From 4cddcc8571715500912b13639f93ff1acc0c4c38 Mon Sep 17 00:00:00 2001 From: Sebas Laverde Chunza <106851243+Laverde97@users.noreply.github.com> Date: Thu, 27 Aug 2026 15:35:40 -0500 Subject: [PATCH 01/29] Improve notebook 00 pedagogy for issue #44 --- notebooks/00-setup-and-data.ipynb | 735 +++++++++++++++++------------- 1 file changed, 407 insertions(+), 328 deletions(-) diff --git a/notebooks/00-setup-and-data.ipynb b/notebooks/00-setup-and-data.ipynb index 7ba1a11..ef68281 100644 --- a/notebooks/00-setup-and-data.ipynb +++ b/notebooks/00-setup-and-data.ipynb @@ -1,332 +1,411 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 00 · Setup and welcome\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/00-setup-and-data.ipynb)\n", - "\n", - "*setup · 5 min*\n", - "\n", - "> 🇪🇸 **Preparación y bienvenida** — Carga todos los conjuntos de datos y confirma que tu entorno funciona antes de empezar.\n", - "\n", - "Load every dataset and confirm your runtime works before anything else.\n", - "\n", - "## What you will be able to do\n", - "\n", - "- Confirm your Colab runtime can reach every dataset the workshop uses.\n", - "- Know which data ships inside the libraries and which is downloaded.\n", - "- Recognise the shapes you will be working with all day." - ], - "id": "s00-00" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "Run this first. It installs and imports everything this notebook needs, and nothing else.\n", - "\n", - "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." - ], - "id": "s00-01" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Section 05 decodes a real video and Colab does not reliably ship an\n", - "# ffmpeg backend, so install it now. Everything else below is already here.\n", - "%pip install -q \"imageio[ffmpeg]\"\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "from sklearn.datasets import load_digits, load_breast_cancer\n", - "from skimage import data\n", - "from scipy import signal\n", - "from scipy.linalg import lu, toeplitz\n", - "\n", - "HOUSING = \"https://raw.githubusercontent.com/ageron/handson-ml2/master/datasets/housing/housing.csv\"\n", - "TAXIS = \"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/taxis.csv\"\n", - "FLIGHTS = \"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/flights.csv\"\n", - "\n", - "housing = pd.read_csv(HOUSING)\n", - "taxis = pd.read_csv(TAXIS)\n", - "flights = pd.read_csv(FLIGHTS)\n", - "print(housing.shape, taxis.shape, flights.shape) # (20640, 10) (6433, 14) (144, 3)\n", - "\n", - "# Section 05's video is 5.9 MB, so don't pull it now -- just prove the backend\n", - "# imports and the host answers. Better to find out here than in two hours.\n", - "import imageio_ffmpeg, urllib.request\n", - "VIDEO_URL = (\"https://upload.wikimedia.org/wikipedia/commons/1/1e/\"\n", - " \"Tormenta_en_l%27Almadrava.webm\")\n", - "req = urllib.request.Request(VIDEO_URL, headers={\n", - " \"User-Agent\": \"tensors-workshop/1.0 \"\n", - " \"(https://github.com/project-delphi/tensors-workshop)\",\n", - " \"Range\": \"bytes=0-1023\"})\n", - "got = urllib.request.urlopen(req, timeout=30).read()\n", - "# Assert rather than print the length: a proxy that ignores Range would quietly\n", - "# pull all 5.9 MB here and still look like a pass, which is the opposite of what\n", - "# this cell is for.\n", - "assert len(got) == 1024, f\"expected a 1 KB range, got {len(got)} bytes\"\n", - "print(\"1024 bytes of video reachable | ffmpeg\", imageio_ffmpeg.get_ffmpeg_version())" - ], - "id": "s00-02" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## All the data here is real\n", - "\n", - "> 🇪🇸 Todos los datos de este taller son reales, no inventados.\n", - "\n", - "Nothing in this workshop is invented with random numbers, because real data\n", - "contains problems that random data never shows — missing values, features on\n", - "incompatible scales, pixels that never change. **Finding those problems is part\n", - "of the work.**\n", - "\n", - "### Included inside the libraries (no download, works offline)\n", - "\n", - "| Dataset | What it is | Shape |\n", - "|---|---|---|\n", - "| `load_breast_cancer()` | 569 real patients, 30 measurements from tumour cell images | `(569, 30)` |\n", - "| `load_digits()` | 1797 real handwritten digits | `(1797, 8, 8)` |\n", - "| `data.camera()`, `data.astronaut()` | Real photographs | `(512, 512)`, `(512, 512, 3)` |\n", - "| `data.immunohistochemistry()`, `data.cell()` | Real histology and microscopy | `(512, 512, 3)`, `(660, 550)` |\n", - "\n", - "### Downloaded once at the start (needs internet, takes a few seconds)\n", - "\n", - "| Dataset | What it is | Used for |\n", - "|---|---|---|\n", - "| California Housing | 20,640 real housing districts, 1990 US census | Pseudoinverse, least squares (section 07) |\n", - "| NYC Taxi Trips | 6,433 real taxi journeys in New York | Tensor factorization (section 10) |\n", - "| Airline Passengers | 144 months of real airline traffic, 1949–1960 | Recursion, forecasting (section 08) |\n", - "\n", - "\n", - "### Downloaded later, by the section that needs it\n", - "\n", - "Neither of these is fetched now — the setup cell above only checks that the\n", - "video host answers and that an ffmpeg backend is present.\n", - "\n", - "| Dataset | What it is | Used for |\n", - "|---|---|---|\n", - "| Storm video | 24 seconds of breaking waves, 720 frames at 960×540 | Video pipeline design (section 05, 5.9 MB) |\n", - "| Voice recording | A five-second CC0 voice sample | Audio denoising (take-home E, in notebook 11) |\n", - "\n", - "If the setup cell above printed `(20640, 10) (6433, 14) (144, 3)`, then a\n", - "`1024 bytes of video reachable` line with an ffmpeg version, you are ready.\n", - "**If it failed, say so in Discord immediately** — a silent download failure will\n", - "leave you stuck at sections 07 and 10, an hour from now, with no obvious cause." - ], - "id": "s00-03" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## See it, not just its shape\n", - "\n", - "> 🇪🇸 Confírmalo con los ojos, no solo con `.shape`.\n", - "\n", - "A shape can match on paper for reasons that are actually bugs — a truncated\n", - "download, a stale cached file, a column that came back silently empty. These\n", - "three files just came over the network; a glance at each is cheaper than\n", - "discovering a bad download at section 07 or 10, an hour from now." - ], - "id": "s00-04" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "fig, axes = plt.subplots(1, 3, figsize=(12, 3.2))\n", - "\n", - "sc = axes[0].scatter(housing[\"longitude\"], housing[\"latitude\"],\n", - " c=housing[\"median_house_value\"], cmap=\"viridis\", s=4)\n", - "axes[0].set_title(\"housing — location, coloured by price\")\n", - "fig.colorbar(sc, ax=axes[0], fraction=0.046)\n", - "\n", - "axes[1].hist(taxis[\"fare\"].dropna(), bins=30, color=\"#4C72B0\")\n", - "axes[1].set_title(\"taxis — fare distribution\")\n", - "axes[1].set_xlabel(\"fare ($)\")\n", - "\n", - "by_year = flights.groupby(\"year\")[\"passengers\"].sum()\n", - "axes[2].plot(by_year.index, by_year.values, marker=\"o\", color=\"#55A868\")\n", - "axes[2].set_title(\"flights — passengers per year\")\n", - "\n", - "fig.suptitle(\"Real California geography, real fares, real growth — \"\n", - " \"if these look right, the downloads worked\")\n", - "plt.tight_layout()\n", - "plt.show()" - ], - "id": "s00-05" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 1 — check the data you did not download\n", - "\n", - "> 🇪🇸 Comprueba los datos que vienen dentro de las librerías." - ], - "id": "s00-06" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO 1: Load the breast cancer data and print the shape of its `.data`.\n", - "# Say out loud what each of the two axes means.\n", - "\n", - "# TODO 2: Print the shape of `load_digits().images` and of\n", - "# `data.immunohistochemistry()`. Both are order 3 — three axes.\n", - "# Do their axes mean the same things?" - ], - "id": "s00-07" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "s00-00" + }, + "source": [ + "# 00 · Setup and welcome\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/00-setup-and-data.ipynb)\n", + "\n", + "*setup · 5 min*\n", + "\n", + "> 🇪🇸 **Preparación y bienvenida** — Carga todos los conjuntos de datos y confirma que tu entorno funciona antes de empezar.\n", + "\n", + "Load every dataset and confirm your runtime works before anything else.\n", + "\n", + "## What you will be able to do\n", + "\n", + "- Confirm your Colab runtime can reach every dataset the workshop uses.\n", + "- Know which data ships inside the libraries and which is downloaded.\n", + "- Recognise the shapes you will be working with all day." + ], + "id": "s00-00" }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "bc = load_breast_cancer()\n", - "print(bc.data.shape) # (569, 30) patients x measurements\n", - "\n", - "print(load_digits().images.shape) # (1797, 8, 8) images x height x width\n", - "print(data.immunohistochemistry().shape) # (512, 512, 3) height x width x colour\n", - "\n", - "# Both are order 3, and they have nothing in common. `digits.images` counts\n", - "# IMAGES along axis 0; the photo counts COLOURS along axis 2. The shape alone\n", - "# never tells you what the axes mean. You must know, and you must keep track." - ], - "id": "s00-08" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 2 — a first look at the downloads\n", - "\n", - "> 🇪🇸 Una primera mirada a los archivos descargados.\n", - "\n", - "One of these three files has a problem waiting in it. You will meet it properly\n", - "in section 07, but it is worth seeing now." - ], - "id": "s00-09" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO 3: Print housing.shape, and then the number of missing values in\n", - "# each column. Which column has them, and how many?\n", - "\n", - "# TODO 4: The taxi data has a 'pickup' column of timestamps. Convert it with\n", - "# pd.to_datetime and extract the hour. Which hour has the most trips?\n", - "# (Keep this number — section 10 comes back to it.)" - ], - "id": "s00-10" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true + { + "cell_type": "markdown", + "metadata": { + "id": "s00-01" + }, + "source": [ + "## Setup\n", + "\n", + "Run this first. It installs and imports everything this notebook needs, and nothing else.\n", + "\n", + "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." + ], + "id": "s00-01" }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "print(housing.shape) # (20640, 10)\n", - "print(housing.isnull().sum()[lambda s: s > 0]) # total_bedrooms 207\n", - "\n", - "hour = pd.to_datetime(taxis[\"pickup\"]).dt.hour\n", - "print(hour.value_counts().idxmax()) # 18 — evening rush\n", - "\n", - "# 207 missing values in `total_bedrooms`. Real data. In section 07 you will drop\n", - "# those rows before solving a 20,433-equation system, and in section 10 a tensor\n", - "# decomposition will rediscover that hour 18 all by itself." - ], - "id": "s00-11" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## A note on how these notebooks work\n", - "\n", - "Every notebook is **self-contained**: it installs, imports and loads its own\n", - "data, so you can open any one of them cold without having run the others. That\n", - "means you will see these same three URLs again. That is deliberate, not\n", - "duplication by accident.\n", - "\n", - "The notebooks are committed with **no outputs**. Every number you see is one you\n", - "produced. Expected results are quoted in the prose so you can check yours — and\n", - "if yours differ, that is worth investigating rather than dismissing." - ], - "id": "s00-12" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Done with this section\n", - "\n", - "Next up: **01 · What a tensor is** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/01-what-a-tensor-is.ipynb).\n", - "\n", - "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" - ], - "id": "s00-13" - } - ], - "metadata": { - "colab": { - "name": "00-setup-and-data.ipynb", - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "s00-02" + }, + "outputs": [], + "source": [ + "# Section 05 decodes a real video and Colab does not reliably ship an\n", + "# ffmpeg backend, so install it now. Everything else below is already here.\n", + "%pip install -q \"imageio[ffmpeg]\"\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.datasets import load_digits, load_breast_cancer\n", + "from skimage import data\n", + "from scipy import signal\n", + "from scipy.linalg import lu, toeplitz\n", + "\n", + "HOUSING = \"https://raw.githubusercontent.com/ageron/handson-ml2/master/datasets/housing/housing.csv\"\n", + "TAXIS = \"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/taxis.csv\"\n", + "FLIGHTS = \"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/flights.csv\"\n", + "\n", + "housing = pd.read_csv(HOUSING)\n", + "taxis = pd.read_csv(TAXIS)\n", + "flights = pd.read_csv(FLIGHTS)\n", + "print(housing.shape, taxis.shape, flights.shape) # (20640, 10) (6433, 14) (144, 3)\n", + "\n", + "# Section 05's video is 5.9 MB, so don't pull it now -- just prove the backend\n", + "# imports and the host answers. Better to find out here than in two hours.\n", + "import imageio_ffmpeg, urllib.request\n", + "VIDEO_URL = (\"https://upload.wikimedia.org/wikipedia/commons/1/1e/\"\n", + " \"Tormenta_en_l%27Almadrava.webm\")\n", + "req = urllib.request.Request(VIDEO_URL, headers={\n", + " \"User-Agent\": \"tensors-workshop/1.0 \"\n", + " \"(https://github.com/project-delphi/tensors-workshop)\",\n", + " \"Range\": \"bytes=0-1023\"})\n", + "got = urllib.request.urlopen(req, timeout=30).read()\n", + "# Assert rather than print the length: a proxy that ignores Range would quietly\n", + "# pull all 5.9 MB here and still look like a pass, which is the opposite of what\n", + "# this cell is for.\n", + "assert len(got) == 1024, f\"expected a 1 KB range, got {len(got)} bytes\"\n", + "print(\"1024 bytes of video reachable | ffmpeg\", imageio_ffmpeg.get_ffmpeg_version())" + ], + "id": "s00-02" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s00-03" + }, + "source": [ + "## Why this matters\n", + "\n", + "We prioritize real datasets whenever domain structure matters. Real data exposes genuine issues such as missing values, incompatible feature scales, and zero-variance columns. A few later sections deliberately use small synthetic examples to isolate specific mathematical concepts, and those exceptions are explicitly justified in their respective sections.\n", + "\n", + "> 🇪🇸 **Por qué esto importa:** Priorizamos datos reales cuando la estructura del dominio es importante. Los datos reales muestran problemas auténticos como valores faltantes, escalas incompatibles y columnas de varianza cero. Algunas secciones posteriores usan pequeños ejemplos sintéticos únicamente para aislar conceptos matemáticos específicos, y esos casos se justifican explícitamente.\n", + "\n", + "### Included inside the libraries (no download, works offline)\n", + "\n", + "| Dataset | What it is | Shape |\n", + "|---|---|---|\n", + "| `load_breast_cancer()` | 569 real patients, 30 measurements from tumour cell images | `(569, 30)` |\n", + "| `load_digits()` | 1797 real handwritten digits | `(1797, 8, 8)` |\n", + "| `data.camera()`, `data.astronaut()` | Real photographs | `(512, 512)`, `(512, 512, 3)` |\n", + "| `data.immunohistochemistry()`, `data.cell()` | Real histology and microscopy | `(512, 512, 3)`, `(660, 550)` |\n", + "\n", + "### Downloaded once at the start (needs internet, takes a few seconds)\n", + "\n", + "| Dataset | What it is | Used for |\n", + "|---|---|---|\n", + "| California Housing | 20,640 real housing districts, 1990 US census | Pseudoinverse, least squares (section 07) |\n", + "| NYC Taxi Trips | 6,433 real taxi journeys in New York | Tensor factorization (section 10) |\n", + "| Airline Passengers | 144 months of real airline traffic, 1949–1960 | Recursion, forecasting (section 08) |\n", + "\n", + "\n", + "### Downloaded later, by the section that needs it\n", + "\n", + "Neither of these is fetched now — the setup cell above only checks that the\n", + "video host answers and that an ffmpeg backend is present.\n", + "\n", + "| Dataset | What it is | Used for |\n", + "|---|---|---|\n", + "| Storm video | 24 seconds of breaking waves, 720 frames at 960×540 | Video pipeline design (section 05, 5.9 MB) |\n", + "| Voice recording | A five-second CC0 voice sample | Audio denoising (take-home E, in notebook 11) |\n", + "\n", + "If the setup cell above printed `(20640, 10) (6433, 14) (144, 3)`, then a\n", + "`1024 bytes of video reachable` line with an ffmpeg version, you are ready.\n", + "**If it failed, say so in Discord immediately** — a silent download failure will\n", + "leave you stuck at sections 07 and 10, an hour from now, with no obvious cause." + ], + "id": "s00-03" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s00-04" + }, + "source": [ + "## See it, not just its shape\n", + "\n", + "> 🇪🇸 Confírmalo con los ojos, no solo con `.shape`.\n", + "\n", + "A shape can match on paper for reasons that are actually bugs — a truncated\n", + "download, a stale cached file, a column that came back silently empty. These\n", + "three files just came over the network; a glance at each is cheaper than\n", + "discovering a bad download at section 07 or 10, an hour from now." + ], + "id": "s00-04" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "s00-05" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "fig, axes = plt.subplots(1, 3, figsize=(12, 3.2))\n", + "\n", + "sc = axes[0].scatter(housing[\"longitude\"], housing[\"latitude\"],\n", + " c=housing[\"median_house_value\"], cmap=\"viridis\", s=4)\n", + "axes[0].set_title(\"housing — location, coloured by price\")\n", + "fig.colorbar(sc, ax=axes[0], fraction=0.046)\n", + "\n", + "axes[1].hist(taxis[\"fare\"].dropna(), bins=30, color=\"#4C72B0\")\n", + "axes[1].set_title(\"taxis — fare distribution\")\n", + "axes[1].set_xlabel(\"fare ($)\")\n", + "\n", + "by_year = flights.groupby(\"year\")[\"passengers\"].sum()\n", + "axes[2].plot(by_year.index, by_year.values, marker=\"o\", color=\"#55A868\")\n", + "axes[2].set_title(\"flights — passengers per year\")\n", + "\n", + "fig.suptitle(\"Real California geography, real fares, real growth — \"\n", + " \"if these look right, the downloads worked\")\n", + "plt.tight_layout()\n", + "plt.show()" + ], + "id": "s00-05" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s00-06" + }, + "source": [ + "## Exercise 1 — check the data you did not download\n", + "\n", + "> 🇪🇸 Comprueba los datos que vienen dentro de las librerías." + ], + "id": "s00-06" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "s00-07" + }, + "outputs": [], + "source": [ + "# TODO 1: Load the breast cancer data and print the shape of its `.data`.\n", + "# Say out loud what each of the two axes means.\n", + "\n", + "# TODO 2: Print the shape of `load_digits().images` and of\n", + "# `data.immunohistochemistry()`. Both are order 3 — three axes.\n", + "# Do their axes mean the same things?" + ], + "id": "s00-07" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "id": "s00-08" + }, + "outputs": [], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "bc = load_breast_cancer()\n", + "print(bc.data.shape) # (569, 30) patients x measurements\n", + "\n", + "print(load_digits().images.shape) # (1797, 8, 8) images x height x width\n", + "print(data.immunohistochemistry().shape) # (512, 512, 3) height x width x colour\n", + "\n", + "# Both are order 3, and they have nothing in common. `digits.images` counts\n", + "# IMAGES along axis 0; the photo counts COLOURS along axis 2. The shape alone\n", + "# never tells you what the axes mean. You must know, and you must keep track." + ], + "id": "s00-08" + }, + { + "cell_type": "markdown", + "source": [ + "
\n", + "Why this solution works · Por qué funciona esta solución\n", + "\n", + "- `(569, 30)` means 569 observations with 30 measured features for each observation.\n", + "- `(1797, 8, 8)` represents a batch of 1,797 digit images, where the last two axes are height and width.\n", + "- `(512, 512, 3)` represents one colour image: height × width × colour channels.\n", + "- Two tensors can both have three axes while those axes mean completely different things. Shape alone does not tell us the semantics.\n", + "\n", + "> 🇪🇸 **Por qué funciona esta solución:** `(569, 30)` representa 569 observaciones con 30 características medidas. `(1797, 8, 8)` corresponde a 1.797 imágenes de dígitos, donde los dos últimos ejes son alto y ancho. `(512, 512, 3)` representa una imagen a color: alto × ancho × canales de color. Dos tensores pueden tener tres ejes y, aun así, representar conceptos completamente diferentes.\n", + "\n", + "
" + ], + "metadata": { + "id": "O-QInfqg3OVS" + }, + "id": "O-QInfqg3OVS" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s00-09" + }, + "source": [ + "## Exercise 2 — a first look at the downloads\n", + "\n", + "> 🇪🇸 Una primera mirada a los archivos descargados.\n", + "\n", + "One of these three files has a problem waiting in it. You will meet it properly\n", + "in section 07, but it is worth seeing now." + ], + "id": "s00-09" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "s00-10" + }, + "outputs": [], + "source": [ + "# TODO 3: Print housing.shape, and then the number of missing values in\n", + "# each column. Which column has them, and how many?\n", + "\n", + "# TODO 4: The taxi data has a 'pickup' column of timestamps. Convert it with\n", + "# pd.to_datetime and extract the hour. Which hour has the most trips?\n", + "# (Keep this number — section 10 comes back to it.)" + ], + "id": "s00-10" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "id": "s00-11" + }, + "outputs": [], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "print(housing.shape) # (20640, 10)\n", + "print(housing.isnull().sum()[lambda s: s > 0]) # total_bedrooms 207\n", + "\n", + "hour = pd.to_datetime(taxis[\"pickup\"]).dt.hour\n", + "print(hour.value_counts().idxmax()) # 18 — evening rush\n", + "\n", + "# 207 missing values in `total_bedrooms`. Real data. In section 07 you will drop\n", + "# those rows before solving a 20,433-equation system, and in section 10 a tensor\n", + "# decomposition will rediscover that hour 18 all by itself." + ], + "id": "s00-11" + }, + { + "cell_type": "markdown", + "source": [ + "
\n", + "Why this solution works · Por qué funciona esta solución\n", + "\n", + "- `housing.shape == (20640, 10)` tells us that the dataset contains 20,640 observations and 10 columns.\n", + "- The 207 missing values in `total_bedrooms` are a real-data problem that must be handled before later matrix operations.\n", + "- Converting `pickup` to datetime lets us extract a meaningful temporal feature: hour of day.\n", + "- Hour `18` has the most taxi pickups in this dataset, a pattern that will become useful again later in the workshop.\n", + "\n", + "> 🇪🇸 **Por qué funciona esta solución:** `housing.shape == (20640, 10)` indica que el conjunto contiene 20.640 observaciones y 10 columnas. Los 207 valores faltantes en `total_bedrooms` son un problema real que deberá tratarse antes de realizar operaciones matriciales posteriores. Convertir `pickup` a fecha y hora permite extraer una característica temporal interpretable: la hora del día. En estos datos, la hora `18` concentra la mayor cantidad de recogidas de taxis.\n", + "\n", + "
" + ], + "metadata": { + "id": "H2elLSdj3VmS" + }, + "id": "H2elLSdj3VmS" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s00-12" + }, + "source": [ + "## A note on how these notebooks work\n", + "\n", + "Every notebook is **self-contained**: it installs, imports and loads its own\n", + "data, so you can open any one of them cold without having run the others. That\n", + "means you will see these same three URLs again. That is deliberate, not\n", + "duplication by accident.\n", + "\n", + "The notebooks are committed with **no outputs**. Every number you see is one you\n", + "produced. Expected results are quoted in the prose so you can check yours — and\n", + "if yours differ, that is worth investigating rather than dismissing." + ], + "id": "s00-12" + }, + { + "cell_type": "markdown", + "source": [ + "## What just happened\n", + "\n", + "We established the baseline needed to continue the workshop:\n", + "\n", + "- The dependencies used by this notebook are available and the video source is reachable.\n", + "- The workshop's core tabular datasets loaded successfully.\n", + "- Real data already exposes issues such as missing values that must be handled rather than ignored.\n", + "- A tensor's shape is only useful when you also know what each axis represents.\n", + "\n", + "> 🇪🇸 **Qué acaba de suceder:** Confirmamos que las dependencias necesarias funcionan, que la fuente de vídeo es accesible y que los conjuntos de datos principales se cargan correctamente. También vimos que los datos reales presentan problemas como valores faltantes y que la forma de un tensor solo tiene sentido cuando entendemos qué representa cada eje." + ], + "metadata": { + "id": "8FxgjBtH2O2Z" + }, + "id": "8FxgjBtH2O2Z" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s00-13" + }, + "source": [ + "---\n", + "\n", + "## Done with this section\n", + "\n", + "Next up: **01 · What a tensor is** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/01-what-a-tensor-is.ipynb).\n", + "\n", + "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" + ], + "id": "s00-13" + } + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python" + } }, - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file From 24092ea09f49b47f9d7dcb41c8326451d6bcc27b Mon Sep 17 00:00:00 2001 From: Sebas Laverde Chunza Date: Thu, 27 Aug 2026 15:58:54 -0500 Subject: [PATCH 02/29] Improve notebook 00 pedagogy for issue #44 --- docs/notebooks/00-setup-and-data.ipynb | 71 ++- notebooks/00-setup-and-data.ipynb | 784 ++++++++++++------------- scripts/gen_notebooks.py | 8 +- 3 files changed, 444 insertions(+), 419 deletions(-) diff --git a/docs/notebooks/00-setup-and-data.ipynb b/docs/notebooks/00-setup-and-data.ipynb index 7ba1a11..b3f9ee0 100644 --- a/docs/notebooks/00-setup-and-data.ipynb +++ b/docs/notebooks/00-setup-and-data.ipynb @@ -82,14 +82,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## All the data here is real\n", + "## Why this matters\n", "\n", - "> 🇪🇸 Todos los datos de este taller son reales, no inventados.\n", + "We prioritize real datasets whenever domain structure matters. Real data exposes genuine issues such as missing values, incompatible feature scales, and zero-variance columns. A few later sections deliberately use small synthetic examples to isolate specific mathematical concepts, and those exceptions are explicitly justified in their respective sections.\n", "\n", - "Nothing in this workshop is invented with random numbers, because real data\n", - "contains problems that random data never shows — missing values, features on\n", - "incompatible scales, pixels that never change. **Finding those problems is part\n", - "of the work.**\n", + "> 🇪🇸 **Por qué esto importa:** Priorizamos datos reales cuando la estructura del dominio es importante. Los datos reales muestran problemas auténticos como valores faltantes, escalas incompatibles y columnas de varianza cero. Algunas secciones posteriores usan pequeños ejemplos sintéticos únicamente para aislar conceptos matemáticos específicos, y esos casos se justifican explícitamente.\n", "\n", "### Included inside the libraries (no download, works offline)\n", "\n", @@ -200,14 +197,14 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ @@ -224,6 +221,24 @@ ], "id": "s00-08" }, + { + "cell_type": "markdown", + "source": [ + "
\n", + "Why this solution works · Por qué funciona esta solución\n", + "\n", + "- `(569, 30)` means 569 observations with 30 measured features for each observation.\n", + "- `(1797, 8, 8)` represents a batch of 1,797 digit images, where the last two axes are height and width.\n", + "- `(512, 512, 3)` represents one colour image: height × width × colour channels.\n", + "- Two tensors can both have three axes while those axes mean completely different things. Shape alone does not tell us the semantics.\n", + "\n", + "> 🇪🇸 **Por qué funciona esta solución:** `(569, 30)` representa 569 observaciones con 30 características medidas. `(1797, 8, 8)` corresponde a 1.797 imágenes de dígitos, donde los dos últimos ejes son alto y ancho. `(512, 512, 3)` representa una imagen a color: alto × ancho × canales de color. Dos tensores pueden tener tres ejes y, aun así, representar conceptos completamente diferentes.\n", + "\n", + "
" + ], + "metadata": {}, + "id": "O-QInfqg3OVS" + }, { "cell_type": "markdown", "metadata": {}, @@ -256,14 +271,14 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ @@ -280,6 +295,24 @@ ], "id": "s00-11" }, + { + "cell_type": "markdown", + "source": [ + "
\n", + "Why this solution works · Por qué funciona esta solución\n", + "\n", + "- `housing.shape == (20640, 10)` tells us that the dataset contains 20,640 observations and 10 columns.\n", + "- The 207 missing values in `total_bedrooms` are a real-data problem that must be handled before later matrix operations.\n", + "- Converting `pickup` to datetime lets us extract a meaningful temporal feature: hour of day.\n", + "- Hour `18` has the most taxi pickups in this dataset, a pattern that will become useful again later in the workshop.\n", + "\n", + "> 🇪🇸 **Por qué funciona esta solución:** `housing.shape == (20640, 10)` indica que el conjunto contiene 20.640 observaciones y 10 columnas. Los 207 valores faltantes en `total_bedrooms` son un problema real que deberá tratarse antes de realizar operaciones matriciales posteriores. Convertir `pickup` a fecha y hora permite extraer una característica temporal interpretable: la hora del día. En estos datos, la hora `18` concentra la mayor cantidad de recogidas de taxis.\n", + "\n", + "
" + ], + "metadata": {}, + "id": "H2elLSdj3VmS" + }, { "cell_type": "markdown", "metadata": {}, @@ -297,6 +330,23 @@ ], "id": "s00-12" }, + { + "cell_type": "markdown", + "source": [ + "## What just happened\n", + "\n", + "We established the baseline needed to continue the workshop:\n", + "\n", + "- The dependencies used by this notebook are available and the video source is reachable.\n", + "- The workshop's core tabular datasets loaded successfully.\n", + "- Real data already exposes issues such as missing values that must be handled rather than ignored.\n", + "- A tensor's shape is only useful when you also know what each axis represents.\n", + "\n", + "> 🇪🇸 **Qué acaba de suceder:** Confirmamos que las dependencias necesarias funcionan, que la fuente de vídeo es accesible y que los conjuntos de datos principales se cargan correctamente. También vimos que los datos reales presentan problemas como valores faltantes y que la forma de un tensor solo tiene sentido cuando entendemos qué representa cada eje." + ], + "metadata": {}, + "id": "8FxgjBtH2O2Z" + }, { "cell_type": "markdown", "metadata": {}, @@ -314,7 +364,6 @@ ], "metadata": { "colab": { - "name": "00-setup-and-data.ipynb", "provenance": [], "toc_visible": true }, diff --git a/notebooks/00-setup-and-data.ipynb b/notebooks/00-setup-and-data.ipynb index ef68281..b3f9ee0 100644 --- a/notebooks/00-setup-and-data.ipynb +++ b/notebooks/00-setup-and-data.ipynb @@ -1,411 +1,381 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "s00-00" - }, - "source": [ - "# 00 · Setup and welcome\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/00-setup-and-data.ipynb)\n", - "\n", - "*setup · 5 min*\n", - "\n", - "> 🇪🇸 **Preparación y bienvenida** — Carga todos los conjuntos de datos y confirma que tu entorno funciona antes de empezar.\n", - "\n", - "Load every dataset and confirm your runtime works before anything else.\n", - "\n", - "## What you will be able to do\n", - "\n", - "- Confirm your Colab runtime can reach every dataset the workshop uses.\n", - "- Know which data ships inside the libraries and which is downloaded.\n", - "- Recognise the shapes you will be working with all day." - ], - "id": "s00-00" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s00-01" - }, - "source": [ - "## Setup\n", - "\n", - "Run this first. It installs and imports everything this notebook needs, and nothing else.\n", - "\n", - "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." - ], - "id": "s00-01" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "s00-02" - }, - "outputs": [], - "source": [ - "# Section 05 decodes a real video and Colab does not reliably ship an\n", - "# ffmpeg backend, so install it now. Everything else below is already here.\n", - "%pip install -q \"imageio[ffmpeg]\"\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "from sklearn.datasets import load_digits, load_breast_cancer\n", - "from skimage import data\n", - "from scipy import signal\n", - "from scipy.linalg import lu, toeplitz\n", - "\n", - "HOUSING = \"https://raw.githubusercontent.com/ageron/handson-ml2/master/datasets/housing/housing.csv\"\n", - "TAXIS = \"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/taxis.csv\"\n", - "FLIGHTS = \"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/flights.csv\"\n", - "\n", - "housing = pd.read_csv(HOUSING)\n", - "taxis = pd.read_csv(TAXIS)\n", - "flights = pd.read_csv(FLIGHTS)\n", - "print(housing.shape, taxis.shape, flights.shape) # (20640, 10) (6433, 14) (144, 3)\n", - "\n", - "# Section 05's video is 5.9 MB, so don't pull it now -- just prove the backend\n", - "# imports and the host answers. Better to find out here than in two hours.\n", - "import imageio_ffmpeg, urllib.request\n", - "VIDEO_URL = (\"https://upload.wikimedia.org/wikipedia/commons/1/1e/\"\n", - " \"Tormenta_en_l%27Almadrava.webm\")\n", - "req = urllib.request.Request(VIDEO_URL, headers={\n", - " \"User-Agent\": \"tensors-workshop/1.0 \"\n", - " \"(https://github.com/project-delphi/tensors-workshop)\",\n", - " \"Range\": \"bytes=0-1023\"})\n", - "got = urllib.request.urlopen(req, timeout=30).read()\n", - "# Assert rather than print the length: a proxy that ignores Range would quietly\n", - "# pull all 5.9 MB here and still look like a pass, which is the opposite of what\n", - "# this cell is for.\n", - "assert len(got) == 1024, f\"expected a 1 KB range, got {len(got)} bytes\"\n", - "print(\"1024 bytes of video reachable | ffmpeg\", imageio_ffmpeg.get_ffmpeg_version())" - ], - "id": "s00-02" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s00-03" - }, - "source": [ - "## Why this matters\n", - "\n", - "We prioritize real datasets whenever domain structure matters. Real data exposes genuine issues such as missing values, incompatible feature scales, and zero-variance columns. A few later sections deliberately use small synthetic examples to isolate specific mathematical concepts, and those exceptions are explicitly justified in their respective sections.\n", - "\n", - "> 🇪🇸 **Por qué esto importa:** Priorizamos datos reales cuando la estructura del dominio es importante. Los datos reales muestran problemas auténticos como valores faltantes, escalas incompatibles y columnas de varianza cero. Algunas secciones posteriores usan pequeños ejemplos sintéticos únicamente para aislar conceptos matemáticos específicos, y esos casos se justifican explícitamente.\n", - "\n", - "### Included inside the libraries (no download, works offline)\n", - "\n", - "| Dataset | What it is | Shape |\n", - "|---|---|---|\n", - "| `load_breast_cancer()` | 569 real patients, 30 measurements from tumour cell images | `(569, 30)` |\n", - "| `load_digits()` | 1797 real handwritten digits | `(1797, 8, 8)` |\n", - "| `data.camera()`, `data.astronaut()` | Real photographs | `(512, 512)`, `(512, 512, 3)` |\n", - "| `data.immunohistochemistry()`, `data.cell()` | Real histology and microscopy | `(512, 512, 3)`, `(660, 550)` |\n", - "\n", - "### Downloaded once at the start (needs internet, takes a few seconds)\n", - "\n", - "| Dataset | What it is | Used for |\n", - "|---|---|---|\n", - "| California Housing | 20,640 real housing districts, 1990 US census | Pseudoinverse, least squares (section 07) |\n", - "| NYC Taxi Trips | 6,433 real taxi journeys in New York | Tensor factorization (section 10) |\n", - "| Airline Passengers | 144 months of real airline traffic, 1949–1960 | Recursion, forecasting (section 08) |\n", - "\n", - "\n", - "### Downloaded later, by the section that needs it\n", - "\n", - "Neither of these is fetched now — the setup cell above only checks that the\n", - "video host answers and that an ffmpeg backend is present.\n", - "\n", - "| Dataset | What it is | Used for |\n", - "|---|---|---|\n", - "| Storm video | 24 seconds of breaking waves, 720 frames at 960×540 | Video pipeline design (section 05, 5.9 MB) |\n", - "| Voice recording | A five-second CC0 voice sample | Audio denoising (take-home E, in notebook 11) |\n", - "\n", - "If the setup cell above printed `(20640, 10) (6433, 14) (144, 3)`, then a\n", - "`1024 bytes of video reachable` line with an ffmpeg version, you are ready.\n", - "**If it failed, say so in Discord immediately** — a silent download failure will\n", - "leave you stuck at sections 07 and 10, an hour from now, with no obvious cause." - ], - "id": "s00-03" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s00-04" - }, - "source": [ - "## See it, not just its shape\n", - "\n", - "> 🇪🇸 Confírmalo con los ojos, no solo con `.shape`.\n", - "\n", - "A shape can match on paper for reasons that are actually bugs — a truncated\n", - "download, a stale cached file, a column that came back silently empty. These\n", - "three files just came over the network; a glance at each is cheaper than\n", - "discovering a bad download at section 07 or 10, an hour from now." - ], - "id": "s00-04" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "s00-05" - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "fig, axes = plt.subplots(1, 3, figsize=(12, 3.2))\n", - "\n", - "sc = axes[0].scatter(housing[\"longitude\"], housing[\"latitude\"],\n", - " c=housing[\"median_house_value\"], cmap=\"viridis\", s=4)\n", - "axes[0].set_title(\"housing — location, coloured by price\")\n", - "fig.colorbar(sc, ax=axes[0], fraction=0.046)\n", - "\n", - "axes[1].hist(taxis[\"fare\"].dropna(), bins=30, color=\"#4C72B0\")\n", - "axes[1].set_title(\"taxis — fare distribution\")\n", - "axes[1].set_xlabel(\"fare ($)\")\n", - "\n", - "by_year = flights.groupby(\"year\")[\"passengers\"].sum()\n", - "axes[2].plot(by_year.index, by_year.values, marker=\"o\", color=\"#55A868\")\n", - "axes[2].set_title(\"flights — passengers per year\")\n", - "\n", - "fig.suptitle(\"Real California geography, real fares, real growth — \"\n", - " \"if these look right, the downloads worked\")\n", - "plt.tight_layout()\n", - "plt.show()" - ], - "id": "s00-05" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s00-06" - }, - "source": [ - "## Exercise 1 — check the data you did not download\n", - "\n", - "> 🇪🇸 Comprueba los datos que vienen dentro de las librerías." - ], - "id": "s00-06" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "s00-07" - }, - "outputs": [], - "source": [ - "# TODO 1: Load the breast cancer data and print the shape of its `.data`.\n", - "# Say out loud what each of the two axes means.\n", - "\n", - "# TODO 2: Print the shape of `load_digits().images` and of\n", - "# `data.immunohistochemistry()`. Both are order 3 — three axes.\n", - "# Do their axes mean the same things?" - ], - "id": "s00-07" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ], - "id": "s00-08" - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "bc = load_breast_cancer()\n", - "print(bc.data.shape) # (569, 30) patients x measurements\n", - "\n", - "print(load_digits().images.shape) # (1797, 8, 8) images x height x width\n", - "print(data.immunohistochemistry().shape) # (512, 512, 3) height x width x colour\n", - "\n", - "# Both are order 3, and they have nothing in common. `digits.images` counts\n", - "# IMAGES along axis 0; the photo counts COLOURS along axis 2. The shape alone\n", - "# never tells you what the axes mean. You must know, and you must keep track." - ], - "id": "s00-08" - }, - { - "cell_type": "markdown", - "source": [ - "
\n", - "Why this solution works · Por qué funciona esta solución\n", - "\n", - "- `(569, 30)` means 569 observations with 30 measured features for each observation.\n", - "- `(1797, 8, 8)` represents a batch of 1,797 digit images, where the last two axes are height and width.\n", - "- `(512, 512, 3)` represents one colour image: height × width × colour channels.\n", - "- Two tensors can both have three axes while those axes mean completely different things. Shape alone does not tell us the semantics.\n", - "\n", - "> 🇪🇸 **Por qué funciona esta solución:** `(569, 30)` representa 569 observaciones con 30 características medidas. `(1797, 8, 8)` corresponde a 1.797 imágenes de dígitos, donde los dos últimos ejes son alto y ancho. `(512, 512, 3)` representa una imagen a color: alto × ancho × canales de color. Dos tensores pueden tener tres ejes y, aun así, representar conceptos completamente diferentes.\n", - "\n", - "
" - ], - "metadata": { - "id": "O-QInfqg3OVS" - }, - "id": "O-QInfqg3OVS" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s00-09" - }, - "source": [ - "## Exercise 2 — a first look at the downloads\n", - "\n", - "> 🇪🇸 Una primera mirada a los archivos descargados.\n", - "\n", - "One of these three files has a problem waiting in it. You will meet it properly\n", - "in section 07, but it is worth seeing now." - ], - "id": "s00-09" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "s00-10" - }, - "outputs": [], - "source": [ - "# TODO 3: Print housing.shape, and then the number of missing values in\n", - "# each column. Which column has them, and how many?\n", - "\n", - "# TODO 4: The taxi data has a 'pickup' column of timestamps. Convert it with\n", - "# pd.to_datetime and extract the hour. Which hour has the most trips?\n", - "# (Keep this number — section 10 comes back to it.)" - ], - "id": "s00-10" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ], - "id": "s00-11" - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "print(housing.shape) # (20640, 10)\n", - "print(housing.isnull().sum()[lambda s: s > 0]) # total_bedrooms 207\n", - "\n", - "hour = pd.to_datetime(taxis[\"pickup\"]).dt.hour\n", - "print(hour.value_counts().idxmax()) # 18 — evening rush\n", - "\n", - "# 207 missing values in `total_bedrooms`. Real data. In section 07 you will drop\n", - "# those rows before solving a 20,433-equation system, and in section 10 a tensor\n", - "# decomposition will rediscover that hour 18 all by itself." - ], - "id": "s00-11" - }, - { - "cell_type": "markdown", - "source": [ - "
\n", - "Why this solution works · Por qué funciona esta solución\n", - "\n", - "- `housing.shape == (20640, 10)` tells us that the dataset contains 20,640 observations and 10 columns.\n", - "- The 207 missing values in `total_bedrooms` are a real-data problem that must be handled before later matrix operations.\n", - "- Converting `pickup` to datetime lets us extract a meaningful temporal feature: hour of day.\n", - "- Hour `18` has the most taxi pickups in this dataset, a pattern that will become useful again later in the workshop.\n", - "\n", - "> 🇪🇸 **Por qué funciona esta solución:** `housing.shape == (20640, 10)` indica que el conjunto contiene 20.640 observaciones y 10 columnas. Los 207 valores faltantes en `total_bedrooms` son un problema real que deberá tratarse antes de realizar operaciones matriciales posteriores. Convertir `pickup` a fecha y hora permite extraer una característica temporal interpretable: la hora del día. En estos datos, la hora `18` concentra la mayor cantidad de recogidas de taxis.\n", - "\n", - "
" - ], - "metadata": { - "id": "H2elLSdj3VmS" - }, - "id": "H2elLSdj3VmS" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s00-12" - }, - "source": [ - "## A note on how these notebooks work\n", - "\n", - "Every notebook is **self-contained**: it installs, imports and loads its own\n", - "data, so you can open any one of them cold without having run the others. That\n", - "means you will see these same three URLs again. That is deliberate, not\n", - "duplication by accident.\n", - "\n", - "The notebooks are committed with **no outputs**. Every number you see is one you\n", - "produced. Expected results are quoted in the prose so you can check yours — and\n", - "if yours differ, that is worth investigating rather than dismissing." - ], - "id": "s00-12" - }, - { - "cell_type": "markdown", - "source": [ - "## What just happened\n", - "\n", - "We established the baseline needed to continue the workshop:\n", - "\n", - "- The dependencies used by this notebook are available and the video source is reachable.\n", - "- The workshop's core tabular datasets loaded successfully.\n", - "- Real data already exposes issues such as missing values that must be handled rather than ignored.\n", - "- A tensor's shape is only useful when you also know what each axis represents.\n", - "\n", - "> 🇪🇸 **Qué acaba de suceder:** Confirmamos que las dependencias necesarias funcionan, que la fuente de vídeo es accesible y que los conjuntos de datos principales se cargan correctamente. También vimos que los datos reales presentan problemas como valores faltantes y que la forma de un tensor solo tiene sentido cuando entendemos qué representa cada eje." - ], - "metadata": { - "id": "8FxgjBtH2O2Z" - }, - "id": "8FxgjBtH2O2Z" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s00-13" - }, - "source": [ - "---\n", - "\n", - "## Done with this section\n", - "\n", - "Next up: **01 · What a tensor is** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/01-what-a-tensor-is.ipynb).\n", - "\n", - "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" - ], - "id": "s00-13" - } - ], - "metadata": { - "colab": { - "provenance": [], - "toc_visible": true + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 00 · Setup and welcome\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/00-setup-and-data.ipynb)\n", + "\n", + "*setup · 5 min*\n", + "\n", + "> 🇪🇸 **Preparación y bienvenida** — Carga todos los conjuntos de datos y confirma que tu entorno funciona antes de empezar.\n", + "\n", + "Load every dataset and confirm your runtime works before anything else.\n", + "\n", + "## What you will be able to do\n", + "\n", + "- Confirm your Colab runtime can reach every dataset the workshop uses.\n", + "- Know which data ships inside the libraries and which is downloaded.\n", + "- Recognise the shapes you will be working with all day." + ], + "id": "s00-00" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "Run this first. It installs and imports everything this notebook needs, and nothing else.\n", + "\n", + "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." + ], + "id": "s00-01" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Section 05 decodes a real video and Colab does not reliably ship an\n", + "# ffmpeg backend, so install it now. Everything else below is already here.\n", + "%pip install -q \"imageio[ffmpeg]\"\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.datasets import load_digits, load_breast_cancer\n", + "from skimage import data\n", + "from scipy import signal\n", + "from scipy.linalg import lu, toeplitz\n", + "\n", + "HOUSING = \"https://raw.githubusercontent.com/ageron/handson-ml2/master/datasets/housing/housing.csv\"\n", + "TAXIS = \"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/taxis.csv\"\n", + "FLIGHTS = \"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/flights.csv\"\n", + "\n", + "housing = pd.read_csv(HOUSING)\n", + "taxis = pd.read_csv(TAXIS)\n", + "flights = pd.read_csv(FLIGHTS)\n", + "print(housing.shape, taxis.shape, flights.shape) # (20640, 10) (6433, 14) (144, 3)\n", + "\n", + "# Section 05's video is 5.9 MB, so don't pull it now -- just prove the backend\n", + "# imports and the host answers. Better to find out here than in two hours.\n", + "import imageio_ffmpeg, urllib.request\n", + "VIDEO_URL = (\"https://upload.wikimedia.org/wikipedia/commons/1/1e/\"\n", + " \"Tormenta_en_l%27Almadrava.webm\")\n", + "req = urllib.request.Request(VIDEO_URL, headers={\n", + " \"User-Agent\": \"tensors-workshop/1.0 \"\n", + " \"(https://github.com/project-delphi/tensors-workshop)\",\n", + " \"Range\": \"bytes=0-1023\"})\n", + "got = urllib.request.urlopen(req, timeout=30).read()\n", + "# Assert rather than print the length: a proxy that ignores Range would quietly\n", + "# pull all 5.9 MB here and still look like a pass, which is the opposite of what\n", + "# this cell is for.\n", + "assert len(got) == 1024, f\"expected a 1 KB range, got {len(got)} bytes\"\n", + "print(\"1024 bytes of video reachable | ffmpeg\", imageio_ffmpeg.get_ffmpeg_version())" + ], + "id": "s00-02" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Why this matters\n", + "\n", + "We prioritize real datasets whenever domain structure matters. Real data exposes genuine issues such as missing values, incompatible feature scales, and zero-variance columns. A few later sections deliberately use small synthetic examples to isolate specific mathematical concepts, and those exceptions are explicitly justified in their respective sections.\n", + "\n", + "> 🇪🇸 **Por qué esto importa:** Priorizamos datos reales cuando la estructura del dominio es importante. Los datos reales muestran problemas auténticos como valores faltantes, escalas incompatibles y columnas de varianza cero. Algunas secciones posteriores usan pequeños ejemplos sintéticos únicamente para aislar conceptos matemáticos específicos, y esos casos se justifican explícitamente.\n", + "\n", + "### Included inside the libraries (no download, works offline)\n", + "\n", + "| Dataset | What it is | Shape |\n", + "|---|---|---|\n", + "| `load_breast_cancer()` | 569 real patients, 30 measurements from tumour cell images | `(569, 30)` |\n", + "| `load_digits()` | 1797 real handwritten digits | `(1797, 8, 8)` |\n", + "| `data.camera()`, `data.astronaut()` | Real photographs | `(512, 512)`, `(512, 512, 3)` |\n", + "| `data.immunohistochemistry()`, `data.cell()` | Real histology and microscopy | `(512, 512, 3)`, `(660, 550)` |\n", + "\n", + "### Downloaded once at the start (needs internet, takes a few seconds)\n", + "\n", + "| Dataset | What it is | Used for |\n", + "|---|---|---|\n", + "| California Housing | 20,640 real housing districts, 1990 US census | Pseudoinverse, least squares (section 07) |\n", + "| NYC Taxi Trips | 6,433 real taxi journeys in New York | Tensor factorization (section 10) |\n", + "| Airline Passengers | 144 months of real airline traffic, 1949–1960 | Recursion, forecasting (section 08) |\n", + "\n", + "\n", + "### Downloaded later, by the section that needs it\n", + "\n", + "Neither of these is fetched now — the setup cell above only checks that the\n", + "video host answers and that an ffmpeg backend is present.\n", + "\n", + "| Dataset | What it is | Used for |\n", + "|---|---|---|\n", + "| Storm video | 24 seconds of breaking waves, 720 frames at 960×540 | Video pipeline design (section 05, 5.9 MB) |\n", + "| Voice recording | A five-second CC0 voice sample | Audio denoising (take-home E, in notebook 11) |\n", + "\n", + "If the setup cell above printed `(20640, 10) (6433, 14) (144, 3)`, then a\n", + "`1024 bytes of video reachable` line with an ffmpeg version, you are ready.\n", + "**If it failed, say so in Discord immediately** — a silent download failure will\n", + "leave you stuck at sections 07 and 10, an hour from now, with no obvious cause." + ], + "id": "s00-03" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## See it, not just its shape\n", + "\n", + "> 🇪🇸 Confírmalo con los ojos, no solo con `.shape`.\n", + "\n", + "A shape can match on paper for reasons that are actually bugs — a truncated\n", + "download, a stale cached file, a column that came back silently empty. These\n", + "three files just came over the network; a glance at each is cheaper than\n", + "discovering a bad download at section 07 or 10, an hour from now." + ], + "id": "s00-04" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "fig, axes = plt.subplots(1, 3, figsize=(12, 3.2))\n", + "\n", + "sc = axes[0].scatter(housing[\"longitude\"], housing[\"latitude\"],\n", + " c=housing[\"median_house_value\"], cmap=\"viridis\", s=4)\n", + "axes[0].set_title(\"housing — location, coloured by price\")\n", + "fig.colorbar(sc, ax=axes[0], fraction=0.046)\n", + "\n", + "axes[1].hist(taxis[\"fare\"].dropna(), bins=30, color=\"#4C72B0\")\n", + "axes[1].set_title(\"taxis — fare distribution\")\n", + "axes[1].set_xlabel(\"fare ($)\")\n", + "\n", + "by_year = flights.groupby(\"year\")[\"passengers\"].sum()\n", + "axes[2].plot(by_year.index, by_year.values, marker=\"o\", color=\"#55A868\")\n", + "axes[2].set_title(\"flights — passengers per year\")\n", + "\n", + "fig.suptitle(\"Real California geography, real fares, real growth — \"\n", + " \"if these look right, the downloads worked\")\n", + "plt.tight_layout()\n", + "plt.show()" + ], + "id": "s00-05" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 1 — check the data you did not download\n", + "\n", + "> 🇪🇸 Comprueba los datos que vienen dentro de las librerías." + ], + "id": "s00-06" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO 1: Load the breast cancer data and print the shape of its `.data`.\n", + "# Say out loud what each of the two axes means.\n", + "\n", + "# TODO 2: Print the shape of `load_digits().images` and of\n", + "# `data.immunohistochemistry()`. Both are order 3 — three axes.\n", + "# Do their axes mean the same things?" + ], + "id": "s00-07" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "bc = load_breast_cancer()\n", + "print(bc.data.shape) # (569, 30) patients x measurements\n", + "\n", + "print(load_digits().images.shape) # (1797, 8, 8) images x height x width\n", + "print(data.immunohistochemistry().shape) # (512, 512, 3) height x width x colour\n", + "\n", + "# Both are order 3, and they have nothing in common. `digits.images` counts\n", + "# IMAGES along axis 0; the photo counts COLOURS along axis 2. The shape alone\n", + "# never tells you what the axes mean. You must know, and you must keep track." + ], + "id": "s00-08" + }, + { + "cell_type": "markdown", + "source": [ + "
\n", + "Why this solution works · Por qué funciona esta solución\n", + "\n", + "- `(569, 30)` means 569 observations with 30 measured features for each observation.\n", + "- `(1797, 8, 8)` represents a batch of 1,797 digit images, where the last two axes are height and width.\n", + "- `(512, 512, 3)` represents one colour image: height × width × colour channels.\n", + "- Two tensors can both have three axes while those axes mean completely different things. Shape alone does not tell us the semantics.\n", + "\n", + "> 🇪🇸 **Por qué funciona esta solución:** `(569, 30)` representa 569 observaciones con 30 características medidas. `(1797, 8, 8)` corresponde a 1.797 imágenes de dígitos, donde los dos últimos ejes son alto y ancho. `(512, 512, 3)` representa una imagen a color: alto × ancho × canales de color. Dos tensores pueden tener tres ejes y, aun así, representar conceptos completamente diferentes.\n", + "\n", + "
" + ], + "metadata": {}, + "id": "O-QInfqg3OVS" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 2 — a first look at the downloads\n", + "\n", + "> 🇪🇸 Una primera mirada a los archivos descargados.\n", + "\n", + "One of these three files has a problem waiting in it. You will meet it properly\n", + "in section 07, but it is worth seeing now." + ], + "id": "s00-09" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO 3: Print housing.shape, and then the number of missing values in\n", + "# each column. Which column has them, and how many?\n", + "\n", + "# TODO 4: The taxi data has a 'pickup' column of timestamps. Convert it with\n", + "# pd.to_datetime and extract the hour. Which hour has the most trips?\n", + "# (Keep this number — section 10 comes back to it.)" + ], + "id": "s00-10" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true }, - "language_info": { - "name": "python" - } + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "print(housing.shape) # (20640, 10)\n", + "print(housing.isnull().sum()[lambda s: s > 0]) # total_bedrooms 207\n", + "\n", + "hour = pd.to_datetime(taxis[\"pickup\"]).dt.hour\n", + "print(hour.value_counts().idxmax()) # 18 — evening rush\n", + "\n", + "# 207 missing values in `total_bedrooms`. Real data. In section 07 you will drop\n", + "# those rows before solving a 20,433-equation system, and in section 10 a tensor\n", + "# decomposition will rediscover that hour 18 all by itself." + ], + "id": "s00-11" + }, + { + "cell_type": "markdown", + "source": [ + "
\n", + "Why this solution works · Por qué funciona esta solución\n", + "\n", + "- `housing.shape == (20640, 10)` tells us that the dataset contains 20,640 observations and 10 columns.\n", + "- The 207 missing values in `total_bedrooms` are a real-data problem that must be handled before later matrix operations.\n", + "- Converting `pickup` to datetime lets us extract a meaningful temporal feature: hour of day.\n", + "- Hour `18` has the most taxi pickups in this dataset, a pattern that will become useful again later in the workshop.\n", + "\n", + "> 🇪🇸 **Por qué funciona esta solución:** `housing.shape == (20640, 10)` indica que el conjunto contiene 20.640 observaciones y 10 columnas. Los 207 valores faltantes en `total_bedrooms` son un problema real que deberá tratarse antes de realizar operaciones matriciales posteriores. Convertir `pickup` a fecha y hora permite extraer una característica temporal interpretable: la hora del día. En estos datos, la hora `18` concentra la mayor cantidad de recogidas de taxis.\n", + "\n", + "
" + ], + "metadata": {}, + "id": "H2elLSdj3VmS" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## A note on how these notebooks work\n", + "\n", + "Every notebook is **self-contained**: it installs, imports and loads its own\n", + "data, so you can open any one of them cold without having run the others. That\n", + "means you will see these same three URLs again. That is deliberate, not\n", + "duplication by accident.\n", + "\n", + "The notebooks are committed with **no outputs**. Every number you see is one you\n", + "produced. Expected results are quoted in the prose so you can check yours — and\n", + "if yours differ, that is worth investigating rather than dismissing." + ], + "id": "s00-12" + }, + { + "cell_type": "markdown", + "source": [ + "## What just happened\n", + "\n", + "We established the baseline needed to continue the workshop:\n", + "\n", + "- The dependencies used by this notebook are available and the video source is reachable.\n", + "- The workshop's core tabular datasets loaded successfully.\n", + "- Real data already exposes issues such as missing values that must be handled rather than ignored.\n", + "- A tensor's shape is only useful when you also know what each axis represents.\n", + "\n", + "> 🇪🇸 **Qué acaba de suceder:** Confirmamos que las dependencias necesarias funcionan, que la fuente de vídeo es accesible y que los conjuntos de datos principales se cargan correctamente. También vimos que los datos reales presentan problemas como valores faltantes y que la forma de un tensor solo tiene sentido cuando entendemos qué representa cada eje." + ], + "metadata": {}, + "id": "8FxgjBtH2O2Z" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Done with this section\n", + "\n", + "Next up: **01 · What a tensor is** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/01-what-a-tensor-is.ipynb).\n", + "\n", + "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" + ], + "id": "s00-13" + } + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/scripts/gen_notebooks.py b/scripts/gen_notebooks.py index 7affd06..bb16fd4 100644 --- a/scripts/gen_notebooks.py +++ b/scripts/gen_notebooks.py @@ -185,9 +185,15 @@ def _normalize_cell(cell: dict) -> dict: and the solution/hide-input tags. """ metadata = cell.setdefault("metadata", {}) - for key in ("colab", "outputId", "executionInfo"): + for key in ("colab", "outputId", "executionInfo", "id"): metadata.pop(key, None) + # Colab may drop cellView when saving a notebook back to GitHub. + # Restore the metadata required to keep solution cells folded. + if "solution" in metadata.get("tags", []): + metadata["cellView"] = "form" + metadata.setdefault("jupyter", {})["source_hidden"] = True + if cell.get("cell_type") == "code": cell["execution_count"] = None cell["outputs"] = [] From ef24faeed264a8071aa1853f603b28bd9f153cc8 Mon Sep 17 00:00:00 2001 From: Sebas Laverde Chunza <106851243+Laverde97@users.noreply.github.com> Date: Fri, 28 Aug 2026 09:24:36 -0500 Subject: [PATCH 03/29] Improve notebook 01 pedagogy for issue #44 --- notebooks/01-what-a-tensor-is.ipynb | 1344 ++++++++++++++++----------- 1 file changed, 798 insertions(+), 546 deletions(-) diff --git a/notebooks/01-what-a-tensor-is.ipynb b/notebooks/01-what-a-tensor-is.ipynb index ec6230a..bf0db47 100644 --- a/notebooks/01-what-a-tensor-is.ipynb +++ b/notebooks/01-what-a-tensor-is.ipynb @@ -1,548 +1,800 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 01 · What a tensor is\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/01-what-a-tensor-is.ipynb)\n", - "\n", - "*Part I · demo · 20 min*\n", - "\n", - "> 🇪🇸 **Qué es un tensor** — El vocabulario, la forma en NumPy y las tres operaciones que importan.\n", - "\n", - "The vocabulary, shape in NumPy, and the three operations that matter.\n", - "\n", - "## What you will be able to do\n", - "\n", - "- Use the vocabulary: order, axis, mode, shape, slice, fiber, unfolding, contraction, decomposition.\n", - "- Read `.shape`, `.ndim` and `.size` off any array and say what each axis means.\n", - "- Take slices and fibers, and unfold a tensor into a matrix without losing anything.\n", - "- Write a dot product and a matrix product as `einsum` contractions.\n", - "- Place LU, QR, eigendecomposition, SVD, the pseudoinverse, Cholesky and Tucker in one map." - ], - "id": "s01-00" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "Run this first. It installs and imports everything this notebook needs, and nothing else.\n", - "\n", - "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." - ], - "id": "s01-01" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from sklearn.datasets import load_digits\n", - "from skimage import data\n", - "from scipy.linalg import lu\n", - "\n", - "rng = np.random.default_rng(0)" - ], - "id": "s01-02" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1.1 Vocabulary\n", - "\n", - "> 🇪🇸 El vocabulario. Casi todos los términos son casi idénticos en español.\n", - "\n", - "Keep this table open for the whole workshop.\n", - "\n", - "| Term | Plain meaning | Spanish | Example |\n", - "|---|---|---|---|\n", - "| **Tensor** | An array of numbers with any number of axes | *tensor* | A colour image |\n", - "| **Axis** (pl. axes) | One direction along which data is arranged | *eje* | Height; width; colour |\n", - "| **Mode** | Another word for axis, used in tensor theory | *modo* | \"mode-0 unfolding\" |\n", - "| **Order** | How many axes a tensor has | *orden* | A matrix has order 2 |\n", - "| **Shape** | The size along each axis, as a tuple | *forma* | `(512, 512, 3)` |\n", - "| **Slice** | Fix one index, keep the rest | *corte* | One colour channel |\n", - "| **Fiber** | Fix every index except one | *fibra* | The 3 colour values of one pixel |\n", - "| **Unfolding** | Rearranging a tensor into a matrix | *desplegado* | Needed for decompositions |\n", - "| **Contraction** | Multiply and sum over a shared axis | *contracción* | The dot product |\n", - "| **Decomposition** | Writing one tensor as a product of simpler ones | *descomposición* | SVD, Tucker |\n", - "\n", - "⚠️ **Warning about the word \"rank\".** In Chapter 2, *rank* means the number of\n", - "independent columns of a matrix. In tensor theory, *rank* often means the number\n", - "of axes. To avoid confusion, this workshop says **order** for the number of\n", - "axes, and **rank** only in Chapter 2's sense." - ], - "id": "s01-03" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1.2 Shape in NumPy\n", - "\n", - "> 🇪🇸 La forma en NumPy: `.shape`, `.ndim` y `.size`.\n", - "\n", - "Every NumPy array has `.shape`, a tuple giving the size along each axis. The\n", - "length of that tuple is `.ndim`, the number of axes." - ], - "id": "s01-04" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "scalar = np.array(3.0) # book: a — order 0\n", - "vector = np.array([1., 2., 3.]) # book: x, x_i — order 1\n", - "matrix = np.array([[1., 2.], [3., 4.]]) # book: A, A_{i,j} — order 2\n", - "tensor = rng.standard_normal((2, 3, 4)) # book: A_{i,j,k} — order 3\n", - "\n", - "for name, arr in [(\"scalar\", scalar), (\"vector\", vector),\n", - " (\"matrix\", matrix), (\"tensor\", tensor)]:\n", - " print(f\"{name:8s} shape={str(arr.shape):12s} ndim={arr.ndim} size={arr.size}\")" - ], - "id": "s01-05" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A scalar has `shape=()`, an empty tuple — there are no axes to measure. And\n", - "`size` is always the product of the numbers in `shape`: 2 × 3 × 4 = 24.\n", - "\n", - "Now with real data." - ], - "id": "s01-06" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "digits = load_digits()\n", - "print(digits.images.shape) # (1797, 8, 8) — 1797 handwritten digits, 8x8 pixels\n", - "\n", - "photo = data.immunohistochemistry()\n", - "print(photo.shape) # (512, 512, 3) — height, width, colour" - ], - "id": "s01-07" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Both are order 3, but their axes mean completely different things.\n", - "`digits.images` counts *images* along axis 0; `photo` counts *colours* along\n", - "axis 2. **The shape alone never tells you what the axes mean.** You must know,\n", - "and you must keep track." - ], - "id": "s01-08" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 1 — read the shapes\n", - "\n", - "> 🇪🇸 Lee las formas y di qué significa cada eje." - ], - "id": "s01-09" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO 1: Build a scalar, a vector, a matrix and an order-3 tensor, and print\n", - "# .shape, .ndim and .size for each. Which one has shape ()?\n", - "\n", - "# TODO 2: Take load_digits().images and data.astronaut(). Both are order 3.\n", - "# For each, write down in a comment what axis 0, 1 and 2 count." - ], - "id": "s01-10" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "for arr in [np.array(3.0), np.zeros(3), np.zeros((2, 2)), np.zeros((2, 3, 4))]:\n", - " print(arr.shape, arr.ndim, arr.size)\n", - "# () 0 1\n", - "# (3,) 1 3\n", - "# (2, 2) 2 4\n", - "# (2, 3, 4) 3 24\n", - "\n", - "print(load_digits().images.shape) # (1797, 8, 8) axis 0 = which image\n", - " # axis 1 = row of pixels\n", - " # axis 2 = column of pixels\n", - "print(data.astronaut().shape) # (512, 512, 3) axis 0 = height\n", - " # axis 1 = width\n", - " # axis 2 = colour channel" - ], - "id": "s01-11" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1.3 The three operations that matter\n", - "\n", - "> 🇪🇸 Cortes y fibras, desplegado y contracción — las tres operaciones clave.\n", - "\n", - "### Slices and fibers — fixing indices takes a tensor apart" - ], - "id": "s01-12" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(photo[:, :, 0].shape) # (512, 512) — a slice: one colour channel, still an image\n", - "print(photo[100, 200, :].shape) # (3,) — a fiber: the 3 colour values of one pixel" - ], - "id": "s01-13" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Same picture, same two indexing operations — see them together. Drag the\n", - "sliders and watch the marked pixel move on both panels at once, while its\n", - "fiber (three numbers, one per colour) redraws on the right.\n", - "\n", - "> 🇪🇸 Mueve los deslizadores: el mismo píxel se marca en el corte y en la\n", - "> imagen completa, y su fibra (tres números, uno por color) se redibuja." - ], - "id": "s01-14" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Colab renders ipywidgets through its own widget manager rather than the\n", - "# classic Jupyter one; this call is a no-op outside Colab, which is why it is\n", - "# guarded rather than assumed.\n", - "try:\n", - " from google.colab import output\n", - " output.enable_custom_widget_manager()\n", - "except ImportError:\n", - " pass\n", - "\n", - "import ipywidgets as widgets\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def show_slice_and_fiber(row, col):\n", - " plt.close('all')\n", - " fig, axes = plt.subplots(1, 3, figsize=(11, 3.2))\n", - "\n", - " axes[0].imshow(photo)\n", - " axes[0].scatter([col], [row], color='#C44E52', s=70, edgecolor='white')\n", - " axes[0].set_title('photo — the fiber, marked')\n", - " axes[0].axis('off')\n", - "\n", - " axes[1].imshow(photo[:, :, 0], cmap='gray')\n", - " axes[1].scatter([col], [row], color='#C44E52', s=70, edgecolor='white')\n", - " axes[1].set_title('photo[:, :, 0] — a slice')\n", - " axes[1].axis('off')\n", - "\n", - " fiber = photo[row, col, :]\n", - " axes[2].bar(['R', 'G', 'B'], fiber, color=['#C44E52', '#55A868', '#4C72B0'])\n", - " axes[2].set_title(f'photo[{row}, {col}, :] — the fiber')\n", - " axes[2].set_ylim(0, 255)\n", - "\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "widgets.interact(show_slice_and_fiber,\n", - " row=widgets.IntSlider(min=0, max=511, step=1, value=100, description='row'),\n", - " col=widgets.IntSlider(min=0, max=511, step=1, value=200, description='col'));" - ], - "id": "s01-15" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Unfolding — every decomposition begins here\n", - "\n", - "Every tensor decomposition begins by turning the tensor into a matrix, one axis\n", - "at a time. Move axis *k* to the front, then flatten everything else into one\n", - "long axis." - ], - "id": "s01-16" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def unfold(T, axis):\n", - " \"\"\"Move `axis` to the front, flatten everything else into one long axis.\"\"\"\n", - " return np.moveaxis(T, axis, 0).reshape(T.shape[axis], -1)\n", - "\n", - "print(unfold(photo, 0).shape) # (512, 1536) — rows are the height axis\n", - "print(unfold(photo, 2).shape) # (3, 262144) — rows are the 3 colour channels" - ], - "id": "s01-17" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Unfolding **loses nothing**. It only rearranges. The mode-2 unfolding says\n", - "\"each colour channel is one row of 262,144 numbers\" — and now every matrix tool\n", - "you know, including SVD, can be applied to it.\n", - "\n", - "You will use this exact function again in sections 07 and 10.\n", - "\n", - "### Contraction — multiply along a shared axis and sum over it\n", - "\n", - "The dot product (eq. 2.8) and the matrix product (eq. 2.5) are both\n", - "contractions. `np.einsum` writes them directly." - ], - "id": "s01-18" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a = np.array([1., 2., 3.]); b = np.array([4., 5., 6.])\n", - "print(np.einsum('i,i->', a, b)) # dot product, sum over i (eq 2.8)\n", - "\n", - "A = np.array([[1., 2.], [3., 4.]]); B = np.array([[5., 6.], [7., 8.]])\n", - "print(np.einsum('ik,kj->ij', A, B)) # matrix product, sum over k (eq 2.5)" - ], - "id": "s01-19" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**The rule, in one sentence:** an index that appears in the inputs but **not**\n", - "after the arrow is summed over; an index that appears after the arrow is kept.\n", - "\n", - "That one sentence is the whole of section 06." - ], - "id": "s01-20" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 2 — take a tensor apart and put it back\n", - "\n", - "> 🇪🇸 Desmonta un tensor y vuelve a montarlo." - ], - "id": "s01-21" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO 3: From `photo`, extract (a) the green channel as a (512, 512) slice and\n", - "# (b) the colour fiber at pixel (10, 20). Which is a slice, which a fiber?\n", - "\n", - "# TODO 4: Unfold `photo` along all three axes and print the three shapes.\n", - "# Confirm that each unfolding has exactly photo.size entries —\n", - "# unfolding rearranges, it never loses anything.\n", - "\n", - "# TODO 5: Write the dot product of `a` and `b` as einsum, and check it against\n", - "# np.dot. Then write the matrix product of A and B, and check against @." - ], - "id": "s01-22" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "green = photo[:, :, 1] # slice — one index fixed, the rest kept\n", - "fiber = photo[10, 20, :] # fiber — every index fixed except one\n", - "print(green.shape, fiber.shape) # (512, 512) (3,)\n", - "\n", - "for ax in range(3):\n", - " M = unfold(photo, ax)\n", - " print(ax, M.shape, M.size == photo.size) # True every time\n", - "\n", - "print(np.einsum('i,i->', a, b), np.dot(a, b)) # 32.0 32.0\n", - "print(np.allclose(np.einsum('ik,kj->ij', A, B), A @ B)) # True" - ], - "id": "s01-23" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1.4 The map of factorizations\n", - "\n", - "> 🇪🇸 El mapa de las factorizaciones: qué método sirve para qué.\n", - "\n", - "A **factorization** writes one object as a product of simpler objects. You met\n", - "two in Chapter 2. Here is the whole family we will use today.\n", - "\n", - "| Method | Works on | What it gives you | Where today |\n", - "|---|---|---|---|\n", - "| **LU** | Square matrix | Gaussian elimination, saved for reuse | Below |\n", - "| **QR / Gram-Schmidt** | Any matrix | Perpendicular, unit-length directions | Below |\n", - "| **Eigendecomposition** | Square matrix | Directions that only get scaled (§2.7) | Section 08 |\n", - "| **SVD** | Any matrix | The most general matrix factorization (§2.8) | Sections 07 and 10 |\n", - "| **Pseudoinverse** | Any matrix | \"Inverse\" when no true inverse exists (§2.9) | Section 07 |\n", - "| **Cholesky** | Symmetric positive-definite matrix | A \"square root\" of a covariance matrix, for *building* correlated data | Section 11, take-home D |\n", - "| **Tucker / CP** | **Tensor, any order** | PCA generalized to every axis | Section 10 |" - ], - "id": "s01-24" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "A3 = np.array([[4., 3., 2.], [2., 1., 1.], [6., 3., 5.]])\n", - "\n", - "P, L, U = lu(A3) # LU: A = P L U\n", - "print(np.allclose(P @ L @ U, A3)) # True\n", - "\n", - "Q, R = np.linalg.qr(A3) # QR: orthonormal directions\n", - "print(np.allclose(Q.T @ Q, np.eye(3))) # True — book eq 2.37" - ], - "id": "s01-25" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**LU** is Gaussian elimination stored as two triangular matrices, so `Ax = b`\n", - "can be solved cheaply many times for different `b`. **QR** (computed by\n", - "Gram-Schmidt, or more stably by other methods) produces *orthonormal* directions\n", - "— mutually perpendicular, each of length 1. It is used for orthogonal weight\n", - "initialization in neural networks and for stable least squares.\n", - "\n", - "Everything in that table except the last row works on **matrices** — two axes.\n", - "Real data often has more. That is what section 10 addresses." - ], - "id": "s01-26" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 3 — which factorization?\n", - "\n", - "> 🇪🇸 ¿Qué factorización usarías en cada caso?" - ], - "id": "s01-27" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO 6: For each situation, name the method from the table above.\n", - "# Write your answer as a comment — no code needed.\n", - "# (a) You must solve Ax = b for 500 different b, with the same square A.\n", - "# (b) You need mutually perpendicular, unit-length directions.\n", - "# (c) A has more rows than columns and there is no exact solution.\n", - "# (d) Your data has three axes and you want to compress all three." - ], - "id": "s01-28" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "# (a) LU — factor once, then each new b is two cheap triangular solves.\n", - "# (b) QR — Q's columns are orthonormal (Q.T @ Q == I).\n", - "# (c) Pseudoinverse — section 07. It is built from the SVD.\n", - "# (d) Tucker — section 10. PCA can only ever see two axes." - ], - "id": "s01-29" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Done with this section\n", - "\n", - "Next up: **02 · Thinking in N dimensions** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/02-thinking-in-n-dimensions.ipynb).\n", - "\n", - "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" - ], - "id": "s01-30" - } - ], - "metadata": { - "colab": { - "name": "01-what-a-tensor-is.ipynb", - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "s01-00" + }, + "source": [ + "# 01 · What a tensor is\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/01-what-a-tensor-is.ipynb)\n", + "\n", + "*Part I · demo · 20 min*\n", + "\n", + "> 🇪🇸 **Qué es un tensor** — Aprende a leer la estructura de un tensor y a seguir el significado de sus ejes mientras los fijas, reorganizas o contraes.\n", + "\n", + "Learn to read a tensor's structure and track what its axes mean as you fix, rearrange, or contract them.\n", + "\n", + "## What you will be able to do\n", + "\n", + "- Explain **order**, **axis/mode**, and **shape**, and distinguish tensor order from matrix/tensor rank.\n", + "- Read `.shape`, `.ndim`, and `.size` and explain what every axis means on real image data.\n", + "- Predict how **slices**, **fibers**, **unfolding**, and **contraction** change or preserve axes.\n", + "- Use `np.einsum` for a dot product and matrix multiplication and reason about the output shape.\n", + "\n", + "> 🇪🇸 **Al terminar podrás:**\n", + "> - Explicar **orden**, **eje/modo** y **forma**, y distinguir el orden tensorial del rango matricial/tensorial.\n", + "> - Leer `.shape`, `.ndim` y `.size` e interpretar qué significa cada eje en datos reales de imágenes.\n", + "> - Predecir cómo **cortes**, **fibras**, **unfolding** y **contracción** cambian o conservan los ejes.\n", + "> - Usar `np.einsum` para un producto escalar y una multiplicación matricial, razonando sobre la forma de salida." + ], + "id": "s01-00" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s01-01" + }, + "source": [ + "## Setup\n", + "\n", + "Run this first. It installs and imports everything this notebook needs, and nothing else.\n", + "\n", + "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." + ], + "id": "s01-01" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dfae2649" + }, + "source": [ + "## Why this matters\n", + "\n", + "For the data tensors used in this workshop, shape alone is not enough; we also need to know what each axis represents. Two arrays can both be order 3 while their axes describe completely different things. Tracking which axes are fixed, kept, rearranged, or summed will make every later tensor operation easier to reason about.\n", + "\n", + "**Learning cycle:** **Predict → Run → Explain.** Try to predict shapes and axis meanings before you execute each example.\n", + "\n", + "> 🇪🇸 **Por qué esto importa:** Para los tensores de datos usados en este taller, la forma por sí sola no es suficiente; también necesitamos saber qué representa cada eje. Dos arreglos pueden ser ambos de orden 3 y, aun así, sus ejes representar conceptos completamente diferentes. Seguir qué ejes se fijan, conservan, reorganizan o suman facilita el razonamiento sobre las operaciones tensoriales posteriores.\n", + ">\n", + "> **Ciclo de aprendizaje:** **Predice → Ejecuta → Explica.** Intenta anticipar las formas y el significado de los ejes antes de ejecutar cada ejemplo." + ], + "id": "dfae2649" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "s01-02" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from sklearn.datasets import load_digits\n", + "from skimage import data\n", + "\n", + "rng = np.random.default_rng(0)" + ], + "id": "s01-02" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s01-03" + }, + "source": [ + "## 1.1 Vocabulary\n", + "\n", + "Keep this table open for the whole workshop. Do not memorize it all at once—use it while you predict what each operation does.\n", + "\n", + "> 🇪🇸 Mantén esta tabla abierta durante el taller. No necesitas memorizarla de una vez: úsala mientras predices qué hace cada operación.\n", + "\n", + "| Term | Plain meaning | Spanish | Example |\n", + "|---|---|---|---|\n", + "| **Tensor** | An array of numbers with any number of axes | *tensor* | A colour image |\n", + "| **Axis** (pl. axes) | One direction along which data is arranged | *eje* | Height; width; colour |\n", + "| **Mode** | Another word for axis, used in tensor theory | *modo* | \"mode-0 unfolding\" |\n", + "| **Order** | How many axes a tensor has | *orden* | A matrix has order 2 |\n", + "| **Shape** | The size along each axis, as a tuple | *forma* | `(512, 512, 3)` |\n", + "| **Slice** | Fix one index, keep the rest | *corte* | One colour channel |\n", + "| **Fiber** | Fix every index except one | *fibra* | The 3 colour values of one pixel |\n", + "| **Unfolding** | Rearrange a tensor into a matrix while preserving all entries | *desplegado / unfolding* | Mode-2 image unfolding |\n", + "| **Contraction** | Multiply and sum over selected/shared indices | *contracción* | Dot product |\n", + "| **Decomposition** | Represent an array using simpler structured components | *descomposición* | SVD, Tucker, CP |" + ], + "id": "s01-03" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "803378e4" + }, + "source": [ + "### Order vs. Rank\n", + "\n", + "In this workshop, **order** refers to the number of axes/modes a tensor has. **Matrix rank** measures linear independence. **Tensor rank** has its own definitions and is not the same as tensor order.\n", + "\n", + "> 🇪🇸 **Orden vs. rango:** El **orden** se refiere al número de ejes/modos de un tensor. El **rango matricial** mide la independencia lineal. El **rango tensorial** tiene sus propias definiciones y no es lo mismo que el orden tensorial.\n", + "\n", + "**Quick check:** if an array has shape `(32, 8, 8)`, what is its order? Can you infer its matrix/tensor rank from the shape alone?\n", + "\n", + "> 🇪🇸 **Comprobación rápida:** si un arreglo tiene forma `(32, 8, 8)`, ¿cuál es su orden? ¿Puedes inferir su rango matricial/tensorial solo a partir de la forma?\n", + "\n", + "
\n", + "Check your reasoning · Comprueba tu razonamiento\n", + "\n", + "Its **order is 3** because it has three axes. Its matrix/tensor rank **cannot be inferred from the shape alone**.\n", + "\n", + "> 🇪🇸 Su **orden es 3** porque tiene tres ejes. Su rango matricial/tensorial **no se puede inferir solo a partir de la forma**.\n", + "\n", + "
" + ], + "id": "803378e4" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s01-04" + }, + "source": [ + "## 1.2 Shape in NumPy\n", + "\n", + "Every NumPy array has `.shape`, a tuple giving the size along each axis. The length of that tuple is `.ndim`, the number of axes, and `.size` is the total number of stored values.\n", + "\n", + "> 🇪🇸 **Forma en NumPy:** Todo arreglo de NumPy tiene `.shape`, una tupla con el tamaño de cada eje. La longitud de esa tupla es `.ndim`, el número de ejes, y `.size` es el número total de valores almacenados." + ], + "id": "s01-04" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "s01-05" + }, + "outputs": [], + "source": [ + "scalar = np.array(3.0) # book: a — order 0\n", + "vector = np.array([1., 2., 3.]) # book: x, x_i — order 1\n", + "matrix = np.array([[1., 2.], [3., 4.]]) # book: A, A_{i,j} — order 2\n", + "tensor = rng.standard_normal((2, 3, 4)) # book: A_{i,j,k} — order 3\n", + "\n", + "for name, arr in [(\"scalar\", scalar), (\"vector\", vector),\n", + " (\"matrix\", matrix), (\"tensor\", tensor)]:\n", + " print(f\"{name:8s} shape={str(arr.shape):12s} ndim={arr.ndim} size={arr.size}\")" + ], + "id": "s01-05" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "edb1356c" + }, + "source": [ + "These tiny synthetic arrays are deliberate: they isolate structure — order, shape, `ndim`, and `size` — without distracting domain details. We switch immediately afterward to real image data to reason about what each axis means.\n", + "\n", + "> 🇪🇸 **Por qué usamos datos sintéticos aquí:** Estos arreglos pequeños permiten aislar la estructura — orden, forma, `ndim` y `size` — sin detalles del dominio. Inmediatamente después usamos imágenes reales para razonar sobre el significado de cada eje." + ], + "id": "edb1356c" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s01-06" + }, + "source": [ + "A scalar has `shape=()`, an empty tuple—there are no axes to measure. `size` is the product of the dimensions in `shape`: for `(2, 3, 4)`, `size = 2 × 3 × 4 = 24`.\n", + "\n", + "Now move from deliberately simple synthetic arrays to **real data**, where axis meaning matters.\n", + "\n", + "> 🇪🇸 Un escalar tiene `shape=()`, una tupla vacía: no hay ejes que medir. `size` es el producto de las dimensiones de `shape`: para `(2, 3, 4)`, `size = 2 × 3 × 4 = 24`.\n", + ">\n", + "> Ahora pasamos de arreglos sintéticos simples a **datos reales**, donde el significado de cada eje sí importa." + ], + "id": "s01-06" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "571ad3c0" + }, + "source": [ + "### Predict before running\n", + "\n", + "What do you expect the shapes to be for `digits.images` and `photo`? What do their axes represent?\n", + "\n", + "> 🇪🇸 **Predice antes de ejecutar:** `digits.images` y `photo` son ambos tensores de orden 3. Antes de ejecutar, predice qué representa cada uno de sus tres ejes. ¿Significan lo mismo?" + ], + "id": "571ad3c0" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "s01-07" + }, + "outputs": [], + "source": [ + "digits = load_digits()\n", + "print(digits.images.shape) # (1797, 8, 8) — 1797 handwritten digits, 8x8 pixels\n", + "\n", + "photo = data.immunohistochemistry()\n", + "print(photo.shape) # (512, 512, 3) — height, width, colour" + ], + "id": "s01-07" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s01-08" + }, + "source": [ + "Both arrays are order 3, but their axes mean completely different things. `digits.images` counts **images** along axis 0; `photo` counts **colour channels** along axis 2. **Shape describes structure, not semantics.** You must know what every axis represents and keep track of that meaning.\n", + "\n", + "> 🇪🇸 Ambos arreglos son de orden 3, pero sus ejes significan cosas completamente diferentes. `digits.images` cuenta **imágenes** en el eje 0; `photo` cuenta **canales de color** en el eje 2. **La forma describe estructura, no semántica.** Debes saber qué representa cada eje y seguir ese significado durante las operaciones." + ], + "id": "s01-08" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s01-09" + }, + "source": [ + "## Exercise 1 — read the shapes\n", + "\n", + "**Predict first.** Build the arrays, then verify `.shape`, `.ndim`, and `.size`. For the real image tensors, explain what every axis counts.\n", + "\n", + "> 🇪🇸 **Predice primero.** Construye los arreglos y después verifica `.shape`, `.ndim` y `.size`. Para los tensores de imágenes reales, explica qué cuenta cada eje." + ], + "id": "s01-09" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "s01-10" + }, + "outputs": [], + "source": [ + "# TODO 1: Build a scalar, a vector, a matrix and an order-3 tensor, and print\n", + "# .shape, .ndim and .size for each. Which one has shape ()?\n", + "\n", + "# TODO 2: Take load_digits().images and data.astronaut(). Both are order 3.\n", + "# For each, write down in a comment what axis 0, 1 and 2 count." + ], + "id": "s01-10" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "id": "s01-11" + }, + "outputs": [], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "for arr in [np.array(3.0), np.zeros(3), np.zeros((2, 2)), np.zeros((2, 3, 4))]:\n", + " print(arr.shape, arr.ndim, arr.size)\n", + "# () 0 1\n", + "# (3,) 1 3\n", + "# (2, 2) 2 4\n", + "# (2, 3, 4) 3 24\n", + "\n", + "print(load_digits().images.shape) # (1797, 8, 8) axis 0 = which image\n", + " # axis 1 = row of pixels\n", + " # axis 2 = column of pixels\n", + "print(data.astronaut().shape) # (512, 512, 3) axis 0 = height\n", + " # axis 1 = width\n", + " # axis 2 = colour channel" + ], + "id": "s01-11" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "74c8da26" + }, + "source": [ + "
\n", + "Why this solution works · Por qué funciona esta solución\n", + "\n", + "1. A scalar has no axes, so its shape is `()`. A vector has one axis, a matrix two, and an order-3 tensor three. The `.ndim` attribute directly tells you the number of axes (order), and `.size` is the total number of elements.\n", + "2. `load_digits().images` represents a collection of 8x8 pixel images. So, axis 0 counts the images, axis 1 counts the rows of pixels, and axis 2 counts the columns of pixels. `data.astronaut()` is a color image. Axis 0 counts height, axis 1 counts width, and axis 2 counts the color channels (Red, Green, Blue).\n", + "\n", + "> 🇪🇸 **Por qué funciona esta solución:**\n", + ">\n", + "> 1. Un escalar no tiene ejes, por lo que su forma es `()`. Un vector tiene un eje, una matriz dos y un tensor de orden 3 tres. El atributo `.ndim` indica directamente el número de ejes (orden), y `.size` es el número total de elementos.\n", + "> 2. `load_digits().images` representa una colección de imágenes de 8x8 píxeles. Por lo tanto, el eje 0 cuenta las imágenes, el eje 1 cuenta las filas de píxeles y el eje 2 cuenta las columnas de píxeles. `data.astronaut()` es una imagen en color. El eje 0 cuenta la altura, el eje 1 la anchura y el eje 2 los canales de color (Rojo, Verde, Azul).\n", + "
" + ], + "id": "74c8da26" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s01-12" + }, + "source": [ + "## 1.3 The three operations that matter\n", + "\n", + "Slices/fibers, unfolding, and contraction all answer one question: **what happens to the axes?**\n", + "\n", + "> 🇪🇸 Cortes/fibras, unfolding y contracción responden a una misma pregunta: **¿qué ocurre con los ejes?**\n", + "\n", + "### Slices and fibers — fixing indices takes a tensor apart\n", + "\n", + "A **slice** fixes one index and keeps the others. A **fiber** fixes every index except one.\n", + "\n", + "> 🇪🇸 Un **corte** fija un índice y conserva los demás. Una **fibra** fija todos los índices excepto uno." + ], + "id": "s01-12" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "s01-13" + }, + "outputs": [], + "source": [ + "print(photo[:, :, 0].shape) # (512, 512) — a slice: one colour channel, still an image\n", + "print(photo[100, 200, :].shape) # (3,) — a fiber: the 3 colour values of one pixel" + ], + "id": "s01-13" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s01-14" + }, + "source": [ + "Same picture, same two indexing operations — see them together. Drag the\n", + "sliders and watch the marked pixel move on both panels at once, while its\n", + "fiber (three numbers, one per colour) redraws on the right.\n", + "\n", + "> 🇪🇸 Mueve los deslizadores: el mismo píxel se marca en el corte y en la\n", + "> imagen completa, y su fibra (tres números, uno por color) se redibuja." + ], + "id": "s01-14" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "s01-15" + }, + "outputs": [], + "source": [ + "# Colab renders ipywidgets through its own widget manager rather than the\n", + "# classic Jupyter one; this call is a no-op outside Colab, which is why it is\n", + "# guarded rather than assumed.\n", + "try:\n", + " from google.colab import output\n", + " output.enable_custom_widget_manager()\n", + "except ImportError:\n", + " pass\n", + "\n", + "import ipywidgets as widgets\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def show_slice_and_fiber(row, col):\n", + " plt.close('all')\n", + " fig, axes = plt.subplots(1, 3, figsize=(11, 3.2))\n", + "\n", + " axes[0].imshow(photo)\n", + " axes[0].scatter([col], [row], color='#C44E52', s=70, edgecolor='white')\n", + " axes[0].set_title('photo — the fiber, marked')\n", + " axes[0].axis('off')\n", + "\n", + " axes[1].imshow(photo[:, :, 0], cmap='gray')\n", + " axes[1].scatter([col], [row], color='#C44E52', s=70, edgecolor='white')\n", + " axes[1].set_title('photo[:, :, 0] — a slice')\n", + " axes[1].axis('off')\n", + "\n", + " fiber = photo[row, col, :]\n", + " axes[2].bar(['R', 'G', 'B'], fiber, color=['#C44E52', '#55A868', '#4C72B0'])\n", + " axes[2].set_title(f'photo[{row}, {col}, :] — the fiber')\n", + " axes[2].set_ylim(0, 255)\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "widgets.interact(show_slice_and_fiber,\n", + " row=widgets.IntSlider(min=0, max=511, step=1, value=100, description='row'),\n", + " col=widgets.IntSlider(min=0, max=511, step=1, value=200, description='col'));" + ], + "id": "s01-15" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s01-16" + }, + "source": [ + "### Unfolding — rearranging axes into a matrix\n", + "\n", + "Mode unfoldings are central to many tensor methods, including the Tucker/HOSVD route used later in this workshop. An unfolding moves one axis to the front and rearranges the remaining axes into a matrix without losing entries.\n", + "\n", + "> 🇪🇸 **Desplegado:** Los unfoldings por modo son fundamentales en muchos métodos tensoriales, incluida la ruta Tucker/HOSVD que usaremos más adelante. El desplegado reorganiza las entradas en una matriz sin perder información." + ], + "id": "s01-16" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "s01-17" + }, + "outputs": [], + "source": [ + "def unfold(T, axis):\n", + " \"\"\"Move `axis` to the front, flatten everything else into one long axis.\"\"\"\n", + " return np.moveaxis(T, axis, 0).reshape(T.shape[axis], -1)\n", + "\n", + "print(unfold(photo, 0).shape) # (512, 1536) — rows are the height axis\n", + "print(unfold(photo, 2).shape) # (3, 262144) — rows are the 3 colour channels" + ], + "id": "s01-17" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s01-18" + }, + "source": [ + "Unfolding **loses nothing**: it only rearranges entries. The mode-2 unfolding says “each colour channel is one row of 262,144 numbers”, which makes matrix tools such as SVD available without discarding information.\n", + "\n", + "You will use this same idea again in sections 07 and 10.\n", + "\n", + "> 🇪🇸 El unfolding **no pierde información**: solo reorganiza las entradas. En el unfolding de modo 2, cada canal de color se convierte en una fila de 262.144 números, lo que permite aplicar herramientas matriciales como SVD sin descartar datos.\n", + "\n", + "### Contraction — multiply along a shared axis and sum over it\n", + "\n", + "The dot product (eq. 2.8) and the matrix product (eq. 2.5) are both contractions. `np.einsum` makes the summed and retained indices explicit.\n", + "\n", + "> 🇪🇸 **Contracción:** el producto escalar y el producto matricial son contracciones. `np.einsum` permite ver explícitamente qué índices se suman y cuáles permanecen." + ], + "id": "s01-18" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "e127993d" + }, + "source": [ + "### Predict before running\n", + "\n", + "For `np.einsum('i,i->', a, b)`:\n", + "- which index is summed?\n", + "- which indices remain?\n", + "- why is the result a scalar?\n", + "\n", + "For `np.einsum('ik,kj->ij', A, B)`:\n", + "- which index is summed?\n", + "- which indices remain?\n", + "- what should the output shape be?\n", + "\n", + "> 🇪🇸 **Predice antes de ejecutar:**\n", + ">\n", + "> Para `np.einsum('i,i->', a, b)`:\n", + "> - ¿qué índice se suma?\n", + "> - ¿qué índices quedan?\n", + "> - ¿por qué el resultado es un escalar?\n", + ">\n", + "> Para `np.einsum('ik,kj->ij', A, B)`:\n", + "> - ¿qué índice se suma?\n", + "> - ¿qué índices quedan?\n", + "> - ¿cuál debería ser la forma de salida?" + ], + "id": "e127993d" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "s01-19" + }, + "outputs": [], + "source": [ + "a = np.array([1., 2., 3.]); b = np.array([4., 5., 6.])\n", + "print(np.einsum('i,i->', a, b)) # dot product, sum over i (eq 2.8)\n", + "\n", + "A = np.array([[1., 2.], [3., 4.]]); B = np.array([[5., 6.], [7., 8.]])\n", + "print(np.einsum('ik,kj->ij', A, B)) # matrix product, sum over k (eq 2.5)" + ], + "id": "s01-19" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s01-20" + }, + "source": [ + "**The rule, in one sentence:** an index that appears in the inputs but **not** after the arrow is summed over; an index that appears after the arrow is kept.\n", + "\n", + "This rule is the core idea that section 06 develops.\n", + "\n", + "> 🇪🇸 **La regla, en una frase:** un índice que aparece en las entradas pero **no** después de la flecha se suma; un índice que aparece después de la flecha se conserva.\n", + ">\n", + "> Esta regla es la idea central que desarrolla la sección 06." + ], + "id": "s01-20" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s01-21" + }, + "source": [ + "## Exercise 2 — take a tensor apart and put it back\n", + "\n", + "Before coding, say out loud which axes you expect to **fix**, **keep**, **rearrange**, or **sum**. Then verify your reasoning with NumPy.\n", + "\n", + "> 🇪🇸 Antes de programar, explica qué ejes esperas **fijar**, **conservar**, **reorganizar** o **sumar**. Después verifica tu razonamiento con NumPy." + ], + "id": "s01-21" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "s01-22" + }, + "outputs": [], + "source": [ + "# TODO 3: From `photo`, extract (a) the green channel as a (512, 512) slice and\n", + "# (b) the colour fiber at pixel (10, 20). Which is a slice, which a fiber?\n", + "\n", + "# TODO 4: Unfold `photo` along all three axes and print the three shapes.\n", + "# Confirm that each unfolding has exactly photo.size entries —\n", + "# unfolding rearranges, it never loses anything.\n", + "\n", + "# TODO 5: Write the dot product of `a` and `b` as einsum, and check it against\n", + "# np.dot. Then write the matrix product of A and B, and check against @." + ], + "id": "s01-22" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "id": "s01-23" + }, + "outputs": [], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "green = photo[:, :, 1] # slice — one index fixed, the rest kept\n", + "fiber = photo[10, 20, :] # fiber — every index fixed except one\n", + "print(green.shape, fiber.shape) # (512, 512) (3,)\n", + "\n", + "for ax in range(3):\n", + " M = unfold(photo, ax)\n", + " print(ax, M.shape, M.size == photo.size) # True every time\n", + "\n", + "print(np.einsum('i,i->', a, b), np.dot(a, b)) # 32.0 32.0\n", + "print(np.allclose(np.einsum('ik,kj->ij', A, B), A @ B)) # True" + ], + "id": "s01-23" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "130e6acc" + }, + "source": [ + "
\n", + "Why this solution works · Por qué funciona esta solución\n", + "\n", + "1. `photo[:, :, 1]` selects the green channel by fixing the last axis (color) to index 1. This is a **slice** because one index is fixed and the rest are kept. `photo[10, 20, :]` selects the color values for the pixel at row 10, column 20. This is a **fiber** because all indices except one are fixed.\n", + "2. `unfold(photo, axis)` rearranges the tensor. For `axis=0`, it creates a matrix where rows correspond to the height dimension. For `axis=1`, rows correspond to the width dimension. For `axis=2`, rows correspond to the color dimension. In all cases, the total number of elements (`.size`) remains the same, demonstrating that unfolding is merely a rearrangement.\n", + "3. `np.einsum('i,i->', a, b)` performs the dot product by summing over the shared index `i`. `np.einsum('ik,kj->ij', A, B)` performs matrix multiplication by summing over the shared index `k` and keeping `i` and `j`. `np.allclose` confirms the results are numerically equivalent to `np.dot` and `@` operator respectively.\n", + "\n", + "> 🇪🇸 **Por qué funciona esta solución:**\n", + ">\n", + "> 1. `photo[:, :, 1]` selecciona el canal verde fijando el último eje (color) al índice 1. Esto es un **corte** porque un índice se fija y el resto se mantienen. `photo[10, 20, :]` selecciona los valores de color para el píxel en la fila 10, columna 20. Esto es una **fibra** porque todos los índices excepto uno están fijos.\n", + "> 2. `unfold(photo, axis)` reorganiza el tensor. Para `axis=0`, crea una matriz donde las filas corresponden a la dimensión de altura. Para `axis=1`, las filas corresponden a la dimensión de anchura. Para `axis=2`, las filas corresponden a la dimensión de color. En todos los casos, el número total de elementos (`.size`) permanece igual, demostrando que el desplegado es solo una reorganización.\n", + "> 3. `np.einsum('i,i->', a, b)` realiza el producto escalar sumando sobre el índice compartido `i`. `np.einsum('ik,kj->ij', A, B)` realiza la multiplicación de matrices sumando sobre el índice compartido `k` y manteniendo `i` y `j`. `np.allclose` confirma que los resultados son numéricamente equivalentes a `np.dot` y al operador `@` respectivamente.\n", + "
" + ], + "id": "130e6acc" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ac9c4637" + }, + "source": [ + "## Exercise 3 — Axis Reasoning Challenge\n", + "\n", + "Let `D = load_digits().images`, with shape `(1797, 8, 8)` = **images × height × width**.\n", + "\n", + "**Predict before running:** for each expression below, write the expected output shape and explain which axes are fixed, kept, rearranged, or contracted:\n", + "\n", + "1. `D[0]`\n", + "2. `D[:, 3, 4]`\n", + "3. `unfold(D, 0)`\n", + "4. `np.einsum('nhw->n', D)`\n", + "\n", + "Then run your code and compare your prediction with the result.\n", + "\n", + "> 🇪🇸 **Reto de razonamiento sobre ejes:** Sea `D = load_digits().images`, con forma `(1797, 8, 8)` = **imágenes × alto × ancho**.\n", + ">\n", + "> **Predice antes de ejecutar:** para cada expresión, escribe la forma de salida esperada y explica qué ejes se fijan, conservan, reorganizan o contraen. Después ejecuta el código y compara tu predicción con el resultado." + ], + "id": "ac9c4637" + }, + { + "cell_type": "code", + "metadata": { + "id": "ecdaf14a" + }, + "source": [ + "# TODO 6: Let D = load_digits().images.\n", + "# Before running each operation, predict its output shape.\n", + "#\n", + "# 1. D[0]\n", + "# 2. D[:, 3, 4]\n", + "# 3. unfold(D, 0)\n", + "# 4. np.einsum('nhw->n', D)\n", + "#\n", + "# For each operation, explain in a comment which axes were\n", + "# fixed, kept, rearranged, or summed/contracted." + ], + "id": "ecdaf14a", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "id": "033d9ced" + }, + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "D = load_digits().images\n", + "print(\"1.\", D[0].shape)\n", + "print(\"2.\", D[:, 3, 4].shape)\n", + "print(\"3.\", unfold(D, 0).shape)\n", + "print(\"4.\", np.einsum('nhw->n', D).shape)" + ], + "id": "033d9ced", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "425b7175" + }, + "source": [ + "
\n", + "Why this solution works · Por qué funciona esta solución\n", + "\n", + "The operations change the shape as follows:\n", + "\n", + "1. `D[0]` takes the first image. The `n` axis is fixed, `h` and `w` are kept. Resulting shape: `(8, 8)`. This is a **slice**.\n", + "2. `D[:, 3, 4]` takes the pixels at row 3, column 4 from all images. The `h` and `w` axes are fixed, `n` is kept. Resulting shape: `(1797,)`. This is a **fiber**.\n", + "3. `unfold(D, 0)` rearranges the tensor. The `n` axis is kept as the first dimension, and `h` and `w` are flattened. Resulting shape: `(1797, 64)`. This is an **unfolding**.\n", + "4. `np.einsum('nhw->n', D)` sums over `h` and `w` axes. The `n` axis is kept. Resulting shape: `(1797,)`. This is a **contraction**.\n", + "\n", + "> 🇪🇸 **Por qué funciona esta solución:** Las operaciones cambian la forma de la siguiente manera:\n", + ">\n", + "> 1. `D[0]` toma la primera imagen. El eje `n` se fija, `h` y `w` se mantienen. Forma resultante: `(8, 8)`. Esto es un **corte**.\n", + "> 2. `D[:, 3, 4]` toma los píxeles en la fila 3, columna 4 de todas las imágenes. Los ejes `h` y `w` se fijan, `n` se mantiene. Forma resultante: `(1797,)`. Esto es una **fibra**.\n", + "> 3. `unfold(D, 0)` reorganiza el tensor. El eje `n` se mantiene como la primera dimensión, y `h` y `w` se aplanan. Forma resultante: `(1797, 64)`. Esto es un **desplegado**.\n", + "> 4. `np.einsum('nhw->n', D)` suma sobre los ejes `h` y `w`. El eje `n` se mantiene. Forma resultante: `(1797,)`. Esto es una **contracción**.\n", + "
" + ], + "id": "425b7175" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s01-24" + }, + "source": [ + "## 1.4 The map of factorizations (Preview)\n", + "\n", + "This is only a preview—**do not memorize these methods yet**. Later sections move from familiar matrix factorizations such as SVD to tensor decompositions such as Tucker.\n", + "\n", + "- **Tucker** represents a tensor using a smaller core tensor and factor matrices.\n", + "- **CP** represents a tensor as a sum of rank-one components.\n", + "\n", + "These methods build on the tensor vocabulary developed here; the Tucker/HOSVD route used later explicitly uses mode unfoldings.\n", + "\n", + "> 🇪🇸 **Vista previa:** Esto es solo un adelanto—**todavía no necesitas memorizar estos métodos**. Más adelante pasaremos de factorizaciones matriciales como SVD a descomposiciones tensoriales como Tucker.\n", + ">\n", + "> - **Tucker** representa un tensor mediante un tensor núcleo más pequeño y matrices de factores.\n", + "> - **CP** representa un tensor como una suma de componentes de rango uno.\n", + ">\n", + "> La ruta Tucker/HOSVD que se usa más adelante emplea explícitamente unfoldings por modo." + ], + "id": "s01-24" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "a1afc435" + }, + "source": [ + "## What just happened\n", + "\n", + "You should now be able to reason about a tensor by following its axes:\n", + "\n", + "- `shape`, `ndim`, and `size` describe **structure**;\n", + "- axis labels describe **meaning**;\n", + "- slices/fibers **fix indices**;\n", + "- unfolding **rearranges entries without losing them**;\n", + "- contraction **sums selected axes**.\n", + "\n", + "**One final self-check:** if you cannot explain what each output axis represents, go back one step and trace the indices again.\n", + "\n", + "> 🇪🇸 **Qué acaba de suceder:** Ahora deberías poder razonar sobre un tensor siguiendo sus ejes:\n", + ">\n", + "> - `shape`, `ndim` y `size` describen la **estructura**;\n", + "> - las etiquetas de los ejes describen el **significado**;\n", + "> - los cortes/fibras **fijan índices**;\n", + "> - el unfolding **reorganiza entradas sin perderlas**;\n", + "> - la contracción **suma ejes seleccionados**.\n", + ">\n", + "> **Autoevaluación final:** si no puedes explicar qué representa cada eje de salida, vuelve un paso atrás y sigue de nuevo los índices." + ], + "id": "a1afc435" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s01-30" + }, + "source": [ + "---\n", + "\n", + "## Done with this section\n", + "\n", + "Next up: **02 · Thinking in N dimensions** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/02-thinking-in-n-dimensions.ipynb).\n", + "\n", + "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" + ], + "id": "s01-30" + } + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python" + } }, - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file From dad440316f5c687ac781a39646219c3e5fbc7c71 Mon Sep 17 00:00:00 2001 From: Sebas Laverde Chunza Date: Fri, 28 Aug 2026 10:41:48 -0500 Subject: [PATCH 04/29] Improve notebook 01 pedagogy for issue #44 --- _variables.yml | 22 +- docs/notebooks/01-what-a-tensor-is.ipynb | 394 ++++-- notebooks/01-what-a-tensor-is.ipynb | 1504 ++++++++++------------ scripts/content.py | 1 - 4 files changed, 993 insertions(+), 928 deletions(-) diff --git a/_variables.yml b/_variables.yml index dd4bf0f..d64c5e6 100644 --- a/_variables.yml +++ b/_variables.yml @@ -179,20 +179,18 @@ sections: format_es: "demostración" title_en: "What a tensor is" title_es: "Qué es un tensor" - summary_en: "The vocabulary, shape in NumPy, and the three operations that matter." - summary_es: "El vocabulario, la forma en NumPy y las tres operaciones que importan." + summary_en: "Learn to read a tensor's structure and track what its axes mean as you fix, rearrange, or contract them." + summary_es: "Aprender a leer la estructura de un tensor y seguir el significado de sus ejes al fijarlos, reorganizarlos o contraerlos." objectives_en: - - "Use the vocabulary: order, axis, mode, shape, slice, fiber, unfolding, contraction, decomposition." - - "Read `.shape`, `.ndim` and `.size` off any array and say what each axis means." - - "Take slices and fibers, and unfold a tensor into a matrix without losing anything." - - "Write a dot product and a matrix product as `einsum` contractions." - - "Place LU, QR, eigendecomposition, SVD, the pseudoinverse, Cholesky and Tucker in one map." + - "Explain order, axis/mode and shape, and distinguish tensor order from matrix/tensor rank." + - "Read `.shape`, `.ndim` and `.size` and explain what every axis means on real image data." + - "Predict how slices, fibers, unfolding and contraction change or preserve axes." + - "Use `np.einsum` for a dot product and matrix multiplication and reason about the output shape." objectives_es: - - "Usar el vocabulario: orden, eje, modo, forma, slice, fibra, unfolding, contracción y descomposición." - - "Leer `.shape`, `.ndim` y `.size` de cualquier arreglo y explicar qué significa cada eje." - - "Tomar slices y fibras, y desplegar un tensor en una matriz sin perder información." - - "Escribir un producto punto y un producto matricial como contracciones con `einsum`." - - "Ubicar LU, QR, descomposición en valores propios, SVD, pseudoinversa, Cholesky y Tucker en un mismo mapa." + - "Explicar orden, eje/modo y forma, y distinguir el orden tensorial del rango matricial/tensorial." + - "Leer `.shape`, `.ndim` y `.size` e interpretar qué significa cada eje en datos reales de imágenes." + - "Predecir cómo cortes, fibras, unfolding y contracción cambian o conservan los ejes." + - "Usar `np.einsum` para un producto escalar y una multiplicación matricial, razonando sobre la forma de salida." s02: n: "02" slug: "thinking-in-n-dimensions" diff --git a/docs/notebooks/01-what-a-tensor-is.ipynb b/docs/notebooks/01-what-a-tensor-is.ipynb index ec6230a..7cc0127 100644 --- a/docs/notebooks/01-what-a-tensor-is.ipynb +++ b/docs/notebooks/01-what-a-tensor-is.ipynb @@ -10,17 +10,16 @@ "\n", "*Part I · demo · 20 min*\n", "\n", - "> 🇪🇸 **Qué es un tensor** — El vocabulario, la forma en NumPy y las tres operaciones que importan.\n", + "> 🇪🇸 **Qué es un tensor** — Aprender a leer la estructura de un tensor y seguir el significado de sus ejes al fijarlos, reorganizarlos o contraerlos.\n", "\n", - "The vocabulary, shape in NumPy, and the three operations that matter.\n", + "Learn to read a tensor's structure and track what its axes mean as you fix, rearrange, or contract them.\n", "\n", "## What you will be able to do\n", "\n", - "- Use the vocabulary: order, axis, mode, shape, slice, fiber, unfolding, contraction, decomposition.\n", - "- Read `.shape`, `.ndim` and `.size` off any array and say what each axis means.\n", - "- Take slices and fibers, and unfold a tensor into a matrix without losing anything.\n", - "- Write a dot product and a matrix product as `einsum` contractions.\n", - "- Place LU, QR, eigendecomposition, SVD, the pseudoinverse, Cholesky and Tucker in one map." + "- Explain order, axis/mode and shape, and distinguish tensor order from matrix/tensor rank.\n", + "- Read `.shape`, `.ndim` and `.size` and explain what every axis means on real image data.\n", + "- Predict how slices, fibers, unfolding and contraction change or preserve axes.\n", + "- Use `np.einsum` for a dot product and matrix multiplication and reason about the output shape." ], "id": "s01-00" }, @@ -45,7 +44,6 @@ "import numpy as np\n", "from sklearn.datasets import load_digits\n", "from skimage import data\n", - "from scipy.linalg import lu\n", "\n", "rng = np.random.default_rng(0)" ], @@ -57,9 +55,9 @@ "source": [ "## 1.1 Vocabulary\n", "\n", - "> 🇪🇸 El vocabulario. Casi todos los términos son casi idénticos en español.\n", + "Keep this table open for the whole workshop. Do not memorize it all at once—use it while you predict what each operation does.\n", "\n", - "Keep this table open for the whole workshop.\n", + "> 🇪🇸 Mantén esta tabla abierta durante el taller. No necesitas memorizarla de una vez: úsala mientras predices qué hace cada operación.\n", "\n", "| Term | Plain meaning | Spanish | Example |\n", "|---|---|---|---|\n", @@ -70,27 +68,46 @@ "| **Shape** | The size along each axis, as a tuple | *forma* | `(512, 512, 3)` |\n", "| **Slice** | Fix one index, keep the rest | *corte* | One colour channel |\n", "| **Fiber** | Fix every index except one | *fibra* | The 3 colour values of one pixel |\n", - "| **Unfolding** | Rearranging a tensor into a matrix | *desplegado* | Needed for decompositions |\n", - "| **Contraction** | Multiply and sum over a shared axis | *contracción* | The dot product |\n", - "| **Decomposition** | Writing one tensor as a product of simpler ones | *descomposición* | SVD, Tucker |\n", - "\n", - "⚠️ **Warning about the word \"rank\".** In Chapter 2, *rank* means the number of\n", - "independent columns of a matrix. In tensor theory, *rank* often means the number\n", - "of axes. To avoid confusion, this workshop says **order** for the number of\n", - "axes, and **rank** only in Chapter 2's sense." + "| **Unfolding** | Rearrange a tensor into a matrix while preserving all entries | *desplegado / unfolding* | Mode-2 image unfolding |\n", + "| **Contraction** | Multiply and sum over selected/shared indices | *contracción* | Dot product |\n", + "| **Decomposition** | Represent an array using simpler structured components | *descomposición* | SVD, Tucker, CP |" ], "id": "s01-03" }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Order vs. Rank\n", + "\n", + "In this workshop, **order** refers to the number of axes/modes a tensor has. **Matrix rank** measures linear independence. **Tensor rank** has its own definitions and is not the same as tensor order.\n", + "\n", + "> 🇪🇸 **Orden vs. rango:** El **orden** se refiere al número de ejes/modos de un tensor. El **rango matricial** mide la independencia lineal. El **rango tensorial** tiene sus propias definiciones y no es lo mismo que el orden tensorial.\n", + "\n", + "**Quick check:** if an array has shape `(32, 8, 8)`, what is its order? Can you infer its matrix/tensor rank from the shape alone?\n", + "\n", + "> 🇪🇸 **Comprobación rápida:** si un arreglo tiene forma `(32, 8, 8)`, ¿cuál es su orden? ¿Puedes inferir su rango matricial/tensorial solo a partir de la forma?\n", + "\n", + "
\n", + "Check your reasoning · Comprueba tu razonamiento\n", + "\n", + "Its **order is 3** because it has three axes. Its matrix/tensor rank **cannot be inferred from the shape alone**.\n", + "\n", + "> 🇪🇸 Su **orden es 3** porque tiene tres ejes. Su rango matricial/tensorial **no se puede inferir solo a partir de la forma**.\n", + "\n", + "
" + ], + "id": "803378e4" + }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1.2 Shape in NumPy\n", "\n", - "> 🇪🇸 La forma en NumPy: `.shape`, `.ndim` y `.size`.\n", + "Every NumPy array has `.shape`, a tuple giving the size along each axis. The length of that tuple is `.ndim`, the number of axes, and `.size` is the total number of stored values.\n", "\n", - "Every NumPy array has `.shape`, a tuple giving the size along each axis. The\n", - "length of that tuple is `.ndim`, the number of axes." + "> 🇪🇸 **Forma en NumPy:** Todo arreglo de NumPy tiene `.shape`, una tupla con el tamaño de cada eje. La longitud de esa tupla es `.ndim`, el número de ejes, y `.size` es el número total de valores almacenados." ], "id": "s01-04" }, @@ -115,13 +132,38 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "A scalar has `shape=()`, an empty tuple — there are no axes to measure. And\n", - "`size` is always the product of the numbers in `shape`: 2 × 3 × 4 = 24.\n", + "These tiny synthetic arrays are deliberate: they isolate structure — order, shape, `ndim`, and `size` — without distracting domain details. We switch immediately afterward to real image data to reason about what each axis means.\n", "\n", - "Now with real data." + "> 🇪🇸 **Por qué usamos datos sintéticos aquí:** Estos arreglos pequeños permiten aislar la estructura — orden, forma, `ndim` y `size` — sin detalles del dominio. Inmediatamente después usamos imágenes reales para razonar sobre el significado de cada eje." + ], + "id": "edb1356c" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A scalar has `shape=()`, an empty tuple—there are no axes to measure. `size` is the product of the dimensions in `shape`: for `(2, 3, 4)`, `size = 2 × 3 × 4 = 24`.\n", + "\n", + "Now move from deliberately simple synthetic arrays to **real data**, where axis meaning matters.\n", + "\n", + "> 🇪🇸 Un escalar tiene `shape=()`, una tupla vacía: no hay ejes que medir. `size` es el producto de las dimensiones de `shape`: para `(2, 3, 4)`, `size = 2 × 3 × 4 = 24`.\n", + ">\n", + "> Ahora pasamos de arreglos sintéticos simples a **datos reales**, donde el significado de cada eje sí importa." ], "id": "s01-06" }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Predict before running\n", + "\n", + "What do you expect the shapes to be for `digits.images` and `photo`? What do their axes represent?\n", + "\n", + "> 🇪🇸 **Predice antes de ejecutar:** `digits.images` y `photo` son ambos tensores de orden 3. Antes de ejecutar, predice qué representa cada uno de sus tres ejes. ¿Significan lo mismo?" + ], + "id": "571ad3c0" + }, { "cell_type": "code", "execution_count": null, @@ -140,10 +182,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Both are order 3, but their axes mean completely different things.\n", - "`digits.images` counts *images* along axis 0; `photo` counts *colours* along\n", - "axis 2. **The shape alone never tells you what the axes mean.** You must know,\n", - "and you must keep track." + "Both arrays are order 3, but their axes mean completely different things. `digits.images` counts **images** along axis 0; `photo` counts **colour channels** along axis 2. **Shape describes structure, not semantics.** You must know what every axis represents and keep track of that meaning.\n", + "\n", + "> 🇪🇸 Ambos arreglos son de orden 3, pero sus ejes significan cosas completamente diferentes. `digits.images` cuenta **imágenes** en el eje 0; `photo` cuenta **canales de color** en el eje 2. **La forma describe estructura, no semántica.** Debes saber qué representa cada eje y seguir ese significado durante las operaciones." ], "id": "s01-08" }, @@ -153,7 +194,9 @@ "source": [ "## Exercise 1 — read the shapes\n", "\n", - "> 🇪🇸 Lee las formas y di qué significa cada eje." + "**Predict first.** Build the arrays, then verify `.shape`, `.ndim`, and `.size`. For the real image tensors, explain what every axis counts.\n", + "\n", + "> 🇪🇸 **Predice primero.** Construye los arreglos y después verifica `.shape`, `.ndim` y `.size`. Para los tensores de imágenes reales, explica qué cuenta cada eje." ], "id": "s01-09" }, @@ -175,14 +218,14 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ @@ -203,15 +246,39 @@ ], "id": "s01-11" }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Why this solution works · Por qué funciona esta solución\n", + "\n", + "1. A scalar has no axes, so its shape is `()`. A vector has one axis, a matrix two, and an order-3 tensor three. The `.ndim` attribute directly tells you the number of axes (order), and `.size` is the total number of elements.\n", + "2. `load_digits().images` represents a collection of 8x8 pixel images. So, axis 0 counts the images, axis 1 counts the rows of pixels, and axis 2 counts the columns of pixels. `data.astronaut()` is a color image. Axis 0 counts height, axis 1 counts width, and axis 2 counts the color channels (Red, Green, Blue).\n", + "\n", + "> 🇪🇸 **Por qué funciona esta solución:**\n", + ">\n", + "> 1. Un escalar no tiene ejes, por lo que su forma es `()`. Un vector tiene un eje, una matriz dos y un tensor de orden 3 tres. El atributo `.ndim` indica directamente el número de ejes (orden), y `.size` es el número total de elementos.\n", + "> 2. `load_digits().images` representa una colección de imágenes de 8x8 píxeles. Por lo tanto, el eje 0 cuenta las imágenes, el eje 1 cuenta las filas de píxeles y el eje 2 cuenta las columnas de píxeles. `data.astronaut()` es una imagen en color. El eje 0 cuenta la altura, el eje 1 la anchura y el eje 2 los canales de color (Rojo, Verde, Azul).\n", + "
" + ], + "id": "74c8da26" + }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1.3 The three operations that matter\n", "\n", - "> 🇪🇸 Cortes y fibras, desplegado y contracción — las tres operaciones clave.\n", + "Slices/fibers, unfolding, and contraction all answer one question: **what happens to the axes?**\n", + "\n", + "> 🇪🇸 Cortes/fibras, unfolding y contracción responden a una misma pregunta: **¿qué ocurre con los ejes?**\n", + "\n", + "### Slices and fibers — fixing indices takes a tensor apart\n", + "\n", + "A **slice** fixes one index and keeps the others. A **fiber** fixes every index except one.\n", "\n", - "### Slices and fibers — fixing indices takes a tensor apart" + "> 🇪🇸 Un **corte** fija un índice y conserva los demás. Una **fibra** fija todos los índices excepto uno." ], "id": "s01-12" }, @@ -289,11 +356,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Unfolding — every decomposition begins here\n", + "### Unfolding — rearranging axes into a matrix\n", "\n", - "Every tensor decomposition begins by turning the tensor into a matrix, one axis\n", - "at a time. Move axis *k* to the front, then flatten everything else into one\n", - "long axis." + "Mode unfoldings are central to many tensor methods, including the Tucker/HOSVD route used later in this workshop. An unfolding moves one axis to the front and rearranges the remaining axes into a matrix without losing entries.\n", + "\n", + "> 🇪🇸 **Desplegado:** Los unfoldings por modo son fundamentales en muchos métodos tensoriales, incluida la ruta Tucker/HOSVD que usaremos más adelante. El desplegado reorganiza las entradas en una matriz sin perder información." ], "id": "s01-16" }, @@ -316,19 +383,50 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Unfolding **loses nothing**. It only rearranges. The mode-2 unfolding says\n", - "\"each colour channel is one row of 262,144 numbers\" — and now every matrix tool\n", - "you know, including SVD, can be applied to it.\n", + "Unfolding **loses nothing**: it only rearranges entries. The mode-2 unfolding says “each colour channel is one row of 262,144 numbers”, which makes matrix tools such as SVD available without discarding information.\n", + "\n", + "You will use this same idea again in sections 07 and 10.\n", "\n", - "You will use this exact function again in sections 07 and 10.\n", + "> 🇪🇸 El unfolding **no pierde información**: solo reorganiza las entradas. En el unfolding de modo 2, cada canal de color se convierte en una fila de 262.144 números, lo que permite aplicar herramientas matriciales como SVD sin descartar datos.\n", "\n", "### Contraction — multiply along a shared axis and sum over it\n", "\n", - "The dot product (eq. 2.8) and the matrix product (eq. 2.5) are both\n", - "contractions. `np.einsum` writes them directly." + "The dot product (eq. 2.8) and the matrix product (eq. 2.5) are both contractions. `np.einsum` makes the summed and retained indices explicit.\n", + "\n", + "> 🇪🇸 **Contracción:** el producto escalar y el producto matricial son contracciones. `np.einsum` permite ver explícitamente qué índices se suman y cuáles permanecen." ], "id": "s01-18" }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Predict before running\n", + "\n", + "For `np.einsum('i,i->', a, b)`:\n", + "- which index is summed?\n", + "- which indices remain?\n", + "- why is the result a scalar?\n", + "\n", + "For `np.einsum('ik,kj->ij', A, B)`:\n", + "- which index is summed?\n", + "- which indices remain?\n", + "- what should the output shape be?\n", + "\n", + "> 🇪🇸 **Predice antes de ejecutar:**\n", + ">\n", + "> Para `np.einsum('i,i->', a, b)`:\n", + "> - ¿qué índice se suma?\n", + "> - ¿qué índices quedan?\n", + "> - ¿por qué el resultado es un escalar?\n", + ">\n", + "> Para `np.einsum('ik,kj->ij', A, B)`:\n", + "> - ¿qué índice se suma?\n", + "> - ¿qué índices quedan?\n", + "> - ¿cuál debería ser la forma de salida?" + ], + "id": "e127993d" + }, { "cell_type": "code", "execution_count": null, @@ -347,10 +445,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**The rule, in one sentence:** an index that appears in the inputs but **not**\n", - "after the arrow is summed over; an index that appears after the arrow is kept.\n", + "**The rule, in one sentence:** an index that appears in the inputs but **not** after the arrow is summed over; an index that appears after the arrow is kept.\n", "\n", - "That one sentence is the whole of section 06." + "This rule is the core idea that section 06 develops.\n", + "\n", + "> 🇪🇸 **La regla, en una frase:** un índice que aparece en las entradas pero **no** después de la flecha se suma; un índice que aparece después de la flecha se conserva.\n", + ">\n", + "> Esta regla es la idea central que desarrolla la sección 06." ], "id": "s01-20" }, @@ -360,7 +461,9 @@ "source": [ "## Exercise 2 — take a tensor apart and put it back\n", "\n", - "> 🇪🇸 Desmonta un tensor y vuelve a montarlo." + "Before coding, say out loud which axes you expect to **fix**, **keep**, **rearrange**, or **sum**. Then verify your reasoning with NumPy.\n", + "\n", + "> 🇪🇸 Antes de programar, explica qué ejes esperas **fijar**, **conservar**, **reorganizar** o **sumar**. Después verifica tu razonamiento con NumPy." ], "id": "s01-21" }, @@ -386,14 +489,14 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ @@ -415,103 +518,161 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 1.4 The map of factorizations\n", - "\n", - "> 🇪🇸 El mapa de las factorizaciones: qué método sirve para qué.\n", - "\n", - "A **factorization** writes one object as a product of simpler objects. You met\n", - "two in Chapter 2. Here is the whole family we will use today.\n", - "\n", - "| Method | Works on | What it gives you | Where today |\n", - "|---|---|---|---|\n", - "| **LU** | Square matrix | Gaussian elimination, saved for reuse | Below |\n", - "| **QR / Gram-Schmidt** | Any matrix | Perpendicular, unit-length directions | Below |\n", - "| **Eigendecomposition** | Square matrix | Directions that only get scaled (§2.7) | Section 08 |\n", - "| **SVD** | Any matrix | The most general matrix factorization (§2.8) | Sections 07 and 10 |\n", - "| **Pseudoinverse** | Any matrix | \"Inverse\" when no true inverse exists (§2.9) | Section 07 |\n", - "| **Cholesky** | Symmetric positive-definite matrix | A \"square root\" of a covariance matrix, for *building* correlated data | Section 11, take-home D |\n", - "| **Tucker / CP** | **Tensor, any order** | PCA generalized to every axis | Section 10 |" + "
\n", + "Why this solution works · Por qué funciona esta solución\n", + "\n", + "1. `photo[:, :, 1]` selects the green channel by fixing the last axis (color) to index 1. This is a **slice** because one index is fixed and the rest are kept. `photo[10, 20, :]` selects the color values for the pixel at row 10, column 20. This is a **fiber** because all indices except one are fixed.\n", + "2. `unfold(photo, axis)` rearranges the tensor. For `axis=0`, it creates a matrix where rows correspond to the height dimension. For `axis=1`, rows correspond to the width dimension. For `axis=2`, rows correspond to the color dimension. In all cases, the total number of elements (`.size`) remains the same, demonstrating that unfolding is merely a rearrangement.\n", + "3. `np.einsum('i,i->', a, b)` performs the dot product by summing over the shared index `i`. `np.einsum('ik,kj->ij', A, B)` performs matrix multiplication by summing over the shared index `k` and keeping `i` and `j`. `np.allclose` confirms the results are numerically equivalent to `np.dot` and `@` operator respectively.\n", + "\n", + "> 🇪🇸 **Por qué funciona esta solución:**\n", + ">\n", + "> 1. `photo[:, :, 1]` selecciona el canal verde fijando el último eje (color) al índice 1. Esto es un **corte** porque un índice se fija y el resto se mantienen. `photo[10, 20, :]` selecciona los valores de color para el píxel en la fila 10, columna 20. Esto es una **fibra** porque todos los índices excepto uno están fijos.\n", + "> 2. `unfold(photo, axis)` reorganiza el tensor. Para `axis=0`, crea una matriz donde las filas corresponden a la dimensión de altura. Para `axis=1`, las filas corresponden a la dimensión de anchura. Para `axis=2`, las filas corresponden a la dimensión de color. En todos los casos, el número total de elementos (`.size`) permanece igual, demostrando que el desplegado es solo una reorganización.\n", + "> 3. `np.einsum('i,i->', a, b)` realiza el producto escalar sumando sobre el índice compartido `i`. `np.einsum('ik,kj->ij', A, B)` realiza la multiplicación de matrices sumando sobre el índice compartido `k` y manteniendo `i` y `j`. `np.allclose` confirma que los resultados son numéricamente equivalentes a `np.dot` y al operador `@` respectivamente.\n", + "
" ], - "id": "s01-24" + "id": "130e6acc" }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "A3 = np.array([[4., 3., 2.], [2., 1., 1.], [6., 3., 5.]])\n", + "## Exercise 3 — Axis Reasoning Challenge\n", "\n", - "P, L, U = lu(A3) # LU: A = P L U\n", - "print(np.allclose(P @ L @ U, A3)) # True\n", + "Let `D = load_digits().images`, with shape `(1797, 8, 8)` = **images × height × width**.\n", "\n", - "Q, R = np.linalg.qr(A3) # QR: orthonormal directions\n", - "print(np.allclose(Q.T @ Q, np.eye(3))) # True — book eq 2.37" - ], - "id": "s01-25" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**LU** is Gaussian elimination stored as two triangular matrices, so `Ax = b`\n", - "can be solved cheaply many times for different `b`. **QR** (computed by\n", - "Gram-Schmidt, or more stably by other methods) produces *orthonormal* directions\n", - "— mutually perpendicular, each of length 1. It is used for orthogonal weight\n", - "initialization in neural networks and for stable least squares.\n", + "**Predict before running:** for each expression below, write the expected output shape and explain which axes are fixed, kept, rearranged, or contracted:\n", "\n", - "Everything in that table except the last row works on **matrices** — two axes.\n", - "Real data often has more. That is what section 10 addresses." - ], - "id": "s01-26" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 3 — which factorization?\n", + "1. `D[0]`\n", + "2. `D[:, 3, 4]`\n", + "3. `unfold(D, 0)`\n", + "4. `np.einsum('nhw->n', D)`\n", + "\n", + "Then run your code and compare your prediction with the result.\n", "\n", - "> 🇪🇸 ¿Qué factorización usarías en cada caso?" + "> 🇪🇸 **Reto de razonamiento sobre ejes:** Sea `D = load_digits().images`, con forma `(1797, 8, 8)` = **imágenes × alto × ancho**.\n", + ">\n", + "> **Predice antes de ejecutar:** para cada expresión, escribe la forma de salida esperada y explica qué ejes se fijan, conservan, reorganizan o contraen. Después ejecuta el código y compara tu predicción con el resultado." ], - "id": "s01-27" + "id": "ac9c4637" }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ - "# TODO 6: For each situation, name the method from the table above.\n", - "# Write your answer as a comment — no code needed.\n", - "# (a) You must solve Ax = b for 500 different b, with the same square A.\n", - "# (b) You need mutually perpendicular, unit-length directions.\n", - "# (c) A has more rows than columns and there is no exact solution.\n", - "# (d) Your data has three axes and you want to compress all three." + "# TODO 6: Let D = load_digits().images.\n", + "# Before running each operation, predict its output shape.\n", + "#\n", + "# 1. D[0]\n", + "# 2. D[:, 3, 4]\n", + "# 3. unfold(D, 0)\n", + "# 4. np.einsum('nhw->n', D)\n", + "#\n", + "# For each operation, explain in a comment which axes were\n", + "# fixed, kept, rearranged, or summed/contracted." ], - "id": "s01-28" + "id": "ecdaf14a", + "execution_count": null, + "outputs": [] }, { "cell_type": "code", - "execution_count": null, "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, - "outputs": [], "source": [ "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "# (a) LU — factor once, then each new b is two cheap triangular solves.\n", - "# (b) QR — Q's columns are orthonormal (Q.T @ Q == I).\n", - "# (c) Pseudoinverse — section 07. It is built from the SVD.\n", - "# (d) Tucker — section 10. PCA can only ever see two axes." + "D = load_digits().images\n", + "print(\"1.\", D[0].shape)\n", + "print(\"2.\", D[:, 3, 4].shape)\n", + "print(\"3.\", unfold(D, 0).shape)\n", + "print(\"4.\", np.einsum('nhw->n', D).shape)" + ], + "id": "033d9ced", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Why this solution works · Por qué funciona esta solución\n", + "\n", + "The operations change the shape as follows:\n", + "\n", + "1. `D[0]` takes the first image. The `n` axis is fixed, `h` and `w` are kept. Resulting shape: `(8, 8)`. This is a **slice**.\n", + "2. `D[:, 3, 4]` takes the pixels at row 3, column 4 from all images. The `h` and `w` axes are fixed, `n` is kept. Resulting shape: `(1797,)`. This is a **fiber**.\n", + "3. `unfold(D, 0)` rearranges the tensor. The `n` axis is kept as the first dimension, and `h` and `w` are flattened. Resulting shape: `(1797, 64)`. This is an **unfolding**.\n", + "4. `np.einsum('nhw->n', D)` sums over `h` and `w` axes. The `n` axis is kept. Resulting shape: `(1797,)`. This is a **contraction**.\n", + "\n", + "> 🇪🇸 **Por qué funciona esta solución:** Las operaciones cambian la forma de la siguiente manera:\n", + ">\n", + "> 1. `D[0]` toma la primera imagen. El eje `n` se fija, `h` y `w` se mantienen. Forma resultante: `(8, 8)`. Esto es un **corte**.\n", + "> 2. `D[:, 3, 4]` toma los píxeles en la fila 3, columna 4 de todas las imágenes. Los ejes `h` y `w` se fijan, `n` se mantiene. Forma resultante: `(1797,)`. Esto es una **fibra**.\n", + "> 3. `unfold(D, 0)` reorganiza el tensor. El eje `n` se mantiene como la primera dimensión, y `h` y `w` se aplanan. Forma resultante: `(1797, 64)`. Esto es un **desplegado**.\n", + "> 4. `np.einsum('nhw->n', D)` suma sobre los ejes `h` y `w`. El eje `n` se mantiene. Forma resultante: `(1797,)`. Esto es una **contracción**.\n", + "
" + ], + "id": "425b7175" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.4 The map of factorizations (Preview)\n", + "\n", + "This is only a preview—**do not memorize these methods yet**. Later sections move from familiar matrix factorizations such as SVD to tensor decompositions such as Tucker.\n", + "\n", + "- **Tucker** represents a tensor using a smaller core tensor and factor matrices.\n", + "- **CP** represents a tensor as a sum of rank-one components.\n", + "\n", + "These methods build on the tensor vocabulary developed here; the Tucker/HOSVD route used later explicitly uses mode unfoldings.\n", + "\n", + "> 🇪🇸 **Vista previa:** Esto es solo un adelanto—**todavía no necesitas memorizar estos métodos**. Más adelante pasaremos de factorizaciones matriciales como SVD a descomposiciones tensoriales como Tucker.\n", + ">\n", + "> - **Tucker** representa un tensor mediante un tensor núcleo más pequeño y matrices de factores.\n", + "> - **CP** representa un tensor como una suma de componentes de rango uno.\n", + ">\n", + "> La ruta Tucker/HOSVD que se usa más adelante emplea explícitamente unfoldings por modo." + ], + "id": "s01-24" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What just happened\n", + "\n", + "You should now be able to reason about a tensor by following its axes:\n", + "\n", + "- `shape`, `ndim`, and `size` describe **structure**;\n", + "- axis labels describe **meaning**;\n", + "- slices/fibers **fix indices**;\n", + "- unfolding **rearranges entries without losing them**;\n", + "- contraction **sums selected axes**.\n", + "\n", + "**One final self-check:** if you cannot explain what each output axis represents, go back one step and trace the indices again.\n", + "\n", + "> 🇪🇸 **Qué acaba de suceder:** Ahora deberías poder razonar sobre un tensor siguiendo sus ejes:\n", + ">\n", + "> - `shape`, `ndim` y `size` describen la **estructura**;\n", + "> - las etiquetas de los ejes describen el **significado**;\n", + "> - los cortes/fibras **fijan índices**;\n", + "> - el unfolding **reorganiza entradas sin perderlas**;\n", + "> - la contracción **suma ejes seleccionados**.\n", + ">\n", + "> **Autoevaluación final:** si no puedes explicar qué representa cada eje de salida, vuelve un paso atrás y sigue de nuevo los índices." ], - "id": "s01-29" + "id": "a1afc435" }, { "cell_type": "markdown", @@ -530,7 +691,6 @@ ], "metadata": { "colab": { - "name": "01-what-a-tensor-is.ipynb", "provenance": [], "toc_visible": true }, diff --git a/notebooks/01-what-a-tensor-is.ipynb b/notebooks/01-what-a-tensor-is.ipynb index bf0db47..7cc0127 100644 --- a/notebooks/01-what-a-tensor-is.ipynb +++ b/notebooks/01-what-a-tensor-is.ipynb @@ -1,800 +1,708 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "s01-00" - }, - "source": [ - "# 01 · What a tensor is\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/01-what-a-tensor-is.ipynb)\n", - "\n", - "*Part I · demo · 20 min*\n", - "\n", - "> 🇪🇸 **Qué es un tensor** — Aprende a leer la estructura de un tensor y a seguir el significado de sus ejes mientras los fijas, reorganizas o contraes.\n", - "\n", - "Learn to read a tensor's structure and track what its axes mean as you fix, rearrange, or contract them.\n", - "\n", - "## What you will be able to do\n", - "\n", - "- Explain **order**, **axis/mode**, and **shape**, and distinguish tensor order from matrix/tensor rank.\n", - "- Read `.shape`, `.ndim`, and `.size` and explain what every axis means on real image data.\n", - "- Predict how **slices**, **fibers**, **unfolding**, and **contraction** change or preserve axes.\n", - "- Use `np.einsum` for a dot product and matrix multiplication and reason about the output shape.\n", - "\n", - "> 🇪🇸 **Al terminar podrás:**\n", - "> - Explicar **orden**, **eje/modo** y **forma**, y distinguir el orden tensorial del rango matricial/tensorial.\n", - "> - Leer `.shape`, `.ndim` y `.size` e interpretar qué significa cada eje en datos reales de imágenes.\n", - "> - Predecir cómo **cortes**, **fibras**, **unfolding** y **contracción** cambian o conservan los ejes.\n", - "> - Usar `np.einsum` para un producto escalar y una multiplicación matricial, razonando sobre la forma de salida." - ], - "id": "s01-00" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s01-01" - }, - "source": [ - "## Setup\n", - "\n", - "Run this first. It installs and imports everything this notebook needs, and nothing else.\n", - "\n", - "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." - ], - "id": "s01-01" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dfae2649" - }, - "source": [ - "## Why this matters\n", - "\n", - "For the data tensors used in this workshop, shape alone is not enough; we also need to know what each axis represents. Two arrays can both be order 3 while their axes describe completely different things. Tracking which axes are fixed, kept, rearranged, or summed will make every later tensor operation easier to reason about.\n", - "\n", - "**Learning cycle:** **Predict → Run → Explain.** Try to predict shapes and axis meanings before you execute each example.\n", - "\n", - "> 🇪🇸 **Por qué esto importa:** Para los tensores de datos usados en este taller, la forma por sí sola no es suficiente; también necesitamos saber qué representa cada eje. Dos arreglos pueden ser ambos de orden 3 y, aun así, sus ejes representar conceptos completamente diferentes. Seguir qué ejes se fijan, conservan, reorganizan o suman facilita el razonamiento sobre las operaciones tensoriales posteriores.\n", - ">\n", - "> **Ciclo de aprendizaje:** **Predice → Ejecuta → Explica.** Intenta anticipar las formas y el significado de los ejes antes de ejecutar cada ejemplo." - ], - "id": "dfae2649" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "s01-02" - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "from sklearn.datasets import load_digits\n", - "from skimage import data\n", - "\n", - "rng = np.random.default_rng(0)" - ], - "id": "s01-02" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s01-03" - }, - "source": [ - "## 1.1 Vocabulary\n", - "\n", - "Keep this table open for the whole workshop. Do not memorize it all at once—use it while you predict what each operation does.\n", - "\n", - "> 🇪🇸 Mantén esta tabla abierta durante el taller. No necesitas memorizarla de una vez: úsala mientras predices qué hace cada operación.\n", - "\n", - "| Term | Plain meaning | Spanish | Example |\n", - "|---|---|---|---|\n", - "| **Tensor** | An array of numbers with any number of axes | *tensor* | A colour image |\n", - "| **Axis** (pl. axes) | One direction along which data is arranged | *eje* | Height; width; colour |\n", - "| **Mode** | Another word for axis, used in tensor theory | *modo* | \"mode-0 unfolding\" |\n", - "| **Order** | How many axes a tensor has | *orden* | A matrix has order 2 |\n", - "| **Shape** | The size along each axis, as a tuple | *forma* | `(512, 512, 3)` |\n", - "| **Slice** | Fix one index, keep the rest | *corte* | One colour channel |\n", - "| **Fiber** | Fix every index except one | *fibra* | The 3 colour values of one pixel |\n", - "| **Unfolding** | Rearrange a tensor into a matrix while preserving all entries | *desplegado / unfolding* | Mode-2 image unfolding |\n", - "| **Contraction** | Multiply and sum over selected/shared indices | *contracción* | Dot product |\n", - "| **Decomposition** | Represent an array using simpler structured components | *descomposición* | SVD, Tucker, CP |" - ], - "id": "s01-03" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "803378e4" - }, - "source": [ - "### Order vs. Rank\n", - "\n", - "In this workshop, **order** refers to the number of axes/modes a tensor has. **Matrix rank** measures linear independence. **Tensor rank** has its own definitions and is not the same as tensor order.\n", - "\n", - "> 🇪🇸 **Orden vs. rango:** El **orden** se refiere al número de ejes/modos de un tensor. El **rango matricial** mide la independencia lineal. El **rango tensorial** tiene sus propias definiciones y no es lo mismo que el orden tensorial.\n", - "\n", - "**Quick check:** if an array has shape `(32, 8, 8)`, what is its order? Can you infer its matrix/tensor rank from the shape alone?\n", - "\n", - "> 🇪🇸 **Comprobación rápida:** si un arreglo tiene forma `(32, 8, 8)`, ¿cuál es su orden? ¿Puedes inferir su rango matricial/tensorial solo a partir de la forma?\n", - "\n", - "
\n", - "Check your reasoning · Comprueba tu razonamiento\n", - "\n", - "Its **order is 3** because it has three axes. Its matrix/tensor rank **cannot be inferred from the shape alone**.\n", - "\n", - "> 🇪🇸 Su **orden es 3** porque tiene tres ejes. Su rango matricial/tensorial **no se puede inferir solo a partir de la forma**.\n", - "\n", - "
" - ], - "id": "803378e4" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s01-04" - }, - "source": [ - "## 1.2 Shape in NumPy\n", - "\n", - "Every NumPy array has `.shape`, a tuple giving the size along each axis. The length of that tuple is `.ndim`, the number of axes, and `.size` is the total number of stored values.\n", - "\n", - "> 🇪🇸 **Forma en NumPy:** Todo arreglo de NumPy tiene `.shape`, una tupla con el tamaño de cada eje. La longitud de esa tupla es `.ndim`, el número de ejes, y `.size` es el número total de valores almacenados." - ], - "id": "s01-04" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "s01-05" - }, - "outputs": [], - "source": [ - "scalar = np.array(3.0) # book: a — order 0\n", - "vector = np.array([1., 2., 3.]) # book: x, x_i — order 1\n", - "matrix = np.array([[1., 2.], [3., 4.]]) # book: A, A_{i,j} — order 2\n", - "tensor = rng.standard_normal((2, 3, 4)) # book: A_{i,j,k} — order 3\n", - "\n", - "for name, arr in [(\"scalar\", scalar), (\"vector\", vector),\n", - " (\"matrix\", matrix), (\"tensor\", tensor)]:\n", - " print(f\"{name:8s} shape={str(arr.shape):12s} ndim={arr.ndim} size={arr.size}\")" - ], - "id": "s01-05" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "edb1356c" - }, - "source": [ - "These tiny synthetic arrays are deliberate: they isolate structure — order, shape, `ndim`, and `size` — without distracting domain details. We switch immediately afterward to real image data to reason about what each axis means.\n", - "\n", - "> 🇪🇸 **Por qué usamos datos sintéticos aquí:** Estos arreglos pequeños permiten aislar la estructura — orden, forma, `ndim` y `size` — sin detalles del dominio. Inmediatamente después usamos imágenes reales para razonar sobre el significado de cada eje." - ], - "id": "edb1356c" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s01-06" - }, - "source": [ - "A scalar has `shape=()`, an empty tuple—there are no axes to measure. `size` is the product of the dimensions in `shape`: for `(2, 3, 4)`, `size = 2 × 3 × 4 = 24`.\n", - "\n", - "Now move from deliberately simple synthetic arrays to **real data**, where axis meaning matters.\n", - "\n", - "> 🇪🇸 Un escalar tiene `shape=()`, una tupla vacía: no hay ejes que medir. `size` es el producto de las dimensiones de `shape`: para `(2, 3, 4)`, `size = 2 × 3 × 4 = 24`.\n", - ">\n", - "> Ahora pasamos de arreglos sintéticos simples a **datos reales**, donde el significado de cada eje sí importa." - ], - "id": "s01-06" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "571ad3c0" - }, - "source": [ - "### Predict before running\n", - "\n", - "What do you expect the shapes to be for `digits.images` and `photo`? What do their axes represent?\n", - "\n", - "> 🇪🇸 **Predice antes de ejecutar:** `digits.images` y `photo` son ambos tensores de orden 3. Antes de ejecutar, predice qué representa cada uno de sus tres ejes. ¿Significan lo mismo?" - ], - "id": "571ad3c0" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "s01-07" - }, - "outputs": [], - "source": [ - "digits = load_digits()\n", - "print(digits.images.shape) # (1797, 8, 8) — 1797 handwritten digits, 8x8 pixels\n", - "\n", - "photo = data.immunohistochemistry()\n", - "print(photo.shape) # (512, 512, 3) — height, width, colour" - ], - "id": "s01-07" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s01-08" - }, - "source": [ - "Both arrays are order 3, but their axes mean completely different things. `digits.images` counts **images** along axis 0; `photo` counts **colour channels** along axis 2. **Shape describes structure, not semantics.** You must know what every axis represents and keep track of that meaning.\n", - "\n", - "> 🇪🇸 Ambos arreglos son de orden 3, pero sus ejes significan cosas completamente diferentes. `digits.images` cuenta **imágenes** en el eje 0; `photo` cuenta **canales de color** en el eje 2. **La forma describe estructura, no semántica.** Debes saber qué representa cada eje y seguir ese significado durante las operaciones." - ], - "id": "s01-08" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s01-09" - }, - "source": [ - "## Exercise 1 — read the shapes\n", - "\n", - "**Predict first.** Build the arrays, then verify `.shape`, `.ndim`, and `.size`. For the real image tensors, explain what every axis counts.\n", - "\n", - "> 🇪🇸 **Predice primero.** Construye los arreglos y después verifica `.shape`, `.ndim` y `.size`. Para los tensores de imágenes reales, explica qué cuenta cada eje." - ], - "id": "s01-09" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "s01-10" - }, - "outputs": [], - "source": [ - "# TODO 1: Build a scalar, a vector, a matrix and an order-3 tensor, and print\n", - "# .shape, .ndim and .size for each. Which one has shape ()?\n", - "\n", - "# TODO 2: Take load_digits().images and data.astronaut(). Both are order 3.\n", - "# For each, write down in a comment what axis 0, 1 and 2 count." - ], - "id": "s01-10" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ], - "id": "s01-11" - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "for arr in [np.array(3.0), np.zeros(3), np.zeros((2, 2)), np.zeros((2, 3, 4))]:\n", - " print(arr.shape, arr.ndim, arr.size)\n", - "# () 0 1\n", - "# (3,) 1 3\n", - "# (2, 2) 2 4\n", - "# (2, 3, 4) 3 24\n", - "\n", - "print(load_digits().images.shape) # (1797, 8, 8) axis 0 = which image\n", - " # axis 1 = row of pixels\n", - " # axis 2 = column of pixels\n", - "print(data.astronaut().shape) # (512, 512, 3) axis 0 = height\n", - " # axis 1 = width\n", - " # axis 2 = colour channel" - ], - "id": "s01-11" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "74c8da26" - }, - "source": [ - "
\n", - "Why this solution works · Por qué funciona esta solución\n", - "\n", - "1. A scalar has no axes, so its shape is `()`. A vector has one axis, a matrix two, and an order-3 tensor three. The `.ndim` attribute directly tells you the number of axes (order), and `.size` is the total number of elements.\n", - "2. `load_digits().images` represents a collection of 8x8 pixel images. So, axis 0 counts the images, axis 1 counts the rows of pixels, and axis 2 counts the columns of pixels. `data.astronaut()` is a color image. Axis 0 counts height, axis 1 counts width, and axis 2 counts the color channels (Red, Green, Blue).\n", - "\n", - "> 🇪🇸 **Por qué funciona esta solución:**\n", - ">\n", - "> 1. Un escalar no tiene ejes, por lo que su forma es `()`. Un vector tiene un eje, una matriz dos y un tensor de orden 3 tres. El atributo `.ndim` indica directamente el número de ejes (orden), y `.size` es el número total de elementos.\n", - "> 2. `load_digits().images` representa una colección de imágenes de 8x8 píxeles. Por lo tanto, el eje 0 cuenta las imágenes, el eje 1 cuenta las filas de píxeles y el eje 2 cuenta las columnas de píxeles. `data.astronaut()` es una imagen en color. El eje 0 cuenta la altura, el eje 1 la anchura y el eje 2 los canales de color (Rojo, Verde, Azul).\n", - "
" - ], - "id": "74c8da26" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s01-12" - }, - "source": [ - "## 1.3 The three operations that matter\n", - "\n", - "Slices/fibers, unfolding, and contraction all answer one question: **what happens to the axes?**\n", - "\n", - "> 🇪🇸 Cortes/fibras, unfolding y contracción responden a una misma pregunta: **¿qué ocurre con los ejes?**\n", - "\n", - "### Slices and fibers — fixing indices takes a tensor apart\n", - "\n", - "A **slice** fixes one index and keeps the others. A **fiber** fixes every index except one.\n", - "\n", - "> 🇪🇸 Un **corte** fija un índice y conserva los demás. Una **fibra** fija todos los índices excepto uno." - ], - "id": "s01-12" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "s01-13" - }, - "outputs": [], - "source": [ - "print(photo[:, :, 0].shape) # (512, 512) — a slice: one colour channel, still an image\n", - "print(photo[100, 200, :].shape) # (3,) — a fiber: the 3 colour values of one pixel" - ], - "id": "s01-13" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s01-14" - }, - "source": [ - "Same picture, same two indexing operations — see them together. Drag the\n", - "sliders and watch the marked pixel move on both panels at once, while its\n", - "fiber (three numbers, one per colour) redraws on the right.\n", - "\n", - "> 🇪🇸 Mueve los deslizadores: el mismo píxel se marca en el corte y en la\n", - "> imagen completa, y su fibra (tres números, uno por color) se redibuja." - ], - "id": "s01-14" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "s01-15" - }, - "outputs": [], - "source": [ - "# Colab renders ipywidgets through its own widget manager rather than the\n", - "# classic Jupyter one; this call is a no-op outside Colab, which is why it is\n", - "# guarded rather than assumed.\n", - "try:\n", - " from google.colab import output\n", - " output.enable_custom_widget_manager()\n", - "except ImportError:\n", - " pass\n", - "\n", - "import ipywidgets as widgets\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def show_slice_and_fiber(row, col):\n", - " plt.close('all')\n", - " fig, axes = plt.subplots(1, 3, figsize=(11, 3.2))\n", - "\n", - " axes[0].imshow(photo)\n", - " axes[0].scatter([col], [row], color='#C44E52', s=70, edgecolor='white')\n", - " axes[0].set_title('photo — the fiber, marked')\n", - " axes[0].axis('off')\n", - "\n", - " axes[1].imshow(photo[:, :, 0], cmap='gray')\n", - " axes[1].scatter([col], [row], color='#C44E52', s=70, edgecolor='white')\n", - " axes[1].set_title('photo[:, :, 0] — a slice')\n", - " axes[1].axis('off')\n", - "\n", - " fiber = photo[row, col, :]\n", - " axes[2].bar(['R', 'G', 'B'], fiber, color=['#C44E52', '#55A868', '#4C72B0'])\n", - " axes[2].set_title(f'photo[{row}, {col}, :] — the fiber')\n", - " axes[2].set_ylim(0, 255)\n", - "\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "widgets.interact(show_slice_and_fiber,\n", - " row=widgets.IntSlider(min=0, max=511, step=1, value=100, description='row'),\n", - " col=widgets.IntSlider(min=0, max=511, step=1, value=200, description='col'));" - ], - "id": "s01-15" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s01-16" - }, - "source": [ - "### Unfolding — rearranging axes into a matrix\n", - "\n", - "Mode unfoldings are central to many tensor methods, including the Tucker/HOSVD route used later in this workshop. An unfolding moves one axis to the front and rearranges the remaining axes into a matrix without losing entries.\n", - "\n", - "> 🇪🇸 **Desplegado:** Los unfoldings por modo son fundamentales en muchos métodos tensoriales, incluida la ruta Tucker/HOSVD que usaremos más adelante. El desplegado reorganiza las entradas en una matriz sin perder información." - ], - "id": "s01-16" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "s01-17" - }, - "outputs": [], - "source": [ - "def unfold(T, axis):\n", - " \"\"\"Move `axis` to the front, flatten everything else into one long axis.\"\"\"\n", - " return np.moveaxis(T, axis, 0).reshape(T.shape[axis], -1)\n", - "\n", - "print(unfold(photo, 0).shape) # (512, 1536) — rows are the height axis\n", - "print(unfold(photo, 2).shape) # (3, 262144) — rows are the 3 colour channels" - ], - "id": "s01-17" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s01-18" - }, - "source": [ - "Unfolding **loses nothing**: it only rearranges entries. The mode-2 unfolding says “each colour channel is one row of 262,144 numbers”, which makes matrix tools such as SVD available without discarding information.\n", - "\n", - "You will use this same idea again in sections 07 and 10.\n", - "\n", - "> 🇪🇸 El unfolding **no pierde información**: solo reorganiza las entradas. En el unfolding de modo 2, cada canal de color se convierte en una fila de 262.144 números, lo que permite aplicar herramientas matriciales como SVD sin descartar datos.\n", - "\n", - "### Contraction — multiply along a shared axis and sum over it\n", - "\n", - "The dot product (eq. 2.8) and the matrix product (eq. 2.5) are both contractions. `np.einsum` makes the summed and retained indices explicit.\n", - "\n", - "> 🇪🇸 **Contracción:** el producto escalar y el producto matricial son contracciones. `np.einsum` permite ver explícitamente qué índices se suman y cuáles permanecen." - ], - "id": "s01-18" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "e127993d" - }, - "source": [ - "### Predict before running\n", - "\n", - "For `np.einsum('i,i->', a, b)`:\n", - "- which index is summed?\n", - "- which indices remain?\n", - "- why is the result a scalar?\n", - "\n", - "For `np.einsum('ik,kj->ij', A, B)`:\n", - "- which index is summed?\n", - "- which indices remain?\n", - "- what should the output shape be?\n", - "\n", - "> 🇪🇸 **Predice antes de ejecutar:**\n", - ">\n", - "> Para `np.einsum('i,i->', a, b)`:\n", - "> - ¿qué índice se suma?\n", - "> - ¿qué índices quedan?\n", - "> - ¿por qué el resultado es un escalar?\n", - ">\n", - "> Para `np.einsum('ik,kj->ij', A, B)`:\n", - "> - ¿qué índice se suma?\n", - "> - ¿qué índices quedan?\n", - "> - ¿cuál debería ser la forma de salida?" - ], - "id": "e127993d" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "s01-19" - }, - "outputs": [], - "source": [ - "a = np.array([1., 2., 3.]); b = np.array([4., 5., 6.])\n", - "print(np.einsum('i,i->', a, b)) # dot product, sum over i (eq 2.8)\n", - "\n", - "A = np.array([[1., 2.], [3., 4.]]); B = np.array([[5., 6.], [7., 8.]])\n", - "print(np.einsum('ik,kj->ij', A, B)) # matrix product, sum over k (eq 2.5)" - ], - "id": "s01-19" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s01-20" - }, - "source": [ - "**The rule, in one sentence:** an index that appears in the inputs but **not** after the arrow is summed over; an index that appears after the arrow is kept.\n", - "\n", - "This rule is the core idea that section 06 develops.\n", - "\n", - "> 🇪🇸 **La regla, en una frase:** un índice que aparece en las entradas pero **no** después de la flecha se suma; un índice que aparece después de la flecha se conserva.\n", - ">\n", - "> Esta regla es la idea central que desarrolla la sección 06." - ], - "id": "s01-20" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s01-21" - }, - "source": [ - "## Exercise 2 — take a tensor apart and put it back\n", - "\n", - "Before coding, say out loud which axes you expect to **fix**, **keep**, **rearrange**, or **sum**. Then verify your reasoning with NumPy.\n", - "\n", - "> 🇪🇸 Antes de programar, explica qué ejes esperas **fijar**, **conservar**, **reorganizar** o **sumar**. Después verifica tu razonamiento con NumPy." - ], - "id": "s01-21" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "s01-22" - }, - "outputs": [], - "source": [ - "# TODO 3: From `photo`, extract (a) the green channel as a (512, 512) slice and\n", - "# (b) the colour fiber at pixel (10, 20). Which is a slice, which a fiber?\n", - "\n", - "# TODO 4: Unfold `photo` along all three axes and print the three shapes.\n", - "# Confirm that each unfolding has exactly photo.size entries —\n", - "# unfolding rearranges, it never loses anything.\n", - "\n", - "# TODO 5: Write the dot product of `a` and `b` as einsum, and check it against\n", - "# np.dot. Then write the matrix product of A and B, and check against @." - ], - "id": "s01-22" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ], - "id": "s01-23" - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "green = photo[:, :, 1] # slice — one index fixed, the rest kept\n", - "fiber = photo[10, 20, :] # fiber — every index fixed except one\n", - "print(green.shape, fiber.shape) # (512, 512) (3,)\n", - "\n", - "for ax in range(3):\n", - " M = unfold(photo, ax)\n", - " print(ax, M.shape, M.size == photo.size) # True every time\n", - "\n", - "print(np.einsum('i,i->', a, b), np.dot(a, b)) # 32.0 32.0\n", - "print(np.allclose(np.einsum('ik,kj->ij', A, B), A @ B)) # True" - ], - "id": "s01-23" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "130e6acc" - }, - "source": [ - "
\n", - "Why this solution works · Por qué funciona esta solución\n", - "\n", - "1. `photo[:, :, 1]` selects the green channel by fixing the last axis (color) to index 1. This is a **slice** because one index is fixed and the rest are kept. `photo[10, 20, :]` selects the color values for the pixel at row 10, column 20. This is a **fiber** because all indices except one are fixed.\n", - "2. `unfold(photo, axis)` rearranges the tensor. For `axis=0`, it creates a matrix where rows correspond to the height dimension. For `axis=1`, rows correspond to the width dimension. For `axis=2`, rows correspond to the color dimension. In all cases, the total number of elements (`.size`) remains the same, demonstrating that unfolding is merely a rearrangement.\n", - "3. `np.einsum('i,i->', a, b)` performs the dot product by summing over the shared index `i`. `np.einsum('ik,kj->ij', A, B)` performs matrix multiplication by summing over the shared index `k` and keeping `i` and `j`. `np.allclose` confirms the results are numerically equivalent to `np.dot` and `@` operator respectively.\n", - "\n", - "> 🇪🇸 **Por qué funciona esta solución:**\n", - ">\n", - "> 1. `photo[:, :, 1]` selecciona el canal verde fijando el último eje (color) al índice 1. Esto es un **corte** porque un índice se fija y el resto se mantienen. `photo[10, 20, :]` selecciona los valores de color para el píxel en la fila 10, columna 20. Esto es una **fibra** porque todos los índices excepto uno están fijos.\n", - "> 2. `unfold(photo, axis)` reorganiza el tensor. Para `axis=0`, crea una matriz donde las filas corresponden a la dimensión de altura. Para `axis=1`, las filas corresponden a la dimensión de anchura. Para `axis=2`, las filas corresponden a la dimensión de color. En todos los casos, el número total de elementos (`.size`) permanece igual, demostrando que el desplegado es solo una reorganización.\n", - "> 3. `np.einsum('i,i->', a, b)` realiza el producto escalar sumando sobre el índice compartido `i`. `np.einsum('ik,kj->ij', A, B)` realiza la multiplicación de matrices sumando sobre el índice compartido `k` y manteniendo `i` y `j`. `np.allclose` confirma que los resultados son numéricamente equivalentes a `np.dot` y al operador `@` respectivamente.\n", - "
" - ], - "id": "130e6acc" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ac9c4637" - }, - "source": [ - "## Exercise 3 — Axis Reasoning Challenge\n", - "\n", - "Let `D = load_digits().images`, with shape `(1797, 8, 8)` = **images × height × width**.\n", - "\n", - "**Predict before running:** for each expression below, write the expected output shape and explain which axes are fixed, kept, rearranged, or contracted:\n", - "\n", - "1. `D[0]`\n", - "2. `D[:, 3, 4]`\n", - "3. `unfold(D, 0)`\n", - "4. `np.einsum('nhw->n', D)`\n", - "\n", - "Then run your code and compare your prediction with the result.\n", - "\n", - "> 🇪🇸 **Reto de razonamiento sobre ejes:** Sea `D = load_digits().images`, con forma `(1797, 8, 8)` = **imágenes × alto × ancho**.\n", - ">\n", - "> **Predice antes de ejecutar:** para cada expresión, escribe la forma de salida esperada y explica qué ejes se fijan, conservan, reorganizan o contraen. Después ejecuta el código y compara tu predicción con el resultado." - ], - "id": "ac9c4637" - }, - { - "cell_type": "code", - "metadata": { - "id": "ecdaf14a" - }, - "source": [ - "# TODO 6: Let D = load_digits().images.\n", - "# Before running each operation, predict its output shape.\n", - "#\n", - "# 1. D[0]\n", - "# 2. D[:, 3, 4]\n", - "# 3. unfold(D, 0)\n", - "# 4. np.einsum('nhw->n', D)\n", - "#\n", - "# For each operation, explain in a comment which axes were\n", - "# fixed, kept, rearranged, or summed/contracted." - ], - "id": "ecdaf14a", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ], - "id": "033d9ced" - }, - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "D = load_digits().images\n", - "print(\"1.\", D[0].shape)\n", - "print(\"2.\", D[:, 3, 4].shape)\n", - "print(\"3.\", unfold(D, 0).shape)\n", - "print(\"4.\", np.einsum('nhw->n', D).shape)" - ], - "id": "033d9ced", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "425b7175" - }, - "source": [ - "
\n", - "Why this solution works · Por qué funciona esta solución\n", - "\n", - "The operations change the shape as follows:\n", - "\n", - "1. `D[0]` takes the first image. The `n` axis is fixed, `h` and `w` are kept. Resulting shape: `(8, 8)`. This is a **slice**.\n", - "2. `D[:, 3, 4]` takes the pixels at row 3, column 4 from all images. The `h` and `w` axes are fixed, `n` is kept. Resulting shape: `(1797,)`. This is a **fiber**.\n", - "3. `unfold(D, 0)` rearranges the tensor. The `n` axis is kept as the first dimension, and `h` and `w` are flattened. Resulting shape: `(1797, 64)`. This is an **unfolding**.\n", - "4. `np.einsum('nhw->n', D)` sums over `h` and `w` axes. The `n` axis is kept. Resulting shape: `(1797,)`. This is a **contraction**.\n", - "\n", - "> 🇪🇸 **Por qué funciona esta solución:** Las operaciones cambian la forma de la siguiente manera:\n", - ">\n", - "> 1. `D[0]` toma la primera imagen. El eje `n` se fija, `h` y `w` se mantienen. Forma resultante: `(8, 8)`. Esto es un **corte**.\n", - "> 2. `D[:, 3, 4]` toma los píxeles en la fila 3, columna 4 de todas las imágenes. Los ejes `h` y `w` se fijan, `n` se mantiene. Forma resultante: `(1797,)`. Esto es una **fibra**.\n", - "> 3. `unfold(D, 0)` reorganiza el tensor. El eje `n` se mantiene como la primera dimensión, y `h` y `w` se aplanan. Forma resultante: `(1797, 64)`. Esto es un **desplegado**.\n", - "> 4. `np.einsum('nhw->n', D)` suma sobre los ejes `h` y `w`. El eje `n` se mantiene. Forma resultante: `(1797,)`. Esto es una **contracción**.\n", - "
" - ], - "id": "425b7175" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s01-24" - }, - "source": [ - "## 1.4 The map of factorizations (Preview)\n", - "\n", - "This is only a preview—**do not memorize these methods yet**. Later sections move from familiar matrix factorizations such as SVD to tensor decompositions such as Tucker.\n", - "\n", - "- **Tucker** represents a tensor using a smaller core tensor and factor matrices.\n", - "- **CP** represents a tensor as a sum of rank-one components.\n", - "\n", - "These methods build on the tensor vocabulary developed here; the Tucker/HOSVD route used later explicitly uses mode unfoldings.\n", - "\n", - "> 🇪🇸 **Vista previa:** Esto es solo un adelanto—**todavía no necesitas memorizar estos métodos**. Más adelante pasaremos de factorizaciones matriciales como SVD a descomposiciones tensoriales como Tucker.\n", - ">\n", - "> - **Tucker** representa un tensor mediante un tensor núcleo más pequeño y matrices de factores.\n", - "> - **CP** representa un tensor como una suma de componentes de rango uno.\n", - ">\n", - "> La ruta Tucker/HOSVD que se usa más adelante emplea explícitamente unfoldings por modo." - ], - "id": "s01-24" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "a1afc435" - }, - "source": [ - "## What just happened\n", - "\n", - "You should now be able to reason about a tensor by following its axes:\n", - "\n", - "- `shape`, `ndim`, and `size` describe **structure**;\n", - "- axis labels describe **meaning**;\n", - "- slices/fibers **fix indices**;\n", - "- unfolding **rearranges entries without losing them**;\n", - "- contraction **sums selected axes**.\n", - "\n", - "**One final self-check:** if you cannot explain what each output axis represents, go back one step and trace the indices again.\n", - "\n", - "> 🇪🇸 **Qué acaba de suceder:** Ahora deberías poder razonar sobre un tensor siguiendo sus ejes:\n", - ">\n", - "> - `shape`, `ndim` y `size` describen la **estructura**;\n", - "> - las etiquetas de los ejes describen el **significado**;\n", - "> - los cortes/fibras **fijan índices**;\n", - "> - el unfolding **reorganiza entradas sin perderlas**;\n", - "> - la contracción **suma ejes seleccionados**.\n", - ">\n", - "> **Autoevaluación final:** si no puedes explicar qué representa cada eje de salida, vuelve un paso atrás y sigue de nuevo los índices." - ], - "id": "a1afc435" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s01-30" - }, - "source": [ - "---\n", - "\n", - "## Done with this section\n", - "\n", - "Next up: **02 · Thinking in N dimensions** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/02-thinking-in-n-dimensions.ipynb).\n", - "\n", - "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" - ], - "id": "s01-30" - } - ], - "metadata": { - "colab": { - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python" - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 01 · What a tensor is\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/01-what-a-tensor-is.ipynb)\n", + "\n", + "*Part I · demo · 20 min*\n", + "\n", + "> 🇪🇸 **Qué es un tensor** — Aprender a leer la estructura de un tensor y seguir el significado de sus ejes al fijarlos, reorganizarlos o contraerlos.\n", + "\n", + "Learn to read a tensor's structure and track what its axes mean as you fix, rearrange, or contract them.\n", + "\n", + "## What you will be able to do\n", + "\n", + "- Explain order, axis/mode and shape, and distinguish tensor order from matrix/tensor rank.\n", + "- Read `.shape`, `.ndim` and `.size` and explain what every axis means on real image data.\n", + "- Predict how slices, fibers, unfolding and contraction change or preserve axes.\n", + "- Use `np.einsum` for a dot product and matrix multiplication and reason about the output shape." + ], + "id": "s01-00" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "Run this first. It installs and imports everything this notebook needs, and nothing else.\n", + "\n", + "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." + ], + "id": "s01-01" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from sklearn.datasets import load_digits\n", + "from skimage import data\n", + "\n", + "rng = np.random.default_rng(0)" + ], + "id": "s01-02" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.1 Vocabulary\n", + "\n", + "Keep this table open for the whole workshop. Do not memorize it all at once—use it while you predict what each operation does.\n", + "\n", + "> 🇪🇸 Mantén esta tabla abierta durante el taller. No necesitas memorizarla de una vez: úsala mientras predices qué hace cada operación.\n", + "\n", + "| Term | Plain meaning | Spanish | Example |\n", + "|---|---|---|---|\n", + "| **Tensor** | An array of numbers with any number of axes | *tensor* | A colour image |\n", + "| **Axis** (pl. axes) | One direction along which data is arranged | *eje* | Height; width; colour |\n", + "| **Mode** | Another word for axis, used in tensor theory | *modo* | \"mode-0 unfolding\" |\n", + "| **Order** | How many axes a tensor has | *orden* | A matrix has order 2 |\n", + "| **Shape** | The size along each axis, as a tuple | *forma* | `(512, 512, 3)` |\n", + "| **Slice** | Fix one index, keep the rest | *corte* | One colour channel |\n", + "| **Fiber** | Fix every index except one | *fibra* | The 3 colour values of one pixel |\n", + "| **Unfolding** | Rearrange a tensor into a matrix while preserving all entries | *desplegado / unfolding* | Mode-2 image unfolding |\n", + "| **Contraction** | Multiply and sum over selected/shared indices | *contracción* | Dot product |\n", + "| **Decomposition** | Represent an array using simpler structured components | *descomposición* | SVD, Tucker, CP |" + ], + "id": "s01-03" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Order vs. Rank\n", + "\n", + "In this workshop, **order** refers to the number of axes/modes a tensor has. **Matrix rank** measures linear independence. **Tensor rank** has its own definitions and is not the same as tensor order.\n", + "\n", + "> 🇪🇸 **Orden vs. rango:** El **orden** se refiere al número de ejes/modos de un tensor. El **rango matricial** mide la independencia lineal. El **rango tensorial** tiene sus propias definiciones y no es lo mismo que el orden tensorial.\n", + "\n", + "**Quick check:** if an array has shape `(32, 8, 8)`, what is its order? Can you infer its matrix/tensor rank from the shape alone?\n", + "\n", + "> 🇪🇸 **Comprobación rápida:** si un arreglo tiene forma `(32, 8, 8)`, ¿cuál es su orden? ¿Puedes inferir su rango matricial/tensorial solo a partir de la forma?\n", + "\n", + "
\n", + "Check your reasoning · Comprueba tu razonamiento\n", + "\n", + "Its **order is 3** because it has three axes. Its matrix/tensor rank **cannot be inferred from the shape alone**.\n", + "\n", + "> 🇪🇸 Su **orden es 3** porque tiene tres ejes. Su rango matricial/tensorial **no se puede inferir solo a partir de la forma**.\n", + "\n", + "
" + ], + "id": "803378e4" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.2 Shape in NumPy\n", + "\n", + "Every NumPy array has `.shape`, a tuple giving the size along each axis. The length of that tuple is `.ndim`, the number of axes, and `.size` is the total number of stored values.\n", + "\n", + "> 🇪🇸 **Forma en NumPy:** Todo arreglo de NumPy tiene `.shape`, una tupla con el tamaño de cada eje. La longitud de esa tupla es `.ndim`, el número de ejes, y `.size` es el número total de valores almacenados." + ], + "id": "s01-04" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scalar = np.array(3.0) # book: a — order 0\n", + "vector = np.array([1., 2., 3.]) # book: x, x_i — order 1\n", + "matrix = np.array([[1., 2.], [3., 4.]]) # book: A, A_{i,j} — order 2\n", + "tensor = rng.standard_normal((2, 3, 4)) # book: A_{i,j,k} — order 3\n", + "\n", + "for name, arr in [(\"scalar\", scalar), (\"vector\", vector),\n", + " (\"matrix\", matrix), (\"tensor\", tensor)]:\n", + " print(f\"{name:8s} shape={str(arr.shape):12s} ndim={arr.ndim} size={arr.size}\")" + ], + "id": "s01-05" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These tiny synthetic arrays are deliberate: they isolate structure — order, shape, `ndim`, and `size` — without distracting domain details. We switch immediately afterward to real image data to reason about what each axis means.\n", + "\n", + "> 🇪🇸 **Por qué usamos datos sintéticos aquí:** Estos arreglos pequeños permiten aislar la estructura — orden, forma, `ndim` y `size` — sin detalles del dominio. Inmediatamente después usamos imágenes reales para razonar sobre el significado de cada eje." + ], + "id": "edb1356c" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A scalar has `shape=()`, an empty tuple—there are no axes to measure. `size` is the product of the dimensions in `shape`: for `(2, 3, 4)`, `size = 2 × 3 × 4 = 24`.\n", + "\n", + "Now move from deliberately simple synthetic arrays to **real data**, where axis meaning matters.\n", + "\n", + "> 🇪🇸 Un escalar tiene `shape=()`, una tupla vacía: no hay ejes que medir. `size` es el producto de las dimensiones de `shape`: para `(2, 3, 4)`, `size = 2 × 3 × 4 = 24`.\n", + ">\n", + "> Ahora pasamos de arreglos sintéticos simples a **datos reales**, donde el significado de cada eje sí importa." + ], + "id": "s01-06" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Predict before running\n", + "\n", + "What do you expect the shapes to be for `digits.images` and `photo`? What do their axes represent?\n", + "\n", + "> 🇪🇸 **Predice antes de ejecutar:** `digits.images` y `photo` son ambos tensores de orden 3. Antes de ejecutar, predice qué representa cada uno de sus tres ejes. ¿Significan lo mismo?" + ], + "id": "571ad3c0" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "digits = load_digits()\n", + "print(digits.images.shape) # (1797, 8, 8) — 1797 handwritten digits, 8x8 pixels\n", + "\n", + "photo = data.immunohistochemistry()\n", + "print(photo.shape) # (512, 512, 3) — height, width, colour" + ], + "id": "s01-07" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Both arrays are order 3, but their axes mean completely different things. `digits.images` counts **images** along axis 0; `photo` counts **colour channels** along axis 2. **Shape describes structure, not semantics.** You must know what every axis represents and keep track of that meaning.\n", + "\n", + "> 🇪🇸 Ambos arreglos son de orden 3, pero sus ejes significan cosas completamente diferentes. `digits.images` cuenta **imágenes** en el eje 0; `photo` cuenta **canales de color** en el eje 2. **La forma describe estructura, no semántica.** Debes saber qué representa cada eje y seguir ese significado durante las operaciones." + ], + "id": "s01-08" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 1 — read the shapes\n", + "\n", + "**Predict first.** Build the arrays, then verify `.shape`, `.ndim`, and `.size`. For the real image tensors, explain what every axis counts.\n", + "\n", + "> 🇪🇸 **Predice primero.** Construye los arreglos y después verifica `.shape`, `.ndim` y `.size`. Para los tensores de imágenes reales, explica qué cuenta cada eje." + ], + "id": "s01-09" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO 1: Build a scalar, a vector, a matrix and an order-3 tensor, and print\n", + "# .shape, .ndim and .size for each. Which one has shape ()?\n", + "\n", + "# TODO 2: Take load_digits().images and data.astronaut(). Both are order 3.\n", + "# For each, write down in a comment what axis 0, 1 and 2 count." + ], + "id": "s01-10" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "for arr in [np.array(3.0), np.zeros(3), np.zeros((2, 2)), np.zeros((2, 3, 4))]:\n", + " print(arr.shape, arr.ndim, arr.size)\n", + "# () 0 1\n", + "# (3,) 1 3\n", + "# (2, 2) 2 4\n", + "# (2, 3, 4) 3 24\n", + "\n", + "print(load_digits().images.shape) # (1797, 8, 8) axis 0 = which image\n", + " # axis 1 = row of pixels\n", + " # axis 2 = column of pixels\n", + "print(data.astronaut().shape) # (512, 512, 3) axis 0 = height\n", + " # axis 1 = width\n", + " # axis 2 = colour channel" + ], + "id": "s01-11" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Why this solution works · Por qué funciona esta solución\n", + "\n", + "1. A scalar has no axes, so its shape is `()`. A vector has one axis, a matrix two, and an order-3 tensor three. The `.ndim` attribute directly tells you the number of axes (order), and `.size` is the total number of elements.\n", + "2. `load_digits().images` represents a collection of 8x8 pixel images. So, axis 0 counts the images, axis 1 counts the rows of pixels, and axis 2 counts the columns of pixels. `data.astronaut()` is a color image. Axis 0 counts height, axis 1 counts width, and axis 2 counts the color channels (Red, Green, Blue).\n", + "\n", + "> 🇪🇸 **Por qué funciona esta solución:**\n", + ">\n", + "> 1. Un escalar no tiene ejes, por lo que su forma es `()`. Un vector tiene un eje, una matriz dos y un tensor de orden 3 tres. El atributo `.ndim` indica directamente el número de ejes (orden), y `.size` es el número total de elementos.\n", + "> 2. `load_digits().images` representa una colección de imágenes de 8x8 píxeles. Por lo tanto, el eje 0 cuenta las imágenes, el eje 1 cuenta las filas de píxeles y el eje 2 cuenta las columnas de píxeles. `data.astronaut()` es una imagen en color. El eje 0 cuenta la altura, el eje 1 la anchura y el eje 2 los canales de color (Rojo, Verde, Azul).\n", + "
" + ], + "id": "74c8da26" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.3 The three operations that matter\n", + "\n", + "Slices/fibers, unfolding, and contraction all answer one question: **what happens to the axes?**\n", + "\n", + "> 🇪🇸 Cortes/fibras, unfolding y contracción responden a una misma pregunta: **¿qué ocurre con los ejes?**\n", + "\n", + "### Slices and fibers — fixing indices takes a tensor apart\n", + "\n", + "A **slice** fixes one index and keeps the others. A **fiber** fixes every index except one.\n", + "\n", + "> 🇪🇸 Un **corte** fija un índice y conserva los demás. Una **fibra** fija todos los índices excepto uno." + ], + "id": "s01-12" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(photo[:, :, 0].shape) # (512, 512) — a slice: one colour channel, still an image\n", + "print(photo[100, 200, :].shape) # (3,) — a fiber: the 3 colour values of one pixel" + ], + "id": "s01-13" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Same picture, same two indexing operations — see them together. Drag the\n", + "sliders and watch the marked pixel move on both panels at once, while its\n", + "fiber (three numbers, one per colour) redraws on the right.\n", + "\n", + "> 🇪🇸 Mueve los deslizadores: el mismo píxel se marca en el corte y en la\n", + "> imagen completa, y su fibra (tres números, uno por color) se redibuja." + ], + "id": "s01-14" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Colab renders ipywidgets through its own widget manager rather than the\n", + "# classic Jupyter one; this call is a no-op outside Colab, which is why it is\n", + "# guarded rather than assumed.\n", + "try:\n", + " from google.colab import output\n", + " output.enable_custom_widget_manager()\n", + "except ImportError:\n", + " pass\n", + "\n", + "import ipywidgets as widgets\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def show_slice_and_fiber(row, col):\n", + " plt.close('all')\n", + " fig, axes = plt.subplots(1, 3, figsize=(11, 3.2))\n", + "\n", + " axes[0].imshow(photo)\n", + " axes[0].scatter([col], [row], color='#C44E52', s=70, edgecolor='white')\n", + " axes[0].set_title('photo — the fiber, marked')\n", + " axes[0].axis('off')\n", + "\n", + " axes[1].imshow(photo[:, :, 0], cmap='gray')\n", + " axes[1].scatter([col], [row], color='#C44E52', s=70, edgecolor='white')\n", + " axes[1].set_title('photo[:, :, 0] — a slice')\n", + " axes[1].axis('off')\n", + "\n", + " fiber = photo[row, col, :]\n", + " axes[2].bar(['R', 'G', 'B'], fiber, color=['#C44E52', '#55A868', '#4C72B0'])\n", + " axes[2].set_title(f'photo[{row}, {col}, :] — the fiber')\n", + " axes[2].set_ylim(0, 255)\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "widgets.interact(show_slice_and_fiber,\n", + " row=widgets.IntSlider(min=0, max=511, step=1, value=100, description='row'),\n", + " col=widgets.IntSlider(min=0, max=511, step=1, value=200, description='col'));" + ], + "id": "s01-15" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Unfolding — rearranging axes into a matrix\n", + "\n", + "Mode unfoldings are central to many tensor methods, including the Tucker/HOSVD route used later in this workshop. An unfolding moves one axis to the front and rearranges the remaining axes into a matrix without losing entries.\n", + "\n", + "> 🇪🇸 **Desplegado:** Los unfoldings por modo son fundamentales en muchos métodos tensoriales, incluida la ruta Tucker/HOSVD que usaremos más adelante. El desplegado reorganiza las entradas en una matriz sin perder información." + ], + "id": "s01-16" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def unfold(T, axis):\n", + " \"\"\"Move `axis` to the front, flatten everything else into one long axis.\"\"\"\n", + " return np.moveaxis(T, axis, 0).reshape(T.shape[axis], -1)\n", + "\n", + "print(unfold(photo, 0).shape) # (512, 1536) — rows are the height axis\n", + "print(unfold(photo, 2).shape) # (3, 262144) — rows are the 3 colour channels" + ], + "id": "s01-17" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Unfolding **loses nothing**: it only rearranges entries. The mode-2 unfolding says “each colour channel is one row of 262,144 numbers”, which makes matrix tools such as SVD available without discarding information.\n", + "\n", + "You will use this same idea again in sections 07 and 10.\n", + "\n", + "> 🇪🇸 El unfolding **no pierde información**: solo reorganiza las entradas. En el unfolding de modo 2, cada canal de color se convierte en una fila de 262.144 números, lo que permite aplicar herramientas matriciales como SVD sin descartar datos.\n", + "\n", + "### Contraction — multiply along a shared axis and sum over it\n", + "\n", + "The dot product (eq. 2.8) and the matrix product (eq. 2.5) are both contractions. `np.einsum` makes the summed and retained indices explicit.\n", + "\n", + "> 🇪🇸 **Contracción:** el producto escalar y el producto matricial son contracciones. `np.einsum` permite ver explícitamente qué índices se suman y cuáles permanecen." + ], + "id": "s01-18" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Predict before running\n", + "\n", + "For `np.einsum('i,i->', a, b)`:\n", + "- which index is summed?\n", + "- which indices remain?\n", + "- why is the result a scalar?\n", + "\n", + "For `np.einsum('ik,kj->ij', A, B)`:\n", + "- which index is summed?\n", + "- which indices remain?\n", + "- what should the output shape be?\n", + "\n", + "> 🇪🇸 **Predice antes de ejecutar:**\n", + ">\n", + "> Para `np.einsum('i,i->', a, b)`:\n", + "> - ¿qué índice se suma?\n", + "> - ¿qué índices quedan?\n", + "> - ¿por qué el resultado es un escalar?\n", + ">\n", + "> Para `np.einsum('ik,kj->ij', A, B)`:\n", + "> - ¿qué índice se suma?\n", + "> - ¿qué índices quedan?\n", + "> - ¿cuál debería ser la forma de salida?" + ], + "id": "e127993d" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = np.array([1., 2., 3.]); b = np.array([4., 5., 6.])\n", + "print(np.einsum('i,i->', a, b)) # dot product, sum over i (eq 2.8)\n", + "\n", + "A = np.array([[1., 2.], [3., 4.]]); B = np.array([[5., 6.], [7., 8.]])\n", + "print(np.einsum('ik,kj->ij', A, B)) # matrix product, sum over k (eq 2.5)" + ], + "id": "s01-19" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**The rule, in one sentence:** an index that appears in the inputs but **not** after the arrow is summed over; an index that appears after the arrow is kept.\n", + "\n", + "This rule is the core idea that section 06 develops.\n", + "\n", + "> 🇪🇸 **La regla, en una frase:** un índice que aparece en las entradas pero **no** después de la flecha se suma; un índice que aparece después de la flecha se conserva.\n", + ">\n", + "> Esta regla es la idea central que desarrolla la sección 06." + ], + "id": "s01-20" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 2 — take a tensor apart and put it back\n", + "\n", + "Before coding, say out loud which axes you expect to **fix**, **keep**, **rearrange**, or **sum**. Then verify your reasoning with NumPy.\n", + "\n", + "> 🇪🇸 Antes de programar, explica qué ejes esperas **fijar**, **conservar**, **reorganizar** o **sumar**. Después verifica tu razonamiento con NumPy." + ], + "id": "s01-21" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO 3: From `photo`, extract (a) the green channel as a (512, 512) slice and\n", + "# (b) the colour fiber at pixel (10, 20). Which is a slice, which a fiber?\n", + "\n", + "# TODO 4: Unfold `photo` along all three axes and print the three shapes.\n", + "# Confirm that each unfolding has exactly photo.size entries —\n", + "# unfolding rearranges, it never loses anything.\n", + "\n", + "# TODO 5: Write the dot product of `a` and `b` as einsum, and check it against\n", + "# np.dot. Then write the matrix product of A and B, and check against @." + ], + "id": "s01-22" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "green = photo[:, :, 1] # slice — one index fixed, the rest kept\n", + "fiber = photo[10, 20, :] # fiber — every index fixed except one\n", + "print(green.shape, fiber.shape) # (512, 512) (3,)\n", + "\n", + "for ax in range(3):\n", + " M = unfold(photo, ax)\n", + " print(ax, M.shape, M.size == photo.size) # True every time\n", + "\n", + "print(np.einsum('i,i->', a, b), np.dot(a, b)) # 32.0 32.0\n", + "print(np.allclose(np.einsum('ik,kj->ij', A, B), A @ B)) # True" + ], + "id": "s01-23" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Why this solution works · Por qué funciona esta solución\n", + "\n", + "1. `photo[:, :, 1]` selects the green channel by fixing the last axis (color) to index 1. This is a **slice** because one index is fixed and the rest are kept. `photo[10, 20, :]` selects the color values for the pixel at row 10, column 20. This is a **fiber** because all indices except one are fixed.\n", + "2. `unfold(photo, axis)` rearranges the tensor. For `axis=0`, it creates a matrix where rows correspond to the height dimension. For `axis=1`, rows correspond to the width dimension. For `axis=2`, rows correspond to the color dimension. In all cases, the total number of elements (`.size`) remains the same, demonstrating that unfolding is merely a rearrangement.\n", + "3. `np.einsum('i,i->', a, b)` performs the dot product by summing over the shared index `i`. `np.einsum('ik,kj->ij', A, B)` performs matrix multiplication by summing over the shared index `k` and keeping `i` and `j`. `np.allclose` confirms the results are numerically equivalent to `np.dot` and `@` operator respectively.\n", + "\n", + "> 🇪🇸 **Por qué funciona esta solución:**\n", + ">\n", + "> 1. `photo[:, :, 1]` selecciona el canal verde fijando el último eje (color) al índice 1. Esto es un **corte** porque un índice se fija y el resto se mantienen. `photo[10, 20, :]` selecciona los valores de color para el píxel en la fila 10, columna 20. Esto es una **fibra** porque todos los índices excepto uno están fijos.\n", + "> 2. `unfold(photo, axis)` reorganiza el tensor. Para `axis=0`, crea una matriz donde las filas corresponden a la dimensión de altura. Para `axis=1`, las filas corresponden a la dimensión de anchura. Para `axis=2`, las filas corresponden a la dimensión de color. En todos los casos, el número total de elementos (`.size`) permanece igual, demostrando que el desplegado es solo una reorganización.\n", + "> 3. `np.einsum('i,i->', a, b)` realiza el producto escalar sumando sobre el índice compartido `i`. `np.einsum('ik,kj->ij', A, B)` realiza la multiplicación de matrices sumando sobre el índice compartido `k` y manteniendo `i` y `j`. `np.allclose` confirma que los resultados son numéricamente equivalentes a `np.dot` y al operador `@` respectivamente.\n", + "
" + ], + "id": "130e6acc" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 3 — Axis Reasoning Challenge\n", + "\n", + "Let `D = load_digits().images`, with shape `(1797, 8, 8)` = **images × height × width**.\n", + "\n", + "**Predict before running:** for each expression below, write the expected output shape and explain which axes are fixed, kept, rearranged, or contracted:\n", + "\n", + "1. `D[0]`\n", + "2. `D[:, 3, 4]`\n", + "3. `unfold(D, 0)`\n", + "4. `np.einsum('nhw->n', D)`\n", + "\n", + "Then run your code and compare your prediction with the result.\n", + "\n", + "> 🇪🇸 **Reto de razonamiento sobre ejes:** Sea `D = load_digits().images`, con forma `(1797, 8, 8)` = **imágenes × alto × ancho**.\n", + ">\n", + "> **Predice antes de ejecutar:** para cada expresión, escribe la forma de salida esperada y explica qué ejes se fijan, conservan, reorganizan o contraen. Después ejecuta el código y compara tu predicción con el resultado." + ], + "id": "ac9c4637" + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "# TODO 6: Let D = load_digits().images.\n", + "# Before running each operation, predict its output shape.\n", + "#\n", + "# 1. D[0]\n", + "# 2. D[:, 3, 4]\n", + "# 3. unfold(D, 0)\n", + "# 4. np.einsum('nhw->n', D)\n", + "#\n", + "# For each operation, explain in a comment which axes were\n", + "# fixed, kept, rearranged, or summed/contracted." + ], + "id": "ecdaf14a", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "D = load_digits().images\n", + "print(\"1.\", D[0].shape)\n", + "print(\"2.\", D[:, 3, 4].shape)\n", + "print(\"3.\", unfold(D, 0).shape)\n", + "print(\"4.\", np.einsum('nhw->n', D).shape)" + ], + "id": "033d9ced", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Why this solution works · Por qué funciona esta solución\n", + "\n", + "The operations change the shape as follows:\n", + "\n", + "1. `D[0]` takes the first image. The `n` axis is fixed, `h` and `w` are kept. Resulting shape: `(8, 8)`. This is a **slice**.\n", + "2. `D[:, 3, 4]` takes the pixels at row 3, column 4 from all images. The `h` and `w` axes are fixed, `n` is kept. Resulting shape: `(1797,)`. This is a **fiber**.\n", + "3. `unfold(D, 0)` rearranges the tensor. The `n` axis is kept as the first dimension, and `h` and `w` are flattened. Resulting shape: `(1797, 64)`. This is an **unfolding**.\n", + "4. `np.einsum('nhw->n', D)` sums over `h` and `w` axes. The `n` axis is kept. Resulting shape: `(1797,)`. This is a **contraction**.\n", + "\n", + "> 🇪🇸 **Por qué funciona esta solución:** Las operaciones cambian la forma de la siguiente manera:\n", + ">\n", + "> 1. `D[0]` toma la primera imagen. El eje `n` se fija, `h` y `w` se mantienen. Forma resultante: `(8, 8)`. Esto es un **corte**.\n", + "> 2. `D[:, 3, 4]` toma los píxeles en la fila 3, columna 4 de todas las imágenes. Los ejes `h` y `w` se fijan, `n` se mantiene. Forma resultante: `(1797,)`. Esto es una **fibra**.\n", + "> 3. `unfold(D, 0)` reorganiza el tensor. El eje `n` se mantiene como la primera dimensión, y `h` y `w` se aplanan. Forma resultante: `(1797, 64)`. Esto es un **desplegado**.\n", + "> 4. `np.einsum('nhw->n', D)` suma sobre los ejes `h` y `w`. El eje `n` se mantiene. Forma resultante: `(1797,)`. Esto es una **contracción**.\n", + "
" + ], + "id": "425b7175" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.4 The map of factorizations (Preview)\n", + "\n", + "This is only a preview—**do not memorize these methods yet**. Later sections move from familiar matrix factorizations such as SVD to tensor decompositions such as Tucker.\n", + "\n", + "- **Tucker** represents a tensor using a smaller core tensor and factor matrices.\n", + "- **CP** represents a tensor as a sum of rank-one components.\n", + "\n", + "These methods build on the tensor vocabulary developed here; the Tucker/HOSVD route used later explicitly uses mode unfoldings.\n", + "\n", + "> 🇪🇸 **Vista previa:** Esto es solo un adelanto—**todavía no necesitas memorizar estos métodos**. Más adelante pasaremos de factorizaciones matriciales como SVD a descomposiciones tensoriales como Tucker.\n", + ">\n", + "> - **Tucker** representa un tensor mediante un tensor núcleo más pequeño y matrices de factores.\n", + "> - **CP** representa un tensor como una suma de componentes de rango uno.\n", + ">\n", + "> La ruta Tucker/HOSVD que se usa más adelante emplea explícitamente unfoldings por modo." + ], + "id": "s01-24" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What just happened\n", + "\n", + "You should now be able to reason about a tensor by following its axes:\n", + "\n", + "- `shape`, `ndim`, and `size` describe **structure**;\n", + "- axis labels describe **meaning**;\n", + "- slices/fibers **fix indices**;\n", + "- unfolding **rearranges entries without losing them**;\n", + "- contraction **sums selected axes**.\n", + "\n", + "**One final self-check:** if you cannot explain what each output axis represents, go back one step and trace the indices again.\n", + "\n", + "> 🇪🇸 **Qué acaba de suceder:** Ahora deberías poder razonar sobre un tensor siguiendo sus ejes:\n", + ">\n", + "> - `shape`, `ndim` y `size` describen la **estructura**;\n", + "> - las etiquetas de los ejes describen el **significado**;\n", + "> - los cortes/fibras **fijan índices**;\n", + "> - el unfolding **reorganiza entradas sin perderlas**;\n", + "> - la contracción **suma ejes seleccionados**.\n", + ">\n", + "> **Autoevaluación final:** si no puedes explicar qué representa cada eje de salida, vuelve un paso atrás y sigue de nuevo los índices." + ], + "id": "a1afc435" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Done with this section\n", + "\n", + "Next up: **02 · Thinking in N dimensions** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/02-thinking-in-n-dimensions.ipynb).\n", + "\n", + "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" + ], + "id": "s01-30" + } + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/scripts/content.py b/scripts/content.py index 2f68612..e484a64 100644 --- a/scripts/content.py +++ b/scripts/content.py @@ -58,7 +58,6 @@ "setup": """import numpy as np from sklearn.datasets import load_digits from skimage import data -from scipy.linalg import lu rng = np.random.default_rng(0)""", } From d91d0b0b73767e8f795cf9b2ade09278cd5eb92d Mon Sep 17 00:00:00 2001 From: Sebas Laverde Chunza <106851243+Laverde97@users.noreply.github.com> Date: Fri, 28 Aug 2026 12:01:07 -0500 Subject: [PATCH 05/29] Improve notebook 02 pedagogy with real data for issue #44 --- notebooks/02-thinking-in-n-dimensions.ipynb | 962 ++++++++++++++------ 1 file changed, 690 insertions(+), 272 deletions(-) diff --git a/notebooks/02-thinking-in-n-dimensions.ipynb b/notebooks/02-thinking-in-n-dimensions.ipynb index f53a32c..cc08613 100644 --- a/notebooks/02-thinking-in-n-dimensions.ipynb +++ b/notebooks/02-thinking-in-n-dimensions.ipynb @@ -1,276 +1,694 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 02 · Thinking in N dimensions\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/02-thinking-in-n-dimensions.ipynb)\n", - "\n", - "*Part II · group · 20 min*\n", - "\n", - "> 🇪🇸 **Pensar en N dimensiones** — Discutir qué significa cada eje y por qué un eje de lote difiere de un eje temporal.\n", - "\n", - "Argue about what each axis means, and why a batch axis differs from a time axis.\n", - "\n", - "## What you will be able to do\n", - "\n", - "- Say what a new axis *counts*, rather than saying \"we add a dimension\".\n", - "- Explain why a batch axis and a time axis behave differently despite identical shapes.\n", - "- Propose two ways to batch videos of different lengths, and say what each loses or invents.\n", - "- Map experimental choices onto axes of a real microscopy tensor." - ], - "id": "s02-00" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "Run this first. It installs and imports everything this notebook needs, and nothing else.\n", - "\n", - "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." - ], - "id": "s02-01" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "rng = np.random.default_rng(0)" - ], - "id": "s02-02" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## This one is a discussion, not an exercise\n", - "\n", - "> 🇪🇸 Este bloque es de discusión en grupo. Diez minutos de debate y luego\n", - "> puesta en común. **Sin código** al principio: dibuja en la pizarra compartida.\n", - "\n", - "Go to your breakout channel. **No code at first.** Sketch on the shared board.\n", - "10 minutes discussion, then share-back. The code cells further down are for the\n", - "share-back — leave them alone until then.\n", - "\n", - "> A grayscale image is a matrix: two axes, height and width. Almost nothing in\n", - "> machine learning is a single grayscale image. Each thing you add — colour,\n", - "> many examples, time — adds an axis, and each axis means something different.\n", - "> Your task is to argue about which axis goes where, and why.\n", - "\n", - "### The five questions\n", - "\n", - "1. Start from a grayscale image `(H, W)`. What is the shape of **(a)** one\n", - " colour image, **(b)** a batch of colour images, **(c)** one video, **(d)** a\n", - " batch of videos? For each step, say what the new axis *counts*.\n", - " **Do not say \"we add a dimension.\"**\n", - "2. A batch axis and a time axis both look like ordinary integer indices in code.\n", - " What is different about their **meaning**? Think about what happens if you\n", - " shuffle the order along each one.\n", - "3. Batching requires every example to have the same shape, but real videos have\n", - " different numbers of frames. Propose two ways to build one batched tensor\n", - " from videos of different lengths. What does each one lose or invent?\n", - "4. Photographing the same dish of cells every 10 minutes for 48 hours gives a\n", - " tensor with the same shape as a video. Which experimental choice maps to\n", - " which axis: frame interval → ? field of view → ? number of dishes → ?\n", - "5. Is there a mathematical limit on how many axes a tensor can have? If not,\n", - " what actually limits you when you are writing the code?" - ], - "id": "s02-03" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 1 — write down your group's answer to question 1\n", - "\n", - "> 🇪🇸 Escribe la respuesta de tu grupo a la pregunta 1.\n", - "\n", - "Do this *after* you have argued about it, not instead of arguing about it." - ], - "id": "s02-04" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO 1: Create one array for each of the five stages, using the shapes your\n", - "# group agreed on. Print each shape with a comment saying what the NEW\n", - "# axis counts at that step.\n", - "\n", - "gray_image = np.zeros((28, 28))\n", - "color_image = ... # + colour\n", - "batch_of_images = ... # + many examples\n", - "video = ... # + ordered time\n", - "batch_of_videos = ... # + many examples of ordered time" - ], - "id": "s02-05" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true + "cells": [ + { + "cell_type": "markdown", + "id": "s02-00", + "metadata": { + "id": "s02-00" + }, + "source": [ + "# 02 · Thinking in N dimensions\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/02-thinking-in-n-dimensions.ipynb)\n", + "\n", + "*Part II · group · 20 min*\n", + "\n", + "> 🇪🇸 **Pensar en N dimensiones** — Aprender a leer tensores reales preguntando qué cuenta cada eje y por qué un eje de lote no significa lo mismo que un eje temporal.\n", + "\n", + "Learn to read real tensors by asking what every axis counts and why a batch axis is not the same thing as a time axis.\n", + "\n", + "## What you will be able to do\n", + "\n", + "- Read the order and shape of real image and video tensors and explain what each axis counts.\n", + "- Compare two real tensors with the same shape but different axis semantics.\n", + "- Show with real data why shuffling a batch can be valid while shuffling time changes the data's meaning.\n", + "- Build a padded order-5 batch from real video clips of different lengths and carry a validity mask." + ] }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "gray_image = np.zeros((28, 28)) # (H, W)\n", - "color_image = np.zeros((28, 28, 3)) # (H, W, C) + colour\n", - "batch_of_images = np.zeros((32, 28, 28, 3)) # (N, H, W, C) + many examples\n", - "video = np.zeros((16, 28, 28, 3)) # (T, H, W, C) + ordered time\n", - "batch_of_videos = np.zeros((8, 16, 28, 28, 3)) # (N, T, H, W, C)\n", - "\n", - "for name, a in [(\"gray\", gray_image), (\"colour\", color_image),\n", - " (\"batch\", batch_of_images), (\"video\", video),\n", - " (\"batch of videos\", batch_of_videos)]:\n", - " print(f\"{name:16s} {a.shape} order {a.ndim}\")" - ], - "id": "s02-06" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 2 — question 2, in code\n", - "\n", - "> 🇪🇸 La pregunta 2, demostrada con código.\n", - "\n", - "`batch_of_images` and `video` have the same *kind* of shape tuple. Question 2\n", - "claims they behave completely differently. Show it." - ], - "id": "s02-07" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO 2: Shuffle axis 0 of `batch_of_images` and argue why nothing is lost.\n", - "# Then shuffle axis 0 of `video` and argue what exactly was destroyed.\n", - "# Hint: put something recognisable along the axis first, so you can see\n", - "# the damage — np.arange broadcast into each frame works well." - ], - "id": "s02-08" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true + { + "cell_type": "markdown", + "id": "s02-01", + "metadata": { + "id": "s02-01" + }, + "source": [ + "## Setup\n", + "\n", + "Run this first. It loads real image and video data used throughout the notebook:\n", + "\n", + "1. handwritten digit images from `sklearn.datasets.load_digits`,\n", + "2. a real RGB photograph from `skimage.data.astronaut`, and\n", + "3. a pinned CC0 video from Wikimedia Commons, the same verified clip used later in section 05.\n", + "\n", + "> 🇪🇸 Ejecuta esta celda primero. Carga datos reales de imágenes y video: dígitos manuscritos, una fotografía RGB y un video CC0 verificado de Wikimedia Commons." + ] }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "# Label each position along axis 0 so the shuffle is visible.\n", - "batch = np.arange(8)[:, None, None] * np.ones((8, 4, 4))\n", - "video = np.arange(8)[:, None, None] * np.ones((8, 4, 4))\n", - "\n", - "perm = rng.permutation(8)\n", - "print(batch[perm][:, 0, 0]) # e.g. [3. 0. 6. ...] — a different order\n", - "print(video[perm][:, 0, 0]) # the same numbers, and that is the problem\n", - "\n", - "# The arrays are identical, and so is the operation. The DIFFERENCE IS MEANING:\n", - "# batch — examples are independent, order carries no information.\n", - "# Shuffling is harmless; every training loop does it on purpose.\n", - "# video — order IS the information. Shuffled frames are no longer a video,\n", - "# and nothing in the shape, dtype or size records that damage." - ], - "id": "s02-09" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Share-back\n", - "\n", - "> 🇪🇸 Puesta en común.\n", - "\n", - "```python\n", - "gray_image = np.zeros((28, 28)) # (H, W)\n", - "color_image = np.zeros((28, 28, 3)) # (H, W, C) + colour\n", - "batch_of_images = np.zeros((32, 28, 28, 3)) # (N, H, W, C) + many examples\n", - "video = np.zeros((16, 28, 28, 3)) # (T, H, W, C) + ordered time\n", - "batch_of_videos = np.zeros((8, 16, 28, 28, 3)) # (N, T, H, W, C)\n", - "```\n", - "\n", - "The key idea is **question 2**. `batch_of_images` and `video` have the same\n", - "*kind* of shape tuple, but shuffling axis 0 is harmless for a batch — examples\n", - "are independent, order carries no information — and destroys a video, where\n", - "order **is** the information.\n", - "\n", - "Chapter 2's notation has no concept of \"order matters between elements.\" That is\n", - "genuinely new today.\n", - "\n", - "### The other four, briefly\n", - "\n", - "- **Q3** — pad every video to the longest and carry a mask (invents frames that\n", - " were never recorded, and you must remember to ignore them), or sample a fixed\n", - " number of frames from each (loses everything you did not sample). Take-home B\n", - " in section 11 builds the mask.\n", - "- **Q4** — frame interval → the time axis; field of view → the height and width\n", - " axes; number of dishes → a batch axis. Same shape as a video, completely\n", - " different experiment.\n", - "- **Q5** — no mathematical limit. What limits you is memory, which grows as the\n", - " product of the shape, and your own ability to remember what each axis means,\n", - " which is why sections 03 and 04 exist." - ], - "id": "s02-10" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Done with this section\n", - "\n", - "Next up: **03 · Indexing and broadcasting real data** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/03-indexing-and-broadcasting.ipynb).\n", - "\n", - "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" - ], - "id": "s02-11" - } - ], - "metadata": { - "colab": { - "name": "02-thinking-in-n-dimensions.ipynb", - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" + { + "cell_type": "code", + "execution_count": 1, + "id": "s02-02", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "s02-02", + "outputId": "78665449-9106-48e0-9a44-793bb25a5757" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "real_digit : (8, 8) float32\n", + "digit_batch: (8, 8, 8) float32\n", + "real_photo : (512, 512, 3) uint8\n", + "real_video : (16, 540, 960, 3) uint8\n", + "video_patch: (8, 8, 8) float32\n" + ] + } + ], + "source": [ + "%pip install -q \"imageio[ffmpeg]\"\n", + "\n", + "import hashlib\n", + "import io\n", + "import urllib.request\n", + "\n", + "import imageio.v3 as iio\n", + "import numpy as np\n", + "from sklearn.datasets import load_digits\n", + "from skimage import data\n", + "\n", + "rng = np.random.default_rng(0)\n", + "\n", + "# ---------------------------------------------------------------------------\n", + "# Real image data: handwritten digits\n", + "# ---------------------------------------------------------------------------\n", + "digits = load_digits()\n", + "digit_batch = digits.images[:8].astype(np.float32) # (N, H, W)\n", + "digit_labels = digits.target[:8]\n", + "real_digit = digit_batch[0]\n", + "real_photo = data.astronaut() # real RGB photograph, (H, W, C)\n", + "\n", + "# ---------------------------------------------------------------------------\n", + "# Real video data: \"Tormenta en l'Almadrava\" by Nicolas Vigier, CC0\n", + "# Same pinned source/checksum already used by notebook 05.\n", + "# ---------------------------------------------------------------------------\n", + "VIDEO_URL = (\n", + " \"https://upload.wikimedia.org/wikipedia/commons/1/1e/\"\n", + " \"Tormenta_en_l%27Almadrava.webm\"\n", + ")\n", + "VIDEO_SHA256 = \"e377fcdd2c79b55bce13c2c24b5dd7e412af39cd400eec548a79d0e59d79dc1b\"\n", + "UA = \"tensors-workshop/1.0 (https://github.com/project-delphi/tensors-workshop)\"\n", + "\n", + "\n", + "def fetch_verified_video(url, expected_sha256, n_frames=16, stride=45):\n", + " \"\"\"Download, checksum, and retain sampled frames from a real video.\"\"\"\n", + " req = urllib.request.Request(url, headers={\"User-Agent\": UA})\n", + " raw = urllib.request.urlopen(req, timeout=120).read()\n", + "\n", + " got = hashlib.sha256(raw).hexdigest()\n", + " if got != expected_sha256:\n", + " raise ValueError(\n", + " f\"checksum mismatch: expected {expected_sha256}, got {got}\"\n", + " )\n", + "\n", + " frames = []\n", + " for i, frame in enumerate(\n", + " iio.imiter(io.BytesIO(raw), plugin=\"FFMPEG\", extension=\".webm\")\n", + " ):\n", + " if i % stride == 0:\n", + " frames.append(frame)\n", + " if len(frames) == n_frames:\n", + " break\n", + "\n", + " return np.stack(frames)\n", + "\n", + "\n", + "real_video = fetch_verified_video(VIDEO_URL, VIDEO_SHA256)\n", + "assert real_video.shape == (16, 540, 960, 3), real_video.shape\n", + "\n", + "# Build a small REAL temporal tensor with exactly the same shape as digit_batch:\n", + "# (8, 8, 8). We take 8 real video frames, a centered 8x8 crop, and average RGB.\n", + "r0 = real_video.shape[1] // 2 - 4\n", + "c0 = real_video.shape[2] // 2 - 4\n", + "video_patch = real_video[:8, r0:r0 + 8, c0:c0 + 8].mean(axis=3).astype(np.float32)\n", + "\n", + "print(\"real_digit :\", real_digit.shape, real_digit.dtype)\n", + "print(\"digit_batch:\", digit_batch.shape, digit_batch.dtype)\n", + "print(\"real_photo :\", real_photo.shape, real_photo.dtype)\n", + "print(\"real_video :\", real_video.shape, real_video.dtype)\n", + "print(\"video_patch:\", video_patch.shape, video_patch.dtype)\n" + ] + }, + { + "cell_type": "markdown", + "id": "p02-why", + "metadata": { + "id": "p02-why" + }, + "source": [ + "## Why this matters\n", + "\n", + "A tensor shape is only the beginning. The same tuple of integers can describe completely different experiments.\n", + "\n", + "In this notebook, `digit_batch` and `video_patch` both have shape **`(8, 8, 8)`**:\n", + "\n", + "- `digit_batch`: `(N, H, W)` — 8 independent handwritten-digit images.\n", + "- `video_patch`: `(T, H, W)` — 8 ordered moments from a real video.\n", + "\n", + "The arrays have the same order and the same shape. Their **axis 0 does not mean the same thing**.\n", + "\n", + "> 🇪🇸 **Por qué importa:** `digit_batch` y `video_patch` tienen exactamente la misma forma `(8, 8, 8)`, pero en uno el eje 0 cuenta ejemplos independientes y en el otro cuenta instantes ordenados. La forma no contiene por sí sola esa semántica.\n", + "\n", + "### Predict → Run → Explain\n", + "\n", + "Before each code cell, predict what every axis counts. Then run the code and explain whether changing the order of an axis changes the meaning of the data.\n", + "\n", + "> 🇪🇸 **Predice → Ejecuta → Explica:** antes de ejecutar, di qué cuenta cada eje. Después explica si cambiar su orden modifica o no el significado de los datos." + ] + }, + { + "cell_type": "markdown", + "id": "p02-real-orders", + "metadata": { + "id": "p02-real-orders" + }, + "source": [ + "## 2.1 Real tensors can have different orders\n", + "\n", + "We will not invent arrays with `np.zeros` to build a shape ladder. Instead, inspect real objects that already occur in data work:\n", + "\n", + "| Real object | Shape | Order | Axis meaning |\n", + "|---|---:|---:|---|\n", + "| one handwritten digit | `(8, 8)` | 2 | `(H, W)` |\n", + "| batch of handwritten digits | `(8, 8, 8)` | 3 | `(N, H, W)` |\n", + "| RGB photograph | `(512, 512, 3)` | 3 | `(H, W, C)` |\n", + "| sampled real video | `(16, 540, 960, 3)` | 4 | `(T, H, W, C)` |\n", + "\n", + "Later we will combine several real clips into an order-5 padded batch `(N, T, H, W, C)`.\n", + "\n", + "> 🇪🇸 No construiremos la progresión con arreglos vacíos. Leeremos objetos reales: un dígito, un lote de dígitos, una fotografía RGB y un video. Al final construiremos un lote de videos de orden 5." + ] + }, + { + "cell_type": "markdown", + "id": "s02-04", + "metadata": { + "id": "s02-04" + }, + "source": [ + "## Exercise 1 — read the real shapes\n", + "\n", + "**Predict first.** For each object below, write:\n", + "\n", + "1. its expected order,\n", + "2. what every axis counts, and\n", + "3. which axes could be shuffled without changing the meaning of the individual observations.\n", + "\n", + "> 🇪🇸 **Predice primero.** Para cada objeto real, escribe su orden, qué cuenta cada eje y cuáles ejes podrían reorganizarse sin cambiar el significado de las observaciones individuales." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "s02-05", + "metadata": { + "id": "s02-05" + }, + "outputs": [], + "source": [ + "# TODO 1:\n", + "# Inspect these REAL tensors:\n", + "#\n", + "# real_digit\n", + "# digit_batch\n", + "# real_photo\n", + "# real_video\n", + "#\n", + "# For each one:\n", + "# 1. print .shape and .ndim\n", + "# 2. write a comment naming every axis\n", + "# 3. state whether reordering axis 0 preserves or changes its meaning\n", + "#\n", + "# Write your code below this line.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "s02-06", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "s02-06", + "outputId": "9c4e6e13-1fbb-412c-a7fe-3bb511e3306f" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "one real digit shape=(8, 8) order=2 axes=(H, W)\n", + "real digit batch shape=(8, 8, 8) order=3 axes=(N, H, W)\n", + "real RGB photo shape=(512, 512, 3) order=3 axes=(H, W, C)\n", + "real sampled video shape=(16, 540, 960, 3) order=4 axes=(T, H, W, C)\n", + "\n", + "Axis 0 meaning:\n", + "- real_digit : image rows; reordering them scrambles the image\n", + "- digit_batch: independent examples; batch order can be changed\n", + "- real_photo : image rows; reordering them scrambles the image\n", + "- real_video : time; reordering it changes temporal meaning\n" + ] + } + ], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "\n", + "objects = [\n", + " (\"one real digit\", real_digit, \"(H, W)\"),\n", + " (\"real digit batch\", digit_batch, \"(N, H, W)\"),\n", + " (\"real RGB photo\", real_photo, \"(H, W, C)\"),\n", + " (\"real sampled video\", real_video, \"(T, H, W, C)\"),\n", + "]\n", + "\n", + "for name, arr, axes in objects:\n", + " print(f\"{name:20s} shape={str(arr.shape):20s} order={arr.ndim} axes={axes}\")\n", + "\n", + "print()\n", + "print(\"Axis 0 meaning:\")\n", + "print(\"- real_digit : image rows; reordering them scrambles the image\")\n", + "print(\"- digit_batch: independent examples; batch order can be changed\")\n", + "print(\"- real_photo : image rows; reordering them scrambles the image\")\n", + "print(\"- real_video : time; reordering it changes temporal meaning\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "p02-ex1-explain", + "metadata": { + "id": "p02-ex1-explain" + }, + "source": [ + "
\n", + "Why this solution works · Por qué funciona esta solución\n", + "\n", + "The number of axes tells us the **order**, but the dataset tells us what those axes **mean**. A batch axis is a collection of independent observations; a spatial or temporal axis carries internal structure.\n", + "\n", + "> 🇪🇸 El número de ejes determina el **orden**, pero el conjunto de datos determina qué **significan** esos ejes. Un eje de lote reúne observaciones independientes; los ejes espaciales y temporales contienen estructura interna.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "p02-same-shape", + "metadata": { + "id": "p02-same-shape" + }, + "source": [ + "## 2.2 Same shape, different meaning\n", + "\n", + "Now compare two **real** tensors with exactly the same shape:\n", + "\n", + "```text\n", + "digit_batch.shape == (8, 8, 8) # (N, H, W)\n", + "video_patch.shape == (8, 8, 8) # (T, H, W)\n", + "```\n", + "\n", + "For the digit batch, keeping each image paired with its label is what matters. The order of examples in the batch is not part of a digit's identity.\n", + "\n", + "For the video tensor, axis 0 is time. Consecutive frames are related because the scene evolves from one moment to the next.\n", + "\n", + "> 🇪🇸 Misma forma, significado diferente: en el lote de dígitos el eje 0 cuenta ejemplos independientes; en el video cuenta tiempo. El código ve enteros, pero el científico debe conservar la semántica." + ] + }, + { + "cell_type": "markdown", + "id": "s02-07", + "metadata": { + "id": "s02-07" + }, + "source": [ + "## Exercise 2 — shuffle batch vs. shuffle time on real data\n", + "\n", + "Use one permutation for both real tensors.\n", + "\n", + "For the digit batch, shuffle **images and labels together**. For the video, shuffle the temporal axis. Then compare a simple temporal-continuity statistic before and after the shuffle.\n", + "\n", + "Because the video was sampled with `stride=45`, these are **consecutive sampled frames**, not consecutive frames from the original video stream.\n", + "\n", + "> 🇪🇸 Usa la misma permutación en ambos tensores. En el lote de dígitos reorganiza imágenes y etiquetas juntas. En el video reorganiza el eje temporal y compara una medida simple de continuidad antes y después. Como el video fue muestreado con `stride=45`, trabajamos con **fotogramas muestreados consecutivos**, no con fotogramas consecutivos del video original." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "s02-08", + "metadata": { + "id": "s02-08" + }, + "outputs": [], + "source": [ + "# TODO 2:\n", + "# 1. Create perm = rng.permutation(8).\n", + "# 2. Apply it to digit_batch AND digit_labels.\n", + "# 3. Apply it to video_patch.\n", + "# 4. Print original vs shuffled labels.\n", + "# 5. Compute the mean absolute change between consecutive SAMPLED video frames\n", + "# before and after shuffling.\n", + "# 6. Explain why the digit batch still represents the same 8 labeled examples,\n", + "# while the temporal story of the sampled video sequence has changed.\n", + "#\n", + "# Write your code below this line.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "s02-09", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "s02-09", + "outputId": "ee1b6d7f-09f3-4fea-e6bb-af871bb1d219" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "original digit labels: [0 1 2 3 4 5 6 7]\n", + "shuffled digit labels: [2 4 3 6 5 0 1 7]\n", + "same labeled examples? True\n", + "\n", + "video mean consecutive sampled-frame change before shuffle: 18.365\n", + "video mean consecutive sampled-frame change after shuffle: 47.814\n", + "after/before ratio: 2.60x\n" + ] + } + ], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "\n", + "perm = rng.permutation(8)\n", + "\n", + "shuffled_digits = digit_batch[perm]\n", + "shuffled_labels = digit_labels[perm]\n", + "shuffled_video = video_patch[perm]\n", + "\n", + "print(\"original digit labels:\", digit_labels)\n", + "print(\"shuffled digit labels:\", shuffled_labels)\n", + "print(\"same labeled examples? \", sorted(zip(digit_labels.tolist(), digit_batch.sum(axis=(1, 2)).round(6).tolist()))\n", + " == sorted(zip(shuffled_labels.tolist(), shuffled_digits.sum(axis=(1, 2)).round(6).tolist())))\n", + "\n", + "\n", + "def mean_consecutive_sampled_change(x):\n", + " x = x.astype(np.float32)\n", + " return float(np.mean(np.abs(x[1:] - x[:-1])))\n", + "\n", + "\n", + "before = mean_consecutive_sampled_change(video_patch)\n", + "after = mean_consecutive_sampled_change(shuffled_video)\n", + "\n", + "print()\n", + "print(f\"video mean consecutive sampled-frame change before shuffle: {before:.3f}\")\n", + "print(f\"video mean consecutive sampled-frame change after shuffle: {after:.3f}\")\n", + "print(f\"after/before ratio: {after / before:.2f}x\")\n", + "\n", + "# Batch: the order of independent examples changed, but image-label pairs stayed intact.\n", + "# Time: the same sampled frames remain, but their temporal order no longer describes\n", + "# the original measured sequence.\n" + ] + }, + { + "cell_type": "markdown", + "id": "p02-ex2-explain", + "metadata": { + "id": "p02-ex2-explain" + }, + "source": [ + "
\n", + "What did the shuffle prove? · ¿Qué demostró la permutación?\n", + "\n", + "For the digits, the permutation changes **presentation order**, not the identity of the eight labeled observations. For the video, the permutation changes the measured chronology of the **sampled frame sequence**.\n", + "\n", + "The continuity statistic is not a universal definition of \"video correctness\"; it is simply observable evidence that reordering real sampled frames changes their temporal relationships.\n", + "\n", + "> 🇪🇸 En los dígitos cambia el **orden de presentación**, no la identidad de las ocho observaciones etiquetadas. En el video cambia la cronología medida de la **secuencia de fotogramas muestreados**. La estadística de continuidad es evidencia observable de que reorganizar fotogramas reales muestreados cambia sus relaciones temporales.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "p02-ragged", + "metadata": { + "id": "p02-ragged" + }, + "source": [ + "## 2.3 Real videos have different lengths\n", + "\n", + "A batch needs one rectangular tensor, but real clips may contain different numbers of frames.\n", + "\n", + "We will create three clips by taking three **different measured segments** from the real storm video. Their pixel values are real; only the segment boundaries are chosen for this teaching example.\n", + "\n", + "For efficiency, we spatially subsample the frames before batching. Spatial subsampling keeps measured pixels but retains fewer of them.\n", + "\n", + "> 🇪🇸 Los tres clips provienen de segmentos distintos del video real. Los valores de los píxeles son medidos; solo elegimos los límites de cada segmento con fines pedagógicos. Para ahorrar memoria conservamos uno de cada cuatro píxeles en cada dirección espacial." + ] + }, + { + "cell_type": "markdown", + "id": "p02-ex3", + "metadata": { + "id": "p02-ex3" + }, + "source": [ + "## Exercise 3 — build an order-5 batch from real clips\n", + "\n", + "Create three real clips with lengths `4`, `7`, and `5` frames. Pad them to the longest length and build a Boolean mask telling the model which frame slots contain measured data.\n", + "\n", + "Predict the final tensor shape before running the solution.\n", + "\n", + "> 🇪🇸 Construye tres clips reales de 4, 7 y 5 fotogramas. Rellénalos hasta la longitud máxima y crea una máscara booleana que indique qué posiciones contienen datos medidos. Predice primero la forma final." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "p02-todo3", + "metadata": { + "id": "p02-todo3" + }, + "outputs": [], + "source": [ + "# TODO 3:\n", + "# Work from real_video.\n", + "#\n", + "# 1. Spatially subsample it with real_video[:, ::4, ::4, :].\n", + "# 2. Take three non-overlapping real segments with lengths 4, 7, and 5.\n", + "# 3. Compute T_max.\n", + "# 4. Allocate one padded batch with shape (N, T_max, H, W, C).\n", + "# 5. Build a Boolean mask with shape (N, T_max).\n", + "# 6. Count how many frame slots are padding rather than measured frames.\n", + "#\n", + "# Write your code below this line.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "p02-sol3", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "p02-sol3", + "outputId": "8aa9d912-d5c5-49ec-eade-d46fc885ae58" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "real clip lengths: [4, 7, 5]\n", + "padded batch shape: (3, 7, 135, 240, 3)\n", + "batch order: 5\n", + "axes: (N, T, H, W, C)\n", + "validity mask shape: (3, 7)\n", + "measured frame slots: 16\n", + "padding frame slots: 5\n", + "padding fraction: 23.8%\n" + ] + } + ], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "\n", + "video_small = real_video[:, ::4, ::4, :] # measured pixels, spatially subsampled\n", + "\n", + "real_clips = [\n", + " video_small[0:4], # 4 measured frames\n", + " video_small[4:11], # 7 measured frames\n", + " video_small[11:16], # 5 measured frames\n", + "]\n", + "\n", + "lengths = np.array([len(x) for x in real_clips])\n", + "T_max = int(lengths.max())\n", + "N = len(real_clips)\n", + "H, W, C = video_small.shape[1:]\n", + "\n", + "padded = np.zeros((N, T_max, H, W, C), dtype=video_small.dtype)\n", + "valid = np.zeros((N, T_max), dtype=bool)\n", + "\n", + "for n, x in enumerate(real_clips):\n", + " T = len(x)\n", + " padded[n, :T] = x\n", + " valid[n, :T] = True\n", + "\n", + "padded_slots = int((~valid).sum())\n", + "total_slots = int(valid.size)\n", + "\n", + "print(\"real clip lengths:\", lengths.tolist())\n", + "print(\"padded batch shape:\", padded.shape)\n", + "print(\"batch order:\", padded.ndim)\n", + "print(\"axes: (N, T, H, W, C)\")\n", + "print(\"validity mask shape:\", valid.shape)\n", + "print(\"measured frame slots:\", int(valid.sum()))\n", + "print(\"padding frame slots:\", padded_slots)\n", + "print(f\"padding fraction: {padded_slots / total_slots:.1%}\")\n", + "\n", + "assert padded.shape == (3, 7, 135, 240, 3)\n", + "assert valid.sum() == 16\n" + ] + }, + { + "cell_type": "markdown", + "id": "p02-ex3-explain", + "metadata": { + "id": "p02-ex3-explain" + }, + "source": [ + "
\n", + "Why padding needs a mask · Por qué el padding necesita una máscara\n", + "\n", + "The order-5 tensor is rectangular, but not every `(N, T)` location represents a recorded frame. The mask distinguishes **measured frames** from **padding values introduced by preprocessing**.\n", + "\n", + "This is an important distinction: the underlying dataset is real, while padding is an explicit computational convention. Without the mask, zeros could be mistaken for observations.\n", + "\n", + "> 🇪🇸 El tensor de orden 5 es rectangular, pero no toda posición `(N, T)` corresponde a un fotograma grabado. La máscara distingue **datos medidos** de **valores de relleno introducidos por el preprocesamiento**.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "p02-microscopy", + "metadata": { + "id": "p02-microscopy" + }, + "source": [ + "## 2.4 From video axes to experimental axes\n", + "\n", + "The same reasoning applies to scientific data.\n", + "\n", + "Suppose a microscope records cells repeatedly:\n", + "\n", + "- `T` can count acquisition times.\n", + "- `H, W` can count pixel locations inside each image.\n", + "- `C` can count imaging channels.\n", + "- another axis can count fields of view, wells, dishes, patients, or experimental conditions.\n", + "\n", + "A **field of view is not automatically the same thing as `H` or `W`**. `H` and `W` are pixel coordinates *inside* an image; multiple fields of view usually require their own observation axis or are organized into the batch structure.\n", + "\n", + "> 🇪🇸 En microscopía, un **campo de visión no es automáticamente un eje `H` o `W`**. `H` y `W` describen coordenadas de píxeles dentro de una imagen; varios campos de visión suelen requerir otro eje de observación." + ] + }, + { + "cell_type": "markdown", + "id": "p02-recap", + "metadata": { + "id": "p02-recap" + }, + "source": [ + "## What just happened\n", + "\n", + "You worked with real measured image/video values throughout the core examples. The only introduced values were explicit padding values, tracked by a validity mask.\n", + "\n", + "- a real handwritten digit gave an order-2 tensor `(H, W)`;\n", + "- eight real digits gave an order-3 batch `(N, H, W)`;\n", + "- a real RGB photograph gave an order-3 tensor `(H, W, C)`;\n", + "- a real sampled video gave an order-4 tensor `(T, H, W, C)`;\n", + "- three real video segments plus explicit padding produced an order-5 batch `(N, T, H, W, C)` and a validity mask.\n", + "\n", + "The central lesson is not \"higher order means more complicated.\" It is:\n", + "\n", + "> **Every axis must have a meaning, and operations are only valid when they respect that meaning.**\n", + "\n", + "Two tensors can have the same shape and still represent fundamentally different data.\n", + "\n", + "> 🇪🇸 **Qué ocurrió:** trabajaste con valores reales medidos de imágenes y video en los ejemplos principales. Los únicos valores introducidos artificialmente fueron los del padding, identificados explícitamente mediante una máscara de validez. El mensaje central es que **cada eje debe tener un significado y las operaciones deben respetarlo**. Dos tensores pueden tener la misma forma y representar datos completamente distintos." + ] + }, + { + "cell_type": "markdown", + "id": "s02-11", + "metadata": { + "id": "s02-11" + }, + "source": [ + "---\n", + "\n", + "## Done with this section\n", + "\n", + "Next up: **03 · Indexing and broadcasting real data** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/03-indexing-and-broadcasting.ipynb).\n", + "\n", + "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" + ] + } + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python" + } }, - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file From 0d4f7a63c9225fc7a69278c28ed2754958e53cfe Mon Sep 17 00:00:00 2001 From: Sebas Laverde Chunza Date: Fri, 28 Aug 2026 12:39:08 -0500 Subject: [PATCH 06/29] Finalize notebook 02 pedagogy for issue #44 --- _variables.yml | 20 +- .../02-thinking-in-n-dimensions.ipynb | 556 ++++++-- notebooks/02-thinking-in-n-dimensions.ipynb | 1258 ++++++++--------- scripts/content.py | 72 +- 4 files changed, 1076 insertions(+), 830 deletions(-) diff --git a/_variables.yml b/_variables.yml index d64c5e6..86604c1 100644 --- a/_variables.yml +++ b/_variables.yml @@ -202,18 +202,18 @@ sections: format_es: "grupo" title_en: "Thinking in N dimensions" title_es: "Pensar en N dimensiones" - summary_en: "Argue about what each axis means, and why a batch axis differs from a time axis." - summary_es: "Discutir qué significa cada eje y por qué un eje de lote difiere de un eje temporal." + summary_en: "Learn to read real tensors by asking what every axis counts and why a batch axis is not the same thing as a time axis." + summary_es: "Aprender a leer tensores reales preguntando qué cuenta cada eje y por qué un eje de lote no significa lo mismo que un eje temporal." objectives_en: - - "Say what a new axis *counts*, rather than saying \"we add a dimension\"." - - "Explain why a batch axis and a time axis behave differently despite identical shapes." - - "Propose two ways to batch videos of different lengths, and say what each loses or invents." - - "Map experimental choices onto axes of a real microscopy tensor." + - "Read the order and shape of real image and video tensors and explain what each axis counts." + - "Compare two real tensors with the same shape but different axis semantics." + - "Show with real data why shuffling a batch can be valid while shuffling time changes the data's meaning." + - "Build a padded order-5 batch from real video clips of different lengths and carry a validity mask." objectives_es: - - "Decir qué *cuenta* un nuevo eje, en lugar de decir simplemente \"agregamos una dimensión\"." - - "Explicar por qué un eje de batch y un eje temporal se comportan de manera diferente aunque tengan formas idénticas." - - "Proponer dos formas de agrupar vídeos de diferentes longitudes y explicar qué pierde o inventa cada una." - - "Representar decisiones experimentales mediante los ejes de un tensor real de microscopía." + - "Leer el orden y la forma de tensores reales de imágenes y video y explicar qué cuenta cada eje." + - "Comparar dos tensores reales con la misma forma pero con distinta semántica en sus ejes." + - "Demostrar con datos reales por qué reorganizar un lote puede ser válido mientras reorganizar el tiempo cambia el significado de los datos." + - "Construir un lote rellenado de orden 5 a partir de clips de video reales de distintas longitudes y mantener una máscara de validez." s03: n: "03" slug: "indexing-and-broadcasting" diff --git a/docs/notebooks/02-thinking-in-n-dimensions.ipynb b/docs/notebooks/02-thinking-in-n-dimensions.ipynb index f53a32c..3abe222 100644 --- a/docs/notebooks/02-thinking-in-n-dimensions.ipynb +++ b/docs/notebooks/02-thinking-in-n-dimensions.ipynb @@ -10,16 +10,16 @@ "\n", "*Part II · group · 20 min*\n", "\n", - "> 🇪🇸 **Pensar en N dimensiones** — Discutir qué significa cada eje y por qué un eje de lote difiere de un eje temporal.\n", + "> 🇪🇸 **Pensar en N dimensiones** — Aprender a leer tensores reales preguntando qué cuenta cada eje y por qué un eje de lote no significa lo mismo que un eje temporal.\n", "\n", - "Argue about what each axis means, and why a batch axis differs from a time axis.\n", + "Learn to read real tensors by asking what every axis counts and why a batch axis is not the same thing as a time axis.\n", "\n", "## What you will be able to do\n", "\n", - "- Say what a new axis *counts*, rather than saying \"we add a dimension\".\n", - "- Explain why a batch axis and a time axis behave differently despite identical shapes.\n", - "- Propose two ways to batch videos of different lengths, and say what each loses or invents.\n", - "- Map experimental choices onto axes of a real microscopy tensor." + "- Read the order and shape of real image and video tensors and explain what each axis counts.\n", + "- Compare two real tensors with the same shape but different axis semantics.\n", + "- Show with real data why shuffling a batch can be valid while shuffling time changes the data's meaning.\n", + "- Build a padded order-5 batch from real video clips of different lengths and carry a validity mask." ], "id": "s02-00" }, @@ -41,205 +41,504 @@ "metadata": {}, "outputs": [], "source": [ + "%pip install -q \"imageio[ffmpeg]\"\n", + "\n", + "import hashlib\n", + "import io\n", + "import urllib.request\n", + "\n", + "import imageio.v3 as iio\n", "import numpy as np\n", + "from sklearn.datasets import load_digits\n", + "from skimage import data\n", + "\n", + "rng = np.random.default_rng(0)\n", + "\n", + "# Real image data: handwritten digits\n", + "digits = load_digits()\n", + "digit_batch = digits.images[:8].astype(np.float32) # (N, H, W)\n", + "digit_labels = digits.target[:8]\n", + "real_digit = digit_batch[0]\n", + "real_photo = data.astronaut() # real RGB photograph\n", + "\n", + "# Real video data: \"Tormenta en l'Almadrava\" by Nicolas Vigier, CC0.\n", + "# Same pinned source/checksum used by notebook 05.\n", + "VIDEO_URL = (\n", + " \"https://upload.wikimedia.org/wikipedia/commons/1/1e/\"\n", + " \"Tormenta_en_l%27Almadrava.webm\"\n", + ")\n", + "VIDEO_SHA256 = \"e377fcdd2c79b55bce13c2c24b5dd7e412af39cd400eec548a79d0e59d79dc1b\"\n", + "UA = \"tensors-workshop/1.0 (https://github.com/project-delphi/tensors-workshop)\"\n", + "\n", + "\n", + "def fetch_verified_video(url, expected_sha256, n_frames=16, stride=45):\n", + " # Download, checksum, and retain sampled frames from a real video.\n", + " req = urllib.request.Request(url, headers={\"User-Agent\": UA})\n", + " raw = urllib.request.urlopen(req, timeout=120).read()\n", + "\n", + " got = hashlib.sha256(raw).hexdigest()\n", + " if got != expected_sha256:\n", + " raise ValueError(\n", + " f\"checksum mismatch: expected {expected_sha256}, got {got}\"\n", + " )\n", + "\n", + " frames = []\n", + " for i, frame in enumerate(\n", + " iio.imiter(io.BytesIO(raw), plugin=\"FFMPEG\", extension=\".webm\")\n", + " ):\n", + " if i % stride == 0:\n", + " frames.append(frame)\n", + " if len(frames) == n_frames:\n", + " break\n", + "\n", + " return np.stack(frames)\n", "\n", - "rng = np.random.default_rng(0)" + "\n", + "real_video = fetch_verified_video(VIDEO_URL, VIDEO_SHA256)\n", + "assert real_video.shape == (16, 540, 960, 3), real_video.shape\n", + "\n", + "# Build a real temporal tensor with the same shape as digit_batch: (8, 8, 8).\n", + "# Take 8 sampled video frames, a centered 8x8 crop, and average RGB.\n", + "r0 = real_video.shape[1] // 2 - 4\n", + "c0 = real_video.shape[2] // 2 - 4\n", + "video_patch = (\n", + " real_video[:8, r0:r0 + 8, c0:c0 + 8]\n", + " .mean(axis=3)\n", + " .astype(np.float32)\n", + ")\n", + "\n", + "print(\"real_digit :\", real_digit.shape, real_digit.dtype)\n", + "print(\"digit_batch:\", digit_batch.shape, digit_batch.dtype)\n", + "print(\"real_photo :\", real_photo.shape, real_photo.dtype)\n", + "print(\"real_video :\", real_video.shape, real_video.dtype)\n", + "print(\"video_patch:\", video_patch.shape, video_patch.dtype)" ], "id": "s02-02" }, { "cell_type": "markdown", + "id": "p02-why", "metadata": {}, "source": [ - "## This one is a discussion, not an exercise\n", - "\n", - "> 🇪🇸 Este bloque es de discusión en grupo. Diez minutos de debate y luego\n", - "> puesta en común. **Sin código** al principio: dibuja en la pizarra compartida.\n", - "\n", - "Go to your breakout channel. **No code at first.** Sketch on the shared board.\n", - "10 minutes discussion, then share-back. The code cells further down are for the\n", - "share-back — leave them alone until then.\n", - "\n", - "> A grayscale image is a matrix: two axes, height and width. Almost nothing in\n", - "> machine learning is a single grayscale image. Each thing you add — colour,\n", - "> many examples, time — adds an axis, and each axis means something different.\n", - "> Your task is to argue about which axis goes where, and why.\n", - "\n", - "### The five questions\n", - "\n", - "1. Start from a grayscale image `(H, W)`. What is the shape of **(a)** one\n", - " colour image, **(b)** a batch of colour images, **(c)** one video, **(d)** a\n", - " batch of videos? For each step, say what the new axis *counts*.\n", - " **Do not say \"we add a dimension.\"**\n", - "2. A batch axis and a time axis both look like ordinary integer indices in code.\n", - " What is different about their **meaning**? Think about what happens if you\n", - " shuffle the order along each one.\n", - "3. Batching requires every example to have the same shape, but real videos have\n", - " different numbers of frames. Propose two ways to build one batched tensor\n", - " from videos of different lengths. What does each one lose or invent?\n", - "4. Photographing the same dish of cells every 10 minutes for 48 hours gives a\n", - " tensor with the same shape as a video. Which experimental choice maps to\n", - " which axis: frame interval → ? field of view → ? number of dishes → ?\n", - "5. Is there a mathematical limit on how many axes a tensor can have? If not,\n", - " what actually limits you when you are writing the code?" - ], - "id": "s02-03" + "## Why this matters\n", + "\n", + "A tensor shape is only the beginning. The same tuple of integers can describe completely different experiments.\n", + "\n", + "In this notebook, `digit_batch` and `video_patch` both have shape **`(8, 8, 8)`**:\n", + "\n", + "- `digit_batch`: `(N, H, W)` — 8 independent handwritten-digit images.\n", + "- `video_patch`: `(T, H, W)` — 8 ordered moments from a real video.\n", + "\n", + "The arrays have the same order and the same shape. Their **axis 0 does not mean the same thing**.\n", + "\n", + "> 🇪🇸 **Por qué importa:** `digit_batch` y `video_patch` tienen exactamente la misma forma `(8, 8, 8)`, pero en uno el eje 0 cuenta ejemplos independientes y en el otro cuenta instantes ordenados. La forma no contiene por sí sola esa semántica.\n", + "\n", + "### Predict → Run → Explain\n", + "\n", + "Before each code cell, predict what every axis counts. Then run the code and explain whether changing the order of an axis changes the meaning of the data.\n", + "\n", + "> 🇪🇸 **Predice → Ejecuta → Explica:** antes de ejecutar, di qué cuenta cada eje. Después explica si cambiar su orden modifica o no el significado de los datos." + ] }, { "cell_type": "markdown", + "id": "p02-real-orders", "metadata": {}, "source": [ - "## Exercise 1 — write down your group's answer to question 1\n", + "## 2.1 Real tensors can have different orders\n", "\n", - "> 🇪🇸 Escribe la respuesta de tu grupo a la pregunta 1.\n", + "We will not invent arrays with `np.zeros` to build a shape ladder. Instead, inspect real objects that already occur in data work:\n", "\n", - "Do this *after* you have argued about it, not instead of arguing about it." - ], - "id": "s02-04" + "| Real object | Shape | Order | Axis meaning |\n", + "|---|---:|---:|---|\n", + "| one handwritten digit | `(8, 8)` | 2 | `(H, W)` |\n", + "| batch of handwritten digits | `(8, 8, 8)` | 3 | `(N, H, W)` |\n", + "| RGB photograph | `(512, 512, 3)` | 3 | `(H, W, C)` |\n", + "| sampled real video | `(16, 540, 960, 3)` | 4 | `(T, H, W, C)` |\n", + "\n", + "Later we will combine several real clips into an order-5 padded batch `(N, T, H, W, C)`.\n", + "\n", + "> 🇪🇸 No construiremos la progresión con arreglos vacíos. Leeremos objetos reales: un dígito, un lote de dígitos, una fotografía RGB y un video. Al final construiremos un lote de videos de orden 5." + ] + }, + { + "cell_type": "markdown", + "id": "s02-04", + "metadata": {}, + "source": [ + "## Exercise 1 — read the real shapes\n", + "\n", + "**Predict first.** For each object below, write:\n", + "\n", + "1. its expected order,\n", + "2. what every axis counts, and\n", + "3. which axes could be shuffled without changing the meaning of the individual observations.\n", + "\n", + "> 🇪🇸 **Predice primero.** Para cada objeto real, escribe su orden, qué cuenta cada eje y cuáles ejes podrían reorganizarse sin cambiar el significado de las observaciones individuales." + ] }, { "cell_type": "code", "execution_count": null, + "id": "s02-05", "metadata": {}, "outputs": [], "source": [ - "# TODO 1: Create one array for each of the five stages, using the shapes your\n", - "# group agreed on. Print each shape with a comment saying what the NEW\n", - "# axis counts at that step.\n", - "\n", - "gray_image = np.zeros((28, 28))\n", - "color_image = ... # + colour\n", - "batch_of_images = ... # + many examples\n", - "video = ... # + ordered time\n", - "batch_of_videos = ... # + many examples of ordered time" - ], - "id": "s02-05" + "# TODO 1:\n", + "# Inspect these REAL tensors:\n", + "#\n", + "# real_digit\n", + "# digit_batch\n", + "# real_photo\n", + "# real_video\n", + "#\n", + "# For each one:\n", + "# 1. print .shape and .ndim\n", + "# 2. write a comment naming every axis\n", + "# 3. state whether reordering axis 0 preserves or changes its meaning\n", + "#\n", + "# Write your code below this line.\n" + ] }, { "cell_type": "code", "execution_count": null, + "id": "s02-06", "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "gray_image = np.zeros((28, 28)) # (H, W)\n", - "color_image = np.zeros((28, 28, 3)) # (H, W, C) + colour\n", - "batch_of_images = np.zeros((32, 28, 28, 3)) # (N, H, W, C) + many examples\n", - "video = np.zeros((16, 28, 28, 3)) # (T, H, W, C) + ordered time\n", - "batch_of_videos = np.zeros((8, 16, 28, 28, 3)) # (N, T, H, W, C)\n", - "\n", - "for name, a in [(\"gray\", gray_image), (\"colour\", color_image),\n", - " (\"batch\", batch_of_images), (\"video\", video),\n", - " (\"batch of videos\", batch_of_videos)]:\n", - " print(f\"{name:16s} {a.shape} order {a.ndim}\")" - ], - "id": "s02-06" + "\n", + "objects = [\n", + " (\"one real digit\", real_digit, \"(H, W)\"),\n", + " (\"real digit batch\", digit_batch, \"(N, H, W)\"),\n", + " (\"real RGB photo\", real_photo, \"(H, W, C)\"),\n", + " (\"real sampled video\", real_video, \"(T, H, W, C)\"),\n", + "]\n", + "\n", + "for name, arr, axes in objects:\n", + " print(f\"{name:20s} shape={str(arr.shape):20s} order={arr.ndim} axes={axes}\")\n", + "\n", + "print()\n", + "print(\"Axis 0 meaning:\")\n", + "print(\"- real_digit : image rows; reordering them scrambles the image\")\n", + "print(\"- digit_batch: independent examples; batch order can be changed\")\n", + "print(\"- real_photo : image rows; reordering them scrambles the image\")\n", + "print(\"- real_video : time; reordering it changes temporal meaning\")\n" + ] }, { "cell_type": "markdown", + "id": "p02-ex1-explain", "metadata": {}, "source": [ - "## Exercise 2 — question 2, in code\n", + "
\n", + "Why this solution works · Por qué funciona esta solución\n", "\n", - "> 🇪🇸 La pregunta 2, demostrada con código.\n", + "The number of axes tells us the **order**, but the dataset tells us what those axes **mean**. A batch axis is a collection of independent observations; a spatial or temporal axis carries internal structure.\n", "\n", - "`batch_of_images` and `video` have the same *kind* of shape tuple. Question 2\n", - "claims they behave completely differently. Show it." - ], - "id": "s02-07" + "> 🇪🇸 El número de ejes determina el **orden**, pero el conjunto de datos determina qué **significan** esos ejes. Un eje de lote reúne observaciones independientes; los ejes espaciales y temporales contienen estructura interna.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "p02-same-shape", + "metadata": {}, + "source": [ + "## 2.2 Same shape, different meaning\n", + "\n", + "Now compare two **real** tensors with exactly the same shape:\n", + "\n", + "```text\n", + "digit_batch.shape == (8, 8, 8) # (N, H, W)\n", + "video_patch.shape == (8, 8, 8) # (T, H, W)\n", + "```\n", + "\n", + "For the digit batch, keeping each image paired with its label is what matters. The order of examples in the batch is not part of a digit's identity.\n", + "\n", + "For the video tensor, axis 0 is time. Consecutive **sampled** frames are related because the scene evolves from one measured moment to the next.\n", + "\n", + "> 🇪🇸 Misma forma, significado diferente: en el lote de dígitos el eje 0 cuenta ejemplos independientes; en el video cuenta tiempo. El código ve enteros, pero el científico debe conservar la semántica." + ] + }, + { + "cell_type": "markdown", + "id": "s02-07", + "metadata": {}, + "source": [ + "## Exercise 2 — shuffle batch vs. shuffle time on real data\n", + "\n", + "Use one permutation for both real tensors.\n", + "\n", + "For the digit batch, shuffle **images and labels together**. For the video, shuffle the temporal axis. Then compare a simple temporal-continuity statistic before and after the shuffle.\n", + "\n", + "Because the video was sampled with `stride=45`, these are **consecutive sampled frames**, not consecutive frames from the original video stream.\n", + "\n", + "> 🇪🇸 Usa la misma permutación en ambos tensores. En el lote de dígitos reorganiza imágenes y etiquetas juntas. En el video reorganiza el eje temporal y compara una medida simple de continuidad antes y después. Como el video fue muestreado con `stride=45`, trabajamos con **fotogramas muestreados consecutivos**, no con fotogramas consecutivos del video original." + ] }, { "cell_type": "code", "execution_count": null, + "id": "s02-08", "metadata": {}, "outputs": [], "source": [ - "# TODO 2: Shuffle axis 0 of `batch_of_images` and argue why nothing is lost.\n", - "# Then shuffle axis 0 of `video` and argue what exactly was destroyed.\n", - "# Hint: put something recognisable along the axis first, so you can see\n", - "# the damage — np.arange broadcast into each frame works well." - ], - "id": "s02-08" + "# TODO 2:\n", + "# 1. Create perm = rng.permutation(8).\n", + "# 2. Apply it to digit_batch AND digit_labels.\n", + "# 3. Apply it to video_patch.\n", + "# 4. Print original vs shuffled labels.\n", + "# 5. Compute the mean absolute change between consecutive SAMPLED video frames\n", + "# before and after shuffling.\n", + "# 6. Explain why the digit batch still represents the same 8 labeled examples,\n", + "# while the temporal story of the sampled video sequence has changed.\n", + "#\n", + "# Write your code below this line.\n" + ] }, { "cell_type": "code", "execution_count": null, + "id": "s02-09", "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "# Label each position along axis 0 so the shuffle is visible.\n", - "batch = np.arange(8)[:, None, None] * np.ones((8, 4, 4))\n", - "video = np.arange(8)[:, None, None] * np.ones((8, 4, 4))\n", "\n", "perm = rng.permutation(8)\n", - "print(batch[perm][:, 0, 0]) # e.g. [3. 0. 6. ...] — a different order\n", - "print(video[perm][:, 0, 0]) # the same numbers, and that is the problem\n", - "\n", - "# The arrays are identical, and so is the operation. The DIFFERENCE IS MEANING:\n", - "# batch — examples are independent, order carries no information.\n", - "# Shuffling is harmless; every training loop does it on purpose.\n", - "# video — order IS the information. Shuffled frames are no longer a video,\n", - "# and nothing in the shape, dtype or size records that damage." - ], - "id": "s02-09" + "\n", + "shuffled_digits = digit_batch[perm]\n", + "shuffled_labels = digit_labels[perm]\n", + "shuffled_video = video_patch[perm]\n", + "\n", + "print(\"original digit labels:\", digit_labels)\n", + "print(\"shuffled digit labels:\", shuffled_labels)\n", + "print(\"same labeled examples? \", sorted(zip(digit_labels.tolist(), digit_batch.sum(axis=(1, 2)).round(6).tolist()))\n", + " == sorted(zip(shuffled_labels.tolist(), shuffled_digits.sum(axis=(1, 2)).round(6).tolist())))\n", + "\n", + "\n", + "def mean_consecutive_sampled_change(x):\n", + " x = x.astype(np.float32)\n", + " return float(np.mean(np.abs(x[1:] - x[:-1])))\n", + "\n", + "\n", + "before = mean_consecutive_sampled_change(video_patch)\n", + "after = mean_consecutive_sampled_change(shuffled_video)\n", + "\n", + "print()\n", + "print(f\"video mean consecutive sampled-frame change before shuffle: {before:.3f}\")\n", + "print(f\"video mean consecutive sampled-frame change after shuffle: {after:.3f}\")\n", + "print(f\"after/before ratio: {after / before:.2f}x\")\n", + "\n", + "# Batch: the order of independent examples changed, but image-label pairs stayed intact.\n", + "# Time: the same sampled frames remain, but their temporal order no longer describes\n", + "# the original measured sequence.\n" + ] }, { "cell_type": "markdown", + "id": "p02-ex2-explain", "metadata": {}, "source": [ - "## Share-back\n", + "
\n", + "What did the shuffle prove? · ¿Qué demostró la permutación?\n", "\n", - "> 🇪🇸 Puesta en común.\n", + "For the digits, the permutation changes **presentation order**, not the identity of the eight labeled observations. For the video, the permutation changes the measured chronology of the **sampled frame sequence**.\n", "\n", - "```python\n", - "gray_image = np.zeros((28, 28)) # (H, W)\n", - "color_image = np.zeros((28, 28, 3)) # (H, W, C) + colour\n", - "batch_of_images = np.zeros((32, 28, 28, 3)) # (N, H, W, C) + many examples\n", - "video = np.zeros((16, 28, 28, 3)) # (T, H, W, C) + ordered time\n", - "batch_of_videos = np.zeros((8, 16, 28, 28, 3)) # (N, T, H, W, C)\n", - "```\n", + "The continuity statistic is not a universal definition of \"video correctness\"; it is simply observable evidence that reordering real sampled frames changes their temporal relationships.\n", "\n", - "The key idea is **question 2**. `batch_of_images` and `video` have the same\n", - "*kind* of shape tuple, but shuffling axis 0 is harmless for a batch — examples\n", - "are independent, order carries no information — and destroys a video, where\n", - "order **is** the information.\n", - "\n", - "Chapter 2's notation has no concept of \"order matters between elements.\" That is\n", - "genuinely new today.\n", - "\n", - "### The other four, briefly\n", - "\n", - "- **Q3** — pad every video to the longest and carry a mask (invents frames that\n", - " were never recorded, and you must remember to ignore them), or sample a fixed\n", - " number of frames from each (loses everything you did not sample). Take-home B\n", - " in section 11 builds the mask.\n", - "- **Q4** — frame interval → the time axis; field of view → the height and width\n", - " axes; number of dishes → a batch axis. Same shape as a video, completely\n", - " different experiment.\n", - "- **Q5** — no mathematical limit. What limits you is memory, which grows as the\n", - " product of the shape, and your own ability to remember what each axis means,\n", - " which is why sections 03 and 04 exist." - ], - "id": "s02-10" + "> 🇪🇸 En los dígitos cambia el **orden de presentación**, no la identidad de las ocho observaciones etiquetadas. En el video cambia la cronología medida de la **secuencia de fotogramas muestreados**. La estadística de continuidad es evidencia observable de que reorganizar fotogramas reales muestreados cambia sus relaciones temporales.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "p02-ragged", + "metadata": {}, + "source": [ + "## 2.3 Real videos have different lengths\n", + "\n", + "A batch needs one rectangular tensor, but real clips may contain different numbers of frames.\n", + "\n", + "We will create three clips by taking three **different measured segments** from the real storm video. Their pixel values are real; only the segment boundaries are chosen for this teaching example.\n", + "\n", + "For efficiency, we spatially subsample the frames before batching. Spatial subsampling keeps measured pixels but retains fewer of them.\n", + "\n", + "> 🇪🇸 Los tres clips provienen de segmentos distintos del video real. Los valores de los píxeles son medidos; solo elegimos los límites de cada segmento con fines pedagógicos. Para ahorrar memoria conservamos uno de cada cuatro píxeles en cada dirección espacial." + ] + }, + { + "cell_type": "markdown", + "id": "p02-ex3", + "metadata": {}, + "source": [ + "## Exercise 3 — build an order-5 batch from real clips\n", + "\n", + "Create three real clips with lengths `4`, `7`, and `5` frames. Pad them to the longest length and build a Boolean mask telling the model which frame slots contain measured data.\n", + "\n", + "Predict the final tensor shape before running the solution.\n", + "\n", + "> 🇪🇸 Construye tres clips reales de 4, 7 y 5 fotogramas. Rellénalos hasta la longitud máxima y crea una máscara booleana que indique qué posiciones contienen datos medidos. Predice primero la forma final." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "p02-todo3", + "metadata": {}, + "outputs": [], + "source": [ + "# TODO 3:\n", + "# Work from real_video.\n", + "#\n", + "# 1. Spatially subsample it with real_video[:, ::4, ::4, :].\n", + "# 2. Take three non-overlapping real segments with lengths 4, 7, and 5.\n", + "# 3. Compute T_max.\n", + "# 4. Allocate one padded batch with shape (N, T_max, H, W, C).\n", + "# 5. Build a Boolean mask with shape (N, T_max).\n", + "# 6. Count how many frame slots are padding rather than measured frames.\n", + "#\n", + "# Write your code below this line.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "p02-sol3", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "\n", + "video_small = real_video[:, ::4, ::4, :] # measured pixels, spatially subsampled\n", + "\n", + "real_clips = [\n", + " video_small[0:4], # 4 measured frames\n", + " video_small[4:11], # 7 measured frames\n", + " video_small[11:16], # 5 measured frames\n", + "]\n", + "\n", + "lengths = np.array([len(x) for x in real_clips])\n", + "T_max = int(lengths.max())\n", + "N = len(real_clips)\n", + "H, W, C = video_small.shape[1:]\n", + "\n", + "padded = np.zeros((N, T_max, H, W, C), dtype=video_small.dtype)\n", + "valid = np.zeros((N, T_max), dtype=bool)\n", + "\n", + "for n, x in enumerate(real_clips):\n", + " T = len(x)\n", + " padded[n, :T] = x\n", + " valid[n, :T] = True\n", + "\n", + "padded_slots = int((~valid).sum())\n", + "total_slots = int(valid.size)\n", + "\n", + "print(\"real clip lengths:\", lengths.tolist())\n", + "print(\"padded batch shape:\", padded.shape)\n", + "print(\"batch order:\", padded.ndim)\n", + "print(\"axes: (N, T, H, W, C)\")\n", + "print(\"validity mask shape:\", valid.shape)\n", + "print(\"measured frame slots:\", int(valid.sum()))\n", + "print(\"padding frame slots:\", padded_slots)\n", + "print(f\"padding fraction: {padded_slots / total_slots:.1%}\")\n", + "\n", + "assert padded.shape == (3, 7, 135, 240, 3)\n", + "assert valid.sum() == 16\n" + ] + }, + { + "cell_type": "markdown", + "id": "p02-ex3-explain", + "metadata": {}, + "source": [ + "
\n", + "Why padding needs a mask · Por qué el padding necesita una máscara\n", + "\n", + "The order-5 tensor is rectangular, but not every `(N, T)` location represents a recorded frame. The mask distinguishes **measured frames** from **padding values introduced by preprocessing**.\n", + "\n", + "This is an important distinction: the underlying dataset is real, while padding is an explicit computational convention. Without the mask, zeros could be mistaken for observations.\n", + "\n", + "> 🇪🇸 El tensor de orden 5 es rectangular, pero no toda posición `(N, T)` corresponde a un fotograma grabado. La máscara distingue **datos medidos** de **valores de relleno introducidos por el preprocesamiento**.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "p02-microscopy", + "metadata": {}, + "source": [ + "## 2.4 From video axes to experimental axes\n", + "\n", + "The same reasoning applies to scientific data.\n", + "\n", + "Suppose a microscope records cells repeatedly:\n", + "\n", + "- `T` can count acquisition times.\n", + "- `H, W` can count pixel locations inside each image.\n", + "- `C` can count imaging channels.\n", + "- another axis can count fields of view, wells, dishes, patients, or experimental conditions.\n", + "\n", + "A **field of view is not automatically the same thing as `H` or `W`**. `H` and `W` are pixel coordinates *inside* an image; multiple fields of view usually require their own observation axis or are organized into the batch structure.\n", + "\n", + "> 🇪🇸 En microscopía, un **campo de visión no es automáticamente un eje `H` o `W`**. `H` y `W` describen coordenadas de píxeles dentro de una imagen; varios campos de visión suelen requerir otro eje de observación." + ] + }, + { + "cell_type": "markdown", + "id": "p02-recap", + "metadata": {}, + "source": [ + "## What just happened\n", + "\n", + "You worked with real measured image/video values throughout the core examples. The only introduced values were explicit padding values, tracked by a validity mask.\n", + "\n", + "- a real handwritten digit gave an order-2 tensor `(H, W)`;\n", + "- eight real digits gave an order-3 batch `(N, H, W)`;\n", + "- a real RGB photograph gave an order-3 tensor `(H, W, C)`;\n", + "- a real sampled video gave an order-4 tensor `(T, H, W, C)`;\n", + "- three real video segments plus explicit padding produced an order-5 batch `(N, T, H, W, C)` and a validity mask.\n", + "\n", + "The central lesson is not \"higher order means more complicated.\" It is:\n", + "\n", + "> **Every axis must have a meaning, and operations are only valid when they respect that meaning.**\n", + "\n", + "Two tensors can have the same shape and still represent fundamentally different data.\n", + "\n", + "> 🇪🇸 **Qué ocurrió:** trabajaste con valores reales medidos de imágenes y video en los ejemplos principales. Los únicos valores introducidos artificialmente fueron los del padding, identificados explícitamente mediante una máscara de validez. El mensaje central es que **cada eje debe tener un significado y las operaciones deben respetarlo**. Dos tensores pueden tener la misma forma y representar datos completamente distintos." + ] }, { "cell_type": "markdown", @@ -258,7 +557,6 @@ ], "metadata": { "colab": { - "name": "02-thinking-in-n-dimensions.ipynb", "provenance": [], "toc_visible": true }, diff --git a/notebooks/02-thinking-in-n-dimensions.ipynb b/notebooks/02-thinking-in-n-dimensions.ipynb index cc08613..3abe222 100644 --- a/notebooks/02-thinking-in-n-dimensions.ipynb +++ b/notebooks/02-thinking-in-n-dimensions.ipynb @@ -1,694 +1,574 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "s02-00", - "metadata": { - "id": "s02-00" - }, - "source": [ - "# 02 · Thinking in N dimensions\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/02-thinking-in-n-dimensions.ipynb)\n", - "\n", - "*Part II · group · 20 min*\n", - "\n", - "> 🇪🇸 **Pensar en N dimensiones** — Aprender a leer tensores reales preguntando qué cuenta cada eje y por qué un eje de lote no significa lo mismo que un eje temporal.\n", - "\n", - "Learn to read real tensors by asking what every axis counts and why a batch axis is not the same thing as a time axis.\n", - "\n", - "## What you will be able to do\n", - "\n", - "- Read the order and shape of real image and video tensors and explain what each axis counts.\n", - "- Compare two real tensors with the same shape but different axis semantics.\n", - "- Show with real data why shuffling a batch can be valid while shuffling time changes the data's meaning.\n", - "- Build a padded order-5 batch from real video clips of different lengths and carry a validity mask." - ] - }, - { - "cell_type": "markdown", - "id": "s02-01", - "metadata": { - "id": "s02-01" - }, - "source": [ - "## Setup\n", - "\n", - "Run this first. It loads real image and video data used throughout the notebook:\n", - "\n", - "1. handwritten digit images from `sklearn.datasets.load_digits`,\n", - "2. a real RGB photograph from `skimage.data.astronaut`, and\n", - "3. a pinned CC0 video from Wikimedia Commons, the same verified clip used later in section 05.\n", - "\n", - "> 🇪🇸 Ejecuta esta celda primero. Carga datos reales de imágenes y video: dígitos manuscritos, una fotografía RGB y un video CC0 verificado de Wikimedia Commons." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "s02-02", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "s02-02", - "outputId": "78665449-9106-48e0-9a44-793bb25a5757" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "real_digit : (8, 8) float32\n", - "digit_batch: (8, 8, 8) float32\n", - "real_photo : (512, 512, 3) uint8\n", - "real_video : (16, 540, 960, 3) uint8\n", - "video_patch: (8, 8, 8) float32\n" - ] - } - ], - "source": [ - "%pip install -q \"imageio[ffmpeg]\"\n", - "\n", - "import hashlib\n", - "import io\n", - "import urllib.request\n", - "\n", - "import imageio.v3 as iio\n", - "import numpy as np\n", - "from sklearn.datasets import load_digits\n", - "from skimage import data\n", - "\n", - "rng = np.random.default_rng(0)\n", - "\n", - "# ---------------------------------------------------------------------------\n", - "# Real image data: handwritten digits\n", - "# ---------------------------------------------------------------------------\n", - "digits = load_digits()\n", - "digit_batch = digits.images[:8].astype(np.float32) # (N, H, W)\n", - "digit_labels = digits.target[:8]\n", - "real_digit = digit_batch[0]\n", - "real_photo = data.astronaut() # real RGB photograph, (H, W, C)\n", - "\n", - "# ---------------------------------------------------------------------------\n", - "# Real video data: \"Tormenta en l'Almadrava\" by Nicolas Vigier, CC0\n", - "# Same pinned source/checksum already used by notebook 05.\n", - "# ---------------------------------------------------------------------------\n", - "VIDEO_URL = (\n", - " \"https://upload.wikimedia.org/wikipedia/commons/1/1e/\"\n", - " \"Tormenta_en_l%27Almadrava.webm\"\n", - ")\n", - "VIDEO_SHA256 = \"e377fcdd2c79b55bce13c2c24b5dd7e412af39cd400eec548a79d0e59d79dc1b\"\n", - "UA = \"tensors-workshop/1.0 (https://github.com/project-delphi/tensors-workshop)\"\n", - "\n", - "\n", - "def fetch_verified_video(url, expected_sha256, n_frames=16, stride=45):\n", - " \"\"\"Download, checksum, and retain sampled frames from a real video.\"\"\"\n", - " req = urllib.request.Request(url, headers={\"User-Agent\": UA})\n", - " raw = urllib.request.urlopen(req, timeout=120).read()\n", - "\n", - " got = hashlib.sha256(raw).hexdigest()\n", - " if got != expected_sha256:\n", - " raise ValueError(\n", - " f\"checksum mismatch: expected {expected_sha256}, got {got}\"\n", - " )\n", - "\n", - " frames = []\n", - " for i, frame in enumerate(\n", - " iio.imiter(io.BytesIO(raw), plugin=\"FFMPEG\", extension=\".webm\")\n", - " ):\n", - " if i % stride == 0:\n", - " frames.append(frame)\n", - " if len(frames) == n_frames:\n", - " break\n", - "\n", - " return np.stack(frames)\n", - "\n", - "\n", - "real_video = fetch_verified_video(VIDEO_URL, VIDEO_SHA256)\n", - "assert real_video.shape == (16, 540, 960, 3), real_video.shape\n", - "\n", - "# Build a small REAL temporal tensor with exactly the same shape as digit_batch:\n", - "# (8, 8, 8). We take 8 real video frames, a centered 8x8 crop, and average RGB.\n", - "r0 = real_video.shape[1] // 2 - 4\n", - "c0 = real_video.shape[2] // 2 - 4\n", - "video_patch = real_video[:8, r0:r0 + 8, c0:c0 + 8].mean(axis=3).astype(np.float32)\n", - "\n", - "print(\"real_digit :\", real_digit.shape, real_digit.dtype)\n", - "print(\"digit_batch:\", digit_batch.shape, digit_batch.dtype)\n", - "print(\"real_photo :\", real_photo.shape, real_photo.dtype)\n", - "print(\"real_video :\", real_video.shape, real_video.dtype)\n", - "print(\"video_patch:\", video_patch.shape, video_patch.dtype)\n" - ] - }, - { - "cell_type": "markdown", - "id": "p02-why", - "metadata": { - "id": "p02-why" - }, - "source": [ - "## Why this matters\n", - "\n", - "A tensor shape is only the beginning. The same tuple of integers can describe completely different experiments.\n", - "\n", - "In this notebook, `digit_batch` and `video_patch` both have shape **`(8, 8, 8)`**:\n", - "\n", - "- `digit_batch`: `(N, H, W)` — 8 independent handwritten-digit images.\n", - "- `video_patch`: `(T, H, W)` — 8 ordered moments from a real video.\n", - "\n", - "The arrays have the same order and the same shape. Their **axis 0 does not mean the same thing**.\n", - "\n", - "> 🇪🇸 **Por qué importa:** `digit_batch` y `video_patch` tienen exactamente la misma forma `(8, 8, 8)`, pero en uno el eje 0 cuenta ejemplos independientes y en el otro cuenta instantes ordenados. La forma no contiene por sí sola esa semántica.\n", - "\n", - "### Predict → Run → Explain\n", - "\n", - "Before each code cell, predict what every axis counts. Then run the code and explain whether changing the order of an axis changes the meaning of the data.\n", - "\n", - "> 🇪🇸 **Predice → Ejecuta → Explica:** antes de ejecutar, di qué cuenta cada eje. Después explica si cambiar su orden modifica o no el significado de los datos." - ] - }, - { - "cell_type": "markdown", - "id": "p02-real-orders", - "metadata": { - "id": "p02-real-orders" - }, - "source": [ - "## 2.1 Real tensors can have different orders\n", - "\n", - "We will not invent arrays with `np.zeros` to build a shape ladder. Instead, inspect real objects that already occur in data work:\n", - "\n", - "| Real object | Shape | Order | Axis meaning |\n", - "|---|---:|---:|---|\n", - "| one handwritten digit | `(8, 8)` | 2 | `(H, W)` |\n", - "| batch of handwritten digits | `(8, 8, 8)` | 3 | `(N, H, W)` |\n", - "| RGB photograph | `(512, 512, 3)` | 3 | `(H, W, C)` |\n", - "| sampled real video | `(16, 540, 960, 3)` | 4 | `(T, H, W, C)` |\n", - "\n", - "Later we will combine several real clips into an order-5 padded batch `(N, T, H, W, C)`.\n", - "\n", - "> 🇪🇸 No construiremos la progresión con arreglos vacíos. Leeremos objetos reales: un dígito, un lote de dígitos, una fotografía RGB y un video. Al final construiremos un lote de videos de orden 5." - ] - }, - { - "cell_type": "markdown", - "id": "s02-04", - "metadata": { - "id": "s02-04" - }, - "source": [ - "## Exercise 1 — read the real shapes\n", - "\n", - "**Predict first.** For each object below, write:\n", - "\n", - "1. its expected order,\n", - "2. what every axis counts, and\n", - "3. which axes could be shuffled without changing the meaning of the individual observations.\n", - "\n", - "> 🇪🇸 **Predice primero.** Para cada objeto real, escribe su orden, qué cuenta cada eje y cuáles ejes podrían reorganizarse sin cambiar el significado de las observaciones individuales." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "s02-05", - "metadata": { - "id": "s02-05" - }, - "outputs": [], - "source": [ - "# TODO 1:\n", - "# Inspect these REAL tensors:\n", - "#\n", - "# real_digit\n", - "# digit_batch\n", - "# real_photo\n", - "# real_video\n", - "#\n", - "# For each one:\n", - "# 1. print .shape and .ndim\n", - "# 2. write a comment naming every axis\n", - "# 3. state whether reordering axis 0 preserves or changes its meaning\n", - "#\n", - "# Write your code below this line.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "s02-06", - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ], - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "s02-06", - "outputId": "9c4e6e13-1fbb-412c-a7fe-3bb511e3306f" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "one real digit shape=(8, 8) order=2 axes=(H, W)\n", - "real digit batch shape=(8, 8, 8) order=3 axes=(N, H, W)\n", - "real RGB photo shape=(512, 512, 3) order=3 axes=(H, W, C)\n", - "real sampled video shape=(16, 540, 960, 3) order=4 axes=(T, H, W, C)\n", - "\n", - "Axis 0 meaning:\n", - "- real_digit : image rows; reordering them scrambles the image\n", - "- digit_batch: independent examples; batch order can be changed\n", - "- real_photo : image rows; reordering them scrambles the image\n", - "- real_video : time; reordering it changes temporal meaning\n" - ] - } - ], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "\n", - "objects = [\n", - " (\"one real digit\", real_digit, \"(H, W)\"),\n", - " (\"real digit batch\", digit_batch, \"(N, H, W)\"),\n", - " (\"real RGB photo\", real_photo, \"(H, W, C)\"),\n", - " (\"real sampled video\", real_video, \"(T, H, W, C)\"),\n", - "]\n", - "\n", - "for name, arr, axes in objects:\n", - " print(f\"{name:20s} shape={str(arr.shape):20s} order={arr.ndim} axes={axes}\")\n", - "\n", - "print()\n", - "print(\"Axis 0 meaning:\")\n", - "print(\"- real_digit : image rows; reordering them scrambles the image\")\n", - "print(\"- digit_batch: independent examples; batch order can be changed\")\n", - "print(\"- real_photo : image rows; reordering them scrambles the image\")\n", - "print(\"- real_video : time; reordering it changes temporal meaning\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "p02-ex1-explain", - "metadata": { - "id": "p02-ex1-explain" - }, - "source": [ - "
\n", - "Why this solution works · Por qué funciona esta solución\n", - "\n", - "The number of axes tells us the **order**, but the dataset tells us what those axes **mean**. A batch axis is a collection of independent observations; a spatial or temporal axis carries internal structure.\n", - "\n", - "> 🇪🇸 El número de ejes determina el **orden**, pero el conjunto de datos determina qué **significan** esos ejes. Un eje de lote reúne observaciones independientes; los ejes espaciales y temporales contienen estructura interna.\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "p02-same-shape", - "metadata": { - "id": "p02-same-shape" - }, - "source": [ - "## 2.2 Same shape, different meaning\n", - "\n", - "Now compare two **real** tensors with exactly the same shape:\n", - "\n", - "```text\n", - "digit_batch.shape == (8, 8, 8) # (N, H, W)\n", - "video_patch.shape == (8, 8, 8) # (T, H, W)\n", - "```\n", - "\n", - "For the digit batch, keeping each image paired with its label is what matters. The order of examples in the batch is not part of a digit's identity.\n", - "\n", - "For the video tensor, axis 0 is time. Consecutive frames are related because the scene evolves from one moment to the next.\n", - "\n", - "> 🇪🇸 Misma forma, significado diferente: en el lote de dígitos el eje 0 cuenta ejemplos independientes; en el video cuenta tiempo. El código ve enteros, pero el científico debe conservar la semántica." - ] - }, - { - "cell_type": "markdown", - "id": "s02-07", - "metadata": { - "id": "s02-07" - }, - "source": [ - "## Exercise 2 — shuffle batch vs. shuffle time on real data\n", - "\n", - "Use one permutation for both real tensors.\n", - "\n", - "For the digit batch, shuffle **images and labels together**. For the video, shuffle the temporal axis. Then compare a simple temporal-continuity statistic before and after the shuffle.\n", - "\n", - "Because the video was sampled with `stride=45`, these are **consecutive sampled frames**, not consecutive frames from the original video stream.\n", - "\n", - "> 🇪🇸 Usa la misma permutación en ambos tensores. En el lote de dígitos reorganiza imágenes y etiquetas juntas. En el video reorganiza el eje temporal y compara una medida simple de continuidad antes y después. Como el video fue muestreado con `stride=45`, trabajamos con **fotogramas muestreados consecutivos**, no con fotogramas consecutivos del video original." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "s02-08", - "metadata": { - "id": "s02-08" - }, - "outputs": [], - "source": [ - "# TODO 2:\n", - "# 1. Create perm = rng.permutation(8).\n", - "# 2. Apply it to digit_batch AND digit_labels.\n", - "# 3. Apply it to video_patch.\n", - "# 4. Print original vs shuffled labels.\n", - "# 5. Compute the mean absolute change between consecutive SAMPLED video frames\n", - "# before and after shuffling.\n", - "# 6. Explain why the digit batch still represents the same 8 labeled examples,\n", - "# while the temporal story of the sampled video sequence has changed.\n", - "#\n", - "# Write your code below this line.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "s02-09", - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ], - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "s02-09", - "outputId": "ee1b6d7f-09f3-4fea-e6bb-af871bb1d219" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "original digit labels: [0 1 2 3 4 5 6 7]\n", - "shuffled digit labels: [2 4 3 6 5 0 1 7]\n", - "same labeled examples? True\n", - "\n", - "video mean consecutive sampled-frame change before shuffle: 18.365\n", - "video mean consecutive sampled-frame change after shuffle: 47.814\n", - "after/before ratio: 2.60x\n" - ] - } - ], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "\n", - "perm = rng.permutation(8)\n", - "\n", - "shuffled_digits = digit_batch[perm]\n", - "shuffled_labels = digit_labels[perm]\n", - "shuffled_video = video_patch[perm]\n", - "\n", - "print(\"original digit labels:\", digit_labels)\n", - "print(\"shuffled digit labels:\", shuffled_labels)\n", - "print(\"same labeled examples? \", sorted(zip(digit_labels.tolist(), digit_batch.sum(axis=(1, 2)).round(6).tolist()))\n", - " == sorted(zip(shuffled_labels.tolist(), shuffled_digits.sum(axis=(1, 2)).round(6).tolist())))\n", - "\n", - "\n", - "def mean_consecutive_sampled_change(x):\n", - " x = x.astype(np.float32)\n", - " return float(np.mean(np.abs(x[1:] - x[:-1])))\n", - "\n", - "\n", - "before = mean_consecutive_sampled_change(video_patch)\n", - "after = mean_consecutive_sampled_change(shuffled_video)\n", - "\n", - "print()\n", - "print(f\"video mean consecutive sampled-frame change before shuffle: {before:.3f}\")\n", - "print(f\"video mean consecutive sampled-frame change after shuffle: {after:.3f}\")\n", - "print(f\"after/before ratio: {after / before:.2f}x\")\n", - "\n", - "# Batch: the order of independent examples changed, but image-label pairs stayed intact.\n", - "# Time: the same sampled frames remain, but their temporal order no longer describes\n", - "# the original measured sequence.\n" - ] - }, - { - "cell_type": "markdown", - "id": "p02-ex2-explain", - "metadata": { - "id": "p02-ex2-explain" - }, - "source": [ - "
\n", - "What did the shuffle prove? · ¿Qué demostró la permutación?\n", - "\n", - "For the digits, the permutation changes **presentation order**, not the identity of the eight labeled observations. For the video, the permutation changes the measured chronology of the **sampled frame sequence**.\n", - "\n", - "The continuity statistic is not a universal definition of \"video correctness\"; it is simply observable evidence that reordering real sampled frames changes their temporal relationships.\n", - "\n", - "> 🇪🇸 En los dígitos cambia el **orden de presentación**, no la identidad de las ocho observaciones etiquetadas. En el video cambia la cronología medida de la **secuencia de fotogramas muestreados**. La estadística de continuidad es evidencia observable de que reorganizar fotogramas reales muestreados cambia sus relaciones temporales.\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "p02-ragged", - "metadata": { - "id": "p02-ragged" - }, - "source": [ - "## 2.3 Real videos have different lengths\n", - "\n", - "A batch needs one rectangular tensor, but real clips may contain different numbers of frames.\n", - "\n", - "We will create three clips by taking three **different measured segments** from the real storm video. Their pixel values are real; only the segment boundaries are chosen for this teaching example.\n", - "\n", - "For efficiency, we spatially subsample the frames before batching. Spatial subsampling keeps measured pixels but retains fewer of them.\n", - "\n", - "> 🇪🇸 Los tres clips provienen de segmentos distintos del video real. Los valores de los píxeles son medidos; solo elegimos los límites de cada segmento con fines pedagógicos. Para ahorrar memoria conservamos uno de cada cuatro píxeles en cada dirección espacial." - ] - }, - { - "cell_type": "markdown", - "id": "p02-ex3", - "metadata": { - "id": "p02-ex3" - }, - "source": [ - "## Exercise 3 — build an order-5 batch from real clips\n", - "\n", - "Create three real clips with lengths `4`, `7`, and `5` frames. Pad them to the longest length and build a Boolean mask telling the model which frame slots contain measured data.\n", - "\n", - "Predict the final tensor shape before running the solution.\n", - "\n", - "> 🇪🇸 Construye tres clips reales de 4, 7 y 5 fotogramas. Rellénalos hasta la longitud máxima y crea una máscara booleana que indique qué posiciones contienen datos medidos. Predice primero la forma final." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "p02-todo3", - "metadata": { - "id": "p02-todo3" - }, - "outputs": [], - "source": [ - "# TODO 3:\n", - "# Work from real_video.\n", - "#\n", - "# 1. Spatially subsample it with real_video[:, ::4, ::4, :].\n", - "# 2. Take three non-overlapping real segments with lengths 4, 7, and 5.\n", - "# 3. Compute T_max.\n", - "# 4. Allocate one padded batch with shape (N, T_max, H, W, C).\n", - "# 5. Build a Boolean mask with shape (N, T_max).\n", - "# 6. Count how many frame slots are padding rather than measured frames.\n", - "#\n", - "# Write your code below this line.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "p02-sol3", - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ], - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "p02-sol3", - "outputId": "8aa9d912-d5c5-49ec-eade-d46fc885ae58" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "real clip lengths: [4, 7, 5]\n", - "padded batch shape: (3, 7, 135, 240, 3)\n", - "batch order: 5\n", - "axes: (N, T, H, W, C)\n", - "validity mask shape: (3, 7)\n", - "measured frame slots: 16\n", - "padding frame slots: 5\n", - "padding fraction: 23.8%\n" - ] - } - ], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "\n", - "video_small = real_video[:, ::4, ::4, :] # measured pixels, spatially subsampled\n", - "\n", - "real_clips = [\n", - " video_small[0:4], # 4 measured frames\n", - " video_small[4:11], # 7 measured frames\n", - " video_small[11:16], # 5 measured frames\n", - "]\n", - "\n", - "lengths = np.array([len(x) for x in real_clips])\n", - "T_max = int(lengths.max())\n", - "N = len(real_clips)\n", - "H, W, C = video_small.shape[1:]\n", - "\n", - "padded = np.zeros((N, T_max, H, W, C), dtype=video_small.dtype)\n", - "valid = np.zeros((N, T_max), dtype=bool)\n", - "\n", - "for n, x in enumerate(real_clips):\n", - " T = len(x)\n", - " padded[n, :T] = x\n", - " valid[n, :T] = True\n", - "\n", - "padded_slots = int((~valid).sum())\n", - "total_slots = int(valid.size)\n", - "\n", - "print(\"real clip lengths:\", lengths.tolist())\n", - "print(\"padded batch shape:\", padded.shape)\n", - "print(\"batch order:\", padded.ndim)\n", - "print(\"axes: (N, T, H, W, C)\")\n", - "print(\"validity mask shape:\", valid.shape)\n", - "print(\"measured frame slots:\", int(valid.sum()))\n", - "print(\"padding frame slots:\", padded_slots)\n", - "print(f\"padding fraction: {padded_slots / total_slots:.1%}\")\n", - "\n", - "assert padded.shape == (3, 7, 135, 240, 3)\n", - "assert valid.sum() == 16\n" - ] - }, - { - "cell_type": "markdown", - "id": "p02-ex3-explain", - "metadata": { - "id": "p02-ex3-explain" - }, - "source": [ - "
\n", - "Why padding needs a mask · Por qué el padding necesita una máscara\n", - "\n", - "The order-5 tensor is rectangular, but not every `(N, T)` location represents a recorded frame. The mask distinguishes **measured frames** from **padding values introduced by preprocessing**.\n", - "\n", - "This is an important distinction: the underlying dataset is real, while padding is an explicit computational convention. Without the mask, zeros could be mistaken for observations.\n", - "\n", - "> 🇪🇸 El tensor de orden 5 es rectangular, pero no toda posición `(N, T)` corresponde a un fotograma grabado. La máscara distingue **datos medidos** de **valores de relleno introducidos por el preprocesamiento**.\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "p02-microscopy", - "metadata": { - "id": "p02-microscopy" - }, - "source": [ - "## 2.4 From video axes to experimental axes\n", - "\n", - "The same reasoning applies to scientific data.\n", - "\n", - "Suppose a microscope records cells repeatedly:\n", - "\n", - "- `T` can count acquisition times.\n", - "- `H, W` can count pixel locations inside each image.\n", - "- `C` can count imaging channels.\n", - "- another axis can count fields of view, wells, dishes, patients, or experimental conditions.\n", - "\n", - "A **field of view is not automatically the same thing as `H` or `W`**. `H` and `W` are pixel coordinates *inside* an image; multiple fields of view usually require their own observation axis or are organized into the batch structure.\n", - "\n", - "> 🇪🇸 En microscopía, un **campo de visión no es automáticamente un eje `H` o `W`**. `H` y `W` describen coordenadas de píxeles dentro de una imagen; varios campos de visión suelen requerir otro eje de observación." - ] - }, - { - "cell_type": "markdown", - "id": "p02-recap", - "metadata": { - "id": "p02-recap" - }, - "source": [ - "## What just happened\n", - "\n", - "You worked with real measured image/video values throughout the core examples. The only introduced values were explicit padding values, tracked by a validity mask.\n", - "\n", - "- a real handwritten digit gave an order-2 tensor `(H, W)`;\n", - "- eight real digits gave an order-3 batch `(N, H, W)`;\n", - "- a real RGB photograph gave an order-3 tensor `(H, W, C)`;\n", - "- a real sampled video gave an order-4 tensor `(T, H, W, C)`;\n", - "- three real video segments plus explicit padding produced an order-5 batch `(N, T, H, W, C)` and a validity mask.\n", - "\n", - "The central lesson is not \"higher order means more complicated.\" It is:\n", - "\n", - "> **Every axis must have a meaning, and operations are only valid when they respect that meaning.**\n", - "\n", - "Two tensors can have the same shape and still represent fundamentally different data.\n", - "\n", - "> 🇪🇸 **Qué ocurrió:** trabajaste con valores reales medidos de imágenes y video en los ejemplos principales. Los únicos valores introducidos artificialmente fueron los del padding, identificados explícitamente mediante una máscara de validez. El mensaje central es que **cada eje debe tener un significado y las operaciones deben respetarlo**. Dos tensores pueden tener la misma forma y representar datos completamente distintos." - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 02 · Thinking in N dimensions\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/02-thinking-in-n-dimensions.ipynb)\n", + "\n", + "*Part II · group · 20 min*\n", + "\n", + "> 🇪🇸 **Pensar en N dimensiones** — Aprender a leer tensores reales preguntando qué cuenta cada eje y por qué un eje de lote no significa lo mismo que un eje temporal.\n", + "\n", + "Learn to read real tensors by asking what every axis counts and why a batch axis is not the same thing as a time axis.\n", + "\n", + "## What you will be able to do\n", + "\n", + "- Read the order and shape of real image and video tensors and explain what each axis counts.\n", + "- Compare two real tensors with the same shape but different axis semantics.\n", + "- Show with real data why shuffling a batch can be valid while shuffling time changes the data's meaning.\n", + "- Build a padded order-5 batch from real video clips of different lengths and carry a validity mask." + ], + "id": "s02-00" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "Run this first. It installs and imports everything this notebook needs, and nothing else.\n", + "\n", + "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." + ], + "id": "s02-01" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install -q \"imageio[ffmpeg]\"\n", + "\n", + "import hashlib\n", + "import io\n", + "import urllib.request\n", + "\n", + "import imageio.v3 as iio\n", + "import numpy as np\n", + "from sklearn.datasets import load_digits\n", + "from skimage import data\n", + "\n", + "rng = np.random.default_rng(0)\n", + "\n", + "# Real image data: handwritten digits\n", + "digits = load_digits()\n", + "digit_batch = digits.images[:8].astype(np.float32) # (N, H, W)\n", + "digit_labels = digits.target[:8]\n", + "real_digit = digit_batch[0]\n", + "real_photo = data.astronaut() # real RGB photograph\n", + "\n", + "# Real video data: \"Tormenta en l'Almadrava\" by Nicolas Vigier, CC0.\n", + "# Same pinned source/checksum used by notebook 05.\n", + "VIDEO_URL = (\n", + " \"https://upload.wikimedia.org/wikipedia/commons/1/1e/\"\n", + " \"Tormenta_en_l%27Almadrava.webm\"\n", + ")\n", + "VIDEO_SHA256 = \"e377fcdd2c79b55bce13c2c24b5dd7e412af39cd400eec548a79d0e59d79dc1b\"\n", + "UA = \"tensors-workshop/1.0 (https://github.com/project-delphi/tensors-workshop)\"\n", + "\n", + "\n", + "def fetch_verified_video(url, expected_sha256, n_frames=16, stride=45):\n", + " # Download, checksum, and retain sampled frames from a real video.\n", + " req = urllib.request.Request(url, headers={\"User-Agent\": UA})\n", + " raw = urllib.request.urlopen(req, timeout=120).read()\n", + "\n", + " got = hashlib.sha256(raw).hexdigest()\n", + " if got != expected_sha256:\n", + " raise ValueError(\n", + " f\"checksum mismatch: expected {expected_sha256}, got {got}\"\n", + " )\n", + "\n", + " frames = []\n", + " for i, frame in enumerate(\n", + " iio.imiter(io.BytesIO(raw), plugin=\"FFMPEG\", extension=\".webm\")\n", + " ):\n", + " if i % stride == 0:\n", + " frames.append(frame)\n", + " if len(frames) == n_frames:\n", + " break\n", + "\n", + " return np.stack(frames)\n", + "\n", + "\n", + "real_video = fetch_verified_video(VIDEO_URL, VIDEO_SHA256)\n", + "assert real_video.shape == (16, 540, 960, 3), real_video.shape\n", + "\n", + "# Build a real temporal tensor with the same shape as digit_batch: (8, 8, 8).\n", + "# Take 8 sampled video frames, a centered 8x8 crop, and average RGB.\n", + "r0 = real_video.shape[1] // 2 - 4\n", + "c0 = real_video.shape[2] // 2 - 4\n", + "video_patch = (\n", + " real_video[:8, r0:r0 + 8, c0:c0 + 8]\n", + " .mean(axis=3)\n", + " .astype(np.float32)\n", + ")\n", + "\n", + "print(\"real_digit :\", real_digit.shape, real_digit.dtype)\n", + "print(\"digit_batch:\", digit_batch.shape, digit_batch.dtype)\n", + "print(\"real_photo :\", real_photo.shape, real_photo.dtype)\n", + "print(\"real_video :\", real_video.shape, real_video.dtype)\n", + "print(\"video_patch:\", video_patch.shape, video_patch.dtype)" + ], + "id": "s02-02" + }, + { + "cell_type": "markdown", + "id": "p02-why", + "metadata": {}, + "source": [ + "## Why this matters\n", + "\n", + "A tensor shape is only the beginning. The same tuple of integers can describe completely different experiments.\n", + "\n", + "In this notebook, `digit_batch` and `video_patch` both have shape **`(8, 8, 8)`**:\n", + "\n", + "- `digit_batch`: `(N, H, W)` — 8 independent handwritten-digit images.\n", + "- `video_patch`: `(T, H, W)` — 8 ordered moments from a real video.\n", + "\n", + "The arrays have the same order and the same shape. Their **axis 0 does not mean the same thing**.\n", + "\n", + "> 🇪🇸 **Por qué importa:** `digit_batch` y `video_patch` tienen exactamente la misma forma `(8, 8, 8)`, pero en uno el eje 0 cuenta ejemplos independientes y en el otro cuenta instantes ordenados. La forma no contiene por sí sola esa semántica.\n", + "\n", + "### Predict → Run → Explain\n", + "\n", + "Before each code cell, predict what every axis counts. Then run the code and explain whether changing the order of an axis changes the meaning of the data.\n", + "\n", + "> 🇪🇸 **Predice → Ejecuta → Explica:** antes de ejecutar, di qué cuenta cada eje. Después explica si cambiar su orden modifica o no el significado de los datos." + ] + }, + { + "cell_type": "markdown", + "id": "p02-real-orders", + "metadata": {}, + "source": [ + "## 2.1 Real tensors can have different orders\n", + "\n", + "We will not invent arrays with `np.zeros` to build a shape ladder. Instead, inspect real objects that already occur in data work:\n", + "\n", + "| Real object | Shape | Order | Axis meaning |\n", + "|---|---:|---:|---|\n", + "| one handwritten digit | `(8, 8)` | 2 | `(H, W)` |\n", + "| batch of handwritten digits | `(8, 8, 8)` | 3 | `(N, H, W)` |\n", + "| RGB photograph | `(512, 512, 3)` | 3 | `(H, W, C)` |\n", + "| sampled real video | `(16, 540, 960, 3)` | 4 | `(T, H, W, C)` |\n", + "\n", + "Later we will combine several real clips into an order-5 padded batch `(N, T, H, W, C)`.\n", + "\n", + "> 🇪🇸 No construiremos la progresión con arreglos vacíos. Leeremos objetos reales: un dígito, un lote de dígitos, una fotografía RGB y un video. Al final construiremos un lote de videos de orden 5." + ] + }, + { + "cell_type": "markdown", + "id": "s02-04", + "metadata": {}, + "source": [ + "## Exercise 1 — read the real shapes\n", + "\n", + "**Predict first.** For each object below, write:\n", + "\n", + "1. its expected order,\n", + "2. what every axis counts, and\n", + "3. which axes could be shuffled without changing the meaning of the individual observations.\n", + "\n", + "> 🇪🇸 **Predice primero.** Para cada objeto real, escribe su orden, qué cuenta cada eje y cuáles ejes podrían reorganizarse sin cambiar el significado de las observaciones individuales." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "s02-05", + "metadata": {}, + "outputs": [], + "source": [ + "# TODO 1:\n", + "# Inspect these REAL tensors:\n", + "#\n", + "# real_digit\n", + "# digit_batch\n", + "# real_photo\n", + "# real_video\n", + "#\n", + "# For each one:\n", + "# 1. print .shape and .ndim\n", + "# 2. write a comment naming every axis\n", + "# 3. state whether reordering axis 0 preserves or changes its meaning\n", + "#\n", + "# Write your code below this line.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "s02-06", + "metadata": { + "jupyter": { + "source_hidden": true }, - { - "cell_type": "markdown", - "id": "s02-11", - "metadata": { - "id": "s02-11" - }, - "source": [ - "---\n", - "\n", - "## Done with this section\n", - "\n", - "Next up: **03 · Indexing and broadcasting real data** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/03-indexing-and-broadcasting.ipynb).\n", - "\n", - "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" - ] - } - ], - "metadata": { - "colab": { - "provenance": [], - "toc_visible": true + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "\n", + "objects = [\n", + " (\"one real digit\", real_digit, \"(H, W)\"),\n", + " (\"real digit batch\", digit_batch, \"(N, H, W)\"),\n", + " (\"real RGB photo\", real_photo, \"(H, W, C)\"),\n", + " (\"real sampled video\", real_video, \"(T, H, W, C)\"),\n", + "]\n", + "\n", + "for name, arr, axes in objects:\n", + " print(f\"{name:20s} shape={str(arr.shape):20s} order={arr.ndim} axes={axes}\")\n", + "\n", + "print()\n", + "print(\"Axis 0 meaning:\")\n", + "print(\"- real_digit : image rows; reordering them scrambles the image\")\n", + "print(\"- digit_batch: independent examples; batch order can be changed\")\n", + "print(\"- real_photo : image rows; reordering them scrambles the image\")\n", + "print(\"- real_video : time; reordering it changes temporal meaning\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "p02-ex1-explain", + "metadata": {}, + "source": [ + "
\n", + "Why this solution works · Por qué funciona esta solución\n", + "\n", + "The number of axes tells us the **order**, but the dataset tells us what those axes **mean**. A batch axis is a collection of independent observations; a spatial or temporal axis carries internal structure.\n", + "\n", + "> 🇪🇸 El número de ejes determina el **orden**, pero el conjunto de datos determina qué **significan** esos ejes. Un eje de lote reúne observaciones independientes; los ejes espaciales y temporales contienen estructura interna.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "p02-same-shape", + "metadata": {}, + "source": [ + "## 2.2 Same shape, different meaning\n", + "\n", + "Now compare two **real** tensors with exactly the same shape:\n", + "\n", + "```text\n", + "digit_batch.shape == (8, 8, 8) # (N, H, W)\n", + "video_patch.shape == (8, 8, 8) # (T, H, W)\n", + "```\n", + "\n", + "For the digit batch, keeping each image paired with its label is what matters. The order of examples in the batch is not part of a digit's identity.\n", + "\n", + "For the video tensor, axis 0 is time. Consecutive **sampled** frames are related because the scene evolves from one measured moment to the next.\n", + "\n", + "> 🇪🇸 Misma forma, significado diferente: en el lote de dígitos el eje 0 cuenta ejemplos independientes; en el video cuenta tiempo. El código ve enteros, pero el científico debe conservar la semántica." + ] + }, + { + "cell_type": "markdown", + "id": "s02-07", + "metadata": {}, + "source": [ + "## Exercise 2 — shuffle batch vs. shuffle time on real data\n", + "\n", + "Use one permutation for both real tensors.\n", + "\n", + "For the digit batch, shuffle **images and labels together**. For the video, shuffle the temporal axis. Then compare a simple temporal-continuity statistic before and after the shuffle.\n", + "\n", + "Because the video was sampled with `stride=45`, these are **consecutive sampled frames**, not consecutive frames from the original video stream.\n", + "\n", + "> 🇪🇸 Usa la misma permutación en ambos tensores. En el lote de dígitos reorganiza imágenes y etiquetas juntas. En el video reorganiza el eje temporal y compara una medida simple de continuidad antes y después. Como el video fue muestreado con `stride=45`, trabajamos con **fotogramas muestreados consecutivos**, no con fotogramas consecutivos del video original." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "s02-08", + "metadata": {}, + "outputs": [], + "source": [ + "# TODO 2:\n", + "# 1. Create perm = rng.permutation(8).\n", + "# 2. Apply it to digit_batch AND digit_labels.\n", + "# 3. Apply it to video_patch.\n", + "# 4. Print original vs shuffled labels.\n", + "# 5. Compute the mean absolute change between consecutive SAMPLED video frames\n", + "# before and after shuffling.\n", + "# 6. Explain why the digit batch still represents the same 8 labeled examples,\n", + "# while the temporal story of the sampled video sequence has changed.\n", + "#\n", + "# Write your code below this line.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "s02-09", + "metadata": { + "jupyter": { + "source_hidden": true }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "\n", + "perm = rng.permutation(8)\n", + "\n", + "shuffled_digits = digit_batch[perm]\n", + "shuffled_labels = digit_labels[perm]\n", + "shuffled_video = video_patch[perm]\n", + "\n", + "print(\"original digit labels:\", digit_labels)\n", + "print(\"shuffled digit labels:\", shuffled_labels)\n", + "print(\"same labeled examples? \", sorted(zip(digit_labels.tolist(), digit_batch.sum(axis=(1, 2)).round(6).tolist()))\n", + " == sorted(zip(shuffled_labels.tolist(), shuffled_digits.sum(axis=(1, 2)).round(6).tolist())))\n", + "\n", + "\n", + "def mean_consecutive_sampled_change(x):\n", + " x = x.astype(np.float32)\n", + " return float(np.mean(np.abs(x[1:] - x[:-1])))\n", + "\n", + "\n", + "before = mean_consecutive_sampled_change(video_patch)\n", + "after = mean_consecutive_sampled_change(shuffled_video)\n", + "\n", + "print()\n", + "print(f\"video mean consecutive sampled-frame change before shuffle: {before:.3f}\")\n", + "print(f\"video mean consecutive sampled-frame change after shuffle: {after:.3f}\")\n", + "print(f\"after/before ratio: {after / before:.2f}x\")\n", + "\n", + "# Batch: the order of independent examples changed, but image-label pairs stayed intact.\n", + "# Time: the same sampled frames remain, but their temporal order no longer describes\n", + "# the original measured sequence.\n" + ] + }, + { + "cell_type": "markdown", + "id": "p02-ex2-explain", + "metadata": {}, + "source": [ + "
\n", + "What did the shuffle prove? · ¿Qué demostró la permutación?\n", + "\n", + "For the digits, the permutation changes **presentation order**, not the identity of the eight labeled observations. For the video, the permutation changes the measured chronology of the **sampled frame sequence**.\n", + "\n", + "The continuity statistic is not a universal definition of \"video correctness\"; it is simply observable evidence that reordering real sampled frames changes their temporal relationships.\n", + "\n", + "> 🇪🇸 En los dígitos cambia el **orden de presentación**, no la identidad de las ocho observaciones etiquetadas. En el video cambia la cronología medida de la **secuencia de fotogramas muestreados**. La estadística de continuidad es evidencia observable de que reorganizar fotogramas reales muestreados cambia sus relaciones temporales.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "p02-ragged", + "metadata": {}, + "source": [ + "## 2.3 Real videos have different lengths\n", + "\n", + "A batch needs one rectangular tensor, but real clips may contain different numbers of frames.\n", + "\n", + "We will create three clips by taking three **different measured segments** from the real storm video. Their pixel values are real; only the segment boundaries are chosen for this teaching example.\n", + "\n", + "For efficiency, we spatially subsample the frames before batching. Spatial subsampling keeps measured pixels but retains fewer of them.\n", + "\n", + "> 🇪🇸 Los tres clips provienen de segmentos distintos del video real. Los valores de los píxeles son medidos; solo elegimos los límites de cada segmento con fines pedagógicos. Para ahorrar memoria conservamos uno de cada cuatro píxeles en cada dirección espacial." + ] + }, + { + "cell_type": "markdown", + "id": "p02-ex3", + "metadata": {}, + "source": [ + "## Exercise 3 — build an order-5 batch from real clips\n", + "\n", + "Create three real clips with lengths `4`, `7`, and `5` frames. Pad them to the longest length and build a Boolean mask telling the model which frame slots contain measured data.\n", + "\n", + "Predict the final tensor shape before running the solution.\n", + "\n", + "> 🇪🇸 Construye tres clips reales de 4, 7 y 5 fotogramas. Rellénalos hasta la longitud máxima y crea una máscara booleana que indique qué posiciones contienen datos medidos. Predice primero la forma final." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "p02-todo3", + "metadata": {}, + "outputs": [], + "source": [ + "# TODO 3:\n", + "# Work from real_video.\n", + "#\n", + "# 1. Spatially subsample it with real_video[:, ::4, ::4, :].\n", + "# 2. Take three non-overlapping real segments with lengths 4, 7, and 5.\n", + "# 3. Compute T_max.\n", + "# 4. Allocate one padded batch with shape (N, T_max, H, W, C).\n", + "# 5. Build a Boolean mask with shape (N, T_max).\n", + "# 6. Count how many frame slots are padding rather than measured frames.\n", + "#\n", + "# Write your code below this line.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "p02-sol3", + "metadata": { + "jupyter": { + "source_hidden": true }, - "language_info": { - "name": "python" - } + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "\n", + "video_small = real_video[:, ::4, ::4, :] # measured pixels, spatially subsampled\n", + "\n", + "real_clips = [\n", + " video_small[0:4], # 4 measured frames\n", + " video_small[4:11], # 7 measured frames\n", + " video_small[11:16], # 5 measured frames\n", + "]\n", + "\n", + "lengths = np.array([len(x) for x in real_clips])\n", + "T_max = int(lengths.max())\n", + "N = len(real_clips)\n", + "H, W, C = video_small.shape[1:]\n", + "\n", + "padded = np.zeros((N, T_max, H, W, C), dtype=video_small.dtype)\n", + "valid = np.zeros((N, T_max), dtype=bool)\n", + "\n", + "for n, x in enumerate(real_clips):\n", + " T = len(x)\n", + " padded[n, :T] = x\n", + " valid[n, :T] = True\n", + "\n", + "padded_slots = int((~valid).sum())\n", + "total_slots = int(valid.size)\n", + "\n", + "print(\"real clip lengths:\", lengths.tolist())\n", + "print(\"padded batch shape:\", padded.shape)\n", + "print(\"batch order:\", padded.ndim)\n", + "print(\"axes: (N, T, H, W, C)\")\n", + "print(\"validity mask shape:\", valid.shape)\n", + "print(\"measured frame slots:\", int(valid.sum()))\n", + "print(\"padding frame slots:\", padded_slots)\n", + "print(f\"padding fraction: {padded_slots / total_slots:.1%}\")\n", + "\n", + "assert padded.shape == (3, 7, 135, 240, 3)\n", + "assert valid.sum() == 16\n" + ] + }, + { + "cell_type": "markdown", + "id": "p02-ex3-explain", + "metadata": {}, + "source": [ + "
\n", + "Why padding needs a mask · Por qué el padding necesita una máscara\n", + "\n", + "The order-5 tensor is rectangular, but not every `(N, T)` location represents a recorded frame. The mask distinguishes **measured frames** from **padding values introduced by preprocessing**.\n", + "\n", + "This is an important distinction: the underlying dataset is real, while padding is an explicit computational convention. Without the mask, zeros could be mistaken for observations.\n", + "\n", + "> 🇪🇸 El tensor de orden 5 es rectangular, pero no toda posición `(N, T)` corresponde a un fotograma grabado. La máscara distingue **datos medidos** de **valores de relleno introducidos por el preprocesamiento**.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "p02-microscopy", + "metadata": {}, + "source": [ + "## 2.4 From video axes to experimental axes\n", + "\n", + "The same reasoning applies to scientific data.\n", + "\n", + "Suppose a microscope records cells repeatedly:\n", + "\n", + "- `T` can count acquisition times.\n", + "- `H, W` can count pixel locations inside each image.\n", + "- `C` can count imaging channels.\n", + "- another axis can count fields of view, wells, dishes, patients, or experimental conditions.\n", + "\n", + "A **field of view is not automatically the same thing as `H` or `W`**. `H` and `W` are pixel coordinates *inside* an image; multiple fields of view usually require their own observation axis or are organized into the batch structure.\n", + "\n", + "> 🇪🇸 En microscopía, un **campo de visión no es automáticamente un eje `H` o `W`**. `H` y `W` describen coordenadas de píxeles dentro de una imagen; varios campos de visión suelen requerir otro eje de observación." + ] + }, + { + "cell_type": "markdown", + "id": "p02-recap", + "metadata": {}, + "source": [ + "## What just happened\n", + "\n", + "You worked with real measured image/video values throughout the core examples. The only introduced values were explicit padding values, tracked by a validity mask.\n", + "\n", + "- a real handwritten digit gave an order-2 tensor `(H, W)`;\n", + "- eight real digits gave an order-3 batch `(N, H, W)`;\n", + "- a real RGB photograph gave an order-3 tensor `(H, W, C)`;\n", + "- a real sampled video gave an order-4 tensor `(T, H, W, C)`;\n", + "- three real video segments plus explicit padding produced an order-5 batch `(N, T, H, W, C)` and a validity mask.\n", + "\n", + "The central lesson is not \"higher order means more complicated.\" It is:\n", + "\n", + "> **Every axis must have a meaning, and operations are only valid when they respect that meaning.**\n", + "\n", + "Two tensors can have the same shape and still represent fundamentally different data.\n", + "\n", + "> 🇪🇸 **Qué ocurrió:** trabajaste con valores reales medidos de imágenes y video en los ejemplos principales. Los únicos valores introducidos artificialmente fueron los del padding, identificados explícitamente mediante una máscara de validez. El mensaje central es que **cada eje debe tener un significado y las operaciones deben respetarlo**. Dos tensores pueden tener la misma forma y representar datos completamente distintos." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Done with this section\n", + "\n", + "Next up: **03 · Indexing and broadcasting real data** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/03-indexing-and-broadcasting.ipynb).\n", + "\n", + "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" + ], + "id": "s02-11" + } + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/scripts/content.py b/scripts/content.py index e484a64..e106ee4 100644 --- a/scripts/content.py +++ b/scripts/content.py @@ -64,9 +64,77 @@ # ───────────────────────────────────────────────────────────────────────────── CONTENT["02"] = { - "setup": """import numpy as np + "setup": """%pip install -q "imageio[ffmpeg]" -rng = np.random.default_rng(0)""", +import hashlib +import io +import urllib.request + +import imageio.v3 as iio +import numpy as np +from sklearn.datasets import load_digits +from skimage import data + +rng = np.random.default_rng(0) + +# Real image data: handwritten digits +digits = load_digits() +digit_batch = digits.images[:8].astype(np.float32) # (N, H, W) +digit_labels = digits.target[:8] +real_digit = digit_batch[0] +real_photo = data.astronaut() # real RGB photograph + +# Real video data: "Tormenta en l'Almadrava" by Nicolas Vigier, CC0. +# Same pinned source/checksum used by notebook 05. +VIDEO_URL = ( + "https://upload.wikimedia.org/wikipedia/commons/1/1e/" + "Tormenta_en_l%27Almadrava.webm" +) +VIDEO_SHA256 = "e377fcdd2c79b55bce13c2c24b5dd7e412af39cd400eec548a79d0e59d79dc1b" +UA = "tensors-workshop/1.0 (https://github.com/project-delphi/tensors-workshop)" + + +def fetch_verified_video(url, expected_sha256, n_frames=16, stride=45): + # Download, checksum, and retain sampled frames from a real video. + req = urllib.request.Request(url, headers={"User-Agent": UA}) + raw = urllib.request.urlopen(req, timeout=120).read() + + got = hashlib.sha256(raw).hexdigest() + if got != expected_sha256: + raise ValueError( + f"checksum mismatch: expected {expected_sha256}, got {got}" + ) + + frames = [] + for i, frame in enumerate( + iio.imiter(io.BytesIO(raw), plugin="FFMPEG", extension=".webm") + ): + if i % stride == 0: + frames.append(frame) + if len(frames) == n_frames: + break + + return np.stack(frames) + + +real_video = fetch_verified_video(VIDEO_URL, VIDEO_SHA256) +assert real_video.shape == (16, 540, 960, 3), real_video.shape + +# Build a real temporal tensor with the same shape as digit_batch: (8, 8, 8). +# Take 8 sampled video frames, a centered 8x8 crop, and average RGB. +r0 = real_video.shape[1] // 2 - 4 +c0 = real_video.shape[2] // 2 - 4 +video_patch = ( + real_video[:8, r0:r0 + 8, c0:c0 + 8] + .mean(axis=3) + .astype(np.float32) +) + +print("real_digit :", real_digit.shape, real_digit.dtype) +print("digit_batch:", digit_batch.shape, digit_batch.dtype) +print("real_photo :", real_photo.shape, real_photo.dtype) +print("real_video :", real_video.shape, real_video.dtype) +print("video_patch:", video_patch.shape, video_patch.dtype)""", } # ───────────────────────────────────────────────────────────────────────────── From 7e9a1b0e3f47b29048341da58a91f4a1570deea0 Mon Sep 17 00:00:00 2001 From: Sebas Laverde Chunza <106851243+Laverde97@users.noreply.github.com> Date: Fri, 28 Aug 2026 13:06:12 -0500 Subject: [PATCH 07/29] Improve notebook 03 pedagogy with real data for issue #44 --- notebooks/03-indexing-and-broadcasting.ipynb | 768 ++++++++++--------- 1 file changed, 398 insertions(+), 370 deletions(-) diff --git a/notebooks/03-indexing-and-broadcasting.ipynb b/notebooks/03-indexing-and-broadcasting.ipynb index 58d61bd..d99fe3d 100644 --- a/notebooks/03-indexing-and-broadcasting.ipynb +++ b/notebooks/03-indexing-and-broadcasting.ipynb @@ -1,375 +1,403 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 03 · Indexing and broadcasting real data\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/03-indexing-and-broadcasting.ipynb)\n", - "\n", - "*Part III · exercise · 15 min*\n", - "\n", - "> 🇪🇸 **Indexación y broadcasting con datos reales** — Seleccionar la columna correcta de datos reales de tumores y encontrar píxeles de varianza cero.\n", - "\n", - "Select the right column of real tumour data, then meet zero-variance pixels.\n", - "\n", - "## What you will be able to do\n", - "\n", - "- Select a named column of real data by name, never by a hard-coded number.\n", - "- Combine fancy indexing and boolean indexing to pull out sub-tables in one operation.\n", - "- Standardize a data matrix with broadcasting.\n", - "- Recognise a zero-variance column, and know why real images contain them." - ], - "id": "s03-00" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "Run this first. It installs and imports everything this notebook needs, and nothing else.\n", - "\n", - "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." - ], - "id": "s03-01" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from sklearn.datasets import load_breast_cancer, load_digits\n", - "\n", - "bc = load_breast_cancer()\n", - "X, y = bc.data, bc.target # (569, 30); y: 0 = malignant, 1 = benign\n", - "names = list(bc.feature_names)\n", - "print(X.shape, len(names))" - ], - "id": "s03-02" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Why this matters\n", - "\n", - "> 🇪🇸 Elegir la columna equivocada no da error: devuelve otra medida real, y el\n", - "> análisis continúa y da una respuesta segura y equivocada.\n", - "\n", - "The `breast_cancer` data holds 30 real measurements of tumour cell nuclei for\n", - "569 real patients. Selecting the wrong column does not produce an error — it\n", - "returns a *different real measurement*, and your analysis continues and gives a\n", - "confident, wrong answer.\n", - "\n", - "In research this produces results nobody can reproduce. In a clinical tool it\n", - "produces a wrong recommendation about a real person.\n", - "\n", - "**In tech**, the identical operation runs on a `(users, items)` matrix to pull\n", - "one user's history before making a recommendation." - ], - "id": "s03-03" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 1 — indexing by name\n", - "\n", - "> 🇪🇸 Indexación por nombre, nunca por número fijo." - ], - "id": "s03-04" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO 1: Print X.shape. Say out loud what each axis means.\n", - "\n", - "# TODO 2: Extract the column \"mean radius\" for all patients -> shape (569,).\n", - "# Find its position with names.index(...). Do not hard-code a number." - ], - "id": "s03-05" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "s03-00" + }, + "source": [ + "# 03 · Indexing and broadcasting real data\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/03-indexing-and-broadcasting.ipynb)\n", + "\n", + "*Part III · exercise · 15 min*\n", + "\n", + "> 🇪🇸 **Indexación y broadcasting con datos reales** — Seleccionar medidas reales por nombre, comparar subconjuntos con máscaras booleanas y detectar píxeles de varianza cero.\n", + "\n", + "Select named measurements from real tumour-sample data, compare meaningful subsets, then standardize real image data safely.\n", + "\n", + "## What you will be able to do\n", + "\n", + "- Select a named column of real data by name, never by a hard-coded number.\n", + "- Use fancy indexing and boolean masks to select meaningful subsets of rows.\n", + "- Standardize a real data matrix with broadcasting.\n", + "- Recognise zero-variance pixels and explain why real image datasets can contain them." + ], + "id": "s03-00" }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "print(X.shape) # (569, 30) patients x measurements\n", - "\n", - "i = names.index(\"mean radius\")\n", - "radius = X[:, i] # book notation A_{:,j}\n", - "print(i, radius.shape) # 0 (569,)\n", - "\n", - "# names.index() rather than 0 because the column order is not yours to assume.\n", - "# If the dataset is ever reordered, the hard-coded version keeps running and\n", - "# keeps being wrong." - ], - "id": "s03-06" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 2 — fancy and boolean indexing\n", - "\n", - "> 🇪🇸 Indexación avanzada y booleana." - ], - "id": "s03-07" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO 3: Find the 5 patients with the LARGEST mean radius, then extract their\n", - "# full 30-measurement profiles as one (5, 30) array, in ONE operation.\n", - "\n", - "# TODO 4: Using boolean indexing, compare mean radius for malignant (y == 0)\n", - "# against benign (y == 1) patients. Is there a real difference?" - ], - "id": "s03-08" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true + { + "cell_type": "markdown", + "metadata": { + "id": "s03-01" + }, + "source": [ + "## Setup\n", + "\n", + "Run this first. It installs and imports everything this notebook needs, and nothing else.\n", + "\n", + "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." + ], + "id": "s03-01" }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "top5 = np.argsort(radius)[-5:]\n", - "profiles = X[top5, :] # (5, 30)\n", - "print(profiles.shape)\n", - "\n", - "print(radius[y == 0].mean(), radius[y == 1].mean()) # 17.5 vs 12.1\n", - "\n", - "# A real result: MALIGNANT TUMOURS REALLY DO HAVE A LARGER MEAN RADIUS,\n", - "# 17.5 against 12.1. Random data would never have shown you that." - ], - "id": "s03-09" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`radius` was one column out of 30, picked because it happens to separate the\n", - "two groups well. Drag the slider below to look at all 30 — most separate far\n", - "less cleanly.\n", - "\n", - "> 🇪🇸 Mueve el deslizador para ver las 30 medidas, una por una. La mayoría\n", - "> separa malignos de benignos mucho peor que el radio." - ], - "id": "s03-10" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " from google.colab import output\n", - " output.enable_custom_widget_manager()\n", - "except ImportError:\n", - " pass\n", - "\n", - "import ipywidgets as widgets\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def show_feature(i):\n", - " plt.close('all')\n", - " col = X[:, i]\n", - " fig, ax = plt.subplots(figsize=(6, 3))\n", - " ax.hist(col[y == 0], bins=30, alpha=0.6, label='malignant', color='#C44E52')\n", - " ax.hist(col[y == 1], bins=30, alpha=0.6, label='benign', color='#4C72B0')\n", - " ax.set_title(names[i])\n", - " ax.legend()\n", - " plt.tight_layout()\n", - " plt.show()\n", - " print(f\"malignant mean: {col[y == 0].mean():.3f} \"\n", - " f\"benign mean: {col[y == 1].mean():.3f}\")\n", - "\n", - "widgets.interact(show_feature,\n", - " i=widgets.IntSlider(min=0, max=len(names) - 1, step=1, value=0,\n", - " description='feature'));" - ], - "id": "s03-11" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Broadcasting, on real images\n", - "\n", - "> 🇪🇸 Broadcasting sobre imágenes reales.\n", - "\n", - "Broadcasting stretches a smaller array across a larger one without copying it.\n", - "Standardizing a data matrix — subtract the mean of each column, divide by its\n", - "standard deviation — is the operation you will do most often.\n", - "\n", - "Run TODO 6 and **look at the result before continuing**. Something is wrong with\n", - "it, and finding out what is the point of this block." - ], - "id": "s03-12" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "images = load_digits().images # (1797, 8, 8)\n", - "D = images.reshape(len(images), -1) # (1797, 64)\n", - "print(D.shape)" - ], - "id": "s03-13" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 3 — standardize, then find the trap\n", - "\n", - "> 🇪🇸 Estandariza y encuentra el problema." - ], - "id": "s03-14" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO 5: Compute the mean and std of each of the 64 pixels across all images.\n", - "\n", - "# TODO 6: Standardize with broadcasting: (D - mean) / std.\n", - "# RUN IT AND LOOK AT THE RESULT before continuing.\n", - "\n", - "# TODO 7: You will find NaN. How many pixels have std == 0, and why would a real\n", - "# handwritten digit image contain such pixels? Fix it, then verify no NaN." - ], - "id": "s03-15" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "s03-02" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from sklearn.datasets import load_breast_cancer, load_digits\n", + "\n", + "bc = load_breast_cancer()\n", + "X, y = bc.data, bc.target # (569, 30); y: 0 = malignant, 1 = benign\n", + "names = list(bc.feature_names)\n", + "print(X.shape, len(names))" + ], + "id": "s03-02" }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "mean, std = D.mean(axis=0), D.std(axis=0)\n", - "print(mean.shape, std.shape) # (64,) (64,)\n", - "\n", - "Z_bad = (D - mean) / std\n", - "print(np.isnan(Z_bad).any()) # True\n", - "\n", - "print((std == 0).sum()) # 3\n", - "Z = (D - mean) / np.where(std == 0, 1.0, std)\n", - "print(np.isnan(Z).any()) # False\n", - "\n", - "# THREE PIXELS ARE ALWAYS DARK in all 1797 digit images: they sit in corners\n", - "# where nobody writes. Their standard deviation is exactly zero, so dividing\n", - "# produces NaN. np.where leaves those columns as plain centred zeros, which is\n", - "# the honest thing to do with a feature that carries no information.\n", - "\n", - "import matplotlib.pyplot as plt\n", - "fig, ax = plt.subplots(figsize=(3, 3))\n", - "ax.imshow(D.mean(axis=0).reshape(8, 8), cmap='gray')\n", - "zero_rows, zero_cols = np.where((std == 0).reshape(8, 8))\n", - "ax.scatter(zero_cols, zero_rows, s=250, marker='s',\n", - " facecolors='none', edgecolors='#C44E52', linewidths=2)\n", - "ax.set_title('zero-variance pixels, marked')\n", - "ax.axis('off')\n", - "plt.show()" - ], - "id": "s03-16" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What just happened\n", - "\n", - "Two real results, neither of which random data could have produced:\n", - "\n", - "1. **Malignant tumours really do have a larger mean radius** — 17.5 against 12.1.\n", - "2. **Three pixels are always dark** in all 1797 digit images, so their standard\n", - " deviation is exactly zero and dividing by it produces `NaN`.\n", - "\n", - "The second is the one to remember. A zero-variance feature is not a bug in your\n", - "code — it is a fact about your data, and you have to decide what to do about it.\n", - "Silently propagating `NaN` into a model is the one option that is always wrong.\n", - "\n", - "The Kahoot below asks you about exactly this." - ], - "id": "s03-17" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Done with this section\n", - "\n", - "Next up: **04 · Reshape and transpose real images** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/04-reshape-and-transpose.ipynb).\n", - "\n", - "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" - ], - "id": "s03-18" - } - ], - "metadata": { - "colab": { - "name": "03-indexing-and-broadcasting.ipynb", - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" + { + "cell_type": "markdown", + "metadata": { + "id": "s03-03" + }, + "source": [ + "## Why this matters\n", + "\n", + "> 🇪🇸 Elegir la columna equivocada no da error: devuelve otra medida real, y el análisis puede continuar con una respuesta convincente pero equivocada.\n", + "\n", + "The `breast_cancer` dataset contains 569 real breast-mass samples with 30 numerical features computed from digitized images of fine-needle aspirates. Selecting the wrong column does not necessarily produce an error — it can return a *different real measurement* while the rest of the analysis keeps running.\n", + "\n", + "In research, that makes results harder to reproduce and easier to misinterpret. In any decision-support pipeline, selecting the wrong feature can quietly change the conclusion.\n", + "\n", + "**In tech**, the identical indexing operation appears on a `(users, items)` matrix when selecting one user's history before making a recommendation." + ], + "id": "s03-03" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s03-04" + }, + "source": [ + "## Exercise 1 — indexing by name\n", + "\n", + "> 🇪🇸 Indexación por nombre, nunca por número fijo." + ], + "id": "s03-04" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "s03-05" + }, + "outputs": [], + "source": [ + "# TODO 1: Print X.shape. Say out loud what each axis means.\n", + "\n", + "# TODO 2: Extract the column \"mean radius\" for all samples -> shape (569,).\n", + "# Find its position with names.index(...). Do not hard-code a number." + ], + "id": "s03-05" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "id": "s03-06" + }, + "outputs": [], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "print(X.shape) # (569, 30) patients x measurements\n", + "\n", + "i = names.index(\"mean radius\")\n", + "radius = X[:, i] # book notation A_{:,j}\n", + "print(i, radius.shape) # 0 (569,)\n", + "\n", + "# names.index() rather than 0 because the column order is not yours to assume.\n", + "# If the dataset is ever reordered, the hard-coded version keeps running and\n", + "# keeps being wrong." + ], + "id": "s03-06" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s03-07" + }, + "source": [ + "## Exercise 2 — fancy and boolean indexing\n", + "\n", + "> 🇪🇸 Indexación avanzada y booleana." + ], + "id": "s03-07" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "s03-08" + }, + "outputs": [], + "source": [ + "# TODO 3: Find the 5 samples with the LARGEST mean radius, then extract their\n", + "# full 30-measurement profiles as one (5, 30) array, in ONE operation.\n", + "#\n", + "# TODO 4: Using boolean indexing, compare mean radius for malignant (y == 0)\n", + "# against benign (y == 1) samples.\n", + "# Describe the difference in THIS dataset; do not treat one feature\n", + "# as a diagnostic rule.\n" + ], + "id": "s03-08" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "id": "s03-09" + }, + "outputs": [], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "top5 = np.argsort(radius)[-5:]\n", + "profiles = X[top5, :] # (5, 30)\n", + "print(profiles.shape)\n", + "\n", + "malignant_mean = radius[y == 0].mean()\n", + "benign_mean = radius[y == 1].mean()\n", + "print(malignant_mean, benign_mean) # about 17.5 vs 12.1\n", + "\n", + "# Dataset-specific result:\n", + "# malignant samples have a larger mean radius ON AVERAGE in this dataset.\n", + "# That is a descriptive comparison, not a one-feature diagnostic rule.\n", + "#\n", + "# Purely synthetic random data would not preserve this real dataset\n", + "# relationship unless we explicitly designed it to do so.\n" + ], + "id": "s03-09" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s03-10" + }, + "source": [ + "`radius` was one column out of 30, picked because it happens to separate the\n", + "two groups well. Drag the slider below to look at all 30 — most separate far\n", + "less cleanly.\n", + "\n", + "> 🇪🇸 Mueve el deslizador para ver las 30 medidas, una por una. La mayoría\n", + "> separa malignos de benignos mucho peor que el radio." + ], + "id": "s03-10" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "s03-11" + }, + "outputs": [], + "source": [ + "try:\n", + " from google.colab import output\n", + " output.enable_custom_widget_manager()\n", + "except ImportError:\n", + " pass\n", + "\n", + "import ipywidgets as widgets\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def show_feature(i):\n", + " plt.close('all')\n", + " col = X[:, i]\n", + " fig, ax = plt.subplots(figsize=(6, 3))\n", + " ax.hist(col[y == 0], bins=30, alpha=0.6, label='malignant', color='#C44E52')\n", + " ax.hist(col[y == 1], bins=30, alpha=0.6, label='benign', color='#4C72B0')\n", + " ax.set_title(names[i])\n", + " ax.legend()\n", + " plt.tight_layout()\n", + " plt.show()\n", + " print(f\"malignant mean: {col[y == 0].mean():.3f} \"\n", + " f\"benign mean: {col[y == 1].mean():.3f}\")\n", + "\n", + "widgets.interact(show_feature,\n", + " i=widgets.IntSlider(min=0, max=len(names) - 1, step=1, value=0,\n", + " description='feature'));" + ], + "id": "s03-11" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s03-12" + }, + "source": [ + "## Broadcasting, on real images\n", + "\n", + "> 🇪🇸 **Broadcasting sobre imágenes reales.** Una operación muy común es estandarizar cada característica restando su media y dividiendo por su desviación estándar.\n", + "\n", + "Broadcasting stretches a smaller array across a larger one without manually copying it. A common preprocessing operation is to standardize a data matrix — subtract the mean of each column, then divide by its standard deviation.\n", + "\n", + "Run TODO 6 and **look at the result before continuing**. Something is wrong with it, and finding out what is the point of this block." + ], + "id": "s03-12" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "s03-13" + }, + "outputs": [], + "source": [ + "images = load_digits().images # (1797, 8, 8)\n", + "D = images.reshape(len(images), -1) # (1797, 64)\n", + "print(D.shape)" + ], + "id": "s03-13" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s03-14" + }, + "source": [ + "## Exercise 3 — standardize, then find the trap\n", + "\n", + "> 🇪🇸 Estandariza y encuentra el problema." + ], + "id": "s03-14" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "s03-15" + }, + "outputs": [], + "source": [ + "# TODO 5: Compute the mean and std of each of the 64 pixels across all images.\n", + "\n", + "# TODO 6: Standardize with broadcasting: (D - mean) / std.\n", + "# RUN IT AND LOOK AT THE RESULT before continuing.\n", + "\n", + "# TODO 7: You will find NaN. How many pixels have std == 0, and why would a real\n", + "# handwritten digit image contain such pixels? Fix it, then verify no NaN." + ], + "id": "s03-15" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "id": "s03-16" + }, + "outputs": [], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "mean, std = D.mean(axis=0), D.std(axis=0)\n", + "print(mean.shape, std.shape) # (64,) (64,)\n", + "\n", + "Z_bad = (D - mean) / std\n", + "print(np.isnan(Z_bad).any()) # True\n", + "\n", + "print((std == 0).sum()) # 3\n", + "Z = (D - mean) / np.where(std == 0, 1.0, std)\n", + "print(np.isnan(Z).any()) # False\n", + "\n", + "# THREE PIXELS ARE ALWAYS DARK across all 1797 digit images.\n", + "# They are background/edge locations that are never activated in this dataset.\n", + "# Their standard deviation is exactly zero, so dividing by it produces NaN.\n", + "# np.where leaves those columns as centred zeros, which is appropriate for\n", + "# features that carry no variation in this dataset.\n", + "\n", + "import matplotlib.pyplot as plt\n", + "fig, ax = plt.subplots(figsize=(3, 3))\n", + "ax.imshow(D.mean(axis=0).reshape(8, 8), cmap='gray')\n", + "zero_rows, zero_cols = np.where((std == 0).reshape(8, 8))\n", + "ax.scatter(zero_cols, zero_rows, s=250, marker='s',\n", + " facecolors='none', edgecolors='#C44E52', linewidths=2)\n", + "ax.set_title('zero-variance pixels, marked')\n", + "ax.axis('off')\n", + "plt.show()\n" + ], + "id": "s03-16" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s03-17" + }, + "source": [ + "## What just happened\n", + "\n", + "Two concrete patterns came directly from real datasets:\n", + "\n", + "1. **In this breast-cancer dataset**, malignant samples have a larger mean radius on average — about 17.5 versus 12.1 for benign samples.\n", + "2. **Three pixel positions have zero variance** across all 1797 `load_digits` images. Dividing by their standard deviation therefore produces `NaN`.\n", + "\n", + "The second result is the broadcasting trap to remember. A zero-variance feature is not necessarily a bug in your code; it can be a property of the data. You must detect it and handle it explicitly rather than silently propagating invalid values into later computations.\n", + "\n", + "> 🇪🇸 **Qué ocurrió:** observaste dos patrones que provienen directamente de datos reales. En este conjunto de cáncer de mama, las muestras malignas tienen un radio medio mayor en promedio. En `load_digits`, tres posiciones de píxel tienen varianza cero en las 1797 imágenes, por lo que dividir por su desviación estándar produce `NaN`. La lección es detectar y manejar explícitamente las características sin variación antes de continuar con el análisis." + ], + "id": "s03-17" + }, + { + "cell_type": "markdown", + "id": "s03-footer", + "metadata": { + "id": "s03-footer" + }, + "source": [ + "---\n", + "\n", + "## Done with this section\n", + "\n", + "Next up: **04 · Reshape and transpose** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/04-reshape-and-transpose.ipynb).\n", + "\n", + "[← Back to the workshop site](../index.html) · [All notebooks](../notebooks.html) · [Handbook](../tensors_workshop_plan_with_quizzes.html)\n" + ] + } + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python" + } }, - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file From 3cfa3cba85d8e84ed71904b3c59d96746d6587f1 Mon Sep 17 00:00:00 2001 From: Sebas Laverde Chunza Date: Fri, 28 Aug 2026 13:36:47 -0500 Subject: [PATCH 08/29] Finalize notebook 03 pedagogy for issue #44 --- _variables.yml | 16 +- .../03-indexing-and-broadcasting.ipynb | 96 ++- notebooks/03-indexing-and-broadcasting.ipynb | 764 +++++++++--------- 3 files changed, 420 insertions(+), 456 deletions(-) diff --git a/_variables.yml b/_variables.yml index 86604c1..be5a8f0 100644 --- a/_variables.yml +++ b/_variables.yml @@ -225,18 +225,18 @@ sections: format_es: "ejercicio" title_en: "Indexing and broadcasting real data" title_es: "Indexación y broadcasting con datos reales" - summary_en: "Select the right column of real tumour data, then meet zero-variance pixels." - summary_es: "Seleccionar la columna correcta de datos reales de tumores y encontrar píxeles de varianza cero." + summary_en: "Select named measurements from real tumour-sample data, compare meaningful subsets, then standardize real image data safely." + summary_es: "Seleccionar medidas reales por nombre, comparar subconjuntos con máscaras booleanas y detectar píxeles de varianza cero." objectives_en: - "Select a named column of real data by name, never by a hard-coded number." - - "Combine fancy indexing and boolean indexing to pull out sub-tables in one operation." - - "Standardize a data matrix with broadcasting." - - "Recognise a zero-variance column, and know why real images contain them." + - "Use fancy indexing and boolean masks to select meaningful subsets of rows." + - "Standardize a real data matrix with broadcasting." + - "Recognise zero-variance pixels and explain why real image datasets can contain them." objectives_es: - "Seleccionar una columna de datos reales por su nombre, nunca mediante un número escrito manualmente." - - "Combinar fancy indexing e indexación booleana para extraer subtablas en una sola operación." - - "Estandarizar una matriz de datos mediante broadcasting." - - "Reconocer una columna de varianza cero y comprender por qué las imágenes reales pueden contenerlas." + - "Usar fancy indexing y máscaras booleanas para seleccionar subconjuntos significativos de filas." + - "Estandarizar una matriz de datos reales mediante broadcasting." + - "Reconocer píxeles de varianza cero y explicar por qué los conjuntos de imágenes reales pueden contenerlos." s04: n: "04" slug: "reshape-and-transpose" diff --git a/docs/notebooks/03-indexing-and-broadcasting.ipynb b/docs/notebooks/03-indexing-and-broadcasting.ipynb index 58d61bd..086c67c 100644 --- a/docs/notebooks/03-indexing-and-broadcasting.ipynb +++ b/docs/notebooks/03-indexing-and-broadcasting.ipynb @@ -10,16 +10,16 @@ "\n", "*Part III · exercise · 15 min*\n", "\n", - "> 🇪🇸 **Indexación y broadcasting con datos reales** — Seleccionar la columna correcta de datos reales de tumores y encontrar píxeles de varianza cero.\n", + "> 🇪🇸 **Indexación y broadcasting con datos reales** — Seleccionar medidas reales por nombre, comparar subconjuntos con máscaras booleanas y detectar píxeles de varianza cero.\n", "\n", - "Select the right column of real tumour data, then meet zero-variance pixels.\n", + "Select named measurements from real tumour-sample data, compare meaningful subsets, then standardize real image data safely.\n", "\n", "## What you will be able to do\n", "\n", "- Select a named column of real data by name, never by a hard-coded number.\n", - "- Combine fancy indexing and boolean indexing to pull out sub-tables in one operation.\n", - "- Standardize a data matrix with broadcasting.\n", - "- Recognise a zero-variance column, and know why real images contain them." + "- Use fancy indexing and boolean masks to select meaningful subsets of rows.\n", + "- Standardize a real data matrix with broadcasting.\n", + "- Recognise zero-variance pixels and explain why real image datasets can contain them." ], "id": "s03-00" }, @@ -57,19 +57,13 @@ "source": [ "## Why this matters\n", "\n", - "> 🇪🇸 Elegir la columna equivocada no da error: devuelve otra medida real, y el\n", - "> análisis continúa y da una respuesta segura y equivocada.\n", + "> 🇪🇸 Elegir la columna equivocada no da error: devuelve otra medida real, y el análisis puede continuar con una respuesta convincente pero equivocada.\n", "\n", - "The `breast_cancer` data holds 30 real measurements of tumour cell nuclei for\n", - "569 real patients. Selecting the wrong column does not produce an error — it\n", - "returns a *different real measurement*, and your analysis continues and gives a\n", - "confident, wrong answer.\n", + "The `breast_cancer` dataset contains 569 real breast-mass samples with 30 numerical features computed from digitized images of fine-needle aspirates. Selecting the wrong column does not necessarily produce an error — it can return a *different real measurement* while the rest of the analysis keeps running.\n", "\n", - "In research this produces results nobody can reproduce. In a clinical tool it\n", - "produces a wrong recommendation about a real person.\n", + "In research, that makes results harder to reproduce and easier to misinterpret. In any decision-support pipeline, selecting the wrong feature can quietly change the conclusion.\n", "\n", - "**In tech**, the identical operation runs on a `(users, items)` matrix to pull\n", - "one user's history before making a recommendation." + "**In tech**, the identical indexing operation appears on a `(users, items)` matrix when selecting one user's history before making a recommendation." ], "id": "s03-03" }, @@ -91,7 +85,7 @@ "source": [ "# TODO 1: Print X.shape. Say out loud what each axis means.\n", "\n", - "# TODO 2: Extract the column \"mean radius\" for all patients -> shape (569,).\n", + "# TODO 2: Extract the column \"mean radius\" for all samples -> shape (569,).\n", "# Find its position with names.index(...). Do not hard-code a number." ], "id": "s03-05" @@ -100,19 +94,19 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "print(X.shape) # (569, 30) patients x measurements\n", + "print(X.shape) # (569, 30) samples x measurements\n", "\n", "i = names.index(\"mean radius\")\n", "radius = X[:, i] # book notation A_{:,j}\n", @@ -140,11 +134,13 @@ "metadata": {}, "outputs": [], "source": [ - "# TODO 3: Find the 5 patients with the LARGEST mean radius, then extract their\n", + "# TODO 3: Find the 5 samples with the LARGEST mean radius, then extract their\n", "# full 30-measurement profiles as one (5, 30) array, in ONE operation.\n", - "\n", + "#\n", "# TODO 4: Using boolean indexing, compare mean radius for malignant (y == 0)\n", - "# against benign (y == 1) patients. Is there a real difference?" + "# against benign (y == 1) samples.\n", + "# Describe the difference in THIS dataset; do not treat one feature\n", + "# as a diagnostic rule.\n" ], "id": "s03-08" }, @@ -152,14 +148,14 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ @@ -168,10 +164,16 @@ "profiles = X[top5, :] # (5, 30)\n", "print(profiles.shape)\n", "\n", - "print(radius[y == 0].mean(), radius[y == 1].mean()) # 17.5 vs 12.1\n", + "malignant_mean = radius[y == 0].mean()\n", + "benign_mean = radius[y == 1].mean()\n", + "print(malignant_mean, benign_mean) # about 17.5 vs 12.1\n", "\n", - "# A real result: MALIGNANT TUMOURS REALLY DO HAVE A LARGER MEAN RADIUS,\n", - "# 17.5 against 12.1. Random data would never have shown you that." + "# Dataset-specific result:\n", + "# malignant samples have a larger mean radius ON AVERAGE in this dataset.\n", + "# That is a descriptive comparison, not a one-feature diagnostic rule.\n", + "#\n", + "# Purely synthetic random data would not preserve this real dataset\n", + "# relationship unless we explicitly designed it to do so.\n" ], "id": "s03-09" }, @@ -228,14 +230,11 @@ "source": [ "## Broadcasting, on real images\n", "\n", - "> 🇪🇸 Broadcasting sobre imágenes reales.\n", + "> 🇪🇸 **Broadcasting sobre imágenes reales.** Una operación muy común es estandarizar cada característica restando su media y dividiendo por su desviación estándar.\n", "\n", - "Broadcasting stretches a smaller array across a larger one without copying it.\n", - "Standardizing a data matrix — subtract the mean of each column, divide by its\n", - "standard deviation — is the operation you will do most often.\n", + "Broadcasting stretches a smaller array across a larger one without manually copying it. A common preprocessing operation is to standardize a data matrix — subtract the mean of each column, then divide by its standard deviation.\n", "\n", - "Run TODO 6 and **look at the result before continuing**. Something is wrong with\n", - "it, and finding out what is the point of this block." + "Run TODO 6 and **look at the result before continuing**. Something is wrong with it, and finding out what is the point of this block." ], "id": "s03-12" }, @@ -281,14 +280,14 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ @@ -303,10 +302,11 @@ "Z = (D - mean) / np.where(std == 0, 1.0, std)\n", "print(np.isnan(Z).any()) # False\n", "\n", - "# THREE PIXELS ARE ALWAYS DARK in all 1797 digit images: they sit in corners\n", - "# where nobody writes. Their standard deviation is exactly zero, so dividing\n", - "# produces NaN. np.where leaves those columns as plain centred zeros, which is\n", - "# the honest thing to do with a feature that carries no information.\n", + "# THREE PIXELS ARE ALWAYS DARK across all 1797 digit images.\n", + "# They are background/edge locations that are never activated in this dataset.\n", + "# Their standard deviation is exactly zero, so dividing by it produces NaN.\n", + "# np.where leaves those columns as centred zeros, which is appropriate for\n", + "# features that carry no variation in this dataset.\n", "\n", "import matplotlib.pyplot as plt\n", "fig, ax = plt.subplots(figsize=(3, 3))\n", @@ -316,7 +316,7 @@ " facecolors='none', edgecolors='#C44E52', linewidths=2)\n", "ax.set_title('zero-variance pixels, marked')\n", "ax.axis('off')\n", - "plt.show()" + "plt.show()\n" ], "id": "s03-16" }, @@ -326,17 +326,14 @@ "source": [ "## What just happened\n", "\n", - "Two real results, neither of which random data could have produced:\n", + "Two concrete patterns came directly from real datasets:\n", "\n", - "1. **Malignant tumours really do have a larger mean radius** — 17.5 against 12.1.\n", - "2. **Three pixels are always dark** in all 1797 digit images, so their standard\n", - " deviation is exactly zero and dividing by it produces `NaN`.\n", + "1. **In this breast-cancer dataset**, malignant samples have a larger mean radius on average — about 17.5 versus 12.1 for benign samples.\n", + "2. **Three pixel positions have zero variance** across all 1797 `load_digits` images. Dividing by their standard deviation therefore produces `NaN`.\n", "\n", - "The second is the one to remember. A zero-variance feature is not a bug in your\n", - "code — it is a fact about your data, and you have to decide what to do about it.\n", - "Silently propagating `NaN` into a model is the one option that is always wrong.\n", + "The second result is the broadcasting trap to remember. A zero-variance feature is not necessarily a bug in your code; it can be a property of the data. You must detect it and handle it explicitly rather than silently propagating invalid values into later computations.\n", "\n", - "The Kahoot below asks you about exactly this." + "> 🇪🇸 **Qué ocurrió:** observaste dos patrones que provienen directamente de datos reales. En este conjunto de cáncer de mama, las muestras malignas tienen un radio medio mayor en promedio. En `load_digits`, tres posiciones de píxel tienen varianza cero en las 1797 imágenes, por lo que dividir por su desviación estándar produce `NaN`. La lección es detectar y manejar explícitamente las características sin variación antes de continuar con el análisis." ], "id": "s03-17" }, @@ -352,12 +349,11 @@ "\n", "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" ], - "id": "s03-18" + "id": "s03-footer" } ], "metadata": { "colab": { - "name": "03-indexing-and-broadcasting.ipynb", "provenance": [], "toc_visible": true }, diff --git a/notebooks/03-indexing-and-broadcasting.ipynb b/notebooks/03-indexing-and-broadcasting.ipynb index d99fe3d..086c67c 100644 --- a/notebooks/03-indexing-and-broadcasting.ipynb +++ b/notebooks/03-indexing-and-broadcasting.ipynb @@ -1,403 +1,371 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "s03-00" - }, - "source": [ - "# 03 · Indexing and broadcasting real data\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/03-indexing-and-broadcasting.ipynb)\n", - "\n", - "*Part III · exercise · 15 min*\n", - "\n", - "> 🇪🇸 **Indexación y broadcasting con datos reales** — Seleccionar medidas reales por nombre, comparar subconjuntos con máscaras booleanas y detectar píxeles de varianza cero.\n", - "\n", - "Select named measurements from real tumour-sample data, compare meaningful subsets, then standardize real image data safely.\n", - "\n", - "## What you will be able to do\n", - "\n", - "- Select a named column of real data by name, never by a hard-coded number.\n", - "- Use fancy indexing and boolean masks to select meaningful subsets of rows.\n", - "- Standardize a real data matrix with broadcasting.\n", - "- Recognise zero-variance pixels and explain why real image datasets can contain them." - ], - "id": "s03-00" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s03-01" - }, - "source": [ - "## Setup\n", - "\n", - "Run this first. It installs and imports everything this notebook needs, and nothing else.\n", - "\n", - "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." - ], - "id": "s03-01" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "s03-02" - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "from sklearn.datasets import load_breast_cancer, load_digits\n", - "\n", - "bc = load_breast_cancer()\n", - "X, y = bc.data, bc.target # (569, 30); y: 0 = malignant, 1 = benign\n", - "names = list(bc.feature_names)\n", - "print(X.shape, len(names))" - ], - "id": "s03-02" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s03-03" - }, - "source": [ - "## Why this matters\n", - "\n", - "> 🇪🇸 Elegir la columna equivocada no da error: devuelve otra medida real, y el análisis puede continuar con una respuesta convincente pero equivocada.\n", - "\n", - "The `breast_cancer` dataset contains 569 real breast-mass samples with 30 numerical features computed from digitized images of fine-needle aspirates. Selecting the wrong column does not necessarily produce an error — it can return a *different real measurement* while the rest of the analysis keeps running.\n", - "\n", - "In research, that makes results harder to reproduce and easier to misinterpret. In any decision-support pipeline, selecting the wrong feature can quietly change the conclusion.\n", - "\n", - "**In tech**, the identical indexing operation appears on a `(users, items)` matrix when selecting one user's history before making a recommendation." - ], - "id": "s03-03" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s03-04" - }, - "source": [ - "## Exercise 1 — indexing by name\n", - "\n", - "> 🇪🇸 Indexación por nombre, nunca por número fijo." - ], - "id": "s03-04" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "s03-05" - }, - "outputs": [], - "source": [ - "# TODO 1: Print X.shape. Say out loud what each axis means.\n", - "\n", - "# TODO 2: Extract the column \"mean radius\" for all samples -> shape (569,).\n", - "# Find its position with names.index(...). Do not hard-code a number." - ], - "id": "s03-05" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ], - "id": "s03-06" - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "print(X.shape) # (569, 30) patients x measurements\n", - "\n", - "i = names.index(\"mean radius\")\n", - "radius = X[:, i] # book notation A_{:,j}\n", - "print(i, radius.shape) # 0 (569,)\n", - "\n", - "# names.index() rather than 0 because the column order is not yours to assume.\n", - "# If the dataset is ever reordered, the hard-coded version keeps running and\n", - "# keeps being wrong." - ], - "id": "s03-06" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s03-07" - }, - "source": [ - "## Exercise 2 — fancy and boolean indexing\n", - "\n", - "> 🇪🇸 Indexación avanzada y booleana." - ], - "id": "s03-07" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "s03-08" - }, - "outputs": [], - "source": [ - "# TODO 3: Find the 5 samples with the LARGEST mean radius, then extract their\n", - "# full 30-measurement profiles as one (5, 30) array, in ONE operation.\n", - "#\n", - "# TODO 4: Using boolean indexing, compare mean radius for malignant (y == 0)\n", - "# against benign (y == 1) samples.\n", - "# Describe the difference in THIS dataset; do not treat one feature\n", - "# as a diagnostic rule.\n" - ], - "id": "s03-08" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ], - "id": "s03-09" - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "top5 = np.argsort(radius)[-5:]\n", - "profiles = X[top5, :] # (5, 30)\n", - "print(profiles.shape)\n", - "\n", - "malignant_mean = radius[y == 0].mean()\n", - "benign_mean = radius[y == 1].mean()\n", - "print(malignant_mean, benign_mean) # about 17.5 vs 12.1\n", - "\n", - "# Dataset-specific result:\n", - "# malignant samples have a larger mean radius ON AVERAGE in this dataset.\n", - "# That is a descriptive comparison, not a one-feature diagnostic rule.\n", - "#\n", - "# Purely synthetic random data would not preserve this real dataset\n", - "# relationship unless we explicitly designed it to do so.\n" - ], - "id": "s03-09" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s03-10" - }, - "source": [ - "`radius` was one column out of 30, picked because it happens to separate the\n", - "two groups well. Drag the slider below to look at all 30 — most separate far\n", - "less cleanly.\n", - "\n", - "> 🇪🇸 Mueve el deslizador para ver las 30 medidas, una por una. La mayoría\n", - "> separa malignos de benignos mucho peor que el radio." - ], - "id": "s03-10" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "s03-11" - }, - "outputs": [], - "source": [ - "try:\n", - " from google.colab import output\n", - " output.enable_custom_widget_manager()\n", - "except ImportError:\n", - " pass\n", - "\n", - "import ipywidgets as widgets\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def show_feature(i):\n", - " plt.close('all')\n", - " col = X[:, i]\n", - " fig, ax = plt.subplots(figsize=(6, 3))\n", - " ax.hist(col[y == 0], bins=30, alpha=0.6, label='malignant', color='#C44E52')\n", - " ax.hist(col[y == 1], bins=30, alpha=0.6, label='benign', color='#4C72B0')\n", - " ax.set_title(names[i])\n", - " ax.legend()\n", - " plt.tight_layout()\n", - " plt.show()\n", - " print(f\"malignant mean: {col[y == 0].mean():.3f} \"\n", - " f\"benign mean: {col[y == 1].mean():.3f}\")\n", - "\n", - "widgets.interact(show_feature,\n", - " i=widgets.IntSlider(min=0, max=len(names) - 1, step=1, value=0,\n", - " description='feature'));" - ], - "id": "s03-11" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s03-12" - }, - "source": [ - "## Broadcasting, on real images\n", - "\n", - "> 🇪🇸 **Broadcasting sobre imágenes reales.** Una operación muy común es estandarizar cada característica restando su media y dividiendo por su desviación estándar.\n", - "\n", - "Broadcasting stretches a smaller array across a larger one without manually copying it. A common preprocessing operation is to standardize a data matrix — subtract the mean of each column, then divide by its standard deviation.\n", - "\n", - "Run TODO 6 and **look at the result before continuing**. Something is wrong with it, and finding out what is the point of this block." - ], - "id": "s03-12" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "s03-13" - }, - "outputs": [], - "source": [ - "images = load_digits().images # (1797, 8, 8)\n", - "D = images.reshape(len(images), -1) # (1797, 64)\n", - "print(D.shape)" - ], - "id": "s03-13" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s03-14" - }, - "source": [ - "## Exercise 3 — standardize, then find the trap\n", - "\n", - "> 🇪🇸 Estandariza y encuentra el problema." - ], - "id": "s03-14" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "s03-15" - }, - "outputs": [], - "source": [ - "# TODO 5: Compute the mean and std of each of the 64 pixels across all images.\n", - "\n", - "# TODO 6: Standardize with broadcasting: (D - mean) / std.\n", - "# RUN IT AND LOOK AT THE RESULT before continuing.\n", - "\n", - "# TODO 7: You will find NaN. How many pixels have std == 0, and why would a real\n", - "# handwritten digit image contain such pixels? Fix it, then verify no NaN." - ], - "id": "s03-15" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ], - "id": "s03-16" - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "mean, std = D.mean(axis=0), D.std(axis=0)\n", - "print(mean.shape, std.shape) # (64,) (64,)\n", - "\n", - "Z_bad = (D - mean) / std\n", - "print(np.isnan(Z_bad).any()) # True\n", - "\n", - "print((std == 0).sum()) # 3\n", - "Z = (D - mean) / np.where(std == 0, 1.0, std)\n", - "print(np.isnan(Z).any()) # False\n", - "\n", - "# THREE PIXELS ARE ALWAYS DARK across all 1797 digit images.\n", - "# They are background/edge locations that are never activated in this dataset.\n", - "# Their standard deviation is exactly zero, so dividing by it produces NaN.\n", - "# np.where leaves those columns as centred zeros, which is appropriate for\n", - "# features that carry no variation in this dataset.\n", - "\n", - "import matplotlib.pyplot as plt\n", - "fig, ax = plt.subplots(figsize=(3, 3))\n", - "ax.imshow(D.mean(axis=0).reshape(8, 8), cmap='gray')\n", - "zero_rows, zero_cols = np.where((std == 0).reshape(8, 8))\n", - "ax.scatter(zero_cols, zero_rows, s=250, marker='s',\n", - " facecolors='none', edgecolors='#C44E52', linewidths=2)\n", - "ax.set_title('zero-variance pixels, marked')\n", - "ax.axis('off')\n", - "plt.show()\n" - ], - "id": "s03-16" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s03-17" - }, - "source": [ - "## What just happened\n", - "\n", - "Two concrete patterns came directly from real datasets:\n", - "\n", - "1. **In this breast-cancer dataset**, malignant samples have a larger mean radius on average — about 17.5 versus 12.1 for benign samples.\n", - "2. **Three pixel positions have zero variance** across all 1797 `load_digits` images. Dividing by their standard deviation therefore produces `NaN`.\n", - "\n", - "The second result is the broadcasting trap to remember. A zero-variance feature is not necessarily a bug in your code; it can be a property of the data. You must detect it and handle it explicitly rather than silently propagating invalid values into later computations.\n", - "\n", - "> 🇪🇸 **Qué ocurrió:** observaste dos patrones que provienen directamente de datos reales. En este conjunto de cáncer de mama, las muestras malignas tienen un radio medio mayor en promedio. En `load_digits`, tres posiciones de píxel tienen varianza cero en las 1797 imágenes, por lo que dividir por su desviación estándar produce `NaN`. La lección es detectar y manejar explícitamente las características sin variación antes de continuar con el análisis." - ], - "id": "s03-17" + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 03 · Indexing and broadcasting real data\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/03-indexing-and-broadcasting.ipynb)\n", + "\n", + "*Part III · exercise · 15 min*\n", + "\n", + "> 🇪🇸 **Indexación y broadcasting con datos reales** — Seleccionar medidas reales por nombre, comparar subconjuntos con máscaras booleanas y detectar píxeles de varianza cero.\n", + "\n", + "Select named measurements from real tumour-sample data, compare meaningful subsets, then standardize real image data safely.\n", + "\n", + "## What you will be able to do\n", + "\n", + "- Select a named column of real data by name, never by a hard-coded number.\n", + "- Use fancy indexing and boolean masks to select meaningful subsets of rows.\n", + "- Standardize a real data matrix with broadcasting.\n", + "- Recognise zero-variance pixels and explain why real image datasets can contain them." + ], + "id": "s03-00" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "Run this first. It installs and imports everything this notebook needs, and nothing else.\n", + "\n", + "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." + ], + "id": "s03-01" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from sklearn.datasets import load_breast_cancer, load_digits\n", + "\n", + "bc = load_breast_cancer()\n", + "X, y = bc.data, bc.target # (569, 30); y: 0 = malignant, 1 = benign\n", + "names = list(bc.feature_names)\n", + "print(X.shape, len(names))" + ], + "id": "s03-02" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Why this matters\n", + "\n", + "> 🇪🇸 Elegir la columna equivocada no da error: devuelve otra medida real, y el análisis puede continuar con una respuesta convincente pero equivocada.\n", + "\n", + "The `breast_cancer` dataset contains 569 real breast-mass samples with 30 numerical features computed from digitized images of fine-needle aspirates. Selecting the wrong column does not necessarily produce an error — it can return a *different real measurement* while the rest of the analysis keeps running.\n", + "\n", + "In research, that makes results harder to reproduce and easier to misinterpret. In any decision-support pipeline, selecting the wrong feature can quietly change the conclusion.\n", + "\n", + "**In tech**, the identical indexing operation appears on a `(users, items)` matrix when selecting one user's history before making a recommendation." + ], + "id": "s03-03" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 1 — indexing by name\n", + "\n", + "> 🇪🇸 Indexación por nombre, nunca por número fijo." + ], + "id": "s03-04" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO 1: Print X.shape. Say out loud what each axis means.\n", + "\n", + "# TODO 2: Extract the column \"mean radius\" for all samples -> shape (569,).\n", + "# Find its position with names.index(...). Do not hard-code a number." + ], + "id": "s03-05" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true }, - { - "cell_type": "markdown", - "id": "s03-footer", - "metadata": { - "id": "s03-footer" - }, - "source": [ - "---\n", - "\n", - "## Done with this section\n", - "\n", - "Next up: **04 · Reshape and transpose** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/04-reshape-and-transpose.ipynb).\n", - "\n", - "[← Back to the workshop site](../index.html) · [All notebooks](../notebooks.html) · [Handbook](../tensors_workshop_plan_with_quizzes.html)\n" - ] - } - ], - "metadata": { - "colab": { - "provenance": [], - "toc_visible": true + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "print(X.shape) # (569, 30) samples x measurements\n", + "\n", + "i = names.index(\"mean radius\")\n", + "radius = X[:, i] # book notation A_{:,j}\n", + "print(i, radius.shape) # 0 (569,)\n", + "\n", + "# names.index() rather than 0 because the column order is not yours to assume.\n", + "# If the dataset is ever reordered, the hard-coded version keeps running and\n", + "# keeps being wrong." + ], + "id": "s03-06" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 2 — fancy and boolean indexing\n", + "\n", + "> 🇪🇸 Indexación avanzada y booleana." + ], + "id": "s03-07" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO 3: Find the 5 samples with the LARGEST mean radius, then extract their\n", + "# full 30-measurement profiles as one (5, 30) array, in ONE operation.\n", + "#\n", + "# TODO 4: Using boolean indexing, compare mean radius for malignant (y == 0)\n", + "# against benign (y == 1) samples.\n", + "# Describe the difference in THIS dataset; do not treat one feature\n", + "# as a diagnostic rule.\n" + ], + "id": "s03-08" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "top5 = np.argsort(radius)[-5:]\n", + "profiles = X[top5, :] # (5, 30)\n", + "print(profiles.shape)\n", + "\n", + "malignant_mean = radius[y == 0].mean()\n", + "benign_mean = radius[y == 1].mean()\n", + "print(malignant_mean, benign_mean) # about 17.5 vs 12.1\n", + "\n", + "# Dataset-specific result:\n", + "# malignant samples have a larger mean radius ON AVERAGE in this dataset.\n", + "# That is a descriptive comparison, not a one-feature diagnostic rule.\n", + "#\n", + "# Purely synthetic random data would not preserve this real dataset\n", + "# relationship unless we explicitly designed it to do so.\n" + ], + "id": "s03-09" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`radius` was one column out of 30, picked because it happens to separate the\n", + "two groups well. Drag the slider below to look at all 30 — most separate far\n", + "less cleanly.\n", + "\n", + "> 🇪🇸 Mueve el deslizador para ver las 30 medidas, una por una. La mayoría\n", + "> separa malignos de benignos mucho peor que el radio." + ], + "id": "s03-10" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " from google.colab import output\n", + " output.enable_custom_widget_manager()\n", + "except ImportError:\n", + " pass\n", + "\n", + "import ipywidgets as widgets\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def show_feature(i):\n", + " plt.close('all')\n", + " col = X[:, i]\n", + " fig, ax = plt.subplots(figsize=(6, 3))\n", + " ax.hist(col[y == 0], bins=30, alpha=0.6, label='malignant', color='#C44E52')\n", + " ax.hist(col[y == 1], bins=30, alpha=0.6, label='benign', color='#4C72B0')\n", + " ax.set_title(names[i])\n", + " ax.legend()\n", + " plt.tight_layout()\n", + " plt.show()\n", + " print(f\"malignant mean: {col[y == 0].mean():.3f} \"\n", + " f\"benign mean: {col[y == 1].mean():.3f}\")\n", + "\n", + "widgets.interact(show_feature,\n", + " i=widgets.IntSlider(min=0, max=len(names) - 1, step=1, value=0,\n", + " description='feature'));" + ], + "id": "s03-11" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Broadcasting, on real images\n", + "\n", + "> 🇪🇸 **Broadcasting sobre imágenes reales.** Una operación muy común es estandarizar cada característica restando su media y dividiendo por su desviación estándar.\n", + "\n", + "Broadcasting stretches a smaller array across a larger one without manually copying it. A common preprocessing operation is to standardize a data matrix — subtract the mean of each column, then divide by its standard deviation.\n", + "\n", + "Run TODO 6 and **look at the result before continuing**. Something is wrong with it, and finding out what is the point of this block." + ], + "id": "s03-12" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "images = load_digits().images # (1797, 8, 8)\n", + "D = images.reshape(len(images), -1) # (1797, 64)\n", + "print(D.shape)" + ], + "id": "s03-13" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 3 — standardize, then find the trap\n", + "\n", + "> 🇪🇸 Estandariza y encuentra el problema." + ], + "id": "s03-14" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO 5: Compute the mean and std of each of the 64 pixels across all images.\n", + "\n", + "# TODO 6: Standardize with broadcasting: (D - mean) / std.\n", + "# RUN IT AND LOOK AT THE RESULT before continuing.\n", + "\n", + "# TODO 7: You will find NaN. How many pixels have std == 0, and why would a real\n", + "# handwritten digit image contain such pixels? Fix it, then verify no NaN." + ], + "id": "s03-15" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true }, - "language_info": { - "name": "python" - } + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "mean, std = D.mean(axis=0), D.std(axis=0)\n", + "print(mean.shape, std.shape) # (64,) (64,)\n", + "\n", + "Z_bad = (D - mean) / std\n", + "print(np.isnan(Z_bad).any()) # True\n", + "\n", + "print((std == 0).sum()) # 3\n", + "Z = (D - mean) / np.where(std == 0, 1.0, std)\n", + "print(np.isnan(Z).any()) # False\n", + "\n", + "# THREE PIXELS ARE ALWAYS DARK across all 1797 digit images.\n", + "# They are background/edge locations that are never activated in this dataset.\n", + "# Their standard deviation is exactly zero, so dividing by it produces NaN.\n", + "# np.where leaves those columns as centred zeros, which is appropriate for\n", + "# features that carry no variation in this dataset.\n", + "\n", + "import matplotlib.pyplot as plt\n", + "fig, ax = plt.subplots(figsize=(3, 3))\n", + "ax.imshow(D.mean(axis=0).reshape(8, 8), cmap='gray')\n", + "zero_rows, zero_cols = np.where((std == 0).reshape(8, 8))\n", + "ax.scatter(zero_cols, zero_rows, s=250, marker='s',\n", + " facecolors='none', edgecolors='#C44E52', linewidths=2)\n", + "ax.set_title('zero-variance pixels, marked')\n", + "ax.axis('off')\n", + "plt.show()\n" + ], + "id": "s03-16" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What just happened\n", + "\n", + "Two concrete patterns came directly from real datasets:\n", + "\n", + "1. **In this breast-cancer dataset**, malignant samples have a larger mean radius on average — about 17.5 versus 12.1 for benign samples.\n", + "2. **Three pixel positions have zero variance** across all 1797 `load_digits` images. Dividing by their standard deviation therefore produces `NaN`.\n", + "\n", + "The second result is the broadcasting trap to remember. A zero-variance feature is not necessarily a bug in your code; it can be a property of the data. You must detect it and handle it explicitly rather than silently propagating invalid values into later computations.\n", + "\n", + "> 🇪🇸 **Qué ocurrió:** observaste dos patrones que provienen directamente de datos reales. En este conjunto de cáncer de mama, las muestras malignas tienen un radio medio mayor en promedio. En `load_digits`, tres posiciones de píxel tienen varianza cero en las 1797 imágenes, por lo que dividir por su desviación estándar produce `NaN`. La lección es detectar y manejar explícitamente las características sin variación antes de continuar con el análisis." + ], + "id": "s03-17" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Done with this section\n", + "\n", + "Next up: **04 · Reshape and transpose real images** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/04-reshape-and-transpose.ipynb).\n", + "\n", + "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" + ], + "id": "s03-footer" + } + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From e08aa60bbfc641604809b14fb2e892c9888302bf Mon Sep 17 00:00:00 2001 From: Sebas Laverde Chunza <106851243+Laverde97@users.noreply.github.com> Date: Fri, 28 Aug 2026 13:53:32 -0500 Subject: [PATCH 09/29] Improve notebook 04 pedagogy with real data for issue #44 --- notebooks/04-reshape-and-transpose.ipynb | 720 ++++++++++++++--------- 1 file changed, 436 insertions(+), 284 deletions(-) diff --git a/notebooks/04-reshape-and-transpose.ipynb b/notebooks/04-reshape-and-transpose.ipynb index 0d6e9fe..5eaedc9 100644 --- a/notebooks/04-reshape-and-transpose.ipynb +++ b/notebooks/04-reshape-and-transpose.ipynb @@ -1,289 +1,441 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 04 · Reshape and transpose real images\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/04-reshape-and-transpose.ipynb)\n", - "\n", - "*Part III · exercise · 15 min*\n", - "\n", - "> 🇪🇸 **Reshape y transposición de imágenes reales** — De HWC a CHW, de NHWC a NCHW, y por qué reshape destruye una imagen en silencio.\n", - "\n", - "HWC to CHW, NHWC to NCHW, and why reshape silently destroys an image.\n", - "\n", - "## What you will be able to do\n", - "\n", - "- Convert an image between `(H, W, C)` and `(C, H, W)` with `np.transpose`.\n", - "- Convert a batch between NHWC and NCHW, and know which axis is which when two share a size.\n", - "- Explain why `reshape` runs without error and still destroys the image." - ], - "id": "s04-00" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "Run this first. It installs and imports everything this notebook needs, and nothing else.\n", - "\n", - "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." - ], - "id": "s04-01" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from skimage import data\n", - "\n", - "photo = data.immunohistochemistry() # (512, 512, 3) real histology\n", - "cells = data.cell() # (660, 550) real microscopy, grayscale\n", - "print(photo.shape, cells.shape)" - ], - "id": "s04-02" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Why this matters\n", - "\n", - "> 🇪🇸 Los microscopios y las cámaras ordenan sus ejes según el hardware, no\n", - "> según lo que el modelo espera. Equivocarse no da error: el modelo funciona con\n", - "> datos revueltos y devuelve resultados seguros y sin sentido.\n", - "\n", - "Microscopes and cameras order their axes according to the hardware, not\n", - "according to what a model expects. Getting this wrong does not crash — the model\n", - "runs on scrambled data and returns confident, meaningless output.\n", - "\n", - "In a drug screen, that is a wrong decision about whether a compound works. The\n", - "famous version in tech: a model trained in TensorFlow (`NHWC`) deployed into\n", - "PyTorch (`NCHW`) with no transpose." - ], - "id": "s04-03" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 1 — one image, two orderings\n", - "\n", - "> 🇪🇸 Una imagen, dos ordenaciones de ejes." - ], - "id": "s04-04" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO 1: Print both shapes. Which one has no colour axis?\n", - "\n", - "# TODO 2: Convert `photo` from (H, W, C) to (C, H, W) with np.transpose." - ], - "id": "s04-05" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "s04-00" + }, + "source": [ + "# 04 · Reshape and transpose real images\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/04-reshape-and-transpose.ipynb)\n", + "\n", + "*Part III · exercise · 15 min*\n", + "\n", + "> 🇪🇸 **Reshape y transposición de imágenes reales** — Reordenar ejes correctamente en imágenes y lotes reales, y comprobar por qué `reshape` puede conservar la forma mientras destruye el significado.\n", + "\n", + "Use real images to move between HWC↔CHW and NHWC↔NCHW, then show why matching shapes do not guarantee matching axis semantics.\n", + "\n", + "## What you will be able to do\n", + "\n", + "- Convert a real RGB image between `(H, W, C)` and `(C, H, W)` with `np.transpose`.\n", + "- Build a batch from three distinct real RGB images and convert NHWC to NCHW.\n", + "- Explain why two axes with the same size cannot be identified from shape alone.\n", + "- Demonstrate on a real image why `reshape` can run without error and still scramble the data." + ], + "id": "s04-00" }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "print(photo.shape, cells.shape) # (512, 512, 3) (660, 550)\n", - "# `cells` is grayscale — order 2, no colour axis at all.\n", - "\n", - "chw = np.transpose(photo, (2, 0, 1)) # (3, 512, 512) — correct\n", - "print(chw.shape)\n", - "\n", - "# The tuple (2, 0, 1) reads: \"the new axis 0 is the old axis 2, the new axis 1\n", - "# is the old axis 0, the new axis 2 is the old axis 1.\"" - ], - "id": "s04-06" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 2 — a batch, and two axes of the same size\n", - "\n", - "> 🇪🇸 Un lote, y dos ejes del mismo tamaño." - ], - "id": "s04-07" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO 3: Stack `photo` three times into a batch of shape (3, 512, 512, 3).\n", - "# Which axis is the batch axis?\n", - "\n", - "# TODO 4: Convert that batch from NHWC to NCHW -> (3, 3, 512, 512).\n", - "# Two axes now both have size 3. How do you know which is which?" - ], - "id": "s04-08" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true + { + "cell_type": "markdown", + "metadata": { + "id": "s04-01" + }, + "source": [ + "## Setup\n", + "\n", + "Run this first. It loads real microscopy, histology, and photographic image data used throughout the notebook.\n", + "\n", + "> 🇪🇸 Ejecuta esta celda primero. Carga datos reales de microscopía, histología y fotografía que se usarán en toda la sección." + ], + "id": "s04-01" }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "batch = np.stack([photo, photo, photo]) # (3, 512, 512, 3)\n", - "print(batch.shape) # axis 0 is the batch axis\n", - "\n", - "nchw = np.transpose(batch, (0, 3, 1, 2)) # (3, 3, 512, 512)\n", - "print(nchw.shape)\n", - "\n", - "# You know which is which ONLY because you wrote the transpose. Nothing in the\n", - "# array records it. Check it by hand — a batch axis and a colour axis behave\n", - "# differently under indexing:\n", - "print(np.array_equal(nchw[0], nchw[1])) # True — the 3 stacked copies\n", - "print(np.array_equal(nchw[:, 0], nchw[:, 1])) # False — the 3 colour channels" - ], - "id": "s04-09" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 3 — the one that runs and is still wrong\n", - "\n", - "> 🇪🇸 El que se ejecuta sin error y aun así está mal." - ], - "id": "s04-10" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO 5: photo.reshape(3, 512, 512) runs WITHOUT error but is wrong.\n", - "# Run it, compare against TODO 2, and explain the difference.\n", - "# Then display both with matplotlib and look at them." - ], - "id": "s04-11" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "s04-02", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "outputId": "f0e058f4-08e2-4d0b-94f1-69ddf3ac5b53" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "histology: (512, 512, 3)\n", + "microscopy: (660, 550)\n", + "astronaut: (512, 512, 3)\n", + "coffee: (400, 600, 3)\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from skimage import data\n", + "\n", + "# Real images distributed with scikit-image.\n", + "photo = data.immunohistochemistry() # (512, 512, 3) real histology, RGB\n", + "cells = data.cell() # (660, 550) real microscopy, grayscale\n", + "astronaut = data.astronaut() # (512, 512, 3) real RGB photograph\n", + "coffee = data.coffee() # (400, 600, 3) real RGB photograph\n", + "\n", + "\n", + "def center_crop_rgb(img, size=256):\n", + " \"\"\"Take a deterministic centre crop so distinct real RGB images can be stacked.\"\"\"\n", + " h, w, c = img.shape\n", + " if c != 3 or h < size or w < size:\n", + " raise ValueError(f\"expected RGB image at least {size}x{size}, got {img.shape}\")\n", + " r0 = (h - size) // 2\n", + " c0 = (w - size) // 2\n", + " return img[r0:r0 + size, c0:c0 + size]\n", + "\n", + "\n", + "rgb_sources = [photo, astronaut, coffee]\n", + "\n", + "print(\"histology:\", photo.shape)\n", + "print(\"microscopy:\", cells.shape)\n", + "print(\"astronaut:\", astronaut.shape)\n", + "print(\"coffee:\", coffee.shape)" + ], + "id": "s04-02" }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "chw = np.transpose(photo, (2, 0, 1)) # (3, 512, 512) — correct\n", - "wrong = photo.reshape(3, 512, 512) # (3, 512, 512) — runs, but scrambles\n", - "\n", - "print(chw.shape == wrong.shape) # True — identical shapes\n", - "print(np.array_equal(chw, wrong)) # False — completely different data\n", - "\n", - "import matplotlib.pyplot as plt\n", - "fig, ax = plt.subplots(1, 2, figsize=(8, 4))\n", - "ax[0].imshow(chw[0], cmap=\"gray\"); ax[0].set_title(\"transpose — a channel\")\n", - "ax[1].imshow(wrong[0], cmap=\"gray\"); ax[1].set_title(\"reshape — nonsense\")\n", - "plt.show()" - ], - "id": "s04-12" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What just happened\n", - "\n", - "**Reshape only reinterprets numbers in memory order. Transpose moves them\n", - "according to axis meaning.** Both give shape `(3, 512, 512)`; only one is the\n", - "image.\n", - "\n", - "> 🇪🇸 `reshape` reinterpreta los números en el orden en que están en memoria;\n", - "> `transpose` los mueve según el significado de cada eje.\n", - "\n", - "And TODO 4 makes the deeper point: **once two axes share a size, the shape\n", - "cannot tell you which is which.** Only your own tracking can. No error will be\n", - "raised, no shape will look wrong, and the model will train — on scrambled data.\n", - "\n", - "That is everything the first Kahoot asks about." - ], - "id": "s04-13" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Time for Kahoot 🎯\n", - "\n", - "**Kahoot 1 — Tensor Vocabulary & Shapes** · 6 questions, about 5 minutes.\n", - "\n", - "> 🇪🇸 **Vocabulario de tensores y formas** — 6 preguntas, unos 5 minutos.\n", - "\n", - "Join at **kahoot.it** with the PIN on the facilitator's screen.\n", - "\n", - "- [Quiz details and facilitator notes](https://project-delphi.github.io/tensors-workshop/kahoot.html#quiz-1)\n", - "- [Import file (`.xlsx`)](https://github.com/project-delphi/tensors-workshop/blob/main/kahoot/kahoot_quiz_1_vocabulary_shapes.xlsx)\n", - "\n", - "Next up: **05 · Video pipeline design** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/05-video-pipeline-design.ipynb).\n", - "\n", - "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" - ], - "id": "s04-14" - } - ], - "metadata": { - "colab": { - "name": "04-reshape-and-transpose.ipynb", - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" + { + "cell_type": "markdown", + "metadata": { + "id": "s04-03" + }, + "source": [ + "## Why this matters\n", + "\n", + "Image-acquisition software and ML frameworks can use different axis conventions. A tensor may contain the right numbers and still be interpreted incorrectly if height, width, colour, or batch axes are in the wrong positions.\n", + "\n", + "That is especially dangerous because many axis mistakes do **not** raise an error. The code can keep running on data whose semantics have been scrambled.\n", + "\n", + "A familiar engineering example is moving image batches between TensorFlow-style `NHWC` and PyTorch-style `NCHW` conventions.\n", + "\n", + "> 🇪🇸 **Por qué importa:** distintos sistemas de adquisición y frameworks pueden usar convenciones diferentes para los ejes. Un tensor puede contener los números correctos y aun así interpretarse mal si lote, alto, ancho o color están en posiciones incorrectas. Muchos de estos errores no generan una excepción: el código sigue funcionando con datos semánticamente desordenados.\n", + "\n", + "### Predict → Run → Explain\n", + "\n", + "Before each exercise, predict the output shape **and name what every axis means**. After running, explain why the operation preserved or destroyed that meaning.\n", + "\n", + "> 🇪🇸 **Predice → Ejecuta → Explica:** antes de cada ejercicio, predice la forma de salida y nombra el significado de cada eje. Después explica por qué la operación conservó o destruyó ese significado." + ], + "id": "s04-03" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s04-04" + }, + "source": [ + "## Exercise 1 — one real image, two orderings\n", + "\n", + "**Predict first:** `photo` is a real RGB histology image and `cells` is a real grayscale microscopy image. Which one has a colour axis? What should the shape of `photo` become after HWC → CHW?\n", + "\n", + "> 🇪🇸 **Predice primero:** `photo` es una imagen histológica RGB real y `cells` es una imagen microscópica real en escala de grises. ¿Cuál tiene eje de color? ¿Qué forma debe tener `photo` después de HWC → CHW?" + ], + "id": "s04-04" + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "s04-05" + }, + "outputs": [], + "source": [ + "# TODO 1: Print both shapes. Which one has no colour axis?\n", + "#\n", + "# TODO 2: Convert `photo` from (H, W, C) to (C, H, W) with np.transpose.\n", + "# Name what each output axis represents.\n" + ], + "id": "s04-05" + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "id": "s04-06", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "outputId": "e20288e2-5eba-494e-e38b-f45b8eb36f66" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(512, 512, 3) (660, 550)\n", + "(3, 512, 512)\n" + ] + } + ], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "print(photo.shape, cells.shape) # (512, 512, 3) (660, 550)\n", + "# `cells` is grayscale — order 2, no colour axis at all.\n", + "\n", + "chw = np.transpose(photo, (2, 0, 1)) # (3, 512, 512) — correct\n", + "print(chw.shape)\n", + "\n", + "# The tuple (2, 0, 1) means:\n", + "# new axis 0 <- old axis 2 (colour)\n", + "# new axis 1 <- old axis 0 (height)\n", + "# new axis 2 <- old axis 1 (width)\n", + "assert np.array_equal(chw, np.moveaxis(photo, 2, 0))\n" + ], + "id": "s04-06" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s04-07" + }, + "source": [ + "## Exercise 2 — a real batch, and two axes of the same size\n", + "\n", + "Instead of stacking three copies of one image, build a batch from **three distinct real RGB images**: histology, astronaut, and coffee. A deterministic centre crop makes them the same spatial size without inventing pixel values.\n", + "\n", + "**Predict first:** after stacking, the batch will be `(N, H, W, C) = (3, 256, 256, 3)`. After NHWC → NCHW it will be `(3, 3, 256, 256)`. Which size-3 axis is batch, and which is colour?\n", + "\n", + "> 🇪🇸 Construye el lote con **tres imágenes RGB reales distintas**. El recorte central solo selecciona píxeles medidos; no inventa valores. Predice qué eje de tamaño 3 representa el lote y cuál representa el color después de NHWC → NCHW." + ], + "id": "s04-07" + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "s04-08" + }, + "outputs": [], + "source": [ + "# TODO 3:\n", + "# Centre-crop each image in `rgb_sources` to 256x256 with center_crop_rgb(...)\n", + "# and stack them into a REAL batch of shape (3, 256, 256, 3).\n", + "# Which axis is the batch axis?\n", + "#\n", + "# TODO 4:\n", + "# Convert that batch from NHWC to NCHW -> (3, 3, 256, 256).\n", + "# Two axes now both have size 3. Demonstrate with indexing what axis 0 means\n", + "# and what axis 1 means. Do not infer semantics from size alone.\n" + ], + "id": "s04-08" + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "id": "s04-09", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "outputId": "5e68d71a-a53e-45fa-e85d-8496a15a902d" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "NHWC: (3, 256, 256, 3)\n", + "NCHW: (3, 3, 256, 256)\n", + "nchw[0] : (3, 256, 256) -> one image, all 3 colour channels\n", + "nchw[:, 0]: (3, 256, 256) -> red channel, all 3 images\n" + ] + } + ], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "\n", + "batch = np.stack([center_crop_rgb(img) for img in rgb_sources])\n", + "print(\"NHWC:\", batch.shape) # (3, 256, 256, 3)\n", + "\n", + "nchw = np.transpose(batch, (0, 3, 1, 2))\n", + "print(\"NCHW:\", nchw.shape) # (3, 3, 256, 256)\n", + "\n", + "# Same numerical shape after fixing one of the two size-3 axes,\n", + "# but completely different semantics:\n", + "one_image = nchw[0] # (C, H, W)\n", + "red_channel_all_images = nchw[:, 0] # (N, H, W)\n", + "\n", + "print(\"nchw[0] :\", one_image.shape, \"-> one image, all 3 colour channels\")\n", + "print(\"nchw[:, 0]:\", red_channel_all_images.shape, \"-> red channel, all 3 images\")\n", + "\n", + "# Verify those semantics against the original NHWC representation.\n", + "assert np.array_equal(one_image, np.transpose(batch[0], (2, 0, 1)))\n", + "assert np.array_equal(red_channel_all_images, batch[:, :, :, 0])\n" + ], + "id": "s04-09" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s04-10" + }, + "source": [ + "## Exercise 3 — the operation that runs and is still wrong\n", + "\n", + "`photo` is real RGB data with shape `(512, 512, 3)`. Both `transpose` and `reshape` can produce an array with shape `(3, 512, 512)`, but only one operation moves the colour axis correctly.\n", + "\n", + "**Predict first:** will equal output shapes imply equal pixel organization?\n", + "\n", + "> 🇪🇸 `photo` contiene datos RGB reales. Tanto `transpose` como `reshape` pueden producir `(3, 512, 512)`, pero solo una operación reordena correctamente el eje de color. **Predice primero:** ¿tener la misma forma implica conservar la misma organización de los píxeles?" + ], + "id": "s04-10" + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "s04-11" + }, + "outputs": [], + "source": [ + "# TODO 5:\n", + "# Run photo.reshape(3, 512, 512). It succeeds without an error.\n", + "# Compare it with the correct CHW tensor from np.transpose(photo, (2, 0, 1)).\n", + "# Verify whether the arrays are equal, then display the first plane from both.\n", + "# Explain why reshape cannot replace transpose when axis meaning must change.\n" + ], + "id": "s04-11" + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "id": "s04-12", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 455 + }, + "outputId": "ba902ff4-6b84-4ac3-fc52-58600cc72c24" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "same shape: True\n", + "same data arrangement: False\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "\n", + "chw = np.transpose(photo, (2, 0, 1)) # (3, 512, 512) — correct\n", + "wrong = photo.reshape(3, 512, 512) # (3, 512, 512) — runs, but scrambles axes\n", + "\n", + "print(\"same shape:\", chw.shape == wrong.shape) # True\n", + "print(\"same data arrangement:\", np.array_equal(chw, wrong)) # False\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=(8, 4))\n", + "ax[0].imshow(chw[0], cmap=\"gray\")\n", + "ax[0].set_title(\"transpose — one real colour channel\")\n", + "ax[1].imshow(wrong[0], cmap=\"gray\")\n", + "ax[1].set_title(\"reshape — scrambled interpretation\")\n", + "for a in ax:\n", + " a.axis(\"off\")\n", + "plt.tight_layout()\n", + "plt.show()\n" + ], + "id": "s04-12" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s04-13" + }, + "source": [ + "## What just happened\n", + "\n", + "You used real measured image values throughout the section:\n", + "\n", + "1. a real histology image showed HWC → CHW;\n", + "2. three **distinct real RGB images** formed an NHWC batch and then an NCHW batch;\n", + "3. two axes both had size 3, proving that **shape alone does not record axis meaning**;\n", + "4. on the real histology image, `reshape` and `transpose` produced the same output shape but different data arrangements.\n", + "\n", + "The central rule is:\n", + "\n", + "> **Use `transpose`/axis-moving operations when axis meaning changes. Use `reshape` when you only want to reinterpret grouping without changing the intended axis order.**\n", + "\n", + "These are the ideas to take into Kahoot 1.\n", + "\n", + "> 🇪🇸 **Qué ocurrió:** trabajaste con valores reales de imágenes. Reordenaste HWC → CHW, construiste un lote real NHWC → NCHW y comprobaste que dos ejes pueden tener el mismo tamaño pero significados distintos. Finalmente viste que `reshape` puede producir la forma esperada y aun así desorganizar los datos. **Cuando cambia el significado o la posición de los ejes, usa una transposición o movimiento de ejes; no sustituyas esa operación por `reshape`.**" + ], + "id": "s04-13" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s04-14" + }, + "source": [ + "---\n", + "\n", + "## Time for Kahoot 🎯\n", + "\n", + "**Kahoot 1 — Tensor Vocabulary & Shapes** · 6 questions, about 5 minutes.\n", + "\n", + "> 🇪🇸 **Vocabulario de tensores y formas** — 6 preguntas, unos 5 minutos.\n", + "\n", + "Join at **kahoot.it** with the PIN on the facilitator's screen.\n", + "\n", + "- [Quiz details and facilitator notes](https://project-delphi.github.io/tensors-workshop/kahoot.html#quiz-1)\n", + "- [Import file (`.xlsx`)](https://github.com/project-delphi/tensors-workshop/blob/main/kahoot/kahoot_quiz_1_vocabulary_shapes.xlsx)\n", + "\n", + "Next up: **05 · Video pipeline design** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/05-video-pipeline-design.ipynb).\n", + "\n", + "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" + ], + "id": "s04-14" + } + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python" + } }, - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file From 7c41803930278b6b75df3eaacce48e2f3c456f7e Mon Sep 17 00:00:00 2001 From: Sebas Laverde Chunza Date: Fri, 28 Aug 2026 14:05:05 -0500 Subject: [PATCH 10/29] Finalize notebook 04 pedagogy for issue #44 --- _variables.yml | 18 +- docs/notebooks/04-reshape-and-transpose.ipynb | 193 +++-- notebooks/04-reshape-and-transpose.ipynb | 781 ++++++++---------- scripts/content.py | 27 +- 4 files changed, 507 insertions(+), 512 deletions(-) diff --git a/_variables.yml b/_variables.yml index be5a8f0..4f845b3 100644 --- a/_variables.yml +++ b/_variables.yml @@ -248,16 +248,18 @@ sections: format_es: "ejercicio" title_en: "Reshape and transpose real images" title_es: "Reshape y transposición de imágenes reales" - summary_en: "HWC to CHW, NHWC to NCHW, and why reshape silently destroys an image." - summary_es: "De HWC a CHW, de NHWC a NCHW, y por qué reshape destruye una imagen en silencio." + summary_en: "Use real images to move between HWC↔CHW and NHWC↔NCHW, then show why matching shapes do not guarantee matching axis semantics." + summary_es: "Reordenar ejes correctamente en imágenes y lotes reales, y comprobar por qué `reshape` puede conservar la forma mientras destruye el significado." objectives_en: - - "Convert an image between `(H, W, C)` and `(C, H, W)` with `np.transpose`." - - "Convert a batch between NHWC and NCHW, and know which axis is which when two share a size." - - "Explain why `reshape` runs without error and still destroys the image." + - "Convert a real RGB image between `(H, W, C)` and `(C, H, W)` with `np.transpose`." + - "Build a batch from three distinct real RGB images and convert NHWC to NCHW." + - "Explain why two axes with the same size cannot be identified from shape alone." + - "Demonstrate on a real image why `reshape` can run without error and still scramble the data." objectives_es: - - "Convertir una imagen entre `(H, W, C)` y `(C, H, W)` con `np.transpose`." - - "Convertir un batch entre NHWC y NCHW y distinguir correctamente los ejes cuando dos tienen el mismo tamaño." - - "Explicar por qué `reshape` puede ejecutarse sin error y aun así destruir la estructura de la imagen." + - "Convertir una imagen RGB real entre `(H, W, C)` y `(C, H, W)` con `np.transpose`." + - "Construir un lote con tres imágenes RGB reales distintas y convertir NHWC a NCHW." + - "Explicar por qué dos ejes con el mismo tamaño no pueden identificarse únicamente a partir de la forma." + - "Demostrar sobre una imagen real por qué `reshape` puede ejecutarse sin error y aun así desorganizar los datos." s05: n: "05" slug: "video-pipeline-design" diff --git a/docs/notebooks/04-reshape-and-transpose.ipynb b/docs/notebooks/04-reshape-and-transpose.ipynb index 0d6e9fe..16e00f4 100644 --- a/docs/notebooks/04-reshape-and-transpose.ipynb +++ b/docs/notebooks/04-reshape-and-transpose.ipynb @@ -10,15 +10,16 @@ "\n", "*Part III · exercise · 15 min*\n", "\n", - "> 🇪🇸 **Reshape y transposición de imágenes reales** — De HWC a CHW, de NHWC a NCHW, y por qué reshape destruye una imagen en silencio.\n", + "> 🇪🇸 **Reshape y transposición de imágenes reales** — Reordenar ejes correctamente en imágenes y lotes reales, y comprobar por qué `reshape` puede conservar la forma mientras destruye el significado.\n", "\n", - "HWC to CHW, NHWC to NCHW, and why reshape silently destroys an image.\n", + "Use real images to move between HWC↔CHW and NHWC↔NCHW, then show why matching shapes do not guarantee matching axis semantics.\n", "\n", "## What you will be able to do\n", "\n", - "- Convert an image between `(H, W, C)` and `(C, H, W)` with `np.transpose`.\n", - "- Convert a batch between NHWC and NCHW, and know which axis is which when two share a size.\n", - "- Explain why `reshape` runs without error and still destroys the image." + "- Convert a real RGB image between `(H, W, C)` and `(C, H, W)` with `np.transpose`.\n", + "- Build a batch from three distinct real RGB images and convert NHWC to NCHW.\n", + "- Explain why two axes with the same size cannot be identified from shape alone.\n", + "- Demonstrate on a real image why `reshape` can run without error and still scramble the data." ], "id": "s04-00" }, @@ -43,9 +44,32 @@ "import numpy as np\n", "from skimage import data\n", "\n", - "photo = data.immunohistochemistry() # (512, 512, 3) real histology\n", + "# Real images distributed with scikit-image.\n", + "photo = data.immunohistochemistry() # (512, 512, 3) real histology, RGB\n", "cells = data.cell() # (660, 550) real microscopy, grayscale\n", - "print(photo.shape, cells.shape)" + "astronaut = data.astronaut() # (512, 512, 3) real RGB photograph\n", + "coffee = data.coffee() # (400, 600, 3) real RGB photograph\n", + "\n", + "\n", + "def center_crop_rgb(img, size=256):\n", + " # Deterministic centre crop so distinct real RGB images can be stacked.\n", + " h, w, c = img.shape\n", + " if c != 3 or h < size or w < size:\n", + " raise ValueError(\n", + " f\"expected RGB image at least {size}x{size}, got {img.shape}\"\n", + " )\n", + "\n", + " r0 = (h - size) // 2\n", + " c0 = (w - size) // 2\n", + " return img[r0:r0 + size, c0:c0 + size]\n", + "\n", + "\n", + "rgb_sources = [photo, astronaut, coffee]\n", + "\n", + "print(\"histology:\", photo.shape)\n", + "print(\"microscopy:\", cells.shape)\n", + "print(\"astronaut:\", astronaut.shape)\n", + "print(\"coffee:\", coffee.shape)" ], "id": "s04-02" }, @@ -55,17 +79,19 @@ "source": [ "## Why this matters\n", "\n", - "> 🇪🇸 Los microscopios y las cámaras ordenan sus ejes según el hardware, no\n", - "> según lo que el modelo espera. Equivocarse no da error: el modelo funciona con\n", - "> datos revueltos y devuelve resultados seguros y sin sentido.\n", + "Image-acquisition software and ML frameworks can use different axis conventions. A tensor may contain the right numbers and still be interpreted incorrectly if height, width, colour, or batch axes are in the wrong positions.\n", + "\n", + "That is especially dangerous because many axis mistakes do **not** raise an error. The code can keep running on data whose semantics have been scrambled.\n", "\n", - "Microscopes and cameras order their axes according to the hardware, not\n", - "according to what a model expects. Getting this wrong does not crash — the model\n", - "runs on scrambled data and returns confident, meaningless output.\n", + "A familiar engineering example is moving image batches between TensorFlow-style `NHWC` and PyTorch-style `NCHW` conventions.\n", "\n", - "In a drug screen, that is a wrong decision about whether a compound works. The\n", - "famous version in tech: a model trained in TensorFlow (`NHWC`) deployed into\n", - "PyTorch (`NCHW`) with no transpose." + "> 🇪🇸 **Por qué importa:** distintos sistemas de adquisición y frameworks pueden usar convenciones diferentes para los ejes. Un tensor puede contener los números correctos y aun así interpretarse mal si lote, alto, ancho o color están en posiciones incorrectas. Muchos de estos errores no generan una excepción: el código sigue funcionando con datos semánticamente desordenados.\n", + "\n", + "### Predict → Run → Explain\n", + "\n", + "Before each exercise, predict the output shape **and name what every axis means**. After running, explain why the operation preserved or destroyed that meaning.\n", + "\n", + "> 🇪🇸 **Predice → Ejecuta → Explica:** antes de cada ejercicio, predice la forma de salida y nombra el significado de cada eje. Después explica por qué la operación conservó o destruyó ese significado." ], "id": "s04-03" }, @@ -73,9 +99,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercise 1 — one image, two orderings\n", + "## Exercise 1 — one real image, two orderings\n", "\n", - "> 🇪🇸 Una imagen, dos ordenaciones de ejes." + "**Predict first:** `photo` is a real RGB histology image and `cells` is a real grayscale microscopy image. Which one has a colour axis? What should the shape of `photo` become after HWC → CHW?\n", + "\n", + "> 🇪🇸 **Predice primero:** `photo` es una imagen histológica RGB real y `cells` es una imagen microscópica real en escala de grises. ¿Cuál tiene eje de color? ¿Qué forma debe tener `photo` después de HWC → CHW?" ], "id": "s04-04" }, @@ -86,8 +114,9 @@ "outputs": [], "source": [ "# TODO 1: Print both shapes. Which one has no colour axis?\n", - "\n", - "# TODO 2: Convert `photo` from (H, W, C) to (C, H, W) with np.transpose." + "#\n", + "# TODO 2: Convert `photo` from (H, W, C) to (C, H, W) with np.transpose.\n", + "# Name what each output axis represents.\n" ], "id": "s04-05" }, @@ -95,14 +124,14 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ @@ -113,8 +142,11 @@ "chw = np.transpose(photo, (2, 0, 1)) # (3, 512, 512) — correct\n", "print(chw.shape)\n", "\n", - "# The tuple (2, 0, 1) reads: \"the new axis 0 is the old axis 2, the new axis 1\n", - "# is the old axis 0, the new axis 2 is the old axis 1.\"" + "# The tuple (2, 0, 1) means:\n", + "# new axis 0 <- old axis 2 (colour)\n", + "# new axis 1 <- old axis 0 (height)\n", + "# new axis 2 <- old axis 1 (width)\n", + "assert np.array_equal(chw, np.moveaxis(photo, 2, 0))\n" ], "id": "s04-06" }, @@ -122,9 +154,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercise 2 — a batch, and two axes of the same size\n", + "## Exercise 2 — a real batch, and two axes of the same size\n", + "\n", + "Instead of stacking three copies of one image, build a batch from **three distinct real RGB images**: histology, astronaut, and coffee. A deterministic centre crop makes them the same spatial size without inventing pixel values.\n", "\n", - "> 🇪🇸 Un lote, y dos ejes del mismo tamaño." + "**Predict first:** after stacking, the batch will be `(N, H, W, C) = (3, 256, 256, 3)`. After NHWC → NCHW it will be `(3, 3, 256, 256)`. Which size-3 axis is batch, and which is colour?\n", + "\n", + "> 🇪🇸 Construye el lote con **tres imágenes RGB reales distintas**. El recorte central solo selecciona píxeles medidos; no inventa valores. Predice qué eje de tamaño 3 representa el lote y cuál representa el color después de NHWC → NCHW." ], "id": "s04-07" }, @@ -134,11 +170,15 @@ "metadata": {}, "outputs": [], "source": [ - "# TODO 3: Stack `photo` three times into a batch of shape (3, 512, 512, 3).\n", - "# Which axis is the batch axis?\n", - "\n", - "# TODO 4: Convert that batch from NHWC to NCHW -> (3, 3, 512, 512).\n", - "# Two axes now both have size 3. How do you know which is which?" + "# TODO 3:\n", + "# Centre-crop each image in `rgb_sources` to 256x256 with center_crop_rgb(...)\n", + "# and stack them into a REAL batch of shape (3, 256, 256, 3).\n", + "# Which axis is the batch axis?\n", + "#\n", + "# TODO 4:\n", + "# Convert that batch from NHWC to NCHW -> (3, 3, 256, 256).\n", + "# Two axes now both have size 3. Demonstrate with indexing what axis 0 means\n", + "# and what axis 1 means. Do not infer semantics from size alone.\n" ], "id": "s04-08" }, @@ -146,29 +186,36 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "batch = np.stack([photo, photo, photo]) # (3, 512, 512, 3)\n", - "print(batch.shape) # axis 0 is the batch axis\n", "\n", - "nchw = np.transpose(batch, (0, 3, 1, 2)) # (3, 3, 512, 512)\n", - "print(nchw.shape)\n", + "batch = np.stack([center_crop_rgb(img) for img in rgb_sources])\n", + "print(\"NHWC:\", batch.shape) # (3, 256, 256, 3)\n", + "\n", + "nchw = np.transpose(batch, (0, 3, 1, 2))\n", + "print(\"NCHW:\", nchw.shape) # (3, 3, 256, 256)\n", "\n", - "# You know which is which ONLY because you wrote the transpose. Nothing in the\n", - "# array records it. Check it by hand — a batch axis and a colour axis behave\n", - "# differently under indexing:\n", - "print(np.array_equal(nchw[0], nchw[1])) # True — the 3 stacked copies\n", - "print(np.array_equal(nchw[:, 0], nchw[:, 1])) # False — the 3 colour channels" + "# Same numerical shape after fixing one of the two size-3 axes,\n", + "# but completely different semantics:\n", + "one_image = nchw[0] # (C, H, W)\n", + "red_channel_all_images = nchw[:, 0] # (N, H, W)\n", + "\n", + "print(\"nchw[0] :\", one_image.shape, \"-> one image, all 3 colour channels\")\n", + "print(\"nchw[:, 0]:\", red_channel_all_images.shape, \"-> red channel, all 3 images\")\n", + "\n", + "# Verify those semantics against the original NHWC representation.\n", + "assert np.array_equal(one_image, np.transpose(batch[0], (2, 0, 1)))\n", + "assert np.array_equal(red_channel_all_images, batch[:, :, :, 0])\n" ], "id": "s04-09" }, @@ -176,9 +223,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercise 3 — the one that runs and is still wrong\n", + "## Exercise 3 — the operation that runs and is still wrong\n", + "\n", + "`photo` is real RGB data with shape `(512, 512, 3)`. Both `transpose` and `reshape` can produce an array with shape `(3, 512, 512)`, but only one operation moves the colour axis correctly.\n", "\n", - "> 🇪🇸 El que se ejecuta sin error y aun así está mal." + "**Predict first:** will equal output shapes imply equal pixel organization?\n", + "\n", + "> 🇪🇸 `photo` contiene datos RGB reales. Tanto `transpose` como `reshape` pueden producir `(3, 512, 512)`, pero solo una operación reordena correctamente el eje de color. **Predice primero:** ¿tener la misma forma implica conservar la misma organización de los píxeles?" ], "id": "s04-10" }, @@ -188,9 +239,11 @@ "metadata": {}, "outputs": [], "source": [ - "# TODO 5: photo.reshape(3, 512, 512) runs WITHOUT error but is wrong.\n", - "# Run it, compare against TODO 2, and explain the difference.\n", - "# Then display both with matplotlib and look at them." + "# TODO 5:\n", + "# Run photo.reshape(3, 512, 512). It succeeds without an error.\n", + "# Compare it with the correct CHW tensor from np.transpose(photo, (2, 0, 1)).\n", + "# Verify whether the arrays are equal, then display the first plane from both.\n", + "# Explain why reshape cannot replace transpose when axis meaning must change.\n" ], "id": "s04-11" }, @@ -198,29 +251,36 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "chw = np.transpose(photo, (2, 0, 1)) # (3, 512, 512) — correct\n", - "wrong = photo.reshape(3, 512, 512) # (3, 512, 512) — runs, but scrambles\n", "\n", - "print(chw.shape == wrong.shape) # True — identical shapes\n", - "print(np.array_equal(chw, wrong)) # False — completely different data\n", + "chw = np.transpose(photo, (2, 0, 1)) # (3, 512, 512) — correct\n", + "wrong = photo.reshape(3, 512, 512) # (3, 512, 512) — runs, but scrambles axes\n", + "\n", + "print(\"same shape:\", chw.shape == wrong.shape) # True\n", + "print(\"same data arrangement:\", np.array_equal(chw, wrong)) # False\n", "\n", "import matplotlib.pyplot as plt\n", + "\n", "fig, ax = plt.subplots(1, 2, figsize=(8, 4))\n", - "ax[0].imshow(chw[0], cmap=\"gray\"); ax[0].set_title(\"transpose — a channel\")\n", - "ax[1].imshow(wrong[0], cmap=\"gray\"); ax[1].set_title(\"reshape — nonsense\")\n", - "plt.show()" + "ax[0].imshow(chw[0], cmap=\"gray\")\n", + "ax[0].set_title(\"transpose — one real colour channel\")\n", + "ax[1].imshow(wrong[0], cmap=\"gray\")\n", + "ax[1].set_title(\"reshape — scrambled interpretation\")\n", + "for a in ax:\n", + " a.axis(\"off\")\n", + "plt.tight_layout()\n", + "plt.show()\n" ], "id": "s04-12" }, @@ -230,18 +290,20 @@ "source": [ "## What just happened\n", "\n", - "**Reshape only reinterprets numbers in memory order. Transpose moves them\n", - "according to axis meaning.** Both give shape `(3, 512, 512)`; only one is the\n", - "image.\n", + "You used real measured image values throughout the section:\n", + "\n", + "1. a real histology image showed HWC → CHW;\n", + "2. three **distinct real RGB images** formed an NHWC batch and then an NCHW batch;\n", + "3. two axes both had size 3, proving that **shape alone does not record axis meaning**;\n", + "4. on the real histology image, `reshape` and `transpose` produced the same output shape but different data arrangements.\n", + "\n", + "The central rule is:\n", "\n", - "> 🇪🇸 `reshape` reinterpreta los números en el orden en que están en memoria;\n", - "> `transpose` los mueve según el significado de cada eje.\n", + "> **Use `transpose`/axis-moving operations when axis meaning changes. Use `reshape` when you only want to reinterpret grouping without changing the intended axis order.**\n", "\n", - "And TODO 4 makes the deeper point: **once two axes share a size, the shape\n", - "cannot tell you which is which.** Only your own tracking can. No error will be\n", - "raised, no shape will look wrong, and the model will train — on scrambled data.\n", + "These are the ideas to take into Kahoot 1.\n", "\n", - "That is everything the first Kahoot asks about." + "> 🇪🇸 **Qué ocurrió:** trabajaste con valores reales de imágenes. Reordenaste HWC → CHW, construiste un lote real NHWC → NCHW y comprobaste que dos ejes pueden tener el mismo tamaño pero significados distintos. Finalmente viste que `reshape` puede producir la forma esperada y aun así desorganizar los datos. **Cuando cambia el significado o la posición de los ejes, usa una transposición o movimiento de ejes; no sustituyas esa operación por `reshape`.**" ], "id": "s04-13" }, @@ -271,7 +333,6 @@ ], "metadata": { "colab": { - "name": "04-reshape-and-transpose.ipynb", "provenance": [], "toc_visible": true }, diff --git a/notebooks/04-reshape-and-transpose.ipynb b/notebooks/04-reshape-and-transpose.ipynb index 5eaedc9..16e00f4 100644 --- a/notebooks/04-reshape-and-transpose.ipynb +++ b/notebooks/04-reshape-and-transpose.ipynb @@ -1,441 +1,350 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "s04-00" - }, - "source": [ - "# 04 · Reshape and transpose real images\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/04-reshape-and-transpose.ipynb)\n", - "\n", - "*Part III · exercise · 15 min*\n", - "\n", - "> 🇪🇸 **Reshape y transposición de imágenes reales** — Reordenar ejes correctamente en imágenes y lotes reales, y comprobar por qué `reshape` puede conservar la forma mientras destruye el significado.\n", - "\n", - "Use real images to move between HWC↔CHW and NHWC↔NCHW, then show why matching shapes do not guarantee matching axis semantics.\n", - "\n", - "## What you will be able to do\n", - "\n", - "- Convert a real RGB image between `(H, W, C)` and `(C, H, W)` with `np.transpose`.\n", - "- Build a batch from three distinct real RGB images and convert NHWC to NCHW.\n", - "- Explain why two axes with the same size cannot be identified from shape alone.\n", - "- Demonstrate on a real image why `reshape` can run without error and still scramble the data." - ], - "id": "s04-00" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s04-01" - }, - "source": [ - "## Setup\n", - "\n", - "Run this first. It loads real microscopy, histology, and photographic image data used throughout the notebook.\n", - "\n", - "> 🇪🇸 Ejecuta esta celda primero. Carga datos reales de microscopía, histología y fotografía que se usarán en toda la sección." - ], - "id": "s04-01" - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "s04-02", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "outputId": "f0e058f4-08e2-4d0b-94f1-69ddf3ac5b53" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "histology: (512, 512, 3)\n", - "microscopy: (660, 550)\n", - "astronaut: (512, 512, 3)\n", - "coffee: (400, 600, 3)\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "from skimage import data\n", - "\n", - "# Real images distributed with scikit-image.\n", - "photo = data.immunohistochemistry() # (512, 512, 3) real histology, RGB\n", - "cells = data.cell() # (660, 550) real microscopy, grayscale\n", - "astronaut = data.astronaut() # (512, 512, 3) real RGB photograph\n", - "coffee = data.coffee() # (400, 600, 3) real RGB photograph\n", - "\n", - "\n", - "def center_crop_rgb(img, size=256):\n", - " \"\"\"Take a deterministic centre crop so distinct real RGB images can be stacked.\"\"\"\n", - " h, w, c = img.shape\n", - " if c != 3 or h < size or w < size:\n", - " raise ValueError(f\"expected RGB image at least {size}x{size}, got {img.shape}\")\n", - " r0 = (h - size) // 2\n", - " c0 = (w - size) // 2\n", - " return img[r0:r0 + size, c0:c0 + size]\n", - "\n", - "\n", - "rgb_sources = [photo, astronaut, coffee]\n", - "\n", - "print(\"histology:\", photo.shape)\n", - "print(\"microscopy:\", cells.shape)\n", - "print(\"astronaut:\", astronaut.shape)\n", - "print(\"coffee:\", coffee.shape)" - ], - "id": "s04-02" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s04-03" - }, - "source": [ - "## Why this matters\n", - "\n", - "Image-acquisition software and ML frameworks can use different axis conventions. A tensor may contain the right numbers and still be interpreted incorrectly if height, width, colour, or batch axes are in the wrong positions.\n", - "\n", - "That is especially dangerous because many axis mistakes do **not** raise an error. The code can keep running on data whose semantics have been scrambled.\n", - "\n", - "A familiar engineering example is moving image batches between TensorFlow-style `NHWC` and PyTorch-style `NCHW` conventions.\n", - "\n", - "> 🇪🇸 **Por qué importa:** distintos sistemas de adquisición y frameworks pueden usar convenciones diferentes para los ejes. Un tensor puede contener los números correctos y aun así interpretarse mal si lote, alto, ancho o color están en posiciones incorrectas. Muchos de estos errores no generan una excepción: el código sigue funcionando con datos semánticamente desordenados.\n", - "\n", - "### Predict → Run → Explain\n", - "\n", - "Before each exercise, predict the output shape **and name what every axis means**. After running, explain why the operation preserved or destroyed that meaning.\n", - "\n", - "> 🇪🇸 **Predice → Ejecuta → Explica:** antes de cada ejercicio, predice la forma de salida y nombra el significado de cada eje. Después explica por qué la operación conservó o destruyó ese significado." - ], - "id": "s04-03" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s04-04" - }, - "source": [ - "## Exercise 1 — one real image, two orderings\n", - "\n", - "**Predict first:** `photo` is a real RGB histology image and `cells` is a real grayscale microscopy image. Which one has a colour axis? What should the shape of `photo` become after HWC → CHW?\n", - "\n", - "> 🇪🇸 **Predice primero:** `photo` es una imagen histológica RGB real y `cells` es una imagen microscópica real en escala de grises. ¿Cuál tiene eje de color? ¿Qué forma debe tener `photo` después de HWC → CHW?" - ], - "id": "s04-04" - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "s04-05" - }, - "outputs": [], - "source": [ - "# TODO 1: Print both shapes. Which one has no colour axis?\n", - "#\n", - "# TODO 2: Convert `photo` from (H, W, C) to (C, H, W) with np.transpose.\n", - "# Name what each output axis represents.\n" - ], - "id": "s04-05" - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ], - "id": "s04-06", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "outputId": "e20288e2-5eba-494e-e38b-f45b8eb36f66" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "(512, 512, 3) (660, 550)\n", - "(3, 512, 512)\n" - ] - } - ], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "print(photo.shape, cells.shape) # (512, 512, 3) (660, 550)\n", - "# `cells` is grayscale — order 2, no colour axis at all.\n", - "\n", - "chw = np.transpose(photo, (2, 0, 1)) # (3, 512, 512) — correct\n", - "print(chw.shape)\n", - "\n", - "# The tuple (2, 0, 1) means:\n", - "# new axis 0 <- old axis 2 (colour)\n", - "# new axis 1 <- old axis 0 (height)\n", - "# new axis 2 <- old axis 1 (width)\n", - "assert np.array_equal(chw, np.moveaxis(photo, 2, 0))\n" - ], - "id": "s04-06" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s04-07" - }, - "source": [ - "## Exercise 2 — a real batch, and two axes of the same size\n", - "\n", - "Instead of stacking three copies of one image, build a batch from **three distinct real RGB images**: histology, astronaut, and coffee. A deterministic centre crop makes them the same spatial size without inventing pixel values.\n", - "\n", - "**Predict first:** after stacking, the batch will be `(N, H, W, C) = (3, 256, 256, 3)`. After NHWC → NCHW it will be `(3, 3, 256, 256)`. Which size-3 axis is batch, and which is colour?\n", - "\n", - "> 🇪🇸 Construye el lote con **tres imágenes RGB reales distintas**. El recorte central solo selecciona píxeles medidos; no inventa valores. Predice qué eje de tamaño 3 representa el lote y cuál representa el color después de NHWC → NCHW." - ], - "id": "s04-07" - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "s04-08" - }, - "outputs": [], - "source": [ - "# TODO 3:\n", - "# Centre-crop each image in `rgb_sources` to 256x256 with center_crop_rgb(...)\n", - "# and stack them into a REAL batch of shape (3, 256, 256, 3).\n", - "# Which axis is the batch axis?\n", - "#\n", - "# TODO 4:\n", - "# Convert that batch from NHWC to NCHW -> (3, 3, 256, 256).\n", - "# Two axes now both have size 3. Demonstrate with indexing what axis 0 means\n", - "# and what axis 1 means. Do not infer semantics from size alone.\n" - ], - "id": "s04-08" - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ], - "id": "s04-09", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "outputId": "5e68d71a-a53e-45fa-e85d-8496a15a902d" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "NHWC: (3, 256, 256, 3)\n", - "NCHW: (3, 3, 256, 256)\n", - "nchw[0] : (3, 256, 256) -> one image, all 3 colour channels\n", - "nchw[:, 0]: (3, 256, 256) -> red channel, all 3 images\n" - ] - } - ], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "\n", - "batch = np.stack([center_crop_rgb(img) for img in rgb_sources])\n", - "print(\"NHWC:\", batch.shape) # (3, 256, 256, 3)\n", - "\n", - "nchw = np.transpose(batch, (0, 3, 1, 2))\n", - "print(\"NCHW:\", nchw.shape) # (3, 3, 256, 256)\n", - "\n", - "# Same numerical shape after fixing one of the two size-3 axes,\n", - "# but completely different semantics:\n", - "one_image = nchw[0] # (C, H, W)\n", - "red_channel_all_images = nchw[:, 0] # (N, H, W)\n", - "\n", - "print(\"nchw[0] :\", one_image.shape, \"-> one image, all 3 colour channels\")\n", - "print(\"nchw[:, 0]:\", red_channel_all_images.shape, \"-> red channel, all 3 images\")\n", - "\n", - "# Verify those semantics against the original NHWC representation.\n", - "assert np.array_equal(one_image, np.transpose(batch[0], (2, 0, 1)))\n", - "assert np.array_equal(red_channel_all_images, batch[:, :, :, 0])\n" - ], - "id": "s04-09" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s04-10" - }, - "source": [ - "## Exercise 3 — the operation that runs and is still wrong\n", - "\n", - "`photo` is real RGB data with shape `(512, 512, 3)`. Both `transpose` and `reshape` can produce an array with shape `(3, 512, 512)`, but only one operation moves the colour axis correctly.\n", - "\n", - "**Predict first:** will equal output shapes imply equal pixel organization?\n", - "\n", - "> 🇪🇸 `photo` contiene datos RGB reales. Tanto `transpose` como `reshape` pueden producir `(3, 512, 512)`, pero solo una operación reordena correctamente el eje de color. **Predice primero:** ¿tener la misma forma implica conservar la misma organización de los píxeles?" - ], - "id": "s04-10" - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "id": "s04-11" - }, - "outputs": [], - "source": [ - "# TODO 5:\n", - "# Run photo.reshape(3, 512, 512). It succeeds without an error.\n", - "# Compare it with the correct CHW tensor from np.transpose(photo, (2, 0, 1)).\n", - "# Verify whether the arrays are equal, then display the first plane from both.\n", - "# Explain why reshape cannot replace transpose when axis meaning must change.\n" - ], - "id": "s04-11" - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ], - "id": "s04-12", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 455 - }, - "outputId": "ba902ff4-6b84-4ac3-fc52-58600cc72c24" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "same shape: True\n", - "same data arrangement: False\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} - } - ], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "\n", - "chw = np.transpose(photo, (2, 0, 1)) # (3, 512, 512) — correct\n", - "wrong = photo.reshape(3, 512, 512) # (3, 512, 512) — runs, but scrambles axes\n", - "\n", - "print(\"same shape:\", chw.shape == wrong.shape) # True\n", - "print(\"same data arrangement:\", np.array_equal(chw, wrong)) # False\n", - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "fig, ax = plt.subplots(1, 2, figsize=(8, 4))\n", - "ax[0].imshow(chw[0], cmap=\"gray\")\n", - "ax[0].set_title(\"transpose — one real colour channel\")\n", - "ax[1].imshow(wrong[0], cmap=\"gray\")\n", - "ax[1].set_title(\"reshape — scrambled interpretation\")\n", - "for a in ax:\n", - " a.axis(\"off\")\n", - "plt.tight_layout()\n", - "plt.show()\n" - ], - "id": "s04-12" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s04-13" - }, - "source": [ - "## What just happened\n", - "\n", - "You used real measured image values throughout the section:\n", - "\n", - "1. a real histology image showed HWC → CHW;\n", - "2. three **distinct real RGB images** formed an NHWC batch and then an NCHW batch;\n", - "3. two axes both had size 3, proving that **shape alone does not record axis meaning**;\n", - "4. on the real histology image, `reshape` and `transpose` produced the same output shape but different data arrangements.\n", - "\n", - "The central rule is:\n", - "\n", - "> **Use `transpose`/axis-moving operations when axis meaning changes. Use `reshape` when you only want to reinterpret grouping without changing the intended axis order.**\n", - "\n", - "These are the ideas to take into Kahoot 1.\n", - "\n", - "> 🇪🇸 **Qué ocurrió:** trabajaste con valores reales de imágenes. Reordenaste HWC → CHW, construiste un lote real NHWC → NCHW y comprobaste que dos ejes pueden tener el mismo tamaño pero significados distintos. Finalmente viste que `reshape` puede producir la forma esperada y aun así desorganizar los datos. **Cuando cambia el significado o la posición de los ejes, usa una transposición o movimiento de ejes; no sustituyas esa operación por `reshape`.**" - ], - "id": "s04-13" + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 04 · Reshape and transpose real images\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/04-reshape-and-transpose.ipynb)\n", + "\n", + "*Part III · exercise · 15 min*\n", + "\n", + "> 🇪🇸 **Reshape y transposición de imágenes reales** — Reordenar ejes correctamente en imágenes y lotes reales, y comprobar por qué `reshape` puede conservar la forma mientras destruye el significado.\n", + "\n", + "Use real images to move between HWC↔CHW and NHWC↔NCHW, then show why matching shapes do not guarantee matching axis semantics.\n", + "\n", + "## What you will be able to do\n", + "\n", + "- Convert a real RGB image between `(H, W, C)` and `(C, H, W)` with `np.transpose`.\n", + "- Build a batch from three distinct real RGB images and convert NHWC to NCHW.\n", + "- Explain why two axes with the same size cannot be identified from shape alone.\n", + "- Demonstrate on a real image why `reshape` can run without error and still scramble the data." + ], + "id": "s04-00" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "Run this first. It installs and imports everything this notebook needs, and nothing else.\n", + "\n", + "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." + ], + "id": "s04-01" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from skimage import data\n", + "\n", + "# Real images distributed with scikit-image.\n", + "photo = data.immunohistochemistry() # (512, 512, 3) real histology, RGB\n", + "cells = data.cell() # (660, 550) real microscopy, grayscale\n", + "astronaut = data.astronaut() # (512, 512, 3) real RGB photograph\n", + "coffee = data.coffee() # (400, 600, 3) real RGB photograph\n", + "\n", + "\n", + "def center_crop_rgb(img, size=256):\n", + " # Deterministic centre crop so distinct real RGB images can be stacked.\n", + " h, w, c = img.shape\n", + " if c != 3 or h < size or w < size:\n", + " raise ValueError(\n", + " f\"expected RGB image at least {size}x{size}, got {img.shape}\"\n", + " )\n", + "\n", + " r0 = (h - size) // 2\n", + " c0 = (w - size) // 2\n", + " return img[r0:r0 + size, c0:c0 + size]\n", + "\n", + "\n", + "rgb_sources = [photo, astronaut, coffee]\n", + "\n", + "print(\"histology:\", photo.shape)\n", + "print(\"microscopy:\", cells.shape)\n", + "print(\"astronaut:\", astronaut.shape)\n", + "print(\"coffee:\", coffee.shape)" + ], + "id": "s04-02" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Why this matters\n", + "\n", + "Image-acquisition software and ML frameworks can use different axis conventions. A tensor may contain the right numbers and still be interpreted incorrectly if height, width, colour, or batch axes are in the wrong positions.\n", + "\n", + "That is especially dangerous because many axis mistakes do **not** raise an error. The code can keep running on data whose semantics have been scrambled.\n", + "\n", + "A familiar engineering example is moving image batches between TensorFlow-style `NHWC` and PyTorch-style `NCHW` conventions.\n", + "\n", + "> 🇪🇸 **Por qué importa:** distintos sistemas de adquisición y frameworks pueden usar convenciones diferentes para los ejes. Un tensor puede contener los números correctos y aun así interpretarse mal si lote, alto, ancho o color están en posiciones incorrectas. Muchos de estos errores no generan una excepción: el código sigue funcionando con datos semánticamente desordenados.\n", + "\n", + "### Predict → Run → Explain\n", + "\n", + "Before each exercise, predict the output shape **and name what every axis means**. After running, explain why the operation preserved or destroyed that meaning.\n", + "\n", + "> 🇪🇸 **Predice → Ejecuta → Explica:** antes de cada ejercicio, predice la forma de salida y nombra el significado de cada eje. Después explica por qué la operación conservó o destruyó ese significado." + ], + "id": "s04-03" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 1 — one real image, two orderings\n", + "\n", + "**Predict first:** `photo` is a real RGB histology image and `cells` is a real grayscale microscopy image. Which one has a colour axis? What should the shape of `photo` become after HWC → CHW?\n", + "\n", + "> 🇪🇸 **Predice primero:** `photo` es una imagen histológica RGB real y `cells` es una imagen microscópica real en escala de grises. ¿Cuál tiene eje de color? ¿Qué forma debe tener `photo` después de HWC → CHW?" + ], + "id": "s04-04" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO 1: Print both shapes. Which one has no colour axis?\n", + "#\n", + "# TODO 2: Convert `photo` from (H, W, C) to (C, H, W) with np.transpose.\n", + "# Name what each output axis represents.\n" + ], + "id": "s04-05" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true }, - { - "cell_type": "markdown", - "metadata": { - "id": "s04-14" - }, - "source": [ - "---\n", - "\n", - "## Time for Kahoot 🎯\n", - "\n", - "**Kahoot 1 — Tensor Vocabulary & Shapes** · 6 questions, about 5 minutes.\n", - "\n", - "> 🇪🇸 **Vocabulario de tensores y formas** — 6 preguntas, unos 5 minutos.\n", - "\n", - "Join at **kahoot.it** with the PIN on the facilitator's screen.\n", - "\n", - "- [Quiz details and facilitator notes](https://project-delphi.github.io/tensors-workshop/kahoot.html#quiz-1)\n", - "- [Import file (`.xlsx`)](https://github.com/project-delphi/tensors-workshop/blob/main/kahoot/kahoot_quiz_1_vocabulary_shapes.xlsx)\n", - "\n", - "Next up: **05 · Video pipeline design** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/05-video-pipeline-design.ipynb).\n", - "\n", - "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" - ], - "id": "s04-14" - } - ], - "metadata": { - "colab": { - "provenance": [], - "toc_visible": true + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "print(photo.shape, cells.shape) # (512, 512, 3) (660, 550)\n", + "# `cells` is grayscale — order 2, no colour axis at all.\n", + "\n", + "chw = np.transpose(photo, (2, 0, 1)) # (3, 512, 512) — correct\n", + "print(chw.shape)\n", + "\n", + "# The tuple (2, 0, 1) means:\n", + "# new axis 0 <- old axis 2 (colour)\n", + "# new axis 1 <- old axis 0 (height)\n", + "# new axis 2 <- old axis 1 (width)\n", + "assert np.array_equal(chw, np.moveaxis(photo, 2, 0))\n" + ], + "id": "s04-06" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 2 — a real batch, and two axes of the same size\n", + "\n", + "Instead of stacking three copies of one image, build a batch from **three distinct real RGB images**: histology, astronaut, and coffee. A deterministic centre crop makes them the same spatial size without inventing pixel values.\n", + "\n", + "**Predict first:** after stacking, the batch will be `(N, H, W, C) = (3, 256, 256, 3)`. After NHWC → NCHW it will be `(3, 3, 256, 256)`. Which size-3 axis is batch, and which is colour?\n", + "\n", + "> 🇪🇸 Construye el lote con **tres imágenes RGB reales distintas**. El recorte central solo selecciona píxeles medidos; no inventa valores. Predice qué eje de tamaño 3 representa el lote y cuál representa el color después de NHWC → NCHW." + ], + "id": "s04-07" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO 3:\n", + "# Centre-crop each image in `rgb_sources` to 256x256 with center_crop_rgb(...)\n", + "# and stack them into a REAL batch of shape (3, 256, 256, 3).\n", + "# Which axis is the batch axis?\n", + "#\n", + "# TODO 4:\n", + "# Convert that batch from NHWC to NCHW -> (3, 3, 256, 256).\n", + "# Two axes now both have size 3. Demonstrate with indexing what axis 0 means\n", + "# and what axis 1 means. Do not infer semantics from size alone.\n" + ], + "id": "s04-08" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "\n", + "batch = np.stack([center_crop_rgb(img) for img in rgb_sources])\n", + "print(\"NHWC:\", batch.shape) # (3, 256, 256, 3)\n", + "\n", + "nchw = np.transpose(batch, (0, 3, 1, 2))\n", + "print(\"NCHW:\", nchw.shape) # (3, 3, 256, 256)\n", + "\n", + "# Same numerical shape after fixing one of the two size-3 axes,\n", + "# but completely different semantics:\n", + "one_image = nchw[0] # (C, H, W)\n", + "red_channel_all_images = nchw[:, 0] # (N, H, W)\n", + "\n", + "print(\"nchw[0] :\", one_image.shape, \"-> one image, all 3 colour channels\")\n", + "print(\"nchw[:, 0]:\", red_channel_all_images.shape, \"-> red channel, all 3 images\")\n", + "\n", + "# Verify those semantics against the original NHWC representation.\n", + "assert np.array_equal(one_image, np.transpose(batch[0], (2, 0, 1)))\n", + "assert np.array_equal(red_channel_all_images, batch[:, :, :, 0])\n" + ], + "id": "s04-09" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 3 — the operation that runs and is still wrong\n", + "\n", + "`photo` is real RGB data with shape `(512, 512, 3)`. Both `transpose` and `reshape` can produce an array with shape `(3, 512, 512)`, but only one operation moves the colour axis correctly.\n", + "\n", + "**Predict first:** will equal output shapes imply equal pixel organization?\n", + "\n", + "> 🇪🇸 `photo` contiene datos RGB reales. Tanto `transpose` como `reshape` pueden producir `(3, 512, 512)`, pero solo una operación reordena correctamente el eje de color. **Predice primero:** ¿tener la misma forma implica conservar la misma organización de los píxeles?" + ], + "id": "s04-10" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO 5:\n", + "# Run photo.reshape(3, 512, 512). It succeeds without an error.\n", + "# Compare it with the correct CHW tensor from np.transpose(photo, (2, 0, 1)).\n", + "# Verify whether the arrays are equal, then display the first plane from both.\n", + "# Explain why reshape cannot replace transpose when axis meaning must change.\n" + ], + "id": "s04-11" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true }, - "language_info": { - "name": "python" - } + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "\n", + "chw = np.transpose(photo, (2, 0, 1)) # (3, 512, 512) — correct\n", + "wrong = photo.reshape(3, 512, 512) # (3, 512, 512) — runs, but scrambles axes\n", + "\n", + "print(\"same shape:\", chw.shape == wrong.shape) # True\n", + "print(\"same data arrangement:\", np.array_equal(chw, wrong)) # False\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=(8, 4))\n", + "ax[0].imshow(chw[0], cmap=\"gray\")\n", + "ax[0].set_title(\"transpose — one real colour channel\")\n", + "ax[1].imshow(wrong[0], cmap=\"gray\")\n", + "ax[1].set_title(\"reshape — scrambled interpretation\")\n", + "for a in ax:\n", + " a.axis(\"off\")\n", + "plt.tight_layout()\n", + "plt.show()\n" + ], + "id": "s04-12" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What just happened\n", + "\n", + "You used real measured image values throughout the section:\n", + "\n", + "1. a real histology image showed HWC → CHW;\n", + "2. three **distinct real RGB images** formed an NHWC batch and then an NCHW batch;\n", + "3. two axes both had size 3, proving that **shape alone does not record axis meaning**;\n", + "4. on the real histology image, `reshape` and `transpose` produced the same output shape but different data arrangements.\n", + "\n", + "The central rule is:\n", + "\n", + "> **Use `transpose`/axis-moving operations when axis meaning changes. Use `reshape` when you only want to reinterpret grouping without changing the intended axis order.**\n", + "\n", + "These are the ideas to take into Kahoot 1.\n", + "\n", + "> 🇪🇸 **Qué ocurrió:** trabajaste con valores reales de imágenes. Reordenaste HWC → CHW, construiste un lote real NHWC → NCHW y comprobaste que dos ejes pueden tener el mismo tamaño pero significados distintos. Finalmente viste que `reshape` puede producir la forma esperada y aun así desorganizar los datos. **Cuando cambia el significado o la posición de los ejes, usa una transposición o movimiento de ejes; no sustituyas esa operación por `reshape`.**" + ], + "id": "s04-13" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Time for Kahoot 🎯\n", + "\n", + "**Kahoot 1 — Tensor Vocabulary & Shapes** · 6 questions, about 5 minutes.\n", + "\n", + "> 🇪🇸 **Vocabulario de tensores y formas** — 6 preguntas, unos 5 minutos.\n", + "\n", + "Join at **kahoot.it** with the PIN on the facilitator's screen.\n", + "\n", + "- [Quiz details and facilitator notes](https://project-delphi.github.io/tensors-workshop/kahoot.html#quiz-1)\n", + "- [Import file (`.xlsx`)](https://github.com/project-delphi/tensors-workshop/blob/main/kahoot/kahoot_quiz_1_vocabulary_shapes.xlsx)\n", + "\n", + "Next up: **05 · Video pipeline design** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/05-video-pipeline-design.ipynb).\n", + "\n", + "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" + ], + "id": "s04-14" + } + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/scripts/content.py b/scripts/content.py index e106ee4..eef4982 100644 --- a/scripts/content.py +++ b/scripts/content.py @@ -153,9 +153,32 @@ def fetch_verified_video(url, expected_sha256, n_frames=16, stride=45): "setup": """import numpy as np from skimage import data -photo = data.immunohistochemistry() # (512, 512, 3) real histology +# Real images distributed with scikit-image. +photo = data.immunohistochemistry() # (512, 512, 3) real histology, RGB cells = data.cell() # (660, 550) real microscopy, grayscale -print(photo.shape, cells.shape)""", +astronaut = data.astronaut() # (512, 512, 3) real RGB photograph +coffee = data.coffee() # (400, 600, 3) real RGB photograph + + +def center_crop_rgb(img, size=256): + # Deterministic centre crop so distinct real RGB images can be stacked. + h, w, c = img.shape + if c != 3 or h < size or w < size: + raise ValueError( + f"expected RGB image at least {size}x{size}, got {img.shape}" + ) + + r0 = (h - size) // 2 + c0 = (w - size) // 2 + return img[r0:r0 + size, c0:c0 + size] + + +rgb_sources = [photo, astronaut, coffee] + +print("histology:", photo.shape) +print("microscopy:", cells.shape) +print("astronaut:", astronaut.shape) +print("coffee:", coffee.shape)""", } # ───────────────────────────────────────────────────────────────────────────── From 677dcfd7a861332fb02cabbf19c700b429fbea73 Mon Sep 17 00:00:00 2001 From: Sebas Laverde Chunza <106851243+Laverde97@users.noreply.github.com> Date: Fri, 28 Aug 2026 14:28:08 -0500 Subject: [PATCH 11/29] Improve notebook 05 pedagogy with real video for issue #44 --- notebooks/05-video-pipeline-design.ipynb | 1264 +++++++++++++++------- 1 file changed, 872 insertions(+), 392 deletions(-) diff --git a/notebooks/05-video-pipeline-design.ipynb b/notebooks/05-video-pipeline-design.ipynb index 286f5e6..0898669 100644 --- a/notebooks/05-video-pipeline-design.ipynb +++ b/notebooks/05-video-pipeline-design.ipynb @@ -1,396 +1,876 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 05 · Video pipeline design\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/05-video-pipeline-design.ipynb)\n", - "\n", - "*Part III · group · 15 min*\n", - "\n", - "> 🇪🇸 **Diseño de un pipeline de vídeo** — Diseñar la forma del tensor en cada etapa de dos sistemas de vídeo reales.\n", - "\n", - "Design the tensor shape at every stage of two real video systems.\n", - "\n", - "## What you will be able to do\n", - "\n", - "- Read the shape of a real decoded video and say what each of its four axes counts.\n", - "- Design the tensor shape at each of five pipeline stages, for two different systems.\n", - "- Apply one ragged-length strategy from section 02 and give the exact batched shape.\n", - "- Decide where a new axis goes, and say how that choice affects the rest of the pipeline." - ], - "id": "s05-00" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "Run this first. It installs and imports everything this notebook needs, and nothing else.\n", - "\n", - "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." - ], - "id": "s05-01" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%pip install -q \"imageio[ffmpeg]\"\n", - "\n", - "import hashlib\n", - "import io\n", - "import urllib.request\n", - "\n", - "import numpy as np\n", - "import imageio.v3 as iio\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# A real clip, pinned. \"Tormenta en l'Almadrava\" by Nicolas Vigier, CC0:\n", - "# https://commons.wikimedia.org/wiki/File:Tormenta_en_l%27Almadrava.webm\n", - "# 24 seconds of breaking waves at 960x540. The SHA-256 is checked below, so the\n", - "# file this notebook decodes cannot silently change under you -- the same\n", - "# guarantee section 11 puts on its voice recording.\n", - "VIDEO_URL = (\"https://upload.wikimedia.org/wikipedia/commons/1/1e/\"\n", - " \"Tormenta_en_l%27Almadrava.webm\")\n", - "VIDEO_SHA256 = \"e377fcdd2c79b55bce13c2c24b5dd7e412af39cd400eec548a79d0e59d79dc1b\"\n", - "\n", - "# Wikimedia answers the default `Python-urllib/3.x` User-Agent with a 403, so\n", - "# this identifies itself the way their policy asks.\n", - "UA = \"tensors-workshop/1.0 (https://github.com/project-delphi/tensors-workshop)\"\n", - "\n", - "\n", - "def fetch_verified_video(url, expected_sha256, n_frames=16, stride=45):\n", - " \"\"\"Download a video, refuse to proceed if it does not match the pinned\n", - " checksum, and decode only every `stride`-th frame, up to `n_frames`.\n", - "\n", - " Note what is and is not saved. `imiter` still decodes frames in order --\n", - " it reaches frame 675 by decoding all 676 before it -- but it only ever\n", - " RETAINS 16 of them, and it stops as soon as it has them. Holding all 720\n", - " would be a 1.1 GB array. Sampling frames rather than keeping them all is\n", - " exactly the decision the design exercise below asks you to make\n", - " deliberately, for two systems, and to say what it costs.\n", - " \"\"\"\n", - " req = urllib.request.Request(url, headers={\"User-Agent\": UA})\n", - " raw = urllib.request.urlopen(req, timeout=120).read()\n", - " got = hashlib.sha256(raw).hexdigest()\n", - " if got != expected_sha256:\n", - " raise ValueError(\n", - " f\"checksum mismatch for {url}: expected {expected_sha256}, got \"\n", - " f\"{got}. Refusing to use unverified video data.\")\n", - " frames = []\n", - " for i, frame in enumerate(\n", - " iio.imiter(io.BytesIO(raw), plugin=\"FFMPEG\", extension=\".webm\")):\n", - " if i % stride == 0:\n", - " frames.append(frame)\n", - " if len(frames) == n_frames:\n", - " break\n", - " return np.stack(frames)\n", - "\n", - "\n", - "clip = fetch_verified_video(VIDEO_URL, VIDEO_SHA256)\n", - "# The cells below index clip[15] and quote this shape, so a short stream should\n", - "# fail here, where the cause is visible, not as an IndexError further down.\n", - "assert clip.shape == (16, 540, 960, 3), f\"unexpected clip shape {clip.shape}\"\n", - "print(clip.shape, clip.dtype) # (16, 540, 960, 3) uint8" - ], - "id": "s05-02" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The tensor you are designing around\n", - "\n", - "> 🇪🇸 Antes de diseñar en abstracto: un vídeo real, ya descargado y\n", - "> decodificado. `(16, 540, 960, 3)` son 16 fotogramas muestreados de 720.\n", - "\n", - "The setup cell just downloaded 24 seconds of a storm and decoded **16 frames\n", - "sampled out of 720**. That is a real order-4 tensor, and every number in it was\n", - "measured by a camera:\n", - "\n", - "| Axis | Length | What it counts |\n", - "|---|---|---|\n", - "| 0 | 16 | frames kept, out of 720 |\n", - "| 1 | 540 | rows of pixels |\n", - "| 2 | 960 | columns of pixels |\n", - "| 3 | 3 | colour channels |\n", - "\n", - "Everything below is a *design* exercise about videos you do not have. Do it\n", - "against this shape rather than an imagined one — and note that the pipeline has\n", - "already made one of the choices you are about to argue about. `stride=45`\n", - "threw away 704 frames. Nothing warned you." - ], - "id": "s05-03" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(1, 4, figsize=(13, 2.6))\n", - "for ax, k in zip(axes, [0, 5, 10, 15]):\n", - " ax.imshow(clip[k])\n", - " ax.set_title(f\"clip[{k}] (frame {k * 45} of 720)\", fontsize=9)\n", - " ax.axis(\"off\")\n", - "fig.suptitle(\"Four of the 16 sampled frames -- the waves are not the same twice\")\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "# Axis 0 is the only axis where order is information. Shuffle axis 1 and you\n", - "# get a scrambled picture that is obviously broken; shuffle axis 0 and you get\n", - "# 16 perfectly valid frames in a meaningless order -- and nothing raises.\n", - "print(clip.shape, clip.nbytes // 1024**2, \"MB for 16 frames\")\n", - "print(\"all 720 would be\", clip.nbytes * 45 // 1024**2, \"MB\")" - ], - "id": "s05-04" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Another discussion block\n", - "\n", - "> 🇪🇸 Otro bloque de discusión: 10 minutos de diseño, 5 de puesta en común.\n", - "> No hay una única respuesta correcta.\n", - "\n", - "Back to your breakout channel. 10 minutes design, 5 minutes share-back.\n", - "**There is no single correct answer.**\n", - "\n", - "> Design the tensor shape at each stage — *raw file → decoded frames →\n", - "> preprocessed batch → model input → model output* — for **both** systems:\n", - ">\n", - "> - **Tech:** a short-video app computing one embedding per video from sampled\n", - "> frames, to choose what to play next.\n", - "> - **Biotech:** a surgical-video model that labels the current phase of an\n", - "> operation from an operating-room camera.\n", - "\n", - "### The five questions\n", - "\n", - "1. Sketch the shape at each of the five stages, for both. Where are they the\n", - " same, and where must they differ?\n", - "2. Clips have different lengths — 30 seconds against 4 hours. Take one strategy\n", - " your group proposed in section 02 and give the exact shape of the\n", - " preprocessed batch. What does an invented or wasted value in that tensor\n", - " represent?\n", - "3. The surgical system adds **three camera angles** recording at once. Where does\n", - " that axis go, and why does its position change how easy the rest of the\n", - " pipeline is to write?\n", - "4. The recommender samples 8 frames out of 900. Which operation from section 03\n", - " does that, and what is lost?\n", - "5. Both systems must decide **which frames matter most**. What kind of mechanism\n", - " could learn that weighting?" - ], - "id": "s05-05" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 1 — sketch the two pipelines\n", - "\n", - "> 🇪🇸 Dibuja las dos tuberías, etapa por etapa.\n", - "\n", - "Use comments. The point is the shapes and what each axis counts, not running\n", - "code." - ], - "id": "s05-06" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO 1: Fill in the shape at each stage for BOTH systems. Next to each,\n", - "# write what the axes count.\n", - "\n", - "# --- Tech: short-video recommender, one embedding per video -------------------\n", - "# raw file : ...\n", - "# decoded frames : ...\n", - "# preprocessed batch: ...\n", - "# model input : ...\n", - "# model output : ...\n", - "\n", - "# --- Biotech: surgical phase labelling, one label per timestep ---------------\n", - "# raw file : ...\n", - "# decoded frames : ...\n", - "# preprocessed batch: ...\n", - "# model input : ...\n", - "# model output : ..." - ], - "id": "s05-07" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "s05-00" + }, + "source": [ + "# 05 · Video pipeline design\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/05-video-pipeline-design.ipynb)\n", + "\n", + "*Part III · group · 15 min*\n", + "\n", + "> 🇪🇸 **Diseño de un pipeline de vídeo** — Convertir un archivo de vídeo real en un tensor, observar qué se pierde al muestrear fotogramas y diseñar formas tensoriales que respeten el significado del tiempo.\n", + "\n", + "Process one pinned real video end to end, then use what its axes actually mean to design two downstream video pipelines.\n", + "\n", + "## What you will be able to do\n", + "\n", + "- Decode a pinned real video file into an order-4 tensor and name every axis.\n", + "- Measure how much temporal information a frame-sampling decision keeps and discards.\n", + "- Design tensor shapes for video-level and timestep-level prediction systems.\n", + "- Explain the memory trade-off between padding variable-length clips and fixed-frame sampling.\n" + ], + "id": "s05-00" }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "# One defensible answer. Your group's may differ and still be right — what\n", - "# matters is that you can say what every axis COUNTS.\n", - "\n", - "# --- Tech: short-video recommender -------------------------------------------\n", - "# raw file : bytes on disk, no shape yet\n", - "# decoded frames : (900, 1080, 1920, 3) T, H, W, C — every frame\n", - "# preprocessed batch: (32, 8, 224, 224, 3) N, T, H, W, C — 8 sampled frames\n", - "# model input : (32, 8, 224, 224, 3)\n", - "# model output : (32, 512) N, embedding — TIME IS GONE,\n", - "# collapsed into one vector per video\n", - "\n", - "# --- Biotech: surgical phase labelling ---------------------------------------\n", - "# raw file : bytes on disk\n", - "# decoded frames : (432000, 1080, 1920, 3) 4 hours at 30fps\n", - "# preprocessed batch: (4, 64, 224, 224, 3) N, T, H, W, C — a sliding window\n", - "# model input : (4, 64, 224, 224, 3)\n", - "# model output : (4, 64, 12) N, T, classes — ONE LABEL PER\n", - "# TIMESTEP, so time SURVIVES\n", - "\n", - "# The five stages look alike until the output. The recommender destroys the time\n", - "# axis on purpose; the surgical model must keep it, because the answer to\n", - "# \"what phase are we in?\" changes during the operation." - ], - "id": "s05-08" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 2 — ragged lengths, and the extra camera\n", - "\n", - "> 🇪🇸 Longitudes distintas y la cámara adicional." - ], - "id": "s05-09" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO 2: Take ONE ragged-length strategy from section 02 (pad + mask, or\n", - "# sample a fixed number of frames). Give the exact shape of the\n", - "# preprocessed batch for 4 clips of 30s, 45s, 2min and 4h at 30 fps.\n", - "# What does an invented or wasted value in that tensor represent?\n", - "\n", - "# TODO 3: Add three camera angles to the surgical system. Write the batch shape\n", - "# with the camera axis in two different positions, and say which makes\n", - "# the rest of the pipeline easier to write." - ], - "id": "s05-10" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true + { + "cell_type": "markdown", + "metadata": { + "id": "s05-01" + }, + "source": [ + "## Setup\n", + "\n", + "Run this first. It downloads one **real CC0 WebM video from Wikimedia Commons**, verifies its SHA-256 checksum, decodes the full stream to count the recorded frames, and retains only a sparse set of frames in memory.\n", + "\n", + "> 🇪🇸 Ejecuta esta celda primero. Descarga un **vídeo real CC0 de Wikimedia Commons**, verifica su checksum SHA-256, decodifica el flujo completo para contar los fotogramas grabados y conserva en memoria solo una muestra dispersa.\n", + "\n", + "The full video is deliberately **not** materialized as one `(T,H,W,C)` array: that would require roughly a gigabyte of RAM. Avoiding that allocation is already a real pipeline-design decision.\n" + ], + "id": "s05-01" }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "# TODO 2 — padding to the longest clip is the honest disaster:\n", - "# longest = 4h at 30fps = 432,000 frames\n", - "# padded batch: (4, 432000, 224, 224, 3) ~ 5.8e11 values. Not possible.\n", - "# An invented value is a frame that was never recorded. The mask is what stops\n", - "# the model from learning from footage that does not exist.\n", - "#\n", - "# Sampling a fixed 64 frames per clip:\n", - "# batch: (4, 64, 224, 224, 3) — fits easily.\n", - "# Nothing is invented; a great deal is DISCARDED, and the 4-hour clip is\n", - "# sampled 500x more sparsely than the 30-second one. That bias is real.\n", - "\n", - "# TODO 3 — two placements:\n", - "# (N, CAM, T, H, W, C) = (4, 3, 64, 224, 224, 3)\n", - "# (N * CAM, T, H, W, C) = (12, 64, 224, 224, 3)\n", - "#\n", - "# The second is easier: every existing per-video operation keeps working\n", - "# unchanged, because the camera axis has been folded into the batch axis — and\n", - "# a batch axis is exactly the axis whose order does not matter. You only need\n", - "# the first form when the model must COMBINE the angles, at which point you\n", - "# must unfold back and the shape bookkeeping becomes yours to get right." - ], - "id": "s05-11" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Share-back\n", - "\n", - "> 🇪🇸 Puesta en común.\n", - "\n", - "**Q4** — sampling 8 frames from 900 is *fancy indexing*, exactly section 03's\n", - "TODO 3: `frames[idx]` where `idx` is an array of positions. What is lost is\n", - "everything between the samples — and for a 4-hour surgical video, that is almost\n", - "all of it. That is why the surgical system uses a sliding window instead.\n", - "\n", - "**Q5** — **attention**. It learns a weight per position from the data itself,\n", - "rather than you choosing which frames matter in advance. Take-home B in section\n", - "11 builds it from two `einsum` calls, and the padding mask from Q2 above turns\n", - "out to be the same mask attention needs.\n", - "\n", - "### The thread running through both discussions\n", - "\n", - "Section 02 asked what an axis *means*. This block asks where to *put* it. The\n", - "answer is the same in both: an axis whose order carries no information (batch,\n", - "camera) can be folded, shuffled and merged freely; an axis whose order **is** the\n", - "information (time) cannot." - ], - "id": "s05-12" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Done with this section\n", - "\n", - "Next up: **06 · Contraction with einsum** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/06-contraction-with-einsum.ipynb).\n", - "\n", - "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" - ], - "id": "s05-13" - } - ], - "metadata": { - "colab": { - "name": "05-video-pipeline-design.ipynb", - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "s05-02", + "outputId": "82ad0c23-36e6-4adf-d071-bc6efd1fdfad" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "retained tensor: (16, 540, 960, 3) uint8\n", + "source frames: 720\n", + "source indices retained: [0, 45, 90, 135, 180, 225, 270, 315, 360, 405, 450, 495, 540, 585, 630, 675]\n", + "RAM retained: 23.7 MB\n" + ] + } + ], + "source": [ + "%pip install -q \"imageio[ffmpeg]\"\n", + "\n", + "import hashlib\n", + "import io\n", + "import urllib.request\n", + "\n", + "import numpy as np\n", + "import imageio.v3 as iio\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Real clip: \"Tormenta en l'Almadrava\" by Nicolas Vigier, CC0.\n", + "# https://commons.wikimedia.org/wiki/File:Tormenta_en_l%27Almadrava.webm\n", + "VIDEO_URL = (\n", + " \"https://upload.wikimedia.org/wikipedia/commons/1/1e/\"\n", + " \"Tormenta_en_l%27Almadrava.webm\"\n", + ")\n", + "VIDEO_SHA256 = \"e377fcdd2c79b55bce13c2c24b5dd7e412af39cd400eec548a79d0e59d79dc1b\"\n", + "UA = \"tensors-workshop/1.0 (https://github.com/project-delphi/tensors-workshop)\"\n", + "\n", + "def fetch_verified_video(url, expected_sha256, n_frames=16, stride=45):\n", + " \"\"\"Verify the real file, decode the whole stream, retain only sparse frames.\"\"\"\n", + " req = urllib.request.Request(url, headers={\"User-Agent\": UA})\n", + " raw = urllib.request.urlopen(req, timeout=120).read()\n", + "\n", + " got = hashlib.sha256(raw).hexdigest()\n", + " if got != expected_sha256:\n", + " raise ValueError(\n", + " f\"checksum mismatch: expected {expected_sha256}, got {got}\"\n", + " )\n", + "\n", + " kept_frames = []\n", + " kept_source_indices = []\n", + " total_frames = 0\n", + "\n", + " for i, frame in enumerate(\n", + " iio.imiter(io.BytesIO(raw), plugin=\"FFMPEG\", extension=\".webm\")\n", + " ):\n", + " total_frames = i + 1\n", + " if i % stride == 0 and len(kept_frames) < n_frames:\n", + " kept_frames.append(frame)\n", + " kept_source_indices.append(i)\n", + "\n", + " clip = np.stack(kept_frames)\n", + " return clip, np.asarray(kept_source_indices), total_frames\n", + "\n", + "clip, kept_source_indices, total_frames = fetch_verified_video(\n", + " VIDEO_URL, VIDEO_SHA256\n", + ")\n", + "\n", + "assert clip.shape == (16, 540, 960, 3), f\"unexpected clip shape {clip.shape}\"\n", + "assert total_frames == 720, f\"unexpected frame count {total_frames}\"\n", + "\n", + "print(\"retained tensor:\", clip.shape, clip.dtype)\n", + "print(\"source frames:\", total_frames)\n", + "print(\"source indices retained:\", kept_source_indices.tolist())\n", + "print(\"RAM retained:\", f\"{clip.nbytes / 1024**2:.1f} MB\")\n" + ], + "id": "s05-02" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s05-03" + }, + "source": [ + "## Why this matters\n", + "\n", + "A video model never receives “a video” in the abstract. A real file is decoded into axes, and every preprocessing choice decides what information survives.\n", + "\n", + "Here the camera recorded **720 frames**. The pipeline keeps only **16** of them in the in-memory tensor `(16, 540, 960, 3)`. That is efficient, but it also means most temporal measurements are deliberately discarded.\n", + "\n", + "This is the concept for the whole section:\n", + "\n", + "> **A video pipeline is a sequence of decisions about which axes survive, which axes move, and which information is discarded.**\n", + "\n", + "> 🇪🇸 **Por qué importa:** un modelo no recibe “un vídeo” de forma abstracta. El archivo real se decodifica en ejes y cada decisión de preprocesamiento determina qué información sobrevive. Aquí la cámara grabó 720 fotogramas, pero el tensor en memoria conserva solo 16. La eficiencia tiene un costo: se descarta información temporal real.\n", + "\n", + "### Predict → Run → Explain\n", + "\n", + "Before each exercise, predict both the **shape** and the **meaning of every axis**. After running code, explain what was kept and what was lost.\n", + "\n", + "> 🇪🇸 **Predice → Ejecuta → Explica:** antes de cada ejercicio, predice tanto la forma como el significado de cada eje. Después de ejecutar, explica qué información se conservó y cuál se perdió.\n" + ], + "id": "s05-03" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s05-04" + }, + "source": [ + "## Exercise 1 — watch a real video become a tensor\n", + "\n", + "Start with the measured data, not a diagram. `clip` contains 16 real frames sampled from the verified 720-frame source video.\n", + "\n", + "**Predict first:** what does each axis in `(16, 540, 960, 3)` count? What percentage of the recorded frames did this pipeline retain?\n", + "\n", + "> 🇪🇸 Empieza con datos medidos, no con un diagrama. `clip` contiene 16 fotogramas reales muestreados del vídeo verificado de 720 fotogramas. **Predice primero:** ¿qué cuenta cada eje y qué porcentaje de los fotogramas grabados conservó el pipeline?\n" + ], + "id": "s05-04" + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "s05-05" + }, + "outputs": [], + "source": [ + "# TODO 1: Print clip.shape and clip.dtype.\n", + "# Name the meaning of axes 0, 1, 2 and 3.\n", + "#\n", + "# TODO 2: Using len(clip) and total_frames, compute:\n", + "# - fraction of recorded frames retained\n", + "# - fraction discarded\n", + "#\n", + "# TODO 3: Inspect kept_source_indices.\n", + "# Explain why clip[1] is NOT source frame 1.\n" + ], + "id": "s05-05" + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1009, + "referenced_widgets": [ + "acfd115b176c40aebbfb902991a77816", + "cca9fe86dd4a47858d5239887ad130e3", + "b9d836bdf16748dca6a165795e63b41d", + "d07f9fd70e9249c0b3914f50fe732f67", + "3a3ad95d2ed74199aefddd8ac1878804", + "2a25efc4365a43a7974bc9c867f88ab3", + "afe5ed25c75f4a88ab3bca368b30ec82" + ] + }, + "id": "s05-06", + "outputId": "bba1f4b1-8845-425c-fcf4-15f5c31a5227" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "shape: (16, 540, 960, 3) dtype: uint8\n", + "axes: (retained time, height, width, colour)\n", + "retained: 2.22%\n", + "discarded: 97.78%\n", + "clip[1] came from source frame 45\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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ePXuqw5KTk0XevHmFJEli8uTJ6vC7d+8Kb29vzf46f/58odPpxN9//61Z14wZMwQAsX37diGEEJ999pkAYHdbhSsACJ1OJ44ePaoZ3r17dxEWFmZ3K1W7du1EQECAGoPUbodxFKdJkyYJSZI0+60SJ9tyeXp6ao41Bw8eFADEV199le5yfv755wKAWLx4sTrNgwcPRNGiRV26HWbEiBECgIiNjU11OiHkeh0SEpLh205Onz4tvLy8RMeOHTXDFy5cKLy9vQUAodfrxccffyyEEGLBggUiNDRU3Lt3L81lX716VQAQU6ZMyVDZiIiyK94OQ0Ruq379+ggODka+fPnQqlUr5MiRAytWrEDevHkByLe4bNy4EW3atEFsbCxu3bqFW7du4fbt24iKisLp06fVt8kYjUb1agOTyYTbt2+rl6Hv27cv3WVr27YtkpKS8Ouvv6rD1q5di5iYGLRt29bpfOvXr0diYiLeffddzTfQyjeg1pYsWYKaNWsiZ86c6rbdunUL9evXh8lkwtatWwHID9/U6/Xo16+fZv6BAwdCCIFVq1Y5LY9ypceaNWvUS7Udsb6l4969e7h16xZq166Nc+fO2d0WUqpUKVStWlX9u0qVKgCAunXrIn/+/HbDHV2Cbv3QS+Vb48TERIdvYUhLqVKl1G+9ASA4OBglSpTQrHf16tWoWrWq5mGRuXLlsrvtA5Bv2RFCZOrrjOPi4gAAefLkwV9//YU2bdpg0KBB+O6773D27FksXLjQ6bwmkwlr165Fy5YtUbhwYXV4WFgY3nzzTWzbtg33799/7DIuXboUiYmJDmNia+HChfjhhx8wcOBAFCtWTB2u3Cbg6AobLy8vu1sv0qtXr152w6zrbkJCAm7duoWXX34ZABzu+2+//bb6u16vR6VKlSCE0LylJzAw0K4OLVmyBBEREShZsqRmf61bty4AYNOmTeq8gPyQzYy8urV27dooVaqU+rcQAsuWLUPz5s0hhNCsOyoqCvfu3XPpGGcdpwcPHuDWrVuoVq0ahBCpXqGjqF+/PooUKaL+XbZsWfj7+6sxSk85V65cibCwMLRq1Updno+PD3r27Jl2gCC/BcZgMKR5pZPZbEaHDh0QExODr776yqVlW4uPj0fr1q3h7e2NyZMna8a1b98eV65cwY4dO3DlyhUMHDgQ8fHxGDp0KCZMmABfX1+MHTsWhQsXRtmyZbF8+XK75StXPd66dSvdZSMiys6YBCEit/XNN99g3bp1WLp0KZo0aYJbt25pOk9nzpyBEAIjR45EcHCw5mf06NEA5CfwA3Jj97PPPkOxYsVgNBoRFBSE4OBgHDp0yKVnO9gqV64cSpYsqXm14qJFixAUFKR2ehy5ePEiAGg6hoDcMbe+zQeQn9WxevVqu21T7nNXtu3ixYsIDw+Hn5+fZv6IiAjNOh0pVKgQ3n//fXz//fcICgpCVFQUvvnmG7uYbN++HfXr10eOHDkQGBiI4OBg9RkGttNaJzqAlERLvnz5HA6/e/euZrhOp9N05gH58nIAmmdzuMq2PIDcubBe78WLFx1emp7W5eqZRemAtmnTRnNrUOvWrWEwGNTL+x25efMm4uPjUaJECbtxERERMJvNuHz58mOXccGCBciVKxcaN26c6nR///03unfvjqioKEyYMEEzTtlOR892SEhIcOn5KakpVKiQ3bA7d+6gf//+6vNZgoOD1ekc7fuO6q+Xl5fdrRIBAQGaOnT69GkcPXrUbn9V6q6yv7Zt2xbVq1fH22+/jdDQULRr1w6LFy92OSFiu403b95ETEwMZs2aZbdu5W0lyrpTc+nSJXTp0gW5cuWCr68vgoODUbt2bQCO42Qrrf0sPeVU9kfbW5Uc1fHH8e6772L16tX4/vvvUa5cuXTNazKZ1LfKLF261OEtlTlz5sTLL7+M0NBQAPItgCEhIejatStmz56NGTNm4Pvvv8eAAQPQtm1bnDlzRjO/sNzSldartYmInjd8JggRua3KlSurzw5o2bIlatSogTfffBMnT56Er6+v2mkYNGiQ02dwKJ3YiRMnYuTIkejWrRvGjx+PXLlyQafTYcCAARn6NhaQOzMTJkzArVu34OfnhxUrVqB9+/YOX3eYEWazGQ0aNMCQIUMcjlc6V4/rk08+QZcuXfD7779j7dq16NevHyZNmoSdO3cib968OHv2LOrVq4eSJUvi008/Rb58+eDp6YmVK1fis88+s4ufXq93uB5nw5WG/pOSVetND6UDpXSWFHq9Hrlz57ZLFD1tly5dwt9//42ePXvCw8PD6XQHDx7Eq6++ijJlymDp0qV2+0JYWBgA4Nq1a3ZJsWvXrqFy5cqPVU5HSZQ2bdrgn3/+weDBg1G+fHn12NGoUSOH+76j+uJKHTKbzYiMjHT6sEtle729vbF161Zs2rQJf/31F1avXo1Fixahbt26WLt2rdN1OdtGZRveeustu2cUKcqWLZvqMk0mExo0aIA7d+5g6NChKFmyJHLkyIErV66gS5cuLh0j04pRZpTTVblz50ZycjJiY2PtksOKsWPH4ttvv8XkyZPRsWPHdK+jR48e+PPPP7FgwYJUE9+KCxcu4JNPPsHatWuh0+nw888/45133lHnnTdvHn755ReMGDFCnUfZ7zP6rBIiouyKSRAiIsgN7EmTJuGVV17B119/jWHDhqlXC3h4eNi9BcDW0qVL8corr+CHH37QDI+JiclwA7Nt27YYO3Ysli1bhtDQUNy/fx/t2rVLdR7lzQSnT5/WXO1w8+ZNu45ukSJFEBcXl+a2FShQAOvXr7dr8J84cUKzztRERkYiMjISI0aMwD///IPq1atjxowZ+Oijj/DHH3/g0aNHWLFihebbXuXy/sxmNptx7tw5TZLn1KlTAJDut5m4qkCBAnbfwgJwOOxJqFixIgCot28pEhMTcevWLQQHBzudNzg4GD4+Pjh58qTduBMnTkCn09klHNLr559/hhAi1Vthzp49i0aNGiEkJAQrV650eCuCcrvRnj17NAmPq1ev4r///nP5dgdX3b17Fxs2bMDYsWMxatQodfjp06czdT2AvL8ePHgQ9erVS/Obe51Oh3r16qFevXr49NNPMXHiRAwfPhybNm1Kc3+3FRwcDD8/P5hMpjTndVauw4cP49SpU5g3bx46deqkDl+3bl26ypJZ5SxQoACOHDkCIYSmzI7quCMlS5YEIL8lxlFi5ZtvvsGYMWMwYMAADB06NB1bIRs8eDDmzJmDzz//HO3bt3dpnkGDBuHVV19V31p09epVzdUj4eHhdvv/+fPnAaRc1UdE5C54OwwRkUWdOnVQuXJlfP7550hISEBISAjq1KmDmTNn4tq1a3bT37x5U/1dr9fbffO/ZMkSu0ZnekRERCAyMhKLFi3CokWLEBYWpr4pwpn69evDw8MDX331laY8ypterLVp0wY7duzAmjVr7MbFxMQgOTkZANCkSROYTCZ8/fXXmmk+++wzSJKU6u0L9+/fV5ejiIyMhE6nU29ZUL7htS7vvXv3MGfOnFS39XFYb4sQAl9//TU8PDxQr169J7K+qKgo7NixAwcOHFCH3blzBwsWLLCbNj2vyHVVnTp1EBISggULFiAhIUEdPnfuXPVbemf0ej0aNmyI33//XXO70I0bN7Bw4ULUqFED/v7+j1W+hQsXIn/+/GoHztb169fRsGFD6HQ6rFmzxmnSpnTp0ihZsiRmzZoFk8mkDp8+fTokSdI8AyIzOKq7gOP97XG1adMGV65cwXfffWc37uHDh3jw4AEAOHzdqZIccvUVsNb0ej3eeOMNLFu2DEeOHLEbb30czJEjBwD5+GG7DEAbJyEEvvjii3SXJzPK2aRJE1y9elXzyuT4+HjMmjXLpXUpzyTas2eP3bhFixahX79+6NChQ6qvqI2Pj8eJEyfsnscxbdo0fPzxx/jwww/Rv39/l8qzadMmrFy5ElOnTlWHhYaGqolqQH6VdJ48eTTz7d27F5IkaZ6xRETkDnglCBGRlcGDB6N169aYO3cuevXqhW+++QY1atRAZGQkevTogcKFC+PGjRvYsWMH/vvvPxw8eBAA0KxZM4wbNw5du3ZFtWrVcPjwYSxYsMDu2RPp1bZtW4waNQpeXl7o3r275nkOjgQHB2PQoEGYNGkSmjVrhiZNmmD//v1YtWqV3RUpgwcPxooVK9CsWTP1la4PHjzA4cOHsXTpUly4cAFBQUFo3rw5XnnlFQwfPhwXLlxAuXLlsHbtWvz+++8YMGCA5mGFtjZu3Ii+ffuidevWKF68OJKTkzF//ny1wwIADRs2hKenJ5o3b4533nkHcXFx+O677xASEuIw+fS4vLy8sHr1anTu3BlVqlTBqlWr8Ndff+HDDz9M9YqIxzFkyBD89NNPaNCgAd599131Fbn58+fHnTt3NN9Gp+cVuRcvXsT8+fMBpHTIPvroIwDyt93KZfhGoxHTpk1D586dUatWLXTs2BGXLl3CF198gZo1a6b5StqPPvoI69atQ40aNdC7d28YDAbMnDkTjx490nS8MuLIkSM4dOgQhg0b5vRKgkaNGuHcuXMYMmQItm3bhm3btqnjQkNDNUmcadOm4dVXX0XDhg3Rrl07HDlyBF9//TXefvvtTP/G29/fH7Vq1cLUqVORlJSEF154AWvXrlW/Yc9MHTt2xOLFi9GrVy9s2rQJ1atXh8lkwokTJ7B48WKsWbMGlSpVwrhx47B161Y0bdoUBQoUQHR0NL799lvkzZvXaZIpLZMnT8amTZtQpUoV9OjRA6VKlcKdO3ewb98+rF+/Xk28FClSBIGBgZgxYwb8/PyQI0cOVKlSBSVLlkSRIkUwaNAgXLlyBf7+/li2bFmm34blajl79OiBr7/+Gp06dcLevXsRFhaG+fPnq6+gTUvhwoVRpkwZrF+/Ht26dVOH//vvv+jUqRNy586NevXq2SU5q1Wrpp4T/v33X7zyyisYPXo0xowZA0De94cMGYJixYohIiICP/30k2b+Bg0a2N3SZjKZMGDAAAwePFhzJV2rVq0wZMgQBAcH4+LFi+o5ydq6detQvXp19RXDRERu4ym+iYaI6JmgvIJ09+7dduNMJpMoUqSIKFKkiEhOThZCCHH27FnRqVMnkSdPHuHh4SFeeOEF0axZM7F06VJ1voSEBDFw4EARFhYmvL29RfXq1cWOHTtE7dq1Re3atdXpXH1FruL06dMCgAAgtm3b5nRbrF+xajKZxNixY9Wy1KlTRxw5ckQUKFBA88pNIYSIjY0VH3zwgShatKjw9PQUQUFBolq1auLjjz8WiYmJmunee+89ER4eLjw8PESxYsXEtGnTNK/hdeTcuXOiW7duokiRIsLLy0vkypVLvPLKK2L9+vWa6VasWCHKli0rvLy8RMGCBcWUKVPE7NmzHb6StmnTpnbrASD69OmjGebodaedO3cWOXLkEGfPnhUNGzYUPj4+IjQ0VIwePdruFcdw8RW5jspj+7kLIb8uuGbNmsJoNIq8efOKSZMmiS+//FIAENevX7dbjyt1ZNOmTWr9sP2xXb8Q8qtby5UrJ4xGowgNDRV9+/YV9+/fT3M9Qgixb98+ERUVJXx9fYWPj4945ZVXNK8qFiJjr8gdNmyYACAOHTrkdBpn2+hsO5cvXy7Kly+vxnrEiBGa+uxMWq/IdfTa2f/++0+89tprIjAwUAQEBIjWrVurrx61rj/OlqHUSVu1a9cWpUuX1gxLTEwUU6ZMEaVLlxZGo1HkzJlTVKxYUYwdO1Z9JeqGDRtEixYtRHh4uPD09BTh4eGiffv2dq/CdsTRfqS4ceOG6NOnj8iXL5/w8PAQefLkEfXq1ROzZs3STPf777+LUqVKCYPBoKnHx44dE/Xr1xe+vr4iKChI9OjRQ33NrXVdd/aKXEflcnRMc7WcFy9eFK+++qrw8fERQUFBon///mL16tUuvSJXCCE+/fRT4evrq3n1r7LvOvux3k5l33VUR5z9OCrXN998I/LmzWv3muukpCTx/vvvi6CgIFGgQAExb948zfiYmBjh6ekpvv/++zS3lYjoeSMJ8Qw9uY2IiOgJ6tKlC5YuXaq+MjarDRgwADNnzkRcXFyaD6wkomfHvXv3ULhwYUydOlXzeuPs4vPPP8fUqVNx9uzZx35rEhFRdsNnghARET0FDx8+1Px9+/ZtzJ8/HzVq1GAChCibCQgIwJAhQzBt2rQMvwEsqyQlJeHTTz/FiBEjmAAhIrfEK0GIiMhtZOWVIOXLl0edOnUQERGBGzdu4IcffsDVq1exYcOGNB94S0RERESZgw9GJSIiegqaNGmCpUuXYtasWZAkCRUqVMAPP/zABAgRERHRU8QrQYiIiIiIiIjILfCZIERERERERETkFpgEISIiIiIiIiK34NIzQcxmM65evQo/Pz9IkvSky0RERERERERE5DIhBGJjYxEeHg6dzvn1Hi4lQa5evYp8+fJlWuGIiIiIiIiIiDLb5cuXkTdvXqfjXUqC+Pn5qQvz9/fPnJIREREREREREWWC+/fvI1++fGr+whmXkiDKLTD+/v5MghARERERERHRMymtR3jwwahERERERERE5BaYBCEiIiIiIiIit8AkCBERERERERG5BZeeCUJERERERETPJ5PJhKSkpKwuBlGqPDw8oNfrH3s5TIIQERERERG5ISEErl+/jpiYmKwuCpFLAgMDkSdPnjQffpoaJkGIiIiIiIjckJIACQkJgY+Pz2N1LImeJCEE4uPjER0dDQAICwvL8LKYBCEiIiIiInIzJpNJTYDkzp07q4tDlCZvb28AQHR0NEJCQjJ8awwfjEpERERERORmlGeA+Pj4ZHFJiFyn1NfHeYYNkyBERERERERuirfAUHaSGfWVSRAiIiIiIiIicgtMghARERERERFlkgsXLkCSJBw4cOCxllOnTh0MGDAg1WlmzZqFfPnyQafT4fPPP3+s9bkLPhiViIiIiIiIKJu5f/8++vbti08//RRvvPEGAgICsrpI2QKvBCEiIiIiIqJsKzEx0a3Wq7h06RKSkpLQtGlThIWFOXzIbVaX8VnEJAgRERERERFlG3Xq1EHfvn0xYMAABAUFISoqCgBw5MgRNG7cGL6+vggNDUXHjh1x69YtdT6z2YypU6eiaNGiMBqNyJ8/PyZMmKCOP3z4MOrWrQtvb2/kzp0bPXv2RFxcnDq+S5cuaNmyJSZMmIDw8HCUKFECAPDvv//ixRdfhJeXFypVqoT9+/fblTmtsj148ACdOnWCr68vwsLC8Mknn6Qag7lz5yIyMhIAULhwYUiShAsXLmDMmDEoX748vv/+exQqVAheXl4AgNWrV6NGjRoIDAxE7ty50axZM5w9e1ZdnnILz+LFi1GzZk14e3vjpZdewqlTp7B7925UqlQJvr6+aNy4MW7evKkpy/fff4+IiAh4eXmhZMmS+Pbbb1P/ALMYkyBERERERET02IQQT21d8+bNg6enJ7Zv344ZM2YgJiYGdevWxYsvvog9e/Zg9erVuHHjBtq0aaPO88EHH2Dy5MkYOXIkjh07hoULFyI0NBSAnISIiopCzpw5sXv3bixZsgTr169H3759NevdsGEDTp48iXXr1uHPP/9EXFwcmjVrhlKlSmHv3r0YM2YMBg0apJnHlbINHjwYW7Zswe+//461a9di8+bN2Ldvn9Ptb9u2LdavXw9ATsJcu3YN+fLlAwCcOXMGy5Ytw6+//qo+l+TBgwd4//33sWfPHmzYsAE6nQ6vvfYazGazZrmjR4/GiBEjsG/fPhgMBrz55psYMmQIvvjiC/z99984c+YMRo0apU6/YMECjBo1ChMmTMDx48cxceJEjBw5EvPmzXP1o3z6hAvu3bsnAIh79+65MjkRERERERE9wx4+fCiOHTsmHj58+NjLSkhKFqeu3xe7z98Wp67fFwlJyZlQQudq164tXnzxRc2w8ePHi4YNG2qGXb58WQAQJ0+eFPfv3xdGo1F89913Dpc5a9YskTNnThEXF6cO++uvv4ROpxPXr18XQgjRuXNnERoaKh49eqROM3PmTJE7d25NHKdPny4AiP3797tUttjYWOHp6SkWL16sjr99+7bw9vYW/fv3dxqH/fv3CwDi/Pnz6rDRo0cLDw8PER0d7XQ+IYS4efOmACAOHz4shBDi/PnzAoD4/vvv1Wl+/vlnAUBs2LBBHTZp0iRRokQJ9e8iRYqIhQsXapY9fvx4UbVq1VTXn1Gp1VtX8xZ8MCoRERERERFl2KXb8bh8Jx6+XgZcvhMPACgW6vdE11mxYkXN3wcPHsSmTZvg6+trN+3Zs2cRExODR48eoV69eg6Xd/z4cZQrVw45cuRQh1WvXh1msxknT55UrxiJjIyEp6enZr6yZcuqt50AQNWqVdNVtocPHyIxMRFVqlRRh+fKlUu93Sa9ChQogODgYM2w06dPY9SoUdi1axdu3bqlXgFy6dIllClTRp2ubNmy6u/W22w9LDo6GoB8dcnZs2fRvXt39OjRQ50mOTn5mX5IK5MgRERERERElCFCCNx7mARfLwP8vDwAAPceJkEIAUmSnth6rZMVABAXF4fmzZtjypQpdtOGhYXh3LlzT2S9rkirbGfOnMmMoqkclbF58+YoUKAAvvvuO4SHh8NsNqNMmTJ2D0718PBQf1c+P9thSgJFeV7Kd999p0ngAIBer8+cjXkCmAQhIiIiIiKiDJEkCQHeHuoVIHEJyciXy+eJJkAcqVChApYtW4aCBQvCYLDv5hYrVgze3t7YsGED3n77bbvxERERmDt3Lh48eKAmEbZv3w6dTpfqFRkRERGYP38+EhIS1KtBdu7cma6yFSlSBB4eHti1axfy588PALh79y5OnTqF2rVrux4EJ27fvo2TJ0/iu+++Q82aNQEA27Zte+zlhoaGIjw8HOfOnUOHDh0ee3lPCx+MSkRERERERBmWP7cP8uXygU6SkC+XD/Lntn9V65PWp08f3LlzB+3bt8fu3btx9uxZrFmzBl27doXJZIKXlxeGDh2KIUOG4Mcff8TZs2exc+dO/PDDDwCADh06wMvLC507d8aRI0ewadMmvPvuu+jYsaN6W4gjb775JiRJQo8ePXDs2DGsXLkSH3/8cbrK5uvri+7du2Pw4MHYuHEjjhw5gi5dukCny5zues6cOZE7d27MmjULZ86cwcaNG/H+++9nyrLHjh2LSZMm4csvv8SpU6dw+PBhzJkzB59++mmmLP9J4JUgRERERERElGFGgx7FQv2e+C0wqQkPD8f27dsxdOhQNGzYEI8ePUKBAgXQqFEjNZkwcuRIGAwGjBo1ClevXkVYWBh69eoFAPDx8cGaNWvQv39/vPTSS/Dx8cEbb7yRZmfe19cXf/zxB3r16oUXX3wRpUqVwpQpU/DGG2+kq2zTpk1Tb5vx8/PDwIEDce/evUyJjU6nwy+//IJ+/fqhTJkyKFGiBL788kvUqVPnsZf99ttvw8fHB9OmTcPgwYORI0cOREZGYsCAAY+97CdFEiLt9xjdv38fAQEBuHfvHvz9/Z9GuYiIiIiIiOgJSUhIwPnz51GoUCHNQz2JnmWp1VtX8xa8HYaIiIiIiIiI3AKTIERERERERETkFpgEISIiIiIiIiK3wCQIEREREREREbkFJkGIiIiIiIiIyC0wCUJEREREREREboFJECIiIiIiIiJyC0yCEBEREREREZFbYBKEiIiIiIiIiNwCkyBERERERERETnTp0gUtW7Z84usZM2YMypcv73T85s2bIUkSYmJinnhZnmdMghAREREREdFzr06dOhgwYEC65/viiy8wd+7cTC9PelWrVg3Xrl1DQEBAhpfBRApgyOoCEBERERERUTb1KA44vBg48DPwIBrIEQKUbw9EtgGMvk+lCImJifD09Hxiy3+cpENm8vT0RJ48ebK6GE/Nk/pceSUIERERERERpd/NU8DXlYA/3wP+2w3cvSD/++d78vCbp57IauvUqYO+fftiwIABCAoKQlRUFADgyJEjaNy4MXx9fREaGoqOHTvi1q1bAORbWrZs2YIvvvgCkiRBkiRcuHABJpMJ3bt3R6FCheDt7Y0SJUrgiy++0KzP9naYOnXqoF+/fhgyZAhy5cqFPHnyYMyYMZp5YmJi8PbbbyM4OBj+/v6oW7cuDh48qJlm8uTJCA0NhZ+fH7p3746EhIRUt9v2Ko65c+ciMDAQa9asQUREBHx9fdGoUSNcu3bN4fwXLlzAK6+8AgDImTMnJElCly5dAABmsxmTJk1S41CuXDksXbrUbt0bNmxApUqV4OPjg2rVquHkyZPqNAcPHsQrr7wCPz8/+Pv7o2LFitizZ486ftmyZShdujSMRiMKFiyITz75RFO+ggULYvz48ejUqRP8/f3Rs2fPVOORUUyCEBERERERUfo8igN+fBWIi7YMENp/46Ll8Y/insjq582bB09PT2zfvh0zZsxATEwM6tatixdffBF79uzB6tWrcePGDbRp0waAfEtL1apV0aNHD1y7dg3Xrl1Dvnz5YDabkTdvXixZsgTHjh3DqFGj8OGHH2Lx4sVprj9HjhzYtWsXpk6dinHjxmHdunXq+NatWyM6OhqrVq3C3r17UaFCBdSrVw937twBACxevBhjxozBxIkTsWfPHoSFheHbb79Ndxzi4+Px8ccfY/78+di6dSsuXbqEQYMGOZw2X758WLZsGQDg5MmTuHbtmprwmTRpEn788UfMmDEDR48exXvvvYe33noLW7Zs0Sxj+PDh+OSTT7Bnzx4YDAZ069ZNHdehQwfkzZsXu3fvxt69ezFs2DB4eHgAAPbu3Ys2bdqgXbt2OHz4MMaMGYORI0fa3Wb08ccfo1y5cti/fz9GjhyZ7ni4grfDEBERERERUfocXgzEOr7iAAAgTPL4w0uASl0zffXFihXD1KlT1b8/+ugjvPjii5g4caI6bPbs2ciXLx9OnTqF4sWLw9PTEz4+PppbSvR6PcaOHav+XahQIezYsQOLFy9WEyiOlC1bFqNHj1bL8vXXX2PDhg1o0KABtm3bhn///RfR0dEwGo0A5M79b7/9hqVLl6Jnz574/PPP0b17d3Tv3l0t//r169O8GsRWUlISZsyYgSJFigAA+vbti3HjxjmcVq/XI1euXACAkJAQBAYGAgAePXqEiRMnYv369ahatSoAoHDhwti2bRtmzpyJ2rVrq8uYMGGC+vewYcPQtGlTJCQkwMvLC5cuXcLgwYNRsmRJNS6KTz/9FPXq1VMTG8WLF8exY8cwbdo09WoUAKhbty4GDhyYrhikF68EISIiIiIiovQ58DMAKY2JJODAwiey+ooVK2r+PnjwIDZt2gRfX1/1R+mMnz17NtVlffPNN6hYsSKCg4Ph6+uLWbNm4dKlS6nOU7ZsWc3fYWFhiI6OVssSFxeH3Llza8pz/vx5tSzHjx9HlSpVNMtQEhDp4ePjoyZAbMvhqjNnziA+Ph4NGjTQlPfHH3+0i531doeFhQGAur73338fb7/9NurXr4/Jkydr5j1+/DiqV6+uWVb16tVx+vRpmEwmdVilSpXSVfaM4JUgRERERERElD4PopFyC4wzwjJd5suRI4fm77i4ODRv3hxTpkyxm1bprDvyyy+/YNCgQfjkk09QtWpV+Pn5Ydq0adi1a1eq61du81BIkgSz2ayWJSwsDJs3b7abT7n6IrM4KocQaX0uWnFx8i1Lf/31F1544QXNOOVKFkfrkyQ5CaZs95gxY/Dmm2/ir7/+wqpVqzB69Gj88ssveO2111wui+3n+iQwCUJERERERETpkyMEuHsRqSdCJHm6p6BChQpYtmwZChYsCIPBcTfX09NTc9UBAGzfvh3VqlVD79691WFpXTniSlmuX78Og8GAggULOpwmIiICu3btQqdOndRhO3fufKz1ukJ524p1HEqVKgWj0YhLly5pbn3JiOLFi6N48eJ477330L59e8yZMwevvfYaIiIisH37ds2027dvR/HixaHX6x9rnenF22GIiIiIiIgofcq3h0tXgpR/82mUBn369MGdO3fQvn177N69G2fPnsWaNWvQtWtXtcNfsGBB7Nq1CxcuXMCtW7dgNptRrFgx7NmzB2vWrMGpU6cwcuRI7N69+7HKUr9+fVStWhUtW7bE2rVrceHCBfzzzz8YPny4+raU/v37Y/bs2ZgzZw5OnTqF0aNH4+jRo48dh7QUKFAAkiThzz//xM2bNxEXFwc/Pz8MGjQI7733HubNm4ezZ89i3759+OqrrzBv3jyXlvvw4UP07dsXmzdvxsWLF7F9+3bs3r0bERERAICBAwdiw4YNGD9+PE6dOoV58+bh66+/dvoQ1yeJSRAiIiIiIiJKn8g2gF8YIDn5Fl/Sy+MjWz+V4oSHh2P79u0wmUxo2LAhIiMjMWDAAAQGBkKnk7u9gwYNgl6vR6lSpRAcHIxLly7hnXfeweuvv462bduiSpUquH37tuaqkIyQJAkrV65ErVq10LVrVxQvXhzt2rXDxYsXERoaCgBo27YtRo4ciSFDhqBixYq4ePEi/ve//z12HNLywgsvYOzYsRg2bBhCQ0PRt29fAMD48eMxcuRITJo0CREREWjUqBH++usvFCpUyKXl6vV63L59G506dULx4sXRpk0bNG7cWH3obIUKFbB48WL88ssvKFOmDEaNGoVx48ZpHor6tEjChRuG7t+/j4CAANy7dw/+/v5Po1xERERERET0hCQkJOD8+fMoVKgQvLy8MraQm6fk1+DGXoP8kFSR8q9fGNBpBRBcPPMKTW4vtXrrat6CzwQhIiIiIiKi9AsuDvTdI78G98BC+SGoOULkW2AiWwNG36wuIZEdJkGIiIiIiIgoY4y+QKWu8g9RNsBnghARERERERGRW2AShIiIiIiIiIjcApMgREREREREbsqF92QQPTMyo74yCUJERERERORmPDw8AADx8fFZXBIi1yn1Vam/GcEHoxIREREREbkZvV6PwMBAREdHAwB8fHwgSVIWl4rIMSEE4uPjER0djcDAQOj1+gwvi0kQIiIiIiIiN5QnTx4AUBMhRM+6wMBAtd5mFJMgREREREREbkiSJISFhSEkJARJSUlZXRyiVHl4eDzWFSAKJkGIiIiIiIjcmF6vz5TOJVF2wAejEhEREREREZFbYBKEiIiIiIiIiNwCkyBERERERERE5BaYBCEiIiIiIiIit8AkCBERERERERG5BSZBiIiIiIiIiMgtMAlCRERERERERG6BSRAiIiIiIiIicgtMghARERERERGRW2AShIiIiIiIiIjcApMgREREREREROQWmAQhIiIiIiIiIrfAJAgRERERERERuQUmQYiIiIiIiIjILTAJQkRERERERERugUkQIiIiIiIiInILTIIQERERERERkVtgEoSIiIiIiIiI3AKTIERERERERETkFpgEISIiIiIiIiK3wCQIEREREREREbkFJkGIiIiIiIiIyC0wCUJEREREREREboFJECIiIiIiIiJyC0yCEBEREREREZFbYBKEiIiIiIiIiNwCkyBERERERERE5BaYBCEiIiIiIiIit8AkCBERERERERG5BSZBiIiIiIiIiMgtMAlCRERERERERG6BSRAiIiIiIiIicgtMghARERERERGRW2AShIiIiIiIiIjcApMgREREREREROQWmAQhIiIiIiIiIrfAJAgRERERERERuQUmQYiIiIiIiIjILTAJQkRERERERERugUkQIiIiIiIiInILTIIQERERERERkVtgEoSIiIiIiIiI3AKTIERERERERETkFpgEISIiIiIiIiK3wCQIEREREREREbkFJkGIiIiIiIiIyC0wCUJEREREREREboFJECIiIiIiIiJyC0yCEBEREREREZFbYBKEiIiIiIiIiNwCkyBERERERERE5BaYBCEiIiIiIiIit8AkCBERERERERG5BSZBiIiIiIiIiMgtMAlCRERERERERG6BSRAiIiIiIiIicgtMghARERERERGRW2AShIiIiIiIiIjcApMgREREREREROQWmAQhIiIiIiIiIrfAJAgRERERERERuQUmQYiIiIiIiIjILTAJQkRERERERERugUkQIiIiIiIiInILTIIQERERERERkVtgEoSIiIiIiIiI3AKTIERERERERETkFgwZnvNRHHB4MXDgZ+BBNJAjBCjfHohsAxh9M7GIzzjGQcY4yBgHGeMgYxxkjIOMcZAxDjLGQcY4yBgHGeMgYxxkjIOMcch0khBCpDXR/fv3ERAQgHv37sHf3x+4eQr48VUg9hoACYBI+dcvDOi0Aggu/qTLnvUYBxnjIGMcZIyDjHGQMQ4yxkHGOMgYBxnjIGMcZIyDjHGQMQ4yxiFd7PIWTqQ/CWLUAV9XAuKiAWGym1ZIeki+IRB9dwOevpAkCcoqnP2e2rjH+f2JLivxAYQlDtJjxCE7bHt2i0OWxPQx4pAl5X1G4vBcbftzHocMze9CHOAbAqnvHgjPHFlf3uckDs/UtjMOWRKHZ3bbs3EcnlhME+Mgff0SRCbH4YmVNxvEIdttu/XfbhYHp+MeMw7ZYduzWxyetZg642oSJP23wxxebMlEOSYJE0TsNez7cxbO528NTw8dEpPNgIDd7/GPkhETn4xAHwN8jAan06X398xcrrNlFftvKcrGXoOzjyCtOGSHbc9uccjKmGYkDs9LHchIHPasmIm9QS2fq21/XuPwOMtyNQ4HV83C6bytnrltz25xeBa3nXF4unF41rc9O8bhSce04q3lqJSJcchOdSCz45Bdt916uYUuLEYFN4hDWsvKaByyw7Zntzg8azE16HQoFuqLoqF+MBr0TiKTtvQnQQ78DKiX4jgmICHk3HKs9GiE6NiHCAnwBsyw+93DQw9JyMtKTDY5nS69v2fmcp0tq8LJJRCQIGUwDtlh27NbHLIyphmJw/NSBzISh6Bzv+K0uf5zte3PaxweZ1muxsHvxBIcN0Q9c9ue3eLwLG474/B04/Csb3t2jMOTjmnUheWZGofsVAcyOw7Zddutl1vlvHvEIa1lZTQO2WHbs1scnrWYBuUwIu5RMjwNehQL9XMal7Sk/+0wD6KRWgJEXqiAb/IdeBokmAF46GH3u0kICCEQ7OcBkxAwQzicLr2/Z+ZyU1uWX/Jd6DIYh+yw7dktDlkd0/TGIavLm9Vx8Eu++9xt+/MYh8ddVnri8Kxte3aLw7O67YzD04tDdtj27BaHpxHTzIxDdqsDmRmH7Lzt1sv1dYM4uLKsjMQhO2x7dovDsxZTT4OE3L6eMEPg3sNEzS016ZX+K0FyhAB3LyK1RIgZEuIMuZCYLKADkGQCYNb+rpckSJKEm7FJ0EsSTJAcTpfe3zNzuaktK9aQE7mTr6daMZ3FITtse3aLQ1bHNL1xyOryZnUcYg05n7ttfx7j8LjLSk8cnrVtz25xeFa3nXF4enHIDtue3eLwNGIaa8iJXEnXMiUO2a0OZGYcsvO2Wy83zpATAY+uPtdxcGVZGYlDdtj27BaHZy2mickCt+MS4edlQIC3Z6rPBklL+h+Memop8Od7qU4vAOyLHPX8PxPk4Fin92ilFYfssO3ZLQ5Z/iyMdMbheakDGYnDntIjn/lnYTzOcp+nODz2szBciMOh8qOf/2eCPIU4PIvbzjg83Tg869ueHePwVJ6FcfSjTItDdqoDmR2H7Lrtds8EOTL+uY+DS8/CyEAcssO2Z7c4PGsxTeuZIHw7TCYv127cM/hWlCxZ7jMYhyyJKd8Ok6E4PFfb/pzHIUPz8+0wWRKHZ2rbGYcsicMzu+3ZOA5PLKZ8O0ymxyHbbbv1324WB6fjnqG3omTpcp+hODxrMXXmySVB/P35vmIF4yBjHGSMg4xxkDEOMsZBxjjIGAcZ4yBjHGSMg4xxkDEOMsZBxjiky5NNggDAozjg8BLgwEL5Yak5QoDybwKRrQGjb6ZtyDOPcZAxDjLGQcY4yBgHGeMgYxxkjIOMcZAxDjLGQcY4yBgHGeMgYxxc9uSTIEREREREREREzwBX8xa6p1gmIiIiIiIiIqIswyQIEREREREREbkFJkGIiIiIiIiIyC0wCUJEREREREREboFJECIiIiIiIiJyC0yCEBEREREREZFbYBKEiIiIiIiIiNwCkyBERERERERE5BaYBCEiIiIiIiIit8AkCBERERERERG5BSZBiIiIiIiIiMgtMAlCRERERERERG6BSRAiIiIiIiIicgtMghARERERERGRW2AShIiIiIiIiIjcApMgREREREREROQWmAQhIiIiIiIiIrfAJAgRERERERERuQUmQYiIiIiIiIjILTAJQkRERERERERugUkQIiIiIiIiInILTIIQERERERERkVtgEoSIiIiIiIiI3AKTIERERERERETkFpgEISIiIiIiIiK3wCQIEREREREREbkFJkGIiIiIiIiIyC0wCUJEREREREREboFJECIiIiIiIiJyC0yCEBEREREREZFbYBKEiIiIiIiIiNwCkyBERERERERE5BaYBCEiIiIiIiIit8AkCBERERERERG5BSZBiIiIiIiIiMgtMAlCRERERERERG6BSRAiIiIiIiIicgtMghARERERERGRW2AShIiIiIiIiIjcApMgREREREREROQWmAQhIiIiIiIiIrfAJAgRERERERERuQUmQYiIiIiIiIjILTAJQkRERERERERugUkQIiIiIiIiInILTIIQERERERERkVtgEoSIiIiIiIiI3AKTIERERERERETkFpgEISIiIiIiIiK3wCQIEREREREREbkFJkGIiIiIiIiIyC0wCUJEREREREREboFJECIiIiIiIiJyC0yCEBEREREREZFbYBKEiIiIiIiIiNwCkyBERERERERE5BaYBCEiIiIiIiIit8AkCBERERERERG5BSZBiIiIiIiIiMgtMAlCRERERERERG6BSRAiIiIiIiIicgtMghARERERERGRW2AShIiIiIiIiIjcApMgREREREREROQWmAQhIiIiIiIiIrfAJAgRERERERERuQUmQYiIiIiIiIjILTAJQkRERERERERugUkQIiIiIiIiInILTIIQERERERERkVtgEoSIiIiIiIiI3AKTIERERERERETkFpgEISIiIiIiIiK3wCQIEREREREREbkFJkGIiIiIiIiIyC0wCUJEREREREREboFJECIiIiIiIiJyC0yCEBEREREREZFbYBKEiIiIiIiIiNwCkyBERERERERE5BaYBCEiIiIiIiIit8AkCBERERERERG5BSZBiIiIiIiIiMgtMAlCRERERERERG6BSRAiIiIiIiIicgtMghARERERERGRW2AShIiIiIiIiIjcApMgREREREREROQWmAQhIiIiIiIiIrfAJAgRERERERERuQUmQYiIiIiIiIjILTAJQkRERERERERugUkQIiIiIiIiInILTIIQERERERERkVtgEoSIiIiIiIiI3AKTIERERERERETkFpgEISIiIiIiIiK3wCQIEREREREREbkFJkGIiIiIiIiIyC0wCUJEREREREREboFJECIiIiIiIiJyC0yCEBEREREREZFbYBKEiIiIiIiIiNwCkyBERERERERE5BaYBCEiIiIiIiIit8AkCBERERERERG5BSZBiIiIiIiIiMgtMAlCRERERERERG6BSRAiIiIiIiIicgtMghARERERERGRW2AShIiIiIiIiIjcApMgREREREREROQWmAQhIiIiIiIiIrfAJAgRERERERERuQUmQYiIiIiIiIjILTAJQkRERERERERugUkQIiIiIiIiInILTIIQERERERERkVtgEoSIiIiIiIiI3AKTIERERERERETkFgyuTCSEAADcv3//iRaGiIiIiIiIiCi9lHyFkr9wxqUkSGxsLAAgX758j1ksIiIiIiIiIqInIzY2FgEBAU7HSyKtNAkAs9mMq1evws/PD5Ikacbdv38f+fLlw+XLl+Hv7//4JSa3xbpEmYH1iDIL6xJlFtYlygysR5RZWJcoMzyL9UgIgdjYWISHh0Onc/7kD5euBNHpdMibN2+q0/j7+z8zG0/ZG+sSZQbWI8osrEuUWViXKDOwHlFmYV2izPCs1aPUrgBR8MGoREREREREROQWmAQhIiIiIiIiIrfw2EkQo9GI0aNHw2g0ZkZ5yI2xLlFmYD2izMK6RJmFdYkyA+sRZRbWJcoM2bkeufRgVCIiIiIiIiKi7I63wxARERERERGRW2AShIiIiIiIiIjcApMgREREREREROQWmAQhIiIiIiIiIrfw2EmQb775BgULFoSXlxeqVKmCf//9NzPKRc+JrVu3onnz5ggPD4ckSfjtt98044UQGDVqFMLCwuDt7Y369evj9OnTmmnu3LmDDh06wN/fH4GBgejevTvi4uKe4lZQVps0aRJeeukl+Pn5ISQkBC1btsTJkyc10yQkJKBPnz7InTs3fH198cYbb+DGjRuaaS5duoSmTZvCx8cHISEhGDx4MJKTk5/mplAWmz59OsqWLQt/f3/4+/ujatWqWLVqlTqe9YgyYvLkyZAkCQMGDFCHsS6RK8aMGQNJkjQ/JUuWVMezHlF6XLlyBW+99RZy584Nb29vREZGYs+ePep4trspLQULFrQ7JkmShD59+gB4fo5Jj5UEWbRoEd5//32MHj0a+/btQ7ly5RAVFYXo6OjMKh9lcw8ePEC5cuXwzTffOBw/depUfPnll5gxYwZ27dqFHDlyICoqCgkJCeo0HTp0wNGjR7Fu3Tr8+eef2Lp1K3r27Pm0NoGeAVu2bEGfPn2wc+dOrFu3DklJSWjYsCEePHigTvPee+/hjz/+wJIlS7BlyxZcvXoVr7/+ujreZDKhadOmSExMxD///IN58+Zh7ty5GDVqVFZsEmWRvHnzYvLkydi7dy/27NmDunXrokWLFjh69CgA1iNKv927d2PmzJkoW7asZjjrErmqdOnSuHbtmvqzbds2dRzrEbnq7t27qF69Ojw8PLBq1SocO3YMn3zyCXLmzKlOw3Y3pWX37t2a49G6desAAK1btwbwHB2TxGOoXLmy6NOnj/q3yWQS4eHhYtKkSY+zWHpOARDLly9X/zabzSJPnjxi2rRp6rCYmBhhNBrFzz//LIQQ4tixYwKA2L17tzrNqlWrhCRJ4sqVK0+t7PRsiY6OFgDEli1bhBByvfHw8BBLlixRpzl+/LgAIHbs2CGEEGLlypVCp9OJ69evq9NMnz5d+Pv7i0ePHj3dDaBnSs6cOcX333/PekTpFhsbK4oVKybWrVsnateuLfr37y+E4DGJXDd69GhRrlw5h+NYjyg9hg4dKmrUqOF0PNvdlBH9+/cXRYoUEWaz+bk6JmX4SpDExETs3bsX9evXV4fpdDrUr18fO3bseJy8DLmJ8+fP4/r165o6FBAQgCpVqqh1aMeOHQgMDESlSpXUaerXrw+dToddu3Y99TLTs+HevXsAgFy5cgEA9u7di6SkJE1dKlmyJPLnz6+pS5GRkQgNDVWniYqKwv3799WrAMi9mEwm/PLLL3jw4AGqVq3KekTp1qdPHzRt2lRTZwAekyh9Tp8+jfDwcBQuXBgdOnTApUuXALAeUfqsWLEClSpVQuvWrRESEoIXX3wR3333nTqe7W5Kr8TERPz000/o1q0bJEl6ro5JGU6C3Lp1CyaTSbOBABAaGorr168/dsHo+afUk9Tq0PXr1xESEqIZbzAYkCtXLtYzN2U2mzFgwABUr14dZcqUASDXE09PTwQGBmqmta1LjuqaMo7cx+HDh+Hr6wuj0YhevXph+fLlKFWqFOsRpcsvv/yCffv2YdKkSXbjWJfIVVWqVMHcuXOxevVqTJ8+HefPn0fNmjURGxvLekTpcu7cOUyfPh3FihXDmjVr8L///Q/9+vXDvHnzALDdTen322+/ISYmBl26dAHwfJ3bDFldACKi9OjTpw+OHDmiuWeaKD1KlCiBAwcO4N69e1i6dCk6d+6MLVu2ZHWxKBu5fPky+vfvj3Xr1sHLyyuri0PZWOPGjdXfy5YtiypVqqBAgQJYvHgxvL29s7BklN2YzWZUqlQJEydOBAC8+OKLOHLkCGbMmIHOnTtncekoO/rhhx/QuHFjhIeHZ3VRMl2GrwQJCgqCXq+3exrsjRs3kCdPnscuGD3/lHqSWh3KkyeP3YN2k5OTcefOHdYzN9S3b1/8+eef2LRpE/LmzasOz5MnDxITExETE6OZ3rYuOapryjhyH56enihatCgqVqyISZMmoVy5cvjiiy9Yj8hle/fuRXR0NCpUqACDwQCDwYAtW7bgyy+/hMFgQGhoKOsSZUhgYCCKFy+OM2fO8JhE6RIWFoZSpUpphkVERKi3V7HdTelx8eJFrF+/Hm+//bY67Hk6JmU4CeLp6YmKFStiw4YN6jCz2YwNGzagatWqmVI4er4VKlQIefLk0dSh+/fvY9euXWodqlq1KmJiYrB37151mo0bN8JsNqNKlSpPvcyUNYQQ6Nu3L5YvX46NGzeiUKFCmvEVK1aEh4eHpi6dPHkSly5d0tSlw4cPa07u69atg7+/v12jgdyL2WzGo0ePWI/IZfXq1cPhw4dx4MAB9adSpUro0KGD+jvrEmVEXFwczp49i7CwMB6TKF2qV6+OkydPaoadOnUKBQoUAMB2N6XPnDlzEBISgqZNm6rDnqtj0uM8VfWXX34RRqNRzJ07Vxw7dkz07NlTBAYGap4GS+4tNjZW7N+/X+zfv18AEJ9++qnYv3+/uHjxohBCiMmTJ4vAwEDx+++/i0OHDokWLVqIQoUKiYcPH6rLaNSokXjxxRfFrl27xLZt20SxYsVE+/bts2qTKAv873//EwEBAWLz5s3i2rVr6k98fLw6Ta9evUT+/PnFxo0bxZ49e0TVqlVF1apV1fHJycmiTJkyomHDhuLAgQNi9erVIjg4WHzwwQdZsUmURYYNGya2bNkizp8/Lw4dOiSGDRsmJEkSa9euFUKwHlHGWb8dRgjWJXLNwIEDxebNm8X58+fF9u3bRf369UVQUJCIjo4WQrAekev+/fdfYTAYxIQJE8Tp06fFggULhI+Pj/jpp5/UadjuJleYTCaRP39+MXToULtxz8sx6bGSIEII8dVXX4n8+fMLT09PUblyZbFz587MKBc9JzZt2iQA2P107txZCCG/rmvkyJEiNDRUGI1GUa9ePXHy5EnNMm7fvi3at28vfH19hb+/v+jatauIjY3Ngq2hrOKoDgEQc+bMUad5+PCh6N27t8iZM6fw8fERr732mrh27ZpmORcuXBCNGzcW3t7eIigoSAwcOFAkJSU95a2hrNStWzdRoEAB4enpKYKDg0W9evXUBIgQrEeUcbZJENYlckXbtm1FWFiY8PT0FC+88IJo27atOHPmjDqe9YjS448//hBlypQRRqNRlCxZUsyaNUsznu1ucsWaNWsEALu6IcTzc0yShBAiSy5BISIiIiIiIiJ6ijL8TBAiIiIiIiIiouyESRAiIiIiIiIicgtMghARERERERGRW2AShIiIiIiIiIjcApMgREREREREROQWmAQhIiIiIiIiIrfAJAgRERERERERuQUmQYiIiIiIiIjILTAJQkRERBn222+/oWjRotDr9RgwYEBWF8clXbp0QcuWLR97OZIk4bfffnvs5RAREdHTY8jqAhAREVH29c4776Br167o168f/Pz8sro4Lvniiy8ghMjqYhAREVEWYBKEiIjoOWcymSBJEnS6zL0ANC4uDtHR0YiKikJ4ePhTXffjCAgIyOoiEBERURZ5dlokREREbmLp0qWIjIyEt7c3cufOjfr16+PBgwcAALPZjHHjxiFv3rwwGo0oX748Vq9erc67efNmSJKEmJgYddiBAwcgSRIuXLgAAJg7dy4CAwOxYsUKlCpVCkajEZcuXcKjR48wdOhQ5MuXD0ajEUWLFsUPP/ygLufIkSNo3LgxfH19ERoaio4dO+LWrVsOt2Hz5s3qlR9169aFJEnYvHmz03Xv3r0bDRo0QFBQEAICAlC7dm3s27dPs0xJkjBz5kw0a9YMPj4+iIiIwI4dO3DmzBnUqVMHOXLkQLVq1XD27FnNfL///jsqVKgALy8vFC5cGGPHjkVycrLT+NveDlOnTh3069cPQ4YMQa5cuZAnTx6MGTNGM8/p06dRq1YteHl5oVSpUli3bp3dci9fvow2bdogMDAQuXLlQosWLdTP5MSJE/Dx8cHChQvV6RcvXgxvb28cO3bMaVmJiIgoczEJQkRE9BRdu3YN7du3R7du3XD8+HFs3rwZr7/+unp7xhdffIFPPvkEH3/8MQ4dOoSoqCi8+uqrOH36dLrWEx8fjylTpuD777/H0aNHERISgk6dOuHnn3/Gl19+iePHj2PmzJnw9fUFAMTExKBu3bp48cUXsWfPHqxevRo3btxAmzZtHC6/WrVqOHnyJABg2bJluHbtGqpVq+Z03bGxsejcuTO2bduGnTt3olixYmjSpAliY2M1yx0/fjw6deqEAwcOoGTJknjzzTfxzjvv4IMPPsCePXsghEDfvn3V6f/++2906tQJ/fv3x7FjxzBz5kzMnTsXEyZMSFe85s2bhxw5cmDXrl2YOnUqxo0bpyY6zGYzXn/9dXh6emLXrl2YMWMGhg4dqpk/KSkJUVFR8PPzw99//43t27fD19cXjRo1QmJiIkqWLImPP/4YvXv3xqVLl/Dff/+hV69emDJlCkqVKpWushIREdFjEERERPTU7N27VwAQFy5ccDg+PDxcTJgwQTPspZdeEr179xZCCLFp0yYBQNy9e1cdv3//fgFAnD9/XgghxJw5cwQAceDAAXWakydPCgBi3bp1Dtc7fvx40bBhQ82wy5cvCwDi5MmTDue5e/euACA2bdqkDnO0bkdMJpPw8/MTf/zxhzoMgBgxYoT6944dOwQA8cMPP6jDfv75Z+Hl5aX+Xa9ePTFx4kTNsufPny/CwsKcrrtz586iRYsW6t+1a9cWNWrU0Ezz0ksviaFDhwohhFizZo0wGAziypUr6vhVq1YJAGL58uXqOkuUKCHMZrM6zaNHj4S3t7dYs2aNOqxp06aiZs2aol69eqJhw4aa6YmIiOjJ4zNBiIiInqJy5cqhXr16iIyMRFRUFBo2bIhWrVohZ86cuH//Pq5evYrq1atr5qlevToOHjyYrvV4enqibNmy6t8HDhyAXq9H7dq1HU5/8OBBbNq0Sb0yxNrZs2dRvHjxDK8bAG7cuIERI0Zg8+bNiI6OhslkQnx8PC5duqSZznq+0NBQAEBkZKRmWEJCAu7fvw9/f38cPHgQ27dv11z5YTKZkJCQgPj4ePj4+LhUZtvyhoWFITo6GgBw/Phx5MuXT/Pck6pVq2qmP3jwIM6cOWP3cNiEhATN7TuzZ89G8eLFodPpcPToUUiS5FL5iIiIKHMwCUJERPQU6fV6rFu3Dv/88w/Wrl2Lr776CsOHD8euXbuQO3fuNOdXHjAqrN5ukpSUZDedt7e3poPt7e2d6nLj4uLQvHlzTJkyxW5cWFhYmuVKbd0A0LlzZ9y+fRtffPEFChQoAKPRiKpVqyIxMVEznYeHh/q7sgxHw8xms1rusWPH4vXXX7crh5eXl8tltl6Hsh5lHa6Ii4tDxYoVsWDBArtxwcHB6u8HDx7EgwcPoNPpcO3atXTHloiIiB4PkyBERERPmSRJqF69OqpXr45Ro0ahQIECWL58Od5//32Eh4dj+/btmis2tm/fjsqVKwNI6VBfu3YNOXPmBCBf5ZGWyMhImM1mbNmyBfXr17cbX6FCBSxbtgwFCxaEwZD5zYPt27fj22+/RZMmTQDIDxF19tDV9KhQoQJOnjyJokWLPvaynImIiMDly5c1SYudO3falWPRokUICQmBv7+/w+XcuXMHXbp0wfDhw3Ht2jV06NAB+/btSzNBRURERJmHD0YlIiJ6inbt2oWJEydiz549uHTpEn799VfcvHkTERERAIDBgwdjypQpWLRoEU6ePIlhw4bhwIED6N+/PwCgaNGiyJcvH8aMGYPTp0/jr7/+wieffJLmegsWLIjOnTujW7du+O2333D+/Hls3rwZixcvBgD06dMHd+7cQfv27bF7926cPXsWa9asQdeuXWEymR57u4sVK4b58+fj+PHj2LVrFzp06JApnf9Ro0bhxx9/xNixY3H06FEcP34cv/zyC0aMGPHYy1bUr18fxYsXR+fOnXHw4EH8/fffGD58uGaaDh06ICgoCC1atMDff/+txrdfv37477//AAC9evVCvnz5MGLECHz66acwmUwYNGhQppWTiIiI0sYkCBER0VPk7++PrVu3okmTJihevDhGjBiBTz75BI0bNwYA9OvXD++//z4GDhyIyMhIrF69GitWrECxYsUAyLdt/Pzzzzhx4gTKli2LKVOm4KOPPnJp3dOnT0erVq3Qu3dvlCxZEj169FBfzatcgWIymdCwYUNERkZiwIABCAwMVG/BeRw//PAD7t69iwoVKqBjx47o168fQkJCHnu5UVFR+PPPP7F27Vq89NJLePnll/HZZ5+hQIECj71shU6nw/Lly/Hw4UNUrlwZb7/9tt3bZ3x8fLB161bkz58fr7/+OiIiItC9e3ckJCTA398fP/74I1auXIn58+fDYDAgR44c+Omnn/Ddd99h1apVmVZWIiIiSp0krG8qJiIiIiIiIiJ6TvFKECIiIiIiIiJyC0yCEBEREREREZFbYBKEiIiIiIiIiNwCkyBERERERERE5BaYBCEiIiIiIiIit8AkCBERERERERG5BSZBiIiIiIiIiMgtMAlCRERERERERG6BSRAiIiIiIiIicgtMghARERERERGRW2AShIiIiIiIiIjcApMgREREREREROQWmAQhIiIiIiIiIrfAJAgRERERERERuQUmQYiIiIiIiIjILTAJQkRERERERERugUkQIiIiIiIiInILTIIQERERERERkVtgEoSIiIiIiIiI3AKTIERERERERETkFpgEISIiIiIiIiK3wCQIEREREREREbkFJkGIiIiIiIiIyC0wCUJEREREREREboFJECKi58DcuXMhSRIuXLjw1Nd94cIFSJKEuXPnPvV1O5OeeKxevRrly5eHl5cXJElCTEzMEy8fPV/q1KmDOnXqPNFlPov7GRERUXbEJAgRPbOUjqyjn2HDhmV18TJNfHw8xowZg82bN2d1UdzO7du30aZNG3h7e+Obb77B/PnzkSNHjqwuFlG288MPPyAiIgJeXl4oVqwYvvrqqwwv6/bt25g2bRpq1aqF4OBgBAYG4uWXX8aiRYvspt29ezf69u2L0qVLI0eOHMifPz/atGmDU6dO2U3bpUsXh+eTkiVLZristho0aABJktC3b1+7cc7OZ5MnT7ab9sqVK2jTpg0CAwPh7++PFi1a4Ny5c49dvvXr16Nu3boICAiAn58fKlasqInr5s2bnZZTkiRMmDBBnTa1c/T169cfu6xERE+KIasLQESUlnHjxqFQoUKaYWXKlMmi0mS++Ph4jB07FgAy/G1yx44d0a5dOxiNxkws2fNv9+7diI2Nxfjx41G/fv2sLg5lU2vXrn3i6yhQoAAePnwIDw+PJ76u9Jo5cyZ69eqFN954A++//z7+/vtv9OvXD/Hx8Rg6dGi6l7djxw4MHz4cTZo0wYgRI2AwGLBs2TK0a9cOx44dU4+XADBlyhRs374drVu3RtmyZXH9+nV8/fXXqFChAnbu3Gl3rjAajfj+++81wwICAjK24TZ+/fVX7NixI9VpGjRogE6dOmmGvfjii5q/4+Li8Morr+DevXv48MMP4eHhgc8++wy1a9fGgQMHkDt37gyVb86cOejevTsaNGiAiRMnQq/X4+TJk7h8+bI6TUREBObPn2837/z587F27Vo0bNjQbpyjc3RgYGCGykhE9DQwCUJEz7zGjRujUqVKWbZ+s9mMxMREeHl5uTS9EAIJCQnw9vZ+wiVLodfrodfrn9r6MsuDBw+y9MqL6OhoAK412OPj4+Hj4/OES0SZJb377ePw9PR84uuQJOmpbEt6PXz4EMOHD0fTpk2xdOlSAECPHj1gNpsxfvx49OzZEzlz5kzXMkuXLo3Tp0+jQIEC6rDevXujfv36mDJlCoYMGaIeN95//30sXLhQ8xm0bdsWkZGRmDx5Mn766SfNsg0GA956662Mbq5TCQkJGDhwIIYOHYpRo0Y5na548eJprv/bb7/F6dOn8e+//+Kll14CIJ8Hy5Qpg08++QQTJ05Md/kuXLiAPn364N1338UXX3zhdLrQ0FCH5Rs7diyKFSumlsdaVp+jiYjSi7fDEFG2t3HjRtSsWRM5cuRAYGAgWrRogePHj2um6dKlCwoWLGg375gxYyBJkmaYcinzggULULp0aRiNRqxevdrp+gsWLIhmzZphzZo1qFSpEry9vTFz5kwAQExMDAYMGIB8+fLBaDSiaNGimDJlCsxmMwC5YRocHAxAbmQqlxKPGTMGAHDo0CF06dIFhQsXhpeXF/LkyYNu3brh9u3bmjI4egaGUq5t27ahcuXK8PLyQuHChfHjjz/abUNa5bSerkuXLggICEBgYCA6d+7s8jM0lDJu2bIFvXv3RkhICPLmzauOX7Vqlfo5+vn5oWnTpjh69KhmGa7GwxV16tRB586dAQAvvfQSJElCly5d1HFlypTB3r17UatWLfj4+ODDDz8EAPz+++9o2rQpwsPDYTQaUaRIEYwfPx4mk8lu+WXKlMGhQ4dQu3Zt+Pj4oGjRompHccuWLahSpQq8vb1RokQJrF+/3q6MV65cQbdu3RAaGgqj0YjSpUtj9uzZdtN99dVXKF26NHx8fJAzZ05UqlQJCxcuTHdMMkNSUpLaYfLy8kLu3LlRo0YNrFu3TjPd09xvr1y5gu7du6ufWaFChfC///0PiYmJ6vyu7gOO2D6/Q7mlYPHixZgwYQLy5s0LLy8v1KtXD2fOnLGbf9asWShSpAi8vb1RuXJl/P3333bTOHsmyIkTJ9CmTRsEBwerdWn48OGaaZ5kPdq0aRNu376N3r17a4b36dMHDx48wF9//ZXq/I4UKlRIkwAB5M+3ZcuWePTokea2kGrVqtkloYoVK4bSpUvb1SeFyWTC/fv3012u1EydOhVmsxmDBg1Kc9qHDx8iISHB6filS5fipZde0iQcSpYsiXr16mHx4sUZKt+MGTNgMpkwbtw4APLVJkIIl+b9999/cebMGXTo0MHpNLGxsXbHQCKiZxWvBCGiZ969e/dw69YtzbCgoCAA8v3NjRs3RuHChTFmzBg8fPgQX331FapXr459+/Y57EC5YuPGjVi8eDH69u2LoKCgNJdz8uRJtG/fHu+88w569OiBEiVKID4+HrVr18aVK1fwzjvvIH/+/Pjnn3/wwQcf4Nq1a/j8888RHByM6dOn43//+x9ee+01vP766wCAsmXLAgDWrVuHc+fOoWvXrsiTJw+OHj2KWbNm4ejRo9i5c6ddR9DWmTNn0KpVK3Tv3h2dO3fG7Nmz0aVLF1SsWBGlS5cGAJfKCchXuLRo0QLbtm1Dr169EBERgeXLl6uJBFf17t0bwcHBGDVqFB48eABAvtS6c+fOiIqKwpQpUxAfH4/p06ejRo0a2L9/vxr/x42HteHDh6NEiRKYNWuWejl3kSJF1PG3b99G48aN0a5dO7z11lsIDQ0FICdzfH198f7778PX1xcbN27EqFGjcP/+fUybNk2zjrt376JZs2Zo164dWrdujenTp6Ndu3ZYsGABBgwYgF69euHNN9/EtGnT0KpVK1y+fBl+fn4AgBs3buDll19WO/fBwcFYtWoVunfvjvv372PAgAEAgO+++w79+vVDq1at0L9/fyQkJODQoUPYtWsX3nzzzXR9NplhzJgxmDRpEt5++21UrlwZ9+/fx549e7Bv3z40aNAAwNPdb69evYrKlSsjJiYGPXv2RMmSJXHlyhUsXboU8fHx8PT0dHkfSK/JkydDp9Nh0KBBuHfvHqZOnYoOHTpg165d6jQ//PAD3nnnHVSrVg0DBgzAuXPn8OqrryJXrlzIly9fqss/dOgQatasCQ8PD/Ts2RMFCxbE2bNn8ccff6jPbnjS9Wj//v0AYHclQMWKFaHT6bB///5Mu/JCec6Ecvx3RgiBGzduqMc4a/Hx8fD390d8fDxy5syJ9u3bY8qUKfD19c1wuS5duoTJkydj9uzZaV4BOHfuXHz77bcQQiAiIgIjRozQxNdsNuPQoUPo1q2b3byVK1fG2rVrERsbqx4nXLV+/XqULFkSK1euxODBg3HlyhXkzJkTffr0wdixY6HTOf9edMGCBQDgNAnyyiuvIC4uDp6enoiKisInn3yCYsWKpat8RERPlSAiekbNmTNHAHD4oyhfvrwICQkRt2/fVocdPHhQ6HQ60alTJ3VY586dRYECBezWMXr0aGF7KAQgdDqdOHr0qEvlLFCggAAgVq9erRk+fvx4kSNHDnHq1CnN8GHDhgm9Xi8uXbokhBDi5s2bAoAYPXq03bLj4+Pthv38888CgNi6das6TInV+fPn7cplPV10dLQwGo1i4MCB6S7nb7/9JgCIqVOnqtMkJyeLmjVrCgBizpw5TiKkLWONGjVEcnKyOjw2NlYEBgaKHj16aKa/fv26CAgI0Ax/nHikVqbdu3drhteuXVsAEDNmzLCbx1EZ3nnnHeHj4yMSEhLslrFw4UJ12IkTJ9T6tXPnTnX4mjVr7GLYvXt3ERYWJm7duqVZV7t27URAQIBajhYtWojSpUunup1PU7ly5UTTpk1TneZp7redOnUSOp3O7jMWQgiz2SyEcH0fcKZ27dqidu3a6t+bNm0SAERERIR49OiROvyLL74QAMThw4eFEEIkJiaKkJAQUb58ec10s2bNEgA0yzx//rxdHalVq5bw8/MTFy9edLhdQjz5etSnTx+h1+sdjgsODhbt2rVL9zIduX37tggJCRE1a9ZMc9r58+cLAOKHH37QDB82bJgYOnSoWLRokfj5559F586dBQBRvXp1kZSUlOGytWrVSlSrVk39G4Do06eP3XTVqlUTn3/+ufj999/F9OnTRZkyZQQA8e2336rTKOeDcePG2c3/zTffCADixIkT6S6jv7+/yJkzpzAajWLkyJFi6dKl4s033xQAxLBhw5zOl5ycLEJDQ0XlypXtxi1atEh06dJFzJs3TyxfvlyMGDFC+Pj4iKCgoDT3GSKirMTbYYjomffNN99g3bp1mh8AuHbtGg4cOIAuXbogV65c6vRly5ZFgwYNsHLlygyvs3bt2ihVqpTL0xcqVAhRUVGaYUuWLEHNmjWRM2dO3Lp1S/2pX78+TCYTtm7dmuZyrb9VTEhIwK1bt/Dyyy8DAPbt25fm/KVKlULNmjXVv4ODg1GiRAnN5eSulnPlypUwGAz43//+p86r1+vx7rvvplkOaz169NA8v2TdunWIiYlB+/btNevX6/WoUqUKNm3alGnxSA+j0YiuXbvaDbcuQ2xsLG7duoWaNWsiPj4eJ06c0Ezr6+uLdu3aqX+XKFECgYGBiIiIQJUqVdThyu/K5yKEwLJly9C8eXMIITRxiYqKwr1799TtDQwMxH///Yfdu3dn3sY/hsDAQBw9ehSnT592OP5p7rdmsxm//fYbmjdv7vCZBcqVQ5mxrzrStWtXza0ayr6ofM579uxBdHQ0evXqpZlOueUsNTdv3sTWrVvRrVs35M+f3+F2PY169PDhQ6fPRPHy8sLDhw/TtTxHzGYzOnTogJiYmDTfOnPixAn06dMHVatWtbtKbdKkSZg8eTLatGmDdu3aYe7cuZgwYQK2b9+u3qaWXps2bcKyZctculpo+/bt6N+/P1599VX06tULe/fuRZkyZfDhhx+qcVL+dfSQa+WZMBmJaVxcHO7evYuxY8di3LhxeOONN7BgwQI0atQIX3zxBWJjYx3Ot2HDBty4ccPhVSBt2rTBnDlz0KlTJ7Rs2RLjx4/HmjVrcPv2bc1bZIiInjW8HYaInnmVK1d22IG5ePEiALljaSsiIgJr1qzJ8IM3bZ90n5HpT58+jUOHDqnP/LClPJQzNXfu3MHYsWPxyy+/2E1/7969NOe37RwBQM6cOXH37t10l/PixYsICwuzu2zcUfxTYxsrpbNct25dh9P7+/urvz9uPNLjhRdecNi5O3r0KEaMGIGNGzfaPVfAtgx58+a1u0UnICDA7jYHpcOrfC43b95ETEwMZs2ahVmzZjksn7L9Q4cOxfr161G5cmUULVoUDRs2xJtvvonq1aunun2P8wrLPHnyOB03btw4tGjRAsWLF0eZMmXQqFEjdOzYUb3F62nutzdv3sT9+/fTfJtUZuyrjtjuf8oDQpXPWYmF7a0DHh4eKFy4cKrLVhIpqW3b06hH3t7emmerWMusB0S/++67WL16NX788UeUK1fO6XTXr19H06ZNERAQgKVLl7r0sOj33nsPI0eOxPr16zUJS1ckJyejX79+6Nixo8MHhqbF09MTffv2VRMiNWrUUOP16NEju+mV54hkJKbe3t548OAB2rdvrxnevn17rF69Gvv370etWrXs5luwYAH0ej3atm3r0npq1KiBKlWqOHzGERHRs4JJECJyC86eFeHsQW7pbWQ6mt5sNqNBgwYYMmSIw3mKFy+e5nLbtGmDf/75B4MHD0b58uXh6+sLs9mMRo0aufTARmedAGH1QLzMKGd62MZK2Y758+c77FwbDCmnqseNx+OUE5Afnlm7dm34+/tj3LhxKFKkCLy8vLBv3z4MHTrUrgzO4p/W56Is56233nL6zBUlqRAREYGTJ0/izz//xOrVq7Fs2TJ8++23GDVqlOZVorbCwsKcjkuLSOWBirVq1cLZs2fx+++/Y+3atfj+++/x2WefYcaMGXj77bfTtZ4nvd8qntQ+4Mr+9yQ9rXpkMpkQHR2NkJAQdXhiYiJu376N8PDwx9qGsWPH4ttvv8XkyZPRsWNHp9Pdu3cPjRs3RkxMDP7++2+X1+vt7Y3cuXPjzp076S7bjz/+iJMnT2LmzJmah1ID8lViFy5cQEhISKpvlVISosr6c+XKBaPRiGvXrtlNqwzLSEzDw8Nx+vRp9dlGCuUzs06MKx4+fIjly5ejfv36dvOlJl++fDh58mS6y0hE9LQwCUJE2Zby9gBHja0TJ04gKChI/TY5Z86cDt9ionwT+yQUKVIEcXFxqF+/fqrTOevo3b17Fxs2bMDYsWM1r1x0dpvBky5ngQIFsGHDBsTFxWmuBnncxq7yMNKQkJBUy/C04pGazZs34/bt2/j1118135qeP38+U9cTHBwMPz8/mEymND8XAMiRIwfatm2Ltm3bIjExEa+//jomTJiADz74wOlrVW3f1pKZcuXKha5du6Jr166Ii4tDrVq1MGbMGLz99ttPdb8NDg6Gv78/jhw5kup0ru4DmU2JxenTpzVXQiUlJeH8+fOpXvWgXCmS2rY9jXpUvnx5APKtPU2aNFGH79mzB2azWR2fEd988w3GjBmDAQMGYOjQoU6nS0hIQPPmzXHq1CmsX78+XbcyKre0ObsKKDWXLl1CUlKSw6tlfvzxR/z4449Yvnw5WrZs6XQZyhU9yvp1Oh0iIyOxZ88eu2l37dqFwoULp/uhqID8oNrTp0/jypUrmquMrl69qlm/tRUrViA2NjbVt8I4cu7cuQzFk4joaeEzQYgo2woLC0P58uUxb948TUfpyJEjWLt2raZBXqRIEdy7dw+HDh1Sh127dg3Lly9/YuVr06YNduzYgTVr1tiNi4mJQXJyMgCo3xLadvaUb5FtvzXO6JsqHrecTZo0QXJyMqZPn66ON5lMad6jn5aoqCj4+/tj4sSJSEpKsht/8+ZNAE8vHqlxVIbExER8++23mb6eN954A8uWLXPYyVViAsDu9cCenp4oVaoUhBAO46moX79+hn9SY1seX19fFC1aVL28/2nutzqdDi1btsQff/zhsFOpfI6u7gOZrVKlSggODsaMGTM0t5TMnTs3zVdPBwcHo1atWpg9ezYuXbqkGads19OoR3Xr1kWuXLk0xwUAmD59Onx8fNC0adNUt8OZRYsWoV+/fujQoQM+/fRTp9OZTCa0bdsWO3bswJIlS1C1alWH0yUkJDh87sX48eMhhECjRo3SXcZ27dph+fLldj+AfLxcvny5+rwf61grYmNj8fnnnyMoKAgVK1ZUh7dq1Qq7d+/W1NmTJ09i48aNaN26dbrLCUC9neWHH35Qh5nNZsyZMwe5cuXSrF+xcOFC+Pj44LXXXnO4TEfbtHLlSuzduzdD8SQielp4JQgRZWvTpk1D48aNUbVqVXTv3l191WZAQADGjBmjTteuXTsMHToUr732Gvr166e+grV48eKZ/kBNxeDBg7FixQo0a9ZMfS3tgwcPcPjwYSxduhQXLlxAUFAQvL29UapUKSxatAjFixdHrly5UKZMGZQpUwa1atXC1KlTkZSUhBdeeAFr167N9KsOXC1n8+bNUb16dQwbNgwXLlxAqVKl8Ouvvz72szj8/f0xffp0dOzYERUqVEC7du0QHByMS5cu4a+//kL16tXx9ddfw9/f/6nEIzXVqlVDzpw50blzZ/Tr1w+SJGH+/PlP5PaGyZMnY9OmTahSpQp69OiBUqVK4c6dO9i3bx/Wr1+vXj7fsGFD5MmTB9WrV0doaCiOHz+Or7/+Gk2bNs3QN8aPq1SpUqhTpw4qVqyIXLlyYc+ePVi6dCn69u2rTvM099uJEydi7dq1qF27Nnr27ImIiAhcu3YNS5YswbZt2xAYGOjyPpDZPDw88NFHH+Gdd95B3bp10bZtW5w/fx5z5sxJ85kgAPDll1+iRo0aqFChAnr27IlChQrhwoUL+Ouvv3DgwAEAT74eeXt7Y/z48ejTpw9at26NqKgo/P333/jpp58wYcIEzcNvN2/ejFdeeQWjR4/WfM62/v33X3Tq1Am5c+dGvXr11Fe0KqpVq6bGZ+DAgVixYgWaN2+OO3fu4KefftJMq7ye9/r163jxxRfRvn17lCxZEgCwZs0arFy5Eo0aNUKLFi008ymvaba9zcVayZIl1WXZKlSokOYKkG+++UZ9SG/+/Plx7do1NYE1f/58zfOHevfuje+++w5NmzbFoEGD4OHhgU8//RShoaEYOHCgZj116tTBli1b0jwGtWjRAvXq1cOkSZNw69YtlCtXDr/99hu2bduGmTNn2j2I9c6dO1i1ahXeeOMNp68PrlatGl588UVUqlQJAQEB2LdvH2bPno18+fLhww8/TLU8RERZ6mm/joaIyFXOXl9qa/369aJ69erC29tb+Pv7i+bNm4tjx47ZTbd27VpRpkwZ4enpKUqUKCF++uknp6/adPR6Q2cKFCjg9JWgsbGx4oMPPhBFixYVnp6eIigoSFSrVk18/PHHIjExUZ3un3/+ERUrVhSenp6a1+X+999/4rXXXhOBgYEiICBAtG7dWly9etXulbrOXpHrqFy2r/NMTzlv374tOnbsKPz9/UVAQIDo2LGj2L9/f7pekevs89y0aZOIiooSAQEBwsvLSxQpUkR06dJF7NmzR53mceKRnjLVrl3b6etCt2/fLl5++WXh7e0twsPDxZAhQ9RX3G7atCnNZTj7XBzVuxs3bog+ffqIfPnyCQ8PD5EnTx5Rr149MWvWLHWamTNnilq1aoncuXMLo9EoihQpIgYPHizu3buX6rY/KR999JGoXLmyCAwMFN7e3qJkyZJiwoQJmnokxNPdby9evCg6deokgoODhdFoFIULFxZ9+vTRvJbW1X3AEWevyF2yZIlmOkevuRVCiG+//VYUKlRIGI1GUalSJbF161a7ZTqb98iRI+o+4eXlJUqUKCFGjhypmeZp1KNZs2aJEiVKCE9PT1GkSBHx2WefaV7VK4QQf/zxh9NXT1tL7fXotjFQXkXt7Edx9+5d8dZbb4miRYsKHx8fYTQaRenSpcXEiRMdfr5BQUHi5ZdfdmnbbTmqi2vXrhUNGjQQefLkER4eHiIwMFA0bNhQbNiwweEyLl++LFq1aiX8/f2Fr6+vaNasmTh9+rTddBUrVhR58uRxqVyxsbGif//+Ik+ePMLT01NERkaKn376yeG0M2bMEADEihUrnC5v+PDhonz58iIgIEB4eHiI/Pnzi//973/i+vXrLpWHiCirSEI8padzEREREZHbGjJkCH7++WecOXPG4StgnxXHjh1D6dKl8eeff2b4dp6nITY2Frly5cLnn3+OPn36ZHVxiIiyDT4ThIiIiIieuE2bNmHkyJHPdAIEkMtZtWrVZzoBAgBbt27FCy+8gB49emR1UYiIshVeCUJEREREREREboFXghARERERERGRW2AShIiIiIiIiIjcApMgREREREREROQWmAQhIiIiIiIiIrfAJAgRERERERERuQUmQYiIiIiIiIjILRhcnfCnX/6CJEkAAEmS1B+dTqf522w2QwihTmtNADBDghACypt5nb2hVxluvWxlmbbzWi/Detm28ztbj+081vMBAkKYHS5HnkeCgAQhtGWDZE51uyAA2xLZbp9tma2311EMlB+zyX67bKczm80O46UOk7fcbnxaZQAkqJsoBCAEhFlAkgT0ej0MBgP0er1d/TGbzTCbzXj0KBEmUzIkSYJeb4DBoAcAJCcnqz+KZLMJJpP8o2yPbSyUzwWAvA5l21Kpg9aZQUfxBQCzkCA008j1xJqj+qr9HZZ4pb4PpIx3Nh0AyNsmx8IECJ1mf4UkyfVNSBCS5XOBgOU/O5qap8xrGS7BDGFZrkGvh6enJ4xeXvD28oLRaERADi/4ehkgr17eh6w/a51OgiTpoHw0tutX6oaz/Vj5XIUQgNmyLcJs+bGvr2bJtk6IVI8JKePkH9v90XZZmn1a+VuSoNfpIOl10On10Ov1ahytqiTMZjMePkxAQsIj6HXydMr+aTabkZycrNZxk8mUsv9a6jEcbJtSRkf/WsdF3V55h9CMS5lPLqxZEyoBSQirOqLEymp+CJgc1l/tMNvjqaPypkyTsmeaIdRzjTXbfU753fr4YHse07Acl3U6QK/Xw8PDA0ajEQaDAUaDBzw9DTB6egBQlqlLqdvQAUg5B8rHm2Tt8dSsCbUalZQdTEApkhASzOaUumB7HDGZTBBmM4RJwGyW/zYJ+TigTC/kGWDZ2x2fKzUFsD4xSZBgcxxRYyjPohwFpVSOY6kxenqibq1a8PT0TFm+TlKPEzqdzlIeSXOgsK/f1tvgmFL7pDSntJnP5pypLCklHvZL1MZMUnd6+U/r40/KPqFdJ9TPNMlssto3BPR6g3osldeuU9cvH9uEehwymUx4lJiIhMREJCUmq9uhHEskyfIJSlb7llXbQN1GsxmA9f5pglmYYRd2IQFm++WkdY6zbjeo80na+W2PcUrZbM/9tsvXzqdtn8hzmh0uXyEpH4iLHLWXUn6X1+/0eA0hx1VY/lI+I0t9SNkm23ao4+11dHxU2zBWn4+zaezKqf6kfG4pdduqIkiQ66Hl2AgAOp0OHgY9DDqd5XcD9JZ/PT084WP0hIeHByQdYDIly/VeWYWlDI/0uZFoCNKUy/r4qBwHHH0Gtm0K69+t61BqHPVHrMfZTuOMsm55P5Yj62x+nU4HvaSDXidBr5djp5MkSJLN525pC+kkCZI6jc1nI0kQknzMkOuUGSarj19uKwJCc6y332a1bJDbAdr2nU79cTSPo/OJ5eSkqS+27S2lTjlqO6QsR5fqsca6HqvbZzel3D51xrZ86lypbGda9cGWq/VR4coxNrU2ke20aQ1Lz3hb2ji4ejZ2fJ5My4B2EWlO43ISxPaDd/Sh2gbZUafBBOcHWFvWO4T19LbrUlg3Pq3LaLsz2i7PWWNap9NB7tgKy+8pjRfNtkI+QWm2xUkSRLOOND5T28SPo7g5agwqJ1nr7bSdz3aZduu2bJe245l6B0b+16wuQaeT4yJJcvwMBgM8PAzQ6XVWn43c0DabdUhOToZerwOgJEkcb7fy+ZqF2eF2OpJ2I8xqmBJIZzGDesy2Wobzg4ejeFt/XtbT2x8ohdVP6nSSJPfehFUTxaYOwTrroVRVB8vSdnisTmQQkKDTnOjkDqC2/PLnLn+GKedfyZII02tOMtr9xsEJQwIkq0a/vL/KdVIS8vZYJ0FSGkQpHQfrvdHZMczxSU1JhDiOjTUhBCSzzb6hk9Syp6xX2Sh56Xqd3MCRdNA0GIWlQZNsGWa9bZLkSo1wTGgrnfy3cFxP1c6VpmFp6QQKZTsF1ASFun+kdYxR6qSyPOvhwubfVLfGbt7UzsmuN0Qkq2OWBzw9PeHl4Qkvoyf0eglmNQGrJJ5TzjnW9S6lmCmxk7SrsfTxJbVeKMswwyo3Ikl2kRE6ndwxUhvRcjJCTuLo5OOkENBUFjXBknrtkdeX0kBxvJ+48vmkTgBINpmgs0puSzoJOr0egD5luzTHMGcNOwfL1+64EJI8nU4dlHZHxTYJpdZ/m0SH9fnAruMgNKl1q+U7O3dYtsdSP6wTW8nJj+SOkV5vOfamTKizSqwox0OlwwkhwWQ2QTLZtGGklM9ZmAUknfyvsNQ5SZLkj8IqZgJ6CGGG2ZJ0U8ttAqCDul5n52frLdb+LtR/1XOteowS2mldaHw7brtImmVbfx62xytHtcPVToH9v8rxyXEbTCmTWaSUy75MSj2zT/Y7Wl5a0jNNSn2xHpnm7PLxSJLgoZcTyJ4eHjBY6q5ep4NO0sltAst5zmw2yUl+s5Drq17bIXXWVlfGmc3ahIft79bzWf9uMplcioPtscjRsqyTAI6W43i/0Lb/1EO3EDBLcrsLQgBmM4Sks3yZpPwAks6s7VPodHLbQycfy+VDgwSh7BOWfRw2ZREC8hdlVhx16JVzv7xseT75cxBQMqHW0zqLF5By3gIAk8nkMHEgzJYEodWXcMpotRnhpC2uiX+qY9VAOB1lW0+cJTtst9vVtkdqcUptnrT6OKkdL1Lry6dVxvRMp8RD24ZINdzWS3NpnenlchIktc6CbXBtg6o03s1mgWRhfxJwtB5njR7b7JWj9ViXQQm6dSJE05F28C2CMq88nzYR46hMyr6nbfymXiEl2H/wjmLsuGPi+GRnfaK0Pei4yroMyuFMGe6okQBYYijkhovSVYal4WrQG6AzSOpVIAYPPXQ6q3UoyTWd3LDSm/SaZTs6IMiNhdR3+tS23dE4246h9TDb7ZY7KNqOsZIsc6Usjhru1nXOfrrUP0e1U42UzrP1bmJbP1JLZFqPV3633nd0EmDQy51D6wOafeZfWVfKMjwsHUqDwdI4sEpqpCQudZYfqxOKJQkCSUr5FtMsL1ynxs+SBLEsS+k0mM1m6IRy2oRVt81yArU53ihxsj6GyP9qPy/ruCsJHWE2A5LSKJDnM1m++nfaIJJ0MOj1MHp6Wo436tIhhARJGORv2U1mmHVWV9kJOSFlvX9qCqi2VyV12zV1QFglC4R9HUmZ3vJtIVI+05SyK52WlMa9dgJH+5r25KcdlvK3bais66wQluSXvALNIqy/IXU2v9Va7Ds5lkSC0kDXSzp46g0wenjA09NDPn5JSr3WQaczwWQyyw1Km/OP3fqV2OtsNw6aeqgea01yo1Zv6eZaHw3kRqAOJj1ghsly/pE/V+UbQr1epybnLcFR59fEQgLUTrpk1oyz/r82jg7DnCFmkwlmvdVxX0hqh0hplEuS/bfWzo6jtuOsCUvZzXB87HN0jrVPgkCNccoytEld7TE2JVEhH3Osy2bfVrBej9ksALN1gs2sufrR+qpKSZLkjo8wA2brY5iATi9BLyRIZh3MOgnC6htTyZLaFmYBs2QGTMJSHYR6LE45pyvbLCdBTGY5ESKXVb6CQZidx17+N+UYpe6u6u/ylYpqmwraH0sgUj4Hm/Omo3Oo/LdkVzdSjvPatmBGkweO2mry7/bHPEdtCrldAzWp5Kj94ej45qht+DhS67jYLdv+tKAsxJLgl6CD3A70MnrC05IE8fAwQC9Jav1VrhLR6XQwmZTkrbx86za6WTLDLJmd9kdsO56uxsL2C1RnMVHbQTrHVwrYtq1sz/vO2pTyVT6S+iMf86D+Ky8TltyCgE4nTy+3pZWjtDbpaLZMq5xeJABKFljIM2jaalZbC9uw2X7+apyVo4ckQZLkPp5OJyztwZRERWr9RzVWqcQwpRyWuwmgtAGUEiu/mF04N6Wcw5RjjPW2A5CPoemgtlOhTRLZtlvS4qw/7Mp8zpbvrG1rvb7U+iiultXVcjqW1nY+3jEtNS4nQYSwyro5KI/S+XcUTKURIZ8wU4an90Oznce6ktk2WJTxykHI9uDjrLHqqKPoqEx2vyutUKT8LY9KpUHg5EDj6CDuykFdnU9tZKV866h8PpZr3+XpIVmNg82Oq/3WBVYNEvtLZyWYJQGdSOnYSjoJOkmnfptqMBigN2gPaNpEjzZxptPp5BII7WeQ0snXXp7rSErMtI0M2zg6ajQ5G6YMV3PBAlYJGW3j1tGVPmmV12FSwvZvR413q9/1ej30evkkaH17mu3JRVu3lSVor1iQY663XGIp3+IhN2Y8odNJasPXYNDDw0Nu5MiXbMrf/uiklASYp6eHpS7IDR/lkkvrToacULA5Cer10CtJEJMJJkt9kyzLlwC1Iat8RiaTCcmWjoK6ZQ5Ovtaxt9QWmMxmmJJNMEPYxc4R9bgoSZAs+4CabDUpCRTbzzZlGw0Gg91xTf0sdQJJSfK3VJLlNjHrDqptPVU69nJbJ5UTm9LLsBourEZBaSAofXdLo9RxOZ1cfWFpFGjKqGlzpdQ55WjkLPkBdZxQt1vSrFRS/03JN8jjtd9IW327bbMNyu9K4lv9ptKSwDN46Kz2CXk9Op1cl00mM0yWixm0cVc6wJbyq8XUnmegdABtvpW2nkZS4i/Js5uFnCCBSOnUmtXjjjyvkghRG/fCwTHI6mNwdL6zPgZrSHIDWE24udjQ0ywCUBObKY1e26vXUj9ey/8qSxM2tw04blQ6bmDbn2sdtRWUz0HTObS+1swqxvJ6HO/bVmdUJ9uTUg5L9YAkyZfEm0wpx0zrBLQQchdBEtrt1+msO5xymZXjriRJEGYJJmGy1GlJjadd8tbqCldJ0qntGKUcwnI+tO1MKtug2Vrr44PNSMkSaEkINUrCpn4p0ziqI9qEhrJwR1ch2141mLEOgBBCk7ywbhY6axdrymGZWNiMU5etubUofeUCbNtaVv9aHdNtk8LOO0zQNEqUz0H921JZJcvnq9NJ8DAYYDQaYfT0kNsDej08lauCLfMYLPVTTrCZIIR8FYN8Prd8yWRVr50nQtL/qEPrW01T65coV2DprZK29glS+46wsz6NdV/Ferz1scV6PZDkq9nMZjN0EPKP1fbKp0hLm0MImIUEyWx1tYh8+WZK4lNokxupsa5Lapysjv3Kj9lsVo9JtuNst1UzXik/Utpcyu3B6pU+yq4s5GShdd2Ul+v4yyZ5PXKEUvYFzdZZnUNS2pOpLMg2OErLS96PhFU7wyo5khrrNqikJJ1F6vPZ7p+uJk2U6R39ntowV5eXGttkmvW5Lo01pKs8rnI5CeL4nmrb+mB/4lN2dLPZ8m2ZzXK1J2r7+7mUv1O7/9u6jLbLs02UOCqXsixHDSRtB9GetsEt5BOZzXpSK7MtRxU5reXYbp+mOyNZlU1NikhQOu/W82sO1pIEne09h0LpEKUcRNQYmyX1EjrbewMNHvItECkNzpQEkfYkLeRGlmS23Auu7Czygd4DHpZvCkxISkqydDC1cbDfEZVOkWs7kF2H0OqEZV0XzbCp/LYfq1CaXTalsTvZyXG0PzGkDNNBqcfKimwaNilDNGWW7yl3/mwNTT/HUlUkCZZvuuXkh5zQUBrPEjw9PeHj4yXfu2tzMEvZV+Rkht7SYFCSYB4envDw0Kc02C0FUDpA1nVf7sgoWyVSLv2Eclua5TJ5dd9TOrDy9UtyY0Vn+ay0l6c7qgvynQOWxhVMgE4AZrmqOzt5O/tcU4IKOTEI+aRq/a28BMlym4xOPdFbL0fZf8wmOcmUlJSkNryU42SyMMuXxlrKrdPp1NtxNGUT9sddtYg2sTBbGk9Ke8C2wet43tSPadYdfQnahrfaiIL9MVjzrxByisBSHsmyrWbLcUxNPlmuslCfN6PUKZsiWh/fre9dV67+UJ7bYPDQyclbveUbfc2xEpBvy5LjJPQCwmzpHNrExq5+OLld0mwVV0kCJL1VB1IonUbL7WYAoAN0wtL5FEK+a8HqNkH52GJzbNSc0lKanpJaVtvy2p5rUnp46mJs6q+rBACT2QS92XJcsOwTQOr3RTs6x8r/6px23pTyWdczR511R+uwf9aZdjqzdcdLiYVlEutEgnZ/s91/rM+L0A6TUsbJx4yUJIZ1p1Cu6im3qglLBZDn0cYgOTk5pV0lAZJO3sd0lnqd0r6zKrPQJup0kgQIy9Vm2l1aw1m9SOmEKW0pq3OiPKMmrmkvx3Gn3TrsmvqRcprRzOPsb+1yU5afst/aPBPNwb/2y0lt2drOsvX+mhpX2zvWHUmd0kmETYyUNVviq1OOgcpwXcrHJCzHWp1IOcbqdXJbUm1XSHIiT/mRAM3zK+Rbog2QhBnJJst5UxJO4+qoLZzSjHUcK3m4dp+0vhrEUXvbumOuJBSV8671PM6u+rRdlrN64Yj6eSjnLQDyBV8CkmRSj8tK3JXEtWRp88s/kmUfStnXNPuLvCLLLuGgHeHgGCagnJO0bXklEeIoCWS9LNvYqsMg1wmzJb66lAOCJibaK/kBydE3j5rxVm0CBzFWpHbucTSvXZ9N+VJFKG0ToZ5f7VdsFW/1lCAfm3SZtK+nlUhJz7IyWgbrabV1yZU2Q8bK5QrXkyCW+6AEnHSa1bHah9soJ2rlG1kl32fZT9X7H5V2qmZ5Qul4W+4FUxr4No00Zwds2wRIWkkQR5euWe84qbE+eDrqOFuXy1kZrZdle5BMbdts57FuFDkqtwRA6OwPXHadPcn6AOP42wylE6AXeruyKR1nvUFnddJIeaaB0vhRDtHWtx5JOu2JQoIO0BugPCwSsDSgpZSHotpezqqc6Kx3OqVD6Cw2jjiKs2s7rivLlTvtjj5fWBqkel3KM1KUoljfE26Zy/K7fAJKtiSKkpOSLHXY0klU1qu2u7UnYr0+pRMifx5yEkNZn9HTE0YPz5T1qi2hlE6UTpKvGJGv/pAfdpaSDLF6todNfK2/PdDrYVWPgZSDoHLLiNJwtjRkpZTnH8jfPsnf0guhU5MtjjsiShi0nQr1GKZzvK8p/6p13dKzVI4ZSvNASXCo9c3yY7nDV75kFJaDDFI68ZB00Bv0MEkmGAxy7Eyay86FfBUAtMc/5aoD7TDJtqVtt/1KbK0jrcTMUUPYfr7UGpvWUyqfp3WDx76xpCZSJeUyWfsTdbIuGUlJJs161M8YUJMtms8TKZ18nXJftU7+9PR6PTwN8qXaSo5Tr9erD0bVqftPSslhqWvyxyon3OQHQSNlXZrvuJThupRoq+2flN+V8to2GBw2/NVEiFL/dTbnNm3D265zaflb+Vi0jVG5AWd1wLD59/GZhXK7mqX+SvZfutjGwO58Dqj7Xco4OZ7W9dXZ88GcsT0HWx+PUpYhQUgpV5wpMbZujzu7fN4yh8P2icPpJDkJoW24plzRkdLwTtlWobS8JEmTcLUmSdC02azD4yyBphluOccI9YsX2+VLKbM7aO9Y/67eVmNTTOvPzDqeyj36tstJ+VveftsYQ6kjTqj7CuzrgWY9kB+ULv9t/zBX27KlzGsbqJQvfaznS+9DEp1ui92xOG2250xJSvnyQijNCEmnNN6hlyzJfUgw6CxtOkm5bUM+d+sNOhgMluOuToIekubzFEJ+iL4wy18iCGGy+oLUcTy0fRLr45VNm9bhNlpuGdMpfROdpZ2qjVJK+0t5WlPKF0l6fUryS5lO/h7P0fFSKZMcD7MZanys+xzydGareiKvU3nmifZHXkfKOUSOrQ4pV1norNYNs1LPtGWTr7TQ1llHdSA1ynSaY1Iq8zkbryQ/5Ntr5Jgrn691f0tpj0uS/f7peH0pfQOrUmtiIYTjfdbZdthup3qutRwTJUgO4+e0rJZDk9mlvTR1tvu+o+OSo76dI6m1oW2XbTuP9d8p01idUNJkvx2ZweUkiCnZpB7oAO1OqLB7WrHQJhoAqztnLR9yShdOkk/cyvJSWhQQZrPlAYgpgbP+NsJRZ972oOrsDSK2v9t3eKwL45gQUC85TuvkZ5sEcfTAWUfT2t5GYvuME2157E+itp+D9kFt9tsNy8HedntSZ/m21HKwgmS5JUKfUk5lOdYdXmE5aFvXGc1JVwjL0TllOXq9Hjqzya7BpRx4tWVKeQ6McuhLbSdPrdOndtLU7qYyzKbxAthcrq+dP+V3q2+SRUqW3nr/0qtX1sg/BvXZGgbNZZnWMU5KSsajR4+QqNdZTqq2J0xtmZVyK8kqpVGrs7whw8NggE6vh0Gvg05vdeCyPphZNk2n18HT09Ny76+HmgSxfhK6dXmt9wFn99Da7gtqZ8Qs5LcICCeNbwe3XzkmqVcZKfuJXq+DpEspt7Bsr+3xQmloKOVVjwM2/R4ByM8NMdsfE5QTOJQHvuqtvt21XMpuMMuHa6WMkhDQ6a2ugDMLCF1KA8Q6fppyCOtvmu2PbrbH9rQa80r8XCPXuZSOuHLcUxqN8voNOp16O52SHLUtk3x1TFJKnJRnsyhvSLEqu/JUfSXWcqMw5RYBuYOok2/pMnpYkoFWlz/rrB5Aabc92uO3bfLCcadbSnnDj1xoOXlnKZzcudJ2+K2PYw4/AWXbhG2HVth9pkoMUz4C5RtDpbPqeDszp9lhX+6U8ts3suSOpeMkiHXdTOmIyrGz/VJQkiT1/GB71WlqjStHt84qMVXn0Wnf0CKE0FwJ4kxq2+Nw31UqL6z3S5v2C+S6rZ4LJTlJo1yhZNuBUM+NDtogzsrj6HchBCClXIXikGRTtyx/K+dQ9QGwluc6aeIun8g05wllG5KTkzXbZt02Us6tjsqeFkfHfLt/kXIadOVzTPnb/lYs+bO0egaGw7qX9rHWUbvQfl12M6l11rZtaV03hFU/QBlmffWvcqWEh04HnWRp60mAh4fB0paQnwum3p5llahIOW5qr15KqQOO22O2sbY9ptjWbUcxkK821UOnE1BuL7Q71+ucz69dp/XnlHK+TWkratcrrNrA2vOu9flaQL7SLaV+Wz8QVlmX0saSr85WvmzRIeXpXlb7t4MjuoD9/uJKnbNbjtN67/h4a9s2Vto51m1C7Tks5TOX20+aaumA0iYCnLdX5HN8aocIZ5+9ozpnfYxNre45WVGq5XBUBlfK7PTY/X/i/nVJkhxXEwQ/kKpm5h6RkVmZdelzumdmZ2d/rez7v8PKyr5BS8v09ulTt6zMjIu7m5kqif0BgASpVDOLrKozDPEwM73wCgIfQBB8qEq3+eij+fVjeB8//jr+/Uj6VdthoMC8Vcytcg7EeUGgz3kYWRTKzhBgaSRQLJfKHG5bnuy+BRPbAFLmUrl+NYXZ9rHKM82ne93KGX36esAmAtmqW0CG7p8zQXJDCTSA0x9/VQag1E8qJkqXRNiWYJJcrJKNo9UGIKtbIIvnRnZKW2/MGE38raLvDA9GIoWBk/Y9l2Cb/jjQyjgAsAexhBhiyXt15RnY7ZldUU7K/vgimWp7XH/0TNsrIyhGEGlQEW7a7+C6Ggz/XMlQfymtZUdvfnwpBD1SrnrKHI9HHI8HzIe5GA0rsJXMxUgSMc+TCofWCGL1lSGp53YKjdVVQ9siME1zozAV8O3aZn09TbJ6Pus2mFYJMOu4vmF9Heo6hd0HUFedXP28YOlTz1sarnGTeW55iB1Z2wgv0pVY8pR1X5ErpVAAGi+XatTzfei3jPj4FJlDo7AEyCocg4GIjbHTA3Qq36sRWd6tvWf933oZVfrqXYVb0FpXxipoc22X6aF7m7VE52ljYCeCyhyIMTijX1RXaSCnjPP5LDxDOr5cX5MoRBJMVzwNvAtzlmA5IBDipAaOUE8umudJ2195bt8WA0qeF5pbtKfRjbxx/VaZtQ2Oo6ndd+z2eJsbuMrids5XY2Me7HcemT5G8ngXqnwFwGvkA6BHoc+FzsdZOGNHZtTtZbk8rz1a/vNwv5+TJl9G9erlb7/lpJZW2+637wFolEnLZyTXPYbxc2v0aQrojtpSn5OSixyTOR5gi1SNd+6gHreUl/77IxisVwL6e17JSSlJ4MNQ44twqWf16KpeLsCytDEKcqjtEyzRHm1cv5dp52sOk4v2mPEtISsqNNbgrEE/3uq70e97zz+SPF+4Z3wjgsSTULkmRtj9+pRxaxQ++S+GWPBJmCZMMWCOchyubHsRvmoxwSpvtTwVRfS80425YDfsB94d1RX7hpD+2VuGkh6H3yqv8qn9Qxz8NT83vC7kZe5eW02nqcaQXPrW44seP+3V55G0x8tGed6i4aF86fqW4ftRJl0gasa0tulrjDW+zoBDjrv13sOcfd1HtLanF+2VVZ77ChbQj0tfzqisR/jTrbreooVb19q05cKjuj+W19enh40gVoHSaLfvWYhDFRZlbLkIDmqaF1Sw7TEkP2FHnhVeuenv9Uzfg4vhPj9WIivXSaONQ5hyDxZZlNXEg7zuDZjt4ea6FcT27kZI4Khof2r+TimVFQ4rIwJFqIAMEXWEqRKF2RSjiDUkARNoFUUv2EyhlqCSawHLFgTQlB3rq35MhoLGgD3LXmaAsWZjWOVE140Q2I67DhhLzBAKcrQomJEJmAIkYCYDgKx+jgBahBgZcs5gInFsVBDAzJvz0UdjbMxQgtOl0kf9FjAoIPX91iu2UNf1lJKMTaixNEypjAX4idJ2mCcc5ql4iAgYlNUL7WrkTCA6YCqeIlVBNU2paV7HDG37ynaeeoBnCld73U47MbqBqtyillht2hT6vq41vZnYAGtvldh7vlMq6jUqdGhj08cEImeYaJVFX9NxJUaKwB4YYQaSm9chEEIEaK30NFIyelpnRe/ErHzXiZvCR9vTY6z9ffA3YKs8+ToEqsYzyZerogAbJ1a6bMFEUK8PC4xnIMeCKh8PBxyPM6a5Gv3WdZUVtFzLNL5lc8niwqycNahvFL6eMnLKYmSZYvESacddusjMdYUeuXLQvr+F3r0MGssEr7D5ZwvLZC7j1I/tEAQwNtdHRhL73Hp5SAurZP2PTWJU1C0xnIdTyMAga1t7z8uSVzPH9F03h+0Z+2TlTVT+AexWm3uPqpp/L3O7+qL2dylrBPJ35u3oPrMI0357X8s+hGKLwz6bAN73LujLGvVT09Y7z5uB3/rJv26mKS/vBU+KQSsAup3CBUAPUaFOLryp9GMGiAmcRIaCgEARTAG58Yqrhvv6B/dpMtrXl2QbdnmUGqOyZWdYdDR2m75Bu8q8nc+VtsaAv6e9sWItn5Wf+7rJHJJ8oj5YF1NqvVoDHiAxkar88fLLYr5JeSRGkCmWk+QOh4MegytbZny+2err8IHrIQhWHPOlW0qp/76nb4ziEPZ9ae+P+O+eQaTXWW4p0KN6+Xd9HWsW3bzSU1F6NWREi6M6/9pU2Ivk3JTb0s9YFrbX674A91Tle/q8Bf8u/K1JN3SRHdlmOPZeutdfvYy4JYPbeu3rwZXP3S7b63TN9R26f2TejNr16Du30vb57dHUfXDjyrv/8enXe4I4IiudpLf3hKS+3Dwzen7vT4oWZce7OvdKgSiodZ/oyABSQGjLSdpudr+trMwZ6wMW3j7ZOrcXSEQCzA8HOW0jzrNEy3YAwfbwWjso1z3wHgy0/bjdetIrYZZHL8xKxPjCTCoj9s97oGCfu0YQ148WvNFie/RtsHf9qmrfvr7+9hdCBKv7nG//xpgmmyqRrPa+b7Suvh3e1dP3QTFeFJCAUp5tV7A2+8RcI/pbLArvQm+r8EGPGDO3fQswK4BiGm4RKG1WYMNTq8gKaHNzaZCYBajGwUpSA786gFCuozUMiLJBQgu28tTlM6zH8Gr/DAlgNjS6Qy8+r5HiIdfls1f+K4C2+nZ3HcAdlV3f24KEEV8ageRC56jeGiMlpi+7yd90FF8PMKgDDq0BCM29ntfavUhhY8jyZWeqp4B4UBfs3cbwVw0xc5wwH+ayimjv2n1Ael08qSJynpFy3T4BEA4EUHBR9K1PAQTn8ULk+7tpCfqx3fKi2hcjmdePicyJMYWXeQUaPtMrWmZgGvFHb5gFxOgMJtgOk769lb+Plau9OfVocmyhpKRHrYYAxyc6ZYVlDKRuYnzem8dWV5NdNfX91lasXOPK33tgC9j+/Z6+RYkuvFgNAZbPXrrVBnu3qYflPZiHvu3bz9qOXTw0+N4bPId0zAP+WOIy9Ca3wsGauc4sHiDmHeqP+7QFAAotlmGWLW9Rt2yafaDIZzVxmWfptp9MATLDROW1fZvs6wjbjvpglwfv9Lm76uo3Gt9GArvf7UKV4RFvzB4F0t1imnoSS6Wxym8tL99mIlLvuanUYZoi5ljfmaaIOEXBV1y3bHC7sbgslkiH12d7Gv6a1MtK36978UV6+dzoHy6fPr++fj2+Ht3by8eX12M8Zu0tqhhd8AEKM2843x08Mqr33cR9KbaQ5L9z+e6qVuadf9d/1ntmmK6e1UUnQ93Cyd20kPAJ9l0vB2CvVRvZ6b8/0hd38hvd82N7j0fsYYX6HEo/+bTnDbYnb+7NrUfm3j2Dy6321vdH+PzXII776as8QYrQirEG8IMjSva/aiqDDiAZI9MXGJ0Qcd8zc9nCsZlFlgmJYmVu0pKFBdaS97Iy1FDCWFMz5bgTLJaH5c9NnXIDoG4xsKYPVDD1gVhnFzchTOLdUYNDtgInpYS8LoXYc85FYe4Fcg9eejc7P54jYZ67fYd7sRrk04QlOQZYwbWHQSEETESlH1NKTaRt/1yfNkaRDjAQqmV/BEpGK9j+/VK2XXPbEHrvgOz6n5W+jPbNRTE6JjdSHO1azsHlYdtenHupGkHmOWIqymAfP4PVPbzmPwqCV3/X8goYNIBa5uu2nzKKlLvBlDxwqcqFbUPw7bdxH4137dG+7q4fXX/upT6PPQHggXkPfob5bu7ZO488u73fKmjU8EbhuwEhMSgHENoV6r3Uz/f+nUDq6+DAsI/X0xtC+m0ydj3U5g/HIoBFyZFl4moURT1GuR7hiWbORXXvDZCtMpkIFGWe2LyJALickpI3a4qmFHglDB44Qt5LbMp1VTGIxBhxT+ncgt2WHoZKY8mSt3SjvHMLgPfBirWx5MqtMgPoWEWTldt21HeHRYxTzX4EATRDOAOpvsbYbH8cGduqXO+PC22PmfTAcgTGNn1n/WUynbfKok9inLcgijXfTIAdz0hEhXdt5BCAgX2p6WzDPqUeWbBIMV6HbcyZvp29rK5K7mhrT5u8EaCnvVEagdy+Xs13HwnOAbFK6+pNnNs2Bg1gbJjQFKM4EUAR4v1Jmlc9+cQMIGVxouPvmfcVxlG7N7z6Rt/4/nwsWXv3n2hluNG6lVWV4mmaMMWIqFsGcxZjo20vkm2GE6IGBE05YV3Wup2QLEaR4JF5nsWQoXmxLmbFQDgcJUaZ1S+ovIpBAnxTADinsqJd508NeGlmYeasHtq174rMSbdk3rjvRvOh/743X0rOA/z2SOrn0F49RnUZzaNAJH1Ixrqq8YvKxVJL5bnsdCUUJvzrVUoq8rGU07SjjoEZ02sfwP2u3rf32l0XBLrJqli8NkzL1f/tvZLXA8NGNYP9Zx4cf5/ujf+t9+4lOx+gT142fm2ev6oeO7pVP+du4vU78ukfnR42gvSr4QBkIpkwADYE1gsIAwX9vVt/wKjzqQhSIpKzxEu8wbGbrDQCqphSl1etL4AOKKBtI1PbjgKu7wABroNvVnWJ7XAsK/t6htgugAshgBpr+mg1qKLQHijtMfIerJiyA67eKD0YGgOwioJLn3RMkEhBSt5OjL5efUyCvj+sT0fv9nX0NDXaZzkUOJ3hIxgocP3UvsfNu7Hr1z7Vdo4BfFEC9WjOELdxeGq2pEY+NtMGUGzlpddUuc61qtQqJgFNpm1/wwuj+8nP32Qr0VQ9GcZ92OUxyLPnDySTdLfsvXzatN9m/+htReBxYNT/HvJKtPQtcxEKZPf3f47oaG/eel5u8TnGCLyn5a8UiCzsLURxbbcTWeDpPMYyxzxdiMIjq/8xEKINN2mMBHXtFnmkdR2BPLLZUGMRSREEQGJMBGIXM6OXFffbuwdsecCrms7h9nm9Osy/KNpuTjox07x5a27118lkFBRYDupwM5VxwQar7iVZqV+QUgBzdak3utVsm+0vvu4jGedlQCnH8X7fh8YnS0BOljaMyqp5khtPaUU2rc0UOiI5Rl1eUhrWn7Q/Hr2cYojh2cdxibnWqaGDG/JmpGD5vuu3TPTP3MJmI4xxj69vlE5styUUAy0YARlIyvtY+pqBcmSobBW1tkRYENjMGSlJxDAb041nqA6d3fP91fffiKZGqV+YGvGG/fHaYpO9cbHfhk/I4fS5eGFMEh+JnVGIGafjAcfjUfAzAymtWNe1NUpC8jRvGzF0MHKyMJsyTvNhLm2W/s0wvkYMcMrViNfN701/9rDdyaSAgKpYD7veyWvHFzfvbMd3X+mi8il1r9cq9ODm2T6rlgaoue/JgsjTmH9OjR1d3oUGCq9pr9drXodhkG4NFV7W1n3T+s1lk8vK6gf8p0/9M6O5kK0s5Y+GY2vvW3OsLbXt20WPXiaUmuy0sXv/zjP/SAV9j4c8nkEdi0fzvlXmI3rio++OaOPvbu8/OH21EcSU2ALQm8lcmf6eIaN/Zu9EldG7lgjqHukm1CgfoB3AfsXIbzO0d7b7ZVsGxnYNdh0+yPdGuTNGb06egB7FeDgUA0hxv9dyQsfQoO+K4hDKhO6FupQPMLJ4Wui/1muHHRPpLJP6bIgEhFCUVV0ogGKPh5idfTd1yZdlXcYqVMSSuwULI+Dge8R3kFdCR6Cj0kYV0BLhvwoxG692DL0QqEFis5PVphT2gPLWoYj2nv35rQD9vRpvobZFdvTUFcnApgwpQHIu21WxlnoymyHDixjpUy/ga7+6pGNatHT9LsJw28byvvW/ArUeIO4qBXv5dXW6lUo17wiEnv+097fvbvOrYKLCdL3Ct8vflgdjMroSLI7DEhBYIsRnN069oLoF1ovBwSk+5TkrFm0+LV3c6e8OoPd1DLFG2bex99u7gm6tsXqTEhyp7aIFNdY+r3kLKOKOB1TjTU9r2mLCgBb3gOHjQt3fvqU4bRRLV9VeIW0r48afGbjJN8dtkK5zfYXxKLfGl+0zt3j2ntTgBHBylBdUVrJt1WoN11558t5uhTqpzj+T0+ZRUWOLbFfl6t/YmOXb5m3oIj50JZtt3zo27/s+2vTBHvZBF8iWgZRrjIRGVrG02QzNAUA5McnoqCgwdW55nCL9vO/9sN1SYXVovUW3bWT0AdbblFGpSQEH9LQyQAyfLh9WHFK3DxOIppK3HI8OHOaElCbFf7Vv7eSJrF42ZgSp7fq6OdTzuf59o10fQNd4qles5Z2wqcMtzGUYofdY9VtYyvGvIGAS3HBUIwgAMGekLNsJbduibYsmaMymacI0TwDLGBQPXqpzlsESjD+tSKt4fuR1Ffqy7b0ahNqnpq8VTBAshmAZ/I5/bvtkq4TZ9fHvvfdq/lUFHxlADDNtacXzFuOLtaymuS5P/0zbPvd+sXRUEcFcZZ+ivE09fKHMGchh0w+jtBE37p3s+nO8/ObmRVfPdjCKiawrDBVvas7kc2brg9peXz9TfZpaUOWDTeW2pbs2355/t66N+Mg/1BBAuBtA9dHyvDzx1/pn7uWxV+ZIz997/z/KcPL4Ebk5Aa6D5Hg/JftdhXPb2BZsVME6em4/ZeTcHpN2b79jXzf9MVRORnUoQox1r6kHUQ3jQlFqGxdv1BVOO+1gnudNHYWY2336Pvmj+XohWYwiYgZBRULY1HW3zXJOMYgDYlO01ukGOBgpclwUE/3OkEBmdlmFXR70eU8f1k4TpByoBFrUrO5OFgFt9heaCWvj1m/9scyZAQ3fJ0B5t4yx4Omfqe7/LXDpUz3HhzuaVBDaKMNbQFZWP1Hg10OMbY9GtKOdcEIdT2Azp5i5rOrt5W9xUawupaju2foOlzbdTVyVoVH5lSds94L7sm4XYXDEQIJfXTa6b/OwsR/zRlevrH8sYDPEgJADiGp8IM+P9vaA+3Lrql59rhwPzNZmgx26il0U8p32d3zQ82e7GUJAnFo67z2uYjcvy9F2QGN03/JBByQHPLK+h9qYZuwqDyj54DF+53+P5MeewL9FV94O0q6IUVE4+rxyd5pXzesx43JDS0oH/9RkpKE8Rf6UU2nckjb2Usvb6rUObCvNSn/khgeWp9xvVjzTGHd9hr7KA63AyuvH+VY/93mO5N1m64y4PjXAv+Adkq1jtoVM5lP1erJy69yTCS5HgQsfWteEnHTxpDGL1bqlgTyuGKZ2ICv9iBdPKnzR6tTOcQLveGBJnbXPklv4gXjJjIwOgBg9DGuBCTw6PavIsTo3/Hj0Y+Q/95SBEX6oMb9y8/7W6Frz7+vZJ3u3Bk6vR4qbwl77XI0PylunaZIT5qZJsQ1BthNuF9ZsOWeKE+IUC6+X4PzSd2yfADgwYghItJbtRxSoGEH22tviC9fOYS9v+2TM61uF7J7C2t5vMbjRaX1ky4v2kuddfV1snozz8phC+VqnsHdU2in4Ll/fMmaA8y4N38KHw3735Vm7dss3Y4Y2jLOa2rpqF+NIy4k2dXX1G41l38TeADLsg/6dBtO3z/l2GbZontc56MeZB/L770mjUbyNLfbHvdFH78yXW/f25sQjxt0R/f2zDCEPG0Guy9IqKWTYZSw8RgYNf23Pc2Ov03wSWtoaT0agswdOTTnKbIfK+07dGaxuW/sC0FwQ53mux4xSjS1RA19ujTQjIVF/b8H1CAB4oDPKf9gXaMfHgCW37G333Xv5Ne3hVhiorrGhGS+M/fWiPAaUc+QtAjyhnWB9ew1XjLZ3FQZQrDbcgFs4Zc6e8it+G+Ew6C8rp67QtHQwGk8L6MicCqjw8WI84/Xv3ppLZeW8qfuOWN0Zx71kq4z+fb8f29ejr2db/7q3vj5bS9lrY1/vPKj/WFEdXx9T/qhsA/Mya1rDAjfHZhrtjXiYV6Ya5YhNOUGh094A0s912dameehKaNliJQ9tBZ8pg2UcqRy+I+XWHqnAdZ/eAWhQQw2Q18UA8mWbIt8aA6uhO6UEClG3pXnFq9anAsbaD+2VUap1qfXy9HY73ZIbfbon0Ms8Rte3XL+bkcLyGsndW6kH5HLR6R+kZpZBVv94QNLJ2Bu4wehgUKuam80XMFI23pNKW+r42kqofxcwZZxs1VAFlG0jJIhhutAtV1D7NePQ88C990w+VsNUVSJs7hs/maaofxPMmNAvFlXZJ6Ddz8dICTnuG+z0nJWbOAJoeZr0P5XYHERxM+cLcQPofSiLQg9149d31pSwltPZ+q0VNn8Z8zSBsx6LfAPA743Z19C6H4tR3rf4dV/ePWWjGkHQnHDV511xjcZksyPH50n4KANiAKmKmve0tvGw/D1/tC/Ghe0zxFhOpjPczDSSuYOFz06W7MnuPWOK76Me/9cxcIYF3hoo5P0qz/v0a3jgHs7x10btaJ9x9zvZB1avFFC5TEVwmEgvkntYlq/DqN9u1Xfv3ZJHzQzFcNLhLxXr1bjAUKOP8Idbc2JUlz7de//es5srvDXAjMtovb16ufN3J0fLDz1+o39GetPeO/f6c4+G/plGja9Nj3uC6D5BbwAZpX2QMgb2dt2nkdLedrYxbt5E/e4NC01Mhz7GhK6uemHd19PXz5QbU9irwLO/6n5onh5F2YUos17x7pnibWABGCPohcheHz4i1PfBJRcQ7oVTDyRv13f0AMrYuRYp+GxB72gPb7ulKaDxjFHOaYK6bEsJ9aQZOY0lbOjDuwNzcsFvGSWQmB9/E/kmhiqQa5tqdbVkZdlK/C1mYNctSK2BDLtXxkuPHh7FUOlpYFTPQY03edT3t/Xb1nt/LIEa4JaIKoAagv9q/9/WpanIoA7qibGjWPTPerAznlOP0bsNta1Ae8YfBnzN8vB/5u7t+6vMAWeW9DxtA3iNroQ4N/xN8i2zr9QzhCDxRsBIBijM8GLgtIwZms++H0zYUyCEMBXlzO8d93W2tlrbbVXT91NOCQwJjjoyIvv23QOVI1rqaX3EK/fzGSUz1tx4fvA6Dzr1Fq8ofXCjKrU/6oOmEECDSspWEgvEuVOvUXKXO2j+QKrjZHSxB7i39ZHCmVMZL8tDjCA1ALdvg9DNFujfbGNX4xBCOc7VjCS2kn6P59xKY5kNmYCs21TVwcNOVgoBiFFO6TDZYv0g92PpB49ViNp5Mk2yUOD7qzcM3qk9/MlCnueU05zIPFR8+7Kri/MELPwzw5S8MmZEQB4rupamaULOGesqp9EZ5rN2iWHp9jh5ZXpPwfP8dxRDzZc3ymMPC46UiP4vEJfg0v29UT1LnJCB8tbzBhRD+7att/qr/21busyAtpH1naztceYYi7R91qd7Y+avj8dkS+82nr28HeX9SN3uvePbXTH9eDFJ+k03mje8ftyOUf08rY7GaI8e+/ujfG4aVZiLGbQp2wCGtm9U5732jMZ/r98fN45QNdD4qzt0MMzh4bJq/R7Jo2/Hng54iz+M8r5V377fv0bmfU0/fN0YfV163BPkepUvqox5KhgBep/2gMatDusHqp9sXAJr1glWDR6h+V1O2QjmGiqrol45sEBP/ui9WhdjiFQmaxFAJe+6J3NyW2Em23vPtV23GI20sWc+za+7hCb19O8Loi2XTcC4+0MAaPiAu/eaZ4ta3tX5hoeP0lDJB1u66H97N9YeVJQ/FZ4jIGBjlXNo+tAbQexaKvt263gzM7a93l/rn+jLr55A0zRttiOMkimFArhR8mr6ChofgjMC9gPc1bEYFtW2ZBd0bAH+3jN9PWzcM3NjEGDIWlPm1l29uHtrpZvPhsD7L4TMsmUvJUYfaG7c3v12W57b50cr5Qymur2rGX+0bS7tcTRe6b8KsGLIgdFiBnQfdsDWk6kp11XY9vv7MdRfpV4hSDwDziieJn1qDR8tv5I6QOsnD8ke8M7Q2IHJZs6jKm82h4j67TU2Rtv98xZxyNfJ8yobu34st4nd/XHa5XGl7Fvv1/b2e3q5/W+QCECAuBKjKBcWRLAHz7V+I89BvVcAJ3Xgc799N5r12G0aK9a1LKPn+oLQilRv5Clocyl5OufxtkqvmI6eHQFCu5ZZj+4t5ecqM1VojqQGOqPYI0nV0c31EALmqa7uW5B1i5WyLIv1KIiAeY4gimVO+j7zdevjBVV+Uib+MDH3Hh5o3ydZvGgz8QDXFO/6vhjFqpu/z9fLbQAgrnKEASRU2R8oSPyKrifJ/qh+RiLZIqTGNPP0Mak0whdASx/j+WKrwNUjoU9EdYtLyavUlSXIKVlcJTkg3tfH4wyph8VB8zRfG996FLa802OY/dRj2Jan1jYWIXujf0w2cpuHv8djRVbKvq/gGkapNNbS2x6mre3bbv8flX2LDvaxf5vn9nnP55RmG8VX2lPbbfJ+n+fIsyWHTflFn+hlgr3kZStcLJCuvL6f9saml+devm3uDRq113f3yu3zG17z7btR5t+bej1wm3/t8+279o7//esMCF9rqPiPTv8Iw8hXxATJG+PHqCJ7E/6W1amACgdSRtsEmjJinTCFWANAgRBjFSCRCDHEIvC1VCD6aNnAqrEexOU8I0R5zweYrC791a0wxqjHeWngQv2LFBD1ex+AdWSplL4wBjYeA59ugW8JJjXG0HaUUi7Mzv4GxpVMDd9j7oEdbfKXInnDQLxA8ycP9EpnD0T93xbsbsumTYSgKuQEIMRNmX25KExDFFoQ0O0egYGYoI3TSDGuHhW09sDEwEnvxeTrMuoTMWRtV4KslTVyAiSYJgBsVO/91IOVUf+gM3Ds1XszVl3ptajqGslcjSDm92J00wjlUla/Xab2hdBPfU8juWwUiQaKezrH9vuorQY4fMu89wm79uWusArsZHVH3qsnVJjZowQ59n1KDAQZXe4CB2wNIULToiCLYchi2xSGQAw5YlLBoaPfUdoFFATd+tJ6vRmfW9e1yWOkKGk2hW4CEaaprq6KkiO0kh2YZQdRyudm/Gwim0F1M7EdHWzv/T1pA3wdL2V/sVyraGZIh04gG38TYzBUGR8Bxmoo3qPtEdjcbdPg2h48Gx0Ny2bMMW+Krk323Y5L9bJh8x02l1y/dvKktlGD7yrd27ONx1W2cZAWyekbVAyPzIy0clk4AVBOPeplaqska+PIKx+Dvh3hJp2cBI2TEQOmecbT6dRsv7X6HQ4zvCK7p6xXrzXp8z5WkfSDzRn5pRXaKL3NO45+iicIPK5o0ETpb+4oKGfAh5u1MRptTfZBlVm3yYYgPMTaSrA+DGBOiAGgrBgyC68OYHBAiU0jIofAtOVte21ufxstYJhszhHq+Jiho9KRvq8dKMcGj70/iEiOpuXgGpDBSEiZECmCKZT52ivCe/Wvz1h70dyv8qgaMsi9LjCC3cgP+gKuyq5Koz7udYPHFKOKs3tjlH993xix5aF92X0d7yrY3fO9rmB1qwaNPSPaqAxqxonKaVb27L6OxswI5VGHCgpWdn3GXoGXAVSpI1gD7p694HAbIItXHj8bNzAjJDb9Iu1rx833EXX9dpt+Hro2oF3XHX93aufcuKz9d7fXtmNr+dZ+GtX+kfk06p+/Jz1qTCFgE2fv16SHjSC5cLDtvT2m0CffSb2BwzO36qq57/Jsz3o3e69kEtUjTcPgfVNMvPXcK6UxRpxOp8at1I4Py5DghCW4YGandGrbBn1zr78soN3XElOvdO4/6D6pfW/DzIESbV74FxcwLgypgm33YvsJYWSyDze727z53PvrDSDtO92E2VXcbCLvu7X2tOSfKTSKdnxI0Z8JbF+ygJneI6mu7IyO4tu1irM8H7s2lPdN6LCqdgzYnjVTaDc94pSNUerpofb79v4o75FAL/0yaOfoO7EpbTvHFqpgH9WEgbKNyQczrrSAImi9eXREm+M2jgSTBiV2tOtpqH+2p7lK4y09DmnfWk2mUFSh1tSNIai+yd+XiWIkKaCwGFdQnhklaVK/f19Of/F81cZ/z9upT0FXOsWEzChnaxEVHulIHNqCcn2PrD2wYC6dM2rZQ/x0j24fpZkGSDktoQFA3AdK1kYPyqhGEAcsN/NwH2D/RySRub7SvWwEWGPC2BhVmm0Dk5c5kvOQB0h+23lc8gNAxHpEqObp6glHX0DlozXPjJxRjCVEpKfcbMvv4yGZgkrUxrPw82ae5+IxV/mBgPkQCNMs225Ph7mcMif5qjcHEWS1aKTkkddjN8krC3Uu5MbUbwadht0MZDrQbVM2nsO1d2sevJFXZSs20C4oIYMJm5OuuMjWjMw1oC4FIE5iPmVQEx/MxqfK6ATm0ATsFv5FdRuUG88eV/yaOeWxRjGWgavhA4CdwjN+vle01dhjyiBE3hmWLVjbYkS5udjWfyxh99s4nnP6RTHTfR55i4/eMnzcwtw+jdraGkRuewz8PXxzr2xfTl+Gr5vxxkd0rirrUGiq5QeGs/YZAoPKoQ3s68rjkTRsLD9q+VXmFi6CAlT1KkMwLxVMzYV2tSbDMe5leSneKSrtNXvu67YmbcB+fatFFF9JHvuGir8/7auU48VkX59H6Xw8JuNnvjYN5+8/yOT0sBHk0TSauCZc+t+eaXvh2SuNo2jb9qwA43p0XvEACXGj3PRCwgstv4/TtiucTqfiVmp/Sd0j7Z0QQvEE8S60vTupn2hj5jZW9hqFeyfdU9j6Zw1DbxWsTgANnmmKcEYH/34jyJBla8KOIWPUhl5B7P/sBAvfBq3OhmU8alDaE5I2bn2gs23+AYzO3RqxofPRdoARXfYKWBOEj0wQMMryjdFWEUw9MgVAY4PXvT7YjpfRwEj41hHoBcs+ILHr3P5WZUiMZyNg5IXmtg3MjEyyvS3lNo++XhHtfH10Po3qZXFImhMpiFCPkDS6rR5Ofb0BWfncmwuADbFBhvp/Wz9tY27dfT3PLbCCq6u35F+Bib7U9Jmtosrqruz5luzzRrHt+/VeSimBQwDUKB1ASElOgrI2C//dz7cfm/63eL2g9Flb3e3zj6a994xXNfcKKd9SOFq+WPmee4SMI4hCT9noYQys9w0kj8uRf1hy/eLpszcOFo9A9gZNNHNst4jSX05CMGusJXaXuBg+zBAH3vZjBsvCh+WtGdhq6QjTeF5fMxKF38sH+5umCYfDAXPjsZr1pBXBKPNBtlROeoxbUeRBoFjHGW4riXY5SGNsjIbaPMIqdxVvCr/FaITL+vZ6/tbgIe763I+V/mdXLRjyuq4gVL9GGSsGxVDK0xcGdMylDoYvCZW3ZDcGvYHQe6J6I6KNSTMNH8Qao7SVlZ081NRgXIdNR5gzcwZxi7tjiBscAmYgAFMcj2efvpY3jubBoGlfjUssed71SP39/R5f93O0z2NPMXxEYfR19N8feXekM+zVd8QP2zJGbSRQ8dQYVr4xfoywxKjOcrPNdlS/Wof9a/abXf39M/v51nv7hq9x349oiz2DGj2vbf5abrCHUR9952vffTTvr83vH4kfdvPSTv5HlPQVRpCGOje3yLnTGHYgFS6B5Jxx21riBY4xbyNS+zTXyZEBxD/nBVf50xWT7Bhcfy65z8uSdyX1eyr95CluomTR1etZ274d95ixb0MrtNtJ20/sW5N8VN4GZLCtjLernb5suYAi6O1+zYuaawYOes4g99UBU0/J6JmS7wtfxmgbzMYokvsV25aJ3kujvunTnjCx5+u2KInZUeuMorCM2tj3vafj3iMFMHoKJVN2wUVNuQ6A7BfujVmo43sLaIxoqf/NqkwUgbQRwva3BbdllGgLvuvvqqQUkTOsDwMIu9E+GJXW+z739RWX+IDYzcVRP2zr0H5nmBHEb+Ni5RG5aXOtjwtQ6vrA+rCn+/LutrXdXJBkqydcurXSWvHMMGYNDHmOL6esUIdYtgPmnLEsi3rSqcs5xnTepz0B22xdGBjcpFzGjm1yw0u2843LnDKlo9b38VW+0XM9bfTzwdNLBVSjfOCULVc31N/2HFezh0lhmPLqk9Prd+t8CxT+41OrLI/q1GyLxP6cHtKSV/RJyrP/WedqqxRQgTqjQNP2naHb7Xy1B4YBf7SvxxSSsuaTS8BgixV1OIiXaX2aMc1Q9u/wE6DGEScPYsBEPiZEP5bKD0jaO/KA9MnLrpxTMRK18r+d455vEWEzvuEODcLJwjIWQuhVVqINoMtFZuzPyyIvgzN4aBubINOFYQpPTzk5DONmWM/fBnihbi26P6dE2o/5r8e7hlX7gL9A5XcBNWadvTPHbWDqwocaGXQ/VT5W51RtRe0/FP5Vn75VzB792dstNmqxZH9vlN8o371re3j7a8ro6zXCM7d0nFG7bvHn+2WM5DBw1xPkRj1GfdHgWmc4GOk+o37ewyDs3FZvjc+wHWxb/trx2MMht8bN0qjtpScfnE/77fh6OXyPpkdlfw02e6S8vyc9jL3w9YamUfo6Iwi7743CXF3trHLyX2HpIri74Dz9ZDFA7dOIQHtGZO6MQF0NSU6QeCHg3+vz6f92e8LuFZknbJ3I8ik17Qa0KrQGZNr74xXIexN0pGTsXStgBj1waUWTL7Hmca8e1heuPMgxhQYG9+o/Uvj876FXiDOCSFmPpKpUVIba9s8olZU0Rt0bHkkC0k0z4hQByGpdSqsCUzT179s6or/+noyVnCxkAMCfZuNBUQGlgGxl8oKG8rBtmz69AQBGYzbq36JoeNoDuvGpq/rDVABtVe/ses2TizLj68xAs1rcgvg24CGT7q23+diVMWp7f80oiVk9xRpatdZvY8D0dHHrc1OH5sMp8bmdB2HAb5u+ugEA+kQEHA6zesjNBUxfLpfNiryfs6V8ywf1S7k76FtvpElmDEQ7niGgmQ8+WR36OSht4S7Qao13sOXZN3vFVZ9hCtJoHlsnGK3knJFyUrnRGtvleXL0ZSYN3ipewzqNQc0tILcHhB4FJb8mMdDEiWnucTU2iGFR4+N07fdg2yuzzIwS1EkuwLxuOFeSMxlAgQqysnwbzACAMyNxUgNILvnb6Rx+IaWPcWaBS+2enSZjmEXeD5gm+bO5azFL7FhocHC0LbijCaKZM/Keddj1LalvxSMAWWhVDLkUWmPPSCZI8GajWK8c+Ngv2zoBla/n3G4pBInXnnzXvAxvGe8byDMrq1W6AkLMpW5EUBd89dpzfsQMIOVUt0Nnxzsdf4EzIjRcw+PJIteU7szrhuuzvu89PY+20/q+28ru+icGuAlxmooy27/Ty76N7NH7wiOdjCMzHNYtPJs6GX+0zrnBUkZKcq1TDXNOdWDb2ABqxHJmzd0ybtXB2tBfq3XZ56WjZ0Y6x14evVzeM36MDAk97ulyxgaJkXk4VZmzqY+X1Y3PaFlm8TnKeChZlLBjZQpsF3htDvbt9hjR8Fm/3dO8Om/pSSNdq23/7bG8hZOGYzJ8ssv7Rh5dCbv1uv/u9pkxrh+/82sMGv8MvDDEJjzqma9PjxtBNqXtK0ij3yL0CYFQ9uCOCMwDXPt9K3lDh72XVMAXYePq0Z8w4PO4ZXXt69U+55+3CWrP9c+LW7f3Ghn1V98/vm/652999s/bX3ZMxBjNpquHFLY/+ZjrS3ZPtgbsM+5Snx1jwd6WGHbHiNZ67K9klGeI0W47orKad1MourrP86z7saeyggeo6+6akFMuQHRUd9++YVnUjw2QOIERwEGDBObsxosQUqqusijmtrLiJeClBtisHVOVsk6ObWe4Ptu849Rw+2KeEN2rrRB9mLd2QpuruN2LFQK0QUhHfGkzx4mGHgeFoIy2uw3rjBp0dUvHagQejPXenG3rR0rjWg1FIuUdBZ7yQ4627eeaBA68rRHt8Zn+uilyQvP19Il1XZt2mzfMpi8JegRrw0XK74Kfq5bQKHpWN1MiRXnm5uhdb0jI2h82DwFv5G6BFPt+BZz0qPUZ0odLhVTcmNmqFbtnMkvdUkqqCDP8KWNWGnNVINVhQa+1ht+R+bfU3k0fHcVm7v0zAMvXpiUn5ITaT/6TW8+PUSwlAEVZ3MrIamACSE4sgk4lpYva41SMJIV/QhRmu8b6fk7Z9D4xgEwRh1mM4TFKSHSJuyD1jTFgniccjjNiiMpHZSxt/kyTnCwnQYAhRgTzOmgYphk+tJZOUbZ0Kx5ReYYZ3pVlD1swczEAIAYJGN/lu+FfI/CgcoNKb6J9x82/Xvb3MbXkwVx4hsyR1kC2F1MJqCdWMUsfZyMUu0fumFsAlB2VuEC61g+yyJPh1R8CaYDpeiVQ324AnSLr//pAt94I4uPMjOZxQEBOQmeBCGDCulpUl4yWouRKMLrckamZsfHqrcYaP/faObhpc7GAd6DD4ZMxVmrppGAbuDnS8HHp3w203eF7HuvfonFr914efdozVtwav9G7j6Z948g2H5M/vBmPmjJ7XcUbHbgMY3MfXl7qZk2utDLqy77vR7QEza2lr60BxUjMsKbwHXLZ3e7PxwxKt9+59YzC6DtLLOPxulfG6Nl7hrlHsvsaI9A/Ko1kkvwo8ar/7vSwEeRrOn1XOCYGBy6rrrcE9Eg5eLRcZm6BNDOQU5nIgWgjVG8RyS0DTf2OweTdr3/dYzpmSiNFZO/ZXrkelduDgcwVbGiNmn4gWHBEn/e2Hdu22oSqALaBAV0/73l6jOq8/eva56DXLt1wLmfWl+dGkUO7+hJMWAQcDgccjnNzYoW1wYK4iTLYjt0IjJEgdHkO7aTu27DxYujyKUogRACZ+3UIBB8TxN7d5OMUTauPr4svr56IwEUACiAxw1fe5OUoZZfpbvlBfbOlC6vXNg9GO6R79OPve6A5mju1rC0tV3DRv2dtHefXfx+2kcvMhInyggGUYIz2es8L3yN7wMPK6sFJ87yBiSDeSGtKCCxu+L0RxOb8xtimGbHVpW9ziTpZt3NUrOJUKhLwnotBw9rPjcIg9ff0YTw8uN8jIys0kDOX/Ct4ug9ISvuNF5YRkHmSOBWvuMyeZqS+/hQUy48Z1fBhKkzRIaqhpNShaXF7bZQelVn/jMTMJUjjrTlqz+7VbYwjWu+QEKgcMw6WILuWX8OvdKtUDLqNl6o3hA+gac/HGHE8HouB0MqcpoAQCZyzxBmb5RjbIlOUtucSmFMCrk9B6a+4p4+2Hfb8OCvYzzAnDK9Ej9N4G0uPOUpg0g1uos27pV5MAIciN/u5Y/OhTyUv1Hggnj+XrSVdvRsjbDGU6XHfvMVJlpec7icr19Z2WUgwDxPtKTU6hejHQPrQ7uXN9lzjJS2OuKWQe6w5MvyMZNdesr7z8e7sHQqMaAFetX0MFszixsC/k1I9IKD3cjIv7mqH2LbJeNx+3Q0/juS9jvOAJ4wUVp8j8dAkN8T8t5TdnseM23g/9XX+e5TLvbz22lPv9+3HcOz8e33/7v32z/vyPI7zeKN+VkNJzXuAg305gRRVVKMLmWwsQMkwqNaHb2+b6/vyUZoY0d8o3/LumAX2b+zeuUWP/XN7tFqv79HJ/XkxGvd/ZNrDtHLv78//8SNyVQjeSv397eRgcG4trP6dW0YRy2d0v89jL9+ipKkkIqCcHNM/b4x3z0jiBaZNvH7it21/rC237t8DhveItICanJG4f14EUM8Y2+8OFQzyl++qrLGBD2pWhT0Y2fXyeLA92+9VWRi9z8yKPH3D7MuYrpjLbsgGOPkVHdub2yuEpU6uvVYP6+femnlvTu/1U2sEUUdnHwhusAn7Vj+P6L0dw63CbfUw8Orz6XodPa2NBGntkW095fu+W7Ut3HmQPPI2IqJm3/6oX/xYVmX6trFG/tD8HtVz+1uUJlmlpXoqV5+64hu60NtibDBJ2wqNAi5Uyfcwseln+49kX/zlesWaMqZpBrMYRMTL7/ZcNfAxFGesdXRK32jMJSMHvmE0z7DTp2K0WA5bfgNUZbEqcd4gisag4MFab7jZtMH4jvGggeHOjKP26fPeU3S87BTUJB6VtdxBX7qbRg8VFA7q/gCffTT9mnd6I4jPZ2Sc+9rUG0Fs7IPz1Cu8PshKf3/SXK/4eWNHjLGczuKV1jhFnGLo6uBOgpEBQowBsTynNyt2b5LRmKejauwOKNt9dBh6Y7T/Xnno7YUHAJjnWV4O3rjox2rL4fsxcI0oj47KBcTIaQFRR/KemRGcJ6Dx9YopjPb3T5gDLNYIwTxiSj1ZJTNpO4nldBm2E6tqXcx7K7ttSsbr2Iwpmo83ghDiTXrek0elL1xebP3avVu2hyutlKcJyDFAAnbr/JqmEni2pwtPb72XUav4uO1TqjOMjDD77dwaJpvxz+0Cy0jBG/WTL2dUj0eUPo8R9t4bpVt1vKdg7/XVqB17+sejxp57vPuW8WPUt7U+gOcVo/bLZ32u3muv+V8+f9U65LpbtBiWdacf+jaM+sA/87XJ9FBZ0rolz27n/WjZt2igXhvn7XXhPt2ixf+Q9A8q6vEjcgfC1NLtCeAnCtBPBst7ZGzw+fnPUbn9+55B7wVXJUeIphCU6cS6UscMULu1RmsD0iB1puaNyrnH4LySOGi4KjFtUKIeFDD2V9abvNgf39kDgmrIqZPU7tQK7tWzPtcqYz1Agbu2B7yGbRwAGOvHdk/u+Lma2ejigBGp0uZvGxWsKYGIQbmuCJoRRBTqbZ4GzFrgWK3aXhEq9WducukZUt/Ooli5utpz5Mrp8yrADdujq305nlb871aQsW4FyrUfse12/849QTS+vqWR9gH9T+c4Z+lP+7S5zVqX1PGqng7ls0LYMXi1Z+1v62o+ak/Tt1yVjMypcUNlbnnMcP6A1bBGeuILzIbRGdscfZDwGLvjZnPTtJQM9Ic65whyEoV5PeWsurqrm3WK8tdeLjCzHCrDW+P4pt/81h5qvXda45YnAZYtapx15ciCWvfGbz/O23G7pbCU5zpjkud93ghibewVY69ob3kgMOJVPR1QWc31Y1gBpc2dRwDL187LR1LfLi//fdoD3aPUj6XmDq8mEunJKZZXoObobFbvCQq02V5lv63ffJyG0Wr9CM/0WwgCev4OoFsN7T8lfyc/VOGOFji0ab3yv0xmFS3vYyCPeppo6qpeYAzavKet29LhUNECugADhba9Z86SVlx1UcHTgRmf5B1nbNCYMSZ/5Bo1Qar3cISQQugqOT7WWI7/bvvHPkPxLnNb2dAmL4enacYUJuGt6qncj0VfxsYTYvC99/th5k3cnRB0qwzUwycmzHPGFKv3k6dX8wBhRtm6V8e4lXX2vPE5M9Rt+W3P46UV7SfcvTHfumlgAXSr6+DeDcVwn373edEefxrlbb9vyZW9Otwrd68to7z3fn9tGvWtpw+5tl+WXBN+vW3v1pjhy+xjgtzrr8yVHd6TLSOc27dz1O57eQIo+qf8KE1t00ikqVzrs99rdqPKkH/+XmG36eJR+vxnJMO12/T1dflqIwiwNTj0wtqubZ9HcUf1z1ne1aWuU6o6QXDLOucVuA2TM2OGla23k67I7BFgWeXr6kAKMgzM9Kt7vm982ptUjQt/88X1pQKY0nfs1x0tjdxfZf0iF9AwUhzU/VsxU97Uez8GQ98/XogbCBkF8+qf9b9HzwDabwbwbtRFvt9j7Goy8G0llD6V7udiH0uccV2uJa6LBc7zgV9J+99ykNMmbKy7PsVYUS5t4PqG34s8Ys6N4Q9+fjHIQIozdLEy4cZTp6Fhq1ffz9XoZPdBUJdiYM0Zy47nmOQ7PsFm9Pw9WhuBov5ZhoJP1jpKUIbmPofWGNN/ls7rXMDvCbqeTkf5NteKEaTO7T7f0f7/AvLtDdUzqgMQtbu+jC8xGsZTqkVtPY0eDGxIFQghRKTUxu6wZ5p5XPRyn58D2lYPVgPeBhQUFIwCijaKmPColNQtm2o+TLINLrDQX08yzB50971enym9ucPTR9/9HvpmyxIpT1b5wwxwF0CaQBqwU1dYS11v0Pym9tx93m6baySKMZxFAQZb7vsw5GvSXg6PAvKx8cNnzuWPoCtvXA0Ok/JBYp0fhX+iHBsNMAJQYmFwoG05zOCUZKsYBYQYy2l4xiuFRwNEdTtJg5mcUWRP+ZIfFX9J/BGqzUSlrTJCxqA7utyTx6PyNVORFzrX27R/uk/7ndzTVX5UXCIGgeuyYFmW0k6gKuKVT4+30Fqeo3t9m2udXF2BEl/Eyq824qrYtZi3ynYfpLzKARhjLMcfT9MMzkBaxMsoSIsa3jmUE+77SJGtYy6p9+Ie8Q9axQOR54gpTgMDBZdYRsW7pSzOea9QbmSH/9vbfu5TezKX9Cs7A73HT/eUL98XVQzSeI71hhKiNtiqe05fFjTAnc7j8r+ZN9x43ZErPv9en3jEkLNpKzAst//9iHFlVGdfdq2DzRm7b3momC7GEjfn/PxzOKbW0fKq8ddqVWr+m7pJbZojzR+ipcEz/Xh4HH4vT+5+tP3IDTvvalN5/gOp9nvrOf2giN1tx/9VBhBg1PQxZnsk/artMCOL7lhp6AUFBoO9L6y8MLhl+Bgl9jOuME+7xvWKAvBxfsbsDcQABmbMqKCmkKbckSGkv99/l5wdaN8BJJTb63ZkYPvcmBx6odSvjozG955iuKfMjf42MQu4kq5ngPUdudbQh3+9a6YBn75ereHhzgoobdvRF5RSEjbU9Y0op6Kw2ApdFeTj+SGPFDNHabdvf6vy1lUwT+IyZrraqaBN3q95S4dTCdZWDD3cC5kqtEo/AHW8mGABHQ0AEtVuWt0ecqtbyZ2coa3jDY8y1BFt+fz9c/7TG+NaQEzdCtuWju05UaDH5W2MmnD0nZ1BQ+nYPyn10b61+4RhWX1AvKbMzAVIE1FZ9TBjzKh/hok9hVReFQJAFPWYXOmLdV3LHzMQApBdZFoxtHXZO5Ak4yFlFoWiAyjFmENGa7yZq/qAL6XpIwPFjvu3HisGzHodd9BPt+TPLdBg2xYyM5hE6cm6YsrM4KSr29nAS3cSh9Z/027ecLnd+n2NAaP6OaL5rAP0+Lwd589Ne5pa3lAI9p7dzheGbYHkzBKTrJ+nTmbLewzbqlJoUZkWhWrckHehzwi/zpzBxMVwJXlWviOeIzY/qsK0R05jpT0U+SNGEMMhjBrvV00qmTE6SrbnG33f7QH+zLnEhmif4dKGPcNUlWn7WCFzxqK8xPK67SnV4ot6b9vWWziml+U9LhouUHXp1qwSHUT6xU4JCiHgcr4ipRUMDSiNXPCDb1Mz/2+NnbXBl+3krO+rHu8l9XDNKY+3sHT1oVpcUwdbBAp22pLWyy8SNn0z6Ptxr1Ys9ShmKIaQWsnNGJGCF4bjOf1v93756t7t79t49++YgbVv9+g3LI8H+evIOHELG937vVfGXp1H37cYD91337ztuLYGkPq8eX/UJIyv5s+QxeAtT7P7j/TqPTp71Dji8+rrs5fPrTJHdLyXrA9vkLJduZvPI/X7e1JPQ7cf/sfU4SuOyK1pNMAjAbqZCIGa8+H9O33AsRGjtzz7I0L36sc56xmRLbB2T+6uvJSJ4iZoK3DlRqkHxt4ndwfSpcwVye4Bkr7/REHtVwxue2z0gKC/NlJeHwES/XX/3WwC5XF2bGjzHeCMNsBgV56tc5nI92C6PotaWK3hBrw1njOMYV4BLVjO3bHLBWBkLtsGmFvvoH5MGRp1W1tkdWv23aLdMrPtZxMMpqRa7WXVJgTX4a6Drecs5sQeMxmNt9VdFPXWAMIsIMpW7PrVH6mnvNwbEG6lPVq8xSdaOmgNcfX4RaVLgkqJ8ZG1mitaWhonLvO49VYYzZ++LH9056j9ezyl5OF+M4vxYK/GJZ+SXzeXhYqU80G8KBI1ASIBWfn0gROFNzqFpKvjeCxRDBIjuhCIU7/5Z/o5VoDvHk1b6xi2aKl00AKLEW/ryxnm373nFQorh1iPfeU2oG3J0W1RamgFaGiq1Mte8QwW26+30j8D1DxW8M7lDiTuXbPrnr56fMDa3z0v3tLk/vwqil8nT5yDGAJ0i5Zii4oXWk9IM4RRqW/1tOrbv5XL9Rmrk1XD6N6MImxnfuzxmx2MMLpmpz4Z72y397R9P8JC8teOY1+XUT/tKWeen99qi3/uVhsB3bI0OGq3PWt8PE5bF4BtsrbYdpF1vSJpkHHDcpa/b1trBG3r29djNwzuQNHy4yWeNgwKYrTeKE1aRtKT6Mz4ZmPd40ZvdOlr7tt1D0v67x5n7LXJv7OniI5o6iGjxM67g4eG8oeZZXsOOQPozvM9OfnffoxHcvVr9A57/lb/j/Lt+/KR+bVnNOlpYYSZ29/jvm3zAIBxHt4+949IY2zejkszj3flz76M8/fN0/uRcf6/TK7/HWmPn/8z0sNGEE/wtzq+nwgNYDSwOyAGoDL0/qi70Vac/rldi10GAF2ZKYymCpTRVNhjCL3QLdchwmAUT+GeNdDnn6kqAr4tzQRxVTahuQU11aW+75s98GFta+pdDDLu3SKstSIDOrX7XnBZHnv16Ouy6ePNhOi9aEZ5jeu2rXTbT9kpwv1KXGHOetkb5AQgZAAKavSEQ/PQ2NDVDeHv+9pf7+l8ZIxkt7fyptCwtg/7qK3LBjxB55NYQkABYnRUr4qU9RSPTQBIC2T5iBHEw4TaDub6KXXbBpy1PD3dZT1WOWc7PtW1rwjFW3E8nLYzSM3zKoDNWr1Hy5v52BlBer67B8wamnEjuvFab19Sr6AdntQB1wwZz2W5AsgN7fstH3v189+HdOv5GrcedeYlFxxvtb41AO/7S5iyL7NrGUsGpkBWnrbXVdu4OaN27LW5nmoBgCQehJCx2w5UkrY7jGTIDv9GNZCPeOvXJi509M93ef01gOcW8Pa009wj8zxageI67Z+r2wBHCt1orG3uVOOV/sd1oaT3QLO/QHXbYs68mYUjuUDGc4ENDXLzLFA389yek3ufw+dVfrZyQb3PtD84c4krAS2/zrGqfezJGS9Px4aUfXywJ0tr1asBXzqxfa5uz+vkno9gbnltO2hj9O6TbemxZ3xstvI0WYHbMcn+OdfGtu2Vtl3VXKP1KeWfIXhjk2CbPiB3Pw4555YX36xPxRqjsb+VdrHRg+kej7bUK+Cjsnd1jK9II8V/12CCDm0U2balgT2dpTdI3Xp+VNdNnXirT4x440i+7xlAvrb+huuIts/t5e/zlM9269mjRqN77Ro9N5JRe/Xauz4q99Z7o7rsPTuUazfm9X90+meX+7gnSOkTrZADonDLENvJIA8Jf6Uh4drvXhCNCLQ8p+7l5uq5100GGThVhkOuLSPo2zNaq4evW8vgZY+W7IkPu0H3pB9MsWjBFjPAZAG9dlzVHFaugrM3OHjlcGsEKc+YEgmNCVFWxXOFdW4o9wDIKDX3GRit/ozyuXXffx8B031h2brTwSl2e0xYYV7pS7BiIJVVROLtkdaMPEOC0kGPjW0Czjr3/q5u/vuIvuE+H0l7TLIaIcbujiPX/7156ctp+w6qvFdAv6ypM354BUOV2QLmgcbg4VmLlLapY62THnncgVpTT1gbaXPEosunLIEys9K87HWuZRXFwRdNVD0H0CXm7XXH6+wR28bERliuiTYn/Z7/ER/0Sk/XM3IqDrsy0Y7VJg14dlv/dpwlAHB1O7Ux9W2ymAFbl3nLp9KQfQ8UwGHr/u/BfwwW1rI3DMtcawJsUsu/tnVo29bQ8+DFPaB0C/z4a+U50jmt5ZvCl1O9Xe04/ei2V7Z8dqsM1vpVGnw4/Urs8bWgZU9JfyTtjYmXfcaLLfaRN0r3HiM+Oy9fmu+DqjbwiEy2bvlnE4MrxHabRW7Lt+d8fWSKZcAF8iwzpjxKHV7Y9u8endzmFZX+pA9ZeWjb3yEE5IG8kb9C3A0x9v3kDSH+Gc9TejxjPLRXtMW7oxYn5EDSc0IaTR1yzrKNqMxZY6pWkR0+0OUz7E8m5MRIyIpFg3hvWrlGr4RN/9Vu28dMUkRwxys7svB1IfHKFgOyvOuPU97DeL0hfvSMr6v9LyK+jnFzetwIL++Q4T3sOaqvxy6jen+tIvw1ZY6u9/f3yt6ru43sXn6PGDj+3jb0v+/139fUaWQo6vP322H227/F3pY8X/T57xk5/O977fp7+7/H2uNnGqiL0tau2Efq8o+kfSvz16Z+HP8j0ldsh/GhrEijaTtgXv5rkymBVRlsQeye8mb3hlZGQIIW5QwgNGC9MAdFk3tEXOszBsLuYm23E9RsAhcA2Fa4AaIM5j0XwTFgts9M21XoneqUZxhVJvcCsQcWzTvYP6J2W+bYKLGX2vJQvCbs3gjI3Kr/Xv579NG+37+bMQKFzTPoRqrUqxpClryqK2gAH+R+f4RcCSTG22P6gJ393swaYJQ3INE+W2EwBurb/hoYnIpRZ5/x74Hn0TX7XFPCsvqV/OA+GUR504bhZ1nR0uccuDU6Bmc54cE4UwG4wmvYjZ2nveyi8edccxgBbJ+K6cbPQ8nltrBhoA36Zm1pc99TWEbCLNN47O61YVO1m0KwncsAykpmH6AVqIpaMwetaYPy6ncqefZHIzJzOXkBjsY97zNuXH/aOvhYsO71yaOg4RZoGj0/KsPetSO32eZ+7hX4ypN4kJ8Hx6PyvPHka2njn52YZfvcnjcSsG9Y6r97mrP4G7WcXFz87dV+24Zdv8VHSQXAho78844QfV4jukZDb9VIUGNB1roxy2KL6LesQRnRnKrk67qXejk7en5Ix7BZ1vEh459clZPa7srTSuwfoNu6db++tzAIM9dTfkzmd23Muat2GSM3v3wZeoqY2eS5ZWBws831A2PPg6O0G2ZAtlOFxABqtDHaEj3CQRtM1/RNwwml3AGGbnmJGCXMENL39SMG4KY+qLHqrDzrm5Qzkh45HUINIuznSnAeWqO2PppGeGmUZ29w8Kl/5x4/GtXv71GWN8+RxWMZ19v/3sONozrfa8Otet0y7IzqXydEIfb6Llrqtd/bsfR19Xyy8tPb/b7FYtRY34R/mcwYzwFfy9YoZL//EWk4Hg43WX17d4Db2G4//0ffu5fXXh493fT9NaKnW+nv6edfFRPkntI1evbW86P8tgDFGVtQO2WkSJJRbedOvO1gi09wG1jaiSxFUPbKrDMIjbbE3Osbe5apX+vbppaX0AYYm8DvwUIDFMHN6o0HjreARnnfKrF78kqrUPq97X15ozL2rvk+8+3aoykbY59FvebBSbf6PBAGoftNkICQ1+u1tMNWN+w52T7A5eSYvm8ln/rX5D9g3n2fjJTQ0fNEBtA62mJsgm/ule3v9fn3ZabEWJxQuPXn87z1acpHT+tj4wOVz56WPe15+stox2VDf+zn3mBFiVq6MqACLpQzoMN9kNbf29ACNqPZ5OEB0q3xHM2zUgdm+CW5DAax0J0Z/Oz5fszaU1C29dvO2XY1ZtM/pPOSilZSxvJRMNm3c9Penfu/Ju3Njf6eGY2qfCOAMkA1FhAzhka6lhePDL6VduVjTDVfAzb+0cnmoinMI5HZ8/neiOSfu9U/Zo0zQDuSdSNg76/t4o2+znKjeaYHfBY8W5TO+iaRGDj7DEmVdjMOm+gKgzHd4xubet7geaN3jU9uZDmLImLytOcHXt7bWPj562q+wWT9osmwzt2iThlv1i0k5VQTR2PK38SR0MsGrtuSWf/bhXLdwhV12MmwT30KgBgbpkngdyZoUGkuyh68J4en0SavmkbYYi8VHg2UBReLB8K8PX2stKXPR5Wu4Vj3yhiqLGH7N6iv9IHy+x3avIWRRm1t5K5H2PaF2J0EtU1bfj2QlW2pm0XhX8NXRzzO46CvSbfwJOC2a/Zl3ajTrXHYKLFy0TLR/qnGEOruN3XwvyFYfLD80n2KQYN5Z/y4PuvlL3OthtW8yp9NoaXIHr+j/03Ypa9RXl7Wt5qvzxvouYEc2tcQvOZzTxfdliHvjWn9Fi21+eo7PutuTB6ZG2ZkL/nefePx9LARpCf+0fdbqZ84w2B2N8AHYMpiOxC7oJYA5hY49czaAIev17bhbVkmMPqy+/54tE8aYEe3hK3VoyuP6oSpgmIL8NomcfPO3p5lYGf1yurRGUHuWe/2+u/eO017b/R5n6oA7MsalVteEiNXW4nGTCKMOSMTYV3Xxvjhn8kYj8NQsLnv3hD4KLOxewZiGL5/2j6ohTXyZVPGqJ73mBczY1lWpGt1d7XYEdX4YV5To21jtw0l2/IrczRGztwC0xEtlz8o6N01gmBD51t6QjGClNUvAmQ9K5dVz3sgxK71yt7wHQIYY2XQ99co7c3LbSZAo9Boi0Z59PVtaIhwcxyJqKHXniZK7JFAaoihxih5SzYN2+8F+50V0/JOI4FdWWgvN0X+CuBb6+gUnjvz1NdjyKebVJWCrwYiPd34Arjt169B6TmrbA+QuUZVLvdl7wFzuzd6rqHfIEFI/bsmk5pnb+KGmnrDRvkkAAM+5svNnIDBaVOCBaBzYthltZ24E/en64O9fPxv3x/NPdQjmksbiixHw3eH7S2nQKGjjyJ8ZSWT6gN9XXp5Wnj7wDjjjxsvLWAe0KbU3+JE5bTlhw2GZ69U+H6y97zxrZuDhIbWAhHAQeL/ZANzhKoKddiu67rhuPZzsXveY9Q+rof19x69jBShHqsbz/LPELX1Crq1kULYPMu2OjNINzH/6PmsY0XGr4aZaibDLDblVyMHVd5rC64mH+vwNYXe0ndul+neRW3v1wTBvZVGse/26nAzbYSgvtvx40Z2oLD9O1krrwXUq8/TbqcvOuyP5qPyTV+eN+b5I5mrfoiNl0VpguP1dnHE4ur71N8dppF+tP+MZCsepZ2nvbJUmVelOjA+xSpompG1+cpZ33OGK/8e3LRxfezrLEboVqfyPVDntOerbaY1O21PqYs94mXIIJsb6as8QfzkzTmLC5sObcsCUAjD7tUBkEnCpEdoOeLtQbQX9MKwQy8ftqncU8bXMeqW+dwmNFF2te0w4NISuExKiYApgKRbtSrPtpPRRkp0BNLArdDJVBvT85zkiIGKkt0T0tadse+j0p6ckZIaQdgZQSy2RRm3SvS1zyrDaye8u9Z9Z6B60uw8K4qT0om731uFC/9x328lxrauNn/KtZxbWMOVAXjlyqJLZ5vcjiMQBT2mdmRdd4/2goEfMaD51spfJ2Okbysfb+aL7yeP5RqB4eqz97vpny6ty4q0nBsFwIwggSJCiKAgDNvo3bffyusNQveAvB+fjVKijWzo0Pazu+Nr/bw2TaQCyk6g+jJUoiYTstR1MOSRvZEdHR84UlLL/6xGU8u7o6WRMicCDYVvQL8LKOFyH9oeO6CkAq+AsrXJmAg5OrS84Q0zysNsrOHmYacdGD82epHv1h4lY0EjDsqYLCrx0ofzXC4Y0pIJQ3dofpQYkNXTIt+4vYk6xqTXuM9BycLGwUhFqdcdb71ThxF9MwYvWY42vm17b7VR/jacsrvajaNf9e7L6kFWyYJk/pEYt/aQ8J5xtE+Fz3SG10Ch8HDqyvCKn8chj/DkPXph5Ruk8sCwCHP1DAwgBGoDRxvdmyfI0JjTMPutItTzw1Ee9tzICNJ7ydk7CofLexb7h7k7ycTn58vK7BSMG4lyY2wdG13qNQY7rynDL8AtRLCdP+Mtq+N3fR/3/SflFjkz6F8iwuVyKSdqMSfnmVvrvavwd5+jlvp+H8nyQauadvlx3023eFT/uzA4lQchSDD1vn0DnNLnM8LsfV3NkGgyglTvGLfJcFQR7C2gap6DiV93hUrMwXJdZSPttGTPSCXJTpys9NRgOAJsj3kmNaS1NdzWfND2qmZQwbi1ghtQ2T7X4w29Rsz105ej10tvUNsm+d5RNsE6Uh73wY/ItgK6NrhXTfzYCJgxANThdVe3gi9Q+Rl5ryjFjSa3s+oG7oFy3U7Sqrf8giyaVOm4LE+XapXnK0Ao18mwhe/rEoC9GnzVbgEu9ff51z7rad62r3NOgNLZo4tGPlnQ7Gq0KABokxrDlI5JiLGczlnKZFZak+uhbLm/jZ1G6as8QZrfSoBU2kIKYloRWNQLJUhiB5Y1pAcwdmm230EZSiYGG2AnrnNEnmpXX0BAahUCn6e3IO4Km26A2f0zgwVAQJZ9juxWlP0JAr3beIxRJ5K77oIh1jq3ddpaZ8dBwvp9qb7tRTDoWfTrrheICcUtYCh52WQbKjVtqs+2+Y3eCY6J2tYUe7/LtPbVgDlLT+Q6XqjMkfXZ0tZiUOJNO4EB0FQ6LPZNU+BMzSOCbNWQzcjkVrfacVUaGiSv1GvPaPNMMNR3+360YPYtGQ08rIrcH4M6q8dd5bDpswqizbVV2RmI2oCphReWdo3T1ptkoBA0dXH9SkDx5WE09Mpc8AS88rEHBtt+84K08natWL2BliY71bn0EcPYYTeWXYDR3lV+1B8jo0qVmnIt+PdDACDHPcuRjWmYN1EEaRymSs12ygWV30xUTjdpK5trX1mMg669rbFaAA9r7xhQSai0LWDIBnFrLPMgg0xoMJd95/2c2KcpqF2sQSMDOYEKmJi665URkb9h4Kup79ckbzzpzXZecRi3q8/LACojK28DvKu/PVZKKoBsnOdWmvk7vg/FSDFSau4BLm9s3RhLFFCNsujn+ujz3tbDYWJbtUwtfeUqN4sPIRFCDLq1xQPE7UKItVXKqP3aywvrj1Eb7Xsvi0eyuXyinhrSHC+eGZwcfvBdgCrzbc7fMzIAuVHs9gwh/p7fzmjGlluK/EjO939NjTaeMUbRA9kwyN8n8xw1I0jvhTSqyx5v6tRGeX+jRHm80PVD9z0xI1g5g/o3hgeQni5WZV+Ret2rFYdwW9kmT5EhYnzvH3It7misrxe0/sJqQ+2jBsf4/IsGU4qi4bi68UVHASaLIG0n99u8DKR8e5sH+dtcl0XVOq/b/rTyCCqPGkHivwuuBSkvUBzGBStbfty0x2OUgmnv8M12jpaSa752L3DhWVUld7zM4fzS69qpDT/yco4BMW50XqAaq7GlDRccyD48T9QG2GeTTK90Mt3LGaZcPR7sXbL3AtDRVKFMjxUL/q68RXY0lBIhCGTAW4gcbXiDBfsqOzpv+0HG2RkSOImjAgGsp0G1BjfR00KMJbcS28iFCFjTimVZQUSY5xkAcDjMmOcDmOtW2NKf+qspi2SxJKWElBOWZdE8Znk6BEzThBAClusVzBLo+ZH0sBFks/+7GDDqRAlOyQJDt3d4xsEI3HpjIHN7Bjs6Rkskrk9eCJDkPVKEgp8E+unPQfcEXxVKHULHdai/1uXZ1FevmQGkL6d3w98FatYGt+r9iKWtgpTKYDzT8Kko7Rw2RpC+jbeAkRYBoBpzDOhU3OkUmZ0+HAovzWS3vzv6uPV9ZAQzRlvyUSZb3kEd0z7tGdT2lOb+vRaEWmnjsrbttHL6+QIA4z7y7b+vXHGpn32ODCD3jCIWEJYaVs2w4GutYl7+c9fUaNQV0RtCNnk1mTp4SAB74Wc1yh2vkB7YUdbce7f6kbv+oeBEkVMGyb/ijGc6vnsAA8BmlbinOeufvq9Q+N12nvg96imtSHndAG8RUCJYmlgyzPUEyW7ebeduBTH11hhwb1O7irJRDHQe3OKZZgQxkPxQsV39MaDNYSpxBcb1GfFCv9q+99zN70WeaXJtvD//LY/RswP+1F/j/TJ2r2u00kqzVIIkjtJIXnpe1ZdX+FlZjxpvl+r5Xv852iLwiIw2POB/7ym1SU+tattSjS/tHNdPyXVYL2Acw2QrQbZ16+sN6G4NtjlIw+flHSpVspDT2zm/L9cJuoxD7fjUcoRTZ7aFGlcP1x29/GRsr/cMoKeJUqY+65/uzWL7xteKyfTBEhR0I2tR51/i6nmcmRF36K+0r5RnlF7rTxTHdFqGSgK5l77XOhWDyLBNXsZzkX2bIoryj2ol6FIde7dVgmjjGCanu/Wcp2ZeRIEBUReHRBZgVT3sqqlSt/4abIH136SOtuDqUm75IrHvocqdA7V1YPee5K9bueB4RclWnxzg1Q1vIV00Vq8tgn+GR0NR69ThjtKmjn9m9ONhtRrICOSygm+mFibXj4Vfo8gFedIxvIaYtAxyW8xClK5xRpBKl9nsEo2hC+53qwu654oglUUgIkK0wL6khuHYekuQ9lMmqJNAvcO5l1sEkBl46zWGGCeNH09TbHhP00cNFOJCA62s8vy48m6/rT9G4RcRwqMSJ3DKmOOEGGLJI4Sg8lrixKW0aJ9nkMbKjCEiHgNijJjng7Zh0rpUQ8WyLLguC6YYyxhZGRaHzn4fjgdQlNfNqUCOu8pILCEKMv7BRpA//vGPePfuHZ6fn6UBIZRBtMTcgZQwZnaNIgiWlREAQyYNqNuom3iEYgTpgXYPiAzA9HWQmyhEQ+RWLk3oWb1g9D9mCsyt4aL3qrA62p8/Jx6Qd2OMJTL4SMkzBcMYoVdsxsLdTWbHOOyJNWXklIYBUj3zG31v+3vfWKFVdqXug+GhoWLw/Oj6LXA62kbRK40tzTil1XnIANCjjy2eRR2nvfL994bmHVjpQeGoj2teQA9ffV7ba7cB+jj/rSFkr227ilhm5MTN2O+9Z/S7KUeFNLr+6ZURIoLtkRNhqJneSBuasnccL7ilMN5TJhvDE7gx4MoqlzslwdMHVETvxDQp127UqVeA2hVsE365ADkBhPJODEFjJgAxhwJgmrHRQK8ZDNbgqL5sE5593ev3epKED5g8aovweq686x7v4MG1zbMqXMtZtPc9DNoMWgy/RyvMjMb5a5B6XmbA1N8fPdMb1f39Pv97fHRUj0fuD5Oh8cGze33M4BKno5HBfdaD90c0v62vbnOhIGCbUeIgjPpkt57uXs/H94whoz7st1s0crzzBpVHxnv1i2xDpZsNZih5U7cSqHVy+QYHiD0wtnbb2YCibEku9kzOGTllBan6nGZuiy57Mm08B0zhYy3TYRc2w4fmie2csDbrhVq+b7eVA+zWq/TDqP7svHisX8m8jRwm8v1c2lbr3dAMtYaaFnvU7Vx98uPtZXfTH14xJ5uoBg7RPVuTBU/t+2ZUh8f4aI2zsL3VLkGITBSGa3knZix5i6Xsf591JrdiXojejCwNp4XhZf++jVF9pD6X9Xuth2L8XLdD1MkisyD7saBtnoV3EgB24Y7dhLXFnJZuan+ZYlv0BSLPloWGbHEEvg+0SoqFiuw3+dg9bLzaK9mel0l8nVzgmGxHZEQWjxQmRiIuPI+1VMvPiERZTeUBbCd/RYjHoASVXpaEnBOmaUaMQRXnWj+ghiowHcvG1/oNnX4n2BLwHvc2D8Gmk3Ix3GddVSnoSunX8rFwA1mP5g4ExBAxYSpzknfwCAGgrF6JkcbYgtAYVs07DzDvDo/JubRL/oSuLMZhCAHH41HbJ/2ZIdu9QwACguqr2rfKf8tCJQJAhClOWr4t9BGWZcHrlxes6woKhL/85S+4XC749rvvxFByPCBSwOl0QM4Z8zTh6fSEw/EIIvMuSSpvlrKF9LKuIFQPu0dx3cNGkI8fP+Ll5QW//e1v8eHDh42uYQNtoLIwZIfBCYSM7jg8PeJyD0jYmxlV8WTPu8kYVyXSPjUE7JUxx3J7hbYyv8pYxM1fVzZcXAtj1rZ/LOe0JVCgCUDap2ABolw9Rsq0/z5SLA2E+GtkFtdyTybscH/tA79rmZJ7019d6oH9KI2Ed9+uvvwNs3KfHlx4sHnrfQOVIYTi0cRMyJnEgk5e0LcrDn2/jNxb+2fb8Ro/s+3LbZ/oFXhvnL5v99K433Yfd8/4OeQqpsmfHNKXs1EURvmqTCI2wWB0XJluzSXsH1K0U//NtdDOufrMKOPbtGxlsAoIZuF3BcdwbZEvq/A13vKGTenK/x5RApnN80xerAIPAAXISSTCu0IgTJEQQwQjwgxDTYvVCJKYQWHFuq6NUddWE7YGP/1OWv7wuGAuak95Xt9xnVfSkLYHfMEn7x3DxEPaKeAb2JDAfW7mn+Xdh3f5WlE0R/P/vgGk5SuDMfBge/geN/3vxeCo7jXfO/d35AMAkHmMUl9/DObkfjIe0o67vZeRWE3cO0zOy6FbRpf+ndFn/95en9h8sa28lofJmZ4APZ8MQWJPleNEbYswcdMWsC7qGEkP5JIpbXs0Zu8zt3RheCbltD2VztHyiNZufa9zXXNR/iX1JFcPLvWwVGT9pscHZezcKzFGbtW3yyNw5eulzwblm3fFrXnUb4EsGGWH1wMdvs2yyNXKXiGxLUSW8fQYyb7LaRNbnDlKPTawcrdl7Wwx7S3McrP5+XI+48ePFydPuZUb7vH+hBpCpfVu5GG6SzWEuIVLVFpo6yWCWGdjeZ9VPqPc5aYPqyK6k9zcr0YG2VZntwOFnjWUptT2VqxKPi9wpYfCD7Xl1jceAin2bdQ2oOQnW3IcHUoF3RhT2dJyfXvFu8MROATd8lS3w5jcFe8CaV+Z1wTkJHELMxgxQDGLeLRTIhBFxCkixFB5WnC8jmSYKm+17vYN2865UjfVETxoYALWtKqTsyzwrKt6RAA4zDMCRZzPFzAD0zyVvopBPRhKdjuLqpD+jbGeImbeTCM9aKtjZHC2GJwRgQgpZRW5hMwanypOCBSw0gIwkJYVCeK1Mc9zofSUEhBiqasZwUMQzxi5HgqdAIIpywKHfkzzjMQZv/n+e4QQ8N1vvkNyMvF0OmAKEVAMG/Uo7ZAiYkoAMUIMRcYlloW56/UKorr15l562AiSckZarvjzX/6Cz18+49379zgcDpjnuZ4truPJxbMjgBQ0B1Lrou4BokBlzd0mXRATYcNyCFQt7mxsBkLMRTiKeQVkCqsIyWVZcb0uQqQQootTRAy2P0usX3tgEK5cQASjuQ2JopfqnHH7ttjxvJbIK8sE1EDklG/Zbw9lVBVs9CxbGAU5iy6ZvqJ9uRU81iYTFIlbA0i5vwOuR7+trXv3+vKbNnRC8BEDCLAFBz6/3lB027C2rYcxucmhhH6le1Qfe6YHYr78XjGtQLPNc6/v631UId4Aslx++zd3W6405o0KUud2W9DmtVFe/bjBrXqQu89wtLpVMvsxNLnlXgfg3CUBWNQXeb6llz3jQNsgUgERGhDZC8quwTvX+8cYKGClAgwPkpr66mN99o0gttsepLj8Shtd+2XlZA8xefrnVkkkavdmEypEZCBQRCR5b01Cf1n8evf73kUbZ+cJ0oxdI9wV3rZEUNrYJ+JKR6Px9t4x2be9AY+1ID8fmMcBK/vyS0W5jtUef+znO4MVJFXo7U/78s+Ovpf90/a7La3+7vgR/PPlyLHaFNck+EFoyu+0vkcNF/3493zWz+Wi3EN5iDEJqmKvLbcNPGq8aTN+LivLY2MkJus2pY0QdJWc9IjNWv9eBJcamTLBJAspTvEsgfbI6GjEgyrOECNIKHhKgLEzZBM17cGGv9Z+J2VMGznFohyxzwftuFlwdXZGEKPgR/BEP/6jZxsM454fycK9fEb12NvSQkQl762xuqXVUpdaUK1yQ2d19uzK1o0hrKb+JJe+fG/ECCEgGk2VZzc7zzucy7W+XOVsPxaPGgjteouxTKij7YQRjjS+SYCdB/bl7RV/+XhGkYREaLaMuc+Sl45lUeaLjPDvKANx/AVqnA2KBVKquoudOkME2fJCNl8khlJQha0aQbQMqrOaJDIsVJstvEU8LBhy/rUaV4hkP5rlo7EachbtKMRY+xA2jKpnKQbxXp/S14obtf1EDq64nqxyqJ0/ZbGvHHBhNBg39EtEICbknPDp40/49ne/RWA5QZHU+7TE77BmghGDGTQZ0xRgq+pCV0rPATgcZyk7Z6S0lDoeosVFql5ahdlry8QT0bZO2Ha7Fkf4Oea9FZv5r2MRI2GajpK3jsXheJA5FQJCrIFvAUGwRFSerX2nxkkwOBhdqoepnsxQ+LuOFLnvoiNr2xytc24DTJvXR4wRNZ4flcVMwY7iwQNC0X+PhwNoEvkzHWeANeh3oS87ejuBSbyEzaCdonrMEGH+9j1iCLhGmVfLmrCuC1asmOcDphAwhxkh1K1BKSfpn3URb5F5Fq+aacJ0OKi94bH0sBFkWRcAwPV6xafPnxD++lccDgccj0c8PT3hcDhUgKINPhxOOB1PuF6vWNe1AFDbf346nUrwEnl3xORLj7oPBjI1xFJWk7Tzr8uKy2VpFNOFV+Di8i5ITwm8mlhqYttjFpBzwrqKK04N6OLrWtlHvbcVIFZn225TXPx5e9TqCCCUVYF+z39pVVVienBQwcMgRsINcD1OtHlmq3x6NO0A5Q6QuFWfPUDVMyq7VqzEHTPeS4HEkuld+n3Zo3LqRJcYCn5f3Vapr4Ctfm/buqvsWFncDml9Jg+ubdtcvnfg0aypxSEJY7A2vNaVl7munngSE1DFbSZc7/V5qT53EyAXxQ8Y9tsjKVNVjr3C1M8PffpuflvQJwZL7gyio+cLrhs8UxWWQb14+7woiXKTs9YFUICLAigaYQ83NH4cYaCpjpPwQCBOUQFZBifds707vyVPU5jgyuqbYoBIC660s2ucot07TVe5sopfYjsE/mG02n1tf8+3AkZzU8H1A/URWpZ+rXXpVtNHyoLd1/rWOVPH35riWzjir6XJ1s8ln+5Tc7EFj1LbnfrdaDyY9Zhc9WTw8b72kikXxrdaKTNqZ1Xqymq6GiSq349FDOBiTJPHRKa2w8ig/lhJqxfReBLDVq1c/5DVUzo951QMURJ6prcsqaEj1Dgl7d+o6LFny7a7anB3+anfXHDBahzJOm71iNycGZlve5eWogZz/74hZPvsdlUfG8zj69Hka+86Y5HvvD4Xj2+BMRdqymDY4RYgqkt+ews2pIPHaI9M/1p5ZnULDv+ZPCBAcWfPE9oGMarCWBTqRs7aSnRLb1ujR4/tlP7dff+u/yxvMJV5kJhxUYVblELtM4fDANnamYWpyIpwMEOA9QUQ9YS6NSektAIIAAM5r+oRDEQSxTtnBiIhTFG8FJhL/8pWsFXmTVDNPIpynZidYR6iBBMVY1Ths44HcWYkls0HM80gjcHQWLAIqhTKtRBs2z+XmA3ruoATI4Soin5AsNgw3AYTJjJjSfVCM10NAHJKDe+3RZVV4zTE6E7gAqompTSyLgsCGKdv3iG8e0Zi9dQJyleD0lMGmDOSLlH4kAv9VlupR4BFyDB8KYHdDbNLn9o2lkB+XhEqnqsBPSXvFgsa3im8Gi2/7Q1DWdziJL4F6aK7jtmk7TCa1VF3ckiMyn5erKsYJ0hxqm33CWpIK5TBeooq6sIeUDFZZqFVv31lmgJirAuggoMD4kTILuA1kW2Rlrl1vpxxff2MT6+vmOYJz+/e4TAf8HZ+w6dPn3E4HBDjhPN1wU8//6QGjIy3t1ecz2/isBADpnnCFCKejie8f/cOx8MBz08nhERYP/0MzhnHwxFPp+cyU949PYmBiwPmeS7jI1OeWp5+Jz1sBPn06VMZNK8UxhiLJ8jpdCqGjRACYphwOBxboBgqwc6zRHc9HA7Fq8Tu91HNffIE5JXAlBPWdcX1epUODlOzPaZX1OuRP959slcYSd08U4l0m4qlkGHBX6p6oEyAx0CugGcwvEJlRiAUhnnbMNCvUlXm0K5wjt6tdd8+11+7LYCFGVTQPXqnhacjAPCIkB8p9rfe60HPSMn29TF6iAPDUqOAOkNIA5K7ueHrUH7bNV++Uxo2z++8N1Y0dgBEB9xu9ReRANlHTkK4NWRJ56A31Fld9uoxqiMN29m95+7/GrDIqF4r/TamZoWJYeooRupWfZBkf72grdIHxhNvHfWrrxQe0NZlbCjar0bvCcWVtTkQHzSqtoAdUvDlUleUrFx3HlKoAIWIkJDrnmA3d8yowmwnxGwBcF+krbg199wDG35lP2/Q/EjZ2jOU2ph74+EoHyorZoOy7gxXO88lNwP9xltJwbTkV1fam3r4z55Z7NR9V/FUkikZWTBYHxS2APgmh9uNHTydMoORyoqrrAqy4sYq16w0IjNIdDxZnysk0MkdU9brSYysBotKYwZqCe6y//R9VMpH8z7gtxUaUDbAXVQdqY868DGZ7J/hsYQ0nOtR3pwBYjlkySu4w7/SY0PzIDf1d1edDJIqKLBH9fzwoJ8ZrReI+xt5bnJpFzTf2lZ2362vR5jEG0FKT9+Z73sGmV6YjXivz6dP/rkmCH/OjuVaO8Yy0PNSAMUI0fOdUf+M6lfiUhT+a9stfS7cNd1+uLkwwm60nfWGac3A0HHsoey6hUsavOWsJomzBE+cJqkHywIo6ZbPSJPQEQOIoXhBcHDbhVQ2gRlZW2gr7SAgTHLiBBGw5gSkBdM0y1aMzEAOyCkVI0gkIMSAOM2i1Nt8hmwpYDUUHA4zJsNErv0eTxIRECRGj3iTqEGBTdmu45az1FHwhegh65oKzpimqTGUNuMRCBGdUa/jHzLv1ZMiG02iBKa1reNZg1tvFqPAQEaJ80CQBatlXdU7PwDIyImBBTieDhUAsbzLOWOeD4gxFLzS01FrSJMTGSu9SYBOIpv/uSj5kkKTR9VPnWFFtzQzj4JUm2CiEljeG0krHJE5OE3R5R0QgnhQrCnVDVWsRi3IVpTrsgCJAWRQFCKoC7ZiuBMvMTGMcJZF7mmaMTknA0L1JtM9EKrv6FG4BftR8eiVeBtZTz+VHSHXdcH1csWSFixKb8u64s9//QsuyxUvr694Pb9hniZM8wHrmrGuC6Z5xjRPmE8HHE4H2boUYumn4zTjm+dnPJ1OOMwTCIykxswpTgjq1TNr4FsTy97RIcQaRNUvSN9Kj3uCLOIJMjr5xAKRLMuCl5eXYtiYpwPO5wtICcQUAi8olmXB+XxGjJNGj53w/v37zWq8Jy5mxrIsWJalBHJJeSkeJwKICCHIXiY7OsfeLZ3GXJhRDworYwilbrb3KbvniTJshdUzkz6qtm8DmzTqop+XAI+OOfo6jfIa/c5oGcXWYKAQumMkxRgwMBz5VK89AhKK5N0w/Ftt6ZNX5vsxGj37iMJ4q8y+LH/dUm/06FdvjNZ8u3M3pgF1la05+s/yGZTb//41yv9+mzEQnD5Rjxk35S9rwuVyaeipF7B7qVHeHXQf0Uvpq0743qrbaEz7FRF5T2pgqcZ43sY6KWUpY9ZT4NS7tgpXbzAuexwbhRuN4WfPgPWIYcsbm6FN6cE9EdX6kK3sDRQW/5u3dG/t9nkScfHEsvaMMtyWx62zjQLTUV32k4H+LZ/w/bYHyFu+KOU/MseqittW5ZHp6RVr/401GJqzkNSM3XsjGeYfv8UvxsqhegT4tlPN1KnpN/vm3vxjZry9veHp6amcEsBcoKmqKFSPGnb1ITNWKIoqx6mXwrb9RKByToxmhmIkhNGKch4Lvu5kNbN4+63r6mj9Nm/r21v6msVNv+x1d/eYWYG3rXAJIRnwa0C4V0qJSmBAm9vSnrCpk1e8qBhMyhNQ3K4GG1EoMvtjII1Gq/dRkXHWDimsNWzekGu95+aIfkbX713jrm9vpRGfvFWOjYcpnqQgPKfUyK+qGlUZ3y8q2rMFNe31xahvXf97rNH3w147/LURLZc2uOv22z5DUGULXmmv2KIxj7C/vq1DXzagnj9EoKnOuzUD63rFcr3qVjeh1RgCDocDMidc3y5AECwPZhymGdMkR3QGikBOZYt4YkYgBkhOvnj3/B7rkvB2uagclTmXWZXRKWKaJZ6CLCLYca2ipJrxpMxHUg7T8cIWK0WAcoPFuLRN+wlctsMZ/czzXPqqxxc9rr+1KMPKj0iFWojmJZMlNgWAlBMSGJgiVjDWbKd3CA/NOcliyJLwzYcPOB1PmEC4vL6Bp4jLumJdrogh4jfffYdMjOViBqcEJGCmI6YQcTwewDnJySgxIueElLIafyBsUL1JQlRPhfN5s7BVZuEN3UFJufSD8OKe77b0yqhHX5v3jB0rWxb3M+NJHQUmxYLvnp8RY8SnT58wTRGfPn/GNE04nk749OULEjO+//1vsbydQWA8P7/DFCOmGLEuC9ZlEUymvEY8PaoBA6iBQqOe4BJDxCGKV8e6rEh5ATiLIU9pJsheGqWRSZwL0oIAwjHOmE4Rx3nG+5yxqpHkklZclguW7xJyyjjMM+ZpLnKsNRyZJJO+DhQQQ8ApiuFmniV2iQ83UDA1AOSEnEmOxg1B6ne54nTMOJ1OzTjcS19xRG4qTEsEZAUKSQV21mOKlmXB5XJBjDOmOJXJaYYQCqFsOxBXrVgYQIwBy7Li/fv3RSGrHVhd8KyM6/WiRpC17Ecyqxgj4Xw+N8zFhBCroPaEPRR6JNthKqBQ5l2WhZxIU2FARAXQ+dQKDDgAhpJXA9qovVYe6xSlel1u1BgJ+wDV9jAWfEu2YmVHSokAMG7g3WTRtL/L39O5u+ZXSkkVaVO6HlHqqiBQIaRFN92nfdXTSl89dt/tz58YsFUm9pUG/zcytFnk4lYxqf1u/dicKATTebblfl1qAYfv771Un7NnVQmpRLupD3f06Q2jRLSN1kxVGG1WlRyg8vEdyp9rl4BFVQx22rQ3huWay6N/jtiow8PWGyBSwasANTRGDXPV9UYQ88CobaOy2lIVNHRHq1m37RsDN31B23u90iaAnAHkSqNuztZ3WwMfIBHDW4De5t17hJSevDG/2gvYJuuSnSnBaI0ajypWPRWVVcj9KVPocFQX6cP783Yby8O8ZVx77F7hEa2C59vmZURTTpmztTwYn7R5j1F+fuxYFa4tf31UybKUUsIf//hHHI9HvHv3TrfWzohTrD3HQFD5agY7O50BikeKvPKJ7N5W2SCy+dXKHtZgzI5aDHWBmbGsC5brqu7z6gmrig1nLrIXjqYsiLqXm9WNH2Uh5y5/55bXFVhPNT/DHxTqgpO2ouRRtlRau4uxR0FvFAAKJw/9fN+Th9n1H+Bpw/MN60vA5OC23aN+qLxu2E9bIHBXbtecqRjQCNYvDDsBYuTVa/1RsIOeHDdFUbRzDOAcEShUbKHNkPkrRoqUVqxqMLGV96bu1PLgfn6NjDz+uVu8dWQQGhlBNnzUPWN8xOin4S/o+2w4TPDb//bG18pe0orzesXCqWCrpHjDtuZxWsFpBTjjTekYFEDzjDBnRApVRueMvCZclyvW5QoGcDg94d3zMygGBFblkVbEKN7aTKJwT2Eq21CWnCF2AoZtn2QW2pgOh52t61tZbTQgc03ii2SwxjgwXcj6SYKlckoSmDhlzAfxYlnXxRk9CIFiUSiDesiY0l5iZNmhDSRbgizWwrIsOB6OmKcJGXKy5Nv5jOv1ijhNQAz49OUzXl9fgUCYj0fpk7c3cBAe+Zu37/HDD7/F89MTfn79jBDAnrsAAQAASURBVJfLGz69fsH55Q2n4wkXyvj551/w8aefME0Tfvcvf8D/43//v+P5+IzT8Yin4wkvX74gpwU//fwzfv75F4CA77/7FjEG8YBhIJAYhgJQFsQ9rjdjwGhrTaNw67VIUn80OK0aRMzTKIPB01RkVJknDMyTeNnOMeJ3v/1B+uZ8wbfv3mOeZ+Sc8TQfADACA2+vr3h7e8PHT5/w4dsPmClgPp0QQsC6rPj88SOen55xPMz49sM3eHp6KhirnNSlW6NyzliXhEABp+NJ25nAyM0BBoZNiagsYMYYi8E/qEFRtoJBjeFc5h9BtpVf10W2MAFq6JP+tS2oWYi2bpmmXMqKIcg7LPezxjmcLJbnNNV4njoHvM5lhhSj6Uf1pYeNIGU1orC3OsHAtmqkkzzL5MyJsWABEeF8PguBaGOPhwMOhxNizCCqxEpEeHl5xV//+uNNhuwTEZWARGQACal51oIaecasO32HeVeFjZByxqJMJcZYzmvf1KWAU7QBBV2e9c+EQG1j2Oo6jRrSC5Y9AZbdb/kbM14vcKpLkbqLhdzst90Dt/3xxuyjMWsSZc8HAzKBWRVumyx9HX1PCOATtG7v7OnzvUW3Asdc18SICkDxRzB796oRUPDXR+CjV07r+976L3+bI4rd848bPUZ9tZ/2lOcKwHzdt3m2MQtQFClL/b5wK0sAt7zlBQnKdbEcbKO61zGSfGSfbnGkon2DwK3xswZ6T4amXDc//f+7yQFA1kDNzRssAEIimYv12gwhXmFpDBQFObdFRWcIKvXd0FsFjxRk3vTXLeWcnVt6bYWfYJX7sx10W4JFt91QfXjGynlt1y2leWtkkv+K8WPAL8uNQRv7vEf9BaCJB+H7PW+mi5svVnJXZW9geyR5PgBnBACXDTItRY6Uuqavu2uuXiB/vf1s8xkoTmyzeFtG/33URks5Z/zyyy+FhuZ5xrt37/D8/Oz2+nIxGj49PQkwv9oqmNR4ihHH41H3IduKa40d4Puo9ZKq8phMKHX9kDljTSuW9SqKAwuNg1mC8GlnFyW/o5M9OrNr/Wlae6lXRsuI9tOECBZNfohp0HvzVeMGKUa7tSigpTrDin08trW1z0t+33y6FNK/lwtPUP7PNbNbRpBN7m5+VGwmp1j52Brc5Wc8UuaCbFUKgREpgmhyMqtTuNj25xPCSlgW3TrkthWV+AIYy+qx0aNwotKn9bktRtnDXF5p7Plo35YeR46U/bbPupbQ+Hr/PLMGRUTGuqwA1J0/qDt/YCAnTMcJ0/wEzhIXI60rGOLWn9cVIPHOuLx+AZLEWYjThNPxiMPphNPhKJ4dDFDOWC9yGs1xmmU7KFVMklZRErMeKyreGVCvz1AUtdonojSuqqQGUIkTYv3HzKp4EubDBOaMxAlmAEmp42NB4iLkWPu+53FqStHD3RSh2CES0FMr7dAKAubZFqwJMYq8XtRgd9VYK4fjEZmAJSdMhxmHfMSa66lEx+fnokBfL1f89Le/4RcKWPKKhVfQFDE/nXC9rvg//+1/iEccGMtyxf/845/wl7/8GdM04/e//z0O8wGfP38GsfRZjBKU+nx50xOyGPNhxtPxhIMq6iIf5DhdMy7FEMBxgug5UftR6DcGQowTmFE8mvz2eMOg0fWv96YxbMpgcFIvnig4dZ4m/PDDD3h+esLLly/45je/wTzNuFwvCET47v17nC9n/Pb773C+XkGB8JvvvpUFRM7Fm2Rdrng+nTBPAafTEafTCeu64vPnz8WQcTqdcLlc1FsxYIoT4jwh54RlWXBdLsg5a9iIjPfv3zdzrd8Ob7LXnpF2Tg2P8MZ2ZvFuBFug/BYp2FbpzBmcsuoBMr8Z0NMagcgiewnCL88vL0gpYYoT5sOM4/EIAnC+XOqWGZJFV3GQuA55Sp++whOkF9LGaLeCphoQ5JmqAImlKkE8OebLBU9Pz2W7ypgPeuZrZfjfZmXn8ui+UaMdXFKGdcsIwu5eOQ4OrWFmW2Vb0W3v99bg5pMZ3MdiYC7HKZX6lJ083PS1F2p9sLCieHRt6+vXnHJCInBqHgOgzRYMsBqwWBVa74Zmk9PGOTb77YAexfWrAZL/OE7FRk8ajEkzriD0j4xAysioMVIGhqCqe8eYhFlIvZJS80BTxl1DCI1+tA1r++KWcWb7zi1DzlD1dOOYmTf78RoDICrYLCTPyggxVsSICLm8r/uAab+9t9po121s9tSPx41QXSp8avA+m1GSy75F7xliPKIXRCMQ3Ld3xFusPgGtddzm2NaA0dJdmy+X0yz6VZNmbth5mi7P/rl782qU/Ny9N88fST0PlYv13qACDa0MXtvMjEcpaMNbyRkbet6AbT82c6YpvyrJfm6NFajH7hUDyK2+GrRrdD3njC9fvjRHK3/8+FH2/nbxwrwM2Wz30u9znMSd+HjE6emI+TChwSDstihap5BgAUZdAc0pFbC2XGX/MyNL+VTnqbVh03cm02uDaz07pcdWLG+lfm5v6PZOn7fPjuVCoeE8jirWYIxtCejnvC/3Jl4qNcFWoJc7rLbTLo9yoof93ouI5mraKftURt9aoEYVRjH01shvNQ81ocEW36R49QB1ymhvvLIAlnbfFAyRB1yCEdo7fX97LFR5Q23PoOeqocdokrnhB/Zw6RlV9Bu67drR9GGH2YYnMA2SyRWf142nAdJV48OMGILKNcGY12VBoCgHNhwOEq+CxSuBbZxQxy4qFjU8GlVhJcgcsMDAJRaOKtRB44+AMyIIYZpA6gUAlu0gIQRA4y2Y8QJl6WDFmldrMGKW54r3MAWESI2nqDxqnhuGt31siaCne0j/2ZaAMh66hc0WWkkNCSCSQNTTBGLlRTmDsixeTlRP9UgpIwbCE0XwHJBjQCbCgQjfEIGYMAUxTqzriuV6RaBQVvLFSCRxVta8YkXGcl2Rl4Soiq3EC1mQkHGYD3g6HGTLz7Liu9MT5ijxIIh1K5IaNkKMADGyYqoYIw6zGDSIJBhp0HctmCgcHrW5GNSo5mNsCG2I7GFmUK5Y5nQ64enpSbe/icL/7vkZc5xwuV4Rp4j3797jME8an4MR+RlzjJjmGc9Ph+KNRDhiShNOxxOOp6MeRWveggmXyxmHaUJaV6R1xS8//4yff/pJgtYCmKYZBPEkgaJsIgJPM8AZZ/XqWHTxwPDgly9finw1eWTe3D62jM3XES/3XoIEYNJjiimxwyum5+gcQRSd14Enm2vCbxmUqcTCmqeIuSwYEtK6ILHQwhSCxg0W3UBirxxu8JKaHjaC9MkrPaMkTFGsbwyUPb0hSHRl1nggwItEhy0KgAYz6pTVvjxToO2Tsa3No0aQ/vlGKQbqUbRE5Vi7u/0zeGZkBPGCMjggCCt9s7S4NQZ44cRog4WVlvYge/C+JWOYeSCc+nftOKkiCE2R7cqzgLUhBBynNk6LyI5tnbxgNVr6GkVnT2jvtXtPGdgzdPTP+Hu3lLn2GRSl2I7C9MC0eQd7M06AXANzb/TTowDa97+nyd6zqP60/lamqC7FZtwgtiOdK3jM4GasN8Csq5fUI5f3fQUaQ8sd0D1UwDfP7L7ezjnXn6ZoWW7DLNxFMyaYsdDv5fdbZfqUuKoDdt/zl36MbZ5a8vE6RqvQvRFEfgTV/M0DhItLpCnb/shKAm36+d5n/32UxnS5/14P0P314Xt7xavCXPStUR1Gr91pz/g5M4K0dex5UjF/8GjWuLp2vHzLsx6vq4niewaOe/ftu20HMRBqnhHruuJ8PmOaphJE3cBaH1/H6DSlBFqueLucEV8iQgx4Op3wzTcf1BuR6p+1WY0YKSVcr9eyimQrvLJCqQpKlIDvUY0tMYZiLIEZNnPry9b3xzxNmFXBsXJ7vjuSTSMj79ca/vw7hW5UIeDBM1Z2awTraa0zgGwKFMWD7IxFB2+4MWtUY4RFMqkKpD5JbT/1ZkeTL/b9FtaReyI5Tfn1iQff/fvVi1RjPpk798AAXMtTns9SoF0rWAiM5Ix2KWdwt6AwwhrN745HVcOlM5aS3e9aPMJJvkwqJqLWlMatIeQxjidPbthvF5zbG+GnKKdIxAAgZ8hJLhFPxwPePZ10ka1uWRCsUl3trQ3v37/D77//Lb7/7jtMFPDx51/wlx9/xMvLC8AZadUZHwTzm8GFc5LT2YJ4BbBuv2C0wcFBogNwbRSynn5FIYinkNYxKC8VniYqmcl/S0If/nhYcsa76iUrWF/yzU7xJJKg55frgvNywWVZ8HQ44ocffsDpdMLL2xvScsXKGZxXnI5iQA4AIhMQCHG28Q1YCVg5gSEere/mI/63f/0v+MNvfoOZAtK6ArnSXkqres0seHs747ouAIWi9B4OB5k5UbxfDicxfpzfzuCUcH57E++fnMQQZp4aMWj7hDevKSFrMNY6p1CDloaAWcMymJzhnBEcNYcQkLJ4s8yHA4IaQJ6ensA54/J2hhkR7IRUQOTIIc54//wMzoxIYpzhnIGcxPyVksSuCQHX6yIBQ6dJvDXsJB4wzi+vWJalHKnLDPCakFcx8hAIMUSkdQWBmsWB4jWyrkWWmdeHN+YUA6t6hQDtFiI7MbGhuU42eRotiwE5Y234Ut68XxEyFdlq2Hldq5FG8k3NFnspU9oRJ/PmcVs/eYIOyUPpq7bDQCst/2/32XpBbYK1CC2q73qhcF0uWNa6BykE2Uc5TfWkmJHyK3swSYi3KB4Kwh1TbcFCL7q5OfZuCCyIwNr5JaChByP6TFF6tIjR6vKIiCxfQIVqUz5vhT6HzfsbwDQo2wwhvcDs32/A9sY40bbRfjwCxLzCxRpR2ABtCGYEaBWvNt8t4Cl33Lj5PEbAsRfe1lab8N6iuQHzRM27G4XEMRX/fgWdhMz93sMtKBsaY6gbV0/jBrxc5fq5+WvSWInpac0AlruWE3JeAIgHj0FNqAGlwERlD73RcQOxb9bfj68ZVzRAm1ryaUA7zVnwoGG5eQCMe+OHN4IU+iNVFIjGJMs1r36s/R7MURDVxjjr+hUIpb5U8m5Yk+7dJOVpWY7Z1HcauxajvlSAo2TUt79dBa+0Wt4sBr6R8t0D4LEiX29vafqWklNfsw6vbfLyqzciu1YPeAUaA3ejIOxW+75KsH2mKi7e4LA1grS9VvPZ9k+fRymmGFrs0oD/9DypralvyENt99d7r6J+oeB6vWJZlmYumGFkFHC4WVgIAZfzBSlJwDSjWauDPWNB2i+XC5Zl2Y3RkVLGggTg2oI6ZpjUvyX7AT09i+pKb1/OyMvl3u9HeLw9szlW9oExGxnOXM4o9KZyatsmrsFtgQ099d+dygrhOzLvDPfJuOmqsNGK1aLBf9v29xhBZK5JAeUOVOdX4adt1uWoU9kyEBA1dlzp525bbdOXrk9b2g+IqmAnJ5taQ7Upu+O5nXk8h0fp5kKBtp61yKK0k2Jnbt/3eKsPEr2H3vpqST23niX2/fr2is+/fMY0zzjMQeJ/XIDD4YhpjuBEsDiDhqdlCwNgseRijPjPv/stjvMB15fP+MO//Gf84dtv8S+//QEAixdJiHg5v+L17YycxQV/uV4BBhZmfPzyGddlxbKuWFLWE9FykXM5sxgUwEBQjy9OoAANiHnEb779VoKFkmzXuF5XnM8XXC5npJQ1hqJhUpHxNmb1ZEuoebDqQ8waozGLQQDEYAqYKGCOAcfDjDUlyZ+B5XxB5IzILMFjJ9lucQh6KIQa4y4p4ZwWnFPCl7Tg0+UNYMIBAR/+9b/guw/f4N3xhPeHkxtY5bG6LWNS74MMLh7DzLJyv+aErNvxEag5WjhdF6Q1YbnWYKdxqkfNykAHLGsq23yCo81yWghRCWotHiESQ8Ocq9Z1wbKsWBLh7XzGl4+vpZ9NVkigVtl6mS9nXJcFz++eEUPAmlb8/FG2d6Y14bosYGIcpojT4Siyxk7bSm3w1uM8i4MAi7HM+sYbBAjCGyUmVpVfPvbguq5IOWG5Ltq3U9FvjD9ZH1hMEluIMxmZUioemHYASS/vzDDh5ygzi5FQf6+rGHrk0JO59vG6ioGnGDraA1cYjOtyrVyDGSGGgtuWdcV1uUrfUMDheCjzxQzh91GXpIeNIEndt8zaSHpsUO2A6p5VntPJWK6BwZwAcnPErVDmkEEhIRCw6BGbwnxlFaG4HtYMNSiVgiPthMK40QqBQK4+JB2dUK/JisBWUJskTbpvPpMexUO09fjgvc4ngFWQOqFtv03IisEHpa0gH3+BUGKxWLmd4UT6zH0f9EP/OXpOyhutEDctAtBufeFymoJ1him9VDpHXOuq29o0iRCwCeYVPg/q+y0xHtSMjHC9UcQYavGbUIQju7ZYXCBRPTT6xhbFXRVJ5UO6EqiAxuJhZAcYLZArSIPSmVKIwjDaMalgwNZu7upR1I7dBpygzgqbm33qQV5zr6OT9p0WVFukZiFFNUREUgalTIqhDG3Qjv7SAwB/O877fzI+1DwPItPXay/oXN6bHwA2CpW1oQZlHJ20JP3fK+H+ORNEABqDobe8m2GXlGegjHMF7n17pE5abib0J1sNenbnOrV0yoM3OuV5q0Q5I1CvvQxL3I85sGcUGbXFC8gebPdtGObGznj+qKTdZHFD8fR0yHSzD3tl51b+QyNIZywxvu3z7cssgTXrCy6ncfn99cYo7mgoZ8EHBmIZNmchAQATsKyEsASEczV02CqqbZ/p5cHLy8sujfdbFoBOnnbJMEaRs13j90x5ppQBQCYagksiKoB0Wy5tPkfX9lJDNxvcwsOx3jP6tmVVFTdb65su2Dkum8e0C6DzzHCSSWmDQBIoL05liwJUvnc59ZVp75Z+s2ftrW3fWi3st52oFYK55G9BvW+fYLzaPwxGTgyzbzCT4AHFzZECECBHsDolh7mZdrWXVDHpF0z+HkPIxpIBxS1d3iNjSLln1/dL2fC/3gBS8KNi4pQXvL2twLoADKS04Jhka0KcIuYQMSn2iCTu+fM8la0qy/kFp0j4+PMv+HmW7TNPT0+YbHWdGd8cT3h/PDgj1DucjieEOOO8LPjv//Zv+OOPf0HKK9ackGwbQc51HhAhc5IjYWNAnCLe3r7g5TMDvOK799/gfL7KKR2rBomcJgm8HKq3kZCnAJIpBtjRooI5jWYkmXe78EQ/PlSwedLAlqReLkziIbESxDOC9US8lLAw4+0i3iOXnHBBxoWTbDEhwkwROa94/fIZ70IEryvyukp8JhIj9mGSrY1X49eTBl8Ogp9TWpDSil8+fUTUuFCypWWWGCDTDIQJT4cZaV2wpoTX8xvOlwsA2WKzrCsuVzViLPLMsl7LyabH+YgYY9mefzwc8HSSLRN27Xq94u18FoNM1lN99JRRIhLjAq54u745GQXwX2QMYogILH4lduKXGGOyxsE8YIoRnLlud9LJ8aZtCSGUCZ7WtXj2p5TE00UnoC0CTNOEy3ItnhJTlN0WCIR5mkvcHGbGcq2LkxQIFAOmKUpQXa2P9ZfJIpvPOedSZ6tPjQHEMExIqhillDDPss3J+imnBAoR0yRzwzxYTBZWnUl4nxmr5ISlemSzxXGR+iUseUFaM0pMJCdr76Wv8ARJ+qkWKRaFvBFsVK1+tgIrHQiYAWAvFQU6y5E7pqGKl0AVLuV55MIEGADpHropzphnseSKRbxlzibEysq0V+EYCNwqLVsBUuOIlDqZPk3VKufra8oIu3y8scKIrNj9ycc4USWrSh3LtBpCXDljgGRuwLVd/edIibizLivC2Cn7FKhYpd1TG0Dkf8qq2wozgvjVvn770F4UcQ94+2uj715gj8DjLTDYA4Y+LkKdmK4fbcsQavyJcn8wFhsDyAOallmX98A8g+WEhRvJQc3x/UInbo6UOtaUkgRgsr4OIYBy0L2sYpE298MNADPa79pyW7ndpj2FoVqxobySGlprysW+kjzql41BUW42z8mX7C9v+qAv0yz1o5gIBo6JbJ5jUw8iA2Rd33iD66Dv+u8tMB0YHZzNpQDXkeLeK+jaT1x/bZJwxv089uan74u6crZfzqOJ1ag9Xvd/4P07RosyGzu+0PCJwbv7xo2x0slc+XO57+6Nxq7M91EX7ozz2ACj1ymDdYtbzZf0SlWwvdEh5wTWBQLKhGUV+rf9zn67pQG1BkO4OdafkgDfh2TKa22ul6fNXCnPjGnCPEj3tsX67yN5TI2Sv2/k7eu1l0ZzJbvx6eVqfzpMS2sDfuDyHdFq91T3Drp36okyVmZYE4hqwPppmuoJhE7ueb5BA8Ld8N/y//5zROS2SQO2VZAhgS/R9JeSrsdsAnXL4olXZoVnWu3H4zQySjX3u9/990foo08tNmz7pcdfzXvwFLJN2ddrXDJsPJ6fTvjD91VmT3rSyWGe9ejQGQzGMU44hKieOhJAdZomEIB5mvHdt9/i/ftv8MP778CZcTo+YZ5mvL2+YV1lwVeUvYxVt+pdLhf86fVP+PLlFW/XK768veCSErKeCkMgTJFwmA9KDqSGMWn96XTC8XTCx4+/4Ho94wDC9csr3nTrQwgRx8MRnJPEOlklSLEt8oYoW3NyWpCTKLPW2+b5ChaUJyEFpN/kuiints0MFLGSbu0xZR1QRdjwRZW5x+kdmAKYCIkZCzNWZo0fkbC+vuJ//o//H36c/ySG7Mx4fnrG9XLB5XzGb777rngZEBGeTicEQAN1XnFZFmRmfP78Gd9+9x2enp7wzfv3OMyzbAFZVuS0qBFjwVkDf66qiCcWD6hlXcvMZYheGKYIcBuAWrZ/CCInqp4RKSUsSb4fDgesa0Jac/FcEHpnOX0LYqQXWZLrPTWoEkXYktUUJxyOR8QQZXxCx7MZxajEUGOK34oSgpxMpri6xHcjZygzrK1y0Lw/GCiximz7iPGbrF7HHgNacHGbB+YtYkYWv+hd8WidrVGNONbfUQ0nV/XmLF45aGUvUQCT1osFEaTESPlaeWTSsgPhvJxxvpw1RsoVgSK+ef8B756e1AbwGI/7CiOIWaITmJMaOgKIbbIQwHKMLkIAZ9ooOtUjQH4vyxWr7kf69ttvHVOvsTGyrlam5I4PyyvW9U2eY+ByueB4OuF4PGKNM9blgHk+AIgoa+AkVjvvasrgEni0tnMsYLwRhMAbQFVWKHYUeVnRa/u0V8yCgsBWUe9AL1ehM4ITY+XPrcB3iuVGcWsE3R1FgTtQmwEmLsYwwCkeN0D/umbYivRefASJ5HxfWRuBwJGSeev3ppkD4HCLRnaDTaIHdihKYnOtPPuI+UOfc3mMx1Ny9IClB9qlrJFC34xfC6rs1Ixe0WmAEeupQ2SRt20fa3BCwyupjxkf9tK9Mc0JTT/0rvS9IXNUdj9uZrD1rdjG54HXbTft6I0pnqb8VhmvSJVYSu6o8bYfct2i0vAWVI48UKB6UNv2d9jSLaGl1x3aHv6m2mejxGpMvWeUGhlHRgrl35sKLGfAVugefndHUWwzL8vDQ8NBnbO3+3dodPDP97RonOeWMusZmcuLut97Y7/N2JXt5VvHp7ysHH8mXK5n2Wc9TzjMBwVp0RkL6+yUtnBbb26lDGvDWs+JhE18sVJPxn5MA31blao6d9v7N5M79leqpjzH8QIr825WXbmjOGZjDLR5CtttnYOnXF4j2XKf3wN+dAQT1qPovXdpUSA6nkQAaDA+9im4MNjGg1Jusz1Gny8eOyGAsshBw67lGEiY4cvKcO8r2F+d2ztpgT1mKEpAMZp0eMHjEZvFd/jknqFt7zn/bJ+z58sjHn2PPfp8t2u4leF8+/SM/+PpBxyPJ1hw9EihrNSv1xXrsuJ4OKjhI+DbD9+ILpESlssVgQiXlzf8+OUNJz1tKk4B83xAPjKYZYvC6+sr1nXBy+sLPn36VJRCCgHTfMR333yL6TgDURXXKAFAn44nXM4XXC9nxBDw7vkZ5/MF57czcF7wm+MTsnoEBAp4no5iyDkcMM0TwOKpIXHVSLaN5IRlXWTcFUcxbGubbAkg1XGi4mZZoKzGU6F9pWeWI37FmAcAuZ5+Rv5D5oNyHWQGAjNiVoMpBeRACAGYpog4y7YbAmHNCygy5qcZn85f8PnyAoLU7/OrebLI6n1iUfTn0xEX3f745csXNFGcrPJKDRbAU4Ki6nZg7S+G5JdyKgaHzIyoOPNwOIjni8aj8vEvYojluPUpRsQwlb42ASGQtRo1g3noWkc7nkgEjbux4PlpKkYHsjx1kSvlpMetE2iKZczNyMMsW2yED+hpatMkW72CxdlcsSzXsl3UeIbVYV1z8R6xfjBaAVAMHZa8fLLYJz546jSJ9yWz8N5pmsRwpfG1zLOGuY39ZSe4rGlVGUtYU8Z5ueLtepFtV1eJI8NgfPnyol4kegxuIFyWi4xvDHg6HvHh+T1iWvDy8SJHjj8Iyh43giTZL/vp0y94ff2Mp+cnPD094enpGfN0AiiCoPshPaiBMlITYEXpAa6XK378248gAB+++UY8N4hBwdxBYwOGQDIdU37DX//y33G9niFHTTLef/gW57crvvvue/z2h98BnEAkijNDPpMqX2RWtoCmo4RRCjE2Hg4eiAGwI6Xci7Jficy4s92/TGU6oynPf5rALOURUPcZV6XZyizq7QBYtPmbkjRY7R6ATRu7jULR/xoBdD/mBlD7QJod6CFqha13wTI3NXN77du5Z/zoFbl7Av8WaNxVCHYUlL08vO5Q+l3QyvjZB3Gx6A7VA2dUB7L8uB2fDeiRm5t21boB/UonczuPRLCZ0VJgDWexlhNpbCu0CkBD8yTAHsBw3B7qkp0xb+rsgKRn/t4I4ueM5dWMO1VgK9tLUJWRUIVkWzjgjTxD0LhDU1bPLZ23hs7yjMbbqUoSNc+FEHSP99ZDqplDpd7OsLIB4miYBDvaHo5BPyC0P849bY/A+t51z6tH/O/XJM/xelnr1YRNXOudedW+z26KbeMK9IrI2Mgxfqf5zlXm9M/0dW2VsraM0fuP8kUArfeBW32UeVFbOpRT3BpCAMaaMlIWMEhEiKEGXg8xCsh1AHZkBNnyI5fI+BrK+wzIUVfMSJ3H4pDPcnUxFkVE96hvkqcvgt8eK1nKXLQLRDZXqNy/ZxApc8HxP3/Pj507zKt89rTWfx9du3d/L+09YwaQZVnaGDExSnyBgrFQt5wUHmAYxPq0Br8vPaf8aa9OGQAn82CWD6EnOTJTFvO8nKNiBPE8irVuxejBtY/7+djjj2oEaeXxvb4cGTkamvFZkWFDlOd6TLXBjYNrj9fL2ifXfvjuO/zuw79IsMoYS18RiZfEl5cXfPn8BZMqS8fDEW9fXvH69iar3mow+fDtt4jThPNVjtd8fXvFl88vOL+dy3aAEAKmOeLDhw/45hsxpEhw5gMAWW1/fXtBCLolKtZtWVPMeP8sR3YeD0c8xyOuh5PwF90ebIsa+Vh5TwgS3JN14OXUkQQi4Bgm0CTba0wTZ+2nQOI1EkmCmbYsyhlDYFhBFN9MjJxXMBOChqjc8itbAIeeDidPCn1mqbd6vES0/ISDbLcAEThYvBIgksV3EI4yUyy0ZhjUEx2VLeWVzr3RF6Sn/QQCI6tOFnE8vsf7b95juVxxvazCn4sRGkiLbiEpJ5RKTAvWPjQUG4AqNwI3rMDmr3mhzLNsQZGeEy8cw/Kvr6/VQOs8FANJeIU1rUicS4DtZV1LvtfrFYsG6z5MekSsGlwi6RHQejRvCGJUM4+QooeSBFm17dUfP36UezGWOJyrbjEzz7o4RRsUAG0MLQDNNrPT6YS0LLher3h5ecHLy4vEn9GT3syAYnkjEF7Pb3g7n/G2XPHp5RVfLm94ywtIT7vJzHhdXnF+O4OI8O7dM54Oz5jmJ1CS2CCfz2fE4xExSwySX375iI8ff7nDaSQ9bAR5/fI3/PlPf8L5/AbOGS8fZTI9Pb/Dv/7n/4LT03sVwJOz3LWKtoDcJA3LCXFifPvhCfPhAM5XvJwlCFGcAw6HGXESZiODe0XGgikSPv3yEz59+gk5yZFVl8uKj58+Ahzw+dMnLNcr/vCH/wQgF4YFCsgpaKAgYQJxClWwKYNA1rgOAyNIVQY6hq55FPcbrjEv1nWV1Vud5FTNiJstH7kTJrbqY9Nfyq6AZyRWOtGt7aonq1Tjin/EgkmRe7cKfVtdGEVQVw24vgObKgr00HvAcH2vu+TLai2YwoDtaDBf743Sh1bo3hK+9Z1bQtoEvgdwta719yOgTrghc3Xt3Rbr4OXjuMFq2vxuGHVfRQV9gwrsgr2mLA9oaXRPT4ZiydBUnIol3d53ctHNi5tgVobdZm5g9ZF0yyDVb88j93xKGSk51/NeiShCchuYylIIARSDHsvWgYo7yuPos9TzJqBs3eWrAlfnhZ8j1UirMRWKcSS4tmPLLwZ8oPJ3dzH39XT0brylzWSnXfXZqrC6+jSGl46HYr8P9wwn95Ntj+Bq5mvKqqC9520bohvUz6ebysMNo4W/PTaAtIat5hnUAOOlyr45O0ZtX6gpY+X3Xp3ByFmPigYgfuQWcwONFanSvoF6v/0LkLhkW2NZzlesqe5zlpNKFIQDzkgvFMbqtTbPkwLDujII2LBV3s9qZGRz1a0PqZImX7MZXthzaq7uzArMe15R2VC/UKGfjbg3A0g1yBZvkRsGL0BXJT0vdsn4tLzn7mtjRwazUfLG6VFdfo2y7H8byC5G7BiFDzucFZVPVkW+ynBTKvo6yurt2LCeUkIGXCBG5zGF1LS59pvukdcFn3ZbbZXN7MoaGUDsdI1aXvv9ZrrDH+07sSPC8qh/1xkuUTFunQI35PBu1Xo5J+Uc5wMOpyfxeIhRTynTWBox4pv33yD/LossynKEKRHhcrni9eUFnFPp97e3N1w1aCMRAakugJjB4927d5jnqRybattWDoeDyPz8HeIknkASeBOIujUnpQXn8wWvry+IgfH+3TsAsnU5pSyBHWfxEDhfr7LqHWS1f5onoSvOZQtLUM8RO0CUYsB8OEoMCFZvqDUjp1V4UqDiEfD8/IzT6YScJajk5XJFXhmX5YK3cyoYW45mR2s4LLQAEIvhZAoEUDVCMRis8VCKIc79JjWCgMQTJOmW6MqfnKc/V2O3SY9AuuU7i8wNQU+bEuEr9GdHCBMhJfEQuZzfcL1cqj6aZVGWo8SQjIdDMz97g2SZlybTSfIvBiBNUwyY4gEpZZkNnEFMSFnkTs4Zb2oAiSEUXU9wJ1Tm1G05mVmMDyTxUzhlMTZoLMqAqkdMk2yZmigCs+EMqa+cXlYX7aPFk1GsZx4hIMKyXut4K/9IOSGmWIw6xhuZ5cQfMeiwxnVJ5YjlVYOLz/OMaZ4xHw7ltLeXly/Fc+Z8PuN8vWBNCUnH+Xiccb2seHv9gst1QdAAuIejBRJP+OXnv2G5ioHndDrih++/x/Ew4+PHX/C3H3/Ely9fytaZe+lhI8jPP/0F6/KGKQLQ/UKcFlzPL3h9+QWyReSAaToKI4iT7mcjtajpOdVI+Onj3/Dp4884v70iLSum+YAPHz7gp59+xromxGnCfJjw4cN7fP/973C+vOLPf/43sfKHgLQkpGVxq5cBCQzOEct1xf/8H/8Db68veH37jOenJ0zTjHfvv8F3v/k9QpghlkyAkQtQMAsu637ORkVyE8MbCopAY5nwebB0z2CkzOCwDXaWk0Zf1n1/1GsR/daSYhiRSTNKQrtuFZ156JLf7MMk71bvlSQNgsPChAm5AjjH6BplYuNt4hQCLTPop4eVtm9RADhKH8spHhk5LyBamzb4evcr9nvGkSKmu7r1IMWvElkdrf5bBcqU/fFRo62SQEpfqgB0z3vPkK1b6DYZ+Mi2P35rBnPPVmPEngWNNsaXndVJV/6WvOrqTQWYVNwVrfVGn+TuiRF/O242viXwUUtWUuqm0eP+8ytL2xb5527c1PxtfH2UbKszkig2vbGTyn5d1LaY5sQ131rXR5MBGSiyh3bKPh0JeBQAGUtdDfRJsL/SnqblBnQdOu5Ih5nLakdZydH+oh0vmVF7jWb8rhNv7KmvtDx2b6VzpDyNymzu+fLkRlu/zXi5FXuXfQCknzpWf6suo+vVmFF5SXuvrf+e8aN5Rr9L4NOW//Hg+f67xTTw/cOD53wSY0uWE6Ug8tUHBg0BNdJfkyMAzk6RQ+EPJtOz4yWN0oqkz9hqnckEm8srQIxlERf5+XBCCHNZPQTgWCgBlBFYZ4LJlVH/OtnR0+FI/ojijbL1uN8Gt2eI9nkUbFMWQrTO2ml1lKgQZRrUq+ZnJ7J4A8t2Xn2tYe/XGkL6d6ohW/edr2tR6EwBmdwpLtaPtwyhta8NMwDGo8lN7lu13myRtb8NTYzn2pCGUD2ER/Ii3KhQf/JfzyMrtuXCe9v6V94qKqLF01HayHkjdfaMX7VHt/fa9xjv3r3Dh+9/W4NAztX7POthDazjldYV0yQnxZyOz/jm/QcIRI1Ykyhkr2+vYJaV8jkecDqdpO90y0BKCS8vL1iWBYfDAe/ff1OOW40xImc54WNNVzCJzIw04fX1Va6vCy4XUS6XpW69WJYFiRnTJEdsz3ESzxYizAeTy3K8qhlhlyRBMqfDhOenJ7x7/w45Z3z8+BHzNOGH73/Aly9fZCFYlekn3e6TUsLnz5+V7qgYWFK2wMxqWIFu08otRrK5zjnLFgaux0LXRcut16IYts3IG5U4TCaqvHB8RF4mZ/wT1EpRT5XJCZN6lDCFcqpLUKxxvV7BLJ4Oh+kJy7rgeDqAU8Lb66VgkmmWYJ0gNPJmy5tLQAU19EBOqdF4JEYH0zRpTMQqi3QN22EeIHMqmKjpW/09zZNu2zEZoDxKF+4NM9dFeu0jzkiZEYIzbABum8829l0ju3NCjWtCTtYxZKtLNWbZ3IjxULzvmn5jOcr6dJIjq1POuF4uiDHi+fkZmVe8vb0BAJ6ejgiTnOqzqPHu3fyM33z4RuK+aIwWZsZ0mCVI6roiaSDhw/GI0+mEaYpIawJRwO+++RbfP7+/BXub9LAR5NPPH5WB6N4zyCkpaVnx45//jL+FH8FMCFHOID4ej3j37TcFpMhxTAnn8wu+fP5Fzi3WyNiXyxtevnxUgiakK2FNhLfzR/ztpz8hxoDr5YqUBFwEIswhqqDLCEEDrAj14fVVymFk/KQTc4oz/p//rxnffveDgC6QnsfegljbL7qnhOQsRpcehPSrNP66vLc1ggjoykgpIIYayK2uFrBs2eE26NQIyDfvqcAeEbslExR7gEOYcG6VN86wM5u9gtEoG6QW68HWm1L2DggqFuS9ezv1rQpy7Z+t8cPGqG4N2iqnLXi8VVdfL726qfm2zhVEsTHHnmSMi3ZpRFtbBc2Bk002W0Uid1kKuW16f1Nue7f3P7FAnra9xPUj2RTVIEjdePV07tu8MXSxtXGrCNT3WgPArwHXfT182uMRIqdI9Y6MvCYg0IbmmvwVFIhce9wIssdzaqyjsRGkF/rtKRX1uFGLBu5jGGzzCiAW8BQ0mBxYePLlfMbxKEGqzucz3t7esCwLnt+9w/O752F+/fjL71wGWGikPO3qMRqLB2LL9O2BzR2vmOkNNeXvKRB9rpaXN6rate3M2R/vPSNI4THU06NUeKNQ3Tim1I5O9TzrFh32imuv6I2eGd7PZ3BOIJrUACMeYEQibwPMYOYCTTKqwcFkLDIueuTgsq74zW9+Iyun1M8BBd2UC0cmZlyvX5CyALplvYAo4N27b3C5HHE8PoOiKc9+Tz4hkAReHMmN0rdZXJXrNkFtf2eELzW0uai1tfnnAyM35Q363GMPn+fueDiD6R4dmjdmUz61Xqxt+aYJdEXpOPh3bK7e4qt7aSSTjU+0O5dJjuqcJlWQ7bQy2yqwxSx737f33MIObZcj2HnGSa/0uMYWFGo/CBva9m3FStu+qs/c6C/9u4VztCGgAadq3+u3sLEarDue1V3xEsp+98aWUd3m+YDTSQwERdI72WQLfOFJjjBmQB2o61g8x2d8++HbEmvL04xhdTN2HI9H5JzLaRxWJ/tu2wzm+QCwHOV5vda4BARWGVrH9XK9IjNjnmWrfs5cTsjIOes9yfdJvVle3l4R9LSNt/MrPn/+jH//939HIML73/4OMUY8PT0Vw4x5AFwulyZIJUhiMljMhu14mqfdAA+rvpTSCs4JmCzGRQ3GSpDtPjHGMsYhyCJn0u0zIDHSiXFE+anFGbFFIB0u80yb5xkcA2AnTQUXIDQDpFtApmnC8XiUIKIsx+vK1o0shqYQQSCs61K2o/W8sbRdDTUmtOVDvHMimSehGNwQ9IQo9bKIrk/7eVbzt36rcyAPFrgbvsNtvxC44fH9e9M8IYbJ1UHutScbtuPdyxczyvVbxS2AqskqKA4wjzwzNh8OB1x1a87T03OJL0JESCyxTK7LVYxc84yn5yeknLBqTBDmuo1JDG+QEx5J+s+C+ebMZQ7dkhc+PR4TJKvlOdUj7GQ8GNfLAmBp8Pbl8gWfPv/UDYpMsPkQVQnUSzUGnHBBYmRdWL0uuWCdsmDBjKRBWJmBdU1WSXkgZ+TkyiWZlOe3L3h+fkKc9Ai9rOdR63nRIlhUYPOY8VcmsQXbo09jliMwXiefuk+lbl9/aPP27w3HyK7Xsz6b61Vp3ipZ27pJwFLAG75YLYZ1Ivu/nHVdQonVJsnQGHKjDQbEh/cG9e2V591+kVoAaIPX+snt29UraHvKcK1by7zMWFQF55ZRbYHoSGW9BVJQ6daXvZNPV9TmyvZaC9qBToEevCX+LRYzQplCMYD4yVzzYz0RypQfyb8aC7xLH2Hssq2Z3VS8jIZ6obfN5lHjSQvrWgU9VD6SACZCDgkU1RhCofBSb9xRSVh40a3YMMO6CZr3P5rntryqnR/+2Nzr9TqcK42gBJAT4/X1Be/eybZIcRWW4HLTFHE+X/HXv/4Vb29vZU/oD7/7Lb7//vvdtnljllANw0B5oUn3wcx3ad5l3vxsYmNgq0y1Cg03el0uxiutF0mfen+fsiqr84GH5kNXnweMDhuFaMMbVUFh9/6NPDK2ZYw++zq1itxjbSn3csZf/vRvOL+94un5HU7P73E8vkOMJxTazaF0YBkBdvKztFV47N9+/CtOpyNy+gCmevS6KH31eNs64hnIGZ8//oRPn37BFCOuixjvfv7rj5iPJ/wv/+v/DSFHIEWAok1RMKqHwYinmHFI+h6l/8VQWZ/bM2YWZ3HlfQUIdjKrD3xclFHHD+/iiO5kvNEzfgugN4LYfPL8NRSXeltN9fltt/NuaHrAu0dSa7++QVXQ9p4cNbliSlPtS9dvPqD/Hq7z+dVrtX5yrQJiz99rfQA0342uRXbKfFT6HcxFlZi77e8xQDO3yWTyljZaLMV4hKty9x6b8tilkf9ypQjsDW99P2eAM/KqHmRAMRJa3S24JADEIFu92fU7GOKhDrk3wvrMjFVj+83T1PCawkv0uePhUDy6ZQs3YX6eYcEiDdtEjRuYUsLheJStLqp/JOe6bzzD2sssit3xcMSKjOvlii+fv+DHH39EjBH/y3/5L3g6PRVjTggB79+/x+l0wukkxwYzM37++WfxULksyCHJ8bMU3JZ9m5PqlV0aSQ0hRYurxKau6X0yXOW91kIZE4mbqJ53LMaleZ6xcsbhMGNZFry9iffM9bqASHYU5GzYkDHNh9o3Ntf0VMqg/DJE2Woz64L8siyIJCcJiXJcT3Ii/w6Fpp1Cj4Y3HD8KATwJVoXjGyCUE2TK+/qt0o152cCgArwkYnInlDp50eC3IF4mpUzihj49BrYxsHAMhYfKhK15Uj2xRuSZ8MTeu8fLs6LfmK6k88jrgxYsFVCjTZBFiel0hAVITWnFYZrKccWABpoNEQuWEqdzoogpmrOABKKOse6mEC9SRp4nOaHrwfSwEaT2hPah+Sb3/JHbH0X4y+gAIFzPGUj73E4iu08ymBbhGNAIuIzz2xtWz2C9nlZnblOn5XrFf/uv/xX//m//hvfffMBvf/ef8M233wEsJ9CEMCEouEHWCaJZ+NMvqhLXNdsBjiow7V4uqxGNsHE5VQDvhFKAYyxbYbwHaiosrEi9ARUD0DJKzD5IqYuO3JXnreNGet6QEGPrfroppGuDWWfvKf6j76PfbdKASLp/zeo68gzxRpxbYFJLRU8Xl8tFV9MjlmXF5XLBslwRgriFTdPcT5dflRi8sR6HzcQc13h7ba9te5lslTnOCYmvQmYQNz5TPAT3VZryQNH6sBpLcvneDqnkFUIsgqTNo/UUaJTA+0wLPf/YIycaLFu1tJe69gGckixNpgzWLSh+PkoetBWANww220pg4wnieYMJf9aVS7/lzvdXMUClGojNG0TM7ZKzBBh7e30DQVcMYkBKsrK1LFd8/OUXnF9l+2PKuh/6xx/x7t07HA4HtGk7Pm40iknE2qNNHqhVfZ60uWRtlbgUsh97XUUwp5QRY8DhODcr8MzA9bpgXSRoWVL3Zh/k8nCY9ag2CZJW+KJTy7ij+z7t8bWhwmdydsOfx0bB0Sd3z5TrD77/aP19Sjnh8+efcDm/4cvnXxCnGe8+/Aa/+92/YJ6PkGDrNuaORgHYAErdE9b1CuYr3j1PeP/+Gev6gre3M5izBsk74Xg4IVDU6PRv4LRijgGX8yteX35BWs8INOPl80d8+pjBOeBwOGIC4/e//wOWlHE4nRDjBEYAghwfyL0XZ9duZnY82QB0xaLSNeRoWIwk4gVTgbWs6GoMFVXwABQjSL9St9f/4/HKFR9rXbfePRWPeONSbuiAiyHUYo3F0EqkEcUzZ0dPm+q5N/v3tu0yw3q3sbnct5MKvKz3n1EBuylWlmefPB5jtONAxqsa3LDlQ+y8jwVD2Wo4AJB6kOSCR0sfbaBUh5lu4olWDvZ92Bh7bmXT5djmt4MrH81kkN7e3vDj3/6KWWNdQPHBrAEcY5wgnow6LzKQeG28l0MImMJU6B3McrKGOyUkq5eJGTd9Gi3MzHMGQU6oQRk3ic+RsuDMlEiP3QWulwuWtJY5HEIsSqNfZDK5cj6fZUFijnh5e8PPP/8NCIR//dd/xXff/QaLnqhiSufxeMTT6UlOoGHZAvS773/A+d17vGjwVyJZTPLbGC/nM5ZlxflyxVU9UwChtRijGC3WFZMqyZyrwSZOhClKvJQ4TZjiDGI5VeTp6QmZs8R50K0Rh/mAeZ6wrCviNEkMkXcJedVT8GLA0/MzDocDXl5e8HY+I0Td6raKMfh0OiLGCdfzWWNQHLS/3nC+XMAATocDTsdTqfv1uoKibNNYcyqxnIT8WHmgerTAFjO4YKcqayE41DybyXBaa0w1bkcqizl287fX44wJMzSIc+URZIYajY1S4kaaIAH32aGaWuriSw38bHzcn2BFjVEYLMFiSYi+4lUzugDlCF6/IGJ0bDRdDfZc5oGVN0fx/ixB0rNywZwRwIjzJIa04OJ5MRdjJnNGXi3khs3RxzjXw0aQaY665cQJKKqfVeF3t9hdKwAtDB7UPlXXpuM84w9/+AM+fPgGX758QYwB0zTj+++/R0oJ/5//9/8Xy7WND9ECQGPwvhMI63LBa7rgfPmCT19+wh/+079gWVaEEPHddz/g/btvMdHUNIgJ6tqn4UVd8DWrt5sRpWkiFBWuKxELgK4EWbqPxZ05I7UTItdVcSvTAkIJ+HO2JfedkdBt6dsKR24BUtOVhYDt6FoL5Nnu82TtIGOUtU90XyYA1ujWOWhk/hL/pC2P7dOD8geMILcNHlvgYoHEPBET5AjmJa3lCgGb1W8ADbj0xpHz+YwYxSXSrn359Bnvv3kHBMLnT7/g06dPxTXxeDzi97//A57fvdf2UwWgDyq8LVBp+yEr0+pXeJr3G4OhgHITB7X/1bqaxfMqECHqPlvh91SCCQMAZ8anjz/jT//zv0s8jPmIb95/wOFwUrdH0vkRmmNRhXZlBSIrYRdQCg9EGJwZ0+EIRsa6LiASy7VpFSOSaK8Z7RD6rjbmWY0vLR22K4F9jg5WtgSuio7NfaG3nDOSzudGSCiIApszrcxBV8syXpuiNANufgP+XAdybdeeMNwGowcxjrgVdC1lVcBghhRA9siaq//y6QKCCStZwXp7Jbx++Yx1uSotybPXywUvLy8qT73SZvOrHZNR8sLWTLCyclrHr3SBI/Wc5WhKWwVb9Dg2c0UuPIKAcNbVRc0rJcb1csWqoNnXzCuNcjrBAe/fv8f7b97hME+FtziqR0E8O+0bffb39YfmtFUKCx13R7WPjCCj9x4xOv86QwjjdJyBtIjxab3i/PoJL5+POBxOmKZZTwYLiPPkjiJk5RVASgs+/vIjPn36CdflAk6Mw/GI4/GIl5cvSJxAkFMh3r97j9/+7vf48vkzfvzxL8hrRqQ6trbqaos3nBPObwv+9KcVnz59xNv5FfPhiDhPePf8DX7/L/8r5vkJnIxnecXYstW6ulaXe1TxgnaHe4aQQYqNKuUIzWddvJEXsqINi9IfOoO+z3OUNmMMgm3La8dsG1vEqLc+VveP21+k7uhxBdGevvpttoD1TfVGvZWGxrpSty1tjsrz322VmMr3wX1vRFFsYziPAISOb7MaR2o/aw393G1iIkCVdS7eIDuNl2fQ0tzoDa9k99dqdo5vuuf8gpd7GgA1W1nGNTXFq6uYp6U7eC6nJIp9COA1Ka6OWDmDILEflgswzXNhrxLjIqmLfnu6GrNgFhCV7ShAjfOQ1CAuC1aM4+GIeZYjU5erGApiDJgnMYCIO/4ihytwLrzEAqnaFpTXtzeknBGjyPIYI+Zp1jgkK86XC758/oLXt9eyLXWaJ6SccNbFte9/+B4fvvkG6yr1vl4uQJItH1OIErIgJSwaA0OMRTO+eYrIx9SsoE+TGfrluNqUUbfMLFesyyrBN2PE+XIGGHj//A5pXfH6+or5MBWFPFDAmlasS8K6rEhpwTy9w/H4hNM84XKV44Iv5zPOb4zn52fMIcpxuykXzxUAOB6OxdBwOhxxuZzBKePd07PE/TgcxLtUj0X+8M0HaUeU03uWZcHr6xtSEk+a04fv5JjfEDDNkxh6lkXkCqsesC6FiC1GJDOXhRGb7/M015hpSmcprViWtdCyyFWd34aRHd2b3K0KvhiWVg3vIAa7ajDIyR2RqzxWFkEtT/HCrnOJFD+HBtMWw0iZCy0GqV7XKEx4OzUr7+SOR5gnSL0uMVGK3FBjyqSnd9kOA/LyhhnTQfQp08Fqf1b9xbbfM1dvFhS8fT89bAQ5Ho54e3tDmEKxlhqzlQHZKgVCLK6DFLQ0mzRZjlc6PYl7zLoueHt9w3/7r/8nQiQJgqMg4DAfcDo9IecEv8jBXdm1BjUFPcea9fihAMYf//3fBPDmjC+ff8H//r/9H5iO74vFTIKyJEyHA0KcIG6wEwC+KZCZBUhLJGdtP8sAmvJYQEMRfti6vVM9f7paqLnsLd3EENE8yCx12IKb2jXbbQ6jFcCGwBuDD5ox34AJD0LsmC+L2u5WVeujrRDcM4J4xfSewOzbJK12a22k2zC4/gbEiZa5eov4fvaGEVNehdG+6rnc8pwEnmIRAuuCT59+wXK9Iqlw4Jzx809/wzQflGnUbUOPtGvTzo7eC9xwTKi44bm2Erm9gZAApbYiYUGPLPARIDR31GBEfhxMYF4uF/zxj3/En/70RxAFUJjwww+/xQ8//IDD4Vhokyioxf4Va1pKEE7zjhFdW/bDHo9HTHPEly+fcVUjkgjsiOv1qgDiiEndVmV7XKxnoLt6euBq/eC/e8XBg/Omb52BlPvr7Bi99ipg3vzGN2ycM1gVDXHl64A+kdyHAsdyS+eaU0J6Wmj36ANAKuaNasil/sWam8rFZptIlU1I7I4J5zYn5gXFPgZGSgGZswBDqR0YGSkt+PzlIxgJUzyUPdcWM8b6tB+jkeLi+4W1Pl5pYASA6nieLxdcdPUs51z2jHvwn8HglJEvSxEyzCgBsFuQI/3ODt/bEXcvb2/4+OkTvv3wDd69f485Tkok5kWle3IH4+hl5T0jAzMXGVKBjtZJ6w7eURb5cSPIqNy9Ot66B4gCkq56tGCMQE5Yzhf87a9/lnHUwOoIhNPphOPTCRRJg9oR0ioeHZfzC3JiiAs64+3LircvL7CT6YkSVl7xab3g9fUjOGWkZUVeGUsCYiQcDkcZ45yLB6qly/kN57dXgAhvry8AEV4Pn/Hdd9/hECMIEzIJVmGd/+THwmiS2jnry2gWRhqZzoJtbZuamEZAICWPag3NSfCBnJzn4vjYhETL8/bHCgWbbMGVo62mLVv8AMC52buy7c/TF3Oto+IDCzjtgfBt7NW2oTrVbQ15X7OAIqpEWwZRu6UphKks9JjnWFSeL27ye7yrjUNnda/1FP7v61w9Dlx99Ps96G9zvcVB474wo8otbGKrxdy+tc1pt79bWYnml9KD8rag8vz8dkZOS1EepR6vRf4vaS1KpmFN8cRdy9YT82ywAKeLbn+xZB4hFk/DYhw8Pz+XYKOvr68Fm8cYC71O04R1XRQTymkwZoA5HA5ipEkrck7ibcIZy/WMly/XYoiZ54h3JCfh2NanEALeafnMjL/88c8AWOOWMLKeyHE8HPD09CReCm6cUso4THVBz5T3Wb06iYGJAqaJQFPE8/Goyn0qcY3enZ7k0AtAjBaKBTPWYpCRyZfVGybh4y+/IMZYMKKPRWLbYs7nM9K6IoVQgmpeLhe8vL0CEFx6OB4Ro5x0Y9ju6fkdYpzx+vKCP/35L4gx4vvvv8d3332H0+mk22zecD6f8eXLCy7nS9kWk9KCDMbT00l0MwR8+PZbwSIkp5QKLhF+HuOMp4N6lSTx7k6reNWcz2elMcHM1s41JTCSjKDNPTafjK0xdToq79CdD4ZZcs5I61p5cwnhoLgzyLzLvDZzzQc5F4xQDRpEnhPY837m+WvU/BZbdsttegOIx0imk5Q5Zng8EMidnhpQT1ssXjGFJ6gRPjh8F1sPZZvTj+pRjx+R+/Im8DUlhImKFW5ZFry+vI2QOBhyMkp7b1uxaZYKX69X7Tx9MjGWVBH2cnnD+fUNtjJSBYIEUNtl5Kp02XeQEtW6ysktzHj98oJffv4R4Tup7+V6xb//6Y84Xy747je/we//8C+IxydMs5mz2q0xBMhCNgsQMyKtrQ4AR+RsbkcMOSO7nnNtO4zYMmSxCvrj+RjVCNILJg+wRsDkVroFCuqksGlbenZTxigfX8+ccwlsVEFNtVL2AvkR4L/37OjdTNUC6oF+U1cHLrYWTUk2kUukb92r/fb2VtqbV2WS6Yq3NxHQWaMsp5Tw5csLvvn2XM777t2XbyWrv1mCAaENv8KxZW5t31geFjzrcl00MGENLjTqo8vlIpHGuRoIzL2YmbFcFwUaCcCCn3/+K8AJx+MJs7pqruuKL58+49Pnj7hqgCqKIvzevf8Gz0/vEWLEGRnLegGQ8d/+238twMJWMaQcCWL27vk9np7eKS3JEXVBI6ybECeybU9xw8BlDKKca++AWc4VSPnxZ3uJWiApLtSoeXSY0Pdl0ONCBJelCkzVUs+kromB+uEso17NISgBMsfzUB4gUNmfWeppSrhZ0WGKCSQAFSogZ+XtJXcVkEQoEdfBjGVdkHLGq/HBQIX+Lb/r5YpzPCOGFUucMGtgsxBc7IbGqFQbHwI1+3jLyq0G2G6UenUyt2PaLtdrMVSyaksFJOhfZvFwymlxk4ccqDCTzjY1Y8yE1zdxzz18/ITn52c8PT0VI10IQQNMqJFIQXPOCafjwUbIzd8xn5N66/i5Ovqx6xUnb/QZ8fHR/L/1+971/r7wz4x1MW9CAnLGcr6Un/Z5fXtD+BIQJmljSrnIQjvCr7SRILqlHdoU1ECAhHRJyiMUdJEQ9broEYHOALLpc3cjrVf87S9/RGDGfHpG1O07fmNrWY1S2U6O7w7UxDKTG+Nfgs6Dyk+YDRfpHneYPKhg17ZnhRCKsu7zbVfEK4aQugV0Q9UaFYvsaWlnJPu/Rp5LiwBqgvYRbGHBe5Pc8nCp9BsKT9yTZ3tpkyfMs7e+2wQXTxlYzLXcArDDxW+Jxaupzb/HF723hdJjZizLtSzA2DaJy+WCaZqKYvhIKtPkhgHEnosYYyVrg41Rm/p82d+CwRMRSVyMX3rbvcFlzjEDX15esfz0k+D3RTwuphhx1OChzOrhl9aKw1x8DZH/E65qADec441tRFQUKXvP8liWBZ8/fy4GOluQmee5fAKMaZpxPrMq+U8gql4g1+tVvEAmyT+lhOV6LTRatrQcDnj3/CwLTOsqHgqKRy+XC8L1ipMFQn07F2MMAJxOJ1wuF/Ga9Ua5GBCpGhPLuKoHJIDSNqERWcydQkCYIhJBdDrAbVuvwTyv12s5+SPq6ZI2Dkf1zpOjeqvhiIjwyy+/YFkWPD09lWfMqHSYZ6wp4enpCc/Pz1gVV5/P5zJeL1++YF1XPD8/48OHDzgej7rlUU6EmaYJ79+/x7vTOzBzGd81rbiuV7y+vuLt7U1im315KYsjIUiwVZHXJ7x//x5zSLhcrsWgw7lu9zgdn3A8nNTGG3C5XvD29obL9a3EOmEWnS4rxiCqJ7rY37quWJdFyV/knMireiyxx31lsQAopwrZnBG6YjF4kAWf3cqfe6mf4r3eWXkAweO1uuDW8RDWLdkuBp1zFxHdy51KYycXzrMYppDFE5wZxYvJ6NfT3b30eGBUz/hXxls64/x2GYLz9sX2Z4hUJo6BFVMKzXWstUC1eaTCL7l5xiy+fgXX11vKkMxED2dxW1JwEg6EKQb87ae/AQy8Xc749PkTcs748a8LmBj/8q//CoQsgXEQAIrFgsWAxBLJcuyUhCVmvL6+4suXLyrAJ8zTAcfTjG/ev8chHkCYAJokuFAB7vo/1WOxfAAvUQDE3WvSM5Qvl4tMbFXgckpIOcHczDeCjjWS801C8f1oLreOYG0cUZ+5lWwFyFs2/cpvD2xaAH8fPN16z0CjjNs+ePJGODOA9Pd7MOWNJZfLpTIzzlgWYF2XqoQ7w1HOCW9vr+ptMjXRx/dW63y9KyimsvrIOmGqk57vlzYPi6R8fpP9polzMYKMlJk+IJK/1/Q1gNPTEZfzWZh9XnFd3vDu3Tus64LDHJHXK758ljgRmTRq+GoujCs4A09Pz8X9728//w3ntzeI4rygzH/XRy8vn/Hb3/4OT6dnMGdcrvWEGs71GNsSBKt537QUEV4W4dt4EzNLHABuDSIxRlAUmqorr4QYYt02xHXPfDE82dyzlVzq+p3tensEdNPvZrzolBlvnGqTBrGiIEeucT2xqqxCTHKM3boK30FmsDMwm+U+a1+yBlEjYsRAQLpizRk5M67rgjUlXK6i0NrpIyCUFQ5bKeLEGo9jrfFGNJAcWGJHmAuodUFg4Zi2hYUoVmUgRFnBYtn3a+1clkWP0ePmLzmZxNz2Y2Pm4LryYsM0ghONvITEl7LVxLe3t8LPj8cjPnz4gMM846effsLb62sp9/n5Gf/6n//FwwdVwjo1QTuVGTBvlL4O1dCzVUhNIfe83vjI1xtBar0AwI5avMWrmYGkO0E7vCSJ6l9OWYKmU7HX4f/P258tWZZkaXrYp6p7PIONPoTHnJWZlVnV3RCAaAACgYAiFPCOb8EH4BPwju/FGzaEzWZ3F7rRhcqhqiIyMiN8MrfpTHtSVV6spbq3eWRVJURaeFIi3d3snH32oMNa//rX/xMNfgx/tGMszY+mqmmaCoMhpl78QtqTvI/c3z9kIfD5/v3RU86vaZp48/oH3t+8p25bnj1/wfMXLyVQNWi1tCAowJbW/mxr+vEBjV54nJN/SAKj6aLm38339B+4cP0Wn4KmxXaQGSL8eP2QTwU+phMvAYAlkBPhR3vCx/v48khGgc2n42z+/ccr1/JYeQ1fsFxSi7BN18ByLY15zfh4n/r49Y+BAcviSL5X5uMxMsdDITxNDOZK78wiTazgeRtPz0Du5fF4pG1lLTOyQbDb7dhsNgDc3d1xd3eXi4dt2/Ly5UtWq5Xcgxkzm1tV8ncwg2k/vton/wrEj37y49fT45i8Ny1uTbpDBB+kOq6fSQn6x8lUBgW0Au69x7FjKs7095IYV3WdFw8/CgCSAMCP3V9EeyPmwlVif6T3L9m9qXX543myWgkTYVAgvaoqYozqQCKvYRhzTnM8nijLMs+Roiiom4aiLDBGNRPiHIunBDwlxq4oqNsWDAyq5XF9fa3M4pBBAWOkDaiqKmU97BmHAWdddk0pXJFbd4wxWY8r5VTC0FgUBAwqMOozIOBcgY9J8N8wec/d3S1dd+Lu9gOT9zx7/oxn11sKVz6xUbXW5gJAmr/pXoOAN6W27sQYxf60LAXsKopcOACegIHHw4FpnKiqKh877bVpvzfGUNhCW6fJz3tVrdRmVdgfwzBmx5M0DuWcpFi/2+/pui4/76ZpNP6wWL23CUgqSli7gmZqfsxis1baj3zIDBI5J8ukrbllJWNgGoRdKyK/UceYADGF5g3ZpcWSWTXBR0L0OJtybxkvmCS8nObFj4HOuZg3g7pLsN2YHwMg6T0JbBQ3sXmdFzbuAjPIYbfRFv6onR+og1dJWSY3ryKPj2QsELzndDop8Bb1PnjVXvzTisr/+4VR0ytNkvAPbxx/9GMRqqqirmsiUlU2oEJ6gRiTmODTjXH5+X9or0pJdVo8lotSQsPye71HRZsxFupaKGq3dze8fPmSy+sr3r9/Lwuw97x7+4aqdNRNnalTrmgg6MBAHrYPgWEa6YeeYei5vb2l74fck5ge5meffsb15ZUKORUYpKqd3BMg4qoKV7icQK1WK9arFX3Xc/ewYxwGPZ4semVZUtUiSjRNE+Pkca7Mi8IyAEhiPgK+SSChOMHc15pDHLnnJg1+ZvbBjyswLDY8PV4CP5j3xSULIbXmzK0Idg4CPwqm/tGqhf4uqUnnMZRyzWiQtgAJEudAch5vOdZZJBLLSscMAsyb7DJgTRN+mrwilYYYBYzSb3lyXNkgJ5wrGYZhnuDLoGB5jfp/EU3AFZZKqtSRgDNyqWGR5M4XNl9D1w+cjl2+nggZBFkG3ZJkKkWcGcBavtJ5EIVafnG+YV/IQm1MpO9PhDBQlgWHw46z7RmvPvuUm5sbTl3HOHmmIFo2fd9zOnU0TcvxeBRaYdc/oQb+sXHQhcD93R3hPFCWNda6DHaEnNBGHJag7KOMqP8oypuft/ep/1K1O5ybB0geaYtgFqlMCxBSSA+z9pzmeWifgpN/HPSax6mwwT7SuEjvWjhKLZ/vR6MmH9EuLHCfJFP2ae9lHoNpzY3Ma6vS7V0haH1RFKzahu50wAwTduEikeZHskVbAt5FUZKE4RJwRBT2Txhmka+4/D99bjZGAWliYkBI/7Hve6XBGzyRKfW3/mgOP03yfQYKyD/L61+ePE/Xo6evp0DAEhj4+Mmm9rHT6cR+v6csSwYFaIx+qCgrum7AWYMrFHiL6TZ8lHSkBC+t2svfR54ktPP1pXXPLOxx07kvPv4E4Hj6+x+DIfP5PL0fT5GN5TGLwpLasK3VyqIFdH9a3ltj9Fy9BkspwIpPDq/HMtk5wDnD5eU5m/WK+7sPlIWlalqurp4Dln/z//l32WXuH9lifvSKURgk3g+8mTq67pGuG6nKkmcXV6w35xRlTYzCHJOWGSH9LkHMjArMO6SeR1C2qsn7dPrej4GLXJW3dt6ho8GYyMe9exGJVTCp+vjRhRmra9qiLRDmvVMXvFxV/ONTQeZp/uqPE+U0r2L+2XLdXd7jj19pfXuikZbWqxxbRHKrCT8GQP7Ycf+hGMOYXP6BBAqkAN78+Jz/4e94KsYOhqJ42l4bo7Beuq6TxLOYnRpSoaXrOu7uPtD3fU6YpkmYj68++zSvFem8n4BQ+dz+cWgjFboC8zqcrjsdJF3eHKnoczXxyX6dEmERyhaAwMfZiraupB0yLEQqpY2hV9beRAyR68831OfJtdDpnB2VTSvjcZpSsSbdazk3ictE32O9WgHaCqIsjmQHn/Y+mIHrcRixTkCAxFKQlhhpTxAWQ8N6vc5jZBkXpr8v13xhzwpgI/F3yHv5drvNTIjSObDSUrpqV5RVqfbzfd5LE9ATvLBCsh1viExx0oJpIWNWixRNI4yLqqqUkTfk1hGJnyyT9zo21QZXwXthDccMIqWc5ezsHGMN282WVSvi/0vgcsmwWbKNr66uFgn53HJeq4uO9+KkgzGz0KaxeIR1X5UVhTrJhBCo65qqqlil57xo3Y1hYWKBye0U4hIqui5e24rSmEigZQLtN9vtPEf0nBMQloCuUr9XWIpSJErHXq6ZxsrzDmrn5DT3ikEERNOcSbGbWcSOaay6wumxx3we4zgS9LMCkjZEhLnunGNMx7XS9jMqqCfzWtkX2pKUrN1jnIHo5b4h5xJwrgRQYDbFAU9bM9O9T/cqOwgt9BebqqZupD01bQ/ee4Z+knUrjhBDBkBS3h/CfE/+szNB/nO9YpCJsts9PkH/pE9LA9yPopm0iBVOLHKMEVEi0IA2RowG0GkxSNSr/X6f32dV/8xao4mKyd952B/4za9/A9ayOdtwPHUzVRqYhoEfvv8Dm+2aTz/7hKZZ8dOvP8ePnru7Ow7HkwTRqNXefs8wiqhQ1GsyFsapZ/IDHz68ZxpGmkZQUaLl9vaW+/sHkkUaztKuWi4uL1itVpxOnq7b8+7dO96/f48xhrKscK7M4k3WWi4uzliv10QKvI8qHin0YRnAEVdYXYxkEDrj8uaYWg2mnGDMbh4fI/aFXSbsskgZbTXQ0EA2Ow2aTA6I5wQ7PZ9pSgmpBlrW5mqw0Qj9KVr5cfAmAVUCnGRTDLMSsklaFPLuORGcx1la5IIPGWVOk2pOLmP+voSGfgwyjeOAVxAuxoBRV5rEbFgGTkKrVIEvrYQv3Tcss06HVEbILRoxzouRD54YPUXhaJqKsqoU2JBNJHq5h6mFpx+mHDBO2mtoEhim9yQwV/nmhXAZSC7v/XxPVquWzWbFH/7wvQRuXcfvv/uOWbjIcHV1xcuXL+mGgfuHRw7HIz4Eqqrm8vKSsizZ7Xa5fzQFBca4zESQSE++209SEXCFY7VKvzdacVs4GKnt2ThOhEAOgKwCIznRJFUQF5dorOjKxEhk1pNJL6O00GBgBOg1ccigkj538xSlNhZckda3FFKmAHbpIPMUeX86luZnYW0xJ4gLVNJZQ1U1EmirI0qMER88wzgSQ6BSMc+0aZW12NdNY8jVijTe97s9xkCx2dL3AzFYpf8aGArqusJYSz9IYJbGcVVXWOO0n1cDT5PU6j1+EDQfnorxLUHJyjicMRAnQtTnPRmmEMV8zDmCsZpEpjuaVok5CJk1ceQ93s8gYIxhac6RmWIzuLjwn0h7V0yJeUzp0rxupWG0OEaq6AQzfx4iu+OB6fvvqaqC9XrFdruVgJjFsMughSCfcf7RHD/EOd2Uf5r886UzwB9PEJdjK86fXzyLfwyYTsd4Mg4X35E+69zMTAwhMLcqp0RMvt9pAh6DzBkDEJZjX15l6WiakoAE/Mdjxzff/J5CW1GNtil8+7vvaepWmGh2dtUKH2mC/PiSlPHlZjDfxMiH9zdEPf9w2PPlZ1/SrtZEYxmBfXek94F2taVtVjK3sZkJ+uSez3dKKq8xCoMrvyvp+6R3m3y/Yl4rFAD5eKEizQZJ6WOcn7kkzuHJerP40AJg0WfIvA7N+7KZv36xnwBPvmu+wjwz5GPxj53xj18JvIwf0evn8ZyKAx+xFZffn/6eE/qn0MzH7/8Ry+UjAPofey0/m4p0w/CU7ZfcqUS0eRCgx6TfTaRqbt/3uYAhcU9kf9hzOnVUda1J64/BdZtjtfn+fHRXn4Ljy4QNnrZtf3RN6H0eh1EBmim7efikYRCXtzwwDsKWICZmYMhxfYhkoM0iukDPrq9zgp7ZQMoo8H4uNKZzS1X5xHxYNS1t02iMnOJaR2BuBfkYJA9eYroQpYiWWk2qSpK+pqkyEJO+M8VZcztO1Ji9ZLNeL9o+ycn2rIMjIpvGmOxAFmPEOWmTEB0r+a9wjkoTdqOARGKnxBAzEwSDflZi0MQ87vuO4/GYres3m404c2li6r3n2HWcug6AtTKNhmHIyWdK/Nu2VaFSzzSN1HVFWdbMcf08Zqqqyt9p0t6v8YErxA0khMDkwbgZbCg1Rkr3TO7tHB+n+7kEsvI+UyysYiVdAwNWxfejkXw0vSc9T0nYBwVZRNuxWrTupvmy1KLI4JSCfM5aYpywlsVcn+b8wkixcVyI94YQcU7av9N3J4BjmibRyytmS1gBxebfhxCyjk0aE2UxYiw0zKwjYwxlO1uGp3U0ac8swZGPi6Dp30v2VXLGTNcoOp4Kppm5CCTPLGYGj4AskwDDRUGhwFMIIu4bQuB07JQ8ISBW26zYbs9oGmGEiU7LlNeAf+r1/3cQBOD9u/fyF12Lz87OeP9+/tnH+E3btpxtz2jqmuuLK/ww0J+OHPYHESYicpwmRk0wk2ARQFmWfPXVV3jvefv2DS9ePGO/P9B1HUUx6aCF4/FIJFI6y9vXr+VEElVkgXP/s7/4S77+yZeCznUdbbvik5cvqKqK71+/5erZM96cnfHr3/5GwAeQYxhPU9f4IEjmOJ3AbChLm/MbSVqC0rcgGsPkJ9kAdHCO48j9/V2e2FI5VJ9rTYqnUZC7tl1rTCQbXtAeRhEotMRY5AFmFwmaxP+zqOtygC83fEEmF0I3+jnrRFdh2dufwAbZWD76rsUTT5tk8vImsUMgV33T+1LCvahtLIAK8oSMS+unhRbEND1NsNI9Tt8jG7BsyuMYRLjTlUqbngNGcS9KfaaCiHsVvZL3yNk55/KpGuTZkpM6MEYEMkF6f4dhUEra3N+tt0Ges1aAMGA1EDZEpmjYjUcwlikFp0ETB+bNJvNlVORyCfrl58GcdKaAd5lU5z800o1B1LX9uJcNqyhIWgfez8Hlu5u33NzeULikmj7hjKWsZGPbPz5I/yqRdVszjY7BQNEWNM2K/W7PYb/PKhrp/733dMcDJs6U29VqpS0cunAbeT7DMBJDpKkb1psNmHk5NDH1bIb5AWLlPka7cGtY3qy0q/KEIfejgBojveTLxMdEmHRMRzLaLwmSJfVKpoBt6ViUvnqZXFgbFmvPQpA1atXGTAzjpH8XwHEcR62kTRmlTxu7jFkBBZumoa4EGBnGgbqqORz3KsoYKMuS1XrNbrfD+xVlXfHm7dsMGKJXXTcNkcjxdGQYRqmGeJ/vat/3DONI2zQqak3eVP000ZQVpY1YRLMjVcl9iDLuY0nEMoyeYZR1L2uhGENZVYSoehTGSiUpRg7H0xM2YQqyZeMeCV7mqSueiuj++JUSQliCu0F/tQQWnv5Nh4SRYHgYLaeuox8mVk0DUWjC1ol9rHzYziBHHnSp/zeNkXkv+xjA+NG8/yOAyPJnS/D6nwZB/tjx5Z7IOiZ7WVWJXtDp1NF1Ez9ujCAHe3IS+TKfvCRpNFJYUSAq6XyMaa0N6PoAfbcjpERd4w9rpUXnH30Zoy5sUaeZ0fYx2Tv2xxPvb95SPX8B1nF3OvKHt2/o/Ejdtnz22ZdsVmeUZSvzG0NuZFR3kbgAxkIM2JiqiLonW4MzqUJYKqiyqP4bAceyh5HuBVb35LR2G/N0/FlrlCmSYAn5L++0f2R8zIGxgDXL/WGhPvTk9eOxMwOWcXmAP3b7Sc9/Hp/LOCWa+dtCCFm/4+MxmxL7xRl8tGb/Q2N3jgH+qdccoczQU9ojphiezMcfnV9MY9Lqc7fZ1WpZfIlRNSv2e+pJrJ+XLbYfnwsLAer8o0WM9zG4sfzr0/szX10CZ47HjmkhqK5E9yfPKgPRcW45jRrc5N8jSllSGYe2rtmuV3gfMMmiPUZNsmSNPhwO6jpmNAmfmdLrtmW72VDXdb6fEm+NeAUGkuhp9LP7XFE46rLI867Vzwcj/x6WRdkwa0kkW9a+n51n6rpms9lkx5hlsjhNk7bOeGKU6zt1HY+7HV51MdLe1NR13vMS8JMc3YZhENZK3QhjwidbbUtdl7N+m7IPU7Kbrr+qasZx5OHhgWEUDZN0Xp3qbMQYpHU5zkKz0zRxd3en90ES/M1mQwj8KIdIiXYCLyzgikJYMd6Lu2BmOM9M/xyPfDROl+tQ0gtJ70trUwIdUjtF9BrbmkicZLwVZZl1S9LnE6glOc2cNyWQISX1KVaeW5SkfSrlpamNJo2N1HLVNI0ULfWZLMG0dL3LsSXaMzMzF8jPtyyLzO7J73du8XmHD2M+9wRifXw/+77PYMYSYE5/pvVnKQS9BIQ+Bovn3Ezm67jQAUtsommalGllGIaRfXd6Mi6tdZTRYF0hDNkUhxvDOArw0dQNoQw/ci39h17/u0CQ62fXTEFRW2Dshydro+asFMCmLLABjuPEEAFrGD9undGdOiFVh8PxjwZUL1++4P/8P/1P7B4fOe72nG3PqF3B7nHHt99+w8Nux3GaOJyO7PZ7WXw1mpmmidevX3N+fs7nn3/KT37yE4qi4P379/z2t7/NaGLbNhwOhxxgxxhom5KyrHj+/Dk3Nx9Yr1eCItcNv/n2t/z2t39H0zT8s3/2z1iv1/zd3/6WX/3q1zx/+SIvcvf392LrFCc264r1es2bN28Ivufx4QNjf8K5mtVqwxdffE5dt9ze3qlQ5YCfPAShlO4eHzgcDgQ/YRDqUxr4xjgmTXJPx8DdB4N55igKsbbSbnJ8EME96W8LmqyL+NFSB0EAADTpC4pIKjsBsqvOx9t/0O0xootUAmjSpHYuB2ppzBg7W/pJosaTKpQx2u8blsF30st+6p6QEd6PNnGNWzIIkv5LNE2vtL+E7pZlhVW3Do8nGkWTp6hVhWXLgfStgVieWWvBVExjSmQjpVYMPu5PbZuV/j3128midTqdsgiaiU9BkIiwPpw1OKcMjQwKGQnwJ0/A4DWBtsYRFwwSYyT4+nEy83T+5b5BzayWFeLUtpSvR0uz1hhKVypQOGKd0cqrgjgBZSHoQqhgQFlWVIX2nE5eAvMglSLvPc5Ib//L5y9o64ah77Fe36dJQlE6/OTZq3CrtQJmWiebWAr4Q4z4acIYy9T3hKrGFnIcq5QxPwxMMVJWKrqWxlqcrzWHg4pCGM3M0tB7An6YOZGYh3fUyloK9DQADMsMTGyCk/OTc44izhUIYwTcSLaMKBaTQ3k7MxKCAmzL6u88g2TNFCR/ZtKkTTlVlIOf6E7gBUVkmgZSG4k4dETMhw9MfuT+4U5755ci0UCMHI5HHna7DM4+7h5JLKEcVodA13cUToQFzQKY7boTDiiMWHk7I2xBg8UD3ogTgA8CjIQExoYEgs5AjzEOb7UqYwJCm5K523WnXLFIyYa1BjPNQVYCm3700NP8gUXCIZT3ZU9uvv96gDTXT4u16uFxn212U4vjetNyfn6OQZyVPv7OPwZ0PBV2TePkI1DmI3Qh8vR3ywQms1+W1/zRjci35o+ALSYa/BQ5+ZFh8Hr9PwY3Pn4tf2+tzWPD6MkU1jF50RRwVtadtJQtdVWWX5NaZtOxdTjm84lx/lQIPl+i0WuR8S/vK9uK7cUlbx7ucUXJME0MvVQTO3/k5s1b2i9raR1DnivaGhaDfGmMER+9AuqBGCcedvfsdo/4aQQDValxyvWntO0WzKK1lKSrEDP4RzQMk9DIneqIWRV1l2Qu4hTUxxhlhhYEL89RAuIigyghz6F/mBERP/q5yWDHH3uuHzGT4pyoz1u6+fFn4oLmbhRszMLzSTfrx9+lD/TJz2bB0/h07Vrsf/MYSde9bLtL4GdyQiDPlSXQEfKfyv796BylaBE1iRq14m1ycvPH2vr67kSIgV7Xw9SCkMUCU4JigoL98yyQGE3BET0Xm67RKiATWbjuJHFvskte1/UZ2Mz3MN3XdL7Ma0hKqNPkfAK2kqI8+d3heOT169eigdfMAprTNHE4HBjHidWq5eLimrZtc5J5drahrios0p48DN0T1oCxLgMFKRa0gMsaDNoMtphbQC4+JuBkmTCn95VlKfpmYW4NSklxuuepvSa9yrIB5D3rzYbLq6ucaKeEtK4q0Dnb9z33Dw+0qxXOOe7u7iiKgi8++yKPuXQuKWFN5yj3wGYNs6qsVaejY7VqaWKTmSggMWxiIIQQeHh4YL8/cH9/n91pzs/PMMDDwz37ww6iYbVa0zStPFdN0LtO9OhSW45LhTNSS4W0PCTLc1uWTCkeUYeaNKbDosW06zpGZUJst1vats33PQE+MUTCJHF12k+NtRJzqqhqukfpepM4Z2I7JNAqWZMHzYmsupkV2g4dQ8hATtd1GfhJbI1UjLI6DpYgwset5/BjdmwaQ/Izn5nr+gMsUkDIDJooLPn03cuxmNaLpBeTnnli7SyZ6su5sJQGeArOxifnmVa2lIvFGCgKS1HUQKUSEYaq8vk+Le+BrUsqypwXJWfJCEwhqLunGC38Ka8/GQTZbFb8xS9/wXq1woTA1cUF//P//K/Y7Q8EA6e+F9GyIAt76SxfXG0oY6QbA/fHgcdhZO89fVwEHhHevXsnkywFIB999/ff/8Bvf/sbtpstP3z/Pc1Pas4u15x/cc4Pr3/g8PYt0Tq26w2ruuX+4Z5AECeKpqFuGu7v7yhLy+PjI8fjkdvbW7yKqgA6cc/ZPTwKTVED27IUfY1/8S/+OYfDgQ8fbvF+5K/+6n+h74e8ECQxonEKNHXNp599Bgbev7/hm2//nuPpkaIoWa1WFIUj+MDhsONwOFCVouQsVS7D2fkZh/1R7KDKgqvLC0UuoWlqrIGeXkEnmye3DD6ZGMfjkeLxgbquGcYBV+gia6QiHtSyt1cV/rqqKYtkFyauPnFBJZVB7iUQIrEClpGDboyZxzxrsAj4F59YM81VcEFhpfItEwPmQAKMaBik/1JF1UQiibYoC4xsQk8psbnQbpICMwSlmgqwYwXxH3omIEwjzWqdUcQcYGiPa1PWJKuvU3eiKitpJRi67HLU1GsmP9GuRE36zZs3PO4eM/0vLRBN02AwGVRcqRK40Tat1J9pFwtcmh/DcMI5Q12V2hIgtrt6qzOzJ/gJY0swkUFFR/Nx9BY7bTmSCuZSBBLGpGVTFio4NOKcLBvWytxIrSmpD3kaR9rSMQ1zEmotWIcAMtZRGKnUOVfx4vlL2lb0PwhwfnbBw8M9x8OeupTr60OgrhuGruft6zcM45DRYWNSJSlgC0cIshlOk8c6iF4EpmzhiFlt2lJaoVxajPRORijLStkrKnTmIyYkho4G80ZAihS8xShUeAOUhZXAsygwqQITPBGDjyIAG4kYBdh0hujnVVAuzsGfDGAV5wz6TENk8kGYMs7pWpVANKPHWUAbcdG2FQGjLiRBvt9GDTK17SZkMMws5qEMCBMjYRqyTlGcJqk+WSu6NDr/pbKuFesYFwCCXJWz8l1T38+isQtQ9UkVZxgYmDf+eRbIYHfGyjVGKAtH4ZyspdpDLGckle00r59WKYA4yhGtCtdVNU1dczx0hHGcE6HFxrpspYuLREISpx8ng4mGLMm4yVU7vWL9nH5eg/7lefYMHHM2Lu+z76FtWsqy4tmzZ7RtK8FU4XLN2Tw5tt47/XswHyed8clbYsrozfKWLwC7JwCIjqGcJ87fY3gKhMjnRG8nPR+i9PMvk8g/VhT5+GWM4ezsDGcdfproBwHNZF1IFdp0AQalb3x8kCfXl196Cc5ZyrJSfa+QL0/enz6aQGE5XFGW7IeO/enIn/3kz7g8v+DNu/dyzCnyeHfPG2PZbFout1uK0lG3W2KMHPoeH7w4KhHpx4Hj8UA3HDnsHpUhOj8fYyzTGHj54pW0yKro5qk7cnd3T9d3YCRZW6831GWDMY6V3eAKy/F44A9/+I4YA1Wt+gx6Lc6V1HUjLXYx4mxJUVSyvgVh3KVEcDlvjZkD+KR7kYNhvcdLob+na42mnQoumDyI0nPSsZYe0yLhBlnDfFi2rspYX57f8rNmASSQ1+U5QV8G8stzNcuYhgSYJeDS5msO45RBgOX5PgUA5u+XuMfkNXSmj09P5nEqBqVjWRbOhzEyGQGxSxWVTHt0OkhK+nVXyteEFgomP2EwutcWVK7AYvBR7L+TICRRBMunadQcAAV2FqyOBHikv7O8B1Ft5J++UpQZ055ipY3WFQ5U72ccR4qyoGlrDKpr5SSeRO/fNA70xyPjKPohEk/JtUp13uOqkmtttfHTlNeJxLzMxRZlfjw+PmKckWuOkevra9E6XIyTOdGNnE4dh8MB7z2rVZtZOnVdM/RemakC+ldVKdecEkBr2asgp7WW58+f46eJm/fvGYaBzWbD5eUlrpy15UCq+UlM1BhD33fsdn3OERJIVNUlL1++oK5rjocTd3d3uTIfYlRAQeLrJE7pp4nj4UCYJtZtk5NkgN3DvbZaeJEFGCYuLi45v7jMRceu63h8fKQsS66vriT/enzE+0ndV2YmQtuu+OSTT1iv17OtsXN4Tfa99+x2j9zfPyjLv6Csa6qy5HA40HWnvJ/WdU3Xdewed5RlKfolyfY4BPwk5zZNE6UCTdMwZJ0RGFRzcZKiS9RYzFqcxvZSsIEYA+OQhPUFONhut0+efRonIQaINheB/liR2UD+jlQwSkHIvEYF3beSdpHMt8I6ccc0BoybAZApkGzUnrb98wT0SyBHKsgudUmWa2r62az5keJOuQLrLFVZ411qsVEmuk9rZKRwRfpAvm/p2BmAXAi9zyANGFM8Yfz/U68/GQT58ssv+N033/Dnn3xGPUzsfnjLX663FGcXWGc5TNIjtzsceX97R38aeTc98MnLcz59dsX1cWCIhtsQeX848LA7Ka0KpvEp71SCe0Gjy9IxDiO//tWvePXqFafuyP6wY9XOi8g0TazWFRA5O9uyaWuiEUGgdrXi8WHH6Xjk5r3n9sMNQz9moUI/TWLFeTzxxWefc3F2ztu3b7HOcX9/j6FjGCaOuoCG4Lm6vpCEy0lg9fbtW1arFc+ePWccPe/evOXdu/ecbbe8vXnP0PeM3cBQ9fSnHmscrhDEM0aIIdJ1e/r+ADhpG4g13gdKX3B7c0NgUtsjGSRtXfHi5UvGYeD777+X5BoNeK3E6ofdPaejxQdP1dRCE9JBxomMnFprMRsoywIfJhLzI+KYggj7JFaHbF7LiFgfmlmmSmmPNVoZ16AzosGPzVXxtDH6aZRJ+4SeqeKtgGG29qqSgBVSjfM66SWZkj7JjydAjEjFOqDCUCHH9nL+ssONQ2AY+nlBIcVeElj1uqGkZMr7CTqtbGkrSFWWjKO0G5glSGWfJj774zELok7jRFEWGvgIshmUjmmZLbTSuQYCxgTMEUqluxGFUmgNFLYgV/vxTD4wqJ1s6jm1Tto6Jp8WzfT00mJmmIJowzsLBJjGSTYUwbulQrmwbI4x8vL5c9b2wP39jrvbB4yFtq2xhRUrOJIStKFUEd9PXrzg4uKCv/nrv2GzXlGVjl/dvKPrjFBAS4d1hs1mzX//3/8P/Ot//f+m7Cyr9RrrDA8P9wJoxoAzhrPNBh+k/aEsC4IKQpWqbj72Y9YAsdYSfIQw8cnLTzDGMI0jZVHiI1xeXEn/r/aKdl3H+5t3RIIkI8HjCidgSbS0VYNBmBKlsdRVQVFWTN6zPx4YlP3y48qkzQF4SgcS+EKElIcbIjZKC5s1qcc7uTmYxXEE/HBW2risJp5RE4RI6oVW8dc0nU1KGufzMNGo/e1ik42Rqizl/TK5c2AbUC2gVD00kFo2YoxMiTGiX5AZIGl+xHl9Sfcj+Lk1ySidFSNgnBGaGNFagnVEo9U85/J65OK8iRq9Pzmp0oQ9RmmrqcpSAbyZ2mycw1iXA9MQUnvhHODbJ21S6XrkfqbLMkbDEyPzT73V873JQUXk6bnGPwYKBE7DQDeM9ONA3bTyvQoANXXDatV+VJmRGytr7ezAkBKUnMBkkGOmsofUi6fj0GhgMwdOC22kuEg2c1CWABkBV8dBk33SMZ8sQX/SKwW2d3d3TOOEMZFqveJ4OgozIx/06WcKK6KUbdPgQ8z2hdKmCNHI/BONhkBVG9brFYfDAat6UcHLeJG5lirFwih5eNix2+0x1vLu/Xt++P4HvXdycSEE7u7uWVWOq7ambWo+++on2KLi5u6O3eHA435HMJH97gMPtzdMURT/52cg+5mPI8fjPXe3Yk1ZV6IV9vrNW7q+k4R4mjDOUtcNlxdXXJxdcTju2R0879+94eHxVuaZ0r4TuGOMZbVacXV5TVXVhOCZ/AhYgop4xijxw5LRmHmhQYB9a1XAFGH/pCqrzJulkJ3OmwW9OlW/l8FwDJoQ8DQ4JrcyJjBS5lbQgSVLjSTJGeR7gj+YJ6NlOd+X8UBytPu43SS95uRBHBpCVHe4PAcTOFRKO3aMi/thaNsa7ydOp15iv+hn7SNk2SidU2ZIugYDfiIY+f7CFjhZPBmUWp5eac1CQYkEm3vvNQ6RPcwayzgOTAZOyFoWjcXHuWhiFIAnaoU9RnUnTMziKC0Hy+9f/F9kaRCfRjYZAEmxGArQDNNAURb5HsYojIhOk+ZE8Z+midIVWANj9Hg/8rjr8FPMbQlFUVDVDXVRgg8cdzvGcWIcB4whtxakhFzaQpQRZQ17bed0xrBqV2CeMjt2uz3dqRNNwKGX8zzsKatKGMRRmLtlKXoc2/Uq5zbGWryyg8+32zwWvSbIn7x8iQiLy3pfOEez3nJ1fpkBnscHAQZC8JmxgpGi2/n5uYxxPxGmwO7hkXfv3ksCy8xcbtqatl2x2WyEDWALZfxM7Pd7Yoz5/qxW8r7EygghUK4qgp/YPdznwvHQnVg1AixM48iHm/ecTscnrOe2bbDGcLbdYIh8uHnPqLp5VSWg6zCMPD48sNuJrhzIM6vqGmctoxYeC+vUaaTk7u6Wh4dHXr16Rfn5F8Q4aVz3HhGNFbeYSgGXGMVqeL3ZYK1lVBapc0VmW1VlJfH0ND25hrTWYBIjyxDjyDjKuC6KUjIlP0FIlufzLFnu2amYlNpKUoyTmX9LQFZjDpOATwNGi98xCGvXRCPFbwX6UqeAH8WSObGiylKKen3XMQODlqRzI+uzsnWt2oFb6TYQPTkFRFALXzO7l4LsEdGrpoif6OOka6tob4kLqgI03jN5zximxRqDdjVMODcpU/E/Mwjyh99/z9XlBXFdcfnpS1w/8MN3v6cbRzj2lCbigueqLLj85AXdONL5if7kua8H9n3HoR+wqzVj0qX4KJ5LC39diwPLNEk/9ma94bNPP+V4PHJ+fsb7mxsMlmkcWW82PH92zXZ7RgyBYRzZrtfgIj+8+QP3tye6YaAqCybtX/M+SsNvnJFoY2TwPj4+4pzjJz/5Ca9fv6ZpW37/hz8wjsL6+MlPvpaKdoT9/sBms2W9lmq/oHojfpo47HbcffjAqe+IMVDYgsKUECynQ6dq/0YHlKo2R6mYt22FdSWb1SVlUXLqDvigir7jwDiIP/rhcSc0sbKiLEq1xdWgvazoho5h6AkEpmnADyN109B1s42r075QsXGdFqie/GwaRoqmyQGz0Uo1OqAz8yMFEQm9U1qZ9PhLgmSMUxAgIfv6WUnXNWgJOqgN1qYtEIhBKureMPqRYF2u9IJSsRbnY3Jyk85Lkkjpr5WKiWzK+vuUcwUymyJXaxb/3+vzhDlPM0YSRGcNfpo4JRYJRlurlAbqyS0Nxhi88TBMc5A1PLUsTWMTYFSRrERTTVogElB5oSwbMDFQOIuDXO0Q0AF8FC2KvEgn68VFYohh7tE0DoyjaVbUda2aEQGGSD9NsmgbCUQ1F8JZx263Y31e8PVXX/Fw/8iXX37Gai0Vkr/927+jKEoOpxOnruM4Hjg8Hrj78IGqLOm7nj/8/ve5glCUBeEoc/Ww21MUJf/u3/4b+u7EerViHAbGaWAaBkmCNaANxijziQxemghFVXB1fsH97QNlUdI2LaeuIwBN23Dz7h2n41GEDX2gbhqG/R5rHT//xS8YxhFXV3zwIkIbhoEpeErXsGoaKueI08DovcxTTQytc5yfn3O+3oiDTQxKT7eZqs6ijz6xI9yy2qlj2VmlTAJh8hgb87OTAF6+L2iV0GBUeDENmUWCao3qCSgTxaA0w6jgRKocyjEFnPPKhAnYQsA2YmJkSdXKIqLGMUacgggJqIwxsQjIyU4CWvOcTsmiJilG0AO9PcmGDWKciASclcpBaicrrJX1TK9XLiElRCZdUl4cZI3KU4rHvVDKQ4wCfhgZSwFDtHK/HMKiScF8CtaX2kV5fcjJYVwkaAkdygfI62p6VmnlSUFQTGPjSTIha944DByHYT6+LoA2jY2P8RNdxCK6foYw09wVJI4psSI9jsV1pQRqcf5L0dW0JjzRzslJl/4ZZjG7xZuerK1PEv4n75JXCIG3b9/mnztrObs4Z5/EyuNM2U/HPD/b8MmLFzgDz64u6E8dMRge7x+4f3hg8oHD0DNMAogE4HA4YQw0dcXnn3+GNZbb23s+//wLPtx+YBx7djvDqm2wxnHz4RYQYPrdmzcC2sWQ1/EYoW1W/PRnv+Av/+JnHPdHvv3972nXG+qm4uuvPufvf/cNz54949n1Jf/xP/5H7h4eGJA2HGcsVVkQi4kpRJwJGAKvXj1jHCYKW8InL/n+hzd0w0BZVlLBHyVxWbdrwtjTDx2n00EYkYmqP8rcMRpITmNPWTq22zOcK7FGguIQjAJoATMJGCzJ5wxsZaH3xXNdVhuFYq0V3UkLMGlq5MqqtOvO1HOX9bJk9iYmXVo6FnRrFnM9kt+bChshPFHq4I+9EuMvjyEFm41hkYSk8aiC03puVVVT5L56ScijXvvkA5OXVhdXFIQoFc26qghBhFHL0kl10xnsZDNbt6rF7jOGeT3FWNpm9URHIAERSYA5EjNQZ7TlL12jNYbaWYIfKYzFm0nuc5yYJi8ttBigJNpCnLcMgOrLhAghEJj34hyH6YIiLQPmyXqSnxMzoJwX5IyjRg6HPbvHHcEEBdbmFu7UWrF8JUH9oihUSFRjHeto25a6rrPlbYyRw2GXBWtTi8Dj42MGBkPw2YVC2AIDXn/WNBXd6Ujbrlmv1/SqS7FetZxtt5RayDnsD4zTmPeFrusxUTQNjvsDXrXejDXUTQMqbltV1czcCQHR34Ou63NLSdu2jGHiMI3KVtnl82zbVpiCQLtZ53aNw+HAcbfPOmPrdgUrw+in3JKy3a4R7TZp0d7td3y4uc0irEVRZPHNEAL39/f5PJ2T3yUmQdedMFqcEsDKUSqLWvTbxK0TYOgH2df2O/a7R1n3Kqn0d90x7+2GyPn2jOvrawZ9fum8sqZhnPVbtus1m9UKiHx4/1ZEN71n6EQg9nR8wFrL2fZcgVfD5eUlhImuH3IbjI8Tp4MUMRMY6r2wOtqmUe0uKyKfZRJHlYKotGYhcbtxmBC0CJ2KQhKDemWQEpO1c1AGsHQqlKUjWFTSwPHkpcWlqJuYgLJeipl9L+LzstAL+8IqH9kYCF48zKIwy6wyXcAQg88gSZobRVFg3Czwm3SnSldQVLMujcSBkhuGMFJVNc7OFufTNOn9lFZMHEwxMeiDAi+CF4yqz5hsj9O89dom/6e8TPxT+KYIzfji4pxPP/uEMHmur6543O1o64bd/QOfPLvi81fXFGXJqRfnCRth6DoRLu16hingmobv37xlUErUbrfLC9l+f6BpapI4qCBfon7cNk1WvG2qmvOzcy4vLymc45tvv6VdrSiLgtvbe4rSsV632m5ypKwqvvzyS4KP/O53v6MbxqcbLXJDv/zyS47HI6dTx7/4F/+cx8dH7h8euLm5oe8HMLDdbqgqx6effoZzBV999TW3t7dyvhFe//Caq6srzi4u+e677zh2HX3f0TQ1Z2dbIpHf//47jImyqVlB06wR1Pf8/IxXr17hbMmpm7C24OLygv/0n/5THmhjL4KZn3/xOTc3N+wed6Km7GRB7dVVIW3o4zSpJ7lVIdMib0zSf2WpFXlu2xXX11dMPnDseo6nI03bSguDdZgo7Rs+23uJgF0EClewXq8pCmFKiGK7oSwsk584nDrGIBMub3o6ufMgTMk4kF0xmEEKG6FQ/RJbiHWYj6p1EmPeMKKyALI9cGJ+xKcJilTFn4IOqdC2pNguJ1TSYVn25aHgSqpiheCZwhzs5Sprvkz9vjDTzD6uKqWfL89Vb0yukiUdF7QlgBioS3XNUCBIAiKb763300Ik9CPtlMXzkERKHIq2Z2fS79v3nPqOfhJrrdRfmJ6Y9xPh8I6NPfH82Qvevr/h1YtnTMNBN7yKh8cdp77n3bsbXFFk8C0GQaYxUDgRHatUvDKmBdwHGq1k9n2HMaL43fUnTkdpHUr9l1bdooImZEYrJbI4FwRvKAsngEeMVHXJGJNGjlYjEaDBWotVil6zakVsbBLbPBGiKqTf1jlChK4XNfXEGCpcIfZxxrDf7wlAUZTEqDoEVnR7ZAONWON003TKXPL5OTnnZNNeAMlBfy6VT0uplt9iE2oxuKzXkRJxH4QGnURy09hKDCLpqTSqD53okYrGE3IVMGJzgCvrmYzPoOBbdnhaggO6/sh3R2XlLOici/G+3MsSgDH/W5lfGK3spsB//nyunKntZ1RgKuf2xoBej03Xkj63uG5StXP5/dqKl5J6SSx4AlLw0WaczlGew0w7zQBRAmtSEqFAwpxHLNeFBfMiJQuZDis/sgmkjDGLIacTifBk7UtJyJM1+Y+94iJhjBkGyfo2cmYJ0M0feXJfBIQH+/gDxk9CQx76J9+b3lM7x7oqCcNEP01MMRKtpQ/hR+fpnOXVq1fc3d1yOEirYVpW09f/8pd/zv/p//g/cn97y8PdBz59+ZJ1e8bxcORv/uZX3N7dcepHhmni9vGe49DPx7eGpq1YNQ1XV1f82U9/RlGWvHv3jl//+teqaC9gx6jVae9lfJRVQd2u+PTTz3jcHbHW8bOf/pTPPn3Jv/u3/4Yf/vA97arlL/7iL9hut/z7/+WvGIaRP/uzn8ge4Qrevf/Au/fvGOPI2XbNs2dr/vD990yTsiOKAmsc52eXfP2Tn7I/dDw+HnjY7dgfDsQYub56xvnZOTcfbjgcdkBkGHqm0WcXsazIaSJYqJqGy8sL2maFbLUOrKw5af8CdUUI0o6cxNpTnJMLC3GOvzJYv2ifyOOYRUL80VhNhZkF8qFrkzxwlwPyuVgyj9/lv00erU+dhOeW3pSLJ2aX/MxSqquB9zMzzBhpQRmmORnbrNY55rFGCniJFVLXFYm1cDqdsg1rYtqFGFTfoiNGw/n5Ga/fvKbrDvLdo8fZghChaVvadsXpeCJGWK9XmakDSYR6kLisLCWpiAFnHUPXidNIUcgjN5HgBylGGVnnJ697ha0IOIYpMHpxWRMQJGBNch8UsCBpSlkF/U+nExhUfHMQkVNN+qKRJDLZVc+tRbIC/vSX/zVf/vS/wCOVZGttTuYTGFDXlQA+XU/hxGa1rmqcsRSl6I3FBUjWNE0WzEz/rus6t67MLKWoDCiyU9rkZ1tRay2bzZbClTnxTsCEtKQYur57whRIrSWESN+NmXkhbfw1k7K1QdiaSXwzRjn2erVe3CcYxoFxmGiVKS8MkKA6d6UyPGI2HBjHkXEYsMx6Ian6343DvAfbp3omUlBO8aQkwnVdMwwDx+MxzwMBh5rcrrB0Nbq8vJR8Ri1bnXMYaxjHPjuHjOMooJrGq6vVimbVcjjKOM4OMbrvFoUyq720h1sn609RFJSFE1BiEWcnodJ5nUmaR4bNZsP5+TlnZ+dZ8FVAoZjbZTabDdfX1wCcTiceHh4YxzGPod1ul0V507zebrdIa5IWo0kJvKyBYrwwA5hABjWNMU9F8aO0kUx+tuZNv3NuZoika05A1ul04v7+nvv7+9zG1LYr2lUr8yPGzGbKAIedTTCcnc8hsZIAxI1UgByJh4vMysoxT/BZjy2J/S4BvqVzjDHzuFy24ABMfmYapXad1CqVmIP/9//r/8g/9fqTmSAvXjznz3/+c6qqFBpMjPTTyK9+81seHh757k3Dh/0XPH/xgu3FBZ21rNoN5WorCYCx+CmCK+hGePP2B2DShUB69I7HI7vd/kllaL1qcVYGmNDIoe9H7h/3/P6HHzI6dBo6vA+cuhFrYH/Y0dRV7oN89/Yt/+1/899igB/evGN/POK1Vy8N/Kqq1HNYaGS73Y7udCJpYCTF4KqSIObdu3d8//338jAnz8379xRFwYebG4qi4tXLT3j99g3Re7brDc+ur2lXLQ/3d1xdX3J2tuH+/oG7u1tiFCGgw37Pt998w9XVNTEaDocjt7fvOB7EbaOpG8ZhUC0DONtsmIZZH2EaBiYTVFvCUTcttQYoabKv25rNasvj4yNNs2KaJpVetJTGcv/hjmEcCRFcWbCuW3zwfPnV1/hx4u72jnfv3ubWEmNEW8MS8d1JrCmDz5oMo4mcnW0wsWS/PzIFqaaGRcU3Ub6jD8oWsWCWgmRzMcE4CYwNCNU25DqEPEv9l8VSIElv0IAj018BtPJtnZGqNYK8pqR4TkJmTQXZ8JKImfad6XjNnQtRWC9GP7cUkUt2fhn4KN38+8UinI4DmlRowhR0YbRuFngVMESoxtaY3PYgVTQE6NAgTxBURyzlyMS5CpMTm5zYISr/xgpq7CeiFSS+Lue+O7P4Lj8EvvzqS+zxA30/8PmrT/n+299xvq2pa+knrmJkvd3SGsv5xQV9Jz7fDw8PrFcbSfit4XDaY50VOneIOCsUNxsjqyJysd5yPB5x1rGttsS1BATjNDIp5XMOmqVFwGnbkDOiy0MQVF0QbKmsGH2udmFHiZFWps12y/bsDGLk5uY9x+4EJhLiSKQQVoezNM2aul5pRVJYX0UpVmdnF1eK5Iueh1QSUzIh4I01hfzcJNqhaOTXdUskcnN/B8ZQV9LiNkyTVG+VVVI0DYOqswdgnLxqNoUMvITgpBKqPeHzPGQGRzShFreouYKfXEck6Z/tpKdRwZogOiNJiiExJFCwa5kMCQNErzEIc0SOrdXU9AyXG9IiSY9RdFvScpIrzNoKIyCIUi+1CmsVPM0F4ujTgNcxIwFcp+KQcndmIDFVHyUA9BkkknUnD5r5Dz1fq4nbvA4l0DaqXswMkCRwx5jFsZgpsSkpW64LM4AsL4fBBiiNMHi8NFZq1ZV8DiigGhbnlthI8xRY/DuiGk/x6YNJ44X53uYbk96/uDtFWfIvf/lzvv70M05dx//33/479sPAGDzDJGc6DRNx8lSF47PLc2oT6YeB3Thy00/sfaAP6Zrk/rx58zYnJ/kyF6+/+9u/58XVJXVd8e7NG7brM9pmy+b8nCEG3t/fUVU1tih4dv2M+4c7jJVYpW1qQoSbDzc0TcP79+94fNxze3cLMfLw8ChrjLU8u76S+65srRADfhp58/oH/vk/+xfc3T/ww/e/5+Huhm/+/htpgRxH/uqv/j1t27LenDFNns1my3a7xdoC50r64cTD/g7CwNCrDhaIFamfwEYeHj/w61+fiLGkaTcE71mvVnk96o4HSmu5PDuTCq+BZrvieBxVpH6cqw+IrsLxcMBPov1QVTVFIbaGUYHMaZIKY4iB1WpD02grVrAQrZ6jVBUFmBM9prRWW0UbElD7I0CEudiQwL68r8+oJtF7xmkSm3JjdFLO7IMZthfqdtIzSWBBAmlmMW2jJLV5jkUMcQiEaRSWhK4FEd3DdUx2xxPd8aQAp86dECi0PSg5RaT4jCgaBEsHj6qpJYEePXVd0U+Dttdooi41W+pDTVlJ8usnr1XsxKSR65uUhSBsQvmftY4wTTmudAjYBx5rtXXWOAVBDMYGpkmZLCGQmILOCttp6gddZ9Ty2lqCtYoy6VqsWgoxeNXE80/WP3l083om40iuyU9BRdVhvdpytj3j/ExBuFFcGeuqkvatGAhTyJXmw+HAMI6U6nB2OBxykpoStqVrR6MF2BgDq6KlqRvKshbQqjsSgoAGSZPOavyQNBG899ze3tL3J3ptta6qKid/xohd/eGwp+t6EVJdtYzjmF1OEgPjbLvNuiMJ4EjJYggT1kk7yjAM7Ha7hW6aMN0FFJDzLaqSuq4J45QtSpNOSGKeCLs9cjgeeHx8zIl2265wtsggkbM2r12bldj/YsSBK50vCrAnEGgYBg6Hw6zhFyOn7sQ49vnfaadISXDf9/TKSE4FRmOk1cX7iePpQFmU1FVyE7H5ObZtm5/nNI0KOJYIoy3ke9n3Pcn6/KTx5el0zK6mSbdwu97QNi037284nY4YLUiu2hUXF+d4H9g9CnvFGsPLly9ZrVY5t3x8fNTiueQ7CcAQACDk3DSxbBJ4JPolBQ8Pj/T9CT+O2nLeZk2bw+GQdU6Sy0sCFmB+BstCkbiZttS1jIPj8ZD1ZVIeneYIiN5MGuMJ4DFYTidxTS2LMj/fp2CMyfoqaZ70fc/j46MYf6SYXOdrei7OCYi12azlPvZeRWZn1yXdEBSQ/s/sDvNf/Zf/JcsKW+EMzjr2O7Gimrzn77/5Hd9+9z2vPvuUr7/+M0EMQ2QKk9IXHWPfMwwjt7e3XF2dU1UFTVMronbF69dvePv2PTHCi+dXrFYt79++k6B9ikL9CYEpiOWmczIopW83Ca0gKvOxl0TeGY7Hjn//v/wHTqcT+8MRUK2AMC+63333e5q2YXt2RlEUWf23qisikV/84s95fBQl5B9+eM3hcODdu3ccDkesPoSvv/qKn/7ZT/n2d7/n+uqKly+f89u//S2bzYb3797xk598zfXVFV98/hnH04FPXjynrgq6U8fbt28ZupGTjxz3R4gy+aOKzRXGEqaJSivcf6/HjdNINDAFjwle2BJlKW1HfgJkERWNlUISlMlTuQIzBRrjpKexLBgO4sGMtXgCprO83u0pCsdpv8f7iVW7ojARCuQY1rBaife6s5axF4RujDMtsjCWar0iTJ5xikRbELUya7UlZ2ZNoMwWNwfYUXzinUFaf9QSOYCIULoCUziy7VtMG6dsnrUVX/mgKKdU9kXzJCBCghFtBYlgndihzUCEBGElaXFOEzTm83squGewQl94qszOPN6yLskCqYWZTpcSxKTinKj3ieieypteE8s0NydFZlO7T5hPCZAFOWgwlNoy0sIuseZcJSeMhKGnHIRW6AnazhZz8AuoOKYGPEXDz3/+ZxDg/sMdx8stJkyMpz2n+yPGOMZ9oKhqhv0dxgh4UpqJ7vCB9XpNDJF1JWJkm2pN6t8ukiI4I5WxlI30pjVtQ9XUDMNE13ccD0eKwrHdbEnU89SSkO6TMbMQL5oAyqULKGFssaggCsMCA30/YZ1he3nNVkrtlFVLWVSLRDf5rUu7iLEW5ypcUcrfC+lNtc7NLSWo3XLpsuicdU6qO6q878qSsqoovOdwOnEchRU2hYAnEkykH0747iiJsc38JGIM2vMpFeMMEumfiTGBEc2ZSdvrlCCp84AcTCaHKIDohaVn4tyPHRYAhgTsMSctIYastSBVBrUKt4Zg5T1FUxKC0Lh9Eg2zKUFaCAKGIGCjkbEQtV1xMtKKEExUg4kwj9nocxCQ2kXy7FLpEeMKRiP9/AlMlAPFfC+EgREzCFIUBYO2WiYbZaHcCyjnFnTVJeCYgr6ikoDjuN/PIIXO7/y9S+AhL3KSXyjxNad4mEgRDYW1BGM4jT1T4v3kkrbeD3VAQMGrnGTaBFY/fWWYIwug6jmmRG/5ZrMAURZrqg+Bja25/+Fb+ocDf3l+hi0FEPPBM1nDu5tb7g4npr7n/TTx9WfXXD+7oDtNvMJwGy1vdifu9wdOXQdRtL7S7QEVNtX1pKpKpiHy+ocfuL68wAR4vLtn06zZbAylKyixnLctIURWmzXnmxXeT0xexPL2xwPDMHJz84Gbm9tszyeBfsAZyzQFXrx4weXlFW/fvaOuat7d3BB9pPcn/sN/+CtpuatruvWatq5EoFEdJx4fd2y2Z1RlyV//9f+WwZC7+1t8mBimjsq1TKO40jhbYEsrQZ1WCPf7R9GcOB0BWY/KquJwHDA+UhXSMz9NntIVfP7ZZ3S955tvvmUcI8YEgvGEKPoy0zAy9JLQT+NIUc0JVlobREvBMvSGqkhVdCmzeB8YxomqamRN1+JE0gxJDBpjTR5DkUW7HyLanvhg2ULYGHUtSYwOozoaOtz8vG/KvJv754VKHViK6aWWs0hMWs8zSyTGLAQdtfLp1fkjsRB98Fp4EPA4A4oxsa+kbcYAQ9/Rdyc9F0uYlpXpORmbtJ3jeDwpEy/idU1zRphuoe849bPzibTRxkzqSeK9xqhdqJ2LIMaA8aIVF1Vby5nU6qs6ZSqebozDB3kAUR9U9DCmyc9HIrDG5vDIWENdN2zWa7qhl7V6FFdGo/fA2JmJuWx7btuG6+trilraLpKWSgiRopBxKDGCoSjKGQSArPOx3qypxomTFl9ScTMl5rvdLjM4V6uWuqnYnm0UlDhp4mc4nU64Qj5/e3srmmNlBdE8sb9NbIeu63CFewJ+JNAhhjgDLetWmFOaXCZ20Ha7zfczAWcJGNjv9/gw8fj4wDh4qroieE+7WrHdbvB+YpwG+n5gv99xeXnB9uyc/fHI1I8469SKvpQ24VVLtEZFRTtu7z4wqhh9EjT1k7j71XXN5fkFXdfRdx2uKMRAw4rAbLVdsz3bcnd3z+l4ygDLmYKvqeUmAXUnZhZDiB4/eUpllBhj8MicS0mxaLTI3HBO44/UMqL3KzmuDMPADz98Ly05Os+tsVxeXnF5fcUwjMpMMcpoCXSnE8EHYWWFmC1oz862tO0qt/okV8nUgrPf73n54hOIKPgdePPDLOrftm2OSVfNis1W9FamURxY+17YhwkAKpxYOkvhxfDixTOmaaRwVgFyq+5MXdZu2e/3HI/HJ0ynpFUj47XMzjeJBZQYFYmNtmTKHI/HDMqcaZ6cWtCapqGuaqYpyHz2speNo7QPrddrZedZ0ZJD1+gghbiyKIUxp9Vkr9ofS8DGOelIWW/WbM82bLcbMVE4HgjB0/czMLgsgvxjrz8ZBElIjCSQSQim5tWrTxinge3Zhs2q5Ve/+lvevX3Hl198RVUVxH7MPXTOGfrBc39/x+l04t07QaYOB1Ewl8ElrBA/eb788nNub25wxrLeNNzePeBHr7oKT8/NYHUCWFbtSiZaWfDwuKMqZTKEMPHF55+Cgaqsuf1wy/HUM/Q9l5eXPDw+0K5bjocd/+E/vlWxQKFZQ+Sv/9Nfk3qSHh8eOD/fYgisVzV9N7JuxT63cI7PXn3K7YcPvHvbs6prqsLy6pMXYte73fLN3/0dl+dnXF5cEFcbfN3iQuD87Jz9YU9V1ZyOHVVdE6Ph2PU4a5mGAbFhlcV2s6ow61q0O5xjU52zPxyo6orBezxSyY/eZ8S/cI6CSNu2GC809aYxlKWjKBx22wiCn5M3QfijhfOLCzabDeMw8uH2hmFMQjmy3Vsjoq2lWpIK9U4WvRgjV5fXROPAqlMNQqtNasB+8tpjWeGK5EJiVe1fNuQpa1YYjDOYomAKkTEIXVj6bmXT9lH90Z1UnnpdcOXvI3ES9XOstpOEgFUQZtLfWSf6AmmBiBGt0Eo1I2iw5ZYV4uCz2KA3hlQhzcmmT+4i8zg2RqKsfN9IlHyfP5sW7jnVkWPYOFeok1OKyVVnSCGQUKoFFBhSNcloj3rWsJ3pn4EJkp6GcwRgYrasMkbcCzxz9ebd20euxpq6qHn/+i39cceqrSkrS1W3mJwkGaLRHkEXKa3BB0tTxEWriHrDa2UhiUxleq2t9OfQ9QeMsbRNRVUVFDoWcrtPNIBaexWOsqqlWlCUmZkkC7DQs6uyEVeEoiRGg/TgaqBsjVi0WdH0qMoap8GO9LV7WSeLUlJOsxDF1Xs2jqOyPQpJ+KaJMI4cbvcM4yCCllaCrVQ5cM5R1RWTD/TjyKib25gqBwtmQATiqDZ4RvRpTI52EyCUxpmI507TJM+SJDSVlM7NrEWRq3M/To6NA1sYBtW6weTMQfR6ci4gAUtS+Je2OpMZU23bcDoJVdVHmdfCJJBBOoMZknQKEJT6RvU7fMgif9ZZHXOpGquJwDiy3qylqmhmzQ4DHPpOgKWUtMeICC+npM3mc0hA0hSS4JfJQl9oBdham8X/jJ6T1+AbbdEJwTOpxSUmWTSqmJEmF080NpjBhh+3shh8gM57Rj8SjUGgjgyLZQAlJZjpOLLwzgmNSWM4Ji0oSG0SWUQpgSDzcrMcGbnNJwE7KFA21CXrdsXLV5/y9//pb7BdTxUjhQngPZ9tVrzarOnHiX6M3O17ToVhdzoxeE+5ueQ09CKAt7gfAHVdslmvsYUFMxJUW+vTT15ycbbNzj9v374jBjg7O+diu+H59SXbzZqqKtgdDpytJab44fX3PD7e40OgbQW0SI5bskfIXhii/DkMA+/evyf4yM9//lMury7wYeKbb36XOTnPn19TVxUmBnb7I9fXV2zPt3z33XfqdrTCOMfd3T273U61tkQ3oiwqLAVjN9HFMffZmygtdVVhMMbx8tUrwBGjWGtaZ7h591YKML2hixLE3374wLHrqSpHUdQYEwl4fJyonCMYGAa1owy9iD+XJSjASmqnAGwM+L6jqVc0WnHth4lpGEUrzLgct/kgBQrRxnAqpBcwThIE6woFX8Upxzphtqb9HGMV8BQAGmTLlaG9kHLX9SGtZQYkEJ9Uo8k6mWEJrEjFjgWIURYFhVa/wegeGnHWENU+M68ZKgyfjresLyxf0ntvdOdRwEWPYY0yWYmybAdILUgxqqBpVMBqAULOSKQUkPRKwEhhxHuf3dIi0hKeijXGFnovxF48RgOTCEGGOKkYpMRnwYtgq0A+KXZI7c6zG1uyUTfGsF5vBMTxPuumpUq4FP7I67z3PjvX7fcHXr9+LXuCJulVWdG2jSaLVqxjkVaHZPOZEihhKnVM2oKRnq0AC6UkvDFw/eyS1Wot4ydGDvudLm+Rru+ZdgfVSBP2z36/p2mFJSIsqkDTtsr4hM1mTVm5nIgm4CVpJLRNQ61MjMfHBz7cfqBuaq6vn7E9W0uh108ZaOv7nq7rAGGQS+KedGci1oAnCFPAj5lplCrzj48iImoQ69roA48Pj7mVrSilfebq6oqL7RmFtXTdEWulBazvJW/quk73QimEPn9+nVkxIQTO3Ia6rnAYzjcbzjYbohFAKiXK4yg54nazoXRFdrTx3vPwcMdo5vaS1XpNs2qzy2JigiT2ToqVUuUvtY80Ta1bT6StW9paWrFkvMn69fjwQFGm9raRtmnoTh339/eUZSkOZCrOnFo0ZtZGyK01wZNj8QReJUCsrVtOp5O0BsWRuqohRi4vRch2t9ux3+/VKEFi4GEQdtBq1VJqcT6Jig9DR6wq+v6kmjgjw9Dnse19oGkqimLWZHEuAR4TfS8sjDT30n8pP1mtVgLCnmbwqq5rzs/OaGtpI7sb7wWIG0Yew4MUbKIwn2OKAYMnThNjCFCV9JrnVaXsz+dn2wxCjtPEMMjYijHg3EpbpcRMZNU0wjQLk67ti/Z1Y7RFsc7r0z/1+pNBkIeHB66vrxdVbPHe/vLLL/j7b77l7vaeF8+e8fOf/xkxwoebD/zud98pHarVfrGJoR+klYJA34s2R98PT9oBElJ9d3fHh5s7fIhC99MF3yL3NYlpWYRO3GaKjeUXv/g52+2Gv/7r/41PP/2E4/HE6Xjk1cvn3N/f8/z5NY/391gmyiLyycsrLBMvXj3n9evXXF9uOB4FgS2c46R9XUkFd67sb1Rl+RKD5fnzT/j5z35B1w389je/5re//RXFqqU7jPzlX/4lxxfPORz2+O6A8RMf3vxACLIZNM4ydSfawkIYMb6jtKIeXhUVhbGYlSC11qbedUEmaUqmaWS93cLzC7px5HA4MYRA0650kAuY4TA4U+CMhSA0zymOYIIyJywi+yfVcGkPQINYGI4njDVsV2tiXBMR9L6sK1wpyadZoPlJUFGSLocpClzRiMODdbkqnpSWMTK0J50o0k840o8jXd8x9AN+kl7BflBrYSvKxn0/st202KJg9BOnceDYd096zOS8ZOxMQTRBfKKUI+0roe9EvEoXkgR+YQQMSA04hllU0E/+SZKYKlRp41tWc33eAOeQXQIRWYTiE5pjyMmH0ee+pKrHpG+jYU4SUlpqhshxVHArzNX57AIRUgIl+0cCQSS4E/GqMbMNeALgBLuovAHGD5xuP9BhaC1UbSSGgzQmOUsSpTNGVOZTMFSWBauiwBUFZVFhcOIiopu7CGgKcu0KbRdRW+eiLCTJC6ofYq0qXpf5ngvdX8E9YwlYvJWfmVx5CBpgGiZXMkVLHA2j9/ggyY4rnArCGdHLcQ7rBzADiXEhN2YiHAZRGa8qtSGMeA2cEpiXgK/tdouNjrOLC6qqxKp4Xdu2bLdbPv30U/q+F2Q/BHSvQeiyI/0gc2R/PDCNI+jamASqbFFqYBlzEB2mkBP/zIrQKDrdWz9plVHPOVVCnXUZpbcuadOgmkQSsCdXBRbPfE6zo1aDyVXZZAl5Oh0lWFDQPRhDjDquQVl8McWl2nce5+qP/n9ag1IwngSzUjBe1ZUKTk8Z2LN2cQ+MU3AhaNJjiVYBvxDy/cxzUYPtCER9vgYRT8x2mdpmFBaFirS2pla51GLnrc7rxMaIPPk+DE9EH8lJkN4BA9FZpvwWS2Lk/NidyKSHMP8z5X4JK9G2qCdvyCBKetPi/OJHByAyM2fkSHfdwNu7Rz559pz9Zs0vfvYzjo8PGBM5Pz+jsqXoDuh62veeLnoe9weGaaJerzGr1xz2B4iR+/v7HCPsDuLqFifoTiedUz2Pdyd1Q/NEL+vz79684fLigs16xXfvX1N8kKLAfn+SttDVilN34HTqcYXly6+/Yr3e8qtf/YbD8SRsAB0DRGkn2B2PGAPjOPDw8IABbm8+SBV1EPbE33/zLXVd8uUXX/Lp51/w8uVLHh4f+ef//IL7+1tev37N2fk5P/nJV3z48IGuO/H4eM9q1fLnP/9z3rx5Q2lKCgJVUWENGRCdQqSuawzwy1/+koeHA/e3dzy7vuD7333DOEhBqK0rwPLf/Tf/kt9//wPffPst69UF1sL94x1dHwh+BAzrdiVV6iw8PeGKirZpOByPAmp4T2EL8CLg+uzqGcfjibYxNM2K1XqDsY6LyyusMex2D9y8e483UWjM00hRWNFK8iN1UdIPgwAj0VO4ina9Ym9gdzwQ/KRFCLWG1tabvJz9aLgqCGIMJhqK6LDK4AnG4Ced12n/tk6ZdWJr6YwBLTaI6HZil5Z5P0vzyCvDLgGgaf66QhwUTBAhWggK8juimcXdpcghLnARcVEpKaBIsKPuKxm0FpHDZWJgdH4+AQkNENXxRgtMGfDWFpbUQrN82SjxHVpMMdbh4ixO+2S6h6hzUZggUQGiw6nT5Uog2bKqSFpxM/NM/kxxTEQKOGXl2NYNriioCqf6ghN1LeyCpm6eMMO7rpOY208MqpeXWkzEhKHVfVX22qaU9pfd7hFrpDCX2lG22y3WONqmZb1aU5TShnF1ecWkbTECqqdWlpip/tJm4Tg7OyOEkJkAwUv8mJJjY4xWuzdM08ThsAeEudM0DYWTNoPHx0eMgcvLS9rVVh00TAYVjsejtP4MQ9Z9WK1WaoLglT0jGilVUVFXNcm69vzygqDndDgcxAUveqqqFOY1MPTS2nN1ccVGXVOWe7xzFj96HvePvH//TpL3aGjXKy4vL1mvznGuyAyXVdtCiLk9ab1e4wqbNeOSZs7+eCCxYdJ9SXFI3/f4yYtLohZwU9tN0PYwYT+UKorbMk2eh4dHOe9exkdZFjgFA5I2xnq9JoTAhw8fsLoOeD9bDZelCO1XlbgIJoBnHEexL1ZwaLVaicHFJN81DAP39/ecTof8XAA2mzOMcZkBs9/veXi8z6wTYWkMnE5H9rtdjqWIUUX5PcZaxtHk+7XZbDKQ41xLWYqjT9/3NOp+mMAOp3o6CaAEibOstRyORwGztRjdNk1m5iTQpHClFN0VYDkcDgC0jYCD4zRkR9JTd+Tx8VHYVWoi8OzZ9YId73PstN/vhT0V5f5OwyithfoqrGPVNKxWK/6U158MgiQ/47SoeD9yfnHGhw83vPnhjQAf7//tIlgS2ukwjsSIboxJ+GqOs2aAPVF+yZvWd7/7AwRRfr28vODm7h4TI6u2oR+FefHyxQvGcWAYRr766kussfzqN79mv3vktNsxdicOD48M/UBhoHvcMe4PvD4eaWxke30hCWJ35PnFBoaOy3XLMA7Um4bCKUXIBMJwkkp4EOXm7WZD3TS8uDjjeDxwOnacHnZ8eP0G7wN+GijxNEYqBY/vf6Cuao7vXmP7PW1dUdTykI0tNTKXJNf7QNk0Qh+qCmEnIG4uSexrqUguSUYNxnA4najKkmq7ZYyI9WpC+XSjKVypA95R1JWAFk5pj7YAWwh9v5QJGFSro64b/X4RJQ2aNBeFy44Uy/70CMI8gFmZu6gJFFrd0UqMkQQoKHIbIgQmjoee+/sHpjDhg7RvdF0ntmfW0WPppiFXmSc/MTzea6uLl8psjIyTtE9ZZyQZN9ISMioIEpj9xtGe2RjTJq+ijVpDnaLR7V71E+QAUjHR9gH0usEooyhq3900sxtUCAo0kPHiXJT6LCPCPBjHmd5qjCaUkO9vCrqXwnLp/k9EYmoTQwAcY9XiS/8nyI+cR1VWmZ6YzitEn1lR0aT8Rb4zWXGmSjYglrFO2DeFK3B2RaLNygJc4oqSohTbUQGiImVVKcvDYkyJsQVTNLiqkgpAu2HVrimrmoiMER+FChsMKooraX96lhGwqHBS1Get+guDUnB9VLG3aAjB5HUKP5JsEL33cz/3IP3m3nsetbKSXolqmECoBMhUZUVZVwpQyeK93W5z9cJ7z+PukcK6rFifnuMw9BkIE8E4k7UlRmWQ9MNI34so2TSO6rZiM0jntWKStDts4Wialqqq2e/3nLqOaGYx0MSgKF2BiXqfkg23bkwh+tzDb7SHRIAUGRcxKpnAlQvYQwAYq4wYEvgdZSFJgGeiI5dloWNsZjRZFoCeAhzOKHgRk0DybK+Ze18NuELXTSTBEEG6DgMUVjYvqS3ELK4crWEcVYDVFvl852TCaLKQaPspcbJKIxehYhGHnNcA/agAUigQqcCKLUuskXZIrxWzmECdtGfOSz/5JwkVWrwjay8tM6AfASD8U7/MrQZJIyuL5WbwIzXjKAi43OhTNpfOTe+TLR1nZ9dU1yXDNNI5y//z3/wb7m/vKUvDF599wtnmnFeffsoYPM16yxBG6vac8+2lzO8YORx7Do97LIHKKThpDMM4CkDhE8Bl+eSTlxz2O4ZxYBgn2WtC5NCN3D4+SkU/RspC+qrGMRD6gcfDkdW6oahFcPj3v/+ef/kv/yXX11dg75imiXGU+ShjTRKkGCQAvbq64u+/+XuiMaw3Gw7HE5dXz5QRNPLl11/z7u07/rdf/Up0v5zj9vaW7XYDiHvEq08/5cP7d8Sx4/z8nMPjnj//2S/ojyNfff45ZenYn468ef+OaOD9+w88dHseH068+f49zolF5jd/5+mHTti6EcZhwk+Rf/Wv/l+UZY2zhn7oCNNEmCZh4OoYkqXWMHQnpsljnKMuK8435wzdSF01NE3F6XDEmoIiWv72179VnSZDUVfc3d5KzBEjU2rp6zumsceYQGlgVTe0q5W20sEUvTA/YqQbRuqLc56dnxNVhLQoCpxxKq7tdH+WfTsVDawx+KR8btVi3BqqpqHvO4bhhLMllSvBOnGDihFXztonGSDQ8y+1rRczt/FgZ/F3S4lT4NOkGEnbAK1x2GgoKpkjUtyaRdEzmKvnHiM41X1yKnafeKGpbZaYKtHKBGUhHpyWiPTPxVo2xSQ2C96KBfuCgJZfQQFgEdrWfxthhKRWyYSwBo2Uks5TbuULs2NQyMUhdSZSbZAEGqcECOAndc311RXGiv7Mar2iDVEAJSesucPxSNB2iASEbLdbykrcXSbdy3b7PevVmqosaeqKQR0+UutA27bUVUUfe+pSwK3D4YCzhejdWZuT9K7r8DHkRHA2e9jn6n7btpyfn7PZbGg0adzv9zw8PDD0A5W2SGRmboxZzyG1K6TEv101bM82WYthtxNXm81mQ9u2Wb/j6uoq76dpf9pu15rkSqxio8v5wW63IwL7/T7rP5xOJyY/cX6+VXZblxPStm2JMfL2zRsdsyG3U1Rqx2s1mW7bls3ZOVVda3Ek5tgqaZIMqomSBC7ruuZ0kmfonBOhWBVsHcdRRG8XuWlyATKRJ8n74XAgltJOLZarxWwXjDi/TJM0ikrLi4jcJ5ZSAoMSq7hpGhVEtaojabW111FXoueS4uhhGHj//n0GUwRg29EPJ3V7SSLAojt5dXWRxX6NcXmcCIhmpUWrrui6HtFqGhn7gWlKwqAFpSMznoOX/KpI7VNdz93pTsdAgVcQabfbUdcV6/Umt9YMwyCuP3re6VzGYeBss82OQwnwSbGs7J+V6n0WRGJm9zzuHoVNV4rkwFK3JjHZ+77Pz3UpSgtkQGq73ZJanJZxVQghn8uf8vqTQZCu69hut5mCJZ7MjzlYlacodSYgO7pM00RZF8QgldhE367LSpOxFCQUbNuWdt2y2mwZp4m7mw84YyiLgnXTUpcFVV2zXrU87nZcnp/x8sVzTgcRcekOB6wxbKqaykn1+tUnz2jblsKe4Sw0ZUmcavU9hq6TStH+8UEn2ZgXn8l7ap2MLgaqQinV6upSMFG6wO3713SnjsJVnHaP/PD7byFGzs+2bLcbaheom5LD3TuOMdIQqZsSz4hVS0EXhTbnbCHUPxOo1zVl3YKqbIMkDVgBHKqyoixrrCsxqFNHUfK8cKpBaKAoZENm1kIQzQuh6UfncGUlVVuSAJiwMQJWWwFg6gf6cYSqZVDkrSpbZWBEeiKzbZ2MgckrQjtqn6wXn/ICsCZQmCCbzHgSOmzwjMMgFR8Mk5/oTsLIcFZop1VVcXUtC30I0hKwPttS1S1jP/L9d3/IC0avStpJUVt6eD3dJKJhiUlgVaAshIANT51aRgXxkhJ5ouQapF8xqBVeSph8nOvcqdKfUGyYq0DLnrW0OUkFOVK4Qjcy8Xcfc8n4SR0nJ1z57/qWRPX1C72bOXkNkK0QtfEhBYjWUFSO02kkKjhjlTGQdUZS8jhHUVpu1h9YcSJxxYr1ek3TtnlcYqzqmBimAMYUVG2LKwr6QdoRnIowrVYbrKs4dj0BS1HV4AqOWMIJRj8waatTiFEWVGCavNAbYZFozmJ2QRN8gqcoS66eP6NuajCizTEMg4iXhcg4zfd9XKhZL3udnwp4oYt4yCri0r3haVdCk76/u8fYmK3prJV+zrQJdMdT7hlOFY5xnBT5noE0H4IKwE653cY5x1rFYfO16zlFBQbWqxXXz5+z2WzYbM5yFeLu/p53N+/p+h6mMQMMycXAWYctE+iarjnmdha9+DwG07hLwGdiHaRriAAWfJiyR72wpcr82XEcGINY4KbjCKiYQD5pQSvKQgekzl0ZiNr2FTU5Nzn4SaysGIJYLS6As8l7JhWUlc9qEJ+GuCZkqa0MBZpm2ECTpASIxIAfvQAR+izSRk3WHEplU51busYEdWIIwWc76uUrj7vlXFQQZTkfpRS7KDA8tcDgo3/oPxdASj4nkwGwwjmcAvHb9ZrDQZg7IQH5QUDzeclaLBpGiPOAaDmVDaZwlE72vtev30KMFK7hu999jyvesjse+fqnP0Ns1kWDIbUwiojbkdu7O55dXVBUFav1hu3ZlusXz/nhh9e8fSc6Y1988RnWGO6GkWkS0LlwkixmbDdK5ToZaOU8MELfDRq8y3P8D//hPzKNIowZiTgLOC1kTJ737z/QViVN3fDNt7/j3dsbPOIIUpYFl5fnnE4db99+4K//178G4OZGRKWlfdXQti1/8Zd/zjfffss4jPzy57/g7/42sl2v+XD3wB+++4FNvcF4x9D1rNuGL199zrHrOD4ewVgBfIcRjwD8dV1R1DVlVQmNvoCqER2BzaZknKRdK5YF27ZkZ+TZ5zbNGPAeitLgXIkNnrubd7TOMZ0O9MMJQqCsLYwnzloH2mJhjLjFWWt4fP8HWe+alheXZ0zTgDEea+eCS4ww9COmqmWMGNnLttpu8Oz8QpYi64heAHUp5JgMJARDdosbp1H3awnCMaiAZswuejFEZf9FXFnjilJatTUZKIsiJ5deAeAQhN0VEUc+HzweGUtpDsQExmvyJeBDwiiFpZgADUhrq6wFS0A3RohTIAOOJrEg51Za0DVY/0zHDItpncLKBFDkAooewWhLURZ7jhBM4jSSxWCzLkOU1nSxZDck3zGZL3lyYYCqrsVmc/LzesvMwDGjjrk47+ODJpdVWVFYx4RhmMThpCgL2qbF+yBJb5REsK4rhr5n6GOeU9Za1qsWYqDrThKDKyN5SgWOhwcKV7BZrVlvtrjCMUwTwzBKy3bpNJFsWG/WHE+nrCOYNCtKbYcYhoG+73l4eMgJfGrpPTs7I/qQ2QXDKFX80+mUGSiFtmcfDocnbS1XV1fZJSUlkPv9XnV5Ivu9MBy6rqdpai4uRLvj4eEBEDvlpmpUCFYKjN2pY9yPrNYt52fnnF+cUWmOsN/viFGsioehp65bhqFntWoZh4Hd7kHlCQKD6r24oqCqK5pGYsHdbifMiaKi7weSTsVut2P/uGO3e6TUtpSzcxGCvbu7k/YIIsWi3SnGyHa7Zb1e52S4KitMhPv7e4ZhoG1aXr78hBgDm/U6n7+zKQ4PlGXF9uxcQIppkrg/RM7OL7i7u5P9HLJN8uF4pHx8ZKV2yH4K3O3vmCbPON4gzKx5XI/TBDFydnZGqzbAXuN6mzsLBChYb6SFqO9FiDrpwkyTtLoIMC/aNcPQS0FM9yTvJ3Xn9Dnubepanu8wglOWhFr3xgBuuxUtJ3XuOTw+SLwJcr8qYfzVKlexXQkjJrn+JCAntXdll5bCsj/shBVSFNR1o7o0PUddx87Pz3NxaLvdZmbOOI48PNznPGkuipWs1ys2G9EJPJ1E72S1WmFtm+Pe5EL0p7z+ZBDkeDzy7t07oTkWBf3Q0fcnPny4y+yPZEEUgmfspeerrQs+efkM7yOXF5dUZcP7D3dcPbvm5uaGpq6xBK7ONlTOcTwcwRWMEe7f3/Dy+XPOtlsedzuuL874/NNPsAbevIdNWzJ1e2pnKNc1VVlgLbSfXmJMIMaRVWWI05GgSN1wksE59gFXWEwIhKGnLUqKwuELUdGvtTexrMoscJgelnOyOHsVDgzOsFm1GGtpqoLtSvQ6SucoC0NpDWHoicFjnfQsRQMlkaKoKYsaY5wwIxIFqyiFVmULXCliimXdYmqpgsuCXVGUFdaWaqlZznRCAONERChGBlW7niYR/jQKXiTdChEOjJlJIpPIYl2krhti0eBMwWGUDWA6DgwP90x+rjBLkiJ0zsTYiFHQ0ULbHGxvsnYGzAVKqZgaRem9BNKAsaIl4wrHdr3l2fU1GMPt7S0P+x3eRx72R4qiZL1ac3l1xWq95ngUcCttmstJGmNNtqZUJsI4TRhncVE2cKe6N7RIe0E3MXmfgwONHXK4kdz6lqyAvKkDbd1ocDQ+mVceEX6KRLrTUSv8sNs/zMlisgrOkcmsLWEW/yXqmARjs9K6fEa+LYMxKYxZBCWidfMoiZQRxogwP1I9Sh/YovKbH2L6c5o4f/mSzz89p9Jqh7EW6ypcWYEpmDTZjDHiqpKyqKkmbVWwDuMcg5VrDlWJD5HJSyCWtDNGP2XB2dIl2y6bBckk2ZY2hmTlJtoY5Ou2zjKFwHjsCDHQrteUVQlWgAzrvFbjS/nehHCXEig9Pj6KWOmizcF7qZClQChGqcMlwKMoCpq6yRWpxMxIAlXrdoWxSRBZxtOlVhM+VvmedLOeQvKSlzExa9coM8kYjHU0bcNnn38ORiizf/j+Dwz9QN8NHE4nyqKmcNJ6kIJns3i+y00uiwan6icL4CMmEGb+e54JyoBKjBNnRPE/VdHkXHUIWmG/+ZgYEzIKra5VIpYqtrbOJpAhMUBsBjfnz2mrmU6pXGfV6qQxljIKeJu1e8Ko41wZPn5RzdS5mCuwMIPIzC1wQdeBBEY5FUyNwWrrzfwSjZ3kShEYhnGec1rdmtkVC/BTz0Vvjn53UIacfD6vXQvQbsY/wgzEpMRKr0mOKQBLCFKlvLi6pCwcV1eXHI9H7u9vwSRBZqBwsx15Pi6z62r6t1o4Y0RE8+xsw1dffMphv+fLL7+AaPj1r3/D27c3fP7VTxKBBGPATyNFWdB1R968fcPxdOT1Wwnmb27vc197UQjjLobI119/za/+5ldY6zg/W/Ph9k4STGYDkRkAkz73oMGas+LO9vrNGzZrEUqtioKvfvZzAp7z83O+++733N8/0J06fvHnP+ebv/+Gi/Mzdrsd7969ZVAHjqETzYvff/tN3kMf7u44Oztj3TZsVi3d0HN5cc4vfvnP+NnPfs7XX/4Zv//uW/76f/2PXJ6tKYuCv/jFX/DXf/0bvvr8a9788Jp15SishxBw/cCXz57RKnNWxFYfOD+/IBDp1fZ3GEds01CWTsUFJ642NTHIOrZerRm2K6Yp0PXSGx6tJrox4tQ+ESxl4QhqmSoiz8qGMgbnDESbbZrluYuG22q14TQM3N/fKXgPtkiOCSVVs2GDAB3WigC6KxxlWfHcCqvUlfWsJaaJfVk4rR0JsOFUtNAqi3XoB3b7HdY6vJHEypSO/f7A0B2ZBs9kHXXZ8DhMMCGiikbYaF7XMRG7dvT9mDWDBOwErACpIcx25MKwjLK/GhGqL6zoVJ1OPT6GvJ8DYi/OYt1ZzOFZrFTXCN3fQkj28FHXqUljBgjBLHRLkLUigb7LIAewpHV0fn/GbHVt6adxPiczA/D5NMN8wPQZEydl6qZiw+L6EhgSwrzehUjbNpxvt6q/E1i1DW1b5+NaayXhbuucoBFFY0qSRK/7clBW9CzCmbTUEkuXmFbDwOSl3TKGCaLHOXFj6box7yvEQFVKwadtW7VRHTnbnuVWg1TEWIo9WmtpqlrGVJTfd91JbV1LFaQNTJMAIn1/4vHxgbKsMovi8vIS76V1Zukikir8xhhZB3ppf1itVqxWa4KXmK/vpGhYFiVNW7Op1sQYMBp6huiJkzDUxdVEknHnBtbrC+qqUkaB3EMR95e915UFZV0rE1bOq21biIbLy6scDzw+CHtlvREWbLuqpQ2+97ONblGIBp33XF5est1u5wJKlPUq+EjhRLOyqYV94wpHUM2w3W7Hbr+nLkVs9ezsAmsl3jqoiGgIIu5JFLtiAY7FfUQ6DwZ2+z3WOE6nDj+F3L6PEcZJlXLHwtFp3CbaVJG6rSkKl51XqqrkcNhLG9XptMjXJo7HA8fjQSDR4Hl8vM/xJuh+qLosxqjhQQiIk5iVe42sbdLF4RfjPT4Z/2ltTCwLEKAoHSOBVjGKO9FSfBbIWinrtbCNktOQgCY1IcysntVK9AHv729zkQ+EZbNaibX38XjIgGFRFLKu+Ymhl9yyqivWq1bnoLB8RJDX0zYzQ+gfe/3JIIhzLtN+7u5uGcaTCCWqN30EhtHj/VFukva5tU3Jpi2Yho4iHHEBLjY102nPtik5Hfe0pYOx4HiY6I8nYghsL6/42RefCSWrtDw/X3NWGRoHtnB8/ly8mYf+lGkvpyiWW8RA6QxOg2lrDN54Xfx1Cw4Rgoi3rCuLsYX0utlSQByddLIvBMLYiaWiMeAdTgeuUMejBrcuJ2bWlXmxG4IIgVnnWG22NJs1rqgxlBhKohHBssFH2s2addtSlBWuqsAWlHWliYuotBsKoWpaw2itJAMBwkmTXCdtLz4MRGsJRmyFZcIY6rKicJbCGAqEJmUNuWeyDtC2NgcUogcQGcOYK+KZWcGsOLzdbpmmWV8g2RdZN4Mek9Jrk6VWr571wXvCxFyxiKnaLs9rmiY+fPjAu3fv5oqt9wSPMjYmcUn5aKM2+o9ENzca0Ns4Jy/OWihU9NdYnJm9tmOU3udJW0JSFTfVaFL7BlH072VBVg/2aWLUBSBtrCl5zPRcayltmSswhdJhl+0cS2eOJeiRrjX3/moUnwTKQPU6chI5i0jNOiGzXe/HrIYnyVICXVSbJ4MnmmwlIMUA9eaS9fUXBJOIuIZgHFQNqJtQqUFO0vqolPaHkQ1jFrRMyZeCbSZCYbGhyJV2Y0zu7U9Ms6UbDwiQkyClEKWSFcbA5I9YpZla3aiGQWjyZ2fnrNZrCh0bKSlIY6Nt29ybm3olC1dmp4hExTTWUqrrx2azoSgd+/0uL+zjKJuW9C4fxJq776XFLAVkxlAWpWqFiL0dztKs2iziO05hZoosqxBqQfbw8MD7Dx84DR0J/3J639N4FFGpIo810ajwWt2B0jowBahGTiRmoEyeUWpFkueeAKiYAn4dI9ZYbJoLKRjE5OotpECikmqiIo4xioBgVIDFRLkQCdRcmuzaViIAllNqchIEFsadXYBbkwbLhtI6ykL6gQMB70shMOl15aVpcT1zQLKoZOr3jGNqIVK2mTJJ5oEJsNTZ4CnQpMlKSOCB1dablFCEGWRKgI9JiXyuEudJLMdwqZ0l5uTkR4wUAA06Cyd6S14TCgjcvHuLsXBz8w6i7B1L0eJUlYzaYiS99ZO6Etn8FdKKeoLSQml4dn1BmEZ+9Tc7bm7u+fzzL/j6z/6Mfuj59ttvhaUFuQiQKj7v378Hg1hFK4VjGEcwJ0mohYDGd999x/3Dg9iga3vXvMbp2FwAauumpapr9vsdv/yLX7LdbOm6E19+8Tkfbu8Ye6mC3t3cYFYtw+FAFTxF4XDTxNV2w+X5BsvE5eWWyat+DpFhGKnKUivTTlzdnGNa16w3G158+hKPpW3PWTct1I7bZoXve6bBsp96vvzyS/6H/+G/4+37O1abFWUc6U+PxDhQRGhcSRl6CiMA5raIFOORsqoonYzXsl1p8Gg0efcUDrUIr6hKh69Fu6nrDf04slqtca7EmIU1s4mZYu+96ktY6dF2TrTArClFP4qIcQV13ZIEhhtjuHj+CcY4XFFRVrUwI0KQvR2jbQ+SaFonLS/DONGNk4iiBoOrZop8jCLambTGjDU8PD5y3B8ZxgFnSx67kWk6ZQZqtCJw3E+GMUAMns53TD6oSKi2xS4KCtYJiDZFAUVskATbx0gcg+47AjIkAHjS9XOaZk0Ruk5FoNUlLlFLYmSpVQQmr+9SjPN5zMo4VslA/U6rMYcrJGkZ81z++LVodZNNAOGz5MVK/5YXP0D2jaD7Drq2pDXJaAtCdmvQz/Sn49PJtywrLc8tAz8Bo62PGENZl7L2EOe4zIgWnWiiVHhfa3uEJE8OQ6X3N6rzGEbuW9VW2UWk76XQIIwtEXgchl7ZGlW20C0TK0FzkIjBGGE0j4NUyLvyhFigyr0fhp6qqnMRw1mHRQTQYwiUKj7ZdR2jm+a2gUKF3leyPtV1y2q1Um0KzzDIvnFxcZHvx8XFBS9evKDrusymqOs6Aw+pUFOUjrrZ5EfhvTjJDIPEIXXd0DQtm428JxV12lasZ4vCUZQCrg7DwPX1NWVZcvNBXGX2h722YAsbq6kbyS80AR9HAbS997Qrmb/n5xfSfrHbUxaiTWG0tT4xX8bBYypHiBP7/Z797kBRlFxdXtK0LcOCfdOupCWnrms2my2n04FoDK4oGIaRTi1fjRHh2nV2KhEHHgyqxzjH0BbD0CsTUH82+UFFSsUl01hDu1pxfX0trB8d/2GhW5Naf4wx+d4CubXDOcfU90zjwBSCtlr6/J5EW1y6HQnbx3A8nhQ8E9A7teQke9tCnThTzLy0VJ5U+PpwOHA4HDILdy4oRwU0RLw0jemzszOSDsjj4yNAbtFKZIIYA+v1KuvWCFDX5nuwXq958eJFjh/FBll14YyKN8sWRvCBQfVcloXoP+Vl4pNM5x9+ffHZKx14cDjsZXNzlseHPfvjkOZO/lMCXWibgovzmi9fXXN1fs75+TWuaBn9xJs37/jmm2+xVvo/HeL3LEJwVh0nwBmhrAZ16Mh9kCGKYE9Q1Dt42rbGRHm/CDLJgugKK7oHRuzzfPC5MtA0LYYC70XAzgfPbrfDWcfFxQVW1XSl0OZYNWuqphZqowWMam1YEbR0pegddNqGURSlanBYyqbCFgXWlUCFNYX0mRupbmCtsDucxQtOo5RzedAxWpyT5CpG6Z8cw5gHdwr2ZaOFKM3yiJhOKW1AuklZLa8KgjrmBD1R+v2o4l5RKZwxbfrkgTwnqzLwyqrGJZHSrqPrO3yYk8LNao0xhqNabyW601IENF3bMkHCkLVG0u9TT9t6vQVjcj9a6uskCvOi7wed0ELtL6syI/NFURCto66FftcPA9cXV0Kb1eTx/e0tP7x7IxUaL4rqXu3tpPIroF96Bq4UsSlrXE60vBcqw/6w43A6qhCptD2llzgoOXWEmJHeVEkOYQHyxJi1VGwCdhIgo6+IUulVJC/GGdhYglLJTjK5iMxaC4ulQUGvpPK+PEYGYzTZ/K9/+hX/9U+/VgaIILRN01K3Yrs2DANdd5Kg2bqsHyEJkllc+/w96bv8InEWZfkBp1XntPGncTKzIcjq8pP3OiY6jscjxjmePX9BCJ6Hh/uMXHsfqOuGoizFrtnaHEhLu85KnsfimYAErta47IYkCZvLYIT8TMZo1/fsd7uc9J5O2iMaPIP3WUcpMRNSUImOudTiltpEpkmedRJwTuwXa8WFZPRTFumTY6hYMSjYaTIgkhxOQvAqZidjUTaYJMIXcjU0qtiusy4Do85IpXfpriI5tpx7MDGDIgk4XY7PcRx1fZf7lda3oJRvYxKjIhB9EMopAmQShcHWNi1N09JoYCnnb6SvOM2tGJUCK8KCvfaerlYNxpKBL+cc46C9vt4rbXkec3L+qj+wGMdpnUgCywlQSe06xortc3IZSVG/iWgQptT8BcMn3c+QvjOztVI1VautJt1vBZ40SUqBhEugFYv7HxMQLeforFMru2SXTG5hrYpCxQhrzjZnrFdr7eM+cXd/x/5wwBYFm7Mth4PoZj0+PKiYoacuC/7l588wsYfo2WxX/Pu/+hvevrmR6zFzG2dZiI5TWTqGUSyHp1HRocWalIWJ9ZXuQRp/JkJdlfz0J3/G3/zmN4BhvV7TDx0XZ2d88dmnDMOJsR/47NUrxnHkV7/5LZ+8+gRj4M2bd1xfX3Hqeuq64fzsjNPDHXUBPkirqAjCSUElEumGnimMWnsxhGmk7zqmccI5KyK5IbBdi/Xg2cU5b28+iDCzqTjsOwpXUhYWF3ueX59hrLa2FhXH48DY91w0JaXzlKUChAtB+TFr0VjW601O6m0l8Ym0Gs2JskEsu50TDYQAWFdIocOk9s0guhqFU80kS1U1VPWKqqox1lG4CldUBBWm9lEel3MluHJmZxlDUdXYVHzC5vYqsSeWNhmixGFOhVlF1PIkIule9srVek1T1RSl2EQe9nuiCQzjwKkTjRjvg7TPeHGqiUHcVXwUjakQw3zPYiREk8Wtl8WDFJ8oZqDAhOxfJi4KDSEQTMzJS2o5TXudMajVropBh5C/2zAH9wl4TfuB1/U5b0MpEA9G9cACm+2Ww/4xs4RjwmzT+3VvjyFAAjLS75ZCxyw+t3gVqj2Q16Ywt+WZQoT1w0cgSP67PuMnAGyevGkOS4X3v/jF/4F//ov/KheWMGRRaefEZdAgrUGd2oxO3mdwrigKmqbO4upJiLQsS+q2pUiOcR8z6rw6ACUWo/daeZY9NLUElFVJIHJ3d880JVeTSlnECSyUOEsSP7ELFZ2ruW08jZGUXJ5OJ2m/0QR2fzzQNC3OFdR1LTHvOOK9gCppbIYg5gG1um4ejkd8mC1nnbbVSwvCwOnUU1Ylp+74JJZer7c8e/YsJ7pJ1HW/32f3krZtcLbg1HU4Zzn1vWgyHfbKJlhxfn5OUZSklrOU5BdFwapdcTocGMae7XbD2fYc70MGJk6njhAjZV2zXkse8ebNG72PAnIVRclmvRE2xTBk0EPuu9yT5KbTNBKjbbdbxsFLm78WrtabFdZa+mHkpDolUrCUXHJSZ77dwyPjMGYQo64rQvTc3Nxklk5VS4tLil1GbTEunMtt/XKv4cOHDzw8PGi+JoyKGKUdzhnhJSVXwCQiSoyLAqzRc3W5dSrFgh8DHhJPyfi11uR/H48Hpsnrv2V9Slo3MDONE2Mm6bKklq3T6UTTNDRN80RXJOnlpZhUWCExj/dhGKQdaL0WMd5FwdEYw9l2y9APet3aiaFxaNJ3Seeaxsz/4//2f/nxYvXx2vVPvkNfop4LTVMrBVx6obvT8GTZMh/9GSO8eX+g7zxfvQq8ePk5BscffvgDv//99wQ/MQ2eft9RFQXbzVppyOJU4qzYpw2aOIzjqDRyq+CCo2orYixzYBx1QUw2hq4odMMUQKCtJSD2mnT1w0RdtaxWW5q6oa6l13S33wvtt66kGpfojSq8VVQVtizAqRsAZKTLmoINc4KSErzRe6K14AqcOq9YU+qmLgmE18TVWEPlZmcVSUhMTvCDUshLVxIUKQbpkZdE0DLGQDcOHA9H8fjWBKpwM9MhWQD3fac2g0rHD6JcPI0yccdJFI2TdW0SAY1RRKTOzs4IPnA6nvjd737HMI4YoopH6cIeYbvd8Oz6WdY+AAGfCt3khRY4J/6Z1qntBKkqX5ZihTuMEzHCtdpMJfBADwYIFezDhw8YYL1eZx9tYy1t02Cdo+s63r9/z827NyIWeTrR9wO7ruPQnyiKktVqTekc69Wa8+0Z4zAQJs9WVZdxFuMcXXeidDVVXdGdOvq+w+N53D0o+BNUf0A0R6QiJInYUtkc1N7OyDPS/CrPsDkJnoOgnIgB3jqsBsNLEGWp5L2kxqWAyySALN/GFDzF/BkWv0vAGlHcdYq6mYEL73nY7yn7HleWHE8nDoddBluW5zIDX3NFOalLA/TjqEraJ3WCEgebVENyzhGYQRJRXleGTyGK+QJKTSIE6gOvX79mGLq88VRVpfZaQkEdRlEcz8CVVgKLohCvjRhJbisS6NhcjU7Csem+D8OAN7PieFmWxEUVoaxKClPRKgskMCfhOak2hkkV39P9T+rvsqHJvRILP0Xe46zNsWTJeC+JdH88kARhA3Gxcaa2lpjP2VpLURZPEHeLBqDmKUCWrPDS2piAFvS6fJB2MKPrVd02lNrGkN/jfaaWegUf+qHHYqjKUsFwsVtLiW5V1/zkq6/55MVLEVE2lnXbMo0T+8Oe9zcf+OH1Dxy7EwY4X62INVkYzQBVU9IqcJLWr/1+z1BI7/E4JYtjAfsGDQAM80bfNE1OUKZpZBondc6SdoFSbfD6YcxVlDCpy4OZrX0nBbCmcZJES+/npCDUEzrwAijJQQQmM8aSAG7UHv2AtBzGqD9XnZ1IJE7SUiNW6UUGgzbtirPthqau+dlPf8rXX3zFul3hh0kEJvueU9dze3/Hze0HTl1Hszln5yo2rQSYx+ORMI0MQ0dTGk79kd3+nhhmIWdnLHUpAnVJrX/T1BxCJ1U8Y5SJCU1dY6zoBjhrqOqSy4tzjIGry0uGfuD167c0TY1FijhVKdaM6/Wad+8H2rZm21ScxiNd9Ny/fU1RFby82HBeSVtn8/Jagr7zDWVVY4g0oaEqLcSJw3HH6fTIYQzYwjKNUqAZpilX48vCsW0L6u1Kwal53hQucto9sK0KphDoxyNDPBIHaOoN67aiMp7KKrsxRFYNTIXDRNlzLU5iNCcOWcZJTOCsCE7jhE0ajWV7+YxmtaUo1vR+gijVaGFdWMWXJIYJEaIX4KSsahEWHnrujzsOxyMgrL+xrBiVIWmCozK1AIwqPjgG0Y8oK1knu14KIgLQQeEKqlKqv64oZhmbKNe7dA1I8+zZs2dCoT6dxMnL1XRDj3WO7dmWYezZnEklu2la1qsNx8OB+7t7obT7QN02RGPoxyGLX7uyIHjPw/0Dh9NRtKISipA0gtKOrIGgUaaBrHky13EWW0gMW1VV1kzzlsWeJ/c6JfniHjJpgSrpiChQ41MLm+77zHuwADBRe0gj+91OW8IUnFiiNqnnAQUUNbbPoLva/KZYKl/k/MXSOvDkpcwiUvFFbcM/BlAUoNTBzz/0SrFJdzrycPdhLtQsGJ45Zolxxm0iqvXVM+mevNOYoyhLXZvFAU+Ynxvq1UrZ0QaCrNvGWKqqYDJL8Hl+Vt4H6qrWCrXh/HyrILI4Mi5FG5OLRzAGtPXVaZU7reNJWySxRMXVUdhrFri+vJKWdq9C/LrPT2NkClNOtstSQIGkMdbUNcmOfhgGqlri77Iscgzh9VwTgCAiolUWSl2tVh8xWHvRMIpwOs6AwTANVE0NbJRVU1MWlYzhMLu9pFYTYwybzQbvRWjz9vYu63yEBeOx1OJy0zRs1EHHmEhVFBJrRBGETSyp5KjX9cccS5VlSd+LvuU4enV2WauL6cBhfyTEwOnUMXqfhVrXzYraiAOpwbDdnikDWGKax90jw9DRtm0+91GFPeu6zkyjYRwwVZ2T/bVasJ+fn6t7zKwZkwvT06S6KJJT1nWDMWI+UBXFosij80XtolfrVY4DDYZ+6HMckkRNDcJ+qaqKaie2vam1XICugVQsSM835VkJGExOPHJPRfw1xpjFepespdPpwDQZBSWbPJ/TK2nbpTGW8pP1es35+bm2hXf5GUsuMBdwExP1T3n96ZogpxMxRnERSMmL2l4tX3kzMHMFZgpwc9/xuPuBwxipXODx4UBdNByGA34caSpZdMqiUPEdRZeD9N8l2uhm3erBVf5Oe4FcVdFkq6sj0RpsWdKu12w2W9q1iGcmrQNrHT5Ghmmi7AeCjwzG4UPBMFmaomb9bIt3wtow1kqrAuIgIVVuEVMMBqFqKsouVnlWq9boecq9mawmWVVFjNISMqlbghZphDaq25BROrExEhiEACYxPYZBNC9OA6nftFDhMGldkqS8rCoMlof7Rx4eRcxWzjVkalfS7cCY3LpjkMV7GkQMKiKIW1RrJCAn6NY6fnj9hv1hL9X2cVL0FeIkaOF6tSIG8U8fp5FpP+XKswhblSo8FNTvWhK9tOUKy0UTknGiUpEcP6W2msR4KcSCa72mcC7T3Nq25Xg8igjk+/c5sbFadb69veVxtxPUXsG3afJMRLyCDPcPd6LiXbfct6Ka7IzldBKhqrKucIUsdHenO/wkdPvj8Yj3I5OX6zJWwS0zV+HLYmb4SG+qy73FqfJjIjlgTm0DKUldMhISdTaElGDPltLzvLGUZZERYgkGXGZYpU1nWVUQcTMW5yluIIWTMVtYR1PVGvikapL0S2OsbpaRzeZM5/bcj7jbHxgV4JymaX7+mtwlemGMwnZIYGxZFthC7fXGMZ9bSvQMBpwhTolRIZVMYwRFL4zDuXZx72y+trTWOWtzq0YCCdJLWChokhAE2NX+UKsixvl+GUnYu74n6O8LTSwNhqIS5on3gWEcxVGBxIYRFo7EejaLVEYNtmJEg0LdUBLQsQAFYxSNoGXFLgNGCrymljHZ2GaASsAWnfud14qOJEY2GizuCZg1KQPKRqTyI9QGYiS316SNr8/P+g70/jonrSmFqosTRUQsqfSVZcU4DuKo4woK63K1sYgw9T0f3r2jLWu+ePUZ27pldCOtc2yqhpdXoi90d3vH4+5Rk57A8XiQsX4MnLpB5g0SKK/LilqdJ4w1ysqToHiYRs41wR+HEWMluKurWo1pI6fTEeMkaHKFgOfiNOCy7a8EciHP11RhBLGqn6aJqqykUq2VnknBa6/0+ckH/DRh9ZhpnU8aNokR5LSC64NUwsZhUHeGkAOnshRb9nW7pmkbCudYr1o+/eQVL1++4MX1M3HTCJHgJlZ1nat8n796Jev7ONJPI7vTicPpxKh719R3vPnrf837u3vKSvat7nRKaQ11XXFxtuX+wwdqV9B5TxgmvvrsBcZanj37BGtr3t3cst2subm9pXCOzapms20heLrTHkKHM5FyGrhei/3k7vGRV88uePHiOZhIW0XpI/ZH2jqyaVppxfATxcpgwwEzWhozYYaeMFr6Y1QGVsAbYam0zkDhoCgoCkuoE3gaFWBdAv6JyGIW1bUB7yMOg42RurJsLlf4aaJqDKu2wFmZUz54nJEA1qnYPBhsKRbiRu2lU4te266xRYMra6q6pahb6tWWgMOYAjcFpqgWjIUVZ6RppLAFBouNyDghEm3NerPC+EBVPjAUe2ElBI9VgdGUlA4YKGQfMIAN8Pz5C7bbLfcP9zzc3zF50awQhlAtjBJ1jZimiZubG7q+V6vqGYQO3rNatVhnOHY9N7fvOR5PXFxesF1vKFyyUW8VGBZae1mWXFxccHV1zR/+8HsOhyNd39GPI6e+I7X7WWs5nY4KDIpOkOyJEvGm9ldIQqPSsmcduXiW3hsmYbNIi2WQMWKTXkjUtTrNf6nQeuuy4KpJ/0W0PVvbbIFxmsFPmUC6XzuTi4JRx5hOL2V5ePASZxnrsMXsrEOIeS+f442PxJeZ70PCYYhonJ2KUibb3Ar7LSVRT+OWOYEg0Wry9yRAnhC0fchgCzc7nxVO9WekUh8V7InIvUrMjQTOgIAlPgg79Ob9O4q7D7iipG5ajAIFfd9JjqEOesKUSvuhrJt1XbHdbJX5Krokg67NiXVXlVV257POURXqzNQd8doumTQVbFVi20a+18wxYWZC6p5eWCN7oHMY4iyQOk0i2Kysg+DF+tZrfLzZrFmtVxLrRjLgktoSymJ260j7y36/y7FayrsScFPXtYgLa3u6FJNKhmmiKVuKVZHXpqIoctU+gwLDII5YaTzrvpdcXMbR6xwWzTJJpKUFSeIE2XNh4WSaQKay5PzignNzTnc6ZV05771qo6woipKmaTkdZf73KroKkuNM4yjueCHF5jYbLtR1LXPGqPCy7t/iguNz/BdCFFv0GCFK7CD3utK2QFnTQ4iZiVGoWH+Mkc32jLKqadQFKAETfdcR9F6leDliaNdrzs7OMvCQnmWh2iDH45FhHDWOGrm7F52Z9apVd58iz/tlcSu7vKilbWL5p8JVKhqlvCKBXSGErP3x8CAAl2iynGVL3rQ2pTwQZDwldrzcx5B1VBJgKIDHXPwc8/z/p19/cjtMotynhcrM//rRy+XPwHrdsNt1KgZlsCZyedHy8vKCfncUcTMj9NSqrKjriv3+QPAienI8nXj54pqmqXUREMXlsmryAoPate72Rw7HA1iriG5Ls2ppmpWwO8oG5ypEMMaBdcLEsLKJZIEjfS0HK1pxj4qcW11sjDJNiqrOFPiQSJNGF9uUjEw+U9aXffteE9VldUNPgCBKfFnQa/Rzu8gwDBI4ALMIpLQMpKQxmpTUwekk9LQ0oItS1KjRAC3ZmOXBEVIbztyuku8LcwKTKgvRwDD1mR6eNA38QjzVe/XwzmyPGeXXgwIw+YUTh0kq4XMl3Gn1wjLTB1P1OoTAp59+SgiB+/t7Hh5E7fji4oJXr15ldDgtpJWCD9M0ceo6DqcjXd9n8dvkzpKo4cmKjygJZtqzly0IMUbRaXmCcAZMQa7kyrNOdnelJJXWMoxDPsdT34uCeqLVk91spaqQvo+50p7aBnwITGFOomOcaaMJ4U/oPiC0PuOeLFZRmRPL5537b5H7X1Ul66bl008+4cXVM56tGtZOGEllpVRQY6jqSgJz3VzHBWAxjiPdMOvNpGQ7CYJ67/PmmFD5uZXqqfjtEgVOwnUxzLoJIQRt7ZkTdmN+DCSlc5vHu2qDmMXYz89dRQf0JRodwnRIrRLpu6KRMZuZHEgbSdJHcYXj4vKSyXuOx2MGfoZeKlJpgwhhkoBucS7pfAWdj/gpPOkdjUShPiutPAUb6Rqdc9kJIEYBXCFiCwE6Y4z4acSrjXTSTSKSHQEgXZ/2u8runq9d1pRUFZPrjt7rPWAOUI20MnqlTVrnMqXcWhHBdYU4Ka2blrIo2e/2HA57yqJg26745JNX/PLPf8nZap2fy/JZGHUkkUq0PI/dXgTKJmUdJiCWGOn6nm4YqGupbAsbTSmiWSdl4aaA9vbHwDhqz6o6ociYSpaMTzfvJDBX1TXBew2YZrDIWpvbpNbtCotRIb6JiOpNVBWuLOjGkWilqJAqZcM44JxU9eSxWYZh5OHhgePxACZSlpWAUEXBOE5SFRuF+fmTr7/mL3/5S66urnDRcNjvefv2LR/e39DWNV998aVUtdZS/Ulrdz9N9MPA3cM9v/v2d/z93/6G6ebvuPvwgaKU4PLD7YlDJ9VKZw21thcWztINI9u25OXzM7bblqZqMaag7yadn4bHxwfa2tE0FX7sCV7swNfrtbTRWNE6scjeppevY9IzjT1D32Ei4oDjJNlwzuraO8/nZFs6TWJ7am1KOGVOG2cy8NcPsqaWRfn/Y+4/ei3LsjRB7NvqqKvee6aFu5t7REZERmSWym7WoGfkH+CcAwJsNkecESBAEgQogAZBUPSAHPLPcMBJE4VmNVFVWSk8ItzdXJg9dcWRW3Cw1trn3Gfm4ZGCXX0RHmb23hXn7rPFWt/61vdx0SPCWdLIgCY2kSvIPtwYAh2U0rDacoxCRaiBwVHrShhTYLveEN3aWFhXAkYjgnTOxhjRDyNsQc4QtiihlIMtqJo5hYhh8tRfDYXR++xqFmJExJwIaV7vzpUAay8URYHReyQd5z1GxUwXDwwG03e1lOQncsETOv4wDKSnZi2MNdTCxkB623W439/TWWPoHgAqV3FTpPhJKufQdO5JQWSaPPfe+7zmq3qFV69fI6aIYRhRlSWxAnj9HY9H3NzfZTp5Tpq4EEKFBTrHfZwZf8J6kL1PnE2ixEcpZmBIzlMCpQWIPtdIokSbzxM+g50hEEmeI2BxYDczpagN/JPXr/H48hK3N7e4ublG3w+Ykid3k8nTXseuNuD5J4VNAa50nt9z248UBsgFBvnzYgywmoBooy2xtJxFWRRw1pEgJ1PYp+DRD33WS2J8iNdVzKwGKsiNsziqUvjFm9/g569/CSncCbtEtIiKokCMie1DkQkviSuMWlBHZiPQmUfrUxsN6wxSJGA6ieYV5OMVi6nOmkppqVeFBCSKxUTHicJ/eo0zOsfoZB7Any9nv6KYqeJcoqprKEM5SoyR9kBrUFTk6lRVdT7TjDHUYshAq8Q2wiiSa5YYSnFcRy6TDsETo0PiK0gb5jRmMVVhBtB72hxnir6MMQbBRxhOiDXfs8js4ZFNASS+JPFMxy0TE303LiBKUq349RPbFxdlmYVRAeTzsnSWTDY4Zh/GIbNohb0igvbShi+AkTyojSLkc1RrjYqL7wRe07XK2pVWKnHvkbhMYgznTG4tjCFivz9wy21kgIPYKGFiq2cW85X4WgRuvfcZJFiv1zzHYxbKF8DPMGN6jmkVnJvniYAm1lKhc5rGHC8T4yJlIGMYBlijUPMeLVp5Mk7y/jFGHI9HCDNH2C4SA+b7w0wfYZEMQ5dj+uX7ilCszBOZ333X5bYjmR9N0+R7QfthZFmH2Zjgf/mf/XfxU48/mgmSH+nsj48+JMGV9oyHL74/DPj0kwu8+fQzHG9vYA35U/ddB9/3OHYDaTQ0K5iqhq3XsHWNqiI6FSmB0yEuIIixFpsn1DsHpeCqEkpbWAZXiqKCMgXRQRnIAPerCqAhdGEICg5A9DDI/pQ3LVtw4EiiOtpaJGPQTgGn0wl3d3cIfkJROjSMqklCDd6oXemQJq6IKg0/ThinEdPkyRKW20NEpDVGqh63bQ+oWT9CHC7kVCb2DPf9J/oWlCDSISAbpDFayDTcM0ke3EvEUPr8EydJimn1ie8xNPNV5HDkBCuB2g88cdgAUGJDWltkHbikLS37a+VQjyCXhCSbjRb2DVtS8vOM0rCR7JVEkXu/3+OHH34gkUwkJE3v9cP1e9zd36MoHBQUyqrCqmnIQmocUTmHy8tLpJSy/VZMCVPwmBa0W0JCpY9P5340rU3eLDUDhYF7LbXWMM7AFIarurTxdP3Adr5TTrAAat/JTiBcaVZIRHFVc4+/gBYhhOzSlFhFUapQ2S0IOo+v9LAu0VitNBILH1VVRZUBPy3AhlmTIDIo4yw5Mu22T/GLP/kFts0KjVaolLRd0HX4QC1LCpxspHDWelXXDapaM/tjImq3UthsNhkMkWqFaLVIIETApiZl/wViLXUgswgsPdPql1UO0lQImUX0sTm5BJH0okI//7lkDM+944R4CVCWcmvEEqzSHFwRo0PjydOn2Gy3uN/vcy/m6XRCCizYNk1ZkI0cQNT8d95xIyj5bk8dVdw5cUriZGBECIvYSFLZURwMGm7xgKb9IU3I+jUqAZaDyBh8FsY12jJjwqCsS171GgADZobAanquRl2WcCWtxcI6lCUB1IEZPEVBVF6tFSICtHOYxhF7TtTF0pJ6+6liX5YOQ6+wXTdAJG2ocegAFpC1lq5LgiWADllnSAhaawVXUAXEM00WmAM3HwL6caBeaq6I9O2JdHUU7UuFK2GNQdu2OBwOvC+4vGcI+CqBouX7NwwDmnWV39s52iu8p72fLBLb7PQha7guKxTOoakqWlNKwZUlrRs/QZ2O2B+POB6Gs8BE5q+AUxJ0JMztMiMHjOM4ZkaRAfDV736H63fvqO/3cMT+cMif/S//4j/CereFshr9NOL+sMfd/S1ZMd/e4+7+Hrd3d7i7v0N7PKAZ7tAPPWrtMI4UjFEITnan3TABiphFUMAUAtq+x9XVChe7Gi+evwCSQt9N+Oqbt/j+2zukyaE9ghimCEDyOAwdFFdfY6IqcOkcpmFESFKVJGeVQoPtOxNVm5GQjEZUBIoYoxf7CK9dTRaCsodYa6GtA5TFMAV04xHQCtvLK9R1QzoaSaEoK2wvLuh8VawRYwreyyhBM0yh9yEiJIUQqTfeOkoyBTSh81charJ/p0QgoCwqFGUJ4whQ6boOh+MewzgiRkBrSqSazRrWWLRdS641xua2U2EHCg2/PR0x3o2UzAnqpxSUStkGNld8iwJD2zJ13MJZOkvJAaMAGPBJKeJ07DHogQtfAdvtluMunQtVIax4z0056VHGIKSIw+GA/X7P8YzP4s4Sv+jg8Te/+xKAgg8e1tA6s5p69De7LR49fYLj4Yjbu1ucjkcMY49xHDJ4HkKgtcXnCX111kVBgcgAsE8RE4MmtLhSZmMQ804S4DlKVorC8rPqLZ9HWhE7CHrWe6JzUHNLQ0BVlHhydYU3L1/h568+wTCO+Obbt/j627e4PR2gzZRbQvN6ZyRbqTnJobZpMxej8leISIkTP2NQ2QKfvnyNLz57Q3vGQK0nVV2jrivEmPDbL7/E5CdstzuE6HF9c42vvvkG/TiQKL4CA4ELNiwStHXIDJAEWEeuEstiB8XHczGF2DHzGlRK5dYEOctF58j7CdFPFNMahzARUxMpMagV+T5RvOX5VKO1SeOl+L4abrskAEsjJGZXKrpWKyLCXJSMAqgFj8CuY37SGE4HwqUUreXeJwx9DwVgvV7j0bMn2O0uAE78EsfDUMQqQIo8ryiXCblQGuEn1q2z1H4PKEQG3J0lgJEss6mApTWBYqK9IO+jteH5YZHKap7PKXB7BifCiZhDMSZYzfM6BdRlTeyyRAXNgpNaYX1JjCfFBYofNPqxywXrih2tlFZw2maR7BCIfR0xF4tDCDlJr+v6rM1C/u5cgbpuMoixWq1IjkAriD2xABhShJPPlESfRGIts5Z7ag1ngGS1WuczmwpKwKpZoyqpEBkitcOTfkqNqqoyQCB5BenpEQCstc6tM6fTiTogEkkT7C4u0HUt2q7FOPl8zaIPJ6ZxExdFMutFwC6l0LUnTIbugwDCdV2ftRQJC0heK2MqLTK0n9H4lRyTTNOY42rZv+Z43ubnL21xx3HENIxZDkN0+ZYAlFgED8OQWTR/zOPvyATBErb90YdRClVZoK4K9EOPtmMEEJwkKOBi3eDxtsKj7RZNXTFliAU5fcQ4eTx59hwvX7+G0haTJxtFEoDT3LNH1DJlLP9nkFWYtQbUjLBqTc+RzVFrA2gSWpN+eRm4peUSjY5iNJfZekyHFrqwdtTf2XYdYgwYh4HZEz73K5Lmw0RBR6IKBQl5BcQgTif0Z/AerihQVxWKYuFdzjaYmZ7OYyqVekE3AcyJ6llSSIkcaWzMOgQyBZavEep9XiAMfiwpSkqpxfejw8CHKVe85HNndiOPYUyZti7fZdnLRmyaZS2VJo5m15ul8KJSCuumQc1o57fffgulFKGEWsPHgMPxAM9Ip1jBLVseYpiZOIqy9kW1R9EBrVQGdxQeXtpinMDjGyJVAhYbjLEmK4QHrvILkCJaBFMUR5h5jYn4ISXMPNh8SIuTAU/TXN2Xz4sZDEkQ8USk2VFmCUbR7xTTdVkcjNux6DAHhH4r68RPI7ECYkRpS3zx2Rv8+osvUHMlmaxeSdgxxZir+SHOVnGCcsu4kNimzwEgeN49PHQEwJhRaVpjYFqoD7RnyPvId5TPPTtoMxvL5LGW66E/HwjPLZLG5Q+lYgTeVxJibqkBA6l6AbRorSEzSv6dABwOewIDADR1jUePHqGw1NYmjkqKW2uGngTDqBpKAV1RFMRW4urXOE0sUhxgLQVh1hgUrsyHV0pAWRYobZErftApM5OUMajKkhhjcdatEUClqqpclRJWXFlWxAaaJkwccAzjgPZ4IlZV4dDUNbXsKQJWDscjjscj9WnvdtSyxZ/Tdi2OxwOOxwMBQzwPyqLMc99oWsMqAZ+8+gT/8r/zL7FZrSCgzPLwFVDteDqhH7osKk2HbWD2Dd2fcRxJlG29OjvQ729vMqvKGIOmbgCFrBDfti0QqeIHkAVc4paltm0RI4ni7vf7HGRRP7aFdQ5d3+Pu7p5aUROBUruLCwBUCdNJZ/qu2MFKANUNPfpxzLakMv9lr+26jgJGntyyNs7AcE40oeY90hoDDWKPCDgSWJB0XTfYrNdQOmXghvj8Cj5G9N2AtuswjgOin7ALB3SnE1ZNjWEYMQwUwJ7Lm9JjVRUYhxHOaezWJZ4+vcA/+c2v4bTFv/k3/wbvvn+fK7njGFAVFttVhWkaEOPE1TpmDbLLTwhsXe9Y3NtYFBVV26RoIIGr0LeNIaCJEmsS+azrFawroTW1rdqiQFmv4IoGtqgRodH2J1Rlye2wBA8a42BMAWVZz0i4RJrimYRzppgy1OIEBi6NsVwQIbq4NhopkmuG2LrLueUXZ50AvyFEKEt0f9nbFRey+mFE27Xo2o6S1UDMuqIs0bG4dEzUjipJZ0gz+JHbAQQoAHg/ZMq8K1icWOXzVoJsrRUia0lkBpeemQrSZhXT7EgX0mzjPU4TJXScmHo+lxWP6zAOpBekFZSmPvTNdgs/jNisd9is10ACjocDvv7mK5xOx9wuEELAIFXHiWy055gCsNaRO0iKrAUilPgZXAcndh/+7sOYbAqkA2cNtZzq7MRETD1lTGZzIZGTgjU6x6ly8AdFAJrmmEWKO9TCNxLlH2BgTdpX4llLpfcTAlvUW2NRFQV9nrLs8kduPFXTYLvZYL1eo2lWsNqgqWtyR3EON7c31GIhIJv32B+OOJ1OUIp1KuLEsQC1OTzevsC2fpTnyjJ5kxgiMgVDziE5xzPIK2dbXDAClQLMuc4MEmY3rpQy0y+f8SDbZ03BQGbjJsUualBZc0RzwVSYekrbHK+B47vlQy2jTEUJa+D3p3trc4uTxBbGOmhFguvGWqzXa2YFEgBUVzUst8kRGCo6aTODgLQWS3JhUbSnUMHU5DUcMkhEenXaGM4tWLB4IXYfmRkFRXNK5rPcD++JYSD3aincKWx2pcmWmnIgbgPi81a0VpwtsiCs7Dv1epWLecfjMRe5ZH+TOSOxmOz9KZH2nIiBi1TAkpUBII/r0k1lZsee658BD7XYAoaBHQXz2EaM00AAWI7JZnYGteV1+XME2KBWpdkaOaWEi8sLWOcwDiOMcVlXz1oLP024vb4BADSrFZ0LvL9Kx0GMEVoyEV5jor0h98p7j7u7uwyEhEAkAAEhhDkk4yTfn84JAlEg8yHOGkjCCJGxU0ohhsikgMguNSG73IiuiGHW8tKW93/1P/nv4acefzQT5I/ESigx59lPdB2fk3V6H9DBcupQ2wRnFNEdtUJV1ri4vMTnb95gd/EISlsCGKJGYQpACxihoSVJIwiXxEYZDRUF+6w4zYtTetJ4lmEKE27ubjGMRJ0zxqIoZwRJRA6VmhN0lRIKVxLLRbHQk3w2v0qzpkFSOoMi17c37EE9AQZniSTprGragEAbhTZsg6nJRcGWDipQcCMBkSRM4twwbyz+LOmb7yEysg3FG21abLga0KyTwFjAWSD8EDzwkXrJkT2qaVMtFXJ1nLJBEeGjJFgZTfcLwkxUeXxz1R2iFs53IkPuih0TqCKtFKlGd22X56gsOtrIFS9y6vuUhNOxKGrgsZKNGYmAjqg1H2Ci8i7XxhTcMCeBMh5LpkRIDILwxqO0gho9VD9rMMhjZGs+aRWhQ1jlcdFIc19hoNYAekQgzeCkYu0NYWKJ80X25sYMemgwRTRQgCUOMnJ/JTggR4CZYkeA2FwtViChOqSIQjuomFCkiKfbTVaIDiGgH+fDbmZWUKCaWItCwCOx9qyqkv3CY6YwCgVQDkq5dzKZpnHMARpYJFHYVrIehFmVWx0WB9YcWMnkW/4dOeikdXO+xhQnGkopQDNbYgkYK/D8N4BCtqCbWBTTWYemadC1bf5ZjBFTP2C73qApKpSOXLQOhwP6sWOXIgL9Su7jzRWPqkYICaf2RL3SxiAEckkyWnpvSdROWl0IiCBNHlPQGlizQrn3HtY5ZvhFTOOUW5qCD7i/u8uVCwImCtRMWQw+MNuAxqNii8D2cMRpf8gHr4y/rK+7u1s+9Clp8d4jJgKModhRIUQc0z5XiCxXqR5dXuH1q9c50aXe+fMKdd/3XCGb0PU9Sq7KdF2HrutRFlVmIrVtC91Ry6UEPTF4aDW3vRVFge/G7xZ7Lo0buPKijYbriAYre0XbtuRmMPQ4HU+Y/IS6brBZr6C1xrE9oe1ahBBzIH3sTgzieCBS2xfZDhpARagDnT2ntsWxbXmez6Ci9x7azi4CwYf5TFC8uSIBYQboaKobvr8KUDSnIph+ygn2fThgfzoipYgp0DyWHnay9CX3DGIcORz3HWJIOBw7Obk/+qBkngL1fopobzq83/e4O/RwCpj6Dquqxn5/wjh4NHWBpqpQ1hWGsYc2jlik1sE5wHIiqdmlyli2N9eaY4ESzXqDCG41MI7AhZg4eSXwwlrHdpVr0oNSlq1USVQ0uhLBltDaoC7XlDSwVTh18it4aD4X6Vpg6NqMIStY0mUhUUHIGkkkGCigK/2M9qWQqO1gGEdOskTgkaq31hKLtF41mFingg8RTN6zPoeGKwtc1BW2u4D21OL6/TtMYUKhCvjoMUwDDodjTg6stZkVIkyjZbKqFFslY2Yc0P49u/qQ1bIUxeYqdt4feMuVQtLArau2LMidQxwltIKaZoCfiicaaaIKvezzyQf4MOF2f4/ht19CJ8Bo2osLV6BhV7Pd5SVK57Db7Uj473gEUiKXKAYNnXMgDR5LTKKUcOpbnFiLQGIZxbHh5D2mwCKISuU4mWywJf5SMM7w3qZ5ac7FMDmlPINLmuOeJKAaAzUaQGFLPHtyhaasUVYVQop4+/YtOS64Araq56KVFiYEN3hLTAGKi2KImDwX/CgQzYya6D2Oxwnv9rckYG0LrOsVtpsVrX1oAjcAeAY9kuwLWlNhKkRYLjhaa+CHCX6cYFbUSrIEOZbUefn3B4U1jruEMUnrn/8zhqzSOcGUNiAVBfwQRrO40MzWrtRGQ/uylyo7qI3ecUGB2itHEmln/Y7cEiqFQkJ2kDfKxcanwUU/TfFH8hPlEsLyVZTjJF5rfhowDd1Z0bI/HWhdcjwIZoo4V6Cu6ix+6hyLTFLAQi0O7JSHlLg1e45XJQcp2NXS53FVUMZBK96b4kRHC/WwQykyTSgVsSSHscc4KSpoKMBPxI4WXTIxUhAwIKWUxdCxaPUuioKBo5QZxDIGuYAseeIir3HOAYE4vKUjZy8wCJFigjLnRYHlnFpqYsieJzp7WdyWcz4A7FDDhUEGhJVKmDwVuOQeLPOt06nlz6K4TWIYYwzpHzKAYYwhpiEDwDEQcBYjOSZprVCyxpjTmoqKkVwIY/CkYeYcYgrwzEwBqLBmjYUrKM7MeRYUJm6/3e12eQyWbdgCLBpDum1GE9t48lM2MJEzTFpsRm47puVA9zoX+b1HNCTsapito7WG9xSTClvmj3n80UyQhwn1R5/D/2lQ/xv1wxPA6nlNS0VKAXj6aIM/+8XnOO7vcTgcOKBWKIoSV1dP8OazL3D1+Cnq5hFQNrRRQtTCDTxX7LUm60qxNYuRqhXGmKzwLdTSwAlf5JseggjJ8PXLppKoKhwSiR7mzYonZozch2UcktKZzqa0wjhOWTAmIhJlmz+P3np22FhODpns8jtJumUyaT1XDADu31dLsGA+BHISu0DwBR2jz+HAiRO6COpbl8OOKtnqrIojn4lEANc4sce6UVyR0nDa5YRK0GIsKusyT+R75MCGLpDHfXazILYB+YuTqO1McZSp6zT1lcmGkZ0ylGLgjPrlp2kCYmKNAqqOtV1HQZkEa1SjpSCJe/lpqFiMSpgWGeBZgg0xV94+WFa86S579mKKDJZQwkHikcjuQBnsEBolwJZ3BGKJoOHc20rtZzSf6bBK3MtOrQ6UpGutYSDCrz4Ha0oza2Ac873IhytS7pFdrpOUEqQt1imDJ48e47Pnz1Cp2UoL8k34YDBW89BFBmjkveZea+nxldfTe7F43AMWUWSavIyjlnEWhxV2D5kmallY9izKuC7vjXwvmfuGqyFLdx2ZPxSwSYJIArHrzRrr9ZqdQVK+TmGadF0PKIW+63BqW1S80YdIiuyFc4AhkUjpo00hQbNQ8TRN5GJVFCzcyMrrhcHQD5S81avs7y7fMwTuUwclvX1PrTJEV50T/RBofk/eU9LpDEY/5jW91BCRPUGplAERAVKcnVtJLOsBhOChtcWqYWAleBIR5QFUWmcKdt/3uRc+A4xa9uDF2koEakoVznBfe1kU+Oz1Z/iLf/EXuNjtCLxJKt/HZYAo+8bEe0EIlKBID7G02XlP9oCr9ZqAi67F/v4uB2ukOj+DdMMw5NdLb7WAJSIaFlOCcRYp0T44sA4OJUIht2FKYLfsFQY0Vs2KE3eNoigx+oFsCT3tRVIZE/egwGJ+om1jjGF2D89vJQ5IKX9O4qSDCgp8tkcs6N9gIUT+t5rtAHMixetXAFoFIIwj/Le/RQp+eYzlPUNmmTOK21co8G5Hj4nBfA3gclPi9bNHONzeIQbPgroGZVGiLByOxyM8O0VBAS+eXaGuK56/XH0qK2itqP0wkl6MNg6TT9RyZ0u0XQ8goV5tsNldoCpXdC+dRUqaHOe02NxbaGNRVGUep3zGM8g+szUJDMnJppzDiujzEcxCZF0iBZUFsuU9rCXXu7IqkdiKlZhZNlOmJd4Qts88B9k+lplBHbdfWefI3SIEZlNGFthMmUG0XEdzsSvlOaf5LJG5FEJgFossYmJsSjuu5j1AJwVhfcYUaV8ilBlQxKoTwGYZO6UwxxSyr8x6PfP5IHtVTD6Dw9SKpeE9F2zUvL+U3HpUllIJHqANCV4mJCDKuARopeFKanttuxanvue5T+2Isqbo/oW8vmOMmekCUHEsJXD7SshrFZFjCEk0UgK0sDRdjh/EAUophaYq8bMvfoanT57CaWp/PrRHnE4t3l1fo+87AuBZK2COAVQWZl6CWlKRFvaRJPXkesMM0yT3YHa7iiFCeW7lYEDRGAttCYjMjBggs0YD66L9/NXP8PNPfgkf51Y+KWZkdnIUNo7NbQtg0V0BY+Xey/xYxpQJdI5nEXDM8Zwkz/Qz+g5Ga1ij8n2IQaj+4oZD1+mcQ+DzG4paQmc2L81xSCwssTM/xCFJEpXERSvDr5334GVRhiMbtcjfkjxfQC7ea/g5zjk4Q6DE6CdoZoNoBUTW6YNWSNrAOCpySNGgKMmhic4AQFuLplmzaOgstg5QzCZaICJyKULroqkjZ6gUp0QnS85vaoUmsFTukbgXEhhoadzibOea50iiPCBGivGNNsSSUbIvpZxLhclzS45oNs3ghuxzOU6UQvyDOFI+X/Zr0eAwWsNoy/GlaFrEnE+lNLOc5n2W2vimiYqKxlJ74uSZBcaaU0pRwVglWscS2wi4t+wckOfGGHk/o+9N7YLcUjb02cbW8PqQ/FqcDqHEqZCAmnFBMJiLqHMMSWeECKnSWpG8eBzH3J0h8es4DED00IrWuHYOzpWEC/AYyRkTY8T/4X/238dPPf7umiA/8ZCEgvqx6QfWWmDw8LOGEKCAR0+f4xe//BX6tsXd7S3ev3+Pm5sbDMOIr7/+HW5urvHzn/0JPvnkC2yuHkMXNZQmL+MQqAKa2EUhMQiSQBNvYlTX82DKJgnMyrPLiSp/EhVeEPjICfEssJORvRTYDou6BAMvzFwZ5QUpwEiMkfrslc4LWGuNIQ5QivynZeFNo4fn10HPieCc9M1Co7lyn0CiYA9of0tEcl5IwpZJc1LPya3hdpMo1m88YQWpi5jtPUnzYBbkSYxuIym2GKUAda5v4IPvQcg8CwxqanMS8Z+yLDEOI77//jt0/YCiZGsqPqyMs/lQk/FYVpOlZ9FoDbiIktlDKZFtk9hzSc+ZBCYxPmB3JNKwEFE3YQKkGLMKsBzc5KUt80fnn8vPBKCQteJjzJu7BP0aQFRJCnNY+r0FrsBIwBGYMigJttYmb2ia5wcSoeqaW11UIscAhAitEpzRQAwIiVoe6qac1wV/z5Sov5lU6+2M9HPblNMGTVnjYrtF8B6HoYWC0EGR0V4fDKw3WR1egoN5zc2snJwYq1lQSajCee6kBICqOFpzeBKZtZPpscQEqerqjAqZBd9i4o0eeX9QSpOWC7f/yMEeQkBCyvNK2CvWGKzWazx98gRXjx5R5aXvkUDsFABwRYEQI9bNBuM4oHIlri6usFqtIH2nJHpF7Wrm0WPqrY8kijaNE4kLevFXj3PfY0rohw6TGtEejxi7IfeVqqLA/T0Bzc450n/RGpurRxx40fwQejkUB6riKNKd4DjwB5i5FCMHfGJrSXMxhMX7hYimac4OcaKT064wTRMwAq5xuZIilEvvPbGAIh3ufvLstkO97yI4pvksEPHqbujhDLnKNFWN58+fz9WEJP+HvC8+3FNl/nlOtOR5FxcX+OSTT3I7VqYYl2UGdKiyNbP05LmHwyELN0q7kHUESCiFbOHetj1u7u4Qhh7QCp6FFH0QATPk/UPsvbWmau/IQpGq6xASW3tyEpodgLjVTmmyIpbzUEumx4GEaDMQ4Oizs4gIWSqARX8lsQ1QagZbAEBrauWBUnl9DwO7O/E55axD8CO+/v73QFQZ5JG9kbFe2hMVa6f4iKqp0I5etkREpXDsPZpmhc9evUDXtZiGHre3dySQlxIOrcd6s8V6S0wMV9dwdYPtxQ6aAzTNrUSRg2hXFLBFDSSgKFZwZcMtAlRQ0bbIgXtSc/AtFtpJ0X4bE/1+BpgSPAeHi0OR9Du4JdZoDVeWSKA5nQAUzqEsLIy2OUksiwIFn2tKaTh2DRLLWQoeB0zBwxUW8Dz3lUVKEafTEcNEZ984TTi2J4QYURYFJd7j3NKiGFCXYoFOBccPi2p7mltTJPjXnHxKPOKcO2cGQxJWiu3WdY3VqqZ9USm0fY/vvv8ep64nK9us7zELZiqwEG0i5y8RI04JbPVrYM2cLMr5Nhc0uOWb41VXsdtWEmYnMKaA0UcM0YPKR1QowdBRQqMMsVMGaltTWAinK2pFFiF9AhC4LRUqa9SopLg6zBp1ifaBFOhnTmkWXCd3FOuKfD7TGWtIa8FQy1pd1bjc7fD0yRMc9veY+gHff/U1jocDEojNq42hynyi/bQuKwatKeYZx5Ftznnf4RXqOMl0lu2XC4eiLJFixDCQGLGzjgBx56iQJDHV5GcbUBbZ1cYwM9cwAEmCryF42BVZG9dVgxQ9OTUmshaPfoLLFWoR4hdAKRDhIcfRKZ8bsvdbZ6mgJGvPGFp7Evvx/TB2FsGW+aKUhmMWt3wvrRJS9PBhbnPJ7FCtmLwbgRQX+22CVimDYBLzSbEscb4hduxU+FFIUZ216QAgRoYmxhVV3qkVTGtNhQk17/tJsc5QZKfIKWHyE5wrsVuvMAwshq0VDAClCPxOccI0dJjafd4feqXQ3b8HQEC+LUpcXT1GUZY4sti47GPek2OisRYNMzzqukZRljnvUIqK29poai+bSJej7wfWWixQViUKN9ujhghu3TGwBXKMRvHSzEzz3sMv7iWxPWJ2NFIgOYMQYnadodyjQkyUSxqruBDpkRIVCBLrpokhhdYzO3jJRNFabNEVt2UiCz9LEu99yEBjbhmXGFWBWF5+RErcEqlYcoK/IxIVKFMinZ6+72fwlwvTE88LgNqjU4ro/YShHzCNI8qqQNAaRilMw0ji0dEjcQ4d/AQoYOxH3N1Sy6RbsGIAxfGgz0BQjAFFUVL7dIpIntg7k/fQxtI5b0y27xWG/jR0mMY+f88QPFIPDKwTGkEAwOyC+bCs8vHHPyoThL8yjFJoCoPVWsO6iKp06DqPmzuPYSSyuyss/pP/5F/izesXMHwQEe19wNuvv8bbt18hBo+qqvDJJ6/x/PkLXDx5jmb9FMqWBDrE2a87gPprR1YKp8VNdGAJviWZkQrF8jvJYCs1Mx9iTJhCRFFUWVmYesgpAJMJq4yBKCCTUjdV+MdxzMG9M3R4CPVffi6K9K4g+6yJK4+idB5lM8fcUyYWUyEEFNadgyRacUsBZpYMp93zxAT8NAMpcjgoPV8fWRIJajz3sc7VC0L8k0q5WkRgDKHbgUEorYDAvuUCpsh/ACXY1jrstlu8ePECz54+hQh3DsNAIls+4Hgk8b27+3v044Dj8QhA5143odkqpXLrCG0ac4JTOjokpXou91MOYAVhE8jmJgEgC2emmYUgLSeyockckrkG4IyqKUBTwiyOSFokSjCVXIXAwsItQWhqgUEm0cxgtkhCZmkAQEpcUUiAcTYnssTYmBNAnXheKQrWqRLnMzorBwKJ2LHdoVqAnHx1RpOuxKsXL/Hnv/kzXO0u8O7br/HD269onPjeeGY0ZDFOUCLlihLr9RpKIbtnZJ0UZhAsaa3ex3zPJdFzBbUX+EBCq+JyJDTEKQQAmg9TOzNAWB1KcXAQgs+2dppBIs48aS/wnoPPc2cZay0eXV3h9atXWSyzZZaH2M4Bs95CYcmpYWIrNmMtVqsViWsCXPWccmISYswaG0KdVUphYJtRGVehW2qt0bYtMSo4EV/qPEiL0vI7UPWSqnoC5m2325xsmcLh/v4+JzabzSZTUqlFqc9MiBgjqpKCkxACJu6jV3xdzhV8iM10ycgA4zgOPN+o8gMFHve5ijOzSkIG1xTPYQBcfUyoywovX7xEVVBQ/+TRI1RVDaPn/ti8D0kSJ4BBCDh1XbakFXBT9j9hzYhgGVXDWtze3hJgumDfEMOO7tk0ThlgXrIUtTXwPuHYniBC0PIdY6I1DwbEUojc2kMnrlKGWtp4/4lproxXVUWVfG5JIQyXtFFygB1nBY757EiZ9SEPmSv0J5itQJUpsQpGJLBP1OBFcJS0gZDb2gJXR8e+w7f/n/8SKXhozdpDvMdIZSiBgr5NXdK+lRLu+jEn0pRIAVfrAldrh+26xmbdwBqHwlW4fPwM2jYoyhWqZgXLYHpMGsparpzPgsXg/YMKaFLVYwco/u4JwuyIeZ+W/Q5mCeTOopdzQjPvdeQEwyxQopdlXQdlNN69f49jd0LdNCRsqixWdc1aFiywGCPt0yLYx1XWfC6GQImT7O1JmIUqa41579Gz9o9UmhOQwUZJXgXom9cKOVDlKufivJP9hQCt2WYR4CJRXnP0X1U1ePniJT795BMYDYzMNj21LW7u7jCME05dj+PpiK7vMLDgYZ6foDPeGGpFGb2nSmQCOcBIzCfFiMw4CLwOAhKPqed1K3Min7V8XtA5NdH4cIFJp9khTHSgplykACc6hkEbjZRC3rMTV6ON1iidIzYgz6eJWQzjNFFLa4owhn+vdBZ3t8Zgt7vE69efoO87OEvvY4zG6XjEN2+/wffff09FQAayZG3GBI656BxFAvqhn2NmLo4tk7EY5rP4eDwCStG11DXE5pO0g6idRXGgQ+YFNBbZ1hMg0MGHrFNwalv4SEUXaUEpdQmXHLVXpkQt5M5lUU1i3Q05xshMII5BZV4WvO5EE0wJSKWo7UKSaimMyfeUWFKq9sZoaIiGXMrX5NheVGIYsdrOGm5x1lkx2mbtITmntSY3JCrocsyZhO3nqFLPrHaJG0OMMzOI31vsR8e8VmjuKc02uFx8kThCClvWOjhnmQ0Zch1OKUUt5koBinIL+kJz/KqA2c0x0XkgCbAUAIWxAcwFZgHaAVp/WTRYs0DsMOLUtdDaoKorDMNALRjbHcUaE4F2Shtstltqx64r+MnjeDpRHAK6v84xu4BBJ8UMEOMsA//U5kWuKBRHVnWF3W6X5y3FpcweVhqAFI/IolkpYonRHoHsyCfMuxzvJ3Jzowz2XP8OYCtwbeia815HemQdt32UzGKU+SkFSGG/pxiyLS45YprZjlnrDEAqrdGeTvDMHFyvV5lNlWJE33UAVAahqOhP4zexsYewObL7Ecfc8n2EySr3moeAijNlSQyQokBVVihKasHJgD1/37xv87ltLbW6CvYjrUD/j//T/xQ/9fhHZ4IkgGhamdomwAdRmJWiINVPHu++f4fHlxcwoIqWVhqmaPDi9eeoV1u8/eq36LoDvvnmKwqQksfN9Q1MseMKGQEGV5ePkEyBzgdEPnhOHYEf/QKIkE0mpZQBCqlezId0PDucQ0w4HdqcMBN9jFR9jbUw2mC7WWOciLpTVRUmP6EfR2w2GxiobK00I1oTho56wk1O2KkfPQTqw9IMrECBAQZwLz6YQqUBQ0JUKkZSpXcOXdfCsoDb3Mcp8qW8UQkVylk6wLUGUsx9mNoYIEXopOfqY6DNIjvoGIvCGG6jSQBXA3VSiIFs6RA8Oj8iS73JxrhgRxht8Nknn+DV85dYrRoCtCYPP0wYu542oBRROIfHjx7hxfPnlOR1Le7uD2RlCzCiizO0AAEAAElEQVR9SrQVaNEZFuXM1NPEYk6a6Lbyn2heSFV9yQQRQCcGsvsbFVHAjKZAQMAMANlSMzEIMfQdjzkoSE6kdyGCdKy4CpVmQCgx/WMGRYjSblkgDzohGYWUqD2DeCO0CSGBKyeSDBEzh74HMjNpiXrGQDa6cZEQI7GIYwKSD9QzjrkHNHKF2jmHx5dX+LPf/BN88eYNSlcAMeHq4hImLe1uaZ2KeKZU+sdpriZT8NFk5Wiy5Zxy5XDZ/0jrxWSwZux7BNa5UEqhqWsCFxfsr5QSgidrvhmwFDG1gLHrCewQTDglxOiZHmnymAmyL0lBXdf45JNP8PjRI6QYs4q22Ewug6xhGCi5MxH7+3uEQKrdfppw/f49lAInZCTUGBmIcma2A67Z0SilWSfGaFJKB3+GVgq77RYVi3fOIBw9uq7LycPS616SMfme7elEAKe1GLqeKn/GwFsPJFIhlwMthgjnbGZwrddrRO4nbflzi6JgGzuq8FHwHbMwcfDhbLx0rTPwKGK1uR0qJdSrFSnGd5RYaw40iOlHDIavvv4KVVFhu9mS0Oo24nJ3OR/+etHCsbivUKSzIvdtprDOYyljJonMer1B23a4uXmP4+mYg7wEFt/mwDNEYkFUZcVCr0e2/UssjB2gDLVAKCWWxAK+8jpnxXdriXJfMkhPgtzSapFgiF7Bbl4mg03UgiUJaICPE1vxugxYGRYSl2RwtncUUWyi7S4dlbAIbpVWcIbaM/iFqMuS5ilrBFXa4DsGlPlIgLgwyYPAaXqLngNSkpFb7GMx4dCOKEyA0R5dt2c2aoljN+D5y8+w3V2gLB2icYjaQSum70IhqJBV8zWoOkzsLNGPIEtq+V6RwWfp3RfgeQZPROsJzDShpKnr+ywma61lq1qV2zdlrAFKQklkrgAYzJhiQDd0iJ60YEpX5r1Q/pR5rIUtwei11rOGmFKkMbAsbsh9c47ZaHEGKnLhQs2ihlLFT3oGBzWIBp8ZPQyCSOuOvF+KEUESpwRcXl7iZ5//HI8uL4EETNOAaRhJZN4HlM5h1azx5LHFfn8gto8wARNpXvR9j+gJQE8MRvuyzsWMqqporgutXtF/UGxlqShpi8xm08zaELDHe4qBiqLgMUx5/1SJ2E9aWLYpEnhrHFCS8LNzZBFP7QWKYkZrqZUqUmuIUZpsKWVfiiRaK7GsHydEJDTNGk8fP8GrFy9wsdtRTDeRm5H3HivL2kZ9h/ZwhFbAm08/gwLw29/9DlMIsMVsBFAUpCFljQjZKjj3mIAiH3LxINueplnoMPqAcrNjt6OEMJCodEwJqlBo6gaJ2UkAsZcEZJae/sKS1pQGuMUDsJpEh1frTWbW+c4jDjFfh+yjogOwpNXn81qpvK7BcY6cpxJrmFzIBFKiokmMBFpobXOuIPEMvU6+/zSvB2YSAConpTJHEAOUo/Mi8H4qa0IEd3Pbokq5oi2OjEYRw7ooi/yeS6aJYUaKFDcl9hfNw7IqmdngkfzsmCfrmWIb0pQahx4x2HwuiwaI5tyDGF8hs5xoi2EAW2loDT476OeupBYbuS+SXykAm1VN9qfWwIDFnFOEihExENOPcrSIdV2QLlnhsK5ZwD30aHsS5JyYaXq8+57PLwIml8KzJ1fkvU604aTdWhi8NL0U72nUTm+sxeXlJVbrNTnngXKF0XvUzZrWPRSqssTt7S3arkPTNFkbL7f5cQHvfr+H9xOKosSpPSFM5NTYD6RxKACHtAfXVYWmrrmLgMadimxT3geNIcvjGEMWckYi9o3mwtL+sIcGaWw0q1Vu4YmcMxEbt8TFukYCtc5oBvFWdcnzFIh+gDOi3wQ4UwD1rGVCZ/fc1CW5H7BoCwW1NZIQLa+JGDC0J9DN90hcaKqYZZYLUdOQBXIzwzDOeot/NHHjH5sJQl+c2yBYrsI5he2mhjEaXTfg1E4YPbDbrfEf/8U/x8VuR71nLIaVIqvnth3++i//vwAGKET82W9+gydPn+L65h5ff/0NQvDwYcLLVy+hix3c+ilgS6oCZK2PmeKsNQ04bTiAcQ5Amvv2+aakuFSyJQq+oK6EhFLyGSMpHsMQqi5D6QOpGDdVnQPlm+sbRrQS24pqRnOnjArGiRJ4LcmZJgFRY0xmskgrT0oJVnq0Zdw5KZqYBidIqo8kChqiIN4J0c9MEMOoOTggFgcQicZkI870U8yKz0ttCJnU8zxQmIJHiERxNNxjmxbBT+EcPnn5Gpe7CwKNjEXwU04O3r9/j3GaaFPSmoUyaZLX9Yqp2BRQ9H2PcZpyf1pT1yhcwW4MdG0S3GlNojx9P1D1XynUTZP7BC0jxeSBTlagfiJ2i3XU5x0jqeKPw4CuJ9qg6L7QIcsU9pjQDz2LYLIugFaYhjBTWXncxBFExigmcmVAmjeIpPgAEkCK0X2AksTIz5UeQHksmTxi65fBkkhWqzP9kyVkE7ILj+D8zjlUdYUvPv8Cv/zVL/Ho8hFV7xOQQsLt+29x/e5tRtfn6oa0gtns8BJ4rRujMwgoOiFLFwupxmtO5ORA6fue2zD8LDScafpEyY1cLU2KKMKWe3Olxxo8LqJwTVVzug7DQoqUJIiNY0LB9o4XFxfY7XYEcnrP4Am3BagHLWtAZoocj0eEIO4TlDBRBYrom1VVZlaZtNjFSKwLxxt/SuTusXTXkfFOKWU7Nvm9/Fx+1/d9bo+RwFDec7/f573COoeqrs/axmJKmLglhPZZCpyLosAwDtBKk5OMMZxIxjOGl/dzAFrXNdbrdZ6nbdtmv3kJDkXoSiqHInhrDLVC+WmhhQQCvGKkhGWzXuPq6gqrZo31eoOry0sKJBaU6MhtZqILIvdsGIbs4CLjuWynFACgLGeXna5rsd/v830z1sKxbgBZZk/o+g5dy2CU1pniL/vpMI44tUf0Y49je8LQ9tTmleZ2wNWqgWVwPwSe75GAUxkLy+eJ7L+TJyajdY6sjoXWKmsBajFXhGIcGSwW+3V27JBKJM6pv8KAE/BIWCGSUAo1expHdKcTfvuv/l9Eq13ED2AmnPzNACi0yp+njMIQZkA3AXBa4eXTDf70i1fY7+9IqHeg/nvrSmwvH+HVq09x9fQ1inqNoqwRYRb6YpH3FwZD1cy8omQh5gq2gFrS6ppAbUHjOGV72WGcEBNV6XwgRw2lFKZAe64ritw+ZbRBlF5wBqCN5mTTUquX6KgIqIaUYBO1Iy5FnaXiKhoYCZhZQopcfFJMsLxXKJ6jImwLRZoICTMonOeHHAppvltLoCSD98v/4pyQLn9ujMXFxRU++/QTPHvyFM5YOkMn1gFaJCjQtPd2fYfbuzveNxo41nuRvaxgNszxcMAwkMhgx8WIuq5p7+OqONkDE1Xe8usI9CSb9rIoc1VesfOKiBDSfYgYhpEZthQTkrvR3Poma4HisfkslP22cA5FQYlH13UsRJhYX4Cuh/SCaP/RXOX+7NNP8dlnn5FjQoiYetqLg2eQB+A4le5H13cYhgHH9oS3b9/i5u4O/TjQnpZSrlg7a3LhTWsSLxTmHDBrL8VFjLxkQdP3JF2/xOBUWZbo2ja7R0mCPnoCHsuyhFEKidluuW0pFyAWLeDBAF7Pmge838u8o8dDAUxQAepBqiPA4fLsW7LdZhacyi3A5224kRkSc3IPLJlQMwgTgmdxfZXBjRAozsLi1fN3mtsfSRNKwWqVddukiCj3V8YfSmftNgEatGH3tHxt8+ty+w2Ph4iAJgYj5D5rRXGT7AVKKXahmgFUpfVZPKU1OVwJeD47HPLYqpkBkCIxzIIPmaFF9AnkPCYEslKuyhIFfx9hfxNGIm3iKecp9IcU+GatIJJTmMEYSczFAlvcl8AximhUxBRJfDkym3Mix08RIrXWISViMU3jJIcZrHFkzKCJHWI5tkMKKIsSIpIsLayW23OmiRxGBXxKgUBtYePNOSq1jaUU+RpmkEtAaKV0ZpRrxQYbzJiAUggxsGUuO1TJvkXTj1eCYuDBsa6K6LUwi5wdpfK8yazpxd6/WIMCggQfKUNT8xqQB4ltzzqO4jzkp5GcuSAAPwucm1lX6L/43/2P8VOP/7+AIB+8FoA1GpaYgAghYfI0GH/+Z3+KX/3iFyRMwxMob2Ih4fbdd/irv/qvEXyP7WaDX/3qV3h0dYXvvvsWv//qt/DTQPTxZPD5L36NzcVLHIeECOqZFvaG0gowRDterVdQUOjGAZ4p8DFEFocj5C1Xs9QsYGo4+Jn8RAl8UQFJY/AjrLOZSi4CZKvVCs+ePYP3Hj989w7DICIx1BNO9laz3SuJWIXcXwilkdjxA1hUoYF8wEQf4Rhc8LIx8sYqetbGEmPDL4L7GBKM4taRMIsN5QoOwJQxTob5/SNPcBGhLMsi20zSIciUU46THLNNtBGmQkDkFhDZ+o0yeP3iNa6urmYadSRWxe3tLQ6HAxKAqq4yK0ArjaZeYb3eUBsDi3meTidAqawaHXxg+j/R+kKcNU1mUSCq9JecWPYD9bZpPbc7iHBaCEQrk0qeNhpFWWbFZDk4AidTQm+joIisuqbR54BFXDrkeoaBqoR06GsCQBAxdD2mgezCRj8CiijY0+QxcuUlsSifjKwPnnrGC7L1rKsK9/s9TscjYkxoarIVDmwHPAwDiSvxQ+uFAFtKmeJbFAU+++wNfv3rP8Xl5SXpaYDuafAB3339Jd5993sAyC0NIhrlfeCKAFHWpOdXRMYk2HGugBZlcl5XWa9hkeQDQGFdBhfksKLrV2Tfm22JF/bIUWj8lNyptBR8ZGo0P5e7urHbXeDi4jInQV3X5euVhDNy8CvBlPfkjy50XfKP7/L4Lisxy3Y9xf3B4v8uHvbCahNmmRx2AjKRpsiSzUa/E9tVYeQUBa1bodBnGioz3tq2RdM08N7jcDgQlZ7RdqkIjdPMviFAsM/XIfeCGHgzcBVCyHuF3MOyLPN9XiL60nKytJ6TcU4pYej7DPQJa0jGUcZVhEqhyELw9avX+OTVaxg7V80kYem7Hvf39yjLGrvdDpcXFzlQkh7mEELWxliyQmQNy/xbAn/OOSgGgE6nE8qyzPNVcZWrH4d5HjliixwO9xjGIWuiIALOFvmAlwCjbVvc394hBmLSSPuM2K3L9QPU3qCtpYREKUpqYoI1BVXxuJ1ynKiiJgCp4vW6TBKU1gA7Fsh9DUxtzuwaji4y0M8BpzB8pqHH23/7r4jZ8CMPDaKsWlBbjDaAqy0OJ9IZCyD2RWENfvOLN/hnv/kVYphwe3+H999/j/v7O3RdC8+A3hef/wyffvY5lCngVQntaihl4f08piElAoWDXHegfZvHMOk5JvKcrHd9j3EYoJi+rK1FP1J7XVJAWTqe46Rb8LDFixI2YvhZ3pN8ILqxzLOUSOnfM3VZdCN0bqlhsFYTkygs2GgpUYtNpvULSwgqW2bKfpFp/0nWbcyMy+xIAQqkfTi3M5d1KO9DSQhbjqo5kF41a3z2+lO8evEc1lqM44Dj8YRpweqU1gRrbdYXavsO4zCiKKT9J8JPHkXhuI0lYegHpm4nhDALGFIooziGoz08JLkPOrO9tCZgMUwTCX+qudV48rOjWQbFy4K0FTwlEkqz60KKGTy0xmQNI9kXZO8oigKn9sTrRZGgLhdzypKA8rZtoZTB609e4+mTx6QFA8Vimx7DSODqOFI7XdVUGdDsui5bZsv5qIzG5APavkPfdei6jsSyRypQOGMzGLpMaHJswKNJZxadsfTetGbkjJHYWe5BZo0ZsUINSCHAT/xaji+l6LdkblysH+Fq85g+ORfTOL5cFIUylMpJd2Zs5QR7TrYyY09iX3WuHbMsfswACUXiigE+qHNtOoktMys30necrwyZeUIDen4dmanAhScC4uYXi+7DMk+Zi2H5wmdgAgsbVAEqgAzyiiYP3Vfa74bsasL7mZ7bvgF2kOTzls7mc6vn7HC4SMjFdSefnVyIKLldbfKezwK5BwlqUTw1vIak9VnWoORsKdG4CuirjZnZXOz8EjnHms+jPFx5HCMzH4gBM8/vGEljRj4bSdxyyEBAKW5LWmTVYgkueoYESDAAxOwZibFzjKVZjNpTK5ywx5Y5mhQKJJWSuSTjMM9p+cf884f5bVLIgHd68KdO85qQM0sK88SaUdjtdsxwCoiI2cUHieYqgWDi7MWgVaJCgAK1JaW8loRNpPL9ibwSLJugBGbz5TFeAJfLa/0v/vf/GX7q8Y/eDvOxRwIwhYgpMEtEFnSiFoK6rqAV0QkjtyJQG0LEo0ePsN+/wLdvf4+2bfHXf/3XKH/9a7x48Rzj2OHt269ZNd6iu3+H/nCH232PdgDakawGf/HLX8KVBabgsdms0awqCmh1gu9HKO9JbMVPUGqu3Mpil+qwbG5UGWbquSaBu3EcMS16uCTI/eabb3Jy8uLFi5zETdOEiXsEUyI6ZYoBiJRwp6hJbM2Q0OhSBwG8WJqqZPolUNU1tQGdTlDa0UYDxb15ESopKEOK+SPb5iFG+o+WdFY9ls1SKtuysZOdoCIQgw+msaMAyxkW5FqAZSklRFZBFjqs0vNGLRXE3XaLZ8+eYbPZ5DaIw3HK7JfdbofJUzAivYPeU3tKx73PSIC1Btvt9uzQnQYScRSEOaWEw/FICTAvKCSgKEq0fLhZa+CcQT9QD50Uv+S7taeWK3mOK5qUyEVEEnvipFIlYh2JK0RVVlg9XlF1nxMl0RSYDyeirHWsnJ1SpOD58lGubvdTTxuAEiHcBOdKAAohhXzokftNOwNU2qC0BWJJQlRPHj/J3zH4gLqs6BAKs/WvqImPbO9mjMFms8EXbz7HullDJYXoaXxlExKnAXHhURyEhDBXkQEg8iHjjEFKmtgt1nIlIcIoCz9OWVPCs34LwFVurbkK1iGF2dVDwgHF9OKUkAN1gGjDWhsET/sPra25uuJZeDRXNqEyoGmMxf5wwO3dXa5AyqHgnMOKgSUBGIqiwPF4xOFwyHNSNuxloCR7jNYadV0R0h5jbrHzPmC/36OqSiyrb5k+y58pPxNAQTzbs8WrJns5mafDMGC1WuF4PBJjKqXMapAAVt63Wa8zSGOtzQCIBEfG6rxmc2VAa1i2CfTeo+F96ng85u87DANOxyMaZpvo9ZrGZJoYjJ0948uiQMFgg2EAwU8TVNOQQF+MZyyZcZqIMacUxsnj/fv3OB6PMIaiENHJECDFGIPNeodHjx7RXsOfCyCPj1xzBtR4nUuiKefEEuRYVutE80nmjASBZVmibVtM/YCubdEe97DG4MnFJYqy5FYp5KTtTN8mgVthArQGhnHAfr/PIBpAwanQc6lSKEkCVey6rstzWtg2ns8AafGSOSHvR/de2BBU9e+XgZecA3zOaKMZGKY17qdp+dQfjR+0UmhKg21j4GyEqyxqp3B/jDiMTIFXGs1mjWM/wmiLav0IL6stNvt73N68x7fffY9pCvjdb7+En3o8f/4KulhB2QG2aKCUBsec0NbCexINNMaRqHvh4BgA7QcCwqEooRbQCdZBWwvFBYHKziGWMpTUKLAugbH5TDSgFuGUAD8FHFmFP8WAru9QclCZUsKYaK0bTT3UtnC5DZLWKoFTwrpJQAZ5BbBSHIjHRO29fhjONHKWFHkkEc2TSl7K951cAog9qjG/f1lUGVShC+CAPSSUZQVnLApX4GKzZTtKqooXZQGMtD8fj0ecTqcMOranFiF4FEWZCxaBW4umYUCcJnjWQ5G903tyzpLrCN4DmlrZYqDfyV4lALK0SgOkYSNruus6aKuxWq0yqEjsQbpvxOox8BMVN5JS6Po+u+j0fU/tgAyEVMwmlPdaNaucdJ5ObRZuN0ZjHAeEENE0FVIkkMc1hlqCJHlIwoAccxwlAEhMCa4soFkLgIo7BohAbR3Sest7OTAMPdq2Q9u1nKianExnAHoYcDqd2OpzQgjDglVBSf8sNE/rPKWU3fMAZDFhcWySfWaZaFHfP+0VCgrb5gJFUSIX2iTB/simoQBiKkgVD/LnzDL7sRpwZtFpzQnbbPm8jDM0swzEolXOEknyM5s1EphBLSAfSdgyoDMDo1AUKUvRUS+ubQZMBGeQ77HMvlPWlcprkd9PRkFzYUqpiBBGZjoajo+lXZTYccviDmmgzO9Hn2GQzj+eGfRz8YlcZogpT2wSKrR2XZvH5Mz4jT8LEluCEuLMhAnsShXO2Y9KYwZcgocxOreDaCQ4Y1inbtFUmTJWRkyJRfFMKWoT9J4AFmUUs6kVtGawX9F9VknBZDMDRS6FUBwLc/6L2cWILxwWM9iwzPMSIyYytx7eazpu06zVIiBG/qWSyzvLz+TeCTvjQRfq/FiAK7LGAeTYBUAGWY/dEcM45Lb39WqNzWqNFLkQogBjXC70Jz5DqW0m5jjBWgWjLZmr8NgJUEJnBaDtLHEh8dRSP3AGav/w478REGT5SCDdgqo0UCni/fsf8M3bL9lbnFgiKSUY7VC6EoU1uLx8hPu79zge7nFzc4O//du/xc9+9jmePHmCtj3i3Q/fIwSP635A8gnaWpTK4tR2ePvDW9xdf4cvfvYFVts1fvjud/juu+/w6tUn+OLnv0CyCnGkDTsiQVsH78GJrCM1XEQCaJJQyUdUVYNnz14gRuDm7g7jNJCS9jDkA09sW7fbLT79k0/x6NHjsxvT9QOjYAFjP2KaBgJjmAY1jGSRqc28YcoB3Pc9moaq+9F7NKsVjqcTb7rsDw2pqCMHLt5PiGUFBZU1LGKMgNZwBVVjJ/a9T2H2184PBcDyhLRE7/OTzyimHE5QgLYiksrBOaOZkRd2Xdd49vQZnj17Do3Z+3u/36PriU0jrBphgpxOpwwqtYcuB37WkoWYBHjZBYQ/n9gllIRDq0zt00qT/aieW6a8n+AcUcfpsJtbpEQkqKwrlJooeXIoVa5C3/Y4HU7oTjMFN0wBq+0qV7XGcUTiA9YYmw/9whJTwFmLWJSw2iCwsNHQsbKzoj5wKIVhompY4h0spYTk0xkd93A45A1BquyVq+CsQ98SK+Bic5ERfQHpxArrxN9DkmatNZxx+OG7H3C4P2QL0MvLS7x+/ZoDSY+UJkBZFthNgBL9ATpIFVTe8AT1jpEE2RS7vEwMhiaGulNKgEeuJMztVQk+UEtT4E2dACWasFHTQSrJm9EGmi2CtVLw45Ar1FgcIgGA0uREsl6vEUPE7fU7XN/donAOdVVh4OpvVVXQPE7SuiEVPgkcpY9XGArTNLKgFWnvEBuDE04/ZTRdKUU0ce8RwoSmaVCWFekAceJPrJTAjCWaxwKgyHVIMCRJvIAFomOyZJZoTcG+tDOsVqt8+MYYc7uMVPWmaYKPov4d8wFPVmknavsBGIDS2G42GPoewziiqmvopsnv/cP33+cWHhH1HDk49uOII1/jMJADjtY6V02zzlOM2Gw2dB8jOYgdDgf4aUQrlSykDIwa57BarXB5eUn7al1TFWlxqEqFRnPyOdOe5wqeANzymKYJKpGDljE0jwTwIocVYm/sjwemBUeUjnRV6vpJHs+7uztqeWHhUrmvGVxhquswDvCeKr2zGB7y/jZNE+sPiJ1lQIgJRVXn54oWioBk9/f3bPFL7hyy/sLZGuSzYRGES4Vr+V8WsoViNsgotaw/GDMQI5LZnSEAU4Q1Cc5EaM292eOE33/9LepqC2cLEMsSKJsNnlcrrNY7fPvdN/BDj+vrWxhj8eLFK4zHHm24RlE1aIcRXdvjyfOXiHCYoADrUDQ1Vqs1XFHieDzh2I84HE9IAIqyQIKGLSqInoxYMSukuYqnWI8rEoCPfN74fBZY6wCFPNeUUahR5XkHLpgU/HtjDJJSGVxuWC8oSNI2UXJfuCInU8qp3IYzMoszpYSCLa2TjtwK4vPcVprBK55vM5BCotKy5qw2SIaD8MAskwUoKi4DSUd88vI1dhcXgCHR6sIaKEOsk+PpkJMrah0MGahU6shnVUDkGKVpGihNIIdnUWmolNlZsgaqps7JraxTWbfLuGEYhjwu0tIs31vWZFFYpERJI7VCjVlQvSgKTKNHALm2nY4Hqpx2PdP5F0Atg/wZMPDdB6DMMAzoug5d16MoHCpnoVJCWZTMJtVQ2qCoyIkjJXJ+iYnO86qpEWPEqT0RWxZkr1oVVd4XtBVhbSri1AxYD6zttNvtcnuPXDu1/YUzvaQYqbjTNE1mE3ZtS+1vbcuaK2T/zekX7w/AMJjMOAUX3MRmmYQTa6T0QOdB6ZzmSTyQIY8FYEDXPYMgeiGqPf/+HLylucNlFTVrbYgWm8ZHEsuQ4P1ELAI2JgAXgli1O7MClKIYVCOxHhoXGTmGp8QVOVFVeWflP2c8J++VMg7g1DKDNrlAUsIaTSxZpXNrKAENhojkKeW5k9K0uN4le0aYXwJ4fMiw+YBBpBJgZ90iQCMt3kdhafsrCfpCIy+ls/cVMCQz3HkuLxlLYj8rzzVGZxvo5b2T+7H8tzwfQGYsUxmXQV/FbeL8uqQZ2MP5IzGSli2rNd8rvWBMcuokrY6MmHwUvJDrS4nvsno4N+YZwWEw0ge/o9/O5+/57/NnLpggy+8j+6XE2845VE2VNSilUEtOT0Bk0VSKH6jNHiFijEtmnMnrNwU/33eef8LwAZBj/WVcumRiSez2U4//RtphPvZYryyqSiH4iFcvnmLdrOFMiRgYZbIFNusNmrqGMcD1u7f4/rtvCeUPAZ9//hmeP3+GorC4u7nG3d0tpqGHSgrWOPgpoJ8CojK4P7WYYsSTF8+grcMP19f4/IsvcPXoKYwtWaekhyksrCsREFkjoiKroCmwOJGC90Th+cUvf43tZodpIicI6V8V+zPZeES00GiytpQEHlDQWWtALF2p51JpYhtMYaR9TlPFWqj7MQSqcGoW9+RNZvQe0zhCa7btSxKsUEKWq3iMyIstJk0+VqUHtUcoKDg3TyLNAbpEu6QbonM7jBzkSnFLSAhnqL5M8u1uh6pZwXB1V8aiPXWZ/krI/tzaoJRCWYmgIlmFFkUBo2gBiv2ndWTZVpYl+r7HYX/IwIws0m7oWSPF5yqE1YQFdl0HoTm6gpSYaSwAP5IWiFDdV+s1NpsNJhY22qzXWU9DxktYK/IdAGSlaYCAshTpBDHWZuHJknseFei+u4IqMX3f8YbjiKJrTA4Oh4GUmYkt5Bm5dpk9JIGUgEY5yFaz/zoJlgYGrTwDPtSuQujsbH21TPykpWGz2cA5h/vrb3D77isWDhYBV+TnC+VQdh7xaQeQnXtiYhYHvWjxXeMMtDH1mw45Pv4ZMZZxcdYxOEYgpzHkAiTWYfLfw2CIqjZA1VBivF6vcTgQWDB5z24odM3HwxFKk7CrJBeyCbtFsjKDiRwkcVBtrTtrzbi/v0cIAev1GtvtFqfTCSlFjON0FqinRBV1qcYZo87WIsDXZOY+Sc0gXNu2s2C01lk1vGkaNHVDbQ1sc10yA0KSCWEv5aSa54Co2fddz9XyMSu+b9ZrrFerPA8lmF4ySyjRPwcxSQ2f2okArsxGChjfv3vPANUGq6aB96THATUr6y/bh3LLQJrFmc1ib6qqitt9LKqq4bGoYI09D+R+pMKQOHAUVgT9j3q/J04kAOD6+joDu8M4oB8G7A97HI5HHE8nToILVBW1MDVNA5VYaV7PZ0BKiQVGB5yOJ/TMfDFGU6U8RowD2dxpYzAxffW432eApChLKG0RQY4lIczuVQRwBbY2FE0lbtPjdZttA2QtLb43rSKa77LuHyYlMXjcffVbYkH+gYcGYBUF9QZAWWhstg5QAYc+Yn+KCAGoqxL/9M//HBe7HQX9HBySpWbA8XiLr37/OyB6ABG//OUv8PrlKxwPB3z99dc4dT1C0Hj07BWCqXDx7AVefvYG6/UWRVHCh4RT2+P9zTX1ffsJ1lgUJVs0t1y9txaIEUM/AEhwroCxNK/v7+9xd3dHWldaoeSEU+jo1hCwLfouYg3tg8+thxIzSPhG4nkklgtFQrICeiqlMlABKJ6P01nFObINa4jELFi2p4GTk2kiXTFbFCBmHzNAFAFhWeCY7VyLgix0qW9+gghGyjza7Xb47M1n2O62lHAPI25vb3BzewfDLKyOxS6bpkHX0t+7roMPE+qyQtPUucUMIG0TIOHUtlTwSHM7qoxVWZYZ1JRigSRRspcJG0RA5uV5LhbZJATtMhtQGZvZrVprBE+aYY4BDwniFRjwVtSioI2BH32mlgMKWhkYQzbN4zhAa4W27RBCxHa7xePLSzx6dEUaTH2PGMlRox8HnE4tNps1DscjAx8tRk8ts8pogNtAARasTYA4UgnIVZUlqqLMbI/z9iZA3CBozCh5tOy4Yq0wgFLWsVEAnAgoOgdnHdr+BO9JtyUDA5hbXkm3pSPwqqqICazWcGoFIM2tImdAwPx5P7ZHy2PJYlRK9ikgZ6RAvh8PX0+FnA9w348+j2Hc/HPZA5dxcUjkjBnjzGiQYhEFK8h7LS1Z9TBlpf8lnT9LEnSxJs7tGJpj2xBgjc0sC7o+HkO9HKsEBX02qJKjn7NSPhwIaffPYCoWCT5Ayc3ijlHhcr52/t+cpj8ALZZjNQOzc8smgDzXM3sE8007v084i9HOHou5TPvd+Tk2oyAMZHwIg8zjtnz+B3+fz9CH33dmdpzPywSVP+9hnn5+T85BnrPfK3y4ZmR82TVJpQ8/Y369whngsgAm5JyRP6VtKtumL+Jj2TeQ0nzOATRPFkCMUooFyGdW1bIIJj//z/8X/4OH3+qDx38wEAQAnAM2a4sXjx+hKkogKsSQiTsERFQVrFZwTuFwvMft7S1b/DgGQp7DOYv7uzv87su/JfVsH1CVNWxRotnusLl8hGq1gS5KvLu9xTCNuLh8hKpsMPQTmtUGo/fwKaAbeqQU0NQlUvQwxkFphxAJ902M/j178QIvX76GNQUh7jkZGTMiKowNMNorrRXamFxt7LpuoadhEEG91xTsBxiNTPWRoD7GmPUmnLUwmg4WOoyJkkkbASG7gqxLIjkxQyUEEk0MiwPOWupBFZRNKrJd12WhO2sLGE3XURaEdMtBqTRpZDzckIqiwNXVFYuTMSDgSVSIhNBoExPNgqZpMIwDWU5x4pZSwmazYaDFw/sxB3Nd10MrTQKEHMgkJKyaVVYPTomCI1EpTymxPavJKOI4DjgeDtjv79i3m6sQoIRWmCh1U0NYC2LrBD1bzknSJgk2zYUh64HIAWjUXM2VIEt6kz33LcufRUmVoYJ1U3p2GkgJWcF9vV4zqynktiD5LKHz03uKOjoySKQ5Ge77jitfOtMeQ4iwdvbuliASQAZW5F4cbr9FfyS/eDk8KSEhwIJ87kmFn+aqzkm69FwDooEzb3xaqzNrNUAONgq8ZaxjEqGyWYArcvWfrLSojSaGGbCKMeY2A1lD6/UWn755g6qq0HV91jPp+z4n//LacRyzHWhRFGSlxvdd2AGzFsSUgQtRtJe5EoI4pIjukGPmQZX3mHEcUZYlux3MSSsw30ul5j5buU5riYJ+2O+x3+9Bzh4mJwPWOlRVifV6g65rs76OdQ5KGwz8ud571GWR96SyrDhhjjlBEB0LYT8IPbvrunx9MVIVlaqiyGr7IrYmc4tAiWoG8CwJ2wqLjcY4cPWfrtcWtE93bceU9sisHBIxlGpRWVbQhphI4lpgWT9FgsSqKjOFfNWsAJCWhgIJP3vvoYxGP/RnwZcwgrq+x+3tHcZxgjYkkn15cYHtbovj6YTr6+vMBGnHIevSAAQ4aR4TozU0yGZXLKe1MUhI8JNHz64/fvK8Z3D1ClLNFP2ZRYCCRS+wLKskJ7DiYHhm2Mk8PX/MAeWsAcFB0QIYzs9eBjIhoH33ltsy//iHBmANxRBQCsMIjh2AP//z3+BnP/uCBJ3BYo4+whggxgk31+/wN3/17zBNA1Z1hV/8yc/x6sUzHO73+Hf/7t9hnAKmAHRTwm/++b/Ay08/Z9DZQtkCARajB+5OR0g9VCnSozLW0hmlNfb39+jaNlfKrJt1cPb7PSYfSIvDWjQrFvlOCWVBNq8JCYHt0B2zAgBgnATsoPGjVheVRe0cM4kAnGn2CLU/sHCmBKUC3Mr9SyzQCi78aG5hJQB4QlKawSVyzzMMzATvF1btDyxyFyXrxID6ixcv8OjRVWZrvH//Hl9++SXu7/eAUmhWq7xnOuegALSikVQUqKuKmZCJ9XoiJ+OW9FAY9B76IQNJ1B6LDHxI7CWgrnyWtJ6KVonMeMOFmNwezExH4yzKqsr7OjmlAENPgHRd1+RMNgVMfuCqMiVWIm6MiFxjLlyBzWaDqir5POQiXPAUXyYq3mw2a1DLbIXLR49QVCWGYcT7d+9yK7ac+QEKJ7bAlcqxMcTWdM4icZHBGovdboeSLWcBZC0rOm9oHg4DMc7o/BDtDyqMQdqz2FkkhojgJ3Z1AdbNClqlszZzHwJCijnGLcsyM2XkOU15hdJukUEQNetaLLYixgsWNe4P8pc5tX7IAJDHBwl+fs6CabH8sXzied75UVAmv6+87gGasjxH5sRX3FfOgYCHVfrz60HWDhIw8yPfFFn7T65WzeyEj78mffD3BXQ0/1xJ0VTlsx1K5fb4h9m3WvxM5d99PME/v5pzQGT5+LBoMSf+/O3nS+G5lKGQj6XHST8Y5OUdlvv6Y2n1x3/+h8CLh/f34fMJBDn77Uc+6yfyd5pYP/Gr5dguWnlwvrtjUWSSa5WXUUumgcIMvEEAxQzsyBpagGXQeBh/JLU4wxasXYmJU0r4P/9v/kd/+HvjPzAIAgDGAJcXFZ7sNiiM4z4gTQGNYsE5Q2ioUsB+v8dhv0cMEatVjVevXuHly5coigI/fP89vvzyS9zd7En53hWISuPVm0/w7MUr6KKELSusdzu4soFOGmVRo1mtMXqPfhoxjD369ojoRwQ/oChraFtjnCJ8TBD15ZACmvUaxpQUlHOF4vLykiriBbWV3N3dYxgI4W/bbtHTT8EiUdhJcXgcPYZpQs99/M4apIWji/wn1U2imAtZTBJuSuBkIgAUX4p7wvJQXCZxSqmcJIsThywsoYZaZlkQ/ZFVnMNIiS0n2vKah9QpoUdut1usVps8n2Iij+yJ3SL2+z1pK6xWmbkgCRSA3A5EB+wwV2GUQlOvGEScLcekEi2BqDbMJuFqDx3yOn+/onCwxmCahiy8FmMCEgW5ou+x3W4z/ZOo7SO6ocvJlVSVJbGlHm7Sj5FqEh2ic9VbEjoZcxEwW29XTPMNOeEVACIlothNI7E/VqsVoaIFVQRFnEy+v2xOdE0U6AlwQwAXMRikHUaAJplLy9aOw+GQNxu5bqUUKjOhNH5WotbiHLFE6HXu76N9wOZNddnugjMQhKocAsqcVTy4Aik9qEtxRrlPUvWnNcGVTAYHcgKnAECjqmr8yZ/8Es9ePEeCiKoiU5pPbcvz0+cAfL2moJ1o+zMrQwBCuZ672xt4BowogaY5KtWMyM40Wuvs4uOllYfBqaUbiQT/QumuWZeE5ursGmGtzWJyVVVhvV4zwNOdtfAIYFLXNSlxQ2EYJ7Qs6JpiRFOXi/mUcjIg84AqpS5///V6zW08ZZ5PlIggJ0HGGPiREkS5XihFySIHwXVdw7HlaN/3eR8ty4oo422HmCLpqpTEnBhHUhFfNysUZYGyKDFMJKQ8jiO3EtDcIlvSnl2l6sySE8VzrTTGaUTwZJ88TJQcOK4uy14kegZKKazWa5y6FkgKFxeXePnyJbVXRRK+vbm5wfXNDQ7dCScW8KRWGUp2Ukp5/y6MRZh8ZrglxRaoaaY8g+8DaXiMCHFu0VEA9z1zoLuglErQmVLKwAvZRc5rUPZXYGYwAkt2DVUcZ+ZMys95+JDKePv918Qy/Hs85vCTqfBI+OUvfo6/+Of/DColHkOi4moSTsHQd/juu2/w7bdfQ6uEzXqNP/81Ca7/8MMP+Ku//ku2Ik5omk0GAPenEw6Dh2ku8ctf/VPsHj/B9vIS775/h9vbayhtsN5u4biNbfI+M2uI/eBhHTFyxmmCtQWqukZSxAoNk8c0DUD0GZhs+w4Fg3DD0MO5EuKQMwwDCbJry0w62uOsc9BaZdbhnORTW6DRBsYR+2epnwVQPzvNY2l1Yx3URJ8ZeL0JM0hpAuFSJKtUma8pzcLTWOzzCeC+cMN7Rsz7Xt/3uQgCILdnyPw0mhhQ51XFuW1LHlprjMHTGsfMzPCerO2tNhBhWqWIJQMAE7PzNGa3g3ls6GE54c/Ua2b+ip6aZiBXxMapEKXz3kjsxpg1R4ZxgFLU3lQWJblIDBOsdQxmFWhbanNWltqK2pbYEetmhcI5XF1d4pNPPsFqtcI0EevzeDrh5u4WfU9izwTyAy072AWQ8KlWGk1VISWw3aXKbcmyRqVgJmucWlJo/QtIUddzXKs4iQnSaucKzmVTPqectZiGPr+vtRZlXeH27g5S7JJCkgDnWmsUuoGJNY8t7wB5r5G9UGbanLd8AOCq9OA5H+5Rc84zJ8lYJNkPgZf5cZ586uVz6Y1pfwUzBnhtKQYq6N+z06LOoEs8+z4fJI6LT088QEmp3II4gzfnD5XY2JnfKz180gIcOP/xItWnDz0fhQdjrFWGjPP3nBkP87suP57GJOVEedm6MQOtABbCo2ev/cjj/Mfqo7/THz5x/p7LUyeP//nVIeFMY0OdDdTiz/Nbx99FffC6MwbSYi5nOSA1X9PHHn8oyVfyesEkPvI+87x78J4fm/spZXFj2rspPhXWtlLUfqXZPpbeR+KLWWye/hQATc9zMAOI6azlZS6uzaLj/8f/9f/wD3xz/m7/oUEQACgKhdfPd2iKGlVBdlk5gQnEyrCWekDHccTN9TWGvs/0yk8//QQvX75Eyf7Mf/lv/wqFM1AqwceA+2OPV5+8xJPnL1HUK9SrLapmi64b4X3Cs2cvsNqs4VPE0Pe4ef893n79O7THAz774md4/uozhKTQjxP2xxbJB/jguQooYEbM7RzPnj5jG0abKeMDU0JjDER7E52KEOAKhwSg6yf005jp89ZorJo6j4VQ2AFkhwmjFQpns3UsHdwWwzDOAU4ksUe5RqEOScJS13XWDJip9QaOe2HzHNCJq/GKx24iP+8lGs0HkuhSyNyRw5T6SqsMJGw2G4RIW77YZ8p/cq0zZVHYDyP37COzRo7HI9oT0UXFiUMplQGTpQtGURSAAvp+YL0Bv6gIWcTgMQ49iXsag3EYodSsVJ6BD07mxpEDi+TzIlzqMcjnH48nbDc7NE2T6ehGWVKoD7OrhlBxxU2k7U9ndDFZspJcW2sRmGngHH03pfVZIgogX68kSySmigyoAbTxCA2aDpl5Q1q21Mh3fAg0pJSwLhM21SzwtGQpSMBjjMtVw+VzwmJdCENk2e4izA+5t8QeCXnDTCmd2TaH/Dui4y6R7JgSgsxfrfNGHGPCZrPFr//0z7DebhYBO1mX3t/d4Ztv3uL29pY0ayKxB+xCl0Kx+FVKAcYaYgFosh8UK0Cy4qT2Ap03cBGFnQEP6l9N2TZNKRKYFIcN+U5L61YR3ZXrziDLRHvU5eVlZjBpnitybwV4rKoK0CQo2rP2C0AB+4YBH0lSnCOGyPF4hOVqeFFQ4nZ3dw+lAOeorSVGAjDqmjRQcsLXtvDjTEV3zqEoK7iiwOl0gojtIaUMItHnOaxWa9Kn6Tok3ueo9UkRoyxGFPw5JHBrUNcV3l2/x2F/IBGvdQPPtsgAsFqtYa3LrCkAec6NrK1xaltMwbMAdcrsurqusxuMUgrr7RaPrh7j1evX2Gw20BBHHbILvru/w939Pb599z2u727RjyOCj/McB/UmW0NaAIlBpJjCWaUrhsAMK50r3WBtDHH2QlQ5uCQcJFArRAi5+EKCcrOWwhKIX+5Bc/ig8hgtA0QJglJaJhtzMBW8x/3Xv/3JdpifemgFrJoaPoxYNw3+yW9+ntle00gsNQWFzWqFqioRg8fbb77C8bgHUsKjiwv8yc9/hrqu8e7d93j3ww8wit43emIERQ18f3OP2zbhybMXePMnv8DF1RP88MM1vvzt38IVFb74+c/RrFYUtLkSpA0iLS00fl3fAtA5iYuR2iGJ0TOh6085iJN2Vc9FFmssQiBR7rqq5/sRI1nuGgM/zQ5jksiLQGqumCmVQfC6IdB0HPrsVqbk3iZqrdHgfVt6rTkwTZzMpQgo/i8sKoBL4N0YQwKuKZ2do+PYn53TordTlMQQU5pYJjHM4IHseWdtvjxXoSgBke9blhW35hIYRQoMBOaWVYWqrLJQOUBnTF2TdkjbtllgVAoPJFBd5SRMXByUpoKG2NzP+ho1UiQdJWMst7cYBgk8NtsVVqsVECh2WzXECjqd2hwzAQpd3yJEj6EfURQl6qrGZrPB55+/QVmUmEZyZ0qg4tIUPIZxwPXNDW5v79D1A4LmFjfvMQjLQguDlr7HxW7HTNwRQ98TqK9Ubm+u6yafxxKviKC8gCB1VYESGC6yLYowZVmicBbT0rVIKWI7IWVGqcQW0kI+TRPQB8ReWvIUVeW1Q0xLHYZFek7Z10d2DCl4zA81Z6r5fc7+ZEBhOf8+eMlHHlr2PP53EjAFD3NI0gZLi18sL1FBwAACqSWJ/OCaF189UtZ4ljx/5BWQxhtaz+ffUf5+/u/56vK+/mAsRO/l4esf/klPTh9cn4BCSs3/zoC9mkEQ+t0S5PnI+//I93jIaDh7nv74eNETxDWT4zw8YG/8EfNC2FgZBFhe29l7Lf6uFERQJKUfa7r52PX+xLUAZ+CXOv+/P/oxF+RnQINYVIlthwn8MNpkq9sQQrZlBjCzCfO6kXnwYH7In4v59LD16f/yv/1Pf/qa/9sAgigFPH5U49mTR6hNAcuWREZTAhAmcvgoODlr2xM5dETPivkRn3/xBs+fPSNNgrs7fP/2W4QwUEWwrtANA8KU8PTZaxT1Dk+evYSyFW73R1w8ukJRVYgp4XDcoz3c4+bdD+i7E5Iy+NWf/1MUZYPBB0whojuduA8zcWBJlrOiEh65siie9XVdo6hqWOfQNNSjLzR56hvtMU4ToC2m4LFmQb9pGuHMbHtJiUOdE+vT6YTCWQQ/25C1bYuLiyuM44j9fs9VZZBI1+LAAZAPmCXQIL+PidovXEFVWqrqk74IMUuYeqp1TgAAcvhRwgqJ7HUO5P+jBENxormh75aA7e4Cu90ui3mKe4Q4JgDEaiiYoqm1RkLI4pFd16E9dQAUHj16hLqu4b3PLTybzSYLnAlrR6rR3TD3/wr4NE1DFkTqup7u0zAroEvABDBrYyL9FqlYExNgzMBTWVJA7FyRxesAYBpmBfWl8rr8e5xGtO0JCSmLbEqFZL/fcx8xuLxLrACqANLmbozh6ppGsyJBTQAM6JAttVR0iEIf2YrVZYu/nHwCuTImAatsPEuApjYeBXqEEDOYpRQ58FSs7ZIiOAinSvOyqkdJGLXsADNTgJK+lHe/fCDGBf1OIQNxKZFFcmQBVDqsZGOm8Uq8funftO8oEChhXZnBAGFHnNoTWcbGCOsKnk/E4FKK3ACMMbT2LYmMOWuZrTGLoaWUuHUuIILEhRPIUto5B7BFb4hkrWgMWWtSS4vjQFvlMZf7sWyPSSmhrld5bKlVgvRgmmZFa9tTS4q4k5RliaEfcgJ9e0/aBTHNyv0lU9HrusIwjAyGUOVSEggCrIesNUL31WSghiqtkTU/iiyAXFiXmWBSaS2YFu39hLqqcFyI/bJsGIwxOB4OEGu1vu8x9KQz1LErAxL1rZdViXEYUBYOt3d3lGClCFdYOFtgd3GJU9uSWLQroY3JYIYAP33fk/MKt445bk8Shpi0Qlpr8fr1a7x58wZlUc+2pgAmBpzFhtbHgHYacbu/wzCMuL6+xfvr93mdx0TaOABNcwL/QgZ3ASBMHpHbHcjlZYI2yCAblCJ7aKXgCouCW4vSIsGk6x+RQUUlDEIRv0tYBkc6A0CRzxthhJzbQC/BUHnE4HHzu7/+B4MgCkBdl6irEogDXj7foHLEzlFc2UdSaOoalxcXcLbAzc173N/fY393j+gDXr96gTdv3qBpKvRdh/fvfsBhf4foJxhloAuHKSS03YRDN6GdgE/fvMHl1WP89vdf4dM3n2Oz3VJV3RVImsTGtTFk0RuZbViWlBwyCCIsyBAjvJ/y3NFaQ3O7WIxxVscPC6tCDiYfVuvFxh28p/bjkPOUaZoIhAU5HIQYGCzqWR8qSRQLKGKDJAbbBERRSuX2MmMddrsLvPnsDVbNGn1P7iICsMoZQFoetN85OwvNE2NEZ60qKVhIMcN7cr0TcUxjDLUBG4thnNmiy6Q5RNJN04o1UFhoOsXEzN3IbRaaWx9XrNvj0XU9mqbBatUgJeB4PObvLs4ziYHI7XZHZ/Y05pa9cSKmj7PUIkMFGY1pJMHxcRiRUqS5oQCoREL4w0D7MhcuqDBjM2tlvW5I9HUKWK022O12+Pzzz2lvi4RWSQV2Ch7t0GP0UwbLh2GCsRaH0xH7/R5+GrJ7lsQbAnYMA1l2L/W1wGdIYR0D9zR2IUbsNmti0JUlteqlGTBGogRIziZrLZw1QIy5QANQUrjebDLwtIwrsp1w38OOE4mGKmIiKV0jKWbGki0IohJyAv2bpjNXyzlpPwc9Ps6SmHcXla/xjI3xI9nTw0SbbnNigE79yPP0R362fM/zZPhjwIZa/r+S//vYZz34dhnLVjSGD77LR4GLB//+8P3PQZCHz/sQWPk4aLF8D8lWJYb6gK3z4P0/9n4ze+Ph7xZ/YQAgfYTukACO5cBFA54by6T9w5edv0dGxOZrz+0/H3zmjIgpKKg0a+U9/KiPpfN/TO4elyyWv+Nrzx/zu+RCCRJ+7G3yPX1gTXN+b/HB7wAsCvDL9UDxn5yDf4xF7t8DBGG1Y/z96Ks/9igKjT/5+WtcrNeA2G3yRDNq6a1MGgnH+z3u7m+htUbfT6ibGp999hpPnz5FVZbo2hO++er3aLsWw9jjarvFptnAqQJ3+w5PX3+BensJV9VwdQVvNGxZIXmP7759C4UEawzW2w2SIls8aI2QpMJL7Iq2bWk0AvtiA3hoFRVjJLprjFitVqRjYguufpCCd4gBSSmMPpBQpCELp5GD7GUVTu7HxAdtDBRgOO5lr7jCe3t7S5WWYYSxlPAANCkDI28ZmbOzh31MIqQkffqUsIZEOhspKVQlicZGHzKLBAAUJw7GWGgjHuXztWutITawUunX2uDps+f45JPXCIEo4sf2hOAD+qGHsxZ10wCK+pNPR3aIKYkuKvRNidsMB0On0wl936MsSqw3a2oVUSYHGDKmrqAEhgTMyIGi68jlwweqlm032wx+UEAxMtOiIPAA5FUvCfvhcOCEO+Z7U5U1hoG0Taqq4hYZk6vYmg/raSLXCM9JpyupOjb5CUPX58qfVJqmaYAITmmtcWpbaDVbaJKeSkRZV3BlmcGcEGZHmJirWRrOFqxXMUKpSBaOzKIwlqqEAnTRfNeoyhIhUPLX2IDGhMyCSinBGpdFOAOP4VJNfHZIMDlQAghNFuaDAtvvYbZmE+s9zXRIAFCGgLkYIyWESdggcxVaKHoSOFMi7TKYkvg7GsPK6ZjtxKKwSDwFdc+eP8Pu4gLH4xG3t7e4vb1FBLW4Ga25oqBIhAzIFQYZn3Gc2HUjoSyLXAmVNVWWBTRrx2hN1ps+BCj+mTByRDdHWmWkgua5nUSAV6m2krPPkINfSUCEdqgNUcj3+z3GaUJROF63ZE/cNDWL4Vme17MOxqNHj5BAria0vxeIMWG93swVVO8xTYsgOAn9nHrzm7pBYqZI255oHlmTHXmMNlg3K9rXEjkfJNB6toaEKQ+HYx4brRWGgRKfEMmdo65KhDDh9vYOEWDHHRISdUWJid0e5P7UdcXsGxAbhO4oQkqZAdM0DS4uLrBerfD02TPSRNEm6yjJviPaHnIGaGMAreFTxOg92q7D8XDAzd0dbu5ucXN9ncUOZzYGVQWNNrBKYxoH3i/rbHUr60ExOOhHSlKkjZHYIZgB+9JmDSilpMI7AxkzmKFhNAHHMcUc/8yAOAnUAuf2kvL7GCNSCPj6L/814gJU/vs+lCJmZFNbXG4cqoLEvJ11RNFNBBJVRQnryGo5JeD9+3cE/EHj009f4c1nnxC78LDH3/z1v8fQD/BTgDbEOAoRKJsGt4cjiqrB4COqzRr/7C/+I5RFhb4b4FyBoixRlBW0Mvjt736LaepgnYGxjosFpLUDpeAjgSAKxKSgc4/2SwAIiYCKmBKip/GTtZz4u8eUclFAzvHJiyI/nSuKA+m5LTJCXDZCjKyFwQ5PYWYiBdaLgNYQ8fCUSCTw8eMn+Kf/7J9TW7IrCFCZQt5vej4rrTWYJtLvIdHlhLbtcGqPQKLWkKZuMoPSWoNh6EnrBFRwKcpiUXRWxFQbR2LWao3ddssAx4DRE8uh60kvbLVasTMSOcARKENn0Xa3y0B7WRDzuCpJjLM9tdlKvu06aEXAd10SECwtdW1L7bBFWVDrqifXPirelNkmdQbD6c+2O6EsKox+wphb/JBb2vp+AJRC01QoSjrzH109xieffEraU1JZTZJDse0x2zaP08h7DTPWeO31Q4dhnMWryRFowvF45DOJQGTN7VSGCzAlO30NrBMyjuS8tb/fU7wUqOWqqipsd1so/i51NVuvHw+HLJ5Pe4WGMgY+hOw4J2zGtmsxTR7GGjwpNK6cnqv0ykCpElrPIIjSBkkTS4m8UskpkVgjFknZBwmfevD3jwEinKwpdVbmV0Bmz+Wf6dmSlPYlzmwXHyosh499tuxlD69NPUgmP86sOL/+D0GLh9+Lr1/+Ku+hPtL+cvbhnJD/AVBkft7y2tL83ReARr4USXAfgDzL900pZaaXenCdajF+D8EO+bcUA87vmzr7Qx7xHMsSrgJ9d/3wvc/f4qcza7WYE3zdD1kf/HkJKcepH2tXWZ6ryzH82PX9+OWcO93IdX0wTT/2SB/96+JtZEUk/l9G3T78/wf3/OH3osJGxugXl/8QSAH+b//5T4Mgfy+LXKUSitLlw+4f9qCBiSGRUGFKc7KgqIpRcIWexPssqrKAUQbDSBu2UgrH9oS/+fJ3mLzHJ69f4+rxE9TNGr/93e/x7dtvUBYNMHpMY4/CB3z/279G8+gJnr18iTSdsO9HJG2xu7jA1XqN/f6Awhl090eqyhiFerNC0dRwZU2baQI22xX3o6YcxDtXYOgnTJMo7EdEKBIB9R73d/dYr7dcMR1htcbd3T1u9/fohh6r9ZrbRmbHAtEXkP5eGZ/gybljw0m6c5YOmBiwampStGeRrxBmgdTRT+iyGjly4KOMwtRP3Ooyo+UxRihLm1dKwAAR8zQgS1RKULQGvO8wDlRpkscyYU5p7kVWSuNid4nLiy3Z/pUadV1h53eI3Pd/e3uL65tr7rNNmfIvWiHS3iMUVmhgGidoQ4kaoY0R0zTmpNNPngEMqsCMQ59prNTvz8wEbRE8AUry2QQcUBKQtVm0ykru4+hRVTWUAkL0maJFvyNgo21blCW1B4ToUVYFAWpptn4qywpICZMfcdofcstMU5FI5jAM8BO1Eag0O3eoBEzTAD8OmYWitUUcR4xhgg8Bpm5QFFxVVsJgoQCJBFArGNMhhAn90HGPOFksi+6J1hrTOEJpEl2VwOWycbCr2TaREi2qdtFcCrOTzmLHtNawSB/bKi+1MRJVhYw1KEyR39tyoi5AiLVzIj9OE4Ka4AML5TL7Q+azPKTSVBZVpmorpqIbrWEFQGAdCRJWpaTk9atX+PSzTxk8stjf3+eKJTHCAATMFbG8h86HnjMWhi1oYyT6vlIshsntbFOcGPAgkeg0RUxhym0oAiAtg4elhg2xWRRCikh+Qj/Sz11JIEmKEUVVoev77NDj/ciMDGC9aiBORykllEWBVdPk9bzdPIZSJrPVrLPENGDmlrTMCBNu4HawKtV53zwcDnDWAoh4f30N5/aIIaDrWp5vFNPGGBF8gNMWkWnyWtPe0o8jrm+us3WkUNnp/Ai0P57IJvLu+j1u767x+PEl7m7vsdld4vWrzxjsqIli7T2vB2odEybIOI5oVg08O7/4ELLA6KtXr/D0MYHxrrAc2EhbSMqBszVkneoV2eWmxM4n2sA5DRMiouuhtzs8ubiCf/0Z+qHH/nDAqT3B+4Cu73A8Hgk49VQh9dOE/V6cGxJ0chSEKHLt0CDNAh8TjKagRxxwpGofE1maIxFgDn4vab+ixNhDWRY1DjG3IEqbXowAPAVwtrCoyopZhYskL/3jFVPoXEk4HSfolGAuC1Qli7CBRCy1s7CimcXzYrVuiMUZE77/4QfEFPHF52+w3uzw85//Cn/1V1/i/fU7JEQcDh5TCLh6NKEoDQrjcbFbwxqLu2+/QlQaxjpstpewGtBlhRgi1lWFNhwxtgfU2y2Mc4C28FNEUglTP0BaQqGAaRgQjc/Ct5KAhUCi7zFEGKcJ9LM2A1wpERugKCyt98iaUIEscK0R/anA7j4cXCbkFoc52ASCjyxyPNE5z0mgAhV76qrB8+cvcDzscdivsF5tUJYVyoL2TdM0qH3g9twJVZXQtS0OhwPu7u9RViV2my3KssThcMh7mFEJKXpYo9G1AxcpHDEonKN57qnAICytpmlwf3uXtZGKkliyfXsiNtA40HnLIFJT094zeY/T8ZgZH846brfWed8QcXKlaL+jOU+FCmsNVADKwmG9IpbmOI4IxkDFCJ0SuuORAGRO9KdxQllWsOzaMw5HaKOZmYkcz4iVdkwJ40Ri+UVRwFmLrj3RPsysObmXRAphRxsASI7ZG7PWSUoJhbM4HI84HI/U8ouEtj1xLOBQlRV0raHYbUrarcDn4mG/J1b0SPNr1ayIQWkMjFIoSgKTrLNIMaJmJ8FTCBzrlajrGhcXF9S21BADVYpa0iK5P+yzS001HGDGA+saJVCzyQSvDBWClIZWFlAClBATRIMBEVMApkRUGiQLfW6jSeeyJm0cLBkRrNGR0iKB+8hDzULTAmAmZoIIcHCeUCb5BbBIDBfEjHlze8DAO0sKuaKuz1o4ZuaEvNePJeZp+Rf14WdRvr5I0NN5gkrvf24Nm5NSNYMqKTNx+P1SypiSPJ2KmSl/F7kniw9H0vz9+IVLgVqVB3v5+ZLOS7qtAM3vnW+BykwfudY/lPunFLEkzTxM2v8gFST/Wj0Y5o99IgM2H7n3Z89S81zLceCDK/mx+09DlBtSHl7kB0yNjz/SgzFbIF1pfq8cA/3IZ0kx+w9/Eo/HEnz5adTpo4+/BwgScHFZ4/nLHW5vjvjhuyPiP0IcIxuzMRaVmy0YKbmhIZPK9vF4AgBsNhtErvatVg3GyeNvv/w9vA9489kbXD16hD/9019jt93h9vo90uRRKYVx6LHZVvDdHu+/GXHoWsAVePr8JaYj0bmfX60AANc3t/g3//pfQWuDZt3g088/x+7xY4QIROWQQA4iRGMdUBQW06gxTUCMmmnBVOkR+uf1zQ2GYcSjR09grcV+fw9XOGw3G6w2a/TsCZ9SRGLhRRHubJoGSpHmBqH3BUZunxAadtM0qGuiAnddj8Ohy5Rp59gNJpFQXOBKSAI52MQQ4JxBWRiezrPmR0zksCHUSCRkkT1rRRckAClQwpJFkJaLV4QwFUJgL3UD+DBgGFqUJYl0tV2L9+/fsyDcgAMH/tISc319fSaedTgccH9/n6vcIoSGOPcP9n3PlXECz6QtgPqSwYkyHyTMkJCeYwmIdrsdj3NA06yyLkHXdvCHQ24b2W43aJoa2ij0fcdJnIGfZo0K0Q24ubnJ1bu27ai9KkZcXFwgxYRts8Jhv4czJEzrY8TpeELbtVThVooCRgaFUkoAAwgU4EY4R4yevh/Q9T269gRrC6qI82PF7TIGdA5ebnfQBri5uclItAL5e69Y2+GEE9q2QzuRFkRdVnB27tfM7jTKzkCbIjo+uZPYHPBAEcsLoHjFaoWUqH1KErDl+wqQQQwFGnfnLIzVIMV8C5QW3lsMg+bWm/hB9QBQ3O6iYE3Blo5EaVbDkOdTUTgolRBBVfVqW2G9aXBqqYf/9vYG1ho0TQ0fSUfDx3nuxCyWNx8GMUYoTcDp8vsJi0PaR4QNRppCPSAJE3QGSYHZ6UaeP45jTp6gCIgV6jOtrRHG2Ox4UxQFKk5mi8LlhJ9YAQBALkFdd0IIE7VOGceVVQr0yWq3Bthmr2Q3o2GYcH19TWAwMwrEElv2+vV6Rf3lzuWWvGEgVgJUnJ1stIE2Cj5M6NoWCizsHCLGfkB3arNAbNu2OTmXMYkxQRmHze4Srqiw2gDWlTi0J6TU4v5wwGazg3UOfddlLQSZC9ZajH5C8gLSJaiUMHQd/uav/xo3729weXmBpqnzPmUZODechC+ZfUZrDhQVdAIMFHRRYv3sOaA02q5HPwyYvMd2s80tcyImeTwc0Lcd/DRAIsLATKkQyOpbxHXp82bXKOA8gIsxZkE9qnixQG9MqCuVQRAC30xmiogWCp07bdY9SinxXACMthRspQTrdAZE/jEfEUA3BCRdcFKkUToHDWrDmMYRiD4zGlarFfquR9sNUEbj3fv3CDHis88+w3azwa9+8ytoa+HHiffYEftDi8IrqGlEUAlFVeHmqztErRGSxlvj8Itf/gbr1Qqnwwm3777Fd9/9FiGMMEWFz3/xG9L4ScDYD6TzkhQxFfj+kGsHucBYS+wcsmlMIH0RcJsPtViNw0jgQAxIicAEaSuoXMmVT7LDVVpTCmhMtsXVzNQgQy9KoAqrsbHrrN2hluwhBRhlcMcFgu+//Q7rzRrb7Q5aG2w3W7iCWn/u9/cErgWPH77/jhhNKaEoSvy27yjhNqRNERh4LJxDjMRy3Wy2MK7IbWgSzyiFzFi5uX3H7BRxCJv1rkSc/Hg8wtmC9wIaj4pdXTQUnOF2QwZSaK1TCLPdbrPtLkCtRNM4YuhnoVQpyIQQsv6RMMioba6GD9TeEzy1TUl8YozBxcWONGvifBac2hZ39/fUXlOuqL3SB+z3BzpPuDXTe49pHLMmi3UOJNzdEZBb1dxaaXDqOuwPewwMcA1dh2kkBkbfdSjKklvvVBbEFqZsWRR4+fIFRJz6eDxBa9obd9sNCldw3IXsrDVOE8ahy+wSa8kCWbTshmFAiAnaWHKXgUJT1dhttnh8eYW2bXFqW7TffYkw7iHmr3SWRigwkywpROUBGLL2hALCQlDRd0iKWjO1sTODBMjPUdqAkm5H+yBEJ0NlQFJywmUFW4tWg6RoH0vK1HniJuyElBYJ/R/Y236sTSAlib+pSC2sAflIeo7+6Gs/8mYfgB6ydyyfcv69zmETJayCtPz3nJzLfFcP3heL18t6Wv5bPis8YABovsYPngtmtyhAJ7mHNCgq39s03/s/MD7CvJR2wZhkIJh5I+BXvq7lOD0AlfKwPYBa8r06H+B8D/kpZ99PXqZI2Fol8jFdsi+kIH3+rsvvE6ESi14vxyA9fM0ffmQA6g895495ozPHnY9/Th6iP2ZO/6Hr+bu2wxij8OTpBj6MmCbgdCQBt78nCAO5ScYofP7ZUzza7VBytSJT1ReVf3KZAKqC+ibfvXtHrQeGNofDgTbkN599hjdv3uDR1RU0FN6/f4e//su/hO47GHYzcGWJqBTGlBC1wu7iAjtGpX3w8JPH7d09bu+PuL0/IMSA7cUF/sm/+BcoygZltcHoWVxRU4KCFEBd0xQMpciiikqjqmvsdrvsypCios39dKIDCwnd0M+WVkiIfGAej8d88Iv9nYJUmGeF9dPphDCNEBFZQf5FsT8lUHCriR0hvdvAOWVZxC9zogaqxKY0945DKXg/i61SBT3lnl4m1cO44iz5UyplJggla9Sr/+mnn6Ku12hPHb5++xYT2772PYmUFvy9Y4yz4OB6jaurK8QYcX9/nxMU0uAgdpGIcQlDQHpeEwdhZVFAawsfPLr2XKRUAr6BHXtEz6OqmqwBIIFK13fZ9rQsC9R1haqmHlmigXqkiLPkFgBOpxP2+z1/HrU71HVNWigxYbNu0HfdLHZrDYZhzGr29D0nFo/ktWIUBWhc2S2rAqT83maaadM0WK+pFefu7pbWt7WIgdT+naugzHlSLXuBgEMAsq2gTMqVS3BqRCmV30i2yikmxDRroEiFXto55HAkyr1HSuzakc7pgNYWC/FWfm4IRCdXpDeDxQGKFNmTnFemWSR+KZ2tD4DFSQGQ4rjJz6WCRoIyJgeaxICinv3j8YimabIDkdgKyvdbJvt+EWQnKGgzO9TMgPC57Rftv9TGZK2DjxHBx1w9BZDBUtKTmZjZY/N82x8OGIcBUEDhyN6bLHGp8llYy0UpbodzZBEdF/3b2+0GWol1twWShjEWZVlnUdNpGjGFCdvtNjMETh1R2yUQFo0RYHa2sZaS6tn5JcEoEAPJjxkId5bsfi82WwKBQ8yAqbg+Fa5ATHOvewgBq9WKNAJGD2sLjNOApIi+Te1hGiJaWRQlbFGgqmfnHrrfrJ2RWKchKU4SZ3FFsg0mUEoYamVZ4vHVEzx7+gzE2nPckqKoPUKRW0r0pC0lAXeMEW3foxt6tEzhN87BcmuZUuRwkVLE8XjA3d0d2u7EgHAFgPcyYxC8x+AnjGLPLCxBblGDokqmLQhMdK7ksQkwivQpxmFA13ccGM/MPoDYWMM4wAdKZDNNl3Ur5EyXuT4OPX77r/9LBAY//7EeCsDFrsDLZzvUrqCW2kTMvhQThqGj+2NIU6PrWtpTx5EFdBOeP3+OTz55ibJ0iCngu2++wel4xKpcYVU3SGlEdzqiLAv0Q4ekFPaHI6J2KNdrVPUKp9OEtg+4ubnHMA4oygK2rPDP/uI/xsXlI7QsctkzE0QZAsNkjxBxzpgSjDXZIpxAM2JvaMNtTmwTnTSBh1i0s1hlac8IJOoOlaA0zXEoYi6kQOwOAVWnQHbKlvcEree2KtGzkbiMQGzSqdGatHwAYBw8g8rk3kLAGt0hpTV2uw1u7/ese5F47ZQwDMB5P8E5to0PszuGCGYXhc2OMiSQmnIBTTQ1pMVS1pwwIsZxglaGW34JqN5sNmdgxvL8J9Fj0vmwhix7T6cTRbCJ2pm0MdhsiRkkzEf5PFqD1IoRYoTR1BaltUZZkk5JCB7BjwwyqbnNTBGQSS2h7LpSlAgTtc1ps9Rl0zleTCllHZOnT57gzWdvYK3F/f09Wf1ai2EccXd7i9VqlTXErLW4vb3F4XDEyPEdaZY0WK8avHr1EleXV/A+4P37axxPRzoDigLeR95/HYzVC+F3mmcFA13rzRrOOnR9T+wSa6G0yTFWURQETGUtwBan73+L6fZb5GQWAgwsVr7i1JL1veYdAecJkwKybCkX9oghIK21hqoxEIYlr03wLVEP35eFz8HJ9Y8k98trUEr8HRWi0kjaQEMvEvIH+hxzio3zfJOSxvO3n+M1evxhEGSZTD/MPx++juH6s9fS0J+hE/lnAjicPSdRQU0c8B62cTwEMxYfjvDgsx5e8jkIMj9HYIf5OUquXt56McbLN12IsOZxfXhh50mwljcUIEGAqTNaRppfe0aK4DMyRYjeXwaVtMwZyjlDpBY0RUEDuQmBcrUMZGXhYA3g/HOTVLOTOHipnwTjPvpQPCd+AkySUf74W/Ac+QN4wrwWdS7U/Njj//5//Z//5GX/nZkgKSrc3XYYhukfAHx8+DCGKNtVVaEpCqJrxgTnClgOnMQykFC4hGa1wlUImPyEU3fCOI1wzmDoJ/zN33yZA+yriws8ffYcq9Ua//V/9V/heH2NylooS1ofxhg8e/wEd/t7fHe8x8XFBY5ti/fvb1DVDX7x+acYE9COHjEpvPvhHcqygituqFWGrdzWXK2oigr9MMIZCzgDo0s4djmgtgAau3H0RJ/XGqeO+k2hFO7u7tD3PZqmxnazIhbEidgvkixKb2UInqiFEnikhMQsAEoYbVZGF5V+q0yebDY7aYA2fL6pRekAVnqfxhF+8rDGAEnP6K02RKe3lBxoo2GsykGH9wFaATYjv7LZES1bXD2Moar0l19+CaUsFDQ0JztC5xf2iwAYXdeh5uREHCnqus56CgdmZRgW5Or7HldXV1kPRETY+p5aPfrhAFeU6IaeD/oVV1Ym3N/fZzeeYRhR1xXu7+8QwgreT7mXnqo9BC6F4HE6HXE87tkZgJMFzza01mRNkKZpUDdEfU3QGeSqV+Qicxg6uMJCK6A7HjGdOlLKZwBBKaA9zuMkrQNSkSIdloTTqeXWD0pKqfpPTICmabg6JQKb9B5VVWW67bfffpuBhr7v4aeJdTNYyNgarJoVamtRVg4hTLy2FbwfIHaRS8cWgFghMCZvy0KZT0jQKuTnMG5NAn6T6JpMmKYxJwd5n4J6cGiJeGpATCFXwpVWuVfbWmI+CMCfEKHUDD7Ixp3ibM8LJIzcdkQUTo2ycEiqwNOnT/H27Vvc3Nxk5sWyRUhAXc8Wt+CgXxIPAROlmisV+BgDhiGyeCtZOsv+WFQVFIBuGICU4DmAF8ZFUkCzWQMAs6iIkVOUJQlODz1WdZNFqF3h4CcPYxIsqFLddcQ+ss7CmALjyJRwpbI4oCsKqGDQdQOtbwb+bm5uORmxGZwBwG1pfT5TxErXTyMLh1JQ0HML39D3UCCgb12vULGWiggTO2vZQSWhLgrYokBkfRznCjS7LYqiYkFjhRA8tQAqnROWyXuYgujZkgTI3K2qCj4G1lEpUBQWSgFFQe4dS7eEw4EAzqqqEH3A4bBHVVfY7XbYbXd5H6P1m6gSmcDtoQo9j03HVpnEcIvcmilrWUSbAW0UNusNqprOU2fLzGK5vb1Fx3sqFLF9PANTh8OBXCOmEcqQhsJqtc5te+tmjdIVSLyftm2bhW9FbLptW2hjMHlpP2OhbWGS8VmQGWv/iDHE8pEAHA8T0mODatOQeDKDh0YblFVJWirWUNtkXeP+/h63w5hjm9/97ivEOOH58xd4/PQJPnnzM3z39lvcv7/Gt998g6eP1tg0NdI0YOo62LrCbrPBFBJU8rj7/i022ytcvrjC02ePsd1ssNlcQJsSQQHJj7AqAnEigXMAPkb0XXcGECqlMQ4DXFlkzQwBA5VSmILnljc6e60k74FYC+M4wigC2Yy1WBdrjONAQsD8XGqtjPkeFU4c66g1QfMu6r1n7TBuSXPUDhujXxQNFDy3RhKwTG2LABWfJgZnHz+6wqtXrxDxFe7v9jAaWZsM3KpDLixcjAmeizeJhcsNhkHaIg3qpkHfj/w5CtYQI2QYfD7nhNGWYoQ2JESfYKmFeaDYYrVeoSpLKoqcyJHPhwmT97BWY7qeULgS0s2Z4qyt1Q8dDscVCleg78cMAC81WIyzWG82MIWD0P7v7+9yRTpy0YTuPVDXDUJKKLXDOAzwMZBzlDbUuug9TseeWmC42Db0A+9hE8qixIvnFP++e/cuF4IKbm85HvbY39/j+vo6M2mfP39OLlbcyur9BKtp3+21Qt/1cE+JtaH1Y1yFHWJI1M4DTS3HzDKpJYZAyq5aAqBN04SmbhgIKhn4oX3+5v17GG2wXW9YX8vAaIUxRc7lJGVNiwyeaBpKCbwxG9R+kCzxYUN/8Oug5/eVJFlsPNTM1vsw7yLAYk6iP54IZiaEvETNTBOtFLXoaAErBIxREG0T0kBZMKyTJLWL9D3vqRz5cGyh1MN2hQ/3zPSxcfrRR/rI3xbflVlr4HFcXBYEHfBQCEphvoVJsJNF/IY83tKykvTihwAVbeQZAro8/BrzoOd7S3Wwmc0hn6WVXOGCLYGUC3JICQvT7ww2nLUoLd1R5qByMe8SgNlpbR43+UoRSAGIAUgEjk4hAEZD64TIBUClZgF9RAL6DTSCvLdSmWn4cOImjm8V4uIOUWHu3L7mj5wTeRD/4JN+8ncf4ktp/r2a/5O18A8JI/7OIAjpNPxjUlh5QjCdtigsoBILqnFio6lHzyhkim3X94iHI3brFXbbLVIKiH7EOHpoUJvHb3/3O6SU0L16jufPXsBVFT7/xS/x5b//S+y/f4d92+HUUcDz/vYeZW1QVg6//+oWm80WdVlAIeLu7gbQBVy9QUjAwAJa3333HW5urgFE1PUKj589hS0rbDZEsS4qxwnuFvVqBVeU8J4C8YEZB93QozsRddiVDlDUWxojTfpxsCidQ3l5SUlB8LmyYAwp/ccwIfiEcSBRQllFSgEhCmiiuAWBgvUQfGYN0HzSMErlPkYad0BHwOgCqIqzuyZonFYGJTAzRgyyQGg+9BW1KRAQEJGSRtSz2KRWCmPfQ0NhGihJ0UkBIWZqOCHkKYt47XZbdF2L0+kArTU2m00WcZQAwjkHlYipMAwD3r9/z/Z0it0UKIE9dS2BPX6CsQWKusLoPca+x7gQf5WWhfv7e5zaE4ZhduvZHwwstxWEELDZrHE47QEGb0QHYb3dYEpzO87N3R1wc52Bj/1+v+gBJnX9kINJZCvLMHlsVisoKKrMc+VKKtVWq9wmImJ5b9++RYieErjMEKJ7OY4jbt5fY7MjKvNmvYNSGtvtmitJpAMhc6ZhNwooBWM1okK2BS7VCKM8SBiWl3hkgWNjuNJNfcUqzVVoJfMozX2NMdHmPOUgMkAcKOT6c8vNsi8+yeG1OFxBFVTSB1BZ8DQZZqDEgDhSBY5XEOTADLzuJFldgpFJRRgjlsMBIUxwJSVVcp2zBs4cHAkThtxCqNJrdAXpEJDvopSCcTZrL2hLdN0Esp0VQdiSdUxiCCgSCbiqSCyYqABbFNiUxVk1HkjMLLG4vLzgMU6cwFA1yzgLRNKG0JbWzul4gg8JNtH7GmfhCofCFbmC4SyJ6Y3jgLYjkeHNZs0ALXK1j75ngaqaXbCEKQQlotMahSmYUh+gLLXgtF1HApKIWRxx6DvcXF8jhYDLy0v6jqyp1HU9tluLsnA4HO9xe39L0zNEbLdbaD3bIZM21QygGUP7lzFSKfcMjmpOBpeCpQTkhCDtS0DbnjD0A3YXO9ijQ9u1VOXWCtYEZoRwu0GKiCrRWeg0VusGrnQYp5E0QMYJMQQc9vdz4loU2KzX2DE77XA4oD128GXMNuivXr3CqSMdkf1hj2/fvkfbtihYW2BblXDc/0/Jpsdxf0R36vA+vYfTMyOsKApst1us1+v8fQFiI/XjgHfv3zPDzWPyI9quY/HkwPsPsg2vdRqbdYW+H9F1/3CRVIAMM1JS7J5iYR1ZDFtF91H2PqUUqrKGb8h++fr6DqTno/DVV9+i7UfAGjx5/BhvvvgZ3q83GNqWdBaSygyAtm1R1yus1g2KkoQwh/4IbQFlLK7f3cMZjXrV4Ltvv8MweTx99hxVs0HhGihnoY1D3GwAJYKf5CoVmMGhtIZCRJjGfGaJw9BqvSK3kWQQfIKfAqIPWaCc9lwufliD6AOmmABN7mCEO9AcjIp0YzQoKZ0yU0cBOkGnyAUU2oNdYeAS6WSkCNKdUlSTJIaAziBKilRhf/XyJZ48eYqrq0c4Hk+YvEfwHofjEe3piNPpiJQCpmmE5rWYE1YAzhr4aYRKwPFwwDSOqOsaWiXc3d1nAd+qrqEQ0XckKiz7kmdr3aP3KMqKYtCyRDx43N/PCYwCt5axEK3WGl0/ICbQuCSKi60x5CijgNFPmalprSVh1a5DSoArHIk4JxLLbtseidsMrTUwvLZkf7y9vc1xmjECWB+h2h513aBpNijLBsaofC7tttvMIhRtEQF3y7LEarVidt+Auirx6WefUEU+EYOiripYbTD2A97d/pCd7owmR6nCkMVt3VS42K7higJ+EnBDo6pqAvj9hBhIt+l0OsGKmxGDRsRUDflct9bSGV2VsFeXaNseI4PRBPAaQOUDci7zL9ImWdfEZnyQq3ygbZAeJFwxP0daTOjvwhiRV334UIk1JZbv+ODfBEpwpV4Bws7glURVfDWDIDmTV5r/bfgLzuK6ibP2HMsDSEojgdaxyq44LKr1I0loTpEXyeVyTM/+/ZF3+Tg4ND//7JnyfvklCfO94L9LsrtkTPB45ruuHrwfkMdBHjl/XoANiZ93Rr6A4BSJtKpSgDAyKG8RDbslM/kcBKGPYc7FIgbNn52kWYpT9+Wkevjggn9KESl6tF2Ltu3go0eIPYb+mIvg1IK8ojg6kR5TyXIJtnCwws5XEiODx5sKtlrPjJwYGc5TJNQ+X+0fATX81FN4fH/qefOqnZ/7wUuyIMsfC9p9/PHfCotcgJC3P/nZSzy9ukTBjiJKadIo0CREOV8q0f9JJLXENA74/vu3OB73GAY62JTRaLsB1hp8+uknePXyFZ4/fwmtLbq2xb//N/8Wtz+8Q1lXKJ2FM8AwdkyGCBhHj/V6SwGOLbC7fAzoEsoVWG22lNikhOPpmGnh9WqFpBRZaxYVLq6eYHf5BNYVSCA65/F4xM3NDQBk+vTQUdUkJKp0ZDFIozPhTCwwl0ijIIk8Inks59+d/1vopowr5Xu6TB7nx5wAStuDJMCzTZ6g5sjXC3Nui5giIZCS8C0TO/m8cZwAkPMHtWEUZBfMooYpJSRNwaYrqO2CDvUxf/Z6vUbX9bDWZd2UFFO2KZbvsd/fYxh6YhyxSGPb9Ti0J1aC1kiBEnQDhcLQoWwLl9uRuq5jsVCNR48eZQtDqjLpDDC1fZcppqvVCtc3N9x3PWWwoqnX5B5hbQ542rbF9e0Ngg84HA+ZzZN7fn2AHydc7nawxuLq6hJPHz/Czc0N9vs90XrXpOIOkCbC9fV1tiol1koNa4W6RvPr9vaGqtquwG53CWF8HA7UeyxCsNJf7ZyDMhrjOGDiAL0sS+wqhctGQ7RTJLEm6nw6axuQQC1XSJhNAYj9K9Hsl8mlUumM9fKxVh2ZIxksgQQjKYOEeY4qnrNx1s7QxuTWpNxWw+8viSoJtVqIW/sSgIlJZd0LuY7lNQsjZm4R4PfXQFwg9g9BkMzskaCGK11KKQoUeYFHrihHZmhRoJ4AqXwksluV/nmtNaymKjKgzvYMBWStghACyqIEQI4kKdEaK5zDZrWGYw0Rz3396/Ua+/0et/f3WG9WaJqGqOjThMCB+TiOGdhtVis61PueE6oAP43oB7LcDNxGYzRptIz9gBgmTH2PEBKMNri9vcHbb76B1govXryAtaQj5IqCaOyabXSnAZp18dpTj6ZZo6xqFlB1RDdPkQUZU96HtTbZZWHZipQ1rLRG27bZXUWe43hsgZSdeFarNZxxzOyjdh9p8bu4uDybV6LdIElvwfooS2FcAZXkM+/v9gRgVqS5oLRmtxtyhpC2rkr2ME97sXO0dxyPx6wX46cJga08xSFHAVhv1lAgvSFaMxqj99gf9kgJ2GxWOZGWM0ha+4a+w7/7f/8/8fhxibrROB5GvP+hQ/qjopI//LBa4RdfPMfzp5fQILFHDWJIBE9CqDHS2Gnuox+GHofDHre3e9Q1MTf3xwFXj3b42c9/hufPX6AsHLrjAd/+/kv0+1uoFDCMPS4uHyGkBGiDm9s7Oq8c7SPE2Cqw3V4B0PibL7/E4D2iMthdPcJvfv3PAePgI2CLck5yDCc6kdg0MbKbnOjppITRTxk4zO11WsRDWThcmCMGDKSAz+aU1z/ZmKZ8flMLC7E3oGQeqmx3ysVUiI36/4+5//y1bUvT+7DfCDOusMOJN1V1VVd3q0nDskhQ/CLJf6phCJD5xR8NA4JtEIYACTJN2BRFdldXdYWbz9lppRlH0Id3jLnWPvdUaHVT0gJu1T47rDDDGO/7vE/IHjQxSuS3sRn0PUfHZ0aXMoYf/8lPefHiFVUG1Elx89PIMHTs93vu7+847PdM87SYD2fAb7vZMA0DfUpMC1FMpSMswHqIYWF/KiVeQ1rJwEJpJZIYI/4QGajO7Mp878na5OmSd0kkLslhwlLzixTE2nN9ohCmUWb0HA9HMRytm8V81NqC4/EoYI5WwmZNJqaZ6bNIBQuzeKLNSVpjTSFGrxFKW+C8Y3t9tdQlyx6XjltmGBmjkdQwTVHaZb2KAfHlS/VRjLL+TuNI1wlb7PFRmMp1U3Jzc8OrV6949fIlRVFJvYwk6wjbTJq4vN8t16/zzLPEGA/jyOF4YEogVq6n5Lidh2XTNDE/fsX89PUCDqgoQEhM+94CgKj/GXT+5fGxFv9yIP6Rn13iHh/plT783lmKcfl9AT4W6Uj+WW70M6tFwyKTySBL/pt8nypFiJKQc5YPKGSk+Ps+eq77PxKT+/w7yzu8mNGfQZu/w0OOxZlpIY/kBalYgIDz7/J7v4bLXlGeS0qe83Mvn0Fdfv8DuGqRiHyQZfKD5/n4ZvXRKzCBAGewZ/mI58+fj0eqWwVwCUkak0AUHUEFIufhYEzhFxoxRx8GYc4P48ipP6W6UdaBpmkw9iz5L6wRaZIxXG1vEkvZEvJ1qkiR7n+/xx8JN/y9/yY//st/8V/9wd/53wwI0rYlP/7sFauqlEIKoW5aa9HKpAldSotZYuGE8mg0PD4+cH//XUqskYZsmEb6fqAsK/7iL/6M128+lZhUpEF+uH/geDyggUoFTAygAnf3d3IBpWSNOUSa1YZXbz6jbrfYWqLTnA/4mI5NWoidD5iyZntzzfb6BWW1woeIi3A8dex2+6WI3O930iQ6j7EKHz3eT+QmxLs5mRMm6v7FwhQXUEiaumdSgAugJH8/b0A56i4fy8tze6kplSY0PvvbXGjbZBKotckysoXejz43WtlLJEd55te4fF9Ch5cbzTmPdzK5KauGHO86palXBLqhP7u/lzYVrmJeWZaVNCjqrOm9vb5JZqjSdJ+yhjvRdUExzo7JzfgYcDGyblboCG6a6Q8n6rrClCJbydPdDEzkQiNLZjJYlQ3nwkURglKsNuskwxmYppmqrHn16hVPT0+UCbQ4HY+LDOju7u7CgFDop3VVUxYFbdOw3+0pi4IXt9fs9/vFRNdN41J8ZkPIDF5kCUJmN1RVvXwOECPLbHCZNdGpb04MGhYHd6U1zs8ExXJ9vFgbbldn+YacR5kGSSTheWKePVuQK3k5b/na/FAnKik15+srT/suHxmku/ThkMYnmcVyBiQAdLq/iOn3Y1yMMMUfIv7gvSzMHICLe2n5XFHun5xolBuLPBl0zjF7h9EFJF1/RCZdZyAlJdn4gF9is10yGk33rrZL4R3UJeAZlmseENp3eh/Zv+VDsPSyMHsGisaz1r8oCrIuPZsPl7agrirevn5NU9fc3d1L05/Wb2OMsBec4+bFLVfbLY9PTzw+PKbnLGnahuC9GNImL5s8uXbzxDgNDF0PqTmLPmCs0LO70xE/n4Gmqqw57A9UVcF6vWIcpiU1yDmJjMQo1ts1dV2i0MwuoNDYslxMfPPxjkkymO+jLIExVjTvSssaNA5uWdNUOr4uXXPS0OiFol7kJCSlUcmDZEq+CU3T8Kd/+qd8/vkXC/Boklws5usrnA2rL70LtNHMztGdZLJOUBIvaQx120hjg3gZPO52+BggSByq0VpiOhPonZOzREYnDWJ0YZGqGgNjMn0WE1opxLu+T5P9OU247XLNlVVNWVbCPFmvIAT+5f/t/0pZTnT9RPAqyTJ+cFv/nR5Kbkt++qOX3F63aGUpbEkeK5gkEQzeJalQIaap88zDw93i0RSB2Xm6buLFy1v+/C/+grdv32ALg3cTX/3ib9i//x7nR2YXaVcNRVElNo8wBeqmom1XdIdO0oAcuKj5q7/9imGaKcqCf/af/jNevXyN0VUCVYNIuLQhpglwjoX0MTBNM9PsIArzckzyg+xXMU3pmtMssp8imQ2LzFCfp4zxvLfLeqoWIN8kfzbS1FSa6IjR4hXkQ2L8hXBhap1i1FVm8qlFdpgb4IiiblpWqw1aG65vb7m+uQEUQ3+SaOjHe06nI7vdE/0wYAsrXhppba9SEtpljZPXrMVrKN1vMcZFLqdJkhulmOaJaXZEzqDDNM9JTmPlHooRWxbMCWwsy3JhO12aCxOVmILCIo/J9ymweG1IbecxtqCwwlLN/jlVJTItNwvY64JPZvcNZVkk6bNhDjN11UAUBmJd1hSJHZfNkuu25fb2BZ999hlwNogWs11h8EzjzDCeePfuHfv9nk8//ZyXL18uEri+OzENA97PSY6u6BKLOfsvSfR5ks6VJevVivV6RVmI/EgrLT55kWXPVkqhbbFEbI/jKOyhwi5rrfchXYt+AXYfv/wr9t/8PDX3sjcRkiQkgx/6DAikE/M7VogPv3NJH7j88uIfHwUvUs2bgZnl9z54/svvqfPrqXjJSEhAyMcaaEWa5EPSgzx/nQUQ+fCP9AKUcMFu+X2PH7x+Pt6Xv/Hhr/zgtf/I505vE868CnnJfDzj2ePz7/LIrIMEJeQ/X8Ci5YXPv7X8YGFv5Hf0ezakBGB8+BvLMf/o75+/UM/e68d+NSYwMf88pRx+8NS5ntPZ++Xi87tkh5ATMmUYOTJOE/M8QYgYZWQ/QEx+Bc83vHz1KtUNHwcHP/jQf5gv8kft7ZnFfa5BP36Efv/jv/wX/5c/+Dv/q4Agz+nY4gfys59+wU+++JSmqiiM6AqFfgwyjRAQ4EOkHuQmGfuB+7vv+PKrX3M6dShFyqKXabYtS3704z/h5vaW6zRBVyju7u65e3/HujBUSqIWd09P3D888OrVS2yacipr6SYPpuJHP/kpykjhbGyDMvI6IcI0O8q65erFDU27IUTN4Xhi8gFjS059xzgMFEVJUWju7u4ZTr0geniZTCrEwR6JMQNSU3NGQ8/JK/lGZSmK4XxDxKVJlHjMbDYY49nN/NLZ/HxuWBrvS6bI5SYmk+CzBk9rTdQx6evd8jzWFMvz5Mcl4KK1xTlPWVbUVZsmRVI0930vjuxKUTW1pJv0vbBkjFqc4t++fZsad5labjYbacAS+yBH0haFpSyLpXkuippxmhlnxzCP7PbiPL5ZrTHaMA8DhTbsT3tCcAmAkSIsG4uWZcnxeExNj1uKnSolJeQEntk5hjQlL8sC7zwgRdXV1ZU0adOMn+YUO1vRnbql+BJfB5meSFSdACb7/R5NeBZHWlfFEgGYGUT557mBzQ3582tHmqZpGhevAznfZpmmhRgZkrklSuG8I6i4AAG3K82L1Tl6LoSQAIiYpq8yFZa43WwQahYuUy7I83v6EOCLMT3XxXQgN/j59xfzynRtxxDPXh5pR73MeZcGM98qefOS/y7NSvPrLe9FJXNFdTbOlN9LrKcLwCY31hkE8VEIsJlRcsmGyUX25ZqZC+h8z4iniYAgIXhmH55tE5mBkqdvSp2B5fPam82JL5sHvbyO0VrkNMFjjaGuqsWEtGlqMQaMEo9YlRXBe07H45KotNvtaOqat28/QSewZrPdivzq4YG+6xa5SIxyDdmUEOC9+GfEGJimAec9Vmn2hx3EQNPW4ovkHDGBafM8s15vICpOp0MCLkQKWBRCC63rmqgjRVlQVSXjOCWNf8RHqKsakxIYhJEiaRq2sAtQt4DEKuL8vDDerC1kgqPOhn5iNDktzCilRB5YpIZGzJ/HJfElgwbzLGvJy5ev+NM//ekischGq/bimsnRmj4KIy3/99VXX/Pdd99JQaSE0to0LbYssYUYgJ+OJ/pO/DxQpMGDTg1ftfgwOecoEnVmnmeKUqIwc4OSm8wsP+y6DrRezHFzk911Sf5ZFLh55t/8t/+Sae6X6NJ/iIcCCqv5x3/+Ba9ebNFavKYI53JXa42Kwl4I0aGieFKMQ89hv+fUndjvO2xpmacZFxQvXr7gZz/7KTe3NyI7Cp6//flf85tf/QoNNE2dGAuKT9++ToarPatGqMr3948MvePVm1ecpsj+2NNsV7RNTWEsL2/fsN8/MI171lcbrl9/gS3XYCy2rFL8acQWFufPLAcx9RVzX6U0s2MxVs3rqfOT0KJtAph9BpHPnhaXEarLvs95CqiV7A9RtmSM0diUPHY5LMl/m9egvD5erp8+PbHWRtgRhbC1Dsd98v+aKMtiSUErqvJsyi6fgOhz4tb5u/kzL6WtVs/WN52ScyJw6k50/bAYU0dYmIGQmF9KU5TFspaeTafjsnaKB0tcCvzL4weJMZaAoK7rluGP9xJxLMxQkeGN05AGCFIfTJMkPnkfFpPq9XZN07SEWepC7yS1pU41glKazz77Ynm/q9WKsqp4eLjn6fGJ777/ltWqZb3eJMZTSAazEtN7c31NVVZpK1SEIKCMVkYAuGlK+6liGMcFZK7rmtNhR1kWfP/tN/z217/m+uaWZrVimh23t7cYY7i9fUndNGht6LqBzWYjZr7pvK1Wq4URksEQ5xxPX/0Vj1/9+/Nk/nJ4ngZuaL30BzIw+djqcPkv9ZHvXlxlMRulnweeP/htFc7vKb/wB9DG8vf5e/nrJbUkN8UfSlby+8s1y0ee4/KzfAD+XEpbLs1Mf9cHjh/74QVo89F/ywtxyRiJH32my/d3/iznoxU/8ncfSpZ+3/s+P/OHPzMXLyTA7gfvI37wHL8TeEnAyN8Lqf/h+f3Y06n0RuJSnMaLN5cdS8418PIry7UKxLMnlzzOA5TMCpYjoJbB5VI3x4tQjWfH7OOf5YdAEM9+pj7yOx99XByMD4eQf5fH//lf/Is/+Dv/i4MgWVKQzSnlIMOnb17wf/jf/yNutlsMQvM+p2XklAS1vBc3u+Wg5pnsNHb89re/5Pvvv8E5KQw2mzXOBw7HI/Wq5fMvvuDlq1fc3txK0R4i333zHV/+6le4447NqqSqSjHcW68pCok1VNrQTzMuRt5+/gXNeo02BVEVRAqcFw2eDwplDUVdcfvyFdMcuH94ZHKOABgrqHVdV8zTxDQO9McOFTwYMWmbnJj1TfN0cfyFvijghCYGUlqDTGHyYn+eqos3QJEmQMYYmTKrc3RV3jzOgIR6RtvPDIBccBeFOJ5fNnSKM3oYvF+a4XMSi1tYK7khyO8zN7/GWOqmZZpmgpcpPKkIyVTvcZqWqah3wiqo25q6qWX9Sg2l6NKV0MHGUaZFKumrQ8BYs7A5hBVRcDh2HLuTTLgKS9PUFNZSVzV1WRK9x1jNqTshFFfLPM7JHM4tFFWJbz6K9jeId0NR1zRNQ6bwhhATYCKT36enR6qqWsCKsigojF1iNPuhlwItnOVO+8ORaZ6k4UiN5dAdsUZoz13XEb2jLCz90MvCEyPr9Ybt1RW73S4lFDULsJEL1KZpGceR4/GY7j+hRmamyJwAlEy7H0ZJgsiJK94HbtrIpvJYI83UkBgyNsdyKs3sRLY2zTPTLKBGgHPTnq5pY3Kzd5YdxOgvFsaz+eJzY9gz0Ko+2JzDAiScAcBcRJ1ppslULBe4iSGCSmyPEJPXgF7YPiDRlkqxmAdmWGKa5HxlTa5zDqWFTg3PE4PyND83xZepJGfDPFlHQpQkEmstPsj7M0YahXma0zGS5jh/LqUlLjgDFTnCFmRvzSBZWdU0dcM0jXjvFi+FaRqwyYRT7u8glPkgRqUmScKKRN+OQRJ0AExhicjmumpbNuv1Ih857PdM05TeT2bxCBAyz+PCPprnMRmmhjQ51QsAOSfQQ3TpldxbyTS3rEpAzGKzWXFmNVgjBbxNLIUPmTQhRIYpefT4BKIp8d0pCkthC8pKojbnccJ7R5WaNu8dx+MpefwYbGEXoO5SZuNjpKnrJY1FWF2y3go7y2Gs5bPPPuMnP/0pTV0LuyFd69qI1GW327Hf7/nqqy/5+uuvmeeZq+trnJdYTe89c5CUKZEIiT9CBpEuQdIcI77dbkUWGSPDaWAYe4xWVGntuwRXi8KKjMyITNGl5lhMLmWfKcsqSbY6/tv/5//97Gf1D/gotOIvf/Y5r15u0UpAY5PiqEHeh9aacRiY50GunRBo6wrvHI8PD+wPR8Y5sdcAFFxfX/PJp5/y2WefcXV1zTTOfPvNN/z1X/8VQ9cxDQIWVYWlLqCui7T+thSFxbtA3bZ8e/fA9Ys3eOC7775jGsV0NOLxbiIqxfWL13z+4z/l9uVr0flruX/LSuJ/vRdAWu4Ln6QhkXGSNaOsKqwVsGsch2VNM0ZTFuWSxJHvBaUuk6gSu1OpBcTO64kyOgHayfw81Qh93y+1wZlFmP5u2adlwDK7eYlIVgtwopfjnNmLmWHZtE36ncS08GdvKJEdPh/mgNRB+T7W2iRzd4XzUtuEGBinGVSSrkSSRCwuQMQyUIq5VkpM3ZjYUTED9yklJ9U7dd2gQAyWjRZ5ZBAzQ2MMddVwNmJ24tcCCwsuD7tsUaDtOWGmaRoiYjKKB2MKkfAaSS2L6V7TRmowrTRvP3lL33V89/33KKXY73fCBAUkrc+dWUNa01YiS6nqOu29iDm7Fk+t4OX9jePI4XBYvOTEGNtxPB7YPz1yPBwAYYDFqNheXdGsWokpTgEBxhi2221ighS8ef0GbTSvXr5ivV6TAxK01tz9+t/y/S//DaQ99vl+mJo6LUbtKjM04iVT+uPT7NwE5sdH2yI5WB8BQfJg5eJXPwBBnn///IQfNok//G564QVAUXwcjMivd+7cP2R2Pn9cJurAh8dEQB95rfjsGObXuGCmX6A/+nJghUA76c38jlf68F1dPuH5L84Gq/kXnz/vDyGF51/lo6LIfc8Pj0nMSNcfCblEwsX7+PBdfPA+fy88kCXN/OB3MwlgOTIxn7cE6v7gtF6wWpRaEnE+fO+5X8zr8cf6+w/vgWef5PdcO5ffvwTULxDiZ9BXPP/6xeOHA3MS6JrfxcK8ujj2HwIm/5sEQS6f7/Kl29ryT//jf8Tnn35GoY3okUJIjIkCrXIsK4mGGZmnRPFWQAzMY8e791/z+Pie03EvGmWladsVD0+POCJVU/P2k7f86Ec/5vbmpZjfxcj+ccff/I//A77bs16v0Ehsr1JCh/WBJU4MJUadq6trXr59S1G1RNXiY0FR1ty+ekXdNrgI7+8e+e7776VwD3NaLMXnAAyFMlS2pFIaHwO6sDgic/DSYCiWRA6hwMvGmBkHWoE1ZwaHNCXiHKxUFFf2tElra6V4SZvr83N6Tm+5NPjLXh55ovBcpiALxuUmk5utPLk2xorhWiqo8/RzHKdlciUxf+USL2utpU2ykf1+D8iEriwqVJRJbZfo9XXTcPvqhST0pGsmAydn462zN0TTNoypmbHpfNbNimEYkk5+BCXmj8YYClvg51kam2QqWpU1VUpYyQyTPAXLrzfPs9CajUyRvBfKuUKJnKUsUVoxTkO6zosUbzzjnTQTL168WMxwM8NlmieRak1S2IlvQiB6B9Gzals0onXvu45f/PW/x4fAqzdv+ZOf/ITNZsv9gwAvh8ORw0Gm9m3bUtcN6/WG25vbhe7q/PlYKa0ZxlF02VoxTzNlVVHV1QIyjeNIa0c2pTRVKkpDbLWmtMUCauYCdpwmptlLYxrPCPR5iiiO8yhDRBI3jIoXi+u52Ljc2PKE7lLGla/tS2Ak/500t2HZIEMUg9ZceBZFAcmXJl//WZaV/xO/jJLiwkfEx0DX9wkEsfgoCUHOSTxhlrmQVwSjF418XiO993QpMWJ53zH7pIghpU3xgnkqIEBkidHnKGZlDB6WcxVjWFhJMskMC4shH7+yFHr2OAwQJOXBh5mitBilGPpRAMN2hUbj3YybHMfDHmsNn3zySZJkzRxPJ25uXnDsOtwsBbhWitWqZRxGHh4fFpPofI7qpuZ4OnA6HYEkNVFQJLNHrSXFpOt7TAIXqrJevGhs8igZx5FAoG4k8tV5J6breYy8XA9i5JjZW0pJjGhV1cn3RiKkc/ylJF2Rznky/UteDFl7L9TylQAZMSzr3KWcy1gxPDTG0J1ODP2wrJl5bYoR0Jqmqbm+vuH29naRASolUZTv3r1bAMz9fkcInvV6lZ6/RinNd999z7E7oROoVhRWpGoX62SWKYmpsjCAZiex8dK8etZNS1XXYkiLXIdiwpjBeyWaFLKMIrBanX1hYhQ5yn/3//qvmS9YT/9QD6PgZ5+/5vZ2LSBdlL0yA7ioZEWYKuMM+kqcbuT+7j3z7DkNg0SkVyXz7Oi6iZcvbvnpT3/KZ59/QVkL4+5p98jf/PyvKHRkXTeUKLybUEDXHTmdTjRNldYl6J1D2ZqoLTe3t2htaOoVUWn2xwPOB6q65vXrT1BFhTKGyTuR2JHiq3UhTWmIeCdeL0oZJifsJZMkPkqL/DGDXJlRITKmvHaeU3tyBK3cEfoZow0lQN7CcHMzSgkIJ9fR2RsnN455LbsEpvO6lMGzDMbmiMaQfjebZsqQJi4yzg/ZFrmWyM+d1+78t9n0OAQBNY21GCseQTFKwsKUDHszadUlxmJaQNM6nyWPGSw8e1B57zH6zHw9s19jirV/zu4zWiR1WQ6jtV6kfQuwn45jXpfFt0rWeqsN3p33JBk8zUujvlqtaJom+aHtmVKa3TxNSRIm5qiRxAhSSmK3EYDTWImLXq1W2CKZJ4bA0KfIY1LCmhIgPCfJTdO41PeZqdQ2K6mfNRgt3kcC4BdLMlXXS7qUGNEX57UiwO3tCxq/Y//NLy6u2SxNVeemLoMjH4zyz/u9/uHEf3lcdPQfPv7OIMgHj2d9/OXgJf6AhQDPAYWlyf7geeVfl/4dYfldlYY6z2CCy6fMAQrwg+c9Hwa59j7WDuce4nf1w+rit59jBD/4yRnD+fChLg7t5Z/9ntf7fd+Rx0X4wweP+Mw4V96UUh++sbSWqWd2qD94/8+vkt8FgmSQio+8pzPscAZD/vhHTEM7xfNe+499XL6XRUFAXEDqBRKLkSH5b8remlNAsyzbU1Xl8h5UOO8B+T2GD17v/Bzpc0ckAviS0aJz3PEH8u2L4/R/+q/+sBzm75wO87HHh3T1P+bxsd9TSjGNI8FYjA4XB3FOH5IFwRI6uBKzN0BHjalrrq+vOZ2e0Js1hTHM48Q8DZTWYIi4ceD9999TaIObJjabLW3Tslo3/Plf/iVf/u0v2d3dE5zDGo3SUBQl6+0GFLTtimEc8N4x7B44WsOLt59AkTSd5Qqj00lOEoarqyvRrncnUAFt5OdVUVMozdifGKZcZE0Sa+kczkei1iKxSck5MUhefC4q1QdUT2M0tirE9JJICDPzNC+LVSTlzadCZ5mYpKbubEj1vAnLRcVlNF9G6MuyPBsepuI/xpgmGiOZgpd14UVRLa/R90O6kMVEcb1eLyZdDw8PS0HmOinAX9zcUllpwDxw2O0Y+56qacQhOx3z7GehlFpAihijbLRdt8hZ8iN/RqMNc2qANpsNbhYGz9N+ByHSnXrKsmTdirt6dl7Pk93ctHjvCDEQ/YwKmk3bUhYlZVFJzFyKVtYoDin+uO97pnHEpwZrv99T1xXEsHiYKKXQaJqqTPpyR12WVKVlu1nh5omnp0eOKTLv7edfkL0bsr+Im2emcUQpw4vbW3HKd06mUrPjq6++WiZvV1ebxUA2RxNPs0wpQwhob7BelpHMiFkVJUU4Co01grUaHSNaRUCmXpLrHimMwpiC2UemVKSHEGRSl6jSIQRpwLx42dSl5TxpPLMwpOhkuRcu74t87M6spzNw+OH6dfnvfA3lKVt+f5nef/mcGSzsp/5M/daa3WGfJvUCVIQA3nl2+x0uPVdVV2hjqHS1MGQuoxdDWvzypqMuPlfXnbC2IKJo2pa2bYX9lQCnuk4RrNYypXv38nNkv4cQwgKYpoOBGwUUjEEcyicvelQ/T2ijuNleLVTJ7tRhjcFYRbtqgMA09bx88ZLtWiIS123NatXy9PRId+oIEYah53Q6CeNjGNOxFfBi6Dsx70tpU5lZkYtfkZIIiyCzbUIYsMYuQE42A/aERN1XkBqjy+v2rEOPqKSPlecVJkxRVgvQmJmKwzBIOpcXSUVta6RPEMZK302MQ8F+v0NpQ5EAq2kcZWNPSQl+cst5VvmzWIM2sF63RLxMX41Ejj7c3/H0+Lis/7K2SjMwjgOn0wmJ71Z0/YkYxbzRFhVtW1PVFUOKds5R5WVZLMfBapPWHjFH9fPE5upKoo1nkcYJYCzSzbIocW5OJrfCsmjallOK0B2GgbIsubu7W+LMrbWcDgdyWtA/9GPV1rStTKBjTKB8ihaUMYAGHbGF+CzFKABIbv7LquX7d98xZ2owkdWqlVSew47v331HUZe0qw3GllRNxZ//xc94vPse3EyhIm1bC7vKdcyjZjidhNFgFNuqpZ8cUUU2q5ZmtUZrYUWut2uUSXHdIS7XWI7StIlVWBgje9bs0IVG6xUhgppmQpT6KEaLMYqQjIqzYW+WU2WgIINuGZQwWqMVeB+frZvi/ZWGMVEYMj6EhZ0IaU6aZKxKn5O08j0ntZ2AygYBa1DZr0RYqwEWxuaY2FWZcTIMPYfDnrIUPyGRoUlteLn2k96HlBvid6J1kY5LwAfxzQkh4MKZXo5KzJjEFppS5HNuvEPM7Nscl64wRqV7MJsnxwQupIcKC0sWzu9T/Ff8cuxQwlorUqx3TlLJ63WMkZAGYUQxeyXGpPRSlNpIal9VCcA8jhyORxl+GWEuamuFoWcKjAlUlawxViuC9wKS2GJJY5xGRdtccTwe0clPxNoieX7Imtq267RWnk38x3FMQGrkcDrhvXjINPUq+aJJouNinEpckizKsqCuK4piy+PjYz4zF8DSeX9eDHdVvsbOYbALrT+fBuJSC18+cm8hXz8HUBYAYulsf0dTG5/96/lzqIvW92IIs8wTL3qiywHN88cHjIgPfpRZ3gnPTzG18sP4wXuR9jh71nykg1+eNDfpZ0ZCPgZaneW7ef1YpBq56U1v5oyBXL7WeZj6uz6WtA8Xf/O7fo/L45XBqt/3ux97wg/BrN/zgsv5yzLr5+9Dxd9xHfzgcUZ5BAj9wQvJpad+WK/m11rWu98B6AloezZW/ejffvD95fOlzxYXIkZgGEaGXvqcd+/fE0Lgzes3InezdiEoyDUhRAOjwDnPPM1S01kZLIYY0c+igjP4pNM9met4YR3qXAenBV/pHKRyebT/eNDnHwQEgfNN/TGjwj/mEVM2sc8az5ReIhTdCW0k9idTJmOcGUefplAKP3u0kljRqqoJbqKuCoKbGeeJwhp0OilunPj+m2+wSuHGEbfZUNqCtq356c9+xt32it/++rdygKxQ9x8fdmR6pNJyAorSMBz33H8TKNYbTN1iLez2gdXVC8bJczyNrJoVbdWio2KaBtpWpmXBTxwOj+weH5jHCW01RVWhCksAtC1YtVdst7Xo448nhmF8hvyH4NGc6f3Z/E9ricGbxjFdUkLpjUazWq+FMp8KhjOl8DKdQ5atokoUzyg08txoZGpzvjEzm8F5LxFOqdmCs7t5pqXKJCbLHmRxjjHS9z1VVUlUZaKK54306uqKqqyYh5F3799xe3VNDB6rhemSTb+qumY/C3skJ7lcMkO0NYtZaJbDZLPJXJzMziXn+InT8UTwnu1mS50nVTFilF5o58MwLOaEZVny+vVrYgw8PN4xjgPzNDCNPdFHmrpdip9hmvDRc0gmhjZRTY2WCFc3j3RuYhx6tErSkLIghsDYHen7nsJaPvvTn9LUFYfDjq+//prj8bhM+3OzMSX2y/v37xeZWVlUTLZgtaTTVOgLE9txGPi2Oyy0cW3MQh2e0rnsum6RPi1Sqo0m9A8JFCwxCoxWHPoT9/fvCTHQlDVts6JttwQ0aHGotkbjcoRqkOvL+4i1YFKBHtzZ3CmDV7nZLYqcQnAh2bqYQj6fHp0LqnEc0z21QPJCLU7XtjjyuyTLUsuk7vJ9AEzDyDiMS+PdDz2zdzRNI5NAH1itNqKfjg3d0Evhf1Ec53SVAPTL+8rTWfl8bdvKRC6xBFQCS2fnGOeJyclrFonCbouCeZoYEssiF+MumQbmybxN7KcYwqKNN1qi9ubZL1Tv2c2YqLm/FxPUsqzph5GyKFi1rdwTRnPcH+iOJ6y1dF3P/fs7YaQEn/xrFDc3N1xdXaWm+Mhq1TJPMrlEg/Nu8czJQDnBSzzrZsPxeOBwOlFqAfG88yn+WGI+ATEGI4BJzJlYEF1cJtCZ+RVjXO6XzEjJE17vZpQy9OEkhqZKUxWGYEUz72ZH1x0lityIVKZKk5F5dqB0kkmUtFWFD06KAjcv6wdK4krLyiaT7zHR59XZcNl7piBNIV5W93kapQGMebIOTVtxlovJtdWdDjgv11sEykIYLJlBlBkCOkJTizn1arWS+yGBlHL9bSF5Ukwx4NyUpt8sBtJKKcrqLC9qmmY5zpktdzweF5nF3+exGE5e1B+fffKWP/3Zn0iEsipTKgicC6ZLmZxJlP5ADB5ioKpWBCKn05HTqRMgwXuur7eM08S3337N5B2ff/Ejrq5vqfWKzfYWpS3fffMb5r5Dx4ix0phN48yrlzcUZUXTttiy5tj3nIaRujKomBPARJIgAFdAFRq0Yo7gfMTYJH/QRua9IWA1SW5rE5BnmZxjGHuc89hC/G+UEhPbfF3nJldYFqlSSIlDRgvgohQiw1EqyXCH1BPqxBzL0dHJ1Jkz1T4SlxQapT6MpBTGoqzPXjwvtMbHKAbakeRPNeGc7NXGiNlmP/SYlLQWjp7VepPkEXZZ7wU0NWgjKTD5e+PkkkHseXBU1GI2LYCHQzlF1s7HVF+J34paAKNsHvvhXhOCDJ8W5q7K6TiaEEXaG5JsJa/tl4bqSimGMRKTt1KuuUzyZLJFgdIWpTTr9YaqrnGTY7Pd8snbTzgej5y6jtPxxPF0kqFhYqQN44CbZ0j+O9452nZFXRWJCRwoiwJr5Niejge5VqaZ99+/o6oFJD0e9tiyoGkbpm5mnCaJwDWW9WYtvjrHwzJ80tqgTYEPPq0BwhaR83mWwokvgfh+9d0p1W0nYozs9k/04yPNBavzdDotBq2r1YrNZvPBRPnM1Pyw2ftYD6x+x9fnJveDZnZphOP5xzF/jue/+xwUuHyK3wV4fOR7Sl7zGSliQW5Y9r3z358bS/W8K8/Qw+99T9JAK0mZMglgUunn8fy32dRfZaCIc231g+P+kePyOwGQ5fc//v4+dkyXr+MP0ITf+RnTH6T677wvxPT9y/eafx4IKESWcQ6IeA6oZOPmmCf4Fz+LIQpjNGb5oElYWPZiiosfW4Sl5vDO8fD4mKTGuUaV383Jj/m9XoLXSyrYskarhe1sjEbb8/EKKQ54OUYqMrtJ1ggVKa2B0hKD4bO3b8kSVzIzkLAk+ZVlubCavHMM/SAAuxGQQ5htcQHPlJLADZWYexnUK6xlDiITLqxFGSN9eFGJ3PqiLi+MRv+ea+ry8Q8Cglwe7P+5j2Gc+dVvvsL8CRRaU9jywgckoo1KOvTc0JxjJo3S4jKthMq82WwgOE6HJ4rC4JzCeclzl4JIMw4Tv/ybX/Hy1S0vbm+5ubnBbjRFWfLm7SeUVcOvf/UbnvY7udCDZxzErLQoxLB1u235/vgOpd5jyoKrV68YX5x40/wMwozVWk4GUmSs6pLNqiDiCFahVY0bjmw3G1w5MXkxzjQxELVhtz9yd/dI267T5miEKTKLB4dJ+ljnn0dNFrZEm6TniwGtxLxMm3STeRaqqVIKN3vZtHWm2svFL84silWzkoi6IACVMXIzWitylmmaGIcR58XDIlMhV+tVioOzi5lYjlvNxXFRVClhJCwgglKKp6cnfPBsUoPeNu1iktquWlwqRKyViY2xUszbJPmpyhKX0ia6vqOsKuqqYrfbMU4yCTudTtiiRGmzuKEXRcHsxAiwLGRKPo4jTgkUKlGHlnbVsN6s6LoTfT/w+PhEjkOdk2nazc01d/d3xBCwWsvzTCPzPDKOwqYY3Uzwjugd0xRkY4qCehZWjBwpLEVhZMJ76Ag+0DQtn7x5xWq1oioLxrGn77ul6MiJOCF4pik55TvHer2+KExsmpy7xbzxeDgREdZOTKBGXVc0TZ38PzxFWdFaWaDyZKqqKmGJTDNd98RKh+TSLgv4MIx8/+4b3r+/w2jL7c0Njw87tLlje33Ly1evZOMxBhOlAJudIwTHOM/SqMVz3CywTB4yQ0CnJgak6MxO/S4lo+QNIPjAHCYiLE3fnPTJ+RoEtfi8zGnCHRXL9ZkL1/y6fT9wPB4FWIyK/tTxtNtjC0PTyj1QlBWboqRpVgJUGkOVjBThbBittKasayAyzzqlXUlagU6bRwhi6OiCpEgYa6hXa/lMyRBaG0tUohsdLyIms5Qjg0UZFMm0Se/mRW+9Wq9ZNQ3ROZSKHHYisdjvnyCIyeYwDBz3B5mme8/j/QNaK05Dz3q1Sv5PE4+PD3gfpJkpSh4fn0QuFyPv379fjoEtC2FepKI9yyiyrGaeE8h66piDGAuWVYO1hkGNlEqzWq/IEhFZE2CYRryf5TmmCZskln0vTJRIoK4rQvSgRAM/DuJRUlVVAkbmNPXKU3FJK9OqJJRyD+WhWRTaDnGh8geOxz2HfaQoJHbUaENUkXHs8F7uo4jI3OZ5whaWaRrlXCdmVWYDkfcAVLqGst+LkFYjyfMGeZ0QJfo7F9EhRvw8E5SnqhvxopldmuDrs4woBKYk78vR2Ku2ZbveSIGTrqW+73l4eOBwOJyvqySBLAqRCIzDgFGKtq7ph0FMx9UP56p/l4esUfXS0OfHMaVslYVIRryTqO6z4bfBaJtePy7nVSJmpZnebK+4vXnBPE2ybve9eCa0ArDfvfte6pdhxHz6KdvtFavVls9/9Gd8+9Wv+epv/4a6ULxcb3n16paqlISWp6cnIoq2XTF3Rx6/85Rty3pzzdwHiqYVo/VphBDwUYEpMFWNm0ciwqCzVvx0QC2MLG0MhCn5QmkxV1dno9B5mlLSWJXWrn7Zz+u6SolAJ5wTMMhai4oiDwypYM2eTXmdcX6+WE8uJvS5QE9rsg5nADqD0VkWdq7tRDKYdM7CyE33avaDEr8NaUic94wpnllrv9w/LnlvZO+bDFAwyN4SEcNBTyTOYgQevUrA4XO5jjBQzl5n4sf2nEk7OycmzTEyu4BW6Z4zKQ63LAghMrppidAtC5HjWJWnvZzNaUNgTEbnGpGA9z6g9chmc0VVF2gilTVcbzZcX91we3PFZrNifzjyzgd0Ou4qSkLYerVh1TboKHIxnRojpRT90BG8AyJVXQkzxBpiiBStYb/fcdg9cf/ue6ZxEGn5559hlKUpa8Lk6MPE6XBczOyVVmKinlicRVEyjwNDGp7IwZN1S8yWC2Yn/kLbzVVix53Xq60aaEu13PcA1hi2SRqoU8+QvbYWyccf0RD9USDEZUP8IdiSf6zO4Msfev7zz5avfsf7vfz+BWMk0Uti/poP5RsXAMIzLOEMAOiPvNfnrxwhBvEbimfjY50YR8L0uZCYqEvvhh8CFj/87Jef7cNjdsnOOYMuH4IeHweYft9nuvifeDYEhbNJ6Pk9nZ9K9vQ0LE7/nqeJYRD5avZbq+uSuqoySiX1Qn7f6foQlpuXoYliiWnP9wNEYcFEICaWaKrJr7f53pDemBiTn49Z1kVIKWj52Adh7U3TxKnrKKuSu/t7fvPllwKCKEVhTUoklSG50ckU2ge0EV/IqixEDukDbnacTmJN0A+DWDdoka1nAKRtGohnw2pixM0TOW0wREcgMXbrmqIUCZYGyJ5MRFx0KAWlVXg/4eeA857J9Kl2PlszmFQj/zGPfzAmyIePfKFmZDtTL/OGJ5vI+RFjpOsG+rGnaFuM1Yvz/KI9i+kkJw2gLvVyQxqlKYsCYxXO97Srmi9/I82uVorjqcN5R6HF8T66wBRmiWlMN5G1lnWzRinD1fUNf/mP13z15Zd8/fVXPO07fIgYYK0VV9uWGAOl1YKqec+4P3DYHXE+8vZHsL1+TVta9vtD8oDwuHlERUeInsHNMrGrG3RVMydjUazBR1it1hLBmxz53ewJXlM2hchifMCHGblLAt7PLPGjQaQ7Wsv0RM6DxpoCN3mMPk/RBd0NKJ3RP0NOBIEZY8RAUSUUcBhkCumcmF9aK1pprQ1GGQpbURTi+C70OIMyhtpamraVRkEpUJphmkWeoxXXV1fUdU3TrKnrmi7dqIfDEe/8YqhbFaUAYmmBGcaRfhyp6oqqKnGzo+96TkPPMEoR0bQtXd8LeGJ0uo6UFEecneAPhwN1XVPagiExU+qrCjdNTNNI15+Y55nDoeX29oayKolErsIGP4ueuLSGzXqF8xI91bYN0zhhtaWwFq0jRM8wDhQq0jQlSlV0XZ+iXB3GwDx1HA+juLTHitPhINr0ssLNM97NPD7cJaBj5Hg8CfWMLPeQwspak6Z/cGlyGjjTR5umWpg5Wivatk2+CKInr+qS4+kg0+dZ4aezX09ZVMSgKGyFdxGLoS4Ksnu+dzPWSkpEUZRYU7BabQgenIsoYzgcTuIlkDY9Y/QC/BXp2iWGpWDL7J4PEwfyICJ4z+wnAUBCXIwbczJLPw4YI/TwpmnS502Ie4rMjWmzslrYVWah3p417/nfxhhWK5FPWRto2pUcm0qo1zlBJhftRVVSt03yfPGpII9LgR2QyWBVlxRVkc5VYts5z2no5flT0yLUcUkVcnOiuM4z0aeUFyuUeR/CkqRSpoQQrVjiNsvcyOBRWlPVJfM8cjocuFqtePPqJeM0Utcy3d+strx//57Hh6+4v3svBrkxpnMWOR6eFvZACJGirJjdxDBPi8xFGi4B6OZ55vHpifV6xfZqSz3XHA9HxnHm9nbDixcCqqCg7zq5jmOkqoUtVyX2wuFwlOOXGAlzSnBRymCNQlcGgk8bpUJpKIsigZiT+KkYu5gWZ6ZQ2+bEpxQ5HhxdNy4brtaa4DzEuDTaCkX0AaWF6TT5iWE4M3KWqU+MDClOd/EEQIr8om6YEwDkvDsX+YgfSZuYeS54ggqEKKyysqySuWBKEnFzMoWM6CDNQlVVlAnkIYRniVr5PquqCuddSi2TQurbb7+F9P7qul68abx3DEO/VLAZOInJM8fNjt3TTqZKMf79EBBI4NLxB9+/v7/nu+82bP/szyhKQxF1Sp+QgrQwcu1nCVJhE3tIMDBhxemCpmnZbjbMk6E3PdM0UFhNaQ0uBB7u3xGCo64KtDK0TUtdrvjxT/4j6rLgq7/9BftjR1Uadk+jeL+kdJD90wOVMfjhwNPhgf3jO9r1NW+/+BMUgce77zDGUNVr6rag0AqvwcdI1x+wVqUiOBvJK2Kcqayi0AZjCibnUaYgKKEl+zgzjRO7u7uFaQCpWU5NVmZ1FEUh0lPv6foTq7pGAUPfU9cCooz9iX4Qdkg/DDRNw2q1OrPtlBHwIQRhgapzipXWemEtZt+OhVHh5TrTORZUC/Xc+1nuWaMZxwGhTZ/X5RACfdcnE1Ivcrd8baYhRVGWWMBpxCcj/SxE0rlhWY8vWbb5+/m95/1HJCYGbc8R50uTvrBavaTD+TxV1ygj9WlUpJjhxI4NwnC2Jpt3yz4OMhGV+mBg0zSU2vDmxUua9YrDYS97llLc3tzg1lu6fqCumiVxqq4qVq2ASPM0sj/sOR6PHI97+v6U5IZyP0iaXsXQDwyD1Cfr7RWKLVVdM/UTWntWt2tsMrX1MTC5eTk+bd0sTR+p1p6mMZ27xKw1Vti33ZDWHLOAXWVRpZrrQLkVIFAWP8XVZpO+vJCX8CGzIHsqnEGD39Ug/52Aix988XHWwvlnv+9ZnhteXn7//P9nEODjz/IxWcMPvy/HIMMmfwAASQVVVYv3W67PQt47Y/YNunxfP3y+zDz4+OOSrfLhzzQxyj02zzPfffcdP/7xj5fzfXkvLs8WL4/Z88+SfybBEcLoFkn1Weqmkg/K+QnPKH0eLGfTXVTEFprWNCjO/a6xKZY4nuPs4TyQI+33ZSl10TD0aahYLRJXldQQWhlickCV0xkxdWI7J1Y3UfzLvJceJMbkmaYS0z4kD7nkmfT09CDeV0rx+uZKjJudY/YzfhroTzNVKex4hU9D9ixLlLhdozRNXaUEr56H3YFpnikKK2BkjLx+/ZK2qXGz4+lpJ0Nr59KgxHN1taUohQEXrOXp4ZGqrNFKc7W9Ev+i5DlF8ByO+2TWLaCu1GuOq6utpAIiQQwh2Tb8MY9/UBAkSwpub2+p65rNZo0xmsPhyG63T/TBSF3XHI/HpPM7P/bHjq+//o7qT37E1aYWqiVn/X7ekORCNWdEUimKxDZAIdrMGHn58jXv37/DR0VZefzg8cGhdZ0QKEHaHp+eFnDmzau3XG2vhalQ1fxH/+gfcXVzwy9+/nP6rpOoQ8S/oK4tRsj8xKjojyfmoLhuN0zHA/dTxMeCsirRRWAeerrjnrnvF9RQDA1lQpH9TfppAqVF6qOlcBEvB4m6nIdJdLNabjjxwIgpCq2irqXhbdscFdlRFE26aQSFCyFRoLRmdj6htzJNrEoxq9JGJigxRIZxwGq9FN8x0a7HcaKwXgCRZHxZWL0UIoOeKJLJ1SIrcBNGF8SoMKbEeTHPOXY9PoJLxmVT8OgQUNbgo6cwcrOcTidiiFzfXtM2LfF4xFQlPogCVxeW4/EACopKGCHHvhN/lvWamIpmKTJsWjTiMvU8nU4yGUlO90rB2HeLoZhzjqE7UReW9ZvXPD0c2T0+iWlrXfPVl79Ba43zYmJKjIzTmGKZYbNuOZ2OdL28TlXJ/aC1StGAMwpF1/d45ygKWWjOk9lIVdWLfAiEsp0X4OxXkXW5uUkXg7QsSQpLHN3p1CUHfrk3rbU8PT0iW4Us1FnzudlsOBxPrLcbYow8Pj5KgRvPE77Xa0Od9MzGGlyMzDGyWV1RV6s0dSzRqiAEWcykyEyAafLfCD5IJGTwywQwRrBpqjeO4zOz3hyr6BI193LzyVryvDCuVyuJv040zmkaWW/WMvGOAW0tPgQxTuY8oZMNM/5g05V4wILgZfKZN1xtRd+9TDxDILizOeslNTEXjT74hQ0To8Rh5sI8hCCUZudxoUrXmSMk4ENbmYRFOPtOmHNqDkGllB5o6pJ21UpBk5KOmkqMULMfiR96jocDfpp4Gnv8tOb9+/echh6A4/HE4+OjxIPWNSrmJkYTo1tSwEKaQvgoZo/CyPM4L9e61iygCEl+NUzj+X0k4Hyz2XD74hZtzCJFG8aR2YlMS1vD5voKHVm8hSSqlSUa1HknE47CLl438zxTVZaqknQDSSMQE9Qc/ygeJT0heFxiDVitcRdUeKUUKpwLnxBy0k6RpuIarRPDaHYLEymzs/Lg4DLiOT+XS4bJbdXiY54aRWxiNuTrPydtZP+GKQG4+foFxAwSaTw3mw1XVzc87XbsdrvlXs/nLTeetpDJ83A60gVh9CxyyGTcW5Ulxogvkw9BDMzTNeDmmdubG1btCjfPbLdbpnHk3Ze/JEzPC5b8/i89qv4uDwUorWjaiq4/JnmHTC61UmJQrA0mFbwmrc1aG0yhFwaPNYbr6xuO+0esbjBa0w8D49hjU0z16GYeH+8FgAie9WbLql1RFgWvXr2itoavf/0rnnY7VAwM80wIkZe3N+JZEhxKS+E3xUB/3PP4/bdsttd88eY1USlc1CKf6o94pcAUVJUhhlnkKaTjFMSkdvfwQPAzdb0GU7DaXOGxjC7S9yM50PM8oCKtk2YB/rLEJQRPiA6twLlpYQtls2KlFEZHTl2HLSxlIRKHGHNzKgMRYNlTbSlrXZZ5aC3SXgG/BfEIQVgJSkv903e9tIJKUxTlwphwbhZmiFLCxvCeoixobCOsxqpMfk6TfBbCImnJrE1ZJw1lMm0PKT1Jab28x8smTmtJYtJW6NraGmxKMMkgZt4bfGJpgQCWRWr4s6eVUQnIIkoKjwpgJL5Z2KiBuhSmlpvlM592OwCM97hRJNZlJVPXeRZz6qooaWtJkOu6jtV6lRiRkb47EINjnke6005q22lERaGzx+hRXnE6CmvVzU7YjNZSrtbo1KBlgNO5mckHQppmrzebBH6b5XjkQShpr8iAUQZotbFLfWmMEf+9YeA0CzPx1auXrOyEUiGBGr9HcpLXgR901XHBE+Lv/b0MAKjnf/vs2fP3LlkJP3ia3/v9Z6yOj/7S+XtZivG7f5cPfvcyhfFcr1wyKv7Y53vOvABtLJgPWRzPj8XyXn7vMy/PenEuLkMbRBqYHU0+efuWbPmagRdhexbYQsDWy2tApf/JgIRCgFQZbslzYyBafb4uovzOAnz8ANs5S/4iisIUgEpBFYaisMkf8ey3JcPJs1wzJN+3XKMrpRY2iU3aGptklCqmJMIElIboBaxVMIwn7u/vub25om5q+k7W3aqsIWqGeWAYJp6enpjGGaMtV1dXlDpACFRFQbWuCQSRyeoLoFchQxFjMLYiG+3mezmmzyJElQ1vXlwzObGuUClVEhR+GhiHAT8P1GWLrixGr1CwsBGnSWSR66ZmHCa0LdAxEJ2nPx6IURgzY3din3qpoLINgmH/tGe9ari9uQXATyPD+MeZrf+DgSB5CqS16MT/yT/5T/jzP/8zuu7Eu/ffczw+LVKBrjsyDNMPniOGwDg6bFlSVGVqUgtsRsQWAETkGJc3NjG71ipiNERd0qyuuHae/e6JEIOc6MnR9T3WVlRNQ93UHA4HHp52oA1ltQMjyRxSSBk++fxTitLy61/9iv3TjnHsORwHfLSs21pQ+yibWHcaeXr3yOrKQ9mBNhjfAi2h3zF3B1RUCelGjP/SBFIrwzjPor81iY4fpdkbx0kmVlExj2OaVGq5iOtqKV4AsmHMNPULK2Mce2IMKWkjNfbTkKKEPUVhaFei2SYq+qGnqCowQnsra7kJrJLpaG58ZfpkE4gUcS6niUjxZIuCyZ1jWIXJYLFGjPwk7k9SE1yEbpqIxjB0Yi6Z0zPGeaYfR1atJBKcjkeOhyP7RL2WxjDQti2r1ZrNtZhp+XzDxkjX9UyjS0i2LGJdN2CtWXTsyyIVAtfX12gtaQxWw+kkk9F5nIhBioxvv/qacZyoioLgHf3pwP39A30vjdeQJvY52te7ka+8Q2tFWZQcd1Kgl1VJUzdC73JnI9uisJLukEANMbmUOL6+7xfgBli03Pk+zFO+3EBmoCcXJcIeCOnzxhSvm6UyMgHIpqi7VHSVQT6TTSaK1tokTzhP4WI0+GRG6Aa3vPeyMsvkPbNSnA/igwO41MAppVDOMc4ir7JleXFtSzMrgJpMU4/H05J6sHt6YhqHZTKd14jsNr+kxHBuODNy770DJAFFp/8PUUz1dJSpQSTgcYsGNKTPDAproUzrVogR71yaXp5Nh52TKNRh6Be6dqZqZwpkZsEspqxWrv/8HFk/Xhay+U6JjqmNxiKgzpBo1HJO/Nnrw0c8spb2KlIXYgB6PBzQQJgG/DyjohjUtXVDmAeOO/FEOh0PAryWFTk9pW0bNCwFu4A3YqCaJ6jjOBFQ2CIZg04SYa3IhpWG0+lISAwKX9VEhGWQJTe73Y5xHNlst6xTehTI5LZO8ZmHw4HVapVYWXGRuGmjQbOsQdM4EtGLhLAsy7TOjrI2jhOQgYSCwhVyHK2mLBMjLe15XdfRdd2yzswXSSsZwAlRQMcMdvggAJDzEzFIE17XNT6e//aScZQBQBAtcVrEZMKdjBOzr0hEiqbImeJrkqQgp+aI4aJIYw57kfI1TcsqmZlmCZJcP7Ku+OQzc14nc7ynNLPTPCemSFwA0+vNFhCT55iujevtFTQNX3/9NTGco1ef1QLxub/H3/URETD9cDiyWTcyzUKjokHFbCZbyj0BeC3sgmkcRf+uhXnmvacfZI+OMa/LIgtRSlgOVssQ5v79e0LwvHhxy3q1ZrNZUxcl6+2an/3lX/LdV1/z+P697P3O8fBwwLsnVGocZxdoViV10/J4d8/u8ZHt1VaGJVVNiErA5O011lY8HY9MEU79UcBm5yhtwTRM/Lt/9+9kUHB9Baqg2Wx49fYt2+sXTJP4PhyOR6FhW8U4TSJ7MDW7x0eGaVo04G3botK9HINKhb1c0zEInLJZrxc2ltYaazTzLNda09TLuitRzzORsHiLXUo4IQOCOTkpLolrEBNwWCQDzpjADZ8YjGfGnZj0jlQpBj4IxTXJJbIpKcl3yuBcwA2dAERB0gxMYSUCu7T4cN5flRJWrNJGpEaoiwks5PSJ/BoCFLIwHqxNKTPOURhDzJI1Y9AhLqywEAJGiynr4EVLP80TwUfaqqVdrSiNxaJ49/VX7B/uWW02vH7zlspqYVkSWbcNbVUyzpKQVRSG43Hi/bt3zNMgSV9a0dQV69UtRgvbJHrH7rCXBip5qCll2G43aG0YpomqbVPKjPhERS/DMO8dfd8tQ4oQJL7Z2gJrC8qyWmSmlw220QLmehewdk5yrRPGGI6nyLpVRMMChsnU/pKKIVP2paVeDE4v2AbLV+n/1WU6x3n9uFxL+OBvLr//3Hfjdy5Jv/PxxwAax+NxMZT+2N99TGqS+y3nxFPrd73m5ef/+75X+fHF+5I3d3HU4/m/i4P9TMJxgT3lfU/WKUuRgLOMuWitaJuafI6VIqVfnt/vmXkh50qpszwlhnlp9FHpnlXJW0Wl50z77vlCiMkOJSYmjYYoPhTeTcwxEBEWly4EXBeZXEATKGyBI6CxxCh7fAyRaeh5erhfPLTqusIYGUjpdGx9MiR3yUsJhCFz936kKktm59jv96kuF9nfqm1pmhVEjdaB4+GO4GbmeeSQ1mtjhBkvaWLTslaFEKiqEq2qNJCUBCkXJpHopv1f6gqf5JhnKYoMxsWqYrtphAjhIyp97nFIbFNjUEpYu3VVYLTGzR3GWFZNKT6HbkIh6gutoagrNuuG/f7A+/cPRD8Sg1tqtj8WfvsHA0HylDU3Zf/9f///4V/9q3+FczPtqlomMYIOffzNKcRgbxhm6rpN7uQ6aRflhOfC8MMFIMSUPhFTVFo0YCpmr/FBAyZF7WpOR5l6t+u1mHAazel0Yr/fY4wYoR6OB25vb2lXaxSKzfWWn/7sZ/zi53/D/usTwUfiaYYgEp2QNKbOe6L3rMuKadpz6u85vXOsVjdY21B4zxw1s3PMwYueNjiUFr37OM3MwVM1DUpJJKPRmrap02JgWDftspn347Akh0jjlDZTY9DpMxsL1lbkJAWlrDRzTuND1sqGJCeppDmxmmHsGVNBqFALPWkax9QE5ihNnfwT0kQ73bhlWREVrNcyYb+9vRU2xRIR6dFGEVAyRYhCa7cusLraUhYFp+7EME7JnDGy2m7wzlPVFafjSYoX5/FKE3Sknya66T4tThIBmI34rCnJsbxlWYkvR3S0bYOxJhkV5uZMLY3zw/2ReZLrep4m3DSzaRtOTzvu7u4oClnM3DQtyQzWWtqqplQ1SuuUDnTgeNzjQ1xABzc5ykTv1EqzXq+p099kM9PcBGUmTQhnI9q6rvBejAnz7+d7I6dmgNCKsz9CfkgqQ0hNiDjeV2UpGsBkXHk47JnnaTGKLKeZzXrLNE4Mp56u65Pcyi4gi1ttaK5bjocDZVEByV0/mR4L8BYSCGFxfkoFnwBlfZoqj+MoKQiIQWTf9zgvBaqPQu2LgUV3TQxsN2uK25ulAc3HInt9NI1MDGMuinODiYcwU8ZAERUhRwFHMZG0CFU8xIBNS4/oH/UC0EjhPT/bVGafIkVDWGQMIWbmh5hnKiPyHjcJgFUUlqjUAmCFGHGJ2p3lCUpp+i5pIX1Ao8QU1IsmE0TPGRK1WSuoy0J+NxkazkPPw93E6XTkcNhjlMZqwzSMNFUlTVEpng3TNC3G1LLBZBNAaVhIwKexFZv1mrKq2O0eZT2dHbcvK1ByTKpK2F9yXgzjMC7+TvLOI6fTgWz4JcV0gdbC9jl+9y32zi5TFGMML16+4LPPPmPsB/ZPO1xi0WTgS7w+Ar3r0FEAG6s1ZdMu+4r3nrqungFIzuVGTORUhLiAj8YYttstu91uMVeWe22k7xLb7+I/fyFpyu8thMAwD+RI3iJFyWUWRGYwyZ6nl3tfK4VOwKqb51SjRbIRanBR4srT9y+9C2Tdi+cUngSMWq1Ztw3WGI4JCLlkplwaDsuxahY2olKK7XZLae0Sa56NXveHA0VRsG5XrNcrmloSK25ubnn/7vvcOT57yOf96I/+4CMfX+cDX797YLupcWUlDChlsdqmY+VxbliaTp1MmGPQhDAve2nbrqnKAjc37J8eUSpireF06gkErLa0dUtwgfv7B4au5/bmBj87Xr18SWFLikrzyY8+p92s+e2vfsthPzDPKQLWBdqqxBagdMEwjJxOO9q6QHmZFvoY6SbPMHs+/1GgWc/4fiBgWNWtVMgFVEXB4/DIdnvFPDuGyTGMAw9HGcicuoHd4UhZ18JSBEI4F/JDNwAiMVUKovMc5734cMU5DQflnreFoSjFcDVLcK2xwpS9SP2IUaXG2HM6nQBhyOUUt8wQWGKTExPUzdmAVK7Zp909k3Nst8Io9EEi1o/H07IOFFWVWB4CLKIUfpAuKiYgWyuRp8Ug7KoQPaT9dhgEBOm7jv50oGxqXrx+mRiucr+EGIk+4qaR06kDJQktRVFQWtHk++RbtNwHnJvNGOPiAzjNkxguA21T09RiJB6jAJXBOQpVoZWmUIoQKvqup61KdAh8/uknuFkAoof7e775za/p9nu++JM/oSxqAjJJHsaecRqoypJxGLm/e8f9/ffiG5PqnKqsCPPIar0mBMf79+9YbzbUVc3Qj6zaNUpJHWhtQT9NS/rh4XBgnEYeErt7nsfFcDok1pgx4rWVU7XygGCeJ6nHkq9LWTbi66fEn2q9XovvQt8zKkUwBpTChUBRVBc+BBZ92UBnukBiUJx7hktWhF4a5kVEsSw6Z/Aky0KAFNWZfnaJv/weNsTH1qjLxyVzQ9KpzibCSqklevjD/56/3+ePIjFdP5Tv/vARP7LOPv+Gcy754Zgf/PwMouS97eJzpt/MQAQXe1SWaLDwOuQRMiPWn1OGMrbivQCGZ4+f58civ+ZztgsLAyq/7bxGkMxNpRbOXh/n857r7mySHNKaM44jIQqgIcMQ8Z1SGuapJyBpfG7yct0OEtyQo7AfHh7T+huTvM3y+PiU1l1FURg2m7XsY7MANUWSac/zxJgGpYW12KLAT5Ewj4m5BsbKsTG2SJKbiXmWnmueZmySGMrPQsZ7GBNQeX1znaTjEhHupvNANdfMwCKt11qG4DIYk4HOmc0ZFwsE72bmcSR4qT+KskQnvCK6GaWNhAUkUDuDqM5lP05LNnn3XoYqxmg+/fQ1WpvzkOfvkDin4u+6gz78xT+AAGYN6dm0kOX/m0b0xJnEFC60j8+eQyk+/+Q1//Sf/seYyCJ1yRdlpqdl0778b5VeyNpkBOiDmE2mydPd++/pj09Mw54QPIe9NM9lXfP2k7fLpHx/ODAMQi189eo12+2WzdUVq/U6vXOFmxy/+tu/5be/+Q3T6GSjRmETikhU/MmPf8TN7ZZvv/kVqJG2KiltizElum6JzQZlxY179uJWjhGvkhDhcDoRED13VdUURSkTUmMJyR+FCC4tFGUlE5Q8KVQqadNjMk31DhccEYmbA42KWWsqRa62EqM39yf60wEVJQ7Q2JJhchhT4NBoK1Odly9e0HcDIUSm2TMM4u2x2ayWzcAYQz8MS/LFQq1O005pnDXaWExRyKTLOdbrFUUhkgiRZxTc399Lk7vdYq1l/7Rjt9uLbr+pl6mLtVZMYdMFOCXWjDA6GgpbUCTATqZTsjA75zidTomu686ocEJp52linlKsqLG4aSQ4vzRA1kgDmSdXSqlEU2aZgvngOBzOyTWZ1TFNk0yDtF7kOVkqdWZbzHKPhQhaJ9YAy3EFibjM0aKZziyskTMD47LgnKaRcZpkAVood9mZWkyEi8IKYDfPS1ywNkao8VrobqumFfNOJfTY29LxycamzTwye1l4T6Okk9gUz+ecZ5pF3lMUFq0lEjKEsCwgWmuqpk5SNyl8pFmOy6akjaIqKjFSTVNem4wYs5lyNr/K61S+BjN4aHWgNoHQPRKHgbqoKNuaoZ9RRQVVzZyu9WgL5gjeK0TdrpdrZZ7PRqMhJvlLCKAk3UYMm/zyGY0x8hzxwngQFlM9n35P2BTjhUmfTU2xxzthTPn0Os55lNJpc9WJqSaeF3mK7dzMPHkKK3Ka7nTCTTNlYTHxPEkRQFUKwHkemGdHU68AI/evOoNJ+XrdJCp03/dpwu1YtWs22y1X19fS+A9DmvCKn00Gcr1zNG0tMrcIx4PI1kwCArQyMm3QinGck/mWpBnc3N6ilOLVq1dkmrxcWwVN09B1p4VNMo5jMussE0tCLYbOAk6Jz9EwDGKqTYprn93iyK6UxG+PY79Il0IQE+R5mp4BBD54pnStnX1I9Dk5A0XaPpa9LR/PzCTK6xtIEeeThCmGnPZjk/ntWUIDIkHKheilCeWZbZJNebOLvazJRVktBVmuAYQFGESHu72irCqGvmO/3zPPLgFSL8WM1BiGvpehxKK5TmawqcgJ3vNv/tV/g5t/yAw91xDChiqLYtFNC1VYkxmGcq7G5ZoDKYbXbclPv3jJy9st63adzOMsIsGyaC3PTToGISCTK22SXNSk+zEyjUe+/+a37PYPWKN5fNpxOol57XZ7zaHr6OeRGCJN3XBzc83N9TUvXtxQ1+UyTBi6ma9++xW/+tWXDN0MMVIow8sXK8o6xQUGh1ERqwz96Ll685q3n/1I1lytGcaRoDVKFxAlYjam4nQax8SKOnFKCUwSMR6Znefx0LG9uqZpV3T9hOB8kaYqKUqz7CdDinKvqpKqLnFuTPIHm+6/fjFlF1C/TN5nBh9FBtU0DXXTUFXtsibKfughkuQPZzA6X4+gCV4MeR8fHnjaPTHOXdKaa4qipl2vF/YI6fo2Vs6b7CORcZrQxlA3zVKbEiPBibR48TRLA7wYpbAfho7ZOYwV8DxE+Xy2LEFpKbYTuJhlxbmZ1FotxbhcOxeNWhqAO+eSbM4nxtGMUYoiPU9pBbw16T7Q+Zo1OsmCFOvVmsIUnLoj0XsZqjjPZrvl9uUrynpFvdqK+e40cTjs09RV7sO799/z/v07pmGgO50WOn5Zlrx8+YK+OwlDKELTtFxfXwPCiB1HifB+8eo1r1+/Zpondvsdj087rrabZY158+aNNGrWcvf+nu+++55xnFBKPA98cIl1tqZtVzw87CThsRRAWQxSt3IujOJGD6z8Ia8QUu+HkBIPz+uUgKAyYS6WvVKuNWOyifoZQJD/TwweztckMZv66sQkOHvPZHZ6kEtKzjEZLHneL/0uuc2yVl007XkQcH7OfNmGZWHMjPi4fP85IHI5KH5mWvoHHh8DZ7qu4+HhgU8++WSRyn3s77K8RtbksAwScjPs/JwYU+GZ1FGh5BiGgLEy9A1RaFXWiFRPRQEfhl6i1su6umDLns9lPqY6HTe5DvXCZn56ekwsbPHRaOsG52d2+x1dN0hyiRWGWlEWKEjGpWqpz2fneNrtaNuW7WYrJqEpRljqniGBxKUkLH7zDd99f0/blHzyyRtA/C+9F5bc1dWVBAuklMkyeWq4ecLNU2KQu5T8k5JblvOUZPz+ImJWn5MNLwGiXKMUxiY5MlSVAAraGIIX8LsuGznGZbHI9A4HYQBn77xpGpdzmJl6lwMSbTRtu0pDZ2G+Z0BbAhdMGqaRgGFZ9+U5zv54TVMvwI9PzGmb/LSk9o7p/IqcyaU0Ma0liOL//W9/+wev+X8wJsgPqaty8GIU5/R8AP7Qo12J3KE2hRQol4AHz1HPZ4ioUulAiSmdbEaGqtTcXt9y1JEnP6BUZJ7E5MW7mcN+x2q9FgOowtJ3ntPxgHcydR3GgWkaaVeyiVdVyY9+8gV1U3L/7p7oEz1dW3771TsIkc1qTfSOpio5HXuOXceqjYR4xD0+UF7f0mxvFtNTG5UY2hlNVOLm7n2kLktx006a6liWaFNkEhaElOuenHb1xaIaPOKv4T3jPBK17L4+CB2sSJT73Nwa77AGXlQFfRcpDdQ4rLY8jifW22v6ELjv9nRB0Xcdq3ZNWdZLAx0D4qmQtL2WFM05z8u5i1H8VHRa6KyVuNp+6IT1UpQ0TU1dl9zd3XE6HtlurzHasN8fFh8ZyZGvaJp0XmqZkJxOR+Y5S3KE+SI3m+Lx8ZG6LGV66mZydK+kIcRlQiF0MJm2iv45LHS+oiiorCyOYc4LuoAK49wLWyKlioAwN3JygdJwdXW1LN6iuRdvlqBIqKclBhYXep90hSFIw6OUYnt9jU7eKNM0puNt6HuXmpYiyZHUQqGMMXI69fS9sAdcSl5RxAVEk3N09r5wyOLkvejxikJ8MpyfcdEvbIuTnxjHaXHPH097Oudp25bg3RJTWkWFKcG5Tq7pQcCfGCLGroU2qcSNWiKxZQP00eNnYczYwuCtwSiEbpgaWEKkKQswAqTEdC/kaYPWCWzIkaneLdcpMaB9oGZi7o8YD1ftCq0Mv/ibv8LWNZ/8+McE73h4eGLWFlOvUEbc6LW2lHVNCl08M7MSRV0pmRRmZkOehsQQ8cEJqJWAqUyBJk20BPmWn6+aZkl1yufVOUfXdUtzIsW6AF0qpgJcG4zWy2edXED43TPj5FLxSzqmBuXDcs1pHcWLKEihPjuRDVhjcc4QgKIsKMpGzk1wfP/uO+YkR7FFQYgQuxOTc9w9PghwOo4L4JOjswWkAucMzgvopwy4ydGu2nTvWFkzEzAok2XYrNbLOtN3HeJFdaAsKwpj2D89CaBWiMfRql0J/VNLg3c6dmgl0b1SuBlWq4rVasU0j+z3u1RseKZRWFFFUaT7CaqqXNiQVms26zWHw0HOv9EyKa5E5pnPs4p6mWbIuuMXU7TM1OiHbmn+vRfgMvuTzHNOPLOJcTPTD/2ztCEAF8JCUc3Sg8sCVc6BFP7SkyriPDGMA9I0yPVXFiWlLamrmmnYs989JiBbWFwxxewdj4fk37IVecY4pQQTljXNGMNnn33OzfUV//ZfP9ernwt5qCrLet2KF8zty2U48c03X/Pzn/8NixcGcg7y64DA210/8e5uz3a9oryppC5AY5SYLmslZt7ZoyGaBEQtXhV6YQ+URQmfBEJ0jP2Jti6JEWbnE9he4BCPlhAC797dy/oZHG8/ec16vZa1olrzZ6s1VbPi22+/ZRoGxm7g1B2xtsVImAvBR+bgGUbPJ+sb2tWavuvwk/jRlNYSoibqs2Gm8zLoGPpegI/ZUZiSqrCUlUabgnq1SnGlIpXwMugkKqFAl6VIQPO9Oc8T09zjvaIs7eKzczwek1ykoCxK2nYl072qxqSJp/ciia0TA0/OMWnvUUyTS+B1TGu0gBfBOybnmaeJfppQxvLy+hVFUWF0QVnUuBCIhGUtCEHYKSIhkXtMwDi7rA15Xy0KvcRAFmWJicKEXbx5TEsdJWVJKwPmLGHOPkVWi9ePUiyAUWZv5b1RLw1dHpLFBbhTPiQARWqnGBwBMRTURZESuQy2kOhwaVQCd3d3xBh5j7BphnGg7zs2mxXeB37zGxk0vP3kM6p2xWpzA0GxXq8pypr9UeoGozWlsZySJCX7mQzDQHfqUCi2my0hytBmvV5TlhVaW25uhIk3+0Dfj6CgLGvWqyQfNQWbzYam3bBqV8zjSNusefNGsd8fKQpDUVnu7+/S9RB4fHximoSBlPAMClsiAQSGU9cxmJ5VkYG0JGvWiqHrhG0a43JMM5DRL7WNSsaQF0a6CGMoS2dz85h9SgTU04ntJF4YSov8yTlh0UR0AqiFCZR2eLnWIUl8n3uIfQhQXK5/kIORLnsdUMo+Azki6Xq6fN5ERFk4Lyr3YYntkdd8JWt9lAuabGJPVEvM+tPTE+/evaPrem5vb/j5z3/OixcvePHixfJeF5A/sVRl36qWr7O/VPauE6P4YvE+G6cxDW3k+bSTwZpL/ZekkEkthrJY2xBjoCjkWnWzu6gfUmqekx5EvOU8p2NHDOKxVxQWWyi6fiDMM1MKT/j+3fccDx3z5NC2wFrxkzodj1RVyapdcXMjKaJl1crAeHI83N8vA4d5nhfGsTBZjhirub29YrWqcc7RtiJfvbra4F1Ivd3AMJxkPRw6eqNRSoZV+b+iKMAYYlTUtfRcuQbw7rzfV1WFTbXIeUAq530aZdCQB5DBB0JQ1LUYo/fDQBxG7u7uGEbHdtOyWq8kZMCdJTI57WqepwVYkdhrI4NgpXDe0fc9XfJXfC4Z04tqQKS1Lq3fMuBbr9cLqzYPOCDdX2kopJTCaLXsd95L/9udTpxOIp+7lI79vsd/sHSYS7BCzsOZciXDDJ0Kt7MmWGvNmzdvqasGg3xIDct0Gn6Inub/Aim+tJAG1WhJlQC42qyZrtc0teHh/j1FKTSfGANDd8Ro8T6oK8tRC3gyjwP379/j/Q2n04mr6ys22w12s6EqLW/evKK0lrt373l8ONJe3/DFZy8Z+4HVyhLCgNWBq00rfiFKpjddP1GtBubjjhgVPoAuKkxV83h3R1SKdrOhqVdSnAWFQVFUkogSlfiChBAIzjH75PGQHJTtwsiJhAiqLKUItyXTPOOcRPz2fb8giyYXesHhh4mpO9L1HZ9+8oZ+3/Prv/kFL1694dUnn1O6idPgMOsr8ZA4HZemBKXph14QRl0thUBu8PIU3horBQjiLzHPIkHRpsCYkXG7ol2VzG5cpBF1XXNzc8Pj4+PSZA7DwOPjA1kmojTL5mRNsUgfQvDLAtW5KfkBsBiliWGvS42bbCyZHVNYjZu9xPRpjTUKo0lO8iFNxWRiMA3CNsnNkNCm7bI4bbYbpmlYJrsyeToj5XVdLwBdpoFl/46c1CCpOSfqpmG73aRjOCwL8eV/ebrWdR0xbWxZz1+WJdMUnzWS541ZzqfIes5UeK0VVV3T9SeyP4KxIrnQWhBc5yLBjexPT+zuPVUlxVAMHj97hnmiahqq0mLmSDcOHHc94+HE1Ytbms1GAEHn8ClrfJwSmydNxCIQtciaXHQLQGStTAh8CPg0YYxK4XxIEjmQFCPxqbCFgQRKTPPENw/f8fTte6bB8fnnHWjN7jgQO4fXX1OuV6BkyklATAlT01XNbVqgZSKQ0e+FljdLCk4MnmkezhRVo4kJ8SgKMXaOUVIUJMayRCnxVJDrQtg+VmvGXtJRondYLQDnnMwxjRbnbqX0omnNBrNhlvhMktnUupXNQiuNdzOTn5AoW4lLAxL6D2GSVKgYFCGIf5Hzco4yJTyExEpJ1OdApOtm9ocdKGG4hURrlWvGoRM1uigMXefouhMmAYV12TB0RyAwDhOnrqcsKqq6XpgwL65vWK/XdF0nm/YkGtl+6Oi6I4UtkhlqyXa9WqRMMcBxfySEyGa9wVgjwPcs9+Z+v18Aq7Isz3R+Lb4DMiEU6VIGN7p0/+bGi3iWQPVDv9zvMUgh2jatNJuT0EGrsrqgkgqAYYzGFtXCdlBIU+6T2/xZsy8AeV5XZCom0+kciX1ZuOZ7J0+W5NqLSVKVktjSZJ7IAv5sNhuUimktTuurMVxdXfH4+LhMMGUNFkC6ruuliLm7u+Orr7/iyy9/sxSxH9YNq1WDRJQ7uu6O16/fUpYlP//5X2MLTVlphmFkHOf0Hn5ooBpRHPoZpUvquk2DlYJCFyid01Dyepl8eZLZax7sRCVFi58Dpmxo2g3jcEIraCrxjTidBqLSGFuw2WwJIbDf77l7f48iYqwUyat2g7UVaMWnP/qUZt3w1W9/KwXj4An7juttS0RD0CgMu+ORd++eePH6NSpCU1iMiUzziFcFqqqZg2Jy2VhUJBl1WbBerXEucBo7pmmiXRc0K2E5nU4H3CyS27pesVpfY5KsTRo92b+HwfPtt98yz0LfzkX4er1htWpTCp1mvRYGgI8XMeUpvlt0+AIkyIjboJAhibGWgDCYjDaAyBvKtCeu1mvxa9KJUYQAWH4YMEbA4LquJQEsgZhiZir78OxmVLru8/Xl45lqPzkZnhlrlve+mFkTUvOrcHNI95hEsUci4zSkq4xlAr3c92ndzEM8kbHJJFeTmZcRN48QA3VZMg0DfvbMfU+3P10wlMICfk/jSNs0lGXF3ft3ifkZOOyflntIIi5FYmyLiqGfadsV/9l/8X/k809ecTwccG7msDPc3NxwPB5p25ZXr17z/fffM00T69WKqmoW5tzd3X1iKwujaPbCABZWzopxdByPPZ9+8gllLb5x+/2J03EgeM/79++JUc5t3bREPK9fv11kt9PUJ4aeXWoaEpMvRmH1XK9lfxXvOZHX5wFVmSLh8x5qUtBA7r+UTsyRGJMPSyTEkCSPXhrti3VIxZCS7QxD74hRg8mMJb/UXtnf8MXtDdFJamGIEt+Zaw0QCbxKTXIGcUjXpAwcpMbUSskw0aeI4nhmB374UJfrdmKGLwa0MZU3P/j95CuFWgAQYsA7YQFmlvFqJQl3X3zxAmM0fV/wy1/+ki+//BKU5mq75fXrV8Ji6HseHx9Tj5c90LIyIN1zQWS9/TDinXgDCrMQAdxjpOt7mralriTcwY0jf/FnP2XVVvz617/ms88/Zeh6adgjfPvde8rC4GbHar1KjE8ZHGzXK2IM3N3dU1ct2+01VVUyDD2n45G6Krm7v8NYy4sXL9isN5Rlk3wEDUpHpptrrDZsNhvKUj5nNt03pkChkrTzmtVqtdwrwjTXiy9LjIG2rRDytEgxffDEJLNpFja79BTWCgtMGvwzUz0E2YOXBK20LuTznsMTMghQN40Mx4nJtFx6sGl0wiaMkcl5ylrTJPbterUWwJ9c940oDeM0sdvvMdZQlSXTdB4kayMAF1El+a2lTUPyYeiXYWp+zyCM5bwea62wtlz6lXmeaVupqbPvSPa3yf8e3bxc0xkAbJuG6+vr5Tj8MY9/MDnMh4+zmVv+kBkMOTN5JM5NCmGjNW9ev+S/+M//c1ZNJYUDikJ9HCnNX2cWSEwbp1wQAZtQKaWQKEQ38f333/Dlb37JPIqGdBxHYtJklZU0DHcPD8s0fppmlC3YbDesVg3b6w0vXtxK8kWIqADHQ8fduzuGfuDN61fc331LYSY2mwYdNP2xZ5oCp77HB7h5ecMwTrSrNSHCsZs4dgNDiGArXn/yKe1qg9YlSonGsaw0IYyMfSdJKtO8UOAlX1kTEl17HiVSrrQlqigw7Qpv7EKbhRTv6OeFWh1DFFf5MNEfnrj//msKrbi5vsF5+O7dHZurG25evGbfT/STo04GpNM0Y4uSoqooq5JxliY+L/LCnhFUb2nUjWwIZ1Mdl7S4EWU0VV3x+u3LTEpE9huVzotcRH3fixlhYej7nmka0+YX0wSxEkTWeTEdmgSxbGq5tvquT5MAWaS9k9g6cW5nARGMMczjmOL9cvywTNlDYFmMJBlD3le+/nPaiUuGgj64FIUl17Q4G8s0JxdLTd0KzT0tavnmzzTBvu9pElvqeDySafPh8v0lBD43R3mRiFEtTapSinEcls0836shSZCur68Ws9HsQ4FShOhT85ckMWlRy3R3YzT66R3u7nvWreHmdk1ZFsmzxEt6jy0lnsxoNpsVwzDiQ6RutyhbEFKhVJalIMrjyDSHZIInRnjWaApbME0DzjvqqpRJp5a8Jh+UuPUbSeVQWmKaZdIjDY82EpEZnWM47Hn/9Td0Tx1Na3n95oan3Z59N1GvVpRNQ2FLZgIBLfTqyGK8S2qgVisBpvLxBym6JIVFjlFZWmxhmUaZNJ2GfvGROcsVDGVVErxMKGOIiz8ISIHiE7hHjAlgHRlG8e6oS0NdVZS2hBAFbZ/mlMaCGPLJKEimDuncO+eWaz1vruJzYgQkiZdUxYayqWVzjmFxO88eFkVRUiW/m/w32bg3R+YKCH6myJrFz0Q28eA9OhlwrtZrXr96zddff5vMeLMHVeSLz7/g6vqKrpNJgDZa0mGGga7rqKqK7Xa7GMTJ36UY4LJCKUNV1/TDCFrR9x273W4BCoqi4MWLWwFdvaOuK+7v7xawQe5DQ2Es3albYkfzPqhUXIAX8fJpcbN45MzzLBTRtGa64IXKnfS0GTD2XtYyhV7i63JhcZaFzAvgG1WmA6cmzufppEpR3frMPAn5GpBrTBm5xsuySpIdIMkqgg80TcPrN6+XqdfhIBT1JbUmsYXOrKFsWGaXtbkoCsah57/7l//1D+QwcjxFtzzP5xjs7A1WloZIoKoK3Czy2nn+oZmqUorr7Zp//Oc/5kefvaGtW0hNdIygFo26SvWIWvaqczMt1y1RofCcdt+z233H8ekxAdqB/WGgHwO2aXjz9m0CDzqeHh+YZ8fLVzesVmvW6w0vX74WrxotssDu1PPX/+Nf8e7du0R7lsFEdBFrNdPkefPZW37yp5+ze/8doTtRVbJ326bGbG/ZDY7Dqcci4H4Mke32imkS0HBwjjFEVtsrtBUGk/hxGJF4BE2IAnSVZZGADXkN5x0ko2NJJhAGZwbTm7bFp+Si46nDx7gMA6ZpIuJZNw0milTX+ZjWMS3ghLHiixRkrRkGJ3KIZHwp65IHJfer97KXaZVTVSAgXjxaiSHhOM9Lbaku7oWc9JLvzXme6YZ+2Tt1Ngg0hUzjE6MAJEVtnlxiDghDIEv4si9VkcBPn5mbWiUvPJlUZ6PDGAIqROTth4W6O48TpS3RKLqU2pYb5Bz5XRQFNt1Lj4+PiXWo2e2eLq78c0xsvkavrq7ZXgujSADqwDjNhBhZrVa8ffsJb99+wtPTjr7viEkyqpTUly54ylImvl0nsgStxRj9iy8+Z73e8PXXX/Pu3XeMk2Oz3fL555+zXq3EI8c5vvnma7777lseHx7xwaW9Qj5fP0iaV6YxaLLHhIBjWmveriIvK8e5bUny3dR8y7zxnIaWQZBLBoXswxex9THTJ5JxriJNn+X7xWJ+C0GppXYdk0RaK0PdlKzaGqMUNoEZIi1Uy2vkPTaDHcTkC5XWG5H6SZyzIkMHF3KXHJOqkuwkt0ZpD8nSEMWFjCYxLGJIsp6UHid+ZT4xQ0js+XPqDkrjEtNC/M3OUoXT6UjXSeLHPE1M80xZlMLG7keRm1QVhZFElP3xiNKK93d33D0+orXlatuyXq/Q2khgQaqXhqFHoZNn30xdGNqq4PZ2Q1OfE6GauoWoeHh4RBnZz9brNdqYJFcVDw3xlpsgKoqiTN4qwtQJwUs4RNozQkq4LIpSmJPRp/4hLP42mbUpTASbvBel/4wxppRFD1FR1eLHInKRcz0gCZcd3gfW65U8dyOR1SIbkbXKO0/fj0xBvG8KrZmGgWEU2UxhLXUt8qTTqaPI9WPy88sJSyqd9yw/AcQPKQ2osn/HOI6cjkfcPBN8SH6SAkpL2s38LC0LWMIa8nPM00RO4snsPKmFQqoJiwWgds4v8lXnRHKc929jDOv1ejFnz72iTj3QMAxMiUGSeyVhqoqEuG1bHh8f+W/+/df8ocd/MBDk+d+SpqJZ5ybrjrWGTz55w/F4pCpL/uk/+Sc0dcWqkYbSIBrJ3JBmhBKkkLsEQeQ1ZAHLhaHJ7zkEFJ6hP/Hdt1/zcPct09QLNTxtnDGKlKMfJ4qiwrlEcXIOtGK9WVG3chO9enHLer1FodP+Fbl7f8/7u/eM/RE/9eANfvS4aaaqDLYyRCI3L2/oul5YD1VJVJrTMFE2a5rVNZvtNdMcsLakriqqUuHDwOxOBDegomI49YyTXJBRW6q6ZXKO2Tu6Uy+mgEE4hbGooKgIwZAtf2YnEbnidSL05KqQFJ55HjgcnuRijgqwmLLm6vpWcuFnoTllmnsi/OFDoKglVtiHQN3UBC/SgNmdN631ao2LYZmc5sbIpQZPzieYQvRn6/UWYwrqqsGNjtvb20Q1fsc4jmy3a8ZJ0NdASNeITGMMEp/l5hnnA9M4UlfV4gatUDg3JbT9fJNaKwkLMUq6xDikiCfv02TG4GeP95GmqWmalmkYcLNbrnOthWZ2PJ1w88xqvaZpah6eHpaFMEda5mm4LGwy6d9sJIL2eDwmgChQlgXDOC6RYSEE2nZ1bl4RBpBMA88JMXnjPbMgrOjn/MSUCma4jKJmQWwXlFWpJMXIfgMsdN9l84wy3drMR8Lje663tThcW4VzI103M8+e2SPSOAUvXl4xTxM+Chtre7XlatUQvGMcBx6eHolK02w3hKAYJ5kmBn+OOPz62+8gBG6uN4zThItCxzVlhbEltqiSbEukAXJPpzjIUgwyj4cDj3f3S6pI3VYYK6al7XZDUdXJ3C8b9qVmNXnb5KzzaXL4eKY8Z2LsnFBrmY4WApopxfF0WhhBRYoVzhGQZSUTY60065Xoumfv0EbL300ymT2dhBGiEEPkqiyxRoP3STgnhTcRYpTjpiElO0S8l/sjH0+FMFmytnfohYkDMvNs2gatBVBzUVhKPoalwc0Rx5LgpYDsFyLykqaRqW3fCWCYPZmCFwmVhuTJcd6MlVJsVitevX5NUzfMzvH1N9+w3+1RWoodAWsUdVVxWiRCRSqsBbRp25bXr1+z3+3wfkoAqiTBlFXN49MelFAv+76naWrm2SWPD40tDKtVS9NU7HZPC4W8647S2FtLfxKKrUtSOpBrTTyJJO7YmCL5ocj9udlI4sI4DjLFmCdCJBlPVthCmqtpnPAuLvdsSJNvmSDqBXxbNO8JmBTWkwDek5tkv0z3dPAhNZwXZnsKmrahKiuR2CS2hzy3FByb7Za6bthebcWssq7oB5FKnI4n5llYQz6dQ9EZl2evmHlGEfnFv/3XeHeenp8p4jlJSpoRqQNIkgOTpBnThcz2guK91B6KF9dX/Gf//D+hKnSihwsIIntA/svz+nc5dVWptpDjjEzX5yN377+kO+zoTye0UvTjxKl39GPg9advFu307umRPpnMXl9fc3V9xXq14erqmqZpxfjdFoTZ8Td/8wvu3t0xzxOESFWU3N0/MTvPj370CS9uavb372EaIGq221rYcmVLtAXjPAubSCmilhSjsqgZx4nTPDO6yM3r12yubmlaaYRDSgGb+jEVv3K+2rYVkAakEYh+ARzEyF3OyzzNqabTjNOUmAflwqw0WtNWhqsCxv0TdWVpVitO4wjK4jDsu4FxdmBL0AVitF5DAtEApnHmNByXs2WMgK1Kc15/jJVmI8r1W9fNUvgPk/iZ2FzAK5XW64lhGhatubEmresyHFHpnspgv8vU8+QBptN14kNItYbj0guoqoXJl4EQN4vBZ3ReqrEoU/hh6JkH8ZMxyB7Xto0U9lWFNRJtmSneTVXz3Xff8e2339L1HSH4xDI9s3By+ZulcgJqaabZURYlV9e3zNFjknTl008/4yc/+SnjOAsDZJoSS1ZAWbSkuR0OR4KP3N7epIFNSE2X5fr6iqoqOCQgeNWuOJ1OrNqWsBi4jzw+PnA6HairCjHp90sjnFkMPnhJLAt+8WF63cLrNq99gWlyS2OllEpMEHUGEdKE+HJty8Oa5xIVYU1kJlBmMadND+8j4zTzy19/xdP+SFmUuCgJcNYomrrk7esbXr64lgFu8sHLIN55fRIfA5uSzfLPMtslBGjq5oIpmtLrko9DBuQW0CexWnJcfFGKJPDm5jZJceS67U49IZl639zcyBAiCqBxdbVdhmN919EPA6v1Jvkryrmf5ymx5SsB7JVekkhOyRxfAaeuFyA315lAlzwyiqKgSOC3UiJbmccxATEWbTT7/UFSj5qWtq2xWjH1IveSmG693MNGG6wp5D3FuPiiqSRtn1LNnvs7uQdIBu5SI9uiQLY/uZ+dc0kiZZYhQ/bjyw19ZhjkNaMuqzMAkVKick2T0xXHcZQaJ5zBknNyUkiM0lxX67SmgKdkPyn2p4lVW1PqwKqIWCVMpuyxMk9T8jeyi6zLO4cPSV6NmFOTUiltrufkVkMr8edyk/i2gBiP2pQeOE2TDN3j2WdEBnrlAswWRYGOZ6wg93cxCcSmaVwY7TEIq7xJ6WBdd1yYtDExUTebzcLmG8dhGajn1Bq0SlLA/mzrkPyZ8vv6f/x/f8EfevwHk8NcPmLMLJCzNkgOol7iLQtr+e1vf421ln/8l3+BjkpM8NKGBiRXZiBtuopkWIMW1DjKxa7TROO89KSYzKrh+uYlT7tHSpRE8GjFOAyYfAN4aK9adruDFL1Gov/2T3tQazF4unsgBNhsrrBFiSkNrz79hGa74d//27/i4WFPoRw6ivO6KQrGLlDWMI4zm+2Wt59+RrSiJSzrWpBB56nKGaoATAS3Z7gb8ONECAM+jrgYUMZgfWQaA7vjSNluaLYb2nZF06yYJkffDRRlQT/OjPMIqsDNkX4UN3xrLf2pWzaQcRrp/UxUinp9TYtCB1C6ICbjUm0sq7JaiofVqmVMaRiC0AamaSBE6MZ+YSnYwiyLbkCaMGsMNqHSxmpsCIRM7woB5yd2ux0PDztUVNRlTVXWzOPE9uqKY4rG7WxyEFYCatRVTVlV7Pc79rs9ZVHw4vqW4D1379/jZjHxmVMRnhkbP/rRjyRayhh8TBF9MSZn50E2UhRt26SbXbNeiVv66XSi2W6WqY1okwsikWaV4j0Pe3aH/WLwWJYFdS3F+DAMgry7gC0UwzwxPz1SlhU+BoqqWJD8oigYpyE1rBPH92JYVC8GQiKZCS6mSaw8ZJpt8M4z9n0Cc6BKbKBzkoqAE2LUGNLCLAu1mJ9GYpSpKDrf3G7Rmipgu22JoV1M6ZjF3f90mhinyOxlwFZVBrynrQuGfsJERxFH1DTS2AKjHaHS7I9H/H7CVg2Fh/E0C7BR1YCjNopD53h4v0OmQRplJpqtgFRFWUi0YFBS0KuATqwYoySh5erqKhXVuVDSC30263tDojDXbYoInSNV2Yhcwdq0fpxTAaoEYnjvQQnF1zuHn+cEjlZEH7BpAgRgqzodRwF9fQg4N/I0yt+GdH5cEKru7GesFSqhisk0Ok2jQhSGjnchTSxI2tBxSU/J7vHTNKXIyzN7z1qLdyHReSPGWqq6ZrVa0fc9D0+PzBm8S3G63nuJ97YFxqbob20Ag/eRaZxxsxTsJtPEiVRlQQhSdDZ1wzx7oefGlNCTpllumvnFl1+y2+04dR1aa2F5JbBntVotkbGr1Yq2bfn22295TFPSdrXicfck4OzQ4b1ntVqxXm/xCYxommaJm3337h2HXu6x7XbLzc0N19dX7A9PXF3dIPTNI5999rlMZULgabfj6elJAEMnmt71Zk2bvIGk+Cnp44i1ct+v1+J5EoLn4eGR9+/fg1Iic5on5mkgJjZck1gtAKfusPge1bVMz+ZpXsyZRdoRMMpQtaK/9ad5ubcJYaFvx2WwINd634mvEVGGCDYpGbSOGBPpjnuO+z3v3n3HixcvePnyFU0rx3+73SbqrUxVZTouBdRqtULSZeqU5vB8wHLpCXL5I5kIyc/m2TMnJg2c0+M+fKgYudquWK9aqkLSPUhNZjazzHuOMJHOYFCeGguQnF4HRVE0vHjxOU21YWfuGLod1kasCVRF5LDboWKkriUpaTh1aBRPTzsB/2/Eu+X66lomgbFEKc0XX3zK1abl8eGBw/5IUZS80jd8++6B7fWKq1VFFTec9pGhl8j4qA1Pj/eEqLi+bXm5XRFVZJg8xkaqAlRUzG7m2B25/26mtjUqG9MahY9gtaUuClRdc/a5mvCzsPKCn2Q6rrIZtejTMx381PVJYlgkqZ34OJTWsi0VdngkzHvaYs1NueVv/4efU7cVbz77HGs9Xz6+JxQryvYGbUs5L96RTaeneZSElLJMF4WANz7ttd7NBAXKWGJKmhtnJxLgVKPM00QwRq4VzdJgZiPCfP4jERcl7U8rqIpk5B081kTpGqI0DDYZ3OOdpLoocPOY/lYTffIXSzKl6BzaC6hEMgyOIWBVpGprAaBdpCoKrq6uGYaevjvhvKcfTolhI5PU+/s7qqbk2B+Yk2mtTolGuZGxtiAEyJI9JQoI5jny9HRHvWqI0fH0OOD9yJdf/pp2teWf/JP/lNdv36ASC292M9M80vcTp1PHNPUcjnusESByvz8wjiMPD+95/eY1bVujSsX7999KYlLfEZxnvVkRgqMfOvw0cVIRY3UCPQJlUS3nRSfPC6I0zBI1fGZrCaOwfL5+qPOANISwMCczqzEPeQRAkIbVGHtmu2QvmbKUPTKvPUHz9HTk23ePMuQYpIZUREqj2awamrqC6NFWobTHB2E7EWUwlr1IUMIYvgRjMhh/ee4u5VtlUeKLKsnF5H6OyWB9SrHkVVniUurJ/f2dTNNXbQKeTxwPJ4rCcjruxYsvSTWeHh9ECpr6ABD5rfPzsq/M88Q89hxyL5b6rqIs2ayaRbZcFYpqvU0sMBkwb1YNwyB1d2Utwc3E4FBhpi11sjsoUMbQvqpkQFUYrBGZWV2VnPlekeA8RmmpY9QEMTO8NNqwsCfrosAmlrX3nroqCFHYo36caVqzAOtFYSm0ITif4q/1Us+LvE1RpUSanDaZ02sy+KGUACw6SbXz8AZYvHZAJKI3N9eLj5hccxIxm+UeMcr6NEyabtbo9gUH54jTCbW2vFwXGB3xPjF6w7mvttZiYsQpRUzDG1mbyyVwYfGFUQIOTfMEXhh+zudrCsSHRXyJTGKzZfZ433c0TbX4Rymyl864AIw6+VxKgqChalqsDwx9z+F0EL/KBFRZLaxAmxJvirJa5Idtu8YkX6YMCooxtVnYS7YsKVMtGmP8gRTsdz3+FwFBnj/Ouc3OOXa7PTFGxmFk9/TEP//n/0xMohRcCPye0X6VksSDSER5OfGOFNMUWSbTmeIvrycZzfJ9mczO00QMHmtE310UluMpGfrZHNMkBpkez/F4xCTK8+l0oqwairIWem9UbNZbbrY3/P/+9f+fr379JSYhtoeul31Ti7HkNM3s9nu211ds1hs22y3333/P6fDEqmlQBA7HPePgiLNmvW4Zpx5jA81qhdYFg/MYVbFZtyhrKG2Bnz3GSFNXllWamBXUIbI/9sxDD2mBL4piWeCzmVBZFouhjkmmcTE1wDEIaDCPEyiWlIC82ZRlmZpEMfksQk6fkSmIzdMRnTSzOi1pqRHTVqj2i07dJ1qpC1gjxmsEWUw26zWff/a5NJWp4ZzCzOFwWBDRaZ4obUGYPUOSjTRVyayAssTNjqooF23/LukZx3lmctNCgRynid3TE8F7jDb4eaawBU3V0ut+QRy991xdXTEMAw8PDxwOBzZXVzifYwJlYY5eipR+HIghUtUVNy9uk8mpNEjXaeGOMRKHM93ezTMzkfVmI0VfEDqyVioxpzTaFrhxwvm4NHKZkbBZr3BEBpckMgBosj9PTEyO/DVeCg6RDUBZGeZ5JMaZiEd5k4qSbL6Ui+JIazSmroT272bads3NjUxUvHfUdcmqqalKC8EtWko1DhxPsogO4yAO/lqhY8T3PfPshf4+TQt98M3LLZvVyNDPAoKlzScazRw8bhgIQdI3tDZiNuxmMQJVSowGUdRpk/NBsuKNNQut1TsHKep2HkXq8fLta/quF2mVc4smUScZU5avzEH0um3TopDEInxgOIlMI2rxJvHBMw1jMqwKUviEkNI+xLw3T9SF0qopjEiG8AJEeRfxUXP79i1NUxODsNT2+z2lLWjbiqos2e/3NE3D5599xu3tLd57vn//PXfv7+iHnqEflw01wtIkZA+ZkJgrVTLEytdaXhOqSpKPrJHPUJaVUCQnSYx4+eIFL1+8xM8zu90T3jvev7+j74/sH54AlQy/pBFxTlghIGyWpmk49V1qsPP0JU3gypLVes3Lly95/fo1VVVxvd+zP+wXvWlVlpgLU81xnNBepqLel4se9e3bt4sf0TRN7Pd7lCIZZVsOhz1aiTHf0IuZ4DQNlJXFzTOlLYUyPA5LYpl3BeN0ZJrcIpfZPT3x9CTrhrwnTVkUydDW8ObtG0kgOXY87Xa8f/8uTQrVQilXSnF7c8Nms+VwEBNpYWPMCysPzj5NKrhF4y7MCAFofQhy7RuNn3NRJgCGMA1cat6S0a823N3dsd8fUsMlLv/5GDbNitvbW1ZrYWrNyaBN5H5umabnxyWT4/z183jhuEycziyQLJfJayfIVO1HX3xBXa1Sc3TJeMkMUp0Sh/QC5ObiNbP2cvEraXUR4g3j+gYVNfduIMQZZ700x26kPx4wCpniazHoQ8HxeGSeZ7ZXG6bhhHcv2G42tG1L01qsWmOZ0cy4eebNj24o7ESpR6LzWAVtu2azsfTjwP3hANqwqipWjbCoXKqngp/YxY5hnPj005cUVtPPUBFodKCqLf3YA5roxHwUWhwSadidjnTHE1oHNMlA1IiMZxwGhmFMzbXj2J1Yr9bSYVuL9zNNWaOd4/HwxPDuO57e77nZrnn1ZuD41LN/6HBTpFjVzMcjD6cnNq9gc/uS3vUYW6ITpV6ixM9meeMkvl7e+4XWPc9eGJBGo7z4NejEesvxjnl6mWUPJvt3JQbn5Cec94tcMNcwMQas0el3xROsLguRJMSIJlIZQ2U03hthN3pPdDPjOKTBjyV6z9yPTGNPmM8mxZliboykIDkF799/t9SgLkSOx6fE6pSUGO/8kroYYkgsKWnus9RcklhSbazESLysxEtD2AiBeRZ6/X6/k/2lKtnt7zGFIXgBHIuiwHlhzSolDNunp6c0na0J0dOuGpSK3N+/5/ERdrsdx4P4jblZvIT2u4mqKoh+RpsohspR9lWjLW4e0EEaWjln4cxmVJ4YLGCF8ZvlH2kJyLWo844+yQG0scvArKzKZYBXpuSPqOS8t+0qyRKjeAl6AYedE/BWmJCeq6sVxhbYsqRN3mar2rJZVxglKVLiLSS1o7Ul1hYMXS+NrTLi6zR0KCGxC0uwrjGJ0SgpbHOSUcs+M/U9Wtul1uz7YWH5eu9Qyctwt5fh7WolPj1lYfB+ZrtpubpaCwOz73l8PDEMPW3bstlsiXHm8fEhSShaylJTadnb3TwDgcKKX9Ccoq8VntNxgEhiRBSgoCgt1liqMvdvApzHIPtZDJqiWCGKoMQkTVLRcRwZhp66qaRmAKn/S5sYDckINlmNjMNAfxIjdKXlfs5reJb7533EJ5P+7EHVnwackwjauqnRTUuMwnzNa4ucD0dZNIzDiRAdZVmQfXfKUiQYwZ/Bs7xnLKb41i4MjwyedN3Afr9b9pm2bShLCZHI5t5FKX5a66pkDA7iiDay7hgrg64MpGxW64WhkmX0+VwuNgQ6s7l9whYjIak0ylL6huNJeijUWR4ao2K9asUXK4VKaG1oKqmX5/S60zReMFQzeASjkxpNwBdJ0Dwzg1vGvuN0OKb6LtdwFU0bFlZMVRXLsN05qWGMtVRlg6vD0kuVSR7kXU7//MOP/xVAEC4KmbNZqnOyyeye9lxvN2zXGzEXCjGZGSV6Yvp7LgqlXNRkKkyeIIkeUy+bgIqiIf7JT37Cl7/+BX2cMNoQ/CwGa/NMUcCpO0qiRoj0fUp6QEz0Hu92qCCmdsQHpmHi+vqGoqiIOtDUDf+7f/yXNGXJ119+uZi/yF4pjr9PTzseH4+8/OQF0+woioqhP+EmoUkO/chhd2KaIsMUeXEbsQV8+vI1ujDMM/gwonXBpy9fEY3i2/d3KGupKqECKTSnQTYgoxR1YYltzlS3TPPI6ESvJhfkOS4yJAniFGbcLECDm7LjshTKJkWWRiXxlcBSSKBFl4s2simHgEumfaTCwscoEb+JYnVZ7IJQsbLZX9uumEOkLOolrmuz2Sya44eHB+ZJvBHGfkA3DdebLWM/MIUx6e0Vm1XLXBi60xFbit5+nEY0nsPuEWMN0zgyO09ZVUvUrIqJooqiP54YlCauhe5VVRVXV1cLKjzP80IP01pjlV2kLihJ61FKpek6yyQ+U/Vlsi1NQjYszWyd3KAU1jKNwzLZKKwVGhqgdIqSVFIAZHNJmfKHZx4t3p99K7IuLxIW0CVT9/L7FSmMJmIJIU1cODMlhPEVOA0DJkys12t+8pMfQ0JsJaUm8Mtf/oJ5GiEW+FFoi10vUyaRVJiUuhG4ubnCJqr3OM0UxuDdTFRa6MNRqPyVVjgV8NNEu2qAyDjJBOs0Hoi6RCUaXSgLIoFJBXQUv5BFq5uSMJSWSYefZqJ3aDxNVQKB2U2cnvb8dv8ASqJ9TVHgk6xgtV7TJZmLMCMkRm/sxSAuJFmSMYZ44WEwzwLMCjuqIPqwMDaK0oiG1mjRT6bCO0aP8TLRz1HO3kfiOFK3K+p1w6ubF8KKKkvKSorcEMLCTPnFL/6GYRjYH/aSruQcwZ/Bt6hET5s31aZpRJ6cJiXPm9PI7e0LrCk4njpiCJS24OXNLdc319zf39N1J/a7HQ93d6gIh8OOfhiSjE+mo/M44700497L19mwVynFMEy4IBvyPDvKspZpTgK4vPfs93u01mw2G169fs0v/vaXyyTQGkNbl0zTRF3XNM0K72V6cLXZUiWvk0xbzV9P08jTzhOiUIHHBCwrBXVdiVv5OIkvg9AmpPCLUmxFH5jCiPNpMohMaUIyIMsUW++FMq1ipCwKqrIS3yIi66YlbBwPDw+oqBmdmDo+Pj5yOBw5HI/UVbUUwsKCEqAxfx6lFGE+e9dkucfiqaGERaFTykeWbGX67jD0EuMcItqI0WOf9LmZmrw/JLpy3fL09Mibt294/eYN63W9mAPu97uFffXH1g3562wcndev3LAWhcE5GV58+ukbNtu1FIWX97k8yVJGKM5Dk8vJ8iIPSs+P0kQtzUzZrrl58ymPj9/zP3H3p022Zel9H/Zbwx7PkNO9t25VNSYCFCFRtCTSkkNW2OEX9ufVW38Av7AjHIqgRAqEQDTQ6OrqukNmnnFPa/KLZ619skCKgMQgiPDpqKiuqryZJ8/ee61n/ccYB9q2o64i0yw5N9fLiRjkGsQUxZ6rNfM4cQwRdb/j+etXlnni/l6sDtpq7u/v2Ox2fP7yiWEe2e56LocTqhE15OvpwDhORCUA6/Z+zzzMTHOiSprrOHM8j4yTJyVF1zZcLxPLvLDd7miMx4YJNU6Y5crz11e0tTy8eyL5Ab8suJDQPtJp+Pz1M/M88vT0wDIl2m6DblqssRwOJ46nC7vdjhgSyzQzhqtYAvqER3H8euTzb440yvDtwz0/vj5zmEXddf18ZkkHkoI5KaavL9SbO5kdEuCKaklsOIvLWWbqloOjcz6UtRKEPoxS3WuMpmtb9HrvkO9fObC4xWX5vCI4IV+k5S7hY6mTFpChqqqsiBIVYQngT0GeG4M0fUmdpcMvIvM3RvYmUsQmWc9i1xBrK8q+bBuUzIHuZ/tuAW1CDDmHwkkwcS3ghdaJCrFIpCitR2+fEWOqFdhLebKKScK4yVY7UGhjf/bsH16f+R/+v/8fmqYDVfHd97/L09N7xmlkGEecX6SVZpKDuGSaLVyvp/zzJFOp73v2+40Qfbs9xmg+//QT43gVVp2EMQrnIikDGi5KI5Cqa5qVyMvZNF4O+8ZYokIsnNxm67e5ZiFFrpeBl9czKXppFquqdba3VSPse4gZGPe4Rdj6FAK7nQC3fdejlOI6DHz88Mh3330jaiPFut6k6JiGER+zCjcICKmUgcpwmRY+fz1Ig15VEaJi09c8PN5RVxarFX5ZmKcJa4XsUhl0UCkyZxXNdZgYp4kmk5xt06xqO+88U5ioK8PLyyvX65WqNkzjFa3JgEJaLd21VWweRP34+vyF8+VKDEEUfFrRbyQPyhhDauVziX4hJEtdVWAFEA5J7D3SugOVsQTvuA4XgvMYa6kqK8+hulUYxxjXdkO5robOVuy3W7qmFgV0BjS8D8zZUliIFqslNLmpa5xbcr6d5K5IzWy9EhwFOAyRdX3XuXU0eQnhF2t8kPMa5LDeTEgkzaR9rrPdUzeicF3D0L3HLwshBoZBsjlKG2CZN4zRa/ip5BleVyX6jais2G63q5UyhEiDYmc1Piv/rW0hOhYnlkcBaoQ4q6xlWdyaWzLP87rPSj15Wpsa18yryrLpJQC36lq5drnVTuy/lqfHp1yiIaqOGCJN3eDcRAzgFwfoTLYI+JAQ8lsbg60lIuF6vbLMM3d3e9quyQUWms3jI13Tcr1cASHubSXlGcMgVqpxHACxZy+z2C6Tkn3geJBnq8q5LSFGXAi3OIy/aa74u8gE+evfpzwEf/2lQR7AruX/9n/577jf3qGzokxn1LS82XITl0W3BMrIOfsGrrz9uaVz3S0jP/7wFxxfP2O0MC7OLYzTwuICaOmiL5vDnBmPqqmy7xW5WTc9m37L09MT+/39Cpws88x4Hfj000/88pe/4noZSRE2rWK3t4QQuVwjdw99DlU0GK0wKkld3rTg5kRCM0+ygD+9fyTpSEiCwpWaohCg73dc3YJDC0sbEk1O4q+qmkDiMgy4KH63afbyMytJ+i7tIV3X0rXd6gMuftxpmojOs9/v2W42a32mC5GxBGttt5JFUFXZQ+hXVUdKObAqH5eLFDtEOdgs80z0cW2rUUr8+z63xrz/8AFtKrrtFls1fPPNNzkoVNDWl5cXCRpTYDQ0bQtJrCyfP/2E957n568sy4w1Ksu2SiCQui3EmQ28nK9v2EQJPW2rWvxwSBaEMiK7kprAmsfHR/b7PcuyrMF/PsX18FDC0i7Xy+qNn3OtYt/3a3BjWbxSShyPR6y162G673vxKXqXlUtSlTmcL+JNzUNQ23WEmJgWyTwQb51hmof1uRM0PK4svhx0Jqlsza+3169kWRSPdbkHJOzLrf7bGAP7OLGPMijVtc2eU0nx19rwp3/6pyzTxLuHB6LzfP36ynWU+92h2G4q6krx8f2OrqtzRZf4f2NU/PT5gI/iqe56u/q0C7A0zx5rNbaquY6By7igjSUAtpINXhmdq9CaVe00O7dW4MU8wEqVoWfXavoKDIEUEqdpZoiWoGqpnNWK9+8/UFU15/OFy+WSD5+SjzMvS67T9mI5WQMvRWbZ5oyQEnplTQkOXTKb3qzVuSUManbLmqUQfUBxq0Et4WLv3r/n2++/Q+pcpU0JrdZQvdL0U8AMuG2Q5Vl0XgLR5H102LoWi9GyrMG7pQkiJXh4eOTd0zv2u3tOxyOfP39mHEeenp64XM4olRiHgWkamcYxD/ezfD5ewtgKu9R0UrdZ1BFv70elYXFLzroxKwOGUjgfuLu7x/vA+/fv+T/8F/+EZZmZp5kff/wNh+NRDjAII9JvtmhlGMeJeVmomwaTmR6xvF3Y3+1JKQhLPgiTFryXbAAt+5r4fv0qbS211uSfo/LeFRLrey7eZKkp1PlZFDD1/bt39F3PdRw5Hk9UVS3BzJlhislzncb1ehVgVB7Ht0pIfsY2O+fw8yTAZ5KgSRkyRL3kowxhaMmTCdkygpLWNoUEw1lbCZidrVrrei+bNFVT03cbYQq7jq5reMhqle1uh5tn/p///X/PnKXC/1te/7aZRCn48OGdMODA//Gf/VdcL2d+7/vvqY1Fka2Yf83+RxmO39hh3v5VPkNNHjLyn4l+4fX1t/zmV3+GjkGY/tmLhU8pfAK3BLq+Wy1KJcvAVpqHhzvatma723F/d0ff91hTIfaxwOn1xA+//jWvXw+E2ZGipOp3fU3TVoTk2N3dQZJq8ZgitrIcz2ecl2u/23Vs+obaGIbhStO2bLuWTadxbuI6jJzOZ9qupWpbrvOMC5q+v+fL1wM/fX4lRU/XNSxLQGtL0BW/+L3fo91s8TFR2Tr73gVoi8Gx324ZrgO/+su/YhoXWmu5u+s5XS54n0hKKhLrtqHuKuquo243NP2GummYc5i5Vpq6aeSwldenkG4qVHKTjezZYuuqrDCyJWy67HkxRmFtM6FQDtlK6dW6GHJgpIClNn8fafYKsaxVUoEuilnFMi2ivslhqCmE9VnXRotiS9tsq2UNuhYlt8nKObIVIq15W0KKzGJ9SilL9LNVJ+cbKKVlXuXNgUopYg6PNRn09Dn0OKbyJiQXyJj6ZwDgzTpi6LstD0/f8P33v8fr4YitKk6XI0rBw8NDZq6vXC5iy9tsNoil71n2BS1V9/v9HY+Pj9TWcr2eeH7+wjhcMKa0RdocOCmAh9ZyaCxtGdFLdtGGmYdG7HqFPEs5YLGEcmstLS4S9ujWMO4YBfCYxhGUgM+bTc84jjl7IHI6Xogh8Ae//wuapuF8PrPZ9IBk1AUSP/z4W17Pg8yajaiDwxLoupanJ7E5HI9nQkw8v544DgEXvNhjkkJbQ9darJXcwhQ8fWupjOLubsO2F7tgDJHg5IDZ1C3H4xHnHLudhK9LsOitUtY5TwCen18IIfH+/aPYXnJ2mLVZnVjXWG0yyy4NW1+/PmOMNAVJNp5Z59QCfBeg/GdKPWGlBfhfZmLwMudn4NIYS92IYlQywsyqKi3zQ2lAsday2WwwJquLM9AUQ650Lf6GJEUGBWQojWU6B6FKiPUtCHmz6WmbFqUV58sZ56R6t66k7W2aRqpMehpj6Tdb6mxzKWGziwvMbsZ7JwRvgjnX60pQsVnXjaqq5PxVVag8aynUms1TlC9FYT+OIz4Di/u7PQrJigve44NYD6uqJsTEdbiyOMfd3R2btlnVETFbaEp47dpOEwLTOGKMoe06AeGCz2c++cy3261kEIZSmJAzFqPk55XzidImWyPDeo5eFsc8udUCFHPYMUimCVrsvkpruk232v+MlXWmb/Lakz8LpRJVZVHK8OX5eSWFTqcTi/MMw0Tfd2hlmX3ifJkkXLqUrOSzVl1Z3r974n/45a//5jni7xoEKd/r3/VjrVL8V//lH/OP/vA/odI2y8xUOZOX7yISvyJpBZQqdU/CTitENmiKFYPsB0yBebzy+dOvGYcj03glBgmscj4Q0DgfaSppjBmHUTbWVrqUfZYBbzY9u92O7XbL/f0D/WYjOQxJkOCUUea/+PO/4vB6YNMp9vtGNiQPOgfjLC7bUSoDKRKc53iYGaeI1fBHf/h7jOPEcbiATtS1zQO5+F7fffiObvuAT4Zx9hIglBUA4zjmKqZE1YmM3piKgMJlZLS0P/gQqSoJ9wOE+chDYKWNeN6DFyQuJi7jjNKWhNyASiuaXC1XVRXTOHEZLvjgiSrmpptceeRFHkyEZV7QSeUgVtYU9BKq+vG7b3l6954P337ENjWVFSQ8Jc0wTgzXK1NuiyGJfP7l+QvzPPLTTz/lhdFRVUUFEX827JbwxHKYE9bWZ9+zJFHrvNGAqDd2+z3zsojkVuu1mqs0wcSYaJoG5zzvP7yXjxNkM/ReGJUcDFjXdZazCqMVotSHFo/oZtszTiPjMKyWmbIBuEVC9Iw2qDzgfPfddyzLwp//8pcss1hL6qbGVnaV8L28vDDPE9qw+uhKveDbwX/dVPJ1sbZaD2BlAS5VwynJYXofZu7CzPE807aW/V2bZYCSNK7FzC3SVReYpoDDkpLhNM5gFH/4O4+0lcgllcphr1oRvITTaWOEdQuyENraMo0zu12PUnKNxmXmcp0ZZ1EC1bUEzno8SWuGMeC8Am1o247ZOXwItH23/n5t29LXFtyAH15JwRGTYfIBp2umaIhZil/Ar8vluoY/Gltqoc16vZZJWIPSgCKHyLCq3Yw2bDa7W2AvogaYxkGG9+AZxgmQPA2AylZoVGZKJLQ3xoiPko1Rqkmdc7S9gJ91VVHVeUDOmT5yiL2FyBljpDElX3NrDXXbykHZi7rAGrENifBcrDAaxdPje3a7HS8vLxyPx7wZy3Vc5onT8SgHapXfV27oij5kMK+XVodsCXJeWMKUbflz9rg3TcPTuyeCD2w3W9q25Xg+8+7du5zNIT7ceRrZbjdUdcVms8VqCbKVQWmLUjBOkxyoUuI6XkU9kBIvL18zyDHj3LzWhPoMglgjoabOebwrgcclpNAK0GHEtlJC0pTSa8aCzTanUg0ZIzw+PmKN4e7+kVI32LYdMUhg8/39PcM48L/82Z/JsFWZ/Izdrqkkp5uVHBCfsiHFxDyOt5YcVaofBdgMSbJkYmK1mIQg+SDG5uwMLXtrYSJ9fAN6l79U3hNyWK6xhn7b50FV1pqvf/XDanX63zpH/Nv+nTHymRpj2HQd282G/+7/9F9TaZub0+yqEGXNKBMlWyFU/joYUgZWilU3zx4xBLwf+Nd/8s9Jy8A8XIg+Ms4jXilQhnHy7O7vMmM5o41ah3tjNQ+P9zRNy2675fH+gd1+h6laUpL3ezlf+Rf//F/w5dNnVEzE3I6VCFR14vHpjrZr+b3f/wOapiNkO9LlcuHweqDvK7rGEL3n+CqsWVtZSI55HrP1z6JtRVLS8DJ7T9dvUbrBLYnX1zPOS22qMorLtNDuNui653qdVvtt8BFldCZgLCkkPn3+xOl8yXuttPrI4ahht9vT9h0hSfOL0pa+l5DgVEC5DEz4EIT8sDeAwHlhtkVRKsO2zYGU0hgn6tR5Xih5XRDFypetBWV/kz0lrCpjldXE0nYQkYpgxGYbJQNgmRfO54scIGBVWxpjpMElP4shOqKXcOrSeuJzcGFZW8ssq1SxlEkDGwqmSVjdBFzHa1aZqPUQTNQrqFGe46TUrSlFK5SS+u+Uv05rDTGtEnWjDT74TDZkUkoptG34x//5P0Vpg62rtTnr24/f0m96fvnn/3pVl9k8S12uZx4eHkTdqjTLvGCUzvkmCyEsxOhYloHLNYcihoTRdn3eqqrh/ftHvPd8+fQVErRh5KlT1F1u6hjn9fPc7bbrAWxxM8qYHD69iJJRSwmAWxZsXZNQvLwcmOYFrWCz29FUlk3X0WdLslKSA1eWmgIQD+PEy+sry+J5eNhzv99jbQm795xOZy6XmbrpCErUeCFn3Dzc37Hfb4kxoFUgRU9Ti9IoBiE43OKIwWO0obL1alWEt6o3tYLefb+R39FWkNUS8zxJ24pStK0AHzGrfWKSprG66TDaSMujrVZrdF0Xe6toiMrfCzFY4gxCvt99biVT2TZWLMkhenyMmb2W+vESrvm25lXUnNVqJynPAEie5OV8YRolz1CrXBmfleC3JpRenn2t1vmrfF43gCW34yDP3zhcOef2Gp33M7H96ewCkKwOAbFyWG2et/b73QrCBLesIIQxhnfv3q9khKhGBIgCsUSW99V10rLp3JJVHaICL1Z/Y0ror2aYJl4PB6qm5u7ujm3fyREqq8NJMIwj07zkti5FVYvtRCt5/lUWCxTit4DAbdOu62yJKNhut/goZLiUjmQLfT6KqyRNrCA5RirfdxJS6kgKqrpdPzujSyOSwRpFCpEUQyZ+AjF4pnnKth5N19+u1+ID87wwDDN935KSwntphVvmGWMEzLsMI7MLGK34+P6J//GHT3/jHPEfxQ7zN+EuVW1zMI/HKpGo6zfgBkDMXjOVfWIlZ0QW/Js0P0VQJQwrFQZHHsD9/oGmqvk8O2KUTJAURe4YFfhF5DqVMTRdR9VIsNx4nbDWMIxDZieEVVjcwn6/o2laNrsehWK763l8uOfTb3/ky6cfVh/cOE6rmiDm4CRB0oRBqGvLdVpIVjPMJ7ptYFkuMQAAtuNJREFUx8UpjteRKirmuMjDahscinf7B+p2h6k6lI0M08D1ItL2eRyFwczoqa0qmq5jCbnuywWarsctnsX5POxP3N/doxUi2Y2JcbzStU1WCMjgW9fyvcZxlMVJW/Ecvhx4fHjg+4/fczh8ZRhFnmeUpDDXu44URe6kreV6GdaAoIJ2Xa9XEomXly9894tfsNm064Ze2QptO/Z3D9A0nF5eeX1+pq4stZVAsvPxgNGKqmtQWiwS8t4TKGGdZYi/LZbjKDkfzjuW4SqHfhR9J5tMQVovuStc8lQ80zTRdR2Pj4+UmsrT6UTTtHz98pX9fs/940MOuvRYW3E4HIhFeZHALY7L+bICES5biK7DeUVD3x4yRNYnNp3vvv2W0/FEpTVfv35lGIafWcHKAdham4MULVW1EXTae+ZZKqqUlrwdlFhdqqqhaQTBLfYylMrsk8gs66rOvtSEczPquuB9oKo0w+gxdqJtK9q2y3kMEv55uV5J1rDEhessLF5tDO8edyzTzMvzJAUBJHbbZh3kE/D4eCf1qcqw3y9cB8dmI8+ateILTygu48gwiTR5WaQGOTjF4XjFVD3aGLrthpgkJLbVCmMN07ywjAPDOHCxluQmkpslnFknfNIseJK+WZoulwt+uTFPSium6xWf/YlGm9wYIixPNJpK66yIEgDMWGE9haVJmb26ErJapzCVKR+K61ZCqcQCp1blgoQJutV2c2MIJXW/qIqKgq54L8VnLINOGVSaJq2DS8oKKx9uKepKl9AwsUe4eYEk3vDD4YXrdaRtaskkuA4CFiwzUimr2W03MogoqQadk8N78d67EGR9zMqxEALG1iuzUdc19/d3K/Dlo+fHn37E2orPnz+vgOGPP/5A9I6ffvJolZsgTM4/MnZNnZ9zorutpM2rNBYM40hSKQeLGeZlWofRJbMbKe8BvgwB62FKSzhtfh7lcGPY7e8IUZL9nVtWwLFpGrY7YcRCHhDv7+/XobfdtuvAsdtu+U/+6B9yul5yZs/C4SC2plI7tyzSsFAsbaDX+wfI4G74GahQ8jV0XgfIYeNaWxQRYpKDeB7ADBVhdEQvw6AIJiQHIQZPcEu2hQWOrwLMl0aBvyUP82+8/rp1pfw774ssPnB0F777+G2WYsvMEGNaAX5UXP9cyuy/Wm23Os8a+UCcysxRwMeinFI8PX3k9dOvMcpQNYa60czeMzk5HM/TTNVUVBk4S0kCwUMMvLwceHp6ZJwmTpeLKAPaRNNsQGvuH/b8l//0n/An//JP+eUvf0XbSkuVVZoQZHaZp8Rf/vKXPL57x/7ujqaWLJLPnz7x/vGexWoOL69cTwPDFPj4zR63TFS14fHpCW0ti3dyIKTCosFBwJGS5vFhx+H1SF9b+q6mMYHLfBX1g4trSGbd1NT1hq6pMHWFRvPNN+/ouoqUcli0ypXlSlPVDVVlpM0rqxp8mNGqBSXPGhn0CEnk3IURdvPMklVxBQDxwecaannuCzhbGZkD66aXK5dVCtEYnFJ5H47E5FeCAaRPT5GtIhm4WlyQOS5GkvdUShM1NxVCXqedy20WJbhdq5XxLPXeAoLANMlcWPI+jNboYEjztK7dIXjqtqFpGsZhWlsrxFYXMRIMIfc+rGCRNGQIOF7C0o0xOQxYrYRZAene5qds9juq2rC/2/Dx29/BBc8Pf/VXTOOVX/7yzzJwLoGnT4+PbLc7TucLqCjVpMisRkrrQdNP0jQUQsJ7hVYNfb8REDx/1nIAdTw/v6CyIsA7z6611LXBzwvTNDNNC48P9/R9n5/9sB7+pyWw2XY5i8HRbTaoqiK1bc6fi/RdyzguNF1LY6WRp2+7bK2X57uoc1CypiyLp6sbPr5/t+670igka1qMidPpyjxH3r//gK0rUWhHz/7uTjI2/IzNgG1C5wZL8tnEYlorAaBar3NnOVSXufOtmr60G6Yk4FVdW6zd0HdtVkU4CfnPqmLy+jVNjtJSAuSyAo338j5kDtA3dQa3MMqqqtefW+XvEYKXwNtlyDOQtF/JnCHqevIcUX5m+V3Kfde2LdYYUc1oja00u+2WvutW4qHMxsXeXe7XrutwXpQS5fsXQKF8TblOMveIksv7QCSga0UIHq0rttvNusecLpe14S/6QJPzZZqmoe9aIbfW50ZzyPXpOqvUvHfYyqwNeau9ErW2mxQAZVkWtLZUVSPtlLMAFn3XUjcf5HtpnYGN0jgE292WaXGcTq+5lVHUGdKgFum6npRzFaUQoFnvmdXGkiQC4nodiBE2uy3zvLC4iZRnrCoHSS+L53q+EpXhcDytWSZ1ZbkOg9wLIRHR1G3Nd9+8JwVPJUIttNY02Wbo/cI0j6vaeBwlM0XI5FbagjY9VS2Knh9/81t22x193/Lu3QNVVXE8HrCvBxEHzAvOz3+rOeI/CgjyN73mxfFyuDLN0jmslc1KjpudJkclUeIdUTcWp6CVJSPkxuhI8jdaUzcd1mpc3zIvE4cXh61ly3PzAsaw5PCdUlFZZ7/R62khELBWM1yHjNqK0sFo8fTtdoIMRwW1Uvzu7/0uXWf44dd/weIcKPG1H48zxmp2u46qqiWJeZ7wy1XSi63ldBk4XWdOQ+A4RdSoudtVqBlimtjcRZKyTC6yTGcWL13dPiq0qdnumzVIU3ycC/M4gRX5oDUVTdPSNmplHMZJfOo+yrg3TCNaSYZG38sgsdtvqdse53xGy6UWttg6KmPZdj2nV0jOMS9y+JZk7By2qHKHeZLBom279aHfbLpVuXB4/sqf/E//I1VVcc2Sxvt3H/hn//V/wzzNNLVi01q0hhCEIX58eiTEwPPz889Cg4rXMYSQEe+bAqQABE9PT0xTRoITLMFhdIWpjAx7VcV+v18VJOXgWfqtBUgSQGmaJl5fXzmeT9R5wZYAqIkQZGEtLC3AZrPh06dPnM/nHEwpB5hiudntdoQQeH19ZZlnfvrxt1zPl3XgKZtKire8FWMM/Xaz/myTJXaidGmgLaBhyoqeRuSXiwdkgYw+MEeXD/0l9Cmu3yumgNaetmnpDTQxcadvtZbz7JnnhXlaMoIeMHVDMjULI4lEaxR3u4qq3rC4yPEqTOXkJ/ra0tUGFxaq84X9bkdKkb5v5XeL4HzecFRFBKYISVv8LMF//XaLv17RlePu8RFjRWYd8r0eQpQKwWyH0Ao2XYdbGpa5yYdYS5VA5+rlYRgoIVqEJMy3UqKYCjGHgeUaUrLyppdqtyqniVe2AqWEMUpxRemdW1ZbRVFYtXVDv5MBJWSbSjkgxyjyyVhUKET5X7Y0mCxzLeCAUrdQ3BDcykSWDTolWUdNrvpbrTLlEJ0SEQnpev/+EWsqrmephe56UUdNw5nryWeJbBQ2MkQq02RljyN4GeiH6ULpfV+WJecKSVCt1lpk41o8p4UNPh6PlAq+8u/2+z3jOK3Xpm1bpiEwzYvUGwYZpsqaPYyXdRhUKqysk9IyWHVdxeF0zsCINDGUoUcOR3FVXqQYs8LlFjpsjMY7n4PyLInI4XjIg5ww59ZYtMmV1ESxpiXN6XTk9fWVupaK9rM654aeYQVlF7cwjAuXyzmzg0pC7N7UTq7S2QgpJEi3NoTyKoMi5HtA3fbcyuZ60SRsT7HtFelsSkkCrPP3CSHgcwhqIrPqVqOU5EGEXMn6vxcEKevm/5oipPz3H3/7E0Yp/uD3foe+7UjRkM8TK+CxvkcFNqtcJHhZU2kjB3dy/bkxqyVGGEXF/cMj83CEuGBIoBx6gRQdqa2YwoL3MtCV56jYJLwPvL4cmSeHdxHnpLFov8/gpraYKvEHf/QLNruKX/3FD1xOI0lLFlRwogn7zW++cjmPbPcvDOMkCfvGcDlfiN5xOlyIscJ5w7/+1YFv3m/YPd6zxERanMxQpqZpKj5/+kRIE3f7XhrpEmzbBrznehyJKXLXbvj8fAag1op5vEL0nI9H0BpdNbnqu2K/28vgnp+LQMp5YorGKLxbSE5UX7OLzNVCVTdrmxqIBcIHYQQLqL+uScj8V+oYoxM7YG2bTJ4JYZZcbsECYj6gKFWKziK6NMMtcrgvKgNA1B5R3omEgCe0SmzaihSkuWo6XZnzgWjxgaZtuX8QkDaEIDlP+RBXGGpUaQkxeS7KcyzF4iP5XmSwQoDYagXpUgYYkixmYnFR6pZvldd3ozTFAtu2cnATP7/MRSV/rcwXKSWc93z33bd88817tIZaWTYbyfgx2oCSvILn5y9cr0fZS33i7v6R3e6eYRhpKgnid4uTtTAfzlIyWLOh6rdSA24NbVNRWcvheOA6XCSjapnlOmXbofee63VAa8VuK3WyxQ4j85dkHHC5UhlD3dRiX5hnqnw/lb3ybrdht+nWPXSeZ4bhQts2KFQGa3PdcAbp6xwoa7VGpYTS5DyrRNe2KGP5xfcfMVaUDcEH2oe9rC3G4KzcpykGShNMynkWoIhevndCSeh3VhVXdS0WGQKVlfy5qOSQYo2VWSF4TLC3TDotwcJawaIXUhLQRWcQMqaQg8ULcCzqU6X6HAB6O0/JPBAziCLPhCgjF4oVdBhKDpjBeckESfO8AvwxwjY3sEUT8j0sIErwgXmaWOaZkNWgpYCh5PMUNfGt9Uhs7UXNdTods1XMrGqrovYuz8rtjEi2RG3W/XxZFlTw+fyxyRZuTd21Eo6vFObNmmCtlbKEnAfost2IvA4Vm54QUJLxVrJ6ZA0TG0tRUhVlWnm/RRlaQkZVBkiDd1yvNxuzkKYSiPz+/RPTNAKJaZrp2g111eBj5Hg8cjiceP/hkW+/+YCCNU9RlD+iCJI2FsvzlxdmtxARFfs4jiI8SEiAbARbNyyLo7KGpq1pWwHtZucwKRGCFAucL2f6rqXreqxRP7OkJgU6K4sqa7PVV85yRXUslb014yjBuVJzLAD2NMna0vdttiCJivtv8/p7CYLEBKfzlf/5X/0Z/+Q/+2N2fY/Nw6pROeSpbIwxEpMgmymVdOySCXLzeFYlyCoJcBKCx82Oy/nANI00bccyjhidqDqTB7zANDlSUkzzLInQmw2VeQGl8E4Sub1znE8nSOL1v7u7o+/8epFTRuuePrxHV/Djjz8SGXOis8N5JbWRWlhEazRNWxOVwQdo64oYBfhot9LzrpAHZ39/z8PjN5zHmboxhKRQxuK8R+XgGPHoBkiRttnQ9zu6vuL59VV+TxSLd1mea/DDRFWJfO58PmcQaIvSstkqrZnHmegctqkx9iadryrLNI14H7iczxwPr8zjuKoSbLZeLMu8yjD7uubufk+ptiJJEK74HMXKsttJ+FZtFWGZmHPF5b/8n/55vqYiO5ccigapqLIcTke6rluTxiXd3kgtl5FaXaUUwzACUpXlvXjmqqrm/v6Ruu1A5xT8y0UAJCU1Z13Xsd1us1XAvjl0aOra/2whP5yON6Y5L3rSLy4L8/F4BHJ7SJKQV7PpiSngkQ10niau1+sa1li+9ng8yaBZVZxOJ2GSjMHmMB2tNU3dsN1u1/frlpABEgGahkFY+s1mw8PjI+/ffWTM7MEwDrJBLQvBy/MVgmxq03SWQ4WKGKNocYTguA4SVlWkvillwKSWStmml8ybqk5sopWarBg4nQYSI9fJc3IJB9QOAorztDDPgcFf+agr6spQJTmEK1Ohaw3WkowhpkhnKrFb5bCmJUQWD1qLdLRpm7WubrvZyKaNwgdZtMdxJLhFDpWVzaoKCV40OjHNw02WqjTbfb8e4FMqbTQ2B75K5Rf5s0uAzvYigRwUprKolERpERM+RpS1dE2bPboy/IoUFZpWgMzS4AFyABQgc2DJAZ1VVVG9aX8qoCDUhCDDTAi3ppVyuJTgQYPSslVYY1YrhEpJWom0Rkd4+fKZaZxXhn8cLlS24tuP3zAMA+Mw8PzyVa5zk7OD5oVhGFebl8pgh3ey3mboe32+mqahaaVyT0DMGqVgnMYVJLqFrrn1cxmGa/YRi40mxpjbX2Nm02NmNQ1fvlzfHK4jhxLcqUSCqYzJ9Ww6K3zEwlPyWFIQCa3Kk3ORLNvKSrNELEnpJfm8WRmdqjLrPQaKpu7Z7e7y4CRNTZfLhXG80LYtdd2wOAmimzOT8jYVvYC7xVsrB4WKaRAbofyzXgfGopZTSvZIOejdmqSS13m41ti6xuThr9hqirVGsgHimnEEZB9JQpmsyIxyKCw8xv/e178LRFHA8XRivF7QJvHHf/RHcojMNgVNVn/koVYpZF9WclBRSg4rcW2pEDWqsSaD96ARdeXD4wfwM34WBWNVJboEWjkqKi6zwzlF1zQMGXC2lVmVDYfDKbOGfvWzw4ZEBrOU5+ndPZWt+PN//Ve8fD1RGZi9w9iavpP8KDNq+rZndgtdrrecF0eIhmmK62HLBc3pMuaWDiPrgxb59nffPhITxBD58nzEWM08TRLkaBR1Yzm8HFkGh7IQErR9Q0qB++2GQOQ6zVzmBa4KW1142O8RZeOFy3Xi/fsNd5uKtjKQ4Hp1/OanrwxBY7d7Hp7erfet1pqUc3dKO4OxOh8S4+pnCsFJyGmIhKi4DBNucRKW6gOzc2z3W2wl+40EBSb6TqTlPrO9vqhEi0qvqtEpoZWRr1skjDHkw1fwgegj85Cb1xZZv6+XgWWZqWqZTcjWnGma1nW529za4Hxw2RMPtq7WNTgBWllRwGqDzuukykqT0tryViHg8wFba0MMEjhY6VILmhWCdbFQxgzKVGw2Oy7ns8yiWvH5p0/8yb/8F/z+7/+RNBP1O/7gd/9BzqiY8cHzl7/6JYfXE95LJtriAimKEmyZZwHVnOfh4Z6u7Xh9nbG2YZ+Dz9u2pWlqoveM44S1Cx8/PFDVFfM08OnzFy6XCyFK2GkMnru77XqwLorcAiobo6U5UZUzg5Cf9o06pnx28txLS6DOih6SEH7OLZAyuKaEoCoHVFUsG1FRW7HeFGDaaI1V4OaJFCWENyZR4rQ5uH2cxhWM2e12AtRPMzEulNreogg2xmK0tGUs80yA9QBc19L4ZqMozN28QNvRtA1J5zVKW6q65/nHZ54PV7qu5tsP7+j7FqMV0bvVtlHXFZJp5XIG3C30vHxWIOUF0zSu2XbFRlEaC918u8dLfp1zAnQM1+tqNy7glc3qpAKYaC01tWWNL8SbXGchRIqiogB74zjIfGEMPt8Tqz0sA6dindqRYmSexnxuKYUTgT6rrWy2mCgl7YTyDN5yd2IIctZZRHF5a34Z1r0dyFadjhADpaGsgBopFQdAXAmc8szLOlSUaRkQC15Ub5nUl6wbyWqZpgMhhEzaNOv3K5kkZZa/v98TY+TT589CCiSpHe66Lqt/xELW1KLA9c7hYqDfbOjajt/89hOaxKbr0Gh8jDikzXC76SRbqrKM08I4z4zjxHZTE4Pjegksw0jbSJZfkwGjtmuwVUPdVDS2oq4XnPdcLlfZq9zCOE4sTlTM2+2Wpq4yqSauh8U5jNZ0fcduv1mVKX/T6+8lCAKwLI4//+VfsttuePzP/zGQ0VJymB0F0QsSDqhS9jCyKkVEHSIDYchhLgV5UkpRNQ1P7Ue2uw0//vov5dBoDcHPdFoBM9EnFhcZrwNKa5quo28rZid1YYWJmyYZ9ITxnpnmmd1uR13V+WGKWKto+y3ffPs9tj5wOV0wlaUJsIwzo3fime4a2qamaaQarm8bQgDTbGm2D2zv7hmmmZgEhYvKEJVeZel10/L6/Ip3HlNbmqbDe4dKSdhVBSFKAjXJ4yOQq4bHYYAEh2limV1uXJAFriSm17XkS4QQOJ5e6drcxR0jm77UIVWSL5tSlsUalJIgVFJCFxArBKZFAKeYa3jJ13eeF1KUNHNjWsZ5YJpHjLW8/yAywyWzkEYb6k3NcB3RWtQsd/sHXAjM00xdN1mBYKQve2U7RZpsq4rKShaJzQfNeZ55ORyAUw4ulCGs7Tpsrru6XC9iZ1C3nIzSuNO2vWzcmV3VSuOd48vnz3nTdhglsmgJbZzYbrZEL97K77/7KM0zURYlozRdI1L4y1XyCrbbHfvNnuPhtKLWHz58XBdAt0gQ0zSPDOPEOIttp7KSD6OVIUTJwrA586Oua7wLWa0hHel1Jax9orDJAZDgMaOlskupRNNW6OVMWFzepDXn00yQ5kR220ZkjihhVFzEpZmKQAqKprFMk9hWhsUzBXKgKaslIypQtsbWG9q+FyAk94qHFPFBVongcu6ASeiscvI+oGzFZlOt0sqySVeVkUDZXP9ogC5naZThUgY2Ab4OxzPzMktdYtvSNi0Pd3frRlbWgxIsVtYhKJuffObFM70sC/MsrSfayoG+yf7ayt4aglCKpm2xts5M/JTtFB6bQbwQ/MqSvM15KcPijYlwhODXg2rZdCU0WK9qj2WaBKWvKrlnizQ3JSIigQ0+MA4jd/f3tF3LdRgY47AOp9MwsO02hCBsiWTiSPhg10kzUmHE5mnC6Cr3zCuKRD3GyJAtRgCj97ntIK3DR3nWmrqhymyAX4TNiEFA6yIB994zjnpVxmy32/VapZSIyRNcXA8pxlhUtGKXy6Gw1lZsehm4peVlkSE4y/vXwD5j8nCd1T0/UzHcvPnlAKy0YVPJALXb32O0ZppGrtcL0zyjjObl8Cq/R/4zt8Mzq0pNauX82nZVwsliDup9yyalJDXr0gYkTSYFYojRo96Eq2qjchPLTFVpvE9ZNg3OzfmgJV+j871X9ueYUlZZFovqv8+08L/+SvkvFyKfP3/mD373F2yaGmPSOtjKtc6VaJQ5g/Wek/s8ootupMjJV2xMBtLNfk9wT1xO4JeRGANGJYwSQN+khE4SzGmNQpUDvjJcrwLEXi7Xn4E6i5vpuhJyXpG05e5e84//yX/Cy9cXXp8/Yw30fY9SAzGpFWydhpFlnuSwRkJXFcP5SgI+vr/j44dHPn36icskuVH7u56+02hE0bnd9gSf2GxqXg5XYnB8++2HrBjR7PYLx+OFYZlBQ7/dM7nAZRjwKRABXRkuw0KDZfRyOJ1cpKkV3z3sse6KOx6JUREcdJXFawnGOx1PNG3LbrdlWRbGnG9Q7Kcqq6zI64PYlQRcmsZxHaBDCHgn64WtDK4yJJcDsDNYOXhPU1UrExtDQucg8GmYmOJAyjOSNooUZQ71eQ2p64a6aUlIuHK33aCXBR/DGuwsAfsJudU0ZFa9KDPlObxlHSUlB02Uoq8kFyKEiLhVJdSyZHQVpl7nOVDuTXnmmqomVZLrIOHztxrp0hSjtQR/GmOzbVZa5qZ5ws0Tf/Hnf8Zvf/yRX3z/C7755juUMuz6HtQGW1m6tmOarhwOr2sz1nS94hZZ6z9//rJajPb7PTZnn4UQeXy6Y7vdEnzIlsLEbnuPrbJqrN/yzQdD151ZXn5g9p6+q9f8CK1zLapK69xTFG5Nc1P9QlrV4UXlUA6cZV2FzMqnEh6fMxeqSgiKN3sReY2QYMtbALaA9TmYNwlI7puKeZqZl5ll0WgrOW0lrxDICuiAUmZVaiglbY3TvPDy/EpVSa14mTWL0liUMHUGS3JzS7Z+l9aTnz5/5Ve/fSZEGOaFtqqALcEvNPl3kxkhMU3SMlfXoso0eW5PCBiRYsoNTGYl+YoqcJ5FLW6qOhPPcd07vU/rnuPyNer7HihKTwEfSiOcXDO72rbK3la+X1FJmGw/rmrJNKxqsd+V8NiUpNZ6nqSC93w8rorJ0pbY9x37/Y5iszFGo1VClWavPPOnkIH3lETNmOdz+euWoSc/91Y+IA1QzapGj7EA8HoFZ4q6rSiTSn6h3F+yjjsnRQSV1djKYExNqcUN4Vb4MI1TVnrMGCWgbyFBlYbhel1nppBn+ioHp74ejuics9K0Nco73DJR1zXff/uOuqrRaK6Xq1g4U2BZJi6XgFukevnxvgPVSzjrNHM5XzkcTxCLEifx9PgoGXVTQJvIdZx5//SIti19nTKpBddx4ny+roRNSgEwWKtRSONWVYtK3+brX9e7v9WM8PcWBEmIF/d4vghLWBmSEll08aunKB5LkrBVJexJa6FzCuPxFmEDBBnNAaYkQ9c/8N338PL1R+bxwhRmQe1UwlpFjIIIumUhAW1bgUpsd2L7kHyIxDBMLIuj38gCoI0ldSJZt5XFmhplFU1f87HdcdweeP3yFTct6GiJIWC1pkZTIZutVUDwGCBMBy7LSFPDZrPl0/MLYZBkf6Urus092/29oHhpYVsrvLuCgq7reTmcxRutEIULgpiGJIen4D0a8dT2XU1b1zmnQALDdN4I6rpeQxGFOZHh9vB84OvzV0qInzGW/W7HNI4rIkki18r9HPBYpfVZzm/rClsJgl/Xza3tpq7xzkmLRFZ7VFXFbrtnGKbVUz+OE7/5zY9UTcWHDx/5z96948uXLxxOR/wbyVll++xvkwFl8YGkND4IW+Odp99uaNqWx8entXe7qSqu18t6YFIomqr6mb/veDxzvV5X5sUtM+M4rBvp/d2Oj998wC2OP/lXf4JfHLOZ2PQ9290Osnzy/v6e4XrlcDjw/PUrWtscZHU7PG62O969e0eMUQKNckArwOvrK+M0MDuRKxpbiRQ5o+dtK33wH7/5yHgdV+l9GSDk8xz59OkT8zKTFLR9t9pt7ndbjNFcridCXIQ9JdHUUiW92Rh8EMR7mZwcCqaZtCxYrXjctcSt9ME3bUNIjrrv2J9GjmMAYzkfj9RaAi53m0jb1lLnln2o4zRjrGGcRH2ijMn1Z8LKGNvgQyQRQUNKsmEW2aQxsn74uEAM6BTFl2pF8lvXzeppjlEYn7ap+Pabb9bMj/Iqm2gZqN9mzshfCqUlt8SHBARcjCQl7LpONjNPMtiRWelpnldfeYx5oFMC3Jpco+bcfGNGqkaSxTNz+vb9leHBOZ83WJH8Nk1F30tI1pjZStnwpQbVL7NUs6HWkDWfVVa1beS5OJ9F3p7Ek7/rpbHl9HqgazsWJwOVNdUK6rRtK5YgNPO8oE2F1qCtkQN+VuHIgCUqg5ABEBmthWEH2O3uRDJ9uaIU3N/fc393x+dPnzgdj+ugZ7NXuTS7VFW1WuC6ThgqHzUqSgaSWJMWkpLMEpHnRpSahflyThg/H6kbqYBsGgkd01ZJ8GIIbLJaY7Uv5T2racRKUtZFW1l8cvz280+MuXKzy3aKeZ5AK0xOoi+NQcXSc7lcJITzDSMFZEWgZB0IYKGyXUACcYuqSJoaEkmlVRJeVSJDloOEfK/xeiGUdVurNVzPOSfNOVYq6JvMaidYKz+VElWhxEP/B0JByj2vNS4kXBCGU2Gy0uM2FxQ1aXlG1rYyMlgDchBIElx7mysk5DxFzf3TN+x3e16+/IbDy4TRiqbO+RJKgF2XpNZ103VZpei5XOasWDVcr4NYxIK0kZTchaoSALZpN/Qxcne/5+F+w6//6ldM88w4zkyz43ga0VrRtVVu9JLDgtKRvpcQ7hgXhuFMv9lwGiM+KT4fRvpB0VSWephpG0tfW6yBptsRAoTkbp+Zjmz2DW2qeTkc87NsGJwDbWm3W+rNlq0HnSxtu2VxC90msWkUw5BgjITJ4IJnjhBoafsNgwskAv46cD5faJpKcj6IknWWQ8PbtpVgaedlHswX8HIZCN6Lai0mmgxKdl1LrU2WsNcC6i+e5CLjkPN88ro2hJjvh0hwAujXdQ0KfAZyx2lh8Z6mCitQ1fZdBoeRtkEXaBqLqSTI1cdQbipCSuCkzheyCsGImgGtsI3BmIq++3mOgKz/0l5Y18L6ksrnIJ/BMgtIqypRZPhYqkD9eo8jGFIGAuaV5fbeU1sreQCLKMTGy5nTy1d++af/syg52w3ffPsdj0/v6FqLoqF+/072E0SlE2KgqmvePb0vjxgoRV1LCL+tap5fXjmeLlTWsttKnbUPkgn1+ctndrtNVizD3d0eW0uWis0qtHE8U6xFMs8065oOkWW5WY+KsqjsgWUvaNuGzaZf98USzl1mNaUk96O0exRVYvle5cCrtUbptCowU2IlDAMBUxvQUtJQSNQyI4hqWGyRxMTpdGIcJ7RWbwJD1RvwR1Mqckt9fFrBG4VJCnSpehb7/tNdj9aGh/sdbV2TYlhD28u1n3PeSkppVTrXVbUC1eUZy0Xj6/svn38pCgAkjDV/zrKfpvX+ExWlWu2IJRektKUU5SJ5nZbn+ue27zXrI1tky8+5v7ujsqIskWr7Zb3nlcrWuST3wPl8XgmR8vmW/XlVq9SSf+Jz62UhNuX+KQBOR1U165zic4tjIa8E7LSrylPUHQ7npvVeKrN4yQCKMaz3WFHFaHUjetyycHmT4SOAlASsNlUDKTEOV4ypsnW64XK6rNaiMuPX1r7ZKwSgnHMIfCHulII5CFEfvcvEnJa5wMt+I/XNFmsSKTgpZtCKxhjsrqe2husgGYTBe55fj3CQs27Xt/h54XS88vTuHU2lcctECAmla96/+4b93YbD8ZBBG09mK2gqS5VtxSvBl6/53/T6ewuClNfheGCcrnRVJ77clDLAYTN7I8PX+spomQSfac7nC58/f+Ef/sM/Am4HEVGLIEAIim57z30M/PCXh3yDhFVqWSpphYmbhGWsKvqmpq5sVjMsuMVnG8gVbUQSP29nHp/e0bc9KEPShrrtUHhsXbPptxy+fiXOC36amYYBo8BqRWWFBTZGWOzoPejA+PqZ4XygysoVl+Xr58NPvH7+UQagCmxy1ESUCsxVTYwNwdzhfKLbbdluH3k9PGNUJDqRPm832xWwWCa5iUqdavSOFOPq8y6hZaW68u5uD0q8jJBDE1G0jXRClzRoMgOwycNyitL84PMgr40hRiWfV0ygLD5AnB0ow+PjO0Hgr0NmwBTDdcxseMhySstut2W/3/P09MRuJwhv23ccT6e1yeWaN7QyUC0Z6LJVRQMovawPVbEMGGM4HV4xRto9zucz5+OJPgfElsOHc2G11pT2jRD8inxLOFmiaSp22w2+bdAolmmkywei169feHl+5v7hgc1mk2vbrmy3DSXwcrvdsiyCSBtjOBxe6bqOn376Lfv9nsfHe84Xi8+DdFnsywYwjiObzYbD4UBbt+z3+/VZ2e12K0t+d3dH3TRUjVQC//rXv+br1698mac8xElF1n0rLMUyL9S1pPk3ShFDZB4c8xClR8RobA1GyaHAWFDJUVmNDoH7KnDXWPr9hk+dtMgonVthnOMv/vwvsgrEst9vGJFmjhA9RleoBGlxLCEy+oBLEJMFdN7UUr7uTeZ4IylqqAxds8GaipiEQbUlFDeJPF5nm1FKkl5OEn9nTOukkO9/YZGLPabIdWcvTEcJySuSUHkeWFVrdWVwfl5zcsp7lo3VoJXcU28379KsFELIQYQ3ufDPw9TSupnKfeFoqhq/lKR3flb9FpxfrRHSmJDyfyPbC4QZVUYUSN5LleOreSbEyMeP31BXNS+vL0zzJAHJzS3Ho+83a/tPXTeM47CyPG0eEiRY0BOdVKAmH5jDslZRNm3L+XjicrngsuLjdDzR9z3H4ykrm97Ua5JQuugF5Fq2bUPT1JLPkoeoorCwldwT5Tkqw9gwDPL5a01USWrO9a3Gz0RNSDMljb0M1zdJc7UqS0riulWKDx8+iu/WVjw9PhF94NOnT5Lh1LYsOXDUjX6tmS3XuKxbK1CrVFYzJPkMY1yBrMwt0NQ1+90uV3lX659zbsHmEGmx/GV/ePR5vU95jYxS9bfdAnA6nWSPzIy7MYakRAUmDFTgbyda/fd7hRg5nQe+vhzZ1JLFo7TOrXLF8y7P3c//f/77SqAkwKxD+duX1ppEi0uepGq6bssYPCnKmhaiqE90SsQgLH/TNNRNR3O4cL0O6zArh5ADKcke8vBwt4bxiURbqkUfn55o65rPP/1E9NICN4yBeRZiqOsaYghUVc2yDFQ2svjE6DzL85nJJz5fF1yCfWuoTcUwLfJn6gN3vQWlWXwiGYupGtwSMBqGQRoPfPDoqqLb9szXibrrabZ3pKTxHlFp1vJMSS5nzxwC1/PIMgT86AkkATrrSmTVfUVKnsU5DodjZp/rHKoslZjeOb6eXiSoFLH0NLUcBHWItHWDUdA2+blMwrBW1nIYRkiKZRE1mvM+kyNxVRxCDtFV6+qAGySjSp5TyXOrxGDN4md8ys9Vku9jK5Vb6SRQUDnQAWK42VxsDkqWuatCGzlgq1xxWeo0jTF5zRHVWNsWG42EyHdtnclBWVNMBmZfn59XtWrZJ6qqyoqTtFp6UgQfhNAyWuOSqK2rKjdgZCLjej4LSagNf/kXf0EqCmwjeSEfP37keh0JPvBwf8/333/Hu/t7sRNoDUkRkjQSfX15XT/j8/XC4hfcIiRX1ez49rvvZP86nbgMI2qa2IEo77QQmSqB8wKlkiRgtKnrzHaqG1CRrQylnj7lsO2VqEhQ1jFtZG0qmQxN0wjgntUKIYgtsRyu27aV7BESU1Z0llGgrmvmZWQcR5rciIS51ZmXvVlA+JFxGDkfB4yRZprNpqPve3a7TVmZ1n1FMr2E/Hk9Cjj4+PSINlZywXK4cFVZuq7h228/IGk2siYapakruzLsP8uUSzcA5TROEkSfLaclc6TrulUJUsCg8jvFKBbCMmekrPwrs04IYo0pjShaa4bcgKi1WtWpdV2vGXoFgCkAybq/VmLbKXb0mGeTQkq+Ve+UOau857f29Ld2lNXemmSHK+RuSrfsQFGRhFUdXVVCHm42EtR7uUhmkigxHafTGWvHrLoyqwKsrPvFSVDeb0oyo5f383avWfMIdWmXuqkSrTXZmpVyNobiejxxOJyZXUQT6foKrRIxaoxSzHNR20iwtK26VWUmM5DHB0dYnJDpSTKuHpv7Vane1jVd18qzp6DebFdwa5onaqtQvWHT14zjQtf2NG3DdRwZhol58Wjj8J++0jayZ4jtSTGNCyEcWZyQyXVt0chaVc5CJQOuKF//Nq//KBW5f9tXW1f8s3/6X/BHv/iOvlZoFTEFBMG8ebgSMWW/lc74pBI1iPeyWD0+Pq431sqOZXkXCUmw9wsvn/6S48snopeDwPU6gDLMs2J7t2cJUrGljWGXD4uHw4lhkiC9EFNeYBJ3d3v6vqduOp7evZcmE1Vq0BIKyemYp4nD5y8cv3wl5NDSTd+y3XRU1ookq1TeJCV+QFORrEU1FS6l3KWdqGzDNJxQfkaFBeUXbKOIymC7J3z7gZgsdSeMS4gB7yemWVBGU/y3+dAji3nMLJ5hcW5VIVR1ha1vcr5lWVBaFt+yaRtt8MuterVkT2w2G7abLefzmZfXV7FapBwelqXGMd58eKi0brAltPTzT5/WRSoG+YyWxf8Mpa8akUfd398LwhylZu94PK6I9DGzwwX5TopVUVIYzrWCKwMC43VcgZ2qqjBa09YNz8/PADl3QRpiygJnrckshSy0m20PMfL8/JWXl+eV3V4m8ViKR88wZ+l/QZS1sfRdz+l8pmma/O/yAp6VT7v9bg04895zPl8YZ7HcFBliZSuUkpRvpZWEUSXZpN69e/ezpPimaaiqSoJdU1w3dGsth9dXTucTRIcxij94qvnYRV6fX/j65RWQuj+Dxs2O5BNGI8G5GznI26ww0lYk/+MkIWZt05F0zdfzwOt1klBSW+ehYMvLywtWK7ZdS22zHNo7kvPoCLZpiO0W3bZEjFjHsNL+FESybY0oLozRogLJB6GUVFYlyNdL3pC05CRuG7v3svYI8yWtQwVwUEqRVMq2LpG5LsuCj4kqs+Pe56aizFK5HGJcZNQhOGl2ytkxMQOz4gVu1j8HrNLJsaivchZIAVEUrNXP5dqWat0igy5WGllGC9ATGYcRnSXWYiW6ZV9ohIVo6zqzHxWRUvkrQ9ZmtyP4IF33i6NtGznU5MH+6emJGMSucXcnLT8vr8/MLq9BRkvehvO0TcOH9+/56dNP60CC1ihd4ZZFWmVCPoyEJIoSL4NZ1/U8Pt7nNoQJ7xecl0YjY43UKHuX35ccMurmjYUupVVCW4CWEjYb/Y2RLuuoXBgJdXzLLpbrVdaGsi+J1UeynZTSdF3Pw8MDu52ovaZp5Mcff5TckzzMXS5Sm/kW3AFWSfS6BmlhcYoaz75R2MQQabs+Mz+RummIycvwF4QFtUbn4T43SSHDdIpKJNMojJbBSYZJnw/sZTCW9V1CYGUYfXk+87ccQ/69X21d84//0T/gn/7n/ym1zuHqqmR93FRSbwNi39rJSt5TUZiWNbn8t2VZmKeJ6frKePzKMhxRSsDRmBTDuHC+TlyXiG1q7h8e2O3v+PTpM+fTWdSQScIWC7O33W7Y7rZ0Xctut8tgSIWWNyc5E+PI1y+fOLy+ihopJsZhoamtAAFdl5U6Nc5Fuu1GLJBtx199+sqXlwsVic5q6kbWpN/9nSfaWkubl62p+w0RK81YkyMmzzRe8U7srLP3uKhJtiXpGmOlMUpnBYXYOYIADwqCnwnB4ZdJnr/gBTBve74+HyR/QxucDxiFPKdugSSH3KauRT1rLMHJvm+rStbSrFQLSRSd0ywAbyIyzQ7nYrZLa6yRlp7iiFL5cPk2aLlYdYuli6xeVVrCbbXR2V4j1648Vyb/DJA1NGS7XzlAlmrqVZHQdGvFaURyPd4GJQqYLpWVfb+VPCsUMcizabT85eclM8LC+EpbW7Xm/ozjlA/z8nyuFoiY2G572k5shOM4rcrZlAlDlzPpirILBf1mS7fpSfkgGLysOykmCYe9l4PSw8MdTd1yHaVt4v37b2janaiovRxa9vs9p5MA2bvdjs1GAt2/fv1KNXzmwYxI6HQOLOXWmlY+53XWL2HOgEpZvZWVbJBVDPk5UpjVmlf20WJXcs6J8s6Yn13PQigVcKSu6zwryB49DINcX524Xi/UdcN2syWE2z9LbTmrJWKaZty0cHe3W/cZ8uddWs3GcZL8uiCK499+emG3afnmw1O2nQjJUlU2527c5oP8f+ReTazkpNwHYVUCSq6WAPPeSflDIfC2u63Maya3bnGrvb0pB6QGVQAqCZQnN0imvMa5eaZYRsrvL7OFPLerUiE38JV5P0SPc/I1CsPD42NWiKb8e0T8Iu1RvFmfQWbzpm3Xf1funbKWF9CkyjW/5SxT7oNCMJU9X7KF4qoOld9d03W3nzHnbIymabPlRECNEt5ZlCjlnrupDG/nprctf8UGNc8zaJ2/b50LCiLX6ykH1NYYI7PL88sLdd3Q9R3Xq6hBNDcSrij8Uiqdp0IO1XWdbdWRYbzKGpIUxY7X9z3X61Wy4ZTKChi//g7FDhyCNJT6IOHX5/MotrgQiQnGcRaCpLY8Pj5AZFW0KxTzMqMVeD+SVJB8KmupzS1f7a1KaJ5n/odfvf6NM8HfaxCkqSseH+75v/93/y27tkGrgI5iiYl//YvVmy7qLCUno2KFPRVkUg4B6++Tsh0jHzzwI5fDJ87HVy7nM8sySbiTU9TNhvN4ZRwkH2R3d0fbtry8SuBl3TREEl3f88OPnwgh8fCw4+7uDltX3N3dcX9/vz485RVjZLoOvH7+yvOXLyzjSG0ELWvbhmUWL1Zu8yOhiVqj64ZkNC46Kq3Z9jsJBAoDw/XKly9f8SmircbULbbaUXXv0FbCuEzunU9JQppiRv6lTvDGKN6GQE0IEr40TTNt10GWRa92gFwRpo1UfV4vV/wiLEtKpeO7VFSKvHpZnBw2lb3518O0hrX5XEn19PgIwDTPNHWdw6Yi1liulzEPFSU9PIcbWk0MgSErNDbbDXf395S6WGPs6ouTvnGxZFyuZ6nq0obFTZkZcKJSCYKAPn99WTfc4D1t3awP4H6/Z7vd5d9vyXLTicvlRErQda0MKcvM3d0dzjn+4pe/lErmLEkvzTixyLIzil5q1MrisriZSGCb21+CyzWbWVrqvKdtO14PJ4ar8K5K65xUbd4g3yLf3G63bDYb7u7u2O/3OYBRMhy+fPnCmBlntyyiQvGO0/nEeD2jFPzhhw2/+9DgloXr5cz5eGAar5AiBo1GM15nvPPc7SRYt60bjJHDYExeJOtKiWw9WRY01xjRVUvb9vzqVz/gFo9KnqYyPN31hGnOVhZFayzWGA7jzGIrPv7ie2xdk3J+TlSKkLKqQYNK0lmeosdoQ4gyLF8uF7StKIHLhfEpQ2FCrcPvnMN+U0wsizDksu54bFXltQlQisX7lQErCrKV2TCKqirhqqW15ZbnUZ5Ha+t1LWnbdh0Q3jYhlWs9T1Nm4UWt4ZyANImE1bdBcVkWvBOmNeYWEAFuRL6oEjgng3RhMoyxNJWs1XVV8eXz53Vw9cGjtZFqy5RWlrLYeRLCxoGWcEJl2PY76rrFGMUwDhxPx2wnFBXQeL1SDvnX60BKkbbrcu2hqLjKobuAOE3fMwxjlukquq5ls5HMHh+XnKvicnNBWKsEnQvoLKOvqmplpWKMPD09ZqDQsd1uc9NVzHXft0O01voWBsqtUrkMdUUdYnQJEM4ZNErT9/0KZGot77tpGu7u7liWha7r+c1vfnNb7/K6bbJNw/u36iRh4nzOb8nSpKxiKnJg2T+NteI3trJex5xyT7qxYbxRSICirmrZI7Ii4a+/5L6V5638s/eB42HgbzeF/Pu/FPDNh0f+H//X/5ZtZTFKC+iZWAPnImnNdVH6BniU61kk6CVjoTSeFMWI3AeO50+/4eXzDxAXUnS5XtUzjBOnyTE72O32bHZbvHccjke0MmhjGKZhPXRqbdhse3bbLdtdy26/xehGQoqNQSVpVwneMQ0DL1++4J1nnkRabrMipG0sVkv+lTIaVRl0pZmcY7t74MtPZ1G1mcSyTGw3LU1WajVNx/7+idN1Qlvx+hudWNyEWwTIHF3AR0NQFaqqWRZPbYXJU0nmhBC8/N07lmVkWSSM3laatm14eHrg68uBnz69YKuexYtKlKxy3G93FLbeasnS6tuO4XwBpXBBAH+rJazYBS+Ac1VJM4f3oCXwr4QtG2OlujxFqtyeFWLJk1Pr2rgSLnnfdkXdCqBSPlwrqlpanjKXI6rInBngspJU8rsMa85HTFRW7JkRser5kKvq8wHVeb8+o8ZIltQ0z5JxFKNovKKoLOdRlHY+A9oKxEqc7Q75CRQAJT/TLuaWhlUxIsCLC8UC8G8+S1VtqHNA82azwVq9rmda25xH4dG6PP8J72UfM5VFK8u8BJYlsH945I//+B8xzS5bcMT2/XD/KAHCIXD84U8IL3+1HljfgvXlGX2rYEAJsSeAVGH5PeM0rH+u/J6F6Zc685ttphxMq+bWICMhn+7NAVL256qqxIqLWmvoi7Uh5L1QaZ0ri0URvQLF65lEglRL2HpVVdjKCtGX78dlcZyvg4BcKfF4v2O73QCs77ccXsvZp5CZgv4pSHr9mTdAwOd8lOsKhlRVJe1nzgkZoE2uMu3pupaSu2itLOxFtVGAoQIwW1tzPl8gKQ7HE21bcbffQYqrmhLE8hyjz1k79c9UH29VEkpL/bI0w7U/Ix1iCKg3is/y/ZdlYbPd5nWdFbguddPmzdnw5+cUu1qhxnFcwRNjjKw1Ka219UvO/LLWytnP2mwv1TloVuasclAv+R/FUvT2v8v2LMqvElheVO0CqmbFmamZpoXrMOVGvUCKC11bo5XBO48Lnq7vxEKvFMs8roC5zvucnLc88zzl/VA+T6Ol+aXYs5fZMU0CSNeZIFWQg+b9WjZQAJy3pIKs0xFlLClKVbbLbYJN23K9DozDSNN1LNPMPGeLYr62tTWEtPD0tGe/39NUkrdTnsPS0uOc4//9Z5/+XeOA3Ld/41f8R3wti5O+73mhbxrWATFEeGOBKZVHJVdCa2GE5cJGNFoQz9wsQ5G+ZcZXZU+dUpCo8RhM3WCqmUYptIK+bThdBqySBP8YE8EteGvZbjZyUO9abFXRbzaYLy/4uHA4nUEp7u72HF5fMVqz2+1WVK8EXLVtz/uP39K0Lb/+q18JqKIUOgV0UxEKQuoC1uSO+xBIKUAKkkuyLGz3WxIejOLDdx9IaK7jhKlbUC2ny5GqqtnftVgrCFyMEa38CnSAbKgakRm6nLobYqRtOvbbjTC+IWAqjarsuumE4Nfq21InVmcGIsbS+LKs4aFaG0rOgstMgLGaWtl8II64ZeJ+v0MCHBeOh6MsEEZnZHompsjpelyl5FrJoJPWnyt+/uGaeH19wTlHZS2b7ZZ379/jXBLJsHNcXqXJwpoLTSXMeCStDHnfb4g+sNv0XC5XYdRytkRZsA+HA/Pi8uG3AAY7fvGL36XkdtR1RW0t0zxyOZ9XtPN4OKCMZEaYusEqtVph/OJWieY0XvMwIDWowyWuSLwicnQiQVtmB9pgbLXWqCVRjq7DvzVSc9s1DX3bQIq8Pj9zPB65v3/g22+/xdqKjx+/5fVV6vHOl3POHpjEB7jbiZSurfEodG3Z3FX02y3n4yvn4zNuXpiXhaAgkDhcF7q2QilPp2umac7KDJ0PVF6Yvbrjbr+ju3vE+0TTWfrG8PHdR4zJAaaL5/Dyyjw7phA4H0fmkDB95DpeqGMjQEgeJDSamMAvjiVbvUqNbYiBlJRsIsZSMoaaupENPecu2Kqi0jKklAPoOI8YbaQ9wxhSKin/EqI2LXMeJBqmSbz8N/Ai0WSFRGEElAKTlT51Xa+bNugVbS8+ZaVvifcxCYNfBge4sT6lKrAAnd57fK6JSyGslX/aWrQ1aB1QOUdpWRzFc+19XC05bp7XqsmShWOt5XgUNZQ1OXROq6wQ8+sG3/Ut0QdOp1em68Buu5WGprqmtpZhHElevOIxDwMF3PY+56UYw2a7p+s6SeFv2/ycyXWfsnz09XAgxsDr8ZUff/wtWieaVrykqBvYKIMemNw4A0XCrFb7kFKsypGQWD2wRcoak+RvbDaiyipBhvM8i/1L5d+HMqCX8N0KYyxt13J//8D5ekZrxeFw4IcffhAFlLXs93sul/PK2OnMgDveyJOVBK764PFBLDApxPUQUcDWInMuQyOAxqAxWVET1kC+m4S8WAfEYrPZ7gle2D8KMxhjrmZ3a/WsyKQjJeD27+qVgOswcjye2Lx/XBWkUCxRSiS2+ZxgjUY6ZMg2FAVR/MaVrdAqSug3sYgyMjuu2T68x1SG559+xTItMi84h9HQGoNVGkNgmq55balQytB2HcoohlHCiBfvWQ4nQkz4KKqw+/0T7b6hqpq1VrDRmm67Z7Pd8vmnn1DmynbT4YaJaZ6521SiCmlF7uz8wvk4SNbG5YQOEy+nGSqp8Z6WyOl0YJoW+q7jgxM59KbfYA14t3Adrkwhcb7OHM8zdb/l4ek9xooiVBtFjC6vqdKisLiR589faeqaEJfVCnY6jfz0+QBasd3cgTZoF+g6qRi32rLP9fPlECABgxHbtLKvhUDb9zjvhRV1i5ApVY3RlrjMEjytpYmr7/scBC6B8SlJYHClBOjK4lt5KZXnFI/SBmtlvbeVpWnrzNrnutqsGCTeWh+MMdhsERR1lltVX8bIuuiD2KqHeBEbYNdKkCDSamiMIsXEPEjoq9KSbqMUBBewRkMQ68uyTHkuk5n1er6sSmWtxJLW9p0cducZbSTbZl6kRnjKga3aKIyp1qDn0nJhrFgvjbXr71NV2faU153aakIsa2HZVxxunrieF2G4bY01mm8/vuPhfs84OmYnIY8oxafPnyVstuuoXaBtbmrat4HPhS0v65eAWWG1vLgQs5L3pijQWjOOLqsac3VuadLJdd/FMlE39Zpl9PZVgJCi1tFarG9KKV5fX5mmJdf2WtkvjcKnwOIkqL+oK7WS34GUMOh1fRYgoJJ2jMXxenglpsjj455vv/0gs+A0cblIta/MxCkDOSWTIxEjKCUZZ5IFlQgp4yGp2F/S+jnM84TWYrtTSVr35F6tUBhiUkRudbE+pnwITrRdnzOVZF1N+T0ui2R6vXt3zzAMnM8nyfbSN0XUssyUANsyoxTiQVQmKpcvSN6WMY1kiQxX5mlCGo42uUnvpuyrqooEjJNkB76tjxeL6O3zrrNduvz8t/tkATmK+mAu91/TrBkqZUYrQFlpp6zrZv3+YmuT0FchTeIKWr0Nmy35i0WdUgDVECRvp2k7+V7Gch1GTpcr8+y5u9tQ2QbvRyF4K0OaEi54dFK52cdJltX9nqYVlcgyz6tjobROKVVyxGQu37Qa13tuhSOs5G6ZR0trXMrK2aK4qeuaWmlCknlbK4NSHVKGMVBVoPqatqnY9Q2vrxfOlyt93xKCo6lrpilwOV0E6K3etGjla+acEJh/m9ffayWIAv7B7/8O/8V/+sfs+w6rEpqs5qhutV5GW5QWVilFfoboyfu++XfL7yEX7q2vNwGRFGbG4cTlfGCZRq6HI3EZqYxlmh3TsmCamnGcwVjQln6z4/PXrzy8e2J3tycCn59f+Pzp8+rf3W023N/dUdc1T09PPDw+oLI8PkWFTgI++HlmHC78+JvfMAwn+r5aQZsUBAQJXiqFYhJJfbfteff4iDGG61VCCZ+fv+CcF2QvQr97YPKJJUhPu7EGk1kPSGhbYW2Nd7d6Yecl2KZpW6wxJYOGFHMOglKgyYqMxDRPVHVL3TaM08JmK4eDcRyYsnqA0lHODTU+Hk8UOV3xC2qj6NqWu/v7PMhrPn/+kiV+dQ5ZCj+TLUpF400K6b3j8fGJpqm5XodVNl8eDmnKkarcu/0jd3d3oDRfn5+5XM+4aYTo6boGpfWKAEvAq8qLYsvT4xMhBO7v78WbmwPv3gYoOSee2r7fsN/v1w2hyYuaqFGy9y5Kjkg5LJUFt+s63CwMpc8LrAS5edF6lk0UOaQXBpmkGKeZuu0EsFmVJrUgxG9kiEVB8uHDB2HzU0kXl970x4fHtfN7nmbO5xPjPNJ1jQQeOsf3Dy2/806YcYUi+oVlGZiGI1+/PvP6fJQFFfEHN3VN3xraHIRUWYOmMDOBumqlLrq2XJ1nv3/iejmR5oG20ux2WzSaaZhWv6u2li9fXxlmT9VWxByy2W76LDuWIM6ifkkpZjm8XpHrGIUl0MbgsxxarBlQut777QadwVWXgQPvAyiDqauVPQCpcLa2kkNyVlBcx1HC55xjnuY1R6PLcmRR+xSmxaxId0kCfwuCmMpmOaDP71GemxJQF0LA6FuNrNY6Vy7mbIQ8jC2TyNtBfN5v20VEEp5oM2jgnCOFhFbkHJv0Zu01b+41aYeCxDSL57fvtlRVzXa7Y1kWjoeDrMQpZdVHxMfIpu8Zl0UCEXNoqMotCDEEYYqN1AyTwzlVSJJsXupnlTCedw/3DMPI6+vLynYYo9bhuQBNJlvqVB7E3j77ZTgoDI4MLKzAbwF/3g5QXb9da33fDu9FOgzQtj37/Z66rtdWKUicL+eVQS5/vgBAZSBa97YoOVlvfeYlS8V7T/QigYZbxWLbtrx7944ffvhh/T7lr3ItF7esiqSVcctKJpOB3yJFvds/rIeUZVlYvCPEuDLnRqnMjAno8/r6d2eHAXj/7oF/9o//Ib/37TdYrXg7BbxlkqHIxzN7mkEQ7z3/6l/9KX/4h/9gbeQyRoL5Umbuyh6awswPf/EnXI5f0Mkzj7NkjSXDEhKmbQhK4YOoSupWZM2zcxxPZxbnCElCa1NMbDeSDbDpOx4e79nu9ihToZRFaZNRGIebR66nA8cvzyynKzF4Nn1NXRs2fZWZdWkoU0BSljFafvnDV5yKNHXFtu9J0TOOV54e7+mbClxA+4BK8kynZkto91xdICSLqSXLIuapuIBGosAt2VoLcc0nSnT9RtRmTkJXU1aihpjXLS/AhNaGFCLLvEhbW4rMbl4H4BAj0ck651JuQIrg83Nd1owSurjZdFhrGa5XkY43EsIoiqfSHhVvwbJ5dpTcMp2/f8jV43q1FpSK2/L1RWUlMn5RKacogZ3G6Nx+Z9Z93uisYtaSowU3RZdCaotV0hhjWRaxsRWmPmX7nrWWEIslUw4nKuZcrnlhiWLz1dqsjWorKahl/lKK3BK0WUFvVMyguM6/ay3zxSi2hvIMkZIQdTGCCtyCOxPjIBlLKdwO3PuHR/7Zf/N/pum2WNvgI0zTzPlyyWo/2evs9SfU6YcVGHirBingx1tLQclmEbWuzIgkyOKFzLC78vSvM6i10tzy1grnsjXpLRBcFA8FRBYVjBy2vQ9cr5LLcD5fqWsJp5xytlaMAk49PNzRtQ1WCaF7vVwywSnzdt93gObr84Hz6ULbVtw/7DPQdrPkSV6dXveJtVK+aZiWhd/85jN9v0NpzTTObDatrMlKAkeX2VNVms1GVJLTNFLyETU5PNVajKl4fj4xLgv3dxLgf7ffsd1tUOp2v8cQBFA2RQklAFLXdatCRhoqWZ8rIH+W8gzdAltjrg9+22SW1t9xniemcSIEt+7dS56dy2choFgOhPeO6/VKCFHUlTkPcBpHjJE8wbppxK6SWIGasv4Xu/HbfcM592+AJEWdUGyUalUASdh6masle8+sKoqS91GAvWLFCcHn7EP5jOfFE5Oi7Vps3QiRO8+M08J5mDFacbdt6LqWtq3zWmVxs+fTpy+Mw0zT3BwBcgaohPzT0hJWVVWuRjcrUFbOzvIZCAhbnsHVUpMBnDL/lvzGlBIRJTXlWiIrSvh3CGLtHa4j2+2OmODzlwPX68g3H+7Z7bZAxC0SPCtklDzXhbgC1vvr//Unv/kb54G/10oQgM+fvxL/UQ4zNQqjivxQHq5iDVA6I3rx3w7WvL0w5CT8W4J0/obcvH3nk2IYpYZWJRm2VQo0tWXxjvu7nuuwMIfA+XLG+8CSK10Tif1ux/Pzs3jYI1wuV2IIbDabvJnAdr8XltLUkCTPoW5bbG35hTUcXr/g3YhRSlKJnXTXn+ZJJEJepLV393uGYRC0zQWMMuw3e74+P4NXGBRumghYQcXDQhUNCmkfSAmUMSxo3JIwWupGiR4VI2GeCQrGecLYWtBLZUEpqhzW5ueZpqplOAgSuOSWhaapqes91hpOp3M+vM3U1Q0R3Wx6+l5aTp6fnzNaaDkcD/jgczd7Sf3NTG1d01WW8/m8LkYqexptZigOh4nPnz+tcjaRkrbUdfH6CgIbU2K4XHDzzLfffc93333LX/3aURuNVYn7u73UWi6S9mzz+9AKtn0nh6IcJvTttx95fn7mdDwwjQMgw21KwlZ//SLvR2mRjSvUOmSN40jTSrBQ13Ur+l1UMyEEVEpM40TKiKpSiibLOVEig/feM2eUuwzkKUlI1vZ+JxJpa7GmQnc9m82GT58+iSQtAzjX65Xj8Yitq9xfP3E6HfjxN7+mstVatdV17/j89TOX62U9CCklTGrxTielsFVLvzN8rDe07SufP33leBhoGktVa3wCF5NYtFIgRAhecm5CcGByMneMnNxX6try4Zv3bPp2DYCalonT6QJaUzctUUV29x3X0fHly5Wm0bTLRELz8ZtWBlWtcqinWgdQWSOSMJhKWAyrdA7tEyDDVjXkQ5zPA1dTVWvuw7J4llnWglvdrGwet8OxVCTX1pJCwPY92ljmZVo9vCWgOcbANMX1YCnBiT/3m5vcEvOW5aqstCrIQbik0YvqJRlJBbd5aA1Z9aWyRQYlwEKRpDZNzdu8EJA2Ga0UwUlYbdf3pMSaem5z8KxsgHJwb9qFaZzWOr1hyFaOPLxLnoFUec6z43w+UTc1Mfp8XWRzlUAwQ93W+Bw67H3MQxu40yINP0qE32jFy+FZNl1Emh1TwnkAUXUYo3Fe7Eshb+hF3VUObTJwslpQTqeThGU3AhYX1uMtW+QOB4yxvHv3LtuEJKvKh7BmDZSqw/LPJivdYox0bUtdVYwZPJ2nie1uR2XF0gESzne9niXENKvxAKIXu1WRvhIL81ck6pq27Ygx5eGLnwEgRY7/9l4DRMYc0ppLUpjQw/GwrnnOFyA+ZuaNtcWNKEDj3+WraRqaquH+4X6tEr31E9xeP8tzoahdboP3H//xP1oPH+vnodTPQBCRsyu++fYXWKO4HL7QtGmtyDVRMQfH9v6Ol5cTTjwYKwvog8NYATLbdsvxcOZ4ugqbnyI+eKbZsb+7p+22UtKm5JBiTUttOgwVL/ETl+OR42Vis2nF6hSEHdZKMsdccGgS//AffEvz8MT5eOV/+ud/umZvqGgZzjPxOmBCoGtr5ug5jAP77yua/T0uh70qjTRJuZDVMjmnJsjzZZXCKxmKE3KPzEHaXmKS/VsbQ5plD/MhiA1vWdj0Unl5Pp/FumG0fOaZjlTmVufpQkRFj8rsu1JQQqTF/joD02p7KutQ0zQSrIkotYp9T9hkCYeVvUJlYFkRUyCmCDGQnNSITtO8MqQhBuqqwRipZo35WjVNk9u7bi0QqYR2BrEfpgTRh1Xx4Be/WhmKFVmUjbKH+hiZvTC0TSWn/esgcnwdkUYaCnOfQ1HzUN10LbvdXqw9RjGO19Waqk3C2hwY6sVO7F0jOSeRnAl0A3+WRepI66YmxRm3FAuyXJ8QvYSAa4WbJ87HA9vdHU3TZuBKoYxYbiSrIjCdFwnF1rm2OhNkZDZaW0Od1Q9CNC3rQdpWFrCUsH4BRyJad3mfza1cGaw5HI9crwNNk2fUzTYHDC+M45T3C7MeUtf8D6Wymq+n7zq8FwuSWwLT7JmdZwmRrm9Y3EzdzLx7fKJaK1kFAHLOs7iFl5cjw7iw6Vs+fnyX1xtpnYzeYYyA5WLpuB3My1o1z0L2vXv3nmWOfPn6jFaiBIgpcrfbsNtuYAObzQZjRRmn31RIC/kacCFwPJ25Tp5pXhjGibatcN5xOL6i9Y0AMMZgEGBnmuZMeGzFZm5FwUxKa9tJWW/fzvN5YUVraUEqivJy2C7g4bI4mrYlpZplycBojKt121g5+xhrqFvJGdnf3TGOEswquSklo00KE6Zse+q7HoXiWGp1jdQUV2/Af2D9/+U9iZK0W/eHlG6WK9lVktQkW8O8LCwuraGwLttfTSgAVwmlbURtnckN0ETgdLny+nq4BcQ3Nb/7iw/0bce8yBrXtfUK4DWmJr17JAb5jPrtjhAizs1rzs00TRyPJ7bbnof7exSlPeitKgeUkizFQh6WnzGOI8MwrF9fgm2VUox5zmizlU7AMwkUtlZazcZxwBjLw76jb43UYldCopMCJcz3cp1YFk/bCLFkrV3//rd5/b0HQb799hu6rpVB1Gp01vBHMiNGRp8o0qUbclteN/XHW8Dj9pCt/z5pUBata5q2p2lmJh9QPhLcJAeWugIPbpnYbTe4w8DiyR6rmXEYiCkx5wEIkxnQmNabQAYcsVjc3T1gjNQviZRcqtWqpubx6YnxemIeBpJJPN495qCoHfM88/z8zMvhlXEc14NqDIm27amaPbZecD5KU41TYC3393dolTBaUOh5lnyPsCzEqCg+zmUaBR2MMMWAjxFlDNZKUJO1lqaVTJAixbfWonJ3t64tddOSSPgo0lRhzSS9nXRjtB8eHmjblnme+f777zNrErhcLytbelP4VJIYXNccz6d1o5MBxaxBVzFKondhYMoAIeivKEYKizePE56AXwy//tVf0m53/M733+PdzHA+0XUiGa0yo7rpN6tKI8bI68vzisTvdjsOry9oEikErpcTi1to2467+we0sbweDwyDIM5d00EGK4zWDFdh7e7v70gx0txLzkJTNzhfDs3VeliXRoiUm3UcS2ZjyJ7Jcviusg1hmWcZqLKioapkEd5sJK16XmZeX1/o+56np0fO1wuJisUJE35/d884DPzmx1/jnGO73ZBUSY+WRXFuIzE0t+cuTyhKW2wNT+/es9ns+NVf/sDnzwdsJRJGnRJB5wrKrOq6Xj2JSFVbfPAcL47dtuZ3f+c9KYU8aAkbs9vvGCaHC1JzrOuapBRdV/PhvYBUbddIloaRyjcVkSwQJWHGtu/ompaLv/J6PLE4R7/ZsNnubgdCowkhZeWRSLBDICeS53DkJIxdzIeioiwp65Opbh33UgGWw4jDjIJVfXHztN6G9+gDy+zeDAKZXfDSRHDbJIW9qfJmIaYpTcpDIQl0ElvVNM60dcV+t18HDaUN4zzJ0P1mDS2DrBz2RD5srMlZAwa3OFQegFJKXKeJYZKAvbbJqf2kdf1WIbPFWhHe+KHl2eWmOCngVF7C5XeS9xBSCWFlvee0Esm2VNF56qamaUW+7bL1x8ebLaRs0DJ0qfXABCUk7caWFEVGGXhi8CzzxJz4WS12YaLEl6/56aefZKtJid1+v3qm52ni3fsPnE7n1cbjlonD4SC2l43kRpACKcoAcD2f6bqetqkZhpFxGIQ194m6bdjseg6HA+M4IbkDMYNcpf5VVA7X68j/8r/8GUqJ7W6axvXZLbbNZZnxy1slSJF9xwzULquSRqIMBFpQptgoG2511JYYoe83eO84nUr+xX/4l3OOnz5/4eXlwP3mW/Ll/dnPL7972avLkPv2a8qA9VYyXZ7h8k1lSAPVbNjdP5LCwng90sWI0YZOW8bF4cYRnDDY2swE31FVhrYR613dNNzdP2JVxeevX7hcrihE7n8+nQg+8u69pql7jAKjLCkpsDWbu3ussVRNxfH1QNQGn5SQGMGTVMJny4CpEiEsnL++UFUN33544P27J5q2YtNWEBPj65HgAufThc/PJ1Kl0NczXSUscZ2tDaXRLXkZ3lOWe6egwHsqrZjnAe+kIaXKAPM0TUQCmoq2tsRoUAnJ7CAx5qFa7r1c92zEnixkmOQPqRSzLbiibQso6VZVXYw3JYrKPztxCyyMyVM31UqepKSzckIC6jWZpc6Ht+A80yJqCDkMKchKgpgrlX0ILC7XieacEak0D/ggpFZpIVTkUNTF5XDJQMjKrqaucZPUcRujV4a2ZEMtyyK2wcyslrYbrcDUFSYlxjlXuOb7WdogKrS9scIk2G42WK1ycLQoY0RV4RmvI8tywHs5lJf2omJLF+AEAeczKy7Aamk2uWXrpBT55b/+E37729+y2z2irOEyXjmdTnR9T9s0kkfhF6zR6x5Vsp9CCDnHyK4s9dtn9O2ZoKmlmrfMhQqxhxotdmFRRFT0m14aVozJyiPyIVA+u77vbrkpmZm/Xq9v1IpkRfCtEWxxiZfXE8f8dZvNhvvHx9WiIg1GDd6HDH7MfPfte777TkI/h2FkcQvGWrrNJq+3biUfl2VeSTObla5Ky2wzzwvLnBVTJO7utjw83Es2V/BM48iyzOjAqjiQe6POZI9nWTwPj488POaw4BBo2yrbOEThVA7DRTHx+HAvBG9Vr8CdCy6DZmmdf8uM8VaFV/ab8ipAe1mn34bTppSyglquU99v2OR2kvX7KBjz/iaAhcbabiUbJMss5kZHt56p7vZ33N8/ytzgHc4V23FaldRvc8hK+0tRQex2O6qqXm08oHBZKT4vCzFOzMvMPE+ZMIJNv5PK63RTeL/5KABR1bR9x/7ujrZ74XQ+0/UdwQthv9lIu5BRKrcm5ipnA/tdL99jbnAh0DUtPlgul3OeuQSQfT1cpZq9btbrI0RcBphMBoONWWeiYt8v+2NREd/mCr2SS0pBQkLpGxoul4GiJBYFfcjgXsLlvLoCdMQYeXzYrzlr5Vw2juOa5fQ3vf7egyDz4iCJ5UIlWchTlCwMY022ZqQ1bI10k/L+24AQed28aijxg8kXgUoWpQObzV4Sq+cZakjZ4ywPc8UwLozDJQcYhoxWO4brmaYR2wJRanq10ogtO60XLpFou466ajITpbGqSCM11nSk2lJbw1y3eWOBy3Wg68Tzd39/x26/5XK98vnLZ8k6sDVzHElJo+qepqtAKbbbLVXdUFWaZZ5YFvndjscjJEmPf7jbk0IiBUGiq6YGrVmWQETjVaLte6q6wS2eqqlY3EKfD9DW2Cz3zIkiSjPNE+dcJTZNM33fM88zlc1px0rkfy/PXylhn4mED47L9UpdVVyHQQCBnKw8DFeAzOIKCq6tzWGXcjWPlwtdZuArbdbNYfGeOg+XYw6/a+oacg2lUor9tufx4YEff/MDSmv2d3dcr1d2ux3H45HD4bAeDk6nIymxAi7DMPDu6Yl5mnj+8nmtzEpIC03IobFGayojPen7zQaUYr/fcTy94paF0+GV6/VKVdXstjuxpCwLGEMM0kChENTaGs3dfs+yTKsHLxSPZ8rNJErj3UJasldUq5wH43n++pmUUlZ2tOsiPU0j4ziwLBM+CLJ9vZ5Z5oV+02LtNgeibdcq0aqq2DaO4KbV7gUapWU4t1VFyv7MP/qHf0DX/5bj6wtVbema7K2PotTquw0fTcvluoCKvLy80tQRa5IwTLXivAh73nc9SmuavuXyfCIph2z1cojYbvv1WQwucBkvVCV4thzAY+B6dlxOAlgapXi4u6OqRRqosiwzuAK0JVJuJYqphJyJtNjn4KeYZfFt268DtvNe1B2INFop+Z3zEkTfdYS8uUIe4pAkdqs1rnKkqFjmCecCd/udbFwxwryQtMLNMy7mw3wtHtm2rlcVUAiSHh9IaDR1bUjJ8/r6ktVUNVXOYTKmsGhpPRSUjSmlXKe93CpXVbm3UgF1c3aEVrKeZztJzJ9HGYJKro7WksNgdc7PWBzCfMXMnjQsi2NxUZ5bxJaXyWAZjrz83MUtkqeSWTajwRioqobNpiMmaTd56yddgWpfaq1LXoRcH2Ml/6UMaXb1D5PZFLcO6EWFJvJ3n5s0ZI0sw5u1loeHB06HA7aq6buO9+/f8xe//NcCWobAp59+EmlyDLx7926tQY5RgljnaSJkwNd7OaQ+a401cvCwOcwXFJWpVgWI+GfFUiPX88awAevwLmqRdj1whBCzjFXyVB6fHpmnOatNPG0n3maUEjultSQmlDJICLD4p63d8eMPv7mpKf4Dv0qGw+KkEciYar1P5Vl7MzDnr5d7PQcTlvvz3zJjpBhFkUD5XjdgzZgatNgn45JbmrSiqSxumNh2LXEUMHecR9p+Q9d2jNPEw/291MZuO4axYxpHhmHk5eUgtZlK8/L8hceHJ7q2I6mYCZ1EVdew6XlXf4uymvFyYfbSdKFiRGXiaFo8yoOyELIV7v27PXUNbQ2KwNfnZ5Z5wWDo7nfsFASVmNyEOwS6zYYZAfetubGCKpncZKNIXlFpI0HBOW9jXiaa2qBTwOrEPA2kpqFpOoBscExYo5lnR993eS2RCmuNZAyFEEnJI7H5EZWC2OOMzpXQgboSO6LSt4rO4B1zttKW57Fc5/DX7A+3e0NyYcKbQ5hSWgCZhDRSKZ2znEwmciIxSjZZCCrPOiorwAZA5oK6riRbwXuivwWZmhw8GUJAaWjbepXIbzabfBB3JBKVlcyGkAEUo4XAUzlHKmR7p8/rcl3X3N3f07Qd0tahmedRFBshrjNqzEq7aVpyYGF24JEIflrV1Sa35ehYJPYNbd1kCbw8L62+rSeyjh25Xk58/u2vmb0XEC1P5yFEokr8zr7h4+amwCrgQ1mvEmm1Q2ht8kF2RvYxkxlqISPn2Uk2Sl3Rd11WTGawI8YclGvzoU8yuMphr6iYlbrt0+XwV8i9cRxWWf75LHPiOMu+VVvF4uUefnl5waRAXze4ZeLT5y+Mw8j+bscffvcNVc7XUTrhvON8uTIMM7MXgK2yms2moakMXdvlOuomX6eZttUSepsiu92W/Z2QeNM4cMrNiKSSs1WAJXl2S0MhlDD+GpttPSWLsTwTzjnqHGBa1k6lFLaSjBijJMcwhoBOyPxrZA4QK/ItpFfpW6UxeUVN8U3+1BvViOwjMpdst4WgXNCaDAbJGe9yvvDy+ioZPk1DVcnvI/uePKfSuqep60aUmv6C0hJ0q41Fx4TJbTYKsmL2VrxRlNu3cx7rPefDjeCJKTEvorwtDUBd3/H47pEQYj6TRFF/DuM6T2ht3iiupcxhGEeSEhDyXt+xZLBXazl/tU2bZ3dRkCpiJk1l3RqnGaMt8zhS1RV3ux3GGvq+w9ojl4tECYxZDVvu86JgX5Z5veeNsVLrnZ95pUXNWhQxi3OrLbK4LpRWOCdW4aZu0Vq+b3kWS75a2VeLKldmtvLvI9aKRVCRcIuAy3+b199rEKTrWkEn3YK2BudlcBA2TBYpldksVlntjbX52WCzNjMUFpIb+FHksAkJoYoKlOXx8b0EIL6+0DSW0/GVZZypG2k18d6hiTTWMC0ifZ6vV7RSbLqG81HjsizRrinRsqCm85W6OuQmD7lJt1upA0tJr5tqyuGN8+IgNyrMy8TlfCElkdq/e3ykaRt++PFHfCyJyjW7rkMrQ4hwvl5xfma76dnues5nSc72UXrfX55f+PTjb+mblspUInvX0G/vaLudMOtaSUWul6osHxwhOqpK/NDiQSUDE7LgpyR5J13Tc797YLPZMI4jP/30EyklNpsNxEjXNqsy4+vrC9Myo61hjp6oEqayoMFHqdEzxuBdoDEVyzKTnMO5RXJLRPmOmyYqWzG6QQ40IVIZRVpcvlMkSLA2ClQiIcjv85fPfPvtRwmHG0RmVuwqbdvy8eNH7u/vMcasEnY51+gcVHjhOgw8Pol08Xq9oJRiu7tJ7uZ5Zs41s5fLkWVZOLx8IUTP9XrJrJVlmSeCW/Jhxa+HrxhKo4SEqU7zsC7ERf1S1C5RReZlzlLlLLf2QVh7J4GKKiVOh1fcLO9xnhfxA+/3aGP48uUkgYBammBK5kmMkbu7Bz58+MB2uxXUdz4xK0fdNDTdDrR4ZGMeuFMSpr5pKn7/975nfH/HeDkSlpHKaNquZbvd0bYdm17q5FKCb7994nq9cjoemaaJ87nYGkRuPA0Dh8OZyyWgBk/XG3Z30s7UtC0J2RyXaebLl2fCkmsSjaXravq+Qelbu0pV1cyjBJTaqiLlFhitTa6SVtRtQ0oxVxEGrBU1lcwNiqQNVd3I5uC95Nh4w3W6VcOhIiYfqIw2aKOJ3q2HaFJa/eeL99S24t3TPW6e5ZCcPNvOYghsm46u0litSFHjgqLv5aBUVZZz9HKYVYlhmHBZAaN9Yp4c8yxgr7WVADS6tMCIR11ksnIIEFl1BuOCMJUuA0Qh3FQqpfK62BitFglnTGldg0OKeBdEPaA0zv28TjaEsmbDkK1Axko2lFJQGjoKwyB2I/n3BYioKruqxFIeKleQx+p8iJKNoAAgKr/pIomPMZKy1FLnTI6iCNPa0DR1Hg7VTc0SxbMvB6zSxiSHnBSTAAchoJTm/uFRpP5vhgQZCGQgPR6PPH99WQEJYVkkG+h6lZpzsflFjAG/zPkzLmGN8tkW5dl2t6NuZfgulXl1U2ONZCeVn73f7wkhsDipLZShJ/urF8fr64HtZsPd3T2Xq4DUzkmquw8hD3pqXTOm3FZUWOy/21eibRqx/ui0tkaojKIVXOOtIqQcDtTbr/03vqvMFIq3AIkAgHXTsds/cHp1KFMR3ULMz4pRMgQ+PGy5LhPjcBVFTw7hMvlwvN/tJHBuWQgBLmdpXru7i8TgMCaR0p6m2VDZdn1PdddRtTUfa8vh5SvX0ysiFKlZxhEfFMdLIKhIVUeO14X7O88f/P639J3Fuxm/LGw2HZfLGQCPp2k0yVa8fj6AXohRag433Zau77J6VyokSbLOkKQOPAWVIepE9JHT8UwI0pi12WwwWKKcgrFaFLU+BPb7LVHLetPolpQkr2eZZ7TOzH9ReSQBSedJhv1NPugqfZsP7Ruwq6hFRVmcZf3xpsrTOTsoxVwLnQ9tKKn/1Ol2L69rV77/dbbcNLXYnq+XgWXyJAaMkoYYSGBgiY7aWipT48Ky2l1jFAB+ZVMri7HyLI7TsK4HIYhqtxBSxojvfh4mmR+Uwud5OSEtjE3bczlfGIerHFZj4Hq5ZlVMfh7IlmNY93FZzzPgHcnfN2V1iGRvVDbhXWSpltxOkVUDGYgVAD5RVZF5nHBuQqVEYw0xZisSnhgiKVXEKJ9tm8HV8/m8ZihoKwrE2wHcUqe01nfL9ZXvWYJtU4JhGElJyJmu61Zgoxy4hIm/KQ9kzQ85u0KtB9MYInPe2/vNhpQSLy8vfP16oKosT++f2O72TLPj9XhimOY8yxiu8czryytd3/L07gFttMxt8dasIWodw2a/5Smvw00jJMeUc+QKMARiN2jbFhNz+H1WBLtloTLyfMr9q6lyhfX1euH+/j7vmWbdY0RNOGeSMlBnVWck4VxEJ4VPktknk7Wiq3vm0eUmwqKmkudodgET0qo6ilG0qm3T5Hr6haZt8r2mMpByU9a+XaPXGmSVslJVgmRDgtPpRIyR0+kMRD4+PGCt5fX1FaVG3r17pOu69dlPUez8281WAtxDWG010qCZLZ1KEbzPyhBHnfM2qrqibpvVkmEz4VsqvMsBfgmRTduw2+/XmUUO9j6r9yfmaZTg/gyqtNnSIr9vWu9z7xbwAm5O14Gu79evc27h+PrKPAtJ2bVNBkjkPi7KtaLwGryDDLiUvJIYAlIuIeSX3FdNLi+ZMjiiaDqJLZDKcL1mGpnKYnP+Ygm3LX8+BHcDHHNOlRCK6WcAdMhEbFGUWWuZlxG3LDifZB11TqxESlFX/39gh3HOsd/vgbwBCK4mrCLlAVBZ0FGCpMhsWK4vK9V18pUkbrU/6JhHliwNTgiDmHz2I1q2ux06RQ4vn6mbhjlJhVZKmTkkkHyktoLQXZ1jvFwJPlBb6bivqprHxweGYciStisxBg6HYx7cZXh2bmEYci90VWWpJaA1pqogRmz215W+7rVWqm15eHzk5fWAWxasrblczlwvA2hD0/WopLleB74+vzBNAw8P99zd3WW/4pCll4naSsCPNLk46i6itKVtxHufVCS4meDksJNMEAlfkJBNbWpCzMFYSVF3zSrpOp1Oa4BQYUyLN/NyuQhLXnqmK0skUbUNlRXVhNEms+4ir9UoNrVFZc+u0sKskBe0MOdAQ+S+cIss3k3dYCtD27R4n73y2tC0W1CGy+lMStB2PdvtlnGQzvS6rjkej+smU2q3lFI8PDzx+voqIVPLQpPrcZUyeXMZ0XqW0KbFYW3F5XImemHHnY+cTkdiFDmmzvepd1NmpeaMuGd5vW5ICapKE6Naq21fX19XtUrZzG8BbtK6U1WyuNuqWQ9cZSOX4KiZ5TwxzPM6bDRNw/lykX741TOvVoDo3bt3jOPI+XKlmic2uy1V3co1CwUEAatlY0jJEYPD2g1drZkG8fOWa3I4vVA38tldzlfquuH+fsu7d/d8+ukzh9NxTfGWQ6hs6HUjdYB393dUTSX3YN1mG4RnXhaGwTPPUr0cJ8flslA3A5u2Fq83Sd5blRjnibbraHPytTEKhUjaE+IFtwRReykN2pIwhKSYZgn0FKZD5+ucW1HyQq6sQqck9g1jQIt0kRhFxYModlJM2Tcs9rpaK+q2JkaHVoautmybBu0XVIK27Xh+PnM5nlgWCZHq+5reGFz0DMFL7WKQMKyq7ejbjpgU6KLqkgF6cSU4T2fgTEtw4aqgkODXlFnyKmfYzE6aZEwla3EMXphJa0gq5cNRWFVLQFaIICBFAc00+RAiG31MYufTOUXeWsn+KHkTSovdKRZ7mIqEWFLohZFISQKEbanhq21eN27Mc8m1KNJZDRKwFwJz9pHP88KyhGwDGTMoU/anfChaGY0M+oSANRWb7Xbd6GOMDNcLPkSG60BdS8VeGbpPp9M6WF0z0CDBr5q+39I0fQ5/rrjb7fO6xrp/zvPM6XJer5nPIM3Hjx+pcgvW4fAi96bS3N/fr1V35ZBRPNPShgCbTS+AbR7ulsxaFx9+ghWcLcqTEgp3Pp9X9uzv+nU8nvju3QO1yU0WP9MZl3v9lg8gwO2tJvff9VrVIloAlRQUyQfqpicpsXSKJXaWZ1qDbhQujNz3PZdomJw0ZCilGQdpIKuqmhTCapdISoLFX18DW9cTo6hm7+4MRluqusEHsS6FFEnGsnt8pN+0jOczbl6wdcV+s+d3/uAPGZfAtCyc/+wvMgM5YHTDcLmgs5ptt9tyOJywNj+UKXK366jbDls3vHt8R1OLFTJ4R3Azs/fi7X6Qg92QveJN3aCVpa0qFi8WDm3kWVDpxvzmKU+ekZTwzrPkmm+XQQGRn1fZkuDyQTTlDLQyCyrETJdWy2DZ98ooHHzK2UmKcchkibZYm1YQuDC5gjHcAj9TTNmGcgtvrIzNbWflXvBUWirFnfP5kM0KEiXBzFbQV6FF0h8C4zSuoPjbTITSPlFA4KJeiPl9qswSF1VCyiw03DJslmkU1dsi4NGySG5NzPtPSvCWVy3auGK1JIQbD5m/Xj5PWc+1jjgXcE6sc1Vl0Yj69vbsiUrWVgISynvVKzlQVWKDrmuz2jGLhW/9zDNA5pxb11W5b3KweK4hDz6i/3/tvVmTJNl13/m7m2+xZWZVLyAaoCjJZiTTw9h8/28w8zo2kmYkQiCaQG25xObb3ebhXPfIpkQjXwYijXFgha6uzsqM8HC/99z/+S/WyWuTm01YU1nSM0wxZwcBEkDR98Ma/yrAiLz2m2dWjVZa0iKjSEiv1ytaGz58eGS324rJqRZj+6Zy9P3A8TIy9xNtbakbJx4zKRKL8f8CtC8G9coKaN/Ulh+++7CuSU3pr0H8GFIZeGitqJTGKocYp1pqZ3EPtrBexNdpnia+fX0hZvjwoebl+YVhHMlZTF6bRuT0Mcj+rnMWRmeKhLQMlZX0bq9nnp/fOJ176qYl5cwwDqXv0Rhr2e23dO2WaRo4n87MPrLZtDTO0taVfJ+3IylljFV0m5Yfv/8eP0+8vr3x4emJzaZjSRkbh/7deaphnGY+f/5GzAlrDcMwU9UOvzwbZAw31srK3BNzhdXLaJrk2frNb37i09cv8vNSxPuIW4AltSTHCXiwePW0rTCQF/B/SaVp2o4PH5vy8+D52zOX64XNbrv6Rmojvfi1DCOWvV6pxTDVotS0Bj6EmMhFpivqiRlV15KgEjwvL6+0bUPwNdvNhsfHJ3LK5V65JfoJQCUm1YusRdc1Y39FqbgyqpbI32VQc71euV6uuMrhmloiTLLYAthKno2qWvxeFmPViNa1nHmsI+lM2y7x5HqV0Sx90+IxspjeW+NQVUnfKnJFNY9AKmv9P1z/pEGQqqpWR9mFuZFywiiZP8oiKA9eTIJE5pJnlsnkcNMeqoX1oZa/A5RZBDkCZr3BFJqUIwYw1rHZHbBW46crp9c3hssFQyZpUFlJXFxMVNaRqpphmhj7Hu/TGmdbVVWh6Gl+/nlcDWZOpyN1LWZLm03H4XAQilKQODmjbo2YnElKskBZjJUSP4bvvv+OH77/HqMt//d/+i+g33h8euTxwyPDNPHHT595fTkzx8S//su/YLPZM3uY58xu/0TTbokhMo+jAATGYsl0+z1V1xSgImJUIafGUB5CTYoZvyDxuiwhxfRIDgxpNTYFVkPE6/UqMaPWkEuiQaYcEpV8T+scOaQic8n4LEuts5bWWkzZjVMOGJWZxoFGSQyWNTKi0Maw3WxFczdHVAp01aIzdVwHz6kfMK7hP/yH/51uv+fbt2codNfX1yPeC+X0ZubYrw3UkiH+pz/9LTnDx4/f8cMPP2CVXjX9yyT3fbb89XzEOcOcptUQUSlZsA8HSZDp+36VHrzPewcIYV4XJK0Nx+ORuq45HA6iXfQLAi8NRCoSmSW//uu3b0V6ktfv8/DwwB//+EehptUNh4eDxNYW/d6m6zBOfr+AWCHIAriYt0bvGZlJlzM+JSrXEEOmrhvadiOH5ZTWQ8WCZVrrpLH3RbvtFdM4r3RjyMyzsDS++/4j12Hk69dXADabhv3DA/v9DqWHEt9mUFR4Dz//7Tf8PGBcJifN/mHH5y9HTsMMWWDQyWeu1x5j5BBbN0IB3m4bNruN3P+AypoQIznP5Czvd2nIYkrEpIhZkbBMXhy8ratwVVWkMB60lQN+YXcZZchJdOvaGCq3NGiKVAzyGuskcjKK8zox4LTGWIfVmk2zoXYVw3zl6/MzwUsDgTPEELiMouM8vl3Z7LccTwPHYcZqxVzNBUyoCFnhcwBjWaIefYpMsziv51T8jhYWTlycziNLdOwiITFGsdttqNtadKkxESaPX5KcykRHrt9N4x1UWqcTOUNVLRRWJ9tbvslWZPLAyhpZGStGY5Rd9ePL1y/PgSR6ZKZpxHstuvDFZNBpclLrAX35+yEExmFkHD0h5PJzikxz2aPKGXp5TSsw847ZcpuunQuApG/yGlOhTCZqTX+5CihWmqilqV8aA+dqhmFY2V+qsHi+PT9DFi3yciDY7XfUbUPf96s30poeMA44Z9jv9yilmEeh9DZNw5JytXgyrb4r+gZ2+LmYWCIRopR9S5f3vClT0eu1L7TziqqqmcaRo7qZ4v05qus6/uJXP2IWqaq6Jc2tdOuFLZqzDD2KxODvA0F+AXiBHFRDEqq0ViINtA11s8OPI1Zb5ksv09nKkpWwNdPQ09UN/dCTkyLmyOl4YrunJE1dUUgaU1gPIZI+EZPHmApjKolFLowkpS1ojbMbUnJUTr5mHnq6pkZlqGrL44cHQsw8fdhzfH3GzyNfPn3BOUtlHW9vR/p+oO/FaDH4gK5q9rsDri4ySiVeHykEnBY/ntooMpF5FNDr2veE4j+GElNxY9tysL4ZRy/GxMvzMs2TrLlaiZcPNxNFSUjy5RBkkeSUeWVjLQOMuq4KOJXXYUzwUeKHfSRmAI0vckGZPAooQkygUmGGRFIZAKwSmZzLvsYq/8sx0Q8jurAFcpSYYaMNysnU3mgr5vcposr3mecZW5iA/fVKjnI9bIlqF3lrZBymMoUVw1JSEhPIwjwr2Q3SLycBnRWKrkyD+2Ekes9cpqc+JHQhVsecy+ReQJm/i1XKPnkLExCmnAwQq7otE/hFJijgkfci0aiq4n3yjnW1MAXm2ZNzkutb5DauxI8arVdgFlh9UNbkmWlEZ2FIO7sM3wT80MbgfeR6Gfj05YVpDuy3Db/96Uec1Xg/rv5iqsg6lt4t57zGn76X4kjqiV9NYHOSWPqmabFW+qXtdotSefWE8H5m9hNGg8qJ2mqeHnd8eHoQCVRKK4guBr7jCnYZo4sHW4/KsOk277wYLF27JdWRh4cDKcU1JvbmjyAsnxhlcPV2vPD8di5AUOb7j0/sdh3TNOIqi7Fdkf+4lTEwzxNLUtjyvZVS9P3I29tZfJ6OPfMcMJUwcLNSJDTjNMnerSaGkPjuozA2Xi9X5jnyfLpSaYnodgUEMMbAlEhZcWxObDcdHz98oGka+nHCB49WGldv1wHEdRiZvXjrtdvlGmmquqLvL/T9lf1+R2Uti6fack8tg+Xz+UxV16vE6eXlRbyNyGy7DalezEpvQwytJfpVQFuJI5a0KdbPcdNtmGfPVIandV3z5dNnCSWoavw4oWKmco6PHz7weHhAFUDmer1wuVwZx4ntdieGpLUAIhlkyFeYqWH2vL2+UTnHbrfj8fGh3NNi+r4Mxoy1VEbgoGmacJWjblsobDhJ8WH1OVkGGcsQVIa6MvwIMaCCIk+KlOcV+DmdLgJaWDmLLD4hC5iYc/EvSoskWZNzWBnnMjyf1v5sHKeSrrWk9JUkKlLpY+3KFvqH6p80CNL3A//xP/1nDm3Dr7//CMXNX2lVjK+EgqzQZOLNpdrI5psRJoV6pxVX3OJ8ZNddJn9Fe5q1sDyUXFAKhViymD0xQ1Ii8VBix05lFTkEzq9vfPz4BDlzncXE0hY9nbPuF+YxS3Pf9yNfv37j8fGhHLAF1fOzRyuEYVKcd3UlB6AQBInuuk1hJrzx6dNnNvsdu92OH7/7ji/PL/TXq2iz2pbHxz1N3cjG7hrqqiGmKDSwlHCHA85aiZWaJd855UzbbhgnmdKoQgd1lUyykk+QxahT0GLZACc/CDKstPiExMQ8DTx/+yoNe2ku6rpeKWQCTskHszT4Rmuqui5xYZHwzhSHnMkhoPRCt3I4U2OUmArlguBmKKyWwHZbMU6Chns/EebA+TQz+4SPmR9+/DXb7ZZ+HBn6nqEfMNrQdTV17eivFzabDdfrdZ1gLuZbbdsCmZ9//lt+/vkPfPr0JzZtR+UqRF8pNMvlUPH4+Mi2a3h5+cb1GoQVUDbdjJiorQcyrVcZ18JAkeskC09VNXSbDd2m43Q+UVU1H7/7KFMwH3h+eRZatbK4quL1VYADobXeMtRTSry8vKypM6pMMhbX88vlTNt1dGbD5XJhfzgwDiMhTMQYeH19FapcPIPr2WxbQs40dS73XmAYLgQjsc9VZSHHwgQAtJhMWisTZJnAgKtqaqW4Xi/MXpI7mrrl3/ybv6Kua/74x6/0fWIY34QZkRUpa3yAP376zDhOhJj49//2BzYbwzxnGtdyPl64DoFY0PjKSgD3HCLOQkoKYxSbrQBmKcXi3u3L5yQNT46ir8yUNJeQSMqCrlBZ0daVMGKMxIPaojGlHJBJkLU0IQuQqLVm0zZohHIJxSA0zBjryEqLoVZMOGvx48wfPr+VKVgqLAdF17XMIZCiTBWTsvT9yGkUT5KMoqrFrV1bYX/MwTPFTJ4jMQZCEulL3Tg5+GiZ9iwgQi6sLxB9ddtWKxukaWua2qE0TOPMnD3JiFlrzrkY4E6FubcYoRkWbbk2ZnWPBznsSBN1o5yLREfc5pdNdjEuFelJ/MWGqN7FCC4gR9aiV4Zb86mLcZ+iMDmQw8GiB18owiLXEm02sqXc9htVph75dtAXYFgOENJMyGa0rntqFvALMcwVmYYkYQAr20IVJkBOMh0PBTQLIYmeumiGF/mKmE7KPdY0zS+Ah48fv2Mcr8VYV9hYQ9+v0pz3k7LlQCBUWvGDWaQxy0ACpdC6NLDDhJ882/2epmnKQVVM+2L5vn/Oul57rueeXSUxsUYbVL7JGKBIaMtB5B+TYLNq1JUqS3VeyCRQWEAxZza7A/PYM/cBV9eoHPFhAiVT9blcw6f9jm+nvjBGZq79ld1OaNN9v8QzSpMayjp/vU44N+DshWnytN0FMOz3B1xVC0FdOZGmWQe2ZgyR5D3GOYbhwvF4ou0afvzuAykl/vq/DhgtRrZaW4mpzJFhEpbu/uEJ6yq2uz1LHPinnz+RY4CYsLWla1u2m45rfyFGAVdd3cg1LwOptV9D0EStFCnIYcI6B0RqYPaBUOCSJdJbZBLCTFn6qpQ0VdVRa0nZef/ZpiQyyso15ARTkOduHMcyaDLlwK/EmyMaYpDULK1Zqdkoofwba9c1GkqELRBzkChaFFGBypJ01g8DMQrIS4ZkSqx4zpIug8JPM0GJn4mzWpgJRcax0Mj7flzXJ2dk/c5ZqPyutivYq7VZWcWJm8GrNhql5HspBMS2WuSKGZEUa61IqvgoZSR9K5WBXEZ8ZZTIaa1SROTP53EsMjNhiGRFeZ6WZ4ryaYtkxRYDZfEZks/Aak3WStbXlNG5+KEYtR6OF/nHVOJQfQirR5181qkwKxPny8D1OvLt+SiDsa7m48fHVR5VVfUaSQpLP3tjvC490k2iGde9YjHsdJWhqiR5ZhhGfN+XYW5TvhYIiU3X4EPkLze/onKVeNWIOEkAv2Juuqy9y2F1nANKazabPXVVM4dImnzxjAtc+gGrFd2m5unpQe6xdX0Ss++31zdeXo68nXuUkQFaXTm+/+6Jp8cDOUWRGMwz2phyv3u8t2XgN60g0PsoVwGkHFW1YbfbcjpesLbisH/kcDgAMAfP+Xrl0+evPB9PfPnyhYf9ltoavn964NKPKyC5f3gUo9ngeXh4oG0qqqYhAW/PL2htqLoGZSxvryeMsmw2Hf31xtS21jC8iqHvpmup61pkG3XNNM28fHuh6ySWfGE5nk/nwvBxIhetHM4J42O36YrsWeBF6R/kGq89h2CNEqtrDEaBT4kcE9dh5Jqvq5H4ZruBquL7jx8lJWsYsNYxjSOX85mmqUgx0XZtke06bGGYn06n9Txca70CWy8vL8U7qeHx8bHc0xqtDwUMlsTAXBYPYwU0fnxqCcHT9wNN067AsTwLnqm/gRDyS4bgCztKa82HDx+k3wgBkOSu8+lMKgx/76fCZnUrg01A6nF9rrz3IoXfbFcPu6pyvL2dyBn2e1lIUgo0dQMkrFO4MkwPKdOPxc/zH1H/pEEQgMpZmqYSJ/Ak/hpYcZhWqmi0CjgiCQFKovcyaGVWAESQVb3ScESrK5TLvCLcCyxdAA4Ui5wm+EzdtNRNyzhNopkKBShBYVSmtmJWeNjt8KcTEU/MiRQkOcBah9FmPQjLpCkxjgNvb2Dth/XA2TYttqpwViLVckaSJpQiGYOzQvcevWcOkRADz7//A027IYTEjz/8wHm4smhEnW2oHjZoJeY/2+2WlCLHt1e6TUdV2cIKSGSVqbuWum6kIYgJqw11cYCunClUV8/1ckSVGDyjFM1mQ20MMWrmCD5FTKWICTbbiqaV77ccwE3R0aNuOe8piRFuUgalHE3jUNkRtEajxLyVSC4JQUZrKmvYNA11ZXh7eeF6vQiQZDQ5Fq1vCBhn2NQdr/2FcY6EmAlRsTs88pvf/pamqZlmz7bbYLU4JXvv+fjhA0+PD6uj9/H0xh/+5m9WI9f9bi/O5Bo27UaSZGKmrkU+s91uORz27HZCf79cLjy/vnA6nxinGbUyfkS76YNfJ7yimy10XcmUk+ZJyYRn8jPDy7TGvRpjGSZhXFRVVaRMNdY5rLErwLVMOpb0m37omfwo0/4SodmYFuuEgaKNRAFe+ysKxZdvX8kp0ZTmYbMRhsnwPHE8vRKzYq8dMfbFwFUmisFI85iiLb4MxWfCODQKV4neMYTAfO25Xkb2+w2n44luu+Hzly88PDyw3+356adfEyP8x//0R0JM2Eqx2be8HXvyeaRtHF3dCnsBkahZpfnTyxtWBQ6dXPe269jud7y8HouZWM1mU1NV4jg/FbfpSmu0zlgtaQQ4Ta5lPRJ/D5EmDXMkoECLMXECQpBUgOPpiq0bafaXDUUbYlTkWGKpvcRta23YbzaSjDCMaET6kY2hNYbsPf31yjzNWCc0RuckNnX2nuQjOQpYrI1lmsRwbvCeiEyAk9KMIeGnQWQqRguVsLCsnDVUWoFaTHU19aaBcnBw1tG2Ej1dVZUIDLVa5QApJvzoCVH8MSTaXBppVxmUqrBRF0laMbpOiJ7b1ZCleUssKSvLSr2w+1TxTAmQRcail/+cczEg1itItWiPF7PpZe15rzUGkW0tbCXn5PAoTaUuTbOTlJUsGutQDN50AVluoAEYbd81zoth4s2/ZPn64P2qzVb5BuRrpdEY2rZZY/0yEk1Mlr2saxqRj2qZEqcCNi+U6uV9L7T5XCa93s98/vwnmb4mYbyRxXulH8bbtpgzbdGYT9O0giuU1+icNEymNIHLNaVc977QeuUaiYmfWqQnfyYcxBjDbrsVWUWUaxTLIVHuqxvTQ+cCSCq9vp+FrZNXYGT5pwBVNzZJOXRn8d2irLVR1Xz44Vdczw3nt8/EaQSVWIwIjVX0Zcq1qy0+aaaYmIYeazSVswxIMpVzlq7bcO0HVCVT05fXN2KSSbDSYrY3DNeSCiSNvZbTMIosUhEl90BK0DYWp2AeB+q65aeffsPv/vp3+BB5enqQaXzl0MZhq5rZB1wtKRrn85mhH5h95HoZqasGvOd4vLJrG7q6kkGWgmqTCHmg6ba03YYcM2hzAzayXBOQZ8iHyDROkrZXercwTyUFoRZ6eliYUoq6tutnYMukNwSJHF0+sWE4iyzVGPFOi7EcWhTGCttkDgMpywHIlnVantlACBMoRZxn5kkitsMSwSsnayona2Iur1niiwvYVdBSicwulO8sjIhN12L0cvAO6yBlMVmFTNu48v7sylrKSYZ2cZXwFaA65wLkZlSWg5pVmqqp2XUt0zQSfMRYVyj7s8iOuUmRcmE9G3MDAULIEAK+mP2WJXetd0+H3HOmsFIy6yBAmH+L4az8jEWWkJQwBDNiLvqbpx9pdFjNJJe0DgVrVHcdaxbp5WI+DiKTDjHyr/7yV9RVRUb63Wke0Vle4NKDypoZuFxEwtE0woQQsMxjrV57tsWfqe97YYoQScAwy+vc7Q8YfYtn7poWH7wAV9pgrFmvCeVaj8NIU9fUTY1uGl5fTwxzYI6ZcRrJamacXwUMAiqty3Mhm3bKG3b7HSmKpCQEMdReQDFXDIJzhh9//I6nxwNdU5FTKOBYQ9e1aFP6zuiL6X5cB5Gn01nkUpp1qHm9XjlfzmL6GRP7hwPbfYMywprYVjWbtub7pwc+f/0mDDVnqP/y1yiluJzPWFMzemG3TeUabrZ7IHHuJQ1xSgIyHDY1Khv+8PmFVIy/u9pRVxqjxbPLR+nzQVE5w3cf92y3DUoh4Q4ltGGRNC1JQJvtVgY+JaFlLMlqXVuT4808fYnf9d6vvxa7goVxmpLI/YdhKKwR8d7y88S1rHPWaqZpLGt7Rd9fGQZhWI/TSNe2WGN5eHh4J8OnMMzDyow4HA5rH7MMrLQWkGphsb1n2caYmOaBcIlMs5ibt5Pncr7w+PiAtcK20LX4ir0fKi0MpLoW+wU/jaTgqZuWvhitbredsPDHCR8kRjyMt+8RiuphGXquEfFhLlJgsK7i4/cfOZ4ufP52ksGtNaR0xCdoastu09K2Nfvdnv1+uwZo/EP1TxoEsVaxP2xlKkMZry2dOSB56wnMrXFRSiIshZIo/ycfdiqUvrQCI6hbkyrN8Lvpjbr9XZDUFRliiMljVnJoU2UKXDlNSjBcL0QfOWw2xMuFfvJMo+dyOrHb7cBZalcRm/hOJybSmC9fvrDb7YQmPHlxfu80tTFiMpPLz7WaRCbOE36eivGWZJWPU2Cz2XE+X7hOA5vdTiap2tB1uzK5DEzjgPcTlZWD1GXoabuW7VZit0IQ+plsAI3orfBUWdHoCtsaTJthu6O/niS+1ViMTmhb83YamPuZgCNpSZnRWZo30hLZVrLIC0ixPBSV08SQIQVSnGm6hk235fR2JBfvjxQSTkszEucZrxXHa4+fJlLwzMXwM8wR38sBYPYe4yQmdpw851OPcTXf//hrvv/1T2Tger1Q1xUxBMI8k1Pk058+84e/+T273Wa9l7RWwrw4nQQIaBq00bJxlAOj+Jyc0VqVlJWJr1+/rJvsOAn6eXh8WrWCosMV+pkg/7NQKXWFNYZaG8ZpFqS1HOAUudBhi2xEKy7DZWVF5ZzIPtNaMdhrNxuCvzlTL4a0u/0O40S6NQwDl4tM7mJp0GLM5Fnc15UyxVjVM/TXlT3SdR06JMYxE/OEsRVNLaZz++0WU1UIwDETo0yymrqWaZdSmEo8LryXtIsYEo9PB5SCD999XGl4z8/PPD8/s989oFTiVz92aFvxerxyPo8llrPoa+PMZlPTtg5V2GNaCe237RY508jzl4FxFqBM8siLGWiOGKUxKrJrOtpGGtnoI6fTC0YJ0BgLIKe1wfpE1lZMgpUWyC7K1HLXOooFKMZqrFE4qwhBDOtyTszjlRQzGEOsDCFlUgxopcukMYlEZejx0Ys0D/nsfYwE6Vw5T5M0slqhUvG3sLr4mWSwFk8mpkhSmZgC8xzXRIG6tjS1W9Nh5DCgqaz8e9cKRVYX9p0qyvGcIBZ/prTQsAvSP00TIaV16qacUFIBVJEcLPTPZQpvtaQd/F1GwmK0pnKmLs1LjhJHvEzSlu5SfptX9tN6SFe3A+zNwLR4EyDXe+yXCGj17mebcgARSUilhfo6+yDykGXvSbAAJ7JH8QsfEPEHEid4oxaPkyI1VGqlmgp7RQCTEPQ6jVvo8Au7JUfxXJmmiUws92TGWsWlH1cJgTGGaUp8/jwKrbZyq4TAe4kn3u33vL29ralTfT9QlQMIC5BQKKzC3KvK2m7ouoq6dr+IJZd7IxFipF/+7M9IBElJYhCrWtah93BaBgEI1q8t7FC1+BndQJD1xLLicKXvUP9jac/qeZM1xjRsdh8A6I9fSUYXgMijVPG+IhJ85LsP33G8DLxeLlzPZ4xxIoUJmWbTst8faFppON/ejuScOR7P4ilWqOubzYbKySEkxbl4MRTGVPLYMhU02pHKpFhrg1GWp4cn8l8q/vp3v+N0utLULcP5yHUYOLiO86nnv/7hKyonfvwobNLvPn7gdPkTL9cRWy7QPM+c9Iy2cuhpsxGGgaswk6wvqgCji4QllqSeSCTFjLMa7Rw+RgEjtHifKCXeFv2wpKMp4EZvX/bWVR5TvBYWuXVTAOnrRajmfp7YbDfYSvxUTApUSqFTIow9IUQwCqOhrivO/RWnspiZtvVqwDyNMmw4bDvmYhqttaIvAMiSQlMWJvHsMoamqSUSOEniQUqWGBWL4TEUUIIsQ7Ji2Fw5SwhyL04hUjv53pW9AUI5F4+DIhkcRzFTVllYTELdz4RY2F5e2BXJx8JUlJSttWcu5db1Z1mf0/K2BHwW3K2kqd2exffSD3lfrMaHPtxMYFPMPD7u6TYbbLgKeBnjyiSL5dmsyuAQRH4Qs4Cubbvhw9MB78XkNsRZ2I8aQBO9JwZPBhlIpFi8jzTeB8Zixr3ZdOx2+3ItoaocwzAxDlcu55FxzJyuE13XoK3h9SgeaY/7LV1b5JwxoTLr/pZCEM8yo9d1Zr/flgFtJHjxToshrkwe5So+7Da0laF2AtypJPtXJqOs5tvLM+fTSeRKrlqNf6va8fThgf3Dgc1uL1HQRpOiLwxCg1Ie7yNEX9hno4AGrvhnOUm62u8PwqoOUUybvaxb2jhsThjrmKNnCjPOWlzZM43R/MWPH5lnT0oRY7UwEIzG1Y6YJ0KUwcJ4vvB6vrDYGOhygEsKjoMMARbfj23XsdtuyzqQyDozjIGurnC1Zb/d8PC45+GwQZFXI+AFXFxMcIV9UFG5ah3ceD8zjfIZW2cliSklhnFk9n4FONq2FX/DwuyUaHjx9mnabh1ACssjril51jmMNUW+KuvA9I7N0PfDKq1d2JgxRL59exbmX9MWY98NKYXS/8QiBxbWxcKskNSkodgUSKKVrSqMrvAknr+9AkuM8BJ7nda1PBbfnQXkEf+TdpVvpaFHa4kIruqKzWZD13VYY0khMAwjp9O5DGnkzLPf71YW7ziO688RKeRMVrrEMT8Qo6Q7nc5XKqfpB88wiMzQupPIqf65G6MqhA6/zmfUgiCnAqDrlUmxGtIos36dWr5JqYX6k7NMtMSj8B0IAkJbveHZ699NhWGijaNpNyj1itZy0xBnwvWMItEUt+QQZ+YhYnJasmpkQyx05cZZlGq59H25USXZJISZy+WZ7bblcNihlJgN6oLEWePISaNiIiUvbAOVqcmkmLEFPBmniapuODSSiuGqmowllwbZFTmRKYcIMc2yXPueuehkN5stxlisE4OicRiYpysxz4znRJhHVIqQI6noA1NOaGtRpiLh0EHo8TgLyjD7mWmeCDFTNQ2uHIKMvuWBL5Px41Hz6dMnTsdXLscTTd3QNWKAFbwXVlCKBD/RX6+QMm3RN4cQmMZpbVi991R1ReUapuR5fhuo24Yff/uv+Pj997iq4fn1jc8vL9i6A8x6wPj8+TOXy4Vp7Dkeu3Jgnokx4CqZDGy2WxZvllUjbIQ6+PLyIvGT2qyHPjFWk+ZfK1bQQQ5lEe/ndUNvmobL0BP7vsiHKoxzVE27uoALXicJJq78otBdoUwwjcY40DZTNU4OR7bQNqcBylQwj4Xu3rS4yqJjQonnZJnQhUJZ1WjjULAuZNKAv1HHEW1gmhPfvp3Y7xoOuy3eT0xGGnDKM1XXDcYKg0Ktsd56NcirG/BxBunBVi8ZheZ0vnA+DYVKLVOdrms5DiWeFo2tGlQxhY05M/YzBssPHx/RWjaoMAemOeFnkRG0rmLbNdRtLdKclFBZ7vkhzlyOHjGP85DAKEmHiiGKX4LVKGex7Y6YRaantF6nzVoZaitO2aZ4fMRxIifx+aiMpXs4CEiWoD+fmFOSXPl0a1TJwo7LOeHnQEozc5DrKlnwmqBzmbhlFAnVOBKFUZQVMSfGYSbGXO5FoQkrJT4c3aalssJmYZ1y5/JzM8oWMzo/k8jFb2DxrlmMxmTtXeiPC9jp5xnP+0ljRuWifS8HhMXPY90X3k3ZRT4jJnFWS0O77Aeo9K4J0SXdSp6H95v3AiKs3h3lEJtCxAcxbSUKeyIV2dbyS6lM1BKLvBwqha56ixH2XsymhQ4ue5msYRFtxNNB4oITtrH8+P0PbEsT93Y8ktXtYC1NzEjbNr+IYnzvNXK9XuX6ldSC5bqLL5CjaztQrP5UMsG8lglUWFkv+/2Bl5dXTqfT6jeSM6DV2iiKWV69DhWWz0k+Y5HfuAKq6gIQLRr7Zc/++zw2/v8qpcTktR8GDtv9OtFbDqN/F7/IRqORocPC2Fl8r375hQLy/3eNx9/51yLIRZua7eGjJDa9fMJOMoFTStJiaiuHzbeXZ6pmw2Hb0g+zRAyiqCtb7n/xBUgpY81VEtti4uXljZyjGNeW2GVyZhyu5JTYbreFxi/gPTly7cVpH0ST//XbVwHCtlt+85vf8n/8n/8XziZ2D1smP/M3f3wBJZ4eTWX4+OGJcZzQOrDd1kynXmw0UChnuc7CGNg2mmN/omktj08fBFAOi5+HEUBcO+K7IZSq5WCacsanwNCPoMW3J8ZETJHZ+/VQvICoy+8X7wKtNTpLj7GkJ8QYUGS6rmLXimdATJ6mFglRrQwqebZtQ7XveD1dqLsOksY1DV159mpXMQw9MQhTuFJwHSe+/OlPAg0Xir14lpTUL1gNWmvnqIq0RiGMtrnEbdpi3ppSkgMB0qFOcyygeGZQApR0XUddOUnCy1nW5pxK0oWYnlaIf0eimLHGBGhCzEwh4lPGx5GQRNqzkP61ygvMvQIcCjF1j2kZIuZfHJ5SlntEretvxMcIKyAi6+IiaUo5r94mC7tlsbQQ03vP4gkoMkyHsmpde5f15eYnqAqzToDy2jmU0cQU5XUBYwhMMbzbm8Tsua5vB+Ld7sBirD0OEzElvn1749vziTlEuqaBeWYMiSqBihlnrEQJp8zsY5EQSK97PB5XqUsIiaquVnNwFk+9IjPsupbt3jL5xPF8JqvMw7bFqEhVkufejq9FjhtoN92a6DUUFuvCXMg5izxFC6D46dNnyImPT08oIHjPy+sLCUUMUZiBiDn7x26DtaYMLRY5w8KeFNPy03lAGQl3EP+8C9M88+tf/cjkPans7cYYnp+/YIzh8elA2xqmMfD2+o1hilwvsobFlKQX6VqsMaucTKXE5XyhbWo+Pgrb5uOHD8yzZ3/YcTydSSialDHOce57YcrWhm5T0VaO7XZDZStyZvVYfM9gXD4rozWDIIzl/gpc+6t8RiETyt5Q1zV9Semx1gF6NSEeJ2FnSfrhpgyAAykGNhsJfDDWksqg98cff/xFz5RSor+IjyKUpKAyHB+GEbL46chAoyRl5bwmUvoC1EiksPj4CHg1kDK0y+eco5jOF/+deda0yyC8eIAt5sDL/SsMTzlD+OCZh54Ypceag5xJ9/s9KHCuZhxlnxmGnroWuVhKkZRiYdvmssbIdZ1mT0h+lcHVdU3dbOi6Bu+FAdTULeKNKOz9afxnLodRWvH09Mivf/ye4Gf87IXSnBNKL3FIt8z2v9tQqUK1k1Vafv/eiwNZ88vX5qJPlbQYyZjPLD4Vt+9r6HYP/Oonw+n1mbG/iKRVGUwxidtsGoZxxsdEYxSmMqgMzgll6nI+i4mNotDiAs4lvFcrw+B8lrhTpTWRIz5H9vs9TgltV1uJYzNK41xLmwLkxO7wxM9/+JnL+YqrMpuuQ2mDH2eMyZhar42TMWalU4oZo8S7jmXiB5mYA8fnVznwW0eaPTZ7tEpYFNMsC+40e1JMzD6iUGhncPWGze4JV7cEwIcJHQOts0STJfo1BXRVgbPs9weeHh85vr1J491f0NGjYmS4nEg+oLJsljFGNJLekXLG1DXD0OOHYT1QVTsxsKqqCuPF0HG73wmjRos8p2k2/O3nb/joufQDZA1KXOxjjByPR46vb+QsKO1y/1jrJFmmyJrEQXpcp8wpRYZBtKGb7YY2tQC3w1GO6JJGpIDtdktVVby9vRHLBGZxck8KmlZ0kD5HnNFs2g6tjRiIpbTevwvgJgkjQs0VBkcgZUnzyD1ieluMN3MQQ6LFi2LxS8iFph5TADKuADJyuJMkFqVkwVYqrwtiXdfkq0d5MYZMIXM5jxJHrDqRekRPXTm6rmXN+VYydc9k+RiUAiSCOeQo5oQpSx560xJCYp4TYxzYtB0+ZIbxzKVP1NuW//Xf/TuGoaepHTnM/O3Pf6A/v+LIPHQNKWV2rmK72RBzwh88ISqUrajbhqxKakmSQ3yKfmU5GK0YJwE35kH+3jhGvBeqsDIBU0eaaKiahpwiClPSeSphhiQBJMlGJrt+5nQ64oeRyhj+6l/9lkPVcHw9Mo+90L9DBOsAxezn9V7TSpdoaCUSKUXZ+EAnAQDmeS7eOoUVUdra4KURbWtNUzu6tsFUtkiSUvlsfenJCqCQY3EhFyAgR2lyrS2pFyGXxp6SLOOZZ48PnrFQIpVWqx/QwrzIWajyKud1evteTrFQ3eXrxT085wgUQLzQ6Je9QUBKysRQgKPlwCvU5rQymN4/uwvmokuDPoWbGXHOrNPDZQIp4Jyk7MiU6wYICDgvet3oC7NFyfUPHnwBXpTKnE9nrBFz0v1+zzCOjLPnw4cPXC6X4g6vuFyuXK5HFDfTbLj9zFhYb4vpaYiBOHv2+x3o23RnAWkkYSmt05zFB0gVVPL9tMday263p6qk2ev7AaGRx3fsnUTTtLLP5bg2LTe2n0QeygToz0gDKdfo6/Mzv/8bxw9Pu/X5voFwN0mMUuLfknUuNP/CZFxGMwsgVwYnWaUiYfq7AEn5qtX3SpV7UZNxKNOAqqgbLUzHEoOdJ6ED5zDz3YcnvoQXVA5MUdaT5acv6661bgWD/Rx4eT6i0FSu4e3lDWslGtjWrjw3lEa9REIrhatrrNZM44Sra37/889suh1aG/7yLz/y8nzkeBwZpkizaTFWGEZOZWBi02k+fzpDnPmwlcNj13a0bcvXr88MwyxyD7dl/yAs1VAm1K5yKCWSQQl3KntXiY/VROYQGU9HUtKEsk5UdYefJWFteZ4pn8+yPy/R4yIFTSWZq6Q4KFMM7CH5mcYplHZUzlBZx4ftjmkcOB/fcJsGP0wMV09M4mex3dVsd1uMMgQ/EqcgaUDWotqG87nHx0ws66YwcxWVq4CSZtE6VDFUVAr2+z3n84Wp+LFlbZn8XACfwjwst5kt7WnMGZPhcrkKM7QkaQHMU0Cp4jNXWWZf6OzGyFAjSeJFylBsHUvix62nXgypQVpntfxS4pUkIHnxd1EgIWPm3aKpyh4ClXFrnylGyWKSuwxuQggicS97ljVGjDxfM4+NyKKtNRjtsE6va1MIYWWuyWHPrkDYkiQk970tB7m4/jxdBjTjKKaaPiRohHEuZpNHQAYxQz8XEFqkWm0jB7mmadkEiQav2xpyQiXFy8sbbV2jnx65nM8Mfc80DWVNN+Q8CQsqyRBsHAbpM0bpK7e7PR92O2DksN2UqT2oMrZVOdE29QqYGCteFI8PB3abDeMwEIJns9kC8OXLV46nHp8ibdsAmT99+UzbiPdEP80M88w4Bty5F1+wEHm7jBy2Hd7PvB3PVE7z+HDAWM0wzVz6nusYaFtHP064aqBpanalH1elr7Iltnm77XCVLcayo7AkiEyjSOiqSoZ1bVPTtK6AOo5PX14IPqCMYdO1fPfxkeF65vXlC48PjwQ/EMNMzom6Evb+9999pO/PzMFzuV45H2cOmy2znvA+rOBkfxUQI6eE6vLK2iKDNpaYBfSbvZikWytng81mQ9u2PDw8CGPKWEJIvB2PfPn6wuvbBa0VD/tt8RBUy2xJepScmeahsI7E32Nhai+BDNoYXF0xjRMhBhpb8/T4BCj66w2YOJ6OZc9eiAOJ3W7H4bBbmZly38sA2xqzMk6ttcK8LYw87xP99YpWiv1+R9t1VFVF3/dcLtc1Lc6HwHa7xZT0unmWJMY4T2uP1XUdtavXs0QInrqu3p3fc2HTNFgjr0E8sBS2TGN3OzkvxShsZWMo6+KI0o6PH3eEEBiG4R9qB+T9/qO+6n9CNXXNfrtl23U4WVnLAlxW14VOvljVlxtqpbcWmHrRkP/3JWZLqHJ4VIX6WnR1WeXSnBaqa3Gf1dbRbHZorfkaZtIUadoOUiSmQFNV1GhCP6BBDmEossqEctgZh4F2s6HbbMUsJliGYVz3CmPgeLyQyXQpEnPEB892IzeguL9bYpZoX2UsKkWaxvHb3/4Vnz99klSREDFWkgf8PJbp9byaELadTIdCCrimgbRocgE0Yz+QQpQJcppRaaaoo/FhZg4zk/fMPpCSaPlDzKiYmfxIzEceKysbWwzkmErTqcnRMwVPipFN+8S2a/njH37Py/NXKmv4eNiS5wuns2cKolEW2rno9qZ5FJ2in7DWUO+3kKVpjSlx9hNVVbN/ODCN4tw9LZTPkDidL1SjTN4ikkjT1kIv3u22wvYIIocRKcCNkSQ090jXtsx+Zp4n8Y4oVH6hrOvS6Gusrdd7UpfJ2XJ/KihTWDmQtWUDizEQkzB19sWsdXFXT3EUpolxPD59IAbPPI1oIBTTKqG9TuvrbaoarSUz3Icg07kyFV0Ot1prkheHeokFPhdKrlA4x3FCK5ETKS3MB2srYrkGWpd0jXmmLRK1XCRn10tP8EHMrawpdPvMGvnlhPEk97M8nxITO2JMJGVJSDFKoRvLzz//zDQJ6DZ7T9/PhJDpGoc1md/9p/9I3Vh2nWMce1yOWGfYNJbtqn3NODdRG0fMNaNPRJQcZrRGJcgp48OMCjPWCtjkfWSeZlIU13ttDG1rgcQ8R+Y5o30E5fnuux+KtlF0yypJUsE0z4h5X4NSRvSUJUVgGmeu5yO7zmHySGsCtTE0RnOaJpLWZJVF72w0tqrLJA+UEnAEFclK4aMYqWkjEbPaSvKMdbJBCiCqy0RQzGqN0dKw51wOVQhAyOJpkQvr4r104GbI+n70nVJCIwkZUamikRdPCLIkuOji3B4LNXT17UiLCV1aWX8pKXnWisxLFQBhkWwtf0cpvQJ2i+RF6SWFZZmS3NJfcr69HwFa9DrmFJZD2WX0whEUE+6cEzHLMVjYIwLQFksA8RGwlpTemTMq+fOF9i1ginjmDMPA73//+2K0p4gpcz0LtXa7E3be4+MTfd9zPL6xRMGbckgA0Kmw/JRa2SMKoXIvfkQLO0OaiYBou5fJz1wODePKInmfSHM6nahrafa6blPo0L6wVWT6In4V3apbrmvH29vbypDJORdd/5+XCaKVeCgcDgdyXsCIG9MoFxBEJI3wPi63rPykYtwpfyZ7u9wVi4H18s1YmSVLb6KV7Nli6m5p2x3Jey7HEzEJSxMtIGlViafZNHlOL6887jteT2fClBlm8YSpXE232ZQpvRIfG7ukNiXe3k7EmNnuOoyKhDCz2+2K342C4s+DkfsNMuM8MheJbsqan//2b0sygKbtal7OF/EQGmce9i2n68C//c1HNJEUJ/a7lutFTKxbC6G/cDyfSOPMw7Zl9/BI1XbYArhoMtpAVab4VeVIacLPIyEFbG0x1jJHRT9B3m3I2jH6QIgJpe26Z4pRrDTfRluskve4eod1LaEAc1pp8REoiYPz0DNeLxgjIKnyiXnIfLkIiHu5jnz+cpHPUWyrGCeRbp7eznz48MgwjJwvEzFmulY07pVzaJvJRaq4eF/ECDHOxYtEF7YCoBVvpx5fepkMpDAWn7sSFby0wEvfm2U9KnmHpCDrqFbC1LNOfGxyhnEKa5xtVqmAsqygitzN76Ngbk45C+Cn9XJPL2Dguxu9sFBVYZotbDpQJVo4oYuHlizNZZipFnNJAUNyEoPlGCUEIeXFhLQqJr/Fs2eNIFbvehlTkqiEzVHXDfM8c7lcVs8vMVZlBX8XTzZQbDaWuqrfSXzKQSsm2rZFKQkRuPYzqEBTG6xTjNMFV1e0XS1JcUEGhX7yWKVXU/1us1nXSGHVJXICaypyVuy2YsbZdUuCSCMsjbAEJlhJaNQKH2aGoSeFSF2M9XVGkkyUDG1c5Zinmc+fP9N1XRnmGabBs+k6+v7K8e3EkRN17UooQsc0XxhnT/DC0PzTpxf+pF4wStabH7575OHpA+J/LQDjw0PLPMvhWYy5hU1jClszlbQmYSIErtcL2shQUeQcAnqFAMo4dFWhVcYaiEFDiLRWYbPBVI5tW6FJbLuW/bYtB/JICBW/+ekv0Nrwp09/5HQ9M0wTbXB0bYvKFaCYphmtb0a6zlpOpwtvrzJoaNuKtm1JOeFq8Tyb5mmNaHWVW5+tYRiYJo9zFVVtCCHx+7/9wjB6jHWSQpQC5/MZ5yx1JevwMI5crldCWAIRxDekadrVoFdrLdJFxKJgHMW/LxS2jrPirXg8vtH3fTmPlPUiiVGsRCYLC+R8PnM89mw2DSkG+kHSa7bbLVkJmNo01cra8SFwvvbMPhQvR0l7XAav2qiV2fbhwwdJYuqvJYlmluSjGMGK/LppanJ270yOpQcLIZavTRz2B9qmwweJrc5lwJdSKoEGMgR2VtbP4AeCH8XUdtv8A92A1D9ZEESh2G03mHJzxkKVXl2uo9DkktalSb3RlxZ0bV2YYUWaRC7z7r9ltZJF8rs/l2b13d8p31foOgZXNTw+fWA8H/HzSCy55sM4MY5zybjX+HmmqxvmGAjek1FFnzXx8PRI09b0/YjVmqSEdi0xP5nL5bpq3S6nE6kYoLbtFqscZNE+ztOIVom2rjHG8qsf/4K3t1eO1xN9fykbjeGvf/cHcsr8+OPHog8E64pZmgJnFQZLIBMmT42iqmtimMg5cB3O6FqMuqZpZJo82jpsvZjuJF7fBuIEKU3MIbDdtShjStSpUPMTxdDWOnJKPH/5ytdPn+ivJ6xKeAUPW8d3u4Y6jvhkuEyJfhpAy8EhZXFcnmOEglzmlIgqM5XUlGme+dOnLyIpMgZlHJdrzzgOtG2Hnye8F7dqpRQff/0TP/30W97ejkyTpCosB7JlgrAsUNM0Mg5XjDEy8cmZsCKekKMq1EfIMayHrBwX+UqZvpUmwBhL3dQlyUOmjU0nyPIwjTjjZMqYMw+HxyI7sOSscI2jvwgVWkOJq5SF01WOppjchhCpqnqlxaUg721BhRcq6Xazpb/2Et1XDrtToa/dDopySI0+UFeWrqnltThHCorKWGkyy9/NWXE+z1wvnq5x7PcdVZVkklwkJUZbSS0o0WI6J8gN1mo2WyOHipiIYeLjD39BTJrr6SyvB/hXv/5AXVm0gvPlwmbbiNyk2QqtcJpxRhPTjNUZ5zSoUKjJmsvriW67IwZxmM4JnHHoLFMksiLERN9PeF+M6pSkmZiqYZ5HEkLN8z4jQ2+Z7oZ5YhrH0tSWGFVdAYZhuIqJU0rUtcNWltO1x9iNJCnYQEqTsC1ayzlkQgFqs1LEcsAmZZTOAhRpSRRoutKAGGm8TZlEa7iBGRQtfspoAqoYAS5rb4iB4KWZEyaaXsGrWy3rJiudc1lztdG4cqCUpAMrdOQofinL18uzFt8xThbmQFqbZNFxK8g3jTrLQaE8p6mwM3xY/DIMKWdcaQgW0EA2XKGqKi2TnxWcKyeDpACMMJGygqKflZSEWJrj5b3frkEhDRFSol1QdliBUqUlDcHHW/JPKIDRMAy/oDA3TctvfvqJa9+LoWeIjOPMw8MHTpczMXqaVpqbvu9FH6/EP2AqcZNKwbeX51USNE3TL+REYg43siQuLFIBYE1s2Gw2qzxJKTFRtUYzXGeGcZApUNvQD1d8iKRUle/tOZ2OQF7ff0oBpW/MkD9XKVUkmloTYsKV+2X1JVDleYDybIs3yyKdXSrl25AE3vcOtz9Y+oxF0gHC0iHfPFQUlsp1uKpl7GcqZ6D4rMhhUfT5kraRaZxlmD0xwEwo+3uibmqa2uK9ImZhPci9kPn2fOR0ufKwb8lZ46yYRCst+yZojMpkl/F+QjcazUxtKrTxqNOFmDND3/P22tO0Hf/L//bvOZ2OPOwbZt/Tvz4zXc5YC7a2tK1iu2mJPnIZPUMvuv8l0c5VJe7bz4TosRZUhDBE+pIWYLT0eZIWZTF1TfQBkxUhKcyaAJiKxFehsseqRNWKNNmUhKcYgkg5Uqa1tnjXWKZ0Jc6RnAJxnsgxiYG3UvTHKyGAn5f90aCypm0r5iD3r8oKhaG/esbpBW0lulgbzTBOkjykAaWZp0BU4HRJvUsik0sg+37x1iHIPaiVJM8kZAqPAuPEqDst618GVaQsWueVmaHKbyQNqgAKWi1ob0kLK4BHLn4dLLduXntiY4pEcAF9EYBNFUlYzqC0FvCOAjIbXYZ5y7NeUh2Vlr0kRMgLo68qr1euGUqVVBthC+q8JP5k5jAzThOqq1HFNwkQOWmMKHPzOqqqejVqBlmDum5D07SM47RKwJdzQ0oyeXfO8fjQyv2pdQH/1eoHpdQtovPzpy9Y41dzfKUyD487urYhI7ISCvBvtKWunPwZCANpv0cGC9Lvp5S4XHsOhz2Vc0jErfSgSmsZXmnF0I8Y6yDHwl7Q4l1hZaC27PPj+cI0T2hrUNpQVzXb3R5XhlAhJg4P3+Ospa5kzw8hsOk6Lv2ZmD0//fojShnGYQKENTQMPW/HC7tty8fvPhTQynB4eAAlsou2SJGmceRyOa8G+q2tiFkY3JL8EtDWsdvuyoHeo3Jm020YhpnjeaTRlseHPVpFxuuIYmS3abheR+knUmI4n0gpsd1vaNsduR943G3RORDmkafDlk1XY51lv+2ojKa/TDSdyH3nwhKy1lI7Vww2W4ZeBpShsEljXtimmTiUvXBUOFdJr1gipUMIjOORmDPfPR3oNh2LxH6eZ75++4bRwh6pqgqy+CB6X4ZP2dC21TvvjVQGjYqmkXtb2ONHvI8cDgd0pQnBs99vqSpbBph2BQerSlh/3gv7Y7fbMQzzTX5f4tbncaJpGna73S/if68+cDpd+dWvJNwhp1uiXVVlwBTJL2ugw+PDAzknMcw9nxmuF/FyKjLE99JFVQ7hErJhUEbkWDEE8Q2sJCAlJQnqMJXCe9YBYMqZWPq3uEQr/yPqfyoIsizYwIpMLzX7mbe3oxiMpogt7t2uuOkv2s/FKCblmzO2pDQA3OinqdBeRfutC6W5TPBYQPV8A7TL//L6Om9giBwcC/uh6OAm70XTnzO2ssSQS/OpCSlSVRWTXyh+GRUCwQeatlmNeMI7LRrIYeJyGUqCh0QKhpRQyrDfNmi1uIPr0lTKtEPpzP5woO5q/vp3f831OuBczbWfMEqMnk6nRVemedhtyPMgqQ+uQs0zVimarsVnxYQ0/pNruPTD6gDvrCN7mcQrJcazMRqUdoR5JvaJ02WibgSZ1cqSE7Rtg3a2oLWJ2U+cTkc00iiFGJn7gcZqamewIWBqRYVjzJYYxQDH+4DCAhrvI9Mkmua6roXGXMa4CWmC/vDzz6v+9nQ6idN9QT73ezG7+uMff2a/f1gnpksG9jJplknykm8vMxNjVGla9Mo8stZQubrEP00sBpHizHyLAhWdrExNMiI7absOZy0PhwPPz888Pj7y+PhYaHqax8cnvn79htECPPkw86tf/ZrL5UxTCZBxvZ5wlUUZI0apl8sauxaK4dh+v7+hqsVbYFmUn56euF6vPD8/s9Dh39Pul+lp3dQYJWZiMmUZ2RpJFAGFLc1fzrJIKRT9GFF6IOdIb3t854sXzYRrt7i6Q5salCVERUaXiWXxaFCa3cOBunF8/fyZ/nLioWowNuLnIElBrUPlyPES+fo2kVPisG0hez48dsQ8o5Ns/M5UVErzuG1xtUHbCmzFtR9RWaOCIiRNfw2Mw4z30nTaAHOIxBxkopcha0vImTkEnk8z9Zdnkh+wSqiFmUTd1lRVw/k6c7qcmeZAzjK181dhjAxToO52bOodxjhUmgjzRIWmdYYuKSat8OWApbV4/sQoB01d8giXmM9MkkY4S+ur0iL1g/KkFBNZ2XRUiMTCAFgYGkYVHw7+x+aPwApc5BzXA/dNm5+LjCULTVjJZpkTxawuoLTIB/M7OYlsZgIeLUaoFO8P79N6YHfFj0Qc2iOokr5jLSqlFVhJOUH5d20WhhcoIxPlLC+vmNdl1DyjVCAlRVZyajFakgsWc9YV5CT/ggygKAyqcm28D6Rl86OcSZA446o000tk9fJ6T6cj1//cE4okxxjHMI40TcN2t+Xt7SKgpZFUAdHpWm4R8fKZL74iy+RVGDQz8wySQOVYzD/FNHXmlmazSGUuRYMLz8/fhIGwaKb7a5HTyfUcp4HF18WX5n8xdDTW/IKB8eeq7WbD998/MQ9ioCnpEdIfrOa8CtYkOUELy+d0Az3WGQrl+pbnYX0uln/8d8+JTLfLPJ2sNHXbsj98ICGJI1XdkvMonkMK9ruufBaBnBW1s1QmkHUuMkzxkrJagdXSQGoBG33w+JTww1QOlhaQ6XabIq1SGFejTUVW4sWhgMrK5Dr7QFXVfPr0R6G314YQA//l//1/2HQNhBPjcGS6jric+eH7AyoFDq1lt9lB1py3A6fzgKkams0GU+IocxKAM0ePj3JgTHHGj2I2H6I8cz4Us+KqRjsLxhGUSANNkZZoBc5YFgmNpFTllemTlCLMs0RQugpSxNmaqb9itaKylma/wx32+GkWSYnWtLuavp8Ic1zv9XkuoLMWz7aQskh+xyCgslJUXSUHwjmgUfggzDzrnCR9eZFM5ML8yEaDqFjFA6OssaEYe1ZNtSAbaGuxtkLnjFGQi+lqzhmNeE+FJJ5VMYNS4k1ntJYUnvW+XTqY0uPqWx+ulQAa1poCaihiCkVapEsfZIhBIuuNswTvy9oh66rR0ocv7EBhyS5xy5oU8wr8LGuBtYaUdfm9XdfAxf9sMTv8hfS9gDT99bp6XlQlkn5h2y2AjKRn7AtgPPDy8sY4TcTynB62O9njtCbngNGuHPwHNt0WkBSYlBL7w46npwe5/4wWgCNnVCqT7CB+XWMZyg1K09S1xB1rRcrlEKi1fC3gnBFWZRBWzGKM2bYtWcE4z1SNmK6qBJfryMvLifN14tc/fqBpN8Qs5txJK5RzxJRFeusDrk6YLJ/R48OugP+ZeZhJwROmmUuUfmS76aiE2IOuZJAZY6C2lg+HLYfDjuA9fZ9wVu6Fjx+fVg+K0+nCMScqV+QOWnEdeipnycWPxodILKD+ct9ZY6gqSdy59gN/ejvy3/7wB/a7DV1Vs+k6fuj29EPP6e1If+lp24q6qphHz+/+69/QtQ1d01FZS2UkBUnup9saftjvpIeYA/1FBqFd15G0MCjnecY4i1OKRqsCZi79wnubBGHkaSV9ihg7R0KcGScxax6HM5vNBqUUh11HW1teXt64nK9YO9E0LarE2ldVtZ4Jh2EgxsA4juXPXZGRynDnw4ePIn85vrFIWpdnZ9nnb14wi0/bTV58OEiIxiKFqarqnfmtW/uQeZ5LTzDw9nZiu92UfjIVwKf0k0aLVyUU5vmSoqTZbjuRyEzj2qvB7TyxvCZrbJGKy3M/jiPXxWelksH/8jkur/N9XDWwer39Y+p/CgiigKYxOCcUMuccb68j03zLWj/s9/z4448F4AiiZcaAUoxeWAzGlGipdxfAGlMOvTJhuhmZqbUpXnxEKDGQK/K9vDjKwp2Ecr6Ez6RfgBSSspFT4HI9CVNFGcgl5z1LRrQcCAIpwH6/4+1yQRnwMfDt5ZmP333EVRW7w550PJWpm/yMuUginl9OPDx0HPaW0+kEGHK2WFPMu7Qg86lMyskQs0wR/upf/xW//5s/8F9/9xmtFA8f9sxFDjP7CVfVpDAT+guUQzhK6Knny1fOM0xKUzdiQHM89pzOS0Pg8VGmyFaXSCZrUcAchFJ5ugxsFWQl0w9razKZsR+Z5plhGun7KykEait+HQCvLye+/7DHOUNOgVorScbBEKK8V2sdxjmaril6QgXK4H2UPHBteC+GWg78CxK6HO5VoZKP48jj42PRr1OyxCfmeaTvL0j2uEQ3uSInWB/kLBue0C9rxnHillVuV8Q0FWqYUorN7oA2RqioKdM0HZtODK1OxxPjOFO5huvpyun1LGaHKD5//krbCo1yv9/jveX1+UUkP70ipojWGWM6dNF3Pzw8sBhiLeCORnG9Xvn5559X2ntVVYzjyE8//UTbtmy3W3Y70RF++/ZtbTJCoTSK54EqByVLCBPKqnKgEv+UYZiZ58J6KRrdy+BJJDat5cpAyhEfAyYlmgRVIzpFYQcoUlBM4wWtoC7T5Lbt+Om3v+F8fON0fOXz85k8Z7aVZnOouPSen7/MvF4Th13Hvtrz+etnTAMvz1cedxUf9xtMDlRWs7WKaTgTrcU1G7T3HC+DPE8o3o4T114mQuKQH/EZYqHvhgyhaLbJYELm2k84E/FJPBr2+y3GOC6XieN5ph8FTG0qjbZWpg0pEdF8+vqKRXFoDY/bCuegAmplqTBcU6LPAWmzM4ki73i3GcE7uUc52C2TBWnAFnnXLTXFe0/M84rSA6u/xPJ1+T0JBH7x9xfmwDKpWyR4y6RllV6lREipmL+J6VgmrvpuW8uUcIluTXlhZN1SvsSQ1BY5XkkKCDI50/CL5ncBaFyJq10Mx957gqjVHV+t7ytE8SJYErMEzBPmRipqCf3OwHS55ssmraA0xLJ5G61FM5+X6y7A+Jy9HGTJTLOXFAcla0OawkpQV0omQtM4EOJc1kjxkrDWkLMuPh95lTlRrsX7xqBpKq7XKznDdruhrsWgue97Sc1oGo7HE0oVc7JpKiZ4al3bSJGkNEsi1sLWXOQ8aQXbVHntt3+GMv3+c5VCQNSnpwcaxCvLNpISs9yTy2tdmimBCG+JFizvJbOCXX8X6FikjsDarN5A8/JK1udHoV3F7ukJHJyeP+PHHpDkKK3ln51tGIaZFBJ1XfGgGy7TKEOhKPHDymiatkMZMX6cpom5xKFbq5hj5NvzkXgIzH7Apx2T92x2e7QFpYuBOhHrGqyxRB14bDvaruX3/+2v2bbCoHg5XaiqmdoZDu2WuG0geNpKyUHcAnHCuYrDtgaViNjVHDqFACqToyeFgDLC6g0xSOT7GJh9ZPaJknwJzDw8bmi3tRhdwyotsUYRkypDsE2hf8v9OI8DhsTj0wMHv+H1eGTqz1zCm0S6DxOTgk3XisF0FAPWcQpMITGHQAgCIjjniEkYVgqonOI6ByFZaJgL4eJ8GdFGM5bDrbWGtqnRzhCiGAiGGDFWpD4sjCQlbMM5xmJuKkOMqqqEQZtEimOdoakcXeuw6MLMnUvfWyarowA2wlp751PEIrs2JQJJsRiboxXGGkkcVJqqdihDYY0Js5WEyDOUyGkFbFdoXb0bFi5UqNt6LYCxWSn2KeaVrSFpEJMk+BXQYnm9y/otv+y69rw/5OUkyWVuSQgrByhhbYyrpOAml7F8ezkyTYF+DNS1Zrdt2e8OjOOZTGDoJy6XHh+ga2sul55hGNnttjgnvdI0D9IL+VGAdWWkLzWWxWMqRBl+aKUZh+sqRRS0lfW9aaWojDCExJdp4HoVf8D9Xoacm80Ga6vVK6SfRi5DTz8G/tvPnznsOyKRvp/k4LlpUWj6YSTnkWmeaGo5TBsEGGzbDqs00YucrG4kVa/r2tVAe55nrIE6OIwVJk7T1oR5ZtO2oDJNW4NWhDCjlMFZw2G/l8TAZX0tqSqy9yaOxxPOWHbFr0/SaVhZDNuu5hJmxsHzh8srrRXAMifomroESkTQmmnyNNuaj9898d3Hj4RZUlGG8cr1esZZt6ZfijVAYc1ot5q/vveWbEskLRl8mHFVJSysIlM39nZ8VghrSN6XpAldLheJPy59T9PU62ffth2Hw2MZXpjCEJnYbpq/c9DP1HW1+g/Ks3Xz80pJDvzTJOk9y3lkkbAuP09AHVvOH0HY1ZsNbduVON6eJXnOl2SqpZ+7yZLlpPzlqxj6NnUBmsueF5PHj74AJwZnK5LWHI+SkLnZdFTWETNcLpf1HNS2cp8tQGdOIofaFtkWShFmGZSayeJKEIPcZ/JMiAeZ+cXe/Uum8t9f/2gQ5O+b/N3rXve6173uda973ete97rXve51r3v9cyj9D3/Jve51r3vd6173ute97nWve93rXve61z//uoMg97rXve51r3vd6173ute97nWve93rX0TdQZB73ete97rXve51r3vd6173ute97vUvou4gyL3uda973ete97rXve51r3vd6173+hdRdxDkXve6173uda973ete97rXve51r3v9i6g7CHKve93rXve6173uda973ete97rXvf5F1B0Eude97nWve93rXve6173uda973ete/yLqDoLc6173ute97nWve93rXve6173uda9/EXUHQe51r3vd6173ute97nWve93rXve617+I+v8A9nwDWdzFfx4AAAAASUVORK5CYII=\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "interactive(children=(IntSlider(value=0, continuous_update=False, description='retained frame', max=15), Outpu…" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "acfd115b176c40aebbfb902991a77816" + } + }, + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "colab": { + "custom_widget_manager": { + "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" + } + } + } + } + } + ], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "print(\"shape:\", clip.shape, \"dtype:\", clip.dtype)\n", + "print(\"axes: (retained time, height, width, colour)\")\n", + "\n", + "retained_fraction = len(clip) / total_frames\n", + "discarded_fraction = 1 - retained_fraction\n", + "\n", + "print(\"retained:\", f\"{retained_fraction:.2%}\")\n", + "print(\"discarded:\", f\"{discarded_fraction:.2%}\")\n", + "print(\"clip[1] came from source frame\", kept_source_indices[1])\n", + "\n", + "# Screenshot-friendly visual computed from the real file.\n", + "fig, axes = plt.subplots(2, 1, figsize=(11, 6))\n", + "\n", + "axes[0].scatter(\n", + " np.arange(total_frames), np.zeros(total_frames),\n", + " s=7, alpha=0.18, label=\"recorded frame\"\n", + ")\n", + "axes[0].scatter(\n", + " kept_source_indices, np.zeros_like(kept_source_indices),\n", + " s=45, label=\"retained in tensor\"\n", + ")\n", + "axes[0].set_yticks([])\n", + "axes[0].set_xlim(-5, total_frames + 5)\n", + "axes[0].set_xlabel(\"source frame index\")\n", + "axes[0].set_title(\n", + " f\"Real video sampling: {len(clip)} of {total_frames} frames retained \"\n", + " f\"({retained_fraction:.1%})\"\n", + ")\n", + "axes[0].legend(loc=\"upper right\")\n", + "\n", + "preview_slots = [0, 5, 10, 15]\n", + "strip = np.concatenate([clip[k] for k in preview_slots], axis=1)\n", + "axes[1].imshow(strip)\n", + "axes[1].set_title(\n", + " \"Four retained real frames — source indices \"\n", + " + \", \".join(str(kept_source_indices[k]) for k in preview_slots)\n", + ")\n", + "axes[1].axis(\"off\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Interactive frame browser in Colab.\n", + "try:\n", + " from google.colab import output\n", + " output.enable_custom_widget_manager()\n", + "except ImportError:\n", + " pass\n", + "\n", + "import ipywidgets as widgets\n", + "from IPython.display import display\n", + "\n", + "def show_retained_frame(k):\n", + " fig, ax = plt.subplots(figsize=(8, 4.5))\n", + " ax.imshow(clip[k])\n", + " ax.set_title(\n", + " f\"clip[{k}] = source frame {kept_source_indices[k]} of {total_frames}\"\n", + " )\n", + " ax.axis(\"off\")\n", + " plt.show()\n", + "\n", + "slider = widgets.IntSlider(\n", + " value=0, min=0, max=len(clip)-1, step=1,\n", + " description=\"retained frame\", continuous_update=False\n", + ")\n", + "display(widgets.interactive(show_retained_frame, k=slider))\n" + ], + "id": "s05-06" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s05-07" + }, + "source": [ + "## Exercise 2 — one real input tensor, two different systems\n", + "\n", + "Now use the real decoded-video convention `(T,H,W,C)` as the anchor and design two downstream systems:\n", + "\n", + "- **Tech:** a short-video recommender that returns one embedding per video.\n", + "- **Biotech:** a surgical-video model that returns one phase-label distribution per timestep.\n", + "\n", + "These are **design scenarios**, not additional datasets. Their output shapes are architectural choices; the input-axis reasoning is grounded in the real decoded tensor above.\n", + "\n", + "> 🇪🇸 Usa la convención real `(T,H,W,C)` como punto de partida para diseñar dos sistemas. Son **escenarios de diseño**, no conjuntos de datos adicionales: las formas de salida son decisiones arquitectónicas, mientras que el razonamiento sobre los ejes parte del tensor real ya decodificado.\n" + ], + "id": "s05-07" + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "s05-08" + }, + "outputs": [], + "source": [ + "# TODO 4: Fill in the shape at each stage for BOTH systems.\n", + "# Next to every shape, write what each axis counts.\n", + "#\n", + "# --- Tech: short-video recommender, one embedding per video -------------------\n", + "# raw file : ...\n", + "# decoded frames : ...\n", + "# preprocessed batch: ...\n", + "# model input : ...\n", + "# model output : ...\n", + "#\n", + "# --- Biotech: surgical phase labelling, one label distribution per timestep --\n", + "# raw file : ...\n", + "# decoded frames : ...\n", + "# preprocessed batch: ...\n", + "# model input : ...\n", + "# model output : ...\n", + "#\n", + "# Then answer: which system intentionally removes the time axis at the output?\n" + ], + "id": "s05-08" + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "s05-09", + "outputId": "1b139133-ad2b-46d9-cd93-f1794f6b1423" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "real anchor shape: (16, 540, 960, 3) -> (T, H, W, C)\n", + "tech output : (32, 512) -> time collapsed\n", + "biotech output : (4, 64, 12) -> time preserved\n" + ] + } + ], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "# One defensible design. Different sizes can also be correct if axis meanings\n", + "# and model goals are internally consistent.\n", + "\n", + "# --- Tech: short-video recommender -------------------------------------------\n", + "# raw file : bytes on disk, no tensor shape yet\n", + "# decoded frames : (T, H, W, C)\n", + "# preprocessed batch: (32, 8, 224, 224, 3) N, T, H, W, C\n", + "# model input : (32, 8, 224, 224, 3)\n", + "# model output : (32, 512) N, embedding\n", + "# Time is intentionally collapsed into one vector per video.\n", + "\n", + "# --- Biotech: surgical phase labelling ---------------------------------------\n", + "# raw file : bytes on disk\n", + "# decoded frames : (T, H, W, C)\n", + "# preprocessed batch: (4, 64, 224, 224, 3) N, T, H, W, C\n", + "# model input : (4, 64, 224, 224, 3)\n", + "# model output : (4, 64, 12) N, T, classes\n", + "# Time survives because the predicted phase can change by timestep.\n", + "\n", + "print(\"real anchor shape:\", clip.shape, \"-> (T, H, W, C)\")\n", + "print(\"tech output :\", (32, 512), \"-> time collapsed\")\n", + "print(\"biotech output :\", (4, 64, 12), \"-> time preserved\")\n" + ], + "id": "s05-09" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s05-10" + }, + "source": [ + "## Exercise 3 — ragged clips: pad or sample?\n", + "\n", + "Real deployments receive videos with different durations. To make the memory cost visible without allocating an impossible image tensor, use a **stress-test design scenario** with clips lasting 30 s, 45 s, 2 min and 4 h at 30 fps.\n", + "\n", + "The durations here are intentionally chosen design inputs, not measurements from the Wikimedia clip. The lesson is the tensor cost they imply.\n", + "\n", + "> 🇪🇸 Los sistemas reales reciben vídeos con duraciones diferentes. Para visualizar el costo de memoria sin crear un tensor de imágenes imposible, usa un **escenario de estrés** con clips de 30 s, 45 s, 2 min y 4 h a 30 fps. Estas duraciones son entradas deliberadas del ejercicio, no mediciones del vídeo de Wikimedia.\n" + ], + "id": "s05-10" + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "s05-11" + }, + "outputs": [], + "source": [ + "# TODO 5:\n", + "# Convert durations [30s, 45s, 2min, 4h] at 30 fps into frame counts.\n", + "# If all four are padded to the longest length, what is the boolean mask shape?\n", + "# What fraction of positions in that mask are invented padding?\n", + "#\n", + "# TODO 6:\n", + "# Compare that with sampling exactly 64 frames from every clip.\n", + "# What is gained? What real temporal information is lost?\n", + "#\n", + "# TODO 7:\n", + "# A surgical system adds 3 synchronized camera angles.\n", + "# Write one shape that keeps camera as its own axis and one that folds camera\n", + "# into the batch axis. When would keeping CAM explicit be necessary?\n" + ], + "id": "s05-11" + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "colab": { + "base_uri": "https://localhost:8080/", + "height": 391 + }, + "id": "s05-12", + "outputId": "46809509-644a-468a-c4d1-9c1dace84e3f" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "frame counts: [900, 1350, 3600, 432000]\n", + "mask shape: (4, 432000)\n", + "padding fraction: 74.66%\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "fixed-sampling batch: (4, 64, 224, 224, 3)\n", + "camera explicit: (4, 3, 64, 224, 224, 3)\n", + "camera folded : (12, 64, 224, 224, 3)\n" + ] + } + ], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "durations_s = np.array([30, 45, 2 * 60, 4 * 60 * 60])\n", + "lengths = durations_s * 30\n", + "T_max = int(lengths.max())\n", + "\n", + "mask = np.zeros((len(lengths), T_max), dtype=bool)\n", + "for i, n in enumerate(lengths):\n", + " mask[i, :int(n)] = True\n", + "\n", + "wasted = 1 - mask.sum() / mask.size\n", + "\n", + "print(\"frame counts:\", lengths.tolist())\n", + "print(\"mask shape:\", mask.shape)\n", + "print(\"padding fraction:\", f\"{wasted:.2%}\")\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 2.8))\n", + "ax.imshow(mask, aspect=\"auto\", cmap=\"Greys\", interpolation=\"nearest\")\n", + "ax.set_yticks(range(4))\n", + "ax.set_yticklabels([\"30 s\", \"45 s\", \"2 min\", \"4 h\"])\n", + "ax.set_xlabel(\"frame index\")\n", + "ax.set_title(\n", + " f\"Valid positions vs padding — {wasted:.2%} of the padded \"\n", + " \"representation is invented\"\n", + ")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "sampled_batch_shape = (4, 64, 224, 224, 3)\n", + "print(\"fixed-sampling batch:\", sampled_batch_shape)\n", + "# Gain: bounded, predictable memory.\n", + "# Cost: long clips are sampled more sparsely; temporal events can vanish.\n", + "\n", + "explicit_camera = (4, 3, 64, 224, 224, 3) # N, CAM, T, H, W, C\n", + "folded_camera = (12, 64, 224, 224, 3) # N*CAM, T, H, W, C\n", + "print(\"camera explicit:\", explicit_camera)\n", + "print(\"camera folded :\", folded_camera)\n", + "# Keep CAM explicit when the model must combine information across views.\n" + ], + "id": "s05-12" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s05-12a" + }, + "source": [ + "## What just happened\n", + "\n", + "You followed one real video from file bytes to a tensor and then used that concrete tensor to reason about larger systems.\n", + "\n", + "1. The pinned WebM file decoded to **720 recorded frames**, while the in-memory tensor retained **16 real frames** with shape `(16, 540, 960, 3)`.\n", + "2. The real-data timeline made the sampling loss measurable: retaining 16 of 720 frames keeps only about **2.2%** of the recorded timesteps.\n", + "3. Two downstream systems can start from the same `(T,H,W,C)` convention and still need different outputs: a recommender may collapse time; timestep labelling must preserve it.\n", + "4. The ragged-length stress test showed why padding can be mathematically valid but operationally wasteful, while fixed sampling controls memory by discarding temporal information.\n", + "5. Camera, batch and time axes are not interchangeable just because they are dimensions of the same tensor.\n", + "\n", + "> 🇪🇸 **Qué ocurrió:** seguiste un vídeo real desde los bytes del archivo hasta un tensor. El archivo contiene 720 fotogramas grabados, pero el tensor conserva 16 imágenes reales `(16,540,960,3)`, aproximadamente el 2.2% de los instantes registrados. Luego viste que conservar, colapsar, rellenar o muestrear un eje temporal son decisiones del pipeline con consecuencias distintas. La forma del tensor no es solo notación: registra qué información decidiste conservar.\n" + ], + "id": "s05-12a" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s05-13" + }, + "source": [ + "---\n", + "\n", + "## Done with this section\n", + "\n", + "Next up: **06 · Contraction with einsum** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/06-contraction-with-einsum.ipynb).\n", + "\n", + "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" + ], + "id": "s05-13" + } + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "version_major": 2, + "version_minor": 0, + "state": { + "acfd115b176c40aebbfb902991a77816": { + "model_module": "@jupyter-widgets/controls", + "model_name": "VBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [ + "widget-interact" + ], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "VBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "VBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_cca9fe86dd4a47858d5239887ad130e3", + "IPY_MODEL_b9d836bdf16748dca6a165795e63b41d" + ], + "layout": "IPY_MODEL_d07f9fd70e9249c0b3914f50fe732f67" + } + }, + "cca9fe86dd4a47858d5239887ad130e3": { + "model_module": "@jupyter-widgets/controls", + "model_name": "IntSliderModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "IntSliderModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "IntSliderView", + "continuous_update": false, + "description": "retained frame", + "description_tooltip": null, + "disabled": false, + "layout": "IPY_MODEL_3a3ad95d2ed74199aefddd8ac1878804", + "max": 15, + "min": 0, + "orientation": "horizontal", + "readout": true, + "readout_format": "d", + "step": 1, + "style": "IPY_MODEL_2a25efc4365a43a7974bc9c867f88ab3", + "value": 0 + } + }, + "b9d836bdf16748dca6a165795e63b41d": { + "model_module": "@jupyter-widgets/output", + "model_name": "OutputModel", + "model_module_version": "1.0.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/output", + "_model_module_version": "1.0.0", + "_model_name": "OutputModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/output", + "_view_module_version": "1.0.0", + "_view_name": "OutputView", + "layout": "IPY_MODEL_afe5ed25c75f4a88ab3bca368b30ec82", + "msg_id": "", + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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\n" 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b/_variables.yml @@ -271,18 +271,18 @@ sections: format_es: "grupo" title_en: "Video pipeline design" title_es: "Diseño de un pipeline de vídeo" - summary_en: "Design the tensor shape at every stage of two real video systems." - summary_es: "Diseñar la forma del tensor en cada etapa de dos sistemas de vídeo reales." + summary_en: "Process one pinned real video end to end, then use what its axes actually mean to design two downstream video pipelines." + summary_es: "Convertir un archivo de vídeo real en un tensor, observar qué se pierde al muestrear fotogramas y diseñar formas tensoriales que respeten el significado del tiempo." objectives_en: - - "Read the shape of a real decoded video and say what each of its four axes counts." - - "Design the tensor shape at each of five pipeline stages, for two different systems." - - "Apply one ragged-length strategy from section 02 and give the exact batched shape." - - "Decide where a new axis goes, and say how that choice affects the rest of the pipeline." + - "Decode a pinned real video file into an order-4 tensor and name every axis." + - "Measure how much temporal information a frame-sampling decision keeps and discards." + - "Design tensor shapes for video-level and timestep-level prediction systems." + - "Explain the memory trade-off between padding variable-length clips and fixed-frame sampling." objectives_es: - - "Leer la forma de un vídeo real decodificado y explicar qué cuenta cada uno de sus cuatro ejes." - - "Diseñar la forma del tensor en cada una de cinco etapas del pipeline para dos sistemas diferentes." - - "Aplicar una estrategia para longitudes variables de la sección 02 y dar la forma exacta del batch." - - "Decidir dónde se ubica un nuevo eje y explicar cómo esa decisión afecta el resto del pipeline." + - "Decodificar un archivo de vídeo real y verificado en un tensor de orden 4, nombrando cada eje." + - "Medir cuánta información temporal conserva y descarta una decisión de muestreo de fotogramas." + - "Diseñar formas tensoriales para sistemas de predicción a nivel de vídeo y a nivel de timestep." + - "Explicar el compromiso de memoria entre rellenar clips de longitud variable y muestrear un número fijo de fotogramas." s06: n: "06" slug: "contraction-with-einsum" diff --git a/docs/notebooks/05-video-pipeline-design.ipynb b/docs/notebooks/05-video-pipeline-design.ipynb index 286f5e6..be48cae 100644 --- a/docs/notebooks/05-video-pipeline-design.ipynb +++ b/docs/notebooks/05-video-pipeline-design.ipynb @@ -10,16 +10,16 @@ "\n", "*Part III · group · 15 min*\n", "\n", - "> 🇪🇸 **Diseño de un pipeline de vídeo** — Diseñar la forma del tensor en cada etapa de dos sistemas de vídeo reales.\n", + "> 🇪🇸 **Diseño de un pipeline de vídeo** — Convertir un archivo de vídeo real en un tensor, observar qué se pierde al muestrear fotogramas y diseñar formas tensoriales que respeten el significado del tiempo.\n", "\n", - "Design the tensor shape at every stage of two real video systems.\n", + "Process one pinned real video end to end, then use what its axes actually mean to design two downstream video pipelines.\n", "\n", "## What you will be able to do\n", "\n", - "- Read the shape of a real decoded video and say what each of its four axes counts.\n", - "- Design the tensor shape at each of five pipeline stages, for two different systems.\n", - "- Apply one ragged-length strategy from section 02 and give the exact batched shape.\n", - "- Decide where a new axis goes, and say how that choice affects the rest of the pipeline." + "- Decode a pinned real video file into an order-4 tensor and name every axis.\n", + "- Measure how much temporal information a frame-sampling decision keeps and discards.\n", + "- Design tensor shapes for video-level and timestep-level prediction systems.\n", + "- Explain the memory trade-off between padding variable-length clips and fixed-frame sampling." ], "id": "s05-00" }, @@ -51,53 +51,54 @@ "import imageio.v3 as iio\n", "import matplotlib.pyplot as plt\n", "\n", - "# A real clip, pinned. \"Tormenta en l'Almadrava\" by Nicolas Vigier, CC0:\n", + "# Real clip: \"Tormenta en l'Almadrava\" by Nicolas Vigier, CC0.\n", "# https://commons.wikimedia.org/wiki/File:Tormenta_en_l%27Almadrava.webm\n", - "# 24 seconds of breaking waves at 960x540. The SHA-256 is checked below, so the\n", - "# file this notebook decodes cannot silently change under you -- the same\n", - "# guarantee section 11 puts on its voice recording.\n", - "VIDEO_URL = (\"https://upload.wikimedia.org/wikipedia/commons/1/1e/\"\n", - " \"Tormenta_en_l%27Almadrava.webm\")\n", + "VIDEO_URL = (\n", + " \"https://upload.wikimedia.org/wikipedia/commons/1/1e/\"\n", + " \"Tormenta_en_l%27Almadrava.webm\"\n", + ")\n", "VIDEO_SHA256 = \"e377fcdd2c79b55bce13c2c24b5dd7e412af39cd400eec548a79d0e59d79dc1b\"\n", - "\n", - "# Wikimedia answers the default `Python-urllib/3.x` User-Agent with a 403, so\n", - "# this identifies itself the way their policy asks.\n", "UA = \"tensors-workshop/1.0 (https://github.com/project-delphi/tensors-workshop)\"\n", "\n", "\n", "def fetch_verified_video(url, expected_sha256, n_frames=16, stride=45):\n", - " \"\"\"Download a video, refuse to proceed if it does not match the pinned\n", - " checksum, and decode only every `stride`-th frame, up to `n_frames`.\n", - "\n", - " Note what is and is not saved. `imiter` still decodes frames in order --\n", - " it reaches frame 675 by decoding all 676 before it -- but it only ever\n", - " RETAINS 16 of them, and it stops as soon as it has them. Holding all 720\n", - " would be a 1.1 GB array. Sampling frames rather than keeping them all is\n", - " exactly the decision the design exercise below asks you to make\n", - " deliberately, for two systems, and to say what it costs.\n", - " \"\"\"\n", + " # Verify the real file, decode the whole stream, retain only sparse frames.\n", " req = urllib.request.Request(url, headers={\"User-Agent\": UA})\n", " raw = urllib.request.urlopen(req, timeout=120).read()\n", + "\n", " got = hashlib.sha256(raw).hexdigest()\n", " if got != expected_sha256:\n", " raise ValueError(\n", - " f\"checksum mismatch for {url}: expected {expected_sha256}, got \"\n", - " f\"{got}. Refusing to use unverified video data.\")\n", - " frames = []\n", + " f\"checksum mismatch: expected {expected_sha256}, got {got}\"\n", + " )\n", + "\n", + " kept_frames = []\n", + " kept_source_indices = []\n", + " total_frames = 0\n", + "\n", " for i, frame in enumerate(\n", - " iio.imiter(io.BytesIO(raw), plugin=\"FFMPEG\", extension=\".webm\")):\n", - " if i % stride == 0:\n", - " frames.append(frame)\n", - " if len(frames) == n_frames:\n", - " break\n", - " return np.stack(frames)\n", + " iio.imiter(io.BytesIO(raw), plugin=\"FFMPEG\", extension=\".webm\")\n", + " ):\n", + " total_frames = i + 1\n", + " if i % stride == 0 and len(kept_frames) < n_frames:\n", + " kept_frames.append(frame)\n", + " kept_source_indices.append(i)\n", + "\n", + " clip = np.stack(kept_frames)\n", + " return clip, np.asarray(kept_source_indices), total_frames\n", + "\n", "\n", + "clip, kept_source_indices, total_frames = fetch_verified_video(\n", + " VIDEO_URL, VIDEO_SHA256\n", + ")\n", "\n", - "clip = fetch_verified_video(VIDEO_URL, VIDEO_SHA256)\n", - "# The cells below index clip[15] and quote this shape, so a short stream should\n", - "# fail here, where the cause is visible, not as an IndexError further down.\n", "assert clip.shape == (16, 540, 960, 3), f\"unexpected clip shape {clip.shape}\"\n", - "print(clip.shape, clip.dtype) # (16, 540, 960, 3) uint8" + "assert total_frames == 720, f\"unexpected frame count {total_frames}\"\n", + "\n", + "print(\"retained tensor:\", clip.shape, clip.dtype)\n", + "print(\"source frames:\", total_frames)\n", + "print(\"source indices retained:\", kept_source_indices.tolist())\n", + "print(\"RAM retained:\", f\"{clip.nbytes / 1024**2:.1f} MB\")" ], "id": "s05-02" }, @@ -105,102 +106,159 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## The tensor you are designing around\n", - "\n", - "> 🇪🇸 Antes de diseñar en abstracto: un vídeo real, ya descargado y\n", - "> decodificado. `(16, 540, 960, 3)` son 16 fotogramas muestreados de 720.\n", - "\n", - "The setup cell just downloaded 24 seconds of a storm and decoded **16 frames\n", - "sampled out of 720**. That is a real order-4 tensor, and every number in it was\n", - "measured by a camera:\n", - "\n", - "| Axis | Length | What it counts |\n", - "|---|---|---|\n", - "| 0 | 16 | frames kept, out of 720 |\n", - "| 1 | 540 | rows of pixels |\n", - "| 2 | 960 | columns of pixels |\n", - "| 3 | 3 | colour channels |\n", - "\n", - "Everything below is a *design* exercise about videos you do not have. Do it\n", - "against this shape rather than an imagined one — and note that the pipeline has\n", - "already made one of the choices you are about to argue about. `stride=45`\n", - "threw away 704 frames. Nothing warned you." + "## Why this matters\n", + "\n", + "A video model never receives “a video” in the abstract. A real file is decoded into axes, and every preprocessing choice decides what information survives.\n", + "\n", + "Here the camera recorded **720 frames**. The pipeline keeps only **16** of them in the in-memory tensor `(16, 540, 960, 3)`. That is efficient, but it also means most temporal measurements are deliberately discarded.\n", + "\n", + "This is the concept for the whole section:\n", + "\n", + "> **A video pipeline is a sequence of decisions about which axes survive, which axes move, and which information is discarded.**\n", + "\n", + "> 🇪🇸 **Por qué importa:** un modelo no recibe “un vídeo” de forma abstracta. El archivo real se decodifica en ejes y cada decisión de preprocesamiento determina qué información sobrevive. Aquí la cámara grabó 720 fotogramas, pero el tensor en memoria conserva solo 16. La eficiencia tiene un costo: se descarta información temporal real.\n", + "\n", + "### Predict → Run → Explain\n", + "\n", + "Before each exercise, predict both the **shape** and the **meaning of every axis**. After running code, explain what was kept and what was lost.\n", + "\n", + "> 🇪🇸 **Predice → Ejecuta → Explica:** antes de cada ejercicio, predice tanto la forma como el significado de cada eje. Después de ejecutar, explica qué información se conservó y cuál se perdió.\n" ], "id": "s05-03" }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "fig, axes = plt.subplots(1, 4, figsize=(13, 2.6))\n", - "for ax, k in zip(axes, [0, 5, 10, 15]):\n", - " ax.imshow(clip[k])\n", - " ax.set_title(f\"clip[{k}] (frame {k * 45} of 720)\", fontsize=9)\n", - " ax.axis(\"off\")\n", - "fig.suptitle(\"Four of the 16 sampled frames -- the waves are not the same twice\")\n", - "plt.tight_layout()\n", - "plt.show()\n", + "## Exercise 1 — watch a real video become a tensor\n", "\n", - "# Axis 0 is the only axis where order is information. Shuffle axis 1 and you\n", - "# get a scrambled picture that is obviously broken; shuffle axis 0 and you get\n", - "# 16 perfectly valid frames in a meaningless order -- and nothing raises.\n", - "print(clip.shape, clip.nbytes // 1024**2, \"MB for 16 frames\")\n", - "print(\"all 720 would be\", clip.nbytes * 45 // 1024**2, \"MB\")" + "Start with the measured data, not a diagram. `clip` contains 16 real frames sampled from the verified 720-frame source video.\n", + "\n", + "**Predict first:** what does each axis in `(16, 540, 960, 3)` count? What percentage of the recorded frames did this pipeline retain?\n", + "\n", + "> 🇪🇸 Empieza con datos medidos, no con un diagrama. `clip` contiene 16 fotogramas reales muestreados del vídeo verificado de 720 fotogramas. **Predice primero:** ¿qué cuenta cada eje y qué porcentaje de los fotogramas grabados conservó el pipeline?\n" ], "id": "s05-04" }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "## Another discussion block\n", - "\n", - "> 🇪🇸 Otro bloque de discusión: 10 minutos de diseño, 5 de puesta en común.\n", - "> No hay una única respuesta correcta.\n", - "\n", - "Back to your breakout channel. 10 minutes design, 5 minutes share-back.\n", - "**There is no single correct answer.**\n", - "\n", - "> Design the tensor shape at each stage — *raw file → decoded frames →\n", - "> preprocessed batch → model input → model output* — for **both** systems:\n", - ">\n", - "> - **Tech:** a short-video app computing one embedding per video from sampled\n", - "> frames, to choose what to play next.\n", - "> - **Biotech:** a surgical-video model that labels the current phase of an\n", - "> operation from an operating-room camera.\n", - "\n", - "### The five questions\n", - "\n", - "1. Sketch the shape at each of the five stages, for both. Where are they the\n", - " same, and where must they differ?\n", - "2. Clips have different lengths — 30 seconds against 4 hours. Take one strategy\n", - " your group proposed in section 02 and give the exact shape of the\n", - " preprocessed batch. What does an invented or wasted value in that tensor\n", - " represent?\n", - "3. The surgical system adds **three camera angles** recording at once. Where does\n", - " that axis go, and why does its position change how easy the rest of the\n", - " pipeline is to write?\n", - "4. The recommender samples 8 frames out of 900. Which operation from section 03\n", - " does that, and what is lost?\n", - "5. Both systems must decide **which frames matter most**. What kind of mechanism\n", - " could learn that weighting?" + "# TODO 1: Print clip.shape and clip.dtype.\n", + "# Name the meaning of axes 0, 1, 2 and 3.\n", + "#\n", + "# TODO 2: Using len(clip) and total_frames, compute:\n", + "# - fraction of recorded frames retained\n", + "# - fraction discarded\n", + "#\n", + "# TODO 3: Inspect kept_source_indices.\n", + "# Explain why clip[1] is NOT source frame 1.\n" ], "id": "s05-05" }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "print(\"shape:\", clip.shape, \"dtype:\", clip.dtype)\n", + "print(\"axes: (retained time, height, width, colour)\")\n", + "\n", + "retained_fraction = len(clip) / total_frames\n", + "discarded_fraction = 1 - retained_fraction\n", + "\n", + "print(\"retained:\", f\"{retained_fraction:.2%}\")\n", + "print(\"discarded:\", f\"{discarded_fraction:.2%}\")\n", + "print(\"clip[1] came from source frame\", kept_source_indices[1])\n", + "\n", + "# Screenshot-friendly visual computed from the real file.\n", + "fig, axes = plt.subplots(2, 1, figsize=(11, 6))\n", + "\n", + "axes[0].scatter(\n", + " np.arange(total_frames), np.zeros(total_frames),\n", + " s=7, alpha=0.18, label=\"recorded frame\"\n", + ")\n", + "axes[0].scatter(\n", + " kept_source_indices, np.zeros_like(kept_source_indices),\n", + " s=45, label=\"retained in tensor\"\n", + ")\n", + "axes[0].set_yticks([])\n", + "axes[0].set_xlim(-5, total_frames + 5)\n", + "axes[0].set_xlabel(\"source frame index\")\n", + "axes[0].set_title(\n", + " f\"Real video sampling: {len(clip)} of {total_frames} frames retained \"\n", + " f\"({retained_fraction:.1%})\"\n", + ")\n", + "axes[0].legend(loc=\"upper right\")\n", + "\n", + "preview_slots = [0, 5, 10, 15]\n", + "strip = np.concatenate([clip[k] for k in preview_slots], axis=1)\n", + "axes[1].imshow(strip)\n", + "axes[1].set_title(\n", + " \"Four retained real frames — source indices \"\n", + " + \", \".join(str(kept_source_indices[k]) for k in preview_slots)\n", + ")\n", + "axes[1].axis(\"off\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Interactive frame browser in Colab.\n", + "try:\n", + " from google.colab import output\n", + " output.enable_custom_widget_manager()\n", + "except ImportError:\n", + " pass\n", + "\n", + "import ipywidgets as widgets\n", + "from IPython.display import display\n", + "\n", + "def show_retained_frame(k):\n", + " fig, ax = plt.subplots(figsize=(8, 4.5))\n", + " ax.imshow(clip[k])\n", + " ax.set_title(\n", + " f\"clip[{k}] = source frame {kept_source_indices[k]} of {total_frames}\"\n", + " )\n", + " ax.axis(\"off\")\n", + " plt.show()\n", + "\n", + "slider = widgets.IntSlider(\n", + " value=0, min=0, max=len(clip)-1, step=1,\n", + " description=\"retained frame\", continuous_update=False\n", + ")\n", + "display(widgets.interactive(show_retained_frame, k=slider))\n" + ], + "id": "s05-06" + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercise 1 — sketch the two pipelines\n", + "## Exercise 2 — one real input tensor, two different systems\n", + "\n", + "Now use the real decoded-video convention `(T,H,W,C)` as the anchor and design two downstream systems:\n", "\n", - "> 🇪🇸 Dibuja las dos tuberías, etapa por etapa.\n", + "- **Tech:** a short-video recommender that returns one embedding per video.\n", + "- **Biotech:** a surgical-video model that returns one phase-label distribution per timestep.\n", "\n", - "Use comments. The point is the shapes and what each axis counts, not running\n", - "code." + "These are **design scenarios**, not additional datasets. Their output shapes are architectural choices; the input-axis reasoning is grounded in the real decoded tensor above.\n", + "\n", + "> 🇪🇸 Usa la convención real `(T,H,W,C)` como punto de partida para diseñar dos sistemas. Son **escenarios de diseño**, no conjuntos de datos adicionales: las formas de salida son decisiones arquitectónicas, mientras que el razonamiento sobre los ejes parte del tensor real ya decodificado.\n" ], - "id": "s05-06" + "id": "s05-07" }, { "cell_type": "code", @@ -208,75 +266,81 @@ "metadata": {}, "outputs": [], "source": [ - "# TODO 1: Fill in the shape at each stage for BOTH systems. Next to each,\n", - "# write what the axes count.\n", - "\n", + "# TODO 4: Fill in the shape at each stage for BOTH systems.\n", + "# Next to every shape, write what each axis counts.\n", + "#\n", "# --- Tech: short-video recommender, one embedding per video -------------------\n", "# raw file : ...\n", "# decoded frames : ...\n", "# preprocessed batch: ...\n", "# model input : ...\n", "# model output : ...\n", - "\n", - "# --- Biotech: surgical phase labelling, one label per timestep ---------------\n", + "#\n", + "# --- Biotech: surgical phase labelling, one label distribution per timestep --\n", "# raw file : ...\n", "# decoded frames : ...\n", "# preprocessed batch: ...\n", "# model input : ...\n", - "# model output : ..." + "# model output : ...\n", + "#\n", + "# Then answer: which system intentionally removes the time axis at the output?\n" ], - "id": "s05-07" + "id": "s05-08" }, { "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "# One defensible answer. Your group's may differ and still be right — what\n", - "# matters is that you can say what every axis COUNTS.\n", + "# One defensible design. Different sizes can also be correct if axis meanings\n", + "# and model goals are internally consistent.\n", "\n", "# --- Tech: short-video recommender -------------------------------------------\n", - "# raw file : bytes on disk, no shape yet\n", - "# decoded frames : (900, 1080, 1920, 3) T, H, W, C — every frame\n", - "# preprocessed batch: (32, 8, 224, 224, 3) N, T, H, W, C — 8 sampled frames\n", + "# raw file : bytes on disk, no tensor shape yet\n", + "# decoded frames : (T, H, W, C)\n", + "# preprocessed batch: (32, 8, 224, 224, 3) N, T, H, W, C\n", "# model input : (32, 8, 224, 224, 3)\n", - "# model output : (32, 512) N, embedding — TIME IS GONE,\n", - "# collapsed into one vector per video\n", + "# model output : (32, 512) N, embedding\n", + "# Time is intentionally collapsed into one vector per video.\n", "\n", "# --- Biotech: surgical phase labelling ---------------------------------------\n", "# raw file : bytes on disk\n", - "# decoded frames : (432000, 1080, 1920, 3) 4 hours at 30fps\n", - "# preprocessed batch: (4, 64, 224, 224, 3) N, T, H, W, C — a sliding window\n", + "# decoded frames : (T, H, W, C)\n", + "# preprocessed batch: (4, 64, 224, 224, 3) N, T, H, W, C\n", "# model input : (4, 64, 224, 224, 3)\n", - "# model output : (4, 64, 12) N, T, classes — ONE LABEL PER\n", - "# TIMESTEP, so time SURVIVES\n", + "# model output : (4, 64, 12) N, T, classes\n", + "# Time survives because the predicted phase can change by timestep.\n", "\n", - "# The five stages look alike until the output. The recommender destroys the time\n", - "# axis on purpose; the surgical model must keep it, because the answer to\n", - "# \"what phase are we in?\" changes during the operation." + "print(\"real anchor shape:\", clip.shape, \"-> (T, H, W, C)\")\n", + "print(\"tech output :\", (32, 512), \"-> time collapsed\")\n", + "print(\"biotech output :\", (4, 64, 12), \"-> time preserved\")\n" ], - "id": "s05-08" + "id": "s05-09" }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercise 2 — ragged lengths, and the extra camera\n", + "## Exercise 3 — ragged clips: pad or sample?\n", + "\n", + "Real deployments receive videos with different durations. To make the memory cost visible without allocating an impossible image tensor, use a **stress-test design scenario** with clips lasting 30 s, 45 s, 2 min and 4 h at 30 fps.\n", "\n", - "> 🇪🇸 Longitudes distintas y la cámara adicional." + "The durations here are intentionally chosen design inputs, not measurements from the Wikimedia clip. The lesson is the tensor cost they imply.\n", + "\n", + "> 🇪🇸 Los sistemas reales reciben vídeos con duraciones diferentes. Para visualizar el costo de memoria sin crear un tensor de imágenes imposible, usa un **escenario de estrés** con clips de 30 s, 45 s, 2 min y 4 h a 30 fps. Estas duraciones son entradas deliberadas del ejercicio, no mediciones del vídeo de Wikimedia.\n" ], - "id": "s05-09" + "id": "s05-10" }, { "cell_type": "code", @@ -284,82 +348,94 @@ "metadata": {}, "outputs": [], "source": [ - "# TODO 2: Take ONE ragged-length strategy from section 02 (pad + mask, or\n", - "# sample a fixed number of frames). Give the exact shape of the\n", - "# preprocessed batch for 4 clips of 30s, 45s, 2min and 4h at 30 fps.\n", - "# What does an invented or wasted value in that tensor represent?\n", - "\n", - "# TODO 3: Add three camera angles to the surgical system. Write the batch shape\n", - "# with the camera axis in two different positions, and say which makes\n", - "# the rest of the pipeline easier to write." + "# TODO 5:\n", + "# Convert durations [30s, 45s, 2min, 4h] at 30 fps into frame counts.\n", + "# If all four are padded to the longest length, what is the boolean mask shape?\n", + "# What fraction of positions in that mask are invented padding?\n", + "#\n", + "# TODO 6:\n", + "# Compare that with sampling exactly 64 frames from every clip.\n", + "# What is gained? What real temporal information is lost?\n", + "#\n", + "# TODO 7:\n", + "# A surgical system adds 3 synchronized camera angles.\n", + "# Write one shape that keeps camera as its own axis and one that folds camera\n", + "# into the batch axis. When would keeping CAM explicit be necessary?\n" ], - "id": "s05-10" + "id": "s05-11" }, { "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "# TODO 2 — padding to the longest clip is the honest disaster:\n", - "# longest = 4h at 30fps = 432,000 frames\n", - "# padded batch: (4, 432000, 224, 224, 3) ~ 5.8e11 values. Not possible.\n", - "# An invented value is a frame that was never recorded. The mask is what stops\n", - "# the model from learning from footage that does not exist.\n", - "#\n", - "# Sampling a fixed 64 frames per clip:\n", - "# batch: (4, 64, 224, 224, 3) — fits easily.\n", - "# Nothing is invented; a great deal is DISCARDED, and the 4-hour clip is\n", - "# sampled 500x more sparsely than the 30-second one. That bias is real.\n", - "\n", - "# TODO 3 — two placements:\n", - "# (N, CAM, T, H, W, C) = (4, 3, 64, 224, 224, 3)\n", - "# (N * CAM, T, H, W, C) = (12, 64, 224, 224, 3)\n", - "#\n", - "# The second is easier: every existing per-video operation keeps working\n", - "# unchanged, because the camera axis has been folded into the batch axis — and\n", - "# a batch axis is exactly the axis whose order does not matter. You only need\n", - "# the first form when the model must COMBINE the angles, at which point you\n", - "# must unfold back and the shape bookkeeping becomes yours to get right." + "durations_s = np.array([30, 45, 2 * 60, 4 * 60 * 60])\n", + "lengths = durations_s * 30\n", + "T_max = int(lengths.max())\n", + "\n", + "mask = np.zeros((len(lengths), T_max), dtype=bool)\n", + "for i, n in enumerate(lengths):\n", + " mask[i, :int(n)] = True\n", + "\n", + "wasted = 1 - mask.sum() / mask.size\n", + "\n", + "print(\"frame counts:\", lengths.tolist())\n", + "print(\"mask shape:\", mask.shape)\n", + "print(\"padding fraction:\", f\"{wasted:.2%}\")\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 2.8))\n", + "ax.imshow(mask, aspect=\"auto\", cmap=\"Greys\", interpolation=\"nearest\")\n", + "ax.set_yticks(range(4))\n", + "ax.set_yticklabels([\"30 s\", \"45 s\", \"2 min\", \"4 h\"])\n", + "ax.set_xlabel(\"frame index\")\n", + "ax.set_title(\n", + " f\"Valid positions vs padding — {wasted:.2%} of the padded \"\n", + " \"representation is invented\"\n", + ")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "sampled_batch_shape = (4, 64, 224, 224, 3)\n", + "print(\"fixed-sampling batch:\", sampled_batch_shape)\n", + "# Gain: bounded, predictable memory.\n", + "# Cost: long clips are sampled more sparsely; temporal events can vanish.\n", + "\n", + "explicit_camera = (4, 3, 64, 224, 224, 3) # N, CAM, T, H, W, C\n", + "folded_camera = (12, 64, 224, 224, 3) # N*CAM, T, H, W, C\n", + "print(\"camera explicit:\", explicit_camera)\n", + "print(\"camera folded :\", folded_camera)\n", + "# Keep CAM explicit when the model must combine information across views.\n" ], - "id": "s05-11" + "id": "s05-12" }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Share-back\n", - "\n", - "> 🇪🇸 Puesta en común.\n", - "\n", - "**Q4** — sampling 8 frames from 900 is *fancy indexing*, exactly section 03's\n", - "TODO 3: `frames[idx]` where `idx` is an array of positions. What is lost is\n", - "everything between the samples — and for a 4-hour surgical video, that is almost\n", - "all of it. That is why the surgical system uses a sliding window instead.\n", + "## What just happened\n", "\n", - "**Q5** — **attention**. It learns a weight per position from the data itself,\n", - "rather than you choosing which frames matter in advance. Take-home B in section\n", - "11 builds it from two `einsum` calls, and the padding mask from Q2 above turns\n", - "out to be the same mask attention needs.\n", + "You followed one real video from file bytes to a tensor and then used that concrete tensor to reason about larger systems.\n", "\n", - "### The thread running through both discussions\n", + "1. The pinned WebM file decoded to **720 recorded frames**, while the in-memory tensor retained **16 real frames** with shape `(16, 540, 960, 3)`.\n", + "2. The real-data timeline made the sampling loss measurable: retaining 16 of 720 frames keeps only about **2.2%** of the recorded timesteps.\n", + "3. Two downstream systems can start from the same `(T,H,W,C)` convention and still need different outputs: a recommender may collapse time; timestep labelling must preserve it.\n", + "4. The ragged-length stress test showed why padding can be mathematically valid but operationally wasteful, while fixed sampling controls memory by discarding temporal information.\n", + "5. Camera, batch and time axes are not interchangeable just because they are dimensions of the same tensor.\n", "\n", - "Section 02 asked what an axis *means*. This block asks where to *put* it. The\n", - "answer is the same in both: an axis whose order carries no information (batch,\n", - "camera) can be folded, shuffled and merged freely; an axis whose order **is** the\n", - "information (time) cannot." + "> 🇪🇸 **Qué ocurrió:** seguiste un vídeo real desde los bytes del archivo hasta un tensor. El archivo contiene 720 fotogramas grabados, pero el tensor conserva 16 imágenes reales `(16,540,960,3)`, aproximadamente el 2.2% de los instantes registrados. Luego viste que conservar, colapsar, rellenar o muestrear un eje temporal son decisiones del pipeline con consecuencias distintas. La forma del tensor no es solo notación: registra qué información decidiste conservar.\n" ], - "id": "s05-12" + "id": "s05-12a" }, { "cell_type": "markdown", @@ -378,7 +454,6 @@ ], "metadata": { "colab": { - "name": "05-video-pipeline-design.ipynb", "provenance": [], "toc_visible": true }, @@ -389,6 +464,264 @@ }, "language_info": { "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "version_major": 2, + "version_minor": 0, + "state": { + "acfd115b176c40aebbfb902991a77816": { + "model_module": "@jupyter-widgets/controls", + "model_name": "VBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [ + "widget-interact" + ], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "VBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "VBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_cca9fe86dd4a47858d5239887ad130e3", + "IPY_MODEL_b9d836bdf16748dca6a165795e63b41d" + ], + "layout": "IPY_MODEL_d07f9fd70e9249c0b3914f50fe732f67" + } + }, + "cca9fe86dd4a47858d5239887ad130e3": { + "model_module": "@jupyter-widgets/controls", + "model_name": "IntSliderModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "IntSliderModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "IntSliderView", + "continuous_update": false, + "description": "retained frame", + "description_tooltip": null, + "disabled": false, + "layout": "IPY_MODEL_3a3ad95d2ed74199aefddd8ac1878804", + "max": 15, + "min": 0, + "orientation": "horizontal", + "readout": true, + "readout_format": "d", + "step": 1, + "style": "IPY_MODEL_2a25efc4365a43a7974bc9c867f88ab3", + "value": 0 + } + }, + "b9d836bdf16748dca6a165795e63b41d": { + "model_module": "@jupyter-widgets/output", + "model_name": "OutputModel", + "model_module_version": "1.0.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/output", + "_model_module_version": "1.0.0", + "_model_name": "OutputModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/output", + "_view_module_version": "1.0.0", + "_view_name": "OutputView", + "layout": "IPY_MODEL_afe5ed25c75f4a88ab3bca368b30ec82", + "msg_id": "", + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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\n" 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null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + } + } + } } }, "nbformat": 4, diff --git a/notebooks/05-video-pipeline-design.ipynb b/notebooks/05-video-pipeline-design.ipynb index 0898669..be48cae 100644 --- a/notebooks/05-video-pipeline-design.ipynb +++ b/notebooks/05-video-pipeline-design.ipynb @@ -1,876 +1,729 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "s05-00" - }, - "source": [ - "# 05 · Video pipeline design\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/05-video-pipeline-design.ipynb)\n", - "\n", - "*Part III · group · 15 min*\n", - "\n", - "> 🇪🇸 **Diseño de un pipeline de vídeo** — Convertir un archivo de vídeo real en un tensor, observar qué se pierde al muestrear fotogramas y diseñar formas tensoriales que respeten el significado del tiempo.\n", - "\n", - "Process one pinned real video end to end, then use what its axes actually mean to design two downstream video pipelines.\n", - "\n", - "## What you will be able to do\n", - "\n", - "- Decode a pinned real video file into an order-4 tensor and name every axis.\n", - "- Measure how much temporal information a frame-sampling decision keeps and discards.\n", - "- Design tensor shapes for video-level and timestep-level prediction systems.\n", - "- Explain the memory trade-off between padding variable-length clips and fixed-frame sampling.\n" - ], - "id": "s05-00" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s05-01" - }, - "source": [ - "## Setup\n", - "\n", - "Run this first. It downloads one **real CC0 WebM video from Wikimedia Commons**, verifies its SHA-256 checksum, decodes the full stream to count the recorded frames, and retains only a sparse set of frames in memory.\n", - "\n", - "> 🇪🇸 Ejecuta esta celda primero. Descarga un **vídeo real CC0 de Wikimedia Commons**, verifica su checksum SHA-256, decodifica el flujo completo para contar los fotogramas grabados y conserva en memoria solo una muestra dispersa.\n", - "\n", - "The full video is deliberately **not** materialized as one `(T,H,W,C)` array: that would require roughly a gigabyte of RAM. Avoiding that allocation is already a real pipeline-design decision.\n" - ], - "id": "s05-01" - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "s05-02", - "outputId": "82ad0c23-36e6-4adf-d071-bc6efd1fdfad" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "retained tensor: (16, 540, 960, 3) uint8\n", - "source frames: 720\n", - "source indices retained: [0, 45, 90, 135, 180, 225, 270, 315, 360, 405, 450, 495, 540, 585, 630, 675]\n", - "RAM retained: 23.7 MB\n" - ] - } - ], - "source": [ - "%pip install -q \"imageio[ffmpeg]\"\n", - "\n", - "import hashlib\n", - "import io\n", - "import urllib.request\n", - "\n", - "import numpy as np\n", - "import imageio.v3 as iio\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Real clip: \"Tormenta en l'Almadrava\" by Nicolas Vigier, CC0.\n", - "# https://commons.wikimedia.org/wiki/File:Tormenta_en_l%27Almadrava.webm\n", - "VIDEO_URL = (\n", - " \"https://upload.wikimedia.org/wikipedia/commons/1/1e/\"\n", - " \"Tormenta_en_l%27Almadrava.webm\"\n", - ")\n", - "VIDEO_SHA256 = \"e377fcdd2c79b55bce13c2c24b5dd7e412af39cd400eec548a79d0e59d79dc1b\"\n", - "UA = \"tensors-workshop/1.0 (https://github.com/project-delphi/tensors-workshop)\"\n", - "\n", - "def fetch_verified_video(url, expected_sha256, n_frames=16, stride=45):\n", - " \"\"\"Verify the real file, decode the whole stream, retain only sparse frames.\"\"\"\n", - " req = urllib.request.Request(url, headers={\"User-Agent\": UA})\n", - " raw = urllib.request.urlopen(req, timeout=120).read()\n", - "\n", - " got = hashlib.sha256(raw).hexdigest()\n", - " if got != expected_sha256:\n", - " raise ValueError(\n", - " f\"checksum mismatch: expected {expected_sha256}, got {got}\"\n", - " )\n", - "\n", - " kept_frames = []\n", - " kept_source_indices = []\n", - " total_frames = 0\n", - "\n", - " for i, frame in enumerate(\n", - " iio.imiter(io.BytesIO(raw), plugin=\"FFMPEG\", extension=\".webm\")\n", - " ):\n", - " total_frames = i + 1\n", - " if i % stride == 0 and len(kept_frames) < n_frames:\n", - " kept_frames.append(frame)\n", - " kept_source_indices.append(i)\n", - "\n", - " clip = np.stack(kept_frames)\n", - " return clip, np.asarray(kept_source_indices), total_frames\n", - "\n", - "clip, kept_source_indices, total_frames = fetch_verified_video(\n", - " VIDEO_URL, VIDEO_SHA256\n", - ")\n", - "\n", - "assert clip.shape == (16, 540, 960, 3), f\"unexpected clip shape {clip.shape}\"\n", - "assert total_frames == 720, f\"unexpected frame count {total_frames}\"\n", - "\n", - "print(\"retained tensor:\", clip.shape, clip.dtype)\n", - "print(\"source frames:\", total_frames)\n", - "print(\"source indices retained:\", kept_source_indices.tolist())\n", - "print(\"RAM retained:\", f\"{clip.nbytes / 1024**2:.1f} MB\")\n" - ], - "id": "s05-02" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s05-03" - }, - "source": [ - "## Why this matters\n", - "\n", - "A video model never receives “a video” in the abstract. A real file is decoded into axes, and every preprocessing choice decides what information survives.\n", - "\n", - "Here the camera recorded **720 frames**. The pipeline keeps only **16** of them in the in-memory tensor `(16, 540, 960, 3)`. That is efficient, but it also means most temporal measurements are deliberately discarded.\n", - "\n", - "This is the concept for the whole section:\n", - "\n", - "> **A video pipeline is a sequence of decisions about which axes survive, which axes move, and which information is discarded.**\n", - "\n", - "> 🇪🇸 **Por qué importa:** un modelo no recibe “un vídeo” de forma abstracta. El archivo real se decodifica en ejes y cada decisión de preprocesamiento determina qué información sobrevive. Aquí la cámara grabó 720 fotogramas, pero el tensor en memoria conserva solo 16. La eficiencia tiene un costo: se descarta información temporal real.\n", - "\n", - "### Predict → Run → Explain\n", - "\n", - "Before each exercise, predict both the **shape** and the **meaning of every axis**. After running code, explain what was kept and what was lost.\n", - "\n", - "> 🇪🇸 **Predice → Ejecuta → Explica:** antes de cada ejercicio, predice tanto la forma como el significado de cada eje. Después de ejecutar, explica qué información se conservó y cuál se perdió.\n" - ], - "id": "s05-03" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s05-04" - }, - "source": [ - "## Exercise 1 — watch a real video become a tensor\n", - "\n", - "Start with the measured data, not a diagram. `clip` contains 16 real frames sampled from the verified 720-frame source video.\n", - "\n", - "**Predict first:** what does each axis in `(16, 540, 960, 3)` count? What percentage of the recorded frames did this pipeline retain?\n", - "\n", - "> 🇪🇸 Empieza con datos medidos, no con un diagrama. `clip` contiene 16 fotogramas reales muestreados del vídeo verificado de 720 fotogramas. **Predice primero:** ¿qué cuenta cada eje y qué porcentaje de los fotogramas grabados conservó el pipeline?\n" - ], - "id": "s05-04" - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "s05-05" - }, - "outputs": [], - "source": [ - "# TODO 1: Print clip.shape and clip.dtype.\n", - "# Name the meaning of axes 0, 1, 2 and 3.\n", - "#\n", - "# TODO 2: Using len(clip) and total_frames, compute:\n", - "# - fraction of recorded frames retained\n", - "# - fraction discarded\n", - "#\n", - "# TODO 3: Inspect kept_source_indices.\n", - "# Explain why clip[1] is NOT source frame 1.\n" - ], - "id": "s05-05" - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ], - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1009, - "referenced_widgets": [ - "acfd115b176c40aebbfb902991a77816", - "cca9fe86dd4a47858d5239887ad130e3", - "b9d836bdf16748dca6a165795e63b41d", - "d07f9fd70e9249c0b3914f50fe732f67", - "3a3ad95d2ed74199aefddd8ac1878804", - "2a25efc4365a43a7974bc9c867f88ab3", - "afe5ed25c75f4a88ab3bca368b30ec82" - ] - }, - "id": "s05-06", - "outputId": "bba1f4b1-8845-425c-fcf4-15f5c31a5227" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "shape: (16, 540, 960, 3) dtype: uint8\n", - "axes: (retained time, height, width, colour)\n", - "retained: 2.22%\n", - "discarded: 97.78%\n", - "clip[1] came from source frame 45\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "interactive(children=(IntSlider(value=0, continuous_update=False, description='retained frame', max=15), Outpu…" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "acfd115b176c40aebbfb902991a77816" - } - }, - "metadata": { - "application/vnd.jupyter.widget-view+json": { - "colab": { - "custom_widget_manager": { - "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" - } - } - } - } - } - ], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "print(\"shape:\", clip.shape, \"dtype:\", clip.dtype)\n", - "print(\"axes: (retained time, height, width, colour)\")\n", - "\n", - "retained_fraction = len(clip) / total_frames\n", - "discarded_fraction = 1 - retained_fraction\n", - "\n", - "print(\"retained:\", f\"{retained_fraction:.2%}\")\n", - "print(\"discarded:\", f\"{discarded_fraction:.2%}\")\n", - "print(\"clip[1] came from source frame\", kept_source_indices[1])\n", - "\n", - "# Screenshot-friendly visual computed from the real file.\n", - "fig, axes = plt.subplots(2, 1, figsize=(11, 6))\n", - "\n", - "axes[0].scatter(\n", - " np.arange(total_frames), np.zeros(total_frames),\n", - " s=7, alpha=0.18, label=\"recorded frame\"\n", - ")\n", - "axes[0].scatter(\n", - " kept_source_indices, np.zeros_like(kept_source_indices),\n", - " s=45, label=\"retained in tensor\"\n", - ")\n", - "axes[0].set_yticks([])\n", - "axes[0].set_xlim(-5, total_frames + 5)\n", - "axes[0].set_xlabel(\"source frame index\")\n", - "axes[0].set_title(\n", - " f\"Real video sampling: {len(clip)} of {total_frames} frames retained \"\n", - " f\"({retained_fraction:.1%})\"\n", - ")\n", - "axes[0].legend(loc=\"upper right\")\n", - "\n", - "preview_slots = [0, 5, 10, 15]\n", - "strip = np.concatenate([clip[k] for k in preview_slots], axis=1)\n", - "axes[1].imshow(strip)\n", - "axes[1].set_title(\n", - " \"Four retained real frames — source indices \"\n", - " + \", \".join(str(kept_source_indices[k]) for k in preview_slots)\n", - ")\n", - "axes[1].axis(\"off\")\n", - "\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "# Interactive frame browser in Colab.\n", - "try:\n", - " from google.colab import output\n", - " output.enable_custom_widget_manager()\n", - "except ImportError:\n", - " pass\n", - "\n", - "import ipywidgets as widgets\n", - "from IPython.display import display\n", - "\n", - "def show_retained_frame(k):\n", - " fig, ax = plt.subplots(figsize=(8, 4.5))\n", - " ax.imshow(clip[k])\n", - " ax.set_title(\n", - " f\"clip[{k}] = source frame {kept_source_indices[k]} of {total_frames}\"\n", - " )\n", - " ax.axis(\"off\")\n", - " plt.show()\n", - "\n", - "slider = widgets.IntSlider(\n", - " value=0, min=0, max=len(clip)-1, step=1,\n", - " description=\"retained frame\", continuous_update=False\n", - ")\n", - "display(widgets.interactive(show_retained_frame, k=slider))\n" - ], - "id": "s05-06" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s05-07" - }, - "source": [ - "## Exercise 2 — one real input tensor, two different systems\n", - "\n", - "Now use the real decoded-video convention `(T,H,W,C)` as the anchor and design two downstream systems:\n", - "\n", - "- **Tech:** a short-video recommender that returns one embedding per video.\n", - "- **Biotech:** a surgical-video model that returns one phase-label distribution per timestep.\n", - "\n", - "These are **design scenarios**, not additional datasets. Their output shapes are architectural choices; the input-axis reasoning is grounded in the real decoded tensor above.\n", - "\n", - "> 🇪🇸 Usa la convención real `(T,H,W,C)` como punto de partida para diseñar dos sistemas. Son **escenarios de diseño**, no conjuntos de datos adicionales: las formas de salida son decisiones arquitectónicas, mientras que el razonamiento sobre los ejes parte del tensor real ya decodificado.\n" - ], - "id": "s05-07" - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "s05-08" - }, - "outputs": [], - "source": [ - "# TODO 4: Fill in the shape at each stage for BOTH systems.\n", - "# Next to every shape, write what each axis counts.\n", - "#\n", - "# --- Tech: short-video recommender, one embedding per video -------------------\n", - "# raw file : ...\n", - "# decoded frames : ...\n", - "# preprocessed batch: ...\n", - "# model input : ...\n", - "# model output : ...\n", - "#\n", - "# --- Biotech: surgical phase labelling, one label distribution per timestep --\n", - "# raw file : ...\n", - "# decoded frames : ...\n", - "# preprocessed batch: ...\n", - "# model input : ...\n", - "# model output : ...\n", - "#\n", - "# Then answer: which system intentionally removes the time axis at the output?\n" - ], - "id": "s05-08" - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ], - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "s05-09", - "outputId": "1b139133-ad2b-46d9-cd93-f1794f6b1423" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "real anchor shape: (16, 540, 960, 3) -> (T, H, W, C)\n", - "tech output : (32, 512) -> time collapsed\n", - "biotech output : (4, 64, 12) -> time preserved\n" - ] - } - ], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "# One defensible design. Different sizes can also be correct if axis meanings\n", - "# and model goals are internally consistent.\n", - "\n", - "# --- Tech: short-video recommender -------------------------------------------\n", - "# raw file : bytes on disk, no tensor shape yet\n", - "# decoded frames : (T, H, W, C)\n", - "# preprocessed batch: (32, 8, 224, 224, 3) N, T, H, W, C\n", - "# model input : (32, 8, 224, 224, 3)\n", - "# model output : (32, 512) N, embedding\n", - "# Time is intentionally collapsed into one vector per video.\n", - "\n", - "# --- Biotech: surgical phase labelling ---------------------------------------\n", - "# raw file : bytes on disk\n", - "# decoded frames : (T, H, W, C)\n", - "# preprocessed batch: (4, 64, 224, 224, 3) N, T, H, W, C\n", - "# model input : (4, 64, 224, 224, 3)\n", - "# model output : (4, 64, 12) N, T, classes\n", - "# Time survives because the predicted phase can change by timestep.\n", - "\n", - "print(\"real anchor shape:\", clip.shape, \"-> (T, H, W, C)\")\n", - "print(\"tech output :\", (32, 512), \"-> time collapsed\")\n", - "print(\"biotech output :\", (4, 64, 12), \"-> time preserved\")\n" - ], - "id": "s05-09" + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 05 · Video pipeline design\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/05-video-pipeline-design.ipynb)\n", + "\n", + "*Part III · group · 15 min*\n", + "\n", + "> 🇪🇸 **Diseño de un pipeline de vídeo** — Convertir un archivo de vídeo real en un tensor, observar qué se pierde al muestrear fotogramas y diseñar formas tensoriales que respeten el significado del tiempo.\n", + "\n", + "Process one pinned real video end to end, then use what its axes actually mean to design two downstream video pipelines.\n", + "\n", + "## What you will be able to do\n", + "\n", + "- Decode a pinned real video file into an order-4 tensor and name every axis.\n", + "- Measure how much temporal information a frame-sampling decision keeps and discards.\n", + "- Design tensor shapes for video-level and timestep-level prediction systems.\n", + "- Explain the memory trade-off between padding variable-length clips and fixed-frame sampling." + ], + "id": "s05-00" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "Run this first. It installs and imports everything this notebook needs, and nothing else.\n", + "\n", + "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." + ], + "id": "s05-01" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install -q \"imageio[ffmpeg]\"\n", + "\n", + "import hashlib\n", + "import io\n", + "import urllib.request\n", + "\n", + "import numpy as np\n", + "import imageio.v3 as iio\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Real clip: \"Tormenta en l'Almadrava\" by Nicolas Vigier, CC0.\n", + "# https://commons.wikimedia.org/wiki/File:Tormenta_en_l%27Almadrava.webm\n", + "VIDEO_URL = (\n", + " \"https://upload.wikimedia.org/wikipedia/commons/1/1e/\"\n", + " \"Tormenta_en_l%27Almadrava.webm\"\n", + ")\n", + "VIDEO_SHA256 = \"e377fcdd2c79b55bce13c2c24b5dd7e412af39cd400eec548a79d0e59d79dc1b\"\n", + "UA = \"tensors-workshop/1.0 (https://github.com/project-delphi/tensors-workshop)\"\n", + "\n", + "\n", + "def fetch_verified_video(url, expected_sha256, n_frames=16, stride=45):\n", + " # Verify the real file, decode the whole stream, retain only sparse frames.\n", + " req = urllib.request.Request(url, headers={\"User-Agent\": UA})\n", + " raw = urllib.request.urlopen(req, timeout=120).read()\n", + "\n", + " got = hashlib.sha256(raw).hexdigest()\n", + " if got != expected_sha256:\n", + " raise ValueError(\n", + " f\"checksum mismatch: expected {expected_sha256}, got {got}\"\n", + " )\n", + "\n", + " kept_frames = []\n", + " kept_source_indices = []\n", + " total_frames = 0\n", + "\n", + " for i, frame in enumerate(\n", + " iio.imiter(io.BytesIO(raw), plugin=\"FFMPEG\", extension=\".webm\")\n", + " ):\n", + " total_frames = i + 1\n", + " if i % stride == 0 and len(kept_frames) < n_frames:\n", + " kept_frames.append(frame)\n", + " kept_source_indices.append(i)\n", + "\n", + " clip = np.stack(kept_frames)\n", + " return clip, np.asarray(kept_source_indices), total_frames\n", + "\n", + "\n", + "clip, kept_source_indices, total_frames = fetch_verified_video(\n", + " VIDEO_URL, VIDEO_SHA256\n", + ")\n", + "\n", + "assert clip.shape == (16, 540, 960, 3), f\"unexpected clip shape {clip.shape}\"\n", + "assert total_frames == 720, f\"unexpected frame count {total_frames}\"\n", + "\n", + "print(\"retained tensor:\", clip.shape, clip.dtype)\n", + "print(\"source frames:\", total_frames)\n", + "print(\"source indices retained:\", kept_source_indices.tolist())\n", + "print(\"RAM retained:\", f\"{clip.nbytes / 1024**2:.1f} MB\")" + ], + "id": "s05-02" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Why this matters\n", + "\n", + "A video model never receives “a video” in the abstract. A real file is decoded into axes, and every preprocessing choice decides what information survives.\n", + "\n", + "Here the camera recorded **720 frames**. The pipeline keeps only **16** of them in the in-memory tensor `(16, 540, 960, 3)`. That is efficient, but it also means most temporal measurements are deliberately discarded.\n", + "\n", + "This is the concept for the whole section:\n", + "\n", + "> **A video pipeline is a sequence of decisions about which axes survive, which axes move, and which information is discarded.**\n", + "\n", + "> 🇪🇸 **Por qué importa:** un modelo no recibe “un vídeo” de forma abstracta. El archivo real se decodifica en ejes y cada decisión de preprocesamiento determina qué información sobrevive. Aquí la cámara grabó 720 fotogramas, pero el tensor en memoria conserva solo 16. La eficiencia tiene un costo: se descarta información temporal real.\n", + "\n", + "### Predict → Run → Explain\n", + "\n", + "Before each exercise, predict both the **shape** and the **meaning of every axis**. After running code, explain what was kept and what was lost.\n", + "\n", + "> 🇪🇸 **Predice → Ejecuta → Explica:** antes de cada ejercicio, predice tanto la forma como el significado de cada eje. Después de ejecutar, explica qué información se conservó y cuál se perdió.\n" + ], + "id": "s05-03" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 1 — watch a real video become a tensor\n", + "\n", + "Start with the measured data, not a diagram. `clip` contains 16 real frames sampled from the verified 720-frame source video.\n", + "\n", + "**Predict first:** what does each axis in `(16, 540, 960, 3)` count? What percentage of the recorded frames did this pipeline retain?\n", + "\n", + "> 🇪🇸 Empieza con datos medidos, no con un diagrama. `clip` contiene 16 fotogramas reales muestreados del vídeo verificado de 720 fotogramas. **Predice primero:** ¿qué cuenta cada eje y qué porcentaje de los fotogramas grabados conservó el pipeline?\n" + ], + "id": "s05-04" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO 1: Print clip.shape and clip.dtype.\n", + "# Name the meaning of axes 0, 1, 2 and 3.\n", + "#\n", + "# TODO 2: Using len(clip) and total_frames, compute:\n", + "# - fraction of recorded frames retained\n", + "# - fraction discarded\n", + "#\n", + "# TODO 3: Inspect kept_source_indices.\n", + "# Explain why clip[1] is NOT source frame 1.\n" + ], + "id": "s05-05" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true }, - { - "cell_type": "markdown", - "metadata": { - "id": "s05-10" - }, - "source": [ - "## Exercise 3 — ragged clips: pad or sample?\n", - "\n", - "Real deployments receive videos with different durations. To make the memory cost visible without allocating an impossible image tensor, use a **stress-test design scenario** with clips lasting 30 s, 45 s, 2 min and 4 h at 30 fps.\n", - "\n", - "The durations here are intentionally chosen design inputs, not measurements from the Wikimedia clip. The lesson is the tensor cost they imply.\n", - "\n", - "> 🇪🇸 Los sistemas reales reciben vídeos con duraciones diferentes. Para visualizar el costo de memoria sin crear un tensor de imágenes imposible, usa un **escenario de estrés** con clips de 30 s, 45 s, 2 min y 4 h a 30 fps. Estas duraciones son entradas deliberadas del ejercicio, no mediciones del vídeo de Wikimedia.\n" - ], - "id": "s05-10" + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "print(\"shape:\", clip.shape, \"dtype:\", clip.dtype)\n", + "print(\"axes: (retained time, height, width, colour)\")\n", + "\n", + "retained_fraction = len(clip) / total_frames\n", + "discarded_fraction = 1 - retained_fraction\n", + "\n", + "print(\"retained:\", f\"{retained_fraction:.2%}\")\n", + "print(\"discarded:\", f\"{discarded_fraction:.2%}\")\n", + "print(\"clip[1] came from source frame\", kept_source_indices[1])\n", + "\n", + "# Screenshot-friendly visual computed from the real file.\n", + "fig, axes = plt.subplots(2, 1, figsize=(11, 6))\n", + "\n", + "axes[0].scatter(\n", + " np.arange(total_frames), np.zeros(total_frames),\n", + " s=7, alpha=0.18, label=\"recorded frame\"\n", + ")\n", + "axes[0].scatter(\n", + " kept_source_indices, np.zeros_like(kept_source_indices),\n", + " s=45, label=\"retained in tensor\"\n", + ")\n", + "axes[0].set_yticks([])\n", + "axes[0].set_xlim(-5, total_frames + 5)\n", + "axes[0].set_xlabel(\"source frame index\")\n", + "axes[0].set_title(\n", + " f\"Real video sampling: {len(clip)} of {total_frames} frames retained \"\n", + " f\"({retained_fraction:.1%})\"\n", + ")\n", + "axes[0].legend(loc=\"upper right\")\n", + "\n", + "preview_slots = [0, 5, 10, 15]\n", + "strip = np.concatenate([clip[k] for k in preview_slots], axis=1)\n", + "axes[1].imshow(strip)\n", + "axes[1].set_title(\n", + " \"Four retained real frames — source indices \"\n", + " + \", \".join(str(kept_source_indices[k]) for k in preview_slots)\n", + ")\n", + "axes[1].axis(\"off\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Interactive frame browser in Colab.\n", + "try:\n", + " from google.colab import output\n", + " output.enable_custom_widget_manager()\n", + "except ImportError:\n", + " pass\n", + "\n", + "import ipywidgets as widgets\n", + "from IPython.display import display\n", + "\n", + "def show_retained_frame(k):\n", + " fig, ax = plt.subplots(figsize=(8, 4.5))\n", + " ax.imshow(clip[k])\n", + " ax.set_title(\n", + " f\"clip[{k}] = source frame {kept_source_indices[k]} of {total_frames}\"\n", + " )\n", + " ax.axis(\"off\")\n", + " plt.show()\n", + "\n", + "slider = widgets.IntSlider(\n", + " value=0, min=0, max=len(clip)-1, step=1,\n", + " description=\"retained frame\", continuous_update=False\n", + ")\n", + "display(widgets.interactive(show_retained_frame, k=slider))\n" + ], + "id": "s05-06" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 2 — one real input tensor, two different systems\n", + "\n", + "Now use the real decoded-video convention `(T,H,W,C)` as the anchor and design two downstream systems:\n", + "\n", + "- **Tech:** a short-video recommender that returns one embedding per video.\n", + "- **Biotech:** a surgical-video model that returns one phase-label distribution per timestep.\n", + "\n", + "These are **design scenarios**, not additional datasets. Their output shapes are architectural choices; the input-axis reasoning is grounded in the real decoded tensor above.\n", + "\n", + "> 🇪🇸 Usa la convención real `(T,H,W,C)` como punto de partida para diseñar dos sistemas. Son **escenarios de diseño**, no conjuntos de datos adicionales: las formas de salida son decisiones arquitectónicas, mientras que el razonamiento sobre los ejes parte del tensor real ya decodificado.\n" + ], + "id": "s05-07" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO 4: Fill in the shape at each stage for BOTH systems.\n", + "# Next to every shape, write what each axis counts.\n", + "#\n", + "# --- Tech: short-video recommender, one embedding per video -------------------\n", + "# raw file : ...\n", + "# decoded frames : ...\n", + "# preprocessed batch: ...\n", + "# model input : ...\n", + "# model output : ...\n", + "#\n", + "# --- Biotech: surgical phase labelling, one label distribution per timestep --\n", + "# raw file : ...\n", + "# decoded frames : ...\n", + "# preprocessed batch: ...\n", + "# model input : ...\n", + "# model output : ...\n", + "#\n", + "# Then answer: which system intentionally removes the time axis at the output?\n" + ], + "id": "s05-08" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "id": "s05-11" - }, - "outputs": [], - "source": [ - "# TODO 5:\n", - "# Convert durations [30s, 45s, 2min, 4h] at 30 fps into frame counts.\n", - "# If all four are padded to the longest length, what is the boolean mask shape?\n", - "# What fraction of positions in that mask are invented padding?\n", - "#\n", - "# TODO 6:\n", - "# Compare that with sampling exactly 64 frames from every clip.\n", - "# What is gained? What real temporal information is lost?\n", - "#\n", - "# TODO 7:\n", - "# A surgical system adds 3 synchronized camera angles.\n", - "# Write one shape that keeps camera as its own axis and one that folds camera\n", - "# into the batch axis. When would keeping CAM explicit be necessary?\n" - ], - "id": "s05-11" + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "# One defensible design. Different sizes can also be correct if axis meanings\n", + "# and model goals are internally consistent.\n", + "\n", + "# --- Tech: short-video recommender -------------------------------------------\n", + "# raw file : bytes on disk, no tensor shape yet\n", + "# decoded frames : (T, H, W, C)\n", + "# preprocessed batch: (32, 8, 224, 224, 3) N, T, H, W, C\n", + "# model input : (32, 8, 224, 224, 3)\n", + "# model output : (32, 512) N, embedding\n", + "# Time is intentionally collapsed into one vector per video.\n", + "\n", + "# --- Biotech: surgical phase labelling ---------------------------------------\n", + "# raw file : bytes on disk\n", + "# decoded frames : (T, H, W, C)\n", + "# preprocessed batch: (4, 64, 224, 224, 3) N, T, H, W, C\n", + "# model input : (4, 64, 224, 224, 3)\n", + "# model output : (4, 64, 12) N, T, classes\n", + "# Time survives because the predicted phase can change by timestep.\n", + "\n", + "print(\"real anchor shape:\", clip.shape, \"-> (T, H, W, C)\")\n", + "print(\"tech output :\", (32, 512), \"-> time collapsed\")\n", + "print(\"biotech output :\", (4, 64, 12), \"-> time preserved\")\n" + ], + "id": "s05-09" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 3 — ragged clips: pad or sample?\n", + "\n", + "Real deployments receive videos with different durations. To make the memory cost visible without allocating an impossible image tensor, use a **stress-test design scenario** with clips lasting 30 s, 45 s, 2 min and 4 h at 30 fps.\n", + "\n", + "The durations here are intentionally chosen design inputs, not measurements from the Wikimedia clip. The lesson is the tensor cost they imply.\n", + "\n", + "> 🇪🇸 Los sistemas reales reciben vídeos con duraciones diferentes. Para visualizar el costo de memoria sin crear un tensor de imágenes imposible, usa un **escenario de estrés** con clips de 30 s, 45 s, 2 min y 4 h a 30 fps. Estas duraciones son entradas deliberadas del ejercicio, no mediciones del vídeo de Wikimedia.\n" + ], + "id": "s05-10" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO 5:\n", + "# Convert durations [30s, 45s, 2min, 4h] at 30 fps into frame counts.\n", + "# If all four are padded to the longest length, what is the boolean mask shape?\n", + "# What fraction of positions in that mask are invented padding?\n", + "#\n", + "# TODO 6:\n", + "# Compare that with sampling exactly 64 frames from every clip.\n", + "# What is gained? What real temporal information is lost?\n", + "#\n", + "# TODO 7:\n", + "# A surgical system adds 3 synchronized camera angles.\n", + "# Write one shape that keeps camera as its own axis and one that folds camera\n", + "# into the batch axis. When would keeping CAM explicit be necessary?\n" + ], + "id": "s05-11" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ], - "colab": { - "base_uri": "https://localhost:8080/", - "height": 391 - }, - "id": "s05-12", - "outputId": "46809509-644a-468a-c4d1-9c1dace84e3f" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "frame counts: [900, 1350, 3600, 432000]\n", - "mask shape: (4, 432000)\n", - "padding fraction: 74.66%\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - }, + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "durations_s = np.array([30, 45, 2 * 60, 4 * 60 * 60])\n", + "lengths = durations_s * 30\n", + "T_max = int(lengths.max())\n", + "\n", + "mask = np.zeros((len(lengths), T_max), dtype=bool)\n", + "for i, n in enumerate(lengths):\n", + " mask[i, :int(n)] = True\n", + "\n", + "wasted = 1 - mask.sum() / mask.size\n", + "\n", + "print(\"frame counts:\", lengths.tolist())\n", + "print(\"mask shape:\", mask.shape)\n", + "print(\"padding fraction:\", f\"{wasted:.2%}\")\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 2.8))\n", + "ax.imshow(mask, aspect=\"auto\", cmap=\"Greys\", interpolation=\"nearest\")\n", + "ax.set_yticks(range(4))\n", + "ax.set_yticklabels([\"30 s\", \"45 s\", \"2 min\", \"4 h\"])\n", + "ax.set_xlabel(\"frame index\")\n", + "ax.set_title(\n", + " f\"Valid positions vs padding — {wasted:.2%} of the padded \"\n", + " \"representation is invented\"\n", + ")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "sampled_batch_shape = (4, 64, 224, 224, 3)\n", + "print(\"fixed-sampling batch:\", sampled_batch_shape)\n", + "# Gain: bounded, predictable memory.\n", + "# Cost: long clips are sampled more sparsely; temporal events can vanish.\n", + "\n", + "explicit_camera = (4, 3, 64, 224, 224, 3) # N, CAM, T, H, W, C\n", + "folded_camera = (12, 64, 224, 224, 3) # N*CAM, T, H, W, C\n", + "print(\"camera explicit:\", explicit_camera)\n", + "print(\"camera folded :\", folded_camera)\n", + "# Keep CAM explicit when the model must combine information across views.\n" + ], + "id": "s05-12" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What just happened\n", + "\n", + "You followed one real video from file bytes to a tensor and then used that concrete tensor to reason about larger systems.\n", + "\n", + "1. The pinned WebM file decoded to **720 recorded frames**, while the in-memory tensor retained **16 real frames** with shape `(16, 540, 960, 3)`.\n", + "2. The real-data timeline made the sampling loss measurable: retaining 16 of 720 frames keeps only about **2.2%** of the recorded timesteps.\n", + "3. Two downstream systems can start from the same `(T,H,W,C)` convention and still need different outputs: a recommender may collapse time; timestep labelling must preserve it.\n", + "4. The ragged-length stress test showed why padding can be mathematically valid but operationally wasteful, while fixed sampling controls memory by discarding temporal information.\n", + "5. Camera, batch and time axes are not interchangeable just because they are dimensions of the same tensor.\n", + "\n", + "> 🇪🇸 **Qué ocurrió:** seguiste un vídeo real desde los bytes del archivo hasta un tensor. El archivo contiene 720 fotogramas grabados, pero el tensor conserva 16 imágenes reales `(16,540,960,3)`, aproximadamente el 2.2% de los instantes registrados. Luego viste que conservar, colapsar, rellenar o muestrear un eje temporal son decisiones del pipeline con consecuencias distintas. La forma del tensor no es solo notación: registra qué información decidiste conservar.\n" + ], + "id": "s05-12a" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Done with this section\n", + "\n", + "Next up: **06 · Contraction with einsum** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/06-contraction-with-einsum.ipynb).\n", + "\n", + "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" + ], + "id": "s05-13" + } + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "version_major": 2, + "version_minor": 0, + "state": { + "acfd115b176c40aebbfb902991a77816": { + "model_module": "@jupyter-widgets/controls", + "model_name": "VBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [ + "widget-interact" + ], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "VBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "VBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_cca9fe86dd4a47858d5239887ad130e3", + "IPY_MODEL_b9d836bdf16748dca6a165795e63b41d" + ], + "layout": "IPY_MODEL_d07f9fd70e9249c0b3914f50fe732f67" + } + }, + "cca9fe86dd4a47858d5239887ad130e3": { + "model_module": "@jupyter-widgets/controls", + "model_name": "IntSliderModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "IntSliderModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "IntSliderView", + "continuous_update": false, + "description": "retained frame", + "description_tooltip": null, + "disabled": false, + "layout": "IPY_MODEL_3a3ad95d2ed74199aefddd8ac1878804", + "max": 15, + "min": 0, + "orientation": "horizontal", + "readout": true, + "readout_format": "d", + "step": 1, + "style": "IPY_MODEL_2a25efc4365a43a7974bc9c867f88ab3", + "value": 0 + } + }, + "b9d836bdf16748dca6a165795e63b41d": { + "model_module": "@jupyter-widgets/output", + "model_name": "OutputModel", + "model_module_version": "1.0.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/output", + "_model_module_version": "1.0.0", + "_model_name": "OutputModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/output", + "_view_module_version": "1.0.0", + "_view_name": "OutputView", + "layout": "IPY_MODEL_afe5ed25c75f4a88ab3bca368b30ec82", + "msg_id": "", + "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "fixed-sampling batch: (4, 64, 224, 224, 3)\n", - "camera explicit: (4, 3, 64, 224, 224, 3)\n", - "camera folded : (12, 64, 224, 224, 3)\n" - ] - } - ], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "durations_s = np.array([30, 45, 2 * 60, 4 * 60 * 60])\n", - "lengths = durations_s * 30\n", - "T_max = int(lengths.max())\n", - "\n", - "mask = np.zeros((len(lengths), T_max), dtype=bool)\n", - "for i, n in enumerate(lengths):\n", - " mask[i, :int(n)] = True\n", - "\n", - "wasted = 1 - mask.sum() / mask.size\n", - "\n", - "print(\"frame counts:\", lengths.tolist())\n", - "print(\"mask shape:\", mask.shape)\n", - "print(\"padding fraction:\", f\"{wasted:.2%}\")\n", - "\n", - "fig, ax = plt.subplots(figsize=(10, 2.8))\n", - "ax.imshow(mask, aspect=\"auto\", cmap=\"Greys\", interpolation=\"nearest\")\n", - "ax.set_yticks(range(4))\n", - "ax.set_yticklabels([\"30 s\", \"45 s\", \"2 min\", \"4 h\"])\n", - "ax.set_xlabel(\"frame index\")\n", - "ax.set_title(\n", - " f\"Valid positions vs padding — {wasted:.2%} of the padded \"\n", - " \"representation is invented\"\n", - ")\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "sampled_batch_shape = (4, 64, 224, 224, 3)\n", - "print(\"fixed-sampling batch:\", sampled_batch_shape)\n", - "# Gain: bounded, predictable memory.\n", - "# Cost: long clips are sampled more sparsely; temporal events can vanish.\n", - "\n", - "explicit_camera = (4, 3, 64, 224, 224, 3) # N, CAM, T, H, W, C\n", - "folded_camera = (12, 64, 224, 224, 3) # N*CAM, T, H, W, C\n", - "print(\"camera explicit:\", explicit_camera)\n", - "print(\"camera folded :\", folded_camera)\n", - "# Keep CAM explicit when the model must combine information across views.\n" - ], - "id": "s05-12" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s05-12a" - }, - "source": [ - "## What just happened\n", - "\n", - "You followed one real video from file bytes to a tensor and then used that concrete tensor to reason about larger systems.\n", - "\n", - "1. The pinned WebM file decoded to **720 recorded frames**, while the in-memory tensor retained **16 real frames** with shape `(16, 540, 960, 3)`.\n", - "2. The real-data timeline made the sampling loss measurable: retaining 16 of 720 frames keeps only about **2.2%** of the recorded timesteps.\n", - "3. Two downstream systems can start from the same `(T,H,W,C)` convention and still need different outputs: a recommender may collapse time; timestep labelling must preserve it.\n", - "4. The ragged-length stress test showed why padding can be mathematically valid but operationally wasteful, while fixed sampling controls memory by discarding temporal information.\n", - "5. Camera, batch and time axes are not interchangeable just because they are dimensions of the same tensor.\n", - "\n", - "> 🇪🇸 **Qué ocurrió:** seguiste un vídeo real desde los bytes del archivo hasta un tensor. El archivo contiene 720 fotogramas grabados, pero el tensor conserva 16 imágenes reales `(16,540,960,3)`, aproximadamente el 2.2% de los instantes registrados. Luego viste que conservar, colapsar, rellenar o muestrear un eje temporal son decisiones del pipeline con consecuencias distintas. La forma del tensor no es solo notación: registra qué información decidiste conservar.\n" - ], - "id": "s05-12a" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s05-13" - }, - "source": [ - "---\n", - "\n", - "## Done with this section\n", - "\n", - "Next up: **06 · Contraction with einsum** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/06-contraction-with-einsum.ipynb).\n", - "\n", - "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" - ], - "id": "s05-13" - } - ], - "metadata": { - "colab": { - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "version_major": 2, - "version_minor": 0, - "state": { - "acfd115b176c40aebbfb902991a77816": { - "model_module": "@jupyter-widgets/controls", - "model_name": "VBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [ - "widget-interact" - ], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "VBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "VBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_cca9fe86dd4a47858d5239887ad130e3", - "IPY_MODEL_b9d836bdf16748dca6a165795e63b41d" - ], - "layout": "IPY_MODEL_d07f9fd70e9249c0b3914f50fe732f67" - } - }, - "cca9fe86dd4a47858d5239887ad130e3": { - "model_module": "@jupyter-widgets/controls", - "model_name": "IntSliderModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "IntSliderModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "IntSliderView", - "continuous_update": false, - "description": "retained frame", - "description_tooltip": null, - "disabled": false, - "layout": "IPY_MODEL_3a3ad95d2ed74199aefddd8ac1878804", - "max": 15, - "min": 0, - "orientation": "horizontal", - "readout": true, - "readout_format": "d", - "step": 1, - "style": "IPY_MODEL_2a25efc4365a43a7974bc9c867f88ab3", - "value": 0 - } - }, - "b9d836bdf16748dca6a165795e63b41d": { - "model_module": "@jupyter-widgets/output", - "model_name": "OutputModel", - "model_module_version": "1.0.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/output", - "_model_module_version": "1.0.0", - "_model_name": "OutputModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/output", - "_view_module_version": "1.0.0", - "_view_name": "OutputView", - "layout": "IPY_MODEL_afe5ed25c75f4a88ab3bca368b30ec82", - "msg_id": "", - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": "
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\n" 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\n" 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null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + } } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/scripts/content.py b/scripts/content.py index eef4982..a48a181 100644 --- a/scripts/content.py +++ b/scripts/content.py @@ -193,53 +193,54 @@ def center_crop_rgb(img, size=256): import imageio.v3 as iio import matplotlib.pyplot as plt -# A real clip, pinned. "Tormenta en l'Almadrava" by Nicolas Vigier, CC0: +# Real clip: "Tormenta en l'Almadrava" by Nicolas Vigier, CC0. # https://commons.wikimedia.org/wiki/File:Tormenta_en_l%27Almadrava.webm -# 24 seconds of breaking waves at 960x540. The SHA-256 is checked below, so the -# file this notebook decodes cannot silently change under you -- the same -# guarantee section 11 puts on its voice recording. -VIDEO_URL = ("https://upload.wikimedia.org/wikipedia/commons/1/1e/" - "Tormenta_en_l%27Almadrava.webm") +VIDEO_URL = ( + "https://upload.wikimedia.org/wikipedia/commons/1/1e/" + "Tormenta_en_l%27Almadrava.webm" +) VIDEO_SHA256 = "e377fcdd2c79b55bce13c2c24b5dd7e412af39cd400eec548a79d0e59d79dc1b" - -# Wikimedia answers the default `Python-urllib/3.x` User-Agent with a 403, so -# this identifies itself the way their policy asks. UA = "tensors-workshop/1.0 (https://github.com/project-delphi/tensors-workshop)" def fetch_verified_video(url, expected_sha256, n_frames=16, stride=45): - \"\"\"Download a video, refuse to proceed if it does not match the pinned - checksum, and decode only every `stride`-th frame, up to `n_frames`. - - Note what is and is not saved. `imiter` still decodes frames in order -- - it reaches frame 675 by decoding all 676 before it -- but it only ever - RETAINS 16 of them, and it stops as soon as it has them. Holding all 720 - would be a 1.1 GB array. Sampling frames rather than keeping them all is - exactly the decision the design exercise below asks you to make - deliberately, for two systems, and to say what it costs. - \"\"\" + # Verify the real file, decode the whole stream, retain only sparse frames. req = urllib.request.Request(url, headers={"User-Agent": UA}) raw = urllib.request.urlopen(req, timeout=120).read() + got = hashlib.sha256(raw).hexdigest() if got != expected_sha256: raise ValueError( - f"checksum mismatch for {url}: expected {expected_sha256}, got " - f"{got}. Refusing to use unverified video data.") - frames = [] + f"checksum mismatch: expected {expected_sha256}, got {got}" + ) + + kept_frames = [] + kept_source_indices = [] + total_frames = 0 + for i, frame in enumerate( - iio.imiter(io.BytesIO(raw), plugin="FFMPEG", extension=".webm")): - if i % stride == 0: - frames.append(frame) - if len(frames) == n_frames: - break - return np.stack(frames) + iio.imiter(io.BytesIO(raw), plugin="FFMPEG", extension=".webm") + ): + total_frames = i + 1 + if i % stride == 0 and len(kept_frames) < n_frames: + kept_frames.append(frame) + kept_source_indices.append(i) + + clip = np.stack(kept_frames) + return clip, np.asarray(kept_source_indices), total_frames -clip = fetch_verified_video(VIDEO_URL, VIDEO_SHA256) -# The cells below index clip[15] and quote this shape, so a short stream should -# fail here, where the cause is visible, not as an IndexError further down. +clip, kept_source_indices, total_frames = fetch_verified_video( + VIDEO_URL, VIDEO_SHA256 +) + assert clip.shape == (16, 540, 960, 3), f"unexpected clip shape {clip.shape}" -print(clip.shape, clip.dtype) # (16, 540, 960, 3) uint8""", +assert total_frames == 720, f"unexpected frame count {total_frames}" + +print("retained tensor:", clip.shape, clip.dtype) +print("source frames:", total_frames) +print("source indices retained:", kept_source_indices.tolist()) +print("RAM retained:", f"{clip.nbytes / 1024**2:.1f} MB")""", } # ───────────────────────────────────────────────────────────────────────────── From 0fe914b4d14064d4fb9514be28ef2370ee5c905a Mon Sep 17 00:00:00 2001 From: Sebas Laverde Chunza <106851243+Laverde97@users.noreply.github.com> Date: Fri, 28 Aug 2026 20:30:06 -0500 Subject: [PATCH 13/29] Improve notebook 06 pedagogy with real data and interactive einsum for issue #44 --- notebooks/06-contraction-with-einsum.ipynb | 2934 +++++++++++++++++--- 1 file changed, 2625 insertions(+), 309 deletions(-) diff --git a/notebooks/06-contraction-with-einsum.ipynb b/notebooks/06-contraction-with-einsum.ipynb index 1b9e120..9bbbf25 100644 --- a/notebooks/06-contraction-with-einsum.ipynb +++ b/notebooks/06-contraction-with-einsum.ipynb @@ -1,314 +1,2630 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 06 · Contraction with einsum\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/06-contraction-with-einsum.ipynb)\n", - "\n", - "*Part IV · exercise · 15 min*\n", - "\n", - "> 🇪🇸 **Contracción con einsum** — Una sola notación para el producto punto, el producto matricial y un lote de imágenes.\n", - "\n", - "One notation for the dot product, the matrix product, and a batch of images.\n", - "\n", - "## What you will be able to do\n", - "\n", - "- State the einsum rule: an index missing after the arrow is summed over.\n", - "- Contract the colour axis of one image, and of a whole batch, with one call each.\n", - "- Write trace, transpose and the matrix product as `einsum` and check them against NumPy.\n", - "- Build a full similarity matrix between 1797 images with a single contraction." - ], - "id": "s06-00" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "Run this first. It installs and imports everything this notebook needs, and nothing else.\n", - "\n", - "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." - ], - "id": "s06-01" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from sklearn.datasets import load_digits\n", - "from skimage import data\n", - "\n", - "photo = data.immunohistochemistry().astype(float) # (512, 512, 3)\n", - "batch = np.stack([photo, data.astronaut().astype(float)]) # (2, 512, 512, 3)\n", - "w = np.array([0.2125, 0.7154, 0.0721]) # RGB -> grayscale weights\n", - "A = np.array([[1., 2.], [3., 4.]])\n", - "B = np.array([[5., 6.], [7., 8.]])\n", - "print(photo.shape, batch.shape)" - ], - "id": "s06-02" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Why this matters\n", - "\n", - "> 🇪🇸 Los sistemas de recomendación y de búsqueda ordenan los resultados con el\n", - "> producto punto entre el vector de un usuario y millones de vectores de\n", - "> artículos. Esa contracción *es* la señal de ranking.\n", - "\n", - "Recommendation and search systems rank items by the dot product between a user\n", - "vector and every item vector — one user against millions of items, many times\n", - "per second. That contraction *is* the ranking signal. Sum over the wrong axis\n", - "and every user gets wrong results.\n", - "\n", - "### The rule, again\n", - "\n", - "An index that appears in the inputs but **not** after the arrow is **summed\n", - "over**. An index that appears after the arrow is **kept**.\n", - "\n", - "That is the whole of `einsum`. Everything below is that one sentence applied." - ], - "id": "s06-03" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 1 — contract the colour axis\n", - "\n", - "> 🇪🇸 Contrae el eje de color." - ], - "id": "s06-04" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO 1: With einsum, convert `photo` to grayscale by contracting the colour\n", - "# axis against w. Result shape (512, 512).\n", - "\n", - "# TODO 2: Do the same for the whole batch in ONE einsum call -> (2, 512, 512)." - ], - "id": "s06-05" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "s06-00" + }, + "source": [ + "# 06 · Contraction with einsum\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/06-contraction-with-einsum.ipynb)\n", + "\n", + "*Part IV · exercise · 15 min*\n", + "\n", + "> 🇪🇸 **Contracción con einsum** — Aprende una sola regla de índices y compruébala sobre una imagen real, píxeles reales de dígitos manuscritos y un buscador interactivo de imágenes.\n", + "\n", + "Use one index rule, see it on real data, and then **change the inputs interactively** to test whether you really understand which index disappears.\n", + "\n", + "## What you will be able to do\n", + "\n", + "- Explain the `einsum` rule: indices missing after `->` are summed over; indices that remain are kept.\n", + "- Contract the colour axis of a real microscopy image and explain what the RGB sliders change — and what they do **not** change.\n", + "- Read trace, transpose and matrix multiplication as index operations on pixel patches cut from real handwritten digits.\n", + "- Build all 3,229,209 pairwise similarities between 1,797 real digit images and explore retrieval with cosine similarity versus raw dot product.\n" + ], + "id": "s06-00" }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "gray = np.einsum('hwc,c->hw', photo, w) # (512, 512)\n", - "gray_batch = np.einsum('nhwc,c->nhw', batch, w) # (2, 512, 512)\n", - "print(gray.shape, gray_batch.shape)\n", - "\n", - "# `c` appears in the inputs but not after the arrow, so it is SUMMED OVER —\n", - "# that is the contraction. `n`, `h`, `w` appear after the arrow, so they are\n", - "# KEPT. Adding a batch axis costs exactly one letter.\n", - "\n", - "import matplotlib.pyplot as plt\n", - "fig, axes = plt.subplots(1, 2, figsize=(8, 4))\n", - "axes[0].imshow(photo / 255); axes[0].set_title(\"photo — axes h, w, c\")\n", - "axes[1].imshow(gray, cmap=\"gray\"); axes[1].set_title(\"'hwc,c->hw' — c is gone\")\n", - "for a in axes:\n", - " a.axis(\"off\")\n", - "fig.suptitle(\"c: in the input, missing after the arrow -> SUMMED. h, w: KEPT.\")\n", - "plt.tight_layout()\n", - "plt.show()" - ], - "id": "s06-06" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 2 — Chapter 2, rewritten as contractions\n", - "\n", - "> 🇪🇸 Las operaciones del capítulo 2, escritas como contracciones." - ], - "id": "s06-07" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO 3: Write these Chapter 2 operations as einsum and check each against\n", - "# NumPy:\n", - "# (a) trace (eq 2.48)\n", - "# (b) transpose (eq 2.3)\n", - "# (c) matrix product (eq 2.5)" - ], - "id": "s06-08" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true + { + "cell_type": "markdown", + "metadata": { + "id": "s06-01" + }, + "source": [ + "## Setup\n", + "\n", + "Run this first. It loads two **real datasets already packaged with standard Python libraries**: a colour microscopy image from `skimage.data` and the 1,797-image handwritten-digits dataset from `sklearn`.\n", + "\n", + "> 🇪🇸 Ejecuta esto primero. Carga dos **conjuntos de datos reales incluidos en librerías estándar de Python**: una imagen de microscopía a color de `skimage.data` y el conjunto de 1.797 imágenes de dígitos manuscritos de `sklearn`.\n", + "\n", + "No random synthetic dataset is used. The grayscale weight vector `w` is a transformation rule, not an observed dataset." + ], + "id": "s06-01" }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "print(np.einsum('ii->', A), np.trace(A)) # trace\n", - "print(np.einsum('ij->ji', A), A.T, sep=\"\\n\") # transpose\n", - "print(np.einsum('ik,kj->ij', A, B), A @ B, sep=\"\\n\") # matrix product\n", - "\n", - "for got, want in [(np.einsum('ii->', A), np.trace(A)),\n", - " (np.einsum('ij->ji', A), A.T),\n", - " (np.einsum('ik,kj->ij', A, B), A @ B)]:\n", - " assert np.allclose(got, want)\n", - "print(\"all three agree\")\n", - "\n", - "# Trace: the repeated `i` with nothing after the arrow sums the diagonal.\n", - "# Transpose: no index is summed at all — einsum is just relabelling axes.\n", - "# Matrix product: `k` is shared and dropped, so it is the contracted axis." - ], - "id": "s06-09" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 3 — every pair of 1797 images, in one call\n", - "\n", - "> 🇪🇸 Todos los pares de 1797 imágenes, en una sola llamada.\n", - "\n", - "This one matters beyond the exercise: it is the same operation a search engine\n", - "runs, and it is the bridge to the distance and similarity questions in the\n", - "Kahoot below." - ], - "id": "s06-10" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO 4: Flatten the digits to (1797, 64) and compute the (1797, 1797)\n", - "# similarity matrix between every pair of digit images with one einsum.\n", - "#\n", - "# Then, for the quiz: normalize each row to unit length first and do it\n", - "# again. That second version is COSINE SIMILARITY — the dot product\n", - "# divided by the two norms. The unnormalized one is dominated by how\n", - "# much ink each digit has, not by its shape." - ], - "id": "s06-11" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "s06-02", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "outputId": "ae45a1d3-f3de-4bc8-ece3-1f7372e3a908" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "photo: (512, 512, 3) batch: (2, 512, 512, 3)\n", + "digits: (1797, 8, 8) labels: (1797,)\n", + "Exercise 2 patches come from digit labels: 0 and 1\n", + "A =\n", + " [[15. 2.]\n", + " [12. 0.]]\n", + "B =\n", + " [[ 3. 15.]\n", + " [15. 16.]]\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import ipywidgets as widgets\n", + "from IPython.display import display\n", + "from sklearn.datasets import load_digits\n", + "from skimage import data\n", + "\n", + "# Enable ipywidgets in Google Colab when available.\n", + "try:\n", + " from google.colab import output\n", + " output.enable_custom_widget_manager()\n", + "except ImportError:\n", + " pass\n", + "\n", + "# Real colour images.\n", + "photo = data.immunohistochemistry().astype(float) # (512, 512, 3)\n", + "batch = np.stack([photo, data.astronaut().astype(float)]) # (2, 512, 512, 3)\n", + "w = np.array([0.2125, 0.7154, 0.0721]) # RGB -> grayscale weights\n", + "\n", + "# Real handwritten digits (UCI Optical Recognition dataset, packaged by sklearn).\n", + "digits = load_digits()\n", + "digit_images = digits.images.astype(float) # (1797, 8, 8)\n", + "\n", + "# Small 2x2 matrices for Exercise 2 are NOT invented numbers:\n", + "# they are central pixel patches from two real digit images.\n", + "A = digit_images[0, 2:4, 2:4]\n", + "B = digit_images[1, 2:4, 2:4]\n", + "\n", + "print(\"photo:\", photo.shape, \"batch:\", batch.shape)\n", + "print(\"digits:\", digit_images.shape, \"labels:\", digits.target.shape)\n", + "print(\"Exercise 2 patches come from digit labels:\", digits.target[0], \"and\", digits.target[1])\n", + "print(\"A =\\n\", A)\n", + "print(\"B =\\n\", B)\n" + ], + "id": "s06-02" }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "D = load_digits().images.reshape(1797, -1) # (1797, 64)\n", - "\n", - "S = np.einsum('id,jd->ij', D, D) # (1797, 1797)\n", - "print(S.shape, S.size) # 3,229,209 pairwise scores\n", - "\n", - "# Cosine similarity: the same contraction on unit-length rows.\n", - "norms = np.linalg.norm(D, axis=1, keepdims=True)\n", - "Dn = D / np.where(norms == 0, 1.0, norms)\n", - "C = np.einsum('id,jd->ij', Dn, Dn)\n", - "print(C.diagonal()[:3]) # ~1.0 — each digit matches itself\n", - "\n", - "# EUCLIDEAN DISTANCE is the square root of summed squared differences, and it is\n", - "# built from the same contraction:\n", - "sq = (D ** 2).sum(1)\n", - "dist = np.sqrt(np.maximum(sq[:, None] + sq[None, :] - 2 * S, 0))\n", - "print(np.round(dist[0, :4], 1))" - ], - "id": "s06-12" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What just happened\n", - "\n", - "`c` appears in the inputs but not after the arrow, so it is **summed over** —\n", - "that is the contraction. `n`, `h`, `w` appear after the arrow, so they are\n", - "**kept**. Adding a batch axis costs exactly one letter.\n", - "\n", - "This is why `einsum` is worth learning: **the same expression works for one image\n", - "or for a million**, and it reads like the mathematics in Chapter 2.\n", - "\n", - "> 🇪🇸 La misma expresión sirve para una imagen o para un millón, y se lee como\n", - "> las matemáticas del capítulo 2.\n", - "\n", - "Keep it in mind for section 10, where a single `einsum` string contracts three\n", - "axes at once: `'ijk,ia,jb,kc->abc'`. That is why einsum came first." - ], - "id": "s06-13" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Done with this section\n", - "\n", - "Next up: **07 · Inverses and the pseudoinverse** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/07-inverses-and-pseudoinverse.ipynb).\n", - "\n", - "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" - ], - "id": "s06-14" - } - ], - "metadata": { - "colab": { - "name": "06-contraction-with-einsum.ipynb", - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" + { + "cell_type": "markdown", + "metadata": { + "id": "s06-03" + }, + "source": [ + "## Why this matters\n", + "\n", + "A contraction is not just a matrix-algebra trick. The same operation appears when an image model removes a colour axis, when linear algebra multiplies matrices, and when a retrieval system compares one query vector with thousands or millions of candidates.\n", + "\n", + "> 🇪🇸 Una contracción no es solo un truco de álgebra matricial. La misma operación aparece cuando un modelo elimina el eje de color de una imagen, cuando el álgebra lineal multiplica matrices y cuando un sistema de búsqueda compara un vector consulta con miles o millones de candidatos.\n", + "\n", + "### One rule for the whole notebook\n", + "\n", + "If an index appears in the inputs but **not** after `->`, it is **summed over**. If it appears after `->`, it is **kept**.\n", + "\n", + "Examples:\n", + "\n", + "- `hwc,c->hw`: `c` disappears → contract colour.\n", + "- `ik,kj->ij`: `k` disappears → matrix multiplication.\n", + "- `id,jd->ij`: `d` disappears → every pair of row vectors gets one similarity score.\n", + "\n", + "### Predict → Run → Explain\n", + "\n", + "Before each exercise, predict **which index disappears and what the output shape must be**. After running the code, explain what information the contraction kept.\n", + "\n", + "> 🇪🇸 **Predice → Ejecuta → Explica:** antes de cada ejercicio, predice **qué índice desaparece y cuál debe ser la forma de salida**. Después de ejecutar, explica qué información conservó la contracción." + ], + "id": "s06-03" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s06-04" + }, + "source": [ + "## Exercise 1 — contract the colour axis of a real image\n", + "\n", + "### What are you looking at?\n", + "\n", + "`photo` is a **real microscopy image** from `skimage.data.immunohistochemistry()` with shape `(512, 512, 3)`:\n", + "\n", + "- `h = 512`: image rows / height\n", + "- `w = 512`: image columns / width\n", + "- `c = 3`: red, green and blue channels\n", + "\n", + "The vector `w = [0.2125, 0.7154, 0.0721]` is **not another dataset**. It is a transformation rule: three weights telling us how much each colour channel contributes to the grayscale result.\n", + "\n", + "### What is the mathematical idea?\n", + "\n", + "In:\n", + "\n", + "`hwc,c->hw`\n", + "\n", + "the index `c` appears in the inputs but **disappears after `->`**, so `einsum` multiplies each colour channel by its weight and **sums over colour**. The `h` and `w` indices survive, so the output remains an image of shape `(512, 512)`.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Predict the output shape before running anything.\n", + "2. Solve the TODO with one `einsum`.\n", + "3. Open the folded solution and move the **R/G/B sliders**.\n", + "4. Notice that the picture changes, but the index rule `hwc,c->hw` does not.\n", + "\n", + "> 🇪🇸 **¿Qué estás viendo?** `photo` es una imagen real de microscopía de forma `(512, 512, 3)`: alto, ancho y tres canales RGB. El vector `w` no es un conjunto de datos; es una regla de transformación. En `hwc,c->hw`, el índice `c` desaparece después de `->`, por eso se multiplica cada canal por su peso y luego se suma sobre color. **Prueba:** predice la forma, resuelve el `einsum` y después mueve los sliders R/G/B. La imagen cambia, pero la regla de índices permanece igual.\n" + ], + "id": "s06-04" + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "s06-05" + }, + "outputs": [], + "source": [ + "# TODO 1: Use einsum to convert `photo` to grayscale by contracting the colour\n", + "# axis against w. Expected result: (512, 512).\n", + "#\n", + "# TODO 2: Do the same for the whole real-image batch in ONE einsum call.\n", + "# Expected result: (2, 512, 512).\n", + "#\n", + "# Explain in one sentence:\n", + "# - which index is contracted?\n", + "# - which indices survive?" + ], + "id": "s06-05" + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "id": "s06-06", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 902, + "referenced_widgets": [ + "4264d1b269004133bdd63da646b2f08f", + "08d53eb8a9cb447ea5281d71f4f6ec9f", + "5abc2d6ff3784fe08d2ea57fbea39d8a", + "ff1ae20acc844ab1bcc28c5e35498647", + "d7e1a368de9f40b5a3dc4f55ee8dabfe", + "6540bc11246c4f798abe25f2c85668c9", + "6fa85a4fcd974dda9e6bb9799653f541", + "69f91f46e5b140228386b5163b14d6bf", + "9d8851769c9749f2afec0d8b231171c0", + "67f060730375472c85d17f336d039fa8", + "ba455c1f2a5646468c18c8cd4255b130", + "70bec5ade61549019934d9b3aee6a45f", + "1f21466d29fc4e0e841a3f734e760ba7", + "66afc8c774444586acb309f92e078b14", + "9d3305439516455397a2c4be86b8038a", + "64384bc8ee094b4ba50a4d09882fb97f", + "9a316c35266b41bf867cfc9cf14cb8af", + "e3114b98fcdd4bc181eb6d3ceb273128" + ] + }, + "outputId": "355d68cc-e4a5-4136-ad87-c1fe72e8c5b3" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "single image: (512, 512, 3) -> (512, 512)\n", + "batch: (2, 512, 512, 3) -> (2, 512, 512)\n", + "contracted index: c | kept indices: h,w (and n for the batch)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(HTML(value='Interactive lab: move R/G/B. The pixels stay real; only the contraction weig…" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "4264d1b269004133bdd63da646b2f08f" + } + }, + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "colab": { + "custom_widget_manager": { + "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" + } + } + } + } + } + ], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "gray = np.einsum('hwc,c->hw', photo, w) # (512, 512)\n", + "gray_batch = np.einsum('nhwc,c->nhw', batch, w) # (2, 512, 512)\n", + "\n", + "print(\"single image:\", photo.shape, \"->\", gray.shape)\n", + "print(\"batch:\", batch.shape, \"->\", gray_batch.shape)\n", + "print(\"contracted index: c | kept indices: h,w (and n for the batch)\")\n", + "\n", + "# Static before/after visual: real photograph in, computed contraction out.\n", + "fig, axes = plt.subplots(1, 2, figsize=(8, 4))\n", + "axes[0].imshow(photo / 255)\n", + "axes[0].set_title(\"real input — axes h, w, c\")\n", + "axes[1].imshow(gray, cmap=\"gray\")\n", + "axes[1].set_title(\"'hwc,c->hw' — c is gone\")\n", + "for ax in axes:\n", + " ax.axis(\"off\")\n", + "fig.suptitle(\"c is missing after ->, so colour is SUMMED OVER; h and w are KEPT\")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Interactive lab: change the contraction weights while keeping the SAME real image\n", + "# and the SAME index rule. continuous_update=False keeps Colab responsive.\n", + "r_slider = widgets.FloatSlider(\n", + " value=float(w[0]), min=0.0, max=1.0, step=0.025,\n", + " description=\"R\", readout_format=\".3f\", continuous_update=False\n", + ")\n", + "g_slider = widgets.FloatSlider(\n", + " value=float(w[1]), min=0.0, max=1.0, step=0.025,\n", + " description=\"G\", readout_format=\".3f\", continuous_update=False\n", + ")\n", + "b_slider = widgets.FloatSlider(\n", + " value=float(w[2]), min=0.0, max=1.0, step=0.025,\n", + " description=\"B\", readout_format=\".3f\", continuous_update=False\n", + ")\n", + "\n", + "def explore_colour_contraction(r, g, b):\n", + " weights = np.array([r, g, b], dtype=float)\n", + " live_gray = np.einsum('hwc,c->hw', photo, weights)\n", + "\n", + " fig, axes = plt.subplots(1, 2, figsize=(7.5, 3.3))\n", + " axes[0].imshow(photo / 255)\n", + " axes[0].set_title(\"same real input\")\n", + " axes[1].imshow(live_gray, cmap=\"gray\")\n", + " axes[1].set_title(f\"R={r:.3f}, G={g:.3f}, B={b:.3f}\")\n", + " for ax in axes:\n", + " ax.axis(\"off\")\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " print(\"EN: c disappears -> colour is contracted; h and w survive.\")\n", + " print(\"ES: c desaparece -> el color se contrae; h y w permanecen.\")\n", + " print(f\"einsum: hwc,c->hw | weight sum / suma de pesos = {weights.sum():.3f}\")\n", + "\n", + "rgb_output = widgets.interactive_output(\n", + " explore_colour_contraction,\n", + " {\"r\": r_slider, \"g\": g_slider, \"b\": b_slider},\n", + ")\n", + "\n", + "display(\n", + " widgets.VBox([\n", + " widgets.HTML(\n", + " \"Interactive lab: move R/G/B. \"\n", + " \"The pixels stay real; only the contraction weights change.\"\n", + " ),\n", + " widgets.HBox([r_slider, g_slider, b_slider]),\n", + " rgb_output,\n", + " ])\n", + ")\n" + ], + "id": "s06-06" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s06-07" + }, + "source": [ + "## Exercise 2 — Chapter 2 operations on real digit pixels\n", + "\n", + "### Where do matrices `A` and `B` come from?\n", + "\n", + "They are **not hand-written toy numbers**. Each `2×2` matrix is the central pixel patch of one real `8×8` handwritten digit from `sklearn.datasets.load_digits()`.\n", + "\n", + "Pixel intensities in this dataset run from **0 to 16**. The matrices are intentionally tiny so you can check the arithmetic by hand while still operating on observed data.\n", + "\n", + "### What are the three operations teaching?\n", + "\n", + "- **Trace — `ii->`**: the repeated `i` disappears, so the diagonal is summed to one scalar.\n", + "- **Transpose — `ij->ji`**: no index disappears; the axes are only reordered.\n", + "- **Matrix product — `ik,kj->ij`**: the shared `k` disappears, so `k` is the contracted dimension; `i` and `j` survive.\n", + "\n", + "### What should you try?\n", + "\n", + "Use the **operation selector** after solving the TODO. Switch among trace, transpose and matrix product and say aloud:\n", + "\n", + "> “Which index disappeared? Which indices survived?”\n", + "\n", + "That sentence is more important than memorising the strings.\n", + "\n", + "> 🇪🇸 **¿De dónde salen `A` y `B`?** No son números inventados: cada matriz `2×2` es un recorte central de píxeles de un dígito manuscrito real `8×8`. Las intensidades van de **0 a 16**. **Traza:** `ii->` elimina `i` y suma la diagonal. **Transpuesta:** `ij->ji` no elimina índices, solo cambia su orden. **Producto matricial:** `ik,kj->ij` elimina `k`, por lo que `k` es la dimensión contraída. Usa el selector y pregúntate siempre: **¿qué índice desapareció y cuáles sobrevivieron?**\n" + ], + "id": "s06-07" + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "s06-08" + }, + "outputs": [], + "source": [ + "# TODO 3: Rewrite these Chapter 2 operations as einsum and check each against\n", + "# NumPy:\n", + "#\n", + "# (a) trace of A -> scalar\n", + "# (b) transpose of A -> (2, 2)\n", + "# (c) matrix product A @ B -> (2, 2)\n", + "#\n", + "# For each expression, say which index is:\n", + "# - summed over,\n", + "# - only relabelled/reordered,\n", + "# - or kept." + ], + "id": "s06-08" + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "id": "s06-09", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 795, + "referenced_widgets": [ + "f1d6be80ffe041c0ad311ed5469ed5cb", + "409af123c59c42dfa1fe448a2ee79c8a", + "36da133a3df24f2bbcf82d3f97d86124", + "482d6755a908430ab2e48cf87d8452e9", + "1ea19295851e48b6bf8769920792e47b", + "50b7096a05e341aa8e78d20f2845b16a", + "97c4528cc0fc4a9b9fe5497d5c9d414d", + "6bd79483d56a4909af42f858716baa77", + "68236406778c40d19786b447bc2b265d", + "c1921f8c3b1849f89d4afed48f2984e2" + ] + }, + "outputId": "467a758d-81c3-4407-e70c-7d4faca5eb10" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "A came from real digit label 0\n", + "[[15. 2.]\n", + " [12. 0.]]\n", + "B came from real digit label 1\n", + "[[ 3. 15.]\n", + " [15. 16.]]\n", + "\n", + "trace: 15.0 | NumPy: 15.0\n", + "transpose:\n", + " [[15. 12.]\n", + " [ 2. 0.]]\n", + "matrix product:\n", + " [[ 75. 257.]\n", + " [ 36. 180.]]\n", + "\n", + "all three einsum results agree with NumPy\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(HTML(value='Interactive index explorer / Explorador interactivo de índices: choose an op…" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "f1d6be80ffe041c0ad311ed5469ed5cb" + } + }, + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "colab": { + "custom_widget_manager": { + "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" + } + } + } + } + } + ], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "trace_e = np.einsum('ii->', A)\n", + "transpose_e = np.einsum('ij->ji', A)\n", + "product_e = np.einsum('ik,kj->ij', A, B)\n", + "\n", + "print(\"A came from real digit label\", digits.target[0])\n", + "print(A)\n", + "print(\"B came from real digit label\", digits.target[1])\n", + "print(B)\n", + "print(\"\\ntrace:\", trace_e, \"| NumPy:\", np.trace(A))\n", + "print(\"transpose:\\n\", transpose_e)\n", + "print(\"matrix product:\\n\", product_e)\n", + "\n", + "assert np.allclose(trace_e, np.trace(A))\n", + "assert np.allclose(transpose_e, A.T)\n", + "assert np.allclose(product_e, A @ B)\n", + "print(\"\\nall three einsum results agree with NumPy\")\n", + "\n", + "# Context: show the real 8x8 source digits and highlight the 2x2 patches.\n", + "fig, axes = plt.subplots(1, 2, figsize=(5, 2.5))\n", + "for ax, idx, name in zip(axes, [0, 1], [\"A\", \"B\"]):\n", + " ax.imshow(digit_images[idx], cmap=\"gray_r\", interpolation=\"nearest\", vmin=0, vmax=16)\n", + " ax.add_patch(\n", + " plt.Rectangle((1.5, 1.5), 2, 2, fill=False, linewidth=2)\n", + " )\n", + " ax.set_title(f\"{name}: real digit label {digits.target[idx]}\\nred box = 2×2 patch\")\n", + " ax.axis(\"off\")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "operation = widgets.ToggleButtons(\n", + " options=[\n", + " (\"Trace / Traza\", \"trace\"),\n", + " (\"Transpose / Transpuesta\", \"transpose\"),\n", + " (\"Matrix product / Producto\", \"product\"),\n", + " ],\n", + " value=\"product\",\n", + " description=\"\",\n", + ")\n", + "\n", + "def explain_operation(op):\n", + " if op == \"trace\":\n", + " expr = \"ii->\"\n", + " result = np.einsum(\"ii->\", A)\n", + " en = \"i is repeated and disappears -> sum the diagonal.\"\n", + " es = \"i se repite y desaparece -> se suma la diagonal.\"\n", + " print(\"A =\\n\", A)\n", + " print(f\"einsum('{expr}', A) =\", result)\n", + " elif op == \"transpose\":\n", + " expr = \"ij->ji\"\n", + " result = np.einsum(\"ij->ji\", A)\n", + " en = \"No index disappears -> only reorder the axes.\"\n", + " es = \"Ningún índice desaparece -> solo se reordenan los ejes.\"\n", + " print(\"A =\\n\", A)\n", + " print(f\"einsum('{expr}', A) =\\n\", result)\n", + " else:\n", + " expr = \"ik,kj->ij\"\n", + " result = np.einsum(\"ik,kj->ij\", A, B)\n", + " en = \"k is shared and disappears -> contract k; keep i and j.\"\n", + " es = \"k es compartido y desaparece -> contrae k; conserva i y j.\"\n", + " print(\"A =\\n\", A)\n", + " print(\"B =\\n\", B)\n", + " print(f\"einsum('{expr}', A, B) =\\n\", result)\n", + "\n", + " print(\"EN:\", en)\n", + " print(\"ES:\", es)\n", + "\n", + "operation_output = widgets.interactive_output(\n", + " explain_operation,\n", + " {\"op\": operation},\n", + ")\n", + "\n", + "display(\n", + " widgets.VBox([\n", + " widgets.HTML(\n", + " \"Interactive index explorer / Explorador interactivo de índices: \"\n", + " \"choose an operation and identify the disappearing index. / \"\n", + " \"elige una operación e identifica el índice que desaparece.\"\n", + " ),\n", + " operation,\n", + " operation_output,\n", + " ])\n", + ")\n" + ], + "id": "s06-09" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s06-10" + }, + "source": [ + "## Exercise 3 — 3,229,209 similarities from 1,797 real digit images\n", + "\n", + "### Why do these digit images look pixelated?\n", + "\n", + "This is **intentional and important**: the original `sklearn` digits dataset stores each handwritten digit at only **8×8 pixels**.\n", + "\n", + "So each image has exactly:\n", + "\n", + "`8 × 8 = 64 real measured pixel features`\n", + "\n", + "The blocky appearance is **not a bad download, compression error or broken image**. It is the original resolution of the dataset. We display it with `interpolation=\"nearest\"` so the notebook does **not invent smooth pixels that were never measured**.\n", + "\n", + "That low resolution is actually useful here: after flattening, every digit becomes a 64-dimensional vector, so the contraction is easy to connect directly to the pixels.\n", + "\n", + "### What does `id,jd->ij` mean?\n", + "\n", + "- `i`: query-image index — kept\n", + "- `j`: candidate-image index — kept\n", + "- `d`: 64 pixel features — **disappears**, so it is contracted\n", + "\n", + "The result therefore has shape `(1797, 1797)`: one similarity score for every pair of real digit images.\n", + "\n", + "### Raw dot product vs cosine similarity\n", + "\n", + "Both use the **same `einsum` contraction**. The difference is what happens before it:\n", + "\n", + "- **Raw dot product** also rewards vector magnitude — roughly, how much total “ink” or intensity an image has.\n", + "- **Cosine similarity** first normalizes each 64-pixel vector to unit length, so the comparison focuses more on the **pattern/direction** of the pixels.\n", + "\n", + "For query image `14` (true label `4`), the strongest raw-dot match is a `1`, while the strongest cosine matches are `4`s. That is a real example of why preprocessing changes the meaning of “similar”.\n", + "\n", + "### What should you try?\n", + "\n", + "Use the retrieval explorer:\n", + "\n", + "1. Move **Query / Consulta** to choose any of the 1,797 real digits.\n", + "2. Switch **Cosine / Coseno** ↔ **Raw dot / P. punto**.\n", + "3. Change **Top k / Vecinos**.\n", + "4. Compare how many retrieved images have the same label as the query.\n", + "\n", + "> 🇪🇸 **¿Por qué se ven pixelados los dígitos?** Porque el dataset original guarda cada dígito con solo **8×8 píxeles**. No es mala calidad de descarga ni un error: son exactamente **64 mediciones reales** por imagen. Se muestran con interpolación `nearest` para no inventar píxeles suaves que nunca fueron observados. En `id,jd->ij`, `d` representa esas 64 características y desaparece; `i` y `j` permanecen, por eso obtenemos una matriz `(1797,1797)` con una similitud para cada par. El producto punto crudo también depende de la magnitud/intensidad; el coseno normaliza primero y compara más la forma del patrón. Usa el explorador para cambiar consulta, métrica y número de vecinos.\n" + ], + "id": "s06-10" + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "s06-11" + }, + "outputs": [], + "source": [ + "# TODO 4: Reshape all real digit images to D with shape (1797, 64).\n", + "#\n", + "# TODO 5: Compute every raw dot-product similarity with ONE einsum:\n", + "# 'id,jd->ij' -> expected shape (1797, 1797).\n", + "#\n", + "# TODO 6: Normalize every row of D to unit length, then repeat the SAME einsum\n", + "# to obtain cosine similarity C.\n", + "#\n", + "# TODO 7: Use query_idx = 14 (true label 4).\n", + "# - exclude the query from matching itself,\n", + "# - find the best raw-dot-product match,\n", + "# - find the five best cosine-similarity matches,\n", + "# - compare their labels with digits.target[query_idx].\n", + "#\n", + "# Predict first: which index disappears in 'id,jd->ij'?" + ], + "id": "s06-11" + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "id": "s06-12", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 815, + "referenced_widgets": [ + "50de1a57580a4e6ba9de46bb2e1a3b7b", + "63c2edfa932a40c2afa36a5903d8ecac", + "8a4f17954ac84b6488eace854ae3c9aa", + "c1e5a275088041b683f87fa3926b1f61", + "4e8d2fe8c79b4cceb87c4aeee701dca8", + "66b51c1690db4f40a3744cdc7d3a1088", + "9e84ef24f01f43bcb725e4b524606c8e", + "3429fba993204c73b9cf03f3e6faa469", + "03dd4538a921434d9cec29eeab10d37d", + "c2402722271343488bd0ecc04b171dbb", + "d611779fb7d24284ab0d6b41abe80f6b", + "949930d4c4fa43f2b3fa1730ae85d261", + "6729a67dc47e403c8ff1b397800f9d96", + "2fb934e286034240bc097ef93a683884", + "41287970de3b44bdb3f9bae195ffb055", + "801bd05623c54084a29f5294746ceace", + "846b7c199d7c49ee8ae3e86c94c23b4b", + "a0a133a4ae18405e85cb6a61e7e66f50", + "dbe8624402164ff6b6584161a1e5433d", + "7c8d9b040a674dd2be855393bf0cac7b", + "63c2631731fd4587a1c2faf3f5f3177c" + ] + }, + "outputId": "a9d1934e-0a30-40dc-ce62-4c8fde05b2fd" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "raw similarity matrix: (1797, 1797) = 3229209 pairwise scores\n", + "query index / label: 14 / 4\n", + "best RAW dot-product match: 1747 label 1 score 4376.0\n", + "top 5 COSINE matches: [41, 1011, 1456, 909, 1254]\n", + "top 5 COSINE labels: [4, 4, 4, 4, 4]\n", + "top 5 COSINE scores: [0.973, 0.959, 0.957, 0.956, 0.955]\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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+ }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(HTML(value='Interactive retrieval explorer / Explorador interactivo: choose a real query…" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "50de1a57580a4e6ba9de46bb2e1a3b7b" + } + }, + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "colab": { + "custom_widget_manager": { + "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" + } + } + } + } + } + ], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "D = digit_images.reshape(len(digit_images), -1) # (1797, 64)\n", + "\n", + "S = np.einsum('id,jd->ij', D, D) # (1797, 1797)\n", + "print(\"raw similarity matrix:\", S.shape, \"=\", S.size, \"pairwise scores\")\n", + "\n", + "norms = np.linalg.norm(D, axis=1, keepdims=True)\n", + "Dn = D / np.where(norms == 0, 1.0, norms)\n", + "C = np.einsum('id,jd->ij', Dn, Dn)\n", + "\n", + "assert S.shape == (1797, 1797)\n", + "assert C.shape == (1797, 1797)\n", + "assert np.allclose(C.diagonal(), 1.0)\n", + "\n", + "query_idx = 14\n", + "query_label = digits.target[query_idx]\n", + "\n", + "raw_scores = S[query_idx].copy()\n", + "cos_scores = C[query_idx].copy()\n", + "raw_scores[query_idx] = -np.inf\n", + "cos_scores[query_idx] = -np.inf\n", + "\n", + "raw_top1 = int(np.argmax(raw_scores))\n", + "cos_top5 = np.argsort(cos_scores)[-5:][::-1]\n", + "\n", + "print(\"query index / label:\", query_idx, \"/\", query_label)\n", + "print(\"best RAW dot-product match:\", raw_top1,\n", + " \"label\", digits.target[raw_top1],\n", + " \"score\", round(float(raw_scores[raw_top1]), 3))\n", + "print(\"top 5 COSINE matches:\", cos_top5.tolist())\n", + "print(\"top 5 COSINE labels:\", digits.target[cos_top5].tolist())\n", + "print(\"top 5 COSINE scores:\", np.round(cos_scores[cos_top5], 3).tolist())\n", + "\n", + "# Static retrieval example. nearest preserves the original 8x8 measurements.\n", + "fig, axes = plt.subplots(1, 6, figsize=(11, 2.5))\n", + "axes[0].imshow(\n", + " digit_images[query_idx], cmap=\"gray_r\",\n", + " interpolation=\"nearest\", vmin=0, vmax=16\n", + ")\n", + "axes[0].set_title(f\"query / consulta\\nlabel {query_label}\")\n", + "\n", + "for ax, idx in zip(axes[1:], cos_top5):\n", + " ax.imshow(\n", + " digit_images[idx], cmap=\"gray_r\",\n", + " interpolation=\"nearest\", vmin=0, vmax=16\n", + " )\n", + " ax.set_title(f\"label {digits.target[idx]}\\ncos={C[query_idx, idx]:.3f}\")\n", + "\n", + "for ax in axes:\n", + " ax.axis(\"off\")\n", + "\n", + "fig.suptitle(\n", + " \"Real 8×8 digits: one contraction -> all-pairs similarity -> retrieval\"\n", + ")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "query_slider = widgets.IntSlider(\n", + " value=14, min=0, max=len(digit_images) - 1, step=1,\n", + " description=\"Query:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"55px\"},\n", + ")\n", + "similarity_toggle = widgets.ToggleButtons(\n", + " options=[(\"Cosine / Coseno\", \"cosine\"), (\"Raw dot / P. punto\", \"raw\")],\n", + " value=\"cosine\",\n", + " description=\"\",\n", + ")\n", + "k_slider = widgets.IntSlider(\n", + " value=5, min=1, max=8, step=1,\n", + " description=\"Top k:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"45px\"},\n", + ")\n", + "\n", + "def explore_retrieval(query, metric, k):\n", + " matrix = C if metric == \"cosine\" else S\n", + " scores = matrix[query].copy()\n", + " scores[query] = -np.inf\n", + "\n", + " top = np.argsort(scores)[-k:][::-1]\n", + " q_label = int(digits.target[query])\n", + "\n", + " fig, axes = plt.subplots(1, k + 1, figsize=(2.05 * (k + 1), 2.75))\n", + " axes = np.atleast_1d(axes)\n", + "\n", + " axes[0].imshow(\n", + " digit_images[query], cmap=\"gray_r\",\n", + " interpolation=\"nearest\", vmin=0, vmax=16\n", + " )\n", + " axes[0].set_title(f\"query {query}\\nlabel {q_label}\")\n", + " axes[0].axis(\"off\")\n", + "\n", + " for ax, idx in zip(axes[1:], top):\n", + " ax.imshow(\n", + " digit_images[idx], cmap=\"gray_r\",\n", + " interpolation=\"nearest\", vmin=0, vmax=16\n", + " )\n", + " score_name = \"cos\" if metric == \"cosine\" else \"dot\"\n", + " ax.set_title(\n", + " f\"idx {idx}\\nlabel {digits.target[idx]}\\n{score_name}={scores[idx]:.3f}\"\n", + " )\n", + " ax.axis(\"off\")\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " labels = digits.target[top].astype(int).tolist()\n", + " same_label = sum(label == q_label for label in labels)\n", + "\n", + " if metric == \"cosine\":\n", + " en = \"Cosine normalizes magnitude first, so the comparison emphasizes pixel-pattern direction.\"\n", + " es = \"El coseno normaliza la magnitud primero, por lo que enfatiza la dirección del patrón de píxeles.\"\n", + " else:\n", + " en = \"Raw dot product also rewards magnitude/intensity, so visually different labels can rank highly.\"\n", + " es = \"El producto punto crudo también premia magnitud/intensidad, por eso pueden aparecer etiquetas distintas.\"\n", + "\n", + " print(\n", + " f\"metric={metric} | query label={q_label} | \"\n", + " f\"top-{k} labels={labels} | same-label matches={same_label}/{k}\"\n", + " )\n", + " print(\"EN:\", en)\n", + " print(\"ES:\", es)\n", + " print(\"Index rule / Regla: id,jd->ij | d disappears / desaparece; i and j survive / permanecen.\")\n", + " print(\"Display note / Nota visual: the data are truly 8×8; the pixelated look is the original resolution.\")\n", + "\n", + "retrieval_output = widgets.interactive_output(\n", + " explore_retrieval,\n", + " {\n", + " \"query\": query_slider,\n", + " \"metric\": similarity_toggle,\n", + " \"k\": k_slider,\n", + " },\n", + ")\n", + "\n", + "display(\n", + " widgets.VBox([\n", + " widgets.HTML(\n", + " \"Interactive retrieval explorer / Explorador interactivo: \"\n", + " \"choose a real query digit, change the metric, and inspect its neighbours. / \"\n", + " \"elige un dígito real, cambia la métrica y observa sus vecinos.\"\n", + " ),\n", + " widgets.HTML(\n", + " \"Image quality note / Nota de calidad: these are original 8×8 measurements; \"\n", + " \"the blocky pixels are the data, not an error. / Son mediciones originales 8×8; \"\n", + " \"los bloques son los datos, no un error.\"\n", + " ),\n", + " widgets.HBox([query_slider, similarity_toggle, k_slider]),\n", + " retrieval_output,\n", + " ])\n", + ")\n" + ], + "id": "s06-12" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s06-13" + }, + "source": [ + "## What just happened\n", + "\n", + "You used **one index rule** three times, but each exercise gave the rule a different meaning:\n", + "\n", + "1. **Real microscopy image — `hwc,c->hw`** \n", + " `c` disappeared, so three colour measurements became one grayscale value at every pixel. The sliders changed the weights, not the rule.\n", + "\n", + "2. **Real digit-pixel matrices — `ik,kj->ij`** \n", + " `k` disappeared, so the shared dimension was multiplied and summed. Trace and transpose showed that `einsum` can also sum repeated indices or simply reorder them.\n", + "\n", + "3. **1,797 real handwritten digits — `id,jd->ij`** \n", + " `d` disappeared, so 64 real pixel measurements became one similarity score for every pair of images: **3,229,209 scores**. The interactive explorer showed that preprocessing changes what “similar” means.\n", + "\n", + "### The sentence to remember\n", + "\n", + "> **If an index disappears after `->`, it is summed over. If it remains, it survives in the output.**\n", + "\n", + "The `8×8` digits are deliberately pixelated because **each visible square is one of the 64 measured features**. Keeping that limitation visible makes the tensor operation easier to understand.\n", + "\n", + "> 🇪🇸 Usaste **una sola regla de índices** tres veces: `c` desapareció al convertir color a gris; `k` desapareció en el producto matricial; y `d` desapareció al convertir 64 píxeles en una similitud entre dos dígitos. La frase para recordar es: **si un índice desaparece después de `->`, se suma; si permanece, sobrevive en la salida.** Los dígitos `8×8` se ven pixelados a propósito: cada cuadrado visible es una de las 64 características realmente medidas.\n", + "\n", + "Keep this rule for section 10: `'ijk,ia,jb,kc->abc'` looks longer, but the logic is exactly the same.\n" + ], + "id": "s06-13" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s06-14" + }, + "source": [ + "---\n", + "\n", + "## Done with this section\n", + "\n", + "Next up: **07 · Inverses and the pseudoinverse** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/07-inverses-and-pseudoinverse.ipynb).\n", + "\n", + "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" + ], + "id": "s06-14" + } + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "version_major": 2, + "version_minor": 0, + "state": { + "4264d1b269004133bdd63da646b2f08f": { + "model_module": "@jupyter-widgets/controls", + "model_name": "VBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "VBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "VBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_08d53eb8a9cb447ea5281d71f4f6ec9f", + "IPY_MODEL_5abc2d6ff3784fe08d2ea57fbea39d8a", + "IPY_MODEL_ff1ae20acc844ab1bcc28c5e35498647" + ], + "layout": "IPY_MODEL_d7e1a368de9f40b5a3dc4f55ee8dabfe" + } + }, + "08d53eb8a9cb447ea5281d71f4f6ec9f": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_6540bc11246c4f798abe25f2c85668c9", + "placeholder": "​", + "style": "IPY_MODEL_6fa85a4fcd974dda9e6bb9799653f541", + "value": "Interactive lab: move R/G/B. 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files changed, 4567 insertions(+), 2721 deletions(-) diff --git a/_variables.yml b/_variables.yml index fc62895..8cf173e 100644 --- a/_variables.yml +++ b/_variables.yml @@ -294,18 +294,18 @@ sections: format_es: "ejercicio" title_en: "Contraction with einsum" title_es: "Contracción con einsum" - summary_en: "One notation for the dot product, the matrix product, and a batch of images." - summary_es: "Una sola notación para el producto punto, el producto matricial y un lote de imágenes." + summary_en: "Use one index rule on real data, then change the inputs interactively to test which index disappears." + summary_es: "Aprender una sola regla de índices, comprobarla con datos reales y cambiar las entradas de forma interactiva para identificar qué índice desaparece." objectives_en: - - "State the einsum rule: an index missing after the arrow is summed over." - - "Contract the colour axis of one image, and of a whole batch, with one call each." - - "Write trace, transpose and the matrix product as `einsum` and check them against NumPy." - - "Build a full similarity matrix between 1797 images with a single contraction." + - "Explain the `einsum` rule: indices missing after `->` are summed over; indices that remain are kept." + - "Contract the colour axis of a real microscopy image and explain what the RGB sliders change — and what they do not change." + - "Read trace, transpose and matrix multiplication as index operations on pixel patches cut from real handwritten digits." + - "Build all 3,229,209 pairwise similarities between 1,797 real digit images and explore retrieval with cosine similarity versus raw dot product." objectives_es: - - "Enunciar la regla de einsum: un índice que no aparece después de la flecha se suma." - - "Contraer el eje de color de una imagen y de un batch completo con una sola llamada en cada caso." - - "Escribir la traza, la transposición y el producto matricial con `einsum` y verificarlos con NumPy." - - "Construir una matriz completa de similitud entre 1797 imágenes mediante una sola contracción." + - "Explicar la regla de `einsum`: los índices que desaparecen después de `->` se suman; los que permanecen se conservan." + - "Contraer el eje de color de una imagen real de microscopía y explicar qué cambian los sliders RGB y qué no cambian." + - "Interpretar la traza, la transposición y el producto matricial como operaciones sobre índices usando recortes de píxeles de dígitos manuscritos reales." + - "Construir las 3.229.209 similitudes por pares entre 1.797 imágenes reales de dígitos y explorar la recuperación con similitud coseno frente al producto punto crudo." s07: n: "07" slug: "inverses-and-pseudoinverse" diff --git a/docs/notebooks/06-contraction-with-einsum.ipynb b/docs/notebooks/06-contraction-with-einsum.ipynb index bb88151..bcb412b 100644 --- a/docs/notebooks/06-contraction-with-einsum.ipynb +++ b/docs/notebooks/06-contraction-with-einsum.ipynb @@ -10,16 +10,16 @@ "\n", "*Part IV · exercise · 15 min*\n", "\n", - "> 🇪🇸 **Contracción con einsum** — Una sola notación para el producto punto, el producto matricial y un lote de imágenes.\n", + "> 🇪🇸 **Contracción con einsum** — Aprender una sola regla de índices, comprobarla con datos reales y cambiar las entradas de forma interactiva para identificar qué índice desaparece.\n", "\n", - "One notation for the dot product, the matrix product, and a batch of images.\n", + "Use one index rule on real data, then change the inputs interactively to test which index disappears.\n", "\n", "## What you will be able to do\n", "\n", - "- State the einsum rule: an index missing after the arrow is summed over.\n", - "- Contract the colour axis of one image, and of a whole batch, with one call each.\n", - "- Write trace, transpose and the matrix product as `einsum` and check them against NumPy.\n", - "- Build a full similarity matrix between 1797 images with a single contraction." + "- Explain the `einsum` rule: indices missing after `->` are summed over; indices that remain are kept.\n", + "- Contract the colour axis of a real microscopy image and explain what the RGB sliders change — and what they do not change.\n", + "- Read trace, transpose and matrix multiplication as index operations on pixel patches cut from real handwritten digits.\n", + "- Build all 3,229,209 pairwise similarities between 1,797 real digit images and explore retrieval with cosine similarity versus raw dot product." ], "id": "s06-00" }, @@ -42,15 +42,43 @@ "outputs": [], "source": [ "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import ipywidgets as widgets\n", + "from IPython.display import display\n", "from sklearn.datasets import load_digits\n", "from skimage import data\n", "\n", - "photo = data.immunohistochemistry().astype(float) # (512, 512, 3)\n", - "batch = np.stack([photo, data.astronaut().astype(float)]) # (2, 512, 512, 3)\n", - "w = np.array([0.2125, 0.7154, 0.0721]) # RGB -> grayscale weights\n", - "A = np.array([[1., 2.], [3., 4.]])\n", - "B = np.array([[5., 6.], [7., 8.]])\n", - "print(photo.shape, batch.shape)" + "# Enable ipywidgets in Google Colab when available.\n", + "try:\n", + " from google.colab import output\n", + " output.enable_custom_widget_manager()\n", + "except ImportError:\n", + " pass\n", + "\n", + "# Real colour images.\n", + "photo = data.immunohistochemistry().astype(float) # (512, 512, 3)\n", + "batch = np.stack([photo, data.astronaut().astype(float)]) # (2, 512, 512, 3)\n", + "w = np.array([0.2125, 0.7154, 0.0721]) # RGB -> grayscale weights\n", + "\n", + "# Real handwritten digits (UCI Optical Recognition dataset, packaged by sklearn).\n", + "digits = load_digits()\n", + "digit_images = digits.images.astype(float) # (1797, 8, 8)\n", + "\n", + "# Small 2x2 matrices for Exercise 2 are NOT invented numbers:\n", + "# they are central pixel patches from two real digit images.\n", + "A = digit_images[0, 2:4, 2:4]\n", + "B = digit_images[1, 2:4, 2:4]\n", + "\n", + "print(\"photo:\", photo.shape, \"batch:\", batch.shape)\n", + "print(\"digits:\", digit_images.shape, \"labels:\", digits.target.shape)\n", + "print(\n", + " \"Exercise 2 patches come from digit labels:\",\n", + " digits.target[0],\n", + " \"and\",\n", + " digits.target[1],\n", + ")\n", + "print(\"A =\\n\", A)\n", + "print(\"B =\\n\", B)" ], "id": "s06-02" }, @@ -60,21 +88,25 @@ "source": [ "## Why this matters\n", "\n", - "> 🇪🇸 Los sistemas de recomendación y de búsqueda ordenan los resultados con el\n", - "> producto punto entre el vector de un usuario y millones de vectores de\n", - "> artículos. Esa contracción *es* la señal de ranking.\n", + "A contraction is not just a matrix-algebra trick. The same operation appears when an image model removes a colour axis, when linear algebra multiplies matrices, and when a retrieval system compares one query vector with thousands or millions of candidates.\n", + "\n", + "> 🇪🇸 Una contracción no es solo un truco de álgebra matricial. La misma operación aparece cuando un modelo elimina el eje de color de una imagen, cuando el álgebra lineal multiplica matrices y cuando un sistema de búsqueda compara un vector consulta con miles o millones de candidatos.\n", + "\n", + "### One rule for the whole notebook\n", "\n", - "Recommendation and search systems rank items by the dot product between a user\n", - "vector and every item vector — one user against millions of items, many times\n", - "per second. That contraction *is* the ranking signal. Sum over the wrong axis\n", - "and every user gets wrong results.\n", + "If an index appears in the inputs but **not** after `->`, it is **summed over**. If it appears after `->`, it is **kept**.\n", "\n", - "### The rule, again\n", + "Examples:\n", "\n", - "An index that appears in the inputs but **not** after the arrow is **summed\n", - "over**. An index that appears after the arrow is **kept**.\n", + "- `hwc,c->hw`: `c` disappears → contract colour.\n", + "- `ik,kj->ij`: `k` disappears → matrix multiplication.\n", + "- `id,jd->ij`: `d` disappears → every pair of row vectors gets one similarity score.\n", "\n", - "That is the whole of `einsum`. Everything below is that one sentence applied." + "### Predict → Run → Explain\n", + "\n", + "Before each exercise, predict **which index disappears and what the output shape must be**. After running the code, explain what information the contraction kept.\n", + "\n", + "> 🇪🇸 **Predice → Ejecuta → Explica:** antes de cada ejercicio, predice **qué índice desaparece y cuál debe ser la forma de salida**. Después de ejecutar, explica qué información conservó la contracción." ], "id": "s06-03" }, @@ -82,9 +114,34 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercise 1 — contract the colour axis\n", + "## Exercise 1 — contract the colour axis of a real image\n", + "\n", + "### What are you looking at?\n", + "\n", + "`photo` is a **real microscopy image** from `skimage.data.immunohistochemistry()` with shape `(512, 512, 3)`:\n", + "\n", + "- `h = 512`: image rows / height\n", + "- `w = 512`: image columns / width\n", + "- `c = 3`: red, green and blue channels\n", + "\n", + "The vector `w = [0.2125, 0.7154, 0.0721]` is **not another dataset**. It is a transformation rule: three weights telling us how much each colour channel contributes to the grayscale result.\n", "\n", - "> 🇪🇸 Contrae el eje de color." + "### What is the mathematical idea?\n", + "\n", + "In:\n", + "\n", + "`hwc,c->hw`\n", + "\n", + "the index `c` appears in the inputs but **disappears after `->`**, so `einsum` multiplies each colour channel by its weight and **sums over colour**. The `h` and `w` indices survive, so the output remains an image of shape `(512, 512)`.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Predict the output shape before running anything.\n", + "2. Solve the TODO with one `einsum`.\n", + "3. Open the folded solution and move the **R/G/B sliders**.\n", + "4. Notice that the picture changes, but the index rule `hwc,c->hw` does not.\n", + "\n", + "> 🇪🇸 **¿Qué estás viendo?** `photo` es una imagen real de microscopía de forma `(512, 512, 3)`: alto, ancho y tres canales RGB. El vector `w` no es un conjunto de datos; es una regla de transformación. En `hwc,c->hw`, el índice `c` desaparece después de `->`, por eso se multiplica cada canal por su peso y luego se suma sobre color. **Prueba:** predice la forma, resuelve el `einsum` y después mueve los sliders R/G/B. La imagen cambia, pero la regla de índices permanece igual.\n" ], "id": "s06-04" }, @@ -94,10 +151,15 @@ "metadata": {}, "outputs": [], "source": [ - "# TODO 1: With einsum, convert `photo` to grayscale by contracting the colour\n", - "# axis against w. Result shape (512, 512).\n", - "\n", - "# TODO 2: Do the same for the whole batch in ONE einsum call -> (2, 512, 512)." + "# TODO 1: Use einsum to convert `photo` to grayscale by contracting the colour\n", + "# axis against w. Expected result: (512, 512).\n", + "#\n", + "# TODO 2: Do the same for the whole real-image batch in ONE einsum call.\n", + "# Expected result: (2, 512, 512).\n", + "#\n", + "# Explain in one sentence:\n", + "# - which index is contracted?\n", + "# - which indices survive?" ], "id": "s06-05" }, @@ -105,25 +167,85 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "gray = np.einsum('hwc,c->hw', photo, w) # (512, 512)\n", - "gray_batch = np.einsum('nhwc,c->nhw', batch, w) # (2, 512, 512)\n", - "print(gray.shape, gray_batch.shape)\n", + "gray = np.einsum('hwc,c->hw', photo, w) # (512, 512)\n", + "gray_batch = np.einsum('nhwc,c->nhw', batch, w) # (2, 512, 512)\n", + "\n", + "print(\"single image:\", photo.shape, \"->\", gray.shape)\n", + "print(\"batch:\", batch.shape, \"->\", gray_batch.shape)\n", + "print(\"contracted index: c | kept indices: h,w (and n for the batch)\")\n", + "\n", + "# Static before/after visual: real photograph in, computed contraction out.\n", + "fig, axes = plt.subplots(1, 2, figsize=(8, 4))\n", + "axes[0].imshow(photo / 255)\n", + "axes[0].set_title(\"real input — axes h, w, c\")\n", + "axes[1].imshow(gray, cmap=\"gray\")\n", + "axes[1].set_title(\"'hwc,c->hw' — c is gone\")\n", + "for ax in axes:\n", + " ax.axis(\"off\")\n", + "fig.suptitle(\"c is missing after ->, so colour is SUMMED OVER; h and w are KEPT\")\n", + "plt.tight_layout()\n", + "plt.show()\n", "\n", - "# `c` appears in the inputs but not after the arrow, so it is SUMMED OVER —\n", - "# that is the contraction. `n`, `h`, `w` appear after the arrow, so they are\n", - "# KEPT. Adding a batch axis costs exactly one letter." + "# Interactive lab: change the contraction weights while keeping the SAME real image\n", + "# and the SAME index rule. continuous_update=False keeps Colab responsive.\n", + "r_slider = widgets.FloatSlider(\n", + " value=float(w[0]), min=0.0, max=1.0, step=0.025,\n", + " description=\"R\", readout_format=\".3f\", continuous_update=False\n", + ")\n", + "g_slider = widgets.FloatSlider(\n", + " value=float(w[1]), min=0.0, max=1.0, step=0.025,\n", + " description=\"G\", readout_format=\".3f\", continuous_update=False\n", + ")\n", + "b_slider = widgets.FloatSlider(\n", + " value=float(w[2]), min=0.0, max=1.0, step=0.025,\n", + " description=\"B\", readout_format=\".3f\", continuous_update=False\n", + ")\n", + "\n", + "def explore_colour_contraction(r, g, b):\n", + " weights = np.array([r, g, b], dtype=float)\n", + " live_gray = np.einsum('hwc,c->hw', photo, weights)\n", + "\n", + " fig, axes = plt.subplots(1, 2, figsize=(7.5, 3.3))\n", + " axes[0].imshow(photo / 255)\n", + " axes[0].set_title(\"same real input\")\n", + " axes[1].imshow(live_gray, cmap=\"gray\")\n", + " axes[1].set_title(f\"R={r:.3f}, G={g:.3f}, B={b:.3f}\")\n", + " for ax in axes:\n", + " ax.axis(\"off\")\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " print(\"EN: c disappears -> colour is contracted; h and w survive.\")\n", + " print(\"ES: c desaparece -> el color se contrae; h y w permanecen.\")\n", + " print(f\"einsum: hwc,c->hw | weight sum / suma de pesos = {weights.sum():.3f}\")\n", + "\n", + "rgb_output = widgets.interactive_output(\n", + " explore_colour_contraction,\n", + " {\"r\": r_slider, \"g\": g_slider, \"b\": b_slider},\n", + ")\n", + "\n", + "display(\n", + " widgets.VBox([\n", + " widgets.HTML(\n", + " \"Interactive lab: move R/G/B. \"\n", + " \"The pixels stay real; only the contraction weights change.\"\n", + " ),\n", + " widgets.HBox([r_slider, g_slider, b_slider]),\n", + " rgb_output,\n", + " ])\n", + ")\n" ], "id": "s06-06" }, @@ -131,9 +253,29 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercise 2 — Chapter 2, rewritten as contractions\n", + "## Exercise 2 — Chapter 2 operations on real digit pixels\n", + "\n", + "### Where do matrices `A` and `B` come from?\n", + "\n", + "They are **not hand-written toy numbers**. Each `2×2` matrix is the central pixel patch of one real `8×8` handwritten digit from `sklearn.datasets.load_digits()`.\n", + "\n", + "Pixel intensities in this dataset run from **0 to 16**. The matrices are intentionally tiny so you can check the arithmetic by hand while still operating on observed data.\n", + "\n", + "### What are the three operations teaching?\n", + "\n", + "- **Trace — `ii->`**: the repeated `i` disappears, so the diagonal is summed to one scalar.\n", + "- **Transpose — `ij->ji`**: no index disappears; the axes are only reordered.\n", + "- **Matrix product — `ik,kj->ij`**: the shared `k` disappears, so `k` is the contracted dimension; `i` and `j` survive.\n", + "\n", + "### What should you try?\n", + "\n", + "Use the **operation selector** after solving the TODO. Switch among trace, transpose and matrix product and say aloud:\n", "\n", - "> 🇪🇸 Las operaciones del capítulo 2, escritas como contracciones." + "> “Which index disappeared? Which indices survived?”\n", + "\n", + "That sentence is more important than memorising the strings.\n", + "\n", + "> 🇪🇸 **¿De dónde salen `A` y `B`?** No son números inventados: cada matriz `2×2` es un recorte central de píxeles de un dígito manuscrito real `8×8`. Las intensidades van de **0 a 16**. **Traza:** `ii->` elimina `i` y suma la diagonal. **Transpuesta:** `ij->ji` no elimina índices, solo cambia su orden. **Producto matricial:** `ik,kj->ij` elimina `k`, por lo que `k` es la dimensión contraída. Usa el selector y pregúntate siempre: **¿qué índice desapareció y cuáles sobrevivieron?**\n" ], "id": "s06-07" }, @@ -143,11 +285,17 @@ "metadata": {}, "outputs": [], "source": [ - "# TODO 3: Write these Chapter 2 operations as einsum and check each against\n", + "# TODO 3: Rewrite these Chapter 2 operations as einsum and check each against\n", "# NumPy:\n", - "# (a) trace (eq 2.48)\n", - "# (b) transpose (eq 2.3)\n", - "# (c) matrix product (eq 2.5)" + "#\n", + "# (a) trace of A -> scalar\n", + "# (b) transpose of A -> (2, 2)\n", + "# (c) matrix product A @ B -> (2, 2)\n", + "#\n", + "# For each expression, say which index is:\n", + "# - summed over,\n", + "# - only relabelled/reordered,\n", + "# - or kept." ], "id": "s06-08" }, @@ -155,31 +303,100 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "print(np.einsum('ii->', A), np.trace(A)) # trace\n", - "print(np.einsum('ij->ji', A), A.T, sep=\"\\n\") # transpose\n", - "print(np.einsum('ik,kj->ij', A, B), A @ B, sep=\"\\n\") # matrix product\n", - "\n", - "for got, want in [(np.einsum('ii->', A), np.trace(A)),\n", - " (np.einsum('ij->ji', A), A.T),\n", - " (np.einsum('ik,kj->ij', A, B), A @ B)]:\n", - " assert np.allclose(got, want)\n", - "print(\"all three agree\")\n", - "\n", - "# Trace: the repeated `i` with nothing after the arrow sums the diagonal.\n", - "# Transpose: no index is summed at all — einsum is just relabelling axes.\n", - "# Matrix product: `k` is shared and dropped, so it is the contracted axis." + "trace_e = np.einsum('ii->', A)\n", + "transpose_e = np.einsum('ij->ji', A)\n", + "product_e = np.einsum('ik,kj->ij', A, B)\n", + "\n", + "print(\"A came from real digit label\", digits.target[0])\n", + "print(A)\n", + "print(\"B came from real digit label\", digits.target[1])\n", + "print(B)\n", + "print(\"\\ntrace:\", trace_e, \"| NumPy:\", np.trace(A))\n", + "print(\"transpose:\\n\", transpose_e)\n", + "print(\"matrix product:\\n\", product_e)\n", + "\n", + "assert np.allclose(trace_e, np.trace(A))\n", + "assert np.allclose(transpose_e, A.T)\n", + "assert np.allclose(product_e, A @ B)\n", + "print(\"\\nall three einsum results agree with NumPy\")\n", + "\n", + "# Context: show the real 8x8 source digits and highlight the 2x2 patches.\n", + "fig, axes = plt.subplots(1, 2, figsize=(5, 2.5))\n", + "for ax, idx, name in zip(axes, [0, 1], [\"A\", \"B\"]):\n", + " ax.imshow(digit_images[idx], cmap=\"gray_r\", interpolation=\"nearest\", vmin=0, vmax=16)\n", + " ax.add_patch(\n", + " plt.Rectangle((1.5, 1.5), 2, 2, fill=False, linewidth=2)\n", + " )\n", + " ax.set_title(f\"{name}: real digit label {digits.target[idx]}\\nred box = 2×2 patch\")\n", + " ax.axis(\"off\")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "operation = widgets.ToggleButtons(\n", + " options=[\n", + " (\"Trace / Traza\", \"trace\"),\n", + " (\"Transpose / Transpuesta\", \"transpose\"),\n", + " (\"Matrix product / Producto\", \"product\"),\n", + " ],\n", + " value=\"product\",\n", + " description=\"\",\n", + ")\n", + "\n", + "def explain_operation(op):\n", + " if op == \"trace\":\n", + " expr = \"ii->\"\n", + " result = np.einsum(\"ii->\", A)\n", + " en = \"i is repeated and disappears -> sum the diagonal.\"\n", + " es = \"i se repite y desaparece -> se suma la diagonal.\"\n", + " print(\"A =\\n\", A)\n", + " print(f\"einsum('{expr}', A) =\", result)\n", + " elif op == \"transpose\":\n", + " expr = \"ij->ji\"\n", + " result = np.einsum(\"ij->ji\", A)\n", + " en = \"No index disappears -> only reorder the axes.\"\n", + " es = \"Ningún índice desaparece -> solo se reordenan los ejes.\"\n", + " print(\"A =\\n\", A)\n", + " print(f\"einsum('{expr}', A) =\\n\", result)\n", + " else:\n", + " expr = \"ik,kj->ij\"\n", + " result = np.einsum(\"ik,kj->ij\", A, B)\n", + " en = \"k is shared and disappears -> contract k; keep i and j.\"\n", + " es = \"k es compartido y desaparece -> contrae k; conserva i y j.\"\n", + " print(\"A =\\n\", A)\n", + " print(\"B =\\n\", B)\n", + " print(f\"einsum('{expr}', A, B) =\\n\", result)\n", + "\n", + " print(\"EN:\", en)\n", + " print(\"ES:\", es)\n", + "\n", + "operation_output = widgets.interactive_output(\n", + " explain_operation,\n", + " {\"op\": operation},\n", + ")\n", + "\n", + "display(\n", + " widgets.VBox([\n", + " widgets.HTML(\n", + " \"Interactive index explorer / Explorador interactivo de índices: \"\n", + " \"choose an operation and identify the disappearing index. / \"\n", + " \"elige una operación e identifica el índice que desaparece.\"\n", + " ),\n", + " operation,\n", + " operation_output,\n", + " ])\n", + ")\n" ], "id": "s06-09" }, @@ -187,13 +404,47 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercise 3 — every pair of 1797 images, in one call\n", + "## Exercise 3 — 3,229,209 similarities from 1,797 real digit images\n", + "\n", + "### Why do these digit images look pixelated?\n", + "\n", + "This is **intentional and important**: the original `sklearn` digits dataset stores each handwritten digit at only **8×8 pixels**.\n", + "\n", + "So each image has exactly:\n", + "\n", + "`8 × 8 = 64 real measured pixel features`\n", + "\n", + "The blocky appearance is **not a bad download, compression error or broken image**. It is the original resolution of the dataset. We display it with `interpolation=\"nearest\"` so the notebook does **not invent smooth pixels that were never measured**.\n", + "\n", + "That low resolution is actually useful here: after flattening, every digit becomes a 64-dimensional vector, so the contraction is easy to connect directly to the pixels.\n", + "\n", + "### What does `id,jd->ij` mean?\n", + "\n", + "- `i`: query-image index — kept\n", + "- `j`: candidate-image index — kept\n", + "- `d`: 64 pixel features — **disappears**, so it is contracted\n", + "\n", + "The result therefore has shape `(1797, 1797)`: one similarity score for every pair of real digit images.\n", + "\n", + "### Raw dot product vs cosine similarity\n", + "\n", + "Both use the **same `einsum` contraction**. The difference is what happens before it:\n", "\n", - "> 🇪🇸 Todos los pares de 1797 imágenes, en una sola llamada.\n", + "- **Raw dot product** also rewards vector magnitude — roughly, how much total “ink” or intensity an image has.\n", + "- **Cosine similarity** first normalizes each 64-pixel vector to unit length, so the comparison focuses more on the **pattern/direction** of the pixels.\n", "\n", - "This one matters beyond the exercise: it is the same operation a search engine\n", - "runs, and it is the bridge to the distance and similarity questions in the\n", - "Kahoot below." + "For query image `14` (true label `4`), the strongest raw-dot match is a `1`, while the strongest cosine matches are `4`s. That is a real example of why preprocessing changes the meaning of “similar”.\n", + "\n", + "### What should you try?\n", + "\n", + "Use the retrieval explorer:\n", + "\n", + "1. Move **Query / Consulta** to choose any of the 1,797 real digits.\n", + "2. Switch **Cosine / Coseno** ↔ **Raw dot / P. punto**.\n", + "3. Change **Top k / Vecinos**.\n", + "4. Compare how many retrieved images have the same label as the query.\n", + "\n", + "> 🇪🇸 **¿Por qué se ven pixelados los dígitos?** Porque el dataset original guarda cada dígito con solo **8×8 píxeles**. No es mala calidad de descarga ni un error: son exactamente **64 mediciones reales** por imagen. Se muestran con interpolación `nearest` para no inventar píxeles suaves que nunca fueron observados. En `id,jd->ij`, `d` representa esas 64 características y desaparece; `i` y `j` permanecen, por eso obtenemos una matriz `(1797,1797)` con una similitud para cada par. El producto punto crudo también depende de la magnitud/intensidad; el coseno normaliza primero y compara más la forma del patrón. Usa el explorador para cambiar consulta, métrica y número de vecinos.\n" ], "id": "s06-10" }, @@ -203,13 +454,21 @@ "metadata": {}, "outputs": [], "source": [ - "# TODO 4: Flatten the digits to (1797, 64) and compute the (1797, 1797)\n", - "# similarity matrix between every pair of digit images with one einsum.\n", + "# TODO 4: Reshape all real digit images to D with shape (1797, 64).\n", + "#\n", + "# TODO 5: Compute every raw dot-product similarity with ONE einsum:\n", + "# 'id,jd->ij' -> expected shape (1797, 1797).\n", + "#\n", + "# TODO 6: Normalize every row of D to unit length, then repeat the SAME einsum\n", + "# to obtain cosine similarity C.\n", "#\n", - "# Then, for the quiz: normalize each row to unit length first and do it\n", - "# again. That second version is COSINE SIMILARITY — the dot product\n", - "# divided by the two norms. The unnormalized one is dominated by how\n", - "# much ink each digit has, not by its shape." + "# TODO 7: Use query_idx = 14 (true label 4).\n", + "# - exclude the query from matching itself,\n", + "# - find the best raw-dot-product match,\n", + "# - find the five best cosine-similarity matches,\n", + "# - compare their labels with digits.target[query_idx].\n", + "#\n", + "# Predict first: which index disappears in 'id,jd->ij'?" ], "id": "s06-11" }, @@ -217,34 +476,168 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "D = load_digits().images.reshape(1797, -1) # (1797, 64)\n", + "D = digit_images.reshape(len(digit_images), -1) # (1797, 64)\n", "\n", - "S = np.einsum('id,jd->ij', D, D) # (1797, 1797)\n", - "print(S.shape, S.size) # 3,229,209 pairwise scores\n", + "S = np.einsum('id,jd->ij', D, D) # (1797, 1797)\n", + "print(\"raw similarity matrix:\", S.shape, \"=\", S.size, \"pairwise scores\")\n", "\n", - "# Cosine similarity: the same contraction on unit-length rows.\n", "norms = np.linalg.norm(D, axis=1, keepdims=True)\n", "Dn = D / np.where(norms == 0, 1.0, norms)\n", "C = np.einsum('id,jd->ij', Dn, Dn)\n", - "print(C.diagonal()[:3]) # ~1.0 — each digit matches itself\n", "\n", - "# EUCLIDEAN DISTANCE is the square root of summed squared differences, and it is\n", - "# built from the same contraction:\n", - "sq = (D ** 2).sum(1)\n", - "dist = np.sqrt(np.maximum(sq[:, None] + sq[None, :] - 2 * S, 0))\n", - "print(np.round(dist[0, :4], 1))" + "assert S.shape == (1797, 1797)\n", + "assert C.shape == (1797, 1797)\n", + "assert np.allclose(C.diagonal(), 1.0)\n", + "\n", + "query_idx = 14\n", + "query_label = digits.target[query_idx]\n", + "\n", + "raw_scores = S[query_idx].copy()\n", + "cos_scores = C[query_idx].copy()\n", + "raw_scores[query_idx] = -np.inf\n", + "cos_scores[query_idx] = -np.inf\n", + "\n", + "raw_top1 = int(np.argmax(raw_scores))\n", + "cos_top5 = np.argsort(cos_scores)[-5:][::-1]\n", + "\n", + "print(\"query index / label:\", query_idx, \"/\", query_label)\n", + "print(\"best RAW dot-product match:\", raw_top1,\n", + " \"label\", digits.target[raw_top1],\n", + " \"score\", round(float(raw_scores[raw_top1]), 3))\n", + "print(\"top 5 COSINE matches:\", cos_top5.tolist())\n", + "print(\"top 5 COSINE labels:\", digits.target[cos_top5].tolist())\n", + "print(\"top 5 COSINE scores:\", np.round(cos_scores[cos_top5], 3).tolist())\n", + "\n", + "# Static retrieval example. nearest preserves the original 8x8 measurements.\n", + "fig, axes = plt.subplots(1, 6, figsize=(11, 2.5))\n", + "axes[0].imshow(\n", + " digit_images[query_idx], cmap=\"gray_r\",\n", + " interpolation=\"nearest\", vmin=0, vmax=16\n", + ")\n", + "axes[0].set_title(f\"query / consulta\\nlabel {query_label}\")\n", + "\n", + "for ax, idx in zip(axes[1:], cos_top5):\n", + " ax.imshow(\n", + " digit_images[idx], cmap=\"gray_r\",\n", + " interpolation=\"nearest\", vmin=0, vmax=16\n", + " )\n", + " ax.set_title(f\"label {digits.target[idx]}\\ncos={C[query_idx, idx]:.3f}\")\n", + "\n", + "for ax in axes:\n", + " ax.axis(\"off\")\n", + "\n", + "fig.suptitle(\n", + " \"Real 8×8 digits: one contraction -> all-pairs similarity -> retrieval\"\n", + ")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "query_slider = widgets.IntSlider(\n", + " value=14, min=0, max=len(digit_images) - 1, step=1,\n", + " description=\"Query:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"55px\"},\n", + ")\n", + "similarity_toggle = widgets.ToggleButtons(\n", + " options=[(\"Cosine / Coseno\", \"cosine\"), (\"Raw dot / P. punto\", \"raw\")],\n", + " value=\"cosine\",\n", + " description=\"\",\n", + ")\n", + "k_slider = widgets.IntSlider(\n", + " value=5, min=1, max=8, step=1,\n", + " description=\"Top k:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"45px\"},\n", + ")\n", + "\n", + "def explore_retrieval(query, metric, k):\n", + " matrix = C if metric == \"cosine\" else S\n", + " scores = matrix[query].copy()\n", + " scores[query] = -np.inf\n", + "\n", + " top = np.argsort(scores)[-k:][::-1]\n", + " q_label = int(digits.target[query])\n", + "\n", + " fig, axes = plt.subplots(1, k + 1, figsize=(2.05 * (k + 1), 2.75))\n", + " axes = np.atleast_1d(axes)\n", + "\n", + " axes[0].imshow(\n", + " digit_images[query], cmap=\"gray_r\",\n", + " interpolation=\"nearest\", vmin=0, vmax=16\n", + " )\n", + " axes[0].set_title(f\"query {query}\\nlabel {q_label}\")\n", + " axes[0].axis(\"off\")\n", + "\n", + " for ax, idx in zip(axes[1:], top):\n", + " ax.imshow(\n", + " digit_images[idx], cmap=\"gray_r\",\n", + " interpolation=\"nearest\", vmin=0, vmax=16\n", + " )\n", + " score_name = \"cos\" if metric == \"cosine\" else \"dot\"\n", + " ax.set_title(\n", + " f\"idx {idx}\\nlabel {digits.target[idx]}\\n{score_name}={scores[idx]:.3f}\"\n", + " )\n", + " ax.axis(\"off\")\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " labels = digits.target[top].astype(int).tolist()\n", + " same_label = sum(label == q_label for label in labels)\n", + "\n", + " if metric == \"cosine\":\n", + " en = \"Cosine normalizes magnitude first, so the comparison emphasizes pixel-pattern direction.\"\n", + " es = \"El coseno normaliza la magnitud primero, por lo que enfatiza la dirección del patrón de píxeles.\"\n", + " else:\n", + " en = \"Raw dot product also rewards magnitude/intensity, so visually different labels can rank highly.\"\n", + " es = \"El producto punto crudo también premia magnitud/intensidad, por eso pueden aparecer etiquetas distintas.\"\n", + "\n", + " print(\n", + " f\"metric={metric} | query label={q_label} | \"\n", + " f\"top-{k} labels={labels} | same-label matches={same_label}/{k}\"\n", + " )\n", + " print(\"EN:\", en)\n", + " print(\"ES:\", es)\n", + " print(\"Index rule / Regla: id,jd->ij | d disappears / desaparece; i and j survive / permanecen.\")\n", + " print(\"Display note / Nota visual: the data are truly 8×8; the pixelated look is the original resolution.\")\n", + "\n", + "retrieval_output = widgets.interactive_output(\n", + " explore_retrieval,\n", + " {\n", + " \"query\": query_slider,\n", + " \"metric\": similarity_toggle,\n", + " \"k\": k_slider,\n", + " },\n", + ")\n", + "\n", + "display(\n", + " widgets.VBox([\n", + " widgets.HTML(\n", + " \"Interactive retrieval explorer / Explorador interactivo: \"\n", + " \"choose a real query digit, change the metric, and inspect its neighbours. / \"\n", + " \"elige un dígito real, cambia la métrica y observa sus vecinos.\"\n", + " ),\n", + " widgets.HTML(\n", + " \"Image quality note / Nota de calidad: these are original 8×8 measurements; \"\n", + " \"the blocky pixels are the data, not an error. / Son mediciones originales 8×8; \"\n", + " \"los bloques son los datos, no un error.\"\n", + " ),\n", + " widgets.HBox([query_slider, similarity_toggle, k_slider]),\n", + " retrieval_output,\n", + " ])\n", + ")\n" ], "id": "s06-12" }, @@ -254,18 +647,26 @@ "source": [ "## What just happened\n", "\n", - "`c` appears in the inputs but not after the arrow, so it is **summed over** —\n", - "that is the contraction. `n`, `h`, `w` appear after the arrow, so they are\n", - "**kept**. Adding a batch axis costs exactly one letter.\n", + "You used **one index rule** three times, but each exercise gave the rule a different meaning:\n", + "\n", + "1. **Real microscopy image — `hwc,c->hw`** \n", + " `c` disappeared, so three colour measurements became one grayscale value at every pixel. The sliders changed the weights, not the rule.\n", "\n", - "This is why `einsum` is worth learning: **the same expression works for one image\n", - "or for a million**, and it reads like the mathematics in Chapter 2.\n", + "2. **Real digit-pixel matrices — `ik,kj->ij`** \n", + " `k` disappeared, so the shared dimension was multiplied and summed. Trace and transpose showed that `einsum` can also sum repeated indices or simply reorder them.\n", "\n", - "> 🇪🇸 La misma expresión sirve para una imagen o para un millón, y se lee como\n", - "> las matemáticas del capítulo 2.\n", + "3. **1,797 real handwritten digits — `id,jd->ij`** \n", + " `d` disappeared, so 64 real pixel measurements became one similarity score for every pair of images: **3,229,209 scores**. The interactive explorer showed that preprocessing changes what “similar” means.\n", "\n", - "Keep it in mind for section 10, where a single `einsum` string contracts three\n", - "axes at once: `'ijk,ia,jb,kc->abc'`. That is why einsum came first." + "### The sentence to remember\n", + "\n", + "> **If an index disappears after `->`, it is summed over. If it remains, it survives in the output.**\n", + "\n", + "The `8×8` digits are deliberately pixelated because **each visible square is one of the 64 measured features**. Keeping that limitation visible makes the tensor operation easier to understand.\n", + "\n", + "> 🇪🇸 Usaste **una sola regla de índices** tres veces: `c` desapareció al convertir color a gris; `k` desapareció en el producto matricial; y `d` desapareció al convertir 64 píxeles en una similitud entre dos dígitos. La frase para recordar es: **si un índice desaparece después de `->`, se suma; si permanece, sobrevive en la salida.** Los dígitos `8×8` se ven pixelados a propósito: cada cuadrado visible es una de las 64 características realmente medidas.\n", + "\n", + "Keep this rule for section 10: `'ijk,ia,jb,kc->abc'` looks longer, but the logic is exactly the same.\n" ], "id": "s06-13" }, @@ -286,7 +687,6 @@ ], "metadata": { "colab": { - "name": "06-contraction-with-einsum.ipynb", "provenance": [], "toc_visible": true }, @@ -297,6 +697,1678 @@ }, "language_info": { "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "version_major": 2, + "version_minor": 0, + "state": { + "4264d1b269004133bdd63da646b2f08f": { + "model_module": "@jupyter-widgets/controls", + "model_name": "VBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "VBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "VBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_08d53eb8a9cb447ea5281d71f4f6ec9f", + "IPY_MODEL_5abc2d6ff3784fe08d2ea57fbea39d8a", + "IPY_MODEL_ff1ae20acc844ab1bcc28c5e35498647" + ], + "layout": "IPY_MODEL_d7e1a368de9f40b5a3dc4f55ee8dabfe" + } + }, + "08d53eb8a9cb447ea5281d71f4f6ec9f": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_6540bc11246c4f798abe25f2c85668c9", + "placeholder": "​", + "style": "IPY_MODEL_6fa85a4fcd974dda9e6bb9799653f541", + "value": "Interactive lab: move R/G/B. 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Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/06-contraction-with-einsum.ipynb)\n", - "\n", - "*Part IV · exercise · 15 min*\n", - "\n", - "> 🇪🇸 **Contracción con einsum** — Aprende una sola regla de índices y compruébala sobre una imagen real, píxeles reales de dígitos manuscritos y un buscador interactivo de imágenes.\n", - "\n", - "Use one index rule, see it on real data, and then **change the inputs interactively** to test whether you really understand which index disappears.\n", - "\n", - "## What you will be able to do\n", - "\n", - "- Explain the `einsum` rule: indices missing after `->` are summed over; indices that remain are kept.\n", - "- Contract the colour axis of a real microscopy image and explain what the RGB sliders change — and what they do **not** change.\n", - "- Read trace, transpose and matrix multiplication as index operations on pixel patches cut from real handwritten digits.\n", - "- Build all 3,229,209 pairwise similarities between 1,797 real digit images and explore retrieval with cosine similarity versus raw dot product.\n" - ], - "id": "s06-00" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s06-01" - }, - "source": [ - "## Setup\n", - "\n", - "Run this first. It loads two **real datasets already packaged with standard Python libraries**: a colour microscopy image from `skimage.data` and the 1,797-image handwritten-digits dataset from `sklearn`.\n", - "\n", - "> 🇪🇸 Ejecuta esto primero. Carga dos **conjuntos de datos reales incluidos en librerías estándar de Python**: una imagen de microscopía a color de `skimage.data` y el conjunto de 1.797 imágenes de dígitos manuscritos de `sklearn`.\n", - "\n", - "No random synthetic dataset is used. The grayscale weight vector `w` is a transformation rule, not an observed dataset." - ], - "id": "s06-01" - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "s06-02", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "outputId": "ae45a1d3-f3de-4bc8-ece3-1f7372e3a908" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "photo: (512, 512, 3) batch: (2, 512, 512, 3)\n", - "digits: (1797, 8, 8) labels: (1797,)\n", - "Exercise 2 patches come from digit labels: 0 and 1\n", - "A =\n", - " [[15. 2.]\n", - " [12. 0.]]\n", - "B =\n", - " [[ 3. 15.]\n", - " [15. 16.]]\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import ipywidgets as widgets\n", - "from IPython.display import display\n", - "from sklearn.datasets import load_digits\n", - "from skimage import data\n", - "\n", - "# Enable ipywidgets in Google Colab when available.\n", - "try:\n", - " from google.colab import output\n", - " output.enable_custom_widget_manager()\n", - "except ImportError:\n", - " pass\n", - "\n", - "# Real colour images.\n", - "photo = data.immunohistochemistry().astype(float) # (512, 512, 3)\n", - "batch = np.stack([photo, data.astronaut().astype(float)]) # (2, 512, 512, 3)\n", - "w = np.array([0.2125, 0.7154, 0.0721]) # RGB -> grayscale weights\n", - "\n", - "# Real handwritten digits (UCI Optical Recognition dataset, packaged by sklearn).\n", - "digits = load_digits()\n", - "digit_images = digits.images.astype(float) # (1797, 8, 8)\n", - "\n", - "# Small 2x2 matrices for Exercise 2 are NOT invented numbers:\n", - "# they are central pixel patches from two real digit images.\n", - "A = digit_images[0, 2:4, 2:4]\n", - "B = digit_images[1, 2:4, 2:4]\n", - "\n", - "print(\"photo:\", photo.shape, \"batch:\", batch.shape)\n", - "print(\"digits:\", digit_images.shape, \"labels:\", digits.target.shape)\n", - "print(\"Exercise 2 patches come from digit labels:\", digits.target[0], \"and\", digits.target[1])\n", - "print(\"A =\\n\", A)\n", - "print(\"B =\\n\", B)\n" - ], - "id": "s06-02" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s06-03" - }, - "source": [ - "## Why this matters\n", - "\n", - "A contraction is not just a matrix-algebra trick. The same operation appears when an image model removes a colour axis, when linear algebra multiplies matrices, and when a retrieval system compares one query vector with thousands or millions of candidates.\n", - "\n", - "> 🇪🇸 Una contracción no es solo un truco de álgebra matricial. La misma operación aparece cuando un modelo elimina el eje de color de una imagen, cuando el álgebra lineal multiplica matrices y cuando un sistema de búsqueda compara un vector consulta con miles o millones de candidatos.\n", - "\n", - "### One rule for the whole notebook\n", - "\n", - "If an index appears in the inputs but **not** after `->`, it is **summed over**. If it appears after `->`, it is **kept**.\n", - "\n", - "Examples:\n", - "\n", - "- `hwc,c->hw`: `c` disappears → contract colour.\n", - "- `ik,kj->ij`: `k` disappears → matrix multiplication.\n", - "- `id,jd->ij`: `d` disappears → every pair of row vectors gets one similarity score.\n", - "\n", - "### Predict → Run → Explain\n", - "\n", - "Before each exercise, predict **which index disappears and what the output shape must be**. After running the code, explain what information the contraction kept.\n", - "\n", - "> 🇪🇸 **Predice → Ejecuta → Explica:** antes de cada ejercicio, predice **qué índice desaparece y cuál debe ser la forma de salida**. Después de ejecutar, explica qué información conservó la contracción." - ], - "id": "s06-03" + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 06 · Contraction with einsum\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/06-contraction-with-einsum.ipynb)\n", + "\n", + "*Part IV · exercise · 15 min*\n", + "\n", + "> 🇪🇸 **Contracción con einsum** — Aprender una sola regla de índices, comprobarla con datos reales y cambiar las entradas de forma interactiva para identificar qué índice desaparece.\n", + "\n", + "Use one index rule on real data, then change the inputs interactively to test which index disappears.\n", + "\n", + "## What you will be able to do\n", + "\n", + "- Explain the `einsum` rule: indices missing after `->` are summed over; indices that remain are kept.\n", + "- Contract the colour axis of a real microscopy image and explain what the RGB sliders change — and what they do not change.\n", + "- Read trace, transpose and matrix multiplication as index operations on pixel patches cut from real handwritten digits.\n", + "- Build all 3,229,209 pairwise similarities between 1,797 real digit images and explore retrieval with cosine similarity versus raw dot product." + ], + "id": "s06-00" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "Run this first. It installs and imports everything this notebook needs, and nothing else.\n", + "\n", + "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." + ], + "id": "s06-01" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import ipywidgets as widgets\n", + "from IPython.display import display\n", + "from sklearn.datasets import load_digits\n", + "from skimage import data\n", + "\n", + "# Enable ipywidgets in Google Colab when available.\n", + "try:\n", + " from google.colab import output\n", + " output.enable_custom_widget_manager()\n", + "except ImportError:\n", + " pass\n", + "\n", + "# Real colour images.\n", + "photo = data.immunohistochemistry().astype(float) # (512, 512, 3)\n", + "batch = np.stack([photo, data.astronaut().astype(float)]) # (2, 512, 512, 3)\n", + "w = np.array([0.2125, 0.7154, 0.0721]) # RGB -> grayscale weights\n", + "\n", + "# Real handwritten digits (UCI Optical Recognition dataset, packaged by sklearn).\n", + "digits = load_digits()\n", + "digit_images = digits.images.astype(float) # (1797, 8, 8)\n", + "\n", + "# Small 2x2 matrices for Exercise 2 are NOT invented numbers:\n", + "# they are central pixel patches from two real digit images.\n", + "A = digit_images[0, 2:4, 2:4]\n", + "B = digit_images[1, 2:4, 2:4]\n", + "\n", + "print(\"photo:\", photo.shape, \"batch:\", batch.shape)\n", + "print(\"digits:\", digit_images.shape, \"labels:\", digits.target.shape)\n", + "print(\n", + " \"Exercise 2 patches come from digit labels:\",\n", + " digits.target[0],\n", + " \"and\",\n", + " digits.target[1],\n", + ")\n", + "print(\"A =\\n\", A)\n", + "print(\"B =\\n\", B)" + ], + "id": "s06-02" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Why this matters\n", + "\n", + "A contraction is not just a matrix-algebra trick. The same operation appears when an image model removes a colour axis, when linear algebra multiplies matrices, and when a retrieval system compares one query vector with thousands or millions of candidates.\n", + "\n", + "> 🇪🇸 Una contracción no es solo un truco de álgebra matricial. La misma operación aparece cuando un modelo elimina el eje de color de una imagen, cuando el álgebra lineal multiplica matrices y cuando un sistema de búsqueda compara un vector consulta con miles o millones de candidatos.\n", + "\n", + "### One rule for the whole notebook\n", + "\n", + "If an index appears in the inputs but **not** after `->`, it is **summed over**. If it appears after `->`, it is **kept**.\n", + "\n", + "Examples:\n", + "\n", + "- `hwc,c->hw`: `c` disappears → contract colour.\n", + "- `ik,kj->ij`: `k` disappears → matrix multiplication.\n", + "- `id,jd->ij`: `d` disappears → every pair of row vectors gets one similarity score.\n", + "\n", + "### Predict → Run → Explain\n", + "\n", + "Before each exercise, predict **which index disappears and what the output shape must be**. After running the code, explain what information the contraction kept.\n", + "\n", + "> 🇪🇸 **Predice → Ejecuta → Explica:** antes de cada ejercicio, predice **qué índice desaparece y cuál debe ser la forma de salida**. Después de ejecutar, explica qué información conservó la contracción." + ], + "id": "s06-03" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 1 — contract the colour axis of a real image\n", + "\n", + "### What are you looking at?\n", + "\n", + "`photo` is a **real microscopy image** from `skimage.data.immunohistochemistry()` with shape `(512, 512, 3)`:\n", + "\n", + "- `h = 512`: image rows / height\n", + "- `w = 512`: image columns / width\n", + "- `c = 3`: red, green and blue channels\n", + "\n", + "The vector `w = [0.2125, 0.7154, 0.0721]` is **not another dataset**. It is a transformation rule: three weights telling us how much each colour channel contributes to the grayscale result.\n", + "\n", + "### What is the mathematical idea?\n", + "\n", + "In:\n", + "\n", + "`hwc,c->hw`\n", + "\n", + "the index `c` appears in the inputs but **disappears after `->`**, so `einsum` multiplies each colour channel by its weight and **sums over colour**. The `h` and `w` indices survive, so the output remains an image of shape `(512, 512)`.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Predict the output shape before running anything.\n", + "2. Solve the TODO with one `einsum`.\n", + "3. Open the folded solution and move the **R/G/B sliders**.\n", + "4. Notice that the picture changes, but the index rule `hwc,c->hw` does not.\n", + "\n", + "> 🇪🇸 **¿Qué estás viendo?** `photo` es una imagen real de microscopía de forma `(512, 512, 3)`: alto, ancho y tres canales RGB. El vector `w` no es un conjunto de datos; es una regla de transformación. En `hwc,c->hw`, el índice `c` desaparece después de `->`, por eso se multiplica cada canal por su peso y luego se suma sobre color. **Prueba:** predice la forma, resuelve el `einsum` y después mueve los sliders R/G/B. La imagen cambia, pero la regla de índices permanece igual.\n" + ], + "id": "s06-04" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO 1: Use einsum to convert `photo` to grayscale by contracting the colour\n", + "# axis against w. Expected result: (512, 512).\n", + "#\n", + "# TODO 2: Do the same for the whole real-image batch in ONE einsum call.\n", + "# Expected result: (2, 512, 512).\n", + "#\n", + "# Explain in one sentence:\n", + "# - which index is contracted?\n", + "# - which indices survive?" + ], + "id": "s06-05" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true }, - { - "cell_type": "markdown", - "metadata": { - "id": "s06-04" - }, - "source": [ - "## Exercise 1 — contract the colour axis of a real image\n", - "\n", - "### What are you looking at?\n", - "\n", - "`photo` is a **real microscopy image** from `skimage.data.immunohistochemistry()` with shape `(512, 512, 3)`:\n", - "\n", - "- `h = 512`: image rows / height\n", - "- `w = 512`: image columns / width\n", - "- `c = 3`: red, green and blue channels\n", - "\n", - "The vector `w = [0.2125, 0.7154, 0.0721]` is **not another dataset**. It is a transformation rule: three weights telling us how much each colour channel contributes to the grayscale result.\n", - "\n", - "### What is the mathematical idea?\n", - "\n", - "In:\n", - "\n", - "`hwc,c->hw`\n", - "\n", - "the index `c` appears in the inputs but **disappears after `->`**, so `einsum` multiplies each colour channel by its weight and **sums over colour**. The `h` and `w` indices survive, so the output remains an image of shape `(512, 512)`.\n", - "\n", - "### What should you try?\n", - "\n", - "1. Predict the output shape before running anything.\n", - "2. Solve the TODO with one `einsum`.\n", - "3. Open the folded solution and move the **R/G/B sliders**.\n", - "4. Notice that the picture changes, but the index rule `hwc,c->hw` does not.\n", - "\n", - "> 🇪🇸 **¿Qué estás viendo?** `photo` es una imagen real de microscopía de forma `(512, 512, 3)`: alto, ancho y tres canales RGB. El vector `w` no es un conjunto de datos; es una regla de transformación. En `hwc,c->hw`, el índice `c` desaparece después de `->`, por eso se multiplica cada canal por su peso y luego se suma sobre color. **Prueba:** predice la forma, resuelve el `einsum` y después mueve los sliders R/G/B. La imagen cambia, pero la regla de índices permanece igual.\n" - ], - "id": "s06-04" + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "gray = np.einsum('hwc,c->hw', photo, w) # (512, 512)\n", + "gray_batch = np.einsum('nhwc,c->nhw', batch, w) # (2, 512, 512)\n", + "\n", + "print(\"single image:\", photo.shape, \"->\", gray.shape)\n", + "print(\"batch:\", batch.shape, \"->\", gray_batch.shape)\n", + "print(\"contracted index: c | kept indices: h,w (and n for the batch)\")\n", + "\n", + "# Static before/after visual: real photograph in, computed contraction out.\n", + "fig, axes = plt.subplots(1, 2, figsize=(8, 4))\n", + "axes[0].imshow(photo / 255)\n", + "axes[0].set_title(\"real input — axes h, w, c\")\n", + "axes[1].imshow(gray, cmap=\"gray\")\n", + "axes[1].set_title(\"'hwc,c->hw' — c is gone\")\n", + "for ax in axes:\n", + " ax.axis(\"off\")\n", + "fig.suptitle(\"c is missing after ->, so colour is SUMMED OVER; h and w are KEPT\")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Interactive lab: change the contraction weights while keeping the SAME real image\n", + "# and the SAME index rule. continuous_update=False keeps Colab responsive.\n", + "r_slider = widgets.FloatSlider(\n", + " value=float(w[0]), min=0.0, max=1.0, step=0.025,\n", + " description=\"R\", readout_format=\".3f\", continuous_update=False\n", + ")\n", + "g_slider = widgets.FloatSlider(\n", + " value=float(w[1]), min=0.0, max=1.0, step=0.025,\n", + " description=\"G\", readout_format=\".3f\", continuous_update=False\n", + ")\n", + "b_slider = widgets.FloatSlider(\n", + " value=float(w[2]), min=0.0, max=1.0, step=0.025,\n", + " description=\"B\", readout_format=\".3f\", continuous_update=False\n", + ")\n", + "\n", + "def explore_colour_contraction(r, g, b):\n", + " weights = np.array([r, g, b], dtype=float)\n", + " live_gray = np.einsum('hwc,c->hw', photo, weights)\n", + "\n", + " fig, axes = plt.subplots(1, 2, figsize=(7.5, 3.3))\n", + " axes[0].imshow(photo / 255)\n", + " axes[0].set_title(\"same real input\")\n", + " axes[1].imshow(live_gray, cmap=\"gray\")\n", + " axes[1].set_title(f\"R={r:.3f}, G={g:.3f}, B={b:.3f}\")\n", + " for ax in axes:\n", + " ax.axis(\"off\")\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " print(\"EN: c disappears -> colour is contracted; h and w survive.\")\n", + " print(\"ES: c desaparece -> el color se contrae; h y w permanecen.\")\n", + " print(f\"einsum: hwc,c->hw | weight sum / suma de pesos = {weights.sum():.3f}\")\n", + "\n", + "rgb_output = widgets.interactive_output(\n", + " explore_colour_contraction,\n", + " {\"r\": r_slider, \"g\": g_slider, \"b\": b_slider},\n", + ")\n", + "\n", + "display(\n", + " widgets.VBox([\n", + " widgets.HTML(\n", + " \"Interactive lab: move R/G/B. \"\n", + " \"The pixels stay real; only the contraction weights change.\"\n", + " ),\n", + " widgets.HBox([r_slider, g_slider, b_slider]),\n", + " rgb_output,\n", + " ])\n", + ")\n" + ], + "id": "s06-06" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 2 — Chapter 2 operations on real digit pixels\n", + "\n", + "### Where do matrices `A` and `B` come from?\n", + "\n", + "They are **not hand-written toy numbers**. Each `2×2` matrix is the central pixel patch of one real `8×8` handwritten digit from `sklearn.datasets.load_digits()`.\n", + "\n", + "Pixel intensities in this dataset run from **0 to 16**. The matrices are intentionally tiny so you can check the arithmetic by hand while still operating on observed data.\n", + "\n", + "### What are the three operations teaching?\n", + "\n", + "- **Trace — `ii->`**: the repeated `i` disappears, so the diagonal is summed to one scalar.\n", + "- **Transpose — `ij->ji`**: no index disappears; the axes are only reordered.\n", + "- **Matrix product — `ik,kj->ij`**: the shared `k` disappears, so `k` is the contracted dimension; `i` and `j` survive.\n", + "\n", + "### What should you try?\n", + "\n", + "Use the **operation selector** after solving the TODO. Switch among trace, transpose and matrix product and say aloud:\n", + "\n", + "> “Which index disappeared? Which indices survived?”\n", + "\n", + "That sentence is more important than memorising the strings.\n", + "\n", + "> 🇪🇸 **¿De dónde salen `A` y `B`?** No son números inventados: cada matriz `2×2` es un recorte central de píxeles de un dígito manuscrito real `8×8`. Las intensidades van de **0 a 16**. **Traza:** `ii->` elimina `i` y suma la diagonal. **Transpuesta:** `ij->ji` no elimina índices, solo cambia su orden. **Producto matricial:** `ik,kj->ij` elimina `k`, por lo que `k` es la dimensión contraída. Usa el selector y pregúntate siempre: **¿qué índice desapareció y cuáles sobrevivieron?**\n" + ], + "id": "s06-07" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO 3: Rewrite these Chapter 2 operations as einsum and check each against\n", + "# NumPy:\n", + "#\n", + "# (a) trace of A -> scalar\n", + "# (b) transpose of A -> (2, 2)\n", + "# (c) matrix product A @ B -> (2, 2)\n", + "#\n", + "# For each expression, say which index is:\n", + "# - summed over,\n", + "# - only relabelled/reordered,\n", + "# - or kept." + ], + "id": "s06-08" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "s06-05" - }, - "outputs": [], - "source": [ - "# TODO 1: Use einsum to convert `photo` to grayscale by contracting the colour\n", - "# axis against w. Expected result: (512, 512).\n", - "#\n", - "# TODO 2: Do the same for the whole real-image batch in ONE einsum call.\n", - "# Expected result: (2, 512, 512).\n", - "#\n", - "# Explain in one sentence:\n", - "# - which index is contracted?\n", - "# - which indices survive?" - ], - "id": "s06-05" + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "trace_e = np.einsum('ii->', A)\n", + "transpose_e = np.einsum('ij->ji', A)\n", + "product_e = np.einsum('ik,kj->ij', A, B)\n", + "\n", + "print(\"A came from real digit label\", digits.target[0])\n", + "print(A)\n", + "print(\"B came from real digit label\", digits.target[1])\n", + "print(B)\n", + "print(\"\\ntrace:\", trace_e, \"| NumPy:\", np.trace(A))\n", + "print(\"transpose:\\n\", transpose_e)\n", + "print(\"matrix product:\\n\", product_e)\n", + "\n", + "assert np.allclose(trace_e, np.trace(A))\n", + "assert np.allclose(transpose_e, A.T)\n", + "assert np.allclose(product_e, A @ B)\n", + "print(\"\\nall three einsum results agree with NumPy\")\n", + "\n", + "# Context: show the real 8x8 source digits and highlight the 2x2 patches.\n", + "fig, axes = plt.subplots(1, 2, figsize=(5, 2.5))\n", + "for ax, idx, name in zip(axes, [0, 1], [\"A\", \"B\"]):\n", + " ax.imshow(digit_images[idx], cmap=\"gray_r\", interpolation=\"nearest\", vmin=0, vmax=16)\n", + " ax.add_patch(\n", + " plt.Rectangle((1.5, 1.5), 2, 2, fill=False, linewidth=2)\n", + " )\n", + " ax.set_title(f\"{name}: real digit label {digits.target[idx]}\\nred box = 2×2 patch\")\n", + " ax.axis(\"off\")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "operation = widgets.ToggleButtons(\n", + " options=[\n", + " (\"Trace / Traza\", \"trace\"),\n", + " (\"Transpose / Transpuesta\", \"transpose\"),\n", + " (\"Matrix product / Producto\", \"product\"),\n", + " ],\n", + " value=\"product\",\n", + " description=\"\",\n", + ")\n", + "\n", + "def explain_operation(op):\n", + " if op == \"trace\":\n", + " expr = \"ii->\"\n", + " result = np.einsum(\"ii->\", A)\n", + " en = \"i is repeated and disappears -> sum the diagonal.\"\n", + " es = \"i se repite y desaparece -> se suma la diagonal.\"\n", + " print(\"A =\\n\", A)\n", + " print(f\"einsum('{expr}', A) =\", result)\n", + " elif op == \"transpose\":\n", + " expr = \"ij->ji\"\n", + " result = np.einsum(\"ij->ji\", A)\n", + " en = \"No index disappears -> only reorder the axes.\"\n", + " es = \"Ningún índice desaparece -> solo se reordenan los ejes.\"\n", + " print(\"A =\\n\", A)\n", + " print(f\"einsum('{expr}', A) =\\n\", result)\n", + " else:\n", + " expr = \"ik,kj->ij\"\n", + " result = np.einsum(\"ik,kj->ij\", A, B)\n", + " en = \"k is shared and disappears -> contract k; keep i and j.\"\n", + " es = \"k es compartido y desaparece -> contrae k; conserva i y j.\"\n", + " print(\"A =\\n\", A)\n", + " print(\"B =\\n\", B)\n", + " print(f\"einsum('{expr}', A, B) =\\n\", result)\n", + "\n", + " print(\"EN:\", en)\n", + " print(\"ES:\", es)\n", + "\n", + "operation_output = widgets.interactive_output(\n", + " explain_operation,\n", + " {\"op\": operation},\n", + ")\n", + "\n", + "display(\n", + " widgets.VBox([\n", + " widgets.HTML(\n", + " \"Interactive index explorer / Explorador interactivo de índices: \"\n", + " \"choose an operation and identify the disappearing index. / \"\n", + " \"elige una operación e identifica el índice que desaparece.\"\n", + " ),\n", + " operation,\n", + " operation_output,\n", + " ])\n", + ")\n" + ], + "id": "s06-09" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 3 — 3,229,209 similarities from 1,797 real digit images\n", + "\n", + "### Why do these digit images look pixelated?\n", + "\n", + "This is **intentional and important**: the original `sklearn` digits dataset stores each handwritten digit at only **8×8 pixels**.\n", + "\n", + "So each image has exactly:\n", + "\n", + "`8 × 8 = 64 real measured pixel features`\n", + "\n", + "The blocky appearance is **not a bad download, compression error or broken image**. It is the original resolution of the dataset. We display it with `interpolation=\"nearest\"` so the notebook does **not invent smooth pixels that were never measured**.\n", + "\n", + "That low resolution is actually useful here: after flattening, every digit becomes a 64-dimensional vector, so the contraction is easy to connect directly to the pixels.\n", + "\n", + "### What does `id,jd->ij` mean?\n", + "\n", + "- `i`: query-image index — kept\n", + "- `j`: candidate-image index — kept\n", + "- `d`: 64 pixel features — **disappears**, so it is contracted\n", + "\n", + "The result therefore has shape `(1797, 1797)`: one similarity score for every pair of real digit images.\n", + "\n", + "### Raw dot product vs cosine similarity\n", + "\n", + "Both use the **same `einsum` contraction**. The difference is what happens before it:\n", + "\n", + "- **Raw dot product** also rewards vector magnitude — roughly, how much total “ink” or intensity an image has.\n", + "- **Cosine similarity** first normalizes each 64-pixel vector to unit length, so the comparison focuses more on the **pattern/direction** of the pixels.\n", + "\n", + "For query image `14` (true label `4`), the strongest raw-dot match is a `1`, while the strongest cosine matches are `4`s. That is a real example of why preprocessing changes the meaning of “similar”.\n", + "\n", + "### What should you try?\n", + "\n", + "Use the retrieval explorer:\n", + "\n", + "1. Move **Query / Consulta** to choose any of the 1,797 real digits.\n", + "2. Switch **Cosine / Coseno** ↔ **Raw dot / P. punto**.\n", + "3. Change **Top k / Vecinos**.\n", + "4. Compare how many retrieved images have the same label as the query.\n", + "\n", + "> 🇪🇸 **¿Por qué se ven pixelados los dígitos?** Porque el dataset original guarda cada dígito con solo **8×8 píxeles**. No es mala calidad de descarga ni un error: son exactamente **64 mediciones reales** por imagen. Se muestran con interpolación `nearest` para no inventar píxeles suaves que nunca fueron observados. En `id,jd->ij`, `d` representa esas 64 características y desaparece; `i` y `j` permanecen, por eso obtenemos una matriz `(1797,1797)` con una similitud para cada par. El producto punto crudo también depende de la magnitud/intensidad; el coseno normaliza primero y compara más la forma del patrón. Usa el explorador para cambiar consulta, métrica y número de vecinos.\n" + ], + "id": "s06-10" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO 4: Reshape all real digit images to D with shape (1797, 64).\n", + "#\n", + "# TODO 5: Compute every raw dot-product similarity with ONE einsum:\n", + "# 'id,jd->ij' -> expected shape (1797, 1797).\n", + "#\n", + "# TODO 6: Normalize every row of D to unit length, then repeat the SAME einsum\n", + "# to obtain cosine similarity C.\n", + "#\n", + "# TODO 7: Use query_idx = 14 (true label 4).\n", + "# - exclude the query from matching itself,\n", + "# - find the best raw-dot-product match,\n", + "# - find the five best cosine-similarity matches,\n", + "# - compare their labels with digits.target[query_idx].\n", + "#\n", + "# Predict first: which index disappears in 'id,jd->ij'?" + ], + "id": "s06-11" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ], - "id": "s06-06", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 902, - "referenced_widgets": [ - "4264d1b269004133bdd63da646b2f08f", - "08d53eb8a9cb447ea5281d71f4f6ec9f", - "5abc2d6ff3784fe08d2ea57fbea39d8a", - "ff1ae20acc844ab1bcc28c5e35498647", - "d7e1a368de9f40b5a3dc4f55ee8dabfe", - "6540bc11246c4f798abe25f2c85668c9", - "6fa85a4fcd974dda9e6bb9799653f541", - "69f91f46e5b140228386b5163b14d6bf", - "9d8851769c9749f2afec0d8b231171c0", - "67f060730375472c85d17f336d039fa8", - "ba455c1f2a5646468c18c8cd4255b130", - "70bec5ade61549019934d9b3aee6a45f", - "1f21466d29fc4e0e841a3f734e760ba7", - "66afc8c774444586acb309f92e078b14", - "9d3305439516455397a2c4be86b8038a", - "64384bc8ee094b4ba50a4d09882fb97f", - "9a316c35266b41bf867cfc9cf14cb8af", - "e3114b98fcdd4bc181eb6d3ceb273128" - ] - }, - "outputId": "355d68cc-e4a5-4136-ad87-c1fe72e8c5b3" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "single image: (512, 512, 3) -> (512, 512)\n", - "batch: (2, 512, 512, 3) -> (2, 512, 512)\n", - "contracted index: c | kept indices: h,w (and n for the batch)\n" - ] - }, + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "D = digit_images.reshape(len(digit_images), -1) # (1797, 64)\n", + "\n", + "S = np.einsum('id,jd->ij', D, D) # (1797, 1797)\n", + "print(\"raw similarity matrix:\", S.shape, \"=\", S.size, \"pairwise scores\")\n", + "\n", + "norms = np.linalg.norm(D, axis=1, keepdims=True)\n", + "Dn = D / np.where(norms == 0, 1.0, norms)\n", + "C = np.einsum('id,jd->ij', Dn, Dn)\n", + "\n", + "assert S.shape == (1797, 1797)\n", + "assert C.shape == (1797, 1797)\n", + "assert np.allclose(C.diagonal(), 1.0)\n", + "\n", + "query_idx = 14\n", + "query_label = digits.target[query_idx]\n", + "\n", + "raw_scores = S[query_idx].copy()\n", + "cos_scores = C[query_idx].copy()\n", + "raw_scores[query_idx] = -np.inf\n", + "cos_scores[query_idx] = -np.inf\n", + "\n", + "raw_top1 = int(np.argmax(raw_scores))\n", + "cos_top5 = np.argsort(cos_scores)[-5:][::-1]\n", + "\n", + "print(\"query index / label:\", query_idx, \"/\", query_label)\n", + "print(\"best RAW dot-product match:\", raw_top1,\n", + " \"label\", digits.target[raw_top1],\n", + " \"score\", round(float(raw_scores[raw_top1]), 3))\n", + "print(\"top 5 COSINE matches:\", cos_top5.tolist())\n", + "print(\"top 5 COSINE labels:\", digits.target[cos_top5].tolist())\n", + "print(\"top 5 COSINE scores:\", np.round(cos_scores[cos_top5], 3).tolist())\n", + "\n", + "# Static retrieval example. nearest preserves the original 8x8 measurements.\n", + "fig, axes = plt.subplots(1, 6, figsize=(11, 2.5))\n", + "axes[0].imshow(\n", + " digit_images[query_idx], cmap=\"gray_r\",\n", + " interpolation=\"nearest\", vmin=0, vmax=16\n", + ")\n", + "axes[0].set_title(f\"query / consulta\\nlabel {query_label}\")\n", + "\n", + "for ax, idx in zip(axes[1:], cos_top5):\n", + " ax.imshow(\n", + " digit_images[idx], cmap=\"gray_r\",\n", + " interpolation=\"nearest\", vmin=0, vmax=16\n", + " )\n", + " ax.set_title(f\"label {digits.target[idx]}\\ncos={C[query_idx, idx]:.3f}\")\n", + "\n", + "for ax in axes:\n", + " ax.axis(\"off\")\n", + "\n", + "fig.suptitle(\n", + " \"Real 8×8 digits: one contraction -> all-pairs similarity -> retrieval\"\n", + ")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "query_slider = widgets.IntSlider(\n", + " value=14, min=0, max=len(digit_images) - 1, step=1,\n", + " description=\"Query:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"55px\"},\n", + ")\n", + "similarity_toggle = widgets.ToggleButtons(\n", + " options=[(\"Cosine / Coseno\", \"cosine\"), (\"Raw dot / P. punto\", \"raw\")],\n", + " value=\"cosine\",\n", + " description=\"\",\n", + ")\n", + "k_slider = widgets.IntSlider(\n", + " value=5, min=1, max=8, step=1,\n", + " description=\"Top k:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"45px\"},\n", + ")\n", + "\n", + "def explore_retrieval(query, metric, k):\n", + " matrix = C if metric == \"cosine\" else S\n", + " scores = matrix[query].copy()\n", + " scores[query] = -np.inf\n", + "\n", + " top = np.argsort(scores)[-k:][::-1]\n", + " q_label = int(digits.target[query])\n", + "\n", + " fig, axes = plt.subplots(1, k + 1, figsize=(2.05 * (k + 1), 2.75))\n", + " axes = np.atleast_1d(axes)\n", + "\n", + " axes[0].imshow(\n", + " digit_images[query], cmap=\"gray_r\",\n", + " interpolation=\"nearest\", vmin=0, vmax=16\n", + " )\n", + " axes[0].set_title(f\"query {query}\\nlabel {q_label}\")\n", + " axes[0].axis(\"off\")\n", + "\n", + " for ax, idx in zip(axes[1:], top):\n", + " ax.imshow(\n", + " digit_images[idx], cmap=\"gray_r\",\n", + " interpolation=\"nearest\", vmin=0, vmax=16\n", + " )\n", + " score_name = \"cos\" if metric == \"cosine\" else \"dot\"\n", + " ax.set_title(\n", + " f\"idx {idx}\\nlabel {digits.target[idx]}\\n{score_name}={scores[idx]:.3f}\"\n", + " )\n", + " ax.axis(\"off\")\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " labels = digits.target[top].astype(int).tolist()\n", + " same_label = sum(label == q_label for label in labels)\n", + "\n", + " if metric == \"cosine\":\n", + " en = \"Cosine normalizes magnitude first, so the comparison emphasizes pixel-pattern direction.\"\n", + " es = \"El coseno normaliza la magnitud primero, por lo que enfatiza la dirección del patrón de píxeles.\"\n", + " else:\n", + " en = \"Raw dot product also rewards magnitude/intensity, so visually different labels can rank highly.\"\n", + " es = \"El producto punto crudo también premia magnitud/intensidad, por eso pueden aparecer etiquetas distintas.\"\n", + "\n", + " print(\n", + " f\"metric={metric} | query label={q_label} | \"\n", + " f\"top-{k} labels={labels} | same-label matches={same_label}/{k}\"\n", + " )\n", + " print(\"EN:\", en)\n", + " print(\"ES:\", es)\n", + " print(\"Index rule / Regla: id,jd->ij | d disappears / desaparece; i and j survive / permanecen.\")\n", + " print(\"Display note / Nota visual: the data are truly 8×8; the pixelated look is the original resolution.\")\n", + "\n", + "retrieval_output = widgets.interactive_output(\n", + " explore_retrieval,\n", + " {\n", + " \"query\": query_slider,\n", + " \"metric\": similarity_toggle,\n", + " \"k\": k_slider,\n", + " },\n", + ")\n", + "\n", + "display(\n", + " widgets.VBox([\n", + " widgets.HTML(\n", + " \"Interactive retrieval explorer / Explorador interactivo: \"\n", + " \"choose a real query digit, change the metric, and inspect its neighbours. / \"\n", + " \"elige un dígito real, cambia la métrica y observa sus vecinos.\"\n", + " ),\n", + " widgets.HTML(\n", + " \"Image quality note / Nota de calidad: these are original 8×8 measurements; \"\n", + " \"the blocky pixels are the data, not an error. / Son mediciones originales 8×8; \"\n", + " \"los bloques son los datos, no un error.\"\n", + " ),\n", + " widgets.HBox([query_slider, similarity_toggle, k_slider]),\n", + " retrieval_output,\n", + " ])\n", + ")\n" + ], + "id": "s06-12" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What just happened\n", + "\n", + "You used **one index rule** three times, but each exercise gave the rule a different meaning:\n", + "\n", + "1. **Real microscopy image — `hwc,c->hw`** \n", + " `c` disappeared, so three colour measurements became one grayscale value at every pixel. The sliders changed the weights, not the rule.\n", + "\n", + "2. **Real digit-pixel matrices — `ik,kj->ij`** \n", + " `k` disappeared, so the shared dimension was multiplied and summed. Trace and transpose showed that `einsum` can also sum repeated indices or simply reorder them.\n", + "\n", + "3. **1,797 real handwritten digits — `id,jd->ij`** \n", + " `d` disappeared, so 64 real pixel measurements became one similarity score for every pair of images: **3,229,209 scores**. The interactive explorer showed that preprocessing changes what “similar” means.\n", + "\n", + "### The sentence to remember\n", + "\n", + "> **If an index disappears after `->`, it is summed over. If it remains, it survives in the output.**\n", + "\n", + "The `8×8` digits are deliberately pixelated because **each visible square is one of the 64 measured features**. Keeping that limitation visible makes the tensor operation easier to understand.\n", + "\n", + "> 🇪🇸 Usaste **una sola regla de índices** tres veces: `c` desapareció al convertir color a gris; `k` desapareció en el producto matricial; y `d` desapareció al convertir 64 píxeles en una similitud entre dos dígitos. La frase para recordar es: **si un índice desaparece después de `->`, se suma; si permanece, sobrevive en la salida.** Los dígitos `8×8` se ven pixelados a propósito: cada cuadrado visible es una de las 64 características realmente medidas.\n", + "\n", + "Keep this rule for section 10: `'ijk,ia,jb,kc->abc'` looks longer, but the logic is exactly the same.\n" + ], + "id": "s06-13" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Done with this section\n", + "\n", + "Next up: **07 · Inverses and the pseudoinverse** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/07-inverses-and-pseudoinverse.ipynb).\n", + "\n", + "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" + ], + "id": "s06-14" + } + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "version_major": 2, + "version_minor": 0, + "state": { + "4264d1b269004133bdd63da646b2f08f": { + "model_module": "@jupyter-widgets/controls", + "model_name": "VBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "VBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "VBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_08d53eb8a9cb447ea5281d71f4f6ec9f", + "IPY_MODEL_5abc2d6ff3784fe08d2ea57fbea39d8a", + "IPY_MODEL_ff1ae20acc844ab1bcc28c5e35498647" + ], + "layout": "IPY_MODEL_d7e1a368de9f40b5a3dc4f55ee8dabfe" + } + }, + "08d53eb8a9cb447ea5281d71f4f6ec9f": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_6540bc11246c4f798abe25f2c85668c9", + "placeholder": "​", + "style": "IPY_MODEL_6fa85a4fcd974dda9e6bb9799653f541", + "value": "Interactive lab: move R/G/B. 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cMX78+OC7bbfdVhx77LFCCLFKycqq/vrLRFYlWfHxx5wbb7xxlXX8e+H6669fZR0hhEilUmKrrbYKPv/yl78UgHjppZeCMtd1xfDhw8W3vvWtAdv612JlfxtssMGgc58xY4bo6uoSy5YtEzNnzhTbbrvtSn83v37/sXlVHHPMMQIQl1122YDyLbfcUmy99dZr3L5cLg8qO/HEE0UsFhPVajUo88ewX//610GZaZqitbVVTJ06NSi74YYbBCB+97vfBWWlUkmMHz9+SJKVffbZR0ycODH4fNBBB4mDDjpIKIoiXnjhBSGEEO+//74AxFNPPbXW5/bQQw8JQLz55ptB2ar6+NqM8SvD7z+jRo0SmqaJJ598cpV1v8q99MYbb4gtt9xSAOKxxx4btO9QsrLuCT3k3yCe5/Hkk0+y3377sc022wz6fsUMB2sik8nwzjvvsGTJki/VnuOPPz74v6IobLPNNggh+P73vz/gGBtssAHz589f5X6i0Si6rvPGG2/Q19e3yrZCzcu0Ou/lqjjppJMGfJ40aRI9PT2BN8SPQj/llFMG1DvttNOGtH9FUYIAO8/z6O3txXEcttlmmzVKgFbH448/jud5TJs2bYA3o7W1lfXWW2+AJAZqQXDHHnvslz5efyKRCEcccQSvvvoqCxYs4MILL+Sdd95h9913Z+zYsUEw3NqSyWQolUqrzWTx/PPPM3HixAFLuolEghNOOIGFCxcya9asVW775JNP4nkeF198MbI8cIjy+8irr76KZVmceeaZA+r84Ac/IJVKrVYa8/zzz6MoCqeffvqA8rPPPhshxABP3tqy4n06VNLpNAAvvfTSWq+UzZgxgwcffJAtt9ySl156iR//+MdsvfXWbLXVVkNeLVsTiqIwbdo0HnroIaAWzDlixIgBwV+r4ogjjmDu3Lm8++67wb9rkquccMIJvPLKK4P+NtpooyG32Q96KxQKq6zjf5dMJle7r2QyGYw1UFtd1DRtgOf/D3/4A4sXL16lXOWxxx4bdD733HPPoHqXXHIJTU1NtLa2MmnSJGbPns21117LwQcfPKDe6NGjEUKsVfrZlY2jqxvbfaLRaPD/QqFAd3c3kyZNolwuD5AjQe269w9o1XWdiRMnDjjO888/T1tb24BzisVinHDCCUM6j0mTJvH+++9TKpUAeOutt9h7773ZYostmDlzJlDzmkuStFrp2IrnVq1W6e7uZvvttwdY6di/4jVc2zF+VXR0dBCJRBgxYsQa636Ze2mnnXZi3rx5XH311Rx00EFDalPIN8u/dzqL/zK6urrI5/NssskmX8v+fv7zn3PMMccwYsQItt56a/bee2+OPvpoxo4dO6TtR44cOeBzOp0mEonQ2Ng4qHx1cgfDMLj66qs5++yzaWlpYfvtt2fffffl6KOPDrIyTJkyhalTpzJjxgyuv/56dtppJw444ACOOOIIDMNY67b6y7d9fX2kUikWLVqELMuMGTNmQL3x48evcd8+9913H9deey0ff/zxgEnDivtcGz799FOEEKy33nor/X7F5f7hw4cPKfNGpVIZpI1dMQNGf0aNGsUll1zCSSedxA9+8AOeeeYZrr766iEvd/fnlFNO4Xe/+x177bUXw4cPZ/fdd2fatGkDMgEsWrRopdrNCRMmBN+vqh/MmzcPWZZXa3wtWrQIgA022GBAua7rjB07Nvh+VdsOGzZskBHWv21fli97r4wZM4azzjqL6667jt/85jdMmjSJ/fffP4jxWBOHH344hx9+OPl8nnfeeYd7772XBx98kP3224+PPvqISCTypdrVnyOOOIKbbrqJv//97zz44IMcdthhQ3IibLnllmy44YY8+OCDZDIZWltbAy3rqlhvvfVWmWpxqBSLRWD1xrb/3eqMdv/7/nr8hoYG9thjD5544gluv/12IpEIDz74IKqqMm3atJXuY/LkyYPG1pVxwgkncMghh1CtVnnttde46aabhqTzXhORSCSQ0fnU1dWt0onSn3/+85/85Cc/4bXXXhswMQEGjUPt7e2D7ou6ujr+8Y9/BJ8XLVrE+PHjB9VbsT+vikmTJuE4Dn/+858ZMWIEnZ2dTJo0iX/+858DDPKNNtqI+vr61e6rt7eXGTNm8PDDD9PZ2bnac4PBfXxtx/hVcccdd3DWWWex5557MnPmzNVei7W9l2RZJpPJBPE2If+ehAb5fzDTpk1j0qRJPPHEE7z88sv84he/4Oqrr+bxxx9nr732WuP2KwYwraoMWKmWrj9nnnkm++23H08++SQvvfQSF110EVdeeSWvvfYaW265ZZD/+O233+aZZ57hpZde4rjjjuPaa6/l7bffXuNLK75su4bKAw88wPTp0znggAM499xzaW5uRlEUrrzyykHBVGuD53lIksQLL7yw0nNY8bz7e2tWx29/+9tBnvRVXQvHcXj++ee55557eO655xBCcMABB6yVxrk/zc3N/O1vf+Oll17ihRde4IUXXuCee+7h6KOP5r777vtS+/x3ZFXG5uqMo6H+fivj2muvZfr06Tz11FO8/PLLnH766Vx55ZW8/fbbtLe3D2kfqVSK3Xbbjd122w1N07jvvvt45513mDJlSmCUVyqVlW5bLpdXa7hvt912jBs3jjPPPJMFCxasVVDmEUccwW233UYymeTQQw8dtPLxr+Cjjz4CVj8p9ydh/Y3FFVm0aBH5fH7QBPF73/sezz77LM8++yz7778/jz32GLvvvvsgo3dt6T8Z2XfffVEUhQsuuICdd955pSurQ2VVY+iayGazTJkyhVQqxWWXXca4ceOIRCK8//77nH/++XieN6TjfF1jNcA222xDJBLhzTffZOTIkTQ3N7P++uszadIkbr31VkzTZObMmRx44IFr3Ne0adP405/+xLnnnssWW2xBIpHA8zz23HPPQecGg/v42o7xq2KjjTbi+eefZ5dddmG33Xbjj3/845C85avj65jYhnxzhAb5N0hTUxOpVCp4UHwdtLW1ccopp3DKKafQ2dnJVlttxRVXXDEkg/zrZty4cZx99tmcffbZfPrpp2yxxRZce+21PPDAA0Gd7bffnu23354rrriCBx98kCOPPJKHH354gHzmyzBq1Cg8z2PBggUDPBVz584d0vaPPvooY8eO5fHHHx9giPXPNb46VmW8jRs3DiEEY8aMYf311x/SvobCHnvsscaXn8yaNYt77rmH+++/n46ODtZff31++tOfMn36dFpaWr7S8XVdZ7/99mO//fbD8zxOOeUU7rjjDi666CLGjx/PqFGjmDNnzqDt/OXtUaNGrXLf48aNw/M8Zs2axRZbbLHSOv72c+bMGbAiZFkWCxYsWO1DaNSoUbz66qsUCoUB3tMV2+avwqwYLPVVPOhrYtNNN2XTTTflJz/5CX/605/49re/ze23377KnNerY5tttuG+++4LsnD0v2YrY86cOav9XaDmib/88suZMGHCKn+blXHEEUdw8cUXs3TpUu6///4hb/dlcV2XBx98kFgstlrJwvrrr8/666/Pk08+yY033rhSb/qvf/1rgEFBdvvvvz/JZJIHH3wQTdPo6+v7WrOr+Pz4xz/mrrvu4ic/+cmAFz19U7zxxhv09PTw+OOPDwiQ/CqZOkaNGsVHH32EEGLA2Lmqe3NFfBnMzJkzGTlyZCCdmjRpEqZp8pvf/IaOjo41BnT29fXx+9//nhkzZnDxxRcH5Z9++umQz+XrHOMnTpzIk08+yT777MNuu+3GzJkzv/IEL+Q/h1BD/g0iyzIHHHAAzzzzzKC3csHaeRBc1x20nNbc3MywYcOGlDrq66RcLg96Mci4ceNIJpNBW/r6+gadn/9A/zra66deu/XWWweU33zzzUPa3vds9G/jO++8M+Q35MXjcWCw8XbQQQehKAozZswYdP5CiC+d+aStrY1dd911wJ/PG2+8wfbbb8/GG2/ML3/5S3bffXf+8Ic/MGfOHM4///yvbIyv2GZZltlss82A//+33HvvvfnLX/4y4PqVSiXuvPNORo8evVo5ygEHHIAsy1x22WWDPFT+Ndx1113RdZ2bbrppwHX91a9+RS6XW20GD/9FGbfccsuA8uuvvx5JkoLJbCqVorGxcVCGoRXvsa+DfD4/KIPIpptuiizLq+0f5XJ5lfeor4X3l77b2trYYosteOCBBwbdp++99x5vv/32Gifyxx9/PJdccsmgVKhrYty4cdxwww1ceeWVTJw4ca22XVtc1+X0009n9uzZnH766avMnuJz8cUX09fXx0knnTRo9eO9997j6quvZpNNNmHq1KkDvotGoxx44IE8//zz3HbbbcTjcb773e9+7eeTyWQ48cQTeemll4KMT98kKxsbLcv6Sv1g7733ZsmSJQPeGlsul4eU8cVn0qRJvPPOO7z++uuBQd7Y2MiECRO4+uqrgzqrY2XnBnDDDTcMuR1f9xi/yy678NBDDzF37lz23HPPQRKhkP9eQg/5N8zPfvYzXn75ZaZMmcIJJ5zAhAkTWLp0KY888ghvvfXWkN/yWCgUaG9v5+CDD2bzzTcnkUjw6quv8u677671w/Kr8sknn7DLLrswbdo0NtpoI1RV5YknnqCjo4PDDjsMqOmzb731Vg488EDGjRtHoVDgrrvuIpVKrfSNfGvL1ltvzdSpU7nhhhvo6elh++235w9/+AOffPIJsOaA2X333ZfHH3+cAw88kH322YcFCxZw++23s9FGGwVa1DUdH2rerMMOOwxN09hvv/0YN24cl19+ORdeeCELFy7kgAMOIJlMsmDBAp544glOOOEEzjnnnK98/v35wx/+gG3b3HrrrRxxxBFD0iBDzZDfeeedueSSS1abt/3444+nt7eX73znO7S3t7No0SJuvvlmtthii0ACcMEFF/DQQw+x1157cfrpp1NfX899993HggULeOyxx1YrWRg/fjw//vGP+elPf8qkSZM46KCDMAyDd999l2HDhnHllVfS1NTEhRdeyIwZM9hzzz3Zf//9mTNnDrfeeivbbrvtoLck9me//fZj55135sc//jELFy5k88035+WXX+app57izDPPHPBmuuOPP56rrrqK448/nm222YY333wzuKe+Tl577TV++MMfcsghh7D++uvjOA73338/iqIMMgT7Uy6X2WGHHdh+++3Zc889GTFiBNlslieffJKZM2dywAEHDHi75XXXXccee+zBFltswfTp0xk2bBizZ8/mzjvvpK2tbY0xBaNGjVpjTv9VccYZZwy57vvvvz9gZc1n3LhxfOtb3wo+53K5oF65XA7e1Dlv3jwOO+wwfvrTn67xWEceeSTvvvsuN954I7NmzeLII4+krq6O999/n7vvvpuGhgYeffTRlWqBv/e97/HrX/+al156iSOPPDKYmK+MRx99dKXyhd12222Nk+QzzjiDG264gauuuoqHH34YqKVDHDNmDMccc8xaBXauLTvssAN1dXUcc8wxnH766UiSxP333/+VJCg/+MEPuOWWWzj66KN57733aGtr4/777ycWiw15H5MmTeKKK67g888/H2B4T548mTvuuIPRo0evUeqVSqWYPHkyP//5z7Ftm+HDh/Pyyy+vlff/XzHGH3jggdx1110cd9xx7L///rz44osD5GRf5V4K+TfmG8vnEhKwaNEicfTRR4umpiZhGIYYO3asOPXUUwel3VsR+qVaM01TnHvuuWLzzTcXyWRSxONxsfnmm4tbb711jcdf1du+VvU2txXTi62YDq67u1uceuqpYsMNNxTxeFyk02mx3XbbDUhp9f7774vDDz9cjBw5UhiGIZqbm8W+++4r/vrXv67yHFfXVj9VVP80g6VSSZx66qmivr5eJBIJccABB4g5c+YIYEAqx5XheZ742c9+JkaNGiUMwxBbbrmlePbZZ1f6lseVpT0UovYGv+HDhwtZlge17bHHHhM77rijiMfjIh6Piw033FCceuqpYs6cOau8zl+WL/v2uGeeeWZIaRcfffRRsfvuu4vm5mah67oYOXKkOPHEE8XSpUsH1Js3b544+OCDRSaTEZFIREycOHHQm+dWx9133y223HJLYRiGqKurE1OmTBGvvPLKgDq33HKL2HDDDYWmaaKlpUWcfPLJoq+vb0Cdlf2GhUJB/OhHPxLDhg0TmqaJ9dZbT/ziF78YlHq0XC6L73//+yKdTotkMimmTZsmOjs7h3yfrooV0x7Onz9fHHfccWLcuHEiEomI+vp6sfPOO4tXX311tfuxbVvcdddd4oADDgju3VgsJrbcckvxi1/8YqVjyttvvy323XdfUVdXJ1RVFcOHDxfHH3+8+OKLLwbVXdW93p81pT1cHaxl2sP+Kf78FHv+XyKREOutt5743ve+J15++eXVHndlPPnkk2K33XYTdXV1wjAMMX78eHH22Wev9hwcxxFtbW0CEM8///xK66wuVV3/e2BNbymdPn26UBQlSMv54YcfCkBccMEFazy3VY3tftvWxB//+Eex/fbbi2g0KoYNGybOO+888dJLLw1KUbiqMWxlfXDRokVi//33F7FYTDQ2NoozzjhDvPjii0NKeyhE7Y2WiqKIZDI5IAXrAw88IABx1FFHrXEfQgjxxRdfiAMPPFBkMhmRTqfFIYccIpYsWbLWfXwoY/zKWFn/8bnmmmsEIPbdd19h2/bXdi+tjDDt4bpHEuJrjLQICfk3429/+xtbbrklDzzwwL9E3/nfxHnnnRcslYaR+CEh/97ceuutnHfeecybNy/0ioaE/BcQashD/mtYWfaIG264AVmW1xjcEwKvv/46F110UWiMh4T8B/D6669z+umnh8Z4SMh/CaGHPOS/hhkzZvDee++x8847o6pqkJLvhBNO4I477ljXzQsJCQkJCQkJWSmhQR7yX8Mrr7zCjBkzmDVrFsVikZEjR3LUUUfx4x//GFUN45dDQkJCQkJC/j0JDfKQkJCQkJCQkJCQdUioIQ8JCQkJCQkJCQlZh4QGeUhISEhISEhISMg6JDTIQ0JCQkJCQkJCQtYhoUEeEhISEhISEhISsg4JDfKQkJCQkJCQkJCQdUhokIeEhISEhISEhISsQ0KDPCQkJCQkJCQkJGQdEhrkISEhISEhISEhIeuQ0CD/N+PSSy9FkqQ11ps+fTqjR4/+1zco5F/GG2+8gSRJPProo+u6KSEhIV8zo0ePZt99913Xzfi3IBzrVs3o0aOZPn36um5GyL8BoUEe8rXw/PPPc+mll67rZoSEhIR849x7771DcqSE/OuQJIl77713XTcjJORLo67rBoR8Oe666y48z1vXzQh4/vnn+eUvfxka5SEhISEhIUNkzpw5yHLoGw0JPeRfmlKptE6Pr2kahmGs0zaEhISEhPxv8Y9//GNdN+G/CsMw0DRtXTcj5N+A0CAfAr6ue9asWRxxxBHU1dWx4447Bt8/8MADbL311kSjUerr6znssMP4/PPPB+xj5syZHHLIIYwcORLDMBgxYgQ/+tGPqFQqX6pNK2rIFy5ciCRJXHPNNdx5552MGzcOwzDYdttteffddwdtm0gkmD9/PnvssQfxeJxhw4Zx2WWXIYQI6vm6vzfeeGPA9v6x/OXB6dOn88tf/hKoLRv6f+uCe+65h+985zs0NzdjGAYbbbQRt91224A6r732GrIsc/HFFw8of/DBB5EkaUD9xYsXc9xxx9HS0oJhGGy88cbcfffdg4578803s/HGGxOLxairq2ObbbbhwQcfHFKbPc/jiiuuoL29nUgkwi677MLcuXO/xNnDVlttxUEHHTSgbNNNN0WSpAEP0t/+9rdIksTs2bO/1HFeeOEFpkyZQjKZJJVKse222w75fENC/pd46623mDhxIpFIhLFjx/LrX/86+C6bzaIoCjfddFNQ1t3djSzLNDQ0DBiPTz75ZFpbWwfs+5133mHvvfemrq6OeDzOZpttxo033rjGNlWrVS699FLWX399IpEIbW1tHHTQQcybN2+N226++eZMnDiRO+64g3w+P5RLELCmse6mm25CURSy2WxQdu211yJJEmeddVZQ5rouyWSS888/f62O/68km83yox/9iNGjR2MYBu3t7Rx99NF0d3evdrsVNeS2bTNjxgzWW289IpEIDQ0N7LjjjrzyyitrbMM//vEPpkyZQjQapb29ncsvv5x77rkHSZJYuHDhgLq33norG2+8MYZhMGzYME499dQB1x1gp512YpNNNmHWrFnsvPPOxGIxhg8fzs9//vNBxzZNk0suuYTx48cHNs55552HaZprbHdIjVCyshYccsghrLfeevzsZz8LBsorrriCiy66iGnTpnH88cfT1dXFzTffzOTJk/nggw/IZDIAPPLII5TLZU4++WQaGhr4y1/+ws0338wXX3zBI4888rW18cEHH6RQKHDiiSciSRI///nPOeigg5g/f/6AWbjruuy5555sv/32/PznP+fFF1/kkksuwXEcLrvssrU65oknnsiSJUt45ZVXuP/++7+2c/ky3HbbbWy88cbsv//+qKrKM888wymnnILneZx66qkAfOc73+GUU07hyiuv5IADDmCrrbZi6dKlnHbaaey6666cdNJJAHR0dLD99tsjSRI//OEPaWpq4oUXXuD73/8++XyeM888E6jJh04//XQOPvhgzjjjDKrVKv/4xz945513OOKII9bY5quuugpZljnnnHPI5XL8/Oc/58gjj+Sdd95Z6/OfNGkSDz30UPC5t7eXf/7zn8iyzMyZM9lss82A2gSxqamJCRMmrPUx7r33Xo477jg23nhjLrzwQjKZDB988AEvvvjikM43JOR/hblz53LwwQfz/e9/n2OOOYa7776b6dOns/XWW7PxxhuTyWTYZJNNePPNNzn99NOBmgEvSRK9vb3MmjWLjTfeGKj12UmTJgX7fuWVV9h3331pa2vjjDPOoLW1ldmzZ/Pss89yxhlnrLJNruuy77778vvf/57DDjuMM844g0KhwCuvvMJHH33EuHHjVntOd955J3fffTcnnXQSZ511Focccgjf//73B7RtVaxprJs0aRKe5/HWW28FAbEzZ84Mxi+fDz74gGKxyOTJk9d4zG+CYrHIpEmTmD17NscddxxbbbUV3d3dPP3003zxxRc0NjYOeV+XXnopV155JccffzwTJ04kn8/z17/+lffff5/ddtttldstXryYnXfeGUmSuPDCC4nH4/zf//3fSlfSL730UmbMmMGuu+7KySefzJw5c7jtttt49913+eMf/zjAVujr62PPPffkoIMOYtq0aTz66KOcf/75bLrppuy1115AbaK1//7789Zbb3HCCScwYcIEPvzwQ66//no++eQTnnzyyaFfzP9lRMgaueSSSwQgDj/88AHlCxcuFIqiiCuuuGJA+YcffihUVR1QXi6XB+33yiuvFJIkiUWLFg061po45phjxKhRo4LPCxYsEIBoaGgQvb29QflTTz0lAPHMM88M2BYQp512WlDmeZ7YZ599hK7roqurSwghxOuvvy4A8frrrw84tn+se+65Jyg79dRTh9TufzUru8577LGHGDt27ICyUqkkxo8fLzbeeGNRrVbFPvvsI1Kp1IDf4vvf/75oa2sT3d3dA7Y97LDDRDqdDo713e9+V2y88cZr3Vb/+k6YMEGYphmU33jjjQIQH3744Vrv85FHHhGAmDVrlhBCiKeffloYhiH2339/ceihhwb1NttsM3HggQeu9f6z2axIJpNiu+22E5VKZcB3nuet9f5CQv5bGTVqlADEm2++GZR1dnYKwzDE2WefHZSdeuqpoqWlJfh81llnicmTJ4vm5mZx2223CSGE6OnpEZIkiRtvvFEIIYTjOGLMmDFi1KhRoq+vb8Bx19QP7777bgGI6667btB3a9OHZ82aJc455xzR0tIiALH++uuLq666SixdunRQ3aGOda7rilQqJc4777ygPQ0NDeKQQw4RiqKIQqEghBDiuuuuE7IsDzr3dcXFF18sAPH4448P+m5N13TUqFHimGOOCT5vvvnmYp999lnrNpx22mlCkiTxwQcfBGU9PT2ivr5eAGLBggVCiNo9qOu62H333YXrukHdW265RQDi7rvvDsqmTJkiAPHrX/86KDNNU7S2toqpU6cGZffff7+QZVnMnDlzQJtuv/12AYg//vGPa30+/4uEkpW1wPec+jz++ON4nse0adPo7u4O/lpbW1lvvfV4/fXXg7rRaDT4f6lUoru7mx122AEhBB988MHX1sZDDz2Uurq64LPvtZg/f/6guj/84Q+D//teYMuyePXVV7+29nzT9L/OuVyO7u5upkyZwvz588nlcsF3sViMe++9l9mzZzN58mSee+45rr/+ekaOHAmAEILHHnuM/fbbDyHEgN93jz32IJfL8f777wOQyWT44osvBkmDhsqxxx6LruvB59X9ZmvC3/bNN98Eat6lbbfdlt122y3wMGWzWT766KMhebRW5JVXXqFQKHDBBRcQiUQGfBdmmQgJGchGG200oJ81NTWxwQYbDOjbkyZNoqOjgzlz5gC1Pjt58mQmTZoU9Nm33noLIUSwrw8++IAFCxZw5plnBquwPmvqh4899hiNjY2cdtppg75bmz48YcIEfvGLX/DFF1/w1FNPMWHCBC666CJGjBjBAQccsFKt+ZrGOlmW2WGHHYLxa/bs2fT09HDBBRcghODPf/4zULtGm2yyyaBzX1c89thjbL755hx44IGDvlvbcTGTyfDPf/6TTz/9dK22e/HFF/nWt77FFltsEZTV19dz5JFHDqj36quvYlkWZ5555oBg0h/84AekUimee+65AfUTiQTf+973gs+6rjNx4sQB9/AjjzzChAkT2HDDDQc8K7/zne8ADLCFQlZNKFlZC8aMGTPg86effooQgvXWW2+l9fsv+3z22WdcfPHFPP300/T19Q2o199Q/Kr4BqWPb5yveExZlhk7duyAsvXXXx9gkNbs6yaXy31p7Xw6nR5gdK/IH//4Ry655BL+/Oc/Uy6XBx03nU4Hn7/97W9z8skn88tf/pI99tiD4447Lviuq6uLbDbLnXfeyZ133rnSY3V2dgJw/vnn8+qrrzJx4kTGjx/P7rvvzhFHHMG3v/3tIZ3TUH+zodDS0sJ6663HzJkzOfHEE5k5cyY777wzkydP5rTTTmP+/PnMnj0bz/O+lEHua0w32WSTtd42JOR/jRX7NtT6d/++7ffDmTNn0t7ezgcffMDll19OU1MT11xzTfBdKpVi8803B75aP5w3bx4bbLABqrrqx39XVxeu6wafE4kEiURipXVVVWX//fdnv/3243e/+x0nnHACTz31FDvttFMgkfMZylg3adIkLr30UiqVCjNnzqStrY2tttqKzTffnJkzZ7Lbbrvx1ltvMW3atLU+92XLlq31Nj4r6vf7M2/ePKZOnfql992fyy67jO9+97usv/76bLLJJuy5554cddRRg67liixatIhvfetbg8rHjx8/qB7ABhtsMKBc13XGjh0bfO/T3t4+aFJRV1c3YML16aefMnv2bJqamlbaNv9ZGbJ6QoN8LVjREPQ8D0mSeOGFF1AUZVB9fwBzXZfddtuN3t5ezj//fDbccEPi8TiLFy9m+vTpX2v6wpW1AxgQHDRUVjWz7z9QfxnOOOMM7rvvvi+17T333LPKlyjMmzePXXbZhQ033JDrrruOESNGoOs6zz//PNdff/2g62yaZhCwOm/ePMrlMrFYDCCo+73vfY9jjjlmpcfzB8gJEyYwZ84cnn32WV588UUee+wxbr31Vi6++GJmzJixxnP6On8zgB133JHf//73VCoV3nvvPS6++OLAmzRz5kxmz55NIpFgyy23/FL7DwkJGRpD6dvDhg1jzJgxvPnmm4wePRohBN/61rdoamrijDPOYNGiRcycOZMddtjhG0uPt+222w4wzC655JJVprRdtGgR9913H/feey8LFixg9OjRnH322Rx++OGD6g7leuy4447Yts2f//znAbp5f8Xg448/pqur60s5FNra2tZ6m5W18V/J5MmTmTdvHk899RQvv/wy//d//8f111/P7bffzvHHH/+NtKE/Q/nNPM9j00035brrrltp3REjRvxL2vbfRmiQfwXGjRuHEIIxY8YE3uWV8eGHH/LJJ59w3333cfTRRwflQ4ma/lfheR7z588f0O5PPvkEIMje4nsvVoy8XnEGDWu3LHfeeecNWAJbG/wAp5XxzDPPYJomTz/99ABPzKqWyy655BJmz57NNddcw/nnn88FF1wQZDtoamoimUziui677rrrGtsVj8c59NBDOfTQQ7Esi4MOOogrrriCCy+8cJC041/NpEmTuOeee3j44YdxXTd4kO+4446BQb7DDjuscqBdHX7A10cffTTI8xISEvLlmDRpEm+++SZjxoxhiy22IJlMsvnmm5NOp3nxxRd5//33B0zu+/fDoYxP/Rk3bhzvvPMOtm2vMt3eb37zmwGrmCuuplYqFZ544gnuvvtuXnvtNXRd54ADDuCOO+5g1113/UrytYkTJ6LrOjNnzmTmzJmce+65QM1Qveuuu/j9738ffF5b/lXP3HHjxvHRRx99bfurr6/n2GOP5dhjjw2CVy+99NLVGuSjRo1aaXauFctGjRoF1PKf9/9dLctiwYIFa30/Qe38//73v7PLLruE0sWvQGiQfwUOOuggLrzwQmbMmMEDDzww4EYUQtDb20tDQ0Ng+PSfUQohhpSe6l/JLbfcEhigQghuueUWNE1jl112AWodV1EU3nzzTQ444IBgu1tvvXXQvuLxOFAz3tek69too43YaKONvp6T6MfKrnMul+Oee+4ZVPedd97hmmuu4cwzz+Tss8+mu7ubq6++mqlTpzJlyhQURWHq1Kk8+OCDfPTRR4OWhru6uoLluZ6eHhoaGoLvdF1no4024oUXXsC27XVikANcffXVbLbZZoFMZ9KkSdx2220sWbKEiy666Evte/fddyeZTHLllVey5557Djg3IUQ4GIeEfAkmTZrEr3/9a377298GmSt8PfV1112HbdsDPMJbbbUVY8aM4YYbbmD69OkDxtw19cOpU6fy3HPPccstt/CjH/1owHf+tquT25100kk8/PDD5HI5NtlkE6677jqOOuqoAWPgVyESibDtttvy0EMP8dlnnw3wkFcqFW666SbGjRv3pbzdX8bYHApTp07lsssu44knnhikI1/bcXHF50kikWD8+PGDUimvyB577MEvf/lL/va3vwU68t7eXn7zm98MqLfrrrui6zo33XQTe+65Z9C2X/3qV+RyOfbZZ58ht9Vn2rRpPP/889x1112ccMIJA76rVCp4nhfYCCGrJjTIvwLjxo3j8ssv58ILL2ThwoUccMABJJNJFixYwBNPPMEJJ5zAOeecw4Ybbsi4ceM455xzWLx4MalUiscee+xLaYS/LiKRCC+++CLHHHMM2223HS+88ALPPfcc/+///b/A0Eyn0xxyyCHcfPPNSJLEuHHjePbZZ1eqB9t6660BOP3009ljjz1QFIXDDjvsGz2n3XffHV3X2W+//TjxxBMpFovcddddNDc3s3Tp0qBetVrlmGOOYb311uOKK64AYMaMGTzzzDMce+yxfPjhh8Tjca666ipef/11tttuO37wgx+w0UYb0dvby/vvv8+rr75Kb29vcNzW1la+/e1v09LSwuzZs7nlllvYZ599SCaTX8u5vfHGG+y8886rXTr2GT9+PK2trcyZM2dA4NbkyZODvL0rW+6VJIkpU6YMyjvfn1QqxfXXX8/xxx/PtttuG+Tl//vf/065XP7SUqSQkP9l/P44Z84cfvaznwXlkydP5oUXXgjeKeEjyzK33XYb++23H1tssQXHHnssbW1tfPzxx/zzn//kpZdeAmrxQGPGjOGYY44J3htx9NFH8+tf/5qzzjqLv/zlL0yaNIlSqcSrr77KKaecwne/+93VtvXBBx/k0EMP5fjjj2e77bb7mq9EjUmTJnHVVVeRTqfZdNNNAWhubmaDDTZgzpw5q5QtrivOPfdcHn30UQ455BCOO+44tt56a3p7e3n66ae5/fbbA+3/UNhoo43Yaaed2Hrrramvr+evf/0rjz766IAkDCvjvPPO44EHHmC33XbjtNNOC9Iejhw5kt7e3sDwbmpqChyJe+65J/vvvz9z5szh1ltvZdttt/1Sq9dHHXUUv/vd7zjppJN4/fXX+fa3v43runz88cf87ne/46WXXmKbbbZZ6/3+z/ENZnT5j8VPReinA1yRxx57TOy4444iHo+LeDwuNtxwQ3HqqaeKOXPmBHVmzZoldt11V5FIJERjY6P4wQ9+IP7+978PSh/4VdMe/uIXvxhUFxCXXHLJgG3j8biYN2+e2H333UUsFhMtLS3ikksuGZAGSQghurq6xNSpU0UsFhN1dXXixBNPFB999NGgdjuOI0477TTR1NQkJElaZykQn376abHZZpuJSCQiRo8eLa6++uogzZef9ulHP/qRUBRFvPPOOwO2/etf/ypUVRUnn3xyUNbR0SFOPfVUMWLECKFpmmhtbRW77LKLuPPOO4M6d9xxh5g8ebJoaGgQhmGIcePGiXPPPVfkcrnVttVPBfbII48MKF9ZWslnnnlGAOL2228f0nU45JBDBCB++9vfBmWWZYlYLCZ0XR+UsrBQKAhAHHbYYUPa/9NPPy122GEHEY1GRSqVEhMnThQPPfTQkLYNCflfYNSoUStNXzdlyhQxZcqUQeXNzc0CEB0dHUHZW2+9JQAxadKklR7jrbfeErvttptIJpMiHo+LzTbbTNx8883B9x9++KEAxAUXXDBgu3K5LH784x+LMWPGBOPawQcfLObNm7fG8yoWi2ussyJrM9YJIcRzzz0nALHXXnsNKD/++OMFIH71q1+tdRv+1fT09Igf/vCHYvjw4ULXddHe3i6OOeaYQWlzV2TFtIeXX365mDhxoshkMiIajYoNN9xQXHHFFcKyrDW24YMPPhCTJk0ShmGI9vZ2ceWVV4qbbrpJAGLZsmUD6t5yyy1iww03FJqmiZaWFnHyyScPSiM5ZcqUlab0XdH+EKL2fLn66qvFxhtvLAzDEHV1dWLrrbcWM2bMWOOzMKSGJMQ3FKkQ8m/D9OnTefTRRykWi+u6KSFD5LzzzuOhhx5i7ty5K33Rw1fl+eefZ9999+Xvf/974JEKCQn5z+bWW2/lvPPOY968ebS0tKzr5oSsA84880zuuOMOisXil4obCvnmCPOQh4T8B/D6669z0UUX/UuMcX//hx12WGiMh4T8F/H6669z+umnh8b4/wgrphPu6enh/vvvZ8cddwyN8f8AQg/5/yChhzwkJCQkJOS/iy222IKddtqJCRMm0NHRwa9+9SuWLFnC73//+y+VlSbkmyUM6gwJCQkJCQkJ+Q9n77335tFHH+XOO+9EkiS22morfvWrX4XG+H8IoYc8JCQkJCQkJCQkZB0SashDQkJCQkJCQkJC1iGhQR4SEhISEhISEhKyDgkN8pCQkJCQkJCQkJB1yJCDOu8457sIV1AqVUgmM+iqjmML5sz5lGy+j4aWJmKZDJKugaLheTKuI5A9D7NYoNC9DKuUR9cVdF2jXC7hujaKLOO4Npoqk04naGysJxGL43kyfbkSvaUCpixItbSx99Qj2HLid6jYKgIdRdHwPA+Wv5W29iKq/q+vX/EsBMLzAA8J8BwH2zYRroOEQFVkNF3BdWxyvd0oksC1TcxKkXIhj1UpoSqgIMj29WC5HomGJjwUkGRi0Si2WaVazGGW8uR6O9DwiCeiKKqOokVpbB1OKtOAkHQcIeEgY7su8VQKXdcxK1ViRpzeXJZ8qUIimUIIBxVBtdBHsbeLXNdSlnz+KValiCZLpJMZhAvFYgVZ1bBcF1d4lCoVsvkipaqJK2kINYKiJkFSUSSJEW2tNCQimOUcxUoWIx7DEeA50PlFB8L1KBcK5HO9RHWVhroU0biOUKBYLgMymmqgKjqe62KVK7iOhWOblEsFwKNt+HDS9c1E0w00tY5A0TXmzf2ExYs+IR6BlC5hKBKKUDCrDtlShb5ylb5CiXg6zYiRI1FVhWIhj6bKpJJJZBkMwyCWiBKPR5EliZihITkOXUs7QBLYwibT3EgknqGhqZ26TAuKHqVSKvDF5/Pp6viMeEwnlU6SLxYx7SqJdBpX6HT3mThOBEmJIpBBBhcXIUnoqowuO9j5XpxyHquUoyGToiGTplIu0NXbR9X2iCWSKLJMfSZNU1MjtiewPLBRQdaRlAiuJ6NKBq5wKRSzVMo5nGoRTXbQJQe7kkdFUMj1osoSjnAxIlGKFZNS2UJWIqh6FGQZWTFQVI3ebB7TtHE9B03VURQZ3dDxXA/HdYgYEXRFRpc8dNnDrlZwXRvhOURjUZCgZFbJl6vYLhiRBLKiE9UNooZGLGIQT8TJ9vagaTqukFm4aCmWFUfyIpRLXeSL86hr1Nhw0w1Yb4NNSTWMJl4/ElOKEUs3oUUSeEhIUs0f4HkenusiPIFpW6QSSXRdR5FlSoUCsiqRSiUpVcpYlgWeTDZXpFyx6OrNUbY8Gppaaairp1Is0LlsCcVCgWGtrYxfbzyarvPPWf/EtKqst/54YvE4S5Z1s3D+MmxbpVK20TWFlrYU66/XiiwLJDwURQIZsn1Fevuq2JZGLmtSLFWJJ9OUyrXxoKE+yX57TPjyo/B/CSeccAKu61IqlUin06iqiuM4fPjhh/T19dHW1kZdXR26riPLMkIIHMfB8zyKxSJdXV0Ui0V0XccwDEqlEo7joCgKjuOgqir19fU0NzeTSCTwPI++vj7y+TwALS0tHH300UyaNAnLspAkKThO/1eXr+o15n44led5wf8dx8G2bVzXBUBVVXRdx7Zturu7kWUZy7Iol8vk83kqlQqKoiBJEj09PTiOE7wGXZIkYrEYpmlSKBQoFot0d3cjSRLJZBJN09A0jeHDh1NfX48kSUFbXNclnU5jGAblcploNEpvby+FQoFUKhWcQz6fp6enh46ODubPnx+0J5PJ4LouhUIhuJ5CCEqlEtlslnK5HJyfpmnBtRsxYgTpdJpSqUSpVCKRSOC6Lq7r8tlnn+G6Lvl8nt7eXiKRCA0NDSQSCWRZplQqDdinf284joNlWRSLRYQQjBo1isbGRjKZDMOHD0fXdWbPns3cuXOJxWJEIhFUVUWSJCqVCoVCgUKhQDabJZPJMHbsWFRVJZ/Po6oqmUwGWZaJRCIkk8mgPdFoFMdxWLx4cfA7t7a2Eo/HaW1tpbGxEV3XKRaLzJ8/nyVLlpBIJMhkMuRyOSzLoq6uDiFE8Nv6qQT732eapiHLMvl8nnK5TLFYpKGhgYaGBkqlEp2dnViWRSqVQpZlGhoaaG1tDa6rEAJZllEUBc/zkGUZz/PI5/OUSiUqlUrwvf+7ZbNZFEXBdV2i0Wjwe6mqimEYSJKEqqqoqkpvby/VahXXddE0Lajjui6O4xCNRlFVFVmWkWU5qOu6LvF4HFmWg9/BcRxisRiKohCJRIhEIsRiMZLJJN3d3ei6jhCCuXPn1sZuoFAo0NfXR3NzM1tuuSWbbropTU1NNDU1IYSgrq6OSCQS9BlJkoJxwvO84NoZhoEsy+RyOVRVpa6ujmKxiGVZwdhQLpfp6urCNE1aW1tpaGigUCiwePFicrkcI0aMYMKECei6zt/+9jdM02STTTYhHo+zePFiPvnkE2zbplQqYRgGw4cPZ8KECSiKghAiuC97enro6enBtm16e3spFouk0+kg93tTU9Ma34ALa2GQd/bmiOgR4vEUmhbBtmz6enNkszli8RR1mUYsz0MVMrge5WoFSQKzapLv68WsVonHEsQjBqVCDs92SCQTGBEDT7homoyuSeQLWfp6uinky5iOoGLbROtStDU3ElUlJLeK5Gp4EghZwhOgLH+wI0Ag+tvky6k9/D3PwxMCITyE62BWijhmBRmB8BwkWaDIMsJ1qRYLRHUF4dYMbMssE0tEMDQFPI9iMY+DTSqRwEWmUi7jmVWEVcUqF8n3dlMtFonXp0mnUlRMm2Ixj9JrIKkKtgeKrqNFYiDJeI5Lxa4iSxK2bRGLxrBdkAQkEikcs4Kr6ZRLZTo7u8jnCxRzvSiSTKlYJW4kcD1BvjeLIzw8SWDbDp7jogqBY1WxqlVUzUGLxJE1DU0VxBNxdF1GNiQsx8S1LVRJQ1UkFFlDSSTJZ3MUilVUzUCNGGiaQiQaoVyxqFoumiYhSRpVt4ptOcSjBhlNolou4JkVJM9GkUHg4UlgJGKokRiWXcJSJDzTQtgOxVKFvlIZVzVIplM0trZQ39CAELVzAYGk6AgEkqojSQrFQhFdU8CWqZYKeK4FCITkEYvGkBSJfL6PaDRJJh4j1txEvpBlWecySqZANV0sy8a0LHTTRlJVEB6KKmNEI5SrNkIC13GQhYckJFzPxLVMIrpGfaKZTCqBbVfpy2aJRiO0j2yirqGBqBFhyeLFzJv7KVo0jhFPocfTSMjYloWEgqopSJ6NWy0irDKeXaFYLSALC7tSQBYushAYkQiqpCOEQiqZQVJNHE8mVd9MJJbGtFxMx6XOSOO6AkWR0bSaQS7LEq7rIikyuqpilkqYhSyO5+Bgo6oyuhYhYqgUyyUk4VGXTCBQkGQNxwXPMcmVCpRUmUopRiwaJRaN4Hoebc315MsGutEIUhOzZ/eSzfdSLTm4lovnWJjVEnoqiec5yJIEyLiei+PYeELUHmKKjOxICE+AAFXVQJKxbBvLdvE80LQInivQDQNNNyibFhWriGfbCM9DURQMI4plOZiWQ6lcoTkVp6m5jkKxgKpICCHwHA9FlvEUkHFRFAUZAUICag9Ef5IghEACbMfBcRwq1Sp6JLb84eRSKheGOoz+V9PV1UUkEiEej6NpGpZl0d3dTW9vL8lkkoaGhsCwFUJQLpeDh3tPTw/lcpl4PE4sFiOXy2HbNqlUimg0iud5aJqGrutks1m6urrIZrPYth0YSsOGDQsMP8/zAAKjtn8O5lXlMfDr9jeCy+Uy1Wo12E6SpMDwKRaLGIaB4zgUCgWq1SqJRCIwQvL5PJ7nBQZzqVTCsiwsy6JUKtHT00OhUAiM0Wq1Sj6fr01Glxtjuq4HxolvyPqTgHg8juM4ACSTSUzTRNd1CoUCS5cuJZfLkc1mkSSJQqFANBrFdV1yuRye5yFJErZtY9s2AKZpUqlU0HWdaDQaGGu+8eNPRBzHQZblwMBLpVL09vYGbY9EIkSjUWKxGOVyGdM0g+tr2zbVapV4PB4Yv6ZpBoanTyKRIBqNYlkWqqpiWRa2bVMsFgcYYMOGDaOpqQkgOA//t/aNJb9d/vb+NRNCBMZkNpslkUiQSCRobW0ll8uxdOlSKpUKkUgEy7KoVqtUq1VUVUUsH7Oi0Whwf/iTHH/flmVhGAbpdJpMJoNlWfT09BCPxxk7dixNTU1Eo1EWLVrE7NmziUajJBKJwOj1DVjDMBBCUK1WMU0T27apVCpBH/Lxfy+ATCYT9IXGxkYSiUSwrW98K4oS3GuyLAdlmqZRKpXI5XIIIQLD058cFYtFPM8jk8kABNv611dVVZLJZNCXPc9j2LBhlEolIpEIkiTxwQcfkM1mgz7hOA7lcpl0Oo3rusEEx/M8bNsOrreiKNi2HfRRf/Lj9yu/z7iuSyQSwTAMKpUKpmkG36uqSiQSwTRNTNMMHAhtbW3k8/nA2PYnXH5f8SfaPv7EVpKkYGzw+4c/afYng4XC0J4RQzbIbaGSjCSIJ9PYpoVtOyxatAjPE9TXN+BYHo7rku3tRdclMnVpPNemkO/ErlaIGAauKyjkC0Q0jWimAT1moBo6ffkebNOiVLFQZcgkkgwf3k7FciiUSkRTSSJ45DsXU+pbBloKV44hAEXVAYEkJIQEgtoftecqkgAkGQkZSRIgScgIZEUgayBkGatcpFwp4nkOhm4Q1aOYxT568314dplqpYSia2hqHbYlUS2XKRYKdHR3oUdi6EYEz615UqqlEnYpT7GvB1WVSSUTqIqKoUm4mkcxnyMSM9CiEfqy3WhGlHi8DuwIvT15IpEodfV1KLJK1IgQicSJxSJgGJgyzC1XWLhgEZViL55dRpIEru3i6LWHULVi4ggPy7WXD0oGmqoSUSRM18KyCzilIrIRwbPTCCmFrEmk9DRCWOQLfVSLJpoisComwoNIJE7BExSqDglXJqIZqLqM5Qgcz8ASKo4jUKMpFE0jYggUoYJTxSoXyPV2IYwoRKMYpIml0zS0tNLx2XxcT8VzLIq5HJZto0V0IpEoDa1tJFL1SLKM8DwkVcWsOkhqlPr6eqIxjXIxW+t0KpRyfVjVEtryzmFbLo7loCoGxWIBT3xGIpMERyJfqlKoCCIRg9ZEA7F4nEq5gOeBjIpwisiqgypDNGqQL5XRFQWvWkbYNsVSloShkkkmEK5FoZijVCpS39xIKpVCkmHp4kW1AbizB8cTJOsEQtExHRmXCkgyuqIgayUMQ0V1ywhsDENGiyaxqmUs2UVBIAsJzxUIAelUPYlMmkZJQdHjWGgUShYeAllTiMZ0IpE4rmtTqVQRikQkHkNRFTzhYlYqoMsYSR1DFjh2Ecmt4lol8uUy5bJJsVjEtj3q6puQJI98Ib/cY6ZQLDqY1TJOMkkxn8WsVgAVWWtA1gWRWJLhI8aT7V1EuWCT685S1zAcRaJmfJtVNMNGllVsq0qlWqWxubH2ECiUapMrPBzHQlZkVEMDBxzXIx5PYtsOlrCJRCJ4HqSTSUplGwkP4VnEYxFyioIQElXTpVSuIvBobm3EyClEogau7YEQxKJR8rkiiuIhSza6JiMJCU3RwKtNxCRVJRaNksvbuLaNbXuoik6lVCIWr0fT5FUaeP9rCCGIRqOk0+ngAThv3jw8z6OxsTF48Pqes8bGRhzHIZfLBYaP67pks1l0XScWiwWGW19fH9VqlVKphKIopNNpRo4cGXib0+k0sizT0dFBT08Puq4HD0/fSFnRU+6Xwf/vifP/gMBgkWWZcrlMuVwe8KDP5/OB57RcLqPremA8+gZNR0cH0WiUSCSCs3xCVyqVKBaL9PT0oGka6XQaTaut9hqGQS6XIx6PE41G6e7uDjy9tm3T1dVFNBqlsbEx8Pr6D37fuCyVSnzyyScUCgVs2x5gCLuuS6VSCQwd33Pqe+d9A71YLBKJRAIj1zeAhRBks1mKxWIwmfK9sv4Exve86rqOZVmBUeN7U32j3Td6SqVScE9EIhFSqRSZTIZhw4Yxf/785Q6ZmufRtu3geg4bNizwhvtGo28wNzU1EY/HyefzweQim80G3mXfYPbbl8/n+fzzz8lkMoERXyqViMVipFIpEolEYIjKsozjOIGBGIvFAu98pVLBcRyKxSLRaJRMJoPjOOTzeQqFAq2trdTV1SFJEosW1Z4Ry5Ytw3Vd6uvrA4PTx/9NauOdF3j+E4kE5XI5MKb9CaTvYfbPw58c+m33DetYLBYYjoqikEwmUVUVz/OoVCpomkYqlQruZ9d1Mc3as6FYLJLP57Esi+bmZgByuVzgUffvNcuygmvu32d+vx47diydnZ0UCgW6urpoamoKrmu1WiUSiaAoCqZpUi6XaWtrQ9d18vk8mqYBtQlY/z7qOE7QT0zTDCby/goP1FZF4vF4MGmrVCrBSs2wYcOC/uRPruLxOH19fYEx7k/G+q9eKIpCPB4PnAi2baNpWvB767oeOAjWxJAN8sbmYSRjUSKaBp5gWd8SPM9l+PBWYvEo+VIZx611cl3VSOgqniewUzE0WaanJ4dZsUjHYsRiCTzJo1iuoHkeejSGrAiies3IiRs6iXgcISkUikViyQTFapV8Tycdn81DizeixuvRYg7xZD2yrCH8gVaqObk810WSFBRZBk/UjAHPrhnlwsa1y+BUcSoFupd9Qba3m1g8SiKWoOxJdC/9gkXzPibf10E8HiVV10CxkAdZRZVlcj09zJ3zMaVymda2YSjCo1ooYFeryLhUSgXq6+uWewiqGJqBJFyKuTw93V20jmgllopiFUooto0jl8l1l8ipOpoqY8QSxKIxYvEojuuiSBKqoiFJMrbjUDUtPMtEeA6uaVMUeTwhwBO4nouiq6iqjuNUiEVixONRhKxTKBXIV8oonoPslZElE1mVEcJBkz2UuEZPuUgyrvF5dw+eUGhoakSrJOjs6aanUCaejpCOJ3ExyJVcPEdBMTRURUbyNJAsrKqNrBq1iUClQj7bB5EYFccllc5Q19CAXS1T7FqGLkDWNBrrMsQyKYSsoyXi6BF9ubRBIRpNoOsgKTqWA6JiUamYVL0qhuqBa1OtVpANHUmSsUwb07QRio1wPSqlIj3dndQ3tNLU2oIeTdUGHkMmGa0NuNneLCATNwwKpRJVFCLJDImIhlMp4llVysU+wCaeqsPQJAqVKpVKGUVTqGtsxLRM5s/5lGw2SzwRI5VO4XrUDAtDx3IFCBfbMilXy/TaFRAu2b4eDE0lYqg4tgmei6YqlKsVKoUSwpNI12Xo6u5mWU8P6aYWokkFW7houoFqGBQqLooWR1INHA9M18S1HEpmAVmRcT0Hz7bBdUnHE1iuRalcJKpFqXhVYol6EkYKixz57j6kokMmnQLZxPU8LNujXLGxXIHjlcB1wLVRNR3sPioVk5IRxVAhGYnhVS0KPX3ke3tpSrWiaBqm61EulYnG4hi6jhExSCSSAJQKRRynNn64roMn3OWDoA7IeJ6EZTnYtoPresiyUjNO6uuQZAVdlVGUmvHgugLTcyiXTSzLJhLRSSYTyJKEFjVoqKvDrrg4poXwbMDGsavLdW4Cx3VRVQld0yjky+AJJBSEZy0fhGUc20Ig0LWVSyD+12hrayMej2MYBp7n0dPTg+u6jBw5kkQiESxxW5aFpmmBAV5XV4eiKHR1dVEulwOZAdSWt32Pq78snk6nicViJBKJwPubTCYpl8t0dnaycOHCwNMYi8UC42RF2Yrv3epvoLmuG9TzJxCVSoXFixfT3d1NIpEgmUziui6LFy/m448/pqenh0QiQWNjI/l8PnhId3V18dFHH1EoFBgxYgRAIGvx293U1ESpVAq820II+vr66OjoYPTo0aRSKfL5fCA36ezsDAxof8KSSCQCr6/vMfQNG98rWK1WA0+e53m4rouu62iaFhjKyWRygDHqrxb4Mgx/pSGdTge/U2dnJwCtra0Ui0U6OzvJZrOk02nS6XRgDDqOg2EYgUHjy098L3ulUglkL7ZtU1dXR1NTE5VKJTiGrus0NDSQyWQCIygajQZe3FgshmEYgVFbKpUol8vB6kp/z6UkScGKgO/h9dvf3NzM8OHDAw+/L8NIpVJ0d3cDEI1GA69nMpkM6tq2HZTX19cH3uZyuYymaTQ3N2OaJrNnz6a3tzeQxLiuSzKZDCZu/oTJn/AJIeju7sYwjMBj338S4q/GNDQ0sGzZMjo6OmhpaSGVSg1YafElYb6czL9PfGmTf1zXdUmlUriuS7VaDVaCEolE4LHv7Owkn88H/deXk/iyJF/m4l9/VVUpl8sYhoGmacTjcarVKt3d3fT09JDJZIJ2FYvFYCyJRqPBKlMulwsmQ/51kmU5kFkJIQJngO/hjsfjgcHvT6L6b+976X1ngizLxGIxGhsbA++6v7Lne+uBoB26rgerTsAAQ92fXPmTiDUxZIM8Hk8R0TU8x6RUKNDT3UWmLkUyFcOyK8iaRKa+nnqpHl2R0A0V1zWJGCY9PVkqlSKNDQ20twwjn8uTKxRINTZQ19iAtVzDaptlkHUsVyKbL6MbtQFDEoKGugy57i4WfjqHaKqH5vZxRB0PBYlIog4ktSZjgZpnXAbZc/Fcp1boCexqCUk4RDQJGYdisZfupUvI9XZSKuaxyxo50Uk2m8M1TYRVptDbgWwnMHQVAahaFCUSpVosUOjtxbFs0rE48ahOqZDHrJQoZPtwXJtkIkYpn0eWBbauIUkqhiSTyxfpWLSYlrZGFLX2I+eLSwCDSkll/nyLxtZhDGsfhStsPOGB5+FJHum6NBM23gjJqZDv66SQz1LKFSjm8lRK5dpNCSieiqs6KJKMq0rYlockS8iSS8xQQBboqktEq0kZquUKZrVEudhHMh6nki8iyU7NW6hJaBhIukbRrJIrVohFo+hGDM2u4spguw7Ck4hoBkg6uCBHJSTFQ1Sq5HMFHDpRIlEky1m+epCknOvF0DSiGkQjOslEAjQDW9RWNCQkbGe5XMGIoEdiCCRM08JyXCqFPDFDRpM9EokUhqriCYgKFcd2UBwHWZKREPT1dCHJGooWo7W1md7ePjynjBGNousKpWIZRVZprFeJRizyhSpmoQcPGc8q41plPNdkeGsTTY119HR3YNsWkWiUaCJBd3cvAonmtpG0to8mn89hWxaaYeC4Al0IFBkc26GQ7WXx4s/JZ7uIRyNkMikaGxtQZDDLglgsQkSveWJ1PYpjOUTjcSqWWTv3apVs7guKVYdUpoGW1pGk4zEcajEElm0DAk1XURSVbF8vkgLxWBzXcbBcF8d2KdsCIUF3vkxE00ml0sgJjdZUG9FIjKb6OsxKEcsyqVoO+UIB17HA87DNKpoG0XgUBYFVKVPq7URTZeKyQ7a7i27ZoWFYO8lKlUSdRDqZQlEisNzQjsZjKHItpkRRNCTJWd5/a6sCnlczpCzLRgiCB5HreggBmqqQTMaQFRXd0KmUq8sNllqMi2U6CE9GkmoeDtezURSVaFQnnjCQJIGuy5TKBYSw0QyFSESn6jloioQkXCzTrMl6IhK6riJ7Ak94VM0yuoCoER3qMPpfTSKRCAyKQqFAR0cHDQ0NpNPpwAhvaGigra0tWDZ2HIdIJEJnZyflcpmmpiba29vp6+sjm83S3NxMc3Nz8IA3TTN40PX19RGJRIKHXUNDA11dXcyaNYtMJsOoUaMCeYVvbALBg7u/jt036qrVauCpBgK9aVdXV2CodnR0BDpc27YDPbFvqPhewEKhQHd3N6ZpBkv4vq64t7cXx3FIpVKBd9HX+voe2/nz59Pe3r58gukGnjzHcZgzZw7Dhg1j9OjRwfK9/299fT1bbrllsBqRzWbJ5XL09fVRLBaD6+DLDHwvY7VaDTyuvtHqGxz+KkGpVCKfz5NMJikUCgOkK77329fT+wazr+f3DUBf+9tfl18ul8lmswCBUa6qKul0Olgx8Y2z/isK/rn4xpU/eQMCiUY+nycSiSDLMqnlsVr+teovwQHo7OxElmV0Xae9vZ2urq7AK++vivia4EgkEujZoTY2+cZbe3s7LS0tLFu2DMuygglPR0cHAO3t7YwcOZJcLodpmkFf8L2vjuPQ29vLokWL6O3tJR6PU19fH0xG/HiE/ka2ZVkkEolAWlOpVOjr66NUKlFfX097ezupVCq4h/pLYhRFoaenB1mWgwmeX8eXSfma7HQ6TTKZDCbGTU1NgTTJnxz4spJqtYqu6ySTNaeLP/HyDeOuri4URWH48OGUy2Xq6uqC+BP//vM92r4mvv/kuv+k0Zc++f/695e/CuVP6H1ZjS9jMk0zmCRKkhRId/zfzL8f/P7n34f+hAgIVmYikUggA+o/nviyszUxZINcQkZBoVypsmzZEhzHJB5Pky9kcWWFxuHtqEYMs1qtBWvJCqV8ns6OHjzXY/SYdlRVIVvqw3RdIqk0RjyJ6cpUHQnJU/A8jSUdfbjVMi1N9SQlkBWZZDpJqVggXypjWS7J+jKpVBoViOgaciyOJ9daWdOAgixLCM/FdRwkrxak5dklCtlezHKearGPjsULqRZy1KXiSI5DpVKiry/L/AXzyCQSpGIasmujywLZc3CrFUq5Ek40Su+yDjRq5XaliGSkiBk6UVUm39NNMhpFRmCWS3iugxM1UNQI+YLN0iU96IaG57jEEwoIC7taIZlqJtUwnIrkIKsCTzhUzSq6YdS07Y5ENB6jfUQ7huzR2xGlUqwnn+2jp7OT7PIlPduysR0ba3kglHB1EDXPhGebOJaFEYthmxUqpQKRSBTXNsnn+qiUS1hmkWLRQjN0LEfQl+3FRcYwVDzXJp8roksqiUxquQTAxrNdZElB0gxsFzwFlIiC5RaRIxJeuUquu5tEMoWpariGisBFMzRct/YgYPnSqqYZGIaO5XkUSwUqpoOmRsgkksRiKUqlIrbtkEplqEtHKeV7EY5JIl2PKks17bysUyiUUHWdaCyGrKqY5TKFbB/RhFfTJ3vO8qCt2gqEpmmYlSq93d24jkehr0DVcgAJx7FIxSMk65IIzyHb141tW8iKiqobZHNF4ok0DY1NmI6H5TjEJB2zUqZcLKAhY5pV+rJ5ylWLcrlCuZhDUWWGjxheWy4zIuRzWTRZIR6PISMhXBlNM1AVmVg8CopCuWwiKRrJpIbc00c1nyWvGhjJepRIimQkRjKSxDQNbLdmCNUlhmGZtSV/17FAU4hFDdKp4eiKCHSFkWgcKeZiROJoig6GgaZGkWwXXZLQ01YtCFuWMSsVioUsZbOMVM1iYJKIUrtPHA/PruJZJtVCjkK2BzXdQkxLICsRXCFqnkJZxXbBrNpoqo6j1iYSjuMhKQLPA0WRqTkf5GBAlmUHIUCSFYTsoBs6sVgcx3aIxaPouobnuriuh+MIbNvDth0UWcLzHMAjETPQdZVUMkpfVkIzFKIRBRmvNpFXa14UXVNRNYNqpYymSciehGk7eK5NxIgRjepDHUb/q/EfaJVKhS+++ALbtmlqagoMzvb2diKRCNVqFVmWkSSJYrHIkiVLcF2X8ePHo2la8EDPZDKBTtqfiHmex+LFi6lWq7S1tQWeqEwmExhHpmnS2NhIOp1GkqRA1+4bXb6Hyw+W6+/1siyL3t7ewKj87LPPyOfzgfTA9+h98sknpNPpIMjRN+6r1WptdSweZ+nSpYHxX6lUguVwXdfp6uoKPPylUimQffjn//nnnwd9MpFIBPvOZDI0NjYCDHjo+0auv2w/evRoFEUhlUpRKBTo7e1l2bJlwQTB9yD29zb21+z6Om8/eC8Wi2FZVmDU+1Ih35Ds7e0NJiOu6wbL/JlMJghk9Q1z35j25Reu6wZBvJ2dnaTT6cAAB4Lr4N8z/VdYfG9qtVoNZBb+aoxlWWQyGerq6shmsziOQ11dXSCr8AMvDcMgHo8HEwM/5sGXcPiBvb5h5q/E+DKa/hpyP1bC8zy6u7uDiYVhGPT19ZFKpWhubg6kDZIkUS6XKRQKwaTIv/9KpRKFQgFVVRk9ejQjRowgFovR29uLqqrBKlL/4MxEIhEYr/5qRkdHB/l8ns7OziAmw189qlartTFO10mn01SrVYrFYnC9/RWm/oGf/u8Zi8WCct/YzWQyQV/xg059L7+/SuNPJPob/P496v/2mqYFQcd+zIZv3PurPr4Ex3Xd4LfyZSX+uOBPvP1xIJFIBBMX/171f18/TsEfF4BgRcCPk/DjK/wJpqqqgS5f1/VA7qMoStDHUqkUsVhsSGPokA3yaqmC4thk+3opFXNkMik8PMqWSX1LK4m6DLJmIKsadqnMF4uXseyLz8BzaGhKEYtFsKwqniTQIlEUPUbZ8rCrZSRFwbFtejqWoQiHVNRA1nRkVUWWazPdBfPnY9k2bcMlYrE4pd4OqsVCbQknnsRRIrjICCQEEnItOQay5+A5Dq5j4ZklNGxcHKxSjnxPJ8VsL5hJNFlC01Ukt0qhtxvVNamPN1OXjJOKRfEcG9ut4jkQTaUwSyXsapVEIo5wbWQhcK2awZPJpKmvyyDLkMtmyebzNc+mDYqaQpGjWJagVHIolXJYZpZ0IoJLF3q6nvrmVqIRHdM20SQZt+ISUVV03cCIRChKEtFYnIbGZpaZJoqqUFeXIRGPUMjVIphlR8JxPNzly0+26SBkcG0JSSh4DvR192FW7VrAh+3gyRJISdSIRkx2cSjjmg4RycWyHRzXBiFwKxZ5ShjRGLF0AhsHFwFIOIDlCISnEdNlTKcKoqYZNktlLAr02TbphgwWDoqq4DpguYL6RBJPAtu2SETjWLaNY1toqkYkamDbtSVmJJAkGVXT8GwLZAU9EsNyPUBBUlQkpaZnzNSlQXiY5RLFchVFNYgnUuiaTCSiY9olvliyDNcxUfHQJHAtE1yBXc6jKSqNjY0oqkIiESOX66W7ZxmpZJL6+np6+nIU+wq0DB9BpqGFUsXCdD2KFZNYJElMjVCpWOQLNc1dIZ9DIBGPR4iNGUkymWD48DZAoloq4wlIJFO1zAKyjKFHlntvHXKlEq4rsGwbs2oTi6eIaCpWpUo51wtC4OTzKHoEWZHwXPA8B8moebgUz0UTVVy3ioSKrkvUJRPguugNDciqSqlcQTMUSlULRdGo2g625WE7HoqmI9QYnuwST6VpatMp5LPkujuo5iQq+U6iepRkfR2lfI5MQyOKqpLt6SLdkkOyyuR7O9FjHolMPZZpI8kWcbUWK4HnAhU8DyrlKo4r4QkZw9DxPLF8ObPm4fY9GbKsYC9fFlVVBcPQMU0Py7Rx3ZonvVQqoWlRNFVFU2pebs8x0XUJRVNIp+qJJ1SE8IjFdErFEkK4aGqEqmmi6wqup6AoEIloeEJCkh2EpBCP6xhGmD0WCB7k3d3d5PN56uvrA4Oxra2NhoaGYAm/WCzy2WefsXDhQjzPo6WlJfDuAYFO29eQ+g+4pUuXAgQGlP/wrVarfPLJJ1iWxahRo0gkEvT09ARa6P4GuY//2Te6fA+8733zM7/09vYGnnn/Id7d3Y3neSSTSTKZDKlUKtiH/4AuFotUKpXgOyDQ4dbX19fGleWeyWw2G0hMfOPKN1Ty+TzVajWQH6TTaVpbWwPDCAgkKL6kQZIkEokELS0tgffOD+7LZrN0d3cHEh3fS+yfuz9BcRyHrq4uqtVqoM31J13RaDS4fr6h5U+aJEkKDEtfR90/cw0QyCIMwwgMLN+Q9A1mP+uGL2PwZR1A4HX2DTo/wNI3+oHA+PeNLF83rapqsBLgZ0DxjT/fe+p70qPRKKZp8vnnnweeYj+I0M8aoygKLS0taJpGMpkMJEfpdJrGxkZ6enro7u4Ossn4Ontfo65pWrBCEIlEgpWCRCLBeuutF8RLQC0wWAgRyGRqQexGIDHqLwvzg4wNw6BarZLL5YCa7KN/4LAfF+GvWvh6dN+I9SU1LS0tqKoaBGf275e+MevLYSRJoq6ujhEjRtDX10dXV1eQEckwDBobG8lmszQ1NaGqKl1dXbS1tWFZFl1dXUG/6h906ccpAMHv5bfXl8n5BrK/itA/S5N/j/lOAX81w5eD+f3OXxXyV3M0TSOTyZBMJgNdeaFQGCAZ8lddVFUNVmj8/pFMJoPJ5ZoYskGeiBqUcr30dnUSj8WIRKPkymW0WIJMUwtaJIZmRFEklS+WLePzRYtqnu7mBmKxGLZj4ngeqh7BshVcT8ZBIles1G6Wvm7MUo6RbU0MGz6cdCaF51SolKvkc1nMSs2LmohEaMqk6O1cjCdUPEmmXTMw0k0gqwhkLNPCMHQUTaFczOKa1VouP88Dp4Iqal5vQ4K8VWHhJ0tobKwnFo3S09VNuVCgpaGOeDRKrLkJVVXIV2xczwahYpsWsViUwtwsiXgMyXXJ9fZSKpWIxqI01NfjWBaObVEsVlja0cWixUvRYxm23Gp9GhqGocgS2dxSqmUH1/FAMklrcdzlP6qmaQjPw3VrHtqSbYOhEY3H8YTAclwS6ToaHRtNU+hY/AV22UFR5eWTpZrXsZAvUSxUqJZcUGQ8IXA9j6iQqJSK9HYXSSQTRNMZjFQDnqSjqTrxmI5DDpHPIWsutlRCqhYxLQvHhXzVRuDRHlVJJ6NIFSgWLSzbwTIVEDWZiGV5YDvURWO4FROrWsaulpEVj1h9CgGYtk1UVhCyiqglGiRiaNiui64qRGJRND2G4wocxyQZjyHLBorkUrFBeIJILIbruZhCoEgyiqLS2tqCoihEDQPXspERZHu68IQgnkySTifIFbN8Oncuuqqy4bgx1KcTyJ5LrqeHdMwgnkrS2JRBIMgXCmSzvURjUZqam3G8WhBjQ1sDRjRFrmhhugLTlegrWahGDOE4uChUqg6WZVMxK8RiCeKJOEYkQqYuA7KGY9ugKCQzGTLJNMV8jq7OblRFwbZNPGEHBo/negghUal0I8sKsqygyR44JcyySbFcwbJrgde1iPcEyWRtqdJ1HDLxOJohoyg21VwnYrnGULgSiuvgCYmIAtVyDuGpeELGkxRMx0WPRGsxIMh4sooWiRNJZohEdSqJFJVSjqV5E2FLGMkMmuTh2BZutYBiF5GUKK5dIZetxWOUKzmQlmsrKxVkScO2KggB1YqF5Qg818OI6DhOzQB3XQ9Jqj3UozENEIExI8m1LEU1D8pyOYJXS/kYj+m4tomqyEQiKppaM+QU1UM3ZDzPRddlTFVGU9Xa6poEhqFTLNpkMglkqUqxUkb1PDRdxTBkFCkM6gSCAKiOjo4gyND3rra2tgZp0WRZZvHixcybN49KpUJbW1vgufL1rr7B5mcrKZfL9PT0UCqVGDlyJKNGjaK+vj4ICvSX0/2lZl9L6xuBvvzBD+byA8f8pehqtRp4iH3jWVGUwOCYNWsWLS0txGIxli1bRqFQoKWlJdC5qqoa9E+oGd7+9Ugmk4HHtFgsEovFAi2xrzlevHgxCxcuJB6Ps8MOO9Dc3BwY67Uga3uAd9kPwvTb6xvS0WiUZDLJkiVLglUG31D67LPPAo9tfX09UJMQ5HK54Br3z24BDPBa+xOP/lICH98T6csl/OBRIPCyFotFCoUClUplgPHuX4dEIkGlUqFcLgeaZt/b7Adf9o8D8L2smqYFhqfv7fSlBv5EwZcN+CsAvtSmvb09MNJ8Y76rqyvIjlNXV0cul2PWrFlomsaECROoq6vD87zAcEylUrS2tgI1Y9fPpDJs2DAcxyEej9Pe3h70B39lolCoORT9CZA/hlUqlSBWIRaLUV9fP8Cr70tXstksS5cuHZB5xN+/r932f1P/WvgrCn56QH8y01/z718/3/OdzWaDyVN/XbhvnPv3i3+t/fgOIJi8+Z75ZDIZBDT73mN/guMHCKuqimmaNceiLAfpRVtbWwPNvy8z8XX78Xg8kDr5K0X+NdP12gqmn8XJP57vZfevna+R9w16f8WqXC4PWCHwJx1+FhtJkoIJuB+wW3MC1Qx7X/I1FIZskAunSr6vh0Kuj3QmSdWyKJsOTY3DUKNpKqaLaZZwKiUcy0SVXaIxnVQyAniYtoMr5OXLxy6lao5i1aJiOstnZNDaNpzx40fR2JBAeFWq1Qq2bZGMx0mNSSIptZvRsS1KxQKm7SJFIgg9QssoSKbrEC7gumA7WKZDOdtNpZRDuC7C89BkFatS4ouF81ny2SI8u4JwbKqlIna1wrIvFtPbk2dYm4vrSRhGFNMysWyXqisQgO15qFpt5lQqluju7CKVTpFevpyo6Tq93d1YjodlOeRyBWQtQqq+GT2WxBIyqVichNRAqZLH8lS8skvL8HoSqQy2U+ugEUPHdFw8AbKQKFeqNUOyvhGzmEeLxMk0NAd6496eDrI93fR091ItV7EsD0nS0XUV23JwXQlJrj2QPEchHktQqpTIZ4vIRgo1aeBKETxPBk9F1WMkUhKVfB/CcYioMsKiljpSgr6eLpKZCM2RZoRl4ppVPEdBU2JIqEiKIJZIUck7yLpOui5Dz7IlRGMRdE0Gz8U0LTo6exk7oh0UAwkHVVOplEuYFRNVFlTLRSJ6DNUwcKwqiVgaXVPxXBPJMLAqKsLzSKdTOLaNY9vomk5EN1i2bBl2uUI8Hicdj7Oks4tytcrwkaNIpFIkEnHq6xsZMWwYMb2W6q6Wls8lHo+QikfBc+nL9+FJ0DqsFU3XkTUdyRW0tg9HyJHaxECVMKIG1WIJIess7epBdi10FPRoLZNLKlNPNBqlrr6e+sYmevuyLF7yBfFolEwmBcKjo6uTXG8fuWwO4bnIioSiypRKZVRFAUHNKAVkSUbXNLK5XsrLyghX1K5fpUKpWCIWj4NVJN+1rBaoqCjE4lFUQ0NC4LkumqqTSWcoV00qpgWKiqwb2I6EQMPxVCLxJLKs4lgVPE3GFg7lkoVtVbFsEyHA1eJ4BgjFxLQczEqeYY1pPNch272M4aMLSFqSalmlKlw8lFpMRr6EYRSQEWiavlybbmDaHma1UvtNZIVIREKI/z9bRm0wrUlX/GV22xaoqkY0FkGULFzXqQWIemrtvhbgOrXlWEWTcT0FSfKQ5drgjHARnhsE+yFA8gSyLIjHIpimjSNUND0COCTiEcxqeZXj5v8S/hJ+X18f9fX1gXE2YsSIID2cH0jnext9LapvCPgPRr+en3bQ99q1t7czYcIEmpqagowQ/hL0BhtsgCzLNDY2BtphP/WcpmmMHTuW+vr6wKjw9aN+3mDf0FAUhUqlwty5c5k/f34wOfCNyUWLFtHZ2Ul7e3ugN/eNSt+Y92UAfraFpUuXUldXR11dHS0tLRiGEeSj9tvgZ56Jx+OB9933BPrBZ6NGjQo0+UKIAQF+UDOgfdmEn+nDzzhiGAZdXV10dXXR0dERXFvfE+p7Of1Ji593ulQq0dvbi2EYgRbfN2b8tH79g+1877Esy3R1dZHJZAJjyb9OvnHt3wO+17a+vp6lS5cGnmP/N166dCnjxo0LgkL9iZQ/CfNTUPrp7PzsPH7Aqp9Zpq6uLpCLGIaBYRgsXrw4CFJNpVLB53HjxgWTkKamJkaOHBlIbHwDK5FIBLrs3t5eJElixIgRwSQNYPTo0YER6euMC4UCkiSxdOnSwJj184fX19cH2uzm5mZ6enr4/PPPAx25EIKlS5fS3d1NX19foHn2J4X+NfLvRV9q09fXx+LFiwOj0pfExONxLMsKZDj+ZMuPafCN5Pr6eiqVSjBZ8q+vf6/4Kf5M0wx+u/7pGv00hb6B6mvOW1pacF2Xzs7OYDWnf556P9Wp3x5N0wbIV3zZjR/70H8lxnXdoH/42ZD8c4zFYkHAsd/3/T9/f5qmBcftv4LQX3Pef0XGX+XzHQtQ85D7k9M1MWSDvJLvJduzDEVykYBSuUo8VU+yrgWhxJCEh1Mpku/twyrnaW3KENElohGDsmXj2GA6UK5WqVRdevMlypZDIp6ksamJYS0NjBnRQn1KxzVzSB6okg2iNqtSZY1SuYJt2uSLJbKFAi3DhlFflyLX14WiG0iujaZGkCQF03RwzBLZriWU8n00NTZQrphUbMGyJV/w3l/fpWfpYoY3N9JYX48qK8Ff1bRY+NlSNFWjIRNH4GIJCVvIeMIjVyzhCGhobMRzPXq6e0hlMtQ1NGF7Hq5lU7Udsvki2XwR03Spq2sknc4gSxKFXB+WVaKhIcGw9naWLnFRgcbmEcST9Qg1gqZoeAIqVRPPg2QsgSQJkqkkmUyZBT199GaLlPJ9WJU8lUKRqmXjeiApGpou1/J+ew627WG5y6N/JQnHc7E9gaQqqKqCKtVSzVVKBSSt1gFsUUuCHk8m0BWoahLVokqlaiGEhaaqWLbNZ/Pm4jkm6YY64qpM2XWQFQdZqWnAJJSa1AFwJQ/JkHCxqVRLKMKt/W4oeJKOjYoiy6iaWnv4VSt4yCCpWNUiyZSG7QgUatkxLLOCoctEIzqqJNHT1UnEiBCPxcnlc1iayeeLFqLKMqNGjSIeT9BYVwe6jlktYVZLy4OR4ss9OiaO5FEslcjm8sSjEapWFT9hUSKRQFYVhJBr2XYUDS2SxBa1fNkKGpYjUCMSmTqJUr4XDRfhVomrMrqhkUjEiBgGejRK1bKxbIERSRBPJpFkhUqpQG9fjlKxSL6Yr70QSdOoq6sjFksQjcXo68nS05etSTQ0FduzMS2TcrFI1DBoaW1nWKSVcrGC5OseixW6eruxLAckD9WCcqmI5zpEjTjlXK6WYaFUwkGQSKXRIzGSqQYkRaFq5UFSkFSdfDWHLCm1HOeSAOHVJE2eAGR0wyAdbUFUNUqFHkrFHJbnMKyvi/pkM7ZpUbQErgepdIRqxaKnO4umSiQTEUDCsR1cR+C5Astz8DxQlZpkBUDTVBSlpsuvmg7lUhVdjyChoKk6yUQKxy7gejaOa+M4Cp4n13Tqtoeuq7VsRLKMIoGqyHhuLcmKRM0IlySQkUCR0FQZVZOJxQwst4rsiFpaRlWgRoe2HPnfjq9T9R9S/lJzQ0NDoOesVCp0d3cPSGXma1l9Q8l/6PupDpPJJK2trQwbNoyxY8dSV1cXeGF9jbbv/fYzlvgvjhkxYgSNjY2BQel7l32DwDRNOjo6yOVytLS0BA/szz//nD/+8Y8sXryYYcOG0dzcHEhkfA/eggULAiPS9xL6RoifGaW/seG/1MiXxvhp4fr6+rAsi4aGhuCFQNlsFtM0aWpqYvTo0Xz22WdIkkRbW1sQ9NbfYPUNeF8qkM/n6erqoqenh1wuF2ji/WV636CC/z9ntj8h8mUMviHmB8ABQQChv0QvSVIQZGkYRjBp8Q0S0zT55JNPgnzYvuTHz3The+L7Z6Hw/+9PxPqnsPQ98b7W3s/D7ccu+PplXzbjZ6+JxWJIkkRHR0egJfaDRefNm4csy4wfP55kMklTU1PgGfWzo8Tj8UBm4ueV7uvrC+5d3whMpVIDtMv+JME34oBACuHLNnzPta+L97Ot9Nfe+15mX2bT3d1NLpcL/vwMNH52If+37+/Z9fXhfsrIkSNHBoG5/gSns7MzkEH58ip/UuNLXny5RiaTCSRJforC/hmG/P/7fbR/FiP/N/BjFPz+4mcy8o11fxLl6/Z9OREQTID7G9O+vMm/j3xJSf9z979Lp9OBlMVfWfH1+P6E2p98+efSXzLj3/9+ILY/Ae8fFOtPlPqvJq2OIRvkhWwXinCoa2zEQcarujS1jkCP12F7CrqsUSr20NvZhVPO09iSobkpg2MLzN4CZtWjL29StlxcAbKi0dTQwLDh7dQ31NFYF6exMYlnZXEdk3hExbYUPLe29GxEI1i2S09vL119vRQrFZrbhpNKJjFQcN0qZimLEk2BkCiXC5jlIp1LFtHbuQTPHE4iVY9leixZspjuri4s06RYKtHS1IRjO8jIRCJRotEE5arL4o5eiqUS8XgULRYFVcbxBN29fVjlIrJuoC8fHHQ9gpAkJEWtadi12ktLSuUquhEhGY+DY2NVi3iOwK5UkTHAc8ik0lgVG12NY2gxpEgUTwicqgWilvYPSSVfyKPKKpoRxUXmH7M+ZsHcT7DNPBHVY9TwFlqGD6OpFXTFwHNlymWTXC5PR0cX2XwBs2LXvIa2RaHoEo/p6FENs5ylms+j6HES6RSKWtMhOq5G1DBIRBqQDR1TALkcxVwOhVrmllI2h7H8IacKF+GU8VwL2waBjCqrOMKjUi2RasigqzKO7WCZFWKqTiKWwHKhYguiukzFtCiWiojlHvRoLE6lkEW4HpFoAl1VEK5C1bGwhIcq1YxLVZXwHBvHsdE0FW/5wG9WKnR1dFBJlonEE+RNCzSdhpYWIoaOrtd0ramYQV82S3dvL5KikKmvR5JA0w3kiAZK7UVU0VgcVYuRK5jIehRhK/QVCtiei6womGYVRYJkMk0iolIq9KIQwbWjlMtFkB1cLKxCFU2LkqlLIEsehVKRUrH2hrVcPotpV6lrqKOttY10qg5ZrgUXxqN1pDMNGBEdVZVq5y0cXMdBUVQMPYJhRIglqlSrFolEiuEjEwwrFRECdF0hm+1i8ReLqJRKJJJJkvEUkgRCkiiZFSTJxbMrSG6FmK6hqDXJk+VUKBYK6JqBIwSqIqEpMpg2mUQKORrBtquoOMiGTjrSQCoVRY8nEFLNe++UHFxbwkPFcTwMZFxXIAlqnw0Dx3ZwnNrv57g1uY/j1AZD13WRZB3d0DELJcrlKsVChWg0Xgu6lFR0TQM8hOegKAT5wj3Pw3M9IhFj+QSVIL2i/0Yx3xuiKgqOC8JzQXjIkiAWNaiaGo7qEY1qxKLG8gltSE9PD1BLgecbH8OHDw8CH1W1NtFetmwZpVKJ4cOH09raim3bgXfMN8L9bBMtLS2MHDmS5uZmGhoaAk20L8/wH/Ke5wXLzb4XuFwu097eTjqdDlK4+enUoGZclkolPv/8c5YtWxYETZqmyWeffRaUFQqFQN/qezL9NHdffPFFkHbRf8OhL2fwc5P7Bo+vI/UD1vx0f/5bAJPJZJBmsf9LZvyXsPheQT+FnW+M9w9c87OA+IbHBx98wMcffxwEPY4ePZqRI0cGL1HyPe99fX0sWbKEvr4+KpVKEOSWy+WCADj/rZ66rgcvnvGlMpFIZIDB7Wd18Y0VP7OG71n2fzNfruHrmf1MO76n3feY+sG9fkYS35DzM3n4GWw8zwu867IsB6sLfgCxHwToe0n9+7JcLrN06dLgbaS+Id7W1hbEMxQKhSA2wTc4/Xzw/m/in0cikUDTNHK5XKCT91Pj+SswsiwHmUpyyx0iftpA/zr5kouGhoZgIuC/5bX/pK29vT0IWNV1PfCm+/ekf66+ht6fKCSTyWDSO27cuMDYNgyDnp4eFi5cSLFYJJVKBRM+f/IDBP3Pn3T4Ei//ZUz+fe4b7P4baf0ML358hx/A7edO96U3wAAvvP/ZX3HxV1v8e8NPM+gb0r5+vv9qQP8+6fcvf9LZX7bm5zH3gzz7B4X797U/Aeif7cUPhvYzD60sfmVVDNkgz2d7iUYN0ukMHbkSKFGEbICkYVVNSqUCXZ8totDdTX1CIx6JoQiZZd3dLO3so6O3SL7kEImniSdSxJMp6huaqatvRAiHSrlEX69FMuKhyBLlUplSoYyiqKQSBtFIjK7OXrq6uimUKzS0NNcCtooloqkMeB6KcJG8Kp7rYZWzlPO52kt/Cnk+m1+lfaSHZfH/sfdfT5JjWXYv/MOBFq49VEZmlmrJ5iiSxv//mUaa8ZLD2z0z1VVZlZmhXUMD55zvAbHRnvOZkfXAp9vjZmVVlRnC3QEH1l57CV4e7lDWkKUpvdF4rvcq8ejoe4OnfLRVHI4lRVlwcbEicXwcr8FxPAbv4OCkVTh0WpNXw5o+mfxlEmrbFusY0jgiS2LqpqfOhw9e7CekoYujXSoDxjpYPOraELhQtQVBnJJEKU2nX1fyDWq5YL5a8evf/Bajew6HDX0TkIWK3//h35FECYf9Aa3BcyMyDdF0SjCZMD3mvDwPBhNXQRS6gKYqCgyGujVY54CyNVE6xfNCml5h+oYwinDThJlaEycBkyzkeDi8MrMldXXHdD4jjGOM09N09WthS4Dve+jQ0mhLFoQEUYiqm0Eu1HR4KI6HE64X4M0Tem3ADQiDIa7O84YLTNXU+MGgA+76mqat8Vwwuh20vkFIGPj4noerHI6HA0kc0DUlT8/3FFVBMpnSK4+6qsDAfLrgq/ffcjjsKIsD//TDj3hobi6HbcfpNGR4d2Y4tsv1FWE4QVuPMI3ocKmNpe46rOPhKYvRPdY0uP6QHJPEKVGg0H1LnCZYYymqhqruMNZD9x1915CfDpRlTuC6zOcL+j7j9uaGi/UFfW84nUrarsT1PdYXF3jesAVJspjAdymrhs32RN6AUT7G9ehVQ6k9FB6dO2gCCVzefP1r4mTCdrNB4RC+3khn8wWLYE0QvG4p8gO7pwem8yXpdA7KsMp8HDWsx/uiodM9+fFEeHHNanWBir3X5IADF29vmM7esTsVtE3Hcbela2L6NsTxU4qyJIgTAkK8YDBghWlEFFvKJsd14ZSf0EYTxSGTaUrTtrTNMHy4rk/fF5RVR1X1WDziUBFGAX7gYls7JC5hMWaI+BxkSaD1MJy6bjAw/sodkoJwafsS33qja98GFt0PRVm+55BGKVHk0zUV1vk3UycwMoaLxYLNZjMa5+QGnuc5Hz584OnpaUzfgCFqTqIFBfRMp9NRKiDmvqIoeH5+Hs1sUlAiaSJxHHN/fz8C/pubm1GrKw2AwMgAC8CUEp9//ud/5ttvv6Vt27FeXTLHRS97nm9sreVwOFAUxTiEiKFNbuDn6SdlWdK27WhMFAAGjHniMgA4jjO+JlmTn0sAXNelruux5EX0wFVVje/ZH/7whzHxpK5r4jjm7//+78ekDnldq9VqTCfZ7/c8PDzw9PQ0GgZhGF7gLwZOa+0IOoUNFPAhdfRicJTXVFXV66YvGX+WFLbIwCJMsRxjMShK7N65dl6en8gYRBogUgaRsYnRDxhlKpKEsdvtRjmVAHLRyZdliRTtfPfdd2y3W06nE3/6059wHIc3b96M76+AtslkwtXV1SidENmRvI5z6YOA7X+dPCKpOkVRjAOXMLiHw2GsY1+tVnRdx7t378bBVhJNgiDg+vp6lM0IEC7LkpeXl1F2cm5+PM/PDoKA7777jizLxhx4keuIOVtKrGTIXi6X46ZKsv9leNB6KPy6ubkZDdzy+f3mm2+Yz+ccDgfqumaz2YzbDTGBJ0kybkvkMyTbCYkJlWFMislEIiSgWfLggbFcSkD4ebmSHEt5X2SjI0z5uTdBPt/n3y/nsAzpkir1Sx6/GJD7viLOUlSYUPUVWgV0vaXb7jBtTb59pNo/Q98SRzNM7/LhwyMfPn5mXzXUKII4YnmxYr26ZjpfEYQxWOh7TVmXRMonVh6Rcjk1PfvdiZvrW9J4yuZ5x35/IEsyrm7e8u1vf82pLGmbDq9u2e2PKGPRUUlVFbR1he1bVvMpWfgtxSmnOhzYvGwxZU4WRxR5jhMk5EVD08uE4+HYYQ3v4LE/FfQo1nhkqQtmqFkfThBD076uVnYnLt4YIuPgKYvnOUwnMfuNIoqSV73SFt1W+J5LoEI8NFkc8fS8YXlxgVEebQdt3qB8lySx+MrgJQGuCkjjK8I0Jgg9pvMpf/O3/45p6mP7it3THXE8DANN33PKS5TbYuwQ0abSjFmS4WVT0uUC3bdU+YnN4x35YUeaZXhhgnJ9urolcCsIDMYalDtFd86wJYgDfDclCR0shuPngv3+gLUOvXaYLwbJiek6TNdirEYbj6Yf9PfHSmPR9EVLXze4oUMaBmzLBoWi74fMexwfrMZ1AsqqwvU1jqOom4rtcYvrMDR7BjFREGJNNyTpGEvbNbRNTZx4vH9/w0+2pa49bt+/JZnMQYXE6ZS6aEgTl9l0RTaZUdQnDnnOw8cP4Hq8vGypqgI/8HECxWpyRTKZEgRzut5DuxptFH6giJOA7fZA4AckkUcSxnRdi2M9wijD6grPd1HuMNk7ZQPW0jQlre1pm4bT6YjrKlarNcqB4+GA4/jUdc/plKONGcyfjsULfDJv0Hf6oU/XaPanBm1jlBdiVIzn+UwTF43BeB5O7FM3FWXZsj0WuCbAjWZDc24YDMkiTYXrOSjl4CqX/HhANzVNnXM6HTA4pFlGXVW0bY1uGrqmJnB9ji931KfdcAHUPaf8gNLw5lahggBaQ58XZHFM12nytgQUeVmiPIXvQ+D7g7HZD4njnr63cIC6aYfG1DjGCyIsoBwP3w/x/RhjKw7HGpwA19EkaUIQKBzHx2LBWlzHAdsTBAqtW4QT7zuNNRajwbEubdtgMWjTg3JwHZfWNihloTdMJyF+GGB0i3bAGUVNf90PyRsWo5rc0DabDU3TsNlsRs2rsE9//vOf+eGHH0a9aBzHXF1dcXV1xWKxGIGNgE0BESIb2Ww2vH37lizLxpbOLMt49+4df/jDH0bDZhAEYzSfAEHRnq5WK+I4Hps3Hx8fqapqZF2FHRVJB/CFBGG/34+AWQCY6Jjlv0Wq8+7duxFMyiAhucZZlo26dwGMwrg9PDxwfX39hSFNWGApPxEmWQxuy+WS//gf/+O4Qr+/vx+3A5LgInpjYZnFeCctqsfjkbu7Ow6HwxdGPwG6wAhYxNAnTLGwpj///DPb7XZkHtfroZlXGE0BMvLfRVGMkqeyLEmSZDw+wsiLXlmOgWwjRKoiIFnAqWxp5BgK+54kCd999x3ff/89VVXx7bffjrITMSBmWcZisWA6nY4RhT/++CNKqVGLLyD/9vaW6XQ6gmthzMMwJEmSsZFU3icZyJIkGeUS8rzPs/HPIzU9z+Py8hLHccbXKZIkeY0wDB8SASmRiofDYQS2MjDKeSoJISLv2O12ACNwlc+sAF0BogKkxSBsrR3fKwHGsqGRMiEZ5ARIf/XVV6OZuygK4jgePQfAOITI8xAdvPy3bHbERC7stzDhspWSTYR4F+SckfdazqlzfTzwhZwL+CLdSAD6uXZ9NpuNUqFzI/L/6fGLAfnqYoWjQn78+JlDDYvrr4mihONuS18e6PIdfXNCd83rNJTz9HKk1Q7JZEYcRaggYn19xXJxRRAm1HWL6Xs8NRjTdN/TNxbtG6qyxhhI0glN0/L//D//g+ubG+I0ZTqfM5/OUF6AF0Z4jotuWo6bFx7KI6f8yOV6ReAHhL7PcXdgv93RlgXF8cg0DmmrkjIvWSwv8ZIMFSZDGkPYM5kMcYBRGqNdS2ct2jjYftC29p2mCxzwPNxoiJ1zO43zytgpRxP6PkkcEIfuAO4t+F6AgwXTEXoOrmPZ7vZ0vSadTekVWNdHOR7WaoxusVrR6x7HcwiiGJQzFL/0HdZoPE/huAE4isPxiOd6hFGEVR55UfOyO9IbhRdGGMfB+B7TizWB49KUBXEY8vHDIMPxQ83l1RscoDzmqKQnCDzcvkZ5lsiNsJ6HUS551eG4MJlNKeuWumrpOsPpVOF5Lr7vEflDbJ7RHa4bgArotaLTLtoOhT+VLXF9i6MNjrXEcUoYJ4RJxul0QGuHVtcUdcdiPsc4PVVbkcQJQZQAHtpYulbTt0NE2vGwxbE919fD6jPJEi5urrh+e0s2WXE41hxPJcf9kSCY4AYJ8WRKNJ3yGxShH2KrgjQxhL4HriFeTAmThKrpcUOP2fKCfndge9hjgcDpmSUDEPX9CNcP6A0Y1GtpksHpW7qmRvcaF0voOdTtEFs5rBJ9rq4vmKYTsJYgiCnygp8+fsbzA5bLBV7Yvkb/DZGSp7IYPAN+BCpCuSGOGxBO5riuR1FUQ/mO51CVR1p6kjSj2B9RnstsvcB1oGsqlILADaiqHNN2WD18/kI3RDNovutXAF7mOY7pCV/BuzUdx9OJumnR1hDHKX2v2W0OPD3tSKcTVusrfhVMmU9WHG2F6TxwI/KqIH5tpDVa0XXgeT7KGeQ4aZa8Jsf0dHowXhrH4iqF6cHiorVLWfXEsSYMLUnsEycB3TGn71t8b0ISh+iupu8a2sZgtRneSzVo3x0GV36ve5TroM2g9XRw6boTQRCRZcNNNPQcWm2Jw1/uoP//+uPqakg1+uGHHyjLcqyi3mw2lGU5xgCK9nOz2fD4+Ejf92P+cBAE3N7esl6vx3WzGKzOizyAUY4gWtT/8l/+ywjOJYniPCe5aRqen59HZu76+npk+rbb7ahtlxxxWXNfXFyMLZDnRsaqqsY0j3PttbCz8nvFrCcaWwEzAobONcainxZw5jjOmEghuerC1glzKqycDDQyCAmIEHAvLLMAL9d1x+pyeV3ACPhkCxHHMf/yL/8yNkXe3t6O8hjRip9reEWSIIbL+Xw+JqcIiysSgfNcaQFE8noEwAvYlq+RoUMYfWHoy7Ic02POBwwxJgo4FIkOwJs3b0a5kJT1SILJ8Xgcy6fka2azGcBoLJQhRSnFYrEgTdNRVrNer3l5eWG73QJDas+5fEtkJHK8BUyLJEvkGwJmRdf85s2b0Ugqw+KPP/6I7/tcXFyM758MSafTaQSzMgjKwCFG0DRNRwOyvMdyrlxdXY2DjuiyJXJQzJwCeEVGJVsROf9EKiTg3RgzypCenp54eHhgOp1ydXVFFEXj6xNJigxG59Gi8jlyHIfJZPLFBuvcmHn+HgvbLp0AWZax2+2+MLPKtkl8KpLcIsD6fEsm6T3njPlkMhnZfSEf/q8z5LP5gruHDT/89BPTi3fMZlMCT2Hamio/cDpt8X2P6SQhTgLapkUrTTxJiedrnDDF+hHz9Rv8IKWsapqmxXccUBZPKcr8BI3G6Qsen+55//4r4tjjT3/6I2Hsc3U1RBu6rsvj0xMv2x3aOswWK7IkoW8b8v0Ba3rKU07ve1hj2Tw/cf/5E31VMY1TDNB2mmQyYzJf4ocZvdH0vaZvS+IkI/Qa8Fx89zVztB9i5xwLKI+m7VFWoZSlroeMWmMdTkWB6RqU0a+52YPGXHcdfdOQtzVxOPx/WRRsN1t6q9BY8rIgThe4StH3DV3f4ZkAY3uMsjgo6rqhLnM2jw/U+ZHH+0fq/Mj++YlJEpLEEfPZbNCsW4fDMUd3BkdB4HngK8DBao0XxVy9fY8fR8T3D1RVw+G4x3QNy2mGQ4CnDJFnMXR05REvcIl9RecpotDFmWcYbdlujzRNS920WGCSZSRpjLIW5Q7mXi/0sdZgtSXwI2zQ0jYNjm6pm56n5ye8yGd5scQA6STjeNgTBCEKRZJN6E1Nb3qUcumNpupqqjLHcx2M7iiLI33TEnjQNj1N2xGEEfPFCq0H41mSzGlby8vLM+lkShqFVF1DbyzT2Zqbt9/y4f/9R/JjSRRq4ixgOk0xFtJ00KnmpyN9U6L6Zkgdqg2qM3SuS2cc9mU7Dkd9cyLwA6rTnv3mAdtpsnQOxtC3BtMbsjRjfjsjSSK6qqbvO4wdipNSZ4qjfBw/JFA+uqrIy5y6biiLCm19ZrOQMIkwro91PBzfAceh1T2hhciP8VWLdmGSzcnSJUWe4wXuIJnphxx93ws4lB1tUxEFHrunF/qyAhzCJMH3fOpaYzqHKIgJPBcVKLq+Iwg0YRziOIosnaCN5fl5w/F45HTKeXnaolyPr1wXrTLQKRiDo6GtO8yrrtsw5IMrz6UqqvFiVhQFs26OshbjmFdW32KMRfeGrm3ROsP3XJIoZLmcg23wPQelLL3u6NqWtqtJ4mFjRzt8phxHYazmlJ+G4qPAH28Ium9fdbIxrquGyMVX5q3v/8Ka/rU/lsslnz594vvvvx8ZbsmBFpNlGIajXlSAtcTLSTLF5eUlURSNLPb5qlji2vq+5+7ujm+//ZYkSfif//N/kiQJt7e3I+i9v7/n+fkZa+1oeJNyG2PMaIaz1vLw8MBPP/00Ajk59tPplOVyOa7rZSAQXbWAXRkcBITIACCMp2hKrbVfsO3nwOs8hUQ0smLgE1Ahkp5zqYhIWs4jHYui4O7ujtPpxM8//0ye51/EUS4Wi1Easd/vR1nIublSwMRXX31FHMd8/PiRqqrGr18sFgCjQVNkHgK0RVctyTYvLy8jkyrHXeLxJCdc2k7Pc9UFGImsJAxDLi8vgUGKIeeV/Mxz+YUARNGPyzFq23ZM82jbliiKWK1Wo7RCzpXHx8eR8Ra99GKx4Ouvv+Z//I//MZ5DooMWMCuDhzz35XI5GoZleJHnJCk9ojl/enpCSoZkwBPgu1qtRo2yDGEyIMpxCMNwHIAFGAPjMZfr6bkHQFhhkXhIG6eYeM/LcwTEi2H24eFhlIKIRlteqxTmyOdJNgVKqXE4eXx8ZLfbcTgceHh4GGVfMkjJIFZVFUmSjP8vQD/P8/E1nU4nVqvVeA4Lq38+LMuwG8cx6/X6i22TaNHldYqxtqqqkQU/Ho+jMVe2dyJJEqmVxHnKtvCX3iN+MSC/u3/khw8fCQKfy8s1oQd1eaKrc5rqRNfULC6XJLFPa1pqUxNkIaGfki0v8OMVjp8ymV1QNy2dbgijmNBT0Bd4DrS6pepKTrtH6qYky0Kq5oTyDLdvbwgin/lyzWZ34OePHznlBUEUoYCmOOE4ltV8DljqqqR7XTkEriIOfI75kTSLeXzZYVFMFms665K3BpwhG702DiiXKArBc1DVwN4abXBDF4VC41BWBb7xiONhOg7jCEd5bLZ7mjJnkvhUdY1yXNI44XgsQWuUB47t2W1eaPqGVlvCbAauAldhsVir6fqWrlP4egD2Wjf4QYLyHTzPx/VDgijjzdtv2T3fYzqNazVd21OV5RD/17b4jqFF45gGXw3m0LrtMMZSNTVojXZ9pqsVc+Cw3XLabXAcS9tWOLhE8esqsMlxfZd4NmWShigMp7xltZqBo9jvTrSdpjdQVi2uq/CCYYByHYtjhhua47oEvkecZFTa0ncapRyO+x1hHPD7P/yeqqlRrovnR0SZIlAuyvcIlCLA4PsBZVkNGeRxQt81PD2/sNs+c3O1Zrm+IIpjetPhBQE4Hk9PG2bzJYtFyGoZUNYd+/0LbhLiuQ5lO5wrvpfh+xkWjzAOcJVmu9ugojlBtCJLMlwMrulxTY3qFZGyaFfz4e6BcHFJi084nRElIR0Vti9xPUizEF01hIFD1VqyNMN11avEYoinbLqOpqlwHNDGYhxnkCO5wzncNi2uF+O4EEQu2rpUnQaj8YIAjaVqK7I4I5umDIP9oImz2lJXDV7g0VuL6Tqs1jTdcMHFDfHCGD+MWcymJNGE6pizO+zZH0+4bo9SDsZ4HPKGfd8SBS7TaUoUxyhX4TiKKIypqiHqqioLtDbUdYsXKNLllOzqOxwbYvoeXItrFL7ygeEG4fodYRRSlTXWguv5NG1LXdXEYQAODBnsDrqXNfRwwxg8lpY0jYE5WRoS+EPOuKMUrvLQemAXe91h5Wsdh6Zq0UYziQagUFUVVdmMuta2HdbBx+ORxWJgywQs/bU/Pn78yL/8y78QhiE3Nzcjkyatg03TjPpiYfEEXErcnyQKCZCRdbmwVGLMFJ3pdDodpSxfffXVyExuNht+/PHHsTYdGGvj5YYtrK2s62UIOK84lxxsMeHJjV2SIgQQyJ8LqBHphVS5C1MuUYDye4RFTtN0NP0J+/38/Dyy7cLEC+gW2YWAgfOiHQFVAjq++eabIf71VcogLLEALAEz8rsFTIjJUv784uICGMy7m81m/DpgLEORpsLVajVq5WXL4DjOWLIkP1uYcmE75XXJ8ZAyH2E7t9stSZLwD//wD6NsRqRSImUSuYIYUbUe2iW7ruPx8ZGnpydub2/HXHkBV47jjFro1Wo1VsJvNpsxpUVY4nOTroCwl5eX8XjLoCGbDzFT+r7PTz/9NA4zYmoUYDz0RkxGmZUYIYVdF/AuEi7xK4jZWPTU8t6cRwEKQJTPgwyfoveWhxwbMehKMo/8XqXU2CkgW4HD4cB2ux0lNXIsz8GrDOIyXIdhOH7+5DiJB2C5XHJzczO+dyIfEZmUGDpl+ySfYSkSE9mU/CNbOYkZPY+tFK3/eSOsJKrI98nXiB/GGDOe83J9k8FMPhP7/X681pzLX/53j18MyDebLcpV/Obr70gWM6ryRJHn5McNyvbMphOiOKbtK4o6J0lTkiBCE5HMViTTG3obgRuhfMV8FeAri25ymqal72uapoQup8hzLq8WWHru7z8ynaZ0uiWvClQZU7UtYRQxWywxxvB0/0Dftbx9e4v7KuMpTkfqouTN7RtcII1CgtUC3/PJixyDhx+mWDfGuBHKDXA8i9NofD8kcjV1N2TcJmnGdDrDMUP7YV2VHIoTy/WcLEtpm5o0m+Aoj+0+pylPmD6kaXpc5VFXNX3TELgKqzvqsh6MrF3D9OKSbJISRiFeMMQDyodCprW+B98OJ4bvhoRxwvriGheHOPDYPK55zCYct8+05QlPGerTkaos6ZoGz3XxlIfnaDzPIYoy+t4QeB5lVdGYjshNcXTH5eWSaeLTlgXH/Q6jh4jC5WqGFwXQ9ei2JvQDnDRG9xbQLOYZfafJ8xpeGxKbph3KUzwXx2h0q2mbms6ClyYkYYhyffqqxWpDW1e0VU1xyumBuukw1lI3HYQuVg+gHjRhNGilUQbdtWy2e055SRjGhHEKykXjEEQprufTa2iafjDstR1d1wOWusmpmxNpkmCMosMhShfcvvuO0DUkSYt1TtxtNhT5lroNeXPlESkPZTsC1bN//kxV1aTRlET1mPZIGmfETo0uW1zT0PclvmuIZxOcbMp0sqL9+IjGI5tMsFZT5hVB4BFGMdr0HA57QOFHKZ026M7ghSmOCgcteG1QysNaB4PCD1NcP6LverreoDyX6NXRXxQVTV1hTUdd95jS0PYdbdu8pta0+K7Ccyzz2RTPVayXS/Io5okXEuVTvcZSLuZzMJqyONGUBY7tqU2H1T2JF5MkKVEY0em/VBs7jkMSh1jTYvsKdIlrAxyrsV2LozWuM+R+112HVTVJlJFlE+qmw2GIJCzLitD38bwBmDiOg3IcotDHmh5rNC4OYegThRnW1rjeMO/2vcbzPfzXJAxtDMp1idyhkMoaizYdYRgAFqXc0bwnQEhA4fDnw2dV/u6v/fH8/IzruvzqV79itVqNkg9pHpQbsuhEJ5PJCNqkclturLJ+F/ZZGGiRPUjyibWWjx8/MpvNxp8rTXxxHI+s5+fPn2nblq+//np8vmLIfP/+/WisE5ZYWEUBOQL2zpv5RActjLk8f1mNH49HLi8vmU6nNE0zxtZtX0vk+r4fNwCit5XPi4AMyWmW+u3zxj+5R8h7A4wrfiljktISeR5SriTATYaScymMAHkxp5VlOQ5PwiZmWTZKP0QucnFxMRozhT2V5BgYNigyFMgAcZ4cI0ymtLXK+y/yEAFsVVVxPB7H90e+R3TbAi7P01Ykfed4PI7V8QIapQJe3kuRH4i+u65ryrIc2VZ5L7755puRRbXWjhX1Xddxe3s7gkfRmhdFMWr9BUy6rjsOPQLal8sB18xmM3788ccxYtC8hgyI50C00zIcCgMsEhr53JybFGXAlUFOElBEgy7lPMKwn5cHyXVPojWl/VVevwwHruuOUaCn02k0x8rmQsy/8pxl26OUGjP4ZfMjD2GZhaUW0JumKdPpdDxnrLUjq3++7ZGB+DyuUIYKec7ys8+Nw/Ka5TXK51uGGrkuyHVLjrfIkkRXLn/3f3r8YkDedR2L2ZyLizUqivnx53v2+y2BMsRJiHLVUOnddwRxwmx9gRumlI1LkExRQYYyIU3X46DIshhHV5zyEqtrTrsXiuMe2xVM0pTLyws2m2dAsVgu2e8LiqomncNkNuN6kgIOL8/PPD3ecblcY7qOx/t74jjksN2xeX7h3dtburqiKXMu1iuKvKKuGhpglUwJkinWjTCuR9u0KD/EjxLQBS/bPUXV8P76HXGS0dU9OPoV4OTM5hmB75OkKVEYkxclD0/PZEmEo3ziKKM3DmV+omnKIR+9LHE9By9wcPCIouCV5dMoT2OtxlHg+4MO21pDEETEcYrWhr6qSJyIwI/QTYODwnE9ssmctikxfUNf54OeOj9QNzWOP2jXm67DBoYgTvGURxz5BJGHHw6A2TQl9emEP8kwUYDpO/b7Qf8ZJgGZq/A9l7qsMKHFCwKC0KPXFmM8sixCG4MtW/pO0/dDlJ2r3Ne0ChejPZqmxvQaG4AjWsi2wVcKo3u22y2TxQIDaOtwKiuq1uD7Dn5gBhCHojfQNz358UheVqzW6wGYYei0JXb9gT0uhw9vGCWE4XARrOohHcTxPcriQLpc4/k+4BEFKd7FNcfdPUX9yHQWsbq4YuVP8YMlURhB1xL6irpoebj7xN3nB9brN8yXa/rmgGsrHGqiLKNra/LdM56nmaxWKHxQLpPJlN64BJ6PcgN0f6ZVc1z8IMT3AlaXbzicapq2Z7la0dYtx6LGcQPSLCOMIoIgJMtmNB1EQYTuW9q2Q6HxPUPXVDiqpm6OuO2w0ne0IXAsTt/g6J7Q9wiANEnwXEUW+xwOPU4YkIURXjbB9QdDUt82ZFygsBT7HfVpS1ud2BcFed2xWizIZjPe3NrXC/ORaZYxmyTYvqYtDxBE+PRYejAtLoP8q7caHI1jG+IgYjKZ0mpD2xv6flhhOgw58K7bkcQJcTSUFEW+D2YY/GazhLD0gH68gHuuj3IVTTm04ilXkcSDoWq73Q43LNcfL+4AQeDj+4M8Reu/lFvEcTQyvf/2GJjX5XI5tnL+8MMPbDabkVETTausdi8uLkbwLEUkwpACY8OlmNW22y2Hw2FkjN+8ecPz8zMAFxcXbLdbyrIcC3gkr/jh4YFPnz5xdXVF13V8+vSJOI55eXnh8fGRr7/+eowfvLy8HEHEOXgQ1k+kBQKIJN5Q4h0F0MkwslwuR1NhFEXkec7d3R1Zlo0gARjBkET1CdMrv+ucQT3P4hY2WPToAuZFUiPJHqLlHgiefixUElnFuXzhXFYgaRRybJqmGZNthF2WNlFhis81xpJqIjFyEj8pDL0A9POs5/Mh4xzoycDS9z3Pz89f5L+fTqcvwP155rfkved5PsqhBCAKAJQUGXm95wypZHRLrCMMYP/q6orNZkNVVcxmM66urkaZjkiOBMj+/PPP/Pzzz1xdXXFxcTFej5RSTKdT2rYdc8PFsCmxiKLNFyAv94hzw+KbN29GKdHFxcUo05GfH8cxYRiOv0uGF/msCbssDLCcE8IYC0AWsC/nr2jNRZoiWy7JEhdp0W63GwdA+fd6vWY+n/P+/ftROjWdTsd8cCl7OpevnDPU8udRFI3fc55NLglZMjTFcTwOijLELRaL8ZgKqJc2YdnqyesU2ZVsus6Tj+S8k0FTNihi1v2/zpD7vsvFekUY+GjH0jUDmxe5Hla3oKAqG5YXF8xWS/AC/GRK5CQEyQV57VDXDU1dkUYufa0J3Y7Ed9jsnyiPz6AHffXN9eWggUYTpSlN09H1lourS67fvMX1XMqywBjN8bgDa3jz5hrHgTIviaOAwAvAAtaQxCFNEjHNJhz2g6xCqyEjG8+n0xaXoVJe95qm19RtR++4zJaXhOmM3nq4vo/uK8qqwRpLGAa0TYXpe7q+5/sfP9D2liSbYR1FbxyU6xLHgw4pP52o25zL2Zp379/ieB5t3+E5Dr5yCH2PNI5QjsIPYuI0pO768cB22qHXGocBuCoH2rZmv9/hK8Xl5TVtGvPw85/pXIW1mrrMQSlO+z113aG8GM8PcfyQbDYnnGQ4DObFrnPo26HdMIlTLt/ckk6m6LbBGM1me0Dr11iqOGW+mOMHIVGscD2Ng4PrgGN6Tn1D3w1rYm0088X8FSz1+E6Ip4YyliSKaauKvmtAKRyGi2M6n+NH8aDT9yPyoiYIXaa+j9X9UKpTtTw/PoHVrFYXJImP61iM6fGCgKrpKMsTVVUTRTHrixVeENH1GmM0Vvdo23Dc90xXV8SzKWVtcQIPP0lwgojNU06lDVE2IXBDsjQjixL6Csrjhu12S9trvDBleyhRbo7ndmhl2Hz8QNdr6rYmSkLevn8zFNEoH20GXWuvhzWv53uEYTQAAQthmBD4Iae8YL8/UrU9Bpf9/jjENc6WuKthvZmmMadTPgweznBxyeKE7eYBy1DklJ92hKGHUgbbG/oWHAtJEqMYcswDxzIJA1zT0jctJ9tT5EfcICZOUjLlE4QJGtg8v+B64dAuGoS01uJ7IY31hsKh/Ym2Hy5MV9dXdF2DtRpXObiAaWva7oCvpsSTaCgB0y1923JqOwLj4jiayBuMahPdD8VJTUPX9SRxRK/1UHSEIYw8vNKiPIOxHfnpxCQNXteVLVY7GGvRysENAhxlUAoswwbGdRVdb/F7S0tHGEYoZV81mtHrxb7H94ORaRnMy3/J1P1rfwRBMLZQCkMnNyphwIqiGEGJgDphxoT9rKpqTHuQtI6ff/55NPBFUcTbt2/HRBMBwsJMvnv3Dt/3RymJMPTChAtLep47LMB7Pp+PRT1ywxcQeA7SZLAQfboAO9HMCysoA4eAhX/6p38aB4pzRjFNU+q6HuUGNzc3fPvtt+O6XVh6SegQMCZaZ5FJnK/3BZRIE6hSipubG6bTKT/88MO4/ZHca0mqOB8G5vP5+D7LcRQdbhzHvHv3boyZk/x1MR+macpqtRq1tsISnmtxJbNa4vQE+J9vJaRpU44JMA47Aq4FNAvrK+x7VVXc3d0BcHl5OcpDxIwqentJ9ri+vh5fp+iX5f27uLhgsViMshxJ8/j8+fO4ARHJiTzn/X7Py8vL6AvYbrdfsNI//fTTaORM05Rvv/129EBorbm8vPzC2BrH8ZhIJGz34XAYk4yAMdFmsVhwcXEx6sFlIyRyjSRJRo+FAOJzE6xoy88lNecSmKqq2Gw2Y4rL+RBnjOHx8XGM1RQQLEbIoijYbDbjwPPmzZvx+QtbLZ8xGSqEeZbUFpERybGQa4xcC4Avhg6RmMlzOB6Po7FVjMXCzoshW7af8rmQIQsY3wcxU8uAKc9HznnxQfySxy8G5N++f8flzS37ouHz/RN12+AHHsobijiM6UmzjNlijRdmGDckiFZ0BPQElG1Fp3u0HW6O9akBr8e0J4rjM215IA19sjQhSRPKsmY6XeAGIR/vnlBuRDZb4PohXdsM5qu+Y7d7Ic0iFospz5vNENOnFL7v4QceVV0QxT7ZJGYyfWU63IF97HpNFgTQDx9Q2w+FOYfT0IBpXI+bm/csr97iOx5dUZGfck75iXQSMZ/PwIFeaza7Haey5fb2HZPJDF3naA0KD+VqPN/FKkM2SVhdLLm8XFPUNX3dE/kB8Wv8juNYXE8RxcmgN+8Hza5lmGJdbzCq5vmRYr9h8/KI0R3XlxeUp5L8cKDpOpIswXcviEOPpmnJ84q2qNDtibLdUzUdXhwzu1wyWczQnk+gPOIopGkcLA5hPCGKJzR1RVUX9MUJo2v2x5r9seFUtWRpSuC7hEHIJA3xXXAxKAxF3dN0PX1RvWrsDX3b4rkKTzmgexxrcZWD5zpYA9Ya8vzEou0wysOPMlYXNyhvhzEdQRTSNxbH8TC2pSgH82E6nWD6BjcI0LVBo2iqiqbpUK5PGCb4fow1ry2QusdV4GDZbp4Jkxm/ufyK1oKhxzqW63fvKOotTbsjiGcYo1DOkCpirOXx+Ymf7++ZL9a8Xb6jeG3HtaYgP21pe0UcZ7h+SDIJiaOY3cuONJ0R+OnrlgQax1AWg3mrbttBhsJwYdjtj8RpNgxRrofvBfgqANchjVMC3ycvCrq2xvdCtG7p2pa80fhKU5RH2rYE09PWQ2FSlsQ42lCecrqqJ/QDPMfiey6+51AVJ/quIwx8+jpHqw7ruSgfTN8OLa9KYVE0vcHxIrLFJZ61hNGE7eYJ+hbteHhRgOcr0n1C29Tk+YmmqomjDm0K/KQgCTUuNV1bUFU9n+9fiNIJb2+uSP0QN1Aox6NphrWl1RbHUYMExRvaMx3lUpUHsBqlAKux2hCHEW1dYRxoO4vnqte8/w4sTKZDIZLrurjqSNV0ZP5QzBW9mpOwlrIsybLJCCaGG1Y73rjW6196Jf3/7uNXv/oVt7e3nE4nPn78OLJRcoPVWo+17gLa5cYFjDdSSW+QeLSmaca8cInlkxQUYbh+/vnnkQUW7a3oV19eXsbf+/T09AXDLDrWJElGdk5u2sJ4CUCTFXbXdRyPR/b7PUop3r59y5s3b3Bdd8w0Px6PoyH0PP7xdDrx1VdfMZ/PR3kA/CWeTZovLy8vubm5GdsihWkWQCpAVUDC+bpdyokkJeTx8RGtNTc3N5xOp4FEeM1yFpZezH8iUzjPLr+6umK5XI4/W5hGYMxBr6pqjATs+579fj8eM9E+SwLFucRLstllKDoHfjIAyfGS75P4OmF0oyji+vqal5eX8eeIVEMGDjE+CtgSY6lkfcv7cG5AFHZY9PxZlnFzczM+J8dx+Oqrr0aDoxgOhb02xvDw8MDPP//Mer1mvV6Pv1drPb5XYoQUIC+/61zvLZnej4+Poz5cnud2ux03TGLsFDApMqfT6TR+HkVSJO+RPH/xSgjDLZ9B0XUL+BXfhHw2ZLMjg9z5e3e+2VmtViPAl3NSjp80Z8pQKue8XAvOdfBlWfLx40fSNOX9+/fjYCLDp3zP+e+WoUJy72UYFfmJvAaR5wCjvl3SjeS6IOk6oiOX7Uqe5+PXygZGhtemabi6uvo/XkN/MSB/c31FWVd8+PFHHnYnsuUNve7R2sF1FXlegGphc+DqzYrJ4g2On9H3Dq11CJOUWRii6yPd4Zm2OmJsRV9uuJil+IsYqzuicGgZbJoaYxTFsaDvh8a35eoSY+FwKjAWiuJEEPq8ffMtcRrDFqbzOV7g4/k+s8XguPb8AURpo1GOSxhGBPPFwPplE3rtoLuOQ5VzOux5enkBB66v37K6eU86mRO4Pm7a83j/mb5viZM5URQQhSF5UfH8+EIyWZO9xjH2uHh+iC4bDrstXd9g6Elnc8I4xGJQrmI+nVKXBcUxJ52FNFVFMAvhFZApVxEE4ZBZ3Ta4nk8c+iSRT+0Ynu4/U1UFL0/3dFVBoCyOblEMSRCOtUziiMBxCVEY47DbHTBdA33N08cPPN05xEnM5cUNaTQhjhN6bfHDhCAYzKORnhElBXUzpOP0bYsxHadTySSLiKKAwFPEUUrgWpTVmF3FsTb0xuF0Kuj7htBXgEfdlJx2O5J40JJ3dUlvenTXUxYFp/zExWT2WhKkmMyXOLbH93qM6ei1pdOGKI5JohCtLcZAlk1pfJ++a2lbjeuHTCdzlKM4nQqMscSxj6XD95whhrLr2Dw9YHRNkkyGADwvYDa9ZXd84fnZEqdLhpIiD2M1FktvLX6S4sYTjJtxdfl2iGCsD6h0id/2xIGPrzSYQbKE7eibmnyf05snlBeSlyWnPOd5s6XrNUp5uJ7PfL5gPl8Rpyl+EBKEIZ4XojuLo4YLxOC2b4nCYftQFQdcxzKZpYAi8hN6HbwaGC1Jkg1Rjroj8FwcLH0zMMq7XUuepBitQQ/pL/XhQG8d+roiTGfgRmg/GKIZcSnyobxrMrtAtx1B55D1FnRD5Ft63VBVJVEWMZtnFKcDbVUxW7loT+FSY00OeFjb0vWa46HicOrwcFBGM3vdxHieD3r4HOu+pzGaNImxNPihj+WSYZQ0YwxXEqfkp9OQItOBDhyqqufDT3f0fcu7d7dMJxk3N0vqpmW7ecFzfYwemPHh4spoMANn1LOCxXXVv8Uevj5ub2+p65p//ud/5vn5mfV6Pd4cPc/jcDjgui5PT0+8e/duZM7kBi6MquQZS3JDURQsl0suLi5G6UMcx+PqWphzafSUBBXRsEZRxPv378mybJQ6CHMvumYBMaIZlUpwAeqiz62qit1ux8PDA47j8PXXX3N7ezvWx08mEz59+kTXdWPsnkhVPn36xGw2G+MYJV1FjIPCSEp7o3gX5vP5GNUocYvCDJ+b7IS1lAr2OI7Z7/d8+vRpbKIU8CksetM0I0gSAGWMGZ+P1poPHz6MAEhMuSLZEdAochSJoKzregQjMpwIcBKmEvjC0CdyJDF5Cosqv08GGEmfkWx0AX9iwBV2WaQCIlcQxns6nY6bDAGpAqTEWHvOcMpAdXd3N+rHBZzOZrPxfJCth+iPgfG8lsZMAfQCPCUi8VznLcBzv9+Pnx0Z9CSBRbYHy+WS5XL5BYCXrd25fEg2SyLPUUoxn88BvkhRkTQXMXQKOG2aZmTiZRMjbLK8Z8LyC3CV90NiF+fz+WhSXi6X43AkpT0SVyqf/YuLiy+y7oExmUhSWeR8lSH//OtF8iTZ++f+C3nd8vdy7sl7UJYl33//PX3f8/XXXzObzXj37h11XY+lWRJ5eO5FkIQmScGBv+jMf8njFwPy0+nIn77/gU8PLwST1QBalIvF0rQdVdtTdx3J0iPMlrj+FPyUKHBRxmDqkijyUPicThqja0J/YIyjeYhje/quBTXU0zdNx2a3p25bZstLLtZXtF1P07YU+QlrO4zumc2mJFnKIT9g0CyWS0xvSKcp03mGcix1U1C3LVXT4voBjlLEQTg0aEbhEPdXnzjun6mrnDhLuXn7Ndfv3oMXUjcax4W+64Z0FU+xmCa4jsFVoBwH33WZToeLUZWfcHXLPAow2kebHscZIvPW6zWe71GUBRaFYxVVVxN3FYphja5ch6KsCKIA5Xq0XUNAANbBVR5W90OCigth6NF0CtdzeffrX5HFIS93P3PcPvGy21MctsSejzIDsEijlC4Jmc8nqMBjnx84FCeaquD5/jOnaILrx/h+DE5AZ8B1PaJ0hhukuFVD5ih019I2OVWxp+0rTkXOLEtI0wmBytBtT9k5lF056H+7DuUYXCfAw2CNwZqhMVE5g/ynNz1t16Kqis3TM7PlBdpqDkWD64d4LkRRQJZOqV4vMmGckGQZBhflh3T9kGVeWoe660m9iChOOewOFMWRPD8xX2Qslxla9xSnE4FSHPdbHj5+YPnmG/zIwXE9kumc+fqaU1kSxTPm0yl9XdFWQ338qSiJkhlBOscN1xDMaDuNmqZ4/gSnbVD0+E6HMj5VfaQ4Htk/f6CpOyptWayv8aOI3ljCOOXN+nIYKtqOJMnQ2qA8NSTWeD5BEA5MsdZo3dI2w/kY+D6n/Q7HlkSRz3w6xVhFZgOCKKYomsH0GsQ0dY01ljSbEEcRx92e4/E0DJDWwQsC6rLk4fmJtm6oqhoOeybzGqsijBeglhBEMSEugRegcOn6fvBOpDNMV9F1JV1T0WkHN5qwWi+J4oS2N7jKJQ598AxWl9SVIp2u8LyQKEkoyo6X7RHfc3E8n9liQZYllHX52nBvaeqGJI4wpkd5LqvVjK7twGiSJH412yW4yqXvOxwHmrql0y2Pjzv2hwN52bFez0gmKVESERUxvudjTE/3+j3OayLAcPG3Y+qE6yqiKBzZwr/2x+Fw4I9//COfPn0ab8YyrEjzXtu2LJfLsZRDdNCylpcbnMQHClgSdu08bUWKgZqmYb1ec3V1NeYHi+mv73sWi8W4sjfGjDKA2Ww2MthVVX1RYCIgN01TkiShLEvquh516pPJhK+++oqvv/561N8KCMjzfJR7yHsgDJtkcsvrE6mLgEppehTJjQA0ef/k5wmzKcZASdCAv5jKztNKRH/9+9//niRJ+PTpEy8vL2NZk4ATa+0Y9Sbbh+PxODKlnz9/HplkYSVFiiBArizLkW2Ukhkx4soWQvwEck6IDEhkQvJz/7VWWDwGVVXx8PDA+nU1JSVH8nolsk/Mk5JrLUA8TdNxGyByi+12O77W1Wo1DpSimd/tdnz48IF3796N5+l8Pufq6mrU0IukpSzLsVVTMtPPDZWyKRA5hQA8+T5hwruuG2Vgcr5cXV2NWdfCZAsjLlsfGS4F/MvnTOQsoruW9JA4jjmdTmMRlpAOwtpvNhv2+/14noRhSFEU3N/fj7Kf3W7HcrkctxniEVFn1085z0VqJXIdGRiur69HQ7FsqcT0WZblOPhK06cULQkDL4VEskmS7ZcMNhcXF+NGRmRiUgQlhmAB03d3d+x2O/I8/8JYLee/DIfymZM8cmHe5ZjIBuqXPH4xIP90/8Dj4xPGKDzXpyoqXN+nV9B1PW6QMplOuLj9FjeeUvQQ+i5hFOOaHt1X0Ba05ZauPqC7AlwPP/Dpuh6jDYEfgnK4f3xgu9kQhgGeH7C+uGA+m7Dbb9HGUOQHglDRdQ1B6NMbQ13VqMDHDwPy9oRVFjfwqeuSsq6puo6ybcHzhszz046Li0tsuaM7Fewe79lvHvCDgG9+9Stuvv7N0OZYtXRVC72mPB2xpmW5zLi+mrOYxeTHmrosyJKELImAnqrKSTwHbRw8D6bTAfzFScR6uaIqc56fngjimLY3GD/iJnSJUx/Xg7br0dbHEoMdjG5d3zObzEgixd3HO+4+fcD0Q4365fUNFxcXfPX+HeXpQHE6Dvne/gtVq2mqBvqhatxRijD2UMqC6rm5WvBGrXnebDnlNbpr2B1ycAO8w4EkmxBnE6beCuXGqChC4eB4LX4cEU0SDttHDqcXdN+jcEj84Ua26hWdhd2hAKPR2qHIG3SgSeKAdDrDWs2xOOEHLmEUDnrypua03XF43rK8usX3LK4f03U1GBcXTVc3VMXAznpBhLZDNXqeV/S+Yr8/UNcaR3UUVc3hdMJag9v1VGVFFXs4VnM6HKnLCscPePzwZ1brK8J0Qll3uFHC4vot22NO1/fEYcIpz+mqkofPH9lvD7z77nd40QKtEjrj4Ucp1gXdaozSKGUp8h27p4/kuyfaqiT0A27ffkU0nXL59h1RPKHTsD8U4Hi4rjewfsYQhCG96SmKCt/vCEKfIFCDH6Eq6buaympMG9JWJ5RjcLBsXh4J4xSDou0NQRgTxxGe6xFHExQpVve4yiHLJnhvBtB63B8Io5g4ienaltPxRGd3lHVD17cYDNunZ467A4vVmjjOoAlwvQDfcQnimM5zKXKHtu4I4iVekNLVJdabEk998v2WTvd4jqFpcwKd0HSGXjdEcYYfKKgtdduzPVYkk4YwaTBo/MAh9BSu62AVtG0zNJd6r7rLvsf3Y1x30LL3XUsQejRtiTY9xlry3NL1Pqcc1CbHizx2xwNx6NO2HbPphLJshtfe9SjHnoHLAWAKSJAb7L89htjDz58/jzc/AaYCzkQ2ILnWAhiFvRSWTvLH5e8ly/c8nu7z5888Pz+PjKDIKqQSXtIoxMAmulVhEYVdEwAprK7o1l3X5XA4cH19PaZ63N3d8fT0RBiG/O53v+NXv/oVWZaN3y+/1xjDer3mzZs3LBaLUbubZdkXtehyUxf2UNjUy8tL8jzn4eFhBCcCNM9TOuAvemxJnxFW/8OHD3z48GFc2d/e3o66dAGdYlCVnGX5PaIDFuB/c3PD27dvx5bFvu/Z7Xa4rst2u2U6nY4A89yoKsd2Mpnw8vLC4XAYQY+0SAqrLdnw52Y+2VLIpuNcQy0V609PT2PEphxvkZPIMCB/d24AFV34uZFWGlflHJRhabfbDY3Ivs+f//znsfq9rusx4lPKZWQbUlUVP//8My8vL/z2t78d30+RSIhBWAy3crylRCsIgpGZ/eqrr8Z0ov1+P36PgGuRdshzFBOuMM3ynojcQ8Dq09PTF4Zh2QKKBlo2A67rjqBV2Gn5WjEGSwKObHm22+2ou5cBT4zDAsbFiCuyGtFpz+dzttvtaDCWrxFDsmj35T3c7XajjE38BOdlWDLoSXrKuUZchjIZWs8z4iWz/unpiSiK2O1243VLkpiEWZf7gQzf5/8vUZW/5PGLAfnz5gBuQJqkmH64AXZNj4oC3CABx2V1/RWLq1t661OWDT0FvemwfY1tcpr2RL5/wOkrlGspq4pApYRhRlWeOOQl2/2Wx6dHfN9jMpu+Nq5NSSIfM0mo6ppJGhPFHkY34IDB4rgei/mMIApotg1RGKI8RRCGlJVPOpnTGyjKir5v0G1NX27Z1yeqrsOlZT5PmSxWXL7/hmi6xCFgMcuoONIWe7omx+iKKIBJFjBNY/J9McSspSmzaYYG4kCR+Irjyx3tfkMcuKSTCY4D+fFEfjpyKk8k1rDZHwmzGZ7rEEc+PZq6LomTJaZTOK7FjxW+Z3BVw9P9lvtP9zhYlqsVN9EbolctYdMNEo2Lm1vSLMFRkMQRh+cnnu4+0ZUVzy8bkiRmMkmxGCZ60J5lUcgsm9Frl+fdiVPVsN0+8fTywGSx5I2jSCaX9L2LqxzCMB6aUk3A3PdQUcj+6Yn87oXVZEoapiyWM9zIJ4o8yqKhqlqORU4bRgyrAIe2KSmKIzN3QhBF+E3D6XAk7OC02bFc3RB4Po7n07Y1VVmB01MXBbrtmK7nRMkEYx2S0EWXRzYvj/S6ZzKZgnLIiyEHO80SHAxV2/GyOeCYHttZPOVRnHLq/Zaf//S/+O5vU8J0SYfLYn3Dcrvn84/fU04rjO45bB55uvvE5cUNcTyh7F2cIKDrDL4aSphcV9F2FXl7oC826K7AUw4qTrhYX/Ptb37H9OICN4w5VS2RFzFVKWXd0lQ12jooBwwG53WDEIYBfddguhbP6Yl8Q16XeMrSVTW7zTMX6yUOcDqe6I3C4BBGg7nF93yMrvAcD8/3aTrDy/MLvuviK5e6qjmdCjqtyaYTOsfF+DHpKoSyxKghX3zaGzabZ/omxxjQfc9qdcl0umAymxP6ASrLiNIY3x+Mz0VxoMPihx6rm5Sq1Vh9ZLK6GBJ37LBqDicxnmswpsM6IdtDyWRRM8fgekONfeh5OLwa1roWz3fH1bLRhla3WDQOw7UhTiLy8sDz/WfCYIqxc7rOo2k9ynrIYK+7mskkwve9VxYcjLaDAfeV1RlW4cMWy/PUaDzS2v4frp5/HY+np6fRgCXg7vyGC4zAUNhAAUnClIl++dwEKsZOkSlsNhvu7u4IgoD5fM7FxQXL5ZI4jpnNZiODLWBW2FZZ8UdRNIJ513XHIpXZbPZFAogwf+fstGRUf/PNN6PRbLlcjikewixKosV0Oh1bIbMsG5sehS19enricDiMANVxnLElUsD9ZrMZAa8ApcHTkI0bA9GTO47Dp0+f+Omnn4AhfUYAnZjXgiAYTbES9/j4+MjHjx9HYJhl2ZhSIyBS5Dti3iyKYkyqkYZM0WmfJ+uI2TWKIh4fH/npp5/GwUFYVAHa0pQqQNF9jW3N85z5fD7qfUWmtNlsuLy8HFlSAZ3nJVJSiCPMcFVVXzTEykZGGGEYJBqPj48jQHddd4zw/F//63/xH/7DfxilEFdXV7y8vPD999+PQO75+ZmPHz9ydXU1NlLKYCmg7Tx1SFomzyMr//CHP3xRknUekygpQDKQyQZBfodImUTjLd/39PQ0aplFEgOMpkTZVolWvGmasaxHfoYMVnIuC/MsUhhJcHl+fh5jKfu+5/Lycow3DYKA6XQ6at9lqwWDtOTt27cjABepmmw4ZDgQnfh2u2W5XHJ5eTluSSQ5RXTl5zGIIl0S7wowbkx+/vnnUbcv76MYitu2HRl68RHIwCL/lr+Tjdi5rv2XPH4xIK/7nihLidMFeaEJgghfefiRR9O1TCYz5ssLmqZnnxd4fozvOqAbTFvimpq+KYbUD9vTNw2uNayWEUmWcP/wwP39Jyya+XzJdJqxmE+YTiYEgU9ZlJi+5+nhgSD0cByfJEkpihzHKgLPxXN82rolDiN8z6PMc5RjMX3PdDLFageHHZ7nkEYR2BrXcwgdhSYkmy2Zra+J0iXaKPq2QZuO/eaBtjzQtjkKS5KkxGGCMQ5V1ZBlU6LpnOlshraGx7vP8EqaJVk2lChVFUo5+N6QuuJHEVXTYFHESYarPLq2o7cdBo0Ttni+Bw5Yo9m+vPDc9YReynq1JAw9siTEYIYNQFHSdS2By/CP7xGGg9O9zVOybE7TVJy6I1pbjocC5TiEXoztO8I4Zmg8bJikMdPpnDiO+enuE7vtE8oLmDcQxwtsGOI7HuBiHIUfh6zjGVl6hW4rTtsXylPB9dWK9WrIaj8eK56eNq9Z2D1VWdD3anhPwpC67fD9iDiK2e9zTN9T5keassRLskG7XpfErgeBg+5adN8SBgGXV5c0jUa3JXlV47kuSRLB6wWvrmpcz8NxXHRv0J3GWocwCFC+i6M0xpT4rsfL0xPRp0+8+dWUvCiYz5esL6857QZNZRz6aN0SBh6z+QQ/CGiPHcrtMcbBtoa+bijzPc3pgNudcNqSKPRYZGuWqzXLxRrDkOPtaU1ZNCSJIgwCcBR90zDJYjxH0zU1jlJ0tsfqltMpx/Ydyh3iH5Xt6dsOPwyYTSfUVUNR1Jzykqw2tH3PdDYjTRKS+RSURXc1KAdXKWyvOeUlRg+gyA9ieuvQajBOAJ6LchzmyxmOtbRNRTZN0XZgjtumpix2bLYt290jQZhw8/Y9k9liOJ9sjR8p5uGM/LinbjvW1xd4rssxL0ArTA+9sZwOBcZriMOQNLH0xqeumuHG2iyJIoW1DliLsQZHvd6MlAeOGjYgrkvXdthWE4UDe+35Q/GPNj3GaJI0wfN9fC+gqiv2+xNlVeG5q+HrdI/rqpFlkXX4wCb+JclhAB7uF3m3f80PabaUdbIYN8UEOJ1OWa/Xox5VQJSYn4TFkxQJ0TdfXFwwnU759OkTHz9+HJNN5vM5y+WS2Ww2AnZZNQuTPplMxkxxYesls1pYLmEIhaWGAWQImy2gHYa2w8vLyzF5RIxfAlBlNZ1l2cj8C9iXRlJjDJ8+ffoi3lH8IDL4JUkygkcYAINIUwSIiaRHpADnqRWSCS4FNee50ucFOlEUjcdL9OmioxU2VlhF0VVLSsxisSBJEn766Seen59HACcDmDCxAvoF0IvUaL/f8+bNmzEj/XA4jDr388FIWEZJ7RF5ieiXJSNcwLgMcJK1LSy2ZMZLvKBE90kay7leXM7HMAzH80aGuru7O5bLJb/97W/HtJc3b96w2Wy+iBQMw3D0K8iQAIwAXHwB8vukMv7y8pL1ej1uUoSpFUkQ8EWuvbCvctzkZ8rvk89SFEWjZEp+txxvKfhZrVb/f8k+Wg9lXOc55wKyz4HnxcXFKK+SuEYBpMfjkaenp3EQ/vrrr5nP5+PWIAxDLi4u2O/3NE0z5rjLoCSpN7vd7ottkQz253p8GfLPS7bO/RFyngJftKSK/Ed8BgLiq6oauwNubm6+kAnJ4HJeUiUDi3xuRHrzSx6/GJB7YYR1FNqC8gOM47G6uKS3PfVxR5JNiIIQawyhq3DQdPUJ1zH09Qn1CsiruqLrewLPZX1xiQpjPt498PnhiapqWC6nXFxeMsliJmk0Tn+7/XZwgRcn1vEKxxkqrKuy5mJ9RZpkNFXFZrtDYWmKoRQnDAOiIBiq61tN6Ck8ZYhjD5wW5UUD2xv5hOka5U04HVtcF/qu4bR54OHTD/hej+9owshjMV+ge5dPn5455S3T5Rpcjzwv8EMfP/Ax/SAzsV2P7QzHw5HZfEYYRVgLbV7Qaoerq7fM1xeURUNRfMRxFVk2RdcN12/e4/khz5sND4/3OI7D+9sMPxhWlpuX/NVgmhB6LqbTVKccFXnEocdsktKcAqaTCaFSvLy8kCVD41txKui7njLvqcojjgtuoMBRTGZrZq/Ni5NJytN2S9O3bB4+4vlbpssLOjMjTjLSbIHjKLq2IcimhL6lJ2S3uafUhos0Jktj0iQicMF1DM3rtCkV0n7gEYchrlMxzWYs5j0Wh6YqaaqcxWKO04PuPFwYohUxuBh0W9PVFcYoDvsDL5sN08QnfB0UHccSBB6+H6K1oe0tdaOpOktmPUIvoK6OuFFCXtZcvbvm5fmZ2VVBPLlEG0ucpqwvL2j2j3Rdw3a7IZumzBcz/MmEVhkq7RDGEU1dUxUn2uKAYxo8NRT0eHjMF1NWyxmOMhwOe/ymYLW+JvEYogZNR6BcJpGDNS7Hw57TbvuaGjGU+Ly8PILRBJ6DNRpsTxwFOCai7zWnU8ExLzCOYrsradoGrOFiveA5S7i5uhq2SkmL6/rcXF7wcP/A5rjD9QNm8/nQ4Ok4+F5EGAa4KhoY/6aiVEeq+kiSRhjdEcUp6TR43YB0HE8v1B8q1pfXzJYLHAd83yMKfNTEp6sNeVGSxSnz2ZJGGyLls5gssdpiO00axWxNhe01nqsoTwX73Z6bmxWucgEHq8Fah6qu6I3B9Tyi0Eclg5ylaRrUKyDQvcF1fdIkw1UBnucQhh5pFnOqKxxH4XsRXacHrflQQzSuP+XmLDcnYVe07qkbg6v+DZADIwgTJstxHK6urkYGfDKZjGUgchOXUhph0kQ+IUyuZDv//PPPfPr0iaIouLi4GOP7BHA6jsNmsxkTTq6urkZAKFGLIi95fn4eExckSUTkDiKTEaYSGCUPkhJyblBt25aXlxc+fPjwhdxjtVrRdR0//fTTWOctw915NreAKJECLJfLMa1DUjhub29HGUue56OEoK5r3r9/TxAEPD8/8/nz59FoKlrpp6enUTMtZr/j8TgC/vl8zvF4ZD6fj4ZbAaqyjhfmWpJplFKjLl9kJU9PT7Rty6dPn8aBQAyCwsSLdlrYdHlu8jUydMjQJIObDBki+xDtPzDmxy8Wi1F2IVIbOf6iZTfGsN1ueXx8HDXd50zyuS5Yfr80yUrqR57nfP311zw+PnJzczNuVdI05c2bN6N05eXlZfQ+TKfTEehLAowk2gholM/Der3m4uICKZCSYijRbgvAE130brdjs9mMyULC7MtWQq5VklLSdd34GQFGD4a1dtRJ397eAoxblZubGz59+sTz8zO+74/FRQLGz8uzRPIlEi15b2az2XgeHQ4H/uVf/oWbmxtWq9UoHZOtkmxEsiwbj6tEksogkCQJLy8v47XkdDqx2Wy4vb39Anzb14Qs+To5P+S5ytBy3pIqx0O8CEVRjIOp6N7l3JLr3nlkowwD52VI58lC/7vHLwbkUZLSa0OUZajeRRuXpm2xrh2SLpLXlVjXEflDykldleiupq1POKbG9DVYgx9EzOZz4knGZrvhw8dPeGHEu4sVoa8Iw4gkTYFBF1yWJdvtHovl4nLNdDrBvJaIOICHSxZllMcTTTk0EuanIzfX14TBwLqWRYHte6ZpiudYMC3a+HS6IU3nODamKDuq/Z62Hyafutjx/PiBzdNPrJcJk1dwOZ8v0Nrh8XmP4wZYFWCtMzRE9SG8fviaukF3DdaFbDoZLnR+QNv2tL1lOl3y1TffobwQ5Ye0bYPtWk4vT5yqj1RlzurikuPpiKMt2XSKMZaejijw2R32aNNxc3NF1zTkTU3fVhybjj4amgvD0MfEAVVxQimH1/BlPN+na3qapiObTEnSmFN5oOs7yrLA8yOy6ZTOU8zSiM7A8/ORw/FEjyYxPb1x8MMpnh9jvRjjdNSmI5xekVpo+iNl05A6Pr6vmM9T2rbi6fkF470WAmmLaxy0Hpo547h/PfF76qrgsH1htl4BCqtbvDjEdYfWR9Mn6L5l+/JMq+H56QFek1f80Ee5kKYBRiv8IKEoa/zA4Ectne7pLFjtoAkIo4hjUXLJACDLoqBrO+qmxfSGqmn4x3/8H0x9S9sPkYCd7gk9hTbDas2pLVb3hKrDzzwcHWOanjBOuVje4CmoqhxjDXEUkCQhgWPo2nbIzDfQ9j354UCZH7m/+8Tj/T049jW6MaRraqIwQFuHrm4IAo/ZZAk47LZ7irJFqQBjLV0/ZNZ7ngvakh+OPJkhC9xRw0Vwks2gq/EdQ1kcAUMQx6/ntcE6Fj8YWEyLJclStK6py4GN9EOPSTZltYxwlc/+kPPTx498/vQBQ8PFxSWO7dD94BswriI/5GzLmjfv3hKqIVe+Kk4EhCg00yzh8sJhf2qp65a82HE6Hrm+WuIHPv7rRVRrQ1U1nF6zdQN/Bq9dt47DawpUN0SOWvC8gL7Tg8naNbiuJY4jsmxCEMTUdUfbdAS+i+uqkYEUEC5mIAGAyn3Vk6t/k6wAY3nGeQqD1FQnSUKWZaP+WZjHsizpum5MTJHvC8NwBDMvLy/8+c9/JgzDMSdamF0BOmLwstZyc3Mzao+FwRaWVvTcogu/vb0d9coCgAVAiTlLoumEsRR9q2Rff/78mfv7ey4uLsb2UQEtDw8PI1iR7G3RWJ8b2jzPG1l00XULc/mrX/1q3CYIIHh+fh4B+vX19Sg/ENAirKP8uUgAxEQpUiKRo8jrFzB7PowKG5tl2UiiiD/gPF1Ga83j4+OYbS2spshzJGlCWijlvZUyJUm9aduW+/v78evPs8VFxiAgSGQzkq5zbqaTbGoZTPq+5/7+fgSJoiMWOYsAbjkfRCZxLusQICvbFXl/5Bz+r//1v46lQOfyD3mtotUXA6+kk4gJUik1ylEkW14GFHnvRNZ1Op34+eef+fz5M/CXNkrZAAlbHQQB19fXAON5I8BR9NUSlSlafhmMxPchIPS8QEkGUMdxRskLMLazymdbBkh5ffv9nh9++IEPHz5gjOH6+nocFmQIkESZr7/+etTIn06n0Xg8m824vr4ei60khvTm5mY8rnJtEBO1DJTykL+XYyREglznBbjLZkc6Bc7jXM/vEXJ+nsdiChA/B/D/u8cvBuRJNiFOEhbrS1rt0TYMN0Lf5WZ5xeXlDYvFFXlRszsc2W035KcjnrKYvsb0Jda8tuplM7wgIC8rXnY7TkXBm+tLFus1noIkDAl9H6OHA/z09DRMpG/ekCQhynXo2h6sQ+BH5KccOsPT4yNVUVC/VoVrPSTADAxIhG46lBnKUNq+Iy8KaA3aSeisoaw8omjJJIooiyMvjx/ZPH2kKreotUccp0yzGevVmrLs6bth8nR9H8dzMbbHdcAPPOraEocBq/WCqjgCGuUqrHJxfZfJfMF8uWa5uuRwPNHWNZNJSujB0+M9xWHDD/mJ5+cH3n/7G3wvJArT1zVsAsbQtg1VVbB5fqJtKuoqxzGatqnwHINyXLTu2R62lE1BlIX0vaZre5Tn4IYeZVEyCSZkswmLixmnvODhccOnnz9yeX3Jcr1gMkko6hp6g+4PHLcPNH03DBatQza9xAsTfD8CN0D7lnjhQu1TNXv8HkJfEccBV9dLHMcMnoSDxfcDPD+i0xrXZTT49l2DF8b0XUXfFGhHobsK04N2ADNIOIpjS9V2aMdjNpsyn6QErqYoD0ThsE5qrUPbaZq2x/UCoizCNwbd9+AMmdqTWUxTn6irivX6emi67GoCk+Ioj8lkThgmfL77nvV8ysVqSZAkOC5o29LUDW2VE3oK37akWcBpfwR3SOB5fHigrSvSNGJ9saLMT+xeXtDaoawa0nRC/HrT3+129G3LNPEIblYo5VI3LekkA2uJwwhtepqqAqPpmoaqrOm7figumi7IqxIcl8D3SeMQzwHT11T5if1xR1eXZGnC3vVwHA/dW4qqpi5yssUcL4zw/BYvjKnsYNRZzBf0XcvpAMpROLjYXtFWliQMwQ7Z6Le3t5R1SVvVHLYbkjgiDgJAYdoW12q0bem6HNcL0b1FG2iaE01zIp5EfPP1LZtdycePd5xOPU1VY7VGOT6uGs5rxwHPc8mPBfv9YdiEeB5t26AUGNNTVYZUJXieT5pmnI4lSkEc+/ihi9U+1rhUZYfSGmUGcH0eZSg3HonFGm/S1uL5LjIE/LU/JpOBdLi8vBzTHUTuI6z2er0es7CF0ZYmQAFwUoctgPfl5YU8z3n79i2Xl5cjCy0ss+d53N/f8/nzZ96/fz8WlAjzF4bhuMb//Pkzp9OJqqrGdbiAU5E9SBSaAM/zWm5JbUjTlNPpNIJxaYEUHfvFxcUISETfLdnRYmoU4LRYLEZZjQBiAaeSHrPf78dECCmj2e/3nE4nHh4e+M1vfvNFq6dovQW0SmpHURQjUJPfpbUeDY6iSxfGUDKsJTVGmkw/f/7Mjz/++IXkRJhIYYglVrDrulFWJBIb0fOLb0AMd8I0O47D4+PjmAAj4FbkFCKfEPArx0hkKl3XfRHLJwymaJjFdCzSpfOyJ5GzyPcDo39AElTW6zWS5y3n22w2I45jPn78yGq14uLiYgTUwtQK6INBby9FQZ7n8fnzZ+q6Jssyrq6uRkOhyHdk2BNfQdu2ZFnG27dvv8jHBka5lMh/5DwQWZlEaQpIldhLOeefnp7Gn3d3dzd+RqWBVmIG5R8ByqvVahwYBITKICKDaJIkvH//ftTCv7y8fDEgCbCXgUGG+PPNyWQy4de//vW4ndrv9+Mgc55hL8x2nudjo6gMvOfJNrL5yrJsvCaJ9Oq8zEjYb+AL1lvuETIwy3AIjGD/lzx+MSC/uL5iOp/jeiFNY8kSHzBYR/P2eo1yQXeDxOCweaQqKnRbo3yPKI6xvQLHDNOTHgyd+92Gl5dn4jRhfbFmvlhi+444DPA9h/xYcjjsOJwOGNOTpjGuO2hIh6kmwDEOdV1RHo80VYnnKuqmGTKElYexirrTVKeSOi/p63owptYl+7IaTJTdBqsa5vO3rFdTmrrluN1z2j3Q10cmicdilnB9ueT9V98RhTP+5//8J8qqYrK8IPADjGNRKEw36M49xw4sLZY4CgkDn2w6ww0iqrohmSWsLi6xwPc/fI/uNX/3t/8erQ1917KYpfSOS5nn9G1H2zv4nsF4PW1T8fLywsPTI89PD3z//T9RFUdM1+C5ivk04/b2BqM1+8OJzhr8OMRzHVSr0cYwXc7ItOX+7p7daUfZlVxerAnjhCzrKcsNDw9P+L7Lze0lWRazms2ZZi/88YefOO2eaMqGpuxoG8384g1KeRjADVKU9fACBVVAVZ8oqpwkdJjNhkzfpu04HHLKSlPXLa6rAAfP9XBciwI8x6K7ivy4JZnNiQOFQuMqhe5r2rogiFJcD3pHkaYxQeDj4FA1HXE6xQ8yjGMoioa600SuM5Q8dRXgkGYpykZEsUddDhnevoK6Lgg8RRLH9K1mOl/x7//+P/JjpJgmPovZhH1Z07Tta653jm5btAth6NCVHX2To7uGn+8+YXXL9dUFxsDnj5+HG2RZ4ToenhdQTDLSbEKSxijb4ruGxXSCu5wync6pmhb/1YneVjVlVWE9j83mMF5UZ9MZyhuyWK2x1O3AxrjKwXPA9h04Q3yiGw1Dr+578lNO3bS0XY9VHkWZ40chs8WSdDbHVR1FWdM1Jzzl0TYlnuOC4w8lX23P0Q6FC3EScb2+GLwNZUFxOrI/nejiGJV1zOcTTHNC6wbTFaRpiBsHFJ2l1iXH4wutdVnFE9brGdvNM0noEYc+yhk6D5Tr0OvB7BrHEafi9HqhHs4ja4eLpud7GDMwU1mUMZstOB4rTqcDUeQxnaZs9i1VVXM8FHiziCT4i+YQ7HhzED25rGr7vueUH8iydLzR/LU/bm9vx6g8ySSWh4CGtm3Hm/B5WYmwmQImBYRIkoaAlNVqNbK/os2VTGJjzMjCwwAEZNUvq/KyLEe9tqzagbHsR5IfZFUtenYBBAK06rrm+fmZzWYzvlbREn/33XfEccx/+2//jaIoRvACjD9LgIOcOzJgzOfzMXFCIvWAseHzP/2n/zQCzsViATAytcL0CUB9fHzk7u6O+/t7/vjHP44xgK7rslgseP/+/SjjEEZWjpFUxcugJLXsV1dXY4xgWZZ8/vyZIAh4//490+l03Gr86U9/GiMiJTLy6urqC+ZVNh2ScCMpOIvFYmTOd7vdmGIjWn5hwCXJQpI+5L2TdAthrYXNhWFolGMhA8h51KZo7M+12RKZmCTJKF8Qs6kkkkic53/+z/95TJaRQUvOcRki5PyVanaR+vR9z5s3bzDG8OHDBz5//jw2a0pxkPwjEjrxNMzn83HAk/e8KAo8z2O3240/Q4YRGQ4l7UdeswwgIjmTngBhomXjJSbT5XLJYrH4QgJ2Lgc5B7PyENPq+efydDqNQ7AManJsxY8hw52w+EmScHV1xfPz85j3L8dGjJYCrKVsSIC4kCoi65FBZbFYjKVWIseS5BvxVMgAIoOWeEHOS7nOrykSl/hLHr8YkE/mc2aLOXXVYLQBC45t8F2HQGkc1yHwDIFvUU5P4IFyQoJwWIfpvsX3XNIkZPP8wHH7zOb5Cd13/Pp3v2a2XKKtRbkeyg2o65ynxxceHz/jeA5XV5fgDCdhr/9iuvADn6bIabuKySwlCiOORUEYJURxxma3w/MsnhuhvIai3NJj6LVDXWl6R2GcFtetMabiuH/g8eGeuthBf2QxDXnzZs03795wfXXDfDrh4emFu/uPuC74/nAiu8riKo+8yTFdTRZ6TGYx9fHIJE2HcpMo4VgUGBwurq7IplPyU87hsCXwfdq2pNU92miCOMR1XIzr0HU9jhPRdYa2O/CyvePu7p6+Nbiey8vzE21VMc0SXNfl+XnLw8MTq9UaP1A4bkynKxzHx6qO1lQkYYSjDWGaUJU1ddty//RMEme0rWUymXM4DiYb5VouLldkacabqxXWGn78eM+xONKcHA5G4yhDky1IJzOy6RQHH9Mo8BxOXc1pW5D4HWDwPJf1eoVSIQ+PB543OzzXx1U+jjOYG00/OPT7pqY4bFksZoRJgOOA6yjK4oTpW9JkiRcnxLMFXhARBQGO1WSTBXE6xzoeXVXh+RFRoggCH6Vc+q7F9Txwhi2LsS2uYgB/nsICxWFHks1xnIC21ySTBeur99T5C9aNSJKAU90ReIqL+YTisANd0RYlVX7AweGwPwxpMJMJurM8n7b0XUvfGgLXZz6ZMZlPidME1AA243BotQx8h9ALwPSDEboD3bdUdUlV1+SngueXDWEUkiQpxg5a+iBKKOuGpuvxXtMNOt1TnA5genyl0NpB5zW+H2CVh0Xj+d6YGe+9XnTQLW3dg+5BWcqypzyeyJKUdJJhtCXPh8QbB9CuQnsOjjK4poW+gbYhL3NOT48cpglB4IAybJ+GNJRMuQTRnF4PMriuyOke7/j229/x+999x3yekKUJnnJweG3CcwfJin1NW1HKfWVpzKuExIJj8V+1meAQ+OGrtOyI42fM5hmH/ETbduR5TRb7TGMf+Evqi7TXnRd9DH+uX29m/phG8df+WCwWY7GPsEKiZT3PSRaWVKLLJMFDCnqkyW+z2fDw8EDf9/zud78bs8hF4yllN58/f8Z1XW5vb0f2VdhLYciljXA+n4/yjDiOx2ZEASVSpCIMl5QTyc8VNlmY9r7vmc/nvHv3jm+++Ybb21vm8zn39/d8/PhxNHQJGy1AT/KxBSxIq6fIIqy1vHnzhtlsxvF4ZLsVL0kzPje5yZ9Hu0k03tPTEx8/fhzZZzFLSr37w8MDnz9/5uLiYnztwigKay5/LqbPuq65v78fj5WU4sjrvL6+ZjKZjKDyhx9+GAttZJCV5BkppRFDnaTnCBCUKEvXdce4SdH2CyiS7YhE361Wq5GRlpQVkRslSTICdgFU8/l81LeLxOScFRV2FvgChAnTb+1QNy+6YwF1t7e3Y3a5+BYkJ1skRHmej4bi7XZL0zSjNOT+/n5k7GUzIUkxAnJFPiGfJ2GohaEVUH48Hnl8fBwlXvKZjON4jERs23asu5cIQ/kMHY/H8fyFL0uEZLA5Z+JlkD4cDmRZNsqiZNgBxuuAGGXF1CrpN5LC4jgODw8PWGtZr9ejWVZMotZafvvb3/K3f/u3Y7fB+aAm57R8RuT/z8G0nGvykOMsm73lcjkaRsV7IUSDXNtksJHPpZyXEp8osp9f8vjFgFz5/jA9OHv6tuDx7hPHw46b6xX0BU1rabsWaxzms4TcazDWJYqnWBw0Lo7v0dQVdX6iOe2IfMVX77/l9vYNWGjKweBT1Q1P94883D/geYrVasmbNzcE/rDCrOoGR3nEUUpd5uyPWxzbE2cBTd/Q255ZmmJdl6YH5XuESULgKbabZ8qipmotjoopi4pjviNOWozROMbStQWzLEbPfdI44t//u9/x/v0t1sD93We+//ED2lZc37zB813K4kQYeri+g4smSwJCBV1dEvgu00lGkqTsTwV3D4/4/mvCSn5is3nm4nKJp6DvaoIwYrZY0tmeT58+4/oZyaRguZgDDtvthl4XLBZTvnr/LUkUc//5Z5qiIHBdXp6e+ac//TO7XQ4qwfc9+q7G2o7VMsMNEozqOVUDwPCTKSpIwBqqsmB3zKnKjsVszXp1Sd0ceHy4pygPvHlzy3y65NvbawIsnx6feTnuaPOWg9NRFnv67orA8wiCGGNjrOvhxj3G3XMon7FPzyShj68C0jRhuVI0neZ0ysEYPOUwe2W6m7bCNobTfkNxnJLN5vhBMFzcscymGevlHIIUL01QXvhqHjkRpXNwE055wWZ3JPQj4jQi8FzariGKfbI0w6qh0Mbqmsj3sX3PYfNESUhjXdLpkun8Eke5uEFKmM75+PMHrOOyurgk8wzdbk8Q+bi1y/5lT1sfyQ87mrpDKZ/Vco3n+vieRzTLCAKP4rinb2uW8zmL5QwvDDAYmq7GVQ4uHk1V8HJ8HKIiO43z2uBZVTW9MeTHgrKuUd7gSzBYJpMpUZwQJwmttvhBSBgNZtNoOiPw3NdiqcmQnZ9l2N5SVzVlVaKtwQ9DkixDmyF+8PC8pSqHXF5XeSjT0TU51WlH07RUZTW8v3GC63T03RHlGoJA8eZyRl2WbJ82WGPYPd2RxAFRGlFR4Qcx6eyKwA0J3RTtZzhhRlE0bDcvfPX2LUr11FWNMS1aOyhlUQq0MaAsYRyCUTRNi+c6BP5QlIXVr5pSS9dpjLFYM1ynXNURhDFpmrDfHchPJUXsspwEDF0Cmvl8AdhxDR+EAUHgD1usvh9j3YRV+mt/BEHAer0eAcanT5/Y7Xbc3t6OjKcwhMIenjfpyTpfpC4CBr799lu+/vrr8canlKKqKj5+/MjHjx/xPI/Ly0vevXs3FtlI2kYcx2PGtDHDhlaeg6zphUkX4+N5Yoowp8LgiVlLkiSWyyVpmvL3f//3fPfddxhj+PjxI//8z/+MMYZ3796NK3MBg8AXCR8SAZdl2disKSy5SDeur6/Hdb7o640x/PjjjwRBwOl0Gktynp+f6fue1WrFd999NyahiO777u6Of/zHf+Tl5WVkTkU3K+ksomUGxmxqef/3+/3Ynnp1dTUy5Xme8+7dOxaLBd988w1KqfEcEEmOADMx8UkbZpIkYwnL3d3dyNZnWTaWuRyPx1HfLM2UwqTudjv2+z2LxWJkZYExFjMIgnF7slgsOB6P4zE4Ho9jwYyYONu2HX0PArbPmW7RyYtuf7FYjGA9yzL+/Oc/4zjOeNzk5zVNw9PT08i4SrKOtFKK7EjO46ZpRunSv25nhYHlv7+/53g8fpGgI1r1862QDKgSOXkexyibCvEwCJsv8ZeixRbgKfp2kXA8PT2R5/kIiuUzcjwexzz4MAzHCvvz9Jybm5tRVmWtHYe+LMvGrclyuRwHMjl3RFoj55pIpmTTdj5kiqlVCrLE0yDHUPwZAtgl5lSOp8jrZLCT71utVsBf5DUyrMk9Q+4R/9djD4f1Ukjke7yUJ14ePzPNEi4WGYGj2ew2GDdktrxivZrjehWnvMNaB20B5WOsQ1tVOLrFcwzpJObmYk0SRWAdlAGrDfvNlg8/fcS2Nb/+9VdMF0OrW9PWPL084XsB68sbkjihKk/gGLquoShzejPotJXn0+ORzZe4KiCvCmgbut5Q1R1Nr1BuiKKnLnIca+nrHAfN9fWS1SqmzHtcpyeLoqGqu2nJ8xPW9Fyu58SThLxqqOuWJFrgKkUaeSwmKXQtGIfI83Fdh/x0ZLsbPhyzRUrX9/Rlzqk4slxMUcri+QrlunSdpX8FDr7bvbI5FfY1+SGKQqbTCUkakcYJ6/WaHQ73nz7z00+fmMyW/Op3f0OeV+RlweFU03eGyTRkuVgw6T1Oh2FSV06AF4Z4yiFOMqqiotcnyqrB9wOSdMJuX7DZbLEWQi8g8nwu5yltU6BNS9E3tNUz2nQoBYEXkE3W+OEUN4xxtSHMLjGexZoDVd1Q25amMWjtkiQxeVFQlBW6awmUQ+APNx+Doe4aHu8+D+aOvqWpSjCGxWpBGHi4SYh1FcobpuPDYahi96yH8hPcoMY6DlGcEgcu/bEmChwmWUBvFX0LtArXcdlvnmlenng5day/qrl9/x3BxRu6KMbxA2bLa5L5BZ211K3GGoNywEOThopD33DavVAUR+Jkyny+xvdTgiAijhKqqqTvGqJ4QsOQm11XNbxGEVoM2lrKouC43/P0+ETTdQRhgvIG2RNKEQUhQRAPud9h/MpCDgbOKE7xwxDjBMRJCsqlqmq6rkb3HZvNjqo3TLMLdBCgQohii9f16H6IUJzO53Rdy/PDZ5bzK8JLj8N+Q1WVpLGP5yp25Q6rG5TTUeQFp90zcZaQTiOSNMS6Hp7j4zodUWBJsoSDbdjvdmDmWN8nP+SsW00aT/DUlGMfolSI55nBqHPYEgYeRg9SlLatAR8/9HFcCIIh3tP0zsi2DOzLQOp3fQ+0hGFMHKe4SmG0RllN2w4X5yiM6LuePC9omgTXNSjljGBbNKIAQejTNv0r8xoBzhhN99f+EDOcSEnu7u6YzWas1+sxBcV1Xdbr9ZgZLEzXeUSZVKmLLlfa+4DxRvj8/Myf//xn2rbl97///SgLkZQJ3/e5ubkZ84WBUR8rP1uYMQFxkgUtUgfRUYuZTUAGDPKci4uLsWBIWGMBIcaYMbVC2Mrr6+vxa+fz+aiFFkZWWGKRA8jzFbAtz/lf66jle4Uxl1SN+XxOlmVju6PjOPz000/8+c9/ZrFY8Dd/8zccj8cx310yu+W5nedUC7AQtly2ByKlEGnReTHLer0ec9mFtRVALcyvAJgkSUYd77kZV9JRRLMvOmgBZrIpb9uWjx8/ju+b/K7VajWaVuX3CoAX464APDk2UgIjg5I8DwGRz8/PPDw8sN/v+eabb/juu++4uroac8DX6/Vo6pXnL3pqYXmlFVTKkeQ5iCxGADww6pxFLiIP0UV//vx5BINitJXXIhIg+TsB/efRlDKYCmMupmEB5MLCixTnPNKxbVs+f/7MarUaox/LsiRJhm29bCkAjscjm82GLMvGc/PcGCqtvDCkv8iwJnIpMXGfDx6Hw4HtdjtuTARMy/VIZE6S7iSAXVj/fx1xKe+FGPllgyNeBZG0icTn/B4h8rfzkiHxdIjH5P/0+MWAXLctxelA1xRUxZa+OTK/nuE7hny/47jbEkzm6L6nMx27Y0FZWXzfwVqDwmBtT18d6PsS19XM5xN818G19vUmaajqkqenew77LX/7737D737/e6omp24qjsc9eV6wWl8ym0xwrEPfatIk49BUdG1P00EUT8HPqLSPEyQYC3l1pMsLmk7TtZq+t2hTo6sa1bYkWYxyhhi5aeiRBj7L2ZwkTvF8n88f76iaiqIsyZJBItD0PZv7ezoN16sMpRUuBqUb+r4mS2NM13E4nsjLhs44TGczrm9umM2nPD09oBhkOEZ3HPKCLAt52e7RpiGLItJJgqVlv78Hx2O2yAh8hgbPsqStGo6HE5/vn3neHvnqN3/gm2++5fr2Lbq33H2+44//6//lx+//zMu+IU5dZssb4mRG01Tsdy/UdYExPUkcooKI2dKnqRp6BjZI+SGd7vn54x26M3zz9pZplnBtF4Sxz7GqeTmUNOaELR2qAwROjzU9gTPHDUL87ALju+hc0dQvBI4Gx9LpDhSs1kv2my1tVVLVDcYEKCfARdF3HcW+QNctKvKoiiNh4DKdpkSRjxd6WNdD+QFN1ZAXDVEa0vXQ9RbPiwh9nziIwdZU+YH94ZHj4RHPj9AtoFtCt4O+JoxDmrzm8PyZ4rjDYvHDhLbpSVZX/PoP/0CbH9BtQd8UhMpSHQ88fPwzj3cfWc2nTLIpcTan0z7xdIlyfXocCCzaWvq6w4tjwiQcJFRtTxgGKOXz8PjA48MDXV1htWExXxJGIXlRYbQhihKMo6jy4jVKLsR1FFEQUDcdWls8o/B8lzCMcYOATjvkdTckiRDgZSnx8pq2qTFdTxi4OKpDGyjLGqIG33MJ4pRZmjCbJDjKYjc9kzQhCQN8ZfG94YJUlx3HY4HG4r6aRB8eXthvB3OtUrDdvvBwP0SOKc9DuUMEqbINylSk8ZxWKfK2omlB656Hx2e++uots9mMw/GA1R29NgSoIRe9bXHgFcwZrB3iEPsefDfEKvt6EW/IsoQg8HCweI6iqmsCpbhcLcF0oBvaVuN7EMU+nhdQljVRlBEEIWVZ0LUaP3AHAkE7X7Bxf+0PiakThvs8K1ma++SGbowZjViycpeHmCGldOe80EPA2t3dHdvtln/4h3/g7/7u70YdshgdLy8vx5u7pDeIBvY8BlAYZ9Gz/uvkDJEsyRpajrdoVoUhD4KADx8+jDF8aZqOmthPnz6htebq6mpclwswEBOlsM5aa5bLJW/fvmW5XHJ/fw/whYRgOp3y/Pw8ylZEMrXdbkfzobynRVGMOvuPHz/y9PTEH/7wB37zm9/w/v17+r7np59+4r//9//OH//4x/EYrdfrsY1SQJbodmUTcl7sJFrjH3/8ka7rxpbJN2/ejFsKaVEVdliYQzkWws5K06XID+RcEL3weTHOeZrGbrejqqoxk16kAv8akEsOt6QByeAlmnaJnNxut+x2u/E8OZdyiOzp8fGR/X4P/AWIrddr/v7v/378DAgrezgc+PHHH/n5559ZrVZMJpPRLzGbzcZhRBhWydWWaE/5f6UUd3d3fP78eZSKyOAhw5IMsFVVjWbT8zx3AaUSWyi/U/T61tpxWJBh5DzzXt7f82FKBoHn5+cxElM2MDIkSRuqgOFPnz7x8vIyvq7n52c+ffo0DmcC2OU8E1Au2zYxav/6178e9d/nkppztlt+jvxz/pkWoD+dTsdBRXwCruuO8a3nID2OByJMPu/iC5AN0HlM5S+9R/xiQN5WJYftM/lhi0tLFis811LkOZ/v7ij6jpvJnKKsyJuWqrNY16coCwIFHi3oGvoTZbEfzIJXF/hBiG4a+m4oC9o8PaDbit/++lu+/vZr5os59ePgDMc6zCZTsighdD2qcmg19D2F1Rbdaqq6J1ms6Z0Y66RDdnpTU7eWvjU4znBQMAbbGbLQJwsmJNEgBdC6xbRDg+PtzTtcz6fteg6nEw+PD4SBx7vbW6w2PD4/c9pvWV1cMcuSocilbtg8PYI1WNOB1VT1EIUTp1PCyYIwiTmdCpq6IotjbKeH52kUp6IlL2pCX5OEPvQNVVdj6orFxQ1BGOGpofynqyuOhxP3D88cTg3f/f7v+Nu/+Tu8IBiq3tOAa+UTTxZMFpf80z/9ie8/fmaSxNzeXDKNY5quwWBom5pjUWOsZTaZsVgtsdZQ5kdCkxGnKW3/yKlq2ByPhJFPmkZ0fQvW4FjLoehpzQlTWjrP4tCB0/3/2PuvH1my7MwX/G1h2mV4qKNSVrFIYljomctGAw3M///SbMyA7EtWFVMdFTpcm957z4PFsvTTGFzmwzzV0IGDQmXmiXA3Mzf71rc+QbA5IUqJ42u8NkOM4H5Y+TWuo+k7Im2YTnJKAk3ncK7DKAVBE+kYOtg+PHP+6gzfNeR5NDZiNlVFlEWoCMq6I8lmeCxV21FVNcGHgcnWQ/76+vmBz59/xKhAnk3Z72qMNlyez5gWMWkWk1iFdg31YUtdldj8DKzGdZ5ifk4aRVQ78MbTVRv220eeHu94dX3Fcr6kaR2NV/ROY7IXaUzb0SjN5lhTpAlZbvGhZ78/kOcZvVd8+PkXHu8fSJKI6XxFZAyx1TR1DT4QPBTTOVXnSDFoa+m6FhNZ0mRCUUwpqxaFxnWOpmnZPW9xaJRN0LFGd4E+KKreoZTBhY66bdHBE8URDkdwPcoqbGxRNtC6ij60GKNwfYu3luAVbes5HmtC0CT5wMy3ridKIqg6lI3pegfOEbSlBzbHAzqJmS8nvHl9wSy3hO6ApWJeZJSbGu8UwRTsDzX3D8+8fn3BbDalaQ11U6OUxhiN8t1QFOQ9MBwfoyPiyBJHYrz69TaXxDHeOfCeSEdYZfDOkaYxsbFMsoKgHMGDQlOWoo30IyMZMICmbdrBIGt+8230r/pVVYPZXJophTXa7/f88ssvYw616JHFYCX15sC45ZCq9Ddv3ozaaWGgb29vadt2BJar1YpPnz59EYkmySYCSEX/3XXdaLSEX6vnBTjJw1t+lmi9pYhEQLMwiV999dUocdhsNmOD6DfffINzjk+fPrFer7m6uhoBS1VV3N7ejppTGTIEoE+n01Fbfpp8IsdHMqwFFAiQKstyNF0Kmy6tlh8/fmS73fLHP/6Rf/zHfxy1wEVR8O23346Z2f/8z//MDz/8wGQy4d27d6PMQlhikRmJFETej7DYnz9/5nA4sNlsRtmBvPcQwggY5f3LICbs7Xw+H1lH+V2S+iLxecAoLxDAJwDv/v6eN2/ejEPXqd9AAJQk5QCjmVSkSCJRenh44JdffkFrzWQyYb1ejzr5oijGnyWf/5QV9t6zXC7H2En5/ev1mru7O96+fctyuRwj8gTgixcABmnPqV55t9uNkql///d/5/b2dkzoEeAqbLzESgpTLCklpyy3yJHkO/Xw8ICU5kiqjWwe5Psg2yH5d3LMTgu+5GdIYpJ8t0SyJBsIYdnluy//vejKd7vdyMK/e/fui+toOp2O8ZeSlnN7e8vbt29Hc6tImWTIOX2/MsidRq/K0H/qT5DPIls5MQefavGBsbxJ/sj3BRiHMbm//Uev3/wkaQ4H7o8bXHvEqECRp2zWz+yet/z8yweiSYFOn0gmYLIzlIrRWnHYb4hCz7IwdM2O8vhEWe75/vvvhwelc3RNh1aK/faZu8+/cL4649vvviXPM56en18E9oYiH6YQawzPT8+4PtDVDV47XNNzPGxIpyuMTXGkOJXSdj1tE/AqxiYZBXPatmO72QMDw2AVpGmEwdH1FuU1h31J6iFO4ePHT/zy4T3guVy9I89yPn38yKcPH0niCZcX56jgaNuK437H7c0n5rMZs3mBiWLcsWS+mJEUM+LJHJQhKMV8tmA5LQaN3aFicyjpugNpEmONotxtwFq8TSnmE7JigseglcEqz+bxiYenJ+rac3Z+yf/tj/93JvMFz+sNXd9RtR1971BxzNvvviOZTnn/80883N7xw/sb8tSyXC6I84Lt5pm6PKIJKGvonSOOI9I8p+uH8p5XbxImeYLyHfuyIoleXO/akuU5WVKzL1uedo88HrasXnU439LbOTpfEuVTPJ7QNfi+pTw+k2Q5Nuppy4o4iVHacNgeUAqU0tR1Q54nuN5RHo50VUaWJuRZQt976qZlX7YkPiIKKa3TJPmErg9DY2MYZCCH4w5FxWH3wHa3HpiM5ZyvvnrLcd8QgNVqTmIhieDs/JxsdkFXH9itn1gkMyIb0TUe5/zQ9BjHNG2ga2pubz4RR5az8xXWxENh1qGiC5pj0+DC0IzZ+0GOlGY5JoLjbo8yhrZ3PDw+cSxrLq+uxza6Y9VgpwVpPoGoB5uiTEqeWkxUo4PDtR1VeSTSmkkxJ4kionR4b/XLg6DpPMV8QZxmxM7h20FvXRQZrQ4cthu8d7i+w1qNayoO9R6tPFVZ8fnDA4+3N4S+x2rNJJ/S9UNCTl13RElKmuWkicEpRVsHdDxBRREq9ASlybOCa2XY7rZsjweUheNxR+cdad1xVSzIpyumucXEMW0/FPQ8rdfM5wVFkaCNIU0zutahtcHYiNANDx6tDdrYl5uhxvtf81+FlbRRjOs9vg9orUamLk9nzOcTsjyld80QK9nWRNEQo9j3jjhO6Lqeru1RKnA8lvS9x9qIb7/9rXfSv96XAEV5iBdFwePj41grLmkJp+wZwHa7BRjLbkSaIPprWf1rrVmv17x//57Ly0v+5m/+Zvwd0vwpqRnW2jEyTv6uAAPJzj4tjhG9ttRmi1EQGFk5YR1lNb7b7cYH9c8//8xPP/1ECIHvvvuOoij4+eef+fnnn8f8dElz2O12fPjwYdQei2b37OxsBORyvS6XS+bz+ZhKI1ph+YxSUGStHU2KYlYTxvHu7o6qqri6uuK//tf/OiZHCBgTM+3vf/97ptMpf/nLX/j8+TM//PADWZZxfn5OURQ8Pz+PUXkCmmXNL4zk119/PQLH0xIkSbvIsmyUWkiUpTDP0uYpx1hAubDyZVmOTOpms/lCRy16aIm0FMOseBfEgCuspbxnAcBi5BOjpjRGnp+f8+23347NmZeXl2NE49XVFYvFYmxyFMb4FCyKjKquaz58+ECSJKNcK4QwGl6FlZatgWw/4jgeP2vXddze3n7RGCkbg/l8PmZ4i6xJNg7CKB+PR4wxTKfTcRsgraEPDw90XTeanuXYCIMuA4Vcw+L1kO/N8Xjkp59+4vPnzyMrLCC6qqpxcyGfSc6bgGJ5zedzvv76azabzWiMlXhSYaInk8nYWirn9OHhYczxF++IaO1PmznFXC5g+lROdOonketPtntyHKSdVgYIMd6KNCtJkvHYiVRFNjC///3v/8N76G8G5KHv2e6e0arHqEAI8PT4THCKNC2wyYSq7nGmpYg9TXOk6XrSWOPrhu36SKw7uromjYaLYf30iFaG2EaUVc2Pf/4/adqKi/PvMNqz3a5p2gZtAnGcEiI/sLZlifdQlw3BO8r6OLBkzpHlBUFbdJzQqwitEqzXNPUB5WNinRGnCc5vaLuGqZkMJSrOo6zFKmh6x+5pTffwwPJs+XLR7vndd99zeXn5cjM+DqD6JUboeCwJrh6+YCEwmU5emDNFFMd0rqevaxavplRNIMtTrErR2hNQPD6tqRtPnCSYAH3TU9WOxfkKWyzpVc720DPXiixK6No9zw8P7Hc7Lt98y/L81VDV6gcjX9O2PD4+0LQNXTdMhMvVGdPJlMerB/7ln//f3D2vwWhmk4K8WKACNOVL7u7LF9aYiCQd6sTRhjgtcF3FZrcleM9yMdwIdNPQJUOqR9PUPK63HNc3ZM6jikCIInoXY+OYqJgRt0uqpiKKDGmi8G2gLiu0tqAtzgcI/aAp9wYLHKsDvVuwWC0xRrHbHUkcBJuibYILhqANSTLBOnD9DmM6UENp0ONjyWKacX55QZEnvLq+5Juvv6EsK4KCxXxC6Gp8X5HECY1zHLbP7LdPxMWCtJgTG02rIfSOrmvZbLb88ssHkmQYmsqqpJhYVKSxcUzfQXnYUzc9hECeJkzzAjxsNlv6rqHIc9bPa7q259vvfoc1Q6vqsawJBGznmU4mRJGmC4rOQ6KHVRvOY42l10PhT9uWgKFujvRBEXREnkT0riG2iiyNMCYHlXN9MccAt5snjtsnqsOBujyQpSnBO47HHVEEaRSx36/pm4Y0SWi6IRbTxAnKRqTzBUlWgDEce0eaZtA7lDbYJCGLDQZP6FvWmwMmSvBNw+5Q8enzLUFpXn1tiaaPXM1fMSsm+HpwynsHTV1SvWS4Hw/DgysQyLMJBEMIagQlg+Z3iCx0zuO9o677l/W0x5qIOEoJHpqm5vn5mcvLC9I0Rhvo+wYXWpSCtm2I44i2tSg1SHmapiMEz+PjE855mqal7xz/x//xW++kf70vkQ2crocFFAsYk8ZHiUZsmmZcoz8+Po5GR9F9Pjw8jA/Vuq75X//rf30Robder0dNpzzcT1MP5H+FETw1cwoQiF+M4sIwynt9fHwcQYkw96IpFz2pc47z8/PRdPi3f/u3vHr1akysAEbZjehpJWZQZAqnrFxZlrx582aMUhTwC4zZ0BLvJq2ml5eX49AgGcqnevr1es1XX33F1dXVCP5P/71E70k5jSSF/NM//RP39/cjuBOwL1sHkYIIWDk10J3G061WK4qiGLcccu5lWBOALABWDLby+YTZlchM0RwL4ykDCvzKVl5cXGCMYbPZjIBIhjsxXzrnxoFcKcXNzQ03Nzejb6EoCt6+fcvvfve7MRHl7OxsZOzlnG02G56fn0ewKIyv6JOfn5/58ccfx+N1mnstOnjRJov+//9b3rhcj3/4wx8wxnB3dzfGCUpG/SmjmyTJuBmSa1iyvE/bcWVYOt26yDGRQqHNZjOac4/H4xfpKcKoS9676K1PIxXFSCpbgNOY0zRNx/Mn9xC5RmR4VUrx3XffMZvNmM/nYyypbEoE9E+n0y8KhE6jV0WyJAkvcpxOdeUyTJzeEx4fH0cfi4B2udbl/iWAXyI+xegqA2HXdfz3//7f/8N76G8G5M8PD0OTXxgeSJGxhADWRqRJgY9S8mJJpyyb9ZrWDQfDGkXnAkWW0RxqqvLIu7dv6eqWuqqYT2f44Pn08Weenu749tuv+ObdG1rnODw+cywr5ovJOOmVxwNVWdK1LU05xKYRHGma4JQhSnLKoEniFGsmaD+sl32SE/oteEAr0AofAspoqrKkbBxFlmHilM55docjSsPd3T273Zbf/93vef36miiyA5ByjslkTl5MUNrgQqA+ljw9PXG+GuKV9i9sgg+evnfM5zmL1Tn2OGifNIH97pnn9Y6m6bk4v8IaQ9fWWJszm8dM5lfUIaFqNVYZsg6a1hGqI/vDlu12zdWbr8mLnKZp6ZwiEF4yoLfUTUUAJkVB1zisjvj6q9cYev7ln/+Z56c19bHE6B76Bu96AtCEBmsMeZYRRXN2hx3NseNYNpTHHdVhR9821G3HxfkK53pc15OkKdfnSyyBQ7Xl0LXkCqK8AJfjdYROCkw+x6/XbNYbEqtomp4QNFpblDZUZUlkFFoFmq5BJxFNU9G2DZEdipgcFdqmxMV8KLBx4NHDBsI5fB/wfY9SHV1X4vqS2fyCPM3oliuWiwUBQ5TGg2HWaBQRhuFmetjXuMpztnsmffctXXnA2mjYynQtbd/zb3/6M7cfb/jq9ZKzxZS2reico+4cUWKp+466PND1geAg1hqco+xaetfjfeDxaUueT5jMLH1QWB2T5jOu38RDKkjfk0yXZPmEXVlR1y1eNbi2o2+P6NChfI+1msgonjdPfLy5o+3DUMKVFtikIE0NKnTgW7Isoa/27A8HNvc3VNtntutnfNuSLoaVv0ljgu+IgudyPidOEtI0wwO7Y02cFfRo0mJKXBQoFXH38ECc5TR1zXG9pul6ijQiT2KKdEo+O4PSMplNMWqI7dzt9oNmXhVMz9+xuF6io4hd6SirjtB07Hcb0iRivd5SlSVZUZClU/TL0DuAkJgsTcamTaPBWs3hsB+1j6AIKOq6ofdDY+kAFFqqqmU2MbTdwNy0bY02GmOHh9j79x9wboi6urt9QCn9MgRE/xd3zv//ed3d3X3xMBQQEEXRCJZFciC11xI755wbmyClnryua8qyHCPyfv75Z+7v7/n973/Pd999N5oeJfFDnhECGpqmGXXOAnSAUSd82rYnUhJh3CQ6T9hmAYeiFxc9t+h5N5sN//AP/zAmvUibp6SnnBpWHx4exjQT+RkCLEUKIswa/GqGa5pmTO0Q4CNZ1KJtlWFB3u92ux0BuWjCRUogZk4BgtPpdBxuvvvuO5RS/I//8T/GBI1TMAIDCLTWjhpwSQWR8hjR47dtO5ZFSfKEmEzLsuTh4WE0IUrTphgcTxlKeZ/CcgrjKyDvtEnxNGs7iqIxlUXOLzACMTEdigxpuVyOA4BIm+TakeEQ+CLFZLvd8u23344gVDTOXdfxz//8z/z888+8e/duNLoKWD8Fr+JZkGtStNBiYhYzpORnF0XBV199NV47p22qwlxLKyv8GtNnreXx8ZGPHz/Sdd24sRJTtsgx8jwfC7Rub2/ZbrfjUKC1Hn0SwjZLbKf8DNluiB69KAqMMWOKSlVV48Ahfy/P81G2NJ/Px+O92Wz49OkTSikuLy95/fr1OOSKn2C73ZKm6WiMlthFGWhlEBPNvFw7Ersp51ReEncr33/Rwc9msy+ut9Nkmx9//HGMDJX3K8f8t7x+MyA/bNekaUzTDF/2xeUSbWL2u5JsmuJtRpzk5FmOV5bt/kBkFdO8wCewefxMcI5vvvmG169ec3P7meAdCs/+sMf7hu+//Yrvf/89KM/+sGW9eSaooUmwbYd1+vEwTCBNVRPboZzosB8amIJOMDYlS2dom2KilK7p6XqPjiJ0kqDVi6vXaFzwxFmKjgYdkooTFMPkqG2MMUOf+cXFxVB8sJhz2O3pun5g5aLhYdw0LU3o2W139M4zXyxAKcqqIstz1Eu2ejKZc6xblE2GgpXygMegMMxnw9SnCFTBkWU5fTAca0WtIvL5BSjN/lgR60D2Un0eJ+lQ6tK0lFWFto4osmgCVnm6ckPTlPgyZTqZkmYzYu95ezmnfnfFp08Nx8OB24dbIgPL+Yw0ianKiqN3hOCIo5c82mKCtZreBWyaoaOIfVWj1hvSJEaFgOta8iTmbJbTNkNEpa9yon5FaOPBUGhiiDJUlFNVD3irMCbGWIXre4yxQ0qIHyrq0QGbxATfsNvuKKYTpos5SVoQZxOc0nRtx6Fq8CrCe0NsE4o0RauOuj6SxJYon5AmCcZa8nxKUUyp6h0+dDRdTd00pJHF+A7fd7Rtw3ZXMrv/yN//8f+BfjEPHfY78B1BGZRN+Ob737GcJlg1ZMhXzTARZ3lCkWuiOKVtBuNmU9WUhwNFluJxHMuKy6sL3r5+Q1UO+eImTrGxIyg7FD3ULdiETlm6oFDWEscJjRuY39RCfdzj8WhjyJIIqwPb4zCQpZM5k7njsEtADTf70MdU20e6ssaGjovFhKkF1/WD/jq2hBaCUoS+B4aEpK5t0HGKx2PjmLb1tM5D0GRZSpQVJFlGnKRopbFac9iueXi4ZTGdoZXCpjmantDXeK8wJsI7R99UuPqADS3TNCWfTPEqwrlA+5KC4p3nWDV4ZambnoBCn0RhGW0wZtDVY/XIhgyM2qBRTJOE0vf4ftDMJ2mMjTRRpMmLjG4/3GRREMJQRCVrZ60shJ48L+h79xKjtvqtt9G/6tdmsxkfUnVd8/r16zEBRNg7eSAKmyuazDRNubu7o+97fv/73/Pu3Ts+fvw4gidp2vybv/kb/v7v/378Z09PTyMjLwBINL2iDZXfJakLkhohD+VT2cYwiLVfAHIB4RLDCMPDWgxhANfX13z33XecnZ2NiSXAyMgJmHx+fqbve87OzsZVvwAVa+2o7xXmXJo1lVIsFosv8ozzPB/lDsAIYPb7/Qg2JPlDgOrxeBzfkxy34/H4BcMoGtnXr1/z3Xff8f79e3a7Hbe3t2NsYJIkXww7AjqE+XXOjckeUtUug5Do8pfL5fi+BFTJcRNG+1TjK/9MWMwkSfDejxsD+f/r9XrMdT+VwciAB79GbOZ5Pg4GwsyLPlzkQ/Le5B5wathrmobn52dubm74x3/8x3FoEUyqor8AAQAASURBVCmKfJY//OEP4/kRRleYbzEEiodBzMXFS3Pz4XDg9evXfPXVV+O5Ok2GybJsNB8KmJZtQ9/3Y7urDMsi6ZBrReIO5/P5aFCV8yTG6xDCaBb+378rwMh2C0MshspTCY+YoQV4i/xIJEiSQQ6MjLkMj7J1kHuLXP+nA71sGkTeIucc+CJtRbZO8jlOnxEyEIoRWTYhsgkSWZxsHeRakvuIxK3KfUO2ahJJ+h+9fjMgL/KEJEk57PcoZYiTlN4p8okhKENZN+i6YZJmNF01lKxkCXFkeVrXlHXHm6vXvLo447AbomqWiznGKLquYXm2ZHW25OxsydPTM9vjgd3hwOLsnLbv0SiqqiTSlkk+wShNW1WU5Y6ubfDOoVIDeihGqZ2nbEqOxxLdd0R9j+97YgCtiNOYbJJhYkucp2wPw4rf2pjWN/QesjwjhJ6z5Zzry0vatuX+4YHgFU3vBq16CENSRT80/i0WS6I4YbvdUTU1cZaTRglOx+wONaV/xMQ582mBVkMKxmw25/bunmN55OxsidbQ9oDNaWrF0RssKXiHChBZyySdUC8WtL1H6UGDfCwr8lzj2ppyt0a7hoSW6vhEuXeEY4FdXLC4vMbgWBUGVhMOicdXQ2tjZAKaQXJ0OHQQlnRSEFFMieIIVKCqSoxR7Pdb6j7gfEdsDElsBxlLmuCD41DV9KGh3d3i64aQLghxgTGayWRKXUyoD1uiTMoYOlwAE8XQBzofCCrQdh3e92y2u8E8mOboKKGsGlSkwDradoj2U0ET5xpjIIkN66c9bdNwdfWKfDLFaEMa53gPbdvhGdJH6tBjplOcD+igsVGEO1Q8P91SHjZkkwu6zg8a/sQSxwX/8F/+EeMa6uMTrh7KBDxr4iTFB0VhEmZRxuFQc3Nzz9PjMxerC9Is4fHpDhMlTGYLyqajajpslNB7RVm3GG0IKhq03D0kXuPdkKXddQ0hvBQRoAazsh8YuLppuLw8Z7E4wyYx2WSOC4rNbsN0NgcLoTmSJgnaBIKBrmroquHBQ68JveW439H3HW1d07sWpRXTxZLpMhokY+0Q3xisJU2TISYzy+hdR2wjrq4uSaKIbZ6hlEUBm90zbX0g1p5yvya1mjzPSLKA71tcV+G746ANx2CjmGhS0DaOx+cNQVmMTajrns12WJcGAjod4jO7pn3RRtZ4H2hbNzJlff9ibMpTXGjpfEsInixPWK3OiF8kcbPpjGNZYq3mRV6I1vql9v3IYb9Ha8tiMX0xVcW/9Tb6V/0qioI0TdntdiNAOmXBxVgnaRSyKhdGuaoq3rx5w6tXr9hsNjw8PLBarUZG+OLigvPzcy4uLri/v2e327Hb7UbJiCSliH5V8sqFqZU1/mlMoYAfYW+FIdN6aNGUVbYU9gjLKLFoAprOz895/fo1bdtyc3MzspYikxD2vSxLzs7OSJJkTAWRY6CUGnXpEgEn72OxWIxlRJeXl6OmWPKT5X3L+xJN9nK5HAcM0QtPJpPRhCqrd0kekYZOAbmz2WyM9JPPIHIGYeC996M8QvTJ8jNlIJOccwHmq9VqHCgEPG02m1FzLsPSKesrRl0xzQk4FNAjv0PkIwKsBdDLdXRqDhTgen9/T9M0vH79eszSFoB/amqVBlUB2zIg3N/fs9/vx5x7kWXFccx/+2//bdTUS5mPbGxOk2t2ux3b7Za7uzuurq7I85z7+/vxWpDff3pNCaCVIU5MnafDjVzbYpiU9/Hq1asxLlQkMpvNhsVigWSni1dBZF9yDcif7XZL3/dj2ovIeiT2sWma8bwJMy1ANYoiXr9+PYJcGYAln12uHfk78vdk6yKAWDZvYk4VM2dVVazX63E4EJNs0zRj86d8X0S+duqLODVd53nO5eXlOIwNfSeH8dkCjKbfU/27tNee6uT/r16/GZBnWUocp1xcXGKjmKAMSV7Q+CNtGFJCdrstwWh6QJvhJO6PFdlkzvnlKyIVODRH6ral7VpcGCIS0zzl4uKCxWxG1/U0bU9Ak+Y5xWTCcnmGazo26w1xnmGjGIXD4uk7i+sVLnhc29L1PcY72r6jPLYEH8hSS1931FVJ3R3ovSdOE4rZjKs317R1z/PmgIoSut6zL0tiYwhoojjhbHWG0Yrj4cBuu2cyG9b322ONMweMsbT1gb53aJtxf/fAsdxzcXVBluV0HnSUsDt2GN+Q5TGRbcktGK1ROjCbTYd2RBXoCNS9p217ujBBR1MOlQffsCoissiArzEmYnF2zuL8ChslVOUwOR83T/zwb/+Cq/e05TMmNBgCTXukxvNpP7BKh+2G9rjH+J7zeYK1M3rnCQRmacK6rjjsNxACxWQ2NCWqgXEIWhMIJA7wnrau6OpuAJFoptMZfQhkdU3TduzKNX1dEeEwOhDFOdPphH6xYN3X9N6B1hgzSFaCd0RxinMtZX3EuY4sHUBaVbUcji3ZJB4aIbXHuRpch2tbdk1FaGvSNMPGQ0nMbL7k+votZ4sFKgS8Aw1Ye0bXt0S2oq47+l4xzafQN6S5Zn0subv7zP3dJ363uKZ3hvlyRZZnwLDJOG4fiRNLs4/prMVGmv1+i3dDQ+Tj4wMPT2vSdDqkRiQpVVlSTOdcXK1I0pTtZjNIwKKYY1VSNY7FfIo2Md5rNIo0TQaz735HW7ckkcGg2B/2dE2FNTMIgbZzxEnMcpFRTCfMl0uauiOLBrPudrslti9V9K5nv9/ycHNLEkVYY2k7R1V51pvNkI3uHJ6AUYpjWYK1pMUU17Y06sj0bEUeW5RWpJGh7mvaY80sWxFFmtevr7m4uGC93WLvLduNod5v6F0gmeTgA9XxgLJPvP/hT2RFweXXOUH1gyZeKYxJybOcuvEEryjLiiStmZkIa8Wko15WzQ1VVTMp0peH17Bib9sGjWU+n6J0Q1k941w/yKNii1YD45VPCrq2J01ynB9aO4VNOx6PZPlgdDtbrnDO8/y8/q230b/qlzB919fXIygTraisfoUVP13NC5AROUZVVV/EDoru+/r6eszIFs2msGSr1Yq2bXl6ehoBFTCu7UVqIWydANjD4TAyd8Igi0wgTVPm8znv3r0ba9wFgJ0mwyRJMmqWd7vdCGrSNGW/34+M3Gl8oqSRvH79+ouCFjEfSv7zKXsnTZPACPhPky6kGEWi2wSYX1xcjGBCJBWbzYZ/+Zd/GWMahYWXNJPTFklhVpfL5QiC5XxL1CUwRlyeZlvL0CJ63FMmdz6fj0CvaZoxfUckC9bakemWa0E+k/zsU+29pJUAX0QbSnOjXIdt2/L4+Dgy9ZISslwuefv27ShTEdZXUkokoaXrujEyUdJwJIbwH/7hH5BSJhnWiqIYgaFsb0QmIfKfu7s77u/vyfOcr7/+mjRNOR6PzGYzXr16NZr8JS9biqtOi5CELZd8f2HSZdCTVCNgBNtS/CPxhnI8Rcct39XtdsunT5/GfybXjXhGTpls2YjIcKKUGmMZhWUWmY7Inb766iuur6/HbYNcd2KalvuEMYY//elPTCaTUVYlUi1jDHmejxpukU6JfEmGKEkOKstylDLJeZCftVgsvigbkg2KDPmz2WwE9qegXvLyJfv//Pycvu95fHz8TffQ3wzIB31XIC8meK05lhUmSnBAnCYYDCrKCGFoxEMrDlXFZDJjdXZJkqQcd1vK45HjsaL3jrbvQSvOLs5ZLJekScrnH35ht68p5lMyB9PZjNl8zuPdA8/Pa5qyoshissSQFyne59TVoCWLshwj67IsQZmYrqmh3tOUe+qqpC33KHqU0awuVlxcnmNMQlJM2R8qbj7f0rnAYj5luTojSzXnq/OXL2iHsZYsL8injmO75ViWFPmE4KEPw8NZa8/VxTmvrl9R1jVV1aF8xqbsKNSEJNU0rSe1EU1XUdUlURozmRW4EHAt9MrQqwiTzoiyFa0H37a4vqapWjANcZoxP1+Szc7pifFBs1mv+fDDn3n/45+pd48kquPibEaUxvR1yUP1kd1uN5giyiNleSCOI4y1zCcpVdVS1w1ZpGE+lMrU1QB2j/tBbzhdLtAmAq3JJhFWGzbrJ5r+wL5ueP/5jsWswMYJcaxQAH1D42va5pmgAkEHrIk4P18xLWIenu5o25Zi9qJH3O5wfQdBA8MGIPaaqu5QuyPZtMSmGUNQj8N7xzzPSI1ivzugwmAQbRvH4VhhjSEyOUlcEMeDHnEAsRbXdvguoFWE66HvAB+YzifkRcH2aUvXtkRRTOsgUtGgn+47dAjopCI0JR17uqDQaIo04+lpze3dI5vdkeXZFWdn57Sd58PnG7Is5+L6NUliKZuaYCKSOCGgaDuPTVKyyRQCpFmG1grlHZHmJQaxw4UeGw032CiOibOCaVGQZXu0VqzOziiriuf7B6yxLIoEUCTKo4KmqRpc0xNr8wLGDRDoXE/TdMNgmqZoY/AuwEsE2X63w0YJkdJgNLEK+Lah7UqMD8R4yrYi9DW7co+JYmySEiURl6+umUwLqu2MfZawnOSEvh003OWBn3/8M5PlnOn5OWYSo7UdokidJk1SQjjQtj3HssYmFflkin4xebZtQ8+wcRse3mHUYUaRATxKB4oixXmFjezLw6WmrhuK3GJ0jFHRIKEy9qXhE5zrUApms+nIWBmjqXcVdfOfxUAwADRhxIER/J3qt0XOIGYyAR0XFxekacp2ux31x6fM3vX1Nefn5yRJwp///Gd2ux2LxYK+78e0ks+fP/P4+DhG0EmboDxQZRUt70HAgKzBRQ4gRULCeL169WqU1ux2O96/f0/f9yyXSy4vL0nTdDT7C+AuimJk7QQYiolMGjKvr695+/btyDyertoFNMjKX1Iq5vP5KBORdAiRAQgjKQBV2OjTTHaRzfzpT3/iT3/605jScn5+PkofPn36xGazGVlAyZwWgHwqm1gulyNb7pwbgaBsNgQ8G2NGvXBZlrx///6LKnv4NVnlNDfaWsvV1RXT6XQ0oM7nc+I4Zr1ef6H5FX265NFPp9MvjI2SYy3Mq3gFZBgQOZOUFUm2vbDykroh2nNJwZH3JvnT8t/JhkVAtCSpnALD+/t7Pn/+zHq9Hgentm355ZdfKIqCN2/ejNsJMevKZ5VSJmFwhWE+zVuX9wR8kRku//3FxQVlWY5yJPnuCnAXL4ZIVOSfS3rRfr//gkUXqYtIxAQoC+MuA7BsbJxzPD09jRKcNE15+/btKJ8RTbkw44fDgT//+c+cnZ1xeXk5yr3k/Ip+Xf7bJEnG5JXTTPLTYVZ+thwnrfW4MZABRAYgMR+L9lw+x2lEpFzXMiyJHOy3vH57DnnfUlUNykR4NaRZeDRRmhElCVXdkRhFHxxJmhDnE5RNmS0vyeYr6qqjdpYey9Nmw7488PrtNdlkQpJluAC390/8+PMHrIlJp1PSdDAaoKCqK6q6JosjCJAkKVlqMSpQVxVRkpMtL4nimLJpiZKOLM1pjxVPt59oNveEdk9ZHphMMvquo+s7mq7lfHHO66Rgszmy2R4pplOuX7/m1dU5WaJZLKZo9ZJfaQcGFzR9HzAmIihF2w3gMSjH5eUVb9+9QWnDdrdHxxPKqqesoDiLiZKCJDE411AedpRNTex7dAU9sC9bvJ5gs4JkegbxApqGEGpMONLVNXGuSPOCtCjovKf1PVoZ7u9u+fjhZ9r6SB4NGn6tNU/P66E4ImgmsxloNWR9R5pimpNnGUU2FL7s93AsHU3rUIShBCaOSbMMGycErTA2QUURxnkiGzNTmr6Y0Vd71ttnqqbj/OqKLM7RvidOLX3vWDd7mgqwEVE2p1hMUIuU3ld8+PxxiFrMYuomYr+rsEoRJxned6Asnes5VjWb7Y4oK1hNZqAUbVuRTDKiWFOrDt8e2Pc1t3ePHI4V3337O7JsgtYxSZrifc/xWNPs95S7gZG2SUqSpIMuWUV0zhPFMVmeDbXyfU8U5fSqx3nwytA5hTIpJsqJsym7pzuqzRPb5zs+fvxE23jOzodEiLIuedrsyYop7776Ch1pysOOtuuxNkZrQwgebSNSG1FVzbiaa+uSx/2GLB1iBPumoW4rDgTi9GXlGDSHssEFTWg71pst6/Uj282axXTO5cUZ1kYUcczt3SNPD8/03WD0rZuWyaRAa8skzzlLU5LZlH1ZkRcF00lOniVYpWm7BqU0x6qmPh4pdzu8Z9gIeEdV7titn7CqJctzmrbClweyyYxiOiGOIrIoYpqnhLak2vekWUKiNCpNcO3QebDIZiib0fcNXesIqsd1DhUswWu6fkgoMtYS2YG59M4Ng7MZioL6/qXSOEsw1qBVIEoMqUtIkmho+fSOw/6I1TnFYkrbOUJQeK/pe/+ix+UlR/slevVFznIsdxx2m996G/2rfgnYEkmDAAQxjJ2u2yXXW/SVy+VyZDlDCDw9PbHf7/n6669Hc5b3npubG/7yl7+M62DRogoolCg1YDQGinxCinzEdCfa0MPhMOaFy4Nc6sIlwkxkJlJwJMy5AKazs7MRGMgDG/gimUUkBACvXr0aK783m83IcEtGumjtxTwqZkw5fsfjcdxACCMugE/kFZIDLkOJ5Dx//vyZn376aQTVIhd4eHjg8fERyRk/jYGbzWajbEAGp8PhMG4qRKsuDZACwmWjIGBnMpmMUgKRTYikABhlRHKtFEXB2dkZq9UK5xy//PLLeGwkY13Ar0gm+r4f2Vs5PqfMpsiPZAvz+fNndrsdf/jDH0agesrK7vf70fwq1wz8GqUqkgpJeBEGWID3aXJHlmU8PDzw/PzM4+MjP//8M03TcHV1heSVy5bnu+++w1rLfr8fGXoBlsL6ynZBAPhut/uiJl7OT5Zl4zAnyUOyUZI/i8ViHD6TJOHz58/c3d2NWxM5fqcRlrPZbNxwyfdRpEGnUiiRR50aisX/IZuWw+Ew/hyR+4i8arfbjd9l0a4/Pj6OA/Zp1OBp7r3cT06ZeRkaZLMgf/dUuiKyFRkSJBtdaz1u404BvQyfYq6VIUVrPZqnf8vrNwPyH374SJymZJMJykbkRUYUxxTTKU3Tsu+OhKCJ8gkmSomzOWkxJ+iEqgt0XaCuh9rzKNJcXlwwX6xI0ylpNuF4KLm5feRYVVxdTFFak8YRBP+iEe9J08HEGfAoNbAtxlqargWbkuqIqu04Vo4i3hMbC82Brh6KC5RJyYoVV1cL+r5kNp+RT6ZESU7QMJ2JFunIdL6gmM5JE4uNI5Ry1O1ws4izis12z2az5dWbd4TgOB72zCcp1xfXnC/n5EnKsW5Ax9hkgu0TTOPQaELfQe8pyw1NeaTIBkNkU1WoKCagUDrCxClBaSKjiZTHxrBIE2YxxHHAa8WhLGlcQ+cM1liKPOHtm1fsE0+o91SHDW1XDUU1baCpS9Jixn4/aMDjxKDCYHDobEMSxSzP5sRpwqE6cixLIpuinSWy8VAzrgI2tag4pqx7grak0wW6gLYs6Dto65KqGrTEvfe43mEURNrTu5LIlUzjnDRSNG3DYpryyfdUxy1ZOqUoMsrygHeOyGiUMiitIBjaumO73pFmE87OL4iSmPaw41iXhHaI1nSuQ1lLVw83qPliQpRYlFWE4HC+o2tqqvqIAuI4woUh+zwozb6qKPsjGMV8NuW43/LLLz+yOH+HsxGYCG0sNkrpmxpUhDYJN7dPUO1xrWM2nWDOUpJ8irYxqIjLy9egLEprfNcRnCexwyq0co5iMhnq7gkcyyMoWM6nbFxNUzZEJiLSEZ3R9C9SisVsDlrx8HhHHEVEdkhneX58wlpFFFmCcuyPe9ZPa47HhtuHJ7RJODtbkeVTssUCbTWu70nShOlsBknMysScnV8QRxF919LWDbFzBO/p2FIf9jw+r4nLljybYBSoAEWRoxUE55hOMrDRsP1IMqqBWGd6saLeb3lS4PsGCKR5iq8PbO8/kk2XkCmUnaLQNG33EuVowFiCD/jeoYkICnrnUEDX94TIDGDdaJQxKDVcQyG8lEKkljjRxInB6hiUout6tLG0XQtBEdlofIBkWYoxw8MDFV6Ynp4QepL0P4uBAP70pz+NrPSpnljWu7vdboxME7ZOwKBE74kxKooirq+vvzDm7fd7Pn/+POYwy8NZ2CmRmQAj+ylGQBls5XcJey/s52mCx3Q65fXr12Pihqy1tdYsFosxe/rs7GwEeMIGyipcAOHT0xNff/01kls+m81Gtv80BlLSNiSSTtji4/E4yKRejLBybE5BhrCQwqDKkCL/TuIWT9l7kUXIeRFQIpns0+l0ZM/lmIomXpo6xfh2PB5HgCgMMzDKQcqyHPXoovOXfGr5PGLKO804F0mKmFJF4iJ6com+k0EDfu0dkIZRkQ6cSlvkOItsSjYqYlY9jQ6U61Gq3U83E8IeS9LNdrvl3//937m6uhq13XKOBKBaa/n48eNo6hS2X4ZTpRSvXr0a/77Ip04bRmU4gkFypZRiuVyOn0/MxsLwx3E8Dli3t7fEcTxeQ/f39+M5FWnLw8PDmKwiA/N0Oh0ZX5EGLRYL4jjm7du3Y6SmHFsB38D4M+W7f2qWFrmL6PlPW0W11rx69Wr0pMg2RNKCbm9vx++fyMeksl6+EyJ5k3Mm70kGHPg1m1yuoVOvw6kRVP6esP1iDJZrVdh68cyI50QIiN/y+s1Pks2uZBkXFFFOlKZk2YQkTVA+sD6saauGYjYhy2d0KqUPKb1Kaeueqj2g+p62OdK3JXmaUqQT5tMlk8mczXbHbnfg080dSZqS5Qmuq0nTguN+Cy6jrnYY7UH1aKvwKOqmZ73Z0bY9i/MJjQsEF1ivH/EEIt+g+yNZqtnXFlTKxdkMHVUsZjHO9cxmS5pGsT/UVOWBvm9ZnS/JpgVEQ5kQ1hJ8w3p74Fg1mMORu7t7+h6UDzjXESeWq6sL3r66ZJqlOOfZ7w40XaBrPYe6I0onaAVtdaDsHbqvWM2nzOcTXNvw6fYWFeB8dc6xi8kmU3qv0L4l8jV5HJjnMTbUNE3JvnJ4k9K6GHSEUoH5JOb61QWLwnB4emJro6E0xqQsbY5rh+mxLEuSyOJ6h/JHknTIb1YTRZrkpFpxfnlJUdZst8cX7dwjk0lBXqQoPIkxaOBQ1aSTKSaOMV6RTjqC11RVQxIruqal7zzL+YxENWinCO0Od/BkFyuU6iAzXMwn/PLhE1YZ0qygKDIOhz3gsVahXmQJXdNho4jDds9mveX14iumyxVNXdLVDUmckCaaKLZ0y5x0uuDqcoaNPIdyw93dlsNmzWG/JXQtsU1QOsPzkrFbBarmwGyWkKcJsbGEvmK7uaNDQ5SST1Zk2YQ4iinyGYmCCoU2GU1vePP6K3aHR0ySkOYLbLSgbiydU2ijCa6lbUr68kiSxmjvOB4ORMYwmQ1RoB5Pkkbkk4zgCiwtwfVj9F/fdy835sDjwwO968izhK5rqauGIs1IEoPWOWW152m3o+4D0/NrJlfvyKdzoiTFaDvm+rb10HxrNMyMQUcJ6XSBMRHaOXTqcH2PUvBcdSSzGGMGmU0+W3B5vqI+blG+ZfP8SNtUdH1LWhTkWUqHIozpRjG51rjgeL6/IfQN9WFPYjwPHxvy+RkX7xb0wWEiS90ZlLLUrSOOUpI4JUsSImuJIotSAed6jDUDqCYwn0+xcTyYveOI3XaL8wpj05HtjmwyvieUGW/mSoNSoLUijiOqSg/6+2QYoJwfbsSLxfS33kb/ql+iO5XSEWFTpbijLEuWy+X4IBZwIyyfpE+IEUwe+rPZjPV6zWaz4f3792NKg2iAxQh5ys4LqKrrIWu+aRouLy9HOYEwwcJwidRB1vhRFI2rcmlVlIGi6zouLy/H4hUBcWIolFW5lKTI70iShNevX4/Ng5JhLYOIFNrIMREwtlqtWCwWtG3Lx48fAUZpw2w2G5k6kUaIPOa0DEZAiazj37x5w3Q65fHxcUxCERmNMPnyOYRVFKAk0h8BjzJ8SC60GDGBEWRK9rYw/CIJkI2GxAQul0uAUUYgelyJ2VutVvz000/j/xcZkfwuYGSGRSv/9PTE7373O1ar1ah9FrCVJAnn5+fMZrPRYCgbk+fn59G0eMpOS9SmRHJK/KRohWVYEuB8uoUQkO6c46uvvhqlHadJK8K4yvdBzNDe+9G7IMZSOefT6XQ8xyIDEcZeZCi3t7ecliKdmqyNMWNhU9u2XF1d8erVq5Htlfcu50xAvwwqkqIkYL3runEYFE+ADCBXV1ejdOXx8XFMOZJNGDDKcET64b0f378cg59//pn5fM633347+ihkqJPzLxnnMizK8RFWvaqqcSiSgXa9Xo9D0Hw+H9OCZNsnngS5tuV5IUON+AVEf+695+zs7DfdQ38zILdJho1TTJQQxSk2jtFK8bxec9yXpGlBVsxBJ5i4IOiE/bGhbRqs8sQ4NC1FYjEmxWiIbIz3is8392ye1yiluby6YD6bEMJQQNR2LfjBNEloMXa4AWZFzna9Y7evmM6WKGtRVpOkMVmiKTcPqOaIVgalAnE+wUYTbJINsoi0oW1L2qaj9xHbzZ7n53tc3zJd5EMkXVUyyQu0idns1my3OwjQtj113ZGlBd71uG5ok7y4XHFxsSJC83D/xOPThk4n9N0RrydcLCcDMxeB6xqSWDObzwh+KI3RAN4zyXNiMtJpTtcnlFVFnjimqUGrgcmomo6uDxhjWZ2dD8UsOFSoOawHDWSc5nTumbLpB3bZxLTe0TcN4UVe4MqGtu5Is4i6bWj7ntlCY03EfL5guTRMZyU3n295//4js9mUd29fE0UW39Y0ZYfzhr6JwAa0sWSTGUZpXLdju9tRHbaAx8bFUPBEjw89vj1ClzOf5MxCBN99hXM9m80RnWfEiWVh5jR1ifIBCFRNTegDrndUdc3zZsOqe0OUZihj6WrPYbemb3omRUyi4ep8wWo1Y3ccGIanhwfK3Q4dPG1b81xt8T7i8uodAcV2t2Y2G77MrmvZPjzinaaYrZifXaB0oK9LehNTZDmKFO0d0VLz+7/7Bz7/oAmmI5stabqaYjbF9RGm04QAxmqMMsTKkukMaw1ZbAluiGicTidE1pImCWka07sOazRxbHm4feT54RFrBg1b17ZsNg3gWcxn9F1LeayxNqVqOrb7A2kRURRT5pMZeb6kmMzBxPQEXIDDcciqt1qRThekkaGpjjRuR+cVVdsTpzFpPidRL0UTRnGlI+qmHIrklcKa4Sa32e6I9HAtTPIMG0V4Ak1VE7pA6wf2LCg1tJbmU5bnDt+W3H76meN+w3R1QV9VBNfiQ0fbVdRNTJrlFJOWqjpiI/MiQ1EE7wh6WCMbrWledIrGWtrjkSjyA4GgDHiPVYoiTYnNoFP2eCKjAI8PDhsblALvHcbokQH9/PmW5fKFJa1LrDVYbX7rbfSv+iUgR0CqPOTu7u7GlfNpKY/ErgkQFhZLHoAieXDO8eHDBx4fH1FK8fr16/EhfaoHldxgSXSYTqc8PT2x3W5ZvBS4ifErTdOhwfaFuRT5hzzEhWkWfXkIgfV6zcPDwwjSRToiG4HHx8cxNk5032JqbduWoih49eoV19fXaK25ubkZUyEkdUbkKrJeT5LkC/AFjFpoMUYKMypSDpFnnKainJ+ffwF2np6eRoAhwE/AtphfhSGWuLmiKEbwBIzM8Gq1Yj6f8+HDB3766ScWiwXffPMNURSNUgSRcgj7KG2kTdOw2WxG46hcN/I5JcpS2PU//OEPOOcG+eXLhuTs7Gw898D4ngV0Pj098dVXX43AUzTmdV2P5+7q6oqLi4sx6URyt+VcPj4+4r3nzZs3uBdjvLC5Xddxd3eHc27MkZffI2k3MoBaa/njH//In//8ZyRrW/LnxTNxqr0X4CffKRk8pW1WAPVpFOSnT59GdlvYeRmyxCAroLauhzJDiXiU2EvRXQvDK74KGehk8yFa+rqux0FZpE4CWOXaks/Sti3r9Xr8nDK0yHdYtl2nra2TyeQLff12u+Xi4uKLa1x03iJTOo3LlGFCBgs5P3KvkUFb5Ejy30pCTNd14/mT75AAcLnusixjs9nw4cMHVqsVy+VylF6J9v4/ev1mQJ7m6bBODo66LvG+Z5JndK6jbBqyKKPuAtQdaazwzlG3La5rQTlwDaHe07d7psmw0t7tdvhwpKkruq5lebbk9etrijTheNiyeV7jfE9fHwftp7W0TfOiETU4H7BJSpwmTOZL0vmKKJ2Bc3z68JH13QYd5xBl2HRFksxo+hZwWNWjgiY4Txxb2q6mqUomRc6kyIkiTRzZgf1rK7bbNda+OIe7gYnP04y+rdnunpgthr+XZhnbxzXHqiJJcorJknXVY+OU6XyONT02gt4ZlIFjVeG7hr6pCd4TG0NXlsxWM0zsULrGWDUYzUJH1zgCEUkWYfKIQ9njg8IHTZxoQtfhe0fbdFRlzbGsafuAjS1t11A1HQpNFKUohig47SBGo3VM2zqqY00SQ54XaG1YLKKheMn1rNfPQ5ZtkpAaQx4HVOugq2iqEmNjismMKLIcNg3755rj/ojVmigqmeQ5IQwPFK1g/bwlSmpm8yVvXl1iooh/+Zc/07YN0zwnshFPj4GmrvHBgdIoC713VHXFer3hcDwyTy9Is4wu97SHiqbZUe7vwWryJCU4x2G7o60biiyjryp821EUc8pyTe81Xhl65ymrmjxV6JCRJCm1OdJXFeXmGXexRweFiS2RVfTdAJZ1FOFxXL56S55ods+f0bplu3siAElqsXowCXZ9Q9sc0PTkqUEbg85TosRy//hEW5cM5e8Qes/2sGO/fWT9eEfzwnjlL+kk5eHAbDlnuVjQdA2bzQ6tY7re0zvFfHlJPs2om4Zja5kslhxqKOs9vesxcczhcERrgw/D9Xc2n2JsirEdXTeU85TNkbyFyKb4ECjynPn8HHPcvnxvMqrDgZv7G/bbNRcXqxfJV4IyCq/0EOkIhM7RO0fv+uE7rC1ZPqE3mjgpWB+PPL//zMV3e5Zth04Hj0bbQWRzZospWRGTxhbUAMRD0MN5MZpgNJE80FBYM4B2FUAFRfABo4c8cqM7CI4sz8lTS99XuL7B+4Fth18LRJTSJEmKtRHWxljT0TQ1Hvdbb6N/1S+RlogsQcp+5IEtK21hFuWfC5sWQhgfyCJn2Ww2X/zzi4sL3r17N6ZbSMGQSD1EHy7ARjS+eZ6PchPRZv/888/c3NyMDLcAdXn4yns6bbYUpldAyansZb1ej8yogAopmFmv16MBMM/zcYUvSRcioRliNO04iIikom3bcR0v2uHz8/MRFMxms/H9CvgSWYOkqAjglajA0xIfAf8iWZFjCYyf5TRv+XA4jFuQU8AvptX9fj8e11PTprCrcvxESy2NmWJU9N6Pn+np6YnD4cBiseDt27dEUcT//J//k6ZpRqmDbGDkvAkLLYB8t9uNtfdFUYz6d4mnk8FEIveKohgTVUQaIwOgDECi1RYGVrTx19fX47GWa17AZwiBt2/fkqbpaO5dr9fjzxGZikg/5HtljBlZ9Lu7u9EkKCbRh4cH1us19/f3o4RIDJ/7/Z6zszPOz89pmoanp6cv5DCr1Wo0INd1zWq1GnP8ZbNzWmBlrR2LuARgS/64yJpEknJ2djYmy0h85c3NDev1mlevXo2lRAJYRRZ0mowk4FikKqLD/+GHH/jd7373RcGS3DvOzs7GDZ0MA/IdEImKEAanki+5fuR3nr432drJ+ZFhRa5tuY8JyBeD7+kQ+x+9fjMgj5ME73v2hxpjLX2fsl4/UR5L5otzJstLnE5RUUJkDb3vCF0NbYMPLV1b4psdEQ0qjcnSlD54bj7fcHN7OziKX11ztlzi6orN0xOPDw9cX1+hdaDIC4IP5FlBnhcobVHGokxMsThjvrogTid0nSfRHvqa/eaJYq5RJsPaCKKh3pVuzTGuuFzNXtbZnuNhg9GOxXyBiSFOLXmaMhQP7dg8PzIpCubzOR8/3hM6h2tqWh9o65I0XaA0rNcbnp/Wgz53VbDvBsbt/GxJ7zq222dmRcosj9EmoLQiLwpK7wgBqmPJZlsxX54RhQqvNTqOCAH6pkMbQxTPccGjtcW6DvuSqW11ROcHBr9re56f1uz3Rzye3geapsOhydOU2EYoAk3f03qH7QLd7khkNEmckaXmZZLUg042tnzz9Vsmk4yqanl6WuNRZFmKp6VtGppji4pSismMpCjouylpVtAcj2gFRkcENF03GCqt0ZTHEm2h7XuWeIo84fLyjIeHNdZoFMNU7nuHMookzfCup+t7dP+rSSSOEkyUwswSB0O90zx+fiZLCwyazcOarm7oqpa+a9lvduADV2/ecXaR0vUKsHjfs1osKVKN9gHtApmN2G+eyYsp++c7gi2Zn0cE1xJCjMfSec/xWFJ1jsnZBb1r6dot86XhsN+QxQEbe7xvqOoddb0jT2MCQ4mN9j3WWKbTnLrqqKqa3gWMrjke1zze39CUe+aTCbNiAsGjAsRRhHpZT293G47HiihSTKZzFqtzksmE1vV0yuJ8wuaoaJoW/6Kx1cHTtH7IDo+Hn9U5wFiaDrp+ME16p6hrRx+1EEBR07uetg8oHRE8HPdbNuvngWGOEqIkR9sIZfRgWjWWpq1Rerg51k2P6gNlU0Hf0dYNxBmr67fUvSdg6DpHnA7fE2Wgcw1ZnpFrSxZboEPrQGQ12hjCC/vtowhcPxQZKQXB47qeSFn60KNCeIm/7ImMYjpJiRNNU5WE4Ib4zRdQ8mt6hWOxWI7JLF3nMNqi1W+9i/51v8T8tN1uR22lmDMlm1jymQUwyUMUGEGnvGRN//79ez5+/Mh0OuXdu3esViuapuHh4YG7uzvevHkzPrBFyylMvGibl8slFxcX4wNVJAbPz8+jjEYY+fV6PaZYXF5ejvpxiTAUrXGapiOLt91ueXh4GLW2P//88wh6hfl7/fr1CDDv7++x1nJ5eTnqmSUi7fn5eWQqBSgIuAJGaYHE852CPdE0S+yhAEj5/yIdENb04eGB3W43ArvT+nZZ8Qtwk+xya+342YVtFL33999/P7KTDw8PX2iFBchLbrQY9iROVIBRkDSnl8g6SUARUDOZTHj16hV3d3dfyAaElRVwLYBufEa8aKfn8zlKKTabDR8/fhyNivf39+NmQSrvQwi8e/eOq6urUX/snBu9DXLcJPVFZEBybuV3y9+TTPzz8/NxkDw/P2e73Y5eBDnWp6C/bdsvoiBFeiNDx6m/Qqrl4VcmV4bkzWYzngMZUKfT6XhsT3PKBZTKdSXX0amRUYCzfJ+FsZaXAFX5OVLmJQZKAa+n31Vh1EV2dbqpEcZZPB5y3xBjt7DzMtCLZE5+vlyrsqWTbZAAdgHVcuxkIBcT+al5WiQ7cs7kPZ6dnY1Ds5w3+Zn/0eu3xx5OprTdS5B6cJTHks12x3y+5PLVayaLS3Z1IChD1/cc9oN+yXcVzlWoviLyLXkRkSUpNjLst3vW60e0DsxnBfP5hK6pOey3PD0O+tPIKOzLeiSOExbLM/LJjDgtKKaBsvVgMg61Q3dHdAADlPsN1WHP6uINNp/Qa0PnHdvjkXrzTGFLVrMpWRJTNR377SNZElMUKU1X0jU1ZVDQ9ex2a+7v70gji57NcW1LkSbERqGVYzkvuLo8x3nPTx9+gV5xff2WvvPc3H6g08mLrrXGh0DveprWE2mLjVPqck9VV9T1kabpiOIcE1oMNTp4DruWKEqxOiLNMmITU/ctNkoHA2Hv0QoiE7HeH3i4fyDRagB0JsYYKJsaFwZRbNn0GJtgtSaYGE+PV5bQtxx2OyIbY21EnhcvxofA826ND57V+RkPT1tuPt/TNjWvrs4xWqG6HlxH63qenx5I0gKjFFlWcLDRoNFNBv3V4VjCITApiqHJM/Roo8mLnOk84WK1oDqW7PclfT8YA73zWBuhrcX5nq7vSEhJ4uilKGdgUNvGoZRFm4gojpnPFvRtx+55Nxg1q8HA1Xct08kE5wJBG5QxVHUNPnC2yLA0tFVFvW9ItKGvKqrdM9X2CZ15EvMWozzOdcM59Z7OB4gisIpsds7+dk+eFOjjgaYtwQeC70ljyJM51gwP+eAZNiNdg/dDSkhZHnl+3qC1ocgjFvM5fRoRaUMspqOXG1/wgaZu2e52RHFKMVuQZDN0PGFfe5QdUo92+4rHDw9MJxMmRYpNFG3boNSQSd42PXEeo01C1/f0ThEwxEmOMdEYJVi3Nc47fAgkyRATWNV7+ralKHKCAmUsTdNT1j1ZUdDWFcey4vDCMM7n8+GG6gL7smG/XpPGBqViimnB1WJBMZkRXoA0eIzVOBUYGnQH5tOawRAdRxatDXUzMKVWKwgaoxU9g9xJKYXRBh0UwXl839M1Q+nRYRfI8hjvevreU0zSkf0YHqaeECB4Rd/5l5vsAHys0b/1NvpX/TplhkVr+/z8zNnZGW/fvuXs7Gw0yEl6iLBNAqBgAFxiCHx+fh4Zvfl8zmKxGGUOAqBkNX88HkmShNVqNTLRAl7E5CbgXzLRD4cDr169+iI+bb/fj2z3crkc9eXr9XpktE+HB8ltFrZdyngE1AqIf/XqFd57/vKXv+Cc482bN6MuXACDAHgB88LkiWb5f29ChAFESVKLSHKEuTxdxwsIub295fb2dgS5stIXoKG1HjcasuI/zSnfbDZjRvpkMhmZ9M1mg/eey8tL7u/v+fDhw1j2JL9LzKV3d3fjMCPvV8xvSqlRwiLnTzYDk8lkjJs8HA5jg6uY9wTUybAn8h8BtXLehEWXrURd19zf34+G3FN2/BSYnrZWihxD8rGlKEni+mRYOH1/p+9xPp+PsYFyzE9NgMLOijlQWG0BjsfjcWTZhY2W3yuGxdPEkdNreLFYjIy7GEGLomC32/Hjjz8ym83GLYbIqURiJBIMGXhOtwTyOQVIy2eRTO8hqerXaEWRdk2n0zFydL/fj1Gmcj+RVBaRDs7nc5bL5ShlkuNyatQExuFfBhPZ8MjwB3whlzv9u3INyZCx2WzGaFeRUcmQJvcBGdDkeyWMuUhd/qPXby8Gygu6Q0+exjRNTd0ccd4zmc6I0pwkm1AY6L3n8eaW54dHijxF+xZcRRw6gmvoG4WeJNRlye3nT+x2Gy4uL3nz5po4tlSH/QDGdju0CnRtR93WHA5HFosBjBezM4KKmJ0VmGxBGxydV0RRTBzF7LcHFJbZdDqkKmiNI7BZr2maiuA9x13JzcdbZpOczjtcV5LOUoLvuLy8oGpbDoeKY9Oz3+047ve42NDOpvRNxSRPiEzARoqL69dMioLbuzt++OlHvnnzLXGSsan2eBeGRJrIcCwbCBDbmK6uaJWnPAS079msH/FdTxpHpGlMU+6xqWa7WeO1BdfSE2MzTaRj4iTHm+FL7Yc4dwiem5vPPD2v+ZvvvqM8ttgkw2vPtG9pm56maunaFqUtVdvSejAmIskLUquw2rBeb9Ev+ra+N6NRTnmYTKaYOKMqW54fbrG+5eLiHOM9hdVsmpYf//InsmLG21eXZGnGYnnGYb/Hh8Bht6csK9JkWOdkeU7dHHlerwcWVGvyJGY5yyn3B/aHIz5o7MuX16RDukmSgOs62qpkt14P5lud4DpF7B1KaaazOcZaHh8eKauWLsDtzT3KKK6vr0iSmKqpQccUkwKDoTzsOey3TFKo6g2urblaXuK7lofPH8mnM15/syC2gO8IIYKgqZuaJE2JY4N2HelEkeZL2mZHnGSUu8chTcc4tNIcDkf63kMAlObm9oGyauidR2Go6xZrY5IkxpoAWEwUD5+5bQdJhlJ0fU+W5Tyt1yzmSzARQcVDulE/GFCzyRRtLKEMtO5I3XkSF/B1T11XtE3DdFrQ1jXbzZb5dIbrOrJ8gnN+kDgZg1PglUcHz3436A7zNKOpS46HA/vDAWMUy9U5ykSs90f2h5KzC0XvHLvtjrqpqaqaEAb2efM8GGAmixX79TMQsJ3C9oGcQFUesCGlch298djUYBUYY3F9Q2QsSTysToP3JNHQJNt1YTRlggcC3cvNHTWs8Y22hACPD4/cfK54/eYKCBTT+csD2L8wew1aW7q2p64b2rYjy/KXTPMYZf+TIodfc8gFwEo2tSQhyOrdOcf79++5v7//IpJPDFd1XTObzTgcDnz48GFcb3/99ddjucrxeGSz2Yz667qu2e124/pdWFBJAxGgI6tk+buS3CHMrJjMhHn85ZdfxgevaH2997x69WqUPLRtO+anN00zmkCF4RZGbzqd8unTJ/785z/zzTffjCkrYnIU4A2M0hut9chgS4636NtFNy6gTI6hgA0BQpLAIaDyw4cPPDw88Ld/+7ej9ETAtgAk+d1yLASwiUzh8fFx1JALABYmcD6fj+U1t7e3hBC4vr4eWfSyLPnXf/3XceOR5zmr1WoEnvv9nsPhMG4zRMsvn1OkBMvlkv1+Px4fGWgEhIk8p6qqEcwJgyoDhrRS3t3djefiw4cPGGN4+/btyIjKlkLOx3a7Hc+BSKm6ruPDhw/MZjN+97vfjQbW0yjKUxZctiCiZZafKcdRzMqy6fj06dMoI5Fh4NRULGytnEeRZMgW4vHxcdxSyR8Z2kT6I3p/0e5LcZOk3FRVNcZ+dl03fufh144BeW02mzEKUQYXKQa7vLzEGDO2Wl5fX4+gV4ysAqLl3J2dnfH09ATwRePq4XAYQfnpdS9Dg2yM5HzIAClDqnwvTqUy8nMFnN/d3fHhw4cxMUladOU6kqFfNhtyzGVz9/9zhrxqWpQa5AZd51BaM1+ecXZxRe8V+7JCm5TjYc/64Q7XtUQ6xgBWa7SDvu7oFbi+5+HjPQ/3d7RNizEKFQKH7Y6+a+i7QXJh9NDI1/QDOJ4uFDYpiNMpJs553hzxVlFMCnxQtM4TjGF+8Ybz11tuP32id55IB3a7NY93j+jgWUwmeGqe7+74CzWvv3rNfJowKVLiJMY5T5JmJFHOcXegbztiYymyoSkxiTT7dk8WZeRpSpZnHPZHbu8fiJOMNMsHzWtTM5/PsEVO37a4vsMoRd90GKCrO0JiiSPLNCuIppq+d5RVjVaB248f2JYV2XRCE2q6DnZ6w6RYkEynmDQHZbEmGtoy28Gc9+7rr5kullSVI6pL2q5EtYE8ywlzQ3Ws6NoWlKZ3HcYojI0JqiMwrGiOhyP39/csFnOs1ZydrTBa4YEogVevLsgTzdPdLZ8/fRoefPkMFWmqxnOoDjw/Bs7n85cb18DwOO9J8xzXtdRtw/lsymwx4f7+joeHR4y2LOZL+rYmNgqrA203mFI1Bu88rveYMGisq+Oear+hns7A9OTplNhGHI+Op82WqzTFanAhsN0OTIaJLL1zTOIEGydUTY93LfP5GbH17LYHguooipx8PsW1nnJ/wJtAXe7ou5rDfkOsIvJFQte9+BqiIekmGIVRCWk2Y3PcoRm+1OXxQF3V1HVP03jqznE8VngfeHh4pCgmXF1dM58vybKc3W7Pdruma4YUitB3hJcc9vCyPrSRoa4q4igin04pW4eOc6J0ioomBBvReUUcGWbzOSpYvOuJ42iISDRQ4nFdTVcf8b2nbUpsFDHJJxg1rBCb+khVl/Rdy7E84j0Y5ejbhr5raaqKY1WTpglxNuFsdc70LLDfH2jaBpzn/OwcbRTvPw7mryTJsTYiSQvSLMaYmO32ibL1+N2RjlvO0KzSGYaIpq0IWEgz4jgZ4k/9YIauq+HaT9MEYxTagH1hzAmBLEmGVa2OiZKE/fqZ4BV123IsK6aznK+++W6Q3MC4khwYQ4PRQ47+ZrPl48dPKDSTac7qfMbqbP5bb6N/1S9hnoT11lpzfn7O1dXVFwkRu92O+/v7MRHhVPMrCRkC2m9ubqjrenwwbjabUeYiDLKke4g0RrKiRUYgzOopi3p5ecm7d+/45Zdfxtg8aVyUB+7hcODm5gaA7777bpQCpGk6Dh5xHI/V8AJahdWW1Jksy0b28fPnz6P2WtJVRDIjIEiGDDmmp5XjpyZOgF9++WXMbwbGFbkwnAK2T1NnAL7//vsx+12SbYRple2GMMkiSTg1tMkm4ebmZtQTX15ejvFwSZLw9u1bsizj8+fP/PLLL1xfX4+gXsyed3d3rFYrJpPJmF4i3gMBlvP5nLOzM25ubri5uRkHgaZpRhmCXEti3jvV7ApoF6OiyJmEdRUA570frxeRXJ3KFJxzzOdzrLWs10M7ryTtdF036qwPh8M4pAHjQCQ6fZFWSPvvfr8f3+vhcPjinDRNw34/xDbf3d0xmUx48+bNyHBvNpsx010kEzJMyjUqkYlSaCZRiMKmi4xD4hNPZU4y3ACjrl100fIzxOcg77tt2/E8yrUs3hGJ8MyyjIuLi1GuI5KY8/NzrLX89NNP/PnPfx7fv2wMrLVjapL4SyQB6DTr/zThSTZJ8p2RzYP8EZmWNOuK7EkkV5JWNJ/P+f7778ehQJ4RMtDLgPP8/MzPP/88ejsuLy+5uLj4TffQ3wzIj8eaODa43uEcWJuyWF2xunpN20HTdijl2Tw/c9xvhrV615BlMaktaA4dQTM0IzrPZrPF9T3z2YRJkbPbrVl3PZGN0D4QRTFZGtO2Pdv9gf2xYuU1+7LFmZpsmrM9trQ+EJmU3iucAhfFZPmCxdWBT5/vOTyvWa2uSFXH/umGs8UZr84v0IXhuAlMk5jlJGW97qiqI96vhqZEDbNiSmg8ruswGhbzCQZHYsHQMy0Gh7PVlkNVcTyWzKcLsqKgrCueN8+oZCihca6nrSqMNviX9IgiUcT09JGj71rypMD1Pc4N0pHnzYbzy2uK6ZS6cdShwzvHYffE8+6JdDYfUjPyKd55ugbiSHO2WmKjjOlyRf/s8aHBuyGJQjPEv/FyU7V2qBav6grlKowKTGcTmqbmxx9/ZLmcs1wuTlZgQ6IBvuXN22usUXz45SPr7ZHCG2aLM/7227ccqorn9ZqHx5uhfAU3SBkicPWQajGkV8RMpjld2/L44NhuNuA8WZyQXJ5jtOFxvcM3Dh0CzkOkLbgOrSDWmrY80pUH0qllUiT4tqVuK45VSdMPW4DOeaq6IYozbDyw7HGa4oNnt1/TtY+4rmZSZMzmGaH3ZJGliFM+/PiRzWZDOkmpj0e6pqLvWtIQCF0HQWE1WA0hKIJ/iRebn9Ed9+yeNjRNy+bxhv2h5Hjs2Wxbgoq4f3hkdbbi/Pw1Z6sVk0lO3XY4X9N2Pb0LZHlO6Gq2hy2R1mgU9cvNPUszlFHECnoXiJMCnUyIswnYYYsyNG/WRNpwdbHEKMizCO9qQqqJjePp4R6rA0lmiSPFbD6l6/ohSrJpqY47bm4+sd4MW6bpdE7oV7RtR3ms0WboBlheXHHx6jWL5Tld73D+Hud2HOoDdV8RxxajNN5DmmSA4el5S5FPODtbskozsiymbffstk/keYHFYWJNXfb0TUXoOqI8xYcXoybQdx1KQ105rDUkSYxSAYIfEoz0kKBSNiXWJkOxk/f4AGfnlyzP5mT5nLpx7Pc7QuDF3OPGB9J+f6DrHNvNnv3+wGw+AdUT/WcMOcBo5BNZQhRFXFxc8Pr16xF8SuSgSCyEAY3jeNRoS3za09PTmGgynU5HbbcwXaLjloezsHsS9Tefz0eWUZoqgdHYd319zfv373l8fBzB5MPDA6vViuvr6zEyUaQvYsQ8zcyWenAZQATQyP1SpDMiaZB1/GQyGQ2H4lM4bdgUZloAtUS6SaKGSH4eHx+5vr4egZYAlM1mMzKZp+VAAqQuLi6I45izs7PRGCtg7jRz+7SMSECeyIeapuHf/u3fOD8/5/z8fPzMknrjvefrr7/GGMOPP/7I8/PzqL/+m7/5m1FycXt7O+Y0y/BwKp9J03SUQ3VdN14XaZqOmd1PT0+jtEUAknwWGdrKsvxCuy6mVpFFnKbViLxBNNwydEk8n7TEysD1l7/8ZdT+S9qHDAWn5TTCqspruVxyOBx4eHigqqpR0y8+AaUUNzc3XF5ejn8k119YYRlg+r7n4eFhZNhPtylyHCWWUIZWeU8Cso0xXF1dodTQuSD/vWwRZNgUqY+YgMUf8OHDB56fn8ct16l5WIbv6+tr3rx5w/n5+cjCi6FWTNgyIMl18fj4yGQyGRt9ZWuyXq9HBloAtQwA8jPkeMt3ViQ68t2Sfy9Dvwwvci2FELi8vOT8/JzJZELTNGPT62l6DjDK8J6fn8c2Ybmmf8vrNz9KlI4IbojZi2xA2YginxLHOSgIqqetGxQeqxV9U9HUhkmiUcrivEPbiDiJOB6H1kOjNUWeEpmX2DLvwBvadqioT9KcQ1lxKOuhkjxKcMpStR5XtRyanrrtyRYBpyzORPTe0neBkEzwJma7u6XabdFKQ3sk1QtSDSjQWUaRWPA9xkDXNZRlxcXsjKA1ddW8rNfD0FSYxtC3RBbmk5TlfIaJIjbPW7ZtQ5JknF9cgTZsN3vK8khqDSb0NOUB3/YE1QMB7R1OQbk/UFPjuwqrAsoMurbH5yd80CRRRug0rmkp9weMNijgcf2Me7xndfGaq8s3JHGCUhHFdApao6KY6Spns1sDg4Ftf9jje02a5aO5yvmMrqtxfUPoOnrXkCYRWvMy/QVubobK4jdv3qKUpqmPQEeaJrz7+mt8MGzWOx4enzkcSl6/fs3ZpCAxioenZ7yCJIlwL1/6pqpI45QkjQnBcdiX5PmEiwvFfrfleDgwvZqglCXPYuwOlHKE4EninMRE1IctoW/QztA3JX1zJF2dkVg1tFX6nsVqybGq2O2eePXqHa9eveZwrCibirJWbA87+r6la0qSJMX1NW3riayjOu7IFzM8gaqpXgKpFdvdjk+fPhFProjyBdpmxGmGtoq6OtA0FWmW0jkwKJI4Zb8/8uHDJ97/9CfKquf61bdcvvqGOJ1yftUMK3HvaPue7WGQFBldEwh0fU9dHlB9QxwnHHdbYv3icrcWG0XY2BCnGVXvMOmC3gzlXL2OUMaQ2CFZJLGa1Bh832O1o6wqvGvxfYPvKuLIcnl+ThRpDvvtUHxRNzRVTdtVuL4mz4YNxqSYkqYxXWyYTSaY2NC0DelkitcWh8bGMXkxGzT+TU1THl9uXhHTScL5+SVl1fD8vMHYhLLp0UaxyGfYWBFcRRJpIg0miWidpuo1vmsHk+9LCZHrB19LFmcEP5QDGaORxk4f3JAgkyYo41AYtDU0Vc3hWDKbzbFRxm5f0bshsQilTx7omrbrKKuKOEooJgVt2xO/aGz7/j9TVoAv2CKRNpyW6ogEAhgj1wQASbKBPCgl+UNYW9Gnyh9hgvM8/4JZlYQHAQHCNEq+tUg7mqYZmdHNZjOymfJzhfESPfupIfJ4PI5FK8L6SSqIGArFuCgGr8fHx9GkJ7GHkt0tx0203qdg4lSyIrITkWPc39+PaR4CfETPDIym1+vr67ERU6rEZWi4uLgY5TvAOMAIiBMWVFb5AgDlnAlYF734N998M67wYdCHf//996Pk5vb2lt1ux1dffTXKJO7v7wG+KEc6jXGUlkQZoiQm8bQ1Uj7DqZ55v9+P+m2RNUnGvFwvUh2/2Wx49+4d7969G1nqsizZbrfje5L3J8z8brfj/Px8TAESudB6veb9+/djDrkwrmL0k62HADqRq/z888/85S9/4Xg88u7dO96+fUue52PMpwwN0jIq4F5iDIXVlkIn+axyvaRp+oWUQky7YhYVMCzbAWG+5fdKvbyUAIlMS86XsNxSsiQJKkmSjDGNTdOM5w0Yc/NlWBSpiphXr66uKMtyNMqKNESiEuW7JrKU03ZdkfbAr0ORGM/lfiWfU5h0iQ0VEC2G2cViMX7mU8nT6dAnchVJCpLjJQPPb3n9dg15NqE87DGawfA3mZBm2VCXHRTeDQU5kyKjSFPKrmVW5EyzFGs8ZlZgyIgN3H7+hEaRRJbppCCJhiktnw6NfE8PTxhtyJL0JS+0xZmE3lu8SuhDhO8Cddvz8PBIWhTE0xnOa451TR7H9MpSzOakWYxyHcoHYt9ifI3vS1To8L7DuYBCsVqecfe44fHxictXbweGjEFKExtNNpngup7gepx3mNjSux5lLNvtkTIEJvMVs8UFvvc8rtd0riNTnr6t0ETMJ3PqpgUfmExy+npP3TZEdOjgKI9HMJaq8ZRdz/nFK9ZPW8qqxvtA09ZoDYTA8+MDjiHObZKlhGKCNjlxGtN7T5TEJHFBlFj2jxXlfsdus0GpeGhu5KXgocjRraGrPE3LULpjLUmSspinLBZz1ps19/cPGBtxcXH+8pAcYsfiOOfdV99QTDf8+O8/8PHTZ6qq4u2bN2STgjzP0NZy2MPdy6ppOpmQRPFLck+gaSuur9+gVERdtTzc3+H9HUU+5N4G74itQWlD8KBDwCoN2tDWFbbSlIdn8Jd0bUXfNlgzRNw9Pa2Zzi8wJmbzoklPs4Q4TYfIOteQxob5bJBn+K6l7VraqmFv9hzcjs45Lq+vKGY5vdaUxz2RDWSx5rh/Js9fESUxdTmAe981Q1smmqwoyKcLHtdH0skFr797xe++/yMu5FQdBAzGaB4f7tmVzzT7I5MsJrXDe+m7Hte0pGbQi7uuJ55lLFcDwMiKCTa2KGWghxDP6DpL0/d0IQxm2TzCuw7X9dRdhWtblLco30FXoX3D1cXZ0A1gAvvdM9pEHA87mqalazsm04LXr/+A1tC0DSFAFMXDJiuboI3l9uERjx7SU/YlKDUEAmrD2fmKvirY7ncomxKnOedXr3nebCkbh0NTH0u0hjiJSWxLHGl8V1KXWybJnCKbolqN61u6riaJoyEa0ge6tmcyMURpQtc21PXgFanqCtSwbt1utiRpjrEKFwJd76m7Hlu3FFONiRJ8VQ2zl1b0XfsCljRtU2Ne2kGDd1gL01lB8ZKt/Z8vRlmGPOxFPyrGLzFDCWssxijRWguLHUUR79+/H5laqdLWemjKBEa2Lk3TkR0VgCJgVlb+t7e3Y86ygEVZZy8WC+7v70etrjDM8tD9NfJScXFxwd3dHff397x79w74tRlSTI6nyR4Seybg2znHcrnk7OxsZDNPJSqy4hZz33Q6HQiMk+QZAdwCMF+9ejUy97Jel+Nwd3c3vkdJnThNITktx7m/v2e3242srOhsJT5PPAHyeyUDO8syzs7OeH5+5ubmhiiKxqbKyWQylqR8//33zOdz/vVf/5VffvmFsiz5+uuvmU6no4xFkmokzvC0tryqKt6+fTsOQTc3N2Meu8hJBGDCr02tsm04Ho/jsCEJMwLE7u/vRy28aJol4k42FZIHL8ZK2fhst1vW6zV93/PmzZtx2JHzJABZDJSn7Lm8X2HcHx8fmU6n/P73v+fv/u7vAMZzb63l9vZ2ZIAF6J/Kt2SzJNru8/NzgNGfIHIyaa6VtkmJ/JTNj/xM+U4Iiy3bCDlOMvTINTqbzfjqq69G86dowE/lMTc3N+N1ut1uxyHKGMPl5eVojBWZyps3b0Zfh1z/wvaLtKht27HEqiiKcTCQoRsY7wdSdCTnTzY/MAzjT09PoxlbPrtsAWSoEGO6SFROs/vlmhJyQbZhIin7j16/nSFXMVk+47B/JlJgI0tkLQpF0zj63jFbzLEKnm5vII6IFUzylCw1dJ3B6sDz0yNVXVEUOejAYjohtkMGcprELw96S5FlaAIGRV21RNMpm31Dp9akhSPNw8sF5fCuoTlusZkmsYYktlAPkXyaAK6liBMu5wWJ6VBUVM2OzeaR6/wc76Gth1rwzzcPvPnqK7K8GMBivSM2irZqQUeUVc9uX1LVPXHa4MqW7aEhWVywOH9H0BPqbge8NHL1HXiwusdoA2iixBJnKcY6XNXQtA2zbDDslceSsvMU8yXWRtzfP9D1wxreWsN2sx1KYowiylLyxFDXO4wNxJllMi1wfSBLUxJrySLDfrfm9uMvBDRpOqHvOtAGm1i8Bwc0XhFMiokBbaiqhrqqKIqMt29eD+xsXVF+/MTlaoVWmrI8YG1KXmR4Fbh+84agNevnDd2HGy6vL4iThN1mR9c1qDCkXEzyCXk+RFEmSUbbQ9054mJKXLXU/S2P7z8OaSCTKcYaQujp20Gmotygh2/qAMGB63Dtkea4JYmnQ4wggd12zXa9ZrG45u7hibaDqnW8envBfDnleFgTHLRNiwIiG4E3GGW4fDunbnZsnp9Zrs549eqKYpLy4dMnjm1NXx6oD2uOVY/WnjSbgPdkSUxd1Tgf0NYAnnff/Z7/ZxIPciUdE0UzDmWgOXR0XtP5Hj25IDUJrlxzrI+oHqrDEeMCkzTnuH6gPh7J8phskpJMM7K8wEYJoEiSnHJ9pHV6KNzpW+I4oXM1rjFUxy1aOTSgfQ9NwATHpMiw2VCWFfAc9kciPUz9kyIizSJcN8hmFsvBeNX1NV3XE7Th7GLQZfZe0bQt+SQjiVPqpqHreuqqAu+4OFvQm4iqd1hlSfMpvTJEWcHsPBBbQ2Q1t7efeLi7YVFotN9hfM52fU82XaFsRBynNL2jdy2JjumcQ4VAlhV0vR9MtS+bHaU1VVUzm8zpaofVMXXZkBcxddUS0MNgamN65+maltgaeqPo+wbnPcZYuqZjs94R6Yiub7FGMZ/nrM7mpOkwpP7nixFcyINWQCr8asISU9mnT59GhrooipGFPY2fEwAtD0JhsMSYJYOQUr82QUoxz2QyGR/Owi4fDgfyPB+NcFIiIkxXmqasVqtRby3MnGjGBZR++PCBb7/9dtRbS9KLyE3EcCrNm5IGIukgwjwCo/5YHuDCbovUQACAFAtJuoZozwXkCNMn2nVhPaU4Rt6jVM5LRnocxyRJwmaz4ZdffgH4omlRPvtp1J0MR7IdmEwmo+G2LEt+/vnnUQIkmdjS3CmA7fHxkR9//JHXr1+TZRnPz8+j/tcY8wVQF6mFHAORnPzwww/MZrOxJVKAkGxbxEx4ulURDXPbtiOb/fT0xGq14ubmZmTDv/nmmzFDW+QcpxsB8UeIYfTi4oK3b98ynU755ZdfRnOsGJDlOpfjfsoEA/z+978fZTJizpTEEQHGIs04HA5fGH7lsz4/P7Pf78fhS0yjAvzFAHxqoJahUbYrwvILgA0hjAO0JPtIdrt83yXXX8y5pz4PpRRXV1fjFuh02JIkGzHTCqMv9wH5fuV5Pv67KIr4+PEjnz9/HhNwZPsi+n75TskwfFptLzp+AdFyHcsgLN9NGfSA8TzJNSjvUb63MvQ9PT2NxuL/PWr1+vr6N91DfzMgr9tBf7w/1pznBZNJQZomtG3H4VDTh540s/R9S2QNxXJObBTKd0MDJY6ma3l8vOdYHlmdLUiSiCSOaJoSrQ3l8cDxOGix48hw2O8BxWJ5jinO6J3i9v6JxVJxnRZM0oQ2MVjt8H1DqA8kSTHkkEeKOIIk0mjvSI1ikaeY1IJv2R3WlG2JsoM0palauqajbxs+fviJi4tLXNtSbtaY0L8wjBrXew6HChc0rfe0naILMUW6wKZn9MTU7Y7OKayJsTqi97x8ATzOgw6auuuwJhBlEUHZQTvee+q6w6Y5SZISUDR9h/OevumJgyWEIXoO36N9hwktfXOgTwyKKWkSky0Gtnf3vGa32fDw8MTD05rl2TlxklJWNcFYoqDow8t7imK00mgbE1swccRx/8wP//4X/u7v/46//bs/8LjZ8P79Jz59vmU1X4w1w44AyjKZF3w//1t+/PE9nz/d0PHIarkgigxRlDCbzknShLapOB6GB5LrHE3v6dcbrt+8ZbY6J78/Y3uswMQEFBCwRpMlZpAhKIO1EV0bYa3CGE953LPbrJnNr7HWMD1bMYkDTe3p2p5ATD6bETlHnE0wJiJNMvpOEdmEqmop8iXzyZQiM8ymMc/PtxR5Rp4U9F3N3c1n/v3P/4ZJCm4vfyJKEmqncX2HTQ4kWUFeDN8LHUDpoRAnied88zf/QF211I2jDxblKlwZ6IOm9YEmGFQ6YZFnVJt7fLOH4IisJjUKnaVo3xAsREkMVmPTBI8GDF3QKJvQlB3KamKr0DTEsaKpdtCVaDOU5CSRwriWSAdCV7Lf19h42Fgc9luqpnmJcgwkeYG2Mcf6AM+KpulI0oLpLKJzHceqxEaWSTZEiNblka6t6XpHmuUYDfvdlv1xT3U4sjuWTBcrsBF5MSWfzDCRZbN+pMgKzhcT/vWf/1/ctzt0OHJ1dY6OMy67CnSKMgatFUp5UAHvIbKWoBQhACfSBwUU+YQ8KxiGlmxIJrCD1MS1HaCxxqLCkFSkI4tuII4j6qqha/shnahuiaOIxnWcLWZMJilpnrA4W2L+U0QO/Grq3O/3o9ZVwJ1oLvM8HxMgRHcsGk5JObi7u+NwOIx6UQHPEgEnQCWO47E2fbVajYDn5uaG1Wo1NoOWZTlKVUR6cKqFFaAhaRPCyu52u7FtUSQMwq7+9NNPY838er0eWXVZlUtsn7COYjwTqcJpRfqpqe40Gk+AiRjWRDsucg5h/+TnS/SbrORP9bPyueHXghNjDE9PTzw/P3N7e8v9/T3n5+ekaTpKaeTnSUKNHDcBytvtln/913/lv/yX/8If//hHnp6e+PHHH3n//j2r1WpMxQHGDcdiseBPf/oT79+/J4QwFhyJ6fA0lQMYwd3j4yNfffUV5+fnY8KKDDDAKM2Q4yrHUIyJ+/2ep6encZC5uLgYry0ZBubzOUVRjBITYVNlMBJjbVEUo69ABsqu6/j06RP/63/9L5Ik4eeff/5CRiHaZwGxwuLLBuPv//7vRz32abThaWSiGIJlgBGwKAOqAERhkGUTdCoJE+OiDANyviWqT86x/Cz5/srGYrfbjRp/59xoZK6qisfHx7GhVnT2x+NxjMiU3yWAVuQzIkOSP6vVahzMxDj7+PhIURSsViv+6Z/+afz8b968Gf0oIh8RyZncVySF5vRYCFkgvQWyMZOGT7lnyLUFjNujqqrGAVS2DVVVjQOORK/KpkJ+13/0+s1PkrJsiCND78BGGSaK6UOgLY/UTUcUG6r9gYf7T7T1nourc1xXst0+0PcZNrKUx4qn+2dC715c6YqhEc/Rdh277YHDYTgYk8WCzXrHoWyIJwvyyYRgLbvjkSQypImi6ntcU9KXFaiIrj4Q6YjQgA2OPLFMpxltV+J8xHSaoSKNcx11dSRNLPPJlLapIbghm9mucE0Nrsf3LfvtmvZYMk2TcXVljCaOEtI8x3cQq5R8uSREKbX39MrQdJ7EavCWAJg4p9cRXoMLiqrtiHRLqhwEz3qzoatbvI6ZzTOquiPojslkyrGqhqbKunkxDQbWj48sz5fE5pw8s2g8x/2WyNiXBBLLLz/9xL//8APHpiebrYjTGTouaPserSL6Hlo/rGO1GmrdkyQdDKt5QhoFPn8u+dO//YnlasXbN28xJubf//xnHp+eubq8YDKbUHctNtJobXAevvr6a7SO+fTpA23Xcv3qisVsTp9mKAWPT090bUuWpShtcAFsWlC9fMGifMJkuUIbjUdhrSE2GrxBm4jdrqZpyoGl7XuUd4QmsNvu6NqaLEk5m+WUUU92P0Qirs7fUCzO2R+OHOsjh3JPbBWBQJYW7LcbilZxFuekeYzzNavVGV2Tcdjt+fjhZ6wOuLbm7OKSqtyz3Txh8yE2K0qSIVqQXyOltAZcD3ooNqrbITs+tQlt09LlmkPZ0TYVVnlCUGgXSLQlylKykNId1uz2O+ZZwmR+hU0T5qslNovRNiJLCoxNKQ8NRnco36BdTxqngzSl95i+JokUGo/vW/4/7P3ZryRJdt2N/szM55jjTDlVVXb1IPCjRHwg//9H4QLUfZAoSlSzW9015HimmHx2G+6Dx/aMpACpCOi+SO2NQnVlnhPh4W4etvbaa62NCzTliS54onO1HyUxx8Po2O+HAW0i0Jo0jsiSFB8ZlvMcX+TnoVSj4VFjMYAKA7fbBe8/fKavR024doZZHpOYOeCpTwPWtgQ/nE25FtBkUWAWK1LlKBY5y0SzLxsCPcF7lPfj9NKQEBUZaaKJYkMYvyFxHvq2Jk7GybptUzGb5RitSc9f1KdDyfpqTZpm6CimyAva9jgCmzzFRBqtFUYZ3NnIqfRA1zdnHeGRNImxfTsWvXlGnETM57OvQMH/zYdsZKJlFd23tNkFQH/8+JG6rnnz5s2USiCt9NPpxOfPnyfW7jLbWMDvKJVLphg0YWmFKS3LcgLal1F+MpxGWCxgMgxKsou8xiXwvTRMiuxD8sIll1vYdwFNwtRd6t83m800jASYwKKAZpGJiORGWFABxmKWk7g+kenIIB4ZaiMg+v7+npubmyn9BZhi5wSY/PM//zP/9E//NLXkhTUXQ+Il0yhso+jHRcf7008/8Z//83/m5uaGt2/fEkUR//iP/8j9/T0vX76cxsMLA2yt5Te/+Q1RFPHDDz/Q9z1v3rxhs9lMxcfnz5+/0jtLQSP3QYCZfBbRSov84XA4TEBbihmJ2eu6jjzPWa9HUkny429vbyegL5nYUjAKAy2RjgJ+b25upnX5pz/9aZIx3N3dUVUVT09P0xoQZlbu8WU03+U9lwJCJoaWZTmxuZeDauSelmXJfr+fpC9Zlk1xn9IlkWdL3kfWqRSQcl/hS/62xAlKFrlMo318fJwiBrXWE1AHJgmadBGkOBQwfHNzw88//zxdW/EryHURU+Rl3CMwFYBS1GVZRlmW0/mKrEm6BRJzKO8t0hRZg8KCS/EiQ6+ur6+n35XC8LKAlfsmRbPIc6QLJtNupfgSz8Yv3SN+ecpK3RAtF6w2W+arLU3v6ZsTgQwXNIlXDG0Ptme1zFgsYva7nqau8H4gTVKeH5/Z745sNyuiKCHLC+I4HSfshcBzt8N6i3aaYfAoE6PjhNliQTGbsSslmq3ltHti//zM7vGB5XxGnIxRgxQzBt/Rt3va8sAsj1Btj9YepQJVWeLPjPf19R2zPOXjp090w0BezMjiFJPGxDoQpRFajdKHeTa67rM8J4rMZHipgkdnETotGJRCB8Pgxo6CQpF4RZTm6DgnRCkqNuNn9AE7tFhbkqmBYejpekuUpygd0w6W0PVobdA6Isty7NCPJr+2pjzt+eabW9aLjLxIaQdH+fyIrVvSNxF5OmP39MB+f+T67jXzxZLyWOF1RBxrvPOgFDp44kgRaU3vHIpAXZeEAXTo2ayXlFXJxw8fSGczbm9v0MAf/un3PD49UizGRef8yLy0vQcVc3t3S1mXHA57Pt8/AYbr7RbvLVkxurdNFFM3DZ1zpIs13TCQpBnz1QaPoimPDG4gUmqMXPQeaxuatiSJluR5weF0IHjI5ilDb6mrA0kc8F5T1Q1dP1BsMt58+5Y+GPZljWc0GqvYoFXKbH1LWtwxywpsMFRtT/AVYThRHp9pyhLvxvXx+tVLXrx+RTM4dg/3bF7mZGnKbLnEqxiUwZy/aFUIdMNAW9esV2fHeTuAU6xmySgNCS3KeVycUB5PuL6FvqXrdrTlPe3pgdPTI3Z7xavXr5kt5sRpilcKFxQ+KAx6HJq1NMQmEIKjyBOaqmf/vMcPHdk5erOrSjrneLi/p21alosFcZKAAhNHhMGRxTFplhPFMXkyRiOiIEvUOYe7w6QJsfZs1wuSKKJvW5a5ppwZ2tMjJimoqx3rzXaUwwTPcpmR5jHOQ13t8a4Z+X0VyJSlLytc25CZgHI9q9Wc7WpNkWYEOxDnoIIl0qN+L3iPx2AHe2YDPdYO04aiziwHQU3AIssinHUQAlVdo84syjiMqSaJFF07grU8y6ircbKoiRR1UxIbjdKaEByLxXqMkf+FGbP/px8SDXZ1dcV6vZ5MTvCFrZXYNJkQKIBaZCj39/eThEA2RdlEhVkWXfflSHIBAsLeycTLx8dHPn/+PMXX9X0/AQYxbImEQdhKiZmz1vLixQuKouD9+/eTZEJ018JeA+x2u682cNmsJXNazG0CvqXAACa2W4CYyCJEZiFgTLTCAg7E2CdAQUyHMtHwcDjw9u3bKWlFip+mafjuu+/I85z7+3uen5959eoVq9WKw+Ew6YTlfeW/pbAApmg/GZJzOp34+eefmc/nU3v+H/7hH7i/v590uyIbkPN++fIlp9Np0p9Lbrz8nMQtVtU4ZFCAvQy2EfmEADcBSpfmOokGFCZXwJMwwBLveHV1xffffz91RoS9FrnHdrsd5y4UxQT+ZA0KuytpJ99++y1v376l6zru7+959erVdM4iCZFrKmu6bVu22+2kbRY/wWVBKtIiYZclB/9wOExJQd9++y2r1eqrBBFZczK0Sl5TwP7j4+OkrxY9tnSaxMAsa1cYYEmXkVx/kbAIeywJL1rr6VmWAmM+n08drtPpNIFg7/107iGESS4EXwoYKe4FzIsn41I2A3zVeYKvk3/kGRFGXb6/pTiR15J1Lp9N2H4xpAOTqVyeQ4l2leJJBhf9b88hH0dXa5bzNWkx5/mwox8ci2VGmkXESmHrMTt6UST40NN2JwIOiDkeTux3JwKaOMkJOsIGzaFqSdKYWV6wuRp3foUin82Iq57EK9I8xxhNUx5QYWD/fE+5Vwxdy9BU2LbB28Dh2DBLU0zkGbo9u6ePLNOIxdWKYD1xGjOcTtT16RyblaAVuL6jretxDLy3ZHFGdO5hLxdz+rM2SaHOI8fH80yKAoPDmBinDe3gyeIEdIyJx9i1trNo7UiJiE0KRjHYnkQHEhPR1x1J7Ed9sR9ARdgQcCHg/Phvz5h6YYyi73oO+2fSNGa7mjG0JZ8/fcR6hTIpi8UAwwuOdUN9OvDdN9/x+u2vifMZP//8Du9GlqOuTujgIXgiHTAaktjg+hZjNEYDbsyQ32zWnE5HHj/fc3s3Jnz89re/5fP9Rz59/sx8sSTNZ1jnWK3XdL0naTq++fYt8edPHPZ73n34TJbPKIqMV6+/5aN6R9931GVJUox+AhNHzFYrdFpgsoI4yWmOz6guoBhZ2eADRge0CaA1cZwzBEtwhq7pcX1NrGdEJqCVRpkYjGGx2dB5Q36qWMQr0nj8vBiDdYY4i8ckHhPwGvqmZ//4CddX9E0N3jH0A2mcoFUgNpokMczyDEWgqWqcTkjz2ZjD5wJKQVAGHY2xoEkS05YVfT92m5Q/kZoGMk9bW/bVgeq4pz7eMzQPGI4sckN2u2ZezEiLHB2Nk3DjKCdJc6IowVuPUYoQLIkaiGMFtsQ1B05PHyEEVFvxdP+Zx8cHrA2g9GiWdp5ZMTItcfplwEdwbhwc1XXjUDBnCW5MPzJaoxycDnu0cuRpgrc9sTYs84jH7kBXNZgk47izZzavoq0bdJSgVIzxjkRpyv2B4+6R5nSkSCIWRUZ/2tPsD4Sh4/bmDu88tu+YR4rO9RDGRCbrLB6FJzDLxum/URQRzhpXY8xomo5S8mKUKfjgcX6Uu/R9R5yMLWWtHE1To/PkDBy7cyyjIs9Tbm6u+POf/kRjBxaLjNV6znw+Q5831b8cTO1iyUiWtvp6vZ7SCyRybD6fT/prAQ2i55XkENnUJE5RYs/kvSS7WjZiacd773l4eJjyikUGIINHhDmUmDlhwQX0yARPiVYUMCwsuBQPAuDX6/W0EcMXQyGMG7aYQiWKTn73kmG7NLdJYoOA4LquJ3b1UtpyGct2qe0VPWuWZWy3W5qm4d27d5MEYbVa8fr1a56enjgej/z617/m17/+NXme8+c//3kCLSIZkfMSEuoyiUa+L66urjgcDnz8+JFXr15xc3PDX//1X/Phwwc+fPgwse8SeyiGu++//54sy3h+fuann36aZEbfffcdP//8M23bcjqdJjY4jmM2m83Eysr9Eib88h8BcZcRdiI7uixk5N9XV1fTGtlut5OMRj6nrGGRP8h0T1ljUkBdGmKTJJn08wLcBDQKMy6fq+u6STsuryNMrOjOJRFmv99PQ6QkfeYyYlMYb1nrl4OhRE8uXaCHh4dJ3/3p06epQyUGaCk0gEmC8y/nBkgBJeZaKY7EJCwgV8y+j4+P03P99PTEdrudoin/5XMkrPzl98DpdJq06i9fvpzORc7r0pwth3gyLn0Al8/aZTfrsoslnTalFFVVURTFRC5IZ0B04v/8z/88ERPb7XaS2/zSPeIXA3KjIU4McZrQtAOPjweK2WgYKPKUZZrw7vE9kYY8z+jasSWRFAW+Dzzc31OdauI4R0VjfOGptXR9z3yeo+MMHSUsN1vm+YwoSng+1kTOj1pcN6CVJ89ibF+DiUhiw8kNxEbjrOW0e2afJKyXOZoeP3RY41BFThxnZMsUH0amS764kiSZplwWWcrnx0devHqBYjR0JXFCMZsTlGI2WzIMliyfE1BjsoUCHacok4zMX9MBhvXmCt/VlFUJGJK2I43O44ujCGyDiRSc9U21c2RZTloUODdWdc5Z4tjgCXjn8IPF2Z4siVmtbsmzhK6pODw9Eic5JrGEbEYYOqpTTZbFvP721yyvXxF0igsRu92ouQyoUW7UtnS+Zz5LybOUPgxYB0PfYcKAVoE0TiCKaduG0+nAdnvNze0t+Szjzz/+wOF0ZGlieucZPGidYiJDNp+ztreYJOf5+Yn3n+55+eKOq/WS12++47/+0z8Szhur0WqUQaBIZwtyb8jSOQcTsfv0M9YNFGmOMoGq6TgeD0TGg9J0zcBgezwxru9JI0USjZKR2BjQmsGPUZ1xXuD9QFmVLBY5WTqjdx5rGVng+ZzMGJTLWK6WaJ9w/7HleDgQRzFpOkaypfmSyCg0jq6tsX1Ap3Pmy/U41RRN27egxymjKPB9T56l/Omf/8CHDz8w2Hqc7hnPSMycod5RHx5ZFIbF1R3KZWSJ53g4kmYpUaxJswQTpcRZRpKmY+Z5bOjqlvq0w9saj2Poe067A319wvaWfddz2O/o2w4Tp8xXa2YLw2K5ZD6bgwo0TYVSBmMSjIoIetRnc44kbaqSNMvQxjD0o6yra2p83+GGFtcPlFVNO1iUicmKObZvqaOAUYpY+3G6ZqyIIkhCT0JPd3rm/Z//O3mScnuz5f7je6rTCa0V+6cd2+sDUZxTLDb4WOFth44KnDe44PF4rHVEsbmIZBtNaM57jGjK1RivCoE4jkiSaJRF+TFuMU4itFEE/Picp/psfIrYblfsdyvapmSxnLNYzMnyDBc8w19iDwEm8CMmQpnEKUylDNuQuELRXMoQmM+fP09yFGnxit5UkiKiKJrYyjiOp01ZAIDocWWTFSZNQMnz8zN5nrNarSbNugBfSXSRwTiii5VhLmIq/fz5M2/evAG+Bl1KKRaLxaSNvQSFIqlwzk1msevr68lMJ8NcBBzKectmLucvQ4YEMFymiwjrJ2BMCiExp8qgFzG3HY9H8jzn+++/n0yYYpCTQ6IjJfVFtM8CgAQ0Xcb6HQ4Hrq+vefHiBfP5nD/84Q/s9/tJ8nI5aGixWEyZ6A8PD7x79443b96w3W55+/Yt//E//kdgTAqRayPSIQHJURTx/v17gKlwEeAq10YMhPBFKiR/J4WRXEsxDpdlOXVWpCuj9ZjBLqBXJre+e/eO3W43SSskqUXApRSFcl+EdRVvhHw/iUznv/yX/8KPP/7IMAyTjEg8W5K9fXt7OxWRu91u6rIIcy2GXlk/kjQjxtWu66YOlXg3RBYVxzHr9Zrlcjl1swSQXhaTEnEqBU9VVZNHQ2RdorMWL8DpdJqKOulaXL6ezDC41F2fTif+8Ic/kKYpL1684N27d1M35+Hhgdvb28mDIKbOS3AumnyR1P1LX8ZloSVF7mVBJvr7y26ddGvkHkhxIYBcUqb+ZWHwPzt+MSB3tqbvIvJZzPHUMQyBYjZOA1N+oG2OJIni+uqGNDO83z0SRTlX13c8fn7k8WlHluTMlht0XGCSOTqOqF3JoXEkmWeeJygTiKJRf6jVCFi1YpyQmRh66wBFFGkibVAK0iTmeGqoyyOHKGKeXON9gx8GQhYRZzmKiDSfk88tOorJ8tHEFsUR2hiSJKaqSqrySF2fmM3mrJcr6qpGxxXPz3uSbIELChUlEALWjQbLwVqOhyNBJSRRTBI8UZLhgkP3LVESoxR0dcOsyJkVOV1Vcjo8c3p+JNMrtDbkszlJntM4T9+12LrGmIgojljMC+rySFkd2azmvLi5omt7nBvIkmTUmNuAK1oOu0eSLOd3v3nLfL2l7FqCUSzmcw6nirIcI+l88ETG0A8DXeMwPsYojQ3jmHETK2azgtmiIJiIwTt2zyODtZwvWSyX/Or77/np3XvKuiQyKW3n6IbAMICKU4KKWW6vCcawe3hgfypJ04TlfMaLl6/5/Ok9JtKoEPDW0XUWHyXodI5JICobOveBoa0xKmY+y1gullTVI0H1KJ2gFCgVETxUx5L6eEC7gbYsiSNDHEV0XTt2K+KxK9F0AwSHNglpOmMYxkz4voiI4gE7DGhlKMuaLM1xC49JUuIsxwc1RuqdjgzbGpPN8F5hvSK4nm4YuyjWDpjIkGQZehyNRKxHEP+nP/yeFy+u2KxmzIoEbwcS3VIkjtd3W1bLiPIYqMtn4tiwWi5IkvFzxB6qpiHNcrQejW6uG0i0Z2hKnp/uaeoGoyP6rmUYPHGS8OL1mzGiNGiSYgEqOjNSc4xRONfjnGUYRu12HhtQCmsH4ujMFNmethtblkkcj8A9KE6nMe5xsGNhOU5+DQxdQ7m3zIqcPI0JXuNcPyYODQN57Hl5vYLmlufHJ+4/fMQNltjEaBQ6KMrjgcEFiuWafDUaR4NPCJEiqFH73Q8DJlKjDl8ZhqEb9efKMJsVtKobr11iRllRNEaSKT1uhpFRZ+YyYHRMHKUoNcoAgnckScyr17cM3WpMeEkj+q4jaI3WfzF1wpdJfvP5fGqti7ZbUkvSNJ3Mmo+Pj1NM3sePH7m/v5/i5WQzlA1URqkLyBGAexl1KDpdAV5iUJPUirZtOR6PU8qKSJuAryQowmrJtL/LnGPRilZVxWKxYL1eczqdiON4ikyTzVuSSYQ5FbZQgKDox+V8JC1GBvkI+JIiRpInBFBLnJ+A29VqNUlAttstL1++nBhOSbWw1jKfz6dz/au/+iuurq6mfOflcjnlS8MXQ9xl3rpcc2F05R4Lk/z4+Di161erFf/m3/wbfvjhh6mLINruy+i/6+trZPjMfr+fsqvfvHnD+/fvJ3Amxly5H2IalYJGOicyaRW+yB2ks3A4HCYZixgORVIhZr/LQkR0xlLEiGxJCsHD4TAVeQLIhaEVY7D8WV3Xk/lXIhTFfCmHFKP/9E//xOvXr6dMb9HBSxzgarWaYhovOwdy3pIoI6ZJeU8plgVcSwxjkiR8++23AJOkRdbcYrGYipbLPPp/GacogFoKWfmzEMKU6S5Fhtz7ruumoUoijZHPKud1d3c3nfe7d+8mo60c+/1+KpBWq9WUM3+p+74svi+jGWXNyLW47HBIcSG/JwWfFCLC/kvX7Ntvv50M21mWTT4Q6YT8r45fvJNkSWDoS5o2pW4Dq9UV2/UNeZrjeou3lvVqRprFPDw98byvz6Nyr7kPB5q+Z7W+YrnZkuZzktkaS4DeMfQdjVNkxCg8ddfjq5pPnz7R9D1xGlMQcLbHWTuml0SaOE5Yb9agxtHfznbUpwPNMiXQYa2jajrawZMXOQ5D56H3nvVsTT5fYq3HB0WSZux3e9zgeHp8Jo7zUXdddZyOLbvje7JiHP3eW4f3gbJu8C7CW0tdlZh4RhrHWOcxKqBjQ1bkpPMxgaK3YZTC2IG+qRi6ljgadYa2GUbWXSu0A0UA70Ztb9AYpTBYtosZiyJj6Dq6xhHHEVmSo4Lh+vYFUZKye34gm82Zr9Y8Pn6kagPbm9coFeGGnjhSDCrgw0CaxeAjCBZnLcUsJ9gY6yKKIh6BVJ4zBI8f3JdBMVmB6sZpXN+9jfjzjz8RgibNcuqupLcOo2MGr8mihM3VDc466rriaXfADgOLxYqnhwe6uiOOIvq2IVtFqCjDRBl+8Hgzw6mctjtRpKPcaZOmoAxPuwN970gzg9EpKjiqY8nheUduNMH29G2NKk9U1YlOJRSLNcYoitTg+4Y40oRgUT6wXs3QKnD/cI9tdmCP9E1HXzfkxZw4zUZ2+czK9Dbw8OkDS6vI13eEYKhOB5I0w8TxKIlR44M4WIdGYaKI19+84eXLl7x8uWU+y7m53tK1PY8fB3zuSBNHeSqpq4q27VmvNhhl0AH6fuDTh8/EZ+DS9x1931KkKV3TgnUM7UDwUCzmBB2Rq9Fs6wNoHWF0TL7aolRMHCejRGfoMSbGJDFOKZx1xFmK0Ya6qlB6jIXs+hY3eNJ5jvMBoyOyrKBtWqyJiQlkxQwTxwSgbzu0CuB7hqbHWsfQO4zWNFUJzqOVIo01wTmqqqaYL0C3VFXN0A2cdrvx95qSdLbG0TGEChcbdGJITExQ4zCgNInx/jymOklQyhDHCZ3u6bqGNFuA0hjRh2oDhPP/xs3GTF/go0a5bmucG9AaoliBGocFJcno9YijvwByGEFt13WTwXCz2UxpJ5LnvNlsyLJs0i5LdvOHDx8mHa38jrTJRZMqbXSRulhreffuHW3bThu5bPiXG+jV1RXA9HeS1HKZZCHZzQIGLieESia5MGDC5gsjV5Ylh8OBP/7xjxN7LUBcJDUiARGWWth8Af6SvCGtcWHSha2UrgPwle5V2usCDkIIbDYb5vP5NGhFuhYAL168IE1THh4emM/nbDYbPn36RF3Xk/Zbrp+Y8v5l9KF0AUSXfamtlnsjQ5DatmW9XvOb3/yGP/7xj1OXQUDgpVH05uZmuh9iHFyv13z+/HmSMlzmQUvX4JJtvsxWl3hFkccI0D0cDjw9PU3xdU3TcDwepyJE4vPyPJ/0ygKwN5sNWuvJmCyyj6qqpi7QJUsta3QYBjabzSQtknUg4A+YWNQ4jvnVr37FmzdvpihFAaTv3r2b3kOkVfKsXRZK7969m9a+yEqyLJvOWUD7arX6yqh4md8uueuytgTQpmk6rUMB/CLHieN4koetVqupgJOOWN/3U5dHALXosWXNC5OutZ6AtRSwYhKXjolEQD4/P0/3QuRNl6y2rH8paKQYv5yEK6boy0FmIi8DJtnJpaFVssfl+0iugbyX+Fb+t6esbNc5nXcE1xNHOd+++Y75fMHQN+jg6LuSVHl6O9C0lmK+Zb19Te8i6s4RZzlpkZHP5iy2d6TzBbvyxECMSiOS2QoVGwiBbujYPz/x9PxInMQEP+CHnmB7XD+wvFpydXONdYF26OnsgI4VsyJDOU9Vlmg9kKYZUZziVIxTEV3vaK1jcJDks3HEfT9mcidRjOl7ZibmeCyZLyqarsehcGhMkuOIqJqKoGN88NRVi1UJWkVEkSKJY5IowvseowwmSYgSw3y1orNuHHseBtr6OGqdI7hd33JzveW0P+GDPwMFReYSjB6HnsRGoYMjIpDmCVlsGAbL8XRivVnz3bff4azHesfz7pnnw4EoTdlc3xB0zGDHXOZidUUSc5YZbUm0o6sDqZmhg8UoNUZZVhAZTWwM3lkUARU4V4ph1OE6R9VYVGTYbjcoE/PTz+953u0wUcZslqHigpgY5z15MeP6VvHpw0+UVY0bOm5uRqNiUx7o6hpPxPw6kC0X9D7COsfy6iXffu9493vH/vBMFjWkueb65orOtpyqnqAivFN452nrluZU084KgrXjNQzj6PjT0OKDIo4MaZLggiX4gWHocEMgIaOznro8ge1oyhOpgZurO5K8YAiczY8JcWRQ2vH5ccfuWHP7JjDf3NGcjqSxIdIxJo3pbBjZW6WxbiAEx2yx5N/+279hsUg57h+oygND1xJ8SZY5vKvwbmx7p0nCar0Y5SCnE5iIm+urERTqgMaiQ48bBrqmIVIJi/mKOEmI8xlEFUFHNN2A04a8mBEnOaiYNM4w50KorGvquiRL4pFFDkBrSWODNhlX12sWyxn39594eHrkVNb0vSOKB2YzRVn1BDx5kRGlBSFAcMPIohMY2oqqLrHDyLb3znI8HMD683OjWSzmeDTOK3rraduew26P9QMOTV0eSRY1LqQMPsX6HqMsSmlipYmimDhJaBvJh40IgS+bvzmbbbUGwrmz8rXxyXlHFMeEoCZ2LE1TvBtQBPI8pZit6bsx01jHX+QV/7cfV1dXE1ATUCEJJvAlFnEYBpqmYblccnNzMwEEiXSbz+dTbJhM0BQmVphnicETVl1AsJj61us1L168mNhIAZli6JTMZWnzyyGxhgI2Z7PZlGYiaROS4nGZviItbmCKWRRALods1qLDFiCQJMkUESdsYlVVUyv/9evX3N3dTfGKl3F10k6/TO8QQCpxdVdXV/z617+ero9EHUqyiIBi7/00kVAMtQKKLmPfJOFCih4BTMBX5k9hoCVi0BjDn//856kzIpM64Qsj+/LlS3766SfKsmQYBu7u7vjuu+84nU6TQfjm5ob1ej1FAd7c3PC73/2O//pf/+vErmdZxt3d3SSRuGR3RboheeyXQFbup3Qt4IsB97JYEsPh8XgkiiLu7u6mogSYujtaj7n6+/2et2/fTlr7m5ub6V5dZnbLOS6XS/7u7/6O1WrF09PTNIBHZDpSNMok3M1mMxWbWmvu7u4myRQwFZ+yNiUtRiQ/wg5Leo50bORzSHFYluVXz4x0vaIo4vb2lvV6zYcPH/j8+fOk7RcJiRQ88voifRLAL68vkiJr7TRQS85juVx+Zc5smobn5+dJwiUTXOWZkCJerrU8f5J9Ls+bFALChst1uyx+Lw3Zl0WaMOnyXnmec3t7O32ey9f7Xx2/GJDPZymxdTwda7bXd7y4vqUfeo5lTXBH2vqEyRNipclmK66KDZvtK/bPj2O2dhQTVMBkKdl8RZTP0Z3FUTP0zZinrAyDC7i2xzrLarEgTROySONdj1aBJDEUxfjF3XaWYj5OKlyognJZMFQtXVMxm6VkaY5OU+I0p3OMcW5RdJYeZJg051idaHuLtpBls9Gh3dYkSUzXnSs3pdhcv6BuevZlw9XVhiw2BDRDY/FhwIUOH/d0rUKHHoLFKI8xkEQK5x1t3zC4FttXZLGmKQcU8cgcVxWRTkfG1kGnFAqD0jAOKKpwQ4cJENIIrTSL5Zqr6zt2++PI7gdHbwfKumEej2PTF4sZTT3gbU0aXbOY5/TWjZKEIqWvHUWeEeHxdsxhH/qe4pxL7fwIqgiBvunp+oHBn0AZ8lk2bV7b7Yam6TgcxmSVolgyK5YYkzIMDucgTnKK2YqH03uGoWcx77jarNHecji07HfP2D//d17oGSpdE1RCNl+TasP9z+/4dH+PVic2mwyTwnxe0FtPQNNUI7BuG0VdNoRzYkzwFhU8WRrjjOFw2DOfFejYUGQZQQ04OwLl025PagzYjr6pSeOEm+2Gq+0VZdtTlxUQ6HpH17QE70kjMxo6u4ZgW4ZGs3+2ZHlBvlyBSYjTnDhJcEqhncO5wLdvf03flVjb8Xj/nvsPP4PvmRcpXXMkT3OGwYEPdHVLYiLKw4kBz7dv32LiaJTDxIpaW+zQM5sXRCrF5xoVx3RDYL6+YvCakDqs0nhtOPUeEwW8DmB7yrLieDhhbUdvHQUps3wE64O11KeKfJaTz+b0gycEzWBHw3Fb1Xz8dM/j4xN5nrK92hCCQQF4S2IM3dByeH5g6DqyLMEOA86OhV6aJRAUh8OJJM24zuf8/PNHtI548/pb0mSMmBseHzgd9hTbhsPRMSSe2dUc4wMqhHNsp6fvB6x1GBNBUCgYzbXIEI4vmddGqzF8HMbuhdYEpUnTCPBEUUzX9aNv4DgOVhmGnqenZ5I4Yb5YEpSWl/y//hAt+G63m8a1SxKEc24yRUqLeLFYcHt7y8PDw8QEw5coQtE/AxNQEvAojJREoMkGLiBCgL1IaC4nfVZVNUUlSvqJgHphAYVVE/ApMglhTQWICLsnA1DquuZ4PHJzczMx/LL5C2iXoTDye5L7LKBQDHaSQgEjeyq52wJiZZMX8C8MZAjhq4i4u7s7np6eeHp6moCMxM6JyVOSTERHL68zn88n9lfOQ3KXJVpS2vUiG+n7ftL3Xk5OvL6+pq5rdrvdpIMWE6IMWZF7L6k2y+VySl4R0+8f//hHJFVGTIJv3rzhxx9/nNJaJANd2Hz44keQJJ1LHf7lNdvtdpN8QlJ0pEsizLoA+yRJuLm54ebmZrr3UjDKfRezsRADYiYuimLSo1/m7Qvg/M1vfjPJez5+/DgZc+WeiFdCdNriqZDflS6CrH25ngK6pUOw2WymeygAVOJBpYCWiaTSZZJ7dwm2Rdoi6UFiPC7Lkvfv33N/fz8N+ZH1LxIhKbDbtp06RZcAV0zfWZbx8uVLfvjhB4wxvH37drpu9/f37Ha7aXJsHMfTe8l3x+UMACnu5TkUeZb8tzxbl2ZY+e9Lbb50ZmR66TAM3N/fTx20f83xiwF5Gkccy4q6arm7NRilSEwg2Ao3lCTJuJk1g6fpFevtCq1T+ranKSv8uW0XxRlEGY4YrROsdTRVTds0FFFO1VT4riGKYm6vb0ZDmXWUzVjlzoocYxRd1xKZhLuba2zfYpRiNcs4tBW27zAqpqpbdBhzioMB50d9bBRHKJPQWUXbQzcEkkixXs4p/YksTsjTjKHvKMsjXdeyXK2wvsTELXFaEMd6Avjaetq2IiiFG2IiDdgOUkWexjA05HFEX7e0XUdiIEsNyqUUWQ4eghu7DMF1RHocKe98RJJmDF1DU3ckRlPMcgY7EKUZi2KJ96OMoaoaemfBaIKKcSHCOo3REWlkmS8LZmnM/lCTJgl9P+ZAJ7Fis5jj+47n+yN2aDEaFssF83lGedrj1fhZXQh0dqA/u7u12YyOY+/pu46r7ZaqrPn8D/9IW9ckWYGKwNlA0AYTReSzFbN5TVcfsW7MPR/yAnU60JZPHMs/kOULFi++R+UbGmtQISJbXaHzOa1v6HzgcKww2hPpiMiAMpbBDWg7jpgfuh4NJNFo3Kurio5xg1ME4sWMbnBE2mGUJzaOyFiG9kDoj1SHJ7777ltW21t6r6m6jnYATRhNoyam7WoeHh7pLGSJYZ4lWB2o6xODs0R5TmwSojghihIiIiLAJwVZpCiPT1gcTdsQP96TpJrb7ZLT0xPKW4a2JlIRoQ00dHjvSPOUEDwm0lhncQGK+RqcQ2MIQTEMYTTY9gNgGNAMQZEUM9rBY5VlvlhRZAUqeDARznmSWBMpiExgPsuwXUfvekKwHPZ7Ah7rPcv1iizP0ZrzoJ1A25ZEJiYMjuPTM2lkMHic1rRNQ3UatXTWa/IsI9BiznngkUkYvGG3O561347NesN2taI6HSB42rpk6Hvc4Djsj6hZTrFxhOCI9Pj95Oxo2Bz1q2vcmdFCqXEN1C0m1uTZ6D0BxgFO3mOSCOcdPliSOAU8wbsR4PcDnz8+UFYn1psVeZYQFymoMXs/hL/EHgJTzri0r8UwJyDuMrGk67qJNZUhMJeT8IS5EpZLEhiSJJnYY9GfCxsr7yvtbAEVMsBHBo2IoU1i1AQAyXtd6mElXk7OS+L2BKxJwSGygUvGTICGbNIiObk0oQrol/cUQ6m8huQ0CxsnLOmlxEAAiZhCpTCSroJo2IV1FonH5eeWz5bn+TTuXtJfhMHv+55Pnz5NcgKRxshwJmHZRSIBTOcjJtCbmxtOpxN///d/PxkABdRJsSHFmsgVJNNZZCVimJQpn5fsviS5SFqKAChhKUVWIPdV1q0xZhrmJF0Z6TrI9RL2Xwqf/X7Pr3/9a25ubqZ7cwlGxUj56dOnrwYDybRNMReKvEXuubC5SZKw3+8nBvf+/n6S9ohOX9axsPvCoAtYlDUiUy0FeIoE7PLPJG9dPoOYEuUaCHgWSctisZjutRRM8tm32+1XHRZh2eUZe3h4mF5LfkbYfcmcB/6H9JOnp6cLEnDLdrudoi8lwnIYBp6fn5nP51MxJ0Xv5fCs9Xr9VfRhFEXTa8jnlkOupzwz0kGQZ0tkQjLUKM/zyegtgP6XHL8YkL9/955j3YGeYYeBpjyyWCTkScCqgPOabgg8Pj5jkpHdLMuGw/Oe6nDAdx2ut1gLdetJjAIVjWPLlSeJFMqP+eD90LFMxpzypq4IwaG8w8Sa5bwgSSK87VFoTBzhwxjbN89juhiqtsX2EW1V4QdLejyQFAWD7TiVR6I4IclnWG9oh/F8oiKGoAnOk0YJaWSItDrng3dY31I3PZ4Ik+TEqcHrnlh5cJo+dAx9Q9c3zLIE7Xt0mhArh+9qdDBgKxQDs/mCeRqjfUJ6jkfUBur6RF0fweQ0ncObBfgY53tWm1tWRUQIDc/PD7imBTWyi/v9iX1V45VBR5pitqIfDFVlWS8CkVZcr5ckRc7tlUYnMT//9GcMA9frJfXhwPH5ER08buho24rBZgxO0/QdeT8QJyk6yQkmoSxPeO8oyyNJmjCbLYjTCBRsNit+9d03fPjwier4TJI5PAkmLTAqoiiWcB24/2ipm56mGwg6ovPgFRg/8PThTwwukF59iyk2zLKcV9//mrY5cP/zP/N8LLlepigNyo1RiJKjHScRGEPbD+hIU1ZHinxNU1X0kePm5oo0jjHK44Ye5zryTKNDh3MVtt1R7p/wQ0+aFiidUjYdXqdksxjXN+g4psgTfAigFGk6TgwFSxTFzPMCR4Q2CXGcYnQERCjNqLuOIqwOJMsthbdsg8cPPQ8//4nn+2diYOga6uN+LHYGT5RmnMoThVrQ9g0kCq0jdBQTxylFOoMQGIaOaLDYqkNFnt4HTk2L9ZoinpEbiNPAcr0ZNdY+sJgvWS8WnA47dLAE36F8ILgB7wfuXtygtKEbepI0xcSKYWgZbEffNcyKiJd3G2bZHK0Up8OeoalxBEIAVMRifYWJEtqhJc6XdHaMjerdQJ5o2sETJylKWVarOctZTt81Y3qO+pKM0lQtrleki1FOFWlPbMbhUcF73BDOWnlDPTRoraBr0JEiTmLiOCKKx6x4PepqQKtRauYs1g6EEBNw+BBIk4RP7z5SNz1V3eE5sV4vWa7js4H0l7Ui/284xLgngEfax7J5CSj79OnTBM4lQ3m3202bqTDgslkLC30phZCEA5nEKZusMLwSzyaGTmHIZrPZNDSm67qJNd3v99O0xcPhMOmAZcMVhhy+tKwFtMuGLEylgFgBV6IVF7Datu00WEZ+RgoMYXMvE02EuRSt7mUCjIAu7z1XV1cTAH98fJxMeyGMQ4WOx+MEPIQ5Pp1OE0sr004lseJPf/oTwDQO/fHxEWCSPog8Sa7lZSqFMMWilxbGWdjr3/72t/z0008cDoep4JBrMZvNuLu74927d5O5UsA0jODo3bt3OOcmv0FRFPz2t7+lqip++OEH9vv9V7pq0TILq39ZsJ1Op6kTYoyZdPbCDgtrK/deZBJiTrzUUEve+2W0JzANi5J1fAmaL1OFLoGvZJELOz8MAz/88AOfPn2angthxKXgleJoSpA7FyMiQRIwLgWaFMxi2JWcconMlGdnsVhMJlKRmsi/vfe8efNm0plfFqPybMxmM169esVsNkMpxW63m4Z1Cfu83W5Hb9Y5aUaK2MshRvIsSDEoxZEcYsiVAvwyQUW+P6QQEQ365Vq4NJNfyqkuk46k2JVzT5KEH3/8kbquJ2/K1dUVV1dX0/fOLz1+MSAvTydMlJFmKW7oORyeSJIFWo9mqqBi+i5Qd443dzdoE1PXJU15ItKwKDLSOEKdjX7aR3ivqOuG1Hh08MzyDIY5T/UJrcB7h7X9OBFvsTgbt8aBK0PXYntHkaWkSYLvOxQQR4bI6En7bAdH2/XoNCN4sDYwX2xIs/k4qr73uKAxcc7gAk3XjWPa84L1Zs3j/jB+UbqRHYyTlChOSPMEG8C2LTpOyWcxenDUzWgAi3UgjQ2xAT80dM1AW44M29VqTqwVYciI44g4MiwWSw6HPbv9AWIPyYrZYgNEhDhinioIDU+PO9q243SqiHSGs4F3nx5xOmJ1dU2SjqkZx+ORqmwpjxWLWUzwgf3zM9lsRcAzzyM6r+nriofP7zEKXr64pa1jTidP17UoHegHz6msWW4LlIlJUsVcKbCWuq3PUoU5czUmXBR5ztu339D3PU9PR/rWMVteobyja2qUNmcdb8Spqvj0tGO5mDNb3VCWDdoNVIcdD88Vt78K3HyXkq0WJMUN9fEb9s8fKJ8+8GK7ZFak6Lbl1JV03YAP4IGm7ei9xzi4ubnFLBa4oSXPZ6R6HDqTZhHKKeqqJuiIoa8ItiNPIh7bliyfMQyO58OJx31JUszIi5QQLLW1qA6c1sw3G5SJcHgGb8mSBKdihj7gnEKrMXdbqQg0BDzunIWtVEQ+34yGXeep9gc+//gHGBpc39LVFYmJqJqO2AV0FJGk4gAfDaN932N7TxrlDLaH4FFmTB5BKeI4YmYSugFQgSiOibQac93HHyFNUtp6nGqZmISmHjiVJ1zfMJ8VLJcLTlU1xiAmYxKSd/Z8Hpoiy4iMokhT7DDQtwpvxnQb6xX9AE4Z0Akq1nidEaIBFWtQGqv0GEmp9JmlyYkiTVrkDEND1VYkeUrwnuPhwOBmFNqglMZEY0qS1gZjNNYOeOcvgEw4M4rj34fwZcT4+EWpxoJYaxQwDCNLW+QFXdthdIKzjjwv6Pqepm5QCl69ejEaQtVf2HE5RE8rLefn5+cJFFwa79q25dWrVxhjqKpq2hQlylDSOgRIXWZ8S8KIgM1L9n21Wk2AE77owWVinrBhlxGEwGTsu0x4kPHowqQK2JdiIYrGhKKrqyuenp4myYa1dmI3RVMszL6ANTHWiT9BmHiRPFxdXbHdbr9KmRAG+/n5eXq/OI6nzyzvF0Lg4eFhei0BJT///DNa62mCoyThnE4nDofDxKA+Pj5OUpvL8//w4QNaa16/fv0V4ymg9Xg8TkkpYpQTk939/f00cl5Y2N/85jd0XTfJlaTzIBpnMc2VZcn9/T2r1YrNZjOxoZI+8+tf/5rvv/9+isJ8+/YtDw8PPD09TbIhye8WBlkYZynuJJ5RAPZl7riklURRNEkdJMlEQKPcE/EcCECWjoiMgRcAK9dXXk8Kz0smVX5WQLmwsTIRVHTnci9EciRgWqRQ8sxJwSTffXJ9Ze1IYQlMzDV8kUPJyHtZd2JklkjS9Xo9MdxSbMv5y7Mi5yag/RIkSyEqPy//lnO5lJOEECbJzeUzdSltkS6UPO+SjCLP1KVRW8gCAe5y/y415vClUyLXXvwl8oyJeV3W8DfffPOvAuPwrwDkL1++AJ2yO41TL73v6btxYAo6oh0Gjo1ldXXH5uqGpu44Ptyze34kjSPyZE6cjSbLzhn6qqctG9qmI51p7DDql/umxvU9usiIsoS6Aus9RTHDeg9+nKrXdRbnxpSPWZHT+fMmazSz2YK6bc6LLaXIZ+TpjNNQoqKMbLaitxpvA7tjRdc5dFycgZIe2V5jiOKEsq5puw4dpRTFnIBB6XGEu1cdzWDH5BRlMAa08sQqkBhFnhjSaJSUtNUe7R2LLCbGMbTdeZS5RScRi+WCsqrYHSoW2xWb9TU6W+C9gkijdMfpUNP2lijJIIwV7alqONaO9d0rFtdv8T7QuoF+CGDHNoq3HQ+PB3yIuX35Bh1B8A22P/Hpww/YvuLq7gX5LCVNFXcvr/j04SPOOtK0oKw6MBWz5RqCGRnZ2QITRRxPJz5/+kTV1Nze3mHPEVvff/+W4H7g06cnsjQjTWOsBcf4UCxWS8oSTnWHSWes1i/x3vD44Ue8a/B9x+OHH1ks19jlnLSYsb6+ZnNzx+HxI1XbM8vH9ZSllsOpAaXxQNuNEp7laoZWir6pSKKcIgr4oUJrRV86XNuQRJ4oeLq+IziHdwFjMpara3bHimO9w+mY2Hk6Ri36MDi0h9lsye16S9v3OBOjkhQdpSiTk0cRi/maIl9AFBPOgC94D94TvMV7QxQV9LojnV3xm7/6f1nMF/yn/+//h8eHPTpY8KPzW6sDJjIQKZIsZbEav6SdHfAOmramO5tylNZ0fTtKVgwENCpo2vpIms0wcUTfeHw/RgM2SrF7esINHbMsQQyP+vzFqM0YE2jbhqoq0ZEmjcdW4tACBGIDo0RbsVjOOJ5qTm1PIMGFmHbwaO+J44xdadEkRFlCajSRhrKsGKwlKzKCTzju9jw+PZDEmuVqjo5j8jTl2Fv6rkWjKdJijDh0I4Oe5xldN8rZRq0oZ7a2J4RxkNA4XGrUlkeRYcRkigBn+cnIIG3WG5wNeA9ZUfDx48czuBRGR086w78c4/HmzZuJ9ZaN/FL7LYz09fU1Nzc3NE3Dp0+fpoxsaelLBrdIFOq6nrSpot+WDTfP82myoJj0hAUTdm2z2Xwl+5B2e1VVX2nO8zzncDh8tdE759jv9xPjeGnkkja4aMzl94CvihCRRsiGL2y3GAclmUIkE9IuF0AnIFBiDXe7HTc3N9OEwsvzkXMVbbdcx6qqePny5STxuYyu++GHHxiGgU+fPhFC4JtvvplAadd1/Pjjj3Rdx6tXryYT4Zs3b/j555+nhBQpmgR0X5o2D4cD79+/n85Bfud3v/sdzrkpOeSyaDLGTAC8LEvSNGW73eKc4+eff566JD///DObzWZidm9ubri7u+P+/p66rqdiLM/zCTAKID+dTtP0zKZppq6HyGdE9y1SIlkPAny32y273Y6yLCegJuD1Umu92WwmaYkUgyLPkcJP1gUwySLk/snPz+dz/uZv/oblcsm///f/no8fP07rUTozAj7zPJ8+m5h5JUVE1p8A9UvQKVGJlxM3hcG+v79HMvblz+S85fPI9FtJYxEfBzD9TAhhmgora/xSRiPSN+Cr2NHT6TSlITnneH5+nrTam81mSiNq23b6nJd54zAWmSKzEROrdJvkuRDmW85ZCgspZEQqJIOkRFYmyTbyM/+auEM5fjEgX85XYFIOxwcMnsUsIY4URiUMDup9iyVhs77CBXV2IO/p+ppZMsoKBjuy0R5D33vadkChUT7g+4HHz5+xbcmiyEniiEiBMuNId2NiVKSo246qOeDRaBWx2+8Yhh4DeEZ2Y+jH8crBjxelyAqSNGd/rHAYBjfKa4yKKase1zsGF/C2x4cxWs+5QGCU1cRxwhAUJk6wHjrrmClNFCdkxQLnDdaOjm8VAstFQRwGkkhhlEX7AYMlSSNiE8aR8ENP37V0SuH9AoXCOXDBYOICFWV0NuCcJw4B66GsWtKswCjGIUJRzMOhIp9vmG9fUNtxI/NDfy4cwnkcuMGYCB00P/3pj6Acm01O1+zRWOazlKo6gLIYo8nzK1arDYfDEe3HDkjXe1Kn0NrgnUdpc84mNZRVzakeGSttIrIsZ7lc8PZXbyjLI6fDPUp5TFKQpHPyvCArcrKi4Hn3RGeh84b19iVtWbPrP7FeplR9w/Pnn5mvFsSxYbZa8ebt9zx9esehPJHHEUUxtjlHyUqM0oqu76mbjtVqMTrss8ByHaA7kaYZtu95fLgn2J5FEXEYWtI4IksL+sGTzlbsjg2HukPFCUkeobzCEkFUYKJ0ZHrjiNlmRTxYUBFexQzWk5gxwSQyKR41mv48hLNWLShNwKDNuPbRKV5lLLYvKWZzqrolX2zo25LPH97x+OEzSaSYzXKMjlABlA+jMdIHkijGnY2SIQTKumZ/KrGDR8UpKkpJ0tlZc+9GU+Wg6FEkcULXD3jbozX0fYvWgThNCFZRNw2nskQbPXaYvCdP4vEeJoqjG8Zpt8HRNg1dP9D2jmPZ4YhZrK8xyYxogN6Ok26DswTbAgNWjd0kbWKi1KGM5un+icN+B8HxYnHFy1e3nKqawQ6EEAGKOIqIowjvHHiN919i5FCcNwwIYZzwmiQRDG4CSSYaGfYQxrkGwXviOD0zp8O0KcdxxHqz5uHx4QyqFIEwtZT/cnw5ZNS3aHAvUzSstdMUzu12O7XgLwGkRJHJZihSEfiSEvHx48dpBPcl+y4pKsLcyhRDpRSPj4/TYBNhwIUtFSmDJD9Ie1rORXTmwmbK+QkjLsBEItnkzy/ZeIlTFAA8ehyW099ftu2FOZS0CfksAgrk9y/Bo4ANMSuKjKIoCpIk4fn5meVyOaXgyOcAJhZagGIIgd///veTtESA3mKxmJJphPHcbDY8Pz9Pf3bJ+ErhI3KY0+k0pZ0YMw6GWq/X/Pa3v+VwOPD8/AwwSX2EbS6KYoqatNZyc3MzXZv1ek3bttMkUMni/s1vfsOHDx8muYzkaQMTuJPiUDTIWZZxdXU1dVtELy9JJqLxl+7PfD6fwLjIQS6lDSJ/EOB+qVWWtSrpJAKq5e8vkz3k74Xtvb6+norJ1WpFXdf8/PPPPD4+TlIkAYQC6GW9XurGLxluufeybsTnIWtE0oXk3kpxIckiVVVxOBwmuY1orKUYks8GTJNSZSCWfB8Iey/PlxQ+8twIuy3XTLog3nuWyyXffPPNBNjlWZJnRJ5zedYvnzkp0ETOJNdc1vSlVEoK30tfjPgvrq6u+Pz58/RMyTP6r90jfjEgz7MCjyHPEoz2KDUQgieKDM0wAsnZYjtKP6zHRBFoxWw+IzMDdd2emeWELJ/j2p4QxtHsKgzYrqXvS1bznBc313R9Q3k6MfhAPpuTz5f4AMe6o20tWVEQJxnOBcqqYTmbMZvPqfbPVEML58xn5z3Bg8ZAGCPcMDFKj+xEFCcE21M3DQw13g447zmeSpbdltV6S9A5z0dL3TmsDcRJhA8QJRnzeU5vQXcNRgdw0cgyDg7nRgOnUuOY9jzP0GGswvT5wazOsVgjSIjIdIKOCwav6b0fiwqlaLvu7CCeY21PksSgAl3fs717Q1Ys+fxcjUWE7Yi9w7YleM9f/9u/5t/9u/+Xumr4z//pP6G15fWLa54eOzbzlKH3HI7jZEQfPIdjySyfoajp7WgM7Ho/GkuzBEVgGCxpGrNYrbDO8en+nvfv37NYLVkRyLKU29trXr+85R/+4R/p+pbV9gWp92RZSprkuPmcU1WBientWADNV3f0raXvavJY050ONKcdw3qNiRcsttfMVld8/O+fiLxl45ZUdUvXD2gTCM6AG2j6UZ8Oo+HShJ7QlTjXj/rGoaHIUoa+pa32kM9AJZR1T9MH6q7HJBnbm1uUiTBpQZwsUHE+3sPgOJY9i1VGXiQ473EWnAFnLFl0NnP4QJLFWH+WqQR/BhxhnObZNmgdo5MMpz0qW/Lbf/d3vPz2V/RtzZ//+Hu63hL6hqvtkvl8gVERQ9vhA7hhYOgHhuGEtaMJ8XgqqZvxdfOFZrVakxYFg/PE2mNtR5rmpEnOarmiqWpiBVmajFn+9ZGurWnrGoXn/v6RJMvIZjPSNCGOI9IkxuiAUREKDzqhrFvq1mKDoVi+wCQLWmto+wSvEpIiHydhuo6m2jG0J7xtaW2L9hZCoGxqBj9w8+IO3w8cTgdmZUaWz2j7lmBGRjFNEryzeKWI45EF8cES8CRJNIHxYRBG60tm7PglKS3bcbOwTgaOGfp+XDcjcKtGM+j59/q+JYrP0+mCn8D/Xw4mZlfa/vAlRUGGcKxWq0n6IRuWAHdhGgWwXaZUCCvVdR3L5XJKcDkcDhM7vlgsCCFwOp0m0J5lGRJzKNPzdrvd1J6/1KVfgiBp6QtbKMBDgKH3fppIud1u0VpPQ1qE5RMgtFqtJi2ygKKiKKboOgEAlzpfAXCXcXGXYFiAxmVR0TQNj4+P0wRQacd3XcfLly+Zz+fc398DX1JDhBn827/9W/7u7/6Oqqr4+7//e7TWvHnzhs+fP0+pK2KSlOE6YlAUwCLdC8l8FmmCRDp++PCBH3/8kc1mM62Tly9f8u233/If/sN/oG3bySApjPlyuZwkTXLdN5vNJH2SwUCn02kCdldXV2w2G37/+98DTIZfef7lusnIe2CSK10aFIUNFr21fF4xFcuk2bu7uwksCqMLTGtxs9l8lU8v/xY5hnxe+Xzyu3LPxawrBWiapvzt3/4tv/rVr2iahn/6p3+a/BfX19eTjEm6UyKxuJyWud/vJ2nFcrmcTJjCyItuXgZ1iUZeChLJP5fC99OnT9Pgrn+ZBX8pfxH5kJgqBYhL+o0M6JF7djmDQAoekee8fv16SvQ5HA5Tos+lV0BkaaI9v4yllPeRZ1o6VfLsA19Je4Q9l+8MubZyHYR8kCJHvgMu7+f/6vjFgHy9WrPbnwh2II0V3tacBksSRVT1aHaczzcoFY9aXjeyEEZBmhVYbwlRxuADkQ+4vqepS2zXErSj72pi5ceNc+iou45j0+BNxPLqmmASqqqm7QOL1RVxmgKKthvo+wqtNNfLjDTPMKeR1Y6cprIa2w308QBBM58vWS7XxOeM4iRJcf3IAsahpcjGQqLpOjrrWaw39GQcuyO+rAlKE6cZykSEIZClKWkaoQk0tmMInr5vibF4D8EYrHOYOGG2WOHCGNOXnB/aqq7ZbLdExjCfLzg2gDI0bU/rNGkaj3F+5ThUYl7M2T3dExuD9Q4dGaI4HvPLjaFuWtrySKJbUmXZXq1ZX99y/eIFjx8/s16viPVAkcUMizm26/DBMV+uKIoZzjuGfqAsWwbr6XtH1fR0zmPShPV2RRIVeNdj7Zj2IYuvbVtMpMnzDKUhy2LevHnJ09Mjn+93uK7Gmgg7jCPlnbckeTZqoYeO3oLBsLh6weHxnvK0I9Kacv9EOltgogR0zOb6Bfc//5lDNbbGojghSzI667DOowM07UDbdWR5RrA9RWJI84S66cH2FHnOdrPGaEt5iGg7i8PQDJrPuxOr7TWv3rwhThP6ocdjUBgSkxMZBW6gd9A1liwpSI3CWod2lghHZDz4HoXC9g1jCOOZ+VCAAq0DWgc8ijhJIVi0jinWhvQ8dl7HCdfbDceH99SnHbvdE21dk+djCkJZ1zRdD0qTJhlpnHB7vcWj0CYmTlOCNoAliQxFHqGUI8li0iRinEypxn+MItIpdTNuYs47iiwnSfKxwGbskCilKY8lSaSYFzPK8kRZdjzuWuJ0zvrmJUQFp8YzOIULMYPTBJ+N0pbEkPixg+Raxak6Ms8MWkHdVLx49Xoc9mMtD/eWH3/6mdu7W0KUs3lxhx00fd8wOyeizOczZnk+xZRmWYr3FqXARBoTxWjzZTR2CAo3hDMAG82ikjKglJ70xcI0CXs2Rrp1zIoCrRXOfmFY/nLAdrvl6elp2txEfy2aU+ArPaww1cBX0gsBmaKRlWl3Yu67HOgiQEH0yzKWWwxpMEo/ZKMW+YpMNxTm8DKDWgxsUggIYyptdGESJbtYBr4IuAMmg6IY0i7j84D/IY1DspZlFDswMXZlWXJ1dUUURSyXy0lqI9GPcn4yzXSxWEzTPeX1BfyJ3ljYaq01Nzc33N7e8vr1a96/fz/p1yWW77KYukxMuUzGkWIlSRKur6+naEApOIT5FmmIgNs8z3n79i339/d8/Phxyl6/TKIR2UHXdVPhdH19zcPDwySPen5+nnTFxhhubm744YcfJl+DvL/ITuR+CfCU6yiFoOTQi/57t9tNa3UYhqnwkdg9uZ//0qMgcZSX0ZyyBoRRBqZrDF/iLOX/y39fTnaVwkzA383NDff399PQI/EUyH2SxByRSN3d3QFMTP1lB0EiLiUB6FLjLgBVQLH3furEXCawAF/Jv2T66+Pj45QRLxKXS1ZcimFh1qU4kMmyUqB88803X2ni//SnP/Hq1SuiKOLVq1dTASzFzmKxmDTfsu4uB/lcDpqS63wpX5FnUdh0ub/yHZHnOTc3N5OhVLoy8h7/2wG5ClCdSo77Pav1jBAy2rqErBh1zkGzWKzwKgVnqbuWKNakpiCfp9S249j0xF3HaqmIDPihR3lLpBVaeWbznCRLqJqG3ju8iUjTgnS2pK56ysZi4oLV9poojuiHnt1uT9V0LBeeNCtYrdf0TQUBlFUMaGw/QDuO686yhCJNiaOIuq5wtscOLb2zJNkIkrqupe17rPMoExEnGUlmSdIxgzpOMkyc4LuG4C1RHGEYp3OmsQHvzkA1wuE41Q1KG9Y6xtmBOE1I84zu0yesdROztFhoqr7BuUDf9lgMWRxRlSfa/Y7NLCNPEnYh4HygahpccDT1kThfkZpAZRua+ojKArcvNrz89hXXr16i4piqbciyhK6u2O1alPJ01lHVHc5D15/OUwcVj4/PlMeK9XpLMUto9nued8/kRcLVZkXfdvRDz3q1YrlYoc6boTLjqPWyPBLHiiSN+dWvvqPrBpq2pci2aCy2bxl6S54l2MGObKOJiOOMIrtmGAKDHXCh5/H+E71XZLMl8/mal2++4/nDDzy+a/A+EEUxaZbju/HhDM4zWM+pLFku5+x2T2zWa/qu4f5xh4kzsmJON1iSOJDNFtRDSZIu8aYkmSesbl5TLK85HXdjTGJqiPBkRjGfz8AOnPxA6DvU0JNmCbEO+L7BxxrbQVvDIr0GFNaDVufpdwGCH+iHHmc7qrpGGUPwgfmsoB96kmwGJmKxueVmvWS3yPn47s98vv9EeTzi7Mh4rLZbbq/P7+E8y8WKLM/o7EBA41ygc5Z+6EjzgsUsJUkiXFDgLb4fvQzBWx4e9gTnGOzI/GRpcmYEEpREXzqHMRrsgM4ThuA57I487kp6G7O8usFR0LSaEBUkcYbWo9RrlIgMOBxZMUMN0A4VWgW0hqHvyLLx2ey6Dj841ustSTZKwsqqZO0dUaRp2wptxpSUvqtZzDMYZLMylOWJOImIImHE+dKCHDyDdfLNdgbp7twqhugMJpJ43Kji7EsiiPeeJE2+RMZFEaMi/S+HpGpIFrCwWQK2RaohQEtAseiNLwH4er2egJnIH4TNE4OZFE2iVS3Lcspjvrq6mlrtT09P08ZfFAXb7XZKZpENXYCgtOLFhCZAU4CcDOMRhlYAp+iURcMqxs5LQ6hod0XWIokOwqRexhHKOciUx8ViAYwFjchtmqaZwMLpdPpq/LgAPUnPEIZXQIeA99evX09TIUXuk+f5NC0TmO6L6LaFAZbhL9vtlsViMWWdz2Yzrq+vJ3Zzs9lMEyFFxy3MvzCZv/vd7yaGXcx5IlkSVvZSHiSGSjHfffz4cTJMrlYr3r59y/v37/nxxx8nYCfM/aUk4ng8slqteHx8ZLvd0rbtNIVVTL0wZux3XTdp0WV6piSPyHkBU+EkwF+kGFIsiGSo67qp2Lo0FgoIFDAvJmYB8PKsiLzp+vqa6+trfv75Z3788Uc+fPjAfr+f3uPq6moC4JLdfxlvKCyxAEnJ7b9MNJLr9unTp+nPZM1fSq9E2iKfU+Raz8/PPD4+IkOPgIn5H6Wvevr88l0hQF8KGJHLSEyk5Lpvt9tJPnM8Hnnx4sVXMYshjIleIlsTKZGYni/1+5fJSZdGU/ERyM/JeYkhVv4tqTfSmZPvrl96/GJAfjwcOez2RGYcNX46PjM4y3y2wDuPtQHvAkE7jALnLMvFnFyNEhcbwKKxPowaza6mrQ4ohpGl9j2DBRdSTBJTxAXEo0kuKZbU3YkkX5AtYnqnSIoc1zscmqrpqOuGth/o+n5kygIok5NEMVVZY7xBJzF2aDkexnD/uqrxvif4Hhd6smzBYpExuHGjDQqsG+PPTJyiTEfdVFRNQ14keOdo6o40dQxdA84SKYUfBoKJsGeJgteGOMkZiPAqkCY5gw/oaIwMnM1mNG09pkOcjavGaJIsI3hH1zbYoUWFgqYsx+xkrWnahs1mS5QlGN9B38NQkkZwfX3FyzevWV1tSGZz9mXJw+6Jtm14vP9MnsFqNaftBsqmQ6mYIs8pq5qh79ntjjw97oCI7c0Vy9WCY3ng8eEeN3TkZ5ZjvljgvEPHhuVyScBTViVPT09oFZjNFqy3G25vr/n86ZHge3AdaIiNJoojjk1FW7Xksw0mzTDpgnRxxcJZjvtPDE1FX5dUxz3zxYbZcsP13Uv6cofy40aZxAmDDWe5gUFrhTERVVXS1BVtdaLuepq2Z3U1I4oTqnbgUNbkaUznNbFJWV6vuJ6tWa42pGmEC5ZIWQgeN7QwVCirsF0LQ4mJM1x7oO0h+HMOPA1aW0rfEpRjvromjjOUGlulzjq87XB9RVeV1G3HfLXGKUXnHHGWYZ1l8AobNPdPO4L1aB2RJzkmwGF/IIoMaRSxWS3xXrE7HuiHjiiOsIMlKEUUxUQeiCMSo6jLI9Z72s5Sli1KmfPgm5L+zKKAw3uF9475Yo5WEd5DUYwSALzDDx1GB6ryxPFw4nhqKFYvcCrl8flAsbwhSjKUTvBe4+yA0o4QLFo50HCqjpyeH7FNTW5SZllGMZ9hdMQxVHx8/MhmtSBNc47lge54YF0eWd3ejDGTwRIYqOsTWZYymxU453HOnpmaceMf+h5tNHmenuUpFucCWqszi3rO4WXcPLM8vZBcQGS+5GmLpGJk1M/ayL8Q5ADT4BYBDjJlb7FYTMkistkKABGgJr9zCZZk0h3w1eAeAaHC/EpWcdu2FEUxmTtFKgNMw4DEyCZstryGDIoR1k0SYsQwegkSLl9fWtnCXBpjpvgzkSnIdEMB0pc6YnldAeqX+ldh4JbL5ZQWcmn4E1OoADe5NnLNhMWXXGSJXRRQfXd3x9u3b7m5uZniIB8eHmiaho8fP04677ZtJ1Z+NptNjKtMSgW4u7ubkjY+f/78VQ76arWarpfIVU6nE/f395NM4fr6mhcvXvD+/fuvrrcUXCILEK2/yFmstTw/P09SCJmgul6vefny5aRTduewAWvttCbkWZZCTsy5YgaN43gq5CQTXmvN7e0ts9mMzWYzAS9hQQXISRykdA3k/18md1zKIMSUCF8A8mWSSl3X07W7PB95ph4eHr4yOiuleH5+nroDkpEv8aKXEZuy1i4jK6ULIlGZWZZNcaGX5lNJJJJCUnT/kpwiHozdbsfhcGC9XgNwf38/SVYEhEvRJc+DMWYybl52VkQnv9/v+fz58wTIj8fjlBz04sWLib0WYqAoiikFR4oGiVkUPbjEMkonQ7oKwPRaYiy9lOWJtlyu6aU34P8vDPnu+ZmubbnarMnTmLarMEmMiSLiVDMz42RB6x2JhqYqMSEQpSnaeKJszizS40Q929KWe/r6SB4HskSjGIgiPf5+koIxBBPj1Tk6zalxoJA/D1ZoOno7bqyE0TA3WEffW3xgHFVu3RhHZy2+74k0tF3Pbv88Rr019di+Twy+t8zyhKurLVXbYeJkzMg2EVpHKD9u7E1dUx4NRZGMTLgG23XYrgdncbZjUJpCxbgAPiiibE6SFXQOht5jfU0a6VGTe2aIqmqMdKtry3I2yj2IDX3bAW4cYa4CdXVCEEA/9KyTiOAHmuMjx32Fd47tcsbVZkmSxAzOjRKgusJ5R5KOpre+H1AqQmlPFAe0SYiilK4/Up9qsjRnOXeU1Rg5l81SlmbO6XTg+cny6u7FmcngLOnwzGdzdATOD+x2Nc+7HVGcMJvNefXNa3xQnMpmvO7GYbIZkQ5EytNUewYHq2xN6B1WJXiTEadzNmlCmsbUxwPl4sRqseTu5StsueP0/Jm268dx7Si6ricyHu/1WQ/ck0QRTVPTD44kzQha01qPVTHWRzSdR5kUqyLy1ZYkX9HbgA89oOjaFt9XBDdwf3rkUYMdeiIDbjmjT2OCHQfaZLMCnRWkXYmKcx4f78mXV9zcvSHJ5miT4H1g6DuU63GuJk0NhIFIxzhrybI53gcCGq0jusESeosyEVleYAgk8dhiPB1P5Pme9dUVV9stbTe28qMkwTqLHQLBe7I0JY41n+/v2R9OoGO813gUaZwx9MN500rg3J6M4oiiyOk7d9aaOuazgixPaUpHXR3PAy48aTEnLuY03qOTjNlyQe8V6ID3PTY0YAMqDCjjOZUHnh8+4aqSBA/Bs1wsyYqcxWINasfj4wEfDPvdiXyWk+QjY+X8gPI9PvQU6WKUbJ0ZGWsH4jhCm3HTaOuGUa8fkS3mo4zFGNJE0zbDaPaUVBk1yo6MNsTxGHeICphoNH+OTEgK55xzH/ozm/VLv0X/zz4eHx9pmmaK1pOoNWEnhXkSoClDeUQCIj9zyaBKZKBsiv8SPAijeLnJyuYuUyNFRyob7SUguzSOycAbaZELEy+smkQo3t7eTgBB5DSy6QrLezwep0zwy6zyS8OngINLRlzOTwCspJoIC77b7aYpo6JXvmTKBQDJ5xMm03vPfr/n+fkZay2bzWaaZnkJ/KQLIBrkS1ZfDGwC1PI8n5Jf8jxnNpuxXq8nYP/69euvsq+BCUw553h8fOTx8XGKhHz79u3UZRFN7mWWu0hk8jyfzk1+Rv45HA4T6/3mzRvKciSHpGsBTFpyAVLSHZH1Iu8p4ErSgkQjLAyzJPmIfEi0zsfjcepsGGOmabJSaIi3Qbo59/f3LJdLXr16NU0uFcnWZZSmrCW5BtIFEROmgErpuAvAF321pBvJGhWWVwoFWWcfP35kt9v9D5pr+XyXz6l0EqSQEQZfCjeJ1RS2XIpU8VZIkSO6/st0k+fnZz5//jw9gyGE6dpLEXB/f08IYerM5Hk+XbfLZ0vWrzx/l3GKl5NzpZgR1lziUOV5upShSPEOX7woEvspRYUYWv+3A/KHx4+Up4r1dhzYUTcNhYnpnMOkMxbLW1RSMFRHmvrE/ukzszRmNb/CxJpi1mFPFQwlvonoTp8I/YE40SRmXOQ3t9c03UA3eGxnabsBVKCqO4bBM/QON3iS9DwBzURjKsh8RjFfoHRM13u8H52Up2MFiSKeZcRZxOCHUY7SdRg9tgdjE+NQRCZmns9JTMIpWE51h38+sb25ZbGYczi2uL4B1+OHhtA1o1k0GOwwEFxPYgJaG2azjPlyxeAtQ9sTTIRTKXXrqOuKRMPt1QqlNRhNP1gG6+mGgYAizSJMYqjbmrY6YduaDI/CY52lbRuiYDAobNPQ2ZZT2bE/VhSzBbN8jfI9zfEZ73IO93Ns34MbqKqSvh8wZtT3DsM52i1NSLOM7WbLLM3H9A6teHh84Pj8iIlvuL65JjIRTVkxdB2Hw5Hj6cB8VYwaXd+DH9NKIl3y6eM9dd3w29/8hs1mg3MB+9N7Hp8OmDhjmcZExBRFzGoz49R04BuCLxiCRs02LBcLjDthqDmd7nn4GWa/+i2b9Yrjasn+6Z666UhCjPdjnInSY/yl957IGLwPHPb7UattYhIVo7M1hpgoyskij4lT4vk1xfaWpve8f/czQ3skCh2p9sSho2sOnI7PNE1LCJ7NZkVzSpgVGUPfonGs1iuy2QLXt6SzFU4nnPoBHWC5uWG1uUYZwxB66vpA35yI0pRuGMiLJc4NxJEmTzNwDltXBDTv759ItQWTcKqeKJIIHyxNfSRNR410bx1lWWGt4+7FC+IkxgPOeapjiTKa4+7IfncgSnLmi80ILN0o+xm/cAwoR0ARJyMoVTiyBDSWod5hq5HFabuW9/cPKJNy9+2v0PkabzLadmR3lNaEvkPbAWMbbN8wdBVld6I+PqO8ZbWYsVrcUmQJSVGwurrGuoDOBrYvvsH2HVdphnMdddui6hZ92LFdbEeW1ftxSqnScMFStu34jJsoIknic4rRKEczJibLU47HE1GscW5A8cUdr1VEGiW0rkFh8UPNMNjzdOAIN1hATe85rru/HJ8+fZpytIXdFkOfMJoyafN4PPLw8DCBuiRJWCwWU/JD0zQcj8evQP12u+XFixcTy/0vNegCLIVBFKAlkXGiXxcZCTANARKAK1IQAW2iMRWzl2SlC6P6+PjI7e3tJF0QMHA5LOgyYUVkKaJTl8g6YU1FF2+M4fb2dmIKBQwJsBXDnTDHch2ACSgKGBDZgzCIApjEmGqt5fPnz9O5CwN+CWAutfBScAmD/fnz52mkvGiDJTJQmFFJW5EOgYA6Gf7zV3/1V1xfX+Oc409/+tM0yVGMqfP5nO12O0lwRE4h+mABc4fDgR9++IHf/e53XF1dsV6veXh4+Or3LhlYAeXe+ynpRaQNInERWZVIq66urui6jh9++GHyHQhbWtc1u91uAnnX19ccj0dms9l07zabzSQ7Ea2xFIlXV1eTH0K6HVVVTTp10fBLxJ9zjrquAaaseCnK5O9lIqqkEYnx+PXr15PMxns/yYlEeiSgWa6TdIQuJRgC6qVoFeOmaMwlBccYw/fffz8V32JwlmJCzK6XkYQi/1itVqxWq6nou7m5mdbj69evJwOzrPuyLNntdl9JYaQIvuxkiaRFugjStZM5A3mes9/vJxIA+CrSUKRnwPTdI4XbZYqRXN9fcvxiQN4PLfNlRp7HuODRypCkBXGSE1RGpyLSKCVNY8pTBa5F65igIlwwJElO195D8PShw7dP5IkjUp6+b4iiJWlagHbUbYn1kMQ5ddNxOtWkcYE7JxzgHU17GkdYG8ViMWe5XuPcQNtanNX0bqAbHMt1xs2rW3Sa8/i841SWWBuIooQ8y/FpMo5xHwJaxwwW2t7xWFasSVmsQAVLcB6jPFkUyI0iM5pjVWItNE1L39ek6xlZdq7EvMKHiChJGUcHKrpuTGyI5hna6FHrGyzt0GMDJLMZ2ve44MYMajx1deS0fybKFD5VxImhO7SgYvIkwbYdPkR0zahnNzpg+4b60BP6lFR76sf7cXMymof7T3SDhc7RtY6+dwRnR6Mi8kWlUMqz2SzwoaNte5ryRJvnbJdrKjR904xGwNTgfUJvHd47jIlIopTVfEXXdjRlTXksmd3Nubu7hTAaWduuI4kgicAGRT7LOJY72tMTq+2cJE3R6YY48pTPPTPVMtQHqmPD9XrF229uubre8PljhqnH3FFtFFGsGWyNiUYNnNEJXdOiydHndBwVpeh0jh1Gw2azf0JFDatoTeoCXd9yrJ7RriVONOvFksganutn1rOM9XKG97BYLlBmlE5UbY3rxy+TLD1i4kdaq7h68Q0vvv01vm/omxK3mKPjBG3AGEWWJgx+9B8kkSGZFSilsW6Yhu8s1hvW1zf8+Mf/Nl7PquXwXKFw1PUJ50aWY3CeoR+jRB+B+WLJfLkkjiKqsqHuOvqmJziwvaere9IsJUpilIIiP8eDqUAUGeJo1LXr4CjSiNV8Rt/W7J/3HMqKxnq8iSkWG+bbWzoihoFxWA+ePI6xfsDRE6meU7uj3T9QnZ5JIs16vSHSEVGcEecLonxGT0ozdOh8zuo2o60q3DCg3EDQe0wUkRdjrn0UJ2iT4PyYJB5F0WT8G4aBPMu4vrqh60eJgu8G7OCITHJOKXKAJYQviR/BM3oavCeNY9LEjFnv3qI1oDSDh6ZtsW74ysn/f/vR9/2UqyytWklcgC9aTJkoeKmZFYZO2F5gYjWFrb1kTCVjPEmSSaogoEXYRNEjS/yetO3FDCkAd7PZ8ObNG7Is4+HhgePxOLFoRVFMWefSghdt7uFwmFg7YAKbl1IaAbwS97bZbMjzfAKBwuDJ7wqYWS6XUwtdjGLCrgqjKeBS9OOz2WwqfiSOME3TydwmIF0AkRQQxhgeHh4mEP7x48fp/aQ4uTSwXYLZq6uriUE+nU6TlOPShCuyDpFXyPVZrVYTgDocDsznc16/fg0wFRkChL33zOfjwLvD4cDNzc20tuI45uHhYWK5pSj81a9+NU38FFmKsPyXkiE5VwGVl1IZuXe73W6SJVzKOaQoWK/XEzheLpfTWluv11OhL/dRChU5D9Hxy4AbOTcBi//SgCgFw6VBdLvdcnNzw3/7b/9tym4XD4DIrgT4isFTqXHokERGHo/HKatcfl6eIQGs4gG5XOeyFqW4lrSf4/E4gW6J3ZTulXQb0jSd1pd4M6SIi6JoMtVePovSicrznBcvXkzPpqT/iITtkhmX4lR8IVLk5nnO7e3tFIMp0iL5npJDfCDy/+X5EPmUrG25NgLsL+VFv+T4xYC8WOZ88+o1sTG8+/iRbgjEzUBhAyR61D5jiY0n0pbVqmCxXKLj8UMZExGrQHt6Qg01xrfM85gkVsRxcgbjMVGaYRJP2/YkUUYcRQz9CFD7rhtlArZjaGpiBgKevJizXMw5PNyPshnn6doBtGG+WrK+uSKYhLq3PD0eqesj2y3TorTOjfFlKLyHwXrqtmelxpjGumrHqLfYkEaKIoso0phSjRMt9/tnvO/ZbjLSNKNpO+rumWy2JC8Kum7A9pbDsSJ4xXw2J8tzlI8Y+pbe9mNE5GLB0+Gerq1RUYJBMzQNfdOQLdYkUURvRyZkNN1lDL1HnY1pprfYoePw9MhRWW5vttxtlzTHHY+Pj2M0krVobXh62lMUCxbLJWmaE8VmBGXzghAsQ1OjI0WeZ0SR4VTWfP7wnlcvX7Gaz3h6KNk9PUAEg2soFgUa8NbiOkdwsF6u0Wh2zzvSJGOzveLFizs88OcffiRNDEmsMElGFGmePj9SHR/I8hX58iUujnDBE2ULfNcSRxnODlSHBw4zTwgd2+0S1w8c9iVoRRwbnB+TFVazHPw4u6rrOuJIk2mNiWOsUzivsRb2zzt0lLK4i9AqxYWO1fqaea5QfUtb14S+ZbCwWm1ZbTdYF/BAmqVY21MstxRpjPKOT+/f8+7H9zSDY7G8YpHnpLP8LOV5JKjxWvt+QNlAUJzNsA7nW4I7DxVQECURC7Ng+Ve/48NPf2Tft2y2G4bG0Dc1eIvte376849YP/od7GCJ44Tlas1qvSVO07HwUhqDYr1Y4rwiMoYYRREZkjwlikY2IUnjM6OisH1LX/cMdc2uPlKdjhwPJ/anipDkzFdXFIsFzltQBt931GVJamJy5cco0bakb0505Y6ueiYzgZurDfl8QXnqqLuBbBFRD4HD7kjbjV/YWZ6SzSOa04m2tbSdx9uaoqnZGk2WZugoQ+tzfnNsvuTlnr8Uh2EYpScBjDYEE86t3FGmIvIv2QCNiWjbjiLNKIqUODZ476iqmoCibTu6fuBwKIkixaz4YvT5v/1YLpd89913xHHMTz/9NMkgRCsqm5KYJzebDev1emqtyz0QacJlgoNEyl2C/MvJgcI+CpN9KScQILtarSZ9s2h8tdas12vu7u4moHp/f09VVV+ZuC5jES8lMZebvJxnkiRTBNzxeJwkMM65Se8qwGc+n09AWuLopO0vco9LNnG5XPL8/Dyxe8KAi8ZYroWAS0kWASa5Sd/3E1h78eIFt7e3HA6HSasrUouHhwfm8znr9Xoy0mmtp6mewiTKYJvT6cRPP/3Et99+y3K55PPnzzw+Pk5FzHK5nNIrpHuw2WxQSk3DoW5ubnj9+jUhBP7whz9M11O6JB8/fmS/3zObzVgul5PZTqQKku6y2+2m0fXX19f0fT/JMGQtCngWjbmsHTHpiYxC5DVRFPHy5csJYF9dXX014l2+ezabzTTECL50b4TlDSHw448/8qc//Ymu61iv18zn86+mpcpak7UqhxQIl8WspPP8zd/8DX/+85/puo7r62vqup6KsK7r+MMf/jBJOSQJab1ec3V1NXWUBBNJ90akG7KmhS2WRBrJdBcNvxRE+/1+YpfX6/X0esCUWCRFqySiSNdJBgvd3d2xWCymZ0jiQx8fH6diTTokUkzIsyLFl3xXSLKKdLukwLksVC6LTflOupSbCLMu7Low9nJNxLsha0GKODFk/5LjFwPyV69ecXt7zX63ox96lErBgwoaozRh6GjKR1y3o6n3LBcFy/WSwWr6tqOpKoKz9G3N0FQYBXEE1nmubm/J5ks6B15pgkmI4hivYpI0ww4D2hjSLCb4gaqqMdqTJoaubrFDJ0lyGDO2sYNSzBZL5ss1Xsc4FZHP1yyXJW3bkaUZsVGU1Qk7WNaz2ShzIBACpGmOVhH9YInjFOcdWZ5RV4bBdfR9zTixdMC5HhMxxadBwDpL3dTUrR0lONax35dslhlZNiN4dTbhjBMRF3mOD5rPj3ts31OXFSFohrYh0ZoiG5n2tu7wbpSZgAGt8IPHDRZvLWk8x7meoR81zVpBVZ54f55utr26IjDGFh2OR9abDXGcooyhamuW+ZJsPiMER1fXWO8wRrFazqnrlqFrMLOU2Swn1I7e9Rx2O6wbsJmlyAsUDo1BA4v5kuPxyIcPH8fYwDzjarvFDpa6bei7isVyxWaxojlU3D8+Ux0fab0imjswCVEyR4dAUlicfaarDnz4eU/XlkRKsVrmo4zGO5QK502pG+9zFLNarRlcB2qcyCituFMFQw3FYk4+W5IkBYM1RPGS1TYmix30Jbu6oe8s+fKKfLUkW24IaDrrUFoxW4xMapoYfNfinOFUduQBttsrAmHUoesYxdg9sc6hvAPnSNKUxWJFlKRY61FmBMuR0egIhrbjdCp58eKad//9v7FarVikK1yRUTcpPgSWyzXWWXwYTaNV03A6jF+MAY33ARMlGDPGRMZJhtIxxmj6+Yz5YjGy8n78skWNkyyDHQjOgeupyxNNXVLXLR7NYr6iSBK0t/SnHSaZodqOUB9pQ8DVT/RdQ9dWBD+QJJrVekGWxBSzBTpKyecpWbFARzFtP+bHK2VQ3tM0Lfq8zpu2J8/nJEXC6VSiUZhR8UWSZbRdSxLiaYOWjUfyeWWjkUMYv0tjnWxSwXs4AzmjI6xtqaqGoXccjzU+eLZXG1brBV3X/6tc9P8nH9999x0vX77k6elpApCXUW59308Z4WVZTqy1tLYFiMvGLmDXWsuLFy8mE59oOS832r7vJzZRUkUESMggGfhi5BOJgxgABdwIYygbrpj++r6fWGtgisOUzyVA8DK3XKIWRQcsrXE5Lj+rgILn5+eJRRcmEJiAryRdSMyhyFwkBs97P2nBLzXGohUWYHipHRaJw5///Gecc9zc3EzM+36/nxJrjDFUVcVms5nArrxXFEWs1+uJZZzP51PyzTAMU0Eihcrls7hcLjkcDvz000/T/RQQLSBL7tPhcJjSXay1kyZd1oIUN2VZ8sMPP0xZ25KlfSmvEHOvmE0vY+7SNGWz2VCWJWVZTsZaYUOTJOHm5mYqgORzy/qRrol0IJbL5VfJOyIN8t5P11sKSDEDCkgUeYZIvi4NiVI8NE3D4XDg1atX/P73v59AtiS0CFsvrym5+uIrgC8zA+Q1JQ5Rzl/WzaXPQJ5PkRCdTqdJZgOj/EeeyePx+NV1l7V/qT9PkmQqWkUeJhIriUCUQlqKKvmOETNuURRT8o1IePI8n4oTSZCRzyysuOwRl0X4ZZ74V3vEhTFdCACReknn7ObmZsrMv4yy/J8dvxiQLxarUXcNZEkKJmEYevp+IEs8ho7ytOO0+0C1f0DfvUBXOV2v8dbxvHti6Fpc1+LsQBJrjEnIihnFbEl3ngoYlKW3mjSfEZxm8AMmGmMRb663NPWRw/MnnO+xzjCcM6KrqjyPw44IBKz3JEphg6LpPcGAD5okLYhMPLKIOsI7y9B1pFcL4jQmSuLztMkZRmc468nziNiNbe7IKBSOwAA48AMqOLJkZPPtWTtrUPTO0jQdSqf0g6PvPXGSEyU51o+JMEoptBpHkw+txVmPdS2hdQyDw/Udynts34+/0/aEoKkbi1IWpQ39cHZ2e4sKAaMAoxjaluNhR/CBpqxIs5Q0STBxTJalaBORZjMCisFbiuUSncR0TYXTis4NY9Z5CORZPk6GDI5IQ54mBHJebu/YH/cMztHUJX3XEpuE5XzJcr7CBc3ueU/fVRyKAyttSDPNy1d3vP/wgd3znuGx5+VdzO1mRQieXTnQdftxFH28wAaDMQt03IE64YaG1nX4oSEr5kSzhOcIurrDBUeeJ7RNi3ejtOrm5ppTeaALY3cgjhPmizm9GyiVIhhAG1AxPiQ4Z6nqis70FHEEcYaZrca4uzin6g3KJAStObUNM51AkoKP8EGxun7NXxcr9oc9g/UQAlFsCDomShIGb6C3RIlBK02a5aT5DB8CSaoxOtBWFYf9iUR5iiygsdxcrcmzGIUl0hH74zjhzvrAzc0dcRazXKwwRlM3DXU9smFVPW5qfuixXU/tT2OOPhoINMsFQ7vicDzQdS3J2egUCBhtmOU52/USggPvyZOE2XJNNltgg4ehoxsGhuGJrusJjJ/5ua7GYlUrsnxkcZxNUGHsiiX5nEwl42RfB/0wdqaiOMF6j7cDx+cDQ9tSpClJHBMCZGnGYb/njXMsF3OCMvR9Rwh+YlKl7SgbnHOO+rz5XsYgjgyNxfvwJWWj7/FhLAiyPMMdK7puzIjvOkfd1uz2R9I8Ic/TX/xl+3/6IYBV9LcCVi+H1BwOBx4fH9ntdrx69Woa2GLtOMlTNKSXkWKz2YzFYjEZPQVgXiaHCJN3d3c3tesFCAsYF7OjbKgCRmVTltb2pQFV2s1d13018ETMc6LLFW2v/B3w1aYvIE8AnGQZC7sqLHLf918NNbk0VookSz6TGAklXUXkECL7kaJG7oMA8EsmsG1bnp+fJwAu2nTRKAuYgRGgSLdDjHYC0EIIExiGUWsrRcX19TX7/X4CysJuLpfLiaEW4+Xz8zPX19dkWca3337LTz/9NHV3X79+zfX19WT8bNt2Ys+Br7TAwiyL7nqxWEzsqHRepJiQ5BRJ2hGG9NJ0ePmPfJ+I1l+ul5gKpRMhHgQpCi7lHZJss9vt/oeUnksZnKxrYWNlfSqlpjx5YYIBbm9vv9L339/fT8/Cy5cvJ2AvXR0ZciRJLsJWy+vKIUWqTNaVaynvI5ntlzIsya0XIHs4HKbXh7GofXh4mK65RJLK9ZBYRTFHy/2UIko6oNIxkudTiuXn52ckyQm+ZL2LgfrS3CkdG+l0zefz6frLMyvnIN0E6RLleT7l1F92DJ6fn6f3uZS//M+OXwzIUaNs4VSWzOcFQSWcWoe3Fo1He4tvD3TljqErsf14k4PKSEw0JhdkCXQGr8ZJh1EUc3f3ChPnOB3hB0tnFSHE6KgY5QqxJthAdXxG+Y6uPo160TjGWkc/WNIY2rZHe48LAZQizVKUiXHK4FWM8wpURJbmKKVp6gpDgnMdXVfT9y3KGJSJ8EHT9xbnRiPpyZZoDVoBalxsWZ5SlhVKeYwOI0OaJvi2pjlniDsMY+JcRBQ0UTwy/nGUoHCARpkI6wKu6djvjnRth9IGZQxu6InOA1tUELbDoIyi94qECKMifBjGLG7vacqKvIiYZRlJZOjqZmRn3QDhnBQQp2w2N2yv71htb0Y2db8H32EGS90NeDtORU2TlHmakCUxbVvT1ie6NifNMp72T6ijJi/mbLKM/ryhEM4pIkYT64QszwkEnnc7kjxHGc16tmS7XUOAzx8+cnh6JM9nXG9XJDNL4zT7+siYNlKQ5huSfAOLI0P5jt6eKBJFnigUEfMioelqgmc00LQVg/VoFRFFGXHcYh14As5Z+m7cyHQ8TnPdHw9cvdEkSUbrOmbFHO8CLtQM3pMmObPVBmUi2sHhnEZpw6lVNMPA3MbMCo3vAos0Jp9vmTHmYR9OR+6WS9R5iFIWz0my2Sg1MjFoTTsM2L7HGIh1RJpn9F1DcC3DYDGRIUlivv/+V3SHI2Ho6LsepTRJElNVNb5R7I4nrq624xrSBh+gt46smLPdbHBDoKxKrLOEMMaTJpEiSwzJdkVTJ2PySABrB5wLODsw9B1FmhDlBdpEJHlGWR4p6w6lx1zzkXUbJykmWUbrK7I0YbaYjQO7VOBUl6NxOZ+RZhmDVTw+7Fhvb0iznGawWHceY6/HrlOi5/zb/+evOByeeXj+TKI1XVNz3D0xX15jkgzvx4JX0ibEYCQAJs9zujN4uWROJL4wBD21I61SIyj3jsVqOabfnCVOXefo2oGuHQheoZT+CyA/H5IzfTgcJp2rxB3CFzZKQJkAbGHaBMRdguM4jnn9+vVUSElyiPzOZeElOfEiNxE2URhsiR0UPakw4PK6l7p3pdTUghYGX9hOMXYJ0ynTAoVll9cuimIqAgRoiVxFGEEYgYnos0Unf8nCCaNfVRVPT08TIy7FgIDry4QKYVjhS9tdwIVkkl9mrcu1kc8lA37u7u64vr6mqqopoUUYTpnYKHIGSQ4RRjnPcx4fH6epnjKoRlh/6SBIZwPGpJ6iKCZpzPX1Nd573r17x/+Pvf/qlSTLsvvB3xGmzcWVoTKzqgXJZgPkEBhgHvj9P8OAIJolUkXEFa5NHzEPdrelRxH4dw7Ap2Y7EKisiHvd3cyO2Vl77bXWfn5+Xkx9Av4l1hDmrHCJahT98rVkQYb+iElUAKgY9ESmcC1hkYhA7z2Hw4E//OEPC0te1/U37LBMtZTrIu8tHRCJy5PiTooRuWdkGJUAfDknIvGSLgqwnHO5bqLLlkx3OX6RoeR5vrDX+/1+MQxfxzVKEoskxch6EFY4y7JFCiNFkBQ+4quQwkGuqSTmyOfI+hEZlADk9Xq9XHfp/Ihm3DnHly9fFjOxmI/lGS+FzH/7b/+N/X7P09MTEj+62+2WIlKK9+s9Qn7OOffN+pQ94jqOVVJU5LkjoFxmJsizRYria2P17+2i/m5A3nYjp90TQ3fm9u6O0QNxZrGOu92c191ecO0Jg5+ZYusoq4Lo/ZyV3E24Zs8UNW3XM0wOZVIu/YiLHkdC20+s6jUxKGKckwzarqVrzwzNiMJjraFaVXPb3zhsUqBMSpgm+mEkAEVVUazW5EWFtilg56i4fqDIM8rMYjUMXUf19rCo6hWXtgNtgUAI82j6U9tS1+WiJ5urKijLjHEY6VpLllpinJMYnPdMIeBRKJWSvOWsG2uxiZ2lMSP0oyPRb+O6B8/kwJoEk6QYm+LGkcFNGAKJneMXOxfI0oQRjTcZAYVXCSGOZEnG7vWZcUxYrUpC8HR9ByFS1/WcpIHBBUVUltXmnrRYMZFy/OUrkx9Yb9aEaAgY0qKmyhJMnGPq+q6lay70Xcvf/+M/AIq27UiKkiwv0dYxDhNpkrJar3EhkCWWzc0t/dDz8vPPbPqBoq45nM4kWcnNnSHRlv3zbh5kYy1397e0/cRu9ws6iWxubuYJnElCWla4PsUPAWsziAEVHVWecE4sOs4TS8uipm8HlLIEP+ehx9HSBohhBmHW2rmweOtkjEM/Gy/HiahG3NQRpjMqesp6TZKXjFPEK0ualUStUEPAR5hCQj8pwmQI08AlznGVPs7JHuPQY7GY4FExUGTFrOO2KcpagplQZiLGiagCSZpRFDnNuZvZ+tOZ3CSsN1teLhcSnfP47gOfv3xhcg6bpASrqTYb8vUGpWDyZ6wybJICayxRW7qxIS1KHrZrEmPo+o4sSbi/vyUxbx0exdLtadqW6GfZknMT+9cdXd9jk5R+HInKkKU50Qcyowm+Z+oiN5uCsrjBx0iaZ5R1TXPpsMZQFCXv373j0o5Ym1KWBf3Qg5619d5NlOUG3EiXWEbv+PL0RJYZqlVN1BDjxOnwSlb+Sl5vSYtqiQO31i6DPK6NYavVatEPXrcnZcMV4DMDIsv5fGJygaJcE4LmfG44HBtCdLTdPDgswr8D8rdX27ZLjrVs7LIJy1AQMS3Ct+180d6KnAV+02LKpikAtuu6BczAbyO5RX8q11SGswhzKkBJGDpJXpHMaWHlJM9cGOK2bZfINhnlfp2eIAyjfN51LKNEwklShrBqwlYDi/lSCpDr4SRiZpV4PgFH8ucapAnbJwWIMOHykuN7fn5eZCBiPosxLiwy/KaZl8mmAH/961+5nkwKsz5aouIEfImB8j//5/+MxBXKz8lxCHgVkCPJPM/Pz7RtS13XHA4H8jzn8fERay1fv34FWBJouq7j+fmZEMIC3GVokOin5TwIwBP2WSRAwvR77795PggIuzZVynq8zny/7lDUdb0AxusukTDJUrwJOD+dTgvQEymGdGSu5Vdy/a+LQVkz4lM4n8/LVNLb29tvBj9JBKOsSdF0S4dD1r8w+rL+b29vl65MmqbLdbjOL5eiTK7jNE3LNRR5inxXecaK4frm5oaPHz8ujPp6vV7042VZ8unTJy6Xy3KtpJMk31uKMsl5//XXXxdjqdybr6+vS3a5rGP4bXiTgGeRpkgnQIop2SMk4lCMw9IJEdb/OrVIOk7XZMT/cclK0/QcTg2rKkMmUCZvkWHBRRJt8MGRKYXXljSxpEVJWa2I00g77BdDSV7kRGsZPbzsTySDx6QVeTWnJzjnmcYL0XksnqE/M/ZngutIU4uxKTrJZ5WBDShboZOCvuvop4nRjdR2PQNf7zFAkeUkWuPShLrMyRLF2DUE51jVNavVFu8V50tP2w3kxRqI80MhsSSJeVsoFUVZ4tzEal3TdbOWPEZH1zYoPVev0xDmUenaME6Bpm1xYZ6Y2I/DGxDPUIBzCucUWiekeUFeVBibMg4jp+Do+pa2azHWcOk6yDQqK5lUTmINucnI0hJDmFND2jNN0+FDIPeWVV0yDhOjC/ig6M4tT/sjj9+PXEZPNwaSrOb42lLXCkUyM6g6pXeB7rgns5GHd4+Eactut6Ppe4q6JqBBJ+TlmtuyZHwzlroIfdNwV6x49/Ejr7tX7ifPFGF3vFCUJfermqhSPn63RUXN8+5AiIpCGRLjSeJEGE4wHvFxwmY5abVCuTvG/kDXT1RZpK4qEtugtcJ1I23TUOYl7Rg5HE4k1rDZVmht0ISlBR1jSpKmlEVFliqKTBPcmbFv8L5DxZbd86+UWYKu5wdC0zkwGTqNBAer9e1bxN8su3BO0XY9ZaZmaUel6NuWy/nIQ1mTGD1LqrxndApPQKs4Gz0tRB/xfkQplu7JNMxDgnQE5xX96Mk0JHk+A0JrwWhsXmLyFSGt0EoRs3liqclzjEnpho5mCqzzYs5hDxGTZqR5wRQUw9ijI5RFjtKGGB3V21ofxp7z5cyvXz6/aSU3c1JQXpBa0KmlLkvGKaXpW6IfqOsth9MJPxmqvODh9pG267g0zdyhUgnn5kLXtpT1ms12y5ow58WnirFzJInGjYFffvmRv/v7H3h8vKcZGqJVHA/PJHnFXZJTVDVJli8Pf8lnFmam73siv7GFM2th30Dhbwyj1nNXS2mNSSyH45FxfGPHe0/bjrgw0o+OcfLEEOHfJ3UCLJnDMrwFfotFu46GE2AhyQnCNEoBJekH13nVbdsug2aup/Jdp5PIRirX/9qEJf9fQJVozoXZApbPzLKMqqoWba5zbtHQeu8X4CmmSxkiJMZDGY4yTRPb7XaRLIjmWvTvMpxIwPd1GoboTgUgS6KGSEiupRHyPGvbdmG8RVoDLMYyYf5F8yxASkyk4reQ+MDn52f++Mc/LqAlyzKOx+MyaVHkHNM0LQY+GVv+8vKy5KXDDEhkCqScf9EVl2XJ999/z/PzMx8+fCCEeYCNFEBKKf74xz8C80AZYYwlqUPeT9jWuq4XKZCAJzGAStfsfD4v12i325EkCbe3t8uak66IsKmS2nGtyxYG+cuXL4tkRHTR1znV2+12kSnJdZQJtlmWsVqtlqQZYZdhZp/l3gG+0WzLuRf9srC18m9S7EomtgB6OQbpKl0nE13fH2IGFgJSigMppkWuJZIgOV+Swd513dItkEJM7t/rzPv1es1+v2eapiXjX6Q4Uviez+el4BVZjEi7BEyP48iPP/7If/yP/5EPHz4spk6ZJ3Cd6S9dMPl9uTflfrzeI6SAvE4LWuJx3+7P/X6/rGfRw8s1kb//va/fDcgvlwGtk7fFEhnHniTJ3jStc/yeCYFMW859yzhMVNsMm6SM44CbRrQK5HlGXlb0zZsrOSrytKAoV3NU3eRpmw7CzLj144X2csAoP6co9B1ojy0mlC2IpsJmK4JO6caJfpwnf6Z5Pucwx4B3E+iB0Y0kieLubsvYN1yOPQooqpKirFAmw5gc9AhEhmFmDrarmhkh6VmP3Xas6nkwwGy+g5lRn8jLEkxO0I4haAIZw+Rmw1x0NO2Fti1ZV7N0hRDfMo493iuUsaRFQVFUbwsBPJ7JTyirMVmKM3Y2CCY5Ok1JAJIRHUaqekPTtQwukCkLxs4Z5+NE1BYfNe0wUm1uqbZ3YAt6N2HSCu80Ux8IKtIPnq4dub9Zc/+u4G6bU2UGHSPZL79yaTtsmlOu1qw3W7KiAmMo6jVj34G1pCal7XtsXhC1oV5ved69UgRDvS7op3kJRpOwfXhH6xWXYeJ4bFDBcbsq2b0e2H35F3pVU64fqPMMlVREndN2LRfTU2UrCKCDQkUY+5F1vaGJHW3b4dycU+v8BDpBvelGvfcoFHk2Rw0a7XDjgTAeMdqR2kCmerZ5QWrmOETvwdoMDQyTI7cZ9i3T3mhQaYafMrTRoBxZbhmGhvPpxGp9S1JsUUbPSZjGgNZE5sFWEUBpQL8NpEnQyqB0SpbXGD8RdUJaVBRWQ/DkVT0PZlKgTYIt13ibE5Qi36TovEK/mTpHNFmlUVlGNLA7HgjBUxUDVZmTaEORZtBPtM2BaexJEs3puOPcXEgTy/sP70jTnNVqQ5LM6UreOZybN4jCzoVy31wYp4m8rFnVG1KTgYsEFzgfjzi3ox8D+0NLVqypV2uKPMWkCSjN0/NnTqcDXX9hmGbQHKJnGFqUCjSXI2mh5rjIZGbdkzcWRh6oAq6u26XyoJw3KIv3LWDetIdv7X7C3FkC2r7nyy87jsczk4tMLuL9nFWulCa+tTb//cWSYywgUtr9wqCKfvdaJnF3d7dsqGKskra3GECF+ZREA9nQ5b2GYViYcQG6AuolyeE6PlEYWjFgSmqKAKYkSRYGVsxhkoZybaYDFtAmrJxIXyTBQxJLBGSJflneQxg0AV9iSBXZh+iOhdkXQCASEAHuknUua16kONdZ3vJZ6/WapmmW8y3HLWymAAtJCxEQKFIN0bUL839/f8/Hjx+5vb1dZBZ//etfuVwuC+C8vb39RooizL90P4RNltxwkQKIvEV03gK09/s9IYRl7P0vv/wC/Da0RwCnMLUCKIUtlw4BsJwLkTfJOZE9Aljys0VvLD8nIF/WxvXwHLmu8jPyR5jja324xFBKR0IIAvkZAeUCFOWPFCcCGOXfr9e2gGBgYZ9lfWw2m+UZKQWGAFdj5mmY3nuqqloKPpk6K7KYNE3Z7Xacz2fSNOW7775bOiCyfuXcXnsvxCwtCUhijBZ5kMhCRIstKTVybb98+cLhcFgiRYV9F3OseALknAtRcL1HyHm7vofkc6WgF0O2dFvk3hcA37YtP//881JYiIxH1pH6/2OP+N2AvB8cd9sbVGyZvEMbxWqzJssziJbTpeFyOHA67jl1AzHZsL5nln0MA945ijwnS+8IKmHXDNisYL29pd7ek+YVbTsyjo6h70mNIipoLwfay4GbdUlZ5VwuisFpgkowpsRmOSQFU9AcLh2XtufxpiZJE7IsxRNpmgvt64FpbPlws6EoM9xwRuFJrSZN0hn82IwkK9G6YRwHtE0oi5IszwGFYgZL3TDy/v17nBtI7IU8m42A4U3nXeQFyig6p5lCgm87ijLHTfPNEpjPC+hZGx4UISp0kmImj1Ia3m44bTQkCVFrsAYd53HqUVmUSokmR+l5+IuPE0W9whxeGceObpjmJAqlUSZlc3OHzQpc7/j48QfqzQNBZwzRYNKSqt4wjgEfPTYpSPNAkpcUiSLJUoapxRBYbzY4NP3k6YaJu6zAo2a5Sl4yjPMQl/XmlnMz0L/sZ0280vTOk0aYgmI8t2w3N0zEeWpnUTJ0R4au4X5d8+ndA9r1/OXXJ9pw4dJ3TDeP3NYl9eaBLkwMw0jfzXKdNEmodUU7Dm9Rd29mvqZF6UDne1ySU3Y91dsDbhodwzACnsP+K857hvGMViPnsSUh8rD5NANFIsemYZgGjNUYkzD2DVbPG67V87XSZY1Rjn7q8M2IjiMRz2H/Sl7fkugSFSPaCqAzZIklhIQYHMFpdHRYpVitbzhHz9RFkiTl5v4dVmkOT5+5HM+4GFht1mzu7mmjJdveMUVD2zSgNDqbo/y0D+Q6QdkeN/VMwzCz7iZlcIHx2MyGXaVQMXI6HLBGs96UWKNn3WZdkWcZPkSM1litaJszXd+gtWV6e5ja1KK0Jk0S1vWKruv59ef/L5MDYxXny5FuHPAB6vqOIk+Yho7mdKSfRjY3NwQ3UuSW1KzRas62H8aOw+lCXuZzF4ERxYT3E+M0Et/AxDWLIe3fvCgWvam0qNM0WRjcBXB4txhzkjSlyEvQB/phoBtGpmlOYDE2nbsIIS6b5f/tr2EYuL29/QYcCsAQdni/3/P6+rpMKXz37t0CAKV9LgDyfD6TZRm3t7dLaoSY0AQcyM+dTqcl/UMApmyEwpBJS/lyuXB/f7/EAsLM7ouUQ0ajC9gWUCuMumzgsmmL1hVYDMNd1/Hp06cFkAmr/7f6dWHQZCy8gGT4dpiIAEkBBtcb/fXfXWdDC2iTgkCY39VqtRhohSEUtvX6PP/www9LprjIcNbr9aLDleMSnbawqiJ1kTUhjCvMBYxMcRXZwvl85uvXrwvQETmPdCNEIiOdASnUJD8+hMBf//rXRRJ1f3+/JPgI+BaG/2+9JQIWhY2Vzsl1Hr50LIDFIClFiRQMt7e3i3RCZCkCluUcS7dGihLpVshzKcbIy8sLdV1/002Sa3htCr0ePHM9YEqKSa01X79+ZbfbIfGSMlBHIhlFGiZrW56b19IuAaPTNLHf77+RQe12u0UCY63l/fv3Syfm+r1EwiT35bX+W9ZU27b8+OOPy3NDBkuJKVOKluPxuDxnpmla7qPrQke6K9JdkOe9XKtrg6jsEUIcSEKK/I50mqRgu5YUialYUoNEdvS3xZoUgr/n9bsB+TA61tt7YmxAOcp1yt3De9p2Yuo9WZ4wjAPnS0tMMmxakGZzS6Y3lvg23j5NMgKaxFiSoqRa32CSimgqPBplICsUhQm0xxeG/kKInnqzZnOzxpsDaoSsrAikKOewNkfrBBU13oe3XGeDSuYx8UPX8fq6Z+wuZHGkzi1Df8G5nik4lAt0U6QfA2030LbdrFcuSkxa4N7YMLRFJwmj6+lGT5kXpFlFWaw4n0+ooPBjYDQjSueAmVl1NNqkJHGeIKmiZugHMmtIbEaIA4wTIfoZpAFDNzB0AwSFtQlplqITy9QPBBRap7ig5gQSpZhQeB8os5ldD96hA1g9PxSarqHwjjA5lM64f/yesrqj8x5lMxKtKKqSw9MTgcD9/S1VnWN0ZHfcMQ2W3CqK1BCjYrPZwuXC637Pzd0tNrNkaTqbQU8Hhr7He4VzlqAj1XZDtJG8qggEFJG8WBHJODVzLF61ueNDvuaXn36k6XvW5Yrt7ZbN+UIWLOdhYGhPUOdUVQ3jium8pxtHbJqSlzmZAd1qUmvYrCqInsn1jGOcyefJ44e5gg2EuWuTmrfhMx2pVthcY7RliIr79YZVaTlejigSUj0yBUWYjiiXoEwGOgWVEqIiBk+SzPKS4MANPWVuIExcDq+Mt+9QGMY0w5YaH99I8aiZxSwAmvn+VSRpyWp7x5hmdOcjWX2L1Sk///Un9qeGv/uH/4BWAWUN0zjLMFywjA68C2ilWNU5eWYxSQZoQpjBqE3m5KDoHK7vCFM/T5I1mixLKLOMm80NRZ7i4xwxdTycUVphjSLGwDjM3aw8K5mZfUXTtozTBNqy2+04nXv2+zNFUVOvV6zXa3I34Xzk06dP2CSnHUasnrgcnpi6I+fLgXlPUtSrLcZGsrfuQXveM7nApBOsCqRW450nxLmzJbIFGfccQlgAU2ITtNJ450jfZAzEWUIUiIyTgwC7U09RaG63NT/84Y88PZ047J7QapYIZUk6pzS5QAj/PhgIWBhvAZKS7y3RZqLPFp2ogLg8zxdAIJFrwMKmiSlLQIqwfNbahSGLMS6MroDlqqqAb1lK+E0fLUBVNtOnp6fF9FeW5SKBEXZPTHKSTiFgXL6vAGTR1orJLc9zqqpaNntJirg2lMpxXf/vtbZ1kV1dGUBlSuc1OE6SZPm7a9YTWMCnpExcS4muzW3CNn748GFJtxG2sK5rvnz5AsC7d++WQTG73W4BKJJ0cXNzw/F45PX1lfv7++WedM5xOByWATTXevXreEdgATsi55Ex9H/5y1+WHPe7u7slxUVMw+v1epmGKeBOWFK5RtZaNpvNUkBKh0DOwTWgE1AmQFOAYIyR29vbpaNzndwjMhUBi9J9EK+AvLeskxDmaaEPDw+LblmO/1qiI2tN7jMpWrMs43w+Lykqf/rTnzgej/zTP/3TwqaLYRp+k0GJ3l1kXcBSDIsU61qC0TTNUpgWRcHd3d03khbJe5d1Kp0deQYDCzuutV6Mv6+vrwtbLgPGnHP84Q9/+EZestvtFlmLFKZyzMBilBVwL+D4Wv4DLF2E/X6/RHLK/SfFg5h9pcMKLF2Q/X5PWZbc39/zD//wD3z+/HnJ3b/2rUgR8ntevxuQ//0//gfyumB0BmMCxmhUklDWCV3smTqNsQljgLKsqVbrGRyHGXx5H+jcRLWtOBxPjP1Alq+xaQlJzRQSbJ4x+iN1XmDchePY0ZyPRBWob25J6orp0ND5gVWa4R1kSfJmRnOk2mDU/IDR1tL0PcGCsQmbdUWLo28bUhKMnqf0DdOIUynndqSoR7p+xGqNDwGbZmSrDV3Q9JObdetJhncT57YnRIVNK27u39MNI2lazJNJp8AUJ45NyxANwxSw9o399JFxGEEH6nxNkRY477GJ4nQ8YtMcbSxWpbgxoqLB+4BO5mJAaY+fNFYnBCztOOHDDMSCsejUUq1WhKnHdT3JpsImmqLMsKlhcI7RKe7uP5Hmaw6HHXlVE6dmBl3DmwO/zlmtao6HPagEFzTDGMAFpmHExYm2a4h+5M//8j/5+N1HHh4fcNPI0F3QaA7PrxTlPaasccFSbipW0dEe9wzdhbq+5dyMTN4wZw968nwO+m/PB5pxoqy3PNwPHI4NqVVMeC7HZ6pMkRclfmjYX87kiaXeVjR9S1FmZMawymsib1NIo8cFMDol1ckcD2kh+oDFMU0NfRN5//EjPlq8HyisoiwyTsdnLpeOaDIKnZAlmsvxTMRg3mIsfVa8scIWFwPdaY8fzpg4kpIxTQNT2vPl5z9x9/4TjsAmTzFWoYKGt/Ht6u15Mb1pk22Wgo1YY2malnx1y+bh/Tw8wlju3z0QXUfftUxvD1JvSlbVisnNrEuSljg3Ys1smuy7js1mNWd5K1Da4IlYo9lUFWWRzMVBCIxdz9SPTG5kfzxgjObm9ubtIeUpyxytFH0/kZgMlCajJHkDKdokZOWam9tHsrxGa3B+YJpGDucz09QRwoQOkf7yAtOZyZ1JwkTw84Awgme12VBkySzx8iNpkmHUnGOUJJY8y4hKz1nsISwtZEm1cM69JeODVpoYPN7NkadKay6XBpNmeGV5eTnz9enIZh0pqw1ZmlJVBUVh2a5LjJ0BjHfzUC6j/l1DDvDP//zPS3tcwK5oN6/ZXWG9xAQpwEJYrLu7uwXgyaYp7KAY7GQy4tevXzmdTsDMUtZ1zfPz82Jik86HsHzX+mNhQuW7brfbBfxdt5mF8WqaZklPkPZ4mqasVqtv0lz+1uyWZdkigRF5gPz86XRawJucm+sYRjGXCosnenU5DtF8X+fqC9Mr31+YRgEnwkqK/OP29naJ7RPd8zRNvHv3bmHLRZcthYDowTebDbvdbgGhommXz5QR6v/jf/wP/vjHP/L+/ftvhip9/fp1kQOJBCWEsBRa6/V6OUcC3EUTLtGH6/Wad+/esd/vl+JFDKGSnHE8HknTlJubm6UbIYUdsIDHa222/L10GwTcf//990txI+dtv99zOp2W4sZay36/B1iArXRWZF0K2wsswDzLMv7617/y8ePHRRN//d2keBMGF1hiOq21SzHy/v37pevw8ePHpXsg8ZbC0ss1le8kBap0L+SzriU0YoQGFmZeitfdboe1loeHh+VaVFWFUmopMEUuIh4HWecPDw+LNEjuj+PxuJAq10BbwLWsYZE4FUWx3ENyrwHfpNbIv0snQiIwpTi5Pr9S5AlrL8X309MTnz9/ZrvdslqtlueAEAh/K0n7Py5Z+Q//+Z/mlvFxR5IaQhiI2qJVIEkVqDDnFicJ2hpCjEzTQHs5cT7u2e93aNeS6shhf2IcRm7qijQv6NHYpEIHhR0HlBrQWuGngbHvSMqcfnT4bsSjcX4eGa5VQmJzxmEeW962DXmWkWXzKPdjN0Di2dzcc3t7R5VlnJ4/48YBE+dWt7aarCpJ8hwXZnvWDOAieZ6RFRXjBOPUEoeZXY70nJuByUGdz7neAY3SCWmW4YKGoBinkddTT1FtSVWKCxMx+jlpIptHk4fg6LoWN02zDCJJiCjG0ePdPKTI+cD5ckFPI/0IkKGZoxGV1aDDW5TcPPEyz3OmbAYiZZFjM4tJEqqqwDUemG9uN00k9s1VHzuUimw2a1brCpvMQ4dG5zBJRpJnxGmcs8n9LOAhQFVWvLw+8/ryhIqezXbNp48fcKPjfOy5nE6kKqPD4LVmXW05v7zw9csX6vqBcUpJihptKsax4dw12LdN7XA8kd3f8fD+A9P0K+7cMEwX+tGhfcqqyLCp5XxuGb3mYXWH8RrnImmqMSFwe3+D8xPH/ZEszeknxdj1JNpw6RpsHAi+Z11lmGxOz1FYdq8vtOc9bZUzNOdZ51iWpEWF84GmH7FpwebmnsvuFZIEN80b9Dj0+KHFRIeJI4enkclNGJuRfn3in43m02qDnzpUtKhEQZhjFANXhsPJEfw8ZVJZS1CGczeQJQk3j+/5/PlX/vrTr9xtS6w2aBUI0zBH9GHpupFhnHj1T4zdBasDfuz48vkXds85MQSqImNVFkxDiwpu1ugHTQhzJv40eZKkYL1Z8/33P5AVKUkyb/qXy4n9cUfb9bgpEG0gKxISM5uC5+Feao53TFPyskRr6BqP047EWrybGIaWRBva4YIfGqpVTVrOMrFX71BxxEQHPpDqSKbiLDtTmmloGYaWXEV88N9omK8zpQUwaKXnqFYFcfLouSxhGgMvrydOl57dsaFpJkJo2awb6iJFa8V6U+Mmj7HmrbieGMaO/a7j/cfvfu+j9N/s67/8l/+yaD5FqykM1vVAHGEnhS0+n8+LlEVAwW63YxiGZbCIbOAC+q41n13XfaOnBhYweM0MinFSJBYCDK2dR3QLIPj69esClOT7iKlPwJl8vujdhfUWdhnm4ljMarImxdApAEMmSK5Wq+U9RRt/3Tm4HqkuQOfahCkmVxlFDr+ZAGX9AwujLyDkOlFGkoikwBEGVdhxKZyuJ6wqpRYGXSZSyu8Biyb56emJL1++LIzyDz/8wDAMHA4Hjsfjcs1g1rg/Pz/zyy+/LNMZhZEW3bowt/v9nnfv3i3yIAG5wzAsQD9N0yUNQ0CoFGoxRh4fH3HOLetW1pQxZuk2iNRHQJ7WmqenJ06n05IOIkC/LOdENjEip2nK6+vrUhRKp0hkLzFGPn/+vJzHX375Ba3nqawiq4DfMsmvr60UQNLpkIIyTVPev3/PTz/9xJ/+9Cfu7u6W7y0FJrDorwWwi4zqp59+4uvXrwu5Udf1NxnuUiCJ9EOmhf793//9UkQbY5bUketkGjF5CgMtx3PdwRAJkfycRH3KedtsNsvvXktCpPDUWlPX9TdSEvn3v90jrmUv8vviKblOWxmGgaenJ87nM7vdbim4t9vt8r1vbm6W+1TO0TAMvLy88P333/+rz9DfDcgdMEWF1xmnwwnvWpSGdZVR1xnjZdZRx+DIkgSrwajINHSzzrSdxyK21Rz3Vq3maVZZljBNEIODoNAagpsYhobmciT4CU2BHyNVnXO7viUMHj91DNMRR0K1epxbe5mhKueLHXykLGuy+pZqfYM1s8Z3ak505xd07HGTYxhHNskMMsZhYHgzoF76Br1fUWzekWc1ePAhktuE4CJN19J3J5K7NXmq0cairKGsK3anlsE58rJgRcLm5h0qWl7OF3p6tKnQOrxplS3DMC/0xOTYJCcA4zDM55NIXlTYtKAfJ4ZuIuiEMnqyNEFbxTB2TFNPltt5OicKoxXGaNK0YJwmXAyz7n9dUKxzDrvPOB/ReYH3A835Qte0uL7HbGvcm3O+GwZSm4ItCMEQgmYMLam2oMQgqfEuMvUOFTUfPn5Hc25IbMPx/JXDcYcZHS5E1sU7tpt3/PLTj/z8y6/cffiBfuwpVpt58JDxhHgiSVP81DOOE3d399w9Rmx5wb28MEwjREeS5Ni64HwyDF1Pms0TXp/ar/NGkhqiBmMsSZbiXCBJCrq2hRjIrQEHwWjqMiMp6zlD3c06/jwvSRJL4/0MdscekyYUecH9Zo3NUg7HI3aayNOSIYyErmGVZjg0iU6wyjL2isv5zOmyZ3UXGLpxzh8P86antMVph1UGUGhtCXFmYKZxRBtItCKrSkbg0g24qMmrNZ9/eibEWXbz4y+/0DnwKp0Hd42S05/StSfC1FLmCbfrjBgDRmmsdhBGjAatFLOZeSAxmu1mQ5YXGGtRCoKfeHk6cjgfMdoSCDg3kWUFNrM07UDnAsbOspjNNsealDQtaPuJS3NBaZimkUmypBOD1WCNxs8tL3QM82Rf5GdnthE/8f7uhrubLSQWqywaRXNpqO88KPMNQygtZmE74lv3zDmHzVJCDCilZ6lRNDx92fHlec8wQV1v6NpZvrYqMh4e7pm6iTB5NjcbVuuSvJjjUcc38PHvL5bzvdvtlo1fBsCI7EDYKwEQYsqUSYoyQny9Xi+teGHJRBIgcX2n0+kbY6TEvonERMxs1xnPMgFQwMZqtVr+XcCARNKJllYYPwF7AuZfX1+/Yeau2UZJ3JDPFXC8Wq0W05rIM+7v7wEWMHxtIBR2UVhyaaFfZ4eLYU202QLu5XNFbiBgSooKMYHKvSKa4O12u+ilJVlE0mWk8BFdsTCfolOXP3J9pRgWthHghx9+WCY3nk4n9vv9InsQE+if//xn/vrXv/Lp06cFhAlIk+6EyIgeHx95//49ZVny9evXRWMt3QBh3CVdQxhR8SuI1EYKEAFb13F3knMu60KkK9LZkHMioFXWrshp5DyLfOva6Ckmya9fv3J/f78AUPmsax25rA8pFKQjJeZSYDE913XNn//852Ut/OUvf/nm/WRdZFm2TFUVlldkT3K/CTAFFlOuTJW9TgMSs6UUgSKTyrKMpmkWfX0Igbu7u6XIlPQbeX5f59Rfg3Mxr0qxJBp/SV2S7Hz5TuIzeXh4AFjuQzl2Oba/Ta659mJIMfrrr78uk3LFHC0dpPfv3y+dgru7OzabzdJJkOfQv/b63YB833QzaxszgqowiUWbHOcnVPDzmG89t73LIqUuM1Izb5Zd05BaQ5IWpFnG4AK2rNDWvFVngcl5iJrUKIIPtH1DjA5rDTFAalLKpGDqJ4JzRD+hTUCFgLGBsspwlwzGmZkZux5bVUCCm2aWLkRFVa9x3RE3tGgzp0RopVDEN/AfsQY0kTDNQ26SJCMUGm0TMuUJLnBpei59R55rnDcoq2ZgVJbodqA7tziTk5c1yti3IUQQg8fqiNITSQJWp2gDhmTWmiuL89D1Hf3Yz+bIVUZRbohdR4wHpqFnGhrSoiDJclwYCUOPzQr8NDBN3SxRSDQwxys6F7DGkOcJaVlzPH5hdJ6bh/doFEwTfuw4nw/k5Ryn1XQdJplZhBAnlDKktkSlDuKITgJNc0bphBBmc+I4OtKkoLcTmI6gHNH3WL0musDl1FOVt6w2Pfvjkc3jwL69cBodZVEx9COb9Q2n155p9AzW4aMmq9eUWvMuURSlZewuhDiyWuXc3Gx4GQf8NFFXK7I0JQZHBPbHPevNhnJd05wHElvQeYXrOvKqpG17mtMR1xkePhVU64LSJKSJnWUkYaA5H5nGeRNKE0tiNW7sOe53uMlR1RWbMkHVGeMbmI8kqOBJrCbUsxmp6z237z9xf/cO7yLBeVTiCcEzTY4Y58IuMBcFIUaietN+RlitN9j1Ftf3ZKmhrgpsohnaI+u64D3QdB2TgzQrSEz6pkkPjH3F6fhCWaRzQtA0YY1l6HsIkegdVqs5hrMbSJOELM+xqaFp5024Gwbc5EnynNXmlqzI8T4QArgYCGbAh4BWYJRCpyUuRHRU+AjaKIw1WAI+TEQ/MQ3jrPMfPFmSUBcl1s4TgZ33WG0YxgnnPLnVs9kU+O6Pf6QLb2PvifPk3fw3s5sxZmllCkCAOdkmjANaqTktxSTMEvCErnW40ZCmBdYW9N2Jvu3py4HUJtzcbrHGsL3dUNUZo2vJ8oQ8zX/vY/Tf9Evay6KNFamJAGZh+CRjWVJTBIxLW18AoOjEBQBLm1qAnxjvBKAK8BUzmnNuibETQCWJGsKgSyyfgBQpBP5WmgK/DYy5Ppbrlr+AYvl7MbMJcyxt/6qqlpg7pdQir5LCUYCxaF9FBiHfQQCDjJVv25btdrswgsISC/stjLwAcgE7AjrkeOQ7iuZ9t9st0hUBfyL/ENOrGAmBbzoSwl7K9V06VG/gU2Q313pmOa7D4UBd19ze3rLb7Xh8fORyuSyacTne5+fn5VhijEtEokhR5P6XuDwp0GSMvVzX3W7H7e3twnRLQSXSHBkx33Ud33//PdvtdpGeyPWSxBH5e1m3r6+vTNO0ZN7L+Plr467cI/KdP378uCTKSIdGgPm1h0D+v7yPpP3IuHZJuBFfwbX5UwoEuXdg9izsdrslblLWg9xnco2AZb6DsOFyfQSQ5nm+pO7IOZJ1LcBXztW1eflaey6SketIQYkklYhGiQIVXbfIupRS/OM//uOi35bzJR1T2SPk36/3CAHr14y5PMPk+SHXWOYqiKfk/v5+6bhJh0OKkd/z+t2AfHvzwG7XoIzBTAmp8RirGYYd49jhx4k8S9lsVhR5Qp5Z8sxwfG1wQ0eaWLLEkmYZephzm4E5s3pyOGfQak5DcMHQxkBV10TnUGhymxGnQHduOO/2pEXk7n6FSgryOscaQ5JYTFUxjj3D6Iipp1AGHzTNscGPHTZ40jxn6o+ECOvViqos3/LAHZpIYjXbdcXqdtYC9ePAOHqMD0DAR8UwTFwuDXkemcb5WIZpRBtNWVWY80A7TtiiJMY5oaUuKvJ0Ivh5omGZGZQKBOdQKiW1GT4qvJuITJgETGpJsoLV5pa89BzPjna4cDkfUdaSpzdYPOhAZhR9NxCDI0kMq+0dVV1zOF7QymCtJoaR4DrypMCPJ1xfgzLo4DAqovAYIlZBkeeYJGd00I6OoqgxRQFK4doDeblmGCZWtSa4gagMk4soZeepqEXJw7s7np6PdM0J6xRNWpCmOdu7RwY/8uXLz8S0JgSDzXJskmESRZpX5OWIThMO54bxTU9U1AWrTc7UnRnOF6J3lHlKnlray4UizanLgmnqGNxAjAoSQ2IyzBA5nc6YbEV72lNbUH7EjQM6Gvq2J6ojxqZM0wjBYZQnTXMa1VKubsjrFc57YghkeUmSBjbrNYk2JFlKkkQmN9+whFmeFCPU6xtCNExxnoIZJgd+wqQ5xDB3Q+KccBKcx/l5LaX6baqreDGcB6Wo1hsSq3n38Tt2L4aoAt//8AecGzifThAVVs+m5tkhmnOuNVkyFxW73StWR5Iypa7q+XP9LOno2g43juyOB1AwjgObmxtWN1u6fiIrSkxSMDnPMAVinM3FOq1IrFna7qM3hBhozx3WmrfhVJpxmDPXnXe0lzNhGinyjKA1JCnG5tTrbC5SdEYWI84F4tBxObzihwkXFeuHT9TazPGhbw/zaybpWgsqQD0QmbxfsuCLvMSSMI47+iFgTIExBTEojDIk1s5+Aw11maOJaBVAOcDhnCIkv3/g8b/l1/39PS8vL9/otSUrW4BTURQLqyYb1fPz85JYIqBcDFzAAqQEyAhLGWNc9NvwW+b56XRa8ocfHx8Xfad8H5GYyERKAeL7/X4Bd0VRLEBEdKLX0YFiCBQmTuIUpRgRAHE6nZZR28ACcoQlF9AsvydgQ6QAEhkpx35tMLtOgJGYuaqqFsZZBsVIkSAsowBVyd6WLGgBQ5LiIR0BYbWFLZSXMMTCIvd9v0g2hNWX3PFrICrFj1zLDx8+8PXr10XiI8dzf3/POI78+uuvC2gVhluiDEWSIiy0UorVasV2u10YV+/9Upicz+clq1zW1bWnQAqOPM+X8yfXAn4rOiR+U8ClvLeMixcQKubZm5ubpRshQFPAqABQYaXljwBykShJKox0GwQgy31y7cWQSENrLT/88ANfv35FKcU//MM/ILnx1zIXAeXr9Xo5v8/Pz4uRd7VafaOxl7Sj3W633KN3d3fc3d0t4+RlXVxrwKUzJt9T/khHQPTXwsA755Z1IdIS6RLJ+1/r4SVlRYgB6Zxcm2hlj5B7Xzot1x0Aub+cc990n0SSdj1467prcZ27DyzX8Vqy9//0+t07yePjB7J8xDnL+XJhaA40zQ4TDUnMaLsTENlualarkua0x2iLG1uUmhinnsRYIr9NdApuJM8sU4gcjye8ixAq8nROZFHK0A8TZTGDluZ84uXrF5rzkXGM3N+t5gdlkkKcNbhoTT+MtKOiuikoqzXalnTtiA+KoetIwhxdFkIgyTL6oSdlHjM/dS1WjSR5CgSUnieGXpoW4z2FVfTDnOaQ5xnGWpr2xHZV49ysYYs6ZbPZElpHMDlGG6wys/HMx1lPzxmr7qkKw+TmMejb2/nBbIhzKsbtmhBGbF5QVLeskozDxXEZfuFw2M3j1LXHTT24kf14YWhPuLGjrnIe3z+yWW85Nx3j2JEYTVqk6MQwukjE4/oTISpSHXh/f4uaGvLUzmbDqFGJYfLz5EhMyuDAe4XRKd3UYtMcoxLcNC9SpTUuBMpqhbaGOwKXbmD/6wEzBELQOA+3dxuKuuZ0eGFOo0lQ0bHZbrHRYbKOfK0Yh55pmkFoYg1ReYxVTAqGoWNqG3QIlGXO6XBAhchqVaPVbJh8+PQRlWRYk/H8dOR4PlK4yNDsWK0TTBipq4Ldbk/QT/jXPdVqZlA264qxm1Aq4f3HP5DkJUEpyjpjGuc1oACUwiuYBo/zjjRLMCYBo9HKvOnBPWmSM/Qj7fnEar0lVYEYRkbnwWSYzM5suZvmQi14wjRP/NQoksRwuZzx04TVilVR8O7jR8ap43zaz4lB0ZNaRfQRw8Q0TmjNXChmmugdRhvqIie1KWmeo1GEOLNZRhm6vmdwE3ldslqv5ymxxnJpe7wKTNESvCKanHacGJ1ne3vH6BzRGKqqpG07xqlHKUNSZFRVQbmuicGhjEINkeA9U9cQnSLNUtAaFzWrcsXxdOR8ntdXcIFp7Dnvz5igMUXG8dxT3muS/G1KrDazr+FtYxInvhj1kiRlcg7nPUmaMAXH7PGMsyzGOVBzMVGW9SzhicOb3jYnuhGTp3PWvI0YGxeJzPjWHv+//fXx48cFgJxOpyVPWzb7tm0BlkmBx+Nx0azCb5M7BShcR40JYBZQcg0yr0Ht8Xjk8+fPHI/HRWpyrTeVIk303nd3dwtYl2mgYn6D33wIAs6vB79IlJ/IB0TmIEXItV72crmw2WwYx3HJNr+5ueFyuSwMuLDTMlhH3lsKiHEcl4mUMJv5Hh4elu8o5rLT6UTXdYtu+fpcCnMvrO2nT5+W6EEBRdKZEMZQjt0Ys5gyRXMuZkJhXK8/S+RIAnyu01pkMqaA0aZp+Pnnnxedsveeh4cHVqvVkjkOLBp0OX6ZrCjSHvksYVBlOJB0JiS6TyZVWmv5/vvvF8nNly9fOB6PSxSiTISt63o20r8Bus1mQ57n3wx++uGHHxbjoMRDykvAnnRvhJ0W1ljkLmmazmlWxyObzWa5HwQICrN/3dG51oQnSfJNwkhZlnz33XeLV+G6kBPgfl2UiIFY1t31HAGR8Gg9T6+VoUDiJxDNvdw3UjDIMUuBdd2tku9eFMUiHZNzIutEDMCyvq6vpZh1Rbry+voKzAXz8XhcfCEC5qX4CCHw/PzM+Xzm7u5uWTvX/oJrmY5IIGUNSldLigxJhLmWgl13n/6PS1aK3KJNRttBUZS4Tc3+FS77njKzaHNEobBWY1Vkig5DIE/m6YjgCT7StQ3jODEBX7/8irIpSXGLHwe6bkKriFrlRG0ZXGSYAquVfdOWe4wCrSJEaNuJ6taCV6i3yYbaJvioGMeJrh/ouwmbOZTSENX8v0rPJsgsZ/KBp6cv3Nw8kqcpHk/wE9MQCM2ZchrBFvPv6EAgEJkfgFkRyDNFdHN6RQyBtmkw2TztsshKoi3wTpFaQxM9l8sZG0eqPGJ1Qp7klHnONHZMQ0vEYlVEZxrNzGamaQG6QNmc1faebT/QDj3t5YAOPSp6dHAEHRn7hsTO4K2sSqpVRVkWROXI8wKbGLp+ousGbKbeTJ0Rkxoe7rdcjq8MQ8cqTVFGzeAwyiACTUATtQWTYnUADZmBtnGLKQilKOsVl77BxcB2u6ZvHYdTzzC2XNqcrCpI0oJPHz9xvDSMcUT7DkOJthnl6h5PwjTuUfi545LytoZOdE2H1ma+hl1DXVa4fqC7XNiuVti8wKuMD59+oFxvuJxbXl+OHPYXYhixjDDNhaUfR2J407bmGY+P76hXKy7nI+PkKas1Ns/pXEDbhKTaYnKHG6e3dtlAczkxDSNdd6GuS9ZVSb2qyYoCN3p8HNDBU6QpKgbG9oyf1hirSHVO1PPETh8cSs3/PXQt0Y2kqcF7R2pL6qrkcjrTNGeKbINNM7YPD5jE0J5eiNM0GyW7DnzAGk1aFGSpoWsGXl5fMNaQmNl8Ob6+0jQt/Tjy4eMn3r17z839HeWqJi8K0jSjbXtMmqGdQk+GtNwwTYF+8jiToYxhxMzRm1Hju4lximRpSZ4lWKvIijm2000ekyXYkKCVevMKTLigyNKMzsG4b+iGibze4iZPPw04bwmkeDcz748fPrC+e0exviGvapSe5TBFUdA0zWJQEsd+JJJlOedzg00MUUVsYhndyDAFXJilRta+eS+sxqqUIkuwVhOimdNZYiDEidWqYnSW0/HM8XjhP//T732S/tt9yabXtu0ijZAsZPk3+G3Eu4AsMQcCS3SaANqff/4ZY8yS0S3gR+L2hIETfbHIImTjFS3t9aAOAYGSwy3mO/mda8ZQ8sM/f/68RPcB30TsSUKKgDVhOCWfXEC2gBphaQWICKCV7yW51NeTQ+V8ihFQouqApasg4Pj29nZJvrgezy6gWjTf8rxer9dLEoaYPq/PiwCLNE2XNBMZpy7AXdhCuY4CSoSNtNYuchABw6vVark+8p0PhwPDMHC5XBZm8ocffuB4PC7XEX4bfQ4s50SYdcnYlmInz3Patl2MicJkSxH3hz/8YSkQn56eeH19XT5HCphrD0NVVXz48GH5HQGmksMuXRj5/+IFkPjFtm1ZrVbLuZeJoaI/l+sqgFXA6dLlC79lzF/LT0SyUdc1x+NxWWeS8pMkCYfDYQGX4ksQ4C4sv0xDFZD99PTE5XJhGAa+//57Pn36xOPj47JuRJMvXg+Rb8m9+bfSFLnH5WeluJNCULokci9fZ/hLAf/8/Lx0YORzrgsM7z2fPn3i/v6e9Xq9zCcQg/blcllkJiJJkVQb6YxIwXb9/teRmXJfyPe/fnaIVEp8Fvv9nv+fX2tjAAEAAElEQVT6X//rv/oM/d2A3PcHyvKGfpx43R9JjZklJdMGHTpQb0MT0oQitaRJRqIh0ZHMzINp0nSOd8M7yiJjcD2vz1+oNpDYHJ8ptLWoJCXJaoLK8Ji3EeIDw9jhfEfwjmq1JbEVKuYonc85xF5hSNCYOXvazcN6xmlkcCNRMQ/YCZo0ySF4Lu2FSzNQlj2pmGh8iyOS2hJiRFuDMhDCHJOoE0VRZ3TtBSLkaYoG0sTiJzenzgTIk5RifYNSGd2lQWmPGwemJBCsBm/I0oJVvaJpetzYMo4OYsTmhhgmUBGlE9ohYEJA52tuHgKTG+hOhxmwmTm+zijIqpLttsakiiQzlFWGthEIFHlBtaqZph2H1y9Mbjfnu29rVuUtaaLZbNbzA8lNZEVOCI5E2bc0F4BIUVUorwlDfJNWvKUoxEheFDRdR8DQj56iqNnUGxIsWXbhOARcGBmdY1Vl5OksG+mGgW7/mcZoivUDk8lJVim51wzdHj+N6DQlRgheURT1nIDTnOmZJSBlUdKHbj5/1mKMplptePfpe46HM103MnY9w6UB36BDRmo8g5sY+4G08hitmcaJ8/nC+XSha3tsXeEnx6UdcbEnYimKcpZCnebhCqPXKJMzqRGvc0hWqLSGJENrT6oT0kTjnOf56y8cDs/oJLK+faTczMWHH+fpp9F5+u5CmDrydE7k6buJ6AaIkKWWoVM0bUOWaJIkI8ly6npN50a66UxzvnDeH4jBUxQ5MQYOpyOTG1lvNoxmwo2B0+WMMZbNzZb17Q3FqgKlyJ0DpRlHT0xzBq9QSU2xVmASkiwhTG7O3Fdz5n+SKrTSDH1P1IokL0mzhMiEf2PhowLCLIMZhgFjLUVVUa9uuLQT596TG0taP5JXGfv9AV0UbFYZWVrRnQ9EDUNM0PkWna+IOsGmv41KFxAg6RxaG4KLWGtwLpCmFqN5K05HQgTnR3wYiaRM04DVhiTVpJmF4InBYZRBWcswTmRJxjiN/PLrM237+9iPf+svYar7vl+kK5KSASzMpYAESWIQuYDIK6TdLxGHX79+XVrZwpLJ+wgAlE217/tFIy7MuKyHa5OvMLaiw5VNF35jvAVEXC4XzufzsqlfZ1RLkXGd0iBMcF3XS4dAvquwcGJME4CqtV6mjQqrLAyqMMECDKVYuZ46qLVeAFae57x//36JVRT5kBy3mCZF7jJLyexSBIj2VeIjb25uuLm5WcDXzc0NT09Pi+5ZgIqY74BFtiFFj1w7KVSESR2GYZnCKOBLJDXOuQXoiqxBxtzLz4tkSbS9cl5FLiJMrFxvMdHKtTPGsNls+OGHH9jtdksHRAzG17pnkeTIGpCEmMvlwna7XVJ8BJjJuZH4w2sfwbWR9Pq5JdKcX3/9ldfX10WXLBGEAmLhNz2z5PkLOIc5alGOQz4ny7IlavR0Oi3SLjlXMcZl2qRkwkv0oOiipaN0LaMSuZnIa+SeFqmWrE+RmchalYJUChC5LvLfMtBJQPR2u13IFinKq6ri9fWVoiiWjHqZGCzPkOu4SQHTwOJlkWJSTNPSqZDnzN96kYSNl2soHTwpIKRDJgXKTz/9tAxh+tdevxuQd+0TaWUxNtCOT1zGyKrI54mUbU/X9YTg0TrBaMjSlNPpSNdeMFqTpBnp26ht1fd4Nw/YiH6aY+IwpGlJnhckaYYKFXm9Jcl2KGto+gY3djg/YKyhqjdsNu/QpiTNalrf4D2oqJhceHvwgraGSSJ9tCMME86NmBjo+4FxnEhtgnezTswoDcqQW02SzAtfhP9aQcCjtSeqicvliPKWMrMEN2F1gneO6AN5kqGyAqPNPNrcD9h5KjiTC1zOE6djy3btscailCcSSExk7AeiN6TWkhUJNkuJyjIGQ1AJ9fYWN174ct7jhw5bl2TaYg2UZcZmU3M47zmdD1RVjbWgVGAYRwoXMSpju77h5XVHdzlidUddG5wbyZKEPE0Y+5YkMaR2ll+oxDARmHwA5gNR1oCKaKPIsoQQI+M44GKkGyeUsRRlTm4U7qbGZhndL3uez3tUmmLMmixqNkXOeH7l8HzAB81DfsNkSmyakK48fXdiv98xdJo8TTAqpcgLtBvp/YnLuSHVmjzN6E4XrNYYZdBZwTh5Jg9JXnD/8Mjp5ZWXqadrD7gho85TXJGSvmVZJ0lCPwwYP7eek/X6TYYdqcuCoA1pkhCc49Jc+Pr1K4lNqesKjEaZjCRbodOKwWtCH8isQdsUbRTt6cjnzz9hrWG1nhMPjE0whUJZRfQTbXOGOFFmKdEPs//BR8gypnFi6kfcNGBNhi3mTe4vf/ozU39CTz2X/QvTm6bVKIV3HqUVtzd3ZOVsrDbaAoZ33lGvV9TrFXlZEIl4FVDGMo2RoBOqVc3hdGaKnn7yNJcTm9sHdFaRmTlJBa0w2pImCeM44CeP0eDCW3SjhkzZWZ8/zJue8xPWGsqyZnJw7kZstaXcPhCVIakLwqmnG0c0iqy6oao3dN2FzmliWlJu7wnKYowMoZgNzYuOfRwpywJrEmQkRERh04QQHKiA8/PwKOcntM1QOgKeosioypzg5hQjj8XaZB7uBRidoJXFu38H5DAnhAjLJeYuGZN9Ps/RodeGrizLFkAjmk4BwRLzJ2BI2v/CFsvPyZhvkZyI1txauwBPAQfXBi8BW9csmAC36zhBiWsTwHE9Wv3avCjvLQymsJmS/CCfL4WBTCuU7y4MnHQPhJGVcerX7XY5v8LSFUWxMPzyHbbb7cLgCzgQwFzXNTc3NxwOh8VAKYBdig1jzAK8BeCITvda5y9M+3Uco7DLUgjJ+RKwPAwDEl8p3Y80Tbm7u1tyuPf7/TdAta5rTqcTX758WYoUWTNi9Hx5eaFpmgWACSA+Ho8LqJQ1J2C8KIplzRRFwfv373l+fl4kK6LL/1sToyTcSNyfXBsx1oqf4Xw+Lxp46eoIa39tHpVzaO087Oqnn35aNNUig5Juivd+6aJIsSLDbQQES2EqaySEwP/8n//zm7xwMQVLF1Ep9Y3EQ9awc47NZk7Fk9QQYFmnWs/TQkVaJCD+/v5+uRay9uSaia9EgLCsWylKJN5S7uW6rpdrIoZfKS7FiyFre7VaLc8C8UkASzEkhYsUADJE7LpTByymXbmvrk228nNlWS5yFXk2XBemsn6lUPrXXr8bkP/64/+k6c7cvPvE/SZhGAKrsqBRnvbg5rxqaymrkiLLQRma05FxGEhTS5pZjIXgHTaxMxg3FqMUidYYYzF5hrLpPBpeJ1T1DBJMYpn8iNaBLDVsNxvSJCVN5vHVwxv4UNpibYlNMhRzrrIOA34MTFNPNHEeUz8FpnGCMEfTETR9N1e/2/s7mtM8FIUQ6NsLzr6NUS0Kogn05wt91zCNPU7PA42armVixKPQOsMWKTEY4qSYgmIYGnwYIHqIYWbbhxEfA2meUpRvxlRtOOx2RB1JUkueRUyakJYFQRf0bYdJNKvbRy77F7qjJ81SUhXRzFKHsR9oLg02yUiSV9Bg05Td7kg/QJqvuH34iEkLTqdnjocdaWpRtiAxJcbMLLGOjjTJiMoQLcRpYnobqJKYSJ6n6CklMMdHJqnl9eWFrCzJSkORlfjBMxk/yxYyTVUnHAdH37ecjrC5385TEzWEoeG0e2J1/x2qSjm3nuh4m4ZpZv0/swTDKI0PA8akGJMyDQN5VVKUxTym/nzgtq6YvGJyGpOUbO80t/dPnA9PtJeW7nxkMClN08/TXbXG2oS8KEEpxmGgrla4sQc1J5fMCv9ZPkWIWJOgtWFzc4Oxhs32BoKeIyK9xqQpSgWsgRjGOcrT9URl+OWvf+bl+YXH45H6/gPb+08Uq1tUjFhtSbRmGCb6ZmbGpiwjhoAbHc35yM8/7tlut9w/3lPUG4zVqKknepjyjs1my/3tFjeNs48hsaDmHH9tM7S2+DhPKOtHR9AjeZHPMpsp4NyASRMwyezFSBz98YyPMIwjEQ/KEk2k73ryJCNPU9b1imkYOR12DH2DMXOhTpg7NX4Y6dsOrWCcPHlhCVHhlKUs1+isousHtMl4ePeBw/7I5XDB5iVpkdNMkWgzLp1jcoqoYBwd1qYkC6vZLs8uyU2fXMBoDXGe2Nk0HUk6D72av1nAzDYUkkRTlSl5nnDc72i6C0VaE9GMk+d8aYhak+Ulw/P59z5G/02//vznP9M0DR8/fuT29pa+7xdWWYxWsrnKhi+b6TVbKEBHwOn1hi6gRFgtYcmu01yuwdo1IyfJCQIiBTyLaW8YhgXwCGMunwMssofHx0cOh8MiA7lcLsvvifxAIgLlPWWSI7CAaon5E/AtTJywhML4CwCVYTbGGJ6fnxeQK+DuuvgRgPv6+roMyRGAJYZLiR28Nre9vLwsA5nev3+/ZH2/vr4uEhUBj1IkyTmWvxONs0gh5LoI8//09LQAGZF1COgULbxIbvb7/SL5kfSL19dXHh8fl27MdcSimIHFWHo9MEmkJRINeD1iXeQeDw8PPD4+zhOGT6cFyMu1k/Uj7yFZ+QLGpDCS63eth767u8MYw+3t7UIYeO+X35HiU7oaSin+1//6X3z58oX9fs/9/f0yHVUYfjnnstZkvQgo/tOf/sTt7e0y1l7kUcIe39zc8PDwsJxfWQsCPIVplqJEjMdSYF7n1IuHQcCudHKu7x9Zb+v1mr7v2e/338gLRdYyjuPiORnHkaqqlkJPpEySgvTx40deX1+XyZlVVS2s/XXXQLoLIoeRiFFZw1IsC3CXwvf6mOR4ZB3Is+z19XUpBoHlGSBdAJlu+6+9fjcgf/rlrxz3ex7u7nlYl4yjoihrMmsIY4frzrT7J2JUBA/N5cjlcsRNgWJzw+3tHUEFLu2ZnDnzeJoiYzuSVYG8SHBaEVAoNcss6qpkVReUhSUxhjCNpGlCkmgyC2FqyfIc5yPejaRFSWICZb1iPQwM5wPj5UDEkiWG4D1KW3zU9O1EogyJThn6iZhEkiynul3Rjg2+78BNuO7CoCMmXYFNid7jvGEaAtrPSQx5ntN3LZfzmYDGx1cyByqZUFmgd4GmOdEeXxj6C4XSaBvJC0uSz8xyWdUMfUuSGu7utpya8ywt8D1DBBc9aIXTFu81qam5uf/Ipi7ww4lVkYCb5umKoyPPKqxNOF0uZGmGtSk+Wp72DfUmZ72pwGakWU3ftxyPPfWmIs0SVkVKmirOpz13qSEvEsbQoUKkSBNQE+otqaIoC8LU8bLbU61qbm9mk017PGCjxqCY4kTQbp5Oua0ZnOHcei7nE/2qRHmHV28t3qGh3X8hVwndZMiLivrmkak7YQY7Z5WHOT5SKYVHUVQbjv0LyiakRc7r8YVgDFnfkKQFVXVD1AZTTOg0pawrXNeQRMPl3DK8acG1MfgY+fr8jDEWrSIaT3M+kuUZWVpik5Rhmmi6HjC8e/9hbr8XGczBlQztSBgd0+TRqWaaeg7nHUZ73NhQlQlpkuC6ltPhNIPeRBO1wqT5PPEyeKbREUNEK7A64ocOFQMGaM87jocdkcDH77/n//X//v/g3MjUXjjvn7nsn1FhIDPQNxf8ODKHf2rSskAn80MmMbMRdxwn+mkiTTPcEPEuYlVK0HYx0SVZQYjMSSVjj01SYvT0TUcMkXNzoTsdKPKMGCJ9e8K7gaAjvTekMZAYaI8XhnagrEu01TSDox08JsuxWU5RVmRFQZamqBi5u7uDOMt23BCwqztu7x/RumDoJpIiIQaNcwGtHTHytglqrJ3199YmdN0Z50dybYkx4H1A6Yh6M97OQC0lT1OM8iRW4X1PiI5xmkhTxTT4WeaiwKYWrS32TUv4f/vrxx9/5PX1lXfv3nFzc7PE7EmKQdu2i6nOe8/hcFjAx83NDY+PjwDftP1FN17X9cI+yr9da6AFjIrx7DqFQVjEa8Z+tVotKSjyeSIBuWbShPGSzOMsy7i7u1va6aLFFUB7ncUt4FSAc9M0i6Zb2FSRjUzTxPl8XuL1ZMMXtlK6AQIQHh4eFgb3On1D2Dspft69e8dms1mKI/lZSbyx1i4DhUQH+/LysshUpLvQdd3C1hdFsQw7OhwOC2N+nTEPv8VEip74+fl5yZYXvTjwjWY/hLAMV5FoSJnKKoyoGFaF0ZfMb5FwXIMnAcer1Yrn5+fFtPr6+rqYECUeUIyM8v/luspEUAHWIQQ+f/78DVN6PB6XToWAf5HlfPfdd8uYeTleYXBlfV4DOCkcRC7xt9NZrwtXWWPShbgeCiXj6GOM/PGPf+S///f/vnSCXl9f2e12y5oROZQcz3WRK2BZjMUixZB/k8JWikM598JQi6Zf/lfOlWjJJSlFrrExhsPhQNM0rFarxffQdd0id5NMeAHADw8PS0dLNPrv3r1bDKgiTZHPEsAt95kUFgLg5eflXr7uiAmoF0mL3FNyLeXZAL8B/f/jKSthmPjy+jNPH//K6uaRor7Bqog1ivW6pq1n9krrhBAVL6+vRD/LMYx+G0OsDcokjP4CLuCGgAv2bXJenIeEZBlZkjM2LcGNJHYeVmKUJssrmtMFrSNZqtHa4aYGm20oi4wQI8GPlKsNQ9eye35ibI/Y6vYtf0GhlQEzyzB0mHPME21J0gJtDA5F0Abzpgt3Q0fQBp2t0Dol6MgwadrLSBgjcR58SZ5nb1V0JMaAeotGm6aO07mh7RrwE2misSaS5xZ0JEQP2pKkOcnbgJr2ciYZU+pqzeEc6IYwt9SDpp8UOiRoLFHNyRRJllCvSqKb8C6gTYIee0JUjN3A1E/4aKjKNd3kUKPHjoHL4JmcoqjucX4ikuBCxCQZN8WW/esT59N+ZmXSjCK3ZGUOAVSIxODwbpy/n3dM40Rd1fhx4udff2bqerbrDTa1uOiZQpgLtDxFKdi97HDB0flAPwaUSdGxod0/keQ1q/UHnJqlDHl1Sz+MTENPolpUackTS1FW+CkS1SzrUcbQdC3ZqmBwjmFyjFOkqEqSxINNMFmG0oa+G/Dj3NazWYG1CT5GhmkkjiN1WeCDo7mcuDQK52G1uiVoRYyavKhYrVd0XcvQD/OkVaW5nI5s6pp1nXM6vHA8PqGVY1XllGVKVWzJkoxpiNhLR3A9Q3sgq2rc2GGTHGNzMhtx2jOOLW5yeDeRWE1qE6oy5/vvP1Gub4jGQpKjTUpqEu6LkpvbLc+//oX9068wjUxtQ2LS2TvBBAq8nzAYlEpJE41SGj9OjP00G7ST+R5QSjH27TxIKnoSFUlTQ5YntF2PnzqMTojBczk3HF5n6VORp+TpnH2SJ4bgJvaHM+fjEa0Stpt7vFI0w4hVgbrcztnnVuO8oh0nTseGLCtIyg1jHHFobFlT3n6gXq/IsgptE5TRTM7hg0epSFQR/fbcmTfuHms1MEvZ5gmoMxgPPuBdpCyruZA1eu4AJYahn2UWNk1BWXaH0yxBU4rQ9Bib8unTp9/7GP03/RqGgefnZ/785z8vWtNrna5M3RTA+/T0tDCY16a1a1mHAA5pqV/rYSUtRDShwowKsBG2T0CE6Iedc6zXa9q25evXr7RtuzCeAuKuM7KFjbweY379/qKTFRZawIvEtQmzJ9/t+nPk+4l055pxlC6CgHoBeyLfEHZWUlUEjF4PipE/eZ6zXq+XpBWJlpPCQ/67KIqFaRX99zRN38RLirTm/v6e5+dnjsfj0nWQSEFh+iXiT2RA4zgu0peff/55GdEurOXfxj3KGhnHcWFEYc4OL8tyMXYKeHt9fV06IcAiabmOWpTYPjkfAjQls1uMtNdpIlI4SdEiEXjC3J7PZ87nM865RcIi11y0z9ej4w+HwzIwa7fbsdvtFlmQREeKtEPWkUwCFQnSdRdJzq/4F+S4RZ8vbLqsBxlV/9NPP/Hly5el8L2WHi2G+LdUFvnuci3k/Mp9Kx4G+d0syyiKYllfAoQPhwNPT0/Lv0uHQArT3W7Hfr9Ha71o52V9CiMt6+C6i3Idh1qWJff390sajpwnuR+vvSdyr0jUIrB0V+Q+lXV8/fkCtOW45XkgU4aBpXv2hz/84Xc9Q383IB+bgfPuwC8//sRd78irZmbPouZ8OjK5gduHu3kwjXM0TYubRrSO9F1H01xQxs5Zxyh8CLhxYJgGusuZfH1DkgWsDpjoGNoLQ3NmGkdSk5Ik6TzSXmtCmLvfXdcxtRM+cTiVgdYYAlpZ0qJEGU3b9VS5J81ykqTE9+0c15clpGoGO4pAlszRhGGapQhE8Dhce8EnmiLOcc5pVlCWa/YTnA4n6jIhhggonPfUqw1RWwJACGAiKoIKUOQFUDGNRwY186nD4Ila4RxoFGlWMA49xlqmyc1MbfTzNEWTkigNaLJUk9UZfVDkNiPNLXFSeAPGZnTB46NHW4MxCdMQGKfApe0YSUiLFTotiM6jrcZ3F/bnCzdG0bSekM6M8evLC+PkuXn3ns3tlqKcHwg2KrRKOJ1G2suFvm2ZxhE3uTeGoSbGwOgG0irBqAQ1znKdPMtn6Q6B0/mVzc2W6uYOe3tLtt+zO5zoLkfW5Q1JMhvprKqIxQYXHYNr0FOYjcOpJQRHZK62V1XJpenovCdfeUKIjM7h+55aJ+gkgzfN8dANjNM0a+uznEgkSRNui3JmWVWEOEF0KOZkjqDngTXT5MiKeQjOMPRoDQTFMI54N9A0nvPxK27qyHNFWZRkqWXOnZ+HYNV1QZZXTDoQvZsnxF7OZHlGntdYZYkmw2YVqh8hzHGg2IR6e0eaV6RFTdQJJsnAec6nM/15T5kpFJa+Gzi/PGGVosojfvAEFXFxIqpZv25tQgzz+h6NpaxqbJoSRkea6zkJZZgHGMVxwHcXkixHe4OaBnSYKPMcXeYU+Vu8mdXk2QzSg58oCoNSmkvwDP3I7d09ebHi3LVMLrJa3VCtbmm6iebS4TGYrKCLGZNLUCZBFwVKG5KyYtIGpzRYQ5LOEaUz8JiZiHGcUDrBh0CaJRwO8/RX2UAmN29eNslx04Q2UGUF1szejXVdUlY5k7swORiHSHu58PR8IEsTrE1xrgetZpnSv78W5u1Pf/rTkoAgG5/kRL97924BDufzeWFypd0rbXth3QQsSTLGdfSZmNYEMAmLKL8rWvSmab4B7QLMhC2XNBExvV1H08lGDXwz9OdvEyNEsy66Xsm53u123wBU0eMKYBSwLQxxWZbfaHMF/F3rUIWFEwOaHJvIPuQYBZRJ0SMyA5FwXEsqRLcrwE9AjXQZZLiMACVhlo0xfPnyhWEY+PDhAw8PDwuTD78xtafTaRnTLkZEkV6IJAFYzrUwrTCzz2IofHh4WNhd8SwIWBQ9OrBkUsu6kPN/Pp9ZrVYLeJaYvetOjAArKVpEeiFANUkSHh8fv2HgrwssAXDXhYUw7LKmRQ8tUi6RJF1LRmBm9kW3LV0eOW6ZkCnFogBDKWqvuxnXTLBcDzEaN02zRDqWZbmASckYl/Mr95TouIUpF+OkFM8C2MXsKOtMTLai25ciR+6Lv5WxPTw8LIko0zQtOvamaf63rpZITeR8Sx44sHgH5P6Xn7nWsGdZxm63+8ZsLs8puQekULtOKJJiT9bb5XLhy5cvy/tIIXh3d/e7nqG/H5B3A9t6Q6I0Y9sw9BOmKMiKkrFvUBrKVc35ONEPA1FFhnFkHDv6cUKbdE5LYTZ56TiHdugY8WNHnHpwPVMTUGbE9bNEwuqZETQ2YRh7xikAinH0TG7EKYsqUkYgyTKiVkz9iLUZm9tHfFS4ELFlTlVvaSNMPhA15EVKc3b0Q0MyZrhxLiCi9xijQCmihaDinOFtE6bBYXSC0SlRtKcRum4AFPVqjckqpmAY3CwRsMa+TQQ1JFkGMaeqcqzJabqRiKNte9zQUBU5RqdYk3BpLuRZwaVztJcTxSahyEtCiLixgbHB+xGTa3wMmGSOs3Nxlv5ENbOeJskI/Wy8dd4zNS1N2825uCHQDiND0BgM+8OJUGfkN2uqas1hf+DHH3/k2HT8U7ni/u6WqWu5HE/UdUl0I317piyK+f0mh9GWdx8+0TRHhr5lnCzV5oaoAj7A6DRlDg/3t7zuPtP5nod3n9jWa8qouHQD3eWICz9T3Tyy3j7QnCeq9Q2jCTSHnmEcsSaivMP5kboqOeyeSdKa9+8/MnjHOHrOp4YQFWVesrnZ8PGHv6M97OiOBy5NQz/2BDO31abRYQLU6xqtFZfjnktzInhHXqVzYoiax8VPfmSaBl5fnhn6nrouCGPAjT1DfyYxBZPr2G4r1qtinhzqHCoqrLZk6RwpaPQEYSI4z9QPtOcTSlXkeYln1s8X9ZYYZlnWNI0ErcnrkjQtUElKNPMQJj9N/Hq68PnHn6gzQ2XD3EmJ6o3t6ejbDmU0RZ2RZLMsB+cgQgyRp+fnWWOuNEVZUZQ1Ji0YxpFp8jRNy/54RBvLar0mzwuyJMEwEP1ElRtsPZ9P70asUSR5SmIN0ziiCNSrFX0/8fMvn+knT9SaJAuMo8f5gMKRVRUOS1ndoG1CjIqiMLRdS1nmGAPj2HI577HJDRGDUmLs7IlEQoiEGNEmJUQYp4nkDZCPY880TmSZJ3jHui6xWU3fe4rCkqYz6IvB0PWBtg8055aum0iTgq4dGab+7Tnnf+9j9N/0q23bhY2TEevSqpf2vwyhkU1fwLawUwJark1QAtrEOCkAWPKxr3XhYhaT9xbZgLBnAiKFrby7u1uAxjXjKgBFBu0IwBD2WYAs/GbcEn21mBXl50QvLCBVCgsBEvK7wrJJG14Y28vlglJqMWhKBrJom2Uk+fl8XpImrouZ6yz363MsIFISKLquW4x0Eht4e3u7MNzC9Mv0SckJlyJMgNrDw8MiSblOQSnLcpEyGWP4/vvvl/zzcRy/MUdKIsq7d++WtJcPHz6w3W65ublZjlciE29vb7/J7haWUo5VWP6XlxeyLOO7775bjkuG5BRFwd3dHX/3d3+3ZFzLOZdCUfTu8jmHw2EB95IIA3zTrfj69esiGZL3kKSQaZq4vb1ls9n8b9MixRtwnSEvMqvrFCmRYMlxypqSiaRyzcXfcDgc+POf/7wAein8pNBqmgZjzHI81wWC935J2BHgK/IaKbbO5zO73W5JWZKiSX5fMseFdb4+VnkuSGH8448/LgWpdG/ku0jxJfeJ5INLJ8GYOQNdOjhynwJL0Sn3gXS+pJsmXYdhGJZCVs7n9QTc63u7bdvFvC7TS6+fRb/n9bsBeXNu2N7eEaeJ5nCkvrnhti7opxGLY3QjX1+f2azX+GkkLwumyXE8vhKiwvvZJDlFRwxhNggOIzEolJ+wOOLU0nVnYlqhXE9i5gep0przpaO5nIjKkKYZShmOpyODh/XjCpMX2CwFHxh9QGvDenvLpe1J0gwX5pSRYRhph4FMRUyqSQuLOkWcG2ZG+21aYZJolNZgMyYXcVNPDB6iIgRNjPOIbaNn1j/ESFWvsVnO5vaBdvDzYCAsWoEbJ6aoSCwYk1KWG3wwHE/tnCqDwU2R4+lMYhQ+Rtquoa4zjA7sds8E4N2nDc5F2lNPVaYUuiRL52hHa2bgQzAkeYkyinEamQKoJJ311kVJ08/6Zh81Nl/h4kBuEvAdQ7sjxlmeUdUbHh7f4758puvaeYR8f8/YnPn5L/+Ld+8e4S1u8cP795RlxTD5mbGOAR8iL4cdzdTyD9s70iSlCBqCYwiO7XaFC2eejkf08UhRb6k2Wx5C5OX5hdPrZy6n8zzkJimIJiEr1zTnV5SJaKPR0ZGllv3xhHMT3nnW6y3RaA6N53A44oLHZhnYjPXNe6rNPd1mz9CcGaaBvJjZMWMtznmGyWHemO+xvYAfMapA63mdhBhIU02WGNw4YQhMfUvXNhijuJz2JMZTVRla+TeGPc4GXmMwOiFNZ4Pm7vWFw+XMpC2b956bB0tRbSmrWYLjw6xvzgqPtXA6n5kmR1FVGJOAngc39f1IkaY8Pj5SJYqpO6PGFpQhyyv2z1857nYQAvVmjUo1Xmu0n+YEkzSjG3raYW4NNpcG9frKZrMlsRlt17HfH+YiTyuGfmS4HNlstzjniUBezA/vebDRbKZM0znH27kJFTU+GqYJ2n5ie3vH/faeYXI4Fxn6EaUN3aUBDKNXlNWaTVXgJkfTNiS+w04W1XXoRNOfIq7SFKtbjFVzctLk0drgw3yvhajJy5lpsQlzkkyYC3trLVqNrOqK1faG82WeKuyGlmFwXNqez5/3JEnJ6CL94Kg8xOjpuokkN8sm+n/763Q6cX9/v2z6AjRkM5LNXHTPoi0WXbkwaaJFFgBynT8tYEY0ywIcROsrchXZVI/HI9M08f79+0V/Ku8tUW6Xy2UByCLXEB23TIQU8HSdaS5g5roQuGbOgaUAENAvm/r9/f2SggIszLu8r0hTJGlFgJGcW3lPKYKUUote+A9/+APO/TaCXlrrwvKJ9EBa7wLar0GNpNyI+U+An+SoAwtI+fDhAz///DNN03A8HheT4b/8y78sci6tNd99990y5EjOh/d+MZLe3t4ubLb8kYJAWPH1er0kj3z58mWRzEixITpwSYaRTkCe5xwOhwWwbrfbxQC72+0WllTMlwL8BZCLXlneT6QPkisunY7rdSJ6Y4n9lOQQY8xiFv1bX8R10STn4unpiePxiFKKjx8/8u7du288FVIcSsKRZKOLCVgYeymQP3z4sBRg8t3EeCgZ7Dc3N8v6FvAtYFTMx5JZLhGabdsug3nESH0+n5drCCxsv7zntRRIvst1x+Lh4YF3794tensxll6vwbqul8xvGc4lhugkSTgej9R1vUwuFRnSdWEtBfn5fF7uA7nPZV2t1+tliNb1FNvL5cLPP/+8eGXEaCz3pwD63/P6/Skrv/66LLTNzS0fP31cdKFWByYcMI+WN9aQ5xmJTbhcWpROUNYyTZ5hnCiLFOfnkx5Q5FmKfdO0RjdnUifW4KYBN40kdjaTnZvZ/b3e3OC9o+tHzl1PsmpYlSvKoqS5XBgnT1XnpFaR+UgIb6PIVYcPIyaZWWsXHDZNyYocpTSKSJokgMK7eQCQ0YFxGJiOR0w6Z59bY0nTnPaNgR6nOctY2xQfNUrbeex5ZonKknQWTWBoe7JiBirtMGLLSFHVTH5eQF0/oI+eVZVzupxnTbsbWNc5l0uD788wXch0gi0MN9WaoZ/ouiOjmxhdxIYEhaasb+jdQJbm9N2ATiIKPY8EH+dR7SGCTXK00wQFbpr1upMLdP3Iui5Zb285nRuGaWDsep4+/0J7OvD0+Rfc0LHe3hCj4v7+YZZfhEg3jDjvsCbBphlfn16o6i+8//BH+t5j0KgQ6F1HWZXUk8NNnmkKpKsV1J720jB0A+fzjvNuxe3jJ7yP2CQjLVY0pxN4R5XoN/a1nyc/TrMB47A/MJGiNKRZgk1TmsGBLdncf6DZP6PTzwQ8aZqg1Zu5iJkpxmq0iuSpZRgieaKp8oQxKNIkxQ0egyfNDN4kvL48MQ49N7c3bDYVSaLIMwt4hr5/k6vMzE839fRtR3s+88svP7M7nSHLCGr2Mqy3N/TdLVEXBCzKGpKsQKkIukcnliSfAXnbzV0ja5jjGk3C7d0jL19GskSTZhVPl19o24Hb+3tut1uq9Zq0rlDWEmLEaI13DlsOrG8f5hjOGOnbZh4w5DyGQN9akiwlTfPZHX85kieGGMH7wHg6MbmR5i3ebr2uqetZ4hTx3N3eE1TKMEKaFnNiwOaGc9PTDhNZWWOSlAMnjHKk1pCFjnhxjG3H2FyIYeLUfKWsMjbbGl3C158HfvgPFV5p3ATBQ4yKtu3QiaHpR2xiyfJy1s1nGcR5MNj8YFYkRpEmirubGudGmilgbEbbThyOHdtNTVSz6TfqtyFkei6urP13UyfATz/9tMQF3t3d8cMPPywJBSIzkZcwfGmacj6fF/AkaScSWSfvJ/psYdVOpxNJkiwscFmWC0ioqmoBASJZkda/gDVptUsbXzZQYYJF2iT/LW1/kYIAyyZ7nUstelWRiAhIE9mDtfab5I3r4T4xxoXdkxSJsiwXlllAirCf15NO1+v10mkQ4F1V1ZJYIiBEwJm8hyRPtG37DYtpzG/DUuQayucXRbGYWcWkKSCw6zp++uknjsfjMnnz9vaWEALv3r1bUlek0BIZwOfPn1mv13z//fff6I2HYaCu62806Nvtdjnerus4Ho+8vLzw/v37Rd9eFAWHw2EB2tfRknJ+BDyK/l8Ap7WWx8fHJVlGro/IUeA3qZGsB0mQkXUro9blvY0xC1P+8PCwTLeU6y9mXZFSiHTodDrxl7/8ZTHPCjC+u7tbWHb5fteTY6WQFDmYFKASl/nu3Tt++eWX5ed++uknmqbh8fFxKaSvJ6kKMC/Lkru7u+XvRRsv90LTNMt3eXl5WcCrAFy596QTc3NzsxisY4w8PDwsEp80Tfn48SPb7ZbL5ULXdYusR66bnJPL5ULbtpxOpwUIS7xnXdf89a9/5Z//+Z8BlmJKZG9yXmTdSHwksFzHa8343d3dshYF4O/3e+7u7hbPx7UPRbpZv+f1uwH57e2G+/s7yrrk8fGezWbFy+ucRvFwd8cwDiRGo2Pk15cnksRSlTmbmxu6wXNuO5yPGGtJ8pLEWPpmwDtHliVM08A4DLjRYzCgE06HHW3XUZQlaZYzTp6sMJT1iqa50A8j4zhBjGxXK6qyYnzT//kQsCZjjvAbyMscYyLeRlZVhokDMGGSFGNTrPnNnDAnMlyISpFpx+nQoIaEzW0gKQzOvsU2vrGoXRvxb/pjpRNcjAzOg56n+2kUYRpxfctkDJlN0MrQ9yOYkcl7ghtnQK48q1W5tByJio+fvsegeT1e2H35EWMSyixhNBnBe7yPXC4dnllGYG2Kw+IJaKPAgH9jj4OPJNYw9B1D15HblEDk0nYwjKwyDSriY8SFgPMRpfQb6PLsnp85vjyTWsvz0xOj86y3txibM3nFGBXRFhgLdZWQFgVoy9PzK+MEebrBmpK6KHGXHq0T6qLk6/OJV/OVyqYQFHlRsVl7smxk6A7snuH+3SeyrKast7SXJ9r2jE6hznJWdYUbR5pLQ109Yk3K8XjBuZHBDZBY0BlZmXD37g+8/voj/eRpux70GTspVtmW1M6pGUpFVIz4aeK4eyHLLfaQEW1GUVT0XcfkJzCWy/nIcfeE1tAcA9W65vHdA/e3N/RdwzC0WK3p25Zzc+FyaSiygtPhyG6/p1xt+OM//OM8mKc0aH+mOXwlrR8oqttZ794OTG7AJPPI+8lFjufTm07S4CdDNKCsYRocGIsPgclHlEn59MMfeP/+jjrPyaoVXYApgjaKxFgulzPRpOgI0zBn/d/cV/ihpTsdUSpw69d0/cD5uH8zVhv2u2cUmpubWxQG5Q0xSRnDCM6jg+J+e0de5mw2W7rBcZuWpNUKqxVD1zAMI9FHTrsX6vWKu21J1zY0x9elWB36mdm6nI+cz0fyIiXPEurbW4rHT5Sbd9SbexJbYs086Xd0E8f9gRADHz68Y7UqGPtx7tZxlRdrYGQCP1KUFc4a/DiDJR8106QYpkiaJRR1SV6kWKXQlCgNMah/7fH5f8VLYtlWqxUfPnzg5uaG5+dnjDE8PDwsGxjA09PTYtCU1BJJYrjOXJa/E42qtPxhBh4yzEXa68KyyVAe0QADbLfbRd8sbLyAZwH1snnWdf1NmovIUWSPEE319ffo+577+/uFqbw2nokGV/SpwgKKZERkAQII5bPEyCgRcyL9Ee2zHNsPP/yA1prX11d++eWXJVZRpDCSiR1jZLvdLiBJvr9oZqU4EZDStu2i9b5mi4WZvc6Qli7H169feX5+JkmSxTAoLKqcd9GxS6GklOLLly+LnlqiBaWYq+uaz58/8/nz56XLIYZJGYLz9etXPnz4sAw3kuhJAZIyzl505NZadrvdIi2Ra1ZVFR8/fuTnn39mGIalCBLtu+jSgeWYX15eFtmFJLlI7r6wtJIMs9/PcbUfPnzg8fFxiXgUqdflcuF0OlEUBfv9fukM/Kf/9J+4v79f0nL2+/2Suy1eCNH7C5N/PB4XpliOT6ISr+Vh1lr+/u//nk+fPi1a72uPgTDvUgBInOfj4yPDMCwMvgxwEq9BlmW8vLwsz4frqEeRJkkBX1UVNzc3CxgX2YkkrHjveX19ZbPZLJ0tiR8V9lyGYUmSS57n3N3dLclPsvbl2S/pPzFGvvvuOzabzfJZ8NtUYSkOJTVIyAAhAkRDLrGdUsDL/XVNRvw/vX43IP/jP/6RVb2mKGtW62rONI7QNPNCutlsOO5eGMeB4OY81hAVeVXi9IQyCWVdzjnHiSUOIzZpaNt5lGuuQBGwOmKBMA0E75jGkWl0DGZkdB5lEgKaABibkOcz21rkKdE7/DT/ztApXDqznCZGEhXwriXRE0k1Dx4K08DURUIw8/hta5n8hM0S9DAPvzEmZez3xNgSvUfBzLi/xRCGEJjcRNQGpQ1BKSb/9qA0KQreWH5NTAxZOg9PqVc1wSQoY1AxvFVsAaXNDOSNYWga3DgR37/n4X5DlqX8+vWFl6+fSa0l+fSO9bpA6RVPLwf6PrLapJi0YnBhTk3xoKwmIQXVY7QjUZq2G+iaE0W1Ik8zxiRhHHjT/s6JKd55jII0zbCJYVWVTH03U5BvD+HD4YjNamxeMno1T1m1KUM/4knQJrDePPD09Rd+/vFHquqWurphtblHYeeJiHFA9R3n568cyoo0K9B6bqv2bTPrjBV07ZokSdE2pSgrencAHKnVrNc14zDy8vLCOE483N9zalpidDTtCYzCYwkKtncfeXj/B37e/g/G9kRwAec82hiUMfT9MBt905TBTajo0cGh/ezeH88HpnZgbGbDrg8T43BGK6DO2KzrZZPRSmG04nQ+0pxOpEnCelUxdAP9MDMcj3f3PNzeEVWg2T8xji1ZnlLWG/w0EJ3HB884TuRVNUd3uokkzdBKkacJhFlqFUzC+rbGjT2f//ovtKPj8cMnousZnIO+ZdIK0re8/hkzkFiDnxQqBqw1BOdoLj1T3zCNHRiFzSzD+QBGsV1vcd7RDwPted5QLDPrubm5mdczsxZQaUU/ToyvO0Yf2N5ZwtSzP59o2wkXIkobPn/5io+BT999ZBw6jrsD+Fn6lCQ5xiRvLdOBoiwpqjXvPnzH3R/+gZubG3RSYFTB5BRtO3I69fz081dc8Ky3d5TVW2LM2CMRlYmxqOggONzUoVWG1QHiHJ/YtQPDCIdLi+4aNlXGelMRh4ngxrmLFf5dsgLwT//0T0su+GazWVq/Mvjn5uZmkSeIZEUkEmJEkyQWMYyJ2WoYBtbr9TfM5LVsQACCGLXgf8/7FvZLNk8xcwqYFBbwbzW5olUXwC4TCaV9Lua/a2mNSC6EMZO4tb8F36JrFUAo7KJkNV9r0YWxllQIkVyM48h3333H4+MjeZ7zyy+/LMNofvjhh/ne0HphaIVBvZYIiPFNrgOwSA5Wq9XynUTeIgyhc25JcUnTdIkLlI6DpE4IOLpOkREDp2Rz//LLL/z5z39eQOb1QBeYQeDXr1+X4kvO5TX7LwytZL8LcyvTPfu+5+npiWEYePfu3RJDeblclnWj1Dwg59OnT0tEoxyznHsB6VmWLZIrYYGNMYuJVbTKAlSlMyHFoTDuYmY8Ho+kaboAQ/mdx8fHhT2W+0G02NfmxGuPgRQQAoyv4xHv7+8ZhoE//elP9H2/aOqvtc7XKS7CRl8XVFLkXWf8Z1m2SKpEmia6dzlnwrKLbEVMzsMwLPr0x8fHxcDaNM0SPfjLL78QQuCPf/zjEn8pa02KDjnvUlh89913/N3f/d0iiZJz0zQNh8OBv/zlL4tMR67JdXykdAOkgJBzKPpxyYAXz4FIW6SrI/fv73n9bkBe1QWH055u6OmGnufdnqg0l6albS9E77jsj1wuJw67VxJryesVZV2jskBarEiyElAweXQ6M937/YG2Gyidxyaz3slEzdgPGMXcOu96tM1ZrW+5vXvER804RRKbEuOEigE39PRtT9+1aKXemGuFMZo8SenbE6MbKRKoKktqN3TnlhZPJEMnGcoaPB5lZhafAHleUpc1TVBM00jft7OZK3qMnpkSxZy84UOcAXmA0QWUn1BEtI5UVYFKI2VlZgCgIclSlDFMbjYIKqUw1jKODqUNRV7gpwmjIplV1IVlU6cc94HuckTzyKpe0fQdMRqarmM1BIKOjG/G1GjU8r6ZivghErwjMYGhOTH2W2y+mo15eY7rz4xjj9WaLE1IDNgkQ6tkNr0CVVlimJNnLqNH6YR2mJiipa5qkrzC0zG4if48oXXOzd0DbvyF82VPmuQzC6vn9JzzsCcLnvZ85OXpme39O8oipVqt8WOH6TsCA02zJ81K0lRTrGpwJTa0jNMwy4vMfJO87nbcPdzx3XcfqFcFzo1z2kZZEgeFISGvtti0woeZEQ5e44NHh4jSkSQxxLeIxps//MDlvEcNOXVRcmlatBt53e24XC4kaUK1yvnw4T3rzQ1lVc4j5McB5yba5sI49m/FmGX3sqNtZ+Oh+f+x91/NlmTZdS74LeF6yyNDZpYkJAGCtLbuN/59Gtus2XYBEIVSWZkhThy9pcsl+sHP9NpR9zaRD/epCDcLy4zMLXy7WmOOOeYYxjCrZijn+eGH37M7PlKuF5hEU80vKJIZxliSPEMbTdR61D+LbZPWWKV48fUcb340aTUjaMv+2LB6dc68WNA0Gx43j+jjnvOLa+Z5zuB6mrbBAnmi8F0gzS3WZKNrTqaIcdRwz92c9cU5/eBH7209zlnsN1u6xo1FmA+0wTN4N8pYDnv0S5seoBs6bh5uISiyckZzHMaB1Bjpm5qm73i89ZOjEWp0ciqrFdpk5BHydcdiuWR9vub1T3/O2du3GJuNwD21RMeLNnTPZttgEoNzL6ZHZpQ3pcaMtofjE2lM4fU9w0sacN93hFCzPzREldANgdxqFqsZeWGpuyNaj0xs74Yf+xj9s95kYFMSAO/v74HxXIi2U9wxHh4epmAN0VWf+gqfDo09PDxMoOjU+1nACjC1ntfrNdfX19PiKUUBMLkgSKtfZAIChI/H41QEyKDafr+frAqF1ZV/l0HJoigmRlFkG6ehKKcSl1M7wFNLRLG8k+FDYedFbvGnSYACgEQ+AiPoFCb44eFh0tMLSAYm8CCgRAD/qSb+1GNaJAki6RCAJZ9x6scshYWcNylG5DyJ9riqqsnpQkCX1pqrq6tpyPdUKiDFmVxLX758mRw4RHYjwHe/339V0AgDK+8XcHR/f8/19TU/+clPJvtD2TcBXeIkIkD31O/9VGJVFAW/+MUvpnmF2WzGfr+fZiZEtrFYLHj37h1nZ2dT/Lx87mmiqoQniZOIdBK89/zud79jt9tNHQcpfEVDLgWDgG+5tqTYkGtXLAS11uz3+0nffzwep3v0+vr6K3nSqTxHukDCCp8OXst5PB2klAAgKcalayNF5ino7ft+SmSVfZI5BhkWF3mVzFaEEKauh8weSOjRL3/5S96/fz9p8sVeUaROkgorMhaRgZ1anMofGQSWf4pMTq5lSQYtimIquE/naP6t7UcDcm3tC3j0rMvRWq4bHGjL49OG3fMzuJ7ixfJut9sxADYpWVQFaTVHqZRu8Ay+RVtDNVuhzA1d2xGiJ9EWN4wx3zYxFHnGfn/AuWF070hSsAmdG+PQtc3I9DgkeNxtGJTB9Q2pVUTn8ENPohNiGOiaI3W9w6dQ5heUVUV3dIRg8MFwqDtsZsnKBNd7YhyBT5JmXL+65nbbMrQNLi0wgNV6dDt5uXl770mJE4jQJkMri9GG6ixjlij6WmH0QFu3DM5hNNRtjRsGirwgUXG0cFOWLJthtKVrG5I0IxLouxoTPfMyJ7qeIhsr2GazYfCBwY2OD1E7TJpiU0tSZDRti3M9Kijc4GibPX0/4KJm0a7RNh01tEXFvj2O0gNjCIzBO1oZnPfc3T2ynBUvXY6U3bHGetjstvjff8/s7BpnSgpSkmKGieCHQOi3GDuQZjlNsyeEgaY5EExOWlQUecXV2Rmfb+/YPN1TLdckyzmZTonBofZb6q4D1+NcT5IUlOUM1VfQjFHz7XEE5rP5+DDc7Q9cvXlDujrDvujiY+c5PDekoSPJZ1xcv2Vz+wf2/YB6AWi8PGxjgLbucE1Lmgai8xy2W6LXoBTaD1yuF6zmBYHI2eUZl1cXHNueummwSTZaJDpPjAorDEZb0zYHiqIkAEVRkljL7c1n7j9/4uL1ijB0/OE3/0qaX3DxWk9sdlAKbVOUSTDGUpY5ru+I0b3MJbQ8bw8sFzMWVcnVq1f87tc5+WxJjP04c7HbUTc76sOe5fIJ5x2uH8izjDLP8f2AG38iijH8Jr4kcyptqOYLsjAWybvjkbwoqeZrLq7nJElO0/fsjwec87gQaJqWY9OgtGFWVcyU4/7mBtf12CTh7GJJNVtgkpyua/EqUpQl7eCw2YyoM3oPeb5gGCLBK2bLBJVYWptzuw+kDczWBmtGWYAbIs4FDnVHCJoiKcb72QeUinjv0GmKin9s1xdVidZj96tpjvR9S9OMwVFKaXrnuVouOL9YkyVwfEn1hPij2Y8/903AnbDfwt5qrXl4eODx8XEasFssFpO7hbSnhZ0Sra8sZtK2FiB0OpxYFAXb7XZiMAUEiPxCWtPejxHqsuCLlEOYa9GdHg6HyRlmsVhwPB4nEC1Wf+JWIQxZmo5e9I+Pj7Rt+xXTLNJDWbDFzeN0WExrzeXl5eSWIm4XUpQIAyyAS/TtMpQpgHlyGHoBW3Ks0zSdjr0AaikUxAv6VMYjAFeGL6XYEemIaKNP5wIErIgWXOYDxFt9s9nw61//mouLi4ldPA1rke86te+TAUix+Lu8vOTz58/c3d2xXC6n4CJhbOU3yHUg1pMihxLXi+VyyXa7Zbvd8v79+8mrWkCWRMAXRcHbt2+5ubmZrjk556fXzOmQsUTYy/EQvTjA1dUVr1+/nhhckTGddj4E/J5644tE4uPHj3z8+JE3b94wDAP/8i//QlmWvH///iuf8FNpijiTiF1j27Y8PT2xWq2Yz+e8efOGf/qnf5pkSaLJF9nM2dnZdH4kpEoGIgXwC9CVAmq5XE7yqt1uN3XM3r59O3WWTj365XjI4CTAp0+fJtvKq6sr5vP5NFchx+TUEUXcW+S+l0JFOg9im3l630shJPewyI/k3EnBLcWXdB7kvItPv8h1xJrx6uoKSbiV7s7//Qz5fE1SLGm7gajs6GSQZMwXa56enri/+YA7bvnbv/4bfvGLX/I///XXDGjmyxXRZnQu0rseFzQmnWHj6OvrY8R5T2LGoblEqzGow8PgR1YgD+MgaLAZafFSDARF3fbo2JNYGNotTiUE58YB0eAwJCRWMwSHUgHvWpqhZehm9IMjRIVJUgKe7fYZbQbO7BKrX+wZUZgsoUpy8i7i/YCOnlSPATAKhXdgDYQYx+AUk6F1QZqnoAyJ1mTGw5DAoDkeG451zWxwzLRGq9EOL5/NcMaAipikHJ1ogqb1Lcd+QNlRyuJDwKDAB4aho23HlMQszej7LbvdhoVJSa2hSGckaY6LBhcV0Y2f6QdHe9xjswLCyMBrazEWkqwAbUgSS9MPJFaxns3wQ0NTt0BC8B1u6DAayjxlns+p1ktsWTD4gOodJh8BUjpfc9g66uaAJ8e7Lcfdni716GJONluwWJ+jioTNfsvT0zPHwxNn52uCTgm6RKeBMsmIJqXrahKbUM4X0C1wocUSOO73+DiwOluh04QB0FmJLRYk2QKtc7yLaGMIAWbzFddvvuHDb/6JencgyTIUo73lfn8g+ogeIsdjx/HxmYv1EjDcfblF6VEKVS0W5JnFZgl5ajkcD+yPHSaLaJuOlp5DNzr/tAPNcY9zY0sxKrCJ5Sc/f0tR5NzefCQvEl5dXeJipNcpmVFUmYbc0vsR2BsNxhrGRNwASpPmszHYJ8tpOociEEPP1fUF/4//1/8T5Tx/+M2/cnf3TLvZYJVjd/eZ/rDH2BRjLMftlrvBk6Up2qgxidMY5vMRJDkfSdKMvnUok5BkM0qVslyu6QfHsXOjX75J0MWMNCoWeUHbDyycJ8uL0Y9fBYpqhTUGa3OyfE6azzkcW56ed7h+oDcZxXwOSYlXCYVOUCojdh7fD2SzGcZoiqoknRXUAzgPRZox9GMwl3cDTX1gGDrSdEUIEe8CNgHzoq9Pk4SoFUoZUpWizIvbAePsx75uGAZHDAl920KMJKnBmkCMnmHoqXSgyMyPfYz+WW9icSZSBGHJREsuThz/8A//wF/91V/xj//4j5OmWYC8GAeIb7BICgRcCwg9ZdgEwAtgFbcEkQkAUytbFk5hgmEEFKcgar/fT/Z8AmRijJMu9k8t1ERXfBrTPc0nwLTvImmRwkWAufxdhvn2+/0EiOV7xNFEOgQC5gRciAZZwImwhsLYyzFt23aSFAi4Fz2vuEMISBb3GWFbhZUUECTuMt57VqvV5IADf3TKEDcX0fJKkI4wq8IcPz09TedDWPO2bafuw/n5ObPZjO12y+3tLfv9nqurq0k+IJ0OKVCSJGG1Wk1gFJgKsouLi+mcnobTnEqWxNrw3bt3/M//+T/ZbrdT98baMd30VJt/f38/pUV++vRpknmIDWWWZeR5PgUIiWOL/F4plE4tFGHsevyH//AfmM1mfPz4kaIoePPmzVeDg6fdC9F8/6lTiAwwn4LaEAJv3rzhv/7X/0oIgX/5l3/hy5cvU0iRHGeRgkiWgMg+BDAvFovpGpXPl/0CODs7m65Duf7lmhAmeRiGr5JMRRcvXZ88zzkcDi+S1PG+kPMIf+wCSPdmNptNnYX5fD6x2gLk5RqX+0yeAXKvCnCXIkcGsKWYFMZcBmalmJFzJve7FONyLP6t7ccndSYVWZ7iVM2Xh3tc33O1OsfmFfnMM18d2LVj22W1XHF9/Zovj08oFIlNxuExq2idQpOgY+DQjJ7IZZGN4CMfB7XqpqPrHcPggEiWJsxmJYNJaYeBZgj0XuGiprQJeZYQXI8nkGhDM3S09YFNfySGBSrJxhh0DdGNmu9DXTP4QDnLycqU5mmPH3qic9iiIEkUrWto+g7nFZGB1EYsDhd6lB9IlMFq/dLuHl1CfNTopBhDhiKE6OmGPdpAnicMLsH5jLxISRNLDIruWNN3A13vXroDENB4ldE4xcP2QN3VpMZQFCXNoadtG543Gxbr1ahJL0s0kaY9cJ5co/TopNJ2o/uK0hkmUeh8AN8xdDV5kbCYF7joGIaISkbgHpTBpAWuUyhrweREHylnK4w1dN2Rrq0pqhmX59fkszNMMed+144DtHNN07Q09FgFzqTY8owqQFfX+L7DhB5rFUGDNyk2q1ienbMdOurjE0+Pc66vv8VmK5KYAC37ukHZgA8eY3Nm8zNUbtg/fWEIAbRCJYpyXpEUJc0QmGULlqvXGDvj2DhQY/R70AadlEQSum6gGwZiek9WLYlDJHhojz15uSCrLE3b4Ppu7F7kKUWRkRUZTVtTtw2HusGmBUFbZsnI+lmtOQ4DbdODiyQ6Jc0tWWa5e3ggyWYslwua44HlqqI+em4+37A8uySdpTw/PFDOz7n6Zk5mM3Z1S9sdKZQeJRrOYdIMm+Uoo7EY8jQldAca11OVBe/ev+fTDzcMTqOjJYma8/lstKtMC9JiRjVfEgAfIYTI8bDHeU/Ttuz3X0i0wSQpWVEwX1iSPBkDepKcdhivMZsVaDMOOudJgh88TdfjvSLLZmiV4n0gKyyLs1F+kyQ5ypQEEh7ujgxmjqoSHBZtSpJsjlIGr1Oa2tN6R1GtSKsKxTCm+uYFwXuc9ygdQUW6vqaud2jlsWZ0NUqS5CU1OKCSl4AyY+BlEMn5gArqBXjkLJYFve9J0w3740DXtRz2O5p6xexsTlYW7HYHnB/IXxaS/903WYSVUtze3k7aZhl0Ojs7o23byXnj7du33N7eTq3w08Aa0XWLvERa5CJ3EOmFLIYSNCMMuyzAAqiLoviKiRTPbSkAZOGVwbRTJwgZ0pKhORkSlMEuAbJyDID/E/iWhfwU5AtoPmXthYGU7zh1XxFfZAGgAgzEOlKAkISp1HXN09MTFxcXU0iQSEeEZRdm89Ry8ZQtlqRH2cfTYVABX8JEiuxGBl6F5b26umKxWJDn+cQgS1Iq/BFMyYCcgCS5HgQQiV2kaNvv7+958+bNV3Hth8Nhkh+ID3ZRFDw8PEzgVFhcYY/Ff1yKKtlOHXVkYFC6OcLuHo/HSfMurxEAXJbldN1I90W6J1IISWEoYFXkU1mWcXt7S57nnJ2dTUWitZYPHz5MSbh3d3csFgt++tOfUpbl5EYCTFaCp1Hvct7k/pjNZvzsZz/ju+++m6RPwOQJLy4scg0IyJfCQfzmpbgrioLVajV1Hk/tAcVtBpi6THLvSKE9DMOUsimAXO4p8V+f5rNeNOtyDcn9LO5C8lyQ7oXo0OV8ir+/7KcUGqeFrRTNAtblGSHSFGHXxeFItPIXFxdUVTU5/Yjd47/5DP2xD1tvStJyziJb0rSOvjmglOFwPNIP42DWfLWm956PNzdUszll07LZbiicZ7k6JyQa4zVGpxx2mzGQJzNkaULbNiR1QtQjgxljpO3GQRmt1KjF7RykmihDncZSlhnVvMDFUXeaFwXRO9qd5/lpg1Ke9eUVeWpxRY4fIoNzuMMBRYIx6Tig+mJJpyJkNiEGTfBHjocjEU0MHo0jugY/NFjjqYqMLAUXO0KIaJsQogGVomxCdIF+aIjtgO07tOunkz9O7bZ4r1AoQoxEpbA2JbwMk1oNSZZTt6MbiU4hDIEszVks1ngfsEmKq3uUtpRlQdN3DENNkaWjB7aOKK2xJiExmuB7oivI82LUkttRr9/UA+X5mrzI2R8ODM6jtCWiedrssCpQZCkYaJ3j4XnL63LOcn2OyWY4Etpmw+a4Jc0rZosVPnisTcmyAqzCxTEZsWlqCmvIsgRjFLumoTCW1dU7Yp5xqI/gO/qmJs0qkiwjyXKaACFqbDqyTyqtMAaquSPNvqBUSpLk2FRTVBVajZ60s3JG3/Y0zYACTIzYrGJ1fs3bdz9BD0fqdg9W4V2LwmC0Ic9TLA4TLDrLyPMSY1IWyzlaaw7NEZNWaOXGwcRoxj9oJDHKKIXRBq9emDULbVdTNy1vzq7HDkcMlLOSpt6z2R153jfYYs7rbw1nxz0Pt1+wRUUfYPCM2ns0yiTgPX3vSLPRuqxrjvSHZ1y/45CnXF+/A6WwScpiccYQj6SJo2mO2BR4YcZMmpNJi05BkedE76gPR9zgcH5s7+0ONZiEqDRZUWLShLbrWZ9dok2C846yKllUMw7HBpVaqllFCOZFCgX9EDFGE4gc9zsOtaNtGubLC4rZgrYLDP4lstwmRCxoRVYUzBYL8tTg2x2+PnIMRxarEqPBe0fXDdT1kaaryfMEHz15bimLBK0jYyTpHwHkaTiEgDUhEYpsZMrHOQ7F8cW1I8bZy73keH5+YhmXP/Yx+me9yWIpsoPj8YhSapKueO+nFviHDx9YLBYcDocpzfL8/HxiqqTVLBIQ+UwZvpPz9adSFpErnO6TaI2lbSzgXDSkSimur6+ntEQB3dKKFtcTYALU8gzfbreTJ7KwlsLaC8gUllmkFQKkRQIhoF7YZhmwFNb6T73NT3XswtzJsRaAnuc565fhatHCa62ZzWYTcBQwI+DmVMfrnJuGYJMkmc6nFES73e4rGYewqgL8uq7j4eFhAlfyeyQ8RUCeMLrC4AKTR7YUeBKAlKYpr169IsuySQojNozCEgvDL/sh/xQAfgri5vP5dEwEUAsgjzEyn8+5vLzkpz/96fRdUjDKtSWsrlhzSpdGBmllcFjOn/xeKThlzuB0cFLkN8fjkcvLy2moeD6fczgc2G63bDYbyrLkZz/7GYfDgZubm6+GluXzBFjK+RbAvt/vp2JEvOKlqyDnQAJugOl3yIC0SKYEnMs9I1kAco2K3KZpGq6urqZOmBxzAcWn96d0iwS8b7fbqcA7tUmUc33Kkp8674hneoyRs7OzqQMifvDSgRF50qkzihSdp50oOYdy/k5ZcznmEiIm92cIgYeHh2lA+d/afjQgX7/6GaDwfU81O7J7emantiR5ztl6QZHA8emRxgd+9/0H/uY//ke++cm3PP1//5Hvv/stb940XFy9Zl68mNc/HYmhoSpHqUTftdS1JclKbFZRzBKKck/XjKzAbruliYb5xRybJGg9HhBtLWleoqKna0dgVJQVbj6jO25IEsNyXo62d3jaztD2Y4uvyBcUhSWxKdZY6uOo1dMqogEVFSqMvuIGR/QdbghE3zErDfN0Qd8e6Y5utEgsSnwAFxReGYYQaYcxKr2ICvty4vM8Q8Ho/WwztDH4EEeNqwX6gE1GvasbAsehI7MR0oS8zMlTQ9M1BBU47Gs2my3DECjLjENd0xw3vLq+pJxl7HqPTXKsSVFOo5OMrKhwfsbt7SfuvnzGpgu6Y0dYVSwXC5qXB1NR5BRlRX8MJFbh48h6KpOS5DOUzfEqwZqU6GExn9N0jnr7TJGkY4iMNSgiMQBRo41Fa0tdN8R0jypWYApaZciTlCTvSPqBrj7QJU8kL/IMnZTMV2sG58mKhO3hAENDpiLEgqbVbDcHhm4cDk6ynLSM48Dv0OF0itKGYfAMLhC0ZXF+xZtvf8ZweODu3tMrUAwsZznea3RUxCHwfH/HejHjbHU+MtNE+iEQE4goztYLlBm9vLf7HZvNDg2crZaUZY7vap4eN1yenxHxDIMbA3eSlBgCIXjCMIDWGGOxJmW2WLJaLtHAcbehVGqUiuiU1ChAhk8cST62zK3WJFqxPWw57O8hRmblnPP1kuf1ku++/IHN4wbjD5yfr0nSgvjysK20HSPk9ViMRO3QOqW4GAO+IpG6bdntR/a8bo44P9pT7fYHGFrSNB+HOneWfrZAGUOez3C1IrEZWmf0fQdxoGsHDnVL0zhcsFRJSWkjqfJoEyBLaX1L8IoYA8FHTJajtSL6gdAeOOzu2Qx7tsuCs7PZS7jTaPOptMJaTVnlnF8smc9yNB2E+NVg1ulQ0fiwtYTgXsCfo206ur4bOwpGY4zm+fmR3WZDmo6ZBUP34wZ2/ty3t2/fTgBTBgufn58nuYJomYdh4De/+Q3/5b/8F37xi1/w3//7f+fXv/413377La9fv57Y5/v7+8lmTFw5DofDJDGYz+dUVTW5UQj7Kgu/sHTCkIv2M4Qw2R8eDofJ1UJAhAylCsMr+yM2jOLAIIu2gCpgAuMCOkSfLYOh4lMtbK2AJZFWCJsmzNypK4kAA7Fgk+JFpC7i0CJFgACD3W7H09PTxB6KbOLNmzcTuJHfJ0ygOJR8+vRp8quW5M71ej0BRnGyEGZaiiQB0wKWBIiJ08lms/mKLRZwKq8VxvPUAlMKIWG2RdMvoMlaOxV8UjQIsJP9EocS6QQIcynMtjDWcn4uLy/52c9+xn6/5+bmZrrWF4vFdC6cc3z58oXVajUx7XIfCHgTj2op9MQDXWQ84jBydXUFMFlFngYlnQ7hiovJer1mdDvbTPsv+nFgGjw9tWQU4CgSntlsxsXFBefn53z+/Jnb21tCCFxeXk6AXPT8AjxlH8TiUeRGoql2zk0dpizLpv8mRdN2u51kKTK4KQy1FNlyf8o9K+da5FLSyRGXFbFHlSJEUjpF231xcTEVeFIEJUnCfD6fujjAV58nnyVrhBTMIs8SRlwcl+S4yMzMaYflx2w/GpCXZ+/wg+PwdEdazAlRUR+P6LamKFI2my23X+6mKeGIZl7NuLy44NOnD3y5+UCeJRTzOdliTZFF8gxSrccIdAUKM4ZvBEbNapK+WCseMFmJna/QegRoI5ulCRF8jCRZjgkDUS7KxKA1pInBGkX/ok9EaUJQdJ3DWs/MJFTVnF2a0wzdi46xx7mARlGkJS5Egjvguppm8KPzhwtkRuNdDxqij8Q4DkH6AEFpXNS4aNDaQjT0LlBVCWlqOB5rmuOeogKt1Et4Ug7WMng1flY/4D00fUOWwXq1YLFY0h56nA9keUpdtxAVZZ6h9JLDfs/QHjEMGAZU8GiTMOYTgk4SkqRC6Y7t9oHbm49cXr4DB5vHB9bn51ijiFlCWZVAZPCe2XxGZg2+b1AqUi082ua4oEiVIUkti7nFu8Dj0zM3xyOz5RjOMQ6wRmCUWmht6fsO3bRkTcv88pJgMrzrcSrB6IRm/0johjEBdX1JCAoXQVvN4Fq6fiBThugVQ+PRZobzT/TOkeUpbVNj0iP1i25bF3Mym+LjmPi4bwbWRcby4hVn12/Y1Vtcd0THnjJTeK9wPUSrMWkK1pCWBd0QedwcCCohzRdkRUVSlvjgSdJAhaU57l6KLM9ut+fTpw8c9ltWiznH5kDXD6zPxuEmwqh3jsRxwNJ7zlYLkjTl5vMNTefI5gsiUC3W6MSQWgOJxQNZPrKHRr9M4h+e6JsjzWHH8XBgMVvxi1/+Lev1Bf/c9WyPDWqo2R2OXF4NvHrzhiRNqdsjum+xNiG1FqNHln8YhpciqGCWJORFQZImtE1N17coBbkBrRV+aMiMBefZP92itKU2zyRpxWy2wjtoh5b9ccfucCCiKcsFKilIjOXx9iNJWpIVFfZluDktNVlZopOEoCx+aMfreqhpt3dsnj6T7Uq6v/1LxnRdw6zKWSxntNk4OLpclCgc1owFlA9/TMYDJrZqDIEZGbXt9om72wPbzYaucWACRTanzDOGbkf0jtVyibEJhxc/6v/dNwnMeHx8nKLqJUq6LEuenp74+PHjxGbCyFJeX1/zhz/8gQ8fPkzSk/V6Pb1OFn5hgOGPkhBhXkWXK8mUp2ykdEFkAZeF83RxPx32EimJADfRoYoGWLTuwpRKS/z/yoHBWjuBGFnYZX/ke6Q4FHA3n88nACxx9AIghBUX0CDgUToJAphP/dslDVSkGeJNDUz78KcuHCKpkXP26tUrnHPc399PBU+WZdPgofd+GqSToc/VajUdVwHeq9Vqch85Ho+s1+vpWMoxkAJEhuaapuH6+noCtDCCdgHcIQTOz8+n4yrHXACxaOxPz3Ge5xPrKwyvyKJENnMquXn9+vV0LctAqmjhpVCQDlHf9zw9PU3HvKqqSTsvxYdYgXrvORwO/OEPf2C327FaraaBwYuLi+n8CHEgcxMC1j98+EDbtsznc4ApcEj+yPUpzP1yufyKJd/tdiyXS/76r//6q/RY0fG/fv2a9+/fT17vYi8q9w4wFaDS5RCplQBVuceESZbz8Pj4iDFmKs7E7Ua84qWIFT24tZbPnz9Pv0fArhTNEhIk9/AwjKm29/f3LBYL/uEf/mHaj/l8znq9nroA6/V6At3yfJH7A/jq/hRQv9ls+Pz5M09PT1Oyr+ybFBVnZ2dTh+fHbD8akJOt0GYgKXruP35gu9mTGccQxgW2c55Xb7/h7GxN1zY0XYeuNW9eX9M1v+T29guuqznEniwxzIqEItX09ZFoDUqnmCShrOa0AwxDi9aGLM9AK9IsZb5c0QePDwoJ9+j7nkNdM88yympGcOCHlq539MNAiAHvHG0/4GN4kagYtPEvgR6aspqRJCmqrNDaAHGUqChNmuQoHxnaDrzH0VLvtxQ6IdgU73tQ0A89/TBQREUITA4b3nusMWgMUSpLrTGaSdYgPfQYFSEaoh5jw90wBrt0/ah9TtIEFxxD8Chr6J1n2O6wiSUET2INF2dLHh4febz/TGoVuS0xRo0aW8bCR2vDbD7j/HzN93/4nr7ZYUzG9uHIYlGQp4ZZWZFmGX3X4P0wLYYxMkpB5kvyssKmGfrFT133Hh09oTtS1w15luCswRHBWAx6DFoJYBNpzR0ogh/162hQKanJ0DHycPt5HL6bzUnNiu7Qj0mrKrCeryiSnKe7e1wzkJVzsqLAJpHFYo7znuA8Ko6Sp/jibRcVo7QIi7IZq8u3dPtHHjf3hH0khp7j7oGyWFDlGY9Po240Lc7YHg98+vKISuacv3pLXq7wQfG4b+m6miIzzIs5fughwtPjA0Nz5LDbsT5bYVNDs2lR2rJcjS20p/sHjBrP//5wwIfRDvPN29c8bw/03ZHf/+H3XF6/5q/+499TLS8xKmBTS+dAW0PX9wyDY56PbFKSJqMlp9F0bYMPgbOLS7796S9YlYbucM/tzSc+39zSucDl1SVpltK2NVZbVFFQ5sV4rm2CyRRKje1z5x2oseugCVilWVSjRritO7puvB8Oh5Zju6XpBopyyfGwAywmsS+2m4YsL7i8vKDpPJvdhrvbB7TNqKo5ylq6APnigvmVJ59d4iPUhwMx9PRPn6ifb9je/cBfvPl7LtZLjFY0TUuaJLx+dQ3GkqZ2TEmNDq0iGI0b4qSfFcAmbGWMo+f7drvl+XE7Hj+vIXq8G2jbmug6uq5hVs5QRLbb5x/9GP1z3gQ8l2XJ999/z9PT01d+xX3f880330xteFn8379/T13XfP78eZJoCNgTOYYstgKQJaFTWuciRTg7O/uK4VJKTd8jrLqw2BJ3LwuttMFFx3t6bYgNorD18Me0RmGqT7Xk0rYX72bRtsv3yXuFLRcgeAq6T72OT9l4YeSlMDgNS5J9EXZPAmREV50kCZeXl9zd3XF7e4uE3JwOg0rBslgsuLq64re//e0k17i7u5sGFYWlFHeTP64RcXLMkNRRYSXl9wvDLeBGgPTXxfG4RpwyrcB0jGBMEPfeM5/PWSwWkxZbCoIsy/jy5csErgU0SmEgMwinwEuuMwFo4lcuADKEwPPz8/R59/f3kwvOfr+fYtTfvHnDbDabGGFxw5nNZtP33t3dTazy+fk5WZbx+PiI1nqSWdzd3U3HdrvdTvfHN998M80O/OY3v+HNmzf85//8nye5jAx6ij2h7KN0UKQoFfB4fX3NL3/5S2az0SXv48ePU/ruq1evpnMt0pCqqqZCV47haWqn3J8iC1oul9MsBDB1atq2nRJ05difDnG/evVq6qp8+vRpegbIs2W1Wk0MtxTnMUYeHx95enriy5cvvH//ftKlywzF27dvp8LyNP1TJDOna4QU0KfPlqenJ+7v77/y3ZdBcikCZTh3s9n8qGfojwbkplzSHY4EMrohYpOcIveYwYOB86s3vH//s9FuaWiJfgSRi+WCv/mbv6aqMvqup+k7nh5uIQaGrma/20IEpVO0MeRFRR8GQlSgNEVZYGTq1TmiEV/Z+KJJGwf9isWSNElo+57jsQHGobEkKdBJho6KBIPWBhcd6BYfNM5HijInSVISHYkhEqLHR0+W5xT5DNWPnsohtMyKgNUBaxRKg7Ga0Dm0MVib4l0gdj1oS3AOTcQaTXRj4qUbHH3X44eewXmCV7hoSIsKsCiTok1G8AETIlG9hEIMHqU1xlryQrNYzSd9lE00bdNgE8NyXtG3DV29Y/eUsLr+BuLIImQ2Ib7E1lujqMqCMrWErma2LtgfjgztgSQtyFLo+gN912BUgOAZ+gHwDK7DmlEDniSGyJgWCgFFYLUoGdojh80DxqTEJEdZg7WG+eIM19Zsn+/pvCdz0NV7Ejs+tG1eMbQ1s2qOxgOOw35DvrgYpRRRYZQiz1JmZYXvOlxuwR9IE4N3NUPXEqJCJZ7EpoQQRz94o/DBkZcFJk1wUTM4TRtSVDanUgNVriiSFBUN2+2B3dPjuJB2nqgjQaXMlpeY4gxVrHC9xzmD0ZbeNfioWK6WmNCyfx413UWZc3V9SXjpVpyfX6KN4Xg4EIOj8wMoRYhw/eqaq1fXrNYrmt7hfKDKxnPU7nfMF2uMiqSpxalI2/ccNg1FVmDimIoaoiJJMpZvVpTVHKJCm4TF2QWHzRf6rWK5OseH8cF/OB4xfUtVFeO8Rt9xdB5rk3GoVxmc7wnes6jmZEXCUcPw4rMOgWa/Z+jHATnnPcF3o8PRoGmbIxHFYnmGTTOqZEFUhm7oCS/38mIxw9oE7yPd4AjKoUJg/3zL49MT68t3zFcXKDegwwBuR5E6zs8qvn3/hnlVEb2j7weCztB2jLa3RtG1R6LrUXlKCH5q7wszJ44P1lpCjDTHGtcNeOcYJzzAJJaLi3NmsxKD4bjZMbiOum3+3fbwZZOhMmAagBN5gTGGb775hp/97GeTd7QAp/V6zT/8wz9M+ua2bbm9vZ2AmyxmAsRE9iEgoKqqibU7tTEEpoHI0wG/vu8n7aoMcwkDJ1IRAZXCTs5mswmky+J7qj0VZlwkJ7KvAjDEBUXAhngUnzK+cjxkyE808adaWZlzON0PYNrP02N0fn4++XMLYyne1SIHeHx85PXr19NnCKsqQESKInGu2G63k82fWNjJoKl0FYCJCRXgLkWPFBar1Wqy4JNjL8XBarWaQl/E17uu66/cabqum1hhGHXGYpEphY0ME8twqjiEyD0vx1QkL3LO5RyepklKN0achOT/PT8/8/j4OA3cCnBerVaT9EjOtcwuCHMq2nvR5ot7ijGG6+vrqQtwOlAYY+Tt27e8ffuWi4uLiZ3O83waKpShSilgxNtd9O5yrpIk4d27d9MApDiXPD094b3n/Px8OpYi1ZIOlNy/0s2Q4k8GdmVIU+4LYALfp0y/DEaLdn+9Xk/OO1rrqdMETB0YKajl/n58fJx85c/Ozr7qJmRZNsmOxMtdWHspfEUOJyy/yIMkCEvA9WkHRY6H/DZgsmgUEP78/Dz9ttPX/a+2Hw3In7c17e6A6QMwggoTd3TPG5arNX/5l39BVizRRqMpxhTMbrxA0sTw6tUrFGOl8OXLLW1zJNGGxCYEF9HKvLSLE5QOhBjp2m609MszsjQbA1GMpCclo22Z06ioSPTozZwmiiHJMXmFn6/I8gqbVaQ6Y6AlhDgaLCtLAJTWKKVJ02yMJx86dvstHsP6/IqimtO6I9okdN0Gu8jJ8xTfjl7lAUBHirJgNqtI8gIXFUpBQsCoSJEYlLd4mxB8oG1rghvwIaJUgk4y0jwjekuvLR6LMqCMh6hxw+h4MfQ9MStIs4SLqwvaZmRmh35gv++oquXo15kYnp8feb7/QlHOKRYJWWqxBvoQ6YaBPnQkWjGb5bimZVkk9POSevvMfBlJipRmaFHBkSWaIrNjamSMpFYRfE/XHnGuo8hyFJpoNUp5uvaAdy9RwLsN2eKMdL4gTSzF+SUWx+75keNuT5pX1Ptncm3I0hLlPChDNVsQ6Dg0DY+Pd5h8QVYsSNOC1AY0EecaVuuCMq34Eve4es7h+ch+94wLipyMpu2om5o0XVBWGUUVUX68BPb7Iw9fHmk6WF+957iL6GFHYRVt3fJw85H2sGd9cYEPkcFHkmJBUq6JyYKeEmcipkzJtGc4PNJ2O2aLhJSUIs95uPvEYjZ2KUIApTRZPuqyj7sdRIfzA9oYIhGbZUSlqNuGNLU8fbllVmRjt6E5oPG0zZFeW8rFBcYaBhcpqznaKOohYPMZCxuwZuxGdM5jdcZyfcYHDIemY15kWJtydn7BYjXHR0fftex2G8LgCUNgsVgyWy5ROmKNIc1zgnMctqNMSilNluaQpHRNS4zS6oukaUaeJOSlAp2O1pY6ZXeop6wBtOVYd2iboI2hrEr6wY0afTdQVRnJEOh3B7rNDaUeE1RdGEh0jVMNWWFYrVdoFH03MAyOYCzBD2jtsTajb1tUdMwu1tPUOzD5Pp+27LXSOOdfirCxCDEqpSwy5rOK1JrxWaNht31GmZTVcvFjH6N/1tvz8zO73W4CJmID1zQNZ2dn/N3f/d3UOpfhSfF9FsZKKTXFv4s3sSzCp/Z2wh4Luye6cgE08j4pvARwnLpYiLWdDAWeDtr9KdurlJqul77v2W63AFxeXk5+1zKMd+rgcdr6luFSkfMIKyss3alOVRZxuTaFjZZrV94nbL0s/OIXnWUZr169mmQDwpAKU5mmKQ8PD3z58oWyLCeJkGi1JfbdWjsVSuJBLhHqcg7FaSLP869CiuR3iERE/jswFRwiWVgul9N+iY766elpikCXAVthfWUQUAqu+/v7qTgSzbnIFs7PzycWt65rHh8fp+eAXEOiV5/P58xms+m8bbdbPn78SNM0vHr1iufn5wlIHo9HPn36NFkwCkMqLLQcS+kaGWMmuYgM+RVFwefPn6cZBiko8zxHYuAFXEpXQIC2yC3u7+8ntl7mBgTgioRFChsB03KMpLCTc3F+fg7A4XCYCt2rq6up8yRMtRSLomMXgCtFhxTRcs6kIASm4y7Xmxwb0dmLFEnYdTk3ci2e2haKjaEMup5KTk4dXc7Pz6dumRzj0wJWyM3r6+sp/ROYJF+nn3c6HCqD2Kf3t3ScjDE8Pz9Psw0/ZvvRgLxte4JXZDYnL0pSN8cSIS5ZrpaslnOULfAx4PqOwTmigt6NTJPREa0g+kBmU6JxdCZlMVtik4wkyfAO+sGhTYI2Y+VkVSSxo3+mT3OCzXHBkGUpZBlhSMjTDKMMfTsQwoslnO4waYmLFnROmit6DG4YiH1kRLwaHyLHY/3SJnMQA75x6CQnyTK0TYnUlLMFzm/wMYDW9F0LKhA1BKNHm7impVgYsiTHO83usKPvdpTrjMxolE0IsaPvOlRwJGlGmWfEZIxAR2uUsTgPo8v5yLqnaY4bBgbnsElCDAptAB041ofxlUaRpoblYgZh9Bn33cCH3/2Gn/7Scv36G2xa8Ow72nrA2EgxK3h9fcn2/pHMKl5frHjc7Ah9Db7ARMfhuCe1lkSNmnWrNK3pOR572ubAYb9B2RRtM9ARHwa6roboXsD5EZ2PNkUuRECxWl/w+vUbbm4+gvc0uycCkfL8FYrIsR0ggDYJJnUMePqmpiwXLKoZMXQcjzv+9VffMS9TfvnTd1SV4vy85Gx2zX675/PdlqFuOTQdZ2HUz/voxrAba0i0GZlzW+HMnMQE4u4Lh92ecIzsHp9p91tmWc7QdkRtCbYgr5aYdE40FV6X9N7Tdz22TMiKCtp2lMj48Xrq2pYhN/R9R8SQJCn94AlNT4hh9BVXhq4bB2+U1tRNg7YJNkk57HfgBroXF5TmsKOLhqHuuc5m5OWKIs9fdOSa5cUrjscNn7//F3xTc3X1lqKc4b3i4vIVl9evufnwO7reM5+vRreixJIYS5YlZGlC6Aae7p7YPD3hvGdwY/Lm2dkZVVni3PCyyGagoK47YtBENUqnbJpTzedgLL2PlPMVAcPj856oLcv1JU+bLaBwwZMpjUoS+qbDxUA1n1MSCSFSlZrlrKQqSxLjOR531M2erjsQYs/l69dU89FCq4+Ktu1pvUPZjPKF7UzThNQkFEXGZvPHRUJsx4AXFrMmRvOyoEls9ThJX5XjPEXbNmRzy8XlGX4YXW/GGY1/36R1K4N4p1Zp5+fnrNfriY08ZZekUyELnnyGLMLCjJ0OMQqYkGE8AaynenBxZzkNEBEAL6BeNL0CmmShF9ZXNLySmiistAyDyneKd7KAf2H34I8yCHHqWK1WE1P7+PhI0zRTO12G+E6lOyKzOPVAPmXcxJVFQJJ8hhQm0rWQ161Wq+k39H3Pb37zG/7yL/9y0go/Pj5Oriaz2Yy3b99yd3eHtZZXr17x8PDwla2kDHSeyocEfMrgm5wXYWeFKRWZgxREAoQuLi549+4dHz58IIQwDSBeX19P75HfKOewruvJvUO02f/H//F/MJvNJi/vy8vLKVH25uZm8nw/7TTI9SRs+6mbjQxQHo/HKQ1VWHABdyLTkYFOYCpoRF98qg1vmmYq4ICJmZXjK/vTtu3UfZFuR5qmU3Fx6nUeQmC3202ps1KoaD0mou73e377299yPB558+bNpHF/9eoVb9684fe///1oYf3iuiLXjhR7Xddxe3vLw8PDxHZLEJJIcgT4K6Wm+0eOsWjGpUskA5UPDw9orbm4uODh4eH/9DyQAU95toQwJnQul8tJay5af7lG3rx5M3XlZFhUCpyqqr5ytynLchq4PS3UTztw8kyTQCOl1DTcLM/BxWLB9fX11F2RZ9u/tf1oQF7kxRhA4hracsZ2G+mHfrTgW60YvKPrdngf8MNA3zWjVlQbiiLHu576uOfpeUMYAsdDTd853r/7htlySdN7usg4QJbmFHlGklhUGB/WPngSayHL8J0n+IDzDj8MuF4zNA21UxhboFDkeYGODh81zoNKLNZmhBBHjXccXRO8D3Rtw+BGWYrVGvD4GBjCQO8dPkJRzajrDHDkeUHMFc4FOt9j05ymH+1/8tme5WpO09Q0+w19u2WvE9JlijKaOASCdxgiRivyLKND0/cdXilMajBYjNJE74gv7H2WavIsw1jN4dBQ10di9CijSI2FKAyPJ08TVsslVhsen5/Zb+5ZzCvmC8hThdWRoe9IjWG5mEPfUdcHitmcPLP46NAEqjwjNSuGfiBGNzqlmICKjiw1RJ0QoqNpD9jMY7RBGyiKjMNuw367Ia8sQ9ex3WywSUqRaooy4fLqir5vqOuW7rjDB4erZixm57QvDxX/Ir2ILtLWe7q8IlkswCRs6wPb5wcOm46LleJiXrJeldSbnsZafFQEp+gcNIMH50kZQ3V0HBc0HTU6q2jcA/umI9EZNi1gOBKHAR0DqTVsDzUmGqrzM8r5ObZc4JICdEZwLYMPNE1PVAOFNWgVqOsjz0+PzGYVq9WKajbjcGiZzRcURcl+u0Ep6Nr2xbKwYbFak+UFKIhxZFvOVmtMcOx3ez59+AFvMtbvfkrIFyzPX2FsgfdjImaajhPs66s3dN2ep9tPKJOgrcH5QJJlLM/OSLKcH377a67Oz0hyg3oMLNdzgndYo7FlCWew247DPVmWEbvA08MDGz1KyYTxUkajlCVJDSZTuCyncz29j2gdwRh8hN55Agqd5tw9b2jabhwQLUrSshqlXsoTVMQkGUrBmC4bCL5FOUhtQlIZZlnJvvHMV9f89Bd/yWy5ABQxKPre0XuNVQnOB46HI5kN5FUxJgKfDJgJW6JebFVDDNNAV3McH7ZGr8jLgix7aXW3gVBqVoslWVay3Te0/fD//8H5v9EmA2ziYiIOB1mWsV6vJ/AhjJz8u7CtojWXVEmxZvvJT34yBc/Ie0+BqrSYT6UGshiLzlpkIBJWJPt7avMn7LnIUaStf6oLFdmDsOfy+eJWcTpAKf7ewhALuzifzzk/P5/AqshZhGk8dWERICjyHfls+OMw8ikLKazjbrebgOap64YAQTkn4gjx9PQ0pV+eBr8Iey4yH/ETF3AltnYiATntNp16ncsApbD6VVWNcxrPz5PTy9PT0yQhms/nvH79mq7rpuFWkUNIB+I06VVCXg6Hw1T4PT09TTri9Xo9/dlsNtN1I+fvdJ5AgLQUPVmWTR0I6WbI+4DJVlKcSZbL5XRcZJBRijEBd8IEPzw8sFgsODs7m2wARXv//DzOpkjBIr/t1GrROcfFxcUkn/nuu+/QWvPtt99Ocg3pMMk5EODdNA03NzfT9SGg/vz8nKIo+NWvfsXV1dXkhiMsuYBT0dKLq03TNNzd3XF/fz+BVHF+OWXQ5X6U43sqDZPzeX9/Pw2Dy3NFOiNyzKUDIoWydC5kAFuGhv/yL/+S1Wo1XZ9STMo5loJRfsPpEPKpJl72L4TAfr+frstTadcwDNO8wmq1mro7/7e7rOAjZZWTe0U7W1CblMfnBp2ALlZs64HjsaHrW7QC5T0aSIoCrTRRRaoy55tv35OolH/+p3/mebNF25Tl2Tkrk7A9tOgkQxlFbSJ+aCAEuqYhK+ekjMOc48kcB7Bi9NTHHV07R+mcPLdYa9DecAwtEAnOESKjxZwbJ6P7YSBGgzYWYxKMSYghMvQ9iVFgI33w9NETXpw2jBntEWeFJYmWthlonjsykzGvLCpJ8K6j7w40zR5rBlQC9X7HxfKcNLF0TqNfPMbNS6teR/C9Q6cKrRXav2gK1ajTjc6TZJpEG6IL1McjyihW8zWL2RzvPE1TM/Qtg+/JqpxVmmKKkmq1pms7Pn34A0X1zPnFFWmiObQ9xJw0z5mvz3i4u8PvdqRZTh9Hv+Y0y9DW0LQ12912TKUsM7KiJH+xddTimasZh0iLhMe+xg0t2uQkqcLqQFvvSdOCMp2PnumLJcvzMzr3GdX20Gs2j7ekeUUyL0njgv2mwxJItMN3NX295bAvKMoCXM/bqzP6bsPh6YHElZR5Rh+hWJ3z2p6ji1cszl4TVI7SKSpqfNviQ0BHPVpOpiW10xAyyuqC0G45bJ6xaQIqoIxmdXUF+QJdrnBqHJBJ7ECelbR1S248odtzqJ+wmaMPgXr7zG7zPEZilwswGUmmpwcieNI0oT6Mzj/HY83F5SvSJCFNM4ok5f7xkeZ4YD2fkScJH7/7Hp3m/PQv/pKQZ7T1lqgTospQUZEnGdoqovOsz98yq1bMyoqmHzBak2YpF9eveP+Tn/H5+z/w+PjA1eWKKi/RYWTqjdI0dcPgA0mZ03YNvWvJ8oRZUtJ3HbvtHjf03N/fvTA/c8qqfMkLcJSzivTFktHFiM005WKJ0zlBaXSuqFZjOE/d9AyHhiTJMFmBSgoGH/GuJ03Vy1yIZte0BAJFblhfXnBdvmO+XjNfXaLTAmUzYjC4YFDKkqVj16lpGtIyxZoE1w1YbRgG9/JwVSijCT4SI1iTYJVm97Rlu9ly3O+YLSqUcmiVkiajVCV4cN6RJKM8J4qx+f/mmwDxGCPL5ZK7uzseHx+nhfhwOEwg+3SIsKqqiUWuqoqf//znaK35H//jf0wa48vLywnECGAU+zGRHYjjx6nbiQBukQqIHEDYdmFGRR5yylqeAjTROcuCLiywfMapnOaUeWuahoeHB6y1k3uKBKKcSnJ2u90EhqU4EM25gCWxOhTAK7//1OVCGEcBCuv1muVyOQEFabFXVfVVkEvbtnz33Xc8PDxwfX09SQxOUza/fPnCZrP5Sjoj56Ku68niUuweBUSJllzAqDDK4oIinyHBLmJdJ1Z1p5r6u7u7Kb1TBuvkXHRdR13XbLfb6bvfvHkz6dFF2hRCYLVa8fOf/3zySZfPAL6SGkm6pRSDYrP55cuXr7or4mMvNopSZMl1LzplOabCtD89PfHq1atpWPg0yAaYXGCEZX79+vVUfGVZxt3dHfv9fhpg/fWvf02WZfzt3/7t5Dsu95Zo5qUgkEFIkSTJ979584af//zn/O53v+Pu7o5Xr15N3aNTGYlzbkrmPbVWPJW1yECtfI8UKGJ5KKx1nucsl8sJcOd5PnVyjsfjdN/LPSaFeZqmU/dAiuE8z7m+vp6kKuJII0WEdEGk0KjrehoSlXkXKaJOHZJOu05S6G2328l6UkD86XNBhkV/7PajAXliNd61HPYbmnpP09Y0fWS+PGPQM3y0JHnK4COur0d3iwh97+gYLfaaZs/56pxqtebV6zc8PD3z+fYLSVlRVjNSm6BM5HDc0tZbjApEAkliKPKcJLFEpbFmrNCcYnQrCQ4VPT50uKHBOfDdkeawxRrN8TAG9QReWmL+JcDBO9CaclZhlafdP1HvdhQqJUlzlFaoRKOzBIaE2fycXI9DjmkeXuwZPcEF0ipH2RGQ73Z39P3AfJbiOs/2eUvw42vaej8CQR1RxtI7R+cdTevJkwLvenyvx0AZq9FG0XtH13qOuwPz2Zw0yyjKAi2DcT6Q2pT9/pnSZ9jMEq3GFnOC6qB31M2O3nmqqoDgyLPxQRNiJMlzyvmc3WaDTuzo9hI8gYixlqIq8H5g8D1ND4tqTp6X1L1DJSNbhBqDWfzQEYIjyxK0sRSFxWaaqBX7w3YEWvaCxawgryrSF82ZenG2GRgIiaVan6HRqL4hDC3H9kjX7jgec9IE1vOKkEW2TzUMA82xHTsjsyXV/JIVM0hfEYsFgzeMs4cKPwwc9kciFpsEUClJPqdzDY3vSYsLBj6SaEPb9+yeHllcz0jTkmgLohonu2OzQ8UB7WtMqHH9gdAeCAR0ohi6I13bUncD82jpvcWPxpPEMBDcOCBbVBXOjd2FLE0IwdPWDYk24AP1fk9lLEWWUqSWzx/+wP7pnuoyod4+ElVCtbyiLDO0Bu9GCcV8fQnDiuAdw7AnzVOUCqRFwfXb91SzOR9+95kvN1/wQ89yNWM+m5HYBDtPMeuE3eEAB/CuJ0TPbr9FK82r19dYm7Ddbrl/eKBtG2yaUM1mdG7g0+fPFGXFfLUmzWckWY61KdoGumFAGYPSisTmFCojRlB67GqUWUFT9/h4JEs11iqSoAkBfKJpo8N2sLg4o5ydgZ3hdYrRFhcV3o3Wo84HtImkiSXPC4hjF0qhRombtmNi64v/vzEJfXvEGoXvhtEpB0/E03U1lcoJ3qN1ilKWSIcaTZrGP/++TS3dw+Ew2cZ1XTcNaIUQpoHM00hz0Zw/PT1R1zXn5+ecn5/z7t077u/v+fTp0xjw9TJYKW1paRcL0Di1Qju17pPvFibtdJh3v99Pw3Onw4eyaIvOdD6fo5Rit9tNns/CgEo7v+/7abBQgL8AfPH2FsD9/Pw8HZu2bSfAWJblNIQqnyOAW2QKMtR2ug9SROx2u4nFlkJHWL40Tb/qWkiLXgZJRbc+n88n8CpseJ7nU8T9aViKHHsByKKtlcE+8TgXwCLHX0CYSIUEhIutpLV2AnEykAl/DNex1k66YClKmqaZgodkOLQsS+7v76ciBZh09MDUaRHwJd0W0W4LaJN9kMJHwGnf99zd3fH69etJ5gJM50uuOWAKgJJrQwaYRb5zOh8g1w0wFRdSvHjvJ19w7/30e0VO8t133/H4+Mjl5eV0ra7X60nuIsdXbEpFBiaFQlEUfPPNNywWC371q19NTivr9ZrFYjHJyM7Pz9ntdpNtZIxxmi949+4d1lqen5+5vb2laZrJ5UiCwaRzIedfigUBxVLkns5yyLUo3R95n+j+5dqUICLZX7n+To/r6VyKdB2kC3UqZzx1QhLpjVxzwEQILJfLqeP3p65Fp7ai/6vtRwPyvtnQDz3d5o7t8y1N31Iu15TLVwxqQd8N2DigbEWepiQalI/0xyMf7j7x/HTDrEo4X63xYeDi8oJvf/ot9/cPNG0zarmbmhAjaZaznJf06yXPT0+kVqPUH7Vz1hrSNKG3FmMNlpQ8M2gsxkDbtfihQ6kI0TP0LSZC0/UYOx78ISqapqdpW7AGpXgJHDIoZbDKEEPEhXGgsu0G0iQjDD0+RoxJyHKFMZZhcCQRsjQlEBj6USOW5QU6WIiBpjmSJSVaG/KiJM9SYgwMg+dYNxyagQGDzRWtGyPIExMxiSWqsV252+2Zr1YkWU5gTITUOh3dLPoW5/wI0Nuauo14n43a4KKiHAbaruVw3FGWJYvFkjQdNfVNU7M6O6cfBg6HIzbNWa5XBG3xITAzc/qhIzEK13U8Pm0oi0Axn1PMFvgY6J1Uki8x1FpzOO7JZyts6tEqYLXi4eGBEDzxzSWY0cqyPhzI0ow0T2iHI503LIqx8AiDRylHYhM6NzAMNVrNOF8viJ2h392z3+2xShHygsVqSZLP8H5GEzRDG6jKFFSCcwGjLUobdocWT6DIK8r5kq7ZoRhYLF+hX72nfviBcjbn8LwjoEiykphWpOUS5Txt19A2B1x7wIaeQjnO1hWLzNMd7jkedpxfXFDNF5TVYgz1URpjFEPrMVoTiZydnU8PTSL0bYOKhhpFagxWj8Oeu+2WYejZbLZ8/7vf8BObMjt/TaEDKQ7XHVAhJyLplppoLImJxEqjYs92v+dQdyzWF/zl3/xHcgUax3F3IDWa0I8+6EmW8vrNO6rZjLRIITgOhz3HpmHo2hHsvjxgrq6vWSyW5GVB7z12l6KM4enxmQ+fbqjmK2Z3T6wurllfvCZLEvZ1Q9f1lOUcbTKc87ghYtMck2YkXqEIRBXpnceHBHSCTlOMetG09xqlKiADZUeXmhDwPuDwo/0ikFcZNrFj8FYYgX9iNEqPCZz+pRhS45OVtukANSbb2oSua0l1ARG63uF8SngZWtWJJi9S+JH6wD/3TQb4np+fJ53xaXjJaYKdML3CXn/58oX7+3tms9nUGn/16hW//OUvJ5btVMstjNrl5SX39/dfgT5J65QgEVmQBVwKWymgVuQgsrAKuBephQzPnQJgkZMIkJLhSQHMMqApoLPv+0lbDH9c+P+vYt9FwiP/bxiGSRcr7xG5xGnh0bYtz8/PrNfr6XsEyIiERsC8/C4BtxJlLv7U0nKXQkN8sWXQUKQNAnKWy+XkUd113TRouFgsphRGGRwUTbBSY4rrfD6fgK8xZgqmef/+PdZaVqsVu92OPM+n3+G9ZzabTfIR6SaIHAJGHboMoT4/P0/H4ezsbPIRFzmSFCbyWdKNORwOL+vlYioAz8/Pp984n88nrbMwxDLsKoy4zAOIi0lRFJPPtoBGYYyliyCMtchgnp6eJvZYBhDltVIEiHzk4eGBX/3qV9P3nV4fwvyfFq4yfCqA+nA4cH5+zn/6T/9p+uztdjt1ZETaIqBdimwpkoU9lzXizZs3o2zzpbDYbDZorbm/v+f7779nsVjw+fNnrq6uuL6+nroCp64up8PDUtQC0/1+2qUSMCyDoQKI1bRGjIWkFEKLxWKSpgmglmvgdL5FnhVSaMm9J0WW3NfCjsvzR4ZWf8z2owH54fkzRWZ43nzk99/9E1We8fOf/RXJ/JKBDLQnxh6jUrTpMDoSGUjTwGJ1jtKOIlMkacqxrhl6/xKpm7M6W5MmGb/613/l5vaW2WzOq8tLlss5zXFPDI6mPuBsQbAlwwDWaPIip9vzMhg32uEpIq7r8P3okGDTlIhifzzSdI7VOiexKXmAth3wfvT1TnVkvpjjuyMQRmCOIbjI4DzOBaLzxLZHFZCnBptYsrygH8Z4+ixN6EMkxDhqGVNLvelwQ0/fd3TtWFEnxpBk47BR29R4NzqZ+L7DhQMqXRBioG5atNHM5nPoRjDRdD3zcg7aQFCk+ehUMzjH4AJ106F6z7HxZLmhLCqsTWibBnc8cqhrkjRjkaRoA5k1DD5gEstitWaz2xPcMLq+oNm+RC4PvaPuenCOoR8YOkiKijRJR3/1occazWy+xLUdfd3z+LTl6fmJlc7QJmJsSZYXOB/pek+WFcyXZ2yfn1/AUY8NDh3jmMboHYMbCCGSZQUmWNqm4f7+M8otydSoeU6ThBgCRVailcH70XWk7wo6LDrNwCQExqFZbRNMEum7yP5YY5XC2BTXKzwGj2F/bFks1xSLM8qLKzqT0EcNKgGlMNpjrMY3nr47UuaK8/Mzcj1w+3nD59sbXr37hrwq0dbS9R2ZNRhrR2Y1jsFWsuAppV5AARRFOrGIZTm2fu/vvvC83XB5ecVqPqPd7wgYqmoxBgc5BXiUtkQ9Oro4F3Bx7OTst0f2uz2uG8jUKNNaLpcE17DbPqO3kcvLC5arJb1z7A97Iop2aEm0ppotKcoFm81L4loySm/6YWC7P1B3o1tKnpckScpivmS93fPl/pH7Lzc0TcfxULO+uCTVCWhDbs14nfctIY5dOKs8zgRUoimzfBz4HjxN078MFztU6PEehj6MLLoa7Q2NBv3yLEjTBGsDxmqUghgiEcXgAoka78PeDSRJyqyq8G7AKsUQR82v84G+d+gkYpOUEDXaJESl6fqBtMhewNhAnhU/9jH6Z709PDxQFAWPj4/86le/oixL/uZv/mZywxAAKwueLLR5nnN2djaBZmm1D8MwaVglfv0f//Ef+fz586QxXq1Wk35XpAECgEUysNvtJtZKNrGpO/W3Fls2ceUQsHaqdRV5h/wdmJhhYRtloRdpgVgtnnpDi+40yzI2m83EuIlzg7DuXddNlmxpmk4L/mkUvXiGy28W+Y4ACNHai55dQKIMEwpzL+yyDKyeht2IPne9Xn+lGwYmVlTs9QT0iwZdQJLsn9gu1nU9JRrKNSEA5tSO9OzsbLJAlO6FdDKEcZdjIoN/X758+QpciauOsMAC6MU7/tR3/nRf2rZl/7IGiguKdHb2+/0kCbq8vPzKheP0M8RzvyzL6XU//PADnz594ttvv52GESWI6k+dQsR2UkB1fMEXcq2VZcl8Pufm5obHx0devXrFcrmcwmhEd39qjSmgUQo70YNvNpvpu6QYko7O09MTr1+/ntJQJbhHGG3Rvj89PU3Xq1wLoqOW4k/ScTebDTc3N3z69Gm6/q6urr46fiITk26MFG5yrcg/xV9cfp9cQ6euJ6fPgT8t2OW8ir5c7kmZaTi1VJXCRD7/1JJU3nvacZOZq39r+/GA/OEHGh344fv/ye3jJ15dv+Pi7bcUy3d8ut3h3RHFmDYZ45GuO1KYlHye0Ro4NgkuOno/jEl+x5qiLMmylLo+snO7MQ4+eo7HAw9GcX15wXxe0TQdfXuArJw8ukf9eBglHtrhXYdXgH6pnl4OjrHjTdZ2LV3vGV0Ppdcc6duGtMhZrpZQJrT1dkx3HDwqAlGRpSWJtgyHQNconAcfxk9R2tD7cTgs+DC1nRKtMKO3B0lqqKqSsswJweNDpB0CbTfQDx6lNHmSEtUYpENqCS+tcqtgNp9jCsNitaJzjrk2FOUMPOg4xqijLMokDC6iYkTrMdJeKYtONGleEdhy2B3JkozzszVpWpBkKVGlDENHUVVUsxmb3ZanpydW6/OXKjDD94Hj9kiaJMyqAqMNaSIe34oiq4jRo1VCCJo8r7g4v+Rpd6A+7oi6HzsKNiEEaNqeqhrZ/mq24svNRxyQVRV5WpBbxc4PdENP2zTMZuVoKdnuOWxrrG9Qrhv9pYMnRMXxUHNsHMuLBYlJiSZB6wKblmg7svbKaLrB0XQ9nmTcfwVZUVK3T+y6jrrzHDrHPDdU1WzUOndHqJZjaqsPGKOpckPKjP2wY7u5Z1tEhjQyeIfJUqrFgsXZGpOmtIcjKgQSDV3bcTwcsImhKGa4IZAkKW3bkSTpy4LjeXx8YL1eg4bZYkkIH4DA0LfYrh6BobVkGrJijikXxKRkCAGlLCFEuq6nbxvqdsAmY+DPLAn8kGWE6FmuViRWv4BZzfDyMKrKisfNhofHR/I85+LigsurK1brczbb7ejPrcyoxzdjx8qHkWHMioI8zVmsO6rFiv2+pnee4+YJ33WEMBZA9B1lNWNdptRNx9Bsee6PDH4s1gaj8FGjtEUrGPqGJNN41zH0HYQw3sXB413HSP54jLZkeYI1LwmISPSWputGu9HeO7RWJOmYYnt/f0dqDCZEvO8hekKMaKXwIdD1PWg9JrDG0UGqGzr6rqc02Y99jP5Zb/f392it+c1vfsPt7S1v377l/fv3nJ2d8enTp2mAVhg5YZTFbUHYbwFau91usnM7Ho9sNptp6OpwOExSgeVyObkqZFk2ATph/YSZFfAhzBcw6cOFHRdN6yl4F9Bzfn4+Dd5JwSDfIS1ucRY51aeLTECkJ8LQnbpwnLphCOg8HUQV9g/+2GEAJscOAU4ClkSPL+uRsIQCKgWcCBgRbTeM1sRpmk5FkLDXfd9PATzPz8/c399zcXEx7U/f99N75/P5tM9yHE4DZARUXl1dTdpzrfXEJorsYD6fT449P/zwwzQ8KyBIQJe8VhI4N5vNV3IhOU/SaZC0UQFoIoWS75ZjD39MbZS0WbnWhFkXiY8UQgIGpSAUFvfp6ekrVl8cbyQQSAJtpIOz3++RMCrRSwvolPN6d3c3dSpOXY2kkBOQL4WVWBlKsSKgUn6PFJD5i3NXjJGzs7OpcyP3jlJjWNbj4yO3t7cURcH19TWvX7/m/Pyc5+fnr2QiItORbpIMX5+fn7NcLtntdtMxkqLz1K5xPp9PXR0ppMW6UeRlQmpJIq/cy3IO5b6S+14GoOVel2tTirRTSQvAly9fpmeF3MPy3eIcJMBf/rsU/jJI/m9tPxqQpzQ83H2mPT5xdr7i7NVrWlIe7rbUrcEmK6xyZJnDd5623tMfazqrMGoMzvFu3DkdDM9PjxzrA33vyPKcs7Nzzs/PyIqM5ti8JFOmzBdzDvWBofEMOiVVCYp0tCj0A2miYBjdVvJZRVblGA31YYeJAV4OytiuNmMwDhpjNHmekWioqoIkNewP4/4N3pMz+iyHENHaYNKMLoILiogavcbxKGMIMeL8gHcDiojVZtQIa0gTTZoYiJEkyZnNE3wIJGmKiwZ0hu9bYnCk2ehI0ylN0/cYNZYNRZGRK8t8MeP++Zl+cBQ6QaHxLowMvI+YpADlSZIMm4xDqIMLpHnGcnXBbnfg+fH2RTt5JMtGdr8fAv0QxjCZ8wsOXc/T0zOL5ZoyL3h8eEYrzeXl9Rhq1Hd471Ba4V0gycyoz3ejJne/P1DvRsAZ/EDXNZSzApsoAoq6aUhqw9nZCmVS8tmScrZFq0hXH4l1QwoUmaG1iuHFei/TOXlekuqULFPU25q+aVBx/M1fvnwhyWesX/2Mrve0LhJzizKWqMa0UmsMWZ4Tdy3d4IlhdApJ0oJoUqLKmK8uKPUvaLd3tG1HfegY8mvKhSEzFhM8RI+NHu9almXKpg5snh/ZhI7t4UheLSlmS9KsHAeXVxbf9TjfUTcH9rsts/mcoR9ompa27Vgu/+jPrBQMrkcbTdPWlFXBbD4jTzOC8xx3z7S9Z795pNk88ur1O6rL16hiCbYiL+dk2bgYuMExmy1IEgV+INNjK/D5+Zno+3HAWGmsTVDw4jjkJ9cGmeBXWrOYL5gt5i+An4lt6ocebQyzxWKUTrmAj4q8nIFO2L+wl5px3qFpjjx+2PKcWKrFAmUsaItJE3SMKO3ZtjVNG1mcXZBlCfiO7dMz+80DxHdcXJ0TQ8HwYmPquoAbWqw24+SlCozBBSND7nygaXtU15MXo2MRCvrYk1hDWeTsn56pD1va9ogioBUE71Aq4t1LAFgCOkno+wavoHM/LvThz31TSnFzc8PhcODy8pI3b94QY+TTp0/TYi9spQw1il5aFjLRIMcYub+/nwI4ZPju6upqAugyRCUsuQAF2QT8CnCTxX02m01DcsKISjv/9P0CUo0xk6zicDhMjDX80SFCwJ2w9cBXi78UGqfvO5VayICggDS5/+SzT4ffTvWuIvuR/Vwulzw8PEzgWe5PcXwRhlEkOJJ2mKYp5+fnbLdb7u7uJkAorhgiBUnTdEpafXh4mDTAkib56tWrybHk1MlCGEIBRZvNZtLtC9Mr0oFTb3ABqKL7VkpNbirSURGXEzmeAurzPOf5+XmSGKRpysePHymKgjdv3kwMqAAz2T9hcaVz8afyI63HFE1jzFQkyrCrWHQKMJbfL+FGMiuw2+2Yz+dTEWGtndxsRCMu/uwiMWqahvV6PYUdCdCXrogcI5HjyP4/Pj7y/PzM+/fvp/tHBo/FCrHv+0lvLdfcfD6fPLlF8nF6nKQrIQOf0ulYrVasVqsJqE5rxEmH5NSlRoY9d7vdVKSmacrxeOT7779Hwqyk+yWsufxusQ0VwH9/f8/T0xMhBF6/fj2BZQHbYoMq8wjy7JJ7VD5TihcB1nLMHh8f2e12k2vOqV78VOsvHRZgklH9W9uPBuSh2/F0+4EitVTrK67f/ZQ+puybiHdjKmZUPb13qAhJkqJwDG1NXlrmszn3d1saBefLc6pZxfrsjO+++wNt1/L27VuuLi/IDxnDYvRtjFqTlSXz5QIfFE0IDO2RfJagiehUM1hDU7c8P95zWcw4HvYjeMtS8AP9MAKebohEmxKioshzjLWj93foSdMxBVRbjU4t9XNNtRyj5gfXE2JGDB4XIs57MOn4Owkom1B3LbZtcK4nojAmYMggOqyG1XKBtSlZMSMzlmPT0jsHaYWixXUj25eEAL7Hpikq9FS5RQUHwTH4lq7NyLOMIi9xLhA8GJOCigQMWT7H+QaUZRgCSr+wMDbB6oRqseTx8Z7bL/ej/tymZFXFoe7oXU9BTtQZr169pz4e2T7vODs7o8xHO6Cu68nSlM45jscd3jv6oSOvqhdWMyNWM4IfE1TLKmdWVaDUi869oXcdaVYw9D2PT48s5vORJV8siUPHcbejb3tsgLOLK6wFk46yGv9SHNnM4kOPMSmLxXq0GTy+DC4pTTWb83lbc3CWqhxZTQ0YrbBGkxiDMZrYBbxX2CwjTSHJSoa2YT5bs1wXPH6CL58+4oaBrNKkBnTsia5BhZ62a1G+ZejGJMhD37PZPpKlGauzK1RSsj92pBlURYZOIr1rCN6T5xl5VrxISzy73Z6Li8sXt4ARHJyfn788hMAPnsuL89GuMwa+3NzRdgNJlrJ/eiB0R5bdkYv3vyCfJYS+Hq+5LCWGACEwq3KGrqbbHqd23R+++wPr5Yxv3r8dW3JRk6Qph+MBHyKvrq5wbnzIfbn5gtGWcjae00O9h/jyQA1m9F+PY/hRUIGgDJ3v0GlGWSmOxwO+7Rjw6OBRvqPrjhz3G+q2JcsLlusFq7NzSpOz61oOuy3WenyeMNQ7jO9Q7oiKDcOw53DQ2KzEmJS+c3T1EZsWDEM/ujvF0QklRPGQhqapWZ6tGIYeGAeYZ1XJal5y8/137LaP7DePDD4lL1YYFUmsJkkM1hiUCkgqb5Ym+H/H48DIJN/c3CAR9t9+++3kkiAAUxYt0VjK0OFsNmOxWHBzczPpdBeLBZeXl/z617/m6emJb7/9dgJc4kUueuv1ev2VQ4qkOJ6Gktzf31OWJbvdbmIohQ0V5k3A82m4jOyrgLUkSXh8fJw8mkXDKn8EwEiBILIF8QoHvtKkihtKmqZf+UwLKyrH9pSVE1AiriHCioq1nTDtMowmLKAAbAFzwpILiFgsFtze3vLp06dpP8XOUcCpUop3796x3+95enqadNFyDOVY7ff7qRgQ5ljSM0ViVFXVxKaLfaKcm67ruLu7Y7VaTUOlokEWoHN9fT0x9PJ7RRokx2m9XgNMXQ3RhD89PU1x8qda41NXHTnuRVFMziaSEnpxccGHDx/4/vvvp2JPAK1cE/J+KeI2mw3Pz89fWRLu9/tpCFeAqDDAAjKHYWCz2UzMvlwL8ncByldXV5P04tOnT7RtO3nLCxP+7bffThKn08Ap6T5IOq785t/85jesVit+9rOfTV2hNE3Z7XYT6HXO8eXLFz5+/DjJV2QIWnTap9f8aeEr1/l8Pp9kY1K4yrGThNiyLDk7O+Pi4oI0Tdnv91MgkHRHTnXi4+zdblrvhAiQe1EKWvgjiy4svsxMyH7IPMTvf//7ScIjVqtyn0unRf4uRenpwO7/avvRgNzVW4pEU8yXFMsLyvkVQ0ip5nP6DoqywHXPY7hGs8OEmiQOGBVIbIZSCVmWjNKNqmTVD5Rlzl//9V9wc3vH4+M9b99/Q9u3FEXJYr3m7u6Jp8121KZmGcaBU5Y8gWAUAUM8WtwBDJG2PuL0gFKatEhHBrn1DN7TDx6jU9AWa5NR0qLHdL++i6RVTpqllFXFbD4jSSzaKKJWEMIoa8hTXJai7OgTbhNNPgxorWjaBmKgbVpiDMyrgsQmGAPVbEGelxTzJUEndNQcD0dUqrAmQ7U9bV1jdU+S56QpaJVCDAyNIwwN+A68Z1bNSYyhcxEXIEEToiWEMVZeaUOaFURGScyxdvQuspwVmGQMGWqOB56ensnKitmLZhqdEXWKCwPGGhbLlOA9zbGhKktskrDZ7ohEnOsxRrHbb5gZxW73TFFVwOgUsl6tuPv8ifvbW5SxZNUMFQPR9/ghslou0May32/RClbrObPFisPTI6H3HJ6fscBiMcemGpMp8jQffaZdpG0aDAOubblYzVgu56RZg99sqBYrOhfpHAzBY14SXdPEoongHFYrjB4HB42xKKVJkpSiXNINB7AenYBJUpIkxfYOfItyR5pNy/G4w3UN1niKBPabB4amxvct3iuW5685u3rD4AP7uifPx0Ck3I66w6EfKIvxesvSjOViBZEXZiXgvcN5R1WWpJmlaY4YZVgs5oQQx2tXKer9Drfx+OixKnAdBoJWFIsdeXXOShvSYs5yUdI2LUqN2vz9bjtN3t/efKR50e4VRUE3dFSzGZfXr8dwL+9JjWE1n/Pl7o4P33/P1etX5EWBJlLMStLsxYdYG6JSYAxVWdJ0A+tyiTaW4+EwWhNWDt92hGEgKavR7987hocHnh7v2e+faY4H0vwOk+YkUfF89z3r5QKLxzCwKAy4Ftc32LzE9R19W9M1A8PQEbzDDT3WWKwtCMFj1Dig3LQ9QRmcB+cDs7TAKIchcNhv2W6faJs9fbej6wxFsSSvFhAGZlUxhpvFF9mStiig6X+cx+yf+ybDj+JnLbrV5XI5BaMI+yQLJzCBMWACbDLcWZYlf//3f8/nz5+5vb3lpz/96QTwzs7OJt2sAAtZQGUBFos20dMKsyryCCkQhLE7lXYIey7t/9lsNu2f/FZhTGXBFQ28gGCJBpfCQ45TCGFihIWNFPmDgARhtCVJUJw1TmUkMngqDLpYT0q3QZg7+X9SAAhjLQBlGIYpZVA6AQ8PD5OV5KnlnTDG6/V6YnPFd/v5+XliGo0xbLfbiUmW4BSRKnz48GHywRa7TJEaiEe66NPPzs5YrVY8Pj7S9/005CghSyK1EAcMYS+bpplCqYqi4OHhgdVq9ZUXtgB6KRhPnTLgj8WTBCUNwzAVZqdaf5E5PD8/T0OJUug8PT1N8iPvPVdXV7x69Wo6fnLdJkkyOeGIrEM84+XYCeAUG0YBonI85Dwrpdhut19ZcwpAXK1WEzMtXvPyGcKuS2H84cMHjsfjZCcp7kBv3rz5KvhmuVxyc3PD7373O969ezdZQEoXQCQdco/Jc0FSTXe73dSxEVAuw9Vyj97d3bHZbDgcDlPBEGPk5uZm8vEXuZZcS/JckOeOnHc5j6eyl1M2XY7xlLnxUmBI10UkQRIKJPee3CMieYlxTJP9MduPBuSbpwei8+RJSWLneJ/RRU01K+n7mq5v6NojvjsS2iOJPxAZKDQEp7m/v+H27pZhtWBoewYXsJnl229/gk0Tfv3r33A87lmvlxwONbP5gqhzjm3HYfNAfzgQlEFZSExFVB7HQGI8syLHeU/X1mTzHLRBo7B2rFjKqFGZIpDAi+41BI/SoI3Ce0cIDpsYZvMZWgXyoiJNLSE1dGGUYsQYiQp89BijyIsM4jjI0PU9MXi6ZpyEN1qRJYatH/De4mOk7hxBG3oyghlt/hJr0UnG4EfmoyxSZosSFxVD3/FwfMCqwNl6wXoxx5uEw36PMyU6mUGIDJ3jeGzZHQ8YE1is1qDGkKWA5nmzG3+fUpydnUNwGKNJ0hznA9FY0rIkxJFpVyEwm5V09YHD8UCSWIq8oHeOGAJRB7Lcst9v0FmCi4EkT4huINGG5XLBfF5xPGwY6oYQFPNFR1HMyTIz2lmGAd+3uD5FqTllNcM3LSGvadUzoW8Z+hqTpCPTXs7xQ6SvHTFasixlaDua1nF9PcMFMElDXlZstjv6oYLEvNz8ijLPSXUg+nGh2NcDdXfAeYUPHmMts8UK127oQ4uPBqUSkjQl7XpC6BmOj3RDoD3uGbqaxGgGNUbZl3kKtiRXC6rZBTaZj+ywTfARDscOkhEQxDCQJCURPzEDdV1jtJkeuqXJMcmonfbDACa+LO5j+9jqBFCURU5ZFpRlQVvv+PL9r8kXz/zir/6eOBwY9MgAw+jn37YHlAq0bY3SitVyNQY9ZX90e9hsNnS9Y322RmcpfV2PA7tFzma349OH76nmC1brNdbMGPp2DAKLiiQvSfKMpnc4ZTA2I0SFyWZklcY7RzEz7LZb+rYlTROKLGV18ZrHxwfatiZJ7GhnOvRoH2Bw7IeWPNXMq5ShD+y3O7wLaP3iMhBHHbkiEILow+30gFda07TtGEhUvnSYQkQbDX60djzsd/RdTdfsqfePmGSBUQNZArMiHQs5BWmaYdToKNS2jsO++7GP0T/rTVL7BAwLCJzP59PA4p8CcmmDD8PAzc0NNzc3nJ2dTaFNaZryi1/8gjRN+ed//mf2+z3n5+fs93uWy+XEJj89PU1DZrLICkiWAUphz8WGEZhYrVMHlFMAK4ypgBqxfBMgI0BOGLlTtk00xDHGSWYgAExAibxXPl9Ay6nNm7Tp5TWnHYGu6yY/87OzswmUyCCrMMcSsCNuGfI6YSKF7dNac3l5Of3W0yFUAVjC0ovziEhPTvXvIifZbrdT9oIwsdIREO2wWNiJTaHoyaV7IdIcGcKU0Bz5f+LxLmBZpEgSENU0DW/evEFcOsqynAZTBWzLuZJzrbWeLClPOw2r1WoKnZJzczrUJ50EYXpFIiT+46IJl+JHXH3kvVmWsdvtJuYVmFItxVMcmPZVPl86RjL4K3+H0TZR/hyPR37/+9+zXC75u7/7u8kNRn6P3Juyz3JdiVxK7gth3EUqIsPFcm6+++47FovFNGMgRa04JAlAF7Zd/rt0A2ROQUKRxFv87u5uKobl+pUuzP39/eSDL7IoOcei65aCRO5tSQAVcC6/X54X8jq5FmU4VfzupcsnzxAZ7JWCWQbDhRD4t7YfDcjvv3yhKOfMyhVBVzxvGsqz8xf2t2C3fcD5dvRY9h19e0QrT0wMx33DD999x93DDan9KeerM5T2Lzd3YFaVrNcrnp4fODu/oJqVfLm7xxZzvv35L9jclzze3nE4Nhz3W6qypCozlMoIx4RoR79z19UsL67RNsNoKNIMtMakAesUvddkeTb6IIdRR05qMHE86DaxFGWJ0aMzRT+0KDMQwsuAjxp9x4/Hns5GksyQpJbZvMK2CfoF/GXJqIPVo50GPoxDrMHOyOYlJstQ3tAcD3gCQSf4OGqcU6upUk20KS7TPN44hr7BLAucH6vorh3wiUVFN/ovu0DnAn0X0HoAZZjNK5JEEXXK82ZD1/ckmWG9PqM57nHDQAwjS5hmCdaMse2ahN3mnr4fwQ6M09SmqfGAVqB1xBrDvjuy328pY0RphY6K5XzOcrXgJ99+S24Nd7d3uBjoDgeKvGS+nNMPAwFFWSTE0OL9QJqWzKolZtZiLs5p/MhEU5Zk+ZwkNcQYyPKSYUiwqWa+VKTa87Q9oHVkfXHBbLnk6CM+vuhBIxACRkU0gWHoSNOcxbzi/vlAXXfgIk2TMZ9XrM4uaB631J17SQpNKbIMp8ENB3JtCdaRRlB43DAOlWr0mLRZLjH5Ch9z8qJ8kfUcx0HCYSDRFptlGK3o2o46qSf/4CxPSVKLsZpqPqNpxzAMY/UIAF3Psa4pi8W0aGmjKauSJNEE13LYNnRuoKu/IaDZNzcs1pfMqjl9V3PcPRP7hi83n/n9737HrMz59t179DjBTF5k3D088v0P3/P55oY3r654dXGGNYo80by5vsTFSN20bJ8f6dqa5XLF2dmKtu2xWYnOKlzt8D7SNMMLaE5IZ0u8h4AmDIY+7NFZxmy1QBFYJXPa5jAmhsYBuprQ9xRZwWG3pWsHzJASleZsfklVnVGVa3o/0LYNITgWi4qiKkjSUZKmjcZ5hwuBp+ctddNSzuf4l8V5DAryqOhpmwatwFqFd6Pkh9BQZorVokCFARUNVT5DBRi6geO+pW3+XbMCfOUXLrIOkZ4ICJJFTlrRMuy12+347W9/y83NDX/1V3/F+fn5xHYJqJc47aurK+bzOZ8/f6YoCv7iL/6C29vbSb8u+lxZII/H44vkbgQFV1dXE5CRwBppYYtUQICZAC7gq4X3dJGW1wIT4344HCZAK1KQ06Ev+Uxp4csQq7T7ZTBNQmHk+8TtRFj4PM/5+PHj9NnCygloEdDY9/3ECksnQECh1noCWDK8KsBSvvO046CUmobvBIBsNpupyBJ2WVjP7XY7FSgxxqmD8vOf/5wkSfj8+fM0cFkUBev1epL2SPKr7IN0WSSWXN5zypaKq81pGqnYHl5dXU2fLyAbmPZbpDzS6Xl4eJhkJ3VdT2FF9/f3E5CV7syppvt0iE/kK9KVFO028JUvv+ispbsisw3i6S8yEkk8XSwWk7RJZEdy7cl3SAdHBmEFgPd9P2n0j8cjFxcXU2KqaM8/fvzIr371K2azGT/5yU+mwcmyLLm9veV3v/sdHz584N27d19Jh96+fTtJ1R4fHyft+8XFxSShybJs6sycuiOJnz0wFeUyVwFjsS0FnLxGWHCZSxApikhMxCFFztdyuZyGa09nV5xzPDw8TEFGIlWTgXBxNTqdvZBC+TTISM6rSGZOLUv/re1HA/LZbMn64jXri7f02SU3B6isBeUpcoMbDL6z9E7RMQ5AeeUYGBj6hqaucb3neGxou4GyLLDG8HT/SO/GJMHddk+WV2SlZn/sKHXO23ffMq8WVNUFz09PPD4/MAwNfTNwNpuhVkv65kDiPL3rcX1DZgwxgFYZmbWjpzFhZEKJJNoSMTjnR7CWRNJUkycGbwxdhPpYY+KGJOT4mGFMilKAVmO8iwJrDFZpirxARYUbOggOrROapiZJDKDROsFj6HpHbAeSoiTLl7TdqGG3iccmJSG21IcDNlXM5kuqIidLDXXn2Gx2tG3PbLkmqgyNIkY1DisqRY8i2DFNcHCBvEjGgcA+AJa221NlBXlZoBPDbvtEepyxyHOqco5H03X9KIU4tugYWM2LMckwRGJwuBCJIaJsQpplVIyDskYpuuMRqzWxTFFKU+SWi7M1Jkaetzu6+ki935BXGQo9OrRERdO2eOfRmR6dN6wlm1e4xnOsD1gF8/kZVVFR5gn3jxuMVWhrMUbT1M/cP9/y6tUFr6/eoE1ObEY5kzYJSWpJrEERCS8txTTXzKuSKkvZP+85HFsG50jyS8r5kmZjqOsGN3R4AjrRWBTBDxSZBadwEYZhLLRskpGVC6rlFTqrUHYGNidJLYf9lqEfqJsdXWxJokcbg+sdrnfswpbDfk98YelNOvrTByJd19I1LVYrkmRkQY6HA+dnHpMkWDtqmp0baJsanViUtuweHviX/8//m29+/lcM0eKaPWF1Rt81bB5uOTzdE4aGi4s1h92Guq05X69e3IUcy1lJag2fbj7z4Ycay7iIWpuik4TQO/KsQBnFdvPMYb/j+tUb5rMlTT3qxINOaZoOYwpUktH5iPeKJCvI85JlUkJWEUOEdEZZZJikJisW4AdwHfvHG9IiQRtwQ0Nz6MBa1mfXvP3pX1Aszuijpg+WzgUChqIoSVOL1hGlXwAPisOx4ePNLT7Acr1iGBylDKy5gUyPAz+DGxfjsirHeRF6ZqVlMc9IrdirWjrX03aeYYgvheu/b6L5lqRHiSiHP0bJ/2kUtQyUCYN7yi4KoL69vZ0W2efn5ympUcDqT37yk2lA7uHhYfJAF+eJs7OzCbicsqrCQgs4ByaJhzCnUkCIlEMGUoXRPNWOn7KtwLRwC5sOTCBXhhNPw4tkARf3DklBPPVfDiFM7hvCKMs+iSxC5A3AxNjLv5+y5gKaTy31xAZRouf3+z2Xl5eTpEReKyz0arX6amj19FgIoyrgUhheaesXRcHl5SXA5F6y3W6n7xLmWAKLhGGW0CAZChaQLyzw3d3d9DoJc3l4eODNmze8f//+qwJKQKRcpyI7KYpicniRfZPh1OVyydPT03S9AtP75TVyjQvgFGZcPOJl/ySsSWQ2sgkI7LqO5+fnSYst7zkFpJIKKudSrAOF/ZUO1PF4nAqw+/t7/tt/+2/8xV/8xcSMn5+f07btlLDb9z1XV1eTfvvi4mL6zSK3+uGHH/jd734HMLmxiF+5FASSaPnu3bvpvAn7Xtf1dI8IKJbE0z8ddpaOlBQxzrlpLkSKzv1+j7Vjsu8vf/nLKaxHip0Y49QNk+tS7v3D4cCHDx/w3k9e81LcnTLoUiScFg8yA3PqRiOhTyJZ+zHbj15JFssLzi/fovM5webYQtMPDY+PX9BxgNDi+3oM5PEBfCTgSMuSpt6zWp5xdn7BsW34eHPHvCopk4zFS/VyrBvyrGR/bNi3DmVTlqsFqJRi/pq5nxHMDK8N2+cb2q4hzqqJJQRPai1+aAhpQpbmDH1H1zb4wRGcJ1EJKnakdsbQappjS4xHsiR5aU1n6LKib2r80BOamqwYsMpiYsSrMYwnBsisJdEao+1LZHiPIZBmGqU8kcihaUjSnDQraeueZrdnpjOirog6IU3nuK4hqgGjCw6bB0xsCbSkiabIxlRHoxOawWMzzePjhvnqEkwgqEDUkWAt6WJJHzrKKiFiR9eUvOLYN2iToE3KsW3JUoW2BpMklLOKLM8ISpHnJf3g6Pv2RRMZsEmK9yOTEJXi7GJFluY0zYG+aZkv8pFp1Ja2PtAMPa5I0UajoiP6gcQqUqPo+pZ6vyHiuXz9ljTRPDxvKMs5rndQKkySUiwX7PyRzJS4RtEcW55vH1jk52OMubbsXgBsc6zphkDQmm3d8Nom6KQgJcX3AYUawT5hTAKNkSTNCCFSJAmXqznH7YHn/kjXdTw+b5i/P2e+WvP0/Af6tqE9HkhTS5qnLIpy1J77gY/3d4Q4Jm/Olxecvf4JOivwOsXHsT1GDKR5SltDXR84dnvOKkuS59S7DWVZ0feBIkup8wybJvgYiMRxeNMmDMOeJB+dVUa7RYM2msH1eO/Gwq0s8N4xtI798UDX9Xz/699yfHjk7U9/hkpL/OEJReTh4w9895t/Ic8MP/vFT7j7csNms+HVq0tSl9HUNavlgsWsIksMN7df+MMPP3B1/YpXr9+8dIs0RiuSxOKz0TLz7vMnmkXDdn8gqyrWF5ckMTL0AW3/f+z9V48sWZalCX6HCVVmqkYucxIsCTK7Z1DTmP8P9FPXNDDofoiMyowM93D3S40pF3bIPIhtcfWsmS4voOclOwW4iPB7zVSFn7XXXnutjMOxo+kSN3cz0IaIxmYlVT3DWkfvA5gKl+fEoaWsF1hj6dodZakwhcLrgCpqXv/277j99g94W0BWkHREk2FiIKkxBVTrsYh83j5T1Sv2xyOfPj9wdbVBvbAxIUS2zzusjdjcoK0hK0uSsRTV6CRTzTKqmSZ3gcwpnDGgDIdjw/HUc248VTn7ta/Rf9fb1dUVr1+/nro3sph9+fIFYFoUhZ0UCcZ8Pud0OrFerycHjx9//HHSni4WiwnYSKiKgJD1ej05O1yC4sfHR9q2ndwZBIALOLkcHJRFVsCVsJ5N00ygW4CzpAKKFhWYdNbCIMpniMZc9klYXwEDwk6K/vlwOEyaafkd2Uf5O7GTk88X0DsOg3dTnLq4gFwOKs7nc7z308CreMBLCI21dhoKFZZXroEcpziTiG5eBtekkBHfeOlKiGOMdCr6vp+GH6UzIHpsCR0CePfuHVmW8fDwwGw2m8CUMNfDMExe6+I7LmFGWutJOy3fKUXEpRWjBOwIeAam7xB2dLPZTIxx27Y8PDzwm9/8hvV6PQH1w+GARN5f2jZ++vRpKoiurq54+/btBIjle8XJR5xjxB2nLEt2u90kdRLZhmjIL4d7d7vdlDYq8iIB9N57bm5upqFakU90Xcc//dM/8fDwMEnCZL7i+++/549//CNFUfD3f//3vH//nqenJ969ezcx8Ov1euqwvH//nr/85S+0bcu7d++mY5ZzLRruH3/8kdVqNc0T3N7eAkwF8n6/p21b3rx5M50jSfcURloKvWEYpr8XP32RiZRlyR/+8Ad++9vf/kLWI8+lPBPSdRI7yv1+z4cPH7i+vp6ehxACj4+P03W6dF6S+0+cm6RolqFOCZYSCcyv2X697WFeslitqWZLDu1oFad0pOuOdMctQ7ND+QYdzqgXxjsFeHweBfiuqFldrXn7Muy4247m8c+PW56fn8iLkjdffYXXmkPbYrSiyA3BDwzeMF/eYouStj/x/PxpZFYGz6f7h/Eh1onlYsZsOUPZnLKqST5yPu7pzqM2SyuFiZ7cKLy1xKGn64+kukapnKE70zdj+9pZTSBiGAhB42PEmjHSPqkISaHiaKOnUoAwYJwhcxbvxxuyKCt8UCjrCLHleNijbE5WLPHBkV6cUVIaSFhiGF9QwXva5kSRZ0TvORxPrK825OWMw+HI6XzGqZKUWozJyYqS3HuOxx3dEFH0RFpqW2HzjJQ0XdNwPB2oizHFMcuzSTPZtmeqak5VVuz7nrKuR0cR67BZThoGQgqECNrY0Qsej1GGzI7DsefTiS8fP3Dcbfnq3VsyZwlpIEaPsQqrE117IqlEe5pTFDl3mzUJw/G4Z7m4oihyumYcHO5bQ1KGtt2xf97yPLsnLytSGsZ4c6XR2lJWM5ZXNUWZobOCpDJ8Gr2njTFolbBGo1IkpFEXqZXCGMVyXnG9XtAPgUMf2B9OPDxa1vmMul6iTjMev7xniJ7NfI7Wib5rORz2NG2LMRllvWJ+dUc233DoAodTj3OKIs/oQ0LHyHyxwKSOT98/k3Ao7cYiSWucU2gLm80Kaw1Nc6KsKmb1DK0U7WlskcWXdMyizMmLnP505tye8cMcYypUMnRdz/ZxT2YNp+ctf/x//2d8f+Lq1Ts+v/9xnD1oG/ZP98ze3LJczamqgj/98Y9sd3vqqqSezej7gaqw3F3f4rKC++dnfITD8UxdzamqOU17YujHNl5ZlMQ4zjxURYaziv68p8hKurblOAT6TmHdDEsiM5ZOvfj/5gVtM5CiJnc16EDberrjQNv40XGnLJitr1B5hjIVbn5FtCXKlZisRLlEnhuU7RkGxrkMq1FaUZQ13is+fXqibT31fMbm+poyt3TNcXyZ55oOjzLj/lxtbjifG1Ic0/7WVwuMBaMTLhuTeUNUnJuOxM8OB/9X3yTEZbFYTJH0YhkmKX4CxGVxDSHw8PAwJTFuNptJ8iIBI/f39zw8PFBV1dQ6lwFHWZy991xdXU0hKg8PD5O29uPHj1M64GKxYLVaTcBaZCGn0+kX4R3CREqrWwYSRT8qDK5slxpzWejlj4BPYYunuYaX9r8A1hjjNAQpkgbgF/9fNLbej9Hscvz7/Z7r6+vJRUZi4i+17CLxuLRgE1mNsKT7/X4adhWQCeMg6mw2m0C5gGr5fWH+5VgElEgxYIzhcDjw/v17ttst33777QR8L20jZaBOpCivXr2a5CzitnI6nSbveRncfHx8nIJp5JzL99d1zWazmWQtAvYuveAvpSvCcsqgorhttG3Lfr+ftMoy+/Px40dCCNNArriCNE2DtZbZbPYLSYi4fgiTHkJgtVqRUprY5stzKIO/4soiAE+cTKTQkPNYVdV0vaVwXC6X0/17f38/DZr+L//L/0LXdbx584Yffvhh0kg/Pj5OGQJ1XfO//W//2wRcF4vFJG+SZM37+/tJdiV6fhlivQwykt9zzk1uJ7KPl5r7SytUmQWQIlSKqb7vOR6Pk85cfPOlg3J5f0phIOy9/JvcHyEEPnz4QNu2zOdzbm9vKYpi0pRfJskWRcHNzc30Pru7u2Oz2UzXS94bItsRIuDXbL96JUko+sFj+x4VM0pnsVrhSQzR05/3+PZArj0zp3AvQ3f7Q0vEsVit0a5giIbcFtSzK3aP93z+6SOfPv5EVRZ4P7BcX+PKAqcV5+OOxfKauq5omoHZfMbt7R1Dt+fLT3/lcGp5eHhGq8RiNef67o6iXtENYJzBZhnzvib259GC0BgcAasDmQkY5XEGnDWkENgdtwyNR1vIC4sHjA4wtHS+wRpHnhsyW1Oa0SNcxUDuDGXmMFqRXrTC/eCplEZpA0qPOmCrIHlSGshzzSiE0KTgWFxdofo5ftiz3+0o8ozr6xsWixXPz1vQhn6IDCHRHU+4pHG1pqwWxKgJweDcHELL8XSk7TqSMlg3QxtLXszomgNFXpPfmNHJwwdsghR+tvtJabRS7Poz51ODtRqlHCTNfnditzsxn83I84IssxityKzhUOzZbg+czyeqqmI5n5MVOV3TUBY5LsvYn05EDZk1hKHH9z36JWDocHiiMRZjFPVsgTOGPKuIwbA7HDkennl6cFTzFc4qtAdvIMsLluuS5WKOKwq6fnSayfMMm2fkuaMsMrRWLwwzxBSIePLSsVzV7E4N/WlgiImHL8+Ua8vm5hsKOrZPX4COGAOH4wGVFEkbFqsNRbkgqJwOy7GLeJ3jAWurUaLUHlFDj/JnDrs9xmVol+FjQltHNwzoF8BinCXFwDB0VEWJBnQEqxxGQ4r+Fzo4WcBQir7r0ECRj9HNQ9uSYmAxX9K2DY/3Xzi1A//5P/+/+ObNa5azkrKqKcqaxTLjt78beHp4xIdE042hWl03RkZHNL/9/R/wPnA8ntCZRVmDy0bHov7YjRaeSZPnxfi7zRnrBzIUhTZE35KhCAPsH8GoRFXOUcbijKNVgaQNOq9IIRKagXPzzPPjDktLWRmyoma5noEuKcoFLpuh82qUOFlH8mP6a9cPNG2Hw2Ksw5icflDc3z8TIqyWK7QG7weMGSf9DR5Sj3XjoGxdz7i5uSOEyO2rV1xdbcaQKfMSrBLCOH+iIjGM4V//sY2btNmF3RLwKW4gwmwLC9s0DbvdDmAKSBFQtFgsuL+/5/vvv+fHH3+cPPrFZk9AnhQBovF9/fo1TdPwww8/cDgcJo9sYSkvnTKEgZZ9vmSVZcEWcC6AQ4CWgFWR3YiNXJ7nk+wDfmZdBQyK9lyYW2mbiw5YwOJlkI7YoIqfuPhe393dcXV1NXlAC1gXgCxhQ1IIXQ4QCvgVJl+Y2qIoePXq1cS4SjHxb0ON2rad5BjSHdhut2y3WxaLxQSEhAGXYB3ROAvAFomOhONcghrpDggTLOdIoualQyD64c+fP09g7NJRZrPZsFqtfpGaKqyzeLvLdZBrAEw2e1LghBD4+PEjt7e3vH79Gq01Dw8P0zkShl8pNYFZGe6T8w1MsiaRwgzDwPPz8ySfkc6B7KuwyPKzl0WarAXiMCPhWXKuRI4EP7sAiS7++vqa8/nM58+fOZ1O/M//8//MN998M0mAqqpitVrxd3/3d3z+/HlyQBH5Ul3XpJT4+7//e7z304CvzEmI7Es02PJMXErXxO4SmApwkXldWjpKkSjr3+l0mrpvopkXdlvkLQLCL0OkpPiU/ZFugqS7StdNZHIiAZOfV0oxn8+nZ+Tt27fT9wojL92P/x7LQ/jvAORt0/Lh40du3QpTX5MbNaZkNkeG8w7fHNChxWUKlRLD0JOSop4tWW02VIsVh+MJFGAc1ikSjhAVRVFjjebzx490XcdX3/6GRZ3z5eMPpKT5/d+uRh9q32Fdxu3Na+4/fmK7O9D7yPX1FVfXV6yvrzGuQjUD3keGoSeEHh96jNbMijku06T+xNCdyEygLitK5/C953DY49ue2aKiyDRRGzQ9xAg+EZIh9CdChJgbUrS0fYNTiSIbbdWGwZPlOdZZBh+wrkCp0QZOkUDD0B3BFQTGhEnSwGK9pNCvePrc0DZHSBBDosgL6tmCerYgojBZTnM80Rz2ZFGTza6w2ZwsX5DbktQdOB8SIZ7x/oyxOSkZyrJmaGeEoCjykubc03ZfuHuTUc1yuq4hpbHN3IZAH+BwbFgsZqSkiFHRDeGlldsCEVRJkTuKuubV23fsdgcO+y2fH57Y7g7kZmR4rbFUsxnKGpph4LDfMwyB+FL9L9YbYEyvnNU1LpuRHGg1YE2LdQ2ogRjPKApSgOg9KfQQRx11UZVEpVFqTHbM8xxtDEWRj3Z/0aNNQhsJiRnwSeGKjMVyzvPxAUKiOXecM816s2K5+Yrbt0/4/pnou7EgNRaXFWgzI6+WtDHj2CW6xrO+vcPWY1gTYQR5eWEJnaes55Q2ce727HZPmBTIrWa9XHI4nbCZpdaKwuZYpenOLV3TkUIipIRRY/svHY+/eJHNZzOqIoeQsHlFuDP89ONf0UZxbk4s44ayKsnKGb/5zbccnh5RybPpe562z8xmC67v7tjtDxzbjt3xTFkUqASFdsxmc3xI1PMlZT1/maOA/emAyyy3r15jtOL5eUfyL8EfIRLxnLZbbFbiVIb3ifZ85Kn5ROgbXr39BovBt0fqvCAqi7EGbwxZPR9dYJRFJcfp3JMVBavqivnylmpxTV7MMXlBVICCzo8LTAyRQXnUMBbBMWhigObckbuM+azmZRQE4yyL2QxL4OHhE8FHkoLNzS2LxYoYE4vlFeVsRlSasq4x1tAHT4wDRieM0zjzq9+3/6638/nMDz/8gARoCIAQyz5JsBSwJIu0uDGsViv2+/0EimTwURg2a+00wPj73/+e+XzOjz/+SEqJ/+F/+B+mAJssy3j9+vXUah+Ggdvb2+nPpdez2NAJYz+fz5HUSSkelsvlNPQpHtjL5XIa5IOfPYwFFF0OMYoLirhIyMChuDsIUBcWXlhWcaqRz5dB1w8fPkw2auKRLQNslx7RAqClnS4Mcdd106ClWCvKOb70Cxdrt6+//npiPIHJik4YdWF3xfVFzi8wXcu6rvnqq6+mePbPnz+z3W4xxkxDfSJBEB9sYfIljCilNDGwAvalYLrUgANTMXHZuRDmX4qgSwnCpfOGdG6kkCyKgqurq+mcnU6niR2+ubnh66+/ngqlS7cTeQ7kvByPR16/fj05eMi+yvfLdWrblufnZ4DJR32/30/DwZd2nvJMSeEmLi2Xa8RisZj86vM85+3bt3z33XcTqJVrX5Ylf/M3fzMVGF3XTZ2H169fT3Z/+/1+cg+SQld8ui/vYekEvHv3bpKRyf0v+yfJrnLt5Jj6vufrr78Gfk7KBaZ3h9yP8vxJIXlzczM5+Ihjj9yHcs4vQ3zkGZPvlnN8WZTLf0tBAqP/vcixJKxJrqG898StR+6FX7P9akDeDz3N0xObN/0YWa4sKUZCt6c/PqOGM9erGYt5QXPc0nbjwc4WK+7efUXjE2dvqOuC7nDgeG4JaKIytF2PsyV1PSN4z/75iaJ0dMcjj/cfKasFZb3G5TUKTV2vePfuN/z4l3/hanPN5vaKq/WSxdUGZXJU1nM+t3RptHYbfM/h3JAXFTZraY4dQ9syqy1FkaFVIoRI8J7ed/jBkBUWbRIxtqQYsUpRZDWZyjAqogkMfUMKAWsUZW4JMY3sbDkjy3K6tkPbisF7/IsUJamIshlqOBJNidKOPjT0qkc7S5aPEpE8y3h6fORwODEMEW0tSlkcFtUO5JnF5RatFdZm+GBQKUPnCp16fNAoNRDiQEiOzI2MaNudyZ1DoejaEVin6Bn6M1V9BUnTdx3O5ZyPe4rC4zKHM5a2i5ybHmsS1iqMs8SUqGeJsprzD//j/50PH37kL//6r8QUOPUNJga0dQzBE1/kDU3T0fcDy9WKwhqi70Ztdho47g8sqhVKhZcqOuCMhuTp2wN9ZukHhVGGsrDUs4o8y8Zo9KSZzZZ4INiMZAxVkaO1ghRx1mKsIkXNMESGIdB7j80MmVOkkOgjNG2kjznzekNWX5FoCL5/aUNpep8wLkcXM5wusT0MydAFhTYO4zRKeZIdKEtFMc84aM+Xnx7JbIEtIfmOPkZMXhLR+BDHYdiqJLNuDPN5YcpiCGg32nW2L6FBdT2bJrljjC9Jm5bXr1/x/PRAN5vz7t0rquUKbQx951ku5yjfEeOAdY6YxqHcIaQX1j5Du5zt4UhmC6q5InYDaEXmPfP52OJvu4YEtF3P827P9dWaq/X1mOhqoXSO/W5HiJGFzqgqS2YVBghDT7d/5JFxCDNgWV2/oppfMXQ9IRmc0RiVuLu947T7zBAaksqw+Zx6dYsrFqN3flSgEyH8HI19Op/Rdrwvi3KG94nDvuF8Hlulq+UCq0Gp8ZxppRh8BDRlNSc/nSmrAlWPPuMuy7FZhnEj2xu8J4aemDx5bnEmIy/+A5HDOLD48PDA119/PemlhRk8vhSSl1HZAt5WqxXffPPNpC+XgU0ZHAQmgCo66MfHR6qq4nQ6TfrhS73zfD7nN7/5Df/lv/wXbm5uePXqFTc3N1NrWTSzAiqGYZi0uBI4IoFFApgu/cpFfy06U1nkJ/ejlyEucc8QRl1An4AFsb0TZlscUASYXuqBhSkWmUqWZdzf30++1cLIAVMy6qUe/FI/C0zfJaBVNNBt204gSZhEAWiiP5efORwOExspHQ+xwBPmU2wfZ7MZ/9P/9D/xww8/8Kc//ekXxYuAVHFJkZCh9Xo9seUy4Cfaaq31pP8XoHYZwCSgW9h6OTcCsES2IMFAUsxIZ0RAtgC3S+tJYZhFPyyacJEqeO8nhvaSdZfCT0C1aOFl1kEKWtGEC4iWZ0k8ti9tReVzL+UcIqGRfZNr75zjq6++4uHhgdPpxLfffstqtcLaMQRJwLUcswwCy75I8SNAWrzERbst4U1SvImc6ObmhpubGz5//jw9f8/Pz5NcR0KRRGa22+348ccfgbEgv7u7m6Qylx3iN2/eTBaWUpBIfoHMKMj+y30sDkhSiMizL376wpBfdq8EiAsTL50BIQ7kj7Djl8XcZTftv7X9akC+3++5unnN+bSnzGvOpzN935EbyOgoS81qXlDXOcftOJ3adj37U0v48kQ0BS6fgbY0/TPtEHneH/myfUY7g80L6tmctm3ompbH+3sGFemf7un6QDm75qtvfk9dr/Bdz+bmFdvHR07nPZ2PbI8t5QquNnN8auh7T8oz+pcX/jZEoh84bh85dQ0ut+RlSVnmhCFgTEY9Gwgv2j6tNbv9lrJacjVfMHiAQOkcxmh835DCMEZrA3mWkeUlTd/jo0FrxdAOhOOBoqoYgW+iH864vIA4tqC0UQQViKHHkF6Ao4MI52PD6dzhY2J/PDNbrIkKytmCwbe0TcMsjCx713pUSpgQMcrg7KjhDqEnpgxtFUVV0O5PpATL5ZLtbktV5JybI9oWzJfXhBf9e9O209Bi04zpmj6MBUdZFeSFxTlLljl8hLIuRleTomJ+tcFpgyEwNAdIgW7wJDVGr1vbsdvvGboOt1i8eG23aAV9GN1w0svDbKzBJsXhuCfFhqosmJcLYEyHdZnDaj1a1jmDyaCezZiXVyg72htqEgnouhZtZvTdwO7QoLUDrYkpjtpnnWDwPO32LOcZ1U1FMd9APNKcDrTdQJ47XFGTdDGGDxlQtqCoFrS9x2YWA2gfUOolMMQkfD/Q9gOz2YKqvmLoGvq2pfGKfTNwd7vBZQWKl8rcWDKXo3XDMHj6+GJXhuL5+ZnbV69ZrVa0bcfpcGBRz6gWK6wz1HWJVddjbLAx7FvPbr/lar3k9fWaDx9GlvFwPLJYLHEuRyvD6XxmNp/Tv1yrtvfYpAkxoI3m6fmJzDmu1iuUVlR5xfF4IgV49+4rVhvH6XjAaigHeSklDrsdY+6twsSe/f2O99/9K0U94+rqBhM6uuMTeTlHuxKtLPgzfmjp+gFX5nx+3NPrmvnN7wg6A8YBa6MTBkPUfkzmjAn3kj9gX9rk//v//i+AYrGcM59XKHpSGGdCYhwLP+tysJb1+o4hdKQYybMClEYpg1Ijczb4nn5oR1cYpVAY8uLXTdD/e9+22y13d3ccDocJrHVdN7GXAjxmsxmPj4/UdT1FtL9//36SsghDLEzpw8PDJC+Zz+eTt7QMzYm0YbFY8Lvf/W4ahLu7u+P+/n5iw3e7HVdXV5OlmQxBiqRBGGNhAgWgyqJ9mRYpIOv5+XmKLJdFW0CdyMuElZXvEpAuhcB+v/9FAE/bthN4F3AowFlApYAl0b9fHp8wdQKMhEEVkCTMrABM2W9p9e92O1JKXF1d8fT0NElZhK2V4xddLzCdLwFAAlwu3WGEkZZZA2ENRZMvRdxyuZwAW9d1Uwy7SHxkKBhGZlSCXSQ5UoJa5BjlnhIGVLIfxLVluVwi9pNN00xgXBh8AevSpfHe8/T0xGKx4NWrVywWC0IIUxF3KYG5LKSqqpqKULmeAhbFJaRtW66vr6fETCk4TqcTr1+/ngoLYW8lxEmkJCJxeXx85O3bt6zX66mTMZ/Pp3M7m8148+YN7969wxjD+Xzm+fmZ6+trXr16xQ8//DDp3SV8Sa65pHzKfMjl7MDDwwNZlnF9fT0VnofDgRgj3377LTc3N9N8iTDlMcYpUEqegc+fP/PnP/+Z2Ww2PZvynIhsRDpc4oYi0rRXr15Nz8llASr3rUhPBEDPZjP+1//1f51cgxaLxfScyGyH2Ig657i9vZ0Kr0sGXror0im5TID9Px2Ql3XBbFYy9CfU6ZndoSV3FiwUxjOrSjIHzfnAcjEnhcT3P/wErmSjM+pVSVXMMRbyosb3DU3Xo6zjzatXbJYLQt9Rmhc7u2aM2E7W87T7gWQeUCbnd79boHRG0pa8Lhhi4MvjM0XTUy/PLNaaarbgcDzT9YHdfs+sLHj16o7j/szzbksfWq5fbSir8aJEowFFVvRUswFtIl3TcdwesKpgXiV86IkpjcxiSGiVyKtyHGxTitP5TJ6X5JnjtD3StJ5kM5ypCC8P5dAPdF1LVjRkZYUhw6CoMo0ZNN2+oW8btBnDYPwQSBGUtsSkCQmMy8mqmq458PS8ozk3FAuFdY7cOpRXHJ8f0AwYN/owB+/phjHwxPcdap6zXC7p+46+bxm6FqvMKFtBgwGfPEqPHupKGQY/hqsYazk3LeezZ319RWkqDqcWjMX3Pcem59gMVIVmvZyTZY7mfEAryF1OP8QxQMblDIPnfG6ZuRlaK9r2zOChaY/kWUFV5wy9H/2tNfi+47h9xGnNfH6FdRp04Hw+ooxjcbOmrOfk1Q0pqwlJoVWiaU7E6EkpMATN4KFt/ejBXlTU1YzcFbTHBuUHhkEzEFFZwer6Fc/9M9Y9Ml9coU3OEA19HIdHlckJQVG6HFdWdENAG4UPAww9ysLQtuy3o/1hQpOSxboZzs1IIaCzGcoWhDRaamZ5hrEWH9tRY59lBN/hXxYsYIoHb88ntucjPo4+uenccTqfiH4YddJWc3t3TQCKPOd6ueJ02nM8HlgsFzSnI7Pr8sVNYIfW9iWwomN/PAFxdC/ynuftEzfXGzabFSlGFDCvZ7RtR1Kacjbj1LTYzJGXkeFwoPcJlEIZg1aWPIdZHIFzih7fHqBz9KFlOB+oFyt8VLg00IWBqqwYtMe4jOvX7ygXK1RWooxDa4PViaghBQuqIxHRZizBjFYoFfFhoCgdd3fX5M7QnDucNRgzPpMxJIx1Y6IwCt2PBa2zbgTkWtENPco48jwjEgGDNRqtDNnFcN//lbfLgS9xDBF2T2zfZJhLXFH+9V//dQJm6/V6Akmi9W3bFmst7969Y71eT6yxOIIIaH16epo0s3//938/ASlhXj9//jyxXzJgJwOOz8/PVFXF27dv2e120zCpaGhFCw9MLKBYtj0/P//CP1kW+0uGVpj/4/E4aXjFBebyZwQIXiYAXoJ5GUAVqzjRoQtjCD8PJVZVxfl8nphQAVXC4D0+Pv5CQyv6WnElEe2shMwICypaZNHCi9vKJfiUa3M6nbi9vaWqqkmWIt8hQTnr9XpyZQEmdlvYagGjwigfj8dpWFFCYLqum2z0xCbQGDMN78rvyYClMMeXXQPxthZwKcWMFBIiJxHwK8x/lmXc3t7S9z1Zlk2+59I1ERZVGOyqqn5xrUUyJLaVAnSBXxQzUuQJaBXm9ZItFiAoTLDMVIg9pHipi6OLMPrWWt68eTPNLazXaw6HwwTGj8fjJPd6enpCaz0NY8r8x+V9Jd0oKdZkvgOYBmFlzkK6O9KxEKb90klGngcZZF4ul9MxSlqpXAuxVpT7Wj5XrqsAculYyM9IENObN2+m63xp4XjpBgQ/O8Nc2pbKu0lmP4CpoJaO039r+9WA/Kuv37BcXXPoEsNw5ri9J7ta0DYNNvaU69uXA7Sczmf6rme1vqJeXLO8eYM3Oce2wcQBRcI4x+uv3lLWGWVuuLq9JQXPrKrxbcfhuKcfWh53TxyOR+qlY/A9j09P3G7uuLm94+H+r7gyx4zu4kQsRbmgns2xruT7/r9MJ/DctKMEIARi9Lhs1Jha57BlTYwQ0uiiEIeW9qgxOscaQ9c0dG3D7d0d88WCc9vQnsdKWqFpQ8cQRpZBK0XXNCQ8rhwt8lJMYB2DHx9037fEocVYQ/Q9KvSooaE/H6iyHKMCx8OJ3eFMUAZbzjAme9GQZxRlSVHkxDSCCG0M2mmMsxhtcLnDmgqtBpoYiPQMITEMDTCAiixXS1J6GRxRPUPX0Q8d1ubYlwHUMCT6MFAWNYP/OTCjHRJ91wGKiGJ3OKC0Yjafk16Cb1xZc2z60YHG5Vhj6QdPjBDTyJYqrei6nqz1mMzSNR0+weH4jFosKWqD1hZ3tizrG8DjnMEkz6f3f2V+tWaxGt1ZonaUVUVR1mjrwIyDum07gkoFo4ypb0DndIOi6wfK6KiqgsV8xqLMmNcZ6+OMGD1eK1xeEZIjkbFYbjg3AzEmlHJYl2OyHD+AIo1+52kEgiEFVBrIbUZsB5zVFC7DKksKmq4PoC2bzQ2vgqI7b8nKGfNFiWLsukQSPgVSHEgvLyZhauSFlWWO1XJFCmMV//i043w+Ef2Z4+HAzXIxAZXD4cDdZsNmveZ43KNTpG/PPD084KyirivOp5a27fB+BP/WGPbbPdYpZnVJkWcQE9frNV3X46zjfGz5/OWRm9fvqBdXDE1HVBm2GNuZ56YhqUBeaLJyhiuXzJfX7HdPHI97hqFnNl9gs5y+PeLyin6I2KwiJMXd3Wu++du/YXH9hixfYXROIjEMHmJAG3BZTl0r5rMBm2VYo7BOsds9cm72VFXG9XpJ351JMWDtOCzb9p7eD2TaUbqCEMG/LEzBexIweE/fDziXYa0GRHYw3tchxF/7Gv13vf3mN79hs9lMrN7T0xObzWZiQK+vr4Gfwz2EDVytVtze3k6aVvh5gOqbb76Z4sHv7u6mVrw4WUiM+na7Zb1eMwwD9/f33N3d8fr1az59+jTpT0UnXFXVZNkmDL5ovWWRFxAkUgXRAl8CxctAE5FqvHnzhtVqNQGfSz9zYeiAia0uy3ICCsKWyvC2gCXRo3rvJ62saHQFEAl7K8O0EpYj5/FyOFUA1eW+XUouLq36Ll1HhIGVcyLsofh2e++n/RB2V66lRLFLMSO+0nJviHxE2G8Bc8LCynUSkCxJpDI0KdpfuT4Af/3rX1mv11OhJz8v+3g54yDSJbEelEFdYYBlCFUAvejJ4ec4e6XUlOQpRYow9HLd5RpdOr0IIBfWVoodsaO8ubnh22+/5XgcB+0lKVZ+/9LRR45F0irlfIgU5XA4TEWadGekiDDGsNvtuL295fr6evI+P5/PfPnyZSpoxMpPOhrSKRK2Wc7Hzc3NdFz7/Z6PHz/y7t27/0rmIs+KPA/y5+rqiu12Oz3nwu5fypLku16/fs0//MM/cHNzM+EUYLqfRVoipIHMcIibk8hVZJ8vnxspnMQVSZJnZchcvke6H1K8Xw6v/9rBzl8NyO/urrFZiSk1T/szKXbkJpL6jjLThKGj7XqGoafrGs6nMxE9DRR0ydH7jtP5TKE99bwmyzQheoJviWaMXK/ygi/NFw7HBucUq+WS9d0t67uvqBajddGxGZnAw/nE26/esZpv2O0aEo7jqSMvF3ifSElxd/uG5M88fPqCSmOq4bKcMZvPGELg3HaURQ1KU9RzssKwu+/Y78/oZClfrNuGrmXoGmIosWa0zRsGj9IZQxgZs0FamVqhtMaZURftshyMwxkHWY5WQOhxugQSRkWsTtSFQ5mCvjvTtx1t15LXC7KiIGnNEBMk6H0g9QPO5KQE59ORqCsikFJP0zZY1VHkCmMNyWjyzGIpaPctu90zRwUpBLIioyxKtqczw9CO7hlEiiLnHAaGYfRAzXJHUVRj8qk3ODcy9l0/6pHzqqbrI4urDUlbdtstOikI48vBaMPgDxR1SRxGPXlmLUpphsGTlxWL2YIhehIDu/0DVhvKPOfqaoWOkf3+iRQHSIHDfkdRVaQYiDFgsvJFE62JJMLQkZSi7zuK3FGWFV0fCE2P7yO91zRtoA8jM+JUpLCBxcLhqgVRKTILzicWqw3+vKU5HcaCyFqGmNFFQ4yj207XnNBaoY3FWkVdZQRlxgh4Irm1GDUWcErbUfOuc/roKBYbtNGUdYmxiuhfwkOMRunx/Bj1Yn+mf14EY4wMfU9VlTw9PHFqR+1d155YzPJxHiEFvB+Zq/blxbKYzcmMoT0fyZ3lsNtRz+a8ur3h+WlHmRc0bcvpfCYOgdxmJPyo8e57jvs9s9mcRT3DGMfp1PP50xdMPme1uWa7PZGUwxU5nQdsIqZIGzWFzXCupGs7ljevyaoZP/3wA0pbSpXozgdu6wqX5Wg9ug+9/s03XN+9IrkSZTNQo8OTToHMGZzV+CGRYqQoC2bzOZhETJ7P9x+BgfX6iqv1HK0S83lNlmekEOh78QQOJDRGO0AzhEAIEWMtWZ4xmy8xL2zJqPvMyDOL0Zq+/4+kToA3b95MYPD5+Xlir8XzW4CmsMCyCIujg0hGLof8hCGUBMSrqyvKsuT9+/dTCMhqteL169e8fv160qOKPvx4PE46WfHwFt2zANE3b94wDAMfP36c9M6X8hQZHhMpSFEUfPnyZfq8sixpmmYKAhE27dIlQ8C4LOAiebkE0fLfIrEQUAdM4Fcs4+S7pFN2OZgo7wcB9DHGyRpPAJwcU1EUExgTWcJut/uF33lZlpOdogykCjEgAFaAyqVlolw7AdNimSjDqU9PT9P+iGuKBAOJJED2SWQJIg9JKfH8/Dydr/V6Pf2dANHtdjuxp/JZl2BYhkbF51skJdJ5GYZhcgO5BNjL5XI63zJDIOFTl8zqpde+Uj9H0QuzKn72Ah4v/bLlfhP2drFYTMOxwkYL0JTiAZjCtGT/ZYhSApOkqyNMs7DREhQlheAYBGcna8Ltdst8PufNmzc8PDz8AkgLyy/nsu97ttsty+Vyuq7H45H3799Pg5ciUZF5iEsWuyiKCXhLh+Uvf/kLWmvm8zl93/P27dupMN1sNvzhD3/gzZs3v7AzlGdLgLeA88uiJsY4PffX19eT1EYGuUUWIwWrPIvyLIvWXgKjZG5C/OTlWb60Gv0/2n69ZKWqGQKsVnOetjsMPZnxqAzmZYb3LYf9mbzI8F1Dcz5yc/eWoixo2obz0JKUGVnhFOh8IukCV15Bf+LURYzpaU4NHz584P2PP/Lu7Vveffs1ti5R+Yy2jyg70HVn2mZPSoqynnP35h1ffT3j0LQcjmea9gN9e6ZpepTvqbOMsp4zNN3LRRjYbY94pbnKlmhrICmKImPoEyEkum4gdxllXnLa71Apsn96pmsbbJFj7VglEQaUAm0Mh9No3+RDIvge5TIsEaVHqUtelLhkcCahoif1DUZrMg1aearC0QxnElCWBX1KZHWFLQqUdeNgmcshKfquJ7cZJjP0w5lyUWKM53Q8cm5OmNRhVPGiJR8Bv80Mu+bMh88fWdYVRVlRL+bUs5pD25FCJLMOQmS9vkYDT08PKAX1fEkkEsOYeoqCh8dHgMmJY/CBvKjRp5Zz6ylyTVWN9naoRFnPRzs/ZTBdNw4pNjJVPVDNZqOntQo8b7fUdUldFRTzGb7tKYqSrj3RhY7FfE54aV1aFDfrGmUyAooiL9gfG7IixzmL0orVaom1Bdac2O47mm6UwgynM1Yr5qUlGfBh4HA8EJViMSsxKPJqgStmpBSJTUdQliyrMDrj3AVQoKJHh4HgB0yZU89nNPFEDGeibwh9QwqefuhpQ8QnjbUVx9aTO0dUlmPbEYYw3h8p0rUNSkNeFETvx6Cg07jw51mGNZqPzw84A2WZsd2fyHPL+uoVziZOTYM6OOa2IHOWqhwDhNqmBRSH7Z7M5mSuoG9brHFYrSgLR993KMBm2RiYpSLajAXAbvvE0PW8fvMW53LWm2uaGHl4+ExWFgx+oCpHrX7A4IoZJiU6P9ANEJQmmYouBaJNFMsbdO5oh562a2lOexbLDUVdsrq5Zb25xUeF1eMzAJbcWVQasDqgdWIIo1d+WeZUVU7nW07HPY8P98zqnFd3V8yrnMP+EUVJlpmX5zZRlhnDMLKULnOYzuKDRxuFzSx5VjBfzEHB/X1DjAFrLDEGUgqo/5CQA0y65fV6zePLu0FYWQFZYtcnrhNv376lruvJWUFAjiyAokMVxlUW9x9++IHvv/+eb775ht/97ndT+p6APwH8whZ+9dVX/O53v+N0Ok3WheIpLgWDODfIQi1OF8KUC4AQ7at8l8R2A5MURRg4AeICwA+Hw+RkIou5LNoyBCgsm4CqSwtG8ZeGn2PXpUgQoCcMsbCTUgzJ4OB+v/+FC4p03ITRE2/t+Xw+gRcJbxLrRLHMA7i/vweYpAQCiIHJlk6KG7HsE721gHhhFcWtQgYQRWZxPB6nOQGx/BNfbNGrixuHMNSr1Woq8GKMU1iSXEdxCxEm9DJpcrvdTrMKonm+dA4Se0MZEJUujnRaLosJkflcSjCkSyNgWO4nscQU8CjAV66pMNOXQ6yX94V0GUTLbozh8fFx2v/n52fyPJ9s+i4LiCzLpntTJCZixSjuL9JtleRd4BeONwI+RX7z1VdfTS45Ih0TiZbMkMhx/ls7ULlWUnTLe0OGxK+urpjNZpPbTQhh2lfZr0tZipBY4iYkQVSfP39mPp9PlqjyLF+mbl5KjeT5FO2/DE/LrMOnT58mMuLSAefXbL8akD/tGurZnBAiKfWEbo9liSKQWYUOER1b0uB5+PQemxXc3WyIKaI1xOg5NifWVwvaU+DYBbTOOPuxyunjwHZ/wsWeFEaXkZvX74h2Rhcz+gZC9PRhlJ5YW1AUS47HA49Pe/7mD1/x5psrPn36wl/+8q9jlb/dMXRHvn5zx1e//S39qeHH776nbTu604n66op6NqMsHUM/urtsHx/Y7/as1iuKzOFfKuNc5TjrCEPkdNqRlxUxJeILu+acIRrDue1pfeTUNLiqpht6vD5T5jVFXRG9IYSGzI7Si/NuR3IGpyNKebTT6DCChfm8JhqDMoZkHL1PqD5SZY7CWog9JrSkoUcNDqVySAN5nqFRaO0IQ8I5g0PRDz06DGQaitzijKLrWqp6TlnOiFERPGiVEfqeFEcbwd53xMMzkQTKkGUGY/VoHegDZT0jxMQwRELyeK9wWUlRZlRlhlFplBbYhO/HoqgoS/qufblp4+hxbTPmxcgCt92AtRnnrufcNiTvSQq0tWyu1iQ0rY+0g+fNasPN3dcMpiYqR0BjpoUpYq0mBD8OABqFtQqlRimIAo6nI7PKkbuSvhtIAbQZ01gLaylu3qBiy/bhJ7BnUgtBZdisQGWJru9JYfz5pvcMQ8D7jmb/jOeE7va0zchcV2GOzjKKYoZPhqQdymqMyzHWU1Y5oTvjrGYYGmKIlEXJkDTeJ0JMVLMZScEw9Dhr0ArGuPjI6mqONoYYBj49PLBRirxaYoCrxYLj8URmDHlWctjtaY8tKYdedxRliSKNUo04kBcZWVZR13OG7szp8ERVWAqbMXQt73/6kcXVNbPlFbe313z8/IXv/uVPWJej0eRFic0yzocjw+Cp50vafizoqnpJSJFATjaHmAaSV7gsEvyAViMzt1pfY1xJHywEjU6KFD3JjYV1fHFYUUahdELrhNKBInfEEKjyjCyrublaolKkOZ0Z+hOwfFnUAiEOJBIpBbTNSAqCj+RFSUqRwb8UWtbihwGt1XieQhgtFM1/uKwA06CbLHyXemMBmAK0P3z4QJZlvHr16hda5NPpNEV0i9/3pRvIdrudgI1YqsnCdyk5kVa9gL/7+3v+8R//kd/97nf89NNP/PM//zNPT08TgP7mm2/4wx/+wOl04l/+5V8mHbwMgUpR8PT0NLHjEkQi7Km4Z0gAz6ULkoBvGaATbbQs9CK7kKJGfh5GUCRsoABVAfrC9AlQkk7CJUMtQEfYa2HkBfSIg4kMTsr1EJAvwUjSxZBCSQCk6JdFPiMMuVwXSVWUAkOukQBAkZ0IiJfWvzi+CDA/HA7TfAEw3R8iF7pkMG9ubgAmgPfu3Tvevn07nUP4eeATmPZVuhTCVovUQAopkTnJzwr7end3R4yRL1++TB7t8LMOXH5HikVx6hEpT9/3k0+/sMSXPveyT8Ksi4ZZroGcE7l3LtNYBVjKNd5sNpOM4vPnz8BYTIvkZr/fT52H7XY7zT6IX7x8j3yvDFuLjKwsyykB9vvvv59kQ2JF+k//9E8TgJeZCrG5HGfb+il8Su4JkSOJTEWKUpGZSE6AXC/5X7lf5Txe/pEhZDmG6+vrqasmMwJS+Mg7RYC2fIc848KWX3bF5GcuZzz+W9uvT+osZtSz1Ri0kluuFjnEljxzECM6BepMsTts2T3fs1zfstttcTW42qFVIsXAsTkTQsJHhyLH5FdEfySkM4ftZ65ri+9a3rx9S7Xa0KmcoHOyfIZ1JdZYiJ7kWwYPu30L6ol6/pF5cx7DadqOxfIKP/T8+OMz7x+e+e1Xb6HzaJuxXi3xKrFYr1mu1mhleH565vP7DzSHEyYzrDYbtIbz7kBIiqooKPKc3WHPbrsnPR9IOrFer4g+4oqMFEfXBZSRsHaUAXRCqYgyBmscqvf0fUdzGnXj+aKmmNf0cRidQoIlxAAktNMkrQkRhgQKjwkBfz7QHHdkRQl5PQ6hVTPqMmf7EMisph8CKgQqZ9ERfNtSFxldZnAaVss5+6YZH4Yh0g0DzSmS5yUqRtouEIG+79BGEYeepAy6HL3J5/M5wxAwxo2ppLFjGAI2K5jNVzgzupugQBuN73vafsAPPUPXUGYZWamIKdF3AaUszhXkVUF/kzieDpybDq0CVo06ba1G3X+IULxYDypb0g2avKjpohsLuyx78SMvSMnTdS0xJrLcUIWM592RxPCikWvZHd0I3L3HaMP11Yr1qqbvzjSdIWYF5WIJxjDQo21JQNP7M8SOul6MjLJ1NOeG8+mZ8+MHrqvEcHxk9/xA14+MRlEWuMxikmXwHt0lqjynyC1K9ZxOB3KnKYucrukIg8cYS1nVPGy3xDR67Cs1vkitShwPe7q2ITCy2tY5srJifzixuR4Y+hdGzlqSMuRZwf3pC41raM8tWZGjteF8PnFuGpTWZJkjqQRGkRUZKZTgO4a+ZTZb8rQ/cuoGvq5qjIKreU13OvK83UJIZOUMpTPqusL7SIoQ+g6VNGHoMXmBcopj50lhINOa2HlmRcK6DGNzTFbhgwGbk5LlfG6xWhFzgwK8D6AUeZFR1eXLwjMuXtYY5vWMul5wtZqT5xmL+QylA13bkfKAUuNijAocTgfKYoGxDq0Vmctou4a2OeF9/2LLGsicQxvz8owC6dexH//et7IsJ/mEaEAvpQICone7HQ8PD1xfX/P8/Exd1xMgEJ9nWfBDCFPrOKU0gX4B0RLaISDwEggKIyugZ7lccjwep8jz9XpN3/d89913fPz4kd/+9rcT6y2fu9ls2Gw2KKW4v7/nr3/96+Qic3NzM0k8hOUry5LtdsvT0xNPT08AEzsoLKYwwLIJSBSALIyeFAUidRDtrYCzSyu7S7mKgLTT6TQlXgrLLwD4y5cvOOd+wfrByKoLoLLWTqE4wnhKOqKw6sJwiv/2JXsv+9z3/eTgIgBdLPMuQ18uddsibxJQKscl+yqFjOiZ5feFEZXzI8y17Nt8PieEMDHFAsyEbZZ7VEKM5L49Ho9T6I0UJNfX12w2m0mGJXIW2Q8pqEQ6Ive4+LRLoSgDxo+Pj5NkRgoqKWBFsiM2inIPig5fCrG6rqeBXSn0hMWXaHpg6uCUZTnpxqVIEAAvDi6yv8K4yzm/BPmXAFf2d7FY8Pz8TNM0U9ElQ6afP3+eAK3WeipEU0oTGJbOlUishJGWgWdhqgWMy70k8iy5d6R4leFc2W8pkOfzOfP5nPV6PUlPxFFGzrc8Y5dOL3KfSadN7i8Z4L3Ujkth8d/afjUgX19dMZst8L7jZrOmPT1SFhlXsyvC4GmaE23fTxPd6/Wa42HH3BUkfabvIs5pvA9kWcVoI1ZxdVXRHh/Y3h9ozi1fTg0OTV7MeNiesPMSneXk+Qyblxit0XgYFPPlGms0NtOkFOj7ltms5h/+8R+4vr5ht9tx/efXHE8HlrfX/HX3XziFRF3NKauScj4nr5cYZYg8cnP3jm7WEExk/eqOtj3zvD3hqvkIEpuWru1pzmd8CBRlTnM6wYtMAhTWGorcgiqZzSsWi9HK7dQNnLuWItOj/tyPL6Y8y3DOju4N/iWJUYZ4UkRFTVI50VqiUaRoxmG/YeB0OI4Mtc5ozyei0iyvNiQUIULXtiyXM4yC82HH+biHYUx1PB4O1PWc54cH6mXA5jN8UCgNWV7RtQ0+qjEddOhG/awZMFrhdEbXd5AMfvDs2gPrdYc1juDBmUSR5/Tdmfbc4JweA3qMBeM47vaEYaCu58SoUAqcBlPkaDfKEuaLq5dBiROZ1VR1SaYjfXOk71sen/dgc9799pqimuOTwZKxP/bcP+159eqWuoKqrPB+fAnVdU1eKJRqqWdnumHgeG4Incf3Pd4P49Cv7+mahpMFkifLcvRyQyoNLtvSdE8YbWi7FhcbbG5wJuJTIrMaHQKuzsj6EoY9Xz5/pjs3FMVynG0Io/wihVGrrJ1mMa/Qw5Hz6Ty6h1jD/nhg6HuMdrTtkYRiv9+x3x2oyhpI44CpAZvnzBYLdofjqHMvCpxxfP/XH7j/fM9qMbb8cpexfXqaWm3ywkUp9mk/gQOtFGWZo2yGs2C1Y1Gu6U5HhvaM9wPLxYJzH3j/449UdUkKA3XhSLHksHtCHU/YvOL69i1KR47HhiLPKEpHiOOcRJZXbNZLYvQQerb3R7776SOuumLzbo52BT4arMnQJsOHAGoserVRIOzEEIkxEeMAiA2X5epqRVHUL4t9R55nGDt2RWKEru/QxpCSGq1SVYfvOoyBmCe0slgLKarR9SglFOOLNob0okn9Dw05wPX1NYvFAgniEZZ4uVxOjLBIVTabDbe3t2y321/4Pl96Totmd7PZTK1lcYyAsQB4fHyctJrCtgkb5b3n6upqWrSBCSj8p//0n7i7u+P5+Zk//vGPHA6HKfxE2GSJJ5dFHODt27ccj0eUUrx582bS5MrPi65bigrRWQMT8Lv0JV4sFpMDikgk5BjENUPAg0StC+sn7Juwd3IeBQgIaz0MA6vVajpvm80GYALUV1dXaK3Z7XYcDocJVG+3W2azGV++fJkkAwJGJThIWG9hbC/3QYCVuLTc3NxMxZJ4bYu0SLoZ8rviLS3uNVKoXA7jXV1dTfIMsTKUwWAJtbHW8jd/8zfTkCMwAWGRKIh8QSn1Cz2+2GdehkgJuJZhR5EyiY79kkUXTfal5eMlUBc9dAhhCnsSi01hYuX7ZGjVez/NWDjnJmtRcbaR6yaFLvxs35fno7vabrebzre1lj//+c98/Phxms/IsuwXVqOHw2HqqIgVpBSQIuMREF/XNafTaRrsFqee7777bgKrMsj8/Pw8ffbr168nCYno+UU6I7pzufc/f/7Md999R1VVfPvtt1PxdWk7KCw4MJ1HOddy/YTR3mw2U2EgVqjWWo7HIyGE6fxeFoWXjirynpJnQQpleT5l337N9uuTOtszs6oic5qqKIiDpyrHQY4uKoxx9P2AD4HNZoPLMh6enyjnV1QLgy1qkh51rsFHQlDkuWVeVZgh56gVRZbx+OE9X796zbHpsYWm69PoSmIHSnLmswIiDD5gnKWe14BHm4gPHbl1Iwg2hs3NHbP1NU3XYnUiK2YkbRlSIDpHmwxP+4br9YZ6ucElzdEdma3mXF2v2O2eyZ4PlNYR+5bnwzP9kDBmrJZm9YyyKLDOjQmAOsOHSNs1GJ2RGYVVCj/0GG0hBkgKl1l0yhjaE1qNbiND3xPCz8EC6aUlHoceHQImUyijQcHgB7qmozmeMcYSBk9sW1w+thTzqiYOAxFPURZYHTg3JzJrSFFztVry/PTE89MTu+dnsrzE5hV5XqFdTjcE2i6gTI6xJV3fcD516OjIc00cIlo58szhB8X2+ZGffvzA6upqTCm1mhAsXaeIL+FPEYU1GeXM0A+BFAKurNFmQNnRC9wVBVErumEgKSjKisxaFvOSeZmTfEujAn1/Zn/cU61ucGX9ovGeMyTD8ezxwZCSGRNVI/hhlK10XU8/RLQx3N1djwEy95HgXoJrDORFCcmSOcfQ92jzorF0ERUd1macjj3hPKaHYiLbw4FPuw/MV2+o5xsUA1UGwSX2T1v8MFBVc+p6BVkJUaOifnH96TkdOnY4Mlr0iwzjeBx9wkPvsUaT2YysKHDG0J1fEsqUwuYFzml6H4gJ2q7DOofPc6zJ8P3Axw8fyV3Opq4pi4JzlrF/saR7enp6YZDGQaDFYsFMzdkfD+MLdlYRfURFT+Ys0Sr6FF+A6QgyDqczXz5/Yr1cUDhHtsyxpuXj50dWm5zoPfv9ka73LGY1Rg1YpxnahqATsyrDmALve2K44dS2lMtrysWapDKUdihl0NrgtB1dbEIipTh2pZTB+0jfD4QwvGgcFcZYjFFAGofHwzCCcuVeWHCL9y3Je4zJsK4ghETTdhg1FpWQcC4bpSvDQNeJztmNz17wjGO9/7FJkI4wbwJILz21xb7s5uaGPM95eHhguVxOseay6MvCKay7sGN5nvPTTz/x1VdfTUBOBhwFiIs2V9g+0TbLcKW1dtJT393dTSynsGiiIRepxPPzMzc3N5NGVFwrbm9veX5+5vHxkSzLpmE2YYsvAZ5zbnIYEc/oS224MNXCpAnQk5+Tn7kMn5FWuoB2YQxl4Ey015dtdLkWlw4RcsyXAHOz2XB/f8/9/T2Pj49TsSMad3E+ERAuul7pFAhwF3nBp0+f+O677yaZj4A4YWyBid0eO6/DL1xbRE8sshsZkhOmVGRF4lgjaaTisCI6c2G7LyUNUrzIccj1e/v2LdZaPn36NBVFUkgA08yCuI9cMrX/lkXebrf88MMPrNdrVqvV9PtZlvHx48eJXZVB5kstfghhSrCVe1N07DJoKwSLgGwB53INsiybGGgJdSqKYuqS/Pjjj9MchQzoPj8/U5Yl9/f30+eGEFgulywWC3a73XS9Lp2JpBCTP0VRTEOdm82GLMsma8X3799PYHu73U4Fs7DScg4F+Mt9cT6fp0TOfytFEXZapCoCkKWokneADEgLsy7nVUC33HeXA9LSrZP9ksJa5GHS4bmUj/3/RUPurOF03EPy/PWvf2G/33O9vuKhfSb60et5CAllxsRKHQJ+6DkfDxT1gqzKiMmTGU3SGR0KqxPH7SOf3/9Ac3imOR2wNsNkFbgZy/VrQrHgcO7xyXLuI5w6HANd04BvIDQoBmKq0UlzPu+JMVHNVtSLK1xZY5Kh6zuuXn3F39kMnwLajlH351NDtbjiKjjqvGR+PHH75hUmM8w2d5h8TqENXz78yG5/xsfE3Fna5jAtGM5lGK0pCsfhfEargDMKnSLR95yajuX1WxIGbYA4Ol9UZQ5DR5GNC//xME6sq8QvHkgSoCQ1avRwHkIgKSYNnB9Gz+m2aTFaE5UiqjHgx2QJlTxaRbIqJ/Q9y+WC/eFE8B6dEkWW0SYFenRPmS2v0DqSFQZjGOPeVY82GV03YLP8Rd5hybKc8+mEsxatFHlR0jGyUcY68rJE61GakrmMoo7E4OlCoKjmzBc1ve9GHaEZJTplOQ4qnfd7jDYY5chrS3Pa8f7DB1RW84e//Tuyak6fNLNyyb5J7I8dCU1ManRw6T0xQF6NL9EQG2KC+WJGPZ+xvlpx3B/Y7bZEP8ooMpczn9Wk5BmGsf0blR2Z2mJOPZtzahoWRUZ7eODjD3/mPIwdn5QgzzUh9BgV6IeGerGgzOcYU4MtCGFMCjXWMCtzurbleNxRu0Dp0jg4GzzOOjSGoWlY1DVX6xWvbm/wIWGsHWUluSNGTx8Sxo3tw9HjO5GXGev1FT/9ODpSzOdzzMULOrzcO23bUlTjoM39/T2b6w15lo2dmr4j+jAOdSqNsxab5Qw+ofAs1ivKesbhsMUZTQwRVKIuMpaLGcvFnNPxMDoezJcjm28VWidS7PBdRMccsgylDPV8zj/+3/4f/O5v/wFXzmiHiHMG70f5VZZnGG1JMeB9YOg7tAZnx9TNqMZMAQEmiURROJQaZT4pRZTWaG3RxjGfrzieGrxPKJPou+ale/ezTZ4EPI0OUv0LQ9RirRu7O+5Xv0b/XW/C2MUY+fOf/zzprNu2nSz7RG996QRyOBwmpvKS6RUA8vT0xA8//DDpWYXxds5N+lHRjwuwFm9pYZKB6X0qLfPFYvELp4mu6yanhsuhLOn6yvv+eDzy7t07nHO/AJiyj6IVllhzATgyHHY4HCbAAUzdg7u7O4BpERdA6r2fFn5JOL30YRb9tVI/JwteamfldwVUCJgQkCAsJDCxnsMwsF6v2W63k+77Mmk0xshqtZoAn7DawlIK0y/s7KW3s8SaSyEl4EiOW3TSooeWQClhsQV4SbEjwFCGDY/HIz/++CNZlvGP//iPE1CXZNfD4TDdD5e6+Ut5TEppGma9vr6eWGcJiBJGXjoAch0ui0iRauz3e/7yl79M4P3yXMr3y314acco10uGniWKXs7rpUxD3Haur8dAOJlBuJSSXM4KyDHmec7NzQ3fffcd2+12cnORYVLBF3Isfd/z+fNn7u7upmJLQLLcP6LNl7/bbDbMZjN2u930bMFYCK5Wq0m3/vj4OBXKlzIscS6SfVkul7x69Yp//Md/nIZLJW9Afu7SkvMyu+CSOZfnT/bl/5sLkjFmkrpdSmrkPpdrLt8ngFyuy+Vg6K/ZfvVKkjvL+XjidNxx/+nj6HKgFP3g8T7S9APaOlxWkBUFeVFQnDLOpyN2+8QMTdKWtg2E1KNxnNuW035Lc3giDA1d17NYXJHP5tj5HbZcUM5vIO85HVtmxSiVODY7lG8oTMRqhQKcUbTNka4L3H+5Z7G64bXSVNrgkyIqhUczv7578ase9XzNuRkH0PI5V8srds/PLNZLTm1DmdV8W67Ae6rZksHDl/ff49sDMUVOxx1aj0xZjBEfA13X4oeOPK+oy4Isz+j8OEBY18Voeeg9oTuT2ZyQRseGrjmx2+6IJOb1jOjTyxSzwXlPHAaKrCaEsagpqorkV+RFic4zDt1ATBE1DC8Wewrfd/ihR+WjjCb4cZFJMZJQxARGj5P/s6sxPAblyfL6Jc49gSmp5uvRNzwEtB5fKEP38hLSirLIGHx8WRTh2mV0TcP+sCfLc/Iix1rxcIa8rCBBjOOgXNQG4xwoMNYy9AM2ZcCog7//cs/VrOLrd7cMIeGyktn6mldvvyZmK1SxxuYV4dwC5oXBGEOR8txBGpntEAassTxvD0SlqecLbm6umFej/7jWoNVoETkOSTrMS8iPdpYUcxIG40q2uz0MR4auJTOKvKppjjvatuXtm2ucCRzaMyiNy0uGqPBRMatqFHA6nXGZpcwtfmgZzluSS+jKctg+U+aO46EZg3GGwDAEYBy6edoeaNqO+XJBN4jLz5iculwuybN8YrOuVis+vP/I+XTidDxCSjhrmdUzVBgt3o6n44smffThredjMdR2LX3X4KzBWo2PHozFFSWH5z0qQh0SWZazWGw4HnY0bfPyErIslwucNXR+GBNxY+DUnAnRUJYZSif80IKKeBVR2lLVC66urjHZaCeqDITgx2c8eTQWzThEnXTCDyNwcaYgz0u0H6VHMKb4aT3KyMaXtccYAew53keyrMC6xOm8p22PxKRYLWuyYgweijFN7fdhCBO7BUzA69cO7Px735xzk9b2/fv3v7Csu1wYxRpRBtekbQ1M2k0BK23bst1u2e/3k65Y3BWEFRXJiuilRdMpgOWydS2R7B8/fmSz2fD1119fFF4jsBWrNQGml5ITcZARf/Usy/i7v/s7xB/de88PP/ww+UGLQ4cs2CGEiYUV5laAjdj/CZsnC7uA1vP5POnSZeBN2E5pyQsQElZRJALiniKdg397bQS4SVfhkuEUO0JJTAQmj/PLjgMwDbDJvkkBJlKMw+EwAcHz+cx2u52095ds5WX0uRTXIjsSVleKELGsXC6XfPvtt5OG9/r6mq+//npig0UTLZ8PI4MsBZnow40x03leLpfc3d1N/uNSyMjnCTC7HNqVfRQAL9exruvJXearr75Ca/0L+0mRUkhhKs+FOPvI/TabzabOpsjCBBjDGLD19PRE0zSsViu6rpuAsnR35HMlnOmHH37geDxOxYpzbhrEXSwWk72l6NfF7eayqLp0HimKgsfHx0lmIt+72+2m49B6DJ+SQWhxeJHCXWQk8r1SjM1mM66vr6frJd8rJOalS48w1cJWyz0u50r05vJZsv+Xw9Mik5KuBzDJe0QeJs+pFLJyb14OCv+a7VcD8r45MbRnhvZE8B2vX79mPp+zj2fOzZG8KFAKsjxn8zIsw8hV0p2PxBAJyvDpaQfkrJYbVrMlq5sr2hl89909eZGzfvUKbzKKasHs6pbBVBSmQpuBFAPn8472cCBXDcYGtIssFwVFZjkd9rTHM+25oz2d8cPAq6++YX59i7GOmDwxGXbnlv25oyxKTFZSZCXWVWAdKq/pk8HkNWVVsd/t6YczxeKGr3/392ht+PH7/0I4N+TVgiEpko+UZY5xjsEPNF07JhMaRZGXlMOoN5tfFSMYToH5rCL5nlapF/u08QKql0VJyUucseVOSmijiTEx9J6mbeljwAN1npEFCH3DkFqKosLmhq7VKAZCiPR+IPmIJtL7iI/j1SnKamRkY8RlmiF5UowEICmLyWYorVk4R3860ncdTdOitMIoNVri1RVd39P2PaRA05xGnfxLmMp4444PU/AetB6lPtpw7hp2xwNGpzFc3RiCT3RdYLVYsrhao7QiU5qmCxTViutXFlMtyOsrdHUN2QqTlSTV4ZxlVhWUZUZZZBS5o2u7l/txlDc0TUPT96NjSZmR0sBmPSfL8jENU0Wa5jyCQOcgafpuoO08pbPMFtcUsysOz2fOXU/bd5ikCSmQa8h1wqSAHwZcPkpg+lZTlHNMPqNtG0iRLLOj/Gb7ROUS87qmLiy7h3tsWb1Y643OMv2QOJ06+mEctI1p1I+3bcusrnHWMXRnbjbXtE3D8XBAY7DGspwv2W23GKO5ub7h5voGqw0nDj8vEueW2WI+WUsZMyZQVnWBSoHT6chz21EWM/Kyol6u6XqPD6Cj4tx4fHK4Yk6MnqH3+BhxWcnN9TU6O/Hx/olDG2i+nFgtlyxnNSlGdtsnFosVs/mSKjc05x3n05HZ1eKFqVBYq+mbE75vxrmLeYnRirLMOJ/j+F3JXDAY6gUIlBij8X7AWkOWF2htp2HkiKXvIk0zcDq25GVJnldUZYFzCu8DIaSXocGMPC8Zhg5rM7QeGcb0H0OdAJMGWgawJARE9LbCQhVFwfX19SQ7EDZbWMbPnz+jlGK9XrNcLnnz5g3L5ZJ//ud/pixLXr9+PYG89Xo9gSJh38TaUBZa59ykgd7v91PcvKRSSqT3pRPM8XjkdDpNmlphRS/TE4uimKzkTqcTy+WSv/3bv8UYw7/8y79MEgsBwdIil8JCPKTFBk609bIPAvDFoUPABvzsvCFgVTYBCOKfLt2Bf+upLCBYJCPCyF76pcvvCsMsoBWYgDnwC72/BD4J0BQJgbhwCLsvoUkitxGGXCQ9wtSLK42w4HKMIvtZrVaTr7kUznVd8/btW6qqmjovYk0HTAx8VVVUVfULH3nZZ9Ghi01ejHHqxoj8RzT5cvxiyZdlGavVivl8zuPjI+fzedKSi3RKAJoUUcA0xCr6fPnstm0n+0Gxory/v58kFZd6cwHOAk5lIHI+n0+g8fb2dmLcR/ndWFBJ2m2Mkbu7O4qiYLfbTUFIco+Lg4pIOKSAOB6PPD4+TudVigEBzdLFku6A/Fue59zd3U3ynfP5PElSRH72+Pg4/bdIYOR5ETZbgqP6vp9yBOT5ukxiFdZdmGu5z6QjIcOrIgUCpmFbKYDk2ZfnWd5dlwWUsOO/Vq4C/x2A/LDfMnQdnz6+R6sEMXLY71G6GFlDm2FePKrbruX56Zm+79hsbogYtvtnhgjN4cRsfkVuAoWNhH7Ph5/+ldPxmc3tNcvNhqZXuGpBVs5omkRelGw2NfefPxH8qD/2caDpGnSpyGzN7umRx0+f0BhWdU1Rz7E20uzvyXJNMV/hjAZliXF40RI1jPnfFm0syTpMVdLFsTozeUWfjui8JjcaP0TKxRMme0+yLQTN/tCQZY6sKMf47TzHWBH725eXpyEOY4ESYyAMPZlOaAUxeJrTGaOgqmq6vqNtO4wetcu6mOGzHK/U6DCjDL3vab2n6XpMeyafz5nNS5zhZehtgATOjNrf06Ehxp6yKEZf8Kbh3AwjuIvQNGeOhz2zdTZ6YKtETBqXV7hM4wdHls0525yHTx/oh448MyStQFmc1YSksX6UDRz2B+p6RpFnrJZzymJsVxpr4eXBHTX4NaBozy1GR4zVZHnJfLFCvzDYXdOQ9wOZUWAt3flM1CWrq1dgKmw+g2wMRQp+wFmoKkfmEn3fYNTILA+DB6VJCfrOc2qPhAT5q2vKzKBVpDkfCD5SVgXZy5AGQOZK6llNOVScdnvaIbG6e4uPPa7I2B7HmHCjE6vqarxX/BjHXs1yYrLYMqfIV7iiJFMarT3Bd5yPe8LQkc8qNpsrhvaINeOi1PUDg4+AxhUlNi8o6zm2nLHeXKMUpFRQOMcx9Gyfnnj96tWLE1HL0PcopVnMFxz2B3zv6buxpTarxwVp8D1lWUyylbquCSm8gIlytPP0kRAjQ0hUNh+LVacIxzNDAKscXRrYN57gBxZ1ic0zuubM8XzmPCRMXvHmzWuSK3nY7tlvd/jtmSozmKQ4PD+iUxylXq6gzDVaB5SJ5E5jnUb3A0pHVIwctkeM0dT1nBQDfUjjcHSWj65AF/rc+NIGz/Mcl+ejPWfUaJ2xP5w5HFraxtP1gbxQgBllP0mhtZmKuRjTy8s3kef64mX7H4AcmDSgP/7448Q2b7fbX7SIpY3dti0PDw8TQJCfFXu7xWLxC33y999/z36/n2K5BXjJYiuDXx8+fJha1wJARWd8f3/Phw8fJm236N13L/MU8p3ws6vHpSuKMN0CUIXdFnAui7qEiojkRT5fJAni+iEgFJis8i514ZctcWHy6rqePNmlKBF2WT5HJA19308DpvP5fIoUl2MS9jvGMflSWMnZbPaLgiXGOIFiscyT3xf2XQZynXN8+PBhGo6TTWRGwsZLAJAwtGJPKedDwKJ0DC5DdYqimPT8y+WS8/n8C625fMdms5l+Xth1YbL/7fAl8IvzIvMAMUbevXs3HYvIoOT3pzXiBSwPw8Dz8zNdN5KWYmG43+95fn6eNNcCoKVglOJNOkeXMw+Hw2GStdze3k5+/MK+CzssYFBAu7gAiXNMCIGHhwfevn07SaqEPV8ul9N+C+s9n8+nLlFd15PtoTDnwubLdb301hd9+mWgjoB2sWWUIlFsBoui4Ouvv8Zay+PjI8/Pz1MnQCk1ucfIvSQDlSKJkuspRenTi3mB7K+AZAHMv1gjXopN6XwIyJbu0G63o2maaT/lHSHXSc6jSIOkUyTPyq/dfr0P+eM93blhPquZzdYERk3len3N4djRDR6lDMZo9vsDh8MBrRSL+ZyynlGUJ5S13A5hHMLqO3aPH/jy4SP/+pd/YnO3pppVND6Q3IzGR8y5pekSJisoy4qyKKirCnpHagJ9d6bKMsIwsH16pD8fmVcV9GeigbLKiOHI8TlgLbh8QcTgHFibodCgFf0Q8LGnUIasrkfGGkXbDyRlyeqCwlqOhxO2mLF59RXOZTx8+YnOe5r+zLE5kz9YfOhZXq15/eotzhUML4zm6FXZjYOdBM5Nh4ovjMUwsJzPuL2+5vDC7gTvSVqR4s+2Od57UhiHfrQ2JG3AWfIqp8jyMfAlRUL/oi93iswU+M7TnAd8iCRtyKsZ86A5nzv6/owP0HcdJE/uNMlomiGSMEQMOqswWcKVEZuXtP0Baw3aWrTOXvTtI5D3YaDvPXlesFrMuVouRvu+tpkeFqlQx4fIMatnBN+ijcIZh1FjgaSNJQImy0bZi9FkPmDRvPnq9yhXEZSFxCiv6FuK3FLlliIzeN8w6EieVQzA0A/EqEZQ3ntOhxP9qqYwOcpojIaoR998eYnv93uOhyNVKnGZwbiMD18eyFRk8/obdDpR1gV/+ec/cf/xntPuCb9aMKSA0o6sqAjJoUNGSBafDOV8jqKnObY4q7lazglDw/F0oDlscVnO8XRmGAJX6xVN01LUC8rFitxHXEy4LCf4gfmL7aFWCe8HVAJr7MT0GG2oqrElezjsJ6DQtS2ZdQyDZzab87zdTjpzbUYQG2LENz3GQDWbYbJEVtZgMpTWKBvpI+Takc2W5GqUqB3bjtyOQ7x9P3A6PaOzlvnqmqvra+rN1zRNSzjvic0ToT1w2D6wvf/E0J346tvfsaxzglPozKBNRNGTu1F/bqzifO4Z2oFt17I/9hTV6gVQabQ+kVKcFssYR294rc1L0qmmKEraNvLw8Ezb9rSdJ0SFUqO0y1iLUumFdRlZxLIoRrvD0NC1PSFG+n50F3r16vZXv3T/vW6SBLhYLH7hg3xzczO5QQgrJYuc1nr6eWGqpEUtMw0//fQTf/rTn6aurACZ/sXV6zJgRqzNBIwKUOz7fooMl5Ai8deWxfvS/USAEjC1zYUxlt8RSQ38PFwoYTNv3rzBOcenT58m4CSFg/eezWYzAT1hNIFf/H+RmAjoEvnE8Xic3FMEBFyCH9lPYZwF/Od5PklpBEDIIKi4nVzKXUQ+IINwUjDI78i+yt/JNZAumwDky4E7Yd8F2KxWK66urn6hKZc14tL+T677pRZYAA8wDTRKwQHw29/+9heMvgyiilRDgJcUMcDEisvPS4iUMLCXxyLXXCRVMpDpnOPjx49orXn37h0xRmazGX/84x/56aef2G63bDabCfAJYywAL6XEYrEAmLzXxaJTOjwiv5EC8Hw+T0WX3KsizxAbP5FCXd4bch7FY363200FY9M003M2n895enricDiwWq0mWZI4oQjwlcJTPl8Go+WekqFaGSCW+0j08ev1mtvbW25ubiamXLoXT09PfP78maZp+MMf/sBisZiAuOzPpZ2hvBvatp185CVNUwaof14j4gTSBaBLB0m+UzpOl8Ph8gyJ9aYc+2VXRLz03759+998h/5qQO77nuAH3nzzjrKy7A57+jAuSDEGhq4nLxx1OTqwFEVBXuQYo9HasFgu0dpRE+m7nufzkccvn3h6+ExZOlbLOS7PiSgihq7pUaczSecMfc9+t0OpRJkbeh3pQ0vojpjZnN3TPUPbcbWYU+Y5zfnMx/ePWGso5nO87/B1jdY5OivQWpGiIi8rsizndGrQQAiRpEY3ieADThucy2laT8oUJq9Z370jL0sWqzXlfE572vPl83u2hy2FD5RlxubuNTev36DQNKeWfr+nAKyCwfejxV7foaOncKN22WUZRZHjY6Qf+glExJSYVQXBjczl+XTEvgxztn2LHyJN05FC4unpgcwZZrMxoRM0aYhYKLRcQQABAABJREFUbajKAmVGN5MQRs1xVlSYZqCs6jEi3Nox0bNw6CynC55uCFRlho8DNq9YXG14/nLA+x6lAiHPyHNHTJpGRYa+w2U5w9Axm1X0XUd7PmEUhKGnyGfMLxbMcUilxCsYXcs1p+ORsqoJcRxUXJQl89mMMHjK5YaynDNb3ZJsTtd7Mjuy45pAXRdYm3BWgZbEu9FvWmtD340yiyEAradtekqnyMocoxWNH2jbhizLp1axLI6mV2R5xub2jr/8+c90s5yb5YrbN47+3KKHUevv+xNYR5aX5OWcoBxDsPR9YkgelRwpQJYVxOjIKsXz/ZauHWcPosl5fNqSlxVVOedw6kkmo4+Kth8lRf3QQ/JoP7oAFsXIbmtjSPw8SKW1oqpL8txxOp/puobjyRJTxC6WVHVNiok+BPaH/fhSy3N2uy390JLllrwYHUiy0pLMOPjrdEE5c5yannZImKJiuVlydZ3YP37hvH/GKciKkiHBEIbRCjFf4BYZNpuPdqMKdJ6TW8PDl/fsHr/wm998g1ED4MdCLVc4Y4m+GxNqU45RnpAi292Wh+cTt7flz4v+i+uJMEZt2720WEdNuDYFIRoeHnc8Pu7Q2nI8tWOHJstwzpBnjqY5ohT4wWOswYdI6D1fvjzQ/ZsAlb/9tS/Sf8eb2ML9/ve/p6qqCTTKcyReypfaZmGNRU8qC2Xbtnz58oVPnz7x5csXqqri6urqF4OFAnBFCy1DhcJWi157sVhMbLx8xul04suXL1hrJ6vG2Ww2uX8IKBQgK9pjYZ4FzAkQFaCb5zmvXr2amF/R33748IHdbjclFL5+/Zp3794BTEmSclwSvS3s9GWSp2i3ReohA5fSPhftsQxbiq5VNPX39/cTwBXW+NLDWsDIpTWhuOcIMylDf+IqIt0KGahbr9eTc4gUDMImS6ElhciltaD822KxYLFYTH9/6b8tm/hBSzEizK0ULnVds16vJ2cf0RkDkz2i3Hf/1jZP7leR/Yjm+TLYSc6vyLOASa5TFAWvX7/mn/7pn5jP52w2G7766qspvEieCSkExDnmUlJ0GZ4krjifP3+ePt9aO7mf1HU9WSHKPS8AU0Jr5JrJdbx0LRJdtkhBLh1zZF5D7gkJ5hIXFilwhMASx57LwVtJ4S2KgtvbW+7u7ri/v5+6IKLh995P/vDSZZLPEcb948ePPDw88Ld/+7fT/SCyFzkuGVKWf5cAsDdv3vxXA5ZSnAnglmdIpDtfvnzh/v4eY8xUHImmXOYg5BmS72/blk+fPk3XQc79r9n+O3zIl/RdiTYa5zIy50hpIPme1XzGSWtiDBR5TptZrNMs10uwhlPbvVjRJXrf050PNOcTVVVSVBmv1zdc372iXm1w9Ybz4OhjDj7gsgS+47DrKHOF1Z7CRrSNoDrwmpgMKUSUzolJoW1BTCe+fHkkOxzxMTIMiet3GZnOcFlJTJamaRkCDDFRViXeD7RNQ/DSRhxP8P39I23TURUVdb5kcTOjnF8zX79i9/SJQWXo8oG6ctR1ji0K2iGR55ZyvqR+ccVIKWIyy3b7yNCeqauK1WIx2gwOHp/g3HegErP5nIRmSFBVOboo8Mnhu5a+a6nrAqMix0PLeddR384xStOe9yxqRZVpDtsDKURiCGRFzmw+G7sTvufcnfBhtJ8LAU7HI/muwGQ9rle4co1SJUrZkTWML8M58wXtYUZ7+EJwBtA4m1FkGWedwGgyNy6QWuU07ZnzeU9d1dRlSZ5ZdO/xStF3PX0fxmJBj3aJo1Z+9HUPIeBDopzXFIs1So3uKy4r8YBxBblxGKtxFuazAmsMWkcgoZWBNLq7GOeIKtHsGtou0DYBPyhOJ09dOrJsnI4v4vgiVlrRnlpiSMyXS1IKbHfPJKX46jffsDueOe72ZPUtJS3L1RPP5Y8c+x3J78jyqzGFMqtILscphRs8u+2RfkjYZCCOz0zbHkgp4FzBcrHm6emBPiksisOpIeDwZAw4sAWWiMscvh8IcexM2axC24Km9xilqKp67GJohdOG9WqB0mCdYRh6snxFXpXkL4C1aDuOp/PPjgsJurbBZjUhRMraUb4E/WBy+kGRaUsfFN1L6E9mHEU5Mlq7LGP7eA/DOGRZZBkkz/n5E9bDfP0V2lak0uDPhmym0U8PxO4I6qWoIaOPA5kDm1d0vkHHntj2KONAaZTNOJ2faduOPCswdpSZGK3I8wLQ9F3EaIdzFsVoM7rdn/jx/T3Hcxj16X1k5hzOKKoyx2jwvh8dVDSjpWTfcz61PD4f6HqPEkeJ/teFPvx73zabzS/CWy5ZW3FPEHmHeEdfX19PA4sCSsV15HQ6Udc1VVXx9ddf8/r1azabzeT2IE4T0jK/TLQUhluAzr91JJF9+/TpE7vdbmo1f/311xPzBkwgSuQcsuBLoS7Sg0+fPk0tfQEoi8WC6+trHh4eACbwJJKCS62rMG6yPT8/Twz+arWaALoE2IjMAJgYWJE6iFZb/NP3+z3b7XbS3ouEJcsynp+fJ7Al+yLnTI5dgMbhcODp6WmSqFwORgrwlo7Hfr+fCjJgKlwuJQbC0os9o4QWybkXxwopYgREi0xA/KxDCJOfu1Lqv7LaFCZdiq9/6+IjAFjOv8g2xEtbgpCy7OdoeSk2hDWVEKzn52eUUvzud7+bZCpS6Ig0R4okiXYXgA0jsJMQJrm2UgTKcORqteLh4WFi1GVQFpiOQ5hb+T3RN4vVpGj05Rmp63rS4gsrLv7cl4D1cDhwPB6nwlCeY3kO5Z679AO/tOoUyVee55OtpkjD5Hw8Pj4yDMM0uCmOL1JMyLGOphc/D9df+vSLRlyeUWHLpViXPwLcRSIm50PsV//6179yOp0mJl+A/6Xu/JJxl5yFh4eHX9gnXtp7/h9tvxqQn5sjzalj+7xldTWjrLJxcVOe2uVk8/kLA9fRDz1ZXZPXM0xeYVWG8xnDACEciSGNummtsM6wXK64vn3FYvOaNmYcHxu0UhRGQRxwKNrmRFKWwgz0uuPQPHM+PlNnAVfPccZyOnc8d2e0zcAU/Pj+E6fjnuA7Nq8+8J/ykqusIpJw+QIfRmYfNIMf/YZLNba4z+czWmma05nD4cinTw84m7NaLJkvZpTFgmpdo7KaPjkW5y2zyuGHM0+PX9idHpnXc1bzJfPlNeZ8wA9nCpPT7Z7oTwOEjsNhx+N2j0+aejZHE8CPISfWGUBhUwTfk2eG1WrG8aBwymOyjGAj/hxQwVJXM7bdE/vnL+T6itQ1pJDwQyD6jjJ3YLJx2NCYl+7F6JPeth6b52jXkY4t1WIgq1cUbkEYICowOiMljdaOoU8oIu25I84jxSyjKnNSTIQwQAqgQBlD33XMtGa1GqfUG8abs/Oje4ixFkViNquwRr1Et3vSi+93woGrKKo52mVEZSBGjBn/PcZA7gxmVmKdnSQbZVlzOB3pup66ntOHwO6443A80/cQQmK3PbJZlwzBE84jK7A/7olAVuQMfSD6OHZ56sXoNELiq2++4c//8h3dYLla3bG4esYV/4Q+Rrbbn9g4RZZdM47pWsoyo/c7Yuqx2pFpS3vw5NYx9KNMxricqHNsPgfjOHcddbXGFTm7cwd5xfr6NVYHrPVoPQYxNd2A1jn18hrftRgDZVGRGU0YBqxTrK4WnLtx4GWxKunjwOF8oMzLl5TO8YXy5cs9RV6wXi7ZHsZuVj8MKGMoihJSYOgaQnLYrKKsLbH3KGuIKXFuenJnqeYbus6ThgarFb49k4YOR2LYwj4odLVGm4IiXxI6TzVfY+y4yDTdmcxUWKWJQ0t3Cig8fXvGo1CupvUGpQvQjhATShtSing/ugEFP8qorHWAQutISoq263nenTmdPZGMrk9YV1BVBc5oqtyOlpd9g9YlWW6JSTF4OLYDrVeE5Bi6SIw9mYv/v16b/5faBESLC4kwfzLIKf7MlyBrNptNQO1Sf3mpm5YQmNevX3NzczMxvcC0QP9bnbEMikp7X3S7p9NpkqcYY/j++++nMJw3b95MEgkBSrLAwy/1x9KqF2ei3W7H+/fvp30VX/Xr6+uJ5Twej5Oc5suXL5PMQYL0RCssunbR2+52Ox4fHyfHC9kX0b1eas2zLGOz2bDb7abzLm37lBKz2Yy2bXl8fJzYeCmChmGYWu6SPCnnSVw+BNTt93uWy+XkPiIAUiQkl+4YMgwosiRhQ+W6iX5e0hJFbgBM+yWAVWz55PgvB03FD1wAkgByKb4uOwMiqRBbQvl+Od9iLylypuvr60nvnFJiv98DTHpkYZwXi8VUrP32t7/lj3/84yRR2mw2lGU5WfyJ6wkwMcW73W4CrnKeZbDzUjYj1n6y36LBz7KM29vbqQCR4koAqgxaCjC+BJWbzWYairy6upqKMOlQyBrx8eNHyrJks9lMLLmw8PLMN00DMHmbyxCoDHhnWTYlzwpwl/8PTPMkAnylCJR0VykG5DqKfO2yYJVi+fL5ENZcCiqRaMm5vXQIenp6mu5D6UjJ/lxapQoBIeTD6XSaOlfiNCTX+b+1/WpAfjy1NMeW+/svnJolv/n2HWPUeM+h7ZivNuQp48vjI4dzw3x1RcBBGO1jklaE2GOt4mq54LB74Hw+sFwuubq+Ji9ndEMkGY3LMk7nnmAalEqczj0p9iRyHp7e89d//SPN7p7VvCBgObUe5zKUybDZeLOmpmHoOrr2iIoDD59+4rh74PXXvyWpRBzG6opkQY1BI+g0MmnWUMTRzq2sRuaUlDidjvT9wPE8Mjd14ZgVM+5ef8Pgb5nPC9rTnqJc0bUNdVnhjKOejWmjvjuRhmZ0BNGGh+0OjWaIkFczEmOSKQN0zTBavGlH6AZCn8hw1HlFGiK+P5DlGbE27A8d56bBZoYQEs3QEOYzdEocjmMioY2j33VRO3Tixfow4YNHKc18vgQUMSQinr5rUVlHFgZAo4wbwbYPZC7HWEeKAWcszlpCGHBGk4xU6WOQi7EapT39EBh6jzWQwjAO72lFZi1FBn7oKWwxuq+YEjBom6OzCrKCulqgXE6I4/CeNbyw/SNAzrIcrXhZREemZhg62rYhz0q0NcTUgVaEFIgxAJHjaaBtFy8LRaAo8he93igN0drgQ6DOC4zW+PbE6XimrCqquuLT/Rdm2Wvyeslsdcvjw/c83n8ixsR8bcj1ApdllNma1iqWywzf9wztEaV6cqcg6NHxIxoGr9AuIysrnFJkZc32eRzInCXN+dwThjPzWlEWFmOhP/ZYq1lt7mhOO9rTjrbvSMbSt2ecsaMfexy9uLu2Q1uL7wfMSlOVNVpDWeQ8PR057Ldcr5fM6xn784G263i8v8caR1nOObUD2pU4ZUgqIzG60hj7MsFuxoIpqRdZmEmcfU8IPSr2qHRiOD0RQyQrr7BOg/fMFwuc6xkGT/SB6AdsVhDDQNdHysxhjUUrg0+aGMfB43o2x73IzHzoX0CNeWFODM5l+MGT0jh+6QdPPwygNRHo+4HlPMe9dLG8Hwixpe06EowFoDK0ned8Hui6sePW9x5lwLn/CAYCJtu0T58+cTqd+P3vfz8NNopcpCxLvnz5wvF4nOzBREsri6a1Y2S7uJdICI+wysKwir4Z+IXH+f39PX/605/Y7XYTiyxgUn5XnCzEPSGlxIcPH3h+fuY3v/nNy/tj+MXxyedfOrqIy4SkIgqoPh6PExNeliVfffXVNJgnjKt4O4vFnHNuKlZkXx8fH6fzIt8hjKOAHgFmAjZEOyxyAglxOZ1O02Bt3/eThed+v59kCCJPke8UkHHJyAvglDmgS13ypd+1SEGkWyFs+yUjfTlYJ10A+ZxLHbLIc0QPf+nzLN8l51C+R+4puY6SNnkZXCQDspfe1Zf6YgHh0q0QbbG4BMlgrHjFy/cJ4z+fz/nw4QNFUTCbzViv13z+/JmPHz8SY5w6RKK9bpqG9Xo9sfRyjAL45XoIcyxadpFkifa+7/vJSQaYQPfNzc3kUCLaZtFzC5su95YAWilcpCtxPB7Zbrfc3NywWCwmK0cB3eKbLoWS3JfCNF9aY0pHQ675ZbrlZdqtSMQEkEuHTHzh5R0jhf9l50MGtuVeFeci+W/RoQuIBqZ78ZL5Fv25yIBE0iSbzJSIfvxSmvR/OiDXpkTpkUlq2o7dfs96vaCaVXy5f8L1BcqOsgBlS/JqjTI1Pih88ATvCX2PTgP4nqrMyPMV8+WKrJwTleVwPHPqTjRdwg+RVV3g+wZ/2qJDx5/++a98/vgjZZVxe3vHbFZzajoWqys8lqqeM58v6NvuRYZS4YzHaE839Oyf7mmPW2wFfeiJKsfk7gWsQRjiCMatxToHSlHPKq6ulnwss3FBV4m+H4MumrPCXq+Z1XNczCmqguViw83NO2JIzOqarmnoh4ZiWNCct2zvP6DyGfXqBnXYYY2hntVk5YyuHXi6vye0A0Y7ysJgMHTHlmQduRuADhM9EUBBWeecuoGub0ha4wfQSXHcN2QorHHYKke9PFx5SmTOjomDjC8dbQ1X6w1GW6IyeGUYGNND+26gKHM0irZpUDFQlBVKWYa+H4NiFAx9R/EyfHluO7a7M+dzSzm7Qs8toW9pm4Y806NO3WriaHRIZgZC6ClUS+o6BpVRlHNSGl+Oi+UVZVmzPzdYl+FcxugnzcROOJejXgqKoR892duuw2UZ1r14m1vDYj7jtGrRaqzSq2KUjTibjUxoDMxmC87nhq71L0MpihgUzpZUpabxLTFEXr99xU9+4NieuV4s+Lv/8f+JM57kx0LQHre44hoVrmkPnqHJUSqRVMts6chnM4Z9QxgSxha0XSKqgZjGl2/X95y6lmo2RxcVKRmetzvOxyfCdU2MOXVdoozHB4t1JdVcg0r0zYnOB5puIPmGLCso8prHx2fyfGA+1ygFxEjox47J9WZN8ANPz88UecZmfcWynqNR7A57DtsdVjliSPS+4dQFitkSpSxNc2I2y6nKkuPpSN+2EANlnmF0Se5uaA5bnh7vSbondzlExyJfEIYWoxKZdfh2ZJ5TSBgSJP8iZ8oICZQp0NqgVU6VlQRVUOYlShm6tiPEYWrPygISI+x2B4jpJZwqp8gDQ79jGMYCr6xKyjLn/8Pef/ZKkmXZmfBzhGlzdf2qUJlZVd3ViuoFyP//G4iXnMZ0k1WdIiKudO0mj50zH+xuS48GZpgD8FNPOZCozKiIuO4m3NZeewmt1SjpiS1lUaC0pu0Dfe85HSq6dqDvFOdzj9KKNNLkefFbv0b/Tb8EBInJabfbcX19TVmWPD4+TlpeYa+kfW8YhkmLKyBHHsRi/BPmTdhM+f3CzooOW4xzRVFwf3/PfD6nqiqurq7GZKe3/HJhP4XxEkD4+vo6GcAEiMqaW7Tboh+VLG/RCcsa/zLf+3Q6cX9/P0lKiqJgtVpxf38/ZZcLgBK9+ePj45TbLOBfWNBLk5lovIWll2MvyRrwa9GPGFyFFRQDn5yLyez9JkWRCDlgYppvbm4mBhyYgMplUYpIj4qimKQAApAkeUUkCDJwSTzdZda2AHEBSALuhb2XGEkB0FdXV1Pp0qXRD5iKqeTYXEpAmqaZzqUA/vl8zmq1mr5HBMBfRl5eXV1NMojLlJHLCMRhGPjuu+8mDf9yueQ//+f/PLHSIgGRzyHGZzlvy+Vykv8IoBaQLudEBgoxVAK8vr5yOBy4vb2d5EwCeOM4nrYsIskRzbh81qenJ9I0ncygct3LNdD3PS8vL1Oykfx9+/2ezWbzzbZLGPzLTcllHvtlQlGSJBwOB56enqZrWZJ8ZDCQYyDHVzYhl16AS0mSnHMZloSxlnMr30MiNxIZkPiPZECWzZgMD8KYi8RLNjn7/X76M5cbvrIsf9t36G/9sh2cRpmEOC3p+yOPz08kqaIsc+LE0nQVST5jvlxiW422JUFlBBXAd2gNNlVoBw9Pjzw/v5CVc+brFG8SVJxRxJbj8466Po9QzTe0pxfq3RPPX3/my8//wvXNmo/3d+TzBTYtMDMIKiEQ05uUIZoR2RzTO+JsRhwrstiTZgmRhuPuhbmOiJIZXd+OiREmweMZ/FtEYdBYG496Zq1Yr6/47rsP7Pent28ihfcQcJy7GpsaImuomg5TlqR5Dh6ysmS2uqWqDpyrDTZNOJ3P5MuGxWpN35xhcG/54ophOGGiCNe0tH1P1/V460dzo1d0zYkITxFH1Bh2hwPKxGACaIjjhLxYUO079vszN/M5i/mCfL7A+YHNboPrO7KsoCzGyblrOrZ1S9v2LBcFQWkMhsGNGnpjzNT8SABQGBsTJRnOtXjGfHStIUti4iSh7x11XVF1DZ9ma6Io4bh7xfcVy3nGrMhIYsXgIYQBFTqG/khf9fSDwnnLuWqIsiW5Tsd88MEzDJ441m9ryMv83VE3rLWid2NCjjKWvIyJ4pi2HVnXwQWMgeVi/PxRFBH8mIt7PJ2ZqbGMYrvd0tTdqM22oLDUrqM3b59VxXS+o3cdV+slQ9PSuMBq8Y7vfv//47Td8fr0I+jA6fxCO0SUc4vyEcMASmusHbDeczpXKKWJ4pTWQeg8Sg00bYvrW2aLiCjKccHQ94EkzTFIq5zHDZoBi3OKqgcVNEm+oHcDfV3hUQw+sL5a03Sel+c9cTyCWte3tHVNcB7vRh3efDZnt9vx9PzyxvAUDElK17ScDye0siT5jCSJ6FEoAnFs36RGPX6wODfKDmJr0drStg0RijgtiJIDx9MBf4TSGLqjJrIJ81mO63qcGzifKnavG7J8TpRqkiwFE+H6YYzkHww2K0jzFdtjO0auakPXdwy+/2a1a+3457RWRFFC04yAo2w0TfMjzhtMPLIg2ihQgd71ZOmoT9WM4OZwrKibjqbp8F6jiFCM3xf/b2Kt/i2/hMWTXO2vX79OMWxiMBRAemnmFDZSzFvDMPD161ceHx+ZzWYTEBQmVIx2wKSh3Ww2/PLLL/z5z3/m/v6eT58+TZnJUugCTA/IS/ApjLkUkUhcmqzJRW8qzJ2Ac2FpjTHc3t7y+9//fjK9wa+xaAL65L3Lw10kKOv1emItpeBotVqxXq8nACE/X/SyArBFciLgREC0ABCR5whAEQmBNE+uVqupglxi8QTsSlKOHO+mabi6uprOt4A0OR7yj8hQhKkXFlnY6cuiG6lKj+OYzWYzsaDz+XyKlJTjKHIAGShEyywSDgHFwroLKynvSQx9IpeRZBCRhAjraYxhtVpN/59sCmTjEkLg5eVlOtaXfoPLZBnZZNzc3Ex//2q14m/+5m/YbDZ8/fp1GjKdc5MOXeQUMqxKTLCwxHItX8pL5FiJLwGYjISy0RDGFpiaUEXeITnrbdvy8PAwbQzkPAmzHEXRFJH48PBAWZZTy6ik0ohJVDThAnCFcZfh51JyJaZeuT5ka3N1dcXhcJh+roBdka0VRTHduzLoXA70RVGw3++n54HcL3J9XGrm5d9lSyRtq5dbExnyRN4l14vEm0oyjAzE8v3wW1//LzTkAyiDjhK0rzkedmx3KXle4Ab3drErrI1JVUbvDVYnhKEjeICe2Hq6psL7jsVqwfLmPSrJODY98cwSZwXrtcHaiPZ8YOgOhP7IcfOVzePPXC0yVouS5WJBsrgmLhaoNuB0htY5dVPhG00WR9QhwZmUPItYzCOW8wIbp8Rao7wjjQ2tD/SuQ0c9XisGPzaPKgbyxAJjlFya5nz/wydOp4qu7ej7Add7fOhxQ42OAuWipGtaHJ7a9fRNR+VGo9kwjEBTRxlJuWKuDJHynA9bvOsoipy2aRiU5n2asnt5pmtrvAlgA77rOO13mHNMls8o5rM3duIMxoG2GKuxcUKRLxnadrQAxgk6MiRZSmhGVsF1LelyidIaNwzMZiW73ZGmrgnzOS4EBkZwX5YlKopoum40RmpDGEaAN1ss8GGMfovjiDSNsNoQJRFQkiU7tocDXdNijOL5+QnjO7pVSfbpHUmUEGmF1tCcKvYvj2z8QOsU2eyam3cz0iJnsbrCRDGdC0Q2wQdwbsAYkRd4okjSVDzDUBNgTG4Z/BsAtm9g0WONYVbmRFFMCJ669iTROHRYG1EU4yo5iXNC0GMkXt3ihx5CP8YjJgoV2TGDPjKkZY4fAo2PiYp3LG/+nsHFNG5DHxxd98z5FGHiNVkSo+wME0649sj5eCApM7pBo4IhUhFhaBicY7FcUs7nPL9UOG/IstmYMT4MzJZLnKs4nCv6QaNNjAsRPgRC79ifxjjNPE0ZBk/Tdm9MWDqm/1Q1fVujPJCNOe02GhNvZrM5TVPTdh1plzL0nuACzbmirlu++31BGlustnSDwyoosvHLt64q8OGNIfMMfuB0rumbmvWqZLm+RpnAZrPB9T1JuiMvF1wvvwejaduOtjmTxBHr6xuSohyHcx3hgyKKUoLXaJOgdTLeJx6SJMUYS1Wf3tJU/MRU9p0j+IBSGmNj4jghTdVo2n77daWgKHJi06ONHhtkhzH5x5hobM5teo7HCj/EaB3hhm48H38pBgKYNNUSdSba58uYwH+t1RTgIqBLGDBhIe/v70mSZGJSsyzj9vZ20oPLA/rl5YWvX79OAFNApjxU4demTklnkQet6LiXy+UEHC6zm4WdFOByWa4jD/80Tfmrv/orjsfjBL4EdAiQWa1W3yRgSGOn/Bz5fcIoSjxk3/fffI48z6fEDWGmnXMT+BaQJMPAZZqIGO+ELZfPKxIaAW1SuDQMYzvtdrudWF4ZOEQHLFIbASwiO1gulxOgFsZR2HgYAaOYV621PDw8EEKYdPdynYj34OnpaTof8/mcT58+UZYl6/V6iju8NBILEBJmWK5LkdiIR0AGHjFoCvgTMC6svQC52Ww2XUOiWb5MW5Hh0VrLfr+ftOtyLZRlOWWUC0CUXHkxBMsAIyBX9O3AxPKLNn25XPL09DSlAsn1e3V1hbSjXkpeZIAT5l0GJ9m6CKCX1BjJSBdWOMsy5vP5NBBIupJIdc7nM3/84x8nidBl7KFkkQtzL/eUSGjW6zU3NzeT9EyArwyucszEoCktqpea8X/NlMs9J7GY+/1+kk6J1EU2JnKfSPrNpYxJNmwydMnAJN8totWXTH+5Ly/N2v+r12/+nY3zo9EyLYjswGbzhYeHJxbzxZh+UcQYa2jPHSaZsSxXlIsVu9cNp8MRqxx1e2b38kiaJcyXt/iooPZ2lKzoiMEzGvRiRaJi6CsOu2e09qzWy/FiXlyBSTjXjia0+LhExyWOGBKDii1dcAwmJZ2tyWJHVtq3Bs2KU/WA3R1ZXHdky1uiSBG8Q2tDmmRoZdFq1A6PzY49fd8QxYaiiN8KezTOeQbf4ZxG6UBRZqBGJnnwjsrVhD7gjzt8GJs8syShuLpltloTuoYoK9EMrFZL2rpmt3mhbyvqrqPZdpzammVcEqUR7b4hUoH+1NH5huX6ZpSeDD1aKerqNKay1A5FgoksjXOEYVwbtv04VbZNByEA4xdlmudE8Ri32A9uLHbqPF73YGKiweOCBgJRkr5lo0NWlPSuoq/31HVFbAucGrBRxHw2Yz6f8fC8w3UVaZ4R68DL0xN55PHuhqHTqEijzcioHo5Hvnz5SjAJ63tDvnrHD7fvmF2taQcPOiaJIoqyeFtvtt880EJQONfRNC0BxdPLjs1uR5KmzGZzFrMFi3lO7/ox0cRagvdYM7bJaq0YnKepW5q6R2vDMDgGN9C2Hedzz+PjBhcgzjLWt0vycgn0xFlMmaR0VUtfeZL5D7CtaQ4tngPeV7SHn8nyniRfMnTQu4ao7VEqgLF0vSVLCpRN6ZstKozSm6ZpONcNSTKWkKSR5fm8p25aUHCoapKkZMBwrEf2wYcBk85QVhPFir5tMdaQl8W0oh/emBZtI5I4wZiEoAxWWW7u3vP16xeeX3d4D3k6MgnWGPbHI9XxOObTRylRPic1GmcNAwatIY4syljO1Rn8gLIxXnVUzQDBkRdzjFLsNlucHyCxnA5b8rxgPivZ9zVDV3PYPWOyjCZEXL9bkZuc4BWuVxzPPf1pR9uPSTxuGJN1IFCWs4kdGpMaOtzg6U8dxliGwaPQRDbicDoR2+jNmGMJriF4NQHEvuuJEkuSZFjTjUMpYLVBmxhtzFh49ZfXxOwJKJAiHln/y+pc4g8FCD8/P7Pb7SYAIjGHEls3DMMkzRCwJwyepFJorSfjnUg9hN2ShA9h6uTfRRctq2cBKWJMvb29naq95QEvzPO/jk+7TIm41C5fMp4CtAWkik5YrjWR6KzX6wlMSYX89fU1VVVN6Q0C+kUyIeD2knG9vb2dmGORtVya3i5lGsK2C0AR0H1ZgHQJZgXQy0ZEfv8lKyt57zIEXWrKF4sFi8WCr1+/TgZSYwyfP3+eJCAiFZD3udvt+PnnnzHG8OHDB9brNf/u3/071uv1pAmX60quMwGNcm4utfdPT0+8vLyQZRmLxWIayoRBF2B7uQmR9BkB0Jeaa4m3FGB8d3fHfD6fzm2e5xODulgseHl5+SZdaLvdTs2il+kqcr0JuywZ4cA35U8S4RfHYxGfnOfj8fgNyL4ss5IBSYai2Wz2TXGRbBskdlBe79+/55dffuHx8XG6PwWkSi47MG2DhJGWIVw+hyTEiJRN7lnZar28vEzD7263m4ZNuT5eX18ng+XHjx8nAC3DweFwmGStcl5hNAfLvSX3k9z/lxGZcRxPcYeXm5jLzc3loCJDgFwv8s/lsft/ev12yQr6bYK3JGmONhFtN9C2442Q9APGeTwBhh43tNT1Ga0Drm/Rumfz+EjbnLm7f0e5XNKZgr5VDCrm3PRkAdLEEmvLsTqx2zxhjCdKU/rjiXy5wscZh87jlcPVB3QaUJkmmJSynGGtYWgdcZwSzxYkqsEPPa8vr7y8vOCC4erujjYYbuOMdJnRdTXaQ6Qi0IqAx1vNWHYDbugJDIQwgBqI4pgoUrhhxLa9e4tnat1oBC0SBu9puxY1AG+xezZJsXFMZBQWj+uusQqs1WSuR8UZz09f0eUC3XeEJiYuZnjXgo3QkcX1Pb3r6bqW6nik7nvirORUHViFiCTOiXKDChXH5gWbWWwSY9++CJum4nQ6YKLRlBmGgDaGqq44nvZjUgqKpjnRA3kIRPFY4670KOHRjOa6WVjyet7z9PhEZG5YzRckb/FDeZ5R5Al5qojtwN16wfbhR3QY2G1eOOw1RVlwtb6inK3QNkNHBUm5pFzekM5WpLMVKkrx7chaJGmGUiOLKtFjYzTlwDA0QCAERvDVO+ra8bJ5Zj5rUe+iN8PuWABkjcJEEUYXb1+0PaOp1b/lmlpc73BuXD31zrGvKrb7hrrtWT2v+Lu//z2zIqKqWoqsIE5zahs4h4yzn3PsclRoSGJH0x7ww4BSA26IcHWFbffYKGJQhrhYEucrQt+PLZZth48jtDKjQahcYKwhSmKOpyPOn7m5XeOGgPHQth3HqiLPS7IsJivXKBcR3Jmr62uKOKGuauaLGcPQ0zSOOB6BZpIWxMpStx1eeSKlidOC7fYVNwx8en9Pkia4wREOgdfnF94lBQrLIs/pB49razBjO2140x0Sxrz75dU10fqa02FPVTkW8wWzsqBIE5qqpqoPbF8NbliNJUZtivIdrqtomwqlMkJQ5OUS1wWa5szz84aqh3KxJo6Tt3vi1wd4URRvGs8KEFf8uNmqqobDsRsN1IC1htmsRGv1liZk0MailGYYWrQbMGa8XvOq43Cs8b4HJZFvfzF1wq/lZfJwErOhMLny4Lo0RUmpiDz8pTr748ePk+5bgIVEcoo/4Pn5mdfX1wkU7vf7CUALYDqdThNQkRQMAaCSHCFM8OPjI4+Pj4QQePfu3QQeV6vVxGQKCBV27V/LIC7ZYwGWIjUQ0CtG0EuGVH6W6JkFoFw2Cgog+/r1K0VRTMdW5AcCiIStbNt2yj4X3ba8N2Erq6qaSnKEHZZadWGF5XPKr8uflWHiUgcsTOWllEWAqrV2kldIJra0dUZRxO3tLV++fCGEwPPzM9vtdpIsiZlPNghSoy4DlRgURcJyWXQkx0QGEzl3Yuh9eXmZAKQwpJdmU/l3AWnC0oumX35d2Ojdbkdd19zc3PAf/+N/nHwC4iOQIVOGMhkahaGXwbOqqm+2M3K8JPlFriUZwuTaTpKE/X6Pc25i4uVnXUaJStxm3/fc3d1N+fyr1WrSf4ucSwZiMQ+LvOTl5WXSysvGIYTA4+PjNLzKexYGXv68yDqMMVxfX3N7e8tut5u05gKaJb1JGHNpq5VNk5iyBcjLkP7w8DCZly/vQbnPyrLkeDxOchlp4BVplBxDGBlwiW4V9lukSZeyNtmeSMurDFO/9fWbAblSmn4IOB9ItOX6+o5hqOk6R5Zk5GmBMhGRDQwMtPWR/XHHvCjQOLYvz5z2B+aL2ZgoYiLSfA5pQtWOMX/OOUxw9NWe8+FlbOdLY0ySETUDyXJNdvWOrg+4bqDrB7RqqE5noqwkMwM+jtHOoXzPfvOCyRQnd+a021OkOSpKWa6uyLIUYzRGBfCj9hXv8MPI0mvlSbMIG5kx5WQYGPyAc93bmjAhimIim9I0NUpbCAPHY4PWMfP5mq5r6F2DsQqlDcaM1fEhBAZj0IlFacXgHSZLWSY5ziQMOuHq9hNDe2ZozmyevrK4ek8SRzR1g/cDm9c9RmnKNMUkMcdTh4kibm7fY3WgOj9xPlZkZYm2Edo40jRjv3vleNhxdXVNGsd470iT9O2LZEMSrSnLOTZVeG3ReiCEnt5pAh791sY6fvnHBDSn45b91rIsZ6gQqKuawTliq5hlFtfWWDUwy2J2mxfq85Hb2zsWyzVaj9pKbzKy+S3xbMX3f/0P/O6P/8CgY7yOwI5AaVyLtlMJRxwn48DkHLyV4YB6m8ZzQjhy2FecDg2u9fC95v52jTV+jGXEY4zUn4/FUNboyTBqbEQIniS1RJ1neXNDpyo2Pz+x/dMT2sT8zV/fE0U9+/2B2+tbVNrh05QwuyJy32H6OZk9EboHwtBhQoVzmlRD77vRfNr15Ms5XsUMbkxBYRjI45gyK0iThMiacVBkvE9O5551WNEPDj0MdAN0XqF7TZym2EShjccNNUoHGtdxPB4wZox7jN+i4Mr5EmMTuj7QucDgA84r4nxG1NT0BM5NQ5JErK6uAMX5XNM3zZjz3o0bhb5qMKnB4eg9BAxBQZwkKG2I0wTTdoSuYV/1hOZMEVk+fnjH//HPf+JcHQhaE/KUJNLkeUyaaNq2opzdcj7XmNjjg2W7O7HZHgk2I5+Pnob5bAQx7u3hIxm8k6bURHg9Jvvsjy2bzY6uG7Xiy+WSLB91z835xODqt4dnzrg/UXg/YCxjW6sdpVYBcH2P638b+/Fv/SWGQQGhd3d3E5OVZdm0Aha2SIyfYgp7enpit9tNJk5Zc0v6hKz8gemhJ+yZyFpEe325SofRcCbMvYBP7z2vr69TPbhkRksEnPx+YUJFUiIgFJgMkCJ9ELB2qWMWRlPAoDSUCtAXllr+jIB6YeTk50vahIDEd+/eTaD84eGBm5ubqazEe8/z8zOSwpGm6cT0SUGKpGVcruHzPOf19XVK0RBpj8T1bTabSfZyOdAIKynv+5IZFMD4+vo6McYSDSemOjGGFkXBy8sLp9OJ9+/fTzGIYtJcLpfMZjP+7u/+jn/4h3+YwJ0APWHFBaQJYJX3Jmy3XAfiGRIz3h/+8Afev38/DRPANGgI6JOBQ5hd+X1N03B/f49kWP/TP/0Txhj+/u//niiK2G633N3dTVsaaY68HBouk21EviXDz93d3eQTEEZX0ltkqLncbMhGRO5J+UfuR9kyyXah6zr2+/30a0mSTFsDeX8i7ZBcftmOyLB8c3MDMElQhNm/zAGXgUhe8j5kwBGTqSShfP/99/z3//7fJy29SJDkfpDyL/FghBCmMiAB0rKVEWmTbNlkKyTDrFy7+/1+2kZJxKNcpxKTKhK6S8+I3PNyLi7Jh9/y+u0a8q7F9Y6o12RZxHx+xfHwTHVuKLKSyCYENGEYaN2Z+SonKOjbI021Z//6wjzPRkNPWRL0yMjpOCfLUpq64nTcszm9EJo9XX2kyGKuru/YnGriBq7efc/y6o7jsaJ9fSXPYqLEYpuGoT9xfm3wUUJ1OvD6+JmhPsH9AhPGNJDbd+/xNsXGOTCK9FXwozygbTA6IY7kCzigMFiraJoxW9t7MNYAYWrvU4A1OYPzHPcdr5sNbQvr9fINCMRYa1AKlAIPDN4TvMYYi1dqbLl8M1tc3XxisbzHu45qv+H18TOvmzN9d8ISY+OYvm/p+5aymNG7BnRgcD2n8xkbxZRFRtANyq5QZrw5FIrVckFTHalOZ8piNiaZ6IjV9RVDcGy3j7RdTaZm5GnCYGKUsbR9j2uGN51uhPMKrWN832Nji/YJfd/h+nE1hNHE1pBGmtDV4Fp8X2HNyHwsFivSPGd1dc3gFce6Z3F1z3d/fcf6/XfcvPuEjgs6r4iUAQPd4GjrBhU03o3ShOBBG02aJoDCuZGhUMqQFQvmsyV959++oFuOx4pFWZDnFq3H7G+lRvmRVhZloHdjxbpRBh1ZFCOT7FA4k9L5lK8PB6rNmR9//Iyi5q//6h0BT983xLklnmeETcZwXDB0iuE8cNwPxLZjMW/xXU8IijA0BBNwqDEKc7BYG5PEKc3BoRnlJmVRoI3GDWNcX5plBBT9MBYYKW2wUUQaLOiUwVvOdQd9S6h7Bt0RG0XveqIkom4bkjQlyXIwhrb3tH2g6T3WxugokM8snXP03Ymm7yjKjGI+xoo9fHng5emRcr5Ca4ONciJtAE9sU1QItP3oOcjzktP5hO564rwgHQJaeYrZDOOONF1DnES0w7iV8MERx5oiSxBldhzFBA9dHzifK/anmkBEkoyGvMgGlqsFfugw/MrKCcMUx6OUpu89rg/UTcdms6euaxZXBWVZ4PqWfX2gPu7Y41gtr0Bponj0LUSRp+kc1mryLEGbsVBJDKx/eTFJyIQFXy6X7Ha7b4pV4Ne2y+vra4Bp3f/8/DwllsjKWiQZsu7f7/eTTruqKoqi4O7ujv1+z2w248OHD1xfX3M8Hnl+fv4GxIoRTExjX758oWka3r9/D4zrdVl7y4pfHrYCisTsKatxAYPAN0Y1+e/LiD/nHLvdjufnZ5qm4fb2dgLhl4Ukcozg14pzAT3AVJAkqQ6fP3+ewAMwtWBKE+ZlQYuA8kudujFmkg5cGkwltlFrPSV2SLyesJfy52X4lc8qg7BsrcTIKey9gHWRo8ggY4zh69evU2rK9fU1EoF3c3PDP/zDP/Dx40c+fPgwDVaXkg45RgKAxIQrUho5ZiIhWi6XE2CUPHlJAvnXRlDREcvxFOmFHPNLDb2ckz/96U8opfjbv/3bCZyJJlq2CiKv2O12WGunrHAZcGU4EKmJSLB2u910310mqXRdN8kx/nXOtpyzy3hESS0R0C0gV5p05T3KNSUxlovF4pvNz2w2m0Dvzz//zNevX1kul9NgKZp+2UDI+RamWoZH2WSIfEg2AeLVEDAtMaDAN5s3OY/A1CAqQ7bEqnrvJ3maSJrkPYkE6eXlhfP5zPX19ZTmdD6fp23Ker2efBgiXRHpj5AP4sv43w7IdZRi8AzBULeKRMf4wdL0DV1T07YVcVoQa+i9I7cQA5uXDbuXLxA6bu8+kMxmuDdTGVphBsPgNSp4otBjIk0gxpqCpms59T1xueTT4iMmnnGuB4xJKIoZlgEVembzjO32lfZYcXI9T49fCMGxXsxJU4PVBWk+Q6VXeGKCTdA6xfcOV1e0teNYB5JWs1iOWilFQKu3Ni803RDejH8x3kPT1hhnyLIc7+BwqNntaupaczoMGN0Sp4q8iFBuwOgRRCoFKvi3uvJRux5FMdqPX8Z9AHRMlGYUKsVEC4KZ8/mnPzN0Ndr3dMcdTjU4FXNqD6QqkMSKvq3p+paBHB80YDmf9+RxQp5klHnOs9Y8vb5gbMTVTYSyKbNZScDh3BmloetarE6I4xRl0hFs+7cQZ+XxITAMo7RnvphDZunrI+e6wsQxURIYXAOuxfiOLDW0qaEsU5zv6MOA15aq9wzekhZLfrj5jut374nSkqbnLXnGEAaLChD6gOsYI+miCGMNNrIURUYYXcMMQ2AYOowdmf3VMuXu5g+czy1Pjy/sdwdC37Fc5ZTzhCy1WBMTBoWNYmDUVwYCphj/23tPkmqKMqXqK2alZX2VEVxJFMOsnHG9viFLA9vtK0lasrpa8PxyYP96pA0xrrP4aM58lo8a+GGLq05U7YF6CHRZynFfjS2Rvsf4gdAPDH3gXNWYyOIJtH1PUAqTRHjX0Xc1kdVj5rZOiE2GNQVGQUChTYnNBnR/ZFamKD8m2xxbB1HEoDSdG/BBQZRBYukxeALKOPJyxW5Ts3+L5sqdB2WYzZfUT0+cDjusMVzfZ2SJpfbDmPWOIU9T4jjDKIMfoFVjrXaSl2McYmbIdMHm4RdmxYL69YU8UsSRwShHN3hSFTFfXJOmc2wyww2W1/2JqrfYPCEtS5I0oswt1mjqzr3JyWKa9m2VrAJu6DjXZ9phoO3geXvg6XlLCIYyz0hiGIb6LWLRjF0DdYe1EYNX9G5s5YzjBGNq4ngsGhoGAVx/MXUCExC9BK/y4JeoQnnAA1Mk2dPTE09PT5NURBjz4/E4sXWXQ4/8OdEJCyCRum0pDBGzl6Q1CPO62+0mra+0J8rvFz2qMMaXGmgxka7X628KXWQlLet6MQ/Kil5W4ZJsIsYvYWnFsCeA75KNFWAroAJ+zUMXcCUs/J///OdvUlcE3F+WtIj2/FJeIxpjSaUwxvD8/Iy1lvv7e4wxk0n0ssRF9MoC5GRAgV8bJkMY69cvtc4iaZKtgmw5BKjKpkHkACGMJvF/+Id/4MOHD1OKz2XyjRx/AY7yvoTNl/cmf7ewoWIcFlnNdrvFOcfV1dWUHiIAToYmKYuRKDs55wLaZrPZ5GcQQ+ft7S1pmk6a9evrax4fH6fGTQFy8/mc+Xw+3TeXLZYC2OX6EPAoxxSYcvll0BEpj7z3ywQqGDcFci7kHMu1K3/mctAUQy4wScCen5/Z7/eTZ0DA9NevX6f3/OHDh28kLSIRuRyWhbkXDb1ozz9//sx8Pp/iQCUdRe4ZGd4koUmaTvM8n+5p2QLJtSESIXm/l4U+lybxEMKUGCPvXa7LS1+E3BdikpbjJJu43/r6zYC8mC3xDoaux4eBpu2oG4er9yzKhDjRxGlE3HdopQltRV/XhPYEQ8usiOldTXMMpCYhiceJuT1uMDoijixJBEHHPGweed2+EBclGQnL1S1pvuZwbPHak1rLLLGcd6805zOnc4tvz1jfst0+Mc/gD3/4G6qmIZ8vWF7f4kKCCznBx2OZStfgd4dx7zwYhs5z8meiZEHGyJCfzzVRZDAmJo3H0h7vA03bUVUtxmgCCmsSBgxeWbRV9IPmdO6w/RjAkKeKNI8wY7giXr0lCGqL9+BDIMmyMWJt6DkfztQe+nagaxX56gPzOrB9+cqsjMlmSwaVEEfQuhbnz8SR4tyccX2Lcy2DH+jbHt927I4HmihhmM/QbzflZruhHQJpuWQ2v2K5nBPCDQaFDxHeeSJtiJIMa1NcGFBGM+BRKqC0p5jnzJM5p+0LL+fRSKQIeNfi+5quPjB0KSpK0Ro+fPrIoak5VD1EKftzR5pn3H36A0lR4o0hmJg8LUHF+KBpG0dV11RVTe8GZvOSJI2JY0uaWNr2zPl8nLSD6+vlm95NkySGyMTMi4I8STlXNYPrUGiMjTBRRJZmGBURPG9fAuWolX77Yooig40MWWRZlMMoaeGeJPI41/HD9x+Yz2coX5HMZ9R1T2ZTFrOCer2gSQ3HbYcZAlnSEFTHYpbR+opoWfBy9MTFGsX4pdkedhyORxSW3nlC31L3gbgoiLN8NCWiOZ2OWNNRlPO3kqaxvMYHRTN40iTBRDOMHSvgg05oBs0QNDqZoSJNsJZuGGjeAPrJgRs0WZaCb0F32CinPh/Z7Y+UeUEaJ5SzAqVv+ed//p/EWYLWnv1xS2cinNe07YBSMVk2J45zNDD4QN11ZFmO9oHe9+jgGYhomjERpjrsWLy/w9qEKMlHT0GyIE0XeJVyOA3sDo661xRpio4itFXkWUxTn1DKExmDNZam7fE+kCQpNhqlLHXX8/h04qdfXjidO+5v31FkMXEcUAH6Prz5Yjy7Q00UufE+6nryt5jMyBgGHXDOv0Ut9rRd///85fn/kdel2QqYNLpVVU0xdpLtKw81AeuSyS3mPdEaS8qEyBbkgffTTz/x8vIygaKrqyvKspxW7qIn3u12U923rNtfX1/J85y//du/nZJDhAEGJsDWdR3b7Xb6Lui6jsPhMK3dhVkW8Cd5yQKwpAhFAJsAYGHLpURI0jUuBwgBtgJWRDYi4EEaHYXdXK/XVFXF4+PjBOrgV2AmEggxOcqQI+ymNJpK/bwxZvRcvWW9L5dL1uv1lDhxmWQihj9howX8CjDLsmxqPRQAJMBHhhwBiT/88MPEVkdRNGXC//DDD1Mtu9Z6yn8Wk6oMTAIsRZKRpilN00x538L2y79LHKXkvEs7qqQFiSlRWGUxq8o5Ed29RGlKk6j8eeccf/VXfzUlzkhGfxzHLJfLSRYkWwXZzAg4vrq6mrY/wHRMhOW/ZKgvc7JhHLTEqClD4mWuuAwbIiG69EKIl0GGUjlHsgkRBl7MwafTaTKlShmVUop//Md/nK5bec9y3YpESWRF8uvyd4tMTPwjst349OnTtHWR+1ye/3JsJNdfzots2OT+k2Fe/A9yfNq25evXr/zLv/zLJJuS9yiadSEBdrvddN1L87AcMzlusrWQ7dX/6vWbAfnqak1b9TTnM5HymAj6OqE/e+r2TN+32MSNRSUYTvsTx92Gtq64Xi2IYwvBE1lNczzQDUfytMQqS1udsGlGUqTsj2f2xwNBJcyW78nn7zB2ASrDRgYVBfJY4+qOorA0p576fGC3eSa4liyNyfNszKFeXEGSc+g1UTYDXUCnGHxDcAN920AYMHGBCpamOrPTG0JYkiaWwTtUAxpGdhjN6TymXrSNwxhF13uWywSlFVFsRylCcJyrFt152u7M3c2cxEKkIQSH0gFjDUMY15LBK/q+RQF5mhKKjNO5pe0bjueaYYBgc1Q6pwuQzUuuSGirPTPVUx1HbffhWFFVe9ZXS2LtOTcVJgx0bcf+XGEUKBSxjegGz2G3px/G5JyizFnOV0TGEFREXfdEGorM4N70yR7GMh+lSaKI1bxAuYbqfKIsc5IkomtGfZXvW8LQsdm8kGYfuLq6ISlmZMs7fvr6jIpynIrxOsakJTZb0LoBR4zyo0zmcDiz2x8JeGwUvU2qMWmaEMWaYeg4HHakWUqcJrRNizF2ZDYHR55GgMboiCReUrcZwXtQA/rNvGesxjtH27Rj9J81JOmYpa71yH68vm5Jsow8HRtKI2vouhOn8wkTOULoR8NknnG1KGh7WOQZ9TwnsjAMM5p9Re96/Fur427/ineBoGfEaYHOZygTce4cjfMoZfGDZxg6qnNDPr8i6Ii29yhtcc7jh7FMxwcgKIxN6J3CBY3ConQ6Fu4UA60f6MhoPZxcS3vc8e5qzs31NeiOOmicghClBJOjVYRWinzmUMFzODzz9Lrhu4/vyLIUbRVJljAMnqpu6PoenWoirfDaU5/3DF1DObsiygqGAToXUMqSRjGDVwzOEaU5NhrNP1Vd4QZPVpTYpMCpjC7EREOEC5rDueNYdfTOEwcYGGVH1mp8F97WhxG8sUdt22Kjty/juuN4PPE///SF//P//IUyX3C9XnF7O2e1HMufdrv2Tf6TcTpVo0SoGxm6sm25vr5hvb7i6ekV5zqsUbTdr7rm/6+/bm5uJjAlYKeqqkk+JBnJIpfYbrdsNhuqquL6+npa/UZRNCWFCHAS2YuAbkllESZTWDNhpoQplxzi0+n0Tca2rPhXqxVxHE9aUXmQirlLkhQus6Y3mw0hhGl9LvpwATsS/SYMedd1U4ygrNYFBIlm9u7ubgIPAnrl34HpoQ9M71+GDIm1kzSMEMJU6nI6jd0Zols/HA6cTqcpWk40ysL4yecQIL/ZbHDOTey1aLoFJInuXJhnGTqACXTK8DGbzaaGVJGviNY9z3Nubm4oy5LVasXPP//8DasrHgQ5BsJK7nY7NpsNMIJVkYIIez8Mw+QNEHB+GVknTZZy7MSrcKlNF1Alw4wMX5fv5fn5eQKGIkGSYVLOo8iAxibobtrqyIAmJkI5hpvNZgLRwhyLUVp+thz30+k0nXMZ8uQ6vrzmLjPAZYAyxkwmYxm0uq5jt9txe3vL3d0dp9Ppm4g/kVLJcBZCYLfb8fj4yO9+97spqSXLssmgKkbqyyjLtm2noU1kSyIDESArw4HWemrklWtJgLwMSDJ8y2cURlvOx+V3hGxtZIsipuV/+qd/4r/9t/82GYrv7++5urrCOcfr6+t0H8t9J2VNs9mMu7s7bm9v+fr16wTI5Zj+ltdvBuRpEuHaHmvV+NBtHUpBmmVYm+CGQNf3GBOjGGjbmrapWcxnRLHBuY4osmN74OGVIWjWs7GufLN/oW8yDgfDuTpRzBbcvf89+eKepLgiYEHH2EijcPRDw9PLI83xhfqwpT4e6JoeqxXGpkTZnLi44vVYs9kesaVhsXRY2xG6HtyZIg74vmVT7UjyBUm+JtaGrjlQnRVxtBxbCUMYdd/O07aOzebEqWroO08UjSysMmeyNGN9PXuLGxpPEAq6pma/hUjPiMoElMYHj+s7tBnlKkoZ+rd4vTA4lAerA8YoBj9Q1S1KWWw8Y38+4LUhX94SJTF97GnqPQRFlnsG19A1B5rjjua0RYWOphqbJc9RTBxFWGPGbOgB0mzAO0cWpyRmzOs21mIUDKqjqV+xUUIap3ivRv24jVjM58yKlMPrifp85N3NgjyxBN9zbk5U54q2rQgEqrbH+IjGDxRXd9yrJX0Yc+fT2RxsStsrnI+pTi273QuH0xnnxi+T23fXlEVGksQE7whBg7JEsaV4Y8j6buCwP+Ocpygc81mJTWOapsMYyLOcrCzpupENCj7ges35dCJ4PwJ1xsKntu1wb3F6VVWzOxypmyeiOOHmblw9LuYZUeyJosC8TLEMPD48sVwsWK6uuL2ZM/ieDaDdim19pN5tOPmatDDYOObUnmm9gwEMFq8SfFxiyzWhcvhhIAmeJLLYKB03QW4gjguSOKdre4bBE4IntTEmnxGiklPV4tVAT4tRA9pkVH2NtzkDCh0H+v2OqmqJ4oRCJXTVgNaBYGOcGs3GcRSIGD0Wr5sNr9sdNzdLsnxG1zeU8xmEiM+/PJLN5xRRQBtHkhkSHYHSaNMQ25TYZtStp2s6hj6QJZbWOTIbMVuu8DgGN0ZtOiytU8RZik6WOJ1wqB3nxjH4QNXUJL0liXKSZCwlclpjrZkeOJfRc23bUzc9+13N0+OB46FmvVizXBXM5ilx7HHOYI0hjhLwA+dzi/c9buiBwOGwHw1l8/nbA++Mtj2d6zH2L5IVYGK0xfQmiQyiHxeWWTTXkt8sySiy4hcmTDKwJRKurutJk75YLPj06dNU4AJ8Yxh1zvHw8MDhcGC/308NnwKyBODs93uenp6mWnMxA4teVQCvxNFJ2ojonwWEy0O3bdtJGnOpnxZQeXt7y/F4nNg6OQ5SRiStlcJiymbgkg3912BKAI+AjdFHo6ZhQwYjGMG8yHAOh8OkHZdsaNlACPvvnJtaSy/LXS5lD3I8hEWUDYMYMJ+fnzkej7x79+6bIUaGFmG5L1sw5bjKIHApi9ntdlMbpbyvDx8+TCDtUsZy2UwpGw9h/YVBlbQYGXQknUaGIAF7AvDk/QvgPZ/P0/UZxzHv37+fohSFPRfG+PPnz1xdXbFer6e21ufn5+nakWFPypIk0/pS6iEyGAGn8jmFqRaJi0iUZJMh6TpRFE2DmgweAuBleIjjeLrXPn36xGw2mwbty1xtuZckGef19ZX7+/tpeJIh4ccff5w+kwxxAugvfRQyoMr9J+9ptVpN97WAXNF+CzA/n8+ToVmGcUmJuTQZy319mWQknhQpbNrv96zXa9br9TTwy3eHDDgypFym70gu/GV7rXyG3/L6zYB8Mc8JrkcPGgaH8y0BT5JmzBdL9FuTXteN8oLdbkfVVlxdzbFWY0yMNYbHx18Azbu7d6j+zHl/xuKITELVtmijmS3WFPM1XiWg4jHNQxkGV1OdDnhXjaxc14+ygywj0XYEYUmKSXKK5T1DFji+7GhDRBMSogGSN02p6wdcc6Y6H+ibHqNjksxSu5rqDFmWoswbW5Bm4ALt6cS5HqgbNaayKIM20NQd5azkZrEkSTxVFTBRQt85jrvRVb9cFGiTEELPEIYxrzz4N+CtQSm0ghA8Ro9lNkkfY2OL6hx958EkDCpjWw0k+Zzbd0uqveX5+Qun/ZGsyCmLhK4+Uh+3xIzgcngzEZ4OR8o8J01SNCeGfpQKNKczVXYep14FxigSreiGlv3xlXK2JM8tEKNMio0zimLMUI+txWpN8J76fCKJNAZPU52o2xqd5Hx+2uD8ibv73/GHd3/k5kPMoR615EmeE2yKjkp2z3seHl/eWro8q6sl6/WKYlaQ5zHe9RgT8L6hazRxHBHHEd4rzqcG7zV973l9OeJ6xXJlSdKMIfScmz26V2/A22FMDCiqc4VilKqUxRzvFcdzxW5/5PHplabpeXnd8fDwSD4r+Pf/wfDDDx9YLksKZzCqo2mOXK+v0cMoJanOO8o85rsPd8zzkv22YF1m7B405+3/5FjtUVHG8rpEdSk+ThiUBZuRLu5JQkf7CrrbEmtF9ja0dZ2nrhps6Oh7j+87+s6RFhYdpbTOM+DovSdoT9939K5GRaCDwSQlGk+aG3x54nR85fnplYDFmZw0Lwm2pHWAH5t5XedQ8YJstqarn6nahryLiNKYvCg5nQZ2hzO7Y8O6WTCb58wXOV51oB1pEjP4MeWmqwKd1ySZQWkN2uC8Q8cx+WxO07Zj/GMxY7AF5foD8WxN7Qz7c8e5HejeGKFIK/IkIYtj/DDgB0+whsE72m6Ummmd07Qtdd3S1p6u0XQNZHHBer1ivkjRZowR7bsx4qvMFXt3pmk7otiQpNHbl2rL8binKArW16vxQXKq6QcHf4k9BGC1Wn3DLF/qg5fL5Tfr7/P5zGazoa5rrq+vJxAvmlEYs47FCCkPQ4kzXK1Wk2FM/r9L46Joa0VDK6t8iUBLkmRKUgEmwHWpKRUGTZg8ARIC0mVNLpITWZlLTrWwm86NjYhSyZ6mKafTaWLmt9vt1JopgOgS/AlgEeAkGnet9cQEi2xBBtHT6URRFHz69GlqVRRJgcTwHQ6HKcpOdOWSuiJyH9Epi3RE3oeYW4XZnc/nkxFSwJ9sQiRjXFhikShIVnaSJJOm/+PHj/yn//SfACZwJcxwFEVsNhs+f/48JexIXN5isZiYevlZ0pAqg8LxeJzO8ePjI33fs16vJxB/Pp+nPwu/JrIIeJ3NZpRlSQhhijcUY/Dj4+Okdf4v/+W/8Fd/9VdcXV1Nx6CqKm5vbwkhTIPQpRRns9mwXC75/Pkzr6+vkxTq7u5uAvpybJfLJcC08ZHjfaltvtSYCxsvQ69IsGTIG4ZhOkaypSrLcpL6PDw8AEw+i8tEGBmgkyRhPp9P176w4bPZjOPxyHa7nZJ7JLlF7h3xbYjMS7Lv5d6W9zWbzSa/iGy4bm5uJqmbJLuIREvuV9GWy6AorLbIeQSMy/uWa/L29nZ6n/K9den5uIyFlE2YaOnv7u6mtBbxQ/yW128G5OurGcr1aFeTJSldHsAf6JuOcvYW/0JE23U0dU1dndBqjAtTyqKU4fXlleZ05v39OzKrOJ1Gk12Z5WNSSN+TlHOSvKQfBgKO43mHwqJ1RFPVnI5nwtAQ2RyTgXYVRbkgNRpP4JeHZ2IfcWocPSnz9Tui4orBefp6T5YluGDxbQVDjw7DCF7jhGWcYbyirjqqKiXJIsBQ1w3Hc8P+UHOqWobBYHSCiSKS1KLN2xc6Dh0Fru9WxFFCfW4xKB6bmtfX/ViZbhhj9OKYIYwxi70fAbMxlsE5Qhjj7YzRJEmEx7DZHHEYsnLNuTqzrxxFmWLiGSYqabpHlsuc2bygr0/gOxZlzmHoKIs5cZpRV2f6psENA2HwWG1YLedkRUbfNnRh/NIxWhH8gNYDzlX0nWXoErI8IsliTBQTBkfT1ei3gpWmbthVx7G5MouZLa7wUYGKZ0TJkg+f/si7D3+gXN3TOHh/k+N1AANtD5vXI798feFwOBJFCXmecHv/jnKWEUKPtoYkMVg8fnirQo8jhsFS1x3Hc02S5OTW8PKy5el1x6luuLpZEEWglSfWlixJmZVzFBZQpHE65rH7wG574uvDC5+/PLE/VjTNmKyx257IigWu97w8v7JeFZTzN0Opd9TnExunSZKcrmtwrsX7FqsLVouSxFjcLOd2lXB8yfj64/+frj0zX8xpzoqz4q0JNULFGSGZoZKSrt5wPp04OU26DpTLOQbL+fBMmqTExtB1A8G0GOVp+hYfKdBggLbv6KsjSRKwDBhtx/sxwDAouj5QVQ3OQ1QmmEjR+IG68+AVyip6p4lVQja/JtDigsIzPmTzWUk39Oizp21aNq87IgNFZqlOO5q+I29bgslwPkHZklmxonY9zplpkNYktF2DijQmnaPTBTqeMV9/wJuMzfbM9lRzOg9UdY01etyYRKNEJngPYZRjDW4EhDqMBuw41vgBskSTxj2JLZjPNHd3K+bzhK7fUVdHghuIzJgQE7zHRhaleVuvGnoXqOoTdX1mvb6mKWKqtn7zGMS/9Wv03/RLjGzCbok5S3TaeZ5P0XQiZRGAKQ+sh4cHTqcTHz9+nNbCos+UB+NsNptW7MAELI0xU8KCsO3AxLSLjvuXX36hLMtJniBSCUlXEMZPItEERAnbLODtfD5P6R1VVU0gTZgzAUoihRGQba3l/fv3RFE0yUTqup5iCkU+IYyxmB+BCQxdpreI7ljkJxIBJxIJYbalgXO5XE5gVwyEl3ISaZ0UFl70+aKdXSwWE4sv50QSOMqynKIghbkXUC7mWEnNkc2IHKPf/e53fP/991OCjEQIys94fn7m559/ZrfbTWzvx48fpyhFYT/FvCnSAnkfh8Nhigd8enri8fGR0+nE3d3dBHhluLrUqMs5995PA8FPP/3Efr+fQNxms5niM79+/cp6vWY+n0+GUtlAiGxGAK21lqurqym1ZL1e8/z8zP/4H/+DpmmmQVaAqXwmGTTkc7VtO4FdiV0UoHg5qF1uqASQCwiWe0iuNdloyXuXa+QyP1+YavELwK/JRJeFW5KMJGbhoiim0h4xYcv1LPeiZI6LJl5iEKXNNI7jKalot9tNGeby94l2Xr5jLiMhLw3YIo8R34FIrd69ezex3bKpEM29/FlhweW+FJb+5uaGoiimYep/ezFQZAKuP2ONo8gShmY8acoYkjTFRgkBg3szwhmjKYqc5XKBMaMDfPP8wtVihtHQV2es8oS+ZtDg1BgHtL69I8qXPG13VN0rWTEnjjOGDoy2xMagVMowQFmmGJ/TnV74+vyVx9cXknyOzua8vOyIZlcksxmr1RWn03lc4dOgrKKrHV1zpj7s2Gx3eD9wdbUmsynGRlg14F2HjdMxyvFNYwxqrCf3gWGAph2wVoPSYxtikWONIokssYnwvadrF2y3e8zuPEa6+QTrFNYqgldYO5aLTEUTGLQaf925AKphfb3mZXOkd5qr63v67sSpcdzMrljffKI5n8nLlCg2MGgCA+dzw/ncsExL4jjBdR3eDqDG1WXdjFGFWgWsNQyDp+sa2sETxRBMoCxz+r5ju30lz2cE30EYTUx4w+vLjoDiVHU8P42tWT/87nvQnsTOWd39wP2HP1DO7rBRyaGBKMnApLjgaKqKze7I49OO87nDRDE3t7fc3KwpyxRtIASNBoIfcH4gDAP2Leqwrit2+/NbQ2eMRhGUZQgD28MRnRqu1wvQmigaC5tC16EZI/7SJCMMA03dcjic+PnnB376+YG68bjB0PQDCkuhU5JkZB1QgTzL6PojeZYBowQothlxEo26Yz/6LIw2mHlB6CPOpmU1/yPDUPPP/7gn2IK8NPQuwlmNCgMmitBJQbq6ZXN8oKr3DCrGKIiMYX57w2voxmz8dk8IYxxgnIyxmkb5N1auQYcerTzN6YgNjjhJiFVK5wMhaHqvOVejsdZoDVbT1R1RlGJ0wtA74mJJV21IZ1f44Uzd1JzOHctFhrGACRTzOSZqYejpBzhWNW0/MAye/XaLV2eSbEm5KMhiA2rAtWd671F6QOFRNmK5uGZ+/Y58tsakK5J8hdM5VbOnqloOhzOD9+SznNXVAmtgbJx9K9PwA10/Gve6vifLLNZEDIBzsN8fiaxlNl9xdVXiQ8PgarQKzFcrXOd5fdpRFAkpUDc1IbwBfB1YLhcMQ0fT1iigaWpspMd77i+vKf5ONKmirZS1tgBkMRjKQ1N0yf/8z//M4+PjJFkQMCcJBjB+b93f30951cKYSfGHAFpJZxFz3OFwmIp/RE/8+Pg4SRfW6/Uk9RDNqSTDSCbxMAzc3NxMD1gBfgJiLrW9lwZRYeOEyReWUQyFkobx+vr6DUCQVbcAFWHbLxMyhPVUSnF/fz+Vp9zd3U3vX8DF+XyeZAOyZj8ejxyPx8lcKEBIKTUBisvjevk5hZWVdJGXl5dpUBKphuirYcyO//LlC0VR8Nd//dcoNTalvnv3ju+//34amiTTWo7r6XSapASSbHF/f8/9/f2kVb7MghYGWMyEVVVNkhJgOn6yEZD8bDnvAjhlkLjUQe/3e/785z/z5z//edqCXLaWSuQkQFEU0zAJTEZVkXZdyo4upQ2iu/+v//W/fpPeIgOlXDvr9XqK7pOXtZZ3794BTPKPS3mHSFTkWpKh6ng8AkybnksmvaqqSWJirZ3MygJCxc8gzHFd1xyPx2ngkpQeGY4uDc3OuckDUBQFy+VyGqouj5F8NjFgS+ymDPnCru92O4ZhYLFYcH19Pf1ZGTjEyCxypMvtk3OO7XY7AXKJ3JTrf7lc0nUdj4+P06ZE8u4vE18udeVVVU0yl9/y+u1NnX1LWx8xaoDQ0jQnktiSxCVxlmOjBNf7yfgQxRGL5aj9qs4VVVUzmy/esitbfD9q0OvqCF3L1f0nZut3DMbSdY627wgECB2u8wSnyLKSu3fXKB/o2orDdsvL8wun3Rfq447Odfzuw3tsukAnMcYoTOihq4iVI04MoR0wRqEY6OozbXUkdBWn7RPHzVeu7j+hdUTfnqnPhmWaENkYqyGJI5hFnOuBpnZ0PShtGEJC7zxt44CA63p00EQ2pihTAlecq57DsaEoM4LqSAbDfJaj0ITw1mYfAsMQMFrT9R1pmpGmEYfjkdVixeDh588vaBORZDl5ZolixbsPf+B6tSCEE+fTM5v9lnPb0u8PZGlO1fbU/Y6+a4mNIUtH9DaEQPfWcFiUc3rn8Lw5m40htglJHOH6M303UJ3P2B6yQhNrjTYQpQmrm1sG19M6iJIEm18zDJDHM26++3vS+Q2P2xNdt6Eo5xQmpT431E3N/rhnd9jTtD3lrGS+WLBart704p7OOQbXMPQQGcgSSxInWG1w/aihjuOE+Tyid4rt9sC5bsb4zDBQVS0bdcJoGOaQJwlGB1QYvwzaAImNCYE3J7QiywvO9ZEBSNOcJCnGbYTtyIuMyEYoDzpo+rYfM+dVzzD0bwUzJ7KsIMsL9rszSZKi04S2T9nv9nz3V/+Bunc8PfxIFFsyE3NyNV0XUCgWaUqWrtl9LahPR7IyxyrGPHejSBJL7w19E+icQ5tAcA6bznAefN+j8KSRJY0K+kOFbxv6ZkDbgFExKEPXK85Nx/JqRdNWtJ3HpldEaYLrFZ33EFkGHZGWKUN34rytiE1Hlnj6t/OdmjGus8hX3Fwv8UM1RhCahLppqKuKNJtjlWNoD+Rxxutmg9OOkBqCCnz87gfef/dHkvKGQRck5RqbLNgem7eG07ERN81S7u9uSGKL9y28rfVBtLwerRVWjR0Bxljarufxacv2dUOaGm5u55SzCO8bXN9RpDmxTWhOB6IYbKTRUczgW7wfsJEmVhllWXA+V3R9O4Yz+dFnkOXZb/0a/Tf9Em3yZc6xMHkCyOX3CEAQdlAY3eVyOcXkCdCUVbRkjEtCgiQXCOBwzlGWJbe3t8Cv2uyHh4dJBtB1HX/3d39HlmUT6Lt86GZZNungLxM8+r7n9fWV19dX3r9/P4EAMUheVmtfru7loS8SAnlQC2gTwAVM7YDy32maslgsps8o/whT2vf9xBZKMs0wDPz4449TsoQkRHz//fcTwJCCHxk2pHhJEiRkgBIAI+t6kQbIub7U5ApLLhrmEMIkE0rTdCqJEnmDsMlxHPO73/2O5XI5STAkg17AnbCfTdNMLZ0iNZGITZEYyc+T3OtLSYYA3dfX1wnYixZYALUktAiQk88rumEB9WIClM8p8ozLVBM5X5elPpLSIkC9KAq22+10jwjb/rd/+7e0bcvnz5+nDYKcn0uT5+fPnzmdTt/kkEtaixhR5VxJBrqcU2H/0zSdipEu4w4vS33W6/UE8CUi8/LYSvKN3CeXcYqX5VYi5xDQKlsZ6RQQ02ocxzw/P0/eC6UUv/vd7/j9738/XR+S3LTb7abvCjFtv3//ftowwa8DspyTSy28fJ88PDxMPQX39/cTuy9/p2j0ZSCX4U0kZCJnEf+I/Fz5Xvotr9/e1IlnPssY2oq2OaH12GDY954sLzDaUvdn9vtxbZAkEWmasD/sqauKq9WS9dU1BkXXNnjXczrsqOuK1XXJ1WqBN5qq7YjzGfNyQefHSduaiL5uaes9J9WTJhFtfWZ/eKTtDnjfsVjN+f5qdOrHWcLyes2p7thvnwltQ5JlJKmhcQ2uqRhcRxh6gu8pshjo2b18Jctz4tU9SZaSFjlppImTmJv1kv2xo24DSQoH046GDx/oneFctUTWMCtL/NDSNj0hCiPTbQzlbMHnr8+ge0KwbxFO0UW0D1gdobUFZXGuRyvFvMw5n05YA+/vr6mbnofHDUkak8VLem9YXt0TXc14ff0zz6+f2Z3O+DDWis/KGeemRmuIk5jYGuLI0Lmx1AfFqOONFEkS4wHtRobJB0OeZxhd4IaBNMkxcQxqTP/QWtOHQDARy+U1xeIOm2TkxQKdFOh8ReUsD7/sOByOY3RcHqBpUNpyPFUc9jWBsZZ8tVowny+wWo+xlFrhncMHR3CBYKFMU2IbMfiBpm5wfT+aPNGcqzMvm1eUthRJTpaOlesPD6/4YWB4d49dp6SxxhjQ2sPgIfT0rhtZ0FVJnGUsVlcMWLRNcAMEeiJjeHd/y6wo0UCRlGy3r7wMGxara6q6IYojsrwYDc5dO7ZlhoYszUiLnNeNJVE5H3//H7Bxyvn4wu7YYjykaPoBdEgwNiGfrdg8P+DbFj90aBxdcyKOFaE3HI8H0mJGHMPgFUZbQgD3pms2b8U2Ok3offP25eFwQWHjlDjJqU5jzbNXeiznsSXa9zAwmiW9Q1lL6wKoBEh5ed1TFCuiKKb3NSaJKaIlbduza3qKJCdOlnRdDYRxY6YB38Cg8E1PvX3CGM+QGPL5jJvbW8rFktpbTFKS5EvcoPBuIPiByGpubtakWcpiOXvrCVBjU6br0SGg3k4nWqGDoqprQtVT1W40WquB2SLn/YcrssKA71HqzbDddG/ZzjFVXRFFCtSAsYEojjFm1Gc2bUvqHGHQWGPpXfsXCfnFa/5mehWtt4AMediKdlyyr7MsY7vdTsUv19fXE9gVfbLob9fr9aQjv2w5lNhBkUQopabEDMn9FrPgzc3NBGZub28nQ17bthPjJtIVkd+I4U8pxePjI3mec3V1RVEUzGazaY1+e3s7mUezLJvi1+ShLnKN2Ww2gaVLqcBiseDnn3+eGFwxeF7Gv12mWwhQmM/nU8Tdx48fqeuaL1++TEPHMAxcX19zc3MzbQkOhwPe+6mCXvTTsrIX5v5S9iOgQ4YL0bsLGJQ0FpF/iI73UvoicZZy3IRF/pd/+Re22+1URiSDnZhyRYojpjnZxlyeI0ma+deSma7rJkOkFFDJZxXJ0efPnxmGgU+fPnF7ezvJfAREy2eWzyHXADBtDoS9/vjx4zRUSb28957r6+tvJDvCessxkuhL2Sj88Y9/nACnmEzlOMvPXSwWPD4+TtcrMMUqXmqahZ2W4fAyp16Ow2VaiRg7xe8gpuDLrY1cE/K/slkBJqO0bHuE0ReNuyTSyIAh2xYZKJqmYbvdYoyZJG/v37+fhs4kSb5J95Gc9Lu7u+ncyCbgsrFXPqPo00Vvf/ndsVwu+e67777Jr5dhWqIaZaCTrZdcLzK0iAFZjsv/dg05wRNHhl8+P4EfgayNLNlijBmqq5q6qjmdzmgF6/UVRZFTn05EUcygBrIsH5lgY2lOBw7nE23fU85mYxpHXeHJ0cZS5ClZUG/xdHA41+z3G56+/Jk8ixiGhuNxRxYb0twyLxeYaKy1rs57znXF8VRzPJ2J44zZfMH1/fW4cm7OpEZRFjmnzSPD4LAm5nh4hS+Wax3x/d13XN1dMwAozXyeAwZjBzIirI04VRVKjYU5u+2R+lzTXc25u7kiqBZjPIpR75ykGW3j2W2fSVOLsYYsuya2FqVitFKM7aGKvnM0TYcisJgXLOYlx1NDmmZ8+nBN2zacTjWbzZaQpxQ3CyIbg4koFkuWzTWZMmTENHWFDYoo0aRxhNGeNIpwQNX1HM8nWjcQlxmLqyXWRpg4wZgUgsHalHQx1rYHBnrnGJwjtxalIo7nirJYcPfx9/hgGbD0WDbHhv3+yKkZ69hnsznlYobNxpXeeb/nfG7Q2nCzXjKbZ+R5SpzEMEDXOc6HM/ZtQtZ6lE2F4W3KBdxbMo3Slt6B6x0oSLKYxbIkzVLOp4rDoWLoA+tlgBBBgIBHG3BDRzf0BAbKeYyJF7hgOFWO/bGh6QcsmjQtWC8KFvNRwoODfDEj0gmvLxu0jkbNtusxaILXDD6Q5gnH6sRQO2wck89XvG6P+C6wvvthjAnVj7QNHKuWpm44dhFDDPPlNWm5oB8a9rtXtI1JswJlFK+b17G1Uxu8UhgT0QcNOgINWgeGwTMAXlvCW+a9C9ANHkxMXi45tEfqc0W5XFDGCdqCCQ6LRicJTnmSbEboaozNyYs1Xzdbvn7dcv/hDoyidS02m1GdG46vB97f3BBlV0S6AhVzrvbs9zsG33BzdYMOPVnkUcaz3++4vr9hNp9TtwNnPxDZwHCusIxehcgYisxyu1gSJzF5GqPUeF+5N7CjUai3RKTIGtCKtulwQ89uW3E81iSpZbHKmS9isnQE1Cpk+H7UnhdlBqrhcN7SVQ439G+m4XGNv6061utbVvMlm82BOIoxkSXPkv/br83/L72Ecfv555+nB18cx2M7c5ZNumuRhtze3k6mL5FRFEUxMVin02nSmQpTLOt5kX4I0JIH92634+eff54a//b7/fTwlog5SRCR1j3J4F4sFrx//34CciKpeX19ncDlbrfjp59+QinF+/fvef/+/fQeREMrGllh/uXhL8zser3m3bt3E0snmeZSHf76+jrF5wm4lAe66JqFzRTZx3K5nDTS33333WTIk1ZQqYMXU6B8PpEgXAIwYfqF3RWTbFmWUymSDEGipRVgK8D18pwdj0fm8zk//PDDN0zlfr/n9fV1GkwWi8V0rYhuV47f3d0dy+Xym0xoSUOR43QZFSlM6CWQEmAmDLMA68PhMLGs19fX32wjhKkXUC+SH9GFy9AFTH+ntH8OwzDJNh4fHycm9nJrItGLEpUpTP7r6ytt2/Lu3bspf11kGZLJLRumsiwn6YdcezCCYvlZwAT8hf2+1IvLkCdgXCQY8/l82gRdXV1NMiu55sVvIQy+bAheXl74/Pkz33333TQ8ZlnG8Xjk6emJd+/eTRsUGZS22y3DMEzmVwHp2+2WDx8+MJ/Pp2vFGDP1E8ggdil5kQFa/n8575c+Djmmcuz2+z1pmk5Dn5i2RZLS9/3kLZBrU7ZUorc/n8/c3t6yWq14eXn5Jgf9t7x+MyB/ePjKYfPM89NXijTi3f0d2lr6MGYLt+0YdVefzyMjpjR5mqHQuK4heAjOvaUe1JzPB1rXUcxnrO9usGnO+TQwDIG267CRpq9bIg1tV7F9eaA67em6M65TeAa6vsXahCSSnM+K47GlbXuU1uwPB3a7LUoZrq7WmFCRphFDe8amEd4Y0ryAEDCRpXMDr8+PnLuBbDZntsgxcYYLnjBE4+fqAk3V0jYVIXjiOCX4wOl8ZvN65LivqE8t61XJfB4RGL+wFrOM2azkeNxxOnVstwcWywXLeUEaW9Tb2rXtBk7HM4fDkcNhbJ+cL0ri2NPUZ4q84O/+5nfsDhXPD89sd3tS47m7zrm++0CUGoYhcFXOSVREXZ3ZbJ/xwaGCA+WJy5JCJ3Qk2LoZj7kzeCIwCTZKyZIZcVyg1Ag4gurZ7l7pzg3lYkVQhs5BubhhvlwT4hnOGU6N5+vzls3+TI8lSUvW12vm85w0iYhjQ30+AY711YwsKyhnGctljsIzhIDzjsh44hiS2FIWKd4NKBwKD29flm3T8/K8fWsRtZgo5eZmxXwxZ329pO899bEijRNMYomjEYx3/QCuo+sdXVeTxRHaGnCOwbf0bmRfmrbh2LS4weNDThqDUQMhS1BxTNsPY2EWiroZGUEVAm7waG05VxVZnpClKXXT4HEoYzE2Zrfds92dmWUF95/+hjSN2R12fP7yldP2QFOd8X6gOrW40FIuR3b7eDqymJdjnTwGrS1GW2ZFyRDlVI0n6AQdAXQoNVbYD4pxW6MjlEkYIoM1EV1zGE3FPhBbNcqDvMH1GpxDZwnhLd50GDqG4CiXN2wOr5h0Sz5PsZEhzVPmg6Wue5oBeiCJCyKtsa6haxrq+kR1jjDeYhVoOx7DJJ/z+WHDYAbi+XuccTR9RZHC4EepWaQMaWxIYoNSA37oaZ0bdUxKobRG64DWoK1+Ay0Nde04HitUMKyv5iRZhPKOLMlHFrwPtL0bdegoXO/xQROUIstS5vMZ1ii6psVQs16NWf1t0xDbsZgo+42GnX/rL6kL//LlC3me8/HjxwkgycPvdDpNAFwYdGGxRF9a1/VkkhRg9/79+6npTyLipOhG0iWenp4mhvpSGiIaTknaOB6Pk/FLstCVUtzc3EzgommaKVVEhgRhHR8eHmjbdgKQAlBkABEWUCL9REst7Ox2u+V0OnF9fT1p3OM4ZrFYMJ/P2e/3HI9HXl5evslZFyAuYFWkHM65iXk+n8+UZcm///f/nu12y5cvXyagdnd3Nx1HAcCytRAWV4CWRAUKeyiABH6NlxQGXvTmIYRp+yHDibw3kSbJ+f369esU8Zfn+ZRoIWY9STa5vr6mKIopAx1+zSCX8yrM8mUOuvyvJKAIIywsqmxL+r6fyp5EQw58k1Ai6Roi+7i8Bi+TQWSokeMhenIxncoQJH+/DJ1S6FNV1cTeRlHE6+srm82GPM/5/e9/P22Tfvrpp2mrdBm/d3V1NUmSJE0ImMC3xEfKPXMJrGVoFMZXjq1o+i8HbNGXy7GQayDP8wnwr9frSZ8v8Y9i5JWBQs6HDEuSdiJ6dmHOpXXz8+fP0yZJNiRiHL/sH5B/v5RIyWe8jFkUCZJ4AyS1Rz7HZWGQbGHkO0WuLxkCRGqjlJpkdWLoFAb9t7x+OyB/euHpl38hdEeyaIY1msP5TD5fUjfjl2PftVg0eZITmYjm1BB6T1+PmvLqPMYc1U3D6Xyi6RwfPn3EG0PjPVGWYtQoixj6jsz2VMcdD19+5nXzTJ4mrJYz4iyldj06iiF4UmPpzh0EQ995vFPYGPLY4osY1/fkNhCqA7633F2viK1l17Xc3n+kbVqUgsP5xKnec9w988uf/xtlGXHz/vfoeAlB0ztPVddsNie8hyFo9vWR/s3A1veKp+cj5+OZ082C73+4Yz5L8L7HKMXN1ZLTac+5OvPwvGF+taIs53QuMPTjWrXuOgYUQRn2hyOoiCSbURQLqM6EoWVeFiRxydBWVMcNp+bEKqSkccni6iN9b8jidASi5yPlzXvapuKw36D12ApaXke8+92YIPPl8wOoQJSvSLKUrCjRRMxnS4zVKOVxviNxHcEYsnKJiTLOVYuKLT0p+zqwO1S87E60DkwyY1bkLJczsiylLHOyNOZw2KGVZ77IyLOMWVmigsc1b/KGxBKUR1nPbJYQmYgQOuLIkmUFcaKpmoq2cygd0XcDp6pmsbri5nrNbFZyd39DZA27bUV7qsnMeJPGyagLVkYRpxEhGBJtyIsUvKdpxiituqqozh2RTcgzw/ZtbTq0DfH9HUVe0A0Dh/MJVMBGMV1zJtaj7t4k9u1Ly9F3PVmWYpTmeDzhAxSznKZbsnnxfNmcWV+nfLq75v56zfxmxcsvP3F4fOLlx59h6PHBsVzMmS9KNtsDTVOhtEYpg9ExeTZGazV1hVcpvQ+0VYdxPWnoMX3H0NRj6Y019E6hTIJNIpJygW/3dF1PHDRN1xDlCqPHTUAwCh2NgD7YCJ1lFOtrdtWOXXXG5IZZPsO7gVlZEseBtukYQmC1XoOr2R1eyPKcvtmTWEN9bFDKkhUzvv/DFensmpd9i056blcJXTswhJoosuhIUZYJIRiUdhil6ZsWNzSURYYymjAE0GBjg7G/lmVoowgE4ihCxwlRnhIYyLOYPCsIQ0eWpbTNHtC43tN1mjy/ounacXiNYqwCmyqur9ZENma326C8p29bSpNyVf429uPf+uvLly/8+OOPkz7cWsvxeGSxWExrdMnnFXZMWCZ5eF3GBgog/+GHHyYJhAB4MXFGUcR+v5+aO4WlFMZOQIcAC2BKTxG9sZjmpE5bWDoBAx8+fJgetofDYcor/qd/+idmsxmfPn36RspRVRXPz88TwBUAIgBB8tEPhwN/+MMfWCwWE5Mpspfz+czXr1+5vr6etLnSNCp18pKmIcdT9KtSnCLHW96zAMabm5upqVFyy29vbyeJj6zlb25u+Ou//muqquLHH39Eaz1FIkqcn2wdxOAqOm7J+BaJRghh0si/vr5OJVGS/y7SG4lblI2DgHFgGrIEYIlk45KpFyAsRTQC9o7HI9fX19zf37NYLPjw4QPW2mmAEC28DCuStiIsucgXxCQpmx75mbvdbpI+yRAn5kUBliILkmFGgO1lJvYY+RumOL6np6cpfef+/p71es3t7S0//fTT1CgpgHG1Wk3MrEh+BIhKJKXcA2KWFYb8cpMgciWR9cxms4nZl/snz/Mp6k8A7mVCkBhOpSF1sVgwDMOk+75MOBKGWkzE0kaqlKIsS/72b/+WxWLBZrMhTVOurq4mY6YAXpEIyWcW2chsNps2AZJQIzIbYdaBKblGmGwZksQcfCnZkTQh+U4QckHAeBzHbLfb6XqRAqjf8vrNgPzDp0/sXr7StAeiyE6oP0lStLFopUjj8eS9u79DeUXf9oz5GIwGva6l7Rq8d3RtR5LmLNc3aJvQDaP0I00sbmgIrqU772lPO3BnlmXM9c0NKorog8IPCmMLImvBD6SzEkNC1zjq6kwILWmajWArgFUa37d0rqVvcpzuUMoSpym7Q8XtzQ2YCI9C2UCZGlIbCH1F7yOCURAUg+sJwWFNRGJj6tbRNg2uc2g9Gg77vuPldYexcP/uiiyJca4lsp7FokBb2B52vG62zMqC6/UcbcbCoEAYI/6ysV2wHzy98xgzfhEFOvq2wtqY+9sVXXOibc4MKFywaFOSzu7pWkddDeTZDUmsSWc98eyaujoym8/Is9GcWNctg5kRgqcoxzVrnGVYZSgXS9zQczrvGYKlWN6SLzwBDUTM4gVpqTjXHU8ve6rGE8UpxWJGOZszm6XEEURWk6YxBNAhMJ8VWKuxVrNYpPRdh3cDxli0VYAn6IjBOZxrSZOU4D1pkRHHlvYt7aYoEn74/V/hgyJOEqLI0LYNjw9fKYqSSEesV3PCSpr2PIQBjCJJI/puNKK2/Xh9WJtg3wyffdMxqABGE1tF1ziG3mKNJUlTXFdzrMaGWkJgcANZmuIHNZp6rKHtBqpjNWqg44gsjWk6hx8UV9crTJzy+csTX17OtGz5+H5Nml2RF3vO0QsmcqxvMjwxWQKRcVjtcK2jq2usMkQ6Bg91VVM5A5EeB4Shw3U9g+4Z2pqua+janig1mOyaYCJQ4LWh6T1Ge6yN0IyFWflsTlv1HLcVNk3I0gQdBiJtyeYL4qwg0EAI6DAyK33bMThIkhSUJuixRCqKE9LIYHxLV7cQPMPgcM5z8+kjt+++p1M5tYsJynKuRs+HtgqNIQ1j/GgImq6r8UOP0W/Z00bj/GjU1loxDP0bS6NJ0wStYtIYtIrRkUHpwKwsCUPA9b+yZipYfFCEEKGwZHmCNp7BBYwFP4yJQCFW4MeG0CSyLBdzwvCXpk6A3/3udzw9PU3xZPJwlvg1AdICiITxFRmGJB8IYBbTl5gmhVWU3Gjn3MS4O+eYz+fc399PrLwAdmGoxQwmRk3vxzp6kb4IeBPDmcS55XnObrfj/v7+m3g2kZWIXvRyvS2/R1h8+fvk/UsWtjGGjx8/frPylzzy7XbL8/Mzs9mM29vbCQABE+MoYF8A1KXRURI3hAUEJnnMYrGgbdupAVWAqEQmLpfLKQ1GwJ0ARdEcSx68AM8QwsTSyktMrufzedI6S4rFfD7/pjxH5A8iDZFfX61WtG07AShhmS9ztOWauGSmBTj98Y9/JIQwMZ5N0/DLL79MYO36+hpgOnZyjKRARs6dSHzkGhBTsQBRAYFybYjJVX6fDJSynZD3Inr+S2PnMAzc3d2RJAk//fQTDw8PeO/59OkTRVFMueJRFE0mZjEpy8ZIzpvcD+fzebo3ZfiUSMSmaabti0g+LuMRL1Nn5LMURcHxeOT19XXyg1xeX5cZ/5d/jwxjMiwIlpQtlmyWxHvxww8/8PHjR4CJWZfrVs6JDKiXcaUy/Aggl2MjbLfci+I5uWTsJQ5UvnNk4L7cIl16JwTky5ZD9P4i2ZPB53/1+s2A/N27j5w2z/hmxfVyxs3NNZv9AZNkRHGM7zuUNcRJjNJ6rNJWvOnPOrwfVwzKKrb7PeemZrFao03CYr4mdYG6H2vIXVvT1w19cyYynvv7G5rOgY5GnXKwtA76zrLMFqSJJQqG0GtQHYOynE6vKCyzoiCOIppTRV2d2Lw+8bLbkyUpNrIU8xLnAzZJWec5cZoyhJ7lbE5fVbx2D9T9Kz6akS/uKbIIbeb0nceYiPV6zn5v2bweqOsObTSDgn5wPL1u8cpze7smjWICA8YEyiJB2zlte2a/27Be5W8Gy0DkLIqAtxFpkmDN2wUUGcoyZ/CjmU8FRZ4nLBcFO98SJRE2Tum6gWMV+J///DPVueZ3v/uem5sl89mMNC4pFzckaQTBE1DkyYx3UU7btQzeM5st0MZyOlZ0pAQdoxNN8D2egb5r6d1AHCWkWU6kNd40qChjjiZJC/JijjYWaxV5FhFHlsEPo/Y8jd4ezOMDu2/F/GBIs4wxUFrjfCCKLF4NdJ0bdfVtO763c8W5agjesFytSLOcAFRNx+F4pO9a0nTPzXqNtuotKcOjVKB3o848BBiGlt710BisMrStwjlI44zrK8u56Wj6FpNbotmSRTEnTyN83zG4DqUgjmLariMozRAMLiiOpyNpGgFj22rXd3g8Xo3V7l3nQSsWVyW9dzw8BLabFt/uuVkX+KEgBEtSWhY3MU114rD9StvVxHFOXpTUhy2n/Z7F8pYoeIJ6yygOHsWYyR1CoG4a0rf30fYNjT8Qq4zZ1Qzn+rE5FsXucGDwA9YmzMuIrq9HNr8scUHT9Z7YaAYPGo3R8Qj065bGVOg4MJDSdZCaBO8D+/2RNNIsFlek1lH5ccBq6zPGZiwXS7777nvO3YAtUvCGpnV0/cBi9RaTFxzD4PEeuq4hvH2RRpEZteIEFOO9qJTCD+HtCxNsZImimNksweqEfujHz+U69tszbujJ83QsKQqG86lhuz3S9B03764piwJ8zyxPCEM/dgSgUAqM1kRFPKYm+b8AcoBPnz5NmmBpItxsNtPDVgCyrP8FoIoOVwC5SEmqqmK9Xk+6Z5EJABPIFOnJx48fJ8ZK1sny+wVwCssuxrPD4QAwgRvR8T49PfHy8jLptxeLxQSYJDLRez9psb98+TKx8ZK3LuBeyks2m83EXMprGAaenp6myEJ56It2XUDedrvl+vr6m1X8Ze62gAIZOrz30wq+KApWq9XEJgrrfz6f+cd//EeOxyN//OMfJ+ZYALCAU6V+bfxsmtEYLit6+RmXQ5IMJGK2lY3GpRxEGHYBU3mefxP1KH9GALQALPm9l5F4Uk4kg9xPP/00bWZOpxMhhKkASiQKkiiSZRl3d3ffGBMF5MOvCRny3wJ0RaZxfX09gdqyLKch4zI+En5tsIVfUz5EJiN/76UMIk3Tid1fr9c45/jy5cu0Wbi9vZ1A7qU5+fX19ZsUlP1+z263+2ZIkuH08nUZTyrtp8Ak6ZH7ZbfbTebJ+Xw+nYPZbPYN03+pU5fBQPLmgWmL4L2fYgYvpV/OuSku8Orqit///vfTMRbWueu66Z4Q8H4ZaShsuPydwATIL6VNck/INS3XkqTFiMZf5DwygMjmTIZ5kUzJORf9uhRx/etj/n/3+s2APMkL1nf31LuxMMTYiDgaUzmsVgT76zTrhgEbRbRuNA1aazCYsRHPDVR9y/X9PXm+YLeryPOecj5jcGfO9RETeiwNdX1gcAN5uUArg1cRNp1jTMoQB4JJKOZL0tiCD9THlhBHRCbCuA7XncGkJPmMNFviuoYoK3h5fuDh5y+sVnOUHTWoddNwdXWFbRqMh/Nuz/l4xuRLTLpEJZ6AxZsCazOiIiZLUuJIs5xdcbVI+fz5me3uhDaKfgicqhp7MGR5RnlbUMwyBjWaJbRZc65Ob6DiV2dzWZbUVc8w1CRphHeOtq3J0ogQhummAg90rK9mJIlB24ghKHqv2R17zp2l7hP+588bgskwSYmxOeW8oO9rAg7CQGxjimWO6XpeNls2DzuyLCdNMrbn4W2yjKnq7q35roLgmZeKuUpI0ojZfMHSWIbBY95YobZtCc6PBsfBE4YBzbgBUUpTlCVJnNA2b8OE1bhhoOt6UGP6SxTFuOBo2zNV1fD14eu4NXDj1uB4rEBp8iLHWENR5mR5RpamRMNoktXG4MNAXVcENaAVQMC7Duc8fe/Z73aEQVOkOZEtiKynKAJlV9P2EWmRU2QlwSvapqGpDygzDhXH44mm7Rg8KBOYxynBtTg3ECcxSZYy+IHX7Y6265mVc4xN8IMnzxPexTHaJLw+nWjqjq+/7Lle5lzffkffPrHZ/MjuuGFoPNfXgeWHOYlVRMqzmBWkyTi4NVqjhvFz6SjGmoBKLNZDrA3GFrgwMOgCHcf4EHB+QEcRUZpwOjmG45kig4UJnA47GqdZzpfEdmTPYq0wYSD0gSwrqbqKtu7JYkdwNSaNUTpC6ZG5D8rjhp4szunaAx5LYgP1cEBpx+l8pG5afJRwONcMpsQNjiTLKYoSBQQfMFYT6wit35gOQEeKyFqGQXKZR6BszTj8hRDQyqCVJn7b6OkhoHSEcx2oMUqxc56qdjRVy2Z7pGs82lpcP/oArNEEPw5yRmuG3r19kUcUZYE2b02+f3lRFAX39/fsdrtvdKjycILxu+v6+npK8BA5gSQWCDBp25b3799TluWUvjFG5vaTsVMpNaVLCPstRrNLdkz01VJQIitmYVHlwSnlN1mW8fj4yJ/+9KdpIIBxCLi5uZnW7dvtlsPhMAF+AVgCNEXeIQDm6uqKH3/8cUqPcM5NKS+S2CLyDDFgSnY1MIEOYZxF5yqM/iXDLDKREMKUMX6ZjiGgtO97/vSnP03yBJGBCBMvTP9qtZqKXcQ0m6Ypx+NxYh/P5/M3iRzyWfI8Z7lcYq2dMuiFoZUBScqP5PwppaYimrquv9HzClsrcgFg0nL/8ssv32wNpDRK2H5pFJU0GBmARFok1xUwyYy6rmOz2UwbFWGm5XoRsCh6ZtnAyIbocDh8Y0a81CVfJpy8vLzQtu20HXDOTXF7xphpw/DTTz9xdXXF+/fvp0IpiYW8v7+fBiiRFEmiiZxP0VXLOZehSt6XMMKiG5fBTxJvpKRLmlxFNiTGZ2DSd1+WbcmwJTruy4zwJEmmwUA+u+Sj13U9DVmyqZEtgRg1RTIjDLiw3fK+LjXyYiS91M8LeJdrUIYSuQ5k+H9+fp4GCtlKiRlWvksuozJl8JTj/796/WZAHqc56+s7npszm5cHiqzA+0DvOvqug7cTfbVacdhvgcB2tx0LWZKYLM+IhoHPzw8kec7tu/cMPfSdoqk7snTAAr6riSMFdCQRdB66rqVzmmKVY7MZvUpZlCU6yjDRyCxrFEQGjcOEhKVVNOctyiqUSUfdb1uPmelJSpLm4HuatsPosYa2amo+//Izv/vuA6FvaXuH29UUS0c082x2J9LZNcXihjSKyDJDnhoIgTJfUhY5z687toczr69b+gGGoDiea/LjnlmR8b68wUYWrRXnc865OqPw+GH8Eh5B6vCmQ8upzyfc0OGD43Q+AX6UfyhPfT6PTERi8EGjTUQUaZJ8gYqORHmO846vz0d0nHFzs6LpNc6NMYHWaqrOgXegDHUf+OnLM23jKIuSvBilLEoF2rZC6/EiT5OIoBM8lihO39huTQiegMcPDVY7kihGB09btcTJWzvpm6QhshGuHwBD1zkGP0qWfIjoHRxOR56fXtAKvvv4jvlyNE8+Pm14eHih7Xv6fqBuzpzqCqM1V8MVNk4obEySlWx3FXV9ZDbPKcvxiyB+Y1XD4LHR+ND7+ac/8z/++Sc+vf/Edx8+kqQarXrSzL7JewqGAbrWQXB47/CDZxh63OBo24bjuWO3ryDAal7S957B99gowsYxWo1GzuO+J4oymr5jeTV/K+24o8yXvD6+snt5YrevSG2gXHxkcT5x3J0J+kxkDZqAxlOkCWE2kMSGLDEEpRkJb0WZWY59jxsatB5LbTThrXlS4VWgGxwmiinKGRo3mpSHFrSmcz1RpDg1DYf9K4vrD0RxQtc0DINDeUWezakPG9qmhjm0TUdk3Wj2bTui2GIjS3j7uf2gqFuP8y3GGrbbDSEtqesWbUfPRBynDE4xK0uiyOB9B2/NopEx6FiPPx8wVhFFFjVaZYEAKKyNUH7Us44Z/wOD7wkuMHjH4Dt8GEjTBD8E2tbRdYGq6djtzgyDwkQaFSnKWcpyluGHFtc7TDKuM6NYoY0iSWParsHovwByYMrvFYOlABTJsRYW7fr6mu12SwiB19fXCQAI+/z58+fJFCqASMpJBLQL4BBQJazWarWaVuWSB30ZTyYPfZHUnE6nSYYgulABMJeV3sYYnp6eqOuaf/mXf+H3v//9xMBLIchsNmOz2TCfzydQlGXZZBITtuzp6WmSowjbLS2fAsplmBCmF5jW4AJeBPSf354DYhyVcyGrfUmCuFyji2FV3tvDwwNxHHN/fz/JdgSMS2ykHPsff/xxygS/TLq51CAL8JJzJID6sjBJzjvwTWa9ADcZ2ICJ+ZRX3/fs93seHh5QSvHDDz+wWq0IIfDw8MAvv/wyDVwC1kW6IEApz3M2mw1VVU2GWgHM8h7lv//85z/zj//4j3z33Xf88MMP0/HNsozFYjFpxoUJl+tGtgWSVrPbjT6l1Wo1SZ1kQNFas9ls2O12E6t+fX3N1dUVnz59Yjab8fXr1+n6kWFTEkouWzgFtArYlY0HMHkO5DwLqJT/T/4OGZ5ksLocoGQjJH6Ku7u7aXgSBrosy6nNVACrbCEupTPyHuQ4CcB+fX2d/ADiWRAZ0WXajchB5D4XwC2SsX/9EuB9WRYkf8dljKIkNYnMRgqmZEgRr4To4y+PpQw8IkGS4eN/9frNgDwv5vi2ARNTdw43BNqu53Q+k2dvbUltQ91WaGtoutHYlRUZ89mM+XzGzz//jNKG67t7lImwyoxa0sHRd2NrngqO0/6IUQFr4NR31FXLfP2ROCtQcYGNF/QqIkpKur6n61qSyKBMTJmVDH1FdarIioIwONq2J44CDoOKUhbrG1arJbvNE/vXJ4YwsNtuWHRLzqcT1bklMoooSkezZlvhlKb1EeiIOC3I8xl+6AiDGVlXBev1nPlqQVX3PDy98vXhiaDADQPn6sxqUTCfFwzDCOqKfCypiaNxfUtQeB/GfOfe4YeeOInQOqDUr9Wvo3Y24IcY4wy981MVvFejnjpNM9q+xQdD2wcen3d0Q8/VakaaGawNb/pYM1aFG0s5W/Lug2a3O9DUHZ4R6B0PB6I4IrEWbS3GWqxNxhIgG79Nm56+b3GuI0siotgQ/IAhUOYZQfkxlSaJ3x7SA3XdMQzQdu1b62FMnJYcjjVfHh756ccfmZUZ6/UVt9crPnya8f7j92y2B15fX6nONW3XgVYjq59nGBORphm73ZnPP/1C05x59+6ad+9uuL5ZovW4dejb0WicZQVaJxwODf+j/YLWGd9/f0MaW2blqEtvqpbTqSZNc9Is4XTuMFpRNRXeQ5wkmNZTVy2bzQ6rDEWWwts9YjtQZES25PHhhabb0vc9v3x+4P79Le/ff2B5VZDEAa3PnLY1VRvQfsH13X9ilq35+tP/QT/09EPADy3LqwXgwTcYOoo4J6gBX7d09UCsB5w70w57imJsw+y6Gp2MedC9H0uhkij5v9j7zx9Lsiy7F/wdYfJqVyEzs1RXd4PkYEi+AUG+P3+A92GAAQYYguwulVkpQrm62tQR8+HcbWGRj4POHsyn7raCV0Z4uN9r99gRa6+99toMXqOzmqYfOG/35FXFYr5gvZzhNBgCszp1rD13DbnWyf+8mNH5hr73+BCg77BFxjD0GFvgg6bMSlzoKMoZx+0zp1ODDW2qmUDz8LTj7c2vqRe3nHuos4yyzMmspu9Skx8VkzZcK4O2iX3MckOWWbr2fJGQqMuhcgHmJlmTxgghRJrmSF5kdN2ZENN8dy5wbgdCNJzbgcftniKvMUGhT7Dd7sgtFPazh6yxGnRqitX3HX3foVC/dBv9F31JcaRoUuUwOxwOY1FZ0zSjzEQK5MSpYLVa8e2332KMGW36hPGSw1qA4vPz83j4Cst+d3fHbDYbC7SA8QAXEC8gVLTHkmoWJw1hlwUIPTw8jN0vHx8fubq6GosuBRxLQZywzwLUBBBNu2ve3t5ydXXF+Xzm/fv3/PjjjwCjHn6z2YzyHNFEC8gVTbsAAmH/pPGKgAtg1MIKMy7jJizhVNYgGYkPHz4wDMPoNCGM+rSBymq14ptvvhmB7FTOIAy7BEGiDRagJKBMJB8SJABj4xRh/aVAVBxexNtewP5ut+PHH3/kz3/+M8vlktvb21F286tf/Wp8bqLhFhAqfvhVVfH09MS3337L+Xzmq6++4u3bt2MDH3mmUugnlpcSnP32t78dgb0UbEq2ROz9xIpT9OsSGDw8PGCM+QLwSYCZZRnv3r0b9eh//etf+eqrr/j666+5vr4eC1qfnp7GOSfSiT//+c9fOIJcX1+Pumfgi+BHHF/k/QXwSvZBGHLJZInkSRoXSRHuer0e557Md7EjlEyEPEtZZ7ImRSoksiOp1Tgej6PsBOD+/p7b29uxuFTG2Fo7gl2RushakSBHpFZThxW5JDidFl9LsCtBgbgCyb9LkCDgW7rrToG/sPVicylB2i+5frlkpaxpsgJlclabG8p6xqk5Eb3HKOgu0V4IEW0MZV1hc0vXtizqmrZteXp+ZnN3w2q5oW06jE7MhFaOtj3S92eGvqPKc7x3bI9HTm3L5vYNtqpRtiRgKLKCtguErsdmNkk8nCM3SX+dWY0xl/RElrF/PtJ3AybPcHhwLcvKXhjciPcu6UC94+3rN+RZgfcB5yNojdIRhefm+pqgMo67Z5ar1DHUDYmBNFYToyO4gDWGr968Yr3esN3v2W0faNuG/X7HbFaQGU1zPifP7DzjErcyDJ5u2GNNkYC48uSZIuKIBIo8J8aAcwO5ycizEqUGtIl0faDrG1AlRZFR1TlN1xOjxdgEIAaX2n7bvCDLM4xNWtygA1pp6nlNPZ/z8tVLdtsjXdfTth1DWVKUGUWRUxaWeV0yrwuMgeNxT1lmZJnFaEVeJ/vC4BxKJfDeDz3apsj7eD5jTUk3tBzPA81pGLv0oQ151eC95uOHPY9PZ7yPnJoOFzxh8GS24OZ6zXJRcW5anPdkeUGM0HWOw6Hh4f6Zh/tnnp9OdP3pwsYotIFZmRxVirxARc1hdyDPcq6u7nh+PvP4fOLVVy+oMnthRJP1XggqsfgaZos5TXvGDT11vSDLS7Ks4t4/0rVJ2+r6gWpWUxRJ16+0Ybm4ZrtteXp+4Ny0BOVpWsfp1PLq5Q3XmyVf/foF+0XO9nELA2RE6jxt3Pf3P9FHy3oxIyeieOC4u2e+XLBYLogq0jUnuvOeGMDSUdpIZeF4OLDbPlMuatbLDJuleoShO6Gzmmp+Aa7nlojCZpbj+UjvO1AZmTXkucWXJSp4NIrNzS0ff9qz3R1YbdZoBTEmiZMPHkOOC47+fKYqFGU9w8U5oQvJglNn3Lx4zdXNS84hp9BgbE6Va6wK9MGBVknypizhkoKPwRM8YDUK0EqjVBwD1hggxIDW6SDSWmOsIkZPZrMkYwrgfMR5CDFybjpm8wV5XoEKVFUxBsgQCcHTdalYNGIpq9kox0P9GyAHxnS+MYbr62tms9no7iF6UpHmCXCTTn3iKvH4+Dh6Tk+9sqUYc1p4JsWEp9OJly9fflE8KgVz0vVPum1O/13+nOf5CHCEcZeiNQExwpx57/n666/HRivCGMrX3d0d4p0sjK0UhomeVe7jm2++4erqarRebNt27NQp/uDw2VVE9M0iCZLvSyAhPyvvIR0rhfUU73IB6nVdj50nJUsgAETAzLQjochIFosFb9++HZsuiSRBCvvEPUVYePFHF+nFVAsv9yaMa5Zl4+cTrbuA3f1+PwJr7z3v3r3j4eHhi2I60daLjaJkD6a+6vv9fmyQJBKEaYMmyYzIfW6329GdRrq1/upXvxrrIab+3cL8i2f2MAzjPMrznPfv39M0zRgozOfzLzTj6/Wa5+fn0bMeGB2H3r59y/X1Nb/73e+4v7/n4eFhnH8iCfnw4cPITgv4fH5+ZrVKXdLFaUUkT7IW8jxnt9vx9PQ0/qzMV8nGSEMruS+pu5B7EImajJ/WepR4PT8/c319PY7VVGsumZ2yLEf7TAk2tNa8fv2au7u7L+aLjPvUJlJeGxifx89tHWWdyL8JMy73L/NHPNuFDBBnHSlqlgBE9oapnEper67rsWGX+oVnxC8G5GW9pKgO7I9nFvWCopoRIyiVWKmmOZPlOfV8xtD3qa35ZVCed89st1v6YaAqZpioIUbKKsdqjcITgse7HkIE0iJ+eNpRL9fMlmvOg+bj4xPFXDGzqRuk0jnH44EQ+pRSLkvO3YAKDYSWogATIsfdA90As/U1WI2Onu32yHB8Bj/QHndYrci0YjGraXrH4CP1vGY+Lxm843DaY/KaalFzPp34+NN7Xrx+hZ7XKJXkCcNw5HBu6XpHVtRkRcXVaolVnuPhkRgC0TtsXlJdKsGN0jR9T987+iHggyOzkGU62b4pndwfQiTP06Tb7w+cTmeKvCDLLPWsZLHQvP/0iPcts1nG11/fsFqVdL2jyHOyzAIDmVVYA0WWtLVGwxA9IfRYbTFGkVtLla+J0dB1PcfjAaKnKAvmswqtAm5I3T9nqxnW2FEa4IeBzFrysk5Mf+sIMYGi7X6Pc4HDcc/p7DmcBtpz4HA40TYNxhZoq1AqZ7dzHI8erTuO544QI0ZBnkEIA1kW2BRlchQZAqdjy/1uy7ufPnE+9/QuYmzOLNeXTpaOtm3IjSIzhuVyQWYL9h8+UlUFt3d3HI4fedo2fHrcYYoFWanJtMYHi7YVzTBgLBSX1KvNMk7nI/rcEjFkRpOVBTbTFxA/MPhL2+y2R5uCFy9foE3BX779ntPpiI89NktylOgHlsuaq9sbNutr+jbg+gHXlNSrkuvX35BZRWUju4/f433P0O5oDx+ZL2sylbEoArieCCzWFfPcYjmAKzExsH9+Yr5JDW+qxZLGZiloOudYk3TaISi0MhAcKir2Tx/oh5br21epP0A74GKgnC2IynA8nbl5cUM0kagDVZ0TlCUrLDom1yCtSfKyouDcKqrFkvX1Hbcv32LzithCWeRJ6qIj0XcYHfHBg4OoANTIaDg3JHlWkaEUI2AB/hcbrcENfdLtVzMimtO5oe/BO+h6R997yrpCYbDGjiAwsxnWKNrL5rxarTmd26RDJxWbav1vgBw+W4Xt93vm8/kXvsRTBkqYdGHNQgij57JouOV3hJ2Ez0V2cphut1vu7+9HACHOJaInl/+KzlmkENPAQFL5kvK/uroaGbenp6fRGk70q1JwKSBOAKpzjv1+PxZ2Ho9Hvv/+e7766quxmYgwxALGhCW+uroai1sF1InMQApfxQVj2gFQALMAEwGR8lrH43EEgmIf+P79+zEz8Jvf/GZ0MJGfmwISAeWi2xUJh8g4po4uUiArNpIia5CiUGFDpx7iAtIFfImN4zAMY6AlNpiSlZhKkJ6fn8fnIgWcEmCJFOTq6uqLWgXx8ZYATbIpwghPg0ApNPzxxx+p65pXr15xOBx4enri48ePoxRHwJvUTsnYyPekWBgYP7eAXZnTkqUwxvDmzRuMMfzhD3/gcDiM7LSAvvV6zYsXL7i+vqZpmjGTsFqt+Oqrr8Zg88OHD3jvx0JW8bwXy0Wl1Mi6A6Nu+vHxkZubmxEgSz2IFFoK8JX/xhhH/fvLly/HYFjmmRRDvn79elxDkjGbZgamYLtpGhaLBbe3t7x+/XqsARGfepnzIkMSsB5j/ILdl6BpGjQDY/AJjO8vWTMJCo7H45jREUmc3PfUcUfGW56lBILDMIzrRupL/qnrFwPyw/FE23uedwfsekbbDpzODXhH3w+czyeC9/R9qkIuygIIuMHx/JSqVa+ursnzEtAQItE7lNZYkzZfm2lCMJxOA7t9i81L5ssr+iE5VAzeQd9RR0/fnRmGE1qDjsmKEK+IYQDfEtyZc9dw2u057h4p6w2Z1diyQgXN4eGB8/YZ3+55fvjE1fUVeaaJIVBUFb73eGWIWqOixhrDYb8jUIHPeHx4YL5cUhYVeVEQUXifBv98OrL/+AmlM169fM1mvaKuDLMypdEJkSLLMUoRQipE6/ksRwk+MESHKe0o9QgxcG5OZDanqmq0Njw/b1GqS04ZwXN7vabvHaemp65qrq9meCluVBHnGnwYyCzMypwit0QCZWFS8VyWEaOi61NrcOc9Wimqcn3JOERU9Dg3MK8LtFHJyrJtx2DBZoYsv2xuTYdRFm0sXT/gQ+TUdjSt59xGjucOP1h8NAyDwTmdCgGHjhAyjC7p2o77+0fevFyy2cxwrkkSm8yQ2YwQe7pzQ3NqeH58YPf8TFHMWS4WaGvIc6grxXxmWa9qNstFagRlI2135HTeE2PGerPh9k7z8dMjf/nuJ05dzc1NTZUV5GbGrKqYVzOCSh1Ui6pmvlhwPp04nTpUgLrMWS4XLJZzXPCgIkPfYbKMQKTvz9is4M1XL0Frvv3uR3a7M89PDSpqDBmZrajWGVmlcPFMWVjM/AYVF2y61/TNnnB+QunUjr7MYWj3dMd7vC3ITMH1qiJ4sHh0HCitYbAaPwzsts9cv24JWUcwycvdmIrBHwnRsFiu6c47zscjmVEMQ0ff9iijUOEGHzVtnxp2aSJZUafgXJHWr4ZAanffdi2FSbUDNoMwWLK6pshuyas5L96+pahm2LxC9T1FkZNnBh0HVAxkmcHEy0YZHFlWjAU/g5MmJXYsdpZrqiEMwYMKxEtHXa0NzkX6PtC2PSFqQoSiLAiX9ZiVOahInhfkWYG14RLQ6iTHMSY5N/UprdyoX56S/Jd87ff7MaV9c3PzRdMUAaLCIolsAZIeWNi+29vbLyzGhAGbsldi4bbb7UaHBCmkmup4Je0vB/X0NQUcN00zekgL+BCg+fHjR56enmiaho8fP47e5BIoiKuLfFlr2e12o6zk06dPY1Hdz5n24/HIu3fvRgB2dXU1pvjls07Z16nloYAQCV7GzNAl6BHQZ4zh4eFhZEpDCNzd3Y0NWKqq4ubmZmSHpzaCIu2RoskpwBTwKABEdPJTnax09pS0vbDuAmQENIn0Rxh8AZDioiPARgCWfG4BO9KA5ePHj7x9+5arq6svgpbpfJHOpdIJVZw1pLBysViMnTanry2g+OrqilevXvH+/Xv+9Kc/0TQNd3d3o1RnPp+PTYCEIRUbSZFhiCf3er0e2VzJDgAjoP/Vr36F1po//vGPbLdbHh8fAUY2WLIREoSJ04mM3el0GiUaUkC73+9HQC3OO3IJ4B+GYZQjCeCU9SeBgwBOkeXIOpJ1Nq27kHuVug4B3rIe27b9Ql4iwFqY9m+++WbMMIiDzLRgWYIvKcCVAGvqciNgeXrJXP35vwmbPpVJyWcSYC+BvAQUU6mWzH0JmGUPkkzUP3X9YkB+PDVgMm5ffkV3fOZhe+DxcUeZa4a+Z79LDQWCmyf5Q1VjlOZ0avABlqsV1uZJWtD0qWNi1xJcpJ6VKDTGaow3tPszxuRcr69ZrNZ4ctrWMbTn5FO8fyYramJITheZchTWY2jQxhFpOZ8ONKc9Xdvz8uUdNlsQM00IAxqPwhNjihJvblLrbq0zfFAsF2til2QSvfPgHWVREFWONYqXd1c873oePt1jbMHmekNhk83ffDbDaE3TtHy8vycG+Pqbr6mrGVkWx0USfRyjvOoia+i6AVTA2NTp1DuHyhRGGzbrNTFEttsdRVFRFCWbzRVd14wRtCJQFvYiz0jAAzRdlw4la+agIjEO5Nam++z6BPhDQDlHvPy80RneXdLLxuCGgCKiVCTPUrOgfkgB1aKejZtqlucMbqBpOhSpfXyMkXM38PHTA6emx2YVWTZjuShQZMQAx/rM48MeF8E5T5GXrFZrzs09T0/PPG2vWC0LdGYpihxjFYqkQ84yhYqOKtfcXK9Yra9RJrVPz3JFZiPLRclqUZPnJmV1YsC5HmugHwZAkRdQFJrB99w/bGm7E0YZZuWa25sNG1UxuCY953pGnmmKvGBW9WR5TntOgUmeZ3R9kjsNQ3KOsVlBaZIEwhrPV29vWC2X/PD9Rx6fHtlvjxhgMZ/DWoGO2AIUgegVuV1SLZacdxmN61NzprwiKot3juA6hqHn1G9RpuB8bsm1YlkospnFdS1Vbsh1i2+2VIsN/TliswKFwtoigVCTk+vA/YcfyG0Cp4tZQcBzOjxjsgUhpsZCXg0sNzeUmabrevKyxmQ5EY0yGc4FekJaP3mRMiAkR6XF5oZf/+5viVozhCTJUppUK+EdHo+PYLMSrblYNCanIaUjWWbQWtF1bgTksrYEHGmtadozWbQYo1E2BdwKg1YZ/ZDC4H7wyW1TBXJrsRdPc6VU6roaPUpZQhgIIWKMxYcL2640z0+HX7qN/ou+pM3527dvx06T9/f3o/Z06i4iKV9hsAXwCOCTg1AONGHahKEVHfqLFy/YbDYAFxeodmRaBdhLF0YBlwIuhIltmoY3b96MoERYfWAEqyKjkT1bWsgL8y4smIAXkaJIseTNzc14aAtzfzqdRhbzd7/73WizOLVnE3AgvtYCdGR+S+GdFJZKtkGKUiUwmkowBPRPHSGmrwuMZ4qw85JRmP683KMA+GlWSsZaGH4JdgR8TUGKBEht2/Lu3bvRJk8864Exi/Hp06dRUlSW5VjUKEBbuppOfbSBcV4VRcHd3d2YCZF5Ie4yq9VqBF7wGVxNLQxFQyyuJ/L5pAOo6KPn8/nIokttw+l0GkGlZBAkWzT1zM6yjG+++Yb1es23337Lp0+feHp6AhgDh+mclmBpsViw3W7H7I0w0CK9kHEXVxzRsotkTILApmnGYGLKYsveaq3lxx9/HAGp1ABIkCzrDBjXihRMC3CV+ScBrBRCAmN317//+7//wvVEAkeRJ03n6dSucnoGyPyFz11Lp6y8FJ2KtGT6s9NnNJ1L02BF7u1/tR5E1iIB1T91/WJAvry+JQw9X/367/jww18I7YFmgGFo+e7773l6eM+LFzecD1tubl8my0BreX7eoW1GUc5AgVEW78WSLGJN8g5WKlzkL5dFkOdcXV2jrEWZjMEHdOiI7ZFzCNSzFYvFhlxHovZ0zRbnW1QccP0J37cEN7BcLFkuX9B5xbkfcK6h684M/UBZzZlfXTGrZ4QIHk20Jc+HM33wzOYVKMep6VitK4qyxuSWulSomPPdu0fyuqKcVVirKcqkT1UqLUCzPfLx/omqnvP2zQ0xgxAUZAWgcC7ZBGI0ZZmiUxRYq5OjxqUhSfAOhWa+mLHfH9nv95eNIQNywsXmrTk3WJvRdT1KpwMvyyxVYem65IRjM0vbOSBJE2LUDEPER83gHN6nwzD4FAVWeY7SijLLsZmmvxx41lqW62RHeT43YwrLuYG27TBGk5mMoe/ph8j2+chf/vKO/eHEcnXF6zdvUrosz8ltznC7orDw7n5LnmtcjKxWJZurF0RSyi/4SAxwPp3ZH3bJ2SMvmc2XVGXOy5dXqI+PnI6fKKuSzdWGxXLOMHTJ7aXrIUBdzyjLOrH2zhO8x/UdZR756u0VXgXywqamSMcTPvQUp44s1/TNmSxTrOdr9IWRCsGjDCgb8HFIdoxBo7MSN2gO5w6lPavlgjLXKOUwGq7WGVX2ht3NgsNxR3JDcRgFVVkQY4cCbFkSXEAZTb1YMpx3BFuhyw3YM/PFHKtqtIkcz3uOhyeeHp948/KWYdDsjx3BBzbrJUPvaQ8/UM0ybLWhacHYGmsUPii6rkf75GX++PiAySzL9RXO9RxVzuJqRlnNybIcg6O0EHLD8fBIkddUqw27s2PwYPLUlIsY6YeIjpYhGHSxYLa5Zr6+YrAlQ3SUZdLr932PVeB9wCvIdPJQjxHyQqG043w4MZvVYz1FjOELdkUYONGVpgMlBcDOJ59zNwTas6PpB5SCtj2xWC+xxuBdqnkYhlTfsJqXDIMHIkVe0nQdy2rB8XxGKct+d/yl2+i/6EvsDP/mb/6G7777bpRZ9H3Pn//8Zz59+sSrV6/Y7/e8ePFiZOweHx9H7a4wVNNGHlO96ZTJnQJdAdkC7ERHO+3kKHpXcWURpn61Wo0NbiQAkPuezWajxZqwctbaEfSIA8X5fB47hAq7B/DXv/51fA2RW0jQKK4sHz58YD6f8/XXX4+yBGEBJe0uum8BCAJu5eAXULBardhut2PbcmH7BVCLhZwAanGiER2zZCHEjUYkCTK2IjOStSWa8al1XNM0I+ARLbNolgU4CZCVYE0CiT/84Q/sdjuurq74+uuvRwY6y7JRDvH+/fuRGd1sNmPxogR28mfpUCrNieq65s2bN7x7947dbkdd19ze3rJer0fAJeBNLCunTinSgfZXv/oVwCijkI6U8uylO+l6vQYYs0QS8ITw2SdeWOnj8Yj47Qv7KnKfoih48eIFu91unDta65EhB8Z5IM4fwnBLXYW0mxf5yH6/59OnT7x9+3Z87s65MaAQe0MptJzqqqeuO58+fRqlQc45ttvtWBQswFvmlwTJIukSRhsY1yQwzv+rqys2m80oIRN5kOjtp0BY7k2CqdPpNPqWSzZFzghZw1J8KzUG8lqS0RO7Q1krTdOM9p0C/vu+Z7/fs1wuR8vW+XxO0zTUdT2SFNvt9hftof8MH/IFrh9Y372hrCq63QP39x/5+OO3PD1+Yjk3GDwqDLi+pW099WyBNRkmK6lms8QsOYheobQixHQYKqPgwjzlpWW1XiVLtkVq2hPRXK3nyb5M55xODu1bTOhRQw/9kePjT2jV07VHiI6qnlMVBSYvGQIELD56gjKpCdDdK2ZlTmYUMSrO3UA3BJTOAUWRaYq8ZBjORNXRD5Fqpjkdnjgd9qzWt1Sl4nza07YtdZXjB8hyS+sHvHfkecHheOb9+0+8frXBD4Fg9dg4KVyK59LmZZjNcrQ2aC3dyCJ9jPRdz/PzHmMybm5uL22oLUqlZilyOMUAfdenglFIXSmjRmvLMFxS/rZAxYy+i/TDxRe+6dgfDvTuUiCRZ8zrGbN5RVUVuKHHR4+OiTHVpIY451M7FpTIZq2USmCSlAFJRXiRp8dndtsz59bR9Vvqesa8LihsRWYis2VB/OoKT8+HT3tcOzCbLVis5ihqYojJoUND15x4ftqlA793/PrXv7mwAzV9n6z+5ouS25sZV5sNXddzPp0psgKlNPv9mWGIGFOwXK6w7UBRQpZ3OKc5nntOhwYfFM5lOA/PuxOLeYnNKqyJuN7hguOw31PVJUoZjNWUeU6+WNI2nv2x57Bv2R9birrC6AYqzXKRk2eW83lAV5q6WvBazy8scIA4EIOhLCp830MIGK0ugVfgcG64fz5Rr1/y6tXXLKqc4Hqa/kwXLc4/p4PQWMp6hlGRfKbJihmDCzzvDuj+iaJQ7A4NvbMsr16lA8YEfOvIygXdwz2hTTKpul4SnWc+m6OKJcdzB9qQmYy8KHk5e0VWlqi8YpNnnNrI4ZQOdRX1xZ0oI1pFOV9hyxnb04lyOSfqSIierm9QAXwMoAI6MziXGJSizCnL/HJwpbFoW3dhJovRsm6aYvTep/1HJ2mW1qBNwBh9kZilQmDCAATKLIFCH5O7S5bZcf6CxhqbnrO5HHR5QXMecO6XNX34l37NZjP6vufly5fjQf7+/Xv++te/8unTp7G99VQuIv7QAt7ksBOLPmGoJc1tjBl111mWjZZjAFdXVyOrLBIBAbV93/Pw8AAwMmJSUCfAChjlBiIJmLqNiF5XWDxhBwVICJO/2+3Y7/cjQBcdtLi7iEZWWMHD4cCPP/7I27dvvyg8nTYAEoArLiHyJXaDzjkeHx+x1vLixYuR2RSWTzTrU8mMsIEyvuJEM5VPTIHJbrcbGU1hRQXoClCfei7neT6+jzhwiIuHBF9T4C92fjLO046U4vDy61//GrE2FN20sOghhFFTfj6fub+/HwuBf//735Pn+dihVGvNarXi7u6Om5ubsWupsMHb7XZkraesu4A6KTQVIOu95/n5eWwNL8AxhMB2ux21x0IWrFarURsvX9JQajabjZ0uxaWlqqpxfkh9hUgnBAhOpSCSoRLLxPl8/oWtoASTwuQrpcYgxLnUyl6ArDQLkkY84pAkfv2S0ZD3mM/nFEXxhRypKIqxI61kP0QOJPcsdQzWJr/4qqrY7/es1+vxM0vwMC1IFWAuWSGZA9PslUiSRNIj2m7JLAjon64tebbyHAXUi52rSHLk/uFL60UJ0MQX/5dcvxiQYyx5lXNXVQznJT8OHback8+WzOqMl9cFN6sa7yOu77DKcD4ekvtJmZNnJWVd0bUDRlkgcjj2yd3AlFib5CJ936O0pihLfIyczmfmiwUPjw+pwNH3WBybxZr5zNKdTxzPWzLlOR33nJsjN7fXCZDPNkQzZwg5igKjc0IYAE9Z5tR1SSctjasZdW0xeUUgQ3lN1JFh2KIzjYuBtg/s9tvEFkRPXV5zco7jYctyXlCXlrLM6QdH5PJwraJ3LafjATXLyDIzMnnThSUbb/p7YuPkoYpf7vPzljz/PAGmkaFsgqJTkwkTwkXb7jSK7CKN8XRdz/Pz9iJZUSl7kWcsFnPmdYU1GqMVXMBJmSfpQIxx7AIphT+yeYu+re96jDWUZUF77uj6ln5wVFXNYlVTVhXGWNq2Y1bnaK3woWe+KHn16paoDPePW0LoyLOK29vXEFqU0qxWa4ay5HA80bT9WKglmsm6nvHVV2/JcoM1F0ZKJWlJOuAjZZGCvxAgz2u0geO5Zb9/4OnpyPE00PUB50mt321Gc4os5zkvbpbkmaZtOxQDVVVSVyXG6iTR6AaaU4dzChcAlWQ+59MRQoYhQ6uOssiSxZ5KUXaIAaPTc+/7FqUCZZm0ae4S4GkVyfKcV2++YrWYof1Ae3ymOx0IXUvwkdX1a6rVDZk1NKcT81nNZrWgOR55enygWnZ03lBlltooXq7nHHsF2mGzAptVDEazqWZ0IXJ4eI9Fc71e83zoOB+eceeezoUkaSkCszKnLkEZwxAdmbGUuaLpUzA4r9apQ6sfMKpmvlqCzvl0/8hdeUUxm0GIOOfJjMG7gM00Whn6zqE1WJPhXbLqFDbDGI33aZ5LEeHz8/PI5gHjARJCMkMxGozJCNFfNLA9g2soCkNR5Mzqiqbr0vMwhixL4Eurz04VylwcQ6L6ovPiv/ZL9quvvvqK8/nMH/7why8cN0QqIMBM5CriCCKHnchOgJHllMMvhDD+ruinT6cTy+Vy7Hoph/VqtWKxWIwNa5RS4+uJRaI07pjqSAWcSypfQJ3Y5k1lApLOFk1013UjcBUgJQ1qBGSJ/AQY2X0pOhQAKrZqci9yRkwbtsjvC/u+3yc7WGHX5ZJ9WXS8kkqX1PrUTQLSXi7BrXR/lPNGfKkleJoGSrIHT/X6AsZEfqS1Hj22hcE9n8+jLryu69FL3lpL0zSjtCmE1BTp7du3IzsrbOrLly/H57HZbEYwJ+C+ruvxNSXbMdX+yn3JmH8+P8PooS4+4g8PD2NhroyhFG8uFgtevnw56raBMUMiLHbXdV94x8slQSQwBoXC+AoQlXqAaWAj80WC16IoRrlLCIH9fj/604vzyXq9Hu95Pp9zdXXF4XAY6x6Evc6ybAxYZA0KgBXwfn9/PxaIbrfb0fZwWrw7PaMlUyLge2qROH2GWms+fPhAVVVfdMKUcZTnJvNqKkWbNumSZynz9vHxcQz6ZR7Iz00LNmUOix5dgnQhf6bF1dN1Jvcj60YKen/J9YsBuY+aECNFZqnma+5ef81/+E//N7b3b3l8/xdi+4gxOUN3xmYZy8WMD/dPhDBQljllVZDnBUpZCOBcAiHOpeYwxiSpxfncEiNkIeCaFhcCXd9hjAKfuutpeuJw5LTtaE4nmsMzfXeka1NKIS9q0Bm2mBF1idY1hpzeVugQCL5BFZZBGfrYQ1alYlOT48nI9RzvLH3X0vqBxg3MMoXNK7L8iDEdT8+fuL6ruF6tcb7Du5bgM/rO4C56wrL3xCeHc56mbZjVn610PrtFuC825sQeDZevfjyopAhDCqO8d1hrvihemerepItUVVX0nce7JJE57E+0XU/X9fSdIzM5KE1eZZRVwXxeE2OygazrOdZo3JAkKDH6VBA5SQ/JpiQLRjYz1zrsPBW8uu2Roq65vslxQV0K8QKDjzgf8SFZVWodubpZYvIcTEoDG6Uo8gytFD6SgrYhcDq1DIPn9vZFqh4PgbbrL5oyi4aJI4MU/8WRxVdaj5IhHwP3D4+8e/ceN2iMLckzi9IR5wLt0NA7z2675/Z6SVlXFFZR5vriNpM0x1lp6YeOYWjBKGZFSQjQtC2uG8gzqea/FCTZlBnJCxgGRwwm+XkTGYaeENJmr42+3EvP0A2Uecnt66/w7Zl9ltGjOZ469m1kvbnh7uqKzXrF0+MjMXiK3OLUjoWqyWY3rPdbhvMJfGS1XDI3BW1UHJuB592JrumpyxnzxTXdfsth+8hyPmfoAufDM8XSsJjP8a7Fuw5vAm07JOcaYzi0Z/omgE8BxGK5IM9yuvOJoqxYXd1SVAVO2VGPqdRFk2c0YFEmOZzEqC5SNpOKMAM4l4LEPM/G1syizZQNUVjMz84r6f+8l7G+IHQCSkfqWUFRJdmMG3rObYs1C/KLdCWt2QQQlusVfT/ggqfrW1brxS/ecP8lX9NCq8ViwVdffcV//a//lY8fP/Ljjz+O9mniuyyuH0IoSAt1ISqmxXxyUEqhqLBXIgcQgDdluqXD4vGYZH5igyjMtRT0CdMMn1PWU6u8KbCcFoOJtEVS3PIz8nP39/e8evWKzWYzggmxFRRduLD1IgMRMmXaTVHYtambg7DXwnwXRcH19fXIEgtAE2AhwEE+51S3LAWqco+73W4cKxlXCYAkSBGGUVhWkdaItnd6Rshri22e3NswDGOr+cfHxzFok/GWZyz7uAAwkbEYY0ZJwLTgTl5bXDJevnw5BoJTPf20iFXuWVjYaXGfjPmnT5/4/vvvx+8JWy5ylr7veX5+HoM9YYJFRy8Elvys1nqUVQhzK5pzuSd5HylUFOmRzG8ZK5F3yZwsioKvvvpqLJoU1vx8PnN9fT367N/f3486bLkWi8WYqfDej7IR59zYCEgkGev1mv1+PzbEEutOCYYFi0hwJRp98UKXgEeyENNi47quR8As57aw18AXDLVIfAQES0ArGEWCcglgpk2OpkXPok+XsZ3uCyLhkWzSlCiQTJJIlkT6I1kJqXP5p65fzpBrTQjxwowprl68QmuoZ3Oc6/npTw/84Y/fUWaGuxcv6buOrjkRvSO/iPubtgVU8gkOnizPybKc5XJ90Sb1tF2HxjIMnmgUgUgIjtmsQKmIHzyzwmBt4OnxE6fDgcPumbLIuL7eUFQ10eRoW9B0DkfHbLmkLGp0pRmipzkFNIH23HA87AFNXnp0HsAo8soyYKCYkRlFG0HnoGwBeovyERMdcXAsZwXYiqq0QGoVHr1nuZjTDZ4yN5cuYHOKIh8fvmzg0+rglKL8rImSTU/+K+mz9PC7cYLKJRsOMAL5ruvo+gFtNDp4Io6yyrBZmqzL1RIXIv3QsVzNKYoM5zqc46Ld9amQjs8HUfLW7seoWTbfaXpVJnuMgawouLm5YTaH3b7hfE5fSkU2q0VyIYkebTV1XoDS7PYlWnvyXNN2Z2ZVQVnUnJueEBRX1y8IwbPZrIkoiNKy2qCIDG4gRmml68nzz01C0iFWobVFa4PSMX1GFdE6EsJAZnNubq5pu47n7Q4XBg6HHYfjkrpOvthKF6lmAGjbLslNlMbmJU3bYVVkNiuZNznzeU5uDJqANfqS/jR0TYsPPmU+AvgAwUe8T8A8eH9hgTXWFgSlaYeOwXvm9ZKXqw3ZbEUXLMtXGVdXN6PtmC6v6NozXdeSU5Mv7lDA9vEB413KfmQZu9ORWimqbkiF1x6CizT7gdura3bDmeh66jzjfHxksV6RG0fUYEPEmsh6WWN04HF7TzcorCkprcH3Z5rjkWxzjS1rbKnBlng01zcvcCrDtQ4XBozJcd5jdWKpfLAYk2FNcjjp2p4YwanUrTYFtMMXzRwEyE3t2mT9qBBxLtK2jhAiNlNEFciLjLsXtxRFxtD1o+2hUorgHd3gIPrxe9Za2m7AZBlVVTCffz7M/jVfUyBtreXVq1ejTtk5xz/8wz/wP/7H/yDPc16/fj26fcjeBozNZgRYSRpb2D5hbgUsCRMsEhQBWRKcffr0if1+z/PzM2VZcnt7OzqCiJYaYL1ej24bP5c+bLfbkY0UpxC5XwksBMALaJTxcM6NoFWymEJcyF5eliXr9XrUD8ueLo4bPz8jgC/S6hKQyDiKJlqA5c/PCEnZi/uLAO/pPi7ARQonBaRsNhvKshzvSe5Bfg8+a4aFaf/5GTEtwpu6Vtzd3bFYLNjtdl9kNUSeJJ9bGHNpDiXyB2FZBejd3d2NBbiyD0wLV6dSD3l+Yospkgy596nVn/w5yzJevHgx+uc750a5kkhUJMMtc1sy45IZAMYOrqLnlz1G3EWmtTACyGUOyfMXuZDMPwGDYm8pTYhev37N7e3t6EkuQaGsA6mJeHh4GN9LsgPAGFQLmbjb7bi9vf0iiD0cDl8Uzcq4C+stDPXUXed4PHJ1dTWy0DKvXrx4gVJqBNASdP+8gFLm+FTfPnVgkS6i02BG1rI816ktp/ysBCpFUYz2i1MHGVnjU536lCmXgFfG9Z+6fjlDHtIG42Ogcx4PeGUZVE4+X1PMr/nhz3+iyjWb61uG/YEYPf3QXoqmWgIaZTR+SLZoVVVRlTPcpWjR+4hShvl8gc0zojG0XZcOQ5V0oDEOVGWOIrKoDO2+Z7Oac3N7S1FXdC6CLcGWnJpAXtUQU9GWJzUumeeKwljOw4lcR/Iio3c9vld4FehCRl6tKas5tJD1C5SJdH0kz1aEYDCZTaDlsOP29QJ8hybDakVRZNS2ohscv/nNV1xt1hgdCD5gKzsyPZJKEYZGimGkCl3SOlOgISk374svCpQkXSYLWKJwpRT90FLkBc43lFXSz1Z1yenUUJSa2hYcTg5UYvOVguVyTp5ZvBvIbAkxEkOyOOqHgcxmI3NRFPm4WGSzghRYFGXGbEh+0s/bJ/b7IzEqynIGKlks/vHP32F04Le//U1ymDED80VF359TY6Tg8F5js5y2Swu/rBekikHDdnugbxuy3FBXJVluUVGjMZcD6bPNkaR802LMOLcNwTsWy5qXr27oOocbIsYWXF3XOJdjbLJqrOucvmvp2obmcKY9l7x8cQtRsX0+4qPH5gUYxTBElHKX6H+OigmIa5XsPZVO/tUhOELwBK/wPm0MKia9syKx/BFNCEmioVRGVIGm7wmqx3RQzNf87b+/Sl72zhNMDkSyWYEp51Qx4F3Sn6sQMeUG7xyzekbTnuifPtH3LSbvKWxB9D4VrJZwzgfMsMcQMLkmDoE4HPBtRGvIskiZGerc0nUNp90T2/2ZX/36b1G9Z3860NmMYVaxvrolyzXdEMiKkqYbCAryvMQqjRsCQ++oZyXnZkApjVIGMJyODeJDbo0hz0va9oTzA4tiPrJzIgebuq3I9/Pccj4d0arC2EjEY3RqzjW7NBvxLoEDWxTkmU4uPE2P5nPjFdmEbZ5dDsL4f9ov/zVewlQBI2CTS+Qh//iP/0hRFNze3rLb7YDP6XdJ8Qsr7L0fZSIC3uT5SutscVz5uYZW5Bmz2WzUc4u2XfZRa+3ISsdLgTZ87hYphIkw38KEAl/cmwAaYcqmtmziT/727duRvRcdvPz873//e25ubkZQIADjf3VGSMAijKmwwwKe5H4lSBCg0HXdWHwm+l5psiSyGwHaMnbixCH3Kl7jwu5K8xjZ+wWQC8CfejtLIyc5I2QshHkX9vf5+XlsL1/X9ch6/sM//ANaa/7u7/5ufI3lcjlmWwQgihf49HMAI6srsigBTjJfp6BOLB5F0iKAeL1e8/btW9qLzDXLUgMiAZfn85nZbDYCevFOf/36NZCcNuR1pw4eVVWNTaQk6Jf7k/N06rgjYHVa4CxzUn5fngMwOqb8p//0n0YmVz7rbDYbC3+nHv8iR5nP56Me/+ft7mOMYxAkBNzU6m8qURJf/bZtR5vRv/3bvx2bJJ3PZ+bz+ahTFxAtn2GaJRCPclkLEgBL0CBSGMFC4sgj9y3rRvaL6dlRFMWYdZE5LUz7VAVQ1/W4LmSOyB4oRc5CDMm8/yXXLwbkMX7WOrkYcd7jdUYxW3Lz8msYBu7fvcf3R56PPW4407VnZrOa9WZF1Bm9i5zOSSdV5MXF9zry4eM9MSTtbH3RMx3PJ5zzWGDoWnSwqSFN19Kdj+Q2JzeKxbzi6uaWvKzofaTMLcrWtC7Sdgc8Tere+PiItTCrLGWZ4Y0mDo4MR+g9bdPjsHQho9pYFlc35LXl1EWC1qgsZ/A9StWAozkf6PefyAvL669e0rtAc1bkaFzQFOWMly9vcd4zDD0aD+GzlZY8sCmb8jmKz7DWjX+fbsCftU9+/H1JwYhrgRw6x+OBqi7Jc00/nHG+papmhBghOmwW6X1LYRXLVY1SMRVi+oAm2Sgm73FF75KNIYDWjBotpSDPk8XdtIBB0kzGFoSY7OO2uz3Pz3uKsmK5mDOb1SiVsT80HI47TFaOHdBmVUZbJalKUWQYnXE4nCmrms6B856isHSDQwF5UVFVqdGRNTYFcpfFaYydpJUToxVDpOlazs0Jf7G8q+qMokgyiRAiwR9xvmOxUFxfJzce7wesjugio2vO9JduqKfGcWocPjYUVY61Gm0NwStmVY2OnhgcLnQorQkxFbYRNRqF0Raip+s7iKnTpHeetkudPLshopTm6nrNcp58v3sHeWZoe3kmM/pzy+Bjasrl0uYxuMT65nmJzgxmrhk6T6gXlLMrNvkC159xzZ5m98zDhx/pjnvi0BHCwHxesX9+IMSeqqzx/YHZrEi9BoYG3wb6LCS9fPTsHz9yvtoQdUZtC1YLw9Du6LqSenbN6uqKzeaaIUTaLhWDodVlLuX4GIhAluUED6dTy/NTcjGoqxl5bi81AClzkGVm1GzK4aS1BhXouhbvEwturSFET1Fo8pAyQEWZc3OzQWuTGsh0kZnJcWGgrmYs5xU719Oej2y3W8QO0WiNGxxt23E+98CrX7qV/ou9prplAYiS6n379i3OOX744YcRHEph52KxGLv4DcMwylCEHQ8h8O7du1GqsVgsuLm5GXW4WutRYwuMKeppgdjt7e0IHOT7AhqkcO3Tp0/j/inMpgANSVELI3l1dcXNzc3IyMoBLnu7gGdxO/nmm29GiY2MT1VVvHnz5gugL4BIXkfAr4AvYf9Epjg9I0SbPpU2CPiSvXm3240FePIcpAhPXGekaA0+W0RamxrliJ5ZAqSpK87PvyeAGhjBvgCevu9HNnrKuD49PY22javVanRpEeY5z3N+97vfjcBa/LYlsyDFkRIQyvvKPYjkYJodkfngfWoAJZloKeQVhxhgBGIyvgLwlsslNzc3I2iTexJ9vAR8IreS+SVgUJrRyGv+vIhRAKQUi04DPgG1Mv63t7csl8sRwMv6kOBHnpW8l5yNYkkpNTmSJRDHFAk0ttstP/300xfSqMViwePj4xh8SRdSCbZEaiaf6/7+nru7uxHES2dTKfQWWY1k/UUfD5+70QojLUXKDw8PY5A87eQpbLasgWn9htQ2yH4ja0oCAJH93t3dYYxht9uNhd0CzCX4PZ1OPD09jZhOiAXx1P8l1y8G5JqQpAFEtFYQFdlFu5ZfX5Nry08//MhPf/0Df/ruHZtlAbHnzdu3rFYr9qcWokNdWK7VekXfDdw/bvEe6npOlpdc31xRz2e0XUfwPbNZhXM9ru9w0aEvD1RFz/nSMa/rO6LJcGii1hBgezix2x/ZbAoW8yUqzIm+IbcBE1r6tuW4O3BuO3qv6ILBzlbk8w3rqxXzeY3JS+ra4YeAioHTYQ/dGRt6+q7hdN7St3OiayiyGUEpYghoY/HBkWeXxg4+kmeJ1Vd8ZnmmPphKKbRSaPO5+5OkF0Wrl9IgHd4HYkzapBhCilLrEuLnzTK/aHetNRe9vqfQJWVZYGwCtzIZnfOUVYlzHcENZJkhRkfTdATnLkylxppkx2gu7FIKCNTlMDXjQSiFqT44fND4mCwt80sQNvhIVIbzuUWpyM3NK9re8+O7e4pixqu7G+azGTp62ou0RxsLWtP2jr4LgKcoSiIObSJ5lgpqwWOswQaDQrpxKbQ2k4BH07uBtmlpzm1ypQ+pcLAdLmOVZSjlyI1nvZyxXl/hBs/2qeF82PH61S1luSYGOBwbHrdnjqcBtKV/PFBXOetVjZ+XzCvLYl6igmF/bOiaBqtm2LLAVomtcsPFXmxw1HXF4FyyjOxaPnz4xOAtWV5wODZcbzasNkle1HQuAVJtOBwbiiKnKiuUUZxPDefTkTh42t7T9mdAcTi3NH3g0+6cmF/nKK2m0EtcOBNUjkNxPO6xyrHarIAuuRfNK5w21LliNS/pzwP9YUdrUkOt9WLBD6Hn4w9/5je//zuyKgfVYq2iLiLGRDbXN+TFjGZ/QOkErvthAKXQmnHzyrKCaDTb5zPnU0NyCwpk2efANgQ/ZjwkdS/g0GhNJI4AJgRHUV6CtixlTqxVzBczjqcju+0BEwu07gg42syynFfUVUVmNURHEG1zDPg+EEKk79z/Ysf8131N2UaxbTPG8N133/HnP/+ZP/7xj2NdzK9+9Ss2m83ouCC/J10kpXhPgOPd3d2oV5XvC6id6j6loEpcVuSe5KAWpu7m5oblcvkFyxpjskncbrdjoBcue60weZKRlD0Xkg+zuDIImBOWTvZ7CVREDiDMvxQrA18AaZHOCNAVhnh6Roh9o5Azkn6X7IEUForsRwpRpbhP2FEJRgTcCigVxnkqnwkhjMHTFIgLayjFqsJmC+svz1nucRq8CSgSUCqA++XLl/R9z/fff09Zlrx69WoszhQ2XiQSwvwL8J2ynJINkbETzbAwz3KJlEKe4bSWQDIXU79t8c0WNx/Jisj7S8GtyHCcc6MGW4p9RZsvhcdT3b4AbsmUS9ZIPutPP/00ylr2+/2oERephsyj/X4/BibGJBJCCj4FcCulOB6PtG3L09PTeL/T+SvAOpEU+gtZkPjsi93k+Xwe30c+c4yR7777jn/37/4dVVWNwYYEVhJAy+uPBfWXOSb3LHNNxlaCWJE4TeeYuClNnX0k2zBdT3IP03m8Wq3GTq+yh8BnydrUjWla/yKZBHnPf+r6xYBcEQBF5LMWS1uLVQaL4vb1b/gP/1tPvVjy//5//t9RGeQmYzafEXyg7xKQzHM7LoTT6czheODlizcsFkuc6y9sa3r458OBs+sJl3byeWY5n04o4HQ6M3QDi/WaajYnK2fsmz61rS+Ts4i6RJV1mVPna8KQgW9Q0XNy8NP2ke3hzOLqjvX1NdX6lmhrrClozh20ELwiRkPfd0kyE3qgx+ae3Hma05bn+4+sbl9jywo3DIC+VJGFBGLKHKK4k3xmOSTqHobUbr64FHPIBjDdoCWlJoAjyz4Xf7jLJhnDZzN+KTbRWlGUGZkt0CFyPnUo4/nTH/+Cc4H5csV8vsD7wGJRUs0ryqrAGsvxcMQHT2ZUCigUQAI6YrWVNt0Ba+NolZTneXKpsJbB+YvrjKasKhYLz+5wYrvdoVWg60oWfsF6c8dxv+PpaQ/es6gLNus5V3lOULDbH2k6R9t19H0gyzQ+cul4mjbUrgup+PdyiJRFYkOInzuEdd3nDl4pEq5xwTAMPXmmyUyFsQqtIj50eK+42iy4vlrx7od3zKsM51qa84HF/AWnZsAF8BgOxzOn5kRUcCw6vPPoEDExoy4MdZUneVTXXQqcDV3r6FoHJLZXa0Nuc2KIWGMo8oyirGCwOBcvQdAJH0JidpWibTrm1zPKsuB0PBD8cNG2a6q6xmYZTdPS98khJMstTnmc72maHt8FnpuW6DpeXF/xd/+Xl+x273n/wx+J7QHTHbmxV/TtMbmf5DmPTx/ZP3yk0JE8RlSRQ4jUZUGZWZ7vP3D9n/+v2NJy9j3OaHy7hfmcvncE5fExQkz6f6tN8gi/2EwVRYl3EVD0vafvHcPgcT6BYFBYq+mHxETO6h74rKfUWifWnSQPSvKmQFFaht4xn825vllTnHOyzPD8dKJtBzJtCYcT1TzHmCQZyzUYXdO2Z7oLq+t8wNgc71P3z3+7vrzkjJj6bn/99df8t//231gul/wf/8f/MR52i8ViBIpTrawAg/1+z5s3b1itViPLKvuMWLJJul40rJBS2H3fs16vR4tDYSyF5RQQJwWl0yI/sRLc7/cjYydt4K21nE6nEfwCIwiU802kh8fjcez0KRaBAvplrAQoyt+noAc+d3MUll9YW8lUCsicnhGiiRYWV0CCAMlp8b+k38U4QGvN//gf/wPnUitwKchcLpdj91Fr7Tj+wkBKMCRSIin2FNB8f3//hSuNFAvKniyWf7vdbgSDIoG4urpit9vx8PAwBmLSdAZgu92Ochl5P/m8Iu0QH3ZhrMV+USz0BNCLvlzOYQFXEjCINET2m+vra25vb/nuu++YzWY45zgejyMglTkiYBsYZTzyPgK+Raoh8jipmwC+CMamxcbCREvwKUHn3d3dGNC9ePFitAWcylukgdHpdBp/V2RXUmwswYlzjhcvXvCf//N/5unpiW+//XZ8Pi9fvhzBf57nY2MwCXQkOJHahY8fP/K//+//+5jFkLmbjC36L+Svsp8Iuy9dO2VcpWZCAkb5PZl74iYDfHFGyJe8hzyXvu+Zz+fc3d1xPB7HuStNlXa73ejQJEy6OKtINkDWhRCwv+T65UWdigvDC4HkzRsUDNHho0JlOa+/+S31rML1DZ9++AOZ7jicG8L9R4zNkx8x6QA7Ho4cDkfms88WSkolCURwkcVshvYutasfeiKGdkid8mIIHPZH8rxIVm15nu5PqVS8qDW3tzfcXN2mTUolgKrxRCLeOYYh2SvOV0tef/0N1fIG8jlnp2kahz+eCLREYmKNlcdkAasgBzIg0xZCz/GwJa8X1FlNNBpNxIcBokURk5WbT5Z4XhgcBc67y2GusFnyOe26Pv3s5eCX4g5gZNKFFZDNBAVaGdqhI8YwaveUShZvRucEP5DbjK5xuN4xDMlb2VwYSqNTO/AsNwTvcCG1C9daYZS6fD+gLkDncNxjs5xZXXM8pg3ncDxjjWG5qokh0A1dkjFcNugiL1guFVle0DYNuVWs13N61xOGZEV4PrfMiozCpiY1VV1j85z94cTxdKJtPYMLzOepGCWzFWWekWmD1pGhb/HaE3zyWTdKU1cztNJ0rrscFhm9c5e26YbjsaftUgdHow1lnlFWOWWp6fsTRpGKGkuLVjkh5MQYLkGaQmeWqp4R1JnDqcPaDGsy+i4km89lxTB4Oj2gosZmBf3gOW3PnA9SeAVaO4yB4+mE9z1KQV2XXF9fcf/YsNtuGYaeofM0547gFdc3qaj13fsP3N5cMZ/POBz2NPsjeZ5R1jWL5YKiLHFuQKE5NSdqF4hLzfk4oGLG48dH7u8/8e2PD5C/5PU3f0s+m3F6+JH24Sf6I6PbTp5ZXHPksNtTasO8LKlnNVlZUuY5X71+xfdDy/3Hd+yPZ+rNLbOrF/RecfP614SLxaHShhgCIXgUCqUNIIdekYK53nE8HXEhzb0EZCCG5OBSUY8HsPdDkudcANPn7oOGED5X1Lfdmev5hpvrEpOZy4EZUiGt7wlYrqoly8WMIrecu8/s5GxWc27O+JBYce8c8Ms2239Nl4CMaWG6MYbf/va3o1ziu+++G50y3r9//4UOWdi87XbLYrEYM4QCnqauHVN/bAGq8n0BB8IQAuPh+fLlS25vb0fANtW8C0g05rP/9Xq9Hl0ypHBQ7ld+dlrUOS0Wk0YrIgkAxnGRDI67BKNTxnkqaxQfcpGWiMOQSFAEZMoZ0bbtKNGQ+StrRYIFGQ8BO5LWn8pShKEU8CHZqGkRm4C4qWRnv9+P9ybyBpFVimZ6anEpry8FtvLZNpvNF24dp9NpLLBt23Yc191ux+FwGAGi6IyloHYq+ZDnIsWsMk7CZIrcQJ6xvK7MTWHbpXmQyEMEVMvvSZAmPuZKqVEWM2Xh5TOKhErO8KenJ/b7/ThHZD4IqFYqebzf3t5yf3/P09PTWD8m2YsXL14QY+T777/n5cuXY+GsrA8JgiRAAEZAKZ1QAd69e8fHjx/59ttvybJsXMufPn3i4eFhtDCVzE7TNDw/P48yMAmKy7Lk66+/pu973r17N9Z4XF9fMwwDb9++/UJuJQG3jL3otGUOi/RK5p4EExKMiZWqBPxiqCHOL1JcKutVMjni/JJl2TiW03up63p0jZFiVwlwZH+QveSXXr+cIY8gCsGketWXhhlcxCwek2fMFhu++fXf49uW4/YH7h8e6YaWu9sXaAXaJvajaVuMsSl9geLcnBNwHQZ8HLAqww09++0T8/mMoR84nFJBYFFV1Is18/kCnWUcTkdMVlDVC0qVcW56VPTMyhrwEDwuJgbN+YDShqyoePnqFdlsQb3cMCiLCxGrNWVV0zaBrhswylOUitxE1BDJokUHRa8sPl/wuD1SWIMOjqE7kc8y8lITcfRdkxqT2LTZGaXohoHqUmwSUalDqI+Xr9Q2Hj5ryoWJEO1TYsBjSsXriNGKLKvoe8d8vmAY0kZalDl5bi+MQbyk+qEqa05Nii4X8wXL1ZKyLKhnBXmmko65+5xeUUoRYhw7FxZlckE5Nw1FobDW07Y9KVzL8SFibWL2n593+BDwXhNjQBFxQ0dVZJRFTWYCm01Fli84nTqOR0/XOnxwdIPh1HSU9UChbQq+tMa7jq4b0MpT5pp5bQkhkuUFMXr6tmd/OGCzDJs5lErjq1D0zlGVc1CWfgicO8X+2HI8pvs3JhJ8h3M91kK9WVNXhjAMlHmGvdrg+h6TzUCnJlBdkxw4FB6jI0WR0fWBGHVqIa8vchsMwxCIZLRNy/PzjtO5xw1grcFmCmugyDUBR12V1FWe5oPOsbakyAv+8R//wv75kaKqOJ09mIKvvnnBdvfAx4dHXt3dMpsvCfGEtYaqrJnVM8rCj/rRlE3pAY23nrKoyNQt3jl++PHAX777QNSW2+vXlDZnR+SkFSbL0H7AGrjbrMkjROcYnOPh+YlyUTNfrLl7ecvD4z3ff/8DUVvuvv49b7/+G2x9xauvfktPTjc4Bj9QZAXhsrNoD3menldUmiHAqek4Nh2BSFZmZIUlBsf5nLrRZrbEmhytFP2lA3B1OVgEeGidE10KxrlIxtr2jMlqovec9ge6c5PkOyqSZ1AVYLRnv3ukbRpmswqlFXlRoAyYzHFuUtbMDf4Xb7j/Gi4BqVOGVy5pzvL73/9+TIl/+vRptKcT8BdCGMHU1dXVKD8RUCIMsoAWKfATOYRoOwXIHw4H8jwfWTJhKYXUEIApzLjU7bx9+5bZbDZ2XZRDWw5dSaNLwx5hawUcVFXF09PTyGi2bTsCE/jsWiH/Lp9pWpA4ddIQhhA+S29E4yvabEnDy88ISGnbdhwnKZoT4CsgVsZOQO9yuRylD8Kkyn3Lvcn4GWNGbbgER6JrF4ZXMsMiKRC2W8YfGAtMpahSWHBpxiNZCWE+xXVlahMp5FVRFKPnumRFmqYZwehUMgSMoFqegxRnStZFAhfJDl9fX3/R8On29nY8p+WepvNVtOVTHfjUyk+ebdM0o2WnZBkkAyKZc7HhEwZWdM///b//d56ensbGQsYYfvOb34xdYV+/fp3qqC7AX+QyMlenRZvCxktA473n22+/5Y9//OOoV5cx/Dlzf3NzM46pZJtkTb5584ZPnz7x5z//Ga01v/71r/ntb39LXdf85je/GQG5ZMLkEq33VG4kFooC1MuJ0mAqofq5ll4sCae1KlMDDQkIJaCW+g/Jpkjx5tPTE+fzeZTqTCUvonaQ5/pPXb+cIUddFOTpEm1m+lMq/kMpbFFz9+pXDOcTP9Kxfbyk9YJnMVtSljXb3R4XYHNzQz2f40MYmwUppVBD4PHhA+fDjuPhma5vOZ3OBBQ3t3cslhvq2Ywsz2mHluZ0onOOsp6RW8VpaPAhsj8fQUUWizlDd6RtzsldYlanwk4/oOLA9uEDrVOYckGgpDI5tS3IQo+KPXroiO0Z5RpcfwJ/ZmiPhKg47Q5s7+8xNiO2Z+Y48sKgbEXwkBULwBJcT5GVqEqjbcapafARdOSiX87Js4K27YjRf2G3BHxRaZ1lluzSxdAYg80z+sGNLGCWWbJcDoeIQuNcsr+r6zmKBOSXy5rMRtp2T2ZrgtdondqUa23w3uH9RZt50V4/b5/ph6TDanvP09MBN3hMllPYIvlLezBaY0xO8I6+HTieO06njubckGU9m6s5t3crFrMMiCxnBed5zvncEHwCT1lRYmzqXpqZ5IQxuBSIhODwQ4sfcpzJaAhYY5jPV/S9R1tDUVXEi9d9DBFtLCFqzseOx92Z/SF10RyG1CreqECewWo1Z7NZMasKnIN4Kf4jJCvOsipRGtquR8VIjIGqzCgLjcLjfUfbK4pyznw5x2QWZRTGaAyKvj/xvD2z3TdElbISVZlTFZoQPZWy2KwgAF3fY61msy5ZLZe4wfPtX97z+LSnGyJDCPTR8/L1Fc1x4N3HT3z95g13dy94enridGq4BOqEAEqZMUBZzhfMy0jfO7rWs1xVbE5rjscTT/dnVNCsZnNWd7+ini04PX5gf/8Tx+MOqwxlkTFoRV5WdNFz7FqujKFcrlhcXbPbHcirJavrt5SzO/LZDdrOaI9nBp827bIucd4TAgQDuc5p2p6uDxzPHdtDw6nrya3G5paoAt47gu+pqpJ5PaMsc9r2TAwBoz/bVxFTNqxrU62CthpjsrGwyfkmZVbQpE62ljy3zOcZRQ6uP3I+pU15cIa27VIQUZZUs5reeWxmCf7fAPn0mgLx6Z+n7JY0D/rTn/7Ep0+fxrS+dOh7fn4eU+7SqGTaa8F7z8ePH9nv96Nu+3g8EmPk5cuXo8xCrF+FvRN/aAFJAsxXq9VYVCbsqsyTGCMfP34ciwSBLwrA5MAVGZ+kzUVrvN1ux4JRAQ4CNgUACygUtj7LspFlk71fPKlFF/6/OiOm9UnCIArgF/ZdwJsAOAHVUiAo9pHGmLFVuARH8voiNxEwLQDTGMPj4+MIwruu4+Hh4YuiwfriaCRspOjgz+czx+NxLMi9ubkZGd0YI8vlcmz0JABa3HBkzObz+cjsT0Hd9DNIm3NhbmXcJNgSRvzp6WksJp0GQWIteXNzM8pThIWXcRHbw2ngUlXVOH9kflRVNer4JRhVSo3FreJEJLIUcUQRW0pgLIzdbDas12uGYeAPf/jDKA+Sz/bVV1+x3+/58ccf+dWvfsXr16/59OkTh8NhBKIyBjK+q9VqzMrIfLi+vh6bCMWYWsW/evWKxWLB/f09Hz9+ZLfbjeBUmG55zlJTcH19zfPzM3Vdc3t7O9o/WmvZ7XZ47794ptP7k7l1PB5H33xZH7I/SIGyFJeKlaHci8xZeRbwuR+MBHHT2gJxUCqKYqwfkYZestam9RZS2yKZo19y/TMA+eWKE0cQIkpdmF40EMnLivLmJaE9cti/Y7f9idNpS3tumJWpm9p2d+D6xR3rqw0hKFyfNLRd15AZiMHxcP+Rw27LrK7RJiMvCrKipJ7NKPKcsipT505yFCeMUvihw/cOXI/rh8vE78Gf6LuOMDii96iQWpw77Tnsdzxvj0RdcHX7BsyAP39MTWuGAaMCse/ozntce8Y3Z4b2TNeccMHztEuV31rFRO/RU+aWaBbszoH1xrJYrFA6J2EElYolfRT57Bdp2GHo0VqNKUJJkUjUmTb/VHg4DKl73H5/oMirBLYVOJ909/Ja3vuL73hGnhc4t6MoUldOYxRNG1CaS9SZJZmL0ZfDYEDr1OmwLAv2+xPH45mymBOCYrt/5PHhGZvl3N3ekuX2opHOsXmBc4ambXnanmi75NPtfaTvU0FoXVXE6FFRU1clw3Kemg9ZMwYFwWv6oSMGj9ZQVyWKQGbT5mmNIYaIsoq6mtHVPSpLmY6h62h9i9ap+dTp1HK/PbA7dpxPA4dzR/ABbSCzKd202aypZxVJk+zwrkOrS/o9eHzwBOc5NwlQ9/1AVc6SF33oCKFDm4L5skKbZP1ptMGoHJOZJP8JgdOpwYWBLLO43uGrjKpMhbNNM6CUT25CZUbTdIDn1csbFJa8/Mi7j5/Y//BEOxyYzf8DdVnh+zPv3r27eN8vL04Pu1Gjut/vCSEwn80py4K+HwjRU1YZ2ixxfqAf0kZXVoZZNedmc0d+teE0n0PwPA09u/2RH959oKwqvvrm15Ta0AV43PfMyorFzSt0taGab5ht7sjma7JqwRBTgydQ2DwdQvECjrQ2aJt84du+Z7s78bw90TQds+sV8/ksBWNxIJLmQpZZjNXsD/uR3ZIDVg5Q731ivrXGh0BmU/fW46lF6SwFgNFTVTNmdc18UVDXJW1zxLn+s3uFc4TTmag01qZgSSmN+TcJOfCly8r/N1AOiZV+8eLFmNJ+fHwcm5bMZjM+fPjA09MTr1694vb29ovnKSlm7z0fPnzg+fl5lDyKtZow0MLqQdKTi1uKtHmfHshS7CVgelrcJY1PlFK8fPlytAMUsCeFZFIIJ/7qwuQ+PT2NemAB8cLmnk4nbm5uWK1WX2hmp9p0+Mx0S/GqAEM5I6adA2OMowZaWtBvt9vRug0+62iFUZeAR5hpuefVajXqwWUtTSUsUz2ujFnf9+z3+3EtPjw88PHjR/I8H7tYCjM5LbSTrqByFgqbKaBZKTWy0dOARACx6I+TtGz2xXhL1kWyGU3TjBKgqbe9tXZ07BDbQmnGJ2Mu2nUZSwnCZJ7LmEjAJ8400q1SAiyRQ8n8keBKGgpJYCDSC3HQkSBEXluemwS10sm0qip+/PFHDofD6E4kzjbff//9COCl2Y84vTw/PxNC+OLnpeBX5uB3333H4ZCcr2azGVdXV6O7iwRCu92Ov/71r8xmM373u9+NYyUg/O7u7ossjHRbnkrBJAiXcZUgToKd7XZ7IZ5OY7H3dB1JliTLMj58+DCeERJEC7MvWTGZ0zK/pR+BBOYCtJfL5ejyIwBcMk9T20SZz/9/tz1MrsiX5naTv18gJSg1FmnlxpBVFbPFhtX6hvMhpaGfn5942u6J2jCb11RlwfF04nzaUxc5uYXgep4ePvL09EBmLDbLyYuSq+sb8rKk7TpO5+T5rJUhL3OyS8RPP3A+HyjygqY7cTwcObdHqipn6Hvm9QzlI/uHA2ejUvOitsU4z3xZcDuzdG4gDE9sn5/Y7rbkxkKInLZ7hq5HR9LvRLC55WqzoT0fePfXPdd3N/z18R0P7z9y+9Xf0bJgCE/obEZhdKr1VAaIhBg4HI8UhUMbhQ8+aZJjQLoWymYz3YCTZY+iKiuMSWm1w/7AUAa0tqMkYXAd3ktldAK3wr6I5Za1qV24sekASIeETZ7jMRWgap2xWMwu39PM6hXOGR4fd3z8cM/T0zOpIHTBLrMYC7NZAUQGp2gGxbGD/WmgHwIhBggO5zvqyjArMuo6R2suEzcjzzKMsZzOZ9zQUVcZs7qgbRu0SpaZRWHJrCKzCcCWeXK0GYY+1QcEjTVJb2xNhtaW3gUenp55fDri0AwuEAnYLFLkyaVlNq8oipQ9afoeN7RolZr02MwSEM9WjzUGjMUaDyokFtcG6tLw4nbFZlVD7AgxEgAfB1QIZLkmLzTakBxQ3AC+R8Uc14MhYnXSlccsEkgaxKYdyIvA7YsZq6tfkRWev/74E48Pn/jjP/6R//Dv/o7FbMHpsOX+/hPDMFxswxRd16IVlwNlhjWavm8vzLBhuZzhfKCqCkJwPD09E4Kj6XvOrcHWBeurVwxty+l8pnSR6nDi3DY0QbO5eYmyJVhLtqipnIWy5fblW9Yv31CtrlGm5twNaQ3omFxzun5kn5TRRAIheprmzH7fsd81oCJVWVPVFcE1KUhUybFFm3j52aRXXS6XI6gSZkORrDC1VoQhHZRFlWFbl+YjgTzLkod9ZsisITMGZz+3Ax/65Iow9A7nAm7oGHqPUppfWK/zL/5Snw+HL66fy1aEpRIN5tXV1agBfXh4GMGvsOXi0DBtE/7x40fu7+9H1lWa/oimVzS20xSyyAdOp9Oo+5QiOym2FCcSKUYTsOZ9auQjhaXipjGVo0hRITBKNITpPR6P/PnPf+bly5fc39/z7t07fvWrX40MuKTVZc4KG3g8HkcLNzkLREog2lfR0cpnnLZVFxnJdrsdnTVkDCUwmTqUiCbcez+2e58CQAkkpjUCAizlvoUZ/PTpEz/99NMoSxFNrhTySjAkOmopwpXPLk4kAvoEMIl8Q8CzZD7m8/koVRHpgjCeErDJeEiGZOo1LXKX+/t77u/vR9AmgF6CPOkuKmMofuLAGABMNdBTH3kZ67quxw6iU6mE/HlquyfyCQH5EqjIfJHflQZJeZ7z6tWrUerz3Xff8fHjR/77f//v/Mf/+B9ZLBbs93s+fPiAWDYK6yxzQMZu6tomwch0jYgMS7T8t7e3Y1GjFOJKMagEs3meOvmKNOnNmze8fv16zMZMtfqi65926pR5Jrai0iBK5okUZk/B++l0YrvdIpaW08yJXFOXHsFI07k+9bCX+SeSHxmr2Ww2zi/ZJwT0/5Lrn+FDHr/YcD/LVS6bLKmTiYsBFSM6L1he3dGetxRZRnAN53NLURYsVmvyPKNpT3RdQ9McKfMl/fnE0J64//QOCLx5+watLYNzlFXSmh6PB1IDFTUWw2VacT4c8P4idA8eoyJuaPHDmXyRQ4DmfMSgyYzCKkuZWVTIKPOS+XxG9C2n3RGdWehPGH+iazx9O+BdYLVYopWmuLtlvVihjEKZyPt339O3Byrl6ZoDPzw8U8+uqW+XbHcHAp9Yz2oWVY7KDFle0buG7faANieuNiuatqGqiv+TXY9MFBn7tKElljUvUoHL7d0LFAalPkfadT0nxs8FNn0vwJ4xtTOmbJW/HEZziArnHQaFNTkxOIbeAwrvHV3n2T0feP/TJ46nM8GlronL5ZyqKinynBg9zbnh1ET2x8D+1NMNEZQlRndhCAK73YHDasZsdoMiEPwAxjD0nuNlky0yQ7g0cMmsYjlLHTbzwlDlGdYCKnXaFKeZSATv8S5FurbICEFRaMVmc8UQNA9PO/ruTGY0dVVwfb3m+nrDajFjVucM3ZkhDGgNZVEmB5whJMs8a4hoXGiBmOZy01NVGbc3a5TJeHF3jTWK4DXGKFCa3jl6NxAVqcNjXVDlqV18USR/fKsDs1lJVZXkucUNLcfjgc1mQ55rfBg4NUeKIuPv/vYrZvOcjx+f2D3uOO4O1MXycjgGjsczEFgslsQIfd+mjInrL84u6WAPEeKl8dDNzRrnB4xNIL4sK/Jqjos9eVaxvn1D5wbKxRKqmk+fPpIvN7z85vcsNy+Y1TOMCayeHxmCp6jm1FcvUNkMFzTHQwr8tFJwkYooY5P3uFKpeZdSDC5wPDYMfWS1WbBYJCYp1YUnz3PUxcKyDyMokoNWDkd1qRTPjMHHiFGKaDTE5GqTAjdNkRusBaUieWbwwWGNxlbVJfiStHhOlhd0bWLHtQLPl4DzX+v1vzojfg7Gp3rtKViVxjuiO07zPR/1zefz+Yu/v3//HoBvvvlmBC3TLNC0uFAAkWSHpjZ7AqiWy+WoOwVGACdMpbT3FoZvKnuRgj+xHlRK8erVK1ar1ahd/f7770d2/3A48D//5/9ksVhwd3c3WqktFotRUiM63sfHx1GrPC1gBEY3CrlfGXthBIU5PRwOvHr1agQGckZIUayAEnGqEKAh7iJi1Sf6d2BkFKeMPPAF2/3999+PxZwyftKIRs4escQTSdGUDZWx3mw2o4xGMiWSkRArzKneWQCmyDym5+d0f5hqwYW51jpZ+Hnvub+/HwvDRVYhEirx6ZYMiQQAQgRMpROQpDWn02kE4saYsZOtPD/5XEJQTAshxQlHsj6iHxepxOFw4Pr6esw4SPD07//9v2exWPD+/XseHh5GX3ypy5CaC7EinBaoSoGpyHiE1b+7uxvHrWmasZBVApsXL16Ma6qqKt69e8dyueQ3v/nNKPMRaZPISq6urr6w0izL8otnJvNMvicaf5ETbTabMdsg6x4YA5ep644Advh8RkyzKfI708yK7AeyzkSGIsGeBF/ybxIcASOx+kuuf5ZkRQYkbbKpkYe6eK/Ei4OKMhB1JK9n3L58S67hh+8CP3z7j3z46XtuX15zfXdDWeQ8Pj3hvMdoxfF44P7Dj7i2xRrL69dvmM1n6XW7jtNF37fdPl8id01wjugHyjynPTUENzCr55zPJ7wbmFcZMVisCiyvN2idCsAUoPAMbcMQD2RZiSlKnrdHmnagUIZZWTMrUxqibTqOxxP74yEtjHlNtaopq5rlYkGZWe4/fs/h6Qk/DNwtNtQW6tyw7x3b5wcyrpiX17jeM1/OKOoF7z58ZLt7Js8zzGKB+lmaQya4/FnSL33fsX0+XLTUkbbpeX7eo7Xh+uqassrJC4tS0l7aXyaaGSO2FAFnNM0J53sWiwSo26bHuWQfmBwBcvpuIBJxLnA8JHs47zxGaa5vrri5ueLm5posS8xHlmuenvfsjo6n7cDp1IBOtodFkRGGBt+fGAZPjKnJincDWscEOn0gdgNde8Iai+sHyqJiOZ/RZT2DS3IFm0FZZCig69vLAirILixPZhQ2s8SYNsaudxS55XozJ/ieeWUpckOWaRZzy6KETPcMfY8xMOAJ0RMi9MOAxhCBiKbvB7qup65nzOoMox3zes56s+Z46iEq9tsjEU930YhbDTE6ijxnVldcX81ROjGzeZGNXtcxOJQesLnBZBm9V4TgL02LBpaLJCvq+8Dvf/sVy/mCD+/uOZ+OKJZ0nbAkL3h+fubTp4+jD/Aw9NSzCh8ccbSUCmhj0Dp5KJWl5epqSdtVLBZzFusV58MzjeuYL695XRQsrm5489u/QamI81DWVxSzK/KyBO+4rtbozDA4T8xrosnoWscQAjN7adoQY6pN0JYYPJ2LBAJtF+gHOJ16ulbxskrawjzLcDojOEMMDmuFnY7j4SeWaqKRHdP+RuP7PtUKaI1zA947YlQoFchznYpyS8tyOaNtJU2t6PuBsqrxPmKsRWFo256uTY5IzfmXecz+a7i+PCO+ZM2nWnJIRWlv3rzBGMMf//hH/vjHP/L999/z+vXr0aLt/v5+1C0fDgd++umnUS/69ddfj0yr1nr0/H56emI+n49MsEgphLWbz+cjszrVM0vzDzl0BaAIg53n+djxERiBiAQM+/1+dBFZLBas1+svnBh++uknHh8f6bqOzWYzgt6maXh4eBglGWLVOJvN+PHHH8dCu6mkYcqYTu9XgGjf92OTEgE58nfJJEhRmmiM5bwRhl2KRUWzLX7ZopOdgpSp/EcKLwWc3t3d8eLFC168eDEW9WZZNmq0n56eRkmRNHUR1n8qT5F5IKBTCjTlzCzLcizilfcWZlsCCilklPEU8Dxl64uiGEG53I8AfckyCEs9ZbV/XrjX9/0YREnhqTTBknoH6Uqa5/kXjKzIi6SD6/Q+5D0F/E2DQwmiRIvd9z1///d/z2q14ocffhiDM8mIv337loeHB96/f896vR4zCBKsyZqVrJYw1GVZcnNzMxYKbzabsdmXzPfr62t+//vfA2lvFo24FF2KU44UxIo0SrI2Mr4yr6WgWdalyEPatuXt27ejC5IEblPpybT2RLT7U8mU7EtTRlsyrbKnSfZIgsvpHO/7fiywFZA/dXaRQP+fuv7ZGvLPkyGB8sR0KfTFo1w8f3WekbNkffuGx8dHsuId2/2BvDT0TcNxt0UrmFUVXdMwdMl1JeYZN+sNy+WKvCjYH44orRN4UJEsM2xWS6yGgUhzOtJpjRt6CIGuPTP0HcPFAnC9XLJczijqGVmxxNgZgxsgDOispAlgbYnXOU55VF6Q10vqqsBagzGatml5eHygbRtO5xM/fvzIX9+/5+bmjt/+6rdk5Zzl8pr7d39lt99zUyywYSBXPVfzmqdjx/b5E0WmqeYLtrs9gw+gMwYHh0PLcrHCBajq2RhNyUEgDS+EPR99NmMk+HjRa+3Hw+Lrr7/CmIjziQXN8wJjNJnNCeFzBJ9f2Ozt7oTWs3ETgYC1JrGXytD3A9vdlvPpfPHxdry4u2E2r1ks5tR1hbUXa65cc7Erx/U9Q9eTbO3AqMhqOWNWriH0ENMkDj61irfaXvTUDWWe0mMoRduA0YBVaF2yyrMExKLHWEXwSe6TJqjGaI1Wiq5rcN7hfMAHGJxP9o7ac7PMcQMoBqxxaD/gmo5czRJLayyZNYSoL5kHh1aRoiyxmeXUnDEmAxSEQFFmaGVx3nEKjv2u4dQ09ENPllvW6xVX6yVGK0LUZHnO9c3q4upTJDlFSBZ6wasLWExMVlWVEFNWSCuFHwb6rmMYIpmt2Cxq8rcvmC1qtAp4As/PqWva4pK52G535EXGcrXmcDySXwKVvk8t6sGTZZGmdViruL27pm36y+Zb0DY5XdMxtzlWL7DOkWc6ZZaUxsecUwtNnw4TbTO6oSNoQ24ybF4S24asqBmCwvcek2WpuZMC7/pL0aTD9WDtDKX2DEMzbopZZjk3KVjN8hxrM0DTNN3I1IgG8OdsimzsxIhRGn1htwNQlTnaGIzVzKoCHwa6vkMR6YcUoGqlCSpQFiU+apIjUqCdNIX5tytdAlDkYPv5c5DviYb3xYsXfPr0ibIseX5+Hn2YJRUtml8BX8Ksiz3ebrf7grTIsoyrq6sR+E8bhgiIEzYYGNk1YW+nQF6Y6qltori1CGA1Jllnfvr0aeya+NNPP/H999/z4sUL/uZv/oayLFmv1/zwww9st9tRx6q1Zr1ej/7aks5/fn4eAaiA3NVqNWp5R5nXBShIwebUc1tAtgCZ5+dn7u/vOZ/P/OY3vxnBwlTTPnXJEKYvhNTSXsCYjJswg6KRFlcQkRi8fv2axWIxjq2wwFOGX5rRTAM4CWTk3qTAThh5OROluE4pNbqJyJwTmY+wnKJJh8+e1nLfwpRPZSbW2rFAEj67q4huWz6/AGR5LXHwkEBGPrNIIKZgcbvdjk49RVGM1n8iQ8nznBcvXoyvKfcgz3cakAlDLQyxFBmLQ4vUAiyXy/H+haGWzyl1AwKuhbGWbAgwzo8sy0bfcWDsWCvSKam7kfks61OKMafyIXlmUrAsgF1YbSmElaJJkQoJaBd5iIyZdASVYBEYszEiL5kC7eneNC0clfGcyldkzxJgL3UpkikRwA6fMzCyHn7J9cttDy83PN1s0wJV6X9KEWMAlcB6NBpMSTm33L3+LVlm8KFn9/Qjz/efOO621IslZVWz26aK3Hq2IjM6FZu5QDs0dH2P1maMmFerFZvNGkWk00kblHybI1mW03UtXd+xXq2oqpJqVuJ9T9sNROXIFLhAah0eFVm1SF0DW8fiasnu0BJMTRssORaDpgmeXpVUmxXFOjB8fM92u+PYaf78/T0ZnspEZqsX5NUcYzPC0NEfPjFb3jLkcG4HHp/veTObgVIM3qOUpa6XeOfpWk/fOYz6vNCnm65oli5Pg9T5MmMYHPN5zvV18mzebfccNgfyfDM+s9StM1y0dvNxw3KXDa8sygvYT81T8jzDGMXhcEKpS4tybS7tjNcURc5iNqeocrwf8C6BTmsTYGqbnsxa5nXFMBiUNjjvmc0rFvMCoyKZKXAuWZmVhaLKNT56jCpRCqzNRg3YcGEN8sySgkDohxZiwNriAl4jRqWfC9EzuAHneoxP3vXaWIwOGByaDmsH+uFE2+5AB9CGoc9phxm2rNF5BbbA+6SnNzaDCINzuODRylIUyfEmxnhpxAOZCajYE0KPdz3O+UtAsMMYy3JRksUkjairDGMgzyBGT+d6jNUYbQhxYHAdSiVdsxsC7bkly3LyrOTp6Smxu9FAjKxXFdWsJFkolsTg2e939EPH1WbD1dWGrm95fn4izzKC1hBTB1Olki3ltABFq9TNMmk6fWLVfYeyBdaW2GEArQimAAxK52RR0XYdbe/I8oy8WlxYwIpTkzTXRM0wRLxPY3puAtv9/jLO4Lynafvkj66SR37bNjTNGR9m9H1HXVqsTlmKIU86bmFCYghjoTSXDRVS4OG9xyp1yeqRJEVRk2dZcnC5ZAeenx/JrKEoy0tTCIvSChUMMWgOx2PqvpvngGI+m//SbfRf9CX1KdMzQr7k3+W/cn4Im/z111+Ph/2nT5/Ggk1hJR8fHzHGjMy3FA+Kr7YAbvF0vr6+BhLrdTweR+mEHPpt23J1dTW22pYDVg5V+bvYCkrR3M3NDbvdbgRHwloLiXJ1dcVms0Hs0Nq25S9/+QuQwMxms/nC6UWAtmja7+/v/0+yEHHyEMZN1qjoxWVMRZ89BbfClq9WK+7u7kbgLPIGuQSkCDs6tYYTyY4QQsAIqCQYkrGo65rNZpNsdReLkTWUsRegKpZyi8ViZBmdc6OTiry/sKBTOzsJ6EQONK79C/stQE1Y+ykAm7LaUow5lSVMtb4SjEnXTJFGDcMwBlQC9uXPwMisSiZ6WlgqwQAwBgA/N26Yeu5Pdf+iaZdnP5XMyHwSaVdRFKMOHlIQInNvarMpHv7S2EjkRtPsooy3MNMyl+V9RXM9dZyR709ZdZmP0rxJHHfEVvF0Oo3PTOobpHZBCk2lIPZ0Oo3AW5h1yRpJAe1UkjbFUNOCz/jFGdGPQbD8+7S2QgLJsiwTyTyRhE2lLzHG0SVGfkfW9D91/f/EkMvNgnxFwCchqvcEk/SgEU2WzdncvMFa+JvhyA9/coTmQNMlPV2eJabLuUC9XLKYzzBKcz6fIDjyskIR+fjhA/WsYhh6drstZZF9brWsIqjAqTkyqxcYZ0FpirLm+WmbdMD1jK7tGfyl2n64bGxZTlCaPkTmizl5yNB5ickLgtG0bUdvSha3b1HKEEJk4SvKxUBZFFRZwfbhIx+f7lHuhNWOEI68f/p/sNis+Xf/8X9jubhlGAz7846//vBX3n7zO/KspiwcMRiOhx1d6wleE0LSqVqbKvBlsspilA1JNtOyrNDaQEwOI+fzCe9TirKelSyXSce43x84Hc8Yc9m0ga7rk1vJxN+0qgf6PhXORpL7SZZbVnZFCPGin/aczltcyFivVxBzuiEdZlWRQYChjxTWs6xzMpv0u/W8psgUx8OBT7snBteynNUs5jm5zXBD98UGNp1vUm3fnM/gPWWe4YNjv9+h1aUyPSsu2uNjamKjL4y9imjt0WEgDh2xa2Bo8Mct/emRoFOTGW0rBt8AKeAL3jOE5CmfF2msk91jR6btqPlPG2t30egrFvPEKhV1TsSy3ScHieftMzZbMJst0Oaie+8COgaUTmAl+HBZF+Vlbekx4BjcQAgKowvyrMYNjuAcznVoUsOgvFjhnaPIiySvUKkmIIQwjm3XdfRDj0aTZfnl8HIMg6Msi7GQJh1C4Fyq/Wi7jFPbUhQ50ZREFemdoR8GIoGqWlAbQ9s1qXFPlmOzHOcjh/2J9tyiVI42GucjD4979vsjh+MJ71MR8eAdzbmnOXcoUlMfSGPVDy3GJuARL422WtNTFhWzOmW+TqcTRV6gbWpkVVYV/dDTtgP6sl0lJsSnbEPwhOBQMT1n5zqa84lys740YPEUeUnXDZTljMG55I7TO6JOTbmM/Teblek1XbM/L/Sc1sXIz+Z5zt3d3Zhi/8d//Mcv2LZpWl7sDEWiMmUef/rpJ+bz+SjXqKqK3W43MmHyO1J0KIVgDw8PI5gUBk6YLwG6EmyI1lxAjwA/kbwImL+9vR2brUhXwoeHhy8s4D5+/MjV1RX/5b/8l7EY+XQ68Ze//IXf/va3o4xBDnhJ5wsTaK0dwYAwpAIqZD8XUCf3L23Yvffs9/tRWpNlGc/PzxwOh1EXOwVg8n7CfE5tJmWMNpvNyJ7GGEepi7RVnzLIcq9iMSfvJ5rw3W7H8/PzqEUWkCo+5z8/I+T5ivOFyJSEiRZQNbWqE7MEGScB61KMJ9pscdoQychUDzyV+4iLirDAMqdEFiHjJtIVCfYAnp+fOZ/PI9Cbz+fjPBGZhgDFqQXmVIohYyoMr8hBJPAARgmPc25kkKeypWnDJZEuSXZIAgCROkkRqARTU/mW6N4lIzCVQYn2XNa+jOlutxszEBLo3N/fs91ux7oQkRZJUAeMgaJkXCRgkaBa5q3YJx4Oh/H+ZE1IMC/jOZXKTOsOZH6cTieurq6+GBNpxCVB5M+LfX/J9c8q6oTPm+znSPJy4yiIgaiSpzPKgNYMXqGLGcVsxWJzy9XVC55+OkFUFMaSGUuVF/TKE4PCB01W5GSZo2kdPgSaczJ4f3F3x9B3qcFO1zL0HTazzOcz+sGhup7FcsH1dcngPc/PW77967fUdc3VekNQmjzvKesSq0BpRTGrGLymyNNnm83nDBGcSrrhth/QOmO9vIJoeXraMYQ5y82C+aymOe25ez1jW8748MOf6I5nNAP7/QPLwzM31ws2Lzuq5VtMtWG7b3l+emS9ydA2Q6mBwcHx2NL3nrrK6LqGrgtj8Y1cElFLl7AULSdHEucCWZ4zX8wIIUXxQ+++SDFZm11SWDnESGbtRVfeXtLBmhj9ZfNOv1uVM5z73GW06wdiGOiaI/2gsCY1/FHWcD4eOJ07QjDU5ZzrzZqqHOhdIGqDshldnzoq7vbPlEXGerOiKHIyazGaS4SdbBaTKkcaT0T6LmmAm+aM832SFCnQ1hADDN7Rdh1d35HnqeNmUeQJfCpPcD1hOODbA749Mpx3uNMj5/aMNoZivqaoN0lvHCKmXKJsxdA7fEiMjs0yciC6wOl8JncZITi8SzKHzOasVhWzWYXDokzB7nDm/v4jwTuUcoTYYXSG4cIWOyjKDFvkuOAJPuAvgNZoS4ztRV+uCEETvMOQM/iBqAJWg/cdg9M07Ynlcp3SZe3nDSnP80sR1IKiIOmgL/abmbUokr2l1mbCzpQ419O2DTZPm+v+cGRwiZXOrMUWFTavaNsO5wbyIqNU+SXr0+E99J2jb1MA4H0KLA7njufnPadjSwiKpuk5n3sGl9yCYtCAI8+4uMOkTIDR6T6U1nA5CELwhBjGDdYFT2hTenPh3eXAJUm8AigV0twCgk9adC5OOsfjAbEdPZ8bkq2hRWmD1pa+H1IXVp8ydvWs+sWb7b/0awqM5JyYHnA/v6aHudgVSov6H374AWBktQQECPgRHbCwYqfTCWMMr1+/Hg9/ARWi/xUAttlsePHiBc45Hh4e+NOf/sRsNuP6+npk8cTiTUCe934E4IvFYjyg5SAX6QkwMpPX19csFguOxyNff/01VVXx17/+le12i1KK5+dndrsdd3d3vHnzZiyC22633N/fc3NzM+rDBUiIVlWkHlJ0KNdUdiEARAC2fIbVavUFQBQ2VMCRSA1k/CXoEB9veXZyFk2brAgIkqJN2X+AMYA4Ho8jo3p7eztq+6cFeFLQKQWIQibI+wrTKQAN+EKWMm1dLq8rYzNlaCVgkrkh4FUkUufzeQR/ks2ZguXRhekC2oShF6mQ1CqIfEHm42azGesfJNPw4cOHkZWeSrtkPKRJoNgqTuekrAlh/6dWlML0ymc7n89sNhvEd14+W57nHA6H0UUG+ELTLWtbgmQhySSLIN/bbrdjUCt1EpKZEvAqc0ZeX9yM5Dk65zgcDqMlqgR48lxlLwDGwETmrAQCkjURdYVkIOT1peOuBM5TFYgEsPL30SAAxgBNsj1TJl3WynS8Jav3S65/FkM+TUF+sdHG1BRIK0VQGpFUmCKjbx1WWWI+o6fm0Fne358xvuP27g2b+RzLAZcbet/TtSfyPEsHrNZ4B8dL1H4+nRgu2s6mOTOfzdDGcjw25HnBYrlOE8Zqzk3LTz/9SN+1LOczvO9R2mIt5LlNoA9FNZtzbi/WckXFLKs59h2tc/i2xQ8OnWepiKtrabuBwQdc1JhyhkXjQ8fqzTcU6zWf3v+Vh/c/0MeG3f7Mj9/+laZ1XH2Vc/XmijCzPD+8J2KpZhsGlyLgk3MMnaPrBmIcqOtqZH5k4knqVDZEKXpJaZ/U3j5GTfAWa5f0/cD51NF1qbBkuVxdUovqsriSVjkx7EknOGqgQsSHyDA0aJVhraIua3xweJ9RZopjc+TpKW2aNs/w/UAkuYo416NNWpBKg8oKOhd5fH7g48cHzueOVy9v+fXXr9G4ZF+YVZybJsklcov3KeBIWrYkHek7h9KWPLNYnZogheA5N+fkNR8i8zojzzOUMvQ+cG57rPKo7oA7PRL7A65vcMOZvmt5fHhAG8V8GFgEKKqSLMtx7RGdg1EFGoXverK8wGhL61r6rqVvk5PJfDYjX6yw1uDdQGEMhUrNbNarG5YLS9c0hNClBkPBYLIKrx0ojVIZWimssbRDy9B7tAqYPDV26ruBrh8Ag4qKoCJRKaxNLi1915LlFu8Gjsf9pXhqyel8onf9mPZsm5bMZhR1Se88LsAsL5nZnNPpyDA4YuwwRhG8S3acw0DuPVVdpwxOkWNUyjzkeXlJS6bCqraRNvMJwJZ5koyhc1zo2Z1OoFNXXFRy/xn6SNcH2s4TtcYozeA6igy0UUnGY1IXXyU6cGPIbUbfO85tQ5SN2Dmyi6Z2u93ifGo2Y43FueR9j2QNYiTqkAqgNYTI2OQhSSF6jMlxATKb03aB7f5M23QMLpBleXKh+TdAPl5yOP38jJjqW38uqRAZxrQLpIATcSuRn50e/sIEAmMxpXguA2P3PPl+nues1+vxHk+nE99///0ohZx6bIvtGyT3k6lWdgouzufzaAsnzKg4rojUQ8DZV199xWaz4YcffuDHH39ErBL/9Kc/0TQN33zzDd988w3eez59+oRSisViMcokBLiI5lqKBeFz5kG07TLe4uwiMpkpWFyv12PTpLZtR7ZcGHZ5hgKAYow8Pj6OLLdICIQJFC2zgEUhAR4eHr4AvsCoHxYgKPKlYRh4eHjg3bt3nE4n3rx5w29/+1uA8f5FQiKSEpEOCIM69S+Xn5OgbSqvEGZcgKGcqcJuylfTNHz69Gm85ymTLMWRU/Asch55VjJ3pKBxqoGXeb3ZbFgulyNAlUyIzPlpPcZUtyz7ugSgMr7TNSaSnrZtRzZ6v9+P73s6nUbyT0CmaKWnxaKyjsSaUggQYfHFMUV+Xu5VxkPuQTpoypew2QKy9/v9+Oep7ETGc6qTnwZcEhjJfiNzSoA38MV7ijxH1oKAaLnvqfROnrHIfIQRl4BTshZy7kimTbJcsrf9U9c/S0Mu11gg9fkfCSFexCuaGC6Vubj0Djp171vefcWL44nT85G//MP/i3/8w184Hfc4d6YoC3RW4GIqtmrblhgC59M+sSf1DGLquBhioCxrjLY8Pm1xPoLKGXzHyZ+J8ZnDfk9zPjKrK+zFh3mxXLJaL9HGYLMMH8F5h4/xcqBfQEjXY7XGRpKmW0WGLmmjY4TVek5V5kSjMFWFixnt0JOtK65MzWLzlsPjB+5//Acetica95F8fseLNy0vrq5QqqU57ynqBXmRkZcFOgT6YSCEjDy34zhLhCubTJZlo14vy1K7eFTqXmmlmY4LgEEpj9aWup5TVfWY9kx6puQX4py/MEAdzg+fAy6tcV2Lc54Xty85n0+o6JjXJYMbGDKNziwxBNTlvnSp6S+pscwWnM8Dx+OZqApwhqftmR/fPbLbHVgvar7+6i3LZc1x/5SyI14RfIcnNdAhJD14mmKa/CJTIsJqsSC3hmHoGPoeTUgaZxTWaDQByFLAEzR+aDF9w3DaoYYTPnrafiBgWC7WZIUloAnB056P5GVNXuWEOAAaE2A4OfCBYBIDrLXBEPDOEbzH/n/Y+7MmSZIrSxP8mFl20c123yICAWROoqqaerqGaKh7qH9/P/RM11BRJRKFTAABRIRvtumusjPzPLBecXFHZCbQ0/WSBQkKMnM1M1URFhbmc88999woIclybK/p6gbreuqqwsQxWRyTmgytslAsagNLG6qgNcMQGFqlg+tJWLQbjsf9GRgo7ODOunINyhJnEcYowBKrjLwosc6fNdWBUS7yDKU03nrqpsZozW6/5VBXKB1hB0dctRitSZPs7Fhz4nQ6sNtaBmdJspTBKcqiIEnPjXe6jmHoJ53obNCvR5q+64OWW/kgOzEp1lY0vaPpO5JMkxUZWsfstidMpLB1Td8PWOUJau4BdLD3XC5moQjVWYwigHLn8Nriz4BgNgtaS7Fok/SoyBCKoiBKYuLz4skQnIOC77kj1jFYf97AQ+AiMh9URNs5qvpI2w60/YBRijgK7+fdXzt1wr+8R0wZJ/levspmvVgsuLu749tvv2W9XvP3f//3/MM//MNobTaVKmitx66XkoKezWafFWaJ/lacWhaLxZial41VWq8LWFgul2Nh3RS8Tl1IhImdAiRJ6cs5iT5dCtwEtKxWgTS6urri6emJ77//fuxsOZ/P+eabb7i5uUGpUB8lhaYC7AUQygYv5zeVEEwt2aQoU+6ByBOn9UnCppdlyWaz4XQ6sVgsxns3DMOo9Z6CQ7lm8ZiWNuEiGxIAPQU1Ig8ARvAnDivivPLjjz+yXq9ZLpd8++23rFYrttvtCP6mlnXAZ/NK5C7e+9F2UhhSAd+f6t8+2efJPe26bmTsnftUVyMOHvJ5Ys0nwY+wqyKXEXAnnyOfMfVDn2Z3hEkWcC37/PTeyT2TIDSOY5qmYbfbjZmUad8SYJRmyPcilRGgLlIbyQLJHNputyNwHoZhLIwWQlC8/tfr9SgPkkBHgiMJGiQgEYZ6GthM6wEkuBN/cnGmEetPeYam64vMd7FJlWubFmfK+06Dc5nDMu9FyjaV0EyL9WWdkvGX7NyUPW/bltPpNGZgBK/JM/DnHH+hhjw4q3yagNIoKHxzjsnAn5mR83+DD+LNxcU187/7H7ksZlyslrz943/l9999z6w0/Pzbr1lerNhXDa7v0Eqx2e2om9NYoHM4nMjS4CmuVHBXGAaHjmIG69HeEmlN31vyImdWFGc/53PleJqEDdwOoKC3DpMo5mUZulW6Hpyja054HWMUlFmGMjHOK4o8obOei6sL8qykah0WhYoLjEnQOiaP5sxXitXlV9zdvaGv7mmqZ5xXtKc9WVxwc7nkYdfhfbAbtMMM5aHtO1B5SJGf97bpgzMtQvlUnHDWdXtLVR2J45Q8K1AqOFCkaUjnSEviaQV0VX2ygVosSuomo6qOKKXJsoS6qgGPHbogt9DBLg5CJ82EdFwAgr78U6QYJxGpVRwOLYfTgfX+mR/fP7I9HEhiw93dLavVkqHvUN6hlGEYLEob1Lm7qFKg9fnhEi0dMPQDXdsTKUVd1dT16dwoKKJrG2yvMCis7XFnP3XXNwz9ELTHTUfTdXTWo6OU+fUMZ3vuH9foqESZFmUOzE1OZ3uc1xTFkiTJcUOL9Q6tPZHRMDhMpAjW1o6mbsL8sZLS7mn2XSgijBLm8wVxlBC6n35iorquQ2lI0/i8kStOJ4/3jjTNAttvOtp9cBACH4K5OMK6YClpjKEscjyhZbwdAgPVNjVGabI0Dqxy3/K8WaN0jMJgu7PXvHdcXq7ORcChK2mS5XgMp1ODs5y7ozr6rsPZYAtoXWAD8zQLrJUeGFqL9466bjjWA8eq5lg1WHtuJLKYo+aK427Pfl9hbUTXQWNDB9Yoy0nj0GQqikITo0grosgHV54oMCTduRutaBPX6zX1eROVDVPSzyg3MoUmiuiHltDPIGjVRZ+aJClt3ZLEOXGc0Q2a46lmuzuilUEbSOLonGnTOPdXH3I5puDkS4D+pXxFNlUBlQBXV1ejfd3l5SW/+93v+Md//Efm8zl/+7d/O/qWi8Th6emJqqq4vb0FAlMuoEGp4L4hhWbCoMomXRQF33zzzejtLcyepLSBUWsrLLmcqwCXaeGd935M4UsXQmGzp+vf878AAQAASURBVDZ7cRxzdXU1SlVkbfben4uIY25vb3l+fh5168K4T9noaVGasPqifxYgIAGqgFfxfJfsKzCm6/f7PYfDYdQ+C9svoGOxWNA0zfieRVGMdm4C1gWIwidvbQHE0yJIYcZFy344HNhut3z//ffsdjuSJOHVq1dcXV19FjwIWy9Acar9nuqtp3ptkZwI4BWWVUCuAEXZX6eZCNlvb25usDZ0h5WgRvZhuTcC6ARITyVZwuSKy8+0E6uwqjJmoukXyYnIIaYNnySIFB2/SLrkvCWQELnIVF4jHTnleoXpF8AtDL7UVgAjUPbej1IqKUbN8xxgbMYlAFQcbGSMJbMgrP80kyUdUY/H4ziPVqsVy+WS7XbLdrsdg6apd7y4uAg7PZUnCYsuz8y49p+DIZnD0nRKAhCRmMg1SgAn4yJ1ATKn5HqOx+NoLSqyHsks/DcC5EGdEkD5T/30vBBPXgkL1dkgXyl0kpLMFsyu7ogf3tFtn7BEWAxoQ55l9IPDndk3raLQ7EUrVheXJHHM6XjgeDxwe31NnOdn2Yej61qs0vR9i3OWPM3whMYu3nvqrqVrWkwc4aylPp6I0wHloW8GUAmoiMQ4rLI0dcOpakizgtXlFZkyNJ0lNp4k1qAiaD3OxORZAUpT2ZYoSqlbSzK/ZXm5ItIVRnc47zgdd1y9uODrN7cMKkfplOqksf1Ab3vaPicyBnWWiQjDIVZbooMTJkYKJGazkuPxwDDU54LAwOaH5i7ZyGRKaksiSNmI6loiOmmyYFiuVnTnhyqKotAMoW2pm+ZcDOcCW3teSGTiyYPjfehGWVUnHh+f2W23RHHC3/7iG/7Df/h3GO0Z+uAr3vXniX92hLF2OC8oYR6FB6sfWUvvQ6eupg3XYrQ5u2yEQqRIxcHP20OMZnCOfvBADCYlTmLSOEJrix0aunbgWLXoqCWb3dD3ivbUoIzChO4vDG2LVQnEOXhw1tPVByKtcJ3GasfQ1GgVGhMZY9DKs908sz8cWS6uSNMcYzqMidAm+K87FzqG2t7ivRRWBqlPnmfkeUlVTaPuCGN0kAJphfKKJI5RnuC1rYITjbOWqm1QHkwUeGevFcv5jKEfOFU9bduAdXAO2hyOsszxSqOjwA7vdyc8nrubmDSOGZw9O5gEOYn20WfFTQrFYB2R8bRdz/FYc6pqTscGEyvypCBLYrq25s3rS/azhFPdo9TAdt+QpQlZnhFFjrIMRcEAaZKg1EDojhmCw5C6D4VRsjnI/BMQJRvx/rAdgYHWEVmmJil3fU63S7vrmDjOMCbG9466aen6niIzoalTngTdvwm1Dn89Pj+mYPzL179kzoVwEHZL1rqbmxs+fPjA8/PzZyl4KUoT9k2AjjFm7E4oIE/8zEX2MXVkGTMgMEowRJMtDKUABO/9WPQlm74AfmlmdH19PQJZ2ZCnDLoU8Anoljl6fX39GbDc7/e8fv2ab7/9FmAEXiIL+LLYVRrjKKXGLIE0zhFWX+Q7u91uTKVLMCS+zMJkKqXG4EPGSICksIMSCEhhmwAxAe3y2peuKALWBPTK+xyPRz58+DA2XfrlL3/Jf/yP//EzycIUcAkYlPkAnxobCfBzLhT413X9WSZmei3TeTcNDEXWEDodf7IRPB6Po5Zc2PQp8JZsgFyn7FNTiz8BgxIIKKV4fn5mu91yeXk5Aucp4JfzEsmWuIjIvCrLcrSclLkx1TVLsClzQ+6Jc27UaEvQZowZu1kK4yvXtd/vx+ADPgVdElC8ePFinB/yXlNcMM1mSLAhbPvxeBw7Lgtwb5qGb775hsViMQZ/YhsqJMx8Ph/PfSqVkfvmvWc+n4/jJ7IqyYhJEWqSJGO3XZkv03VBQL2sC8KAS8apqqoRsMv/ovv/bwTIQzMgYcY/MR3itCLM+Flvfv6XUoTCKR9u1Ozqhld+wNGT5Qm7p3d8uN8wX1yyWCx5en5iu90wm8+IzmxWlmVkeYYiaJvbPjiwVG13Dg7Cw+QJRXES4RyOB/q2BQ8exZAHV5a279judkRnrXGS5swXl0RxRhZnOOWpXEtzXNNVe4rMsFxdkScJOnJkZiA2CUYb2sGhGGg6x9A1JNpQLuYMjacbBpbLS7K4QyvH4B1DdyRNUspyhteGZplx2B/O/U0cSn8qQJEoWSI+ebhCdKlI05yua1FEpGlxBkpiE/mpGlgesLquP5O9TCuxyzJIEtq6puuGMZgaPFR1RXO2E1I6NGUKTHo2LrbykHdnOUPXBllMWaTc3VwGP+G2Jc8j8jSia/aYNDiJtG2HNhGJOnvO63OFeWdROmwqaZLhbEVZFngsfTNgtMboaNSYx3FCfTpR+wORCU2DrHO01Za22uO7FttboigOgNi29EPLsTpRNR0qssT7IKuo60eMGUgzQ9+ldIOCqCAtV0RxHgCxrenbga6tUHEBJqFpGyJJOw6O9nTg6f6eoRu4vL6aeBtHaB0epggDscKd2e2+b2mamiRO6KOepqqw8jcqJs9DQ5HeDnjv0EqRJQnYwCC70DuXNIrOmY2BtmpotSLJ8tCx0vbgLIpgfamUwsQx/hzcKB204d43tG1N03TkWUYcpxilsK5HqdCcyhiN9Y5u6DFRTJJqTJxS1wNV1VCdGo7HOjTFimKUC4FtucqYlYa67okjxR+/f6JrHVmUoOOBODFEsQ6ZEvzY9yCoVoI3u2R8JHUKnwq89vs9d3d3gR1JrjjsjzzcP3JxuSJLC4a+wegYZz15kVNXDc4qFosLtE6wLnQNbdsO7yzaKLI8ppzl5HnM/rD7XL733/nxJRCfAukvWfMvv5d0NsD19fXIaOd5zsPDA+/fv2e1WrFarbi/v2e9Xo/uG7KJC+iVQEuAppzHtCBOAO1ut/vMC1sY3q7r2Gw2Y7GaaG7l86aARkD55eXlyIrJ/7Jhy9oogFo0w8MwcH19/RmLKkWH4hstXuXTwlkpWBMZjYBf+beQMHmef8auCggWXe7xeOTm5oaLi4sRZAqrKm4tIj8QHbS44EgwJTKOtm1HVxcBusLGy/VJel8KQSWQePnyJfP5fAwQhEiaNvaRjKIARAlQ5LolwyEATDT0AsQlWzHNGMu5CksrtVkCtiQLfDweR7tG0RGLnl3mntyP+Xw+zhtZi6Stvdj0TXXwEpB0Xcft7e3YHGmqT5/KqKYFmlM7ScnoyLhLhkjuuRB8Ux9uuS9T9xK5Z1PZjNwHmftyffLMSiAmHUSnRaUSGMqclYyO1AQIO344HMbGUfI30qlVpD2///3vx+dDMi1yDV+uK3LdEihLsCbPuUh+Xr58yWKxIEkSdrsdHz584Pr6esw6yfyRwGeqO5e1RtYQAfJi2SrP7Z9z/AWA3J+Bt8hU5KtCKf8noFwGRhZVuWlOa9L5kqs4RpnQ6OcPdmDz8I7/8qtfk2WhYC8r5wGQJyl5ljMryyCX8B6PAq3wSpOkGdY1xHFGmma4YaBrm7OjhcIOA63zFHlOFKV451lvn1lvnmm7lsVqRd/1WOc5LHYUxZzZLDTp0YNjnnjwlm73yLY5khYzoijGV3uqzuF1ikOh4wilYpZZhqLC9pYsHtCJIc8NeZrSNRWR8mjb4LsjrouI0jmXy5TIOLyHvEjJMtEo63HCSzGEPNBpmnPYHzGriCRRnE4N+KB/lQmaJA7nhjHFE/RrOd6Hh2tq8wMK6xQaTdd7rG2x526Gy8WCq+tblPecjge00eMDO61aloVPFpE01ZSloiwHVhcD6JhjVRFHmt36kaE70aaaKE7oB3CDo+ka4iiiLDK0smRZSpycmYwhdM4U3TwKtNHnBcriPSRJStfU1Kc9eaKJIs3Q1FS7NVW1x/Ut7twWnWqgqTcY7el6Rzmb8+brn+NVjB0GuqairZ9JYsd8WZAWy9D8atBo44iURvkGTJBnQfDAT7SmSHOcs3RdRcTAooiwzZHTfs1yMSfJMoahxbvh/Hthk06zFKU8th9w1uEjf5YLKcoyP2+8oqNzZElK8EK39H2LshCbGG89TdvileJUncLGFJlzWhVm5QzvQ+t6lMF6S1Zk5HmBV4rBebxTaBUzny3x3gfb0G4gzXI0DuX8GZCr8yY7YAdLmufERYaJUoahJU46orglihxZUoBXeDdQ5jF9X5FnBk3Ey7trNpuat2+DXjSPDXmeUM4K0jQ8EyYJgHx0EjA6KM7P7JSk46cNN2Rxz9ICW4RGWZ8sshLSNKRs7eCCi0qUMp+t6HvHoWpGTaDHo1TI3nRtDapncF3wqP/r8SeAW44pGP8pUC6b/lTXK5vjlK3+8OED/+k//acxVT2bzcbfK4pi1DkLWJtKMiQbKOy6aJ9FtiByE2E2n5+feXp6omkarq6uxs18u92OnyvrnmTshF2T1LjMwakLhqTJ5XMl5S1pcJFTyCY/bRoj4Lcsy9GlQvYI8Ws+Ho/juGVZNrKuaZpyPB4Bxs8Udl1kBcJmCmiX7qZf3iv45LUt/69WK+7u7gBGTbMUnArolM+QcxOJhLV2dNgRtjyOY56enkY98VTXK04lojEWAPglCyvnLKBJ7rOMj8h3JMu82Ww+c0WR8RbQLRaMUmQqxaCyL0t3VQkUhP2GT0wy8FnzHFmHJCgR+Yo0R5rWPEwt/KbadwHoUgsg81KeN2F/ZU5JNkPYdpFrSCMjcVkRFxgJJgWQTsG6AN7FYjFmHtq2/cx8QgLIaSAm4y7nIdIaKdCFEDyXZTkG1gCvXr3i6emJH374YXym5dmXZ1sY+qmMSu6X7BFi/9k0zfg8CFiX85Exl3OSAm5xIVoul2OgNu4RE/ZfAmKRLP05x18sWZG1dLrAnl85/3zClPtPRvyicTFRjMPjopjF5R2273m4/8iHDx94en4gjeHF3TWLyxuKco6Jg61f3baB7RwGhsEFYOmC9jiKA1OdJDlNVTP0HXGcolVIqa0WSyJj6HtHVTX0XY/RmrubW4oyB6U5Hk+cjidcZxnahtPxiPVwqhpmsxXHraK3HhMnoQBPGY51x3x1gUki8llJaJCSsrq4JUsy8sRgooHUBKYyyhJOVY1rj/RuwA0t5dKRFXP0PKO3UOYZSRyxr/djlC8R+H6/H1OP8oB5F+zfuy7YBYYmL4GZtq4jzRJm5Yw0zbm/v0efx2lqwaS1xjpH3wevZ+dhVi5CgV7fMpwrzeM4Ci3EzyBfItKpxl3SRABFMUMRHrrZTBHFKdYvcW7g+fEhgJvBY/2AUzFV3XGqjqzmc5IkpchzlAoWfc4N1FUVPM1dYCya6oQxCnGNCbNQnVusW7ytOBz27LeboB9X0NmOOE5ouhZvW2zfcKqODDbC6iUmTkDFQYrjDF1b0xx3MOTYtqFc3TDUQZrS4rBNje0H4iRjfnmLJ6IbPJYhNEByLfNMEamSqu3pqi2n44I0vUFhqevACjjnWSwXRIMGbc7OHR6cZxh6YhO6e9b1ifhcF5ClKUortFE0VUekFVmc4uyANgrvXHAz8Yr7+0cccHf3Am1S5rOCJHEURZCKBbbd4fH0/YAxCU3f0dYty/mSuk6wztK0DWkfMXQtSRrh/UDX9eRFQZxo+m7AAUZpmrajOwP+tguNuLQOVptD33KwLUkcsmdJEmGtYl7O0Co4ZSwvSubznMuLFWmsaOsDxoRC1q5rx801dDn9VPAVRRFZnuPOG880ba9U8F5vmk+saV03wCdg2Pf2rH9UNE1L00iDC01ZzkhiRdM2NJ0lSuOQmfrr8SfHl3vE9OtUTy6btRyyeWkd2rwPw8D79+95+/Yt7969I45jXr16xfX19ZiujqLosw1Q7NWmEgIhC8RVQoCxsMNSACguG1rrkbWFoE8XwCZpdmGGpYumbL4CXCVDKWy3zEXRygsoka9S7yOSj77vubi4oCiK0QlGAKhYtslYCRsn5yOgQv6frvfTjEGe58znc/I85/379yOTKuB3ynaKjEFkAOJiIU4WApSnJI1IK4ERlAs4FumDjM/Uuu/jx48j8BSWVjqhSpdWyYgAo0RF2H0pspMsxXSOide2+F9L10oBoEmSjOBV3key1NNssACvw+EwjtfFxQVVVY2El2jRpZ28/J0AcckCXV1djcTbfr/n5uYGCBrr3W6Hc47VajVez1R2JGMt80LY7Km0RYI9CcIkUJEA5sOHD3jvefXq1QjMp7IdKVid3kdh/pfL5fhzYcklmJRAQOataNHlnETKI0HvVN8vEpapt7hkjeq65vLyksViwfX1NdKlc+qu4r3/zCFlukfIPJVxjKJorI+QcZTnRFxaBNTLdUs9iWQ8REIzbUAmJNGfc/wFgFwxmc8TllyY8fA7P/mXk0XZ4fHOo0xMOl9xrTXfHA88Pz2xfn4ijhUXl9fc3r0kzkrMWbPqhgHlPfU5EinynLbtQGmyLMfZYaySjqKIvMhQZwlI3dTUp5q26zmdWvaHA8Yo2rajbhq0Co1KuqanrVqaqkYp2J9OrNdb6kWLiRJQBmVi+sFhdETv4bB7Ii0TlGpIopiqaolsjZ4tqNcNy1XOh3VFmiTMykWw5osrmt7SOc2LNx1Xr39GrNNgx9Z3aBgBtywOEsWJpjHPS7yH9Xobosg2NBnKsoK27Wm7itmsRGtF3VRobSiLMjSniT55qIpGDRSz2YJuCMWh2kTBc9wr8mJG1zVY67A+ZDXiOB6bSEgkLxaMRVGcdWmWJE0YbNChKzsQRwlVU2Fti3cWRYpWilPT8fS8pe87siSnac/tel2PtS1aKdI0GVNIfd+eFyPQ2oR7bRXDWYseG0V72PL47nu2myfiNMekM3yc0Vmo6o5EO067A8ftM3l5SasqntYPLFY3ZGmCd2ED//HtA9UmMFvXL3vS2ZI4yRm6ht3zE/vNhnw256Y+sLi8Aw9VZYk0xNqwzGBZpFRdQuct+/VHjBYtfE/fBxY2SQxZljD0PZ6wYTkbmgZpRG8eii9D8KNQPrRwj4wOenyCRZ9S4Lyn6wes11SNZb3bokwBOqcfDNZZLi6WKA1ZnpMpRdN2IcvUdKHDqA1NdK5vrtg8P/L4+AgMZGlCXTUhAFeaqmrH4kYPDM7RtgNdZ/n48YE/fPc9s8XV2c/bhPcd3LlAG7QKr8VJxnwxPzcAiijKjDSLwQ4hqEpjrHVn/WXYgJz3f7IxZXlGlqSjY4KApBAQBFtG790IosJGlWBMxOm0J0+PKBVhhwGFx2hNUZTEcUSSGnanHdYNlCbh4WHDty/v/vyl9N/o8efoxv+c3xFAJulorTX//t//ex4eHnh4eCCOY25ubnj58uXIak8LMWUjFFAgYFWY8SnDOt1wRXJxPB7ZbrefAQ5J6QsjKrKAw+HA4+PjmE4XRnYqJdlsNiNzKoWW1lqWyyVPT09cXl7y+PhImqajjEW6cDrn+Oabb/jqq69GUCGExxTspGk6urFI90W5vqenJ+bz+Wcpe2EyF4sFU4eWsixHV4up97WM7bSBkYyPsPZSxCeBsqT/ZaxF+iF2jQIyhdkUeUMcx6OUZ+oyUlUVj4+P4/WK3GX6DIvHtQQJMp+EPJJ9SrK7u92OH374gaenp1G3LFlfucbtdstmsxkDs8fHx9EbXWRy4pYzm8148+bNCMq6ruPx8ZH1es18PufNmzdcX1/jvR/157KHCkPunOPp6Wmct3KPRZ4iNRECOEWqBHwmMZn6dUsGSq5d5qpoyp0LtTPr9fozmZW1luvr63FuACPLLYBUHIxevHjB4+MjHz9+BBgzLHJuEtTIPRHw3nUd796945/+6Z9YLpefafJlLZgGQGmajoGJBAwSfMs8lXGYOg3Je8keITbS+/1+xI3TmodpwCLyYXGtmboDyXlKnZ1kniRgN8bw/v17vvrqq391Df2LJCt/CrjlNXFcmf7OlCk/f6fEUg9c7+gtOBVzdfuK//j//H9R5inN8Zm0WICOafuBIs3QKLz2dG3LYAM73XQdJkmIdJClaG+IY3DDcGa2GiJtUMpTHUPK/lg1eBTa6PNAWpquRXmFMTFFXlIUM0wcgzbMLix3rzxKG/rB45WhqjsWSUoSp1itKBYF1nUcD3vapsUNA66r2T83DENFfVDUzYGqrinzORfLRSgS1BEmLVB3L+iPG+L5JSrKGM4shLDOU32epJrCxJYUlxSyeOq6oyyDeKLvOqA468IqbO8JTYRClfXUyF8pTdNWHI5H9vuKrm9IkpTeWrI0ZjZf0DYRgw3dDtM0Ris9Tjh5OKcbkqSanPNYO2C0IlKayFg0PVlsOFUtkODRPDw+87zekWVpCHx0DAg7YskzqRYf6PsOcRnRKiw8RmkiEzM4wPUkcUzVdNT7Pf2xoq97Wt2xuHlDsVii6gFsTVO1MHhe3t1iyhtMUeK1o+1DpiXPS2zv+fjwgTLP6Nue+dUt5WxBpD2+2UN/pNk3bDWUWUJSlNi+prcdFk/fDXgVM1/dkEYJUZGh6bFWnV1VUvquoW1q2job78npVI2twOM4wbbngqM4gIPkXKQzDJ40jXG2Z7B9sH00CcoYettRt5ZyfsHTtuL+YUecLrDeAJbZckEWB3vHNInxzhNpw+lUh14AsaHrG2bzC5arJXV9ZLc7El9djIum1hplFF670EkVTVkssR5++PEtP779iLWQRClpGpp2+eHMEOG4iApiEwrxdvuKOEkpyoSiTMnyGKWCPalSmtD4nvPcDQtv13+yUQPGTUe0mtPzBE/XDdhzR9Qoisd1quvCYp7n+dnTX53B+znlCmg0UZSc1zxFVXV8/LiG//ufv5L+Wz1+SrIir00Z8Z/6Xo4vC7KEkXv58iX/6//6v47BvrhMCIMrm6eABWHcpkVXwpIKgJe5AYyaYmF/5Xe/dAgRMClr59XVFV9//fW4MQOjVliKOoXZlgynAFORZIgdrVivXVxcjKxmlmW8evWK4/E4ZgPsP7NHiMuFNHhxzo1FllPwJMBSAOswDCPDK9LG2Ww2yhmkOLBpGvb7PdttKI5+/fr1KNlZLpefdTkVhlZaiIs8QsZEPkMAqAAhAajAZzIbgPv7ex4fH0fJhAC9qTe4OF7Itcl9kvOajpk4ruz3e06n08ho393djc4507331atXo8+7uJ5I4DMMA/f392Ngcn19zWKxGOWmfd+PQZ6QViKpmMpCLi8vx98BxkyPvO+UjZWAsKqqsQZBskQSDIr8Qxj6KUMs8iEJVJfLJc/Pz3z48OEzu0ypm5BAS56Pw+EwSkQkuBNf881mw+3t7WdrrzxTIoMSk4rvvvuOP/7xj+N7SWG+4Ar41CBst9ux2+1GIF6W5Zjpknssx/TeT+fDdLx/eo8A6Xgrz4Mc8j7C9kuwJ/9LgC/zXAKv9+/f8z//z//zv7B6huMvlqzIIXry87/OHz5l0ac6QWHOPKHbtIJI43uHNxHpbMHd65+hsLz7/rcM/YneWYpZyWAd/Zk17vqeqm4pzlaGcRydm6xYmqZjGDr6LjTw6drm7LyhaOuaYbAUZUFWlHR9kL8sl6HNeHVqGHqP0TFJWkKUEGUF17M5i4tLtpsdVdPRdANLHXN1c8PQO/anE8qAZwCd0hwPRNrTt448Tbm5ukXRkmcxVxeKvu2IcNTHE1lWMJ8vsfWJ3fqRVZxRrmZ0TnGq+vMmIwAiaNceH5/oup75fBaq0aOMqmrAt2gTgo/jqaIoErQxtF3IGBR5gXeKuqnHjW6qq5PXTtWGP/7wjqo60Q+Wm5srtJk4aDgHUUhBevzInmitx8VWHqJP/qf+DGTA+wG8JksjutbgXUqSpbSOUIBaNTjvOZ4aijwlSwqyvCDSHq19cM/xbrTZc95jvQ8Fu34YH1rnDFGckURZcEdpepphwOc5TQ1pmZAXVwzHJ/ra0VUt1eHE7dVr8kXOtunBhsAtiXJubl5yfH7g6eMD+92e6+OJ2WzO9dWKlIF8VdL0PdpW1McnvOpx/YDtQ/TfNi3t4IL0ZH7JLM1QLqKt+7M0J0WrJXjougZFYCOM1rjB0tQ1Q9eTpAnmzBpGkSYSFsFZ+g6styRRxOAHtI7Jy4K6s7RDz2xxQTmruL9/Jsn3JFnJbJZjPeF/5+iHUFgZR/HYhCdKYuq6YrNx5FnGmzdvAptgEpI0GdvPY6Afzs4+TYtWNYdDw3Z3oGsts/mSYh4kaE3XsHm653jYk6c5eX6FTyM225qHxzWohLQwIRMTRTg/YO0Qnvdzt4M4Di40dRP8y6MoOTsZhLqHkdVTMJuVtF1wbHKOzwpARacrzJ/WOkiBgMFaBhuCPyla1VEEXhFFKdYpdruaDx+e/88uo/+mjy+Lq6bA/Kd+Pn1dwJOsVbPZjG+++QaA7777bryH0jVTNkrpKCmabNGEix5UmMVpkxHRXw/DMG7y8n4XFxejjEU2bHHBEHtG8RQX1llrPXYC3e/3o2zDGDOm4EUbLXaNRVFwfX09sr7H45E8z1mtVtR1zdPTE3Ecc3l5OYJb8bOW614ul9zf34/fSxt2AbVTECfdJmUcpKGLgL0pwy9NY6Tg8/e///04Hi9evPiMTRVWUu5nWZbjHiH63ekeMQU7AmBE9zwt7hSJhzjHiEe7yFYE7E0BvryXBHXy+TInRLM8ZWrFJlU6cop8aCojWa1W45iKPOLu7o7Hx0fev3/Per0eO6/e3t6OgZtkEPb7PcA4F2U+TgtnxRFFsiUCOuVeQ2g0JEBSnFxkTOU6p+4vErhMveElc9L3PcvlksViwfv378nzfJQyCbssIFlA6lQSUlUVz8/P5HnOz372s1EnL4GZSGQkAJFnb7fbsdlsxjkrQac0Y5LiWdGur9drPnz4MNZiyD2U7OhUqy3yrLquRwZc7BHlOiTbJIGrzFNxWpkWP4tmXGQ/EgRIICDPuZyPzK3NZjN2Hv7Xjr9IsvJJmjJdTKev8SevwSgfDz/3wVvZKI2P4rGRSxpHvFSOtjny8O6PnE6fKn0j5dAouupEWx3JY83qckXb1HRdy+mwY/38hLOWNEnOOumEtqnxGKI0AT0QZynFrCTqz13eohilIopZEtgwFWG9pnPBbcLkBSYrGXRDUuaQBnbeqphDc6CzjujciKecLZkXc7I4wfYDBvCuC8VxJhQbrlYpeWIY2hbvwUQJH95/oLgc0GlJubhEq/j8EGbn6C4wOVdXV6wuVjw9PTNYR1aUDIOiqkNBalO3LBYz4tSTlxFpNguFj9rBualO23ekaUJzlrAYbRiGjjgK1cpN32OiAHqqU427sljbUdUNiqBlbhpLawyxNgFoK01bN3j/qWNXYFsGLi5WAMEb3Fm8Dx7W2gTPcUntnPbBvSXSJsgu+hrUDO8t89mMOFIc9hvqqkKrsMh2w9nSMEpI4jRYXvrzoq40Nig7GHrLqWrQyYLV6oaqCw/I5WoORMRxQVRY7u/v8XnBN8sFsfcYExEVBdieNz/7OX1b8f0//Vf6rqdvevbdmlms6Osj81kRZBrWc//uB5bXL0Ap+q5iaIPUJzTF8bwoFjSnioQYZSL6rg0WjGkcznewONtjjGdWpjR1RF2fKIuS+TxYjPVthzGhe6dzHqUiFJrIpDhvabvQGTOKIpT2pKnBRJo3b96w3dW8//BIXoTCtKFX9NoRmdDkKYoMznmSJApNgoYBrcA7S9c0Z+1mQlu32MGSlTOGoccxEGWh6ZF3ivrUs92eUGQo3dF2NjREcgNtXbE/nIJtZO/Y7E6kqeZwbDlVDSZ29H0MlOcaAk9wHyLYQ1qH8YZIh67AgemRTT/IrIZhwLoeYxTlrOD0UKH41MBnytIKAyisS99bum7A9S39MDCctfWRTkjiCFyw++x72O8bNtv6z19G/w0fn9cUfT7GXx7/kuuAML4CyoERZMgG+8MPP4xM+RToi/REWsRLR83dbsfj4+OY8pb7LQycvCbFbFPwMmW/5BqFZRTAKEV5n7KOapRLycYtIE+crUSXLexwlmVcXV2NcgzZ1H/88cfxdek0KgWJI5lyOnFzc8PV1RX39/cE16xyBGsiSVgul+O++iXTLAGqjItct4AWAWFSrHg8HkfwKy3EpbOlAFVhEMV6UAC+WNddX18DjGBVwJ+AN2FM5fcF/Aso9d6HTrxn9lTOQ1jQ6dwRsC5zUz5LAi4pnm3blufnZy4vL0c98Ww24927d2M2QACYgK6/+Zu/oW1b/uEf/mE8v+fn5/FeSlbCWsuPP/44Nn8KHYHbkcWVoEBY5GkWaCrLENnFfD4f5/xsNhvnvDjgyDXL8yFgcap5loxGHMf87Gc/Y7PZ8PbtW2az2di5VYIyyRRJcCP3XOay6LqlCFLmvdQMTG0AT6cT6/V6nINSLCnBl1h0DsPAZrOhrusxmyFzUe7l9H8Z56lUScC4AG657xIozOfzUT/+5XtKkDl1T5F7LMGMBBqi45cxl7VnvV7/6wso/38x5FOJynRx9eef8RM/UyjngwTFB/tCpzReaSKTUF5ccfvqax4fPvL0+EgWaeJI0zQ189mcNFJo19Oe9jy8rWibhsNhT++Cx2yW58zmM4yJ6fuQ0u9tR5okeBTWebIsBx26GOZ5ifPQtz1t1xHFEUM7ULUtZj5jUJ6qbZktF+z2Rzxg3cDT+uGcvjF09ZGua0jmodmRNprDfs+xqkiTmGZQXFzcUmQ5RZqg3ECc9NRNTTNYTk3DcNgTbdYsLnaUy1uKbEbb9iNrIQ0mlFIUZcGH+0fybIYxGb2DvJjz8LSjtxBnBXZz4vJqHgrXOLdVjwbSLAXvA2PbtqNtD+rc5EYpvFJ4NFEUn51aNM52gaFMUoZ+YL87cLm6CPaBxp6ZUU/fd+dCnTmHwwmtD2RZjNYhK+KsC8B1GIXDoZgpGljNSw7LgtNxSxx5iizCDjXHQ89yNsP1A21Vk+UJRZFies3Qu/PiC1lW4JVjGDrQil60grEiKQuazhAnJXoYeF5/pMhhMS+5t7BaXFJ3J/7xN/+VcjYnz5d8/8NH3rz+KnQMjRNefP1zHh+eePrwgcPuyNVFyWG7pT5uaU4ZVzc39EPDw8cnojgnSlK8C3psozVZkqDimDTJQuv43pEY6JsKRQpKkyYJSRRT1w373YbZbE5ZxNw/bsjzBKUsxniGweG9yMQUCh2CSRvaxisLfW/Bd0QGosTgXMRsseJYdfzjP/6B3ebIdnFEo0njGSbSoIbzQiYuFZ4kNkTGkKQpSRTcZ6yzGBM8WD1hgwvzM9QXZHnO4dCjdIqJHFCjNWjj8D48a8MQPMq1TrFe0bSOQ9VS1S2p90TRkrLIiLXG2wHF+V6rsMgabRj6UPuglcZ7cE6jdcIwdChtadsGa8Nibgc7smJpmlFXpxFkSQGbgK8kjegGS911VE0FXqFNRGQUOEtvB5yDofc8PGxJsvL/7DL6b/740+L/P2XDvwTnUy359O+11lxcXPDVV1/x4cMH7u/vR4asqqqxKFDkGwIOZWPv+37cUGVDF8ZWUvuim5aNWTS80iRH0vMCsgTciC2hsIGPj48jaBFN+3K5HCUJ0glRPv/q6mrUtE5Zxyn7KYV/FxcXlGU5noO4X0wtBN+9ezey0+Jicn9/P9YOee+5ubkZLSHFQULGoaoqmqYZ3V2mumM5wv6QjM+PBDtiX3d1dTUykQKeJNBZLBaj/EUcVwTUCdspQE3A1Gq14uLigv1+PwZDAnjE1lcs+6RbqBQMyj4qAFK+SoAhbeIFZIo0Rtx0VqsVTdPwX/7Lf2GxWFCWJX/4wx/4+uuvRxvMb7/9lg8fPvDu3Ts2mw1XV1dst9vRE//Fixf0fc/bt28/c7gZ617ObigS4EmWQu6RBJEy38X2syxLPnz4MLLoItMQ4Cn3TEDvVNYjAFvkF1Kc+atf/Yr1es3z8zNKKa6vr0dALuBTAKsw8XL+8tnT9wVGSZA4o0j2aFrELWuyZLOm1ppi0SkWo1KbMGXeBYCLREaCMnlfYb2nRanDMPD09PSZC89U/y7BqwQMIvWR85OCz6m9pgTafd/z8ePH/xZFnVP/8c9eZWqH6MfGQX4E5VNtuZLiHs7RTFAc0FpPP1hMmjNfXvD4/gc+vHvHq7tbtLO01RFvB7IkYr9d8267JYljnHeUiznXd3dkWcFgg11bfmYF++FToYl3IQ0dmXCD27YjSVNMFJ1bd1cM1qG0A22JY03f1ZxODf3gqas6NMTBBSZwgKE70TcNqoxRWJqmom5O1G1NWlygbIxXEV5p0BodZxAlKOtQamCxSvBRjHU9m+dHMAWz+SVVHcCEFOD0fU+a5UQmPkdlniTzmCQGpShnczbbDWU1A1NyqnqsdQx9R6QUisB6zuczTNDZnCd2Rl7kNE2Ps0EDqY3BE3yfh6HH6E86qbJckMYloSHQgEKRxAnaBAu9kB7UAW/r0FLdB8FKSCvqYLFnzr7B4Im1oshjlrMMXIJhIE8jkjhl6LuRPYnioF1XKnTGtErhnccpC3jQoRW6dBbVsSEvC7K6obWKvnfEScxgG57XH1m++orZYhmavcwzPm6e+C//+f/LanbB+mHLKs+YXawweURW5Ny8eMnm8TF4cp8Uh01FkcX0w8B6swZi+rbFth1ZmpCXM/quYbvd0DuFdobDfotJNd709F2Mtx2RDnUKFhfcQZKYummoztpFrRV9X1PVexQalMY6i0KHe2xd8MQ3YTFOkhRc6ByqGNCEbpppVvLqxQ1PTxuauuWwP5EmMbMidHf1zmKiOOj0XRdY4Sh4vBsNURQkadqEgM17R3U6hLngQ/EnKgTQp7pmt92z33d4p8mLBKUcbRs6fioVo1SPVxqPoesdp1NDXXfESUIcR6HuwCisdfT9QBwZjFch6NCKrgnuC+oc8NnBhoJWoGn3Z4CtaOqaquqI4yysQ54xXT9tBS0p+lN1omrac5CfUWQ54nEPoWusUZrdfncuiE3+kmX0v7vjn9OKTws9/zlt+fTvhC0TP/Aff/yRH374gdevX4+gUkDher0em8x471mtVrx69eozKzkp4JP3FQAjIFLAtrB61toRJE/BiEgphKkWiUQcx+OmLTIIYNQB13U9Aoqpdl4AqIAJAfEC9MUBQ86lKIqxKYmcqwAasYgUeYK0pJfCNAFmAmTEzk0AzPF4HBvPTDXiIpX5Uo8rbGMqHd3O91DGWrS50/s7DcgkWyXZCdFSi65a7PUEJEm2QRxTpl7nsmeJZGmqEZYi0uAANhslKRKY933P4+Mj33zzDcvlcrTWe3h44H//3//3UR5UluXomFOWJa9fv+b+/n6UuWw2m9FK7+np6TNWXCRPwqZLsLTZbEbJp7DiMrbAZ50ip50txYJvyhJPZRUCnAXYC0CV501kfHINVVWx3W5J03SUjAjQFhZbAKxkeSR4E6AtzjdyTlO3HAkqttvtyJ4rpT6Ti0wdmCSTIXKtKTiWgHu0uD0HNV86t8g4KKXGujeR3Ip95VQnX9c1RVGMa4Fcu5zHtBGQZLRkfhlj2Gw2o7zmzzn+oqLOALjlQfpTX/JPzLgfQfsnYC7vcj5UKM4KAxUTZ4ru1KKilLuXX1Hvt3z4/nf09Q+0bUNRZMzmJZExHI8nuqHn7uUdaZ6TzxZk+SzYrfWOOA7OC85ZVBTTDz1u6OmaJmiAr69ZLJYcD0fq04mht8RRRJZmxGmKSjNclKBx1F1DfdjhvaI9VSRJaDNfHfakRc78ckHTJCRJRNc17LZbnHekSUzX1MRxwvG4o0ivzsGII0kj4nTJdn/AofEmYj4v0RqOhw3FfInCBh280mOxTeodSRKRJSmDB2c74kijtOX6esnusOa435EkhqrWRErTNgM+MhTZjP2uwlvFYrlitbpkt93gseG8vMdbh+GTt7gdHGRBm972DX2nKLMlSaLou54ozjGRwXXdWBxn7YDzA0oF3a0/N2xyzhOPTSSCv3hY5JtgezdYri+XFLlG+RAcpLPzYhzH5GiSvKDvG4YhdBa1NjRBsrYLpcXGgx+wbY1yPYrgWe69BxXhCWDVWc+H9++5zEqyNCWKPV55sjTld7//PbcXN5RJwbvvf8d/WP2PLIsZPzy8R9Pz+uU11ekwaosH51HG0PUDVVOz3R/o+o6vvvqK9MUNXXVi9/TEse1o7EfWu4aXX/0dST7j4cOWoW+5fXFLnhdk2QycJYozyixkb1p68jRDA6fDHnVmhz2gz9kBEyVEkQGtMEEXxmB9KHTVBoclijTetSxXJd9+85rvfvc9wxDazsvjaaII/JmxiaLQ3dN7vA+Nioz51AwCQm2Hdfa8LtgzUI84nVq883Rn+ZFSjiKf07ct6+cdCk0cxZSzJUobusGBV/TnjqF5kVPOSpRWOBc2Dc7sR3Rmo/q+PTsQxaCket6TJDHehwLNOEnPYMeQZUHG1PctihDYVfVpBObAmNLEe4Y+2IjO8znL5eqsn63w3gWXKKVwridJIc0/gY//no8pwJZ/fwnEv/zdL7+fvjZluyStLm4mb968Ybfb8d13341sblmWLJdLoigaGfLXr19TliXz+XzcFIXpEkAiTJY0Tttut9ze3rJarUarQ2lHL4WDot8WOcZutwMYbffkb8uyHLXhWZaN4AsY2XPpLDpl0eQzpAOi1noEytvtdgT3086j0mxFHFdEWiMA5O7ujt1uN4IsYQDlHIqiGFuUX1xccHl5yXq9/ixjMZUSTfWzAmrERUUkIlNLyqk9roAkYV2FUZzaHmZZNhaXil/1zc3N6B4zLVoUWUlRFCPolwBNGGPgs+LfqePHdK4Kc/3DDz8wm81G4A+h4Ps3v/kN19fX5HnO73//ey4vL5nP5/z2t78F4Kuvvvqsw6vIIiRjs91u6bqOb7/9ltevX3M6nXh8fBwDrPV6zc9//nOKouDt27d0XcerV6/GAkbJ9E1dggSA7vf7cVynz+JUsvElYBdZmIzNxcUFv/jFL/jNb37zqWj//DfyvgJ+p/drxClnKdVUFiKAX+w9Re4kunlgdEUKTl4hoBUXIPkdmW/yTE/rDmTNkMyNyEkkIJWgTIKRaUG4/FzOSeb08Xgcg2EZx+m1RVHEcrnk4uKCtm3HrqcynyTw/28AyD8BcXEY+NxVJRxS2BkA+5/qyr06+68Ie352aTE6oihLlGvp8jkv3/wMNQzcv/ue5/WOwyEwELNZTlnOePUm+NCiDYMPxX3axMRJYMq8czRtg/MWcBilyNOEw7bnsN2gXOgC6YeeJIrPDSZSlDGYLKfqHVVzwg0DqfF0XcOqDJrf/eOONI2pDjWU5Tmt5bFDR5pG7PdHui6wa95ZFvM5+IGuq2mOoXK9nJUoo2jqhs56ojRnHmcY5TBYkig0IWnaKkgHkuADba0liQ1nBTeDdSSxZlbMuL2+4OOHjxgDRkGapHirONYtkQrt2k8nqQwPi3+aJmPQFEcGoxV90/L8/ESWGb7JbvFRYCOHrmfoItKkwFmF8544CY4oJuJcBFLStA2bzQbvLWmSnQF5eKDk4UySsIAEO8NgZWday6sXt7TNMUgj7BA8z62mH8L3gTlqafsO5T1GxQxdh1aeoW7p6grXNai+wnYNUWSI0wzVGJRJiJOCslhw2j5z2O0pcVR9i/UDl6srrlZbFrOCMk45bB74/W9+xdWLO+rqhO1qbN9wsZydJT6ezWaNMjHL1ZKkbkiylN12y/P9e07bJ7oupMQshroZ6IYfuby4JjaO9vhM21R0ZUTsejrrSOMIMHgF6JAl8IUD7BkMurN1IIERVyH70LYHtDaoOKfvfLg/ylE3DSZRZGXK4XQgzxNuby+oq5rTseJ42lOWEVmuWS6Dbj8wZip8nndkWXJeEB3GKKLY4J0P35+b4gwWUBqtI7zrmJUls9mJ06kF7UlTjyIA67pp6SOLikIL+zjpMSYGBWlehDqPJA7Bnj9rSiODNnpSNd9/WhzxwSrSG7puGIFBZiO8Dz0Lkjhis9my3R5YrZafLZLCgkwLwdI0IfGKLM/C3BqCc0+kNSpLoPbMipzO9szmf2XI4U+B+JfM90/93vT45wo/heWU4k7nHEVR8M0332Ct5fvvv+fp6WlkOOfzOfP5nG+++Ya7u7vPWFIBDAIKhY2TzVwYys1mM4IMYcwvLi5GIC7gWkCUOFyI/vz+/p40Tdnv96O2V+QJYk0oXS2dcyyXyxEkiPRFivaEyRadrwAkYSMloJy6aAhTJ6ygANa7uzt+/PHHz7TDzrnPgJwEIAL65XcE7IjjysPDw1j8J9cvAEtYU5HfiKa8KIqxUPX5+Xn8uZw3fAJdAjKF0ZXXX79+PWaNhekWhlRA39QdR+wHlVKjP7iANZFESBGpMWasJZEsy9Tx5+rqiqurq7F5znq95u///u9HYC3Xf3FxMQK9p6cnjDFcXl6OcgtxM5GCRpFLyHwSq8H9fk/TNCwWi/EZkQBLGGS5t/JzmefCVEtgK9r+qce6vC6ZAsmIvHz5crT2kyJguXcCWqd6bJF0CLMsQF3ux/S+yt/NZrPPtNuSVZmy0bLGS4YFGLMV0qlz+mxP5SQSpErQJ372kr2QMRA5U5IkPD8/s9/vubi4IM/zsUZFiog/3yM+NfUSidF0fQjy3RnDMIz9Av614y+0PQTRhQugHn86LrwTecoI3j8VdH56Nz++nT5HEsordJyRpHOubt/gmo6PHz5yODXkacKLNCfLiuCZnGdsdluyosQ6D2fWTauItm3om5aubxlsj/NBh7xarYjVC4a+p28bnIMkCpPHKMVxv6NuWlSa4nUogrRndq5vakyaEmFp+wpHaHAEKavlnOf1lup0wkQps7IkXqXB9SKJSZKIpq5xUcTusKPvO7phdZaxRGR5ih1ahramyGcMzQGFIs0SBgvR2aax73qGc7CB93jlsP2AToNc5vbmgqY6EUeGSBuwDlA4GzpZZlkZfM51TFPXdF1N0huSKMJoHQCTcwx9i7Ud3TmtFEfQNh11NXDYDWh9pMhLktTgZxF976mbmiyN0YZRSx7F0figOufHB1ErTSwpWQVJEp0DpgCg9k3Dzc110AafbROtC106nQPnOrwdwAUXjKFr0PixuBfbUxiPa3fU9QnrHCYpIE7oLWR5QZ5lDH3PttqjfMtitSRNS+yg6JqOi6IgWc7Ybp54Wj8TJxFZBMPQgE6p244kTUizGQMak6QY61ikoatXVzdst/vzQ+9oe4uPEhwVrq/IohVXi5w2tpQx6L7BenBZjlMxaIfXjt55miYsTnESYyJNEsV4FeZFnAQ/+NPxFIpnuo6hIzQoageetzsurhcU2jAMPd4FWceLF7d8eH9PkgRw3/cdbddSFhlZlp4XnvDI5nlK14e/t27AeEUUx6GtfNcH1tokZxYqPO/lrORiNcNaR1V3ePXJ1mv9vMfh6YYGO7TESc6szIjiiHJekBcZWivSJPnE3usQBPR9j7duzLIMQ3+uS7AYHZ0X2yqw+XHKYLvzxhSfsymfuhsKUzLtrud96HBqTBjvyJgxezQMobC1nM0gjSlnGSpyzOZ/1ZDD5wz5l2y5/Pxfkqf8S4ewdwJI8jzn1atXdF3Hjz/+yOFwGAssJYVcFAWbzWYEvfC584JIBwQMLpfLsVukgDWRTkhmaLvdjs4XYn03dXGZ2sxNr+vi4oKnpycOh8MIfq6urkYGTZqaJEkyMqjyHlrrsS1727bMZrOxGFWAr1yX/N2UzZ5qw+/u7sYsw5T5kyyBMMxRFH02RnL98r5TFlqK7ESGsNvtzvLG8jM9tDSsEcAkYFks/QT0y5wQVlx0x1L46r1nvV7z4sWLcU+Reyj3Yvq9BAoQmjs9PDyMYyZNf8QuUph/mT/CajvnxmJFsQlcLBasViuen59HH3lx0BFNv8xHYbUFtEk34fV6PeqYp9acEtRIkyHRZotGXoC26O4liyDXIAGZ6Lh3u93Yll76tUDIsDw9PY0WhXJf4jjm9evXvH37dhx7mQtyX4Vc895/1vRnyoSLJGpaayDP/Xw+5/Lycjx/Oa6vr0eWXIKuNE1HEL5YLMZmRTKvp2uE1ClIlkXWdWH7pbmUBDNTS0yRv8j4ynyTgEjGR4I3GW9gDOBFnibnbIz5vx6Qe88XgPvzQs6Qtp4urvI7k9e+MGbxZ0s8AKU0yiSkucGoCN/ktDcNy+uXXFcViyLl5ctXxBEMtqOqT3T9EBw7+j7YHdYNWhlwwbPcDR11c0JrT5ul7J9m9ENIW6w3G46HE1qb4I+cZZxOFSjN5e0tURYY5DgNEZrtGog8SRTRRZ5Ie7IsZbmcE8UxSmusCz7Fi7N9UFVXDEN4AFvX0LYNbW+J49AF0XlPVpTkSUjBn/YbImPIsxSd5AytBpOChqapSNOcyBiuLlZY57DOY11o963oWcwyvv3ZaxTBdSKKEp6e1jhnORyOFEUemEDXYiIpQDjb87gOYxS31xcoH5xZXtzdkmcph8OW4/HEZn2i7zTex7x48ZJlMqPrHSaKqZsDduiYzYNHaxInI9MTHlbH0J9ThM4x9AM6MufWtIByxEZhopQXt7c4a4mMQXmPtT1eWXADQ9tQ7bcYHN4O2KFHY3FdS3vaUu+fKZIEhaJvKmzXsz9U6HxFPltxqI8oHTzn/TDw9PCAtQ0qSri8ecHl1QvyCKIkojruUNqQJQWH447BOJIsxymDimMOTQdExEnBgKY7pyZX13fUhxqv92TOsd5s2O82oB2FTjhsnlktZ5y2az58eMcf/ukfwUfc3b3mb/7OoAZLXMwxicN5RRrHmCgljhOSJDgTVXUFkcIYjdEJaezJs1nwLq8GtNGstxXHaiA6DcS5pap7stSiNBRFzus3LwAbHFV0sFzM0pgkiek6j7Xh+e36HqXAmOgzC6yuCx7xsYpRWmFMRN+H5jt4mM0z4sTgnKKqW9bPG4o8o531tF0Lg6YeOoz2KGVZzAsGN1AUCZGBYeiwUdgkijSj7RqiOEOb0EBJWM1+GHDeo5ynOlVnAJNhTLgurYMdouhQkyQedYjAyIpJsVSeZzgHZZ7TtC3z2YzTsaLrGvJz1ivPM64vV3RDThSbP3cZ/Td/TAH3T8lSvtSHfykX+Cn5yvSQeygs7e3tLXd3dxyPx7Epi2iApVBRALY0V5HPFZ21OE5st1seHx9H+Yqw7sL45Xk+2t29fPlyBANTcC4SDdncsyxjtVqNoGbq7bxarcZzlEJLAcGScpeAUcCMeFmLNEWkIMLiCVi7vr4eQak4m0Cwd/ubv/mbz3S+UuwpbL4wgQLyp1Z5xhhevHgx3r/Xr1+T5zm73Y79fs/z8/PogPL69esRoArTL/Z6ArjFZUNavgtgE2AlTiHyrEog8erVqz9hX+VomobtdjvKJORnXddxOBxGKz0BZ+IFLzIIkfKIS82HDx/GLMjd3R23t7djUeZ+vx8lFLvdbiw2lesT1xcJ/gXs3d7ejlkJ5xzPz8+s1+sx+7Fer7m4uGC9XvP27Vt+/etfA6F1/P/wP/wPWPupY6sENgLIZV5KIx5hjYXNPRwOYxD1/Pw8WmxKkCBzSKxG5f1ljsvck/s8lXlMGWsB4xIYys+nNQvTjrJVVfHw8DAy4PL+AqS11qPrS1EUYyZEnncxvxCJioBpef7l2ZP7K51m5b1F/iXPm4wLfOqWLvdUgiyprZjP5xwOhzHrJUHKzc3NeI5/zvFnA3Kl1GegXFjvKRMifuOfQLv67N9SzvmJWP/0nqEWVIevJsFFGfnyiq//5peU85LINqRpRHXcUtUHokjTdz3P1ROH/Q7bt1SHA8qb0ExIw9A1tG1FHGuag8arZ5yP6Ieep6dnrA2pFmt6Iq3JkqCD1ngiIEljkjSmtz24DmcNnQvgpJwVRFlG23UcjieStGB1mTMMnqJYkaQlu2ODVwnWG9J8htYJM2OIYg06OIIoPInRpNpwqmqa45a2LMiMQvkET+jCaJ0njiO6tiGJI+qmITbmDMhU8D/vGrI0YjFfElw4FM4u2W5D84CqPpIkC5QOKXhrLWkcnSe8YrUI53ixWuJ80Lp3XU0UGfK84N3piaf1Ca0z4iRHRYokMcxnGTpaUZ8ORCYmSjKUU3RdP6bJAtv6SQds7QBabIWCl/zQ9ZTxbIwmbd+drfIsfdcwdBXNcUtz3JElBuUd3vY413M6HDlt17iuwsSapmo57Dc8bR553g1cpF8HS0sLUZKRJCnHpwfqpgp6ZKvoBsP17Vf07R4VDegsJzMpWXnF/OIW2x3A9+TFHFSM2h/PkpCcwRuyoiROMorFNVmp0eWR9fMz2SLhwsw4no44B7vtM90/VTw/P/Hrf/h1aBxk4Wdf/4I8zbl98ZJi2ZDO5wxOYUnOGvOz/Mc7sjQUCrkB8rQkiXLiKKGpdzyvdxxPA82g0JGhbhT3jweOx5osHShLg9KaPM+AgchI9sKy221H8BEWyQZjNEkSY4xoyhXeBdY6MjFD7+iGGq0jinxOUaQMvUdrWMzDWO/2J9IoYndswPcURcQquWD9/EwaQ5Yq5vOEflAhuMgj4jg4xwzDgEt8KB7WGm+nrheB+Y5NQtdZTqeaODaoeYo2mrbuKYsCpy1d2zGfLxkGO6bnhemYOkn0dqAsC7I0Ic1SNs/PODdQ5Blpkp6zZo4iy4gHTdWe/txl9N/08c8V6v0Uc/6lPOVLpvyn5C7T7+WerVYrfvnLX47Ffnmef2aNJgWXu91uBF6yWQujHEiBePSHhsCECVCVbqDCiE3PR/TaIoGZ2hjO5/NR2rLf78mybNygpVW9dPsTGc5UhiBZG/gEREVGIMybSAOEHZSW8NL6e5rGFx2zWPfJvbDWsl6vRwcLAV8i0xCmU1xuRH4hDKTIPsqy5Pvvv+fx8XEMGkQiJM42oskVgCpBkgC5qQ5YGNgv9b3z+ZyLiwuAEbQLEy46XhlvIQ9EliPZB9HQSxC2Xq/H3xdGNkkSnp6exqBJwP3Lly/HQEikFLPZjMvLyxGAlmX5JxmVaXC1Wq1GG8yHh4dRKy0NnSSwub+/5z//5/88ZnJ+8YtfkOc5r1+/ZrVajS4wAgClqY4UNorOX+ROQjo8Pj6OOnfJbtzf34+1DPP5fMzMyPMmc0yKpSULIC5AMhYyryTjIcBZgmMZG/m3gPLtNuw9u91uvJ6LiwseHx9Hhl0sFMUOUxhuyTzI3BVveRl7WS8kKJPnS4IyGUdxn5Nzk8B/ukdIpkM6hGZZxuPjI2I1KhIsuQYhB/6c4y9kyMel6AzGPwH1z5nzf45JH/98fNOwyCpQCucdSsX4SIEDlzRE5YIkn/P+j2/p5hkRA9unNUkScaobOjuglCNJUuJljEbjnQU7MF+tMNEKhcUOFusD1B6spUhToihYoJ2OVXCSMIooSUm1QtuOWGlmWYFJCoo8+EZb53FeUc6XDAqaxmJPNUk6J0lLokQTZwuUSak7QsobFbSncUEWxQyuDUB2rqkPe4amRntFtd2yXz9RV0euXn3F9YuUKEpBhc6bQTUe2AS8QinN4AbiPEyK/WHH8XCiiTTiTR3HsLooyIqgD7e2RWvIspT2nL0Y+oEsS2mqPUr35HlJFMe0bUXTdUFWkiXML5Y4lbLd1xyqI0WdUTUxWaZRGJI4p8xnVKcTWkXM5wusC24vkYnAhIUVBdZ74vPCOwxhUR2swwJd1zMMPW1TkSYR1vX0zYmh2ZO4mqE/4q0Hb8EPDENHe9pTH7cMbU+DpauPNNWRw35PXWsuMOgoJclKsljjjyXPxwpQxFlGOV/RDhqTLrl/uicxHZHRJGlO4xOyPMXECdVhQxbNmS+uUGlL1pxo2z2744nbqwVJPuPQG3qX4LOUeJFwfWm4dgO//adf83D/lt4O9F1Io3qrmOdzDJrDes3f/3/+33z7Nz/n7us3zK8uIU7pSPHqHADjcTZkJYzW53tqOeyO7HE8PD7z7v2aDw8H0mzJzYsXROkF/dBSNxUf73fcXKfBnzwxRLFsdmFT56y10zo6F4smZ3Y8ZB3atqYfLLNyTlkuzmnODlcFf/A4Thh6xak90rc1ymuyTFHmBu1STqeaxICJPLPC4Iac+aLgxd0Nz/me7WGLoiOOgl7d9XYstFRa0Xcd1anidDqMwd7V1TV5Pme3C5vr1dUlMON0rGm7liTu6AdHUZaAZ7PZjP7HArBkEVdK0VR16N6rI6qq5u27t6yWK7zzpElKepYJdG1D27VfVNH89QB+Ekh/KVn5KSb9S7nLlz+bFhUKcyvFbr/73e9YrVYAPDw8jEWgIv1IkoTLy8vxXKy1o60fMKa25XthxKS9toBFAR4COISplAY1shkLsVDXNYfDYdy8hcUU2zUBygKwRTYhv7vf70fru+12y/PzM6fTiTdv3vD69esRKAijCIwBisxrcckQGz7RIUsQenl5+dn5CyMIjFKGqRWcgKGpHCTLstG7e7vdjoGDsK5yD8qyHCUDy+VyBI1T0Cf3WoCesIwytqK7FsAlHthTv3MZM9EPC1AXe0iR2+x2uzFzImREkiSjfaMEcOI/nWUZHz9+HEGaMLwSuAlTfnFxMfpxN03Dbrfj9vZ2LF4UuZJIpbz3/OpXv+Ldu3cju9w0DWJbCbBer/nf/rf/jV/+8pd8++23XF9fj/NXAOZUuy3Eg3OO7XbLdrvl48eP/PDDD3z8+JGiKHj16tVoIdk0De/fvx9rLySokjEVPfa00c5Urw6Mmaf5fD52K5XxnzbvEUZZggdhnQ+Hwyh5EQ32arXizZs3Y+HxVGIi4FkCUwnCpYtpFEVjMfBms+Hjx49jQy45hyzLRpDtvef5+Zm7u7txzsLne8TUJ/50OvH999+PQaoEDzIHRVv/5xx/AUMu331ZyDkB1Xwq6gzfTxfe8Z0IymbPWPh5ft17BUqjdIKKPSYrmF/eMHQndn9/otqviV1HpBx5NkPrCOtDK3HnLLYP+uS+aYIbBzq0QrYD81mBUQZrQ5q/LAqMCeyCVoAJYKera/zQY71nf9B0/QUXl5ckSUSal1g0/eBRJg6aUmuhc7R9i1aaNJuz2VZE8UDVWLLUoExM31sirVDGcKw6SmKSJEyCquuJURjbg/b07YmuPjL0DSrO0TpY8QznFvRGGxIT0w2O6lgH/+iywLuBWZnStSeSOCVKMryCpm3J8/hsD2fphwEzQBxHJGmMcg6UI4oU4FC+BxQmUszjnCiO6azn1SuDMXua/pl26Ki7mn7IadouBD1dByoizQq6wwEIVnHOWfxZGyc2frJ4GGPoekVvPc6FyoJ+GKirikiDdwNdc6JvTwzNEdsd0UMdPODbE/v9lsH21E1HVx9wVkOcYPsOnMU5i9YxDh06gWoFypDE8Th34zTj4voOq4KH/fPuib5dE6cRKp2hsgWDNrh+oLGa0mTkyxuyheL58Z6Hpweq/Zo8iVikc/aNxxRzLi6vmN046sOOoav42d/8e9qhQ/uWly/vqOuO5exIe2pwXUdbVbz/4Q8kiUfHFmU65pc3JJnB02BdjTKabuhgUCRRShybAMwNKBNzcXnBw7rCqRNVa3EqxZKiTESUWNbrA1rtKMoYCKBbmU9WYMXZoqvrepZZSRf11FVF34cIXxggbzVpmoAKbe27ocNEEQ6omuYsEdGgLHZoQnFqGnN9eQFYUJaiiLm6eEWSFmSZoc4MzicMgyWJAWwoHI5jPMGm9HDcs9/u6Lpm3CiDu49ntzvQtsH6LU1S+qHB6KClb9uBq6tL1puncd2SboGy0MrCHthIzW63pWnasXPjbDZHT4qptC4wkSFLP+kY/3r8Kdv9JRv+z+nK5fgppvzLQ+5TlmWji8n/8X/8HyO7Ni0K9P5TMxO5x8KmAiMrKa4NAjzEx1uAuJyH2P8JQG7blpubmxGUA6MDg2itBRROC0c/SaTyz0C5bPLAqMuVoELAqjCTU/98KXyUdVVS+dLdUjzLZ7PZyKKLFKRpmpGhn56/jPE0EJoeURSN2t5hGPjqq6/G6xZAKcBZqU8e4CJzgU/FqKLblXH6bI84j6Ow6GIxKWBTOo5Oiz1F0rPdbhmGYdS4izWmyBimQFMKDKdATM739vZ2HIP1ej0GA6IJn0pkJJuwXC75+PEj9/f3o+e6WBaWZcnd3d3oftN1Hf/u3/278by++uqrsZmTFCGeTif+8Ic/jBIVrTXX19ejdEWCKZFoiY2fjKPImUSjLfdFnpE0TXl6ehqZbGAM1GReiHVo0zRcXFyMMg6RG027pUpQJwGGgHEpvJ++r7DeNzc347iLS1GWZeR5PoJ2+X1gBMEi+drv92w2m7Hz5rTHgLwubkkSCMp6cHNzM44NMMpTJFCdduoUW8O6rpnNZmPHZxlr2SckyPtzjr+4qHNqe/hJxvKp4HOqJVdnC8RP2vPz70y7e06+GxdtpVDE5OWceRGjbcXV7R3v//BfuZrnvHp5R5om7LYHejsQxRFZnuOVwfYDdVPTNTXt6cT22ODtgCPCmIg8L0hMkK3o86DNdCjgUkDfDSitqeqGU1dxOB5J0oQoz1BRgorSoHw3hkjH6NbjMYChGzx+8Gf/c4+OZ1ijUXGGHRqO+xNJpmlbj7VNKCytQ7rUWYsfGpI0wvc1tq1QbiCJIrwzECnqqjrrzZuzNlajCClHFKRJjFJBCzzYDt95TBwTxZ7B1sQY4iSha1rcAINzNLWnyHOCXzhkyVk3aEPxaJom5EVBvz+wmJfEUY6JYp6envF2CK3Tz+4gOgnt7Nu2JzIRWVnSNBVtWxNHEJkIa9xZixxkM2GTc7SttEb3eDx5kRP6sDRY1xPFGmNjXB8W9aGuOB627HYbrPd0Fra7BqUSsszgVExvPU3TY33Y1LaHE/lihVMakxShyG8YyE2MiTMwBYNTlKsbfB/hfY1JE5bXr1Be0R5jvDIUy1uyxS0WTdQ6bPSWXf2O9/f3uDjFZreUiyvyi1cYHXPqf0SbhFerJbv9M97W3L24Y73ekRU9Q92xf34MDHS95+P7HxlshY4teZmjohjtWvr+iO+g7yxZltP3R46HhjKfk6cRmAgdZcTpM3kxo7Mxi4sL/HlhWF5cs9+17A81RZlhbXiejQ7FtN67c/GtQaFpmxaFpussh+MBlCaKwybdtH3ovpmEe+YczOclkYk4HZ+xg0VphVaGtmlJ0ojlakU5Vzg/gLGgHFeXl+wPIfhMYs9VPqMfDGWRYhQMzmJUhLNuBBd1XWNtT55nXF1dM5steXo68PAQ7OSSJKJuaowJ5EGw3DynwFHc3FzT991n1l3D0OO8JTaG+bxkvz+CV2gMsyLHe0dZFmE90wp1ttH03cCpav78ZfTf8PFlIedPacP/uZ/9c4WgXx6yR8jvzGazkUV7+fIlv/3tb1mtVnz99dekacpmsxlZuakbgrhtCEMqzKs4gQgQnnbqFPD6pWOHsN/CqAvIEOmJgB74BHTlELAk4FneSyQK4uQicgpJzU/1uVLYCYzNhkQPO7UVFImNAONpMZuw8gLKxNdbCu6mFnsCRkQKIvaE2+121MtHUcT9/f1nLP3U41n09mVZjgzytKhSAqhp1kKkRTJXRPow9RSfjo1YDAbHrzB24poic0GApYy1NBmSsZL3F7mSaL6lOZOw3FPGVSnFxcXFWAgoko7T6cTbt29HV5dpEbFkSi4vL8c5++bNGx4fH0ed8uPj4zjvfvzxx3GcZrPZGJgI4yzSJCkYFftGCdTEN12yRAIg5fN3u934XAHjPJKiXgmYpLi467pRTy9adsmeTC1GxXdf2PKpLEQ6pS4WizEIVkpxc3PDdrsdQfjNzc2ZIJmNQarcF3mGxJmoLEtub29ZLBY8PDzw4cMHIGRzJMMKjBkFeSbu7u7GQFhciqSgU/oAiH+6rEPyVQLJ6fomtSf/2vEX2R5+0o0LMJ+6rXi+1I5/krR8DsqVV5+97ydXFtGnu7McwTP0lsE6Lm+uaY433KzmFPMgi7AKejec3URicJ5yMSPJM6oqI04y8vkcP/TgQ+Ho0PeUqwui2HA47kEpZvOEpq5I4hgdWdK8IFt5lt4Clt46XNsTMt5Bv6u1BquIzo2NejvgvKFua/qmJUpSrHfYuiNJC+rW8rw5BG2s0SRRhMLTtQOddfiuxg01XQ9v7++5PpxIy0uK+W1wsDgzy9Zb2m4gihIiFGkWczqFjqVBT6vOi+UAeExsuLwMnutNVRFlCUUWB6Y9Ttnt9sSLZdCndw1t05AXGThPUzfnjUrRnGpmsyUk8PrFFVlkOB4PeNszL1coBX3nOFUVT48PXKyWxMmc+nREYfAOlFFnPVno9Ng04lYAaSLaM4c2BjvYoHMfOhwKE8X4JIeuQ5mE7eYDH374A3EaEWUlp8bh4xlJtsJFM1SqqDZrus5xqg7cfaPp2oFZukAnnl4/oaMI5T1929H2PSqNMVFJ0bV0x5bDbkOUzXFotI5pOk/beXb7luLCYZUmWt5x9dXfcqy3OHegbRtQNSjFsfF4pciWL9FdSrV7j0MxX1xzbDWLm6+JdIwbLMXyI49v/wDPlt36IzpyXK+vSYsZq5cJXqfg7JmpU9RDHXTcxuD7lsikxPmM9XOFd575fMbq+iXFLCHOTSjUbAfyWUF1PNK0A1EU07UO5S1pqkOAYj15ntG1gYFKs4IsLWk7R9W0tP1Akmq88rRtKCJLYhOcX6yiazuM1lgsURQTR4amqTidasrcgg7PSJKlgOZ5HZxotOnx3jEvZ5SzBUlkQqMqZ3HnjUeYhovliiSJ0EZRFgvqFrb7hv2p5vpqidcWZRRlmbPbHVFG4Z3i4eMDi2VJnqUM5/TsYjEnWDwqrHfn5+lEU1fEUU6aFHiviNPQ0bZuTizznL6F3S4Ubcd/LeoE/hRI/5T85Eu2fPqzf44N/6nPkPcSza+1lru7O/b7PTc3N2NxHnyyW4PApkn7eGmXvlgsPisK7Puey8vLUX4genCxhxvn4cXFWCgmgE6sAgX8TM9ZGF4JBIQZlpS5OF5IwZqwicLkC4juuo53796x3W5H73VxhxBgIoBXzkWKGQVkCviQAOH6+pr9fj8y9nmejwHFZrMZmVDRfEsRpjDOzoUGMIvFgjRNefPmzSi3sdaO2QdxNfn48SOXl5ckSfKZjl4cPoQlFxZaALLcc7neqWWg6OnFreTp6Yk//OEPo85f/NaleVKWZTw9PY3nJH7pt7e342dOu0SKw42AaWFihf2U65NahaurK5RSrFYrvvnmm5F9F5tKAdcC4CUg8N5zcXFBXdejk4zINn744QceHx9Hnb7Mlzdv3nyW3Zk+F9PixzzPxy6yy+WSu7u70cJRAjEpUBSdfNM0o6xEgiuR3UzdjaQBljitCGAvy3Jk9GW+y/2TeS7OJ+KeIg25AB4fH8cAS8ZmNpuNWRkJ3OS9RHsugaNkhDabDYfDYcx0yHMt2SprLe/fv2e1Wo3a77quR/9/maNZlo1ymGkGSTJyVVWN9QSbzeYzNv9fO/6iTp3ChH9aFKfa8k+vyVexPfykM596l5//F/25YpSwCNBXKI7HE+vthjRN+bt//++Zp5r2VNE7T6o0XiuG7sThsEWbBJOlRGlG4jVxnNG1DdXhQGQMJopp7QkfZ+SrBY2HfughiYkgpP4jKFcXQYqiPZHxdHUT9KJ1TRS70KhFO4Z2YJ7GKJNiiWl9RDV4+q5Fq4iszOh6Q9PUWOeJkgxtQpfFYYAeT16u6OsD2/WaLIG8THj54obX33wF1tFUJ2ZXS6wfGLwDFRquDM7hXWhOMpuVDHagrk/EccRqtUQpzgzOEZTHDQN4i+s7EhMecB2FBilN05KmGUmSgVdnwBwKOVHh33mWhW6NJkSI/Syjqfa01YlI31GWOY9PFadTQ5LG5EXO0A+hvbkC5/xovyjFeBIFG2UwsaYferxzeK1wWCwB0OooQjtN7zvQMTpK6Zqeru8wiaFqe5wpSfJLnC6pXIwxnt7HDAPB6s84Bm3p2oHYxDgfB39y5WnroDVeFAUmuWDoLfunH2nbnsz2DG4AB0meMvQxH+4/oosV5eUt2sSs7r5iv7vn+P63eG/QKNzQ0bY91iuUbWk2T2zef0eaZniTQlISLW65vryla1qa3sDTGp3t+Ppn36LVwOZpjdcprTVc3ljysjizswqTpMRxhu81rR1Q2Zz+YGmqhrJIKZdzLm5eMGiFilSQZClPkiV4W7DfV+AzlAqNhsoiIU0NRhv6zuGsR+uYuupwTmF0QtfXdNZS9w2DD4t/N5zIkpjLiznD4GjbE1qf/ck9aJXQdxUOh1OOtus4NXXoH6o0zoVUfFcd6boa63tubi+Jk4jqUKNNkFEB5zl4eWZ5IpTynE4dxwpOJ4t1mrzMiZOINI1J0wQ79BBB1zbBsacP9p1KgdGcAXVEFEeYWFM3Nev1BjdAmWVolbA/HME4VDSgDJQO1rsT734MAH82+2tjIDn+JZb7p177KTnLn1PgKYfWwatZivL+p//pfyLP89FhQv6+bVu22+1nzXO89yMIEd2vFHdKwahImaY6UgEFU4a5qqqRzRbQJ3pW0eHCJ0eKaZt7ARsipZjKa5RSYzAgxW2z2YzXr1/zi1/8Auccp9OJm5ubkYUERtmD/C9Bh4CIq6srIDDqAgq/BDdfNv0RAC0M7NTxRpwvBOwLi3g6nUZpyXw+5+PHjxwOh7H7ozCQwAgmJYMx7ZgoLOjULUO+ymdO2VsBkpLlECed6fnKvBLgKiyxFKnCp4Y74n5zd3c36o3v7+9Hhw3JsIjN77t37yiKgqurq9EVZrPZ8O7du/EZEc22nPfz8zM//vjjZ8HQYrHg5uZmlOJIbcTf/u3fopTi4eFhBMkvXrwYAS0wFjlKQarYOArwvbi4GHXScu0iz7HWfib/Er281D4I6BbmX+7TNGgU2YrUQ1xdXY0ZCblHcv9EZz2VY8m/5Z5KIb/3nhcvXows/FRDL/ISyWgopcZiZVkTJECQdUCCFXlvkUPJ3JfnQoIK8dAXBl7WIMmEyHxdr9f88Y9/5OLiYrwn/9rxFwFy+AS4g7Lkk4uKsOHCoH/OnE+LP38qHflTCzV46zEmxjl4++493fGZ/9u3b8iSBGsdzoW221GcYYcWrzVV1dDZBmNiFIbeQt05tPJn6UOCJeJ5c2R7aIgihcehVMTz7sDQdRz7AYcjTSJiA11dY4eBoW1RxtC3DauLS1IdEyuNMoq6a9GuI9MROoY06QPwxdJ1HqdgebEkjjWH3Y5T25DlBdY6Tr3FlAuc6vDnhWu3PUAdsbz9Ggj2hqCIkwB0bN/jnR9tlpqmpqr8ZLEMadAoTXEuaP3cYGibhiSKqZvAqs7mS+qm5/5xTRRpFrMZtm/xWhHpeIwGQ6pX/FVD58flckEUmdCtUQfNnTGaLFuS5cESqu160ixjvliSxDFPz8/nyX1u9IDBaMfQd1g7YM4pYkMUZA9EJCZH6wFLhbUDWaQo85TVcsWgFMQz8uiCuLzDkWG7gfbUMnjD8uKK6yQlix0nd6A5PWJcTtz3ZGkBWtF1Fev1R8zyNSrOMCojzS/Ybd/R9eFa07wkVRHtDBwDrt2S6wuavsdZy93tNySDpesrumONfXhgcZlgXcTj++9pdm+h23D74obOa1aXL+lJ6M0Cn/QUFy/Jl/d01Z6yAOVCwfJhewT9gOtbZrOSNA21ANrEFPMV1segM5I4pema4AwymxFlVwzKsNkeaF1/lgMplHJkeYwdFG3f0z3X7CI4HHMuL5fMigJ31iFa6zhWNW5wNG1P1/X0juA6hCKKFCoKDjqhKCfjdDyFItxe/IMzButQKix0282OoR9IEwVG0bcWbSCNU/S5q6u3DsYOnZz1qx6tP7Gc0sGzqlvqSrHdHTlWDYP1NF2PMQ6toK4rsiTH+4H5vMSfC7y9OrOYwviUBc47DocTziuMSUBH7A4nHp6e0RHks4T5Ys6ptrz/8Mwfv3/H3d2KNHvxly6j/90c/5xkZfrzL3/254J5+JRqlgZBh8OBv/u7vxulCyLlSNN01H9O3SXgk75VJFHCtD4/P48gXo7NZkPXdaM0QFg40ZWLVKRpGq6vr0egI44vwmILaysyCDm3q6sr4jgeta7SsEr8nwUcLRYLNpsNVVXx4sWLUZYi1yrAQphjsYSTrIGATtGRS4GsaK1F3y6fVdc1Hz58QLoniqZcGEnJJEhhnLyHMOtTyYwAOynQE03varUiTVMeHh5GxlGCkinYExmRgChgzAaI/GTKlMKnTpXTDqcC0K6urnj58iVJkoxFhRI4CSDvuo7Hx0eWy+UoQSrLkufn57HAUcCgaPUFLErQIVaNfd+z3++5v7/n+voa5xw//vjjOLdevXqF957r6+uRtBI5x2q14nQ6jZ8humgZe2G7ZczETUcAvlhISqMfgOfn53HM5FkTnbhc92azYb8PXc4lOyKBgDxPU19/CbIkyBUHpLIsPxvfae2DjPPz8/NYPCsAXeaqPK9Tn3kJaKZSkakfvRTvbjYbjsfj6MBiznVtYoErgSt86iMw1Y9LgLjdbseCY601u92Ojx8/jvIhsTN9+/Ytv//973n58iW/+MUv/oVV8tPxF7isTP1jYaopD0Bc0pHwucvK58z5nxyTbp8igxk/RD7PK9qm5bg/8uHDRwyAMlzfvkIdDdDibIo2Mcok1Meaw/FI2w54wA0Ehlgp6HvWx4/hXI3CGIhNT5pEVE1HGkUcqwD8bJ6i3IDtQ4vuvnfYbuB4auh7GKzHE1EuVjSdY3uoGTwkeY4aCjrviExMms0hMcR5gjaGvotoW03dtWB7OiArZlzMM9LIo7znfrPl8tUF1lmOxwO9N3g0aIWz0A2WNE6IzpNlsMNZg2fOE/ZcdR3FGG1AQZzEuL7H4XEedocjSV6iTExb1ZgoAROHwKMfSM4pSiCAZWNo2w7r1HkBj0ITmeGTrU/QUUa0ZwCHidjtj5hY7BSDbV4Sp/SdpW0HjPKkWUqmMgbnz1X3NkwFF3ykI62JlaGpj+zv37JbP9P3Az7JMMmMnhyrU5QpGPoarxLQKbP5gqvrJSoFrS2dOqHaAe0cZRGKkXTsqes9280zXkfM8zlpuiLLVygT4f0A9FgG+qEhNgOP736Lsh0mW5IXS8pyhbr9BtudaPoG5wZ8tWFoO/bv/4mueuL166swv01MMb+idim9L3BqYH79huv9jnr7RNU8s8gzqv0zxip+9jd/QxRpDpsntn1NbDRpkuCqIyouMOkM5jPwUBYJxJpBeQ7HmtOh4VBXJFlMHGmUczgb2t0rfbYWdD33T89s9zsuVktWyyXz+QKtFXE8sK/2nKoKpUwoyHWGrrcMg0fnaiw2MiqA5aEfRm2l9w6lNEp73NnreLfbEpmUNMuDq0tnwTnSLCNLI/KsDHPWh+jen+tQps1EvIem6Rh6x35fs37e0jQDqBgwDENoAmJijVOWJI9Js4Tdbo2ODJyZoOycFo2iiN3+QNv25MWCOC44VT0fn57YH04UZcYqW6BMzo8/PvCbf/yOzWbDzd3yr42BzsdPFXPCJ923yA3ktZ+Sr/xr7/tTshZ5X2lh/+OPP47v+/Lly7GdtYBnY8woNxBgLaBJQIG4qsjvi7ZabAEFhIiOWLTdwgwej8fP2N/VajVKEgSciD5aSBXxiZ6ygQJwxMVjuVyOAcPT0xNv3rwJLktnkCNjJcBvqveWQscpSBRQLTIHuQYZU7HBEyZ02lBIWMOpX7QUyAkYEmA8DMOooxU3EpF3GGPYbrcjAynXITp+YVSnhZNy/gLGpgxvVVV8/PiRp6enkf2UIEUyGtP26IvFgru7u1GmAoxjINaVYiMpXuHiWiOaaDlnmUtRFPHDDz98prGfz+djMyspfBT9/Nu3bzmdTnz99dcAI5ieSqlub2/Z7XZjIWFZlqO2+pe//CVxHH8WIKRpOgZG4uYi2nsJavb7PYfDYfTclvGX4G4qNbm/v2ez2XB1dTVqvQXon06n0TlHxlnkRDKfD4fDKKmS8ZW5LPduGIZR9y/PhdQcyHMj9RqSFZkCcnn+pOZCsiTb7ZaHh4cxUyKfJZkxYJRqrdfrsVZBHJzkXCWAFMnM6XTiw4cPo+ZeGiz98Y9/5Fe/+hVPT0+8fPlyBPr/2vEXuqx8bmk4laVIQefkL8aff5KrfFpAhTH/UmM+LrhnOUwUxWRJymqx5HaRk0eew+HIy9dfMVteEiUZx8MTziu6wXKxWBDlC9JTzXZ7pDrVdN7j0KRJSLfUpxNouFiuMIkh0h6NQ5mI1y/u6PqWuqoxirF7ZZaFQpn1Zo/1MI8KTvUJ6yy1O+KUwWsdJBpdRxJpbN9hlWboW3Q6Y6gdDtCuY15ExLGhb3q8jcAPpHlGmSe0TcXt3S3Xd3e0TU1ctJh0jvWhXb0y0RkoepyzASTb0KxE/KMlzT94sfgKD7aODEM74JXicDzR72rK8oKqaVEmJkmDU4r3IZgZ7EBR5KzXzywWC9q2I4rPBSEuaMGdD7rIwE6kQB404yqmLAu63nI6VWRZSO07G5rKQNBDn6qauFOsLpZB091btA4NoqztwFq8cvi+pznuePf976kOG3rn8RlkscamKZ6UoYeuV6TJjPnFHUW8YLmKaQdLWRh2pxNDW+NdsOjzKIoyIYk9uA6wOGdI0hlxNmcYjrSnI0bHEDkO+yPrhw/U+wNuOccohS5y2m4Ak6BTxd3VJbEGW9V898ff8+6f/hOrZU4WXWG9Iytm9B5UlHGoB5yzlLGhvLhhdf2S9//0kaf7j2zWTyRlydXLVywvFtjecdzsUa5jUeQMdUOclZi0JPIWUxakiad3nkM7cDzlKBWzXFyQ5QlZorB9Q11VtF2HcwNxWhKZmN62PG+3HI5HusHhiMizHNSnoilMaCTVdwMDgdmOtEb7wM7UTT1G3rLohcU2PP9RFJEmMZHRgMM7SxQZfC8OJ1CWGWVRUtehKCeNE7x3wCeG3J/ndNN0bDZHnp9PtN3AfLbk6vKaWZmgzUB1rFksl9RVQxJ5+r6h7zqyuDg3xtJ0XUscJ7RtR101GJMSJyXWGZ42a3anmnKx4PLqkiSb0bWW3//+R3748R0XFwsuLi+C28xfD+BPrQ2nr/0UOy4/n/57+jfy+pfvK3IF2SizLOPi4mKUVex2O77++uuRdZUCrL7vWa1Wo4Z8vV6PTKnoZKXoTLS9wmIL6/jmzZtRnjK9BmGhn56eRjAv7y0ARwClgDLRO4s3tqTqBThJUag4WEgHybquefny5dhGXvyPpwBVQLZoykW2MO1cOgU0U8/ztm1RSrHb7Xh+fh5lM+IvLsAKGIsGHx8fWa1WY4GeMKkiwRFbwmkgI5Ic8YcWRwzJCsg5inf05eXlZ04sAoYlQBiGgcPhwO9///sx+JEgRMZn2n306uoK6YjZ9/3I4H4JGAXEyn2U4EUCK9GgR1E0WgvudrvRBlK6kYqNo2RPqqriu+++49e//vWYTZDPk6yFdBIVlvzu7o5f//rXvH//nsfHx7Eh1tXVFV3XsV6vR6tE0WJLsCHfy/jKPBfddJZlI8EytWYUZxlpLS/kyFQCJAGZAG4JeOSapf7AT/YIGUv5XoIHqb+QMRDXHq31WMgtLkPCXE/fR+a0BFGiQ18ul2ONiTGG3W7HxcXFGGhP3WnEVlHqMSTDJEWrEhRLB9Sbm5tRS/+b3/yG7777juvr69El5s85/qKizs+//9zu8Kd9x89WhvAZWP984ZWvnxZc74NbR9e2qC407em7DtsfuXxxjTaGOE3ZHwMjrUwCusOdmdUsn5GmJSYqsDwzqI4sDl0uvXMs0rDQJVlKlickkaKpdiyXC3SSUpYzVNpQFjmxNqF5SVny4uIK+8cf2R1O7LqYRpVEaUTvPHGSodBEsSNNY0wcUc7m9HXD4/MjcXpksVyRZiVJAs73uK7huHnEu55IebbPllMcIv2r25fYvsekFu96kggshqrpSOKMNMkxOJwdiJMI332qfA7A5dxWd7Djg9IPPc4O9HYgENCG46lhu3/EOk83OKyzFEVCkmiargEcWRIHzS4h6s2LGWkSmhOBH/WDsgHgFadTxXa/I4kzvIpp+pZTVVE3DUkaJEdhYfC0503KnTcxawfsYImzPGjIUdi2o2tOPLz/keeHj3jbsz21qFKzmnmSvKDvFVonZFlM7FMi15HER3pf0bZ7+sHDkKL6GO811UksrjxlHhMpRzYriZOcoe/oraNtatyiwTYN7dBz3Facdgcy7cm0w+ie/eYeF2X0vWO3eeJqWbDMUmLbsH77O9rdPS6/JI4Bo1kslljnSPMEdIwdetq+ZugHlpeXtNe3fPf0Ees01ip+849/4PbNC77+6jXFytDs1wxWsdsdGZ62oBSn7TPpck7THMgvfxZcUJoFOlmymK+YzXOS2OO6iKFMqZsGpTVxkqCUI89D2vv+/h7roR88d9fXSBdP74MD0eAk+A5sOUqT5wV5XqC1xw72vCjbERRYa1Han5t6zPjqq9fY4VwM3Vm08uhIo7WiKHL0meWygyPKIoahP0sMzFkS59Eqwg6erhto+4G6aZglOXXTUZYRsyyl0UG25qyjGRqU82gTkaahg2jf9zRtizaGum7pe0eWz9Am4/F5x8PjDpRmdbFiNl9gB892e2Sz3lIUOTc3l1xdrSiKvwJy+Hxd/ykGfLrGT3/vL9WbT0Gk2OoJCyfuFGJ3JsyxaKJFMyxsmHSqBM6Su09MschKhNE9Ho8jQJf24WKNKMDo5z//Ob/97W/Z7XYjoBQphIAiAR2iEZamLBJUCGgS+aEALGMMj4+PI0v94sWLkQWfSg5Efy2fJ+l20eJOWcypLlt018KmSxAgjZWEmRZfc9HfQ5DJTGUjwiCKC4d0Upy6dRwOh5EZF0b1eDyOEgJr7djcRhhNySgIABTWXP6+rmvevn3Lx48fseeMXFmWI6Mtc0XkGvJ+MpfkuiU4EVZ3Kl9YLpefOZhIIaPMxc1mM3ZUFeZ+vV6P+vjn5+fPXFp++OEHttvt6O4zrV8QwNv3/RjAXV9fc3d3N0p7hmHgV7/6FV999RU///nPubi4GIORzWbDw8MDSik2m80ouRGrUCl0lSZDkvEQ/3iRlMj1A6MOvu/7Ucstz6WAeDlEFiKB4NS1RuacvCbSreVyybfffjveg6ljkGQnptIzycCIDG2qOxcpjQQDq9VqDGBlPsrvi4e9SKoEoIu8RQIAKbr+8OEDHz9+RCnF1dXV2FxovV7z9PQ02lre3t6OFpL/2vEXa8iFKQ9SFWHKpy4q04X0c//xT8z6J1vEL6Us3ntQDqVCUx2nPFkaob1j/XTPxTylHywcDhTza7IkoW1OVHXL8XDAuYgLHeMxtF1PFOdc395QFKGrlB2CRWDb9dSto+trnOvBN5SzC+q+x/c9Jkopl1csFwusyTjsT8R9RLK4Y1GCVxFXeRncK7oWBwxDKE7t2gatPE45Gj/QDR58Ta087WnPanVBFGm6vuXucsnQN9iuweCIkJS9pqkPWKWZX1xgu5pBxef0vibPU/AhyxCZCBc5okgTx9EYGYLCK4XtB/KyJE8TDoc9dRNkJ0pHNJ1jt2/I8pzCKbrBE1mH8Sp0CO0Dg7tYLEmimOOxwmiDO7PzUpwZm2hcbI2JOJ1O/Pj2A85FvPnqNWma0bRhsSsWOXGs6bsWo8y50UpgcDyhc6d1A33fgBvQWNr2xGG35unhnqD61+goI1/cEOUrVDTDRCXLxQXzPKfabzn0LYf6SBoN+N5x2t6jfEaer4jjkn3fcDidGIaOxfGE93tevswwacHheAzd9NIO39dEfhnqE04Nq2LO5SLjsNvgmw6yJT62OGWomorINdxvH9m++55Z5Lm9nINyrLdbfK7I6hOGktmFAtvSK0uWR1S1wmUxP/v2G4xr+P6Pf+TU9eyPLWXjicprGhKqQw/aEGtHEvUMXcXT/T3Ht3+gvP/Av/t/zIiyHNcfiNKcLNekmSKJPCqK0ComzQxaGZI0CUWYSUSSSCe2Hdv9npvrG6LIkKbBR982DtvbUDytI+KIcxONsHmeqWv8WXY03ehNpGialiSOuL5ccaob6qqldz1aK9I0ochz5rMS5R1902CURikD9GHtUMFS1HsYrMOYBO/DYppkgVV5el4Tx54sWzAMnlNV0w+OOAo2mFlWMCtnHKvAgkVxCNK7zgIafMTp2PJwv6ZtexarnLav2R+gPra8/fEDaZZwdbXizZsb0kxzOu3h8s9LSf73cnzJaP8Ucy6/J8e/9Hs/9d4iPxBgBnB/fz+m+rfbLcvlcgSGp9OJ/X6Pc270OhY2bLFYjPZyUyAgIEvAoLT0Ftbs4uKC1Wo1akkFUIjeW1juqawDGGUYosOVYrfdbsfpdBobFg3DwO3t7cgQCogWcChSQWFH5ZqEvRTwK3Z3ouk1xowSEpGDiYPIbrcbHS2Evd9ut2Oxn4AfCWCk6E0YXmkJL0GTBAPCIMv7Ho9H/vCHP+C952c/+9kIcoWplnGb6oyleFJY8Wlr9rqu2Ww2fPjwYbzuKIpYLpcjiBKwKzaN4qQhYHm9XgOMny82elIMqZQaLTX3+/3YZVLmhxQPzudzVqsV2+12tK2cBkzee373u9/x448/jraJSimen5/JsmxscCXdmQWIit7+b//2b3HO8bvf/W6cN9fX16M9pTxX4grUdR3v37/nu+++4+3bt/wv/8v/MhZ5in++nKOAZMmEyFexrbTW8vT0xHa75cWLF2OGSsZSsj4y54qiGAMsYMxGCRstLLjM5ziOubm5+awQU86jKIrRFnH6DE3XEJljAtCDQsCOAPz+/n6sq5DshkibZC2Zz+ccj8ex1kKyTHIcDgc+fPgwdvaUbryHw4E//OEPY1feb775hjRNORwO45rzLx1/MSCfgm5pAvQl6BYd+RSkhwkyZdZlAD9nycM3Gu8dzjuct6SpYbUs2T0ECcnV1QVxuSKbL8MipTWRNsRR0D8f1husiqg7h3WG1ER4ZciKGd46rHMMLsgWtFEkKmawsNnsKfKEMs9x3rPdrDFKE8cpVbPl0DzT+Ygom9MPCkOKUQmttdRdi1KaJE5I8xRne8BRLAJ4Oe4eqOs2+HR3PUqBcwN5mqDcgHKWssjI04z52WT+49N7nElYXV3ivEInM5IoQUdhsg9tj7UDSkXnopkgD+r7nr7vzpXGGZ33bNZrFss5nP2mUaGp0uCg6xxRovEkOK9xDqyHLI5xbsAO0p7YB32wtTjAe4tS/z/2/qtJcizL0gW/cw44oNTM3JwFyagsktXV1XJLZP7/S7/1SI/0dBfJqiQR4R5OjCkHPWQeoBuuHhnJrky/9E2IRLi7mRpMFTgA1l57rbXH1k3bd4Sg0ErhPXSdZeih6y2nk6UsFxjtSJLRJKmNYrAtUVKMsXNEaG2mtvDQ93g34IaGfmgZuiPWNsCACoF+sNQd3CxvyKprhrjEqRTnFV3vsd7QhYh97ci0JQWMijAE+mZPCBaTBsqqIDMRu4d7Tu9brm/+huVtRZ4bUD3H/ROn3hM5TaJjlkVGvd/ibE/TNsxmK5KqYiAhSjLsqiUNPbqNebt7YnG9oCxzoqrkN999D/mOvHrGXKXobkupDCc7xk0aWqLIc+wOBBxXN1esdMRwTgh6fNoxm69Zf3FLsA7lehJ6OD7R2A80xyPN3QOn/ZZl8RIVOqAhzT1xMpBGhlhFxNqQxhprHSYCE2nQUGQJL57f0rU9RukzEz1OdbU2cGpOnE4NyjjKoiRLU6IoRikzav5NQI/TpwA+M3LN5xVNW6O1QmuYVzmRUWg8UTTeHNMsJUsjhqE/612TT4z4OV2GEBjsaMzM0hyjo3N7tqCo0nG4lxujUrvB4pqBJIpYLdfYYfSDSBsdAi6MnZlhsEQmxdnA09OBw6HBmJgyz9HKo5Wn7xu69sRiVvDVVy+5fb6k7444+4kR+sv2u3KTy+3HJs4/BbT/eN/y5yVTnqYpq9WKDx8+AHBzc0NVVWeZ3SeGTeQYj4+PiGFM2GdgArGXpjExEFpreXp6oiiKid2UISqiMZYYwEuNuLC7IgMRACTXh8QWiklTwIYwdxKrFkKYQPN8PqdtWz5+/IjWmpubm+k4CLgRPfclwyzHVwC+SFCapuHh4YHlcjkdU2BiYMUMeGmYu3xvYrYUPf0l+LpkTy913FLsiMFSxrWLREJA8qVJUQCYSAkElF8yyLJGhLUWvbOwp5dMrujvP3WXP+nQQxh17LPZjCiKuLu744cffuDFixe8fPlyYowF2ItUaDabfTaMaDabTWbSy+JMhhZJOkpZlvzqV7+azq+w/lK0STciiqKpYJKJmtJ5kXP4s5/9bDoPSo1JI3I8RPN8mS0vjPDlECGJFZRCRgpBkUlJ0SX6fknxORwOU9ygJAwJW32pF5fujVJqKkIkkUe0/XLOBNjLtNsR43xaG5f7FTAu6S5y/Obz+ZQXLsdG5Dkih5Ii4bLjJPcC8QZIl2O3202fUz6PdB2WyyXffPMNr169mtb5n7L9GaZOAc8jMy6g+8c3y+lGKj9wjjb81Lb8JGsJ4Xdv2Bd7AxUYbEvfHMmziMWswOgwxvwZw+AGTBxTlCWhr9EE+nagPh3xRBBleK/pup5+GONuCKOJrB8cJo7Jiwzle0LX0/c1udfUpz04zzBYGAbitKBrWwYf0ZOQx4reKh43ByIV6Noa6z1pVtEZz9C1DH1LGmnKLCWKctJyydGPbaygNfPlHO8szX5PrGPqumG72ZKmj1zdXDE4x8fNhtnqCtfVxElJasabUlDjQCClFNoYTBQROs6Rgh3H4x6t1bm1cx7k0jbnG8xo5uitZxg8Ac3gFU1rcR7qbiBKFbFVWOcJBKx30HXk6Whu8mH8+iVLYbRhHLM+nm8TxSRpyfZw4nFz4vrZ1Rj5GEaJgPMWYxRKe4a2wxNwPmBUQKOwQwfWYYJFhR7oGfqaoW85HA+opOBQt/RWE4cY5w1eKZq6ZVDjjcuZFJPN8cNACI481qRG03Y9aMtsWWKbG4b9I7vtlsfdlu9/869YFZHPR125URYTPM3ufhyC41vKQlMUCdZoehtIdEKeLkjTgv7Ukoaaxc01/q++5rC5o1ovSeZLmmPDEBI+vnvL4XFLu31AmwSVxGijGboD9eYD7eYO63vSPKWYzamWcwYbRsNriMmq5/TEeGfpfYdTc7RPKABrT2we7gnJHcEsMXGHiRqUdmgTk5zz61XQ9DjQHqM1UaRxWcpqCY9lgdYxRinSOCaJSoIybI89zo5RnFpF5FmC7R0+MYAmeI/X49qQm2oI4yCdvCzwwdJ0J9IoJk0yokijGFmsqqzQZvx31zcoFTD6kwZWG2FBxg6KUhHGpERRilIQR4r5vKQsMpIkpm5a9vsTcWzI0pzBeZq6ITm3I/tje97PeCMaH8aG07FjtzvgPcSxwUSKskxJkpR33++5Ws25ul7y8796iYkd292R+flh+5dt3H4Mui+3P6Qt/7Fm/HJ/f2iTmLw8zydwK4YzYb4kXhBGICTaWWmdi+xA0isEsErMoLxGYgyFaRf2XEDtpRluGAYeHh4+A/6S3yy/T4CGJDSIyUzrcUjLpfHscDjw8PBAnufc3t5ireX+/p6rq6sJMF9GwAETuJKvC4spLLYMlZEugsh1BEB9MvWPaRUi7RCwJp9XAKOwrJca9cvsaTHLCSsqjPzj4yPPnz+fGE1JzpH7iAAaAfhKqQmIX8qYLnPAL823lwWcmA+BqSCR/csxlIJotVrx/PnzKVrz6emJf//3fwdgPp9P5khgkt+IpEeAmjDGAiYvp7D+3d/9HY+Pj6zX66mbI6krDw8Pk7lRuhFt205a7kuj73K5/MzkOpvNpmMv19klsPz48eO0TwHcUpAIKJdNiiBhla+urvj48eMk9xCZjZhEhXUWzbqA38tCT7okl/IpGVQkqSqXMqg4jifdt1zDl2y4FEOyyTqRjpD8XbojYtDebrefseXH43GKLRWZ1iVJIFKZp6enz5h9mVZ7PB65vr7m2bNn/N3f/d2kvZeOxx/b/kxT5yUo53xgQQybwohL8sqnH/6Ezz8B+U/Gzp/eAsF7hn44G2+OBDy7/Y44L1BJiTf9lNQw3sA9w9DR925MD8FjNGijaZrxJqIYs4fbpkb1GqMdwXYQeoKzDH2gPe4o05RYx/i+pe4sbujJyxmpyVHRaKq0tifSoNIUE8ekWUFdj05/o2HoIDjHejmjWlzj1agly9KY/bFn8/SE6zvy2ICH4CKCNWx2LYPrGXqLQfHu7RtuXkRoUzAAmDF5QjO2QLWRUd6fUi7iJAbUxAZeX12jNTgXyLKU3g7keUqWwWCP+DBgXaBpLGmmSeKxBRhHECcpwY03kDTLsG6YLsCJAfEOGOUqXdefGRTNMHiatmcYPPMyRZnx/Q79QGA0m+rY4IYxV1xZdU7VAOctwXXY5ohtjvTN8ZwZHaOTjNk8IyvKcXIqCmcdXdeQyrTRoJkvrxmOA74ZcM6jI0NRJoQkxmQ5oX/Ow7EluD2hb3j37b+SZAnFqSROPC9unnG635DoQFlo/v1Xv2W1WmDiCu00OqsYfEyWLkjSGVVl6Q8/cHd/z+rmGTrSlIslPiv46vkMq2L2j3vqw5F6u8HajihLKWblOFF2aMiimPLqGYMNBKXxQ48feobBcrQJy/gZ2eoWh2EYWqyP0SqmTAz94QObzYFd/2vmL74hXZQY3aGV3AADwVuUCUQo/HkIVxRnZCE+gwQFeFQYCGHs5kRRNGb5G03wlq7taLuELDcM1hK7CBUphvOYeoIi9uPAJ7nmQgh4Z9E6JvieQCCKmTomkVGgPM6OBk+lx/z6MeEnJvhxuqy1nsho6qahbRvGi8eiCSgVcP1A5+F4PDGfVQTgdBgZonlZkmYpbnf2VsTjFFBtxsz63f7IfnekbbtznGqPUSnteQiW1oZ5lZFEHmtb8jTBXDy8/rKN208x4/Lnj4H47/v5P8SUyyat5Pv7+ylNRYa1COCQpIZL5rTruonhlFhCGUIDn0C+sNoCdgRcSvqIJHYIEBSW95LhFSmfPOglgk0AlnOOq6urie17eHiYJBEPDw9TYSAEiOiULwHzt99+y+vXrydQcglOhOW8lMiIGU6AWhRFPHv2bPq+yFPKspxa+sI8igRDUiYkDUMYegE3l+Bb/pT3dpkeI8dajp8ANflssi85zpfMrHMO59xkGBXJg+RMi1lRNmHSxTgYQmC9XnM4HKaC6pIVF7Au0om+7ycWWyQ6L1684O7ubopV/Od//meur68nuYgcSwHk8/mc/X7Phw8fuL29nYBimqY8fz5GqIp5Us6zDLCScxfH8SRlkoLlUrIkjK/8WwonGXb19PRE13W8fPlyYuMvuxByfMVkKj8rRY9EEF6aKQXYS5Hbtu0U3SnFm0SCSrdEzKpynoHpddIVkuMoXR+5HuXvIqO6NPgKmy3TdOW9Xq6DEMYUIekK7fd7uq5jPp+T5/mUNS5mXpFniUdA1pr4EiRS8ZLdFwmM/hOfEX+WZCWcU1MEVI83zE9/B2lDfkphEeCtLkD5pezlUrLy6feMYDz4AGhQCce6pxsC1rboxyfKWcDEgcF7TARZMbaWg/UY3VEUKQMRaZyh02SUqdh+HDXvBxTDqNewmsQEmrqhPh3QLsaoMALMKOJ4OlK3YF1MGhRGa7QKQI8NHbHJiMx5+lmsyLOYVy+fnycjdmw3j9RDTxUl5HlFlDToKGG/P7E/NOMN3TqSOEHrFE+C6xUqKJwbaOuBj+8+sFi+xDlPSBTEBq80kdGEvsM6d056CMSRYVbNaLuOrukJQHQ2EXVdg+0GkjgiMmPmd5GDDho7QFM7khTaxlHlCd4FTDJeYC546rZhsBZDRFFkeD+y8gAej3U9PjjariMEh/WWKI6wzlE3DbNZRJ5lDLb71B1Rmjg2Y8Khd+dBQIa0KAm9xjaWrhswYdQmB+eITMyxbnj+4mfc3Kzp4xgXpexOPXYAk4y6aLoGoxMsKZ3VKGKioElMSpqVqCglLiJWz2Oenk4Uacvx7jv652tCXxJnCVmkuX9/x3KW453m7v0bhq5Fm5y4uqFY3NCGkrrXDEpj0gWx33EaelbLa2YmYyAmKm/IF88gTihmDaeHj7z/9b+yf7zjVB8xScyz21ue375gVi7IshKPom5rgj/QtHu2+w26gCS/JytviPOCpumoe0Ual6TlDbG3qGBHg25bE6uByPVEzoC1oJMR6SowcUTXtFjn8DrCmIg8z1guC9q6RusW5w1ltcAFTZGPWeNdHwgu0LU9UKCMwhGIdcqp7jkeO7TRVKUhThSxAW97Ih2IjUarT61wFSCJDd4P6Dg9S54sRisIDu9HUG6tw6No2p6udURR4HBsOTR7lLJkaU6kA0PXgNNnM6cjODdGfRpPmSVoozgcdiOrHicEBR5z9px46nqg7zsiPabyzMqcxBjq/sDL5zc4O0419XYYC5Q0Ivg/DBr/n7ZdPgv+0N9/zJT/eB9/SLYirJiAPK31BBxlgIowjsLGia5bHq7CIgobJ2tSgAJ8yjyOotEXczgcpoeuvMdLMCe/6zIKT0CKAJwsyyYdskznbNt2ilgTALLb7SZ2XFJY5PfKEBNhrt+8ecN6vf5Mqw1MbK+AOmEKZ7PZNIAFmACsgKjLyDwZMy/AVLoBwmheRimK8U0Y/+k6P59zkXaIgU6kAQL0ZYqqnAdZLwIA5ZxL7rcMVRKAJ8ckiiKOxyOvX7/m9vZ20kYfDofJCCu680uQJ+tDiqc4jinLklevXk3diY8fP/Lq1asppzyOY3744YfJu/D27dupIzCbzaaJriI5EZmPyGkErMmwHgHo9/f3/Pu//zv39/cTc/vy5UtevXo1gcYQwrT2hPGVdTSbzaYCUD6nnBMpri4LnEvdtQB0yaOX6yGKIsqynJJJpJMi2fQi55IiQMDwJciX60iOj6wfMXb+GGgLKJe1IoXYZdKPFHbAFCUp15B4JSRBSWI5Rdd+mZsu3oztdjt1C2QNwqf8etGWl2U5FZFd1/H69eupYyb3mSns4k/Y/sxJnXCZpPKJEb8E1pda8ZFN/5wd/8Sw/5g1v2wrhdEfSVXNiF+85un9t+we77i6uSZLU077Pc4fCFqzWC2ZVQUGR6wVzelI1zdY2xK8xejxoT8MDX4YDaNXqxnB9njfkSiNijVJWaBweAdBazoX2Ox2DC4hzZIxdrDv8LYjqLEidL7HBwg24MPAYMfqbDa/Zn21xESK02HPpmtJ44goyjBRwmJdkBRz6ma8Wc6XS4q8QusM5QPKn9hv3nA8HTi1lm/cOMHSnY+5wuExRHFKCMNZBxVRZHNMeR6i0FuCGkHz0PU412NUIE4TwFKftuDHYT77Tc1+V7NalwRv6DpLUppzC86fIxAdzo8DV4DPjEZRlE4FmzFnXTLni0SDD/4Myue0dYtGobTBmAhUIIrMOUs9I05SBmvBxKRpiUtO2FNPczjRHU+4vqWte+LDhh++/SWqvGb14q8o4hQTFEY5jB6TNvxwQilNMCmdBzd4Mm0wFOBibJSQXs+ZP38iT+Huza94evdLqtUN5fKax+2Bf/vXf+F6WbBel6yqBUVecTy1ZPFApTSrxZJOldS9x6lAmuekixUPdc9ycUuRr3mqwZhrqtmcKG/xA8yWD+zu3zN0PYv1FYvVc/Lla4rFLVFc0DuLTnYEm1IaqPv3NKcnus1bTiYhWz4nDBblalzo6ZyjGwJZkjCvFjy7WrFIY4oRAWP7HquTkX1WEd4rNrsjKENeLsYozcFSlQV5qgl0OJ+gDCgdKIqYskjZPJ2o2w7rBparguUqRxtDZwOHeuBwskQmwkSeOHYkyXkiJwGjNd4FnPPYfsCYcdBV37UjMO/tKA8bBqz1pGmGiVKsczSnE4e64XRsic6RlUE5kkTx+vkV89WS+vzgqesjkTaURYYODj9YoiwGHE3d4LxHndOITJTiB0Pbjd0wj2cxz3h2s+T25orddkNX13z51ZdooBvacex6Fp/vjH9hyC+33wfEZbvUistrfmofv2+/l/8ppZjP57x+/Zq3b99yf3/P8+fPJymEaMBFRwxMhkLRHAuTKyysALPr6+sJQF6mTcAnf4QwZsIqX8omZJ8C1C9TXvq+Z7lccnNzgzFmkkRI7nMURVxdXX3GUK/X689yr70fpzteFgSXx1j+k6Lksh0vUYPCqkqahIAiyXGWlBoBKdvtdhpmI2kqYmqU4yKMODAZYOVYyDkT+Yr8bgFXoiWX93XJigpou5ywKFIQkSDs9/tJLy3//vWvf01Zlrx+/fp38shlRLrIEaTjcimhiKKI6+trXrx4QZZlfPvtt7x584arqytWqxWbzYb/8T/+B1dXV1xfX0+s/OFwmDoTMqBIQLkMQtrv91xdXU0xnMKuCqu8Xq/5+PEjXddNEXrr9XpK+xEALedNkmpkHPxqtZr08pcdHsklf/bs2WR2lA7AZTqMJLUopSZ50zAMUz77pc9CjKdVVfH4+DitScktl6LteDxOx0YkJSL1kXMuQPlyaqoYqaWgE7mYAHrxA8j+L8F0mqa8fv2a9Xo9DbwSvbpo6SVBBZiKEClMRBojBWsIgcViwfPnz3nx4gVPT0/Udc0333wzyan2+/0Ud/jHOn2y/RmA/Hd1359rwC9zyH+ctvL5dgnG/6COPAQMkCSjfmiTpMRRzLyqyNKc46nmWDe8ffMd1bwk0VDlCWU5As6mPXE4bInyI0kxp2sGjI7HVAcdESLF5nFD43riWFMVGXlREEJBtVjgVUzvHrE+JYoLCFA3NaemxsQxs8WcNFOjdtZ5DvWB4+k4siIqoIJlPivxdhiBpw+0T3uCVnz5+kv6YeD+4ZHBWuarK5IkJ4kLgnUwpDh74HB44lRvaerTOC1UDaAijBolBDrEyBAd791n2bhZmhFnCXV9YhjsWR84tn7SJGJ/OGF0TJ7GPNgjh8ORxbJEMWqCtdJEUUxyZjC61qKNQatPMUzSkpKb+6irGwfOGKNouxrrZUrdJ52T9wHvPJaA0gGjY0JQpGmOiQwKTT84cqWJIkNne4aupW8b2tORxMTEqmOegTcduT+SJ4FBabq+xw567DIMLU1T0/UDSaw51B3HzmFNQZwokmw0G+bLK+gfyecF+8MjSZnxvHzBanHLx3e/YuhrsuyK62e3EGXsa0twgY/vf2BpDSqd07YDUeSJE8tidc3u6UC2uEGlV9THLZFVFDrG+YbBDsSRYbVaU84rls9fcf36r8kXL3HRAq8TPBalE3zjmcUR3bFjd/8dx+EHhrYlO9zTK43DoRnTYOrdFq0V1x6++uZrqiIHZ8dUFKPQYYxyTLOSw6njzfc/0A+evoebZ88IFop8QZau8d6OEhI3ELAkqaEoUtLEcDzWOD+alnwYgc7xULPfnzidekDhg8PojCIrsNajQiAETfCj8Rf01BIewYoYki19b8mzlGHwDNbiXGAMSBqHU/V2nK4ZR5oiK7m5WZEVOfNFiTER79/3dGcJQNd2ZEl8XnrCrjqiKAHGgVshaOq2o+16jIlYLldURYlSEdaOBtvr6yv2uy1NOxDlY5Rae44++8v2u4kpP/775df+FDnK5faH2HJ54EriRRzHE9MqD+hvv/2WxWLxGWgIIVDXNbvdbhreIlFvl0zd3d3dxM6J+S6EwHK5/EybK4BTJgNKnJxIFgRwCsgV1nCxWExsmvee+/t7jDF8/fXX9H3Phw8fprg7kYpIitEwDJMG+3Q6fbYWBUgAn+mLRd8LTENWZJCRsJGSHS1ac4kflNzmS426dBgE1Iu5TkyEwqYLOF8ulxMIM8ZMwE0KJCm8hEGHT8A8hDAZBYEJlIt0QH7f8Xiczp90HaQ4EeAqwEoi8UTTL4yyFGFFUVDXNavVanrGbbfbadjParXi+++/p+97iqLg+fPnE+ATPbiw8nVdT8fr6uqKx8fHKUZxu91OEhRhfOM4nkD+ixcv+OqrryYWXT63sOTL5ZLD4cDd3d1kKN3tdr8j1RC5lPeev/mbv5ny4i+LJMkv3+/3/OY3v5nkQi9evJikTALI5RqQ9SRFmvgsTqfTdN8dB8ONrPXkD7owhV4WkrJv0aELAy4Fr2APeX5IN0s8G5IcFEXR5AUoimJaf99///3U/ZGu0KXOXbo/lwSCSGCkYL70CTx79oznz59Pg5uKoqA6B3T8/93UeSkxGW+En+Qnn1qQv/97n/bx023LH29GgTYGFRTe9uMUQDfw/v07hq7hr/76r0c2IU2ZrWZsNo/UzZGHDzXzMqcsSqosIjg7AqB9R1sP3N6+Is8LbF8ztA3NcUNbH5nPClZVSmY0vRsnbFbLawYbczxZun4ckqOCZzmfkZcFxmjadszxjLKIk22xkaIsZ6QG8AOr5ZwiS3BesT/UYCJsCNgAcZpRzua4EIizFGUM5pz73FvwYWS3+7bBuwHvejApkRqzudM0Y3N6oGtPFHlCCA5tNPWppm1acIqgR6Aux/10auj3LXkxjmGnU8SRIk40+/2Orl3iw5hYkCee7XbDs+trCIbN5pGiKAmAc59MFNL+kTbl2CIKzMqc9WrGx7s7Pn74gavVN8RxRB1G/funBaOIk4QoTkEZunZsefo4ItUBF2tCGE2gfd9j2575zYwyjVgUmsY2bN/9EqtjsmyG0gVt7XAhYrVe4DvDad8yKytsb2i6nu1+T1YpluUCH8WkyxW4W24iR3t8YraYkeWK5aLgq589Y1bMSNOKwY/hlLPVCJqf9ic+vvstSV7igyJNDUMeY3TCYn0NUU6Uz3j2skLHKQpHW2/ojg+kxnF9tSKezUhXz6FY0egK5wu0SYkMDFiGYY/2PZmpMAGePn5HXm9Q2/eEJCUvc5JIE7zD255qsWa+WBCCxxiNtf0oNbKWotDE2mAUuH6gaQb2+5q2/Y7NpuHm6pr5rORw7FEqkKajBC3NDc4F8jwmL1Pypj97ABzeB7Qes8etHbX6fTeglKMqx4QYpSPcMOD9WLgbE5Ek6ZRl/Onh3KJURAievh+7Kr21GG3QkSGJR42qJ4wJKMFSFBlZYYgSWCyXHA/1aGLGM/R2/Lwmom4aojRGRxrnA6kZ41Gb1nNqB47nARxlOaearQgh4uH+ibrpef3ylr7vqJsTRVlwe3vDYC1DfRp1+H/Z/iho/qm27U+x5b9P2vJTv0/uQZdygzdv3tC2LX//938/ZYkLm1zXNe/evWM2m03jw4XBFqDw6tWrCUA2TcPhcOB0OjGfz6dJmSLLEOOlROPJvsQ8dqlNFyZZgL3EqV1dXU3DekSicpliIhMWhcmUa0UA9CW7La18AcVZlvHw8DABBJHUSN63vF7etzDpfd9PKRnCUiZJMk0qlPedZRmbzYbb21sAHh4epiSLT2lGnwa8RFE0aahDGGMkr66ueP/+PW/fvmW9Xn+mWxaGciST0qmbIZKEyyhH+WwCtm9ubiZQNAwDb9++nQC6aH4Brq+v6bpuAtkig9lut8xmsylrfrVaTZ/pcDhMTPhqteLnP/85VVVdDOYboyiVUmy32wn8yTmVTsfV1dWkwf/yyy8nQHw8Htnv95O2v6oq1uv1FO8p6/2yMLnshrx7947T6cTj4+OUmy/sskhMRDt9mesuIPbSTFvX9XTeHx8fefbs2QT+pRiWtSaaaSluhZUXwC/67ku5jOj1xTcghZgULiKfuozZFJmPdEIuoxYlxQiY1qLIeERbv9/vJxPz5ZCppmkmaculUVTkXaITn81m03X58eNH6rrmyy+/nDoUVVXx4sWLyeAqa+KPbX9GysrvxhWe/3aWqgjYFg355zrxy++PF9vltM7fzS8nBNzQkeJxQ8cwtHRdS3vcoYPn+tkztI5ZLVc87p7GyVes+fD2+1HPVVZUszmxUuSJxnoNVmFcSzTmsaBcQ5VFFFHBcjljViS0zYFTN2CSgqLyzKsKfEsUB5RJabtuTDXR0Pc1EZ4iycjzjFkWo80VyVmPqLVGuZZUK+KiQuH5+ssvxq9jsf0w6mt7y+7pHh1FhHJJGsejftYFYh1RFTl5FqFxGDxGGYIfL0I72FGS4Rnb8OdzlaYpaTLqyqX95z14a1GA0Yo40gy9JzKBNFZ0g6VuDuTZDZGJ0drRdw7noChKiqIENHZw4yj7881DWrCyeMdYp8D11RLrNHW9Z+gadtsN/fMVtu+Jzzo1kTylSU5vR2a0bzvUuYAY3MjEd32LMoYkzWmjcbDP4XiAd9+Tlgva3tE7aPQTOio4nhxdHzDuGUUWs64STBjI41GmMNiWoY0Z7MBsucbMMrrIczSeOI1ZzXMW85LjcUOWRTx/9ZrOxjRDhMkKVlfP6QaPKQ80dQtY2rZmsIHOpSRpRp7PURrm85K4iqjrBoYTw/GB0DyRGwuZIZ0VRFVFa1JUVhIoCcGACaPmW6cEn6BNQlUVuOFAFFt6uycvr7hezkjjFKUMdq1p+p7BOt5/uOPq9hbPGD8ZxQ1xZDBRTtc2tE1LmmWUPuJ4Gnj79o62gZubgFaByFjKSqGiQJLGaAPWd5gI5ouStusn1muxqM4+iNE46tzY/vQhYOIE6wOHQ40dRgY6SWOKshinzAZPcc6z74fRwOn9wOZw4nhsUWbMKU8CpGlyBjY9xsB8PmM2z0jSMdf/8fGeu7sH9vsdeZqSZSmx0URa0dsW5y22B9Q4JbbtB5w31KeGum5RKNIsJ4SIuuk5HI4UeULdtlg/vvcsz7HWsd3v6AdHkvxpU9j+T99+H3j+KUPnpeTk8nW/7/u/b9/yMJXEDWFHATabDVrriYmUrOfvvvuO7777jvl8/tn4bwEClw9PkaLEccx6vaaqKk6n08SoyT6EaRWNqgBHibhL03QCBSKzkGeEgBVh7b/55psJcAgYFq25AFoBoPKzMuhENmHfxewnx1Ra/AIMxVD36RnxaWKnJHDIKHaJjDydTlMyjLDN3vup8yDn5TJVRTqqlwAHxnhK0T93Xcdms/ks5/2yQBMWXgDzpXxIjIJi4pU1sNvt+P777yem8jK1RgoPOceLxQJgArWXudrr9Xrqkkj3YLlcslgsJoPvV1999Vkc383NzZSpLskqAiDl+Av4XS6XU/a1ZJ8LEyvyFlmrIieS8yydAClI5vP5ZxF9VVVxdXX1WWylFHBv377lxYsXk9RKJFNxHE8mWSkyjscj33333aTZl4JYALUYZQVMy9RWOTfr9Xp6jQBrWTsiWdnv99N6S9N0yv0X0k/epwDmp6cn9vv9dE5kTUtxoLWezpOsn7u7uyn6UYy2l2v9kmmX3ycJLJJFL1IU6bDJ9Fz57FLoy3r+3zqp83dvjiJn+bGznunroidXCn4M7D9pzz/t39qe+nTAa8thv2XoWmKjKdYr0nScjJakObPZklm1IMtSdk8P5OmM5fya+nigb55GOUJRsVpdk2lFvXvgtBulGwBVamiDHwfvtBG261FBU9cndpsNaVaitcMoh0ZRZhFREuO8QweFRhPTgwuUScpiOTsv5pbD6cBpuyOKEyI8ke3JVY9Wmqbt6NsxQ5224/DwSDv0DFc3zKs5WaSJlB+zo3UCvgPf05yOpKHEhR59PmTL5Yq+q+mHhuY8VcoFRwjQt59a6iFAliZEUQYKvLNExpCnijSBSieYSFPNKvpuTJ0xJhrTSfR44W2etgQVxgxp/Wn4g1Ta06CLYaCazXidZCwWOSEMo9mwGVMF0vMkOe89XdueTZ3QdWOlr9U5bikokiQeB8fMZuSrG1oX0HlG3XcUHqpZRaVjrFMcTx3b/R7lwFjH8aFj9vwZr1Ylx+ZI3beYGNARcQaxGiiLHHxMGE6wf2Tz/geSeLyxD7YnLSpONhCXV+TRnLRakpRLbNdjXEJqarSvqY8bjPFo60hihe6PI3s8HPCto9vvqbsd7eYdvn6iDwNlXjBLDVYNzFYzQlZwaDUoA64nRAGVGJIoxQ4pRVVQlrd4reicZnX7Fev1S5Iop+4GbPBwOoIxHI4d+31NVmQopUmSmKHv6QdI8wylYVZVmAisO7Hft/z612/YbI+8eHbDapkDGu/HgjnNxsKzLAeCt/R2NECejifc1RKlzDg0yp91uE5jfWBwcGxrHh+32KGnKkvifmCwliSJxzhJpRmsH6UsymAd1E1H0w6YaJROxXGC0oqqLM46dU0UKYxWKODp6Ym3P7yj7wdWqyvWyxU4R3AWrQ1RFBMCYwFQlDTtQNuPN966abDOEiU53gdOp4bjcUvwHTfXK5Qai4w8z7A+sDsc8V6TZdU55ecv2x+SpPwhvfjl139K3nK5D9mP6F1FiyoMnuh90zTl/fv3E9ASE9zDw8PEagroqeuasiynceabzYbNZjM9RIui4HQ6fWYgFIDy+Pj42bRIYWAFYFweB9E/Cwssmc2i94VPCRMC7EU60bYtd3d3tG3Ls2fPJqZeDI+XunIBo5eFxdXV1TT0SICeSAQEdMoxvoybE4AhQOfSFCpJKcKkSu774+MjwGc6ZPlTwI8wpQKUhK0VWYAUDlK4iDlQwLesAwH4wqQuFgtWqxXDME4GlXMlciXn3KSxluLj7u6OV69ecXNzM4F0MdBKQoZ0F2RIksTliWRCjIzS+ZDscVkvcn7EzChrQxhhMbXKOhZNsmjZJYFHZEvyvUsmGZj03SKlsNby8uVLbm5ukPHvcgyMMdO0VDE8S0666KnFoyFeiN1uxy9/+Uuenp54+fLllORyKS+RroTIbiSVSAbjXCb9iPZb1uXd3d2UtiPvQ86tFHZiDhX2WdazyKCEdT8cDlMBJQXu/f0933333dRBkUJL1vHluq+q6rPi43TuoErBdjgcJlnO7e3tdLzFe7DdbgkhfJby88e2/xspK7//+5/AtejJL1uPv/uzv29/3nuCc6SxYf/4kaeHjzT1AaUCNzdXREaDjnB+zP7Miwo3eOpTh3cKpWK80/Rdx2azZfOw5elhy+6wx3qLtcOZrcjIs7Flrg0UeY4xMdl8SYHmsDswDKN5sm47tI6o5ktMPA4VOR73HA57FtWc+WLU0b6B6eIfF5Qly1KO23siHVM3Dd55mralbhriJAUUGR3GeIw7sn/cM0QRu/uPnA5PVPOIh7u3xNWcNizohwIbDHnyyWQ0aqo6TGHIs5xDfxjbUM6eE2BiurbHDhaNJvgwxkKaQByHMXM7S6mqDK0hSSPUGdSPFWJGlqXoSGGdm7R0cuOSC+t0Oo1JAudEjtksYVaN8XF9X2PtQGRiZBhMYHwINHUNekzgUOebrNIa1zu0USNrqgxJteTVYk1ZlaPrvczwOsZahUkyqmWFZRwQ5eMebwcePnzPvbNE0QjukzgiSwtmc0M8i8kTxenkCd6Q5yvqk+Vd85EXV1dUs5JnN6/pdMGuccRVChQ0taYdItqQEyUxVT7H2YHh9EhiAt3+gTdv35PNVvTNgftdw/Zw5PHjD1TGsU4DQ3dkqI+0fiCaH4nTCmUD7qQwcUbf1wTbEOt+TAXSA2lumBdrorzE5Gtm6y8w8Zw4njELsD3scGaL8z1ZpIjSkuicOpOmGdYFtBmZ5tnM0FlD0x6IopT5ImPrj+Mwp2YgTiOSPCJJNV1rQYWRqU4Suu6IdxYT6XGA1/nhrxhNm4SxE+G94lR37LZb9scaFQJ5rnDdQO8GrlaLkZn0flyXatSIt+1A1ztMHJFl6Rn0gHMWrRV5mlIVBfv9hr494Wd2elBqHVMWFbNqRnM64RET18jYxVoTJynH+nh+fzXHY0Mcp6RZcdY6HtjvD1RVgnUO6wfmswJ9zkYfrEfrmMEqTqcWXv45d9L/87c/phG/ZL4v//3H9imvFSIgTVPu7+/5+PHjpI0WDa+AwKenJ8qynAxlwn6Jbvnh4YG7uzvu7u4+GxEvcoU8z6eUCpEaCJsqQ2EESArbKQz24XBgt9tNWdHyOVer1QS0+74nz3Oenp6mNBdJT5FUE9mEyb+/vydJEj5+/Djt/8OHDxPLLtMzBcjBp8mj8jm22+0EcqTVLykpUlwAE9MtSSySKiFgWfYtIG63203s52q1mkC4pKmI4U6A22KxYDabTWBbZD2ScCLnXs6vyHHkvQngFflGVVUT43x3d0dVVROAS5JkSj2ReMBhGHj37t1n4FaY6dVqNfkRRPdcliXH45GmaXj+/Dnz+ZwXL14ATKZUOfcyNVVkI8K0GmPY7XZ8++23zOdz6rrm6emJ7XbL+/fvJ7Dfdd1UoEhRKcdQTMhy/i712CLHyvOcm5ubMbL4nCqy3W6n11/KQoR9F3AqsZHSKRDWW4yvp9PpM1ZauhbybzmXIsGS6w74zIAsOnOJepRjLIWimIgvC0xh3i+z/AX4Xr7/siwnTbcM9Hl6ekIy+Ofz+SR5kWJT4kDTNJ3uF1I8iynaez89b2az2cSqSxdDrkFZd8fjkS+//PKP3uP+LFOnsNx/bLuUs8CnuMRLDbl8P/zI1DlpxvBoFei6GkWgbWq220d06IiimKwocd6w3R2pqjlVUZClMdc31xR5Shon+KE/XwTDGKOIQ2kYhp4QPPVxT9dolIIoiWjwpGmOamrAsDvWbDZb0qzAB0+cpKjQEDgPfmhbitig6WlPW+7vHnh8vB+nDqYZfd9h9NhC8s7TNh1NW5PEGaezRm2+WLJar6mqGWkSQe84PG45WM/d2/ekSeDZ9SsO23vijz8wezan7y1BKax1JElE17X0Zz2W956mrrG9xatAO3QkWUyapQy9pWsajI6JjWFWZNStZ1alJKnCKkUUQ9vVrJfrEbA3CufsuV0XEUeGfuhouk+Zu96PNxxpx03TuZRHq7G7oCIwOsPafrpJWTtgogit1Tj901rSLMX24wCaEMA7j3fntUJEsbg63/AcWWuZzUs84F1gGBQeg4kroihDZR1DW1Mf99zdvac57lkuKhbLObZJCP2R0lpSY0hURh4bToMdp5H2DQ8f7rm5/k9Emebu/ROHwZNQcxwSQuTxKkapEudqvIIXr77m9Gion35g8/iR7/7jf/Hi1Wti7dgde7xKWFY53XGP9Zr18hpsR1cfyaqK++/+hWx2S9NqBq+xWLI0wphAW2/x7Y4iS1nMK6xKaC2YuCCt1vgQE0UpsYsIjSXSnijWtIMmLZJxyA4RkVaEoFFoqiKlHzRPTzX9cKLpHIOz9PVACPcMtiBKluRZeT7P7rOIP601JlIkcYw+D6sahvHcKTN2S9CjZv94akdvhtJjUaAUkY6I05SmrUlz0NrgLDRNz+AcURIRxQlpEhGUp+16okiRJilZmrPf7WialixNOBwabO8pijkEhUQlpmlK6wa6riFKDG3XkOQFgbHIs95zqhvariNJq9E47Ryn4xE79MwX1+RliTYDyij6fqDvxwQYpWO63tMPjr9sfx64lu2nWPWfModeAkT5vrxehoScTieenp4QY6JodrfbLfP5fALXkiyRpunEagqoFvZN7lGHw2Eyegp7JrF/SqnJUHmZwy37ESZT0h6OxyMfPnzg48ePE1gSE58MdhHTmICBp6cnVqsV19fXzOfzicF8eHjAWsv3339PkiQ8e/aMp6cn3r9/z/PnzyepjIBQYfrkGSFMsEhApHUvunmR8QhLKPF0SqlJziDsqABG+V0ibWnbdiKn5Nh0XcfhcCDP88/yrwVkC+MsMYbCoso+pMiQ9y7s8iXAW61Wk4xIzI4CkITtTNN06mKIT+CHH37geDyyWq1YrVaT0VMArxwTOWYC5J8/f06e53z//ffT+xb50iUAVUrx1VdfcX9/PxWB//Iv/8LXX389AXRhpCVeUxhcAfq//vWvpymtwrJLwSNrRzpAcv5lqI68VuIrLz8PML1fMc8KO9/3PY+PjxMwF625nB+51i6jPmWTQkykKsCkIb9McxGpmUjQLiMQBexL7r8w4/K7pdgApi6ZdBU2mw2n02kyqPZ9PxV5wsyLt0O8TJd+CykmpACTgkhkRcMwsFqtpqLvci7BZfKNSML+2PZnMuSjPvwnvDnTNn7/88QV0ZB/Ys/Dp339RLs3hADneL2h72ibEyE4Xr58zi/++uc0bUvXO+rWorQeteXtQNdDY8b0kaxKSU1JNS8Y+o6qyPHB0/cD+8NulGx4j3OWtmsJeCJjCEEx2J7mtKfKU9arl0RRPFU9URQTpynz2YKu66mbbgS6fUuRaspXt8yqOUmactjvRuPA0NF3A48Pj3hvubl5hlGeqsqZVzllno7sm4EoCrjMEXrFMQpksSJWjva0Y797pFj2DKonK/OzNqlE60CapXivGbox4SJNU7wLhD6gztNSq6oiJClJqnF+fOAY7ZlVBUWRsG8t4GiaA87PSAwUZUYcGZwbUMpTVjlxEnPYH8834dEw6pz9zMAzMRDn3FDbDbiz+W90iI8tJGctcZ6gI3PuHoyM6/ktk+clOgJ7OpGmW/rBs1hdMfTteGO7f6So5uTlkj4YTrXD6wxtPMFFOAfpzHBrYvaPHxnaPbvNI22j6Zr9WJQdd2SzG0ySM8tjvvnqNaeHd7T1ib5veKx7jscjty+f402KixOS2fWYhOI93UFxrLdUi4hqtsTWG542j8TGoX3DcHri5fUrTL6inF9xPJxwXUMVB9rjI83+nsf797SdJU4/sN21WCKII/KqwLuBbr9jnmlWZcKmb9mcBrad4hRyXnydg85p64auUzjy0bioBjaHMSKQPMJ0nizLUcrQD+OU29msZDav+PCwxbqBOIvG4lU5ettzPNasZiVRpDkdazbbw9nwOU6JjWNFFEcYbUhEL2oHnFf48/XuQyAoNUZ0Ok8ImnJWkmYG1DgIKooigg/0w3COAnXEsaGoEgKBvm2JYz12p8JYpNWnE3mWk8YJXW8J3qBVfNYs9tjBoRnbkXEUkxUp2/2OZZqhTYT1fowsDUwtz6Hzk4GnLFPW6yWz+QwfGnabJ0KAoXd0raUfanyIcH/JIZ+232fevNz+mJzlx1ry3wfwpU0uLGIIgS+++IL/9J/+0wRAhdkW+Yfou0WzHccxy+WSruumNrvIEkRvKw9reUALEDkcDpRlydXV1ZSPLMWAyCfatp3kLhI39/XXXzOfz6dkDQEXbdvy8eNHvPc8f/58isCTYkKmkAp7K8N9hN08HA48PT1NI8CrqqJt28kMKmkvAtaFDZWCJoQwvS/R4EqBIu9BpBIytVOM/CJpsNZSVdVkAP2UoPR5xrUwpqJbFuAi7KakiMAIKsWIKd2Dy2jFy8mLAtbFqPnhwwc+fPgwjU0XNlaMrwL+ZrPZBJbFvFjX9cSMHo/HqSAqioKf//znPDw8cDweaduW/X7Pfr/nyy+/nIo3YUu991McY5IkLBYL6rqePAEiIRFgv1wu2e12E4O/3+/Zbrd8+PBhmoa62WwAJnmMyEmKomA+n9P3/WfG3Z///OdToSnnXwpMmWpaFMW0XuS1kli0WCz4+PHjZ8kjwHQdLJfLyewqUZwC7qXbcVnUDsPwmV9D/ryMP5RoRVmbYkoVo6kUgFJsXBp9xV9xPB6nIWFSLGmtp6xyKUbEHyCdo0s9vBQZUmyKdO10OlFV1ZSCI8ZxuYcIgy/dvD9l+zMmdQo78WOW+9P26cYKnwycnzLHP31PGPPxdUFeECAEfx7oMTD0LbFRJJEi0pokTjGRGVsTegDjKWclwfd0bc1mt2F72BFHEa4f9dHejvtRenwox5Hm+vqKvCjYbp9GvdJ8hjZjq8d5z+PTjm4YqMqMshzZ8eE8KbFrG5yNsW1NUIZT06HCCCKNBqU0TXPEewvBUxajTKC3jqwoUFpTzpZEWcVgHXleoOOEOC2IIk+wR/JEkSQp0cubMVM7WAbvx9a8dwQzLg4fwsjcnyvNthsvvlk1I4kT6mNDHEc47+iHHqMivHcYJ2Ngx3OptWexKBk4kSTjOHtrWyJlRqBve5rmRJImjFrihODLM1sSnavCjiROJ0NrCB7w+OBIjcFHmqFuSYociMfPcaEJ1VqhNQxDizExKI1X44LJ85JhviJKPrK6SsizFNvX9F1D33UYExNHJXGWUWQZQcd0wY6A8jRggmK+mjFbrqi3d+y3Hwk0DEODaTzdDtp6B1FBnuW8fLbiGPfsdw+8f/eWloQwOFy7JykSdChQtqOTB/XQEgeLHRSpMVjv6O3A8mpBVWbc3qworq5oqKgHxfrVNyjnxvfytOG7d3e8/f5XrOYzbl+84nC0WAwmL7ChwgDKeYw3NPsjh65lP3iGqOTx7h06S1ldP8f5DEVBls0IamSs+sHTWYhdgN6S5WPqyWB7MAkeBTqQphFJXpAVxQjIz9rrEAJtN+D9eOPdbracWkfw441VG0MSR5hIj76AOBnjLG0gcmOaiXUegiJNMrzzGBNh1Kjp7voepcdhUPWpo+s7+t7iQyBJDXGiURoikxCZkYVv25FBrKqKPCtwFpQy+BDR94EQDFlWkqUpx+MO7x3z1ZIojanbFtSYMhF8GCVpPpxv4h60GAUVVVUQJRHOWZpunD0wZqhD1zlOtcV6izKSR/7/7O3HgPqntsuvC9j+fYD7x/uQ+4WAu8ucYgHZl1P9hGGTWMG6rqcpe2J4uzSXCWCI45jb29spT7lt2wlwiFnr4eFhAvGz2Wx6CF8Ox7lk7oHJgCnAUh70EjsnIFprPckkRJ8sA4WEIZSJmF988cVnEh4ZuCPMovyOyxQJkT/IqG+RMghbL8BamGsYu2Gr1WoCJgLYRDcs7L4wldIxEF2yGFNF9nIpQbg09XVdNzGYPwZrAqAvAwRkK8tymnYpHRBh6S/XSJZlE7iXzyw64Mtccem0SCTidrvleDwikzdfvnxJlmU8PT3x5s2bCShKxKOcb5kgepn3LkDPWsvNzQ2z2Yznz59zfX09/c4vvvgCyQAX3fNvfvMbVqsVr1+/Zr/fT8f5UoIVwpgWJMZYrTUfPnwgyzKePXs2XVMi77hk++U6kFQZOc6yfmTNlWU5FVTyO4Wx3+/3Uwa5nDeRQ116EeTzyzoQEC7fk+tEui2X61eArvd+8muI3ERYfukyXcp8pGAWYF4UBVmWfXb+pfslBa6cVyk+LwsRKVQvGXZZr1LoSgH54/X6+7Y/M4f8c3D94+3TjTVMwP3TTfp3b7AiYQnBnwH5OKFTBY/RhihJSZMUbWIGB0Pf8vg0Vi/WB5Q2RHGCiXLyck61XNO1LX1Xs9tt6E4t1juUjqjbjr7bEymNiQxzazkeDhxPJ+I0IU5j0AEfFOV8zuv1DVFk6PsO63qsdehIYdQYi3g41cRJQVAJcZqNBlNGVrDrWrq25di3LOYzFmVFuvCUTU/XefLZklVe0vUD1jt8cBwdhKEnNYE4z8hNwXJ+hXIdT7sf6E57nH7EDQe8mhO0xwOntkHHOb0dOJ6OpFFEksSoMOptjVZj698ozsMPR622MUTGkFhF1wfW6xlWQZYnFGmC7QdUNMYr1u0Jzpnk1nl8GFAhYJ0jKI8yMPQWrSMSBSYyaKMJytO0NUFBkVe03WgSjSLD6XTA2oAmwg0ahULrGOc7tB4NnsHDqe0hVpgkp3eKU33EMTLtSZry9Phw1sp7qrknSecEHYHJSMsFcbKgbwfG8eo9RTyDJKc+/MDm4QdU/8hq3rC6fs78JiPLHJpAkiwYhiPvf3hHWq7GIq9+JMRjF8WphMRUKBNwAfIoxUQBTaDIE169vCH0R+bljBevv+C+iTHVNZku6UKCxmLKJTZOSWdrXn7xM4osIcpnFLEhyhYsbp5TLZfgPN3uQDjt2L3/DfX+kUNT45OCoBXe1nS7O/LylqJ8iQopTd/RDTVZUhLw2GBRwdB7R6JTrPX4vqfpGk6HHZEOVLOc2bwiSSPAM7TtKN/xPV1nUTqQFxnWdtStBxRZklKWxSiF0eGc7KMYrCP2ARPF54m7njRJMIyTcPu+JclhsC1JFk0PosFZdJKQm4wkVSgTiAwk5/g4Zy2zvCLPC+q6oR9gf+g5HnvqFpyPKMpiHExkDEqFMWklSdgfjxiT4gbP8bilrOYksWI1LzlFFusUXfBEOmCKiNV6zNT33hM8GJMy9D3tEKg7z7EZaHtPmvOXjc+Z7z9m3vx9DPiPnxGXr5f/LgGaPOCF6ey6jvv7+8+0sAKiJDpOHurSzpYHprBmwkJLIsfhcPgskQRGn5CMR5f2tDDAopXe7/eTzEO0tvJZhLFvmobVajXFqNV1jYzvluhFYemEdRO2W/TQzjmenp6mQSfCrMqxk/H2wmZetvgFWIgxVICqABw5rsI6C5ARuY3IcUTWI4yvdBQuz5VovX8MnoR1l6QKGRgjGmx5D8K4XgKcEMJUVEg++/F4/ExPLfIeay3L5ZIsy6b3KtGOAqaAqcOx3W4nk+FqteL29naafimv67qO77//fgKxMk79sgiSzrFENIpJ9IsvvpgMjF999dUUlyesumippaj82c9+NpkmZU3f3t5Oa2C323E6nXj37t1UQEjxNAzDJN2SJBwB7bIW5HwJWyzXk+SZ/zixBJjA8WXXQrTyYsS9TJMRYC9rT3T7l0k6Il/qum4qSi/TgIQZv0xI+bEHQiIopTu13++n+FLv/ZQMIx0bkbBJYotcK7PZbJonIMcEmHwYcg+4jGqUIkcAedd1f7Kx8/9Wysp4oV9mi38C4p+iDj9pxkV3Pv6Ykq8ShCVXAcWZLfUO7R2KgA4KpQzWKfKiIk9nmDilG8YR6+PIdU3TBZTWRHFBtShR/iwZ2e7omnEq5HF3YJzKF4OC7W6H1ob5YgFK0due/eFEQHH1bIlX0Tlk0KOMJi00kQ9kRUVrPZUax217r9AmwdqAMTFFmqHrmmQ2EDf1GNNW5GjnSeeGkR3WWBsolhkuOJI0om4O7Hcd1gWu5wsCMXFS4tox89sPA119oD1tSLMbuu5EVuTsjzVZZkArlGZMrMABmjxLaboGvMOomCSLiaLzWFrvcB50FMPQ4b0jS2KCsyRRjBvGynewA0oF4iTGuUBkUk7NkarMOBz3hDBehCYy6GiUH4y556A0eGB/PKFNTjc4TN8TmfPrXCBOEoI3ECIIA9pofBgfCHlR0nvF/rhnUWVk1YLH3RF1csyrhC+//gZrOz68f0dz7h6kyQkdl+TLG7RKico5NjOEoDHKMzSPeBUIaqA+7nl6eMvHt4/kxTuef/WSl1++ZLUaDZpxlqJ1zfFxw2K9YGh2RHlKllboeCDoAe0dIQ4oBu7u7zg+fc88tlR5Sogs88UCqyI2p55ylrKrHWmsmCUZWaVIihnZbMnVeo0bBsrlNaZcU61fcP38NSaK6Zqedr+jffyBrt7jXc+xPmB8j26P1B8HHr7/LdXilqAq4mxFlGakVU5VXHPaW4yZo3RG23UEFZFmJXXTst2daOsTZZZxe72kLHPiNEJrhxsS+m5AK40LPf0wrpumsah2lCtppYmjiOAt3nqM0ueklWFcX9rgvCV4h8aTxhHBWpI4ItKB3lniKEcrhfOBU93jvSGoMEpezqAtKA/Boc5MxXgdD2y2NYeTZ7dtyfKMrIgpy2TsWEUGAhMQOh5qinKOURFFkaGVx2hPWUQMvUUF6PxAHBt0pClnY/Gx70ZzdAiaprXUraXuHKfO4oIi/EVDDvyuF+hSivJjIH759x+D8h+D8B/vW4DDJfiXVARJCBH2VgCjDKyR6YWiRX56epqAw3a7BZge0hKbuF6vUerT9D2AFy9efPYZ5QEtQ1PEJAafkkoEsIu8QpI4JBLRWstisfhMKnP5XkUjL1+XgkTMc6INPh6PXF9f07YtZVlOsXzCMEtKBDCBYGHVZRS8HFNhBgV0SaEjwEPkO3Ks5XvSzhf2UQCwgFNhROUYSea2ABkBaZJfLgD1Utcr4E+0/vP5nNlsxtPT0yQx+au/+iv6vuft27efxQ3KOpBEDtEMXxYIo7l7z/39PW/evKEoCr755hu+/vrraYqrSCoeHh64vr6mrusJKEqnRj4/wIcPH3h4eJjOubwPpRSHw4GqqjgcDhMbLa+bzWbT71yv1xRFwfX1Na9evZqy7ne7HQ8PD5OcQ0yoXdfx8eNHfvvb305rOc/zCSiL8VGuU7ke8jyfukoSdSmdIwHOl50g+V3e+0nPLoXUpaRJCithx6XgkjUhwFeKCUk2kTV5mest60DW82Xim8hkHh8fJz+GGIlFviTrTIqs/X4/FUWSMqO1nrT08jukuJrNZp/NAQCmzy5Ft5BNf8r2Z2nIL3PEx4MwguzPc8Q/achFMz5eePL3UdMs2vGgztoaBeAJwY0Pd+cJ3uNcoK4bPJooSXFBYf0IEHvrKKuS5tDi7MBsXtD3A8pZ0jgjWY83CO8CUZTRnPb4s0ERo8bIQQV5kZMBXd8TxQnr9Q2DHxdNlhcYpTnVDdYHoqyiqRvirKRuegbniUhxKoBKUcREeUKsgKjB+YFDa3HWE50rbxwE12GMRqPQRlPkJcHNaU4DWbmizGZoq9Emolzc4CPY1h1vfv1LvkzWdIMiy79EK8XxWDOvCsZUC/BegXf44ImMJuBpmhPKKKLonBrhPFpHeEbgJFGFh8ORWblkMZ/RDx5lx+zlUW8FszIm+HFoURyngJ2qfgDnxnb/MNhR0tF73rx5R5HtyZJs1OlH6mwOVHTNwKA9wSd4NTLuqIA+J7GENKOpa7yOyas12mw5HjpwPVfrkr/+279hVqX8x7//ksPuPX1S0LSOYn9PdfWSbHZDnC4wUcnYIihI41vyBG5Xa/7dxWz0R4a+4dtf/4q62fG3v/gb4jghy3KuryPu3t1z2O8ojcdvN2x3HVfPNTqux0JCe+xwoj5uRpd47ClNQmCg6Rz22IDKaNqBwaVESYLJS/LSkxRzrm9fM89imqajWj+DbEW2uIF4hkNBkhCXgSLyHJ++ozveU1Yl2WxOVhVsdvtxEJRXOLWjWnTkyTWRH+j2A9iEef4laFCpw/YDeT6yN/487CrPYqoiJUsiolhhtIFE0UcapTQeTdN6mtqgNaRJRBxDHBuUVhijp+x4rTUEj9bn+wOBJInJ0pgyTfBOUxYJSgXOlyBdN9B1lmHwxHEGgbMu2IySNRWItCGtxodU8IG2HdgfatreMDhQgydVo2wsnLttaSbRaWNXJY5jojjC+R6CI4nGoqEqErr+bPQMPYPr4GwCd72n6yx2CCMgrwfazuGDJknHKMa/bOP2h5jvnwLl8NNA/qekLAJUL9vc8iCXaYCibxWtq6SlCDgQvbjIJCQHW1hBMdMJuy7/yYNcdKo3NzcTwBZDmyQyZFk2aVcvpxReflZ52AtQERAlsgoB5AIYBPAL0JIUDQEVq9VqirH7t3/7N/7xH/9xmhwpYG82m312DAXwCkgWbbUAIjkG8KkNPwwD+/1+GiwjQENA4TAMzOfzScogBdHlM0LkA5LZ3XUdv/3tbycJwSUAByY/gIAmWRvCjEqBo5SaEmAuZQj/8A//wHw+55//+Z95enqazLbb7Zbr6+vPJrwKOy0suUT1vX//nq7r+OUvf8npdOIf//Efp8SN29tb3r59O/kOtNZsNhtev349seVKqenYiUb5csjRfr+fwLDEY0qiTVmWvHz5ctLwi7RCWFsphkS7//DwMJ0jKfokRlE2Sb8R07OYHaXYEemKSLqASa4i3Qg59pd58dIlkO+J7loKMjl/Ih+5TMyRIkQkJiItknUj8qPLzoxcX/I7jDFTB0dMvfv9fuo0SaEu71cy6OX1UrDKdXm5b7n+pZiQ43Kpzb/UlwsYl47Mn7L9mYOBJDHl9z2ARKby+VCgQDjD8OkLlzM7QdjxAAqFicaWOWE0niljGJyjGwb63hJFhjxOMedpldGpoWsO7IcjXdcADqWgKkqyrEDHBj8PxOkIAvqhJ7hRTnE8nfDtGNdUVtkoR3AeOzjioiSOk3HoDgadJDgV0/QNJIbOajwRRo/VovPQNxbCCECitCRirBZRjuD1OT3EYYzD2RNaKep9h9aGZTVDuQ4T58TpnKhI8H1GGTqINCo+8LTf0j19JL0qOB325FWJdx6l9Bkgj2CY83kaLzBL27TEw0AIUDctxkRoHRgGi4kMaaqpG6hPB+4+bojjDGVi0jgB5VHKk8RmNNj1Fl0PRHEMOJwb18bYSozRWhGCx1pHcJrTcWDzeMd6ueLm6goIWBvO5jxLEo03Kx35CZCjw9lwocb3GsUkaYFW4zls24btU0OWjDn1RlmSyNKc7gkqwvWKzYeGojuyunlFCCecA28HjIbgY5brl/zsr1uu1yv69shu94hzHfuHDa9ff4FJUoyy3L6Oub+/p6sH7u7f0HlD33uCHrs11WzGs5sVCZ6bqxuSSLO5e4+2imo5G1naJB5lUUlO0ztOvUdrR5JV2OZInM5IiyUmqyCrSNIcpUeGVxtHMzTUhycGZ0mLHO9mVOsrknKGi1PWcUJezUjLGWlWglP0fUPiQbctzdMHUh3RhD3lPMENY453WeTYuSNL03OXykE4m3GSEfh2XY/SnqqssFbRdxBFI0NeFsk4UXQY00m0UWdDzJFIQxKPQ3nSMifPErI0wXtNkkRY35+ZmpzBevr+POQqjIzJYD2R1pigCMqTxRF5lmGMZugdBD+Beh/ctJa7viWqNc57IhPR9T1aQZ5n47oc+nPhqMmKHEWEiQaKOCGK4f6pJ2BxcsNVmtjE2H7AO+j7cYqod2MKUPIn6gP/T98u5SZ/SLIiAPX3AfY/pCmX1wmLDUxMpDxwLydLygNRMsdF5ysP5csJfqJBFnmFMH1iUBOmEj4NQ0uSZPo9AkCl3S4SCnnQC9i7LB6kFS9gWECCMNbCPktU3WKxmIoJMWtK61809NvtlqencVjefr+fYtmkAIBPkx0/PSP8pLsNIUwyFwEbov+V4/Hu3bsJqAg4umTfJXHrMtdZGM7L1BTR5x4OBx4eHliv19MAJzl2ApBFfy3n/VLHLozoZSegaZqJjRawLFIYAcI//PADTdNwe3v7WZEn2umbmxt+8YtfcH19TdM0bDabKbv8Zz/72SS9+eqrr3j//j11XU/mR5HbSESwGHWfPXtGkiS8f/8eay3r9RpJoREp0o/lSTJBUtJDREIl71PiHyVyUro119fXE9AWA6TIdEQ6Jefr8fERYwzb7XaKOxRZj0RzXl6/IhMRAK+UoqqqSeoiXSGRq4jcRDoxl8k10qkQcCwFtejEBaSLnlw6M7Im5L5xeV2IoVK+J7Iv6cQI0y7+BpFNyeukcJSMdmHE+77n/v4e8bLI/mU/4oW4nDr6v0FDfsl0T7fI841S4PWPb6hnucpnP3QZf6gu9iHfGyPRMIbOWqx3lPMKQsdiUTD0DUafx3GbcVx3dzpQ759ouxZre7RWnJqWxWI1jrfNS/KyIs5S+qEjynMeHp7IZitefPkzmmNNZCKKPMc7Rzmb0/Y9Wht8UGP0Wmloh4BTMSrJScolsyRwOrYEZXBWGISxckocrNejNjBOxlYjxoxRhF3D8bShyFOqvBjjEVWMjWJUiLAu4tAFohiMSgnxnDTviUyCoWE4bMiXz+lOB2bzBUk8TuqcVQvs0GN9wCjI0vQMxgeur5+hzJh7W1Uz4jjBeVBmzFc/1QP9kBAnDe8/PJBlJSFUhMqgh4EkMQy94/j0SN20lPlzvAsozGggbU4YozGRxtoBNJRFBd4RrOHp/onTvuXF7S2LRUFvxwLMO080xrqj0figCVhCGNvAgx0Y/HgzV5EmzVOyOMIohW2faA572uMB2zUUq4qr5Q0P9xt8V9N2NW1To7BkswXomMQk2MHjvaNpe8r5AsJAX48xjYfdlqHuME6Nefdpynx9Aybi1//6K+4/3kMS8/Rwj1WB+XLN/K//hjTk5NoTpRU6zYmTknrzkfvNgdVVTp5rIgOYhKbzDF5zqBuUijieWhJjWK2u8cGgGQ2PcWQYhp6uO9F1R+rjFqXh2YvnHKuCpKgYVMzsqiTLc5KiJCvnY8JI05GnMc9WS5xvoW8JfQOmxGjo+prAaKRdr5eU5WzMe9di+AWjwOjxvwDEUcR6sSBNSrrW0bQts3lBkae07UA/eKyHKFYksSZPDbGBSEMaJxitUWo0kPrzPICsmGFMTF23nI4NKhiOh/rcbo9RGBQGbx06Nec0Fo89S6mKPMUrhR329IOjKKspwUed70Vd26DV2M5rmiNxEhEn5swAcgYHliTLcN6SFRFRPAL/4BxuGAhOYXSEwo+FnVNopVDeE+s/s9H4f+j2U0D68oH4U6D7p9jwP6YvF4ALn3SoMsZ6tVpND20BSwKCd7vdBMqF0RYjn0yZFDNgnud8/PiRxWLBN998Mw10EcAxn88/AwdxHE/ABfiMsTwcDlNbXoC8/P3m5maa9nh/fz89uCUesCiKSUsugBs+GQaFnZSECClUDocDq9WK4/H4WS76YrGY5CcieZHC4/b2djLOielTCgM5XqLn/eGHHz4bTy5gSFjg0+k0gUL5noB8YV2FdZQi5O7ujv1+z6tXr6bhPvJZpVMgx1s22delFKIoikm7f5mS0nUd19fXXF9f8+HDh8n0KcyxpKKIDl46F7K2pGjabDZTyoxSY+62DJX6n//zf/Lu3TuiKOLu7o4QAldXV/z93//99P4uWeanpyceHh64ubmZtOESDymFipxPY8yk45c1dhkxKUy71prXr1+z2+0mg6kA86Iopo6RgHzpEon2WQpc0dVrrbm5uZmGHgmrLYWPAGu5Dlar1ZRDXtc1y+Xys1xx6UaJr0L2eakfFzAupmZh3g+HwyRRqut66vpIAScdlks2XAC1dGOkQJD7kHQm5O8SVSoSLPmMwqbLn9IlkGMnwFsKT1n7cg39KdufkbLyIy349Ke6+J5IWi6/NzLk55/gUosuXxFo7v3ZBOIcuIF+6DjVRzznQQfOk6UZhIDzntjEY8yftyQGGjcwfuyR0WpODU9PW7K84K9+/nNms4o4zTjWJ66f3XL7/Pl4M9weeHx44FSPJjal23FQImCtZ/AjGN/XA14r6tahjz1RnGKdIktTsjzFDZb9YUfftCOTH2fESUHdOjoXo4Mm1mGUK5ixpd+6QDcEsjQBlREnM+KkYEDRDY4ijVFJieprdh/vaE4HVD2QX79GJyucdTit6HtLUWS4YQQ6GANa4wN4AqfjEYweh/KYGKU0WkMcJ0RRTNM68ixnVs15/+6Rx6cDKjGopKDMY5I0pmv2nI5Hum7g4/0DWZow2LHSLMpsupj6vkUpQzmrGNqW4NW5UGk5HFuiJKLtLCEobO9xQ0CVKVorvHJYbwle4dWoR4/iUYe83d4TRT1aWxLt6K1ns93hB8usmBGsYqgHZsWC42kgDMOYGNOcaMKAjmJUmpLEMYtFTEQgSjPieAm2YBcbgnP4YeDp4YnF1Q3FYkk5n/FFklNvaupDzak78fj4nnxWEK8yjD+xvfuBIp9T5gV1iAgG8sU1XTfw8PjEYhUz8MiQgFYztFZor/DnhkDbtLR5Qz7P0TqAGzjua97/8D11s6fKNM7WeNfhjGE4Xy9WQTlfoKMInS1IZrcMqkH5E27o2B57FosMtKLpTiTVEusbtNOYOKUsCuI4mx7k2uhRQ63H4hjliaJxcmZzajFRSllk5DlkjRnf/FkH6Hyg6wdQkKWG+SwjjcfuQJ7E2K5HG01kNO2ZSQhh9EhstzueNjvSZEbX9nRdT5YavBvZSDf0DPEodbJ2wA4DnHXixsDgBrqu5tmzObNZhfIdztmzXCaBELDe4rzFtQOZSs+MWoHWMVF8whhFmkY8v71GaU0cxZhIo82Y2z4M54m3HlRQRFFMVcYU+V8AOfyuFEW2S3D9h/Ti8u/f34H9FKkqkgqJdwOmrwn7Jw/Itm2nh6I8JIFJonJ3d0dZlvziF7+YUhOOxyMvXryYwOFms+Hjx49Tfrc8pOFTUSDvRR7wMrxFAEhRFFg7DrASGYaw5CJVENB5yfQKiJcWvoA5YTazLJtY1ffv309xdzIQRoybIkOQVI1LiYaAHAHf0/3gooUv+czz+Zw3b97w8PAwpdKI3EQ+d9u206RUmagq2lyt9aTpn81m0/np+356rQC6yex9lh59kkZ+MhHK8XXO8fj4OAE86R48PT1NRj9Jg6mqitPpNBUGIi+4TAIRbbfIMmTCquj17+7uuLm5mcbap2k6jXIXpnw2m3F1dYX3ng8fPkyFnzDFMl7+7u5uAtvSZbnM9Rb5RdM0n00d3Ww2fP/991PW9mWcoFwnSikWi8X02ebz+XQtiXF5uVxO52U+n0/H5bKwlON6yWrLdpkPLsWhrAm57i+TbaRIWywWk8lYChEpPC+vCWstj4+PPDw8TJ0aiYC8LCakiLyM2xTvghhNjTFTTr0w3JdSNyl25T2KdEd041mW8erVq0nedWlQliJSNO7Cqkth9Me2P/tJIvfKyxvs+Svn739u7AzTPz9n0T9JVca/hkm2EsZhMP1A37UMfUcaRRzw7PdbVss5RhviKKI+1Rz2Wx7fv6PebzmeTmgdk+YFPiiKKkVpy2a74/37j0QmIoo1zlrSvCSoiN4F5utr6nbg7uOGoR3HvC7XizF2T6ekRYKKEspZgYpSerejqWtmswTvHMf9gVl1FvprRRyND+/9bksSjyZOEyegDTqGsprhfcEwdLjBUkYF+IjeQwgRh+OJHk2cFaSJIU8zSnNNvd3w/oe3FDNFVeS0amSYbe+YlTPsMJo5i7wkqIA2Bj+MaRhKG5TSgGa32zNfLDAmwtqOOE6oyhKlPEWRMzjHw9OWYlFg9p4iX44AuyoxOmIYPHXT8/7DB6y15HnKF/mrkdXWBq0j+k6MDo40GTPRjdE0Xc9NlBInjtOp5XTqadRAbBRZUYxZ1XaMczTK44aeWENwPbbb03cPhP6ExZGagOsbhrbDKMXQOtKyIs8q0jgijnsed+Ok1iiPqBYF2tVks5IkytABNI5ZlRPrilQZXNvzw9u3ZJ3lKslAa6yzdF3DcrXk5cvn/PD+O6os4mo943a9YPvwgSKfkeiIYHtMFJGkGXGe4Iae/dMdwfW4esfp2JNdZaTGkcWGITWsFxWRCujg8K6jr/ccDjuO+z3fffsrgu959eKawjh0Yjg2B7zSdG1PdbVidfOc3e5AWl2hkjUmdwTVQF+zb+5Rg2I2zyDR9KEjcg2xi8f1aAwKi1Z6vILP13Xfd3hjUAScHQhBj5NwQ0c1q8jyjCQZgfX4EDAM1tEPA1VZYLTn5mqJCpb5bE4eR2z7ntiMA4aiaBz6AwrvFIdDPWbbF5rB+pFB9wNxXJLEQJSOMabOMpwBEWEUwnlnUSrQ9S1jHr5BeTNGpOY5Knj6rmP3uCNOEuI4AhXGtapGgBPFGnAUZUGcxYQwJgd565BbWmQ0GkbjuYI8i5lVORfhG3/ZLrYfPyMu//wxEIefZtF/rL0WgCIP9stpfSEEttst6/V6eoAej0e22y1v376dElNEBhBCmDTHj4+PvHnzhq+//noCscKsWWu5urqirmvevXtH27aT/hiYWNkoiqascMlAXiwWE+gR1lnAovfjFFHRTAsQk0Eu8hlFOiCfW8CzyHGEaUyShM1mw3fffcdisfgsfk/SPEQCI0kb0r4HPtPzbjYbVqvVBGRE7iCstjDa8/l80tELwBa5QF3XvH37dtIDiwFTPr+Y3oTZlAJKmH+JZZR0DPk90iUQucFllKDs81LKcilTkuEvIv0QllqSeZbL5cTIi+YZRvZcmOCu6/j222+nfQnQbNuWq6srvvjii8kE+uzZM549e8bHjx8nYCfvVyQowzDw8PAwMfL7/Z7r6+sJALdtO5kx5XxKx2e32/Ef//EfeO8nzboUlFIYXl9fc3t7y2azmVJD5HyJdKPvexaLxWfmS+k+SOF52Z0QWZYURRJhKAk3EtsphZW8VmQnspYkilGAuRRUUmRJ4em9nz6vSFUuE2EEGMvPStEs71eAtyQiyTUna+rS/HqZQnTJ2Ms9RtaOFItyTUoRJay4UqN5VvLU/5Ttz5KsXBIXn1jyHyeunLczTX4J4H+kd5l+Yjzg59alNrjQEbwb2+feosKoM7Z9z+FsQMjygvuPTzzcf8QPLbZrabtxIp+939I7T1XNSPIxr/vDhw/4oWe9XGDSeBw6NAyjMXSW8fzFK9q65+n+gaE/cTwd8UBjjzTvH8hnK5brZ8yLOctqxqnuUM6SRRoda7rmQFuPUojT/okkSUmTAuVH3Wlbj7nl1hqMyYgjDSGiKEsiHdG1A33bsFytMMbT2oGgHd42qDgiz3Kq2Ypqec1suWK+XKF9TnAeHScj22w9Co2OYqzrQXFmLXtmZUZeFihlcH7UjkdRgtaGYbAMNmDiiNkip5iNbdt+cLQd41TOKj1XljMgJq1r+qFjt9sSxwkmiojjdGQbTQpJhFaafqhZrhfM5hVt09E2PU034DH0NrDZHplVJb1T1J2jiMf9OGexw0DwAypYcANlpmmoeXx6w2JWoHTKvErZphFuCAy9Y0g8SRoT5xUvr1ckmye2+w3Hes/peEfXbKkKw1dfPGdeZnjrqU1KmVcEBbPFihvnqZuG3fHIOs/YPDxQH0/kScLzl9cMnDi2W/IsBWfp6gZDxLsfvuPD/QNXr75mcfOcLE8xXKFx9E1Nf7wnr56Rm4aEmkx5QugIfQ0RJCbGqJ7j4cj9wz1JpHl5VVCWaxazgtAf6XyCCSmdVaBS0DFd70iLOVFcEIhBZ+gkw+uRMdn3LZFPyJQh8WOmvonHxBLvBpzWxIkBH3A20IXxZjacW+vegfeGvju76J3H+4GgPM4NDEOE9Yp+cAQfMLFivVqwmOdASxKN2fZJEmOtw3uHMaOxO4lSjnXLfnfADo6+6whBk2UJz67XrJclRW7IEkMcQdc2nOqeYfB4Hwhend9DRxyP+vGAw3tLHI9r8HQ6QRiHIuVFQRxHHI4H2nYgySzONxDUaHrVgSxK6AePNwFnuzFLPzmbmV1HCANJEpPECo0fMzr/sv1eZvungDX8LqP++xh2+d5lWsXlv4VBFHC52Wxo25aiKKbBMKItvwSBIj0RcP727VuGYeDq6uqzmDUxKn7xxRecTifu7u5o25bD4TC9RpjFm5sb1us1i8ViAicCtOu6nmIWd7vdxAoKUJG8cgFsAjxFKiAM6dXV1WSKlD9Ff7xYLFiv19N/wvgJsBFALgAWmDTAkuhxqe8WFl0YxyiKprH0opOVaDyJklsulwBTN0GKDtExX342AYVXV1csFovpMwo7Lgy3FDZiqhU5gQAzGIFqURQTkypMsgAiAXKihy6KgpubG+bz+TSgSaQQkqgibLZMf4TRECnnSxJs7u7uJmZfMsSFtRagrbXmu+++48OHD3zxxRfc3t5Oo+2Bqbsgg5bkPMkak2OolGK/3/PhwweSJJmST0SOJNfDZcdFuiOi9xfTpXxvlLJ+kg8J2yz/lqJM1qdce5IuIqy8SGdERgafTLyyX/EEyECdS+OkFMPS0ZKi83Q6TeZT+boYatfr9SQDiqJoujZlP3J9yXqWHHK5LrTWYxjDeZ9y/Pf7/fR5ZI1J10g8DbIGxRMiv0uuezmP/k98RvyZOeSy/TjmUGQq4XdfwmVu+SVwDxNnHs7/U0qd2XKPdz2ubwm2BT8Qa+iHDusGsjSjyjPKL7+gyBLq05EsTej7gXc/3LPdH0jNqMMN3tN1Lfvthru3b3hxc8urr75gNluQxslkaClnc16+/oI4ijkdNvTdCafAd573d3e8+fBAVT1w+/wlRhvapkOhSNOCKI447bYEHEkSs56P5tDgOobmSJrnLArN9nCgGzzKjjd7ExniucZHYUxdUZ6qKgjBgQ043+OHBucTHBnz5RV//ff/iMoy0qpi6DMGa0n82FUIQREnKcZE9ENL1/e0bUOW5Wc2ZjsVPXEsGttPkUNBecoyYr2acfewZ/CO1gaOTcvalmD7kS1kII4jXrx4QZ5n5Hk6jmb3IygOQY3a3wBJErFczlivFzR1R1WVmCjGDQMQU7cW62uKsiTOYyLriSMwWuPCaBDWwdIed/j+SOgbuuOOeJaRxYYWT5IYjl3D02bH4XTimYmpsoIkjZhdX3P14iV3H99xf/cW+hbbHPn45jvaWUaRz0iTGalJQSmiImcRXfPwq1+z/e1v6YeBfugp8hSrA71v0bGimlfM5wuSJKWaLYnilNOx5t2HNzxsHvnir/6Gq+cvx4rZqPGYaU+ZDPjugX6oSYuUdnfPbvOe1bwkZJqHd/c87Wv2+x0vXzzjejUny1Lq0456/0isA5ExqCjl+bMvIZvTDoG+s7A/UK0rvLOcmtEgGRVXuHDg2Fm6oSNSGbPSgPNgxnVj1YB3EtEVGPrR5Gz9OL12nLqpCVZzOrSc6gO9zcjymDTNSZKUoYa2HVBaY4xiVmZEZjx/RRbRNu14Tp0/y1TGrphShqZpsW5MREnShCTJmM1yrm+WzMoYHQaMGWVdwXVoPM4OOAunY8cwgDFj/OiY7qLo7Hgz7vqe+4d7ZlVFWVQ479nvR9lVlmV4p+jc+KDWIeCdI0kzmqYefQ1akyaj+dW7Gh8GvO+IozEaMooC0Z9moP8/fvspQP1jjfgfAt3yffhdEC/M+OV+RMpxObhDQLCAy2+++eaz1BPJjd5sNhOjJua2p6cnvv/+e16+fMk333zzWfqG9575fM7XX3/92cMaxjXy7t073rx5w2KxmNrZl/FxURRNsYoih5CiQkZyl2U5TWgURk8micqDXgCmMHoCcmAEWev1mv/yX/7LNDhL9nXJrov8QDS1AkCdGwceCRC8NGsKIBd2/fr6ejIvdl03yT/kfct7f/36NUVRTIysgH8BKAJkrq6upthAAfbCcksso2j8BZxKMSbr43A40J07djI9UgqrJEmmCZySNb1er6dBQq9fv+bdu3e8e/duSsr49ttvpzz4yzhIif/713/9V375y19Oa1DMhMLaLhaL6T0I+3w4HHj79i339/f87d/+LS9fvpwkDwKehVUWmY3EGa5WqylXfbvdstls+OKLL7i+vibP86kbJJKjKBqf0WmaToXTfr/n6upqKihEYy0yDZGMSIdGGOfLnH0B8QLgL7s3zrlpKukwDJOeX5JwZOiOSDkuYwclnUUArWxa62l9SVyjHNPb29tJfiKdL5GlyXsWcC6eCblWBGgLMy7nWpJnZGqpXKNiCJWOlEwHluJGOkdSBFwOK/vfYOqES0AtenL5+ngDVSiJMeTTU+rT/Vdd/sQniK/OLxK9lIfe9uy2T2weH/C2IY0ChkCeRMznJUmSEUcZfdeO0wKThMxDbw2rm+fEScLQdrR9g7UdQ1fjmobNwx2bzQPb44G/+sXfcfPqJcpEGB2Pw2yArCiYLyu6YWAdF5TzGz48PPH0dOThw8cxczsovA908QlFoG5OpGlE34zJDuvlFWleooJGq4Fj84geGrI0JyYiBENqIlzTsj3t6W3N9XpN0xw4HvYcmy3W1YS+Rw+BKBjm8wXPXr6ALKP3kOYZrvEYNcbUaWMm5hGtp3SaIoto6o66aSjLGUWWA5rDsabte5I4GUFf0xLHhqvrBYe6pWlqdJRwOsLglsSM6SlRpHHnP7MsmTRn3gV6P5wJw1FPtVrOKPJAU/ccjw2rqxVZnmLP7Izz8PC4I4ojdBIwWX5uaUJkDENrUWHADjU6OIo4Rjv4/tffMi/HqZLKe2ZlRts3tENHaw/k0RqrB/owatTLakFiIrpDRd8+0ew/8PD+niI7sVh6kjjDZCk6jZjPr/gK+Jf/+b/4b//v/8b1asVsXlLNc7I8Y3VzhfOeNCvI8pI0y3HO8+zFC66ur/jNb3/Lu2//nbptWF8/w3XN2OFxPf3xjna349B6dlnC4fE9H374lnB9hfEdXT9ws1ixXqQ8u16AG9g9vqeuWyLlsf2YlFMt1xR5Sa9i9rsH6rrlJo7x7YbTtmFfK6rFM8r5iixb4dsn+uM9NnaoOWACWi47OzB0LTpLMEpzbGqyLB1NmMDQDXinyfOSU90yuDGBpO8CRT5D+fGh2XTt+RIfp2wqLHGk0Spgh54ozun7ZjQDa0jSDO/gdBxTf/LCkCQpq+WSxbIgyzRRFLB9Tz9YIm3x3lLkGcZktK1ldxhZujTNQDmyPMM5T9OMUoaqLIijMapx1GG2YxEbZxidnjtF0TmV6Jzk4UaGXSlNmkQoD/qcNGRMIIoCcexJ09GoG0d/iT38qe2n2O+f0o//vp/5fSZR+U/Y0/v7+8/YXAG8AqIk11qmAA7DwO3t7QR8BPwIe/7x40ceHh7Y7Xb85//8n/nyyy8nbbU8WMuyZLVaTeBwuVxOGdNi6gMmphSYJlmK/OLq6moyRmqtp6EmorsFJi2tDMm5ubmhrmt2ux3H43Fi/gQIL5dLvvjii8msKUBHmL3LzyDAWyQlx+ORpmkm4AtMenBJr5DBN8+ePZteL8kl8h4uoxQlxnG5XE7Ej4AW4LNc7Z/97Gfs9/tp0ubl/u7u7ibGUdJFBHhKYSSFiTCVv/zlL5nP51MxJ6z+5RRLYCoqZrMZX3755aQB3+12vHv3jjzPub6+nobRXI5r/+///b/zX//rf53Y3sViMQF2Mf9JSohzjtevX/Ps2TP+4z/+g1/96leTmVZSSUTqtN1uqeuaPM+5v7/n+++/n3TuTdOwXq9ZrVY8e/YM7z339/cTwBbGVszKAHd3d9OkURmKJYZfGZYkzHJd159p9C8NkSLhED/BZSShdCgu16W8LoQw5XPL2hbdthQ6ci0JUJbMeBmyJV0KKeCkQJGEmUsmXoygks9+GZt5OVm2bdsxeON875DrVLpOUhhK4SDF3+Xxkc8hx0pA+OValY7QH9v+9NjDz/TfcMlyj7R24EfcyPk/zQTkRyp8/ClhOpRChXEi5AjGx5HrRoHWga5vOO0fmRcpkfLsticePn7AB9A6RukIj6LVEXk5p6wqejuycEmhSMsUZ3vak6ZcL4lfv+SHD+/4X//f/85m/8Qv/vM/sFiucTfP6Zqeuj4QRZo8npGnBVGSkxQLkqLi5tbjBk+SpuigOJ2OnI4n+q6jMIqiSDkeD+x2BwgGvTtwOtYYA8q4Me88VjT1Ce8Nbd1yOB34cPcDzneEv/4rtrstT08bmv5AmiiqLKVKM7IkZ/XsitnVgs3xyOm0ZbaqCGEED4GAMRFKj6fDmGhkv51n8J7WWkyaoZOcYxtQOqDQKJXgwpj17pwjNrBezdkfTwzOj52JpqFve5JsNIMmcUJvhzPDqc6LdiB4jw+j/ltrqOsDWkMUlVzf3BDFB0yssa7FRCO7WBU5fWvZbo/khWE+T4iqGPwI5uq+YxhOdG2DswNFlrEs5/x/fvkf2L4lzRKurq8pqhJjFL6z+OAIvud4eMR5Q5YUaKVZLhfYTOGHHL+u+PDDt+y3Wwb7gI4j0llFlCRUiWG5XvLzv/45XdNSNx1ZkeKDpyhztInZPh2wXpEVM/K8BGMIHtIs5x/+4R94eHjicb/nh/2O69WSap5x2tacjg94UraHZpwMO9T09Q7bJWTJDSaKyEtD78IouUgScmewnIiMwnjLYbdjt9+jiy1xPsf3B/ZPD8zKiDxLOB12uFCSpjFxEmM0KJ1iiGBw2L4lTsY4S40mBIezLfYcKdp1e/o+Gc2eZtR5BzxplpFlEVHIyfMYVCBJcpT+xKKIJyTJIpJEo9Vo1PIukKUxu34/JraYlCwv6bqBpq5Jk4hAIEk181nCrEqwQ03fBWKjMCbFek/ddqAUeZGiI09xbHk6tuR5RhQbFvMF3oExMX1v6aKO5NzO1yYaPzfj1NmR7Rl15HLzVIym0Tgap8caoxm6gbar8cFiDCSJIssMZZFg8GMq1F+2H3mKfnf7sV78z3mNPOzkQQ2fRqmLpltMg09PT7x//35iHOVBK8N45vP5BFoEMAmT9uzZM7TWvHnzhv/23/4bm82Gf/qnf2K9XvPixYspY/hyyqNokoui4OXLl9PDXKQFAmolQu1wOLDZbCZ2TVJYJMVFEkkExOz3e969e4dzjr//+7+fxqlf6qHFQCcj2He73aRFFiZe7tVyPET+IEBZZAlJknyWOnEZ1yiaYolUlH+LzETMqJIBL59RpAKXJkWl1GfZ57e3t5MsQ/YrIEzY7aIoJsmM7FvMoJLikef5lDsupleZsnk5kEZ0yWIEVkqxXq8nc+TNzQ3fffcdT09PE1ssg5ySJOH6+ppf/OIX1HVNXdcTuypg8OHhYfq36MelSPqnf/on7u7u2Gw27Ha7KW1ns9lMUqjtdjt1J2Tio3Q3REojkotLA2MIgc1mw3a7nboTXdfx8PAwFVvb7XaSg4iMSPTPovMWECnXnXSkBOjK+rsEm5exhVIMSA73pbxD0mnkOpFuj3RAgEnrLmkt0rHJsozFYjF5IoTVl4JGOlNyHiT7XfwHMmxL1k7btqRpOvlAxJgpXYbLeEaJfpRugQBwkf3IfiWCU7waf6greLn96YBcjckCIJB6BOGf8sW5+J6e5Cf4y5SVT684v8tR6hKU7GXUl+Ih0kSRoSoLND2uqzketvTNiVlZURQ5h/2RoXcok7LdHjFpTrVY01tH1w8UZU6eJ/jgRgY2MhituL29obUNm/t3vPs2YXj2kr4+kKUFwbW0TpO5grKckaQ5th6F++urK7RJONUtwTEC5dkcQqDrWryzlIs1z154IjW2oI6n33JqTuRlRDv0NNsdx+PAfD7GEKkoJs5ilPYsVitMFFHM5qA8cWxYzEqqIqM9nbi+ucGf9fSneoOOc4IqONVHqsUK7wZU8MzyAmNS2qZhPlsSpylmAG0KWptSNz113TKflaSJxvUNKljiOME7T5UnVNl4A50XKU19oD01LKsZQ9/hCUSRRilDf77Rjq3TYYxW7GrKYgRvfd/Q1B0+FAzOc9psiCJLUSSkkWJZFvSNp24bToea+4+WeX4LfsC3LSFA3/WgFIO1ZDri+tlzrm9uOR72GKV5fNqx2dd4pZnNb7AW3r99R5QUXF3fEquEajYjjiN8WnA69CyuXtE0NVGasnl44OHxjpsIUDluiIl0zMuXLzDa8Oa7N6SJGbsb0WjOjZKUdx/uMElBtbih6QfwCiJDkma8/mKB+eEdv/mPX/Hr9z+QlQnGaLwP9P0erQxFldJYS98d2TwFXtxeUS3XqOBIkxIdVzhl6EyEzxI6Z+maHcfWkZgOf/+BJN2SG0hVy9DuicxL1qsVx16hQo/yA8EG8BajwfqGwZ7AxtgjlGVFbAy+b7AB8rQgiR2DbQgo+t6RJBl1eyKKAkkKymnSJAWtSJKUuh1w3qFQOG+ZzeeUVUYcWZpjTZJkHFzDMPRYZ1FaT12tu7sHlArMqwLvLVmRUJUafEPwPdvdnuvrK0YPi2E2W/L49ITrT4AhLzOqqmC768jynK4d0ATSpKAfHKVSxGmM9ZY0jiZtrDB4WoE7T6dVgNFqZPONQuvx8yjlz4x+RJbFZI1hNS/IktFYGpm/AHLZfgzKL8HXjx9KP2Xs/KlN9iHbZYKBGCBhNGxJJJ0kG4gExBgzDYWRjGVpuwuDJ8AkiiJevXpF3/d8+PCBX//615P+W9g1+W+xWEySGOccNzc3GGMmllGSJITZdM6xWq14/fr1JLn45S9/OSV/yINdJBdiJpTc5qurK6IoYj6fTwZQiZWTZBhhHWV0ulJqYkOldS/gQ2LpsiybjpOwh/Izl3nqot0WoCcM4/F4nKZjikb3MsdcGFQBT5dM/GX0oLV2Sm+RSZbz+Xx6zX6/nyIXRbN8mYMt+vsXL17w/PnzaeDO/f39lEUuGvDvvvtu0mCLFEgK8/1+P3Uj0jTl/v6eu7u7CYTJcZTuyW9+85vPcsRFxvDmzRuSJGG9Xn8G2NI05Wc/+xlaa/7t3/6Nd+/eTXF88nkkbUe08/f397x48YKrq6vPwLQUTcImy1qN45iPHz9OTK8Ur1EUTZNc5XoSmYicf9HbS469MMXe+ylGU4Dr2KFMJzNumqZYayeNuhR4InkS06fsV7T32+12whJSPKZpyvv375GkGMkll+JHugNiDtVas1gspoJVJFbiE5DJtMAk5ZHCWorWy7xy2cQICnxmzL1MbpIiQ2Q1IleSwvFP2f4MhnxktdV0Y5yg+HTT/PTay+SVi5uuRCOqIJ3tcWpnGKPT8GE0mnlHCI44jsYW9JByOO5xzo+t7SynLApOhxNpnKCUoT7syYC2OVF3A9Z5PJ5+aAl2wA8dvQ4USQI4VusVqMDQNLiuYXvfsVit6ZoBR8QpTun6MY1Fm4Rh6DgedqgoQ5sEp8KYFZ1G4P04sl6N2cRd09C1HbNZxT/9v/4vTvWR3vZnVjniJiQsltdkWUHbtQx2IAQ3GlJtR54XoBT90GNdTDeMsYu742kcyW4dh92evLoir8aIv7ZrmS/neN+fR5V7kiglTjIcGhscaTRnc9/w9t0jbd0ynzdUVUyajBMLqzIeJQzOspoVNLFmOSuZFxkqKJz1WOdpu56ySNHaoOCcdDDQdT0+eAY3Lt40GfPZrYX98ci3373DcmI+j3l2vSCLKyKjGbqepm7I8pTDrmW3y6iyhL7tiBUUVcXQ9URJSnAGlc+4evUl7sNHgvcUSUKU5iRZQZKX3N3fs3t6JNgjQzuA0iyWSxbLiv8fe3/6JEeSXXmiP11s9T02BJYEkJmVVWQVOXwzlG6Rlv73uz+94aMMu8giqyqRmdhj99V2U30f1K/BABbJpMh8YtNEUpBARHi426J67rnnnmO1oqsrVouUx0+fUhYzFI66KmmakijS+Kai0x3KQpomnD+6IEtjtOrR2mLjmNXqlLoJjHDXO4yNSJMMG4cq3diIx4+fcvPhiv/n//kDOol48e03nD+6INIx56sVSWK4uX5LuVsf3WkiwJAkU8zkBJ2t6ImIXItKena7NZWv0ckcRUdTN2wf7jBGkWpNU2wp9hvyyRlVU+PaA6qzaBOGEaMY0kzT9QXUhsxGaN/ju46+rWiriiTqmGbh/nZtd2RNgiwjihVtasjslOlkRidavbbBuQ5tIDKaJI1IEktVFRgbEccp+XRKVRzdHIxhMs0pywO73QajYZInKBUzmaYs5ulx7cig7ykO5VHHmWBtxHy+OLJWDXVdUVclznvubm+xWvH8qyeYxNK13dG5QeO9o+sDqGiaGo9jbiPiKLjDOBfue9d3ocvk1VFJF+Q1GnDeM8lTFEtm0wmRNWHeg38dUP7vfvxLwPtLTfi/xqCPpSrC+ArwzPOctm0HUCzOCbPZjO12+88Gt8YAQZwSBIQIWAI4Pz8f2vNVVXF1dTXonOGTLlnYT9HojjXBAlBEKy2fTYYX5/M5//2//3eKohi0yHIeRNIizK+A+nGKobg8yLDidrv9zF5xPp8zn8/Z7/eDW8dYNiKOEmOJwPX1NT/99NPgwS2ssDDEwsiKM8ZisRj0xFKojM8LfErblGFNYXcFvIks4fvvvx9An8iKRJYiBZHop+XcCJiWYCNhob/66ivev38/fE4BS1mW8eHDB25vbwc2GBgGYUVfvFqteP78+SAFkftA2P4xa/r48ePPotbjOOb09PSzz2utHQC7fP7nz5/z7t07fve73xHHMX/2Z3/G48ePhy5EmqZ8+PBh8BeX8ykzB2O3oDRNWa/Xw3UUVxkJ/DHGDIOoUvyN5xWkgJT7R4otYJBFCVM9diASBlmGbWVmYhxIJWBePoOA1qIoBuA9m80GXbYU2lKESXdJXH5kcFjetxSDIlNbLpfc3t4Oshkphq6urrDW8vLlywFYi7vPeM5Cikp5fuX9SzdpbPko11zWKfHlFweif4twGB8/34ccj6IH1FF7+ilnExS9AGt/9B73x6/6T97k4I9/7/HoY+iHWB46FA6PwztH37bUVTlo0G7qhrpuefHsKQaNVhHeWZq2YTHPeP7yGduipGlLoigOPsLGhJa8iiiqAzUdOE2cGM5OHwHQ9R3RsTXRliXvXr+nQ/HVS0W/VTg0J2eXaHr263sOVRMSI20U2MXIkKTBZUSJJMdpyrKmqDrOTk7ofURMhrIxUZzSth1N29GrliiNMC6iqTvafUXXQmwVSZpikwTlFF3tsTrj/m7Hxj+Qpgmnq3POz85R0Yy6/bRB9A5CAqZDe4Mipio6vMuoa8ObNzf89PqK2XyO9w1d65hODSq3HIqavnOgHPP5jMkkRxuIbU7dhtQw3QfbunCDe6w1+FYRx5qu82zXG+qmxTvD6mRK3/bUbc9mXfHx/S29qonMCfHllCjOiFNFnMaoQuE8tJ2n7yBNMw6bdQBDyqEwmCQH15MkM16kM5LlB5zrmc0WREnO1c091zf3RNNzHk8WfHjzE5vtjqZruV/fk6YRq9mUWR5RzWKsdhgUZ8sVbT8NA6TO4XtHnsbYyNK2Fu9ayjLYhkVJzmQyZTI54fT0jPcfrih2a1ZnZ0QWbGzBZPRtR28iTh89Jp7MOXl0zovv/pzziwsSm3C+XNHXB+rywGp5zmKxZDo/QycTpsvHkC6odYYxKZn1aK+IohzjNY2NcdUWq3pIWpTvmU4nFI2j2t4wyXLmqcGpCld5GnqMalHUeK/C4C0QRxFV14SimB4TeQ7bLZiYzhWk6YooC/7w9rjYOg9RHJ5JmQMohP3wPUp5IqNRKrAK0yyjabvApO+r40BPRBwl3KxvaJrg7pDlYTi46yu22w1JErOYh6EdsXhzzg+tf7GQ6/0t1euPWD3FmbBOmaNsq+/dEaCNhwJ9kNTEMdrA/rBHa0vXtbRtYEvavsESPoN34F2PdwqrDbPJhDQOhQGANnpI9PzPIxz/kjZcrsGfOsaAfMyoj19rLHeQYcayLAcQIBvvy5cvh01SAPdyueTbb78dAmK+jPMee1HLUOjFxQXAZ9ZqRVHwww8/4L3n22+/Hd6zfO/9/f0QuCLDcQIGBUzJ7zgcDoNfuMhnpCAQgD1mQgXwi9ZWwKUwqsYYrq+vAQYHkUePHg0e5cDAcMvnAgbGWoDIDz/8wKtXrwZmXzzAp9PpoA8WplkAurw/kZuIdECYcpHxiO5f5CtnZ2fDZ314eODt27fDzzx79mwYoJMuxBj053k+yDq+1CUnScJf/uVfslqt6Pt+YCs/fvw4DPBNp1N+/PFHHh4eaNuWu7s7sixjuVwynU4HZxRgGISUe0qYWjm3cj2lYyKR9RcXF7x584bNZjNIckQeIY43T548YTKZ8OTJE/78z/+cx48fDy4kMnQrmmn5HCcnJwPoHt/HwuIeDoeBIRbALRp6CQySAkK+T86h3E/jjojct1EUsdlsBmZ4/DpfFp/jOYCiKD4b+B3LpcQbP0mSwZZUHHk+fvw4SKFE+tO2LQ8PD8O1evr06VCIyACmMWa4Ft57Xr16Nbzu2L1GpFoidxmvPaL/liFguU9lZm68rkmBK8+8DIPL8yXdhH/r+HcB8nAc7Qy94pOryjFdk8BEfVKTu+NPftKOezzKBdYJHaQqwrpr5XEKlA8AHT6FLsRRwvmzE5bLZQB9rWexPKduKqJYY5qa4rAlyWcksaFuHF3dUDvHYj7n0eVTisOerqnpqp75MuHp05co7WjrhjhLqA4lrqlC0qXvmU9nHMqa5rChb3u6qmFz+0DddKAtSZ4zmc6PmqSOpq4JMfPh3MQ+Yr25Y3coyfMVk2lKV9fBhm+/I80y8nxOFOXHdvyCro6hb6HrieKIuqopu2PwUVERG09VVES9w97esTxNmc3mn2yguoY4Clowf3RfaVooSs8f//g7/umffiTNF9SlA9fSdTXaTFguplgrenQXgHiSUhYllStR6HAdh2LLA44sm1JVG5wHoy1d66mrnr4viGKL0QobJWRZShwl3G929K2m7zVN04L2pJkFA0VRY42idxqUJctyLB1NVRHZiCgNiXVaGfJsxomytE2LUoZd0VA5S768YDbNMa6h7zqqKiyqRsN2fcft9UcOkaLa3DNJI5I4Jksy+q5l/bAGBZtNeAAvHj2i97C+v+ZhvWG+OOH5i6+ZTBfMjwvu9dUVr3/8noe7EAKxPL8kmS1Cwqs1nF1c8Jd/9X9w+fwZl199RZSkRCbGaoNrSxazJZPJHK0TZoszTDbFxhNKb6la0NrQ++B8Ym3MdDqjpqO3jvawJoksXdPg+wrX1Ny+2zBJEh49fUHV9BTNljRLUTi6tsVi0B7yRBGrhq4NA45aq2Bn2HZYHaEddE1DnARWQ+kwd5AkER5FWRUobdA6eINbq4ljgzZgrcY7R9N2tJFjs9miVXBYiZPgpFI3YbBqPp8RxxHO9TjfkWUpzvWs1w+43nF+djG0NWUhb0fBEtPpBGs0znfM5xPSOCaJbZCcHNcdWTCTzBzBW8p8HtJq7+5uAH0EGT4UEk2LM4rIR6AMinD/gMLGNswq9NKGdPyn6eHnx5j5HgPqMSD9Utby5SFs2hgo/CmQDgwsX5IknJ+fc3p6yv39/WCnJxrRuq7Z7/fDYJjIVkRG8vTp08GqT2LLBdyLflc8m2VDXiwWg0RA7P/EFlH8yefz+RAOIzIB+Rzee+7v7we7O5F8iJZY2vOin10sFsN7FkZTGHSRqVhrh/cpXtgiPRlb2o3BhPiG/+M//iO//e1vB922AHJjDKvVanCcAQbWcgz+xtderu10OuX29nZo3QtrKe9fNLcCcO/u7gYJxVh7f39/z+FwGGQdwAAGZdhUJDBa68EnXthssZyUQkLAvbDsImv68OHDIKEQlxCxTby5uUEpxf39PdZanjx5Msgm7u/vWa1W/OIXv2A+nw9Dnu/fv+f777/n9vaW+Xw+6NkF4D5+/Jj/+l//K19//TUvXrwY0itFJiIdCHGGEX9vYWxFbtG2LXEcD4mjcj+MGd6maXj9+jV5nvPVV18NRa0MF0uRIIWgsNtfplJKYVLX9aARH0tngKGgERnM2PZS3O3ktR8eHgaduWjLpaOyWq2GwB/R5EsIVN/3PHr0aJhpgE/e+rJHiOxLnh95fQHnst5IkScFkzwz19fXg0xJnl0ZIpXu2xjkC5CXbt2X6+C/dvz7fMiPDLASnfhxK/IKtDJBE84nX9hPwM0HMOd9oJyGIU81eJUrFVwftILg1OIHL+qu6XAO0jQE/jTd0dUkzml7T+RCYt9iPqVzHt83TPKcvlO0Xc/kqFVLk5SqKmmbkq7rSZMIow2vXn/PNy+/Zjab8c3XX/Ow2WC9I7MGO0npncKgSW2O7ztu7x44FHvWhy3KK+Io4XDYctgfAhvne/o+DIAVRUlR1JwsL3j65AU2Uux2d7R9TaQnOKvQxhDpDG8NvQ+hOn1b43xws9jvd6i+4/7uI8VuTZql/Or//D+wx1aRMWbwc+97h04iPJ66aumcp6wcf/jDD7x+/RG0ou0a0CGFsW5r2i4M/y2XM+pyR3MES0pBksbHSfrAjGsFJrLEVh83F4cxGpxCK4+xMd71bNcHsknEcpmB65nNU7799itmtwknqwVaKfquBdWD7snS5Dg4l6BUhHMQJzGRNpSFI4mjIBdJZP5AE6fB9tHaGO8b8smEJJ1hlKerdzx7/vwoA2qZTzKqwyk3715Tl1turz7woQrs2mw6o3eOpm2w1tAfrfOc82STjDyN8YsFtw9rmu4VRVXy9MkTJnnO6emKN69/4I//9Pc8vnxMksZ0Pvi6T+KUm6v3LOY5jy/PWMwndF6hPLRNSV0VIfyqbUiyHB0bvFL0OHrfgdJo7TDe0bUFrq3wrsZox2w5Ze/2lLsGqLFKkVlHdSjZ339ktZziesMkSXHdnrou8LREixXQoX0LXQW9QUdxkBa5Dq1j8IrYxPRiRxq8SIEQ7hTFQavYu/BsW2vIgH6SkKRR8DSHI6sfo1FUZUXdtiRJigc26w1VFcKWwJEkEWkaY63Cez1Ytsng0BgY9MeFeb/f03cds2nGoWhIYsVinjOb5Rjl6bswXNy77ugKZNEqomlajNGkabCqWq83R6/pGXUdgEUcJRgT0ke7I3NiDChlMSasfUliadsGpf9TsvJzji9ZuC+BOfzLYH0MxsdgVqQmIt2QFr7Yuomzijh/SPCLbOzSip/NZoMbhsgrhNUVjfB3333HfD7nu+++4+HhAWDYkEV/KqD3+vp68LWW79vtdmy32wHUSqt8v99zOBw4PT3lxYsXRFHEer0eGNlx8MnY1UJAltj8ye8V2cp/+S//ZfgZGdgTdlf+Lq38siz53e9+x6tXrwbwIddqfC5OTk6G4kOKiyzLBs3y+DzI8J8w3vKZx4OHk8mEk5MTvA/hMH/2Z3/Ghw8fOD09/YzdVUoNEpWx24Vcn/1+P8gyhBUVDbYUK1IcCIiv65pvvvlm8NKezWbsdjvevHnD4XDg/fv3gwxG3FqkOyEF49iOcbVaDTKYoij46quvmE6nnJ+f8+rVK/7u7/6Or776auhqyADq27dvB6ZXBg4F7EqojgRVCes6HtQVMCjuIPLeTk5OcM4NUi1gAOm3t7ecnJwMhey4o/GlHESAp3Q+BHjKOZVzLe9LhnqlaJTvVUoNgFsKQnk2gEG2JdIu6Tadnp4CDKy7FCtSvMo1GrPU8v/b7XbIEZDiRGYzxl7+wo6PbR1FYjSfz4cCX5JV5TNKcTj+3V+C8/Gz9G8dPxuQm+PDHE5wSIT09Efmq6ftHVk+xSiNw6P0cWCTEE7TH8G81oRBT9cHNwwgSeKw8dUNfd/QtlUAo71DK0NkIyb5JGACpYiSmBQwJqasDiFF02Z0fUscp5RNR5xENK1nMs1puwZ38Cjv0erTjZNlGZvNPYfiwJs3r3n+/DnWGC4vLnC9p9rv6JzDobFpjsJxtpqzXMy4u1vTOs3l029QNmE2mdKtWnaHoCEsq+IYId9x/fEN7398R7XdkE9i0D1NV/Pupz2PL58zn53hXRzCTnzP+uGOsi7JpzPQYeGxGu4ebqjLPX/27M85PVmRZMfK3RmMtfSuP94oKYf9Fud6oiij73Y0zYHziyXaZCRpTpampIkNbifGhYG9yKBJqKqCvoe6htlsSpIs6bqWpqkBz2Q6o6kKrDVHXV8augYOJtmU+7uCrvM8PKw5OZ2Q5SkH3/H1txc8f3lGklg2m2Pb0jvSLGK+nKK2hMFSZ+h7aNoGb0JbzHchMKarDySxxShFFiuKvmazXWNUhHUO0x+TsvqaONKk6Zy2qciSmNPllEfLGduHa5r9GV1Tc3t7w9u372i7jsvLxxhjaaqSuuvYbndoGwaEpvMTrm4f+P0f/sjf/H//b36XZ3z94jllseOwXfPobAV9w/WH1zhtQxHXOz68ectXL57jqse0ZYxXlmJfsn/YUO4f2N5ec3PznmyaE8eGsq/p2x3YDNeXVPsD3nnoGvr6wH57zzS1QVNve05WOa6DtixIrEa5lu//8A80bYOJMiaLJRhD71rSPLDPWZaggK6piZLZcRZEY6OU6XyBc5qm7rFxFmYZmorNZs3Mz9DakGY53muatqZtO7SCySRBaUWWZ0zyhK5tjouTPuo0Qyejdw46x36/QxuF1kH2EcfmqPXuBw1kRT20Ir8EbsLQRJHh6ZML1psDk3xCluXEsSaODOWhHhZJrcN8B/ooRfFhViWwJSV1XZEkMWUZ2NTpJLSs26bBuSC709oNDq3WGpr2GGBm/tOIHBgAytgzXDZoYdZEXzmWlADDpvanXgcYWECRK4zDPaRdLnpX+CRdEFAorWQBIMLsCTsrGnT5/RD2iMlkMljEvXr1im+//XYYGuz7fgDCsp8IM3tycsL19TV93/PixYvBGePs7IzdbjfIX4S9fffuHa9evRr0vQKKv//+e549e/aZF7mwg6JBl/Olteb29paiKHj+/Dnn5+efuV5IW16Gz7bbbegcHUFDVVVcXl4OQER0vmNfa2GyRcsuCaAC7uS6zOfzYcjvcDgMTh8CYEXfe39/P7xPgF/+8peDd/zDw8NQdAjgFXnMmNkXvbLo7KUjIoWBMNhyv419qoVVF1u8k5MTzs7OuLu7G+RNV1dX/Pjjj7Rty7NnzwZ3jrZtWa/XWGt5/vz5YH3593//9/yP//E/mEwm/OIXvxhsDC8vL+n7njdv3gzPhHOOn376aSgMxKJyt9txf3/Pdrvl5uaGjx8/DlII6eyIFEhAuFzD9Xo9dArEXlO6ElIY/cM//MNnlp0CYqfT6VCsAsNzIoBS/NXH5xkYrBQljGn888JWy309nU4HwkX05+KTLoWWDJSaEeko96KsDXLP3N/fD/8mx7hoF63+/f090+l06JDJvSnPxtiRSdYk+T1iC5kkCUVRDLIkuUfGXSP5/VEUDffveED0Xzt+/lDn8QEIgRzN4E6gtdgmeYxSRJEJ457HAUetgwewxuOdP0paPkldvAvAoCr29E1FWxVUhz3aNRilKYsS3zfMFnOMgv3hgDaax0+fUFU12nqaaofvGyCASqU1JjJUdYF2BjA0TYlGsd9t0dozPV8G1teHxXRfhqnyJI4BT9/1GGtpu56ybrFpwWy+QClDbC2r5ZTOWeg7yqqlaWuyPOM0PiXNMmwUUZQHtFJcnl3y/e9/R1PviGzKbD4lSyyzPCeNLE1VoOjQ2oTiZJbhTY+xKkgcIkPftXz7y29ZzWecn58xXy7odQjPcd4TaY3r3VFSEs61VprIGvIs5vGjE7wKHtvnFxekaUxdVSjVomiPIS49eOi6hiiaDPKBcLOGh85GhqI4YLXCWENZlVhvieKwaHsVcXu35e7+jqrreFY/4vHlnCSp0cqSJCnOecpKs9s3FFWNiXJMBNpYus5T1z2dg67vMbRkSUTd1cepA+jrA03XoJyn2T9w/fYteE0U5wEIW0vdVLRdjY0MkTbMpxOq2BIrR5okTOMTsjji5HRFlk142KyxNgwIp9mxdfnxhqrtSLMJy9OYb77+hr533N3chBCZtuHNjz/i+pYXT/+M+WKB15q67airlnKzIdNw//Edv/tfnrMnT+mcotiXVPuCri7Yre/Ybte8e/+as8sL8sWKutjhbYyyCX3vqauKti7DwHNVcL+roK+x9FjVofoa5Xu8DxtuVTVs1ht0VLI5bIM0ajojn6Ts92EoZz7N6RpH34OJIuIsxWsPOiFOY+qupPdH9kJrougYo2yhLA9obQFH09bEsSXJk6Cndn2QkHQcF8+g/e76HqstvfccdjvW6w35JKFuKvI8BnWcIxksm3QYbnafGNMxkJPNWGvDo/OU+XQGSpEmGc67o2bW0fUtkT4mEzpJigsdrLAh9mRZStd9Svxr2/aoeQwFrj4+U0Yruq7GK4htjMdTNfXPnqD/j36MAfN4UEyGAIVdE2Zt3OaWc/jlxgoMbKEA2LIsBz3xWIqwXC4RRxFrLS9evBiGxsT1BD6x2uLxLYytAALRJT969GgAMKIR3m63AwgZu1GIznWxWCAOEcJQCvvYNM0QMiPDfTIw+PjxY377298OQ4PSWp9Op4NfOjA4pAh7bq0dBtm6ruPXv/41q9WKy8vLIW5dCht5bsZMubyGaJiVUiyXS548eTKAEDnGLX6RR0hHYuxPLZH3X55jAUFKKa6vr7m+vqZpGl6+fMmzZ88G6UOapvT9pxh5GSSUgJWx1EiuqUhKgIH5l6Hd3W7HTz/9NMgwBIAJ8yyvK0OB8h5E2nB+fs5kMuHu7m6YPcjznJubG969ezdIg87Pz/nlL39J13VcXV0N5+n777+n6zpevnzJcrn8zHlGAqrev3//M9UvAAEAAElEQVTP3/zN3/Ds2TOcCyFHAvbu7u54eHgYQquWy+Vwj4tmf5xuWpblUGzJPSNFo9y3RVEMspvtdstkMhkKWrnHhYGWe0Y6C+bokCVgU9xU5B4UyZQ808J6j8Ohxk4l8t7kZ6X7KQC6rutBUvPlXiBuK//SHiGzFZeXl4MzkXQoZEZD1igpWMfFvkinpNMiRIMU8PIcynMhn0sAOTAUTz/n+NmAvO97uram75rgXNL39H2LVuroThBi7uPY0rvgA26NJYpi4jgishZ0YMs773Aemqal69ogTanro9NDges6rHfHn01pqmCz5LoW54JXslM9k3lOUe+pNjWJNUyyCX3b8nC/Js1ynPOsiwPz5QlxbKmKgti4ICtoCt69vabpa756/ozLy0uMtrx/957tfsc8n6K7Hm0M1phj+I7Ga4XSkGUJZeOomy0eg3MtxaHl2BMg9Rnz+YL97sBiueA3f/nnNPWBPMvJ8ikoTd97eheKjLJqiG1CT8d8teT8yZOQaOk9xlqs1iSJ4XS1CEBZB7avaRq2+4LVSUqSxRgb0fXtpzZS3xEZePn8Cc4rvLJExuFdRWQ7IusDCG8bulYNPxvHlul0MqTLidtGlmZ4wgOKDx2Pvq/h2MlIU0uWBYnBYV9RHDoOh4qmPgTP8brGe3NMbfQcDg35JMcYi4lT+q6lrh3OKeIkRvvQ7agLjdYReZpR7osw8Nn3lNsH2mJDWdQcigptLFk+DaC4rel9YDWt0mRJzGqaM89iZmlEW1cUdcVsueD00SOMiSiLmqptOTm7ZF/uuX+454+vXlNWPc+efsWfffcd2nVUZcFXTy85WUy4+viRn17/yMWjC549f85iuUJ7TTmfM8tyvv/hBzbrLcuzR2SzBWcnl7i6pdhvuMtznArDWP+///v/5sXXX5NNpkxmU5I0pS8rdF1RbzbUbUueTYILTFujI8OhPKD6huVshoknnJ2f0KgpUZIznc3wBnb7AzZJiaIUYxKMiYGINE/BTImSGa2HTvU0aNJsgqlBqxjnQ2dpsVhi7KdWYBQZIiL6fnf0ZnXEsaZte2JrqA9h07PGUlcNrXNEsUErRXuUjOR5FrzVTQD8x1vquHiFGQilzVHb/YnxCIv6ESQb/Zl2FHpwnsgqdJ7Qu3bYmEP6pj4y8R7nepSCPM8Aj/d96ODh6Ps2/H75vSoMhLreYaym74Ozkfc9sf15oQ//0Q9hTOVPAUzCRgqwlfayMNgyDCbuJPAJmAtoko1SmD4BX+KbLdHf8ntkM5Q4dgG6IlO5vb0dmOP9fs/p6SlxHA9aZLEB/Omnn2jblm+//ZanT59ijOGnn34a7ODGftkCVgQQiA5bQK38LvgkzVkul2y3W05OTvjrv/5rqqoadOMCKoSJH7uInJyc8OzZs0E+MQ5FOjs7G1wwxP1lt9sNTLQwq6LpFoD07bffDjII+XcBfTIcCww/K/rc+/v7z9r9YiM51pQLcBEQL98jwFO82qfT6WBLJ9rj/X4/WOSJdEKkNlIcSeCS6PbFe9q54DUuA7QibZFBQgGVwCBxWSwWw7CiFICr1YrHjx9jrR3mDB49esR+v+fu7o5/+qd/oixLXr58yW9+8xu8Dx7yL168YLVaDTpySYEV60IJ7/nDH/7A/f09FxcXzOdzzs/PB9ccuZYfPnzgf/7P/8l3333HdDplsVgMYU1lWQ7R8pPJZGCmkyQZgKNooh8/fowMLQqjLW5EIjUSADmdTodiSoCvAFTpPMm9LA418mwLcJfhT7k/RTcuMifpYkn3SnTrcp3G3RnpcoznUeSZk//kfpb3K+9jMpl8JnmSQvRThoYf7oUxEy/nYbz/SAEhYF7+TWQ5Au6lSPp/PRio7ZojO97i+j5ITvruyMj2NE1NUxVorUJIjD9qgK0ljhOSJA5suTVEcQpoHIEt7/vuyI55siwhnqTQlezbktl0xs53uL4nijR9r9jt1+gIqqZAa0+eT0i0xrmWw74mijSL+YRsOufq/oFskmKNpa8L8vmEtqu5v7/mYf3A2cUpZ+fn5NMpRVmT5DnFbs92exyyyadYPHUbFgYbx9g4YbaYYGNH1XYciord9pam88xmC2wMWR6j6JlMU8pqQ5SnPP7qMfPFKX2n2O5K7u/XeA+96inbmp6OxJsw+GockQJtY5SxTOZzksiAtpR1T921TFY5bdNQVc1w02njqesS8ERxhOshPVo97ssKpRxVuafravI8JdIKazzeQH9cNIVZ2W53xyoS4vhTmpaxwUbR04dER2vY7vbEUUaW55yczplfTdm931GW0HchgdEYz8P9mjiaYnSE6y1Nreh9jTIx1kZ0vaftAquaZQm+b+hdjzExceKp9gV4RRKn7DcPeNfz6OIM5RXr7Taw7L2najq0mVI1NYdDQbkvoO/o65KbpmI5zVhMM6I0MMBN17KYLpgtz2ialkNRkTZzlifnwVav63n1x+85WS04XSwoYosCvvnmm6A9bkP7eDqZcSgb2rqj3B1oWsd8ccLL737F6tElNpmwWpxiUaxvb0jiFG2Cbr7rWu4+XqP0NWcnS85OT4i1IrGK3ji6siIxOTqyHOoG1SsMEc55NDGHoock5dtfPsNGEV7BdrfBRF0oZiYzpvM5Xe/pvMHoFEeMNQnWxkQRmMSgoxSnK5wnFDTOszscAmMNeHUMY7DRsBH3rgOlyCcJzvfsiwN5Hha7/eFA27TUOgrZBceW9mw+xVqFMQpjg0e71hqDwavggIRyg72qLJiBdeyGRdX7LgyHc/TCV0GiAgalPc4FT3GtNXk2pT5uxHVd4RVMohxUsDVEQWRj0iQNTjkB+R+HljVGeWwc0bcdimB3miTxz11G/0MfoscWAC2MuWxkkoQpGk1xYxD7O9FkCvAaH2OGL8uyAQy3bctisQAYGDZrLZvNZkj9M8Z85vEsOm4Z7ru+vh6AR13Xg1+3DOpdXl4Og3gSALPZbFiv18xmM1ar1QBYRY4iaaHiPnI4HHh4eBjsAkX6AQyvm+c5L1++HHyyJTJdNnoBxOO/yzOgtR5+n9j2NU3DycnJMLApDi5y/uGT64qw4SKZED2yACnRZ4+DgaSbINdS3pcwggJapAjYbDYDMDo/P2e1WvH69euheyFF2e3t7dDFGIMa6a7IfSVOJyLZkG6BaKZlMNM5x+PHjwEGRlrkNVKw7Pd7ttstMkgqumPpVIjeWK63XNPZbDb4lXddx+9+9ztOT085PT0dwOWvfvWr4Wck1Eh0+KJxXq1W/PrXvx6i7kVDf319PUg1pLB5//49WmvOz8+HICvpPghIFlmWsOQQ2GOZq/jNb34zPGNyXeQ5kTmLcVElryngXK6PrMlSXIlUBRgkS8MecSzORRYjA9ZScErnQ+4lcZUZA3IpGEUaImy0MNTjPULWnrHWXZ7TsfZ+DLSlCJA0Vymmx0PQwGeDvnKu5DWk8Bw7G8mg6791/GxA7voAxIVV4sgUh5Zu8N5uqgqPp6mbo1a7o6sUpdqFATUUJkmYzxfYJMNoTZwmxEbjIktddBg0SaQp6z1VUVCVJfVhT0vHLE8oDlu86ymKPU3bYUxEPp0Sa0PbFFRFyWp1gvM9rm+ZT3OqtsK1MJtEGA/X1zd0rgftyScz8smMpoeqddSdZ3uo6OqK7aGk7j1plqOOTFzX9/i2IY4j4lQz8R7valxXYJXl8aMlcTIJC6CGJMmYTFLK2hPnU7yKyKcz0uk524OjqmqSacoizom0IjaGpq7ZH0p045hMLXkc0TQ9kTXB77sqwIA+7OlVj7EpaZ4EOZBXVGVNGkckaU5ddfR1x93dA+vNjtn8hCh2tH2D9wq8ZT5JiSONt+64cEdUZUhEVDpYKXa9/8RE2dB6arsWrSOSOEGrA8YoFI7ZLOPrl89wzmO0IYlTkshQ1VuSOAUMrnPUZYtRMV3raOviaFfp6PqapuvIvcE7Q9lUWBsTG0tTbNhudnx8/yM//P4f8S4MGyVpSpykeNfinMJaTZZPyPKcSTahm7ckkUF5x2H7wHa/5W79gI0Ni9WSk5MzWufwbYtzYGxE5AlyjyzHdQ277ZrNZo3rO+7ub7k2ml/+6pdM5kvOHz3BWsvFoydstgfubh5o+h2bomJ58Yjl6TmT+ZI4m6JMhO88cZoxmy85VDW/Pj0jS2Me7m/56dUfefvTT+zu70iThDhPSNKc6XSGtcnRPaelU4bV8gTtHNo7bj5eMX90yv/5539FlKZstjv4+B6T7qjrkruHNVmeBabbeXoMndNYFew4sRAnUXA6MhFGhSFTe9TCTWdz2jZM9ddVGIDNsyxo5YoK5x2zyQlt24B3ZFlOXXfUVY+NQkx9XZX4vmM+naEURFFwLfEeXN+jlEZr/xk7PV5shYH4xN4ZnA9FaNe2gyROOYVS+shehzYo6vj5cFRVSdepY5BVQ1k2aGtoe0WSWPr+U4BZ03bHtrSkC4fi1UZhwHOsO/zf+Rj7W49bx7Lpifc28Jkrx9h2TZg7CauRFrPovEXiIUBXUhIPh8PApAqwkk1e9OMCvg6HA2dnZ8OmPZ/PB02wMGEfPnwYNvTpdDoUAPJ94ustaZV5ng+AcexkItp3+V1aa548eUKWZQNbK9IUYceVCh7GolEty3KwT5SCRcKDqqoapBYChKXwkWHHMRsoRY0E3khnqa5rbm5uBh2wgA0BGvL7RXcrGvixPEBAtbxf0VoLeBFLO4D5fM4vfvGLgWEUkCddALmfpIATWZAAI5EKyfsTaZLIJUTi8Q//8A/DTIpogaV7I7p+sdMTBhkYkk7v7u6GYdbz8/Ph2grIlNkB6bys1+sh2VO033/xF3/BYrHgyZMngzPLer3m6upq6ABcXl4ObjgS2iSF0nK5HOwxsyzj5uaG3//+94Nzi3QcpFCVIXiRS0laq/eet2/f8vjxY/7qr/5q8C1/+/btcB/c3d0Nxakw1l8O6gq4FH33+HlcLBbDsKp0pcQ9R+xrZ7PZcA7HXaQ4jocOmDyX8trCxI8lIrIfjCUjIsMSxlsY+HGh9eX6NAbswDAULHkDUuBKUSvONmOSQLot4+FNkUONh7H/rePna8j7DqODz7hzGqUN1ho0YUAzy3PMMXbaGotzHt85+jAGhTu2jl1RUBxK4jQln8yYTWckcUznodUGjaOuGopDQVkUtHXB7uGOvi0wJwv80Tdce4U1IXTEGkuaZqzfPxAlKWkcsd48sN18IMlSDlUZvMaVJ0tSLi5P2ewOzJNT0smCsoVDVdH3ULZQe8X+0NB1e2rvmeQT8iwjy6fEkaWsKw7bLUmahkHHtubZ5TkKw8lsQtt5Ou3IYkVsoTHQ4MF54shS1SVaBYeHomyCN3o8Q3lH3VaAxh9t4pzzTPKc3W4NznKyyDEm6DPL3YbetMTTGOc6lDEo5zFKE0UJzmnKqmW3O7BZb9nuK1ANPTW9c2xsSZIonj++YJLHJGkATV3vMSam7Ry988RJROf6cK0NOBVu3rpqMdpgbYzWMYdDeD2tHJePVsSxxXmP71tq11JXPUZbvNdorZjmGVV1II5j1tsNRVuRxpa66diuNZNkTmwzUEEypI7BLrvNltsP7yj3G+LEokzGvm5Z5HP61oViUGnKugvOQE4RmYg4iphMUp48fUxVF7z78I71w5r7bUnd3dF0+hiwkBGZCK9DNRxFMThLmC1oub655uOHK84fXfC7378iTRMetgfSOOFhvSdNclYrhY0sySRjMp+yK/ek/YxUe7quoTkUtOWO3f6Boj7w9fNfcnp6ysXhkvl8xvb+lrubWz7e3WH3CauzGJvloHJ6a1F5YMY3pWeaJPRthdcxbddT1A15kpPOTlj0hviw4+rjGzYPa6ap4elXT4MUK81QZCgboUyCsSrYFTY1OE/bd3g0WlvyyRTvw7no2mPUtNZoo49yLoNvexQ+DIvaAIbXDwWKCFywCAwbWIyNw3ClADbXO0k4wPnAiusRGB/WoZGGT5iWrutpmk/JcPf390CITlYo6jroXbu+Y7ffB6bFe9q6YXV6Rtv11K2nqxqSOMFhqasKo9WxCG9o2h7bOeI4om5brNEYrUnimKIof+4y+h/6GLdvpWASzfB4UExAsgCGsaZZwO5+vx8Gp+bz+QDSBNCJjlws9+7u7j6TyUjLXRg9kTI8PDwMAPHh4WEImBGpihQAT548YbPZhAHf4wCaMLnCPArgl2E4AXZCXGw2m2FgUAYCRaMtTLMAUXm/IgUZB95IWIoMqo5dG+TzCmvvnGO1Wg2vJ4Nx8qyMB0Bl4LEoCjabzTBEKBKGvu/ZbDakacpXX3016N+lEyJ62S9lSOOhWOlQCKMq11Wp4L8t4FGY8LEMQms9FCriPCMAv67rIXlVpAxjvfx6vebdu3fsdruBYRZ7Pzl/WuuBfRfZj1zv58+fU1UVb9684e7ujvV6PXzu+Xw+gPvxEJ+A+a7r+PDhwwB+//7v/54sy3h4eCBN0+F9n56eDjIq0YWLhESKz7GO/te//jUXFxc8e/ZscHS5urri6uqKNE25uLgYwLK1dkgzPRwOnyWxjosbsWHc7/e8e/eOu7u7oVMjw73yemP5UlmWQ5EJDPeqgGAB5eOCfOzHLQOlURRxd3cHfHJeks6HPI9jffcYeMvvHe8RY7Y72OrOByDfNM1wzW5ubgAG3bwU0H3fDx00uX8vLi4GwC1FCnyydfzSTlTY8bHrj0jV/q3j3xcMpBiqbXEuCNryhslkSmpjmq7D2gitNF3XB4cIjn6nXUfddcfNN1QaZRXAoe8D2x1pqJsW1x01hFVJFFvm2ZTEaNa7PU4bfFmSzhbEE0uSpmw3G66ub5jECaXRxHHC5iE4niymU5IkYr9dk2UxJklDAmIWGOvGGaJ0iu4dJm44O0/Jk5x3717zw0+vsUazWMy5ub1hMpkFazelSM7PyCJLPJvg0RRlzd3H98wWC6zveLj+wHQ2p297Iu+grnBJRVU0FEULOibPEjyGpulpO4fWEWVVoa3FRglNEzT2fddwOOxZzjSxDYuy9h6sx0aGzvcYB8GazuCdonEdvYOyajkUDW0Hdeep6oaiKsnSmLSFQ9mQH10y4jiiK1uMiTgUBfuiJM8z4sxglUJbS9002DhDKUNZ1oBBYyjKijybMJ2kNHXLfB4fK2FpEVmatqdrOoxOmM1zmrZjU5Q436CPOt04SkIXBo0xKcpa6FrqpsA7z3w24eL0lJiOfDKhweDjCSdnj9iudzRV0I7XdYPyniyOj2Exms5Dp2B6suK71ZKqrKmLCteFja3zoKymqYPTj1Y6dBLwaG2ouwqvNDqKefbViyHZrm46smzG+/dXnC5X5JMcj+fk/ITl6ZK7zZp3716zOByY5Qus97i+5HC4xylHlKe0ytMpz3QxJ8sSposVi4snNL3D6YhN0dJay+n5M8wspNpubq+42a9JjePs8QUmzbi9/8B5HDFbXpDPPCaKuH+44e7jR6yvmc0mLC5mVG2LNzE2PNh4H3TbRhnSJKVtgvWiP8bWO+eJY/OZ9jKwYDWxjdBHEN00QQ7VNA1964lMSlVXONfR9R2lL/G+ZLlYEk0nR0tFiRhTnyUAfzr8oDEXQCf+5E3TMJvOUSpYMNrIsN/viOPouHnUGB1hbARe0zQ9Td3hMaAsddNSt5666UEpbOtp2z4MyqLwSuNQOB/Cq1AGbYMWvXeOrvtPJ3I5hHmSzXs8gDudTofNWjZbkR7AJ4s1AbnydfGslqFQ2dhFwyngQphFiUs/HA5DAIwkPH748IE0TYfWvbTVhWUee08DwyCaWCqKBdzjx4/JsoyffvqJ77//HvGIvrq6GlITZTA0juPBSq4oCt6/fz/8/ePHjywWi6G9LdpfKTbGmmwBBFrrYVBSnBzkvAlgHOvEBSyKfGGssZXnuCxLDofD8DrCCGZZRtM0HA4HptPpZwBE2Oj9fj/EjwMDSy+ATJh0uSaSoioSDpEpyPdIF0JrPTCu0vWQe2xsmyfFhzDqzrkBaI4dP6Io4uLigoeHh0FLLIB87KUtrKeE8UgXZqw1ls8oxY0cws6KVOLrr78mz3M+fPgw6LvfvHnDyckJ0+kUgEePHnFxccHd3R0//fTT4Ekvn0nuZ7kPgIFJl0RTuS9k1uDy8nIAyGKFaYwZhnWvrq6IooiTk5NBQnV7e8u7d+8GSeHl5eUg6xkf8pnl3pDrIAWRAFIpXKUzJddMwLJo9GU9GIP8oiiG4lJcXf6tQ75HnhVZg8adJLkuURQNw6ty/4+TNeX9yyF/F1ck2QPHa56cm7FGXToN41Ckf+342YDcWhHRh/9XR/tAY45tRmshyZkoRWSlZeIHf3LnQnpk27U0bRvU4+rY7lWBmUOb4OHtHJ0PntZnF5eU+5jd7RX36w1d2zCbLwKI22/JsiRYMnYdq8WC7th6K8uS6WTC7e09jes5vzhlMZuxWJ3ivKZ1Cd4kHOqWJIqYzubH1umGPE04W0x5+uQRd3e33F7f0LYV2/UDVx/eo5Xn9sM7nj57ymK5oKxKDkUZNNDOU5U78Iq6bWmrPWmWk2UzXNfQVjssGt+XlFVJbDJ0bNFGEScmOIO4hK53tH24uEVVY6OI6rDn9uaa08WMxFrK2hHpMKyoVYhuatuWyBjBN9g4oXVQNT29UxzKgqIsUUCaTshTTVnsubutWSwzIhsFhtS70P53PW3b4I1BFQ5rjwmMScpqtaJtm6EVFG7IHmsNWZ5yvPSEYbmgtTWmw+jwQC/tJOjRrzt6Urq+w/VH9tHIQ64xxgZnDNfTdQ1pnHB5+YjFPEebiOtNgc6WmHROb3p0lmK1AlvSdy3K6ONreryGsmmIspjlasVyCcVmj1aKPMtojm1o1/a0XYt3fTi3R0Y1SYM/dZYkxFHE0ydPePniBR+vPvD6h9dBwmANr378gda3/OrP/wznFavVKYf9O67ev8dcwCRNcV0LvWM1XwQ5Tl3TNDXGgI0NapoF7X0yxdsJN5saF81J5udYrzFa4eOMantN5AuyNAzl3t+9DSE4iSbSBmzPcjah2c4p9nt+fPWOX80vmawiMMF33PU+yLx0R2Q0kbV0bT94fo/9ZEU7KKzdbrfD2hjnfJBU1RWTfMbhUAV/fOcoywqlfdCK947ttiBLs6HFL23Ef+3wns80erIATyYTkjhhf9jhfWhPl+XhuJmEbp1zHnWUc3V9T9s6bBzTHsH5fl9yKCrAkiRRANttR9d90kLXdYP3jji2QWLjFL7/TzAuhww2jgf84NOGJQN9wMDOyuYFn6wPhc0aDxjKZj+2rhNW8/LycnC9uLu7o21blsvl4PstoLrrOk5OTqjrmo8fP1IUBdPplKurK/q+5/LyksViMQzcCXAV3bgMiAoYOTk54fnz51xfX/Px40eapuH+/p63b9+ilOLt27d8/fXXrFarAfAK03c4HACGoUEZIhSHH2BwGpFIcmm9CwCXYka+T2QBHz9+HNhXsb4bW9cJWJG/C8AWAHU4HAZpkNgfHg4Hrq6uBp26AFk5TwLMxlpzOUcSPiMFglxX0afLtZX3Key7MKPyueW6jwszub+EVRd7zSzLePr06WDpd39/P3yW8T0hhcuYAVZKDfIh0YOv1+tBvlSW5ZDuOS56vP8UlCPscpIkPH/+nF/84he8e/eOP/zhD8Pv/cMf/kDf9/zVX/0VzrnBEvPNmzc8efJkcNPp+34IZRJQKF0H6R7J77y7uyOOYxaLxXD/SrqmyGu6ruPm5mYoPEW2IQPG+/2e3//+98OzMC54xmBTimv3b+wRwV56M6z1os0+OzsbJFVSTI0HiqU4lmfmyz3iX9ovZL2QtUZkMiKbkvck3bXxAKdIVWQNkuJCJGLyfqXrNradFFZ83M0an5eftYb+3G/U2hzF/QxgPFwcHXSg1lLVFU1dE0XhDRlrAwvFMfjHgLURRN0xdEReG7wxNOWB5hg4YuMEmyRUuwM2TkkmU4rDFmsMbdeQJRFpluGahrrr0c4xzzPuyzK0u7Wm7Rz3my2L01N6Ig5Vz+3DjihKsXEOJsFXYZE32pClOYvpHNc3tL0jTVKePP2K5fIkyAzqmrvrK+7vb7i/u2O7feDs7Iz5Yo5XgGtpup6P77d4B1ES8/5tgTKW5ck52XTJ2ekFcZZTFTuUTYjjjF41dF1J1zk6r5gtlmy229CGj2OKsmKWh8Vqt71D9Q2z6ZSq9STxLGjxI0tVtzR1i83CtXK+x3lD3fZHHbijLQLrcnl5zjRP0Ko7MtPHm9ma43BThzWaJImDZrbtaVDYLCaymqbpgDpIFoyh6VqyNMV7R1kemM3mOO8+6b4MaKVIkpgo8kdW0WMjWC4m5NOcoiypygNxEg0x0s6FICpU8FpPs5zirg+pnjYmzqZM9YTKBQZ53zryJCWKY2yUUZUH2qYE50ligzXQ9Q1lU5M1Fdp7vOvQJjoGXXkiq/GRpe8bqqbGdR3aaKwxAdgruPr4gb5vefvmJ16+eMmjyws+xpbVasEvfvULyq6h6epwv1uDNprLR5fcXN2A62nKgsN2x36743y2oq0bvFY0VUVXbKELgVjahBmLxgXXE5VMqVxENFti45g8TpguV9h+i+13HHa3HB5u+enV7zjsNzx68pS+86zmKQmPuXr3gYe7DbfXtyzPn5LGdgi+EZtSpdUxZDfMh4hWmxH7KW3BIDPoqOuePM9omw5rLFpbUKE1vN8fjjZp5uiW5CmL5rNBmzHLMv5TQPcYtMmiJ+AojmParj1uHCGALLB5lshGGCObbMN2t6fvg4OT0TFF0VKWLdtNwf16Az4iiQx5GgpBAK00WonDjMFGFq0V3juc/8+kTjnkugiYgk8SI9Effxm6M7YDk814DMLGXxP7wrHdWpIkA9Ml0e6iXU7TdHDTEDAhgGqsfRYvbAhSGBkqFBAsremxC4cUDmma8uLFi8HruaoqPn78yM3NDTc3N4P/9Gq1AhgA4+vXr4eN+6effsIYw+np6cDsisZ8POAqQFyYQ/HpFqZ/MpkM4Mc5N2h1RYMrOl8BdF8OugnYFWb+6dOnn/nGj7sffwrsi/vJOFJ+fE1lSFRAj8gJZI+Q+0aCY8b2cScnJ8zn80HGIddWwJrcI6JZvr29HQogGZ4Vlxth70XuInaaff8pOVQ6L8KSSsEg69E43ElsN8f3rhRkXdfx6tUrfvGLX/D06dPBBec3v/nN0AkQnbExhqdPn/L+/fthfVuv12w2GxaLxRBIU1XV8DsFBMrzIjKovu8H33Jr7RA4JEBXnGF2ux3Pnj0bho1fvHjBmzdvBjnM5eXlZ3rpcdE11mKL5eH4WYVPlovCMMvzKJ9Xfl46nfIcSyEyZr3He8OXs0Xy719eq/EwsxRecog8SM6/zLlsNpuhEybPf1EUw3kbkwvyHMl6IoPlYqU5fsZ+zvHzhzqPN5+8uDHHgarjhtz1nrb1tE6hvMI4jdcGo42sqKBAm6BDNUcGHRdAkCNIW8JGH05gE8fUbYdVkGQ52XSG6yrKqqDvOnbbHVmag1K0TUOWpmg8X331DG0smIjFesO+rLhf71kuZuiiYTLN6IuGqq0omxavLPt9yWwyPRq/t1QdNL4nzyesHp+xWMyJrWHzcMf11Ud26zvubj5S12GYcbFc0DYd/X5HZKPQSYgsfRKx3uy4KhuSdE2933N++YRIW3b7NV3bggkPkzUJELT58/mcumkxSpFPUpqqoK0K6qrioT5QHXZ4E5ObnGlTY41F0R6rU3OsOh1V3VM1LSaKUTY8QOfnZ1w+OsPooO3XypGlFqWhc0EyEZUN2+2eKNZA6GJoQoqi956yqKjq6rhxBe1d7xwcXSjCwyppXn24xt6hFbRdkMQ47ymrLXGchMBOnTLJLEbBZJqTT3Lq6kBbN0RGo3Uc9N1xQts5PJo0SkmU4XDoaRxE6ZQ4y4ljS1dV6LZFO0/bl2jXE5sI1x81ZXWF7j11UbFvNuztcXjjsMc7hzUaP7gKgfKW2gdLyL/+v/4vmjYwbR/ff+DZ86dURWjL7Q97Ts9Oubm95vbhgfOLc/J8QmwTNvdr7m5uWU4naGC73jBZlhSHA16rMLDbNnR1Qdd0pLnBak8UWw69p7aaRhuq2uOjBG+XOGdxvToOL/XMJx2v37+mLPbgSs7OzsjiHBc5EgvL+ZSy2FMc9uhohlca1bWkUYS1MRA6WY5PIMnYoPOWjUcW3dB6nFDXLZPJjP1+jY1iuq7FaE3T1NTHQKm2rUmSKbUvh3AGATZjxu5fYj6Ugr53wyIvC57rw9yAshaljmlpOg6hwCisCYy299B1juJQslxmGB1TVQ1N7ehaT111lGXNfl8SRxl5KptuSB3uO4eLXHi+CXpnvA/3/X8en2lKhSX/ktUdM2rCSH3JpAvAEIZJgPm47SvATWzUpKUvATEyGLbZbAZA1jTNwJqJRlZmDg6HA3d3d0MSpeh4BcAC7Ha7Qdqy3++HzzKdToeURdHEvn//nvv7ez5+/DgAqdPT08FZQzSqYrH48PAwSET2+/1gsSg69bEuX86tOHcI2zwechXnENHwCggSec/YtUOAp3QtlAq+6I8fP/4MeIkEpe+D57sAxrF8RO4DkaeUZTlcJ/F/FkAzLtbkGksXRGRpAkzlPYuERzTLUmBJcSBFkwB6YNBpbzYb+j5YKAs7PE48lWJHGH4psESHLeeoLEt2u93wvbKGCT6SYvG//bf/RtM0vHv3jrdv3/Ly5cthmPZwOPDo0SOurq64vb3l8vJySOK8vb3l48ePg8Ti/v6ek5OTYShW7m1575PJBGPMwH6PwaXIM4S9F322WHoKsBf9uYBT0bTvjzM3AjbHryfdAbnHRJ4hazMw7BEyCyBD1yK1EjmLPGPyjIqDjRQDY5nbGIx/eUiX5Ms9QiRwYj8o95IUc+P1RuZYTk9PB8mVkAgi5drtdgMol0JWCkt5ruXzyOv+nOPf5UPu3Ccm1drQ1m2a4A3edS3aRESpxdoIr3WIsR9igALgVvp4MpUC5UISZR9sDfUxBk8bRdMEpmWxWLK++0i53YO2xGnQHe0PW3bbHdNpuBkP2z3L5YrZbHlM9Iopm45vvr1gdyi5vrlhMknJs5BUud4V7IsDnuP0/6Gk2O1YzOfoKAydJfmUfDpnulwxPz2l7zrOFxcsH7/k/uYjj7a33Lz/keqwpe1B2RhrP1kCJUnMcr5ktTpjvzvQdI799oG6Ljl99Ji2bLi6/oi2MZP5jPOLJ3ReB8mLshhC/Dd9jO/DA1iVFZPMUFcFXrekXY3G47ouRLK3LZMso2oqirKnaTVZltJ2jq5rOVktOT1doY5BLM554jSlbGpc1TCfT4htcNBJs5iod7SNwytF1/a0dYePFLGNwlCvDn7i2hj2xQ4ULBYztNXkJqekpmmEwYBAvQaLyziNyfOMuulxx8THKLVEJuiAjQ3Dw8qE4dDOBfCVTeacXTwOMecmI7YxUdvSdQ7vw/DVftOE9MrIkGXJcYE/xqj3DW3ZUuBJTERTlazv7jEmDAt2R0s8azSxtcRJTNN03N7ckqQZF4/Oefrk2VEWpPj97//Ab3/798xnc6q2Js4SLp88YTaf87DZ0veernX0bc90MsV34XP4vseooHee1hUOT2QMrXccDnu26w1e3THdrJktz4lml5g0PFElju12j+8ceZSynD2l3xvaXUuSLJlkGw7Fmg/vXrOYpZzMUkq/B7/DmPg4fOvxfY8yHtf3oAKLWdVlOO94tFYDcG59+xmTJWxnsOgKxXffu6POvMUT0zQV3jk615IkBms1jSK430wm/0xbN15ox6zClyyIAIpwBY7sRN/j/Sc9sutDZ85GEd5puh6U17Rt8Ln3TtG2IZDL2og4Tui6AMCNtsNmEjaEEIKmlMZ7aPqOrg+e5+o/XVYABkAzZs1kw5WNToYsZZMfs2DjgawvWVkB5vI1eV1jDKvVipubGzabzQDIBJA+PDwM2tDNZjNoZs/OzobXuLi4GKQes9lscKwQ/2r4NES63W5ZLpeDjGQymTCfz1mtVpyfnw9M49OnT/n48SMvXrzg7du3gxe0sGcCMMUe8ezsbLDAW6/XVFXF48ePKcuS9+/fD2FBT548GTSv4/MuoFA8qSVNUyk1AFN51iTIRgC8/F006CcnJ1xcXAznXUCsgJHFYjGAX5FVSFEkRYwAt/GswNj1ZbVaIRZzSqkBkI2ffym6xr7aUhhI50Dex3hIuOs6ZrMZjx8/HoC6OGWIlOJwOLBerwepj/yOMcCWDpy1duicSLEgMgbpPEhheHV1NQwFP3/+fPhMv/3tb/nbv/3bYV4gy7Ih3VNcWUT+MJvNBomGaMO32+1gwTd22bm/v/8MtMvAqYDA9Xo9MP+LxWIIG8qybOgo/fjjjywWi2GuYcy0y9/lGspzLedSmOEkSYbnX55zrfVQyJyfnw9dJhmMHM9NyLUTKci4wJbvk3P5ZfEnx5/aI+R6y5ohgHn83Ij0bUwoSLEt3SO5D+R9y3WRzoBcu3FnUGRlY3b/3zp+NiDvOjfcfHJSuq7HdcGqzPngp6KVQWkDImtBH0+aOrZ5wR03Te893o21yjWuqXBNyWG3oSkP1FVoFbRVySSNaNqO2/sN56cnXFxc0tQVbdNSFQ3392s2mwPKBKu+Q9WQzqZoG2PiCIemc9DXDQ54dPkYj+f25o6+a3g4bOiaipNHl5gkI8rnxLNTksU5Pp9R7PbMZjn53NOajGQyI0ky3vzwT2wPQa9atx5lg/6095rJJGYyS8nzKaA4VCXb3Z43P35P1XR4bVHG0NV7lOswUY6OcvLJAmUN63XBwbWksSGOEkoPCo0x4PAYdUwQbDvwGqsjXO847AvidMJ0sSDLJlxd3dK7nrOTBcYoDkXJfldQ1C2z6ZwkjY8DGY4o1mhjSTJF37bM5ymHQ0XR9fRti7EJrle0eOIkxjuHMoYoSqnqBnOoyFJpXSu0CR7x3ge5U3gQAis/ny8oyhqvSozRpGmMUQEstu1xGFQb2qah6z1N63Emwk7mOBNjk5w4nVH1B4qHe2KtmOQJd9f3HLZr1CRjNlmQTXJcXxFbRbvv6OoWr2BdPBBpi/I9H95+ZLvdMM1zFJ4osrRGUxbBO793nmw6ZbE6Jclz+r7n/PIJm32wUZtOpxwOO25uromSmOcvXlKWNf/4u3/kxYuXpEnC+cU5z548ZXt3S7lf8/Lbl1w97NjvD0xmU2yk6bRhuTohjlLef/zI+4+vKX78I6eXL7l48Zf4pGE6tRzajqJsqfuELpnT+5RdobBY4mRFHMVU1Zo//uM/0j1/wsl8wiTvODQtaazA9eAdRusgvehdKHz6HnX0AncODscBs+i4AcriIgtQcAMKQ7RhEDdIngYWhQCUZ7MFzgVHiNkkGzbB8Hp+KHAEmI0X2n+NPXfewVGmF2wTAzsTwF5PFCWhwNAObTRKacqipuu3NG1HmkoCXdhEImuPr6FRKqT8OuePLFCEUhp1lNx5/2mo7H/3Y+wcItdJNqUxGyUb859iVeXay/WXTVFYLmHTJDRFAOjd3d0gRWiahpubGy4uLgZQJnaHt7e3PDw8DABOpBMClOX3yFDZ06dPgTB82bbt0FoXv2gBDTJkt9lsBmZTXEKyLOMPf/jDwHCKPaEws9PpdBg+FUZ4s9nwxz/+cRgiE0ApQ3NRFDGbzVBK8fDw8JmEZ7fbDQBkLCH6MgxotwuhXvLe379/j3OO8/PzATwLEJTOgIAmAU6SYCiMqoCTsZREPpfgBwGTAqzHzjzyrEtxIIOLMhgqv1OAj4B0GRQUeYR0DeR+FG36zc3NAKzev3/PZrMZotzn8/nAAkv4jzFmCBxyzvHmzZvBf957/5kVpQwITqdTTk5OyI97xNOnT9lut0PojxR/aZryzTffcDgc+Lu/+zt+8YtfkKYpl5eXPH/+nNvbW7bbLb/61a+4vb0dOjRSzIrX+Zs3b3jz5g2///3vefr0Kd988w1JkgxWnTIULUBdikxhxMuy5H/9r//F4XBgtVoNxZrooOXaybMhYHMsERF99ViGJuu/PPsiA4JPVoJyDaWAEO37bDYbOiDjtFg5vtwjvvzal3+Xe0vWHwHnUnwJeTB2hRnLgkL43Se5jny/vN7Y5OBLDf2YMf+3jn+XhjywRkGY33cOvEOpMFwWa40yEUJ+h2CO40YrYvGxe8IRjNP3KB98lH3Xgu/xfUNbFTTFAd8Fm8PZySnzWc71x3eUTUuU5uR5RlmVaGu5eHR5nEjec3d3D0pjooSmrtHeUbcdTWsxUUwcW3Qc0ve00kwmGW11YLc50EUG1zWYPkWp4Edt4hQdZUxXE5SCpqmJZqfh5BvD/cOWstiTJgnN/R1V59BEeJ3iSHB9sDxsm5qqrJnmwRrrUJSUZUNZlMyzhP6wpdUl04Uis1OiKMZPknCOjUInCU2SYXRgLo33WOUxSuH6nqb1xDpCEQbnsjxjtphidEHX5lijydOUsq4xSvGw3vLh6gFtH3j06JxJbun7HVESE1kwWoFXWKPIkgjtFF3vQWvuHx7Y7XZcXJyR5ymJ90RxRl/Dfl/gPOE8o7DW0DTdcWDR4L2irju6tiAE4nRkWUwchcXTKI3rO9o2bPDq+HvrzlH1BAtCldD7jr5qsaohtppEK6ZZTGI9qsmwXXBvqYo1ziq0cVgbo9qapjpQuY7tekNdNrx+/ZrNZk0cxcRa0zY1dV0Gv2lrefzVC84fXfL42TPOzs5BQV3WoZhynudff8N0MmW/XfPx43vWDw9cXFxyfnbG9e0daZZxsjpBeUdVlLS+Q1vNdDbj7lBh44jpfE5kHH1d0pYeE6UsFqdMXEtPz3a34cd/+FsePfszaFum2Sk20mAVzlcoq1mcnWP8FBUZIhbsd5b9+gP/+NvfMcsNRnsmq3MMPX3X4bqOjhpvk9GCK0/s0RrN96gjQwASm23x3pEkGW3b0XeOxktSbbA47Psw4GmMpu0deZ7iXEOWBobQH1lPo/XRWeWfg+2wQX8Oyj+XOYBzPsxw8GntiaMEfGCy8D7MOmiLUsewkbqkamrazhPF6ZE08Md04cDqecfRlaUdAHscJ3gPbdPRHIGHzNT8737IZiVgbKw7HTPb8vexOwV8fo3l72MQAJ8Gy2QITJxB4jgeLBLfvXtHVVWDv7d4VD958mSQsdzc3HzG9om+Wlrj42AikUdUVTVINMYtdLHLi6JoCHRpmmbw4jbGcHd3N1j+3d7efgYkhX0UWYEMm0qaofisTyaTQQYi8hjRxgqQgE/OMGMp0FjLOx7Sy/N8YKvla8KuyzDk+/fvh/M3nU4HUD4GJaIlH2aGtObu7o7NZsPjx48Hy0TRu8twnZznLwcWvfeDWYAUE6JNF0eUMfgXhlV02SJRGHcTRI4h0hBhZkWSIgBcWE+xm7y/v6csS169esX9/f3AzMtwonQ+Xr58yePHj3nx4gWXl5cAw3ns+57vvvuO+XzOw8PDYDH4+PHjQbqSZdngGS6uLtZaFovFkKQpQ6pyn4i+Xp6LzWbD3/3d3/Hy5cuhoBGQLJKn8/PzQfaklBq80//2b/92cNI5OTkBGM6xXGO5vuPnVa67Hu0RMsPhnBuK5DE7LcW3yEBE7iL2nHKvyHUdd0T/1PElOz7eI748xh28QCTVn61fQe7bD1IoWV+EjZfujNxf8nllfRM5ljix/HsIm5/9nUmcoLXB9Z62adFKYyNLZDXtUZtrjKXt2sCUH9lwvA9DYsf/R/ngZ80Irruevm3QOLwPNoptXVCXB6yBNE2Y5jnGauI0Z3lyRo/mYXegqDt836MJYMDamL73Q2gRLoBhazROQecdsYY0SqnrhrZtSJI4yBiaEqNnuLYkyyfEymG1Q/mgkzdxhrYRVgcw4qMMogmtipmuLvnq2TNW63t++vEHXNuxrzqKqkArh8ZRHXbUTT2EXizmC4pDwdXVR3TXYqNw/rrDPQfVE6cz4igjThLKogDvjoCgoe8bzFF2EoJUXCiSkuOAiDVUVQl6TVPV5GkA1d53RBai2OLQ3N7vaF2Fjid4n2OUoSxbzMSgTY82sN+vSZNJSGF0hrbzlFXHmzfv2W73PH58wXw5YzLJUMpy/3DHbndgsZwSW81kkmPM0SKz70mTKX3XUBbB6rBsG9LM4oynbSFOcyKbUNfHFqM2wUGmh6LxGG9JogyvW6pDge0PlHXN/uEaXxoOruGwWVPut7RdQ7EHoz3K99x0LX1VQNdyUJq6abi9vWe32fL82dPQ7q4qDlWBdz1pHA/pZU+fPuPs8skx9KBmV9xye/eAQ2OjBI/G2JjHF4+4vbvh7U+vefb8JSfLE9IkR2lDUZTBTQZP6x1lVZBOMk7Pz8inE1xXs1qdcTAb2naD0ild4zg7OWGRd9zebaiuf6Q/bMgWF0TTFdg55WFL29TM85goyojbCbqzLOYXbG5vOawrNtcbTk+m5NMVfdfg+lAk9Y0mnVq0MeGZVSqAXMIcBF4znX5ajNuuHToqxhxtvvQnm1NpARblYVj4TR9mSJQOsrXPJvaPzKTy4etfLp7h8J/9mwyUe39sTaKD57334flXnxwUuq5De338/qBl1zok4tb10VZU3o/WaCPx7f4zz+VQgEdDW77rWqyNqJr2Zy+4/5EPcWyQTU5autLOFkAkG/yYRfqXNk85pKUvx1jPKWBwNpsRRdEAbIBB/jF2OhDAJYyYgDLZOEUaI/HgbdsODGtVVaxWq0G7K8BX2MCxjELYbAGdp6envHz5kru7O/74xz8OWneRliilhqHD09NT8jwftMPv3r0b7lGxRZT3KDr0w+EwMOUCZAQ0yJ8CHuU/8UuX7oKwqFKUeO+5ubkZPos8swI0pRDYbreDvETWiaIo+OGHH1iv1zx79myw+tNac319zWazGQoLCaORAkv00KLnF5s8Ad0iKxLphBQG0kWRNUhrPTD3VVUN4Np7z8PDA9vtdgBNch2Do1I9FBZVVXF9fc3DwwMvX74cChZhnpMkYT6fc3p6yosXL3j69OkQ077b7bi+vh6KDzm3T5484erqiu+//55vvvmG09PTgfk/HA4j1zI3FGiSFtt1HWdnZ8OQoQD08/PzIXn26uqKw+HAcrkcUmqluyPzOwKSV6sV19fXQ1jR2dnZ0C0QOZJ4est5lvcmn0fWeXk2xelmrMmXIkrYcBmoHc+byLowlrMJeJb//3KYE/45Y/5pj1Cfvaa8Z3nGxSlmHBgkhbGsSbJ+yPsZ32+y1o2teEUfL2Beir+fc/w7GHI93PAiO+l7h/PQth1K96gkxEtrbdAo2j5YxhmlQzS1O7LicjL7Ht+3+LYF1xEbRd101FVBU5VoPPQu/IlntzvQe1ienpMlCd73ODRJFNFUNZv7YN7fHhmtQ1ES5SlZPmGymGPiCGPC4JzSljhOSZIc4xV5llBOMpIkIkssk0STRhAbj6KlKva0+5LFakWepXS1IUpyXF3TEdGpiF3Z03rL8vwJXd1yd3cXgkgs5GkMcYbVEQ+7Er0rieOgl56kE/qm4W67QRmD9o7S92wf7lE2ZXlyStcH8ANBa2/QNG1F34fY8BAxDq53NE2H95r9vgRtiI9+yUqFG8pqRZ7GrBah/eX7CIXCuZ4oSoPNISmKoLmOY4vSjshojI45lB3z2RJrU25u1ngMjqDNzfIwcLle3+HxZGl03DhCUlrThmG/LM2oyjbMJRxjybXSVFWJ68IGmKYJfe9xOriU9A7qHmIdkUSWnoLdoaS5u2P9cMfd7Qfy1GDoaOoDbRPSF1VkcXj6rkE5Rx4FjX9TlUe7zDmPLx/x9OlTuq6lPBx49vQyMPt1TRInzM5OidMUGydUTUtRVfz4+i2b9ZqLR4/JJnMA0tzjrWFaljxs1txeX1EeDtRlQRQZmrYBH0Bt3dTUXUPT+nBe2p48ndI5TdFucCYnX+SoqGCzORBHmrPlCfvtjuL2LW21ZaGeYVRB1fSUdYtrU/R8jo09TdmQasM0ndGnM4q+ZLcrOHcKjaFrGmzSocwnezmtzDFlN0iitNbHZzawflmeoxqD1jHOaVAGrzzK66C7j4L8JbIxTd2QTyZUVUueh+6StUe7slEo0Pj403q7T5P2//z4JHcZFOVKBVb/yGo7f+zC6UAFOBdIBWUM2sg8i7DrwW3GGoPWUJYVbetoW0eeHQdLuyDPCwyhZbM7/Nxl9D/0IXuEMGhjI4AxazlmoqT1LT87HuIUJlzaygKaxUZPEj7ldYUh9N5zfn4+MHfAIJUQdloYrf1+T36cZ5CkRmPMZ+4M4iKS5/ngty3gVQC49579fo9zjpOTkyGBUOQd8j4EgDx69GhIxwy2oXYA18JaSjCRMWYYxFuv15/pVO/u7rDWDsmjY82saLPl/MghoNV7P3hcf+lqI9KQk5OTEKp1vE4CpMQxQ66hzE2JXrsoikFr//Hjx+F1helu27A/eu+HlFNJnBRWXAKbxsO/MmQnoF1kNGMdubDmIhMQH+q7uzs+fvw4yA9kKHLsniL3mbDKArpXqxVPnz7lxYsXdF1I13zx4sXQYUmSZLjnZFi0KApevXrF3d3d4FgDDOFRRVEMg7/7/X7Qs4v8QQp/0azLnxL4I4y+xMuv12uiKBrAutg/P3nyBGBIxKzreiiEpLCSAkI6SGPmV4pXuU++1HEL0JVzKtdz7EACDF8XtlnOnchS5H4aXzut9VDQyjUa399f7gl/6u/j9zv+U96z3DfjrpKA8vFaJT8n3ycFqcxhyLWVZwxkjwjF0M85fv5Qp29R2tL2xxtWfGT7AMzpwegOE9mwRfZu1IQOYksN9EfdpTpunMqH5Mc4sjilqMqOtqtABQ1v19TEiUUb8Eoxm58y9SGivSh3NG7PfDpnvoyYr84oy4LdZsPm4YG+d0RJRNfVVMWeXE9QGtquA68wrieZ5GityLIk2NrFEUqDsmAMKNVjtMd4T+d6vOvBK7SK6Gm4W29J0hk2jqg6KBqI0iWTWUw0PaVvW6JIU1U76nJH17Vcvf/IfrshiyOyyLKaTZgv5pjNmqqtOBy2oDx914Pa4XOF1RGgMD48HLE21KWiqfvjxteidIS2hv2h5O27WzaHmuk0489/9Q3aRDRtH1IVjWKSRJwsMx5fzKhqML6grgraFqqiYuMPLJY5Rge7wqYqaWlJEofrPZMs4eLiMd9//xPv369J8yW9D/KNKM6IE0fTejyOKOqZ5DFZFuHdHq08URJjbRiqM9rT1jU4eyw6wufxWIzVgEHpMODpsFRtxyzP6dhyd//Aw9Vr1rcfwLdMLs6I0xhrcnQeFusoDo4bbWeZZimJ1dA2aDVnv9tjtDkyKRsmWc5qtSRNA8u03a6J04gkTXA4yuqAVoqHuzvevP6R7375S75++Q1RnKKMpSGk2s7mM4qqoG0r+rbi4faKaRbj6hLvHcp7lA+f++5uw3R1gdcJTQPFoaRROfEiAQ/J3FNsNxx2a9qmpulhdziwyhKMK+iLltxa4kSF7geaOLEcdEXfNExSS3p2Qj2z3N5dURQtrvN0bQM4tApPqlYhOMf3BKmRViEQTGvqtg6++UkakiwbaHtNUfd454ispW7bIFvTIcGz8444yyjrljzPjvrrBmvMcM7l+HKR/7SgyiL6eUtyzJyPvy4biVaBMZd2cRQZeqcGS8ambXF1TzzJkQHsqq5IIoM1PliBAm3jqKsebS2d88cgoBZrDXES0nDb5j9dVuATsywgWsCSAGrZzITtkms11o2P9ePyNflz3CYeO2iIXZpsnMvlcggTER2o6HpPT0+HaPX7+/thwEySEceuJBA2ZgFS4ok8Bp/C6o0Zs7FWFRjizYWVFNCyXC4HVxhhC4uioG1b3r59OyQ7ilRB0h7ruh504nJeZ7PZZx2HMTAW7bm0/qMoYrfbDSE08/mcv/iLv8AY85n8RSLjRYcvUgoJCvLec3JyMgAUYfrluk8mEx4/fsw//dM/8ebNm0FLLkOGImWQ6yhJp7vdbgBiYzcOeQ9jchA+zXCMJUDi7CHn/8OHD1xdXeG9H0Kdxs4aY+cQkbS0bcvJycmQXiqdgMlkMjDaMjgpRRp8SnC8vr7m1atX/PrXv+a7774b7lEpMhaLxXA+m6bh+vp6mKsZz05UVcXV1dVnciiR/CyXS5wLQUibzYbtdjsUYfv9fnABkQJTdOPA4Ogj3YdHjx4xn8+5vr7+TD89vpflWZVnXe57kZ+Iq450cqQzIedYruW4gJJOh5z3cdE+Bshfzp38S8e/BMDH69T4s4hcZhx4Jbpw7/1QKKRp+plL0XiOQRxtZEhUnmnpVo0HU/+142cDco+nb1vatgY0StrbMOhJvVe4zoPv0SpEagPBNu7YYg4QPYAu3zvapqat9xjfgqvo6gaFIktTuqqkaRuM9UGCUdegU6aTOc73JGievZgxnWZs12uM0kzimB5I8hxwTCZh0KauK9b3ZfBMN4bpZEZqwLV1YJ+1Zr5ccnJ2ho0jojTDH10jFLBaLuh9jNJRACzaokxC13miJCGKY0yckpvgyFE7RadiGq/ovSaanmMny3ARkyXJ3R3lbkPdVNxs9iz7DBslRHh613F7/YHIaE6Wc+rtVfhalhEBfavwNgmx9SYeFtIoTWnajodNwf1Dya7sub3bY0zEy+ePyLMUrcIQbd9WrJYJ/5+//Ibeaaq6Zru7x+qGNJmgVY9yjjRL8c5jlT7GqDuMcUSRZnVyyvmu54ef3vD7P7zm2VcXnJxMMMbjvGG9rciyFGN6rNkTW1D0aGvQqiPLLE17OA6GerquJ7UR+DDQWdeKJMnwR21vHKdENmNfHWh9TDRZgNZ433GyyMmymMl8SZxmeN/TVgWb9QbtLNPJjKauqOqQBjbNclaLKXG6Zr/Z0HcdZVkQGYW2iqLuqcqK3vUkJiShtlWBN4blfMZue8fpasazxxfEseVw2BNnE/aHgq7ao3yNjjRKOVxfc/PxHb6rj6x/T2Rt0CYbS3HYc/3hPV/PT9gXFUXVo02KIwIPcZowyxdkJxfsH+44dB+IJo7Z4pQsjrnfPGCTiHySkxiN7Q80h4pqd49yDk2HTRTGJiRlStXU7LZbVHrAJgeINCrOA2usLV0TujBaq2Pr7gik4oim63l4KOl6RV339KpDq+B33zTBrz3znxY6gyYyYQpf49AEsPyntX4Kpb5kwf0Ayj+Bc/gkeANPf6z5PwXGyGIriz44mqqlbcP3OhS9B2NiUCY8v1ZhDRgL2jhcF8J/2g609zSto0dhjEZj8Q52+4K2/Xkes//RDwE0Mqgr537crhdJwJjxhM+B7PiQlrAMdo2H9sT5Q1jOsfvDbDYbNt5vv/2W2WzG/f39oH2GT2zWdDplvV4PThoirZnNZgMjL2B2tVpxcXExaJmFoQeGQCEpAgW0S3Ei8pXFYjFIIuQzim+0DCKmacrNzc0gqbi7u2OxWAzn0TnHhw8fsNZycnIyaNuF/R37j8vnEV2vMO1i93hzc4Mxhm+//XawiAQGQPrXf/3XgxZ7s9kM70+KI2FsBQgJ2BK2drvd8sc//pF/+Id/4OXLl4PDjXQ0BBzf39//M2s76QyIHlnkQ2MGc5wQOva7d84NsiLpXOR5zmKxGMC0yFiMCVbDwiLLz65WqyHlVSQ0UojJQLHcGyIv0VqzWq1Yr9ecnJzw1VdfDaE0eZ4PjL2AVCms3r17N3QQxlIHYVjfvHnDYrEY0jjld4rmWgqF+/v7QZqyWq1I03S49yUtN5BN288SUNM0HYZY67pms9kcO9Xp4JgjjiPjGQh55qSIkoAsAePwiUX/0gNfwK90iODzwkqejzHw/5dY8bFM5UsGffynDHNKATtep2Q9ke+XrlwgbZLP5F4ixZHnV56Z8domXaix3O5fO342IG/blr4TP9L8WA04etdjGC+qDrBo+6k97N1xUExzHN7yIQmyb6nrkro4oFyLb0t2mw379Q7XVsQ6hMxMpilRbMmPPWNrDR6NJ8ZER+0ommya4/uesq5ZnqxQ3mN0iP4+HPa0TbAIbKqanQOlj4w4hshaTk9OyPIcY2MUEQqLNTFd00MPk3xC5xTOQZ5Owdpg6db3+KZjt39Aa0ucpNR1i/NwKGtMq1gspqTpFJRiulTkkyVNsaevSj68fsX9rgnyGAUnJysW8zmb+1turq+xkWEynTKdzZguliRJRNf3RHHExaNz5osFD4WnrFvwMVXd4VUEBNbyw9Wa1ckJaEPXlCh6siwliVPOTmc0HRwOiiw/palLJtMJ1nic66irGnyIs0/ilKYJCZb7ome92YdCIUq5vr0inSRk05Q40iH8pWzpezBK4/ua+SRhMc+IIkvveqxVIRXRhrTIug5uFkkS07vmONTZ4r06ahpDkJL3Fbt9xWKSszxb4ZsVi/QErQx1r6icRusIpxUm05goRSVTrGlo/YZ8ljKdJmgbhnarqqapK7qupapqbBxTtw1VVRPFlt55yqqkqzsUhh1QlgUvXzwnTWOc6/CA7qJQOLqOpghT+uWhYHPcLD++f8eTx4+DBrNpqOuG2WLF0yeO67sHqv2GDkOkE0wc0xPkNspGRElKPp2TpDl5PqWtClxbUdUtaZrjFdxe34ZFN89wTUXd7LE4XNMQRzFZlnCyWuGj0Bo3StE0FV2n0UlJXTfEiUEpGwpv74gAE2mSKGz2bae5u39gt20wNmbhIZ8kmC6ABGsUWoP3jsgY+q4nsoY8S3B9jTYqKET+JNPhj0X+P2dIw/9//r0Qfo9zPZrP44sF6H1icY7ODW1z/D0e58UOSweQrcOfVqtAzXtpeZrgQuMdeE8UxWh6DkXNfn+g636epdV/9EO0mEqpge360vZr7KQxZjrHrJX8KQWWBKEIIBN2W3Sb4lks9nXS9gY+KwYA5vP58Jqnp6fAJ/ZPBvWAATAJsIRP0etjJk8KD5EZTCaTASSJ5nmsfxf2N03Tz5IxBUzmeXD8OTk5YTabDaDv1atXrNfrARycnp6yXC4Hz2pxXRH7OmGp4zjm8ePHQ3KpAKSxc0XTNLx//57z8/MBzAPDQODFxcXgzSxSHNETC1D33g/yHHHBkU6E2AJeXV0Fh6Wj1l905iKJELtCkQ4JWykplAKYRLcsxZkAPCmSRCK03W6ZzWZcXFwMLLAMiI5nCKR7IQy2sNfyPkW6IkOmZVkOn3MswRjPA4iU4bvvvhtY6jFzKmmoY3vOOI558+YNX3311XAdZGbhxYsXXF1dsd1uAYYOhpwLYWNnsxlZlg3XSQpkKdQ+fPgwfF1ef1zATSYTzs7OhgJX9PtSFMmwtPyMPFcCqKWYkGJSrts47EfO+5idlmFiuS5frgVyfAmwv+ys/imNuRSw46+N5Vljpl+kQV/+3BiEj+U48p8Up/K6Yhl5OByGLIGfc/w7bA9DOEqWpcRxhNZqYAICCPejDdMjfsCftSB9sFXjmIjoaVFH2z6FpmgaDoeCzWZL3xSk1nNz/ZHOzdnuN0ynCyZ5jnctaZZhTMzdwy3GRkyOJvJFUTCZLYitoe1aqrpCmZjl6pSqPJBGEV3XUhRl+HpTE0XhQbQ6ojwcmC1TNJayqImSHmt76kOJNRl5vkBpg3dQ05KkKeu1oyz3NF3PdDonSVI8x1CDqsa7nmk+JU1iDlVJWbZM8wmRzVCTjkPZ4vuG5TTFdQe2D9doOh5/9YLqsOP+/pqiKNlud5y3HSdnZ6EomMzI8gy0Ikkz7jdl0DWmafBXryraDvaV493Hex62lsR4VqsJs1gsjfog2clj4t6gFVhjiCJF3wW/ed/3gMJ5T900VE3PZnfgp9fvcS5lvjzFpAmHcs/d/ZbVck5iE1arCV1b07UtFS2WHmscs9kF9ZF1WiwWNK0DH8KdnANjIrRRdF1LU3dEcYxSEEcxaZwRmYJDsWc+zbl4+phIHTBtyWZb0BKj4hyvDU51ZFmwTeyNxtuOWMXoRLE97Eisx3eeqmkoiyIAOxuD0aCE3XLsDwWZjjg5PyNJU3bbLbPpjMViTtd2JDbGKUdd7sKP4ujalrIo2B4jg5eLJW9fv6bcH3j27BlN13F6+YTFyRnL00e0/+vv2N1dkc0WmMhjVUocZ5gkR0cJCk3TNfRYTDZjtlzSVQXrh1vi2GAMbA8V+32NUYbpJCWNDdVhTQdo49HKMckyVDINC/c0ozcGpS1oRV23KB0Hdx/v6boGVPD3tn2Hc4quc9RVx3odijGUYTKZBd35cRYA7+n74CtfNiVRHIa/G++JbJC0fdlWHC+Qcvxr7cnxYusDdf7ZzwlzKUuPOKEYHQC4q+pw3ylHFIVQrNjo4Aw0DJ3qkK+gQduIyBp61x1djsQGzPGFlfr/tocMRokbhmx0X3qNA59tZuPjy3/7EryPAUzTNINGOThsbZnP54MlnSTp3d7eDm4V0qYX+zgBLNZaTk9PBx2vSFgEtIzZZrGHUypYo8nAp6SEih2h98HCMMsy7u7uBrZ7sVgMrK58j4AW2cNkkM9ae7RTDRICGba7u7sDQsDRfr/n5uZm8NZumoaLiwvEgk8GKfM85+HhgTzPh98vEpmiKHj79u3AUp+enn7GxsvPj+cABFQLOAYG1rmua9brNT/88APOhWRRCcS5ubkZtOlnZ2cD4AMGtnS5XA5DcTJEKzKIMWspA5giZxLwLxKgxWLBs2fPAAZ9NHwaQA4EYzbcqwLM4jhmt9sNn7GqqmFGQHTBAtS6rhskRJeXl4O0SPzpx448Ih2R50WKFnFLefXq1aBPb9uWp0+fcnp6yvn5OX/zN3/D7e0t8/l8+LxSgEjRKF0X0f/L3ITousXKUmROwtzDJ9mVFGKLxWK4B2VNFQAv94YATZGlCQMtmn353CKpkt8j3yeAXz7DuGvw/8Ye8eU686UWXBhtuffkT2NCANNYUiXXW9a2se+63LfyLMhryOf8Mm/jXzp+NiCP7CdhfV1XWBtjjD7eCJ+sDT8tuGEQTI3czDweVI/vuyBxcR2a/mivF34+yzJYLmlKg2sOOO+oqlB5dk3LJJ3h+5am8TjXEkeWNM/IshnO9aTHVlRdN8RRhvOA7okjQ+8dWZISWcNi1dN2HftDaDEpbUIokDKkUYxTnqYoWd9dM+scbRf049r3KBUFkNdVfPXsKX1XcnV9Q6w1cRyxLw7gQxC7sYambHj75i1GK5J8QlnUpOmMPJ2w3+4w6ZI0tmSTGNduUVrx8f0b/umPrzldTjl/9IzYKh7ubvFth0VhY0vnWso63DQ2mZJkOUmakeeaNN3iqei8Yl803NzvWbmM1SIlitMQ3HL0afbKYExEFGdMJzm9azFakU7icB3V0X8ZhfcK7w19b2ga2Bcl82XGfHHOzW3LT6+vWD8ceHxxwaNHM7IsxnUFzjXHzU0fZRDi1xnsJ7sm3LRd35IkwQLR6Iim6cjzmKIo0cqQZ1Om05bb+x37omAxn1NsJ7R7R+MqMCmT+UWwSHQlxgaG2/eOPJmQpRO6dkdZb8iSFKKGPJ+SxjGOMNRaViUIqHM91kGU9RilaeqGYn9gtVweF6QGFJgoom5arNb0bYPRhrYO1fbJasX56Rl3V9fc391xZS3PXn7N6aNLbDyha0qeP3vGfrsmoaU4rPFeM8sXqChG2Yj9vqSpqjDQq2NUFGMxmLSiKPdkacyjy2dsN1uca6jqjsQ46i4EHelE47wlS1KyxYIkibHGYpMEm84wWeg+lVVN14dQIE9opTp6rNX0PfR9hDUx3mnaBsqiw7tjimd3BF7Hll0cW4rCMZmkaOMwnUeP1oPP1wtZP4avfPb18D18ts6MZQ6yWI8X7cBu+GNLsQwDw0fvdec6jFKkSYSNNN71QRcex0dRncILm9Z35GmEsTr8PGFYFB+K1K77T5cVYAAFwmSOBx7Hx5il+lPtZfnamHGSzQ0CYBDwLHrbsiwHPa4ETonURJg/kVYIiy3DeNJiFkAgw5Wnp6cDMywATIC4MGBlWXJzczOw4MMMw7Fl3bYtX3/9NW3b8uHDh6H1vd/vP2vbV1XFDz/8gNZ6sDcUxnG9Xg8s7nQ6HQDAmzdv+N3vfsfJyQlPnz4liiKur68HoCMsswR4iW+6hMIIG++9H0D9ycnJAJ6FwZdDLN3Emk6Y/vH5Gx8yPHo4HDg5OeHk5ISPHz/y/fffc3d3x5MnT3jy5AmTyWQ4d3VdD0yz/D65X6QDIxKgcfCUnDPpzsxms2FgdrFYsNls2O/3A0O7WCwGxlZkRV3XkabpIJMR5xlxgZHPKmmdcp2lCyLnU0KkTk5OhiJCKfWZBEWkHyKRODs749GjR3z8+JHr62ustfziF78Y/O7ruubly5dD+JXcP2KPaa0dPOPHsxrAUOTlec6TJ08G33qxG5SiVIo08aaXcyyDzePPN74vBGyO7U5lSFgcgUTWIQBYpCJyfaVoHMvYvmS//9R8yZffI9/3p37myzVlzICPOxyyr0jxKedBigV59qUwlY6LdHK+XLPGhcu/dfxsQB4qok++lEoZ4jj4Y4/tYKQdHZiNsIk63+P9EZwTWClch3cteIcxx+AhpcjTFOsd5BFtGVGW58SJIkliNJqmroLncuFp+5o4iXFdRFWFeN1BB1ZXJGnCanZGXTco15MlIfGy7zu0jYiNZXZkusJkdRj+6/qaNI85XU2pmo6u3dEfKtpmR7m/A2WJ0wyloO9b0iRiNslxDlCecr8HrYPEIjL4znDYFrRdw0mUMJnMybIpJoop2y3eZpg0o/EOYyfkS80vFivevf6RN+/e0PSeP//uZWBK9lv6riVOLEVdYKwiiixV3+F92CB650jzlDix7OoajwFtg/7dQ+9C2711FShFXdW0fUUUJ+RpRt3UWK3oWkXvOpRX5LnGmgjlNV3b0/cWpWK6rmb9sKd1jijKubt/YL+9Q/mUyKacrDJwCo8iyyYkSbD+S9KgxwqFjgPFMHwSHoDZ8aEROzJ/XAwTJtMp631MUR1YzBJsPqEuSqI0Y3fomegIFRnsJCVKs2NwVEnjPUmcor3Hxgd672iqBqUNcZrivCOOUw7FgapuiOLo+LBrfO847LagDPPpNAzklAeMDQyg61q6uqRsayJjiI8P8OFwQCtNHFnOzk9p6pq7uzsunn1F3Xb42OO84+xsRUzLZrulrRxeRWjfEkeaxjuK4sB+tycyliSKUKoP4TR2QjKxpFlghmwy47Bbs1lfs232RMZgkozGObTzRAAeIhveU5RlqCSl0wZjLfuioWraYzCSgrYnTsKCF0UxTdujtUVj6F0IDup7T9M3hMRLG4ajrSHPU+qqZTad4HwH2gV9tvo8kfHz458P5Hy5CMtiGRY7h8cFAP3F4iuLYtjoK8qqAAfWKKzVGCxZGocMBBc6RdYozJHtqJv+CDxk7fI412NMinPgeo9Whq77eZZW/9EPAbcCnGTD/dLqcAzAx9cT/jnjJZvjeOBQtKbiDCFx5NJKF7mJ2N2lafqZ1ECG6WS48vz8fJBCjKOwBfB9qdUVYDGdTgeGVzThVVUNDKTIBOQ9iK5dKTUwqkmSDMB5s9kM0gEBzKLJFUmAAPjVasVqteLVq1e8fv2aruv4zW9+M0STiwVjWZYDcyeMnnhcC1Muuuex7lUGYUWCIdISAfViCycAWa7H2IdamEJxVJHBubIsB+mFFD7y7Mp7koJEQJ48zzKsmKYpi8ViuEfGxaDYEG42G8qyZLFYkOf50M0YF1jijy4SG3FYkdcR4DouPqSgkoJO7ueu64bPJey42HIKSyrdAGFa+74fZEwiiSrLkuvra54/fz7cD845Hj16hFJqmHeQZ0fAorDfY59s+brcT+IOtNlsuL+/HwoEeS8CpuU+i6JoYMyFBZZBUCkyZKhamHMZjh6TJvLaYg0o/4m0RrpaAnJFcy7svFzbL1nzPyVPkZ+V/8aykn9tjxBnHPm9ci/LejMeZJUOkRStf0rzLkWIsOk/5/h3RMx9cg/3HpqmCiyTCWBJNiyPpIqoY/rfcbE92h6GgU+HOrJXSnmapsb1DdDRtRX4DqMVvTZkWU6SBubZO8X9/R0PDxuWqyXWOrabhiSb4bwmzXNOVqcYo0nSAFDCIgZ1VRJbS9t11Eff4OwYHNEdq7a+qVmvt5Rtw6MnT5nN5+Aqiu0GHScoZdkpi7YxUZzi0UHS0YXP1HUNbe+ZzzIOh5K+C0xpnib4rsWYKXg4v7hgNltSNg3z1YpJP0d7aOsD+/KAAebTOV//2V8xWZxy/e5Hfvf7n/j6+WMuLx7z4f0b9oc9dnZCFsf4vqftGpSP0MpjjWI2TZnkEZt9jweq8kCdgl0sSI/tob5t6PqeummCl7aHtu3p2g5lQ0GhtKJrjr7Luqeuw2BcU9XHNqKj7zp22y2r1YInj5+y3+4pDjVXV7dYvSKyHbHtw/yADxV227VobdAmJopjtPLHeymkKE7y6Sc/bO+JogTneyJjiZqIyXROcWgoq54sX9IkBdNZzL5YhyLChrj0pvP0WFSc0dNTdy3aQZRkeFVhbIy1MW177DTYmDTLQoJjlhPFEVpbnOs47DfM5wsmWYLrWpzrqYv6+FA2WGuIjMaKJlVHuM4xyydYY3l08YjdJqS0/fDqB7LFGZNOoX1LRAta8/7de5RNyJRh93DDaZrjnCYx0FgTCpLJJDiflBXKZhidoCKDN5YkjynqFuwkSFAUaOvwbY1NUoyN6FwYzk5wxEkU5DwuXPuqbgge3oYIi9Ycq37AB2lRFAUnFdd5jAlFeN+GQVVrTGClj25McWzJsoTeKSjLo0OJ/WxhFKeIL//7U0yHLLb/rA05SFPUAD4EVA0slXfEUUyepfQeeqeIrMXjUISsAqMlUdhT10GfmppwHtyRRdfaUB1KvIc0Sdntip+/jP5vcghLLvrQf4nFkk1zbF0om+cYpAvAE8Avm55YAgqD7Jzj5uaGu7s7Tk9Psdby8PAwsOKikRWbPQGYIh0R8CRa8izLBicUuY8EyHz11VdDsqAMVcq9Kz7eAjDkHhcAK8N5wmaL7/Z8Psf74AQiw5+np6fDhi56eoDFYsFf/MVfsFqteP36NX//93/PN998w5MnT3j9+jX7/X6QJQhbKefWWst8PmcymQwyjqIoyLJsGGQUsCXdBrk248ATAWLiCy9uKPJ3KXS6rmO9XnN2dsZXX33FZrPhcDjw/v37z0CaHOLFLSBQAKHIKoqiYDabfXYPiYxCOg7z+ZztdktZloN7y2KxGOQ/Y9mLMN8CrMVfXM7V2FtagOQ4rEjWpe12OySfyut8GSA0duiQgkOKmSdPnrBer3n37h1/+MMfWK1WQyElzPfr16+Hc3V/fz/cO19qyYXNFoAu71N81EUbL+deCi4B58LsSxCTPHvCwsvvGw8vy3Mt/z4O9RHpjqwH8v1JEvY0+bt8lrHc5OfsEeOOqZCsYznKeL8QK08pEsN6H5556ahJITH2QB+7v4y7DOPsAfl/cSKSAujnHD8bkIdQjaAX11o8PGuSNND3xhg0hqMwBbwiDFwF0I3WqCM41xwvhusBT1UVwe2kKui6CquDJ7YnuJkAaGVDAFGckEQxbVNjlGa5mKNUhPMaraCtCpQ1TLKUJI5omvoYmtOxbxt839J2fRj2PF7syEZ4746DfQ11VfLu7RvQgZGPrGW2XBFFCY2DOJ3QdxkeQxJnaG1JrKcpW4rdDp9NA0i3MV3XHqupAKZ0dGSG24YszeimjqoMcgS0Ic5m/P/Z+7MnOa4suxv9ncFnjylHzCBZY3fLpJZMsmv6/1/0omumvhq6WKwiCRBAzjH77OfcB4/tcLCrv2aZfU/qdrMqgInMyAgfzll77bXWdi5mfRzMl4urV1xeXfOn//MPfPz0QGyvSJKMj9//maAcRtnT9xggDi1KGfA98yzk1ctzojSkLGvKYodVHXkanQx23WmR7ijL4ZwcdhWL+Ryle5q6pm1qoijAGjt0R05G0lkeso4rsmwAVj0QJiHzeYjREbHVbNdb9tstTyFcXc6I44TBnBnivCIKA5yHQ3Eky8244FtjORwK4jhhtVpQUg5yFa2xdtB2O+/IZjO6tqSuD8yjnDw/o1J75vOSqlhjU+gaaJ3GhMnQ0XAtXdOi+iF2sOt7fH/SSCqNc56n9Wao5j2kaUY+m1HVFU1dg2sJLFTVkc51eK/GjtHT4z1JHLHIZ5hgkHMlUYRRirqq2JxMO3E0pOPcfvrEm19tyWbzU3cAyrodptCqAFNW7Nf3hEFI5TVl4/BO43pD1ztc5zkUDYHRzGY5aMexqtEKitoT5ZfMl8/Q9NCVtOUWR0VRl0RK0buWIXMb0Bprhi5L3XYobQcj7GnyrjUBQWBxThFFMmlRYa0iCIa8bq9Be4WShdB1dN1g8myaijA2JElEK3I1D777vHjKIv1zecPPj2mr9PPCPESEDgzGwM7Dl3nYTdNgjSaJErzS1F0HGOL41BL3Dqs9gR008K53eOeHVBxvQPXYwJwY1JaiqGi7wdD9hQbnX/Hxc0AtG7cwTFMgAp83zSnYECZNNmy53iJHkRb/tDATkCbgSkCFsHir1WrckCWeT0xkEkUom75kMQsYHPeI06Ysg4LKsuSHH34YZQeSdiJgRl4bGLsE0s7ebrejrGZqapMBP9MBSgLsjsfjyNSKlnu32+H9kGn+/Plz/sf/+B/89NNPI6D49ttvx2Ew0/a7FEez2Yw3b96Med+Hw2GUD0yNiCI7aduW7XbLcrlEKfWF2W+ql5VO9Xq9HqMHBZgsFouRkX18fGSz2RBFEc+ePRsTXoTxlUJCTLuSnS7yjCRJxihAGW8vYMh7z3w+H4faCKMuchUxqIrkSRJqZK2Y6qGn96RzjsfHx/Hc5HnOfD4fZUECyKayjmmsoZwD8SRMuzqPj4+jMdUYw/v37/ntb3/LfD4fnw0pcuI4HlOBBFwKWysFoEiRxD8xjaYsy5LZbMbZ2dkIlkWGI39OJWXyjElxJpIoeaYF9EuHQbotU9A+fZ6mJleRHYksRt77VOoh113+/v90/OU94nOhJfeJfB55P1JQTpOKZP2S9UY+0/QzyH0j/ybPpzwzYnT/JcdfwZBPxx0Po6i97+m6dqx8RkZAD7nRzp205UoGYytwA4ve956qLNBuYNbWmwfqYo81nu1+TxanGG04HAqKYoPC07WOxfyMy4sretewWd9RFQcWqzPCKCXQirI40LYdSZbR2WCYNqgtSRxTK48mIIpj2pM5qHwoaKqawBrm8xlJMqRZaBtSNTUmn3E87Hn4+Ik4zcAEdGmHjVriJDtt7INxMLJAX0MfUB2H4UOgyGczetejVExg9MBWtzuiOMH1QwtdRRZrFb0KsVGMdx11ccBGmlB3PH/9NfXugcf1jkh7mrrBmwrfDeAytIbOc2rft0SB4cXzFReXK7rOsd+tCawnTwLoW5q6pms7giAmyzT3949stmv6vufZswtMFHE87IbUCTNtBwbkeUzveoJI87Q+0jnP6vyMJBmkQ1HQ4jrDdnNku9uSxIo4zCl0T5IY4sQym82xYUB7e48HsjzHqIF5Lcs1h0PBfD4nChPKsjoZ6yxhHGCsJ7IJYZzTHBoUEJqYVu1Y5CEfbh/p8bRtQOMMibVYk6CUJs4yVNBSbTYUxYG+2FPsdhgzJCAcjiVKw+rsjDTLMNbSFT1BoIkjS1Md6XxBnM5AD5vpfDbn6e6OYnckVAYzM7RtMxSN+vOgju12i1aaVy9e8Mfvf+DTT++G/Hlr0cB8dcm//0//H9broSXZHPc83bwjypdD7n3rKY8lRdHgsDivCBdzTDiAxLqrh46TNwQ2QYc5gYFAdxx6TVPfYzwkoSYILEEw+BBc3+OMp6wa2s5j7XA/axuiTwtZGA0egq5tORx2oIaknjixKHp63xGYE9jyQ4woFlrnqKsCj0HR4XBAOADe0yI7TcH4p5ryL82aU9ZkbFkqUHrI6h+AwWdnvPyM1prAWvI8xQPr7QZjLUkU0fUO6HGuG+4zrU8TRwewV1ct2g5zEZx3dE0L+FNUZ0cYfR6z/q/5EFAt7dspSBMN85Q1/jlrJX8KEBAgKoBYYvokBk5SM4RthUFnvFwuub6+xjnHw8MDx+OR8/PzkekTs6bob4ER6IoMIUkS6rpmt9uN8pYgCEb2UyIRhYmVaZpinpM2vgBv+R1i+hLphZwrYcblvpWIQQHGwhILkJBugMgwrB1Gt+92u9HEKgBDQMfPWccgCHj16hXX19e0bTsOlhGtvRQ/YRgym824ublhvV7TdR0vX75EcrmnAMd7TxzHY5qNAO++77m8vBzZWTHUPj09sdlsvpBFiHZ+sVgQBAEfP37Eez8aAyXBRBjvKIpG2Y1Ie4SJTpJklAfJe5zP53z48AH4PPhmylxLkbZer0eDrWi3d7vdKDG5uLgYTY/yWSWJpCiKMXKx6zoWiwWfPn0aYyOnxYLIG5RSrNdrlFK8efOGP/zhD/zwww/jEB/vPefn5/zX//pfeXx8pCgKjscjHz58GLsFUjyJnl4MtXLfCTiUQlbuqSngFwmQYLqpkVdMwMK2T2Mb5WjbdpTuyNAt+BwhKPeJgHzxDUzZbficviTvQ54POf7SHjH9t+keMV2bpEMr94SsQ1IozufDkD8xOEsHQj6DdGqETJDnfbqXSZHvnBtJgl9y/BUMucL7IUfcuWECJ9qemDIzXnw9ApAe1ClerHcoJZoddRrMM8hf2qYZ9MpdQ1MXdKrnsN/QNw2zfIE+yVauLi/RylAWDb6H0BrM2QV9X+O6lrLd0YYhHoU2wfC14kAUxnjricKAMMwpjkeenjasHx847vY8PN5RHgrms4xXL59zfn5JNk9QaAIdMp+lnC/POBwPbHY7Hp62/PDDB0wQ8/U3X59uNo8NLJEJOJvnBGEM/dCKXG923H5cg9aszh1VVZDPzzDGsH68Q2vDbJYPEXR9S+2g7GviJCKZLamOO/bFgTxbol3L44dHjKt5/uwFPojo2yGaUCWOvm8IwxhvFXXXYJQhCjRpZIntbGA0taOtC+xpiI1RhizJKbKaumkoiiO7XcRyMSPPc6I4wOoBYHY9mL4jCFuCsOXVqyUXV3OORcFsnhEEmqbWpBF0TUHbFBhtOBYlszwgSzOapsX5liC0nCcXrFbLYWCRHuIllRkyocuyZL3ekCQxbd8RR4PB1GiG2Me6x9qIzsQ415FGOW2xgaAmjeBQbfB9jFYR1mf4tsA7hzeOtjpSFntccaQ9Di1NjWNfHOmdJ45ilqth4MLxcKCpa/IswvuOp6cNddczX/Vk+ZzAhEQ2oCpLyuJIXRTkWUp1HHSSgR2ekbIseLx/GJmf1y+eUxd7qmJPGKeDcTZKmZ9f4pWhf7jjuH+gKfdo5Yny1WDwBIzVeBuRZnPSLD8x7J4gHe7FoIY0XTLLzgiNxjd7lOvQvaU+eKq6oGqKwdvR93SupW4rjkUHRMN0U60x1hAYcG07JPa0PcoH9H2LsRDFhig2WOvRThOf0pfADcN/bEDflmRZirZQVw2u79CRQtnPm9BntvPzYvqXtIFT0DZ+nUGTrrTGnKQ2bTsslIE2Q3fs1Ha0ajCaRmFAEscEYYK1A7GQRCFt67HaDIOwTpuBdz1tUxEH0fC9vaNphtamNpowCIa0mX87xusi7PY0tUA0vl/uEWoELNOUDvic5wt8oVcW1n2z2VDX9ch6ZlnGs2fPRrZUrvnl5eXIeAuQAEZm+ng8jqBECob9fs96vebu7o7dbsft7e0I/t6+fcvV1dXIEgv7eHFxMf7c/f093333HWEY8tvf/nbUxwrzLaZJOUdPT0+8f/8epRRXV1fjlEtjDPf392itR5ZUWFiZVCnSl81mM7LR7969w3vPq1evxs9ZluXI3ElWt4AzAZLC5IvsRGIgxWgqLPXxeByZ8vl8PjK6Ym4T0BWGIW/fvuXZs2ccDocxzlAYXtHeS/pHnudjHJ+w1ldXV6O5Vu4BAU7H45Gnp6dxuufUYCrTMsUHIEym6MSFYRYpBHye0imdkOPxOAJ/Kfj2+/3IAl9cXIwacNFBi2RKjJpSuEnqi7zm73//ew6HA9tTCpcUimLKTZKEt2/fjuBaisk4jrm4uEApxc3NDdvtlqIoUGpIMplquY0xI4MPjJ0b+AyU5/M51tovukTyeeReE2DsvR+B/pQZF0A6TSWR+0ByzKU7JJ8DGAu5tm2ZzWZjsol8fil05PdNDft/aY/4+Rr0869PAbnI3qaGXikyJKlHJG0C4MWLIhIcWbekiyRdMCls5DOKXOiXHL98MJDSuL6jaXo61xPaEIdDqVMLzTkGZYrGe8fQyR0YcueG4TreuYF5O+WRZ0lG0dX4tho28yyjLo+EcQIo6vJAGkdEUUYSpyilCYIEayKGaLIKRc/+sKeua4Lg5GqFgQVua/rqiDWWOB2YsWK35bB9wmrPq1fP+N1vvyYOQp6eHvhf//N/8v7HH3n19huev3zNbDYHP8hq5ouc1dkZz144vv/hPd/9+Qe+/fYfef7sYmjbK00ap2RpTh5rumPHbrtn++kTh+MREwTs7z+xOLuiPW44u7himSbUTUd13KC1oWlbomxGkCZ0ztH0PfnyDDXLodwRaM/j7Sfeff+ev/2b3zJfXVA0DbYqSOMlfecwkQGr8Ap6P8gGnLIYNRQx1hiaphwKiGgY5a5MzGo1w5qe/W5L3zQcd0fiJCRJYmxoafsa7RU2NDjlqJqC8/Oci4s5T08ObRz4jji1ZNGSNIxIwoj7+y1leaQoQlbLGV4ZtNFUVcOxKE7ypo6mHhJf4iDArM7Y7g5stnt65zCBwoYJzne0bYNRDtX1GCxxPKfvS9KzHKc69lvHcp7T3j+h6QcJVK3pm6FbsmtLDrt7fLOj2e8od3uM88yyHNM4MD1t73D+lNXregKgq1qqvuNxvaF3YIKMNMkoqx1NeeTx4Y4sjgmspW8HyY8eFDas1w+DeSgNOR4LOtfx6s1r3n34ibo4cP3sGhumFGXNze0txWHPw+MjNx8/kCcRZXkgiDNmq2vi+SVpHqDilDBOUdZS1TXahsRxTu8cUZdgwgUqnNF2DWXVoJxjEcckZkFx9HRNQ9vV6K6l0x1111JVLTYK0VoNeeHa4ekIwsGkqulQKhrYddOSxIYogChU6CAkCkKM4TTNVqNOg4WiKMR1Pd4NXgClFDawOOfHBV8Wzemf08X353+Oi7HzKG3RSuMZTKagx03J4QmswYwzgz3aKparOWGYoOhR9GRpRNPIwu9P8w4sStdYC2kSkkYx9sT6tl0DyhCFFof5pcvo//WHbE4C/KTokmQOAUzS8p3+nPw5TVrI83xk9gRwidZZwLewq/K1Kesn7LAMU5kOKJK2skhYhMnb7Xas12uMMbx9+5a//du/JYoiHh4e+O///b/zpz/9iV/96le8fft2nAgKsFwuubi44M2bN/zxj3/k22+/5X/+z//Jq1evRiZRplGmacrhcGCz2fDx48cxYu/u7o6LiwsOhwPX19fkeT5GPQoglZ+X83J2dsZyuRyTSay1/PGPf+Q//If/wMXFxZgeIeZFkcUIaBFgLrpgYdcFqAsIOT8/xxgzFkO73Y40TcfzP013gWH9vLq64urqamTtRccvY+/jOOb29nYEq5INL+9BgLDIGwSoy3h4GUAj73XKAgvYFsb/7OwMGJjP1WrFzc3NyAyLrAQYs+5lIqaMkpe4QSmIJJlD7mnRid/f34+gXbTrZVlye3s7TnuVrpF0SB4eHpjNZuPgoL7v+frrr/nhhx84Ho+8fPly7AR8/PiR3W7H/f097969+yKV5/z8fIz+lOmhMihJugfT50mumxh9JSpUjJtt247PksRjCqgWYCo68Gm3SYq+JElGgCt/TsG1nDe5z+T6yr0o3zvVkMNft0fImiLvVX637BFSLE87dnK/C7Pt/ZBoI10nKRSmcYhyX0tBKkXkL2XH4a8A5MYY2qZldzgAnvTiYvwAznuch8AYlFYo79Cn1AylILSD8ct1A9jp3TDWx7ctXVPTVAWhDSCIaNuOKAajPF1xAHrAcnd3j7UB+WyOU5q+61GcWneqpHU1qh9yteuqIrSGvuuoyyNxGNE3KZ0fzCLlfou1hkpDYKCtSoxR/M3f/o7D7sC+2PDD9wVZnnN2tmK1OsMQntJiNC9ePGOxOuNw3HN3+wl8R1vXaCBPM1aLgT2pdltUdyQPwfuGpizZ3ZU0h5z2+IQJU3QYn0BqgLKWpi1Rxy0miNFBRBtY8jihbSp6AvL5OR7L7cMTL3/9exZnK2wcDqxp60FpqrIiiAKM0bRdPdRFKI7HgjixVHXF4Xggzwe5RBBoZlmOpsZqRxylgMbjKMojXoWghyz6qumwKhq6IwaS2JAlhr7vKI8VYZwRRSnRVUJVDdr09++fWO+2nF8umKsMbQKC0HAsi0GjH4RoPbCrymgsGqWhrGpsEBCrId0jiUO864Z4SasplceYkM519DYmWV5SVntssCOLLRpNUTQ01QOHY0uaz4jCkPT8nPVTw93NJ9qqJwsCknxJ4xxKQ9tWdJ3HqB7ftUPyTNfz/uaJh8cnLq4uiOKEpqkJrWa3eWL79MDz3/6Oy/NzfN/j2pYsicF1GDPkgEc2wKuYtnWUVcVqsRjSPZSn62uMVcznGUp5qroi3++JQ0vflOzX9ygFL1dnqP5IuR8WL6cCirohy+aDQVVHzJjRdWYAigpQjs7VQyHWdSiniINwyF5v94T5DGMtcaTplQe6gfVWPVqfDMt9i1KWw/5AcTigUCxnKXEcEAUDqxwYjTWapnUobfF4ojjCIakoCo3Bez2adaeLrPcgkzqHv0+ncoIk7chiOvwp0riTudR5FAqtLT0O3/dYbSm7gsFT7PHaEwSaJAnw9HjXYUYgMXgLjAmwQY/3HXFsyZOQIFB415Km8SkWUqN0SPdvkzoBRqZtqm2e6kaFkZq26H++sYnRasqU/3w0ddM0I/gWQKTUMPQkCIIvGEHgi4maAtCFPZ2CDNEo7/f7obt1yhyfyj/+/u//ns1mw+Fw4Ntvv2U+n3NxcfFFbrf3njdv3nB+fs5+vx8lF2Iay/N8jE/cbre0bTsme5Rlyc3NzcjKCrsmm76w1gLgpZ0vUX1KKZbLJQC3t7f8/ve/5+LiYmQp5XsEoAvQAkbNsYwyl7x2YflEFjFNuhHWVL4uXYwpaJsOjJH4vTiOef78+Qhiv//+e9br9ZgkMmXBp0z3FAiKJlrOjwz/ke5IGIZjyokMEJLkE5HmAGNRJix9HMdcX19zf3/Px48fxxSf2Ww2Fh3SDZCiT1j6n376ibu7uzGPXJjT9XrN4+Mjz5494/r6euzaSLEgz4V0kuSePzs7GwGlgOOphn+3242xiI+PjyilOD8/Hz0G8pyJZlxkFfDZYyOH6MOlOyW58U3TjD8r112e96lETc6HTACFoUgVUD69h0WHLt6CqSb756Z/udZy/Jz5lq/9HJT/pY6qKDmAEViLrly6b3Ivy3MFn5NTBFyLjEyul9zjskZJcT99Ln7J8YsBudaa3vXjTeH9MDRG3qz3w2RK3DAeW2mDVm4wcyrou0GrOkz3dAShxfUDq+U9KDSuH3SbeTbHu4ayrgjjiDCKUNqw3uzwpiDLh0zxznmsScGGKNPRn0ynbefY7bZUZUldHYnsYJbRQYgNwmGcbBSx223Zbrd4NwwnOjtfEUYhaq3ZHw6noSsaGxiiOKXrPb3TKBPSNx1ZmvPv/u4/sdtu+eGHP7F9esL1mqentcwuxWhNFMXYICAMItwp3aHaP7HZvaPueharcy4urojzodg4bNaoMCWbrfBW09Exz1N6VROultyvltx8/Ejd1JxdnFPpEGUAo3EMRljvNThF0/S4vke5YSR423r6DrSxlFWN1j1RGBPHAXmWQD8MN8rzGcrAw9Mdm+2W2XxJ2zj22yNJ7vE+oDhWxNYOcZLO0NfDJERjQrQ2nK1mVE3L7d1H6vpkFPHglULbgN4N8ZnaWIwJca6nKA9EUTo6k8uqRNuBCcuSJV55giigaT1RGFIUFdoG7MqGPIpJ5hf0fUNbFTTlYYjmdIpSd1TFjrowXD+/RtuI5cU1dRSzCAMWyzPK+kjXVCg8VVmiSPA9PN48UHWezaGHMCdM52AC+tNDfnvzEaNhnue4vqWsK+IopCgHEGwslHWJ87A8uyDOYlzn8JWn7zqOhz1ND1GSsTq/IJ8vefnqLa9ff8PD3S3r+0/43Zrddkty95HlFYRBjjt6vEno6wYXBSTRBc4bjqdnTVlD35e4vhz0233H8VAOUZwM2lsVR1zONVEak81nODReeZJAE1k+p7moIepQjHWzPCdJYpI0GKJPu0EOFIQR2sgQlwJ92kSVGiKitLES1vRPjs+kh5r8/fO/ef85Nu/z19WJFT8tvpxkLFrjtabrPO7k3u9dD6cNpHcd2uhToTBMGB4SVwaCIQ4tvWuJY4PWIfksReGGn9OKOIloT0ZSKSL+tR9TtlE2Ktn8P8dUfmbGBZhPUxhkgxSmTFj1aeydsJXC9AnbKvIPkXhMNZ4CBKZmMdEIC6gTNjEIghHECgsrr3N1dUUcxzw8PIx67SkbKMWEtPHzPOc//af/xHq95o9//OOop76/vx/vYzH3CYiU973b7cZBP5JTLfIXmewoSSxa61G+I9//7t07qqri+vp6vD6i659mJIvpcwoMBVyJNls6ENLpmM1mI1i/u7tjvV6zXC5pmobtdju+TykcpkkvgiGEhayqio8fP44m3Km+dypZEIZdCqg0TXl6ehqH7YhRU7ok4luQokqkH8vlcpQVCNiW1xVJiXQ1Li8vRyb//Pz8C1mGFCJ93/P+/ftROy2mWpHiAfz000/jNZJ4TIlgFNAuxaVIXQTESZyiAP/Ly0sWiwVfffUVX3/9NTc3N9zc3LDZbFiv13z69Innz58TBMFo6pQCTMCvZINPjasCqvf7/fjcrtfrMZc8yzJWq9UIbmWYj1JqvE+kUBBvhUhjpl4GkdMAY1dH1gA5Fz8H5D8/fv61KTCX156uS1O5y/Trcr/J+iQysmlCjPybrEnA+HeJNBWz8LQwSZLkC+ndLzn+ikmd3fhLpWqXXzRQ9KcWwSnyrD2NqDZao5SnR6G1wSqN8sMHdWoA9TZJ8V0zpKR0LUoNQ0hMMExALJuGNJ+RzpccyxIdDL/PdUNkX5AM7XtNP7Suz1rK4kh5PFBWB9qTgdFj0SYaNnsdkM9WBJEljmI8AwtotWZ5dkEYJhyLI4f9kaKsmS+WaG0py5YoTpnNzpgvL+l9hI0s51eQZJf0fYXrK7q2Jgw1YWAG3TQWFcRE1g769Lal63qe3r2jOe6J6Dmu7wizjNnZBb1q0M2BfJ6Rhp5Et+hE0+meeRLyZDT3d7eDmSBfYMIQyo6u7wf2tmto+56uc/StIgoGw4zrHHE0I4oH9qepG0o7LA42CPEKqqYiUxlxEgI9Smm2m5LtpqMsetLKs1jGdK1nuz1g1DB4KYoC6qajbWuc6kmyiKurFW/evOCw26BPiRvzk0a/rVvqqqXtBj1yVTYURYm1MWmaMJ/PORz3eO/Qp2hN5zscHm1A62EipMKxPewJzhKyxSW+b6lPEZKtc0RxwnmYcCgqDvuC7W7LbLni+tkzfvrTH8kMRFGIAY7VEWsYEoSSHBNn9JXGRJYXFxnGKPIsxIQBRjuatqY4Vrx48erEeG2oioqr60u6tuGw2xFGIXXbkuZz2s4RhIZsltN7x/Gwpy4rknyOR1HVLVULcWxJF+esVIjXAfFsSd/UHA8H1uv/RRjnBFFOMjtjcfWcZR6iu5K6PkLbEeiAUGkaV+H7EquGBanznkAHeGVoG0cYeaw1JHFAmCTYIMHh8K4ZRt2rgYWOwpiuU/R9h/dD4ojzHdbGaD0M0fHejYsuMC5oQ8uxnWgRO8xkjfrMZIAA6y8NPJ8X5r9k6hkm/37+WWHSlVKn2MpTqtPgxRy06n6ISvTOnwYZDbMOho1gAOhhZAcgroZEGXB0p3jLQYrX0veKrv83QA6fdaOSNiKsr7SE4fM1nEbpyWYsTJhsyMKkBUEwmiMFuAvjLYBANLwSaycmuOnQExl8Ipvt4XDgcDiMA4YEaIkZWACUZJzLZxT9sORR73a7cXqnsLai75aM7TiOefHiBfP5fCw8xDApiTDTVAr4bDj805/+NGp3Hx8fx+hGYNTFS0GSZdkY8xgEAZ8+faKu68EPFEVj+odIBKb/EzmBSBdkimNd1yOzLQyhgGdhTMWQKAC5KArOzs7GuEORB4hhUpj6LMt4/vw5X3/99WicFG/AFNwJ8BEmW+6J1Wo1srFyyD0njLowwYfDgYuLC5bL5airl2FBSZJwdXU16uM3m804cOkPf/jD+N6FbZbCR3LMZcLr5eUl1trRMCza+uPxyJs3b8bzdDweefHixZhcI+dFgLjk5XvvR2OxdCjEbCtAWc7/bDYbZTaPj4/jNZzP5zx79uyLYkA6BsJSTyUhwqrL/SXFooBruT+nueVStE07XJKWI/8+NXkLQJY1Q7pX04L858D653KVn5s8p+z2z++Fv3SI1Ei04xJROf13+HIWgsjm5GuS3iNdPvl801z8qUfmXzp+MSCXEyth/HIxpydSaz1oT1GnDOkhFkzhT1UHg9REDaxVVdc0TYfVmuJY0VYlWZKDchSHPWU15DsrrdE2Ip8nnGXZwDKffr6qapTSg6HlJJVJwiXKhiTZjDgKaJuWw/5AXVeEwcnUE1i6rkVbybPsmc8XJHFEWzfs1lvam090/fAwr9cbsjRnPluQZzOCMOJ42NMrT9V6Xn31W7SG73/8DqOGdv/QQHDUVcPTw4bDviGJFZqeJAxYLs+GCn6/o2tqur7l8eGW43bN4uwCkze4IsQGjuNxj/E1T3cfwHU8u76kLgr2uz2z9AK64UYuu4rAepq+RUchzg967bLo0FYRBZqziyWHQ0nXObzStF1H1/VoZQnDBI+nako6X9LTEycZx6OmqWtuPm2p3t/wu9+95Ou3zwkDR1sPGjmtNShFGGkOxwITQpoYXr++ZrsJ0QrquqQ4BCifgtOkSYZTiuOhpK5beufZ7/YDi+s6nOtO3ZWAojiQJpZOXN7GYwNF13h6rzgcO0KbEKZnnF11dF1NVRzAeZIoZTFLUM+u6LwfQZSxIUVxIFAO13V0Vc3F8yvibI6JZphkwVfPfkM6n9M0Fev1PVZ7TKBxXUVgI2bzBVkS01Q1ynusBlx3yuRWhGGMMjFRlNE7T9P3XMyGRJ8/PDxyf3fH8yhBK0tZt4MMpT29xzAnXlxjo4y23KNRrB9u2W6faNueKF9CVxJqcG09dHBaj9YByjQYjoS2g3YAQFGaY4KAonYoG5OmGYExxIHBWtC6p+s72q6m62p6B2EYsVyt2Kx3dH3Lcjnj7GyJcy3GnLR+WtO1n9ud0+EJfd+NwEpxWvD5rOP7vLBKa/Ev6wSnx3SxPK1Qw88P/4r3Pb0bIhaddzjXo/TnlJZwMtBDKQhDSxhamqZFaU/vGoLAEMfBoHX3HTI3QSmF0h5jNb1zjCHo/8oP2ZyFRZsCIgGhU8mBXPtpCokAGAHcAh6UUkMq1ilTWik1AmF5XWnn53k+vm5VVWPUmwAapdTIaokRTqQ2UwmESFqmKRSLxWKUIjw+PvLTTz+Nm+3j4yP5aWiYvK4MCWqahl/96ldorfnuu+9Gdk427qqquL29HadyCst7dnZGHMfj0KC2bbm9vWW73Y56YTGmbjYbAD59+oT3nhcvXnA8HscUE9mnpWCR4kIkPMIkR1HE1dUV+/3+C5ZWQJuA8qqqRvZQBu80TcOHDx9omoa/+7u/41e/+hVBEIyvL587SZIRWKdpytdffz12N6qqYr/fj8+qyAZkGE/f92PuuwAjub/EUyCgVoocMYrK78zznOfPn9N13cgWSxH16tWrUbcMjNIlKSTLsuT169cjGE+ShJcvX47dioeHh1FyI0XjYrEYh+DIsyGYano/SoRe3/cjw3x3d8enT5+Iomg0qwLjMKooisbJmmLwlJ+RAVZCqIpOW0CxXF+516XIEPOtaKNF6jLtoIhsR97D+fn5OADq7OyMi4uLEVhPs82nhbi8rmjq5dmbFgXTPeLL/eKfZ8rhMxj/SxIXYDzPArCFQJKfEamb/G9aPEvhOu3+TEMK5E/5vl96/FUpK/KnVFWfBzRotA5OH6wfUlhOE/dcf9oY3cBoO9cTnhILrA3xUYKhx4blEP/ne5q6Ik5iyuZIFGUDKx8EcHogjLGnEeKKMAzo+uHGbuqatusIgiE6sK3rwdAYGvK5JQdwfjClRiFtO8himrah6x3K+mEkPRBnS549D9kfttRlwf6wGeLhupa+qQltQHk8EOQh+TwDC8qGXL/8irZrydIU7zuqquQsjEiXe+q2ZT7L2Dw9Uh/3hFbx7PKati7xruPp4YHN+gf+f9//f7l+/oKLq2uq/Zb0N7+iLQoOxy2Pt5/omgodhlRFOTCwqxKlEqyN6DvN03qLDgyJHca5H44lTd2Sz3Jcr9nti2HaZ2gxVtG0PZvdnixLSfM5DkfnWnbbPb2Hpu6om5C2tRyLQYaEsiilaZuGumo4P18QBIbNboPzLWW5R7ctSTpjNovRzGibIaJQojKN0jjlqZuWp6cd2liSJEXrYcCOMoo4DQmtGTwJbQOJGVI+ggCtHFoPqSNJPqM47lG7mrP5jFg1rM4vqeOAzcM9sXXormX99Mj+WOJ1QFG1VGVFXZQoZ/EmJEoyrp+/IpsvKVrNsdV0OqBo3OlxsewPW5rScTaL0VazOjvnsN3QuZqubfC+Z7NeY+OILM1JkwxtQ+q2R+Fo6mGc8Nmpxbzf7ch2W6LU4eqObH6BDUIa59E2xNsem54m4BV7jIJZZDFZRNlW3H/8nrZtuHjxBq8Cul7he7DzGdY4ZrGi8tB0itliSHEpdnvybMFseUYUx8RxSBBZ6qalLgv6viWITlFgxg5SdAWLxYyLi3NmsxmbzRNKMwxiwo8TOKdMxXTRWywWKKM5Hosv1pO/tND+JVJj0JX/P7MeAziWxdyfNo32VHwHdF0/mJ+NpXNfDrIwpyhXYxTQD2PO/DDUTCmZSuhQSqQqw6Rign/mrfwrO6bXVADHVKM5NXNOjVXy39Pvl81fBp3IhigbpoxLl9b/arX6YrDLVGcsG75M1puyysJMi/xDBv2IKW+afS6vId3h2WzG69evRyC/3W5HGY0UoDKcZ7FYjLKZt2/fjkBJjIFxHHN2djayww8PDxwOB9I05fr6eoyju7295enpif/23/4br1694vnz5+x2O/72b/92zBL/+PHjyL5LZN9qtQI+p8s8Pj6OBk7RpFdVNX7+7XY7miXlvEmuuMhRJGFEjLxSMEzjHLXW4zm8vLwkDMNxdPsUHIvuf5rSMTWb1nXN/f39mKgjQNsYM2p3p1pdAUvTZI3ZbMZ+vwdgtVqR5/koSbm/vx+LlMfHxzHOUdh+GVWv9RBi8fLly1GiI90CAcrAyGrLdb+8vGS9Xo/3kveex8dH4jgeTbqS/gGMRcnZ2dlopN1sNuN0WkmsEX255OoDY/GQJEOXua5r3r9/T9M0vH79enxGnHNjmo9IWcQkLEz+crnk7Oxs1EiLFGg6uVXIF3meV6sV19fXLBaLsciamjinYPvne4R0mSQTX15zukf8kkMKtb90/FwKI/etsPXSMRLmXr5/uv7I55W9SN7fNPN/GpEohfe/dPxVps7pB5qadb44We4E2E4nZHgoAtqmoQe0DojDZEjLMAHRbI5yLW1TUR1aXNOilSK0ltViSZycYmd6R993dG1Lc6polFJExpIlySkBpqZr2qGt7npwwwQ+hRp066cHODgxW2ES0mFpXUHnGghSWh9Qd0Nq8nyZY4KIKj4QxTGBNhTHI23dUNct231Bu9+zuHgBxqH7hKbv8TrERDM0iqJcY6M5RBplWoJ8QU5ImM4JLOiuhzCmrWpmZ4bnzuNPEwCrsuLu9hPKd1g8rjny8HA3DD+KIlRSst2smT+v0aZD6RTv4eHhiYuryyEKLwixUcdms8MzRAa2fUuaJRgHfdMPA5TaDl22pFmE84q6aeicBQWz+ZztZstPn255fFozXwS0zaA1s3q4JlVVEUYDY+D8MMFxt9+itT1ljGeURyU3EFEYoJVmvz+wP1QcDiXaBmhjWC5y0jRCMWRYG63puhpFP7I43ndkWUrTempawNB00B0asjxllq5I6i31ccftxw9UaUgWRTzefKDrFfnqkkgrXBjRFBWPu4I0CkgXl3iT0PmAquuo2o4o9LTlUDDK4lVsH9nRMosDHh+f2Dw+EFuD0RpjNF3fk2pDGKVYG5GkGabt2BcHjIY4DMAPgyW++/7PeG24fP4SE3nyhUKpwVSrbYSyDa6FbH7OLAmINeyfbmmbiigwoB1tseX+pz+Rz1coFRLakMwmHI8bDvWO1rfMF2fMz6952h4xiSWdXxElc8I4wQYB1ioOhxJPh7FDpJ82Bq/M2Ba21rBczrA2IIoCjIG+b7DajEySABbZjKVwr+sax2d96HQowzQbVhazz6BtCtC/HH/8xcKIRwPO+5GVb9tmKKRdjyGi63q8B2MNjKlQg2wHNbDeKAazp1f0rh+1kl03zF2oq0F37LwijCKiMP2ly+j/1YeAYPhsgpINTcDZVMsp11haxnKvTMGyyCTl/pFBN3IPCVgQBk6A3FR3PpW8SNTetAiQ9ydt9ml8mYAcKRSmUhjv/WjmFF2zaJkFhIqu9+rqagSRojGdSiAkdnDK5AszKb+3qqqRdRQGryzL0TQqwPXu7m5k+kVn/eLFi5HdBEbjoYDuKIp4enoCGOUskrEtmepiNJQOhMTiKTUYSbfbLe/evePh4WEEq5vN5otYvKmO2Vo75nJLxrjkznvvR4Z7u92O+d/Cqgqg9t6PEhK5JqKbdm5YX+VawGc99lTffDgceP/+/Wjo/PDhA33fc3Z2NnYMjscj6/V61KALMBPGV+4BeX9pmo4dC2G5BfTL90jHQSQhEvkoBY0A7vl8zh/+8Ae01rx69WpkxAVzyf0qGfwC7h8eHkbWWYqLH3/8cfxZkUdJ56nve1ar1Vg8SMdAJuHK90v3Qthtee5FmhQEAWdnZyN7LNdGvn+3242ssjFmLJKlIzb1EEz3CFmrpdj755jnqaQFRFL9JXiWtUIKyenzLb9T9qTPe4Qd35esF7LOyPMrYF68KfJv/6/nkA+Hx/NllMywYAyay+E73Giv+vzB3WAyO+2sVVkO4LntsVGACTSeIWnERj3adbR0BCbCastxf6QuCwJrKYoj+8OOrm0Gpj0ICOMhJjBOEyJrMRpsGOCdQ6sBXFZ1cyoQIrTXw1TMOMGbEK8j+qpEhzkdmmNV4bueHk/dG5wKMUFM3w3TGR8eHsnzBh2E0B6pjg/0fUvZaVSQ4omG6ZdtR3UsqNqeHvDKsj2UBCZkfp5z3O84Nnu61mN1TLJMibOMxeqSp8fH09n23N4/YH3P+SLFhJamUeRZhs0HVqGuGvAVJoioa8XheCQuMuLZjDiJ0dpQHPccjwfq1tJvOuI44vx8RZJGBIHGObh7WHP88QZjLErDfJkThBZjFGXdsNtvqZqKsOk5FnvqJiJILKvlkrYtqCt7enAscZzQY/Cux2pFMp/h+56mqYbJn64nSgI2XUtV12x3e+rW0bvTBFc1I40DotBSVeWQNa3MEK85ICas1gRW07SDsc/G6WCEOrYEQUhRWZyLKYqGx4/vOZuldE3HbHnBbJbTE7LSETc24f7uhvRsSWigJMI7i9egfM324QaPIk1iXN+gVUeWJITW8/T4yI8//IjVUDhPnqZESQS9IsESRiFBEDGbzXG7PU1ZgoL0qwhjLBcXl3z/44/89O4dXee4fvWWrqpoek3VasJFTpxkdFqBb3FdRLK8IIpjjrsnHp8eqKuC9XrDer8Hb0iTGddX1xRPZzRdycP2nrLt+Lv/8F+4fB5Ruw7CBKdTdJgSphnKaKq6GvT5Wp2YyeRkQob9YZiMN4AEw+G4petrnNM41w0m3dOiOV0QtR4miTbNoAXV1pAkgwN9urAKiwqMDMNkiUVkIT9vPw7/7U955JzWpn5c4IMgoO5PbJtuqduBHcfLgupGqRWnz+6QYuDzlLa+d5RFRdt2DIkuAa5v2e+ONNE/NQz9azx+3j6W+0GSB6apDnIdp7JHASjeD8kdslELYAHGpIPpZhsEwchISlEo6SXSZpZIPNHAyusIoJwCRnl9MWYJuy86YWDMS4bPaRXyOb333NzcjDF5bduOUXYCTJRS4/RR+axyDoWdvbi4GGU505SQPM+5uLjg7u5uBBifPn0a2UlhtGez2chmi2FQpAi73W5kpmWY0eFwGAseyYK+vLwcz5lzjpubmxHwaq3HTHVr7cjGi+5bDItZlnF+fj6eY7kvBJgLMFutVmPhJR2WOI5H6ZEYXKfyAMmKFsmkrCdyLwr4lWJQjLqSoiMA8Hg88v79e5bLJXVdc3Z2NspzBefc3Nxwfn4+AjUprLz33N3dAYwpL6KPD4JgzKWX75VJqPIeBcwtFgs2m80oOxEz5PX1Nd999x1//vOf6bqON2/ejNIdKZZkqJUUvqvViiRJ2Gw23N/fU5blOBkVGHP7RWLy8PBA0zT8l//yX8ZzKOy3FHaSEz7VXovExjk3DuuazWYjcJd1WPDitHslz7qQXDIgSAoa6bTJWjLNuP/5HjFlz3++R0xJgCnhI+8tPM09mU4NlWssn226r0kxJq8ra4TMNZAuh/yezWYzelD+peOX55B7P7Ru4ZSM8nM2axghrU7mMXXKH8cPZjytNYhspR+GBTnvKesGS0/TDsOCvIe6qthtHimPO/qT+Se0w0WryoK+KmmqggaFDQOKusYrxfnJaOP90D5P4oQ0y7HKY5TD09P1LVVZ4JViYSO8CYlnKTqa4YCu80TZOdo5mqbiUHZURcF+c0/f1lyerVidXRBHMW3X0rsG3XfU5YHHbYUOc0yYorCUVUF1LNgfnlidnxPGCdv9MJTHnJ/Rdh1FPZg7Z1lMmKbEwYooXtDpmK5t6JqS3nt8U+CUIk5zsiTh2YuXEGXghs+KUsMAJixd59jtj+SLmjCJSLJBf181BdYavPIcTpvB9YvrYeCLhqJs+OGHDzStIwhCnr+85vmLK9q2om5bkjxCbTe0fY82apCRuI40m3M8NFRlRecdeZ4QBJCbGNd27HfbUy5qdJIlefq+RasQazxhYFHK8Pj0RNvV+P6M5SrDGE1gDXV1xJght1orMySyoDBaYc2QaV3XjiCOqJ567jYHwuiMso04HOH569/SrRaotuTNaonXEfsGVJTQO0u2OKNTliCJ6FxHEwy+g/pYUGw3bNdD243zc7QZ8u+zyOJQfPr4Aed65qsz8jjBWEtRlRyLkrkyzBZnaH167ygCY6jrI3V5ZLM94HvPxdn5MCRi/cj1sxd0dYn3mrqCHRuUsWg8Tedoa4fzAV7HNCqiU0OEZG4MXTcYTOv9E9/efkLhOb9e0mqPsxFt56lahTeDCTrMlkTZkjDO6Lqeum2xgUX1iiCMsEHEsSg5HI60bUcce6BHG0/TVCeGXOPdaXLZqaU/LKQaN8raeoLgtCn6wegN6osN9J838fxTecq4mA8rE15MnUqdJmwOum7B2b3rT2sY1HVDGOqT/EWfGI/hc/T9sNn3TmRxBmsCimJoMxdFhbUBSZKS54Mu9d3795Rl80uX0f+rj5+brOT4px2Pz98jLNdUCjkduCFgUkCzsFxlWfL09DQCXdlEsywb2SkBNrLhKqW4vr4e2SphJ+fz+RcAQWQXwMgqz+fz8ee6rhuHwEwnIz49PdG2LZeXl6MUYvpZiqLg8fHxi4mlkuyx3W65vLwkSZIRkF9eXo4xgjI8RfS9AlhEMiEJJsA4YOfNmzdfdKumrGLf96OURQrtxWIxRgwqpUZA/erVqzFT+Xg88t133yHTud+8ecPr169Hpng2m43TPKWQEaZaiibR4k9Nj5vNhsViMTKqch/IfSHdhPv7+xFIiZxDgPVUDiGAaqpTrqqKJEm4v78fTY8SHfj1119zfn5O13Wcn5+P5lJhqSWmUgDoNCd9s9mMkYNXV1cjOBPD67t37+j7nvPz81FeIzKYi4uLUaYxfe+SALNeD9Ozr66uxlSfly9fjhGdRVEAn7tT06Jnegi4lnt7u93y6dMnAJ4/fz6+hsittNajnEYKiKkUS66JMOwCvgWoyvmTuE25Ft0Xe8Rn+ZkAY1kDphKY6R4xXWumKSnTPWJa7P9La9H0uZD3LtN6ZT2Te0ze/3S/kmJNiuqp4VhMtt9//z1lWf7zC+fk+KsYcjVpFw8b4hAlNqXzZfqfc6comb4f9N5mSGFAgdYKGwRobWjbaoiP8wzj3Ks9xfaRh5sPPN3fYa0mjiKMVjy2DW1dYbQadKseTGAp6xplDEWkifSKzXrNh48fWS5WvHj1kvg0QMW7lsY5DsUBpQIWZ8NUzyDICGLD4VhSFSV10+Gblr5tOBQtbenYHxvq4oDRhtVidmrzhRgT0Hs1SFriBfuqo+47sjwiTRMOuqY4PtEcFVbNCHxPsd/y4/qeJMsItCafzUjSjKbvaNsO70OWV68IrGa3ucdrKDYdPZrlxQV5HJKmGVU/LJqu76F3dLQYPTCvRVEO6TTzjCgynJ3PCUI7jFRXlrpuKIoD++2e+WJ20hFblA4ojge6vqVubvHecn45sC7GekzQM59nnK1mhJHB6iFBw9qAuq5ODLambR0oQ5JmPD3ds1mvWcwXpGlC33U4N0TPWWuIY0OcpARByH53ILKwXGYs8gTlIQxO+um25XgoyHIwYUCep8yUpnhY43GkeUY2m/PwuCFJO85XX7HZ7MlWlvn1NV25xhrN3XrHw/4ABZRVRxAkBMmMRms63xEHKW274+bmju3Ne1RT0rmW/dMtL16+Ik4s2inuPt2xfnjkbLXixYuXrOZLPIrvvv9+SDfRAWGU4vEcDkOkosZxPGz507d/wEYpSTzj+fPneBxPmy1VeSA+bJmdJzRa0VUFvdIYY7FGY+LBBNv0Hjs7J+vdMIW0KdGLGXkUs3na4rRnMc+5OFtw9foVKp6TLc+oW0XrA6J4QZDM0UFE56E8MU9pmtDVNb33VE3LbrcfFyglKTfO4f3gBRm6Ru6LRXBYkA2cWrpt2zCbDSBhf9ifFjN7+hmFPi10UybiM7hTJ8nKX5jYCYME5vT1vh8IAOcHeRtqAE+DkVxTVTV14zAmZGC5PYMOXJ+8DYOJeDANy6ALzW53GFIeohRrQwIb4T0URUUYxpRFxb8dnw/ZI6aa8ekeMdWRS3dkmsIAn1lJkbIIOK+qamRLP3z4wM3NzRetcUkVEeYdGAejSErCarXi8fGRd+/ecXZ2xtu3b0cGcArI5V4UNl3GsEs2tgAUicqT1JYpczwFmGL+E1AqAFvYadH+Amy3W56enka9tJgCRZLjnOPZs2cEQTCCQWE/ZUS9JHZIZJ5IAKTIkM8hSTKXl5dfDFUSlnuz2QxRwSe5idZ6LISkCLi+vh51/xL+IINVBOjJdZh2JbQeAhnu7+9HqYt8zmn8pcQchmHIZrMZZRGSRCIdFJE1iRRnPp+zXC65vb0FGOMa7+/vR+b+6emJ8/NzXrx4MeaW39/fs16vR+ZfWGg5xAT5008/8enTp1H28PDwwFdffUWapnjvx1xyGRglhudvv/12vO+FPd3tdqMca7vd8r/+1/8aP/erV68Q3fnxeBzBvLDWAuTlHofPheM0X/zs7Iw0TUc9/nK55PLykrdv344+Bil8kyT5Qg4mwDnP8/E61nU9TgsVE6QA3mk04FTTLd0P+VNkOovFYizc5N8FkE/B8LTQm8rkfr5H/KWv/9zEKedJij9ZX4SJl4JQfo/ck2IalsJaOnJyvkTadjgcxjSmX3L8ckCuTvHBSuFPRs7eOfqT8QIG2YBznq5tcV2LxtO3DW3fEQYWrU+WK+dxg4aDQFustrgooXy6pTzsKA8bjrsNH9//RJokxFlEGGg83aBRd0Nr2WpL09QUZcFsMWdzewt1zfnFOdY8o65bPn54R9P3zOdLlqszwjDGtw0msoMhVBka5+k8VK1mc2zZPD1hcSRRQFk72g6CeIY1pwWlfaI5HEmSkDiKiZOYQ1ngjSeNElTbExhFEIbkV+doLghtTF33BEnMIonZ7Xfs948op9CzOaFWdF2PCQKCKDzF9WVkK03nNc4p2u5IFhtmixnegw1CTJYThikNGucNYRASxgm7sqBuWlAn5sR3gzlSQxzEpFFKnqQ431E3Q262Z9Dmda1mf6jZrAuMfUDpkCyNeH59idE9i3nMYpZiDSQnk4c1DFIANE3TUVcN3kOepsznc56eHmnbhiBITg9qBN4RxwloxdWZ4rDbcXu7oaksTVmy324JrCYIDEapk/a/o2kbrPJDlJ5rCUNF1bT0fU0SJ0RhS9cbnE7QyRlhkGF1iY0SHu8/UTQDe1G3DmuGDH20JU5zlLHkWUiDo6kqDvsds9AMsYu+4/H+hijQrOYz7j/d0tQtThnKzlM+bQnDmBaLCWPQltY5Ag3WKJwaBuNUheN4fOT165yu72m6nvOTNnR9f0tbDv4KZbOhiLUhNg5H+dHxqDmUBUmSswoC2uOaZr/BmJDFPCBLFxT7PXkeE2cJXlnCKKXHoMOQPM6J44wgiEFZ3OleapoGpYeM8rYbhhMdjwXJxG0ehxFNU9N3Q9HdtC1pkhAElrYZzNvDZvhZfyeygtlsQd0MxkhrpcU4ZPW7U1yq1kPh0TTdSYNyAuenZWiY7OpRJ/ZDuaE7pPyJUcUBQ7JKYAO874b4VK/ZHQ60rSdJBlJgSGJxaBOMINJ7BWisthg1GD+bph1iEjFwOjdd17HbV3Sdoaz+TbIyPX5u2JoOxZCNULSWSqnRUCUM2VTGJGyZtLWFFZeM7h9++GGcfjkdzDM1VAmLvVwuubm5oWmakcmsqooff/yRruvGSZtRFI1SmSkrLuBBmEphTMWoJ+BbDG9iyhRDnBQFwswK0H/x4gXAyOIKAz4dsCIjzgXYSsScpFsI4BHd+zTpRrTRUgwJwBVWT1hKKZrkfaVpOk5KlZg9YGSSd7vdaA6VyMUXL16MBYQYGuM4HgcriSZe5AkwMPrL5XJkvwUASsScFAGXl5dst1s+fPgwTh8V0Cz3jrz+dFqnsK8SnyiDieQemY5Ij+OYm5ubUWojUiEBf1JA5Xk+ArhpMg7Azc0NYRiyXC758OHDaPps25a7u7uRfZX3PJX/CjAsy5Lj8cjXX389FmFXV1eIsXd6PuW9SYfIGDNKneSzCYstIDzLMna7HbPZbPwsAuRFiiImTrlvBGQK8ywabJGqyLMr/o/pMyPPp9xn02FYUkxIGo1IpqZ55OJHmT7XU3/Kz7uo03VoaiKdmjSl0yHPlBQ8Mg132rGTv08Li+n7kM8Fn6MOZa1o2/b/fYZ8sEZOgtdPsgO8ww9i22E0tg3w3fAwRIFlc9wNzGA/ZIdjNDaIqOqavu6ZpzntsSJSlmK3pSn30Fdo5QjDAH1KCknynDAagvTNyTTa9cPGO6RyBNR1ycf3P1LsN1w+u+Lq1XPKpuXm/oGyOODqhizN8dYQ2ICmPKDjOUXT07iQY90Tpwuisubh5gN3dUFdFXRtxdkio+taqn0Nbcmja7Cm5+JixdnFFcnigjBPedxX9HU3mC51SNf2pHFCbDN0V4N3JKHB5gl0BdunLTfrB9J0hlKaqxdXmDhndyhouxajQ6J0ydmlplp/5HB4RPuWpu/Jr15zfnENOiCKMqqjx2tLkKSYUzyTc47ADBKiqihIQovXw3lr6oYoDjg/W9H3LYfiSJaGlLOUsnJ0Xc3x0LB+OhKYoTNwvoxJUzN8BuOJ45CubWiaGmsDqrJF0RMHEW3f0vctSRINshqgadpTy2pI2emMg9AzzwN++80LVH/kaf1IcThg9DPw7sTMDmkXURLStDVhHNK05ckc3JGmlrbxJEFIqCzlsaLKUoJ0we7wQLI6I8ly9KEkbhv6psL4hjwOaXyAsyGt62mqll1d4A4bfN+htWG+OidOQqq6IjCayGiqQ8H64QltLUm+ovIB3oYks3OioqOqew7HmsWiQQUQeEPXNbRdz9O65FjVrC5bztIUjMH3w2b4eHvD46cbbj995Nd/958x6RnaatI0AjPoWI9lSd12pHHG8+fP2N5HrDvHoXxiV1TDOPk4Ij9bgdHsjiV0W56vXpNkOdHsEm1jojAmsCFGW5Q2tH1P1bQYE+K9Ok1pVUN8o9a43mHjmKouSOJhk/TCgvQtNtC0pxz6rrdjy7LrWkSiEkXxCBw+JzGAVgFdOyS8LGYz/Im1dt6PoYIiRZPFVg/U+eAVQaPVECmpAGs1xgy5+Oo0tXN3KHAdrFaDzlxpT981gDkx5RbnOuIowZyK70NxPLH56pQhH6CNxbUebWKK/Z6uN/9kvfzXekxZMNmYphuVGCeF6ZJpjCK7gM9T8qqqGjO0D4cDkjYhDLN8nySFzGZD51LaxtPkFgE8VVXx/fffs91uefnyJc+ePaNpGj59+jRqtfM8HwGOMH+SclJVFXmejyPMP336NA6LkdxtYctkr7y+vubq6orlcnlKJxpGz4uGummakf0VbaxIUkTfK/njYuwTQC3SkDzP0VqzXq/HSald1/Hs2bPRUCrATF5/kGEVX0gE5PMKOJQUm4uLi1Ejm2UZ8/mcsizHouHh4WE02Z6fn5Nl2Qhyfy53EEZXzKMCkgXoiUlyyvpGUcR8Pud3v/vdyETvdjtev349grKprlzWHjHtCZgWzbUkeeR5PrKyFxcXZFnGdrsdv1cKAgFvUpxIkSfnX+Qowq5K1+Pu7m5MeBHzuqS9THX2Io0R6c/j4yNFUXB9ff0FOLTW8vT0xIcPH3j//j1///d/P5p/5b6Vjo3cVy9evOD29na8fhIVKsOOjDGjd0A8CvP5fCz8RK4hLLXIw5xzY1dHWGspCEUeJAWHMOUCyqUgl+mqUrRPPSJf7hH6C2OxDMGaSlbk73IfTWVocm+Ijh0YDdxyf3g/ZL6LvEjAv9yPAsDFiyDrg6xN8sxJYS6fR9KXfsnxyxlyLx9smHbXtQ3OdQTGoo2hbobWsPf6tIEHlMc9yreDfrurTxFpmr4fUjhmizmubih2GwwtfVMRhRbVWWxgSbMcbQOCOGC2XODoyEyI6zr6rkP7gbFzXY89Ga8CO2iZ//zdn5gtH3nz9Tf8+puv+fDhE9WxHLKwm4bHu1uUjZifeZyKafoO7w1hNEhNzi/OKQ4hfr5A48mzmK46ctc5jk3Psaxo6h1Oa5wJOAsiFhdXhEcPXYPxoLwmTRc0VUff1PRty27/wPH4hHPNMHY8UvSdx3cVVVPz8Kkk2sZ0XhPGMxThMNQh0KjQst+3rB+3bKqaF/k5r8IEbULAoLSn7TqUVJN9T12WRPMUawKcg6Kscd5ONr8B2HTegesxBtIkIsscfa+G3OyypjweCXTIxXlOFEEYDJrbruvwyp/YTI1ScsMm5EFOT8sw2TAeH7Cu7dBBSOc91oaU9YEwgOxiTt+9Rque/W5LcTgwy5NxQy/K4rTRDcOVhrx7T5wEJGnAZtMOXoMoZHs4UpYVJgipOig6w3yxYnnxDPqC7XFDtX+g6nri/JzeOhpnMTqgqQrWNx/ZPj4yXyy5fPmK3X5HuTtis2AwVDY1VV2zyHKibM6x1yTJit6mlJ3mUDSEcUvnHE3vCZwjjEOCIiJOcorG49DMliscnuJ4JE6HSLG6uGW/fmLzcEN2rlnkOX1TUzUN28ORqqzIspTl2Yqn3Z7HzYY4mxN5RbPfY5THasfZs2fksxl1r2hVQp4vsSZAe0UYBASBGUBv7/DKMUzetXQnj4c/LVpN02CsYZZntO3wfM1mMyST2zlhDqDvO5T+bJoZpF0D8/L09ATqs0v980LKqEG3RtG2DVE4TLXtusG0CXyxqMp/D4PHNIG1KOMxdUVRHnHO07pB9+pR1HU7TIRVCmMN6uSHGTYSGcEOUfgZjOz2e5q2HcB4e7p3+6Hrdywq6qanKKpf7KD/13BM2WnR+k6lJ8KaydckA1rAp2zqAtglClCYYgFUokUVA5lkdstrT2PzhPEWNks0vP/4j//IarXiN7/5Db/73e/48ccfx+mLTdNwe3uLTO0ERqlIFEXkeT5mdctwltlsNprDplNAp9F719fXo9wDBtCQ5/kogRE9tcQJCks/zdn+6aefyLIM59zIzIrGXbLP7+/vR/AsshF5dkQPLka5oihYLpdfgCwBRPIzU4ZQTHeSb+2cG4fsaK25uroa2/lTM6+aFNPy3ufz+QiixBApqSzS8QjDcJwemWXZCN7kPMlrACMQlUNAmejkn56eRiZc4iqnUXer1Yqrqyu6rhvz3kW/L4BcZCI//fQTDw8PrFYr3rx5w2azGQs2SVkpy5LLy8sx7UVeR4YFJUkyniOZ9ipZ6pIMc3Z2NsofROsvnoX7+3u896NeWQy7Mkjo4uKC9XrNw8MDWZaN3gEBui9evBgHBsl9JHKN6ZAquV9l7Z5qugVkSySoFCsyMAe+zPyexgLK8yzF6cPDwxe/Z6oPF9Av94cA92nRL+9JAPGUUZ9KsaQokX8Hxq6DPKvy3uXnpHiVIkWGOknxLc+oFC1S4MuE2F9y/FWTOvHDYAyULE4dWg3SlbIs6b3DdpDGIa5vqOoj9hQh1vcdoY1wDtp2yPrVuqd1JV1/5Gl9z2b7xHwWo0yECWKcUeSzGS9evWa3P7DdrrE2xJqYNLaEVjHPZxSHAqM0aT5nuwkoiz1d3/Fw94QxIW++/ooXL15QFhVV3dHt9hz3Bf7DJ3yvuXz+hjxLedpVFPWGptmxWOTM5xmuH3SmSRITaEWWL1nf31LsthTHB1pX8uFhz92m4lg5Lq+ek0eW6rjFxj1oi4ljoiCkb2p0E9AeHFVT4a3jfLUinc8ojzX7my3dpiSrQ5IkJVCglSZzjq4+opsdsfHE+ZIYzWK5IoxilNJoMwx2MdYQhpYgMERxhA2Gat2EEcoE7Hc1TacJwwilPHXXo9uGvh9a931bEQYRF+cJfb+maRucH8yrxlqs9TjXABHGKDyDodfaASTbMABv2O8PxNkwcVNrzSyfUxZDO7Y/DakZNLqeNE1o2uHGvbhYEccRP/zwA59ubvDPLpnplDiO0NpQnYCTUiWz2dA16WmJwojIenw06EnX+wP74jjko0cJx6KimQcsV2f0xQO370uO+x1N1xElM0zYkQQRWZJxdDUP97e0Xcs3v/475osl20OFjXPCOKZXjnSeEc8Szp9dEWczysJhgjkPm5K79REdxKgg4lhWZOc5cRKhvSLLPc+fXVI3NXVVEcYDQ34oGlqn0Tbmxcs37LY7dk8PYCKSbA4O2qYlVgoThlRlSVXVrHdbKu9puxadJlyfnw8ysWqPTTKiNCNSISqYEaQZkQlRKLT3p2dQDcbpriGKY7xS0Lmh4EVhtKZvG9IoZjbP2e2GFIXVakVdV+A13oFWlp5hoZ1OZZSFUFjMYCJL8N6Dd7gelPcEgaVpKh4296R5zursHBPYkTWSn/vClHNqde53W542jzjXkc9ywiiia/1AAvQO5zxxFOM6MY8OoeafAeAwv0BAgLCzVd0CGq0toCiKku3uwG5/pK4bqrpmdQJk/9oP2ZjkuouuVs5xURTjBijgS8x48HkKpmxuU92mMMXr9Xoc2S5Sifl8zldffcVms2G73Y6MnkS1zefzkRmez+ejDrfrOm5ubjDG8Jvf/IbXr19TFMUoQ9hutyOAePXqFfP5fMySliQOGcM+TWSZz+cjmBM97M3NDY+Pj5RlyYsXL8bBOGKck6QNyXgWkKu15uLiYgRhAgCEWZwCB3lOrLXM53NWq9U4WGgKSqbMp5wj6VZorb/IIBcAL/phAZNhGHJ1dTUyxs65UWYgwBX4AsiLmU+SaoSJFmZXhhxJMs0UlAtjvd/vub6+JkkS/vjHP/LTTz/x6tWrMZ5PAJfch6KPFyAlr5ckCdvtlv1+PxpwD4fDKFuSiEBhjqWDIbnpT09P3N4Ok7L/83/+z+PEUNHZAywWC+bzOS9evCDLsrG4eHh4GDsKkgp0dXU16s5ns9lo3JSpr2KolWvw9u3bEWhLh0Suhdz7UhA+PT2NsookSTg/Px9lFBL9KNclz/PxOkmHQlhl0UjLsyqFkdwjEscoHSAx28JnYCxgXDogwBf3pUi2fm7YlKJdEoRub2/HHHn5mnhHhJUXeYlct81mw8PDA9575vM5cRyP+4oUlkmSfJHmImuZrEHTCcSyDkjHR9YxMfput9tRk35+fv6L1tC/ajCQMYMBatB6DpuU1oqmaQegjkMZRd831OUBrYa0g961uK4Z/l3HKKepqpLmuKVvC3bHBza7e5qupm1D0jjDsaHpepzR2Cil27bYcHXS3oWYIKRz1ZAZHqe4rkcZAyamdUfCKKfvGpqmpS4qZrMleh7hi5qlDVE6ZLve8qlqaIuK+eUzqqalajuU82TpnDjO6Lph6qH3w8Jy/jwlnl2B66nLDbc3P1Ds76n2G77939+ye1rz+u3XJ8F9x+6wJUpygjgAq0kXc3QIdbWnaUrKtiMKYuIs52/+7nKY2lnuqY571ptHVN/BYUZTFXR9S5wkrC6vuMiXJGcviOMUYwOMtWjlhhSbriMKh8jAwNqBNVeGMM7gcKCse9q+JQgVQdcR9gML2bYtURjiG+jKmjQJyfKQwHiyNCDLAozuAQfeYa05meGGiZlt0+C9Igwth82GxnekWYp3njAKaeqBueidIwwsaRpTHAq8d0TxIHvo2o40TXj79vWkdalPm73COUPXQb+viaKMbJ5T1gfKsqbHE4SGKLYEYUDTdHgU2WzGcX1ku9mRnUUsFkPruNw9Dnnb2mMteOvRNBjlSNOILLlGGUPZdTx78xXvf3hP0VaEScxhu+Xl29e8/eYbVDjH5DkqOuNYPREvrol0RTYPUbbHhjFRmnHY7ajqBudavGvo2/q0yOWEyZyibCl7w1mesLQB6+2W7dMtaZah+56qqvHGEmc5DZ7NZkuPJVte4vBDgWYjiv2W7gkOVU/dbLA2JM0VzsRE8ZwwitE4jNKAH8571xK4ADFott2g9fauxxozFERqiOGUlq9EuI3t/VP7UhamKZM9ZR2mxkxZW8QYc3PzEdedUpeUYTZfnIoxTd+LqccSJRGud+x3O242Gz5++ADK86tff8NsNqdpW4yBrnE0zdC5M8aC+xytp9Hj9E5hIrU65eJ2PcaG0PQoZUizGXXT03Wetu1PRaFmPstZrea/dBn9v/qYSgym7WbRz043YGFmpwzadIMXeYiwTNvtlvV6PZrTpsBANnhhr6uqGgGUAEVh1ac6Y0lBEenGYrH4Iodcaz2C6LIsubq6GuUp0pqXdru0rL33I3j33o+Mtkg7/sf/+B88Pj7yzTffjOdst9uNoDwIgnHIUVmWI9AQVv4//sf/yHa7HRnp6ZAdiWLMsozr62vyPOf8/JwkSb74TKKrFkZd2D5hkkVOMc1lFoA9zSQXBl5YX5F/CAgXiQUwgngBaCJVEnOrdAMk/lF03yInERAt1yvPc37961+P50Y6MFJAiVFPsrQltUQAmpjvhE2XQu3p6YmrqytWqxXz+XzMURfAOM2mzrJsLMKapuGrr77iT3/606i332w2fPPNN/z2t78liqJT0lg8DvYRPbcwy1mWjWB26rVomob5fD6y533fj0z2lP2W7oZoyZVSY7a8sOxnZ2eEYTjKKIqiGCUo8/l8LJanSUTynInWevq8Tg3bP49ylGsyzn85JbJMwfY01lAkTtO9Ybq2AKzXaz58+DAWieJXmEYPShdDptOKQfrHH39EKcXf/M3fMJ/Px/1LPCCyNgn4n2rFh8CDdPxcsg5Mjaez2WyU40iRKkXh/+uAXDbEui5pm2YY3nMa7qJOKSp922K1pW5K9vvtYLxrKnAtRnva1hGEIYG2OIYR1SaApj2iTE8+ywnCmCDOaHsI4oRsNqfpIZ2tCGLF+ukJbQPQUDc1uvPM85ztZktgLXE2p3M9RsHm6Z7iWNHWLV3bYtOcNIgxXjFL56zyBXh42jzxuH6gNRoTxWBC1t6TpgvSfE6W5+yPDfdPa7QOUDpgNjvHJBmv0oyufKQrNjx++IGH+490bcvbX/+OJEuIfEDV9RyLAt8PJrjOKWyUk2RzjNLkac52u6dxhihdsJovOW7vebr5wMPDLU9371HA2cUF2WKFtzGNM5gOOElEuqajax2tczjXkaTDNFSUp+88XQ/OQ1HU7I8VURiyWKTMFhEmDNDe0fdHuq4fctLnGflMEScRUagJw44kVgShIgoTlFcnY9zgs/VOnxZ1g/OgtGEw+g5JPLIZSIXv/GAaDAJDUzVYrcnzlOOhBDSr1RLnhkmJWg8yBq3tIIvoPG3fUlcdbesJgphDsaXtPIFJsFaTZckQ42cMYZ5Dm9G7gqppmc8WvHz1hq46sN880tVHMBZveur+CH3H1cWStqnoXEeepgTxnMsXmsPmAadK6t6Tz2YESULVQpwvaHzKYhWTzefkUUe5+cCnD38iiQxX19f4oqZV0PuO8/MFSRqx3R3w4RxnE1S0wKQlm+OOWTIYux6e1hT7R5LIkgQhm8MG17ecP3uO1xHz86vBVNq0aDMASh1obFyjA0fbFEShwrkh7SeIo8EgGszQStF3wyAtaw1aaRwQGEOrO5SBzg3xkt67kS2S9mJdN6d8Z4e1n8GRLNiybghYEbA+NcbIYua8H4HAarGkbRseHu7ZbLcsl8sTWIpOgKxH92p43qOQnXc8f/6MbJYRxxF1XVE1HdYE9P0AoLu2H+I9R8bFY06rn7SLvYe2a2m7Ifaw7QEUXe9o2o6+Z5joqw3GBjRNS5ImxMm/jeqEz0BbNiuRi0zTSmSzlHQGrfVoiJINXDbm6VhvYbIkGk9a/TKoRaL0kiTh4eFhBAGiIR6M5U8jwyngXCZiykYqjLEwlZLiIfIA0T4bY7i7uxsnV87nc/b7/ZheIWy8SCUk3vD9+/fc3NzQti2///3vRzAqyS6SXCEARQCusLKSpX52dsbT09OoY5cc8svLS1ar1T8xNwp7KkBBXkfYUAGwUkRIOsRyuRynQoo8QHTTkhsuhr0p0BUW/OdmOHk/0t0AvgB1UjDINRDGVIq36bTNqZlV7p2pEU/uRQGSh8NhZD/lPhBQJrIgOf+LxYKvvvqKqqp4enoaOzmiDe/7nuvr69G8KOD81atXrNfr8ZxKzrsMWgLGjkccx6zXa969e0cURbx8+XKcCOqcG1nz9Xo93s9y74uRMsuyUTYjpIKk6ojB9vLyEmDUt4seW4pWKWqksEuSZOzYyPWQ8ybXTApv+DwETDTY0r2R3yndMCn8pkTNtIs6lZf8pT1CXr8sS1arFW3bcnt7y3q95uzsbIzwlGdZ7iMxB79+/Xo878JuT30BAqThs/Rumjkvz4B877QTJB0IKQi01uO6JZNYf8nxVzHkp9Mz3NB2MGnWp6o0DAOca9AorBoMYI2rwXVYBW1TY7UhzoYPaLMI7x3b9SNZFpFEK/SZhV5xf7fm2z/9QJwEpNkcpS1d7yjrjjDJsYGi6yvCKMEzZJmnWQ4MBq8kW2C0O7FujxRlxcP9I/mlZnHxDNf2hFiyKKUsjrRtQ7l+YL/dEaUZQTLn3acbUCFptuD62SuidEHX1GTzmDDJaXpP5xRtr4iinMiAefYCqz3rzSPvvv+O+eHAxYu3nM2XtJ3hcCjp2x5rIqw1J9CiSbIVvYtp6pogMNRVQTw741Irmrai2D+htYEgpdPxEFsX5XROUdUt2rcEyRD5pGxIGBjaviGKLK7vTiyHp+/c0HVAUXcd292es/MUa2OMUiRRQHWo8HaQ6KAM+SwhDDRB0KP8AKC1BuWgrmrCMEFh6XuFsQkOT+sUNkrp+5q+GwwdbVMPnwFHFIVUVcF2e+R8uaJra4yBOE5owoaqbMjz+ekG70etl9GWMAjYbfcoNGVRUR5r0tmgU3auoW0rtIHAapQOAEeSRITqDFdrbOhQZshOP798RhQNSQtVN6TCxGGKjSwX+Qtubz+RzmfEaYrXIbPl+fBwNmuqDiKneFhvwcxZZBpjI0IbMEuWuPoJv72jKBqaBjaHhqIzNN4SpSned/RdS123NL0izi8p+5j6cUfd7jEW3n71K2z8E58+vUdpOL96RhIbet/TNw3JfI7VIb0KwVma3uCdxvmU+fwZiS4wSYhyNVp1dBT0/Y6my0n6FWGwpKgayuJIPsuJ44C67ijLGitRgO0gKWrbk3a370mzbARRA+AydN3Atjx79oyqqsZJgcKQyaY5jaNSaohu8t7j3WdGbDab0Z403L3z/PTTew6H/SmW7MinTzdYO7TYZdpmevIoCCCyYURVttR1R1O3FEVJ07TE4ed27NA+/ZwEM4BIS9sNHpHNdg9K0bWetjtSVS1d508Gzx6UIssSzL95OsdDNlEBPcDI+E0H8UhhJiBIKTUmjwi7LKPlHx8fx6QQYV4/ffrE//k//2fUMgtwmLLjYrACRm0vDEBAsse11jw9PXE8Hrm5ueHq6oqrq6sROIi0pG1b7u/vRyCUpik//fTTCJZfvXo1yiqkMBAtvLyPaSLGw8MD3377LVdXV7x584blcknXdWOes7Cxcm7kXEylAGJskzQHMbnKuZUkETGkTk2Hcn6iKBrZaNFRi/RIJm1eXFyMYEzOx3R4iwxAEiPtdMS4RKYCX0gBpOAQsCesu4C1OI4pimKMJBQAJIBLOhrSFZhmQ0s0IjCaGIWdlntOAJP8PjFOisFSa81sNuPZs2fj4CFh4KXIXCwWfPjwYUwt0VqPTKiwrrImSQdB/pRuwGaz+aILJAWTMN6iRxZWvO/7MWXFWstvfvMb4jge78Xnz5+Pz4nci1NGWj6Dc25k6UUqI8+GAFMBoZL4Ive1fDYzWfik6JAuhkQjTs+XmEpfvnw5zgmQPWK6J/x8j5jKVsTcK7r3zWZD3/ejUfvNmzdj4SuFFjAy/yJTk2dAOgRTg60ky3zeIz4XDZKYI59Fii8pAMRwPi0+RZL1S46/KofcaIOyHu/1OMs6CERr49Hq1A7rOozWaB3QlC1lW4Jr0Taga0q871AaqmpPVZWghkEkRln225L98YjH0jSO47EGnbDdHwFNGEUU1ZGuPfLy+QVdV1PVFcvZnGOxH0aEWEuaJcwXc3b7wfgRRhHlsWC28vQOUJowstCUYGA+z6iaHcfdA1kUcZZF1K3jsL7hpikI0wUmSDk/X5LnEceipiordvsNxpWkpkdrw7PnL1mdLbl/fODT+x9o6prz65ek6QqaGleXYCyBTrEYsmxG32nqqqPrwYYhKoS2daSrK95GIbeffuTpac0//OE7Xr/1PB57Xn4dcfH8HNQAtl3djVFw1poh8rBvicMcayxtPTCh+CEyrixLcIqmLvEuRBlFmgaUhaUoa5paobSlLHs0IUkcoTEDHa7UwDx2Duh5etpS1C1pMhvAJh3OKbS2NE1HYAfjL3jKshoGCvWDNEUpj2eQwXR9g/f9yRQ4DJMZ0jCGJA6UIYoCsiymaYcpi5LBkUQRXdtTFjWKgDDQ6M4TKE9gDXGWQeSJTYvqDkTZnDDN6PcP5PMZsVPUrSeNY/I0oTnsMKonDBR9V1H1gIpJ8jl97YjTOVGcoFSIRw/DZ3yPDmIwAXWn0CZiubzAhBn3T0fqtuNYO3w3TIxtu440yWg6qFpQ8QqdXWLanmO7pXaKt7/6DfPzS562O/787j2z2YIkG4zGrml4uPmE1zEmynF+qOBjG0NX4bqeODQUx5Ku6QjSlCBSKN1TNzVpN8SIDsbOfsgUdz14h9YhMHRIjFEYY1GnAV/TQRxDWk5AGAaEJ23d1PEuQFxAghwj+yELkR2YjP1++4Ve0Z86cJvNBmsNNghI02QcwuCcI7QWa9R4LzRti1cGCFDK4P0JVLcdaRKPbFPvWsBjrTltMBZjA7pjRdc72q6naTqSNMPY4GRiHmIqi2NJnsek6SDr+rfjs2RFdP5yCDAX8COgT+4LkWYImJsy5kVRjOyobJQyaVFAvLTBt9st8BnM1XXN69evR6Z0uVyODKQxw7j21Wo16j1FRyzMq/xOuddF+rDdbkdDYtu2PD4+fhHtdnl5yWKx4HA4UBQFm83mCwb41atXXFxccHt7y48//khd1zx//nw05gkAE6Zd9MES2ycMtCS7RFHE+/fveXh44B/+4R/41a9+xW6349e//jUvX74cwZC0+IWhFqAuHQmRqUzP/ZSxF1Aj+mRgTPUARhmRnLupDOnh4YGqqr5gCwVAT2UN3vvxekvm9VQ+ICy+dFtFliSfS+sh11vkAwLk5L4QM+WUpZeCXEC7pACJOXO9Xo9dGNGTZ1nG8Xgc18NplrUYjeWzTjsBU0+NFDaS1y7TMgX8S9EkRlb5bPLfApp/97vfcXl5ydPTE9999x2LxYLZbAYMxfDHjx/Hn50CWwGRURSx3+9HTblEZE6jC+X9y/mXZ0gpNXoZZA2Qomg6PVV8C9HP9gjgX9wjpuuISIFEuiTSHJHniPxHCgIxR08ZfWBkwqfsu6xLMrxJzOFyfwijbq39YvKuvA8poKfzCWTWgJAB/9LxV0lW3ERT050qOaOHXHKlwWBQWmGCiDzLwXX4psN3DSbQQ4u8b1C4YagPYI2lKBp6eo7HPQ/3TzRdw1fffM1hf6Cpe4KoxbmaOE2IE0V/Et9bq3G9py4OHLSjaRu63hNHIb2C0AakswXbzSPKWOY6YHGsMNZiwpAgiTGuxVZHQt3x62++Zrfd8rTekaQLZnlM6Bvads/25pHGaQ6HR86uXqBtwm635en+lrNFyvJ8SecMHkU+WwwpMZsNbbXj7vsdSTYny5dczhdgLE1X41SAaxsqB/PFnLpu6XxPmuc0lUcbRxaHXBiNswn2ds3t3QOttwTpA0F2xuzcE0RDNOSAlT34AYQmUUAcnKYSOjfkw3uHd5wY+prddk2e9CSRIbQJ83lCzxHwON/Rt47eesAMsoZT/OXgoDdUVcduV3Fz/0Tf35HkGYvlkvl8RhIHlFVN1zXM8pwoiqmqkt4NzFGSzDAaojgENcQseu+wVp8iH09RSq7F9z1REjFb5JggZLM+gPbo06Ap50/mEGPwPYRGEWhNGGiiICCNYrrao/sSbXpm4SX745by/Z/RRrFYrKgbj0VDX/H0eEOx36KtwaSgoxCvFPPFiqaAIMjoO0XfGYxk7OsWTE/bK5oW2qoDDM4bomRFp1tM7Li7/cSLs4zzizkoTe8N89UzgmxFkK5Yf/ozN9//T+7We77+9a+5fvNb3v10yw8/vueP3/8E/hN/8zeaxaIjTHM8HcV2Q5qdgQoxTtO3B3AFne+wRpMkC3od4JxFq/C02A8F0CzPQCmK4xFjgtOiFp82mcFsqZSiqkq0VicpQIsxATDECxozdM2m2jlpH8vxc23gl//1OZe3aRrqpgGliNOUtqtJswyPJ4oCZvmM+4fbUxpLOAwOMwFNVw+yk7an62v6rubhcct6faCpO4LAEpyFo55R9Z62rXBOnVi9oYAcGLchNSawId4pojBGmxrXOpqqpW4arrNzjNYE5p9qHv81HtPrLUBLwJL8KeBENLVTI9pUPjHq/PXnSXgA+/2e29tb2rblt7/97WicEqZXQJDohwVcHY/HL4ymAgysHYbYrNfrEWCIDldAlbBhSil+97vfjeYwmWAoLe2PHz+OKRbPnz/HWstms+H29pazs7MxR1rAfRAEoxziu+++G6UvwnxPc9q99+NYd+fcmOgiiSHS6v/w4QOfPn0aGfEsy0bTovysnFv52Wl+u1yzaczfer0egZrka0+fawEyU+Om6PqlayGTIUUzLoZYAfdt245GO5k0KTp9Yean50LkD5KKITIckdlMBybJfScgUoDTVNssQ5ykSJEow91ux/fff/9F4o8UG3d3d+x2u7FbINdhtVqNkp+ppnkq3RLQJvnUcr3ks3769InLy0uur6+BAZxKLGOWZfz0009899133N/f8/vf/55vvvmGH374ge+++44//vGPeO/59//+34+DgASwDulYn4ulaSqJ/JsUX1MZj+jL9/v9F8ZgIU+mk2d/DmCBEciKAVOY52mxBf90j5geU7ZdvBXAWKBIESIs+s3NDdvtdkxxERmc3OcCqGVyq8ijLi8vx/er1OcBViJVk0JIihPpDEmaETC+v+fPn4/33C85fnkO+aRtIJuyMfqUCzy0nG0YobXHdS2uaWhP1UUYRLi+pjgWVGUxgHFjmC+XLOcrXA9Ga6qqxQSWRBuWyxX79Mh6vaXfbtHa07uSvusJA0eephTHA01doLVDq57FYkbVtGhthsSEOOBXv/4VP/zZs3564v5xS1X2vPr6a5aXF7QKWqOo/MCCpYGmOR65XM5xXlGUW/ryQJqk2FRRtY67n77l4e49YZzjHGRJwnH7yMd6T9fUKN+xylOMb1F9Q+BajO/oipJ9vaFvl2TzFdam6EDTdkegY5nNcL6l2BUEZmB+675D4dBhTJTm/O53v6PYHrm4vGJxfjboDxUEUUSHItSGzg0jw9MsIUlivOvRQGgtoTFEQUDXtRhrwDGYRSuLMxHeDOcdrWlaR1kOALluKsrjkGASWoNTDKbRyOB9z3x2RtsHfPx0x93dGnQ4sOM1xIHC0BOFIXm+ZDab07YlbTPE02nvca7D4U4PSofWIX3fEgYRAtsGOyHDsJtT67GuGtquoestfd+i1PC5D32NYojFVK7DuxaFIowi6IdhVs47FufPeP7qLcftHcXxSO8UXhnqtqCpCkKraKojaZwP0pZuMDsWVU0UzphlQ4GhQo3G0bsK5yxNF6L00GnYrnekaY5JHGWn6XXM7thyOeu5uLri40NB0R9JnEbrmGh2wayp6duCavueXdkS65D87Bv+dvkNl9cf+PjuT2w2W25vPpDPMl5/9Qbb91T7A+iI1iu0K4lTg/I9gQ3QKsbGC7LsirrWzPWpRe1aZsmcrhtc93EM6jRyXgzb1mqUhrZrmOWLcTHP89kwqr73rNdrDscj8QlMTR36sm4477F6Ms74tHZw+v8wDMjzGeXxiGf4mf1hPxrehLXz8bCxZnk2DCXSZhhz3xmiyJGjaNqesmiJopgwaHH98LvaU5qPMTnG6pFxHHS+JceiQOvTQBHniaOIoqhIsqEz5HpPWdcEQUiWZsBnBu5f+zFe54kWVEAuMG5aArqFgROWse/7kVUGRp3yarX6Im5OgNT5+Tl5nvPw8PBFioOwi5JfPmXqhnSgepR3JEnC73//e7799lseHh64u7ujLEt+/etfc319PTJ5AvajKOJ4PHJ+fj7qrYVZFFb23bt3fPr0adTOZln2RToLMGrTp4NKJI9dpAbC9gmIEGOeGA2lEABGvfm/+3f/ju12y7Nnz7i+vh6j5wRsTjtXs9lsfI9yDeR3iuZX0l5EImHtkDUuQEWMkhLzJgyzdBekYBGGWSZbyv0hIEi+X3T706xvua8EAE0nsU4jR3/+ueS9CxMshV6e52M3Rc6HaIEHT8znCaQyxXK9Xo8dOSEN5F6UJJQpQC3Lcvw8ksIhQHga63k8HseMecmGV0qx3+9ZrVY8f/58HGb1/PnzkYEXWdV6vR6Z+ouLC87Oznj58iV//vOfx7zy+XzOr3/9a5xzbLfbL7TfeZ6PoFI6IPP5fCySpwZm+VwCTKW4OB6P489LYSWRhuKREOmOTFGVwlx+7udrhxw/B+hhGI7dJ7ne+/2e2Ww2ptHIPSlGVZnCK+9PZGzCYk8jOuXrUnhI8STP1/F4ZL/fj2uYJLMcj8exmyNm2TAMxyLh52z/P3f8ckA+jNYc2EjnThvrMF1PLq5G4/tBq+y8IggibD6na0sOm4aqqHB+SJlo246qLomDmLqqWcyXeKcG8BAokighsAkPj2vqomSxnHHYbzjuHHma0BlFFqekkSWJVoBjX5SkSUqa50RBgPKOrilYnq3QWvHnP37PH//wv3H0dL4nnuc4143t6+22oiwKwjDi/PwMlObh7g7noe47Xq4uuKoaOjQ2TNBqALUP9/eU5RZ/AriR8cR2GFpiFFitMFbTNCXN3tPVFfuyIV9dEiQzTJSweerpHbi25O7jHcbCbJ4RBQHWhqyWK87mc/Iw5uPHG7q2oSwK1H7PcvUc24FDEVqL0gH6lIZS1x2RjbDGnlJXFIoeGxisioiD07V0jrqqWC4NaRJig2EgT1VXw/XqDNYMUiWtFGVZ0XWACjBBQJbnhNGO3bGgbRxl2dA0HdEix1jDsSiIo4ggtLTNMNWzrgZfgbHDgBmlwPkWTQje0HaOpqkIwuFhqqqStmtI05zlctCYOz+8b/BYPTC1paqGIk0brPZo14EbmHa8I4wSmqoHG/Pyza94vNHc3XyEXlH3Lfv1I2VZoVDDJM6+ozweaPE4O4xoD5OUIBqyso3WuK6m62p676jagCRICIIUEyZ4bUhnMyKTcHfjsNGCsh00yE1TcP9UMX9REGRQ1B4fzjh/+TU3zYbNoSJ0BV6fkSRLLl5mBNmS7dMN9d07Pt3dUdZ7ri6WNG1Hnq/ovQLfkIZzdBCgdEia5Zg4J7Qh5ekzBVFE7x19D20HURRjbHhiyfpBImLNafEZOhaDoUjMUYMPou3qk6TEEpwWNnda3MTI1//MpPPF2sIQTaiVJk5Snh7X9K7DA9ksJ46GhbYqK6q6AhTn5xfDAts0tHVNYO3wjLXtUOAnEdYqiqInjnuCIKHr6pPedkuahoRRgrGfNZSH44G6bkizGcZUBDY4beIa13mMHnwzfd9xdrYiy1OSRGEDzb8dXxp5xbwNjKBxWrgIEJUNtmmaEVxMEyYE2IiRa8rCCgN+d3fH8Xjk7OxsjCqU+DYBStKGlyznPM9Hlryuay4uhvvpf//v/80//MM/jOBAwIXcx/IeZUKm1ppPnz6NLe+3b99+keYh7O3t7e0oARFwO5XyTFnF3W43poKcnZ2Nn+H+/n4EOpKnLbF+ApTF3Pfu3bsR0G6323Gwz/T3CfAR3bRobkVuIO9fWEkBuGdnZ6M8Z1iXq/F6SaEgwEhAsGTGi+lQwLzWmrOzs1HCIcZEYVdluuk03WQK2tq2HTskUrCJzOP8/HzUMQsTPY3LnMpcBNzJfSlMvbWWb775hg8fPvDTTz+N3yODe4BRSiTj0aW4FNZc3rcUVgL4RcIhmnspnj58+PBFASUG6NevX4+Z9UEQ8Pr16zF3XAB+lmW8ffuWPM+5v78fDb9lWXJ9fT0We/I8ivxJohPlmZJiWd7/VOsv/y5dFGGS5frLcCXxQgiBI+bUn+fTizRkWpz+JaZcWPUkSbi7uxsBtHRWRP4ma4UMxBK2ejoISK6PxE5OU3dE2y/rhOj+BfzLELL9fj92auT9TYsLGQQ2lfT8S8cvBuRNXaFPsYehDeh7d6LuW7q+JzgZPZVSGGsJ4wTV1zgcZbHHOX+a7rXl7nGN6zvSOEZ7TkN9BmDc1i1xFNG2HTZKyVYLttvHoXqLU3brJ7ZlSQhc5HP6fohvM0GArhusNiRxMhjRqpKqOlCepka9fP2czdOOTz+9IwgML+xrtNFcLGa0dcmuLomzbIiFi4a4pM7DbrulqYYEhiQa9NnW11gTYAOLPsvYHR1hEOP9nDSOcG1DWxmyMGS3WVMcjwTW4NqOKFR0VcHjzXvOL59j6ahdgzaWSCl0MEzSzAJDYBXeabb7La5tic8vSPOEx/2OeNWA8/Rti1LDlMEwMKcs8hBroG16uq7HBCGtaymrAyhHEFpiG7KYhWgFbd1hI01ZFKRJCmWFSkOU6vHO03cd3UkLrrUZpCjO41WPo8crh1IeazR9N0TF4SWXOkQrw+FwJJ8lw2RIpQapg9IMiZktTVfhfE9ZVjRtMD7M1eHAPJ+jtcX1juNxx2JxRhwZirKkrCrSJMcYS9dBaDWlVmjl0UZhtB8KFO+omxrlNMZEdDrChCnL82dYY3m8vaOvKkIb0gUDUxIHAa711P0BnQTE6Zy2MQRJDIHBElKXJc32cWB8kwZMTtt7ludXJDHEqcKZgWHNkjOun/+G48O3dN5xfjbj2JY8fviBfPGCQIeQLHF1R5yvuF8/sVIRJsrRzuF0jJ6/YJmdY+eXqJ++xTVb/vTdO0LjqWc7VqsldduQJCEXV1c4DB5P35U05QblwNAAMUYFOKdpW0UQBvTO406F98CSDbGTTV2Q5zPaVrSooE9MulKermtIktk4QdCfvAqcFurwxIzKoiXHMNzpxJMrzWy+4PKqo27rIVv/tBkbM2jhjR7y7l0/GLi32x0aj4qg7Tv6ztM0PWGc0baevlccy4blYsX52QsC63B+SJuI4pAojknShMPxSF23ZHkOGDxgtKVte3CKumzwXY9WEFrFYpYSR4YwGjTu/3Z8HqwhoEeSB6aJBwKmxaAnm6okVyyXS47HI7e3t+OmCZ8TXAS0SoZwHMdj4ogwfE9PTyMrKeYveU/CloucQ4BvURRkWcbXX3/Nw8MDP/74I2EY8s0333whV5CR6pJEIUWCxA8KAydMuDHD5MHLy0t2u90oD/k54yhAXzZ4AYQfPnzg2bNnKDXEIwrTJwZZAUje+1HKc319zXw+Z7fbjcOSBDQICBNTm5xPkR0IuweMRs3lcjkCmyiKKIqCNE3H2EN53ak8Qyk1Sk+EhZ52zqaJTCJvEWZYmNdpl01+h4BakbnIe95ut6OBUcyxoq+fdjHkd8vrSiEk4HyQ5lUj2JROytXVFcYYPn36NAJikeSIhlwSShaLxZg0ItdGDKoifRDgJnIikZUIg//69esRdF5cXNA0De/evRsTdOS6zefzcZiOFDNitJWJqj/++CNN0/CP//iPWGtZLpecn5+Pevjnz5+Pz3DbtqMxUQ4pguR8T2Uu0mGU8ya+CvFdyHmV1xZD9H6/HwsV+bdp9+znJsjpvbJcLsfEoKkmXxJw5N4RJl6Ml/J7RH8ungKRtJ2fn/PmzZsRdwhwl6QkKZRF2iMSKDHKynmTAnS5XH6O09W/jLT5xYDcaDB6iEAbRmEzakt7V2PCYBhR3TWnh88PJqtTNF5Z1UP0oXNUVU0QWuarBbMkp64b6qrBtTVJGDBLMsqup+laLq+vsIHiuN0wy2LCszO2j/dsHm6JVU8URoRpgq89uJ7NZk2SZcxnGSoMBkY0DNB40iQmfR5T1RW6b6m260FHlyYo74mjBGKN0havLMpG2CQjV4a46ymKI1ZpDJ6mOFB2jnw2I09jsiwiimKavqc4Vtx9WpNEMXXdstvVaK/oe8duvyUtWhyOQ3kkDgNwA/jwXhNEMUmc07Ut5eYOH8fDeXYN+JbOlaSzmC6MybJhYA44tHIYDc53GEBrO0wiVKcbvmnQypOkAappCUNFEkfEaQi+A+exNjrpZjXWKKIoxlpNWVaU1RABGJkEZSxd29J0Hdq40ybsCEKNtWrQsbse53u6pkUl4amCbmibgDA0p99j8X1PXdX0XUPvW7q+paw9de2JIk2SWLqm41iUzOcrgkBzOG4xZgBMTaOJw5DQBoAC7YjCgCSO6P0wxKr33WBEZshp75wjDIITKA/ovMJrQ5wMbHZbVtS2oe1qlFf4vkNhwLVsNw9UVYlRUNQ1SRLRFh2+7k8Z7gUYhdMQpTOMWaBsRdM3aANeRcTpBQf7iaqDqjiQhpq63v3/2fuvJkuSJEsT/EREsV5kyM1RRGRkZGVXVnfPFA3R/Px5W6KZfdneHdqi7sqszEAeDgxeqFDAPshldfXY3q2Y1+pSyqQwN7tAVUVU5PDhw4fp94/Ul29R2QKbOez6mscPzzxvHzCZ5yLNuXz1DfvW8rw9kG2+4UYX6OGZf9k1nJpHxvaR1CRcXEe/2bys2B4OKOfJtQL6cxVA7KBqSPHOYJ1Ch8hupUU+Nf9SKnbATZJY5PmZZYuFtlmWnm2gBuDzpsuceeLL4hk55G/Bn2UBIVCUBZvLK07NEWuj1Mj72MRos7k4Oyy0jKOwNRkqnLtodo5x9DivGW2gbfpYz7Jcs1yteXF7Q2ocxgR2++jfe31zifOe3e5AUVUUecXj04797kCalqRphrPDmRFyjH3L1cWKy01NYsB5C/rfO3XCZ4tLSfMCEwCfu28IcPu1k4KAZSlgzLKMy8vLiRUU+YGkj8V+7NWrV1PjD2kWcn9/PznuCOBp2xbvPU9PTywWi4ldBiYwUFUVb9++nRjA7XY7aclDiF7YovWdBxYCACTVDXzhGS1sm3ifH49Hfv7558k1RPyiBVxKUCts/NxaTizrhHUUBl1YfWvt5A0uqfx54ZyAZgE9ci2ijxbphHyPXLtIG+S/cu1JkkxNaKy1E/AV8COgdx4EyJyYu1FM9SNn4C/fI/NB9MwC+OaASTzrRT9+OBymsZfPEytG+b2Ae2A6F/hcOCrnOreyk2sTJlbWwzk7KgEhMAV6YrkoLOy8kFR+lkBBQPnT09MEkMWFZrfbcXV1NXm6X1xc8O7dOx4fH6drFPvEp6cnLi4uEPtPcXQR5l86qkpXURk7uQ9zwCzzSsZ/LvuQOSUZn7mntwQ0opmXfUD+P687+e/tEfOsmsyZqqq4vr6e7qm8TrJE4pgioFuCYMlSCAiX2pA0TVmv16zXa16/fj2B++fnZz59+sTLly9xzk2seVEUPDw8sN1upwyD1EpIBuzq6orLy8vJwvH/nzZ+fvxmQO6cxY5SYevJ0hydaIw2ZFlO8ArrzxW5zkU2CYVRhqyoKOoFXduQVxv+7k/XpIkG7zg23bkYrMeNPRrHOJzwI7HLpdJcL1aYoefh43vKTFHmiv3+yC8/b1mullSLJeuLK7qmIQSNciNh7Bm6Fm9H7DBS5BlaGzbrNdWiZn848vHDHUpHg39lYsfRsqjIKsPgLM/7Lf0QO59l54U7Sj9yuqZjGC1FUUVbPGPoe8t2H+38ep+yqi4Zu5HWbembI3ZsMYnmarUgaEiKlKrOcbbh+WlPCLDeXIJtJpBy2A6sVgvyNPB0eGa37bi6fsnXX33L1ZuvcUlNUWQMNsotEqOjq0iegHJRVuMCwziwrnLUzQXbY9SclUVkQFRQpEWGVoqmbUizJNYGhMCqrtHanGUInq7pz02AVOz4yZnJMFFvq1VAK0vwA1kWvcpRoI0iEB++LM1o0QRihBlQpHn0h7c9dK1lu+spCosyBT5Ee8e1iuw8nMH96M9MxAKFPke+sYNsmhq0B/BYGyU0xoAPUV880bI6QekEbVLyckGxMORpXIifHh8j0PSQJJqu3TM6RZpl5FnsBGmto7cON1qUyeiHlrxKUSalaw7s9w/o1HPzek1Vb+j7AVMHyJZ8uj8wNgfSbEmSe5r9HdXFJVVRMKgUNpc0uyW2O5GkI8qd8OORYOO9CyZleXFLqTbYfoBhy/7xPY/7LcVyzVWWs99tGZ0nLTLSosbkC9IsYXCaMMJgPV3f0nQ9C21IkoA2Cf1g8S6Q5QY7fN4gZWHLzx1infP0/We7sl9X0P9aEyiHvE7+LyzLbrfj4f6ebugoipwy/2x1V5Yly+VyWvgA6nMtifXuvNh6nDco7enHEesiSBmdZeg7BjoWdU6WJ9jtQNu1lEPNMDiuri4ZBsf+cML7KLnr+2hNGQHGgFJwdX1JkmqsHUiznBD+nSIHJtmCjI0w4rIpS+p7bgcom3JZliwWiwnA/Of//J8npkrcKIQVAyattcytzWbDOI58+PCBLMsm1vn777+fOiZeXV1NsgI5V2FaZfMWNny5XLLf7/nll1/QWk/WZcLazwGxdMecg0hhksW3X6wZu67j+fl5smsTll4s0+Sz5tpv8cu+v78HYpMXsfmTYj3xCt/tdjw+PnJ7e8vvf/97vvrqqwk4C0AWuYyAKgE6AuTlOZRxEVZUwKJ00xTwJJ1TZWzl7yJ7kKDMGDMFL5Itm/vUz9eQ+efPAxEJOtq2ZbvdUpYl19fX0+8EgMocEfAubhxzG0G5LpmXTdNM3zX3o5ZAM0mSqXW9gPX7+/upEFb01PLZksWZ20mKbl70xsfjcQr63r59OzntyGd8/PhxAuR5nrPb7SYZE8DFxQXPz89TpkXYX2G4ZT7PPfnv7++njrd5nvP8/Dyx10XxuUZLpDUCNiXrNPf4FwmZZK2+3CO+lDrN5Ry/dlmZiJzZ8es9QoLm5+dnPn78iHQelfP13k/jM98jFovF/5crytzhR7TgMjeEBS+Kgqenp2lN6vueFy9eTBIiCUKkG+e8X8Lt7e2UOZnXOfxrx28G5Ha0BE/Ulp4joeADwYfIdAJBQaI0GIVToDDYvkUlKdcvX2NULBwJBIa+pW8PdKcTzva4caDME1LlIThSE51DrPU4O3B7scT4NfeffsHbDhVGyioDRj788hPPz4/kZc3oPEWWsN/e8/z4hLUDnz585PLigizP+P7nH9lsLqjqJaONjPXDw5YsyzFpxtVNRrWKUhTnzv6xWpOmEYQ5ezaATwJK57S9p7MtQRlO3cDu2LNY3nB5s4he7R7yxYbm9Ejb7Hl6vOdhf6Isc9Is5+n5mSzVLJYVfdfRHJ/5+O5HvPdcXV1h7cDp+MBquUL5Lvpka09d5SyXC46jQRtDZhJSG60ACR6Fw449wY7gAtor8hQWJHif0Q8jwY0MYyA1CpNmgEd7h7MjeVGdJ1dBXcYHre96mqaj23Z0XfRQXy5qijyDEKjynDw1FJk+d/fMSRLwfkTrFO8jo2h0ZJbavmGzWmGto21aMAqd5PgQQdEwetICilzjrYoNgZSnqs5WR3C2G0oZh8/61SLPMD5ESc05gBy9I0B0wnEWpSwEG+dznrEpbhiaBsYxundUFWb3jLOO4EZMUNRZgg0GnSi861EEnIPEpAx9ZGa7tiNJRxa5ItWB5nDg0B1Jqkt05vFJTr40XL78mvb4M8Zn2NGR5pahfSIMWxIqOtuilaHIlzw+PeCGkXpRc3jKOQyGQIbJc6p6Q6YsN2//gGufWF9ecP/hB45dx+nYYhLDGAJFvcFkNQMFKi0JqqQbArtdw/7QYkMgLXOUTuj7EaccZV5itKEPnvS8aYuWM00zQoC+H+i6YWKyBCjMF9X5YjtfdOc/i17xeDzy+PREnmeYqpwW0LIsp6Id2YyyLGO0Dh8CysfGVybJcGPAOkfs8BpBd72occFilGO00U9/tVpyOp7wTqEwRJvOAWehKGoUCW1zmIDDMDiWq5rVqkYpT5oXFGWFtf9e1Al84dAhbJnMB/m3sMrCnsHnhiXSyETAu2ighQmTeSCsU1mWE+Cx1nJzc4P3nvfv30/sp0gqfvzxRx4eHiYHljzPp2Y/4zjy7t07rq+vyfOcx8fHqWB0HEd2ux2fPn2a5CFSLCkBg5yTgNx5QxoBYBJ8NE3DbrdjtVpNTiyieT8cDpxOpwkwCRh+fHyctPZN07Df7/nhhx9wzk264P1+z2az+cLtQcCJ+LtL5mLOgIo2eD5OUhApwFX03PMujeJiIwWW8h5hqsU9BviCpZcUvtzLuq6nQEfAsATxwkRLE5i5g0cIgd1uNwHBuae5MMwyv6SeQEC2yEZkvZJrmv88d/oRcHxzc/NFACedNeeNcwSUyjXJMyC2kgJOpaA4SRL2+z2n02kCxOJ48vr166l4UTJD4lk+l2SUZTlZby6Xy8liEphIDKUU33zzDW3bcnV1xbt372iaZsroiHPPPKgUmdJut2O73X4x10XbL/8WEC7PlgQRMo8k4zUPyOWQ8Z5Lm+T47+0R+/2e+/v7Lywg582IxFsdmJxi5kWj8+yNaN+bppnqRaRgWRyFBMxL0CiBpsiMTqfT9Dx0XTc1spNCZQnef8vxmwF5YhKCio7RiUmm9I+zFq0NXnmsczg34sboea1C7BCpTYpT4AJYq7DWoUICugA9grLngfB417HbPp8joio2fFEOrSxvX15zschoTkfa7kSRZWgN42hp2hPD0NG2Pe1xHxvHnFpQcDqcSHRCkhqs9zzvfubly1e8uH1FUS25+/TA867BJCPOG9q+4+bmgnpRUy9qcOdUXZoxnFPmbTeQpBk+aJpjj1cJXmdkZcXy4jYC/tGhAtxe3TIOJ/r2QFL9xNA3FEXK0B3Z7h4Y+xMXFyvevHmNG0fa/YneOo67HbvdlrzMKLMcBSTGcNjv0A8PXL52KBKGvicvM5Z1xalpo7RlCDTHPcGNLMoSAvhxJFUZ6zrn2fUQLEoZlIJh6MmzqC+LnTU1wxDo+44sy1nUFXmW4X3D03bH/V0sgFsuStarmrJMKYqUzbrmYrNEqUBdplEWYWKjoa5t4hh1GWmWst0PUV+uY/fFRGmquqaqErw/0DQjl0GjdEbbt3TdSOo1q3WBd5BmGUoZgleEMKKNJgWU1piz/d1gHc4RtczOR/9yHwtTVfB46xn6gSLV5GWNKaDvOpKioKwrCB27p2d8c2TzIsFZhfIZBEWa5IyDI8tyrFOsNlcs1peEcSDxI0WmWdUlvXUc9gMqP6DTmtQYlpc37NtHnD9yOh5o7h9R1SdWVxuqRcHQtigPZV6jvOf4dMeyrkiLklTV+KDJUkOSZjRNj8pW9O2JorrgxRsY2y2Pz89YP3L14pb1+oJidUEfCrLygry+5HSw7A5HHp+OZEVGCIaqWjD0RwiKLM3wwWKtYxjGmf7TkSaGcYxBmbU+BlvnhXauG/01E/LfW2hlMRb/3bhZx26unKUOwvDJZiFAYBh6lDb0/UDT9BRlBkrR9RZtUoIaGJ3FJPrcMCo2toLAcrkgy3N++ukXLq9u6XtL1w70/QghYegbQFHXFahAUaasVjUmORehak3XWw77I797++K3LqX/Zg/Z2AWAzBvCCGsp80MYQ/md6D5Fuzn3mxZwI/NnHEeenp6mrMmcZZVufOKrLH8T/2lxVhCtsjDmAkzkGp6fn3n79i2vXr2irmvev3/P8/Pz9Peu63j58iXL5XJykpB0vrS1F6eFEMLkm66Uoq5rrq+vJ9kNMGl6m6bhxx9/nDTPbdvy/PxM3/dcXl7yzTffTEGCtZbn5+cJvAvjLXaLd3d3fPXVV5McRZxgpHBWgLzIAAT4aR2b4sgYyLMqICUSRXYCmQK2BNCJLOjjx48T2yjNc8qynDTMci8EZMv19n0/jZ3IAoTNFPcc8WaXQlnJPohuXZx5hFWVtWgu0xH5y7w51ZxhFkZXmFMJViAGkaIt9t7z8PDA6XTi9vZ2YlxlrRJwaq2dCm9l7otsRcZUXGrSNJ2KUvu+53A4TM49Nzc3LJfL6dqFIX58fJzGQK5P6gDEhlHYXik+fnh4wNrY1fPi4oL1ej0xzRIkis2nECGS1ZlLmOa6cZHfzAse5R5LZkzu96/3iPkxl7DIz9JfYC5FkXEWcP7rPUL+Kw2OBEhLzYtka6RmYO6JLwWjf/3rX3nx4sU0xyTgEZmXuKlUVcV6vZ4sQ+V7d7sdv/vd7/71NfS3LbVn3+qIbPDeRhM6FXWkRge8G1HeYfuOYexRHoL3aKXQSRr1l9bhh5E8L1HB4YaYYq7zkqwoefhwx49/+wt9e8K66D18sdmwWa2xPpDokkVds1muaU4NeZqjk8B6s6EbTmilGNqRw+GEUpr0xS3OeQIKk2UoY9hcXtINA0+PzyhlePnyNT4k9O8/UJQ1TTuwP+74+PE9v/vdN7y8jcZ7aZqRmIRhGDnsDwzn7qCLYkHQLaNTqGzBsXc4lWHJGbH4EMiSGjsCWcL1q4y+PaHDSFV35OmC/fYewoh3CVmWc3v7Fd5bxqGn60bapuXu7onFcsHj/ZZysSFteo6nI9XFgsHGgsq2bdltnxnHjs3Fktxouq7BJRqlNTpYCFAXJWFRcDg2pDolNel5TBOKLOPUtYxDT56nEWw5B1phzhGis5ZhiIuEHVvyTLFaXVBkCb//9ivKIuWw32MMVFXJMPSkqcboCu9z0DAOceFv2o40jV57SptY2LdYY/Q9aZZiTEqeJxx9yzg6jIG+G8iyFKVjdKvO8pcsNQwhatdTneBVBNfWKVCaPM9Q+pz2BBIdH7rd846DDlxdXLJZrSlWG/K2o6yPBHtiLLPIto8tyhvGcaBebmi6HhyU1ZpqsSErarq25f7pA7cXa0LqqYuU3q1xRFCQpzYW3SYF5WJNc3pAa8X+6R6aA4en99TLJc4a+m4guIBRhv64Z//4kbRaQhGoFxWpdhwPzzhrSVCYJKcfO4xOWW4uGdoTP737hXp9RVmvweSMY0KZVkDGsWk5nnqabiSvakLQNKeWYEf0eTHViaLvu/PGsSK2orfkZ5chay129DjvqapkStnNF9F52nHOgsnGqLXmdDrx8PAQ7VBXK1ABa0fWy9W0gUlqV9i/yHgEjNF03YAnNqPCg/OBiOcDJjH4YM8OmuEstYkMjTA8ETj4SZMYNxAwRuODwyi4uNiwWtfkeYpzseHQ8dTxtDv+1mX03/QhKVr47IggrJKwZ8KYzdl0Ad2ysTvnvkh/i5xEtJt//vOfJwcP8bS+uLiYGEOxzhMQ8msPaWGZlVK8fv16YsoE+F1fX9N1Hff392gdG/mEEPjpp5+oqmp6/7t37/i7v/s73rx5A/BFkaSwt8IOi0RFHEzkkGdBGNwsy3j79u3k1CIA6+npaQI0eZ7z+vXrCVQL0Pjw4QOr1YpPnz6xXC45Ho9TcaMAo6ZpeHx8ZBxHLi8vp0LXuf0cxALB1Wo1abElAyasnzDnkhEQgDUfa7kPEmRvNhvyPOePf/wjZVlOFnzS4VTuvwAZAVKio5/LWlar1Rea9KIo2G63U6HfPBgTRlnGaO41LqBerk0KLufrUwiBx8doLHF9fc1ms2G1Wk2NX4QtFYAv4yKdREWaJHOhaRru7++5ubmZQP7cck/0+eJMIx74j4+PHI9H7u/vWa1WUyZG7pf8TXTOok+X4E0IFdHsX1xc0DQNP/zwAxcXFyyXy0l6JDKj4/E4BZdyjXM9/DiOU2Am5yvXPy/SFEZagtBfA+25lGVuyyj/luv79OkTWms2m83Ebq/Xa66vrxnH2KRL5oZksGSsxXFHMmzy+fO5IPNrXuMwrxuQInEZKyET5DNkfkgGJoTozCI1Iv/a8ZsBuT9bhkkaruWzRkmR4sYepQKJdmAUaKKvcwj44NHGYILCOUsIoNCkaUlZLyiSwNEk7HYHtrsdb1/dgra0XcOHu/e0fcN6tcEDRqc0hx2GBLMoqYuC0Y6k2pJnCYtcUxXl2QElm9wb8qpm9IE0L3m9XHDz4sjj4w4X4NvvvkPnBdvtDuscwWuennYMw19pm57Nak1VleTnlIsxhs1mzWq14NCMmCQlq2tOA+gkMv+DU3QWxsEzugiS0rQkTxb0wwOnwzN9M3J18ZLL61sSHehOe5rDniKrKYqcQGB1EaOyw7mN+OhSLso1SVbS9j21iu4YkV058rx9xNqePAu4RGN0wLoe5QxpkuC8p2/3MZhZ5aSpIU1iy/XgfdTFJgbrPIkxOBtbvAcVW4YnBhZ1xs3Vkot1zXq94PbFJYtlQWrAuoEsVRiz4Gn7TAgZGE/THtAkFEXOMMS5tFqtoid9YiiyHHT0FvckrNZr2qFnsA7rE5I8ZxhHNpdL0jR28gznosMQosXjOPbTQ5QkmqJISduEfrT0w4jzGuc0wcc6A+cHhuaEPctUAglJWnHqPSZfUNRrgj2Rm8Aw9DS9xQ49JitxY4/RCVmWkiYJRb0gqIRdt2UcRhITyJKA0QHlHX3bki066nLBepmSOI19Ttlbh9GaqkjZN0fu3v3EcnVDvrhEEc62ioYiSxlOB7rDE1frS7QZwR7xvYvOI0FBiNakKEVQCS+//obeWTqr0GmFySrqNAOledod2R8aTm28L1lRR4cVF0hMcrY7hL7vMEYRgmMYerwP5HmB1pEp73uLPUulor4/fLFxykb962KdXx+yAR+Px9gcZbNiUddcnT2Pxc5OdKySKhQbVus9xiR4rxgHxzg6XAgM4wiKc7dXRSQUPNbGFL0LUFU1aVbgrAUCZVGglKPvBprmhDae29sbXtxe4b07A3rL/cND7LLa2d+6jP6bPiR1LHuEgCBxGpkX74nsQDYt2SiFWZLNWgo4BXhtt1uen58n5rdtW96/fz9JG+RzBHBLWl3OSbyxZZOVTC9E5k+CAZGUiNXg3//930/AWFg+kQlIMaFYKcp3SfMb0cBLcaowcQJU5g2UBIyJ/aG4P4jLx/F4ZL/fT9cBTEzqbreb5BbimS7aXSkKPB6PPD4+ToBKrAvn9oRzH2XRSwu4keyGBF/yXxlbuf9SXCs+8q9evZqAkny3MWbKdAjwVkpNqf++79lsNsBnmYkw4SFEb/N5sacUTF5fX0/68Pl5SXA3Z6+FaRUPeLlGuc4QYmGtzG1gCqrm+n4ZMzlvmQdy79M0nXTjwnrPLRhFyiI2eeLb/vz8PGGOsiw5Ho+T24p0f5R7LpIWkS/JvZbvkuuW+62U4ttvv52yBFL7IPr9p6enLwqMBZALmyxrsTDN8/sr0hvRn8+DNhnPX9cZ/XqP+PV+IffucDiw2WymWo+bm5tJWy7jKt9xOBym+yvnLQGDnKsEfnMZnTzjIuWSNUjAuBR2SzbMGMPr16959erVF9mWT58+TYHTbzl+MyDv+wbnxCva0nX2vEGmdJ07N1LR0dXiLKmM9mkJYM6D7mLXEYibmvexGNRaTJJxffOSRV1xebGiG1v6sWd/PLB73vHh/jk6aXg47faM/UjwEIJFm0BdlxgFwY2slwsW9ZKqXlDUNW0/EIaRarECbRid5+LqmqJa8vy8w6QJX339lqLMuft0x0JV1GXF4+MD2+cDRiVYO0avY2Moq5wszXBuIEkNy6JCZwsyn7EIGb3VtN0IJiEY2DcNWZKRe02aaHRaUi6hXlQcd4+4XIMf0CRk9YrmdGDwsXhnUeSE4FmcYlTqg8Oi2R9P3GpNlmeMDpq243Q6kiaG68tryiJl7E9oYxiHAZOk5FlKGCxDeyLHszxvQOfmnoSgGIeBvCjwzuFtZNR9cARic5iySLl9sWa1iKm7qixIUoVSA2lR4DpPmmcslmt616N0IDcJTXMiOBjHgnDWcWVZhh8dbdPQtC31uiJLDG3vsM7Sdx3eZ1g7RieM/YHr6wuSMo1NpIwhTZNp845FFucKfusw5wXDOU/bjrS9Q+FIjQNvMX7EW0uqNQ5FcArrNWm+QgVDWp5oDhmehNPxGesDBE2WGOw44ggkWc7Qd4wOfDAM3UBVlNFRJcRC0ixN6AdIGUg4oX0A69A6NoAKwXO5iYUoD7/8wmJ9y/VrQ5LX6CKjWixwp5quO3F4fmJ9tccoTX86Yi3kRUmS5BgTHWSctWiTs7684sXgePvNd6TlklM3oJKErmmxY0bAMNpAP1ic9bRtR12WpJkhSQ0oT9c1k8+x1opxsKyWa0DHdLcLKAxBnQt2s3TaoGQRlgV+cl3576Qo67rm1atXvH//fmqSsV7FFtTPj08MfT95wopmUz4nsn+KJMkZbXRx0mfv/WNzQhmFMjoG516KidNY34AnTQ1lmWHdAMoRiBknYxRppqkXNReXK0LwEwvbtj0P90/YkKBNxr8fTH7Uwi4JIzV3pZDNUuYBMG2CQvjIIc+0yAaSJJlkIldXV5MN4X6/5+npiQ8fPkzAUTp4zhmweSfC1WrFarWaWlsLkFqtVtNm+uLFC+q65vHxkTRN+f3vf09VVbx//36SW0iXP5HFCNCsquoLWzzROUtmRzZp0XaLp7EAmSzLpvMToC0sqrh2iCWeWC9KlkCAl9gkilyiaRqOx+Nk4ycOJAImRSrhvZ8Y0bl0RcZLMhafn70vPejFSm+1WgFM9n/CQIuGWlheAUPSeEeAksieRG4kgY8UFIr7ypyZ3m633N7efuFhLpIOGVdZj+auJjIeIkWYA1gB1jJ3xNkFmJh+YNIaS8ZDAL2w8nIfu66bmtXI3JT7Lse86Fmel8vLS9q25eeff+bi4oK3b99OmSPJiLRty9PT0wRShZkWf3EBpLJGSzboD3/4A2VZTg4sEmQBE6gXrbXMbQlmm6aZPn/OWgMTgz8/JHCa7xFz60m53l/vEcvlkrdv3/Lzzz9Pz60UM9/d3U22i78+f2AKzCRDINcpgeA8SyffL+MiIH+eyZDzk+druVxyfX09zRdh5D99+jRd3285/i+4rMToUCZOXOhiFNm1DcH1kCaE4AlKY0xM86e5OrMegTQ1JCbHW4c2Bu8sIXiG0XJqOo5NT10ucSFlfXFJZz2tu+fw8Uh7GCE0mOC4XK8oqhGjon/t6dTSPY8EO5AQ2D4+RTH9Ykla5jTdQF4u2Fy9YLm+pKhLTJpTLxZY77l7eKAoC+plSXks6NqeJM25uIg3+Oef3tP1R+qqYL1Z8PLlC7Sq6YaRQEowjnZ/Yn31FUab6PjRO9LMoLyntyNFVTE4Sz/0aALryzXBdgxDw7HZgrcUWYLRipAkNHaE44GrPALpOo0WXf3QoUzKrhPNvmUcHH3f4b3j9sUNdZWz296TGI3WCqU0WRaLJcehI7ie075Fh4FwTvOHoGkHhx0tRRWL+dIsIJ0yI7sY3UaSOqPIFAGP1o5x7Ghby2DjBO5HT8BRVBljP2JMgrIKdODUHEmTuPG0TUOiE9q+RxlNkiYUi4p+9FR1yaltKYqcapHTnjqGoY+gsc7PAQTTJvj5QfZAwPmRhAwCDL3l2LS4oMhTTcg8ynuyzFCWFWYsY8FxtaDtPHlewWBRaUlRb7BhxGRbiiSjGyxd16BNGbMIWWDoWg7NjmEMJGnOelFh3cj+dCK4lMzUXK4WpMbTHT7iW03iDbY/Mrqe/rBlkSZkOuH+4YlffviRtFxy9bIiSRNMkuB81MSPxwO7p3vW2jD2PjrGDAUmL1HK4m2DGztMXbA/NOT1msXmBhcM3o1oRpwNHE8tbQeDjVIOj8fakUCO0gaTqOhK0vckiSbLou9u20T7P60NwxnIg0KfA21xw9BaT5165xKWEGYV9vF/0+InrcPFsi1Jon3Z3f0dxdmdQTZhSS/KBs5ZcnA6WawLFHl0zomWi8TmU3B2+vFnNjBhsD0m0aRpvObRJSStIk0LzCohTS/J8hRwtG1DnheAou9GrIsuNUX5m5fRf9OHsEeyiQkDLtpoKfiab2bCSAszK6BBxlcAuTDRx+NxYurEn9k5xy+//MJ+v5++V1qGax0DR0nnCwCUorB5u3bR515cXEyNb6TQ69OnT5RlOfl7i12adOz8/vvvaduWuq4nsCRpdrnW7XY7NSsBJu213CPRwEogIc18hNkW5lHAg9jgiaNHlmVTUx1jzHTP5fPluRFd/NPT0xQQSDZCAKPYL/46aBJgJoBynmGQ18gYyueJbEQ0/CJ1gAjWJWsgYyMZBemAKEykACQJEqRzohQuHo/Hsy1qMzVyASb5xK+zdjLfpID4cDggRbFy7tLFV86truuJHZcgRvTDUqQq5yDzWBh1uf40TadgRDT8AvjSNGW3201e8HK/D4fDRIzc3d3x17/+NfZWefPmC6AtdQH39/fc3t5OgaYURMtzKgWgu92OxWIxZZckWHTOTb7bUucATOMmWa550x2Zh6Jtl8BJ1gW5//I+CWJ/vUeInGRuBiCBvRRZzmUp2+2WDx8+TE46cxmSrDHSZCnP86lbqMjZ5HvnZME8UJLgRbIukvGYZ5BEYif1AsBkiynv/y3Hb95JssRgncWOZy9JrSB4AudCqSQjJbJtYM7d6zx2sCiVUBTlWV6g6G2HUoYQouPG0I/ngTFkeYFJUpSpUCRkleH6lWa8ani6+4Wh3XMaHOs65/pyydfZW+wY2G1PNKcDeWqwQ0cIDm0SHrfPPD1v8WiKT5/YXN6w3Kz5uz/8gfzmBmMg+JH2NOIJLJcVdrTkacGLmxccj3uc9ygdmeL9/khV5eRZbKiz2z/TuR1O5WTlEpUGAhlVVUCS0nY9Vb0kSXO8tjjlsOPAqW8oM8PyakNvTxgMZV0Q3EC1KIi2fiMYRVqkjEOPCxaVGlYXlyyTinq1jKAwTem657PtoGK/2+Kso6pLkiwFNGgTvcbTjForhq5haE+keU7wHh80KsTmMYdty2KxxGiFVxFsGW2w1mGDJUsTTK7wAZx3pKmKUp0z5AlwDhYcwUfP73EYMecN1jmPc57RBsZgUTohLUowKdYHtA7c3l5yarYo7VguKtpDix/jgjPaWGiY5wXWjhOrEbVsPcbEFGxdGqqiIMtGnNvjgsIUBRAbLyVJRl2uWOUJu/0elVX0VmNdQKc5JitYX93Q6YHD/on97pmABp1i3YhSBX3XERzUZUGRxw6kWnm67oQddhhV4n3sYNsPA/34SKqhSAvc6BltR9931EnNzdUVQR+xQ8vTwx2Li0sWV1f45ZptWtB2T5jQkyjPeNqS6hStIVOKMI7RN115ijymmHsH1WqDzkpcUKRJxvHY0vSBU6vphgylFVVdkhcZVZ2RFznaeFCBpjnF8QwBk8T6idHac0e9Bd6Fs4QjkOVRUhDOBW9xUYoss7SiV2reEhliR6horynAa7lcUFUV/RCLZ9qmxbro8e5mjhAR2EcJirVxHVJGM7pYt0FQUYo0DAQfSIw5f7/YxQVCiAyVVgqtiAGmCqxWNVqn0XnJQNu13N3do5Qmzyv63vHwsKVpxvis1+lvXUb/TR+/li/MbezmTYFkcxQwJwGcuOj8WmogMgV5nQB42TAXi8XkHf7x48dJfiCs1XfffYd0Aj0cDhNDJ8zV4+Pj1Fzll19+mVqQG2O4vb2dNnVxvJDW4nme8+rVqwlUyfnudrtJMqK1ZrvdTtcjRahzbWrTNCyXy0m+IC4PTdNQFMVU8AmRJXTOfeHDPpcEyTVdX19jTOz+KMBTWrwrpSZ3EJHZwOdCx3kHTtFiz/W24hUuGmV5r7i4zLtQSlA2bygzD9aENZ77c8+dOAQACtM91/q+fv16Ks4VvfuczRVXDmG/5XsE3AtDXlXVpIsXICbXKkyw1AHIfZZ7Ku4rMs6iFZbPl/sohbOfs416YuTl++YsvQQ0MsaSVXjx4gVSpHt3dzfJmVar1XRtIgERKYV839wLXAK4cRwnlllA7+Fw+MLLW+69SHRkHIDpmZB5JxKi/X4/Bc4y7kVRTIGDzG25VzJ35nUMvwbkMlfW6/Uk/2qaZrKalLH+vEd8rlWaF4nPs0i/tkGcZ3Hnc16eS1nX1uv19NxJ8Pvx48dpfeq6jk+fPk0yLHlO/rXjNwNy5QPaQ9f3KK3JizI2GAmQZwXYHvyIUQmoBIJGoc+v0SgSvI92ZF4prLOMdkTZHhghWOzYMXSKtF7gncfZgDIl1fIFaWq4uPmK4+6e0/YT/XDg/unIqnasl2suL9ax4QyOrotNZy4uNly/uuWw33N/d4/znrHd89PDR4wfyVONNgnLqmAYR7phgGApioQ0SXHB4pViubmgyG653Gw4Hbcc9k98unugKDL6vqXpBpJiyXH3EVN0hGTJYvWCZujI05TFckPT9rFwMQWCo7EtKlEkScrF9Qtc35PoQFZVeN+jkhSTjtjREawl1YHtfospSkYfbbLq1YokTRlHxTCMLJc1WRY1/VcXF5RlwWAtbdtF+QyBxarGu4E+S3A2RrbHQ0PfW/KyIk3hdGoZO4XGQZISlEERO1065/AqBxRBaRQx06HUuStj7zAqIclTvHK4MBJCdOaxo8XZ+HANYyAEDUExeM+ps1jjWJgcraEoPdfXBYkeSIyirkqaU8epPXHlNngXmdm6XnA6Hei6nqqSxiSaoe8Zx4GizLi4WPLx/gFFQGlPkmiwAa0Mabogz0pQGdZ5slTTOw8qwaY5Wg8EFR/U7nQCNPXmGkzOYDU6ScjLghBUbFhjj7Gz6dgy9D1axeLCNLPY4UjXPjIoh1msKYqa5WKBtiNR+x3YXNT0KuHuw08UyxXapCyrDfnqisNPP3K5qKI1ZbMnKAXK4FxOO1qMSSjrmnqxoqgW6KCol2uCThisJwQDOiNJNGWV47QiaVqqIkPjKMuMsszxvkOb6EiUFyV5UWFMGvXkOjlvliMoR5JqfLDkaU6iFEZpgrUkSpMWGd5bQApr0vPiFAuFlYrXrLVG4VHEYCJ4y9gPJCoFryjyikW9oOtafPDkSYr1EZArpdBJFj/PGFzwaKNBq3MfBI1WhkQnEM7F58qTnM/FO48Uf3rnCM5SZCUKTaIDbdcydF103kkL+qFlt285HUf6QZHnKcvF6rcuo/+mD9nAxG1BrO6ASTYhG9+8qGsuY5qn6ufFYPIaYZCFffxc0xMbwrx69Yqnp6fJH1wK4MSHXECipKyvrq548+YN2+2Wjx8/Tuza3d3dBOBEEz1nryVFL+ctLeuvrq7Y7Xbsdjs+fPgw6aElrS+OKEmScHFxMbHkojWXtPpcViBNXIT5FocRYe8EIEoBnxSUif2avE4kOVJsd319TVmWWGsnJlq02XIfJAMpbKnIFUQeAUyAT+aAABj47KY0l2MIOypB1RwQyXhDDNTm4F18wgU4lWU5aevFIzxmzE9fWOuJW8hcIiSF3OI+c319zYcPH6bPluBSAkcJFufFjALI55Z4p9MJYPJDF8mSgFPxmp8HNvIMSHZD5sF6vZ6yMnPZx9XVFQDv3r2bPOClK2fbtlN2oGma6f5LcCOZSJF8iHxLALuAVql5EM22OJOIB/+8EHJekyFjO3+OBexLoCf3UcZwbjcpf5/L1STYk0MkSPI8yPMo5ysZB1mP5JmaW2vO9eLynfI3GU+RM0mgJtkDCfSASZoigZS4u2y3Ww6HwyTvEvnWv3b8ZkDeNk1shR2IFoJKg4nd+5TyBAyE6HgQgsa5gEpSUp2e2XBQ0vbaOgY30tsebVt8f+L56Y6+P1LXKW13RGcVu+0Bq0tMWhDSjCSvqU1OvbggdM+45pkiY9rcy0WOMprO9jzud3TDwHq14vbmhte3L9BGsd+fOJxOfLz7wP/9//bId3/3B65uXrBeLtju9zw0J4x25LmmHyxKx0LSp92B1eqCN29/x/t3io8ffuaoI3Pe9i0LbRibJ47HPSZfkSpHICfRBbbVVPmSbnTYACaBYYAxONIsp8wKVGnRYWToDpHNzEvyosa4gb5pyRJwfYdThq4fyV3ABYUOMQLP8oLFckGeGYoioSoXJCZncA1prgko7DAQyEAFglKoxDCMA4fDnmEYOR53543HM7YNOzSry0uKvMCphNj/RHE6NTh3buaTZPEzATtYnAuQagwGfXYW6doGrROMScgygx1dDLZU1PN/uH/mw/0jZb3g22++4vZyTTqe+PrNFaONXTzzIifLIiNMgLpeYkeHUtG1x7kQpTEqMq5GG4ZhJC1jtqIsUwZn0cFhVGyNrjAEDIMDk5QMwwHv+s8p9SRhHALKZFxe3ZDpeO1JjF/HbQAAUn9JREFUuWT0KY7AaC3DeGQcB5JEo0NHdziAt7EodrRYd0C5HoMl9D3W9/g8h7zAWk/TW7zRFMqRlQnLRU02KLwd+enH96yXC5wuSKs1aVaw3255fXtFWeQM1uJVLMDNy5qqXuF1rM+o6zVZnmPd2ZtY55hE4Q4WPzr6vkVrT1VqylKxqGOGJS8rgreYJGG5XpElOUZnpMbjEk/Ax4B67M/Fn4E0hbrM6dqePC0o84Jjd0Bpj1IencROtHjQxpBoQAf8aLHOUtUVfR9Zhaooo6WoD5xOHWkWGbrD8cjmYhNnVprQtyPuTLJX9SIG+yF6TwdiwGWMpiAnM1lkzLsDSaJIs2Xc7D0YlRBcwNuARpFosXlr8c6yXCzwQRFUyv39jnEE52ODqjQrqMvqty6j/6aPOaiTDU9StXO2a77xzYvD5o4rAszEArHvex4eHibAIUWHT09PU7pZANL19fXkcDH3QwamBj/CmEvh4MuXL3nz5s0kLTkej/zyyy/8b//b/8af/vQnbm9v2Ww2U4t7YcLm4FIa9Hz77bf88MMP/Pzzz9N9EDAo3s9zCYX8vqqqqbBQwI1kF4TBlWBC2FsBJyKRkGyhpMsF0ArTLd1Jhe1MkuSc8YoA49caftHcimvM4XCYbPqEdb6+vv6iyVAI4Qv5x9yDXsZTNOjyOwFFc924gDZrLR8+fODDhw8sFgv+8Ic/cHNzwziOkw3kOI6Tb7uANwmiJDMxt7YTQClSjrquJ8kQfFl0KPdFxkQCSxl3AYYSHAiA9edsobCwopsGpqyKzHUBffPCSLGyFOZ8rsNfrVbT9f31r3+dXEck4/H09MSbN2+mgmYZS6kLENAsDXCEYZas036/n2ROUhMxb3A19ysXhl0CHRlveX7l+ZbAROaiyFvkXsu9kfkvgbvMBWkK9/79+0ne45ybsl7ee/b7PVdXV9NninxG7o3cZ5EZyXojReMyr+V3c4Z8HmTI58t8mAPuT58+TdkqYKod+S3HbwbkRmtMkpDnsYhudPZsexiZT0LAKI0ibrjKpOg0i7xXCCgFiTkXVOmo/9Uq3hAbAkmakuYFQUE3DLjDDjsoyJLIeBEwOoEkpciWJHXCkMLYPGOdBe9wKpCbPLIVZUnXNHR9T2aSWOi5qFFBs1ouePniBX/529/4f/6X/webi0t+/90fePnqJRrP03aLHTvqqqIsa56fj1RVyXb7jFGO5bKmPa54fIiaxFPbcjpa6mVDtbyAEBgOimp1hVIduJEMReccKgTSPMHoinHs6XpLO/aMfUeeRGlQXixJEkOeJozHPW27xSee02FHvz+QFDU3b74lMTHAsaOLjYHSBK2js0TTHNDaxjbxRY6zHmPiBHHe48+t60ciWEpl/HxkKpVKUKjogmIMBFmsNSEkOBvZzUDAeY/Dx26VIRb+6nOxojykAEmaoXXA2u5caJLRtiN3nx74+d0dSbbH24Q6qyjyKJHROnZ0DQFQHqU0x9OJm+srvA/0XRsLEEPUj8dF7Zx2dAF39sy+vFixOx4I1p2vL7Z3t15h0BR5yTj2WDeiNWedMWilybOCXK+xY4/XKSavGfZDZPJNwvF0oG8OFKsaO54YuwN5klKUBc3Y0zctozFkWYI/A/c8y3FB4VSCTmvqRQUhgvXCGN5+9YZRr1HphiRJOe6hWF5TLFKyNBZTV2WOP420/QmTpPhRo0IJ1tM1gdXmEqM1KohtZ4H1CtU2+DDgXM9qUVDVGVkaHXna9sRiccFue4gdVlHnpmABHxxKx669wXuK8uwP3Pd0bcPFeoEPDhTc3X/iaffIzc0FaZaRJjnOGYKLchHnHTooTJqSnwtxP3z4yPF4YLlYos4V8sfTidvVDcMwQgikSUqe5ewOB563W1bruBkkaULbtWRZSl2XtO2AAoxSpHl2tm0dzprjQF1XaJ2cF+S4HimlMSYB1Jk9i8XQaZaz2USv+Y+fdpNUSumoVTfm/7d7zP9Ih2zG8xS8bF4CMgSECas515vPWSlh3+T9wnrNW3aLpd78OwXgC+AUvbewf8KmXV9fU1UVp9NpYtzFzk7S4q9eveK//bf/xv/xf/wfXF1d8ac//WmyOBSHlcUiSqyenp4mXbaktA+Hw7Q5CxBfrVZTwdt+v59+FpZQAIOkwgUUCcMt90xY9jzPJ+cV0R9LB8tvvvlmAjUCjISlFF36nOWUbAN8znYIuBLWXsYCvuwlIK45c0u7eVHiXCIgYy1B2Bd7xBmICciWMfrll1/44YcfJkZaJBRyznM2VCnF4XDg9vZ2AsUyz6Q4VkCfgCzJlohFoFyfnCNEZnaejZDvE4Aq4yX/lm6nAtKPxyMXFxeTVaVo/iVDMZceyRyW7xa9sjC1xhh+//vfTwy0OBBJDY5IY6qqmiQoAjglGD6dTlxeXk6BoXzOnEkWsCk1FRI83t7eTvIcAc0yBr9mwCVQbNuWy8vLaR34+PEjj4+PvHz5cvruefGyzDf5G8SswH6/nzIDUh/y5s2bKaMiAax0rb24uJgCQwkGhE0HvjjP/54rjaxLc7ZeHF8kUMrzfOqg++HDh2luzufGbzl+MyDXWp3BlyUAo40pM1TABkdiFCpEpB1iJp1AbBiitD4XVXn82IMdwQ1oBVmaErKcm9tXHHZPeO+oFwX7XUPwmrE94IcenQ34LMUQ0waJtqQm4TQMNIfnc+fLDDtaTJrw8vaWru04Hg7sTyc+fPzI1WVkQoZhwHnP12++oihKDocjP33/N7o2mubXRUY/DFRFDjrqnxd1ydgP/PjDXylyjSZwcXHF0EPX36ODAhudL+4/vcckCevLS65vblld3GLbDtcqeguuLynrFZ4U68DhGRnJEkO5KkjOBXb9GDX5+8MRxiPPzw/orECNPUViUC6QpgmJMYQ0ww0DVlmyVJ3dRgzaG5TWJKkh0RmJ0aBGIMe5jnG0LJcrloslp8OBcRxI0dHT2p7boicpOo2Msw+Koe/ZHwayomK5WmODx3mLVpAahQ8Oh41FgueFzzvOD5XHGE2apBijY1DSDnRN4PS4pzt+T52W/P1/eIMPKdiecpmTGMObV694fnxk6Ht2+z3X11d0fTPZ3yUuFuwF70mzlLF3UTKhNBebFc6PdKcGo6PeOEki6+88qDShKEvAMXjL4XgkOBszPsZgR01WrwlpTZqVqGSk63rsMJLpEuNOjMct/XFPnmiqMkPjSbUnM4GgHF0XnQGqeslidYEzNX2wbNI1V5sV7WlL02xxpCzWFwSzoVy9BJ1SLZegAsaduN1UFMnIqT3QdyeGvkFpQ5YPlNeX9C6wPexwQ0+1iN6/iTEMzsVHXkFgJMs8m4uSssjQxk+McKKjBaHRcRGZnvvgSZLPOruqKs8LlOKwe2S7f2YYRu7uH9ntd+RFymJRsNpsSJOUcZTmLuqsIY9VnT4EHh+eeHh4YLlasj8cYzdWHaiXFWmWcNzvMWlKlked5OPjIyiD0opEJbSnFqMVq+UCjT5nUgImUfGcz9+XpjnGiBd53Hits5ggGtrA6dSw3e4xSUpVZfS9I8kKdk9bhq7HmDyudwryTJEk7r+zYv6Pd8i8EEAzd7T4tQ5zrqucs43w2d9XAJ2AltevX/P8/Iz3ntVqNXl7z+3l5PtkU51bCIrcQuQGb968mTpnHg6HST8uenTnHL/73e+oqordbsef//znyXtanFlEytCfXYD6vucvf/nLJAsR/bdY5nkffZzfv38/ab1fvnzJ1dUVp9Npao4ljXbmgFACDukCKMxr13UTo/nw8DCBOknby30QSQQwgUH5fGEwxYtaDpENrddrlsvllIYHJimMgD0p6BY5wX6/pyiKqUmPfI+AHZEASLAlDLb8V4oVBcBKIxvxJf9P/+k/fQEa0zTl66+/nhw3np+fp8JGsb+bM9FZlk3jDEyASjIgcg5yj4ThFUC73++/yPKM40hd1xNAE6cX0WELmytNqKToeJ5JkkLcxWIx6ZRFznJ1dTUBe5EWyXzQWk8OQeK+k6bpNKfmunlpXiRzRlhyAaTzZznLsqlAeu5uI1INmStyT2SOynlLJ1ZjzNTEahgGPn78OMm3VqsVl5eXZFn2BaM9z6oB3N3d8enTJ9br9VSMKvUDAsAlaBfZmcw3pdQU9MjzI9co4yf/lgBf7qU8a/PsiniLy+/6vifLsimLNy+WlpqX33L8ZkCe5inuvJmO1saUcKJjvaA2aKPAudgkJDiUD5jE43xA6YTEp3gfcLZHeYvyDkNsO25NQr1a4YNjt33GusiuPj3c0/YjXqcEnUVWHkcSHMb1FNrjxiPONox2YLFYknrzhf3WsWnwY+DUDXQfPuHRvHhxQ11UdG3Hoq7RZyeR4+6Zw/aZJM9YbdYkBtCB1bJkvzsy9g1dd+D56Yhyjtdvfs/q4hJPSlYmKDzH045FXTOOPY93H3HnqufFxS1ZeYFHMTRRp2pUSlYW5FXGsdmjsaRlztidGIMHO+K8wpiM9tAzdh2roiBTHhMstu9I0gVZmpFnOWiLMYqijDIha32UpihNCGCSBG0M2qfgNIftkf1+x+osbRjHHh8sxmSYNMe2I0FpkiSjKEuK0tP3lrY90HYDx9ZybEfyoiDNYuMdbUwE5N7hgotsvAtY685gKAYLZVHgffQLv7l5wf2j4/npkbvTln/+5x+5vFhyfV3gvaU5tZTlgqrMUZcXDP3AMLQMQ0eSGMYxfmaSGLwHr85ODw6s99ihp8xzyjzFDyZ6g6sYJJpER2178OhUYwaN9rHIT8vnAUEbSHISnZPmJZelYfv4xL5rqHJDsq54uj+Qm0BZFZRlxuPjMwRFkcVC0XGMhahKpwRdoLMFTncUi4KkrHFtz6l/pg0tV+1ItdIMfQeZwmQFN6+/4vj4nt6O6ODpTicyo7i+2GA05FVNmWsYocoj85wmZwAeYiYjz1KKMidrTyRZyaLOqOqcLE2BQFWUpGkCIQbLUav6OXUXWwEXtG0//S5WmC/IqyihcXguLq+wfuCX9x9YrTekSRk7zaaxzsOHgBsHxn5gtCMfPnyK8zUoun7g8ur6vMBG5tukCavlEqUN9w+P5EXJZn1B2/d0fYd3gbJeoFWKHWLRZ6oT8ixBaRWDMG1QWYY5r3reu/Om2ePdSJKkeA/WesYxgoU084SmR3Wex8fnyI4pi9GBNNXUtaGqflvBzr/1Qxi9OSifM95zICZM2JyRkxTxnG0Tdko2Uu9jF0gBcnd3d7RtO32HyDTgc+pb0srjOE6uKXM7Nml00jQNP//8MwCvXr2amrhIehuYQEWe55NOWBjx7XY76cVFg/7NN99weXkJMLlciOxDgMkwDGy32wn4AFPRm1JqsgGVAkbRpct9lvsnXTOl+6W4h0hhogBwAYMCNIAJrAo4EQ32brfj+fmZ1Wo1yR/kvgrgn8sRRHbTNM1UcCf6eZHYCBP667kwt43MsmyymUvTlJcvX04Wk6fTif/3//v/zfX1NTc3N1PBrXQCFSs/cQAR0DRngOdBgLjQiETk1xKaObM8dzSR654zw1KYWxQFVVVxf39P13UURcHl5SWfPn2a9O7ydwHccg/ESUfOWSk1NRUS7b5o6jebzeRykuc5X331Fff399O4imONFJ5K0yC53rl9oYBq0YSfTqcpMBSGXHTkAjAlYzGXqUhLe3GKkYJW7/3kWBNCmMZOfNXlPGQdkIyLZIjevXv3hXXkzc0Nz8/PUzFrmqZTcPLx40fKspysIucNmiRDJNK6ubZdALcESPJMyLnIeEswLG4tkm26u7ub1hX5bBnr33L8ZkAeiI4GSkf3FG00WqexIx7EQjaIxYicC1MIseAvxCIw5wLBOxKt8E5FyYVWmLQg0QFtUvbHI7bvuViuqHNDGHucdnTjibFtCcHR9C1js6NMDXkCq2VFalJCcGidoM4FColJKauopU7ykqfHRz49PLPdH7l9cctyUVEUJUPXY+1Iog1933FoTxz3e25ejlT1gq63GAOLZYExL3l+fOD+4yd+fv+Ol68zkqrAZAZwLItrlNZ0Xc/48ETQC06Nw/LIdZKxri9pOgVuwCQGrRSjdbRdj/cddZXig0YnefRt1ylXN7cc6GiP25hdsANhHPDOcjoecC6m2z09Sjv6UaGNociWpEUFGJwN+KDwwaNMErXrVcUwDpg0BRR5kfO8fUIbR53kpFka3XPSjCzP8QFG21Ev1jhfsD2c2O0OFINjvVmRGEiXOahA17YRDBbEueIDzo1nUB6BsB9Hqqrgm2827A6Gp8ee5+dnPnx44MefPrJcfRXZnuGIwtO1R8qyxmhNaN1kVeacxfvIZg9Dfy4SjBt0N3RYF0izlDJLsFmCxscQTClUkpCqDKUcCsiyBOdH8iwGNYPrcTolmIy27UmSFKU0dVExVi3DyZDiyMucLjfkaUWaZQTv6JojWZqSpDmji77o3husS+idITUVSZlzcXnNsijoe4vabc/OKz01Hmc7kjSjaTuCt6RFyfPjMw+ne3y75XKVk6d1zJbomq45EpKK65sryqKMtoSJxntNYlLSPKNODCbTWDuSZTH9nSYJdhzI8zR60DuHPjNIskhZZ8mUMJ4W5wRkxMZfSZrjacjzkuVyw3b7QFXVJElKYgzkMTDUWnFq9lPbcoXGWk8/WB4fn9lsLnh6eibPYwZuGAaKvETplN3uiHXw6nVswBC6nuAhSVLS5Bz0O0tqFEmiqcoccKB8DLyNwZh5w5qMcRjp+7OXuUnxqSbPS9quZ7fbx/oYpbHDOT1uYxOyqiqoyoQ08fz78fmYg2wBevC50cdcazpn1uaNOOYAUYCJML1SYCiFlKKRFSAoQFQAhWzUc9mFyAukeE0Y5Pv7ez59+sR2u528tMUWUeQRYmG32+14/fo1y+VyYsVWqxVff/019/f3/PLLL/z44498/fXXEzsPTPrxtm25u7ubigGFkZTiPAEGApznoEKYPWFfX758iVKK/X4/aW5FXiH+2PNMhbxPtLwyPnPNrIAJYewhPu+Pj48TUJV0/Nz6zVrLer0mhDA1cur7nsvLy4nNFn2vADlhkCWrMg/g6rrmu+++m+z8Hh8feffuHX/961+n8RG9vbSGN8ZMwYCAQMkWyJomwYmwvnIvJIsATKB77pIh4Fnu23yuCziWQEoax8zBsIDnEMIXc1SkMxIkyPyvqooXL15MwY40C5J6AZnHYvVXliX39/ccj0e6rpsKnmXeC0i/vb2lqqrpeyVIEYAqjHlRFBN4FyAv55fn+aQzl4yH3DcZz/l8lWe+KArW6zWPj49TVsGYz57xxsSmUYfDYQrMRU5yf3/P5eUlDw8P0xhI11itNc/PkTj55ptvpsBlHkTKmiNrkJyfsPEyt2VeSgAjQbAAdFkXnp+fp2ueZ0NEO17X9fR5/+ra+VsXWR/U1N68KFNQJrLkgNaefujOP+uzpCU+1HlWoI2ObgbOo1C4841FaXwAiKxq8OBtbBnvbcfVRcXlusIpwzAGTBo7Um4f7zntNUPX0DdHhjwlzcx5gp7dhpMEoxMuLq7Ii5q19ZT1ip9/+pHv//I9P737yH/+hz9xc3OF7YfYHtwY/BjbwqcmBevYPz9jUWd2sYhOJufOkp8etjT9kc3VDSjohgh2q2JNXaaki7eU5ZKuPfLw8A6ln1lZQ1A5th8ZnaGoF6giI00AnWGdi+BFa7xzGBzGB4q85OWLWw7HLc+P92wf73lz8RYXPErHIkVjEvKiABNHRpsIHkFHG8pgwDm00gStSbKMoqgiCDlraEVbbdKEzOSx26rSsSA3QJYXWDdiEktZVvSDo2k6TBpt6YoixWAYhpa6ju4c3n1uVxs9rRPcWattEkWK4uXtNbuvYsDVtUd++fCJb353TZpmaG2I3teKrmsoihJUeS5Giq4hXecIwWHdgPIRMOZZQZ4ZcmJTmyzVFFm0v0uTLMp69NkL248416OIwWO09dSYNKdPUobuBCouUkYrumZPlgTKPEG5gPdn/1Ln2O13jIMjSw2Xl2vabqDpYyfQ0UOa1XhVMAaDyTOyaolJM6rlhrSoCf1IkaUsqwxlMk5jS5HGIlSdFIx7w2PTYpsW7XuC7UhUwGQ5ZBpVFFRpjkOjVEJWVKgkjXUBSpFl8XmJm4EhMRpFtAbEB5rmSPAu2lXa4Wzj1KCm510YNUuSGPp+pB9Gut7StBalC07NgLURKD8+PdEPliwtI7gu82g7mabstjFt2nbtObUfWK8vadqeelExDB1ZXqJNQtMOHI8deb4AZei6IdYyoOIzmyakSQ7Bk2UpRivyIjnXQ/izvj0GBd4HdDQGoqorQmhxFsYxNkjyUVTObr8/O9l4xtHG5yWNkqTlsqCuk9id+N+PLzS8IuWQQ7S8c6nK3LJP1gcB7wI45pKNucxB7ODEolCAoIDUu7s7drvdBJ7F5m3eMU+Y96urq4lZXSwW/O1vf+Of/umf+OGHH/jHf/xHXr16Nckm5PMFeFkbO3bKJi9AQwDqp0+faNuW6+vrCbDJJi8FdsImfvz4kbu7Oy4uLiaNqgBSAXLCSM6DHWHDi6Lg9evX7HY77u7ueHh4mBw55jphAS4CSOd/A6bPFFAu+tq5Zho++43LmM3PQ8CkAPrj8TjdL7Fe7LpuksjI6+cOGXIe8t1v3ryZOns2TcO7d+/4wx/+MAFICSSEkYeYjRBHHGFsRQolGb+57aOAZQHiv25qJgytBDhyH0Q3Le+TgGCuBZ/b6G232ynQub6+pmmaCWBLwa6co8yVPM+nTMVcCy0abTnfLMsmL/PT6TQ9M/Prk/kkQbKMsTy/Iv+SRlLzWgLJSIidpbD6cn/lfsn8nGdv5oWckplKkmTKJIisSqRBUiwtwbaA4ouLiyl7JWBc9O0ilQKmhkwSsMu1SFZCnkWZ03Pnp7nMTpxrJNCVAFqpaCEqz40UFwvQX6/XU4D4W47fDMiTJMeLzZg2KB0jzSTNIlhHkyeR8fYu+jzHAs4Y6VosWsXGKc6OWBcZ03GMvuV1XeOHnsv1BY2GZr/Fp7Fxi3cjYYy650zBosi4rF9wOh7ou5rlakHbnWjaU9Qxp0m0OzMJ1nmUDZi0ZHV1y42Fp/2R7//6V6wL/PEP37IoczabCxJjGPuB06Ehzx2r5RptNMu6YnSOvh/RKBZ1TZ7koBMOTc/p8ExZL8iKktEqugHSsqLeXNANcOg7THGB9YHjqcOHjjQtokNDAYPq8WeNft825PUCbRJMqUEF2uctdowLlVaK427Hw/1HLl7uSApN1zuKqorjcgbUijMQUSaCc51Gi0IUwQ3RZQ9FVVckOralV8qz2qzY7k5TAx6TpERv7Vhcq3RKIKFposY9zUqGEbreMg4Dw9Dx8uWGPDUM/UhZVuc0/2ftoDH6HFVrlAIfevLM87vfvyLLNb+8/4XRjrRdh3UJmjin0jRjGMYzKC8oivzM8iaEkIKKC4ts/lkaswAaHa8vOFKj0OYz6+H8iPYWZwfC0KODJXhLkqRnZhR0WuCVxo0Dd8/3vLi8ZJHnEBzt6YB3A2lqwKS0TcvT03MMhM4FZAFNUVVgDWaxZHX1lnx5SchKtErJiorgAzYogoqZCoLl+fEXqmrN4dRRLVZkWQR/qzrnWUX26fDUw+0Vm9UC62NDrrSoMWmBTotYc3C2rvTnReNsMUM0JjWYc8ATQnxeu7YjTWIQE3VwUvQYbTqjo4wBxNM1MFpP23keHo9AghsddZVjEs/hcIwFkiqh7XqyPDkXcec8P2/Z7Xd4F9g+77HWUVUrLi8vGK0HTAzqvOJ0POEDrJZLutbSdyPDIE3Geuw4UuZ1LG42huA9WkF0gvGxSFjFq/YuetkrBXl+LugZLW3TcTidSLMMnRpMEos8D4dD7Ii3ktR7RpYqvGu/cKb4H/mYFz4JGyfFawLUpufuzITDZ8ZcLOKEkRKGTVg3kXmIR7j4Qks6XLTNklYXb+q2bSfXFfEFFhAn3ytyCXHveH5+5p//+Z+x1vIP//APVFXF1dXVBC5k05cU+WKx+CKIWCwWE1Mq7cxFqysBRVmWXF1dTTIXAbLCCgq4EnAiAFAa3wh72vc9T09PE8DTWk+2i2/evJl8kSVd/2stv3zu3FlkXmgroFks+i4uLnh+fv7CP12AvfwfmIpphVkVL/m+73n9+vWk4RaWVsC3MKSSdRCAlGUZf/zjH8nznJ9++mkqlpXCWAHzEjyJxEZYdwFpIoURACgNZYRll/koTL3MVZG2CCCf64IFQI/jyOPj49QJVe7DnPk/Ho+T732sxamm8xL9+M3NzXRvBZALSASmbMTd3d1UvCzFnMZ87kr79PTE4+Mjr1+/ZrPZTCBTAlT5//w+S8bq1/UeElDI2MyLqWXeSEAyd1eRIEbcYu7v7wEmzX2SJFNnXckcSUCQ5zmPj48T6/309MQ4jlOPAQHbgi2kmZU876Lhl/OfA2Y5dwlUZPzkegVgiw5cpEvyPEudgFznfr+nLMvJyUiyCjJvftMa+lsXW2MK8C46q5hoMaVNRl6U5wdD/JdjY44Ywced3IeoFXfWMTpPlhRUtWG33zJYR54a3PnGVGVBHhZ0DNi+4fh8QpmUJM0Y+4E0zVF+iGwlsdFLURRUdc7aLXB2YBh7TGJIsxRP7KYXvGW0nqxa87s//AnnNfcfP7D/L/8vXr245vfffMVXr19yeXXFdrtl+7xltVqzvNyAD9RVTVkGsjRHAdZYblGcdt/z/v17bl6+5qvf/wcsKVblkBZ4k+AUlKsb+jYF2xAU/PLue5TyvHz1iuUyjY1RgsEkKcPQ0ytDkmWkSYoyCUVVU2S37B9HlDJ457m/+8jX3ZFyucEHjVEQnGUcfSzkNETAi4kAXWs0kRlXITLq3kcZRWJSmuMRbTx5UaD20Ss1Lxx5lkSGmugZDpH1rBc1g1OYNspQzqV6jKPDOyhXC5ztCYjPp5pYhDRLSc6NppTWrJY5z7snPCN5pfnD333LelOSFTmjtRjlgJTEaJKyYLvbT2k056LUwAc/pSLlkAYRiTHRzs4odJ7iCRDOjJsdCK6jbbYwNFRZhkkMXicEneAAndX4oGmbI3fvf+L0+Inff/01frQ0pwOn5sRys6Zab7BK4562uHEkWIsnsFovGYOh2/akxYLlxQ2mXGNN9OYf7AiDZTg7KSwrTXPccvdhx9XVNWW9JBkDzbEF71DecblZYdtL3v/4PU9PB64uX6DTEpWW1OtLiuUleb0mmIrBBnRCbOTk3NkVScG5WVBiUg5NEx1Z0jgvijyP80PrqaNuWRaE4GibhkDU7Vs7Yr2jyGva1vPjj59oTj2b9Zr//J//jiy1WHciSTTeerSJDivPD1sOpxPdqed4PDEOjnF0JEmUwZkzS7ZYLEmTgrYdGAYHKiF4w27XnBdLSLMzGFAKiO44idE4YkbmLIlH6xh8xEAsOvfELnY2KsFs1Nlb5/BjT5pllHVFnuVkecbF5iLWyuBYLDKs7TA6Mu3/fnxmz4R5FXAowEiK0USHPJclSCpY3DUE4EgRmNiaaa0n/bMwztJxMsuyyddbwIuA83kbeNF/yjkCk1xGUux/+tOf8N7z/v17/vf//X/n9evX/OEPf+Drr7/mxYsXE9DZbDaTc4Q4rgjjKpv8P//zP/Pzzz/z5s0b/vjHP04ARv4O0bdagJtSih9++AGlFG/fvv3CXUMYemGv5R6LfloK2cQqUECz3Avn3KSrFtbu14B8DtIFeAoLK/d/u93S9/0EOuYFunLt0o1SZBnymRJsic1gCJ9tMkVOI64YAtTX6/VU0FtVFf/wD//AxcXFNLeEwTbGTK43YtMnAbMQNfPAY150KfNW2HW5fpkbx+NxYoPn90+CBSnY/fnnn3l4eOC7777DWsvhcOB0OnFxccFmswGYdN4Cfi8uLqb6CAnUZJ5LsDkv4q3rmsPhwLt373jx4sUUEEpHV+89V1dXNE3D3/72Nx4eHri5uZnsQcVRSGwM57KyOYCcFz1ut9vpORNpjGQx5DokOBY/dhlDkb40TcNf//rXyXHmf/lf/heyLJuYchlv5xwfP36c7p0UoEpxpcxL59xU0Ns0zRQ0eu+ntWMegEvQMZfk/FqiIoG63BMJJCW4kzkBTFkKCSAkaAemBmIyt37TGvqbXkUsiAo+xAJObQhnABmbcyTkeQ3OMdiB4Gy0PlSxU6cfv7Q/6oPDmASVJDilccHz8PBA83xHfzzg2wND1+Bth7MRgKSJwQBdc6A5nuiahqEfQMH+uGN9seb65hKtA6fTITLMxjCGGEBYb0Cn5GWJMjl//IeC25dv+PjuR+4ennh6eOT58Wtev7yJxV0uelp6rVgsV5RVhbP+3Ngn2uFtlku+fvWaMs059gMffvqBtFyxevEGpR1NtyOokovrS5RdYoeGcdhzcXmBsycILafjA1lRkZsSgiLLkghAQ9Q5G62xKEbn8IBJDIlSdM2Jjx/ekdcbyuoyavkDFHkBxhBUijYpWp8BKArvY795hcJ5x2AtudKYzJAWOSGMmCShWiwIPi74geiCEYUdmlgp4KODhi/pBk/TbBlGy6Iq0UZxajrqqiBNzXnjjIBZn9ko11pQgXEcsNZRm4SigMO7O969+8TNzWteVhdRd5l7gnU4a3HOorWZGo5YG2UvkYwOZFl6fljc56LOccA7RZakGBVI8xTrHG3fnbMJAaUCuPj5QWVonWCSHBv5Y7JiiUlz0sSwqku605EP738h0YbRWUyWkZU19XLFYnNFXtb0pyPW9ry4vqYbRvoh0I+OAk2SFQw+4FXAOU/bdZgQ8HhCkAr2I0Z59k93ZNrR2yPtOf2oPCRa8fLFLduHe553B2zQJFkFWUm5uiSrVugkZ/AaiBaS/TBGuVLwGBMDsTgbNONgScsCHz5v/M5JajeNwDaNnVAHO5ClGUlqGIcxdrYtVvz483vev7+nbQaWizUmyYmy7QRtoud3nme4AG3bsdvu6NuBth/RyvDy5Ssur29Y1ovJwSVN4uv7wXE8WZr2SNtG+UmW6RhkuTPLegZ03jsCSbxOHceYEH3qDcnZRjOcU+OWcbQ4pxhdBONOnhOtWSxq6noRrbnShKbZc2oP5Jk66+57gvt3yQp8LgwUYDW3zRNgLqyTpLKBKaU+Z+iASTMur/n06RNPT09TYZsUe82dOiAWsokbheiBd7sdV1dX3N7eorWenC7mjKyckzQ5+cd//Efevn3Ljz/+yKdPn7i7u+P+/n5qV+6cY7vdopSaUtNzRxT5/ddff02e5zRNw/fff09Zlrx8+XJiA5VS3NzccHl5ORUiXl9fT2BV2Hi5F1LsKYfcRwEJAvSlSLWu66nDp/d+khPNJSvzQlgZL2EUhVEV6UWSJFNxrAQfwqjPP0syCKLnn3f3PJ1OLBaLLyQhMg6ilwe+6EZaFAXb7ZYffviBly9f8tVXX01gSECfzD/RBQvAm8ts5lp1AZ+/LsgUNlfeJyBvXqws4yESLZFDLJdLTqcTP/300xeZH2FPLy7i3iaSjdvbW/q+nwpRpZur1CzIucw1/iKJUUpxf38/vU6kJDIWb9684e7ubgpmBDjKfBVwK/dq3qhoHnTIWMgcl7/L/ZV5IfNcgmhhy8XC8ocffuCnn36a3Ipkrs6Dc8lyN03D09PTxHQrpXjz5g0vXryYpF7zWhSZZ7I+yPXKecp6IuuPZPTm1yjgWYIamS/zugxZb+QeS2Dz5s2bqcBTglCZz+437hG/GZCjxR0BnA+M3ka5QRe1oCZNwBgSJb7UIXbbdGfrOQJFVpKXJcNoQSvysmYcLGN/om86Hu4+MRyfGE9b+nbParVkc3F5btd9ou2jDMEYRT8OeG/p+5ZfPmxR2vDN19/yu6+/YVFfonOFSjOCM+ikxrqCcYiSGWMsq+KSor4hSRf0x2cePv7MP/3Tn3m6f+DyYsnF5QpU4OHuDucgyytCgM5FqYU+sw2r9YKyKtmfGnZNR9ufyIylrDT9vmEYW/yoSHQGWIau5eryAqjZb5/YPT1wdXnFcp2BhrwoSTIDWpFmGuUSMDXt9kCa5+R5RuIc2WZFc9yz3+3Q6YK0WJDnGdbHYEEZE51BVPShximc9WgfUATsMGLHkTRJCVqTVTXivV0uDV03MngHYcAYRRZ81NV7hTIaO3akGjarimHoabuexaIkTRTBdQx9T5nX6CDaao/OIisU9W4ZWaYYh1P0860q8kQz2o6Hx3vq9YLN5ZK8LLG9ZRxPpFaTZnH8pYjTWottOo7H5pzGc1NREdbhXcJoR4bxbPeVxA2ka1oGO7Ks86hJX2ywXYbJcwgGneSEoAg40iRQ5EtOuiDLCso8I3gfAW6Ws1iuqNcbTJpSZEn0dQea5kCWpKTWM+4ajOlJk4QyTwgOBgl0FGgV6PuGsiq5vb2kb3dUueH+0ye2Tw+kRgEBpRVu9ORZzu3NDcO33/DD99/z8dMnQl7xd//4NfXyEpUUtIOlt4G8LKNXftMBCqM1i3qB0QrvYRhHApCXOXYYzgFYDMS8t2ShIMsjuD6dTpFZT7OpkMh70Crhwy93NKd+YgqKIseOQyw6trEXgVKB5hSLudbLDU/DjiI1LJZrXr9+w9XVFWka3VH6vqNrB/oxsNsPfHw48fiwZ1F1XN+sWKgEVGCR1iRZRpKk9MNAP47RY34c8Vi0j+Gk1AzYwYEKJIkhNnxN8HjGrqcdLaPzYGKhqfcjhANlbljUBVla4dzxLL9LaE/H/yur6L/pY67DlBS/aIWloGoOwuV18yYakp4XkFTX9QRSm6bhw4cPE2smLhNXV1eM4zjJU2QjFCa26zp+/vlntNZ89913fPfddyyXy4lhFotAASTyfvEyT9OU4/HIu3fv+C//5b/w8eNHrq+vub6+BuDDhw9TERd8WeRnrWWz2UwA7HA4fNFxU1i8edOYtm25ubkhhMDz8zP39/cTYJfARhg/KTAUra3og+V7D4fDJO0pimJ6/VznOyfLRF4grhJybyTTABGArlaryaJPvl8AirxGgPTFxQV939O27cRmirxHggv5brm2uU+3jL+MhdQIyNhXVfUFizmvSRCgKiyrWP6N4zjZMQrgElZYxlE6lUrmQ65T5CISzAlDLjpmuddzl5vVajU10BGgqpSatPUik5KsjRQrz+UgWkepp4A/kTl9+PCB+/v7LyRjwki/fPmSP/7xj/zlL3/h/fv35HnO//q//q9TkbMEtXVdczqdJmZbaz1JrGQuQJSEirWlzJl5QSzwheRKrBclQ/PTTz9xPB7J83wqVBW3kjm4F+36ZrOZxnS9XvPVV1/x4sWL6T6Kv3nf9+x2Oz5+/Mj9/T11XXN7eztle2QupWl6zooO0xyVezbX/8v1ihxF5rVkVORc545Q4i4j4yksvFzLbzl+u4Y8S7Ehcq2egDFqcldRKhZSumAjvjAxEjU6Y9QapfPo2oCO2k7nGJ0jSTPKqmbfHgkEgg8c9jvSMFCvVpAk9C7QtgPPhwPeeUYbyJOMNM+oLta0pwOH04G+s/zy0z3B5nz7h7eUWYJJo7PE4BO0WWKyhM4GrOtJNRy6Fl1ccru+4ubqhn/RCQ8PH3Cu5++++5r1ZsPz7sSpG3i4f2C5WuGDp0wLlIli/s16Q7VaUl9uWBxbPtw/cNg9sFiWXC4M+2OLbR8w+TJqmFPomo6uPTJ0Azp47vv3YDvefvUGT0OqAy6MZ9bUoUygqAq6g4pWdHg2dUW2ii1w9VkjbH1ko1FJZMeThKA0wQV0ADuMGAJaO8bzQ6UTgyegTY7WUTOdmYzBHRmGWLRHcIxjj1IJwYO1I2M/QFCUecLlZkE3pBitKfMMa4mfP+ZonWG9hwBudDGr4jXjENvFKzSLcoG1mlQnrOoFj9uGu08PXN9cUpUXlHmFSYlFl3YkSTOc/VxYk2VZ9CS2Hq3MtEkEO6LOmnHtFT5EFsmcN76uaclTQ5aleFVglcfrMjLkOone8n7EMVJkC8p6gxsa+m5PmikwGXm5oqhXpGXF6IZz4JGBC6RFjR0dmJQs12hzYL/bYoeWzfqaXRPZlixR6HDuDBk8aVYw9CcCmpsXL0iUA2/xdmQYekyqMSrg7cCiLvn977/heGg5Ni1KJVgL/dAx+gSVZXTjyP4U3WaMTlDaRPlWFps1nU57TBJlRdZJ0ZWmOx5icStgdAxspKtfXMCPtE3L9fVLtM6RVvZpakgzHWUqjhhw6Fg9e9wdCU5xe/OS5tSxrC85nRrW6wuuLi4hOLK0wOgYODhr6VrH49ORu/sj9/cH6spSLirS3JMXcfwSnTIOFmd97OBK1IgHXNTepwkm0SRK4w0kSZQmjZ1FmwTnPaeup+kH+tGT5SkERfAO7zzNaU+RKwgOYyBJzsGgjef77wdT2h74gnkSqclcRiKSC0kPz9Pm83+nacpisZiKuX6tExX2q2kattvtBOykeE28mwWsf//993jv+eMf/zjZ4AkgnFu3SWpctMiXl5e8ePGC//P//D/59OkT1lr+9Kc/cXl5ObF4nz59mnS6wj5K9871ej2dy4cPH9hut6zXa1arFfv9fipElKyCBBzCFP/yyy9Ya/n222+neytMtPy7ruvJixlgvV5P3y3XKe+Zs5JzRlCAiMiB5mBbGGQZP9HCC8sqIEeCrK7rpqJE0cobYyZQJ5LCuTxgbi0nem1gckoRdv7p6Yn3799PTiESbMwlT7IPCIMtdpkCpASIz+UyIqmS4EiYznln1XmNxHxOi0RGgg8Bd1VVsVgspuZLMj/DucZongEwxkxB2nq9/kL6AUw6d3E3AaZsi5z7XLppbexw+cc//nFqoiNAdO4uI11Y5xkuAcJpmk4ZJWF8RU4jNRPy3A7DMBXSim/96XTi9vb2C725AGphrOX98LmL6cuXLycmXSQu19fXE9iX+TsMw+RR/+nTJz5+/MhisZg09aKXl9fK8z0H5HM9+DyrMWfXhbUXUC4BiKw5sjfK8yjvFWvE33L8dttDpUjPKR6I0gkXPDrETRxnz5GGdHaUKlWFMRqvwFmP95YkNfTeTg/5crlkmWsy32Nci+32XF1t6K2l7y2HU8NoA1W9iOl1H0jLmrxacOoGXr39hiJdcP/pwA8/fmDfnvjd373k4nYNicbaAZMpjMnRCqx1jIOld5qgMkhTyizl27/7jzyv1zzd/cgvH+5QJuPi8oJqdByOJ07NHmUS0Cp2+kszVJKSVxVVkpLkFd4Y/vzXv8WCm+WS1CsOzYH2dKTIKxSBse857Pb0XYPtO/b7e+4+1hg9cn1zzcWmpBssdjxixyj5OT498fTwgDvtUN6yuBzIQ2AYerKVJkkzUJG5V8z0fNqilT7LVTzgJylCmhWYJI0OKipFm4QAJCohyyzONVF2EsC7Ae8SlNfgHMF6XAgkqaGuCkxy7ugY7DlYixmEoDhLnOKCz9lFRikYxgGFij7l48jFZs1NM3JsP/Hw+MBh/5ruoqbMU/KipGstox1ROrYtlw1Gqsu7LrbBjtXYLdo7bB+ZDKU1bnR4FwMdtEapyJBmWUWa5jinot7aBBKjMVqhgoEsxeYVl1evyBJN2+4gDAy9I80XKFOikoIsr0gTKIzCdW1soLTfk2clxTLnNCo+3D0RvGNRFagk0AzRsnHsGmCEMLJ7fkCFDhsU3vbki5IkSVFZQp5nZ1bCY70jzXMyH1hlNS/efsdiecHx2GJVQrG4JC0KnvYdp1NLUIaqzNEmjX7440gInnEcqKucU3OKHTiLgqZpGQbLarWYgNPpeJoWl6IoeHh4QOto9fbw2JClhuBHkqSgOe04HncQxhhI+8Aw9lgbqOsFy8WS1fIC7xQPD09nmzTP89MTSoVoeejBO8U4BJ6fDmy3J5rOUtUJo3UxGC4D4zCg04SuG+j7MWrB3YD3Dm0C1nqS9LNTgtYak8Q1KoQYFLZ9z+HUMDhPPzqWGOzoSBJFmhjA03UNzo5kUnjYD7GldPHvgBwiyJNUu8gh5s4oAq5+DWbk38AX8hNJpQvDOLfna5qGFy9eTMBvv99PBXHw2ftYHEy++eYbiqLg/fv3/Mu//AuHw4G///u/59WrV1/YuwlzKVrduQwkz/NJu/zx40d+/vlnjDGTvGS3201+xMIwSmZAvJvF0eG//tf/OoHLEMIksZnbuG2320mas91ueffuHVrrySNdmGMBGY+Pj9zd3U0OEMLg933ParWa5CFzuYuwe3Jf5WcZQ5HKzEHo/H6I04ZIjUQWI2MujiESoAi7KoHAPAibv08CiF+D/MvLS16+fDl5ve92O66vrydAPpfZzK9L9oimabi6uuJ4PE4WmSK3mAN1AdlyjiL1mIN4AW8i9yiKgtvbW7Ism8ZAsgBzm0hhwedjKwxw3/d8+PBhqkmQjLL3fgrOQgg8Pj5Owa/468/tJ2UsZL0W+cY333zDer2e6jnkudput5PP/dwVRIIi6Up7OBwmWYnIkDabzRSwHA4HxnFks9lQliWfPn3CGMNms+H+/n4K2oU53u/3XxSSSpZjuVxOAaVzjk+fPnFxcUEIgYeHh2luzrNsDw8PU3C8XC6nLIiw8HmeT/dcgPJcJjUv9JzL5SSIaNt2WmdkXkoAKQFW27ZTICPPqARjv+X47baH5wkqUZicqHTzkwdLKYMxRGbp/LC7YSAQzlVlgNZofLRuCOd2vBhIMtJywWG/ZX+MKauubQFFUVSRbXWBoDT1cklZL6m8Yuw68qxi4yusSnj3/md27QNvf/eSV2++JctzlG9RxmBMHjuHKoPJElRSYvIUZxX58oLbLGOxqDg+3/G3v/3ExeWOV1+95vJqxegD4xh4eHqi7eND+unukd3+xMXVFWlaUOQFr16+YhgtTw8PGJOhVIL3AwMaozV1VVEVOePQ0hz3ON/Sdh1//vM/0zUHvO1J84KiXDKOA34Y2T89sn16JnEjduhYHk9s3iY4bxn6HkdPkuboRKPPGYsQHMFZQKPVuUX46OiHeE+TJCMEjbVgVECd7ZSjEwV463BqwCiNDw6TpaANmujUYfvYhlyZs51enmN0YBzO1fHG4FzshOi9i64xxLb0db0gnAJd23E6HTEmWvGlmebVy2v+9sPPHA9bxmFD1zqSEOdWkhgUsShPdH/ygMeHKkbezsdF2dkR5TxJkgIO6x1uDKRpTpqPUUOtom6QAGPX4v0Y328UWgeSNHZEdTonL5aYNMHZnjQPBDJ8SNBpTZonpElA+R6dxUxONnrStKBaLPmH1TU3r/cU9QadaOokIStSnO3p0KzqFGMVtttTlxoTHPie1BQkiSYxSWT3lQZlyNKCfhjJ2o6qXvP7//Cf8ElN34NTim6wDL4/W/n16CSlyD3OOxgddZlPbBLqzE6Z5Kxn7MmydNJ5SopZirrElzk6KPRoHbh5ccHtbSx8fHq+54e/ecoypSgS8tzQDz1JksfAVBkI6pxq7nHOcjodURq6riVJon7fWc/x2LHfR+efJNFo7VHKRw1iMtAcTxD688YVpo1Ia0WqNc55CCoGYHY8r1MKf16jfIhdVIdhJHhN8J6maanyFK+ZNtBhGMizlNVqcc4MwnK15Ozd+j/8Mbfgk418zpTP3RvmxZyS9pafhcUEvvgMeV9Zljw/P09OB03TTAylMOui3567nwhgAqbU+Xfffcc333wz6VbnjL2w+AKkpIBM7Oeenp7485//zPX1Nd988w0vXryYNvp5Q5j379+z3W65ubmZANPbt28ZhmFqFCOM81zOIkyydIRs25Z/+qd/mqQi4jAjAOHh4YGHh4dJKnE4HPj666+nQk7gC7mE7Odz8CnsoLCvv7YgnEtSgAn8ymfNLRWBLzo7CoiSwk25z3PZkshiQggsFosJiB4OhwlwZlnGmzdv+Mtf/sJut5u0w78ulpXxlOBOpATzYELAmASQMk8lkJAASbTlArrmkiz5DgF04uIhBaByPfJ5EqjK3Jp3Zt1sNtzd3X3hXjP32pfCZOnBMdeUy+t/bV8o92e5XPIf/+N/nDI/8/ERZxo5/3mRpoBZcaIRqYvIioSJFu2/zF+R/Iibjtaaly9f8vr1a4wxPDw88Oc//3lqRCTnKnp7uW8iSREHIqm9mLu47Pd7ttvt5G0vc1CyQyKjES9/YcllTktwI58nP0tgI++VgFJkNZLVkHGSsZRCbOCLn/+14/8CILecHZtRKpyjmhGt82kRDEFSYVGWAJ6gHMPo0Sq2sB7HHje4iZlSCtIsP7tvVFzfvsK7kSIBFVwsUkTTD45+cBT1km70NBbG00BIai5ub3Cjo9KWby+uubhZ87cf/onv//YjQx+4vfVUq9ikRmWKIo3aRK0cu+cHnnZH6iIhS6vo9FBcUNaBtu3Z7o6k5R2bqxUXVy/IihVP24bjcWD/uOf56RGtA6+7geubmJa5vrxifzzx/O4dXf9EvVyzubwBNGmakWcZeM9+51lUL9hc1Dw/P/Dp4zv+8pf/xsPdL9y+fMWLF6/QKlpErJc1jx8VwzCiPPz808/c/O4/sNQK5yxD21KeQZrWOnq/K87NmYZzt0xLGEe6rsVjMYmOMg+jSUyGCgoVHFoHcJ6x78DHIjiCJ08VWVaBjw2fxtESnCfJYnfMqsqp6hJnC/ouMrLoBDuO54LgZNK5SYoHIM/Tc0dSyDNDelmwPyxpDjuGvmPMC3ya4myI9oLMbbk0w2Cn7nV932HPLEZWVSii64/WGp1EP2nnQ/RrRzOMPYemox9GjAokaWySFNwQ3TbcgHcjPoyMzoFOMCo7/wxK5wSrQWU4NOPQkSpITYYygWq5ibZ8WlNVC75aXWC9xqnI0NcGcIqTyhgPGfSasT0QRoNOFJebJZvNksEFXIhdQ63zpEWBLiqyXHHz9RVltcSUC5zPSAuDd5pTMzB6x2g1TdNR1kksxjQGo8LECFVlwTi2U+ouMkCaslxMgY4saIvFgsViwdPT01RpP9qR5bLm9etrkvQ/8fz8xGm/5+HxE1mWcH11hVY1Q2uplyVVtWQcHW1z4uPHj2dtZEsInsUibshaRQBtneX+ccvp1DOOFmVi3YFiQXM6sqhi2tJZKWQyWDswDJYszeLzc+5NIMW/BFnTzhrTs8d4cNA0XVwDVE+RJhiTUJYVeW44HlsgRWvD6diACpOs5t+Pzz7hcshz/mv7uDkTJSBqXlgnet65vEKCwqIoePXq1RdSijmrLIyU6LIl1f7q1atpA766uuLFixf85S9/4Z//+Z/p+543b96wXq+n9La4JyilJts1AVpSeCfFe8/PzxRFwfX1Nbe3txRFMTU1kUJQY6JH8suXLzHGcHNzw36/5/Hxka7rWK1WXF9ff6F3dc7x/PzMq1evuLq64uHhgXfv3vFP//RPvH//nrdv3/Lq1aspCFqv1/zyyy8To/rXv/6Vb7/9dgLZAqrl/v9arjLXUgt7LGB2XsQo4yJAX94voFMYWQkUZA4I+yrOJ3NfevkMOT+ZO7LuyGeKRjvPc7bbLfv9fvKhFmAvet35XByGYSIShCEVfbi8Vu6JMKcCCmUeScHj3K997roxLyKdO8RIsCXjJMSG7IXr9XoKTqQAd14kO9fDS1OseeHk9fU1V1dX03nLuUkhbpZlfPvtt1PHSPnb3JVFXJCEgJkXW4pVoIDRPM+ngGq1Wk17hEjJlstlzJo+PHwRCEmBc5qmPDw8sNvt+PTp06QnB6Y6A/GvF795sVqUz5/XQFhrubu743g8TuclAaV0cJVxleBeQL4w+/O1aJ4pmhevSk2KuO1IgCiypDzPORwO0+8OhwPA9N2/5fjtkpWzPEIGJE0TvLcoFfA+Om9IRGGtix7AGvCRCZcHPxBlLcHFB1AFjVEaZaIlXzAJ6BQXLFWaoWtFkg64fcNgwTpQOsGrlLaPreHHQ8uyrEiKDKMUm6sX/I6B+4efebq/J4xw9cJTbBzJSlHmFatFhhty2ialPXaQpKhE0x+PQE6xuOZtXXM4fuRw2NIMR3rreP2m5M2btwzW8MMPPzOMln5oafqR7e4wpc4uLy84nQ7c3d0ztA3d6Uhe1NSrJUYbts/PpCZBG2BQLBdLLv/+T3jbMDQHhubE3/7yZ4bB8vLFS7IktpvfH490bUeiDUPfE7xj6FtMUZJmWXQYSTQqxDbkMIIf6ZuOvhvPnt4jaaYxRuO8BxdQZ7YgEFDBo0MgWEtQnmFoGbqWPFOYJWidkWiFJtCPIy5AUZWgoyYrTRKyrKAbRhgdAY1JTZRXpClKS5rJEgL4c+Mlkxhuri8Zracq/xDTWW6kawJ1ZuJrgiJLPhdIeB/17ZJSHIaBcN4MnPOx+ZQPoDRGp4xEtr4oErROcMeY9TA6+lVbOzCODQaHGxqGocHbkb5t8QzoJBZiJl7jtKLrR2wwVIlBJxrbhyhlIuDsQF5Gj9ugVHRTyRRJVpCfGylp5UE5BuNJE0+ZawqVk6cKHUa0CjgfsxDBE2VJJkVnC3SxoChrssUar1OsStFZQakLwginoScEQzeMOKvQ58ZACqa0ZphJf+z42XJK2Bup/JeKftlMm6aZGB9FoO2OKEa++/Yr+teRKdxtn2lOpzg2aUqR5lzdviB4Rdc2E6txfXPFMHZnG8V4Dl3fU+Qp/WBpmx5jUqqqxoWBqkpYrytUcORZifeO06nFe8jS5CyB8iQmZlW0Emb2rGU/Fy9rrUlUgnUOrQxpVuD2Q8zAOB8DvSpDK8M4WoyJzNY4iEuIpu87spkf8f/IhzCzsuFLil/2hXnB2RyAyeYHnyUUMg8l8yrARECC7CfCVs/dEAR4AtPGvN1uJ9ZZKTUBgI8fP/Lx40fGceTVq1dcXl5OxWzSdESKMUV+IqBnuVyyWCzY7XYTUBrHkd/97nd88803WGv5l3/5l0n+Il39pEX79fU1x+OR9+/fTx0dxaJRGERh+0Re8D//z//zVKR4Op34r//1v9J1Ha9fv56kMaI/BybA3HXdVDApIHLuLuGc43Q6TcyprAG/zmTMtevA5+d11vhF2Mm57EF09aLbFdAtxZjwuWmRSORkPOcFhGmacnt7yziO/E//0//EbrfDOTdp8OfstsxJ0aPL3iz3RBwzZC7BZx2zkA0CgkVXLkWlAs7n0iaRgQjQlsBHAiR5v+iTgQnsCqM9L2aV1wr4nDOxkr2Bzw4wMufleZHARSw/5e9ynyWAledWxlL2A8m+zG0t55p/kd8IuN3v9xNIl2ZQIt0ApiDvj3/8I19//fUksxKmWc7r5cuXEyP9/PzM4+MjL1++nOQhcp5S3zEMA6fTaWLmRe4jEhcJ5kQjL8HuvGBZnrF5gChjMv/OPM/Z7XbT3BfnGQlc5N7MM0Nixfpbjt8MyOcVvxHti5A/+eJhtXaIms4pVeRRWk9aVa3P5vFJirURNCqirWKaFxBqFpsNu7tfGJue1MRun4vFgtGfsHiarseGjjStMCbBBcWxG9AkpEnsxGjSgm+++j3Pj3f88uP33H+658XXe9a3LevLFxxChwqK1aKkSBRZnrHfPtOOjqv1Bb45YZRmnb3APQ4M44Gnh0eSZIFOFlxcveH25SvyomC/38W0dx89leuqYrlaslmtKbOcu/sHHu/vWG+ueP36FQZNkySkWUJzOmG0QaU5y2VBZpac9hlDP6CUIzVZtD7se8o842hgtCPGRd1xkWeoJENnKSg1tT03wURQ6AbGvmXoTrghFgx65Rn7nsj2pZRZLNwMZ2mJ1ipKWHDgHG7oGYeGw+4BP47k+YZAjjFgux4VAlAyjpF1KItY4Df0js6NaKOp64K26xjGjiLPMQpWqyX3d0+0TUcA7DhyeXGBdYG8KGlOTQTbaSw4NEnGMHRk6edaBpn0c8YsSVNyPF3fk6QJbowShCzPUNqg/LmTqVZkeYFzMbDzCrRJYAi07Ymu2WPHlkTBMLaMtkVjMcqCiuPdtB4bErIyJ9M5SiuSLCc5L4reWkYbWXrnwY+KrMhJTEATwFu65kBwPRcXK5alwXYnhr4huBGdlyRFDUmBCRqVpCRZhU5L8nJJWtR4pSmqJVlRMYwB56L+2VsVu2c2AygTdehKQYh2msaoc6bqnK5OPqfqjej8h3Fq/SwpZ9GtrtfraeM9NgfKKiWEkbxIeLG85Ppyw+l4IjGGYejBQ5EW7PbRlurh4WFKkWZZck5Hp9OGam3g8WlHPwScN2SZRhnFzYsN11crrq8vOOyeYgbCe7ROsNZxOol8RZGmmrI2KC1gL1ohRhYkdqeNmb4ErRx9H4PERMWFNG81p6Y5B3Bxnr1//2EiJRSBJP93DTl8Bl6yR4hsYa4ll6zM3Ev7/9PeuTQ3blxR+OsXAJISRUljaZLx1MiT8f//KV64KuUspuIoiTyi9SQJEI8GkEXztiBv4iySqUr12cyjSBCPRve5t889V/SYIoeUhfC3Gs6pbOD09JTb21uqqopZU7HiE425kCIhSCJtkeM65/j48SPr9ZrPnz9zc3PDx48fefv2bXQ5ESIsJEnawEvjEVmw1+s1bdtGnaxzjouLC969e8dsNosFp6J3Pzo6YrVaRf3tly9fuL295fz8nPfv378qfBNduribiCe0yACmxW1TSYhoZkXKI1nq6TY8vHRFnRarCcmWvwupma718nxF8lDXdWxQJAWq4sQkz1CKLsWvWtxzpjIHOQeRHPzyyy+xuLHrOs7PzyNhlqyoXJsQbgk8hKxKllP07kKQpMOlNHyR650WcMq1CyQoqaoq/r7ccyHqU4I9bWk/LWiWY02t9OT9ESkEhHmrLEv6vn/lKjMtyhQCLxlg+fd8Po+/K17106ZbIoWShMvUG1/G1XSnSgLE6Q5L13WRjIt0TMi+dJ01Jtg0ih1lURSsVqu4U2SMeVWw+vT0RFmWUdK1WCxeBWwva0SQh0mQJdIaeYcvLy95eHiIc5DMPxK0SpAnTbPkGfz2uiXIl0JWSRpUVRUz4/P5PAaS19fX8Vzlnvwe/EeEPGwNBu2l9z3et4TW7OpQiAEQmtAoFZw2et+jNQcNs0WpMS6g/RikLC7L6H2DUmGg5llGP/Rsnx7JnSYvFhydnGGspRlGbNmwKWvGocPkGTbL6ZoO5TTaOXzdYbM5Rab59l1BrhU//eUn6qbibb2nerxltDnFYkkxP8banK6tUYwslydoa/Fdi7OKRV5gHey299R1SbnZ8dPTnzle3fH+w0f++O49599ccHd3y74ssU7RtA3lzY4352ecnZww9D276oabf/4d7z2nqzOKPGdWFOw2T1hrGIaRum7xBnoKZosFmpF9VWK1IVs4nBrYH81pmz1lteP+fk3XtbjCgn55ebz3mE5jrGbwDe1+x+g7nDbooWX0NePQgPEoOzK0oWi2H/rgLqE1XVMz+JZegRoH+rah3LToAdq6x2YnKAzeN4zDQNfNyG0GWrFvPH03ULfDwYUCTlyG71u22w3HRwusDtkCrRR3v95ztFzibIYaBnzbHrKfhrZuUC5owo8Xc7LM0NRNzLiELPvLNt3LpDLSNC1t25FlOeWuxDiHtaF4dRhGhmEMhaxjKDZWmEOAaJjPj2DwdAwwhAY0Tb1j8DXG9KG7Zd9htGWxOGIYQ3OZvAgTvnEZzhr25ZbOh6z9blehXYZzFq0GjA7Fsk1dUVcVWoOxGYuzI5wzjH3YfjXZjLZX+FHh8hk2m4HLyYslaIdxDjc7Cl1Y1YA2LuxW+JZ671Eq3A9rXZA0qeAJPx76AaAC6ZkVs0mhkH+lLxUylOc5ZVkeJqeXBgp931Fk84PMx4ddsDEEd1nm6NqaEUVZVux2JQ+Pz4eCrHPevDllHMXbNvimhyx5R9N0tO3IMGgGNZA7zTffrHj//pKhqdnvKo5Pwham0Yaq7Ch3+8MC6dnvG2YLmSgPtQzGhPKVMezuKVQI7Pt9qIlQKiQL+o68MChC74WqqmibCu87xvGQARx7dtvd751G/68hREwWMCFfQk6mxXqyuE0zc9OiQVlA5fsipYKXDF/fh859WRbau5+enr7KQEoBmmjARZ8qhXJSnHx1dYW1lh9//DESU2k2JBItkaqM4xjt6yRrulqtcM7x/PzMfr/n+fmZH374gbOzMz59+sSHDx+4vLzk9vY2WqA1TcP19TWXl5ecnZ3FrfCff/6Ztm0PtqFFJPNyP0Q7C0RJjWQHhYwsl8u4q7Ver2NRp5AoIJ67EFWpCZlquoW0ybULgRNbQXFJEaI+lS3I/YWXQt2pblyK3yS7LMHHMITmONOCWK01t7e3UQ88rVGQ4kiZq0RyUdc1fd/H4r6pzAZevNolQ59lGdvtNo6PaRAp90jmRiFj4sk9JWzT+zj16ZZgUYINOXcZr3I9m80m/v9UrlJVVZx3xQhDCKkQcrlOIYKixZbvTEmhPAfRfU+DYBlH0wJHCZil1kLGELzUF0pypSiKqPWeXoP3PrrlCDmWP0V/PY5jLPa8v7/n6emJy8tLLi4uYoA8DTxkZ2ZqxZjnOW/fvuW7776LO1yr1eoleXQ4vgRRVVXFLLfssMg4k7Et90aCWZnPJMiWa93tdq+KweU4klX/d/gPNOQEbaW1OJcffvBQMHWYjENU71AKBu9hGA6uHwQ7RBWa0xhn0MaiBwj1gSoU2VlH21XAyNG8IBuXtHVF01Q0957l6pSjYkbTdGQG0ApFT9c0oQm4UYxjsFO0ekm5ued4prm6+oD3Ff+4uaHd3nE0c4xDx65r2G6eOT45w2U5mTPMikXoOmoNJ29WFKbHqJ7B++C6YDLuHx8pqw604/3Vn8jyGRcXf+Tvf/srrd+TW8tm+8htV7OYzciKgnd/eMuvd4/c/brmyz9vePPmG779NjSYGOmx9tDZVMEwOKxzzGcuVvYWTkPf4Zzh6GjGoKDalTw+PPLm+A8h0vYee9h9GHpP03Z0XUWzr8B7MpPR9QNts8PlB6u9/ZZ278GEYkGjCno1MvgO+oGensxoZrMCNfYoBoa+ReuBsR/puprRQNd7ckKTqKZuqPf7g2OJZhiDo4qzkM9CIUpZblnMl8wXcx6ft7jc45yOW7FaB20doydY+bV4H8is12JZ1b/Se8nLpI1B6UPn085jnUMZjdaBXI3RossQNEPjocCwhcEH55cBcpfTNxW+H1kUBXq5ZLf1PG0e0SPkbhF8xYuCQRlsbg8ZaIX3PfM8uKJ0TU3TtezLHVk+C/d2cLRdi2EkdxljMaDUSOYsq9UZ1gY5hD1IuPAjFkNWzDBZgXEz8vkx3aCwLse6gmq/p/MDSofnEPy1e7wf0NqQuQxnJRgIk6SzLk46U62bZEngpYPftPGKfMYYS9e1KEZGwjXMZsG2sOsajAajgwd43w1sNzseHp64v39kPp9H3Wx4htJYJFhUjiMoY8nyOf2oaPuG5bJgsShQqqeuSswh4y+yONmlcS7Dd56ue9Ea9r2PuwHh914kdtaEBVArDQRyMeoBYxyz2RylusOY0RwfB2cCraBr+2D3mhBJmThaCJmWBUwKC6d+3bKY//Y48syEGAGRtIkcY7FYAERd8Hq95vT0NNokigvG9LdlgRQ5xtPTE4vFgu+//562bbm+vmaz2USt7ePjI8/Pz5yensbfF09xYwwXFxevisvEBvDu7i4S5U+fPkXt++fPn2NgIo4Ui8UiFnqu12u+fPkSyfrV1VXM6k6vR7KB8/k8Wj6KREgK7SC8q/f396+0vpKhlcy4kBI5Lynwy/M8ZoIliy3BlARbQlolGyvkRQiqZM9jvcrELk+y4VOSba2Nx5GdBPFrF1miONIopWKXVDm+BBDTLLAQq+mYE0I6LaqcBoRCtOR6ZVxKQaesOxJcifRktVqx2Wx4eHiIY91aGwPVqWZZaiJk11FkS6LvFgIv35N3Jcsyzs7OYmAp40/eNyHk4m4jY0LcX6bSFJkLZadZpIpyL2TnWZ6zuCjJOyprrxxPMv/i2CLvnFyHfHa6kyPvuuyWPT09cXd3x3q9Zj6fc3FxEc9TxtfLHB6CCwlyvPexmBuImf/pd2WHTrTw03N4MSdRr3iFBLBynUAMMKfPV3ZIlstlDIYke/97oMapICwhISEhISEhISEh4X8K/e8/kpCQkJCQkJCQkJDw30Ii5AkJCQkJCQkJCQlfEYmQJyQkJCQkJCQkJHxFJEKekJCQkJCQkJCQ8BWRCHlCQkJCQkJCQkLCV0Qi5AkJCQkJCQkJCQlfEYmQJyQkJCQkJCQkJHxFJEKekJCQkJCQkJCQ8BWRCHlCQkJCQkJCQkLCV8S/ABEYkqvAJTmsAAAAAElFTkSuQmCC\n" 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NH/7hH/JTP/VTAKytrbG2tna/9n1fY7l/nQey/b1tGwQBn/jEJ3jGM55xn1nkD8X9+VBgQSi/BbjmmmtqdUw+IN72trfx3//7f+dZz3oWZVny9Kc/neFwyJve9CauuOIKPM/j9OnTXHvttecltdybYnZvy8W+SlBVVfH0pz+dN77xjfe47mWXXXaf53NPN/yF7vfzn/88z33uc3nSk57Ee97zHtbW1jAMg/e+9733mED0YHAh43GhuDey++2s8bXAAt8uPPaxj60Vsx/5kR/hCU94Ai9+8Yu55ZZbzlHcJpPJBalfpmnWyTHyBb+5uXneepubm/eLFL7yla/kr/7qr/jgBz/I933f913wdt8M73vf+3jTm97Eq171Kv7Lf/kvD3g/V1xxBTAv+bIfuq7XZOjUqVPnbfftHtMTJ07w+c9/nh//8R/HMIwL3u5CcfDgQYCHtGYkwPd///cD8LnPfa4mlHEcnyfY3BuksNDtdrEs617HEvim4/lAr8XHP/5xoii6T1X4obo/HwosCOW3CGtra/zkT/4kP/mTP8nOzg7f9V3fxa/+6q/yrGc9i6985SvceuutvP/97+elL31pvc39leIvBBdffDFBEJwXsvrn2u9f/MVfYNs2n/rUp85RNN773vc+pMdzf3Dbbbedt+zWW2/Fdd1ace10OozH4/PWO378+HnLHmqldYEF/iVD0zTe+ta38tSnPpXf/d3f5c1vfnP9t5/+6Z++oBp4T37yk+uC4o94xCPQdZ3rr7/+nNBelmXccMMNFxTuA3jDG97Ae9/7Xt71rnfxohe96P6d1L3gE5/4BK94xSt4/vOfX1fWeKC4/PLLufTSS/n4xz/Ou971LjzPu6Dtvp1jCvCnf/qnCCEedLj73iAtWvcW7XqgKIoCOFcR/vCHP3xOXdJvBilEqKrKd3zHd3D99deft84//dM/cdFFF33TLPcHei0++MEP4vs+z33uc+913w/l/flQYEEoH2KUZUkQBHW2HcDy8jLr6+u15C2VtP3KmRCC3/7t337Ij+eaa67hF37hF/jUpz51ng9yPB7j+/4D8ltc6H41TUNRlHOUvWPHjn1bO8v84z/+I1/84hfrjPuTJ0/yiU98gmc+85n1tbn44ouZTCbceOONPPKRjwTms8mPfexj5+3P87x7JJ8LLPBvFU95ylN47GMfy7ve9S5+5md+pvaSPRC/X6vV4mlPexof+MAH+P/+v/+vfjn/yZ/8CUEQ8IIXvKBeN4oiTpw4Qb/fPyeT9e1vfzvveMc7+E//6T896Ixhic997nO88IUv5ElPehIf/OAHUdUHX2XvF37hF3jJS17CK1/5St7//vefp/jdUzTl2zWmEh/60Ic4dOgQT3jCEy74PO8Jw+GQVqt1TiQpz3N+/dd/HdM0eepTn/qg9n93fPKTnwTgUY96VL3sgXgoAa6++mre/OY3c/3119dK/S233MLf/d3f8frXv/6cdW+++WZc160tWPfnWkjs7u7y6U9/mhe96EX3WqXgW3F/PlgsCOVDjNlsxoEDB7j66qt51KMehe/7fPrTn+YLX/gCv/mbvwnMQx8XX3wxr3/96zl9+jTNZpO/+Iu/OM9L+VDgDW94A3/5l3/Jc57zHK699loe85jHEIYhX/nKV/joRz/KsWPHHlCJgQvd77Of/Wze+c538sxnPpMXv/jF7Ozs8O53v5tLLrmEG2+88SE/3wvBIx7xCJ7xjGecUzYI4Bd/8RfrdV74whfypje9iec973m89rWvJYoifu/3fo/LLrvsnHqiAI95zGP49Kc/zTvf+U7W19c5evQoj3vc4/5Zz2mBBf658YY3vIEXvOAFvO9976sT4h6I3w/gV3/1V7nqqqt48pOfXHcS+c3f/E1+4Ad+gGc+85n1etdddx1PfepTectb3lLXa/zYxz7GG9/4Ri699FIe9rCH8YEPfOCcfT/96U9nZWWl/l0mb8iuI3/yJ3/C3//93wPUIcPjx4/z3Oc+F0VRuPrqq88r5fbIRz6ynmgCdUmXe2s5KPHiF7+Ym266ibe+9a1cd911vPCFL+To0aOEYchNN93En/7pn9JoNM4hh9+OMZW46aabuPHGG3nzm998r5GYyWTC7/zO7wDwv//3/wbm3Yva7TbtdpvXvOY1wDwh51d+5Ve4+uqrOXr0KMPhkA996EPcdNNN/Nqv/do53vVjx45x9OhRXvayl11QB7LTp0/X1z3LMr785S/z+7//+/T7/TrcDQ/MQwnwkz/5k/zBH/wBz372s3n961+PYRi8853vZGVl5byuPw972MPOUYvhwq+FxIc//GGKorhXVfj+3p//bPi25Jb/G0aapuINb3iDeNSjHiUajYbwPE886lGPOi/t/2tf+5p42tOeJnzfF/1+X7zyla8UX/7ylwUg3vve99brvexlLxOe5533OU9+8pPFlVdeed7ye+oaMJvNxM/93M+JSy65RJimKfr9vrjqqqvEO97xDpFl2Tc9n2/W2eBC9/tHf/RH4tJLLxWWZYkrrrhCvPe9773H8jsPtmzQ3btlyBIR+0tYAOLVr361+MAHPlAf06Mf/eh7LB3yN3/zN+IRj3iEME1TXH755eIDH/jAPR73zTffLJ70pCcJx3EEsCghtMC/GdxbpxwhhCjLUlx88cXi4osvFkVRPOjP+vznPy+uuuoqYdu2WFpaEq9+9avrDiwSn/nMZ+71+39vP3f/bn+zde/+Off2c/duNv1+X3zv937vBZ/rZz/7WXH11VeLtbU1YRiGaDab4ru/+7vFW97yFrG5uXnB+7kvPNAxlXjzm98sAHHjjTfe62fcdddd9zpO+0uvXX/99eKHfuiHxMbGhjBNU/i+L57whCeIP//zPz9vn1/5ylcEIN785jff5znevWyQqqpieXlZvOhFLxK33377fW5/oTh58qS4+uqrRbPZFL7vi+c85zn32JGGu5VwkriQayHxvd/7vWJ5eflev1f39/7858Kil/cC/09BURRe/epX87u/+7vf7kNZYIEF/g3ga1/7GldeeeVD3qjh/2W85z3v4Y1vfCN33HHHOeryAv+y8e0Pui+wwAILLLDAv1J85jOf4fGPf/yCTD6E+MxnPsNrX/vaBZn8V4aFQrnA/1NYKJQLLLDAAgss8NBjoVAusMACCyywwAILLPCgsMjyXuD/KSwE+QUWWGCBBRZ46LFQKBdYYIEFFlhggQUWeFBYEMoFFlhggQUWWGCBBR4UFoRygQUWWGCBBRZYYIEHhQv2UP7e/++5FFmOEBq+10BUKl//2s2EccTSxiqG54FhUlUqZSkgLwiGO4SDXXS1xLIMwjBA1t5UlIpW02NpqYdjOURRzu54QkRJY3Wda17+Ki552OOIcx1NsxACUGTfZIFg/i9ivkxa44SoEFWJgqDMM/I8hapEVxVMU6fIE6ajAboKSTQlmk5IowBTVyiyhOFoiNvqolouoGCbJkkUEI73CMa7UCQ0Gh6KZuI1uyyvH0IzHAo0cqGAqtFot8mTDMuwGI7HJHmJ47pQ5hTRlMnuJtsn72TnzJ1UWUyr0UJXDMIooUIjrwrSPGc4nhLEKbmig+6h6R4oKodWV1lqucTBkLSMQTdAaGyf2qLKSybDAWk0o9dp0mp5CF0QRDGaZqCrFpqmEc8CijylyGLCYEp/qcfS2kH87gorG0cI4pCv3fhFRD6h7SjYqopWKoyDhGEYM5iFeK0Whw4foSoL4iig02qhaQqWY9Noeti2iamrWIrC3uYWlajAUugsr9LqrLG8chjTcgmDKTd//QZEEdFqNwijiKzM8FsdJmHFLFBQ9QaVUBEqlJRoqoptVBSzIXkwRC1S1pZ6KKJkZ2+PpAS/0cQydZb7fTTTJCshV01QbVAsqHQUVWUyHRBHY/J4gqMLymSCVhYEsxG6poCqIFSd0SRC0SxMe379dcMmiBJmQURVlWiajm1bKIpCURRYpomhgq0KqjSiKnNEVeB5DoWomEQRYZKj6Taa4eCYJq5l0vA9qrKgKEuEonHXXdsgOmRJxmD4dbxmxqMe80guveI7aa1ehuqtYLg9LL9NxbzVZVVVVGWJEII8L2g1mxiGQZ6kZEVKu90kCEMqIQiDhCBM2dkbM0sLVlcP4rsuu5tnGA526fe6XPGwh5HnOTd+5cscvfgIvX6f05t73H7baVQ8ZtMZa+sdHnbFOoapoFBhWjqzWcjOTkCS6gz2YooSDMsiTWN6XZ8fedaVD/1T7f9hvPKVr6xbvPq+T1VVfPnLXyYMQw4ePFi3RK2qirIsyfOcwWDA3t4eqqpi2zaz2azen6IotNttVldXcRyHMAzZ29ujqirW1tb4qZ/6KR75yEeSZRm6riOEQFGUs89kUf8r9yX/X1VV/f8sy8iyDCEEqqpiWRZZljEYDNA0jSAImEwmRFGEruv139rtNqZpAmDbNmEYMhwOGQ6HFEVBq9VC13VarRYHDx7ENE2EEFRVhaqqdDodkiTBNE2GwyFpmuL7PmVZEkUR29vbHDt2jBMnTpBlGa1WC1VVCcOw3k+apgyHQ8IwBEDX9fqYDh48SLfbZTqdUhRFPT7Hjx+nKAr29vYIw5B+v0+73UbTNMIwRNO0+mc2m5FlGWmaMpvNWFlZ4eDBg/R6PQ4dOkQYhlx//fVkWYbnefVnTCYTptMp4/GYdrvNJZdcQlEURFFEp9NB0zRc16XVauE4DoZhoGkap06dQgiBaZqsrq7S7/dZX1+v74sbbriBoijodrsEQUBRFHQ6HWazGbPZrP58VVURQqBpGoZh1H8vy7LuVLO1tUWe57RaLSzLYmVlBdM0KcsSRVFQVbXej6IojMdjgiAgiiIMwyBJEqqqYjKZoGkaqqrW66mqiuu6aJqGaZoEQcB0OqWqKjRNw3Gcbzynrfk7Udd1kiShLEuqqqq/P/Iz5bW1bRvbtmk2m5RlSVEUKIrCbbfdBkCSJOzs7NBoNLjqqqv4ju/4DtbX1/E8D9/38X0fmPcIL8uy/rw8z+t7OkkSsiyrx7mqqnoMd3Z2iKKIAwcO4Ps+p0+fZmdnh+Xl5fq7eP3113PFFVewsrLCyZMn+frXv16PzaFDh3jEIx6BaZooioJlWUwmE7a2tkiShN3dXcqyxDRN0jSl3+/z/Oc//4KePxdMKLeHE1p+i6bfoCoFu9u7jMdTlldW8ewGeVGhkFMVJUpVMh2NCUZjLMOk4djEwQRD0Wj3OlSKQIgClYKd3S1UoZJmJWlRoboWB1Z6aEWCyAMoLSpFQSgaKhoIwfwxJBAKKIpMtPjGixQEVZWTxFNEniLKnFTkJJqGUgni6RjHVIgnQ6JgSqvdwtA1olmFQGAaOo7rEMcxVRpRxgGz4R7BdMTqUpd2q80siBkPh9i+j2qaFIDlNDA0n2QWUZYVOuqcfEcJnuujUZFQcSYI2d7aZjgYUmQJwSyiYTcpS0EQRuSUFGVJVQkMBEUSk4kU7AzDtNDUHNdzMIwuaR4yDSaoisBQQbNMcsdnMpwwmsZYrofrWDieIAhyKl1DVCqZ0KmqjEazgW1ClcaUSYgiSrIyRTF1vHab4eaUrFCp8pQ0CBmFEalQ6fS69FdWaXc6xHFMnGZUqollW1iuTVmWJFFEpUMQRVRlRlkWWLaPZVoE4RRzvMv6waM0221KDKZhieFChUJRlBR5gSIUVFXB9RyitCArchRRoJQFZZZRpTFN18F325iGys72Fq7ncGR1lVa7TRxG3HLLraiGhd9dwva6CDWnrHIcw8ZAhTRALWLIY+IkJoumaKLANAwMTQdFw/abOL4gLRWcRo9SMYjTgoar4vfV+oFmWgZCVPMvl6YSTybkwRTF1DBEhiIyBBVxFKIUJUvtFgKdvKgo0pjhdEIwHdFs+DQbDcqyZKXfJBVtbLeLejJld+dWgklCmeWUaYzmllSiRFVUEJBm84ei7TpQijmxLCswQNV1yjQmzQpUTUdXdXJL0NQM0rwk2hlRZCmq52BZFpbtkmYVUZzQ6TbYOLiCbekgBGVeYuo6ZVmgqRW6rgBnXwQIyrxAYU4u8iynyAvitMCyHZpNnzSNL/Txs8AFYmdnh1arRePsvbO5uclwOGRtbQ3XdSmKApj3UK6qiuFwyGAwwDRNPM9jOp2iaRpLS0sIIWrSt7m5iaIoJElCURTYts3GxgZFUZBlWU0C7t6eb38S3H5iKX+vqoooisjzvH6xappWkyL5sptOp3S7XQzDYDqd1qSn0WgQhiFJkhBFEYPBgPF4zOrqak3m9vb2aDQamKZJVVV4nodlWTXBURSFRqOBoih4nlcTk+l0yqlTp9jZ2SHLMiaTCb7vUxQFs9kMIURNCOTYxHGM4zj1y9r3fQzDqLdXVRVd19F1Hc/z2NvbYzQa4Xketm3jeR6z2QxFUerxKMuSdrtdE+0kSc5OFHMMw6DT6XD69GmKoqiJpyRPS0tLrK+v0+v1iKKIJElQFKX+LEmedV0niqL6fCRpmk6n2LbNkSNH6HQ6KIrCbDbD8zyA+vpLEtloNIjjmDzP6/HJ85w0TWk0Gnieh2mabG5u4vs+GxsbNWm66aabMAyDXq+H7/uoqkpVVTXhS9OUoijI87y+3vI+kM/fRqNBq9WqiSrMCZ7ruiwtLaHrej1xqqoKRVHQNI3JZEIQBPVkQN6rkjT3er35c+zsZ08mk5qst1otiqJgZWUFIQS+72NZFpubm4xGI9I0JU1TPM+rJzNCiJq8uq47FwDOXmuYT0ziOCbLMlRVnYsBeY6maWRZVv9NEkLbtknTlDAMWVpa4siRI/U5yvukLMuaOAN172+5H0VRyLKMPM+J4xjbtmm32yRJcsHPnwsmlHajQ6PVxtJNZuMxW1ubc8bdaBAGMUEUUFUJBzZWqKqcqIqwLY0kyknDkKV2E9VoUFQVQoU4S/Adi7bfpel5NFsdBqMxpuOg5imjM8fodpbA7qBYbWy3jYKUKaGURFKoddxelPOLgshRyhTHEBRlymBvk6LI8BwXQzPZOXUX490zFFmI5TpkaW9OYpKEk8eOsbO3x9r6AShL8iikTFOS6QiK+YUp8hxL10nSiNPH76K3ukSlCZJ4TKu5yiyaECc5GwcPopk2Tb+BYztoisCoCuIo4a67jpMEQxApnuWQmilVJeYvXlGCqqKqOq5h0247JEXKLBpRlQoia4LSQTcVLMel4ansbu9imwrD3REKGu3+MtMwYJIU+F0fz9JJi5SysshKsPwuSmljaBmiiKEsmI0GCNultG3sVpul9XXSOCAJxmhZSZqnNFo+LdvB7y3hNbtUCii6juU0UHUHv9XDslSScIxtqhRJQBhOMFWFsqqIo4QiK8ip2N3dot3vo+k2XqtHLkwc38d3FOJoRpbmGBSUSUhphjiWT1nmKEVOEYxI0hlN16TVaJFEAaPhjF6/S7vdYXt7k6997Svouo6Chus4KEIQBjMKoaMgyBA4pko42UFUGY6hUqKgmiaigDTKSCnwGg0ECl6zSb/RJRMGUQ6FLlCFieM0yfKEYBaQZwq22wBFMIkChGLj9xrkyRSlTCjTKWWZUakFg/EARhnNVosgmIKiICoQQUWRF4yHQ/IkRtVtFEcBXaPbX4YyIA0LpntjWr05QYjTBCUMMS0bUZVYtkm322awu4eiCqqqIM9BNwwM20IoKpZtkyYpqqqj69BsNIjjAlMDU1ewbZOyhLgoCMOY3lKD1bUlEAq6quLaNr5rM5vF6FqJKgqoBCoKVVmhmxqWZaKpKmVRUpYCBYUiz3A9B/Xsw3uBhw6yD7RU3U6dOoXv+7RaLYIgqEnU4cOHa8Ji2zZRFBEEAf1+H13XzyFJnufR7/frfe/t7eG6Lnmec+rUKfr9PrZtY1kWvu+fQyq/Mcn/BuSLUxIOqUrt7OyQ53mttB0/fpzt7e36ZSyJVJIk3H777Wxvb3Pw4MGaFKVpymQyoSgKTNMky7JaybrjjjtYX19HVdVapYuiiDiOOXr0KKZp0mw2cV23PuYwDLntttuYzWZUVVUrtGVZkmVZTQ5UVcU0TXzfJ8sywjCsiRaAaZq4rovv+2xubmKaJtvb2wAsLy8zm80Iw5But4tt2xRFUY9Ro9GoFURJ0gaDwXyyZ1m1+iqvnyRvnU4H27bp9/s1sZKqpCShtm0TBEE9RlJhLMuSMAxJ0xQhBJubmywvL9dqb1VV9VgFQUCapqiqSpIkWJaF4zg1kQyCgCRJaqInFe7l5WV6vR6nT5/mhhtuwDAMABzHQQjBdDoFOEe1Hg6HVFWFaZrkeY5lWeR5XqvDrVYLIQTNZrM+zjRN6wmK53mkacp0Oq3vKUVRCIIAIQS9Xm8uIlVVTfZUVa0nXZ1Oh+l0es49nec5e3t7xHGMYRg4joOmaaysrNTjuLu7y/LyMgBxHKPrOpZl1d+9paUltra26v3K+9a2bQBc1yWO45pYtlqtej/yM+XELAxDVldXOXjwIEIIdF3Hdd1zJotyIrdfobVtG1VVKYrinEmn7/s1yb4QXDCh7PXWcE0D8pThYIiiwurKEpVaoRgq7X4PjRLf9aiqlLyVcWJ6ijRLObi6jqaoBHGI02xg+y6+2qXKE5otH8cwUFWdfr+PqqkohsHu6ZMoqkWjfxC/t44iBLbbRigaFZyNdguUs4qlKEuqMqcsUixdQakyknDM9qlj7G2fQVEEsesRBhGD7S1O3nUzRRqyvL5OkiQYpoOlaeyeOcMkDKEo8WyT4c4ORRoTTCf0+l2CyZg4VDB1E0oYDMcE0zGrG0tomsIkDMkTjWlcUlYZvZV1llfXUTXmio2q0Gg2OHTkMMnUI5gNScOI0WhAlmYoQoCmYtoWKhq2CoaqoJpQVYKsLDDVFMvIEEKhyCOKeIJvCzIbdsoY027SbfUJtgqGQUQ79VnqNHF9k2lUgapQqiqW2aCqUio9w7Uc0jRlPByRqxZOlNBsNun3l9iOA3TbwtKbtLsdTL9JoeqohopAQ1EqbNtFtxzSXJDlMeF0yrgI8Sz17KzcALWiQCFLc9A0irJie/MU3aV1Dh0+zNJyThyNabZsbMtkOBjSsT2qUiHJI4QCllJRlilxPMW1VFb6HZI4IE1jbMdB0Qz2hmPCOMdvdQGBpuqomoGuaSiolFnB3t4ug50zKKJA0xT6vS5ZUlIVGU3fIxOQlykqgrws2Tt9mmZvCWUakxQKnf4qvtUiFSalgCQrKYWCEAq7e0PyMsOxLJRKIREq06jA0CCKcmzDQPeX8ZUmRS5YWluhlYSkaUIQJoRRQFhoKFWJoXtYto0oQ4LtEZoKvlYS7G6z03DprB2hv6xgWfMQjgI0/AaO74GioSgaosoBQVVWlKpAVXSKvEJUOUmSUhQloGCaBr1eB03XUTWBqqmoqkaWlkRRihAqtmWT5xmartBpN4iDGAWBphYUZYymKRiGTl7lmLpOHAeIqsI09fn4qxqVyAmjAt81LvhBtcCFYWVlBdu26xedqqpsbGzUSszy8nKtyMlw5Xg8JkkSDh48iKqqBEFQq5xSlel2u7VStLKyUqtsJ06cQFVVVlZW6Pf7tUKzX4Xcr1JWVVWrTDIEHQQBJ06c4MyZM8CcFE+nU86cOcNtt91GmqYcOHCAOI6xLAtd1zl58iSz2Yw8z3Fdl+3t7Vo5Wl5ersOjhmFQFAW7u7t1uE/TNOI4JkmSmiCur6+zsbGBpmkURYGqqrTbbS6++OI6dCzJkCRamqZhWRaqqtYhatu26zCoDPfCXAWKogjHcXAch6IocF2XdrtNnudMJhP6/X5N3IMgqFUjSbJ0XcdxHJIkYW9vD0VRCMOQVqvFysoKcRzXn9nv92uVzzCMOoTsOE5NxPI8Zzwek+c5juPU6qm8dkmSoGkaZVly8uRJVldXueSSS2ryKknpzs5OfU5SUZPqojznlZWVWkmWoejd3V2iKKLdbgPU6plU0NI0ZXt7m83NzXq8l5eXieOYoihoNpvnqHp5nnP8+HGWlpbq81peXj5HAZRqalVVbG1tURRFPb4yvK3rOrPZrFbA5b4PHjxY3zdBENQEXqrq8tpvbW3V12F3d5eTJ09y4MABVlZWcBwHmE+0ms1m/V2R4wXUSqKqqvX+peoLYFlWrbbK+05VVdI0rcmxfAZomka3261Vb0kaZfi+qioMw6htHKZp1iquHA8Zor8QXDCh1DUTTVEZjEbs7W3TbLokWUSl6bRX1kDVUcoCzTSIJwmj4QTLMuj2Whi6ShikaLaD1+5RaSpZloACO4MAJc/o99uYlk6z2QBRsbe1SRimHLioxDJ0Ck0HywbNli5MVAWEKCmLuTIiypxgtMswnpFFY7ZP3kEZRxiaQpYmbJ88Pr/hWk30KkMUKXqVkwUzojLAVDXiyZiqLEhmExpmB882mSbhPAwoKqJgBsxnFig2k8GMbHsEeY7fMMizCF01cdsbhMGQRrdNmicYikDXVCpF0Gg1edSjHkUyGzId7jIZDdk9s8loOKDMc7KiJEtidE2nLHTKXKWiQOQpuqZCmVHlEbpuEsUzpuMBWVoxnSYYZ5XIYDpEM1QqAcPhBFM10B0Hyymo8mLuOTVt8sJAcRXScgaoxFFCcvoM3X6GlmeoGnNyW5QYmoVAoKoKpmlSVBVhHJCkJb7XotPtkSYZeZHS7vQoM4MknLDU6WIbBlmeEyY5cRTht5rohkE4naCqFn6rOx+fskSgoJsWhmkQBSGUCVkQEUSbVJVAVBlN18R3baoyI0kiDNMEzWAWpPSWlnHbK2R5xmw6JolClLIiy1KmsxFhlLKzu8Pu7hYHD6xz9OgRmr7PdDTCMnVs0yIJQ1zHR9c1XN+lmWQIRadEJZiOmWxv0l3WsJ0OiqFh6S6pqaFogm7TJUtTkjQhz0qgotluY1sqqnb2C217NDwV123gWjaemIcmWpUgSiIUIUjDiCicklcpaj7BNzIQFZoumO2OSWYTgvEQZzqh6fWxTJNCCNIsx8gFaRRi6CaVXoIQVJVAqQBUiqJEUb4Rqi/LCl3REIqC67mUpSBNChzboiwyiqKiKhWKsph/58oMTYV228O0NFpNiygOcF0dXVMoAFGVUJU0Gi5pmmFZGkVVkRUJlmLguu4FP6gWuDDIl8FgMGBra4tWq0WSJKiqytraWu3bMk2zJkhSJZFeN8dx6HQ69UsK5qH0oihYXl6uQ2FCCM6cOUMQBLWqIhUTGTaGb6iU+8Ogg8GAOI4Jw5Bjx47VikuSJBw7dow77riDXq9Xry+J53g8Rtd1xuNxHXqWJC2O4/p4pcIlj2Vvb4/Nzc2aiEjlSvr/ZFgS5sQGoNPp8LjHPa4Om+/t7XHq1Cl2d3fJ85yiKEiSBF3Xa5IsCbMcZ0mcoyiqfZqTyQTbtuvwtWEYVFXF7u5uTRpd162Jq2maFEWB53m1ohQEAXEcs7KyQp7n6LpeExpJYqV/UI5dkkiRoF979Hq9Xk1E+v1+rezKayPVbulTlF5PeU1M08Q0zVr5DoKAra2tWv1uNBq1TSCO4zo8Lf2gnU6HLMtqwi7D9pPJhDAM2dzcZGtri6NHj3LppZfSbDZrhVYqrJ7nYRgGjUaDKIpqr670Ba6urtbKrFT+5IQhTVOSJKnHWpJkSbwcx8HzPDzPq8mgtHjIkHsYhudYIOSYG4bBzs4O0+mU4XDIZDKprQ1SPZXRATnxkcR2v4IvJ4PymDRNq+0UZVkSx3FtZymKop7QSIIq/cJS0Y6iqCb1QE22m81mrTLLe9e27fv1nL5gQplGMaiCvZ0tTENFN3VmUczSxkFaSz2KQhBPZpzZ3OHMiWMYmmBppQtKSZJmaJaNZnmMw5Ti7IR1sL2NWqYsdRoYto1CSSUqjt1+jLwsWTugkk4H7J4AFQXHb1DoBRXKPClHUdAUoMyp8owqT1HyCJEFTHY3OXPsTqo0puW76LpKlQTsnTmJq6zimSqq4aGWBVkcITBQTYNwMsFteog8gbIgT2IQFRvra/i+y2g4YjAeEqU5QrExjBagMRolDAbbaEqC77loboPeygEcxyTLU4SiUCoKtmlguy5pNGFpeQWlKkijkF6/i+9ZTIZjJrMpeVGR5xlFXpCnOaWoqDJQDIXh3pA0nT9gkqKYJ5ioBnajQVqFVGkOeYlR5aiiIp3FDJUpKwccXMegUAoqoZADaa5iqh4aJWWeo2k6aRASIhBZiuGZKJpCmpZYuoFlOWRZhu/6FFlFliZYpoum6fN8K+ZhTdO2SIoI03aoFI0CFUUzEGL+sGq1m+RJTBhnGIZDq91GNw00XeP4ydNUeYJjaGRxQplEpLMROiq9XhfDaqKrsLl5miJ3abXaDEYTTMdk7eBRslIhzguiNMdrLVNVA0aDHbI8ZzyaoBkmvV6btfVlDh46gKHpREGAblr4jQa6qmLqNlE8pSxzxrOAoqiI4xTH9fFtkzCcEQ63UYwADBtVUygLgaLOH7IWgrKMEEWGppm0mk0cx8Y35ypdlGYkuaAC0lJQFoK8UFF1E8W2cCyb1YM+wXTEeOc0yaggSwv6/T55HNLq9cjzjMneFv1DU4LBDqkncJtdglmMYXgYtoNQclJSiqKafw9Nga7poICuG5RlhWHMZ8KKqpFX89m+bZtkTklZVhRFSZ4VBEGI66koqNiWQVJmOLaK4zZwrB6D0YCGbxOGAarCXLnU5usaRo7rmuRlgZqpNHwT2170VXioIVUgGVqVCQmHDh1ieXmZoiiYTCacPHmSO++8E13XWVtbq8PbMnQtw7ww909WVUWv18O27Zog3nbbbRRFweHDh5lMJhw7dqx+0cmXFXCOMiKJWFmWtQJ1++23k2UZzWazJpUnT56sFUAZHoyiCJi/IMfjce2VkyFKIQQHDx6k2Wyyt7fHYDCo/V+WZQEwGAzY2dmpVVrXdWt/qfSSyRCr53kEQcDa2lodvlxZWaHRaLC3t1eTWplUtN/bqOt6rZo2Go1aXZKfW5YlSZLUJFuGXnd3dzl06BCu69bqrhCiDiurqlonQMn1ZUKO9Bnu99RJYiqJggx5ymORSpYkS1IJlOPd7XaJoogoijBNs/ax6rrOXXfdVRPmJElqhVhRFJaXl2s1+cSJE6RpSrfbre0SR44cqe+BOI7pdDoIIdjZ2akTnXRdZ3l5mUOHDnH06NHaPyuJkQz5SpV5MpmQ5zlRFNUEMAgCBoNBHc6XZFh6D4FzEmM6nQ6u69YTEelVBGqfr/Qk2raN4zhcdNFFjMdjtra2GAwGpGlaK6n9fp8sy9jZ2eHo0aPs7e3VFpTpdFon+cjPkpMUqRQqilLbEOTvUkWX128/mcyyjOl0WqvTkrzKsLfjOOzt7dFsNpnNZrWPUk5IDMPA9/1zfK/fEkLpWgZ7mydJ4ohGs0mYpphek2Z/GdWwcCyd6d6QUyeOUyUJS+tLqJpKnKYoukVR6OQlRFnBNIiYTkYkwYhLDq2xsXEAw9CJw5h4N6LIcizLZKndRqlSTh+7jTTPwXLw+usoqkFRzGmlYRlE0zFZHKCr8+QSvczQyxy9Ktjd22a2W7HU77Kzs0c0nWEdPki37aMqJUGSk5+dUVLpKAKmwzHiQMHkbLbg0vIyjmURhQFhGHPi5Can94ZcesVj2LjoUjzHYzQ6zXQ2QFcLUAs6ilaHQqpqPmPIhYBSwW147G2V4Fj0l9dQVYWtE8dIkwjbtVCNDkVeMZ2GREFGHFWgzD2IpqWTpTHjQYTfauD1V1GtFqpq4jgWeTVCUaYINUZJI+IiJcthlA7QTZWNi9apVJPxNCZNC/LMoNJUVAFFVtK2Xao4JZpNQRQ07R6ogjjNaNotKlUHRWDoKmYpsA0dx7VRVI0sS/BcF123oEpJAEM3UTWdoqpQBDRbLRRAV1RQKjQEm6dPYNo26wePYDsWN954nKbnsXrxERqOxW6RUrgmfqNBu9smyRK2d3awbJOllRWyvKS7vI7X6hNlkFYQZQqzuEAoGnmlkeaCKA5I84y25+G3WrTabVA00rxANQzajSZKNVddVBRKkVOW2TxcIKCqFOI4Q1VUdN2EIiKJA2ZhRBRHFHmBczaMZehzI77ruVhqRRFlzGYVtu3MLRtZgYZCkQtmgYIQBugGoihx/AaqblCiYFguTrOL7VoEk1124pAqBdtrYhgqRRKiJFPMRp8siRhXGmWlMBiM2ThwAKGqqKpBls4zuuPZXAnyfIeyrM6+RObmetPSEIizfp35g7cSVa26lGWBopioqGiKgm0bqHXhsRLbVjCMeTISFVRliWXq8yzzlk9VhYRxjqKpeJ6JppQX/KBa4MLgOA6nTp2aT9rOeq1832d5ebl+Ce7u7nLnnXfWLz6ZALBfaZNes9FoxGw24+KLL+bw4cM1QZUeO+nTq6qqJoamabK0tFS/+IA6wSOKotpvtz8cvrOzw/b2NsvLy2xtbTGdTuvQraIoRFFElmXnEKLBYMCRI0fY29sjz3PW1tZq1SoIAu666y42Nzd55CMfyeWXX14nwcgsYKmiylCvvM/lv41Gg9OnT6NpGuvr62iaxl133UUcx7UqJsPGMhNYkm3LskjTlL29PTqdDktLSzWhk8kZkhxIdTTPc7a3tzEMg0svvRRFUeqkDklSJXn2fZ84jusEJem3k2qVDJfLZAzp8ZRJHfL/ctIgQ83y9263C3DOOB0/fhzbtjl69Ciu6/KFL3yBZrPJwx72sFo99X2fdrtdq6BnzpzBcRzW19fJ85zV1dVaGZRqZBiGdRKStAbIY2y323S73Votlx5CIQQnTpyox1tOlICaDMvzl+qo9KrKfUtyLLP7dV2v1UY5nlK5zPO8ViFlpQE5AZLj32q1cF2X0WhUe0tlEpJUQYFzMsl3d3c5cuRIPdbSwzmZTGrVVVZEEELUCiXMJ49yciJVRkl65fWXlgl5DeWxykmBVEOlDaLb7dbjp2na/Qp3w/1RKMMJe1tnyPOI3NSJ0pLVtRUwfKK4oEpnJOEM19YwHRffNQmzlKxUyNKSJCkZzsbMohRN17FNm0OXP4wrLz2IbwnSZEpVFTQbPku9HkLMb/LpeEyUJASzIcfu+DoHFIVGs4MoOTurnjDePUOeBFRlhSIUsjjia1+5gdN33U7DsVBUlXA2Y297l+Fgyt5wRrvhz32VaUFSzs3VlmHheT7jyZDNU2dYP7jB2voGvu8RxxFRkhNFKbMwpt1dod1bJitBKyv8To84mxHOUrpWm05vpTYUq7pOlCRoiopQoN3pM+4MqIocx7Npi3lZI8e12Dp9mmBnQBxlFIWCprtkWQGKCopBUWg0vQZREhJMY8wmaKaJIgyUSsF0PAxdZzbYwjENykShoAKlYuv0STxfR3dtlCyBQsM2dTRVRVNdKBI0U8fxXabDAMPwKPOUXMBwNGO1v0qlGlimRhxFhHECFeRJSKfXoChLLEPB0DWqXMV3HZIoQDt7MytCQVEgmAVsnjzJ+vo6rmUzHYyIwhlQ4jd8VlfXWVteoipTqrIizRIMQ8M0VIJgyiwO6S510TQdoWjYvo/XWCIuNXTDmpPEMETEKTuDCY4qsP0mcQgr3R6e69JfWSbLK06ePInvenTaTcbTGcFkwmQ0IphNUVQwDYNKCDRFIwhCUMDUNHTDYHPrFFmazsNAVUWepSgmDLdmBEGIrus0mj66qVHmGYpQ8LwGeVESpSm67WDYHig2QrHQhINQdYoERJFQZQFlkZHlCVlWkmkumje/n7MwxUFhb2eT4e4p1torRKlBOM1AMfEbKru7Q2zTQFcNUqGgazpBnhDHKaZp4LnqvJKA8Y1sv7IoyfICVdUBFd9vkKWCoswpipyiULAMjbIoUTUNw9CAikqIs+RSgJgX9UJRUAToGhieRZykoBoURYnnGlRFdr8eVgvcN4Ig4MyZM6RpWr/INjY2MAyjTlwJggDHcfB9v052kQpHHMc1QZKqxXd8x3dw5ZVX1uG5qqpq354M7Q2HQ6IoYjKZcPPNNwPQbrfrxIYkSdja2iKO49rzFscx119/PbfffnutsM1mMzY3N9nd3WVvb492u12rp9ITJsnRaDTi+PHjHDlyhEOHDtFsNgnDsA7XzmYzlpaWWFpaqs+v2+3WSppt2ywvL9fPaZnpLJMVer0e3W6XsizxPK8myZ7nceLECTY3N4miiKIoamIiX+KSkMoxaTabtWdNZvZKj51lWcRxXB/HyZMnaTabtS9REkJpZ5BJR1IplVnkVVUxGAxqa4MMjctrFsdxrVLL8K9MvJDliiQURalV50OHDtXZ9kEQAPMEmAMHDrC+vl4fo1RBDcOoQ9bSbyuVa6kqy7DwbDYjCAJ2dnbmz8uz/tF+v4/neaytrZHnOXfddVdNAsfjMaPRqA4jS7VRJknJxBnpEzx16hRJktDr9WofZVEUnD59ulYuZbkeqUZKVVlm7UsVEahVeulrlJnRcmKgqmpdLUCS5c3NTTY3N2m32/V2qqrSarXY2tqq1VxJWKWlQd7r8t4A6gmG/D4AtY1DTgjlGO/3SwL1GO0v6yX9kpJAykmfvDekun4huGBCGY62qfKIdrPBNM1p9VbxOmsI1UWnYDjaZjrYYanj0et4CKERDgrCMGcWJsRpyTTKsByf9Y0NDmwsc/GhZSw1pUyG2KaLrc9vDNf2CMKY8XjC5u4OvZUVVlaWiUXJZO8MelVg6DZpnpFGM7ZP3UGVxywtrRAnFcfvuosbv3wDwXCXyy86itdqoQgF13EphcJdJ7aoqpKGZ6OYBpWqUxYF8d4eimnSanepSoFlO1iuR5wXpIVgFiUMxlM0zaLZaCGqkjwJSOMZaxtdlpeX2BUF3d4BGo0+quOhqRpJkpFnJabXgKoCxcR2W9xx69cZ7+4wHmxRpvMkE8t1WdvwUIRGFGXMphGD4Zg0nd8kZZJQiALXNTEdkzgcUSU5jtegMkxU5spRd3WFZGaTVhVqOaXMMsoy5vSx42wcOoinq4giRyFBVAVlCapmUFKRU2C3HNAFwWyC47ewTY9M6BhCx1Q0ZrMRaRpTFBWmXTBTNCy3gY5PmWWk8QzdAENTCWYTTN04++CKCGYztrc2MTWNVrtNu+ERBRNmszGGPZ85j0djfEsjDmYMR2P63TaKpmGbJoZrMS/5aWDbDeJMQXcaVDFMw4JKUagwaPhtmq6DWqZkiY7r2ECJalpMw4SigE53Bcc2idOEKEnZGw6ZjOZe1l6vS6+3jON6ZGlBcfrMnNhaGpqmoNsauqrR7/ZxPJdwFoGi4jk+09lsrvRpgsl4j8HeDg3Xx9INNFUjDQOiaIbXaOI3OjQbLmESIDCIxmOKosLQdTRdQQN0ZOmIArfVRPcVkukOVVUSJwGarlBEBXEs0AyFsoQwSMj0jGbDRtM0olga0qEsQVHUWimxLJMgDAmChCwtMAwbBQPXcZnqEVVVgFIiRDVP6ikqHMOgKs9mYipzEiln05qiggqVKFBV0A2Fhu9QVnPPmGWpWF7zgh9UC1wYhsMhWZbVWcxLS0v0er36ZTQcDtnd3a2Xy6xpmQEuCafruhw6dIhDhw5x8cUX14ksMowuQ22z2YzBYMDm5iZra2usr6/XGdvSYyczn48dO1arVHEcc9ttt/GFL3yBwWDAFVdcQafTAcDzPIQQ3HHHHXWms6yTKBUt0zTp9XqUZVn73OSLVoY5JVGQql4cxxw8eJDV1VWEECwvL9d1GCXplWE++QL3fZ+vfvWrbG9vs7u7W4eUXdfl8OHDtYduMpnU2b6y3qMsUSSzoeM4rs9FhivX19dr/6EMWeZ5zu23385FF11Uk779SpwkHpLYa5pWhzmlj1J+ryeTSU3ibdtmb2+vJjvyuki1ar8qJus2njp1Cl3X6Xa7dZh2MpnU2cyDwQDHcZhOpwwGg5p0S8sAfCO7PE3TOgFmf61TmZwihKjD1fLemc1mFMXc6uO6bl0uaHd3l8FgUCfeLC0t1feA9JHatl0fi0zokdnOsk6lrG8qs7m3t7frElOSEIZhWGeOdzqdmnyPRqOanEtfI1D7Y+WYSeVURgHiOD7HNzk7+66Q/lTpzdx/HeW/UumX31VJFD3PYzwe1+qkJIty8gGcUyNWejTvrtQbhlHX15RjKBOTLgQXTCjHg23aTQ+30WSczXAbXTTTI01LkvGI7ZOnqeKQRqdPy/PY3B4yHs/Y3gkoMLAcj7W1ZZZX1un2euhaTpbGaHoKZUGWJWeN3RaO4zEcTjl+6hRxntHuL1MWJe1Wk6Kal7mpqoI0DJiOBox3N9nbPk0STGm0ltk6fYrsrA8hL+a18MIgOvvidMkywYnTu7TbPq1uF9XUqURFFsUIRcVwXFQUskJQlALL9ciLiqwoyIu5itpwHdJggqmoqCpYaotSlOiqgag0ykJDUwzCIEazPQzDpMhL4jDE1DT6yysMRwNuvfUW9na2abk6j3jEI/BshyTKUFWLIheMR2M2t3cZDMfs7Q7IiwxVVyirnCiKyWdj8lKl0e7gNtpoukmWzxMejFaTtqZgujbBcICeCihKtk9v4jcaaIZJVQXEcUFRaXNvnagoRcVSv4ehKJTjGSItMDWDyTRC1XU0jXmYWNVQqLD0eSZxnkRoikBQkRfp3HicxFimgWUac4uCoaJQYRgap06fYDKboNsOmtvg4EWXsLLaYX19g2A6ZTIdcOdtt9PyHRyvQZLEJEVOTkmr3cH12wjFRFN1csVgGgUEiUA3NNI4RqlSWg0HTeg0XIuqyoiiEFVRCMIUgYZjWQRhyGQyIg5mqJqK1/BZ7i/NC/AKjSRJMQyFoxdfjGkZqKrAcS2qqmQ2i0izkkp10V2TPC/R/Q5Nu0FLAde1KLKQU8fvospLfMebF3jXdTRzHmIuspDpXoLtNTB0BbUqKJSKokgok4wsz6iKivW1DcymQxxPSdOYI4ePUCLQdIMoCslThTK3qDBJsgzLced2g7LEcVyStERTIS7mpnfXs9F0nTTLMA0dVdVJ4owwynC8HNsysCwTXQdBha6pKMo8W7wqS1TFoarKuXVBmd8/mqrX4XJt7mpA11RElWNo4LkWpuVgGgqaIu77wbPA/cLOzg7tdptms0kcx7UyliQJ4/GYY8eOkSQJy8vLNBoNzpw5w3A4rDNpXdflwIEDbGxs1ORgf+KIVGFkKZy9vT3uuuuuOnxeFEWdsCOTDCTB29raYnNzk9lsRqfT4cSJE3XmtlRBZDKNrLl4/Phxer0evV6vVrWkiiiVIunLlGFXqdC0Wi183ycIgnOKhQN16Fx60WRyjyxJI5WrtbU19vb2+OpXv8qZM2fwfZ9HP/rRuK5bEwtZHP706dPs7u6yvb19Toha1jIsy5JOp0PzbJMBGRJtNpsoioLrunWyUlEUnDhxglarVZNKWSdS7lfWmVRVldFoVH+mJIYyvC19g7I80/66gvuziKVfVYaKYV7y6Pjx47Wi67oul112GRsbGxw6dKiux/jVr361Hm+pwFVVRbfbrc9P/kjVVCaGSWIsS/tIv6pcF+ZWjtlsxnA4rK9ns9lkdXWVAwcOABBFEZZlcfnll59jLyjLsi4VJMmtDM/Le0iS0TvuuKO+l6RqJwlZlmU14ZTfB6kYyySnoijqBgLSFnLJJZecozymaVorodLnKu8jGTGQdhFZo1TX9XoyI0mpzMCWvmeZHS8V4f0JYpKQShueJJLy/peKblVVdY1UGRqXk9ELwQUTSlUVNFptUmGSCYus1AjGU4okYnTmGOFol27DRldtjh/f4Y7jpxinOWmp0ur3WF09RLuzhO24iKoiCWPGVYTVMTEUlcE4QAiVteUO0SxmOpnhuw0uO3wIv9MmCiM0wyGMU2xNJy5z4iikSGK67SamIqjSjMHpEwR7W7imTlxklJVCnBZE6TyETFmBKgiijCjPKVWLRkNDVApVpWG5bfIsIYljlNGU/prArEDXFGxLw7IUXLcxD2PHEaWp4/ourqlAZTHSdJxGiySHbJahmiVt10UxNDTdxLUtvIaLocHDHnY5jgnBaIfpYJtut0+eZcySMWWVgmqS6TqttXWc3hJOt0scBuRxyHBnkySa4fpNVFUjjxNKPUQzC0SukSsCw3exfRdLr2i4OqdPb7G9tUOaFfR6Bb1+F0VV54XTS4Vc1UlzUBTBLAWTijQt0YwMR9eJwgjRalFhYnldRFUg1IQkFziy09BkDxBzo7jr4PpNqiI769cpEGXO0lIbUcUMhiN6K10cv4Nm+eRpgShVlpc2aLX7bO+YmJtnQIXxZMp0OkY1VPxuC9tv4DZ7lJVFmVYIRcdyLYrZiLLI5tYLzUcRFabhoJGhKCqmaRBFCcQFSRpRZDFZGjMZj2g2PNqNPrPpFNfxKQuFMArJ0pRKVOiGjolGFCe4nkcY5cSZiVBMStVFd3QUB3JNR+glWRoTzmLUUmD7K4hqXhAaUWI4LsbZUPhsOmY6HFGWKVlRYdoOAkiikCyJyJME27DZPRlj2TZlWRAEEzzDotXrQqGSjMbYjkVYQZKphLGFYVuY1lzfFKjYzjxJZxYGzMIQNwjpdNqIMgc0TMNG02ziJGM2y0EU2I6N5cwfnpWoQICqVKgaVFUO1bwZQFGWiEoBoZJn2XzdSkU3TMoypyrn++u0PSoxvw+KPL3gB9UCFwaZ0bm/UPJoNCJJEk6dOsVwOKyVkDvuuIPbb7+9JgDLy8scOHCAfr9fK0mynIj0Mg6HQ4QQbGxs1Nmrvu/zqEc9qs6YlkRBKjAyDN3r9WqV8cSJE+zs7NSeR0l0JJmSxy9fyrLMilRupOIVRRF7e3scPHiwJgCSGMmi4pJoNJvNOjQqCYlMYpCF3fdnqsvEj0c96lF1HcTd3d060UKSIfmCXl9fp9/v0+v1akVyc3Oz9rpJj5wkIzLs6ft+TVJ83+f48eOcOXOGJElYWVlheXm53lZaCGQCkSxUvj8jXGZnwzfU3v3JPEmSMBwO6+3k50t/nkxSWV1dpSxLdnd3WV1drccvTediwdraGt1ul83NTU6cOIGmaQyHQ8bjcV2gvNFonKMSA7XPUGZRy/GQSTJysiCJpyRrcRwzHA5ptVqsrq4yHo/rSYQkarJOpeyctJ/YSeIkO+DICUaSJIxGo5rYyvtLTrDk5GY8HrO3t1dPWuQ6MhFOKvjHjh2ra3HKJKJ+v19/F2XdSFm2SobUJaQHdjqd1j9ye6C2PyRJUntoZcKNVDWlz1gm9EhvrJzkAecUpJeTDbmutAfIJKQLxQUTyuXVZeK04q7NLczWGn6jxWw0JAvmP2UWIiqTySRka3tEUWm0ui18w6K7vMHy8ga6bpPECSoCQ9epipgsrqhIiaOUVqtLJeCGG25kbX2dluOy1F/C8n3SokSpIAsjtk4eY7C3TaPh43selq6xF6UMd3bIwhBHUzBVlXGSzZMX3CaublPmJa3miLIEYdlMkog0r3BzQZFmVKqKqltgOORxRpSUKJoOytxf47s2rq2jKgJV1dBUjSpPMTUXpSwZDUcYto3qmJSKiqroiKqkKhJUVVAIgeM0EapKnCXkRYamzTOiUVSGoxGGbuA3moRJxvbukDQHzXIoFfC6XTr9PkUc49oOd915G9t7Ezq9ZUx0wskUzXOwHRutVLEUE802KBWdSVjguDZeo0GyNyFNC4ajGaZhoJs6VVWSVzmqMBGaRVbMC8jnZUVcRKiGQ1WoaJqO4zbRDJUwnJGWCkkYYTk6lqsTpRGu42KYLmWlkJcFeZoSBPPyRq5jsLzcx3Isjlx0hLVDh3DcDju7U6ajKZPWGMtr0Wh3sRoNUHV2j98JSUjTL1AtlVa/i1B18kql1e6TDMbMpmNEVdL2dJJ0HobQDYsim1cFKMsUqpQsDimyElOFVBQgVMqiwPN9VldXMTUNx3YJZiEnTm/iuj5+wyfPE0AQZwmT6QzNcChLDc305x2CGl2EUCizHNNzidOIjBLH9YimAU6ziWWq5GmCKkp03SIMJyDAMB3aHY1KzCcvaZYSRSFFmmDrKqoCeRwy2tulqEps26MoSoaDKe3OvOHA5YrLypEeozKjQiVOYqw0xS8sqlIhLxRUVUNXKzzPZTILiNMMv6ioihJNU6lKQDHIcwijDMty8Js6fsNmOo0QZYlj2+hKSZ7GZKlAVCWlECiVgsLZjjhVgaop865TpkUwC9F1E8+zUDUQlYrQDDAWWd4PNTY2NkiSpFa3Wq0Ww+GwbtsmXyKj0YgzZ85QVRX9fh9N0+qQtcyclb5CqY7JuofSuH/ddddx8OBBXNetFU/p5QvDkLvuuqvu3CPJXRiGbG9v1zUiJVHyfR/XdevPk/5LmQQk1R+Z2GAYRk2eZNFnqazJYuBSkZF+MKnA7O7u1vUYgfPUHPmClmFwqfJJNWswGNTJIVEU1S0ELcuqC2SvrKzUNRhvueUWdnZ26Pf7AEwmkzp7VhJEWc9SlsGRJVxkzUnTNOv9S5VSqlr7C1JLEq/rej3msl5nGIZ1tq9MLJJZwHJs5SRhf/b75ZdfztGjR/F9n62tLYbDYZ2tvF+BlEp1q9XCNM2aCJdlSa/XY2dnh8lkQlmW9flJC4VU7CSpCcOwvmYyIUVud+DAgVppnEwmHD9+HM/zaLVa9X4kSZRESSqvUgmVRFp6Vz3Pq72ulmXV5Fe2/pREVdZalYRQEllZDkm2XZSF8GUN1F6vV7e8vOiii+oJkyyuL5N3pC9S0zQajUZdI1aq8JLww5wQBkFQl/FqtVqMRqOaEMsJh2xRuZ9oSmuAVOplaSj5/ZHfg/3+ywvBBT/RTcvhtmN3MppkXHLgUmxTY5LFhNMBSRLQ7XVoNDyKKgMTml4Hr7dGodq0lzbQHZ9wFlKVFbauoqkKaRSxl8RE013yLOGKKy7j+Ik78Ro2yyt9VG3uoTi1vU2UpHR7S7QaDaLZFKWsyOOEsCgo8pzJcI/Tx4/RbTZRKoW8qGj1ljGcJorpIjDIsgjXayCKjEwVEJaURUYYhKiooBvEaYZuKCRpimVb5GXF7mCIQUWWz2v3WYZJnKYkQYCS62SexXg8mocRe8tESYLp5piWS55n5KWJXmpQ6QihkCYpk/GA8e4Wg+0B0+Euo+1dPEun4bm0O218x2Zs6GRFhqpUmKZNJaAqK4Ru0FvfQLEMBoMhwSxkd3ebXruBgomhlVhaRRlPEIWOaxlklk7pWyiiixAqUZgSjzJUTZv34DZUDFXBdSw000BUJZqqYTkeeRBQJRlRVnD69Cn6K30sx8FrNJiMR6ROjGvbmLZGViSomkGcRvPWiGeLzU/Ge+gqqKpOFCVoukmz3SWOM8oqoN/vMRjOmM1GGA2PKJ33NV9eOcxsZ8Tm5hauU+F784e66XgomkYwnVIkISYZbd9herZsUlaYhEVKw/VRVAF5TBamjIbb6GiYpociVIq8otls0evNFdc8SSnKHMMyaGhtDNNBNQ0QEEXz7NYkzlD1EL/VwXBNSkVHaBUIjRKBrluYpSBVC3y/jeO0SZMYoUKeVRR5jKbojKMcy9BJZjnBYERRlpjW/PySqMTUDObKIeh6iW4YoCr4boMsL9nc3GKwN2B7a/dsXVGTyu6DaqNUUKQlVXH2QaIIDNMkjJKzDxWFIAhpt9pURTFvp8j8/sqzYj7x67WxTYNet4OqZJiWihAFeZmTZCmOrWBYNkUcUwGKqhCGAVVVYp6tZSazKm3bQVW12rRuGFr98F/goYNt29x2222MRqM6mSJNU8bjMXEcs7S0VJM1wzBYWlqaN5Q4W5zccZz6pS9fbFKBGY/HZFnGIx/5SO644w6azSZra2vz5MnplNOnTxNFUe1NlO3/ZOKKLLZ+xx13nBMWX1pawnXdOqS9v6c2UKt50ncnQ36yw4t8cW9vb9dKnCRpMrs3z/O6huF4PK67osiQoSSskoAKMa92MBgM2N7e5syZM7VX1HEcGo0G3W63JgIydCgVUBk6lD3Ed3Z2mM1mbG1t0ev1AOri19JPJ+tpNptzb7G0C8iC5TIrWYZyJVmSBcsluU7TlGPHjtWEsNls1v3GXdetSwXJEKysq1gUBYPBoA6LSpVZlg6qqoqVlRV2d3drUiyz9Tc2NtjZ2amzumXZGhnOlV7Ou5M6mdQis6ylEikLt9u2XWdZt9ttlpaW6hC9bAEqw9Iy6Un6gaVi2G63a4Ilr60s6C1JWqvVqsPNMqNcqppSdZ/NZrVCKScXYRjWoWapHMqJkud5dTepnZ0dTp8+XZM0SXDlOOyvGymTqWSYWrYdlTVIgTojXk6mpAoqt5ffLfm8lYqtVCRlVEB2ZpL3gRwnaQ+Q37ELxQUTyjvuOna208Bl+K7JbDIgnA3IkhDLMmi0GyRZRFmVOB0fzWrTWT0IRgvH75JkOboNtq5AEUFaklcZUTxhPBywfmCZIBohyOj2Wmimhu16nNzcJIhjdN1gNh4yG+7R63ZYXVpiOh0TTafYlk3DdfFcC8+12R1Ozmb/tolzBZFrVIpGrhQomo5rqJR5jKbMFTfLslCEyiyJKbOYbq+DZVt4jQZFoXDq9A6uoVBmIcqcepLHMaaqoFIyHg6Ishij0cLxHTTLQNU1KlGRF3OTdVEpVEJgmDmWY+E3WiiVYKm3TDDe46TjkczGUOWE0yllniGyCA2Bho6lWRRoFIpCiiBMUnJVp9nr0mg2mNoaIkuZTkcUuUWLAsXQELHA67ZpN2w0RaCgoqg9trZGRHFKUSmEUUKjYWOZFqooyZOSsixQTQPPcqiygjxOUIDB7s68LzTMyy1pBqajolsWil5h6gqGaRFGMbppkSYl2zu7TCdDLjp6kGanhyhLXNehEhp7O0MOHGyiayrtls9kuofRcLBbSyR5hV4ZtFqrBK09mn5OIQI2tzYZBrCxdpSGbWCpJXEyJS1nuKrJmeEWSrOHavtYBliqICtyDEPQ77cw0EhTlbRQaPs+lm3O+4YjSPKMLEsoiooSDd0xiPOKMMzQNAdFqzAdi6QQmCWoikqlKMRZSqvRolIgy2LKIoOqIAoD8rIgzRI0FfIspcpzbFOn2e7RbjbR12G4vcfO3h6j0RjbtrCbNlEwZToOcEwVz7NwHBvXcTF1i8lkWveVzdKcY3feyuqRAzTWGyhlhshytBJM3ZqT4STB03Qc2yaO55UW0jwnjhNsU0NVNDQNqnJe5mWuZJVomoLvOUALz7VQFYFQVSzLBlSqqqSiwrEddNMkDiMU9RvGdJnt7jg2aZrXDzhddy74IbXAhePWW29lMBhw0UUX1ZnQ8mVuWRbdbrdWJ2SxY5kFLlWj/V5CGUaURdCPHDlSEztZCkdmPcue0FLBWl5erkOT0oMnM8t932d3d7cOZcvC6BIydC1f8DLjfH+IcWlpqQ5NS8+hJNBATQrlC393d7dOjGk0GrXvTBJWqa4KIerEG0l+lpeXGY1G3HLLLXWYUdY93F9jUipqMnQoSZQk8o4zr+M7Go3qCZUkyKZp0mq16uQOVVU5c+ZMXWdRhs5l5xcZApdqmFSShRBsb2+f00FGtuCzbbsuESSJi2VZRFHEmTNnGI1GXHrppbUKLUPmW1tbHDlypCa2o9HonL7ZQgi63S6dTqcudXPy5Emm02mtYsvELqmwDgaDWhGUCp+s8SjVTUk6peosxyzLspqICSFwHKfuYy7PT7YHlWQf5sqlHOP9RE76XKV/UaqC8jsj+5hvbW2xvb3N3t5efe/JxDTZH9113XqCJJPkZIedW265hYsuuoiNjY36vpOVEqQ/WKqv8vskE9GkL3R/4XOpPkpFEzin+44kvnJ9qczLGpRy3KbTaV1Uf3+2+v7M/wvBBRPKLM1ZX1tj4+A6ozBhb29AnszwHBPTMsjKgqjI6PS6aJZHLlzsZg/UNgUGqq7S77tUWUA4HJCGU2bTIWU0wjI0XNdi88xJev1ligIGoxENodFsd1g/eJAwCPnKl2+g5Tdoug7jNCWKA7bObPLwh19BHExpeS6+63LsxBnyQqHlttDdJortQQmqKbAcD62MGO2MMSyXXm+ZMi3J84LJdIpqqBxw11BV5uHKNGUwnkLTw0LHNj2iMESU844zYZRQ+Q6lBo2l5Xp2UlUliiLDKSVCFJiWd7aY67zOpt9oYyoKtmlSZRm7WycpwillPCOeTkijADQDzdAQeYznNsEysE2DJHMJQhO1zCmiAEfvk8cRO2dOsxNMKKqU/nIPQ1XJkxjLsnEcizQtqYRCs+lQVhUiKSnynKo0MXX9bI1IQZalJGWBa5qoqn62wHoKCgwHe/QMnUJAXpTMZiFOIdCNEoV83vJPNcnzjMFoQoXC8soaAo28mIdBUQ2yvMIwLFRFIy8KkjRBMTSSdIYl2hSVgm15LK0dooinVNUulqZSWCXoLqJSiKMQUxPMkoBjp05g6g6e06TIJ1hGiZ7My9mIZEpVRPiug2s1yHKVUsxwHA8h5sX3PW+eLRonEaqq0emtEEYFhuWyvNZhZ2ebOBU4jothmjRaPXTLIysryjJHIDB0QZlHZFlImgYU+dkWWmlKLip0ReAaGrapYTUaNLy5NWAwDvCXVsBt4vsepqGTZwmiyEhmE/JkShgH5CKl13Zpdtr0l/rs7e7hOg6tlodSxZTpFN1sIUSBKBJUIciLkpwCJU7xTBfHdQmTFFHNQx+26aGoKqoisO35pKyqcjTO9uz2PIoiAFHW2ee6ZZOmGWkyrw2HqjCZjM++jM2z9//ZkOPZbEPgbIa3haoq9Yt+gYcOsoXi0aNHmc1mbG9v17UJpTKVpilLS0s1QdtfT0/XdVZWVsiyjMFgQBiGjEaj2ofoeR4nT56sO7Ts7e3Vniv5mdddd13dulFmjZ88eZLv/M7vZDab1Vm9d955Z50AIb1qMrQo/WmnT5/GsiyWl5frAuKTyQRd1zly5EitBEk1Ub7493szZbhXqmYrKys1gZEK3/5uPjJTWhZ6b7Va9ctXKk5SDZSldKQPTSZWSNVIkhzZWUV2zTl16lTduWdtba0mW7JLjlT+JFmWoXeprEk/oEzUkWRDTjIVRWFnZ+ccv6bs1CLJi/x+yqQiIQTr6+sAtRomFV/5XpOlpSQJlhnBMplLHo+0H8jPkkqe7IRkGEat4JmmWSti8jz3E0gZkt6vXu8vb7O0tFQTrgMHDrC5uVm3eLQsq+5+s79PtRwrWfRdqtpSjZNqs7z+MvN/OBzWExmZDS4z86UqKtXcbrdb2x+2trbqVpv77y2gnpDIRCZplZD3tSSOsnSRvBelii+JnyzxI6+3nITJ6gWmaZ7jg5ZjIu8D5W7P6f3q74Xigp/oChW9bgfPcxhMB6RphOdYqKKgFCVVIVjdOIzlNTDcFpXawHA7hLFKEieYeoVSVZhqySwLCMY7pMEESxMcOngI1zVBAct2yMOMRrvH6voGqq6RZSnDwS55GrN28UXE0QxNmXvfojBEVRU0Khq+h6YqJElGVpnolotQTfJyPlCFgBJ1XsZHqHTbfdBsDAvyIiSKYrymA6JEEYKiKLnr+EmyAnTThQIM06LIMuIqZhaMcWyDfr+D4booVOiqgiKqef0/TaHhu5iWSYVKo+GDYpIXJYploggFVZtL6oqm019aJtJhLxxTFCnBeAiaxmw8oigVTMtHM20sv4HpuTiWjqnoTOOATAi8RpPVAxrBZIyqCsaTeR9lYxbi+w0azSaO66BpBaLTACEYVTPiJCVNNEJdo9luYpk6VPN2lgqcnclraJnAtC1GoyHdlVVMy6ZUCpI8IM4CGg0dlQKIiGYJk9EAQ1NYXl3D1FVEVVApGlFWEgx20TSd/lIfw5r7eKqyoKpSpuM92ktrOG4DVdOxjBaa47F54jbcBnitLobVpdVsIdKYeDZgNpswGU9J0inLSwqWBUk4YHq6pKwESRayvNZnqdNGVXR0Mc/gjJMEw3BQlPnLGEWh3e4TRTFleTbZJC8RVY5peays+bRa8zahSZJTFaCiY1kGo8Euul4RBWMUCoTIKTNBWYKtG5imRpllmELF1zVUkUEGQZyjWRqddou2amDZ83IsFDmthsdwd4tgYJCjMZ5NyKsJ3aZP72x9vXA2xbNNlLIgD6cU5RhTb2CqOWqVE6XzIvNFpWEoFr7nk5clihaRZTllNVeuy6rA0HUsSyXPBapaMptNcOw2uqbNr08JChqWZQAlVVmSVgWqahBGCYauo6o5lmWfnXmbICDPC3TdqNWBRcj7W4elpaVanZRkEr5R0unQoUO10rWfkEmiIBUPmbwha1IePXoUz/NqwibrOh48eLAOV8rSOuvr6zXRkiE1+dJqNpu1d1KqQPtbFcrjlMpct9utFUtZFkgqhzAnP7fffntNguS/skj2dDqt+0lLoiozX6Vi12w26xe8vEfly1WuP5lMUFWV1dXVelzyPGc0GtWZ1jLRRNYPlD5FSVb2l/oZj8fnvOBN06TZbNJut+vMXjkWg8Gg9tsZhlG3RJR/lwRI1qq0LIvBYMD6+vo5xEUmtQB1+RlZYmljY6MOo0v/3c7ODpqmsbq6Wrfkkz+DwYCVlZWapJqmieM43HHHHXUyjmzjKRNIJpMJw+GQJElYW1vDsizCMOTUqVO1L1FWGJBlbGQfd0mIpJ9XJj9JoijD2bZtc+DAgbr/vAyPS4VOEm1ZUWD/fSdJsCSykkzJ+86yLNrtdk3qpErdbrfZ2tqqJ1jS7tFut1ldXa0Lr0tiLEmn/C7sT4DbrzjKkLyMKsjjlYRPJmfJUk77KwAA9aRdjq0shSTvEzmp3H+euq7XEzNJ9i8UF0woL7noMN2ldc4M9hgNh+iGhmZoqFVFUVS0u0s022uUmoXl9SkUl7TUCLOEIk8wqAgnCZ5RkAQDZuMtbA3ardY8+7QqWVlZY2dvQlooHL18CdudFxmP45DBYAfPs+l0Wxw/McG35w82yzZJ04hGy0NXFChUhDLvBV2UFbZlkecCUZYUacpwMmU2meJ2Vlg+cBGe2yQPIoaDEVEcceDw6txbUoTs7A1Ic8HRIxdhaJAmIRoapmmhGyqqDr2lDgcOrpMWJVEJnuVgqvN+xrqh4bgWJQUK2tmQiIGm6ZRFyWQ8YDLYYTzeY3WpP5/9pelczV3qoVKSpAnTSUg4CYnKMWGcodgWnZU+fruF0A0MXaPQdRRVp9VZotHqEoUBQTglTkJG0xmDUUy7k+JYJpZl0mraGBrolAxG82LW42mIYZmAjirmSpR6thWfqWtkqoKqzMNIYRzh6AaW49Nd0plMxpiOhSgihKKRZhnj6Yyjhw+gm3MCUeQZWSmIgpCyFFi2jWE4VCUUZYpChUbFaGcLv73C+sVrpHlJUSr019cZTk5TVDM0w0dUYJo6aQqnzpxidzhm+dDFZLlBVSkUVUA0HiKEiqZr86QQx2U8GON5CpbVQDd1skwQBjN29/bI8gJFVRFUjMdTLCdG1QwsR6DZGpZpn31BWMRJiCoEiIwqzUnTErWImc5GFHkCVBiGju+6lElKlkwxsDAUsAwTQ62YTSYonkcWJZTZXNlTdQ2YJ7oIRaNQNEyvRcew8Rs9lJ1NiiQkqaDh+7R7bbI8IowC8iRBtVNyMcZvL2FpGXk2IwhzTu9N6fX7uKs2hm6gayZFMW+RqKJSlCUKAtcz6XYaDIcJilKBKKjKAtu0iOIcBYWsAss0SJIQTTNwHHvuYTLmLzzdMFDPhmaqsiRNs7rWnyQA8zJhOWdtPws8RLj88stZXV2tw3L7vWV5ntdZyPKFJYmTbDEnE3ZkOFS2wJMt6aSKJUvjXHnllXiex2g0IgxDdnZ28H2ffr/PnXfeWXuyHMchSZL6ZSxfjpKkSLIoSxONRqPa63j48OG6/M/u7i5RFNU1GqfTaZ0Uc+mll9YqGMw9bbLG4srKCkeOHKlrPcoEIOkTky96oH6RSo+dDOHv7e3VhbYl+V5dXa2LXMvQvizyblkWq6urNfnb76/r9Xq0223CMGQ6ndZK8GAwoNfr1aFJmZEvIcPl0icnVWVJKuX+JfGSpY1c12VlZYXhcFh7TmXYdzQacckll9ShZxnGl8qqJMjSZynHaHt7m16vx2WXXVZPDg8cOMDwbIe5/Yrz/sz+I0eO1Ne6qirG43F9HvJa7O7u1iql3H7/tZYkTCYQyRC3zJj2fb/u7w6cU/JKtmmUZExm+MsEFqnYSRVbZoBLVXp/EfL9Bcd9369tC1IlLcsS3/dZWlqq1Wq5D2k7kT5F6bFdXl4+596VdUj3T3J836fX67G7u1uPhRxzWXJJTpikB9fzvPoaS7V8fwhdJgfJ+21/sfbl5eULev5cMKFcWeqzubvFLbcdQ3O7aKZFUVXowCSYkQodq7lGf/0AGA2KUqWipN330MqUZHCGIpoQlAGWknPpkQOUeYJt2ZimRZYmTCcxUZyzdvBiWq0eUZIRRQlZFqPpCpdcdjGarmJaJn6rSTCd0uv3KKsSRWXepAMwTQu32cX3mjQbTbKsYDqMmY2H7A6GOL7PyqFLaPZWsXULzW5w7I7bUBXwXAfPcwijmNOnt1laPYTluJRphK6bVGnIcDQkL1Nsz8Jv+vMafYaGa5pMRgN6pkcWxziOS1mVlJQYlj0vcp4maLqBY+m4ls5mMObk8bvY2z6DKgpsDRQhKLOUssjxTBPdB08ziZMMpcgRSsnuibvYOlXRbLZZ6q3Nk3YqZV4YXNex3BZOs0MURqRZRpFl5FmEKGJMU8e0DTy7gaFUiKpie5yRVzCdBpSFjm3qVJVgMB7j2g6uZZHG4bwWZ5owGg7xWl2yPEc1TFqdHrYpKLKKIp9nVjeazboOYrfbosjnZRKKCjqdPpqiMxpN5yZnR0PXBIoiIM/Z3TrFgUsfjm6omLZJq7nOYHyI0WiLRmsJUVSIszUzs0pgNVpgN2n3N1B0myyZoPijuV/TUFFFjKpBkSbMhntshmdQdJukKJhOZwxGYzTdRNMNGs02nc4ytjsPbdu2S1nODdS6phGGAZoKpqkQToeoVUG71aQsDRp+h6KYh9Adx0NXFYSZULkOVVEyGQ0Zj0b4rkt+to9tFMZEWU4ehVh+h1KzsP0WuuMQBjGm6aGbDSJsvHZJlYVYWkWczMhFwcraMlkckcYJrb4OhkCpAoSYIaomeSEYDUPiuEIXgtW1ZQxTluuYt1eMo5im51EK6C81cV0NTQVdV0GA5/rziYBQEQiiuOT220+AChvrKxw8uE5/eYWtM2fQlHkdNONsiaZ5/TqPqhJ1CEfX778/Z4H7xtraGpubm9x0003nJG4AtWrSarXqZBHpIVteXqaqKvb29gjDsFYUL7300rNJVXb9Ypch8KNHj9LtduuaeGmaous6V155Za2gdDodxuMxy8vL54SXYe6zbTabtZolWxWORiN2dnZoNBocPXqUpaWlmmjccsstdekXSTKPHz/OxsZGHSKVCtNgMKhD6u12G6AmLZJsy5eo9JjJMF+SJLXPUCpRd9xxR12vc7/3UiZpyOxmGfZVVZVjx45x7NgxOp0Oy8vLdThdqkm+79NsNuvxkzUKZYb7/mxpIQR7e3uUZcl4PD4ns3w0GtWESiZByQzxTqdTt8Ts9/t1KF6SQElaZQFxmT0srQyygLlUX2UR76IoOHXqFFdccUVNmFutFoPBoP7c/cRRZmlLtVgmJMn2gTIpSypy+3twyxaXe3t7tUey0+nUHXXkdZLXUXZdkiHbyWRS1+2UXYzkREr6OyXRLoqi7gXv+35d3icIgrqwusyElmHvIAjqckVA7VU2DKP2jR48eLDui768vFxbLuT4SAuJVGA3Njbq751cTxZZF0LU6rBUcmFONGWLSKmE3nzzzSiKUvdEX1tb48SJE/V3UVZLcF23Lqkkvbiapt0va9IFr7mzs8NNX72ZOFdpeX0QCqAQZzlJLtAVE7vZp9I8dLOJb5gQBwhKbBVykVAVIZ6tYtpdNHLKYt7XN4hiJuMxUZrQ6vRZWlqa992M5j2/VbXE81x0y2A4HWF7Nl7LI80TVltrVEXGOJygVAquNQ8nmJqKb+t4OsSTCcPd06RpQH9tlSOXPBy31SbLKtSyQMQRaRrR6fi0mhaKKFFEhe/MyeVgNECkMX1Px3LtefcSw6bV9mk2GwTBPGRdKikqKqpaoevK2XNLcD2XPC9J1BBdt7F0kyScEUwnVGWG4zp0Ox0OHzpAmYScvONmtga77O7sYGsaagWO5eA5JobexnRskjIlSOJ5mabTJ1A1B9P28QqBblq4vo/TWkIxM3zmNSLTZEI4GzCezVAo8dttep0GaVYxTcfk0dkWUgkoAjRzPkPS9PnkwHYs0jCgyFIG2zt0l9ZBd5glCZquYxoGnttgdjYM4HgNFM1CVQVxnOPaDnkWkKQlqm4RBiGz2YQ4DlhZ7WD6NqPBgCLLGW6eZnDmBO3Vwwg0bL9Hf+0waSloNftooiScjBkN9gjCmEZvGbOxBGabQrHQWg1U1UOvMjSRQFqxt7fDdLhLNA3n/edbXforaxiOj9teore0SpEVCGVeAgd13o/aMHQcxyAKQ5JkRpbGOLZBXuZU2RjH1rB0G9U2QHXQdIvxOEBRdMoiQ9FN/FaLIstI4wTDMFEUDdvTiaOINE8Jz4afGp2ctNRwGhGNVhdbtzBR552M0PHcFrlmkMQTikqn0ny8tk+rU5IlMZqm4lgqmpKSRCOcRg/T9HE8lzjO2B1OMWybpZW5pzbLYjRNochzyqqgEvOQR7vTII0TfH9e8Nf3zpZhUSCOU7JcYWc3YDKbEUQZlQIHDswnNoZhkGfFfHLANwznMgwzJxvmIuT9LcCZM2f40pe+VGfOyhfLfgIhQ67SEymLR0uiVxRFnVggf5dZv8PhkDiO6ff7rKys1GFTGfb1fR/LsuqCzK1Wi/Rse1Kp9lVVVb/EpbJkmibj8bj2fG5sbPCwhz2sJpoyZCdb6O0Pecskn729vbrHsgwZ373Li5zEyHC/DP0FQVCXPZLHZRgGQRDUHUg8z6Pf73PxxRcTxzG33HILx48f5/Tp07VXUXrfZNa2TJQZjUacOHGiTnyQZFCSaRnOTc9OMieTSd1hRRZ2l3U3ZUauLPmyvzezrJ04nU7JsowzZ87U7Q+lqrrfvyj9inIcoijCdV2yLKsL2s9ms7qV4sbGBs1mk52dnXqdU6dOsb6+jhCCRqPBgQMHzilwPx6P2d3dZTab0e/364QomfG9vxNMmqZsb28zGAzqMjbtdpv19fU6fL66ulrfy/s7vxiGcU5XIumjlNnOksRLC4JMIJM+Umkl2N+NSd7TslrFZDKps64lQe50OrXaLScS+3uDyySkbrdbt/6UZajkvSeTk6Q3dHd3F9u260x96TGVvbplFYFer1cT3CRJ6qiDVKjzPGdra6u+fgBHjhypiapUafcnBklSLgny/XlOXzChPHZykzjJMewmRVpQZQJhaFSKgdNeZuPiK/F6qwSxQFczXF1DEwVVHjCb7ZDHY6oqpSh0NE2lKECgEoUhx04cR1Wg1+2wsrrCUq/DcDRGFRmGMb9ZNF1DKJBVFZ1OhyAOKSlRNJOiEBRiXmcvSBMUBaaDLVaWesy2A4LxmDQc4fo2By5+ON3VI6iqSR6lZLMJ02CMquYs9T1Wl1pkSUUcBPiei+ealOW8bE+Rh6ThhEbDR9c1mk2PIivY3dvB9jymcUJv/RDNpoNuzsMghtWgLAxUvSLPA1xHhaLkjptvJUtnWJbFpZddTrPdot/rMRsNWDt0EaqmkiUJwXCPcDJGVaY4lo3j2iTZDMe1cewGpqaiqA6DUchouMvWzjZ+p0N3aZVme5VCceaJNq6B2XCwGi57OyfZHIyJ4pSu26LdanJQ0dnaG5HE2dls3IzKdWg0bJIiJQynNP0Gnuswnk4pS5XhmS3Wjl5GYZso+v+fvT/7kSQ7s3vR3942jz7GkBGZWZlVRbKaZLOloz6AJOBe6P+HoG6x2RSLrCGnmDx8tnm+Dx6fdaTuw0kBfd7KgEJWZcXgbmZue+31rcGirgvcoSc/Hmmr+tS5brgoA9q2ZnX/SJploE2SLCdLMmzboesH9vuEsshoioI8SdCWw90Pf2E6XTD4DoPpsLx6zWq95XgsmAcOZXLk5v1PGJiE4TmddQpd14Z7urPNjDpLaKo9u/t3JJsH+qbh1as3vP31d0zOz8F0aTvNdpfQ9YpO5WTJgcBz6YaWpm7wvJ6uK9HUFPmBskjpK03fltA3xOGczeYR0/FRhoXrBtj2aRdvBwGGCsmThK7tODu7IM8SNusN8XRC5LoYrkdn2iRpTq+g7yre//A9k+kM1/UxTZs4nhIEMSoMSVAoNM70nDTZ0eke11F0w568qlEqY7BsFBYDHVHsMbCi7Ts2+xwvKpgsarTR4wcWhtGjlKYoS7SlcV2Hrj09BNu2o2lqur7F9S12uw15WTMMMU1rsz90BJOapMip25K6OYHPLH2qg2w7Bnt4Ajb9E7Mj+Wdf/Jz65fjC4+eff6YoClzXHUGk6KNmsxm/+c1vWC6XIwsi9Xh1XZMkyajtkgVWtGdJkvDjjz8CJ43m1dUV5+fnbDYbgM96g4GR7crzHPg3E4QAB/n9Epp9d3c3MlJhGPLdd99xdXU1VtFJjqZSanSPyxhUtIr/ewyKBJNPp9NxYRVW8+XLlyOQy/N8/D5hxwT0/elPfxo7qn//+9+PTON2u+Xrr78e2UxhVkVbJyNUCZwWwCbs3d3d3QiOnldj+r4/utBvb295eHggy7Kx9g8YO9ElTDuKotEpL+AkDMMRCN/c3PDtt9+O1+G57k7AvkREtW3L7e3t2IedpimHw2EcX8s4X7SplmXx/fffjxFKEpX08PDAfr8njmOSJOGHH34YAaTcjwLC5JxXVTW2DbVty9u3b/nd7343AmLRbcrY93g8jo02ImMQNl4qE8UlLnrF1Wo1jqxFViCMt0QEiVEqSRJWqxXz+RzXdUf2Wu7Dtm35y1/+wnw+HwPap9PpeD3kWCwWHA6HEUSKyeY5iBMDlgC6zWZDFEVjRqyMokV7/NzFLk1KwmwHQTBOGuR17vd7ptPpCDLF3CQs7nO9sIBJAcL/r8QGJUVJNJuB9hm0iTZsLNemHTri6ZJouuSQFlStJrIdqvxAWx6hTsh2K/qmoKkraFuWL6/Zrk+ZVUl2QJsWi/mU86cInK6pCTyHzXrFbDEhzaGuoWsHHMfDd33S5ojve3Rti+LEeoRexH6X0nU1rmPSFDuU6WAZPecXSybLS+LzFwxY9G3P0JSU2Z66SlBDS+jH+J5LmSX4ro83XXC2XLI77E5u6rKh61p82zq1/eQlWp06v+umJS9KLk0LQytU31LXOa4dQTugLUXf1Wwfb2krhe/avDg/mZHaoaduGh4eHrDoCaKIaDLh/MU1Foq6qMjSlOMxJQwDFAOLxRzXcVjEU5pOoTCJYrh7XPPp40+kRcZVb+K4C0zbxnI8BlqswOLydUCy3dDkKatjySQKOL+YEkQuu33O/d2aNElR9AyqQ6ueHqjakwOyfdwBOel+T1fXaOMkfNZ9ga8M6qJAcYqRcPyYoa1JN/d0bUcURwxPO1HUKWKoa2uyoqFtDTQGjhOS5ylNmvLXP/0L3/3f/1+KuiaeLji7uGJz8w58i7bKKbOEqze/okeTFzXKbFAd0HRURUqd7tDlBtvqubw8Jwojzi+uCeIIy3E4pAW2EzKZzsnykqoqmU4jhq7BHDSGa9G31UkzSYdjDrSqQfegho6ma0iOKYckI4h6MDRt0DGNY9TQ0tc1huVQlSXH3R5DnxbIuulJ8grH86gGC8tfMPXmJ+Oa3dP3LXWdcdiexouTyQLXDVlcXOJHU2z/5PS0XIuqSCnKlMvrrxj6jrYFSzvkVUtV1Pj+gO/aNF1PVQ3sjylN02JZBoYCxUl/1Q89lmEDp38/dRJ3OLZJ158Y6m5Ts92uOD+bAxbDYHJMCw7JEW28JH4aX5mmhWWZNHX7NHaySNPqaQHRTzqoX0be/96HBI8/r1iTMaswJJLLKHorcYFKN3JZluOCvlqt+PDhwxgrIsykgDTf93l4eODs7Gwc28oIWBY8aTMRjV8YhiMwkMgaARcCVC8vLwHR29aj9gwYAaSM6WazGRcXF2Oos7x+GQfLwiojccl9FGNOXdfjuNSyLLqu4+HhYazFu7q6+gy43N7ejuai2WzG9fU1wAh8JaMRGMfckiOolBoNHD///PPIYIkj2fO88T1+8803IzAQM83V1dWYK3lzc8PhcBiZZWGamqbB8zweHh4A2O12YwxMURRIZaCA/eVySRiGYwi3mExEGwr/tmHIsuwzg0uapqRpyj/90z/xX//rf6Usy/GcfPjwYQRPWZaNoPZ5i5K8juPxODKi19fX43udTCY4jsPxeBwlFHKfCUv43JwlkVbPr6/8P9G4ims/iqIRpMvGS9ICJNBcQLpENUkigTT7iDZTRuICLl+8eDFmW4qJTVp/Xr9+PYbJy4ZEpBdyH4q0RACjsI7AaNqRfxfALCBQgPV6vebq6mq8L47HI/v9HsMwRre5BJcL4y1RXfLskL//0uPLqxdtG9Ny6bBB2UwWZyhDccwSprMFlmFSVw0GBn2d0Q0NXXlEtRlFkZ2aOyyTcL4gbzo+3t3zuFozm0dcXl4wm4ZYWmGZJvv9jv3+QJalhLGP5540LJcXL/DcgDLLKNICy9RkSYJlGdiGSdvU2HrANgf80AZV4fgB2C6hM8UJzylzULqizo88fPyJMttg6xbfd5jEC/a7gsd1guNHJ51oe2JTmjKnaTtMw6Kqa/Is4eL8DN/3GHKLMsu4vHyN78es7h7pFaeKxq7n/MUr8qrg4fGOLEt4dfWGKArpuob9NkMZGtM06IqctimJAodpFJL5Ll0Uoy+vTrtfFHVVU5cV2bFhWyZYrsGgFY4Xc7a4wA9DJtOIXZKzvvuIaR2Il+d0aoLvhziuR9vWTM7mGKrl5v0PHJqCl7OIOAqJowpLw+P6xC4lyYGmbbFtg74D1/aZTGYn53Se0VYF/synbRtQYGoYhha6lqFtsEyDQ1qy3myY+CaOY1DXDWro8TyXthsoqxMw8z0X33VpmgRlOXQDlGXFZr3hMjij62F5fkGb71Cq4pjsCGOf2WJKa0c0KRiORdt1JMct+WGDbgtMepShmD0FMQ8oHh8+sqTDVBa0Bao38cwB5Zs0dc1ut8G0TKqyZpMeSQ47bEujhpahb/BcG9uyKLKau7s1bQ+sDpRVyWwSs5zHXJydobTC8wPiwKOvKh7uV2jTYjY/oxk6lOEShhFaO5iGQVWkZNkBP/BxXBOUjV+a5HnGYb1nn225uHpJOIlBgee6WIZJgUldN0RBRKcUbd0TT6d0dUdXtUSeT5KkWIZBmRfs9wcuzqcoBvqe071V1wxKYT21cqgB6qpEq1NfsmEqPO/UtGE7Fp5v4/keVZWiMOnaEwt5YqEGTNMYd9zCMIiI/bTL/vI4il+OLztEdyfnWRiOJElYLBbj6BMY6/oEgAnzZNv2OGJ9//49d3d3LJdLrq+vR8e1ZVlst9vR7TyZTPA8j91ux/X19Th6lN7lJEk+c9DKyFGYHN/3R91lHMdjJEyWZbx79278OUEQMJvN2Gw2rFarz4LFZXF+zjSmacqLFy9GZjJJEl6+fEkYhtzc3ACM8SuvXr2iLMuxb/yrr74aY3vW6/WoVXsebTOdTgnDcNSlSiC3gITD4cDDw8OorQuCgLOzsxHMHA4Hbm5uRq3kMAyjoaRpmtH089NPP1GW5dhMI6yimKPEbfzc5DSbzcb8yqqqmM1mIxiUcbkYbSScXkxVwnBLZI8AddkgSF6hjPqLohh1r33fc3l5OQL5/X7PZDIZtbCSSSogT5hUYNRGvnr1CjhJOMRwJHpfMdHUdc12ux2vyfF4HNtx5L2JnCLLMm5ubui6jru7u7EKdLlc8uLFizHtQKKubm5uME2Ts7OzMeQ/juNRDiEg+HkfeBRFZFk2jphfv349AnMxDsnnTu77uq7HMftz1lB+hzj1gdF9L4HrEmskP1PJc/rJhCXykyAIRsYcGM+jTAuej7plbC/PD9nsfenxxV/pBRGDNvDdCVVj0vY92tD4T+JoQykcU2OYFmV+pG5y6uKAQUXfNVi2y3Qxx3Bs/vrjX0mLiqvXXxG4FkEQ4rk+TVVSlCUPqzUouL6+Qpuaoirpux5bW4Suz379SPoEOJeLOZ5z2tVlxwTPtnEMDX1N25X0tHjhhLTUHFcHmi5DK9jvbrh5/7/wnZ7lPGQ6iZhEUzabI8esZh7M6QbY7XenHMini99WFZY6MW2u74M2qNuByeyMt1//ig4DPWjaMueYZty8f0+ZJZieR5mk+H7IMEBPiwZ22y2vX79EK8iPJWV2oK9A0eE4Jp5vkyUK27YAxalpp6EsG+J4iunok5u7yLGShCiOaUMf2zLYHgo2qw9UXU3Y1PRLC8Oe0Wmfwezoh4rw7A1Fck9a1ph6wHNNLi9m1HVGlhbkBdR1i9YGNR1FWWHZFtQtZZ5w3D7iTSLoK0w9YGhF6DkYDBz3W4r61L7Qdx22F2I7Gsex8QMDy/bJshLTDqjaU8Za0SoGHDzb4XBMeXX5muR4ZF6f2ll6pVitH9l+/BttfhzbYwxL0/UlfZOg+gFHVTiRzVA7qLZhMZngmJo8O6IMTRS4OKqnrE+Vgv2gKItTp/fm8Z5PH97T9R2m5WIapygo7do0ZYnn2UzCOdvdnuMxpe9PtYld20EPdB3H7Z6+KNB6eNLURGg0xtCQpznQY3k+Q9eiTQvXcWi7FttxQEfk+YG2bbFsk+XyDMfxqOuedx8+8uH9j7x4ecXZcknf5CfnfuSTHVJ2dcP5ixc0w0BTFvS6wFA9y1lM3ZlkecN+v2G/23F5NsO2bEytGYaTNvKQHJ8enuEpR1Vz0lb2JgYGoDFNi65r0EaP7Wi07ROGE+q6pykrLNPANP+t9k523M81lP3QcjpZvxz/nodkLQogeR5BIhV5YqAQXZgYpYTVk8Dy77//fjTfyAIlGYl5nnN3dwfA69evR6AqYE6YS6l9FKZOAIbo/oSpEf2d9F/L4vn4+MhPP/2E67osl0tmsxmTyWTU5F1eXtL3/ViHKABZQKtE8Eh80WKx4De/+c1n2tIsy/jxxx/HXmUZpcrXyOv4+uuvx/ggYcmAMdBaepvh30LVJUhbwqQFiEynU+I4HsOvJZPyOVgQ84sEq0tAvYxOr66uRudwnucjCwmMTmIB1ev1euypljGzsGfCYK5Wq1E/K2BFQIlE5kgWpmQnOo7Dfr/n6uqK/X4/5jrCaTT//v37UY8o4EWeBwJaptPp2FQkrmcZxYZhOG4UntcVbrdb7u/v+emnn8a4H9GQit5WWofW6zX7/X6MWJIwcRkti7P7efKByDyAkTUU/aV8TiaTycjYOo7DxcXFCMR/+uknfvjhB7766qsx11UqEqVx6uXLl5/F+SilWC6XIxO83W7ZbDa8ePHiM0d2nuefMY3PdaRyfYHxXAvzKHWhotOVcybTDLn/5Dktjv//k+OLAeVkvmAym+GHM8pKcUxyBj1weXXB2dkV2nC5X23YrNekyQFTc+pObk7dzm44QWnFZrdjtd0yDX3OLl9ga0XouViWYuj6serq7TdfY9maAU3bdLiOR3pMyPZHdtstRZaTZTnTyZSq7nAcG8MwT07XScyxOHI4JqStxg1MysYnDC/wHJvH1Q33n34kPTwQv5gQRQ7nZ5fMZkvuVwcM08FyPJR1qpozFfQGWBqW52dUeYJlaUzbQWmLeG4zP7sgjCZ8ur0jjmPO5lPWqzvqbM//+tP/4PLVW5YXL9GmhUIRhh7J/sBhv+cjLUPfUhUpdA2trYkCn6Zt2R52NEONF3vUVUM3dPixz/GQgKmIJhHxLOZhtePTx0+cX5xx8eKMQUPgJege7jY3lE1FWbYUOQSTMxzXp9cm2ofYtenTW6q+wzEhjFxev7rk9u6RfZJimhYok6YdaLse2zgFdZuOQ12lNEVC39QMJnRthxpa6iKhGwbqJCGKpyyuzlB9RV4cmMRT6AyquqWsOyzbxXM9mqZFa8C2iEObps5wTA10tE3JMIA2HM4vX/Lw/ntsz+P68hzt2tR6oG1zyuSIZxq4Ro/tKg55iakVtzcnF/1kEhEEPu9vbui7H6nKFj+ICCcxbdty3O/xbZOvX11g2TZl1eJ6LgzDqUknz2maiuN+T5WXuI5HGM9JiwKtTTzXwbNN+rakzjO26xV6aKmD4JRr2Q6keUWZJniTGNPx8MIY+pphgNl0xm6XYhkWbV/T11DrAUtrTKV59fIleVnQNg3HzQbXtbD7CEtrDFratqRrUwzLo2kzmsKkLPb4kyu++/UbfvzpE4d9S1OW0PeYpotmeAoYtjimx6ddv0HXNjD0T4t0gWmGhEHE4ZDS9Q1B6OCl9inbtWhJjjn2kxFHa2NkQoQNEbDRtPUTWP1FRPnvfUgskLTeyEj09evXXF5eYhgGd3d3Y6+yMBLCPMmCutlseHx8ZDKZcH19PQIQWaSkZk8cvsIgua7Lfr9nt9uxXq9HVnA+n1PX9ahfy/N8HF/KAhtF0ViRKK/z/fv37HY7Xr58yWQy4fLykuVyyd3d3RiSL0BSWE/DOPWSC7si4ExCpuM45sOHD2OV393dHVmW8T/+x//gzZs3XF1djaxMFEXsdju22y3AuKALCyivWaJl4jgeF2XpVhYd53w+H0fdV1dXvHz5cmSvhmFgtVqNzGaWZcxms7GTXILpJaxd2N23b9/y8eNH9vv9Z+Hj0utdVdU4xpTcz+egSjSzu92O2WzGfD4fZQIS8STsmeM4Y7yTbBQnk8k4qgY+a/+5vr4eo6PEsSws2vF4HEGStMIYhjGOyaVx58OHD6NjXZp5xNzlOA5ff/31mD7ged64AZDNkmg+Pc9jOp2OTLfECwl4W6/XIzssrK3cu9PpdLzWAlgXi8XoOJcNjIB9rTVv3rwZ3fbSqgOMgF+imUT/KNOB2WzG73//e77//vsxr1MMOPIZEwnAer3+TOcMjEAxjuOR+ZWNIJzkBpKnKq9HNp0CWCWLUuKK5Gu/5PhiQPni1UvCKKKuh5NBpu/o+pqzacDQZnR9R3ZcUxcpaqgxLQ/XnjD0PmEYPIXkbri9+YhhaF5cXxHFE/TQ47oOdZFwd3fPId3jujaeZ4+oexgGPNejrkryJHlyiJoYTzmIddfRFg15WpIfdgwDKG2THFNUk5EWG6LYIg4s9rst+eGGKn1kFtu8uJjy9vU1r7/6lvuHPZvtGi+aYZsWyoBh6CmLFKOvWc5ijLYhCgOiSYwbhCRZyWIec3ZxyW6353/95V/5/W9/S+AqyvzkHC+6E81clQ3aVBhGycP9kffvPvBwe8+//uuW5LDHMsC1TM6Wc6IopG0qBm1geS6G1tRdhx+HGMokrwrW+w1ZlbFcnmE7Ho5Tc3+/wrI1V9eXvH11SeyHdH/7mYft6uTiLlvariOen2O5DrYXo1oTUxtk+zs220fmkclkGlNVNXlesXpMyMoa17bRysCxbFqzwjAUVXakyvZPH7YeQ0NZpAx9jWNpXNcnjAJMy2LoenplYbkxqoWmq+iocUyLtj+1FYVxiO+EWKqiKY/YGrq+YehqXMeiVSZnF9f89g//SLq9ZTafcMhPY67AsejLAtXm0DWkZUrf1Nzc31HkCVcvLjkcUj59+MR+t8UxbDw/wNCnPEk/9PDdE3vpO1M8P6BuOwzLJDkkJMcEhoHNensSaXs+HXrU0ZRVQuV5dL5P15T0dYVh2uge2rohz0vSND8ByyTBTI9M5jNMCwyjO7mniwNVWdPVNYHr0/eKOq8ZmuyUaxkHRL5HnifsNlvKfUfjHjk/m6O6AjU0NOWBOHCxbYO0bdkf1hS9yVU45fpqSVnsmU5jtD65trVWqG7AcS20Pkkdur6DYXhiEv9t5z+ZzFmttuwOByzbIYoD1tsNh33KJHBx49MYW/Q+VVWNYnYZraVJQj90nJ2dffGD6pfjy443b96MBg2Jtum6jsViMTpE9/v9CCSEher7fmQI1+s179+/xzCMcWwn7mFpeZEgZcmZFOepOFJF8yYNHeK27bpu1HI9DwwXB7MAic1mw263I0mSUU/3zTff8M0333B7e8vj4+PI8Mn7lPe0WCxGcCcj6TRNWSwWvHjxgs1mwx//+Ef+43/8j/i+T5Zl4/l5njFpGAY3Nzf88MMPfPz4kX/6p38amSHbtrm4uGAymYwmF8labdt2dC+LJi/P85HB8jyPT58+Yds2X331FV9//TVRFPGnP/1prIeUDEPJpBRdnGma7HY7Hh8fmU6nY2h4lmU8PDyM+ZeSoyjd36LxE1Ak40zRjUqNoLDGwGfVhRKVJOyihJaLfOX5plF+/9XVFf/4j//Ier0ek1skekjG55IJ2TQN7969I8syXr58yW6346effhqZZ2GZ5T6VphoxQEnjjlSN9n3ParUatbzAOLYXbaw0y0jclWhksywbX+vxeOR4PLJYLMZzKeBPsltFUykA8rk5J01TVqvVONoXRl1kAmEYjqByu93SdR1v377l9evX5Hk+SkyEwXxepygmnOcJGvJ5n81m3N3djZml0+mUx8dHdrvd+LkAxp8rjO7JiNmOGa8iX/jS44sBpeMHTGdTdrsD7bEkPTwShS60GYbtMihF4Ju0jcUweLh+9KSnOjm0i+Sew+YBRcuvfvX1idptTvVuRdlwf3vP4+oRyzlVKWl1EoQW5WkEoBjIioSemng6p6wrfKUwHZf1w4YoiolnS+oyZ3c8UDQDdaMp8gLbNdF6y8d3OfvtI6bumEUG337zNd999y1xHLPbrvnrX/+CafaEkU/bNU+sUI/ua3xL4VqKvhuYz+aEcczmmHJMU2ZnF6AURZERRS59X1LVJW4Q0NKRHY8YzoxhUGhl8vj4QFHssRybf/i//oHd+oF0fyDyXY77A3/+1z/j+yGzxZS+VXTdQBR6dKqj7Xos08CNpxhVTdNU3DysaGoIvQmGabHb7hiGmssXlyznEf/wm6/54acPrHYZxf4DSrdU5ZHZ2RXe4oJe+9RoGrNiXz6S5RvOKg/DNHj58hrTOvLp5uHUTd504JzGPF3fUBcZ2X7DbBbRozGUoqkL4tAnigPc6QLDCYjCkLLIMW2fXjtsjzvUYOAH0almsKixrdOItGk76EoC10F1NdbQsX24JZwscZyIpleE0wtWdzcc0opwMqfPCnpvwO5cku2OqjqSHneURfPUmX7B0Cp6II7mqF4xiyKWZ0vcwKNXPU3b4NmaoW3I05OOsqwbBk5arqwoSY8Zu/2eOJ6QZAXatPCDHq0Utu3geD69NsibBkNpTDegbx0s32cxM/GzgrI81TBanosfhKhBc3gavcRRTJWVMJwYvL5pqfOCwbQxPJcybdFmj0HLPLLJjynH9T37hw9MYh/TManLAm06+MsIy3LotU1Vt9zf3/Hdr35D6FvUTfXU5mOi1IkF7roG0zr9d1XWOLbCNAyUftpEDoqmaU+O8CJH2QaTScD+eCTPatK0YB47dF1L02iCwPrsQSXxIN2Tduf/ZOf7y/Flh0TbrNdrjsfjaOZo23ZkEmUB9n1/HO0KsHh4eODx8ZFhGPjtb387RrQIOPrw4QN3d3cjoJKFTTqrgbHxQ7L4TnWcztjdvFgsKIpi7LOWakTJOPzb3/7Ger0eR3q/+c1v+MMf/sB0OmW9XvOv//qvY+ahGDvk3rKf9L9VVbFcLkeW8HA4jHmOeZ6PlYHixAbYbrejC1drzf39/QjQ/vN//s+jczkIArbbLf/8z/88hrgLKyjaOFmUhcGr65qbmxvquh4du9Kq8urVK87OzvjDH/7AX//6V9brNdvtdjznFxcXo74SGEGNgGTTNPnqq6+wbZv3798zDMNoNJJsRQlOXywWAGNzjxiLpJ4wiqJRRyvjcGDMW3zeKS2SChkJw2nMLa7o/imR5dOnTxyPR2az2WfObBk3ywZHRvsCkmQjM5lMRh2ssKvSiCRNP3KfpWk6snBSxSlNT5IdKRscYV+Bka0MgoD5fD5mTkpmqJisBPDLuFumMAJEBWDKyF7ex+Fw4PHxkbu7u5HxFDZxuVyObG1VVXz69Inf/e53hGE4vq//XfcqkUaSQCDGnf+dKRa5geTBpmk6Si5kMiFRRTKBeD4+FynAlx5fDChdz2UyickORz7cf6JKE95evcEYSh7uVnjRjMVsimXapHmLYTnUbQcayjKjKwvMvuLVxRkvX5xq+8q8ZGhb1g8rfvzhZ84WIZdXZ6cHUV2y3W3xg4jF4hS1UlQZfVNySFySIsP1Ynpl4UczlGFRNTm9Mkjziro3sayQ3WFHnm9pqgw19CyXEbPIpy5azuYTFvM5dd3wuLqn7UouL+coyyQ5bFnMJxhmzzz2CCyTvslxJyGOfdIf3T880A6KvChp2ZIVCa9fn2oGLduiHzRVlXFIj2Ad8cISp3+qonQsLi4uuL5+zdlyxvb+gdX9HQ+rDS+u32BaLnlRctifGmQm0xlBFLLbbijrFmUGeE5IwEBdlmzWB/KyZhKH1E3P7d0dRVnwzduvmQUOb1/M6duSTZ5RHT/SNDmoHlObuP4Mw55iBho72DMUHWleoLqGvtNoQxGEAev1hnVZooee0DuF0Ru03N9+JJ6EuGFIndUYSrOYTXCjANNz6A0T23HZbPeYpkc72DSDRd+0LCYTLAPqJiMKQyzHpypL1KAwFNx+fMe++gl7/orLl2+J4zldcBrxmv6UY5HjRKcHpKkGXN+iXJds13cckz1BMGO6OMOyPAIvoGlOml4DjR466HrqsmDQ0A8tQ9uRHA/c396RpBnadvH8EMO0UUrjBgEL08YPTtFRruvgeQGGfQJurnfKHA0nMUPfsd1sUbZJcHGJoRTevH8aiXUEUYTjumwf7zE1xIFLfkxQ9NiWSXpckecZddnQdwrX9fAiB9+3CEKH+TTmsTriT33KAg7rR8LJhKHqqLKK5XWI7cxIhwDDjijLE0gOI48uLVEMVFWBNhyUqbCUie979N1J+G2bp0aoYeioygqFgecFWKZ9SlcwGlDW08O947A/Ui08LLM/1aE+jVG01iitcD2HtumwTAttMJpDfjn+/Q5xPe/3e25ubkZzSd/3fPz4kTiOxwUsSZLR4SkaSBmxvXz5klevXuF53qgTu7u74y9/+QtnZ2cjCBJ3eBiGYy6lAChZwESrJ53hMkoTh7MwS9JEAozsX5qmnJ2djYHbd3d3NE0z1gTudjvOzs5GM4fo/MSMkSQJNzc343hXQMc333wzjsxF7yhjWAnaFjB5fX3N69evubi44Pb2lpubG+7v73n9+vUYOyQj8eVyOQJfYb6es5Wr1YqiKMZ8zU+fPlEUBb/+9a+J45jXr1+PLTXS5gJ8lvH5vMtazuFz44hkecoIV0DJx48fP2PODMNguVwSx/GYC+q6Lo+Pj5/F2Yjr2zTN0VAirmUZf7979448z8dmo8lkMup5hQUWQCfv5fHxcWTvZKMhKQDSaCObFGlyGTelT8zmx48fx/giYfvk5wuzKQysGLhkU1XXNdPpdGQzLcsaDTrL5XJ0xE8mE1zXZbVajVIDqW2Ue1AYy+cANAxDoigasyclM3K1Wo3McpqmvHr16rNWobIsx82XJAOIC1w25uLKF7mBOLzlHMn9LzIjqeNs25bdbsf5+fln+kn5d/ne5/3taZqy/MJKsy8GlF1TU2RH2jolPTwwi0I8U5PtdyS7HZbj0zkDSVGR5h2mqRn6DkP1NMWWrs1wHHVycyuFMQyYCrKy4ObmI9Dz29/+FjewKcuM3WFLXdUsF+d4jkuy3xN4AYfq1CwyKBPTm1L0NmYQUJc16XFDkZXUdUvTQdO2qLrF1AMuFm2bM/XmxL7LYjol8ANW9yuO6ZEiTzmbxViOx6e7R3b7jPOZhzkM+KZxYsw8BzX07A8bkqzCME1m8zOm8ynb7SOGPglhq7qiqFv2u5SqyZiGAa4Nebomy23i2Me2gb4nPezJkoyH9Y7VJuGb7/7At7/5O8Io5u72nj/98V9499NPbA8NF+dnLO2Qqsw47NYUZYFhgGV7TBYWRVbSDgPKsDBsl/uHNV078KuvXrOchbT9gklW8njIyNs9zVGTmQo9tFj+AsNycSYv6C1NldxAfUTTU7cdpmMynU3Yr9dkWYGpNWCiepMyKch2R1zHJk22uI5BFPnYnoXhWAyGTVWUHJOCeBoytJy0e6bCd3zaJuG4X5EeWlwvoG1AdRU2JabuyZseXXZkxy3n199guiFKW3z9d/+RfLeiazIsXdP0FZuHj9zffsCzLYLzK2x/irYivCAGFIMyqIuM3rAIPJ9WQVtWOK5DXfd8+viR435PW9dMJxMMy6Jqe0zTYtAGdV4yP5vg2DaubZ9yFlEYhoXn+oTTGUXVUu04BaHjEEZzdLgkzxIcx8Q0HYosI6kaDD/EcAPiKGYS+Xz68DNtmbOYxniWRi2mtE3L8ZBTFBVqGOj7gYeHR9L9EVNDV9c83N9h2Q6npu0BuhLdV0SuRdMqkqqk7RW39w987b1iNp1yOO7p+pquUxhaUzVPbA/6adwNXQ+mYaPV8CRq7wgCD+gxgKIsmIYhWimGNqMqG3A0vm/T9wrTdPC80yZh6Adsx6JrT6xn2/5iyvn3PiRip6qq0V0ri95msxlHrsLkyNhMRpcyspRRm2jSiqLg/fv3APyH//AfCMNwHOdKNZvrumMVniyWMgqWfLyyLMdRtkQMtW07AhMJmRbQtFgsxkxGMcMsl0tc1+XTp09st9txsZNFVVjX5z3ky+WS5XLJ4+Pj6FKXYG55DxIufTweSdP0s77s/X4/Vv+tViv+/u//nt///vfEcczHjx/57//9v/P999+z2+3GjmqRD4jpQgxP0kIjkS23t7e0bcuvfvWrke0U5k0c3JLdKKBoOp2OY0kBncLcLZdLVqvVCBrlEBe067pjtuRkMhkbdgzDGIH1fD4ftXQCyESTKK08MrIWFktkC/v9ntevX4+j2X/4h39gs9mMETR933N3d8eHDx/wPI/r6+sR+D3XKQr7/Lz/WoK2f/7559FMJEYeeb2iNby4uPhM9yksn+d5I8hbr9djs4yAYGm9kZQBadNxHIc4jplMJp+57gWkipZW4pgkA1TySWUDIcYtYQLlukpwf9/3fPr0iV/96lcsFosxDkvOh3xWnpuQZEMh2laRBsjvOUXPTcbfKcymsL5yjeX3C2P7vO7xS44vBpRlkvBQHqnyA4ELljFw3O/4+f0H7CchclJtSQtFpyzy44HABtqcoT2Qphtm0wmXF2cM/UCdZbRVzer2E4Fr8M0ffs/Vy2sOx+0pD0+b+JGPZzv0dUuZlRi9hnY4AUorptMBvQrpe0VVNxRVT9crlDag6VFtzzz0sC0NtBRty1C36N7gm69/zTAodocj7z+8I44CXl9fcXf3yOb+jmiyYBp60JbkyZ7suGe+mGJoRZbnKG2esiCjKUVZ0dQVkR9QlgUom8Ox5JjmOGaHayqa4khalMzOX+J4HpYG3cP64f7J/FLx+//0X/n1r7+jbhu0YXD1xieYLJmeXfG//vxn/vLzR16cL4jjOU3XMWSKIs/IypwwiLicz1FDz3G/ZWJboC3yumVzPHB5NmcyCYABQ8E+rSnaHfWhozZb+r5AuTOwAxz7NXpQFOU7qjqh7hqarsFxDCbTmLZqSLP6BCoHsLVLsj0yiQOGpsb3bQzToOta2iLDCT3yssUNplTtQF2XNG2HrU/AZb/bcHf7gSxZM4mnZEmDY1ucL2NmEw/X0mjdkR92lGWB4U6pmh43XuBYBvn+gU637B8Sbj/9zGI+ZRbPqJqBY9mjbJ/BC2jqloKKtGqYxyf2+XDcnRbYvOKHv/4Nhp4gmmGgsM0TUzd0A64XMpg2vbbRpqZpKgLHIppMaNoTvOqajt32wDEvUaaNtkOU09MogwYY9KnjXA8Dpm0zDC1912A6BtpUFFVC19cMiAtSUdctWVKgTJt4HtGrAWUYnAo9bZq6RA8Gg2FyzHO0Y/Lq9TVnixDVJhhDyiTwSaucoXcpq5bb+zVvvroijiOKKmMYwDBM1NCcgGTfgdYoDGzLxbZMyjJ/EoA3OI7N0PfQ9zimQ1Ofet89PyIKIlAdDAZN3VLXDW17euDVdY05mLRNR98PWNYvsUH/3ocAtTRNR6nBbrfjb3/722jUKYpiXPT2+/0YNSP1dovFYmw+kciZjx8/4rou//iP/8ibN29G16yMpYUZlNYdAUXCcgCj/k7YM9HEiblDxnXPXcDffffdGKj9448/MplM+Oqrr7i5ueH29pbpdDqOvqVd5uzsbNQNShB6HMcURUFVVeNYV8K9JaZFqgAlakhA1jAM3N3d8fHjRw6HA//lv/wXfve7340A5ttvvx2zMP/n//yffP/992MOoejwBJhIWLUA3uejy+12y4sXL8ZsRNGXCqsk+jZhnwSYCBsq4Fwig+Q+EFApY3bJEJU2FxmJS1zTc22hvMdhGNhsNnz48IHD4TCOr8XdLCHxhmGMbnQBf3Jtd7vdaLZ69+4d5+fn4+ssimJsZpFrL5pay7JGqUGWZfz5z38eNbJKqdEYIxre58ybuKsFIAvwEyAppi05l88be4DPIohktCy/S1IC5M/nWa1yvp9ryQXQCmD9+uuvOT8/H8GuaJjF+X1zc8M333zDbDZ7Vln7/w8iJbnheUe3GOSEvX4OuCU4X15bXdef3T9lWY4Ob3HQf+nx5YAyTTiUCbbR47suh8Oe3XrHar0j6BWVWhHOrwCPrq3J0x0YHQ4ldbmnqQoWi7enKJNugEGxW9/zeP+Bb7/9mrPzGbv9jiTZY5qaIIwwtUGeZeRpSVMWNHVGkeUo0yWKQgbt0imXqu2pOwNteThhRFxOeFxtGOgIggjLUECHVjB0iu32gFvVWLbNu0/v8VyXs8UZQ6+4ubmFAZbLBU1d0VYZq4d7GDrmixmmbaPKksl0guG5OJ4PSrNYnBE4Fne3d6x3KUXVEngefdeTZCmmHz25egOa3sCzberiyP3dJ5pWcXF5xbe//g2YNmVRUWcnwW/Ztly8eo0/mfLh3TvuPn3kfr3lxeWc2PXA2NJWBcpQNG2D6zhYrkvb1swW54S+h+5r9scUyzSIoxjXa3GdkkNWcLu6palyphcNfV2hwzMMP4J2ydCW7NcVju9gux3FMcXzfdI2p+1BDYqyqvE9myLPKbKUIPLxPIssK+iNlsFQtGZN3Vt4wYSqbqHu0FpxTLZUxSNlvqPvO6Iw5je//vWJodSa6cRHDzWeY+GEc4psT7JbM70MMbSiGXq0Pu3K8rJks35EKQijCMNxoG+p+5K+bSn3p+aHvuswDAvbccnSDdqySfOSw37P4vwCz3FYrzekWcp8OsELpwxNdxpn+zGDYWH0DXVRkecphtJYto/taMqmpSxq9tsDThgRRDFB72PZFtM4oDZh97CiLHO6psK2TR6PW1AdhalYP9xw3G4xAMd2UYZF05yCyr0wwlIDpuPQtz2mG2O6DnQOluqZY3A4bNkdj/jbHabzAT8puPZmhPE5s9gla2y6TpMkR7JsQhR5GPoktG/qHsOwGIbu6SF9iqnq+2Hc8Q5P7KhhmGhlMnTqKUKmYDqNiaMZjmPRP5l5Bk6j777vME0brWvqqiXPC4qipO/hm2+++eKH1S/H//MhAeDCxGy3Wx4eHsZIGKUUi8ViNFDIuFBYyLquOT8/H/VjwzDw8PDAzc0N33333WhqORwO4/hPFknRnYlJRMa9AmhkwRIQJcaBYRjGnwP/5jwVds9xnNEtfHFxATBqBS8uLsaoGsksPDs7G3/HfD7/rEby/Pwcz/P48OEDm83msy5vAeHT6XTUAEr4+6dPn8ZR++9+97sxdkkAfFVVvHnzhul0yg8//MC7d++4v7/n5cuXo7NdAqObphmZ4rquOTs7G5kicWsLc+i6Lsfjkbu7O6qq4sWLF2OtprTqdF3H/f09QRCMXy9h8hJiLu9TwtdlzC0sstRUiitY3pMARDH1yAj4d7/73Xg/zefzUUsZRRFJkozgWECdsGdlWfLw8DBKIGQzIyy1SAfEwCdxS1KDud1uubi4wPM8VqvVaJgRd72Mu2UTI405whDLxifLMjabzVgPKnICYaWljUgAqdRg2rbNzc3N2BAlhkNh+sMwBBhZUbnOcj/KRkJ6yX/++Wf2+z2e53F1dTUCbBnpiylNmEeRpwDjqBr4LH5LAKecc5F0SPC6SBbkmf58cyfGI4kGy7KMvu/57rvvvuj588WAMj8cOR43GKpHK0gOCWowmUzmaCtgGAzSNKfqCpTSRL5FkyX0qiJPD8RBdKopTO+ZRBFZmvGXP/+ROPZ5eXVO3basVjtMSxGEE7RSZElCWze0TU9dl6fgcstG294TA+Qy2D6WZUAPVblDDSa245yaPvqOQUNWVdiWgbZs8rLmkGywHQulFR0db756zWQSs35c0w+KyWyGZdsU5SneJTkmvLx+AUBdn+jmJMu5XF4RRBF13eKaLkW655BkpElBFE8wFAytxrQjZsvXHBuD/bE7ZSZaiuxw5OHulovrN7y4vKTvQaHR2mCz3pCkB/oBXMcjigJ++9u/Iwg8/vynf+F+tSWOQizLoSkyyroGx8VzXYIgoqpLkuRI26vTed9vsEzN1eUlSmu0GphPQuqy5JgfSB/f4cw7tO0wuA7a8bCjBfq4p61STHWqy2RQaNOmTDM63aLpsGygbimKnMvrc9BwSFK8iYcXnHSuyrCx3ZCuK+i7DEP3HJNHmirhq5cXBMG3uJbNi5fXtE2NtgyiwKUtUkzVkxU51fqe7LDGj2ZobaOHga5tKKqS//WXv7Lb7rm6uqbvG9qhQ5kax7UpqpysbOnbgTj0cS2b5JCe2E5D0VQ1XhiznC/I0xRtWDhBTDWYuP6UUJtU3UDddid2va4Z+gHTNjCMgSzdsr9LaQeNP5lj6J7At/A9C8OAKPQJXc39asfh8Zb9+pGuqYnjiLoq6boS1zLpu4p5HGM7DmlWoiwbL3Bx/Rg3jEiynEFr6AaqPMMeNL4f41kmWdGgLA8vgKYbuLt9IMhblPeBr2cvmYZnDDmUVU+S5RyPexzHfNp4wXQ6Q2uLYSif8tZsHMd+2u0z7uwty4JBYxono0+el2R5wXI5ox8a2rago8ZSNl1XY9unR8xq9Tg6aNfrLbZlU9dfPkr55fiyQyJ7RA+13+8BRn1h3/djU87znmKlFMfjceyzFvbveDzyz//8z0wmE16/fj1qKWU8KSxaXdcj0yGOW3Fgi8ZLHMSSUygGGBmbCktlWRZFUYx5lbIofvPNN0ynUx4eHkbTj4yWxTn+1VdfAYxgSDIwxRxj2zbH43HsZJZoHAG6AlD3+/34Hvb7PZ8+feL169djdiCcFnQBNQKoJpPJKAn4p3/6J25vb0eGThZoMcsIcBNXsrSjWJbFy5cvx2s4n89HQPDw8MBisRjZPDHSyPhXRpTCZqVpOo4sJT4oz3O++uqr8f4QJ7yMrsW1LFII+dlv3rwZgdebN29GnZ0wazJaXa1W7HY7oigaGTT57P/xj39ks9mMbTECfDzPoyiKUYcoJiAx7Aggj6JodIxLTqUwk8Ks1nU9GpPkPBiG8Vm6gFx3ab6RzZHv+9zf33N/f8/j4+Oos5QNlrB/YmISllP65CWYXACgADKRKohJRuKmPn78ON4TAvZkkyI6Ws/zuL+/ZxiGsabz+Zjadd3xPYpJTlhF0b3Khu/8/HyMBnoeGC8M7d3d3dgm9fDwMGqsv/T4YkC5frjHtgwO6YHA9/GDiDytcNwQw41wgim9YdHmFa5r4RoDreFTJwW+5/H2zRu2ux0a6NqKzeaOILD59a++Jgx97h5WJFnGbDY9uUnblrIoybOMoesxDYMiT0/aAdtFmy6WF6HtiKEZGIwcxwug36EMDYamG3oc3yOvSgxtYZsOVV2jtEHdNAxDz9tv33J+cU5VllRVg2meomS6bqDoana7PShFEIakWYppWSjDQJlPbUGWjzJ7miIjzSq6Di4uLvFclyJLccOIZjCoWpfeCDCdgCxr8PRA3VRAP96sxyTBdjuaumLoKqpsT10XKD/E9EICP+Q3b65p0x0fP75ne39Dnu+JPAfPdajKgiP92DpxAtZwSHL6QZHnJaweicKAvmuxteZiMUUPa7aHB7RlEkzn9JULhs1g+yg7Yv/wiGMMoE6xMkoPDFrTDR2mbdIrMMyT3iTPCtzQx/ZC3CCmVyZ5UdHQn3q2lcY2TbquwDLBNmziOGLoFbPJnLbt6RVUVckw9JhP9YZZVpAdK66TLa4J7dP5qsqMvKzY7I/M5mfE05C6SCnrirLpcRyXtu9wTYOegbooSYoC1zGp6h7LMrh+9Za6LGkHhRtMmA0GpmlRNi1uGNMpRXFMGFD0XX9yQmubYegwLYUuGh5Xn9gdMqLZEtON8COHpDuFABtDTra9J9sd8HTP4FlgayLPZvAsqkKj1IBt2ijDwLBsBsPCDSaUHThhhDeZg3t6YBpA03bUPRxWW3zHxXYi4pmBGmocc6AsCtI0Y/P4wHV24HzxAtvzqDqLaRWCUuz3B45JSlnVuF6E41howxwXK8eyRpbR8/ynawI8PaSzogIFhqGBnrqpsO2IrMzRhkPT1mh9Wtzv7x8wtHliPNuehpY4nnzxg+qX48uO29vbUTP5fIETEBPH8QigZCGVlhTf9/nVr37FZrMZQYA0p/zud78jjuNTXW6SjGNbWXye5yM+Z4Skc/h5vZsYfZ4zKL7vj6BEcgWF+en7nr/7u7/j6upqHFvLIi7mGXnNsiDL+FVGkAJun7/Wq6urkaWL4/izrFTJfDRNc1xQBexI04uEcQszG0XR2Erym9/8hjRN+fnnn7m9vR3NSfI+gVE3KMBagKUYicSUobUemVkBnLPZbAxvF3PR3d3dCCqeR80I0wqMIDNJkvH1CqsmrmVgdAyLW13YZgHywk5KJzUwsry73W40OAkLLqaVzWbDYrFgPp+PYezCAj4HbCLLEF2fbdu8fft2HAmHYcj5+fmohRW9oGyg5F58Dr6AMUpnsVh8BsYF3IrDXu4v+RoxQcl5Ebe4VDiKhECApiQjSFTW7e3tCP5kQmBZFlmWcTweeXh4GA1oErElgeubzYb9fk9Zlp/pXaWqU66tGKBkXC+Mo7xuYTMlDzbLMlzXHTeXMmYXnatsRCaTL39OfzGgDDwLpTRN02I7Lpbl0raaQWnKusUzDJSpsW0Dz7VwTc3t+h5bW3z7m9/S1RVpcuRsuaCqCmzb5LvvfsXF5TmPjys2+x1l3dADZVFTlQVaGdimRdUUJNmRpsrpUZjhkkk4oXwauSZpjq5rzK7DMgxQCtu16foBNwzw2pambrFMlzKvGTDxfRfTVLx9+watFOvNlrptaYcBwzTJy4K+KSmrhvPFkrppSdKMeDrFsV160+NutQOzJo4iNIowjEmTUzduPIkxzYimVSjls80G3GmMMhya+ohhaKaTKcfJFG2a1E1HUZQobVCkR2hKrL4gO9xxPALhlPD8itD1eTn3MJuY9WPJfVZhYGBom6RIKfME+imGaRJ4p5HJQEfTlLRtQ1nk9FmJY2g81+BsOkH1DdqAus9pDrcMVc3gTDEtFy+IcL2AOjvgey5d19P1JZbj0rUVHT0dA3XXcTgmrFZrLuwrDNujKGu0bVNWFVUDqteEXkjgOez2G6qy4OXLCxwvwNQmtu2RZnuatiAvCyrbwXdMjL5DKU2a7Nmsb/i2KdDKp2tbtGExXV7yf/+X/w+qSWmy3Um/mRwwLBiUietGNC3c3a7YbrbMZwvariHLK169eYUybAbdndqA6pqmA9f3KIsDXVGjtAYUQz+gNBjawDIcVN9QVxVt23D14oL5vMV0PYJ4xiHd4wUhGmjyEt2Dawz0pqLT0PUDQ1NRlAVZltLUFX3fYpgm0+USbIeqqbH9CLQGbWC5Dm17Yn9ef/UVBnB7c0Oe5iR5SlUcMVVDnScErk08mUHX0FcZQ51guwaW6zFfTKnqnvVmhzZOMT/7Q0YYnhYfd3La8dqOQ1UVTw+W/unh3KOVQRD6VPWpNtO0NNNZzHzmYViaQAcM/NvYqu875vMZx2NKO1bWzfC84IsfVL8cX3aIo1rGbWJUEV2YOO+lCcW2bR4eHtBa8/vf/340gVxcXIzj5r//+7/n+vqa+/v7Ua8nYzSpSBT9lix84jAWoCYsohg5ZLzted5oIBB2SUCPvB/TNPnVr36F1prVajWCTFmQhf2SRhIxlYiG9O7ubgwXB4jjeNT5zWazMdwdTpIB+TsBwBKrI2yPLNDC/j037Uhmpozn27YdwQIwxhYJcyWAW8LNBaTKaFr+vwB40XRKK43o+gSsSu+0yFTE2SujTak7FJbZdd2xVUdGvFK3GAQB6/Waoih48+bNGHckY2gJ5JZ7SRivw+HA/f39Z5pFwzA4Pz/nv/23/zbGRJmmyeFwGCUX0u706dMnVqvVaFDK83wM5RdtpGj+xETzXCsqOsjnUWVSyfnq1SuWy+XIJkt9orCrwmjKhkSuSVEUJEkyjrYtyxqrJCXXUkxlAtIcxxklPR8+fCBJEtI0He+f5130ohsWsBdFEefn52ODkcgRxKAjxhm5hvL+no+vBeyKJMQ0TRaLxRg1JTIL2TR0XcdyuRyLBiaTCYvFYpSLfMnxxYDS9z2UNrm+foVpO6AMdN3TKqjqiv1+h+m59Gj63iErWy6uvsJ3HeomoyoPlHVFO7RoPXD98pqz5ZKmbimrFqVN/NBmMplC13PYH4g8F9MDUw90bUFXK6q6pW5bqqahswfSLDmZJixNviuoipS6bXA8j/lyyfJiSdMPHJOcpockLwhcB7TJbDYh8Lynh0GC43gMSrM/JliWS12kDP1A07bc3NwSxP4pzHrQdIPJepthBybacAht9fRgtU7nRw90A+RtR9XDYMaUnUmRVix868SY9RBNZgSTC0zbIctPgbqf3v/I46cfaYsdussxlaJsCrZ1jtKa9HikL/YEVsur8ynKsOi6nsix2B+Pp6xNy2IymWEaijCKKCqbumlQhkNbV6RliWN1mAuXs/MLDNejrGsO2ZqmLrEnPaYxww492vmcTVPQ9h1Ka5RhMHSnhpu2KUnSnM6zCPzTqNZPK4LYpVcdBjV929KWNYeigMkC13WeoohiXly9Zj6d0rcdetBoY0LT2BiG81T56GIZFo47YCYJN5/eU+QJQRwSxlMczwVaAj8g2z1QOTZVamFZJklygMEgzXLuHjYYhsvFxSWmZZOkR65fvyWeTDgcjqAUjjI4piWm4aJMl647YKHwXJehramKDNfWMPTsDntcy0Brl7JpcRyP6TQgnk1wnIDtbkvdnIwCjutjKYO8yHn49AlDKbQySKuC/fFIWVfwVMdpdh3Dfk80W4BRM1QF0ygicC2qqqUzoM4TJssltmXz3W9/w/GQ8rC6Z7seyPYbhl5haIsiy9C7LR9++gvRbEochJRdQV1rLMPHcwOStKGqWw7HDMt2sSzzVHNq2+inh6zn2liWiVIdfd/Rdg1xHNJ0ikOaU9enHbFj29RVge3alEWJ47hPAMEiDINT5/M05vzsAtf1WK83X/yg+uX4skMWfRnXCuMHjMYPaRSRMOZXr16Nrl1hjQQcvHnzhvPz89ERLfV0sghKZJAsqDL6luBqAWVJkoyxKPv9fgSCAryurq5G9k9GhRKcvVwuCYJgZGqkPUbG0gIEmqbh/fv3TCaT0RksrJMAUwnddl331Ff/VFsn7TZiUpHxv4zpJURcaghN0+SHH37g/fv3I3MpY05hFWVca9v22DYkwd6iobMsi/l8PjKA0mbz3DXtOA62bY/ucRlhisNZ4oKWy+VopBDgJWYSAdoCFIWlFDYKGJnBoig+A+RxHPPq1SsWi8XIWj0H3PKnsM+73W4MKZ9MJmMIOpyqQTebDcfjcWSSD4cDUnV4e3uLZVlcX1+PLva3b98yn8/Z7XZjfq2AbQFaws4JABV5hzjkxWUtBh1hKNfr9RhxJcxfWZa8f/9+ZHhFAiEbJQFh2+12DMSXik1hVIUNl9H9P/zDP7Df77m9vWW1WrHdbsdxv2hDv//+e+bzOdfX1yMYtyxrzLUUeYTcD7JhEHDqed54v8pY+7kx7HnmppyL5yywyBdkQybGtIeHhy9+/vwf5VDWdYcXhGRlhTINeq1wAx/dKkzbpGlrDNuhbBpm83Pm0yVVUZFVNZv9kaqt6IaeyWzKfDbDdn1+evc3unbAdgNs1yGeTtg9bnlcPVKHIXHoEPgeemhpqgLfcgiCkH44ffink4AySym3a/J0T5UcoK9Aw+J8wfmLc2bLCx4e93x8/4lBGyzOzplNAy7OJliWeXKuui6OFxDGE7aHHKVt+gHqpuZhVXC2nPHy+iVFVZHXHUVf0hoGC8+k6aDnFPZctxXxdIa2DPK0ptEmg+ETRpc0yqYpj3RNQpHlmEbH8uKKYHJBq1y6QfHh3U/89c9/ZHf7E66uOZtFKEOzzTPu6w+YpkXXdxRFius6xFGEabgcDwmDpdGxTz9A2/W0VU7StQSTKYbl4FgOXjChyFLSbscuK2g+fGI2m2I5AVoZGH1D0WaU2QO9GjCdiPOzJZPI5ubu0ynYfTlnt9lRlwUogx6DpoOq7tnvU9wow/ZDDMOiqysi18XXisMxwcCnqmt2ux3DYGKbIZ4boTTk6RHVDPR1T1v1KCy6VtEwMA0DgjCkbmtgwLIcLNfA8ny6tkE7LdrN6fKUpten0TTw/uMHtruEcHLamR3Tgu3hyIurK1zPJisLMC1s26GtGwZl4IURKAPH9VAM9O2pp7zpa+qiO3V7a43jB0STGa4XYahT7MR6sybdHfB9l8hx8fVA2/TstwdUD5ZhoJUCFG0PTT9QD2DZNgoTyzIYGGiqAq2fHHyqp0oO9N2ATU9ZJNSFRbJvsL0AtMn5iwvCKCDdTmiyHb5pUFc5TZXz/ue/snhxgTtdoD2bvm+outMYqW06yqKhbjPi6am1px96qrKi6Fq67tTj3TQ9XdfiuhaoDj9wqJp+7JhP05R2EeHYLqZhAQ1KnTZNpzB0xXI5J4pOQvyqrMiy5IsfVL8cX3bIyEtGdMLSCAMoo2cBGRKnI1mKEu/Sdd2Y/+h5Hj/88MPoHPU8j9lsxmq14v7+nslkMo4FxfEqTlJhEiUsWgw9ojtUSnF5ecn19TXn5+fc39/z448/jn8/m824vLwcx6/SGjOdTtlutyMbU1UVd3d3nJ+f89VXX42NKDLCloBvGfXVdc18Ph9HprJ4i/tV2DphvsQwAaee7h9++IE//vGPfPz4ccxzFDb03bt3IzMsLJRkcAoIltBuiXARXZ5oSIV52263HI9Hfvzxx7E1R8a4wvQppXBdd3SWf/z4ka7rOD8/H4O4xdErfc3b7XYceYvu7nkqADDG6kgwvWwahJ0UzSwwuonFICPAU86DbFiEzXyen6mUGltxptMp5+fnHI/HUWspMgFhR0WCIGNuYXcFiMtGSUbevu8zn8/H9yr3rmyG5H2L+1tYyucZmDK6lvGyuL+LohgNQKJDlvckjTqyiTMMY6wQlVpSYYbLsuRvf/sb19fXY+WmMKviuJYmnudxVrKZkdcn95QAePncC/CUa/c8DF1YXJFQSAassNfSaf4lx5drKDdbBjTadlGGjWVY+FHMgKYfGhg6bNvBn8wwnRB/skQ5IfmxpmoVaZ6D1oTxBNcPGZTF7d0jHz7dcbY8JzDMkVqt6or+JNZi7JA14Hg84IZTvDAka1uGLMELXdLiwObhE12eUJUVk9ij72sGBtAG0+UCbUccDzllWXJ5dc1yHjMJT7qapm3ph4GqbimKk8ZSaUVe5LimxewpcNa0HNLdAcOdUWUKpU0sy8OxXariQJom9ENDlh1QtkWSNwzmDMsPccIY1SjMvsDVCjU02JZ1AuBtTdUOgOaw39LWBZHv4GqTtu3ZbLas1xuiMCaeTlAKLMc+1RR6Lq7pYRuKY5JSbE4xMK7lEPgBXhjQaQPD9WlQKGWi7ZPRo0p3HNaPdCrn/OwM1bVoU2PS0lR7KCwc1yGMffpwwuH4yON6gxNM8HyHoswwlMJ23JODHk1RVmy3O8LJjHkUkxUFdncKHactyNM1u2PGdnfkm2++IwgiTMvFtiBLd6TZkeKQUbUKyzHR2magJy1KDNPEMiw2mzVB/BLDMOn701h7UDZoB8eLWWXvSFePrO8/sNsfCKI5lmmdjD1dx8WLa6aLGUWenpgYNG3boQ0DLwhpup48T/Fch+N+S5k2hJ6N6lryLEFrxWQ2ZUCz2e3RSjN0DdvdnuS4Qw8Dr19d09cVeVrw/v0nyqImCmOaHjzPQemT3is6W7I9JARRxHQaYxsGbXMy/aR5TpLlbFaPWK6PxqCpc/LjBsfs8fyAvi1RtkMQRafoHttEtzPSzQqlezytscOAKt2R7Ff41lOdY3PSpg6DRmmLrlf0/UljpbSmaRvqujq9Nwa6J5G+0g56UFiWxg88HMfCcU6RFEma4Z/NaZqOvlMwaNq2x/NcmqZmOo0xTYu+bynrnCL/BVD+ex/SciOaQWGvgNHV6bru6GQW1m2329E0DWl60qlPp9NxAf706RM///wzl5eXn2nu/vcWjzAMMQyD/X4/AkrRGEokze3t7aink7YOGdOdn5/jOA77/Z6iKMbwdGF5xEggzJ2MGPM8H+vuvvnmG2zbHvuTn+syHcchz/MRzD53D8toWRZgYSvlXAqQEaC03W5H4C7M4+Pj4wiw5/P5CPTiOCYIglFTKpo5AdsSgC1fL+ywjLKTJOHx8XEE2TJyFdAibT9xHI+awIeHhxEwyohVDE7yfY+Pj8xmM+I4Hs0iwrIeDoeRRf27v/u7MShcTE1ibBIgKkBOzqXWmoeHhzEQXTYPz40/4oS/vb1ls9mMmalpmtI0zXj9xV0umkQZ10rckaQZJEnymWNfa81isUApxXq9HjMYhekGePv27Zjd+uOPP37WoiS1ogJyt9vtGMAu2lphVsUwJdmb0gAk11BY6ziOR81r27aj9lc+P7KJkMag59WXwjiL/lIyKWVDJJ9xYWxFAhCG4WjgEqZaGrDkZ4umummacaMlgDbLsi9+/nwxoPz55zvmFxd4hsJ3HDw/wLFt7h9WVFmJ5UQYVkBvBFjBnKzsqZv8KeD2gOfaTM6umU6WuJ5PluV8+HTPoBS2Y6LoYejIkgN1lWHbin5o6IeefoCqaUnzHHd6TtH0JHVKXw44hsZoErRq6bTDZPaKxdzGtio83yeeLCnr06JpOzbL8wXBNMaPpxiWgWFrkqzkmKSgGx5Wa+aLc+hPVXRnLy64PlsSuj5ZUdJjgXYYtMYyHdqqoExKqI8EjkU4v2C/39O2HWEYU+NjOR6673AY0FbHWeThGR1t33A8HinanKY3sEyDs7MZoflrkvUdh/UDZZmjnRA37FGGSZKWWIbGcQyqvIEBzMjAj32UbZLXJWlaUFU1hllhmjam02OpFsv2ScoeZXsE/gRt2NQlFGVKUZXQNrR1h+85OF0FFDjdgdCCoqhYRC6r25ysH/D8EM9zKIscSysM42mXU1RoI2H7uGF2fobteeR1QV/VmEph0xBYPcPE5ew8xnAG8irheMzZPD6QHfeYWJimQ9O0FFVFXacooyaMfCLf5/HhE6Y7RTkhfjTH8UJsx6NzQqyh53AsyZKK84srwmmM608w7An94BNpB7SirWuqLMPUkBU5dd0wnc0JwoAsz9G2wXQe0dUJbd2i+pa+bejblng2xbVtHh5XJ0BludRlhaUVi8WSIk+4e3xgtz2Asultl7OLl8TRBNO0aPuWpqzwA5dh6JlcXDJdXKJQlMWJWdEKsu4R3WmSosbuDS4vLtG9xyQKqMqMtiqxDU0cxLiOiRoGHHuKb1k4tsV2dUvfVhhDS769Z/XO5aU3RbkmtuVRNoq6GxiUhWGoU1Xk0yLdcxqD1V2DbhSe74DSmJZN1be0fY/lWISRg+UsiYIQtEYbFn1zGuUorWnbBssKqesT21pWBcdjwjB0TKa/aCj/vY+//e1vXF5ejtEgAmRub29HEb4sRkEQjExcURQcDgd83+fy8nKM20nTlHfv3qGU+iyrL0mSkT0RUCl6MwkFl+iZLMtGhlFGlsvl8rNmFDGZyO958eLFmDEp43txvWqtRzZSzAMvX74ctYuSsSkARnRuMjb0PG8ECOIoBj4DcxL6LSPV/X4/6j8t69RyZlnW2PYiOkRhIiWTUMaKwDjWtm17ZH4kg1FAxnM9qm3bI2CV/FAJnxfdnrB1z7MGpe5QAJaASgnDlkaV/X7Pw8MDl5eXYxi9OMXlHMxmM168eDG+5v1+z/39/XgdhOEVlk02ML7vc3t7+1kguMQgua4LMOpOhf2VexX+rWNaIn4k+kgqNaMoGu+rxWIxtjPJ/dC2LYvFAsdxuL+/HyUOwnSenZ2NGxyJALIsi6+//noEwaJrFPnE5eUl5+fnAOO1EI2ogNu2bbm+viYMQ6bT6SghkfxHucckwshxHB4eHsbN0nq95scffxzPlWykZAQuOlL5Rw5hZwUEiyZSxuJRFI1B5nIfiF5ZoqzEDCfnWhzxwsx/yfHFgBLLxbB9HC/Cf4oO2G92FGlxii7xJwx2xKADjnl7cm5poM5wjQEj8HEtiyicst7u+fDhI8ckO7maPAfTVLRViWNCVRxR1DiOhx8GVG3P4+aA50do06LTGqV6sv0DVl+geoVlm6Bmp25otyXwcmCga+F4LFk/PlIWKWHs0QwddTcQ+B5FXbLZHmjalqppqasWPQz0TcUkDnj58ooXizld2fD+0x27qqMvE5Q1Y+J7GPrkyg5MOFssMFRPXRTss4LpPKbWEW4YU1YDdV8Q+hrHHGiqkmOe0SoPP57geDFKtdAVdI5FPJlTZCVp2aJtGz82acqausjp1ECZF9Rlg+ub1G3DfLHAcmwuX1zRtj2rhw2fPt3gOFtev7piMI2Tcz1tMIMYrUxM2yeYLKjNU0NCke4Zuhbbj3AcTd+3NMkjOjSYhz6utSBLDny6XaGVj+fZGAa0VYmhNUWeUVcVzlNuV5LmTC/OwLJIqz1tleGaA5FjMJlGXF8tqLuC/f7A6vaGtqxomoK27lDaYza/ZLtPULpmNnNom5psdY/jTajyLa5lUqRHtDKJgwgjmDA4Nl+9/Y6PTYnvdQy2wo9iPG9Blp5G84YJ9AWhY2BosLXHal1QVwWu54AacD0H09TMphHJvuH240fqosSyLbq2Yb05NSPZlkGeHdHKpu4Hit0RP7CJwjnLF98STuYMhsVgmJRVQ9d2eIbGUGAykBx3NE1HVje4XkSwmOF1A1pBZ/nYRYplaIYeonhGdtyhdIvWBqZlwNBTJAl109MMp/FOrzVuOGE29BTJlv36jio/YjgBNAXKaqj7mq518bwAO6/RajjFBBkadeL2MU2Duqho+x5tmJRlBdqgH06Zp45tM5vEdN1ph2saCq0Vgzo14kCPUoyO2Q8fPp7Gdq5L3VS48S/B5v/eh+gEgyAYFzAJcRZwIV3MWZaNeq+6rkcAIwDg8fGRn376if1+P7KTYkwRQwwwfr24wsW9LKBAXLSykApTI45VYARt9/f3o/ZOmBOJ11mv1yMjI+HSUgv45s0bLi8vKctyrAEU1i0IgtHFatv2Z53eSZJwdnY2ygIEHAkj97zxR7IhhekShjfLshEASjxRnufjOZamlaZpODs7w3EcXr16RdM03N3d8fPPP+N5Hm/fvh2BjOgdZcwrndTymqURSFqIJK9wMpngOA6Hw4EPHz4gVXqiIZT2l7IsR2ZPGCuJ5qmqakx6mM1mvHr1amTTPn78OII30U6enZ2x2WzG3M/nek0x3yRJMrrwoyjC8zx+/etfj/3ZlmURx/GYSvBvhr5+HPMbhjHWSoqOVhJNFosF2+2Wn3/+eQT3bduyWq1GTbHE+UiPuPTev3r1iul0+llWpvx+AddiVBE2WAxDAqrlfYreVhz70srU9/3YaiSmJzkfAIfDgdVq9ZnzWgBe0zSj7lXSE543IMnnV5zt0sz0vPd8Pp+PjKe8T/nsyibK87yRqT0/Px83LALyv+j586Vf6AYejmvTtjVplp56gJuKrCgx/Bl51WNaYPYDeZnRNzVKtagqQZUZjqVwHYftdsNhf3JlhWHIq1fXeLbBYb8lTY40ZUrf1piGoqorDEPTtgMoEy/yiKcznMkZk2rgffGezc0HtB3QWwFuPGPApGxTrHrAMhQKYOg4HLZ4rk0YepiWxjAV/dCz329BDUziCXf3WxzrlG94OGyYzkOm0xjDNNlnB/peE0UxuwqiMCCKI0yjQbUNylDkZUlXn3Yutmkw1BXz5RKcDm30+J7GVgZN3dNj4Udzsgr6wcAwHWzHoc1qmrqlKmuSrKCoWizbIisb2qo9jYC1oi5rVN1jupq66kmOOb4HnheglMbzfIahP0VWZBm262O7+hRb05ZUVYlpu0SzGYUJu9UHdrsDhlI4boBpKBzHxbFNjocEt26JJnP+4e//jmHQ7A8ZUeCjw4jH1SPQ0w0DGJqma0mS7DSmPzvD86c0xUB9yNltD9RNyle//g22odlstzRlheu4ZEWF50assz2D6ogxyIsK06ixtI9jGVRJTnnYUR43eH4Mg4mhoe+7U9yO6nnx+i2uBfnxDq+NqOoK0zYIA+s06m0L2q7A9zSmYRAZPpZjsd0daMocYxgwlGK73nBY37PbrGjLCj/wMJQmTVJmixmWbbLbb+naHqVMBkxmiwswFUWniaJLqk5TZCWD0VHV9aluUIHvWExDH8sOKbuSouyompwgMAHjBNYWl5jpga6psQ3Ffrtm/XDHdBoRxVM816VXp1zQXp+aetquo1Ytw6Bw/RBTw3G/ZfW4orIeeJnlhP5JVlLWCtePmTFg6AHDhEH1T05uhTIMTNPANAy0etoRD2BgoHowlcJzHbq2o7M0kW/TdQVtUz4tjJ+PYJQ6xTGdHoAVTfNLDuW/9yFRJ9IHLaaCLMsIgmAEg6Izk8gQYRMFSDw+Po5gI45jvv766zGO6Hg8jtotMY8IyyGL5Gw2G/P78jzn06dP4xheKuBkcRM37TAMY62fMH2ySMtocDqdcnNzMxqOpHpRjC1i/plMJqMpQsCCvF5hZUVnJ+HiElItYEXYP2kYklGj6DCFXZT+cmHx5HzIeZVYnLIsRxZYXNlBECA96xLv9NxsIqYSeX93d3c8Pj6OxiLTNMfg7OdVhP/pP/2n8XxGUUQcx2OWoZh2mqYZx+/n5+djnuTxeBzd/L/97W8xTZPVajWCUAFVArSl1UaupYC3/X4/NtwA4zWQ0frbt29Hk5YAVGGsZeMgekDZoDiOMwbSA+NofbVa8fj4SFmWo2v7eDyyXC7H7xGGXCk1tim1bTuOuAWwCYgTkCUB7GJakoggYXHl3pH7+fHxkbu7O+bz+WebEAGoMmoWcCsbnu12y/39PYZh8Ic//GHUQwtLKq9Zfo4AQfk7iYx6rukUOYloiEUnLEHycp/J5u05qy+bx6Zpvvj588WA8qQhqaiLFNf1ORx25FnB8uwFXjyn0Q62bdF3NUOVMzQVbV/Q5wesIcOwTyGcSXrk53c/MwwDX7/9NYv5jOywY3V3h22ZBF5M4HswDEThBMt26YcObbmEsxnx4hxtOvRNhtXXHLeP+FONYcdo2yNLc/a7LYmZ8urqDNuxqKo1bZ0xX15g2OB6NrZjUhYZ69U94ZN25uP7e2hamjKjLlJ8b/HUQrCiqTpmi3N2ZYvWPVEccUgOGDQsJh7aHDBMjWV4JMcjbVmRpDfMFmcoUrRtnXIMmxZtGLj2jGYYsA0wTJ9hUFiGRdG01HXLYZ+w3x9p+5a66ymqFtu0cWwHU2uqtqXqenTZUhQHtDbxvfDfst36juurc1zX5JgUrB7XnAGeHqiqhDSvMJwI/zLGDWPcNMI0XRh6lDLoesjSgs5zKPIcw9xTtw3TxRlXL5bUdQOczCUKTsYqx6XRNXXboLqWtuvQ2sRxQ6LYxBk0h4eSsjjiWjb7xx1VWlAWFXmacdgfuLx6xeLMp+0MmmbA93x8x8XswejBBg6re+aLcwYM4uVr6Gq63gY1kJUledMSn7+gG2rs3qU/rFFD98S6lhT5gbYp8Cyftq3RQ4frGAS+Q1E+dWZnBmm6Yf1wix5altMZlmEw9D2DGui7juRYPO16HeJJSDw9Rzk2RV3T9Yptop4eTAOmNVCUHWVVY1smhjJpeoO6M6gbhWlZtK0iz+unUXEPDNQtWNqiaQp2mzVFVbCwFlhOiDYttGlimDZ5XZ2C+nuo6o62qGnrgq6sMNyQsysPO55T1R3BU27coHrQHfHEw3UtDFoMY8C2DAYAqd3qOtqmwdCKrmkwlYExnGoz6Tq0GrAsTRieYoaGocEwbIaBcZfddT2z2ZxhUJRljdbmqaLxl+Pf9ZD2FTGD7HY70jTl8vJybNwQo4Qs4iLuF31lEAQcj0f+9re/MQzD2DEtAd+2bX8WdSMLrugNF4sFZ2dnn+nM1us18/l81EOmaTq6nKX3WcDY5eXlyLKK7vHu7o4wDMce5eeOZNHN3dzcUFUVZ2dnY86lsEXACMrkn91uR9u2fPr0ibOzM4CRjZHFVhpFhGkSUCnnbrfbjaNzyQ6U7xMHtMQaCegSplcW/NevX+N5HofDgbu7u88aZsS4IZpUaTZ6Hg8jGaKiXxQm9NWrV+MIWwC7MKsCKGU0Ktmbs9kMpdTIFNu2PTJn0gy03W559eoVl5eXI0v8fFwtQEeAKjBqAsVRLsYo+RkSiSPXQO5P0anKeNZ13dFwJmP8w+HA7e3tZ9dYNJfCfMuIXhzetm2PLLdoQQVIyX0lbLqM0YXFFzOUnEO5X+SaiRFKIn2kfUYA2nMj2XPJieu6vH79emz9kfMIjGYi2ewIcJfrKiYd2SCKwUauh5wPGXvLBum5TljysJfL5aif/N9H6/9PxxcDynAyIS9zlIYkObDZ7Tk/u+Ti6hVWsCCreqq2Y7t+hKFjqHO6PsdsC2xX4bsOSvVs1yua+pQZNp/FFHnKbrvlsN+xXMywTZNj1eB7IfPlGV44wXIVWQ2D6bBPKyxrgL6nyBLqImNxYWN4Ph09xzwjedziDUcu5jM0Pcnhka4p8HwLjJ66zkm6ga6sub+/wRh6AtfD6HtCz8E2es4XEy4vz1hvt9x/uOWrl19jWi53737CjGagoGkbDAvKssLWJv1gkBz35FlCXdXYjo/qckwUaZqDtrFND88LMQ0bA+jqgboe6JoOQzl8+viJKjmiDAvHCbCNgaKqsB1F13Y0T13lvT4BKMO0KLMjj6sttu0SRfFTZEFKXmZM51PaweDThxu6quBiOcegx+wasqSlqDuiMMR1fRzXp6krTMslOR7IspRJG9G2DU1WoU1NEEUsZjHJ4cjDakdVdtD39F2Pss1T7mHbo9WABqqipB9SqqLDGBSW4zKdzqBX3H+84VAU7A8J0ON7Hm3X02OCaVIXFbYNhhrIkgNpU+BZLvvDmvzwiDIs3Muv0LS0bU0PdAMYroepB5xoQX7ssJyAts7ohwI1tISBBYNJXVU0bYsxKIqyoaxKjoeUx/UWrQ0mscvF2TltdWIyTMOgbRvoB+hPYmyUSRDPsf0JNQ5to3GDJce04KePa2bTCXHo0XQNTQuGtmjqlt5SDINB12kGTEzTxXbccSwhD6Qg8Bm6miRNMQ3NfL7AMB32h5RBG7h+wCFZczgcmUynLM9OYL+sWpJDRt+U2KbDbD5henaO1oq6qugGC61tDD2gdI9pDDi2haUVzlO4cDf0WFozaDANnipTTw8wNQz0TcfQ9dRNSZLsGChRDKBNDENTVfKwbem7gbY9mXW0obEsG8tUX/yg+uX4skN0W7LQrtdrLi8vefXqFWEYjho8MYWIAUWYIBlBr1Yrqqri+vqa5XJJlmVj6PP/nr8n7JawcKIhFCZURrUvXrwYQWiSJNzf3wOMoEN0is9HtAJ2P336NI44ZWQo2YZXV1djjZ2wXt9///1odBHgJI5cYe4kkFxCtQGOx+M4xpTfIYaI58zVu3fvxq8VgCY/S7SWz7VuEvh+d3c36jOlOSXP8zFn8qeffhpBtVwXiXyJ43i8RgJu9vv9OO4WA5SMjxeLBbvdjru7u3HDICyVhNsLYJHoJflTxqTDMPDu3bsxsFzOvZwvYXyFdTwcDuOYVSoGDcPg+voaYARgou2TWCJpRRLXuOSYBkEwRlCJq7ooCna7HavVajSQvXjxYtQqipZUZBYCJsVoJlrfKIo4HA78/PPPY0uNbAwE/En8kLxfAYnC8GVZNoI0kR6Ypjl+RqS1KggCDocDu92O+Xw+ZqaWZclutxvZ2eVyOUoyBPQ9102KBEL+FL3o84xOMY4JkJT7UeKRBFxqrUc5iVwbWX9kkyGpA196fDGg1IaFYZgoNVAUFaZpEc8WDIaFNm0cBtbbO3brByaxj+5KTBpU11BnNX3ssLq74/bmE4PSnC1nWIaizAuqsmS33bGYTtnt9hyPKRcvJvjRHDeY0A0WU+UxGAYdp1GcNlocf0I8zen7AXvo2W83pMkOSxu0WcOPf/kr9BVVnhJHDlrBZDaj7jrKtCLPMqqixLWgq0tM4wRUbdNgcXFG33Z8vLulKVtMx6XqOlzHwY9Chr6lb1sM2zktqkVNZylUP6DpiXyPQRm0Vc4xP3AsClx/QtYeKe2cKJqjXQ/LNtFKoYG2rTkcjyziGEs9xRGplrop6TtFWdS09VO1lDYwbQsv9LEMxX674XG1IY4jPO/0QZ3NZ9iOg+tHtHXDfr1ivTq5EAPboSpabm7ek0UR1+cnkfx2sz51LZclWhtUdU0Uh3SdxXa7A6V5cXmFZxtYdBzzFM0p8HroT2NNyxjo6obD5pHj2RmW36Fx0JycxEobJElKXtYURU16TAnjEM8PQJ10eKEfYk0sDtt76qbApsFSQN+T7LY83Lzn68kcNdR0bYk2T7mVA+C4HkNX40dz0uMBsKirElufxrllXpMXNW030A9wf/fplAkaT4iiCZbt0zQ1bZ3TNS30p+aZTj9F/miJZvDRtkOvHDB9esPDdFxM18cdbKxsoGo6ek5xHX3XYZuasqrJko7AdzFtm7kX0rUdQ9fStzX0LXl6IAhChqdMxyzLORwSLq+vcIOYrKgoipKhahl6TRhGbLdbyrJCcXqwTOZnZMcDeZGgsxbl5xjpAe3EVMNArQa0tgmcAD10T4ykQ991KAWe69B0DVo9hZQbmq5rUL3GsCySLKXvB7I859OnG+JphFYDtmc9LTZPLMjTtdlud2TZKSNuMvWZxN4XP6h+Ob7sEPZNnLwSZiwMieu6fPz4cXTgykIpETdi6Pjw4QNwAnuSqVcUBY+PjywWi9Ep++rVq7ELWRpTnoMwaRwRcCLNH9IFnmUZ//Iv/zICT2nykUxFCa0W0CK6PXFIv3jxgq7rePfu3WfaOQmuFoAgoFGYIGDUTQKjaUekAfI9ki0ogAlOC+9ut2M2m42uaPkZIiUQdk3GtDLC32w23N/fj+BGArIFlEiQ9d3d3chIJUky5mteXV1xcXExsoZiMpEg6rZtR0ez9IjLvQCMLKWMYJum4fHxkYuLixHIS4yM9HgLG3g4HMaMz+fO5Pl8Po7In3//ZrPh/fv3Y0SSsL4CYCSAXYLmgXFDIudRjC7DMPDp06dRoyixNiIpEAApjN/za/Vc0yt/SvyV3PfCpIvBRkxkIlEQLa6ALQm0l8B6z/PGFqbtdsvr169HJlXY8r7vR22yjOwlckqyWZMkGVlYAbMC/mSzJ2Bc7jvR58omQa6rMI+iuU3TlPfv348JBJKqI4ymbBJFcy3xYP+vNOUkaY6hT7rGYdDMlhdcXL+mHRRZnlNVDY/3t3R1iTU4pzBkoKpz1HBiKe5u76iqgsVySVOX3N3eoJU+sXm2Q9cNHA4p+2PG/FJTdQrbcCmqgbQemJ7NoNc0fU8Uubx4U7PdJRRZxmTeke3uSTY7fvP6JXagyPZ3tEVKHFqkWYVpGZRlg+P5BPOIdLPHVIppHGKoDkt3GIbCtSwCP2CfZuwPCdeX1wxKsdvvsJ2TI7apKrqmocx6mr6hswYCc2BoOyL/SROxT8izlMfdntnZGbQtddlzzCqyJMEIAvxwhutET0CnIJ5EXF5fkycVYd1R5Fv6vsayHRzLpciLp/iEBqWhKEv6usJ1bcoq54cffmCxmHFxcY7v+VR1hRp6FvOIOLD54fu/0nUwWSy5mM0IgpDD8cjm8R7XsenpqdsKwzZo64ambZ5u8FPd1na9weQEHOfTiKZuSbIK09TUXQ/dgAIMBpoiozjuUdpkNg2h7jmmKVpVONGErofd/nDKe9Qmpm3jBR7r7Y6qLDlbnBFGDvQng1dkxXz8+RNlnlNkCW1dkCZ7cGJcw6VrO0zDwNCKXhmYXkgcL9jlBxQGm82K4zFlvyspG8XqcYNhnJiIy8tLJrMZtuUQdB2Pq3tszycpTwHCxgBZdjgZG8LTB9G1HXrTATvC8SdgBbTaoGpaLNPk8nyBZmA28Wlqi6Yy2W/X0FVobWNbGtv1GLqBsm84HnY8rO5I04SmbTk/u2C3vqfIC4ahxrRtZucvuHzx8sl4tWJ/OCUK+K7Dvh9YrdbE0RTTtFGqIp6dY/oBtqnZHw40XU8YL3H9mLKoaMocMwywtYHqetTAk8RBo7AwDYVpGiRpjud6aEORFdmpdao+mYy6XjGZn/Hy9VuKPCfL01GnNQxgmhZZVrDb7fnhbz9hmiYvX59zfXXGb774UfXL8SXH4XAY2TQ4AULpTRZG7ubmZhzxyaJyPB6BUy3bx48fP2ue+fDhw8iYyEK22+3Y7/dcX1+PerCyLCnLkouLi3FMGMcx3377LZvNhiRJRjD6+PjIt99+y2QyYbvdjnEtEuUjRpazszPW6zVa63EcK4YEMREJ8/Py5ctRVynsl7CKaZqOC6zU+4njVSJn1us1FxcXYzak1OKJwUk6rgV4f/XVVyN7KLmK0rIi+ZCy0Ms4X0b7f/7zn0d2VYBk3/djTNKf/vSnMQtUGGBxZYtTWECAACrR+717946Hh4dRDydGGdGXPh89AyNYFCAvphpgzBLdbrcjEyvnXYDRxcXFyO7J6PyHH34gy7IxgF3qKiU4/rkxJAgCptPpOEZerVbs93u22y11XY9NR1EUcX19PY6su64bG3/EBS8mMGlpkjGvsG3P5QbC8r548WIcKUurzHq9/oylE/ApI/Tb29ux+eny8nIE+BLeL1OBtm25vb1lu92OG52+77m7uyOO41ELLI00ooXtuo7pdDqmFhRFQRzHn+mNxQgEjMysAGDJC5VNmADu5XLJ119/PV4b2TiIlEOyYv/85z9jmiZv377l1atXX/z8+WJA2Q8GxgCmaYOyiOM5YTijaqBuWuqqgL6hqwrKwiRyYvTAiTHyTkCobRps02A2iem7hqLqsA3rSeQa03QD+2NKUbf02iItWlqzomoVh6LFaRWdtugMkwybwZ/TGi7ruw+cL88x6gyzLZl6DpZ2CZgQuTY1HWWVkWU5y3AKaJqmp6kaHNskDt2Tw9yCII7x/YAiK0iyjCAIiaczyqo6GXgcB7Ot6POMpmxOUSldjXJgP1T0TYLraGzHxrYNVqtHLMcncifUdUtenz6oVVWwuvtAND/nxeVX+K6P6WiiOGLQmnC+4JimGJVGM3A47DANd7zB29ahaSrKqoSmwrUMmqYlTROG4RTr8erVK2zbIsuOKD1w/eqarlesHtbc3z8ymdZcvnjBIvK4Xa2o2xrTNhi6nqI8bQTiIEQbiqbtWMyXJMc9x8Oei7MLQs+nqmqKqmRoWkzDwnE86jxBdx26a2jyBGexwHcNkrJiMAa0afLx5obpdMny7Jy8qmnalryqaIaWLN1jWy5JoglDm6YqUZ6JMjRpltIDaZLwcP+AP3uFP6no6grXsmjaiiwvTs0uvcJ1A7pm4Pb2jnc//CtJVjKbvcYNFlxczccuVK0hyRuaKsU0FHXb0TenpIC+62iblslTFIvruZi2Ra81vRXQWxNMJ6AzHCxD0/cVth7wAw819FjmabNg6p6+LXHMgbPFBEP3pIfdaYyT5eR5QnrcMNARBT62OdA0HUHggLJxfB8vjDEcD8PWeEFJXTfUWUZVlpiGxXTis1ies17vqOqBulXY3oTlYsJxr7B0j20MOLZJ2Rj0XUtf12jTBTXQdy2oUwZfU1fYYUDfd5imwcCJwTSsU72pNg3yoqTr4fziiqLsaHtN1ytQir47fV/btaRZiuN4WJZN27V03Umj88vx/84hWYDCMIjmT4CLgCBh66RPW9ga6YuWkZoAFwmt3u12Y9ewMDAyAnwecTIMw+jSfXh44OLiYmR5xFUt4dMSKJ4kCVdXV8CJYZLRo9TICTAQY8jhcBiZsrIs2Ww2o9ZNWCthiOT9Pg/Ztm2bu7u7UT/63IiQpikfP35kPp+PjUKO4zCdTkcAJiYlATPCEjuOM5pLxMwhr0vCog+Hw2h4EhPV27dv6fuTofLm5obZbMbLly+ZTqfc3t6OP+e59lXAhugnBWS/ePFiHBsLeyfGIwHZApLPz89Hpk3G9MJoXV5ejiYu0R5KX7fkjtZ1PcoEkiQZx823t7csl8sRsAnIESOTOLml6egvf/nL6L4Pw5CvvvpqjNHRWo8gVZg4YRefu/5FGiAMrWVZ47haxscCpuI4HnNHJcheZBIXFxdordntdiMDmWXZGKkjmmAxziilxkxQiQWSDYMwyqZpMpvNuLi4GCODxO0ujnkxOMl4Xd6btF4JOyk6Zdnoib7WcZxRAylpA33fc3V1NRrqnutMBegLQyqbLtlcffFz50u/0HVDivSIaRoEgUMQhvT9qeO4bSss02AShjRpQux7+I6FBhwd41iah7tPmIaJM3RM4oC+H5hMJ2g0yf6A7/n0bUeWFTTKpBtseu1SNlDUHY/rDbbv407mFG1PVnYYg8aLT203VCVGU2APJbQpQ1/R9w191xKEPm52GtecXV4zDJzMBAxM44i+7RiGnn7oQZ9GsrvtnqzrOXtxjR/MWK8eyYqcwDFpygzXdYh8n7qsCMKIvklPY+KuxhgUZVlR91C2A2fnEZ8+3o0PdVRP17as1idXmuc4qOkCZfi4vks7DMxnMzaP9+zuc9LDjv0hJQxnOK6PaRr4UUhZaur/H3t/9iPJkWb5gj8R0V1tN99iZZDMzMqsqaqei557e3noh3nsv7rRgwYubjfQ3TWdyKysTCaDjAgPd7fddF9E5kH9Uxpr7gMLyHqjAgTJCF/MdDE5cr6zFM1QXekccRSxWi2ZpCn395/48PHjWPlltGK/P3Bze0eczvnjH/6Rb775lrqsuH1xxyyd0FpLnp+5//Rp0H1EMWEc4ayjqmoWizXWKQ77M5/uH5hMpjRNjcIRhx5dr/CVxikDrqMuzhTnLba7GQwibU0UemTnPc75+EHM4ZRTlBXTxYSu7+iKgtDTLOdDeHJblUOFYG8o+jPaN7x+8xp8Q54dwTZo11LnB9Y3t/RdR11kNFoRmBBPw2Sx5JQ14E/58q9+w7t3f0vZ+TTd8EGz3Tyy2W/AtUxiH2d72qajryp8B33boXAs1sshvH+Soo2hw9DohNKGNH1H1zuCyMfTPc7W1FWF7VrSyEd1Ja5vuVqkeEajVUd23lFVLXleUFc1s9mUV69usbajbgbWPgwjkmRK2bTsDmeq1nI8F2htsCjSyYTI9zidjig/JpnOWSzWlI2j6y3nokTX4AcGo8Cojjo/Ek9umaUxRaNo6pI4GnSP/bPWcT6PaZuarmvou468KJikKafjGescOlT0zlF3PWVVEyUTlAmwTQMotIKm6wiCiKocAtJxPVo7JlHEdJqO7s+fj7/cIUBBwp+FYQLGRXI2m40pGzJGk43q999/Pwr1F4sF1toxHHq3243jYBn1iTlEwOrDw8PYZCPtHjLqEzAlGi8BlsKayXj58+fPvH79+kdmgssQdGG/tdZjdd7r16+ZTqfc39+PET5i2BETgoSWy4LqnBsjYtq25cWLF/z5z38ex6gwSD3kZ8ZxzHq9HgGwnBsJ597v9+z3+1HrKPrAsizHqB65RpKl+OHDB7799lvevXs3gojNZsPLly9HpvIPf/jDWJEp1/N8PvPdd9+N2YPCfJVlyXq9xjk3xvyIyQN+MLwI4BcwKWybtAtFz9FvAk5kJLtarcbzJQ1IwMjoijNdMh2lr1sAUJZlvHjxYrw3RCsuhhlhqP/mb/6GX/3qV6MjWjrnpclGxuUC1i9d+SIhmE6n46ZGxsHyNQIsBTRJsLf8v4SXw8DeC5iUikVh/WWzI5sR0UQKKyu/XwCmNCVNp1PW6/V4vqUG8zJuS+KzBJCKy17c8sJ4CgMpGk4JtxeQLC04RVGQJMmPJhjCoov57dI9Pp1Oxxasn3r8ZECpVIgfTijLPeksIQx8cFCVLSjF7e0NdXaiiiNCo1hMU7RugYDTfvdM2aZ4/pTY96nbmsAfqtnCMMC2LVXX0rU9KpqwORRk3ZbZHHo36BJtV9LmR4w/JYhDnFMkcYimx1eWReRB76EpycoD5+Oexd0cVXW0dcvHz5+4ffmS6XROdToOjFHb4LRPltUcjjlWhdSdZnssSa5eMl28psOnbh2e5+N6S1fXqNCi0GjPJ4hjCC1dUaC0Q2uP7JxTtI7F9Q113bLbb1FaEYUBWXbEM4bEN8wnIV19pqo9oonPfD7HmIDIN8SB4bDf8Om79wRhQhjEKGPwg5ge6NB0eJhwgjLDQ12XBfNpyldff8X2cOS7jx+5Xq0IzCByn0wXzBYTXrx5jTMeH542lL1iuZzTds0QiK7NqB8ynofnBRRVQ4cmmq7Q4YY/f/ee6eTEdDLF8zR91eA5jeoVaZxQlj3a9HR1RpXtadI1tu8xWI67LdPZHZvNgaoDlM+r129p24K2zmiqEq308H5ty93br2jqM7vtE6/fvuXmds1u/8Tn7YnqtKM4PVH1mr6vMV5AHAbUdYVzDb2G6WrNv/9//8fhw8l5oFLaXU1TO0prIYXYhJT5nrKrcFWG63rSIOL0tAXbE09jwmnMfLlEax/PD4cGqKzHKoO1LYEHrqkp6xxsjacUHj1F1RF5+jnbMkFJDaVzhAEYE9GlQyZknEw4HHb0VtH2ltV8iu8HlN2wEIdBSFXVWOs4Hg7MJwnJZELZdOjYEE8W9MZncXNLXZWkccjnT99z/+E9qV8xiRS74BPT1UtMGBL4IT0tPRYcaKsIg5i2s3h+QN8PjEbXdJhJgFMdTV1hjKWqhqYdh0Fpj7qq8Y1HZ6DthpGq6x3HwxlP+7TUpEnI9fWSNAm5ubn+yR9UPx8/7ZARX57nY7i5mC2UUrx8+XIc40qEj4wet9stRVGwWCzGeBxhlITdEyAgi5BUNa5WqxG4SsiztM8IeyOvT0ApDG7f/X7P3d3dOBb+/vvvefv2LfP5fDR1CFN6Op3GTue2bcdWEdFoihtYFvvLyBYBgRJ3IyaSpmm4ubmhrusxniiKojGcXDSQ4soVNlSMEWEYjqYgCdCWvwNGbamM4cXxvVgs+NWvfsVut+Pbb78dnfFVVY3d4ZJNeX9/T9d140ha2Kc4jsfrJVIB59yYR/nNN9+MwEBYKAHllzmRoiGV0TXAZrNhuVyOLJrWmnfv3o1RNgLkxOH/9ddfU1UVj4+PfPXVV7x48YLNZsPj4+N43QQICVsoJjFptfmP//E/jiy61prtdju6j4UBlJxKMetEUTSazCTLUnTDsok5nU6jtlPY9iF9oh8BnGhwLzWXwkTKtRMmUTrJ5f0sFosfjZflvYlcQPI3BRQK2L29vR3vqe+++47379+PKQphGI6xR5eObtmACXspbKJs7GSjIM/CJcMoJqrLQHNha3e73Y/ade7u7sZ//9TjJwPKohrAXlm3JOmEOIkpq5rTucIPFMfDnqI4c72a4WtLW5/xA01vW56eHsmyM+vVnDSJaNuKtq457necsxLXWwIz5B3OZit0uqJtHdn2QJrOmUwSitCgbUtfFaheEUUBzoMk1CSRj+sqplGANgla9ZzOe3o3jFOqosK2Hb5WfP74Hfb2luNmgytL+q6hNxqcoigr5muPTimsFxNOr8CfU9mGqhlOl6cDOqtAGboeOmcompbA6/EjH1cUnM8ZZdniJRNQ3hAXhKNvO/q+xfZDZmNVnFH9EtcVdE1A300IwzlJmHDab7j/8IHPDxuOecXL+RUWQ17W+FbTuh4HaD/ENx6RcST+hPPhkd///vf87//H/8Fv/vo3fPPn7/jw8Z6rxZI0Tdjtt/hhRJzGvPvFL7A6HJoemob1ekmaTgn9EIdlt9vjeT5t3dE5hYkrFqsV6eoab7MfDElofKPx4qHTuWvBWYVjyC+syordZsPq6gviKOB2+Ra6lmMx9LfPJkuavqNsLOq5RjGIp+xPOV+8vmE1n7CYetT1keVihu16nh4+8f337zkVNVfXd8TTGb2JsQ68aMJkMiPwgyFLUYEOfK4mU87ngs5q2k7jlUfoGtp+CAT3oikzz9CcHqEz6EATOsX1ekXVFiTzKdEkwYtjlPaHBpy+p+1qrKrwfQ/tWjQWZyu064ZaSu3Q7cDQnXdn0Arje2y2O+q2obcQxgnJZE5ZZWil8IOQ2WJJ3ZRk+YkwjJgmMXvtqPMT2o+IkwS9mNDUJaeqZbffMb+6w/N9lqsVSV3x6cN7NC1X84T//n/9d2gPzOcRX3s+XZ2BitChh9KAcjgUyngDs+iC5w+bYawyncwwyqCMxksHI17g+5RFQ/CcWWa0IggMdavwfY+8rinLmiIvUTi6pub1y1vSSchivcQPfw42/0sfMloty3Ks3pMWnCAI2O125Hk+ZtqJLrLrOj5//szpdBpHjZd6MhH2e57HbrcbTSVi6pAFUwBsVVWjU1rMQMIAXZo/xHVqjBmBqjGG9+/f8/Lly1GnJ4YOGDR/EvMj2jrR1AnzIsyQjPTkNUluo5wTYW3E/S1OaGGqYGDVxP0q43Dpthbjyf39PefzeWwVEbZOnMaS6ycAdbfb8T//5//kP/yH/8C/+lf/in/8x3/k/fv3rNdrptMpT09PI+v1V3/1VyiluL+/p6qqUVMpQE7ilwSICQMq7KkAWwHVwGjakD/P83yUJAgTKxuDJElGF7OMu0V/ezgc+PLLL0dpRVVVI4t5f3/PN998M8ZWTafTEbxc5jteNvOI81o0uCLBkHgrYX6lM/uytlOMScIQi07zsp5QgJmAy8s6SLnmsqmQyCQ5r5KPKpszccLXdc3pdBqB4tPTE1mWjcBQpBhlWbLZbLi5uRkd3Wma8v79e5xzrFYr/vN//s9I29ElGPynYeZixJJNn7CbohuV50LqMiUCSoDk5WbxfD6P5iHZlL19+5bpdDpmbP7U4ycDyiwfwrqTyRzth2RlTdNB1ysCZ9hvtjTlmVevbyjzA9vticlkgu17Hu+fUA76fmjc0MbH+I7zKedwOJImKZEfkdctk2RJOp2SNRbV9/jGUuUHzrsNka8xXo/2FX0YYrscZVtm05CmKYjCiMYpiuxM21TcXq8JPcP5dCQOfF7eXBN4GuN6Ak/x/v4DiySlUfpZrO2RJgmdH+G7iHA+p9UGOkPVWrrGEcUaHcaoMKV3PrbtqLuWtimJqHB1RbY9gBcyn0dkeUmUGuJ0SlnkONuB0uT5meJ8JAk0V4sJzmjO2ydoOvzbV2w+f+YPf/hHTDTh1bs1Wnnghyg76Flt3w+jU23wtKLvKnrtCKOQojjzpz/9kf/nasnXv/gaesvm8wNhHJKmU9reEoQhZW356uuv6HvL0+YRpxQvX7xguphwPh855yVpCkVZ0qFIrCWvakwUs7y5patKOnr8wMO44aHc7jNsZ3FOcT5X6BDKvKIqTiwXc4LAgFZo3+fNl7/Ai6c8bp44nE/4xmI8j+l0TkeL8lKUF5HXBYEx9H3L5vGB7LgjjQLmyxWub9k+fmZ285bJdIYXTTFegFb6eTTQUNcVodaghggiz1espj7K1vRNgfIHpl1bi9YKazqyYsNuv+Hu6oqr2xvm6xUmDsEYoniCVgHWVszThqZtSROfKh/MQqFR+FrRViV113J4esIzhiAMBm2hGjSgxveHkXbgMQk9lB8wmybkZQl9ia97ZtMpge+jbM96FnI+PhCnc/I2Zz5fEAcRTeWYTkM0DWV+wKhBAjGPNJ5r8HTHOg3JM8MsTUmjiLYu0V5FEE0xgUGpQe9ctx1tXQ2sczdMEYjCH5y+VcXV9TUoReAHdP0AeI031Cxa49M2Df5ihjHNEKBe5XTtALYDPyAIfJJnXd3Px1/2uIyNEU2YmAmcc2PTyLt37zifzzw+Po6s1MePH8eFXBzaopHb7Xaj87SqKtbr9QggxLwgGYXC5oiZQcCLsHyX7S6SRSgRK1EU8erVqzGqxPM8Pnz4MIIRYWsmk8nIGIkLVVgZCcQW9tBaO2rQJJdRHOuiZzufz0wmEyaTyQgGhQ0TXdnV1dU4Zm/blpcvX/Lx40d++9vfEkURv/jFL0a9ngBJAeGXXdSijcuyjH/4h39gvV7zm9/8hr7vx7pC0apKFNNf/dVfjXFPSinevHnDcrkcGsmex5wCCC7d7NIeJCybZA+K6UR0jmEYjvrA1Wo1ZjD6vs8vf/lLkiTh/v5+HOMKuw2M7KhUL4pZRkLq1+v1+GeShyrA8DLqRmKXBOQFQTDKLgQ8SazRZZbobrfj7u6Oly9fcnV1NYJJCR+31o5yB2nikXgf2VTJuZVrI3FKEn8llZHyuheLxfhsSSSRMPmLxWI0BtV1zWq1Gis45ZwJSw6MIN8Yw3w+53Q6jSYwAYs/aP2Hz0xhWOUZkGdNjDVlWXJ3dzfej3Ivie5UWGlh2uu6Hjd0wNhfL5utn3r8ZEDZdj1JGrO+vqKxmsP2SJIMrljX1tDVLOcJQeg4HjJc31MWitMh53wuWK+WeEGMMgFtr0knC7Qe+poDPwTtEU/nJNMpcRzxuPtM7xQPn76naypO+z2L6YRGtSjTkPiapj5w2HwiiTxS4+McdHnP4bjD9h3z6RRsx/mwYzKf43s+SeBh6JmmMYHv4wc+9jmg2dMGbQw6iNCdotcBVQ++8ujRWDRVYwlCH2tCUCFdW4Bt8KylqHJiJSMOjVWarnfUTUdvobdgu56mqtlunljMA6axz3n/QN1CazWqtdws1xz3G9Ik5Rdv/wY/Srj/9IDSHmVRoFyPwxH6CozCVuWQFel6lLOsFnOss9x//MirN+/46uuv0M8Ph1UQRgnGeKxWE7K85vbFCzCG3W7L9x8/84uvvyKdzrl7+Yrz+TiYg8KQzvV4UcRqMsMqn/NuQ50dUNaiXE/X1XRtSRhEKELa3KKcpq17quKIWkRYG9BbQHvcvHxJ1RnMOeNqPiHwHMbz0F7MdDEEuFddg+or7nffUWU7+roaNKTRlHQ6xxqF7RqiwMMoTdf3WOXwPDXEGT1fO6cUSRJx2h8JtM88UXR1i5s46grK3ZnsvCfff4+tHwlNief14EGUxGAUKI3xAqxVgCOOAlTvoWLQqqftMor9Z6ZJwinPeXz4TNN05PmQf7d61qIFkQ+9JYgM2vZUeY5GgVb4z6HpQRiSlzltZaDVeApmsaKpWqrzE/g+hoYoCqnLM7NJQGctdX2i6Arq/EyVHTGux8fSFSfK4xFWCwITYNuGxFPY51igvuuxTmOVIwojjNYob2AvB11NP4YgK+Vo+xZrO4oiH/Skz7o6ZUOapsU5hqxJ32O1XvDd+/coLH7ouJlc4Qc/s5P/EocsmuK0fnx8HJkKYd4Wi8WP2kMkY/BwOIz6M2nQELOHjCmlCUeqE5+enpCml7qu2e12YxOOjByrqhq1lWIa6LpudL5KvI9E8ciiDozso4A0YAQCsuhdZipedorL38kiKsBSTDrC0MkYUZgoGWOWZcnj4+PIxopeU0C3ONAnkwlffvklURTx4cOH0TgiPz8IgnHRltcp+ktrLd999x3v3r3j17/+9cjawgA0JNPwfD7z+vXrsYnl/fv3/PrXv2Y2m/HmzRuOxyOn02kE0NKHrpRis9mMr0eicWQsK40yMMQeZVk21vTJ2Pn169ejEetyvC6RVHJ+u67j6elp7CgXNniQcQ330OX1AsaNw2WAd5qm4/hV9K9iBtput5xOp3EUftkScwl+Lu8X2cAA4xhbNkhZlvHp06fRBS9sp8geJJ/1UiohcgnRMIoWVMbHMhUQMCo/S5qb5F6TZ08MTGKekedQzquw7yLzEIZVwvMvz58kGcjXX8YbCfg8nU7jMyJfL/FVf/rTn8Y/Exb8n3P8ZEDpe2qIzPEDNvs9TduxWPhM0wivVRT7ksU6palrtFGEUcJpV7Dd7NE6xPgxjdM8HQtQjmsTEEYJ6+tbpsmEsmoJD2eUGTqktXre9R42pEmM7RoMlrIoOGU7Ek/hmYq2PBOEhiBOUdpnZQLO2RHbadq2IQzm2K4lCQOOWcbN7TWeBjzvOWDbkSYT6rohSad4z1EwXuSB9qnbwayTTuZ0Gooio9Ylft1hgmHBdI0dMvqcA+VQ2hDGyQAgnz+sPOOTtw1tVWHbmjgOub1Zg2v4/PEe8PCjFDdbUpwOVMWJX//6l1y9+ooeH0xCVTV4wYnqfKLMjtimY5qGEBjoFbbrUH2LH3p4vqGuSg77HVfra37xi19y//CJ+8/3pE7R9BA7jXOadDpjhUH5Ebvtho8PT9zdXPHi1VvOf/gdTVdwtXo2k3iGIJ0yvfKIkxm7T99R7j4TGk0QRhRVzfl0ROspDk2R1zhth/fsKwKj8IxBOU2vFP6zc9m5FkdHnM6pWkXdKXztkU5jaBraKibyZjx8OFI1LYv5kqIsCBKPMDAo15GdD3QmYXUzGcapDtrOYgKfHlDPGaN//P1v2Ww+4fka34vRKqHYf6YrM1azCJ8FcZhSZBlJGg/n0/Px/YAoiofHpof8dKTKdhjV01YVx6cN+WFLthn0jfn5jBfEQzh/EJJOF0Pgd1VgtEYrD/38IdC3zVBZqcEPQmzXUFcFJ9fjGw19Q37OqdqOzkKYTsn6Fm2nuKak6x3Gi0mMxjeO4/GRb/7x9+jecr1esLn/SN00HLZ7zocDYTIjma3pXYAJG3od0FqNU44o8LDPHypKOZy1dF2Pb8ywAbODiW1YWLznD7IOY3y0AYcdMkGHJk6urpYc9lvqqiBNE2bTCWHoU7c/N+X8pY/Ldo7tdktd1+MYVfqYr6+vx8VY+pwvO48vx34C7m5ubsZxn7hQhXXzff9Hhh0YxtLiApZxdhzH46IvGYei+xI9mJhBXr58iVTJiT5STA+TyWRcBMVcISNNYWalZ1yYI9HNyQIso1LRVco/4poVEBPHMS9evBhBs7jWhR3M85y//du/5e3btyNzJpmZwoQ1TTO+ZgH1AvpE9yiB8b/5zW/4+PEjHz58GIG3sJ2XjODj4yMfP37kxYsXfPHFF/z2t7+lbVuurq5G9m4ymXB9fU2apnz8+JHNZjPGJsn1EQAmYEmidORnXI63p9PpCJKlHlFyD4V9LooC3/d5//79qK0VWYGAE7nuL168GFlT+b3AqHP8+7//ex4fH0cAKxFPZVmyWCwARmZ1MpmM972MmoHRJCQOetnc7Ha7sV5UGFoBUHIPycZD5BnAj2oTRSoirKmws9LZ3ff9KBWQpAVhrAUEH49Hfvvb32Kt5ebmZmx7enp6Yr/fk6Yp8/l81DDLZkTeuzDPItkQ9lHMVxJjdHluxza9C1ApJQFSaym62yiKRiPTTzl+MqDs25yuDygKnzyvmc0XTGcztG1o24LlYspyOefjx/cE4Yz1as2H7/6/5EVJOltgwgkmnOOwZPkZP29ZzUK0F+EH8TNwcyjl8DSEvqGzFgcEgY/WEAY+51PJ/ukzN/MJQdDRNy020M/tKBE6sHhhRBAY/CDEeOb5w6TmdDxQFhmLxYooTYiShM/3j3hBQodG+yG941kvA8fjCXRI7HtoP8CPYkxb44XDh66tapazhLo/cd5vaYsD3jQmSgajRO0cdVVSFiWe7xMFHrrXHE4ZN1cLJklKfi4IPZ8irzDKoykyimzP2zd3JLMryrZG+z6z2ZRz/ogDetejtKKpKirVE3kaozRtUxMHhul0QhBFdEqx3z934M6XvHz1CuV5fN5saHpHWQ+GEYuPVR7T1RV4HofthigbAqjvXrziu7rB8/RQwdd09KZHBymhN0Hvz5yre5TXEUYJ89mCvHgCWoZnUINV5IczxfFAazxc3xH4z/EcncILI7JzSdNWeH5JlM6praWsSuzUUBY5RhvOZUWcTGj9Dj9O0NpDOUeVnejqAj9In1uHShrXAZredkRxNMT51DVeEnE+7vnumz/y67/+JbOJj+16Yq9GpfDFmxuayiM7PBAEAfPZkBHa1BVFVdPUDc7B+XTCOIetcw7bR/IsBzRd26KUx+r6hvX1HVZ5BMkUzwuZTaf4/mDg6bqWtm0wWuH5hqZpCXyNAuqqQGlN6Ad4emCKzocTbV3jRzFBFOJw1MWZ3LVM4ojI03RdjcHgmo71NKS7veLT9x95+HQ/GG5QaKcoTkc2+hOT+RpvGpDnZ1zg4Yy+WJw9POPTthXOWnxviGo5no5DW5DWeJ4hjEKCQHQ5AUZrjPbxjE+PHf/+9Zs76nIwrSk9fDBr89MbGH4+ftohpgBx7kqntox81+s16/Wa9+/fE0UR19fX/PnPfybLspG5jOMYabORnyEL9WUl3CUAFU2ZMCfH45HPnz+P41MBjZfjcBlJy5+JDlAcxaIhTNN0rHwUPaKALak/lAXe9/3R+SoLvowhJWxb3qvEuwhrKTozYaYOhwO3t7dMJpORbRIWSwDZl19+yWKxGKOW5vP5GAkk41Ax0AgoKoriR6HpooOULMQvvvgCYwwPDw/jKFiCyYHRcPL09ESapsRxzOvXr0eN6KX7WSQCAsRECrBcLkfQLWDJWjv2b8vPuewnj6KIw+EwjliFbZNRrkTuFEUxjnsvWbTz+Txqa+XaXUbXCIMtOtjD4cAf/vAH/u7v/m6UZUiN4bt370YgHgQBy+Vy1MkWRTHep8L2CkgTc45EW93e3o6mEzHjSIuRMNUSbSWbEgFn0rwkmyZh2WV0LwBQpAiyqZCxsmg+X79+zfv378cNi1yPw+Ew3hNSuyngURhveQ5F2ypd3VIrKnpKCee/rAUVYAuMI3U5r8LCysj9px4/GVAmkaPvCs5niOMZt9eviIII29RozxJFMfvDke2h4Iu37wjCJVVr0UHAYn1FPF0QTFe0VYn1ehp8Oh1grOOcF9x/uufz/Sdu3C1Ga/q2oreOJJ0QhAGL1RJLj3MNTV1SZidav6PrevKypbUaHYRURUnRdFwtZoRJSt1YwjihKEps73h83DKdrYjSCXE651x8IIgb2q6ltXDOSog8XBtQupww9lCBh8NhAkMyTYlnM7SnURpcW9IUZ2zXMJ+mRFFI0VQorTFO42lFZ3u06/G1oXcdN4spsW8o85K2HXZxSTjkXdZdx4cP75mvVpzynLLRXL/44lmD1hNGAVUOSRLSUKHUsANxKkC1PpM0IIkjtO+DdTRlQ1s31E2DU5bXb14N4+39CeP52LIdKgsZgMDq+pa+sxxPOaq3LBaTQXdZt5T5mXh+TRKmdCamayw6WuDMhLw6spwHzKcxxve5//xEiIcyPkYZzsczx+2OxXyGbWuqNuN8OtB6KdPFEqN7aIYgbdfXaAWzWcp+t6E47ujrLX1dQ+tIZytMEJAmw0NQNR0fvv2G1UsIp1cUpyPpdEoQRWhvSCNoe4t6Xoy+/PortGpYzics5hP6tuejV5AEPm19pixyeutYr6/AOjwU2+2O3jncfEZZ5Gg11BB2VYNtLYEfEqUTTJRinaLpLcYLCcOEJF1gvAClFWVTgtMY/1norBR+GONchRcY/MBwOh8JPA+tPbTySNIpTd1igpYkTTGej2NoI9Kqp60y2qal74amp/x8wlOayFN4Gs5FRTKZUWw3FFnJcbund1AXJ3Q8p60Kmi5AhR5hNLAIwyKvKYqWKAwxxsN4Hk1T44fPgnfTD73kZsizdM4OVZZmSG9o2w6loCwLnOsJIw/P6OH5xo0fZj8ff7lDNIvH4xDC//LlyxHQiUZLdGdff/31j2r8rq+vR3PNPx0LA2NUzYcPH0YGUUCB6CulQtDaIWdUQIQYLIQhqapqZE8l+1FyMEVvJ6BPRpPSCCL6S2H8xDgiC7EsqjKeFWOGZGRKYLQwZfCDiUeYS2B02GZZRtM0XF9fE8fxGBT+7bffcnV1NY54X79+PYIFWbTFmQyMbTsiS5Aga2G55Jwopfjqq68GTfp2O45v5edorbm7uxtBr7V27EmvqmoEzJed3fJ6yrIcDTS+7/Phw4fx/Uve4tPTE8vlcry2opuUZiC5l4SNXi6XI6Mm70HOs+hdpdXmj3/8I2/evBnbcYQFk6gqkSb4vj+akVarFcvlcjRsCVMt95P0hPu+z9PT07jZkKB0+MGsdvl6RLogTPd0Ov2RrhIYWVpp1xFGULTFcl6FHRfALD8LGNlzAeYytpbzKmNxGYk/Pj6OQfsSYC4bD7mWIgmRc5dl2ajvvMymvAWX1agAAQAASURBVJSHCOgFfuTuvsxcdc6NP1OmEP8i1YtXq5TKQtn2vH7xivVyTZ7vUbaizPb4sxk9hqubtyyv3gwB1G6oBwzTmMXVC0yccqg7Gny8ZIbxY5x1ZOWZ4/mIVhbfALZFuQ6tNLe3t/hRRNk0dFjiJGQ2TajKHK+xxFGCn8Q4LyJvHY119GiidIIfJ5zOx0H75oekU0WWZYMeo26wWhNNFhSNoyhqwniCMkP8CRg84/DNECyOVnjGww8SJrOUc14RqIAqq1C2xlOW9WqJbVvK+sjCDG0yYeCjcGjlUK5/dv9aIt/jXJQEUUQ6mVGXg1NsezhQtg1VWzNZLKkbxe7JI55dM5mEgwnH1Zz3FYk/QduewDOUdT6MktUw+g7CgM6BVoqyrgiqis55xGnKF+++wPI977+7xw9SgiDGS+ZUdUsQBqyubvj0/bdsDnu0Ubx4+ZLssCHLM7abJ4LlG5wXYI3i7vXXeG3PH//H/8X9/YHFImR+lTCZhRTlUHvdtx1l1pCfC6ZxBH2HowMsbVNz3O9JI58gCFG6o20yutZyLs/YtqCtK9qyYRon3L66pewtVdPQK0Pf9SjnqPMzn7//lrsvApQy+J7CMwoTxrS2x/cDrBq6uFfra9IooKlPHPeDyUerEmN62rohCgM8PScMYvq25f7TJ1ZXa4IoxngGQ0tZnuj7DqUM8/kKLwwHlju21K3DMPSWO+3T4eMFMVVTccyG3uskComTkNDzoeroW7i+vSGIDKdzxvlc0NuC3g4PfZ5lTKYxYTKjbxy2b56NPzX5aY/thly4tmmo8hxfe/S9Hbrdwwm7wxmlfLq257A/0OI4H/eoeEmhPFwS45ke03YQeBitsdbR90M8lnND/aR8eHpGw/NuWsYnWmscljAIh3G3MURxNPS4q2F8DpaqHqoyw59NOX/x4/r6enS1vn37dgQ8AsKkBu/Fixfc3NyMi4jEpdze3o56L8n7EyOONKoAI4MlgPPVq1cjcBU2Zj6fUxTFj7ISZbETMCJZmMIciaHofD6PujnJxBON3z/t+ha2BRg1kzK6zLJsbA0RYCC1jpcaPBnHXuowxRgii7UA7cPhMMoJmqYZGUqpVBQDETCGrMs5k8pJ0bddmlCkaUgA+i9+8QsAvvnmm5HJTZJkZMBubm749ttv2Ww2GGN48+bN2Prz8PAwGi601nzxxRf0fc9//a//lY8fP7Jarbi6umI+n49RQ9IodDo9G2qfZQAwgKLdbkccx+NrFnB0+W9pOPryyy/HcwyMcUV5nvPtt9/y5ZdfjtdbXMcynhUT2c3NzahH3G63Y1yUXHsx30iA/HfffTe61OUcS3WjAFP5PbLxklGyjLWFhT8ej+OmTIC/gNKXL1+OLLy4zeVcSfTSZDIZR9zCUl+yu3Ivy3MkG4DdbjdKD7bbLdZa9vv9mBd76V4XMCtGLGBkjOVr5Lm4LBqQz2sB1fJ8CuMqG5fLjNCfevx0hjIOyA9nXKdZzubEnuFcnWmbHdo4OhS98pkvr0iSFYfNnl7oYi/ES6bgRRg/pqyeKIqSeRxSlQVdWZKmE3wUkedRFRlGu2GEZkChuL66wmhHhCKfxTRFRuTHODf0Q6MHOtpi8QIf44d0ziMrO8raMpsmKNTQ6ez7VFVO1dQs1lecTiWdq1lM5sMouWxwTUvTZ6ANrvVQfQ2eJfAVnmuITE9dHrBtReIrqlZhlKauG9q6xNBhtIezoPRgcjgdD7imYjYJ6G1PGEYs1tfkecXm8Ymm76j7Duf5WO0TRwmebpjEBs9A4MmCrag9mCdzmix7riGsmE1SJkmAo8cEIX1RUtY1WTk81PPlEDYc+SE31zfsdie+ff+Bm7u3KC9CY2ibDs+PSSYLdo8lTTO8zjZOOGcn7t9/Azph/fY3EC7oTYg3WeOClGOxJeoc+pwPtY91gTaOsqzAOcq8HOKbPEOgPHAWsGy3T7BYoCOP0ICnejQNbVWguor8+MR8EnP38i1gKE9n6sZhtMPRUdcVp92B6VVIaBTKNey2nynrgunyijCZDf3eJsDDEXohaj5nt/1Elp85HP/MbJKQhh5dWeG6DtNZ2mIYW9luGN8naYx1FusClJ5C7/C0T9cqWgdt3RNO5nRVj1OaBkXbWqbTFOf5tG1D3TuSyYLA0wSBRxT62KahLHOeNhvSSYJFM50v0MbQ1A0Pnx/YH3a07QSjDKHv42uHVYrTcU9+PpEk8RCY31uSyYQ4jMnOBVmRYZ2jqWpev35D5BuqOuOw21DmOV6e85jVzF/N8ROLpzWeMTRti1aWKErwvOFDRRv93JrUPJtq3KAFBWAYZTvrCEIP6J+lAi2nU0aenYniAK0H5mcQW6qf/EH18/HTjiRJRgevMEQCVC4Xhst4mq7rqOt6HBfLeLssS7IsG/MKJS9PgIA05MgYTkaI8t9SpzedTn8U8SIgQ7RxsgjWdT3GnkikibA619fXIxsngE3YTGl2EUZNXj8wphMIOyXjy8scRWFiZAE+HA7jaxHAcX19TZZlfP78edQOyuZKopHERCOMjozghWEVpnM2m425nALChXGTayMj9Lu7OzabDX/84x95/fr1CJTk/EiYu4yXkyThdDrxzTffjKNhuT4ycpWRtAAmGWsK4yeki7CG8s/j4+OPKgIFeBRFMY57Z7MZb9++RSk1NtpI3qFEUF1fX4+yhcfHxzGM/XLTIeBuuVyOjJ3kOYoRRq6D/H4BjtIrL9dU/n3ZnCSbA2DUEgprK4y6ZHdKcLmEgz88PDCbzXDOjSYyqTSVjYaMyOW97Pf7sdFJaz0a3gSYysauqirevXs3gk4xVIn85M2bN2MbkWzOROMq4E8C8i+bdeTZl42SbCLlz+q6Zr/fcz6fxw3b7e3t+LU/9fjJgHK/3bE7ZCTTiCrPWUxiAm+IWrF47I4FWeUzmSVkWcVhu6M8nXE0OKcHwKUNOE1dlmgmuL6lrktc1zJLJnRa0/YNTZkTJhGTWYpylr5rSNLJoJPzFctpxGO2wXZDnJFqO6J5DlpxPh9RxuBFKUXjKBuoG4dvApxqh/YX36NqG86nI06nNK0FExIk0yGgGg9rFE3ZUpz3dFGA5zr8UDMJQlyV4SnI6gNJFDCdRAQmJQojqjyjKjPybE+QLKjqDrwpXQ/GT1jMJgR+z+GwxQtiuqanKEr2WU7nFMr4hEFKlnVcrSAwmturJU0f4fkRx/OB0lYspwltUVBkh4HBayrMPAKjqKuWsLeYICKeKs7HE2WZ4wU+8/kC1w/dzy/vbjjsd+SnLeq5ncgLE4IwYrG8pW96qrqgdxodRDQWurpg++mPaC8gvHqHiRakqzUvvv4l3/1Dxi7LcXj4CjQGpXuUp9BG45SmapqhMrKH7HSi9VOmk8mztrCjzCuiADzVYPsz2fGJJj+RXF9h/IRz1eFMjPYGV3SaTuntjqquWfmGwAfta7T2h35wP2LotfaH8bd1BJMIrTqmOFrb0ZQ5p4fv0b3F9D3n44G+6cgsWG0oypymrSibYoi98gJm6YLA+HRtQ9N2nPMK18G5rDkXLdPVDaH28SNYrFcEns9iuWI5W5Kd92jb4hmLrx2nOmexnGE8TVlXTGZTtBkaqKwrWa9T4tAwTafURUF9OtNgcXbIjZzOr2lsx3QyIcvOlG2H9j2sDkB7GO1Yr+bEgU+ZZ3Suwamhc73MKvo2GcC97QiMwveGHnelYTJ5NtppO8Q9GY11HcYoPK1R1uLo8MMIrKXrGsLQw9kWrTTZKePx85bj6cDqasVqOUNpD8fQsvXz8Zc9Hh8f2W63zOfzsWlDQI5zbtTSLZdLTqcTT09PYyOKLKzCYEjenoyvpZvb9/0x4FxacWAYo8nYVSJfhGmUsbAEqcu4L47jMe/ysk5PgK2M70X3JiNPAagChERfBow1fTI+ljBpcaxLZI84cYX1E22f1E6K2UhAX57nY0ai6EDl/XmeN46hZRxqrR1ZWun8rut6BCGXQHc2m7Hf70cdpzQDJUnC69ev2W6344heDD2SNymA9NLhW1XV2HokWYLr9Zpf/vKX/Pa3vx2vuYB/AdMyfhVTUtM0o6ZSrn3f92RZNmYjCosmLTiSe3jZ/CIaSLk35B/53WKcks2KsNwCaGTjcH9//6O4o0sdpmRWipb0cvQs70VMUtJlLVFQcRyP+ZDr9ZrVavWja6215nQ6ja52MQYJcLPWjud5Op2O1xx+MIBJPqdocqVtSFqfjDGs1+vx/hTWERg3I/KcClgV8Crj9sufJSaxSxZTxvYyOhcGWioyD4cDNzc3YyMU/KCx/SnHTwaU2+0WP0jxtOZ82jGfevgGlGeoreGQFSTTK4wfk50PFKcjSWBAB6AMTTcwMnVjaeoGgyMJfVyScK5ylKfpu0GHNZtOcZ7G9zxs31KXFUkUEvohXd/hG0XgGZxtwfXU1bDb9KOQtulJ0jlxOqepW/KqwzfREEBe1ni+YZKmBM7i+Ruq1tJ0Fj8ICaIYR0DeZOggIDEheVnTtQ2+cUSBT2AcXXWmqEpU33G1XDKJQjJbE3geaZoSJRHH0xlVK4jWxNM1fdOSmJjQNGw333M8nvFMjdYBT7sjp8qyuL4lTqZobSiKnNPhzHIW0TcdVZ0zW66pS0caKsq65PPH96yXc+bTOefTs4nFDeP8IOkJ0ymxDtDGw7aDeD3wI248H6UMy+WcX/3ya/74x285bB6YzK/RfkBdDNS354cczgc+Pm5YzGeE6YqgqOnKkj/+9n+w/qLl5df/D2bXN+ivvuKw/cDTd//A9eKaJPZpzzn1saC34DxNUVdUXUoYxazDObatiJMpQajx6Qg9RZkP4vO2yvDosF0DzuHQbA9ndueSdD5HxzF1V5O1Hc4PWN7eECQRTd+S+B6eiWk6Bc5gdIhSHmiFxeJsj0ITpAtWzuJZKA5H7j/9GVsXlNkRTynKqsGPIrxweEyM0WhjKIsaRYuOPDrr0J5BPT/IYRTSKR+tFV4QoIzGGIVWjiAMsVFE34R4yic/7yjrAt/zWF8tOec5oPD8geHr+w6tHZNpzCQOCIzP2VUop/C8kK7XNL0CP0FbS68TrNfT24baalqnCaIYFPi+IQw8cBGH05kgCbF9R344otIUrQye72E8/awbUuOHtfE8wNH3HcbXKGvo+iG6yhiNe5ZWyKLftjWhP7SqODs0TIVRzH53JAwCbvXgIv/5+MsfT09Po17qcDiMkS0Clk6n02g6kLHeZe6emFhkYQbGRXKz2YxfI65jWbglVFxG5NLGIdl8zrmxIUbA3nQ6HWNhZEwnQFbAwGWzyWX/NjCydGJ0EWAn7/+yDvDq6ookScbFWHL+9vv9aF4RJ664fB8eHtjv9+P4UNi029tb0jQdWVKJ05FztlqtRlPR8Xjku+++G3M7D4fDWEdZVdWoERUQ0Lbt6MYWgLVer/nrv/5rfve73/Hw8MByuRxBm4Cx4/E4jrInk8kYZfM//sf/4Msvv+Sv/uqvePHiBb/61a94enoa9Z8SiSOjdmDs/BbjUNu2I0gXwCebjcuRthzb7ZbD4TCOcQVICuMqgfjCNMv9JMBN2EUBTxKDBbDf7/nw4cPY7KO1HjM3L1lp2WjAD7FBl7mXlxIJAcbCOEsjlIC14/E4vv6bm5sRKIpJTMba8/l8ZKuB8T1dtgNdjqIlokqSA4Cxoco5N+Z49n0/piiIyUae6cu4K2HG5XeJRlTG2pcbCAHYYhISDWoURWy32x85x/9FGMrXr15RPwd8a9XTNgU4i9Mep7wGP+Xq7hV107D5/InTcc90kuJUj+eFNL2hPNdkWUHfWfq2oy4KqjwbXL9BTB94NGVNHKb0ajgxbV+SZRVJMmExn9FVQxRPGEaD29RBEESk6QynNJ07MUmn1K2mbRWbfcbNYopVPq3VBF6A8nxsW5MVBdqbk0yGRhulNGhF2zs6HE55aKPQvSM0EAeGyFOUZU1TDjqTyDia8gy2pW1qwiAgSVL2x5zEW7BeXOP8BON1aFeS56cBwIYhtrU0Vcv+3BCvXjBZfzk8fMWZvrXc33/mw/uCh4cD2ktYXV9hbUlbn3j49C1GdaSTGM9TXF2/4/HhkbZpMUZx2GcsvBSlPcIgIkqn7A5bdrsdTd9xd/cCh2K1WvL1V/D3//N3tMWRKAzoO4Xnh89MwIqsLPFjWN++w/awe/gEXcf207csV1e08wnxJOXu9VuePn5LXjYkcUgYxhgvR/VgURRlQ1lWTOYhZVWQpBWx7nFNBspx3GeEz0xYX5do5wCPZLrkmNUcHj+AF1IpxWQ2GUwrVjFbrkmurml7B0GI8kJ8PyEwCZPJEi9IwBhA4WyP6zXWgtExykyIptf89f/27/hjEPI//9v/SV229E1NkedoBdP5lHiSMlsu0Br6rqWlxvU9XTs4nouyoK56rLFgNU2VoRR4yqfMeipAA9nxRFsVJJGP0qCNfs561BjPUOQFdQNJEjOdTKgrBa4n8BWKnmQSUTUNp6rB+FNqB13t8IKEJrcYIvwoIvAMXdfT2oI4TajLkk8Pnwg8w2I5QwcegR9wPNfosCMOYozyaLserRVRlNC2DU3T4vsGrQcGCjUkMYhLchB6O8CNbsCmaZlNFvRdSZImFB8+4Hk+4Ea92M/Hv8zxxRdf/KgZ5LKzN89zfN/n1atX1HXNx48fxzElMBpEBGhKxI30GItDuK7rcZQtbIf0EadpynK5HAGKLM6i2ZJeYOfcGDbdti3b7Zb1eg38EBsjDIxozabT6choiQZRRniX+YNimiiKgqIofhQldBmSPZlM2O/3eJ7Hy5cvfzSuFkZIzomwmavViuvr69H00bYt33//PX/605/49OkTvu9ze3v7I5bwMs7o9vZ2HFEbY9jtdiNAEN3ndrsdpQjSaX59fY21lv/23/7bCMqF3ZRIqKIoSNOUV69ejcamvu/58OED19fXo1v4iy++4LvvvhvNHjJmvox8KoqC5XI59qFfZk1KRqQ4jeWYzWacTic+fvw4jsuFvRYJxtXV1Qh45HwL+3nZBiNg61IjO51O+Tf/5t8QhiH/5b/8F4qioGmaMfJI3p+44GUELi1HYnwRraBscuTeEaAoWaCiBxUAKPeduP2lUlRYedmsAD/aKF06xsMwHFMAhIWX50yYzY8fP45Mqcg3DofDuJmS9ybMqoScC9spz75oMQVEX7L68IPLXOKNvv3225E1lmdKNhk/9fjJgHI2mVN3inp3Jo00cWjovZCushR1RTq/BuNj24ayKlDKDiO8pqGzCmNCqrql6xzKges69psnjG1YLxd4GrRnaK3F+MONtdntqLoeY4aL0FtLZDRKa5qmxrnhzRptSKOUsrX0ztBaQ2s1Fo+y7ml7R15UtJ1Fm4C6bnBGE8cpjfXAGPoe6m4w/aSzBXkJzTM7lkQ+s9gj9BRa9Sjb4GtLEmqaIqPrWsqioCryQRuj9DAm9lKsDqibnufGYw6HM54J8J1DGzsAQD9mtn7JsWRYnFuHj6IsSq7WC25fvCDPat7/8Q/c3s1p6gOLWURX9ez2T8+xSkvmsyXbzQ7P86laS1X1BOGzUzNQrNdXnM8ZDw8PQ2BrkrJYLLi9u+btfs+f/vQtlo4gnhNPUiaTlGSa8vnhM63z6FXEav2G8lzitw2N7Xj69C3z1YLZ6pqX777i+2/+wHbzgcg3GM+j63pQCocmL2rKuiVoGroOAjooD3h+wOGwo8zOxAHs2oJpOsHzY5wOKeqO4+6JaDojCH2UiXA6QXsajKPBcL28omoa0BF1M9wnQRCiMFgHyqnnWCqFUwqlht0oOqRXIenyBb/42/8dpwPOpy0fvv2Gz//rfxBouL67JgpCXNfT2QblhjLN6jmG43g6szscAYOXpEymK7zQ4HmOqs6x2hDHCdopNJY4jrBdPdQweoamLnnabPCCADD4/qDHSiOPQ9tilKWpS4o8p2otWeXwwhVBusYQ0OHjrMJ2NXV1pmoKatWg7DBBaGzH025Dbzu0tcyXt1gcVV3iGBYL3zNDixPec/OJxhj9bKT5YReun2saBzApI5HnFqxn3ZC1LU3boM1gyhP9kbUWbfToCv/5+Msfs9lsZLnEQCFgqqqq0akrI2sYGB1pvJH/FrDWti0PDw8457i6uhoX98sIl6enp7E3+HQ6jWNCARyyKIn4X+4FWWSBMdJGQJowjJIVKW08Ag4kn09ABQwLtOgEBUjIiFu+TirmBJwKeJWFGAZAIUAP+FFczM3Nzch6ytfnec7NzQ2vX78myzJ+//vf8/LlS6qqYj6fj5E1wvpIP7b83LIsRxYtDEOur69HxlFGpKvVilevXrHdbvn973+PtXY8B+LM//Tp0zgmvb29HcfxXdfx/fffs16vub6+5uuvv+YPf/jDyGYLmJSNntwrYrYR3atkm57P51EGIGNwYQqfnp7GPMdLNk7O5Xq9HsG0SBwEhF3qNS9BDTCOctfrNf/6X/9rjDHs93v++Mc/8t//+3/HGMOrV69+tIERZk36wsVMBYMs4tJhLgaty7QAyVWV91CWJQ8PDyMTGgTBaNqR2CdhTi9jm2TjJf/IBkUYZrkHJRhePmvX6/UIeuU5lWkAMD6vwqxe3vOX4+rLvxeAfllVKpsZkZ/IzxBW819k5L2YL3naHkgiD6jJ8j1BYCjrBi9IiZI5TdsTKEXX98RJjKcafG0GU44f4cpmqGVT0DcVZV9yvRzGHofTgUORs7i6IZktyIsKqwICX+FHEZ11HI8Z/nJGkiY82ee2mDCk6XraqqV3Ct+PSSZzjDdk+MVxSt20HLocQ0fvLIdTxvLmmrtXb/i8KThkOdYqrAOnDFE0wQt88uI8uLJp8LSibUsi42GMZj6bksTJMAbUP2RQyQ45nXh4YULVWKq+IzAM49SqYjGLyPITsySiqBrmyxvieMJhlw+6n7YgsAWBgXe//Cv+9f/2/+JPf/gjrq949WJFfoa2jijLDusYQEnXERgP6zRV1ZJXLVaXXKcpnjEM7JAmCodx0cPDA6v1ijAKmc+nfPXlax7vP7LdPjJfKko/JJkt8XyPII6HusiyxvSK2eoFp92GrsqxVU5+2hFPp+ggYnF1x9OHb3na7EknKaBomxarFVZbmtZS1y1hFBH7EKiOpqrpq4L1YkYYwOlQ07Qdje3ZHCvqXrG6fsV8vaKzFuUnBNEc3zPQN1RVQ9P5pMlk+GD0QNmeMNBoZXH0aO3Rd89jFCxGQVs3KKWJ0xnWNiTLW/76X/87miLj9tUXTKcz6uMTYWSoioq+6ejpKLKcqtnRdQ6jhw9G39MEQUQ0TUgmEXXX4ZsecMRxRJqkYMG1LXEU0Lc1+8Pjc7iwAhoi7TF5/jBWztLWPXEYDzFZleVw6gknC1a3C6rep2gMdacxfkIUhxhX0ysHjaLJ99DVhL5Plpcsr9bM0ilPnz/x/ccP3NzeUDcVYbjE9zyUs7i+Y5ImeJ6hbir8YBh1O9djbY82mrKqCMMArYcP6+FDxwI/iOCd02ilqJqKIq8uwqeH/zaeoX0GLD8ff9lDTAwyFpZ4HensTtN0ZC2Embhk92SUKtEwMsa9uroadX7n85nb29uxfk4yKCWcfL/fjz3FMrqUvxPmUpg1YajEHCIRJpKJeHd3x9u3b3l4eBid2gJQZbyeZRnww+Ip4FYq8iaTyRjALcBCxnvCjMnvFqODMHRSa1gUxWgckX5n0c55nsdvfvMb/t2/+3f89re/pes63r59O5p7LoOv5XfImFkMRQLCBETJCPT+/n7U5i2XS371q1/x4cOHMVImCIJRwhDH8cjCyZhf2pCqquJ4PDKbzUZg/P79ex4eHkbW+DK0W0w0wqIJYJTaTWlaEieztB7d3t5ydXU1giT5fmFsBSDLhkW+TrSSMgKX+wYYR8/iPF+tVvzbf/tvKYqCt2/fMp/PR+mG6Ibbth11qwKKhcUTkCebL2FThW2UKB25ZzebDYfDYTwHwjjLNRLH+WUSwnQ65cWLF6PhTeQa8j3AKJkQxvZ8Po897R8/fuTbb78dpwkS2i7nTKpH67oetcOXjK6w2HIOL2Ox/qlZSSYQSZKMQP+Stf7nsJQ/GVCGfkB5zqmbEqM6yqKgbTyaWuEHM6aTJV1naeoTDosfRSRhTLbbk9cNc9fjbEtTZNA32LZikvp4occhO1G0LdYLiOcLGmfYZzVBOGUyn2GdI8tyzucTs0nK1WzKfLnAdh11C671aOuGXvmkccpsMiXwA87HAwpLXVco0zCNPdq+pahrFsoQJlP8yOGHPX2viJIJSisUisj3aY2iV0Df42yHF3pY4JQXJOkETICziiiJedpscG5w7aE0WV3Q946qKLE6INCG7eaJNAyYpROyw5beWup2AH59WxF5kJ9yymzH1Tzk7s1rrl++xEQh2tPMpgl1leF7hqbR1J0dOr57hVIBT5s9xbkkCGPavqY+7pnOU1aLKXWRg4I4jlitVpzPZ7qupWkquq4migN+9Ve/4He/+yPONni6x/UNZVURhUOtn+s7OjST9Qt6Z6gfP9BUJZ+/f48zIXevvuDu1Tsev/8Txe4zSQpJnNLacmhbsT3HLGM6i+mbmq7MMAoO+yPGC/H8gDD2WOgrTuca/BlZW5PM16xfvKNtS5Tu0Qp075ikCdqFFPmJvqzxwxDfGFxToQKfrjrSxh6hP+hplfLQ8mDZFq06qragtxalHFGQYBykUcKd0ry6uebx/e/585/+wMcP73G2HzS8YUiUpPTaMpstiOKI1lqU9qi7jt62xIHHcpFS1i3aGIyyoBRGOU6nA107VG8laYrnGXwvAq0oinJgdfuOwINJ5HPYn9kdSzqXsJy9orI+HQYvjlHOG5IIlEN7kE7nUGuOxR6lwLke4xkWsxld07FYrjmdnzidz5j4zNX8C6qmoO0qkmmKZxR+MGh4By1WBmiC0H/+gBEhuaFteqwV4fcPo0hjDP3zB1sUx9zcBHz69GncGQtr/fPo+y9/CMASVkPGxbLwSEe0hC1LP7F0fAtgk4VOtG5hGHI8HkdGQ0ZlspBLHNHpdBoX1NVqxXq9HsfaYr4BRnYtCAL2+z3AyBIJMySLt5gYpP1D9IvyfoXJlLgZ0aBJP7cwm0mS8Pnz51GzJgBBTCZy7z48PIwxQbvd7kcjUxkzS2TMarXi3bt3vHnzZlz0hTmVkG4BNAIWHx+HjaQAlt1ux3K5HLWX4tq9vr4ezRvyc5Ik4W/+5m/4+7//+xEQiGFF9IoCxiQTVBzt3377LcYY3r59y9u3b/n222/ZbrdjJuZl/I1kRArTpvXQYX7ZRCP6QnHPLxYLXr9+PQJAYcQERJ3P53GMLOysXD8BexITJSy4vEeRK4jG8fLnvnr1ij/96U/8wz/8A99+++2oHRRZgzGGxWIx6kUvEwHCMGS1Wo333iUzKqYfAcGSSiDyEbnfBDhvt4OkDBifDwGql9IMMdHIZkPAtGyAmqbh6upqDJmXzYTcA2I0CsOQPM/H/nlh44XtvMw9lZ8vrvPLcywJBXd3d6ORS0oB/rnHTwaU58OR4+GAH3kU+ZGmb1gur4YRrZGFRFO3DfP5lESFtF1J1TtM12NdT9+UtFVGEii07nEo0A5tfOIgxAUxyXxNfq4JkhmeF6L9AK0cfVaSlw1l1dBPw2ctTI3SIb7xOB7OmCRFeZqyONH7AUVxousqjC3xQkOahjjXP4+kHQ7wgwjtNWRFRt12RNqjzAt8v6evKwwD+4KzODwaa2nRWBPQODMYNkyAU4Y0nRAlMV1vUajBhevU4CovMsriTJp6tGWJpz3KpkF7mjSN0F2Balpoc+LQ4+WbV9y8viNdrtidzxzOR07nI+3TntVqSl5UnLKKKExwrqVrGh4fd+x3R7744gsWiznbw5bPD5+w3YrAeMwXM6aTKdr3WC6X9LYjL3I+P3xmtVyxvr7iZndgtz2BbcDWKAtxGHM6HrE9hMmacLIk6QYT1W7zgfy457R74vruDYurW+5evuWhznAWjPEIgpC6blDaQ6Hp2o68PhHH4SCKb3tuXnxB0zvy3ZHA1zTOI47nvPryitnymul8yvnwgG96XG8JXE1sOlzf0lKhO0VX9EPPuYJGVWjdYlWD8RV+kIJS2L6nbztsW9KUQzVaMpnhlEdjHV4Q0TYVVgeDiSuIB1NJELJ5esJozdt377h98YLd/oB1PUrroaidIbcULNo42irHWcvxdKKqWuIwocjLZ7G4xfMVvlb4vofvD0YYP/QJAp++qfA1nE8Zu+2BU+lYv3jN06FERz5+MgET4VqL7S1WWXA9nqc5bo8U+ZnEs/iex/z6GuUUj4ctfdMwX6zYH3fs9jsWtxXx7Aposa7hdNoThh5JEg/suzEozfNOu3oGjupZ99M/j0CHMTdA33cEoY9WCueGyC8Rfisl4+7+ZzD5L3Tsdju22y1xHJNlGV3XsV6vR+ZJQFPbtmNOoYjyZeERECHMsiyGsqBeOrilXUQWWvhhZCpjbQEYnueNRgNjDFmWjTmPl3WEEvsigEQiTiSqSBhW+f5LnagwMpdsjTCiAhgEQMmCeVlll+c5eZ4zm83GcHcZzYpWUQxCURTx7t07vvjiC1ar1QgADofDyOqKM1wqHuu65v7+ns1mw9dff816vWaz2fDx48fR9btcLsdRsvR9Z1nGx48fubq64vb2lhcvXrDZbEYAKCB5v9+PoFteb9u2PD4+cjgc2Gw2vHr1itvbW96+fTs2ygiIuIyXEnZawFJd17x582YczYrxI45jfvnLX7Jer1ksFqPGUkazAlxkJCubmX861hb2UL5OzrOwfhIpJU76y/tAQrzDMOTz588YY/jFL37BmzdvxvP0T1+TRD8Jc3o8HseKUIl5El2kmGDEZCbNMrLBOh6PPD4+UhQFr169GtMBJOJHnjFhCD3PG3vPxfH+4sULpN1Hait3ux2bzYaXL18yn8/H7xdN5aUc5DKe6fL/ZdpweR0kA1Web7n/RcYh5+qfq3n/yYDy8fERZ3tWyxVdW2KxaOMTxj6en5DnJb5WZOcjnoMwTekqSGZLfM/HNQXF8Ym+PjOdBSg6/CCks5Yg8unRuBY6Z6h7hfJi2h7askEb6HqHc0NQdl23NF0HStG2Hb3rqfuK0PexneVw3OL7AVVREAYa21iiIODmek3ZdjilKeoGVIBC09Q1eZZxOgbo+QSjHV1d0VU1uIa+qyBOsSg6C34yRwUpeePANnR9TzKdEpkhw+98OpNnOfFqTRwHOK2o2pIwMBjtyLLTYHLoezAegecojo8c9xm+8VldLZhNU3pnKeuKuhz6z7VWlFWFY4HSPp6vCKKUusw5nwuiMCaJGnbbHaubFYvljNP5yGaz4c2Ll8OHb9tgnWU6naC0o+3qZ72Qz2q15ot3XwDfk+VDP7QOUwLt8OjYHg/0KsLEaxp8nD8hma4JfIvqag6bR66v7nj15i19vuO8f8L1DqUMddUQeD/sqBVQFQWdAz+Z0jjQ+LTWxzY9yo9RYcrq6obeKoqyBqUo8wzaknzXsL239F1LGGjaWUrmDw1LcRJj0glxtcKaiMfHB67u3jCZXQ35pJ2lbwq65kzgW6BFMWgsdRDQ1zVa++RVTV+3BFFCEqdEvo9SQ0/3dDZjOp3Q9478fCaMY/q+HRg74xEEHk9Pn9kfzqB9rDMUJsfZAXT5wdB7bYxHnCQ4OzTKaKNI4im9hux85Hg40llI5it6P6DrFPPplF4pnOrobEXXNYM0Q3fsdhsOT5/x2wblDUzSdDYjiqecspbS5RxORzw/JgxjyqrATBusrYe6Ut8DFG37LOzWAI6yGPrHwzR5XqSHD/XKDKadqm4wRtN1/ZjHV1bNYDqCUV/pnENpPeqffz7+ssf9/T3W2jHLUMCCsELn83kM2Jbg8kvjiuTRSbwNMC6iwvzJIiTjQmHIxCwAP2TbXTJ7l98jOkUBcQIQoijixYsXP4r8EcBR10P5w36/H7WgwnoKKyUdzqIxlHG/gFsBar7vczgcRj2gMJailRSQICNYWZD3+z2bzWY02AgTJQ52YBw5wg8uYtFxCjOZJAlPT0/c3t6yXq85Ho88PDzw9u3b8VyIqUXOnzCE19fX/PKXvwT4kVlKzBYSji2udoklEt3c4+Mjd3d3vHv3jizLRhZWzreMuAV8iSv9ksUUcCQgUMbc8r5FC7vdbvnw4cPImEkck+hgpR3JGMP9/T0vX74cv0bGxeLmv9xkyIjZGDOO4qUrXjS0u92OxWIx5omeTifiOB7H7aId/Pz583jOhNmWsbecV8/zxpH76XQa9ZZyn+x2O6y1YztT27Zj9/olQJZ7YrvdjtWacl6kwel0Oo3AUqpAJTtTNkfyHuX5kmdEdJnT6XQEhZK6ILmu8pwKYylgW+5dGaHLWPxfBlA+fRougl6RFRV+HNMDfjwhSNa0naIp9py2n0njEPw5ofYIsgLbHOlyj+Z8j7E5xilm0ymvX72ibDrysqVxjrruOJ0K+l7T1j1aGZQBrMP3Del0Qjqb03Q1baPQTnM+l+g4YLJMcJ4mK0rKqhpYpTCkr0KqOiMOE3wTcKp6sv0J60+4vlkRZgNbpW2Dq0tsZbAtdK3FdhVRqMAPmc5m4A3RQ8qL6KxPUZ7pm4IoWGA8D4ul6Xqavkd5hiDy8TxFVmTkpwNe26B8g3M9WXbCD32UUxTnI9t9wSkrWF/fEnqO8rTH9SWH+5i+a+mailN2pmkdTQtl1Q8Od98n9Od4aOh6fDX0wp4Piru3g0j5vD9QlyWbpyeSaYQfetRNge+HTOIpu82Bb/70Db7nsVpf0fWWP/3xW/bHHasrD19FTGcRVRvS9TnK1vROY2bXLJdzVL2hb898+uZ3TIKAq6sl2+mEzeMDbVfhlEFpB9o9Rw86nHWczyfwPIIwZRJM8JMb0BmJadFBRLJc4yUzvvvwPcftPbHpmIcetjpyOj5SFDld37NeL+nqEK0sZX5mNpswnS9p8hwvnuElNVsH9Jb17QvQjrorKfIjSivqtiGZLOlb0GFAHMX0RQHK5+PTHtNrznkDbYVSPU1TkqQxxg8oy4qmabm9uxuqHrWmLIfKucPuyH5/JErmxOkUhUFrhQ40vm9AOcyzfgfXk0aGyLOUpyeaqqFoau53O6LJmpsvvqbVMUEH1vV4zzrkoK9wdUZVHCjOW+r8yCSOWC5uWMynRFFMulpT1S2L29f454y+W9E3JXlVkecFwaJEG01vHV3fo9WgJ/Z9jzwvCIOANJ0wfK4MKQc4Nezkz2e0sTxr7ofPCGXwtIfvKaqiQGlD09YoNLZ34Iaf0/9szPmLHzLSlTGhgIokSUZWriiKsQfa9/2ho/15nC2ZjsLazedz3r17N/6daP8kZ1HYx0tzwWw2G8elsogej0fiOB4duDKWFy2jhK9fmoiEBZNsQwFZ4moVBkvGm2EYslgsxu8X7Z6wmnd3d6MBREaZnuf96Hcej8dRGykjfNGinU6nMQ/yxYsXIzDv+55Pnz6No2kZlQrTK6BORpDCFgtr+MUXXxDHMbvd7kfB2cIYyjV6eHjgH/7hH0ZQ2XUdv/vd79jtdmOg/GKxoK7rH+kQp9PpODKt65p//Md/JIoibm9v+e6778awdmGjBKBcsmFiCJHqQgFZQRCwWq1I05Q///nPPD09jZpY6dqW8fD19fU40hato3SKy/0pv/PFixfjuFbigSRsXnJKJeBcKTU62iWUXQwyMqqW4PzXr1+P8UfCfG42GzabDZPJZMy+FIbvMhtT2GDpxT4ejyOYFS3qV199NTKA/zRQXRzpp9NpLAy4urpiuVyOz0ZVVbx8+XJMWRA9r7RcCeMuJioB9cLSitkMGLW7aZo+a/V/aM6Rzwi5rnIehe2XDYOA1p96/GRAaQLFzfoKrR1d2+ElHsqEaC+kwxBGPrZo8ExPEHj0zqBMhMJRnzcErkb3R6aJxtkGUGgTEEYRp/yE0h6+b8jOBUk0w6DRWOoiw9oG7Wvm8wmT2ZT9pxNdp+jbjqa1XL+Y8fLda7KqIfu+pCoa5lOPKPVxXcV5/wQYrDUUVctjdma2fk2RN/RdT2gg8Ryz2MdXcM5PZOeCtitIbtd4oU9RNZgwIkqWVG1P0zVkeUUaGoIwxIsMdZkPETtJissONE2FiRtc33Lab5noBhWnA+PadSRJBE5hmw5sjwZsV3PYPtJXIYm5Id88PMc4ZORZRplXnM8VQ60jwHDTKg1OdazWMxwtZVWz/fzA7e0Lgtmc7HQgaAKMb+nxcW5wmPs64Hp1xf6w57DdM5/OefnyBTj4/R/+hGcsUaBx2hCEhvPThjSakUxeYcMptq/o+gxX7aiOJ87bT9x8+ZLlesF2M2O/OQ76U0/T9SXOhRjjUzU54AgmHsp4KD+idRFN17D98B0miPly9oZW1zRtRRAaEt8jDX3K+sQkCrm5XuGcIohjtFGUxZnAOZzTbB8eyb/5Ducn/O2//nek61vaOic778EM7GAcRUPzDUP7jNIedV0MI3FnWd/c0NVf8N//z/8PnXM0ZUlb5VR1gR/4g0FIe3RtS3HOWF1dMV+u6KpnjVpriYMErMLVHUEaYjyPKA5wODzfEAU+uB7bd3i+IvYc++2WzXZP3jpMEJMurzBxSl1D29V4ThFHMa1r8GxBW+0ptx9oyxOr+ZR0ktL1DqsjCCYcS0tRdZhkShrEFKcTnpdSdTuc1sRpTBCGRNEE6yAIfJrGUpUVXdsSBgGTdDAmNPXg3h4+bA2oHmvbH1zhGMqyJg4j4tCnqXOapsNoRds68qImyHLiJELpn8fef+lDYmtkIYMfcvmAcREWPaswNUqpMTZFzDrCUEkA+fl8HhdXYXsEgIgRxPd9FosF8/l8dCkL8Hv16hVff/316LQuimLUtgmbBQO7KBWHUg95mTEpGkpZnJum4dWrV2NUkIwaZdyXZdmoFZXXKiBGjBvist5ut+N4+3IsCvyQVPB8bp+enkbA9/T0RJ7nnM+DjEZem1LqR20lcr6ur69HB++nT5949eoVy+WSw+EwGlnERCEj95ubG3a7HU9PT8znc968eQPA//pf/2tklkRj+PDwQJIkTKfTH8XWSAj5ZrNhuVxyc3PD4+Mjj4+PP3Iby3+L4UlA5GW+4XfffTeaguScRFE0jmIlVufu7m689zzPG0fJzjk+ffpElmX4vs+///f/npubG6qqGgGQMIGXsgTRvgrrLYz2f/pP/wnn3Ci5EFbuUjd5Op3G4G5hvC9DvoWhFl0kDAy9sPPy90EQjOdNtKCr1WpkTuXZEz0wDBrh7XY7Gr6kQUqyROWZkOt/PB5HralINeQ+FhmIbFrkNUynU/I8//9r65Hn6tIVLrpbec2XRjx5Zi71yj/l+MmA8u7lLa+eM7TKosREM2wPyij6OkdbTd+cWC+nzJdX9C6gqyuMc7RVxqkp8OgwniZKEpbra/BClArQYU/XKsLQo6lb+t7R9S3YhrYuCENN33dDBIk/PBwOqJuOMEmYrVb46YTAtEymCzZPB4znoRicvJ4xGM/Q2Z7eDtmGSntYO3wAR3FIXRm06jHaYm1DXpxQun9eNBXnPMO0liDSlHVPWZTkWc3V8pogiLBdMYzyUFzf3LA7FnRtTV0U2LqlrUomqwmhH5AV52HX6odYq2j6QWumnMN1Laf8gO5Tgld3ZIcd79+/x1rLdDbndM7w/R2v37zG90M622OCgMlyTnE80FQFxmgmacw5K8mOO9arJVU+1PSd8j3L6xWTdEIUxs/n2TFJUuqy4dPHB168eMFqteIXX1v2xyNadawXU3zjUR4y8tOG2B8yLp1W+PGSvqmIop7iuOH7b8+U2YH1YkKTl5R1A1pjnaIoS5o6xfMCuq6htxatFEEYYb2ARqmB3Y1TPC+lUzGrq9d4pobqzP7hnr61TKfXrO9uQHs0XY/ne8zWQ0Woth2fv3/P54ffs7peMElTjAbbtRwPBzo7ZDvapsIzhniS0PaWrirxjUcYeISzhLZsuLpaEIYeuvFY3d7QlDlVVaDRdE1D0xb0XcvpcODh8wN+GKGNhzI+vhfgBQHag7ay9B7E/gTXljhnMSaCzoK1tFVBXpx5yM9k5zPHc044XTJdL/Cw2CrH9JrufKC1DlUHtFVOWRzpno1yy/mE5XIFOkB1Cu0H5E1HlZUjUAw9nyiesD8/gfKpykHuEYUBGoifR4ayoAnAkA/wMAxpn5mdy4w4+dCUvDoZcw9s1Jm67jiecpQxhNHzyMb7ucv7L328efOG169f8/3334/h2qKxkgifuq65uroacx/FKCM1h8DIMt3c3IwjaslklAXoMjhZ2EUZ4V2K+iXwfL1ejwzQbDbj4eFhXOxEiyaARkawwnyKEUSYFPlz6eoW9k8iW4QtFJB3dXU1vn4BdS9fvmS/34/NKWJeubu7G7Wdwi7KmFdGrl3XkWUZ1lrevXvHbrfjT3/6E9ba0XUcBAFffvnlCOjCMGS9Xo9h6gJcz+cz+/1+rHfc7/ecTidub2+ZTqcjgytsU1mWfP/997x+/Zqrqyt+/etfjyPb1WqF5w31kZe94TICF2PPfr/nH//xH8f2FwFhMp6VvEYZ3wooky5p+GGjISyebGSEsRNG8eXLlyMz6/v+j0Lmv/32W+7v77m9vR1BqxiVhCUToCPspGhNJfKoLEtubm7GyKBXr16NEgTJYpR7Yr/f8/HjxzHP8VIb6fv+eF2kH1wkI3Kfy889n89jXutsNuPq6mpk+Pq+H8fmAvgk11TyMqWhR96LGJMu9cJiIhMnthjTlFJjdacA/Et9qrjCLx3s/3ef05cjeGMMp9NpnD7ItRVt6k89fvJXXl/fjG8mjAKc6+m7Hi+w2PbE/nDg8PAty/mcpmuo6pYqO3E+nejqkqquCUMDgWE6m2OChN25xipLS0gQJ3StxfgNnnbc3V6x3z6QHYshxLwdYoHqusR/Ngr0dggIV15I2To65xHHU5I4xSiNpzVVWRIEPlESo32DNj5pOqVtHVobAj8iikJyA21X4QcaXAu2JY5DjNG0/ZCL1vYd590OS0CeFWAVUTzB2metD4owHHZonvHIipKqdtRljbYWZXvqoqLMK6zVZEVDGIQ0bU9T1wNLZTS9Btu35NmgpdhtNsznc8IgeH49HX4QD2YQozBROPRH+4am6OlsRxSETCcxGktgFJMkIplG9PSUVYnreuqgIvRCFvMVKMNDuWHz+EQcxcSTCS9e3GJdz35/wLme1WTO67srHvY5+ekzgVJof4omwYQ3aJPT1TnH7Q7blqTxlDQylGWDtS1h6NP3HU3TcXd1RdOWVK5HGzNEV4QLymyHCXzCOME6D2sjmq6l7TtiP8EGEUYv8NKEmgSjI3rP0VrLJImxwRBaf/3iK7xgQtv39M+GGRj0jdZBb4d0giAISadznFIEocZ2LVmeYWxHYHpwLVfrBd9t7oknK6rzgex8xgtCFsslcRLheYZTlpHnFWVVUWRn2rYH9ODs9kN6O7jSl8s5RVnStQ3aKJq6wfc8ojBgMU1pyoKubkjjmOViMQDxpqTY3NO0A+h11vLwVGL7BqV6FosZs+kV2inCMMGPJnTOQ5mAumrIyxrPD+iqikN+oDxnaOeYTBJUYGjrlq6puU4TMMGzps3/kehcFtSyLOkunLSDHi2gabpxweiex5AoizEBeV7hnCE7V1i3ZzabECfhz8acf4HjxYsX40jvsmZNRsbb7Zb7+3tWq9Uo4D+fz6OR5DKiRLrAj8fj+PMlR1IA3MuXL3l6emK/34+LsPR+C5gRMCUaTWk/kcw/0RwKsyisigSfiw5RGC55P5cjyEtdmES9AOPCLgBGQMblYimLsIwIRQso+sQ8z3/EBl1m/Un7kHNuiGJbrcYRuozihZkUHaUwf1KFJ7E9otOTnmjJT5Sg9uVySZIk3N/fc39/P8YevX79Gmst2+0Wa+34Z09PTxwOB4Dxeslrk2zMpmnGayGjadkYiExANiIC6qIoGrWn4mCW+wx+6GiXqB9gHK+K216Mem/evBk3H5dVg/KZI9pdaTICxtd3Pp9HsOSc4+bmZmRvJW1AQLzcV6fTifP5PILCyxBwkUhIQoG0L8lmTPSi8/l8ZAVlbC1M4tPT0490vZL5DLBarUZjURRF4/Mp979oieWek/tKWGaReYg+Uq6BaCzlPMj3y7WUDZncw/L8iFxE3rtsoOT3LhaLsZ3npx4/HXoqj9PpjLU9i/mcc9nTNS1B3ENbUJ0eyU8b0mi4aMo811x5GnyPuqvp+5bldMVscQVeRFv1lK1FmwTtp+i+QXuK8rxF2yHw2ZhhR1g3LZ4KKMoK6waDjhf6KOPTodHW0FmL70cY45FnOSr26NqKvsnRRqM9H4uh64bq4jwvMRq0VijtSNIQTw/sldaWKPQJA48qK2lbR6d9ul7hBx7G89BAGMQoY3FonFJ0vWWz3VHkJW3roUxLV1dEnsH1Hc4plNI45VN1ChME9LZCazMA4CwnijxiP6DMMtp2GJljB93hYrFmtlyxXN9wzDLK7IT1Woqypusc56ImShImSUx+PlPlGW2T4geG/enEdDlnMpvR9T1t09B1LW3bsFjdEBzPoBWbzZZbPyCdpKyvVlhn2dw/4lvFejHDhCHnVnGsjvS9Q3lzonBJOjvRnE609ZFZ7DONDPkk4HQG20E6mZAd9oNbPEzRRtO1JTw/BLZthpYcZznlGS/8EKcCAj+mty2trenRTGZXhOmE2jq6RmMZRiBF17GYxdC1TPyUq7sv2O03HE8n5rc3hIGPH4ZMplP8IEaj8Xyfuuuom5rAGPxAUZeKtqtRtiEIPG5vr2lPe0w3xGX1ncW6lv3+yOZwYL1eoU2A51uoG/wgZH01R6HJ8hzrBoG3UR2+hnkSUlWOwPdovWew1tQ0lSEM/EEXG8c0bcVpf8Apj95BU1cEYUA6mVB3J+IoGsxgYUCW5fS9I53OSdKUzT5H9Qo/iHBVTWctoW9QenBf//rXv+Fw3HAuz9iu47DdcH1bYiJN1w8VqMJKifBdPnirZ/2TLJSSVTlqjfp+zKuMopS2A2s1ZdXhVEVdt4BCa/OTP35+Pn76IfpGiaFpmmZkp47H4zhWFSbi0uUro9Hr6+uR7RKG558GK8vCI4uZsJ8yThOgcWkYEVOHsEvC8snvEMZIAqBFFyc6TQEp8vMuweHlqFGAmDAz4miV7+m6jsfHx3GcLqBBFnRhfYThEgArrFaWZePvFWByGbsjkUkS/SNsoVwPGblPp9MR5MjvPxwOrFYrFovFj3IM27bl6uqK/X4/ajBlzHl7e4u1lk+fBq+DZEVeal9FryoMalEUI5icTqcj+JS8UWvtyOTJRkDMJcIQn06n0SAi506Yxfl8Po51hQUXs468tzAMefXqFZvNhv1+z93d3bh5kOBxYGRKZYMr8gaRAwRBwMuXLzmdTj8CVDJm3m633NzcjN8r43CpdBRwKgBTNjTydXL9xfkuIF+aaiTY/JKtl4gu6ZGP43jU3ErtqOSEhmE4ykYE/Dvn+Lu/+7tRhyrueglwF3PR5UZBYqeErZfe+EtNrIzCRU8telRhosVYJddMnrWfcvz02KCsZP/0idDXpInPuRxYjdNuMzAS5wPatmMfdxIltLYnnM/YV3usb8irltZC2fSUWUXnfKoWpmFE23Ropanrgvx8oDhVQ9VcmuJHEVkDJhgYlKazVG1DEMeksxlhlKC9AD/wccojTWJ87aCv0UAQRUxnc5rOYq10DnccygNR5I8xJkHg4xnNfJ5Q5OfBPdzWNE1N2ys6NEpFGD8AXeEZH+0ZnLXUTU/oGzoUTe3QxiMy4dDMU+R0bY2nh50bdUuQBDgvolYB1gCmI4o0280j02mEcz1VXRJ4hkkywfN9egttD/FkiRfPULXjcf+RuVNgNRafIJ0RmqHruyhyyvzMd99VvHr7lrbrqJuOyWJFbDS7zWbIQQx82r5nub7idD7x8PjI+taxP55I0pT11Q2BCThvD3RNy2S+IFaGwx+/x48McbDEOk00mWHrmKoZ3Muu70hCnyjwsMoR+gG1F9K1jr6zJHFKZxSNc1g7LFJxkrCzPbYqaaqCTjmsbei7nLLcoZV9rsoMaLqeIEzplYJW0TlD2Rpco6mLkkC3OKdpupaqyDFeNLiTjSYME5zT+GE0tLZ4IbgWTU+cJJzqjHOW0WUF6WRKGCX0Zc7N3QuaDx9xSmGCkChNiearwYHeHQinHqFTeL5Pfs5JpjMWizm279AKlos5ge/Tdy2B56FR5EXO0Ns9VDPePwyxF70DtBkqLJUiUj1tWZAsE2Yv1tRtRzpN8LyQru3w/ZDrqyuyYtAEZflwj3qeQSnNZJJA21B7hs+Pj0xnEVN/gjOQn/d8/vSe6eqWMJmMi4cwUlVVjQL35HnRkA+nSxF70zSEUYTRUNY1i+UVzvkcT0eOx4LORpR1h7X8s7Q5Px8/7Tifz2ObR5qmI4DZbDajwUFGtmLWAcYsPhn7SdyPGDuEuRQwIQYWASGTyWQchUvMimgYkyQZtZICXCVvUqQTAgwXi8U4ahRDgMQg9ePn9ABG5/P5GLsiRh0BM5djcHk9lxo4AWpy38piLOBSQMdlN7OMBiW3U2Jcqqoa2UUB3FKllyQJZVn+KOIHGBmzqqrGiJpvvvmGr776amSOBQw8Pj6ODUAC9o/H4+iK3u/3TCaT0XS02Wyo63qsPfzd7343srzCeAmLJk5g0T4C48hVjEuSOSlTCjEJSdSUbDYvTSRKqdEgIj9fwPnlBkIc3uKqvjRTyUbgcgws94tsLCSwXaKe4jimLEtevXrF+/fvgR8abeR8SOWgTFhOpxOz2YzVajVqGler1cjiiQZWQJ3WmsPhwIcPH8b7SkCdfBaWZcnV1RWvX78eHd+XjUt3d3fjpuR8Po/3oGRUyrTn/v6e+Xw+ah+lG/6yOKDrurGwoCzL0dEvAfL/d5/TohuW1yryF4m9EmAs3/tTj58MKI/HgqrumM/moCyBb4Ae+oYQR6QUrfHwjSFOpqTplKItKU41bVMTxTG99siKms3hTJhqojQh8A1lUWH7Hh9LnR9omwzblWg9LNq+icDrMcEU5SVUXU/ddcTp8AFl+x5PKQLfx3YNs2mC6hsOuz2+b5hP5ygTkmcnqqYnmkw5Z2cCY5jOYnw/IInT8YSHoY9WPX3f0DQVcRLhaug6DXqIQymqEkJFlmd4k4AgiMFB03T0vcLzI4Jogu+HFFnGqa3JCjC+Iq9rXBSgdQReRBBMCMMUZVvqqqYoz/iD44Y4iKj6dqhvLFse9yeWLy1542idDzqmLDriaIgE6qyh6Ft0b7l79YrQKO7vP1O3HdPFCj9OSNI588WcoqyxCoq6QQU9y+tr8IbQ7GNe4bchk0WKp3vuXsxwvebpeMJzGtf3xLqnKbbUeODPScKQeHpFkW3I84pZNCEOIzSKrq4pck0ST6iqhv3+yJU3RGIonsNXVYMxHpPJDN+LCb2OKt/Q2RLtSrLtJxaTySChaGs6fEzg6HrFZLqk71qsVQMbVvZMIkMSxGjVsN9umMwW+EajULRdj1MK21t6NOghH7GzHUoPVZxdB1Z52N7QDOiOIE7QnofTGqs1XjzDBlOU1ujEoZsK30Q0bUVlKwI/5FzW9F3DJE0p25666Qi8AejVdY3SGttaDqcjp8OB0/nEfLEkCoPn8dHQux0EPqf8TNeUeIHBdS2h8bm9fcFquWa73XLYb2lazeFc4QUx1+sV10ZTNw1Nk+NshzaOx8d7ZosvWcxnFE1Dnu05HzZMF2viKEJfuDpF52OtpbpgKmDoIL9sa9DaYJ3FqCGT9vFxQ1131HVPXjRoz9D19jlM/ueR91/6EH3efD7/kSFEFgZhAEUjKVmF4kwWEX6e52y3W9I0ZTKZjAyKjKCFUZORmTDZAhhlNC0jVQEJAvT6vmc+n2OtHd3c8/l8XDTFoXs6nUbwKMBAGCQxTQj4TdP0R+YC0btJrqAstAJshA0S1668JwmDr6pq1NbJ+5IRovxsue9F5yiA6unpidevX/9oUZbubAFVUnf59u1bPM/jw4cPY1yTBKvL6FUp9SNWTUCu6OokRunNmzc/Gn8Lqyqsk7zf2WzG8Xgc3cYiP5DqQDmX2+2W29vbkfW6ZPAuI5ik5hFg8yzRkhHtpQ5VWmzktcn1ESZSRtby/uRnCnCTzwwBdpf1nZf/LZE+AqQkNktrPW6SZfQvIFnubwHcMuaWDZUwkPv9nt1ux/F4HCUOcp9IzI+YxWSc7/s+r1694urqisfHx3GDJ8HlNzc3o5NdNnJaa+7v70fNpZiVJAhfNmgyzhbwK9Kky3gnubbyzFzmyzZNw/39/Zi0ICYpIQ3+OcdPBpRl2ZIkk+eA8oGinU5isB7nzQ5bVVRZiTNnJuuhP7mqK7RyzBdzlBdwrPco7ZFMFySzFQ6foqgpsozQ88BWnPZPhKYjigOyvKSsWlRkUP4MHc6oOjhkBRZFlKSEUYC1PUWecTqdiVTP+mpJtt9g+3aMPPH9mDByGK8ZxsjAfLl+XgChrhua2ieJY3zPEEc+0AM9cTrDj31MDY0NyIsKz9NY11JWBbPUR2sPOkvbODqrUCbADwLiNCWMQ7RRDBUvmiBNKPDBi1B+ihd4YDxcWxJPZ+yzA1Hn0NpRNR1onzCeUPeO+fqGm5df4PyEyjYE4ZS2OGOUo20Vba94cXXLehYSeT3adlil2R5OaKNYThd4UUzZdKSzJU1dEMcJVdvxtD/QdT3OeOyOR169+oJjVhFFMUHgMV1fs6t7doczaeDz8mrFxw8f+fxhi4tvmM2vmIUxXjijzDecs5JABRinCL0A2ztM4FG1JXU9sF6FrSD2cc+JQp11qMHvTp49UWQnlG5wriYxFbfzW5SnKOuWzvY4m+KcwXYdRil8YyCKwbYor6OnwjMeZVFw2O/xoyUmAJRGeT5ojacNvQKjFfRgjCKKUvq6xKCw1jFdXtEXGW2ZDcxkEuH8aGAn4ylt2xHOA0LX43pwVUFCiFOW1rac8pKqc5yyksDTJEFI29ScjyfSNBiyXZ1ldbXm9ds3eF5AHAY0TTWM2XtH0zYkcUTVNjRlxWp9Db3iw/uPtF3HZvtI03ccTxVRtGB9+4K+qdGBTxT4nE8FSg9u8uubFX3fkmUVvXL0TYvWFq0GLZd6Zg1EuC3jsvaZ2ZFg82HX/UNfs1LDSLGsK3w/4PP9ns12T1V1NI3FOY0xAQ79M6D8FzjKshyDwYVVlC5hcSVnWYYx5kdO40tDh4APif9RSpFl2cgGWmvH8OokScYGFFlYZRwnWZdpmo7mINFrGmO4vb0dzRcyuhXzjQBS5xyz2WxcAEXnKQyfaCOBcXwri6lo0gS4XLbmXOoCRQsojI2AJ/nZMuoXZrXruhGQXY7L5XvatuX6+po3b96MC7OAWjFitG3L3d3dmLkoi/t2ux3jfwSkCjgTICRgRGvNbrfjiy++GF33kgkpG4I4jrm7u+P9+/e8f/+eOI5HLaZUFQpohx8yR+U9iVtaonrgh1xFOY7HI+fzefx/z/NYrVY/YruF6b7sxhbWU4wjEly/2+3GWk0BryJ3kP+/BI4C9mTUP6SiFKPbXPTAYlSRVqfL8a6AU3F9n06n8Zo3TTOywAKGb25u+Oqrr8bPRZEWCPMt16ooCq6uruj7nm+++WYMmZcK0CRJePHiBU3TjD/rcDiMm7TLDR/8EPcjG0M5hyJJuHz+JLBdjHJyz8s1lE2K7/t8/PiRx8fHcVMk94D8vp96/GRAWTeW16/u6LoBmKyubumtoa0syll22x1ZXaFT8LwAYzxsPzAVk3SC1Qbfz5ksr5gurjHhhLLqsa7DGA9NS5UfqaqMxc2S+WKKOmRYk+AFKdo5tB9jnaKshgBp43v4YUCL43DYs91uWSQhN8sJbZODa7HO0jlFaxVtB3XTYHwI4gk6iFAmQBsfZTya3hHFE+qqJooS8nxoe+mbDj9MCEMPeo+q7YniBOVqtPZomw6jzOAs14NA0/g+TmnqqqFrWozxMIGP8p9T85XGYdB4hCagczXWQjpb4D894HqHteBQWBwmCClrx3J5y3RxR20Vnc6Zr9Y8ZGfyvCSdJKTThNZ27I5nkgBiMxiF1uuAx92evCyZuAVGa4xn2H/eUxQVYTQjKzKS2QwVWJTnDVpVDP8/9v7sR5IkzfLFfrLobquv4R5b1pJdPd2XM5wZgpiHwfz/uABBkMNmd3VNZeUSqy+2664iwgeNT8uiL3hvDFF8SwUSlZXhYW6mqqZy5Hxn0Sbn1LQMKmJ1dUP96YHe9VwsZ3RXc45NTeOP7Leexd09y/UNpWuo255sXpAVGSaPqZsWqxVpHNE0JcdjYFAe7yxd3WAihx8GVAj4rqIpn1CuRYWWtj5yOZsxTzX10KJDz9D0tLFC2wyvY2wUM5pvHFmeoH2grhtc2xDHmsPmkfXFLSYqsWmODwYVNJGNsDrCO4XHoTzkxRKlAl15oA+BF69+xw///P/k0+cnrl+8IMsTfJQyJHNcNOdQbvA+ENuYPEsxcQb6SFMeCQSy2Zo0jhiahqZpOR2OKO8IzmFtwXq1QNuxfahue2g7qipQVyeGvifP5yOrOgTaviWKM06HI58efqapB+bLJS70tG3DajHnxd09nQu47sTT44E0T6kPzxituLiYkcQ5dX1E42i7Dh1l6NChlKduGtSXh7fseuUBHUUR1lgiG+HdQBTHpEkyMpdA2/UEpTgeR4fnzYs73r/f8PjwmeAD1sZEUUzw4atF6dfjb3O0bcubN28mBubm5mZiwwRUyihXRsEySpYRpThxLy4uplBkAVYwhmlXVfVVCLUweKLVk8Va/r8weRJ7M5/Pp9w90VoKoyKLoYBLYT9lIZUxbF3XE1CTzY/EoJwv7GJ8EHZNFmJZgIHJZCGMj4yjgQkMnWcArlYrPn369BWLI6BI+q4vLi6mcf7l5eUUKbRYLKawbQl3FyAgMT5lWU5NRrLgC5Mn41057wKoxfEMcHNzM5kuLi4uqOt60lIK8ywtPDJeF9mCaGLFRCQbA9Hcif4OmCJmhKkWdlyAtWwCqqqaxtVyzs/D18VlniQJT09Pkytf6hLl7wmAkusiph9p33n79i3//b//d96/f8/9/f1XTU7nZijZjMhrivbwPP9Toqvks0fR2Fwkmx35mdPpNElLBHSe38MiT5C4IDnnq9Vqah6SBiXJhNVaTzpY2SAJ6JTrLT3wArKlJEDuJwkuP2fiBUALUXA4HFgul7x8+XLKJBWQf55u8K3HNwPK+1evifOMiBilPelsQfCKylVEsaV3AaKE2fIC0GgNRmvqpiebRVR1Q3CeKE6JsgXNkKBji4oUyyyjPXzmdNrRNDXzy9+TLub0xxYbJdgoIho8sY0h9MTG0BIwUUQ9DHgzYIwaDTT1idIOqNDhQ0fZtETtwLHuOZY1wXm88aSzOYNNOLZ+jC+yCXU/sDs12Kjg4vKOrntHGud0nSMwcKo7ameoO4fRMcEPuCHQVA0XizmxjencwGm3YfCeVM9QWLraofXI+tg0gdoTgkWpmN5B2Y6g2itNluXM5wuGuiJKNTbSBBdw3tH2cHH1kiy/pDzsyWdzmmNHN7QE13M7v6IocnabJ5q2QznAQN/19L7DDx0f3v0EDNy+uKFvS+rqSN90tCXEswt6H5HNc9q+pamOrJdXVLWnHRR+gDSbs14PbB4/UjvP8uKa66qnajy1D9TlhiKCrJhR7Z7YHHfkywLV1ZhIk+iYZR6j1BiGrb3GY4l1hMNjI0isp6lOKBdzuZrjfIRPDOsipS6fqRqH1RHL1FKfPuFVhI0zyGYMgyWyBvBUxy2hO2Iiz+Ch7BoeP/zI6qZHRYZ0cQ1ewaAhBMYM/YDzAZum5GZJZCzPTceL11cc9kequuE3r25p6wOntqNpWgYXk2Uz+s4RRWNUkB9qbDSOu4rZfASOytMOAz70JEk8suDeERlD3ziavhyr6YqU5XIx3kN5PkoGhkCeZ3SuJytmmCgCZbi6ugJisjzHuYa6beiGAa16dHAM7Z6h3lDWAd83dIOjT3PM+pJ5MQJ571rQBhvcZAryMEWMiOtXemOtNmjOaiy9p+8HTlXFgOHx+cDz855XOuP2asFqvSJNnlgtC4o8QysNLvBrDOXf/nj79u1XI0xp15AFWZzQ0oIjIK1pGhaLxWRmEGOEGAYEnB0OBw6Hw6QREwOHjPtk4T0HV2KoEP2WLHzycwI+BfSIztN7z3w+n3IHZYGUPME4jrm5uZkkFwIYBeAIayggUHIv5f2IIUJ0pNJZLe9RwIe4ueXPgck0Iv9NonNk4ZfIn81mM0UDCdBYLBbM5/PJESzv76/M/8CPP/4IwP39PU3TTIBLtHEyPhYdooDGc93j1dUVnz59YhiGibUU57gEts9mM7bbLbvdjuVyOV0v0YQKWJER7DmTKbpT59xU7ykGH2GtRRsrGaaySZBIG2BiBWUj0rYtv/zyCy9evJjkDufjW2C6b8Q0JGzob37zG3a7HWVZ8pvf/Ga6r2S0fZ5ScG4mOhwO03k9Z2CFdRaQJQy5gPKLi4tJwyjZpwIqBcxKpBIw6RqFCZQpjYBuuYaiTRWtpIBKAdMCGoEJyEqEk8g7hG3/t/IAcXM/Pj7y+PiIUooXL15weXk59YbLtf+f3fR/M6D8d//+f+FY7mnaChjw2mAtxKkm4FFm/CKiFV3XcNoHdtsNp+dnjC84HUuGwVEsligTYXSGUim27dCqQeNpTge8d3R9QHWjtm1oa6w9Yk1M31ZUdUlwjiJP8WHsOg4xrC6uybOc/cNHurrCqhbnB0wcEecFgx9vQqsVykBWFOh4RlvVDJ3H2JS6KdnsS4o0JU1SgjKYKCEOip6RrXnc7shnl1ginGvQSpEmhijSDEM31iTiieJxnNo3Du+AoCirGhVbmi6McT/agrUEHQgokjzDqvEhemgq8jQlSzMSA2kes2/GjLhhGDA2IjIJ7SmQZTFZOiNOI5RR9M6RpTMg0PQtbggMgyOORqbp88f3qDCwWi949fKOofF8+nhkUBm1iljYBYvZip///K/MZ5d4s8JmM0KSUbdHglIkScLucOTF1SUvXtzz/v1HuqbhtDuiiphZbAkmsDluubm9xjJqFKMIZnlGVqRsnp9JTEI9BMLg8EMHoSEMDYsiJo40cRyx2R7YPj1Q5xHVfofzjmy2QBlL2fQk2YwwFOwPn/Bf3MOu73FtScTAbmjo+xaM5cPnR/7Tf4nJ11cMfYOxCoz5q/ZPRzjXMwxj5ztRQusVZe9Z397zyy8/8fOHT1ytcoxSuL6h7xUDhlPZMvQDfdcQhgpcy8Pnj6RxjFGK9WJGcB0MHcs8w+BxbjRKaROzXq/47u1b7Jdu77I8sd09Uddj7I7SijQrxjYbGwGGxMZEcUGWppSlx7oB5z1VdQTvqLoToS1ZLOaYPOdY1rSuhaEhaE2qPHESQRzjupquq0nVCBYl8sR7PwnAgw9jTaVzaK0Iw4BGE7zi6enAse542pbUdU+aHsiSFKVhvZ7jnScvUlzf0Q8tu23H/cvX/1MPrF+P//3jP//n/zzlycFf41pEo3auK2vblt1ux/PzMw8PD9PIbxiGSc8oo8bzCsTzjD3JrRRjiujAxLEqsTK73W5iPmez2cTYyKIl+shz3Z8Yd4SVE4amqio2m830Z+cBzmK8ES2eLKjqyzNL2DVh8gRUCJgTtiyKov+NaUcWf+lnFgAmLlmtNUVRTCyQgCSlFMfjcaoGFBAkAAfGZ7qMwiXk+t27d3jvubq64u3btzRNw/v37wEmxni5XPIv//IvU3WmjL3l86VpOrmnxagyDAPb7XbKuNRas91uub+//2rzIfrZh4eHr0w6MvYV45GAs8PhMBmIdrsdUnUpGk4J+RbGU4xbkn8pr6215sOHD/y3//bfuLi4mK67gFq5PwR8w1+7udu25e7ujh9++IGffvqJy8vLr7IovfeTs/8cwH/48GEy1azX6wl8CQMq7nZjDJeXl/z+97//ivV8fn7+CvTJZ5X7RjZcRVFwPB6/SjAQEN11HavVitls9tUG5NwMdm5Ak42O3E8SxyVgW75b5yUH3vuJAX96eppYfgn9v7y8nFzo8vufn595+/btNz1/vhlQVoOj85ZTqyiPJ64uMi6WKfN5yjY2DENPkiYkkcbgacojdXmkaWrqNsYrw2q9ZD6fYbQidAOeFqMDrm8oT3u6tkZZSxigiGdczHv2hy1NtaEdArPlHXFiyPKYKATc4EizOdnihny2HEelTcXhYU/X1/Rdj/OGJB5DtNu2YRgaqvpItj5yfXczRgsFKOKE4emB3e6InzvsIkXZaDQnxAnHbYWOY5armMXqBU3ZcNh8JIQEbQJdf8IN0PU9RseoKCWgqeoS5wdQmnxWMDho65pgawoViGLL4Fq6uiLLI1zfoZVCEUji5MsY1hOnllcv5wzdnsfP70jmC/qupjoeqY8n0mhOU9fsDwda50nShOAhaE3vG4zyY/5iMAxDoG8dkUm4ur/heDjxtCnZHzZYD8bG3FwsmM2u+emnd7z8bcGpqYiyGU4lBDVmDw5tRdc7lpfX9MGgd1v65yegJ0ljFosZx8MWrRXr5YpP5UcCjqA8QQeSPKNrxs95Ohy4vLyjqUs0nixJSfOM4AORTVgsVmRRoD7swA+ovqFIF6xnKzyw2z8zSxKMjej6mjhLGWxEpC3KR2y3A0/bPTZdEAZFZCKGricQofXY5KOVBq0YXBjHwGZckBerFX4YcMqwurrllx/+SJQM7HcbPm/2OBWjo5S+92hlCENHXe7IU8P99WJc2IHIeJTSmChGq5E5ns8KFosl6ovgffP0xP50xAdIs5QQPFEyZ3BwaloiDyF4FmlBFCVoG1NVLb3rRzA7DBijR01yGO8jmyUo76ibiq7pqdpRI2TxvLy95vr6mk5FGK0pTyXFhfsqN04elKJT0nasUIysHQUGLqB1wvPzlvefnhhCRJpkHA4l/VXPxXpNc2ow2nB9e0lejJWkgV/7vP/Wx7nhQyoU1+v1ZEgR04EAKdFGSoWcUoqrq6sJUIp55bwGT8CjRKKsVquJFer7nvV6PbVsgLSC5SyXSxaL8ftQ1/VU+Se/Q4wFwt6cTicuLy+nXEb4awbheXfyeS3g09PTlD14eXnJ6XTi8fHxq7H3ucNbWB4ZzwurK+He1tqJuRGW8zwfE5jilkIIpGnK27dvaduWDx8+MJ/Pp2shGlQZpYou8XzxF9AqrmcZw79584bdbsfT0xO73W5igq+vr1kul/z5z3/m+++/n0wusmmI43g6n9fX19PIW8BzmqbT9ZMx688//zydbwnRliic3W43BaHL9ZCNwL/NLpVsyjzPp/zFzWYzRdwIi3cek/P8/MzT09Pk6p+KEs7im0TjKlpY2fQIGIJx5P+v//qvpGnK8/PzxMQJMBYwJkD/7u5u+ryykZL3JQHtq9VqMiZJbSYwaUvlPhAtZQhhAoECqgWYnqcJyPWX81jX9fR9FD3ly5cvp/copjgJR5dNkkgagGkjJON9AZnGGB4fH3n37t10/Xe7HS9evODq6mrS+d7f34/TqP+JUHP4nwCUjYPWxWCWZLOYNLd0/Wls6dCKNLFkeco8T4kt7B53+KFnVuTkRc6pasdF3g34qqTtO4I3ZKn54ozuSNOYfgikNiFWEa7pqE8HZvOILI1JU0MSJbRFBq3jVFaYkBHliqZ2XxysYyh23Y/C88TGRNaMoeEKki8dyrHRKKXR1pBkcyKfkMQnnp6eCLQoWjAweM98NsNvK9quwyYLPBprkjGSxSiU6oljhVNm9N2YBK8ihsFRNyXd0NEOA3mxQmnD/jBQNTVtdSBJI2Ll8K4jVl+q/1xL/KXS0aNR2hFbTZRb+nBiv3nHZfQa7TzaDbTNifIUCEDddURpwbHu0ToiTxYYHzCuonMHrE0JfkApQ9d5oigjUBNnEaqpwPW0ZcMpzrm6fsOPv/yZw3HDyVkyp7DKU6QFaqg57fd0faB1YNKci2tFVliaco/WnsUy57jL6JqGZTEnS8e+7VN9JMSafLHAh5rgLV1VoV0Hfc3psMd1NS+KBdksQylNFltCXxKUIShNXowxGv0XMXRsLatidGH2ztM7R1QUGDWafYqi4MW9Zn5xS5HN6OsOO3MQHH3fjTE2dgT0Pni0NRAcKMX68gr6niJPWMwLtAmEoaFYLLi4rajrdsyhVHYEpH3L6Zhh9cge910HHoL3qBDAe/CBoe0w1lJ3oxZHfenTzmZzivmSKEro+jFih2Egstk0Jg4mpe49vmvQ2pLkOapTOAaaqqRvG1QYyOOULEuxUYSxFlSLUxbnPLEKPH76TBonFBfXBD8KsL1zxOlfx0HnD/SR0Ri7uVER3oONElTfU9eO4BKSdHQK921P13REWnF5MWpIl6sCZRwwTK08vx5/u0MYF8nRkzgRWcAEAMhiIQYBycUTJ7IsUMJGSU6dgEhh0mTBFS2WaBhlNCuu4TzPvxrdiaFHDDoytpTQcGHHZWwnUTUSIfP582fgryYFGY8/PT1NWjxh8c7r5uQ9n7+2AEVhCGXMLmHvck7k9yilpsxLCagGpsVdRqTiXhc2qqqqCRAJQBODlLCXwlqdN6BIPiX8tTpTTEdpmnJ3d8ef//znyakvwEeuo8T7yLWTVhqJq1mtViwWi0n2IHIJYWolYkeC4YW9E1OShLFLdM75KFc2L13XcTwep/tSAKNIAM5//vXr11MYuWxeBJzKNZX7UjISQwiT+UU2L8JirlYr7u/vp8pRue5d17Hf76d78TxrU+QLsjmTcbe8hrCv5xs1Ye3l9eWekNcVBlvYRAnUl++XaDrP242GYaz+/PDhA1mWTbpcAdTCLJ5LPs4jgYSZFAmKGIVELiDvT3Sz19fXpGk66XeBaWP4Lce3Vy/evWW3banqgeq4pesaXNCodtxJLeYFs4s5hA5Dz9CVBN9hDBhriOKxNzYyGqU9Q1/Rt4EsLrDmizEiiseecGWp9keeHx44HDYU2ZqLyytskeH7cUwxOEtzbJjNYrJijg8xh8MzoWswSn3pRRlPhmI0Z3TtOFpfzAuKWcbgB05VD11HojxtN9D1PbaH/aEhiyKarsaHwGw+o3Q13kYYbbBWM89ndG3DbjgS6SUqJLRNTVZkaG3o+5Y40cxWOfrkyecXLBYrDqVj//Ez2+cHokhjLYS+oasG6tOe4Dqub6+5urnm46enEQBHGq0GoijQ9jVttcW5QBZrZnlCHkdkyWhS8iqm6UCZhGAznB2bajAJs9mKtjmijWVwAWVibJzw+u1rOv8Lnx6f6HowNsGs16wvr3l++oTJV1T1ievLSxLjCcOMYn2Fc57N4QQajNVc3lzQV5bD5we6viHPEo77A6c4Zl5k1M2JOElJipwkK9hsSvbbZ5bXCfX+EWsGrIaqLPn88TOPmwPGGmZ5ivJQzFZoY7BpDpHBaMcym6ECxHGC0garA1GsiKIxTD6OYrJco3TCqR1o65r50BIrTz90eB1AGfrOjw02OqC1ggAqQNc1tKeK2GqSvODFyzd8+vATBs1FEVMdD3gX0AF8GNCRIY9mKBzGao5DS/zFSWq1+TKSH3j4/DAyoXjSIiefzWjabpRE2JQuGI51x+AhzedEUZhiRxrXE4IjspbFakEcW0yn0WYEtFV5Ik9jlNF4ZUmKBd1xj440M5uPxqDNE/Q9Hz9+ZtYrvr/7A1GUMPKpf3VzyljEWjsGwbfdGOLvHSFAns3Z7hu6PhCIiKICxUAS63ETEQZMHmN0gTEBlENpGNyvDOXf+nj9+jWbzWYyU/zb3L/VasXV1dXEQEmsjQC68wYkCfAWQCM/c66JFMPBdrulKAru7u4mDdl568dsNpvGh7vd7ivQAUxtMaIVBCatobjDhRU8j3TZ7XYT6yeOcBk1C8ATlrDve25ubiZnuzSOSKXoer2e4nBWqxXH45FffvllGvkKKys1eN577u/vubu7m0bR59mXkqsoY0npYpbmF1n8z0GvAOrlcvkVeBE263e/+x3OOT59+jT93aurK25ubvj8+fM0cr+5uZnArICQ7XY7gfMXL14wn8/59OnTlBW62+0mt72A39lsRlEUPD09sdlsuL29nTYBwpS9e/eOx8fHSXfpvZ9G3eLYl80M8FXtocgQRPZw3hxzHlMlsgV5JgnL+G/jb47H4wTq3759y88//wyMOEBCz4HJkHW+8RC9sKQBCCv/4cOHrxqe5H6S8y/g3ns/ucoFoEoOZBRFk8lN7qWu66aSAWGUZVQeRRGLxWLKMO37nl9++YWmaabeejnEPFlV1XS+RbIBTOy56GUFZMpriF5ajvPN1v+sjvKbAeXFakmaauomMLSX7LfvKXclRTSnHz5hjCZPIpr6REhyLB5CzzD0tE1F3wf6vuXx8weu7lP6pmW/q/Cuocg0A4qqGb7Q2TD0DUPb4F1PXTYoFRGCIRDGdpwAVduT9A43AFphdETjAoTRHT0Kq48E9UQSj1EyIXT0LbR1RZKOMTXBezQDPngWixlJOuD6EmMVfdtyPOzRKmU+W9CrnOAhsiNg3G23zNKGy+WCNLZAoG8rIjSxAlMkKJfgnccmM5LikvVNy65q2O227DefiQzooWM4edrqSJ5FzBYFy/WSw6mmdzVZmuPxtE0zxur0J3Ce9SJnuLnmcNxj8ATA+wGtI4yN6D34YIh0go7GUbTWnjhOSLOUKE5IioL+NDBfFBwODVV1YGsSsDFpFnNzfc2xblGuIlIrkrQAFIO3HA5brIU0MRjd0zQHquOJfujxXU8SxXRRxH7zzN3dHZ6M6/sX3Lx8Q9sNPDzs6bodDBWqP+D6HqugbFtM0zBLC65vXuBdz2nXkOcLdJrRe5ivVwTvcX2PVpqyLml2FXVdUhQJi9mMWZGjTURTN2jtiK1laGtCX6N8PUoBtMbo8OXhFXBDy9BWWDsGGMU2olPjQnh9uWR1dUU71Bx3TzT1nuCHcbPiwZoxXxUXeHp+HhdhE+GHnqfHB05lxeXVNev1mmI5Z641xWzGMHiUiaiHPSa1eJ3S9J6GCGUjBpMyDA7tLZ33aGtJY0uSGJJZDmEg1ikhfDFRRJbeB1ITUQ+Koewp24AxMXXV0Taeug3YYEjzNYvLF+SrK/L5AqXVpP3abrfTw3wEGjFV3aGNxquANYa2b2jaL4YGaSUJHXnmSJIIHFitUQSiBKI0oawqnp938P03P6t+Pb7huLy8nBzQUkUnTuJzlvJ8NCouZjEvtG3L+/fvefPmDU3TsN1uJ+ZHXLvnuYSycMq47Nx1LCO8vu8n5keYo3ODweFwQCk15TyKrkw2M+emDKmFEzZM8gRlPCgMoyzkUjkpDI8AG2EytdYTcJXR+2w24+7ujuPxyGaz4enpaZKAHI/HqelmuVxyeXk5GUvkHIlZRz63sGTyHuHrbNDzogAB7cI8iS5ScjmXy+UkMXh+fp60ky9evJgYRGEo5fdst9vJCay1njYcEkYuJo+npydevXpFlmW8evWKN2/eTA5kuc7C5Iq2Vq7R/f39JEeQ7FLv/eQmFxAov1t0lcvlcmLMhT2TUb0wrAIe5Z47N0rJuRIN72az4ebmZpTydB2bzWZixiVVwBjDbDbDOcfDw8O0Wer7nk+fPnE8Hrm9veXy8nJiO+UekU3IeYvQ+bWUay4je8kvFX1jlmXTuPmclRezmbCFknUqwHA2m3F9fT1JWORe0VpPoFOe07LJkpG1fEdEC22tne5VSQ+Q8yzfSWHQHx4e+Id/+Idvev58+4B8OFDkSw7lkW7omS0WhG6JdSVKWbI0JY0sSWKJjCKxitQaokiBd4ShJ04TDrsnMAk6XhNFFpRGRwk6Kui8IY0MLnR0fY33LVpp4mROCClKJTjXorAYFeEGR9N19IPD0TIEB1qDN0Q2IXjP8ekRR8Tl0tJ3HaEvGZqa9LIh0wptFD2O3nXEWYTzEVr1RHE0Mp1K4XoHBmITsVisCSGhOuxxQ0vrO3Kr0UTM8jnz2YmyrGnKsTXIpoYQRhq8c4ZT7YnzNdf3r/G+x3c1KtKY4NHes1rMmC0ykiyiWGREscK3iuViSdv3PH7+yKl6Zr48sLqYE9uE1XLO6XQgDANJmuK7gVhHaDOytGlRoJ2iLzu0+tIvq0aN3qkqRxe7Mtzf3RGpiI+PRyrf03QN+WzJIo+I1I6mP3F6/kia/JbB5NhFTDQEfHtAhYBRmqaHNCmYXybUxx19WTHLC6rjCXwgTTLyYsGr735LVbWUp4a+btG0GF8ScPiupa1ripWaHrblqaKsW6L5jKYdODQdXsfMioLejQ+KfvB4ItpgsT4m2Bk6maOtIWKUJzjn+PDuL7jQ8spCsbrBE+P6hthENHVF155ITCCNYrq2JfgxQaA16kvosSYrRoNN41qOxwPH3Y7T7kBkDXEyBv2W9Yn5YoHREadTST8MFLPZyEYu5iSzHB/AO3DNgPeaKL9AY9A2wQ4eqxK0icAYosTQDgNDCCyynCQxKD0QlCKEcbbftd3YtZ1lpHHG4A2HeiA1hmx5R9+3BNeyyC+JTEZzOtCGGJNfYIoVyiZEcTRpe4DpAUhQBA+EcUE0WqONpukaBt8z+B7QDENDEkGWRaNGenBYrQnGYDWkccJPP73n6fn4//Vx8+vx/9shjlMJVpbmGVnwBJxMjPNZTI4YLdI0nXImhVE5NxYIkyMaNlmIpNVExnHAtPALQ3he/ybAQYwCwLRAn2f4yUItY08Zl8qiLK8lgFnGtMCkVRQtmQDOxWLxlfHhvFdZxtNFUUxB4RKzAkzazeVyOYWEy+dYrVZTUPTpdOLi4mLKZFytVhOIk1YhMfwAE8CR8yYmkTzPOZ1OE6v7+vVrtNZTbFHbttMYWQDX4+PjBHjm8/nUdnV+TWRcfzgcKMuS+Xw+aR8FAP3+97+fnPdi9JHngjiRpdGn7/spUmq1Wk3ZjOe6VDnnskEQMCXgSu4BcboLe35xcTH9TgGeApgk5sd7P4XqS7amnFPv/bQ52O120987HA6cTqdJiysbA5GLSH2kbHIEmIr84twMJtdSviPSRCXXUQ5hpmVkLZuosiyRDFKRqQjTfDwep02dtDcJwyrX43w0Le/3HGiLVlmMTMLkSwi8jNdlOpGmKX/+8595fHz85ufPNwPK8vCexQ0E9myPO+Zpjo40p2NF23ZYo7AGkixls9nStTVJHJOmCqXG3WBwHVrB0NUocuKkIM0LbBKTzS+I8wVGDZRNydCWoBxJmnN5cU8UzYniGU3d4b3CeT2NzLTVVGVDVVcw9PRti/aeqqxGnSSKpmkxsqjZCGsMgxvohx6lwbkBbR1NdyR0J9bzHD/0KG8Yuo4kn2OTHK0tbdeh6IgixdB5TqeB/a5iOYcoMqB6ImNxbQPOkMQKpxLQEV2IcBourl/QnJ7ZfNijbUIWW6yG9XoGxnM47jkedxgbCK2naTqUipjP1jT1M+XhmchW7OOACglFmtA0FVFiyeKYYGKchnbowdjRyRwZcANxYvGMTEPdD2Bi0jRnlli46tFRzL/8suF5/0ycRqRErLKEd88f2dWPxMUlenaDijLi2cB2/8Rpt2GRp1gbkecZoSk5tgNVWTFLU+rDEaM1KopBRXRDIM4K7l6+otru+PzhJ5pydB5maYSNLEVe4Jynrsau8+VyCcFjrWEVjdocNzh2hz3PTxtmRUE+KzBRRpwtISooe0WmNCZKsQZ2u4/88tP/oO9LoshwHSBZgMkMbdPT1iV5YlCu5bTbEAL4qKOtW/q2JnhDEuecjiX/41//B9Y3VJtH2qrC9T2R0XjnWSxWXN++wMYR1iQMgyPNU2bzOdksBw1BGXw/RhrliyW7/YlBebaHE9nMEqUzEpON+ZqBMXkgePq2RwVH13egBuLUEkcRXdVSliecH8YUA5Ww2ZbY2SXp8oYojfFtTVdtUd5SrF6wWF2zL490KiVfXeO1xRj7RYvXMfZ0j7qiYvZX/Q/BYOMEN7QEHINr6fsGZQtQHmMU83mBMVC1FVZZlDKgxi57rWP67teR99/62G633N7eEkIYI6i+jMCen58nACOjSPlv5yMvCaKWkRwwBX/LOFTadM7ZkyzLuL6+nhY5YZoEpMrCJuyeGGNkJC7jNWFQzhe2c/evABCJV5FOaAlyl4By0bwBE9slgEKig4Dp54SZBL7SSr548YLD4cD79++nzyZOX601+/3+q2BwicqRceV2u52AOTDpSGXsLddDgJKAEgF1wgiLNk8cuTIW/eGHH3h+fp5Ay2w248cff/xKZiCj691uNwV0i5ZRRren04k8z6cMRBndC4B/8+YNm82GX375hdPpNIFpeR0B4Vpr1uv1BLaESRZn+cPDw6TXlQih8/GsALTn52f+/Oc/T6AnhDD9TomXko3FZrOZWLmqqqboIwGM//RP/zSZkeSelY2DMMci6bi9vf0KSJ4387Rty3K5ZLvdAuNmReoehS0EpusmY3IZMUusT1VVE0AUNv3p6YnZbPZVoL3EVsmm5HA4ALBeryemX9hiYe4FDJ/nk4rGU/I0ZeMlr7FcLr9KcpDvovz5+Wbq/+j4ZkD5lz/9P7jrKi4vX6B8RpHOKI+KaqdwQJKMUTsqwHG/IbhRT5BkEYPriIcEYy1DMJgAUWzxUUzQlt5rkjQfs498hfcDSnvmswIXDJExWAVtNUZH2CQncR2RjWBooStxXYcbOpQHBujqHoMl1jFdMxAngavbGw4bjxvGTmR/2OJ8RJpmeAaqckvfNNC3RMv5l6iKgXhwOB9Br6ANdB7qckfXVxjXAY6h78Eo8iKjGxqKJKXUgQFHZGJ8G2OSmLgoKMsaHSesb17j6pLEODKr0WHAO0fbtDjleXx6RqHR1vLx04asWFEsr9BRzub5PbvtnoAhSZYYa9Ctg6ElL1KCDTSuow+e4AzmC9j37UB1atA6Zb/ff2nyiVBDoO4rlA7EqWa5SNmcArvtlmV0iVMOq8C1Jw7PH5knc6q6G89xvqBpDrjek8YZCuidx0YpYFHaUsxnNF1J19es1Bt8iIiTGasrWKyXPD9oXNtwcp5TOT5c0GqM7jGGuqpYFOnY7JOk2Cgdu837HqsMs2LOcrVkvpzjfcD3MLQBqxRGG3zf0PYdp8MzVjua047/9//9/8b10zOXr77j8v63JPkaqwwGxTD0DG01ZngajR8c5WHLx8+fub655fLqipff/R7lGsrZkvq4IzUwyxP6Lw8spTReaWyUMjhHnCa0/UBf1ixWS5LI4l2HiRTKRCxWF9RNT9k7uqFjaEpQli54XOfQPlAUOSaC3WbH0FXEMbhYE2JDX9V0TYvWis4FTKTplSXP55g0JyhYri4wJuH5YcNgxu+kJab3EVU1kKhA3zniJCW1hrKsv4w3FcZoUIEQRre61Zp2GEjiGGMtXoHSCmsUSWwosgjnO07VgTxZjhu7vscriOKUrv8VUP6tj3/6p3+iaRqur6+n2J79fj8tumKaETmD/IwszgJsZDEShlJAljBywvoopaacQPk5MRvIGE20fKLjlHG3gBAYWSkxqtzd3fH09DSNNIXVE7PIZrOZGE9jzPT7pOZP2DgZw5+zqAJMZ7PZpB0U84GMVAVAS1vK3d3dZMwQnZ9oi4HJIGSM4f3798xmM9brNXEcTzV7wORuBiYjlABmWeylElOMTrPZbBohn5t2hG0W1lNApQCFpml4fHycMj8lD1LkBwJw5frI5xeQ2TQNr169mkxQNzc3XF5eTqPv7XY7ZUuegw8BsnIthbkVN7bUSa5Wq6+c7OfAuu/7ySxzOBz4X//X/5WHhwfevHnD69evJ2OLmFYEcIsOcr/f8+7dO+7v77m5uZl0p8vlkv1+Pxm8RAMpoFGYRWnUORwOE7ssge7GGNbr9SQPOWf/5fOIQSyEMPWqS3A9MLGrwhTL+ZcYLKXU9Hs/fvw4gVXRp0rcloB9yQOVe1A+k2hLZQMmGxj5uXOTnsQpybNBAL4Yqr71+GZAufn4ka7u+Y//1zVXRUaaZyTWoIZ7yqePNIcnCGbMY6pOaB1RFBnFYsGhPGCGAVxgaHqU6chWCh9ZtI2ITIqyPbMixQ8DsbW4YImsJtYGTYfyNTYe8/midA44LtZrDrst3ekZa3IUYJQl6IimC0TBEqlA2wfiKCWez6BM0arDdQ2hOqKSFcqmuLanacC1gUSN3cltZDmVJb0LBJ1igiE42JcNVbmjrY8UpiNODWkRYWNLkuXEdUUcW6L1grKuSYo11VOF8z3N0NJ5xdBrrJ2xvnqBbzYsC4vyA27wJCbH68Cpqkb9XTB03nDcV1xnK5reg83R/UBdQ5JFpFnMLI/49PEdWRZjAINmXmQoAqEfiCJNUAmfPp7I0oT7mxueHh6pDidWyzVGw6AHHIHlomBwLcdjSd3kXwLhEyJdUW8+khYrSNaopCDK119qCfcMpiXKRhOWiTNsOmfAgzXsTs+QpjTDQJIsSLMVaZKhk4TZYgFNS9e2uMGR5RkBRdU27D9VJFGE1Z795onFcsViEaOMpWkb4qRgdXGND54QILKWsqzIogjte067PafjE0kEkfVcXy2xOuZwbKgOz8yqOeVxRpIusDoG71HKogC+xPHEUURwDZHVKK1ZXFwzu7zFdw3NcUu1f6A5PtOXB5wbCC5grCaOU7AGdMBohfcDYwxkT1O2aGXRZnRdgyKymkWRcTiVJDYwDC1dPeCHwNNuwzMeawwqDGjlcQHaY6BXnuN+x9B6Vpdr+gCnpidbLEjzgtl8AcHTtzVJmrNYa05lSzNo7t78gWK5gjCyiB5FXTejMWlsCyWOI4Zh1LcOQ0sUabx3eB8Ag3eQxAk2yknimNh6FAN934DSeGU4Hr+MxgHnYfUlXPvX4293fPjwgaZp+K//9b+yWq0mBsg5N0XOhBD49OnTBEzm8znL5ZLj8TiZeGScKMBIhP4i7j9njmSkBkzmCmF8xH272Ww4Ho/TzwkTIouVgAmpmxM2RoK7ZZQpmr3z2J8oiqb8TGHxJBLmeDxO8T+SAyiVfPK6Utc3m82mphABBgI6bm5uaJpmGn8K8BBjioBw59wE7s5zLKWdSEaWv/zyyzS6BKYgaQH0Wmt++eWXqZrv06dPU3e0nDvRkjrnJk2iMLjGjGvxfD5HKv1ms9kURyMAT1hXkbXI2Fc0t1mWTaHZSZJMo2zZGMjot65rjsfjpNF8enpivV5P43DZYIgh7Lxq8bzOc7/fT/fbzc0N1lr2+z37/Z6yLCcTi9xb53pUAU3npqyrqyuurq4mgCj3obB/ElguYEsAmLDWwhTKZkPGxWL2kk2H/Gzf95P56fw9hhAmnfB2u6XrusmVXtc1i8ViYkaFlU7TdIq+6rqO3/zmN1NtpGiVZUwu3ylhI4Hp+ySbN/l3YcflOyXfM9kYiHlJ5CmXl5ff/Pz5ZkDZVy3vNn/h6vIF8+U1dV7RdJ6qPJLNMhgyPIrtdkdwDpSl7zvapiUwatdc09GWHY6IYuiIc4gSi/ZQHg8MfQNuwGhDnOZUxxJtFCEMHI87SMER4/xozMlnc7ZPj9RVSTTLSJIc7QN9XxMlMXpoUcGTxglJnDAMnoBCG03fN/jygI0WgCFKZvgQcdhVLAtwzmPt2DEdJ/EX46vHu56mqXBu3DHrtsE7z+A8TdvjgyaJcxaLnKo8YAeHtREKUKHHDTXOGZSKR7ARW5SOyOcpDAPBG4YAp6bEOT+28aDQg+dQ92R1j8cyqJh0fkNZn6janijSRPG4A3/8/Inl1TWzixuyfI4bHAaFG1r224q2aairGqsMVmuOuz2RVizWS1Aa5wYImtVySXWqqduSKJ1hiyUrE7Hd7qmPTyyKNb0fQFmy4oKqqWnacSdfpBFplrELO9p2IE9Tfn6/J18bvPPUbQexI44MThu0jWjaI955HIo0jkEp0ixFDY4ksoCjaysOB9BRRpRkDC5QzFKSOGG722BjQx8CVXlAZxlNVVNVO9JEYZKUOM9RIWB1TJrM6XBU5Z6oPJDNSuJ4hYkztPV0Xc1wqsZ7QGlmixWLi1viYknv1WiWweFVxGx5yf75gc8fP+HrmjROSKOMoGu8cgQ1svgKTfCB9rAnTjOMUWhlidKE/eFEGAZCV5MqTx5pTm0NfcuimNMYT1keMdowyzMIDqMDWWbZb/fsdwcuL69YXdywORxIjGK+uqH3hqZpcUFTNR7nFSaeo0OO0hEhXRLNlsT5jCRN+fJ8+gIixpgTpTUE6Lp+ZMO/PFz9FyDc9T15npEkGZExrJYxxmiqqiegKcuWT5+3pFk8Msuu5/b2xTc/qH49vu2oqoqnpydubm5Yr9dTKsDpdJqAoOT9CWsn5hcBRBKVE0Lg+vp60oHJonje9Std3rLACRgBJnAzn8+nMGVxOgsj928zHAUQANO4TcAH/FXPK6Pbc4ZNnNPyu6uqmkDPuX5MjBmSwSi6PnkdAZTCHqVpOjFKi8ViYljPY3REx2atnXSEAiakgUgMJMKAffjwYWL+5NoIqJSg7LIsJ4AoulZZ4OW8rdfrKcxegKMAyv1+PwWWi5ZRzBly/fI85/Hxka7ryLKMH3/8kYuLiwnsnJuEBOAJsJJzLu0351WWoruVkbfUGm42m6lb+ng8MpvNOJ1OE0MmIFZ0iXK/yM9KDaxsWmRkL9dgvV5zfX093R9iDpNz9fT0xLt37ya5h7C4wGQMk/tAGoVEhiG5jefMquhQJUJJ7gFhwgUAisxETENXV1eTWer8fAsLKay/AOg0TZnP59P38TxrVO4dAbESBn9uEpL/fi4LEcAv7TnH43GKKJLvwf39/Tc/f769y7tsKfKCar+n7xwqTonSnP32kSxPCW5GVdcMfjwp/VAR0HSdxyn/xUkNKniGpsY1JapocPUB7wx9UxOcI9KGKElo62oc26nA6VRT9Q7VGzo13gAMjqAsi/UV3eDRKObLC1SA52qLjgxFnnLYP4Mf8EOPazpwHqUB7QnBoRVEXyJ0krhgGGDoHcFD07RoY1hdXOJ1QjuAUppIW1zQZGlOcCWR1VibcipHQ8RxdxyNSVGCUuOXd1Zk7MojWZxTJDkBh+tKVOiwVo2xO3EEPqLre7zSaGOIo4Rj2eHqhrLq2R1G0ND5QNV1tF7jjye8a4jWc9Ks4PPnj3x+3vL3/6eM6+sbjts9Q9tirKI+7cG7cSzRdSzXF6R5zum0o3cN+WLFwNhS1PeKi/WSzfYTPS3XN/csF0uawVMf93j1jvnlPUmc0fU9i4tbTpt3VE2LYqDraqJIsz+WJEnOcnVJj+Z0rOkGxyyKWawLbu/fUG0eqbc76qqkR2P6AdsPo9s9Tceg/MNhDD3PU2xiGbyj71uapqKpK5zrsDqmaxv6tqQKLcG3XF+tiSxjd3ZwaNTIjKaaxvVj4HvbcdhtWK5T+nisxczma5wbNxE+eLJ5TpTkqCjBJBlpmvFxu+PH//EDs1hhiPAOvAv0bTe2BllNkkdEsaVxA3jwPvD48cOXUUZMPlsSJQV129L3jufNlrbrmC+WxElKEUVYOmaZYjlbQ/A412OUIo4sWo86jyzLaTvHhw8PVF1PvlihMAyDI6gBk80xSYJWBoWlSMaRUZzGdEPL6bQjzgxhMGTpqNcc3LghGhxje89pT5wkWGsm4bjte2KruVgV9IOiyGLSNEYp6HpFWXpOxyOnssPYmOFQ0/UNXfdrl/ff+jidThRFwW63m0ZtWZbx/Pw8LbCikxTABX/t9hUntmjAmqaZmC1xyQqYFK2kgFQBNdL+IiNvaeAQoHhxcTGxKzLilfH7ubZSGCMBZgIMBJCd5ztKhp4AMvirs1VAqAAjaSqRxVzAkoSWSwC5/L3z15MFXM6P/Pc4jicgeTqd2G633NzcTBE3zrkpkPzq6oo8z3n//j2fP3/mP/2n/8SLFy+mUb5oTSV+p2kaLi4upupLaTKSo+97rq6ueHp6wnvP3d3dVMsorJjkCzrnpp+Vkb1oDgU8iQFGQFIcx1xeXvL69Wuen5/ZbrcTKJe6QdE3nk4n9vv9FIEjGwYxWck9JyygvI73fqpaFGAITPmMcg3atmWz2Uw91+eSCwH6i8ViMvkI+7rZbPiXf/mXaaQs95psTgRcy/sVVu/du3fTyHm5XE4GIDE+tW076R5Fd1oUxRTGL/e//BkwbXDevXs3dZ/LdRRpgrxPAbjCiEs00nl9pXx2GfnLtTxn9UVKIH3kIiERGYlMAiSA31o7xXvJff4txzcDyvpUslquaE8lBMX1aknveyI9OreGriWOLUkaU5URp92WNJ8TJ47O9+N4rOsZ+h6jDMZ3qL6iOp6ITYr2HUU2hm6Xp1FAjYmwUUpZ1TwfSpZmjpkV6Cii7VqCh8XFFXXTYaOYbuhRXlE2DREem2jiVFOdWvzQEwaPVoo4isbu7qBxfTu60L3Ce40xCdYO+BDwAYr5kmK+QscFu2PHECxKwdB2eBUwOiafzXDesNuXxEmCG+BwLMlTy+A8XV8Rp3Pq05bBw4s3v6frHEPfkc5Hpql3A8GMWjSNJlEaHxy9DwQz6hCthbbtccFgswUDDZmNaMtHnAsMTrFcXdJ1PR8+f2K/3TDc3VMft3x+/4Gbm2tc33F9dcVisaTpHME7fPCcqopDdeD71SVZko0jWOdYLgtcmPGw22GLFfntC65f3PHw8TO7h3dUp4qru+/QJiLOl3B8BB1QOoxtKH5g6FuGIeby4poexfF4oq4bojTFpjMubt/w8MvPNPsdZXUkTRKSJGZkvgaUHWjbhqY6onxPZMH7HufARqMZbOg6cD3l7gR4jodnovWCIo/Ryn3RKo292dbExFHMbrPh8+aJTlsuXynWIeX65p44jUcNqDYUiwB49vsDUZ5jo4SgDP0QSPyYlff29Vv6ak9/8liTcOoPfHx4wHU9i4sVKl7gtUHRE9uYpmsp6wo3OKpTyWKxJIkzyqpitz9gbETTtpS7J1arC/phwMbjYji4AQhEkSVNEwjuy+7b4hyUdcn17QuSPKJ1UNctQVnK45HEg9IxF6srvPOU5QkdBnS7wwRNs/cMi4h8cYHzjq7r8UERvBorQlWENtHkKHfOodXYMJQlCbP1BVU9YI2iH2q8M3z6tKftAl2raNqBooe+73AMaNt/84Pq1+PbjuPxyMXFBcfjkRDCxHyIgUTaRrIsoyxLNpvNxHjIgixaR/grWyfGE8naE9esaN2k+1pq/sR0IaPFy8vLr3q9hYWBryNKJJhdxnfyO+XvyD/n7lrJnxS39Xne4LkRScbV2+12+rz7/X7SJgpDJ4aJ3/72t1NUzmKxmECGvN65rk3OsYzhxZAhoFQkBeJslkrBd+/eTUByv9/zyy+/cHd3R9/33N7eslwup1gaAeGn02linwX8yOhbOqZfvnzJy5cveffu3RSD8+rVK4wxk65Wop/kHMsG8fr6mhDGusyqqiY3+N3dHT/++CO73Y7T6TRdN/ir+1pGxDI+F7Aj10vO52azAUZji/RVC8Mm51jA/tPT0+Q0fvPmDSEE7u7uvoq6OW/ikfYd0Vh6P/ae//a3v53On5jOJIfz8vJyGpMLiBaX+jCM3eer1Wq6P6S/XoxXsmESCYbcf+faSWEJJcv0/v5+yp0UXaTcuzKuF2f8+QZGetcFSMs0QZhQuQ/lEFArYPXi4mLahIl0QbI2ZRMpGzu5jt96fPNPfvr0ERtFNG3H311dkVpFaB2JVXQG/GjgJE1Trm+vqeoOZS2999R1RzFL6PvRvJIWBXFk8X2Da3vaUGOt5nA6kBc5QRuOVcfV5SV5nvHu3ftxQZ+VXF/ckGYFh+2WxFiSJMZ5hXcDzjdo9AgyMLgwEGcZfd9BCMRRRImm7XpMsPS+o+q2JLNbIpsTxwk2ilFq1M54FMbEBGVAW6JEY4mIo5KhbWjcwDzVtL0j6T3pbNw1tN1A157Q6xmH04EoSsh04GpVsDudcNWW2EYsZimZ1RyONcdDSVDjzj/N5ljrQENd1ZjIE5QZgZRz4zg+yVE99HWDtQk+OOq2ZznLWK2veNpsCc7z4eefOO22fPzlJ7qmwkQJN7cvSPMFnQvUbYfWimy+5P37n3j3/j0vbr/DoIi0oW06siyl6HqaqsU5RT5bUhQlXTOaXOrZgmJ5iYoismLNfvMXQtcyi6MJ8LRtDypiszuSrVNMrLFpQtkFkuKC1fU9m8+/0LmexBZjq4xSY+6o0igVSKymbXuyWBPUaHLpmwETepLEsK8PHLbPrNdL1qsZRZF80eWOTTVaj6OYoT+x8/Dx/Xsenp+J5wvSfEZRzKnLJ5Q1oDNMZIjSGW1bY5Icm85Qynz58mu6vicExcXVLU8fW2wxx2PY7ksub264vbqkWK5QSUpQX+Iw+oG2abi4fYnRGpyjKY/jdVQe17dkec7hOI4fSg0KjW8Mp6alakrapmW+mDFfzOm6dtRV2Qw/wGKx4v7+Do/hcGowSUqUZByPJUYPaDzh8ImmqmmrE11b47aWokhI/B0/N0d+/7/8XwgqhhBBsOyrBq8arDXkeQq40bGNxlqNG3riyBDpwNXFjK6raasxiWG7r1EqxdqYoBTKakyI0EpjTfTND6pfj287xI3cNA23t7fTv8tiKWMyyS2UNhRhLmez2eQEFVDY9/200AjDJ5q/0+nEzc3N5C6WjmjJw5T8RgFWAqjOXbHnTTzi1oURDIpRQoLIhXUSVvE8V1AAkozcxVAg8S0CmCRGRz4TjGycsEjiqBUGdblcTiHq+/0eYGJWhbUULSAwnU9xjnddN42OheVdLBZcXl5OHeo//PAD2+2WH3/8cfrZly9fTtdDsj8XiwU//vgjP/30Ey9fvvxK8yfNMmVZ4txfm1wErM7n82nEKTIEAdHncgelFM/Pz1xeXk6ASPSjt7e3fPz4cbo/ZJQuYEbYOMm2lFHvucygruvp9S8uLqawcAF/kiAg1+7nn3+eOsKLophYZNGaygapbduJkZR7U+JyYKxjFNMUMG1+Xrx4wWq1mu4baeKp65r7+/vJcCMMtLDgAsxlUyX3rORHNk3DcrlktVpNRjm5N1arFa9fv/7q3suyjP1+PwE4uQdPp9OUBytmn6qq+I//8T9O5x6Y7k0xHQETKyrGOBmDX19fT80/4pSXjZLcU+dJCN96fPNPvv3uNbcv7ijmC66vL9iXJ7wP3N3csJwX7LZbnh4+Y60milMW6xWdV5R9T5TnJHlOpCKq42hND36gb0r84DAqom8G9tstURRTFJa66UBHxGnO4Dw+KLSC9XKB1/YLYu/QeYrWAe968iLH9T2LWYpyKcp3aJNgY02UJPgw6iKPp5pMQ3kq2Xcd1y8cUWpHnZjWdH1HVWmC0cR27CHv+wEXRuMx3hFch2cgBDvqM52naVoGN9B2HfgOtMEHOBz2pGnKm/tbkqc9u8d39IPjcjnDzGK0tpR1R+8964srnEoZQo/WChUpgmsZ+p7IKPqhozwdWMQZAaiallwFgta03UDd9Axf9J/GaKrTie3jI0YpPrx/z+XNHcrEdE7RYyCOiCLDfLnEo3l+eqA89RT5JXmxJjIxBE8etTw8fuYpzri6uCKKM4oiJ/iS3dM7UIri9hVpsaA8pNT1jtyMrUT1qaQ8lSwWd1hd0zUVdVMSjEZFKfnylrs3f+DdD/+CMqP+JCZlvRoNKz4ENNA3NccvgJG4I8tn9FWHjhKGEDhsP1MedhjVcnP7glcv78jThPK4Yxg6yrqibWqG3uH6ge1uw6yY8Yd/9w8Uyzmahnr/ARvHpLMY70fHqg+ONF8weGjaZtwURRGu70Hrsds8zdntN2ATXr5+y9XlnCKJiPIZnbI4pYljiy9LlPPgPE3bEhlDmmWEviVJLHkWs9tvCUCep5yOO0Czmq2ItSGZLejiHhtZlvkSNVfM5nMGD+vrJXG+oDzuaTpH7+Hw8Iksz1msluyeHmhOJcPgcMOYD7nZPBLw5HnK4+cPXH//D7z8TU2URkQmwmGom5aPnz+S5im/+91bDO7LCEoRWTM2F2mFHxrSWTbmU/qIsta0HWOMUW4pFjmzWUbfqLGC1YdvflD9enzb8fvf/55Xr16xWCymyBtpdFkulzw/P/Px48cJmEn/cdu25Hk+6e8Oh8MU0yNjbTGXPD8/T+YZ0RaeM5xKKdbr9aSrFDAio0ZhZc7d4ue5ejKePhwOGGOmDMRXr16NuvWzgGsxJQiwE32ngIDzEX6SJBM4FQZGflZGnxKR8/nzZz5//kzf91xcXEzNLwLWZLwuYFYWbDlPwmpdXV0BTOdJNH/yHmSBPx6PfP78GaUUP/30E3d3d185wCUDVCJ5Hh4eJmAvQNs5R5qmfPr06asYJwEhDw8PaK0nZkxMMRLvI73uq9VqutbCZMVxzGq14re//S1/+tOfMGZsSRJ2dIoT+/JZzwFpURRTjScwmcOUGjuq37x5M43zhQ2XWKhhGCZz0X/4D/+B1WqFUmpqSBLmWDSUIuuQsbRockUykWXZ1PTzm9/8huvra7Ism8xFwKSpFO2hmKtk05OmKXmes91uJ5OagDnJsxTzkph3pN7Sez9FE8nEYBgGPn78SFEUXFxc8PnzZ06n01eyjoeHh+l3ffjwgb/7u7/j+++/n6KIZKPy7t07iqLgD3/4w/R9kM8ugFHuO7l/BbxLvqZEJglTeS5B+D86vhlQ3r++I4RxxLjf72idY7c7jpqZLOGweea0P5DlOSaKKVZLokERZTPiJMMCQcVou+VU1lw4j4ktSQTaB6qmQYVAUzc0WUeaz0iLOd3gUXoEbSp4lB+oypqh6/FtTZ0aNGAVdPUefM88Nxi1pC1rnGswxhIlKYMfsElM3OUkSUHcNLiyxfU9Q9/SDx1BBYw2BO/xShFQDAG6vqfrA1oFgh/Is4QijkhiSNIYm+coGzNUPc71JHGMC2CMxQ+OrqnJU8v9zQrvOn74yzt0v+By9ZZ8tubj5w3VqWO5jmkGNdYfogk6IUpjjKkIw4DqBprTnvliTZrEDGlKaBrKqqGqPN45IqtYLlYUSYpWgTSJGFrDbLagrFpMktNjSWYrLJqh6zhVJ2w0J81Knh8fKE8t11cGZRPiqED3e1R5YvPpI0bHpMm4APjheWTrmj3H05wktuSLOY17xhiFTSyzWfHFhVhzd3fLcXC0XYXDjfWQKFaX9xSLK+K0oC1PDH2P0ophcPjQEltLFGkuVwtCW7KczWirA0NZ87Q74J0DHchTzeXFgouLJVpp2i8A8Hja05QlaZIwdC2n0wFtDJcXl8zilM2njxzqHS/7itX6Chsu0ViSOKL3BqUN/ovAO9JjaD8EomQ0BDXVkd2pAhOxvLyiDw3b8kgSHLPlFWmS0Pft2CU/WNCOWTrDDz1N1WCTiFk0R0cWHUdkRYGxET54No8bmqpDGzu2GzmP854QWdphoNrtqNuWaL/F2IS+D7Sdx9iIp82Gumu4e3FL2zSAwXtFli+wUUK2uEIbw+2LW377h++5/d33FLMFQaUoEo77isfHAz//8sDF5ZrXrwNFaui7CqMUCoVBYXRg6GvckI5NQ4OjPAWa1tMMHa3veXN/QTFP2DclWZri1f/+M+fX43/++O677yYAstlspgVZgrplMZexoOQ4ihnifAwqOj5hKmTxkf8Vx7IYSs4NFKKpFHZTomuEfZLFUdg9MU/IGFPMEsJoyWvJP8KiyGKnlPpqbCsgUXRxEpckuZwC/iRUWkatdV2T5zn39/c45/jXf/3Xqb5wPp/z/v17DofD1MIirKowcQIQxHAi+joJMj+dTl99XhmjilaubdvJxCOh1QJEBEALm/X58+dpHHoeqF1VFR8+fJikCOfOX6mNTJKE5XI56RnF8LHZbCjLklevXk0srpwfgOvr60kvKaBLxsTnOtWLi4vps5zrSgXg53nO9fX1lOcpznEJGpdzIezf1dUVWZbx8ePHKY1AzEnSKCRSCdmgCBAW6YEARXnN6+vryXgjrKHoFGXDJRsgqdEUXaKc79lsNgG6z58/T1mcok+U69z3/Vc989LgJCkC8mcvX76cWHMBeAL4jTG8fPmSf/zHf+T777+fxvxKKfb7PZ8+feLHH3/k+vqa3/72txNrK8zx+UbsvPHoeDxOAHwYBt68eTMx26KH/tbjmwGlsprN8w6vNHU30HQDHj3S8wSGpsUNHbttw2x9SV4UzNIZSb5gcIGubIhSy2yx4uHpE03bUKTxqEO0mjgadVh911G3PVmxwMQZVXlEm4QsTVHecdpvqTo3xqbg6ZqaLI3BdxwPO4zqyC+XLOdLtg0EYnrX0fYDSWRxg0KZGJTh8vKK2h9omwprYwgO9eXB6b84olIF2sb4wQMerQwX6zWZ6rE0DF3J4Acio6nbBue/xGYko6YtSXO88yRJitWaaihJLSzyBK3GrM2qa+kHT906yrrHpglKJ0RJOmpFQ4/iCzjqWtqhpKnWROmCPE1oB0vXOKw1eKVxPmCimM12RxZb5rM5SZRwansO1cBffvyF+dULlrYgzhdEeUHdBVAd88Ulp8Oe4Psx8sVrZumKzKSssoSn3TNRvuTu/p7Z8gKNZ3fY0rUn2rYmjhdjBFCb41xNfTpQN2OG19PzMzcvblmuViRpQj94TDCcTi3xoFldviDJZpT7HUEcxD6Q5inVdodRirzI2e82JCbBRAkJjnlmaZqBJE94+5s34/XyjuADLnwRX3tPmsT4oeN42H5xM+vRHf70xI9/+RcWlzk//emfMPGMl7+NidIF2kY4pXBBoYwlTzOGrkGpUUi+/dJ/u1hfkC9WPH3+yOrqhvLwwPt37+k/NdzelmT5jKo8ERlLlia4pkVFo3M6SyNILKeqZHV5weWLO6qm4VRVxDbl1XdL0nRG33t2xz1t19P1A4dhoGp7imJGkmY8fXz/JXJpSRLnpHnGXXyLU19Gfg6idEHnFUm6ZHCKOBiC0jArOIQZV7oANAqFc56uc+z2FcMwdqH7AB415mrGyRitpNSoHQ4DznVU1ZGuVWx3o0TC+cBiteDyeo0JA4GBJJ3TDb+acv7Wh7WWh4eHaRwsuitx3YpA//n5eQJJkkU3DMMUGL1cLvn06dMUxSILkrh2zw07opUUFkf0dzLmlN8v2kuJVbm5uZmYHGEvJRtSQIz83LnjWF5TzBwCZmThlvcqYOW8meQ8Eka0gTJyl+YSccYKuySjZskdlLGyOG3lfJzrNkWHul6vyfN8ijKSzwAjYJC6wzRNJw2ouLv/9Kc/cX19PekeBdhIFqIwhMIuCVsl2ZXS9CNB2GKmEVAjo1jv/RQ7lCQJDw8PkwbznHmWDcbNzc1k/BJmVMbZz8/Pk6Zxs9lMAFNAlsQnff/991+5r8UkI6Yr0beeO5wfHh744x//yNXVFf/8z/9MHMf84Q9/mD63uJjPwb3cJxLldHFxwXK5nBz2IjPouo77+3tms9k0ThdAJq8tm67j8cj19TUvX76cwHKapvzud7+bDDdiipNNh8RSrVYr3r17xzAMX4XOy7hdJA5yv+R5PrHU4tIX4CyHbGCkJlc2WqLXPO/plvv9PKNVEh+896zXa25vb7/6edmMfNPz51t/cLa8QMdzrInGkVlwzFcXbJ6e+OX9j1wtZ/zn//N/5H/8+DNdUCyXFzgd0TlPP4COZ2jn6NH0/YDVijyx9BrcEAiMwl6vB9LEotICFSW0oaLuHW3XkXQVTbmhd2MIeAgO5RVWx7SuI7iWst4wzw0un+HR4wi12qI2A9d2jVKWgMYpxWq9YNYFXN9igiMxFqMUrvf4XjHg8UFjbEYUa5RVRMaQqB7Tj4zXqSqxScpsDWHoMVqTLS/wvsfEI7r3taPx0LgvZh/niY0huIFh6L/siGLa9sDpdKAwMflsTpLO8CoepQGhw/WO6rgnaAOuwZol6ISQjNrNyEZUrSNPLLN8wW7zRGIMqDF+KLaa+SwnzUataNX0hDgQzTPS5RXlPlDtKzwJXV2y5wmdLjBZwfr6lsR4nv7HnzjuH3hxfws2J0Rz0gJiNcYS9V3Per0iFEt0B6090buGy5sLTlVD7TzLxQVxtkbplK5z9IPDBMXdq9/w88ULyu2WKIkxCvphYPu8g9ZRbU+U/sR6seD5cTPqK4HZckGxnpHOckJw7A8HVDRG4oxmrJ4wBI7HA37oyPOUwcPdy3suLtf88pcfKPKE1/f3HNsBNQwkakDpgbZ3YAxJlIwi4cBoQMpitIKqd3g3NoF8//3vWM0LDk+PfPjwRLndoX3Dh/JEms3RxhJQhMETi77UDRSFsCaeJDM474iinDgxzBdLmranwaCziDxJSAaI4pim6QhKj8AuOJYXNxitsTajmF0yeM3j05am7zFZQZ4UEOUYLMpkY75lUKR5Rpwl6HzGqQ6sg8EaS9953DBQVSe8d0Q2ZugDPnJorfAEtDVjtWWIiLQwWeNDrjyVOBfo2g6tAlkWEzqH8wMhjPfjr8ff9liv11OmnixEFxcXPDw88PPPP3NxccF/+S//hT/96U845yawIYyFMDribpYAbXk90Y8JkDrvIxZRv7Bz5/E6wuKJtrAsy4ndBKboEq01d3d3U/yPhDyfa+pkXCcMi/wOYYqEQRLmTCJpZMQvC7IYlkT7KA5kAajCLp3HDYkmVSQB0pSilJqilySY+7zZR0CqsF8S8SOs8Xl4vABZYVfrup5aZVarFdvtdmJ8pc9bWNDb29vJobvZbHj16tVkbjnPuhSGT/SkYp66vb2dmMflcjm1Igl7BfD27Vuurq6m3ysRYhI99Pz8PEX0fP78edoArNfr6Zp77ydAKn9f/tnv9xMz6JzjzZs3XF9f86c//YnZbDZ1zItcQRhOYSTl3hFQKKNfYUf/8R//keVyyePj42SKCiHwpz/9aWKw5frLvSFB9MJuS6yOMKMCzmWjIoYa2SCIthTg8vJykkkI+/z58+ep6UmMPfK9OK97lKmAsPxyXwlAPGdE5T6XzZawqgJG5V4Vs5joZ8+Z9vPIrm85vl1tGS+YZTGb52dO+xPL1Yo4n5POe/LZnL5rybKcly9f88vnUasRxQnGjbmPhIihramamjRLiI2mSCJ8GnEqG9zR0Q89WZ4xm+W4KKXsetoBeq8w2pImEa5rgAhD4FQd8aWDsMQkGUYH4MtJqko8imKZ8/Ts6Lpm7LrOc/oBumGgamq870jiHEM/gjTAmtE04F1HPwS8jjFpQXBjMHXb1USxIQwRcRKTZjFxZEnjiKqqaTUMzmMiBdow6JRdVZI8b4g1JGmG0Zb9YWwbiLOcWV4Q/Cf6vsaYQMDTdD2dA20ybAxkDV19xMaGxSyj7prRdasMThnS5IsBSY+6zyRbEMeG5vRI2zWsr25ZXL4gLtY8Hjv6viUNnv3hSGQMg0mwxSXzVcu2/gk3tETGEwwMJiZdXLC+XHNoGjZPn4jvfkeUrkHHtH1FNww477DxguX6FtUm7DYbvFKYxLJIVwRjcSphub4njpacmgFlFMMQRtOLSel7RzvsSY9bormlbwND3ZMWS2JvOJY1bhiZyzxPiJKIqqkonxr2pxplE2bLccQxfBFn921PpCJ0avDBcdgdePPmJT70LJY5p0PL50+PzC9uOO33HDaPvPjtEqsjDqeGvqtJ0mLsZ09STJyigCxJqfdPVPsD83nBi/t7Hj8+QYiIvOZisSCgiLOcYnkxMp5fANdht6M9HXl8fEYFsHFM1rlx86YsUZTQ9I528CR5QQDiKMLbQNP2KBOTJjO8U0SJZpnmWGtQOsVEMx4ftrR2gYoTOh2RpAtUlIGOKWtHpzzzxZwkMVgLaVrgfMCPhd04P3Aqdyh6rBnDzSNrGaXGEd55tLWgFP0wGi1QiiTORxd6NhB8Rdc0nA4Huq5hniZj/3dbsziLPvn1+NscEgvy8PDAbreb4mYWi8UEHvI85+3bt1MkiiyKIr4XY4GEogtYkYVHDAmiKxTnroAnGRsKiJQxr+gYZXwqYE9c0A8PD9NrCRsn7I4wV/BX16ox5iswImYjAbICkCXfMsuySadZluXE1Mgiq5TieDxOWkOp1DscDhNbJmDovFFI3rOMmKVeUaJmJK9QgM556LnEzCRJwvF4pG1bbm5uphxFiW4Bpg5q+TsCtOV6CDO7XC65vr6mLEseHh54/fr1Vz3fAq4kDqjruqnNR86XgA6J55FR7r9lccWUIvmWYp6STMPzeJo0TafoIEkNWK/X0/0ijNn5qPpwOPC73/1uYs92ux0fPnyYMhyfnp74/e9/T5qm0+hWInXkuss53+12U13imzdv+PDhw1dgF5hAu1xbCSpvmoaPHz8Cf60ilb8jJiS5DvL7/q3+Us65ZEAKqBR5wmw2mzZqck9KPqaM46V7WxhF2QxKEYCEtAtQlzBz0RgLyBSJhWxURIN5OBwmYGutnbrav/X49pF3tiZJC1Yqo2sHQlCcTkcCkBUzUlPw4y/vuLx5wWI+5+PDI4vVBcvVBUmwKB3z6fSI8w2zWUbXjcn6Jk5AWVwY8/lQiq6tKZsek1lQGqUNWV4wW8xo3YA2hiLJ6Msd+6cnokRxs5ihTY7WPYP3bHd7ouhLFIG1dG1NcJ40iuhaT9VUaFsR3IAOPb47gauZz2LM4Bl8jQ9gooTBG5T90s/ZDYSmI2pbDOEsx6klhFHP47wHbXBoIpuQpJ79/kTdtOjUwuCYz5cMfsD5serOuUCep7TtieAbkmiGsjBgxzpABarPyYsZZbWHMNCUDXFWUBQ5dVON7nVr6X1gfyiJrWYAqq5nf6y4fTNjtljjTUrflzw/H8izOVFWEGmLSlK89oRsNo5B1djDrjVsy5pVvuD21W8pqvEh11VHknSBznMsCf2xIsniL+HxBTZVJOkaE51QagzJlvHEvJjRNi117VABiihisb7hu9/9Pe74xP60BQYU/ahZLFLU0BIamK9WGBVRzGcYa9kfj9h0iQqgjMUHRlBaN1itRj1rW2HjGG08m91mDOzW0HcdaZZwOmkenjY8H1vu3sZ0bc2n9++IizmdA7xCG4uJUrq+x7oY73rq6kR52LCtN5SzgqurVyRpyny+wjYLQvhSXagM5akkyWckWY7RltV6zXIxx/cDbdvRdi2H45Hn3Z7eQ5SmxGlKPzguLm/wfuytX68uUDYQpylxFAMRSkM3BLRR1G3N8/Oe07Fjvrwhmy0pqw6lwAWPQhEwZLM5xXyBdjVDuWHbPGNuV+Bv6fqGuu2p6pI0jUArlsuMJDEoWvjipznvfA4hoL/oKq224zSj79FfmJS2bUh0QBHY77eoXzWUf/NDxqvC2siiLJpFay1/+ctfePHiBcvlko8fP3JxcTEtGgKghJGRCsBzBk0WMhHtyzNQgM5qtfpKU3k6nXh4eJh0ewKuxF0qrJLEysjCK9pLYQllpCnvTQAhMLGT8j7l7wrbKEybjOFlXC5mEln0t9stVVVNwFTieM7zJAUwyvsU8CFRNzLOFKOI5AbO5/PJ/Ss/JyBRzEu73W5qRJHPIZrX8/GvjO5FQydM3eFwYD6f8/bt26lhpSzLCdRJX7eMQcUUJJsHATnClEknubCvAkL//u//foqNkkOCvIXNFWPWcrmcAtFFEiGAVRzwAsbFqS3B7HJviBtdtIK73W6Kdfrll18mHa/UW4pGUTZLko9ZliXz+XyKHZJYpvPInePxOLGBEiQv97TISKTuUgC2jIZvbm4mAHd5eTlthEQ2IgyxMI8PDw9TA9J8Pp9YegG6MI7Bl8vlxOy3bcvd3d1XWbFyz2qtJ5AuumJhKL96Tn85/3LPilRAQLnck9vt9qvx+v/R8c2A8ur1P9DWNd5HoN6z3x0o5jmrizWP1YHtbsfm9MD69p5Xr1/z4eNn/vTH/xfff/93LC+uybIYFWri2JNlEcGNi2hsE2yckuUz0nRH09RjPVi6YD63aPMFUWuNiRNilXBqHFZrinlBudNkacx8lqFqxeAymqbleDqwmFviKKPIZzyXY+iudwPBO6yyxCahpsP3FV2oCa5ivUjoqo5D6VBaY6OEYQgoo+k9NAMor9AorFKs16OwuWtqtElQyhAwYAzNAE45utZRlTWxdWTJgsvLC8qooayr8c/qE4rAajHj08MT2rdcLhJ2bYsxBdpEKO+I8xlar9kfn3n3819I0zVl21LcvyBJYo6nkkU6J44swUIaGbzriLM5+RDwKsZh0dpysVozDHB8fiDJClhejEYPP4AyxElOezrQqz12dkkyu2aIM9zQo3RFfXzkxCfMpUJnM2ySMdMGEyk2uy3GDSRaUXcRu33D0A9kWYJNM1zf0LU1Nh6I4oSq7gjtwGo24+bVd+w+/pm6r+iHhmVu8SrCtQOhT9mWjqv5jMX8glPTcmoGSJakWUFWFATvOBz2VOWJMitZz2cMQ8fD42duri5xwQOaxXJJGBzefelh1+PoeHUx6q7K44EBRRTHWG1JI4O1ZnTue0UcJ+gQ4dKETXng+fED2yfL5eqG29tbfv7TP/Hzu09Yd+Lm+pLr2Tha7NoGviyeGggobByjtBlH98slXd/Rdz11W6NVT9uUHB57rBmTCIZyh40SXFLikhlxlNP2HU1XcSxLut7hicmzJaov8TWk2qKNplPgTTy6s+3Iwuu+ots9sN1/ZPeccXt3ycX1PWkKUWqJOkM2y1ivZ0Q2oIPCD+ErLZsszNZaBtez2+7YPG+pyooheLJ0SRLFPD8/Yoxm/aVC7Nfjb3t89913X7W0SNTI1dUVZVmy3W45HA7c3d3xm9/8hl9++YX//t//O//wD/8wBW6Ltk+YEMlyTJJkquETJ68syjLWFUAiGX2iP9xutxNTKtl+YraRwOiiKKaqRGFgxCAETFouGdVLELOME4UlFPAn4Ff0lPI7z6siz2NlZFQv5qCrqyv2+/3EnElu5nq9ntit9Xo9VTvKd0DYwu12yw8//ECe5xPrI60qeZ5P71nMH6KXk0OyCIdh4OnpaWLPxL0OTIB9u91OkTrCmAnAFMB5Xp8YRdE0mhbgut1u6ft+YhOlE/2c1ZUonDdv3vDu3buJnZ3P5wATU3c6nVgsFqxWq4mVlMpLcWILwDudThNg+/jxIy9evJiAkAB6kSIIgyfa0v1+PzHfYs4RYHxeM1gUBT/++COfPn3CWsvV1RX39/f88z//M3/5y1+mYHWRBQhYF3kFMMUgrVYrVqvVxPaKE/50OvH58+fp/hIDlUhD5Lo0TTO1M52Hw8u9KZrN8yxOYeF3ux3b7ZaHhwdevXrF7e3tBGjbtp0iuwQQynkUcCm6VZGwPD8/8/j4OG1+5B6RzyElBN96fDOg1PkNsalpTjVN66kOR46HZw6nPYf9kcV8iTGa3geMNfzmu7ecjlvq4w6tPXEEi8KwicFqj7Fgo4goSmmHgPNg7ZfA8hCYfaHNvRt3w84Hmq4jmc/J9AjoglI479AqMPQddVPjfEDbhECP84osK8iy4ktrjca5Hu8GYpOSRjmbbkM7jICyK0tmUULfdqAVQzcwDGNkkQoaF8bu5DiKYNAEFFmWUp6O9F1DnNgvu5wYZyKGoPFO0X4ZWzZtO4IHa/CAC+Po0nlPHEVcrOaUpwPl8Zm2uiCyI2NI8KACJrKk8Zz1xYrPnz5x9yKlqvac8ggbRawW81FUfNhjlCfNZgwthDijWBqUjTFRgo4iskSRx4b3796R5jOUggGNMZqAwWNwPpDamLauyC4SvE1wOsHqsSbyw09/ZnCe2+/+jq53NG2LG1ryOMWanN3mmaAy0nyJth2zWUHf1LRVxdC3RBqsttAMNJ2j85b5xR0v3n7Psd6zPTzRt/tRTxgnHOoWbSNUFPG427I5dcyWt2OveJRxaBoIgbRYj0I+H9hsNjx9ek/XtSgFT9sNeT7j8vKa4/6AdwNaj2NxHxSL5ZIuBD5++JmqHXj7u99zc/8WMxvrH7sAaS7O0FH8PUZJDRyOB7abDVfXr7m+vafcfaQ9fObdpwdO7cDdy3uiOOZw2BIZ+8XBF+O8Q9kxsgogy1KyNCZpFEYrFlmMtRFN1YGC02nPqe/HIPv1NdokY0NTWVLWNZeXN2SppetKfvr5Z6I4Z75cMaAw+ZL5zRsWxQW9d7SnDa7bUz6/4/Mvf+T1339PnlqMDsSx4fbmmssrSNOYeRFjlMMaTe/1xFTJKOx8DPj48MjTw8O4CdWMUVthoGtrEjM+4B+fn7/5QfXr8W2HsCrCNMgCdDgc2G63k1tUtFbff/89+/1+0gRKzIyMPWVBk/gVYQGF1fm3jSAy5lssFl8xG/LnsgDL4gZMYeky+j0fY5//btF0idtWHKznbuvzRVSYTRjbSaQ2Upgo+UfMC2K6Ec2ivD9hD+WzX1xcfNUvfT7OBqZR9/X1Ne/fv+fVq1dTg5GMeefz+RQuLjmNAvbknJ9HAf30008TEyxg4/zcCoC/urqaAI2woD/88APDMPD73/9+YgSFzTPG8PT0NLGL1topMkZ0qxJ3dG5uur6+5ne/+90Uji/mLRmPi17v6emJw+HAer2eTCjnLn+5Xk9PT7x//34yaD0+PjKbzbi5uWG3203ZpcK2r9drvPf8/PPPNE3DH/7whynXUca8RVFM4EkYau/HzvCnpydevHjB/f391CH+yy+/UNc1b968IUmSKV5oPp9PoF/OrSQIiIzinOUVfaswxBJkL9+74/FIWZbc3NxMxp8ffviBJEmmWKg8z7m9vZ2YeAGgj4+P/OUvf+Ef//EfJ0YyjmPu7u4mcClGMgHV8h2Q75XoeEMIfPz4kY8fP06tSXI9ZFS/WCz4/PnzNz9/vhlQRvMrXDgw+Hh05doI5zvq05GXr9/w3Xe/IwSH71varuPNm1dEFp6eHnB9w+bxI27o6OqSrgws1pfkSjFbrOgPNSEwVrrF9osDV9G7scUlhEBd1Wx3jpvZgjhOOez29O1AwKC/hI+bOCG2Cf0QULpj3OyNO7Min6FGDzfgybKcLJ3jhkDvS1I7MpUqsigdCM7hCKA0XTeg6egHjx8GrJXdrR0jIIYBPzgaV4HJpmxCHaW4wYGO8H7sQbbGYKwhyROyWU7fd0RxRNfVKKO5uliOD6unj6xvX6O1oxs6IqsJw1i1t1rM2Xz+iG9LtHdUpw2X17cYG9F3JW6oRwq+a1E4vB/I0pQsz1BmBOEBj1aeRRFzPG7pyzkko4bRxjlZsaA6jrvzKGiyy5JolpIkGa5OydOCoa2oqh1NXWJnF7jBodxAvlwTqYg2TdDzGTbS4D1Wa9rBjTFONiIQQAWC9hTzBUFHo/GruMRmSy4TRZFaLKNe57jf0ruOwcPT/ojOLomXL4hnV5zKmnbwKN8QRZrFYgnDie3mibouefXyjiiJMDZmtb4cFx/vGIYW5zyD99zfv+LF/S3N4DmearabJ7af3nN1eYNRgTyLCT3UTc12f2A1n1NECcoYrI24v3+J1gZtLBc3d5T7l+zUmLdYVvUow4gjiiJDqUDXNATrUMZijGUYxvtjFEVX+MGMjHBw0AeUa/EKND34nr4deHr8xGyxppgtuLm9wXn1ZaNTcCwbri6X9IOnqfZ4rWnKA0+bDevb78iKJarv6E7PKLdnUWh+/5vXLOczmr6nacYHm44i4kjjuprOebB60oWda7LG3a+jKkuqU8XQ9wRGY8VytcBaTZEnVIdxJ346Hb/5QfXr8W2H6NdEwyag5Hg88pvf/Ibvv/9+0gC2bctvf/tboiji06dP9H3Pp0+fJibvdDpxeXmJ1Nttt9uJ9REtmCxSwgZWVcVms5kW4e12OznNBZQIiySMk+gfhb06PyTq5xxgyihTwJUwMPJ7JD9QgK+M8oTlEtZS3o8wQAI4uq6bTAznsUgyhpfImd1ux8PDw1eZkRLRE0UR6/Wajx8/fiU9uL29nd6XsIHyvmV8KuBHxvzi7hW2VKJrkiRhPp9PdXnee66urqa+dNETyvhfnMZyvcQFLwaTc1OTnAMBHqK7EzZa8iUlIP+8wk/cxtKII6kBi8ViAkYiFxBm8vHxkbIsefv27XR/XF5eTgBHNLrOOd6+fcvr16+nmKHHx0fev3/P7e3tJLsQRnmz2UzB9JJS8Pr16+n6393dTWNdybN8fn6e2PhzaYfcE3IfzmaziaGVDc15bqO897qu+fTp05RFKd3qMG4ApRxAmF0Bzo+Pj9zf3zOfz6c/Ezb4D3/4A8vlchrDy2ZAWFB5jxJLJBFF8r0QkCoRTHKN1+v1dN9LHujx+O3P6W8GlM/7iv5Q0w2K1eqSKGh22yMv7u/43b/7A1E8MjWhb+mamlNZslqvWMxHR9Iv795Tnw7E1tI2HXhFHKcYE4Hq6PvxBszyUW/gnUdFCvvl5vWNBg+RsigTE0cpOimYLy5IsjlxtiCixnc9yn8BcSiUMRgzUuBd37I7bFFRxmJ9idYpNko4HlsWRYRv7dR+4hVTVIOyEQGwwaN0IIsMzkTo0NNWJ/puBMQ2scRZhI5TamKcilA2Bt0SgqKuG9qpuSHj7uULytORoe/Z1EdWxYrL1ZLUag6bR+I4ZXmdksYGjabqB7q2ITLqS3h7zd3VNZtjRXfasVhf0HY1OgyksSEygTB4rAm4vqJtKxZ4jLEE4yEMDF1JGGrKw45oZihmC2JrSO9e0ZUHPn78hWWUUO6fyJQmURbC+HM+NBzqmoeHD1zqhCLJiKOAxqN1z6vXV3RHQ71fcNyc2O+eaAbDom4oq4q06MiKC1IHkdK0bcf28zP7KrB+8ZZy+wN57FHdwOdPn2iOB1ZXV5RVi44K0vk1xGsaZvg4IU9m0B1om2eWixh0AniGviVOLKgxFzTNCo6HPV1b0w8tqNE4luQ5gx+NXafjntUsJ8LhuxLXN+wPW/LlDUErfDBEcUrTnlBxwfLyFqsVnfOEoFms1nhlKZueOMlJkpS7+3sCo0Fsv9/hugGrLBfX12gToa0mjmK6qh7fq7bYZMy/bMvR6efwRNYSpznZDNJiST5bcixbuiGAHmOjfNOiI8t8GdN2PWVZE7QiwrI9Hqge/0I8XGCURw0nFCfSwnB5uYYvzux+0IQwoJzHqJiqPLFaFl/A4GkaLUmDymSS6Ee2Z+h7dIA0S1kvF2RJhMpT9k9b9oNjtVp884Pq1+Pbju12+1VOXwiBx8dHXr9+zb//9/9+AmKyEJ1Opym4uyxLfvzxx2nsK4ukABhxTYtpRowHUqsnPceyOMkCl6Ypq9Vq0vCJ9ktYp/Ofl02KOIDPHbGif5M4FhnFnYd7y/FvKxCrqpr+/XwEKa9xzjyJTg3GNeDNmzcTEHp4eODi4mIyqzw9PZEkycQOybkV4Dmfzyf39G63oyzLqXbx3KR07s6VHEIZeZ4bf8QAs1gspjad4/HIu3fvsNZOlYbCYIqLuCxLPnz4wMuXLydWDcbN3nfffTfVCT49PfH8/DzlLp5OJ/I8Z71eT6C7ruspD1IYPmGRP3z4MMXqVFVFHMeTpEE0mOfjdGnugXEjICYaYfx2u90UAn/+34V1E8mEbCi6rmOz2UzpBXK9hQGWUbm83sXFBUqpryom37x5AzC108jG5+bmZgKVwsQCEyiX2K3zelBhDOUelftIAKywuRKfdTqdJgnHfr+fmpTkvpL7XTI0JaJKJkRighOmWzY38pwWNlWYfYkRk3tdKj3Ftd/3/f9/TDldN6B1RJLkFMWchAHNmsuL0VgwBMPQd3R9jyfQ9h2xMSjGxSnC0GPJ44J5viJKC7wfx8HWxuMI+ItLMIkTQpbibYrzI4XtrCWNEvDQ1C2KEVjapGAIFhMVxER4VdMNNWgDStM07RTOOoSBsh5ITUycZnSdJssX1M2YhxlQNM34/kNk6Iaepm5Yz2OUiqj2O07HR/LLhCgyqEHTdS2ua0mznDxLIYoYCGhjcF6h0ShlSeKcoRt3GFFs6VqHsVC3FX4YxizO2LBezWnKA31T8/Gnv5BECa/efk/A0lWOwQ3kRcr9ixuq3YH1PBt1gF2FcrOxxaQ+oec5WZxj04RG1ZzKlqrccypH8K0UDL5lcC0w0LUV3qQE7+kGyKKE+5evqasTeEe5fURrxWx1Szv40QBiI6J4oO9qXNuwmt+g9cB+/8xf/vxHfv/dHfdXS66vCy6LW56ftjw/nNgda5p+IApjTqQ1Y82jDaBtwaDnqKiiHxSHckd72HPabMnjmPpUEqmIOL8kzi8Ids6gC2pf0fmBZZKjfIk2nr4e6NoW5wb6vsPYhDhOaNqeummwCozRNG1HMZvTOzc+QGezsVGnrujrmv3lNXa2oitb7pMFcbYgCwYbJZg44vrld/z0rxu6ruf6/gJlLavLa27uXvLzn/+Z4KEo8unaxakd80OPNU+fn9k8PY1M+DC2csTxGM0VJxFaq7G2UsdoAwRHPl+g45igI5J8wfZQ4tAUizXb/QHQKDWmLDRNhwcW6zUheEKAWZ4wyzOMaqmqA7vTI11fc//qNcW8oO962haOZY83A3leQAZpmlDk2fQwF7ci8IXJ8Hg/fo/L04m+60nT+ZfRlqdpaooi49Wre3oHzv8aG/S3PmRkKAsZjEzJ9fX1pL8T97QAFQFi5yHI8veFvTrP4xPwI4HhMlqWEHIZOcqCK/pLARQilZAFUMwyMlYWACS/v+s6ZrPZ5HgFphGdLJpVVbFcLtFaTxWJNzc3/5tIIwG1YvYQJhCYHO+yyAoglZgfWZwlfuh0OlHXNT/88ANxHPO73/1uGnVKa8vr168nqYG8D/l9VVVN7t1zl/nxeJyAnMgIZNwvetZzF/vbt28nF/12u52CwOU6C7CXcyBtM5vNhj/+8Y/8/ve/58WLF9OI9eHhgQ8fPkxNLnJNzgGugBRhZCUwXNi90+kE/JWUEcAuwEcC8MWcIizg+TmWtIHze0TalaQlSNp86rqexuT7/X6KZAKmlqE3b97wT//0T/R9z+vXr7HWcn19zatXr/jjH/84jeHFVCXpBqfTiY8fP07u/67ruL6+ngw/skmT748w3+fMqGSDAhPbL/evRFEBX2kWZ7PZ5Pw+nU6TZOPt27eTmUj0mPI7gCmkXu4ZCUeXcwFMo3B5Tfm+y+ZrNpvx3XfffcW4fsvxzYAyi1Pms5RBdZzeRdTHBqUj0mLB4XTiVLUoPEPb4t3ALE2JI4PrA70bSLOcx8+PWBvzd3/4e7AR+7IDAvMiI0timi+u277vyOYRvTa4wTEMPUPf0VQDzenEoFPiNCONZlgcHoXzQNDj6NB5hsFjYjW6fdsO5zyjcTkw+IFmaOmcIs5z7DHBaM9isaTRHceyxNgI5RRleWLW1RA89WlDV+0pI8vFcmSUQgClRu1hkiTU3tH7HhMbrLIYZXBAIJDGKXk6uuuOpwNomM1zcIE4tigNKM9ytcQYi7FbmnLP5uEXZotL0lizr1owoxko9D3lcU9kDXgweJazHKsVITj6rsYmY8xLUWSoOMK5jn6oiWxEHBuMgfJ0pDApeuj4/7D3X7+SJGl6N/gzM9fuIY9MVaqrqruHwyGxxILL/59YgAsusBzODLt7urpEqqNDunYz2ws/r3fkfBdfEmjelQOJFnnyRLiIsMee9xFPjw8kWU5cJMwWS65fvuDh/p6q3KEVLLI5WZFTNTtaC0EQ0w891WHDajYjyVOeqiN+qLi/+4V1/pLFPGXXHQmTDB06TFTQOUM3eBLUtFsbrMLEOYdeQe1J8jV9u0d70FjCIKQcFErHJMWaICnwYYwJInxV0ncdZXuk0AN+sOyeHum7lqvra4r5gr53LFc5WhuMUXRNiwKOx4rL65fPzjiwfU+epkRZysPdA//yj/+D34Upyfkr6vqA1xFtCyawKO2ZrS+4fPUN1X5DlOa0Q08Sx5xdXBKnOe9++BNvXl3R1OM43rse5cYvk+VqRd02BKFBGTgexv5WbQKyLEUbQxRGo1TiOW/SKkXTdqDBhxavQkyi2RzLUQcbBBCl9F6hghjvRgkGymL8QN80NGXHLI+5WOUsFyFhGvP662/JFwtQ5nnxs4SpxlnHfrdjUURj+1A3alW1NpRVhcfT9+OmSAOH3YHddkfbeor5GXEUjhuVpmWWZKyWSzoLh7L+7C+qX4/PO5IkYbVaEYYhb9++ZbvdTouNtJAAE7AR3Zcs5mma8uHDB4Ig4N//+38/mQ2AyZCz2+0mNmg2m30CFMRVLEzLaQSKgFFgYonESCNsifxvYDLXiJNc2BVhXyR6RrL01us1wGTs2W63E7tyyoKehnWfxu0IAD4FI2JqESAjGk/J9xSQdTwep7GmGFjE1NB13dTRLGBanM/iXBdQKUzrqdZNWMzD4TCB87u7u6kmb7Va8ebNm6myT0bk4iqX8acwnMvlcgLoYoSRekB5L6dNMcLmnY7hBQRJTNDjsx5aJAQiiRE97un4XJ4dGR8/PDzQdR2vXr2axrjCTAvgFLD9+vXrKT5KnovZbMbNzQ3//b//d8IwnCKTxGwkxpqzszO++OKLyW0u5yoj+z/+8Y98+eWX06ZIzlWYcpnEaK3Z7XYTk5/n+XRfBGQLCGuaZhq1i6FIdKCn10Q2ZacObxlzi6lOWpW+++47Fs+Ra7JJkViizWYzNeicSk3kMywbDKmv3Gw2UzSQgHyRSqzXa4Zh+D8z8g60Hvt6vSMKI+7LljBLGMIF+8bTND1te0R7R4DCRTG276mrA+vlnOLFkuN+RPrHpuHyakWQKDAh+0OJ60qGtsaHAUaNTR3+WVytlUJrT9eWDH0NkcaYGD/0DEMDXlOXR3rvxsWtbemHAaUs0SqBoaCrdtT7I4kOCFKF1aCSAN1HJMmcOOxJEkegW45VjXKGeTFHBQFNvccOEEcW40PKw5bL9RVgUDrARGoENcAw9KBGZhZr0eEIFIe+Jwo8Q9s+x9uMX/IaaKqKKAw5HDYMviOZZ+gkJ8hmlPsdv/zyI2fnNfPFHBgwJkEZw2y14u7mhjCIMWHIMIzaoiiOKMs91ikUCUWxIDAhZT8QxykqMGjlCQwMfU2aBESRJksN3veU+x3zNMQkMfP1mu1hQ+Q6XFtR1nuK9SX52Tlo0ENHU5cM9Z7t5hatz0hDzdcvr2mqB55u75jPclSUcv3lS/IXOcXFb7AqxROhvaY5HLCDIzApVsdYnWDJSJJzmv4d1nocjrpvSVcvMfNzOjWK8gNdkhhNTA3UNLsHAl1h0Ry2DxwPR2aLFU4lYMa2nLoqxxpP9Sz+73uy55iFUGu6umb3+Mj5ckkSBdx+uIGuYj2LOW4fsD4kLc4wUYTCoVzExcuv4eIVfhjZ0DAOWZ5f8Oabb/nhf/0TNx8/sprn2LYnz1OiIKKuG3oLoXL0Q0cUjiO4dbxEa8N2O4484iRltV7jvKPrWhbLFXFeYExIMV+hYkfZdhRZgAlCus7RNgPaBBAY8nRJVdYo78b+dWNGF7kzYOH88gVnV1dk8zMIUjAR1vU4F2B0gNaj4SAKY9zgGbqBQI+gU6Gx1mESA86jFeweNxx3O/ouxNMD9q9uxd5hAot14PjV5f23PmQRloVQIlDCMJyYHBlLC+AThuns7GwCnm/fvqUsS16+fDnF1QiQbNv2k+zGU0e2MEkChmScK9pFYe+ASUd4OjIUJkbYVTEdiOtczDKySEqGpZyf6BhFA3ZxcTEt1KdGG9Ftwl8nY8I+CSMpIHQ+H6UZ4tqVhpr5fD71Ou92O3744Qeurq4mY4WAzbOzM96/fz8ZbYTVkuxJAS7z+ZwgCKbQcwFCArjFgSuRP7vdboq3EUe6AIzj8cj5+Tnn5+fTPZL7//DwMGlZv/rqqwkMLxYLoijim2++4dWrV1xdXU3XSDS5wiDKvZeR8qkxShqCBNgIIJRrr7Vms9lMwObx8XECunK+SZJMjLRILWTDIyxgWZY8PDxM8oOff/55CmSXnm1pHxKt7Zs3b3jx4sUE2OM45vLyku+++45//Md/5N27d6xWK9q2nUxEkkoAfBL+L5uAp6cnPn78SJqmnJ+fT6B5vV5TFMW0CZIcTglPP41MCoJg0lM656aN2mlNopiIZDMiGxRhj8UEJ+kB/1ajfBrIDnB/fz/lnJ5KP+Tfy3Pzv3N8NqC0fcnT/o724T3b3SNOh6Srl9S+wFuLCjWx1gTKoaynLFs29x9omy1XlxcU6Zw3X31J2dTcPzzilMFZjzcaUCxnKceNIY4McRQShgHdoIiTmNYYjFYE2hCHikE7+q6mqw8MfYMfPNVxi9cBg/WEJiRNEgbr6IaBMI6I4pjDMGCH8fWcd/Q4mt6S5ku0O6B0TxSPLvW27SlyTWAMXXNksI40TkiChE3tGfqxbjGKU6JAo7Siblr2xxqnGxId0/QRPvMoo1BG0bYNm8cNq/UZSZqjdIDtLYoQrRRt39EOLba3tH2AVwEmSgmjhqraEycBi3lBmibYoUOZgNX5OY/3j+AcZ1c5OozQUYgOFcoPWOcoy4Ysi0jzBUEY0w4dyoxVjkEQUPsapUatpdeO/b7il7ctb15dE4QJ8+Wavm0IghCvLPvmQBJnFMUKX5Yoa2ltS9PuMcx5eXlGc4APh3v6pqNPUorlOVF6RuBXdGFO70KsMwy9AxSHY01rO+JkRj5fs2uPeGU5u/6anS3Ruy33mw1ny5fkUY5O5hDGtNWettrh2pJgqMkCyyIJGZod3g3kxYwoKfA6RmMBNWaPKkifI1J2u3EH1pQlvh83TDjH0HUMXUtTHfmn/9//l3y5Jl2/wBQ1xvcoN8aNGBNhYgPG4oYWEzR0Q0PrPK+++Jqvf/Mdu7sPPD484u2Aa3sGZ1mfnXP9csXuuGPoG47HA5vHLUEQUuQFaZ7y1ddfkaQZcZbStB3393e8//CBznqWZ1dUnadYXqKUoW4aBluRpjPCKMJ6jVIhQZIRWo12ARbP4D0+yiAJ6elpWoM2M7zKsCpAoSeWf2RCexZF8pwr6VDaECcZgQlobccwOLQ29G2Lt/Y5gzLAGEVZHsjmOW3b4Vw0GpiiADzEv8YG/c2PU6OCmA3EESt6RwEDIsy/vb2lrmtevHhBURSTe1cqHAVwwRiwLNmRp+aaNE0np7hoHgUIiVtYKTX9jJgcxHAj+rk0TScNHzAtfsKGyuIpus5TQ4Jo7STrVl5XWEkBonVdT002YiSBv2oWm6bh/v5+ilESQCNMpgBhMaQJOBNmMk1TlsvlJ1WJl5eX3N7eYq3lxYsXUxuKMFTee47H46S1E3OFvJ9TdlPGx4fDgR9//JEvv/xycp8LiyYtOpL9Kc56cbErpXj58uXk8K/rmjRNpzabU9bsNO9TQKVEGIlW9NWrV1P7zf39/QRO5bpXVTVpU8WQI4YhAeeycQGm504pNbm1t9stwATwhAkUw8nxeOS//bf/xnK5nDIgT58hAarCugqoG4aBb775ht/97nfc3NxM90ne6+XlJW/evJkkAPv9nsfHx8kRXxQF33333QTum6bh5uaGX375hWEYuLy8nACmAOxT1v10cydjavkjxQLCOApwFDAvnx0ZmcumRJ5XkSWdhu+LtESed9mcLRaLTxqITjNWP/f4bEB5ePyFUFl++uWf2D195JtvvyNbv2LQKdb1BKoliANCPUDX450iiFOiyJMXxRgkbhRfPs/lL64u+eGHH3n38wdWyzXn6xWL+Vi11NRHbJwzqBS8I80S2oMhQBMGGmUMvR3ouxbvLGEUMVjL8VgTRglJmtF7xW53HAPH3UCWpzR5htKeQAXgFV3X07Y9wWDHerhMEQUxaVbQbI8YE5DGEbW1ZGlMlsTsnzZ0bUNVHYkDcB6SbNRdHI4l3o6h7+V+h48X9EPI0LWkeQa6wXpFWTfMVmeAJghC4iinKvd03cCxrKlai1cpy2JNMgtp65qyOhI3NUVxjgkMJkjorWOWpmy2O8qmIS/GusmyrlBo6rofGbOmY1Z4XszGvEY9hGjlmc2W9GXN08OWx6cnVJgTRCOIb9qeQ1mzWI4Gpv123PHhe1zf4DB4N7YbKRWQJKMT++HxIwEL9NCSRCFdWxOakMCEeG+I0hllqwlMhA7i52rLCBNFDJWlORwxyqCDmGHQFNmCziry+ZI3szX5+QW1CtEmAROhlAPf0zUlfjjy8nrJKlX8+McfeXh64MUXX5HNCwZrCY0iCEcwI19U2+0ODzR1AyjwnrquyNKUJE354S9/BgWrxZzmsAMdsVqdkVDjG48PIiB51g6CUiFlc2S/3VKXDSZMOTu/IMFyPG7YPG2Io5A4TSmrksF5BmvROmB9dkGazdlud6ggRFnHMFgG5/FdjwlDLq+uWcyXPD5tufv4kcenLeuLHWeXL8Zr7DyhgqpvaFpLnOaEJmcIFQExSZwSJFDVHXEUYFyLd4ahg9gHBDokMGPLpNKKIDBow5hsoMA6GCyjZKAfaLuOIs8JAoNtPc6OX3C9dfQ9xJFCqQBUwOChtw5lNK7rRs3xr8ff9Li/v0drzZ///Gdub2/5/e9//wlLJWPf06BjGUvPZrMpBue7776j73tevHjBH/7wB3755RfOzs4mLaYwXmLukPGxROEIoJQ4HtFYim5LMgmdc1MTCTBlKZ7mOp6Ow7uumxhXcfNKKLm0AGVZxuPj49S5LaBN3Nr7/X4CSrvdbtIBSlakaBSFtQWmhV2iYCSWSSk15TpKr3NVVZ+cwzAMrNdrnp6eqKrqE8ZTgJ+Egdd1zTfffDOxWBIMXpYl9/f33N/fT+PoNE0nY9VqtZpe47THWcbUAnzTNKXv+ykKRthScQMLgMiybMpXlP/vNDBc7rMAXemCXywWzGazyXF9GpztvZ8C4V+/fk1RFPzzP/8zDw8PfPXVVywWiwn0yD2T8b1kZoosQcxTcr//+Mc/otRY07nf7ycdqbi0Rdco10WA3dPT09hWF0UTI7vf73l4eJiYbqk0lPd2eXk5aSJPMyIFhIZhyMuXL1mtVtzd3fHu3Tvu7u64vr7mxYsXE5NojJl0kHK/5fMkveGS3ymOcWGB5brKJkeeNdlMiCP+1OEtz6Tcd2GVZTN3utGUc5UN2ucenw8o73+ia/f8/PZfwCqW169YXn3P+9sDzjWgE5QOaZsdiYnIZjFNs6dtOtqhx6E4HCuieDTabDZP1G2FdQO7/ZY4NqxWCx4fHmnqPSQFPgpwgwUcbuixvsP2LZ4A78aRuHmO4emHgaquyYOICA1owNG1NfPFjCLOaA4bjtX+OcBbEZiIYrak31s6pxisQmuF0ob+eUxjrcPbARPFjGuqI04MeZZhNFQOWgttOzAMnjAICIKYITC4KGBwPV3XkCcJcbLibF3QDgOLICKKUpQFBVDXYELabsy9DMMY9PilHGczHrdbDrsD6+WSYlYQRBGOCHzPbLlk/+EDj0+PrM8vCUxAYCLKfQlWk6czwjDCmABnHWmU4ZxF6xHknZ9dcPP4xP6wJYgsUbIkCCKqumW5WpAVKWnxwMPdR4KqJJ2FxMZTu5762c27WBUMg+W43/IwVLi2RLkenKcqa9pesbo+Q5sIwpQgGtlUZQxoTd329ANYZ1CKZwc17PqWsvMETlPMZnRdS6NbohS09RijyeOE2KVsb27YPrUE85im78aR/dkZaTGjadux4UY5mqqkqo5EYUzXjh3odT1+qD2Ox6cH8ixHGZjNF+w/fkTj6eojTdsRakVge6J8ic6WdNaidcRgR/PCsWxROma+OCO2CUYbgnAM07X9mOtmnSVNcowJeLi7QWvN9fU1r15fsViVtE2L1uOXtQ4CgjAY2ULvWK7OWawvmC83VHVDWR25f/8LihGI664jzXOC0NM2O7Z3R3rnKbIZLRqCGKMVQ9eg9UDbdGM8ER5ve+zQgusxyhFFwQgKtcY9jwC7tn8O+h/HIVkWczzsqQ97YhPQ9w2K52xCPZqeUM+/A491jrqpydLP74j99fi8Q9jGH374AeccL1684NWrV7x///6T0a6wXuLulmxCAXwC/gSYCUOUJMlU7ViW5Sdd3rJYw6ch5MKGGGMmA40sbPDXnMf1ej1VPJ52gcv7PBwOU6/xaTizOM9Pe7NhBMpiapBFUoCpsIOnmjcx7eR5zsXFxaQ3i+N4YgcFcAtAEnOPVOI9PDyw2WymMaz8vWguj8cj9/f3U+e2MYbtdjuBMmGFBIALWwRweXk5tcSIYUTc72dnZ1O95sePHynLchqNihtYHLve+ynbURhCkSP0fc/Lly8ncHIaFC7g7DR6STSBIqUQtvHUHCb3RZjPjx8/8vDwML2+uK8l/1KyL8uy/CRzVEC7MJb39/dTxNJisZjqB6WNSDYGRVFMzvDTTY6YWeSaCCt3dXU1McvSjBQEwVSR+PLlS7766qtJVynPlzxLIpc4Pz/n7OyMs7OzKbbp7du3ABOzL9mrUu1orZ3kHjJNED2qfEblcyXgUsxkkit6GlYvnw2J5RJGWjYGwCfufQGowGSsEz3x5xyfr6Ec9rx790fSSLE4f4OLCn6+2dF1EXG4IAh7QjRNfeR42NMbRxCEtGoUOyfG8fT0QJJmbHd7fvObb7m6vCROE8rD+AUzn83YH7aUdU2/fyKcGZQK8UNHGChoe4auJZ+tibxGux7s6MpumhaUxvmxqjGOI4o8I41D0iyma0uavqNtO+ZqFL56IAgjKufprcergM5aMAGDc3T9GNStAO3t8/uAOApBKbJihg5Hmt6pCHRE35QYDUkW0weGQ90SBhpjYJZmFEXOvq7H0bwO8V5h+5629wRhDsoTRSHaRDin0HHMYnXB4+MTVVWy2exIsxkWS9X2KOWJiwWLdcd2ux8jnXTAZrNjMV9gB0dXlfT96NbDKxwK78c/m6ctCovR0DU1STbHGOidpawr6q4lTmZksyV5U6K9Z/9wj1oMJHHGwUDfWKyFJE6J85g4sJT1Edf34D339w9E6ZzzNzH7uqPVOVkYAeMHMYwiwiiiK+txfBqOY9q+TlDkfP3d7yjvf2GzeaKyPeHZGbExaKPH3um+I6SnSAKqcs/Pu5rD4UixOCfO5qADZrME17cM9ZFjuadtxvzJum44HErW64tJO6L1yM51fcdiOWez32KUYvd4T9MObO5vOD7c8Pqb74nPXmDSFXG6IEnGSJQ0yUiSObgOXTvCKOT29pbVIieNI7Q2z5qWHqWD0bHXNNzd3eE9zOZzwueNl3fPOWzOEicJ3kPb9lgUSZ7jlMYrhestSWCoyx0fN/cESUSYpARhQpylRGGI7+Dj3S3pbEUxK7DdkbvHj2jlubhakXczvPYEaNqmxg6AGxiTyYPRxe01dTvqROeLAqU9rQDd5ZzdwyO7zQNdW6JI0Wo0wjnvMEFAGCmc1nilaU9aQX49/jaHtZaff/55irKJooi3b99O2jdhQKSlRlinUzbv/v6eLMvYbDb87ne/4+XLl2RZNv38crlku91SVdXUxCMaLcm+k3BzYcsEcJ4GKMtCKFpEYRzlz6lBQUwSoumSBV9GngJkBTgKEASmEbIATVk8JbNQTDUC8KSnXJgpWWCFzRGQKGN3GU2en59PeYoPDw9TcLcs1EVRcHZ2xtPT02ToeXx8ZLlcMgzDNKKXayXXSaKfgGlkKUyq1PGJ7lLG28AUcSRaPwHd4gKWMHQBJjc3N1PPu5h7Tl3BUiEoYF9AjDCtv//977m/v+fh4YG+76eQdblGAmxk07DZbNjtdtOYXYDhaZuMjIelFUnc1aIPlE3Ker2eJB4PDw80TcPt7S13d3d8//33nJ2dTV3sEl0ljUACuKMo4sOHD5NBRRg7YetWq9Unnd6LxWL69/JHGD3RUYrLGpiSDaIomvIuJapIGHsBeh8+fJiyO7uu4/7+fpIpCNssjK0AfPmsyZ+6ric9q4zMZWNzf38/bRblvQkwlo2EUn+tivzc4/Njg8pHfFuyWp1x/eW3+GCO6xJUEBKnMUO/wT67P8PQoJ4dhF0XMLQd64sVJghI0pS6qTmWey4vr/HKcXF+gfOe8liRzxdkc0XZKzw9YRTiCPBlwHZfUR626DinsZBEIVoHlNVzdIDVxLkhTlKCIARrUb7DO4sJNHEas9+PlKBzA05ZnANtxpYS/8wcERh6N1DXJfPFkq5pyaJxHBgFmmS9Is/nLM4uMVXL/ljiogCcoS8rcD26q9EmJDKWNB1Hi211oIo9eT7GYbSdBaKxeUZHxEmB9e1krHB+IEoUcZyQZAWPD/cM7z5ggphitaZqLCY04ENmi3P6tuWwL1ktxsy3un7WPQL90NF2NSaKwCniMKLIR8Hw9mlLFIc4bQiNGh3dQUqaFWyfNjg7EGc5WT6nLw8c7u9R3cDV6zeEsaYbDNaBdpq2H6N6lApYnV0y9D273YEkywmTlN19hc/nBGGI1qDxaCAwGvUs1TBBSByEtGGK1jnL5UtCNxbXG6cItUO7mq5sGdoKQ4dxNfVxS18fqOojs/mC5cVrqsZT9yXzWUb0vNsLTEAyF2Cnn/VlA9476rp8bkBI8N4SJzHr1ZLQaOrDgbuHR7Qx7B/u6Pua6990rK41nfeks4AiT4nDgDgKsF3FZvOBIi+om5r99oHvfvPNGDXVOhbLFcPQk0Yxi7zg7uGBm48fR6lFnlNXFUpr8mKGf15gB+uJstHcE+mYIClQ+x193WCblijUoAO6oeP23T1N27BYzbm8eolLa1zXsX88QFfgu5JEDYRxiLcVdb3FuJ7YBzTVEetirBvoepipmDFYCwZrqdueVRhh3fhlG5iYNNK82284Hp4o9494vUD5Dq0sWRJhns09WgXEcU7ffX4cxa/H5x2Hw4G2bTk7O+Obb76ZxmKS3ye6PwFBwuBIR/GLFy8mbaOEGr948QJgqsTb7/dT/ZwwUaLzqqpqYkHE8CPMyfF4nMa7Egkk7wGYxqqSPyjjN+AT44GMcYVtKstyqkAUp624fefz+eT63e/3Exsphh5hfwRYCytz2ucs53gaqSTMqYAxAQV5nnN3d8dPP/00uYOlUg+YDB/S35zn+QTkgWn0LgBY+rSNMVPmpYw4Jbg+z3MeHh4+cT1Lf/owDFPzy2kEkIA2gIuLiyn7U8LKP378OAEcAdTAJ8yyjGj3+/0UlA5jFqrcH9HpyjWU56eua8qyZLlccnV1RV3XtG07saoygpdO838bnyTMmYBDMcSIG/7m5gZjDPf393Rdx29/+1tevnwJMAE1YZAljH0+n1NVFY+Pj/zud7+bxtHr9Xp6jufzOTc3N7x9+3ZysYujXBhWeT5FT6m1np5pyUOVTckwDFPbz3q95tWrV9OI+u7ubnLECxssz7s8d2VZTtdFfk42I8LIywRBnvM4jvnXf/3Xqe3p9N+IZvhU1ylM5uccnw0oq90TRZISJwuS9Jy6C0iLJW3V47yjbkpsuSPod0S+JQ813jrapgI3cCwPtG3DcrXkP/7Hf+CHv/yFfmhZLudYp1idX/GnP/9IM+xGkKECwlARhR5rB4YIXJaAd2NvdjTqKwNjRq1XFBHakZ30z404SsPwHNCcJgGzxRzvLHkWE0UBgxvrD0dNYoAKNGmYkaTjbqHve9zQs99uKLKEeDWnrgaiJCNIUnoCBq3pDXjtCHSI329o25J5qCkWKb1TNNWRclsySwNmaUKUpjRlzWBylFZY66nrnrJuiGJNnua0XUdVdVT1I8t5RpKOPbhtU9MN44LuTYgO47FxxTnmi5yuKqmqkvlshnnWTzgGFI7t9pHeOWbLJYYxqmi5XPLh3c+0Tw1xNiPPFyRBjI40eRJyKI80lWI+v2C2WLJvWlTvqLdPdGdLwkiT6ZQ0Hhcg21miQNNVPcvFkrOLNcemJ81n7PYl3ZASakMYBmRJQqwtOEeRpYRhg/Ue7xVxmpIWK4b9AavGoPowiDB9y1BvUFqz3W7ompI4UHjbYNsKjSIKU5br12SzSw5lhRoAOuaxYrN5QrmBIE6Jo4jzswvs4CadSBRFhJEhDDWHQ4lWivlsPsortMF2PQMdgfYctve4nxTb3YZXX//dGO9TGIx2DF1NW+2pqyPaaNarNZvH/tmkoGi6ButhsVwRhQFdXVHEEeV+z1/+/CdW5+cU8znL5fo5MqvERAk6TFAmxCQRcTB+wVgV0ccNyjnqYwnWkhrD/OyK+/tbuq4dwfjxgNLBGHTeHckihQ4VQ91RHQ/Mzq5RzmG7ZswWRWOHnjiJMYHB2gHrPVXdYp2i6x1KecIoQFnL8bBjt3ukOm6oyweSLMDQkYSaNI4wSmG0Aafw1lAe28/+ovr1+Lxjt9tNY9s8z+m6bloohbWQxg0J1rbWUtf15ORtmobz83P+83/+z/zxj3+cGCAJ6P6Xf/mXSfQvIcwC1KS9RV7rtJdYRooCCoVJOR3PSQi6AF1hJgXACYsoDTqii+z7ns1mM/0bYAJ58hoS33LqWF+v19O5HY9Hnp6eKIpiun4CFk6DqGXULyBBxplixBFzgxhg/u0oVMCvGCEEQMn1eHp6QvrK5fqenZ3x008/8fDwMOlMhUnKsmyq8xOgL6P9zWbD+fn5BETTNKUsy8nwI+Py6+tr6rqmKAq22+3EAMooX8Ch6FcF6Kdpynw+Z7fbAUzvV6QNWmseHx+nZ0HG3MLwXlxcsFgsJkb0cDiQJMk0Epd7eHV1NYE0YAKD4roXLavc61Oj1dPTE3/+8595fHzku+++m2QF8nMyWhfdpQA5YX6dc8/5wNHk0t7tdvyv//W/uLy8ZLlcslqtprQEeV9iApKmG/m8yYZGmPTz83Nubm6QPnUxrsln4rTSUkLjgQl4Ap8ARmHrT41Ycl+kQ/3p6Yn9fv9JooLocoX5Fd3m/5HYoJv3bzk7f0EWr6grDYVhkc+Jwpbd5oaur/BDja0PKAbQIXeP9/zhj//CYlEwy3KUHkHecrVgsZiz2TxxfnGBRnNsOq5evSFLI+5vPtIdawYLRRZhEoONNEOo2Vc1KutY5md458iSGK80JnYcO0WcpM+h5qORQHkz9mAHhrwoRsOBMXgs3j/vjPF0Q8/h2Q09yzNm8xl13YAHoxSBVoRG4/24u9sdSmwww4cZRIa6Kom8wemQfrCEgWaRRagwZq979rc12XJJHBkUjv3xAEmINiFYT9n0HI8NOSEXaQ7kOBWz3e4o65b5c//00+MdYTDq9cIoJggztI457h7xXqNN8Oz2tahAzh3wsDts8MagQ0OjKuZZzvX1Fe3xa969fUs/WLrqyOqiIEhC+r4iCsENNdYO5PkC8gZ1dk7Z7GmaA2Q5+WxOqKPnlhlNFCkCFIPT1J0jny8oFgvKrsUztkl454mMJjIe2w2s5gWP25qyPoxh5C5lsT5n3z3R9DUOM418G9tAsyV0FfgGPXi6tsY7z2yxIMnnxMU5jpwkS5+zNytq36GAJI7QmvE6DeOHu5gVpFnMbD7D47D9KCcw2oyjaWWIowylNUWecn6xRivPYXNDWZe8evOG3dMH7G7LYrEew+CPG7zrePvLT+z2O16+fMlyPobPmsBwe3PD23fvef3yBWfLOVGgmRcpKggo9zuGvmXoW+bzJXmeYp0mzgvKxtI7qJsBrQ1RvkDHM7zX7O0TTVOznC9IkhBTnFGVB5S3BK7D1hVRHFPu9zhl8VmMChPCMCPLlgzO07Y1gRk/S1FkRmOO1thu1FvuD0eK2QLrPGkSohnH93VV4t0AvqNrduT5ilD3nC0ztB8IdUQWJ/RNT3VsOP4KKP/mx88//8zV1dXEMApzIr3KIsSXxT4IAt69e8f//J//c+pbFkZovV6zWq14fHzk+vp6cut++eWX5HnO+/fvqaoKa+00SpSFXoK51+s1zrmpRUZaS8TRK87z09gScarKiPrUZSqZkwKqlsvlFC8jTOypmWe3201NPvK+5GclIqgoiomJu7m5meJ5gIlpFV2jAHLvPS9fvpzYu6enJ8qyJM9zLi8vubu7mwDUKcDYbrcT6JFrJ/pEGQ3L6FYA32w2mxpxfvzxx4mlknYeOQ8xh0j+5NXVFVVVUVXVBPzkmgugEzZN2EEZ68o1EtZYzB2r1Wo6V3HYn5+fT88V8MkY+DSiylr7CagWaYHo+8Q8JPdbxs4ylj8cDszn8wm0yz0U9lheX0DRbDbj8vJyGoMfj0e++eYbHh4eJtmBMKbWWv7yl7+w2Wz44osvpgijIAh4//49P/30E1988QVnZ2fTMyobE9nQSL6nGMDEgCTgXDI5vffTmHqxWEyjeHmuZDN2mpwgQF6MQnJtgyCYmo9ONZeSZLBYLJBKT7kH0uYjryPnIqHqsmET2YFoUz/n+GxAmaUzzi/fsLz6gschxOgAE4yL89Aa6BQtnqHvsPQ0dc9u80Rd1oRhQNsP5FlO13Y83t3DM10bJilBnHLYP/L1t7/l8uKK2eyS+7tbdvsNdbnjfD4jXC8Z6gNV12O7GuV7FAqjNWkUorVncD2B9kQmoBsChsFiFEShIo0CfB/R1nA47BnMhiEApaOxMlKNKF8rTxCMphs8DH1DECiGvqMqS0ChdEg/+NEpG8xI0oym0/ghJIwW9Gxp6pry8MRifUaRhijlOByPdG3HbLkGk+Odx2qFAzqlIUxAG5zTxElK3Xq0iWnKLWfzlCRPqT+27A57VllOns/G/mplaJoBP/QsihjnLG3XgjN459BBSBQE2LZBK0VzPGDQzJLRcHFxtkINPY9PW5rDnjpNWSQhgVZEQUjdtAz9QJoX6CAiX8ywpqc8HgiVZjG/JE3mOFfhMZhQE2h42N0TNxVffv0lcTrnWGq8NpggJAoMSjnsMIxj36Jgnqfc32441A3WO16/OiObLyk/vsU3NU45TGSIHGjTEWSa2kHbdvRtT1YsKFaXxMUKFczQUYrBs9/c0ZYVrT0Sj3eQoelp6j1NN76+NgodBDilsMPYxBCaMQ/1WB7JUkVRLAifc0WbukYbj9OG4/Gef/7//L95+dVv6XyAPbtCK3i8+8D25h2KgSQJ6e3I9o3Ay3K+XnFze8tffvgT9vUb1mdr0ucNkXcKbQxP93cc9ztevHyNUSHH3RM+SGnqDhMWWB3S9o4wKkjSnEWYw26PShKSWUGQLEmLGj90DNWWzj8Rhgr8QF0e0GHC9Zffcn79JU4ndHag7hqCMCYvsvG6aI9XisF5Ptw+cHP/wJsko+8HZsUomDfwrGXrSdKEOArwviWJPatFQhx6jNJoAuqmomkGlP/sr59fj8888jznxYsXvHz5cnJAG2OYz+eTpkpGZrJYPz4+cjwep/GfjMBvbm4mhkK0XqKrvL6+Zrlc8vHjRzabzeQ0lso9GaELmBC2RgDNKZtyyqBIHqWMzuXnBOSexslIqLSMcAX0CGgU3WBZlszn82l8LczXfr+fGl7Oz88nrdtut/ukFvDf6tME7ApQloX9cDhMoPzt27fsdjuur6+ZzWYT8BFwNJ/PJ52dXBMZu4vmUlg7AeqXl5dYa3l4eOBwODxLc9JJiyp6OpESyLhYGKb1ej2NiIEJ5ApA/P777ye2U/ST4hqW+3Rq/BFH+RdffMFiseD9+/eTRlYYa2PM1OAiz8R8Pme9Xk8sq7yGgL7TZhZxvgvje+pu7vt+CowXdjPLsimXVK736d//1//6X/n2228nF79SipubGz58+DCNzkUHKcDr4uKC9+/f84c//IGvv/6ai4uLT0bDQRBMZinpCZecTUlCEMZRAGEQBGw2m+n9yjhdnldg2mhJza3kg54aiySd4fQzYq3lw4cPUzZm3/fTOB7+mv8qGlKRbEghgvyeuq6nZ/Fzj8/+Rl+urliev0Snc1SfMDDw8PAB7VqGds/QlvihB+txWEZ1nOKrr35D7yx3DzvSqGKelsRRxLGsSKKUqu5wzUC+WJEmGVoXrK5m+GBG5/9EdbhjsD34kUnU2mGMw/U1UZJjh466POL6gcCBiSJCo+i9ojqWRGHDclYQGMcsSakPAUPTYruawOTgDVYb8AoNRNoQaoNWYPuGIAyJIwU4mq4jCBOiOGOzr2ntjnlY4IOYIJjhnMEEM9p6YPPwgDYdWRo+xzd72n4cD242e4pliu0GlPY4o4nnC1rbkOQhTTeQFRG+t+ggAhWwOxwxOHQYkOYpQRg+5wGObJM2Bu8HgijGOouzkM9mzOcLquOBrmmZRykmMAxtzaE8Mos1gVLgOozxxKGiaVv2T3egHNcvX7OvKpI4Yeh6KBTpfMbRHonIGGood0d2+pHVN1eYdcJuv8MOPXXp8Cak6lt8GKGiDD0YnH0el3iLRoxRMXjP+WLGZpZx04xak6d9xPVyRXMXUHcth+ORLEtZzGcYo2nqhvsPu2e2Nuf8xZfEqws6DBaDdo4kVERxRHXoKXdbzosIAgM4IhPgQ0+eZ+jAYL3D4QmeRwOh0Qx9T2DMWKXpLf3QcTFbEUUBbvBsdju6ruenP/yJ490tl198xXAYM8ru3r/jhz/9E5cXa373d9/zw7/+69jFHYf0XctyNqNIv+Dj7S3vPn5k8HBxeY1yCmNCjFakcUTft9x9/Ih1GhUYFmfnBF4xdDAMjqd9y/o8I9UBDj0aqPIZw+DxxhDGMU41ZOuMgzHEkSXIAtyTIl1f8ea3/4CZrRjCBK89IQlD61FaY4zCO/v8JRfz4eaRsm7HbNcoGs1N/ZEiNXgFUZoRxAmz5Zo0L1gsYpLYEYeaMBhzYre7iqoaSOLis7+ofj0+7zg7O+Py8nKScDjnuLu7mxgNGYXKGFmO7777Dmstt7e3bLdbiqKYDBgSyFxVFavVahqBit5SxminNW3CsAnAkdYPcZ0K2JAxexiGLBaLSeu53+8nAHLqXD1lMk+BhTBuwOQITpKE7XY7mYVOm0kEuN3f30+vKYcEXj8+Pk5NN7LIyqIvneJiSJIF/TROR1zboqETgAxMo89hGJjP5yyXy0luIDpQyVYU0CjXTN67BJS/efNmuk8y6p3P55NrXyk1Aa/f/e53XFxcTB3Vx+NxAjvC4srG4t+agyQzcb1eT2P14/E4jdVvb28nVktYRGFZ3717N2VHvnr1amLD5BpIZuV+v+fp6YnlcjldK9H9Sd6ovCeRa8hGQsCm/E7pEJfRf9u2/NM//RO3t7d89dVXk9v5l19+4Z//+Z+5vr7mP/7H/8i//Mu/TL9fqiqzLOPDhw+8ffsW5xwvX778JFVArv27d+8mlu/i4mLaNIhGVeKU5B5J17tkpcrnRbJWsyzj/v6ei4sL/t2/+3dTULo866LRPNWrKqV4//795JAXuYWEtQPTJkV0vNLwJEYzuWaSq/q5x2cDyijJmC/W1GEMA4QGhr6i3j1Q7+8JXEPoW6IgwHUNt/cPdFZxdnZFOptR5BnbxweqsuTtz+/oupZvvv2OMEoo+54o0GhlsdaRZAsu44hj+UBTbhis5/7unt1+T5KlrC7OiNIZJkoJdEBbHWn6mjROcL4nMUAU4PqW3lXgYtrmSHe0Y4VfpDBqQLuOtrPPGpeASFuU9wQ4AuXR3o6j3OdGlSBK8CoAE9AN7WjWmZ9hdIEJIuJgRttsxwiZ/kDXNGOwczr2IxsVEsY5h2M51jYVESbICOMFQZSDSaiann4o8TpBBxlxnNGYkqo+sJzFZFlOHCd4/Gi4WKxpjCGKEwyjazgIYxyKrrMcywbvDeiAMDBEgWFXHvnxh7+we3rk22++RocjwArDMVi+air6pqLvGhZFjsdQVQf8fAUKknyG0mPtYtdt2W837Lf3RPkMowGtCcLRKJRmhiSf4XWEQ6FNQBAYolBjNFg3un+VcmRpxNXFkrJu2bUDT097iiBltrggqC65//gzgR1IlZtiH7phIIwyssU58fyczmQ8HSqiOCINQpq2QRtDUcw4PnwArTFBiMNjtCJShtV6MV7Ppma1XBKFIcc4Qz8zqFqp0XkfjGHw/dARR+MmpC5b+rbj8PTEL3/+X/S2JcqW/PTzW2Kj6OuKIn/F9cuX49hmf2A5nxFGPWXVsChmXF5eky9WeDRlNeq6jBsYho5hGEeCyntCrTChpj3uiNKcvh1obUOoEowbiIwmeQ40915Tdz1pXIAeOFY9/fFItatYLp7r6/KcNL8YQ+KDHBNnBNrgE4XbV3g/dp0ro8nSgs2m5ebmkcVyzmq1IopD2uqA7TtaY/BKEUYRq7NLunYgzedcv7gkSQK0HiOe+sFinacfBvLs1y7vv/UhY2Zxbgsw2e1207hVwEHbtlNUiRhYZrPZ5FT+4YcfaNuW3//+9xMTd5pzJ33HUuk4DAMfP35ku92SZdlUaSdMmDBoshgK6BPgc6pRBCbjgjCQokUUgAlMi7MA29NuZXEAS4SOgDFxosvfn4IgcZcnScLhcGC32zGbzSamTcaw4sgW4CwZlaJjlJpK4BO2Uxgh0ScCU66lRBPJaPN4PPLHP/6Rh4cHfvvb304/L6Hywt7JuBqYmGJxlcvrdF3H09MTj4+Pn2RtRlE0aT/l54FPrpWAWTHk5HnO9fX1pB2VzMblcklVVbx79+4TwLPf76f7vlwup3G1yAnkfsgoWTqz5VmT93R+fg6M2kDRNIoZTKJvTgPv5TWF5WvblsfHR/7whz8wDAN5nvPDDz9MkTxFUfDFF19M4G+1Wk295PP5nBcvXrBcLqfrfH5+Pj1zAorlmYyiiMPhMH1uZCIgjKa8LwGcsmmQfvjtdjtpM8VoJcyzbDikBlUArCQUPD4+8u7dO9brNefn55MTXwxfsvmSwPWiKHj9+vX0HgRQyrheQOjnHJ8NKJum5f7hgXCdkYZLvPb0Q4fqG7pyi3MNcTJ2WvconAq5eHlNFGc0fUc4aKJkxtPdE3d3T9ihoet6rl6+4OLlK9TQcXf7lm+//09U9YA2husXr6iPW55ub3ja7InCmPXlBedX11hCmmeXaBQHNEeLViMTol0LtsGo/rnbWnPcH6j3NUkWkWchQeix1PSdRSmDUQNxFBAGmqFriYwii2PwDtsPmCDBOo8JQ7Q2FHlG2w1422LMgFOjXiYpMq5ev6J8+onddsdyteT87JL5fIHSAb31WDTV8YAeHPMww6QLUAmz2RW+2dJUT2izIZ8FoGOybE5XD8xncw67LdvdnlWYEkbuOVxXY0xIc6w4qoooCkGFNI2lbY/keYoJQsI4JEsTjNEk2S8cypr3N3fkSYT1Dm005+fnbI9HAqPom5qmadHGkM1X7HZ3ACxnMwKlicKUtvXsywP7/T2rWI9yA2vR2pGkCeeXZyRpStVAEIQkKiVJIpJ4rC3UymEMWN+DVmTzlMWqoHo40nYDtx83fP/iivxFw353h6am6zu89YRRwtnFS6J0Qe0iHg4txfkZ6TzHo+jsgGsdZhioyzFBoHU9rqrwQ0eRJpRViQoM6pmB8INlsB7lFQr97KwetT05iiAMSeIEvCeOx0y+j+/foxXTF5OOCh4e7nBty9XFanwv/cCX33zDj3/+C2XdcizHPnV7KImzlLPLMQWhqVt0FNBWLRjNYr2i73qG3mJUQH2siOIQOziUilA9uK7kw3ZD37xmffEKy7iBcIkhjDOsc9AMHDaPNHVPFDuyIqVYXJHPL0myFUm2wAcjE9sM42ik63toFEEcgQto6pLDvuTLL78gCAzOWfIiI4sKbFfSVAe8UpxfXBKHMUlWcH5xRZxlhGmCCQ1da9HaAxal/vdqvX49/u+Pqqq4vb1lvV5/wnQJGyVxKbJIC8N1Wp+Xpik3Nzfc3NxMjTCvX7/mzZs3DMPAhw8f+Pu///vJsPL69WsOh8OULxhF0Zip+urVpGUEJsZTxtvS3iPMn2gMZXQpDB/8lYk5jeuRMbe4riUaSXSL4sI9bf8Q8FEUBV9++eUUn7Jer7m6upqYMTEt7ff7CcwKiynsnIzLZbQs/dhS/SfB13EcT6NDcSELOAamka5oOcXwI8zm4XDg/fv303lKuLYEeAvzrLVmuVzy+PiIUmNfuICbth2NeZvNZnouBIRlWcaLFy8m3a0AOQnXFlAnQF7ul7if27bl3bt3fPnllxhj2Gw2ANNGQbId83wsLtlut5MZB/7qRpYMVAk4l02KhIuLsUvuocgQ5P3JGBj+ulmRce7FxQW//PLLdA/EPHNzc0PXdVxfX0+azO+//54//OEPUzJAGIaTA15Yeen7lvd1fn7+SZXo8Xic2FH5vWK6qaqKFy9eTEBQ3qNIMR4eHqb3N5vNJkZYQL+ARzHfiGZSzlf0k99+++200RL9ppiNlFJcX19PI3jZ/Emzjjwb8h3xucfnA8q65ubmI68XL1G6Be/oyw317o7ANlwsc7TqaA4HwjBifX7J+as3PG4OxFqhho66rPE6xClNGCUYrSn3e1arJUHo+fDzI/P5OUl+DiogDGJeXH/B4XFLnBacnxdcXF2xOr+gGUCVLV1V4bylaSsclvlizdBs6euK1SIiTyO8ddRVRVkdCMJ8NMYYy9BVaOeIogRlHBqwg6MZGoxyZElAP1i8jpgVo1NLhwrreb7oPX1Xoto9LhyZF+87snmObzOqY0mgDNvNFufGqr8gTokw1N0OhcO5DhMExOQkJscHIZHxeN3gXIv3EWk+w7ZHum6MOdpsNsyWa7zr6PuGLJvhBssQ1ByPDctlhNYh1jp6a+n6PVFkyFxKHCfMl2f8h//H/5O3b3/i/u6OrQY1dESBIdTjF8ngPE/3D+SzGWmWov2oASqPDS4rMEFK21jwaswIdTVDu8PaAO8gMhAHY4WmR5FlBa3XJDYijSOiMMD5gSDUmFBhB+j6DustaZ6gH/Zo62kbS9OFrOcvWVy8pq9vOW4fsc7RW9BhSpgtGHzMwYZEKiJJZ3gP/nk3Nktymgg2NzVd31L3Pa5vyYoZvR1D89MsJU1zcJ5uaPHWY7EYowFPP/STxiuKIkKliMKYly/WHPd76mNGnqcsFnN0FPLVV19x8/Zn2r6j6TvqtiWK0zHjlJ56cFRtw2qxQoVjM9EiyzkrZgxDTzv0dF3Ler1kfT52cR/345dMV7dY14zB6VlMpC395sjx4SMhns5ClC0oFudY1+C9JkkisiwntAuadk87GJbpkuX5G4JkBjpCK4W1A85a+n6gajq8VkRxhveGzeZIYCKWi9FApBgIjBlNPl2FUgFhmDCbzVmtLoiimKyYE6U5YZJgrcPaDqUcRR6RJuazv6h+PT7vEIZIWCBxlG42mynaRdihKIq4vLzkiy++4OHhYTJoyBgUmIwbojMMw5AffviB5XI5jXujKOL169eTA/ny8pJXr15xeXk5mSnEZX5qyqjr+hN3tLTvHA6HicWREHFh04Qlk58Vo48sgDLqFcAqo2jJGZSRqLWW+Xw+MWiSCSk6OlmcRQcqQE4+/wJqha0VI4aMN51zPDw8TA5yafgR8LTf76csSqnF67pucuImScJ6vea//Jf/wl/+8hdub2+n+3MKOkWmIGYVASmHw+GTSsrTmKjTcHgZc4sUQDSpwrrKdRS2Ugwo4sIXOYWct8QASUapZIdKpJKwXgBFUUzsswDbJEl4//79tJER/d9pS9KpbvY05B6YWEkJDJfxsFQnHg6HCaRFUcR3333HTz/9NGWfSp6nAD1hsIX1r+t6CpEXXWnXdZyfn3N1dTWx2lEUTaac1Wo1sdyPj4/TZ03A8mq1mhh6CeOXayobPDFgSeLAKYMoGzsxQz08PBAEwaSJhL/KBoShDMOQ+Xw+BfDPZrNPcjnl83TKtH/O8flNOeWRMF/RdyVd76ibjsD3GFuxKEIu1jPKwwYfjjqp1ime9jWDisnSlP3DDcem5+lwpOw6Zkk4OkVtz/bxiaSvab3nX//4PykW17z58juStMAow8X1a6rqgApi2gF0nFNkEf2wwVtLFMckaULXdtTHHce6RoeKLM/HC2QtWT6jqWrAA476uCUrzgjNaLKYJRlKOYa2RjlLoBQmiTFBRNUNmMBQD5a+PJKQYYeBrm2ItAFXo1WANpp+qHF+GEfDz9rAp8ctbTcwHCvCbIEyMUkxZ7Adzg4oZehaDxYCZ4jjBOs6rO1w9CRBQlpkNM2R+XzBYb8jjgKq6kg2i9HPY8muH3DW0Q8OpTWeUew9XoeAMI4YHOgwZn15ze54pOkckVHY5giup+57jAmZFxmb7Y6uqVku5mB7vALvevquJwxjyrJCG43Gctg/EseGolgDhjguSNIIZy22H0iyiNksZ56syGYzomBsMhqGYbzGdU3XeQZv8HjyNCLEsmtKHrY1i1lBMjvHDQfa7g47WJRJ0WFGryKsToiSnN6C7yyBMigHBo1WEBhD1bSsF0vieD3qboOQY+eYz3LCcPzyCbRBBxqlR/1kPwwjQ/msR1mtVhyPJcpZzs4iZlnKfD4jUNd8+dUX1E7xsN0SBPDtd7/h8eGOrutH171SKKUZBjdmfG53tIOD6tlVaQ5YO1AU+fRF+Pi0xXvFan1B7wwGh7Nb6Ae6pmGoWrzShL5je/OWm5//QjZbsjq/wtV7wmyODlP0AIaentGItjm0hIeeyy9yrIrGjYFS4JlGXM750fhmQryHD+9vCMKQy8tz0iTADpbA6GdXPuTFAq0MvW3R8VhjqoMQE4QYPS6c3vbMZhm2dyTpr4Dyb30IkJAFUkwS0mBycXExgUlZ7IVRyrKMu7s7qqpiu91OlXDCGN3f30+mln/6p39itVrxm9/8hizLUErx6tWrKSD8dGEXNklAkAAqacyRRUvG6DLylvMR17fE3khIs7COkpcoI3kBsRIpJGNsAWOn7I4wnhIeLgBSqvCKopiukzhoTxd/GXcKsBV3r4S/S7/3aeSRBLGfskBy7uIwF2bvxYsXE0sqo9xTCcB8Pp8CqgWYCMgVgCosGjC17Ej94ykwlXsmzuP5fP5JPqaYTOQayTMjhprHx0fm8/n0vAggEmZRRv1FUUzAWr5r5F7IlEfCxYXNlWsqzLM440VDK9pc0ZKenZ1NMoLLy8tJJ6iU4ttvv51aoIIg4O/+7u+4vb2dgL38XmGypVtenssgCKZzlU3F/f090o5zmp0qeZ/yb733vH//nr/85S/M53MuLy+p63pyccvrA9NGTl5Hfqecu5i55PXkZ96+fUsYhlxfX0/6Zdm4yEZKXPECLk+fz9N+dQG0n3t8NqDMi5TZLKXcP1K7scIvCCEPLevFDDvUBIFmVsz4X//6A3HZs9IpxeoKE6VEcYYJY6q2Y3F+wZevrtDW4oYBYzSHfYkPNU+7H3HmFhUkfPHme5SJSGc5VsHHhyeqHhaXPecXZ0Rpz/FQcjweWZ+t6cpmzFeq9ly8PCPNQuIkxTsDGIp5h1Y9TVlyOFTMixVg8W4gjMJnd1eEGwb8MLrQkzTDDAOH3QYfpMR5hG3HnVHXjPR6kheEUYw2AVFk8MeetiwxbqBve7q2w3uN4zkAPEpYFAWbp3vKY8XieZQeJyG2amiOPUEAOlAMtqfpjrT1kUhZlvM5+/mMoW9p244oLWi6GjT0rkc/t5J4B855wjCirhva1vPi5TVNZ9kdKxRwrHuqzpKtFmRZSl3uAT/GEvWWMIwZ7EBV1cyjxfNuraKs9qyWZ6R5TFPVVNWA9pZqtyEJQ4piRRiCsx3Hfc/svCArZqzyawYTY0wwMsp23OnWjaPuHMdq1EPm2YzkVcpxe2BoSwZlsUHE6vIVffVAms1wTjG4gM5F9F7jdYDWIUU+o7MOpaDvx/vtesthu6Vre7wyoBOS/JlhCHeoMGZAkwchYRyNY+dnDU/bjAA6iBLquh57dQ876rKm7Vq2mw3H4wgErR3IZ3OIYuqq4cX5OW1TUpdH+naO6zvOz854fHjC6PEDfChLqkZjtBrdz3XJb7795jl5QFFk+bOjOyYt5mMvfKE47Pa0w+i+HsFnSmEVOIdt9vTHgIae9rAhnS/pLRg7GozQCUGScvbiNUE2hyBBacOY7+/xgXxBGbRRBIHCM2Bdx/n5iuUiHz/vWqEU43X1ijjN0NrQtjVROCZBoDVt14EOSNIIpRWJ1zjnicPos7+ofj0+75jNZiwWi6laT9ghEeALMJjP5/zzP/8zRVFMEUHSfCLsyuXlJV9++eXEWMhIWhyqoreSAHXJszsdI8ooTdghCRl/fHzkcDjw+vXrKUxbxpey8B+PR/b7/QR+5FzEhSv6NRmRi05QooBkARVQLfmYomUsy3KSAcjYWICSMKJFUUyuamHqpGNZmE0Bq03TTHFMohUUQCFaOgF7AqSE/ZQKxaZpePPmzTSihrHzWwxAMv6Fv9b3ScOXmDCEVTsej5ydnf1fImm22y1RFLFYLD7JJpT6w9lsNt1bYdmE2RRwJOD5iy++mDYfMMp+RF8pTKIwwqd5pMI6CkgXgPP09DRdJ/k50ZSeGqpERiDgShIMJCtytVqx2+0+0U5Kw4+MgOWaX19fT7mgone8vLzk9vZ2MvoIyy5a4Kqq+P3vfz+BT4lqkjG13C8xP4lRS57b00xYmRhIxI+YdGAE/K9evfqk211YdvkZ0bmKYclay+Xl5fR5l2skcgCRUwhrf/r3Iu2QDYB8Dj73+GxA+eXXr8mLFdt6oNxuiKNRw5JoR2AK/LNOoC5LVus1Z9dfkCwuqAZ43GwIvSMrcr769jfstw8UqzVZHI0L5rFku32k7iqG9ki2mmOt5XA4cnF+TpJnpLNsbAwhBBUzW5yRpHOaqiIvZriuoqobtNIERo2Ll1HPQG/M9tOBoq+PdNVAHHn8YKnKIy9evmC+WLA/Hhi6Hqc1je3prUOhcNbS1TVRFqBsN4an+9Fha/oe3zcoW+MHCFxL35bEgSYgZLfZsStrgrQgjlIGP+rwoiRltbqgbEanszIKE2l8rwmiiCiE1g141eOUY3ANSRQQBJrz9RkmMnRtT9uMbncTgAk1tu8ZvCUMAmITEoYRTV+NFZI6oGmPODzz+QIdJ2TzJZ2HvhvQ4bM2qWnRJhjzPYOQoXcM/eiAdtZSN3vi2hDGGm8D5sWMKJjjfY/xAx/e/oXLl6/JF3PCOCVOUuIkA6UIjKYfGuwzuCnLmropQWcjoEwMRWFYFCnz1LBYxpR1BVGA0QXoiCSdY61jaEYdYRDG6CBmUKCcJTYGoxROWULtCDUEypOGEaGO8C6gaS1xtuT6y99S7e6YLZZEETjU2DftHdb2WGcZhp5ivvxkUQu1oqpqHp724yKrx/FMFgSE3nNfPsDFOWerFfePD2AHHh/uicKExWLObrMfQb+3aAV13dHWntksA+dYzGY0TYtRhvunHTe3D1xcv2K/3Y1O8Ww1mgCqmiCCNJ+xyJbki4H97ontdkPT1OSzOXV9IM1neKsJw4jV2Zrv/sM/kC6uiJMZigDnLMNgCQKI45TZrHiOIwoIIs3D4w3Qc3V5jcKCd0RRjAKqYWQH0iwhjDT2WXg+fnG6sR4Th/cKjyVJnvuKh1+bcv7Wx3fffTf1cz8+Pk4NKcI+yMIhpoJXr16xXC6nejfvPbPZjN/97ndTVp/k5B0OB56eniZgIeNciccpioL5fD4BQAGHAoJkoZVRtYyQxUwhzJMwYdJ8I6Dxiy++YLVasd/vJ7AooBKYQJ0APFkUZVzadd3EKspIUfSCEvQszJyYJ9I0ndpuhGETF7SAHGEF5bXEaHJ5eTnpF4XtFSZIxsanjTinGjwBt+K+FbDCNBEvAAEAAElEQVQhmZMyfj01Jcn4FZiilyQY/BSoCGj+y1/+wps3b1gulxNTKQyw/D4J35YaRGPGxqOu65jNZtOofblcTvpL0TkK8ymAXpgwYAKGAshEoynXV0xDUjP5zTffsN1up1G1HDKWFoZVTDMiG5D3K1pZAbAy/r+5ueHFixdToLlICGQDJjIIMUzJNZ/PR4yyWCym/+/+/p4PHz7w8tmAKRICiacSp3qWZaxWKzabDY+Pj1RVNZUPCCMuVZ7/6T/9J1ar1XRfhPUU2YNUM8pG8Pb2dspIlUMMUjKiFzmAvI4A3H9b3Xiqp/zc47MB5fX1JSpIIB64e3ogDQ3hMJDGAeVhx+CgrkfXVxg/j5o9KDT9MIAbyIsc9AXWO3wYE2YZXhve395z2D5xdX3O97+9YHH5iihbgdZ8uL3h7uGBi+srrs5ecjwOdL3n7m4DOOq6Y7k6o97BfftAFEac5WdEYUxVN5hwIIoUYZwRxpqPux3bzYH1eonrBoam4bB5AhxewWAHvA8YnEcpTVU3Y6SQYmRwsMRhgHKaLM0JAg+2I/T96AbWnig2hFlCU42ZWkqP43cdhFg0Bk3T9tjWkkQZTXXEpBFeQdOVHPc7skQR5SNwSZMI34aU+x27xxviKGZ1Nna0ll3DCINgPivYb0cWMQhD0izDBCEEnraFzXaHe27K6QbP+dVLuuEDh/2W2Giy7Dknzo1xNXZwKO8ZrON4rCjmM5bzJRjPdv9AYAzL2YwsWVMfDxyPB3AWO3QM3ThyS6IElEYZgw4DqrYGBeBYrc5AhXR9RdNB0zgO1QFnLbFOyWNPkgJxShwFRMPAcnWFrY80bUOaGQKf0NhxJOtsR1PuCaOYOM9YzDN806N8jVEehWcYLPiB3muawYz5jbMBTIDzPb21WO9QRo8bhDAkzwvcszNZa01Z1hRpyuPTHd3gsW5gOS9QWlM3FTrI0EqP+ZpJgut7mvJIHKUorzhfLTBekacZj5sNfdsTmZAgUBjgsN1iu46zswviKKXuHA+PTyTzNa31OB2hAgfBQFIk9M5S2bGisxt6lpevaYZ3lHWFiVo6e2SxWhCrmKRY8/Lbr5kv16gkR5kIYyJC7Qn1QBgoynLcyS6XC3QIXVfx8eM75ouE6+szAu0JwogkjbH9QBiOemLvFUEQ4duGdhgwetRXrtY5OhCxvieKDEoBJ4HVvx5/m+PVq1eTLu7u7m5ioOI4ngKcpRkkSZJPmEExoog28rQKUSnFhw8feHx85NWrV/z93//9BCK11rx79467uztevnzJ9fX1mMDQddzc3ABM2rPtdsv79+8n/abkJwqLKgBIFtvz8/NpbP309AQwvVdgAo6i0Tx1BQsYlZH4aaSRaM6KopgaRYQRE9Al4Ltt22l0LcacpmnYbrekaTq5ptM0nZjF+/v7yQwi7KS89+VyOQHz01xCYQQFxKxWK4Zh4MWLF/R9z3a7/aS5RkCTXAsB3hJQfpqHOJ/Pp6BsYVsFaAtTBX91d5/mD4ruVu6DsJSncTcSQyNA5eLiYtp4iLZTni/R6QqbJyYnAW2nppvTWCExbsnvOjVahWHIbDabzFmSsSq1lAI6xUBVVdUEfiWTUYxrSZJM5y2gUCocJXpKrm3XdVxdXU36xLu7u2nkL6yhsPcCemUMfX19/dyc9teN03q9np7D3/72t5PG8XQkLdMzYaolbF3MUcvlktevX08bFWHvxe0v7KbcU2HsLy4uplxSSYKQ74HPPT6/erGxpLlC4ejrPUGuSELPPIvY7o845xmakqY88vVXv8EEmmNXc6wtURyiBsu+HhhsRq/mHFuH7Y8EvmO3ecRaT764xGQZu8rjqiNJosFZgiDF9pYkm/P9776lajp+efuO3W7D4VhhuwPn8wXXr15R7g7sDhsOTUUyX3D5IiEwBm8t1b5haAeK+Ywizxn6llAbuqZlc/+IDkOCMBrNIYNFG03TDfTWcWxaXBCh4xjlOoJoRlYU4FsCraCvGYZubOKhQxs1upaTBB1H6Gj8t5gIrSNCrYCKNNI0/ZEoS9E+YBhqHA7nDH4AHXgC71BuYPd4j8ESBUuUgmI+p92VGB0SmBCTB7RVw273CMqDVmgToQNwfuDm7oE4yZgtlng/Bp5bb0BHpLN0jG7yjjAPRhdzmtI1NW09xlN0bc/ibE0QhFTP0SDWe6y3eKMJo3g0ZCUju6ZTx/X6imR+Ru81sTYo7UiSkLZtiKKQxbyg7xXdZmQt67olDBTLWcIQKqqm5VhXhKs5QRgzO7umbw+wfeDYeIxKyMKMphugtyRGYYcxt1QHhm4oaYcDXb1naGuGocBrjY5TGusxcYz1hqbr0cFAoMe6UGtb0jShrVvabiA2Y62cs6NZRucJWRISOFgsX+Bsy/ubW65fvyZKIlaL+bNQvQen2D8dWK1D3FARRhGa0ems9Xgfs7Sg7yqGvsQNPXV5ZGdCZgvFYrmicZaP734mDGOybIExESroacoKbUI0EYMLiPMZbdcRz18QZA1dXzLYjnK/ZX3xkvOLc+bLczqrCb1Bm2AKL3fO0vXj/TQhxLHG48aRtu25vrjgYjWjPB6wQ82ZWqGVQhs/OreVx4QBYz+6JYgM2uiR5Xw2sWml8M7R237Sdf16/O0OcQvDaNCRlpzZbDaNwWUc+v3330/aw9NFTcCC/D4BAlKHJ1EmEpOTZdm0SIlx4h/+4R+oqooff/yRzWbDbrebmla+/PJLttvtVFu4WCx4/fr1NMqTWsTFYjGFgouO7u7ubgLMokU8NbbIWFLORYCIgA9h3UQbKKytxBuJ4UYWbgGnp8YfYcWE1RFQC+Oo/P7+HmBi4MT1LQBD+p83m80n7JywchJILWybAFzRTMroV0b4okuVGJ+2bScD1ePj49TeIvo7OU9xdadpysXFxVR5Ke9HXMESCSSSArnOkh0ahuEU93R2dkaSJJyfn1PX9eRqVkpNsUwSESTM2CljKzFI4tiXEb6YgwT8CiiUDYOct2wURMMpQDqKItbrNcMwTI50YGoGEl3o09MT5+fn07MjgMoYw2KxmM5Bnr2yLKfcTGHsf/rpp0kucToyPzVxiSRDGHx5LoXtv76+nkbWp+y9PCMCDOUayWfVWjsxrhLXJCD9VHt6Gskl11MYd/k3p1KXzz0+G1A6QtJsiWNLkSqMbwiDCOUsSQBVXfFw8xavQrquQZuaKJkz7CuUdtjBMQwhzocQOpxu2B1umIc9gYKXX36FKda0PkSHBXGUoXWA9j1dG7PdPTD8+JY4WxFGCW3X0w8Oi+b2aUecZayvX9LVP5PPllgD68tritmcpmr58O4t2/tHlHZcvrzG9R19UxHFCVmasjseODzuRhBYZCgUUZTSdYq+beidp7OWxNuRkTSKOEtw/ejsrg5b+vpAtJoRBp4hGONWnLOEYQCBxipN3zuc79ABNOWBtjxAnKEN6HxOGkfU2mBMSN8NpEGKtp7AWWZphLcdiyKjPB6J/WjweLjbEEcZ3g40rcVrRdNVRG2MMgN5mE0POCogjFP63tP1jijO0GrsrjaBQjtL19bYvqcZRr1VkufjiNmB1iFZNmc2t1RVye5YoRnQ3oFSzOZzUIZeBST5nCCek6QrOhKOdU8UR8+udkfbNmMoeagIQ4VzLUpZjscdu0OI9wl971jNF5yvlzC02G5BtjoD7fD7lt7HOG0YXIfWEMeGtteUZcVxdwflexZBx/bplqqqCYsZaZFjkhhrxzifkamxGFfRVCVRqBm60aASRskI2IzBugFnB+azGd5a+q7BKgVWobWharuRySXEDZZmsCRRgtEB5aEkUAHKmOdaz5KmbYnjAK8VOg6ITQyuHl3nUcjm6YHtseSLb75lMcvpHx65/XDLcnVJko1d7VEUo3VA0/aoviEIUsKkoKs7DvWR2ARoNFqP5oM4m4FO8D5msJqhblHxONbu+wGlFVESk3pLHBtMEBFFAev5nLPVksW84Lgf6LymKo9E8cj8hNHYgKOUwQQBhrG3u66rUTKQFzjriNNx948H/K85lH/r49SpK2zaqeaq6zrev3+PhFlLPMx2u/0EcAlDYcxYLydu76+++mpyoUposrBGAiD+9Kc/TWM1AQcAd3d3ZFnGy5cvp8o3rfWU71eWJT///PPkaH79+vVUEykGH9Fiim5NTDXCnInZRZiqLMsm9kligKqq4uzsbFrsJWRcXOAS8yLXQICanKuYZ0RD2bbtdM2Byc28WCwmNrDve25ubqbXEp2gZEkKWJQxpph8ZIwt+ZfyHkX3KX8v70s0glrraRQtek95Pk51qpJXmSTJdA7CXgsglut6OuKHsZZSwKo4vC8uLqbR/NnZ2f8lbkgkARJTdTgc2G63U8zO/f09x+NxcnNLnE7XdRMb7L2fYnVO75PcTxnvi0xAtIOySZDcUXk/p9mlp8H0cu0kbF6uv0gq5Fzu7+/Z7XZ8//33zOdzbm9v+eWXX6b2pdPEAmlKEi2u6Hjlnor84zTaS+6pyENEcyoxUlINGkURq9WK8/PzyRQmxQHi3hZ9tLC68poCzoVJPQ36/985PhtQnp2dM5/NwbdcX57j+or1Yg3WUVZHqqrEaMV8tWa7fWIeJAz2gNEjPR2EKR5DnsxZzBfsHn7ieCjp7IE4jLEq5NgpiFLybEWc5OOI2bV417FcnRNGmqYtGbzl6sU1v/2739PWNf/yh39+Hhdadv1fyIoF88Wc2XpNvljTtI8k6ZzzywQCOLu+4OH2BqtbZlnM0HbUVctutx1dy8pjtCYKR+QfxwFpEjObpaxXC3SUcqhrrPekgaJvLX1TEweaKAxIAkNT1yMT0/fPvdAJ1re4wGCUo3eWuixpm5pifU5z3KO0IYxSBuupu4HQKIxyVPs9TXVEMcYfeWt5etwwGzxWRbRdSxzPsd7TO0Xd9BhtSZMOM2iC+WiyMSbmcKipy4Y0m2FtTxKndEpxLPeEgSJJIrwJaaqKze7I+WqJCTSYZ8d0HBNECav1BdY6mrokSyOyNMEqR9+3bHYlUbHg7PVvsERYH7Evex43G7788jWBicfYIDdQ5DlhCN2gyPcxg69om46mqUkizdC3uN7QNy0wECYZs/UFSRLQDvdEVlE3DSEtJopRxhN5jbKWItEMLey3jzzc35MkC7p2IMdgtMEO4J1lvshR/Y6mqdEK+q6na1uMdnTdaLZ5etpweXU1mWWs8qR5ztNujwosRbHAO8WH9x/56otklBzEMfvdnjiK2W/GhTBO4ud+IHDOkqQpQZKOoDpJWKRnVPst3lnyLKPqPe/e/kIchwRYZmnMfvPIfn+gmJ8xm605HKqxVScNGPojioRZnqCYEyrH092Bv/zygbOX3xGkMwYfoHWMUiG9c3hlRlOOGY1pTo1f/v3QYZ0hDCPOztbMZzPAYwLNLJlR1SV9b+n6jiCMqOsW50Ps0BOFGpQeu9B7S9t24D1a6ef4II/3n6/N+fX4vOPy8pLFYjHpqCTSRMaM4vg9Ozvj8fGRy8vLiYUQJkgYu9Vqxe3t7TQql78T7eFp9Z+MJ+V3yAjz9evX/If/8B+o65r/8T/+x8QMNk0zjTvPzs6msadkIhpjePXqFR8+fJjAkYwHn56ePmEQZVwrC/F8Puf8/HwCg7KQCng7bYSRcbeAXvirk1YW7+PxSFVVnJ+fczgcptcUFkxAqJhAgInlvb29nYLGBRgKqynaO7mG4gA3xkxh8QISRUMnrUKn4EKaasTBLtdCxpi3t7dUVTVlezZNM/272WzGF198ATCBj8fHR37zm99M411hQ6MommJ8vPef6Fzbtp162uX1ha0U0CZARrSN4mqWPvKnpydubm6mikxgcuQ75yaGUX6PjOvlukkt5cuXLyewLHFOp6N/5xw///wzv/nNb6YouM1mM/2nBK7LNRGAJTpFAbb7/R7n3PRs/vjjjxPwl4BxCShfLBZTU5IwksAEyLXWfPz4kR9++IE3b95MFZnyHIr7W56TUzf6ad6nMM3ApAE+DTWXZ15YdvlMy7N0Kj0Q0HzaqPV/d3w2oOzaBtxAHAajxi2KiaKYvutxKOq6pZiNfamPT4/Mlufkizn5PGd/bEZHKpo0SQiVpVIQBYan+w1fvHzDobZki4C67BiCjkKnzLKEvm/wSpHPcrQaQA1Y21EUc+IkoZiv+H9dvhjdyIctXoU8PN6jw5DWhxxqT1KsOb9OaauaxfmKYpHTWo0yKcZbHh5/ZrCKMIyI45DsObphNh8ZvcOxpmtrkjAg1KN7ONABQ2/RJiKKQoZmpJQVf+3KtMOAx9P3A3QdQTr+DGoMI63KiqFtyGaWpqqJ0znEEKU5Q9uSZREGKJuS0GjiPMMPHdunJ3ZPG9KsIMhTtEnoracfPFFSYNojbb2jPNSkicH1jsCkaOXZ9CVv377n4vKKNM2IopC+H6NnnNI4ZQijkHSmGZwmylOUH9tsrHeYJKK1FpSimM3xacJ6URAbT31U7PdP7I4Hrs8uifI5Ub5gUDHbw57eGqzTWAfOuufFqaPtPfN5zhdBwLsPt7RVQGggigxpnJFlCUM/oI0nzwpc5PBZQlNbyscnjBmwrubd27eocMX64jUaS5EoNpuW42FPls8oijNUnOGtRnuN8gN1VbJFkaiGUHmapqWpjjRVjVHj+HBe5JQ3dzjrMFFIlOYoP+Cqlm4YcHVNlqaEQcD26YmH9Ll5IEkpj+WkQZHWAWsti+WCxI6aqzxLRp2uH0iigNaoUX/sGZmQ45G2OrCYzVgVGVEw8P72kVmxoipLdocD86IgMD2BUhybmjApOFvP8M5i7SWHw475xStMlIMKQQVoHTzHN43Mubfq+b9bmqadKujCMCIINCjHYDuatiFRESYIUUrTD6NBwgTPm6GqwUUBgQkwZuyxH7+sOoIgIo5llPgrQ/m3PsTMcZpnKK0qwJRUIO5lCU1eLpfTiAz+GgcjY9rb21u+/PJLyrKcwsLFdSrxPMDErgFTRImMb6+urhiGgf1+j1Jq0ngKKzabzXj16hVVVU0Lo5hCYDSRyMIpur0kSSa2TeoahU0SfZhU5Ml1EAB4ymjKAis/Jz8jBiZhA2XEL+BV2EkxbIiWbxiGKUpHjBjyXkRHJzmc+/2eLMumc9Va8/DwwI8//siLFy+mgHcBWXIIqyYjVBkfC2slo0vJGJTcS2EFd7sdZ2dnExvovWe3202ubvkj10pYyCAI+OWXXz5hxoRBlmso1Z0yjn58fJy0mT/++OMUdg7jd9zT09PUSiTu81OTiIRxi2ZSZBuSwSgGoffv30+aYamKFEAl7H0Yhjw8PFAUBV999RVpmk5MsrCtEpkjY2zRggJTEPlp77jUhYqEQyKO3r9/PzHVEgEk5yBh5BcXF9N4eb/fT5rMfzuqBibgDExuftmojN/Vf+1eFxZcnmVhOgXIn+pg5bkRpl8+Y/JeP/f4bECZxAG77QNPD7fcvH/Hq5fX3D8+YnvHoazxJgAUHod3A4f9DhWmhKlGeUtkEpSDrtxze/+B4/Yj1WFPmhaESUE8v2J29grVWJyKqFuHsxX0Fa6t0a5Fqxb8DNtbHu4/UtUNq4sXRNmMZrDoZMar7/6O1995wjimaceAatdZgktN1zQsz9c47QmzFUNdc3i653AcAVUQhdT1HmMCsixHoQhDg3MtRnsiA9iBfuiYLa+IBkuAo26HsYHG9URhxGY3aoMCbXDWMdgB4/0zIHneOQFoTZwkRFFIPfRoo+jqligMqZ5diS5QownCGLRXuDxnvz+OYHUYDUKd0nilyeYrvOvQBh7vLXXTEwaWrhueW3oGkiih63uO+wPBc9xMVVUM1pHmOdoY0Iog1hSLgMG2JEnOYlnQtjUmCHAosjQjimJ2jw+UZUs8SwmCmNu7e7LVBd///u9pdUSYLOiJKaseE0a4Z32vc1DkOeCp2wrHwMXlGfPFnON2z+PTw2iAikLyPCMKxzga78GqEExGWiw43N0TaU+9e2B3/5FiHbDf3FMUEV7V4HswmtnyDKMzdJjTd2B6SxwatNNUxy2EFqeHMRLK+lFyUNekZzHRZcJufwQUQRQThIamKlFhSJwkeOcw2lBkOXmesXl6GkXpz2OyLM2ev4AO9LZnsI77u3suri7HnMu2ATyhARPFRHFCWbd0bc96sSbJCpr6iB2enaGh4fJ8TZqEfHx4wmFQgcIE4L3FDRV96wh8gtIBs+WSb3/3d6wurumcIvAKPQwYb4mDkMCoURvatig8YaAnE4OMq7Qe2eu2rVDao7RCM2qOCxWyP1R4O9B2PVH4155lpTTeO9q2G41NfY9zniAYmc9fj7/tkSQJj4+P3N3d8fPPP/Pll19yd3f3SW8z/JV5OTV6yFhcmKrb21seHx8nwCMO6MvLy0kXJ7otaQIRACIL883NDVVVTU0pogn7/e9/z+9///sJ5AnQknH45eXlxJSKUWW32wF8kq0oYE4AyL/VmUkXt4BDAU5iWhLGUdiYU0evnIdkXYou87QpRc5bRqECfkWzKlo7YYJEUynvVfqvhUEV9klGwsJqCdN0OuKU8aiYd9I0Zb1eT3mcAqCiKJqij5bLJWEY8uHDB9brNf/wD/8wnZ+AL/nsC5gTICWblRcvXkwu5fv7+4l1LIpiOg8ZcZ/WKUqLzu3tLefn51OW4+l1FtAr6QQyjha5gmwWhEmTRIDLy8upu12eEYkFEuOQ6EOLopgqRq+uriZQLPFVh8NhYv3EBS7SBmAyx8g9aZqG6+tr8jyfRsfCiksg+c3NzcQ4ymdMNmGn5/7v//2/5+rqagL1pxmusiGRzaG4/eVZkvOTzcqpBlg2OrvdbjJjiev+9Fk/zTkV8Pl/JDaIYaA6HNhvH4mjUVjc1ANt16KDkCTNSZ9dg7vDkeq4x4QxsbXsjg1Nq8jSOR5DxECgHNYOrM4uCPIF+fkLwmzJPAvZ7Q6YIOZQPjKUT+SBJQkcaRwSaM9uf2C3K/H6lrbtuHj1JUGS0fUOk84Ig4AojsnmCm89QzeQRgnl4UhSpNRdy/rFiuZwJJ+taTvPu7/8gc39e2iasQtZafxzfMzhOO5e0iSmyDP6Y40dOtI4GbWDQ4xyCnoP3lOVo+5iMVuA93RVj4k6gr4nzPUYCK00cZqCcyRZRn2o6fsWxUCWxLihw9tqHBsahbeA0oxeZU0URhwPB/L1Bb1tiNKCvvdjzmKYs7p4SVcdwEPfD3g/EASGPEtQNTR1yTHQaKVpm4Z9eaCYFQRhiDEaOwzkUYxWcwY7ULYd6vnV+2FgODrSOEUHEU+7DUkUEaiA1fkLZhcvWF++pqYgKZY0B0sQhMxnM4xWZFkKbnxInXPg1fhFYAKyNGWxSFnOX2H02DGulEPhiaOIrm+xXoEzRMmCurM0+w1915GlIUNb8lQ1rIovMP6Z5k9yehcwWMM8mVMPUlVlOOyfcO2OsAhR2tHWNd5aAhNiPTRNP1YHRhFV05DNZpRVRRxFqEqzXq0Zug4YZRLr1Zr37z7ycH+Pd47FM4srUS51VZM8Mxtd12G0xjtLHBmcG9gfS4IwQltF0/R0nSVOxuirprN4N96DMAwIw4Dlcs795sDjbsf9U8vZeoUJNE1zxPuBOClYLOYYrWj7Hh2Nphi8xXYNA5Y4T4niAOWCZ1CgSZOMw3H/zOAcCaMQj2N41tWiRsONtQplYvqhYrfdgjZcX52RpDHGgLV/DS0+OzsfjTnaoLUiiuLP/vr59fi8Q2J8JDJoPp9PblvJmJM/UnMojIWYYfI8n5gJWcQuLi7I85yLi4vp3z89PREEwTSeFYZEFr/dbjeZUZqm4YsvvpiAkvQSC8MoWjfpJ5aIIal1XCwWtG3Ln/70J25ubqaFUhZC59w0mpfu491uN7GBAqBPgaNkCy6XS6QVR96TBLLLAi3AarvdTtpTYRWFMRL9nrwn7z1xHE9MYNd1k55TNGxXV1dT6LXoISVHU9z4Ah7rup6C3gUMCEAXBkrijQTQC9AMgoCnp6ep+/rq6orLy0tevnw5jYX3+z1BEEyVjQK+gQlgPj4+TkBemO1TjSQwgUFhOMXlLsYsYXaFzRMgI5pAiUsSdlie1aZppk72qqo+iUESR7eMdOfz+fRMaj1WCkukkkg+RK/rnGO5XE5MujCfElIv+YyiG7bWTpWMokmUZ1fc1rIpEdAnEURPT0/c399zcXExgWG5RtL3LvKBU8ZUTE2n9Yry7wQAn3aXy8ZHPsMCOodh4OnpCaUUL1++/IQ5l3t4eXk5Odpls/a5x2cDyuNhS3U4sHm8YzZP8M6itGGwfsoYzLIRGTtrKbKYcvfEdrvlcXckiHPy0LBcrqnx3JZbkizh/MVLOh+Try5w8QzvNS9er9k83NFUDa5pMapBx5az5Yq2OnLYPGDQRFFAs39glxjOXrwhigI6Qtph4FDtCIOIOIrJsgKvDT62VIOnmK8ZnKM9tCTZkusvvqUsK+4fHgiSOd2gOFY96/WCOI3QRjO0HcpoAhMS6H6s4kti+rYdx4bWjACgbYmimEgyxZSiyHNUltHr57G41rSHin1Zo5VnZgeyPEG5Do9+1m/C4MaID9d35OkYOTNQ0/UObULqusHbgSgYe7FRhjDKiHQKPmNIMza3N9RNTRgotA6I4wR0RF2P4LeuSuIoIOkjiiJ/XvDVMzsaMJ/NsE3DdrshMM/F9kkOOiTJcowetTA6DMdnoTgnX1yh4zlptCKIM9zhQBhqijwiitSknevaHm0M3sFuu6cdHJdXZ+SRIQwMtrcoPbrN+mdnZpxkOIYR+BnL2YsvuR0adHWkae4wgeZscU4WKprGEkUJJsywQ0wcrwjTgqY6kCQaNxxpqyPrecrLF+cctw90FaDMGBelA5wKGJwiSkd3vDYGozVxEHJXHpkXBSaMOByPGAKKLMcozXazIY0TFrM5aZJSxxVJmlDXDenzTrhp6udWigi85fFpR9971mcF2azAm5bBaZQz7GpL12niKEB7T9M2BG1LHCcsVwusibh7fOKXmy3zLML4ga5uiFYG1de0lcYoh9KeKFREsaHtqlFz62u0hjRJ6Vro+oE4CSd9jsSBiIEjCCNQhrYZ0CZmtztSVR1V1ZNmAUGQEJgA/axBhdG0NMabCJD0E9j89fjbHbvdjt1ux93d3eTaFbAhY2xhUqy1E0h6enri6elputdnZ2cAU2i49HJLALr3nq+++oq7u7uJpZFnZbVaTTmYwASq7u7ueP369bRACWsqTlWJ3xFAIqaK3W5HlmV8/fXXHI9Hbm9vp+iVsiy5uLiYQKzkUAqLJIBStI4CbmU0LiNDyRaUayRjQInZER3bqWNa4lucc1No9qmZQca/cq1PcxdF4ygu7Y8fP076TnEqK6UmvZvkGEr+42mWphhIhMkVFkzAglxXYe3EfLFarabRcJIkU2XgfD6fnMPAtBlxzk1B3S9fvpyYUgHepxpaAeFiOBKD1fF4nKQBkikpOkzRpaZpOjHmp/d5uVzy5s0bnp6ephG4/CcwGdEEmMk5H49H5vM5URRNZhwJ9H98fJyApLDwMqYvioI8z6fmHgHYwviLXlmujURyybMFfFLnKJFAd3d3vHv3bmJ+5RrJGPpUGyz6VGEshU2W72Z5TmTsLf9bRvsyWpcyAtFRC/g+NaEBk5ZSnlHZiH3u8dmA8uH+lq6uWc1nZLOEY3lktXrJsRyDtIMw4lhXbB4ewXsuzs6ou4G2d+SzAqUDtG94uv2FP/3hn2iHkq+//xYbGJTJqQY/up+jhOViSVseaOOYplW0dUmkwHYdD48PtNWR1WJJQMc8drjmicMjFOtroiBHqYDARDg3snnHqiGIY+JZgbUDTmnawRIkBVmaUlctUb7i7PoLtvcfKMsNra0om4YkDfFKc/XiJfPFmn5wY3mjswx9i/cDTdugbE9TNxjvWM7nRGHI/jAyPCowBCag9+NDrz0oPWoWfQAEisWsoGs7nPf07YBRnjgNGZqW3jvQhiTNsd5wODQMdU/TtLR1RbJI0EZRW0dgYnQAQRARPptC2m7ceSoN2ji082gFw9AzDJYoCLm+PGeWpez2Hf2/6ZYNAkMSxXjXY3RAEqeEUUIUj2LqMMmI0oS2McQq4cWb7xgIiZKMtuto6pI0NuRpQKAdVX0gT8YPU9t09L1lGBy73YH5LGUW5yjnsbYnNBFJEhMEBcdjSd/2JFmEyTR/fvsBR8jlF99ydnVGEBne/vQL7XGH686wgx/1jkFKb2PwGT6ImS0DvC2pjzV5GmGH0QRUVRWg2O8PzGYL+sGSz1eEaczCeuaLJd5ZkniOtx1939K1IVmSPbOpEIXjl3NVVlOkRJ5mKMWkayrLkqurKxwjoOrtgMaRFTMGqwnTAotBD4bOesIwweSK3h7o2pYsMBgTsdnt6NyOdL7m8uoVy+tvx0aMaotvd1S7B55u3zP0FV9/9ztmWUBvFGEIRg/ksSIIDc53VGWJ7Wr2ZU8cz4mihKjvGIYW8PjnoHdnPUEY4oEkzTgcOh4et9R1S9t7UgKsdSilUWqMmhSgIbq2ru3o+g6tf40N+lsfMmJer9fMZjMOhwPn5+ccj8cJ1JRlye3tLQBXV1cTqDrN+fv48SP/+I//SN/3/N3f/d0EUk4Ducca0uME2MQs0ff9BDRlEZUx3N3dHefn55+wJ8KCCWgSwHS6aEpeZFEUvHr1itvb24mZEcOJUorXr19P5g34a/2dgC9r7fPnnEnnJkzm6ThSmEZhasUMIUwpMIFXie05XfBFjyjGlaqqWCwW0zUUMCoAerPZTDo9eT15bWGfwjDkxYsXUwOLRCVJTqaMdk/zIQW4CoMmEoKiKPj6668nPaAEzks2qQSCn4675X1sNhsWi8UEiGUsLYy4aFlFm/njjz/inOPrr7/mxYsXRFHEn//85ymrVBzMpwyvMWaqkjzVmNZ1PYFI0SQOwzBVNUo0lYDR06zNU+d0HMcTu7ff73l4eJieITEJHQ6HaSMFTM+UhJrLfRYgLCC+68YJmNwT6TSfz+e8efOGV69esd/vp8nBdrvl48ePdF3H73//+yluSJ4PYfOdc1Oo/OFwmGo6hYmGv0pZ5LMOf9V3Sq2qMOSyETiVqIhuUvSZws5+7vHZgLKtK4xSfPPN1zRdxbGqGIaOYeixzlHkCaHRlFFEmiYYMwplC0KsgrZteLy94+Pb99TlltXFkjTPsV4zeM3+UBKmCaEyHPaj4ziNDQM9TXtARyH7p3sYelaLGZFR3H58h9GedLGk8ZYsK9BpMo4r1bgz0yakqhs8mm4YI3wGN9YKVtVA2Vl0UnD56muSNOVpfcbm/iOP9x+5392TDzHz+YzXX31Nns2oyprGblHegx2wfUPf1ig7kIRjJE6aF+ggoBt6rO1pe4fWsFrO6b0fHdV4kjSmG8bsxThs2WweSJOQPE/QyoB1RIEZDTWBYbAOZQKSrKCqO1JtMEYRGUUYGzQxzdDjtSHQARAxX13wdLNjGFq09qTZmDfZKIe1HUEQEwSawOgxYF2ND1aRpdTt6KpM05Q0Lei7GhMYmrrBO43NPEGUki9XJFFMOljyYs5sfY3VEV1vcSi865kVEYEeW1KU8vD/Z+9Pf2RJzvRe8Gduvi+x5X72qj5NUmzN1XYBDaAB5p/Xl4EEjKbvQE11k6w6dZZcYw/f3czmg+frzNLFjKoH7G/lAFFkMTMj3N0i/LHnfRaeXSfKo+8sg1G0/UBVNnRZQJBEeBqapsLzMrpu1LQM/cDp2BFEAauLS/7HP/wDRRpwdfaGD9Zimo56v2NojyjPJ4pzdFzgG5+2g94ZAqVxziONE8IcDus72rYlzWfs2g1WeWT5nPVuj/VjGgPts5tZe2OtJYzOR19Ju4eeROCzWc56/UhVl+jDc+5XFDGbL0B57Pa7ZwOXx3a7IU6jsT0mStAqwCoI/JgkiznVLd3gEWdLsuU11WHNafOIRhOEiqEdMyb9ZE24eEs+u8SGMa6OSMOAp7uf2D3dEf/+t2h6BteinE+gQ0zfYFozyhww7LdbHrYVNzcJWvt4ynseeUREUUx5KunNMJqzdILyNHf3T+x2JV030JuBIAxHDaX2aNrmWQdkCIKRrbh/eMCYcZSTJAkfP373i7+sfj3+14doG3/7299O1W4SBSSVcwIAZLQ9n8+n32+ahtvbW3788UdOp9PUgyysnLCF8kCXEbHoKSX6RUKafd/n8+fPeN5YR7jf739mUplYb9+fRr9iBhPtobA+cRzz7t070jTl/Pyc+/t77u7uWK/XtG07dYsLkH4Zjv3SwSoPeqnfk+sjQE9G4GIeesmSiR4xSZKpolDyAF/G1ggzKFWM8v+9bK4RUAljOPXXr19/ZmoRhknAgYy5pa6v67ppLCvf0zJOlnGqjOqFdZZcx6IoOD8/nxgp+Evfu2RQCvMn/xRALs53MftI7MzLusu+76fO+Ovra/7+7/9+kkxIbJLIB4DJYCXSBwHnwuD6vs/j4+OUDiA6wtlsNrn+XzYiiS5WfkYYy5cxUcvlksfHR6qqmjJBhWFXSrHdbqcNz3q9njSWMq0BJm2sGFyKouDs7Iz9fs/T09P0MzLyf3x8nIxwcRxPxpyvX7/y8PDAv/k3/2a6F6K5FHAnr7Ver3l6euLt27cTow1/cfYfDodp7QtT/+3bt2kTIiyyRC1Jvab8TlVV3N7eTtKQLMv43e9+94u+f34xoFwt53RNhzEWD0Xo+yhruFwuOJ5KQu3jNPiRz+J8hfEUfWcx1jL0A8fjqIHI8gSlznj74QOLy1fo7Ix95WGVT+Asvu057h5JY0XkD7jYYWigb3B+AlZhbUhjLEpHfPl6j3f/hA58BqNYvQ0Jkjl+EHOqGrTvwPMJI5+6LBH2VivYbnY8PW3I4oxZNmfxakZ+9prl1R3+D/9IsLmlyEPCQFF2DnxDlM6YWY+6LvE9y77a0R43zPKcPMs5VQ3bY4kxHdZZZvP5qPkLYpIsxHgxzva0dc1yXnDanah3LWezCN/TVKctq+IM0w50zQiioiQmyQO6wdL1Db3pCAJN3TU8Pa3pBodX9sTFgBfkaC/BmlEDmmQFcZRRHu7p4wHfD5nPFzS1GllSM+CMhzHw8HjPrJgxf259McNAN1iOx4o8zwjCFN/XOGtHZ/swYJ0izBdks8XIakUpvYUwTvH8AOtgPovHSszAQzGO3vvBoP2A3hqOVU1VGZrecNi3rOYpUTR+8Xd9h7UDgxmBSTGb0/UNVX3i4vqSY92weViTzF7hGVitvvLtcMfQrUmKSzoV48f5CMy7nvV6j68K7KDxUDTVEWt7ojgnilLun/YMKI5lA15C63x8pUnzBWEUY02FcQMKTZjMaU4ntG/J8mI0mvia5Txnt5qjtcLYgSAOR0d9ktD2AxyO7PcHFosZzlj6fgzCT/MZSVagvJjOeIS+T+g0zWAJPB8/jFldviIMItYPt5iuwvcUvjW02zv6HrKzdyi/wIYeWnn48ZqhbjGmYehODDg8ZfGiYowYcgN974OKcDrgeGq4so4oGgXnip7AD3FWMQwQhjG+9nAKnjZ7vtxusMaj6wxJHBBpjyJLgHEzpTzwfI+669huDzxtT1g3jv3K5teR91/7kEBpYfjEnCFBx/KAieOYy8vLKY9SQMB+v6eu6ynj8ePHj9zc3JCm6TSOg/GBt9lsJjAokSvCYskIUNiyT58+TQ05wzDw/v37qcHmpU4wiqKJTYVR7/b09MT9/T1ZllEUBW/evOH8/Jybmxv+6Z/+idlsNsXaCJh6mak4tluVUy+46OvEoCCtNAIMi6KYdGV1XbNaraZw9pdOadH5iVtcQJFoIQUQd13H/f39NPKdzWbT+cp5FkUx5YGKfm25XE6AVK6lGEXm8/nPGE9xCBdFMWlZxREv55jn+VTlKBo9yZu01k7aSQEuAmYEJIu2sG1bdrvdNDIXZlBASd/3LJfLSSf5+vVryrLk7u5ukmFcXFz8TBcJTJsMaZx5GZcjgE3GulIx+NJUFQTBlOUp4EjGvxIsLtc+CIIps1HOTzZZwjJK+L4wpRLp9LLvXJjJl+P+KIq4vr4mDENub2+nTYJSo1eg73suLi5+VtsZx/Fk6BGQLdIHydoUFtLzvCmySNzmct9eNgsJs/j4+Mjnz59/plOWdQ5/aZsSycjT0xNPT08TqJfqzF9y/GJAuT/s2G+O3N3f8ur1BUk06qDCIGOeZ5RNzaE6ECQpOknxwoxEJfSdAlcT+gGbsmToG+aLBWcXVyzOb2hsyP5Y43sWbVpU1zN0NUoFuHbH9vEL+8090fmCwPdROmF/bHBWo4OEh9tPbDcPODfQDQP/++UNNojQXoCxFjOM+XqgSdIMY9rnGzR+CHfbA4/dlqKYsVjMybOMdPmGVwSsrt+QJZrN+o7ddk1rBpYzn8XZDfqwJvQMtikpt0/U1ZHT8ci+bEiKOWHgo3pL1w74QYhGQduiQz0CSU/hmRaVJOwPA33jSOOMp+Mth60l9WP6smIYLG7oSOMIYxVaga89eDZItG3L8XjCVi3JoIhnhiDSmEHjByHODnhegDUeprc44/B9jzQJMcZS9x0qCPB8fzQjoUjTGGsMNgzwfUvXG3x/BCiLRYHvebTt6GhXQYznJ4TZEqV9rA7RuOdrbvE9jzyNxwBupcaOcOB4PJAXc9q+51AeqZoWYz32+xNtV9B2LU1bg3IcTkfSJEf7/vOIPoZ4ZFOuX71ity05lJaz2Q1hOgcND/c/cOUpdBrhrEeazOntnjD08RRYO+Dh8EPN0Go8P8aqmDAtGPZbwjijaSxPh5Kri0vmyxjldURBRNc5yrInLc5wTuNcTxgFaMAZQxQFxNH4cEuLnGN1ojMDy5kI2C2PDw8sZwVnqyW745ZTWeIAPwixbqAdPOJ8SZzG9E2Pw1FWFVp7eFFGlM3x4oiu3FEfNiTK0R7u2BlQ6TlJlIKKKebnRJF7jpEo8aMITE9THvA9S9c2KC+isR7Ky/HDBPWcFSmO7GGwIwMbxHgeKOWom47doWQwHl2vUPgkSYyvx1SCqiqp65IwitG+pmstx6qnGzT9wPOX76/Vi3/tQyoLv379yrt370iSBBjZi/l8PsXUJEkyPZyFpZL2ExmrrVarybxhjOF4PE56Mcm1k9+9v7/n6elpcs2K/lBA2ufPn6eu8LZtpweugAGJvBF9mzxY5bXW6zW3t7fM53NWqxV5nk/VeGIukL5mGYGK8UEelKK9E12k6OrkXF5W8QGsViv2+/2UNbjf76fRqRifhEEVV7CMZkWHKW5eYajk2s7n80mjJu/h5bUVgCTmIBlTyt8Epgiyl+Hfcg4iNRAHurCboqeUvyOu9JdRPy9fZ7/fM5/PpzGrmEi22y2vX7+emm+Esc7zfArQl4lNVVW8efOG9XrN6XSampa01nz9+nVic/9nBl3YX1m/4l4Xw5BsaIRhvLm5mcLURYYhAP6lSUpYX1n/soE6nU7T2pHzv729nQLbxcQG/Ay8FUUxsdHABF6lu1tG1VIeIBsZMdkopaaRvYz15Z5KmoHoLGWD9vI8Xm4IxbEuGxa5NrI+BGALYyubBNlYiKTgJWv/L2LK6QePtnecTjvmi4wo8gm0x+7wxOLskqE27E8ti7MLBptgOoVSA33bYU1LkUWURYoxIWcXl4RJzvZQ0mPG/uzhSJzDabNB03N7v+WnH/8Hti85W87pXUhjIwI/JpsX+DqgqcaE+SKPMV1FeXiirw+k+YKmPoEL0X4MeDSdwdeKIIhQnsbDZ7lc8O3bPU3TcTgeaNqGKAy4XM1Ynd9g7Yo8j7m4ekN1KknCCGsMShn8JKWrdlh/i44Ljrs1YRByfnVFEKZUxxNdZwl9S+j70DvK9kgy8wnjkABAQZqlNF1NVZf4oaXvLG09ECeWvu3A09hnfUMQZbRtg7GGfhiIk4zlaoVxCquD5yD1jr7tCKKEoR+wXU+SZJzUuFtJwpCh7wh8TRxHgEdvPTwvJIkLhs5QlRVa2TGuyFdEoSYKDMr2RASEOmK2yFFBio5nZMtz/CCm6Q1h6P8lM6vtnoXbAb7WtF2HMoaqromTFOV5aO2RZSlBeMIbxgq/UcsxAxx5ntF1A3Xd4isNeAR+jPY0p/aEF3isLpasH9bMkzNeffgt5eGWP/73e9YPX5lf5uTJFdqmeEZRZCF9vyctHH7n0ZYOpUKq2hKnCqV90rzAehqrQOuYphm42zwwzzVnqxTfj+mNIUkzVhcJ+90DZVOhnaNvavquAzWG25fHisD36QFrBrQHZ8vF+ABeP/L65oYsyThWJzZPa+IgQUcpde+oBwiSgn4YaJuBJCno2pbhWROWxAmRdvgYmrrEqROq2+H5AZ4Ppq/H6k+X0ncGrEW5AZzBOo1TPkGQoP2Ettd4QUaRF3jKo6mbZ7bnL6Md8KnrE4GnUYwso3WOwRjSeMxvVQq6rgXlnnf+FoPidKyp64GyNHStQYeK2XN48K/HX+8QIHY4HKaHlOi4Li4uOJ1OHI9HLi4uJnAnkToyChWN2NXV1eSwFYZEwJCM825vb/mnf/on+r7n/Px8Go0DU2ZhVVUTOyRROOLElbGsABvRbL0MXD4/P+fz58/UdT2BuiiKuLi4GLXI1k4ZlhJKLcyPRLsIi7rZbAjDkJubm2k8KNE/Mv7ebrdTfqaMXcV1Lo05opMTk4+AH9EzShSSMJcy6pWfk9GsaODE/SyjVGGmZEwO/EyjKWPnl1mhwo69dBgLwJNIoZch5MKaicNaNgLCuIo2Vf6+AE6RBrRtO5mQpCJTNiXABP7k311dXXF3d0eWZXz8+JH9fs9/+2//jdvbW66vr39maJrNxuraoih+FnlVVdUEiCRySABwXdf89NNPzGaznzGAeZ5zfX3Ner2e2DZxZotpRVzbAtJk3cko+u3bt2RZxul04uHhYdKMyudGZBHy/qQKUjYZcq1fyjrEJS73XnSMMu4WucbLbFRZp5JnKa8jmzEBmrJOZX3IxEBkEbJu4S/pBC+bpERmIiatX3r8YkDpXEAQZfhdw/3jI37geHVzw7GqaLqSMAmZr86J03O8YKxZM0OLpwx+YNnc37PfbykWK+J8gfUjAj/l8X7Lbl+xms9wnaE73HFY3/LnP/6B8/Mlb9+9J0pTBjROJwwqwssWxHGE8XzSfE4YOOJwRpgk7B7vCOMcggJrDZ4fo/yAwRiUFxB6Pj7grGKxnPP69RVhsAGlUIxfHmVbEyajkaYbFLPFJUXhkWejluN42lJVBYdtiL/dcfUuYnV5jecsOgjp2oG6rGjbAe11hOGAUgaHxnUNeB555HM8lXSmRwVgGIj8gCAc4xuCQTEr5qTFjNaMIaVRnJHGIX2WUZ8qmnZsutF+yKA01aDAjjsxHBgz4KwjjBI8HWJsM/Y244jikDCKaZue24cnrl4VZFnBfvNAdTLMi5g0DkGNsgWPCmyLrQf2R4XSKUG6pDhLieOUqh9HN2EYEQR/EbZbO+5++8Fg3fjAi9MMlEdZlVgLWRZzdbnE83wCPRpA2qYjCDWnU0nTtKPBJkzBOZpmIPB9giClMx35LANjqIaB86sP/P7f/N8Z6orN02e69sBhdwsqx/YB7eDwAkeeBwwbQzVYwiinai3oMeT4VJWE8YJifoZVAcZ4+DpGoeh68HSIpafpFVEUks5WVAeFaSvKtmOW5Zzjs1kfCcMI7WnsMMocMJY8zajynNu7e+Iw4vxihQKGrme32TJbarSOGPoOGw5EUUjd1PieoneGrmsJtIfSPs4EJPmCpqmoThtipUjiGH8Ys2M1muroOO5P9E1DlLiRTY0STD9gjcYLI4piTm0CAj/CWqjr8cvmL/EuCbiOrvOx1hFFMXmqKE/fUDpEe+N9V8/B/2Hg02uf3jjKU83pVNO1FjN4WAuB+jWH8l/ieGmyuLu7IwxD3r59O4VzJ0nC+fn5pNET0CMA5Pb2ls1mM5l65GEmGizJdRQ92D/8wz9wdXXF999/PwECafeQ8aFSamIDJe9PnNoybhUm8WUjiLBEZ2dnvHv3bnpAynkKkyNj3+VyyWKxmEDU8XjkdDpNI7zvvvtuCtMOguBnoeVynsBkhBFDhGjSRD4gDNFut5tGxfP5fAIU8tAWVk8AsAAtGWEKcJDxrNTziQZTALE00Hz+/Jm3b99OofSHw4H5fD5dYxmfW2sn8C1GkcvLy8lsIsDjJcv2Uq8nrKoYhESCUBQFNzc3072R1xH3tEgt5Dq+bCUahmFy7Xddx6tXryYHvGRxbjabCagKmJZ4KsnmlHvV9/1kTBHNo7B3oi99GQYuDL0YhoSVvLy85PHxcbr2AqSttWOpxWzGly9fpjE2jJuep6eniQ0VNlbYTmnueZksIOHrMiF4GTslU4RhGCZzlsgnBMCLfGE+n09rUfSOwOTqls+ggNkkScjzfALkLzdr8r7F+X88Hqf1KmvyZRPVLzl+MaCsaotTEX4QU1VrNpsts1mBdQNOWXx/zO7qCdFEWNvirMX3La5r6fuKfJYzO7ugHBye9cnTgrMLH8/b4LsO05wot9/49uP/YJ5qLlYLVqtzwmJJ4wJaF4OK2fUdJojoVMbgZySx5nKVkCYJWRTC0JJkC7pmwAwdng4YnMZ2AwpLGmn65zy97757x3K1pGt7+m5kS41pwHdk84K+7eisxQyG+rAnTVI6C86Px9zMs1cEjC09uIEoDNnvdpzr8YPUNRVWGTwP6vJAXR9I8hn5fM4wNJzqFryY0I8J44wsW9GY0TUfxCFpkWNOJw7HPZ7nyOJkHFUXOafj47jb9H2shTCIyYoC53ljdaDvozwP5zyKxZLy9ITyFLNijPsJo5iiGDCf759ZhICH+1t2nkG9Oie7OkP7EAaK+nhk+3TPYBVOp1y8+o40isifu7tx9jmX0OL7Cmvdc0xRhFJQVTXK80jSBPOcbm6GDjMYotDn4mJBEITU1YnQH7MzZ7MCT3so5YPzaJqetmlwph9TA0KFCnz2h5I4ifAcNPiE+WvOrv8txkZ09kjXrTkeE/zwkjQM0GGOGnaU+wPOQq80nvUx1qPveqI4YrZcstk2KA1ZOiOKMmbLBM/rOVQ1hoCBAGcU1vjsyx5tDUGc0A0W7YdEYQLOoy7HuszQC+m6nihKyLIZ++ORU1WxHBa4waEMbPcbwOPqzXuMDuncQOjHJJFP39YMbffMnDj6oWe72VEkIbPVGYaBw/6eoeuI0xn563f4nkfTtFTVkeVyST5f4muFp0OGwUMHMcpL8cOMw3FkVkfTwF80SMK8dN2YIakUhHFMmo5lBmN4uSOKI6LIofTI9HbdgA4i+r6mrjqOxxJchMJ7Zn5+eQPDr8cvO0RzJ6PYx8fHSbcGTIzDy3srgE6YMhnxiRYyyzKurq4m/WRd12w2G/70pz+R5zmXl5dcXFxQFMX094T1FBCltZ7+rpgbZFwo2jFhAyUP7yVL95vf/Ibz8/MJbEjDjWgNX479uq6bRsUyTr24uEAp9TPtmeRoimkCmFgkae5ZLBYTQybOWzHkyDg/juPJCCRd0FMj1jOIEXAhY1eJm3kJOiSWSQDHS4OMMGESsySVlJLtKSB3v99P2Ypaa96+fUuSJMzn8wmMi/ZPALK8hqwfef8CeOW6isHmJessruqXbmEBbC+jlYIgYLfbTRWSwiq/fft2AtVi1BEHtjBnAtxfrldh9aRCVO5zHMecnZ1NQFjWk5jS5P7L+pP7J3pMuW6y+ZI807Isp79hrZ1SEt69ezcZd2RtiDtamMBhGHh8fCTP84nF32630zl8+PBhGmufTqepi1ukCfI+ZdPzssLyZTWihJ4Ly/8ycF4+U2KGFIZa1qCw8yIJEVmFfOZ/6fHLAWVvCbRHViyo6g1fv92yWi3ReqRtq87gFMxmC/LZivXDPU3bwVCxe/xGnITMz17RqBjjpbggoTegsBRZgOpa9o8P2KFivpiNuZazJa1R1KcOlSSouKB3ISpIGbSiUw3x7Jws6Ahjj6qpON3eobcnVtcd6eoVeAZne8IgQOsQT1m052M8wzB0BIHPbJbQND6KEWgOg49jIM9TTjjwFU0zUuTb0w6HHU0q+YKL9zGe6UmOW0J/zJzcPD5Ql3sGHNX9V8qhY1FkDOXA0De0hwajeuIk51CeUJ6mrI44PNoWgjCnp6cZepK+G99TPzC0HVEUY6wZW238He3QEwwtbW/o6XA6JIhznNL4foqnFcqoZwB7oipLhlmK0gadJJwtl6RJhOlqPJcQKMvT/S0X85ihy3F2bEjp+577+0ce1ltWV++J5jd8/69fUyxWtIMlihOiJCEIfJqmngJ2wcPacaEbC0/bb1R1w2yx4Hx5xmK5GLMv1dgGVAUKZwd832MYLKbtcc9s69AbqqrmaX1ivT6g45izyyWLswWeGiiKFN8qTo1HNP+IfTrQVD9iXUl7+Ik4aojzc5p6rO80/YCKAnoXkSVzjB2wg8GasXv6WFYsFiPLcupbjmVFkvkcy4YomlE2AzqM8Ajxkhna+AR02LYlDiOKWYGnFH3XE4YB2g/IixTjFGnuc3HpeHp6IHhcsyhyfO3jrGG33ZDNFnhhSlws8bXHEGisp/G8GKV9yvKEhwMd0FuPthso5itC32e32WKVpTpkzBYrZkXOab/mtHvidLigUxEXby5IgxzbK5rW8PSwo3djluhgLA77HPCcA+75i2rAuecxTJCj1HMwftsyy2KiKECpFmcNMD5sIi8gjmKCoHn+clLPsV6KwA/+WV9Wvx7/60MAynw+53Q68fnz58l4ICM6AS6LxYLb29uJkZAeZRmHCxMlZgAZa4rBZLVaAUzs3EttpoBUeSCJZlA0b8Icvnr1agJ7EkckD0NhOuWBKlmLL6N0gCmqRhivtm2nVp2iKMjznO+//34CFDJ+v7u7m+rzvn37Rtu2U7D1yygWiV0RM45IBUQDKUD85X+SJJnGrQK8ZLz9EsTJuFb0ky8B7HK5nO6DAHFhEJVS3N7eToHpMlrtuo7b21vu7+959eoVi8WCf/fv/t0UpSSh7aKXE8kBMDFe1lqenp6m2Kfz83POzs4m9liAjdxj0dsJ+Oy6jrIseXh44PHxkTiOub6+nlzlEmYuzvy7u7vJgb/ZbKZgegGEcv+FcROdqegNj8cjZ2dnk/b2cDiQ5/nEYAqLKoyvADAJ8RewLfdIPj9jYseMm5sb7u/vp42AsLNPT09TTaSYfV6OmUWjCH8ZKzdNw3K5nOofPc9jt9uxWq2Yz+dTJqys3w8fPkwMokwdhFWW9SkNVsLaS1ORbHiAacQtpiX5vMl1EAOQ6Jrld8TA9EuPXz7yRtP2PVE8tuL0fUnbmpGNGiygsdbQtSX7g0N5A6ZrOR0eOR2OvHrzhnS+wNcFx9ajbg1KdaRxQGss68d7hr4izjP2VUOxWNHphL5zdHWFqx02HAiS0TyDNcRhhJ+kRJ5Hedxxd/uFqulYXt+g4pxodgZDM7pT8fGswihH7yuUBw5D1w/POzFDGEVof2Rcuq6m7Xr6fhRNF7MAvy7phw6nHEEUEvoBioxQQ7FYoT2FVg6dzHi4/0awO5JZh7YDQeDhlTVxyDNoNbSHA9XhQFzA4XQiCHLy4gytWqrjV0IFYZIQDgalYL/fEEYBCgd2rL/bbjf4eo6OEpq2pap2JJ4ijDMUA0ppdBjge5q0Kdg8fiEK4M3NDWEQYPFJ4pA0DYl9w+Uq5/hkOe02fLU9aZFzfXPDfHmJ0z8R5ZrF5VtWN++IiyX4EQ5HGIXPu8Jy0qaIQ2wcPUi/r2KzPbHZlvSNg2tNlkR4yhD4ijxLnoXx3QgkhwHth1g7fmEpz2Nwjn3dsbnd8eOXNf/23/2O5WIEeufzBSo2lCqlVGdUZk+oPaCkLu/RvqJvNX29A2twThPmS3RUUG/uqI4lnraEOhg1JFGA52uMM3z98pm/+Zv3dMbAYNkeSjzfUuQps9kl9Fu0OZHPZwxNy2yWjdfC06T5jCyf43khx6rBMaCjFKMC7tdbwigkiMZx4Ha3py1LUi9iFsccq5qh7SEYpRrWAcojCCJevZlhupb10x1BEhFoj1macDxVrNd39NaQJSFZ7OPRYfqGph7NXtlsTusM67s7HrYnZqtLwjDC2AGInnVTox6ork8EQUwUJXRtT101bLYV9vnhuFjMCEIfN9QoxhzYsfhgHMNlWUqS1hwPHQ4HKsD96sn5FzkkTy7Lsmn0Jg8hYIptkcYMcbTu93vev3/PYrGYHkAC4NI0xRjD/f39FFcjoEd0eMMwcDqdxrrR5+5oYQOFLTwcDpMeUsKx5fXEeCKASbR6/7O2TB76kv/4ss94sVhMWXvAZDaA8aEuZhUBF1+/fmW9Xk8/H4bhFHcjoEWujYAcARZiRJHrI+9fAuLlWnuex3q9/hkbKgHbMu4UyYGMTe/v7wnDcBr1wwgcBAxeXFzw+PjIdrud4n7evHkzjWFnsxnX19e8efNmAkEih1BKTSNqGceKzlSOYRhYr9es12uapuHNmzfTCFwcwjLufdnMIlo8uW9lWfLTTz/x448/8h//439kuVxOAFAAOfzFaQx/MbWIJlNG2UVRTNWi4u4WYCxrwhjDTz/9xO9+97tJ8yv1ldKxLSNkqalcLBaTDlY0xC/BoFyzu7u7STtZFMVkMprP52RZNjngBXDKuUVRxPv376fNmMQgCeh9eHiYSgZkPci5930/remvX7/y9PTE+fn5FD0l67ooCsqy/FmUl7js1+v1BP4lTP6lzEU2bcBkMNrv99P7d/+ML+pfbsqxBtMZBhMwn6/QylDXLWfLgiwuMG1H2fZY21AdSpJAUx42lJsNxSwnLeagQ/wwJQ8TmranaWqaw572cE9b7ZkVOZ3TRJ2iuH7LbH7Bfn/CDg2+cjSnDQw1HTUaRXPcsr3/zOUiYqj3JGHI+eUNUTF/pnQVSsEwdDjTgxqd373uSZKAwfPo+3YClMPQonU4mj5UiLWwLkuOhy0Xl+ek2Zyhb8b4m+cvJWsMvQMvTHEKOjPgpSHnrxOibEVbnWgOG24//xkV1KRJxND1tE1N1zUsizkqCtgfW1Ael9evgQYd1KSRpRsGPE+T5xmn44Guacie22JmszmPT7eUpwPnRYYfxxgvRHmWvh8d0lqPVLkKNcobqfnTYc9wfobpBzpjCH2PSDvUUMHQ4CvD58+fePv2Hdev36J1jAWC7Iy/+fCK19/9hg+//dcMOkL7AWAo2wo7QN/1jO7/6HnsLTqWHqV8kjjH2S3H4wHMPVlaUKQpvgbnLJ43jsth/KceC6pHNstpUAGzleLcxezKO758vUf7f+S3v73i4izBzmcEWYQqUtT8At86/H7N0H1h6Neo4YhnvBFcKUs1DBTZDGtCHBqsJQw1kR9QZBlKOawbG3uMMQzOYJxlcA7lBQwuxLiIwVkwHl3Top3htNs9b1occZqS5TOc0lRtT9UMKM/DCxOK5TnVaUvddSzmOTezsTf2uN/jnCZNUtp+3NApNcYwtWYAzyOIIoxzqCDETwpOXUdgPJZpyjD0bNcHojbHVwNFFjHPY6zpCLWiaTp0ZDiWDevtkX7gWaIQslhkeJ7DOTONmoZhIAxgrE00dPXA09OGvh9YnV2MwdNlSXN6ojpp3r9PSeIMC/RDh+c5wlCTZCHOaYzppy+xX4+/3iEMkTFmAntVVU0M17g5GHPnpBlFHMuLxWIaxYreUUClNPBIXZ6A0pubG1arFbvdbhpbS+C4GCb2+z13d3esVqtJ93hzc/Mz44uAEwEWIrMQVk5AgIAXGT+LS1xGdTc3N+R5PgWBC3iUUeXL30mShPfv3//sYfzDDz8QRdGU5yiAVeJ2ZBwtgddimhHN5Ww2+5kbHEZz0t3dHYfDgdevX//snF/2Q4v+T8CYVGEKsJXRsYxePc/jz3/+M9999x3v3r2bAGGe53z8+JGPHz/yd3/3dz9jzOTeN03DmDEbTppN+AtbJU014nLP83xiWwUwCsMlYEUc6yBVq2dTOsBPP/1EEAT8/ve/5/LyktVqNQWfiwlM7vNLSYOsoa7ruLq6+hmbKvo/Abfyvl7mj8q/k98RFlXMOev1evr/5b2IUUcAt7T6iL5wuVxOo+7tdgswscOyGRJtskRhyUhcNkEvHe2i63wJaGWNy+fleDzy9PQ0bbKiKJo2Ry+NcCJNeLm+ZBN4eXk5bYoOhwOHw4GPHz9OhiB5v6Jzfil3+KXHPwNQKqzzaXuPIE7ROqGpGsysp2sbfKVxfU0Wr4gC2Dzes1vfscxjrq6v0XHCsW5QQ4UOfZTtMF1Fc9iihg488KKYwE84D5bMVjd4XkQUQ6ADQh/mic9ut+VYPtJWJfffPjPLYky6wtMes+U5frpChRFKBZiuf2b7elTgmM9j/MBH4aH9ANW2oxPaWvwgxAyGvqtxUYRWAft9yeNDyWAdnleRZgFxPOpOlH0O5rYWqxTWGfAD8ELatiMMc1ZXMzxrqQ57vGBOO0TYvsFTPUPrsDiMZ8Z/h+V0OoCn0H5MlBb03ZaubUijmHC1YP1wz3q95vJ6jDu4uD6nNxWmKxn6jjDNCMIYp0KO1QhQPe3RG4MeNAbLfLGEoaRuasKmRPkRmgFtO6JAE/kW7RmarhoD0uOM1vg4z+N/+9//b7z+7iPGi7Aqph88AuvjHLRNS13V+NpHeQqtDVmWTF8Gp9Me3w/J8xXff3hL2/Q8PW15uH9C2YH5PCZJNYEfYAYIgmgMhW97omeNkDE9WRYzR9FaTV5EpGnE6VRSVy35+0uOxz1BmDA/m+PdJjQu4VSFdEdFHkWkiU/ZHuiaPU1bU4c5QWsJ1ECgFdo6bG84nk4kaUI39DRdh/MUSZrSdy2BrwnCgIgIS47yApq+wbM+yob4fkicpvh+RNX3+HECvs+AAj/BBT7O02AH8pnPMHQcqxPzeYEOQuazBbd39+y3TygUq8sbnFIY7QiUjw58tB8RhPHzmDEmm69wZmCVXkG9pSwbAq2YZRG+dmh/HGXP44woLgiDhLZTPG5qBiKSPCFKYmZ5QJLENM3YWiIAZDAd/dCieo+y7dkcam7vnrAGijwB1VPVx3Gz1DVcNC14Ghxo38cPxlKBTPm07VjJaH+NofyrH/LQlLGrBIYLCyObAzHE3N/f8/DwwGw24/Xr18RxTFmWE+MnejWJOhFDh+jzzs/Pp/GtgDyJdLm9vZ0YqtlsNjWAnJ2d/awaTh6S0h4iLIqMV4GJoXw5/hM93m634/7+fnogCpMlD1oZ771kP0XjGUURNzc3E4AT9kYAsVwz+XkByALQsiybgKcYnu7u7nh4eJjA46tXr6b3LOyuAC/JyZTRsbxHGVELsBGwKONyAcYCRIQF9jyP//Sf/hMfP36cQIWMyUXfWJblJCeQKCEBH+J2LoqCv/3bv6VpGu7v7/n27RsSBi75oRJtJPrHl73QRVFMIGU2m5FlGYfDgbIsJ9AtTn3JSBSg8xLQi4RBzvVl5JHILGTTIddCAtbFRS3MqphlXoZ+SwXhyy5u2SjI+vS8MexeGHgZWS+XS758+TIlHtzc3EwaTGGjpVZ0t9tN/edyTWRcLwyjaBolGSBN0+m9yWtImLz8UwDn6XSaGE3Z3NR1zXa75evXrxOLLRphiXsSzbVcD7lm8twWXekvPX4xoMxmK0xnMW6gbluqamCod6wWOWGsYVDkkY9qS0zTMJx2YGvStKBqKsI4RwcRXX2kLSuiICDxDHGecH/7yKFscPHA+c2KN6+vORxH7dxqsaQ57aiPa5Tt8E1J35yoNw/83d++xtOafLEgm6+wXkFrEvzAp+87TvsDcWoZGkdVgg4WpKkHzqL9Dk/5RGHCMIxu5HF0MmCcw9eOtncY52Osoiwt3dAwUzGpUuSxj8JiMFjn4HnXq/2QNJ9hzUB1qjAGut4nXb3j5vuQuy8/ksceOs457rY4W1Kf7ogiRdeNI/XBWvpuoD9VNENLn2Zjh7OnWG/WDE6RzM/I8jnXry5pyiOe9hm6gSyO8MMMrSPQCqscSo27tPOLFTFzvv7wTzRty9wZbF/T10cMEUFSEEWad9+9Y1fXWD/gUA+EieL7739HmOcQJkRBhvViIgL61nEqG6qqwQ98/DAhjgOS2KeqDs87yYDV2eJZV+RIkxzfS1nM5pxOFU3dkqQBET5+EJDEETiFMfovO6+hx9OKMAooAGMcv/14SeD1eJ7j4998x7xIMd2Jru8o0oSz1RLT9pS+wY875nGFH7Ysi5peBTxuOqJwxtA5wshRl0e6piPWAW3b0WKYFTNUEDLUirbvqMs9STbDaR+8AF9H9Aa0n6Ijh8JgGXBeTKcG/HSJF4d0xtG3NdbzOBoPZ8IxT9NT6CDlcNyy2R2Io4QoTbm+vuJPf/6BIAxZqXPq+oSLesqqx/Ni0mSO7Syep6m7jjTNwNiRIVcx/aAwXU992PDq9TWBr4myOX5QMJ9fYb2M3XFgfeixKiZNM/xQU+QxdXUkjkOiMGIYDM6N4zbte2O96rHjx89PHI4tV+fXZKlPFBg656iNo+8NT5sjOhhlA1E4jol8bwxEH9nPsTP81+Ove4ieUTRqZVlSVRVnZ2cTkyZmFzEBiNZPOpUlgkWaYWRc+Pnz5ymW582bN7x9+5bD4QCMTS9iShFmQ0KS/+7v/m4yz4jZQBg3cbZKZ/LxeJwYnZc6THG7ik5MxvhBEPxsHC7mCWFnBXS+BJMChIqimBg0YXbPz8/5zW9+w6dPnyY9qOT4ySi867qJNZT4nmEYJqColOLx8XHSRErlnhh7BLyI81tAs4Ce6+trlFL88Y9/nPRwcr9kvB5FER8/fpz+ZlmWpGnK73//++l9CFMlcTPH45GyLCembGxASyYdaRiGXF5eTvKGLMvw/bEnXDIoxbkvWYfApJ8UkCcAV9bBv/pX/2oCt7/73e+mzY2A64uLi2lULGBSHM1a60mHKZskGdcLmJdaQzEZCRASkCbXQO67MMFyvcXRLqBczkEc8QLegyCY2HyRH7x584Y//OEP08ZE6kPlvojsRIw+spGSzZlkoO52O96/fz9FPYVhOIXoHw6HiQkVADybzSjLclpHL1ttZBO53+/58ccf2e/3XF9fT68t8pe+73l6epqioiSkXvS88pmRJqVfcvzyppzzc+pjw9A2RJ6PqRPa40BdnzC2JwgTVqsF9anmtF3D0PH6+hJfeyjlqE8HBlOSJgV2cHQNzOcztuWJ3eFEkC4oFm8J0wu0XxBGHaHvEbgGbXzaY89hv2a3fsD3HLNZRpKm+GmGizIqlRIlK+g0xg2YrmLoxi8XVErfOXbbDc4tcYlPb3o8BWNPseZUVhxPDcY4ms6wXIwasDAKUYOhbiqqbqDtS24u5/TaodVoXvD9kf1TbjSUDM4RBj5ZmnA41Zzqhqru6YnR6ZJBGWYXOV5U0JaPWFfTtRXltqarj8xmGWXXgOnpmppNVbGYzwn9gH6wbDdbOuORxBlFmlMkCUoF1I0lDhVRqgmMh3FjW7RTjjSOWc5itndfCMOAJApG41DfEWjD+umePItYLs+5eJ0Tza85tdAT4HsxUbFCxymDBUVE3zkOhy27wxHPU8RJQpzEpGlMGGnatqKsTs87srHeb/zyNGjPRymPm+sVdZ1hrRlNQtonCH2w5jkqyBBFIQ417So32y1JmlPkCUEY0PclVV0RhI6ua1DWsSjmtJ1iUST0bTE6s81+1L86g7U9u90apxKipEBnc1A+h6altYrAOoxpOdUDxdk1g/WwTmOMG9+TH9JYiQ/SdNahlI+nIUoMlprWGSrXs+9qTu2Rt1cXJFHIsfPolYenE6wf4Hs+Sd7hzMB6+0CWptxcnRPFPsl9TG8GqrJmMMN4DZWhPm3p64rZ4hwvSmi6Hk8HY+j6MKCtRxBn+NqnrqsxpifOUEFOR0JnI4Zeszt2nGqDH2oMYwaorxUDo0HK1x5121LXDWEckqYZ+3LHT58f+W//xx9Jw4Krf33G+VlOnsF2V3NUetT27o70Q0/fd89Gj0tWqyWPDxs85caYpF9H3n/14/LychrNCUshzJC0gVxcXHA8HqcQ8Ldv305GmMPhMIEJedgtFgt2u90E/F4yjMLqwMiOHg4HNpsNDw8Pk7NbRpsSdP2y/UOYEnmICwg9OzubnLjiTgam7mUJdD47O5sY0r7vJ0avbVtubm5+5qAW9kmAjowh8zyfxvllWeKcmxy619fX08hRxvzr9ZqqqlgsFtOYXvq6l8vlpL98enqa2KY8zyemTB7e4kR/yQCJSeTbt2+TiUlMQlprHh4eptrEd+/eTUYlYBq5C7MMTPrY7XY7jbLlfohJ63Q6cXZ2NskEBOQKGHv9+vVk1nkZPSOxQS+jgmQc//T0NDUbCQgXsCUGHLl+8/n8Z+y5fC8YYyadr7xvyV4UCYOwhhcXFz+TREhbjGxKZH3KJkSiliSfUQxKb968md6jXFNZX7IBWa/XU6e8hJfL+5BrKBpMYdzlWsu1k+sknx+5vjI5kA3Vy9xW+fcCkOX9yQZQuthFm/nDDz/wX/7LfyFJEl6/fs3l5SV5nrNeryfmdbPZTEYyyeu8uLjg9vZ2kov8i4y80ySkrxu0VSgz4DAjoAsjut6g3YCnxoaMtm04P19hbIevPdrBcNg+EEY56Szjab+j6wx1daSsSi6v33Dx+m9JZzcoPx61lkGJYmC737G5+5H2tKXa7xi6gThLiJM5yfySYwf39xVhkTC3PQwdaijJgoGhOXI8bCkW18ThnLbeUwWKODoDpXCewlooy5bHpxOnUwMoosgDxiaFq5s5u90WYyzWeXRNw2bjiIMVYRrQD452GPC0RxiNQeFN22L6Fl8pAg2e9mi7ln5w+PGM/X5HkcXMLt5ispC+H3PO4kRhhor62NIc1nT1jqaqwUGg/ec8w4GmakmSAtMPzPMlQ9+BcgS+Y1A1ddWQZDNQAYNV+GHEYj4nCRxfjgeSSJPGPnboOJY7qtOOU12zO9Xo1iNxBW/e/2ue9jU6Sslmc5wf09uIsupYr7+y2x+wDmbzgutnsKQUONeB84nigMxkDIPlcKhom54s7Tg7P8M6xdCPI5JFmlJXJVXVYAfF6XiC5y+u8QEwZiLWTc+prHja7Kjrlovra25eXbNa5iQJhIHlbHXG7edvdPWO8/Nz3tysxmYh5/D7msPdlqOrmfk+OgzoKouyCkeA9WL84oKw2uDskQBHGkWgAja7E4HyiKOcujowzA3a98iSDC+94Fh3GDfQYfFsRGMtvU6xvkOHlnZf0bY9q7MFtRnQvgdBTO88vFCjzUA2h/X6kfV2x/l5gQLSLKPrPH766ZbZ2ZLA7yhCn9jTOOXwdUMYJkBIU3eYHpLQYzCGOM1ZXd3Q1CdUENMT0BGSxitskLM79ZRNTzcMdKbhUuckSYin/6Ln0lrhec+7+Odw+dOx5/brnv2mYvXhjLOznDTTBIEl8H2iMMIMPWXZYF2Pc5bT6chiMWe5Gtmzsmrp+g7l/xob9Nc+ZAwm2jhhLeShLg+k0+lE27ZcXV1NbGHf92w2G+I4ZrlcTtEm0qbx5s2bCcS8zI10zrHZbPj69SvH43GqlxPQJBWFX79+nfRyojETnaY4XcMwnB6eEtIsIzkJlRZWVKJulsslr1+/Zr1eT+dc1zWPj4/TyFMYmZcROXVdI73PAgIE3AozWRQFV1dX0xj1ZYi16EolCFrG0aLjE9aw73tms9k00haTkPRhC2sahiFnZ2eTrlWaXIRFPRwO01hYYnk+fvw4mYDkvlg7Vk7+8MMPbLdbnBurJW9ubn4W2yPgXjSnUoUoUVAC+CV2SdbBy/idl3mhIo85Ho9TR/br1695+/bttEEIgoDz83M+ffpEXddcXV3x9u3badNgjOHbt2/T6F2qOcWA8jLPUVg0aXsS8ClJAtIcI2BU7tFLHaa8fxlLN03DxcXFZDKSTZCEwyuleHh4YL1eT5mmEnr/5z//mbOzs8kUI8zzy4zTlwC873vyPOfVq1cTcyxAUoCm3GsZZcv5vIxjkjUnI33Juvz8+TObzYaPHz9OGmr5HZG2vBxpHw4HVqvVlBwgG5mXZq3/1fGLf/LibI7qO9rKwODTBB6+Slksz1BKYwbL7rRn/bSm71scc8Zu44D7u89EQcDFPKHc3mPbliyZUbYNYRSRzVfoKUbI4WFpqpbqtGFoKxwBYZiSngX4K0cUx5S9RccL5vMZZdgweAmDFxF4Db7nGLqavilp2hqFx/w8oreW094QJzFxkuB7IcZ5lE3FqRroBh9fByhP0w8OHXgs8xTPqwmjmK417LYHjoc99XxGGAZYp/B0gMPgsCgUYRA8t5JY/NAnTkKCKKAbenrrQzBjW3VkRcGqiFk/feNY3jErcrIs4Lh+JFRuBL1BiFKapqqJwxDf0yhjCbSir2tOegxgz/IYQ4vpK6qyJEs1UTTDD3KCeBRTYzuSMMR1HfvtGHPE867NCxO2p44km3N99oG33/8rVq2ldQY/CnF+QlnDT1/X7PZ7FI6r6wvOzheEcUgY+ygz4KmBYTDPQa8Jp1OLcxpPeTw+HWl7xdnZgiDyONY7dKcIPJ80jfH9kK5tcNYShTG+H7PdHVmv99zebziVDQ/3a5qu5VT1zGY581lCUXiYoaRrc66vrtlutpTlljyNeP/mmlmSUS1nlKuYze3/m3K/Jy5WeHnMyU8Y0PhBRnHxjtBW9Js/41mD5zQ4D+s8jqcK01sC5dG1PVE4hnbXZUVrLHiOtqnohxIvUmOgvrHEyYzhuGW93mOMotcZcbJk8GPKqsdXMda1eHpGurimt1uaoSeNQvL5jO22Z7/dU9YPXJzPubpe0g8VFouOfJoS6lrhdAZ4EEfoYKy/jGxB7xRhfkaUzgjnN6SrG8reY3OsOVQ9/dCTxposjkjDEDsYnH2O/+gtzo05hE3X0bUGjwjT+SRRzs31BbN5iLEnyrLFU5CnGU21B8c46o5Duq5htx/rTS+vVjytdzRdi+VXQPnXPgQgyuhXNI3n5+cTA3Q4HHh4eJhGWaKb+vr162RA2Gw2E5gQF+1isZjGdTDq+QTgiLkkiiIuLy8Bpvq7NE0nsCOjRnkAtm07sYry/q21k+YsSZKfVfgJCyTMozz4BZiJu/Xp6Yn9fj+N+kXb9jLnUqJvxEzycnwoI2Bx8c7nc+7u7vj69esUni6xL2JkEH2a5EKKO1kyG4VBFMPR8Xgkz/MprkV+T5zxAvBlTCnh5+I4v7y85De/+c1k8pDfr6qKT58+TQBL2CkZcYuhR0C/jJFlsyEmjouLi2l8K9dbQImwjDK232w2PD4+8vXrV06nE9++fZv0movFYhr9C+v9+vVrHh8fOR6PZFnGhw8fpvH35eUlX758YbvdMp/PJ9ONrFUx52w2m+l+yn8EBInmVTZWsm6E4bTWTlpQYZFfOq7lXIVplHugtZ7SC14mC2w2mylq6erqatLNCmgXjaOATEkfEM2n5HIKQy2gbrfbTeckusqX+ZPC1Dvnpk2PsL0iEXjz5g2LxWLamACTxEXen3xuNpsNi8WCm5sbHh4eps/1Lz1+MaCcFRGnLSR+iK88uiqhPJXkxYwoSnB4nE5HhqEjSWKSJMaYga9fv9HVNeeLOd7QooaGyLMo1+GsYXlxTZQWbA5b0DVhmOGsZugGhnbAGo8kXhAmKf5Q0dcnNvs9Va9IDzU6T5kvL4hnF5SHHVHg0F1Nd+ywfUNz3FIdTyxmC6Igp3EOa2ogpO879seG/bGmaXuMCfB0gPJCHODcmNldLDLSJMUZ8JWiaxoeHrd0fU8Ua9IsxCkYeoPnOZTSqNHiOzJZaUye9Xja0A4lsZ9ijOVQN6RxxGxxw33wlSwrSNKAamvAg87CYrEkTXP2uy1914J1BL5PGsekcYAdeiyKrmtAj408ge84Hp6Iwog4mqH90TTU1DXWQlU13B83ZGnMxeUlZzc5g844u/zAzevfUCxu6G1AsSooAk1nWjb7ktvbHevtuHO+vDzj6uqcMNJ4ejRceL7CmWeXmdbUdcvucMTZgMBPGGzH42ZP3becX86Jw3GXFWrJCA3I0wzbj2zYpy/f+O//8CcenvYMRlO3BmsUs/mMsmwpTweWyzOcs/haczjsyJMVYRQyDCfa5kQQzDlbLZhnMW2heX2d8f/6LydcfyBPCqrKw/PU6EYPEoJsyXBM2B82VCpi4WnOZmecgNad0NbQ9QZaw6ANVvcEvocxHX3f0rUnTnVHFIZo59NbR28UbrCUVUuQjVrEY9PgVEBrFEonuKEnnV8yVANVM5AmGXGao6uKbLakrWsOh5Ik9Wm7E03fcjjWeGFBEC2Zz1dUvWXoW9LQI1ABXR8QZnPi2QXJ7Ix49oooP+fh/shmd2R/Gk0CV5dXZEmIh8XZcVNkrcX2lq7viCJN4EcYFKHvo5zm4vyc65slvj9QlieUtcRhxmGzJQg8lB/iGLB2wLoBz/NpmhNZNsPzwPcV3q9NOX/1YzabsV6vp4eVuJ/n8/kUSSJ1g/KAGoaBH3/8kaqqePv27cQeipbKGMOrV6/GSKvtdmL55KH6MgdRnKtVVbFer6eHoozKZXwu2ixh/cR1+rJHWYBr13UcDgf2+/3EzL3UHsLIXK1Wq0n/J+Pz29vbSbOY5znABCxkpAdMMS4C/oTNFAe5GG4+f/48Pfil2cVaO/WLCxB/OVoVRhP+4qKW/Mntdjtp12REKw/60+nEfr8nyzJubm54/fo1WmtevXrF+/fvJ2AjrNgwDBNT/PT0RJIk3NzcTPFML1ktYUQlu1PuiQAnARPSuS7aw5fAUu7zp0+f+Pu///vJGPVypC0bjg8fPkwM7na7nYC0aEPDMJzC8efzOa9fv+Y//+f/PK1TiQmSc5CxrjCwnudNmyZh+cR0Jb8n91MMUiI9kJ+XUfnpdJoMZBL0Lp+HYRhYLBaUZTnpOKWSUeQHst4lZFzMXtLqIxpceW8v8zAl2qgoCr59+8Zms5k2bDc3N1NywEuHvXwOhQEW9z6MG7TXr19PbKcASPmOkE2WbKzknOX8/+VyKIeWutwyyxLa6ggY8qIgiBOCMKJtOowZa+KWqzlZmvLt9pbTseTy7GyMkxkGbN9x3B9IF4rr6zf4+YxqsOyPW8Ioxdoe23sUacGyOMMNPW1Tcljfsn564OH2EzpO+P43/xtdNxCbgUBBooFIo3pQxoHraU57Ttsn+sGyP19x9eYjvhcw1CeGMCArlhyOFt/zSNOEUzU25dStww8DBuuwdhyLD31PFMYURcrZ+Rm3t09s9jXZENINA0WeYNUYU2StBQtajQs4iWOSqEb7IQ6fb3c7smKBR4gfh1zcfEeoFb6uads9rek4breoweH8nqY/0LcdcRThhxGGI01bkwwFaZYzmIGybgkjjQ4j4sjjcCipyhLlRSSZJkoyes8RxDGFf8GhHFldoiVV1/Dq3e+5efdb6gaOm4o0X9C3lrZsOJz2PK43VHVPMS+4OL/g/GyJ7yvM0GOGjsb1BB6E/qjF6ruerh/w/QBjfPbHkqbrsWpANYq4bBn6AIWP8w2ealB0dEoTaB/nFKeyxjpF1zvqpscSkKY5aRYTJwbtaTQeg3HEccAszzkcWuIk4ng4UuQ5nhdS1z1FkmHtic4W/O2//r/y3/+P/wfOqfHLwPR09ZFAORZnl9jjLbvDmjiP8Zwh1LBYzDjaPV1ZMhiHMxblKzwP7NCjXE8W+fgqoD8c6IYGz0/w8DBWU9c982UAytJUJWGwQIcpbd3jhTG97ZgVV+z7A9vdkSyxGKMI44TIBig/4Gw5I8409bYnDDWnsobGcX45g6EiCyL2+zUqgtaz5LMZH9/9LYvL96hoQVJc0ZHQ9zuMHeUp81nBxfly3Ajxl6gNa8dNjXN2rO8MY45Nw7dv9zg3cHFRMJ9HDEOJVjCfLxk6i9aO2TzGODieTnjaEXkBq9WCfmhxztC0NcaODUC/Hn/dQyrUZrPZlKM3n88nwCJs1ssKRjHbXF1d0bbtNB7ebDbTOFlCzaXtRMbWeZ5PlXpVVfH4+MjDwwNfvnwhjmP+7u/+buqOlvGisG8ynj4ej2w2G/q+5/r6mvfv30/RLVEUTa5grTV5nlOW5aSXFHAkwFdYo9lsxtXVFZ8/f2a73U5xNPP5fAJ0wiDJCFjAnzyUv3z5MgVcx3E8AToxWAzDwNPT08REitNYKvNk9J7n+dRJLTpCYQb3+/0ElsS5K6YWMTrJ/26ahu+++47vv/9+GumL8UQA+ePj48QKih5O5Azwf3aJy4ZArqNEHsHYmnM8Hn8GRoXVlntprZ3GpgIOgemckySZ9LmSLiD3U5zcck9Egyr38t//+3/Pf/2v/3WKLZI15nnepAPe7XYURQH8JWdUwLhE3ghwk3Ui4+iXlZqyBsTABkymtDAMp7U2DAPn5+d0Xcd6vZ42MPKZCIJgClmXdf/SRCQyj91uN+VbLhYLvv/++0mvK93ZslETZ/nLtqqXEUgvo4NE//n582estVxdXbFcLqdN1Hw+n8bYi8UC58b+blljcm6ydmWj+EuPXz4ctwPzPEa5DjPUpFlI30Oa5ig0x9OJ7W5H6GuSJGa9eWIYOj58+ECRZjRNje1HtN73HavVnDjLOXYdYbRgli9Q2iMMAgbb0zV7Yt+glaM6PrLfP9A2Ry5vrlidXxPGIYuLS9pBcdje4dqaJItx9DRtjR06zNCisfihYv/0jdlsTrR6RRAHJKFPFvlcrOZor6FqHWFkOR7rsZXFeJzKljDwiaNsZAdNi+97ZGlKFOfsj2PLTZzEaB1hjcEMI9DxlI9jdAsv5j6zPOZYdtxcn1HWPY/rR+bzAuN85vNrLhYFT09/4s9//squPBFEKYvVjKbvQFniPCeJxvFhEPk4HNYORGmA73z6weBpjVKaolgCJVGU4WuNdf34MPfACyNmyzPy5SuCKCGfr7gIZ9hwzpenns12P45lwpbQeVR1w2Z3whiYL4pRkJ9m9H2DHTys6XCux3YQFjFxEGGsoamb5+rNkLpuuX98wNMBcRbiByH391tMP/Dm1TUXZwtCX6E14DqctfR9h+8rLq/O8OOUfvBQfoJ1oGg4P085Wy3xlU8QZmwet1xdZ/jaIwj9MXx/sARBj1M9OgpIipztbkNx8T0f/w4ebv/M4VSjTfW8xhVBEZLNznj48gP0A8p19O0JrCGKfJ7uDsT5jMA6NBrtBQydAeURegFpmlG3B2zfYUyPsQFRnFEdd1RlRawU5dCTLFKU7fGcwSmF9UZzj/YLDk87NnHDfD5joCGepRA7KqWJ/IwgXtK1FShL4Ado1eP6I2poKJ++YVMfHXh8+JvvuHnzlpacIDsjSGa0ZYcdRlb14nzFYjkjS8eebmPMCI4Zv6i0p/CDgLbrqGrD4dhSViVR7PH2/Tl54eNcizWWvhvrSZNEU7cNSofgDYRhjNaiKatJs/nzA0nh6f+v3za/Hv9/HhIPIgyfuEzzPJ9MN8JOpGnK4+Mjfd/zt3/7t+R5Pum1RD8prJGwPS9zKiWjUsDJfr9nu91O2rmrqyuSJJnG8MLeCVP4ErzKCPr+/n4a+ckIOkkSLi4uJvNBHMeTMUcAtBiExPEtkTAC2sSQ8pKleslSSr7gbDbjeDxOuraHh4epsUZaY+7u7vjDH/4wRd+IqeRlZJFcL7kn4hiX11VKTeHoopeTBhjRWcrridwgDMPpGj09Pf2fNJNislqtVlxeXk73/qXRpes6ZrPZNO6X8W8QBFRVxe3t7c/idL59+0bf97x//34Cp/Ka8n6DIODVq1cTEyssJ4wmMWnIieN4ilMS3aK8R2HUXoaGX15e8m//7b/ly5cv0/0GJpAzn8/59OnTVBEqzGgcx3z58oU8zydWXvSF8t8ljkc2NrKh2O/300ZMaiVfyiQEmAZBwOPjI+v1evqZ+Xw+jZ+11lMN40vdrKQDPDw8TPKB3/3udxODKy56SQ6QukthwAVEvjQuCdhvmoaqqqZzSJKE77//fpJZyIar7/tJa/2yolEc76LtlWslIPaXHL8cUGJwbuDh9gueGwjjkGQ+w/cDTseK0/FE33VcXd6QpQn1qSTwxw95lKQ45XHqWnan4xR70DQVhpzQj5jnCc5B4CsOdcPm6Z7HbxVRqDgct/jKEmcBs3yG9j2GvuHu6yeOVcvxeCKOM86vL8lnGW19IvQUeRJzeGoJwpCmOvDt85+51BEf334kX86xeHh+gHUefmWJYm98+NlhjOh52lOfal7dnFMkGfp5pOwHPkGYsN3cctjvsfaCIs+JgwDPYxwZOkXXDZRVg689kjii7Xq0Z3n/9oKua2mqise+I7qYE6cxfpRQLFdo5Zj5KX3dYMoDYeQR+R5RqOnamFPdcqhKjNLEi5w0Hx172hvd3lGUkCQrnDL0Q0vX1ORRSNsPNIPjw80H/DCjtz71oLjblpy6Hb0ZR2bpfA6BZnfccTrWKDQ3V1fMFilJGqOVpmt7ykNFGPikcYanLMqNY37noGt7mrZjsJqua0FBnIWcXSzQns/6aUdT9lwsFcoGOGvwAocZejrToTzLcpWQLzLmZ5bNvqJsR+A2n51zvswAR1N3LOc522HLw/0DQRTSdS3a0zgDQaTwI8Wh2hMGMVG+4P5pjx8suXnzNwQP30gqw+7QUDYd294jjzPibEE7VOw3jyyUT5zEbA972r4jUh54PtbzUV40AnVtMNZgPR+0jzOGwUJvHUGcE6cFdVWR5SlFEqI9C7Yn9DWEAX6koSsJ4zm+v+fLl/VYoenB4HpUFPO4PqF0RBqvCFRMbDV1vWezeeDq8hKlAtLAYroS63wWyyV1Z6kwhJ2hdw2mN/jao8hi8vmSOI7wfYV6/nLq+x6txvCjQGm09miajrq2bLc1w9CzWmUsl2NuqCbEe+5Az9IEiDiUO4a+wbkBTzv6vmZ/2HFz9YY4jNCe/zxu/ZWh/Jc4nHN8+fIFYDLYSOSJhGXf3NyQZdkExmT8rZRiu91O+X7S3CI6w5c5fXVd8/DwwE8//TSZGoRFlBiXvu/56aefptFnHMdTJaBoyrIs4+HhgSiKKMuSH3/8Ea0133333VTvKK/70lkuY8iHhwdOp9PU5iKjcDH2PD09/axRRhgruVbidBbjhbhxP3z4MEUvDcMwhabHccxqtZoexC/jjuQ1RT94Op0mFqooiimW6WXPMjAxrmLW6Puet2/fTo0ofd/z+Pg4McwyGpWObGnsef36NcvlcnJEN00zGVwkUF3OW8bCwrZKzqZ0t2utub+/pyzLyaQjRhYBJzJqXi6XXFxcsNlsJhC1WCwmtk/Yx2EY+Pr168S4iqxCoqFkPRZFwcPDA0EQ8OHDhynTdLfbTYYbcc/3fT8FlKdpyna7pW3bSXv5UrcrEoWXI3ABaHI/BFDJWhLALfdWxvBBEEzxUnJNwjDk8fFxApTyelVV8fT0xM3NzaS5lc3P2dnZFA/1EnjKZ2m5XP4sTUHiqmS8L58PcXpL89P5+fkkA5GffbmhE+mLSAa6rmO73fLmzZsJYGZZNv38Lzl+MaD84Ycfefr2I6Y9cXN5TjEr6CwYx9h7vD/QNS0eHqEfYntD17S4xFCejpyORw6HLc5TnL+6Jp0X9LXC9R7G9FjjcGbguDtxf/eFutzj+zAMCuM6PF8ThxFtN1BWe7rOgKcoyxObzdMYqN2/x54tRvCZhHSeZnV+BQp6Y1k/PXDqDdmi4EP8d3hhgTUR2vNwbqB8rg3UfsgwKI7Hjs36xG574vpizqtXS+JoNN2cLed8jmOOxx0Pj1tW5yuiswWerzB9T912nI4Vu92R46Hk5uaSPJ9xLGuyJOHf/F8+8vi446cfP3N3/0Dy9ozLVx9AO8r8nFU+p6sbdvs1fV8z9DVhEhGhcNGCvKqp647eReBnRElKGOSkyYwgCAkjj8NxQ719YjZf4LwANMzOCly0oDI+60PD/dOBY92RFQvOzs9ZLDKyJKSpK5zrOTsryNKcPI/J0gAUGGtAW1ziEUchYRCgnCXQFmMH+sFwOtXc3T+CF5DPFrx//4qLyzPSNOHpcUcaxRRRThQG9MYwuB7jDMPQEEcBnq+wrqPvHE1j6IeOqmnphh6lOnwGtCvI4oi6G1isLqnqI9ZYwuDZTAOUZUWaJWjPULcNeBodRDw8bPEsnF195LtVwfaw5u7rN7a397RVRXms6W3DxStN17c4zxEnCdZ6KBUQRwl+ltMMPtYDpQyOBoWH58ABgR9iVYSKQszQ0VWP9IMl9C1DfQIN7eARqBwVeERJijEdxfkl+097vt0/Mj8vCKKIOM5wxPRWcWoteTIn8qAdKgZT0pR7tPUJPA9UiE4L7tcnzP6B2WVBp1rCQONrRRRpPN8nChVRoBj6GuN6fBWMDVCewvc9PK1wQNs2HA8NTTUwLzK8wMfDkMUxnjIw1DTdOFLr2rFWcXygZKwWM7q2pe8MF2crjocx2inSmvkze/Dr8dc7/vEf/5HPnz/Ttu0E3ISBETe15BlKfImM42RsutvtUEpNtX0yYpaoE/mdL1++TEDsZce0RKRIQLpU/T09PU06NHkP8tC+vr4Gxofl/f391E4j42MBjzIib5pmApUy6l2v11xfX09ALAxDzs/PJ+bp7u6Oy8tLLi8vJzZGAJf0J799+5aiKCazyH/4D/+B+/t7/vjHP/Lt2ze+++67yZUsRpO6rn8WvyJ6ODG0CAsoDl1xnosRZ7fb8fT0xHK5nIDO5eXlBCYlhkkMQi+bZuRvC5MsQEic4wKSX5qbxDAk2tVv375N49C/+Zu/mTIL7+/vSZJkIoBeNtCIE14Al4y7+77/WfWlHDLevri4oCzLaTz70kwjur2XDTW3t7dYa7m5ueH8/JztdsunT5+4vb2dIrGMMbx582Zaa9L8Iqyo6HoFWL48XuZMSkyVrHcBabJmhVGUFqGLiwsOhwNfv36dTE+STCCRSnIvhBmUfnLZIMlmarPZTJ8BGUmLyUrG7i/Bn2RoitlLQusldeClCU4ApWhe4S9aXmHepYZyNptxcXEx5cmKXvWXHv+MphzH02ZD7PU4txqz6bKCvjckcYTnHFfnl0R+RFu2eM7D9ANm6Gnbhq5tqZuGOCvI5kvKtgedkCcpSoFxHc1py+PnHzkdNpxfnBPnKZ0Do/yxbcNZbOPQgY+1LUr1zNKEyD9DOUXmg1efWF2cEWiNzReEUUrXdiTKUXcDpj2yufsTV5crkrnG0z7Gjgv6dCzxg/iZOm4xg8X0im1TMbQdynPcXC9wFiLf42w5w9OO3WHH43pPUcywjJq6buhxz+DldCw5nDqukhlx5LCuJwwU56uM+jTjuN/QOkPiZRTLtwThGWEY4yUNr89f0zQlh8OG0NfMZgWv/ZDTqeLb12+kWUKSxmRFQRQmFPkcYzu6oSEsFqzCiCBMcSrE+T1tr1gfOw6nikPV44U5by/n5HlEEofkeYKnoDr1zOcJQeBzscpx1mC6Fu17aOUgcGg/QCuHp0ad6HyRUVYlvenIsjlZ1hE86zLiJGa1moPzMMWAfnNNFMVoX+HpgTD2R7d8p4jiGKwhCDrKsuJ4OGBsSByFtF3LZr0l9nwuLy4YnGWz32HtgDNj57nvR2RJjHWjEaapaoLQpxlaPA3FIsdww93dI5/XPS7zObv5QBhHpNpy/+c/EgVjn/dqWWA9f9TkdDWhH5GEGRpNXZXUzmDxaeuWcOhIo5auKbF9hxdGeF4AOkLHOa7Z07Q9njfG5kRZSKRHlkjHAUQhztOEswXxYknZ7smMIQ8jjHWkcUI3OHCKdLGkPhrUwSfSIcpa7NDjhwlhlnP93W/xkxWDTcGLqMoWco0fxqOJzGmUB3ao6LqGJAlQgcYNA56vRoPVc/ViGAbEiQMXEsUZzrMsZhlJlGCGliDoaL1ujNYyAVl6RjO0Y6ZlP96XIk0Y2pa2rvCwBF5AHv9qyvlrH8YYHh8ffxblImNF0ULd3NwQBMH0cBOwKDWDIsqXAOqXUSXCaP3444/sdrspUkciXIRFEWOKMCiSRyhMZ9M0XF1dTQ5tYfVk/CwxQzc3NyyXy4lNEnOOjGiFPRQ9o7Bsb968mZza0m+92+14eHiYdJHCBApjJAyujG/lgXp+fj6FSxtjUEqN32nPYHfMWb2YDBlBEDCfz6de8E+fPk1ATxhS0Z12XUdRFBP7JdpEYYuOx+PUj/7x40fyPCdN00lnJ0amIAi4vLycwJ2AiZdB3gKapEZQGm1kTC9j8vPz84nN+u677yZgI2YsMeYIsJK1tNvtJrd527bTOry+vh4zhDebafQqa0XWjkQSCXD1PG8aJX/9+nXqwH7z5s0Ejv/pn/5pctfLPT4ej5O7W7SfAmDl8/AyWuplVaGcX1VVk4RBon3E1CISDJEsLJfLieGV9S3AVOKa9vv9tC5EiiLrRqoPpf7ydDpN61FG3PI+JDFBNlJikpJzk/cm70EafkRqIVpR2RiKzlM2V6JVlc2BjO7/RTSUHz58T7W9J9EDV9fX4I1ZemEUjUHeUTQKao2l73rapsUaQ1meGExPWdWUdcPF4oZidob1fOreobSlqrY0pyN9dWK5SEizS6Ikxyqfrh1onaaIZ2MMURLgegVeSVXvcDiW8xnKKrq6ZvfwyPF0JEszPO3hPPB1yPJsiVUenm85K3JMU7Ftv9GpA158RhxpiiIGNPN5zn5/ZLc94QPOasq65vOXWwY3cH62fHYzK4oiYXAt2+2a7Tzj9c05nu+jjUEbiOKUrh0wxtIPA0kSY11PXZckccrN1ZK+PaB8H+XHqECzr1r+8U9fubq65GxVUJyfE+TnhL4iTiK08ghnAzpZYJ0jTmKSNKep2zE4vW0YrEanGUlk6I3F1zHLIqZqOra7Ej/wef3mnCybEyejkSeOn4XaTUOeRONGwVMEgYcZLH4YEUUBvRnojR0bhozBOUPdtqiTx/5QUZYNvo757uNv8dQYNL/dbXh8emC1XJHGCbN5TJzEeJ5jGDp0AApLbQbK+nnsajTaC0mjmKodA8nz2CPwUoosIdDeOCLvx7XW9R2+F6B1i7WOOBrrGttuIFUJfuiDZ+lay/J8zgB8/XbPp5/2dI1mnixxhOjQcfU2o+8a2mqDFySkYUjZGJqqwppnRyAGgyEIY0ynGPqexjVYO3AqD9jKEc0SkjREtZrBwfFUP2c7jufemQ4zGEyrsMYRKIfSmiQp6Oo9Q9szNB3O82h7O64RrTlVNZ7yWKzO0cORoTrgunHM/vrdd1xevcYlZ3h9SDdYmnZgeb4iDH3Ajg1OXc9gBrTm+UtVYXDPI8OXXbQhvp/CPET7IW1XEQU+5eGIdePOP04ShgHKsqPue5YXc8IA4kCh4wg7WHAOT6uxL3w2w8P94i+qX49fdvzmN7+ZomZevXo1MT2SvRhF0dSxLA8OiRMRdkY0kIvFYhqFiftT2lZE0yUPGxnfialFRpHS4uKcm+KEZCwn8Tei4/J9n4uLi4l9kYf13d0dwKRDFDAlWZkSjyOv9enTJ5xzU56gADhrLev1mtVqxZs3bwAmMCp6N7kuor0Th/fr168ncC3O17Is+cMf/sDr16+5uLiYjCgvx8virpewdDEVSaezMF+iP/R9n9lsRlVVbLdbwjCcInUkMkb+tjBgAvYlUF0cxXJuAiAkwFtYaKn9+93vfjeNZtfrNff395yfn5MkCYvFYvr78rflukltpIAhGWPL9dRaUxTFBICk1UbW08uIJNnQCHMumxdZM58+feKHH36YcjJh/M569+7dxIa/lBu8NOUIEyfjamFPxVAkbLNEZAkLLgasl7pbYDpfAVtSZSggVExOAhCB6TMn7K0xhvfv3091p8JuN00zsdNynUUHK3mWL1MC5GeEjRXQ+rL9RjJDha0UJ7v0e0tsmBiLXmpAF4vFP+v75xcDysXZOe++/xtUeyR/ZuJa4wh8jXvO1xI6ue07doc9cRRS1RWDM+yqI/OzM/wg5XjouLhaoVRDWR3xTIs2J+r6QJxm4CVYAvx4RhL6uF6RFmdkaQS9ozq1BMRoHG3l0FFBGmeYrEUFEU+Pd3z6esf52ZJiVpClHnGSsliuMENNtT/QtD/gxUtsOCOZg9YZRR6hdUgSh1wsI9ZFxLdvj9RNhw48TnXD/eOGKI5ZzgryWULYa1ZnOWV9QtGDMmPoe5LgKYMZLHEUYK20huR4OHxfg+spipBXNxfoIMLhUzYd3x5OfLuv2Rwf+Pgx4vpqRZjnpLFPPzQYZ4jynPN0wfFUstufaJzD93O2FVgbYik47feU5QlnLfOZT6E94rTgTbHAOoXvB2g/pO86Yj8i0gpjegINWTLS7HlR0Hf9805X0w2GwYBTPkmS0rcdp+OB9dMd+8OBbnA07UBZNqRZRhRHRElEFI0M4+Ew5rQpDV1X4ZQBZxmsAuvouoFT06OcJvACAt/j6iKh7RqqtiLJMrIkw/SWtj6iPEcUhZxOJf3gcIHCC+KxqtFzJFkGWI5VRd205NmMIArQeLx6c4nSPo93R+6+VTSZIituaBe37HZ/5rB95LDfcf3qA+fnN/SnA5HvEejxIT14Gtv24A9ob8wthYE0j7EuoTE+ygeHG+s8o5jd4Yl23bJcrojVMD5UL96gomcHoPbwTIMfxGgV0pYttV+hAnA6H3vovXAcnSufKC7ojjVBEHM6rkmCkMD3cXgcTjWDDunNQJTmxEmCYkDrUSvsHPjaQ2uIwmAc1XtjmLlS3hilhDQzhERhhlIa3RiM6RjMgHouB+gay3Zbcjg0KM/DWUUURgQ+YDq8wKPrhjGwPU1I8ogo+GdIuH89ftEh1YHScCNMnOgZF4vF9D0trm0JORbH58XFxcTYvXr1aspXFGBS1/UEMoCJMRLHrrhZJdtQTCNxHE+gyvd97u/v+fTpE5eXl9MDPU3TqbVFgqbFoS61jbPZbHqYn5+f8/j4yE8//TQZhMqy5O7ubnogzmazydgj5wlMIE/yLOWBKqyUPJglI/Dt27eTxq2ua25vb6cQbufc5IYXcCjZgGmaTgynMHpybYCJibTWTrFJMjYXllTqEyXC52XGqDjhpb9a7rWMeWXcvN/veXh4mFzvArzEvCKAVfSwAlwli1AMIaK93O/3wF/aWm5ubqZcUTlvGbMK4BFnNTCdh5jGgEkiMJvNJvbx/fv3eJ7Ht2/f+Pr16xQttFwueXp6mq7tu3fvuLq6mnIjX/ZxvzSPyT0U57gARBm/R1E06TBFA7rZbLi6upo0riIbEPArjvCXcVbC/AmbK1rWw+HwM8b+cDhMPyufAdkgyfsSVlgA/f+c5ypM5kumW9aBvAeRvUhGq8gVXuamCiAGftZ09EuPX/yNnmQF5xc3fPthjDXJ84y+6zHxSMXGaYrvQd93bHY7dOATRCFJEvPl4Y4wSbl5+5amMrTNQFt1+BpsVxJ5FkOLryxD1zG4gHRZoOIFSmfkYYEfhijPgeegrQh1zCzwaEIffEUQz8gLnygriLOcp4dbMD19P7Db7/B8zbdvX3lzc4kbOpphT5jXJEvDbl+RzK/IltckacysGEe5s/yC5SLnYb3ncb1ju2/pjWN/OBJHmqvLFZ5WBIHP6XSk7RqcHZ536mNjSBAEpFlC29QY21PVJ0B2ZS11VZFkIcZp8HzCeIaOZoSZo7WOL3dHVJBydbWCHoz18LVH3w7gFL0K+fZ0YLv5QpYVpGkOWIahGysxA00axQwuwHkhSZIRBBrnDGAxQ0OoLaEO6eoKP9SEgYfCJ0lTnAWFT98PWDd2nvdGsz+VPDz8SOT7vH1zzWLl6K1i/eWeummpyprNfo/2NfPFnMurK5I0R/sJX748EASwXM1Gg1Pg4awFNwZj/8NPn/jzn77w8cNHrq/O0F5HlgUslguiKGXoHbXpGfoGM1h6o2m7jsOxpO0OGAMXyzld5zDW4IcBnorYbrbsNi3KC7HKcn1zxavXNxTZGQ/f7jltH3CBIcquma2+47A54kxLoBUehiT0OVvOSBKfMPFxFjrryBJN2Q9Y1+FrS+B5aN8j1D5GQW8McZqhVU9TH7F9hXEwmA5PDRz2a5ZXOXGc0NcNykAU5fh+QtNsKXJHO9T4eYrpezwd4QcBOA+Ho2odqu3wvLFDOD5/ZP7GYj2NH8VY4zOfz8Ymo2EA5wjDAK18rDV43ggonTXACDS11gTeuBtXaKwdGWJjW1ADXd+SxBHGQtN0NI3j8elA21r80GN/OFLkEQpou44w9PF9jygKCMJ4zKe0v9w9+Ovxyw6pT/vjH//Ier2egIYACNEsSuyJPICyLOPr16+kacr79++n1hMxzojmCpjYKmMMq9VqYqNe9kdLtI5E1IgZJUmSCXQWRcHt7e3EWkmf808//cTbt28nB3hRFKxWK7bbLcvlcmJHhcSYzWasViseHh4mwDQMA7vdjiiKePXq1fQ+ZCQq4EFApOgahaUUZumlrk7GswIQpHbPWjuFwr969WpilESDJ8Dx7u6O9Xo9nbuMP8U1nCTJxCSJxlLc0i9HnFVVTeBFQKv8ngBZ+duikRSm8+zsbModlS73zWYzRe5I3mgQBPz4448/a+8RcK2UoigKfvjhB/7H//gf/OY3v5k2HnmeT2tCWlvkekt0kxjDrLWcn59PzKUAofV6zXq9noDZ27dvef/+PbPZjC9fvrBerwnDkKIouLi4YL1eTxpVGFl40c7KOPllnagANFkTwpDLzyilpvct+ZRKKTabzSSHEGZfNlN1XTObzSYJg7CwLzMcRcohZqfb29tJjyt6WemgF6ZQrslL45KsCfksihYa/gL6hYUVqYtsBJum4eHhYWIvd7vd9DkSt73oN+U9ibv+lxy/vHoxX1CnOwanMU7RtN0kXG7blrqpSNOE3ozxNflsxnIxoypH0fDlq9c4pQnCAGMNw9BC1+KGjqo9jSNO21M1PfnyFWGS48VztJ/jdAzKo2orAk8TximhrygPDXGaYoeevjf0g8UPE5LZkg9FzvbxG6fdmqo6YYaBpqpp62E0kTBgh4663GG8jLaKibM5nipwpgUNWiuur89YnK+42lf89PWWpu3o+p6mqbg6nxMEmr7vyNKQMPTwFPTWMgwWrX38QOOsRqkA5Rmc8wA3BoFrh3OWfjA4Mw4AlecRpymeX2F7S9M57h62DHZgsUyJYw/DMzNsHUGccPP6LVFy4HgoccqnaRu6biBNk1F7GgbEcUoYRuDAmAFrepwbiCMfa0A5SxpHOGVxSqF9//majhuAumlo24Ywyhic5vOXO3789COX53Oury9ZnZ1xefWKt+++52m95nQs6cyoK0myDMWoV/3x0z23X78yXyR8+PCam1fnYDWm7cEpojTF80K2u5p//NNX4iTn/DwkKyKiQHM8VHSdIYpjBtuBg7I64fujZqbtKjbrLZEOyJIE1Vu8fgACwmDGl8+3lHVHbzoeHje8//CexXxBHF3wRR1oDg12yDm//A/k0Rmf//z/pDVQtx35LMcMHW4oifWACh3d0NLXawJ66u7A4B3xUkvblFgd4Mc+3WDxPM3gfLwgpa4b2u2eJMuYzTPK1oEdd+n7tqPvHUmUkWQzTt2RthswSqOHHqMUZhjwnhuSjB0Iwozj4RF/6FHao+0Mbe9Yvb7m1Hv4cUgah2gsBoevPZwxaO0R+D7a1yRxxOl0QCnQHs+uQIWnNL4fAoq+754F+aMG1Nc+XW9oWkM3wPZwQqmAAM3xWLI/ROh59lwS4PA8je97+IHHsTxiI/v/8zvn1+Off0jGn+iqRKhfFMXP9JECUBaLBcvlctKZvXv37mctMqLdejmSlLBv6fQWc4Y8mAVIijNVwrkFIMrodDabMZvNuLu7Y7vdThV7AmaFoRK20/O8iVGDv4wefd+f2mDEtCG1inVdc319PbE1AtTkPGQsLzmMwvwICJTw75fB18AEKsWQ1LbtBI4l8giY/m4cx7x//54syybTkwAMeU+igROzioBNYZEEZIiGTzSSAmClcahpmgmc/vjjj/zxj3/k8vJyukavX7/m+++/5/HxkcPhMGU1SrSU1po//elPfPr0ieVyycePH6e+dzGGiIt8s9nwhz/8YapnFGZxt9tN0gE5B2EOZTQuLm6pp3wZH/Xjjz9O0Tl3d3d8/PiR1Wo1STf2+z3DMPD69WvSNOUf//EfpzU6n8+ntSamFskAlQadYRimIHtZqy/H48J0r9driqKY9MSSFPAynFy0iMLkyhqRMbXEKAmAEyZQNnpv376dmHi5t8AE4GWNi5b5cDhM7KQYgF6GmYv85OUmTcbpwvzLa4i+82U80stA88PhMK3lX3L8ckCZzdgFCUGUslzllMft2LkM9H2L/0y1e57CDzR924JS3N7dkqQZ83xBVbdEUYL2FV1XjgxT35NECYfDke3uwOWb7/DinMZAYEF7GuMc4Bj6HuN6kjDEmg7fB195eGHI0/0jxmnCPGHoKopQ4SsFQz8+SPuGN69e4WmfzoDyA/zQpx9aFhcXGM/jsN1Q5At6X+G8kWlxrqZuOrI04zd/+zfsDyceH79RlieOpwPnZ0us6THDQKD90bFsHPXuQBBEhEGA7zkU4/gwjqXTs8H3FVEYEwRgLGz3FUrFzGcJh1lE3YwVf3HiY9xz44iOCENNoD2GweBwrFYzzs7OKE8NXduz3x9o2oA0jcnSmCKL8LWj62qUCkj8CD/QaM/HOTO6eoOAruvRoabrx5q9fugoG8vp0LHbjS0VOkiI4jl3dyceH0t87VN3PakdcIOjKGKS9Iq+H9DaZ7BwOjU8Pm3ZbZ9YPx0oqzEXM41DgkCRRRGhH7CYrziWFXGUcLa6YrMp2RxqFhcZ+AFWwWA8jDXUXUuUJNTN+BAKI81iFuMMVGVDXVcMfU8xm+HMCKLzbMFs3rHd37I71JR1T9tZ3rw+5/J8wYePbzltF9SHFjVY8jTHKUdZHcgXM0LbsX26pVpvmS0W5HOfVtfsjw3WOPLIMY9CtD1SnQ4MnsfFIiBKFgxmFKMXS9A64LB9IAgiurahawaGp1s8IIxiFBHYgcXZBeXhkf3hyPnVJcb1hFEKHni+ph96XNcRRDFpPqM7dXjaI19ecP36AybKSLTCD0MiH5wbcHYYzVTaw9rnUOdhwFof7SlgdHkD4J4bc6x5HnuDMQ5rPYpwxmAcXW9wTrPb74nTjMCP0FoRxyEKhacUvbXUdUcQRCgvoO1atPbw9K8M5V/7yPN8MiSIC1UYDWEghEUUZsXzPL58+TKNWauqmvRYbdtOD88kSdjtdqzXa96/f/+z3MGXTMxL0ChOY3mAffv2bWLVpMFGNGowmnnevXv3s1ggYWAk2Pkl8yrjPmBiEX//+9+z2+24vb2d4oouLy+nHmX52+KgFgZVGLGXoE2ac+I4ntif7XaLUmoyXEjcj2glBQQI2yPnLrWC4lLfbrfTeFhYS2GDRSMnDJowVAIg5Z8CSuq6Zr/fs9lspi70NE0nQ4v8XbnOUh8pBh7R0d7d3U2ucnE7izNd9LGr1WrKOby8vOTx8ZHtdjtlhQrIMcbQNA1pmk5mGImxemnE6bpuSiMYhoGiKFgul2w2mykmqG1b3r9/z9XVFb/97W+nBhkxkiilOJ1OnJ2d4Zzj4eFhqt6cz+fTxkZYRWl6ko2KbAKEkVutVmitJzZURvkPDw8AP3OoX1xcsNvt2G63vHr1atISi8FKxvoiTXj5mu/evfvZ51FYZ2Gt5d7LBkjAqABKWa9yveU1ZbM1m80mcO3cWFcpkUcyMQB+Fuz+UsMqQPaXHr/8J72QwSm2hyNvri/ohzUAQ99RNzVZXuDUGJdiraXpWjabJ/phYJ4UKOfhKUUc+3jK4lwDbsC5Uch//7gliFP8KKVHsdntyYiZJzm+9mnbGjs0OGPoXIdpT2hXoVVDdSpZ338jmV+gkwitA3bbB5rdI/VxR9ucmL1+Q+hrWqfonaLIUqI4oDoc2awfSWc+20ON72e8efsGFXnYfox32R2O1M0dnh+xXJxxfXlJ3xwIgxDlII1iOtVinaLue4beYazD2R7rK+LIAzyGvqMfOsIgwphRL+ispZjl+IFmVjia1nJ5mTGfv6Npx8o6TzmGocLXljhQZHGE8hw2UDjUCNwGS6BjnE1YLDLM0KOfsyvNMMoJgigCpxiGHnAMWpHEY67kYAHPw1hLVTd0nWV37NgfeurKcTrU9N2A0gOOE4e9oW0V+33N4XhitUwIAg22xvcscRZinKM7NKwf7vnxz18wRhPEGavzFUkMcRoQ+IokDYmCkDSLWO/WBIHH1fUNZfXA529P+KmjsylpFAERQRxjVIsXaLxeM5sXIztrFc4MLBc5s3mGw4Ea44gs0JuB88sVfhjzh3/8E9vdEaUqAn+PVh7LRcHZ5SX9bMD0A65LWF0vqasdgWvZ3f4RhUHZivZ4ix85Et9HzzTOemR+SqwcDAOhp3h4fGR20REEEMQZiYXKOKLEMHOWpumIA49WGQ67e6w1XL1+z2AG+qEn8X3QIUM3ShEcDj/0xo53HNrXeF6EZyxhEoPNKNI5V6/ekBYLjr1PFAVEUYBmwCqD7+tnhhp8PyAKI+qmwpqBMAxwzk4PQNmtj/ltCpSiaxsCfwSGx1NJ0/RYfIbBEcURDo8gEIdpSBCEdO2oI/X9cX3xPO435leX91/7kAeQ5MnJg0TYOgksBiYtnISbi7tXMhLlISMPzuPxyO3t7dT965xjvV5PAEyMAMLkyd+Xvykdzy9z9SQjUuKM3r9/P7F6wgZJtM7Dw8NU3ej7Pt9///3EPkl9YF3X+L7P2dkZr169ovn/sPdnP5Ika3Yn+BMR3W03X2PN5d6sm7cKdYlucgiCLwP0391vMwBn0D3kVBWrbvHmnrF5+Ga77iIyD2KfhkV2NRjZKM5TKuCICA93MzVVUZEj5zvnfHU9aNXECCPX4zTUWsAO8BH7ZK0dSp8SBSRB5tfX1wNzJeysADQpoQsQAD5y50q7xlOzxQcT3Ad2WO7paabh6eIvOtjNZsPhcBjKydLWUXSo6/Wa9XrNcrkcGDOt9aAVFQD+3/7bfxsYUYnCkRK4MKnSxzxJEp49ezYYoUQecOo0BgZgNJ/PB/ZcQJswYzIeITB70vLxv/7X/zqYrkQPeXZ2Nrj/pYz+9OnTQabw5s2bAWSJuzqOY+bz+eD8F91sFEW8e/duYLEli1LK3+IGl995fHzEWsvLly8HACyfTzSukmQggE/0jwI0xWH9/PnzYWN0Gjkk10wApGxoBJQLmJWflY3QaVC/nItEdkkHo77vh1ilU62lJC/I+f4yuP5Tj08GlI/rDY4IFeU8bg7c3a8wvqOuanbbDWkcdGqTyYQ2jtlt93gUZ+fn5MWIrrW43lGXe7LUhEUnMVgbsdmUpOmY6eKMKEppekffdbR1SbXf0FtFmsYkxqLoiZWFOLTL2+0eOWwPzCYF+biAY6xNZVuausT1HXmWkR6jV6aLBdu6pfNgekuWpDgAZ5mPJ+y3G1brKcvzM9IoJor8cYf1wJtX76nLmucvnzMpzsgzg1bBwJBEMb315FlO0+zDBINCeYftQ0u6vMiITERdVaHDkDLHPp0dceyYTnKyzNJ1FjdJcU7R9x6tQascjyWOFGkc0/UtRmusd6EHs7dopemdReNJssCIeluTRGC0pmlqtDLDTjNOE7quD2ypjgBFWdY8PO6o2x7nU6IoIS8McZyxud9RNZ6u74mijMl4QdOsePvuPZfnBWmckxgTIoCUoWksfVvi+ppxkTGdnaHiGBMpsgym45jzswlFlqFQNPWetq1wzlOMMiazCevtiu9/fM16l5LHKdPRkifXZ5jI0HaWvJiQpRFFPkJ5RV01R7Ygo7M9vXXBVJWkeOXBO66uZsTJ1/z80y0PDw/c3mzQ3jAbz1HaQFSjTY/JctLoc0b1jurxLWk+xUQJWkNbbUkOCVXnMfGItunwymFSj/EN2jtsXdJXO1Q6xXmPUQalY0yUMZ+fcdi8p96XKA1ppMBWtNWergOlDD0wmZ/RRB7be9IiwXsHOugSI61JjSaKU1RaMJ+OmJ5fc/38JQ6F0pooNsEY47oQHm8SwuamQ+vwWYwJRhyZ6E8npw+TZYWJDCbWRDrCWQ3e0LUtDnfc+PQopYnTIPWITCiVx1GCVdBbi9YJRZHhKjhUzSdPVL8dn3Y8PISNvgCK9+/fD4vier0eFrrT0qRSiqurq486q5RlOWQ5CiharVbkeT70jj5lxySG5pTpE9ZFundsNpvBJCOLrJRqZaETMLVcLocFVJzaAqRmsxmbzWbopiKAQXoqv3r1irIs+eKLLwbDyS8XajGqnPYDF3AnAEoYT6UU6/V6ML0IyO26bnCPS+lcyuWySAvAPAWCwPCcCYgV4CDa01Md5SlolvPd7/fc398PAPm0u40EoAvYmU6n1HXN69evubq6Gpiw017Sci9FAynsqOQ4XlxcDI5+kRMIMJrP56xWK7755puhN/l0Oh064ojpRrS6wMCCi3FHpA7ibgZ4+vQpSZLw/fffc3t7y9u3bwGG9AG53mmaslwuhzxQYeCEYRYXtoAmiU+SjZJ0fBKgJnOfbEwk6P9UGiHXQO63AHUZx3LIGDu9nhIC//nnnwMMrytRXzIW4EPAuXze0zB2ecY+nqc/sIrCVMoGSt5L5BISxC+AUUxL8hxIyPvhcPjk+efTS97TBVEU8+LLv6Za37KvLL470HU1u8094zxiVFzR1h3OK7a7A5PJmCSJ0Dqi744tp/jQ0NwTmJau75nO5kxnM1SU0ZctrtnTKsW2a1ksLkm9wtPSNmusb/B9RV/vsU3DbDpjMrnk0HnqrqbrjkCyGDG9CK2rrNf4KONQW5yHPEkpq5DvNRmPSPKYvBjx+mbD+5s3ZEVOHE+IE8PE5PTdjNuHDW9ubinGE64vJzgP6BiUxvYebTSJUSSxpustcWyOZT6Hsx2xCU7Ft+U7ttsNi8WSxWJGXYddddB4aGxvgzs6zen7EMOkdYTzCvB4r+k6T9dbemuxtqM6xjSlSUqepmjliWKDtS4kPB3NMV33YdfjjhqiLMvw1tE0PavHPX/+8w9Yr7i4fMKTJ0/J04wkTtgs93z/w1vWuxYHLJajI8O8p65b9HxE17WsVg+0TU8+mpAmERfnM5ztaLsNRV6wPFtijMJoj+0sne4ZjyY45wOwsQ1dWzObKEbjJSZVKOXY7CrariRJUxLTAR1Pry9RToH16FiTj1IUnqo6hMxLIsrKYcuK6XhMkmiUarlYpoyzFzw8TNhuV8SRwtmOJDZoE+OsRysDaPLJDN+VbLMJJl+iyoY8nxKrFNdu2ay3rB5XPL++YN95YizTccH5rKLe/EBaGPouoyMJMgNtcBa00jyuHwHHZLagLffs1muK2RWYDGM88zNPlyi6vmIyXlI5Q9MbdJTgXLj/kTFYFxMXE86fvGB2fknpLDryaHOclHC0fUesQ/cb6xxx4un6EHOkdUJdd3hvPyo3Slmz7/ujgSrC2z6Mox72+47O1nR9QxQb8jzBHRsVVFVJYhzOhue9KAqatiNKII4z7u/uP3mi+u34tEMCjf/whz8MukRhcVar1dD5QoDIer0emLdTV+ppGQ0YTBWLxWLo0CIM5G63GxhOKcWJlk8AY9M0LBaLoT2dxKSIlu3JkyfDgi7h1s45RqPRkE84nU4Hl/jr16958+bNR6BL+mXf3t7y6tUrJpPJkIEon0le/9TMIODvNAx8sViw3++HUu7Z2dngFJbuN9JdRWJvpHOMgEbRQZ6Gwgt4lt8TMCDspJSURTZwGkIu7FbTNNzf3/N3f/d3eO958uTJR/mM9/f3fPPNN0PnovPz86G8W1UVy+VyeA2J4cmyjKurq8EgJZIJuVYyhiaTyVBalXOZzWaDG1gpNegyRVsL8Pz582FMnYZll2U5MJQC/k4d3hcXF0P492q1+sjhLoHjwgROp9OBWZY4HwlLb9uWx8dH7u/vB+ZeDF0SP1UUxQASTzvpCDMJIaqqLMuhz72AxYuLi8EBfqrhlOdEniMZ0y9evODy8vKjCCI5JxlH8v6yoRBW8rS9qMzTcu6nulG5l6JBPj0nAfKCO06BqshRhM19//79J88/n+7ynswxccbL3/8NzeaS3XbDj9/8Aw/3Nzy7mqFsA7ajqnviJCeOErKiCLSxU7heoTRYF9yoHjA6IskiFksYTaak+YjOOhbTAut6rI8o65rEtxhrsc2Gw8MrvKtoqh3j8YQiKzBpQe8NXiksPSZOWV4+ZZwHcNH3nn3dgUmBwPBFscZWPa31jI1m9XhDVe4psjGrumR/2DMaZcRR6DtsbRvKd9uOm5vAyPVdD3mG9R6nFEkcgXMUhUyM8VHj46hrx3a7x5iE5fIsGFyS6Lg7KnFOhNhht+hsT1PVKBUc1kUxQquEtu04tDVN23Eoa3aHAyhFkiZMRgXFOCdLY7quAe1J4hiDIjIR+305lE5k4cizNJxf0+C94u72kc2mou1B6dUxwNqTJYrieoRzS77/6T2rbcVoPGI+v0abhrKsaJoxXV3xcL9iu9sxmcy4vn7CYjHGudAlYLEcc329QJuIw+5wHPiezSbkvU0mM5yPiVNHFDdUlQvxN0d3eW9bVtuSy2VBYgxda+nqNrQ1nBSoSBFHEeNRRlVaHtYVm3WF1wZcyWRsmM9SVALGO/KnM9SzCX3fEiUKZ1uSOMF68ITsRPB01vG4q5ldfs7Lz74ii1SIPUof6d0dduqIkowsT0lMSB1oesdqsybp7lEq437TMppdkeVjXGdx/RgdFaweb9A6YjxeorxjPJ6ybxy960nilGwyJ00XmLwg1RmHBrb7Bu81UWSIkohYQT6dQpKxORyIRwVeedq2RnnonUVFGuct3jmyLCFJI7abHcZE1HWDtSEnVRbxpmlOQnFTtIrC5giHiUJ70a51HOoK5TtGxYTpuMB6hzH6GCnkUGgik9L3YTJNs4K297TNzSdPVL8dn3ZI7t/XX389CO7/6Z/+iffv3/Ps2bOP8giFgRT9nhhPTrMF4UPJ9fz8nNlsNixGonsTBhQYQMbd3d3wPhKlI2XyUxZPurKIQ1k6kwDD+UlkkTGGu7s79vv9UALc7XZD9qMAjDRN2Ww2vH79msvLy49yB6UcKQv7qeNXPsdmsxlAgpTMy7IcutLIdToFzLLQC6PZtsG02rYt+/2e3W4HMDB+EuYuIELKwxKgLUBZwIVcO1noxcgk90sCrJMk4enTp1hr+e6771itVoNLXuJtJMD+/fv3bDYbZrMZz58/5+zsDOcc2+2W8/PzgWHcbrcDQHl8fBwC2qVsKm52YalF8rBarYauRKJBFMAorNxkMuFwOPD4+DhoU733gxFGGMsXL17w8uXLAUwK0DqNgILAMm82G66vr/nd7343sJJyz+U1hcUUQ5BcS2AAi6eb6TiOh5aK8tmlS5KAZHltYcTruh7ioISdNMYMmk6JDwI+Sh4Q2QowlMJF5iFjTjZW+/1+MGFJuLqwkDKexKAjkVmn9082DPJ+YjITZl8MZ596fDKg9CYmG6WMRyMe3mmK2RmLy2eYfsLTi5w81vR9Rxpl7PYbvHdEJmY0nmB7j/Ka3nas13uc04zGBdaFC6m0JkoStrsdWVGw3jyG8m1bMk4zprmirSuq/QrjWjbbNXmRUYwmFJNzfDSh8xnOGuo6BXqyPEQW7fc7iBSjIkZFOZ4UnKbtSpz2QEvdwW6/pSx3jCaXFPkF++2Ks8WIaJQQJQleBbORNlDVoUerHqcD4ldKYY+TTRSZ4+C24Tocu0GEcODDsAOK4w95XJKHJkxAuLkJXeeD6adTtF1DWTWsVmvqpkNpjYkj8nHBbDLGGEUcGbSGPEtJ4rAwqGN+tAiRpfyitaYqK/IiI5HMUOeYThfkowlxEtP2Dnv8HGjL2cWMHoV6c0vbdRTFjLPlGXHUk2cjiizjcbWm6xxxHFiDoMnKg6s4Nmgdhl00mx0XgcAg4DVxlDEaRezLFe/fvWe7a2k7aFof2iYajXId80nKeFQEOYGGyWRMmiV4AiuwPmywTqMNGANlVVFpSxon7HctRZYzHudUdUPfd0SRQ3kfYpzywNZY53C2B+2ZLc/40//8f6Ov9mzub2irPU1rSccXPJ9ckOcph92O5WKGtpb7u/dMlhavV2RaM85SkqSgVQqMw7oIM5px9vz3dL0F2zIbF+zrA9vVLY2PMAaSxDLOIopU4Y2noyfWhigK0ULzyRTjezQF0+UMT8R6u+d8chUMMa0NXaM8pCbC2mCyiaOEtrGABOQGLa6U2uI45vHx8aNsNqVUAInWHxnzPiQ22BbnG7J8yajIOJTHSTY6aoK8IooMh7Iky/Mgsu8c6uMuaL8d/wqHhElLxMp8Puf6+pq+7wcWUOYsMTXI/HTqTpUoFgGaUiqUfMrRaDQAAHHzSjC0LKLyc3I+wtacskrCmEpJUZzTwFCqO9Unbrdb9vs98/l8MAmdnZ0NZiTR2gnLudvtBleulPuFMZJSn+g9pcONBKRLfuLp64q7GRjKiLIIS+lb4nJOO/eI9lBeX85RSo7SWQUYHNoyT0uovGROihRgsVgMn1uAj4CEq6srvPf8/PPPg5P8/Px8cAoXRTH0fE6ShNlsNrz3qRlIur2csoGyGRBA8+bNGzabzQA+pFzsvWc2mw1RRSK1EPAsrQiFQZbPKddlu91SFAWTyYSyLIdNhfd+YDaFwRUm8fz8nP/wH/7DYKCRrjcyBsUlLeD55uaG8/Pz4ZwlLkeeJamsvHz5ctDcCpCUZ0QAf1EUg8ns1C2dHpu+yP0VJl8ie2QMymc57d8u+aMyzuRPYVeFkT6dp0/d33K/5BqJKUlIA3n+BcgLYykMrzC2n3p8MqB0XuG8Q0cx509e8O/+4/+d289e8uM//2d2m9d0OixSi7MZD6s1RntGozA4lQZvPTiPUpooTojjjLpuOJQVUZSwL2u80jRtTVGkWGfRHrTq2Ny/ojyUHDaP9F1JlqZMp3M6r7BEOK+JsxHOR0yLKU29w2vPtmyom444yUL2nUqJkgVtrelcSuUbEt2SZCOyvKRvS3bbBz67ekGvDBpL3zX0zhFHEbPpmNv71XFCcUfm8oP2RS6+LL5t2w9WfSlxAMeST8Nu1w03Uwbh6SRijKHvQrxLWVWUh4q2D/mTi9kMC2RFwmw2xdPRtaE1k+1b8BZrPVpBluWDLkd2MaduxL4LOzDnPfPFkiiZ03SOqirZbcvQvzmKUAryUczz7Ire9Wy2O8xRg2dMAJ/ehuDz2WzJsxcvyLOMsqyCU1h5mqZHqdAiUesw6QTm9hhPo4L79/b+jrv7e5J4jFaG8Sij7YNrcLfv2B+mnJ9NyEcJearpmhLvw2tGkcZ5hXKKJEuo65beNuSpPkZGOZI0mMOKIsL7IA2w1uJd0NRExmCiCB1F9G1D7yxZMWE8mTGeL/n+27+w31TMz665urpmPCp4vH/Au56mLpnqCaPlS7are2xVolTENMuovWKzb9juVvgeIp0yn5+xfvsD+/UjjTNE+Zj55VOs76GvsLZHqZjEaNq6pa89ymm0ykmyDG990LQuLhlNCkwxpTtOIiYyoEAHqp1Yx3gfskXrujvqdO3Qf7msuo8YCWFIZIF31mGtp6lbvHMoDdpAnuaMpyO8t/R9R9v3jPKExvfgOuL4gwO26y3WWc7PF588Uf12fNpxWlZ8/vw5/8v/8r/wu9/9jn/4h38YHMmiq7u/vx+MGaJpFIbpVKwveYWiKwSGTEsp5SmleP/+/dCCT0CmGCGAQbso5gRhI6UUe6rZlPEoYE/AppT2VqsVf/3Xfz3owWSxF1B4c3PzUW6mLNi/nKclyFk6mEjfawnDbppmMHachkifMjqnpUdhTYUUECBRFAXL5XIAyZKJKOBWAsiF7ZTSrYABOW9h2s7OzobOL2LGEeZL7ukXX3wxGLRkPRHwK/d5sVjw+eefk+f5ADBONxDwAVhJGLa8h4TTv3//fljzJINR4qp2ux2Xl5dDe0eRWsj4ks8nnYpEDiAROLIJkKzNU2AkBizRH4quU9IKFosFf/7zn3l4eBgY1/F4zO3t7WBqkh7hDw8PQ+KBXGNp8yljZ7lc8ubNm6EFZ1EUA3A/jZSSrFBhLwVwClY4OzsbgPXp83pqPJI1WsahbFbkGoqmWcCn3K9Tna6w6HIOWmtGo9FHDQ/ExS/Mt9wTeT9JV/jU49MZSmVCCc1ZEm2IR1OS8ZLJ+VO+ff0Du9tXfPH5S8x2S9uU9F0LhB7ZaBMiSKxlMp0xGgXRat109L0lioIpoDneuCQK7FmmDVmas9se2NdrsBXX15ekRU5PRJSNaa1CGU2cxCgVYZwjUTGJclTbA9gO24ZYHm88vSrwUUE2WtD4HuNqOqvwviBSEGlPX1csLpckkQ+uXhUxGY9waK4vF1xenjMapZiTCep0Vy3xDNERhMkgOc3wGo9HtO2H3CqZQE7bLHVdQ9M2RFFMFDuKccw0HlOWNfPZnN5B01UYE3ZnWVoQGYVRUShP8qF1k+zYRfQusRZBP6ExkWE0HrN0KZ3dUtYHlIpwHrre8bDakKURZ+fnmF4xmWS0bUlsQll1PJrR9dD3nour5yjv8E7zuNqg8KRZcoyjUajj4qJ1dIzWCIDO6GOAtm+ZTgrOL2b0ncJ7w8XlBW3XcfP+Bq86Docdu32ObTVukjOdFJSHmt2+wkQJThuqqqQoDNNZQZppYq1DVFOahgex7yjLPXGcgCcE8ztHdFw0mqYLJeUoo+81u7LC5hlRMuLlV3/D9cuvcF5RjMZ471gmM2wfOiL1bYsCsvt7IiCKDQ+rB3R1IC16JqMxxmsOmw175iTtkjyNSLzCtRt0PwU8WewZFzHzacbt7R23d2vOLp+hlKHar6jynNnygjQxtBaiDkYqQhNSFbyHvuvJkvQ4xiIiE9O2bmCGoygNP9e3TCbjjxymstMeYitcT99Z2jZMWtqAUp7FckaeZ9T7ijwviF1YBJ1t8OJUPE5+eI/RhniU8tvxr3sImyAgRBiey8tLfvrpJ96/f8/vf/974jimruuBZZMoE1ngJVYG+MjgIXIZWcilxCZsofzss2fPhpKyLKanbJ/8W2s9OH9FG3Zq6pGImX/JMFbXNU+ePBnYMGFtIPQrl5BuAX1iqoEP87R8rtPFXBghYbXk37IAS2chARoCoATYiAFlv9+zXC6HjZmUF4XFEjAMYZ4WA4cYVk7n6f1+P4C56XQ6XA8BQcL83t/fD3rIvu8HQ458/tMYmadPnw7X4v7+/iN3/y/HkXxmWddEEzqbzbi6uho0fk+fPh36sIuzf7vd0rYt0+mU2Ww2SADkXgj7KmV7Af6SyygaQAFZovcTcH8aoA/BsCQl7T/+8Y98+eWXSFSVSB5kXZQ1+e7ubhgD9/f3HA6HgV0HBvOaAF4ZrxJ6LnmU8/mcd+/ecXNzw/X19eCyFoZYKgSyqRBwLQBP/l/utegiBYDLOBQzmJw/8NE8LXO4jF35v7OzM4qiYL/fDxtC2TDIs3CaeSrmnE89fgVDGR7mHodtHb2KMcWUxdVnLK/fsX5cUbbQrjYhpLPIKYqcuvPUbUvfdyRRTDEaUdUNm82WvuvIiwmL+Zym7zD09G1DRIRzPbZv2dclaZwyLhKur88pxhNaB97kWBWzW5XEynHz9g378kCeGYo8tAk0fUWmHZ21IZjbl6Q+Yn49QymDMxHGFHTOkaZLug6qw4r13Xuun17Rtwf0EQwn2YSLLGc2nwIO37foEypYdtungblJkuFcSxTpYdDJxCHRKjI5n1L6aZqGPquxQWkXOvB4RxRHaN2hjaVzFUmaE2UJeEvfdcxmU5I4oms9tu+wzmKtw3tFmh5LGUmEs25wE4Yj0N3aGLzvuL17YLdvjzqeDOs03/34msjA32YjinzEZJxRV1EoY0eGtukxkaPrHJEOTGDd9rjOkqYa7x1ax7RdR2Q0SRJATHD2ZceJDeq6Cp1l8oiz8yneqWP5vAHdcXmZk+cLjNFoHLbv2KwrxkVBXVlW65retyR5MMBYG6KRstigcTjfESWGpmtQ1qGP4Mth6fvQBrJtQv/vuu7pbNglL+YTHAll60i8waiMYjalblqqzuE9OBvK5JiMeDIh0pqCAuc1RZHjikeyckNXbkj1I5uH92SmhzzCz8bsNw9EaYb1FfS7oLnxLarT9JXHKM/DzSvmoxFKxWRmhHYlbbNjNr8mG8+YL89o+lDKTuKYtgv32h8ZYWNCqXv1uCEvCsbjnCwb0TQ7nHdDCU52xQHw62EHnuUpVbnBmJgkNRgDo1HoflJVNXXVMRqnGBXKZ8oaVo937Hbb46ZP4Zyl7jps/8nz1G/HJx6nhhDR5xVFwdOnT3n27NlQhr2/vx9aKIq7W5y+Up6VKB7RBp6aRU4NKNKFR/SY0oLwVIf5+PiI1ppXr14NGYaSbyjzngAk0bo9efJkAI/y/0VRsNvt2O12vH//nhcvXgxgTZjPq6srFovAfsuCDwyuX6kKnW7kZWEXPaMsxgIyRSYgYdsCeDabzQC0hX077RYkJUx5j7ZtWSwWgz5SgJks4JL/KWuF6BNPtacCQG5ubgYdnoCAb7755qOytmgUZQMg4FKumWQsDubMI4gQkkTOW3SrMraqqhoY3cvLy2EcCLHy5MkTiqIY7n/XdTw+PjIejwc3tkQLnUbqCDMp11FYR7k3wgbKhkC0mXKdl8slwDAeRV8qm6fT8q/WepCiyXtK60NxN0dRxN3d3eCiFwPPafi8SBROw//fvHkzbG5E6lHXNcvlctjgyWZOQvdPN1KyAbi7uxs0j8LiyrURQkjK1sI+i8ZSzl+0m5LvKXpgkaMJsLy7u2Oz2QybPjHdCfP6KccnA0pFcGUTso8xScLy4pqrs3PaQ8Xj44YfXn/LNFdEpufz331JUYzodiXeO5JjKeKwr1itdkRRQpLFnJ0vSeOY9vGeIknQ2tO1Fd714IKBoOl7rG3xCjrn6TEoDPuqZbXZMV8kjPLQ5ztRNVFbUe8bNqsNVdtT9QqVTUgmZ1wuxozHBdYb8nyE73v22wdUU0Pbsl0/okyL7w7EUYyzfWjVZ3uyIiFOQvxOnGSY425ddkcyyGV3EUVmYARlRyBxG+FGhUkrzzKKUY4/akolcNRECud6vLdEcUSe5VR1j1Keqq7orSXJYvq2QisCAG9KOBohjDHEaYSJI4wOk2GaTBmNgotLROZ939M7R28VXmmMibGupXdgLVRVx9nyCW/eveKb717x1ZefU+Q5F2eLABDjGLShaXva1qHoSdMJvQvt+SbjFKUCsHHeoQhZmFpHA82utWa3Cy6/1oL3wUjU2o4sMxjdAS1nT2ZMJnNWDyuaw5azp+cUWUbTdDysD7y/39G5EH4+Hmcs5475JGU6Som0Z7sv6SpHMhoTJzm2K7F9oPh725MmCU1zoK4qfvr5FkcIgd9uS87O5uR5StX0pInGVh1Khx2cMorD7kB7CA5od5xYtlVD2Xj8+oDqe2KVkiZnOFXSO8V+v6Wv9swWU7wvSeIInWbEsWU+SbF1T3vY0eiCPElJI3j301/4/dd/JCo0Lm5Io47IOM4vr4iTgu5wwDtwLsQkaR16MU8mM7I0p9xv2W4PKBUzKjT48LPeO8APu2TZ+Wqtj2HkBmMUUaTxWtGjMREhnD+KuHl3A32QEKjI48Y5oyy4c/EW7wPj0VtL3zmqsv3kieq349OO03w6YQSvr6+HHsf39/f89NNPgxbwD3/4Q+jQtNkMGqzFYsF2ux1aMwrjlaYpt7e3gztZjATClom2DxgAEjCYLoQdEcZFNHSPj4+D81tMK8vlcmDipMvOZrMZgO/j4+MAfOT/BZgIKJPuI/pknhaWS1gtWYhlnj7tsyyObAEKeZ4PpVcBZsKACiskAeBSuhYmU6pPAmQFGAADiSBs73a7JU1Tzs7Ohn7bskE4jYwRw8VpCfjy8pKff/6ZP//5z3z99deMRqMBvAhQOO3qIkA0y7KhBZ+AcwHSp8BYa812ux0MOKK7kyxFAdLPnz9nNptxd3fH4XDg+fPnQw7ow8MD79+/H67ZZDIZysDT6XR4DzF0SUSTgG+5ntL56fvvvwcCMBMjkLyXaE5FvymvLeyk3Kf9fj9stARsCltrrR3kIqK9lM5GSZIMgHW/3w+/F8cx3333HX/7t387bJxEl/rkyZPhug+JN0cgv9/vmc1mZFnGbrdjvV4PJX951mTTmGXZRwzlL6Udp2Yz2WTEcczr168/YiAFrM7n82EekXEvHYY+9fhVgFImLFTovhGhibziyz/+z5gk5X/7fzra7Q1ZxrHWX9F1LYkxWO9wzvO42pClIy4vr4HgBrU2sJflYR86eRhPFCm6LpQK2rpjNJkyGk9Qcc5hX4N3eHXsHxtHzMYFk9RDf8Dbln1z4O7uBm8SFlcvGZ0/xUUjIGa/PuB1iveatu1wWJRviExLljv6bsv9+7dcPMuIopyub9FxCjjwjjxLcNailMb7/iNNzemOparqATDJzZVdF4TygrSutH1gE2WHF3Y6kGUpSZIHd/eh5dWrt2y3e6bzxZGO98xnY7IsCU7u3Q7rHFmaYEwUmEGlKcsSgK7rqevmKCgOTjkU9M4FfaZSjCcTmk5xqGps19C0KaPJiOnsirIseff2ltkk5Xw5JR8V1G3H/WpLVbc0jSVNI6yHOA4GqaYNfdtF65Fno1DyjBM61Q3CdWstkckJ/ZcastQwLhRRrPC+o+9rLs7mGG2oYwXeUx12jEcFVWNxKqJsPHcPa5TWHMqgdUiUI4shm+TH8G6I44S6bKnKFo/DGIhMhDkaf9IkIStGlLXmcGjw7oC1cHV5hjGe2rWcLeY0TcnhsAUV2hSOJmPapqOqSpTWJFmMVZby0NI1jvWupG9Knlw+5cnLF7x/+w3b21eoakMaX9DUe7JRxnq/5ae79+RaMYpj9KjARIrlZMzrn35kXvwtvWqwymCbR1y/oO16rHJYfwy8VZqefiib4MH20DQ9TROAr/eKvnfH8anZHw5k2SikLNijeUJptArh5t47ktTQ1JbpZMzV1RnWe+q6oiwbDGC3BxbnE5I0IcsS0iSibSqa4wSuTYRzoNRvnXL+tY9TAb1osUQL96c//Yk0Tflf/9f/lc1mM7BYkql3ClAeHh7I85wnT558ZH4Qlu7UdHC6OM9mM2azGXEcDyBVqjNi/hBQKaXgm5ubYaEV44iYFoSVEgAkZeA8z4d2hy9evBhY1tMQZulhLGzL6Z+iHQvz9AenrnS3EeYOGMKupZwv10HMIVLGFt3nbrfjxx9/HILExfAiRiJx+Er59LRkL4u3lNFvb28HMHda/VJKMZvNBg2lsMTS1/xwOPDq1Sum0ykXFxdDFMz9/f3gDJbSrbCCQoYIOBWGUYColNdPZQJSJh+Px8M16bqOy8tLtNYfMZzS/lMc0Le3t2itB2OOjFeJsZJ7IoyajLnTL8m2FJ2vrMVPnjwZgPHFxcVg0JJzFinA6f0TYCnubJFUfP755/z888+8f//+I9e/bMRub2+HTclkMhlY0e+//34oF8tnFnZfxrX8n7DXAhBl0yEbLWAYd1EUsdvtPmJG5XOdMrkCuqfT6dDBR5z28ixI3JGMX3lPMUfJffnU49M75eCP6jfAB5NN5y3WQzIa88Vf/TW2qfin//z/QNk1N/f3TCZj4ji4uSMTs91s8M4zny+OtD3gQxrldDyiK/e0rkcRwAxKUVUtoNFxgiP0nE7SBHRMZlKW05C7ZtuGnh7rHd47PDBfzMnn5yyvP8cnE2qf0HeO9lDifYn3liKykDrS2GCsJtcZVdPQVHuaao9OC6I0xRNK8Fqpo/va0nQBMJooorcWow3eK+I4xTmLUnYYEMIISJq/taFtojGhHGxMTN+7QahtjD4u8hG+dyRxRNP0eK9J05wiLyjynCIPgNp2LbZriePoqPsTjU4wZewPO2azJb31rNZbyqolSWJG4ylVVWLbGlzQtqVJwmIxJ8sq8B3L5YTeBvDRlEE+kKeasmrIj6xr33XsDxVN06P1iEN5YDErSJOEOCJsFnxguQ+HkixLMSaiaVqMiVE6pnM9nYtZrWus9Sg0DojSmOXZEu9rtA990Md5EuJ3fB+YNa1JsoQoSem6Lc47stRQNz3OKvCaumrxPjieHx53rFcHtNJEkUZrR5JoettjtGIyHfHERtzc7lk9rNisdmy3FW3ref7iCnzPu9s7nlydUVclZVmR5TmT6YTpJKJpapzzJPsd07GiGVlwEft1xQ/f/8C3P77nr/74kme//zdM53P2b79n9+CwLnSsyXTNer8JWad5RlGPWYwWvHj+lGq35tWP37OrOz77+k/EyZTpZIpW0uHAY7TG+dA/W2tLnhegI8qmZbvb0zuLiSOSNJRbktSQZ8Eo0LUtbdOilR6YngBMQlZnYKcOFKMFy0Uw4W12e/CetmsxkWI6ycmzmKap0DpktE6mE9zWYV1ohdn39tOnn9+OX32csoeiIfvrv/5rmqbhP/2n/4S1lpubm8EtKgv4er3GOcdiIfO0H76CtKEawJZsVGXBPxX1S7alGFIEfMprCTNzdnbGfD7n2bNnH5nBTgGCgB4xIUjWpZgfxJ0rgOY031GMCQKKpbQon1nAmLihhcWCD1FDAvpEPyjs7CkTJJ9Z2Edx/sqXuLGlfC5laAECANvtdtBdPjw8DK3wptPpEOV1qq2UloHOuSFv0jk3uNQlXkkAX9d1A1iSGKHFYjHE/8g1BwbQInE18jmFET2N2pFzuri4GMgVccWfhogLg3earZhl2VC2BgaAL9rOh4eHQd8oIFXu42w249mzZ7x79467u7uhXWPTNHz++ec453j79i1Pnz4dci6FjZvP58N7SeKBsOIS1P7NN9/wpz/9iT/+8Y8sl0vevn37EciXawZhAzOdThmPx3z++edst1u+++47DocDf/u3fzuwmcKkn7qwT00zMra32+0AImWzImNKwOdp+oJ4JAT0i6xNGOCHh4dhkycbyNlsNmzOhJ2WblRyn//HlLx9yI4MoFK+PF55VAQ6Trl48jkvvnjg7U9/z8PjLUkakWeToKVsWg5lzeLsnDhN6Ww/lGm97+jamoe7G9AK7zxl0zCZzpguLilGI5RR7MsDJrXM5ueUZUd5WJHGGU4rjFb0bUXXNWgFSZZydn5GMV1QVXts62hdyuJsRqsUXVORRJaIhiiqiVyLtQ1JauhKh29bbHPgsLlnch4TmzhkFCZjDKG3dholWIImwwEaTd97tIrojwNGdJXy0MiNDjl9Ho9jNp9y2JdD5h/KhdJqVdE0NXXdEceBcTRacXG+ZDwuiGJNlmicDc5aCPo05xydaY90t8Oj2R9KOquwXcjHdD5EwBiT0vclTdXRddB2jq5tqcuaokiYTAvms5wkjigPLWkUdIZN19F0lt6F3tTjYsRu31K7hv1+w7gw2N5AnOKdQXnFw8OaNA8Ta101NFWLR6OzjH3ZsdlbHlclh7JDA4qeJPZMZxl5FowzGk+WxsSAiTXaKDprsX2HxhEpSxwrDlVHbx1pmpNkOSgT2lTqjMf7FatNSdMF3WiWRuRp6DI0GafM5gFkT6cJcXxGkiT84z98z83bFYfSYVXEF79/xmH/wNv39zy9uiKKsyPT7IlSTdMEbZZCo50jiyO81TDJuLw658cf9/z44y189pTJ+CnqymHimHYzoT2smI3GcLakaUKJb7Vbc/70jGk0I59MuLl7ZHb2hKfPvmJ09pKzJ19SW0V9bBOmtA5fvcdEMXUT7lfTOtaHCgvEWYzXjq5usE6RZyOmkxh/LE+naUp/BJj+uKgrHTYp2oQILHSK63qqfQnek6Sa0cgQRz3l/pG6Cnml3ntUpJnOp5RVC7vg/P/t+Nc9Tkvev2QrBWQ9f/6cr776iu+++47b29shl09YuKZpuLi4GOajXwr9xUEtjMd8Ph+ie2TTLHq2w+HAbrcbyodSpj7NV7y8vBx6iAv4u7q6GhbEUzAp5WZxJcuiul6vh17Sp5FAojcT7ZyAJXl/cZELKyras9PAaQF7y+VyAFmyyEqfasl2lLKm1pqrq6shLuk0Cub0eoqW8RTEnUbGnEbqSAcjYbmEnSyKgvl8PmgzxZF/qjOUtUgimgREjcfjQTcr5yC6PWHiJGNUHPnb7ZbHx8ch4B0Y3PUCHmXNkzEn5y+fX0CQXGvR1MrrGWO4vb3l8fFxAD+SlCJlcummNJvNBk3hf/kv/4XXr18PTO/XX3/NdrvlzZs3PH/+fJCcyRgRplbGityn2WzGkydP+Pbbb/n222/53e9+N4BNYd/3+z3T6ZTLy8vh/q/X6yHTczqd8u7dOy4uLvjss8+4uLjg+fPnw30U8CfjUECh3NvtdjvoTEWqIFrT2Ww2bLZEEiCmttPoICmjy3MnbLCYiERiIfIC2TQtFoshPUA2CJ9yfLrL2+vjxBTME6FPMhzjnzFxwvnVc6gr6sMtb3++Z7PZcra4wjvPYV8ynkyYLpZY6zFe0zU1kXK0VcXrn74NoEBHHMqaJM8YT0KKfZrn1F2Dq0p822DbGt93+Laibko8jjSNqQ97mrpmMh4xzlN8X7O9f8Nm3zCaX2GSCXav8U4T+x7VdzTVjn25wVZ7unJH21WsNnsOZQCmphiRJAqjoGwNbpKQZTG2hzg2WG+xNsQqWR8uUBTFVHU1TFQSTSFiYnngQvmkw9rAPIaJSGFdYN3yPPT8ds4fd3RBDzebjYkiRdNWdF2IYpJJzFpH11m6rmcymbDblWy2e+K4oKoabm8e6K3nfHnG8jwAlihKiOOC7W7P7eOeQ9nRdT1Jr5lOFlycTfHeMR0VzGcFXdOitA4TAAHQeufIsxilc/AWXI+zPV2n8dYxmy5xTpHkQSBclSXeQRQnHMqW28cdj5uKza6m6wJLmMaK5dmCs7MFRZbQtn14XdfT2w4dJ3igblqslQy+DE2H7UtQGeNpDsbhsKCiYDwCNruS3aEnTlJGRYK1EaPc4JzhUDbgIc9TtHY8i85oKsv3P7zl/vGBXbmh8w1f/u459X7N3d095+dnpKnmcCiPyQUxbdvhPYxGY7I0o6pqvD+wWI45lGesHtfc3++ILsacX33OxdkZD6+/4+bHmsNux93jCmU0V9fPqJ1lXXbMxiOWT18SjQ9MF09IJ5ckozOcSmm6D4JtZXRokRlrlI5o+p7trmK1adnuK6bjjLxIcb6ndx0RMUVekOUJt7c3x5aianB7S0qBidSx1JSBD2WcSOsgn4hjxpOC2SwnSRSH7Qatg1C8qmvarmM8neI96OiDa/G341/v+GV5SkCCHBJ8LZovCb8+7aZymh0ooE5MJ999993AAArbM51OB31k0zQDkyZSC1nshI2SmCBhc04NAcvlcjAlCgCUEtzpl3Q+ORwO6ONcJOX9uq6H8rKYbk6NLwLSxAwp87Qx5qOoIlmQ5TOc5jwKoJZIIAGIpy7dxWIx6AsFwJ6adeS6SCtJaY15OBx4+/Ytfd9zeXnJ5eXlYBCSa3N7e8t+vx+kA7PZbDDHiPlCMiGl3aGco2gdTzV5UhkTV72Uu8X0IZFR0rFG9KzClJ6fnw/aRQE+wm7Jcy5xN8Isvnr16qPyvZwTfMhcFOAmpW0xnJzmKEqG84sXL6jrmr/85S9DAL5zjj/84Q/sdjtubm64vLwcnNfCFAvbLHpNCQA/Pz8fdMe3t7dcX1/z9OnTQaf63XffsdlsuLu7Q2s9gEUBms+fP2c8Hg/gUthHudanbKKwrwIkpd2j/J5cT9Ht5nnOu3fvBuAoZXEBnqINFrAspmFhVqVDkDQBEMApgFZkGsKif+rxK0rewxR1rNEHZOmVBuXpgSxJKWZzpvNzNqsluJLddsduX1L3lj8+f0qaJaxWa5S3GDy2b3i4f8fj4wOL+ZI8L5ifnZFmOV1vOZR7ur7DRBGpMcRJQrPf0XcW1XdsNhvq5kCcGHCeIsko1yVdFDQf1f6AJmKRnIOucdUNq8cH6qoiVppyu6UpK+gtbVVjtEJFiqbc8fbHvzBZzFg/vOPqs7/BxufsKs31dU4eZTgfHpiud5R1NUTghB1G/dEOVwClTDjOeSJj6G3Hw8MjWsUsFsugKe2DztEYjdYRURwdBbiHUA7NsqCZbEK5XHbXQZOmSdPQu1xpTZZNqCrP6nHHu3fv2Wx2JEmMUYokNUxGGV5pWqvZN7DaNDRdCDLvt3vu7yKm44zxKLBTUZQRTUOZu6wq2qZkNCqIE4PCMkpT0iwijRWx0eRZMC85Z3E2fBltMNqgTETbOd7dPLDaVXR9CMdXKjja8yJhPC4wRlHXJV1bohVEJiNOAphs2zbkLGqFVp40MUTGkqWKy4sp43FM39dYl2ClpJ9HGO3pu56u7XFtje9T+kaTGE3fRaSJQWkf7mfXcf10xmSe8+d//oa3Nzd885e/MJvkPL1cUO3XvHr1MxcXl4OTVOtwPyaTydEFX6EUTGcj8lEeOjW5Du876q6ltSMWoyXXLxRNXVE1LT5dcahrLtIR8+kSGyX0UU6xNKhRw9XTL5hcPSUpltTW45RBKR/0yp2l64NhSqtQYt5sdtzf1ZR1w+XFgtGooGv24Du0iTDGU5b7oR2fMBSD1gdCtJO1OOtI04imbvDOAp7RKCPPU/I8JTERbRwRRaGEmaUZ1jnqug3SAyfj9bfjf8RxylT+8vtSelsul0M482azGcwKL1++pCiKITBZNhbv37/n7u6Os7MzRqMRFxcXA2gTcCOlXAE+Mu+tVquPYsuyLGO1Wg1u2t1uN7AxEp1zd3c3aNw2m83AZom+SwDht99+y3K55Pb2li+//HIYt8JIic5MOvFIqVIMPrL4ymIsjufTFnfS0tEYw9nZ2QA05VzEmCnlQzHniEHnFIieatyEuZWfu7+/59WrV4OTWEq8El8jnXnEfS/l2ffv3w8AHQIQmM/nA0CV0qewvdK56DT2SUDyaRapAO+2bXn9+vWQyyjMtTj7pfuNtNyU3xPmU0qq8vnlfcXwJW0zBfSexiuJ417GkoBr2SzItW/blufPnzOfz/mHf/gHXr9+zT/+4z8ObONut+OHH34Y4nzERKO1HjI8BYTP5/PBRCvnJJuNyWTCF198EfDFsSIk4+ry8nIYT+fn54xGI16+fMnV1dXABsOHbE8Zl3Jf5Fm5ubmhLEuur68/apUqY02MbsJKfzRPHzdCovcVd7gwjePxeJBhSKqHfE5h3+WzSTn+U49fBSiV8gFIosLfj5pKhwEcrbPoJGF5+Yy23lLt7tjtDjg8FxdnRJGmqg8cyi1ZHNG2DfV+w93tW2bzGVfXV1R1ExY6a9lsVmRZTpHnpFlGrENun3PB0YTz9G1JV+8o0glN31H1PbFWGAxppPFZQpKPySLPan0PRkOzw1V7dk1PW7WMiwmRzpk9f85kNMIbz8P9O+pyTTSKuHv/Dm8jnvxhznq9wfr3XMwmxJEmLXKcsjy+vSGKDWeLOXX7odXTqStPHqbwEGiiWB2zwYJrXX4+SVKSND4uCIq27YZdlOw2q6oKLJ3Wx1zBPjhwTXqcEMB7R1Nb1o97Xv38lsMhOO5nsylFnoaIoa6haj2bnWe9a6g6j1Ixva3xONabDZtVwWQUdIPeO5x27PaHMBnGER5LlmjGRYpShjSNKLKEKApmLmst/ZHV7tuOyMSkaR56k2vFfDGn7h2Hhw2RdhRFxuXFkvPzJYvZGFxL13QoDfnRQWedCrIBpWltTRyHFpVJBFdXSy684snlEo1CmziE5nc9dC1xbBiPM6wNgD3LYvI8JjahJWFRpDjbsdtvWS6WpA6atibPLX/62y+YLwrevr3j7t0tl4sJ02kwR223H4wOXVsHNtSFRUcmU+cUaWJ4/vya3nasHlfESUKcjWl9Rz5ecv35HyBOiGcL3r9/x+j8ms9+/7fMF5dEERy2azpvyUdT0tkZ3uTsV7ujLELT9Q6lNf74ZRU4PIeyZr9vyIuC+WyBNgalAeWCCz9SQ8yQGBxEFwcfTEvOWSKjAU8U6WBiig1JYtDak8QG53vyInQy8gRpgYmSo7O/wZiInu7XTD+/HZ94/Etl71OmUliKJ0+eDK0GN5sNEOJehI3a7/eDiXC32/H27VvOzs549uzZUCKTBVDGvWjuRIcprFPTNEOpWkq1p1pG+V3p0CQu8rIshz8lgP3zzz8fzA/v378fTCnv3r3DWsvf/M3f8PDwgPee5XI5aPkAfv7550F7KOVOAVCn5VYp2Z5mWMr/yzx9CgiB4TMK2wUMJepTA5OwPgImxHV+f3/P999/PzBri8ViYF6ltC9h2wJ8BCQ8Pj7y8PAwdDaSr81mM8TxCIiV9pCix5P7IADnVGMn+kytNWdnZwOwlhDzJ0+ecHl5OTCbAiblvE9zK6WsKxsPMYo8efIEpdQA6qXkK60ZhSmXsrj8XeJuNpsN5+fnZFmQHRVFwb/9t/+W5XLJq1eveP36NRcXF0Og93q9ZjweD0YlAXdiVpHrmqYpn3/++ZDvKdIQ5xzj8Zjf/e53gw7x7du3nJ+f88c//nEwlm02G6y1Qz6lMYaHh4cBTMu4EFArz6nkdEpM0ansQsCinLeAa9EZy/kLG3naXUfYZNmoyO+dNigQGYr8voyNTz1+lSln0E0Go/fxX4GtVErjI088Krh89jkGzzf/XPLNd38myw3n15doBW1VkhhNU9c83L2jOexYLs9YLJYkaY5DUdY1h8OeqjowmYxRytG3NUmcgLVopTHesy/3ZLHG24jJKOP8/IIoChOH8j31YU/rd2TjGetd0MxlccLF8go/v6BtGtbr8IC2uifzLaNkwtXlNZPJiPevv2Nzd0+sNdM0Ijeeyjs2j7cUBqaTKc5rxuMJxqQ8Pj6QZTmT8ZhRUbDfbYcHXm4sBC1QVZXs9iVaQ1k2PNyvsdZxfX2NMQXxcYchh1J6yP0KO5L94KYLWY0VxkSkaSiNN01D13ZsNjvKskIrzdlywdn5gvPzZRB6zyZY27N/3HD/2LLdlqAUo8mINJlCX+H6MphmlALlSeIAILrW4m0X+qfbhjxPj4HlHm0gSdQR5LX0XU8SZ6RHtjIyYfD0XXAbF2nE+WJEoh1RBEliGBUwijtcu8FEGq0s1nt653HdcfI/gsS+Cz2ox6OYIh8xnc1Yb0rqqmW/26E1jEYZoyzB48jThOVyyqjoKYqcNA3g1/ZNAFaxC3FM3uDxZFlE20KRJ9SN5a++fM4oDVFF3vU0jSWOI87Olsdw5+CQxNkQqWQ9zlm8DwBYaYOnZz4rSJMIlGK+PGO3vqdFMb96QVyMePLF74KswmmidE6UTTAa0skZTnl66/FJTucUnYc0SlFK43wIiVc+om5De8Sy8rSd5rBvmE5nx/7HEc5qnNXEsURMfGAoBEwORohj7BPeE2kdpBrO4mxHlsWkiSbNYsbjnP1ujVLQtj1JkuG9IooTeusoy4au7znsyl81Wf12/PcPKdWemiDgg5MUGIwyL1++BODv//7v+ed//mfyPOfp06eDo1cyEG9ubtjv91xcXAwLNzB0hREdGzDE04i7GhiAqSyuV1dXH2kcBUBNJpOhlCoM6Pn5+ZBbKCYQWSifPn3KbDbjxx9/HECOsC7WWu7v7wdtn3NuYKHu7u4GHVpRFINO7HTzJICrLEu22+2gDRVDxrNnz4YFWuZpWczFQS7h5m3bDuBJsgHFtS3M22n5XsrHEtW0WCwGWcDd3d3gfhczlYA1AWTA4FaXHEeRWBVFaCoiYEG0hyJPEGZVmDBxA8tmQNhZ+QynPcfl5+X1BYSffk4JCz/NdBQDisTjnAbin5+fD98TgC/X+zSEGxjYYYkM+vrrr4e/y/lEUcTl5eVgeJIYKLmOMg7kXnrvh9KwBIOvViu890Nw/ldffTWAMwljl4xLue4y/k/1uvI+AgzFXS/Gqfl8PlSKTgH5aYch2ZAkSTJcCwHI8vzL5xIGUu5xkMSFHvNN0wwtJ2WzI9ISMah9yvGrGMrTeBzv9THYW2OUCuYc5VFJRDqaM798wdnjI5Plz6zvX7F7fMR4h4lTtFI0dU2aZCRRxGI2I89y2r7HHS+yx7NczJlPx/RHF15dHejao8PvhJFbLqdEsQaTMJos6Z3DdjWxish0jEknqL4JjvJkTJylpGmYdDpuKebnbPc7fnj7lp/f3/NiU7OYjEiyOVrtg3PVWRJfsyxy7mzLw+MNOtIQJ6z3JdYbnI+pypbeglKGJE2DLOD4mWSyBYbdSSgBh4fk3bubY9nnM6zrcM6SZTlahZgbedCjKCJJE7q+BiTAGJIkoiwrwLDd7I47JMdoVPD0+prRJCcyhjgO7Q2d7+m7jkhrNJ440qAseWI4v1iQx2d415DEgYqPTSjTe+dIotA9CII2RqugOUyTBO8t4IhiTVP1dF0LXqOVoWtr2rbCeRU2I86jlWKSWrKpou8OaCy61rRqj8oLnI6IkgylI7wPICVLM5I0pWk7bBTyE41RpGlE31W4rmG/2bM7lDjvGE/GXF6cUeQxDs14XDCbBoOPUoRSe2ro2p6+rzAmI8tSXN+RpAlGG1brNc5CEo84W4yJ44Q0Niij2GzWNE3L8mxO24QOPPl4zG6/J0uSwOy6cF3kGSpGYZddVlVw5icpfV8T5SN01xH7nOl0jIkSykZTN55IK0wU09kOk8bEWUF7aInjnLYLE3leFFjnKeuGpnW0naVtNMYUeL+hbRvatsOYgqapSZP4uCv3lGU9TIze+UGmEcBI+Lc6yqcjpei0Ik0iJuOCNI0ZFQn7/RbnQw/5rmvIshDunsQpTVsBGtu73zSU/wMOWahOhfQCKk/BpYSXX19fc39/z/n5Oe/fvx+YvdOomDRNieOY5XI5lLhPwaq4tIVFO+0BLEBDwIGwM6edPuQQc4GwQxJlIoBB8jF//PFHXr9+zWq1GrSS0ktagPRkMmG9Xg+RLtIbWg7JhxRG5lTbfrq4yvnLQt11Ha9fv/4/aCfFOCEslrynfCZg0AtKGRpgtVrx+Pg4sF7Pnz9nOp0Ovy9smWgwxe0s73V9fT1Uw4T9kzVCDBYSsC2xPwJ2ZYMh0TxSBTvNqjx15J/G6whYbJpm6Lkt5XkZF5IrKg5luY8S9yMgdr1eD/3fZ7MZ19fXFEUx3EfpoCPg7PR3ha2UCCZhAUOlLzDRpx2dJPP04uJi0BFPJpMh+/P0uTktEZ8GxMt1FkZUJCTCZJ92TZLzyrJs2FiJHle0kZKaIM+P3Dv5nuiY5dpaawf9qJigBGTLPT3Vvgs4Fu2k5KmKTlmeU3ktMSsJaBXM8inH/yVAGSYtQOmjL8eH4GJj8UrjVcJo9oSXX/bEseebf/xPbFcPVPst+WRGmo+oqpbJeMZ4FABTVTf0tifJUu7e32JdR54mKDzeByNGXZc4r9BOBz1clHB2do5XLujECNoCtHSB8STZmN4b0skCnypMnEGa0mlN3ddQhLLotLigZoSzHh/PeP1+DW1JkcyI45qb9+842J7nv/8j82TKqux4f/eOOB+TJAVZOmJUOJq6p22PbftMRBxHgw5IXGyB8k/o+5YoMhRFhneGNCkIPb73TGfjoIVME/a7kr4vKQoBcA3W2WHCBYV1HW1X07Q1eDPs6MLANmjlyfIYrRW9bUmTmKbp6FtFnsbMRg6jIzrrmC5GRNqy261pu4pYQ55dQAxaMUxGp4tKkhwZh77DE1r0tW0QwedpBoR70jYVcRwNtLzWFroG1dX4dk+9u8XQo+KErsmhm5OO53Te0ViI4owkzeh6x/bwiEJR5AUmOrZtdB1JDFkKSaoY65ymtWz3e7TRXF5MKfIcrSxGA67DK7C2JVLmyG70OPdhE9D3lsikRDqh7Tr6tgHfExlFFINzMB1PaI6aqiRJmC/m7PcHtD4uqtoQRTH9sR95HMchUNxaoMfahvG04LCzOB0TZROs7WhdRHfoiNMxSaZpmhJUxGg8I81ymqbnsNvQtQFQO6fZbGoeHtdUdYtWEZ3t2W4PlIcw7qyz1E1J0yZozVHX21NXLUU+osyOi4t1oSzug9SgERbjGEnlvcdo0BriyAR2V3tW6xXT6TSYq0yEc8FpXlUN69UGhSZOEvLi/4KE+7fjv3ucztO//L48s/J/y+WSP/zhDyRJwn/+z/+Z+/v7wQwgDubZbDaUSSWpIssy3r59i4Riy3gQjaO8j7CJl5eXgwZMWEl5PWBYzIR1k+4yp4wfMJSuxcjw5s2bYaMeRdHw76+//nowWLx7926YK08zJU8XcAF5wpKdEgDC/p0yZwKkBOxkWTZkS0oItVyroigGVlda/p0aQYQ1EgOnEAenAd5SnZJSv7WW5XI56EulBCqvJUBZQJHc79P+4PKngIc8z4FAEIg2UJhKOXc5/81mM4BjOT/53Kct/Pq+5+7ubrh3vwyBF8B5Ck6NMQOolHKwjGd57VNto9wrAZgC/E81sMIgSnanGH3Ozs6GtqOnTUlER3iaEAAMBihhVIUVlOsoY0zyNgXAyWc77eq0Xq+5v78f8EHf96zX68GlL4ylyAhE1yutKn8ZIyXPkQBR2XicxhLJJl5rzcPDA4vF4qNzEl3y4+PjsHmRZ+5Tjl89owutrbUO7Nsxhtr7ECeDCkHIXmmK2QXL5jlP159z//NfUM6SGkMaJyRxjDEJcToiNgbrtvjO03VB+Prk6RNs29C2NfvdDucto0lBU3c4r7g8W+JR1E3Ljz//yGw6Ca0I44w0zwPLZQxRllPWDq0j8jin9YpGKZq2pe080+VToijl8XHLaFYwm07xtmM0vuD+5mdev/sB3IGm3mJ+/p4ksiyefcW4uGZVNby/fcfl1WfESUYUtRx2LdWhYz4d41wzxC6IRV8mkzzPSNPR8cHKmUzDbqPtGpzrKcuK+fwqAK/IUB4a4jg7DgyFMUkAkE2DMeoIXBrSNCaOMrrW0rTQ256mPdDWeybTEdPJGGs7mroPwd8uYjoegU8YjxxOaUyS0LQ1680jVbXjxbMnaBNK3X1vSVOZoBTOhR1RyD5U1E2N8z1aE7IOoxilNHXVsC8PJLEhzSKChMLi2pq+3WDLHd1hTbe9paz3RElKOlrgfR80eNkMTE55qHAe8mwESuEd1E1D7Ax9HwLFQ3l/xKjIsTqlbh03799j+xbrKpSOiCKF6x2d9aRZSpYYenfsHhDFRDqm7xuyNMN7je0hUjmtszjVB11naylLzXgywxuN9W4oG4VySghb9g763hHHBud6QBHHIdeROGKU5LR1TZbkdLbnYbXCmJBBqqIMg6NtO7Isx5icru/p2g7nNPtdSdd0eBfRWMtuX/PwsKGuQvvIw6Gj63uc03jn0MoPANA5+4EFUZ62DRpZYWSarkX1iq7vjiyHB+9xVjaVQfRibYeJwLmOstojOKbrWuIow3lITUTV9UDIWDOx+UgG8tvxr3dIGe0UOMpxysRJSW8+n3N9fc3nn3/ODz/88JHU4dQ8IWAAGFgWaX0o+j7J8hOQcnl5CQSQ8u233w4ATHSTwACEpGuJ6ADl9+q6Hpim+/t7FosF8/kcay3T6ZQ3b97w5s2bYQH+9ttvieN4CDzf7/e8e/eOp0+fDsBtu92y3++H19nv95RlOZhLhGkSMCisqQBEKSVLv24BIsJCnZpP5PoI2BEGVMLTBYQJkJNSp5QqJRtSmEYppQqYk3L5Z599NmzURbJyKn+AD6XuU8OFgH4xFO52u4EhlteQknFZlkN/bmGvBUgCA0soTmkhUaRsLmYaAVFiXBFA9+bNmwG4irFHroMA99OSt4BHYZlFbymbKmH6DofDcG+lVJym6bGD2GQAZgJYZZyfNgY4zeQVXeVpVxpgYPrEyCWvK6BfSs/b7XYwnTVNM5jaTvMpT3WO8hyeZqDKtZFqQNd1g04UPshf5Fk6LYeLXlSuj7yPyFwgSFrkGn/q8atm9F+WTjwenA95d0oBBqU0Oo3p6x6djXDJlNoX3N5XpNry/MWUJI3R45ze9TR1iRmNcdYRmYjHh2DB75oG13eUhxDfk2UFVd0SJxlxmhLFodvGjz/8wGG/Y1zkWNNgIkMcF8RxcC9nowle9XidkeQz9k1D2XZ0bQde01tPWVWUVUfbK0wyRmnou4qLL/9Atljy9ue/UNeObnPPj99+S9V5Lr8Ycbl4xu3qkXuTUUzO6XpHVTUc9hVdM8HTkufpsDuVB24IiXZ2KGWkqSFJNWmv0dpgraOuOvq+oyhGKCKMUSgF3hus7dBKh1ByG3aycRRKwt4HOcC4CCX33ioaHSamrm1IsgzX2xA4r8HZnjyLiRKNilL2dcfN7SMPjxvOlyN+9/kLkgiKPKbvW0KHnQhre6IolBMUiq7pg3vXRERGk2WGpq6pqh22d4zymDRNUNpQ1hXKd6h6Q7+/xTU72uZAVx+4e39DFEdM5g1oTZoX9LVGpwqjQu4hsSU2CXXbUDUlSoVzKvIcE0UfchNNgokLFvOM/X6Ld20I544ytIlxNvQYV0rj6amqEmMUcRbyFruuo2kbtIrx6kOkhdUOHemBOQ9OzDHb/W6YDOuyJklTdBqz3x3QOmY8ztgfdnRtiPRJYoPC03QticsoRmPaVjIsIU0zdK6pq4qubUJXH23QaGIT4zF4FbOrguyi6S1d79gfGrrOsz80eBXyOm1vydOQ2RpFBtf1GM2xfJZhe8+hOoSuP8YEjbT3PDw+0h2ZF9FRGq3puz5UCmKF0YbeecrycFwoa+yxZ7zRCXXr2WzLIZw/y3KyJP01089vxyce/4d5+ri4ykJ7qrOTsGQpm0r3ki+++II0TYfStPT+lUVHolLEFS0sjwBDeU1ZqP7yl7+w2+0Gp6uwTFJaF1ZQKjlSBpTFU5hPMeAIe9m2LV999RXL5ZLvvvtu6D/+z//8zzRNw1dffcX5+Tn39/dDqV0AjbTbE7OjMHTAUBYWdkxMMMKoyTN+qnsTJkdIFwEvSqnBoS5Vo9N7Is5sYdi22+1wX05dz6caOGGSJND74uKCv/qrvxqc5gLCRDIgGwRhAgVMivFGQuKlFCsZkhL8LR2ABGjXdc3bt2+J45izs7OP7pu8j7i2BaRImV+0l5KzedqdSGQNp+BOAN6pOUXKz3KtxchzuoESxleAmIzL2WzGbvdhnj4cDsOGSRjS6XQ6fN7TcSqAUQw9sraLblLkHqeVgFNtq4SgSxSVaGyli88pQD5lS+X3BVDLtRSw573n7u6OrusG4CznICD1NJheGFrRlwpwbNv26AP4wFqf4pf/3vHpweaDW/CDtuD4H1h3ZCb90fntLT4GZVLG5095+sWfKB/3fPdP/xt//qf/xnQcE8WK0WRKVe1x1tI0FXUZ2KXlchnYpN6itSGJY1arLV3vGE0imqZkZ/dUdUldlswmYwwe7yzz+YQ4TYiSGOsVne3wog+0PbZpiVDhgxtF39TUdR/a2k2nmDTCaYNKE5quobie8Lw44+r5hruf/8xqd0P701um5y95fv0crSY8HLZkoylpnpAVGc57ur4nzz9QznLNZDJ1LgSMGxP0j0pz3JEEx3fT2OCeTcPEvF5vhsEpOryiGNPb5oMbyyv2uz1pkjEqxrRNTVHk9L0iTWOiJA5gxGhMZo55jwalDZv7Lb1LsBpe3zzw6vUd3lk+e/mS5XJKuX/EWY9zMkkUKOXwWJy3ZGlK23Z01jOfTom1oq4PeGfxtgVvSaIMjaezns5pXNNhqj3dbgW2oq5aOgvz2Rk6CuOqrUqaw4Z0muD6Y9eVztHsPKQ5tg/Xib7F9x7SjDjN8LajqSr6pqa3W+I09POOTUEcGfo+9JNWWtN1DpRFKemPa3l8fAgTkgoPmbM11lniPMYYUPbYFjPN8ITYIqMV8+kUjRpKHo+PD6goObJ5FWmcEJkUY6Ast6xXB7zyRElCaqEYTcgLh+072ibkgoWnypOkYdcbQDBoHQMxdXugbFqiNGEyHWF72G0rUND1PT2eRAGqJ05TJtOCODah57vyoWavLM55uq5nPp8Oi3ccx8PiWlUVeZERx6EM5G0wSCgNve2IYnMco2EnH0cZOkqpG8eh3NHUHbZ3pElMEn8IUv7t+Nc7ThfU03laWKZTc46wHFEUcXFxwVdffcXDwwN/93d/x9/93d8NodzSpUX0XqfmkVM2KU1THh4eBv1WVVWsVqthLM1ms+H9l8vlR+DqlHX6pbZStGnCmMzn8wG0SFzMkydPKIqCL774gh9++IHNZsP333/P1dUVz549Q2s9BHmfGjWklCyHMFOnbRxPgYuAXgEYwjxKqfO0VCivJ1mb8rMQ8hWF3avreshYFMAqQEiMNQKmbm9vB4D56tUrfvrpJ6y1fPnll5yfnw/dVU4BmTCU4vKW+Bsx2AholDK4ACAx5wmIFM2dAE/pfiSgWvpQW2uHEu5ut/tI2ynjBT6AIynrCuA+1ZBKKVsAqtyDXxqvBCifaloFjAkoEoBlTOgGI5ILrTV3d3fD2JPXF5C13+95eHgAGGKSJpPJsDmSFo7yXMnn/SUIFqZaNmp93w/pCqL/Fe2u/IyckzCLsrHoum4IH5drPB6PB5nHaDQa5A2yuZDrL9crjmOqqhr0nqJ/lg2SbAp/zTz9q2tOMl+dZh6Fb3o49hCW0PPeOaI05+nL31FoRZ7FfPfP/x9Wq7f8/ncvmM2mHOoW+tCNY71eM5tPj8YLS5qktE1NdwyKVkYFF7HiuHAZrq8uKUYFeZrQOQfO07UN4Gl6y2gyZzJKqOsmmAzaEusjjAJlNFFs0JGh94rz82Vge0qLjhLSJMcoQ5ye4ac10+k5unugbR7wHg6bB8bL55AkqBiSuEDjwkLrepSKhglIKHEpM8mDaK0DBcZodrsNoa3iBIiOgzFEQgiVL2UVEWWPopzdPmhasyynPmZpdm1N6JNu6Z09ZjWaAHabdnBymdQQRZooMjzcb3hz+4qf39yC1vzV719wfnFG3zXgHdaC86DN0WmnINLhnjRNTdcHyr5tOpyG3X6P0UfR96GmQ6GTKHSTIaL3Bts5vFU0VU/fQ5TmZOOcuqxYrfdkhaLc10Rpi1UWpSvS0ZxgEAnlW6M8tu9JdIL2jupQYvsGnAv3aV9S3lYkSc7y7AytC7SO0Do8xG3XovBkecJ4PKLrGlCOLM3pe0d7nDTAkyQRcRLjfUQSx2RJEhhlZ7FdYG9xEEWGLE3pbMvjaot1ilrXeBdMVOfny2AGcpCkGdYpNus9fhK0kH0X+rtro7C2Dy0/8xCZ0TUNELHbHdjuDmx2JW3rKMYJ89mEJIpoqgOHsqNpDGXTk2cJxsSMJzFpEge2MzZ47UOP7yNbHscfokJ2u90gWpcFfLfdkeXCMARNqDEm3BulyLLj4uw1STJC6YRDtWe13hFHMUliyPOYNDW/Acr/Pxyn8/QHUsB/9Kdo9b744ovBAf73f//33N/f8/XXX7NYLAYQ0TQNj4+PLJdLJF5GmE7ZLJ8yc7JwPXv2bFjwTnP9vA/Pg+g0JS9PGBIBAFLycy4kYQAfmSSEPZRyuCz03ntWqxVnZ2eDo1lMBgKiToGizNPyXjJPCtAwxrDZbMiybGDyTsGedLiRfsviFj81Bcmifxr/Ijo8OQ8BhPKnzP1RFPH+/XvevXvHjz/+iNaaP/7xj1xfXw/MmFxfYbvgg3NX9I6nAFeCreM4Hhg0AXDCssnvyDiQbMyyLHl4eBhMHsJmictZAI28zmlJ+rTMC6Ht5Lt370jTlIuLi+HeS+laNhnS4lAYSQGmEswNDJIC+To1tMhnkXVYjFOSySqbp7Ztubq6GsrGYtp5fHwcwKJIH+TrVAMr7Ukl2kiyQyV4XuK5JCdUJBZaa6bT6aCZPDU6WWuHzY5oejebzdCQQPTMm81m6H4lwFrugaQhyOZOxvBms+Hh4WFgs0+B+KcevxpQeh/yKEMWZfj36aH4MHl5H8rhOknJ5mfMr18we7xhe+8pW4/3huV8wWq9pakq0iwLC72Jubiagvfc3b6nGI3JR3Aoa+q6pWpqlHM4b4nyo1M1jumbFms9cZqEyJzdlt12hzEJOs6J44JRGtH0jvVqRd20XF0/Yz6b0/cQ6544ydA6obUaFUfUTU/vPXE6RnlLTMLF6IIssVjbgq24PFtisglVC02zp6lamr4h6wPWPt0ByI2Ujg+hfOLIswKlQhk/0OE9TdMOsQEy8YoDLAjDW5rGEycZfdfhvWI2X9A1IQDW6NAuszkOetGhyMMmovG+K1Hao5QD3zEuErI849/87R/JU4O1DV3rqPqOKInRGqwNE5V3KrBTUUwItw6Sgbqu8D5oV70PUoiqKolUBBY0CroqtPXzGkzGOE9QxtLWOw51zWpzIBtZCuXo/SNxrEiyiEO/p/cJJp+ioxTvHX21xzYG3TY4neCcRR1D36v9hp9+es1kumA8maJ1MEOhAuNqjMI7R9PUgDuWuMPisdvucc6GsO4kpu+D+14rFWKsnMP1gd103oN3GBNh+5bKdhRpQp0mbLYHeu1I04y2s+wOJWmWYOIcryN26x3OWUbFiDSJ8c6iAG1CZBQEtjEwHEGnXFZ1AJWbA9oYirTAKM8oV/z+y0v2h4Ykgnfvt2FizWPGo6Bh1VqTpCnKt0eJRQgnH49Hw0I/Ho9ZrVYAQzeV0MnJ8vDwGNiefERVV2gVWBqje7bbHXk2IYpSeq+o65be9hRFxnQ2YjrOAEvb/ZZD+T/yOC1vn35PjtOSuLAji8WCJ0+ecHt7y+3t7XEjFZzcj4+PgaU+SZt48uQJ3nvevXs3xLuIblxYJykpi/5Mvi9s2Xq9Zr1eD6yUsH193/Pw8EBd1zx79oz5fP6RM/s0a++0f7Z8xvF4PJSnrbVcXl4OhhphlSQsWzRrp/O0lDD3+/0AOk4DwEUj1zQNi8VieG/JcJSSobSzFOZKrmdd18N7Ax8xbPL5RI8tmZUCCsQVPhqN+Hf/7t8NZW75PAKyT92/Yi4SPaaUtMUsBQxr0+nvnmoBZcMhvysGDtF1iv5W7l+opBUDUBfNoLjET+Oadrsd33333aAhPdUmnmaayjmLXExaIcp7icZRAN7p+nn6ngK0pONTnudDD2vJS5VWm6I1X61WA+ssAPNUs3yq45RnTFoYrtfrQS8sEoE//OEPg+729evXg05UuvYI+32qdwU+0ilPp9OBQX14eBjallprub29HeKxhI09BZByb6T6IJKH+Xw+aHYFGH/K8StaL55OTh9PUh8BSyXhr2G3FRmDdRHj5QVf/vW/Ic0ifvjn/8LbH/+CtT8xm41omhqTFUwXS+IkYTqZgHd475jOl0RG07UtoMiylMiEdn593xwDRFfUhxJjIvJizHq1Zr155FBVjCZj2rZnNj9jNJ5RjGc4q8iocLah2b4nUT1pXmA6j+sbsAblNPQG4zWZSVDeEOcG7QzjWU4atTR1g3INtl6TJDGTfIRd5pSlJk0j0ixB+Q8Ttvd+KCMGU5Oi7xzT2YS6boijdNByGBMNk17QzaRDDIMMjL7v8UCcZHhv2e3LY/tKRZYGM0R3zN6S0sdpDIcAXefAWsP5ck6aFby0sDvs6Zo967YjSzxaaZoOeh9YwTRJ8fREcUycHLUZvQ0le+VxkSb20XHHHIBUV9Xst/ekSYR3lnLzwGH7iGtLnOtJbUvdbFA+bBqK8Zwkn+J8RLk7YLs1eaEpJmOsykl8Q1pMQ3cj1YaSfGPpieidJ0tSurrGNSXGt+w39xx2a/I8w3sHvsdoT9e1GKMxURT6mFclSZzQHRnHLA1/T5MY23ckUYQ5OuexjlhHdH0f3OHes9tu6Y/lm6LQZGlCm4bdhdaKyXRKmuV4HZjlOEoZjzS7/YbDviJLg/uxaytQHqXC5NS2HXGcMhqn6CijqvdYH/rHZ3FMUFZaitzQxyFyKomeUFY9203DeJyRJDp0WYoCexFHwVgVJl+FNpq6CuX60Wg0LBoyfieTMFa7NuR/egdGh0y63vZYG0xm89kZVWOpqya0nHQOCAHqSjk622B9z2/Hv+5xOk//EkyeHvJ/wooJ0Dg7O+NPf/oTeZ7zD//wD3z33XdYa1ksFkMQ+NnZ2RBBAmGxl/KpLD6ngE82wtIZR3SDDw8Pg6FkNpvRNA3L5ZLpdMpkMhnOTUwNsggLCBDAIiyXlKWFYZ/P50NZTxZMifU5Pz8ftHPCqgnQEPZMytoQWNz5fD6UCKWkKpEu+/1+kIYE02U+dNERg4wAm+12O4DDU2f1aZu703lafk8YVelQJCVlAaynulIBYGIoOu2Iclr2FIZXXlvWl/V6/REjJ/mgcj+FSRZtrcT8iJNZWEQBmWK8kVK6bFRkYyGaWe/98H7ymgKmfhkQfzgcBnZcQLiskeKUls95Gvsk90PAobRyPA2qV0oNBjLZQEhZebvdDmysAE95pgToCoMr40+A5umYEvArDPbhcGC9Xg8VIRljwkgKWy3MrTTMEKZYmNVTY5xoS2Wsnm6w0jRluVwOOEPakJ7OEVKK/9TjV2koP0xW/9KfcAo0T8srOorAaEwx4eqzryjLPTc37/j+59fExvLs6TVPLq7JijFJmnIMOghAJMtRCqyDJPFHoS3sNxsUYXdwdnbGcjYHFH0XKOC+61jMpownY6qqYb9Z01Y19X7H4VDSWgtE+PbA4/s3aJOA0tSNJc5GmCwhH4+AmLyYk+cT8iwmTlrySJHECt+Da3ZYpai9ZTS/ZD7Ow8/FMXmWcdjvybLxQDPLZDKZTNEmAkK7xKbpP4iIoxAwnecpRTEiihLu7x8G8fapvsc52B/qo1ZkjFaEEjWBqY3jiN72w6QCfFSSCg9gTpYmTKxm6UAf2+WVhx1NXUKvUFGM9TGr1Y7IaC7PF+TZCKM1cawpqxLnLNZ1dF1LU5aYSKEBfCi3J0lEe9jQ9Q2b9SN1WdLanrbriCPDZr/BdVuackvdGqbLS4pxiJ/p+ort5pF6V9PXY0bTC4igtQ29d9i6wnsYzc7QUYHCEx11fqlquZhllE1PtXvALedkoxGHfcVuF6I+ZtMpCQmRiYOz2wYdY5Fnx2EdWMksS8M17XuauiJPUpy1aA3d0QBze/vA/nDg8uqKOCnIsxFKxcRJTFW3dM6hjaJ3HjAhJmo0oa6r44T3IQAX7PHhz4Je1nmSKKLrLQ4VZBU6JjKhS5HtWirXksSaNIkYFSnT8ZjH+wecs4zHI84vlkzyhL49HI1c7jjZhUzO04VM2KgPUVHh+Z5OZ1RVeRxXCV3XIzmVzmmc47jwlwMDMRoVeGfZbjdgPOPJ7JMnqt+OTztO5+l/6U/5GTlOWUpZdEajEV9++SX7/Z43b97w3XffEUURL1684PLycihdy++J9k8WbsmtVEoNIdxaay4uLjg7OxvYotVqRdu2LJdLZrPZsKBWVcVutxuME6KPe/v27aDvE0OEsDnwoaWcRASJKUhK9cIgyftJCLrMy8KySdh527ZDhxM5xKksbKY4maVN3+3t7XA+p7nD4taWCCSZe+FDCLmwk6fazcHgdwTzy+Vy+Dn5HemNfspAignp6upq6C4joEXAo0TQnKYtiBZ2v9/T9/0A+CWGR0r3p12MLi4umE6ngzZTAKE41uUeiGbRez/0OZdDAPByuRy6N52fnw8ZkdLyUfSzAupF5ymJATK+hVUUfacAa7mWSilubm7Y7XaD+1+u02n17nSt3Gw2TCaTod/1qTYRGAC5lL/F0S7Pn4wZufeS8Sgmpel0OmhkJ5PJ0K5RjEG/BKUyT8smSTYycnjvmc1mA1A8PZ/TNpdiDpJNj0gVVqvVYGT71ONXMZQf/q4GABm+fWzF6D9oLIe4AoK00jnQScb07IoXv/9r3r17y+3790RKc3ZxxWJ5QZzmOAW268BZqjpEkKhgJifLc2zf0zY1HijGIyId2j4eypL9/kDT9DyuVsGQ0PasHtZ4FF3T0RwamrLCOc/t4yPea4rR5OiUDUydQ6OSHdkoxbsCowyH1TuuL57RrXsm05g3jyEGKDIhS/DAI63TPP/yr5lcPsNrA87Sd33I/Ts+vEKTh84QYfDd3YZWTG3T413Ql+22O5I0IstT2rYhimLiyFCWB/o+DEDZbUVRSj6asdmuMVFC37UYk2BMiBHSRw2TaENksMjEFmIDaubznLruQicib0mSmPXDDmc7egxaJax2JY+PG6aTEeNxH8rYZYtzgb0bHYN+27YJTmA02gSTS39kATU969vXvHvzY5A3ZBNUWnBoelxrsWXF+u6ONF+y2a7JpzuKYkTsY5Io4t3Pr9jcRVxel4zPauJ0RNdUrN7f0DQ1509ecnb9AkzMYd8RG80k1ozmCXWf0lLzePcauMA7S9uEnVmWRAHwNhUehe16lFZoQucZ7xxt2wTdYx80m+Bou5osSkEpeudwHtrO8+bmAUuCMiPy3JKmMXmckClNBlRNS99aus7SW4fNC87Oz7i/veH9+xvOzuZ07VGGoOBQ1fS9I4o81nnquqcsW77/4Sf2pWP02QSlDW3nUF2P8vHRie/IsoLJpCGKNMUoZVSkIbe174F4YCeTNMXzIcjYWkuSpkymU3ZH52VgAzzGhE0CcNzxOtI0tFbcbDYkUU7XB6Oc0ZosDx2JnGvZbffkoxH391u+fPrkkyer347//nE6T/+SqTwFlac/c/o9YWLOz8/5+uuvefXqFW/evAHg+vqai4uLAUwKMyhxP/L+wiIKWynZicAQOSOtBiGU1O7u7vDeD8YAAT63t7cDMy7nKyxKHMeD4SUY4B558uQJ6/V6KANOJpMBZEmA+F/91V9xdXX1UelaOpmEVrhBvyYgpiiKQdsnLJqATmGSTnuCi4NWWFoBYuPxeCjtn+pNRQsoTOFut2M6nQ5sl3RzkdK6lMoFAAj4kesj+Yaz2WwobYvLXMCLfFa5lgKcTsu179694+effx6YVJErCDt4c3MzfCbpfS3n9N1335HnOc+ePeP8/Hwo/b97946qqnjx4gVPnz5F69CBSDrvhEzcMK5ubm6AD1135LoKowl8pA0Udk7K6acGIJEcnLqeu64bxnYURQPDLJshYACPAiBHoxFXV1e8e/eON2/eDLFY8vPCnAuYFfbvL3/5y7D+ytot8gdho4uiGLo5SWtIYWaFkRbWWZ41Aa9ZljGfz4fSv7DyElUla/6pnGK9Xn9kINJaf2TmWa1WjEYj3r9/z2efffZJ88+v0FB+AI0f2jCKnvIUZH7QWSqOaFApTBxhO0fTKaJswp/+7X9glCesb38mzkb0DjTH+CHtQni3D7X0KDJkcUzfdRij8XGM7cKE1avQU/iw3VI1LU3XY5IIBZgopm5qlDMU2YjRaIZJEjAx589+h9IRVdPh0LStIx+NcV4R5TEox3p9T1fXGBWxX92j6GkOPWW1o206puPJsdOKYTQ7g3pLu8sp5lfUnafvQ5B0uCZuyKDUWh9jNhKyLGe3O+CPXUXG43EYOCY4h6uqwfYe5/ww2EWv4ZxnfyhZb2vuHx747OVztAlAO40MVaWJIh1MGCe7ECnbnGqODocD3oU4mNhotO7JE03VhIeld553N3ccDjVFMcJjCCyy6FRO3ZmeJIkD89V1GBPhnQcX4U1EvdvTbnd0VtFFluXzJXEa03VQlR2u7nn5+2fEs2t0ETIxXd9QjCY0ZcfD61dU2x3zyw3T+ZI01uhuj+oqtrc/MylS8tmCrilpbIfrOpreM11cEWdT8klMV+9Js5zxOKPvGpqmJi4jtDa0XXDfnZ2dceRYiZPA5KXH8pMyGq8NfdviI4M2KSaK2e0qkmxCnE54825Fmi8ZdZ7pVDGeGpR2xMedeaQt23ZPGke0bcV4vGA+n/L4+EDb9GRZGkw+Ckxs6PoD/W7HbJrQtC1/+fY71qs9o8kZeV7Q2Z6uqqjKHbPZlMViyWbzyGq9J81zxpOE0SgL7GgbtJPeK4yJiJMopC0cHaWywIROTnpgZqztUcrQNC1d96GLThwH57z3njzLiCJN7wJQNSY0QDAqQkdh7mhqy5u3D/z7P/3h06eg347/7vFLF/cvjTj/kqbyFHDKAiaL2H/8j/+R0WjEzc3N/yEo+1SDJwxnURRDOVdcuiLRAQbm6pS9k7IghDLgZDIZTDgvX74cWKVTgCVzl9YhoFneQ7Rk2+12YNCm0+lQDpay9Xa7ZbFYDJ/1lEkUjaVE+IjuTRbrUGGaDCVYrfWgc5QF+zQUXEro0rnnyy+/HECDBKqLOeS01d1psLZkQgowlNLvafVAWKs3b96w3++HihZ8AF4CWgXsy++e6vOF/dztdkNYexRFvHz5ctBGiv70s88+G9heAV7j8ZiyLHn16hWbzYbr62uWy+UAFruu4+bmZtDqCdspjm4BoLPZbNDsTiaTjzoxSWl/tVpxcXExjGG5XuKWlnK5GJYE9IuxKk1TXr16NYy7+XzOfD4fxq/oS0VS0DQNk8mE5XLJ/f390JLyVEspbK6A/3/6p3/i8fFxaBYg12+/37NYLLi4uGC1WvHw8DBIBQR4isP/tIIgAFvGl5SwpQwuDLekEIhj+5cs5SmL+0vjsFzPuq55/fo1//7f//tPmn9+Zacc0UuqATSGQ8DmKag8mnYAhQMfnMzeR6TjGXGk+cr3fK8cTbNDHYOz27ZBeaiqkqauWS7mAaz0HX1bsd2ucX3Ikez6Dm9DbEzbtWhjuDw7w/rAjEynU+qyObJ/ChPluCghG884u7qi62C12VG1HfOkYDqbs1pv6b0F1TM3Mbaq0F5hvGIyStDqGFxqIiIFrqsBxSiJWd29p/CGbLSgyCesNiVJkqK1oq4tea6J44Sy3FCMxhwONV3rKA8lShnixFCWDVGc4o+mhQD4NNa1hD7LPd5z3IVp2rbj9n7Lm7dv0Vrz/Pk1fdehfI9zlqbp8DiUD0xBEocdrTwk3bEPdmBJm/AerkMrR5YFABGlGdvKY62nqmsOhyC6H2VjptMZip62CTpIK+5EjgyWB+Vc6GRjI+IoRxPRlC1Np4gXGfttx2w5JopHdK2mayx3t+95OjsjihzWeZSP0XrEsxdf8uf7W969ekPTWg7rDdeXCxJ60iym9TW71TscYdfVtxVdXVM2HXVVMbl6iU4KdFpQlfaYfTY9XtPqKM5OOOz0Ua6QDSUqEwVG2juP7W0I9Y80je1I44RslBPXPVkRc37xhL/85Udev73nyy9eYqKMpg9NAKztSGKDRlGkCQ5F0zWs1w9MRqH9mgIiY0jThN6GMPUss7R1R1lWPD7uqKsebRJG4wk6iijLA7fvb2jrGucT4sTyuKq4u1+R5iMm87DBUMrjXE8UB5AscVVVXWOdI4kzrHVHgBhMSiEiKWK/LzGaQaflvR964n7IfQsGra4LZhytQ+92cyzRaZ2y27W8en33a6af345fefxLYPKXRp3Tv8vPyQImGjD5HVnEZIEGhpiR8/PzATCI0UZMIqd5hKLnuri4GBiW+XxOWZYDaJSS8nQ65cmTJ3Rdx+Pj41D2XSwWgyNXKcX5+fmgyQMGZkeyDk/PN8uyj1jPoih4fHwcAKAs1eWpegABAABJREFUruJ4ln7HYtAQUCYdsYCh1CifR0qTofrTDD9zc3PDq1ev0Frz+eefDwv7KcATk4W8h2Q4SvyNgBrR00l5V2QHAjgloLyqKoqiYD6fAwzaR2Ej5X7LtZQyurB0Uu5eLBZDGfo0rP3du3cDqyZMWRRFfPnll9ze3vLjjz/SNA2r1YonT56glBpYZWnzKQBJnM5VVQ0tJSV8XNooSgJAURQDSyzGGckyPQXGp59RysWiDR+NRlxfX/OP//iPvHr1asjxFOZQ3OxyjeU+ignps88+G0DkaeC6nOPhcBg64Ui2pYyrN2/efGQke3h44P379xRFMcgBhHWVZ1DGZVVVA9ssmlZ5Txkju91uAKSn87Q806LDBD5iKCWVQZjq3W7HDz/88Mlzzq/QUP6yVBJAo/xdQGVgLj/W7ijvgzkFjVVAlBLlnstnn1GXG7758/+XzeMKPQ9OtjRO8F1NX+1xeUzXKrabNbv9lu12w6gYMZlMsTajrisclkk+o+sdcZoSKx1MQSYmLSJMbDEmpreKfdvjk4gWhY9idJqTJQUexeNuR2tbjFa0VU2R54xnC7RSlIcD1vVUXU+WzRmNCiZZinJ9MHJ0PffvbliqhHy65LyY4GwwUpxONnmeUbcNu31JFKU0fU3nNKvHNePJmKzoGUcpsQm5i01nSeIQb2RtT1kemIwmcATpcRyhI3PM9QugXek+MLNK0TYd+92WSTHG4WhtTdvURJE5lqc7nDtqU43ChpfFq9AJxRMm4bJpmE7GVOWBritJE4VzDZGJiU3MYdvQ244sjbE6RAFppTFRjCcAGH88H2stnfNYEs5mFzyWNf3qlifLBUrFLJdXrB9X9D9+y9d/M2a3OZAnOUmc8uSzL9jvNvzwz3+mOjTEWrO9t9i2pBiPyEcTXn//HWdPO0wS09UHnO3wKqL0e5Ymoq1b8rig6+vQOeb4EDWupWsrksQwKhIe12sm45wsiykPFYp4kHgkUY51PY6etmug6Qj92xU61rx8+YTtvuX16xsW8z2z2Zw08RR5gvKhI41SnjSNaY7tDJMoAueIjsyOViHkHmuxXU+epkxGY9brmqZVxPGM3j7gcUDP/lBSVS1KacqqZb2t2OxqDlUDxhDHUyKjUcf3lg2fJ9xrhSIyEUoFV76wUWW5A8Bah+09XoeWit6HiVeYImFLnFfsdgfqtqFpa5QyRDpBK0/XW7xTPDzuqT/dPPjb8YnHvySg/yWA/D/TWMrviylBFsqXL19yOBz4u7/7O+7v71kul4PxoOu6wdEtusjtdstqtWI8Hg+5hMJkLpfLj4LJBWiKnlF0g8KIS9lOjAtKKVar1cAOShu68/NzlFJHOZEbQJ44oeV7bdsOvbin0ykvXrwYKitSsrXWDq707Xb7kfbw9vaW6XTKaDQanMjCSIlmU4Kjp9PpANTkswmDJsBc9Hx1XbPZbAZWUa6pADVxgAsY+FiCFv4uZeXZbDYEZkspX9gtKeOL5lXO71SXJ5sH0dhB6KO+3++5u7sb5AIXFxfc39/zzTff8D/9T//T0A87TVO+/PJL1us1//W//tfBiHV3dzcwfJPJhG+++Ybnz58PpXQByAKihKGVErZEBMm1mU6ng7lLWhxK7JFcE/n8EMCgMNlpmg7Ad7vd8tNPP7FcLlksFoMmET5oO4U9FqOPSEPk3E4NaKLrfXx8HJ4T2WgIWy2GGtGLCqMuWlfZ1P3y+ZV/n4alyz2SOEHZLAjTLABZ3OXCjnsf/CYC5CXpQOQCzrnhnn3q8atK3h80kh9ig8Lh/4WfOTk8qFOdzjF/L8Iwmp8TxTnv3r4m9j1RpGm6mkRD4y2vfvwOd3RmJUXK1ZPr4JB1mhhNMZ3R9cGEkjpPZGK8D7o3rQxpkbLb7imrkt46auvIWeAJ+jVPyC1s+zYEdCcxbVlRHQ6M83OydETVNqx2K2azGd6nFNMFWZZg4ihoJfsdzijSIqJtazarB9Jizmx6Ttv3GKNPxLUJSZzyZnNLmo5xRERZTpS1PG525OMR3mgyF9HUPRqHtZ7pZILynvJQstvvKIqcJInZ7MNuxaFQ2qC0wmPRJgSnRzqhM1mQHziHihRZnh0zIz2hnWJP29ZoowYa2qOw3pMVBVor0tgwG2eU+5jUOEZ5RJYa6upArzRd26E1JEkE2tO1oR2j1qCMhLv39LYjyRPG8wmrjcdaRRQbHlfvuFoU5KMxsTWQGH7+8XsiZbh5fcfvvvgrZucLTBbx5OXnvPnpFdvNI6NEc3tYM8ri40OrqfYlru3I0pR8PKYuD1Rth1aEmCLb4NgBPZEO0VYoGBUp+0PJYbsNQMt3NO2BrIvQBpwP3ZWstbQuMLAmMSRJjvIK7zpiY9HaYZKILz5/xnq9Z7PZ83i/JtJzRvkUpTVGa6xtsLY75oAG2UAcRygC0IxMRF2XeAis81ESUlYN+31NVfXEUUYca5zrAnPqNN5bLJreKnb7hsOhJstT0jQmiXTIFO07lDGh3H10tPe9Reng+vZOk8QZdbMFBdb2rFdrus4fTVyhRC4L8amGrKxbyrqmdz15nmJMRJam4X2do+t6tts9xajgt+Nf9/gl4/gvMZD/Z+7vX5bKTyNWlsslSZLw6tUr4INhRMpk33777eCgLYqCFy9eDKVAYOhQAwwLnQArYa3W6/VHETlyPsKyiKlFSpoS6XPqrl6tVgPDM5vNBsZGQKM4xYVpGo/HH/UzDikG9cCOSTkUGExAq9VqKEmKm1sW8rBG+KFcLJo0CRwXpkkAg5Qt5XvyOtLBRtgnkSIIS/zL+yYu6iRJhi4vAtSzLBuigOT3BUD+0iEPDIAkz3MWiwWr1WoAeff391xcXAy6ziRJ+Pbbb9Fa8/PPP/OHP/xh0Nl+8cUXfP/996zXa6T3e3HU2QMD85vn+RCeL8BGAKZ8PrlGwjzvdjs2m81HDK/cXxlzouOVzytl8NPA8TRN+eqrr1itVqxWq6FTlFSlZNMjY120nnL9hJEty3IYq3KPy7IcgKJ02xGQfpoo0Pf94ByX7MdTE468r+gdRVIiryE6Whk70lxAStpSKTjNeY3jeHB2ywZKxrNsqrquY71efySd+O8dv8KUM/ztZDcb/v2BnQz/FlD50WQGoBRKabRTmDiltw06zXn68gv263tev34HrieOI6bTIBx+fHxkuZhzeXlONp5SjKf0NoSoG2OwzhHZjq5vsW3LfrdjNBqxmC/Y7w/st1u884yKnCTNUVmGjzOU69it17RtT1XWpGlCkhhsd2A5yakTSAwcDmFiiM0xLDyK2e3WFOk54InSiHm6YLPbEaGJspwsjdmuH7kcL/C+o+9avGeg6pMkCnFKtgutz5XmbDllt1+z222JE4PyKU3TExtNnmSsV3vOz5Y8f/E5u+0GpRxaAT6UUWVnHB5WHcwhVcMonZOmOoSZpzGub0NIvO2pO2m7JDpP0c+EHbA2mvF4Qtf1REYxHaXoJxfU1Q5vO4p8Qte2RMYwnS/obQAlTd1i7bE7R9+gjcf7HluXKGwYKwqUivCEbkDrn1fcvHvLLI5JE49JPc1PFf/7//v/xeX8grc/fcPFxf9MGsXc7VZcXS7YRn0wAPUdaRKh8TyuVjyudzRdxxdffs5sOmL/+MD9ekPtb1jtOp68+AN2o9luH7m8PKcYTShGE7JiElzbTtH2PVmSYruG7fbxWMYFfWRu4zglikBrcC4kEiivUISe5viGs+WEz15cc3vzEDRMx+fBGIN3Pc55kuxDN4Ouq4kihY7iYyxUMyzCqHA/guO9o64rmqYkSxIiDTfv3uGdJ45zolhh0XTW03Q9HkU+KsjyjLDpcwHsx4okiQl94AOzqXXocR5Y64Su7fn/sfdfT5Zc6bUn+Nvbt+sjQ2akQKKAqiKLZFv3bZt5aeu/vl8vZ+xeTt+6rEIBSCBlyKNdbjEPHp/nySSqCMyQb3AzGCJDnuNy7fUtkeYRWg/vbXitPS6OBua+qceHnYjene2xfU+R5yyXJ4QAVVUTvBvatEJAact8Uf7sG9Wv2y/bfgpMfq6vlO2nGBD5Tx5WSZLw1VdfsVqtePXq1QgmpAv79vaWs7Mznjx5wnQ6ZTabjWPfjyUObtTZSWvNyckJu92O9XqN934MCxe9uMTWSB+z6BOlt1i+TzSKotuUbEIxMEhYtoAQAYcyvhR9pmjMxUD5eSj0+fn56DoW/aEwiTK6vLi44De/+c3ocD9mgSUjUcapMooUd7q418XYIWBAHMHACEgkP1DkASInmE6nPH/+fAQLom80xnBycjJq7sS4IYyUgBSRD8h5IYyovL/Xr1+Po2A5P/6v/+v/4vz8nG+//ZbLy0uSJGG73XJ1dTUeE2GmxYV+f39P13X8/ve/Z7FYcH9/z/39Pc451us1v/nNb0ZG+urqiul0SlmWY8qAHDOJ7hFXsuxzYGQURcM73k8f35f3nvPzc37zm9/w7t27EazBRymA935cHMnxkN8p9z4514GRFTxm78VI++bNm/HaEbZafqcsrI5Dx+X4yr1VGG0BusJay36Q1y7vVV6n6E7bth0bq+Q1lmU5pi8Iwy9/WynFcrn82fedXzDyhgEsfhxzfz4GJ3wONvk4Vnss1FFhyOJTKiLPS0I/Ybo456uv/4Ef//JH3r15BcFh7QlZFvP02TMun1wQpxn9UIQzOKdtwPaWthuYEBU8aWzoTUS922GUwvUenGU2mQ592Fqj0pxDZ6kPG2Is1tbMc03b7KjrHmMiat+wPD3DOU9ve0yk2O+HWJwkcUxmM5xt2fcHum4w0qCHh6Z2nnw6J0tyotCTRIHge5quJ8tSnPc450liA/qjdmU2nbLfz7m7vSVNInSYErxiX7UYnaGU5uFhS1HEKBhNFCaKMFrj+p6721smRUzx8hzbe/a7hq42ZOkEZyFoTaQTVAhM8pzJdMJuNzguk3QY93R+0BpZ68aLwFpHnsV0bcdiWhAtiketpcMFB049MrER1lmatiVSQ3i5tz2976gOO2gbaNcEb4dQbaPRJsNkhiwp2K235GWOdTVoz+XpJbf2mvkko9k/8M0f/zvz0xPq3RbXHpjPSmIT4b3lcNiznC1AaXSSsN1seP/jK65VoG1aLJp9N5jCzk4vcASq9Q1NFohDTxs8SWSIYk3QhtgkwzmjPMF5UP4xq1KhdYSzLU1dk6Y5rtc4N3SzH9qa+XLGoa2I45Tnzy5w1lHXBw5VzqSJKU6meKcGoG401g6d733foVQgjodRt/eWNH3s/Q2OyBhs45gUJdPpgd2+QRuF1g68p6oGJ72yisRqsswRFORlSTGZYGKDUkMuaBRF6GgwVrVtN45tAv4xADhhvzvQtC0myVAqUJQTosjy4cN7lGJsCxEt0MeHMmRphjaGJDbUdUNsBqNckWfstjuW85y0+LXL+z9j+2vM5F/7+k9t8tACRqPNyckJf//3f8+//uu/8urVq/FhmKYpL1++5NmzZ5804Qh4ktzC45gTCbKWPF3n3DjCFOZI8h2FeTnORRWweXFxMQI0YwwPDw/jw19MNwJixfEsTKdU2wEjOJNRuTysj53BWmvm8znb7ZYPHz4cRWmFsflGKcXd3d3Y6/35yLnve66vr5lOp3z11Vd0XTeOHQVIyr4XoDifz0czk/zN4xYWYclklC1jb9Ecyn9yLERPeDxKlmMg/ebH2Y6SnXwMwmUhoZTi8vJyPH673Y7//t//O+fn52MXtkQHiYNdag+TJGG1WvH999+PY38B6F3XjQ7qh4eHkSUWja0whNIYI+eI7Lfj/S1jaAF+4oI+PT0d9+nLly8/MctMp9NPqkXldwk4B8bRtDC8AsLkeyeTCbPZjO12O2p5j88VGYfLe5PooOPaTnkfwAgmhd0XELrdbsdFHzAykW/evEEpNRqOjg10x5piYT9FF6zUkEG82Ww4OTn5JJLp39t+YVPOsU7y4+eGI/3o6P6r43AIKgB+bNPxThFnU6aLC3ITc3d9w6H5lljDfHFClg2Zk+vdjqy3KB0NQcpRhLdhyDHsGjrbEinPyckJi8nwgKt2W2w/VDZ2dcV+s2G/r0gmE4gTejs4xm3bEsWGRAdcWwGGKItYnsy4uXkYdnKa88WLLwZWTUOkNW3T0nc1m/2GpmtQxpBkJXEa0Ry2pElGV63RSUocxTijcX6IagnOkZoI6/0ja+tQWJ5dnYPrKbIUozR9sAyd046iyIl0wLtA2x5oOzXU/ilQwaGDx3YWRaA+1NR1xXZTYfsGrbbMZnPmcYlSsN0e6NIIE+txxSajF++GcYH3juwxYzEEj4k1J8sJwQfW6wfScja4f60jaI8PHuU9tq3AdTjvhjGs7akPO64/vMMET6Et9WEAsVGyRCUZvbNMpzNUvWezXtHVGy6ePCFOSrrOowjMZwWbh3tu7++JDSjbYExO1XRExqBMTogSUJ5sUlKUJfvNjv2hou96tvsKpwy5SqGvmJQFoYyJ+opm7VC9pUxSdFCoKKX3iu4xasKkGemjOzJiuFntNg8Dy6wdbTPE7+z2Fbt6z2y5xNkOEzmyLOXqyQW73ZY0jbGup20bJmXxOAJzjxqxBBNrnHX09nG1+Rj9lKYZ3mtCUHgPeZFzejpk5nXW07QNJydLlNrT2o62q7HWM5t1pFnMLJpQFCmRVugowjs7aGitpSMQHkcg/nGxY71H49jt9yg9NAIFHAQYGpHEYBfGG5CMmSSLLopiYmOI9ODw7pqGQGC5OKEoU55cnpCk8S+7/fy6/aztb5lv5Gv/Hqj8HJBKBmKSJLx//37Uey2Xy5EZkexGAWCiKzscDmM1o5hoJB9PcnnFfLJer9ntdqPJS74mY2gBoGKUOD095cOHD2ObzVdffQUwjpAl9FuiimSULMYFGZ1LQ4+AYQFYAgiP98fz589H5u+YoRK2R9gj0aaJXvHzh7lo6cTAJGyQ6OMEaB437Bw7fYFxNC3j9GPzioz0ZZ8JoBHgfDx+tdaOZhFhlcXUczyulcBsyad89uzZqCUEWCwW3N3dcXNzM4JcWQgc92MDYx+2HHNhrmVfy8/OZrNRnyugGT5GHR1LFETeIAYWaac5Zvc2mw2Hw2FkawUsP3/+fAx0t3boGJ9Op6OJSc4dYY5lKiMG1+MUBGHbxYEuv0+0vsdRRIvFYsz7ldGzvDcB//I7j0Po5byUfXbcu/15heixZhkYNc8Spi+LRwGdZ2dnTCYTnj59OkoFfs72i6sXj7djV/ewDYacT7WWw6fDp/8cftbEGK1RU+iV4urL31G1DQk9p6cz2maH8xbbWSp3oK72NIcDTVWTRCkQsF1NXe9Ikoj19VsCMW1vubu9pa5btIqYTWeYOCEExcnl5aBZjCNUHGGbA0lUoJQmNlBOckyZYZ3HxAlxAibOmC/OaNqGpmtIzJCn1/uWrJyh44je9aTKk5mItj5QRfdMy4JYz/GRgTCM5zOToTUsFzO6vkdHw41U05MlEb/58jlZmgOa7e5A27SPWpyMKFK40A8avEgRm4g0MVxenDCbTmnbjvPTJYFhdLNeb7i/q9BRwRdfGExmWMxy0iyjaw8kacGkmHD/GEQ7n8+JjMG7oRln6JA2jycuWNsRac3V1ROcD0SoocvbW7yrsdWBw+ZhaF/pewg9tq1ptmtstRlGE/Weer9ntdqgyyU6KairNVle0tV7Hu7v2e/uKeYnTGfnXL3wFLmmqdYkRUmRTqnrLSY2mLwgLhP2hxoXGXRS0nUHoiRmMTshK3s2mw2b9QbfMASo95aH23ek25Q///lfub+7QwXD73//T/zv/8+Ict4Rz+booEnjmDwvMY8XXOd6bO8xUcSkmI83+t12TdtrtlWL9YH7Vc2hrljOJyRpwWw+pSgTTATmMcmg1gLKFH3fUVXN0CsfRYQAznr6rieEQZeDUmgdE8cJIWgmZcZ0WmBt4Pr6Dh8MSRoRxTFJ7OmtJ4o800lG7g1FHhHpQAjDw6TMcwKBKDJoMywgeLxGVVDUTYuznsk0I06S4dhHhqZuKMuSJEnHG7Q4DoVVyvOU2CTEcTpMKsJju1KaYV3H6XJBUcRY1/3/c/v5dfuZ20+NtH/K1HH87+NRrbArMAC1r7/+etRsXVxcjMHJ8oCUHElpKwHGaJgkSXj37t34UP3w4cOo7xPGMITA1dXV+PAWg4YwgDLWlTxFARNxHLNcLkfHsPwuSSEQIACMo1ppJinLctRnDubJASSLAUkMQDAwrL/97W9H56+wh2JEErZTXquA1aurq7ER6OLiYgR3Dw8PIwBTSpGm6QgyBJBOJhNub2/HHnV5LwIWjzWtMrYV4HtsGhFmcrVajb9DxtwCtAQA7vf7sac7SRJ2ux1FUdA0Dbe3t6zXa5bLJcvlkt/85jdMJpNR0yq6TSlHkK5vGd9KLuRisWA6nbJarUaQKuNxiar6H//jf3B9fU0IgX/6p3/i//w//0+Wy+VoehKWUha38vNiPknTdGSCxa0vWsPD4cDp6emY4yjJAJJIIAsn+b2y+BBQLPvzY2Qeo2ZTkgTm8znWWt69ezfqf481keIAP16kCFMrWtVj1/fxJppjSQYQNlUyLz9v1hOWWeR3sliQxY4YKyW+6Vjz+nO2XwgoP7q4/w2YHJnLT9nL8AmK/Ph5FISgCVrjTY7KHOXZE542FasPr+is47CvaKo9vbNUTU3ftwMT1/agPRqP0TAvC7QKdLZH6YhEK6ZFwel8SZoOESgmGkbMuYmIjaac5CRZShIDWuMclJMJ5WxGZT2r7Y40mbFYnoCOmc4vqW4/DKYFIkyWMzE5LgwawTwKYHsSAl3b8LDbElzP6dOXzNMcHRls8/gA9Z48TXHWoZVmUhbkecZ2u0MFMCbDO0eZJ3Ayo6oq+r4ZTDaRHmKRwuDES+II7xzz2QkoRdc3NG1DkqYkWUZQNfu64mG1JskjisJQllMarSjSnL7rmE3n1G1F27REUTzqJ70Pg+vYGLy3oBVBKXwYIm0O+x1RpLC2pTtsCM2OfncHUUB5i8JR7bZsHh7oqorWdzT7Fev1Lbf3D5xmX6KiBBPnFLMT3r59Tds2qMigTU7rE1Q8ZdOu0cpgshlxccLk5JLmcEea5BTlAr2raJuKxjriOGO2WOCjCTqKMUzJ9Jwvz17y4w/fst4+8Oc//4m+b3n7+i19azHKEFpPbmKefvmS5dUlKk7pVI4xCTpS9N0wcg5BAwqtDHXdstls+eHH91zfV8TZlCfPrtgfNOudxdoDp6clSlmSWKO0J+AJDJqWJE0f2Uce2QUDgceVr6YophgT0bQ1dVMT6YS8mLDdHOi7mtmsQGURKpzw8HDAaEeeG5IkZbs9cHE2JU9itocdibHkRUw4EoTLw6epK/b7HUopFoslSZJy/eE9h0NNlp/QtT1d15LMikfWYDL238oKWh7exhj2hwPxJCFJDN9//wNxnKBQQ/6mUnhrCXbIKP11+4/ffsq9/fnXfsr5ffzx56BSwFGWZZyfn/P111/z9u3bYUG12z3eo/oxY1LGssK8ieFFtFvCwkyn07HKUVggGXlLa4dU1AnDMpvNWCwWo24uy7LHnvkBAL5//34cQcpIT9geOV8FILx79w7nHC9evBgDnyUTU0a+Atym0+mYRwkf2R8Zb0vEjjh1BaDKOFIikuRvC1gUBvNwOIxZhGVZjoHwohGUXMa6rj8xNX3Uvn/UVB4vBiRCRsB+0zTj5+S4bzYbbm9vhzziR1PRw8MD19fX43UugP3t27cjwJd9K3maIYQRQJ6dnY0ssOw7YcbkdwnjKufYxcUFf/nLX0aHeN/3fP/992N+szjXv/76a54+fTo6/wXkHbcFiTlGGpi+++47bm5uPkkukPrFi4uLkeU7vgaOQ+oFzB3HRck5Kmy47JeyLFmv1+PIP89zrq6uuLu7G49pmqZsNhsuLy/JsmxMFJDCkWOtbN/3HA6HkY2Ua+bt27ejmUekCtJ5LyBeWE9hVOW8kQVfkiR88803n+xLuU5F9/xzt1888h4OvMQGfaxeDGMu5fEN7Mj9DaDC8NUQAD38T0Uoo1FpICpnTE7P+OO//DMr1UBbUWQxsY7Ik3yIf3AePOA9TbV/DFQeOo9nswnWgXOBNI4xJqFtuyFf0TsIiio4wh7aJuf88oLJJCfJCmxQeAwqSui7hqa2NN0Qi2Nizd3DgdWmAQ1ZEkEYLqK6a4kDzLKM/W7PvrX0TYvtWzb3kJYlxeyEEMeYOIYQiKOILMnwVlG3LW1XYRREepAE1Ic1WVY+un8d82WBVgrnW6JghkzAZIiY8cHifUtwCSZJMAZKkxHFMZdPLggq483bO/bNgaabUNcN+Ii+s5h5hu0D3ktYeRgyAxnMH1qbcXXWtA4fwIchZH23XaPxqBjaakdfb9HNlsRVJFpRV1vq+kDdNHSHLb4LOK3om6Fv3PsAytB2Hm1S0ih/rGkMpFnB8vwpXpUEnXD9YUOepkTJhC4a4g52rcJFMWfLK7K55v3bV7x/9y3zScRkcc66CsTlnPmLZ+SHCtseeKZjDv/63/AqsDg5xbuIvuqh76k3W/7nv/y/CL7Cq4rFxSWkjrZdY1LonaPpPGmSoZUhBEvXtmhj8Eqz2dfEfcqTaErQGZHxfLjeEnxCnkdMpglpEhHCMA7M0pS+t8QGinz6qIMZusPrpqHvA5OypOssvbVYZ0kzTddbmqZF64C11eC4TjSzSUHTVKjIMZ3lnJ/OWS6nxFqhowalLLEJQwZmMuRENnXDdrtmu9ngnH28mU2JdMT9/Yo0zYbA9L4nhEFHnCQpJtY8PNxRlsUoUJeHpFJqGHF37Qgygn8MoVZqyKJUjtgZiuLkl91+ft1+1vZTDOTnhpy/pbH8ayBUWJWiKDg9PeW//tf/Ogr9pbYuTdMxZ1DYPmGqZOQt+jthto0xn+j25G9uNhuqquLp06cjsBS2REbkEogtr+3u7m40w4jeUBgqqevb7XajrlNaeiTiSKJ/BIwJ0D2OVhEgKWycMFonJycjYy9RRzKClX0h4EvYsziOef78+eiSljzOqqpGUC5RS8650XErjLGwpwLuxMUrbJbsC2PMyBzLfhYmq6qqkZEUzWld16MDXK5tiT6S45Pn+RghFEXRaNY51p0KwDo9PWWxWPDjjz/y448/MpvNRkPWdDodAZ5MPf7lX/4FgLOzs/Ec6vth4vTP//zP4z598uTJOBkRZk0+FhAoCRTAyFLKwkUYc1kYSI+2ADr5nZJfKWy0mJpEKylgVyQIx4H+4rhO05TZbDZ+frFYcHl5yenp6fh6jo1b8jqE/ZaRv3SKSxSTtB/JAkbim8SVf1zfKMdSrnOZEkiSQp7nn4DK43Pu52y/yOWtlNysHptw1KefFwD5U2Pwj1pL+Zx61A8q0BE6Tjm/vKKIHKeXT3j7l/8PT06mPL16wm6/x7QdOo7I8gIfBn1blGa0VcX99gGjYOIi4jghTXOUtUQmIjcxeVFiYkPf9gSlONQ12/2eJM/IQ4k2CSFKAYU2CVp7nI9wTtFbR6zAtg4fFaADKsmodnuqpsEHTx1aXNcQ6oY0ifFdRaID9BW2OaC8I4ljPNA0NVlsBrZ1KPmh73pcbgejDoH9/oC1ESoyRMZjbU+SJjhr6bseryPaNlBkGc73JHFMkkSDOYnApCgJSlOW8MXzEoUZ6ijDYxYWCmMSrPdY5ymLKShHVe8JJmAi8xgpZBiaUSTbyg1B28GSZhmRGkBNCHZgStvhBtgddqwebqmqA72Hza4lhIQ0neBCRNtZ+s7S9ZbV9kC5mBGiAhOneOdJTUySlfRqQuwVi7MrCDtMVjA/e47vW7yz5JMZ2eIKHzR6s+Ngv6O6viWdlFA+Y7p8wvTkCYftlt3dO55/NWW7XzErYkyckBV7+qpne3eDAnbbFd/++X8Qoo5ymg2vx9d0raZrLSZO6dohdihLC8oiISWmmG7IyxqTTUnLEq9hOj9js2nZ7CrSbIazAZ0Nx9c7TwgM+/lRs9r3nvuHNUpHRCYiihzW7omTGO8dURyTZzn39/vHc+fjWKQoTpjOyiGPUjviNCLLcrr2QGw852czvG/Js5i2qjBm0Mq2bcNms6Wpa4yJWC5PKMsZb9/esd5sePnFjN71GOMBNZQDxBHO9pyenj4K3ocbnPMO5y1JHDOZlOy2OyBiWs6GSKayAM1gBFIRkfW07a8M5X/W9tEc+berFv+9z8vXxCiSJMmom7u6uuJPf/oTFxcXvHjxYtQpHrMs0qMsbJCwjALuZDQpETeSSSiM3Xa7HdtDROslAEnGuDLKleYeWQSL01jczAKmhBkUvafkXorh45hVlPct4E5eIzDqP481b6K/E0ZNRosCJEUDB4xB3JPJhC+//BIx9BxHykh6h7V2rJ88Hrses4Qf79N2NEfJ6F5YYwH2UmUorKRz7hMHPDBG8QgTvFwux3GujJrFsCEsn4CSy8vLUVsqjDIwRtq8fft2zAiVtpj1es3t7S2/+93vxkxOAXJVVXFzc4NSamQvlVLjeSEaRQFOAvrEOAgfNZt5no9GHolF2mw2oxZVxtnHixeRSnRdx+3t7Wj8EfZQFg5yXt/e3n7SciNB6rIfhIUX1tYYw+Xl5b8xQgkoX61WoxlNOs5/+OEHHh4e+Prrrz+J8BIG0lrL+fn5uEiQxZGci9PplPV6DcBsNhv30XHouSxSfu72i1zeH8Ej/JQx56N28tPPw6DL+vTzH9lNggc83nnqtuX0/BTfveBiPsGrgFMaSyByPd61mCQnL5fEaU6TVRTTGb4fquS6tiMpJ5xMSzbbNTrSBOvQAeIkIylKJt4TlMM7OwSPO4eJAiaJUZEmfvQW9d7hlWNf73FNQ5ymtG1Pkhbsq8FsUhYxWZLgncN2nrqvaPdrkizi7ZvvaL1ifvqCs8k5Rmv6AC5Y2t5iooQ4UZg2eqzf0hgTkSTRECFDwnw+ZbvZEFxHniS4vidNEzbbHelsTmRyDvvdAACVflzZQiAQqwiTKL54dk4Wa7q2JovPmRT5YNrZbdltNnzx8gWH/RaCxrswOLS1VDqJ8FhhtAGGfE/LMLK0toMoIoozSCYQrXn4cM+7N6+YLudUXUSvS9LsBGsKnGmoGs9+e+ACT987ouyEgEHF2ZAV2fR0zqOnEzJT4Po9m/t7TOYJKsL6iLoLtNuaYttCWjC9+prF+p79h46mbjBxgwuK9SGAnjE5CexvvyUyCSab43TMs9/9FtdbHt695cOrf6X/ULFe3bO+X/D+9WvOvkjxKiF0NaBpOw1BYaMY1+Sk2ZR9PaQNnJ6dcH71kiTXxFlM8Ip8UlLvDzStI4kNbeMochFAa0xsqA8NkUlIkxJjHIe6xnd2yPLsa9LUkiWGIk5om34oCVCK2KRESlE1FSZqSbKCqq1I0oyustTN/lF071iczMiyGbHRdMHjbSCJh4fbycmSsnhGFCmSJKeqA/cPBxyaJI9R2lGWBdvNntho9tsdJobpbML+sGfomp8TaYXTgTg1bDdburYjS6dok6A0+ODo2pY0K3l42D2Orj7N0/t1+4/ffsqY87e+B/5tXuUxOBWmTdpMuq4bKkqPvuc4Fuf8/HzM1pvP5yOIa9t2dMGuVqtPHtrifBWQJABNRuLCIgrQFQAmodaiETvOqpQ8SGGsrLXsdjuyLOP7778fH74yvhSG6pj9lHii41xAGcEKMBFQKc7i9XrNyckJxhi22+3I9h47bgVcvHz5coyAEaa3qio2mw2r1Yqvv/6a3W4oGRCZiQBi2d8CMoERvIshShYEAoLfvn3Lq1evODk5GR3foiGU0b+M97uuGwsMZN/L+Ho2m42aw5ubm5HlknGpADZZjNzf34/GLgHwoq88Ozvj+vp6NMAopfjDH/5A3/e8efOGv/zlL3Rdx/39Pbe3t/zwww98+eWXwMdIIOlbFxa7KIpRjnB5ecnz58/Hc9J7P7YhiblHfkb2oxx3Me9IfqPUc8q+EVbwOHZJjpG0HuV5Pi6yxJQlIE8qJ0V3LGykXEdybNI0paoq7u7uAMb9NJlMxugkidBaLBaj215YUDnfVqvVaCYS45KA3zzPubu7Gysnf+72CxnKz8Xb/7Zd4fErfByPq/HjT0Do+KEfMvZQWOvY7Hb01vHk6oqTSUa9P9DagENh2x273YZ8EnDKECUpeZTQVAf27YYszgixIcqnhCTDmRodG7TxBGtx/ZAtGHlPnGiC7airA+3hgLeOxEQoq5jnCaiMDkMXDNvWY+kp84IshqbaobQmn0wJwVK3HWmckJZzNvfv6eqGWZzy4vlzzi4v2G+3nAVPbBICCh1B3zY4Hx6DoXOcd+x2G4oiZzab0vcdTddTVwc0YNuWVEcE5+AxI/BQt4/5mQXWWVCKopw8rtCqx8iZwfwxnWRs1g3e9ZRFTl0PF9jiZPE4choMN3GcEumIQ717vNkN+idCwNuhWpGxVUehtHnUfIbHTMkcHvVyzitClFNOnuD1hBbodYFXCUWek8UBGxqq/QFlHCbO0JGmOmw51DvmpylpkrO7fz+AMmfxBOI04fzJGW/ffuBhdcvy6iVKp1w8+xp3WNG7DmU9XbUnChOqqqHbvGH97s9MJwXO5Oh8QXbygkhFVG2Ef/+BxcVTJrGl2te8e/OeqoOzywOTaYnSiihOyIopfTe01xAU9cEym2ScXJyTTZfs+xZre3xQJFkMLme3qwje0fUGrSfM5wVaGdpmWATVdYe1Ae8VddNjgdZuSRJD2/eEMmexnA+tRsE9Xvga7yO61uEKT1UfBiNNGC5p5yxKB+r6ANpzdXU+LNzUx4VckRfExmDM8BDa7Ru2O8XuYNFRTJwOC4ko0vRdiyHBO4tOEur6gFIMQNIN31+WE6rqwHq9IYtLoihjs61ou4ooDZSTkn1t+f6HD4Dj8vLn55v9uv387a+NsD+/T3+utfxrustj9lLA4Gazoe97Xrx4wWw2G8elwGjwEM2khG3LaFVGp8ed18ctOQLSjqOH9vs9+/3+EwPKsYFBmJwQhpBvCW4W7aMYT2RceHt7O4LDL7/8kqdPn44MnTzQ5b0KKJtOp2NGopgtuq4bMzKVUuODW0b+AkYEcIhpQkwkVVWNo00xZzw8PIwaOBnpn52djcybgAoBKgJKBQgft6QIcypAUNhRYSGP24hkYXB8DggjK4BdNHeiwTscDmPt5u3t7SeB5FmW8fTpU3744Qdub2959uwZURTx8uXLkRUVXafE6azXa16/fv1JcLw4o5um4fXr1+OYe7fb8erVK5qm4enTp2MslJh0RCYAg6N+Pp9zdXXFbDYbQbaM7r33bLfbcRGhtWa5XI6st+xrkQHIfhd2UoLDBZyLllUkA7KAkmtAridh1iUn8vnz559cjwIU5fqQhclutxsbiI4XEsKMy9+X8/LYNS7mqYeHh1FyInpPYS6rquLbb78lhMCzZ89+9r3nF2goP+ohP95oBDg+fseYPfkxq1KA5MdROZ/8vPyM98P3aSLevfvA4eEtX798OgRPqpgoSolycK5Fm4zdoabvPdZKF6jn0NY472n9DrU+UHcVaRyRxDFddcC2LeuqRiko8hjXtRA8iTF49tRVTTmZ45wmTqdUrePQOZw25GVJ1FgMiixOiXVMmk84HPbs9y2t91RNR68N0/MrkthikpgffnzDk5cz+r6jVwrrFWmUgDK4R43GAOiGOsRhNLMfV5MojdERvu+wtqfrepyvifOSrg9sd2vOzk4IOsIHP55YwypUP7rbAybSLBZDVVjbtY90e0YcG/aHiq53hGAppoNbzG3W5NkADp0dahS1iVBhMOs47/EuoEJEYiKcdXRdS19t0N6S5gXBFMTJEp0vUfGMrqppe00U53z19W8oSk0V7XH1DVaroV4xTWn6PauHa1x2RxSdkGYnRPEU6yEykOcpdAkny5Td6g2TIsWplCIpuXrxB+rtHVXXsL55z3wJm/fvuX//DbPCY6ITorQkX1zRR3PQMfOLwPz8PdV1Q2w6lG2pdg2nF55qfUO9dphoGLNNZqf4KKOYDXEQaRKTzwpCXPCwa7h5WONwZFlCsA6lQStN03e03YHtbs1sXXKymJGl2dBSYzuqQ8X+UONDwLqAdUO4eJ4anPNopVHGUB0aum5gjdWA8/FucIoe9vvhOjExXduTZjHTcoIxEUYbQhgWIx6HiuOPzm6GTMyudWw3LTc3D2jT0XaWqnL4x+KANO6YTAsIjt4OJq3J440/NjFaD130aVaSJFPW+wPvPtwCntPLEzwZP/x4wzff/sBslvHs2enPv/38uv2s7XN2UT73U+zkT33PT33fMUAVNhDg9evX3N3d8dvf/nZ8cEZRRFEUI4MmY2Fh0USLKKzMw8PDmAUoIPA4f1JabYAxrFwcrDLSlN8pD1/5WLR80qyy3W7H16DUkJ8oDuzvvvuOr7/+etSXyehRGFBpL5GRvnNuZNUk5Ps49/BYkybaP9EbCoATVu/YFWyMGTV1MpoXN65oP7uuGx28q9VqdJuL2ejYlCLRQMAI0sWtLGBKnOjydwQ4JUnC3//9348aQWHZBBy3bcvd3d24b+TnBbhIZuPp6ekYYQSMLTqiR/zw4QNnZ2e8efOGN2/ejEycRFXJe7q6uuLdu3d8+PBhZFq32y1Pnjzh4eFhZOeMMSyXy1GvK89ScUJvt1uur6/H13J87ookYrPZjI76PM8/JnrsdmMNp5zDcpyPczBlYXScVSoFANvt9hPNa57nY8f2MTsu16gAdNE8ikv//fv3o/74GKAL6yqLC8kzlfNMgOaxMejNmzcAXF1dAfDdd9/xP//n/2SxWPDy5cufff/5hdWL4d+AxOPRtYBG2UI4vmF9HJsff++4Ino8EImJmU0mLNKndE0PKF58+RuatmW/vaNrI6I4Z1FkbPYV1WpH0zh6F6FQpNmE1aGi6zsWizkuUvgoIiSOy4snWNfRt91jQHVM8ujG2+8OeAOtq2k7R5I5HBFNZ6m7HnxH5SyxNiTFhCiZgg1oGso8Ik4UyhucM8RFxtnpDNd1kAamswm77YZkegoYeuvwQeP8kAfYti1t144VYuFR6+iHXYVzliRL8P3Qh73Z7fH7jiSd0FtFbxncs4+B1V3ffUzh73uUkiDYIUdTohnyPHu8qTkm0wXr9Yr1esNyucCHgHNhvKG6vqEoEoqyoK7bwVykDd6GweHvwTUVr7//M5u79/SAmU8hLXAqw7uYznVkkxO033NxmoCJKBPNrt5BB2mcEpmEcpLiXEV92BLpknk5oZic4lVFVx8wRtHXNbvNPaubGwqjSSYXFCeXREnJ7DTlIlXgHe3untu//L/Zb97z7B9/h380/cT5nNrGVB6KYsnli9/x/d07bu7uOewesAqevHyJiRW79QN9tWWaZ7j9niibQNei/RAxFJGxPcQcdjFaGbSOyZIYr9ohK9U78qIgzwt22w0/vn3LZrvj+fNnzExOnKbYzXYINo9SfOhxfRhahsIgQdhsN6SxGW9ag04soPSjxssPmkhnOxSKSEc0VU2Wp8ySKWU5Zbt5QClNpIdrUQCl1prq0LDZ7Lm729M0HU+ulsxmS4yxtM2GybSka1qKIme/rUErYjNcPwJE9vsDzimyYoF1Ee9vr2mtY3GypChPOBxa/vu//Cvvr295cvV3lNNfm3L+o7efMt8cs05/jYH8qe34e4//L4yi9CfLiO+3v/3tOCYVNlCMDPf39+OIVCk1mmO6ruPk5GTUpMVxzNXV1QicZIEsD3TJFZQxuIApMdgcZwYKwBEmStg2yV0sioKLi4vxwT+fz1mv16Oe7DhG6DjTUtp8jvezfJ8EaMNgABFtnoBqAQgCXmScKCNp+fdwPe1pmmYEWOL0vr+/5+HhYcybPG65kSxM0R0K83mcOdk0Dd988w23t7d470czEnysXRTAfnFxMe5/YUsF/E8mk5E9liin+XwOMDLRVVWxWq348OEDcRwznU45Pz8fx7gC6Ha7HX/+859ZrVb8b//b/zZqOWVxIsf6N7/5DXd3d1xfX4/j+K+++mqMX5JudwFMcgzlmEplowA/OV7HrTFFUbBer3n16hXr9ZqXL1+OrUwyJpbFg1QUyjFbr9cjqy5/Q3S4cozkOgBGY5Q8+2VsLT8DH7u6JZR/tVpxfX1NXdc8f/6cxWIxjvclJ1SuO7muRA+slBpjkyR268OHD/T9oIufTqfs93v+63/9r7x7947nz5+P++7nbL9AQyktOR/B4LHL+9Ox9nEExadMpWzHN7gQAn1n8W1PHBmC8+gQWMznRHHKELMcoUyKa2twlnySc1HOSLMZH27uCMZjooQo0hRJwUmWk+YpRZHibY1yM6aTEhVpmqZlNimHUStwcnrGN9++orOB3gVUElH3njhJUVlBmjiSfEKkPKHvuL+7JisPTKYLcm0gDjjXsNveo7G0h54HegKBs4tn4D1giZTHJIamscRxiokiVBgaWMzj6HFY4T1qGL2j60RH5LDO0vYO62C12WFdjTaaAMznBUmiaLuGKNLEsXm8ifWUk4LYDGHuxw5E7wO292w2O5Q2oBPqrsM86nTE5RaComv2dH1PrspHqr8jTzKUsijn6OuK1d0H3r9+Rd/WbBpLERaURYryAyNr4glxpEkii05r2maF8vdEXYK3MW03uJxjo5jmhiTW5OUU8Dg09X7PbHKgRbPfVDzc3GH6jlz3BLdn9eDpg2a3WTMvYk7yhG71jrsf/gc66jHRV5AYsqxAaU0cJUP1ZlMTdMTls+d8c/0DD6s9Jk35b//yJ1589QWnizP6ztN0lu7mDudvSNJ3TE9PmF1dMTm37LuWtplRzC6YzmdMSoPvaroup25b4iTBGE2RJ7x91/Pu+prOeZ4+8Symg6HG+zBUiDpw3qN1RECTZzlpkg7jZq0B93hzYAyeL4qcF8+fEoLGuUDQAUyEBvIsRYXAYX8gi82QZ+qGjnnnwuP4XFFVHftDM7QemRjrAnmW4KIYHcV0fcVuN8QLpWlKmg95cqEHpTR11ZGmJUplfLi+Z709cHZ+yunZOT5obm83rNdb5osZT64uKMtfm3L+o7efAo6ff+5z1/ffYjOPR3chhDHuRsa6AMvl8t+EKss4UIwQRVHw7t27cWwnoDTP8zEmR8LE5/P5OGqUZhqA8/Nz/vVf/3UMlY7jeARlwiJJbZ21luvr69EIcWxeESAiLGgIgadPn36yP8QcJI5sYPwdomsTQ9FxdZ4AMnH8Smi5/A4BJscd0/K9s9lsbPcR0FkUxQgUHx4ePhltyn6R+7SMYmW8KsdLtI8iC7i+vh5HxcJsfR6OLmyZ6PVEDykASoxVok2VDnPRQ85mM5RSPDw88OHDh3EfCEt3LB0QEPXdd9+Ni2U5lgKI2rYds09fvHjB+/fvub+/J8sy/vmf/5nf/e53nJ6ejufn+/fvR2b57OyMp0+fcnl5Of6e+XzOcrlkMpmMPyO6W1mM/Pjjj7x9+xZrLc+ePRsNNXI8hKEUHaiAwuNAcgGUAujLsuTLL7/8hD0WTaoYnCRq6TgGSY6NmMtEYiKMsLDBoumUeKEsy8ZIIVnMCdjWWvPmzRvW6zWXl5c8efKEEALX19c8PDywXC558eLFf5bL+7gl53iM/REojtiSz1e8x6Px8Mnnhx8fQJUnkCURRgXubz9QZk9pe0uUz8jykqbecaganK1QKiUvZ7SdJUknzE8XRJGhfVwtN11P3Qaa7oC3NSfLjKbvCVaRZCUnl88o5h3v39+w72PykxeYEBGZbGBj2hb3OH50zmIUKN/Ruh29Daj9jtA1lJMJaZbh+pbzxYS+rYiCGyoYyylaK3abe7LJDF9MAEXXehSaJDaER+Aw3Cj040qwo2la0Apn7dCqYjTbvh80jDqhag9sdzWLkyWdDTS9JYrNqGmblEO47HazJ9IRvXfA40j88SIdIoDg9u6B+9WeZ8+eM52m1E2PUoG8SOm7Hk1EmqQo/OOJ7XHOYvsGfE9wHYfditvrd3jbDaHncUk6OSNK54SoZD47YZJlbO+u2TVb7GGLdo5q955IFxTFCa3vOdQ1yu+ZbLck6YGz51MOdcVkUhKpHN9WaFNQrfdMTMZiMWX9cIeeOELqIMmomgOhtfzwf/+AXd9wOktpXOBhvSYKGdm8Ii06isKz7zuSTHGIA/nJnL/7+79DBct6X7HZNpz1hqg8x1YB13ekxmNCR9vsuPvmT/DmFX/4f2Qk8xLXK5L4jKLUZBnoxKCYUjfDA0hHmiSNiZNB2vCwWnN6ckFkItIsIW0tXWVxLhAnOXGsKfJ8qBp1juAdCsbxoQC7oUpRc3G+5FC17HcVDkuRpxR5zmw6wfU9rrdEaQYMNyfUo3DfOpSKUGh8cGR5RtO23N6uyLJTehuwfU1AYz1kScxkMqXpBielSYbFgHPhUeNTD2PzKALl2R021PuOd2/fcna64OrpOYtFTlXtgMXPvln9uv37208Bx5/LTH4OQo8/L98vRgUBKe/evRtjUsRRW1UVh8Phk/xGYZguLi5GNgUYH+TywDs9PR1H3FmW8eTJE+bzOW/evBm1hPIQFi2csJJyTci/+74fH7wSOyR/Q1hV59zobF2tVmPIubw2+FifKGBHmEQJvpZomOl0OjKJAiqEsT07OxtZVwFrEsmS5/k4rhVGTf6OgBXnHNfX19zf3/Py5csxk1KMNMdVegKoZT/I6xEwLZ3VolGVqBwZFRdFwc3NzdhkdDzel0goAZnCwJ6eno5SBPiY0Sid5+fn52Ml5bG8oW1b/tt/+29jSHrf99zf32OtHQGfsIhirjk/P+d/+V/+F0IYoqXW6/WjF6EcdbbH7uo//vGPfPfdd/wf/8f/wXK5HDW6x73xUk0oBhWJP/Lec3d3x/n5+TiGF0ONuKUFWAu4k4WWLHyECZVz4smTJ+z3+3FBIK5zCUCX93p8ncoYXTbvPUVRjJIByUsVY5qYw4SxFIOPaIKlRej9+/cj6N1ut+z3e3744YcxuWG5XHI4HH72/ecX51B+/P/HIHM1tuKoIyz5uVPwU70lfNRcEjQh9Hhv0VFguZywvVWUeUo+PyOeLIYw8V1MnmX07ZAb2NY9jVW4YPAeTBJTlMmg53M1WiviCBQxu90ayoRJUdC3Ne/fvWU2O6HtHKv391iVEuUFvlWoRNPUnrprMSYhiTO6MORfxvmSxRkcNjdUdUdrtxhzQKmAIYDt0Hims4jYROzWK358fw1Gk5cTImUG7VySoB77sLUextU9YczRUkqTxhm973m4v2cyHWI4AmB9oHfQdOC8wfpoyN/0Yaj36yxd1xLHKbazeOdwrn98MITx4Q9qYMW6QN9p1uuhA9YYi1IdJo5o6orIpIOZI4oeW3oigvdY2+K7itDV2P6AszXO9jSdpVE5z0+ucNmcXmX0LnCoO3oM+14TWk+pFalJ0cHTHB5QScJsMYGm5+bNax7WEeeXvyMtSrTu2a5uaP2K6MwxS6FuIfgO6x3TvEDnE3RSgguYfo+a5Vzf1uRJxGz+hDcfrmHTkpcXJDoho8d0PXVXEYWKrt2yrzbMFjMWl5eQpigU233P4uk/0PUBbEvkKuzmA66xHDYbNg93XMx+A0CctCRZR5LGZNpglCE1Q8C/ioas0WmZ8ezpFT+8eosxZoh6KofWm9VuzXZbMykj0rhEa4N6zAWNhkz1T8Te88UCYxRtWxNFsJgXRBr6LqYoS7I0I40/irN1pB6lDI9j7xDo2g4TpcTxMMabzydMZjmOQO8cddNh+47lbMZ8Nqfa7R4f6gbvh7DyrrVE2mB7uL/b0raO+aIgS2OSWLPpKkwUeHJ5we9//wLrD/T216ac/4zteDz914w4sn3OUv41vaV8TVgSrfVYeygsoMTgbDabMZZHdHKiKZSflXHpse4vz3O22y2TyWTMznv9+jWLxWL8GBjHoDKGFV2jsKTCuF1eXo5xK6J/k/cqf1d6ph8eHnj9+vVoWhAQcJxJKK5p0bKJWUPYzJubm5Gpg48LP3EdH2vusiwbY3lknC9fl5+Xh/+xY1qYyuOKRgFnMrYWMCp5mfIajuv+REsKjO5i2S+ir5T3KFIEMc5IKHnTNPz444+sViuePXs21k7e398D8OTJk1HjKu+tKIqRTZbPzedzbm5uRuD55s2bMT9R3qOwbjIy3u/3LJdLrq6uxnHubrfjxYsX43uUHE4x+9zd3bFYLEazlLR7HTPmsv8kdP/Fixd8++234+uYTqcj87xarZjNZiObKK/zeMwtx325XH5iqpIIJjHziEFNWPPjAH65jiUSSYDucrkczzeJTeq6bmwv2mw2I3iUBZE40/u+5+bmZuw0l78vzPmzZ8/4x3/8x3ER9HO3Xzjy/jxK4nM39/FY+9/GCv31bQj0Dli2mweC78lTg3Udzvc0+y1ZMYiQszjBd91gXOksweQEFXGoapxnMOB0lrpuiLOExMS4friIne15uP2AUQoXZ0Q+0NUHAhkh0kRRStda1usttmsIWmEizWq1pdptiSPFfFJQ5lPyENhuVwSlKMopbVNzOOxJjOGwWbHd7Yce6eCoDhV9faDer5kmJVrFuEfTDATQw9haRhZ13bBcDmOaJB2aWpIkIUt7DrUlhICOYpqupml76rbHpIEkVUR9h9F6qBk0KXmR44IFPibvaxRxFgOKtu1I0oKub3lY17z4wgy1lAqssygNSnms61E8rqC9x9uOxjXEWIJv6Ls9tm9ou47I5LQN1G2ABJwK7No9qTE4D3GxQEUtoXUYDXmsab0nnhYo/4TV6xpbb1lvvuf7b/47J8+eEhvHybzE71pCs6btdmhdM5sv6Q+W1sI0mTKZnEGv8aFnMZ+hri5pu5rF5RXqXNF5w4e3P7K7uSFJUqIso5gUNNWaw/17IixZmWPSnOlsgkdTdz2NS4hml7igsK4m6DkZCWr/htXtPS7+Hr14jjYVWudonRGbjAhPGg9Z/EEFsjQGctqmI4k1Oni0gjRPaXuP1lv6zrLzB7J8GKu5xx5v54dKxcdUK4L3Q9d2knCoNsRpShJrvE/oYsVkkhObwYzT9w2RGRDpsFIepg4hPIq/dQRolAITK6bTCXlmaOqW1cOWosxwHlarNdHjjfV+dUtve+IkATW0B202B1arDdYOkUVxrDA6UO3WnCxKvvrykpOTjPVmP2RT/rr9h26fA8S/Nur+XBv5+cd/a/PejxE5wh4550YwKGJ/AXHCyIkuUJgdYfjkYSagU8bVwsAfO7jlYS990uJqNcZwf38/Gh5msxlFUYy6SGCsFBRQ9PBYOSuasv1+T13XbLfbEaRIxI44qYGRCaqqitPT00+cthKofaxfFKOHjFRl5C35kGJoOQ51FyB5rG0UJni1WgGMr1HAgnwfMOZOCpiU4ybAUsDJMSAXQCSSBWlYkdcqLnVxzf/www9jF/kf//hHXrx4QRzHnJycjPpPCYNfLpejQWuxWDCfz3HOjZ3az58/p21bnj59OoaZ//jjjyPQFLZtv9+PcTkCxES3KXFQs9ls3IcC6rbbLTc3N2OUjoBuYYFlf8q1IS54ySOV60Pc4wLKhKEVFlgWH8I0y3+y/8TxL+56SQ84/rfs/2OmVa7rY7AaxzHz+XyMIhIQLqyq1kOd6d3d3Sfjd4D1ej0ywWKgi6KIzWbD6ekpv/3tb0czlbDOP2f7hSPvTz7DR3byo27y+Gv/9uNPReMff/dwITgb2B9aNtvD0Md9/8ACg04svh9GtnGakLqEpqpJUw2xIYkSrI6xzqI1eG+xfY23NbHKh1F1fSAKGuUa4jhFhZjNakVbO/LZgjTOUNrTR5Y4ikjjHBMnRHGMsx2JmYP3HJqKrJgxmZ9Qtx1d37PeNtzd3jEo3jxxlA11fgfz2FdsuLu+JU5mZOU5LklRkSfSMagI+3iDVSqgCGgd0XcOp3nU0eQ01R4TRWRpQmQyir3Cdmv6XtE0ARNbJnlM1/Rk05xIPbLFStH37vFiGYwYPrhHreXgMnPe43zAdR2HqmY2nWBihXPt47HxxGmKc5a2H25UcZbiOk+7P9Dtt9iuBe9JkpQ2RHzx5UtOz5Y0JqPqFNVh6C23bqhHjPScul7RBksaJSRpRJxNYZHRbi3cbun3D9y++hci9sR5RBnnPFRrsknG9Ye31E1FlhcU5SlqckrnU9pQkpSapt/ysN1xdvUFbWehmHM+vyCYlGq9YfvhDR++/RO9bTHpcGGeL5fki5woymj6Dh8O7Hb3bLotvV6wLK4w+YL9TtNHc/KTl0RG0Xc7rq8/cJZMiEKNCR0RMUoFfPAoo8AHuq5HmZ7IGPI8YT5PB1e56tAmJ0kT8iwjS4emnKZq8LOUzjkSElwP220NQVHkiiI3BG/xTqEJaAZtq/eWONYoPXRoKxXwvsfo4fhb64ZrUg/JBN5pXL1nvV0BPUbFBNvR1sPHbdeSZTG274m1Ii8KrO1RCobw+wDE9Fax2dQc9ge0UaRxRhYburZmOZ/gnSdLNa6tmRYlSumfe/v5dfuZ298aY39+7/0pneXx7/kpNlN0jmJukKBnYDQjiKFCAIOMOAVUHjN1wrrJqFoctB/122E0WwibeBzoLYyNmG9OTk7GhbmEZjdNQ9d1rNdrPnz4ML4nYZ12u904Tnz//v04ApW/JQ9iYUXl54+NGRIFdOyEFme2vEcxqpRlSdM0o1ZU9vdxQLbsH2GMJKj7mKE7DvU+NgWJhvOYCZWQeJEIiNbv66+/Ho03bduOVX2iz4uiocNcQIwwjAIQr6+v2e12fPvttwDjYuLm5obZbMbt7S1VVVEUxaiXlOMr+kVhOEUWcXV1hTFmdDL/6U9/GveNhKBLpaa8z/V6PYLXsiw/qciUHNC2bXn37t0IHI8XXxKz5JwbNcKipRTTi5wzotk9lj1I3aFoh2URUxTFuFiQRQIwSg4EyAu4lXNJjicMCxiRGEhBADCOweX4iGNctJSiSxbGU/6+vD6p3xTAXtc1Jycn43kkYPfnLjThFwabfwSHn2VKfgYgP5p3+OzzcsMbgOjxCEUFRRLHXFw+5eHtt1gfkaQF1e6AdQdMmjOZTZlOJ8Takyho2o7ODuXtkUlovKNtWoL3LCYZ3rWkqgdlcVox2KGHNuVNVbE9HIjMFOMCqBalHDoEQohBabq+xmCJTGAymbCYz7m5/sC+OtBqhTYZUTBopZguzmm7QaS9mM9RKiVCg9tx2L7j9uaB2clTPAqvNeghOD2OYlxv6VxPHGvm0xneD2BCaY2OIrwd9EBGK5JYs91vMDoh+MB6dSDLUtLE0HeePInHE6DthizL+FFYXtf1Y4tCOgLvgEPpADrgQ6B3lqbvmWcJTVuhowijY4igaSwoiON0OOIhYPIJNDW1DdS7PcFa9lVFftLwcP2K4kyRJwvINEmiyZKMvuoJXuFVSuM9vlVM0wmpnmN1z/z5lOlmS4Rjd/0Nk9yRTJbcbA+8+uZP7F+csz88UEwX3N9vmJwvOS1mhGxJ5QJKaZLplJDnVDojOzml8gVJ+ZzJdEpabMEG7t+9YX33hsXZOXm+YHr6FcX0lECEq9d4e4+yDaHaUj+8pkhn5Gee0DU419NrjVUJSTpnMp1xMisotScNHte0WJWgo5jIJBz2e3aHmkWck6bDg/BkMSHSLV1/YDqfkPSO2Szn7m5HXTVst3uWpyVxkuOVpu4c96vhAaNMQpwMhQAES2wigmdorLF26LDvO0I0aCT7rsM5j1IarSO63tEc9mx2FbYf5Aytq8lSxfMnp6T5cLP0zqN8IDURtq1J8xRjFLvd9lGuoUHFBBLaylE1wzm1mOVcPTklTSI297c8vboY6kNdR9cZtLDzv27/4dtfc3T/NaMOfDryln8ff05AAAzxPc+ePeP169cjaBGtnTBG8/l8BGOi4ZKxojCOMu6Ur8GnrSQhDNmHwnLKA/v4P6XUyMAZY0Zjy7t378acPmF8lFKjflKqEo/BomjKTk+HOKtj1u9YGynMkIyAZWQvQO64BlLAwf39/RimLU5p2b/CGArwFv3psZlGXo8cM2m7ybLsk15tASXwMWtSjp+MngVA73Y7zs7OeP/+PRcXF6MRRhp9qqoamS3n3GhmkRHxF198wcPDA0op3r9/P4LGzWbDH//4R7766qvRoHN7ezsaP4TVDiGM+k2A09PT0X08n89HveabN2+4ubnh4uKCsiw5Pz8fTT8SpSTNP7Kfz8/Px+MlLGWWZWNrjywmBLDL/hPDS5Ik42heoou6rhtNVYvFgpubmxHgSRan/M5jA5W8PwGQci1JGL2AdxkvS+6pgD9ZvB0zo1mWjQHtAqrFUCWNTsYYNpvNJ45zAaVVVQEwn8959uzZuAiQrFBhtuW6/LnbL8yhlBvMR4AZHk04ct/61LxzvH38np8GvAEVoMgz5vMFq+uUSTkhS1P2+5pdVfH6xzvKSU4awSRLUCiazY7tZkUyWUKU0XWe2MQUWYHrYX3/nkgFyiJhOpkR8MxPT3nYNPShwcRTCLDbbmn7jnxSUE6m+EfWdL9dsT/smU5nmCiwXM5ZK0iMYb3e0PQ9L7/8ktMQuL65w8Qx8+UpJsqJvMK19zi75/WPf+Fss8bZwXmNjknj5NEcY7Fdi1Weqq7wzmEiM6x4m2EkoVUgSgxx8ATXo1XEtMx5f/PAYjFlucwJQaO1GfqmtaauW5TS48UgN78kiceIgSQxj2C0oWktTVNjbYJSA2B1AWwIjz3MCSYyZEVJ31us9WRRjFbQVge6uqLarolNQuwrsnAgsSvS3JClQ/6l8xE69Byaiqa3JJFhtzvQhJjKNST5DOsbJudX+O6eur5j/fCOr85PWEyWvHllado9z59/wWRxxqay9F3H3Ye3zM4jul6BcpjEU87PcVYzu/yKh+sDrovITYENW0ANN8f4S5ZPnnP58g/Es2f00QSUQkUlVIrzS8Pu/v9mf/MG1/bkmw90ShNUT+Mb2v0G3/c8NYblbEpuIiLv0DEo7+h6S5JGvP9wx9t311w96bh69gyc5vT0itgwxAR5i9KOokiYTDLu77c0reNwmKD0nLbtWa93bHf1wDQHh4kKpuWgWyRoglc460dmQUZsbdvRtv14bVrnsdbR9X5w+W93lNMJcRJR5lOePj1FG4OJE1arBw67nEhpbNegiwwfPHj1yFwnOK9R2lA3NYe6IU4Szs/OKbKCvm+ZTmdcPjlnvXrA2pYkKdCRGoxnv27/odtPaSB/ys39c406f20rioKTkxPev38/ZiLudjt2ux339/ejQUXMBdJFLB3EMnKVCrybm5uRXZKYkrOzM1ar1WgeCSGMBozpdPrJaFNG1wJkJaRbRttt2/K73/0O7z3v378nSRJOT08/qVrs+55vv/2WZ8+ejQ9hYGRChVEFRrAlndwSWC3jd2FhRXf37t07Tk5OODk5+WQEKvmOst+FfYShMWi73bJYLEZnuRiY5PUKuyabMGuyb2UMLABKDFObzWYEu8AYoySASACKGGck/Frel7jCLy8v6bpuHEVfXl4ym834y1/+Ql3XfPnllyyXS/b7PW3b8vbt29FtDYxZk33fc3V1xZs3b8a/J+9L9IdPnz7lq6++GkGt7LPD4cDTp0+5v7/n+vp6zGmUTWKJ+r7n5cuX4+8TSYIc/yzLePv2La9fv+b58+d88cUX43uU1yPadTlPb29vR2ZXWEKRUgiQF13ucTe6jJmNMaPsQ9zmcl3KOFoYbom0EuD//PnzkaE/7rCXaKvjrExh+5UaWppEc/vkyZPRIb5YLHj+/PlYjykyDjHQ/ZztF428YQCDx3rJj0BSxN8g5pt/e28KPzE6H6BnHEU4BcFbFJ6uqfnh1Su+/vorzi/OSLZ7Cluy2TxQbbbcNhXL+YwiiQdmxu7Z7zYk6YTz0xl9W9NUOw7rW0ykWJQXpBG0NpDEGZcXC7Tac2gCNnjiSDObnRCnMd3jza7IY7RrSWYlkyLHBMtyuqDME6zX1O1gQHFAkmZMFwuU1o+tKhExCtsOTuqubQm2x/sOvMOgMVGMUrDdbJiUGUr5cfWugiJOYpwbVuXWOR4eDsRpTJ4n9FYRx5rge3a7DZd+wnJ5TmI6NpsD52dnHPYblNKYx4gnWWWL4LwoisdojJjzswWvfnjLh3dveX41f3zggB4OOCEo8qzEo+g7h0IRx4YEz0G5ocrSe/q2Zz6dkRnPJLbs7l+xvvmRrJiDLllve6azBbPcsF+1ZEVB10bsq4qOiGU+Q6cZxdkl2m9Js4g4DhRlxKRM+f3fv+Dy/BlND71KWZyf0wfDarem6TpUFA3xS0VMlpZQ5GAKLp6d4JSB0FNvbwjdhpNFyfmTU7LzF7h8yUFNCEyIjaFXDmsLdF9TplNu37ylrbdsN+/RRclkWhJrjbI9RVFSlBN626EjTVPvoW2ZzBSxzgjO0TUd+13Nt4fXrLctzy6fUpQph0NNlkGcOZJUE6eKokwpypS+d1jnCI9tRyI0b+oOrR2nJzlRnGJdj7UMmZVRQpalWNujo4goMljXEOmYpnXUVU39OBIxxpClOW3WUWQZWRJRFilJqkjzlDhJefNmRwjD60jihN4Ohq80yvCPf693gbrp2O4OWOuYTBaUxYLdvqGqdpyezGjalrZrOD8/Jc8zVps19uhB+Ov2H7P9nLH1T+knj7e/ZcwRFkwe9nVd88033/D3f//3PHnyZIwoEQDZNA0nJyfjCNxay3a7HZ3BTdOMWYWSZXjcj/z06dPxoSYMz2KxGM0eArSOXcEwMC+SsycgTFgqYSaPawTl9wsAExAmAFGpIWNQjEfHbSky6gdGZ7ncX4XR9N6z2Wx4+vTpGFy+Xq+5uLgYAaSAHLlPKzXkdU4mk1HL9+TJE7799ltev37NixcvPjmWcrylR11YruMOdAE5bdtyfn4+ApLb29vRYBVFQ3WfGK0klFzAruzHJEk4Pz9Hgr0lm3I6nfJP//RPXF1djcBReqo3m82oyRTQK+kAxhi++OKL8RyU9pblcsnTp0/HUbcsIoQRFBlGnuejrvPh4WHsgJdxb1mWTKfTMe5J9KOyCBEWd7PZsN/vWa/XPH/+fHSPSwSPmLWki1z+vgBUkUHIAkNajuT8l+877rIXPasQAcfVjhKvJb3waZqO+03+/f3334+SlOM+eQHMck7J+5PKzMlkMrq7z8/PR92rAE3RSv/c7ReOvH/aFXj87+NGHZHrfLw3fRx1f7oFnO2wXYNrKrq2pu9bettyd3c7ZPHlJb5VPL16Tr1d8+P3f+Hdm/dMJxOUjiinCVmZ0rqebn+PImBouTiZERvFclrQVHuqznHYHZgvS2bTkii2oBOatiOKDU3bYJRjkiYUZcrZNCcvC4IPdH2Ha3akJiZPC84WE07nE5x3dIcNMYH9/sD94cB0umA+mRF8j0YxLQqmk4yIoZFGhYHOtsGRJCniuHaP1XqpSUenXQiDAWM4uSLiPmAMRHRMypSmHcbeISi8V9jeY0zMfL5gsxl6PIs8G6lsiTIAaNqWIk948fwJvW3RyrHbbpgU0aCZi2N8cEQhIk0LdofDI4CRCJuevuuGKsrpnCgrsUFze3eHSjKirEQ5z/Z+hwsJu73l8BDz9OqS56cT6raiiD2N84TuQN8eOH/yAj3N2KuWvq8pE8/JyYL9bsV0MSVfnKJdgc5mzE8uqdoevV7TtzV1vaXb1xibMF+copOYLIG8KFlvtrjDjm79jqhdU8aQzQpUnuLLEpUvqTqD1gptYpTJUD4ly3POzuYE7Whcx3x+zsXFUxQRnohtVdP2jtc/vmN5dk7XO3Q0jL105HHBQ4Dl8ozVuubNm1sOO8/52QlZOihvTdKRFQkm1vjQMZ9POFQtdVXR1A2z+ZRIR+PYou0CAYVXEdvdhupxLJjnGUpprOuIk3hwcXc9Koqp64b1psI6R1HklGX8aPYaIqGmk5LFoiAycP9ww3a752H1wLQcxO/m0TznQ6Dp2qGRp3f0FprGsT8cCAyLq6ruORx2pOlwH9gfDqRZTlCK7W5H21ny/Ofnm/26/bLt37tP/xRrKdvn33O8yXixrutRm+ic4+bmBmCM3Hnx4gWbzYZvvvmGV69ejbV4wi7JyFU2iWVZLBYcDocxZ/Hs7GzUCsr4XBprZGE8mUxG8CMsorBcAlxFWylTmt1ux7t378bxvDBH0i8u4OvjPTh8MrqU8aOAwGPjiwAEmRQAY51dnucjqBNDznK5HFlUGVVK9/Qxg1aWJS9fvhyZyfV6PUYGifNXDCUy1hYNnRw3MSxJoLi0zoj28vb2djSx3N3d8eLFCy4vL0dAK4xZ0zQ8e/aM+Xw+TsDSNOXs7IzNZjM6jWV0f35+Pr4Hyb+U13h6ejouDIqi4OHhYQzwljiqxWIx5pUKoyZgX/SNElQvIFNc4HI+S6f1d999NwbaiybyuF3o7OyMh4cHXr16NTbxyJhb4oZkkXBycsLhcBjHyOLeFn3jca/3ZrMZDV+SsSm6XBlzi2Z1tVphrR2BtozgAWaz2agLvb29HR3s0+l0dOvL9SvyDnH413U9XndiHpPcyxCGDFGZKgj4P44w+ve2XxQb9FM3mKOv8lNu7k8ZzE/bc2RzztI3NbbZs7u/oT7sKPKU0+UZUWQ4VAesgzQviGPDtnugyGbYqmd9v+Xh4Z6ynJJkBb0fVgF5kZGlCQRH09Q0hxUmStBZycP9LYdDjSfiUHWgY7JiMox0Q8N+v8XXa1xTUuYFeeyIdIRvD9SHmjhJSNKCRMTP1tLVQ9QPVU27XpOEhl2/IzcRtjkQYema7eAm3juSMkWbmFk5rM6cbdntDkyKySCY7txQb+g9qMGdm6UpBE/X1iQmJ0uhLCPivKQociDgnccH6PtBK3k4HOhtP6645cIRYKKVImjF86fnzOclzjYE3z/eIDLm88eGge3uMcbCY53DmAjvhlV+UeZs05R0umT5/CuKMme93ZAUE5KsIJ+mdD3c3j4wyQDb0O0+UOQZBQ6jLWUcUc5yzCxhkie0rSebnNG773n77jVPL8/JsymtVVhT4pITkvkFXTKnCx0uURT5dJAP+IbCBPrNLav9ay6dpdPX3K82bB/eYZoH0naNx5MWCardcnrxkr3do00xRCxRo5NAEhlMFnFyNictMkimLC9+w3R6ibWKqusI6RbnW5TJ6Z0CNEVZYPseo1PiJGE6m9H5mqaNcK7j3btbrAs8ezL8HusCfe+JjWE2n3I4bAjBEoKjbmrKssCYQYZgrSdyChcUm23Fze0DXVWzWMzp+gNd3zGZFKQ6wlqP0jHedRzqoU4xigyRMUSRRmtFmsaUZQ7Bcdjt8FnG9999x/3Dmqur51xcXNAcKowaAKUxBoymyCfcr/cEDNt9hXWeclLSW8vd/ZrDfsUXLy4Gs1ykifOMpukJHtJ0wqHqf8nt59ftZ25/6z79t9jH45/9KVApD8i6rscu7KIoODs7Qxo/JBomjmO6rqMoCqqq4u7ujtvb23E8LmzNcetMXddjX7RouuThJ0BQeo1hAAgCbKUbW/q39/v96LiWh3Xf92Nn8rG5QdhQGe2JE1xc68KcStSRMJWif5PxsOwzeT8y1hcGTvIGgZHR6vt+ZPekrUVGofI7j13cMrKVaYXoKJfLJcDYUiT7V8bvMnYVt/QXX3wxBopLNuRsNqPvez58+DBGM202m1GWIMdFQLiAw9lshnOO169f8+zZszHvUwD3fD4fNbACjAS8SVblZrMZWe/7+/txlCzAUUDk06dPRyAurLBoT9M05fLyctSBXl1djWYZkf+IHlaAtgBykSAsFovxfJH3ZK3l+fPnn4yghSnf7/cjkBcZmSwyhLkU09CHDx9G0CkATxjUY63qscxAzgV5fVKpKCz/n/70J25vb3nx4gVXV1fj+Q2MWtfJZDJGOUmUkDC1Nzc3bLdbvvrqq1HLLDFYoo/+T8mhHG4sn7u5f2qTcffHnm7Jqxz/9RM3tChStLZlu3mgPmzZ7zckkSPNciKTc/+wIc8n5FlGFMXMFye4ztLpGDsZTo7tesWhOeDcEMkzXAQRWsFhvyJNc4r5CWnb8+3tn3EOgtLoyDCdD+Nq/2jsKfKcdp/zp9tb6qZmNp0TGJim2XRK8NA0g9akaTtW6zWz2YLZfE4RGYzbsnr/jvvWcv/uHYtlQlevePfmO8qzP2BDTZIBZU5vLc3j+MQ5h3eerunxeLTRZHnObrWhdj1ZbijzlKrxTCcxm6qlLBOca0jSEu0tTe0eVxY5xmiqphuyDh+1PnJh7ff7QWsRIMsivDd4l+FcRGdbPEOqvonjIby6HcTfOoqxfTvkD3Y93kMgIpkseXb6BK0VUXFPMZtTNz3WK9AZxewU5TpsfaCudrz94S/Yrma5nDOZlfTU5GlMYhd4Dz0Ga2F9v+Xdj+/5wz/+AZXM+PG+wiUlte5xVYMNmkBJHFmePP8N+zuN3d5w/foVr1+/wbd7dr1m11iq/Z6MjovSYHTg5t1rJr0dWMX0jLoz9L4nSTSRstTVPVpZzs9OycspNp4zP3tBnJ/iSTC95eDfEYWWpMhwISFOhggqpTSEIfrkdBmzP9zifDXkeRrDZnfAmDuIJuTFBK0D3lmSOMHanhDARBEmGkZu3jn63qF1hNIG62C92bHb1+AD1kLAUpQ5Hmj7njxN6frA/tBgnSfJUrI0JUniIQdSafI0QQP7/QFjNG3d4WwgjjNMZIh0RJal9PXhUZZhiaOBbYyimENtWa93j69L01vLbrMlTRRJlhLFmiQ1WOvpup4QNC4MY/Jft//Y7XOzzV/bfgowHhtxPv+cfJ+M9Far1RjOLExgHMfc3t6OzlYxvogTVkawq9VqBJ/HAd5aa7bb7chItW3LH//4x1GLGEXRGPkio+yiKNhut3z48IG6rsecwRDCyDzKOLuuax4eHlgsFiOT5L3n7du3dF3HmzdvRsbp+++/5/Ly8hMzhwBSuU/La5CRogSUy/hVgqelzk7qDIUNkrGmsFBilBCQIazhdrsdo3oEIAtglFYdAeICBsQQIqNz0UUCY/2hADUJSRdgJZE+Aqy//fbbsZNbGMnjZh1hQG9vb/n+++/5L//lv5AkCdfX12PkjgAv0cN+8cUXY6/1q1ev+P7774cK4rYdA9Wlg1opNWorhQmVrEnRocqC4/LyclwEXF5ejoydLChCCKMsQlqQ5PyWVh2JkRJZkLjZZX/J+Sf5oXL8hSmVzNBjI8xqtWK73Y5A01r7CaMuRiVhbWW8LnFawmoCo0lNdLzHWZp5no+aXHHzAyPrLSYqCeOXqkhJJTgelwuT/p+ioRxuLscg8d98lU81kp8368jnfsIjHgLe9RAcXVvjXM/F+Skvnl5RNx1N74majuqwxvcxcRyRmJj56RxzOufkfAnBDTpMb9lstzgvK7gG6yyR0WgVYbsG7wPLeYqJE2IzrHbjJCIvS5I4ZX+o6TtL21ZkJlDOS6bTkqYeBM271dBGcnd/x3KxwKGwbU1wGbGekeeG2PQ4daCzFtUdyKMY2+x4uH1PNvuagCMywwVm4og4NmidUtcVJnoU6zYViUkwUcR0OiNSgShyKN3TW8vJyYwPqy06clTNjhDmREZTTnK0DljbkRcZgUBTt483n+FmFD1mXyql8LanOmzQSmP98JCPlCYEP578eZGhQsD6HvW4AiJAnpfEbkaaTEizkuXpKV1bP94sDuTFDBXlHCoHUYmKM7QyJNqwQLG6fcd2s6Kt19T7O6rtima3IZ9fkqqYk9mE+PKCarvDWcv9aosiZZqnWAJpMSVEOd52tPtrAobT0wsemg33dx/QrqLZvGe+eMLZ6SXF9B9Y3d9TGjCuYr16T7275+H+FhVN2FWeHihmJd719PsNp0VEMknZrPfcN9d8oedcvFhQNRW9NVhyTJLTBs/DtuVkVtJ1aqg8JMJZR1kUzOcz3n54oGpqVKToXcehPrDZwnJeYLRhu9vysDpAAGMiklgRJ4Y4MkSPNwEXYAiYUnTOQ2SAgA+aybQky3N8GGQNQUHfWQ7V8DAvihSTGNq2JjbDoiuJE7bbDQRITEpVtWidovE0TU/XWvRjjmmWxTRdi9cRaT4fDEJ+eIDHcYy3jkNVUdUHzs6fcHp+RmQsu80D3nmaxtK2Hus0zv8aG/Sfsf0tMPm5rvL435+zkz9l7hENmDi3r66u+OKLL0ZAIixj3/djnM/Z2RlKKS4uLsaHr3ND9Z4YB4RVE4ApQdZioBBWUrRrEgItGY+iM5vP5+MYT8wRNzc3Y++1ME8SqyJAVkCAjNOvr69ZLpcjMycPefn+w+EwggiJChKXuYSKi5ni7OyM29vbcZwp+0AiWaRDGhj3o9x3ZWQuTKVEvYiu7bjDW37PmJxytGgQk4YAlfPzc9q25cOHD+PoXFhmYcYEeAB8+PBhDInf7Xas1+vRMCQ5k8+ePRuzJoURE3ZTzFgCmj5/DQK6Tk5OOD8/Zz6fc3t7O46DJWNU9qMAe0kJEMBureX+/n4E519++eWYKACM0oDVajUyhcd1nZPJhJOTE96+fTu65yXsfbPZjKNmyXGEj1FCAlDl/D02SwljK69DmpvkGAKfGGcltkpYbnntq9VqPH9Ecyns6LHsQjSXwsIKmBdWWAilw+HA5eXlaDySUbvojoUJ/7nb/w9NOX9r5ft5ppnoKI/bdIZtaGEMw8PIWYLtwVuSSBFpRZ7mlJOSOM2Imp5yOh0MFdWe1XrF/tBDCCg/6C+V90zKklgbTk4WFGXB6uGe3hVEJiLLh0qq+4ctQSmms8F80XYtXd9hu47KNbRRTN36gXULkCbDwXR9A3im0wLrAq11TBdLZidnRCah7XrSLMOkOVlZosKBIlXMk5IyekqaBlxXYestzrUohgPb24F1HITnw0UymU5o65bYGUL4GJKqgscYTaQVikCWGhbzApVqIhPobUMSGbIsoW0bul5hTEReZEQ6wjpLWRY4Z1EY0kcdzRB63mOSBOM1bdOR5Rlt23w8nkBkNF3TohjGpb0CpSKmsxPy8pbJvKYocmxXUdd7bB+jVUo+nVDmOV7HNN7SuogmeOZnM2bLc/YP7zjsrultS7W7IdZ+kChkE86WKWf5FbcfXvP69ffsbYRKSpJQYVSB8h1VF4bX2ln6LlBkBkdAGc3J6ZSzxZT5kwva+JSWnBd/97/T7tes33/P65s1r1/9T06XC2bzcw5NwCcpTp2gA5jO4bTndnXPru3pkilvf/wLJJokX+LDnCyb4kNH31fUraN1oNqeNC8xkaHtW+IoAR1IUsPFxZK8LPHOEpxDqQG4OdexWW24v99inSEERWQMSRJj4og8yyEE+t4SO/MI5DyJSdGxIooMcRSDgt72TONhnFN3zcDAGk2SRSSJJjYJkVZ492iySVOyJMPa4Zrte03fQ5rkZGnKbrsijg2LkwWrzRodmUeBPnTdAB6cV4/a4I40jZlMC5QKNG2LCwrnFF3n2e9a6i6gouSX3X5+3X7W9u8xlJ9HB30OII+BpbB9x/WGohMUDaOMsauqGp3Xh8OB+/t7do+tSjLeFYbIGMPZ2RnT6ZS7u7tx1CsPdwmvluxJYa+OAaF8DB/HzH0/yChms9nI/iyXy9EII2yQaC/l4SsMq7BfEt0jEToC7iR+TcCMuGWP35+MVQUoSG6juGYFXBx3LMtoUgCMjNfl5+WhLsYkeR3Cgh4fN2GvBNgKMFwsFkynUxaLxTjqPRwOI5s2m81GACiA1jnH+fk5p6en3N/fs16vx0DvKIpGcHh6ekpZlrx7947vv/9+ZNVEXnXMyMq4V8xDcRxzfn4+dm4LiP7Hf/xHdrsdb9++5d27d3zzzTecnZ2xXC7HTE95z9Iwt1qtxjaa77///hNmT/IgRQMqwEkafo5bbtI05erqajR2CUsu0XsylpcFloBKOYflfJCRtwA5YJQCiPZU5CHi3Jcc0+NOdXmtYgiS816ihuScFsbx9PSU9XpNFEVjxaNEU8nrkZ8TFljOa1lcbbfbke3+udsvMOX8bTAZgjjA5d/Hru+AdHwHiRsaukPAD9mPSZqCSTEmxQdN07SsNjJGM2iTEJmCpJgxWZ7h+pbtZsXmYYW3CmNi1tv94HQ1EX0/RBm0fTeMa/uGyMRMF0sWJ2cAdF1N7wMGjTYxdWdprUInBVmcYeJsYO2so20bqt2WMs+ZTaeUp579oSNOCrJsMsTrPOY6rtuGKFjKYkKuc85PnnDYfeB69Yau87huh0oqiJZsDweS3GDtEKUzLYoBMIaAVqCNRuuAMfHASUXDPk0yRejg4mJJ3XkmWYZrO1Si8a6j71uSNMdZRWcbhhjOFqX10AfuFalS6CgizVN2u81QYThZUrf20Wn5eMJHGc5G6EihdMQQwA4KTVW3hCig05z1tsYxMF5lOeH9+7c0bcvSe7L8BJNEFPmEtFjQTJ6AdejQMoknqDTh4fobqutrdndbzp485eLFc5I4Qscxk3rC9bsPJNmMfAa2usFoA11BHs3RkSPKY6Koo2kPJLHi5fNL6Guef/kFK5uhyzO0mrDvItJsTsgnxJMFL7/+A4mJSMoFmZlwcvmC2ckpvne06zW799+xXr1nvbnHmgdQ4Nst+eSE5enXeCY01qJMIMxiet8SkdC4njwZLv52v2e7fkAHx8nJgtlsOnR62462aYm0w3tLkhsmk5z1usO7QJ5mlHmO8o7YRETa0NsO68NgimkroigwyTKUd3jXYa0D79FRRFe1Qz5qmmCSCJMotAmDFOJwQHlYnpwP7VKNY73u2O5arE/IiogkSR8zIz1FWdK0HdYqtFHU9Z68mDCdpGgi6sZR1w4TBSaTgdXvbY/rPUrFdLalagPbqmdf9yTpz75P/br9zO3njLuPt2O28m/pJ+Xj4wed6B6lh1pG18YMkWcy7hbTgDwsJfRZ3NwSuC3jPammOzsb7tMCHOXBLSBMXodoJyU8XHqdZ7PZOMIUw4cAY2BkC0XzeXFxMTKawvgICBTgJGNvqWeU/SUA8ri+DxhzAp88eTK+LtE9ih5PxtjCKIlp8jgMWwCo1A/OZrMRVB033xyzvMcMs7QDiWYRBuA5nU55/fr1yBCLFrUsy3EfCrCVn3/79i339/fc3d3x7NkzXr58+Ul25evXrz9hi4WtPQ6jN8aMLv0vv/ySvu/5+uuvaduWyWQysuDH4P/v/u7vxtdmjOHq6orz8/NRgvHu3TseHh64v78fgbT8vsvLS+AjUyjaStl/wkILsw1DbJXkTsq5JWx2nudMp1MeHh7G/FVhh4WtFkAp71WOoTD9x73tAuyF6RS20xgzOuuXy+XIyK9Wq1EPKRpYOeektlTe6+FwGDNCj2ODxKA1n89H6YScf+Lo3+/3oxno52y/QEMpehoJLj/W6/DZePvzaCAlA/Hh5x4BycBQWoLr8dbifaDtPUk+ZT5Zoo0e6v9CQCvNvrJoHVEUJXGcEpuYPM1pmxbb9qzu7tAM1XSb3Z40yylnM3QUsasOBK84nS9xxENUURIxjTKsC5isJHUQVERnA4SI3gaytCDTEbHrMZMKVABjiExgOclQOqE6NGRlQRZptAlst/fs1zum0zkmLoEI6pRIG3zXsL59wyw9p2kXaKM5VBV5EqE1GDPoOI2JMC4CAnGk0ebxBhMgKAM60Nt6kAk0LTgPYQC/MGhIm6YlTRKcDWSTjP1hT1lOxtW2D4NOzwcHUcTN/YaqDtjeEccGHzwhKIIf4o9Moh6PpENpTVFOaZxif9iST09Q8YTdzjKfJXz1u98TxfD29Y842zGZ7PCkzC9eEJcnmHKK0sNovtmnRHHAmMCbfcuPP7zh7btrTt79wN//098xmRREcUyRl+zWW5SymDwCk5NncwID2PWhYbW65u7dN8zjnjzPIFa0NrCpLSEOVLZnVpZMypJyccr05JJJ/ARrHdPz58TzC04vX5KkOXXdYJdr8iSir9d0tiJoQ+xabr/7M33QxOlfUPGcxdk5y4tT9g8HsuQMoxf0XY7WPWlWsFnt2O22lFnKk7MFZZljEgUhoe9SCGBDx37vqQ4tWg2yg+FhokY2PtKa4AfpyVC/6DAqkMURwTnyLALvhoVbGOKm6qZHqSGGKDJDFJRSnkgpkjR+PBccq3XFZh84VJ68SMlzTRQ9PigfxfSr1RpFRBKnwzUdLIkJqCLCW483mixNSIuYyDA6dr1XVFXHvu6pe49TEb3/Ndj8P2P7fKz9Ux//lDnnrzGXx5pBYSiFYZKYn2PjyH6/R/q65eEoMT/SeS1/b71ej2HoURSN1YxiMhHXshgp8jz/JKJH/p/n+ciySY6lAJfpdIrWmv1+P7KAMt57eHhgPp+PD2TRovV9z/X19fi65cEsmj3JJTzWzgnzJKyUMIOSXSmgDfgkPqau609qDXe73Ti+FZ2nMMVaa25ubqiq6hN3N3ysVjxu89Faj/rLw+EwGmRkXP0P//APxHHM999/j9QWwtDDLTE7AlBFLyvH6bvvvuPHH3/khx9+4L/8l/8yjmnLshwNT3LsBJTI/rm7u+PHH38co28EYB0Oh3H/S6yNMMzPnj2j73suLy8/CeSWCkxhySWWyHvPN99880ms0cXFBU+ePOH29naULwjYzfOc+/t7NpsNRVHw5MmT8RwWk5Ww9RK1I+yr6CWPj5McN2C8PoRdFGAp16I03gj4Fm2nnEPCVIYwNEfJ3xfnuxwjYWTv7+8HOdPRuSDHRph3kT8IaBWtsYS7i+b5PyU2CD5nHY+1NvJ1Hp3cPE6/H4HjCCiPfhfh8RsfKXEVoU1M11uariPtoGstaT6YcNI0w/YNTX2gdpq2qTAGEpOQLyb43hFFMX3f0NseDVRNRV21j460GZGJSZIM66CcTDBRxG5foYiwKqbpOrwyNK0bHpgmovODLiw2OXGWYF3HvqlRKEzkSRJPHEOkLMF5ut4yK0sityAv5synZ0RBoQxYZQnbFe++/wvZ5Cl7lbF88pTqUFGkC5Qyw1iwH4w5UTSAy4fVA8uTU7wPdH0/mGKcR0URkYbqsOX2Zk2WTegsmEij1VDDl8SDK7htLHk2wVr3SUUaQN20NK3jw4cHbHfHxfkFs+kE2weCD9Rdj40gDZoQWVAORYwLjjhJaeqINJ0SRSX7/Y7gapZzxcX5GavbN/Tths3DFk9C220oFlfMzl4QpXOcDZhIEWUzTl/8gWlccDI9oa42bPcPXL9+Q/bll5R5gTlLCUHTtA3buxXb1w9cvOjR6ZTewmSao/qKIs1AGdabA9Mso24daZzgtMbEKT0RVmeYfMZkfsbJJCOgiaenmMk5mIzOKtAGr4FYo2JDmucU8zkmn2J1xCTJKOYLsmJBlqYEt6bf7OjSHhKDjUqSKEfnmjxLKbMUreNhgYAjUvpRHzmYm4wyqEnJft9TloHehiELVUFwA6s/sDEBYxSRhixNyI1iMskIzpJlMb31xHGKtcMo3fuBNW66A947lI8wOpCYmGk55OrZ3tK0LdZqrHN0nSdNhhgtay1pMjjMjTEobQjeY20/xFhlCXGkcT4iyzSbaoWzHeCHCQMRXdtT146q6um6gI4Nxvy8EO1ft1+2fQ4W/5arW77/b32ffF0emvLQE4ZJKvPESNL3PVVVjYJ+YaWO3b+i+VNKjQ+w5XI5AtRjgCXGCBkZys/J3xeQImNzYRJFR3escZMHpDhspQJwsVgAjA/t1WrFX/7yl3EceHV1NWYRHv+OY9B3f3/P2dnZCLhlVCkPe3GsC2spoEGAobV2jNYR5/nxfVrA0ps3b+i6jqurq1FDKK9HXOfHTl8xZ0gFogAIAdNPnjzhw4cPtG071mg2TcNyueTy8nI8plEUjVWEMsbf7/dsNhtevXrF7373O4qiGM1MTdNwe3vLDz/8wJdffvnJMe37fpQ3rNfro0zkZGQXBZQXRcFyuRw7uwVoHrujhSWW80xihmR8Lc07YiaSDm4xsUgIv5ipZHEh14UYX4RFns1mY8qAvJfjliJhzYW5FoONGLPkHBVmVMbNEo0lzKOASVkUCVMqjKIw3DLCl5G2/F0ZvYu7XuKOhO0W5l/2oUSCCWMqC4ifu/2CkfdPxwLBx0ggpaQp54iNfASTj7+FEVY+InkFmMjg/fAgyycFbVOQFwmub4mTbBy79fWO7d0tXd8MjTPeMZnMWC5PybOc2fKEzrY471ivN0xPSrTSRGo4oJohnLvtexSKoAwmK3FODQaBOCbLJnjdEbzGuYC3nrbtiSLPdDbh5OSS7WY7iGcjTWd7docNmTVkSUp1qEhmCyKd4ELMwQ5jQ58uyCYXnCcZ8brC7x+w2QLXn5OmCQrNpJyjwtBikhiD1hFt03CyPBsSAL0jywqUivBBkSQ5IfRkWc+79w/k+YzT8wlZpohNIDKG/X7HoarJ03OcsyRJOoalR5Gmtx1xbIAMnOHD+w+4XnF1eYF1Cus8wTpMFEOAiAgX3Hg8rXegFVFsSLKMqQtoDtTbe+rqgf1mxWJekmc5m02Fr9fc7fc42zA9vSComFgbrO1oXWC2PAff0exLilVCWx/QNpAVKdFE8WI659W3r3j97Wuq3nN3d4/TmsXylD/84R+IFcSTGUlRsjU5tlpTVR35qUGlEUql7BrYVRZjFW3nqFvHbD4nEKOj4QHlnaPtaqpqzXa/ZnGyoMwNJitwUUq6OCUtSsrFEu8Vfd2QRoblvMA2O3y1xeoSNVnQNBXBB+aLKXlWEscKrYae7cREWAJd60DBdDKFy5jZ1LLf18yXE9I0ZtfW9M4TJ4Yk0RSJJo6GsXduNFGkMXFMCBalFXle4oLmcGggRGy3B5zrmExjCAZne4o0GZkfgifPYjoLfbcDrZnrKbbvx2O92+9QCrq+QXtNZAZ2O08Mh64nTgY2NU01WRYT/JBZqTxE2qCCw3YQvALnifhltV6/bv/+9rdigT53dB9/7q8BUGFcgDHKRmJH6roe+5ilD1iMJzc3NyPgcs4xm804PT0dY4bEtCAgTB5oYk4R0CWbjIglKFwe4PJ6ZEwnWZaiIRMTi+j+pIN5t9uxXC7H33H8HqSNRPIQ5W8LmBSdqIxMh1ayejQfSXTSsXtY3tObN28oy3IEasJw7na7T/6WMFkCDgQUCNB6+/Yt1tqRtRMGWQCxfJ9swpAJWyjAR5iu9XrNcrkkz3PW6/WYV2itHY+PADjv/ViTKM1Ioi2UBcDvf/97/vSnP/HnP/+Zruu4vr5GqaH68n/9X//X0U0ucgNp8Dk7OxvZzKqq2O/3o+tYQC4wxtuI1lOA7dnZ2QgMRTdaluXo/q+qasz+FEe5sNjC0J2cnIyv65ghlEUMMILbxWIxnktZlo2h4XIuyTE+ziU91paK3lLG2seh43KOCRCUxYWw9OLYljG1XLvb7XZcqMj1EkIY9cEyTpdO8mOznYBHWZgcL05+zvYLGEq5AQHITUtA5sdQ85++mX10eR//vhACwXtwluAsu/2WoAIoiExEGhePwkyN6x2ub1G+wzY1Wkd4G9iud9zeDgzeb776iiTLODQVi9OEp8+ekaYZ9zd37NZbut5iYjdoLJ2H4OhDxLbu6IOh6T06ifBhYGGKLKPvOg5NS31oQBnyYk5nI1qXYhUkicYUBUEHGuvprCKoDKUdJp7QuEDXB9K4RKdz6tWKdr9jff2Gs8Uz2rohS1P63pKmOV1d0XtHksSgwYVhjKG0Jkkz4iQb9t1jJ3PdwHJxwg+vbri+WUOsOT3NhqDzrmG7WdE0PbcqIooUzlmms5IkiUANZpY8n5GYnOAj6kPHg96y2ze40A+5k13AWYhUSqw91g3yBKPBumE8vt2tgZos74nVwGpW2zV5nBKFiCgYpvmMza7DdR3tboPSHpMkqCQjz2JipUmjGBOd4uY5Blj7wOZ+Q2wKypNTyvmC4DSrmzVte8/7V38mnxecLxO63Q2JyVksn9ColGJ+Qas167trQrwhDiUujojM6bAP/cD8VYeKPJ+QpBO0CnT1gXdvf6A6rClih+0rjIZgDL2O8FFMOVug4oR0fkXTenCD2ar3mjTLabuaTPc4V6OsJo4zirwkTtJhkWMijHlk+5UnjiOsdTT1EFlVPjYDoQKRUiilaTqLx5OnhvksJzGBSZ6DtYNcIo5ompagBgf4fnfg7nY1MMe7Ch15ijweFkp9h00i8NB3Hd4PutnIeOq2JqiIsrwgMQ7vHVorYhVjvcV7S9dbsjwdtUNdt8fjUXHE06fn6CgiSSJsN9yQVPDDmN5DpGLKPGZSxj//9vPr9rO2z8Hg8Xj7p0bgx9vnZpzj75fuYYmxkU3MJPJzwjwKQyXhzQ8PD3z48IHz83N+//vfj+PJi4sLvvjiC/I85/3792OYtTz0BMDIKE4czcdVcgJqBQQJMBUWBxi1d8LyCLMlY2r5PjFE3N/fs9/v+fDhA8vlcqzHEzb2OLJF9tdx2LrEtQggqeua09NTvv32W96/f48xhvPz8xE4i5lE9JLOuXEUD4wRSTLilnGraCqFrRL9pex3AS4y5pRKQAFconGVvwODBk/yLHe73ThKFT2jANMnT56MTvjb21vu7u5GQ4jkOb5//56mafj222+Zz+ecnp6ObLN0pguwv7m5GRk0kSsA4/GXmDvZ51VV8cMPP4yRSRKRJKD6OEhfYqgEpIvuUcbjIueQsfAxwyhgEhjPlaqqKMuSyWQyticds3zAGH91PGoWcHocxbPZbLi+vsaYoXtbaz2agYSBPE4oEHAq4PZz2cmx7EKkKaL5lNemlOKLL74YAa4YkuR6l98l7/Hnbr/Q5S3jbdFNfmzDefzqT3z8E8xm+LjyDSHgrCV0HW1TEQ39H+x3W6ZFgYlj6kPFw90dqw9v2a7vqZuWJMlBG4pygvVDj3ZeTDg/O8H1ljQvsF5RpjnL8yfc3W3ZrnasNxsWyymtC8T5jCibUEzmoBPa+zW27cEHmqbCAJHWGAVOearDjt02w3uNimKINElhyGclfVsPY+o00BGBSqjrlk71ZIUiTQ2z+Sl2t+H1q1eclSeUeYa3Dm8h0inODmP7NEmJTIR3/eN+1hiT0LY9zkOeD5VeJk6ZTAp6a9Em4uZ+RbEoSDKYz3KyPOf8PKLvA6v1luvV/f+XvT97kiM7s3vR396+fYw5IjORmRgKqCpWcehusVvHWqZ7TSaz81+f83LtyvQgU2tqsdlkkaxCYU5kZszhs/ve98HjcwTQbKloh32fys1IoIDMRIS7h++117cGnGuJ4s8IQ7+rfqTTXdZ1JxRHadrGkZcd8NzuDuz3OYVpCAOPIAkx+qh9tR3Ij3zI6pS2XlKkawJtMaqmLnNooa1ABwGjZEQUBdxv9uy3GWmRMxjH1L4iXIzxoxCsJY59gsEc0zryNCMrGpQJQHfjbrB89tljUBVpfs/F+ZTYc9y+fcFi9oA4HuIlPlGckESX1GWBayuqw4pCNwwvx4S+xrOK+WQATYlyNc6WrO9ek2c5r158i1INDx/MGCcB6WoFWpFXDZPpmMF4To3Bi87xtIfPjLbYssyXLAYhcehTUeI3Gb4XYD0DqkUri8OiVMca4DRNXdM2lraF3TYljFsGg4Qw1GRH3VnTOoqyIgwCFvMJD85naBrO5nN26y1Ga+zxYaO0wbaw3+VsNzuCyNG0DUZ1dZ1xGOBFijDwaJoa29YoQKsuB9O5lqKsQIHvGzzjMUwSsC2r9RJHJxlxrkUpfdQG+ZR1TpwEBJFP3VqaugFru8+Q0Si6TVwU+IwHMUny48j7X/L45+J//hgb+cdA5qnDW1gMAW7ytRL+7fs+h8OB29tb3r5927NWwloOh0PatuXt27c9QyejQhnJXl5ecnt7y3K5ZL1es1gseoYmjuM+kkf6moVxEhAhoFlibaRHWSKCJpNJvyjLIq217kfj0sIym83Y7Xb84Q9/YDgc9qP601BsaS85XbwFfJyOLOX1iybSGMPt7W1fHymjWTkfq9WK+/v7fiwpDBfQ6wwlW1GMPOPxuA8HlyB1YdjktQnDKQ7rw+HwkXFFwItUPYoOT6KCRHt5cXHRXzMx7QjDJvKGUybvyy+/7GUNl5eX+L7Pq1eveiZRwI6AO2EbAa6vr3ugO5vN+vFs27a8f/++z8gEePjwYZ8YIOH2MiYX45W8XzGcCECX9y73CnyI8hHHu4yOhSVfr9e9hlg2RxJPJCHqZ2dnXF5e4tyH9h0BfgJYZfy+Wq16dloA42kWpfy7co/LRktG8LIBk+SC5XKJc64PQhfAK53hSZL0ZjCZFgiAls99EAR9veQPPf6kYHMBk92Jlt3shz87PY6PKXCq11P230vHvCmluiBk26KweLQ0tiYwiirLWGUHPK9jVN6/fUddFnh+hKocd8sdZd0QRjv88BiVUhQcHpwzXyyI4kFnIgem8wVPP/+CN+YV6WFNmmVUFpY3KypnuLz+jMXZJcMooqkbPAeD0OOwvYdjr7htLGEY4aoC66DMS1rncDYiCg0OxyAZ4CWG3XbHbD4j9BVZXYCqaOuKcJAwmZ1x9fgZ08uHjMYTUnsUXVtorCXwfaAzUzR1TdO2hGGM7wc4GtrWdgyXNlRl1dXoDUKm8xHvb7fUjSXNS4q6wQ81URQQxQHaGKxrqaqSwA8wXgB4+H6E1obyyFxOp2Pa1lFVDWXlaK1mtUkZDQdklSWoHHHcdZC3VY1yDbau0TYn8ipWq1ecz8bEYUQSGQqjydIM8JmGE5LJnIezS7b7LavNPfc3dxTpku085uHlnND4eF7McDBG+4bzq2ve3bzj7fv3PAoDDst7PGAyjVmUU1b7AUkcd65m7bHdrnhz857F9WdcP/2SMI6YLubk2zVFds94GhDYDaEboFxOk20IDIQ6JN3f8fbdLRrLk4sB4/EQo1rqNIU4JK8sg2SM8WOs8zAmxjkPVIjzDDbwyG3DrnK4UGGalrbpWHFnW5qmJPA9lFbUVY3SCmUMdV2B9bCNR5E1pIcNVZkRJT5YupD3upNCaK1YzMaMBj6e5/C0xTca2x6z57Q6Skhgs9nRWneUOnjM52MeXMwZDQxR6BF4sN2s2RYZTaOoK9uFndOitMO5lrYF33g0dUO63+Ibn/Col9sf9ihasqyiqpruM64cYRhgixpLi3MtYRiAa2ibHK0aggA8z6J+9OT82Y//lVP7fzUK/+eApSw0pyYDGaf6vt/XzslCKY5hAS+3t7fUdd2zW9CxbcvlsgcnsoAtFgu++uqrXjMpBp1Xr17hnOtrAAXgyThZXOMydhXNn7SOCMMl0ULCKgloldcKnaNcmLRnz55xfX3dO4JP2S0BOgKyT0eTAnoE6Iq+bTAYcHZ2xrt37/psQ2GgRLsnmkcx1wiYPAUg0+mU+XyONLrIaHO1WjEej/sMQQGeUh0pY0xjDPf39/14WYwZkoEojTGz2YztdstyueTNmzccDgfOzs549OjRR4YnYwyPHj3i9evXvHnzpm85Ukoxn8/JsqzPupT7ab1e8/r1ax4/fswXX3zRSyG22y1pmjKbzT4a2cuoWmJwXr161WtbhSUVBvOUzYUPHenCSEsgudynAhTlPhEGXMD2aU6ksMFSHSmxQ/K9pyyxxGLJ50eu7Sk7LpmYIlkQIHp9fc1wOOw3FKvVqmfF5d+ADzmkwlxKaoJoiYX1FFB/ylBGUdRvDuWzJBtHMRD9KfpJ+FMYyv4Z8+mYW/70+CCTakUBmqdg8nQFcR8Ulc5ZimxHdtiQHzZoW0NTMRoOmUxmJMmY2XjK/fKeKArZbPb47+7x/QAFVE1Jmu5YL+/Y3d5ydn7OV3/1lwynMxyO1jlmiwXpIT02wtQ0TqH8nO/f3PKbf/wN49FbtNK0jWU46CqqDoc9YegR+h5RHKO0h09NkkTYuuKQZti8JM89wiiiQVO0Fa6tiIIRzra4tqBpUpqy5t3WJ/AMj7/4KWY8xY8TTGnAdpZdT/sEYYB1Nda1lFXXdpPnBat15zyLk4SqrjtzjtZobVG65exszHp7IK8KdO6R5yWxF9JWJVp3i/rFxVl/07atpSxqcB5N3TIcxuAM2+tL6qZlOpuCBqUaytqS3W+I4pAgMWijCIw6sloW1xRU2Q5ja5osJfcgHDloG3yj2FQpaZGjkxh/vkAFhllySTwYsrwLKIOAprjnzXfPmYxGjCcXRH4MWjM8WzB1lt9/8zvul0uGw4TJeIAxGhMZFudnjEYTksHwKAfQ6M2G19/9I2m65erplyjncNQEpiZ0O7K778i8AFUdeP38H1lMR9jqwKFu8VvLZDJgMdaU5YbVZotqcwKtGQ1HLB5+QUHE3WoLqiYcjKmLlM2+JYxHJNOHKJWx260IqJmPvOP1dZ3ut64Io5DWtbSNIwwCfGPoPCwKTcD2sKd1GU0bMhpNMbrLnGtai288ksQHapLIoGiPoEFj226E7/seRdFQ1w1JEhNEMZPxmPl8yGQao11FU+eEJsAYTeB3mZZlmVFXDUHgo00HDuumpiwrAr8zFcwXC5rGcjhkOAv4hrq2VGW3+bFti/KhPd6fURiQRB37Dg1KNxhj8Y3DNz8Gm/+5j/8VG/m/+55PR+Sn+kmgHztvt9s+X1L0kbPZjMFgwGw24/b2ljiOWa1WvH79uh8ZSqj17e0tNzc3XF1d8Td/8ze9Ls45x/n5eV8rJwDW931evHjB3//93/cspej1pLdaMgBlrA3d6Fb+TWGQxDwk43thKk8XanEzf/3114zH4z4kWgCO53n9yF3A33A4JE1T7u/ve4ZP4nzElKO15uLigvV63es9RcMnDFkURVxdXfUA9bSNp67rnnESacBisejZp6qq+tpEcf5KjJGwWgKwBeQBPRgRNjKOY+bzee8YHo1GvHv3rj8Pv//975lMJn22ptaa8/Nz2rblH/7hH7i9ve3zLoVpE2e21D8CrFYrvvnmG/b7PV988UV/DwiIvr297fWvv//975nP530WqXNdDuh8Pqcoij6+xxjDZDLhyZMnWGu5u7vrJRBFUfQO7vl8jlKqHzGfMqCi2RR9oQBM0bIKKN5sNv2oXFIKsizrz6fECIkmVD5LAj7DMOzBv4DH6XTK+fl5/5k4reIUuYNEY8lrkk2DREdtt1vOz897eYqARfka2RwJmBbmUtz2cr+cRjz90ONPc3mfjq/dKaQU9vL0D08NOB9/r1Ou65BuLU5Cb5uSLN1ze/OGcexjlCXdbThsNxgvpG670XhZaQbDMZdXAcrzqauStq0YTwa4YkrgHDd3N/zH//D/4afbv+Lzr79mOl0QmIg8T9Gex2g8pEUzOY8ZLy7ZbDPa2hFFIU1Vs98fyNM9bV1gjc9yuWYYDxlPZqTlDlRLWeckgyHaNbSto84Klrfvubl9zYMHC1xTdvVk2QrjWQa+T6xDzs8fML26IG0qwrbEGB/nOr2CZ7wOYOPwPJ+qaTGepagq/DDChAPqVlFmnbbR91UX+aMbFosxy/UOa7vcvyzNmA0jPM8QBiGNtThniaLw+PBpcK5jtKqqQCkYjyc8efKY/SHD87sNgFINgfFZ7nJublfEQ4/haISixfc9DrsCXac0dUEcBIyiIf/497/GaIiikMlsxmicsN6ntJQ0LqfKLQofozyuLh9SDSPaasj2/iX3tyvKwqE9g4ljkvGQs6tzyqrkV//9f5BnMa0tOTufMT9bUJY12oTEg2mnx1Qek9mM+WLO7f2ady+fE4URF9OEQ77j/t33FE3ANs2JA8V+9ZaBOeNsluBFBm86Ik4iQt8jzy2e7+MHHm1VUBUleX7AepbN8gbP+MymQ3arnKJJGE0XJIME40KKIsUVLW1Zof0Wz+uqE5sqw3htl21a1530wBjqsu5MOaMheXkgSTyUsgRBiHMcHdgNnnH4odeZrjyojg+RPKuom851HwQJy3Xn1kzigCj2OT8bkyQezpZdS5MHZdVllQ2HA8JIk+UN99tt93CLDcbzaeuasqrwTUQySAgCn6ao0NpgfL8z29CFqkee7qYPTYPngedpaDsTj3UNvg9hAElsCHyF0T9SlH/u41O28Y8d/yv95On3y3hNmDnoFrjD4cCbN2/6EOzNZsN6ve7HweLCHo1GPHr0qHeFN03DdDrl4uICgHfv3vF//9//N//6X/9rfvGLX/RsocSniJHi4uKC8/PzPlRbFkcBtgLGbm9ve1ArWYtVVfUaN2Es3717x7t377i6uuojbMS8I4zQ5eVl3xsto2phuWSBFRODOKAlt/K02vY00keA13K57Jmw/X7/UdWinGvROAp4kt8rpZhOp3z++ec9EyWHaCTfvXvHcDhkNBoB9CDj1BWcJAn/9b/+1z4uR3SPm82mB8qi0wR49OgR+/2esix5//49Nzc3PSiWzMmHDx9SliX/6T/9p75a8/LykouLi/5rR6NRz7YuFgvOz895//49z58/76sPsyzj9evXPbiPooj7+/u+denUjS33y2kovRh85J7wfZ/5fN4Hkc/n814XKAyvMMKiXxWH82lwuATsK6X6usrTGkfR0UreqmxyhKUOgoA0TT8y7UiAv4z/Ly8vSZKkZ7jF9S1GnSRJyLKM9Xrdb3ZOg89l7C2ufnGry3U8HetLAoEwpcLAymsXLem/iMvbKYdyHyr3JKu8n3i7Dkz2Zh0BkUcTj+Mo+FSqc4IfwaVt22OEiUcY+cRxSF3n7A9bXFURxxHWOvK8Qnsh+7QkHk3RJiIvD91INwmp8hqigFEYECSG3z//lm9/+z/RVFxcXDMcTTotndbEoyHD4ZjWGcy+4PrRQxyGqmpo65ZkOsUedykKx3h+Aa3DeIb3t9/T1Dnx0KeoDOnhgDFDBsMJSvmMxzPCKEEbw2g2ZTgbkSQB8/EYz1nm00lnnshqdtv3xCNNWZW0dt7lC1oYDhOKLCMMErwgQIWgzIhdrjikBYM4Igg8qjxHK4fxDYMoZDKKqOuGINDUeQFWo1QAWmG0xreSzt8J5y2Otq3xfUXTFORZC0RYFOvNkjjpqv9m4wFlYcnTkvVyzWSo8BKfJk9p65byONJxDi7OH/Aiek6RZRwOOXnV0KCIohFFUXPz+hWD4YzJZMFwEBMGPmVQ4xETBl181G6zxru/YXq2wAQQRTGfPXmMcnD77oYwjIjCiDAIMUHE7f2a4fiMJJ5QFDWN8pidJYzHZ3z37Xe8efmCw3SAcy113XSxNVpTNY4i37JeNVxfLkhGU/A1QThARTHUEc7lVLZmvy+xdYG7e0cYDxiYkn26BfuIwSDGVQZPW9SxQlQraNucuk5xlYFcEYchrqloy5IgMORFTlXH4AKMCcjyAu37hLEmCiO0UYRhzD7twH9rG+I4YjiMCcOWti3AeF3Hd1XRWkcSBDgcWXpgEIdoDdEgIIkdnqrZb7cMBzHBcfxogpDDIQM8kmFEkhSkuSWKhmRZxSCOqGuLwxFEAU51cVZta/FN98BzR52kUhqjNW1TEfhdZm1jWywW42vCwMMRMBlFaOXwfzR5/9mPT8Hk/2rkLSzkP3ecmnlOBf+ymFdVxX6//0gLKYuYACUxIMiC+ale8ZtvvuFXv/oV0GnmJpNJb26RTElhKT/77DOAnmWZzWb9Qgr0kT2e5/Hu3bseTIpmzvf9nuGU6BvP85jP572zdz6f9+N30dfJ4i3aSDkn4/GYNE17E46YiSQGSRZkGSWLMUh6wcMw7J3FYugQ3aOMW2VcLYyStLTI9Vkulz2wmk6nffXl3d0do9GI0WjUj0pFEuCc4/Lykt/97nc9wBEWVJp3vv/+e8bjcQ++BADJSFxrzWq14v3791xcXPSxNJ9//jnOOV6/ft1rI+X83Nzc9K5ruQ8ePHjAbDbjt7/9Ld9//z3z+bwH28Lutm1Lmqbc3d3x8OHDXs8pP1uui7WWzWZD0zQ9UxsEQc+mi3nrVL4hJi2JwRK27nQkLkAc6O9nAeJyD8Vx/FFfeZIkjMfjfnMhZh6RJwgAlftEclslSWCz2fRMN9C/D+hMOMKIy2fqUxkI0DPgkkQgnw2RYohRS6YHopuU7z+tEf2hxw8HlPS5QD0J2dtunMNJQJBzcII7+9+I5tKpDmwex4C4Ftc2aNUxWsPhkM3dgbqqiXzDZDwmzTLqssFTsN+sQGusKijqmqoOKSuDrUtsXeLqCs+DiwcXrNdLqnTPbnVLW+U0DTR4eCYgTQuS0aTLrWwdykRoHeA8TRKPcLYhquPjhWhJdwfCMOBf/+2/Js0PlHVJaxXTc8NwOO9CvouCpq2p65KyzBiPu2T61lm0P6IuM/ZZyTiJaIqSnIbJ/JqqshRlwWQwxbqqc9UqzWg0pXYKqzwaRnz/6h2r5YbhMGY8CojCljj0GA5DFA2L8YCyKpnNpmRphrXQuhZXWQbJEZgXBVVZUVY1VdMZLyaTQacLLWvWm4rvXrzCCyuuHoyZjqaEvk9T1hRVyX5vWd5bogdzyrQbBceDAVUR0jYxZqS4fPYVd+/f42lNECeYMMYECe9vb9nerNiHKav7ZRdaO5+isSSBx+L8nCjyefvqOVVd0lQ5rvJptUIbmE4neNojigy+rzB+wHx+RlG21E2Lc6rrYz+OsQax5uF1xR9+81tevX7J0598wYOrawbBgPPFlPSwosy2YB1aeXg6IBot0IMFrUkwtib0KzbrWxpviEdBnuVslrf4xuDjsd8sGS2eUDUNrtyhzABciTEtYaKo6j1UHnEU4RqwdUFalIynIwZxQFMWGOOhDBgfjFEMBiGz+YymsSjlUdcZOIvWjmQQEicBZbFlGMfE8YCmSmls20UIxdFxHFYwHA4IQsNgGDIZdXoYj27R90334PBHIVXdkqYZdVNR1SV17Xj75g3n8xmT4RlN3WKtQ2tF3dRdh3hV4mxLGAUYT2FtF2tVVy1Kc1wgNUo7PK1wnmMyGTJsHaNBjKPtLN8/Hv9/O/4YCyn//c9FCp12AAuoFJOJRAP5vs90Ou3DkD3PY71ef+R6lQVaXODCjlxfX7NcLnvAcKoPE6PPeDzuF+JTxkVyJE97krfbLVEU8e/+3b87CdXvXv94PO7jYWRxP63eE11oWZZ9N7SMuiXqqCiKHrjK+ZhMJn1mIsD333/fAzpxagvjCPSRNYvFoneuy/kXg1Oe5/04VBhfGXlKU8of/vAHfN/vdZ5BEPTfI40/cg7FmCTgcjwe8+WXX/L27dseEAnwe/v2bd86I+Pr8/NzoHMuX15eEscxz58/74GfALUgCJjP5z3jetrnXvTEg+szIQV8PXnyhP/5P/8nz58/5+c//zmPHj0iiiIuLi7Y7Xb9eRKA02VLD/q2lyAI+p5v0QvK+4duvH5xcdGPs08bjWRzJGyryCHyPGc+n/ftRjL+lVHwcDjk7OwMaZgR8C9h8nK+pXVI7nsZhy+XS8qy7IGnyARO34NEZImLXTZwcs5fvnzJ+fl5n+15ypo75/r7X/SUp1pPkWHIBknOrTj0heH+X206Pz1+MKDUzgEt0oKjRBKpFK37kDXpoHPCiKqyE0nisGi6qBoFWGdRrivMtq2lLHLc8Y3fNxbjh5yfX6BRxwWrZDCMuH74gM0hRwcegW/QSmEbh8KnrHKUazBGcX5+hTE+vh92zFDd8PrFK1rV6UqKsmaQpaB8dvst+6zADxOCKGY0HBInESYAbAO0eH5L1ewZDKf4rUc8OMMLIurWkucFZZthQh/VGqoG2qbEVoo4iUFBlbV4KmC7OXBY7Qgin/mDC4aDQdc7rXU3TuzbKFzXEZ412Dbh5mbD73/3Gm1CmqbTPc6mIZ5WZGmO9mA8HmJtN5Zsmxrjeb0pI8tSoDNsNJ7F82C/2tE0FuOF+H7XELTbFbx7fU80UFydXWBMQhBVRHFAXufUjaOqLEEQkbstrWtRyuH5ETZ2BOGYz/9yyuD8hiAICKMh9+sdN+/vmV4+JRpOuHv3hmK/Z7vbcXv7lskg4mI+JpxP0K7lfL6grisaW1MXFaEf4keK0Fe8z3bUtWFxdsZgMGUyuWA2X/DixWvS/ZrxZILG4CcJVVkTjSZcPn5KlO346i9/ydn5BYNoyDQKuX37PWfzS+I4YTC+IJ6eE02uyVSEMkOiEURJi1aGFI8qXeK5Am0bfA883yffvGcyGrMYjGjcgSbNCAOFtgXOOpqyaz2qUk3tujYkz3ek2y14Ia0NmcwHXSyPf9TJILEVnUwhy1KsazEeeBoUrnP2ewPqpgGl0Noj8AOU0hwOe4zxiKKA8WSIdSX7/Y44jpjP532HsOzqJfon3KX84bu3BP7g2IbTVS5a2+KcBc0xQihA6+7eds6y3+8xxifNDkf2pcLRtey41oID3/OYjkedqUx3mrKmrfnx+Jc5/pjD+9TkcLpInCZuiOFGFplTENq2bc+oyQIZBAGXl5c9u2atZTQa8eTJE7bbbR8oDvQLr4xxZbQslX4S9izOXXFMHw4HlFKsViv2+31vYpHuaTFdiO5LmElxqQrTKGycmF1kga/ruh/fSwf2arXqnbeXl5f94iqg6dSkc9rv/fLlS/7xH//xo/H/bDbrWVtZsEVDJ+N00eLJexXgLCN5GaOK7m2z2fDixQsGgwFXV1c9syTgSICTaD2F9RRQGoYhf/M3f8PFxUUPeMV8c319zWg04s2bN2w2GzabDe/evWM8HnN+ft7fF1J7KDpPOa9BEPSbiwcPHjAajZjP55ydnfHdd9+x3W57jWCSJD2oevbsGfv9nr/+67/m6uqqd5wLaJLAcjEMwYcRrlw/afER4CTgb7VaMZ1O+01Imqb9uRCQLsYkuc5SEyojYckZDYKg14EKW3uaJSm6RrnnBeDJ6xNWUFz5cRz394SYl+T9ChsvoHA0GrHZbPjd737Xf65O80mFhRQ9qZwbybiUDcZ0Ou0B8Gkbk+d5PfMv2l/RVf6Q40/QUIp70KFRqGPdokPheRp3zJXspty2c3cfaxDd0arq9NEdrrpfPe2ONYzdGK2ua4q8wNOG68fPMFpzf3eP70dMZmd4nsUddjhbEfpJ5wtvoK0bzi8umM3mpIc9dZVT1fDw0WeUZUqRlYyHI8ZJTF7VxJ5iOBlStzVVW+O7mnK/5v79W5rWkYymLM4f0LYlVVmhdacRjUOfw35DmpVMZ1fMpgu2hzWb7ZI4GTAeLwj8EcM4JjEedZFB06I8TZpmYBuKdINrCpTRtEGEPzwjGV7g+ceQ1rYl8CXhviErWl68vOd//ur35LllMj0j1xV10xDHhouLCZ4qaZoK61qCMKAsSlCKpm26/m1UB2Q8Q9Mcd9bKw7aaLC1ZrQ+MxyG+nzCZDJlOJ9RtgVI+ZVlhaQgij3bdkKUt1g4BQzIYoNqCKs8J4wEt4JwijIeMW2jqtqvaaxSzB49JQp96OCAMA9qmJg4DVvfv2C7fk29ueffckkQRs+mMQ3pgv99hfA+H4+LBA0wYsNvcs08LdvscbUKuLi+ZT2e8/P4Fv/2H/85iPuPy0WcMFuc4NIHv8ejJQ346/xkPnz5DmQBf+7RFRmB8ojDG4jGcLvDiEa3yKBuHNorWWtqii+pxg5gkmJCtc5y2tHWOpwP2yx2xb3j21S/IqhZLF5FTNAWBF2J0yyAAbI5WEcbzaeoKpxWB72GbzsASRRqlQHuKKAywtjm6tj2M0USBB6OYKDy6SBtLVbVUtqLIKoIwxAR+nxs3n09RGqCLIKqqku1mS+h3rRGnfcpNH4niEQaG1rVMFhNm4xFhYDqbOeJOVMde5JjRaIBSHll2oCjz40Otc7Zr5aHQvaxaAX5gOqf6EazYH97o9ePx//AQsAUfgOWnzKT8nbAW8n3CXgC9HlLMJI8fP+6jfISdEpe2MH4Cguq65urqisVi0evx6rrm6dOnfb+wtKAIIyRGAWG29vs9b9++7XMaHzx40IMned1xHPdu4cViwWKx6KNZpBXnVCMmI1OtdQ9GJFBbAImMjwVMCjAVhjTLMr777jv+y3/5Lz2zJUaZJEm4urr6aLQohgwxy5wad07Brji7xfAjRpfZbMZ8Pu9BuuQaCjAUcAP0kU1iuJHrfwo+pcpRgNx4PO5jbOI45ubmhvv7e1arFb///e9JkqS/jtvtth/hStSPvAYxOD169IizszO+/fZb/tt/+2+cn5/z2Wef9VmUQRDw7NkzFosFX3zxRc8EirZRxrCyIQY+ymiUqB4Zz69Wq/78a625u7sjiiJ+/vOf905nOc/CeEvLjDB1wnzLhuS03edTzatcNwnGP3VMizYxy7J+gyVmIAmNB3pGfLVaEQQBs9ms7xWXjdJpZJXoi+V+Pq31lNG9SCzk3pZ77tRtfmrEk42h/Pm/GKAUrZSzzVEP2d2Uret6r30/6H4PYBUOhz0+sHQvumywTQdAjaexTQu0WFdTNzW2aTHa9MBHGU1L16qR5SWDRDMZDfGMoW5aTBRhrQfKg+MDczAYUXgdharQrFYb0u2W0ddfd86ntkU3ddeJ7MAPAgIT4ZsLtvsD96stZ/M54/GMNN3jGqibkqIs+0L29+9ueXB2xdPPnlA1KY2rSDcWr62wUYltDU1dkR62lHVNEIeUVYVtalZ371gvb/npz37KdDohOWow5KJq7XXg2tbUTUWelbx+/ZI4CYiSgMEgYJiEaNPgB4o4CQmNz263oapKlIIwColUF2KulMdgENPUJW3bAIokjsnzFt+PqOuc9WrHaHKGH8BgoPnlL7+irArGo4iiPKCwBJFmPBljbYXWEXXdMdCe1zFRtjVY1403jedI4oB9vScKPMaDiDAagm0xLmQyfkxR5CShz2ePLtje3bC5e0u6WbLbbnj7+g0m8I8fsC4aab3aMJ6NefrkCVnZ8ubmjv/yd/+FxXzO44dXKFdDW3D37jVhaNgdtoBHud+THQ789fX/QWQcVrdUWU6+WbPf3LPZLllcXOL5iqot0a5EqRbXHlDWQpuR7pZ4tiQKHHpgaI3B1gpPtYxjw93b74lCH+0nRMMx2a7Eqho9fYBvNE2ZoXWLCQPqqsIp8P0QayE4LvLdLj/HMx5+0AWGyxjaeJrBwCdOAkbjjhU0xscYn7qEqu4MUm3T7XA9owgjH88o4iQCGozxGAwH7A8HyrLqNVNyuOOk4OJixuFQMp0mDJOQ4SCmyDt2o7WWwI8+cv6Foc9oNOTt23dHxqLqGyI+7K4dxoDCx9PSpNWx5D8ef95DFotPzTUCPITVgH+aM3n657JwCdshX3c6rpbFRhjC08BnCZQWE43o7wTYSke0HPf392w2G/7yL/+Sy8vLHkR+uM/CHtxtNhvu7+97N6yAP9FT3t/fs16vefPmDQ8ePODLL7/sF3WJaJHgaDF+SAaj3LM3Nzfc3t7yy1/+sh97CvA4zbeUhTnLMr7//vuPwq6lHlC0k8J4iebzNE9STEzy74szOcuyPtZIAKX8vL/927/tY46EIRWnsMgDRD4g5+50LCrB1ZvNpq8FFGcz0OtZoyjis88+4/b2lvfv37NarViv17x48YIgCJhMJr27+f7+nvl8zpdffkme57x+/Zr/+B//Yw8gRSrw+vVrwjDsQ/J3ux37/Z4nT570gCZNUzabTZ+HKWz2qfZP2Lu2bfvsUQGWAsLkfb569ao31wjTBx1IFamDAEUx3wiYNCfPacnb7DbWST+GFkPMaTPPaTapyEMk6P/UBCZZj6Lz3e12fQ3i6fRA2Oarq6u+oUc2O6LVlSSX0+d0FEVMJhNevXrFgwcPPoqrkirHU0b3FJP8y7i8rcW2TdfRa7t8ObmgKA89GNK5vQXZKqzr8iZdvyMGZ1tQHnVdUhcpVX6gzA4o52iazl01mUxQxqOsKxZnCzwvwA8NbZ3RtF3H8CDx8YKYQ1qg/ZC6KmhQlHlBXefMJmfEcQjOstqseXdzw/wYJlsWGVVdkhc1GMNgPCWJDGE4YZAMCJJJtwhqw/niHOssyWBAXmY0dcO3g2+4ffOau3ffkwwjTKBpyorDekmmM5TqXGxptmd72BNXQ4zvYzzN/GLO06fXfPbsKckgQR8fqsY3tK3F+B8WgcA3xFHLxfkYP0iYLy6ZzSa0bY2ixtMNgU9nhGgbPM8cR6fdw6ILWlUYHzwFjhZrwQ8jlDJMpxOW9zvulnfMLwY8enhNmbQYL+xGmGnK4c2KrKwIIp8wDiky22UWHttWlGkIo5BsV6BbB21GXdRQlqzefU96yPGDhBQPpT2KMkV7rgP0UQSRz2g4ZOBfET26Ji8yfv/7b1lt1njax/cj7ldLXr95xwNr+fInP+Hxsyums3P+w3/4/5Jut7RFynff/p7LizOefP4UqxRllVIVNbu7e4o85/vf/yN5caBuFbvNnny3ozxsePv2FbVrePjsM6z2qesUq0Maq6jKEtfUVIc9ri051DmGmkDVYEua1lGVFe/fr0AH6CAkiGPiwYDRdNx1tEczoiimqugY02iIMopoMKJuHHnmCLWHw1IeI59kgWnbhqIsiONug9A0lsB0Aeae1rTWkpcdG91aS56m7HZbRuMB1taEJsC5FpkuBEHU1SG29qMFXpgTz2v57NE1aVYQRTHG+FjbALa751THJJRlQZdRWdM0ptdgeZ6iaY6tKnXbjbeh01QqaNujbkdpiqJE/RhE+Wc/ZMGRhVYWolPnNXxgHmXhgI+Zy1OtlbCHMg4UPaSwkTLaFMAkTIqwgFJ1eOqSFUZsNpv1WZTL5ZLXr1/3BhmJRxEThISAR1HUa9QE3El/9KkJ5de//jUvX77k9evXPbtYliWr1apniIS52R7TDWRse3l5yZdffslPfvKTj8CvmGZO43hOo3HCMOTi4qIPZYcPY/JPR5ECnOT1yn8DPSiQLMfb21tub2958OABn332WQ+YpLZReqVl7C3jfWHIpAFINKHCrJVlyZs3b/roJaCPvxHQLK5q0So+e/aMPM/59a9/zXK57EHz3d0d33//PW3b8rOf/Yyf/OQnnJ2d8X/9X/9XD6R/85vf8PDhQ77++uted5jnObe3t6Rpyq9//eveIS4Ezn6/5+XLl1hr+fLLL3vXtLC3os2U3NJPMxpFD/nmzZveWJYkCcPhkPl83oecS2uRUqoPQpfoKTkf4uSWaykATrIvgf5aCustfy+fsd1ux2az6cfcsiGT+0k2TyJH+qfPaY9nz55xOBz67FNhJ+U1CQMuzwMBvPK+RKog2mRhI2WTeBoC/0Oix+T4wYCybRrquqSpC5qmom1qtIKmqbvxlq2AzqVsjOmYvyDA0wqruoq+1nY0dWtbqiLDVgVVfsCWFcZB4IeU6Z7BaIhtKpxrMYFHPIgpm5zl7YrQMxjlOpHuIaNFUaQp49mCuqpQriQONa4tefduDcry81/8jPl8wft379nt9wyiGM+YoyeoPXqILNoYkkFIUXURN21TU9gKzze0jc9kPKXIS559/ozzRVfTNxyOMUGItY66sewPWfe6Q5+zwRlXjx+hjY8FAj8gMIqzxZTBMKHR3Th3t9sxVjFhEh5vwAblXGdUCjRfffEEiyaMEqLYw9oW3zM462jbitpC24oDeNCPo7RWBIF/3Hl43fWhpWly/GDEYBgRhD5FXrFd55RlQ9sWhL4DV+NshXOK/T4nigNMEKKKlqKwNA0EvsHzWoz2AU0cxeSHA/vNEltVFLslu+WKLK9wyiOOBxzynMZ2D1rf8xhGIbNRzCTyITTUbcPF1SUPP/sMaxVZVjCcLkjzlPV2w29/9x3Pari+vOb/9W/+ltXyjodXDxglIXe3N7x//5ZnX37JxWCCrVrS0Yhvv/2OmzfviIcTBpM515cPyZIh+21Imufcvr/jv/yn/8Sjz54QJgOG4zG2rCDP2K7XaK/7mBR5Smg0aXEgUJbRYMxkOmFex3h+xHA0oqhL0jRjtligMGgvwPNiBsMEE0ywnk9NS6N8gkFIUReY4+I0m87wg4DiuFP2g4A2PRDFEU1rj8y+oq4716unDWVVd0H3vqHYdS7AIPBRynVmHwXQdX07B0p1BqBPNXZN0+J5jjgOGI2S4whRUZaWOO5C9btRX0VZ1iilsdYd2VKP+XzaNwBZ21DXgJUxyocWHq27h1nTlsQndW8/Hn+eQ8a/p+YXWYhkgYUPeX+iB5MFRAKWpedYmJUsy/pRYRiGpGnKeDzugaswXBLnI+BIwI6MqxeLRa+hlDHd69evAfjrv/5rzs7OePPmTZ9FKYvtqelFGJ3TwO7TYGepSvzqq696zd+p4/Y0n09A79OnT3vQKOzOxcVFHznknGOz2aC17hdlWcSV6kKif/azn/UmE2nJOQ2zll8l21EC4QWQCTiW6yNZg6PRqI+BkQxLGbmf6l93u91HY3wBWqc1giIJ2O/3rNfr3sBzd3fXM1yDwaAHdXK+kyTpjTCidX306BFffPFFLxEQcLZer/n1r3/df82///f/nru7Ox4/fsxwOOTdu3e8ffuWn/70p/09I07vly9fMh6PmU6nPHr0iNFoxGq1Ik1T3r59y3/4D/+Bzz//nOFw2DOoaZr2jB/Q6yPzPO/NW4vFos90nEwmfVuQRFgJwBc2Xdhn+JBnKpuIxWLRO/Tl2u12u/7zIPpXOfenAFgYdvigfTzVXMqG4rSp59NJg4B8SR6QSCnZmJ0azk61lcYYzs7O+p8vgPJ03H2a6CDPEJEY/JDjBwPKuqmo64q2rWnbhratcTicbUmzPfmhy7myrSUIQwLf73/VxhBECSiNw9LV9lnAEccR4SCiOqwZJEOaMqNtupq/PC85ZAWtamhdJ5yOPI8s3aMVxEnIZLbgdrNjMIwpMof2Qpqm5O7uhn2649HjR1w8uKR1YKKYdLOlLrfM5wuSOKaoO8OK5/skoxGz0ZA0L9lnObfr96AN0/kZzvk4VxEPAtJcM39wwfX1Q5QOSdOa97f3tMpSq7YzBxmHBVwNvvIwYUA0GDAcxGjjsT+U6DBAhy1pWjCda4IwAFqqqsb3DVhFFATUdUlWlGjtURR7kijAj3QXu2IdTWuPFDtH+r0hDLusz7pu8FU3enJYhsMBu/0e328YDmMuLua8efuWLLM0lUcUhdi2pCxqbBOC9SlzRdWUoAM8P6BuoGkdw0GEao9xA+GAOq9wFhSazWqF0fDo+kEXAK80VdUSxWOcgkOaku5TctuS7zfcNCXz8ZBkGOFHIU4rJtMF04Uhy0uyPOfs4pqyyFjdrVjf3nH54ILz+ZwwDPiLv/gFv/u9x+GwZzKekZcNaZqTpgVRPOL6+pKrR0/xkxGLyRlVmrG6u8HzIvzwNW1b8+b7F0SB4eHVA+IoZKQVpSu7XFAT0qLRrcYQ4mtF0xryVvP1z/8aPwqom4b39zdY6wjChPF0Qd0orApARbQ6wo8HeL7CT4KuyUlXNM6hbcv+sGc0GtM0Nfv9DqW944MEUNLhqlmvDiSDIXlekBclvvFp2k5iMptNGY0TtAZj9DFLVOPh0WKxzuJJhNcJEwUO3ze47l1i27rTQmqFUt6R4+z+P0mSI7OSk+e2u29Vl9zgFIRB2L2muvuct8cucM/rslbbusY3mjgKPn3M/Hj8PzzEJS0gSwClAI79ft+Ph8WFK2NAcbnKcerwFhB0OBz6KB5ZMLMs6+vyxJTj+x/0vBLHs1wuGY/HHA6Hfox7c3PDfr/n6dOnXF9f92NbAU5iThAAIIBgMpn08TxStyfua4mISdOUq6srnjx50jOR79696xfosiz7cZ6ANxmXCgO43+/7c3Q4HDg/P+/1fDLCFGAqeYYyqhRnt4DPtm1ZLBa9QUKig0TrJz8D6OsU5fdXV1e8fPmSw+HQywhkxC/6UnGvC3AR45CM9+XcSrg50J+7J0+e9LE0Mv5XqquxFPC93W5p27aPEhL2TPIkhcW+uroiz/M+r/Lhw4ecn5/3RqB//Md/7I050hcu8TmPHj3i6dOn/T2TZRk3Nzc9w9o0Dd9++y1hGPL48ePevXwqp5CNk4Boud//1b/6V/3PuLm56RlAAZvwoXtdfq6AtFOz2m63YzKZ9BuTU/AHHzYkd3d3/X2Y53nPJEoslYShn3aFy2s/1TJ//Jz+kHnZT4j5wJSfSl2Gw2EvHZAaVHkfQD9ul3G3AEkBxLJp+RcBlLat6ao4Ols8noenAacps5S67HbDznYmm6YsSPc7QIHnMRpPCeMEPwwJoxhrDPmhRTtQtiHPMrJ0T7rb0pR7JsOItszRgc9hvwNliAZDkiCkrIpjjmTFYbcl8ruaRKMVYWDYbrbUdUk86Bb1VhnSoqRsYZuXFIeUfV6zOFsQhCHDJKBuW6pjYv14GNG2BYFpSAYJnz0653DIUbarzhskIVYBno9nYs4enLM9NOwPGaNpRNI2hJ6iLkvSrCSwhmkyxFlNVdV42K63vK1pMDgXEMbBkZ5uaJuWwXBEXTvKQ8b793cUZctoAo1tKIqa0dBnMRl22X5VcTRpNCjtYR1Y56GNpm5rAn3s7K5rjPHxjEHhcLbm7GzMT7/+gjQvMZ4hMB5ZnuP7MW2jaGqL0QFNC2WV4SlFa2uKIscOfJT1qNuGKEmwdcp6ueHFt7/h5Xe/YziMccpjMBqjlOmAjTH4QYjv+YzjIaFvMB6U+YHV8p43q65p4vziAX7cdK7mI1vn64A4jmjKgjw7sFytuLu/I44jnj57ymR+TlrUDIYT4oHGNks26x3xdMLk/JzBeEI0HNPYTgEcxRHhYMBf/vJvGA4Tbt684vs/fMOL598DEA8ThuMpYTyktT5OQdVapuM5kae4vV+yrQt++f/+kmQ8Ybs90JqE/W7Dcr1hNB4wHI2waJSJcF6IChI8X2F8j7Io8UxAay3G97GuC3T3vG4MorRmMEgwnkdVFTit0MqhcMRRwv39Fk91FZrpYY9WligeHI0zAdCFqSsFWncxXVp9MFzIg0geJMYYWttQZBkKR2ubI5MVdPdNh2xRWnVd87btQLFTZFkFSlE3mjD0qOsWhepSELKCOInwPE3TNqA6tv5DANmPx5/rOA0hl2t66kIV9lK+RtpDgI/GyqIPk/w7YcGkcm673VIURc8SidFAKdWDDemZljifIAh6JikIgj42ZTAYMJ/Pga5iT8LTpeFGnMjC+snPFUZINJlPnz7tzUDibpX3JfE6AqqlutAY07Nc4l6XUT98qHMEeqetLL5N0/Ru96IoePv2LUVR9K0reZ73DufTvEQxEMmYUzIBBUDIiFrC1Nu25cGDB/zlX/5lz2pK7eVpTqIAV/n5MuqVfue2bfu8wru7O7755hu++eabvh5wPB5/ZMyQ0eupFCDLsr7pSICuMLbSICQsp8Qv3d3dcXNzw2Aw4Cc/+QmLxeIjA5ZoCiX0fjKZMBqNPtrMDIdD/s2/+TeMRiNevnzJb37zG37/+9/3cVBy3woQkxB9Y0zf/f5//p//J5PJhPV6jed5bDYb7u7u+g0K8BFjLIYy0UwKEy8aVLmvZDQu4fYyQoZu8/3+/fv+2u52u54lFuMM8NH1P2UMgT6KSPSg4v6XHnvZUEgH+CnLeQpiTzNR5Z4WIC06YJlcnKYC/CnHn6Ch7NovnFNoz0cHAc5ZbFMzmUwpsgzrQHF8sa772u5mb1iv1vhhxnA0ZjgYoRVobfBQlHlOWRQUeUqR7nBVSmssbZFjbYQXxkSDiDCIKIuCzT5lkgwITML93S1hHGKMxtU1jfW4fnjBep8RJCNaFbDLLa01VNbHi4ZUu4xXNzfcHQNrry4bwjiiaWoGcYznaXzX8sXjhzSNgyIjVJamPIAHXlvhXHfy2rpgtUuJw4A0K0F5tE5T1XVnLPIMddPgLLRtzW5zYHA5xTdN10KSalQ0o24qlKfB2q6hxCqyvGS7S9luU2rr0VCQ5hm+0SSJJvQjppOIWHejYd83HNKS1nY+JYxDGUXTdhmNRVGRHgo0Pod9xmg0IQwUTz97wGazQ1FRVdDUnSnIuYDRIOZwKCgaS5pnOE8R+oY03dOM58TREGVqXNMxla5paIuUJDAMkgGNFzKYnlEVHVCuraPMKnylMN6x0H4U8+DhJfPrByyXa8q8JK8cy82O+XyGHxjMkVXzlIenO33hZrdhudny5fkFr96+53DoAmzf3dxzvjjnbHEGGvw4oMKyXN0y0wrPGep0z2F3z2a35MHTx8ymU/w4YDQesluvefPuPZWDtPXZHRyT+RmBB7Zq2KQ7Yq9FhwlJZFhtV+h4QDxZcKYDGveS5e333Lx5yZdff43TCmcMzjOguw98Wze41mG07gxmAcTxAOc6N75zrnNXW4trW0IToDXUVUHge7jW0tYK1xqatuuC9wLXhZ07g6c9nHVdVNcxgUGhPgKT8rCSsYa1jqKoMMYnikLS9MByuWQ6nVFV3f0cRhF11eCcom464X/sR9Su5LDPiKKYulXURU4Y+DilaSw0LWijsbSEgY9vPPK0+JMeVj8e//tDwJQsShIbI0Hgp+yULERyD1RV1bthBbAJYyNsVZZlPSMprFqn1W4/xE8d9Vvb7bYf10rQ9GlG3pMnT/qYFKAfs8rP2mw2vHr1iru7u34EKuNWySAE+Pzzz3sgJYu6RBgJq1RVFZvNhjiOP1pURe8pIz4BXqvViuvr636MfjgcenZLxqECMsQ8IoYfeS+SNyjd2KJtFDAoxpnTEak4tiU+SALigyDgiy++YLVa9eNwef0CqsQccgo6d7sd06N3QMDpqa5Q9KhKqZ4xlPG8NOXIz5pMJjx+/JiHDx9yd3fXyyCWyyVnZ2f/RAsowG69XrNcLnnw4AEvXrzotY6vX7/m4uKCi4uLnhW09kNdopxHObeff/45i8Wib+ZZrVa9tlLOx9nZGfP5vL9mAozFeS6mJWHq3r17x8uXL/mLv/iLXlMsWsJTraH8XuooBXjJOZVfBWCKO/1UzyzXSq41fGAkP9UxCyiUcymfUQHNAnLFFHR7e9vLScSZL//eaVqBaDil1vOUuZTPnrwv2USIdvqHHH+Cy7tjJ2TnJBekRRElA5LBCGM66ta2XbcwgG1aqqarb7MotDG0rtNUKu11OZLOdY7XIGI2n1PsHEWe0zQlzlqqLCcIA1QQcNjvaNuW5WpJejjgaU1RlmjfYz6fMB4OiAcjWhXjTERZOZLhEKU1u13GxfklD+Yz7u5uef/+hu1mxWa1RNE11FTpZ1xcnFO3NXnRtY/sd2uGgxGHLKPOBpgwIgkS6mwP2idPc8paM0wCrPIpCw0YqqbbUTgUeVkQBDFFkbLbWCaDEO110UpBFIJyR8bnw43qmYCytuRFjfM8mixnfzgwm4xQaPaHPZ5Xk8TdLqS1HnXdkJcVTjuc1wWA51XBbDTG88aUZVfbWNcNWkMSh0d9a4hSLVqb466sxPMck2mCw/Hq/R1BqDt9qAe+73WMo2dQaKqypCpLxqMhnz97Rno2w3k+68KxuPyM9WpLUzc0bU2ZF2ijCYyH9hyVdeim4ezqmqtHTzhs9mil8FSXWZdnaWcGA7RSNHWN0hqUZjye8uTJE2bTGXf3d7x985Y0zQj8HdvdhjAJWczOaRW8efOO7XbH9fk1kQHbFF1bTxRyKHJa25IMIqLwgmQ0wXkRjYq52RRE08+ITQTOsr59Rba/ZRD7DOOA5f0rklHCZB4QRh7TyYj1e83qdsPN+JYno8uju1nh3DGbTDf4ng++pqpbbGuPOsYPUSHyYCjLjn3QyiNL006rmhV4yqNtKooyB93SZhlVcWA0HHb94DIO+WeYQHlQyY78cDhgPJ9BklAUXcQIypLnGc5B20IUDWhtS5YVlEVLnHiUZUORN2x3Oa01BH5Ia+n0ndqjaVuqukFp8IMukaFpLNb9aMr5cx8CJmUBkYUCunaa0WjUs5by50AfhSPmgdMQZFlg5b+lIk8Cp2XRzbKs16BJ1d/d3V0/+pasv/Pz856dkjFlVVV9vMl2u+Xq6oqLiwtubm548+YNy+Wyr6mT0fv19XUPjMTEIX93GtGSpmk/8q6qqs8slEX7VB+X53k/xpefB/RGDaBnzuQcSei0aPYkLkdY191u1+veTsFFnuf965B/U7IShc0U5lEyKwV0yveIjm82m+Fc11AjUTryuZbrCfQj+clkwldffcWDBw96E87Dhw/7zEthrISlE3AousjPPvuMzWbTv5Y8z/t7Qdg1YdQErH7xxRe9wejFixe9nGC9Xn/EUr98+ZL1es319XUPvsSBLkyysOBimJJooMVi0W803r1712sbkyTh9vaWyWTC2dkZUdRl8r57946bmxsmk0l//wl7LLpjAWKnMpLTSCcBerKhUUr1Rp/TzYtcb9E3SpOUsIRyfLrpF6wlznIxuA0Gg76FSqkuCF2ArQBGuc9EppJlGdvttnfDizFLWPLTgPpT9vOHHj8YUHYfnk6PpbV3fNB4+JGP52mqxmJbRxCERzNAZwJwgcNzDq+pkXIcpboIkTp3lFXHbJoowgsjPD2mqXLWuzVR0IWa+ybA1RVl6kgCg5qMub15f0TXEZv1nsWDC9LCEsUKVzRER3ayzEqCMML3A8bDMa6tCEPDT776iuvra9L0QJln3L6/Yb1astssOTtbMJlOuoYb3YWD31qHCQKKosIPY8azC8bTBZPpnKbMsc4QRwFlU2FdQesUw8mY7a7LNaublqppiMKI1XKJaob4QYD1DMbTBL5PVTfYusaLPFrb4vCoakfdQlOXOFUxnYxYzMf4nqWpKooC4thHeZqmqDBG41uv6z3PLF4cHj+ULUHooxTUdUYUBdRNhcNhjH/i7lIYXzP2E5rG0TaOZOhz3k4YVgPSwx7jOeIoQnvHCA2t0Z6HH/idjs4pRpMZrRdRBo5DBbsK4iAhjg2eyanKlMo5YmNwniMvSwZ1hae78O7AGKIwQNkW6xvypiLPMrAt2tNdhmTgc3/3nl/9j//RuzKrPKdtLF9+9SX/43/+PYPxCD8IMUpxcXbB8m6Ja2p2uwP7zRZ/MMLWDRZHkR+w+YG2rvHwiZOErIkYTye4YIxKpgRhwCwaYOor/HZLna9Yrm757tu/5yrfMJrMGEaOzx495PbNW159/5bZ5VMmZ49orMK2thvjKw/tKbR2KG3xjMFrPoTfnoqkO3G1x2g0oG0bjPEpyw9xJda1DMcRuJYyT/tdrjyMPjXgfPqAkAURIEli2uOi1TQNw+EIT3tobajKhra1bHcHqqrBeAbnPLK04rDPuX2/pKocUWCIAvm3Ok1t5zLURF5w1AQ5fsSTf/5DQM4pCDwdzQlYkGgnOUSnJSyRjNnE1SpGGtFdSsTNer3uR6Oiu0rTlDAMmc/nvHnzpq8nvL+/5/Lysl8E8zzvwd3hcOgBoOjToijiF7/4Ra/vy7KMt2/f9nmIl5eXzGaz/j0K+yYGFtHHLRYL5vN573A+bRWxttOfiz5QQFMcx9ze3vY60dNRqLC5p5mOcl6FbZIgb3HBy3sWg4YYouQzLNdOro2ck1OmSXSup4yTvB5pNnnw4AFlWfYh6lINeMqGigHIOdfnZYoDX/IcpZ1FwLqASnHeR1HUf58AWDkHAvpOjS43Nzf85//8n3n06BE/+9nPemf7L37xC/7u7/6ubxRSSnF5ecnNzQ1N07Ddblkul720QGQXEnUjyQVVVbFYLD5ihQWEy2bn7u6O3/zmNzx58qSv3nz27BkvX77k22+/5eHDh1xcXPSgTMCwAFbZ/Mjz+VS7KckFWuv+/hUQf3pOpFVJCgLkOP05f+w5LTIEMcaJ/EOe06c1ibIZ6RrTqp6gStOU3W7H27dvWSwW/Wf5dMwuXy8s7KeRYv/b588P/UJr3YmrTfcPrqpuUG0XBu15oKwC7aGU1xd9KwW+CbDOHgGHBdc1fLijbiyOEzbWYRtLmAyIh2PaOqNMDzStpTiWvdd1TeiHzCZTLi4eoDyf5W7P7pCyzyqMX5IMAqhrsrKgseDuV10bQBiSZjWHsqVWMFxc8/DLCZHvs7p/z+3NO1Z3N+w2t4DjfDEnL45ZULpz2DYa1vd37LYHhqMlV4+eEMQDymxP01RYpdF4KC/A9xOm0wllWZPEIdgua7DIcu6qFN8YwnFDUGQYr9OdGe/IHlhLUbXkdY0JQ2gaRuMRjx5eEYc+ztUY7YhjQ2MdySDB82q22wOhMqB0V7FuNVjH4ZBiSo8wDAjj6AgaQGu6FhZjUEpC6elMFs527uHyQBQalKeIgjGubUiGMclgQFkcaJoaTxnCKMEPQsqqixIKkjG6bchqhYkGBFFCaLzuXrItdZ1j6ABptzsqUU1DmeUcygrP06SHPVVRdDFDbY1tW9oGPBS4lr/4xc8pq4Jvvvktr1+/YjweEccJVVNyeX3FarOmrCpGkwlhELFbb7m/u2McBaT7A77VpPsDTjvKPKXND9RFQRCNMNoyiD1S5VF5hrRWuGgAocLqkDpXGL9lPKh48fo5Zbbn6edPmQwmjAeaXaDQBOy3G8o8A+Oj2ho/CLrkgLalsQ6ODwLPeP2H+EOwb0UcD7DW4Xmdm761Fu3p/mHf2powHNA2Bv+o5ZGd5aeC8dOR1KlDVBY0ASO+9mmabuyhOPZ0G2hbS9s4DvuMyWQGrjPPVZUjzyt8v+CwzwnnMcZomqYrN7CNBdQRvJa4tqU9eaD+ePx5Dhlnwocwc2HEZKwrC/2n94eMXoWJPF1MTnWJ79+/7/WDspiL2Udq/mQ8OJ/Pubq6whjDer1mt9v1o0gZtQrQu7297fuZT0OYz87O+PrrrwnDkPfv3/PmzRvev3/PcrlEKcWDBw/6UbyAJs/zuL29Zb1ec3d3x9OnT/u+5dPzI+yfAE5ZSMVgIsabyWTCbDbrQfNpRqc4qqUDejqd8tlnn33EFCZJ0oM+0e/Jazj9LO52ux6ESQ2gAAV5LsA/bTqSrEMBkOJol4xC0YKKfi8Mw15rKS5meQ+inZURqAA5uX9kLC7Vlp7nsdvt+jgdAXGnhrC//uu/pixLfvWrX/H8+fNeAlDXNY8fP2a1WlGWZR/SvVwue5mE6GJFfygxVvJ6JWdSpi0CisXwJP89Ho95/vw5aZry1Vdf9Sy5yEI2m00/Sj7VUcp5kPH/6ahbdIuiBZbrLZMBYX/lnMg9croBk3P7x8Dkp89p+UzL65ENhUQ+CfCUf2+32/XstVwv0d5ut9t+0yOvQ7ST8p5EAvJDjx/u8q6b/g17nunHFK3tZvSeH+IZA9pQO9D2BNX2yFvjdKcNa+oGdYxId8pRNQ2j4ZBVvmO32eGbAFzL4XDgsNthzAGtNNkhZTyecv3oM0bjCWnR8NlnZyw3e8oqZzQcEMYJu0NBUZeA7grjq4r5bIqJYwITMJjMGU5mTB9c0tY1j86fsHiyY/3+NaubFyzfv6RobAcMw47FC3yf+WzB2aLhcMipW8vbV88ZTeaUrWNzeEkQJ8zPLxhOzsiKHSgfT0FV5ESBR1V2FLSfGIq6QAcDNBbXdO7aui6JooDdfk9LxGg0oG4sxlNcXl7gG+/otLf4ScxmfyCJfUbjCHRDMoy6DMCGjhUtKxSGwA/wA5/WOnzPUJQZaEUQhRjPBzzyLIVj9ItzXcRQEESMx0MOaUnZtIShh298wtA/MmweFkd91NhFgzFnl4+oqhoTjohbS3HUdWZpyt1ujWpbZrMRKgBPN7RtSVuVZJstLgios5LdenMcj+c0dYXCEYUBQRiyOxxYNisur6747LPPjh8e+N3vviEv5t3D5Jvf8uzzL8jLgjQtMH6I0Yb5bEaZFqi6IAy62KAsy1BG4xtD1Tbs9lvS97fc3N8ynF4wePATlF/jlGO/22OrltlgQjgMye5KPFORxDOyw5a3r75n+vVPiHyF76WgfcJAY9sGYxy2aXupQF13Ga64Ft8Y6tp8FEUikS3T6QzbKpqm23DUVY2zAU1TU1UFJvDwjQfOY3SMDTndIX86Qvn0gXWaRdjpLLv++g7ctjhrUUoTBBFV3eJpQ11b2hbq2lLXLb4f4PsRbetorcN4PlpD01TUVbd5UUrTtq7rXT9KXn48/ryH5OjJdT1dGCQfT4T7n7IPp3pJAZrCXJ9q4iaTCe/evWO1WvXAVIw6Mt4W7d7Tp0+ZTqdkWcaXX37JcrnszTwSXyOvebVa9UyTuGyn02kPSuu65vz8nKdPn/bRM2/evPloLCys3dnZGRcXFz3Qff78eV8pJxqyBw8e9LpSed+iKZPx4HA4pKqqj+ojgZ7FFCPSZDLpF/mHDx9+BMhEAiDRO57n9UBcxpICjkTzJ98n43r5O6DXtJ1+roXZPdW2CjAVhk30dHVdMxwOefjwIWVZ9sBVGF7J5RRXugAzea2bzaZngVerVc+yyuhWNgW73e6jeCH5N/7hH/6h12/+6le/4quvvuorNoVJl55zYapFtytArGkaNpsNh8OB9+/fM5vN+uBzgM1mQ13XPaC+u7vr2cvdbsfz58/5q7/6qz7WR86hPDcFDMvmSN6/THPk6+SeKYqCxWLRA3DP8/owfeneFoAqLPlp9eanYFI+l6efT3lOy+uVz+OpNlZr3acFiJFHXpNs8uT+ErmG53k98yyvQ75HfuYPPf4khjIIfHw/OH6gGtrG4mmPxnUVcVp7qKPW0qE6x7fqGErruoeXs7YbWzpLVRU0VU6dHUh3G6os5bDf07aWwSDhZrmkKBu+/OqnpIcdVVniaZ/DIePVy9ckwy1F1RIMB2g/wCooGoutGqzWXF1fk6Yp6/sVeb5n1VacP3yMCSL8eEQ4ucAbLijzkiAKCcMJIx0wGI5orWO33YBz1JXCtI6qbRn7miBKOIsGVHXNIc/Yb5bs8wKHpq0yDtpSpgeCeMJkdo61is12T+QlRGFEoQ1aeWjj8LTFaEXbNNhWE5iQIivxtObiwQXDYUHgQxSGJHFAmhXc3q3Z7nKGoynDYUjTVoRxgvE7+YELLZ722a73NJXFOU16KKkaxyCOsE4Thp3m47DPCaMWT3cMWacPccdRVwWuqydsncb4hjAK8I2Hch3robSHbRvKxlI20GLQ0QiPAuNHjEYeuzRnHPr4seEmXVLWBbbRRKFBq5Y48LlfpaAcm80a1zjub2949/YtvvEZDwe0tmYH+H5APBoznMy4uLomjAe4smB2ds5XnsfPf/5ztpsN9/e3LJf3XF894lf/+BuKsuTh1TUXFw+wTcPm9h2PPnvMi7eddGJ6NsdHUwcRZ+dXVO9u2O/XvL9/z3i7Z/zgp8TzZyhGpE1LU3u0gU9DjGbAYHjB0A3Ybd/x67//ex4+mKHcDt8b4RtH2zT4YdeF3bYtTXvMcVQdKE+z7KOoEYc7jrcNVVWD6x5sxg+paxnNVTg6ZyrKMRwOmIw/NER0q86Rbu4fTt1//7ExRqd/tl0dquoeYMbrQHbbNoRBt5mUcWp6yMjyCqV9QPU7eBmjaK1paou1XSRREATgFK7t9JOh/yc0v/54/KBDnKjCWMjiJwzXqeng9HtkIRGQecpQShyOGCSyLGO32/WjttvbW4qi4Be/+EXPVEkG5fPnz/vx+HA47Bd8WbyAfqQt4dZt2/LkyZPeADGZTBgOh311oIxGR6NRP9qT1ykgTjqSxcRzOBz6PEOgZ9YkrmaxWGCt7UGysJnC1ghLKKBIjDWe53F1ddUHp8dxTJIkPdDZbDaMx2NGo1GviZZFXMDecrnsx9pirBG2S9zhAoJlzCzjZYkLcs71I3gBkqfnWtjUqqp6oCOSB2ln2e/3H2VkCgiReKAoivog881mQ9M0vaklCILe0S8xSqd5kqL3u7i44K/+6q/45S9/yXq95v37931G5X//7/+dPM958uQJ19fX1HXN+/fv+fzzz3n58mXv+Bez2eXlJa9fv2a73XJzc8NqteLq6qrv3BbwLAwk0F+L1WrF3/3d3/Hw4cP+nJ02Owkz92lqguhz++f0EaSfjqNlA3Ia4eWcYzgcAp0GeDab9azyH9vwy3EaAySHAD5hNmWyIAkOkqMpn3XROp+6v0WPKf8tsgmRnchzQSQkP/T4wU/0MAi7kVtvKtD4fkDjHEnYGSScEzR7pGkV9B3gJycI20LboGyD51rKpqBId9CURGHAdDxkEAesN2sa21C2jl2a49qWaDAkjAfkWUmW5QRR0rGctqWoK0wQEg4Noa9J8xQURJHh9t2+M2JUGdoO0cpiPI8WzWCyOO4KNP5w3mX1JQtm0ZTJeMybNy8psgzneRSNQbmWtirZ7bbduGQ2ZTqF5bKj7SNaysOaQANlQBSOOJsMcUDrPJJ4gKPEU6CdxdMKZ1uqqmEyGFNW7VEOUGN0w9k8IYlCmsbSNF0u4x+ev8UEWz777JpxogmjkPEoQOsWZztH9WgY0zSKurG8fPmWNDvw8OEV09mIJA5xruFuuWY8HhH4utMsHndcxgRo5bPbpWgVUDYlQehhPIjDAK0UZVngeRpqRdVYstrhrI+nY6q2otilYAJ0UxAGCtUUhKqkrPek2wONr/G0pbAN2WZJu19TlRX3d0vevb3pXlcYctht2O+6ZovpbM7Vk6dcPvmMi8tr2rYh3ezIipIgGjCZLTDGx/c029Wa6WTOg/NzvCBiMptT5hlZnmE9sMoRxBGTxYzJbEpbZtDMOOz2DIYzgjjh8SDhzds79tVvcWnKYP4Izx+ilCOvIJokJCZErxSeHWHrmvt333L/+gXTccTZ48+xxyKAIkvRoSzcXXWltd3D19OQJKof2QyGyfGhH3a5pMajqhuMH+B5hjTNjiMe3WWWKtePzOu6RtEZmHBdzM8HAKl67aKMUz56WGmFPm4IAbRnCEOPPC+OwMM7ccRCnRf4QYznHYOZdfdwc92+kaqS6CEf3wRUVUlRlN3mRf8oovxzH6ctGwIkhUU81UvJ4vLHFiz5c1lUT3WCMjKO45jpdMpgMOD+/r4fccrfD4dDkiTpR8cCXsQEIHmPvu/3wC2OY16/ft0vzmI2EKZd6uycc71ZZjAYEEURs9mMFy9e9AunLORVVbFer4njuHcA393d9Qu5jOjLsuw1l/L+xYRzes7ETT4ej/vWHxm1np2d9WyfsIu/+93vCMOwD+MWh7L8LBlLi9Hju+++43A48OTJExaLRe+Av7297Xu8BeCJY1xr3Yeui75RchtPzVDwIfhe2FTRKQpYFtZMzqHUMgp4Wq/XfQ/7zc0Nr169Yjqd9iBTXsdiseDZs2c8e/aMR48eUdc1y+WSLMv6HnAB1lLXeHl52csP5L6Re1Ou32Kx6IPdJUUgjuM+Tujly5fked7/fOiisSRCSBjCqqp4/fo1L1686Jl0uQaiAf50gyUbgCRJ+ggl2dRIVqowmMJ8Sqe6bLSB/vN4ygie3mc/5Dj9PqDfoEiKg2zohWU81QLLxvJ0dH/qQJdGKRn/Cyv/Q44/wZTTienrpsU1LcrXaLr4FuNptAK0h3dsxVFoPK26nDrbjVGVtZ3Jom1pqhKjHJWtqbKUuszxbI2ma4jZ7Q8EUYzxfZQJiIYTlHNk+wN5WlCXFZ4xFGWFqWKS0YjxcAieoqoKPBPg+6bTCFZdIHQU+oQGRoEiNmBUZ2JI85rJdEqUJLRVjj+cURMShB7hcMH03JLuumaBu9W2Ywx9ReMUbVFRNZYoDBjGCappOCyX1M6iXYurC5yOiIcTjB9ibYs6VqF3tHKJ6jKWsG2nLXRK0zRtJ6zGERiLpxustoShYTgc0bZgG0VRWqbDgKaxoI41d66TFYShISYkLRqCIOHFi9c0taVprphMRxijWN6vSQ8pk+mAyWhAFHULkgOSZECR72jaznBlPI+6qdgfKpIoJgiDLprGM52TvbJo5ZOEA5rdgcNmQ15kvHn9PaGxKFtS5ill3QGKDIttKrRtibQiq0qqqsa1DV98/pQnTx6z3W6wTYN+dA3OMhyNmc0XzObnKM+nrmpevHrDarXlq6+/xikfE8TMZguqIufmzRuCeAjaUOQFWZl3zuWyoKkKrLKYIMDikQyntGVDS4mJJlSHA/mh5frikmyXkt38gTq9Z3T5mMaO2GYlvjEwGqF9jSs0STjGqIRdvmLbHji77pIO6qrEUuP7tnPGdzNgnLMdiNMKpboPuB8EaO1jLZ0WVnW1pLa1tNbim4DyuHC0uOODqlvUrOuuUx8dwz814XSShn/6584ptDyonO2/pns4HdtWPAWqi8CytsugdNhOF43r7j/d5Y3WVXGMsbAMEqkOq44PQEOa5z/4QfXj8cOO015tGdudhhSfshSyUMi9IguRMBOy0MifSUzMKWMjo1xZQIWF2W63fe6lMaY3pYxGI8bj8UfxPvK6ZEEWdk1MAxLOnKZpX8tYVRWj0ag3CokhZbvdcn9/z/39ff+zRRMpRhoxNCyXy57pkTGlMI0CuORciAEE+EhXJsBGmExhgoWhk3MoIPTTAG0BKmLqCMOQP/zhDz2zLPWWt7e3fXezxAAJYBLgLiyTXG9xHwtDJ9f5tKnI2q7fPMsyXrx40QOOPM/7a9d9bj90PYtMoW1bvv76az7//HPW6zVN0/D06VMkxuj8/Jzz8/MePD9//pz7+/s+oicMQ87PzymKghcvXvSFCXmekx+fDaJPBXqmbDwe941MIpsQ1/9ut+Pdu3fs93uurq76TY7v+z0gP9WaijnmyZMnPdhWSvXg61TjKsz+aZi9nFO5psJgC4stUga5J051zad6TOCf/CrHH/vv08zK08+ufI5kLC6v59OfLe/l1DQmG0H4IHUR4PxDjx9evUgNdMHEnqexztFWH8LMgzDo6gy7dYW+p7dblXCum76pY5B5YDQKTeFqqrrAeN1/G6PwjKZqNNPFFThH05Ts0oLpZMh0fkGRF2w3Gw7bLUVZEniKqsjwA4/I17RFSuNS4nhIPBxifc0gSfADH8/XmACM51CqQWNpm/KocQvwvLAbdXshygRss5paRUwvZsTTS4oix/cUebalqQuW9/fc3t8T+gXDMGA+PaOpc7b7LUW6R2OpmzXUeybTOaDwbEPgezQlNNUxjNg1aNM1ktzfb3l7t8YPVvz0q6d4dIBCK0USGc4XA64fjKhbg6231FVDkbfsNhnjcUQXYg15ticMHFjHfDZjPF5wc7PEC0a0yjAY+PhhQtU0pLlF64YkHjMYeBRZThgYgsAjz2uUa6nLEs9TYBRNWxKEBqUVTXt09SufvGlIhglZ9Z7Xr1+xunlJnm44v5gThoYoNIS+QnseWmmapmKURBgs2jZkacrcdYtcXeXMpmPi4wN3t9swGA3xfI/WNeRFyvJ+yes3L/n6q695+PARJkxwrjMajScTVpsNRXEgy/acTUfYssA2NUZr8qpkdb8kmpzT4lMEEZvUYkYXDEaaeNpw2Kw5pDucMhwOK0aBh642OJcx0AqjA4yzhFEXLm5Uzfl8ymyouLt/R1m0uMZi6xo/6tqLtDZgOwOb0V1vjdaavMyxjSOIEupWU7eGvOoAXVvXNG17DD/vgKMfhpimJo4jHEUHJI8jEPinu9gPD6QPv//YAX6MYOCDCFwf0xo8zwCOIDDH9+BTFBUWj2jo42kP3+8mA0Y7PO0oW0eeNVi67ve2tThrjwkBAdtd+YMfVD8eP+yQ6ybspAAIGbuJtu5TJ/en/wM+MoGINk/urVNTwfn5eQ/KDocD0+mUs7OzXmMnZodTw4TUA4rTW4CchGgLGDy9lwXECHCTgGoBOUDv/BZ2RV63dGHLWHqxWPSgRJgwAWESXyOsblmWPcATdgfom2CCIOhBkgDPJEk4Pz/n6uqqZzWlzlJCvAXcS8alc46zszNmsxlv377tAZQYR6qq6sfsp6NpOZ+y8AsjKeySsMMSFyWvU6Juvv/+e96+fcvhcODBgwd99JOcZ2G6BGw414Xkn7rY5/N5PwZer9dMJpP+PpOu7u+//55f/OIXvWFJjul0ymq16nWSZ2dnPYsqGw9haOUeljggGR+v1+u+rUmMTXLPeycbbJEyaK05Pz9nNBpxe3vbAyrZhAk4k8+D6Be11v1rk1xSids5vUdP82DFiCOGr1PjmBynz2nZwH36Z6fP6k/NO3IvyedVdK8C0IHeECafr9P4MJGCnGppJZVADGQ/5PjBgNK5zkhT1xUmHgBdrVsQhDg6dk1h0Ro89aEuqFujuixGnMU1DWWe4ZoMbQvqskDro/uzLmmamqE/ZL1LGQyGJPGQrNhz9TgmSSK26xVxEIJvCJK4aw2Jw+MNtSXPdhjPMByOCbSjzg+4tmE0GbOYzwmTBC+IcKprEZmOhox0jNIB1oLvR1hLZ+IIIzwTEOqAsnXUSlMrBZ4mnCYMfY0ZXeAPb0nXK7Iyo9kcmAwC/CAC5bi/u+liVHRFQUGQJPjaYGuNpzzwDEEQdjtk45MVNXfLA8tVSV5sUcrw5efXxMYDWtq2ZDYJ+T/++ic0rWJ72GKbgigArVq0ckRBiG1a8DVKWbRniSKfB5ePWK0rvv3uLVXTcnEx7nI8i5a6KVD4BGZPYCDwFdaWJEln4AmPu7K2dfiBOS4umjCMjzu6iCAYsN+XNC4iGEyo25ok0izGZwwmU0wYQVuz326o65bJdE5V5uRVQRIaZmdzhpOC9d09dVVQFBl+MKOsS4qyoHUtTivqtmKzWTOdTdnvllwsplxfXnRsbxWQFyVVeqB1DcbXNFXJerXie1oGg2OWXNuFc1dlye3btwzHZ6zWexoCWh3hHISDMfPRgiLbs3r3BlO0jCZTAq3YbO+JhgOiAPzWUWUZxWGNtpbAtIR+xKFIyIqCw26PGaQ07Ih1TF02oDR11eB7umMvnUFrD4xjf+jqyKq6IWoVvlG0dYNtWqKj2S0IAtoK4ijsJBOoY7yP7lnFP6a9ER3lx0Dy9HPeHn+VwF76h2DHzDSUZdU35FgUnvHxPIMfeBij8H1QdGH6dXPsJa4tFjCme5Ae0pyq+uHuwR+PH3YImyZ5i0DPBgE9s3Zq1DiNL5FDjBYCosQRLLmOMkKTrMbBYECWZTx9+pTBYHAMxO/GjOLAlfG4OL2NMX3ciSQWTKfTvm5RRvRt2/ZmFllsJfpGahH/WLam1rrXqo1GI4bDYV/peH9/3xsjAG5ubnq5wHq97tkyMTeI6UdY1DzPubu74/7+vs+f/Prrr3tg2DRdkPy//bf/th8rn0YCyTURgCyLfxRFPHz4kNVqxe9+9zuapuHy8rIfXZ+6cOV/1nY66qIoevZMon5EKypViqKv3e12AH1TjUgYhP0UbWpd130bkhh4FosFs9msb6CR7E7JPBQjh7DAi8WC9XrNxcUFjx496qNpBECKhrEsS+7v71FK9eBV2LWiKPrx+nK5/OieHQ6HjMdj0jTlzZs3PcgUMCRASgxHUiUpDUuSELDdbhkOh/1GS9jK08D80w3Fbrdjt9v1mw0BsaKBhQ7YCZiUz9tp1qXcp58e8qyW5/OnX3O66RPgLJ9rYdCLovhogyibDAGUwnDLSFw2hfK1ooMWbegPOX4woCzLEttCFMXHbKyasqk7E4iTSCGH1v7xIdX1fKM0OHckPjpAVBQZrsqwVcbqfsVuucKohjLd4qhpXcMgiTBGY4xHEkfUTUFV1/hhTBxHWBRRHB/1hxaLpsgPYDtn836/A62O40WPyVGPFwYxrvXwdAitpikbhpMYpwzOakwYc/vme+q2oclyymrHYDDqFk8HRVnTWo/FYkoY+lSN5vLRkPb8mny75u3Lb/FKD0XIIPaYTMZk+00XiRF3URCz8wvCKKGqLfP5jLPzcw6NR1pUaLpmEYehajze3e6YzWeEAShXMxwmJFHAbDagrFr8YESe6+5nR4amrimOzFIcR9S1pShK7lcZy3VKlIy43+64Xa4ZTBIi38M6jyyr8HQKbcVsEjMcDNFeN5tPkhiUwbaOsswxno9V4vyvUSjCKOrAJT67Q870bMb51QVmEaCBvIbcGowXoROfUGl0PMTzcqhSRtMYDLi6Js1S8uOOKR4koKuustJ4lFVN4+U4HZJ6e6qy4NnTzzrW3DZUVY7nQesa0vRAlmasl0uCIODm7RsenF8QBgF5XZGMJvzk65/y3fOXZNv7Lkt0MMN6BmVCjB8Q+CFhMiTwI84vr6izA2WdMZmesd1tWK22TMZDfFqgorUl6ri7W8zn6HBIFIdoXBdAXuTkRUkUJShlOh2qNTilCCODw2O13vL9i7d4Xsh0PmY+H2NtF0RvPIVzLb7xKLKCYTJAqy56qJui/+/rDP+5r3HOYl2Lpz7o6z6MczSe1xnt6qoCOgd46+gNXcbTGK+Tujhnj7WPncGoU4x27EJT1xwOGVX1o4byz32cNmUIA3XKVgrgkgVKRrrwcbRUXde987Oqqt48IcYEAaKig/R9vw/uFh3fKZAUsOec6wOYpU1HxoACAMMw7A0mskBWVdVrD0Xf+PLly37MK6YfuWclikhqG2Uce3V1xWaz4bvvvutHqYPBgNlsxna75d27d/1Y/MGDB30O5NnZGZeXl33O5il4reuad+/ecXZ21o+RxcU+n897h2+apiRJ0huFBDBIxmKe59zf37NcLkmShOVyyfv375lMJv3oXtg1ay2z2ayv/AN6ECasoJxTcSmLTlXA5W634/z8nIcPH/bM6un9IhpWGQ8LEynM9OFw6FnB4XDYs7CnZhORRRRFwU9+8pN+3CsgzVrbj6zv7u4IgoCXL19yfX3dRxuNx2P+4i/+gt/97ne9AUt6qoXJDoKAwTHh4vr6upc4zOfzXgYxnU77z4mwcIPBoO8Zl/crTn8xSUrihmxwZAOzXC759ttvMcb0fean8UICWtM07V+vbCb+uQ29HP/8dOkDkDwdx5+CU9l0CRCUaYSw+fLr6YbyU8ZTWNV/MUDpnMLzOoD3QdT5IWJG3Nydlup4UjjmGh4dqziL49jPbAxZ0Xa5dm1L2xasVkvQFZvdisX8gmkQ41xFGPqk+QbPBIyn0053kgyxx7q/Mk8Jos7lrGxLFPjkZXEEgMegzijEti3G+DStZbfeMLQG48cYL2I0noHpzDGTyZjRcMTdcolSHp4xaKe7OBTfx7YN9ZGlORwyoighSuY46zO9dEyHMZ4q2N6/5d3dHRezMRdhSNtUHPY7PL3k/NLHGB/P92jaBj+KoG4I4wFxMqR+n1HWlu2h4rtXd4wHhouzIdr4VKJn8Q2jYEgchRijCAIPRYDxdBf1gqKuG6oWtvuc16/fEyVzHj35guX6Ha9f37GYTTibzxkkDtd2upnttiKJDbPZlIPtRlJF1aKcPV4vh+8HtE1DXXXOMIUlCQfE0Yg0u2c6Dnjw5CHFUrFZ7Wh1RJTMqJoWdIPyPArADIZE8YhDtaMsC2LPRymvq6FEkRclqM6sUlY19nDgbDBhPByx220ZDcfd2KXIiRJFWew7hrxtaOqa7AhMry+v+P7b7/ju2z9wdXXN4uqa8+sn3cMiK6jTDSZMsIVHOLnEDIZoP6QqatraYb2QwXSAG4xY3b/HKstgtKBq1tSVIxqE+J7lsE2Pe6iGQRwTjTvjgh8FeF5EEIZUdQOqxvc90P6xOaZB1xxNb1DmbQdOdcHZ/JymLTu9iAPbtmgFzlbEyRSlOoApj5/TB82nxz8PJh1W9Dju48BdYSqVoptEKNfVXzY1VlmCwOsC8j1NGAUo1Ulcus91TRBF+H4Xd+V7GqW6kX1zbD/68fjzHaeC+9PfnzIep/fG6T0ii8sp23Eabi16QKnGW6/XnJ+f96xcHMe9dlGy7yTjUEbvcRz37KIs2vJ3YiSRUXvbdhWIp/Emp7V5s9mMyWTSdyULOyfMpVTLOed6l7SA3OvrayaTCUqpvptaGlSEUdRa920tEikTRVGfOTgcDnn79i1VVfVRNMPhsG+fETe1gB5xeIs5o9c5H0elEoXz4sULBoMBn3/+OXd3d3z//fd9DJIwijLGFIPLfr/vQZ2AbGE/T0G+AESp7BuPxzx79qwHslrrPvhdqQ+RYgKEJfxcWpgEwEivtIzA5fWMx2PWx4pj0dIKmy3OcwGnAI8fP+abb77hN7/5TV/z+PjxY7TWHxm8xGgzGAx6ACvAeT6f96NspVSvXS3Lsm+KWq/X/edgMBj0SQKnhiZhKIVBFqmAbMbkOgiIvLi46MGZsNQC/gWsynk7/d8pIIR/HkzKZ/T0608/07Jhk9/L9RP2Udh4eX/y/aetbHKfn5qS/kVMOVHYjQG7D8mHRhxr2/7G6l7gETkDVhKysTjXHoOpa7SyoCzKOcbDIaGGpjhg65Si3NG2FWWe0SadtjFzNb6GMPLR2uu1NTc3NwySAcMw7KJImhLPtWgc0WBA3TSExx3XeDTEeD6eBj/wycqa4rDCqZaySCkOKzwTYIIA33MszmYUZYFSmrLIqesWbQxBYMjTghffv2A0GqH9CE+H+NGI+9UeL56j4wRP5cweaEwQ8uLV9wxCw8+/fkZdpGSHPYHxqHGk+f4YtQTG75pmgiAkiALsoSCvLVnRkiSdccQdXfbWNZR5DtowiAc0bUWR1wSBR11VBH6IUhqFh20stg1oWsN6kzH1Anx/xIuXL9msMpSLuDgf43kWhSPwA+q6PQqKQ+qmxdqGummP4x3FZDxCab+PtknTgjhKGI8n7PMlh6wgGU/Itvc4L6BuPZJ4Qlk1qNBDaY88zRj4msCMyLcVvoLWpvhhRBgF6OND+XA44FRnBtEmoK0b0t2epq45Ozujsd3Dr6m6a51VBR7HvM3jCGY8GjGbz3j98sB6veHhl19h4gFVkfHkyUPWtzeUdReDk4wXBMZQO8dmu6NtGozSaN0FlatwhHUlo/GAKJlyOGzYZWs8V1E7hY+jBSLPEEdR110dBJhkhIpitDGkeYFXdeHspgXlWYwv42oPT/vUtaMquyDxpmnxfHVMV/CI44CyiIijgKI8oD0PeQZ9+qD6VHvz6cPrNCLG4Y5VqLb/fnlI1nVDesg7htJ18go/MMSRT54XaA2hb9AojPGxNqNpakIddeY4Z9E6AFfjLNT1jyPvP/chzJ64NkULJwvMqZgf/qmz9NSQc/r3YqQR1lLCraWxREbjxpie0RIQ+ObNm14HKIuX3JenodoyXhX2RACn6COLopOCCMAzxnBxcdFX2slrk78/HA58++23vZ5PXtvd3V0PkJRSXF1dEYYh3333HXEc8xd/8Rf9vyUgVZhBoB8zC5O62+16faQ4w4F/IhcQM5CYb8Tt/ikgaJqG1WrVM57Pnz9nuVwCnUZUdHKnY2ZhPQWgSbamaDUl71DGr9PplP1+T5qmTCYT1ut1z2ZKi9GpIUpiZkS3KhrCU/Zyv9/3zxoB1Nvtlrquuby87PWJAnoFsAmLJ/fZ2dkZ3333Hcvlkq+//po4jsnznGfPnnFzc0NVVWRZ1l9X51w/nhd5gpwf5xzT6ZThcNgbxU5lHsIixnHc52fKvXEaDSXASzYu8tmQ9ymfh1Pzm8RenTZDfZT5qz6O6vpUM/nHzDinz+XTz+vHz+m6H8WfMo5SXSmvTc69gMbTUby8L+fchwi6H3D8CV3eXs86dHN2d7xwcmHscUHreMmmtVjbdkDAdeYXhUUre2S6KpSyQNsxG4OEMhuSDAzW1thG8e7tGxrrGAwDmqYijIegfaazOWEUYYyH8U0HYvYHlO+h2po8S7FHlD0edUny2Jbd/kBlLQ+uHzJMAvbplnSbor2Q7crgBZ1mUimPtrWEvqbIi27Eqy1NXWI8v4vXGQ6RLumz8wcUdctwOu/efVOT5jmeizh/8hWj+QO+/8Nv+Obb13zx5JrrhxNu3r5GRzGL8wCjFXlV4lqfui2JIsN0OuRmuQQMTVNj2861HkURddGd36rKMb6mrjv63ngK6Pq2O/1EF9tSlg112XYLuDLc3d0zm084W1xSZDlvX98RGEUcWQKvxp8NaZumCyn2DNrrQtGxXeh1muYMh6OOsTouItrrckijQcxgOKGslgzjAX40ZDw1HG62OMALYqzTWDxUqKloKG2DFwzQ1mGrDM8E1FWOa7pWGZTCDwLCKMYYnyzd4WnNdDYjMB5VXmCdI88zPKNQ9thP7Qc0dUMSRfie4frqit1my9t375i/eYsXj8G2BIDvG7777jmD6Tn79S3JeEZlPRQtbdMQDUfghxR5jg5GGG+AFwX4saJ2mu0hpcwyRsmUusoJlEEbn6atsbYhCn28MKCw0DYtad4J533joT0IvE5D2TTgG58uXLzrwra2C0SPht0971yL52mGw5jBMKZpDjjX9tfjdLz46Yjl9OEkep7+odSlx9K1W6l+JCWLZ5erVmE0DJKYurVo02k4rW0wWuHpY+NO66jrphN2a4W17VGW4CjKkiiK2e5+dHn/uQ8BjJ9Ggci98OkiJSL8UwbzdHGTheZTzdlwOOxZsJcvX34UfyPmjNOAcnHZSo+wGAGEOZtOp/3rXa1WtG3L48ePGQ6H7Ha7vmVHAqqFNRItpzBeAoqEhRmPxyilmE6nXF5e9mNbeW9Z1sVvPX36lMViwW9+8xt++9vf8uWXX/LkyRNevXpFFEU96yiGDGttP9J+//59t6E9yagUECTOYQF/pxo8oNccCvMnwFop1bOmDx484HA48PLly/5nS0zRaeyPgD4BFsJACmgRgC9ufNFGCoCSqkyg/zlAz6hKTqGAjE+zD0WjKWBMIpkWi0X/tZIWIPekgBsBsr7v8+jRI1arFa9eveLly5fEcdzfk0EQ8M033zCfz3tzk2xQmqbpGUgZW0ucjnRbi35XAuvhQ+WlgGTRkIq8Qe5fGRfLfXeqQTzVLooDX9ZGCVc/lTkIOJV76XSzd3qcJirI134KPk/bsKIo6mUqsokRc5O8xlO3utx7g8Gg/3v5VRzqf8rxJyQLezjXHG+shrLq2Ctcx6zhOpUU7qifsl2ECK6ra+tAZIOylrqpyNM9TZHSVnkXNdS0aG2wrUIrQ+taPM9QVQV1UTI4LqYWRV3kaCxJFBJHIXVVoHFkeQ5tQ1XXaKWPhiDVAYumoixzDnlOXh0fCm3DYDgmjAc0aKJk1GnnvK71I/AshzKjtp02FKdo2u4BmyQRTnsMh12lVhLHtOOWIs8p2xY/GtLagPt9ju/F/OyX/4b3L7/l1bt7wsfnGOPz6tUrBmdPcE2DhyMOfBwetrVcPZjQ2CuyvKQqDoT+gFESoY83Y5HX5HlNsc0ZJjVRZMjbmsDXhGFwrHLs8kKHw5AwhvHIp2ociQmYTSP0NGS92rFbr7l9f8flgzGjWQxOdTmUugsS36cZUTKkqit847Pfp+y2KYuz6fFBWHZmC+OBsowmE7bLlKoqGU8uSN09k1FBka+xZkRegTJRlyFqK6pqh7YWz1pUY4+B+JosS8mynKpuGU0HTKez4wO4QVPTNiVZ5rAOyuNu2NYlk/EYE0UEvumAmuuaE4KjzivPct6+esWDh0/wA5+6sWRFRVqU+FXFfnVHHA8onaGtLcr5OGupGsc+bwgDQxTH5E2BVpCWjmRyzWzxCO1qmnyLag/kdYaqCpyr0R5orTDakDUNRdUQKp8gDFBep430tI/VDt/XoDoDi+97KO3Q3pFdtG1XW1k1eNrQNAWDYURZFV18j+Ujd+P/TlN5Cjx1J8KkrdseQHie6x2AdV1j2w4kGt9R1BV+EBEEBgV4yhH4Hs5ZmrZ7MEVxhNIO3+8e7EWaUhYV7THm6sfjz38IaBQwIwuyLIDyNbJZOB3hya8i9j8cDn2EzKcLoYzTBGgVRdG7tYFeR3c6LgXIsuyjSCKgBwvCVqZp2ptQJEBdwr6Hw2G/uAtTeZqtKGyS53m9flPyAmVkLK9BciM3mw2e5/G3f/u3PH/+nFevXvHs2TN83+f58+dcXFz050cW2bZtub6+pm27rmhh9aRSUkbuWZaxXq/7kaqM5AXwCLN2mqkojOJ8Pkcp1Y+k371714d3yzUVw9But+v1mMYYdrsdm82mj+bJ8/wjM8ZkMunBx2w26/9MYnbELS6stzCgco/IcyNN0480i5Ijefq1Yr6RXFCpqBTwKazr8qh3HwwGpGnK8+fP+5B7OZ9yP97f35MkSQ9+4IPBRNzvUu8oOsjZbNb3dctrFN3nKdiS6yL3vWRQymt1zvVM5Gn0lTxz5b2LlENae05fp2yg/nfPaQHL/XP6ZPwu94BzXSi+yEfatu01pdKcdFo4Ieyt3CtiGjoN9Zdx/p9y/GBAKS9ES2aeha76rSKm2zGJy7S1xwgSiUd2DttamrqlzFI8B3Vdstus8FRLftgzGU2p6pb3799jPA1OMYgnjM5GHA5Ldqs1w/GYIEqwdck2O+CHIYExKN2FO8dJ3DWS+IYsS7m9u2W32+KalrOzGUmcEA9G3egYi7KW5fKebH+gVQY/yhhOZoRBRF1kXaizAVvWtGVNVXeMaxTHWB10esy65JDleJ6PQh91jIbGeSh/ilaOtspRpmVx+Zg8MCzXW7S1NFWDaxpoLX7g0eA69omKs/mIwegx1kK63xJHGl9bmjKnrmqCMCGONPfLN2SHnIcPHzCIE/L8QBgGgOxKA84GMU3bMBxFbHYZo8mY0TghTQ/4XkmRWbJsz26nGMSaNHXEceeAnM3ntA6U5zEejdDKkOddTdZ4PMZ4PodD2hlGggDPh1DFeP6QtoXEG1C4JeOhz5vbO5xfU1UGE4FJEjztkQRDKrsj36TYdMdhuwUaNpstTdMymow7RlRrWtswSAJsW7Ld3hPEI5Tn45mAQZRws1qhrCNYGIq6pm0avCgC59isNxjt8fDyitv371nfv2cynYFzLC4f88vRnNVyRZbl3L95TjI9w3gReZ5RFA3KJKAMg+EY7UfUVQ2upcIn+P+x91/Nkhxpdjb6uAidcuvapaBazwz5cc4d//8tacYZdqMH6AYKJbZOnaHD/VxEvlFZmCa/mnN67hBmZQWxRWZkhMfy9S4RJgTRmCRQlOaeaveOzpUoq7CBQeFp2obOOvKiou0gQPUyiwC6rqJ1PSDcbrc41xAnEUlmwTcYozCqT0owRhEGffd7XZVo3dJ1NUEQfsIqHDsJ/5ae8njBUgcwqejBq9EBSvUPA2s+9gcHYQ/Kw8bxtFgSBpYoCLCaHugrMFpTH6paveujjmxg6bqWtjmAlq4j/g80MPxyfN5xnDMpTIO4viUG5NjhfXwIEJORojycFosFSvUh4AJC3r9/PzxYxcCy2Wx4fHwcDCnC8Ih7Wh5kAnpkLH1zc8NqtaLrOi4uLgZ9ohzOOR4eHoYuZ+knlopEedALgBanqrxfcd+Ks1zAq/xsYTsFWFxfX7NYLAY3sTSeiHP6eOR4dnY21C5uNptPwsRlpJ2m6ZAj+fr1a9I0/Xfh2QJEu65jMpmwWq0Gjeh2u0VCsrfb7aCBlJ8dRdGg3zPGDNpQaTQSGcFmsxmkBMc1hPK+oI+W+fDhw+C6Ft2pOMXX6/UQ0bNYLAZGWVzt4/F42HAIOybB8vJZiKHKOcfl5eWgHxW5wGKxwBjDixcvuLm54e7ubmgyur6+HtqZ9vv94PwWjaWYkZTqm5QEGMv1LWNocTrLaPi44UtA1jEoFiAuMUHOuaGeMk3TYbPz87GxADppj5KfJ3+LrvX4Hvw/HccTJ2C4l4UdP9ZKSwRX0zQ8Pj4OxqWfs5QCtoX5FA2l3EPHsVOfc/wHAGU/GuscOAdGW4zRJImFo6BkCTh2rnd5wsFI4A5uUK2h7X+I62o6V1PXFbvdFqM189kJV5cXOAdl0aDpK/ratqBuG5qqxNg+k1E5R7Hrb2KMIYtjiqLj5uGJ1eKJrmlYr5ZYpdisH5lPT7h+9eWhv9qQxBGT8YhdXvKwWPPdt/9GlGR8+dXrnuWzltgYJvMReV6R5x2Pj4/cv9sRJRnj2SltXTCazCjyHOgFz65r6dD4zmICi4lS9sUGYxLS6Snvv/8A9Y4XL7/A2qDXBU3GdB6SZEQbKPB9k04YWIJJQhpblG/RKsDaPoYpSVPOz05ZrRasV2sCMyfLxiRJ1L+GrsH5jo4SG9T86teXrDe9FiiMDLPxiCzSdHXBel2QFwVlEcM0oapbWteQjUbMZhOKssZ1GqUVSZKx3+c8Pj2RpglB2EfHeN8ShYaqdkRhBmV3CGKfgi+ZpIblZoFxMVFkUXV/g+5dTr56oN2uafcbqrIPe6/a/qYbjcc459jvdrR1QxdqqrpiudkynZ9zcnoOylDlOevVinK3Yz6Zsl1v6Lq21+Hme1arJXXVcHZ2irKazfKBk5MZqIDGgQ5jtLHstivauqKucrLZBbFJ0YlFxxnpaE50YFtsMsJaTdga0vSMSTqDrsS5hnHiaHJHXe8oyz0j39G0DUVdsNs7TDjqZQK2z1717WH82IJW9Car2PQbiQAiGxKGAeDQSvfRTdWeUZayz1fDhs+EwSeuvY+L1ccKxo/39M++xnuUMWg+9sV63xBae3BsK6IwIgj7zeNkOibNMqAjiQPK+BAGjKJz/TrQtjVBHBCFATjR4ygCazA2/OyF6pfj8w55oAj7KKNQYYCO9WPyIBGWWv7f8VhPQKbEkMh1dnp6yvX19eAoBgZHtDA7xzo1qfUTVtB7z+3tLY+PjzRNw2KxQGvNYrHg5OSEr776ahgdSrvMfr/n/v6ef/mXfyHLMn79618PJgprLefn50MA+t3d3TAuPTk5GUwckjkpr0Xen1Jq6KcW88+3335LXdd89dVXBEHAer1mOp3ivR/GswIAwjAcGDcZ5coDPssyrq6ueHp6GsCSRC0JQyafl7WW3//+9wNjKgHpwvBK9WVRFMzn88GVPZ1OOTk5+cQkkmXZUGl5nO8pAFYArwR9TyaTYYMg0gQZb8u49enpaXBlixFHwMcxsBZWbL/fs1wuOTs7G0LOJYtzt9txcnIyhKJ3Xcdut+Pp6Ymqqri8vBzc1GdnZ4MWWFjC9Xo9aEhPTk4GZ7pIHeScCQsqphwB7gIGJYBfAKRspLbb7QDCZOwtemHRg0oAv/yOIAg+qTQU3fF4PB4aoQTU/zxdQe6Vn7OVP1+nj1lNWadlYySvS0oB5FqWZilx+ct6cJxUIN8j7x8YRvafe3w+oDSGpqzYbPeMsqzfndLfAN65g7brEBV0OCnWGJTzNM718UForDGURU1gNPP5CevVEhM0KO1py4IsTdlstiRJ1scC2YCuiwiCOZvNmrIoiEJD09T4rqapW/arB7IkpU1iNvuc7fKRQMPF1Rm//dVXeNfx9u0bHu4eyIs9F8+umUymKDLiJObs/IRsMiNOxzw8PnH74S1aObSCLM6Yz04Ig4BuvyJ/uqGrKvLdE8XqlmxyQnN2QTqa03jFuliD1jhtSCYnuLZFGcvJxTNcvqFee+LshDfvP3B29ZzJyRm1a4msoa08ffGdIbCWuqnA6/6B7hz4jmKXkyQZnXPEQcjF+QyjahSGpqpxXUsQ9iClo+tHkErj6JhOYqLAUVUlXVOSxDGjZ6dENuQvf3nPh5sbVpsNp2c962cDxb7I6Q6Zjd5prFFMp1Oc7zU6KEVmEpIgomsbLB7rPUZZOpPiQphdfUHjSrJ8S1uWxE1OmzfsNw+EcUrb1XRtzSbPWdw+khjN2cmcsANPR3swg9G2ZFHEdlNyc39P5z3j6TltU+Pamrvbd/i25vT8HN91dE1Nlsa0bUlV7ZjOx6zXGzoc88NDJk1CbJRRlA3L9YZ9VfC0XJJFAZvFHbvNktPrL8kmU5xt0aqhrHoNa5r1rvvxfIwiodMxTdVvfALdkoQRytUoYJ/v8GGK0z1b79sGa0DrvjkpiPr7Q1tD02zBN0xHM9LIkMSGyAYYpel8jVIGceDs8z111RLEYf/9WvWf1aCHky/9dNE6FnV/sli5vrGnc/RspbZY62m7lixODg9kcKpjMsn6qUBXE9i+PMBoTdN2B11tjbWecRaRRAFKecLQUNUtaRjRuX+fv/bL8f/fIayD1NIJ6DnWdB2L++WhJOMvuQ5EhyYaOOm4FgCRZRmr1Yo0TYcqOtEzCuiREbYwh4vFYqgf3G63Qy/0xcUFf/jDH3DO8de//pUPHz6Q5znPnz8fRrFJknB5ecl0OiXLMu7v73n79u3weqWPW1hPyYfcbrdD0PbFxUW/djk3mBNEXymj+2fPnpHn+ZBf+Mc//pHnz59zdnY2gBkB1gIoZHQvWjbvPev1egCMSZJwdXU1vFYBkMcAT86tmEiOnctyjoMg4M9//jNv375ltVpxcXExgM7dbjeAIfmcxWkv0Uxa62Hkf8xkCzt3fX09tKUI0yvnUHSF7UFb/+HDhwHEC6AUdlt0hKvVig8fPuC95+TkZLgW3r59S9u2w++T0HSROwjI9N4P4/osywbwK80+j4+PQ7LAarXi5cuXw7mTEfexrlI6voVBlnYkSSmQ8b0AMXm/x4HwAhYFXDrnBvB63NMumxT5zPf7/VDvKecd/laL2Weu0z/778csvYBkYUTFDd91/bosUVOiAxXALJILeZ8SxSW/43OO/4ApR1M1NXleEMe9GaCf7Td4NK3rsyYDG9J1fTYhOJxrD203HaHVONdRVQXeedrGobTl5OScar9BBb3mKs9L7h8fGU9nxElKXpWMgzGdMjQOXN1SlSV1kdNUJVWVk0UxNorR1hIYRWAti8dHtuu+xP7kZMZ8PuXx8YmbDz+x2Y548eIFXo3oihznLaMsxtorUIr9dsu7t29Y3C95un/A6r5mUvmO2ShCaQ0YfLNjfVvwvvgOFURM56eMpzNsFLH4sCBIxqTjE0oDmTXYJOX87IJ33xseF0v+n/Nz7GQExuONonMe71XvznaepqlxXUcYaZTub1jnFYGNmEyy3jDUZAQ2IRtN8HQsVg/Y0KJ0wNPjlihN8VjKsiAODMYbtmWBU4Y4TpjPRlxczrl/vGWz2+B43ucL2pDW+X7EHkYEUUxR7Xp9nDVUdY0uNNr2gbxpHBAoRx15yrIBQkrXEoQp0eiMcV3QFHs601K3LWVRUHclWTZicnbBer0knZwxDgzT+Yy6rXGupms7qrLFu5btvmK1L7lblpyczjBRP87XruXh9gOXFxdMRil1kRMHlraBFiirvA9QH4/x3uA7R6g8ZZHT5iXJaMLzF885v7ji7OySp/s71k937LZrwsU9o8kc5zrqtqFVIXndkY1S0nTU63ObPhTcuQLvS7quZr/NCQNLWzcsnp4Yn50QTyMukozOa6LIEBhHYDVNVQEBvlPsthu6tmE6zhiNooPZqo+JCnWKx9PUJVHSj0uUtigM0tN9vJB9XJ+OGUqJ+PrY6dx/bd+KA7342XvQSuOUomvbPiaqqfF4tNVQd1ircHX/2UinuPf0rnYLaRIwnSRY0y9+SRpjbEPnNLr9RUT59z5Ez7jf7wc9HnzU1crDQR4+AjCPHcYS2SOGEdH4CQMoD608z7m7u2M+nw8u1mPmQ0Kvi6IYNHwyohVWMQgC7u/vWa1WQ+vO2dkZd3d3vH37lvV6zRdffAEwOIEFKCulWK/X/Pjjj0NrzbF5QUa/omm7ubnh+++/JwiCIZw7DEPevn07sKC73W4Yy19eXvLtt9/y8PDA1dXV8PPEyCYgRMbiErci43MB8qL5lFB0YQJlVGyM4f7+fgjDFkMJfMwVlXig6+trbm9vWa/Xn4BHMVdI0LuAKdGXihFG9KwSoi6xSjL2lo5y+frjgHupt1wsFkynU6Io4uzsbJADtG076F43m82waZCcRxkVf/jwgWfPnjEejz8JRZf3XpYlk8lkuI6AIQR9PB7z+srgDO8AAKTzSURBVPVrnj17xtXVFTc3Nzw8PHySNSmGGgGVk8lkANICAuUaAQYmUhqVLi8vhy5659wn+klJFJCRt7DDAlrl3hIJgRhe4G8HmP+twPK/5faWr5Pr+fjrjv9ZQPuxIQ8Yrj/ZFMl7kHsxy7Khk11ijo4d4J97fH714uGCE7QrhyTBa61wXhzgDqM13h31Rpq+RaPVhjQZ0WoIdN/tqzAobQmSlNZ7xrM52XROVTeowKJdQO0cYToijFOM8njnKIsdu80am29p65q6aolVQBSPieOIKM6IDmO4zvWjz+nJGdqE1E3N+/cfGE+mWBtS1Y4kGTOZXRCnp4RhSd3G7HcrvK9pqj068qSRpW06AhuTRAlK9YxAVZa8+/F72t0KVV5AYJmcnNM1O8I2ZGwT0gACE6LWmvk4ZbNcsNtuuLh8hQpDytofHM19VmFdO9rKE4UReIXCMZ2MDotzRRhWPagMQtquJYosQRzxtPYURUO+b9msGtIJTKYhm9WOJlIY5UnTmLJqKasdxsZcXp6y3V2zXDziOYzLbIBrKuqqo3MNYRiw35co+p1uURQUZd67+X1PtTs6+mu473fe7nK014xPnoFv2e7W7J/usUHEyemcza5gtVkyUVO+/tVvebp9T1CXpEmA71q6pqT2HU3Y6zLX+x0mO+f17BmjUYgJE0zg2TxusDZgPpv3Y++84PTshHy/pW5bTBgQxSlN5xmNMrSC3c0N+82WZDKjaVq0VSgbcfH8C8J0TpCMybYrmqrkzfd/IoxHBPGYZHLG2eVzRoGnLTZ0ZU2gQwLVUbV7tK9RGurOEcYJnVdUZc1MK7I0IsxmaBOhtKOtczQe0AQ2oGr7dAStQVtFEPbMHnwMsxU2qV+k+wXSeU/Xdnj984VKwGMPLvuFx/874NkvSrLzPYzMPXjVm4lEk2nLEucArYea1V7S4g7aXfrXbyDNIoJQE8ch0NG2fYc7ODrX0v6SGvR3P8SJe3p6+okO8eM6rQcABAxsivzzcdxPlmWDqUDMCUqpwaQhvdKiPZSxZZZlA9Mhrt7VajWwNAIsBdzImFJGml3XcXZ2Nhgj3rx5MzBP8oA+OTkZTC7Cmok5Q0BV0zTDyFwpNYxH//KXv3zSMy7sozzjxMCw2WyGWsDVasXl5eWQdiDnVEwtMj6G/r6az+dDA0sURcxms0+yLOM4ZrFYUBQFu92O1WrFZDIZfp9oMSWcXsD69fX1AJ4EUApbJsynxBxB33str0PG6nIcyyCkPUa0iseO+vPz84HpBfjDH/7A+/fvB6ArzCQwjE03mw2j0WjQVYpuU3JKpT1nv99zcXHBdrsddLXiyBaH+rt37wa5gXym1lpevnw5XGvb7ZayLPnTn/40sIWz2WyIhBKgKtrBY5Akn4kAUbn2ZSMg144cx9mswkz/PP5JNmof1+n2k03bMZD8W+7un4PGv8VoCtA8NuuI0eu4sUiYTvkjLKn8f4n0Eg2raEhlPflPAZSCZuVCl51g20rxeM9J9g2L/vAiDoX0zqNVS9c5iqpCe0XdOOp9RXZgeMqqpq4LlIaxCZlMJyTa9kYBYynLCu9hPJr0hoUgJEoz0mxCFAWUedmHWIt+5WACQPfu8yQwjEcpTdXwFD7w8PiAx7Neb0iTjNl0zngyxvmO3S6nbBSvf/UHqqrg9u49gfEY29dLlkXF4mlFUTpCqwhUwOnZBUmaUuU5Zb5ls1mzfrzn5PIZIS0qC2hLT1Ptebp7SxJZTBzw9PDI6RcO2o6u8+z3BSiNswqPJi8K9vsGGyim0wyUo64dHC7ytksJwgRfVxR1TtF2GKvxxFRVx+3dhvLdHf/4T19yMjnBtXuaruq707VCa0dZ7wijmFcvLxilFkVHnu+IQ4vWijhKaT2s13vqpqVrHV3boSRf1PU93y7u42HCIMBYoO6TSPdFSxymxKMLLp4VGKNoyookSphNMjyGqmlBWawN2W82WBpcUxNZy/nlFToao+MZJ6/7+szl8oG22qMDg6MmTkbMT05xnetlBnTgWqw2OANpOsGGcR9nheLs9JTNcsX97R3XcYIxIa6qUUFK3rYQjUnn19hoRL1b0N5/YH37hrrpSCanaF8Tag9BBrVHqRBFSKgKXOBoqoowyyCMKRpPfLhp48AQBgqUo2kr2qYE9CFWZc7dzSNBYHj+/BnQt+NEYYhz3bDDl1Gb1pq6PjiyjaFzHcJCyuLWr0efF3B+/LDx3vdso+sftJ3rP2fve31W6xzh8AAFa3vg2DQtSnu8b4gigzEeaHGHn6cUaKPQ7peWnP+MQ0as4tr8uE63wwNErg1hH+FjZduxO1n0c/v9fog9ETe36MPm8/nw4JcHmfd9nqCMdaWrWzSK4kYV8CoPZmBg8KTz+/7+Hu/7nEEBktPpFGDQ6v3ud7+jLMvBKCQgIM9z7u/vBxbPGMPl5eUQrL3b7Viv1wMDCT0AE4D44cOHodf6/v6er7/++hOtnzzE4SODJqHuAkLk72Pji7iU5XXK7/rhhx/453/+Z+bz+SATEK2csJ5hGPLll18yGo0Qo5Q8k8VBvlqtBi2rgAFhIUVTKK9HNgve+8EoNJ1Oefny5SB7EHAGDHE0YmgBBpAn7TZJkgwVnI+Pj8OGA3qjlAB4ufaOpRgCbuR1X1xc8PT0xPv37z8xPInUIAxDzs7OBlB5e3vLzc3N0Kwk17xsRuSekDVUxunCUE4mk2FDcWzSEcYvDENOT095//49YRjyxRdffGLAOb7X5HfI1EDus2Pn9P/NkPPvdfA/by/7GAN2zLoe66d/Xvcprn3ZXArAFNmL/Fy5n//TXN7HlCsw7Oh6fcHHqKB+snYYeLuuByBdRxTGgCOKUnRg8V2Lch2+rcD3FXsm1P0HemhS6Np+t6DxRGFA50EbTVE0eOcJAos3AQ5LEKdMowTloXMd2vQNNLv9jrqtaTFEXYBThtnpM4J4zHazZLNZolE0VUlTFDRdya4tSOdXONURpBmnz14TBgFxHLLbbjiNYuL5ijAM6Nqa1eM9UaCYX17TNSX77ZZ9nvP9t99ysVoxPTmFOuf8ZM5++cjtu59wbYULYhZPj5TlHm1GhMGY7aamKGtGszFV3bLPS8qiYjIZo3RBGNm+4i605GWN2mwZZWPCJKWuC3a7PShLWTvywrNaVb0+z/fatmKXMxlHTKcj1ts1zjes1gsmkzOyLAI/pqlrtOrZ4ySKaZuWXV6xXOdEccRoPCaJI+o6YjROCKxG0dEe2GmlDWFgKLUnyUbsNmseVwWXp1NGs3OUL7h7+wbrK1TX8OHDHVUHjTfkeUWZV/jWo8OM6XzEs+evyVvNulLgNE3ZoHRIWa2o8y1ns4QgirA2YLVcoXwvtXh8esIEAWmUMJudUDUttevNBV3bkSYp7z68I356IpuCihyz8xlNo8CEONthU41VnmqzoCsMsyyi7Aoe3/0FXEc8nuEJcJ0ink4IyME6ylYzO7nuN05Nx/T0kmw0IUliotiy3RW9Q1tBGISgNd1hM3D9/IqTkxM2mzXagPMtio8mCmMNWplP9Ed9aG3xiXv3eJcr7OPPgeTxAvXz78GLqaKjriu6QzOV930oubWWzh9reCxtWx+qF1ucV3g6tAlomo/C/q5zgCIIDL8cf9/jWBsHH3Mm5cEmD5vjz1zGWsLUeO8HFlI+MzHoSDPJbDYbjAUytoWP+YXGmIEVO24aEYAGfBI9tNvthoe2AMyLiwuSJGG1WrFa9cYzGZHKtS/gLcsyXr58OTCS6/WaFy9ecHJyMrCz9/f3hGHIs2fPBp3pfr/nX/7lX4YgcRm7LxYL3rx5M4Cl+/v7wYQShiHr9foTY4yYVMRxLKNEycjUun+2CeMoYKwsyyFWSECXVN5NJpOhEtJ7z9PT01C3KMYJAazCyO52u8FVPZ1Oh983mUwGPa0AOQFawlKt12uenp64uLjg5OQE5xxv3rwZrhHRPooG9bjT/fT0lNevXw8JAcJMG2OGGKiTk5NBN/r09DQwrPK5yM8RAJ7n+bC+vXnzhvv7+8HdL+5w0TdKE816vSbP8yF66aeffho2OHLNyXsTZlf0nVVVcX5+PkRUSWi9aGZlSiNSgtevX3N2dsZqtRqYf1lf5R4Qlr1pmuHnSj7nz81xxyzk31qn/5aJ5+dfI9pXWdeFbfy51lKY2OPXLGHmgu2OHe6fe/yHAeXPUewnVYtaHXIo5esN6IPTUBuqqsHakDiIcW1LVeRATRQE4CKmowxtNFVR4l1LWfXRC0ZrbBASJSnlfovCUZctTdXbxzvcgSmFKIoJoxgPPTulQ5rtFhVFNCpml2+xXhGlMzqvCOMY2o7NasluuyNKRhQdNKqj9TVOxdTO0HqL15pdDt4ENCpBm4g4mXAWjfCuod7vaFoIRiGvvg5Is8mh91jz/t07Hm9vSAPY7taU+Q6dZEz3O/LdDqummDBlvy/Y7guy2YwwisjGju12x3qzYZdr4jji9HRGV7VkWco+byjKTQ+gXYO2h27TyHJz9z2PiyfmJzH7fc5+rwlMb2hqBKx7hdGGssyZz0+ITqZsVuv+4jCaKA5YLVas13tWm4KkdQRhQBgZJpOMrrH9yNspqrrPo9TKkaUxZdXQtqBMzLYomLSWJJhigzHLpxU/3r/ldDJi9bRifvaMLErI4hF3jWe9WzGfnKPiiH2jqVyfZ+qqEl96rHakSUKd11RFyWL9xIf3N/i2IY0iojii8zWjMCUIYibjKXlZkS8eSeKYOAqZTKcUf/meNz/8wNULx8llf122ba/ljRKFqw1xEhKqjlESsV09UXUlTb7hx3/7F8I4I4xGXJ5fouKOx8UNu2LD7PSC7NU5++UOYwNsPCWIU0xoca6lbSu0hjCKCIIQT995GwT28EejtUOpfpxtMB/vvz7wdRh5VFVF0zaEUfKJvut4sZDvPR6bHx/ysz7uinvg2DYN1aG7u9cfeaqqb8vSocZ3R0G7yh/YcU/nWkIbonVAENg+RqVsqKo+QsMEIUmc8cvx9z0+uvM/yo1+Puo8HoMJAyYTKGGAZIwnGjpgqEycTqefjNWOx4kyspRGEvl/8vsFmEojCTBo+tbr9QA2ZWQrVYnCvj09PbHZbIbecHlv8qAXtiXP8+FBKJE3MtY81mL+9re//STk+s2bN3z48GEY20rV33a7HZpgoiga/l2A9XQ6Zb1es1qt2G63ZFnG2dkZzjnG4/GQqymsj5hpkiThw4cP3N/fc3Z2NnyvABEB6/JZFUXB6ekpURQNcU7y/h4fH1kul0POo4zvZVQsI1dh+IBPDDjGGPb7/QCiwzDk4eGB29tb5vP5oC+U8aiEqs9ms6GqUD4Tcf4L2Nvv9xRFwXK55M2bN0MAvmxchBWcTqcURcH9/f3wmc1mM/70pz/x3Xff8fr168HM07YtWZaRZRl1XQ+Zo2maDucgz3P++Mc/Dk7sq6urwdS12+24uLhgPp/z8PAw/L7jelA5//L6gAH8H+dP/hwUCqCU++zYCCRMq/w/ceXLfXsMHP9P6/TxPS7AsS8z8UOAvuhohTmV7xUjlrxOeS8yyj/WPMsG7XOPzw82P3pfx+7AHg0fxFTKow8Pus7141CU6vWOVUFdVzjXYZSiaXvnt7WGtiyp8z3V7hA427YkSdQzfdsN3jtsGPYMZRAwm8/6BS+MsIHBGkVbNbTO9WHobYtXmnQ0JcxmZCalH7xpVKDwzpPXJbvSUewKuro4BGAruqZBeY+rtqwXjnXusckUpbbUTUu53zOdzUgnY/K8Yb/rOJnP2W0rNnmFVoZpNul1oJNTbm9u8L6jKnbk5Z4oiIjSjCSNmZ9fMZ5McZ2jax2ehqbp2G535HlBmCZM5yM22yXb7R46zXrb76ovry9wLqfznpsP79jvK4Io5Pr5FbN5xGazp/MtnWooqpa26+OZTi6mdE1OkfffGycZo9EEhaYuy4Htcm3fVW5waNXr6pbLDQ9PS9qmIkmeARYbaIr9QYzvDNb24MhqMKY3a5koYrNaE6xLTrKI9coTpicE4RN13XD97AqbTHBBRqxTWhWjlinJJMNrR65CmrYi365Zr346aK9i2q4gtb1A8P3bt+R5ziTL0DagcZ6qapmFCVk2JrABgelwdY2rS5q67AN9pzP++uZHlDbM5mcU2w37RtM5A9qilKGoHA0RdnxG0Hl8fY/ynmq34f7mHVaFfPj+W+IowKaaVZ7zzR/+meetoVEZUTbGJjOS8RxlLEVZom0fz5PEGQ7FPi8oiorxOCQIDEWxo3MNStlhg+YOD6Ie9KkDk5TSN1N1BNYCHxtuhJmSheN4Efv5IdpK57tevKJ6+ULnOhQeY01vSlP9eE/pgChSw4Oub9Jyh9/VoZTGmp4Ryfcl202O1oYsnRDHI54WC3bbp89efn45Pu/4W2J9EeT/rQ3GcVOHjERFi3ecU2etHZgmqewTULDdboeYGWlSsdZyeno6xKrIA0vMKxL2rJQaTBPHD8rj+BIZkx+PTsVcIQHXu91uGPNLXauMx8UBfXZ2NriW5ffK17x7925g1QSMCli5vLwczB4SyyL1dvv9njRNOTk5YbVaDY5qMaW8ePFiAL1v375lt9sRRRGvX7/m9PSU1Wo1AAhp1qnrmmfPng3vwzk3yAaEpRW3u7CNx3roxWLBw8PD4BAHhpGvsJQybhcWTbS3Em00Ho8HmYE4fl+8eDG4zY9dz9PpdFhbhPmV75Vgcdko/Pjjj4OEQuQOAnqERRVd6rE+9eTkhO+++w6t9QC8xQAka6IAqslkgtSCip707du3GGP49ttvB9C42+34r//1v/LrX/8apfoa0CzLmEwmn2ysoB/Vy8/K85zJZDKwz8f3lZyX46xXYfaBwVkNfLJOC/j8OVN5fK/K5/zziZL8DBmxyz+L01uirOSzPh6Ty2uV9yYd9mJKenh4GKpPP+f4bEDpkTGYvBP6+BN/jMQPuWWHXajRhyZv71Fa9eaRpqasa9rO0dQ1Vblnu7jn5t2PNGVBGFpCa1l3LU1V4X3vLrVBQF5XmMASmQ7v4MPNDVmS8eLVK2wQYIyldTW7vMBhiEczTBAzjWfkZcNuV9B4RVsVlHnFbl2yWW0ot0uuLk45m8+IgoCqaahaUNbSpYZtmRNnmkg7mqJg+bhG+QvCOCbPC4rNsq+CPOhYTJjQ4VGR5fL1CNfVLO7fs120tF4xOztjksbE6YjKOeqqIsk8TdtibUBVt2x2ORfjjDgOOLuYkWQJYZBQVQ3Ffs9qsWI8PdyADdzdrXFOURSer74JQGvC2GKCjjRLmExSrO3HmGEYUhY5HR6tLK6DyXTC8umBtm0YZ2N0HNDUFajeZJMkKVGUsH544PbulukkZj59jlWaLjB458j3Bd4pTKhIshFp3VCucpJRQrBJuXncEMfPaMNLoumO356d0hVLojjizc0jZdNQNhsUAensnBKwgYau4+n2HcsPb+iKHUutODu/ZDzN8Epze39Hvt1xfX3Ns8srAhvy15/eHJiwmCjOyPOqrw00isXTXZ/T6frIkrIqKeqaKt8RJ1MinVBuNzgTEFgDGFyQUTcdKp0TljWqLVC+I1SwWW7Zr5cE8ykXV894/uoV47MLqkbhTUaUnRJmM5QNKeqG1nVEUULdNjil2O0K1usNURTiXIsHOteQxFFf5ekd2mhU+3EB6Jz0CytOTuaUdTUsGt57lO4TFY51cwIwfr5QDQDkcHN7PF7G064F5Y/YIEVRVgShBvqSA2sN4Om6hrZt+oihbIw5hN6v1xuMsYRhTBQllFWFwlDX5WcvVL8c/78dSqmBFRSdmjy45L8fP8BkDCoavmOQ+fj4yI8//jgYX+ThJQBUHpbCgMnv++mnnxiNRnz55ZefRLIIiyng4uzsbAjjFoZlv9+zWq0GZlJaYgTkCBgZjUZDJaRoNe/u7gZ2c7/fD8HjwugJ4xRFEV9++SVd1w3ZmN73kTWSFyngVeJtRHu42WwYj8ekacrV1dWQ91iW5RBfNJ/PhzHjzc3NwOD99re/HWQAAmAlhNx7P7wPuUfFCPXw8DC4ro8biMTFHUURt7e3vHv3jul0ynw+H9hj59xw3sWBLiHs8vNub2+H8zObzbi8vBy0lD/99BNlWQ760NlsNqw7bdvy/v173r17R1H08purq6tBE3pzc8Nms+Hly5c8f/6cMAz5/vvvh4gp6ZqWUfb9/f0wer26uhqYMwHxxpi+Hvhwzco1LiH+4uyGHrxJWP3p6SnPnz/nq6++GkbnxpgBUMpnK4y3sNciJxC2HD4N/hbNsGw65DrfbDYY01dlCug9DhiXTd3xOn0sXZJ789jhLcexbhI+brTkGjvOxJRzJOzucaSS1JvKZEISAIAhGP5zjs9nKI+wpD6wGW1bH6w4fX902zq6ukIrR1OWtAoCqw/ifnBdH0MSmBAbp+wfGrbrJdvVI7fv3lLsC7JRQhhpOJg9NAqrNWWpKMuCbDzi4d1bLi8veHY+JS8qfvzh3+hQnJ6eMRpPaRuHjVOiOKRyis5rqs6y2rUsn54IDr3i+8qjg4wwcazXe6p9znySEUchWTqmaCrScIxXhiDojT6z5ByjNEaFOOeZzidsths2yzu0MsRmTuMctYI4SfA+JJvMaL0GHdDuH4mThOl8QtU5omREGPZucec1YRyibUBeVv3osKsIAw1xRBSmzMYR9aSm62o631P+J/M5223NalXw4f0CbWOePTvn/HROWxfMZwmTUUwc9tFHeI82Ft95yqKhLCpGWR9/8Pj0QBrHWNMv/EEYEMcJE+Dq/ITdZkG531PsdmxXawKriUKL832QelEWRAqiqMUYjwlcH5CeZuS5o3YhOj0j7GqiqMKnE+5v39Gpw8Oua3qgG2UkyYgwDIio+VCULJ8eGYUapTyrxT11HjIbZ9y++0DTdKggZlnUhKGmVSE21PT2HI9R9F3yaO5vl9SV5vziks7Ds+tn3N3ecvv2B1zjyE6u8b7X/NpkTBilNE3MriyIshkXcUy1fqA0AePRnNmkIt9uGE8zkmzEZH6GjjMIIrJ0SpyOsTam8wptLKpzKGNp65a8bFhvtsMuOzhokquyIoxC2qFnVVMfGCVj9bCRU8qTZSM6108FjPnIRkrHttYWoxVt2/WfOx8DhIZ/Vj1zj1J96YDrcL47gFlL13mCKGa3LdjvCyZGWj5aus6jjT5MJXp7nvSRV1VNU3eEWYpSlqbxbLclZeU5PAd/Of4TjuONwrFLU1gkAQSiDxPGStgO+AhQ7u/vWS6Xg65Q4lvkwXo8XgcGBu2nn37qN3mHbMfvvvtuAGoyhpWHl/zetm0Hh7G8fhm9pWk6uMVlzJpl2SfGA3ETi2lFRv5pmrJer4cx8XG/uTiVJbRcqT6PULItm6YZAJY80CUcWmJkBNgKoBP3u2zExuMxp6enA3v3008/DU7li4uLof5QTCEyapXoHqlwHI/Hw/g5SZJPtJoymnz27Bmr1Wpo1pER7bHbOM/zYcQrv8e5vtZyv98P52w2mw06zHfv3g3XkFwjMrYXuURRFDw8PAwNMI+Pj+z3e6bT6aBJlaxQATvCav9cO/j+/XuqquLZs2d473n58iXv37/nxx9/pG3bwZEu16noSPM8HzSQq9UKa+3goBfn/ng8Hsw8cm2JPlgAnzCHAs4kbP7YFCnmsZ/3YR9v4I6be47H6Mc6S2EVZer7tyKGfg4ojw048nuEaZbOctGYyv11LIkCPsnslDD6YwZe+uU/9/gPdHn32XRK9Q+gtu1Hw0opfHdwNGnTM5FKk1d7FA6MwiuFCSKKsibA9tPxpqPO97TFDu1rAmtwUULTOUZJhg0V+X6PUbqPdPFgbEBoQ7bbFT9uN5xfnnF5eYkzlvunBevFE+2+xMQR4Cn2GzqTkXeOfQFhOiHYFzzdvqOuCqpixziL8Cphu9nQlVtu379jNk24un7O9OIam4Xs7td0KCI7xrmOIEwIVUTVFhjVMY4MroKnxweK1QJrQ2anM6Jgym5fUdcTlImYnV6R+4bd9pa22uPCmBeXX2GjjDDMKEsNNsBGMVVd07YNcWjBO4o8J9QZbdfQ1DVxEhAnKWVVEASe6TRjv2/J85b1siBLck5PMn7zzSvGmSW2mijQBIGmrgq0Nr35RntGaYb3HVEcYEx/wbVdhzUBRocE1hPaimcXYwwv+NO3f2K7XqFfXuOdQxtFVbeEcUBVNcQqpaz2eCqy1FCWkIYRATnbTc54HFP4iCpIGWcZwS4n8x1mvyFKoVOG1hqqpqYtC+ompykLrA04u7rsNy9NTRoFVPuC9dOK2dkZKhqxVyHR6Ixw37F+eiAvG0ZZ3eeItg1N7bh/yGl8wsXzEK813vfO0Nt3b7n56S1f/f6/Mbv6EhuFxHFA2VRsdjvqtmM0yTgbn/HQddSNZ7FcgVPY0YjZ2SmtUiy3BbOpJclGJNNzbJASBhHG9BFATeeo2w5rI+q6pXOOyFr0IUcSpwhsSGADyk5yAVuMVZT7CmN7IX7v1uzTE8Iw+mSnq7Ui1JauaWiqglGaocwhYuJwRzvv+8apw4NXAb7rm3g6pXG+RWuFVgbnG9ABeblhvy8YT2b9iFu5Hsja4PBzDGEY4zrPfr+mqRtsENI0jjAKcV7hlKWqFXXzS7D53/v41Ij1se9XwCIwPHjkgSj/TR6UApKgB6PS1SwPLYnqEZAlozVhciRfcr1es9lsBlCpteb+/p6npyfyPB8MQGJSOAYD2+12ABQCpJRSLBYLyrLkzZs3zOdzXr16NTi3b25uPhn7iTZTGDxh8+7u7gagcX5+ThD0bWXiYL64uOD+/n54oAZBwB/+8Ich7kcyGiVoW0CSmFWOcyElGklA8Xw+Hwwti8WCLMs4Pz/nd7/73dCDLn9ElyqvS0KmpcZQQMJxr3nbtjx79gyA//W//hfL5ZIvv/zy3+lM5WcLYyYxcKLvkxgjAWwSZO9933p0DNhFvyfANwgCnj9/jqQICEP88PDAxcXFIGcYjUaMRiMeHx8HdlnAeV3X3N3dAfDy5csBCFlrefPmDT/88AP/5b/8l0+c5XVds16vByPPcSal1GiORiMuLy/x3g/vcTweD7FOwqDL6xA9qbD1xwH0wvIes/LyNdvtdmA6RW96rGcUMCnMoZiCxHD1c9OcsJfCXB6PxuVePmY3RSYiTnfZBBynBkRR/8yQa1/kBsdB75IW8LnHfyA26NCCgwfXuz6jsGch9nkOCqJAEwaG7XqBUQ7X1iil6byidT2Fn4Uxq7t7umJHvl0TWEMXhERJglcd2WRENk4p64J4FKI82K5jnGZ0TQddR5J0NHXB7e0ddw+PfP3rX/Pi+pr72wcCE1A3Leu7O7RNmJ49Jwg06rDrSJKA84tzmrpvMEniCNXV3OqA1eMt++0j7bbGPS6odcjzdEJkABy+c8TJmKrqaH17uEneUlVbxpOEySSmKlvy3Yabt0/kuwk2TKAsiMKYURajAtj6jv1uT2UbXpoAYyOU6oOplT7oBDy0dY0ZRVgTUtdb1ts91gQoffg6OOQAeuIo5PTkBO+3tE1LXZYUecfJszlxxCFYuu0Dpa1BYXq2yvX6DmX6CzFLM5zzuK6j84q67ructa6II8X1szNw33B3d8fj4yOnJxOcC/HekecF3gd0be9ENtYwzSI0LW2liKOQ/X5Hks3ARuyqhpP5jLPLa272jxTbJzbbLcn0Ahd1KJNhleLh5h33Nze8fPGCk4tz3r17i1UWE0R0rqXuGtLJFKdjnEkpfcwq7yjqjrJuKZuGJApIspSsbJmfnOGBME6Js5jtZkuSjZlNpqyWK1b3N5gw4yybUGzW5HVNsS+IgoDReMzN0z1F06JHYyJUX5WoWs5fvCBOMmoizOiSKIgxKMLQYky/8ep8R2ADnNc0TZ+MMLjurCaKQtbrFVEUEtiQqix7ZlFpuq45JCr4YeEKw0MrwsG9d2zCMLpvq/KupSxy0tEY5xSNAM+jHa/o2gIboBVUTUHT+KFG0diAoizwqENouUEdcsKCQPftS50jjvqR0X6/Z73d4pyibQ9i8LJmn5dstnu22x1x9Pkdsb8cn3ccBzaLBlGMAxJ1Iw9OYeskF0+uAwFONzc3FEXBZrMZHpxifJhOp0MI9ng8Hka04/F40GseM1u3t7f8/ve/59WrV3z48GEAkDc3NwRBwMXFxSfasjRNub6+HsCgsIjGGO7u7gYDjHzP119//Ul2nmRoQh8vdHt7OzCn0+mUsizZbrf88MMPbDabgWWS9yAsj4xUhcX7uVYOGOJmBFgI8P5b+YJxHHN2dvaJkWK32/Hy5cuBbTxOUJHfJWBIQIiAS2EKJWJI4oNevHiB9573799zd3fH+fn5AFLE8CT3vLSnHDvGBTSKEejk5ITr62u+++47NpvN0DUu0UNa62Hc/cUXX/Ds2TN+/PHH4XqTa0v0lse6PWHH5HcLuJXXLOyhtD+dnJzw+PjI7e0tURQxGo0G8L/dbgdd593d3cAcHoOuV69eDZpIkVsAw3sRA8uxK15ilmQcH8fxMP6WTZiMuo8ZSJGByCbJHK3T8tkdj6Nl8yS/V3SOP1+n5Z6uqmrYyInBRkbVwtbL9wswlntcgK/on+V1Szbqer1mvV7/55hyuq7DaIUxmqaWiidP25Y0bQ3KYbWl6/p2E2MUTVXRKYPSEa7x1HWBr3LKesXq6Zan5T3TLMHYFKcNta/46vol603OLq/R2jIZpRjd6wCbusUaQ5im3H54h/MO17Tc396TjSa8+uI1q/WedrvH7Uvefv8X2rLl6tU3jKxhubunaysuLk7xaFwH1gaE1jKenrN8fKQuNywe37La3vP03RueHre8/vJLyjqn9I6qc2SjOWmcoEJPo0p2O03TVaRJxCjJGI0n7LdruqKAqgS1Jh1lNCWUywcC7bl89RqyObOTE4w9uKwa8LqPUzFWE8URXilsFIMJ2RdN7/41YMoSj6FzDoXGGksSG7IswvsOpVviOMJaR9fWYCPswVjRL1AeE1i61nN7f8f182doZchGI8q8xAYBdVkRJ2PaqiFJYsqyoK5bnr+4xtg+DqJuEuKuxRhLU0PTtHRdztn5CVGc0HW9/i9OAuI4YrXfUdYtaTZmv7xhv9eczuass7SPHXItcaDQscFEAcZrPrQVYRzy7PlzdBAxml2Qb9d0xqKigGevX/D89StcOKMLTtjuKzaVYjQ5QUcRyhjS8ZS2KDGBZTpOeFwu8EA6mrMrOvJ6g4nHXF2P2Oxy9utHsvGUMJnQlBXjJGG9z3l60uyLChVGYAzPrl7Q1TX5+oHKa3zdECYZkdEEeCwe7R3WaFzXUtYlUZKAh65raZsGDTg8WZaSxBGr5ccMMa0NRtuDzOTjiEIW5CAIPnEFAn2K+SG83HVd341blFxcXjGZnRIfFh3ZCRvTN+TUdU1R1fz00xs63/D8+TVRktC2jrZtqOs+pSEIPuqmtfnIiMVxRBQewp6bhq7zNE1LHGc4r9nvCx6fluRFSedaptPJZy9Uvxyfd8iDU9gR2VwI0yDshADP49ByYbCO8ybv7+95fHz8pJ2m6zpevXo1BFMrpQagIIBSQOHbt2+B/tp6//494/GYr7/+ejCwbDYbvv32W8qy/KQzW5g2YWOE9ZRxb1EUA7D84x//yMPDA998880AZtu2HWJ65GG92WwGZjVN08GZLYBGon2qqhr0ZGJEkS5p+SOaRAEXMm631g6aQHk4w8eMVxnPChMlGsrjKjxhGgWUCKt1c3PDq1evhvO83+8HACEj9izLhpzL169fD2N5+fwFyEs8zOXl5eB+l0Dv47icyWQymJ5OT0+5ubkZWG9xe4sUQKYmr1+/JgiCQ/TZZjhXX331FV9//fVwDne7HUVRDAYXY8zg8g6CoJdgPT4CfT7ocX3hq1evBt2faFhF5yn95cIWW2uHqKjlcknX9dFx0gZzDPxl/Cz/Xzbvx7WkojWV6lDZkMkfYLhG/q/rNB9d3V3XDdFU19fXnJyckCTJkLBwrI8XEPmXv/wF5xyvX78ezE9y78p1dJwQIBsK2TDKz5Fgenm/2+2Wh4cH9vs9XdcN9aefc3w2oOwDyjuqqqY9oPT+ZDYckipwrmG3WaBdR9WUeFdTVb2xwqqAsszxpqYsVxTVijAK0DYgCEJar0knE7wJiLMTZnra5/DZhKbdsa06smTUj0dUSJzNwDXk+zV11eLqFq0s2XxOMp4xGc9YP61pthv+8q//kzYM6YxB2Zjm0KIznc1pOs1ytUGZhPHlF0SBYXJxRb6+pVjd8fThDd9/+795+eXXTOYzCqfIqx2t73MXVRiRTU4wxhPaAJzCt3CWjLG+Yft0y9PtOx5/2hDYftE5vboinszxyZQgTFHK0B1iccoyx5o+JNpoRdN0OAdKB2x2Ozwlk3HCaBr2gLDr8K6v7wsjuLiYESchUeiYTgOCgH7cerjY69qD1VRljbYBXtEbpNoOBRj9MU6gvzkc1uq+tjCLUQrquuP6+pK27bBW93E+zgGGtvW0TU0z9cRZQlFtKasGbULiJBxaK9IsoysT9nnBLAq4unrO9vEDT/ctXbVHBxFt52mdYjKKsc8uKZuayWTG9Rff8PaHH7GRY7tac/XyBel0SqunEJ3T6Y4XX0WMoopq9YHVesfrVy+oGkfZtngaZtOe2UtmHTqaYNKG3a7EBhDFLQ+3hzq2eYtrGvI6ZzY/xQch12df03qPtgHWxpS7LapoKOpemjBRAY3X2DhlkiSEdnKoH236cbP3vY5V9SYppcAG5uCg3mGsOTAk62GRGljHo/wyYZX+nVhb9bmTSimWyyVPT09kScrTYsG+rA4O0RHeO+q6QWnd57y2DbeP91irubq4ZjyZUJR98HrXQtt43FHOrfcOrc2BOdXDotm0LUrbngFvHGiL76B1LcYGGNsRRSHT2ccml1+Ov89xrPk6bhURI4k8wGR8KQ8UGcfJuEzAkDS9CCsisUHiijXGDBo9aaLJsozNZjPoxmSsJvE0WutBLzidTgcn6f/8n/9zeMAf1wRK1/ZisRiMHpJFuFqtWC6XvH37ln/913/lm2++4fT0dIg7EjAgBpPj61RYVKUUDw8PvH//nh9++GFwpl9fXzOdTofsTJENyEhWQqIlv0/WTMmYFDZURqHCIEVRxLNnzwYwdjxuFRAiGldpyBF2SoCNjC4FCAKDE18cxXVd8+LFi4HtFOAiP2u73Q65lqKVk9gdOS9idhIjzIsXL7i/v+fm5mZwQct7k8B5yb38+uuv+e677wbw9cUXXwyfgYzWf/3rXw9s33K55Ouvvx6Ajvd9r/lxNJFcWxJn8+7du6GWUsxjp6enWGu5uLj4ZDMi6QRSTSrrp8QXHbfbwKfOavlbNlWiT5TXfqxVlLGzsIsCMo/H0sc/W2vN3d0dd3d3jEYjHh4eyPN8aIMSEClj6rZtubu7G4DybDYbgKdc78eB5PJ6jiOQRI4g15u8PmHHhZGPoug/B1B+fLHtQK3LiQ8PgcYaaOoa15aorunNOW1DEo0wRhOM4t7IozumsxGjNEV7y/u3N7y/eeA3v/s1aIuj7+yO0yleeZSJ8MrTOE+cjsB1JKMWXAMK8v2G9XpLZwJmz56jlSW1MbN0wn6/4/3dLYvlB0wcY6IRD+9vCOOMi8sXjKbn7HcF6fgEbUNqFN5mTOaXzEYRWaj58PYHPrx9w0nrOL1+jU0S6lpR1i3Ka6JojHeOMEwYjyYsF6ve+dqUzM41yne8e7Olah1BFtOZlIYIRUhRtRA2WN1SVc3BAKQIw76iTitD3XmapmNfVnilcL7h5CwlCA0Ky3bV4lzPEihtGY1TwtBhdUUY9lIE17lei6pDukM2ZOcBpTFBRNN0REE4XGTWWrxSrFYrZuMxbVsThIYkjWia/SFtv8W5tgcQShMGAavlFuU1u21BnIVYG6JNr3kMAkUc9c7pKAoIz07x1QKMJxtPOb+6xqiOvNhTNTnKeIy2vHx+xvLREY0ykizDq4zZ+TVdvaSoPVHrWW33hKMZsYlIxzGT0xNU88Tq/h37fMe+8pSdpVUBURphlKLIC8raE2SnqFJTqiXNfsMXL19i4wfu7t8SJZYkm7ArSpTrGGdjRpMzigbyqqNuApyHyeyaVOUYt8doh/N76vqJupmQ6TO0grLMCcKQJAopqxblHVEYUFUdcRTStjUQHEVwtIfRFj3wO7AZ0sghIzEAdeQKHDSSB21zlmXMpzM2u0OD03rFfH7C5eUF2+2G9XrdP0CAJIk5OZlR1SV3t7fYMMI5RV40FEVFUVR0bSu9WMODWRY67z1N27Hd5RRlRVW3tG5PVfVVoh5F03aMR2OC4Jcu77/3ISBD7mPR4MnDUMZyx4YRGdPJQ1XaYpRSw0MN+siXd+/e8U//9E+f6LekL1xGwjJyljGt/A4xiGitub6+Hpir8XjMbrfj3bt3g6EjiqKhY/v6+pr5fM5msxkAmgCF09PTwcjy448/8uOPP9I0DS9fvhx0ddIEI+afOI4H5k3kJqKr+/777wcN3jGrI852CeoWY48YSo61fxKA7pzj/Px8cJPL+RLXtbwXOQ9yDwlbepxBKZ+fGJmOP1Ot9dCvLQYliRRK03QAuwJGJcMSejmAOJuPWVIxq8RxzOXl5TBSn0wmPH/+fJBQyFjWGMOrV6+4v79nPB4PMVBXV1eDi7xpmkG3KGykGJJub2+HyCYxqEgMlIBAaTiCvn/7yy+/JIoiPnz4QJIknxiKRBfZNM2wuQI4PT39BOzJ5kvOuYB40WXKJEc0rdIyc6xLFtOWbNCAgSmVNAQ5jt3bAlRlqiDjfJEULBYLzs/Pefbs2SdRTPLzxTUurT1itpI/cq/Dp+u0vH7ZVAijLckKAuaF5f9PCTb/6AQE5R1wCCw3Guc72rqi833VHTagrErqtsTgcW1FH0np2W83uLbFhiFhYNgs96x2O9ABm23JZOpZbzd4LEFgWG5WnJ2MiEJFke8ZZRn7skCFEYGJmJ3M+f7P/5vNZouOItKyIYoDnDHY1OLLPUkWMe1CFusnZtZi45C8WPH+rxuS8RwbjTk9mxGlls02Z71ZoJodo6AjSkf86je/ZbVecPf+DUW+5+zqJWE0gbrAO1AqwZqAOBpRV5621WgbYiJLqxznr74hyBJub97z57/8yHmleCoVX/z6dygd0nbQVQf3tdVoBb6riQODDSxlXh9usoPwveuoyj10lsB4RqOIp+WOutqhbUBVtsRhQhQaFC3eebrW0TSesqx4XG6I44w4G4FyKBtS1y0u6hknpXq3njWKKAp6/R99X3fPlkqHrKWuO6ztF7Uk8WSjhrrq6MPOIYoCmqhjlRcYHRCFmtBAaDTRZIJqPJGuUVXDaHbG09MNk9mE1hlap5lkI7Rr2S06ooOhyJmAJBujUsPyaUYQRTincd0hRzEwqDCgyCFJxiirWGxq8rJlVzq8CtDa90YWZ2hchB1fYad76iXsKs9Xv/4Nq82G24cFfrUnHU0ZdQ1dWXL//h3YFBVkdB1ENgJnDzpdxT5fYeKYaXjWZ4vWZa+T9VJl2A565P5m7QhD88m4RFhIoy1RFGO0pig+bTiR+1IpqT1Vn5hutNGkacJ2ux4WlySOaNqGp6dHlOKweTCs1ytc1xEFAUZD0/ahyq0Da2K61tM0jrKsCawmCEOMCei66hOXorW2N1G1Hfu8BAxJGtLsaqq6oSxruq5lNIoZjUN+Of6+x7FjW5iQ4xBjYSSFUdlut4MLXJgNpdQwHj7WW0rG4nq95uTkhPW6L0AIgoDVajXE+ez3+4HZkrHw2dkZ//qv/8pqtRr0isd91WLKECby7Oxs+O/ffffdABovLi4Glmq1Wg3sY5Zl/OEPfxgc1Pv9nufPnw+xL8eOWjHnfNQh9xvpL774gjRNeffuHd9++y37/Z7dbsfvf//7gb0RgCHnTwBcEATDQ1y0bfL1An7H4zGPj48Ds7ff74fmFwHiwkIWRcHj4+MAlAQ8yucnpI6wydLhLAz0MWAKw3AYg8r5Ho/Hw2uU8XXTNAMLLJ/bcVWfMWbIhLy/vx8aZ7qu79323g8gXULcj/MMj938wtYKcygOa2m6kXYheW/yvsbjMdPplOVySVEUw2d+c3PD09PT0JBTliVv374d2En5fQKWjDFDbaVoPMWYImNu2XABn0RMCaMnjD3wyWcgsUw/vy+PmU75//JaRCMq67RsBO7v7wcW01rLarUa7kthsuW+FbZYzG1ybct/lw2DXEtiYBJttWxCpDteYoWkZehzjs9vylGK7nAy8NC2rgc+rv87sAEawygb09blQa/mDgaeEqs7+l7oiKqMaOqaMt9yc3dPGAX8+je/Y7/NKcsa5yqSzKB1x2hsiSJD11SU+RajOlrXM2JREPdB52fnrJ4eaDwE8YyTi5AoiQmiEFXuMFXIi8k1J9MRT4s1aWQIIk9RFhRPO/aNY/H0ntnZNU0Hm9WCNNJkJ1OqtsMEhvnshEArymLP/Q//mySdMDk5Jx1NaH1LB9TlniDOOL84Y73bEqcprXVo3TE+e0arLHdPW1arDdn4hM1qy8l1RzQKqdsOTx/ZYrQijSPi0ID3KN/hurYPmDYG7yvWqyfGcUMchYxGKU0X0bR9fmHbdLStAiK0MgOz5V1NUZQsF3tWm3viLOX84pzZdERFy2K5Yj6fHvLA+higOA5RGsLA9Gxk03+ObddgvB1c/ijFeDJC25DF0waUA9XH0Git+prKzpNYjQ0UYaAZZzFd3WFcjjFjJufPUG//Ql1vmYzHNA1Y5Vgt78m3K5zyBGOLsp5kNEJ3ligc452ia+0BDLco3dC0mrqBsmgJlQaV4G2Mihqebt/zuy+vMfGUxsF4es4kOyWIp9z+VXO/fMNv/nDB6YvfELx74Mcff+TfvnvLy9Jzeloxms7w3Z62XhEEY5S3uHoFusDpmiwbQ5gCCVqHfbZoVZKkvdZqv9thbUgURgQ2Ivct1hq0VuR5yWg0pqp6DTFKY3RA7T9mAh6PUeTw/PsuWNlZi06uqipCpWm7ltE4A+XJsgQbGMqqIApi0sNDXrUtShmatqMs9tzeLVgud3St4/LidAAudC1tWw96pKLsY0nqpj0ATo81Adr00VJ5XjAejwiDAPuLyfvvfgwbjCMR/zEjI2BT2mGETZMH6rGxQ5ib3W7H+/fviaKIf/zHf2Sz2QxMkrivxcjSNM2gqzz+nWEYcnFxwePj4+BUvrq6GqJrBES8fv16ACwSQC3gqqqqoa2l6zoWi8UAMiXy5PT0FGl8+f7774fGGnG7AoPW7urqivV6PbTFGNPn0iqluL+/Z7FYMJlMWC6X1HU9gBX4KAmSmCD4GKdzPPJ8enoaIm2kD12AimjeRIOplBqYrjzPeXx8ZLVaMRqNePbs2TB6bJqG09NT0jRlu93Sdd0Azo/Hmcf6PgGsSvXZkZLzeLyOiFNcDCjCrh0brWSU/Ne//pWqqpjNZgO7J854pfrQeAHR8vqOo2vkNck5EAApYCeKIt6/f89vfvObIVVgNpsNAPXPf/4zi8WC//pf/yuvXr1iOp3y/fff86c//YmyLDk/Px+kEhLtAx+7yIURFP2nADaRDMimSl6LAE0B5fv9nslkMuRtyvk7zm491lQeH8frtNx7sk6L9EA+LwFzo9FoAOoyohfZg5zHPM/58OEDT09PgwZZ3pdcNwLci6IYtJMCOIWllqzS6XQ6ANfPPT7f5X3Y7fa6KU0YBqj+MYbrOqyJ8a5BYXuwF8W4FjarJdtmBa7D2IDT0zNCG2F133+cjkYYFFaHvGtv+gqkLCTPl4SBIUticC1dUzPOYpI0JNUJne/BaRQYXn/xmh9dw/2HO+7uF/z2H/6J6y+/oFaekg7sYYTbNFyezEBbVsslOnCM0pAzHXKzWLB9qvAYpnFGXW75cLOhKUsC7ZmmIa7co1xNbDyqbljfraiLE1SQkYxPaOoGHYC3UBZr6mKNMY4u6vVkUTriH//xn+iKCmVC5vMpSiu0tQQ6wHeajn7smGUJXdtgdUASBiRhSGAbwKMx+LbuMxsbx2Q8RZsp+6Imz8s+DzLfY7UjsgZnDUmksUHMaKS4uAzIq/csl1uSbELbdUxSS2gccRSSns7JshG77ZKyLGiqAqccOE/d1PjO4L3Bm0Nvuu4zCEMLs3l2eDg11HWJowfFaRpTlzs0LcbTv37X9z/jQpwKsfGEL775HXdvv2W726Kw1G3NfrvGGkWV74lGLc411HVFU+4JwxGjtI/v0IDvctoGyq5BYdjvS0pXctZqlI2p/Jb1riZKUpxSfLi54YvL3wEhOp5x/vLXPFLwtClJnCOZfsU//X9+zfnl9ywe3/HDD98TRYZXXz7HeahVjFEBlppsmmAUaB0RxHPG40uKsm+1aZoaG6Y4x2GXaHA0RFF82LFD5xw2MCRJzGKxJI4TnPOUVXnY+Rv00eIni5FzDnPkQD1aubA2YDQaszyMuIoyZzyeEMcRXdeyWC7JspSzs1OssVitMcpSxeHBEe6oyo6icHStGWoeeye6QR/WGmF99gdtkes6tDZUdU3dtKD6HMy288xmc2zwH1Lb/HJ85iHXg4AaeZACn7AYcq3Iw3qxWLBcLj8BDUEQDKBEmAprLX/961+5vb1lNBoNYz0R9Mu4WNzgwn5FUcQ333yDc453797x4cMH/tt/+2988803w2sTB2xd11xcXKC15unpaRjhCmi5v78HGIwYb9++HfR/4/F4qHoUBufDhw8DWypZgCIZESBrjBlAWZZl/PM///PAssmYVM6HMIFZln0SdC5jUhkRyhhcxtjT6ZTz8/MhNkhqII8ZQfn+yWTC9fU1ZVny9PQ0sLej0Wj4OgleF2ORuNpF0vDzfEKZJCRJwsnJyScGD7l2siz7JHdQwO/x1CRJEn73u98NDnml+tzO9Xo9MK9ynoXtks9QwJL8XLkWt9vtcP3I65WKTaUU79694+rqapAufPnllwCsViu6ru/n/u///b/z/Plz7u7u+Ld/+zfiOOabb74Z7gUBrCI5EOOYGJrEWHTsShdAKBE78kdkAVLZKOPi+/v7QTMr67RsLuCjLOTn96x85g8PDwBDL7wYpuQakPtS3o/8bgHOAhRlzC+bLgGF8hlsNptPrgu5Do6v2dPT0wFwf+7x2at62wgzotCoA7qtDh+WwWoNDrQNiE1KoBW7dYNRms57NsWWrmnxXYPrFKPxpNf1lTUqCLBRyHg+Y71fH7LNluxXOW484vriivF00gdWu471fs/J2SVZluK7lvXygSRNOT0/4f3bG374/s+gPZcvrok02CRit1kdAq77APT5yQl3d3fs8oLJNGU0zfDKYoMIayzLBaw2K7JJjFaecZbiYktwyLdsmr6Lmbpi8bTAP94zO7vCtzldlBIbT77N6TSMohlGaby1NHiuri64uXtguVig0hOm8yu0Vn0kUhSjTUBgDxpVozAmwNPiXIkNLEmYMM402kFbN7RNTRQmeEDrfgeucHRtR6s1dI7dbk9HX4XoVR9E7Vw/xrSmw/uAOE6p64bddk8U94yfcx6FJtCeztf9z3XQdZa2c9T1niiyWBuw2+8OIt4xdd2QFzmu8YRBROcV1iiMBRsorPYYOtq2AtcS2oimqkgnp1xev+bh5h3Fdk9ZlNRVDw41lqZuqNoNySTBWEs8GvX1m11LWxc0rsKFBZ0eE9uYk4vnKLfDpgFJMqNpYDS5ZrOvOT1LMS5nefuei+SSjohwdEYyvWC53eCDGTa2eJUyfvYropNnmJu/8vD+O3744SeMq5iOpz3D0dWMsivGkxlhkhFEFt8UKB+gfUtgA7yzNF1HEKZ909TBkBNFIcZoiiIf2EnpplXKUZbFsNg3h+o6DjvTzn2sVPz0xlc4HKBJ0hHL5RptbM98RwnWWHbbVf8wm0Vk2YjN4cEUBuGB5XCgAowNaDtF5zTz+RmB9YfFvyaNEqI4wFrDw9MOlCIMQqIooSgalNd0jUN7jcIRR5bZbESaBthfNJR/90MeCscax+MxqYAhYSNkjCbX0G63G8KvnXNMJpPB9Srh2ScnJ0M8i2i7ptMpz58/H0KuBRBcXl4OzN7T09NQZfjjjz/y7bfforXm9evXn4z95Fq+uLjg/Pyc9+/fD+PhY2ONtXborz52mUv+5HG2ZF3XPDw8DCykMIPSEiIPZ/nZAM+fP+fDhw88Pj4Oxg95MMvXyrhczqkAetEfyihYHtgCVmRcLcBFfu5msxmYTgE9IlWQny85ktL8c8xAH5v2BLAJYybvd7vdEsfxYGTJ83x4bcBwXQiAFgZM3ldZlkynU169esXbt2/ZbrfkeT6ARWB4v7PZbGA5hf0UFu/YgPXs2bPBJCXj3vl8zna75eLiAu89Hz58GDSEEvAu0VFi9hSH9Nu3b/npp5/47rvvcK5vGJLrMMuyIbBdwtCFWT425hwbsYS1N8YM0T7yHo8rMSWKSGKQhEE8DhX/d9Olw/UumZwiyzi+PoU9lkrMoiiG83lcjyr3rqQSyORB7gtr7SBdCcNwmA4cm5Hk+p7P54O+9nOP/0CXd4fRBnPQkvT6q56BSJME5TyeDmN79gygLCs8ChMGbG43JHHMdDZFo2jbfhyehCFpklF2LfE44/TynGK3ZTaZsXOO/WrJxmomoxG+C/Aa8J58t2M+m+INgEdpSLOEly+vKfKCarukWISEUUgUBBAnREGEsRE6iAnjXm8ZZi1lUaItVMWWattnks3HCePROVEY0zjH4uGJpmyIdMhq2xFoTdNU7HZPOKPYb9bEgUF1Bc3egrJkUYrrOly+Jk5jdsWGptrTdDHT0wm1Dkji+MD0dmjlca4hCCMC20f6eOfpXIunxQYebT1ZFjMaR3RdibUySpaIiRRj+ou7amvCOEYZS930lX9eGWxgeh1coMG5A1CrUcQoZSjKAmNTjAkOLmRFVda0TUHtSspaU5aOMIwJQ8t2v2E+PSOOEopyx2QyJrAhTd2/vsAEtN6RRAHtIbbIK4c/9IRXVYX2FmsSvIkwYcpoMofGYdqOap9TdBVGW7qqgbClrXPysgDjqH1fc7nbbfrWnrbBBw4XwPz8EucjWtXh2w5rx4xmLyi6DeCYJIpqcw/VjnF2CiqknV/y9O6B5umGMIHzF7/CJmds1zGT5yNsOqdevuOnP/9P8tWeq8tTJiczbBCSTSZs9wVOacI4QKkIRYPREdA3xThtULojjoLDpkz12tRDFmlvqFAHnadlv9/h/SFHTevDxkEPi6A8RGQx915KFBXOw3g657ky1G2NG77HMplMDxEpa6qqoesajOq70auqoW36DWJRNtggJko8z65fMJ/272mXb9hu+oKBoqxwDkbjCYvlhrwoMTrAGkuRFzjnoWu5PJsxSoN+M6Y/f5Tyy/F5h1wPelinP4ZPH8egiMEDGIwOYRiyWq2Gbmr4yFAJwyRC/aurK3a73TCGXS6XRFE0AFBhz7fb7SdATLRaX3zxxVCH+PT0NESZyINcAI2EYosmU1hFydKbTqfDuL3rOu7u7obYmfV6PcTkiE5M2DJhB0UOJIYGMX5IUPfp6emgaTvWwMlYWB7ixw5qee2j0eiTcHD5HmFtBZxI3I4Yfo71yKIzlDGpOKgFkIrOUbRxwkxJdaaMe4MgYLPZcHJyMsgZZPQtYFUczH1hQjcAHwHAEoEk14441AW0Su+4aEgl9/DYpBSG4cBqy2sXx74AYUknODk5GSQGaZqy2WyG8G+lFKenp7x58wbn+q7z169fD9KGV69ekWUZy+WSf/3Xf2W9Xg+1nWEYMp1Oh053kRwAw4brOApIInZksyAg8Xh8HkXRwLIes6HC8B8zxcf3qvztnGM2m/Hll18Onx/0jOZsNhuYarkuZQMooFbG2DItePXq1XBvbrdbVqsV19fXg6ZXIpmEnRdmWa7jy8vLwVj1t8b2/6fj80feXUvdtLiD8zgID12tqN4M4Ry+7aDtejbCBERpRlXXeB/x5Td/6HV4eJSHrqtpqj2GDteU+BastpxNZqzahuXDLZF1WN3x/u2/sZ9OyMZTRtMZxW5PGAT4pqJtmx5AdB5rLOPRiJcvXrDLCz68e08Yx8RxQuc6wighCjRF01B2DdpaxmmKUr0xIbQR+b4gDCOSdISyln1Rs95sWWxqxtmE2im2laepdjR1zvnFOdk4Q5Uaaxy7dZ+RlmYT1GgCHqq9Y79WJGlC16xZryvOLl/w4vlr0tMroiSm8YbGt1ijicK+6lErDV0fw3M+H2OsoWpa0jTqJQdOEQaGuqmpqhKte9ZhdtgV9w7HGtcZylrjvCe0FmvoY4mUQ9GglTloLcGYHlAoFKENyZsSbQzeQRDE+M6z3VcslzvCsOPkNKSuO5rGkWURRbmjaWrKotfwRGHcjzrbGq36AHbnfR+RVNUYBW3nUU2LAdABXluUDRhN5+hshELx+HDfv0enoVXs1y2efvFrmxbnPbuixNgJxT4nzAK8itgXDWW9Znp2wnwyoaod0fSS9XpFcL+ibT0madivbzmfzdFBhJ6dUCwnNMUeH+zA7VEqpvVgw4zTq69hOsUaS7u7Z7F+YDQ3GG3Y7ba0TpFGMTbOUDqmaj1eKaqiZpeXZJMUGyo8mqrqH6BtUw8PNnm4G2OBHtzJwnMcEwT8zd3ux3zJHlis12seHh7wdH1+6uFhf9yH24czj6mKPa3rqKuatlVYQqqq7gGqUngcVV2SxpooCtg8LWmbGUVRE8cZSlnW633f6W4DmqYHzG3bYAPD6fkM5xu8D+h3h78cf89DGIvjkfexKUf0dccdxxICrrXm97///QB6BDgJ8yR6N631YMiQEZ8xhr/+9a/M53Om0ymz2WwADvJ9wnKJk/zLL79kt9vx448/DtmQorcT3a+MQEXXJsBHqvvEjZ3n+RB2LsHQ0v1c1zXPnj0bsjQl6kiYJgEh0tWcZRl1XbNYLHj27BmvXr3i4uKCNE2HcyhA6Dibs2kazs/Ph5imLMsGQ4mM8mU0b60dHvgyqhTzjIAPGW0fTyDkz7EDXQK3j3V+0AMJkQycn59/wqgJkD029RzHEh1rHkUnKNeMMKfyeUiVoVKKm5ub4T10XcdyuQQYchK990M+5G63G+oKxWl8fn7OfD4f2M3lcsnt7e2wIVoulwPreXJywtPT0yds3fHfL1684OTkZDDKLJfLQb4g2lOp/pRNl1znxxpC+YzE4JUkyXAujjWGIjk4Xqfl+Fvr9LEkQWs9vFdZm4XRlHMkgFpiguTelNciofqyvldVNTCdd3d3g85SNker1WoA+SJ7EDAvAP/nuvz/t+M/MPLu8M7j8cRRAA7wEIVhP0q2Ggx0yqO8pmqaPg9ydoIV6rXcU+ZbmnJPXW2IAo2JNb6rUUbhvKOqCi5nGYEb8fTwga4pSNMQrR3v3/5A9Jiig4h8t6UutiyfFlRVwepxwflFf9M8rVbMT85oOrh/d4vWFhuEnJ6dc55mhy7kvvopsJYkSmiamp3bE0eapvMs1wVOGbZFTdsFvPjyD1gborwinZ1T5kuenm7Z5lt82aJNyN3DA6NRTJbF1PWeH79/j/OO6WRC2zXMZjPAEYcxhpbxOCVKE5zWxDbsR8m+Q9FnXHZ1gXZgvCKNFM5HbAuP72rqxhOHFm0N0OFc7zZs2pY4SZlOJuRFwX5X8vhYsM87wiBgOh6hDaRxRBoHpJEiTQxx2NfrGRMdLlB3WBhbsjSlaTqKIocgwBjYbp/QpiVOJyhvqCtHVXWk6Qil9IG2j/uxKWC0IUksuu1wzuO86xtblKJxGle3GO3wXYsJAk7OL2iLkmq3JSlygs2SttnTtTsC64njkM4rfFuhvafzYEzYu8PbhrDtyDJNXtU83D1Qdpog7XA2Znz+jL1fkec30NVYm7NfvefyxTOUjsF7kmTK5v4W2pZyM6PalXRdQDw+JRtNIIl6o0l5yfrxLftyw2a9ZYTHxFkfiK4zTDSlI2G3rXhaFJRNy7MoIrWWIq8JTdA34lQFFoPWH4NwvVcURT0wFjLOOF60ZKH6uc5FGAFhalarFeNJNozPRPwvLFC/EDuc7+OGQKNtQHMwi5VVRVmVh35vT9N5wsgyylIWyxVN7Ti/eEaR13SdIgpT6rqPwkqSmLZ1nJxMSJIQbSBJM+qm45fj73uIeQL4pGtbQKKMuuWhLy0vJycnAyApioL9fj8AMvleGVPK90nji4Rdi87xhx9+GALFZRz68PBAWZY8PDwMUTJPT0+cn5/TdR1v3rwZRsji5JbfJdEl0ryz2WwGFk3ib4S1/NWvfjW8j/l8zm63+yTk2hjDzc3NEG1TVRV//OMfh7GosJJyzpRSh9zW7BPTyzGbJudSxtHT6ZTdbjfIDY5BvQBmiZqZzWbs9/shTHq32w3Mn4w+BRTIiFYArTDM8s/HRisBpDLOT9N0uD7KshxYPtlQHLufBfzJ+xTmVWQQwn6HYcjV1RVFUbDdbj8xMIkp6JgVh48Mumxs5HyI1lXex3GOpIAnay2LxYLXr18PrynLMu7u7mjblvV6PWyOkyQZgu2F+RX2erVaDfeHALc47lu7NpvNYACTz02uHRn3w8f4n+OGG/k8RIN4vLn/W+v0caRPEAQsFguenp6GDU5RFMPndtyZfvx5HI+6gYGVPt48iiFM3tezZ8+GcyryCamIbJpmSFgQl76Azc85PhtQhoHFtR0OTxD0mrn2sFv1XUPbNjR1Rdc04PqHkA0iqralqhzeK4xKcL4Eel2W9x373ZrV4hGlFVk2IUsSvO+4vjzhbJpS5PmBvQmIo4R9sadpKvL1nmq3oakaXOew2lAWFUEckZc1rDe8eP6KOB7z/sMdRVXRugXb/ZZn1+ecnJ4QBv2CoXT/sC6KkqZxROmEXV7RACaYYNOUeHKB0gbXOs7mF1TlhnB8xmb9RGShqfaslju2H564fnbF+eklvvbUdcl2tWG9WaE8B5ay5enxicl6w8Vc0TYNUZAQRwGb7Y6mcig6mmLHKEnx3kHbMYpjwJDnFUb3pqi6Kjk9maGNQateOddUFVEYYs0I7wx394/c3i5pm4azkwmz2YhRFnJ5PmcyTtEGwqiv00uzmP2upWlrjO6NIkoblDK0jSOOI8bjEbCmqjqUDnBdn1EYRTWTadQ7e4MQpTTQoU1vTLHKoI2mqBqaGuCgD2o8VvfRN9559nlBZGGcjbE2oMj3pOMxrtmxeLjFtBWp1rjOYGyI1SF17chGE+JkxPzsgnyzwjd70khxMplTtpblZk8Qa6IgIBmdUuwfqXZrljd3xKcLLl9cEWanNFVNFGYo79k+3TCZzgjnlkBlREGvW6wbj7cZzhbMzp/jq5S7xxuchpfnz8mmJxQ+Ix6dE9gx+eOSp8Wu78D0hjCIaKq8l4j4PnMSFFkWHRb3vrJQoWkbD+rjgi4L888XKPn34/8m0RA9g+Bpu3aoMpNF83gsqpQm3+dUTZ+r2htqLB6Nx4P22NBgrcO53oW4Wm8P47Qr8rzqGU3naNuOLEtRyjMaJ0ynI4zpMzOrumW53PDVq6vPXqx+Of7fD2lZOXaOyohMAM5x1Ig8FI9ZS2E4xHHrvR8etKJTFKAgGZHCeIkze7/fU9c1y+VyCDWXMap0RhdFwWKxGOJ63rx5M4x8t9stL1++HHIchRUTwFTXNaPRaBizSnTQbDYbGMPT01PyPGc6nbJYLAbWc7FY8O7dO16+fDk4xKUdR8CGRKjc39+zWq04PT0dxrxxHA9GGGB48IsOTTZrYrgRk8zZ2dknZjrRLQrbeHt7y/v37wemUzJAr66uBkOUVOZJlZ+MXY9DtI/lAPCxllF62eM4HhzQAppls3HMgEpPOfBJmDswjLjltQjjKBmmwojJdXdcNyhA7+LigvV6PVw38/mcrutYrVbD+5SGnN1ux9u3bzk7O+PVq1fDZkAY4IeHB+bz+WC4kUifqqqGjYRsZG5vb1FKcXl5yWw2G+KIwjAcmqGOo4FENiCfGTBoKOXc/PzvYwD983tK/v14nV4ulzRNM2iBRespNZSiET1mUne73XDu5Do4BrDHkUHz+ZzFYsFqteLi4oL9fj8ATzF7iTtfWF2t9bDx++qrrz5r/fkPxAZ5Ot9rJZq6BN/1uYkYcDXatz3TiMPTmz68UqANdd0RBQnOtSgTEYYdPop5v1zzp3/9F9LYYkPFYnnL2dkZo3SM0RlRGKMJCIOIJIkYTyZUzZ7IBuw2e+qmJYlTus5jwhCMJYhj0tGYu7sHPJqLq2tqr1lv9jRVze3dIx9u3vP1119xcXZOGASkSUpRlIed25Q4TWicxRBQ+YBGRXQ6pj1UyRHGdAqykwwVzKj2K7QL+eLrKa7ZYzWUZc18fkVg+wU0SZ8oywptYLHIObs6Zb8vadoGG/agcbNZc3t7SxAqLk5ntFVBrT1KW6wC72smicV3FVHgCa3CdQpzAMVNUxNHMVXdA3zvPXgHdARGobwiSUJOTyckkeH6aoaiY7leEUX2wD61pGlCFAXsd73wviwrojAhCAu8B2sjRuMpZVWDMoRRQlO0aGNompZQWZSGqiqRruqu83RdfTBrlH3jUqeIkxhMQucKOhyubdms1igc+jwiCmNMMiEdz3D1FsspHYqyKfCdIggD8qokMAmz6RnKxmw2/ahn+vwCrTyRNVQttE1JNskYpzGoGeXDoUWk3FHd1zzdvOH0OqAqGrqyRntFvd+yfnjPaTJmNE4IqMi3u95p3/XMqPf9Zigu97y/eeCL3/w3bJihughlItbbitW6YLuvmZ+d4pxivysItD806PTRO2EYYIyMlhRaifuuIwgOjYqH41gveSz0Ps5Fk65k6EXWNtCMsxEnJyeD+UIy6T6Ku6FuW6yNQfVVnfVBI6WNAnXYdR9YZqUUYRRhjO2vOQ4hwEUPVsoyJxvFXF6ekaQRWivKqmG52rFa55+7/PxyfOZxHBd0zJyJw/eYLRLgAB8jT+RBLGPQKIp4enrif/yP/zEwJI+Pj1xcXAwaK9GgiYhfXLNhGA6AQdyoYqYR1uTDhw9473n+/Dld1w1A7cOHD7x9+5bf/va3XF1dDcAlz/Ohrk86muUekPck4EfG0aenpwNb6r3nt7/97RCRJBEzos2U6kJjDE9PT1xfX7Pdbgcw6ZxjuVzy4cOHYTR43KMtD/XRaDSweMeuX9HeSSqC6C8FeAgrm6Yp5+fnJEnCixcvAFgsFp+wzqPRaAC3/TpdfpKrGIYhk8lkkCyIAUNMSgKSxEwiwffiBBeGuuu6IRZM1hpJBlBKDc1FaZoO1ZVyHcrGRV6fjMiDIGC5XHJ/f8+XX345mERkLD+ZTBiPx1hrh1YYAYPv37/nxYsXg9YVYL/fc3d3NzCTwGA+kfMlwFGSAf7hH/5hiLoSs4psgIQdlS53uaeE8ZP1FRg2SXLNH4PIY9Ao9xUwsIuySXr37t3AuIqJ6fz8nM1mM7C2ElwvZh8ZT8sGSj67n+fQyvsXKclxdaZ89kVRMB6Pub6+HuQlsvkS2cLnHJ8NKKtyP7wQ72rK4tBXqaCu+7pA7bp+Eq48oPFe933bsqhpizMBRkU0JiAbTfjy619xdjqlrHMW6yVFXVOs1oS2oCkadpstgY765hjVEEUWozyB0WTZiDiKCKKIunXEowmYgDhJ+Oqbb3harHEOfvWb3/Du/Xse7h+IknNWyyd++ukGowLGo57uV8B8PiNJU5rOkWYJJp6yqzStiqlqT+uh6RT5YksUhiRxhgocTtUo43HUJOMU35S0FDRViQkjzq7OOH/2iv1+yz7P0VrhdUjjHMYawihiX5Tsdluca0jjEXWVY5XvtYfGE4Ux+zzHKcXZbDQ4uttG0TY1aTTCO9cDSOfofANKEUeWZ1czpqOMMLBMZ2PixJIkIUW5Y5SlYDvyvCCMArbbFXiF96PBzUfnqav6sPOxFHWNsYZ631DVNSrqQUjXOeJ4RF0XWPuxmaNte5OJUqoHeFGIylvyoqaoHNAQaEfXVvgiR3tPWdVs9xWoCIKMeHRCtVtAW7FZr2haTxBndE2FMRFKQdPUaG/o6pbQhhjjsMoTWI0rG2gK0rAmCwOassP7DucdZ/MpNw9P3P70hiQ9wcQTdNQ7nylSdqsFJ8/2GJ9Tbx2+AxulaO1QyoH2mDDi1Ve/4v7hkdHsHIclNAFFXpLnULeOqulAGcq6Puhg+872ssoPO3jI84LABiRxSFFUNI07AEnRTX0cOclO9G+xlNDvrufz+RDxMplMOJ3Pqauqr508GAFksewfxg0oA8pSFDVN69HG0nmPMgplNAp9kC2A0uBcx2w2wbkW7xqsNQQBVHWBp+XkZEo2Sqnr/kHz8LBgtS5A/RJs/vc+JDtSHiQScCxsg8S/HGujjh86Aj6PXeDj8Zjf/va3gw7v6elp+Fs6n1er1ZA3eWx0MMYMDlN5eEtTSpqm/O53v+Ph4QHnHP/wD//ATz/9xM3NDWma8vj4yF//+tdh7CxA5fT0dACTo9GIJEkGYHHc5f34+PhJlM9x3NZ0Oh2YHhnrP3/+nBcvXrDdbvuK3yNzk4BgGU93Xcd0Oh00kcLeRVE0GIBkdC7gQ/SLxw/6Y83i8+fPh17r+Xw+TBIk81DyDwVEihygKApOTk4GTd2xMUYAhIBNAXgCaOU1H4+pBQjJ9EKA2/F7EYOOjLvFHT8ej4ew/NVqNYDn458t16eAdDnPMl4WY5YE4Mv5Ojs74+bmhh9++GHoHBcdbVEULJdLnj9/PjDqMtKVny+g+de//jW3t7eDhjUIgiHKSSQMcl1Izqi1djB0KaWGHnUJ3xdsBB+NPcebuP+bsUXyU9+/f890OmU+n3N6ekpZlmy322H8LOu0XG/yOe33+0+MOsLmw0fdtFzHon0W2YSM8eX8Hm8Ibm9vh2arzz0+G1A2TXGY1/cB172zq6Iq92jX9g9t37tCOwdhnODxB4dgiPIepaBtGpxrqJuOqnacnD0jzVLGUYTJtvz1r9+zfHrA+A2R1X2oeVuwWOfkRXEwqtQESpHEYR/TkqUUVcNkfs705Jyqc8zmcy4uL7i9u2dfbDk9n1FWOWVRM52cUFUlD3cL3vz4I6MsYTYbc3l5Rp531C3ocEy+W5NNr6l8xHrfgAnQAezLgiCK2BU5dC3Ts1NUl/J0955ys0XjSKKQpikp8xwVRcxmE0ZRyPzykrqpcTpgm+do27vFi6KkbRtevXyBpqUqthhzMNIElq6raeuCuqmxyoECrQPqtgd7nevQxgw0Vj9AVoSh5uwkpR717BOU5HlN0/Uam7otidOIqi6xxvTAsO1YrZakSR/foL0iLwo655hmCQSGIMgPu54QGziqxZbdbs983lcFOucHUDmMar2ia2sUiqZu2e2LvmIzVCRBh7GeJIogijHaYkyE1zEm6NBhTpBM6Mo+mmaUJuR1g8fSeoUJItaLBbu8RuuAcZZSNyW1q7A6YT7KCENPvf3AtgzwZQt07PMtiQHrPO++/ythesr1l78iikJsGOK9piz2LB9uOQsjykbTtA6VjAitxfiiz9dM51St4vz6S8JsRlW3oGqaDvICqqaj6Xo9UlXXoKQ6saMscoJQatw0ddWSJBGbTU7b9CY4lMcYTdepYVH/eabZRwciw8J2dnY2ZOBFUc+w3N3ewmEBl4dQf3iqqh6AxGaTo01EEEb99WY0WiucU2htMEb3bUlNTRjZPg4q7LMp0yxmNE6IY0sY2b52Mghpmo6yaCiKlvCgW/rl+Psdx5V8Aqx6OU8xMITysBPWQlgcyawU9uN4RH5xcTG4qbMs49/+7d+4v78fHtJi6lgsFsOoV+JwRCsoGXinp6ecnZ0Neq3r6+shK/Li4mLoEBem8/b2lu+//36opru+vh4e/sKCCqASICjNNdLc07Yt5+fnOOf48OHD0GWeJAnAYCA6OTkhDEOePXs2nEvpbZZxe9u2Qw6isJnAsIGWcyYPYjkXwvYJ03d8hGHI2dkZ4/H4kwgnYaHquh40pDK+Po5ikvFrnucDE3fMmAnDWFXV4Lw/Znbl85M1RNi3uq4/MbBI4LkA9WOQLv89TdOBFZXRNHzMW3x6ehquEdHoSayVdKfLRkVGzPIZAPz5z39mNBrxzTffDMBTdIx3d3efGMGE8ZOcSMkNffny5fDa5PMRmcZx1JLcS3Ju5X3305c+BF0am46d4sJIHzPlchw7xeXrhemW7EnnHO/fvx9G15JCcGy4EZ2sXBNy/4rJ53iDKOyvSAkEyAvRIOBc8lrruqYoikGe8rnH52sorR4oVa36TDnnHVZrAmvxbY1rOwIb4hU0dUMUJ4TZCO8VdVmhdK+pkw/DeU8QxGgbo4MpNs6YnEGUXfB0/4663hMaxfnZhFH2iq6F5WJDW5VY7WmaEm0tt3e3LNdr1Lv3TE/OmZ6c8pvf/Jqz83NsoNhst2ijGY8Tmrrl5PQUhWK1WqDrirJqeVosSNKIyXhCvt9RrjZ0JkUHGc6MCIMYHUZsdnvSbIwOAlwLTVdSNCVpbBifzFg+lWRJitWOaXpK01R0eHRo0a7F6745ZHJ6iYqmeA/6QOcncYzvOvblniiwxEmMR6O0Bd+RxDFGe+piSxCGdL7FO4XXHetVRZaNUHi80lgb4unomoYoDAgSRes6us4TaY33Xa/Tcx3K9UxdEPS7WWOhbVrarutZ5s6B6d2RXlu8d1xcztnlK2ygmE7G7Nc5Xdf3ejvnUUoPuqz+Aq16ltJBEo36Baxb05fsRHSuRmvDKJ0wDRSb3RYVZlRViw1jVBAzP79k3e3ZbBbsdmt0mNA2PdCvqxKrA9K4d0cb49nvN7i2OKj/FMV6w/LxiTgwBCqkqDaUZUGSxFydX3C7Llg+3jE5O2cye0UynrIyIfv9I+ddS7NbYW2IRmE7aOoWaAlDi7GWxmvm01M6LMZo9ruSXVmw21uaFuIkIkkj0jQmjEKUcdRtTVVX2NBijKUsa/b5niCI6NrebKUUxFGEQlGVJTbL8DisNYA/nG/R5vR/8J6u6zU9skjXdcm+c+RlyfzQcAEfxy8K1YNlbfBKUbctse1d9E3TYMPDiFTT63p9P/aGHsQaA9p4ZtMMa2KCyOK6htu7O5zzRFHGZpOzXO6oWkOcfn5H7C/H5x3Hesjj2BcBWTK2k65tYZBGo9GwLstDCxiAqIxuJZZERt43NzfD+FhAp7CD8rCWh+qHDx9YLBb88MMPnJ2dcXZ2xj/+4z9yeXmJ1DcaYwaWRFprJC6lqioeHx+HsfputxsepvKAlNe52WwYjUYDuBAJQJIknJ6e8vDwMBhtJPcQ+MThHscx5+fnA2ARlkrG9/LwFeOEgHQBWvv9fnjIyz22WCyG7vNjjaqwcgJGBMDJ5yebcvksZENZH3Jp5Wt+Pnq/vr4ewNhsNhuq+2TDINeLjHIlzF3ehwTfC9snQEmYYRm3C3gNgoCrq6tBC7nZbIbXK7IKyfyUjatICuQ1S5WinA8BNlmWcXV1xWKx4O7ujvPzc7744ouB8d5ut4NbX0Cuc479fj98tiIpEHZSfn+e5wOAlyzM0ah/Tsk1fNznXhTFYKAacNHhfUl802g0Gn6HgDu5t2SydCxBEVZapBDCPB+75mWdl/Ml4F+YaNHlHr//47/lOjXGMJ/Ph3u66zrev3+Pc25gwJ+enoa14nOPz48NalrcYccap1nPmnhFEBloSryyBKaPAukfTQFg8U73zAwK19TUTYX1Ja4rqaucxvazfNc0dE6Tjs8ZzS6ZXb5it7yn2t6xzjc4XzIbjTk7nVPWBXVd0RWek5NTzq+v2B26Xb02PN284Y/Vlt/+7vekozHzSca+KCmbiiQxRIFhn1dEaUacZFxdXlDlWxZPt+T7AqVhu98TpjM2i59wdkw2u6arOiJryEZz8rLBW41WjqrdoR2Eacapuca4jsA6uq5EhyG+bWnKijQyrDYrVJQQOzidzwmThKY1tK1jPM4IA0WazpmMp7Rtx77I++zIg2mpLDa0TY5RmuVyQ+c8o/GYoqwpXYtva0wQQtCgTK+tqFwAqnft4j34w0VZdkSmj0yyStPUDdZYqqZ36pZlR5rEtA72ZUNeVXRBRBomJGnF8+sRYdjnKSZJQlkUdK07sJMRRkNVbWnbQ4itUhRVjg0dp6czPtw/QtthdL8xwXfYYEQUhKAj2q7rNbkKgigGH6JtiDWGrq4IoxQTpRSNJoxCjAnJy5Km3uK6lrba0bYNCoUxLa0rKXb3dFYxnZ2QxiHteEzXNgTGc3U1ZVnuuH3/E8rGTOMxKp1SuQ99D3e1QzUapTRVk9N5z3g8JckmJNmYyERE2YjGAV6jgwTbKdIsYltuyLIQaxyjLCKOQqx15HVFFCdk2RilA7xvscZS1//f9t6rS5LrytL8rjDpKnRkZKSCIgGSXVO15qV/fj90r5pp1lSXYBEYggSBVBEZ4SFcmZu6986D+TFYstesyR7y0c8LEpkRLkzdfffZe58KH1ps1KWUplFEbAw4T2wjtI12YDPC+25hUnica1BaAQGjDSGA0VBX247ttIYkzhnnY1abFXEUkSbJzo2vMTZGGYv3nQ5amy770ihNpCM0GteWgCOJEwjgncPoCNe4Lpw/TtGmY6OrcttlsQbDw+OGu7s1ZalAGaaTg09+UO3r00rA08/B+KpnImURGuYaDrW4AkxkYQJ6Zk0WTmFwZrMZR0dHXFxc9HO+1+s1ANPp9KOYms1mw+npKc+ePePx8ZGHhweUUrx7946qqvi7v/s7JpNJH4cii1g34KKLlsnznMvLSzabDTc3N6zX6z7+ZTQaMZ/P+ygeyUCczWa941mOjXPuIwe5ADj5ftIalpnj0iaUqJi2bZnNZn27U3Rtw4k3w1gerTXz+bw/Ztvttp+TPtRWDg13wohJiVRBwKIAePk+wpQJyBWtoriYnz171sfjyHQheb+hHELYUAHD4rgX9/UwGF/YSNHvybU0jEkSwJumaS93ELAjx0eAvhhaZNOzWCz6aKAsy/rJO8YYnj59SlEU/PTTT1hrewAo7WWJmxKwJbmL4uwXZ/vQlBbHcZ91Ki5zaasLEBa9uQBf0XUO3d7SHh/mlErLWcDeMCMWfnaMi6ZXfi5JEiaTSQ/K5R4GekmKHJPhRmJ4vQtzLPeyAM+hHECioeR43N/fM5/PuwEpO3nIp9ant7yrCm0MaZxiVWe4MTrC+wawXYNVK0Lo3N3sAGc3RcfSuBbvHY2rCa5ktXqkqgrcyLIullB5tlWEinOCSbFxxvQ4Jjo8ol3doNs13teE4EiyBJtY1mXBh/mc2WTC6dExX37+is2moNiW3Mzn/Mtv/08unj3j4vI5p4czfFPhfEMUB9LQGUcWixXLdcHF6Sl1VXJ99QatPEW5JW9D52w3CywtyeikY9O2gVF6wKZ22CijbQOtr4lsjFURqm1oXUHAkuQ5sdK4YkFVbHBlSVW3pLOSgMYFTVXWxFHMZDLC6G56CsrQek+cjmkaR9t6Iq9xPoBWbOuS9WbVsVhNTUBRqy2b9Yrj0zMw4Hfau7L0OKeJUwM79qnd6Vg0Bo3CO08dKqyJSJIUoz1V1XYt2Kblx9fveX+74MmTc/7T11+RJRHxyQQfOpPWaJRR1xVt65iMp9RVjY2EOVCEoHch6XrHWiZMpzmrzQa9C3VXaHww1N5ibUZdPRJcDbsbpvKQZmMuLp+TxgnYlG1jML7L6lwu7olji6Vms3gg0rsszc2ahgIbaUJddp/Fu04PW3kyY1HKkyWKy+NjfDxh8bihyVJaM2J8dEZZ1RxPM06OphRVSeMDOkrIxxno7uadTY92D1ZDkk6IYs22Lak2Fd5VTKcjRrkhz6KOdcxyHh8fmEzGOwOaxhq7ayu7jrVVGmMC1oLRCoJCBXh4uGcyzdEYjE1xTmGNxuhuJnjbNOi4Y2qur685OzklYHh8XKK1odk9yKe7/Liy3PzcHsky7u4WTCZjAgoVNJEx5EmGDlAUGxQNUdJ9Vq0MCo1rfcfemk7GUTUNWZpxfp5St/D6zS2oCKUDUZySZ/uW99+6hu0vYShlgZM2myxgQ4evgCEBpMK6iElGHNWyuArAiaKI09NTjo+Pe+2c6DFFfyaGidlsxtnZGV9//XXPCn348IH/9t/+Gy9fvuTly5d9K3y1Wn0UFXR/f89yueydum/evOmZIPm80noWBrAoip51E4AjLN6QeQP6rD/R0lVVRVVVPZMlMS5idBF2SACVsJzy/YUREh2cfEYp+S7D9re0qyUXUfSGw/a4tGIFMAoIEQfyDz/8wNXVFZeXl/z93/89WZZxfn7e/7u4sEXLKsdmyN4JkyasqWSKwv9sBpSgcgHGcoyzLOPFixc9wJLv0TRNH4IP9Ky06CcFDA/HQQrQF6CdJEkfUH5/f9+znsfHx31g++npac+uS+6pOKS7+L7uuwgLK9ecAP/RaNQzc6PRqJ/rLiBa2soiK5HvLuBOPvt8Pu/fTzYQAv7EkJMkCQ8PD7x7947z83OgM2DJ5xUvg1zvck/L6EcxIQ3/HuiZ2WGSB8joX9W3y6VLcXl5SdM0/Pjjj/3Pi4ThU+uTAaW1BmUsSmvqtgGtsDaibhpirdHKohXoKAZjcK4bhGGMwru2G7MXHJExaGWZzg7wru1wZ9BU2wJUTmjLbr71bvxgqhO0PmA5X3etQKtRwRCnCa9evaLallTbLfOHR4rtlqPDA7I05cWzZ8RRzPu3b7ibz/niq6+YjbsIk21VcXpyysNjQRQd4JuKn17/mVEaMc5HbIuasigpNgumB444q7hZPXBwcsbs8IQ8PaWsSpqixUQJSZrRNIrNpqTablCh4fggJYtzwENwrIuSUC14fLilDorDkwuyOO3gXFAd6+YaXNugdOhCn5XFRhalLcqCUg5tLUbHhNDlplltunF8zqNtgg+a1jmMD9RtjXMB7yPKKpCFCG27vw8hEBsFOPyOsUySBILGmBjvm+79NLimYX77yM3Vgof7isRkfPOLS5rGE0cQWYMeJWxWS9brFWmakGUpi+UCCMRxBPh+J+edQ/mW46MDQmhpqy6iyBiN3/Vs0yzHuQqaDjx73zF3xCm4EfHkEBtn+E1D0G03Uahy+HpLUxZoV5KmY7RSFK7a6Rc1rmnIpofkoxltqZgcJhyOc7abe7xyTA+OSA8uifInKBNzcx0RCJzMErJc0TpHtV1R1hXaxmSxxWZTtqsHjk7OiZTpmDttUHRh8YGG0chyeDDC2rDTIxsmo5hqu2U2me4eip1LGgVNW5Pn2W43v2K9WRBFitbVfP+nP9D6mjx7RZoaAoaqbFFWdZOITESSddq416/foLWi2bkAG9cwG08py20nyo4jNpsuvuXg6Jg4SWjqpstnTTPKssG7gLVdkDl4FKojuneLilZ6ZxYyaN217ZfLNTaK6IZUjalXHXudpAneN2Spwej2//2Bs6//XyWL2jBeRDR4stjJ3wk7IQu6/LywL1rrPspFFklZ0GQRFVApC6qwh8LYpGnKV1991c/Mlukcx8fH5HnOq1eviOOYH3/8kQ8fPvCrX/2qXyDLsuT8/Jy7u7u+NfunP/2pb0cK+JOJPTJN5ezsrH/9siz71mSWZdR1zXq97sfNyeQYYWwF1Nzd3eG95/z8vAd4suESJneYLSg6OfmvGB5CCP2kIGF3BbgJoBXGSHR+w/cRQAQ/j+gbauWGcUFN0/Qu6Pl8ThRF/PrXv+4lB8K6LRaLPsszz/M+KkneQzYbAoxPTk56ecSQAYMusFzAvGhEJdtWIoKEaRZmTlg4AXySBSlOZWFfDw4Oetbw5OTko+k2h4eHHB0d9RsBkSqcnJz0c8+FrZXPIy36s7OzftMl94Ocy/G4S8EYglpxhsvEmuF5Fwe2hO2LFrdtW7799tt+szFshUuUlxjTAH744YdeyynH4fDwsJ/6JMdwPp9zcnLSRwXJpksYRtloyLUyTEEQACzPB8nkFA10nucsFgvatu1lHcLQfvLz51N/0MQG53w3JNC3RDZGadBWo7TC1Z3mDu8xNsIHiEyGDy1tVRKaCtU2nRtcRYymM7RWrJaPRDZmsbjjcTGnCQYVpXgV0Hisq0hVQ1M8UG4XTCZ5fxHH6e5Crmu265L31x94WpY8eXKB0Zrj4xNQiqqsuL163zmqs6xrWZjAwTRjuShYlxUfrt/S1lum4xmn589RZty182lZbh5p24q3q3uKxQnT4weSyRkRKdulxnCC1jHj8SHj6QFVtSKbJtCW1FU3i9oFzXqxZP34SD6ZECuH9i2+aUniGD3RaOWIkpg0i/HOULUBtEajieKI4GsiH7F4eGC5XHA0PWI6nfBwf0esLcamrIuqa1dqS5pF1HXL3d2au/uCOC3J8hEm1sSRhUjT+garDVVdEicJBE8ca6zVjMcZxmhQgcl4hncF1+8e+Hf3R45nM05PRzTNijTVpFnCxcUTtpuC9XpJlp0QdrOqO4ZSQWC3CelYzUmesC0Str7FqoCNLMYa2rrBa4iSiEDX9le78ZotAUxMOj3CRgnZ2HTh9sWas4MxD/NrmnrDOEtJoq4llieaquk2EpGNMTYjGR2Ba0kmhnQyomo9j4sbRpVnGqdooyGKObl4QZbF2GaBTT2bxQesURyOM2xkOZjEOK2oiharIYkjjFZUrsWYiCgxxAlko5TpTAKOHXmaEwhENkIBxnbaVFBkWdrv6LVmJ1xfsa22rDYr6rYFBe+ur/hyPIHgiKwm4Lv4qLab9PCwWLBab5gdzNgUW0ajMd47vHJ4BQeHh9Rty/zunsOjI6yN2JQFrg1MJmOU15QEjFbEsSGyXbJAkiYQzI6t6GLC6rrFWmhbT1lWrFYF48mUsmxxoebmwx1t06CwxBby3DDK97O8/9YleijRXAlTKUBTWLQhsJHWmbQKhyzKbDbDmG6yTBzHPDw88PDw0G8OpWTBKoqidyVPJpNenyfM5mq14u3bt1xeXvL8+XOUUpyenvaL7bt37/o2poR7i/ZP/r2qqn52uLiUgT5iReJfTk9PP4qQAXqN5sHBQQ8ShAGT9qh8x+l02gNs0RgK+BCAOnRqi0lCAIFErkg+oswSFzZOjpmwm5KB+Pj42IMUMcEIAzjMRBT2TzSE0Omlf/rpJ968edODWZldLmzcs2fPWK/XLJfLPj9THPnD2DHJ5xTwLj83bGcPAa6424fgW+QB0+m0D5g/OTnh5uamNxrJTHhxngsIlPGVAr5msxkywUgyKGVD8+zZsz6YW15vGFEk+Y7CssvvShtYWsyj0agjanagWQCfXOuySRAGvqqqHtSLEUqc7yIxefv2bc+ay+vKBmC5XPLw8NCPxZQxo9KmBvoM1Jubm35Gt5jDZrPZR+5yuSaEhR/qNoWlFqZUpktNp9OeHRdNtHzXIVP7KfXJgFIp06WJKAXadRquXUs1dN+mc35a24HOpkW7rt/vXEOkFa1SYCyoQJqNaZuGxfJtB2KsxvoS56Gqik476BracoVuK0aJYTxKuvw774ji7oKObESU5MTJhKAtH+4WrMuG89NTsjQhTVJc3aCVoiq2rFdLNqsl508vcV6hlWOUR5yfH3NzfcXN/AYdT0kmY6K4M68cjXMIcHs7p26jrh3oFCdnzzFKg2/Qpsvia0JDUdVkjSVSBkxK42uy0ZTEn1Ku7tCuQbUVNBXldkNgp3FwHdNYtSWxzbDpBFQX2eIDKKUxUUKcpNhoC6abABRUYFttGUUpcZoSJzlJkmFiS6BiPDE0bcz9cs26qDk8OsToTpfYtC0QSNMMo3ebhuDRWmFMtLtxUr748gvu7uHhbsv19QPf//ENk+lnu4uzofaQJilajdhuN70Avqq2WGuoqo5mN9ZA09LUFVkekSeW0Bi0UlirUdYSqQhUizEaaxVJbAFF7UswCZWvcMpitWGSjWmKAr9dkmURTRZhQkacpNRNy3a9YDSaoFUX0RS8oWoNrUpQSc50fMg0zyirhvr+jqKs8a7F11u0thTlFpuklMUDP92+x2/vODlIyUYxWdTNrQ/WcnZ2ShIlOAfOKbS2xGnK1BiitGP2xuOMOIpxbU2axbT1zw7OON5lSCpFFFlC1QWHG5PgfUuWdze1iWJOp8c8Lu/QSmF0124Opts1r9bFLjoksC1K6qrl8WGJ0TFab4liQ1WVpEmGNhF3d/eMxzNmBwcslyuc80RxSmQtTdV2m7ooYpQnRJFGAdaYXYoAOxevoW2KTq9pLFGkMKak2Fasiwql15Tbsmvlu5bJOGU6SditQ/v6G9aQPRGQIAuOLPJ/aQYZBjIPF1kZ+yYgbegSFxC22Ww+0mANDT6iI5NFTOZ1a625vb1lu93y5MmTfgKMMGBFUbBcLlksFjx//rxfsMfjMU+fPuXdu3dcX1/3ekVZfEejESGEfnLK7e0tzjkuLi4+au0LcBKmTAD30EghsTPCIMrcb6DX/InmUL6TMJdDx7OYOASQSIamHIssy3r2SfSYj4+PrNdrTk9Pe5AwnGn9lyypbAzG4zFff/018/mc+XzO+/fv+fbbb5nNZj0rNnT2C4Mn2ZuiCRRwLG710WjUt4WHwFmOh4BoAZbCggk41Vp/lCEqWk75LOIkl/GNcu3J+8mMeGEKb29ve0AlAFYc2EVR8Oc//7k3tEibWq5tycwcGmnSNO21mhLFJJpPAftRFPVudbkeJXJHrnu5X6ADYzI6Uu5DuQaVUr2UxDnXSyzu7u76jZwEqous4fb2ttcti7FKTFDDcyaudqB/TzlHwwlIwuqKnlU0wJJT6pxjMpn0x+JT69NNOXSCT20MoyjtRrT5AMp3LlVjQIFzAWU0aZKjNLjWoQHXeqq62RkFXBcMHRSubXF1yXiSc3lxSFCWqgnY6Anr9ZrF3Q3b9YJyvcIlFue6L536zk1uTMTx8QlRMmJ2dMqb16/5wx++449/+on//e//N2bTGa7ZvW/rqcotJlEUiyVNoNMpRkmXQRnHfLiZU5QrjqYTgoGygigZkWZTzkfPSOIxq9U9D483aHuPjUZ4Whq/Ip1MITakWdSFPhuDTSJC2xK7QLuNeXJ6znJ1z/zDFWcvHznIT6nbFqUNNrLEqSXogFYJRluC0ujYQNAE17UX411GojGGsLtwPA3aGkZphgsatMGYiDhRxK0mThyj0Zj7hyWL5QbnHWkaEeJAVZWM8tGO2ezal0kS0TRtB2It5Knl1ctL1qstNzcfePP2PV98ecbpSYJ3jjjRlOWWJEmBlO12w2Qy7qYk7cwiPnSMm40MY52hTTeL3MVd5ExkbccMBvC+Rvsa52q0Ao/BRClOW1oA1QGbersmizV1pLEmoFS34Xh4fKBpWsbjnCxLKBebTpeqLFkyow4xOo5JxzOiNCOfHGHjrJuik3QO5GW5YZxacJpolPLwfkO1WEKzoV5bZgczxiYlJCl5nOLQxFGKiVOUinYanRQbaeq6Io4MRoOyBte0bDYr4ijGaLUL/u1CZu1Ag2OtYbPZ4nemmaY1FKXDtRofKa6urxjlM5qmJUnj3UNccTe/Y1uVbMuKsqoZj2fEaYINoI1Fm4jVaktVeUZHY6ra0TZdtFNVbkniTlYRxxZrDHked2kOoe10nEp397rqNF8uD5RlS1N7iqJEiRNys4VdTFQ+GhFnltk0YzKJsGbf8v5b1xA0SeahLIDS9h5G+gzBkLTwhlM35DXl72ezGc+ePetfL45jlsslt7e3rFYrVqtVr0+UrFNZ7E5PT0nTlJOTE3744Qd+97vf8d133/Gf//N/7tknAT6if1wsFn07WKJ10jTl6uqqn4IjmYvSthsGft/d3fUMlGQ0ykxvARCiFZMWc1mWPH36tA8w//zzz3t9mZiahpNLZOGWtqswj2ma9gYPoNesic5teK7kmMnv3N3d8fj42IMVAc3j8fgjwDYE4gIavvzyS5bLZZ/Z+Mtf/rIP6h5OfoGuxT9kueRYy/eU1xWAPNRbDn9+mCogvyfX3TDDUUCQOI/v7u56bacwfgKCJD5HNg5igpLXkWO12Wx6QDcej3nz5k0/IWy9XnN0dNQDU2F3hfmVc5jneR+tI8YZkYGIKUbOk4SBD1v/1naTguQ4iktbzFdv377tI4uEOQwhcHNz07vYy7LsI3zgZze2RCgNjW5Af0zl+wwBpXwOOUdKqd64JO+12Wx63bBoZGUClXQIpEPxqfXJgDKyGS6EzmQTxT21rE2FtwlGa4Lv2K2gutnNSisUHZtJFPB0i5VvYTYZY3CcHh9TPN6xXT6QxhFoi28cdWNJtOZwknMyzdisRoymI4rtirrunKNWqPfW4U1AxSOOzp9xXlT8/ne/4x//6X/w6vlTLp+ccnx4gAqB25sbgut2enmWoWxEVTfYXWvF2pj317fcXr/h8PSc6eyYslHUISadHFNWmq3bouNDyhqq1T1aaSazQ0bJiMKVWBVRFSXJaEwUWaLRGLfcOeHjGIVi+fDA/OY909MXlFtHmo0A089RxpjO2KQNRnfueRfcLhMG8vEIHSyboiAbjdjWDZtNwXiSEsUJWluUjonjiFGwFEVLUXXj9LxXrFclrq14dnmCVgC6b7HKgy4Ejw/tjq1sGY01X339gtEkwYeSqmloWkukA1orksSy3W52u7SwA6Yx3juSNOldonk2Io46Z7tRjmQX8G207VzKrsFVBabddrO9rUbrCBQQrQgBbt69ZpQmXJ6e4Zsty8U91mowEU1QzO8fIHT6ziiOGU8nhEqRpEccnL8gmhyhvCLOchoXqNpuAxBFhpurPzOdHlG2gcnsEN9W5DHkacTd+w3rhw3m6TmTWUTQMcl4RpRNidIxKkoJyuDZ6Vd8p+80ymB1jNv9PyHg2i4KSkLqIewethB8F+PUth0Qb73i/n7N1YclcRRzdjLF6M5BW1fd6EttFGhF6wLvr69oG8ficYUPkOcTJtMJAU0UdSae1bIgjjNCsGxWBXXtsDaiKgsIgTiKaJXrZAtaYa3a5YmCNgbvG8qyRkLrCbDdVqw3G+I0QVtDlqesVxtG44zDwylxYkkSi6LsIp/29TetYc6ksIPCcohuchhsLgBTGBcBRWVZ0rZt3zI+Ozv7KAZGWtjSzpZA5uVyyWw267WIoqmTxVk+l8TZ/PM//zP/9b/+Vz777DOePXvW6/Wurq56xkeCyQWMicbt3bt3vH37lidPnvTubmGYhEWUSJvFYoHWus/5k8+y2Wx6lnM8HvcaPQE99/f3XF9fc35+3k/SEW2aAEkBJQKohrOPpdW5Xq8Zj8e9SUeAkQDKIcATwOe97x3hL1686IGWAL6hjlPYNtlI/OY3v2E6nfbaR4mWEfAs3xu65/1QMyebBwFZQ6ZQ2qliMpLjLBKKoREshMCbN2/I87zP9Xx4ePjoOry9vf3IaS1h8WmacnFx0bu7h/ICuYbevn3L4eEhdV33c9iF9X379i13d3e8ePGiH8cooFSuJ/nuspkaAmBhc4VpF5A3DEiX3xFnumhOhR2WCCWJJqqqiqIo+uvGOcfbt29pmob7+/ueZRYjmEgdJJophC6wXTZe4vgWzaYwoZK7KZ9NDGXDGDHpAmRZ1gPR5XLJZDLh+Pi4Z9BlM/mp9emjF02Mb12XiUgHjKI4RukuA9C3La33GGMxWnXGiwAETdV0rek4zVkXBUppNtuCZr3GEMhjS9Uoys0CHyAbTWiDxzWOarPGtV2+YVRFu+ykY7bFChcCURzh0TivqRsPdsTF8y8IKuL63Wv+8McfuZvP+eUXrzg+PCDPRzzcPzI7PARtmWQ5cdy1xtumJbEJTVGy+ekN6/ktaZyTJBN0luAIYA0nT19Sru6xumW5+jPr1R3GNuQjhVIG7TPwnma7BQ9JFGGMJZ8eULgVxkaUTc3tzRWXX6zJ81k3x9o5VGxQyqKURauIoDo2qGOFFJ6d/T+KMNpS12UXip2mtE13wSutQWl82LV5rGYyHVG1gU3RUFYNJra7lg5koxznd7mGKvTsgzaKum5xLjAaTwi39yzXc9Lc8OLFV4wnMT60+M6h0T30tYyj6kw2zgfa1n30MCyKDUpBYmNiq4lNTO3a3ejHBt+WlJtHIt+QpClGRXgTo9DYZEzTOnxTcnX7Dl0VGBSbYoOJY2ZHR5hsTNl6ys26C78/mNJ4w2O1JYpy0vEh3iZo7ymrEu08rWtp24qm3rB6WNBsFhyenOA3VTdbNooYZTFPL57yx+/+g8fFlldfzQg2I58dk02PibIpDRGtA7fL/PTOoZTMVY3Yrgrs7v6wVoOC4B2ubdEa4iRivVrRuposzneAUmFtzp///Cd++NNrnpyf8/LFc4wpUKpGEWidwQPvr65YrdaslwVN44ijlKODI/LRBLQhTTPSdMRqVbItJbh6hUxk6sLLNV2+pcOY3dSdyKB12F2LBq30brNhKIoC5zRNE3DO07gW7Q1JGjM7OOT09JQ8y6ibLcYE8szQNAKi9/W3LFkMBSQKKyMsniz+wn4MF05p7cksbqVUb3qR15L2rzB9sshL0LiAuOPj476VKwwK0AMca20/cu/169d8++23fPjwgW+++aY3Vszn897QMhqN+lBpYUaLouD+/p7b29uPYnHkODx//pzFYoExhsVi0f9Z2trCJgrjJED54OCgB0hVVfH+/Xt+8YtffAQmhd0SMCn/FbAH9DE5f8mgCigaZhHKJuDg4KBn1oQtE02mAERpN0sUjfy3bdtebrBYLMjznM8//7xnIIdM71DLJ+asYTi3tGKFeRVNo7RMhZUUZusvJ9II41rXNdfX1z3zKHFEx8fHH5mksizrDWBiRJH55cMJQPKa0iLebDacnZ2xXq+5v7/vdZDPnj3j3//933l4eOCbb77pW9Cz2awHh3I8RNowBM3DcP5hpuOQ2RcDi4BuAZrff/893333HZeXl3zxxRf9eYafc07fvHnTyzrkepbZ7eI+z7KM5XLZRywNpxkNs0aH15xcb8NNjtzvIk8Rc9gwX/To6IgnT570IfnCdotO9pOfP5/8k9oQVOhCj9GAoqoatNEkaY5zLaEqIXQLiqvbzplrI+Ikw7kuAy9Oc6pizXq15O7dW+rlLdvFLa4pODiYgdJU5ZaqbQmuiyhZLhZ473h/9YbJdMpnrz5jMjkkGI9JUrbOouIRbQk2TcmSA17mR5xffMbrP37H6v6af/w//pnPX1ySxIYkMcxvb8jHM9Js1EWjeEXwgdgYTo+PyNOM28cFi7sbspnj+GDGslrSNJrp5IwsOqQqFt3Uh+OcbbFicX/N0dEZsYkwWdwxMTsdoFIp1eMDXnXThiZxRL3dcP3+DRfPc6I0xcYd8FQmAhPtwDsEp7p2d+hAZ900HVM8isnsFKUDOYaybAla0bgW4wORNtB5jYkix2ScU5YtVVORZTGR7XbE47wL69aqczEnsTjcAloZtnWBNhVZoijWD9w9lEwPJ5yePycfa5rtPXXV5XcmSYT3bqCzqsiyn+fP5nlOEMdxUwGBJEmpVhWbYksca/I0xVd5pzWNUiKd4JQl4EjTGWnSscsRM7Zl0ckqkpTp4RHZdMZBmjCeTvF1RdOURDbicVXhg0RkRGzaFrTtNiohUDVbDo8OSRJLFivWiwV8qIitpmkbvDYED8+enrN5vGe5eGR+/8jlySWTw3OidNLNCyeAc6w2nYvUaMMoH2FMwPkOcKVpRtM2ndbYOXzYPeiThOA9m6IgiROiuGsVERTFpuann95Tlg2j0ZgoSfCtTKDoWtPrYktnEjc0dcd6Xz695OLpJaMsQylP8IqiqFmuaq4+rNkWNZNJzuFhThQ5kiwjy8YobfoHu999Rhd8N8FIW5x3u5ZbRNt2OurWN5RNQ9N6rAeru8Dzw9mE6XTMahUoqzVpktE2gbbet7z/1iULiyw2El3TaV2znlUS5kEAgrhDhy5dYTGkhSgTQQ4PD3sTjZjHNpsN9/f3eO95/fo1BwcH/OIXv+jjWoQVFIAk2YS/+tWveP78Od999x3z+Zz/8l/+C19++WXfYr2+vu7bobJ4Srvx7OyMPM+5u7vj9vaWg4OD3hkrbdTT01PW6zVnZ2ecnp6y2Wy4vb3l7Oysb+UOQ7a11r3uTVgzmf382Wef9bOyhakbsnICTAS8icRAWCClVD8qUcCKsGLSVhQgtd1umc/n5HneO4iFURRDh7Cv0Glmh6MUV6sV8/m8zwoV48UwVkqArLRn8zzvj6/MIh9mkso8bgGFwgbKNTOMUhJdbJ7n/bUin/n4+LgftiDTkIRBlZGScv4F0IiGtK7rXvYgcTvDgHYxl7x48aI3V93e3vbRVnIPyDlaLpc9mBd5goAtCekH+t8RmYEEqMvnkIie9XrNn/70J8qyZDKZfJQdKUYqyWuFTo87Go14/vw5L1686Dctkim6WCy4urrqpQkylz7Lsj6YX57TQ0e3gE4BzQLwgd6AJ/8mjOnR0RGz2YzFYtFrXQXEfmp9emxQHNP6QOsDWnesS+sdiYlBqT7uxFhDpCNarUBFBBeI4s6A0AZPNhrT1hXbpqEuS24+XBFRY2NDgyZ4uFsuaeqGPM3Q1jA7OqRtKh5+umfxsKbZWr765Zccno+wWYQPKU3IsemYyinqtiIyGWVwnDz9iufPX/H6D7/jx59+5Ox0zFdfvMAFxeNyy82HW6az2c7en1FUG4yxHJ+fMD09Yv6wYr5cYVzB8WTEw6KgKW6IbIZ3WxSOaltTb2tcWbH0jstnT0ls6Ngd7Ylsl7dnpzlumzDKM7xrOTw7Jo4s2lq8Nhhtad3OkRnFBG0ILoCDpmqItKPdneA0z3GATcegFEnkqPyKsqkxVqHbEtsmQKfXa6ua2BqODsfYCCJjSRNLXRXU25JknOOa3U3TeqyJd3NjY1TQZHHGNA/ENiL4gnfvrjk+PSTPD0izMbQbWtcQRwnOiRjb4FpPXbd0U3gqsiTBB4hjQ105CNI2iFmvb8nSmCQaoZIpoW3QOz2iQXdzok1KmsxI86MukNx2Ld5JNmM8O8JrqEOLzTJ0lGCaBO8CSZ5go4LNekUaATaiqAPGaiyestwwzXNGo5S6WnJycooKNTo4tHId06g1idVcXj5hPEq5uZ1z+llA64RNUVM5h0ljimrLuqjQOiKOwNaOfBTTNG0/kWjbVKRptmONHD4EsihmtVqzLbYczA4I3lNsCk5OnrLerGgbjzGKySQnibtczrYNJInlcfFIU7VcXjzj4WHJND9CG8uzp8+Ik4gsiwm+pa4d27Lldr7i3dWS+4c1l09PSPMIEwWyLEFjaBtH3bT4AK5tQTniOEJpTRxH1FVFksQYo2naFmNjNnXBuqppvCJ2CqOhLNasjSNLIbIB7zVKdWAmsvtZ3n/r+mjUKT+P/Ru2Z6WtJwBhyLoJ2JxMJj0rst1uef/+PUAPRLz33N/f961RY0yv8frhhx+4v7+nLEt+85vf9NE7wlLKoi4gwjnH8+fP+eyzz/j973/P999/z8XFBV9//TXeex4fH3n//j2Hh4d9G1xGHj558oSzszPu7u56QDubzXh4eOjnLctCKyMdZdToy5cve3AomkGlVB+ILg7ji4uLj35OFmoxp8j/S0akOKQlUmZohBFALeBvOJpPMgejKOqBgwD9siwpioLpdNpvAoQ9E4YPOqZQNKIC7s/Pz/tweGlVC9ssTJZsNIYyCQFAcrzk/2XWuugQ5fWGXSgB0nIMpS0sU46EmZXrQtzSogGU+B2RVgggl/Mi8gHJbRzG5Ejb98WLF0wmE66vr/nyyy97l7fE4kjslJhgxNEux1cYdpkVP/z7x8dHiqLoZ2Ov12uePHnykbtbIpMEmAsALsuS58+fc3d312+4Xr582TPYshnZbrfc3Nzw9u1b5vM5L1686EdoClBvmqY/dgIYpSMhGw65juQ8iGZz2KYXPaWMZhTGVq7PT63/BYZSE+1mAWutQSl0cDS7TMPOwbvrYYXOUau1woUW5xps3Dl99W73ErljEl/hto9ot2U0HdE0jvndHVXtyLIxXnXu1SzPKdZrnr54RaRz3r994F/+9f/ms1+cc/7yBBUbGtdgkhitLG3TzbfetoYoGpGODC+++IZRnvFw+xPXH+Y8vXzGxcWUxXrDpliibdTF1GgDxhLnGXmcYJKcdV1yc/2eg+Nj4qBZPa4wUdaBp7ZmuViwLVZUmxU/bBeU20e+/OpzTian1M7TVhVt1VCvl8xvb6mX9130jPddq1spol0YvNaqc+2GgPMtGrXT3Hm8c522Lk4wNkLpCKVjUBpjPEnSUIYtcWwJweHaEk1nCvKNw+tAlsSgcpq6QSlPFBm878J9tTGEXQvAGE1kI7zb5Q56j1GaJ2enrLaO+/t7Hh8WnB6NGM8SlGmpqhKtbT+JRynVPwzHo8luh1zusjYlK68LZm+9R2uL84EozjE2olitqRpIEtWxrTbgo5jJ9AQTapI0IfiSuvZE8ZRgcmwSdTPBDbht516rypqj6ZRNbbi6fcRqOByNiCpP0zY0ZYFSDVXZsNElVjc0vmEySol0RJp0D2fXOhrXEmcp43DA8eiQk7OnFEVFowLp5BivDevNhs22ZjRK0DamblsCoTOvWUNRbDC7h9hyuSaOJfpE9YHOMq1D667dF/yCKNKde7tcsV4vIbQQoCgq6sqR52MOZoccTE9Yrbb9zvv+/g5rj9FA8IqmCcznS+4fNlR1oKwdaZYynWjapsVqzWaz7VjEpsYHh9Y7XV6SEOg2S9YaArsRnsGzXK0pm5a68YyCpm0ceW5QeLbbNa5tGI1zkh27kUR7m/ffuoR9kkUd6NvSw3iYYd7kMCNRTBtKqX76CtC7uGezGU3T8OHDhx5MymuORiNWqxWfffYZxhhev37Nb3/7W7755ps+b3IIriQiRoDRdDrl66+/Zjwe93mKL1++7FvXcm9Iu3nYCk+ShO12y9u3bzk9PQW6cGjJ2GvbtgeZEqq+2Wz63EtxrVdVxXq95sOHDyyXy49c8sJaCqiUYyjh6EPN3bC1PMz/lEVb/n2o4ROwJy1PWdDh540C/MxCy3Eb/ptoKS8uLvrcwvv7e05PTzk6OkLyIoemK3k/Aayi4xy2V6XNLHIKYZlFHzjU6grDeHh42OszBSRJS1mOjbiUV6tV7/qvqoqrq6te9yifRSa3yGZCJAaTyeSj4yrHUVjt8XjMxcUFRVEA9HFQch1MJpOPcicFWEt2ZpIk/eQkAdoyyUcYcq11v3kYxvLIOEjRLcqQgKOjo15zLPeqTHsShlLuMzEuybQeuQeNMT1Alvt7aPKSa2UYdeS9Z7FY9KywvI/cx9IWn0wmPcAdRhj9f9UnA0pBrM61u1iULrJELuzuZrF45yB4gg/UbdPpKF0X7q2DgxC6iJgoxsQZ+fSQx/mW1EFdN3gPaTpC6QgXFDodkU2PIBpjywJLzFkYc339nn/5t//gcnHKi89/yWiUoWyBMeMubkbHVElCrC1Oe+zokLPnlslkwuPtW7797g88f/mMo9NDvLJUjefq5o7xaEpVbri7X3B+cUEU55yenLFcFdzP54Sd2ziomiZAliQ8e3pJXRUsF3d8+NDy7u07qnLFq1cvmM4OyfIptDXb9YL7u3tUtSX4ltNtycxq6qrEqS1RkmHjqDPG4PG+BlQX9K48VbXdPURigtfdeTC7OcpaowK0dd2d1ABYjY3ANY7gPFVZY+KOSU7SFGuhqRza2E6HWrc474jyCOda0jTZPRwLttsCpTVRrDk7OWC92bB4vKMuj9jGltSCNVE3McX/rNGSm95aizUWH7q52q7tYiYCHROG0mT5iLatCejuGvDQNmWnrcRjjCeKuyzLKBoxnp7QtiVRGwghAZMSZxnWevAVJlOkyhJMyWh6wK+OnnCx3JKOpwRtSTJL01ZsVwXbaUq5WhOaLXGqgJYszjHWYkzW6VJ1RJJklFXH3F08/4L84JxlY2i9Yl1UBK0oioqyasjzbnHRSiJbFEYZ6qYkS9JdRIPqb16JMZHoCdldVtWW2Szn1asL7u7mPDzc8v0fKkZ5wmyW471DYcizMaBpW8dmsyLLclarJcZ0O/vJeESoYbXa8vjQPcCjJIHQkCQRWZaw3axpld8BjEDTdC2nODE4FzrtpO/Ga3bPBb9zxnbyhrZRu4d/RZZ2Ot+udVIxyjuhd1vvtF7O/88Pmn39VfXzc/rnkGgBM8Ogc2HtQgh921TAgvy9MDdilpA4HgnflpnVAhIl808AKcDbt2/57W9/y93dHb/4xS969myYjTkMmB6NRrx8+ZLpdMqHDx/4t3/7N7744gvOz897EHp1ddVHyMzncy4vL0nTlPPzcxaLBTc3N8DHZgWZ3FKWZT+V5Mcff2S73fLll19yeHjYs2mr1Yrb29ue+ZHxjcLUiWFhqDccsr/DMYai0RsGkItedairk0VbgJf8v4A2Yenkd51z/ecVqYJ8VjlvT550SSkyC10Y2yFokfMwNJvI+8g1I+BEWp+j0ahvAQtoE3AyNBkJCy7GGfkdCfSW4zMEPwcHBxwfH/P4+NiPORyNRtR1zXK57EGY5GrKtSespBxjITJCCHz22WccHR31OsyhPlgAngBxue4FRKdp2gNLiXsSSYUASJmgVJYlBwcHfPnll9ze3nJ3d8fvfvc7xuMxBwcH/TGV57s4yCVsXnSOwkJLSoFEIsl3zfO8/0xifJPrSUC1gP6/dO9LdJNoUaW1LYyyfC/5s+ikP7U+GVCG0I3/kygTOQFd7fKVMCjlu7nROuC8zOascU3VLSBht7uLYjAR0+NTmrokTrp5wSEoNkVF2XhsMqIJivtVRcAwnp6ig+Yog4OzY968ibj58A7XKp5dBvJDjRorkihiOpmhQs39/AO1chxMRpR1jbcTxtOnBBQPj4+0quL84ilPTs6Zlor53ZL5Q+ckbzEcn3T5gsdHKcW24OZ2zng64+Aww1jDKM9p64bQVpyfnTOZpDzc37JZ3/PDH77l9OycNBmRpjlWKfI0ZblZU5ed2PvJV/+Jtq1o6wqbZJ3jVwGqS/h0rqYtG1ztaMoC57v5zs45bNSN2sN7VPDgHG1ZUvmWarsBVxIfnkBQhB3t3qIwtstIzEcJPosptyVeGVzwoALRTsundoyZc44ojnFlQ54nHHh49eIJdV1RlVvqNMPuTEM20qjdBdi2bnBzb2ldZ/CJo5i6rUh1p0Fyre/1RevNhtWmpGkMVnezpPFd+6htCpqqpG5LPAEbx7jQolC4tpvSgo7YNlsSrYlsgk0tk2SEMgZtLCfnZ3hlQXVTbBQxa2/Z5BpXtOhQo5zhcDJiOsmpXMAHRe0D2ljiOGc0zjg6PsNEGd4kpFGGb2CxqXEBNusSTzf2MTKWJOraNgcHY5q6oJt1bnc376R/MMguWLQ70poJwZPlEd988zlFcc7i8YH1eklROEZ5Bh5GkwlpOmFbdFM+Hh4eODvrTD9J3LUsvA8EZbm/X7It2x2wL0mTKdV2TXCd+7Iqa4zpHOZN43cLf0Qn0Ax9BqVS3Z+iKKJZbdDKdue5cdS2IUssaZoRRTFNUxJCFx+2LSuiuJNi7OtvW0MAI23GIRs11EzJ4isaNGE6ZCESdkMif6qq6mOGgD4/TxY7mbgis39l7N8PP/zA+/fvaduWV69ecXx83M/qFsPIhw8f0Fr3Ll9rbe92vbu7I4TA8+fPOT09pSzLPgRcWvRnZ2fEcczp6SlFUXB1ddWDEwloFlAjmsL5fM5yueQ//uM/uLi46CfHCABdr9eUZcnr16/55S9/2QNyYamGmkFpGQ9ZV2l3Ah8B9aHJRJjf4+Pj/t8EyIpWbjj5RVhRMZAIUL2/v+9bz5Kn6b3n888/74GDsJ7yeQQoSItWWEp59kj7W64DuZZkFOOwvSvfTzSXopkUtlKOg7zGsJUtbX3ZWBjTzesG+mtUMkZHoxHr9bpv1YrRRt5LWt4SNXR2dtZvYMRwIkYi+a9c4wIKj46O+nMgz2mJz9luu6liaZr27yutfPl8f/d3f9dripfLZT+PXhII8jzvx5FKrJWcT7lHAebzeQ+KBdiLRlSYZmllS2t6yKjLvS3nXDKPtdb9eZb7V5zvoomW6002ZZ9anz7Leyfg/jmfSnUuXud3bk1FVZcE73ctVxkftcsfVKCM6rLr0ARtsHGCTTJsktE0GywwGeWdY9uVNK2jcqCMQSnDw6ZklGSd2zdKODp5QpZFLO7nfP/tv3LydM3B05rpyQWWiDw1VKMMjcdrQ9EE8uwAGyWMD0bc3//I3d09det4YUY8ffYV+fgEZSIe7u+oHdw/LLDGcHR8zMnRIcE5lqsNj37OxeUzJuOc+YebnYu1RKO5vLxklD5n9TinqRtuH6/J0hHT0Zg4MoTgKauqGz0ZfDcFpnddOiJvUK7FhwZXlxTrDaENaAL4FqMigvdYQLkGQtcS7yYptoS2pdwu0aEkMhDZWWcMKj3ltmA0meCDpyi2pGlCUJZ1UaJQpFmM25k8wi6sdrMpaZqWqqkZjzIm0xmXlxesVmtio2jrBp1mbMstWZYS9M+5dtLacc5htKFpa5zvNLh13aKNJVB3wCSOSVpP2zqCsqAcjXM05Zpmu6auNuBb6nZLUDVGNSjj8a2nKEtipcl0TvBg4gTlPZoWHaANnqquMUGTj3dTEpqS4Coi3XJ4MCI3J7TVBt9s8UoRlCFKUpzSxCbFpCOy8SFJPsZHKXE2IeiYEBJU6wlBUVaeug6keQo76NWxDV2kliwI8hDuHIKd01FMC3Ecc3Nz87PuTAW22xVHhznHx1O+/OIlxWZDsS6wRuNax8HhEeuiYLnsxPjye0nys6PTB1hvtqw3NVonjEeWOHEcHY04OpqiQkDBzoWb0jaBzbrctXoirFV01LeMKutixDzdRjCOU9qmwDloG0dVNzRNS113Y1q70OauVVRut4yyT58Ru69PK1nQRa83dPEOGbRhy1Xc3kODyHDhF4PEUHc3dHgP9ZBa6z6ORBiv8/Nz8jxnPp/zr//6r7x48YLLy0vOzs6ADngODSfDCSqHh4d8+PCB29tbmqbhq6++4tWrV/10mPl8Ttu2fSj02dkZJycntG3LcrnstWfT6ZSrq6seVCilek2aaEHfv39Pnuc9izqMTxrGLP3l8RKGSDIzh6BvqO0DPgLxbdv2E2iGQdNAn7EZQthp2TtGVBz3cnyTJOnbz9J6Fi3gbDbjxYsXLJfLHhjleU5RFD1wls2H975/LTH9CNMl5iI5BiIhEKAhgHqYpyhufil5TXnvYWblsOT3RMcrn1uA4tHRUc+CD80iwnIKOBXgJpsJAVXyO9INGs6pHiYRyNol51vAtRxjuR9ETzqMfZLpRF9//XU/jlGumePj426K2cMDHz586M+rMNHy/qvVqmchpYN1cnLCyclJfx6kgyWpAMJeDvMx5TiL/EWuGdk4yvUizw0BqCIXEbPWp9YnA8pu95PuPkSDc13o9S4xBpCIlF1YeduijdpFytBN1nGe0Hb5kVp1jE+a5SRpytWH10S+Js1GHMwOMVFMUTuaTUldb0myCVGcUzbdmDmtDB7L0eETpnnOn//4Hd/+6z/ycrth/XBDlI1Jx1PyJCNg2RYFNopJspR65UgSzWn0nMVjF73z4eqKh8eKJ08/4+nlc45Pz5nfXuPahrqqub66Yjoe8/LykndX17y9uqFpOo1jZC1pmlDedSe1bSpqq9F2RB4rkmRMbC2ptdgwohilFMWKh/tbqu2G8ZMUZ22nYWwbXNMQG03b1tTbFbQVkYpRwVNXa1oVESUjdKipii1KQWQtvqkIru5C5+uKZblCeUccVyg7BRqaqqbNUoxLUEHhi5qybDtwpyAbZSxXayLbxfp0870Dd/MHoiQhihJ01LnHT44mFMWGyFqsNYxHo/5hIg+n4Y44SVOo6LSEUWf6ycejnX6022xoE2NwuxB0iwod09iqAhW6fMa6LijWDyRRwOxcbpAQaFhvFqRZgjZd3mHblBTrNc61lGWNiWriJEZhsNqxXCwo1iviOCI5OCSxJwTfEscRcTam9ga07TIm45Q4naJstxEy2Zi2DShvcKGlLD3b0uODwZqIKIoxBrTqJCJS8vCTlpKwShJ4LJo2afm4tkWpgDWKbVUwm+TokBLbDvQLG1BsShaLJZvNhvPzU2azCWFnAgKN0obVeklRNLRtJ6UYjRNevbrg9OSA6zfviNMYpfRu8SqoyhaCpiwr0ixDKXBt0+XO7oYbdA97u9NnOrwPlFWNoqUsR4zymLppWW7XNE2327cGtr745AfVvj6tpE0ni8VfMpTC4giYEC2WsCLCRMmfgb5Fmec5f/rTn/pWZxfh9rMGTjRewznDcv+fnp4ymUz4/e9/z3//7/+dX//619zf3/dgUu4JmQMuesw0Tbm8vOzH7b19+5b7+3tevHjBy5cvefLkCdfX17054e3bt0ynU169esXbt295/fr1RzpEYeEkb1Fa0wIQ5M9Azy7N53O22y1Pnz7tWSBhhUQf2EVn/dw2lba/vJawiwIABGAKmydZhwIGZJGX8yetStE/jsfjfrygOK6BfiMqMT8AJycnbDabng0UXaIA0b98TguTWZYlcRz3U2zku8izSq4nYWrF2ALsZDAly+WyN4mIE1ryFAX8yO+tVqteUjH8TuK8l9eS35FNkRhZ5LPLeZZzOZwQIy1qiYoSTe4wikdKgJRoWYdGKPnsMgZ0yAaKPEI2BDL5Z7PZ9DFJMg3p8vKyN5sNg9ZXqxWbzaY//5PJhC+//JLz83N+/PHHXm6ilGK5XPbfR65tWVtkIzBcY2QjKOcIOo20sPiPj499tudwI/Ep9b+gofQ457G2u0i7XWnb07F5nhMnCa5tujxF1Z2Ibpwfu3akAl91JhLbZVv5GqzVHEzHVJsFVbmhdo7pwRFxmlDVdQc+taNtaoy2/fzw0eiAqnhkmk/49a++4d/+/V9YfPiRPDY05YrV8h4Tj5jOjtC6GyhfFCvsaMThcY4vUhSeh/tbQqt4/ec/U2xbvvjlrxlPunFVb376MzayrB+XlOsV0/GIcZZyfnrC9e0d//LP/xez6QEvX74gshaUx9q8C3W3pjPBJDWP9ze0WmNoSdOI6SSndC3z21tmz1qi1NI6T2wsmkC1LaibgnKzhtZhtKOqSqDCBKg3j1SbCmW6mytEhrbu8kC1gSyJaRoH3tE2W5JoRNtWXZu6qUnRgGVTlBRFBbqb1ey8Q5vu+G6KFVkyJs9y3l/fMNUxIbQ0mw1NWzObTfDttmt1VgWz2ZTtptg9LDUhtP0DyHuPNpooTiirugvQ1jVaWVT0sylAGwvO4x1sXdOFgmNIkwxfb3G+ZTIaQbPlcfUB37TEUU6aZSSjMcFqrDG7EZ273Vi5pa0r1ssFcVIxnYwJOupc6dbQJgkQGM2OdvqVqpNf6AjVgrIxSTZCRxnp+IA2aOI071rhZYFSnUEqYHA+AJo4TkiiGGtBaY0xutuAeU8Um36n3D2EfmYsxZnXMUEaYyKqqkCrbipVlkZYo1k3FXY3SSmOLJv1mtvbObe3d0wmUybTCVFkaNuweyjsWt4obJSSJIomVBwc5ownKa4uCc6h6TS5wcO2qHDO71jGBmN+Hqlnzc8gpakbtJL4FA3B0TQtsY3I0k6HWq5KlNJMpzPSNKOtS5oBg7Gvv00J8yVgYhj78pdj9IaspbCYwpoL4BFgIu3Jg4MD1ut1r8M6OjrqAaQshtJWE3ZnMpn0wd7/8A//wD/90z/x/v37vj0rUg+JIzo+Pmaz6QYknJ2d9UBkPp/jnOP777+nKAp+85vf9GMFf/jhB6Io4uHhgdVqxXQ6ZTQacXFxwdXVFf/4j//I4eEhX3zxRX9shlpOyf0T7SR0zKnkQl5fX/PixYseQA5dxwISBGxIu1TYRXHQyiZbzC2iQ5W2vWgnBRwL0wj0eaDD9rOAoNVq1cfIvH37tge10pI+PDzsmWUZSbharT6SQci1It9NNN1DE9LQvT6clCOAGLrNsnx/MTs9PDz0LmeZbiPHXdzxonkUQCMt5aEkQwCmaBLlOpNrToCktLv/MhB9aKaS7ywdIWHXh7r/Yb6k3CvyM0Pn9xBEyusPiQF5TQHn19fXXF9fc3Bw0OuO5XwC/XmR7+y95/j4mOl02q+TUgIih/mcQxA5NORUVfURaJZrTo6btZbHx0eUUhwcHPQ6SgGdn1Iq/K/Az33ta1/72te+9rWvfe3rL2o/q2Jf+9rXvva1r33ta19/Ve0B5b72ta997Wtf+9rXvv6q2gPKfe1rX/va1772ta99/VW1B5T72te+9rWvfe1rX/v6q2oPKPe1r33ta1/72te+9vVX1R5Q7mtf+9rXvva1r33t66+qPaDc1772ta997Wtf+9rXX1V7QLmvfe1rX/va1772ta+/qvaAcl/72te+9rWvfe1rX39V/T9xlnXMh/jcigAAAABJRU5ErkJggg==\n" + }, + "metadata": {} }, { - "output_type": "display_data", - "data": { - "text/plain": [ - "VBox(children=(HTML(value='Interactive lab: move R/G/B. The pixels stay real; only the contraction weig…" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "4264d1b269004133bdd63da646b2f08f" - } - }, - "metadata": { - "application/vnd.jupyter.widget-view+json": { - "colab": { - "custom_widget_manager": { - "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" - } - } - } - } + "output_type": "stream", + "name": "stdout", + "text": [ + "EN: c disappears -> colour is contracted; h and w survive.\n", + "ES: c desaparece -> el color se contrae; h y w permanecen.\n", + "einsum: hwc,c->hw | weight sum / suma de pesos = 1.000\n" + ] } - ], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "gray = np.einsum('hwc,c->hw', photo, w) # (512, 512)\n", - "gray_batch = np.einsum('nhwc,c->nhw', batch, w) # (2, 512, 512)\n", - "\n", - "print(\"single image:\", photo.shape, \"->\", gray.shape)\n", - "print(\"batch:\", batch.shape, \"->\", gray_batch.shape)\n", - "print(\"contracted index: c | kept indices: h,w (and n for the batch)\")\n", - "\n", - "# Static before/after visual: real photograph in, computed contraction out.\n", - "fig, axes = plt.subplots(1, 2, figsize=(8, 4))\n", - "axes[0].imshow(photo / 255)\n", - "axes[0].set_title(\"real input — axes h, w, c\")\n", - "axes[1].imshow(gray, cmap=\"gray\")\n", - "axes[1].set_title(\"'hwc,c->hw' — c is gone\")\n", - "for ax in axes:\n", - " ax.axis(\"off\")\n", - "fig.suptitle(\"c is missing after ->, so colour is SUMMED OVER; h and w are KEPT\")\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "# Interactive lab: change the contraction weights while keeping the SAME real image\n", - "# and the SAME index rule. continuous_update=False keeps Colab responsive.\n", - "r_slider = widgets.FloatSlider(\n", - " value=float(w[0]), min=0.0, max=1.0, step=0.025,\n", - " description=\"R\", readout_format=\".3f\", continuous_update=False\n", - ")\n", - "g_slider = widgets.FloatSlider(\n", - " value=float(w[1]), min=0.0, max=1.0, step=0.025,\n", - " description=\"G\", readout_format=\".3f\", continuous_update=False\n", - ")\n", - "b_slider = widgets.FloatSlider(\n", - " value=float(w[2]), min=0.0, max=1.0, step=0.025,\n", - " description=\"B\", readout_format=\".3f\", continuous_update=False\n", - ")\n", - "\n", - "def explore_colour_contraction(r, g, b):\n", - " weights = np.array([r, g, b], dtype=float)\n", - " live_gray = np.einsum('hwc,c->hw', photo, weights)\n", - "\n", - " fig, axes = plt.subplots(1, 2, figsize=(7.5, 3.3))\n", - " axes[0].imshow(photo / 255)\n", - " axes[0].set_title(\"same real input\")\n", - " axes[1].imshow(live_gray, cmap=\"gray\")\n", - " axes[1].set_title(f\"R={r:.3f}, G={g:.3f}, B={b:.3f}\")\n", - " for ax in axes:\n", - " ax.axis(\"off\")\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - " print(\"EN: c disappears -> colour is contracted; h and w survive.\")\n", - " print(\"ES: c desaparece -> el color se contrae; h y w permanecen.\")\n", - " print(f\"einsum: hwc,c->hw | weight sum / suma de pesos = {weights.sum():.3f}\")\n", - "\n", - "rgb_output = widgets.interactive_output(\n", - " explore_colour_contraction,\n", - " {\"r\": r_slider, \"g\": g_slider, \"b\": b_slider},\n", - ")\n", - "\n", - "display(\n", - " widgets.VBox([\n", - " widgets.HTML(\n", - " \"Interactive lab: move R/G/B. \"\n", - " \"The pixels stay real; only the contraction weights change.\"\n", - " ),\n", - " widgets.HBox([r_slider, g_slider, b_slider]),\n", - " rgb_output,\n", - " ])\n", - ")\n" - ], - "id": "s06-06" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s06-07" - }, - "source": [ - "## Exercise 2 — Chapter 2 operations on real digit pixels\n", - "\n", - "### Where do matrices `A` and `B` come from?\n", - "\n", - "They are **not hand-written toy numbers**. Each `2×2` matrix is the central pixel patch of one real `8×8` handwritten digit from `sklearn.datasets.load_digits()`.\n", - "\n", - "Pixel intensities in this dataset run from **0 to 16**. The matrices are intentionally tiny so you can check the arithmetic by hand while still operating on observed data.\n", - "\n", - "### What are the three operations teaching?\n", - "\n", - "- **Trace — `ii->`**: the repeated `i` disappears, so the diagonal is summed to one scalar.\n", - "- **Transpose — `ij->ji`**: no index disappears; the axes are only reordered.\n", - "- **Matrix product — `ik,kj->ij`**: the shared `k` disappears, so `k` is the contracted dimension; `i` and `j` survive.\n", - "\n", - "### What should you try?\n", - "\n", - "Use the **operation selector** after solving the TODO. Switch among trace, transpose and matrix product and say aloud:\n", - "\n", - "> “Which index disappeared? Which indices survived?”\n", - "\n", - "That sentence is more important than memorising the strings.\n", - "\n", - "> 🇪🇸 **¿De dónde salen `A` y `B`?** No son números inventados: cada matriz `2×2` es un recorte central de píxeles de un dígito manuscrito real `8×8`. Las intensidades van de **0 a 16**. **Traza:** `ii->` elimina `i` y suma la diagonal. **Transpuesta:** `ij->ji` no elimina índices, solo cambia su orden. **Producto matricial:** `ik,kj->ij` elimina `k`, por lo que `k` es la dimensión contraída. Usa el selector y pregúntate siempre: **¿qué índice desapareció y cuáles sobrevivieron?**\n" - ], - "id": "s06-07" - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "s06-08" - }, - "outputs": [], - "source": [ - "# TODO 3: Rewrite these Chapter 2 operations as einsum and check each against\n", - "# NumPy:\n", - "#\n", - "# (a) trace of A -> scalar\n", - "# (b) transpose of A -> (2, 2)\n", - "# (c) matrix product A @ B -> (2, 2)\n", - "#\n", - "# For each expression, say which index is:\n", - "# - summed over,\n", - "# - only relabelled/reordered,\n", - "# - or kept." - ], - "id": "s06-08" - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ], - "id": "s06-09", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 795, - "referenced_widgets": [ - "f1d6be80ffe041c0ad311ed5469ed5cb", - "409af123c59c42dfa1fe448a2ee79c8a", - "36da133a3df24f2bbcf82d3f97d86124", - "482d6755a908430ab2e48cf87d8452e9", - "1ea19295851e48b6bf8769920792e47b", - "50b7096a05e341aa8e78d20f2845b16a", - "97c4528cc0fc4a9b9fe5497d5c9d414d", - "6bd79483d56a4909af42f858716baa77", - "68236406778c40d19786b447bc2b265d", - "c1921f8c3b1849f89d4afed48f2984e2" - ] - }, - "outputId": "467a758d-81c3-4407-e70c-7d4faca5eb10" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "A came from real digit label 0\n", - "[[15. 2.]\n", - " [12. 0.]]\n", - "B came from real digit label 1\n", - "[[ 3. 15.]\n", - " [15. 16.]]\n", - "\n", - "trace: 15.0 | NumPy: 15.0\n", - "transpose:\n", - " [[15. 12.]\n", - " [ 2. 0.]]\n", - "matrix product:\n", - " [[ 75. 257.]\n", - " [ 36. 180.]]\n", - "\n", - "all three einsum results agree with NumPy\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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"_view_name": "OutputView", + "layout": "IPY_MODEL_c1921f8c3b1849f89d4afed48f2984e2", + "msg_id": "", + "outputs": [ { - "output_type": "display_data", - "data": { - "text/plain": [ - "VBox(children=(HTML(value='Interactive index explorer / Explorador interactivo de índices: choose an op…" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "f1d6be80ffe041c0ad311ed5469ed5cb" - } - }, - "metadata": { - "application/vnd.jupyter.widget-view+json": { - "colab": { - "custom_widget_manager": { - "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" - } - } - } - } + "output_type": "stream", + "name": "stdout", + "text": [ + "A =\n", + " [[15. 2.]\n", + " [12. 0.]]\n", + "B =\n", + " [[ 3. 15.]\n", + " [15. 16.]]\n", + "einsum('ik,kj->ij', A, B) =\n", + " [[ 75. 257.]\n", + " [ 36. 180.]]\n", + "EN: k is shared and disappears -> contract k; keep i and j.\n", + "ES: k es compartido y desaparece -> contrae k; conserva i y j.\n" + ] } - ], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "trace_e = np.einsum('ii->', A)\n", - "transpose_e = np.einsum('ij->ji', A)\n", - "product_e = np.einsum('ik,kj->ij', A, B)\n", - "\n", - "print(\"A came from real digit label\", digits.target[0])\n", - "print(A)\n", - "print(\"B came from real digit label\", digits.target[1])\n", - "print(B)\n", - "print(\"\\ntrace:\", trace_e, \"| NumPy:\", np.trace(A))\n", - "print(\"transpose:\\n\", transpose_e)\n", - "print(\"matrix product:\\n\", product_e)\n", - "\n", - "assert np.allclose(trace_e, np.trace(A))\n", - "assert np.allclose(transpose_e, A.T)\n", - "assert np.allclose(product_e, A @ B)\n", - "print(\"\\nall three einsum results agree with NumPy\")\n", - "\n", - "# Context: show the real 8x8 source digits and highlight the 2x2 patches.\n", - "fig, axes = plt.subplots(1, 2, figsize=(5, 2.5))\n", - "for ax, idx, name in zip(axes, [0, 1], [\"A\", \"B\"]):\n", - " ax.imshow(digit_images[idx], cmap=\"gray_r\", interpolation=\"nearest\", vmin=0, vmax=16)\n", - " ax.add_patch(\n", - " plt.Rectangle((1.5, 1.5), 2, 2, fill=False, linewidth=2)\n", - " )\n", - " ax.set_title(f\"{name}: real digit label {digits.target[idx]}\\nred box = 2×2 patch\")\n", - " ax.axis(\"off\")\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "operation = widgets.ToggleButtons(\n", - " options=[\n", - " (\"Trace / Traza\", \"trace\"),\n", - " (\"Transpose / Transpuesta\", \"transpose\"),\n", - " (\"Matrix product / Producto\", \"product\"),\n", - " ],\n", - " value=\"product\",\n", - " description=\"\",\n", - ")\n", - "\n", - "def explain_operation(op):\n", - " if op == \"trace\":\n", - " expr = \"ii->\"\n", - " result = np.einsum(\"ii->\", A)\n", - " en = \"i is repeated and disappears -> sum the diagonal.\"\n", - " es = \"i se repite y desaparece -> se suma la diagonal.\"\n", - " print(\"A =\\n\", A)\n", - " print(f\"einsum('{expr}', A) =\", result)\n", - " elif op == \"transpose\":\n", - " expr = \"ij->ji\"\n", - " result = np.einsum(\"ij->ji\", A)\n", - " en = \"No index disappears -> only reorder the axes.\"\n", - " es = \"Ningún índice desaparece -> solo se reordenan los ejes.\"\n", - " print(\"A =\\n\", A)\n", - " print(f\"einsum('{expr}', A) =\\n\", result)\n", - " else:\n", - " expr = \"ik,kj->ij\"\n", - " result = np.einsum(\"ik,kj->ij\", A, B)\n", - " en = \"k is shared and disappears -> contract k; keep i and j.\"\n", - " es = \"k es compartido y desaparece -> contrae k; conserva i y j.\"\n", - " print(\"A =\\n\", A)\n", - " print(\"B =\\n\", B)\n", - " print(f\"einsum('{expr}', A, B) =\\n\", result)\n", - "\n", - " print(\"EN:\", en)\n", - " print(\"ES:\", es)\n", - "\n", - "operation_output = widgets.interactive_output(\n", - " explain_operation,\n", - " {\"op\": operation},\n", - ")\n", - "\n", - "display(\n", - " widgets.VBox([\n", - " widgets.HTML(\n", - " \"Interactive index explorer / Explorador interactivo de índices: \"\n", - " \"choose an operation and identify the disappearing index. / \"\n", - " \"elige una operación e identifica el índice que desaparece.\"\n", - " ),\n", - " operation,\n", - " operation_output,\n", - " ])\n", - ")\n" - ], - "id": "s06-09" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s06-10" - }, - "source": [ - "## Exercise 3 — 3,229,209 similarities from 1,797 real digit images\n", - "\n", - "### Why do these digit images look pixelated?\n", - "\n", - "This is **intentional and important**: the original `sklearn` digits dataset stores each handwritten digit at only **8×8 pixels**.\n", - "\n", - "So each image has exactly:\n", - "\n", - "`8 × 8 = 64 real measured pixel features`\n", - "\n", - "The blocky appearance is **not a bad download, compression error or broken image**. It is the original resolution of the dataset. We display it with `interpolation=\"nearest\"` so the notebook does **not invent smooth pixels that were never measured**.\n", - "\n", - "That low resolution is actually useful here: after flattening, every digit becomes a 64-dimensional vector, so the contraction is easy to connect directly to the pixels.\n", - "\n", - "### What does `id,jd->ij` mean?\n", - "\n", - "- `i`: query-image index — kept\n", - "- `j`: candidate-image index — kept\n", - "- `d`: 64 pixel features — **disappears**, so it is contracted\n", - "\n", - "The result therefore has shape `(1797, 1797)`: one similarity score for every pair of real digit images.\n", - "\n", - "### Raw dot product vs cosine similarity\n", - "\n", - "Both use the **same `einsum` contraction**. The difference is what happens before it:\n", - "\n", - "- **Raw dot product** also rewards vector magnitude — roughly, how much total “ink” or intensity an image has.\n", - "- **Cosine similarity** first normalizes each 64-pixel vector to unit length, so the comparison focuses more on the **pattern/direction** of the pixels.\n", - "\n", - "For query image `14` (true label `4`), the strongest raw-dot match is a `1`, while the strongest cosine matches are `4`s. That is a real example of why preprocessing changes the meaning of “similar”.\n", - "\n", - "### What should you try?\n", - "\n", - "Use the retrieval explorer:\n", - "\n", - "1. Move **Query / Consulta** to choose any of the 1,797 real digits.\n", - "2. Switch **Cosine / Coseno** ↔ **Raw dot / P. punto**.\n", - "3. Change **Top k / Vecinos**.\n", - "4. Compare how many retrieved images have the same label as the query.\n", - "\n", - "> 🇪🇸 **¿Por qué se ven pixelados los dígitos?** Porque el dataset original guarda cada dígito con solo **8×8 píxeles**. No es mala calidad de descarga ni un error: son exactamente **64 mediciones reales** por imagen. Se muestran con interpolación `nearest` para no inventar píxeles suaves que nunca fueron observados. En `id,jd->ij`, `d` representa esas 64 características y desaparece; `i` y `j` permanecen, por eso obtenemos una matriz `(1797,1797)` con una similitud para cada par. El producto punto crudo también depende de la magnitud/intensidad; el coseno normaliza primero y compara más la forma del patrón. Usa el explorador para cambiar consulta, métrica y número de vecinos.\n" - ], - "id": "s06-10" - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "id": "s06-11" - }, - "outputs": [], - "source": [ - "# TODO 4: Reshape all real digit images to D with shape (1797, 64).\n", - "#\n", - "# TODO 5: Compute every raw dot-product similarity with ONE einsum:\n", - "# 'id,jd->ij' -> expected shape (1797, 1797).\n", - "#\n", - "# TODO 6: Normalize every row of D to unit length, then repeat the SAME einsum\n", - "# to obtain cosine similarity C.\n", - "#\n", - "# TODO 7: Use query_idx = 14 (true label 4).\n", - "# - exclude the query from matching itself,\n", - "# - find the best raw-dot-product match,\n", - "# - find the five best cosine-similarity matches,\n", - "# - compare their labels with digits.target[query_idx].\n", - "#\n", - "# Predict first: which index disappears in 'id,jd->ij'?" - ], - "id": "s06-11" - }, - { - "cell_type": "code", - "execution_count": 7, - 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\n" + }, + "metadata": {} }, { - "output_type": "display_data", - "data": { - "text/plain": [ - "VBox(children=(HTML(value='Interactive retrieval explorer / Explorador interactivo: choose a real query…" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "50de1a57580a4e6ba9de46bb2e1a3b7b" - } - }, - "metadata": { - "application/vnd.jupyter.widget-view+json": { - "colab": { - "custom_widget_manager": { - "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" - } - } - } - } - } - ], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "D = digit_images.reshape(len(digit_images), -1) # (1797, 64)\n", - "\n", - "S = np.einsum('id,jd->ij', D, D) # (1797, 1797)\n", - "print(\"raw similarity matrix:\", S.shape, \"=\", S.size, \"pairwise scores\")\n", - "\n", - "norms = np.linalg.norm(D, axis=1, keepdims=True)\n", - "Dn = D / np.where(norms == 0, 1.0, norms)\n", - "C = np.einsum('id,jd->ij', Dn, Dn)\n", - "\n", - "assert S.shape == (1797, 1797)\n", - "assert C.shape == (1797, 1797)\n", - "assert np.allclose(C.diagonal(), 1.0)\n", - "\n", - "query_idx = 14\n", - "query_label = digits.target[query_idx]\n", - "\n", - "raw_scores = S[query_idx].copy()\n", - "cos_scores = C[query_idx].copy()\n", - "raw_scores[query_idx] = -np.inf\n", - "cos_scores[query_idx] = -np.inf\n", - "\n", - "raw_top1 = int(np.argmax(raw_scores))\n", - "cos_top5 = np.argsort(cos_scores)[-5:][::-1]\n", - "\n", - "print(\"query index / label:\", query_idx, \"/\", query_label)\n", - "print(\"best RAW dot-product match:\", raw_top1,\n", - " \"label\", digits.target[raw_top1],\n", - " \"score\", round(float(raw_scores[raw_top1]), 3))\n", - "print(\"top 5 COSINE matches:\", cos_top5.tolist())\n", - "print(\"top 5 COSINE labels:\", digits.target[cos_top5].tolist())\n", - "print(\"top 5 COSINE scores:\", np.round(cos_scores[cos_top5], 3).tolist())\n", - "\n", - "# Static retrieval example. nearest preserves the original 8x8 measurements.\n", - "fig, axes = plt.subplots(1, 6, figsize=(11, 2.5))\n", - "axes[0].imshow(\n", - " digit_images[query_idx], cmap=\"gray_r\",\n", - " interpolation=\"nearest\", vmin=0, vmax=16\n", - ")\n", - "axes[0].set_title(f\"query / consulta\\nlabel {query_label}\")\n", - "\n", - "for ax, idx in zip(axes[1:], cos_top5):\n", - " ax.imshow(\n", - " digit_images[idx], cmap=\"gray_r\",\n", - " interpolation=\"nearest\", vmin=0, vmax=16\n", - " )\n", - " ax.set_title(f\"label {digits.target[idx]}\\ncos={C[query_idx, idx]:.3f}\")\n", - "\n", - "for ax in axes:\n", - " ax.axis(\"off\")\n", - "\n", - "fig.suptitle(\n", - " \"Real 8×8 digits: one contraction -> all-pairs similarity -> retrieval\"\n", - ")\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "query_slider = widgets.IntSlider(\n", - " value=14, min=0, max=len(digit_images) - 1, step=1,\n", - " description=\"Query:\",\n", - " continuous_update=False,\n", - " style={\"description_width\": \"55px\"},\n", - ")\n", - "similarity_toggle = widgets.ToggleButtons(\n", - " options=[(\"Cosine / Coseno\", \"cosine\"), (\"Raw dot / P. punto\", \"raw\")],\n", - " value=\"cosine\",\n", - " description=\"\",\n", - ")\n", - "k_slider = widgets.IntSlider(\n", - " value=5, min=1, max=8, step=1,\n", - " description=\"Top k:\",\n", - " continuous_update=False,\n", - " style={\"description_width\": \"45px\"},\n", - ")\n", - "\n", - "def explore_retrieval(query, metric, k):\n", - " matrix = C if metric == \"cosine\" else S\n", - " scores = matrix[query].copy()\n", - " scores[query] = -np.inf\n", - "\n", - " top = np.argsort(scores)[-k:][::-1]\n", - " q_label = int(digits.target[query])\n", - "\n", - " fig, axes = plt.subplots(1, k + 1, figsize=(2.05 * (k + 1), 2.75))\n", - " axes = np.atleast_1d(axes)\n", - "\n", - " axes[0].imshow(\n", - " digit_images[query], cmap=\"gray_r\",\n", - " interpolation=\"nearest\", vmin=0, vmax=16\n", - " )\n", - " axes[0].set_title(f\"query {query}\\nlabel {q_label}\")\n", - " axes[0].axis(\"off\")\n", - "\n", - " for ax, idx in zip(axes[1:], top):\n", - " ax.imshow(\n", - " digit_images[idx], cmap=\"gray_r\",\n", - " interpolation=\"nearest\", vmin=0, vmax=16\n", - " )\n", - " score_name = \"cos\" if metric == \"cosine\" else \"dot\"\n", - " ax.set_title(\n", - " f\"idx {idx}\\nlabel {digits.target[idx]}\\n{score_name}={scores[idx]:.3f}\"\n", - " )\n", - " ax.axis(\"off\")\n", - "\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - " labels = digits.target[top].astype(int).tolist()\n", - " same_label = sum(label == q_label for label in labels)\n", - "\n", - " if metric == \"cosine\":\n", - " en = \"Cosine normalizes magnitude first, so the comparison emphasizes pixel-pattern direction.\"\n", - " es = \"El coseno normaliza la magnitud primero, por lo que enfatiza la dirección del patrón de píxeles.\"\n", - " else:\n", - " en = \"Raw dot product also rewards magnitude/intensity, so visually different labels can rank highly.\"\n", - " es = \"El producto punto crudo también premia magnitud/intensidad, por eso pueden aparecer etiquetas distintas.\"\n", - "\n", - " print(\n", - " f\"metric={metric} | query label={q_label} | \"\n", - " f\"top-{k} labels={labels} | same-label matches={same_label}/{k}\"\n", - " )\n", - " print(\"EN:\", en)\n", - " print(\"ES:\", es)\n", - " print(\"Index rule / Regla: id,jd->ij | d disappears / desaparece; i and j survive / permanecen.\")\n", - " print(\"Display note / Nota visual: the data are truly 8×8; the pixelated look is the original resolution.\")\n", - "\n", - "retrieval_output = widgets.interactive_output(\n", - " explore_retrieval,\n", - " {\n", - " \"query\": query_slider,\n", - " \"metric\": similarity_toggle,\n", - " \"k\": k_slider,\n", - " },\n", - ")\n", - "\n", - "display(\n", - " widgets.VBox([\n", - " widgets.HTML(\n", - " \"Interactive retrieval explorer / Explorador interactivo: \"\n", - " \"choose a real query digit, change the metric, and inspect its neighbours. / \"\n", - " \"elige un dígito real, cambia la métrica y observa sus vecinos.\"\n", - " ),\n", - " widgets.HTML(\n", - " \"Image quality note / Nota de calidad: these are original 8×8 measurements; \"\n", - " \"the blocky pixels are the data, not an error. / Son mediciones originales 8×8; \"\n", - " \"los bloques son los datos, no un error.\"\n", - " ),\n", - " widgets.HBox([query_slider, similarity_toggle, k_slider]),\n", - " retrieval_output,\n", - " ])\n", - ")\n" - ], - "id": "s06-12" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s06-13" - }, - "source": [ - "## What just happened\n", - "\n", - "You used **one index rule** three times, but each exercise gave the rule a different meaning:\n", - "\n", - "1. **Real microscopy image — `hwc,c->hw`** \n", - " `c` disappeared, so three colour measurements became one grayscale value at every pixel. The sliders changed the weights, not the rule.\n", - "\n", - "2. **Real digit-pixel matrices — `ik,kj->ij`** \n", - " `k` disappeared, so the shared dimension was multiplied and summed. Trace and transpose showed that `einsum` can also sum repeated indices or simply reorder them.\n", - "\n", - "3. **1,797 real handwritten digits — `id,jd->ij`** \n", - " `d` disappeared, so 64 real pixel measurements became one similarity score for every pair of images: **3,229,209 scores**. The interactive explorer showed that preprocessing changes what “similar” means.\n", - "\n", - "### The sentence to remember\n", - "\n", - "> **If an index disappears after `->`, it is summed over. If it remains, it survives in the output.**\n", - "\n", - "The `8×8` digits are deliberately pixelated because **each visible square is one of the 64 measured features**. Keeping that limitation visible makes the tensor operation easier to understand.\n", - "\n", - "> 🇪🇸 Usaste **una sola regla de índices** tres veces: `c` desapareció al convertir color a gris; `k` desapareció en el producto matricial; y `d` desapareció al convertir 64 píxeles en una similitud entre dos dígitos. La frase para recordar es: **si un índice desaparece después de `->`, se suma; si permanece, sobrevive en la salida.** Los dígitos `8×8` se ven pixelados a propósito: cada cuadrado visible es una de las 64 características realmente medidas.\n", - "\n", - "Keep this rule for section 10: `'ijk,ia,jb,kc->abc'` looks longer, but the logic is exactly the same.\n" - ], - "id": "s06-13" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s06-14" - }, - "source": [ - "---\n", - "\n", - "## Done with this section\n", - "\n", - "Next up: **07 · Inverses and the pseudoinverse** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/07-inverses-and-pseudoinverse.ipynb).\n", - "\n", - "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" - ], - "id": "s06-14" - } - ], - "metadata": { - "colab": { - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "version_major": 2, - "version_minor": 0, - "state": { - "4264d1b269004133bdd63da646b2f08f": { - "model_module": "@jupyter-widgets/controls", - "model_name": "VBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "VBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "VBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_08d53eb8a9cb447ea5281d71f4f6ec9f", - "IPY_MODEL_5abc2d6ff3784fe08d2ea57fbea39d8a", - "IPY_MODEL_ff1ae20acc844ab1bcc28c5e35498647" - ], - "layout": "IPY_MODEL_d7e1a368de9f40b5a3dc4f55ee8dabfe" - } - }, - "08d53eb8a9cb447ea5281d71f4f6ec9f": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_6540bc11246c4f798abe25f2c85668c9", - "placeholder": "​", - "style": "IPY_MODEL_6fa85a4fcd974dda9e6bb9799653f541", - "value": "Interactive lab: move R/G/B. 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\n" 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"nbformat": 4, + "nbformat_minor": 5 +} diff --git a/scripts/content.py b/scripts/content.py index a48a181..93807c5 100644 --- a/scripts/content.py +++ b/scripts/content.py @@ -246,15 +246,43 @@ def fetch_verified_video(url, expected_sha256, n_frames=16, stride=45): # ───────────────────────────────────────────────────────────────────────────── CONTENT["06"] = { "setup": """import numpy as np +import matplotlib.pyplot as plt +import ipywidgets as widgets +from IPython.display import display from sklearn.datasets import load_digits from skimage import data -photo = data.immunohistochemistry().astype(float) # (512, 512, 3) -batch = np.stack([photo, data.astronaut().astype(float)]) # (2, 512, 512, 3) -w = np.array([0.2125, 0.7154, 0.0721]) # RGB -> grayscale weights -A = np.array([[1., 2.], [3., 4.]]) -B = np.array([[5., 6.], [7., 8.]]) -print(photo.shape, batch.shape)""", +# Enable ipywidgets in Google Colab when available. +try: + from google.colab import output + output.enable_custom_widget_manager() +except ImportError: + pass + +# Real colour images. +photo = data.immunohistochemistry().astype(float) # (512, 512, 3) +batch = np.stack([photo, data.astronaut().astype(float)]) # (2, 512, 512, 3) +w = np.array([0.2125, 0.7154, 0.0721]) # RGB -> grayscale weights + +# Real handwritten digits (UCI Optical Recognition dataset, packaged by sklearn). +digits = load_digits() +digit_images = digits.images.astype(float) # (1797, 8, 8) + +# Small 2x2 matrices for Exercise 2 are NOT invented numbers: +# they are central pixel patches from two real digit images. +A = digit_images[0, 2:4, 2:4] +B = digit_images[1, 2:4, 2:4] + +print("photo:", photo.shape, "batch:", batch.shape) +print("digits:", digit_images.shape, "labels:", digits.target.shape) +print( + "Exercise 2 patches come from digit labels:", + digits.target[0], + "and", + digits.target[1], +) +print("A =\\n", A) +print("B =\\n", B)""", } # ───────────────────────────────────────────────────────────────────────────── From edf1dd892b27c2e73ece22dbefbe9bc5868f0ac6 Mon Sep 17 00:00:00 2001 From: Sebas Laverde Chunza <106851243+Laverde97@users.noreply.github.com> Date: Fri, 28 Aug 2026 21:16:07 -0500 Subject: [PATCH 15/29] Improve notebook 07 pedagogy with real data and interactive pseudoinverse for issue #44 --- notebooks/07-inverses-and-pseudoinverse.ipynb | 2706 ++++++++++++++--- 1 file changed, 2262 insertions(+), 444 deletions(-) diff --git a/notebooks/07-inverses-and-pseudoinverse.ipynb b/notebooks/07-inverses-and-pseudoinverse.ipynb index 381b13d..4caefe7 100644 --- a/notebooks/07-inverses-and-pseudoinverse.ipynb +++ b/notebooks/07-inverses-and-pseudoinverse.ipynb @@ -1,449 +1,2267 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 07 · Inverses and the pseudoinverse\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/07-inverses-and-pseudoinverse.ipynb)\n", - "\n", - "*Part IV · exercise · 15 min*\n", - "\n", - "> 🇪🇸 **Inversas y la pseudoinversa** — Resolver un sistema de 20.433 ecuaciones que no tiene solución exacta.\n", - "\n", - "Solve a 20,433-equation system that has no exact solution.\n", - "\n", - "## What you will be able to do\n", - "\n", - "- Say when a square matrix has no inverse, and predict the error before you see it.\n", - "- Compute the Moore-Penrose pseudoinverse and verify its four defining conditions.\n", - "- Say what `x = A⁺b` gives you for a tall matrix and for a wide one.\n", - "- Solve a real 20,433-equation system that has no exact solution.\n", - "- Apply the pseudoinverse to a tensor by unfolding, solving, and folding back." - ], - "id": "s07-00" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "Run this first. It installs and imports everything this notebook needs, and nothing else.\n", - "\n", - "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." - ], - "id": "s07-01" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "HOUSING = \"https://raw.githubusercontent.com/ageron/handson-ml2/master/datasets/housing/housing.csv\"\n", - "housing = pd.read_csv(HOUSING)\n", - "\n", - "def unfold(T, axis):\n", - " return np.moveaxis(T, axis, 0).reshape(T.shape[axis], -1)\n", - "\n", - "rng = np.random.default_rng(0)\n", - "print(housing.shape) # (20640, 10)\n", - "print(housing['total_bedrooms'].isnull().sum()) # 207 missing values!" - ], - "id": "s07-02" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 1 — square matrices\n", - "\n", - "> 🇪🇸 Paso 1: matrices cuadradas. La inversa solo existe si las columnas son\n", - "> linealmente independientes.\n", - "\n", - "Chapter 2 §2.3 defines `A⁻¹` for a square matrix, with `A⁻¹A = I`. But this only\n", - "exists when the columns are linearly independent. A matrix with dependent\n", - "columns is **singular** and has no inverse." - ], - "id": "s07-03" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "S = np.array([[2., 1.], [1., 3.]])\n", - "print(np.round(np.linalg.inv(S) @ S, 12)) # the identity, fine\n", - "\n", - "Singular = np.array([[1., 2.], [2., 4.]]) # column 2 = 2 x column 1\n", - "try:\n", - " np.linalg.inv(Singular)\n", - "except np.linalg.LinAlgError as e:\n", - " print(\"LinAlgError:\", e) # this error is the expected result" - ], - "id": "s07-04" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 2 — non-square matrices\n", - "\n", - "> 🇪🇸 Paso 2: matrices no cuadradas. `A⁻¹` ni siquiera está definida, pero la\n", - "> pseudoinversa sí.\n", - "\n", - "`A⁻¹` is not even defined. But we still need to solve `Ax = b`, and in machine\n", - "learning `A` is almost never square: it has one row per example and one column\n", - "per feature, and there are always far more examples than features.\n", - "\n", - "The **Moore-Penrose pseudoinverse** `A⁺` (Chapter 2 §2.9) is the answer. It is\n", - "defined for *every* matrix — square or not, singular or not — and it is computed\n", - "from the SVD (eq. 2.47):\n", - "\n", - "$$A^{+} = V D^{+} U^{\\top}$$" - ], - "id": "s07-05" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "A = rng.standard_normal((5, 3))\n", - "A_plus = np.linalg.pinv(A)\n", - "print(A.shape, A_plus.shape) # (5, 3) (3, 5) — note the shape flips\n", - "\n", - "U, S_, Vt = np.linalg.svd(A, full_matrices=False)\n", - "print(np.allclose(A_plus, Vt.T @ np.diag(1 / S_) @ U.T)) # True — this is eq 2.47" - ], - "id": "s07-06" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It satisfies four conditions that define it uniquely." - ], - "id": "s07-07" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(np.allclose(A @ A_plus @ A, A)) # 1\n", - "print(np.allclose(A_plus @ A @ A_plus, A_plus)) # 2\n", - "print(np.allclose((A @ A_plus).T, A @ A_plus)) # 3\n", - "print(np.allclose((A_plus @ A).T, A_plus @ A)) # 4" - ], - "id": "s07-08" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What `A⁺` gives you depends on the shape, exactly as Chapter 2 §2.9 says:\n", - "\n", - "- **More rows than columns** (too many equations, usually no exact solution) →\n", - " `x = A⁺b` gives the `x` that makes `Ax` as **close as possible** to `b`.\n", - " This is least squares.\n", - "- **More columns than rows** (too few equations, infinitely many solutions) →\n", - " `x = A⁺b` gives the valid solution with the **smallest norm**." - ], - "id": "s07-09" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 3 — what about tensors?\n", - "\n", - "> 🇪🇸 Paso 3: ¿y los tensores? No hay una única inversa tensorial aceptada por\n", - "> todos. En la práctica se despliega, se resuelve como matriz y se vuelve a\n", - "> plegar.\n", - "\n", - "This is a fair question with an honest answer. There is no single tensor inverse\n", - "that everyone uses. Several definitions exist (based on the Einstein product, or\n", - "the t-product for order-3 tensors), and they are active research.\n", - "\n", - "**In practice, in machine learning, you unfold the tensor into a matrix, use the\n", - "matrix pseudoinverse, and fold the result back.** That works because unfolding\n", - "loses nothing — which you proved for yourself in section 01." - ], - "id": "s07-10" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "T = rng.standard_normal((4, 3, 5))\n", - "M = unfold(T, 0) # (4, 15)\n", - "M_plus = np.linalg.pinv(M) # (15, 4)\n", - "print(M.shape, M_plus.shape)\n", - "print(np.allclose(M @ M_plus @ M, M)) # True" - ], - "id": "s07-11" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**When a tensor problem is hard, unfold it to a matrix, solve it there, and\n", - "fold back.** That is a general lesson, and section 10 is built entirely on it." - ], - "id": "s07-12" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 1 — real California housing data\n", - "\n", - "> 🇪🇸 Datos reales de vivienda en California: 20.640 distritos censales.\n", - "\n", - "Predict house value from district features. 20,640 real districts, 207 of them\n", - "with a missing value.\n", - "\n", - "::: {.callout-note}\n", - "TODO 3 asks you to trigger an error on purpose. If it raises, you did it right.\n", - ":::" - ], - "id": "s07-13" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO 1: Drop rows with missing values. How many rows remain?\n", - "\n", - "# TODO 2: Build X from these columns, and add a column of ones for the bias:\n", - "# ['housing_median_age','total_rooms','total_bedrooms',\n", - "# 'population','households','median_income']\n", - "# Target y = 'median_house_value'. Print X.shape. Is X square?\n", - "\n", - "# TODO 3: Try np.linalg.inv(X). What happens, and why?\n", - "# THE ERROR IS THE EXPECTED RESULT — you have not done anything wrong." - ], - "id": "s07-14" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "s07-00" + }, + "source": [ + "# 07 · Inverses and the pseudoinverse\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/07-inverses-and-pseudoinverse.ipynb)\n", + "\n", + "*Part IV · exercise · 15 min*\n", + "\n", + "> 🇪🇸 **Inversas y la pseudoinversa** — Aprende cuándo una inversa deja de existir y cómo la pseudoinversa sigue resolviendo problemas reales: sistemas singulares, regresión con 20.433 observaciones y tensores de imágenes.\n", + "\n", + "Use one question throughout the notebook:\n", + "\n", + "> **Does an exact inverse exist? If not, what does the pseudoinverse give us instead?**\n", + "\n", + "## What you will be able to do\n", + "\n", + "- Diagnose whether a **real-data square matrix** is invertible and explain why duplicated information makes it singular.\n", + "- Compute the Moore–Penrose pseudoinverse and verify its four defining conditions.\n", + "- Explain the difference between **wide**, **square**, and **tall** systems using real California housing observations.\n", + "- Solve a real `20,433 × 7` least-squares problem and interpret the residual rather than pretending an exact solution exists.\n", + "- Unfold a real image tensor, solve a wide system with the pseudoinverse, and fold the minimum-norm solution back into an image.\n", + "\n", + "> 🇪🇸 **Al terminar podrás:**\n", + "> - Diagnosticar si una matriz cuadrada construida con datos reales es invertible y explicar por qué duplicar información produce singularidad.\n", + "> - Calcular la pseudoinversa de Moore–Penrose y verificar sus cuatro condiciones.\n", + "> - Explicar la diferencia entre sistemas **anchos**, **cuadrados** y **altos** con observaciones reales de vivienda en California.\n", + "> - Resolver un problema real de mínimos cuadrados de `20.433 × 7` e interpretar el residuo.\n", + "> - Desplegar un tensor real de imágenes, resolver un sistema ancho y volver a plegar la solución de norma mínima.\n" + ], + "id": "s07-00" }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "d = housing.dropna()\n", - "print(len(d)) # 20433 rows remain\n", - "\n", - "feats = ['housing_median_age','total_rooms','total_bedrooms',\n", - " 'population','households','median_income']\n", - "X = np.column_stack([np.ones(len(d)), d[feats].to_numpy(float)]) # (20433, 7)\n", - "y = d['median_house_value'].to_numpy(float)\n", - "print(X.shape) # (20433, 7) — very tall\n", - "\n", - "try:\n", - " np.linalg.inv(X)\n", - "except np.linalg.LinAlgError as e:\n", - " print(\"LinAlgError:\", e) # inv() requires a SQUARE matrix. X has 20,433\n", - " # rows and 7 columns, so it cannot even be called." - ], - "id": "s07-15" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 2 — solve it anyway\n", - "\n", - "> 🇪🇸 Resuélvelo de todas formas, con la pseudoinversa." - ], - "id": "s07-16" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO 4: Solve for the weights with the pseudoinverse: w = pinv(X) @ y.\n", - "\n", - "# TODO 5: Check your answer against np.linalg.lstsq. Do they agree?\n", - "\n", - "# TODO 6: Compute the RMSE of the predictions. Which feature has the largest\n", - "# coefficient, and does that make sense for house prices?" - ], - "id": "s07-17" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true + { + "cell_type": "markdown", + "metadata": { + "id": "s07-01" + }, + "source": [ + "## Setup\n", + "\n", + "Run this first. It loads two real datasets:\n", + "\n", + "1. **California housing districts** from the public housing dataset used in *Hands-On Machine Learning*. After removing the 207 rows with a missing value, 20,433 real districts remain.\n", + "2. **Handwritten digit images** from `sklearn.datasets.load_digits()`, used later for the tensor example.\n", + "\n", + "The housing features are standardized before regression so their coefficients are on comparable scales. The target remains the real median house value in dollars.\n", + "\n", + "> 🇪🇸 Ejecuta esta celda primero. Carga dos conjuntos de datos reales:\n", + "> 1. distritos de vivienda de California; después de eliminar 207 filas con un valor faltante quedan 20.433 observaciones reales;\n", + "> 2. imágenes reales de dígitos manuscritos de `sklearn`.\n", + ">\n", + "> Las variables de vivienda se estandarizan antes de la regresión para que sus coeficientes sean comparables. El objetivo sigue siendo el valor mediano real de la vivienda en dólares.\n" + ], + "id": "s07-01" }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "w = np.linalg.pinv(X) @ y\n", - "w_lstsq, *_ = np.linalg.lstsq(X, y, rcond=None)\n", - "print(np.allclose(w, w_lstsq)) # True\n", - "\n", - "rmse = np.sqrt(((X @ w - y) ** 2).mean())\n", - "print(round(rmse)) # ~75980\n", - "\n", - "coef, name = max(zip(w[1:], feats))\n", - "print(name, round(coef)) # median_income 47748\n", - "\n", - "# X is 20433 x 7 — very tall, so np.linalg.inv cannot even be called. There is\n", - "# NO EXACT SOLUTION: no straight line passes through 20,433 points. The\n", - "# pseudoinverse gives the best possible answer instead, and lstsq agrees exactly\n", - "# because it solves the same problem.\n", - "#\n", - "# The largest coefficient belongs to median_income, which is the sensible\n", - "# result — income predicts house prices.\n", - "\n", - "import matplotlib.pyplot as plt\n", - "pred = X @ w\n", - "residuals = pred - y\n", - "fig, axes = plt.subplots(1, 2, figsize=(9, 3.5))\n", - "axes[0].scatter(y, pred, s=3, alpha=0.2, color=\"#4C72B0\")\n", - "lims = [min(y.min(), pred.min()), max(y.max(), pred.max())]\n", - "axes[0].plot(lims, lims, color=\"#C44E52\", linewidth=1)\n", - "axes[0].set_xlabel(\"actual\"); axes[0].set_ylabel(\"predicted\")\n", - "axes[0].set_title(\"predicted vs actual\")\n", - "axes[1].hist(residuals, bins=60, color=\"#55A868\")\n", - "axes[1].set_xlabel(\"prediction - actual\"); axes[1].set_title(\"residuals\")\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "# No straight line fits 20,433 points exactly, and the residuals show it: they\n", - "# are not tightly clustered at zero, and the predicted-vs-actual scatter fans\n", - "# out badly at the high end. LEAST SQUARES MINIMIZES THE AVERAGE SQUARED ERROR\n", - "# ACROSS ALL POINTS — it says nothing about any one prediction being close." - ], - "id": "s07-18" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 3 — the tensor version\n", - "\n", - "> 🇪🇸 La versión tensorial: despliega, resuelve, vuelve a plegar." - ], - "id": "s07-19" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO 7: Take an order-3 tensor T of shape (4, 3, 5) and a vector b of\n", - "# length 4. Solve the unfolded least-squares problem for x, then fold\n", - "# x back to the shape of a mode-0 slice. What shape must x have?" - ], - "id": "s07-20" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "s07-02", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "outputId": "d32c7648-9d20-4201-d78a-2e8baab081b4" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "housing rows: 20433\n", + "housing design matrix: (20433, 7)\n", + "digit tensor: (1797, 8, 8)\n", + "interactive charts: Plotly enabled (hover, zoom, pan)\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import ipywidgets as widgets\n", + "import plotly.express as px\n", + "import plotly.graph_objects as go\n", + "from IPython.display import display\n", + "from sklearn.datasets import load_digits\n", + "\n", + "# Enable ipywidgets in Google Colab when available.\n", + "try:\n", + " from google.colab import output\n", + " output.enable_custom_widget_manager()\n", + "except ImportError:\n", + " pass\n", + "\n", + "HOUSING = (\n", + " \"https://raw.githubusercontent.com/ageron/handson-ml2/master/\"\n", + " \"datasets/housing/housing.csv\"\n", + ")\n", + "housing = pd.read_csv(HOUSING).dropna().reset_index(drop=True)\n", + "\n", + "features = [\n", + " \"housing_median_age\",\n", + " \"total_rooms\",\n", + " \"total_bedrooms\",\n", + " \"population\",\n", + " \"households\",\n", + " \"median_income\",\n", + "]\n", + "\n", + "X_raw = housing[features].to_numpy(float)\n", + "feature_mean = X_raw.mean(axis=0)\n", + "feature_std = X_raw.std(axis=0)\n", + "X_scaled = (X_raw - feature_mean) / feature_std\n", + "\n", + "# Bias + six standardized real features -> 7 columns.\n", + "X = np.column_stack([np.ones(len(housing)), X_scaled])\n", + "y = housing[\"median_house_value\"].to_numpy(float)\n", + "column_names = [\"bias\"] + features\n", + "\n", + "# Real image tensor for Exercise 3.\n", + "digits = load_digits()\n", + "digit_tensor = digits.images.astype(float) # (1797, 8, 8)\n", + "\n", + "def unfold(T, axis=0):\n", + " return np.moveaxis(T, axis, 0).reshape(T.shape[axis], -1)\n", + "\n", + "print(\"housing rows:\", len(housing))\n", + "print(\"housing design matrix:\", X.shape)\n", + "print(\"digit tensor:\", digit_tensor.shape)\n", + "print(\"interactive charts: Plotly enabled (hover, zoom, pan)\")\n" + ], + "id": "s07-02" }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "T = rng.standard_normal((4, 3, 5))\n", - "b = rng.standard_normal(4)\n", - "\n", - "M = unfold(T, 0) # (4, 15) — one row per index along axis 0\n", - "x_flat = np.linalg.pinv(M) @ b # (15,) — min-norm solution, wide matrix\n", - "x = x_flat.reshape(T.shape[1], T.shape[2]) # fold back to (3, 5)\n", - "print(M.shape, x_flat.shape, x.shape)\n", - "\n", - "print(np.allclose(M @ x_flat, b)) # True — 4 equations, 15 unknowns\n", - "\n", - "# M is WIDE (4 x 15): infinitely many solutions, and pinv picks the one with the\n", - "# smallest norm. Folding back to (3, 5) is only meaningful because unfolding\n", - "# lost nothing in the first place." - ], - "id": "s07-21" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Time for Kahoot 🎯\n", - "\n", - "**Kahoot 2 — Einsum, Distance & the Pseudoinverse** · 6 questions, about 5 minutes.\n", - "\n", - "> 🇪🇸 **Einsum, distancia y la pseudoinversa** — 6 preguntas, unos 5 minutos.\n", - "\n", - "Join at **kahoot.it** with the PIN on the facilitator's screen.\n", - "\n", - "- [Quiz details and facilitator notes](https://project-delphi.github.io/tensors-workshop/kahoot.html#quiz-2)\n", - "- [Import file (`.xlsx`)](https://github.com/project-delphi/tensors-workshop/blob/main/kahoot/kahoot_quiz_2_distance_pseudoinverse.xlsx)\n", - "\n", - "Next up: **08 · Recursion with matrices and vectors** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/08-recursion-with-matrices.ipynb).\n", - "\n", - "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" - ], - "id": "s07-22" - } - ], - "metadata": { - "colab": { - "name": "07-inverses-and-pseudoinverse.ipynb", - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" + { + "cell_type": "markdown", + "metadata": { + "id": "s07-03" + }, + "source": [ + "## Why this matters\n", + "\n", + "In real data work, `A⁻¹` is the exception, not the default.\n", + "\n", + "A classical inverse requires a matrix that is both:\n", + "\n", + "- **square**, and\n", + "- **full rank**.\n", + "\n", + "Real machine-learning design matrices are usually **tall**: many observations, few features. They can also become **singular** when two columns carry duplicate or redundant information. In both situations, asking for `A⁻¹` is the wrong question.\n", + "\n", + "The Moore–Penrose pseudoinverse `A⁺` is defined for rectangular and singular matrices. Its meaning depends on the geometry:\n", + "\n", + "- **tall system** — usually no exact solution → `A⁺b` gives the **least-squares** solution;\n", + "- **wide system** — usually infinitely many exact solutions → `A⁺b` chooses the **minimum-norm** solution;\n", + "- **singular square system** — no ordinary inverse → `A⁺` still exists.\n", + "\n", + "### Learning cycle: Predict → Run → Explain\n", + "\n", + "Before every exercise, predict:\n", + "\n", + "1. Is the matrix wide, square, or tall?\n", + "2. What is its rank?\n", + "3. Should an ordinary inverse exist?\n", + "4. If not, what should the pseudoinverse mean here?\n", + "\n", + "> 🇪🇸 En trabajo real con datos, `A⁻¹` es la excepción. Una inversa ordinaria exige una matriz **cuadrada y de rango completo**. Las matrices de aprendizaje automático suelen ser **altas**, y además pueden volverse **singulares** cuando dos columnas contienen información duplicada. La pseudoinversa `A⁺` sigue existiendo. En un sistema alto entrega mínimos cuadrados; en uno ancho elige la solución exacta de norma mínima; y en una matriz cuadrada singular reemplaza una inversa que no existe.\n", + ">\n", + "> **Predice → Ejecuta → Explica:** antes de cada ejercicio decide si la matriz es ancha, cuadrada o alta; cuál debería ser su rango; si existe una inversa ordinaria; y qué debería significar la pseudoinversa.\n" + ], + "id": "s07-03" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s07-04" + }, + "source": [ + "## Exercise 1 — make a real-data matrix singular\n", + "\n", + "### What are you looking at?\n", + "\n", + "We take **seven real California districts** spread across the dataset and all seven columns of the standardized design matrix (bias + six real features). This gives a `7×7` square matrix.\n", + "\n", + "Then we deliberately duplicate one feature column. The observations are still real; the duplication is a **teaching transformation that mimics a common feature-engineering mistake**: supplying the same information twice.\n", + "\n", + "### What should happen?\n", + "\n", + "If two columns are identical, they are linearly dependent. Rank drops below 7, so the matrix becomes singular and `np.linalg.inv(...)` must fail.\n", + "\n", + "The pseudoinverse should still exist and satisfy the four Moore–Penrose conditions.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Build the real `7×7` matrix from the indicated rows.\n", + "2. Check its rank.\n", + "3. Duplicate one feature column and predict the new rank **before** running.\n", + "4. Try the ordinary inverse on the singular matrix.\n", + "5. Compute `A⁺` and verify the four defining conditions.\n", + "6. Use the interactive selector to switch between the original and duplicated-feature matrices.\n", + "\n", + "> 🇪🇸 Tomamos **siete distritos reales de California** distribuidos a lo largo del dataset y las siete columnas del diseño. Después duplicamos deliberadamente una columna. Los datos siguen siendo reales; la duplicación simula un error común de ingeniería de variables. Si dos columnas son iguales, el rango cae y la matriz se vuelve singular. La inversa ordinaria debe fallar, pero la pseudoinversa debe seguir existiendo. Usa el selector para comparar ambas matrices.\n" + ], + "id": "s07-04" + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "s07-05" + }, + "outputs": [], + "source": [ + "# TODO 1\n", + "# 1. Select seven spread-out real rows:\n", + "# row_idx = np.linspace(0, len(X) - 1, 7, dtype=int)\n", + "# A_real = X[row_idx]\n", + "#\n", + "# 2. Print A_real.shape and np.linalg.matrix_rank(A_real).\n", + "#\n", + "# 3. Make A_singular by copying A_real and duplicating one feature column:\n", + "# A_singular[:, 2] = A_singular[:, 1]\n", + "# Predict its rank before printing it.\n", + "#\n", + "# 4. Try np.linalg.inv(A_singular). The error is expected.\n", + "#\n", + "# 5. Compute A_plus = np.linalg.pinv(A_singular) and verify:\n", + "# A A+ A = A\n", + "# A+ A A+ = A+\n", + "# (A A+)^T = A A+\n", + "# (A+ A)^T = A+ A\n" + ], + "id": "s07-05" + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "s07-06", + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "colab": { + "base_uri": "https://localhost:8080/", + "height": 252, + "referenced_widgets": [ + "fdca3be0680e4b7aa100fbe7c6a4c334", + "17277ab54be14e1194408f20b0791762", + "0783e7c769aa424cb4266e1c13901323", + "0b4be2195dc74d27b9cd4dc08bdd20c8", + "90ad6f4badab467d96d39c81990cb801", + "67bd03a0e8aa4b55ab9cf30c28815164", + "1031cd5b62c54b11868db183a5d0c3d5", + "299e6e4f0b36489f97ddac85f1ac2375", + "4a710dbc9eed42e9897e754448c30ddf", + "be0cefa7e897453fa49466720097c3a5" + ] + }, + "outputId": "865390cb-b142-4aad-d4cf-ca627dd9f560" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "original / original: (7, 7) rank = 7\n", + "duplicated / duplicada: (7, 7) rank = 6\n", + "Moore–Penrose conditions / condiciones: [True, True, True, True]\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(HTML(value='Matrix diagnostic / Diagnóstico de matriz: switch one data-design decision a…" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "fdca3be0680e4b7aa100fbe7c6a4c334" + } + }, + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "colab": { + "custom_widget_manager": { + "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" + } + } + } + } + } + ], + "source": [ + "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "row_idx = np.linspace(0, len(X) - 1, 7, dtype=int)\n", + "A_real = X[row_idx].copy()\n", + "\n", + "A_singular = A_real.copy()\n", + "A_singular[:, 2] = A_singular[:, 1] # deliberate duplicate of a REAL feature\n", + "\n", + "print(\"original / original:\", A_real.shape,\n", + " \"rank =\", np.linalg.matrix_rank(A_real))\n", + "print(\"duplicated / duplicada:\", A_singular.shape,\n", + " \"rank =\", np.linalg.matrix_rank(A_singular))\n", + "\n", + "try:\n", + " np.linalg.inv(A_singular)\n", + "except np.linalg.LinAlgError as e:\n", + " print(\"Expected inverse failure / Fallo esperado de la inversa:\", e)\n", + "\n", + "A_plus = np.linalg.pinv(A_singular)\n", + "\n", + "mp_checks = [\n", + " np.allclose(A_singular @ A_plus @ A_singular, A_singular),\n", + " np.allclose(A_plus @ A_singular @ A_plus, A_plus),\n", + " np.allclose((A_singular @ A_plus).T, A_singular @ A_plus),\n", + " np.allclose((A_plus @ A_singular).T, A_plus @ A_singular),\n", + "]\n", + "print(\"Moore–Penrose conditions / condiciones:\", mp_checks)\n", + "\n", + "matrix_choice = widgets.ToggleButtons(\n", + " options=[\n", + " (\"Original real 7×7 / Real original\", \"original\"),\n", + " (\"Duplicated feature / Variable duplicada\", \"singular\"),\n", + " ],\n", + " value=\"original\",\n", + " description=\"\",\n", + ")\n", + "\n", + "def inspect_matrix(choice):\n", + " A = A_real if choice == \"original\" else A_singular\n", + " rank = np.linalg.matrix_rank(A)\n", + " cond = np.linalg.cond(A)\n", + "\n", + " print(\"shape / forma:\", A.shape)\n", + " print(\"rank / rango:\", rank)\n", + " print(\"condition number / número de condición:\", f\"{cond:.3e}\")\n", + "\n", + " if rank == A.shape[0]:\n", + " inv_error = np.linalg.norm(np.linalg.inv(A) @ A - np.eye(A.shape[0]))\n", + " print(\"EN: ordinary inverse exists.\")\n", + " print(\"ES: la inversa ordinaria existe.\")\n", + " print(\"||A⁻¹A - I|| =\", f\"{inv_error:.3e}\")\n", + " else:\n", + " print(\"EN: ordinary inverse does NOT exist; columns are dependent.\")\n", + " print(\"ES: la inversa ordinaria NO existe; hay columnas dependientes.\")\n", + "\n", + " Ap = np.linalg.pinv(A)\n", + " reconstruction = np.linalg.norm(A @ Ap @ A - A)\n", + " print(\"pseudoinverse shape / forma de A⁺:\", Ap.shape)\n", + " print(\"||A A⁺ A - A|| =\", f\"{reconstruction:.3e}\")\n", + "\n", + "matrix_output = widgets.interactive_output(\n", + " inspect_matrix,\n", + " {\"choice\": matrix_choice},\n", + ")\n", + "\n", + "display(\n", + " widgets.VBox([\n", + " widgets.HTML(\n", + " \"Matrix diagnostic / Diagnóstico de matriz: \"\n", + " \"switch one data-design decision and watch rank change. / \"\n", + " \"cambia una decisión del diseño y observa cómo cambia el rango.\"\n", + " ),\n", + " matrix_choice,\n", + " matrix_output,\n", + " ])\n", + ")\n" + ], + "id": "s07-06" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s07-07" + }, + "source": [ + "## Exercise 2 — one real dataset, three geometries\n", + "\n", + "The full California housing design matrix has shape:\n", + "\n", + "`(20,433 observations, 7 columns)`\n", + "\n", + "so it is **very tall**. No ordinary matrix inverse is defined for it.\n", + "\n", + "The pseudoinverse solves:\n", + "\n", + "`w = X⁺y`\n", + "\n", + "which minimizes the total squared residual. It does **not** claim that a straight line passes exactly through all 20,433 observations.\n", + "\n", + "### Why the interactive charts matter\n", + "\n", + "The two full-dataset charts are now **Plotly charts**. You can:\n", + "\n", + "- **hover** over a point to inspect the real and predicted house values;\n", + "- **zoom** into dense regions;\n", + "- **pan** across the distribution;\n", + "- use the toolbar to reset the view.\n", + "\n", + "Then keep the same seven columns but change how many **real rows** are used:\n", + "\n", + "- fewer than 7 rows → **wide** system: more unknowns than equations;\n", + "- exactly 7 rows → **square** system;\n", + "- more than 7 rows → **tall** system: more equations than unknowns.\n", + "\n", + "The **Rows / Filas** slider redraws an interactive Plotly chart so you can inspect each real observation and its prediction while the geometry changes.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Predict why `np.linalg.inv(X)` cannot be called on the full dataset.\n", + "2. Compute `w = pinv(X) @ y`.\n", + "3. Check it against `np.linalg.lstsq`.\n", + "4. Compute RMSE and inspect predicted versus actual values.\n", + "5. Hover over several districts and compare prediction error.\n", + "6. Move **Rows / Filas** through the wide → square → tall transition.\n", + "\n", + "> 🇪🇸 La matriz completa tiene forma `(20.433, 7)`, por lo que es **muy alta**. No existe una inversa ordinaria para una matriz rectangular. La pseudoinversa calcula la solución de mínimos cuadrados.\n", + ">\n", + "> Las gráficas ahora son **interactivas con Plotly**: pasa el cursor sobre los puntos para ver valores reales y predichos, haz **zoom**, desplázate con **pan** y reinicia la vista desde la barra de herramientas.\n", + ">\n", + "> Con el slider **Rows / Filas** mantienes las mismas siete columnas y cambias el número de filas reales: menos de 7 produce un sistema ancho, 7 uno cuadrado y más de 7 uno alto. La gráfica se actualiza y permite inspeccionar cada observación real.\n" + ], + "id": "s07-07" + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "s07-08" + }, + "outputs": [], + "source": [ + "# TODO 2\n", + "# 1. Explain why np.linalg.inv(X) is undefined from X.shape alone.\n", + "#\n", + "# 2. Solve the full real-data problem:\n", + "# w = np.linalg.pinv(X) @ y\n", + "#\n", + "# 3. Compare w with:\n", + "# np.linalg.lstsq(X, y, rcond=None)\n", + "#\n", + "# 4. Compute predictions and RMSE.\n", + "#\n", + "# 5. Because the six non-bias features were standardized, compare the\n", + "# ABSOLUTE values of w[1:] and identify the largest standardized coefficient.\n", + "# Describe it as an association in this dataset, NOT a causal effect.\n" + ], + "id": "s07-08" + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "s07-09", + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1509, + "referenced_widgets": [ + "ff5f9361100346188b16a1af2d273f9d", + "7dc550d7552f41f5af38fe47dd4e28f9", + "193b5af8c8374bd8b18d137cc4d48ca9", + "041bd6d03a9c4496851ee9a468d02352", + "1e74b2d33d574a8390182f018928563a", + "b4b80f5ec0bb48e2b6916fac7eed1c2e", + "b6b977f07c49401b9629e9be5b51fb4f", + "66aad08b71814b96a99ad75d198f96f9", + "ad6b01e7bf014fbcb9bd66ed5ebc500c", + "b380451182ae4bb189d75ff33fe38a6e" + ] + }, + "outputId": "d82324b0-7941-4d44-8719-bd3596dde7ef" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "full X / X completa: (20433, 7)\n", + "EN: X is rectangular, so np.linalg.inv(X) is not defined.\n", + "ES: X es rectangular, por lo que np.linalg.inv(X) no está definida.\n", + "pinv == lstsq: True\n", + "RMSE: $75,981\n", + "largest standardized coefficient / mayor coeficiente estandarizado: median_income 90,686\n", + "EN: this is an association in this linear fit, not a causal claim.\n", + "ES: es una asociación en este ajuste lineal, no una afirmación causal.\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
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pinv chooses minimum norm.\"\n", + " meaning_es = \"Normalmente hay muchas soluciones exactas; pinv elige norma mínima.\"\n", + " elif n_rows == Xn.shape[1]:\n", + " geometry = \"SQUARE / CUADRADO\"\n", + " meaning_en = \"An ordinary inverse exists only if rank is full.\"\n", + " meaning_es = \"La inversa ordinaria solo existe si el rango es completo.\"\n", + " else:\n", + " geometry = \"TALL / ALTO\"\n", + " meaning_en = \"Usually no exact solution; pinv gives least squares.\"\n", + " meaning_es = \"Normalmente no hay solución exacta; pinv da mínimos cuadrados.\"\n", + "\n", + " print(f\"{geometry}: {Xn.shape} | rank/rango={rank}\")\n", + " print(\"||Xw-y|| =\", f\"{residual_norm:.3e}\",\n", + " \"| ||w|| =\", f\"{coef_norm:.3e}\")\n", + " print(\"EN:\", meaning_en)\n", + " print(\"ES:\", meaning_es)\n", + "\n", + " long_df = pd.DataFrame({\n", + " \"row\": np.tile(np.arange(n_rows), 2),\n", + " \"value\": np.concatenate([yn, predn]),\n", + " \"series\": (\n", + " [\"actual / real\"] * n_rows\n", + " + [\"predicted / predicho\"] * n_rows\n", + " ),\n", + " })\n", + "\n", + " fig = px.scatter(\n", + " long_df,\n", + " x=\"row\",\n", + " y=\"value\",\n", + " color=\"series\",\n", + " hover_data={\"row\": True, \"value\": \":,.0f\"},\n", + " title=f\"{geometry} — same 7 columns / mismas 7 columnas\",\n", + " labels={\"row\": \"row / fila\", \"value\": \"median house value\"},\n", + " )\n", + " fig.update_traces(marker={\"size\": 9})\n", + " fig.update_layout(height=390, legend_title_text=\"\")\n", + " fig.show()\n", + "\n", + "geometry_output = widgets.interactive_output(\n", + " explore_geometry,\n", + " {\"n_rows\": rows_slider},\n", + ")\n", + "\n", + "display(\n", + " widgets.VBox([\n", + " widgets.HTML(\n", + " \"Geometry explorer / Explorador geométrico: \"\n", + " \"move through wide → square → tall using real housing rows. \"\n", + " \"Hover, zoom and pan in every chart. / \"\n", + " \"recorre ancho → cuadrado → alto usando filas reales. \"\n", + " \"Pasa el cursor, haz zoom y desplázate en cada gráfica.\"\n", + " ),\n", + " rows_slider,\n", + " geometry_output,\n", + " ])\n", + ")\n" + ], + "id": "s07-09" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s07-10" + }, + "source": [ + "## Exercise 3 — pseudoinverse after unfolding a real image tensor\n", + "\n", + "Now use real handwritten-digit images.\n", + "\n", + "Take the first 20 images:\n", + "\n", + "`T.shape = (20, 8, 8)`\n", + "\n", + "Unfolding mode 0 gives:\n", + "\n", + "`M.shape = (20, 64)`\n", + "\n", + "This is a **wide** matrix: 20 equations and 64 unknown pixel weights.\n", + "\n", + "### A target we can interpret exactly\n", + "\n", + "For each real digit image, define `b` as its **mean pixel intensity**. Because the mean of 64 pixels is a linear function, the uniform weight vector\n", + "\n", + "`[1/64, 1/64, ..., 1/64]`\n", + "\n", + "is one exact solution of `Mx = b`.\n", + "\n", + "But a wide system has many exact solutions. The pseudoinverse chooses the one with the **smallest Euclidean norm**. We can fold that 64-value solution back to an `8×8` weight image and compare it with the uniform solution.\n", + "\n", + "### Why the heatmaps are interactive\n", + "\n", + "The weight maps are Plotly heatmaps. Hover over any cell to inspect its **row, column and numerical weight**. You can zoom into a region and compare how individual weights change as more digit equations are added.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Unfold `T` to `(20, 64)`.\n", + "2. Build `b` from the real image means.\n", + "3. Solve `x = M⁺b`.\n", + "4. Confirm that both the pseudoinverse solution and the uniform solution predict `b`.\n", + "5. Compare their norms.\n", + "6. Fold `x` back to `8×8`.\n", + "7. Hover over individual weights in the heatmap.\n", + "8. Move **Digits / Dígitos** and see how the minimum-norm map changes as more real equations are added.\n", + "\n", + "> 🇪🇸 Ahora usamos imágenes reales de dígitos. Veinte imágenes `8×8` forman un tensor `(20,8,8)`. Al desplegarlo obtenemos una matriz ancha `(20,64)`. Definimos `b` como la intensidad media real de cada imagen. El vector uniforme `1/64` es una solución exacta conocida, pero existen muchas. La pseudoinversa elige la solución exacta de **norma mínima**.\n", + ">\n", + "> Los mapas de pesos ahora son **heatmaps interactivos de Plotly**. Pasa el cursor sobre cualquier celda para ver su fila, columna y peso numérico; también puedes hacer zoom. Después mueve **Digits / Dígitos** para observar cómo cambia la solución cuando añadimos más ecuaciones reales.\n" + ], + "id": "s07-10" + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "s07-11" + }, + "outputs": [], + "source": [ + "# TODO 3\n", + "# 1. T = digit_tensor[:20]\n", + "# 2. M = unfold(T, 0) # expected shape (20, 64)\n", + "# 3. b = T.mean(axis=(1, 2)) # one real mean intensity per image\n", + "# 4. x_pinv = np.linalg.pinv(M) @ b\n", + "# 5. x_uniform = np.full(64, 1 / 64)\n", + "#\n", + "# Check:\n", + "# - M @ x_pinv reproduces b\n", + "# - M @ x_uniform reproduces b\n", + "# - ||x_pinv|| <= ||x_uniform||\n", + "#\n", + "# Finally fold:\n", + "# x_image = x_pinv.reshape(8, 8)\n" + ], + "id": "s07-11" + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "s07-12", + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1832, + "referenced_widgets": [ + "054e3f7b9415416fbc519836762c5895", + "60e78b631a51493ab971c652ef29e22b", + "3c9d0ed5aaee4eddb11fa2c32420e3c1", + "487e686339c641ddb83c434a1efcd133", + "8739ba147ec048ada637b10565ddcad8", + "f31c87db8f0341a1ba014921658e3e36", + "e7c39b9033114256afe03b2d9843cae6", + "66d49545b54b4ed58a547e360fbada3a", + "4a6446977f0347f0a75bcd874d3d7ff3", + "6fdee2acb4a24c1c8cd9da09636bf898" + ] + }, + "outputId": "5333d8c9-5b9e-4ce9-ceef-8fa3f1723d85" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "tensor / tensor: (20, 8, 8)\n", + "unfolded / desplegado: (20, 64)\n", + "pinv residual / residuo: 2.107e-14\n", + "uniform residual / residuo uniforme: 0.000e+00\n", + "||x_pinv||: 0.102328\n", + "||x_uniform||: 0.125000\n", + "minimum norm check / chequeo norma mínima: True\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
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col=%{x}
pixel=%{z:.1f}\",\n", + " )\n", + ")\n", + "fig_digit.update_layout(\n", + " title=\"Real digit / Dígito real — original 8×8 measurements\",\n", + " height=390,\n", + " yaxis={\"autorange\": \"reversed\", \"scaleanchor\": \"x\"},\n", + ")\n", + "fig_digit.show()\n", + "\n", + "def weight_heatmap(z, title):\n", + " fig = go.Figure(\n", + " data=go.Heatmap(\n", + " z=z,\n", + " zmid=0,\n", + " colorscale=\"RdBu\",\n", + " hovertemplate=(\n", + " \"row=%{y}
col=%{x}
weight/peso=%{z:.6f}\"\n", + " ),\n", + " colorbar={\"title\": \"weight / peso\"},\n", + " )\n", + " )\n", + " fig.update_layout(\n", + " title=title,\n", + " height=390,\n", + " yaxis={\"autorange\": \"reversed\", \"scaleanchor\": \"x\"},\n", + " )\n", + " return fig\n", + "\n", + "weight_heatmap(\n", + " x_image,\n", + " \"Pseudoinverse minimum-norm weights / Pesos de norma mínima\",\n", + ").show()\n", + "\n", + "weight_heatmap(\n", + " uniform_image,\n", + " \"Known uniform exact solution / Solución uniforme exacta\",\n", + ").show()\n", + "\n", + "digits_slider = widgets.IntSlider(\n", + " value=20, min=5, max=60, step=5,\n", + " description=\"Digits / Dígitos:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"100px\"},\n", + ")\n", + "\n", + "def explore_tensor_pinv(n_digits):\n", + " Tn = digit_tensor[:n_digits]\n", + " Mn = unfold(Tn, 0)\n", + " bn = Tn.mean(axis=(1, 2))\n", + "\n", + " x_min = np.linalg.pinv(Mn) @ bn\n", + " x_known = np.full(Mn.shape[1], 1 / Mn.shape[1])\n", + "\n", + " res_min = np.linalg.norm(Mn @ x_min - bn)\n", + " res_known = np.linalg.norm(Mn @ x_known - bn)\n", + "\n", + " print(\n", + " f\"M: {Mn.shape} | rank/rango={np.linalg.matrix_rank(Mn)} | \"\n", + " f\"pinv residual/residuo={res_min:.2e}\"\n", + " )\n", + " print(\n", + " f\"||x_pinv||={np.linalg.norm(x_min):.6f} | \"\n", + " f\"||x_uniform||={np.linalg.norm(x_known):.6f}\"\n", + " )\n", + " print(\"EN: both solve the same real equations; pinv selects minimum norm.\")\n", + " print(\"ES: ambas resuelven las mismas ecuaciones reales; pinv elige norma mínima.\")\n", + "\n", + " fig = weight_heatmap(\n", + " x_min.reshape(8, 8),\n", + " f\"Pseudoinverse weights / Pesos pinv — {n_digits} real equations\",\n", + " )\n", + " fig.show()\n", + "\n", + "tensor_output = widgets.interactive_output(\n", + " explore_tensor_pinv,\n", + " {\"n_digits\": digits_slider},\n", + ")\n", + "\n", + "display(\n", + " widgets.VBox([\n", + " widgets.HTML(\n", + " \"Tensor pseudoinverse explorer / Explorador tensorial: \"\n", + " \"add real digit equations, then hover/zoom on the weight map. / \"\n", + " \"añade ecuaciones de dígitos reales y después pasa el cursor o haz zoom \"\n", + " \"sobre el mapa de pesos.\"\n", + " ),\n", + " digits_slider,\n", + " tensor_output,\n", + " ])\n", + ")\n" + ], + "id": "s07-12" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s07-13" + }, + "source": [ + "## What just happened\n", + "\n", + "You used the pseudoinverse for **three different reasons**, all on real observations:\n", + "\n", + "1. **Singular square matrix** \n", + " Duplicating a real feature made two columns dependent. Rank fell and the ordinary inverse disappeared, but `A⁺` still existed and satisfied the Moore–Penrose conditions.\n", + "\n", + "2. **Real California housing regression** \n", + " `X` had shape `(20,433, 7)`: far more equations than unknowns. There is generally no exact line through all observations, so `X⁺y` returned the **least-squares** solution. The interactive scatter and residual histogram let you hover, zoom and inspect where the approximation succeeds or fails.\n", + "\n", + "3. **Real digit-image tensor** \n", + " Unfolding `(20,8,8)` produced a wide `(20,64)` matrix. Many exact pixel-weight solutions existed, so the pseudoinverse selected the **minimum-norm** one. Interactive heatmaps made every individual pixel weight inspectable before folding the solution back to `8×8`.\n", + "\n", + "### The sentence to remember\n", + "\n", + "> **Inverse asks for an exact reversible square map. Pseudoinverse asks for the best-defined solution when that ideal situation is unavailable.**\n", + "\n", + "Or, by geometry:\n", + "\n", + "- tall → least squares;\n", + "- wide → minimum norm;\n", + "- singular → pseudoinverse still exists.\n", + "\n", + "> 🇪🇸 Usaste la pseudoinversa por **tres razones distintas** con observaciones reales: una matriz cuadrada se volvió singular al duplicar una variable; la regresión de California produjo un sistema alto que necesita mínimos cuadrados; y el tensor de dígitos produjo un sistema ancho con muchas soluciones exactas, donde `pinv` eligió la de norma mínima.\n", + ">\n", + "> Las gráficas interactivas permiten inspeccionar la matemática: en vivienda puedes pasar el cursor sobre observaciones reales, hacer zoom sobre residuos y ver cómo cambia la geometría; en el tensor puedes inspeccionar el valor exacto de cada peso.\n", + ">\n", + "> **Frase para recordar:** la inversa exige un mapa cuadrado, reversible y exacto. La pseudoinversa entrega una solución bien definida cuando esa situación ideal no existe.\n" + ], + "id": "s07-13" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s07-14" + }, + "source": [ + "---\n", + "\n", + "## Time for Kahoot 🎯\n", + "\n", + "**Kahoot 2 — Einsum, Distance & the Pseudoinverse** · 6 questions, about 5 minutes.\n", + "\n", + "> 🇪🇸 **Kahoot 2 — Einsum, distancia y la pseudoinversa** · 6 preguntas, unos 5 minutos.\n", + "\n", + "Join at **kahoot.it** with the PIN on the facilitator's screen.\n", + "\n", + "- [Quiz details and facilitator notes](https://project-delphi.github.io/tensors-workshop/kahoot.html#quiz-2)\n", + "- [Import file (`.xlsx`)](https://github.com/project-delphi/tensors-workshop/blob/main/kahoot/kahoot_quiz_2_distance_pseudoinverse.xlsx)\n", + "\n", + "Next up: **08 · Recursion with matrices and vectors** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/08-recursion-with-matrices.ipynb).\n", + "\n", + "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)\n" + ], + "id": "s07-14" + } + ], + "metadata": { + "colab": { + "toc_visible": true, + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "version_major": 2, + "version_minor": 0, + "state": { + "fdca3be0680e4b7aa100fbe7c6a4c334": { + "model_module": "@jupyter-widgets/controls", + "model_name": "VBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": 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residual/residuo=1.10e-14\n", + "||x_pinv||=0.098159 | ||x_uniform||=0.125000\n", + "EN: both solve the same real equations; pinv selects minimum norm.\n", + "ES: ambas resuelven las mismas ecuaciones reales; pinv elige norma mínima.\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/html": "\n\n\n
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"language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file From f4b6c9041afe894b2edfe8770c9a506a2666a3a2 Mon Sep 17 00:00:00 2001 From: Sebas Laverde Chunza Date: Fri, 28 Aug 2026 21:38:07 -0500 Subject: [PATCH 16/29] Finalize notebook 07 pedagogy for issue #44 --- _variables.yml | 20 +- .../07-inverses-and-pseudoinverse.ipynb | 1911 +++++++- notebooks/07-inverses-and-pseudoinverse.ipynb | 4113 ++++++++--------- scripts/content.py | 52 +- 4 files changed, 3588 insertions(+), 2508 deletions(-) diff --git a/_variables.yml b/_variables.yml index 8cf173e..884111b 100644 --- a/_variables.yml +++ b/_variables.yml @@ -317,20 +317,20 @@ sections: format_es: "ejercicio" title_en: "Inverses and the pseudoinverse" title_es: "Inversas y la pseudoinversa" - summary_en: "Solve a 20,433-equation system that has no exact solution." - summary_es: "Resolver un sistema de 20.433 ecuaciones que no tiene solución exacta." + summary_en: "Use the pseudoinverse on real singular, tall, and wide systems, then inspect the geometry interactively." + summary_es: "Usar la pseudoinversa en sistemas reales singulares, altos y anchos, e inspeccionar su geometría de forma interactiva." objectives_en: - - "Say when a square matrix has no inverse, and predict the error before you see it." + - "Diagnose whether a real-data square matrix is invertible and explain why duplicated information makes it singular." - "Compute the Moore-Penrose pseudoinverse and verify its four defining conditions." - - "Say what `x = A⁺b` gives you for a tall matrix and for a wide one." - - "Solve a real 20,433-equation system that has no exact solution." - - "Apply the pseudoinverse to a tensor by unfolding, solving, and folding back." + - "Explain the difference between wide, square, and tall systems using real California housing observations." + - "Solve a real 20,433-by-7 least-squares problem and interpret the residual rather than pretending an exact solution exists." + - "Unfold a real image tensor, solve a wide system with the pseudoinverse, and fold the minimum-norm solution back into an image." objectives_es: - - "Identificar cuándo una matriz cuadrada no tiene inversa y anticipar el error antes de observarlo." + - "Diagnosticar si una matriz cuadrada construida con datos reales es invertible y explicar por qué duplicar información produce singularidad." - "Calcular la pseudoinversa de Moore-Penrose y verificar sus cuatro condiciones definitorias." - - "Explicar qué produce `x = A⁺b` para una matriz alta y para una matriz ancha." - - "Resolver un sistema real de 20.433 ecuaciones que no tiene solución exacta." - - "Aplicar la pseudoinversa a un tensor mediante unfolding, solución y reconstrucción." + - "Explicar la diferencia entre sistemas anchos, cuadrados y altos usando observaciones reales de vivienda en California." + - "Resolver un problema real de mínimos cuadrados de 20.433 por 7 e interpretar el residuo en lugar de suponer una solución exacta." + - "Desplegar un tensor real de imágenes, resolver un sistema ancho con la pseudoinversa y volver a plegar la solución de norma mínima." s08: n: "08" slug: "recursion-with-matrices" diff --git a/docs/notebooks/07-inverses-and-pseudoinverse.ipynb b/docs/notebooks/07-inverses-and-pseudoinverse.ipynb index 381b13d..561a2bd 100644 --- a/docs/notebooks/07-inverses-and-pseudoinverse.ipynb +++ b/docs/notebooks/07-inverses-and-pseudoinverse.ipynb @@ -10,17 +10,17 @@ "\n", "*Part IV · exercise · 15 min*\n", "\n", - "> 🇪🇸 **Inversas y la pseudoinversa** — Resolver un sistema de 20.433 ecuaciones que no tiene solución exacta.\n", + "> 🇪🇸 **Inversas y la pseudoinversa** — Usar la pseudoinversa en sistemas reales singulares, altos y anchos, e inspeccionar su geometría de forma interactiva.\n", "\n", - "Solve a 20,433-equation system that has no exact solution.\n", + "Use the pseudoinverse on real singular, tall, and wide systems, then inspect the geometry interactively.\n", "\n", "## What you will be able to do\n", "\n", - "- Say when a square matrix has no inverse, and predict the error before you see it.\n", + "- Diagnose whether a real-data square matrix is invertible and explain why duplicated information makes it singular.\n", "- Compute the Moore-Penrose pseudoinverse and verify its four defining conditions.\n", - "- Say what `x = A⁺b` gives you for a tall matrix and for a wide one.\n", - "- Solve a real 20,433-equation system that has no exact solution.\n", - "- Apply the pseudoinverse to a tensor by unfolding, solving, and folding back." + "- Explain the difference between wide, square, and tall systems using real California housing observations.\n", + "- Solve a real 20,433-by-7 least-squares problem and interpret the residual rather than pretending an exact solution exists.\n", + "- Unfold a real image tensor, solve a wide system with the pseudoinverse, and fold the minimum-norm solution back into an image." ], "id": "s07-00" }, @@ -44,16 +44,56 @@ "source": [ "import numpy as np\n", "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import ipywidgets as widgets\n", + "import plotly.express as px\n", + "import plotly.graph_objects as go\n", + "from IPython.display import display\n", + "from sklearn.datasets import load_digits\n", + "\n", + "# Enable ipywidgets in Google Colab when available.\n", + "try:\n", + " from google.colab import output\n", + " output.enable_custom_widget_manager()\n", + "except ImportError:\n", + " pass\n", + "\n", + "HOUSING = (\n", + " \"https://raw.githubusercontent.com/ageron/handson-ml2/master/\"\n", + " \"datasets/housing/housing.csv\"\n", + ")\n", + "housing = pd.read_csv(HOUSING).dropna().reset_index(drop=True)\n", + "\n", + "features = [\n", + " \"housing_median_age\",\n", + " \"total_rooms\",\n", + " \"total_bedrooms\",\n", + " \"population\",\n", + " \"households\",\n", + " \"median_income\",\n", + "]\n", + "\n", + "X_raw = housing[features].to_numpy(float)\n", + "feature_mean = X_raw.mean(axis=0)\n", + "feature_std = X_raw.std(axis=0)\n", + "X_scaled = (X_raw - feature_mean) / feature_std\n", + "\n", + "# Bias + six standardized real features -> 7 columns.\n", + "X = np.column_stack([np.ones(len(housing)), X_scaled])\n", + "y = housing[\"median_house_value\"].to_numpy(float)\n", + "column_names = [\"bias\"] + features\n", "\n", - "HOUSING = \"https://raw.githubusercontent.com/ageron/handson-ml2/master/datasets/housing/housing.csv\"\n", - "housing = pd.read_csv(HOUSING)\n", + "# Real image tensor for Exercise 3.\n", + "digits = load_digits()\n", + "digit_tensor = digits.images.astype(float) # (1797, 8, 8)\n", "\n", - "def unfold(T, axis):\n", + "def unfold(T, axis=0):\n", " return np.moveaxis(T, axis, 0).reshape(T.shape[axis], -1)\n", "\n", - "rng = np.random.default_rng(0)\n", - "print(housing.shape) # (20640, 10)\n", - "print(housing['total_bedrooms'].isnull().sum()) # 207 missing values!" + "print(\"housing rows:\", len(housing))\n", + "print(\"housing design matrix:\", X.shape)\n", + "print(\"digit tensor:\", digit_tensor.shape)\n", + "print(\"interactive charts: Plotly enabled (hover, zoom, pan)\")" ], "id": "s07-02" }, @@ -61,164 +101,74 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Step 1 — square matrices\n", + "## Why this matters\n", "\n", - "> 🇪🇸 Paso 1: matrices cuadradas. La inversa solo existe si las columnas son\n", - "> linealmente independientes.\n", + "In real data work, `A⁻¹` is the exception, not the default.\n", "\n", - "Chapter 2 §2.3 defines `A⁻¹` for a square matrix, with `A⁻¹A = I`. But this only\n", - "exists when the columns are linearly independent. A matrix with dependent\n", - "columns is **singular** and has no inverse." - ], - "id": "s07-03" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "S = np.array([[2., 1.], [1., 3.]])\n", - "print(np.round(np.linalg.inv(S) @ S, 12)) # the identity, fine\n", + "A classical inverse requires a matrix that is both:\n", "\n", - "Singular = np.array([[1., 2.], [2., 4.]]) # column 2 = 2 x column 1\n", - "try:\n", - " np.linalg.inv(Singular)\n", - "except np.linalg.LinAlgError as e:\n", - " print(\"LinAlgError:\", e) # this error is the expected result" - ], - "id": "s07-04" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 2 — non-square matrices\n", + "- **square**, and\n", + "- **full rank**.\n", "\n", - "> 🇪🇸 Paso 2: matrices no cuadradas. `A⁻¹` ni siquiera está definida, pero la\n", - "> pseudoinversa sí.\n", + "Real machine-learning design matrices are usually **tall**: many observations, few features. They can also become **singular** when two columns carry duplicate or redundant information. In both situations, asking for `A⁻¹` is the wrong question.\n", "\n", - "`A⁻¹` is not even defined. But we still need to solve `Ax = b`, and in machine\n", - "learning `A` is almost never square: it has one row per example and one column\n", - "per feature, and there are always far more examples than features.\n", + "The Moore–Penrose pseudoinverse `A⁺` is defined for rectangular and singular matrices. Its meaning depends on the geometry:\n", "\n", - "The **Moore-Penrose pseudoinverse** `A⁺` (Chapter 2 §2.9) is the answer. It is\n", - "defined for *every* matrix — square or not, singular or not — and it is computed\n", - "from the SVD (eq. 2.47):\n", + "- **tall system** — usually no exact solution → `A⁺b` gives the **least-squares** solution;\n", + "- **wide system** — usually infinitely many exact solutions → `A⁺b` chooses the **minimum-norm** solution;\n", + "- **singular square system** — no ordinary inverse → `A⁺` still exists.\n", "\n", - "$$A^{+} = V D^{+} U^{\\top}$$" - ], - "id": "s07-05" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "A = rng.standard_normal((5, 3))\n", - "A_plus = np.linalg.pinv(A)\n", - "print(A.shape, A_plus.shape) # (5, 3) (3, 5) — note the shape flips\n", + "### How to use the folded solutions / Cómo usar las soluciones plegadas\n", "\n", - "U, S_, Vt = np.linalg.svd(A, full_matrices=False)\n", - "print(np.allclose(A_plus, Vt.T @ np.diag(1 / S_) @ U.T)) # True — this is eq 2.47" - ], - "id": "s07-06" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It satisfies four conditions that define it uniquely." - ], - "id": "s07-07" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(np.allclose(A @ A_plus @ A, A)) # 1\n", - "print(np.allclose(A_plus @ A @ A_plus, A_plus)) # 2\n", - "print(np.allclose((A @ A_plus).T, A @ A_plus)) # 3\n", - "print(np.allclose((A_plus @ A).T, A_plus @ A)) # 4" - ], - "id": "s07-08" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What `A⁺` gives you depends on the shape, exactly as Chapter 2 §2.9 says:\n", + "Each exercise has a **Solution / Solución** cell that is intentionally closed. First try the `TODO`; then open the solution to compare your reasoning with a reference implementation.\n", "\n", - "- **More rows than columns** (too many equations, usually no exact solution) →\n", - " `x = A⁺b` gives the `x` that makes `Ax` as **close as possible** to `b`.\n", - " This is least squares.\n", - "- **More columns than rows** (too few equations, infinitely many solutions) →\n", - " `x = A⁺b` gives the valid solution with the **smallest norm**." - ], - "id": "s07-09" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 3 — what about tensors?\n", + "> 🇪🇸 Cada ejercicio tiene una celda **Solution / Solución** cerrada a propósito. Primero intenta resolver el `TODO`; después abre la solución para comparar tu razonamiento con una implementación de referencia.\n", "\n", - "> 🇪🇸 Paso 3: ¿y los tensores? No hay una única inversa tensorial aceptada por\n", - "> todos. En la práctica se despliega, se resuelve como matriz y se vuelve a\n", - "> plegar.\n", + "### Learning cycle: Predict → Run → Explain\n", "\n", - "This is a fair question with an honest answer. There is no single tensor inverse\n", - "that everyone uses. Several definitions exist (based on the Einstein product, or\n", - "the t-product for order-3 tensors), and they are active research.\n", + "Before every exercise, predict:\n", "\n", - "**In practice, in machine learning, you unfold the tensor into a matrix, use the\n", - "matrix pseudoinverse, and fold the result back.** That works because unfolding\n", - "loses nothing — which you proved for yourself in section 01." - ], - "id": "s07-10" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "T = rng.standard_normal((4, 3, 5))\n", - "M = unfold(T, 0) # (4, 15)\n", - "M_plus = np.linalg.pinv(M) # (15, 4)\n", - "print(M.shape, M_plus.shape)\n", - "print(np.allclose(M @ M_plus @ M, M)) # True" - ], - "id": "s07-11" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**When a tensor problem is hard, unfold it to a matrix, solve it there, and\n", - "fold back.** That is a general lesson, and section 10 is built entirely on it." + "1. Is the matrix wide, square, or tall?\n", + "2. What is its rank?\n", + "3. Should an ordinary inverse exist?\n", + "4. If not, what should the pseudoinverse mean here?\n", + "\n", + "> 🇪🇸 En trabajo real con datos, `A⁻¹` es la excepción. Una inversa ordinaria exige una matriz **cuadrada y de rango completo**. Las matrices de aprendizaje automático suelen ser **altas**, y además pueden volverse **singulares** cuando dos columnas contienen información duplicada. La pseudoinversa `A⁺` sigue existiendo. En un sistema alto entrega mínimos cuadrados; en uno ancho elige la solución exacta de norma mínima; y en una matriz cuadrada singular reemplaza una inversa que no existe.\n", + ">\n", + "> **Predice → Ejecuta → Explica:** antes de cada ejercicio decide si la matriz es ancha, cuadrada o alta; cuál debería ser su rango; si existe una inversa ordinaria; y qué debería significar la pseudoinversa.\n" ], - "id": "s07-12" + "id": "s07-03" }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercise 1 — real California housing data\n", + "## Exercise 1 — make a real-data matrix singular\n", + "\n", + "### What are you looking at?\n", + "\n", + "We take **seven real California districts** spread across the dataset and all seven columns of the standardized design matrix (bias + six real features). This gives a `7×7` square matrix.\n", + "\n", + "Then we deliberately duplicate one feature column. The observations are still real; the duplication is a **teaching transformation that mimics a common feature-engineering mistake**: supplying the same information twice.\n", + "\n", + "### What should happen?\n", + "\n", + "If two columns are identical, they are linearly dependent. Rank drops below 7, so the matrix becomes singular and `np.linalg.inv(...)` must fail.\n", "\n", - "> 🇪🇸 Datos reales de vivienda en California: 20.640 distritos censales.\n", + "The pseudoinverse should still exist and satisfy the four Moore–Penrose conditions.\n", "\n", - "Predict house value from district features. 20,640 real districts, 207 of them\n", - "with a missing value.\n", + "### What should you try?\n", "\n", - "::: {.callout-note}\n", - "TODO 3 asks you to trigger an error on purpose. If it raises, you did it right.\n", - ":::" + "1. Build the real `7×7` matrix from the indicated rows.\n", + "2. Check its rank.\n", + "3. Duplicate one feature column and predict the new rank **before** running.\n", + "4. Try the ordinary inverse on the singular matrix.\n", + "5. Compute `A⁺` and verify the four defining conditions.\n", + "6. Use the interactive selector to switch between the original and duplicated-feature matrices.\n", + "\n", + "> 🇪🇸 Tomamos **siete distritos reales de California** distribuidos a lo largo del dataset y las siete columnas del diseño. Después duplicamos deliberadamente una columna. Los datos siguen siendo reales; la duplicación simula un error común de ingeniería de variables. Si dos columnas son iguales, el rango cae y la matriz se vuelve singular. La inversa ordinaria debe fallar, pero la pseudoinversa debe seguir existiendo. Usa el selector para comparar ambas matrices.\n" ], - "id": "s07-13" + "id": "s07-04" }, { "cell_type": "code", @@ -226,60 +176,171 @@ "metadata": {}, "outputs": [], "source": [ - "# TODO 1: Drop rows with missing values. How many rows remain?\n", - "\n", - "# TODO 2: Build X from these columns, and add a column of ones for the bias:\n", - "# ['housing_median_age','total_rooms','total_bedrooms',\n", - "# 'population','households','median_income']\n", - "# Target y = 'median_house_value'. Print X.shape. Is X square?\n", - "\n", - "# TODO 3: Try np.linalg.inv(X). What happens, and why?\n", - "# THE ERROR IS THE EXPECTED RESULT — you have not done anything wrong." + "# TODO 1\n", + "# 1. Select seven spread-out real rows:\n", + "# row_idx = np.linspace(0, len(X) - 1, 7, dtype=int)\n", + "# A_real = X[row_idx]\n", + "#\n", + "# 2. Print A_real.shape and np.linalg.matrix_rank(A_real).\n", + "#\n", + "# 3. Make A_singular by copying A_real and duplicating one feature column:\n", + "# A_singular[:, 2] = A_singular[:, 1]\n", + "# Predict its rank before printing it.\n", + "#\n", + "# 4. Try np.linalg.inv(A_singular). The error is expected.\n", + "#\n", + "# 5. Compute A_plus = np.linalg.pinv(A_singular) and verify:\n", + "# A A+ A = A\n", + "# A+ A A+ = A+\n", + "# (A A+)^T = A A+\n", + "# (A+ A)^T = A+ A\n" ], - "id": "s07-14" + "id": "s07-05" }, { "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "d = housing.dropna()\n", - "print(len(d)) # 20433 rows remain\n", + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "row_idx = np.linspace(0, len(X) - 1, 7, dtype=int)\n", + "A_real = X[row_idx].copy()\n", + "\n", + "A_singular = A_real.copy()\n", + "A_singular[:, 2] = A_singular[:, 1] # deliberate duplicate of a REAL feature\n", "\n", - "feats = ['housing_median_age','total_rooms','total_bedrooms',\n", - " 'population','households','median_income']\n", - "X = np.column_stack([np.ones(len(d)), d[feats].to_numpy(float)]) # (20433, 7)\n", - "y = d['median_house_value'].to_numpy(float)\n", - "print(X.shape) # (20433, 7) — very tall\n", + "print(\"original / original:\", A_real.shape,\n", + " \"rank =\", np.linalg.matrix_rank(A_real))\n", + "print(\"duplicated / duplicada:\", A_singular.shape,\n", + " \"rank =\", np.linalg.matrix_rank(A_singular))\n", "\n", "try:\n", - " np.linalg.inv(X)\n", + " np.linalg.inv(A_singular)\n", "except np.linalg.LinAlgError as e:\n", - " print(\"LinAlgError:\", e) # inv() requires a SQUARE matrix. X has 20,433\n", - " # rows and 7 columns, so it cannot even be called." + " print(\"Expected inverse failure / Fallo esperado de la inversa:\", e)\n", + "\n", + "A_plus = np.linalg.pinv(A_singular)\n", + "\n", + "mp_checks = [\n", + " np.allclose(A_singular @ A_plus @ A_singular, A_singular),\n", + " np.allclose(A_plus @ A_singular @ A_plus, A_plus),\n", + " np.allclose((A_singular @ A_plus).T, A_singular @ A_plus),\n", + " np.allclose((A_plus @ A_singular).T, A_plus @ A_singular),\n", + "]\n", + "print(\"Moore–Penrose conditions / condiciones:\", mp_checks)\n", + "\n", + "matrix_choice = widgets.ToggleButtons(\n", + " options=[\n", + " (\"Original real 7×7 / Real original\", \"original\"),\n", + " (\"Duplicated feature / Variable duplicada\", \"singular\"),\n", + " ],\n", + " value=\"original\",\n", + " description=\"\",\n", + ")\n", + "\n", + "def inspect_matrix(choice):\n", + " A = A_real if choice == \"original\" else A_singular\n", + " rank = np.linalg.matrix_rank(A)\n", + " cond = np.linalg.cond(A)\n", + "\n", + " print(\"shape / forma:\", A.shape)\n", + " print(\"rank / rango:\", rank)\n", + " print(\"condition number / número de condición:\", f\"{cond:.3e}\")\n", + "\n", + " if rank == A.shape[0]:\n", + " inv_error = np.linalg.norm(np.linalg.inv(A) @ A - np.eye(A.shape[0]))\n", + " print(\"EN: ordinary inverse exists.\")\n", + " print(\"ES: la inversa ordinaria existe.\")\n", + " print(\"||A⁻¹A - I|| =\", f\"{inv_error:.3e}\")\n", + " else:\n", + " print(\"EN: ordinary inverse does NOT exist; columns are dependent.\")\n", + " print(\"ES: la inversa ordinaria NO existe; hay columnas dependientes.\")\n", + "\n", + " Ap = np.linalg.pinv(A)\n", + " reconstruction = np.linalg.norm(A @ Ap @ A - A)\n", + " print(\"pseudoinverse shape / forma de A⁺:\", Ap.shape)\n", + " print(\"||A A⁺ A - A|| =\", f\"{reconstruction:.3e}\")\n", + "\n", + "matrix_output = widgets.interactive_output(\n", + " inspect_matrix,\n", + " {\"choice\": matrix_choice},\n", + ")\n", + "\n", + "display(\n", + " widgets.VBox([\n", + " widgets.HTML(\n", + " \"Matrix diagnostic / Diagnóstico de matriz: \"\n", + " \"switch one data-design decision and watch rank change. / \"\n", + " \"cambia una decisión del diseño y observa cómo cambia el rango.\"\n", + " ),\n", + " matrix_choice,\n", + " matrix_output,\n", + " ])\n", + ")\n" ], - "id": "s07-15" + "id": "s07-06" }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercise 2 — solve it anyway\n", + "## Exercise 2 — one real dataset, three geometries\n", + "\n", + "The full California housing design matrix has shape:\n", + "\n", + "`(20,433 observations, 7 columns)`\n", + "\n", + "so it is **very tall**. No ordinary matrix inverse is defined for it.\n", "\n", - "> 🇪🇸 Resuélvelo de todas formas, con la pseudoinversa." + "The pseudoinverse solves:\n", + "\n", + "`w = X⁺y`\n", + "\n", + "which minimizes the total squared residual. It does **not** claim that a straight line passes exactly through all 20,433 observations.\n", + "\n", + "### Why the interactive charts matter\n", + "\n", + "The two full-dataset charts are now **Plotly charts**. You can:\n", + "\n", + "- **hover** over a point to inspect the real and predicted house values;\n", + "- **zoom** into dense regions;\n", + "- **pan** across the distribution;\n", + "- use the toolbar to reset the view.\n", + "\n", + "Then keep the same seven columns but change how many **real rows** are used:\n", + "\n", + "- fewer than 7 rows → **wide** system: more unknowns than equations;\n", + "- exactly 7 rows → **square** system;\n", + "- more than 7 rows → **tall** system: more equations than unknowns.\n", + "\n", + "The **Rows / Filas** slider redraws an interactive Plotly chart so you can inspect each real observation and its prediction while the geometry changes.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Predict why `np.linalg.inv(X)` cannot be called on the full dataset.\n", + "2. Compute `w = pinv(X) @ y`.\n", + "3. Check it against `np.linalg.lstsq`.\n", + "4. Compute RMSE and inspect predicted versus actual values.\n", + "5. Hover over several districts and compare prediction error.\n", + "6. Move **Rows / Filas** through the wide → square → tall transition.\n", + "\n", + "> 🇪🇸 La matriz completa tiene forma `(20.433, 7)`, por lo que es **muy alta**. No existe una inversa ordinaria para una matriz rectangular. La pseudoinversa calcula la solución de mínimos cuadrados.\n", + ">\n", + "> Las gráficas ahora son **interactivas con Plotly**: pasa el cursor sobre los puntos para ver valores reales y predichos, haz **zoom**, desplázate con **pan** y reinicia la vista desde la barra de herramientas.\n", + ">\n", + "> Con el slider **Rows / Filas** mantienes las mismas siete columnas y cambias el número de filas reales: menos de 7 produce un sistema ancho, 7 uno cuadrado y más de 7 uno alto. La gráfica se actualiza y permite inspeccionar cada observación real.\n" ], - "id": "s07-16" + "id": "s07-07" }, { "cell_type": "code", @@ -287,79 +348,256 @@ "metadata": {}, "outputs": [], "source": [ - "# TODO 4: Solve for the weights with the pseudoinverse: w = pinv(X) @ y.\n", - "\n", - "# TODO 5: Check your answer against np.linalg.lstsq. Do they agree?\n", - "\n", - "# TODO 6: Compute the RMSE of the predictions. Which feature has the largest\n", - "# coefficient, and does that make sense for house prices?" + "# TODO 2\n", + "# 1. Explain why np.linalg.inv(X) is undefined from X.shape alone.\n", + "#\n", + "# 2. Solve the full real-data problem:\n", + "# w = np.linalg.pinv(X) @ y\n", + "#\n", + "# 3. Compare w with:\n", + "# np.linalg.lstsq(X, y, rcond=None)\n", + "#\n", + "# 4. Compute predictions and RMSE.\n", + "#\n", + "# 5. Because the six non-bias features were standardized, compare the\n", + "# ABSOLUTE values of w[1:] and identify the largest standardized coefficient.\n", + "# Describe it as an association in this dataset, NOT a causal effect.\n" ], - "id": "s07-17" + "id": "s07-08" }, { "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "print(\"full X / X completa:\", X.shape)\n", + "print(\"EN: X is rectangular, so np.linalg.inv(X) is not defined.\")\n", + "print(\"ES: X es rectangular, por lo que np.linalg.inv(X) no está definida.\")\n", + "\n", "w = np.linalg.pinv(X) @ y\n", "w_lstsq, *_ = np.linalg.lstsq(X, y, rcond=None)\n", - "print(np.allclose(w, w_lstsq)) # True\n", + "print(\"pinv == lstsq:\", np.allclose(w, w_lstsq))\n", "\n", - "rmse = np.sqrt(((X @ w - y) ** 2).mean())\n", - "print(round(rmse)) # ~75980\n", + "pred = X @ w\n", + "residuals = pred - y\n", + "rmse = np.sqrt(np.mean(residuals**2))\n", + "print(\"RMSE:\", f\"${rmse:,.0f}\")\n", "\n", - "coef, name = max(zip(w[1:], feats))\n", - "print(name, round(coef)) # median_income 47748\n", + "coef_idx = int(np.argmax(np.abs(w[1:])))\n", + "print(\n", + " \"largest standardized coefficient / mayor coeficiente estandarizado:\",\n", + " features[coef_idx],\n", + " f\"{w[1:][coef_idx]:,.0f}\",\n", + ")\n", + "print(\"EN: this is an association in this linear fit, not a causal claim.\")\n", + "print(\"ES: es una asociación en este ajuste lineal, no una afirmación causal.\")\n", "\n", - "# X is 20433 x 7 — very tall, so np.linalg.inv cannot even be called. There is\n", - "# NO EXACT SOLUTION: no straight line passes through 20,433 points. The\n", - "# pseudoinverse gives the best possible answer instead, and lstsq agrees exactly\n", - "# because it solves the same problem.\n", - "#\n", - "# The largest coefficient belongs to median_income, which is the sensible\n", - "# result — income predicts house prices.\n", + "# Interactive full-dataset scatter: hover to inspect individual real districts.\n", + "scatter_df = pd.DataFrame({\n", + " \"actual\": y,\n", + " \"predicted\": pred,\n", + " \"residual\": residuals,\n", + " \"row\": np.arange(len(y)),\n", + " \"median_income\": housing[\"median_income\"].to_numpy(float),\n", + "})\n", "\n", - "import matplotlib.pyplot as plt\n", - "pred = X @ w\n", - "residuals = pred - y\n", - "fig, axes = plt.subplots(1, 2, figsize=(9, 3.5))\n", - "axes[0].scatter(y, pred, s=3, alpha=0.2, color=\"#4C72B0\")\n", - "lims = [min(y.min(), pred.min()), max(y.max(), pred.max())]\n", - "axes[0].plot(lims, lims, color=\"#C44E52\", linewidth=1)\n", - "axes[0].set_xlabel(\"actual\"); axes[0].set_ylabel(\"predicted\")\n", - "axes[0].set_title(\"predicted vs actual\")\n", - "axes[1].hist(residuals, bins=60, color=\"#55A868\")\n", - "axes[1].set_xlabel(\"prediction - actual\"); axes[1].set_title(\"residuals\")\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "# No straight line fits 20,433 points exactly, and the residuals show it: they\n", - "# are not tightly clustered at zero, and the predicted-vs-actual scatter fans\n", - "# out badly at the high end. LEAST SQUARES MINIMIZES THE AVERAGE SQUARED ERROR\n", - "# ACROSS ALL POINTS — it says nothing about any one prediction being close." + "fig_scatter = px.scatter(\n", + " scatter_df,\n", + " x=\"actual\",\n", + " y=\"predicted\",\n", + " hover_data={\n", + " \"row\": True,\n", + " \"actual\": \":,.0f\",\n", + " \"predicted\": \":,.0f\",\n", + " \"residual\": \":,.0f\",\n", + " \"median_income\": \":.2f\",\n", + " },\n", + " opacity=0.32,\n", + " title=\"20,433 real districts / distritos reales — hover to inspect\",\n", + " labels={\n", + " \"actual\": \"actual / real\",\n", + " \"predicted\": \"predicted / predicho\",\n", + " },\n", + ")\n", + "lo = float(min(y.min(), pred.min()))\n", + "hi = float(max(y.max(), pred.max()))\n", + "fig_scatter.add_trace(\n", + " go.Scatter(\n", + " x=[lo, hi],\n", + " y=[lo, hi],\n", + " mode=\"lines\",\n", + " name=\"perfect prediction / predicción perfecta\",\n", + " hoverinfo=\"skip\",\n", + " )\n", + ")\n", + "fig_scatter.update_layout(\n", + " height=330,\n", + " width=650,\n", + " margin=dict(l=55, r=20, t=55, b=50),\n", + ")\n", + "fig_scatter.show()\n", + "\n", + "# Interactive residual histogram.\n", + "residual_df = pd.DataFrame({\"residual\": residuals})\n", + "fig_resid = px.histogram(\n", + " residual_df,\n", + " x=\"residual\",\n", + " nbins=60,\n", + " title=\"Residuals / Residuos — zoom and hover\",\n", + " labels={\"residual\": \"prediction - actual / predicción - real\"},\n", + ")\n", + "fig_resid.add_vline(x=0, line_width=1)\n", + "fig_resid.update_layout(\n", + " height=280,\n", + " width=650,\n", + " margin=dict(l=55, r=20, t=55, b=50),\n", + " yaxis_title=\"count / conteo\",\n", + ")\n", + "fig_resid.show()\n", + "\n", + "rows_slider = widgets.IntSlider(\n", + " value=20, min=3, max=40, step=1,\n", + " description=\"Rows / Filas:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"90px\"},\n", + ")\n", + "\n", + "def explore_geometry(n_rows):\n", + " Xn = X[:n_rows]\n", + " yn = y[:n_rows]\n", + " wn = np.linalg.pinv(Xn) @ yn\n", + " predn = Xn @ wn\n", + "\n", + " residual_norm = np.linalg.norm(predn - yn)\n", + " coef_norm = np.linalg.norm(wn)\n", + " rank = np.linalg.matrix_rank(Xn)\n", + "\n", + " if n_rows < Xn.shape[1]:\n", + " geometry = \"WIDE / ANCHO\"\n", + " meaning_en = \"Usually many exact solutions; pinv chooses minimum norm.\"\n", + " meaning_es = \"Normalmente hay muchas soluciones exactas; pinv elige norma mínima.\"\n", + " elif n_rows == Xn.shape[1]:\n", + " geometry = \"SQUARE / CUADRADO\"\n", + " meaning_en = \"An ordinary inverse exists only if rank is full.\"\n", + " meaning_es = \"La inversa ordinaria solo existe si el rango es completo.\"\n", + " else:\n", + " geometry = \"TALL / ALTO\"\n", + " meaning_en = \"Usually no exact solution; pinv gives least squares.\"\n", + " meaning_es = \"Normalmente no hay solución exacta; pinv da mínimos cuadrados.\"\n", + "\n", + " print(f\"{geometry}: {Xn.shape} | rank/rango={rank}\")\n", + " print(\"||Xw-y|| =\", f\"{residual_norm:.3e}\",\n", + " \"| ||w|| =\", f\"{coef_norm:.3e}\")\n", + " print(\"EN:\", meaning_en)\n", + " print(\"ES:\", meaning_es)\n", + "\n", + " long_df = pd.DataFrame({\n", + " \"row\": np.tile(np.arange(n_rows), 2),\n", + " \"value\": np.concatenate([yn, predn]),\n", + " \"series\": (\n", + " [\"actual / real\"] * n_rows\n", + " + [\"predicted / predicho\"] * n_rows\n", + " ),\n", + " })\n", + "\n", + " fig = px.scatter(\n", + " long_df,\n", + " x=\"row\",\n", + " y=\"value\",\n", + " color=\"series\",\n", + " hover_data={\"row\": True, \"value\": \":,.0f\"},\n", + " title=f\"{geometry} — same 7 columns / mismas 7 columnas\",\n", + " labels={\"row\": \"row / fila\", \"value\": \"median house value\"},\n", + " )\n", + " fig.update_traces(marker={\"size\": 9})\n", + " fig.update_layout(\n", + " height=290,\n", + " width=620,\n", + " margin=dict(l=55, r=20, t=55, b=50),\n", + " legend_title_text=\"\",\n", + " )\n", + " fig.show()\n", + "\n", + "geometry_output = widgets.interactive_output(\n", + " explore_geometry,\n", + " {\"n_rows\": rows_slider},\n", + ")\n", + "\n", + "display(\n", + " widgets.VBox([\n", + " widgets.HTML(\n", + " \"Geometry explorer / Explorador geométrico: \"\n", + " \"move through wide → square → tall using real housing rows. \"\n", + " \"Hover, zoom and pan in every chart. / \"\n", + " \"recorre ancho → cuadrado → alto usando filas reales. \"\n", + " \"Pasa el cursor, haz zoom y desplázate en cada gráfica.\"\n", + " ),\n", + " rows_slider,\n", + " geometry_output,\n", + " ])\n", + ")\n" ], - "id": "s07-18" + "id": "s07-09" }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercise 3 — the tensor version\n", + "## Exercise 3 — pseudoinverse after unfolding a real image tensor\n", "\n", - "> 🇪🇸 La versión tensorial: despliega, resuelve, vuelve a plegar." + "Now use real handwritten-digit images.\n", + "\n", + "Take the first 20 images:\n", + "\n", + "`T.shape = (20, 8, 8)`\n", + "\n", + "Unfolding mode 0 gives:\n", + "\n", + "`M.shape = (20, 64)`\n", + "\n", + "This is a **wide** matrix: 20 equations and 64 unknown pixel weights.\n", + "\n", + "### A target we can interpret exactly\n", + "\n", + "For each real digit image, define `b` as its **mean pixel intensity**. Because the mean of 64 pixels is a linear function, the uniform weight vector\n", + "\n", + "`[1/64, 1/64, ..., 1/64]`\n", + "\n", + "is one exact solution of `Mx = b`.\n", + "\n", + "But a wide system has many exact solutions. The pseudoinverse chooses the one with the **smallest Euclidean norm**. We can fold that 64-value solution back to an `8×8` weight image and compare it with the uniform solution.\n", + "\n", + "### Why the heatmaps are interactive\n", + "\n", + "The weight maps are Plotly heatmaps. Hover over any cell to inspect its **row, column and numerical weight**. You can zoom into a region and compare how individual weights change as more digit equations are added.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Unfold `T` to `(20, 64)`.\n", + "2. Build `b` from the real image means.\n", + "3. Solve `x = M⁺b`.\n", + "4. Confirm that both the pseudoinverse solution and the uniform solution predict `b`.\n", + "5. Compare their norms.\n", + "6. Fold `x` back to `8×8`.\n", + "7. Hover over individual weights in the heatmap.\n", + "8. Move **Digits / Dígitos** and see how the minimum-norm map changes as more real equations are added.\n", + "\n", + "> 🇪🇸 Ahora usamos imágenes reales de dígitos. Veinte imágenes `8×8` forman un tensor `(20,8,8)`. Al desplegarlo obtenemos una matriz ancha `(20,64)`. Definimos `b` como la intensidad media real de cada imagen. El vector uniforme `1/64` es una solución exacta conocida, pero existen muchas. La pseudoinversa elige la solución exacta de **norma mínima**.\n", + ">\n", + "> Los mapas de pesos ahora son **heatmaps interactivos de Plotly**. Pasa el cursor sobre cualquier celda para ver su fila, columna y peso numérico; también puedes hacer zoom. Después mueve **Digits / Dígitos** para observar cómo cambia la solución cuando añadimos más ecuaciones reales.\n" ], - "id": "s07-19" + "id": "s07-10" }, { "cell_type": "code", @@ -367,43 +605,192 @@ "metadata": {}, "outputs": [], "source": [ - "# TODO 7: Take an order-3 tensor T of shape (4, 3, 5) and a vector b of\n", - "# length 4. Solve the unfolded least-squares problem for x, then fold\n", - "# x back to the shape of a mode-0 slice. What shape must x have?" + "# TODO 3\n", + "# 1. T = digit_tensor[:20]\n", + "# 2. M = unfold(T, 0) # expected shape (20, 64)\n", + "# 3. b = T.mean(axis=(1, 2)) # one real mean intensity per image\n", + "# 4. x_pinv = np.linalg.pinv(M) @ b\n", + "# 5. x_uniform = np.full(64, 1 / 64)\n", + "#\n", + "# Check:\n", + "# - M @ x_pinv reproduces b\n", + "# - M @ x_uniform reproduces b\n", + "# - ||x_pinv|| <= ||x_uniform||\n", + "#\n", + "# Finally fold:\n", + "# x_image = x_pinv.reshape(8, 8)\n" ], - "id": "s07-20" + "id": "s07-11" }, { "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "T = rng.standard_normal((4, 3, 5))\n", - "b = rng.standard_normal(4)\n", + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "T = digit_tensor[:20]\n", + "M = unfold(T, 0)\n", + "b = T.mean(axis=(1, 2))\n", + "\n", + "x_pinv = np.linalg.pinv(M) @ b\n", + "x_uniform = np.full(M.shape[1], 1 / M.shape[1])\n", + "\n", + "print(\"tensor / tensor:\", T.shape)\n", + "print(\"unfolded / desplegado:\", M.shape)\n", + "print(\"pinv residual / residuo:\", f\"{np.linalg.norm(M @ x_pinv - b):.3e}\")\n", + "print(\"uniform residual / residuo uniforme:\",\n", + " f\"{np.linalg.norm(M @ x_uniform - b):.3e}\")\n", + "print(\"||x_pinv||:\", f\"{np.linalg.norm(x_pinv):.6f}\")\n", + "print(\"||x_uniform||:\", f\"{np.linalg.norm(x_uniform):.6f}\")\n", + "print(\"minimum norm check / chequeo norma mínima:\",\n", + " np.linalg.norm(x_pinv) <= np.linalg.norm(x_uniform) + 1e-10)\n", + "\n", + "x_image = x_pinv.reshape(8, 8)\n", + "uniform_image = x_uniform.reshape(8, 8)\n", + "\n", + "# Real digit shown as interactive heatmap so the original 8x8 measurements are inspectable.\n", + "fig_digit = go.Figure(\n", + " data=go.Heatmap(\n", + " z=T[0],\n", + " colorbar={\"title\": \"pixel\"},\n", + " hovertemplate=\"row=%{y}
col=%{x}
pixel=%{z:.1f}\",\n", + " )\n", + ")\n", + "fig_digit.update_layout(\n", + " title=\"Real digit / Dígito real — original 8×8 measurements\",\n", + " height=300, width=390,\n", + " yaxis={\"autorange\": \"reversed\", \"scaleanchor\": \"x\"},\n", + ")\n", + "fig_digit.show()\n", + "\n", + "def weight_heatmap(z, title):\n", + " fig = go.Figure(\n", + " data=go.Heatmap(\n", + " z=z,\n", + " zmid=0,\n", + " colorscale=\"RdBu\",\n", + " hovertemplate=(\n", + " \"row=%{y}
col=%{x}
weight/peso=%{z:.6f}\"\n", + " ),\n", + " colorbar={\"title\": \"weight / peso\"},\n", + " )\n", + " )\n", + " fig.update_layout(\n", + " title=title,\n", + " height=300, width=390,\n", + " yaxis={\"autorange\": \"reversed\", \"scaleanchor\": \"x\"},\n", + " )\n", + " return fig\n", + "\n", + "weight_heatmap(\n", + " x_image,\n", + " \"Pseudoinverse minimum-norm weights / Pesos de norma mínima\",\n", + ").show()\n", + "\n", + "weight_heatmap(\n", + " uniform_image,\n", + " \"Known uniform exact solution / Solución uniforme exacta\",\n", + ").show()\n", + "\n", + "digits_slider = widgets.IntSlider(\n", + " value=20, min=5, max=60, step=5,\n", + " description=\"Digits / Dígitos:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"100px\"},\n", + ")\n", + "\n", + "def explore_tensor_pinv(n_digits):\n", + " Tn = digit_tensor[:n_digits]\n", + " Mn = unfold(Tn, 0)\n", + " bn = Tn.mean(axis=(1, 2))\n", + "\n", + " x_min = np.linalg.pinv(Mn) @ bn\n", + " x_known = np.full(Mn.shape[1], 1 / Mn.shape[1])\n", + "\n", + " res_min = np.linalg.norm(Mn @ x_min - bn)\n", + " res_known = np.linalg.norm(Mn @ x_known - bn)\n", + "\n", + " print(\n", + " f\"M: {Mn.shape} | rank/rango={np.linalg.matrix_rank(Mn)} | \"\n", + " f\"pinv residual/residuo={res_min:.2e}\"\n", + " )\n", + " print(\n", + " f\"||x_pinv||={np.linalg.norm(x_min):.6f} | \"\n", + " f\"||x_uniform||={np.linalg.norm(x_known):.6f}\"\n", + " )\n", + " print(\"EN: both solve the same real equations; pinv selects minimum norm.\")\n", + " print(\"ES: ambas resuelven las mismas ecuaciones reales; pinv elige norma mínima.\")\n", + "\n", + " fig = weight_heatmap(\n", + " x_min.reshape(8, 8),\n", + " f\"Pseudoinverse weights / Pesos pinv — {n_digits} real equations\",\n", + " )\n", + " fig.show()\n", + "\n", + "tensor_output = widgets.interactive_output(\n", + " explore_tensor_pinv,\n", + " {\"n_digits\": digits_slider},\n", + ")\n", + "\n", + "display(\n", + " widgets.VBox([\n", + " widgets.HTML(\n", + " \"Tensor pseudoinverse explorer / Explorador tensorial: \"\n", + " \"add real digit equations, then hover/zoom on the weight map. / \"\n", + " \"añade ecuaciones de dígitos reales y después pasa el cursor o haz zoom \"\n", + " \"sobre el mapa de pesos.\"\n", + " ),\n", + " digits_slider,\n", + " tensor_output,\n", + " ])\n", + ")\n" + ], + "id": "s07-12" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What just happened\n", + "\n", + "You used the pseudoinverse for **three different reasons**, all on real observations:\n", + "\n", + "1. **Singular square matrix** \n", + " Duplicating a real feature made two columns dependent. Rank fell and the ordinary inverse disappeared, but `A⁺` still existed and satisfied the Moore–Penrose conditions.\n", + "\n", + "2. **Real California housing regression** \n", + " `X` had shape `(20,433, 7)`: far more equations than unknowns. There is generally no exact line through all observations, so `X⁺y` returned the **least-squares** solution. The interactive scatter and residual histogram let you hover, zoom and inspect where the approximation succeeds or fails.\n", "\n", - "M = unfold(T, 0) # (4, 15) — one row per index along axis 0\n", - "x_flat = np.linalg.pinv(M) @ b # (15,) — min-norm solution, wide matrix\n", - "x = x_flat.reshape(T.shape[1], T.shape[2]) # fold back to (3, 5)\n", - "print(M.shape, x_flat.shape, x.shape)\n", + "3. **Real digit-image tensor** \n", + " Unfolding `(20,8,8)` produced a wide `(20,64)` matrix. Many exact pixel-weight solutions existed, so the pseudoinverse selected the **minimum-norm** one. Interactive heatmaps made every individual pixel weight inspectable before folding the solution back to `8×8`.\n", "\n", - "print(np.allclose(M @ x_flat, b)) # True — 4 equations, 15 unknowns\n", + "### The sentence to remember\n", "\n", - "# M is WIDE (4 x 15): infinitely many solutions, and pinv picks the one with the\n", - "# smallest norm. Folding back to (3, 5) is only meaningful because unfolding\n", - "# lost nothing in the first place." + "> **Inverse asks for an exact reversible square map. Pseudoinverse asks for the best-defined solution when that ideal situation is unavailable.**\n", + "\n", + "Or, by geometry:\n", + "\n", + "- tall → least squares;\n", + "- wide → minimum norm;\n", + "- singular → pseudoinverse still exists.\n", + "\n", + "> 🇪🇸 Usaste la pseudoinversa por **tres razones distintas** con observaciones reales: una matriz cuadrada se volvió singular al duplicar una variable; la regresión de California produjo un sistema alto que necesita mínimos cuadrados; y el tensor de dígitos produjo un sistema ancho con muchas soluciones exactas, donde `pinv` eligió la de norma mínima.\n", + ">\n", + "> Las gráficas interactivas permiten inspeccionar la matemática: en vivienda puedes pasar el cursor sobre observaciones reales, hacer zoom sobre residuos y ver cómo cambia la geometría; en el tensor puedes inspeccionar el valor exacto de cada peso.\n", + ">\n", + "> **Frase para recordar:** la inversa exige un mapa cuadrado, reversible y exacto. La pseudoinversa entrega una solución bien definida cuando esa situación ideal no existe.\n" ], - "id": "s07-21" + "id": "s07-13" }, { "cell_type": "markdown", @@ -426,14 +813,13 @@ "\n", "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" ], - "id": "s07-22" + "id": "s07-14" } ], "metadata": { "colab": { - "name": "07-inverses-and-pseudoinverse.ipynb", - "provenance": [], - "toc_visible": true + "toc_visible": true, + "provenance": [] }, "kernelspec": { "display_name": "Python 3", @@ -442,6 +828,1049 @@ }, "language_info": { "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "version_major": 2, + "version_minor": 0, + "state": { + "fdca3be0680e4b7aa100fbe7c6a4c334": { + "model_module": "@jupyter-widgets/controls", + "model_name": 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"nbformat": 4, diff --git a/notebooks/07-inverses-and-pseudoinverse.ipynb b/notebooks/07-inverses-and-pseudoinverse.ipynb index 4caefe7..561a2bd 100644 --- a/notebooks/07-inverses-and-pseudoinverse.ipynb +++ b/notebooks/07-inverses-and-pseudoinverse.ipynb @@ -1,2267 +1,1878 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "s07-00" - }, - "source": [ - "# 07 · Inverses and the pseudoinverse\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/07-inverses-and-pseudoinverse.ipynb)\n", - "\n", - "*Part IV · exercise · 15 min*\n", - "\n", - "> 🇪🇸 **Inversas y la pseudoinversa** — Aprende cuándo una inversa deja de existir y cómo la pseudoinversa sigue resolviendo problemas reales: sistemas singulares, regresión con 20.433 observaciones y tensores de imágenes.\n", - "\n", - "Use one question throughout the notebook:\n", - "\n", - "> **Does an exact inverse exist? If not, what does the pseudoinverse give us instead?**\n", - "\n", - "## What you will be able to do\n", - "\n", - "- Diagnose whether a **real-data square matrix** is invertible and explain why duplicated information makes it singular.\n", - "- Compute the Moore–Penrose pseudoinverse and verify its four defining conditions.\n", - "- Explain the difference between **wide**, **square**, and **tall** systems using real California housing observations.\n", - "- Solve a real `20,433 × 7` least-squares problem and interpret the residual rather than pretending an exact solution exists.\n", - "- Unfold a real image tensor, solve a wide system with the pseudoinverse, and fold the minimum-norm solution back into an image.\n", - "\n", - "> 🇪🇸 **Al terminar podrás:**\n", - "> - Diagnosticar si una matriz cuadrada construida con datos reales es invertible y explicar por qué duplicar información produce singularidad.\n", - "> - Calcular la pseudoinversa de Moore–Penrose y verificar sus cuatro condiciones.\n", - "> - Explicar la diferencia entre sistemas **anchos**, **cuadrados** y **altos** con observaciones reales de vivienda en California.\n", - "> - Resolver un problema real de mínimos cuadrados de `20.433 × 7` e interpretar el residuo.\n", - "> - Desplegar un tensor real de imágenes, resolver un sistema ancho y volver a plegar la solución de norma mínima.\n" - ], - "id": "s07-00" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s07-01" - }, - "source": [ - "## Setup\n", - "\n", - "Run this first. It loads two real datasets:\n", - "\n", - "1. **California housing districts** from the public housing dataset used in *Hands-On Machine Learning*. After removing the 207 rows with a missing value, 20,433 real districts remain.\n", - "2. **Handwritten digit images** from `sklearn.datasets.load_digits()`, used later for the tensor example.\n", - "\n", - "The housing features are standardized before regression so their coefficients are on comparable scales. The target remains the real median house value in dollars.\n", - "\n", - "> 🇪🇸 Ejecuta esta celda primero. Carga dos conjuntos de datos reales:\n", - "> 1. distritos de vivienda de California; después de eliminar 207 filas con un valor faltante quedan 20.433 observaciones reales;\n", - "> 2. imágenes reales de dígitos manuscritos de `sklearn`.\n", - ">\n", - "> Las variables de vivienda se estandarizan antes de la regresión para que sus coeficientes sean comparables. El objetivo sigue siendo el valor mediano real de la vivienda en dólares.\n" - ], - "id": "s07-01" - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "s07-02", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "outputId": "d32c7648-9d20-4201-d78a-2e8baab081b4" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "housing rows: 20433\n", - "housing design matrix: (20433, 7)\n", - "digit tensor: (1797, 8, 8)\n", - "interactive charts: Plotly enabled (hover, zoom, pan)\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import ipywidgets as widgets\n", - "import plotly.express as px\n", - "import plotly.graph_objects as go\n", - "from IPython.display import display\n", - "from sklearn.datasets import load_digits\n", - "\n", - "# Enable ipywidgets in Google Colab when available.\n", - "try:\n", - " from google.colab import output\n", - " output.enable_custom_widget_manager()\n", - "except ImportError:\n", - " pass\n", - "\n", - "HOUSING = (\n", - " \"https://raw.githubusercontent.com/ageron/handson-ml2/master/\"\n", - " \"datasets/housing/housing.csv\"\n", - ")\n", - "housing = pd.read_csv(HOUSING).dropna().reset_index(drop=True)\n", - "\n", - "features = [\n", - " \"housing_median_age\",\n", - " \"total_rooms\",\n", - " \"total_bedrooms\",\n", - " \"population\",\n", - " \"households\",\n", - " \"median_income\",\n", - "]\n", - "\n", - "X_raw = housing[features].to_numpy(float)\n", - "feature_mean = X_raw.mean(axis=0)\n", - "feature_std = X_raw.std(axis=0)\n", - "X_scaled = (X_raw - feature_mean) / feature_std\n", - "\n", - "# Bias + six standardized real features -> 7 columns.\n", - "X = np.column_stack([np.ones(len(housing)), X_scaled])\n", - "y = housing[\"median_house_value\"].to_numpy(float)\n", - "column_names = [\"bias\"] + features\n", - "\n", - "# Real image tensor for Exercise 3.\n", - "digits = load_digits()\n", - "digit_tensor = digits.images.astype(float) # (1797, 8, 8)\n", - "\n", - "def unfold(T, axis=0):\n", - " return np.moveaxis(T, axis, 0).reshape(T.shape[axis], -1)\n", - "\n", - "print(\"housing rows:\", len(housing))\n", - "print(\"housing design matrix:\", X.shape)\n", - "print(\"digit tensor:\", digit_tensor.shape)\n", - "print(\"interactive charts: Plotly enabled (hover, zoom, pan)\")\n" - ], - "id": "s07-02" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s07-03" - }, - "source": [ - "## Why this matters\n", - "\n", - "In real data work, `A⁻¹` is the exception, not the default.\n", - "\n", - "A classical inverse requires a matrix that is both:\n", - "\n", - "- **square**, and\n", - "- **full rank**.\n", - "\n", - "Real machine-learning design matrices are usually **tall**: many observations, few features. They can also become **singular** when two columns carry duplicate or redundant information. In both situations, asking for `A⁻¹` is the wrong question.\n", - "\n", - "The Moore–Penrose pseudoinverse `A⁺` is defined for rectangular and singular matrices. Its meaning depends on the geometry:\n", - "\n", - "- **tall system** — usually no exact solution → `A⁺b` gives the **least-squares** solution;\n", - "- **wide system** — usually infinitely many exact solutions → `A⁺b` chooses the **minimum-norm** solution;\n", - "- **singular square system** — no ordinary inverse → `A⁺` still exists.\n", - "\n", - "### Learning cycle: Predict → Run → Explain\n", - "\n", - "Before every exercise, predict:\n", - "\n", - "1. Is the matrix wide, square, or tall?\n", - "2. What is its rank?\n", - "3. Should an ordinary inverse exist?\n", - "4. If not, what should the pseudoinverse mean here?\n", - "\n", - "> 🇪🇸 En trabajo real con datos, `A⁻¹` es la excepción. Una inversa ordinaria exige una matriz **cuadrada y de rango completo**. Las matrices de aprendizaje automático suelen ser **altas**, y además pueden volverse **singulares** cuando dos columnas contienen información duplicada. La pseudoinversa `A⁺` sigue existiendo. En un sistema alto entrega mínimos cuadrados; en uno ancho elige la solución exacta de norma mínima; y en una matriz cuadrada singular reemplaza una inversa que no existe.\n", - ">\n", - "> **Predice → Ejecuta → Explica:** antes de cada ejercicio decide si la matriz es ancha, cuadrada o alta; cuál debería ser su rango; si existe una inversa ordinaria; y qué debería significar la pseudoinversa.\n" - ], - "id": "s07-03" + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 07 · Inverses and the pseudoinverse\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/07-inverses-and-pseudoinverse.ipynb)\n", + "\n", + "*Part IV · exercise · 15 min*\n", + "\n", + "> 🇪🇸 **Inversas y la pseudoinversa** — Usar la pseudoinversa en sistemas reales singulares, altos y anchos, e inspeccionar su geometría de forma interactiva.\n", + "\n", + "Use the pseudoinverse on real singular, tall, and wide systems, then inspect the geometry interactively.\n", + "\n", + "## What you will be able to do\n", + "\n", + "- Diagnose whether a real-data square matrix is invertible and explain why duplicated information makes it singular.\n", + "- Compute the Moore-Penrose pseudoinverse and verify its four defining conditions.\n", + "- Explain the difference between wide, square, and tall systems using real California housing observations.\n", + "- Solve a real 20,433-by-7 least-squares problem and interpret the residual rather than pretending an exact solution exists.\n", + "- Unfold a real image tensor, solve a wide system with the pseudoinverse, and fold the minimum-norm solution back into an image." + ], + "id": "s07-00" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "Run this first. It installs and imports everything this notebook needs, and nothing else.\n", + "\n", + "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." + ], + "id": "s07-01" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import ipywidgets as widgets\n", + "import plotly.express as px\n", + "import plotly.graph_objects as go\n", + "from IPython.display import display\n", + "from sklearn.datasets import load_digits\n", + "\n", + "# Enable ipywidgets in Google Colab when available.\n", + "try:\n", + " from google.colab import output\n", + " output.enable_custom_widget_manager()\n", + "except ImportError:\n", + " pass\n", + "\n", + "HOUSING = (\n", + " \"https://raw.githubusercontent.com/ageron/handson-ml2/master/\"\n", + " \"datasets/housing/housing.csv\"\n", + ")\n", + "housing = pd.read_csv(HOUSING).dropna().reset_index(drop=True)\n", + "\n", + "features = [\n", + " \"housing_median_age\",\n", + " \"total_rooms\",\n", + " \"total_bedrooms\",\n", + " \"population\",\n", + " \"households\",\n", + " \"median_income\",\n", + "]\n", + "\n", + "X_raw = housing[features].to_numpy(float)\n", + "feature_mean = X_raw.mean(axis=0)\n", + "feature_std = X_raw.std(axis=0)\n", + "X_scaled = (X_raw - feature_mean) / feature_std\n", + "\n", + "# Bias + six standardized real features -> 7 columns.\n", + "X = np.column_stack([np.ones(len(housing)), X_scaled])\n", + "y = housing[\"median_house_value\"].to_numpy(float)\n", + "column_names = [\"bias\"] + features\n", + "\n", + "# Real image tensor for Exercise 3.\n", + "digits = load_digits()\n", + "digit_tensor = digits.images.astype(float) # (1797, 8, 8)\n", + "\n", + "def unfold(T, axis=0):\n", + " return np.moveaxis(T, axis, 0).reshape(T.shape[axis], -1)\n", + "\n", + "print(\"housing rows:\", len(housing))\n", + "print(\"housing design matrix:\", X.shape)\n", + "print(\"digit tensor:\", digit_tensor.shape)\n", + "print(\"interactive charts: Plotly enabled (hover, zoom, pan)\")" + ], + "id": "s07-02" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Why this matters\n", + "\n", + "In real data work, `A⁻¹` is the exception, not the default.\n", + "\n", + "A classical inverse requires a matrix that is both:\n", + "\n", + "- **square**, and\n", + "- **full rank**.\n", + "\n", + "Real machine-learning design matrices are usually **tall**: many observations, few features. They can also become **singular** when two columns carry duplicate or redundant information. In both situations, asking for `A⁻¹` is the wrong question.\n", + "\n", + "The Moore–Penrose pseudoinverse `A⁺` is defined for rectangular and singular matrices. Its meaning depends on the geometry:\n", + "\n", + "- **tall system** — usually no exact solution → `A⁺b` gives the **least-squares** solution;\n", + "- **wide system** — usually infinitely many exact solutions → `A⁺b` chooses the **minimum-norm** solution;\n", + "- **singular square system** — no ordinary inverse → `A⁺` still exists.\n", + "\n", + "### How to use the folded solutions / Cómo usar las soluciones plegadas\n", + "\n", + "Each exercise has a **Solution / Solución** cell that is intentionally closed. First try the `TODO`; then open the solution to compare your reasoning with a reference implementation.\n", + "\n", + "> 🇪🇸 Cada ejercicio tiene una celda **Solution / Solución** cerrada a propósito. Primero intenta resolver el `TODO`; después abre la solución para comparar tu razonamiento con una implementación de referencia.\n", + "\n", + "### Learning cycle: Predict → Run → Explain\n", + "\n", + "Before every exercise, predict:\n", + "\n", + "1. Is the matrix wide, square, or tall?\n", + "2. What is its rank?\n", + "3. Should an ordinary inverse exist?\n", + "4. If not, what should the pseudoinverse mean here?\n", + "\n", + "> 🇪🇸 En trabajo real con datos, `A⁻¹` es la excepción. Una inversa ordinaria exige una matriz **cuadrada y de rango completo**. Las matrices de aprendizaje automático suelen ser **altas**, y además pueden volverse **singulares** cuando dos columnas contienen información duplicada. La pseudoinversa `A⁺` sigue existiendo. En un sistema alto entrega mínimos cuadrados; en uno ancho elige la solución exacta de norma mínima; y en una matriz cuadrada singular reemplaza una inversa que no existe.\n", + ">\n", + "> **Predice → Ejecuta → Explica:** antes de cada ejercicio decide si la matriz es ancha, cuadrada o alta; cuál debería ser su rango; si existe una inversa ordinaria; y qué debería significar la pseudoinversa.\n" + ], + "id": "s07-03" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 1 — make a real-data matrix singular\n", + "\n", + "### What are you looking at?\n", + "\n", + "We take **seven real California districts** spread across the dataset and all seven columns of the standardized design matrix (bias + six real features). This gives a `7×7` square matrix.\n", + "\n", + "Then we deliberately duplicate one feature column. The observations are still real; the duplication is a **teaching transformation that mimics a common feature-engineering mistake**: supplying the same information twice.\n", + "\n", + "### What should happen?\n", + "\n", + "If two columns are identical, they are linearly dependent. Rank drops below 7, so the matrix becomes singular and `np.linalg.inv(...)` must fail.\n", + "\n", + "The pseudoinverse should still exist and satisfy the four Moore–Penrose conditions.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Build the real `7×7` matrix from the indicated rows.\n", + "2. Check its rank.\n", + "3. Duplicate one feature column and predict the new rank **before** running.\n", + "4. Try the ordinary inverse on the singular matrix.\n", + "5. Compute `A⁺` and verify the four defining conditions.\n", + "6. Use the interactive selector to switch between the original and duplicated-feature matrices.\n", + "\n", + "> 🇪🇸 Tomamos **siete distritos reales de California** distribuidos a lo largo del dataset y las siete columnas del diseño. Después duplicamos deliberadamente una columna. Los datos siguen siendo reales; la duplicación simula un error común de ingeniería de variables. Si dos columnas son iguales, el rango cae y la matriz se vuelve singular. La inversa ordinaria debe fallar, pero la pseudoinversa debe seguir existiendo. Usa el selector para comparar ambas matrices.\n" + ], + "id": "s07-04" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO 1\n", + "# 1. Select seven spread-out real rows:\n", + "# row_idx = np.linspace(0, len(X) - 1, 7, dtype=int)\n", + "# A_real = X[row_idx]\n", + "#\n", + "# 2. Print A_real.shape and np.linalg.matrix_rank(A_real).\n", + "#\n", + "# 3. Make A_singular by copying A_real and duplicating one feature column:\n", + "# A_singular[:, 2] = A_singular[:, 1]\n", + "# Predict its rank before printing it.\n", + "#\n", + "# 4. Try np.linalg.inv(A_singular). The error is expected.\n", + "#\n", + "# 5. Compute A_plus = np.linalg.pinv(A_singular) and verify:\n", + "# A A+ A = A\n", + "# A+ A A+ = A+\n", + "# (A A+)^T = A A+\n", + "# (A+ A)^T = A+ A\n" + ], + "id": "s07-05" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true }, - { - "cell_type": "markdown", - "metadata": { - "id": "s07-04" - }, - "source": [ - "## Exercise 1 — make a real-data matrix singular\n", - "\n", - "### What are you looking at?\n", - "\n", - "We take **seven real California districts** spread across the dataset and all seven columns of the standardized design matrix (bias + six real features). This gives a `7×7` square matrix.\n", - "\n", - "Then we deliberately duplicate one feature column. The observations are still real; the duplication is a **teaching transformation that mimics a common feature-engineering mistake**: supplying the same information twice.\n", - "\n", - "### What should happen?\n", - "\n", - "If two columns are identical, they are linearly dependent. Rank drops below 7, so the matrix becomes singular and `np.linalg.inv(...)` must fail.\n", - "\n", - "The pseudoinverse should still exist and satisfy the four Moore–Penrose conditions.\n", - "\n", - "### What should you try?\n", - "\n", - "1. Build the real `7×7` matrix from the indicated rows.\n", - "2. Check its rank.\n", - "3. Duplicate one feature column and predict the new rank **before** running.\n", - "4. Try the ordinary inverse on the singular matrix.\n", - "5. Compute `A⁺` and verify the four defining conditions.\n", - "6. Use the interactive selector to switch between the original and duplicated-feature matrices.\n", - "\n", - "> 🇪🇸 Tomamos **siete distritos reales de California** distribuidos a lo largo del dataset y las siete columnas del diseño. Después duplicamos deliberadamente una columna. Los datos siguen siendo reales; la duplicación simula un error común de ingeniería de variables. Si dos columnas son iguales, el rango cae y la matriz se vuelve singular. La inversa ordinaria debe fallar, pero la pseudoinversa debe seguir existiendo. Usa el selector para comparar ambas matrices.\n" - ], - "id": "s07-04" + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "row_idx = np.linspace(0, len(X) - 1, 7, dtype=int)\n", + "A_real = X[row_idx].copy()\n", + "\n", + "A_singular = A_real.copy()\n", + "A_singular[:, 2] = A_singular[:, 1] # deliberate duplicate of a REAL feature\n", + "\n", + "print(\"original / original:\", A_real.shape,\n", + " \"rank =\", np.linalg.matrix_rank(A_real))\n", + "print(\"duplicated / duplicada:\", A_singular.shape,\n", + " \"rank =\", np.linalg.matrix_rank(A_singular))\n", + "\n", + "try:\n", + " np.linalg.inv(A_singular)\n", + "except np.linalg.LinAlgError as e:\n", + " print(\"Expected inverse failure / Fallo esperado de la inversa:\", e)\n", + "\n", + "A_plus = np.linalg.pinv(A_singular)\n", + "\n", + "mp_checks = [\n", + " np.allclose(A_singular @ A_plus @ A_singular, A_singular),\n", + " np.allclose(A_plus @ A_singular @ A_plus, A_plus),\n", + " np.allclose((A_singular @ A_plus).T, A_singular @ A_plus),\n", + " np.allclose((A_plus @ A_singular).T, A_plus @ A_singular),\n", + "]\n", + "print(\"Moore–Penrose conditions / condiciones:\", mp_checks)\n", + "\n", + "matrix_choice = widgets.ToggleButtons(\n", + " options=[\n", + " (\"Original real 7×7 / Real original\", \"original\"),\n", + " (\"Duplicated feature / Variable duplicada\", \"singular\"),\n", + " ],\n", + " value=\"original\",\n", + " description=\"\",\n", + ")\n", + "\n", + "def inspect_matrix(choice):\n", + " A = A_real if choice == \"original\" else A_singular\n", + " rank = np.linalg.matrix_rank(A)\n", + " cond = np.linalg.cond(A)\n", + "\n", + " print(\"shape / forma:\", A.shape)\n", + " print(\"rank / rango:\", rank)\n", + " print(\"condition number / número de condición:\", f\"{cond:.3e}\")\n", + "\n", + " if rank == A.shape[0]:\n", + " inv_error = np.linalg.norm(np.linalg.inv(A) @ A - np.eye(A.shape[0]))\n", + " print(\"EN: ordinary inverse exists.\")\n", + " print(\"ES: la inversa ordinaria existe.\")\n", + " print(\"||A⁻¹A - I|| =\", f\"{inv_error:.3e}\")\n", + " else:\n", + " print(\"EN: ordinary inverse does NOT exist; columns are dependent.\")\n", + " print(\"ES: la inversa ordinaria NO existe; hay columnas dependientes.\")\n", + "\n", + " Ap = np.linalg.pinv(A)\n", + " reconstruction = np.linalg.norm(A @ Ap @ A - A)\n", + " print(\"pseudoinverse shape / forma de A⁺:\", Ap.shape)\n", + " print(\"||A A⁺ A - A|| =\", f\"{reconstruction:.3e}\")\n", + "\n", + "matrix_output = widgets.interactive_output(\n", + " inspect_matrix,\n", + " {\"choice\": matrix_choice},\n", + ")\n", + "\n", + "display(\n", + " widgets.VBox([\n", + " widgets.HTML(\n", + " \"Matrix diagnostic / Diagnóstico de matriz: \"\n", + " \"switch one data-design decision and watch rank change. / \"\n", + " \"cambia una decisión del diseño y observa cómo cambia el rango.\"\n", + " ),\n", + " matrix_choice,\n", + " matrix_output,\n", + " ])\n", + ")\n" + ], + "id": "s07-06" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 2 — one real dataset, three geometries\n", + "\n", + "The full California housing design matrix has shape:\n", + "\n", + "`(20,433 observations, 7 columns)`\n", + "\n", + "so it is **very tall**. No ordinary matrix inverse is defined for it.\n", + "\n", + "The pseudoinverse solves:\n", + "\n", + "`w = X⁺y`\n", + "\n", + "which minimizes the total squared residual. It does **not** claim that a straight line passes exactly through all 20,433 observations.\n", + "\n", + "### Why the interactive charts matter\n", + "\n", + "The two full-dataset charts are now **Plotly charts**. You can:\n", + "\n", + "- **hover** over a point to inspect the real and predicted house values;\n", + "- **zoom** into dense regions;\n", + "- **pan** across the distribution;\n", + "- use the toolbar to reset the view.\n", + "\n", + "Then keep the same seven columns but change how many **real rows** are used:\n", + "\n", + "- fewer than 7 rows → **wide** system: more unknowns than equations;\n", + "- exactly 7 rows → **square** system;\n", + "- more than 7 rows → **tall** system: more equations than unknowns.\n", + "\n", + "The **Rows / Filas** slider redraws an interactive Plotly chart so you can inspect each real observation and its prediction while the geometry changes.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Predict why `np.linalg.inv(X)` cannot be called on the full dataset.\n", + "2. Compute `w = pinv(X) @ y`.\n", + "3. Check it against `np.linalg.lstsq`.\n", + "4. Compute RMSE and inspect predicted versus actual values.\n", + "5. Hover over several districts and compare prediction error.\n", + "6. Move **Rows / Filas** through the wide → square → tall transition.\n", + "\n", + "> 🇪🇸 La matriz completa tiene forma `(20.433, 7)`, por lo que es **muy alta**. No existe una inversa ordinaria para una matriz rectangular. La pseudoinversa calcula la solución de mínimos cuadrados.\n", + ">\n", + "> Las gráficas ahora son **interactivas con Plotly**: pasa el cursor sobre los puntos para ver valores reales y predichos, haz **zoom**, desplázate con **pan** y reinicia la vista desde la barra de herramientas.\n", + ">\n", + "> Con el slider **Rows / Filas** mantienes las mismas siete columnas y cambias el número de filas reales: menos de 7 produce un sistema ancho, 7 uno cuadrado y más de 7 uno alto. La gráfica se actualiza y permite inspeccionar cada observación real.\n" + ], + "id": "s07-07" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO 2\n", + "# 1. Explain why np.linalg.inv(X) is undefined from X.shape alone.\n", + "#\n", + "# 2. Solve the full real-data problem:\n", + "# w = np.linalg.pinv(X) @ y\n", + "#\n", + "# 3. Compare w with:\n", + "# np.linalg.lstsq(X, y, rcond=None)\n", + "#\n", + "# 4. Compute predictions and RMSE.\n", + "#\n", + "# 5. Because the six non-bias features were standardized, compare the\n", + "# ABSOLUTE values of w[1:] and identify the largest standardized coefficient.\n", + "# Describe it as an association in this dataset, NOT a causal effect.\n" + ], + "id": "s07-08" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "s07-05" - }, - "outputs": [], - "source": [ - "# TODO 1\n", - "# 1. Select seven spread-out real rows:\n", - "# row_idx = np.linspace(0, len(X) - 1, 7, dtype=int)\n", - "# A_real = X[row_idx]\n", - "#\n", - "# 2. Print A_real.shape and np.linalg.matrix_rank(A_real).\n", - "#\n", - "# 3. Make A_singular by copying A_real and duplicating one feature column:\n", - "# A_singular[:, 2] = A_singular[:, 1]\n", - "# Predict its rank before printing it.\n", - "#\n", - "# 4. Try np.linalg.inv(A_singular). The error is expected.\n", - "#\n", - "# 5. Compute A_plus = np.linalg.pinv(A_singular) and verify:\n", - "# A A+ A = A\n", - "# A+ A A+ = A+\n", - "# (A A+)^T = A A+\n", - "# (A+ A)^T = A+ A\n" - ], - "id": "s07-05" + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "print(\"full X / X completa:\", X.shape)\n", + "print(\"EN: X is rectangular, so np.linalg.inv(X) is not defined.\")\n", + "print(\"ES: X es rectangular, por lo que np.linalg.inv(X) no está definida.\")\n", + "\n", + "w = np.linalg.pinv(X) @ y\n", + "w_lstsq, *_ = np.linalg.lstsq(X, y, rcond=None)\n", + "print(\"pinv == lstsq:\", np.allclose(w, w_lstsq))\n", + "\n", + "pred = X @ w\n", + "residuals = pred - y\n", + "rmse = np.sqrt(np.mean(residuals**2))\n", + "print(\"RMSE:\", f\"${rmse:,.0f}\")\n", + "\n", + "coef_idx = int(np.argmax(np.abs(w[1:])))\n", + "print(\n", + " \"largest standardized coefficient / mayor coeficiente estandarizado:\",\n", + " features[coef_idx],\n", + " f\"{w[1:][coef_idx]:,.0f}\",\n", + ")\n", + "print(\"EN: this is an association in this linear fit, not a causal claim.\")\n", + "print(\"ES: es una asociación en este ajuste lineal, no una afirmación causal.\")\n", + "\n", + "# Interactive full-dataset scatter: hover to inspect individual real districts.\n", + "scatter_df = pd.DataFrame({\n", + " \"actual\": y,\n", + " \"predicted\": pred,\n", + " \"residual\": residuals,\n", + " \"row\": np.arange(len(y)),\n", + " \"median_income\": housing[\"median_income\"].to_numpy(float),\n", + "})\n", + "\n", + "fig_scatter = px.scatter(\n", + " scatter_df,\n", + " x=\"actual\",\n", + " y=\"predicted\",\n", + " hover_data={\n", + " \"row\": True,\n", + " \"actual\": \":,.0f\",\n", + " \"predicted\": \":,.0f\",\n", + " \"residual\": \":,.0f\",\n", + " \"median_income\": \":.2f\",\n", + " },\n", + " opacity=0.32,\n", + " title=\"20,433 real districts / distritos reales — hover to inspect\",\n", + " labels={\n", + " \"actual\": \"actual / real\",\n", + " \"predicted\": \"predicted / predicho\",\n", + " },\n", + ")\n", + "lo = float(min(y.min(), pred.min()))\n", + "hi = float(max(y.max(), pred.max()))\n", + "fig_scatter.add_trace(\n", + " go.Scatter(\n", + " x=[lo, hi],\n", + " y=[lo, hi],\n", + " mode=\"lines\",\n", + " name=\"perfect prediction / predicción perfecta\",\n", + " hoverinfo=\"skip\",\n", + " )\n", + ")\n", + "fig_scatter.update_layout(\n", + " height=330,\n", + " width=650,\n", + " margin=dict(l=55, r=20, t=55, b=50),\n", + ")\n", + "fig_scatter.show()\n", + "\n", + "# Interactive residual histogram.\n", + "residual_df = pd.DataFrame({\"residual\": residuals})\n", + "fig_resid = px.histogram(\n", + " residual_df,\n", + " x=\"residual\",\n", + " nbins=60,\n", + " title=\"Residuals / Residuos — zoom and hover\",\n", + " labels={\"residual\": \"prediction - actual / predicción - real\"},\n", + ")\n", + "fig_resid.add_vline(x=0, line_width=1)\n", + "fig_resid.update_layout(\n", + " height=280,\n", + " width=650,\n", + " margin=dict(l=55, r=20, t=55, b=50),\n", + " yaxis_title=\"count / conteo\",\n", + ")\n", + "fig_resid.show()\n", + "\n", + "rows_slider = widgets.IntSlider(\n", + " value=20, min=3, max=40, step=1,\n", + " description=\"Rows / Filas:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"90px\"},\n", + ")\n", + "\n", + "def explore_geometry(n_rows):\n", + " Xn = X[:n_rows]\n", + " yn = y[:n_rows]\n", + " wn = np.linalg.pinv(Xn) @ yn\n", + " predn = Xn @ wn\n", + "\n", + " residual_norm = np.linalg.norm(predn - yn)\n", + " coef_norm = np.linalg.norm(wn)\n", + " rank = np.linalg.matrix_rank(Xn)\n", + "\n", + " if n_rows < Xn.shape[1]:\n", + " geometry = \"WIDE / ANCHO\"\n", + " meaning_en = \"Usually many exact solutions; pinv chooses minimum norm.\"\n", + " meaning_es = \"Normalmente hay muchas soluciones exactas; pinv elige norma mínima.\"\n", + " elif n_rows == Xn.shape[1]:\n", + " geometry = \"SQUARE / CUADRADO\"\n", + " meaning_en = \"An ordinary inverse exists only if rank is full.\"\n", + " meaning_es = \"La inversa ordinaria solo existe si el rango es completo.\"\n", + " else:\n", + " geometry = \"TALL / ALTO\"\n", + " meaning_en = \"Usually no exact solution; pinv gives least squares.\"\n", + " meaning_es = \"Normalmente no hay solución exacta; pinv da mínimos cuadrados.\"\n", + "\n", + " print(f\"{geometry}: {Xn.shape} | rank/rango={rank}\")\n", + " print(\"||Xw-y|| =\", f\"{residual_norm:.3e}\",\n", + " \"| ||w|| =\", f\"{coef_norm:.3e}\")\n", + " print(\"EN:\", meaning_en)\n", + " print(\"ES:\", meaning_es)\n", + "\n", + " long_df = pd.DataFrame({\n", + " \"row\": np.tile(np.arange(n_rows), 2),\n", + " \"value\": np.concatenate([yn, predn]),\n", + " \"series\": (\n", + " [\"actual / real\"] * n_rows\n", + " + [\"predicted / predicho\"] * n_rows\n", + " ),\n", + " })\n", + "\n", + " fig = px.scatter(\n", + " long_df,\n", + " x=\"row\",\n", + " y=\"value\",\n", + " color=\"series\",\n", + " hover_data={\"row\": True, \"value\": \":,.0f\"},\n", + " title=f\"{geometry} — same 7 columns / mismas 7 columnas\",\n", + " labels={\"row\": \"row / fila\", \"value\": \"median house value\"},\n", + " )\n", + " fig.update_traces(marker={\"size\": 9})\n", + " fig.update_layout(\n", + " height=290,\n", + " width=620,\n", + " margin=dict(l=55, r=20, t=55, b=50),\n", + " legend_title_text=\"\",\n", + " )\n", + " fig.show()\n", + "\n", + "geometry_output = widgets.interactive_output(\n", + " explore_geometry,\n", + " {\"n_rows\": rows_slider},\n", + ")\n", + "\n", + "display(\n", + " widgets.VBox([\n", + " widgets.HTML(\n", + " \"Geometry explorer / Explorador geométrico: \"\n", + " \"move through wide → square → tall using real housing rows. \"\n", + " \"Hover, zoom and pan in every chart. / \"\n", + " \"recorre ancho → cuadrado → alto usando filas reales. \"\n", + " \"Pasa el cursor, haz zoom y desplázate en cada gráfica.\"\n", + " ),\n", + " rows_slider,\n", + " geometry_output,\n", + " ])\n", + ")\n" + ], + "id": "s07-09" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 3 — pseudoinverse after unfolding a real image tensor\n", + "\n", + "Now use real handwritten-digit images.\n", + "\n", + "Take the first 20 images:\n", + "\n", + "`T.shape = (20, 8, 8)`\n", + "\n", + "Unfolding mode 0 gives:\n", + "\n", + "`M.shape = (20, 64)`\n", + "\n", + "This is a **wide** matrix: 20 equations and 64 unknown pixel weights.\n", + "\n", + "### A target we can interpret exactly\n", + "\n", + "For each real digit image, define `b` as its **mean pixel intensity**. Because the mean of 64 pixels is a linear function, the uniform weight vector\n", + "\n", + "`[1/64, 1/64, ..., 1/64]`\n", + "\n", + "is one exact solution of `Mx = b`.\n", + "\n", + "But a wide system has many exact solutions. The pseudoinverse chooses the one with the **smallest Euclidean norm**. We can fold that 64-value solution back to an `8×8` weight image and compare it with the uniform solution.\n", + "\n", + "### Why the heatmaps are interactive\n", + "\n", + "The weight maps are Plotly heatmaps. Hover over any cell to inspect its **row, column and numerical weight**. You can zoom into a region and compare how individual weights change as more digit equations are added.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Unfold `T` to `(20, 64)`.\n", + "2. Build `b` from the real image means.\n", + "3. Solve `x = M⁺b`.\n", + "4. Confirm that both the pseudoinverse solution and the uniform solution predict `b`.\n", + "5. Compare their norms.\n", + "6. Fold `x` back to `8×8`.\n", + "7. Hover over individual weights in the heatmap.\n", + "8. Move **Digits / Dígitos** and see how the minimum-norm map changes as more real equations are added.\n", + "\n", + "> 🇪🇸 Ahora usamos imágenes reales de dígitos. Veinte imágenes `8×8` forman un tensor `(20,8,8)`. Al desplegarlo obtenemos una matriz ancha `(20,64)`. Definimos `b` como la intensidad media real de cada imagen. El vector uniforme `1/64` es una solución exacta conocida, pero existen muchas. La pseudoinversa elige la solución exacta de **norma mínima**.\n", + ">\n", + "> Los mapas de pesos ahora son **heatmaps interactivos de Plotly**. Pasa el cursor sobre cualquier celda para ver su fila, columna y peso numérico; también puedes hacer zoom. Después mueve **Digits / Dígitos** para observar cómo cambia la solución cuando añadimos más ecuaciones reales.\n" + ], + "id": "s07-10" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO 3\n", + "# 1. T = digit_tensor[:20]\n", + "# 2. M = unfold(T, 0) # expected shape (20, 64)\n", + "# 3. b = T.mean(axis=(1, 2)) # one real mean intensity per image\n", + "# 4. x_pinv = np.linalg.pinv(M) @ b\n", + "# 5. x_uniform = np.full(64, 1 / 64)\n", + "#\n", + "# Check:\n", + "# - M @ x_pinv reproduces b\n", + "# - M @ x_uniform reproduces b\n", + "# - ||x_pinv|| <= ||x_uniform||\n", + "#\n", + "# Finally fold:\n", + "# x_image = x_pinv.reshape(8, 8)\n" + ], + "id": "s07-11" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "s07-06", - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ], - "colab": { - "base_uri": "https://localhost:8080/", - "height": 252, - "referenced_widgets": [ - "fdca3be0680e4b7aa100fbe7c6a4c334", - "17277ab54be14e1194408f20b0791762", - "0783e7c769aa424cb4266e1c13901323", - "0b4be2195dc74d27b9cd4dc08bdd20c8", - "90ad6f4badab467d96d39c81990cb801", - "67bd03a0e8aa4b55ab9cf30c28815164", - "1031cd5b62c54b11868db183a5d0c3d5", - "299e6e4f0b36489f97ddac85f1ac2375", - "4a710dbc9eed42e9897e754448c30ddf", - "be0cefa7e897453fa49466720097c3a5" - ] - }, - "outputId": "865390cb-b142-4aad-d4cf-ca627dd9f560" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "original / original: (7, 7) rank = 7\n", - "duplicated / duplicada: (7, 7) rank = 6\n", - "Moore–Penrose conditions / condiciones: [True, True, True, True]\n" - ] - }, + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "T = digit_tensor[:20]\n", + "M = unfold(T, 0)\n", + "b = T.mean(axis=(1, 2))\n", + "\n", + "x_pinv = np.linalg.pinv(M) @ b\n", + "x_uniform = np.full(M.shape[1], 1 / M.shape[1])\n", + "\n", + "print(\"tensor / tensor:\", T.shape)\n", + "print(\"unfolded / desplegado:\", M.shape)\n", + "print(\"pinv residual / residuo:\", f\"{np.linalg.norm(M @ x_pinv - b):.3e}\")\n", + "print(\"uniform residual / residuo uniforme:\",\n", + " f\"{np.linalg.norm(M @ x_uniform - b):.3e}\")\n", + "print(\"||x_pinv||:\", f\"{np.linalg.norm(x_pinv):.6f}\")\n", + "print(\"||x_uniform||:\", f\"{np.linalg.norm(x_uniform):.6f}\")\n", + "print(\"minimum norm check / chequeo norma mínima:\",\n", + " np.linalg.norm(x_pinv) <= np.linalg.norm(x_uniform) + 1e-10)\n", + "\n", + "x_image = x_pinv.reshape(8, 8)\n", + "uniform_image = x_uniform.reshape(8, 8)\n", + "\n", + "# Real digit shown as interactive heatmap so the original 8x8 measurements are inspectable.\n", + "fig_digit = go.Figure(\n", + " data=go.Heatmap(\n", + " z=T[0],\n", + " colorbar={\"title\": \"pixel\"},\n", + " hovertemplate=\"row=%{y}
col=%{x}
pixel=%{z:.1f}\",\n", + " )\n", + ")\n", + "fig_digit.update_layout(\n", + " title=\"Real digit / Dígito real — original 8×8 measurements\",\n", + " height=300, width=390,\n", + " yaxis={\"autorange\": \"reversed\", \"scaleanchor\": \"x\"},\n", + ")\n", + "fig_digit.show()\n", + "\n", + "def weight_heatmap(z, title):\n", + " fig = go.Figure(\n", + " data=go.Heatmap(\n", + " z=z,\n", + " zmid=0,\n", + " colorscale=\"RdBu\",\n", + " hovertemplate=(\n", + " \"row=%{y}
col=%{x}
weight/peso=%{z:.6f}\"\n", + " ),\n", + " colorbar={\"title\": \"weight / peso\"},\n", + " )\n", + " )\n", + " fig.update_layout(\n", + " title=title,\n", + " height=300, width=390,\n", + " yaxis={\"autorange\": \"reversed\", \"scaleanchor\": \"x\"},\n", + " )\n", + " return fig\n", + "\n", + "weight_heatmap(\n", + " x_image,\n", + " \"Pseudoinverse minimum-norm weights / Pesos de norma mínima\",\n", + ").show()\n", + "\n", + "weight_heatmap(\n", + " uniform_image,\n", + " \"Known uniform exact solution / Solución uniforme exacta\",\n", + ").show()\n", + "\n", + "digits_slider = widgets.IntSlider(\n", + " value=20, min=5, max=60, step=5,\n", + " description=\"Digits / Dígitos:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"100px\"},\n", + ")\n", + "\n", + "def explore_tensor_pinv(n_digits):\n", + " Tn = digit_tensor[:n_digits]\n", + " Mn = unfold(Tn, 0)\n", + " bn = Tn.mean(axis=(1, 2))\n", + "\n", + " x_min = np.linalg.pinv(Mn) @ bn\n", + " x_known = np.full(Mn.shape[1], 1 / Mn.shape[1])\n", + "\n", + " res_min = np.linalg.norm(Mn @ x_min - bn)\n", + " res_known = np.linalg.norm(Mn @ x_known - bn)\n", + "\n", + " print(\n", + " f\"M: {Mn.shape} | rank/rango={np.linalg.matrix_rank(Mn)} | \"\n", + " f\"pinv residual/residuo={res_min:.2e}\"\n", + " )\n", + " print(\n", + " f\"||x_pinv||={np.linalg.norm(x_min):.6f} | \"\n", + " f\"||x_uniform||={np.linalg.norm(x_known):.6f}\"\n", + " )\n", + " print(\"EN: both solve the same real equations; pinv selects minimum norm.\")\n", + " print(\"ES: ambas resuelven las mismas ecuaciones reales; pinv elige norma mínima.\")\n", + "\n", + " fig = weight_heatmap(\n", + " x_min.reshape(8, 8),\n", + " f\"Pseudoinverse weights / Pesos pinv — {n_digits} real equations\",\n", + " )\n", + " fig.show()\n", + "\n", + "tensor_output = widgets.interactive_output(\n", + " explore_tensor_pinv,\n", + " {\"n_digits\": digits_slider},\n", + ")\n", + "\n", + "display(\n", + " widgets.VBox([\n", + " widgets.HTML(\n", + " \"Tensor pseudoinverse explorer / Explorador tensorial: \"\n", + " \"add real digit equations, then hover/zoom on the weight map. / \"\n", + " \"añade ecuaciones de dígitos reales y después pasa el cursor o haz zoom \"\n", + " \"sobre el mapa de pesos.\"\n", + " ),\n", + " digits_slider,\n", + " tensor_output,\n", + " ])\n", + ")\n" + ], + "id": "s07-12" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What just happened\n", + "\n", + "You used the pseudoinverse for **three different reasons**, all on real observations:\n", + "\n", + "1. **Singular square matrix** \n", + " Duplicating a real feature made two columns dependent. Rank fell and the ordinary inverse disappeared, but `A⁺` still existed and satisfied the Moore–Penrose conditions.\n", + "\n", + "2. **Real California housing regression** \n", + " `X` had shape `(20,433, 7)`: far more equations than unknowns. There is generally no exact line through all observations, so `X⁺y` returned the **least-squares** solution. The interactive scatter and residual histogram let you hover, zoom and inspect where the approximation succeeds or fails.\n", + "\n", + "3. **Real digit-image tensor** \n", + " Unfolding `(20,8,8)` produced a wide `(20,64)` matrix. Many exact pixel-weight solutions existed, so the pseudoinverse selected the **minimum-norm** one. Interactive heatmaps made every individual pixel weight inspectable before folding the solution back to `8×8`.\n", + "\n", + "### The sentence to remember\n", + "\n", + "> **Inverse asks for an exact reversible square map. Pseudoinverse asks for the best-defined solution when that ideal situation is unavailable.**\n", + "\n", + "Or, by geometry:\n", + "\n", + "- tall → least squares;\n", + "- wide → minimum norm;\n", + "- singular → pseudoinverse still exists.\n", + "\n", + "> 🇪🇸 Usaste la pseudoinversa por **tres razones distintas** con observaciones reales: una matriz cuadrada se volvió singular al duplicar una variable; la regresión de California produjo un sistema alto que necesita mínimos cuadrados; y el tensor de dígitos produjo un sistema ancho con muchas soluciones exactas, donde `pinv` eligió la de norma mínima.\n", + ">\n", + "> Las gráficas interactivas permiten inspeccionar la matemática: en vivienda puedes pasar el cursor sobre observaciones reales, hacer zoom sobre residuos y ver cómo cambia la geometría; en el tensor puedes inspeccionar el valor exacto de cada peso.\n", + ">\n", + "> **Frase para recordar:** la inversa exige un mapa cuadrado, reversible y exacto. La pseudoinversa entrega una solución bien definida cuando esa situación ideal no existe.\n" + ], + "id": "s07-13" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Time for Kahoot 🎯\n", + "\n", + "**Kahoot 2 — Einsum, Distance & the Pseudoinverse** · 6 questions, about 5 minutes.\n", + "\n", + "> 🇪🇸 **Einsum, distancia y la pseudoinversa** — 6 preguntas, unos 5 minutos.\n", + "\n", + "Join at **kahoot.it** with the PIN on the facilitator's screen.\n", + "\n", + "- [Quiz details and facilitator notes](https://project-delphi.github.io/tensors-workshop/kahoot.html#quiz-2)\n", + "- [Import file (`.xlsx`)](https://github.com/project-delphi/tensors-workshop/blob/main/kahoot/kahoot_quiz_2_distance_pseudoinverse.xlsx)\n", + "\n", + "Next up: **08 · Recursion with matrices and vectors** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/08-recursion-with-matrices.ipynb).\n", + "\n", + "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" + ], + "id": "s07-14" + } + ], + "metadata": { + "colab": { + "toc_visible": true, + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "version_major": 2, + "version_minor": 0, + "state": { + "fdca3be0680e4b7aa100fbe7c6a4c334": { + "model_module": "@jupyter-widgets/controls", + "model_name": "VBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "VBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "VBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_17277ab54be14e1194408f20b0791762", + "IPY_MODEL_0783e7c769aa424cb4266e1c13901323", + "IPY_MODEL_0b4be2195dc74d27b9cd4dc08bdd20c8" + ], + "layout": "IPY_MODEL_90ad6f4badab467d96d39c81990cb801" + } + }, + "17277ab54be14e1194408f20b0791762": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_67bd03a0e8aa4b55ab9cf30c28815164", + "placeholder": "​", + "style": "IPY_MODEL_1031cd5b62c54b11868db183a5d0c3d5", + "value": "Matrix diagnostic / Diagnóstico de matriz: switch one data-design decision and watch rank change. / cambia una decisión del diseño y observa cómo cambia el rango." + } + }, + "0783e7c769aa424cb4266e1c13901323": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ToggleButtonsModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ToggleButtonsModel", + "_options_labels": [ + "Original real 7×7 / Real original", + "Duplicated feature / Variable duplicada" + ], + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ToggleButtonsView", + "button_style": "", + "description": "", + "description_tooltip": null, + "disabled": false, + "icons": [], + "index": 1, + "layout": "IPY_MODEL_299e6e4f0b36489f97ddac85f1ac2375", + "style": "IPY_MODEL_4a710dbc9eed42e9897e754448c30ddf", + "tooltips": [] + } + }, + "0b4be2195dc74d27b9cd4dc08bdd20c8": { + "model_module": "@jupyter-widgets/output", + "model_name": "OutputModel", + "model_module_version": "1.0.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/output", + "_model_module_version": "1.0.0", + "_model_name": "OutputModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/output", + "_view_module_version": "1.0.0", + "_view_name": "OutputView", + "layout": "IPY_MODEL_be0cefa7e897453fa49466720097c3a5", + "msg_id": "", + "outputs": [ { - "output_type": "display_data", - "data": { - "text/plain": [ - "VBox(children=(HTML(value='Matrix diagnostic / Diagnóstico de matriz: switch one data-design decision a…" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "fdca3be0680e4b7aa100fbe7c6a4c334" - } - }, - "metadata": { - "application/vnd.jupyter.widget-view+json": { - "colab": { - "custom_widget_manager": { - "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" - } - } - } - } + "output_type": "stream", + "name": "stdout", + "text": [ + "shape / forma: (7, 7)\n", + "rank / rango: 6\n", + "condition number / número de condición: 1.789e+16\n", + "EN: ordinary inverse does NOT exist; columns are dependent.\n", + "ES: la inversa ordinaria NO existe; hay columnas dependientes.\n", + "pseudoinverse shape / forma de A⁺: (7, 7)\n", + "||A A⁺ A - A|| = 1.132e-14\n" + ] } - ], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "row_idx = np.linspace(0, len(X) - 1, 7, dtype=int)\n", - "A_real = X[row_idx].copy()\n", - "\n", - "A_singular = A_real.copy()\n", - "A_singular[:, 2] = A_singular[:, 1] # deliberate duplicate of a REAL feature\n", - "\n", - "print(\"original / original:\", A_real.shape,\n", - " \"rank =\", np.linalg.matrix_rank(A_real))\n", - "print(\"duplicated / duplicada:\", A_singular.shape,\n", - " \"rank =\", np.linalg.matrix_rank(A_singular))\n", - "\n", - "try:\n", - " np.linalg.inv(A_singular)\n", - "except np.linalg.LinAlgError as e:\n", - " print(\"Expected inverse failure / Fallo esperado de la inversa:\", e)\n", - "\n", - "A_plus = np.linalg.pinv(A_singular)\n", - "\n", - "mp_checks = [\n", - " np.allclose(A_singular @ A_plus @ A_singular, A_singular),\n", - " np.allclose(A_plus @ A_singular @ A_plus, A_plus),\n", - " np.allclose((A_singular @ A_plus).T, A_singular @ A_plus),\n", - " np.allclose((A_plus @ A_singular).T, A_plus @ A_singular),\n", - "]\n", - "print(\"Moore–Penrose conditions / condiciones:\", mp_checks)\n", - "\n", - "matrix_choice = widgets.ToggleButtons(\n", - " options=[\n", - " (\"Original real 7×7 / Real original\", \"original\"),\n", - " (\"Duplicated feature / Variable duplicada\", \"singular\"),\n", - " ],\n", - " value=\"original\",\n", - " description=\"\",\n", - ")\n", - "\n", - "def inspect_matrix(choice):\n", - " A = A_real if choice == \"original\" else A_singular\n", - " rank = np.linalg.matrix_rank(A)\n", - " cond = np.linalg.cond(A)\n", - "\n", - " print(\"shape / forma:\", A.shape)\n", - " print(\"rank / rango:\", rank)\n", - " print(\"condition number / número de condición:\", f\"{cond:.3e}\")\n", - "\n", - " if rank == A.shape[0]:\n", - " inv_error = np.linalg.norm(np.linalg.inv(A) @ A - np.eye(A.shape[0]))\n", - " print(\"EN: ordinary inverse exists.\")\n", - " print(\"ES: la inversa ordinaria existe.\")\n", - " print(\"||A⁻¹A - I|| =\", f\"{inv_error:.3e}\")\n", - " else:\n", - " print(\"EN: ordinary inverse does NOT exist; columns are dependent.\")\n", - " print(\"ES: la inversa ordinaria NO existe; hay columnas dependientes.\")\n", - "\n", - " Ap = np.linalg.pinv(A)\n", - " reconstruction = np.linalg.norm(A @ Ap @ A - A)\n", - " print(\"pseudoinverse shape / forma de A⁺:\", Ap.shape)\n", - " print(\"||A A⁺ A - A|| =\", f\"{reconstruction:.3e}\")\n", - "\n", - "matrix_output = widgets.interactive_output(\n", - " inspect_matrix,\n", - " {\"choice\": matrix_choice},\n", - ")\n", - "\n", - "display(\n", - " widgets.VBox([\n", - " widgets.HTML(\n", - " \"Matrix diagnostic / Diagnóstico de matriz: \"\n", - " \"switch one data-design decision and watch rank change. / \"\n", - " \"cambia una decisión del diseño y observa cómo cambia el rango.\"\n", - " ),\n", - " matrix_choice,\n", - " matrix_output,\n", - " ])\n", - ")\n" - ], - "id": "s07-06" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s07-07" - }, - "source": [ - "## Exercise 2 — one real dataset, three geometries\n", - "\n", - "The full California housing design matrix has shape:\n", - "\n", - "`(20,433 observations, 7 columns)`\n", - "\n", - "so it is **very tall**. No ordinary matrix inverse is defined for it.\n", - "\n", - "The pseudoinverse solves:\n", - "\n", - "`w = X⁺y`\n", - "\n", - "which minimizes the total squared residual. It does **not** claim that a straight line passes exactly through all 20,433 observations.\n", - "\n", - "### Why the interactive charts matter\n", - "\n", - "The two full-dataset charts are now **Plotly charts**. You can:\n", - "\n", - "- **hover** over a point to inspect the real and predicted house values;\n", - "- **zoom** into dense regions;\n", - "- **pan** across the distribution;\n", - "- use the toolbar to reset the view.\n", - "\n", - "Then keep the same seven columns but change how many **real rows** are used:\n", - "\n", - "- fewer than 7 rows → **wide** system: more unknowns than equations;\n", - "- exactly 7 rows → **square** system;\n", - "- more than 7 rows → **tall** system: more equations than unknowns.\n", - "\n", - "The **Rows / Filas** slider redraws an interactive Plotly chart so you can inspect each real observation and its prediction while the geometry changes.\n", - "\n", - "### What should you try?\n", - "\n", - "1. Predict why `np.linalg.inv(X)` cannot be called on the full dataset.\n", - "2. Compute `w = pinv(X) @ y`.\n", - "3. Check it against `np.linalg.lstsq`.\n", - "4. Compute RMSE and inspect predicted versus actual values.\n", - "5. Hover over several districts and compare prediction error.\n", - "6. Move **Rows / Filas** through the wide → square → tall transition.\n", - "\n", - "> 🇪🇸 La matriz completa tiene forma `(20.433, 7)`, por lo que es **muy alta**. No existe una inversa ordinaria para una matriz rectangular. La pseudoinversa calcula la solución de mínimos cuadrados.\n", - ">\n", - "> Las gráficas ahora son **interactivas con Plotly**: pasa el cursor sobre los puntos para ver valores reales y predichos, haz **zoom**, desplázate con **pan** y reinicia la vista desde la barra de herramientas.\n", - ">\n", - "> Con el slider **Rows / Filas** mantienes las mismas siete columnas y cambias el número de filas reales: menos de 7 produce un sistema ancho, 7 uno cuadrado y más de 7 uno alto. La gráfica se actualiza y permite inspeccionar cada observación real.\n" - ], - "id": "s07-07" - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "s07-08" - }, - "outputs": [], - "source": [ - "# TODO 2\n", - "# 1. Explain why np.linalg.inv(X) is undefined from X.shape alone.\n", - "#\n", - "# 2. Solve the full real-data problem:\n", - "# w = np.linalg.pinv(X) @ y\n", - "#\n", - "# 3. Compare w with:\n", - "# np.linalg.lstsq(X, y, rcond=None)\n", - "#\n", - "# 4. Compute predictions and RMSE.\n", - "#\n", - "# 5. Because the six non-bias features were standardized, compare the\n", - "# ABSOLUTE values of w[1:] and identify the largest standardized coefficient.\n", - "# Describe it as an association in this dataset, NOT a causal effect.\n" - ], - "id": "s07-08" - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "s07-09", - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ], - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1509, - "referenced_widgets": [ - "ff5f9361100346188b16a1af2d273f9d", - "7dc550d7552f41f5af38fe47dd4e28f9", - "193b5af8c8374bd8b18d137cc4d48ca9", - "041bd6d03a9c4496851ee9a468d02352", - "1e74b2d33d574a8390182f018928563a", - "b4b80f5ec0bb48e2b6916fac7eed1c2e", - "b6b977f07c49401b9629e9be5b51fb4f", - "66aad08b71814b96a99ad75d198f96f9", - "ad6b01e7bf014fbcb9bd66ed5ebc500c", - "b380451182ae4bb189d75ff33fe38a6e" - ] - }, - "outputId": "d82324b0-7941-4d44-8719-bd3596dde7ef" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "full X / X completa: (20433, 7)\n", - "EN: X is rectangular, so np.linalg.inv(X) is not defined.\n", - "ES: X es rectangular, por lo que np.linalg.inv(X) no está definida.\n", - "pinv == lstsq: True\n", - "RMSE: $75,981\n", - "largest standardized coefficient / mayor coeficiente estandarizado: median_income 90,686\n", - "EN: this is an association in this linear fit, not a causal claim.\n", - "ES: es una asociación en este ajuste lineal, no una afirmación causal.\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "
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Pasa el cursor, haz zoom y desplázate en cada gráfica." + } + }, + "193b5af8c8374bd8b18d137cc4d48ca9": { + "model_module": "@jupyter-widgets/controls", + "model_name": "IntSliderModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "IntSliderModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "IntSliderView", + "continuous_update": false, + "description": "Rows / Filas:", + "description_tooltip": null, + "disabled": false, + "layout": "IPY_MODEL_66aad08b71814b96a99ad75d198f96f9", + "max": 40, + "min": 3, + "orientation": "horizontal", + "readout": true, + "readout_format": "d", + "step": 1, + "style": "IPY_MODEL_ad6b01e7bf014fbcb9bd66ed5ebc500c", + "value": 21 + } + }, + "041bd6d03a9c4496851ee9a468d02352": { + "model_module": "@jupyter-widgets/output", + "model_name": "OutputModel", + "model_module_version": "1.0.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/output", + "_model_module_version": "1.0.0", + "_model_name": "OutputModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/output", + "_view_module_version": "1.0.0", + "_view_name": "OutputView", + "layout": "IPY_MODEL_b380451182ae4bb189d75ff33fe38a6e", + "msg_id": "", + "outputs": [ { - "output_type": "display_data", - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "
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pinv chooses minimum norm.\"\n", - " meaning_es = \"Normalmente hay muchas soluciones exactas; pinv elige norma mínima.\"\n", - " elif n_rows == Xn.shape[1]:\n", - " geometry = \"SQUARE / CUADRADO\"\n", - " meaning_en = \"An ordinary inverse exists only if rank is full.\"\n", - " meaning_es = \"La inversa ordinaria solo existe si el rango es completo.\"\n", - " else:\n", - " geometry = \"TALL / ALTO\"\n", - " meaning_en = \"Usually no exact solution; pinv gives least squares.\"\n", - " meaning_es = \"Normalmente no hay solución exacta; pinv da mínimos cuadrados.\"\n", - "\n", - " print(f\"{geometry}: {Xn.shape} | rank/rango={rank}\")\n", - " print(\"||Xw-y|| =\", f\"{residual_norm:.3e}\",\n", - " \"| ||w|| =\", f\"{coef_norm:.3e}\")\n", - " print(\"EN:\", meaning_en)\n", - " print(\"ES:\", meaning_es)\n", - "\n", - " long_df = pd.DataFrame({\n", - " \"row\": np.tile(np.arange(n_rows), 2),\n", - " \"value\": np.concatenate([yn, predn]),\n", - " \"series\": (\n", - " [\"actual / real\"] * n_rows\n", - " + [\"predicted / predicho\"] * n_rows\n", - " ),\n", - " })\n", - "\n", - " fig = px.scatter(\n", - " long_df,\n", - " x=\"row\",\n", - " y=\"value\",\n", - " color=\"series\",\n", - " hover_data={\"row\": True, \"value\": \":,.0f\"},\n", - " title=f\"{geometry} — same 7 columns / mismas 7 columnas\",\n", - " labels={\"row\": \"row / fila\", \"value\": \"median house value\"},\n", - " )\n", - " fig.update_traces(marker={\"size\": 9})\n", - " fig.update_layout(height=390, legend_title_text=\"\")\n", - " fig.show()\n", - "\n", - "geometry_output = widgets.interactive_output(\n", - " explore_geometry,\n", - " {\"n_rows\": rows_slider},\n", - ")\n", - "\n", - "display(\n", - " widgets.VBox([\n", - " widgets.HTML(\n", - " \"Geometry explorer / Explorador geométrico: \"\n", - " \"move through wide → square → tall using real housing rows. \"\n", - " \"Hover, zoom and pan in every chart. / \"\n", - " \"recorre ancho → cuadrado → alto usando filas reales. \"\n", - " \"Pasa el cursor, haz zoom y desplázate en cada gráfica.\"\n", - " ),\n", - " rows_slider,\n", - " geometry_output,\n", - " ])\n", - ")\n" - ], - "id": "s07-09" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s07-10" - }, - "source": [ - "## Exercise 3 — pseudoinverse after unfolding a real image tensor\n", - "\n", - "Now use real handwritten-digit images.\n", - "\n", - "Take the first 20 images:\n", - "\n", - "`T.shape = (20, 8, 8)`\n", - "\n", - "Unfolding mode 0 gives:\n", - "\n", - "`M.shape = (20, 64)`\n", - "\n", - "This is a **wide** matrix: 20 equations and 64 unknown pixel weights.\n", - "\n", - "### A target we can interpret exactly\n", - "\n", - "For each real digit image, define `b` as its **mean pixel intensity**. Because the mean of 64 pixels is a linear function, the uniform weight vector\n", - "\n", - "`[1/64, 1/64, ..., 1/64]`\n", - "\n", - "is one exact solution of `Mx = b`.\n", - "\n", - "But a wide system has many exact solutions. The pseudoinverse chooses the one with the **smallest Euclidean norm**. We can fold that 64-value solution back to an `8×8` weight image and compare it with the uniform solution.\n", - "\n", - "### Why the heatmaps are interactive\n", - "\n", - "The weight maps are Plotly heatmaps. Hover over any cell to inspect its **row, column and numerical weight**. You can zoom into a region and compare how individual weights change as more digit equations are added.\n", - "\n", - "### What should you try?\n", - "\n", - "1. Unfold `T` to `(20, 64)`.\n", - "2. Build `b` from the real image means.\n", - "3. Solve `x = M⁺b`.\n", - "4. Confirm that both the pseudoinverse solution and the uniform solution predict `b`.\n", - "5. Compare their norms.\n", - "6. Fold `x` back to `8×8`.\n", - "7. Hover over individual weights in the heatmap.\n", - "8. Move **Digits / Dígitos** and see how the minimum-norm map changes as more real equations are added.\n", - "\n", - "> 🇪🇸 Ahora usamos imágenes reales de dígitos. Veinte imágenes `8×8` forman un tensor `(20,8,8)`. Al desplegarlo obtenemos una matriz ancha `(20,64)`. Definimos `b` como la intensidad media real de cada imagen. El vector uniforme `1/64` es una solución exacta conocida, pero existen muchas. La pseudoinversa elige la solución exacta de **norma mínima**.\n", - ">\n", - "> Los mapas de pesos ahora son **heatmaps interactivos de Plotly**. Pasa el cursor sobre cualquier celda para ver su fila, columna y peso numérico; también puedes hacer zoom. Después mueve **Digits / Dígitos** para observar cómo cambia la solución cuando añadimos más ecuaciones reales.\n" - ], - "id": "s07-10" - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "id": "s07-11" - }, - "outputs": [], - "source": [ - "# TODO 3\n", - "# 1. T = digit_tensor[:20]\n", - "# 2. M = unfold(T, 0) # expected shape (20, 64)\n", - "# 3. b = T.mean(axis=(1, 2)) # one real mean intensity per image\n", - "# 4. x_pinv = np.linalg.pinv(M) @ b\n", - "# 5. x_uniform = np.full(64, 1 / 64)\n", - "#\n", - "# Check:\n", - "# - M @ x_pinv reproduces b\n", - "# - M @ x_uniform reproduces b\n", - "# - ||x_pinv|| <= ||x_uniform||\n", - "#\n", - "# Finally fold:\n", - "# x_image = x_pinv.reshape(8, 8)\n" - ], - "id": "s07-11" - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "id": "s07-12", - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ], - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1832, - "referenced_widgets": [ - "054e3f7b9415416fbc519836762c5895", - "60e78b631a51493ab971c652ef29e22b", - "3c9d0ed5aaee4eddb11fa2c32420e3c1", - "487e686339c641ddb83c434a1efcd133", - "8739ba147ec048ada637b10565ddcad8", - "f31c87db8f0341a1ba014921658e3e36", - "e7c39b9033114256afe03b2d9843cae6", - "66d49545b54b4ed58a547e360fbada3a", - "4a6446977f0347f0a75bcd874d3d7ff3", - "6fdee2acb4a24c1c8cd9da09636bf898" - ] - }, - "outputId": "5333d8c9-5b9e-4ce9-ceef-8fa3f1723d85" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "tensor / tensor: (20, 8, 8)\n", - "unfolded / desplegado: (20, 64)\n", - "pinv residual / residuo: 2.107e-14\n", - "uniform residual / residuo uniforme: 0.000e+00\n", - "||x_pinv||: 0.102328\n", - "||x_uniform||: 0.125000\n", - "minimum norm check / chequeo norma mínima: True\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "
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col=%{x}
pixel=%{z:.1f}\",\n", - " )\n", - ")\n", - "fig_digit.update_layout(\n", - " title=\"Real digit / Dígito real — original 8×8 measurements\",\n", - " height=390,\n", - " yaxis={\"autorange\": \"reversed\", \"scaleanchor\": \"x\"},\n", - ")\n", - "fig_digit.show()\n", - "\n", - "def weight_heatmap(z, title):\n", - " fig = go.Figure(\n", - " data=go.Heatmap(\n", - " z=z,\n", - " zmid=0,\n", - " colorscale=\"RdBu\",\n", - " hovertemplate=(\n", - " \"row=%{y}
col=%{x}
weight/peso=%{z:.6f}\"\n", - " ),\n", - " colorbar={\"title\": \"weight / peso\"},\n", - " )\n", - " )\n", - " fig.update_layout(\n", - " title=title,\n", - " height=390,\n", - " yaxis={\"autorange\": \"reversed\", \"scaleanchor\": \"x\"},\n", - " )\n", - " return fig\n", - "\n", - "weight_heatmap(\n", - " x_image,\n", - " \"Pseudoinverse minimum-norm weights / Pesos de norma mínima\",\n", - ").show()\n", - "\n", - "weight_heatmap(\n", - " uniform_image,\n", - " \"Known uniform exact solution / Solución uniforme exacta\",\n", - ").show()\n", - "\n", - "digits_slider = widgets.IntSlider(\n", - " value=20, min=5, max=60, step=5,\n", - " description=\"Digits / Dígitos:\",\n", - " continuous_update=False,\n", - " style={\"description_width\": \"100px\"},\n", - ")\n", - "\n", - "def explore_tensor_pinv(n_digits):\n", - " Tn = digit_tensor[:n_digits]\n", - " Mn = unfold(Tn, 0)\n", - " bn = Tn.mean(axis=(1, 2))\n", - "\n", - " x_min = np.linalg.pinv(Mn) @ bn\n", - " x_known = np.full(Mn.shape[1], 1 / Mn.shape[1])\n", - "\n", - " res_min = np.linalg.norm(Mn @ x_min - bn)\n", - " res_known = np.linalg.norm(Mn @ x_known - bn)\n", - "\n", - " print(\n", - " f\"M: {Mn.shape} | rank/rango={np.linalg.matrix_rank(Mn)} | \"\n", - " f\"pinv residual/residuo={res_min:.2e}\"\n", - " )\n", - " print(\n", - " f\"||x_pinv||={np.linalg.norm(x_min):.6f} | \"\n", - " f\"||x_uniform||={np.linalg.norm(x_known):.6f}\"\n", - " )\n", - " print(\"EN: both solve the same real equations; pinv selects minimum norm.\")\n", - " print(\"ES: ambas resuelven las mismas ecuaciones reales; pinv elige norma mínima.\")\n", - "\n", - " fig = weight_heatmap(\n", - " x_min.reshape(8, 8),\n", - " f\"Pseudoinverse weights / Pesos pinv — {n_digits} real equations\",\n", - " )\n", - " fig.show()\n", - "\n", - "tensor_output = widgets.interactive_output(\n", - " explore_tensor_pinv,\n", - " {\"n_digits\": digits_slider},\n", - ")\n", - "\n", - "display(\n", - " widgets.VBox([\n", - " widgets.HTML(\n", - " \"Tensor pseudoinverse explorer / Explorador tensorial: \"\n", - " \"add real digit equations, then hover/zoom on the weight map. / \"\n", - " \"añade ecuaciones de dígitos reales y después pasa el cursor o haz zoom \"\n", - " \"sobre el mapa de pesos.\"\n", - " ),\n", - " digits_slider,\n", - " tensor_output,\n", - " ])\n", - ")\n" - ], - "id": "s07-12" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s07-13" - }, - "source": [ - "## What just happened\n", - "\n", - "You used the pseudoinverse for **three different reasons**, all on real observations:\n", - "\n", - "1. **Singular square matrix** \n", - " Duplicating a real feature made two columns dependent. Rank fell and the ordinary inverse disappeared, but `A⁺` still existed and satisfied the Moore–Penrose conditions.\n", - "\n", - "2. **Real California housing regression** \n", - " `X` had shape `(20,433, 7)`: far more equations than unknowns. There is generally no exact line through all observations, so `X⁺y` returned the **least-squares** solution. The interactive scatter and residual histogram let you hover, zoom and inspect where the approximation succeeds or fails.\n", - "\n", - "3. **Real digit-image tensor** \n", - " Unfolding `(20,8,8)` produced a wide `(20,64)` matrix. Many exact pixel-weight solutions existed, so the pseudoinverse selected the **minimum-norm** one. Interactive heatmaps made every individual pixel weight inspectable before folding the solution back to `8×8`.\n", - "\n", - "### The sentence to remember\n", - "\n", - "> **Inverse asks for an exact reversible square map. Pseudoinverse asks for the best-defined solution when that ideal situation is unavailable.**\n", - "\n", - "Or, by geometry:\n", - "\n", - "- tall → least squares;\n", - "- wide → minimum norm;\n", - "- singular → pseudoinverse still exists.\n", - "\n", - "> 🇪🇸 Usaste la pseudoinversa por **tres razones distintas** con observaciones reales: una matriz cuadrada se volvió singular al duplicar una variable; la regresión de California produjo un sistema alto que necesita mínimos cuadrados; y el tensor de dígitos produjo un sistema ancho con muchas soluciones exactas, donde `pinv` eligió la de norma mínima.\n", - ">\n", - "> Las gráficas interactivas permiten inspeccionar la matemática: en vivienda puedes pasar el cursor sobre observaciones reales, hacer zoom sobre residuos y ver cómo cambia la geometría; en el tensor puedes inspeccionar el valor exacto de cada peso.\n", - ">\n", - "> **Frase para recordar:** la inversa exige un mapa cuadrado, reversible y exacto. La pseudoinversa entrega una solución bien definida cuando esa situación ideal no existe.\n" - ], - "id": "s07-13" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s07-14" - }, - "source": [ - "---\n", - "\n", - "## Time for Kahoot 🎯\n", - "\n", - "**Kahoot 2 — Einsum, Distance & the Pseudoinverse** · 6 questions, about 5 minutes.\n", - "\n", - "> 🇪🇸 **Kahoot 2 — Einsum, distancia y la pseudoinversa** · 6 preguntas, unos 5 minutos.\n", - "\n", - "Join at **kahoot.it** with the PIN on the facilitator's screen.\n", - "\n", - "- [Quiz details and facilitator notes](https://project-delphi.github.io/tensors-workshop/kahoot.html#quiz-2)\n", - "- [Import file (`.xlsx`)](https://github.com/project-delphi/tensors-workshop/blob/main/kahoot/kahoot_quiz_2_distance_pseudoinverse.xlsx)\n", - "\n", - "Next up: **08 · Recursion with matrices and vectors** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/08-recursion-with-matrices.ipynb).\n", - "\n", - "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)\n" - ], - "id": "s07-14" - } - ], - "metadata": { - "colab": { - "toc_visible": true, - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "version_major": 2, - "version_minor": 0, - "state": { - "fdca3be0680e4b7aa100fbe7c6a4c334": { - "model_module": "@jupyter-widgets/controls", - "model_name": "VBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "VBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - 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"nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/scripts/content.py b/scripts/content.py index 93807c5..f8704d1 100644 --- a/scripts/content.py +++ b/scripts/content.py @@ -289,16 +289,56 @@ def fetch_verified_video(url, expected_sha256, n_frames=16, stride=45): CONTENT["07"] = { "setup": """import numpy as np import pandas as pd +import matplotlib.pyplot as plt +import ipywidgets as widgets +import plotly.express as px +import plotly.graph_objects as go +from IPython.display import display +from sklearn.datasets import load_digits -HOUSING = "https://raw.githubusercontent.com/ageron/handson-ml2/master/datasets/housing/housing.csv" -housing = pd.read_csv(HOUSING) +# Enable ipywidgets in Google Colab when available. +try: + from google.colab import output + output.enable_custom_widget_manager() +except ImportError: + pass -def unfold(T, axis): +HOUSING = ( + "https://raw.githubusercontent.com/ageron/handson-ml2/master/" + "datasets/housing/housing.csv" +) +housing = pd.read_csv(HOUSING).dropna().reset_index(drop=True) + +features = [ + "housing_median_age", + "total_rooms", + "total_bedrooms", + "population", + "households", + "median_income", +] + +X_raw = housing[features].to_numpy(float) +feature_mean = X_raw.mean(axis=0) +feature_std = X_raw.std(axis=0) +X_scaled = (X_raw - feature_mean) / feature_std + +# Bias + six standardized real features -> 7 columns. +X = np.column_stack([np.ones(len(housing)), X_scaled]) +y = housing["median_house_value"].to_numpy(float) +column_names = ["bias"] + features + +# Real image tensor for Exercise 3. +digits = load_digits() +digit_tensor = digits.images.astype(float) # (1797, 8, 8) + +def unfold(T, axis=0): return np.moveaxis(T, axis, 0).reshape(T.shape[axis], -1) -rng = np.random.default_rng(0) -print(housing.shape) # (20640, 10) -print(housing['total_bedrooms'].isnull().sum()) # 207 missing values!""", +print("housing rows:", len(housing)) +print("housing design matrix:", X.shape) +print("digit tensor:", digit_tensor.shape) +print("interactive charts: Plotly enabled (hover, zoom, pan)")""", } # ───────────────────────────────────────────────────────────────────────────── From 796607c680d2c9ae970bde32b5e15f716838e7e5 Mon Sep 17 00:00:00 2001 From: Sebas Laverde Chunza <106851243+Laverde97@users.noreply.github.com> Date: Fri, 28 Aug 2026 21:48:24 -0500 Subject: [PATCH 17/29] Improve notebook 08 pedagogy with real data and interactive recursion for issue #44 --- notebooks/08-recursion-with-matrices.ipynb | 2331 ++++++++++++++++---- 1 file changed, 1887 insertions(+), 444 deletions(-) diff --git a/notebooks/08-recursion-with-matrices.ipynb b/notebooks/08-recursion-with-matrices.ipynb index 0aa8f4e..3e450f3 100644 --- a/notebooks/08-recursion-with-matrices.ipynb +++ b/notebooks/08-recursion-with-matrices.ipynb @@ -1,448 +1,1891 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 08 · Recursion with matrices and vectors\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/08-recursion-with-matrices.ipynb)\n", - "\n", - "*Part IV · demo · 10 min*\n", - "\n", - "> 🇪🇸 **Recursión con matrices y vectores** — Aplicar una misma matriz una y otra vez: Fibonacci, autovectores y un pronóstico real.\n", - "\n", - "Apply one matrix again and again: Fibonacci, eigenvectors, and a real forecast.\n", - "\n", - "## What you will be able to do\n", - "\n", - "- Write a recurrence as repeated multiplication by one matrix.\n", - "- Find the dominant eigenvector by power iteration, and check it against `np.linalg.eig`.\n", - "- Fit an autoregressive model with the pseudoinverse and feed its own output back in.\n", - "- Recognise that structure as the skeleton of a recurrent neural network." - ], - "id": "s08-00" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "Run this first. It installs and imports everything this notebook needs, and nothing else.\n", - "\n", - "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." - ], - "id": "s08-01" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "FLIGHTS = \"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/flights.csv\"\n", - "flights = pd.read_csv(FLIGHTS)\n", - "\n", - "rng = np.random.default_rng(0)\n", - "print(flights.shape) # (144, 3) — 144 real months, 1949-1960" - ], - "id": "s08-02" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Recursion means defining something in terms of itself\n", - "\n", - "> 🇪🇸 La recursión consiste en definir algo en términos de sí mismo. Con\n", - "> matrices, esto se convierte en aplicar la misma matriz una y otra vez.\n", - "\n", - "With matrices this becomes: **apply the same matrix again and again.** Three\n", - "examples, increasing in usefulness.\n", - "\n", - "This is a demo — read it, run it, ask about it. The exercises at the end are\n", - "short." - ], - "id": "s08-03" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1. Fibonacci as repeated matrix multiplication\n", - "\n", - "The rule `f(n) = f(n-1) + f(n-2)` is one matrix applied repeatedly." - ], - "id": "s08-04" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "F = np.array([[1, 1], [1, 0]])\n", - "v = np.array([1, 0])\n", - "for _ in range(10):\n", - " v = F @ v\n", - "print(v[1]) # 55\n", - "print(np.linalg.matrix_power(F, 10)[0, 1]) # 55 — same answer, one step" - ], - "id": "s08-05" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2. Power iteration — recursion that finds an eigenvector\n", - "\n", - "Multiply any starting vector by `A` repeatedly, rescaling each time. It\n", - "converges to the eigenvector with the largest eigenvalue (Chapter 2 §2.7)." - ], - "id": "s08-06" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "A = np.array([[4., 1.], [2., 3.]])\n", - "x = rng.standard_normal(2); x /= np.linalg.norm(x)\n", - "for _ in range(50):\n", - " x = A @ x\n", - " x /= np.linalg.norm(x)\n", - "\n", - "print(x @ A @ x) # 5.000000\n", - "print(np.linalg.eig(A)[0].max()) # 5.000000 — identical" - ], - "id": "s08-07" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "xv = rng.standard_normal(2); xv /= np.linalg.norm(xv)\n", - "checkpoints = {}\n", - "for step in range(1, 51):\n", - " xv = A @ xv\n", - " xv /= np.linalg.norm(xv)\n", - " if step in (1, 2, 5, 10, 50):\n", - " checkpoints[step] = xv.copy()\n", - "\n", - "eigvals, eigvecs = np.linalg.eig(A)\n", - "dominant = eigvecs[:, np.argmax(eigvals)].real\n", - "dominant /= np.linalg.norm(dominant)\n", - "\n", - "fig, ax = plt.subplots(figsize=(4, 4))\n", - "theta = np.linspace(0, 2 * np.pi, 200)\n", - "ax.plot(np.cos(theta), np.sin(theta), color=\"lightgray\", linewidth=1)\n", - "for step, v in checkpoints.items():\n", - " ax.annotate(\"\", xy=v, xytext=(0, 0), arrowprops=dict(\n", - " arrowstyle=\"->\", color=\"#4C72B0\", alpha=0.3 + 0.7 * step / 50))\n", - " ax.text(v[0] * 1.15, v[1] * 1.15, str(step), fontsize=8, ha=\"center\")\n", - "for sign in (1, -1):\n", - " ax.annotate(\"\", xy=sign * dominant, xytext=(0, 0),\n", - " arrowprops=dict(arrowstyle=\"->\", color=\"#C44E52\", linewidth=2))\n", - "ax.set_xlim(-1.3, 1.3); ax.set_ylim(-1.3, 1.3); ax.set_aspect(\"equal\")\n", - "ax.set_title(\"power iteration converges to the eigenvector\\n(red = the true dominant eigenvector, both signs)\")\n", - "plt.tight_layout()\n", - "plt.show()" - ], - "id": "s08-08" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is how PageRank ranks web pages, and it is why eigenvectors matter far\n", - "beyond Chapter 2: **repeated application of a matrix converges to its dominant\n", - "eigenvector.**" - ], - "id": "s08-09" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3. Recursion on real data — forecasting airline traffic\n", - "\n", - "This combines recursion with the pseudoinverse from section 07. We fit a model\n", - "that predicts each month from the previous 12, then apply it *to its own output*\n", - "to forecast forward." - ], - "id": "s08-10" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "y = flights['passengers'].to_numpy(float) # 144 real months, 1949-1960\n", - "p = 12\n", - "rows = np.array([y[i:i+p] for i in range(len(y) - p)])\n", - "X = np.column_stack([np.ones(len(rows)), rows])\n", - "w = np.linalg.pinv(X) @ y[p:] # least squares, exactly as in section 07\n", - "print(X.shape) # (132, 13) — 132 training windows\n", - "\n", - "history = list(y[-p:])\n", - "for _ in range(12): # recursion: feed predictions back in\n", - " nxt = w[0] + np.dot(w[1:], history[-p:])\n", - " history.append(nxt)\n", - "\n", - "print(np.round(history[-12:], 1))\n", - "# [465.2 429.1 455.1 491.0 527.8 589.4 679.7 661.3 575.3 509.5 438.6 470.7]" - ], - "id": "s08-11" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The forecast above extrapolates 12 months **past the end of the dataset**, so\n", - "there is nothing to check it against. To see the forecast next to real numbers,\n", - "hold out the last 12 months, fit on everything before them, and forecast those\n", - "same 12 months back.\n", - "\n", - "> 🇪🇸 El pronóstico anterior se extiende 12 meses **más allá del final de los\n", - "> datos**, así que no hay nada real con qué compararlo. Para ver el pronóstico\n", - "> junto a números reales, se retienen los últimos 12 meses, se ajusta con todo\n", - "> lo anterior, y se pronostican esos mismos 12 meses." - ], - "id": "s08-12" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "y_train, y_test = y[:-12], y[-12:]\n", - "rows_tr = np.array([y_train[i:i+p] for i in range(len(y_train) - p)])\n", - "X_tr = np.column_stack([np.ones(len(rows_tr)), rows_tr])\n", - "w_tr = np.linalg.pinv(X_tr) @ y_train[p:]\n", - "\n", - "hist_tr = list(y_train[-p:])\n", - "for _ in range(12):\n", - " hist_tr.append(w_tr[0] + np.dot(w_tr[1:], hist_tr[-p:]))\n", - "forecast_holdout = np.array(hist_tr[-12:])\n", - "\n", - "import matplotlib.pyplot as plt\n", - "months = np.arange(1, 13)\n", - "fig, ax = plt.subplots(figsize=(6, 3.2))\n", - "ax.plot(months, y_test, marker=\"o\", label=\"actual\", color=\"#4C72B0\")\n", - "ax.plot(months, forecast_holdout, marker=\"o\", label=\"forecast\", color=\"#C44E52\")\n", - "ax.set_xlabel(\"month (held out, never seen while fitting)\")\n", - "ax.set_ylabel(\"passengers\")\n", - "ax.set_title(\"forecast vs. actual — last 12 months held out\")\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "mape = (np.abs(forecast_holdout - y_test) / y_test).mean()\n", - "print(f\"mean absolute percentage error: {mape:.1%}\")" - ], - "id": "s08-13" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The forecast reproduces the seasonal shape of real air travel — low in winter,\n", - "peaking in summer — because the model learned it from 132 real training windows.\n", - "\n", - "**This is exactly the structure of a recurrent neural network**: a hidden state,\n", - "updated by the same weights at every step." - ], - "id": "s08-14" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# The same shape, with a nonlinearity. W and U are the SAME at every step —\n", - "# that is the recursion.\n", - "W = rng.standard_normal((4, 4)) * 0.5\n", - "U = rng.standard_normal((4, 3)) * 0.5\n", - "xs = rng.standard_normal((6, 3)) # a sequence of 6 inputs, 3 features each\n", - "\n", - "h = np.zeros(4)\n", - "for t in range(6):\n", - " h = np.tanh(W @ h + U @ xs[t])\n", - "print(np.round(h, 3))" - ], - "id": "s08-15" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 1 — the forecast, and what breaks it\n", - "\n", - "> 🇪🇸 El pronóstico, y qué lo rompe." - ], - "id": "s08-16" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO 1: Change the window length p from 12 to 3 and re-run the forecast.\n", - "# The seasonal shape disappears. Why? What does p = 12 encode about\n", - "# this particular dataset that p = 3 cannot?\n", - "\n", - "# TODO 2: Forecast 60 months ahead instead of 12. Plot it if you can. Recursive\n", - "# forecasting feeds predictions back in as if they were observations —\n", - "# what does that do to the error over a long horizon?" - ], - "id": "s08-17" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "c2SUj_xUZhNB" + }, + "source": [ + "# 08 · Recursion with matrices and vectors\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/08-recursion-with-matrices.ipynb)\n", + "\n", + "*Part IV · demo · 10 min*\n", + "\n", + "> 🇪🇸 **Recursión con matrices y vectores** — Entender una recurrencia como una actualización de estado repetida, observar cuándo domina una dirección propia y comprobar qué ocurre cuando un pronóstico reutiliza sus propias predicciones.\n", + "\n", + "Treat recursion as repeated state updates, connect repeated multiplication with dominant eigen-directions, and test recursive forecasting on real airline-passenger data.\n", + "\n", + "## What you will be able to do\n", + "\n", + "- Write a recurrence as a repeated state update `x[t+1] = A @ x[t]`.\n", + "- Explain why repeated multiplication can align a state with a dominant eigenvector.\n", + "- Use a controlled synthetic matrix to see how the eigenvalue ratio controls convergence speed.\n", + "- Fit a real autoregressive model with the pseudoinverse and feed its own predictions back in.\n", + "- Diagnose why recursive forecast error can compound with horizon.\n", + "\n", + "> 🇪🇸 **Al terminar podrás:** escribir una recurrencia como `x[t+1] = A @ x[t]`; explicar por qué las multiplicaciones repetidas pueden alinear el estado con un autovector dominante; observar cómo la razón entre autovalores controla la velocidad de convergencia; ajustar un modelo autorregresivo con datos reales usando la pseudoinversa; y diagnosticar por qué el error puede acumularse cuando las predicciones se reutilizan como entradas." + ], + "id": "c2SUj_xUZhNB" }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "def forecast(y, p, steps):\n", - " rows = np.array([y[i:i+p] for i in range(len(y) - p)])\n", - " X = np.column_stack([np.ones(len(rows)), rows])\n", - " w = np.linalg.pinv(X) @ y[p:]\n", - " hist = list(y[-p:])\n", - " for _ in range(steps):\n", - " hist.append(w[0] + np.dot(w[1:], hist[-p:]))\n", - " return np.array(hist[-steps:])\n", - "\n", - "print(np.round(forecast(y, 12, 12), 1)) # seasonal: winter low, summer peak\n", - "print(np.round(forecast(y, 3, 12), 1)) # smooth, seasonality gone\n", - "\n", - "# p = 12 encodes ONE YEAR. The model can see the same month a year earlier, so\n", - "# seasonality is available to it as a linear term. With p = 3 it can only see a\n", - "# local trend, and a linear model has no way to invent a yearly cycle.\n", - "\n", - "print(np.round(forecast(y, 12, 60)[-6:], 1))\n", - "# Errors compound: every predicted month becomes an input to the next\n", - "# prediction, so mistakes feed on themselves. Recursive forecasts are trustworthy\n", - "# for a short horizon and decorative for a long one." - ], - "id": "s08-18" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 2 — power iteration by hand\n", - "\n", - "> 🇪🇸 Iteración de potencias, paso a paso." - ], - "id": "s08-19" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO 3: Run power iteration on A = [[4., 1.], [2., 3.]] but print the\n", - "# estimate after 1, 2, 5, 10 and 50 steps. How fast does it converge?\n", - "# Try a second matrix whose two eigenvalues are close together\n", - "# (e.g. [[4., 1.], [0., 3.9]]). What changes, and why?" - ], - "id": "s08-20" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true + { + "cell_type": "markdown", + "metadata": { + "id": "xvXiblBeZhNE" + }, + "source": [ + "## Setup\n", + "\n", + "Run this cell first. It loads the real monthly airline-passenger dataset and the small visualization tools used below.\n", + "\n", + "> 🇪🇸 Ejecuta primero esta celda. Carga el conjunto real de pasajeros mensuales de aerolíneas y las herramientas pequeñas de visualización que utilizaremos." + ], + "id": "xvXiblBeZhNE" }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "def power_iterate(M, steps, seed=0):\n", - " v = np.random.default_rng(seed).standard_normal(M.shape[0])\n", - " v /= np.linalg.norm(v)\n", - " out = {}\n", - " for k in range(1, max(steps) + 1):\n", - " v = M @ v\n", - " v /= np.linalg.norm(v)\n", - " if k in steps:\n", - " out[k] = round(float(v @ M @ v), 4)\n", - " return out\n", - "\n", - "A = np.array([[4., 1.], [2., 3.]]) # eigenvalues 5 and 2\n", - "A2 = np.array([[4., 1.], [0., 3.9]]) # eigenvalues 4 and 3.9\n", - "\n", - "print(power_iterate(A, [1, 2, 5, 10, 50]))\n", - "print(power_iterate(A2, [1, 2, 5, 10, 50]))\n", - "print(np.linalg.eigvals(A), np.linalg.eigvals(A2))\n", - "\n", - "# Convergence speed is set by the RATIO of the two largest eigenvalues. For A\n", - "# that ratio is 2/5, so each step shrinks the error to 40% of itself and ten\n", - "# steps are plenty. For A2 it is 3.9/4 = 0.975, and after 50 steps it is still\n", - "# arriving. Power iteration is fast exactly when one direction dominates." - ], - "id": "s08-21" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Done with this section\n", - "\n", - "Next up: **09 · Convolution and deconvolution** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/09-convolution-and-deconvolution.ipynb).\n", - "\n", - "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" - ], - "id": "s08-22" - } - ], - "metadata": { - "colab": { - "name": "08-recursion-with-matrices.ipynb", - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "QIrgXPtMZhNE", + "outputId": "faa44d1a-9aac-4c25-c78d-b6a995526354" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "real months / meses reales: 144\n", + "range / periodo: 1949-Jan → 1960-Dec\n", + "passengers min/max: 104 622\n", + "interactive charts: Plotly + ipywidgets enabled\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import ipywidgets as widgets\n", + "import plotly.express as px\n", + "import plotly.graph_objects as go\n", + "from IPython.display import display\n", + "\n", + "try:\n", + " from google.colab import output\n", + " output.enable_custom_widget_manager()\n", + "except ImportError:\n", + " pass\n", + "\n", + "FLIGHTS = \"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/flights.csv\"\n", + "flights = pd.read_csv(FLIGHTS)\n", + "\n", + "y = flights[\"passengers\"].to_numpy(float)\n", + "labels = (\n", + " flights[\"year\"].astype(str)\n", + " + \"-\"\n", + " + flights[\"month\"].astype(str).str[:3]\n", + ").to_numpy()\n", + "\n", + "rng = np.random.default_rng(0)\n", + "\n", + "print(\"real months / meses reales:\", len(y))\n", + "print(\"range / periodo:\", labels[0], \"→\", labels[-1])\n", + "print(\"passengers min/max:\", int(y.min()), int(y.max()))\n", + "print(\"interactive charts: Plotly + ipywidgets enabled\")" + ], + "id": "QIrgXPtMZhNE" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "enmPFvDxZhNF" + }, + "source": [ + "## Why this matters\n", + "\n", + "A recurrence does not need a mysterious new operation. It can be as simple as reusing the **same update rule** over and over:\n", + "\n", + "`x[t+1] = A x[t]`\n", + "\n", + "The important idea is that the output at one step becomes the input to the next. That creates **state**.\n", + "\n", + "This same pattern appears in several places:\n", + "\n", + "- a second-order recurrence such as Fibonacci can be rewritten as a matrix state update;\n", + "- power iteration repeatedly applies a matrix until one direction dominates;\n", + "- recursive forecasting predicts the next value, then feeds that prediction back as if it were observed.\n", + "\n", + "The mathematics is similar, but the consequences differ: repetition can reveal structure, or it can amplify error.\n", + "\n", + "### How to use the folded solutions / Cómo usar las soluciones plegadas\n", + "\n", + "Each exercise has a **Solution / Solución** cell that is intentionally closed. First try the `TODO`; then open the solution to compare your reasoning with a reference implementation.\n", + "\n", + "> 🇪🇸 Una recurrencia reutiliza la misma regla de actualización y convierte la salida de un paso en la entrada del siguiente. Ese patrón aparece en Fibonacci, en la iteración de potencias y en el pronóstico recursivo. La repetición puede revelar una dirección dominante, pero también puede propagar errores.\n", + ">\n", + "> Cada ejercicio tiene una celda **Solution / Solución** cerrada a propósito. Primero intenta resolver el `TODO`; después abre la solución para comparar tu razonamiento con una implementación de referencia.\n", + "\n", + "### Learning cycle: Predict → Run → Explain\n", + "\n", + "Before every exercise, predict what repeated application should do, run the update, and then explain what changed and why.\n", + "\n", + "> 🇪🇸 **Predice → Ejecuta → Explica:** antes de cada ejercicio anticipa qué debería hacer la aplicación repetida, ejecuta la actualización y explica qué cambió y por qué." + ], + "id": "enmPFvDxZhNF" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_HweadqaZhNG" + }, + "source": [ + "## Exercise 1 — turn a recurrence into a state update\n", + "\n", + "Fibonacci looks scalar:\n", + "\n", + "`f[n+1] = f[n] + f[n-1]`\n", + "\n", + "but it becomes a two-dimensional state:\n", + "\n", + "`[f[n+1], f[n]]ᵀ = F [f[n], f[n-1]]ᵀ`\n", + "\n", + "with\n", + "\n", + "`F = [[1, 1], [1, 0]]`.\n", + "\n", + "This is our cleanest example of **recursion as repeated matrix multiplication**.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Start from `[1, 0]`.\n", + "2. Apply the same matrix 10 times.\n", + "3. Compare the loop with `np.linalg.matrix_power`.\n", + "4. Move the **Steps / Pasos** slider and watch the state grow.\n", + "\n", + "> 🇪🇸 Fibonacci parece una recurrencia escalar, pero puede escribirse como un estado bidimensional actualizado siempre por la misma matriz. Prueba el `TODO`, compara el ciclo con `matrix_power` y usa el slider para observar cómo evoluciona el estado." + ], + "id": "_HweadqaZhNG" + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "GDonVFm-ZhNG" + }, + "outputs": [], + "source": [ + "# TODO\n", + "# 1. Define F = [[1, 1], [1, 0]] and v0 = [1, 0].\n", + "# 2. Apply F repeatedly for 10 steps.\n", + "# 3. Compare the loop result with np.linalg.matrix_power(F, 10) @ v0.\n", + "# 4. Predict which entry contains Fibonacci(10)." + ], + "id": "GDonVFm-ZhNG" + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "colab": { + "base_uri": "https://localhost:8080/", + "height": 426, + "referenced_widgets": [ + "f1abd382ef8948dc85623896de0b4145", + "6fd73fcbb8d24ab1997a0c34f82a2865", + "29cc84b86e0340428b21bfa0fe878b2f", + "e5f65c4e14624af79085fcbf09b1322e", + "8460ac0e378240f4a90f394b548fbbff", + "8e202737fdd24f998080a7e351c99d10", + "1d38e2b9d06e40c4b9dcae2c39f4620a" + ] + }, + "id": "3TCadKpPZhNH", + "outputId": "63158d68-2f3c-43dc-ec7f-eee048b0254f" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "loop / ciclo: [89 55]\n", + "matrix_power: [89 55]\n", + "Fibonacci(10): 55\n", + "same result / mismo resultado: True\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(IntSlider(value=10, continuous_update=False, description='Steps / Pasos:', max=25, min=1, style…" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "f1abd382ef8948dc85623896de0b4145" + } + }, + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "colab": { + "custom_widget_manager": { + "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" + } + } + } + } + } + ], + "source": [ + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "F = np.array([[1, 1], [1, 0]], dtype=object)\n", + "v0 = np.array([1, 0], dtype=object)\n", + "\n", + "v = v0.copy()\n", + "for _ in range(10):\n", + " v = F @ v\n", + "\n", + "via_power = np.linalg.matrix_power(F, 10) @ v0\n", + "\n", + "print(\"loop / ciclo:\", v)\n", + "print(\"matrix_power:\", via_power)\n", + "print(\"Fibonacci(10):\", int(v[1]))\n", + "print(\"same result / mismo resultado:\", np.array_equal(v, via_power))\n", + "\n", + "steps_slider = widgets.IntSlider(\n", + " value=10, min=1, max=25, step=1,\n", + " description=\"Steps / Pasos:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"100px\"},\n", + ")\n", + "\n", + "def explore_fibonacci(steps):\n", + " state = np.linalg.matrix_power(F, steps) @ v0\n", + " seq = []\n", + " s = v0.copy()\n", + " for k in range(steps + 1):\n", + " seq.append((k, int(s[0]), int(s[1])))\n", + " s = F @ s\n", + "\n", + " df = pd.DataFrame(seq, columns=[\"step\", \"f_next\", \"f_current\"])\n", + " print(\n", + " f\"step/paso={steps} | state/estado={state.tolist()} | \"\n", + " f\"Fibonacci({steps})={int(state[1])}\"\n", + " )\n", + "\n", + " fig = px.line(\n", + " df,\n", + " x=\"step\",\n", + " y=[\"f_next\", \"f_current\"],\n", + " markers=True,\n", + " title=\"Repeated state update / Actualización repetida del estado\",\n", + " labels={\"value\": \"state value / valor\", \"step\": \"step / paso\"},\n", + " )\n", + " fig.update_layout(\n", + " height=290,\n", + " width=620,\n", + " margin=dict(l=55, r=20, t=55, b=50),\n", + " legend_title_text=\"state / estado\",\n", + " )\n", + " fig.show()\n", + "\n", + "fib_output = widgets.interactive_output(\n", + " explore_fibonacci,\n", + " {\"steps\": steps_slider},\n", + ")\n", + "\n", + "display(widgets.VBox([steps_slider, fib_output]))" + ], + "id": "3TCadKpPZhNH" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vQSIq5gwZhNH" + }, + "source": [ + "## Exercise 2 — when repeated multiplication chooses a direction\n", + "\n", + "Power iteration uses the same recursive skeleton:\n", + "\n", + "`x[t+1] = A x[t]`, followed by normalization.\n", + "\n", + "For a suitable matrix, repeated multiplication tends to align the vector with the eigenvector associated with the largest-magnitude eigenvalue.\n", + "\n", + "Here we intentionally use a **small synthetic 2×2 matrix**. The goal is not to pretend it is real data; the controlled matrix lets us change the eigenvalue gap and isolate exactly what controls convergence speed.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Run power iteration for 1, 2, 5, 10 and 50 steps.\n", + "2. Compare the estimated direction with `np.linalg.eig`.\n", + "3. Use the **λ₂ / λ₁** slider to make the two eigenvalues closer.\n", + "4. Predict what happens as the ratio approaches 1.\n", + "\n", + "> 🇪🇸 Aquí usamos deliberadamente una matriz sintética `2×2` porque queremos aislar un mecanismo matemático: la velocidad de convergencia depende de qué tan dominante sea el autovalor principal. Acerca `λ₂/λ₁` a 1 y observa cómo la recursión tarda más en alinearse con la dirección dominante." + ], + "id": "vQSIq5gwZhNH" + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "0c5M2KsSZhNI" + }, + "outputs": [], + "source": [ + "# TODO\n", + "# 1. Implement power iteration with normalization after every multiplication.\n", + "# 2. Track the angle between the current vector and the dominant eigenvector.\n", + "# 3. Compare a matrix with a clear spectral gap against one whose eigenvalues\n", + "# are almost equal." + ], + "id": "0c5M2KsSZhNI" + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "colab": { + "base_uri": "https://localhost:8080/", + "height": 433, + "referenced_widgets": [ + "0132a07e0e3d42ee9a803d790fa64dab", + "daf0b9104c794fffb841948421df0968", + "1376dca24ec349ea871d201ddba907c8", + "d4dd3c9ead6c4c3ca24a1e6f1fc5dc76", + "6f1421ae3f024c948f476f9a58a803ec", + "bcd39a4cda2b484faa471afb91ca1b91", + "15800829ef5244fe8bf617fb454fa69d", + "2b013b6aa7c048ecb4ff129fa656d411", + "637ad86817de48328c5db67e4fc269fc", + "99a6b78cd0ac4494a4682024039196c9" + ] + }, + "id": "QNw9UFIjZhNI", + "outputId": "e4c99dad-fa97-4c3a-f4ff-c8875e187dfb" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(HTML(value='Spectral-gap explorer / Explorador de brecha espectral: move λ₂/λ₁ toward 1 …" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "0132a07e0e3d42ee9a803d790fa64dab" + } + }, + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "colab": { + "custom_widget_manager": { + "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" + } + } + } + } + } + ], + "source": [ + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "def power_trace(M, steps=40, seed=0):\n", + " eigvals, eigvecs = np.linalg.eig(M)\n", + " idx = int(np.argmax(np.abs(eigvals)))\n", + " dominant = eigvecs[:, idx].real\n", + " dominant /= np.linalg.norm(dominant)\n", + "\n", + " v = np.random.default_rng(seed).standard_normal(M.shape[0])\n", + " v /= np.linalg.norm(v)\n", + "\n", + " rows = []\n", + " for step in range(1, steps + 1):\n", + " v = M @ v\n", + " v /= np.linalg.norm(v)\n", + "\n", + " alignment = abs(float(np.dot(v, dominant)))\n", + " alignment = min(1.0, max(0.0, alignment))\n", + " angle = np.degrees(np.arccos(alignment))\n", + " rq = float(v @ M @ v)\n", + "\n", + " rows.append({\n", + " \"step\": step,\n", + " \"angle_deg\": angle,\n", + " \"rayleigh\": rq,\n", + " })\n", + "\n", + " return pd.DataFrame(rows), eigvals, dominant\n", + "\n", + "ratio_slider = widgets.FloatSlider(\n", + " value=0.40,\n", + " min=0.10,\n", + " max=0.99,\n", + " step=0.01,\n", + " description=\"λ₂ / λ₁:\",\n", + " continuous_update=False,\n", + " readout_format=\".2f\",\n", + " style={\"description_width\": \"80px\"},\n", + ")\n", + "\n", + "def explore_power_iteration(ratio):\n", + " # Controlled symmetric matrix with eigenvalues 5 and 5*ratio.\n", + " Q = np.array([\n", + " [np.cos(np.pi / 6), -np.sin(np.pi / 6)],\n", + " [np.sin(np.pi / 6), np.cos(np.pi / 6)],\n", + " ])\n", + " D = np.diag([5.0, 5.0 * ratio])\n", + " M = Q @ D @ Q.T\n", + "\n", + " trace, eigvals, dominant = power_trace(M, steps=40, seed=0)\n", + "\n", + " print(\"eigenvalues / autovalores:\", np.round(np.sort(eigvals)[::-1], 3))\n", + " print(\"ratio λ₂/λ₁:\", f\"{ratio:.2f}\")\n", + " print(\"final angle / ángulo final:\", f\"{trace['angle_deg'].iloc[-1]:.4f}°\")\n", + "\n", + " fig = px.line(\n", + " trace,\n", + " x=\"step\",\n", + " y=\"angle_deg\",\n", + " markers=True,\n", + " title=\"Angle to dominant eigenvector / Ángulo al autovector dominante\",\n", + " labels={\n", + " \"step\": \"iteration / iteración\",\n", + " \"angle_deg\": \"angle (degrees) / ángulo (grados)\",\n", + " },\n", + " )\n", + " fig.update_layout(\n", + " height=300,\n", + " width=620,\n", + " margin=dict(l=55, r=20, t=55, b=50),\n", + " )\n", + " fig.show()\n", + "\n", + "power_output = widgets.interactive_output(\n", + " explore_power_iteration,\n", + " {\"ratio\": ratio_slider},\n", + ")\n", + "\n", + "display(\n", + " widgets.VBox([\n", + " widgets.HTML(\n", + " \"Spectral-gap explorer / Explorador de brecha espectral: \"\n", + " \"move λ₂/λ₁ toward 1 and watch convergence slow down. / \"\n", + " \"acerca λ₂/λ₁ a 1 y observa cómo se ralentiza la convergencia.\"\n", + " ),\n", + " ratio_slider,\n", + " power_output,\n", + " ])\n", + ")" + ], + "id": "QNw9UFIjZhNI" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "McaT-XufZhNI" + }, + "source": [ + "## Exercise 3 — recursive forecasting on real airline traffic\n", + "\n", + "Now recursion becomes operational.\n", + "\n", + "The dataset contains **144 real monthly passenger counts from 1949 to 1960**. We hold out the final 12 months, fit an autoregressive model to the earlier observations, and then forecast recursively.\n", + "\n", + "For a window of length `p`, the model learns:\n", + "\n", + "`next month = bias + w₁·previous value + ... + wₚ·value p months ago`\n", + "\n", + "The weights come from the pseudoinverse, linking this notebook directly to Section 07.\n", + "\n", + "The crucial recursive step is this: after predicting one month, that prediction is appended to history and becomes an input for the next prediction.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Fit with `p = 12` and forecast the held-out 12 months.\n", + "2. Compare predictions with the real values the model never saw during fitting.\n", + "3. Move **Window / Ventana** from 3 to 24.\n", + "4. Move **Horizon / Horizonte** farther into the future and watch recursive uncertainty grow.\n", + "5. Explain why `p = 12` is meaningful for monthly seasonal data.\n", + "\n", + "> 🇪🇸 Ahora usamos 144 observaciones mensuales reales. Reservamos los últimos 12 meses, ajustamos un modelo autorregresivo con la pseudoinversa y pronosticamos de manera recursiva. Cada predicción pasa a formar parte de la historia usada para producir la siguiente. Cambia la ventana y el horizonte para observar cómo la estructura estacional y los errores se propagan." + ], + "id": "McaT-XufZhNI" + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "rppgxuRqZhNJ" + }, + "outputs": [], + "source": [ + "# TODO\n", + "# 1. Hold out the final 12 real months.\n", + "# 2. Fit an autoregressive model with p=12 using np.linalg.pinv.\n", + "# 3. Forecast the 12 held-out months recursively.\n", + "# 4. Compute MAPE.\n", + "# 5. Try p=3 and explain what seasonal information is lost." + ], + "id": "rppgxuRqZhNJ" + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "colab": { + "base_uri": "https://localhost:8080/", + "height": 495, + "referenced_widgets": [ + "fa02676fe93742d99e80dc111bcca688", + "345d2801dfbe4be88007a5ac1177d077", + "e919f45b91fa4ef5ac941f7343843b72", + "7051e0c10e7046fdb8b2a8487f2e5e07", + "785d1c37218a46128093a6c00a9e910a", + "7b7d992f2e9c476f8226061fe3f202be", + "8b1e42563cb548f1a65933d77714c8a7", + "bc117a3012a847dba2b92be39c38b737", + "6c5e98e04f9d4389819dc13d4985b35e", + "f5abb0241ed842e8a2bf5802023f34d9", + "f68be5cb6f6348a99e0b3c314bd2e20d", + "602fdb35b6ec48bf8df5c1ebcace87ed", + "e9496f42027f42289478d8e9cb42e178" + ] + }, + "id": "AJU4qS0EZhNJ", + "outputId": "1c982dd1-0b9b-4716-c064-9bfaf35182b2" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "train months / meses entrenamiento: 132\n", + "held-out months / meses reservados: 12\n", + "p=12 holdout MAPE: 3.1%\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(HTML(value='Recursive forecast explorer / Explorador de pronóstico recursivo: change mem…" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "fa02676fe93742d99e80dc111bcca688" + } + }, + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "colab": { + "custom_widget_manager": { + "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" + } + } + } + } + } + ], + "source": [ + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "def fit_ar(series, p):\n", + " rows = np.array(\n", + " [series[i:i+p] for i in range(len(series) - p)],\n", + " dtype=float,\n", + " )\n", + " X_ar = np.column_stack([np.ones(len(rows)), rows])\n", + " target = series[p:]\n", + " w_ar = np.linalg.pinv(X_ar) @ target\n", + " return w_ar\n", + "\n", + "def recursive_forecast(history, w_ar, p, steps):\n", + " hist = list(np.asarray(history, dtype=float))\n", + " out = []\n", + "\n", + " for _ in range(steps):\n", + " nxt = float(w_ar[0] + np.dot(w_ar[1:], hist[-p:]))\n", + " hist.append(nxt)\n", + " out.append(nxt)\n", + "\n", + " return np.asarray(out)\n", + "\n", + "def mape(actual, predicted):\n", + " actual = np.asarray(actual, dtype=float)\n", + " predicted = np.asarray(predicted, dtype=float)\n", + " return np.mean(np.abs(predicted - actual) / actual)\n", + "\n", + "holdout = 12\n", + "y_train = y[:-holdout]\n", + "y_test = y[-holdout:]\n", + "\n", + "w12 = fit_ar(y_train, 12)\n", + "pred12 = recursive_forecast(y_train, w12, 12, holdout)\n", + "\n", + "print(\"train months / meses entrenamiento:\", len(y_train))\n", + "print(\"held-out months / meses reservados:\", len(y_test))\n", + "print(\"p=12 holdout MAPE:\", f\"{mape(y_test, pred12):.1%}\")\n", + "\n", + "window_slider = widgets.IntSlider(\n", + " value=12, min=3, max=24, step=1,\n", + " description=\"Window / Ventana:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"115px\"},\n", + ")\n", + "\n", + "horizon_slider = widgets.IntSlider(\n", + " value=12, min=6, max=36, step=6,\n", + " description=\"Horizon / Horizonte:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"125px\"},\n", + ")\n", + "\n", + "def explore_recursive_forecast(window, horizon):\n", + " # Fit only on data before the final 12 real months.\n", + " train = y[:-12]\n", + " w_ar = fit_ar(train, window)\n", + "\n", + " # Start recursive forecast at the end of training.\n", + " pred = recursive_forecast(train, w_ar, window, horizon)\n", + "\n", + " start = len(train)\n", + " future_idx = np.arange(start, start + horizon)\n", + "\n", + " available_actual = y[start:min(start + horizon, len(y))]\n", + " actual_idx = np.arange(start, start + len(available_actual))\n", + "\n", + " fig = go.Figure()\n", + "\n", + " context_start = max(0, start - 36)\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=np.arange(context_start, start),\n", + " y=y[context_start:start],\n", + " mode=\"lines\",\n", + " name=\"training context / contexto\",\n", + " )\n", + " )\n", + "\n", + " if len(available_actual):\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=actual_idx,\n", + " y=available_actual,\n", + " mode=\"lines+markers\",\n", + " name=\"held-out actual / real reservado\",\n", + " )\n", + " )\n", + "\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=future_idx,\n", + " y=pred,\n", + " mode=\"lines+markers\",\n", + " name=\"recursive forecast / pronóstico recursivo\",\n", + " )\n", + " )\n", + "\n", + " fig.add_vline(x=start - 0.5, line_dash=\"dash\")\n", + "\n", + " fig.update_layout(\n", + " title=(\n", + " f\"Recursive forecast — window={window}, horizon={horizon} / \"\n", + " f\"ventana={window}, horizonte={horizon}\"\n", + " ),\n", + " xaxis_title=\"month index / índice mensual\",\n", + " yaxis_title=\"passengers / pasajeros\",\n", + " height=330,\n", + " width=680,\n", + " margin=dict(l=55, r=20, t=60, b=50),\n", + " legend=dict(orientation=\"h\", y=-0.23),\n", + " )\n", + " fig.show()\n", + "\n", + " comparable = min(len(available_actual), len(pred))\n", + " if comparable:\n", + " err = mape(available_actual[:comparable], pred[:comparable])\n", + " print(\n", + " f\"MAPE on {comparable} real held-out months / \"\n", + " f\"MAPE en {comparable} meses reales reservados: {err:.1%}\"\n", + " )\n", + "\n", + " if horizon > 12:\n", + " print(\n", + " \"EN: after month 12, the chart is beyond the dataset; \"\n", + " \"there is no real target here to validate against.\"\n", + " )\n", + " print(\n", + " \"ES: después del mes 12, el pronóstico queda fuera del conjunto; \"\n", + " \"no existe un valor real aquí para validarlo.\"\n", + " )\n", + "\n", + " print(\n", + " \"EN: every predicted month becomes an input to the next prediction.\"\n", + " )\n", + " print(\n", + " \"ES: cada mes predicho se convierte en entrada de la siguiente predicción.\"\n", + " )\n", + "\n", + "forecast_output = widgets.interactive_output(\n", + " explore_recursive_forecast,\n", + " {\n", + " \"window\": window_slider,\n", + " \"horizon\": horizon_slider,\n", + " },\n", + ")\n", + "\n", + "display(\n", + " widgets.VBox([\n", + " widgets.HTML(\n", + " \"Recursive forecast explorer / Explorador de pronóstico recursivo: \"\n", + " \"change memory length and forecast horizon. / \"\n", + " \"cambia la longitud de memoria y el horizonte.\"\n", + " ),\n", + " window_slider,\n", + " horizon_slider,\n", + " forecast_output,\n", + " ])\n", + ")" + ], + "id": "AJU4qS0EZhNJ" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "50T2ppHXZhNJ" + }, + "source": [ + "## What just happened\n", + "\n", + "You used the same idea — **reuse the current state to create the next state** — in three settings.\n", + "\n", + "1. **Fibonacci:** the state `[f[n], f[n-1]]` was updated by the same matrix at every step. `matrix_power` compressed many recursive updates into one matrix power.\n", + "2. **Power iteration:** repeated multiplication amplified the dominant eigendirection. When `λ₂/λ₁` moved closer to 1, convergence slowed because the dominant direction was less dominant.\n", + "3. **Real airline forecasting:** the pseudoinverse fitted one-step linear dynamics from real observations, then recursion fed predictions back into the model. That made long-horizon errors capable of compounding.\n", + "\n", + "### The sentence to remember\n", + "\n", + "> **Recursion is repeated state update: the next input contains the previous output.**\n", + "\n", + "That is also the skeleton behind recurrent neural networks: the same parameters are reused across sequence steps, while a hidden state carries information forward. Modern sequence models often use more elaborate mechanisms, but this state-update view is the essential bridge.\n", + "\n", + "> 🇪🇸 Usaste la misma idea — **reutilizar el estado actual para construir el siguiente** — en tres contextos. Fibonacci mostró la actualización matricial repetida; la iteración de potencias mostró cómo una dirección propia puede dominar; y el pronóstico real mostró cómo una predicción puede convertirse en entrada y propagar error.\n", + ">\n", + "> **Frase para recordar:** la recursión es una actualización repetida del estado: la siguiente entrada contiene la salida anterior.\n", + ">\n", + "> Esta es también la estructura básica de una red neuronal recurrente: los mismos parámetros se reutilizan a lo largo de la secuencia mientras un estado oculto transporta información hacia adelante." + ], + "id": "50T2ppHXZhNJ" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GyOmjAugZhNJ" + }, + "source": [ + "---\n", + "\n", + "## Done with this section\n", + "\n", + "Next up: **09 · Convolution and deconvolution** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/09-convolution-and-deconvolution.ipynb).\n", + "\n", + "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)\n", + "\n", + "> 🇪🇸 **Fin de esta sección.** A continuación: **09 · Convolución y deconvolución**." + ], + "id": "GyOmjAugZhNJ" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3" + }, + "colab": { + "provenance": [] + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "version_major": 2, + "version_minor": 0, + "state": { + "f1abd382ef8948dc85623896de0b4145": { + "model_module": "@jupyter-widgets/controls", + "model_name": "VBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "VBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "VBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_6fd73fcbb8d24ab1997a0c34f82a2865", + "IPY_MODEL_29cc84b86e0340428b21bfa0fe878b2f" + ], + "layout": "IPY_MODEL_e5f65c4e14624af79085fcbf09b1322e" + } + }, + "6fd73fcbb8d24ab1997a0c34f82a2865": { + "model_module": "@jupyter-widgets/controls", + "model_name": "IntSliderModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "IntSliderModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "IntSliderView", + "continuous_update": false, + "description": "Steps / Pasos:", + "description_tooltip": null, + "disabled": false, + "layout": "IPY_MODEL_8460ac0e378240f4a90f394b548fbbff", + "max": 25, + "min": 1, + "orientation": "horizontal", + "readout": true, + "readout_format": "d", + "step": 1, + "style": "IPY_MODEL_8e202737fdd24f998080a7e351c99d10", + "value": 6 + } + }, + "29cc84b86e0340428b21bfa0fe878b2f": { + "model_module": "@jupyter-widgets/output", + "model_name": "OutputModel", + "model_module_version": "1.0.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/output", + "_model_module_version": "1.0.0", + "_model_name": "OutputModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/output", + "_view_module_version": "1.0.0", + "_view_name": "OutputView", + "layout": "IPY_MODEL_1d38e2b9d06e40c4b9dcae2c39f4620a", + "msg_id": "", + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "step/paso=6 | state/estado=[13, 8] | Fibonacci(6)=8\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/html": "\n\n\n
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b/_variables.yml @@ -342,18 +342,20 @@ sections: format_es: "demostración" title_en: "Recursion with matrices and vectors" title_es: "Recursión con matrices y vectores" - summary_en: "Apply one matrix again and again: Fibonacci, eigenvectors, and a real forecast." - summary_es: "Aplicar una misma matriz una y otra vez: Fibonacci, autovectores y un pronóstico real." + summary_en: "Treat recursion as repeated state updates, connect repeated multiplication with dominant eigen-directions, and test recursive forecasting on real airline-passenger data." + summary_es: "Entender una recurrencia como una actualización de estado repetida, observar cuándo domina una dirección propia y comprobar qué ocurre cuando un pronóstico reutiliza sus propias predicciones." objectives_en: - - "Write a recurrence as repeated multiplication by one matrix." - - "Find the dominant eigenvector by power iteration, and check it against `np.linalg.eig`." - - "Fit an autoregressive model with the pseudoinverse and feed its own output back in." - - "Recognise that structure as the skeleton of a recurrent neural network." + - "Write a recurrence as a repeated state update `x[t+1] = A @ x[t]`." + - "Explain why repeated multiplication can align a state with a dominant eigenvector." + - "Use a controlled synthetic matrix to see how the eigenvalue ratio controls convergence speed." + - "Fit a real autoregressive model with the pseudoinverse and feed its own predictions back in." + - "Diagnose why recursive forecast error can compound with horizon." objectives_es: - - "Escribir una recurrencia como multiplicaciones repetidas por una misma matriz." - - "Encontrar el vector propio dominante mediante power iteration y comprobarlo con `np.linalg.eig`." - - "Ajustar un modelo autorregresivo con la pseudoinversa y reutilizar su propia salida como entrada." - - "Reconocer esa estructura como el esqueleto de una red neuronal recurrente." + - "Escribir una recurrencia como una actualización repetida del estado `x[t+1] = A @ x[t]`." + - "Explicar por qué las multiplicaciones repetidas pueden alinear un estado con un autovector dominante." + - "Usar una matriz sintética controlada para observar cómo la razón entre autovalores controla la velocidad de convergencia." + - "Ajustar un modelo autorregresivo real con la pseudoinversa y reutilizar sus propias predicciones como entradas." + - "Diagnosticar por qué el error de un pronóstico recursivo puede acumularse con el horizonte." s09: n: "09" slug: "convolution-and-deconvolution" diff --git a/docs/notebooks/08-recursion-with-matrices.ipynb b/docs/notebooks/08-recursion-with-matrices.ipynb index 0aa8f4e..1cf3b07 100644 --- a/docs/notebooks/08-recursion-with-matrices.ipynb +++ b/docs/notebooks/08-recursion-with-matrices.ipynb @@ -10,18 +10,19 @@ "\n", "*Part IV · demo · 10 min*\n", "\n", - "> 🇪🇸 **Recursión con matrices y vectores** — Aplicar una misma matriz una y otra vez: Fibonacci, autovectores y un pronóstico real.\n", + "> 🇪🇸 **Recursión con matrices y vectores** — Entender una recurrencia como una actualización de estado repetida, observar cuándo domina una dirección propia y comprobar qué ocurre cuando un pronóstico reutiliza sus propias predicciones.\n", "\n", - "Apply one matrix again and again: Fibonacci, eigenvectors, and a real forecast.\n", + "Treat recursion as repeated state updates, connect repeated multiplication with dominant eigen-directions, and test recursive forecasting on real airline-passenger data.\n", "\n", "## What you will be able to do\n", "\n", - "- Write a recurrence as repeated multiplication by one matrix.\n", - "- Find the dominant eigenvector by power iteration, and check it against `np.linalg.eig`.\n", - "- Fit an autoregressive model with the pseudoinverse and feed its own output back in.\n", - "- Recognise that structure as the skeleton of a recurrent neural network." + "- Write a recurrence as a repeated state update `x[t+1] = A @ x[t]`.\n", + "- Explain why repeated multiplication can align a state with a dominant eigenvector.\n", + "- Use a controlled synthetic matrix to see how the eigenvalue ratio controls convergence speed.\n", + "- Fit a real autoregressive model with the pseudoinverse and feed its own predictions back in.\n", + "- Diagnose why recursive forecast error can compound with horizon." ], - "id": "s08-00" + "id": "c2SUj_xUZhNB" }, { "cell_type": "markdown", @@ -33,7 +34,7 @@ "\n", "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." ], - "id": "s08-01" + "id": "xvXiblBeZhNE" }, { "cell_type": "code", @@ -43,143 +44,103 @@ "source": [ "import numpy as np\n", "import pandas as pd\n", + "import ipywidgets as widgets\n", + "import plotly.express as px\n", + "import plotly.graph_objects as go\n", + "from IPython.display import display\n", + "\n", + "# Enable ipywidgets in Google Colab when available.\n", + "try:\n", + " from google.colab import output\n", + " output.enable_custom_widget_manager()\n", + "except ImportError:\n", + " pass\n", "\n", "FLIGHTS = \"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/flights.csv\"\n", "flights = pd.read_csv(FLIGHTS)\n", "\n", + "y = flights[\"passengers\"].to_numpy(float)\n", + "labels = (\n", + " flights[\"year\"].astype(str)\n", + " + \"-\"\n", + " + flights[\"month\"].astype(str).str[:3]\n", + ").to_numpy()\n", + "\n", "rng = np.random.default_rng(0)\n", - "print(flights.shape) # (144, 3) — 144 real months, 1949-1960" + "\n", + "print(\"real months / meses reales:\", len(y))\n", + "print(\"range / periodo:\", labels[0], \"→\", labels[-1])\n", + "print(\"passengers min/max:\", int(y.min()), int(y.max()))\n", + "print(\"interactive charts: Plotly + ipywidgets enabled\")" ], - "id": "s08-02" + "id": "QIrgXPtMZhNE" }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Recursion means defining something in terms of itself\n", + "## Why this matters\n", "\n", - "> 🇪🇸 La recursión consiste en definir algo en términos de sí mismo. Con\n", - "> matrices, esto se convierte en aplicar la misma matriz una y otra vez.\n", + "A recurrence does not need a mysterious new operation. It can be as simple as reusing the **same update rule** over and over:\n", "\n", - "With matrices this becomes: **apply the same matrix again and again.** Three\n", - "examples, increasing in usefulness.\n", + "`x[t+1] = A x[t]`\n", "\n", - "This is a demo — read it, run it, ask about it. The exercises at the end are\n", - "short." - ], - "id": "s08-03" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1. Fibonacci as repeated matrix multiplication\n", + "The important idea is that the output at one step becomes the input to the next. That creates **state**.\n", "\n", - "The rule `f(n) = f(n-1) + f(n-2)` is one matrix applied repeatedly." - ], - "id": "s08-04" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "F = np.array([[1, 1], [1, 0]])\n", - "v = np.array([1, 0])\n", - "for _ in range(10):\n", - " v = F @ v\n", - "print(v[1]) # 55\n", - "print(np.linalg.matrix_power(F, 10)[0, 1]) # 55 — same answer, one step" - ], - "id": "s08-05" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2. Power iteration — recursion that finds an eigenvector\n", + "This same pattern appears in several places:\n", "\n", - "Multiply any starting vector by `A` repeatedly, rescaling each time. It\n", - "converges to the eigenvector with the largest eigenvalue (Chapter 2 §2.7)." - ], - "id": "s08-06" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "A = np.array([[4., 1.], [2., 3.]])\n", - "x = rng.standard_normal(2); x /= np.linalg.norm(x)\n", - "for _ in range(50):\n", - " x = A @ x\n", - " x /= np.linalg.norm(x)\n", - "\n", - "print(x @ A @ x) # 5.000000\n", - "print(np.linalg.eig(A)[0].max()) # 5.000000 — identical" - ], - "id": "s08-07" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "xv = rng.standard_normal(2); xv /= np.linalg.norm(xv)\n", - "checkpoints = {}\n", - "for step in range(1, 51):\n", - " xv = A @ xv\n", - " xv /= np.linalg.norm(xv)\n", - " if step in (1, 2, 5, 10, 50):\n", - " checkpoints[step] = xv.copy()\n", - "\n", - "eigvals, eigvecs = np.linalg.eig(A)\n", - "dominant = eigvecs[:, np.argmax(eigvals)].real\n", - "dominant /= np.linalg.norm(dominant)\n", - "\n", - "fig, ax = plt.subplots(figsize=(4, 4))\n", - "theta = np.linspace(0, 2 * np.pi, 200)\n", - "ax.plot(np.cos(theta), np.sin(theta), color=\"lightgray\", linewidth=1)\n", - "for step, v in checkpoints.items():\n", - " ax.annotate(\"\", xy=v, xytext=(0, 0), arrowprops=dict(\n", - " arrowstyle=\"->\", color=\"#4C72B0\", alpha=0.3 + 0.7 * step / 50))\n", - " ax.text(v[0] * 1.15, v[1] * 1.15, str(step), fontsize=8, ha=\"center\")\n", - "for sign in (1, -1):\n", - " ax.annotate(\"\", xy=sign * dominant, xytext=(0, 0),\n", - " arrowprops=dict(arrowstyle=\"->\", color=\"#C44E52\", linewidth=2))\n", - "ax.set_xlim(-1.3, 1.3); ax.set_ylim(-1.3, 1.3); ax.set_aspect(\"equal\")\n", - "ax.set_title(\"power iteration converges to the eigenvector\\n(red = the true dominant eigenvector, both signs)\")\n", - "plt.tight_layout()\n", - "plt.show()" - ], - "id": "s08-08" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is how PageRank ranks web pages, and it is why eigenvectors matter far\n", - "beyond Chapter 2: **repeated application of a matrix converges to its dominant\n", - "eigenvector.**" + "- a second-order recurrence such as Fibonacci can be rewritten as a matrix state update;\n", + "- power iteration repeatedly applies a matrix until one direction dominates;\n", + "- recursive forecasting predicts the next value, then feeds that prediction back as if it were observed.\n", + "\n", + "The mathematics is similar, but the consequences differ: repetition can reveal structure, or it can amplify error.\n", + "\n", + "### How to use the folded solutions / Cómo usar las soluciones plegadas\n", + "\n", + "Each exercise has a **Solution / Solución** cell that is intentionally closed. First try the `TODO`; then open the solution to compare your reasoning with a reference implementation.\n", + "\n", + "> 🇪🇸 Una recurrencia reutiliza la misma regla de actualización y convierte la salida de un paso en la entrada del siguiente. Ese patrón aparece en Fibonacci, en la iteración de potencias y en el pronóstico recursivo. La repetición puede revelar una dirección dominante, pero también puede propagar errores.\n", + ">\n", + "> Cada ejercicio tiene una celda **Solution / Solución** cerrada a propósito. Primero intenta resolver el `TODO`; después abre la solución para comparar tu razonamiento con una implementación de referencia.\n", + "\n", + "### Learning cycle: Predict → Run → Explain\n", + "\n", + "Before every exercise, predict what repeated application should do, run the update, and then explain what changed and why.\n", + "\n", + "> 🇪🇸 **Predice → Ejecuta → Explica:** antes de cada ejercicio anticipa qué debería hacer la aplicación repetida, ejecuta la actualización y explica qué cambió y por qué." ], - "id": "s08-09" + "id": "enmPFvDxZhNF" }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 3. Recursion on real data — forecasting airline traffic\n", + "## Exercise 1 — turn a recurrence into a state update\n", + "\n", + "Fibonacci looks scalar:\n", "\n", - "This combines recursion with the pseudoinverse from section 07. We fit a model\n", - "that predicts each month from the previous 12, then apply it *to its own output*\n", - "to forecast forward." + "`f[n+1] = f[n] + f[n-1]`\n", + "\n", + "but it becomes a two-dimensional state:\n", + "\n", + "`[f[n+1], f[n]]ᵀ = F [f[n], f[n-1]]ᵀ`\n", + "\n", + "with\n", + "\n", + "`F = [[1, 1], [1, 0]]`.\n", + "\n", + "This is our cleanest example of **recursion as repeated matrix multiplication**.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Start from `[1, 0]`.\n", + "2. Apply the same matrix 10 times.\n", + "3. Compare the loop with `np.linalg.matrix_power`.\n", + "4. Move the **Steps / Pasos** slider and watch the state grow.\n", + "\n", + "> 🇪🇸 Fibonacci parece una recurrencia escalar, pero puede escribirse como un estado bidimensional actualizado siempre por la misma matriz. Prueba el `TODO`, compara el ciclo con `matrix_power` y usa el slider para observar cómo evoluciona el estado." ], - "id": "s08-10" + "id": "_HweadqaZhNG" }, { "cell_type": "code", @@ -187,112 +148,114 @@ "metadata": {}, "outputs": [], "source": [ - "y = flights['passengers'].to_numpy(float) # 144 real months, 1949-1960\n", - "p = 12\n", - "rows = np.array([y[i:i+p] for i in range(len(y) - p)])\n", - "X = np.column_stack([np.ones(len(rows)), rows])\n", - "w = np.linalg.pinv(X) @ y[p:] # least squares, exactly as in section 07\n", - "print(X.shape) # (132, 13) — 132 training windows\n", - "\n", - "history = list(y[-p:])\n", - "for _ in range(12): # recursion: feed predictions back in\n", - " nxt = w[0] + np.dot(w[1:], history[-p:])\n", - " history.append(nxt)\n", - "\n", - "print(np.round(history[-12:], 1))\n", - "# [465.2 429.1 455.1 491.0 527.8 589.4 679.7 661.3 575.3 509.5 438.6 470.7]" + "# TODO\n", + "# 1. Define F = [[1, 1], [1, 0]] and v0 = [1, 0].\n", + "# 2. Apply F repeatedly for 10 steps.\n", + "# 3. Compare the loop result with np.linalg.matrix_power(F, 10) @ v0.\n", + "# 4. Predict which entry contains Fibonacci(10)." ], - "id": "s08-11" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The forecast above extrapolates 12 months **past the end of the dataset**, so\n", - "there is nothing to check it against. To see the forecast next to real numbers,\n", - "hold out the last 12 months, fit on everything before them, and forecast those\n", - "same 12 months back.\n", - "\n", - "> 🇪🇸 El pronóstico anterior se extiende 12 meses **más allá del final de los\n", - "> datos**, así que no hay nada real con qué compararlo. Para ver el pronóstico\n", - "> junto a números reales, se retienen los últimos 12 meses, se ajusta con todo\n", - "> lo anterior, y se pronostican esos mismos 12 meses." - ], - "id": "s08-12" + "id": "GDonVFm-ZhNG" }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, "outputs": [], "source": [ - "y_train, y_test = y[:-12], y[-12:]\n", - "rows_tr = np.array([y_train[i:i+p] for i in range(len(y_train) - p)])\n", - "X_tr = np.column_stack([np.ones(len(rows_tr)), rows_tr])\n", - "w_tr = np.linalg.pinv(X_tr) @ y_train[p:]\n", - "\n", - "hist_tr = list(y_train[-p:])\n", - "for _ in range(12):\n", - " hist_tr.append(w_tr[0] + np.dot(w_tr[1:], hist_tr[-p:]))\n", - "forecast_holdout = np.array(hist_tr[-12:])\n", - "\n", - "import matplotlib.pyplot as plt\n", - "months = np.arange(1, 13)\n", - "fig, ax = plt.subplots(figsize=(6, 3.2))\n", - "ax.plot(months, y_test, marker=\"o\", label=\"actual\", color=\"#4C72B0\")\n", - "ax.plot(months, forecast_holdout, marker=\"o\", label=\"forecast\", color=\"#C44E52\")\n", - "ax.set_xlabel(\"month (held out, never seen while fitting)\")\n", - "ax.set_ylabel(\"passengers\")\n", - "ax.set_title(\"forecast vs. actual — last 12 months held out\")\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "mape = (np.abs(forecast_holdout - y_test) / y_test).mean()\n", - "print(f\"mean absolute percentage error: {mape:.1%}\")" - ], - "id": "s08-13" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The forecast reproduces the seasonal shape of real air travel — low in winter,\n", - "peaking in summer — because the model learned it from 132 real training windows.\n", + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "F = np.array([[1, 1], [1, 0]], dtype=object)\n", + "v0 = np.array([1, 0], dtype=object)\n", "\n", - "**This is exactly the structure of a recurrent neural network**: a hidden state,\n", - "updated by the same weights at every step." - ], - "id": "s08-14" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# The same shape, with a nonlinearity. W and U are the SAME at every step —\n", - "# that is the recursion.\n", - "W = rng.standard_normal((4, 4)) * 0.5\n", - "U = rng.standard_normal((4, 3)) * 0.5\n", - "xs = rng.standard_normal((6, 3)) # a sequence of 6 inputs, 3 features each\n", - "\n", - "h = np.zeros(4)\n", - "for t in range(6):\n", - " h = np.tanh(W @ h + U @ xs[t])\n", - "print(np.round(h, 3))" + "v = v0.copy()\n", + "for _ in range(10):\n", + " v = F @ v\n", + "\n", + "via_power = np.linalg.matrix_power(F, 10) @ v0\n", + "\n", + "print(\"loop / ciclo:\", v)\n", + "print(\"matrix_power:\", via_power)\n", + "print(\"Fibonacci(10):\", int(v[1]))\n", + "print(\"same result / mismo resultado:\", np.array_equal(v, via_power))\n", + "\n", + "steps_slider = widgets.IntSlider(\n", + " value=10, min=1, max=25, step=1,\n", + " description=\"Steps / Pasos:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"100px\"},\n", + ")\n", + "\n", + "def explore_fibonacci(steps):\n", + " state = np.linalg.matrix_power(F, steps) @ v0\n", + " seq = []\n", + " s = v0.copy()\n", + " for k in range(steps + 1):\n", + " seq.append((k, int(s[0]), int(s[1])))\n", + " s = F @ s\n", + "\n", + " df = pd.DataFrame(seq, columns=[\"step\", \"f_next\", \"f_current\"])\n", + " print(\n", + " f\"step/paso={steps} | state/estado={state.tolist()} | \"\n", + " f\"Fibonacci({steps})={int(state[1])}\"\n", + " )\n", + "\n", + " fig = px.line(\n", + " df,\n", + " x=\"step\",\n", + " y=[\"f_next\", \"f_current\"],\n", + " markers=True,\n", + " title=\"Repeated state update / Actualización repetida del estado\",\n", + " labels={\"value\": \"state value / valor\", \"step\": \"step / paso\"},\n", + " )\n", + " fig.update_layout(\n", + " height=290,\n", + " width=620,\n", + " margin=dict(l=55, r=20, t=55, b=50),\n", + " legend_title_text=\"state / estado\",\n", + " )\n", + " fig.show()\n", + "\n", + "fib_output = widgets.interactive_output(\n", + " explore_fibonacci,\n", + " {\"steps\": steps_slider},\n", + ")\n", + "\n", + "display(widgets.VBox([steps_slider, fib_output]))" ], - "id": "s08-15" + "id": "3TCadKpPZhNH" }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercise 1 — the forecast, and what breaks it\n", + "## Exercise 2 — when repeated multiplication chooses a direction\n", + "\n", + "Power iteration uses the same recursive skeleton:\n", + "\n", + "`x[t+1] = A x[t]`, followed by normalization.\n", "\n", - "> 🇪🇸 El pronóstico, y qué lo rompe." + "For a suitable matrix, repeated multiplication tends to align the vector with the eigenvector associated with the largest-magnitude eigenvalue.\n", + "\n", + "Here we intentionally use a **small synthetic 2×2 matrix**. The goal is not to pretend it is real data; the controlled matrix lets us change the eigenvalue gap and isolate exactly what controls convergence speed.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Run power iteration for 1, 2, 5, 10 and 50 steps.\n", + "2. Compare the estimated direction with `np.linalg.eig`.\n", + "3. Use the **λ₂ / λ₁** slider to make the two eigenvalues closer.\n", + "4. Predict what happens as the ratio approaches 1.\n", + "\n", + "> 🇪🇸 Aquí usamos deliberadamente una matriz sintética `2×2` porque queremos aislar un mecanismo matemático: la velocidad de convergencia depende de qué tan dominante sea el autovalor principal. Acerca `λ₂/λ₁` a 1 y observa cómo la recursión tarda más en alinearse con la dirección dominante." ], - "id": "s08-16" + "id": "vQSIq5gwZhNH" }, { "cell_type": "code", @@ -300,64 +263,149 @@ "metadata": {}, "outputs": [], "source": [ - "# TODO 1: Change the window length p from 12 to 3 and re-run the forecast.\n", - "# The seasonal shape disappears. Why? What does p = 12 encode about\n", - "# this particular dataset that p = 3 cannot?\n", - "\n", - "# TODO 2: Forecast 60 months ahead instead of 12. Plot it if you can. Recursive\n", - "# forecasting feeds predictions back in as if they were observations —\n", - "# what does that do to the error over a long horizon?" + "# TODO\n", + "# 1. Implement power iteration with normalization after every multiplication.\n", + "# 2. Track the angle between the current vector and the dominant eigenvector.\n", + "# 3. Compare a matrix with a clear spectral gap against one whose eigenvalues\n", + "# are almost equal." ], - "id": "s08-17" + "id": "0c5M2KsSZhNI" }, { "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "def forecast(y, p, steps):\n", - " rows = np.array([y[i:i+p] for i in range(len(y) - p)])\n", - " X = np.column_stack([np.ones(len(rows)), rows])\n", - " w = np.linalg.pinv(X) @ y[p:]\n", - " hist = list(y[-p:])\n", - " for _ in range(steps):\n", - " hist.append(w[0] + np.dot(w[1:], hist[-p:]))\n", - " return np.array(hist[-steps:])\n", + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "def power_trace(M, steps=40, seed=0):\n", + " eigvals, eigvecs = np.linalg.eig(M)\n", + " idx = int(np.argmax(np.abs(eigvals)))\n", + " dominant = eigvecs[:, idx].real\n", + " dominant /= np.linalg.norm(dominant)\n", + "\n", + " v = np.random.default_rng(seed).standard_normal(M.shape[0])\n", + " v /= np.linalg.norm(v)\n", + "\n", + " rows = []\n", + " for step in range(1, steps + 1):\n", + " v = M @ v\n", + " v /= np.linalg.norm(v)\n", + "\n", + " alignment = abs(float(np.dot(v, dominant)))\n", + " alignment = min(1.0, max(0.0, alignment))\n", + " angle = np.degrees(np.arccos(alignment))\n", + " rq = float(v @ M @ v)\n", + "\n", + " rows.append({\n", + " \"step\": step,\n", + " \"angle_deg\": angle,\n", + " \"rayleigh\": rq,\n", + " })\n", + "\n", + " return pd.DataFrame(rows), eigvals, dominant\n", + "\n", + "ratio_slider = widgets.FloatSlider(\n", + " value=0.40,\n", + " min=0.10,\n", + " max=0.99,\n", + " step=0.01,\n", + " description=\"λ₂ / λ₁:\",\n", + " continuous_update=False,\n", + " readout_format=\".2f\",\n", + " style={\"description_width\": \"80px\"},\n", + ")\n", "\n", - "print(np.round(forecast(y, 12, 12), 1)) # seasonal: winter low, summer peak\n", - "print(np.round(forecast(y, 3, 12), 1)) # smooth, seasonality gone\n", + "def explore_power_iteration(ratio):\n", + " # Controlled symmetric matrix with eigenvalues 5 and 5*ratio.\n", + " Q = np.array([\n", + " [np.cos(np.pi / 6), -np.sin(np.pi / 6)],\n", + " [np.sin(np.pi / 6), np.cos(np.pi / 6)],\n", + " ])\n", + " D = np.diag([5.0, 5.0 * ratio])\n", + " M = Q @ D @ Q.T\n", "\n", - "# p = 12 encodes ONE YEAR. The model can see the same month a year earlier, so\n", - "# seasonality is available to it as a linear term. With p = 3 it can only see a\n", - "# local trend, and a linear model has no way to invent a yearly cycle.\n", + " trace, eigvals, dominant = power_trace(M, steps=40, seed=0)\n", "\n", - "print(np.round(forecast(y, 12, 60)[-6:], 1))\n", - "# Errors compound: every predicted month becomes an input to the next\n", - "# prediction, so mistakes feed on themselves. Recursive forecasts are trustworthy\n", - "# for a short horizon and decorative for a long one." + " print(\"eigenvalues / autovalores:\", np.round(np.sort(eigvals)[::-1], 3))\n", + " print(\"ratio λ₂/λ₁:\", f\"{ratio:.2f}\")\n", + " print(\"final angle / ángulo final:\", f\"{trace['angle_deg'].iloc[-1]:.4f}°\")\n", + "\n", + " fig = px.line(\n", + " trace,\n", + " x=\"step\",\n", + " y=\"angle_deg\",\n", + " markers=True,\n", + " title=\"Angle to dominant eigenvector / Ángulo al autovector dominante\",\n", + " labels={\n", + " \"step\": \"iteration / iteración\",\n", + " \"angle_deg\": \"angle (degrees) / ángulo (grados)\",\n", + " },\n", + " )\n", + " fig.update_layout(\n", + " height=300,\n", + " width=620,\n", + " margin=dict(l=55, r=20, t=55, b=50),\n", + " )\n", + " fig.show()\n", + "\n", + "power_output = widgets.interactive_output(\n", + " explore_power_iteration,\n", + " {\"ratio\": ratio_slider},\n", + ")\n", + "\n", + "display(\n", + " widgets.VBox([\n", + " widgets.HTML(\n", + " \"Spectral-gap explorer / Explorador de brecha espectral: \"\n", + " \"move λ₂/λ₁ toward 1 and watch convergence slow down. / \"\n", + " \"acerca λ₂/λ₁ a 1 y observa cómo se ralentiza la convergencia.\"\n", + " ),\n", + " ratio_slider,\n", + " power_output,\n", + " ])\n", + ")" ], - "id": "s08-18" + "id": "QNw9UFIjZhNI" }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercise 2 — power iteration by hand\n", + "## Exercise 3 — recursive forecasting on real airline traffic\n", + "\n", + "Now recursion becomes operational.\n", + "\n", + "The dataset contains **144 real monthly passenger counts from 1949 to 1960**. We hold out the final 12 months, fit an autoregressive model to the earlier observations, and then forecast recursively.\n", + "\n", + "For a window of length `p`, the model learns:\n", + "\n", + "`next month = bias + w₁·previous value + ... + wₚ·value p months ago`\n", + "\n", + "The weights come from the pseudoinverse, linking this notebook directly to Section 07.\n", + "\n", + "The crucial recursive step is this: after predicting one month, that prediction is appended to history and becomes an input for the next prediction.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Fit with `p = 12` and forecast the held-out 12 months.\n", + "2. Compare predictions with the real values the model never saw during fitting.\n", + "3. Move **Window / Ventana** from 3 to 24.\n", + "4. Move **Horizon / Horizonte** farther into the future and watch recursive uncertainty grow.\n", + "5. Explain why `p = 12` is meaningful for monthly seasonal data.\n", "\n", - "> 🇪🇸 Iteración de potencias, paso a paso." + "> 🇪🇸 Ahora usamos 144 observaciones mensuales reales. Reservamos los últimos 12 meses, ajustamos un modelo autorregresivo con la pseudoinversa y pronosticamos de manera recursiva. Cada predicción pasa a formar parte de la historia usada para producir la siguiente. Cambia la ventana y el horizonte para observar cómo la estructura estacional y los errores se propagan." ], - "id": "s08-19" + "id": "McaT-XufZhNI" }, { "cell_type": "code", @@ -365,53 +413,216 @@ "metadata": {}, "outputs": [], "source": [ - "# TODO 3: Run power iteration on A = [[4., 1.], [2., 3.]] but print the\n", - "# estimate after 1, 2, 5, 10 and 50 steps. How fast does it converge?\n", - "# Try a second matrix whose two eigenvalues are close together\n", - "# (e.g. [[4., 1.], [0., 3.9]]). What changes, and why?" + "# TODO\n", + "# 1. Hold out the final 12 real months.\n", + "# 2. Fit an autoregressive model with p=12 using np.linalg.pinv.\n", + "# 3. Forecast the 12 held-out months recursively.\n", + "# 4. Compute MAPE.\n", + "# 5. Try p=3 and explain what seasonal information is lost." ], - "id": "s08-20" + "id": "rppgxuRqZhNJ" }, { "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "def power_iterate(M, steps, seed=0):\n", - " v = np.random.default_rng(seed).standard_normal(M.shape[0])\n", - " v /= np.linalg.norm(v)\n", - " out = {}\n", - " for k in range(1, max(steps) + 1):\n", - " v = M @ v\n", - " v /= np.linalg.norm(v)\n", - " if k in steps:\n", - " out[k] = round(float(v @ M @ v), 4)\n", - " return out\n", + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "def fit_ar(series, p):\n", + " rows = np.array(\n", + " [series[i:i+p] for i in range(len(series) - p)],\n", + " dtype=float,\n", + " )\n", + " X_ar = np.column_stack([np.ones(len(rows)), rows])\n", + " target = series[p:]\n", + " w_ar = np.linalg.pinv(X_ar) @ target\n", + " return w_ar\n", + "\n", + "def recursive_forecast(history, w_ar, p, steps):\n", + " hist = list(np.asarray(history, dtype=float))\n", + " out = []\n", + "\n", + " for _ in range(steps):\n", + " nxt = float(w_ar[0] + np.dot(w_ar[1:], hist[-p:]))\n", + " hist.append(nxt)\n", + " out.append(nxt)\n", + "\n", + " return np.asarray(out)\n", + "\n", + "def mape(actual, predicted):\n", + " actual = np.asarray(actual, dtype=float)\n", + " predicted = np.asarray(predicted, dtype=float)\n", + " return np.mean(np.abs(predicted - actual) / actual)\n", + "\n", + "holdout = 12\n", + "y_train = y[:-holdout]\n", + "y_test = y[-holdout:]\n", + "\n", + "w12 = fit_ar(y_train, 12)\n", + "pred12 = recursive_forecast(y_train, w12, 12, holdout)\n", + "\n", + "print(\"train months / meses entrenamiento:\", len(y_train))\n", + "print(\"held-out months / meses reservados:\", len(y_test))\n", + "print(\"p=12 holdout MAPE:\", f\"{mape(y_test, pred12):.1%}\")\n", + "\n", + "window_slider = widgets.IntSlider(\n", + " value=12, min=3, max=24, step=1,\n", + " description=\"Window / Ventana:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"115px\"},\n", + ")\n", + "\n", + "horizon_slider = widgets.IntSlider(\n", + " value=12, min=6, max=36, step=6,\n", + " description=\"Horizon / Horizonte:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"125px\"},\n", + ")\n", + "\n", + "def explore_recursive_forecast(window, horizon):\n", + " # Fit only on data before the final 12 real months.\n", + " train = y[:-12]\n", + " w_ar = fit_ar(train, window)\n", + "\n", + " # Start recursive forecast at the end of training.\n", + " pred = recursive_forecast(train, w_ar, window, horizon)\n", + "\n", + " start = len(train)\n", + " future_idx = np.arange(start, start + horizon)\n", + "\n", + " available_actual = y[start:min(start + horizon, len(y))]\n", + " actual_idx = np.arange(start, start + len(available_actual))\n", + "\n", + " fig = go.Figure()\n", + "\n", + " context_start = max(0, start - 36)\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=np.arange(context_start, start),\n", + " y=y[context_start:start],\n", + " mode=\"lines\",\n", + " name=\"training context / contexto\",\n", + " )\n", + " )\n", + "\n", + " if len(available_actual):\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=actual_idx,\n", + " y=available_actual,\n", + " mode=\"lines+markers\",\n", + " name=\"held-out actual / real reservado\",\n", + " )\n", + " )\n", + "\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=future_idx,\n", + " y=pred,\n", + " mode=\"lines+markers\",\n", + " name=\"recursive forecast / pronóstico recursivo\",\n", + " )\n", + " )\n", "\n", - "A = np.array([[4., 1.], [2., 3.]]) # eigenvalues 5 and 2\n", - "A2 = np.array([[4., 1.], [0., 3.9]]) # eigenvalues 4 and 3.9\n", + " fig.add_vline(x=start - 0.5, line_dash=\"dash\")\n", "\n", - "print(power_iterate(A, [1, 2, 5, 10, 50]))\n", - "print(power_iterate(A2, [1, 2, 5, 10, 50]))\n", - "print(np.linalg.eigvals(A), np.linalg.eigvals(A2))\n", + " fig.update_layout(\n", + " title=(\n", + " f\"Recursive forecast — window={window}, horizon={horizon} / \"\n", + " f\"ventana={window}, horizonte={horizon}\"\n", + " ),\n", + " xaxis_title=\"month index / índice mensual\",\n", + " yaxis_title=\"passengers / pasajeros\",\n", + " height=330,\n", + " width=680,\n", + " margin=dict(l=55, r=20, t=60, b=50),\n", + " legend=dict(orientation=\"h\", y=-0.23),\n", + " )\n", + " fig.show()\n", "\n", - "# Convergence speed is set by the RATIO of the two largest eigenvalues. For A\n", - "# that ratio is 2/5, so each step shrinks the error to 40% of itself and ten\n", - "# steps are plenty. For A2 it is 3.9/4 = 0.975, and after 50 steps it is still\n", - "# arriving. Power iteration is fast exactly when one direction dominates." + " comparable = min(len(available_actual), len(pred))\n", + " if comparable:\n", + " err = mape(available_actual[:comparable], pred[:comparable])\n", + " print(\n", + " f\"MAPE on {comparable} real held-out months / \"\n", + " f\"MAPE en {comparable} meses reales reservados: {err:.1%}\"\n", + " )\n", + "\n", + " if horizon > 12:\n", + " print(\n", + " \"EN: after month 12, the chart is beyond the dataset; \"\n", + " \"there is no real target here to validate against.\"\n", + " )\n", + " print(\n", + " \"ES: después del mes 12, el pronóstico queda fuera del conjunto; \"\n", + " \"no existe un valor real aquí para validarlo.\"\n", + " )\n", + "\n", + " print(\n", + " \"EN: every predicted month becomes an input to the next prediction.\"\n", + " )\n", + " print(\n", + " \"ES: cada mes predicho se convierte en entrada de la siguiente predicción.\"\n", + " )\n", + "\n", + "forecast_output = widgets.interactive_output(\n", + " explore_recursive_forecast,\n", + " {\n", + " \"window\": window_slider,\n", + " \"horizon\": horizon_slider,\n", + " },\n", + ")\n", + "\n", + "display(\n", + " widgets.VBox([\n", + " widgets.HTML(\n", + " \"Recursive forecast explorer / Explorador de pronóstico recursivo: \"\n", + " \"change memory length and forecast horizon. / \"\n", + " \"cambia la longitud de memoria y el horizonte.\"\n", + " ),\n", + " window_slider,\n", + " horizon_slider,\n", + " forecast_output,\n", + " ])\n", + ")" ], - "id": "s08-21" + "id": "AJU4qS0EZhNJ" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What just happened\n", + "\n", + "You used the same idea — **reuse the current state to create the next state** — in three settings.\n", + "\n", + "1. **Fibonacci:** the state `[f[n], f[n-1]]` was updated by the same matrix at every step. `matrix_power` compressed many recursive updates into one matrix power.\n", + "2. **Power iteration:** repeated multiplication amplified the dominant eigendirection. When `λ₂/λ₁` moved closer to 1, convergence slowed because the dominant direction was less dominant.\n", + "3. **Real airline forecasting:** the pseudoinverse fitted one-step linear dynamics from real observations, then recursion fed predictions back into the model. That made long-horizon errors capable of compounding.\n", + "\n", + "### The sentence to remember\n", + "\n", + "> **Recursion is repeated state update: the next input contains the previous output.**\n", + "\n", + "That is also the skeleton behind recurrent neural networks: the same parameters are reused across sequence steps, while a hidden state carries information forward. Modern sequence models often use more elaborate mechanisms, but this state-update view is the essential bridge.\n", + "\n", + "> 🇪🇸 Usaste la misma idea — **reutilizar el estado actual para construir el siguiente** — en tres contextos. Fibonacci mostró la actualización matricial repetida; la iteración de potencias mostró cómo una dirección propia puede dominar; y el pronóstico real mostró cómo una predicción puede convertirse en entrada y propagar error.\n", + ">\n", + "> **Frase para recordar:** la recursión es una actualización repetida del estado: la siguiente entrada contiene la salida anterior.\n", + ">\n", + "> Esta es también la estructura básica de una red neuronal recurrente: los mismos parámetros se reutilizan a lo largo de la secuencia mientras un estado oculto transporta información hacia adelante." + ], + "id": "50T2ppHXZhNJ" }, { "cell_type": "markdown", @@ -425,22 +636,1072 @@ "\n", "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" ], - "id": "s08-22" + "id": "GyOmjAugZhNJ" } ], "metadata": { - "colab": { - "name": "08-recursion-with-matrices.ipynb", - "provenance": [], - "toc_visible": true - }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { - "name": "python" + "name": "python", + "version": "3" + }, + "colab": { + "provenance": [] + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "version_major": 2, + "version_minor": 0, + "state": { + "f1abd382ef8948dc85623896de0b4145": { + "model_module": "@jupyter-widgets/controls", + "model_name": "VBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "VBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "VBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_6fd73fcbb8d24ab1997a0c34f82a2865", + "IPY_MODEL_29cc84b86e0340428b21bfa0fe878b2f" + ], + "layout": "IPY_MODEL_e5f65c4e14624af79085fcbf09b1322e" + } + }, + "6fd73fcbb8d24ab1997a0c34f82a2865": { + "model_module": "@jupyter-widgets/controls", + "model_name": "IntSliderModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "IntSliderModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "IntSliderView", + "continuous_update": false, + "description": "Steps / Pasos:", + "description_tooltip": null, + "disabled": false, + "layout": "IPY_MODEL_8460ac0e378240f4a90f394b548fbbff", + "max": 25, + "min": 1, + "orientation": "horizontal", + "readout": true, + "readout_format": "d", + "step": 1, + "style": "IPY_MODEL_8e202737fdd24f998080a7e351c99d10", + "value": 6 + } + }, + "29cc84b86e0340428b21bfa0fe878b2f": { + "model_module": "@jupyter-widgets/output", + "model_name": "OutputModel", + "model_module_version": "1.0.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/output", + "_model_module_version": "1.0.0", + "_model_name": "OutputModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/output", + "_view_module_version": "1.0.0", + "_view_name": "OutputView", + "layout": "IPY_MODEL_1d38e2b9d06e40c4b9dcae2c39f4620a", + "msg_id": "", + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "step/paso=6 | state/estado=[13, 8] | Fibonacci(6)=8\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/html": "\n\n\n
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**Recursión con matrices y vectores** — Entender una recurrencia como una actualización de estado repetida, observar cuándo domina una dirección propia y comprobar qué ocurre cuando un pronóstico reutiliza sus propias predicciones.\n", - "\n", - "Treat recursion as repeated state updates, connect repeated multiplication with dominant eigen-directions, and test recursive forecasting on real airline-passenger data.\n", - "\n", - "## What you will be able to do\n", - "\n", - "- Write a recurrence as a repeated state update `x[t+1] = A @ x[t]`.\n", - "- Explain why repeated multiplication can align a state with a dominant eigenvector.\n", - "- Use a controlled synthetic matrix to see how the eigenvalue ratio controls convergence speed.\n", - "- Fit a real autoregressive model with the pseudoinverse and feed its own predictions back in.\n", - "- Diagnose why recursive forecast error can compound with horizon.\n", - "\n", - "> 🇪🇸 **Al terminar podrás:** escribir una recurrencia como `x[t+1] = A @ x[t]`; explicar por qué las multiplicaciones repetidas pueden alinear el estado con un autovector dominante; observar cómo la razón entre autovalores controla la velocidad de convergencia; ajustar un modelo autorregresivo con datos reales usando la pseudoinversa; y diagnosticar por qué el error puede acumularse cuando las predicciones se reutilizan como entradas." - ], - "id": "c2SUj_xUZhNB" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "xvXiblBeZhNE" - }, - "source": [ - "## Setup\n", - "\n", - "Run this cell first. It loads the real monthly airline-passenger dataset and the small visualization tools used below.\n", - "\n", - "> 🇪🇸 Ejecuta primero esta celda. Carga el conjunto real de pasajeros mensuales de aerolíneas y las herramientas pequeñas de visualización que utilizaremos." - ], - "id": "xvXiblBeZhNE" - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "QIrgXPtMZhNE", - "outputId": "faa44d1a-9aac-4c25-c78d-b6a995526354" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "real months / meses reales: 144\n", - "range / periodo: 1949-Jan → 1960-Dec\n", - "passengers min/max: 104 622\n", - "interactive charts: Plotly + ipywidgets enabled\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import ipywidgets as widgets\n", - "import plotly.express as px\n", - "import plotly.graph_objects as go\n", - "from IPython.display import display\n", - "\n", - "try:\n", - " from google.colab import output\n", - " output.enable_custom_widget_manager()\n", - "except ImportError:\n", - " pass\n", - "\n", - "FLIGHTS = \"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/flights.csv\"\n", - "flights = pd.read_csv(FLIGHTS)\n", - "\n", - "y = flights[\"passengers\"].to_numpy(float)\n", - "labels = (\n", - " flights[\"year\"].astype(str)\n", - " + \"-\"\n", - " + flights[\"month\"].astype(str).str[:3]\n", - ").to_numpy()\n", - "\n", - "rng = np.random.default_rng(0)\n", - "\n", - "print(\"real months / meses reales:\", len(y))\n", - "print(\"range / periodo:\", labels[0], \"→\", labels[-1])\n", - "print(\"passengers min/max:\", int(y.min()), int(y.max()))\n", - "print(\"interactive charts: Plotly + ipywidgets enabled\")" - ], - "id": "QIrgXPtMZhNE" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "enmPFvDxZhNF" - }, - "source": [ - "## Why this matters\n", - "\n", - "A recurrence does not need a mysterious new operation. It can be as simple as reusing the **same update rule** over and over:\n", - "\n", - "`x[t+1] = A x[t]`\n", - "\n", - "The important idea is that the output at one step becomes the input to the next. That creates **state**.\n", - "\n", - "This same pattern appears in several places:\n", - "\n", - "- a second-order recurrence such as Fibonacci can be rewritten as a matrix state update;\n", - "- power iteration repeatedly applies a matrix until one direction dominates;\n", - "- recursive forecasting predicts the next value, then feeds that prediction back as if it were observed.\n", - "\n", - "The mathematics is similar, but the consequences differ: repetition can reveal structure, or it can amplify error.\n", - "\n", - "### How to use the folded solutions / Cómo usar las soluciones plegadas\n", - "\n", - "Each exercise has a **Solution / Solución** cell that is intentionally closed. First try the `TODO`; then open the solution to compare your reasoning with a reference implementation.\n", - "\n", - "> 🇪🇸 Una recurrencia reutiliza la misma regla de actualización y convierte la salida de un paso en la entrada del siguiente. Ese patrón aparece en Fibonacci, en la iteración de potencias y en el pronóstico recursivo. La repetición puede revelar una dirección dominante, pero también puede propagar errores.\n", - ">\n", - "> Cada ejercicio tiene una celda **Solution / Solución** cerrada a propósito. Primero intenta resolver el `TODO`; después abre la solución para comparar tu razonamiento con una implementación de referencia.\n", - "\n", - "### Learning cycle: Predict → Run → Explain\n", - "\n", - "Before every exercise, predict what repeated application should do, run the update, and then explain what changed and why.\n", - "\n", - "> 🇪🇸 **Predice → Ejecuta → Explica:** antes de cada ejercicio anticipa qué debería hacer la aplicación repetida, ejecuta la actualización y explica qué cambió y por qué." - ], - "id": "enmPFvDxZhNF" + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 08 · Recursion with matrices and vectors\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/08-recursion-with-matrices.ipynb)\n", + "\n", + "*Part IV · demo · 10 min*\n", + "\n", + "> 🇪🇸 **Recursión con matrices y vectores** — Entender una recurrencia como una actualización de estado repetida, observar cuándo domina una dirección propia y comprobar qué ocurre cuando un pronóstico reutiliza sus propias predicciones.\n", + "\n", + "Treat recursion as repeated state updates, connect repeated multiplication with dominant eigen-directions, and test recursive forecasting on real airline-passenger data.\n", + "\n", + "## What you will be able to do\n", + "\n", + "- Write a recurrence as a repeated state update `x[t+1] = A @ x[t]`.\n", + "- Explain why repeated multiplication can align a state with a dominant eigenvector.\n", + "- Use a controlled synthetic matrix to see how the eigenvalue ratio controls convergence speed.\n", + "- Fit a real autoregressive model with the pseudoinverse and feed its own predictions back in.\n", + "- Diagnose why recursive forecast error can compound with horizon." + ], + "id": "c2SUj_xUZhNB" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "Run this first. It installs and imports everything this notebook needs, and nothing else.\n", + "\n", + "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." + ], + "id": "xvXiblBeZhNE" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import ipywidgets as widgets\n", + "import plotly.express as px\n", + "import plotly.graph_objects as go\n", + "from IPython.display import display\n", + "\n", + "# Enable ipywidgets in Google Colab when available.\n", + "try:\n", + " from google.colab import output\n", + " output.enable_custom_widget_manager()\n", + "except ImportError:\n", + " pass\n", + "\n", + "FLIGHTS = \"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/flights.csv\"\n", + "flights = pd.read_csv(FLIGHTS)\n", + "\n", + "y = flights[\"passengers\"].to_numpy(float)\n", + "labels = (\n", + " flights[\"year\"].astype(str)\n", + " + \"-\"\n", + " + flights[\"month\"].astype(str).str[:3]\n", + ").to_numpy()\n", + "\n", + "rng = np.random.default_rng(0)\n", + "\n", + "print(\"real months / meses reales:\", len(y))\n", + "print(\"range / periodo:\", labels[0], \"→\", labels[-1])\n", + "print(\"passengers min/max:\", int(y.min()), int(y.max()))\n", + "print(\"interactive charts: Plotly + ipywidgets enabled\")" + ], + "id": "QIrgXPtMZhNE" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Why this matters\n", + "\n", + "A recurrence does not need a mysterious new operation. It can be as simple as reusing the **same update rule** over and over:\n", + "\n", + "`x[t+1] = A x[t]`\n", + "\n", + "The important idea is that the output at one step becomes the input to the next. That creates **state**.\n", + "\n", + "This same pattern appears in several places:\n", + "\n", + "- a second-order recurrence such as Fibonacci can be rewritten as a matrix state update;\n", + "- power iteration repeatedly applies a matrix until one direction dominates;\n", + "- recursive forecasting predicts the next value, then feeds that prediction back as if it were observed.\n", + "\n", + "The mathematics is similar, but the consequences differ: repetition can reveal structure, or it can amplify error.\n", + "\n", + "### How to use the folded solutions / Cómo usar las soluciones plegadas\n", + "\n", + "Each exercise has a **Solution / Solución** cell that is intentionally closed. First try the `TODO`; then open the solution to compare your reasoning with a reference implementation.\n", + "\n", + "> 🇪🇸 Una recurrencia reutiliza la misma regla de actualización y convierte la salida de un paso en la entrada del siguiente. Ese patrón aparece en Fibonacci, en la iteración de potencias y en el pronóstico recursivo. La repetición puede revelar una dirección dominante, pero también puede propagar errores.\n", + ">\n", + "> Cada ejercicio tiene una celda **Solution / Solución** cerrada a propósito. Primero intenta resolver el `TODO`; después abre la solución para comparar tu razonamiento con una implementación de referencia.\n", + "\n", + "### Learning cycle: Predict → Run → Explain\n", + "\n", + "Before every exercise, predict what repeated application should do, run the update, and then explain what changed and why.\n", + "\n", + "> 🇪🇸 **Predice → Ejecuta → Explica:** antes de cada ejercicio anticipa qué debería hacer la aplicación repetida, ejecuta la actualización y explica qué cambió y por qué." + ], + "id": "enmPFvDxZhNF" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 1 — turn a recurrence into a state update\n", + "\n", + "Fibonacci looks scalar:\n", + "\n", + "`f[n+1] = f[n] + f[n-1]`\n", + "\n", + "but it becomes a two-dimensional state:\n", + "\n", + "`[f[n+1], f[n]]ᵀ = F [f[n], f[n-1]]ᵀ`\n", + "\n", + "with\n", + "\n", + "`F = [[1, 1], [1, 0]]`.\n", + "\n", + "This is our cleanest example of **recursion as repeated matrix multiplication**.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Start from `[1, 0]`.\n", + "2. Apply the same matrix 10 times.\n", + "3. Compare the loop with `np.linalg.matrix_power`.\n", + "4. Move the **Steps / Pasos** slider and watch the state grow.\n", + "\n", + "> 🇪🇸 Fibonacci parece una recurrencia escalar, pero puede escribirse como un estado bidimensional actualizado siempre por la misma matriz. Prueba el `TODO`, compara el ciclo con `matrix_power` y usa el slider para observar cómo evoluciona el estado." + ], + "id": "_HweadqaZhNG" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO\n", + "# 1. Define F = [[1, 1], [1, 0]] and v0 = [1, 0].\n", + "# 2. Apply F repeatedly for 10 steps.\n", + "# 3. Compare the loop result with np.linalg.matrix_power(F, 10) @ v0.\n", + "# 4. Predict which entry contains Fibonacci(10)." + ], + "id": "GDonVFm-ZhNG" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true }, - { - "cell_type": "markdown", - "metadata": { - "id": "_HweadqaZhNG" - }, - "source": [ - "## Exercise 1 — turn a recurrence into a state update\n", - "\n", - "Fibonacci looks scalar:\n", - "\n", - "`f[n+1] = f[n] + f[n-1]`\n", - "\n", - "but it becomes a two-dimensional state:\n", - "\n", - "`[f[n+1], f[n]]ᵀ = F [f[n], f[n-1]]ᵀ`\n", - "\n", - "with\n", - "\n", - "`F = [[1, 1], [1, 0]]`.\n", - "\n", - "This is our cleanest example of **recursion as repeated matrix multiplication**.\n", - "\n", - "### What should you try?\n", - "\n", - "1. Start from `[1, 0]`.\n", - "2. Apply the same matrix 10 times.\n", - "3. Compare the loop with `np.linalg.matrix_power`.\n", - "4. Move the **Steps / Pasos** slider and watch the state grow.\n", - "\n", - "> 🇪🇸 Fibonacci parece una recurrencia escalar, pero puede escribirse como un estado bidimensional actualizado siempre por la misma matriz. Prueba el `TODO`, compara el ciclo con `matrix_power` y usa el slider para observar cómo evoluciona el estado." - ], - "id": "_HweadqaZhNG" + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "F = np.array([[1, 1], [1, 0]], dtype=object)\n", + "v0 = np.array([1, 0], dtype=object)\n", + "\n", + "v = v0.copy()\n", + "for _ in range(10):\n", + " v = F @ v\n", + "\n", + "via_power = np.linalg.matrix_power(F, 10) @ v0\n", + "\n", + "print(\"loop / ciclo:\", v)\n", + "print(\"matrix_power:\", via_power)\n", + "print(\"Fibonacci(10):\", int(v[1]))\n", + "print(\"same result / mismo resultado:\", np.array_equal(v, via_power))\n", + "\n", + "steps_slider = widgets.IntSlider(\n", + " value=10, min=1, max=25, step=1,\n", + " description=\"Steps / Pasos:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"100px\"},\n", + ")\n", + "\n", + "def explore_fibonacci(steps):\n", + " state = np.linalg.matrix_power(F, steps) @ v0\n", + " seq = []\n", + " s = v0.copy()\n", + " for k in range(steps + 1):\n", + " seq.append((k, int(s[0]), int(s[1])))\n", + " s = F @ s\n", + "\n", + " df = pd.DataFrame(seq, columns=[\"step\", \"f_next\", \"f_current\"])\n", + " print(\n", + " f\"step/paso={steps} | state/estado={state.tolist()} | \"\n", + " f\"Fibonacci({steps})={int(state[1])}\"\n", + " )\n", + "\n", + " fig = px.line(\n", + " df,\n", + " x=\"step\",\n", + " y=[\"f_next\", \"f_current\"],\n", + " markers=True,\n", + " title=\"Repeated state update / Actualización repetida del estado\",\n", + " labels={\"value\": \"state value / valor\", \"step\": \"step / paso\"},\n", + " )\n", + " fig.update_layout(\n", + " height=290,\n", + " width=620,\n", + " margin=dict(l=55, r=20, t=55, b=50),\n", + " legend_title_text=\"state / estado\",\n", + " )\n", + " fig.show()\n", + "\n", + "fib_output = widgets.interactive_output(\n", + " explore_fibonacci,\n", + " {\"steps\": steps_slider},\n", + ")\n", + "\n", + "display(widgets.VBox([steps_slider, fib_output]))" + ], + "id": "3TCadKpPZhNH" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 2 — when repeated multiplication chooses a direction\n", + "\n", + "Power iteration uses the same recursive skeleton:\n", + "\n", + "`x[t+1] = A x[t]`, followed by normalization.\n", + "\n", + "For a suitable matrix, repeated multiplication tends to align the vector with the eigenvector associated with the largest-magnitude eigenvalue.\n", + "\n", + "Here we intentionally use a **small synthetic 2×2 matrix**. The goal is not to pretend it is real data; the controlled matrix lets us change the eigenvalue gap and isolate exactly what controls convergence speed.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Run power iteration for 1, 2, 5, 10 and 50 steps.\n", + "2. Compare the estimated direction with `np.linalg.eig`.\n", + "3. Use the **λ₂ / λ₁** slider to make the two eigenvalues closer.\n", + "4. Predict what happens as the ratio approaches 1.\n", + "\n", + "> 🇪🇸 Aquí usamos deliberadamente una matriz sintética `2×2` porque queremos aislar un mecanismo matemático: la velocidad de convergencia depende de qué tan dominante sea el autovalor principal. Acerca `λ₂/λ₁` a 1 y observa cómo la recursión tarda más en alinearse con la dirección dominante." + ], + "id": "vQSIq5gwZhNH" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO\n", + "# 1. Implement power iteration with normalization after every multiplication.\n", + "# 2. Track the angle between the current vector and the dominant eigenvector.\n", + "# 3. Compare a matrix with a clear spectral gap against one whose eigenvalues\n", + "# are almost equal." + ], + "id": "0c5M2KsSZhNI" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "id": "GDonVFm-ZhNG" - }, - "outputs": [], - "source": [ - "# TODO\n", - "# 1. Define F = [[1, 1], [1, 0]] and v0 = [1, 0].\n", - "# 2. Apply F repeatedly for 10 steps.\n", - "# 3. Compare the loop result with np.linalg.matrix_power(F, 10) @ v0.\n", - "# 4. Predict which entry contains Fibonacci(10)." - ], - "id": "GDonVFm-ZhNG" + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "def power_trace(M, steps=40, seed=0):\n", + " eigvals, eigvecs = np.linalg.eig(M)\n", + " idx = int(np.argmax(np.abs(eigvals)))\n", + " dominant = eigvecs[:, idx].real\n", + " dominant /= np.linalg.norm(dominant)\n", + "\n", + " v = np.random.default_rng(seed).standard_normal(M.shape[0])\n", + " v /= np.linalg.norm(v)\n", + "\n", + " rows = []\n", + " for step in range(1, steps + 1):\n", + " v = M @ v\n", + " v /= np.linalg.norm(v)\n", + "\n", + " alignment = abs(float(np.dot(v, dominant)))\n", + " alignment = min(1.0, max(0.0, alignment))\n", + " angle = np.degrees(np.arccos(alignment))\n", + " rq = float(v @ M @ v)\n", + "\n", + " rows.append({\n", + " \"step\": step,\n", + " \"angle_deg\": angle,\n", + " \"rayleigh\": rq,\n", + " })\n", + "\n", + " return pd.DataFrame(rows), eigvals, dominant\n", + "\n", + "ratio_slider = widgets.FloatSlider(\n", + " value=0.40,\n", + " min=0.10,\n", + " max=0.99,\n", + " step=0.01,\n", + " description=\"λ₂ / λ₁:\",\n", + " continuous_update=False,\n", + " readout_format=\".2f\",\n", + " style={\"description_width\": \"80px\"},\n", + ")\n", + "\n", + "def explore_power_iteration(ratio):\n", + " # Controlled symmetric matrix with eigenvalues 5 and 5*ratio.\n", + " Q = np.array([\n", + " [np.cos(np.pi / 6), -np.sin(np.pi / 6)],\n", + " [np.sin(np.pi / 6), np.cos(np.pi / 6)],\n", + " ])\n", + " D = np.diag([5.0, 5.0 * ratio])\n", + " M = Q @ D @ Q.T\n", + "\n", + " trace, eigvals, dominant = power_trace(M, steps=40, seed=0)\n", + "\n", + " print(\"eigenvalues / autovalores:\", np.round(np.sort(eigvals)[::-1], 3))\n", + " print(\"ratio λ₂/λ₁:\", f\"{ratio:.2f}\")\n", + " print(\"final angle / ángulo final:\", f\"{trace['angle_deg'].iloc[-1]:.4f}°\")\n", + "\n", + " fig = px.line(\n", + " trace,\n", + " x=\"step\",\n", + " y=\"angle_deg\",\n", + " markers=True,\n", + " title=\"Angle to dominant eigenvector / Ángulo al autovector dominante\",\n", + " labels={\n", + " \"step\": \"iteration / iteración\",\n", + " \"angle_deg\": \"angle (degrees) / ángulo (grados)\",\n", + " },\n", + " )\n", + " fig.update_layout(\n", + " height=300,\n", + " width=620,\n", + " margin=dict(l=55, r=20, t=55, b=50),\n", + " )\n", + " fig.show()\n", + "\n", + "power_output = widgets.interactive_output(\n", + " explore_power_iteration,\n", + " {\"ratio\": ratio_slider},\n", + ")\n", + "\n", + "display(\n", + " widgets.VBox([\n", + " widgets.HTML(\n", + " \"Spectral-gap explorer / Explorador de brecha espectral: \"\n", + " \"move λ₂/λ₁ toward 1 and watch convergence slow down. / \"\n", + " \"acerca λ₂/λ₁ a 1 y observa cómo se ralentiza la convergencia.\"\n", + " ),\n", + " ratio_slider,\n", + " power_output,\n", + " ])\n", + ")" + ], + "id": "QNw9UFIjZhNI" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 3 — recursive forecasting on real airline traffic\n", + "\n", + "Now recursion becomes operational.\n", + "\n", + "The dataset contains **144 real monthly passenger counts from 1949 to 1960**. We hold out the final 12 months, fit an autoregressive model to the earlier observations, and then forecast recursively.\n", + "\n", + "For a window of length `p`, the model learns:\n", + "\n", + "`next month = bias + w₁·previous value + ... + wₚ·value p months ago`\n", + "\n", + "The weights come from the pseudoinverse, linking this notebook directly to Section 07.\n", + "\n", + "The crucial recursive step is this: after predicting one month, that prediction is appended to history and becomes an input for the next prediction.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Fit with `p = 12` and forecast the held-out 12 months.\n", + "2. Compare predictions with the real values the model never saw during fitting.\n", + "3. Move **Window / Ventana** from 3 to 24.\n", + "4. Move **Horizon / Horizonte** farther into the future and watch recursive uncertainty grow.\n", + "5. Explain why `p = 12` is meaningful for monthly seasonal data.\n", + "\n", + "> 🇪🇸 Ahora usamos 144 observaciones mensuales reales. Reservamos los últimos 12 meses, ajustamos un modelo autorregresivo con la pseudoinversa y pronosticamos de manera recursiva. Cada predicción pasa a formar parte de la historia usada para producir la siguiente. Cambia la ventana y el horizonte para observar cómo la estructura estacional y los errores se propagan." + ], + "id": "McaT-XufZhNI" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO\n", + "# 1. Hold out the final 12 real months.\n", + "# 2. Fit an autoregressive model with p=12 using np.linalg.pinv.\n", + "# 3. Forecast the 12 held-out months recursively.\n", + "# 4. Compute MAPE.\n", + "# 5. Try p=3 and explain what seasonal information is lost." + ], + "id": "rppgxuRqZhNJ" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ], - "colab": { - "base_uri": "https://localhost:8080/", - "height": 426, - "referenced_widgets": [ - "f1abd382ef8948dc85623896de0b4145", - "6fd73fcbb8d24ab1997a0c34f82a2865", - "29cc84b86e0340428b21bfa0fe878b2f", - "e5f65c4e14624af79085fcbf09b1322e", - "8460ac0e378240f4a90f394b548fbbff", - "8e202737fdd24f998080a7e351c99d10", - "1d38e2b9d06e40c4b9dcae2c39f4620a" - ] - }, - "id": "3TCadKpPZhNH", - "outputId": "63158d68-2f3c-43dc-ec7f-eee048b0254f" - }, - "outputs": [ + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "def fit_ar(series, p):\n", + " rows = np.array(\n", + " [series[i:i+p] for i in range(len(series) - p)],\n", + " dtype=float,\n", + " )\n", + " X_ar = np.column_stack([np.ones(len(rows)), rows])\n", + " target = series[p:]\n", + " w_ar = np.linalg.pinv(X_ar) @ target\n", + " return w_ar\n", + "\n", + "def recursive_forecast(history, w_ar, p, steps):\n", + " hist = list(np.asarray(history, dtype=float))\n", + " out = []\n", + "\n", + " for _ in range(steps):\n", + " nxt = float(w_ar[0] + np.dot(w_ar[1:], hist[-p:]))\n", + " hist.append(nxt)\n", + " out.append(nxt)\n", + "\n", + " return np.asarray(out)\n", + "\n", + "def mape(actual, predicted):\n", + " actual = np.asarray(actual, dtype=float)\n", + " predicted = np.asarray(predicted, dtype=float)\n", + " return np.mean(np.abs(predicted - actual) / actual)\n", + "\n", + "holdout = 12\n", + "y_train = y[:-holdout]\n", + "y_test = y[-holdout:]\n", + "\n", + "w12 = fit_ar(y_train, 12)\n", + "pred12 = recursive_forecast(y_train, w12, 12, holdout)\n", + "\n", + "print(\"train months / meses entrenamiento:\", len(y_train))\n", + "print(\"held-out months / meses reservados:\", len(y_test))\n", + "print(\"p=12 holdout MAPE:\", f\"{mape(y_test, pred12):.1%}\")\n", + "\n", + "window_slider = widgets.IntSlider(\n", + " value=12, min=3, max=24, step=1,\n", + " description=\"Window / Ventana:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"115px\"},\n", + ")\n", + "\n", + "horizon_slider = widgets.IntSlider(\n", + " value=12, min=6, max=36, step=6,\n", + " description=\"Horizon / Horizonte:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"125px\"},\n", + ")\n", + "\n", + "def explore_recursive_forecast(window, horizon):\n", + " # Fit only on data before the final 12 real months.\n", + " train = y[:-12]\n", + " w_ar = fit_ar(train, window)\n", + "\n", + " # Start recursive forecast at the end of training.\n", + " pred = recursive_forecast(train, w_ar, window, horizon)\n", + "\n", + " start = len(train)\n", + " future_idx = np.arange(start, start + horizon)\n", + "\n", + " available_actual = y[start:min(start + horizon, len(y))]\n", + " actual_idx = np.arange(start, start + len(available_actual))\n", + "\n", + " fig = go.Figure()\n", + "\n", + " context_start = max(0, start - 36)\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=np.arange(context_start, start),\n", + " y=y[context_start:start],\n", + " mode=\"lines\",\n", + " name=\"training context / contexto\",\n", + " )\n", + " )\n", + "\n", + " if len(available_actual):\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=actual_idx,\n", + " y=available_actual,\n", + " mode=\"lines+markers\",\n", + " name=\"held-out actual / real reservado\",\n", + " )\n", + " )\n", + "\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=future_idx,\n", + " y=pred,\n", + " mode=\"lines+markers\",\n", + " name=\"recursive forecast / pronóstico recursivo\",\n", + " )\n", + " )\n", + "\n", + " fig.add_vline(x=start - 0.5, line_dash=\"dash\")\n", + "\n", + " fig.update_layout(\n", + " title=(\n", + " f\"Recursive forecast — window={window}, horizon={horizon} / \"\n", + " f\"ventana={window}, horizonte={horizon}\"\n", + " ),\n", + " xaxis_title=\"month index / índice mensual\",\n", + " yaxis_title=\"passengers / pasajeros\",\n", + " height=330,\n", + " width=680,\n", + " margin=dict(l=55, r=20, t=60, b=50),\n", + " legend=dict(orientation=\"h\", y=-0.23),\n", + " )\n", + " fig.show()\n", + "\n", + " comparable = min(len(available_actual), len(pred))\n", + " if comparable:\n", + " err = mape(available_actual[:comparable], pred[:comparable])\n", + " print(\n", + " f\"MAPE on {comparable} real held-out months / \"\n", + " f\"MAPE en {comparable} meses reales reservados: {err:.1%}\"\n", + " )\n", + "\n", + " if horizon > 12:\n", + " print(\n", + " \"EN: after month 12, the chart is beyond the dataset; \"\n", + " \"there is no real target here to validate against.\"\n", + " )\n", + " print(\n", + " \"ES: después del mes 12, el pronóstico queda fuera del conjunto; \"\n", + " \"no existe un valor real aquí para validarlo.\"\n", + " )\n", + "\n", + " print(\n", + " \"EN: every predicted month becomes an input to the next prediction.\"\n", + " )\n", + " print(\n", + " \"ES: cada mes predicho se convierte en entrada de la siguiente predicción.\"\n", + " )\n", + "\n", + "forecast_output = widgets.interactive_output(\n", + " explore_recursive_forecast,\n", + " {\n", + " \"window\": window_slider,\n", + " \"horizon\": horizon_slider,\n", + " },\n", + ")\n", + "\n", + "display(\n", + " widgets.VBox([\n", + " widgets.HTML(\n", + " \"Recursive forecast explorer / Explorador de pronóstico recursivo: \"\n", + " \"change memory length and forecast horizon. / \"\n", + " \"cambia la longitud de memoria y el horizonte.\"\n", + " ),\n", + " window_slider,\n", + " horizon_slider,\n", + " forecast_output,\n", + " ])\n", + ")" + ], + "id": "AJU4qS0EZhNJ" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What just happened\n", + "\n", + "You used the same idea — **reuse the current state to create the next state** — in three settings.\n", + "\n", + "1. **Fibonacci:** the state `[f[n], f[n-1]]` was updated by the same matrix at every step. `matrix_power` compressed many recursive updates into one matrix power.\n", + "2. **Power iteration:** repeated multiplication amplified the dominant eigendirection. When `λ₂/λ₁` moved closer to 1, convergence slowed because the dominant direction was less dominant.\n", + "3. **Real airline forecasting:** the pseudoinverse fitted one-step linear dynamics from real observations, then recursion fed predictions back into the model. That made long-horizon errors capable of compounding.\n", + "\n", + "### The sentence to remember\n", + "\n", + "> **Recursion is repeated state update: the next input contains the previous output.**\n", + "\n", + "That is also the skeleton behind recurrent neural networks: the same parameters are reused across sequence steps, while a hidden state carries information forward. Modern sequence models often use more elaborate mechanisms, but this state-update view is the essential bridge.\n", + "\n", + "> 🇪🇸 Usaste la misma idea — **reutilizar el estado actual para construir el siguiente** — en tres contextos. Fibonacci mostró la actualización matricial repetida; la iteración de potencias mostró cómo una dirección propia puede dominar; y el pronóstico real mostró cómo una predicción puede convertirse en entrada y propagar error.\n", + ">\n", + "> **Frase para recordar:** la recursión es una actualización repetida del estado: la siguiente entrada contiene la salida anterior.\n", + ">\n", + "> Esta es también la estructura básica de una red neuronal recurrente: los mismos parámetros se reutilizan a lo largo de la secuencia mientras un estado oculto transporta información hacia adelante." + ], + "id": "50T2ppHXZhNJ" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Done with this section\n", + "\n", + "Next up: **09 · Convolution and deconvolution** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/09-convolution-and-deconvolution.ipynb).\n", + "\n", + "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" + ], + "id": "GyOmjAugZhNJ" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3" + }, + "colab": { + "provenance": [] + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "version_major": 2, + "version_minor": 0, + "state": { + "f1abd382ef8948dc85623896de0b4145": { + "model_module": "@jupyter-widgets/controls", + "model_name": "VBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "VBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "VBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_6fd73fcbb8d24ab1997a0c34f82a2865", + "IPY_MODEL_29cc84b86e0340428b21bfa0fe878b2f" + ], + "layout": "IPY_MODEL_e5f65c4e14624af79085fcbf09b1322e" + } + }, + "6fd73fcbb8d24ab1997a0c34f82a2865": { + "model_module": "@jupyter-widgets/controls", + "model_name": "IntSliderModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "IntSliderModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "IntSliderView", + "continuous_update": false, + "description": "Steps / Pasos:", + "description_tooltip": null, + "disabled": false, + "layout": "IPY_MODEL_8460ac0e378240f4a90f394b548fbbff", + "max": 25, + "min": 1, + "orientation": "horizontal", + "readout": true, + "readout_format": "d", + "step": 1, + "style": "IPY_MODEL_8e202737fdd24f998080a7e351c99d10", + "value": 6 + } + }, + "29cc84b86e0340428b21bfa0fe878b2f": { + "model_module": "@jupyter-widgets/output", + "model_name": "OutputModel", + "model_module_version": "1.0.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/output", + "_model_module_version": "1.0.0", + "_model_name": "OutputModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/output", + "_view_module_version": "1.0.0", + "_view_name": "OutputView", + "layout": "IPY_MODEL_1d38e2b9d06e40c4b9dcae2c39f4620a", + "msg_id": "", + "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "loop / ciclo: [89 55]\n", - "matrix_power: [89 55]\n", - "Fibonacci(10): 55\n", - "same result / mismo resultado: True\n" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "step/paso=6 | state/estado=[13, 8] | Fibonacci(6)=8\n" + ] }, { - "output_type": "display_data", - "data": { - "text/plain": [ - "VBox(children=(IntSlider(value=10, continuous_update=False, description='Steps / Pasos:', max=25, min=1, style…" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "f1abd382ef8948dc85623896de0b4145" - } - }, - "metadata": { - "application/vnd.jupyter.widget-view+json": { - "colab": { - "custom_widget_manager": { - "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" - } - } - } - } + "output_type": "display_data", + "data": { + "text/html": "\n\n\n
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\n\n" + }, + "metadata": {} } - ], - "source": [ - "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", - "F = np.array([[1, 1], [1, 0]], dtype=object)\n", - "v0 = np.array([1, 0], dtype=object)\n", - "\n", - "v = v0.copy()\n", - "for _ in range(10):\n", - " v = F @ v\n", - "\n", - "via_power = np.linalg.matrix_power(F, 10) @ v0\n", - "\n", - "print(\"loop / ciclo:\", v)\n", - "print(\"matrix_power:\", via_power)\n", - "print(\"Fibonacci(10):\", int(v[1]))\n", - "print(\"same result / mismo resultado:\", np.array_equal(v, via_power))\n", - "\n", - "steps_slider = widgets.IntSlider(\n", - " value=10, min=1, max=25, step=1,\n", - " description=\"Steps / Pasos:\",\n", - " continuous_update=False,\n", - " style={\"description_width\": \"100px\"},\n", - ")\n", - "\n", - "def explore_fibonacci(steps):\n", - " state = np.linalg.matrix_power(F, steps) @ v0\n", - " seq = []\n", - " s = v0.copy()\n", - " for k in range(steps + 1):\n", - " seq.append((k, int(s[0]), int(s[1])))\n", - " s = F @ s\n", - "\n", - " df = pd.DataFrame(seq, columns=[\"step\", \"f_next\", \"f_current\"])\n", - " print(\n", - " f\"step/paso={steps} | state/estado={state.tolist()} | \"\n", - " f\"Fibonacci({steps})={int(state[1])}\"\n", - " )\n", - "\n", - " fig = px.line(\n", - " df,\n", - " x=\"step\",\n", - " y=[\"f_next\", \"f_current\"],\n", - " markers=True,\n", - " title=\"Repeated state update / Actualización repetida del estado\",\n", - " labels={\"value\": \"state value / valor\", \"step\": \"step / paso\"},\n", - " )\n", - " fig.update_layout(\n", - " height=290,\n", - " width=620,\n", - " margin=dict(l=55, r=20, t=55, b=50),\n", - " legend_title_text=\"state / estado\",\n", - " )\n", - " fig.show()\n", - "\n", - "fib_output = widgets.interactive_output(\n", - " explore_fibonacci,\n", - " {\"steps\": steps_slider},\n", - ")\n", - "\n", - "display(widgets.VBox([steps_slider, fib_output]))" - ], - "id": "3TCadKpPZhNH" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "vQSIq5gwZhNH" - }, - "source": [ - "## Exercise 2 — when repeated multiplication chooses a direction\n", - "\n", - "Power iteration uses the same recursive skeleton:\n", - "\n", - "`x[t+1] = A x[t]`, followed by normalization.\n", - "\n", - "For a suitable matrix, repeated multiplication tends to align the vector with the eigenvector associated with the largest-magnitude eigenvalue.\n", - "\n", - "Here we intentionally use a **small synthetic 2×2 matrix**. The goal is not to pretend it is real data; the controlled matrix lets us change the eigenvalue gap and isolate exactly what controls convergence speed.\n", - "\n", - "### What should you try?\n", - "\n", - "1. Run power iteration for 1, 2, 5, 10 and 50 steps.\n", - "2. Compare the estimated direction with `np.linalg.eig`.\n", - "3. Use the **λ₂ / λ₁** slider to make the two eigenvalues closer.\n", - "4. Predict what happens as the ratio approaches 1.\n", - "\n", - "> 🇪🇸 Aquí usamos deliberadamente una matriz sintética `2×2` porque queremos aislar un mecanismo matemático: la velocidad de convergencia depende de qué tan dominante sea el autovalor principal. Acerca `λ₂/λ₁` a 1 y observa cómo la recursión tarda más en alinearse con la dirección dominante." - ], - "id": "vQSIq5gwZhNH" - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "id": "0c5M2KsSZhNI" - }, - "outputs": [], - "source": [ - "# TODO\n", - "# 1. Implement power iteration with normalization after every multiplication.\n", - "# 2. Track the angle between the current vector and the dominant eigenvector.\n", - "# 3. Compare a matrix with a clear spectral gap against one whose eigenvalues\n", - "# are almost equal." - ], - "id": "0c5M2KsSZhNI" - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ], - "colab": { - "base_uri": "https://localhost:8080/", - "height": 433, - "referenced_widgets": [ - "0132a07e0e3d42ee9a803d790fa64dab", - "daf0b9104c794fffb841948421df0968", - "1376dca24ec349ea871d201ddba907c8", - "d4dd3c9ead6c4c3ca24a1e6f1fc5dc76", - "6f1421ae3f024c948f476f9a58a803ec", - "bcd39a4cda2b484faa471afb91ca1b91", - "15800829ef5244fe8bf617fb454fa69d", - "2b013b6aa7c048ecb4ff129fa656d411", - "637ad86817de48328c5db67e4fc269fc", - "99a6b78cd0ac4494a4682024039196c9" - ] + ] + } + }, + "e5f65c4e14624af79085fcbf09b1322e": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + 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We hold out the final 12 months, fit an autoregressive model to the earlier observations, and then forecast recursively.\n", - "\n", - "For a window of length `p`, the model learns:\n", - "\n", - "`next month = bias + w₁·previous value + ... + wₚ·value p months ago`\n", - "\n", - "The weights come from the pseudoinverse, linking this notebook directly to Section 07.\n", - "\n", - "The crucial recursive step is this: after predicting one month, that prediction is appended to history and becomes an input for the next prediction.\n", - "\n", - "### What should you try?\n", - "\n", - "1. Fit with `p = 12` and forecast the held-out 12 months.\n", - "2. Compare predictions with the real values the model never saw during fitting.\n", - "3. Move **Window / Ventana** from 3 to 24.\n", - "4. Move **Horizon / Horizonte** farther into the future and watch recursive uncertainty grow.\n", - "5. Explain why `p = 12` is meaningful for monthly seasonal data.\n", - "\n", - "> 🇪🇸 Ahora usamos 144 observaciones mensuales reales. Reservamos los últimos 12 meses, ajustamos un modelo autorregresivo con la pseudoinversa y pronosticamos de manera recursiva. Cada predicción pasa a formar parte de la historia usada para producir la siguiente. Cambia la ventana y el horizonte para observar cómo la estructura estacional y los errores se propagan." - ], - "id": "McaT-XufZhNI" - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "id": "rppgxuRqZhNJ" - }, - "outputs": [], - "source": [ - "# TODO\n", - "# 1. Hold out the final 12 real months.\n", - "# 2. Fit an autoregressive model with p=12 using np.linalg.pinv.\n", - "# 3. Forecast the 12 held-out months recursively.\n", - "# 4. Compute MAPE.\n", - "# 5. 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\"\n", - " \"there is no real target here to validate against.\"\n", - " )\n", - " print(\n", - " \"ES: después del mes 12, el pronóstico queda fuera del conjunto; \"\n", - " \"no existe un valor real aquí para validarlo.\"\n", - " )\n", - "\n", - " print(\n", - " \"EN: every predicted month becomes an input to the next prediction.\"\n", - " )\n", - " print(\n", - " \"ES: cada mes predicho se convierte en entrada de la siguiente predicción.\"\n", - " )\n", - "\n", - "forecast_output = widgets.interactive_output(\n", - " explore_recursive_forecast,\n", - " {\n", - " \"window\": window_slider,\n", - " \"horizon\": horizon_slider,\n", - " },\n", - ")\n", - "\n", - "display(\n", - " widgets.VBox([\n", - " widgets.HTML(\n", - " \"Recursive forecast explorer / Explorador de pronóstico recursivo: \"\n", - " \"change memory length and forecast horizon. / \"\n", - " \"cambia la longitud de memoria y el horizonte.\"\n", - " ),\n", - " window_slider,\n", - " horizon_slider,\n", - " forecast_output,\n", - " ])\n", - ")" - ], - "id": "AJU4qS0EZhNJ" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "50T2ppHXZhNJ" - }, - "source": [ - "## What just happened\n", - "\n", - "You used the same idea — **reuse the current state to create the next state** — in three settings.\n", - "\n", - "1. **Fibonacci:** the state `[f[n], f[n-1]]` was updated by the same matrix at every step. `matrix_power` compressed many recursive updates into one matrix power.\n", - "2. **Power iteration:** repeated multiplication amplified the dominant eigendirection. When `λ₂/λ₁` moved closer to 1, convergence slowed because the dominant direction was less dominant.\n", - "3. **Real airline forecasting:** the pseudoinverse fitted one-step linear dynamics from real observations, then recursion fed predictions back into the model. That made long-horizon errors capable of compounding.\n", - "\n", - "### The sentence to remember\n", - "\n", - "> **Recursion is repeated state update: the next input contains the previous output.**\n", - "\n", - "That is also the skeleton behind recurrent neural networks: the same parameters are reused across sequence steps, while a hidden state carries information forward. Modern sequence models often use more elaborate mechanisms, but this state-update view is the essential bridge.\n", - "\n", - "> 🇪🇸 Usaste la misma idea — **reutilizar el estado actual para construir el siguiente** — en tres contextos. Fibonacci mostró la actualización matricial repetida; la iteración de potencias mostró cómo una dirección propia puede dominar; y el pronóstico real mostró cómo una predicción puede convertirse en entrada y propagar error.\n", - ">\n", - "> **Frase para recordar:** la recursión es una actualización repetida del estado: la siguiente entrada contiene la salida anterior.\n", - ">\n", - "> Esta es también la estructura básica de una red neuronal recurrente: los mismos parámetros se reutilizan a lo largo de la secuencia mientras un estado oculto transporta información hacia adelante." - ], - "id": "50T2ppHXZhNJ" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GyOmjAugZhNJ" - }, - "source": [ - "---\n", - "\n", - "## Done with this section\n", - "\n", - "Next up: **09 · Convolution and deconvolution** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/09-convolution-and-deconvolution.ipynb).\n", - "\n", - "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)\n", - "\n", - "> 🇪🇸 **Fin de esta sección.** A continuación: **09 · Convolución y deconvolución**." - ], - "id": "GyOmjAugZhNJ" - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3" - }, - "colab": { - "provenance": [] - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "version_major": 2, - "version_minor": 0, - "state": { - "f1abd382ef8948dc85623896de0b4145": { - "model_module": "@jupyter-widgets/controls", - "model_name": "VBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "VBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "VBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_6fd73fcbb8d24ab1997a0c34f82a2865", - "IPY_MODEL_29cc84b86e0340428b21bfa0fe878b2f" - ], - "layout": "IPY_MODEL_e5f65c4e14624af79085fcbf09b1322e" - } - }, - "6fd73fcbb8d24ab1997a0c34f82a2865": { - "model_module": "@jupyter-widgets/controls", - "model_name": "IntSliderModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "IntSliderModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "IntSliderView", - "continuous_update": false, - "description": "Steps / Pasos:", - "description_tooltip": null, - "disabled": false, - "layout": "IPY_MODEL_8460ac0e378240f4a90f394b548fbbff", - "max": 25, - "min": 1, - "orientation": "horizontal", - "readout": true, - "readout_format": "d", - "step": 1, - "style": "IPY_MODEL_8e202737fdd24f998080a7e351c99d10", - "value": 6 - } - }, - "29cc84b86e0340428b21bfa0fe878b2f": { - "model_module": "@jupyter-widgets/output", - "model_name": "OutputModel", - "model_module_version": "1.0.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/output", - "_model_module_version": "1.0.0", - "_model_name": "OutputModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/output", - "_view_module_version": "1.0.0", - "_view_name": "OutputView", - "layout": "IPY_MODEL_1d38e2b9d06e40c4b9dcae2c39f4620a", - "msg_id": "", - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "step/paso=6 | state/estado=[13, 8] | Fibonacci(6)=8\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/html": "\n\n\n
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plotly.graph_objects as go +from IPython.display import display + +# Enable ipywidgets in Google Colab when available. +try: + from google.colab import output + output.enable_custom_widget_manager() +except ImportError: + pass FLIGHTS = "https://raw.githubusercontent.com/mwaskom/seaborn-data/master/flights.csv" flights = pd.read_csv(FLIGHTS) +y = flights["passengers"].to_numpy(float) +labels = ( + flights["year"].astype(str) + + "-" + + flights["month"].astype(str).str[:3] +).to_numpy() + rng = np.random.default_rng(0) -print(flights.shape) # (144, 3) — 144 real months, 1949-1960""", + +print("real months / meses reales:", len(y)) +print("range / periodo:", labels[0], "→", labels[-1]) +print("passengers min/max:", int(y.min()), int(y.max())) +print("interactive charts: Plotly + ipywidgets enabled")""", } # ───────────────────────────────────────────────────────────────────────────── From 3cfa61c2b1dcc51a93e27e19b0e1a095d5d82d2e Mon Sep 17 00:00:00 2001 From: Sebas Laverde Chunza <106851243+Laverde97@users.noreply.github.com> Date: Fri, 28 Aug 2026 22:12:33 -0500 Subject: [PATCH 19/29] Improve notebook 09 pedagogy with real data and interactive deconvolution for issue #44 --- .../09-convolution-and-deconvolution.ipynb | 1985 +++++++++++++---- 1 file changed, 1513 insertions(+), 472 deletions(-) diff --git a/notebooks/09-convolution-and-deconvolution.ipynb b/notebooks/09-convolution-and-deconvolution.ipynb index 1686e27..398b83c 100644 --- a/notebooks/09-convolution-and-deconvolution.ipynb +++ b/notebooks/09-convolution-and-deconvolution.ipynb @@ -1,477 +1,1518 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 09 · Convolution and deconvolution\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/09-convolution-and-deconvolution.ipynb)\n", - "\n", - "*Part IV · exercise · 15 min*\n", - "\n", - "> 🇪🇸 **Convolución y deconvolución** — La convolución es un producto matricial estructurado, y el desenfoque se puede deshacer en parte.\n", - "\n", - "Convolution is a structured matrix product, and blur can be partly undone.\n", - "\n", - "## What you will be able to do\n", - "\n", - "- Predict the output size of a convolution in `full`, `valid` and `same` mode.\n", - "- Say why what deep learning calls convolution is really correlation.\n", - "- Write a convolution as multiplication by a Toeplitz matrix.\n", - "- Distinguish transposed convolution from true deconvolution.\n", - "- Recover a blurred image with Richardson-Lucy, and measure it honestly." - ], - "id": "s09-00" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "Run this first. It installs and imports everything this notebook needs, and nothing else.\n", - "\n", - "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." - ], - "id": "s09-01" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from scipy import signal\n", - "from scipy.linalg import toeplitz\n", - "from skimage import data\n", - "from skimage.restoration import richardson_lucy\n", - "\n", - "img = data.camera().astype(float) / 255. # real photograph, 512x512\n", - "sobel = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], float)\n", - "print(img.shape, img.min(), img.max())" - ], - "id": "s09-02" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The theory\n", - "\n", - "> 🇪🇸 La convolución desliza un núcleo pequeño sobre un array mayor,\n", - "> multiplicando y sumando en cada posición.\n", - "\n", - "**Convolution** slides a small array (the **kernel**, or **filter**) across a\n", - "larger one, multiplying and summing at each position. It is the operation at the\n", - "heart of every convolutional neural network, and it is also how every blur,\n", - "sharpen and edge-detection filter works." - ], - "id": "s09-03" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x = np.array([1., 2., 3., 4., 5.])\n", - "k = np.array([1., 0., -1.])\n", - "\n", - "print(np.convolve(x, k, 'full')) # [ 1. 2. 2. 2. 2. -4. -5.] length 5+3-1 = 7\n", - "print(np.convolve(x, k, 'valid')) # [ 2. 2. 2.] length 5-3+1 = 3\n", - "print(np.convolve(x, k, 'same')) # [ 2. 2. 2. 2. -4.] length 5" - ], - "id": "s09-04" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Three modes, three output sizes. `valid` uses only positions where the kernel\n", - "fits completely — this is why convolution **shrinks** an image by\n", - "`kernel_size - 1`.\n", - "\n", - "⚠️ **A detail that confuses everyone.** True convolution flips the kernel;\n", - "**correlation** does not. What deep learning libraries call \"convolution\" is\n", - "actually correlation. It makes no practical difference, because the network\n", - "*learns* the kernel — but you should know the names are inconsistent." - ], - "id": "s09-05" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(np.correlate(x, k, 'valid')) # [-2. -2. -2.]\n", - "print(np.convolve(x, k[::-1], 'valid')) # [-2. -2. -2.] — the same, with k flipped" - ], - "id": "s09-06" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Convolution is a matrix multiplication\n", - "\n", - "This is the connection back to Chapter 2. Any convolution can be written as\n", - "multiplication by a **Toeplitz** matrix — a matrix where the kernel is shifted\n", - "along each row." - ], - "id": "s09-07" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "col = np.zeros(7); col[:3] = k\n", - "row = np.zeros(5); row[0] = k[0]\n", - "C = toeplitz(col, row) # (7, 5)\n", - "print(C.shape)\n", - "print(np.allclose(C @ x, np.convolve(x, k, 'full'))) # True" - ], - "id": "s09-08" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So convolution is not a new kind of operation. It is a **structured matrix\n", - "multiplication** — one where the same few numbers are reused across the whole\n", - "matrix. That reuse is exactly why CNNs need so many fewer parameters than fully\n", - "connected networks.\n", - "\n", - "### Deconvolution means two different things\n", - "\n", - "Keep them separate:\n", - "\n", - "1. **Transposed convolution** — the upsampling layer in a decoder or GAN. It\n", - " makes things *bigger*. It is **not** a true inverse; the name is historical\n", - " and misleading.\n", - "2. **True deconvolution** — recovering the original signal from a blurred one.\n", - " This is a genuine inverse problem, and it is where section 07 comes back." - ], - "id": "s09-09" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 1 — convolve and blur a real photograph\n", - "\n", - "> 🇪🇸 Convoluciona y desenfoca una fotografía real." - ], - "id": "s09-10" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO 1: Convolve `img` with `sobel` in 'valid' mode. What shape comes out,\n", - "# and by how much did it shrink?\n", - "\n", - "# TODO 2: Blur the image with a 9x9 averaging kernel (all entries equal,\n", - "# summing to 1), mode='same'. Display it next to the original." - ], - "id": "s09-11" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "m_jYVv4wfdAQ" + }, + "source": [ + "# 09 · Convolution and deconvolution\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/09-convolution-and-deconvolution.ipynb)\n", + "\n", + "*Part IV · exercise · 15 min*\n", + "\n", + "> 🇪🇸 **Convolución y deconvolución** — Ver la convolución como un operador lineal estructurado, distinguir correlación de convolución, entender qué hace realmente una convolución transpuesta y recuperar parcialmente una imagen real desenfocada.\n", + "\n", + "Treat convolution as a structured linear operator, separate it from correlation, understand what transposed convolution actually does, and partially recover a real blurred image.\n", + "\n", + "## What you will be able to do\n", + "\n", + "- Predict `full`, `same`, and `valid` output shapes for a real image and verify them interactively.\n", + "- Explain why deep-learning “convolution” is usually cross-correlation and show the kernel-flip relationship.\n", + "- Write a 1D convolution of a real image scanline as multiplication by a Toeplitz matrix.\n", + "- Explain transposed convolution as an overlap-add linear operator that changes shape but is not a true inverse.\n", + "- Recover a real blurred image with Richardson–Lucy and measure improvement away from boundary artifacts.\n", + "\n", + "> 🇪🇸 **Al terminar podrás:** predecir tamaños de salida; distinguir correlación de convolución; representar una convolución como una matriz Toeplitz; explicar por qué la convolución transpuesta no es una deconvolución verdadera; y medir de forma honesta la recuperación de una imagen real desenfocada." + ], + "id": "m_jYVv4wfdAQ" }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "edges = signal.convolve2d(img, sobel, mode='valid') # (510, 510) — shrank by 2\n", - "print(edges.shape)\n", - "\n", - "psf = np.ones((9, 9)); psf /= psf.sum()\n", - "blurred = signal.convolve2d(img, psf, mode='same', boundary='symm')\n", - "print(blurred.shape) # (512, 512) — 'same' keeps it\n", - "\n", - "import matplotlib.pyplot as plt\n", - "fig, ax = plt.subplots(1, 3, figsize=(12, 4))\n", - "for a, im, t in zip(ax, [img, edges, blurred], [\"original\", \"sobel\", \"blurred\"]):\n", - " a.imshow(im, cmap=\"gray\"); a.set_title(t); a.axis(\"off\")\n", - "plt.show()\n", - "\n", - "# 'valid' shrinks by kernel_size - 1 = 2 in each direction: 512 -> 510." - ], - "id": "s09-12" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "See the blur trade-off live: a bigger averaging kernel removes more detail,\n", - "and it shrinks a `'valid'`-mode output by more (`kernel_size - 1` per side).\n", - "\n", - "> 🇪🇸 El deslizador muestra el compromiso del desenfoque: un kernel más grande\n", - "> elimina más detalle, y en modo `'valid'` recorta más el resultado." - ], - "id": "s09-13" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " from google.colab import output\n", - " output.enable_custom_widget_manager()\n", - "except ImportError:\n", - " pass\n", - "\n", - "import ipywidgets as widgets\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def show_blur(k):\n", - " psf_k = np.ones((k, k)); psf_k /= psf_k.sum()\n", - " blurred_k = signal.convolve2d(img, psf_k, mode='same', boundary='symm')\n", - " valid_k = signal.convolve2d(img, psf_k, mode='valid')\n", - "\n", - " plt.close('all')\n", - " fig, axes = plt.subplots(1, 2, figsize=(8, 4))\n", - " axes[0].imshow(blurred_k, cmap='gray')\n", - " axes[0].set_title(f\"{k}x{k} kernel, mode='same' — {blurred_k.shape}\")\n", - " axes[1].imshow(valid_k, cmap='gray')\n", - " axes[1].set_title(f\"{k}x{k} kernel, mode='valid' — {valid_k.shape}\")\n", - " for a in axes:\n", - " a.axis('off')\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "widgets.interact(show_blur,\n", - " k=widgets.IntSlider(min=1, max=25, step=2, value=9,\n", - " description='kernel size'));" - ], - "id": "s09-14" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 2 — transposed convolution\n", - "\n", - "> 🇪🇸 Convolución transpuesta: hace las cosas más grandes, no las deshace." - ], - "id": "s09-15" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO 3: Upsample this 2x2 array to 3x3 by adding small * kernel into an\n", - "# output array at each position:\n", - "# small = np.array([[1., 2.], [3., 4.]]); ker = np.ones((2, 2))\n", - "# What shape do you get? Why is this called \"deconvolution\" in CNNs\n", - "# even though it does not undo anything?" - ], - "id": "s09-16" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true + { + "cell_type": "markdown", + "metadata": { + "id": "YBkF2AOQfdAS" + }, + "source": [ + "## Setup\n", + "\n", + "Run this first. We use a real grayscale photograph from `skimage.data`, a Sobel edge kernel, and a real scanline from that image.\n", + "\n", + "> 🇪🇸 Ejecuta primero esta celda. Usaremos una fotografía real en escala de grises de `skimage.data`, un kernel Sobel para bordes y una fila real de píxeles extraída de esa imagen." + ], + "id": "YBkF2AOQfdAS" }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "small = np.array([[1., 2.], [3., 4.]])\n", - "ker = np.ones((2, 2))\n", - "\n", - "out = np.zeros((3, 3))\n", - "for i in range(2):\n", - " for j in range(2):\n", - " out[i:i+2, j:j+2] += small[i, j] * ker\n", - "print(out)\n", - "# [[ 1. 3. 2.]\n", - "# [ 4. 10. 6.]\n", - "# [ 3. 7. 4.]]\n", - "\n", - "# Shape (3, 3): it GREW, by kernel_size - 1, which is exactly what 'valid'\n", - "# convolution shrinks by. That inverse relationship between the SHAPES is the\n", - "# whole reason for the name. The VALUES are not undone at all — you cannot\n", - "# recover `small` from `out`. The name is historical and misleading." - ], - "id": "s09-17" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 3 — true deconvolution\n", - "\n", - "> 🇪🇸 Deconvolución de verdad. **Recorta 25 píxeles del borde antes de medir.**\n", - "\n", - "::: {.callout-warning}\n", - "**This is the hardest thing in the workshop, and it has a trap.** Deconvolution\n", - "creates strong artifacts at the edges, where the algorithm has no information\n", - "about what lies outside the image. If you measure error over the whole image,\n", - "the artifacts dominate and it looks like the method failed. **Crop 25 pixels off\n", - "every side before measuring.** Do this before you run it, not after.\n", - ":::" - ], - "id": "s09-18" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO 4: Add small noise to the blurred image, then try to recover the\n", - "# original with skimage.restoration.richardson_lucy(..., num_iter=50).\n", - "# Measure error BEFORE and AFTER, ignoring a 25-pixel border.\n", - "# Did it improve?" - ], - "id": "s09-19" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "QSazhIfXfdAT", + "outputId": "87c28960-3f94-41ca-f2e5-e773145d5fcd" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "full image / imagen completa: (512, 512)\n", + "real patch / recorte real: (160, 160)\n", + "deconvolution crop / recorte deconvolución: (256, 256)\n", + "real scanline / fila real: (32,)\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from scipy import signal\n", + "from scipy.linalg import toeplitz\n", + "from skimage import data\n", + "from skimage.restoration import richardson_lucy\n", + "import matplotlib.pyplot as plt\n", + "import ipywidgets as widgets\n", + "from IPython.display import display\n", + "\n", + "try:\n", + " from google.colab import output\n", + " output.enable_custom_widget_manager()\n", + "except ImportError:\n", + " pass\n", + "\n", + "img = data.camera().astype(float) / 255.0 # real 512×512 photograph\n", + "patch = img[176:336, 176:336] # real 160×160 crop\n", + "work = img[128:384, 128:384] # real 256×256 crop for deconvolution\n", + "scanline = img[256, 220:252].copy() # 32 real pixel measurements\n", + "\n", + "sobel_x = np.array([\n", + " [-1., 0., 1.],\n", + " [-2., 0., 2.],\n", + " [-1., 0., 1.],\n", + "])\n", + "\n", + "kernel_1d = np.array([1., 0., -1.])\n", + "\n", + "def convmtx_full_1d(kernel, n):\n", + " m = len(kernel)\n", + " col = np.zeros(n + m - 1)\n", + " col[:m] = kernel\n", + " row = np.zeros(n)\n", + " row[0] = kernel[0]\n", + " return toeplitz(col, row)\n", + "\n", + "print(\"full image / imagen completa:\", img.shape)\n", + "print(\"real patch / recorte real:\", patch.shape)\n", + "print(\"deconvolution crop / recorte deconvolución:\", work.shape)\n", + "print(\"real scanline / fila real:\", scanline.shape)" + ], + "id": "QSazhIfXfdAT" }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "psf = np.ones((9, 9)); psf /= psf.sum()\n", - "blurred = signal.convolve2d(img, psf, mode='same', boundary='symm')\n", - "noisy = blurred + 0.002 * np.random.default_rng(0).standard_normal(blurred.shape)\n", - "\n", - "recovered = richardson_lucy(np.clip(noisy, 0, 1), psf, num_iter=50)\n", - "\n", - "c = 25 # ignore the border: deconvolution always creates edge artifacts\n", - "err = lambda a: np.linalg.norm((a - img)[c:-c, c:-c]) / np.linalg.norm(img[c:-c, c:-c])\n", - "print(err(noisy), err(recovered)) # 0.1157 -> 0.0815\n", - "\n", - "# Deconvolution REDUCED THE ERROR BY ABOUT 30%." - ], - "id": "s09-20" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Why not just invert the blur directly?\n", - "\n", - "Because it fails badly. Blurring destroys high-frequency detail, so inverting it\n", - "divides by numbers very close to zero and amplifies noise enormously." - ], - "id": "s09-21" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Self-contained: rebuilds the blur and the noise rather than reusing names\n", - "# from the exercise above, so this cell runs whether or not you opened the\n", - "# solution — and whatever you called your own variables.\n", - "psf = np.ones((9, 9)); psf /= psf.sum()\n", - "blurred = signal.convolve2d(img, psf, mode='same', boundary='symm')\n", - "noisy = blurred + 0.002 * np.random.default_rng(0).standard_normal(blurred.shape)\n", - "\n", - "K = np.fft.fft2(psf, s=img.shape)\n", - "naive = np.real(np.fft.ifft2(np.fft.fft2(noisy) / np.where(abs(K) < 1e-3, 1e-3, K)))\n", - "\n", - "c = 25\n", - "err = lambda a: np.linalg.norm((a - img)[c:-c, c:-c]) / np.linalg.norm(img[c:-c, c:-c])\n", - "print(err(noisy), err(naive)) # 0.1157 ~2.49\n", - "#\n", - "# Richardson-Lucy got that 0.1157 down to 0.0815. The naive inverse takes it to\n", - "# ~2.49 — roughly TWENTY TIMES WORSE than the blurred image it started from.\n", - "# Cropping the border does not rescue it either (~2.51 uncropped): this is not\n", - "# an edge artifact, it is noise amplified across the whole image." - ], - "id": "s09-22" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What just happened\n", - "\n", - "**This is the same lesson as section 07.** A direct inverse either does not exist\n", - "or is unusable, so you use a method that finds the best stable answer instead.\n", - "The pseudoinverse does this for linear systems; Richardson-Lucy and Wiener\n", - "filtering do it for deconvolution.\n", - "\n", - "> 🇪🇸 Cuando no existe una inversa exacta, no te rindes: buscas la mejor\n", - "> aproximación estable.\n", - "\n", - "In biotech this is routine: every fluorescence microscope blurs its images by a\n", - "known amount (the *point spread function*), and deconvolution is standard\n", - "practice before cells are counted or measured." - ], - "id": "s09-23" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Done with this section\n", - "\n", - "Next up: **10 · Tucker decomposition on real data** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/10-tucker-decomposition.ipynb).\n", - "\n", - "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" - ], - "id": "s09-24" - } - ], - "metadata": { - "colab": { - "name": "09-convolution-and-deconvolution.ipynb", - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" + { + "cell_type": "markdown", + "metadata": { + "id": "UMs6aGKbfdAU" + }, + "source": [ + "## Why this matters\n", + "\n", + "Convolution is not a mysterious new kind of multiplication. It is a **linear operator with repeated structure**: the same small kernel is reused across positions.\n", + "\n", + "That gives us three important connections:\n", + "\n", + "- **convolution ↔ correlation:** true convolution flips the kernel; cross-correlation does not;\n", + "- **convolution ↔ matrix multiplication:** a Toeplitz matrix can represent the same operation;\n", + "- **blur ↔ inverse problem:** once convolution destroys or suppresses information, “undoing” it can be unstable.\n", + "\n", + "A transposed convolution belongs to the second connection: it is the transpose/adjoint-style partner of a convolution operator. It can enlarge an array through overlap-add, but it does **not** reconstruct the original values by itself.\n", + "\n", + "True deconvolution belongs to the third connection: we know or estimate the blur kernel and solve a difficult inverse problem under noise.\n", + "\n", + "### How to use the folded solutions / Cómo usar las soluciones plegadas\n", + "\n", + "Each exercise has a **Solution / Solución** cell that is intentionally closed. First try the `TODO`; then open the solution to compare your reasoning with a reference implementation.\n", + "\n", + "> 🇪🇸 La convolución es un operador lineal estructurado que reutiliza el mismo kernel. La convolución verdadera invierte el kernel; la correlación no. Ese mismo operador puede escribirse como una matriz Toeplitz. La convolución transpuesta cambia la forma mediante superposición y suma, pero no deshace automáticamente el operador original. La deconvolución verdadera intenta recuperar una señal perdida y por eso es un problema inverso sensible al ruido.\n", + ">\n", + "> Cada ejercicio tiene una celda **Solution / Solución** cerrada a propósito. Primero intenta el `TODO`; después abre la solución.\n", + "\n", + "### Learning cycle: Predict → Run → Explain\n", + "\n", + "Before every exercise, predict the output shape and what information should be preserved or lost. Then run it and explain the result.\n", + "\n", + "> 🇪🇸 **Predice → Ejecuta → Explica:** anticipa la forma de salida y qué información debería conservarse o perderse; luego ejecuta y explica el resultado." + ], + "id": "UMs6aGKbfdAU" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "elfkGTiPfdAU" + }, + "source": [ + "## Exercise 1 — convolution, correlation, and Toeplitz on real pixels\n", + "\n", + "We start from two real pieces of the photograph:\n", + "\n", + "- a `160×160` crop for 2D edge filtering;\n", + "- a length-32 scanline for the matrix view.\n", + "\n", + "For a `3×3` kernel on a `160×160` image:\n", + "\n", + "- `valid` should produce `158×158`;\n", + "- `same` should produce `160×160`;\n", + "- `full` should produce `162×162`.\n", + "\n", + "For the scanline, full 1D convolution with a length-3 kernel should produce `32 + 3 - 1 = 34` values.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Compare `correlate2d(patch, sobel_x)` with `convolve2d(patch, flip(sobel_x))`.\n", + "2. Verify that they match.\n", + "3. Build a Toeplitz matrix `C` for the real scanline and check `C @ scanline == np.convolve(...)`.\n", + "4. Move **Mode / Modo** and verify the predicted image sizes.\n", + "\n", + "> 🇪🇸 Usaremos píxeles reales de la fotografía. Comprueba que la correlación con Sobel coincide con la convolución cuando el kernel se invierte, representa una convolución 1D con una matriz Toeplitz y usa el selector de modo para confirmar `valid`, `same` y `full`." + ], + "id": "elfkGTiPfdAU" + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "W4pZ396DfdAV" + }, + "outputs": [], + "source": [ + "# TODO\n", + "# 1. Compute 2D correlation with sobel_x.\n", + "# 2. Compute 2D convolution with np.flip(sobel_x).\n", + "# 3. Verify that the two results match.\n", + "# 4. Build C = convmtx_full_1d(kernel_1d, len(scanline)).\n", + "# 5. Check C @ scanline against np.convolve(scanline, kernel_1d, \"full\")." + ], + "id": "W4pZ396DfdAV" + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452, + "referenced_widgets": [ + "6919773a5f424929a218e688c37fd897", + "0c73ae284a094f0dbeb5f9d8a4a28b53", + "f986b5345f574416abe533a93ae3ac55", + "00f17bafcda144e0af306d4ad3464b05", + "8aded483293f49269adf02c8125e6ce7", + "2cd870b8a3734d628fd39b3ba099c70f", + "48f38061dc4e414ba31ec1b4ce6e57d4" + ] + }, + "id": "llmJoCZJfdAV", + "outputId": "fef8a8ba-ac53-4a1e-9d34-a1105195d9cd" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "correlation == convolution with flipped kernel / correlación == convolución con kernel invertido: True\n", + "Toeplitz matrix / matriz Toeplitz: (34, 32)\n", + "full convolution / convolución full: (34,)\n", + "C @ scanline == convolution: True\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(ToggleButtons(description='Mode / Modo:', index=1, options=('valid', 'same', 'full'), style=Tog…" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "6919773a5f424929a218e688c37fd897" + } + }, + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "colab": { + "custom_widget_manager": { + "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" + } + } + } + } + } + ], + "source": [ + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "corr = signal.correlate2d(patch, sobel_x, mode=\"valid\")\n", + "conv_flipped = signal.convolve2d(\n", + " patch,\n", + " np.flip(sobel_x),\n", + " mode=\"valid\",\n", + ")\n", + "\n", + "print(\n", + " \"correlation == convolution with flipped kernel / \"\n", + " \"correlación == convolución con kernel invertido:\",\n", + " np.allclose(corr, conv_flipped),\n", + ")\n", + "\n", + "C = convmtx_full_1d(kernel_1d, len(scanline))\n", + "via_matrix = C @ scanline\n", + "via_convolution = np.convolve(scanline, kernel_1d, mode=\"full\")\n", + "\n", + "print(\"Toeplitz matrix / matriz Toeplitz:\", C.shape)\n", + "print(\"full convolution / convolución full:\", via_convolution.shape)\n", + "print(\"C @ scanline == convolution:\", np.allclose(via_matrix, via_convolution))\n", + "\n", + "mode_widget = widgets.ToggleButtons(\n", + " options=[\"valid\", \"same\", \"full\"],\n", + " value=\"same\",\n", + " description=\"Mode / Modo:\",\n", + " style={\"description_width\": \"95px\"},\n", + ")\n", + "\n", + "def explore_mode(mode):\n", + " filtered = signal.correlate2d(patch, sobel_x, mode=mode)\n", + "\n", + " print(\n", + " f\"mode/modo={mode} | input/entrada={patch.shape} | \"\n", + " f\"output/salida={filtered.shape}\"\n", + " )\n", + "\n", + " fig, axes = plt.subplots(1, 2, figsize=(7.2, 3.0))\n", + " axes[0].imshow(patch, cmap=\"gray\")\n", + " axes[0].set_title(\"real patch / recorte real\")\n", + " axes[1].imshow(filtered, cmap=\"gray\")\n", + " axes[1].set_title(f\"Sobel correlation — {mode}\")\n", + " for ax in axes:\n", + " ax.axis(\"off\")\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "mode_output = widgets.interactive_output(\n", + " explore_mode,\n", + " {\"mode\": mode_widget},\n", + ")\n", + "\n", + "display(widgets.VBox([mode_widget, mode_output]))" + ], + "id": "llmJoCZJfdAV" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4msnOeU7fdAW" + }, + "source": [ + "## Exercise 2 — transposed convolution changes shape, not history\n", + "\n", + "The phrase **“deconvolution layer”** is often used informally for transposed convolution in decoders and generative models. That name is misleading.\n", + "\n", + "Here we use a tiny `2×2` patch sampled from the real photograph so the overlap-add mechanism is visible.\n", + "\n", + "A transposed-convolution-style update places a scaled copy of the kernel into the output for each input value. When stride increases, the output grows and gaps appear between placements.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Take a real `2×2` patch from the photograph.\n", + "2. Use a `2×2` all-ones kernel.\n", + "3. Implement overlap-add for stride 1.\n", + "4. Move **Stride / Paso** from 1 to 3 and inspect the output shape.\n", + "5. Explain why a larger output does **not** mean the original pre-convolution image has been recovered.\n", + "\n", + "> 🇪🇸 La convolución transpuesta reutiliza un kernel mediante superposición y suma. Puede aumentar el tamaño espacial, especialmente con stride mayor que 1, pero aumentar la forma no equivale a invertir los valores perdidos por una convolución anterior." + ], + "id": "4msnOeU7fdAW" + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "N3KU8_VUfdAW" + }, + "outputs": [], + "source": [ + "# TODO\n", + "# 1. Extract a real 2×2 patch from img.\n", + "# 2. Implement overlap-add with a 2×2 kernel and stride=1.\n", + "# 3. Predict the output shape.\n", + "# 4. Repeat with stride=2.\n", + "# 5. Explain why this is not a true inverse." + ], + "id": "N3KU8_VUfdAW" + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452, + "referenced_widgets": [ + "95dba5e40c0f4f38908cbedcfbecdc91", + "775cbe4a23944f9980afc1e178c4a6f9", + "8c451ec653384de4832ee4ba6f299c00", + "c01546199ab748e09c326a91126464ec", + "ec03610a9d75430b86b510c37f400577", + "8634e0be1a12454283d18d1eb534d23c", + "5a05ac456ad743349fc75099b903afa4" + ] + }, + "id": "LcIHWex0fdAW", + "outputId": "a36f19b5-76f5-419e-d519-d273cc469225" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "real input / entrada real:\n", + "[[0.02 0.02 ]\n", + " [0.024 0.02 ]]\n", + "stride=1 output shape / forma: (3, 3)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(IntSlider(value=1, continuous_update=False, description='Stride / Paso:', max=3, min=1, style=S…" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "95dba5e40c0f4f38908cbedcfbecdc91" + } + }, + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "colab": { + "custom_widget_manager": { + "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" + } + } + } + } + } + ], + "source": [ + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "small = img[250:254:2, 250:254:2].copy() # real 2×2 pixel patch\n", + "ker = np.ones((2, 2), dtype=float)\n", + "\n", + "def transposed_overlap_add(x, kernel, stride=1):\n", + " h, w = x.shape\n", + " kh, kw = kernel.shape\n", + "\n", + " out_h = (h - 1) * stride + kh\n", + " out_w = (w - 1) * stride + kw\n", + " out = np.zeros((out_h, out_w), dtype=float)\n", + "\n", + " for i in range(h):\n", + " for j in range(w):\n", + " r = i * stride\n", + " c = j * stride\n", + " out[r:r+kh, c:c+kw] += x[i, j] * kernel\n", + "\n", + " return out\n", + "\n", + "print(\"real input / entrada real:\")\n", + "print(np.round(small, 3))\n", + "print(\"stride=1 output shape / forma:\", transposed_overlap_add(small, ker, 1).shape)\n", + "\n", + "stride_slider = widgets.IntSlider(\n", + " value=1,\n", + " min=1,\n", + " max=3,\n", + " step=1,\n", + " description=\"Stride / Paso:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"95px\"},\n", + ")\n", + "\n", + "def explore_transposed(stride):\n", + " out = transposed_overlap_add(small, ker, stride)\n", + "\n", + " print(\n", + " f\"input/entrada={small.shape} | stride/paso={stride} | \"\n", + " f\"output/salida={out.shape}\"\n", + " )\n", + " print(\n", + " \"EN: shape expansion is not value inversion; information lost earlier \"\n", + " \"does not magically return.\"\n", + " )\n", + " print(\n", + " \"ES: aumentar la forma no invierte los valores; la información perdida \"\n", + " \"antes no reaparece automáticamente.\"\n", + " )\n", + "\n", + " fig, axes = plt.subplots(1, 2, figsize=(5.8, 2.8))\n", + " axes[0].imshow(small, cmap=\"viridis\")\n", + " axes[0].set_title(\"real 2×2 input\")\n", + " axes[1].imshow(out, cmap=\"viridis\")\n", + " axes[1].set_title(f\"overlap-add, stride={stride}\")\n", + " for ax in axes:\n", + " ax.set_xticks(range(ax.images[0].get_array().shape[1]))\n", + " ax.set_yticks(range(ax.images[0].get_array().shape[0]))\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "transpose_output = widgets.interactive_output(\n", + " explore_transposed,\n", + " {\"stride\": stride_slider},\n", + ")\n", + "\n", + "display(widgets.VBox([stride_slider, transpose_output]))" + ], + "id": "LcIHWex0fdAW" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "udqLELfjfdAW" + }, + "source": [ + "## Exercise 3 — true deconvolution on a real photograph\n", + "\n", + "Now we solve an actual inverse problem.\n", + "\n", + "We blur a real `256×256` crop with a `9×9` point-spread function (PSF), add a small controlled amount of noise, and attempt to recover the original with **Richardson–Lucy deconvolution**.\n", + "\n", + "There is an important measurement trap: boundary pixels are where the algorithm has the least information about what lies outside the image. We therefore compute the error after removing a fixed border.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Blur the real image crop with a normalized `9×9` averaging PSF.\n", + "2. Add small reproducible Gaussian noise.\n", + "3. Recover it with `richardson_lucy`.\n", + "4. Measure relative error on the interior only.\n", + "5. Move **Iterations / Iteraciones** and observe the trade-off: too few iterations under-correct; many iterations can begin to emphasize noise.\n", + "\n", + "> 🇪🇸 Ahora sí resolvemos un problema de deconvolución. Desenfocamos un recorte real con una PSF conocida, añadimos ruido controlado y usamos Richardson–Lucy para recuperar detalle. Medimos únicamente el interior porque los bordes contienen artefactos propios de la falta de información fuera de la imagen." + ], + "id": "udqLELfjfdAW" + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "evDam-pcfdAW" + }, + "outputs": [], + "source": [ + "# TODO\n", + "# 1. Build a normalized 9×9 averaging PSF.\n", + "# 2. Blur work with signal.fftconvolve(..., mode=\"same\").\n", + "# 3. Add small reproducible noise.\n", + "# 4. Recover with richardson_lucy.\n", + "# 5. Compare interior relative error before and after." + ], + "id": "evDam-pcfdAW" + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "colab": { + "base_uri": "https://localhost:8080/", + "height": 441, + "referenced_widgets": [ + "312a87d3c33b44379702ae4610625b9d", + "0e8a7e4a0c614052aad776390df7f3ca", + "40c66bc0a7394bb5a65421a0af065beb", + "f5a846f445704bd7a0daa67fe192814a", + "4960f14d63a8435b9be8d7de8b6757bd", + "530735ccd92a4e6b8c0bce6fea156a4c", + "d744de219eb848d896266aa8cc33aceb" + ] + }, + "id": "fevJ1to2fdAX", + "outputId": "6120c30f-72ed-44a9-9233-729b6912af68" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "blurred+noise error / error desenfoque+ruido: 0.1891\n", + "RL 20 iterations / iteraciones: 0.1309\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(IntSlider(value=20, continuous_update=False, description='Iterations / Iteraciones:', max=50, m…" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "312a87d3c33b44379702ae4610625b9d" + } + }, + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "colab": { + "custom_widget_manager": { + "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" + } + } + } + } + } + ], + "source": [ + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "psf = np.ones((9, 9), dtype=float)\n", + "psf /= psf.sum()\n", + "\n", + "blurred = signal.fftconvolve(work, psf, mode=\"same\")\n", + "noise = 0.002 * np.random.default_rng(0).standard_normal(work.shape)\n", + "noisy = np.clip(blurred + noise, 0, 1)\n", + "\n", + "border = 20\n", + "\n", + "def interior_relative_error(candidate):\n", + " ref = work[border:-border, border:-border]\n", + " cand = candidate[border:-border, border:-border]\n", + " return np.linalg.norm(cand - ref) / np.linalg.norm(ref)\n", + "\n", + "recovered_20 = richardson_lucy(noisy, psf, num_iter=20, clip=False)\n", + "\n", + "print(\n", + " \"blurred+noise error / error desenfoque+ruido:\",\n", + " f\"{interior_relative_error(noisy):.4f}\",\n", + ")\n", + "print(\n", + " \"RL 20 iterations / iteraciones:\",\n", + " f\"{interior_relative_error(recovered_20):.4f}\",\n", + ")\n", + "\n", + "iterations_slider = widgets.IntSlider(\n", + " value=20,\n", + " min=1,\n", + " max=50,\n", + " step=3,\n", + " description=\"Iterations / Iteraciones:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"145px\"},\n", + ")\n", + "\n", + "def explore_deconvolution(iterations):\n", + " recovered = richardson_lucy(\n", + " noisy,\n", + " psf,\n", + " num_iter=iterations,\n", + " clip=False,\n", + " )\n", + "\n", + " err_before = interior_relative_error(noisy)\n", + " err_after = interior_relative_error(recovered)\n", + "\n", + " print(\n", + " f\"iterations/iteraciones={iterations} | \"\n", + " f\"before/antes={err_before:.4f} | after/después={err_after:.4f}\"\n", + " )\n", + "\n", + " if err_after < err_before:\n", + " print(\"EN: recovery improved the interior error.\")\n", + " print(\"ES: la recuperación redujo el error interior.\")\n", + " else:\n", + " print(\"EN: at this iteration count the recovery no longer improves the metric.\")\n", + " print(\"ES: con este número de iteraciones la recuperación ya no mejora la métrica.\")\n", + "\n", + " fig, axes = plt.subplots(1, 3, figsize=(8.6, 3.0))\n", + " images = [work, noisy, np.clip(recovered, 0, 1)]\n", + " titles = [\n", + " \"original real / original\",\n", + " \"blurred + noise / desenfoque\",\n", + " f\"Richardson–Lucy ({iterations})\",\n", + " ]\n", + "\n", + " for ax, im, title in zip(axes, images, titles):\n", + " ax.imshow(im, cmap=\"gray\", vmin=0, vmax=1)\n", + " ax.set_title(title, fontsize=9)\n", + " ax.axis(\"off\")\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "deconv_output = widgets.interactive_output(\n", + " explore_deconvolution,\n", + " {\"iterations\": iterations_slider},\n", + ")\n", + "\n", + "display(widgets.VBox([iterations_slider, deconv_output]))" + ], + "id": "fevJ1to2fdAX" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "n9x9uzxpfdAX" + }, + "source": [ + "## What just happened\n", + "\n", + "You followed one operator from forward use to inverse use.\n", + "\n", + "1. **Correlation vs convolution:** deep-learning libraries usually slide the learned kernel without flipping it, which is cross-correlation. True convolution gives the same result when the kernel is flipped first.\n", + "2. **Toeplitz view:** a convolution of real pixel measurements became an ordinary matrix product `C @ x`. The special part was the repeated structure of `C`, not a new algebra.\n", + "3. **Transposed convolution:** overlap-add changed the spatial shape using the same local weights. It behaved like the transpose/adjoint partner of a convolutional operator, not like a true inverse.\n", + "4. **True deconvolution:** Richardson–Lucy used the known blur kernel plus an iterative model to recover some detail from a noisy real image. The result had to be measured away from unreliable boundaries.\n", + "\n", + "### The sentence to remember\n", + "\n", + "> **Convolution applies a structured operator; transposed convolution applies its shape-changing partner; deconvolution tries to solve the inverse problem.**\n", + "\n", + "In microscopy, astronomy, and medical imaging, the blur kernel is often described by a **point-spread function (PSF)**. Deconvolution is useful precisely because imaging systems spread information before we ever see the pixels.\n", + "\n", + "> 🇪🇸 Seguiste un mismo operador desde el problema directo hasta el inverso. La correlación y la convolución se diferencian por el volteo del kernel; Toeplitz muestra que la operación sigue siendo multiplicación matricial; la convolución transpuesta cambia la forma pero no recupera automáticamente lo perdido; y Richardson–Lucy intenta resolver un problema inverso real bajo ruido.\n", + ">\n", + "> **Frase para recordar:** la convolución aplica un operador estructurado; la convolución transpuesta aplica su compañero que cambia la forma; la deconvolución intenta resolver el problema inverso." + ], + "id": "n9x9uzxpfdAX" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MSfwCollfdAX" + }, + "source": [ + "---\n", + "\n", + "## Done with this section\n", + "\n", + "Next up: **10 · Tucker decomposition on real data** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/10-tucker-decomposition.ipynb).\n", + "\n", + "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)\n", + "\n", + "> 🇪🇸 **Fin de esta sección.** A continuación: **10 · Descomposición Tucker con datos reales**." + ], + "id": "MSfwCollfdAX" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3" + }, + "colab": { + "provenance": [] + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "version_major": 2, + "version_minor": 0, + "state": { + "6919773a5f424929a218e688c37fd897": { + "model_module": "@jupyter-widgets/controls", + "model_name": "VBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "VBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "VBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_0c73ae284a094f0dbeb5f9d8a4a28b53", + "IPY_MODEL_f986b5345f574416abe533a93ae3ac55" + ], + "layout": "IPY_MODEL_00f17bafcda144e0af306d4ad3464b05" + } + }, + "0c73ae284a094f0dbeb5f9d8a4a28b53": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ToggleButtonsModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ToggleButtonsModel", + "_options_labels": [ + "valid", + "same", + "full" + ], + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ToggleButtonsView", + "button_style": "", + "description": "Mode / Modo:", + "description_tooltip": null, + "disabled": false, + "icons": [], + "index": 1, + "layout": "IPY_MODEL_8aded483293f49269adf02c8125e6ce7", + "style": "IPY_MODEL_2cd870b8a3734d628fd39b3ba099c70f", + "tooltips": [] + } + }, + "f986b5345f574416abe533a93ae3ac55": { + "model_module": "@jupyter-widgets/output", + "model_name": "OutputModel", + "model_module_version": "1.0.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/output", + "_model_module_version": "1.0.0", + "_model_name": "OutputModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/output", + "_view_module_version": "1.0.0", + "_view_name": "OutputView", + "layout": "IPY_MODEL_48f38061dc4e414ba31ec1b4ce6e57d4", + "msg_id": "", + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "mode/modo=same | input/entrada=(160, 160) | output/salida=(160, 160)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
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null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + } + } + } + } }, - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file From 820f73084ef6cdb87b9ffe1b3f3f823b8e7f3a3d Mon Sep 17 00:00:00 2001 From: Sebas Laverde Chunza Date: Fri, 28 Aug 2026 22:29:14 -0500 Subject: [PATCH 20/29] Finalize notebook 09 pedagogy for issue #44 --- _variables.yml | 24 +- .../09-convolution-and-deconvolution.ipynb | 1421 +++++++-- .../09-convolution-and-deconvolution.ipynb | 2818 ++++++++--------- scripts/content.py | 38 +- 4 files changed, 2505 insertions(+), 1796 deletions(-) diff --git a/_variables.yml b/_variables.yml index 1b30f5f..3ed59e3 100644 --- a/_variables.yml +++ b/_variables.yml @@ -367,20 +367,20 @@ sections: format_es: "ejercicio" title_en: "Convolution and deconvolution" title_es: "Convolución y deconvolución" - summary_en: "Convolution is a structured matrix product, and blur can be partly undone." - summary_es: "La convolución es un producto matricial estructurado, y el desenfoque se puede deshacer en parte." + summary_en: "Treat convolution as a structured linear operator, separate it from correlation, understand transposed convolution, and partially recover a real blurred image." + summary_es: "Ver la convolución como un operador lineal estructurado, distinguir correlación de convolución, entender la convolución transpuesta y recuperar parcialmente una imagen real desenfocada." objectives_en: - - "Predict the output size of a convolution in `full`, `valid` and `same` mode." - - "Say why what deep learning calls convolution is really correlation." - - "Write a convolution as multiplication by a Toeplitz matrix." - - "Distinguish transposed convolution from true deconvolution." - - "Recover a blurred image with Richardson-Lucy, and measure it honestly." + - "Predict `full`, `same`, and `valid` output shapes for a real image and verify them interactively." + - "Explain why deep-learning convolution is usually cross-correlation and show the kernel-flip relationship." + - "Write a 1D convolution of a real image scanline as multiplication by a Toeplitz matrix." + - "Explain transposed convolution as an overlap-add linear operator that changes shape but is not a true inverse." + - "Recover a real blurred image with Richardson-Lucy and measure improvement away from boundary artifacts." objectives_es: - - "Predecir el tamaño de salida de una convolución en los modos `full`, `valid` y `same`." - - "Explicar por qué lo que deep learning llama convolución es realmente correlación." - - "Escribir una convolución como multiplicación por una matriz de Toeplitz." - - "Distinguir la convolución transpuesta de una deconvolución verdadera." - - "Recuperar una imagen desenfocada con Richardson-Lucy y medir el resultado de manera rigurosa." + - "Predecir los tamaños de salida `full`, `same` y `valid` para una imagen real y verificarlos de forma interactiva." + - "Explicar por qué la convolución en deep learning suele ser correlación cruzada y mostrar la relación mediante el volteo del kernel." + - "Escribir una convolución 1D de una fila real de píxeles como multiplicación por una matriz Toeplitz." + - "Explicar la convolución transpuesta como un operador lineal de superposición y suma que cambia la forma pero no es una inversa verdadera." + - "Recuperar una imagen real desenfocada con Richardson-Lucy y medir la mejora lejos de los artefactos de borde." s10: n: "10" slug: "tucker-decomposition" diff --git a/docs/notebooks/09-convolution-and-deconvolution.ipynb b/docs/notebooks/09-convolution-and-deconvolution.ipynb index 1686e27..ae04705 100644 --- a/docs/notebooks/09-convolution-and-deconvolution.ipynb +++ b/docs/notebooks/09-convolution-and-deconvolution.ipynb @@ -10,19 +10,19 @@ "\n", "*Part IV · exercise · 15 min*\n", "\n", - "> 🇪🇸 **Convolución y deconvolución** — La convolución es un producto matricial estructurado, y el desenfoque se puede deshacer en parte.\n", + "> 🇪🇸 **Convolución y deconvolución** — Ver la convolución como un operador lineal estructurado, distinguir correlación de convolución, entender la convolución transpuesta y recuperar parcialmente una imagen real desenfocada.\n", "\n", - "Convolution is a structured matrix product, and blur can be partly undone.\n", + "Treat convolution as a structured linear operator, separate it from correlation, understand transposed convolution, and partially recover a real blurred image.\n", "\n", "## What you will be able to do\n", "\n", - "- Predict the output size of a convolution in `full`, `valid` and `same` mode.\n", - "- Say why what deep learning calls convolution is really correlation.\n", - "- Write a convolution as multiplication by a Toeplitz matrix.\n", - "- Distinguish transposed convolution from true deconvolution.\n", - "- Recover a blurred image with Richardson-Lucy, and measure it honestly." + "- Predict `full`, `same`, and `valid` output shapes for a real image and verify them interactively.\n", + "- Explain why deep-learning convolution is usually cross-correlation and show the kernel-flip relationship.\n", + "- Write a 1D convolution of a real image scanline as multiplication by a Toeplitz matrix.\n", + "- Explain transposed convolution as an overlap-add linear operator that changes shape but is not a true inverse.\n", + "- Recover a real blurred image with Richardson-Lucy and measure improvement away from boundary artifacts." ], - "id": "s09-00" + "id": "m_jYVv4wfdAQ" }, { "cell_type": "markdown", @@ -34,7 +34,7 @@ "\n", "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." ], - "id": "s09-01" + "id": "YBkF2AOQfdAS" }, { "cell_type": "code", @@ -47,126 +47,108 @@ "from scipy.linalg import toeplitz\n", "from skimage import data\n", "from skimage.restoration import richardson_lucy\n", + "import matplotlib.pyplot as plt\n", + "import ipywidgets as widgets\n", + "from IPython.display import display\n", "\n", - "img = data.camera().astype(float) / 255. # real photograph, 512x512\n", - "sobel = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], float)\n", - "print(img.shape, img.min(), img.max())" - ], - "id": "s09-02" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The theory\n", + "# Enable ipywidgets in Google Colab when available.\n", + "try:\n", + " from google.colab import output\n", + " output.enable_custom_widget_manager()\n", + "except ImportError:\n", + " pass\n", "\n", - "> 🇪🇸 La convolución desliza un núcleo pequeño sobre un array mayor,\n", - "> multiplicando y sumando en cada posición.\n", + "img = data.camera().astype(float) / 255.0\n", + "patch = img[176:336, 176:336]\n", + "work = img[128:384, 128:384]\n", + "scanline = img[256, 220:252].copy()\n", "\n", - "**Convolution** slides a small array (the **kernel**, or **filter**) across a\n", - "larger one, multiplying and summing at each position. It is the operation at the\n", - "heart of every convolutional neural network, and it is also how every blur,\n", - "sharpen and edge-detection filter works." - ], - "id": "s09-03" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x = np.array([1., 2., 3., 4., 5.])\n", - "k = np.array([1., 0., -1.])\n", + "sobel_x = np.array([\n", + " [-1., 0., 1.],\n", + " [-2., 0., 2.],\n", + " [-1., 0., 1.],\n", + "])\n", "\n", - "print(np.convolve(x, k, 'full')) # [ 1. 2. 2. 2. 2. -4. -5.] length 5+3-1 = 7\n", - "print(np.convolve(x, k, 'valid')) # [ 2. 2. 2.] length 5-3+1 = 3\n", - "print(np.convolve(x, k, 'same')) # [ 2. 2. 2. 2. -4.] length 5" - ], - "id": "s09-04" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Three modes, three output sizes. `valid` uses only positions where the kernel\n", - "fits completely — this is why convolution **shrinks** an image by\n", - "`kernel_size - 1`.\n", - "\n", - "⚠️ **A detail that confuses everyone.** True convolution flips the kernel;\n", - "**correlation** does not. What deep learning libraries call \"convolution\" is\n", - "actually correlation. It makes no practical difference, because the network\n", - "*learns* the kernel — but you should know the names are inconsistent." - ], - "id": "s09-05" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(np.correlate(x, k, 'valid')) # [-2. -2. -2.]\n", - "print(np.convolve(x, k[::-1], 'valid')) # [-2. -2. -2.] — the same, with k flipped" - ], - "id": "s09-06" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Convolution is a matrix multiplication\n", + "kernel_1d = np.array([1., 0., -1.])\n", "\n", - "This is the connection back to Chapter 2. Any convolution can be written as\n", - "multiplication by a **Toeplitz** matrix — a matrix where the kernel is shifted\n", - "along each row." - ], - "id": "s09-07" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "col = np.zeros(7); col[:3] = k\n", - "row = np.zeros(5); row[0] = k[0]\n", - "C = toeplitz(col, row) # (7, 5)\n", - "print(C.shape)\n", - "print(np.allclose(C @ x, np.convolve(x, k, 'full'))) # True" + "def convmtx_full_1d(kernel, n):\n", + " m = len(kernel)\n", + " col = np.zeros(n + m - 1)\n", + " col[:m] = kernel\n", + " row = np.zeros(n)\n", + " row[0] = kernel[0]\n", + " return toeplitz(col, row)\n", + "\n", + "print(\"full image / imagen completa:\", img.shape)\n", + "print(\"real patch / recorte real:\", patch.shape)\n", + "print(\"deconvolution crop / recorte deconvolución:\", work.shape)\n", + "print(\"real scanline / fila real:\", scanline.shape)" ], - "id": "s09-08" + "id": "QSazhIfXfdAT" }, { "cell_type": "markdown", "metadata": {}, "source": [ - "So convolution is not a new kind of operation. It is a **structured matrix\n", - "multiplication** — one where the same few numbers are reused across the whole\n", - "matrix. That reuse is exactly why CNNs need so many fewer parameters than fully\n", - "connected networks.\n", + "## Why this matters\n", + "\n", + "Convolution is not a mysterious new kind of multiplication. It is a **linear operator with repeated structure**: the same small kernel is reused across positions.\n", + "\n", + "That gives us three important connections:\n", + "\n", + "- **convolution ↔ correlation:** true convolution flips the kernel; cross-correlation does not;\n", + "- **convolution ↔ matrix multiplication:** a Toeplitz matrix can represent the same operation;\n", + "- **blur ↔ inverse problem:** once convolution destroys or suppresses information, “undoing” it can be unstable.\n", + "\n", + "A transposed convolution belongs to the second connection: it is the transpose/adjoint-style partner of a convolution operator. It can enlarge an array through overlap-add, but it does **not** reconstruct the original values by itself.\n", + "\n", + "True deconvolution belongs to the third connection: we know or estimate the blur kernel and solve a difficult inverse problem under noise.\n", + "\n", + "### How to use the folded solutions / Cómo usar las soluciones plegadas\n", "\n", - "### Deconvolution means two different things\n", + "Each exercise has a **Solution / Solución** cell that is intentionally closed. First try the `TODO`; then open the solution to compare your reasoning with a reference implementation.\n", "\n", - "Keep them separate:\n", + "> 🇪🇸 La convolución es un operador lineal estructurado que reutiliza el mismo kernel. La convolución verdadera invierte el kernel; la correlación no. Ese mismo operador puede escribirse como una matriz Toeplitz. La convolución transpuesta cambia la forma mediante superposición y suma, pero no deshace automáticamente el operador original. La deconvolución verdadera intenta recuperar una señal perdida y por eso es un problema inverso sensible al ruido.\n", + ">\n", + "> Cada ejercicio tiene una celda **Solution / Solución** cerrada a propósito. Primero intenta el `TODO`; después abre la solución.\n", "\n", - "1. **Transposed convolution** — the upsampling layer in a decoder or GAN. It\n", - " makes things *bigger*. It is **not** a true inverse; the name is historical\n", - " and misleading.\n", - "2. **True deconvolution** — recovering the original signal from a blurred one.\n", - " This is a genuine inverse problem, and it is where section 07 comes back." + "### Learning cycle: Predict → Run → Explain\n", + "\n", + "Before every exercise, predict the output shape and what information should be preserved or lost. Then run it and explain the result.\n", + "\n", + "> 🇪🇸 **Predice → Ejecuta → Explica:** anticipa la forma de salida y qué información debería conservarse o perderse; luego ejecuta y explica el resultado." ], - "id": "s09-09" + "id": "UMs6aGKbfdAU" }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercise 1 — convolve and blur a real photograph\n", + "## Exercise 1 — convolution, correlation, and Toeplitz on real pixels\n", + "\n", + "We start from two real pieces of the photograph:\n", + "\n", + "- a `160×160` crop for 2D edge filtering;\n", + "- a length-32 scanline for the matrix view.\n", + "\n", + "For a `3×3` kernel on a `160×160` image:\n", + "\n", + "- `valid` should produce `158×158`;\n", + "- `same` should produce `160×160`;\n", + "- `full` should produce `162×162`.\n", + "\n", + "For the scanline, full 1D convolution with a length-3 kernel should produce `32 + 3 - 1 = 34` values.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Compare `correlate2d(patch, sobel_x)` with `convolve2d(patch, flip(sobel_x))`.\n", + "2. Verify that they match.\n", + "3. Build a Toeplitz matrix `C` for the real scanline and check `C @ scanline == np.convolve(...)`.\n", + "4. Move **Mode / Modo** and verify the predicted image sizes.\n", "\n", - "> 🇪🇸 Convoluciona y desenfoca una fotografía real." + "> 🇪🇸 Usaremos píxeles reales de la fotografía. Comprueba que la correlación con Sobel coincide con la convolución cuando el kernel se invierte, representa una convolución 1D con una matriz Toeplitz y usa el selector de modo para confirmar `valid`, `same` y `full`." ], - "id": "s09-10" + "id": "elfkGTiPfdAU" }, { "cell_type": "code", @@ -174,105 +156,109 @@ "metadata": {}, "outputs": [], "source": [ - "# TODO 1: Convolve `img` with `sobel` in 'valid' mode. What shape comes out,\n", - "# and by how much did it shrink?\n", - "\n", - "# TODO 2: Blur the image with a 9x9 averaging kernel (all entries equal,\n", - "# summing to 1), mode='same'. Display it next to the original." + "# TODO\n", + "# 1. Compute 2D correlation with sobel_x.\n", + "# 2. Compute 2D convolution with np.flip(sobel_x).\n", + "# 3. Verify that the two results match.\n", + "# 4. Build C = convmtx_full_1d(kernel_1d, len(scanline)).\n", + "# 5. Check C @ scanline against np.convolve(scanline, kernel_1d, \"full\")." ], - "id": "s09-11" + "id": "W4pZ396DfdAV" }, { "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "edges = signal.convolve2d(img, sobel, mode='valid') # (510, 510) — shrank by 2\n", - "print(edges.shape)\n", + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "corr = signal.correlate2d(patch, sobel_x, mode=\"valid\")\n", + "conv_flipped = signal.convolve2d(\n", + " patch,\n", + " np.flip(sobel_x),\n", + " mode=\"valid\",\n", + ")\n", "\n", - "psf = np.ones((9, 9)); psf /= psf.sum()\n", - "blurred = signal.convolve2d(img, psf, mode='same', boundary='symm')\n", - "print(blurred.shape) # (512, 512) — 'same' keeps it\n", + "print(\n", + " \"correlation == convolution with flipped kernel / \"\n", + " \"correlación == convolución con kernel invertido:\",\n", + " np.allclose(corr, conv_flipped),\n", + ")\n", "\n", - "import matplotlib.pyplot as plt\n", - "fig, ax = plt.subplots(1, 3, figsize=(12, 4))\n", - "for a, im, t in zip(ax, [img, edges, blurred], [\"original\", \"sobel\", \"blurred\"]):\n", - " a.imshow(im, cmap=\"gray\"); a.set_title(t); a.axis(\"off\")\n", - "plt.show()\n", + "C = convmtx_full_1d(kernel_1d, len(scanline))\n", + "via_matrix = C @ scanline\n", + "via_convolution = np.convolve(scanline, kernel_1d, mode=\"full\")\n", "\n", - "# 'valid' shrinks by kernel_size - 1 = 2 in each direction: 512 -> 510." - ], - "id": "s09-12" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "See the blur trade-off live: a bigger averaging kernel removes more detail,\n", - "and it shrinks a `'valid'`-mode output by more (`kernel_size - 1` per side).\n", + "print(\"Toeplitz matrix / matriz Toeplitz:\", C.shape)\n", + "print(\"full convolution / convolución full:\", via_convolution.shape)\n", + "print(\"C @ scanline == convolution:\", np.allclose(via_matrix, via_convolution))\n", "\n", - "> 🇪🇸 El deslizador muestra el compromiso del desenfoque: un kernel más grande\n", - "> elimina más detalle, y en modo `'valid'` recorta más el resultado." - ], - "id": "s09-13" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " from google.colab import output\n", - " output.enable_custom_widget_manager()\n", - "except ImportError:\n", - " pass\n", + "mode_widget = widgets.ToggleButtons(\n", + " options=[\"valid\", \"same\", \"full\"],\n", + " value=\"same\",\n", + " description=\"Mode / Modo:\",\n", + " style={\"description_width\": \"95px\"},\n", + ")\n", "\n", - "import ipywidgets as widgets\n", - "import matplotlib.pyplot as plt\n", + "def explore_mode(mode):\n", + " filtered = signal.correlate2d(patch, sobel_x, mode=mode)\n", + "\n", + " print(\n", + " f\"mode/modo={mode} | input/entrada={patch.shape} | \"\n", + " f\"output/salida={filtered.shape}\"\n", + " )\n", "\n", - "def show_blur(k):\n", - " psf_k = np.ones((k, k)); psf_k /= psf_k.sum()\n", - " blurred_k = signal.convolve2d(img, psf_k, mode='same', boundary='symm')\n", - " valid_k = signal.convolve2d(img, psf_k, mode='valid')\n", - "\n", - " plt.close('all')\n", - " fig, axes = plt.subplots(1, 2, figsize=(8, 4))\n", - " axes[0].imshow(blurred_k, cmap='gray')\n", - " axes[0].set_title(f\"{k}x{k} kernel, mode='same' — {blurred_k.shape}\")\n", - " axes[1].imshow(valid_k, cmap='gray')\n", - " axes[1].set_title(f\"{k}x{k} kernel, mode='valid' — {valid_k.shape}\")\n", - " for a in axes:\n", - " a.axis('off')\n", + " fig, axes = plt.subplots(1, 2, figsize=(7.2, 3.0))\n", + " axes[0].imshow(patch, cmap=\"gray\")\n", + " axes[0].set_title(\"real patch / recorte real\")\n", + " axes[1].imshow(filtered, cmap=\"gray\")\n", + " axes[1].set_title(f\"Sobel correlation — {mode}\")\n", + " for ax in axes:\n", + " ax.axis(\"off\")\n", " plt.tight_layout()\n", " plt.show()\n", "\n", - "widgets.interact(show_blur,\n", - " k=widgets.IntSlider(min=1, max=25, step=2, value=9,\n", - " description='kernel size'));" + "mode_output = widgets.interactive_output(\n", + " explore_mode,\n", + " {\"mode\": mode_widget},\n", + ")\n", + "\n", + "display(widgets.VBox([mode_widget, mode_output]))" ], - "id": "s09-14" + "id": "llmJoCZJfdAV" }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercise 2 — transposed convolution\n", + "## Exercise 2 — transposed convolution changes shape, not history\n", + "\n", + "The phrase **“deconvolution layer”** is often used informally for transposed convolution in decoders and generative models. That name is misleading.\n", + "\n", + "Here we use a tiny `2×2` patch sampled from the real photograph so the overlap-add mechanism is visible.\n", + "\n", + "A transposed-convolution-style update places a scaled copy of the kernel into the output for each input value. When stride increases, the output grows and gaps appear between placements.\n", "\n", - "> 🇪🇸 Convolución transpuesta: hace las cosas más grandes, no las deshace." + "### What should you try?\n", + "\n", + "1. Take a real `2×2` patch from the photograph.\n", + "2. Use a `2×2` all-ones kernel.\n", + "3. Implement overlap-add for stride 1.\n", + "4. Move **Stride / Paso** from 1 to 3 and inspect the output shape.\n", + "5. Explain why a larger output does **not** mean the original pre-convolution image has been recovered.\n", + "\n", + "> 🇪🇸 La convolución transpuesta reutiliza un kernel mediante superposición y suma. Puede aumentar el tamaño espacial, especialmente con stride mayor que 1, pero aumentar la forma no equivale a invertir los valores perdidos por una convolución anterior." ], - "id": "s09-15" + "id": "4msnOeU7fdAW" }, { "cell_type": "code", @@ -280,66 +266,123 @@ "metadata": {}, "outputs": [], "source": [ - "# TODO 3: Upsample this 2x2 array to 3x3 by adding small * kernel into an\n", - "# output array at each position:\n", - "# small = np.array([[1., 2.], [3., 4.]]); ker = np.ones((2, 2))\n", - "# What shape do you get? Why is this called \"deconvolution\" in CNNs\n", - "# even though it does not undo anything?" + "# TODO\n", + "# 1. Extract a real 2×2 patch from img.\n", + "# 2. Implement overlap-add with a 2×2 kernel and stride=1.\n", + "# 3. Predict the output shape.\n", + "# 4. Repeat with stride=2.\n", + "# 5. Explain why this is not a true inverse." ], - "id": "s09-16" + "id": "N3KU8_VUfdAW" }, { "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "small = np.array([[1., 2.], [3., 4.]])\n", - "ker = np.ones((2, 2))\n", - "\n", - "out = np.zeros((3, 3))\n", - "for i in range(2):\n", - " for j in range(2):\n", - " out[i:i+2, j:j+2] += small[i, j] * ker\n", - "print(out)\n", - "# [[ 1. 3. 2.]\n", - "# [ 4. 10. 6.]\n", - "# [ 3. 7. 4.]]\n", - "\n", - "# Shape (3, 3): it GREW, by kernel_size - 1, which is exactly what 'valid'\n", - "# convolution shrinks by. That inverse relationship between the SHAPES is the\n", - "# whole reason for the name. The VALUES are not undone at all — you cannot\n", - "# recover `small` from `out`. The name is historical and misleading." + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "small = img[250:254:2, 250:254:2].copy() # real 2×2 pixel patch\n", + "ker = np.ones((2, 2), dtype=float)\n", + "\n", + "def transposed_overlap_add(x, kernel, stride=1):\n", + " h, w = x.shape\n", + " kh, kw = kernel.shape\n", + "\n", + " out_h = (h - 1) * stride + kh\n", + " out_w = (w - 1) * stride + kw\n", + " out = np.zeros((out_h, out_w), dtype=float)\n", + "\n", + " for i in range(h):\n", + " for j in range(w):\n", + " r = i * stride\n", + " c = j * stride\n", + " out[r:r+kh, c:c+kw] += x[i, j] * kernel\n", + "\n", + " return out\n", + "\n", + "print(\"real input / entrada real:\")\n", + "print(np.round(small, 3))\n", + "print(\"stride=1 output shape / forma:\", transposed_overlap_add(small, ker, 1).shape)\n", + "\n", + "stride_slider = widgets.IntSlider(\n", + " value=1,\n", + " min=1,\n", + " max=3,\n", + " step=1,\n", + " description=\"Stride / Paso:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"95px\"},\n", + ")\n", + "\n", + "def explore_transposed(stride):\n", + " out = transposed_overlap_add(small, ker, stride)\n", + "\n", + " print(\n", + " f\"input/entrada={small.shape} | stride/paso={stride} | \"\n", + " f\"output/salida={out.shape}\"\n", + " )\n", + " print(\n", + " \"EN: shape expansion is not value inversion; information lost earlier \"\n", + " \"does not magically return.\"\n", + " )\n", + " print(\n", + " \"ES: aumentar la forma no invierte los valores; la información perdida \"\n", + " \"antes no reaparece automáticamente.\"\n", + " )\n", + "\n", + " fig, axes = plt.subplots(1, 2, figsize=(5.8, 2.8))\n", + " axes[0].imshow(small, cmap=\"viridis\")\n", + " axes[0].set_title(\"real 2×2 input\")\n", + " axes[1].imshow(out, cmap=\"viridis\")\n", + " axes[1].set_title(f\"overlap-add, stride={stride}\")\n", + " for ax in axes:\n", + " ax.set_xticks(range(ax.images[0].get_array().shape[1]))\n", + " ax.set_yticks(range(ax.images[0].get_array().shape[0]))\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "transpose_output = widgets.interactive_output(\n", + " explore_transposed,\n", + " {\"stride\": stride_slider},\n", + ")\n", + "\n", + "display(widgets.VBox([stride_slider, transpose_output]))" ], - "id": "s09-17" + "id": "LcIHWex0fdAW" }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercise 3 — true deconvolution\n", + "## Exercise 3 — true deconvolution on a real photograph\n", + "\n", + "Now we solve an actual inverse problem.\n", + "\n", + "We blur a real `256×256` crop with a `9×9` point-spread function (PSF), add a small controlled amount of noise, and attempt to recover the original with **Richardson–Lucy deconvolution**.\n", + "\n", + "There is an important measurement trap: boundary pixels are where the algorithm has the least information about what lies outside the image. We therefore compute the error after removing a fixed border.\n", "\n", - "> 🇪🇸 Deconvolución de verdad. **Recorta 25 píxeles del borde antes de medir.**\n", + "### What should you try?\n", "\n", - "::: {.callout-warning}\n", - "**This is the hardest thing in the workshop, and it has a trap.** Deconvolution\n", - "creates strong artifacts at the edges, where the algorithm has no information\n", - "about what lies outside the image. If you measure error over the whole image,\n", - "the artifacts dominate and it looks like the method failed. **Crop 25 pixels off\n", - "every side before measuring.** Do this before you run it, not after.\n", - ":::" + "1. Blur the real image crop with a normalized `9×9` averaging PSF.\n", + "2. Add small reproducible Gaussian noise.\n", + "3. Recover it with `richardson_lucy`.\n", + "4. Measure relative error on the interior only.\n", + "5. Move **Iterations / Iteraciones** and observe the trade-off: too few iterations under-correct; many iterations can begin to emphasize noise.\n", + "\n", + "> 🇪🇸 Ahora sí resolvemos un problema de deconvolución. Desenfocamos un recorte real con una PSF conocida, añadimos ruido controlado y usamos Richardson–Lucy para recuperar detalle. Medimos únicamente el interior porque los bordes contienen artefactos propios de la falta de información fuera de la imagen." ], - "id": "s09-18" + "id": "udqLELfjfdAW" }, { "cell_type": "code", @@ -347,80 +390,113 @@ "metadata": {}, "outputs": [], "source": [ - "# TODO 4: Add small noise to the blurred image, then try to recover the\n", - "# original with skimage.restoration.richardson_lucy(..., num_iter=50).\n", - "# Measure error BEFORE and AFTER, ignoring a 25-pixel border.\n", - "# Did it improve?" + "# TODO\n", + "# 1. Build a normalized 9×9 averaging PSF.\n", + "# 2. Blur work with signal.fftconvolve(..., mode=\"same\").\n", + "# 3. Add small reproducible noise.\n", + "# 4. Recover with richardson_lucy.\n", + "# 5. Compare interior relative error before and after." ], - "id": "s09-19" + "id": "evDam-pcfdAW" }, { "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "psf = np.ones((9, 9)); psf /= psf.sum()\n", - "blurred = signal.convolve2d(img, psf, mode='same', boundary='symm')\n", - "noisy = blurred + 0.002 * np.random.default_rng(0).standard_normal(blurred.shape)\n", + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "psf = np.ones((9, 9), dtype=float)\n", + "psf /= psf.sum()\n", "\n", - "recovered = richardson_lucy(np.clip(noisy, 0, 1), psf, num_iter=50)\n", + "blurred = signal.fftconvolve(work, psf, mode=\"same\")\n", + "noise = 0.002 * np.random.default_rng(0).standard_normal(work.shape)\n", + "noisy = np.clip(blurred + noise, 0, 1)\n", "\n", - "c = 25 # ignore the border: deconvolution always creates edge artifacts\n", - "err = lambda a: np.linalg.norm((a - img)[c:-c, c:-c]) / np.linalg.norm(img[c:-c, c:-c])\n", - "print(err(noisy), err(recovered)) # 0.1157 -> 0.0815\n", + "border = 20\n", "\n", - "# Deconvolution REDUCED THE ERROR BY ABOUT 30%." - ], - "id": "s09-20" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Why not just invert the blur directly?\n", + "def interior_relative_error(candidate):\n", + " ref = work[border:-border, border:-border]\n", + " cand = candidate[border:-border, border:-border]\n", + " return np.linalg.norm(cand - ref) / np.linalg.norm(ref)\n", "\n", - "Because it fails badly. Blurring destroys high-frequency detail, so inverting it\n", - "divides by numbers very close to zero and amplifies noise enormously." - ], - "id": "s09-21" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Self-contained: rebuilds the blur and the noise rather than reusing names\n", - "# from the exercise above, so this cell runs whether or not you opened the\n", - "# solution — and whatever you called your own variables.\n", - "psf = np.ones((9, 9)); psf /= psf.sum()\n", - "blurred = signal.convolve2d(img, psf, mode='same', boundary='symm')\n", - "noisy = blurred + 0.002 * np.random.default_rng(0).standard_normal(blurred.shape)\n", - "\n", - "K = np.fft.fft2(psf, s=img.shape)\n", - "naive = np.real(np.fft.ifft2(np.fft.fft2(noisy) / np.where(abs(K) < 1e-3, 1e-3, K)))\n", - "\n", - "c = 25\n", - "err = lambda a: np.linalg.norm((a - img)[c:-c, c:-c]) / np.linalg.norm(img[c:-c, c:-c])\n", - "print(err(noisy), err(naive)) # 0.1157 ~2.49\n", - "#\n", - "# Richardson-Lucy got that 0.1157 down to 0.0815. The naive inverse takes it to\n", - "# ~2.49 — roughly TWENTY TIMES WORSE than the blurred image it started from.\n", - "# Cropping the border does not rescue it either (~2.51 uncropped): this is not\n", - "# an edge artifact, it is noise amplified across the whole image." + "recovered_20 = richardson_lucy(noisy, psf, num_iter=20, clip=False)\n", + "\n", + "print(\n", + " \"blurred+noise error / error desenfoque+ruido:\",\n", + " f\"{interior_relative_error(noisy):.4f}\",\n", + ")\n", + "print(\n", + " \"RL 20 iterations / iteraciones:\",\n", + " f\"{interior_relative_error(recovered_20):.4f}\",\n", + ")\n", + "\n", + "iterations_slider = widgets.IntSlider(\n", + " value=20,\n", + " min=1,\n", + " max=50,\n", + " step=3,\n", + " description=\"Iterations / Iteraciones:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"145px\"},\n", + ")\n", + "\n", + "def explore_deconvolution(iterations):\n", + " recovered = richardson_lucy(\n", + " noisy,\n", + " psf,\n", + " num_iter=iterations,\n", + " clip=False,\n", + " )\n", + "\n", + " err_before = interior_relative_error(noisy)\n", + " err_after = interior_relative_error(recovered)\n", + "\n", + " print(\n", + " f\"iterations/iteraciones={iterations} | \"\n", + " f\"before/antes={err_before:.4f} | after/después={err_after:.4f}\"\n", + " )\n", + "\n", + " if err_after < err_before:\n", + " print(\"EN: recovery improved the interior error.\")\n", + " print(\"ES: la recuperación redujo el error interior.\")\n", + " else:\n", + " print(\"EN: at this iteration count the recovery no longer improves the metric.\")\n", + " print(\"ES: con este número de iteraciones la recuperación ya no mejora la métrica.\")\n", + "\n", + " fig, axes = plt.subplots(1, 3, figsize=(8.6, 3.0))\n", + " images = [work, noisy, np.clip(recovered, 0, 1)]\n", + " titles = [\n", + " \"original real / original\",\n", + " \"blurred + noise / desenfoque\",\n", + " f\"Richardson–Lucy ({iterations})\",\n", + " ]\n", + "\n", + " for ax, im, title in zip(axes, images, titles):\n", + " ax.imshow(im, cmap=\"gray\", vmin=0, vmax=1)\n", + " ax.set_title(title, fontsize=9)\n", + " ax.axis(\"off\")\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "deconv_output = widgets.interactive_output(\n", + " explore_deconvolution,\n", + " {\"iterations\": iterations_slider},\n", + ")\n", + "\n", + "display(widgets.VBox([iterations_slider, deconv_output]))" ], - "id": "s09-22" + "id": "fevJ1to2fdAX" }, { "cell_type": "markdown", @@ -428,19 +504,24 @@ "source": [ "## What just happened\n", "\n", - "**This is the same lesson as section 07.** A direct inverse either does not exist\n", - "or is unusable, so you use a method that finds the best stable answer instead.\n", - "The pseudoinverse does this for linear systems; Richardson-Lucy and Wiener\n", - "filtering do it for deconvolution.\n", + "You followed one operator from forward use to inverse use.\n", + "\n", + "1. **Correlation vs convolution:** deep-learning libraries usually slide the learned kernel without flipping it, which is cross-correlation. True convolution gives the same result when the kernel is flipped first.\n", + "2. **Toeplitz view:** a convolution of real pixel measurements became an ordinary matrix product `C @ x`. The special part was the repeated structure of `C`, not a new algebra.\n", + "3. **Transposed convolution:** overlap-add changed the spatial shape using the same local weights. It behaved like the transpose/adjoint partner of a convolutional operator, not like a true inverse.\n", + "4. **True deconvolution:** Richardson–Lucy used the known blur kernel plus an iterative model to recover some detail from a noisy real image. The result had to be measured away from unreliable boundaries.\n", + "\n", + "### The sentence to remember\n", "\n", - "> 🇪🇸 Cuando no existe una inversa exacta, no te rindes: buscas la mejor\n", - "> aproximación estable.\n", + "> **Convolution applies a structured operator; transposed convolution applies its shape-changing partner; deconvolution tries to solve the inverse problem.**\n", "\n", - "In biotech this is routine: every fluorescence microscope blurs its images by a\n", - "known amount (the *point spread function*), and deconvolution is standard\n", - "practice before cells are counted or measured." + "In microscopy, astronomy, and medical imaging, the blur kernel is often described by a **point-spread function (PSF)**. Deconvolution is useful precisely because imaging systems spread information before we ever see the pixels.\n", + "\n", + "> 🇪🇸 Seguiste un mismo operador desde el problema directo hasta el inverso. La correlación y la convolución se diferencian por el volteo del kernel; Toeplitz muestra que la operación sigue siendo multiplicación matricial; la convolución transpuesta cambia la forma pero no recupera automáticamente lo perdido; y Richardson–Lucy intenta resolver un problema inverso real bajo ruido.\n", + ">\n", + "> **Frase para recordar:** la convolución aplica un operador estructurado; la convolución transpuesta aplica su compañero que cambia la forma; la deconvolución intenta resolver el problema inverso." ], - "id": "s09-23" + "id": "n9x9uzxpfdAX" }, { "cell_type": "markdown", @@ -454,22 +535,800 @@ "\n", "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" ], - "id": "s09-24" + "id": "MSfwCollfdAX" } ], "metadata": { - "colab": { - "name": "09-convolution-and-deconvolution.ipynb", - "provenance": [], - "toc_visible": true - }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { - "name": "python" + "name": "python", + "version": "3" + }, + "colab": { + "provenance": [] + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "version_major": 2, + "version_minor": 0, + "state": { + "6919773a5f424929a218e688c37fd897": { + "model_module": "@jupyter-widgets/controls", + "model_name": "VBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "VBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "VBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_0c73ae284a094f0dbeb5f9d8a4a28b53", + "IPY_MODEL_f986b5345f574416abe533a93ae3ac55" + ], + "layout": "IPY_MODEL_00f17bafcda144e0af306d4ad3464b05" + } + }, + "0c73ae284a094f0dbeb5f9d8a4a28b53": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ToggleButtonsModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ToggleButtonsModel", + "_options_labels": [ + "valid", + "same", + "full" + ], + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ToggleButtonsView", + "button_style": "", + "description": "Mode / Modo:", + "description_tooltip": null, + "disabled": false, + "icons": [], + "index": 1, + "layout": "IPY_MODEL_8aded483293f49269adf02c8125e6ce7", + "style": "IPY_MODEL_2cd870b8a3734d628fd39b3ba099c70f", + "tooltips": [] + } + }, + "f986b5345f574416abe533a93ae3ac55": { + "model_module": "@jupyter-widgets/output", + "model_name": "OutputModel", + "model_module_version": "1.0.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/output", + "_model_module_version": "1.0.0", + "_model_name": "OutputModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/output", + "_view_module_version": "1.0.0", + "_view_name": "OutputView", + "layout": "IPY_MODEL_48f38061dc4e414ba31ec1b4ce6e57d4", + "msg_id": "", + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "mode/modo=same | input/entrada=(160, 160) | output/salida=(160, 160)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
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null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + } + } + } } }, "nbformat": 4, diff --git a/notebooks/09-convolution-and-deconvolution.ipynb b/notebooks/09-convolution-and-deconvolution.ipynb index 398b83c..ae04705 100644 --- a/notebooks/09-convolution-and-deconvolution.ipynb +++ b/notebooks/09-convolution-and-deconvolution.ipynb @@ -1,1518 +1,1336 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "m_jYVv4wfdAQ" - }, - "source": [ - "# 09 · Convolution and deconvolution\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/09-convolution-and-deconvolution.ipynb)\n", - "\n", - "*Part IV · exercise · 15 min*\n", - "\n", - "> 🇪🇸 **Convolución y deconvolución** — Ver la convolución como un operador lineal estructurado, distinguir correlación de convolución, entender qué hace realmente una convolución transpuesta y recuperar parcialmente una imagen real desenfocada.\n", - "\n", - "Treat convolution as a structured linear operator, separate it from correlation, understand what transposed convolution actually does, and partially recover a real blurred image.\n", - "\n", - "## What you will be able to do\n", - "\n", - "- Predict `full`, `same`, and `valid` output shapes for a real image and verify them interactively.\n", - "- Explain why deep-learning “convolution” is usually cross-correlation and show the kernel-flip relationship.\n", - "- Write a 1D convolution of a real image scanline as multiplication by a Toeplitz matrix.\n", - "- Explain transposed convolution as an overlap-add linear operator that changes shape but is not a true inverse.\n", - "- Recover a real blurred image with Richardson–Lucy and measure improvement away from boundary artifacts.\n", - "\n", - "> 🇪🇸 **Al terminar podrás:** predecir tamaños de salida; distinguir correlación de convolución; representar una convolución como una matriz Toeplitz; explicar por qué la convolución transpuesta no es una deconvolución verdadera; y medir de forma honesta la recuperación de una imagen real desenfocada." - ], - "id": "m_jYVv4wfdAQ" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "YBkF2AOQfdAS" - }, - "source": [ - "## Setup\n", - "\n", - "Run this first. We use a real grayscale photograph from `skimage.data`, a Sobel edge kernel, and a real scanline from that image.\n", - "\n", - "> 🇪🇸 Ejecuta primero esta celda. Usaremos una fotografía real en escala de grises de `skimage.data`, un kernel Sobel para bordes y una fila real de píxeles extraída de esa imagen." - ], - "id": "YBkF2AOQfdAS" - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "QSazhIfXfdAT", - "outputId": "87c28960-3f94-41ca-f2e5-e773145d5fcd" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "full image / imagen completa: (512, 512)\n", - "real patch / recorte real: (160, 160)\n", - "deconvolution crop / recorte deconvolución: (256, 256)\n", - "real scanline / fila real: (32,)\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "from scipy import signal\n", - "from scipy.linalg import toeplitz\n", - "from skimage import data\n", - "from skimage.restoration import richardson_lucy\n", - "import matplotlib.pyplot as plt\n", - "import ipywidgets as widgets\n", - "from IPython.display import display\n", - "\n", - "try:\n", - " from google.colab import output\n", - " output.enable_custom_widget_manager()\n", - "except ImportError:\n", - " pass\n", - "\n", - "img = data.camera().astype(float) / 255.0 # real 512×512 photograph\n", - "patch = img[176:336, 176:336] # real 160×160 crop\n", - "work = img[128:384, 128:384] # real 256×256 crop for deconvolution\n", - "scanline = img[256, 220:252].copy() # 32 real pixel measurements\n", - "\n", - "sobel_x = np.array([\n", - " [-1., 0., 1.],\n", - " [-2., 0., 2.],\n", - " [-1., 0., 1.],\n", - "])\n", - "\n", - "kernel_1d = np.array([1., 0., -1.])\n", - "\n", - "def convmtx_full_1d(kernel, n):\n", - " m = len(kernel)\n", - " col = np.zeros(n + m - 1)\n", - " col[:m] = kernel\n", - " row = np.zeros(n)\n", - " row[0] = kernel[0]\n", - " return toeplitz(col, row)\n", - "\n", - "print(\"full image / imagen completa:\", img.shape)\n", - "print(\"real patch / recorte real:\", patch.shape)\n", - "print(\"deconvolution crop / recorte deconvolución:\", work.shape)\n", - "print(\"real scanline / fila real:\", scanline.shape)" - ], - "id": "QSazhIfXfdAT" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UMs6aGKbfdAU" - }, - "source": [ - "## Why this matters\n", - "\n", - "Convolution is not a mysterious new kind of multiplication. It is a **linear operator with repeated structure**: the same small kernel is reused across positions.\n", - "\n", - "That gives us three important connections:\n", - "\n", - "- **convolution ↔ correlation:** true convolution flips the kernel; cross-correlation does not;\n", - "- **convolution ↔ matrix multiplication:** a Toeplitz matrix can represent the same operation;\n", - "- **blur ↔ inverse problem:** once convolution destroys or suppresses information, “undoing” it can be unstable.\n", - "\n", - "A transposed convolution belongs to the second connection: it is the transpose/adjoint-style partner of a convolution operator. It can enlarge an array through overlap-add, but it does **not** reconstruct the original values by itself.\n", - "\n", - "True deconvolution belongs to the third connection: we know or estimate the blur kernel and solve a difficult inverse problem under noise.\n", - "\n", - "### How to use the folded solutions / Cómo usar las soluciones plegadas\n", - "\n", - "Each exercise has a **Solution / Solución** cell that is intentionally closed. First try the `TODO`; then open the solution to compare your reasoning with a reference implementation.\n", - "\n", - "> 🇪🇸 La convolución es un operador lineal estructurado que reutiliza el mismo kernel. La convolución verdadera invierte el kernel; la correlación no. Ese mismo operador puede escribirse como una matriz Toeplitz. La convolución transpuesta cambia la forma mediante superposición y suma, pero no deshace automáticamente el operador original. La deconvolución verdadera intenta recuperar una señal perdida y por eso es un problema inverso sensible al ruido.\n", - ">\n", - "> Cada ejercicio tiene una celda **Solution / Solución** cerrada a propósito. Primero intenta el `TODO`; después abre la solución.\n", - "\n", - "### Learning cycle: Predict → Run → Explain\n", - "\n", - "Before every exercise, predict the output shape and what information should be preserved or lost. Then run it and explain the result.\n", - "\n", - "> 🇪🇸 **Predice → Ejecuta → Explica:** anticipa la forma de salida y qué información debería conservarse o perderse; luego ejecuta y explica el resultado." - ], - "id": "UMs6aGKbfdAU" + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 09 · Convolution and deconvolution\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/09-convolution-and-deconvolution.ipynb)\n", + "\n", + "*Part IV · exercise · 15 min*\n", + "\n", + "> 🇪🇸 **Convolución y deconvolución** — Ver la convolución como un operador lineal estructurado, distinguir correlación de convolución, entender la convolución transpuesta y recuperar parcialmente una imagen real desenfocada.\n", + "\n", + "Treat convolution as a structured linear operator, separate it from correlation, understand transposed convolution, and partially recover a real blurred image.\n", + "\n", + "## What you will be able to do\n", + "\n", + "- Predict `full`, `same`, and `valid` output shapes for a real image and verify them interactively.\n", + "- Explain why deep-learning convolution is usually cross-correlation and show the kernel-flip relationship.\n", + "- Write a 1D convolution of a real image scanline as multiplication by a Toeplitz matrix.\n", + "- Explain transposed convolution as an overlap-add linear operator that changes shape but is not a true inverse.\n", + "- Recover a real blurred image with Richardson-Lucy and measure improvement away from boundary artifacts." + ], + "id": "m_jYVv4wfdAQ" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "Run this first. It installs and imports everything this notebook needs, and nothing else.\n", + "\n", + "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." + ], + "id": "YBkF2AOQfdAS" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from scipy import signal\n", + "from scipy.linalg import toeplitz\n", + "from skimage import data\n", + "from skimage.restoration import richardson_lucy\n", + "import matplotlib.pyplot as plt\n", + "import ipywidgets as widgets\n", + "from IPython.display import display\n", + "\n", + "# Enable ipywidgets in Google Colab when available.\n", + "try:\n", + " from google.colab import output\n", + " output.enable_custom_widget_manager()\n", + "except ImportError:\n", + " pass\n", + "\n", + "img = data.camera().astype(float) / 255.0\n", + "patch = img[176:336, 176:336]\n", + "work = img[128:384, 128:384]\n", + "scanline = img[256, 220:252].copy()\n", + "\n", + "sobel_x = np.array([\n", + " [-1., 0., 1.],\n", + " [-2., 0., 2.],\n", + " [-1., 0., 1.],\n", + "])\n", + "\n", + "kernel_1d = np.array([1., 0., -1.])\n", + "\n", + "def convmtx_full_1d(kernel, n):\n", + " m = len(kernel)\n", + " col = np.zeros(n + m - 1)\n", + " col[:m] = kernel\n", + " row = np.zeros(n)\n", + " row[0] = kernel[0]\n", + " return toeplitz(col, row)\n", + "\n", + "print(\"full image / imagen completa:\", img.shape)\n", + "print(\"real patch / recorte real:\", patch.shape)\n", + "print(\"deconvolution crop / recorte deconvolución:\", work.shape)\n", + "print(\"real scanline / fila real:\", scanline.shape)" + ], + "id": "QSazhIfXfdAT" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Why this matters\n", + "\n", + "Convolution is not a mysterious new kind of multiplication. It is a **linear operator with repeated structure**: the same small kernel is reused across positions.\n", + "\n", + "That gives us three important connections:\n", + "\n", + "- **convolution ↔ correlation:** true convolution flips the kernel; cross-correlation does not;\n", + "- **convolution ↔ matrix multiplication:** a Toeplitz matrix can represent the same operation;\n", + "- **blur ↔ inverse problem:** once convolution destroys or suppresses information, “undoing” it can be unstable.\n", + "\n", + "A transposed convolution belongs to the second connection: it is the transpose/adjoint-style partner of a convolution operator. It can enlarge an array through overlap-add, but it does **not** reconstruct the original values by itself.\n", + "\n", + "True deconvolution belongs to the third connection: we know or estimate the blur kernel and solve a difficult inverse problem under noise.\n", + "\n", + "### How to use the folded solutions / Cómo usar las soluciones plegadas\n", + "\n", + "Each exercise has a **Solution / Solución** cell that is intentionally closed. First try the `TODO`; then open the solution to compare your reasoning with a reference implementation.\n", + "\n", + "> 🇪🇸 La convolución es un operador lineal estructurado que reutiliza el mismo kernel. La convolución verdadera invierte el kernel; la correlación no. Ese mismo operador puede escribirse como una matriz Toeplitz. La convolución transpuesta cambia la forma mediante superposición y suma, pero no deshace automáticamente el operador original. La deconvolución verdadera intenta recuperar una señal perdida y por eso es un problema inverso sensible al ruido.\n", + ">\n", + "> Cada ejercicio tiene una celda **Solution / Solución** cerrada a propósito. Primero intenta el `TODO`; después abre la solución.\n", + "\n", + "### Learning cycle: Predict → Run → Explain\n", + "\n", + "Before every exercise, predict the output shape and what information should be preserved or lost. Then run it and explain the result.\n", + "\n", + "> 🇪🇸 **Predice → Ejecuta → Explica:** anticipa la forma de salida y qué información debería conservarse o perderse; luego ejecuta y explica el resultado." + ], + "id": "UMs6aGKbfdAU" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 1 — convolution, correlation, and Toeplitz on real pixels\n", + "\n", + "We start from two real pieces of the photograph:\n", + "\n", + "- a `160×160` crop for 2D edge filtering;\n", + "- a length-32 scanline for the matrix view.\n", + "\n", + "For a `3×3` kernel on a `160×160` image:\n", + "\n", + "- `valid` should produce `158×158`;\n", + "- `same` should produce `160×160`;\n", + "- `full` should produce `162×162`.\n", + "\n", + "For the scanline, full 1D convolution with a length-3 kernel should produce `32 + 3 - 1 = 34` values.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Compare `correlate2d(patch, sobel_x)` with `convolve2d(patch, flip(sobel_x))`.\n", + "2. Verify that they match.\n", + "3. Build a Toeplitz matrix `C` for the real scanline and check `C @ scanline == np.convolve(...)`.\n", + "4. Move **Mode / Modo** and verify the predicted image sizes.\n", + "\n", + "> 🇪🇸 Usaremos píxeles reales de la fotografía. Comprueba que la correlación con Sobel coincide con la convolución cuando el kernel se invierte, representa una convolución 1D con una matriz Toeplitz y usa el selector de modo para confirmar `valid`, `same` y `full`." + ], + "id": "elfkGTiPfdAU" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO\n", + "# 1. Compute 2D correlation with sobel_x.\n", + "# 2. Compute 2D convolution with np.flip(sobel_x).\n", + "# 3. Verify that the two results match.\n", + "# 4. Build C = convmtx_full_1d(kernel_1d, len(scanline)).\n", + "# 5. Check C @ scanline against np.convolve(scanline, kernel_1d, \"full\")." + ], + "id": "W4pZ396DfdAV" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true }, - { - "cell_type": "markdown", - "metadata": { - "id": "elfkGTiPfdAU" - }, - "source": [ - "## Exercise 1 — convolution, correlation, and Toeplitz on real pixels\n", - "\n", - "We start from two real pieces of the photograph:\n", - "\n", - "- a `160×160` crop for 2D edge filtering;\n", - "- a length-32 scanline for the matrix view.\n", - "\n", - "For a `3×3` kernel on a `160×160` image:\n", - "\n", - "- `valid` should produce `158×158`;\n", - "- `same` should produce `160×160`;\n", - "- `full` should produce `162×162`.\n", - "\n", - "For the scanline, full 1D convolution with a length-3 kernel should produce `32 + 3 - 1 = 34` values.\n", - "\n", - "### What should you try?\n", - "\n", - "1. Compare `correlate2d(patch, sobel_x)` with `convolve2d(patch, flip(sobel_x))`.\n", - "2. Verify that they match.\n", - "3. Build a Toeplitz matrix `C` for the real scanline and check `C @ scanline == np.convolve(...)`.\n", - "4. Move **Mode / Modo** and verify the predicted image sizes.\n", - "\n", - "> 🇪🇸 Usaremos píxeles reales de la fotografía. Comprueba que la correlación con Sobel coincide con la convolución cuando el kernel se invierte, representa una convolución 1D con una matriz Toeplitz y usa el selector de modo para confirmar `valid`, `same` y `full`." - ], - "id": "elfkGTiPfdAU" + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "corr = signal.correlate2d(patch, sobel_x, mode=\"valid\")\n", + "conv_flipped = signal.convolve2d(\n", + " patch,\n", + " np.flip(sobel_x),\n", + " mode=\"valid\",\n", + ")\n", + "\n", + "print(\n", + " \"correlation == convolution with flipped kernel / \"\n", + " \"correlación == convolución con kernel invertido:\",\n", + " np.allclose(corr, conv_flipped),\n", + ")\n", + "\n", + "C = convmtx_full_1d(kernel_1d, len(scanline))\n", + "via_matrix = C @ scanline\n", + "via_convolution = np.convolve(scanline, kernel_1d, mode=\"full\")\n", + "\n", + "print(\"Toeplitz matrix / matriz Toeplitz:\", C.shape)\n", + "print(\"full convolution / convolución full:\", via_convolution.shape)\n", + "print(\"C @ scanline == convolution:\", np.allclose(via_matrix, via_convolution))\n", + "\n", + "mode_widget = widgets.ToggleButtons(\n", + " options=[\"valid\", \"same\", \"full\"],\n", + " value=\"same\",\n", + " description=\"Mode / Modo:\",\n", + " style={\"description_width\": \"95px\"},\n", + ")\n", + "\n", + "def explore_mode(mode):\n", + " filtered = signal.correlate2d(patch, sobel_x, mode=mode)\n", + "\n", + " print(\n", + " f\"mode/modo={mode} | input/entrada={patch.shape} | \"\n", + " f\"output/salida={filtered.shape}\"\n", + " )\n", + "\n", + " fig, axes = plt.subplots(1, 2, figsize=(7.2, 3.0))\n", + " axes[0].imshow(patch, cmap=\"gray\")\n", + " axes[0].set_title(\"real patch / recorte real\")\n", + " axes[1].imshow(filtered, cmap=\"gray\")\n", + " axes[1].set_title(f\"Sobel correlation — {mode}\")\n", + " for ax in axes:\n", + " ax.axis(\"off\")\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "mode_output = widgets.interactive_output(\n", + " explore_mode,\n", + " {\"mode\": mode_widget},\n", + ")\n", + "\n", + "display(widgets.VBox([mode_widget, mode_output]))" + ], + "id": "llmJoCZJfdAV" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 2 — transposed convolution changes shape, not history\n", + "\n", + "The phrase **“deconvolution layer”** is often used informally for transposed convolution in decoders and generative models. That name is misleading.\n", + "\n", + "Here we use a tiny `2×2` patch sampled from the real photograph so the overlap-add mechanism is visible.\n", + "\n", + "A transposed-convolution-style update places a scaled copy of the kernel into the output for each input value. When stride increases, the output grows and gaps appear between placements.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Take a real `2×2` patch from the photograph.\n", + "2. Use a `2×2` all-ones kernel.\n", + "3. Implement overlap-add for stride 1.\n", + "4. Move **Stride / Paso** from 1 to 3 and inspect the output shape.\n", + "5. Explain why a larger output does **not** mean the original pre-convolution image has been recovered.\n", + "\n", + "> 🇪🇸 La convolución transpuesta reutiliza un kernel mediante superposición y suma. Puede aumentar el tamaño espacial, especialmente con stride mayor que 1, pero aumentar la forma no equivale a invertir los valores perdidos por una convolución anterior." + ], + "id": "4msnOeU7fdAW" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO\n", + "# 1. Extract a real 2×2 patch from img.\n", + "# 2. Implement overlap-add with a 2×2 kernel and stride=1.\n", + "# 3. Predict the output shape.\n", + "# 4. Repeat with stride=2.\n", + "# 5. Explain why this is not a true inverse." + ], + "id": "N3KU8_VUfdAW" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "W4pZ396DfdAV" - }, - "outputs": [], - "source": [ - "# TODO\n", - "# 1. Compute 2D correlation with sobel_x.\n", - "# 2. Compute 2D convolution with np.flip(sobel_x).\n", - "# 3. Verify that the two results match.\n", - "# 4. Build C = convmtx_full_1d(kernel_1d, len(scanline)).\n", - "# 5. Check C @ scanline against np.convolve(scanline, kernel_1d, \"full\")." - ], - "id": "W4pZ396DfdAV" + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "small = img[250:254:2, 250:254:2].copy() # real 2×2 pixel patch\n", + "ker = np.ones((2, 2), dtype=float)\n", + "\n", + "def transposed_overlap_add(x, kernel, stride=1):\n", + " h, w = x.shape\n", + " kh, kw = kernel.shape\n", + "\n", + " out_h = (h - 1) * stride + kh\n", + " out_w = (w - 1) * stride + kw\n", + " out = np.zeros((out_h, out_w), dtype=float)\n", + "\n", + " for i in range(h):\n", + " for j in range(w):\n", + " r = i * stride\n", + " c = j * stride\n", + " out[r:r+kh, c:c+kw] += x[i, j] * kernel\n", + "\n", + " return out\n", + "\n", + "print(\"real input / entrada real:\")\n", + "print(np.round(small, 3))\n", + "print(\"stride=1 output shape / forma:\", transposed_overlap_add(small, ker, 1).shape)\n", + "\n", + "stride_slider = widgets.IntSlider(\n", + " value=1,\n", + " min=1,\n", + " max=3,\n", + " step=1,\n", + " description=\"Stride / Paso:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"95px\"},\n", + ")\n", + "\n", + "def explore_transposed(stride):\n", + " out = transposed_overlap_add(small, ker, stride)\n", + "\n", + " print(\n", + " f\"input/entrada={small.shape} | stride/paso={stride} | \"\n", + " f\"output/salida={out.shape}\"\n", + " )\n", + " print(\n", + " \"EN: shape expansion is not value inversion; information lost earlier \"\n", + " \"does not magically return.\"\n", + " )\n", + " print(\n", + " \"ES: aumentar la forma no invierte los valores; la información perdida \"\n", + " \"antes no reaparece automáticamente.\"\n", + " )\n", + "\n", + " fig, axes = plt.subplots(1, 2, figsize=(5.8, 2.8))\n", + " axes[0].imshow(small, cmap=\"viridis\")\n", + " axes[0].set_title(\"real 2×2 input\")\n", + " axes[1].imshow(out, cmap=\"viridis\")\n", + " axes[1].set_title(f\"overlap-add, stride={stride}\")\n", + " for ax in axes:\n", + " ax.set_xticks(range(ax.images[0].get_array().shape[1]))\n", + " ax.set_yticks(range(ax.images[0].get_array().shape[0]))\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "transpose_output = widgets.interactive_output(\n", + " explore_transposed,\n", + " {\"stride\": stride_slider},\n", + ")\n", + "\n", + "display(widgets.VBox([stride_slider, transpose_output]))" + ], + "id": "LcIHWex0fdAW" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 3 — true deconvolution on a real photograph\n", + "\n", + "Now we solve an actual inverse problem.\n", + "\n", + "We blur a real `256×256` crop with a `9×9` point-spread function (PSF), add a small controlled amount of noise, and attempt to recover the original with **Richardson–Lucy deconvolution**.\n", + "\n", + "There is an important measurement trap: boundary pixels are where the algorithm has the least information about what lies outside the image. We therefore compute the error after removing a fixed border.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Blur the real image crop with a normalized `9×9` averaging PSF.\n", + "2. Add small reproducible Gaussian noise.\n", + "3. Recover it with `richardson_lucy`.\n", + "4. Measure relative error on the interior only.\n", + "5. Move **Iterations / Iteraciones** and observe the trade-off: too few iterations under-correct; many iterations can begin to emphasize noise.\n", + "\n", + "> 🇪🇸 Ahora sí resolvemos un problema de deconvolución. Desenfocamos un recorte real con una PSF conocida, añadimos ruido controlado y usamos Richardson–Lucy para recuperar detalle. Medimos únicamente el interior porque los bordes contienen artefactos propios de la falta de información fuera de la imagen." + ], + "id": "udqLELfjfdAW" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO\n", + "# 1. Build a normalized 9×9 averaging PSF.\n", + "# 2. Blur work with signal.fftconvolve(..., mode=\"same\").\n", + "# 3. Add small reproducible noise.\n", + "# 4. Recover with richardson_lucy.\n", + "# 5. Compare interior relative error before and after." + ], + "id": "evDam-pcfdAW" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ], - "colab": { - "base_uri": "https://localhost:8080/", - "height": 452, - "referenced_widgets": [ - "6919773a5f424929a218e688c37fd897", - "0c73ae284a094f0dbeb5f9d8a4a28b53", - "f986b5345f574416abe533a93ae3ac55", - "00f17bafcda144e0af306d4ad3464b05", - "8aded483293f49269adf02c8125e6ce7", - "2cd870b8a3734d628fd39b3ba099c70f", - "48f38061dc4e414ba31ec1b4ce6e57d4" - ] - }, - "id": "llmJoCZJfdAV", - "outputId": "fef8a8ba-ac53-4a1e-9d34-a1105195d9cd" - }, - "outputs": [ + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "psf = np.ones((9, 9), dtype=float)\n", + "psf /= psf.sum()\n", + "\n", + "blurred = signal.fftconvolve(work, psf, mode=\"same\")\n", + "noise = 0.002 * np.random.default_rng(0).standard_normal(work.shape)\n", + "noisy = np.clip(blurred + noise, 0, 1)\n", + "\n", + "border = 20\n", + "\n", + "def interior_relative_error(candidate):\n", + " ref = work[border:-border, border:-border]\n", + " cand = candidate[border:-border, border:-border]\n", + " return np.linalg.norm(cand - ref) / np.linalg.norm(ref)\n", + "\n", + "recovered_20 = richardson_lucy(noisy, psf, num_iter=20, clip=False)\n", + "\n", + "print(\n", + " \"blurred+noise error / error desenfoque+ruido:\",\n", + " f\"{interior_relative_error(noisy):.4f}\",\n", + ")\n", + "print(\n", + " \"RL 20 iterations / iteraciones:\",\n", + " f\"{interior_relative_error(recovered_20):.4f}\",\n", + ")\n", + "\n", + "iterations_slider = widgets.IntSlider(\n", + " value=20,\n", + " min=1,\n", + " max=50,\n", + " step=3,\n", + " description=\"Iterations / Iteraciones:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"145px\"},\n", + ")\n", + "\n", + "def explore_deconvolution(iterations):\n", + " recovered = richardson_lucy(\n", + " noisy,\n", + " psf,\n", + " num_iter=iterations,\n", + " clip=False,\n", + " )\n", + "\n", + " err_before = interior_relative_error(noisy)\n", + " err_after = interior_relative_error(recovered)\n", + "\n", + " print(\n", + " f\"iterations/iteraciones={iterations} | \"\n", + " f\"before/antes={err_before:.4f} | after/después={err_after:.4f}\"\n", + " )\n", + "\n", + " if err_after < err_before:\n", + " print(\"EN: recovery improved the interior error.\")\n", + " print(\"ES: la recuperación redujo el error interior.\")\n", + " else:\n", + " print(\"EN: at this iteration count the recovery no longer improves the metric.\")\n", + " print(\"ES: con este número de iteraciones la recuperación ya no mejora la métrica.\")\n", + "\n", + " fig, axes = plt.subplots(1, 3, figsize=(8.6, 3.0))\n", + " images = [work, noisy, np.clip(recovered, 0, 1)]\n", + " titles = [\n", + " \"original real / original\",\n", + " \"blurred + noise / desenfoque\",\n", + " f\"Richardson–Lucy ({iterations})\",\n", + " ]\n", + "\n", + " for ax, im, title in zip(axes, images, titles):\n", + " ax.imshow(im, cmap=\"gray\", vmin=0, vmax=1)\n", + " ax.set_title(title, fontsize=9)\n", + " ax.axis(\"off\")\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "deconv_output = widgets.interactive_output(\n", + " explore_deconvolution,\n", + " {\"iterations\": iterations_slider},\n", + ")\n", + "\n", + "display(widgets.VBox([iterations_slider, deconv_output]))" + ], + "id": "fevJ1to2fdAX" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What just happened\n", + "\n", + "You followed one operator from forward use to inverse use.\n", + "\n", + "1. **Correlation vs convolution:** deep-learning libraries usually slide the learned kernel without flipping it, which is cross-correlation. True convolution gives the same result when the kernel is flipped first.\n", + "2. **Toeplitz view:** a convolution of real pixel measurements became an ordinary matrix product `C @ x`. The special part was the repeated structure of `C`, not a new algebra.\n", + "3. **Transposed convolution:** overlap-add changed the spatial shape using the same local weights. It behaved like the transpose/adjoint partner of a convolutional operator, not like a true inverse.\n", + "4. **True deconvolution:** Richardson–Lucy used the known blur kernel plus an iterative model to recover some detail from a noisy real image. The result had to be measured away from unreliable boundaries.\n", + "\n", + "### The sentence to remember\n", + "\n", + "> **Convolution applies a structured operator; transposed convolution applies its shape-changing partner; deconvolution tries to solve the inverse problem.**\n", + "\n", + "In microscopy, astronomy, and medical imaging, the blur kernel is often described by a **point-spread function (PSF)**. Deconvolution is useful precisely because imaging systems spread information before we ever see the pixels.\n", + "\n", + "> 🇪🇸 Seguiste un mismo operador desde el problema directo hasta el inverso. La correlación y la convolución se diferencian por el volteo del kernel; Toeplitz muestra que la operación sigue siendo multiplicación matricial; la convolución transpuesta cambia la forma pero no recupera automáticamente lo perdido; y Richardson–Lucy intenta resolver un problema inverso real bajo ruido.\n", + ">\n", + "> **Frase para recordar:** la convolución aplica un operador estructurado; la convolución transpuesta aplica su compañero que cambia la forma; la deconvolución intenta resolver el problema inverso." + ], + "id": "n9x9uzxpfdAX" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Done with this section\n", + "\n", + "Next up: **10 · Tucker decomposition on real data** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/10-tucker-decomposition.ipynb).\n", + "\n", + "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" + ], + "id": "MSfwCollfdAX" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3" + }, + "colab": { + "provenance": [] + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "version_major": 2, + "version_minor": 0, + "state": { + "6919773a5f424929a218e688c37fd897": { + "model_module": "@jupyter-widgets/controls", + "model_name": "VBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "VBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "VBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_0c73ae284a094f0dbeb5f9d8a4a28b53", + "IPY_MODEL_f986b5345f574416abe533a93ae3ac55" + ], + "layout": "IPY_MODEL_00f17bafcda144e0af306d4ad3464b05" + } + }, + "0c73ae284a094f0dbeb5f9d8a4a28b53": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ToggleButtonsModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ToggleButtonsModel", + "_options_labels": [ + "valid", + "same", + "full" + ], + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ToggleButtonsView", + "button_style": "", + "description": "Mode / Modo:", + "description_tooltip": null, + "disabled": false, + "icons": [], + "index": 1, + "layout": "IPY_MODEL_8aded483293f49269adf02c8125e6ce7", + "style": "IPY_MODEL_2cd870b8a3734d628fd39b3ba099c70f", + "tooltips": [] + } + }, + "f986b5345f574416abe533a93ae3ac55": { + "model_module": "@jupyter-widgets/output", + "model_name": "OutputModel", + "model_module_version": "1.0.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/output", + "_model_module_version": "1.0.0", + "_model_name": "OutputModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/output", + "_view_module_version": "1.0.0", + "_view_name": "OutputView", + "layout": "IPY_MODEL_48f38061dc4e414ba31ec1b4ce6e57d4", + "msg_id": "", + "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "correlation == convolution with flipped kernel / correlación == convolución con kernel invertido: True\n", - "Toeplitz matrix / matriz Toeplitz: (34, 32)\n", - "full convolution / convolución full: (34,)\n", - "C @ scanline == convolution: True\n" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "mode/modo=same | input/entrada=(160, 160) | output/salida=(160, 160)\n" + ] }, { - "output_type": "display_data", - "data": { - "text/plain": [ - "VBox(children=(ToggleButtons(description='Mode / Modo:', index=1, options=('valid', 'same', 'full'), style=Tog…" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "6919773a5f424929a218e688c37fd897" - } - }, - "metadata": { - "application/vnd.jupyter.widget-view+json": { - "colab": { - "custom_widget_manager": { - "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" - } - } - } - } + "output_type": "display_data", + "data": { + "text/plain": "
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+ }, + "metadata": {} } - ], - "source": [ - "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", - "corr = signal.correlate2d(patch, sobel_x, mode=\"valid\")\n", - "conv_flipped = signal.convolve2d(\n", - " patch,\n", - " np.flip(sobel_x),\n", - " mode=\"valid\",\n", - ")\n", - "\n", - "print(\n", - " \"correlation == convolution with flipped kernel / \"\n", - " \"correlación == convolución con kernel invertido:\",\n", - " np.allclose(corr, conv_flipped),\n", - ")\n", - "\n", - "C = convmtx_full_1d(kernel_1d, len(scanline))\n", - "via_matrix = C @ scanline\n", - "via_convolution = np.convolve(scanline, kernel_1d, mode=\"full\")\n", - "\n", - "print(\"Toeplitz matrix / matriz Toeplitz:\", C.shape)\n", - "print(\"full convolution / convolución full:\", via_convolution.shape)\n", - "print(\"C @ scanline == convolution:\", np.allclose(via_matrix, via_convolution))\n", - "\n", - "mode_widget = widgets.ToggleButtons(\n", - " options=[\"valid\", \"same\", \"full\"],\n", - " value=\"same\",\n", - " description=\"Mode / Modo:\",\n", - " style={\"description_width\": \"95px\"},\n", - ")\n", - "\n", - "def explore_mode(mode):\n", - " filtered = signal.correlate2d(patch, sobel_x, mode=mode)\n", - "\n", - " print(\n", - " f\"mode/modo={mode} | input/entrada={patch.shape} | \"\n", - " f\"output/salida={filtered.shape}\"\n", - " )\n", - "\n", - " fig, axes = plt.subplots(1, 2, figsize=(7.2, 3.0))\n", - " axes[0].imshow(patch, cmap=\"gray\")\n", - " axes[0].set_title(\"real patch / recorte real\")\n", - " axes[1].imshow(filtered, cmap=\"gray\")\n", - " axes[1].set_title(f\"Sobel correlation — {mode}\")\n", - " for ax in axes:\n", - " ax.axis(\"off\")\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "mode_output = widgets.interactive_output(\n", - " explore_mode,\n", - " {\"mode\": mode_widget},\n", - ")\n", - "\n", - "display(widgets.VBox([mode_widget, mode_output]))" - ], - "id": "llmJoCZJfdAV" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4msnOeU7fdAW" - }, - "source": [ - "## Exercise 2 — transposed convolution changes shape, not history\n", - "\n", - "The phrase **“deconvolution layer”** is often used informally for transposed convolution in decoders and generative models. That name is misleading.\n", - "\n", - "Here we use a tiny `2×2` patch sampled from the real photograph so the overlap-add mechanism is visible.\n", - "\n", - "A transposed-convolution-style update places a scaled copy of the kernel into the output for each input value. When stride increases, the output grows and gaps appear between placements.\n", - "\n", - "### What should you try?\n", - "\n", - "1. Take a real `2×2` patch from the photograph.\n", - "2. Use a `2×2` all-ones kernel.\n", - "3. Implement overlap-add for stride 1.\n", - "4. Move **Stride / Paso** from 1 to 3 and inspect the output shape.\n", - "5. Explain why a larger output does **not** mean the original pre-convolution image has been recovered.\n", - "\n", - "> 🇪🇸 La convolución transpuesta reutiliza un kernel mediante superposición y suma. Puede aumentar el tamaño espacial, especialmente con stride mayor que 1, pero aumentar la forma no equivale a invertir los valores perdidos por una convolución anterior." - ], - "id": "4msnOeU7fdAW" - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "N3KU8_VUfdAW" - }, - "outputs": [], - "source": [ - "# TODO\n", - "# 1. Extract a real 2×2 patch from img.\n", - "# 2. Implement overlap-add with a 2×2 kernel and stride=1.\n", - "# 3. Predict the output shape.\n", - "# 4. Repeat with stride=2.\n", - "# 5. 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+ }, + "metadata": {} } - ], - "source": [ - "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", - "small = img[250:254:2, 250:254:2].copy() # real 2×2 pixel patch\n", - "ker = np.ones((2, 2), dtype=float)\n", - "\n", - "def transposed_overlap_add(x, kernel, stride=1):\n", - " h, w = x.shape\n", - " kh, kw = kernel.shape\n", - "\n", - " out_h = (h - 1) * stride + kh\n", - " out_w = (w - 1) * stride + kw\n", - " out = np.zeros((out_h, out_w), dtype=float)\n", - "\n", - " for i in range(h):\n", - " for j in range(w):\n", - " r = i * stride\n", - " c = j * stride\n", - " out[r:r+kh, c:c+kw] += x[i, j] * kernel\n", - "\n", - " return out\n", - "\n", - "print(\"real input / entrada real:\")\n", - "print(np.round(small, 3))\n", - "print(\"stride=1 output shape / forma:\", transposed_overlap_add(small, ker, 1).shape)\n", - "\n", - "stride_slider = widgets.IntSlider(\n", - " value=1,\n", - " min=1,\n", - " max=3,\n", - " step=1,\n", - " description=\"Stride / Paso:\",\n", - " continuous_update=False,\n", - " style={\"description_width\": \"95px\"},\n", - ")\n", - "\n", - "def explore_transposed(stride):\n", - " out = transposed_overlap_add(small, ker, stride)\n", - "\n", - " print(\n", - " f\"input/entrada={small.shape} | stride/paso={stride} | \"\n", - " f\"output/salida={out.shape}\"\n", - " )\n", - " print(\n", - " \"EN: shape expansion is not value inversion; information lost earlier \"\n", - " \"does not magically return.\"\n", - " )\n", - " print(\n", - " \"ES: aumentar la forma no invierte los valores; la información perdida \"\n", - " \"antes no reaparece automáticamente.\"\n", - " )\n", - "\n", - " fig, axes = plt.subplots(1, 2, figsize=(5.8, 2.8))\n", - " axes[0].imshow(small, cmap=\"viridis\")\n", - " axes[0].set_title(\"real 2×2 input\")\n", - " axes[1].imshow(out, cmap=\"viridis\")\n", - " axes[1].set_title(f\"overlap-add, stride={stride}\")\n", - " for ax in axes:\n", - " ax.set_xticks(range(ax.images[0].get_array().shape[1]))\n", - " ax.set_yticks(range(ax.images[0].get_array().shape[0]))\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "transpose_output = widgets.interactive_output(\n", - " explore_transposed,\n", - " {\"stride\": stride_slider},\n", - ")\n", - "\n", - "display(widgets.VBox([stride_slider, transpose_output]))" - ], - "id": "LcIHWex0fdAW" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "udqLELfjfdAW" - }, - "source": [ - "## Exercise 3 — true deconvolution on a real photograph\n", - "\n", - "Now we solve an actual inverse problem.\n", - "\n", - "We blur a real `256×256` crop with a `9×9` point-spread function (PSF), add a small controlled amount of noise, and attempt to recover the original with **Richardson–Lucy deconvolution**.\n", - "\n", - "There is an important measurement trap: boundary pixels are where the algorithm has the least information about what lies outside the image. We therefore compute the error after removing a fixed border.\n", - "\n", - "### What should you try?\n", - "\n", - "1. Blur the real image crop with a normalized `9×9` averaging PSF.\n", - "2. Add small reproducible Gaussian noise.\n", - "3. Recover it with `richardson_lucy`.\n", - "4. Measure relative error on the interior only.\n", - "5. Move **Iterations / Iteraciones** and observe the trade-off: too few iterations under-correct; many iterations can begin to emphasize noise.\n", - "\n", - "> 🇪🇸 Ahora sí resolvemos un problema de deconvolución. Desenfocamos un recorte real con una PSF conocida, añadimos ruido controlado y usamos Richardson–Lucy para recuperar detalle. Medimos únicamente el interior porque los bordes contienen artefactos propios de la falta de información fuera de la imagen." - ], - "id": "udqLELfjfdAW" - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "id": "evDam-pcfdAW" - }, - "outputs": [], - "source": [ - "# TODO\n", - "# 1. Build a normalized 9×9 averaging PSF.\n", - "# 2. Blur work with signal.fftconvolve(..., mode=\"same\").\n", - "# 3. Add small reproducible noise.\n", - "# 4. Recover with richardson_lucy.\n", - "# 5. Compare interior relative error before and after." - ], - "id": "evDam-pcfdAW" - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ], - "colab": { - "base_uri": "https://localhost:8080/", - "height": 441, - "referenced_widgets": [ - "312a87d3c33b44379702ae4610625b9d", - "0e8a7e4a0c614052aad776390df7f3ca", - "40c66bc0a7394bb5a65421a0af065beb", - "f5a846f445704bd7a0daa67fe192814a", - "4960f14d63a8435b9be8d7de8b6757bd", - "530735ccd92a4e6b8c0bce6fea156a4c", - "d744de219eb848d896266aa8cc33aceb" - ] - }, - "id": "fevJ1to2fdAX", - "outputId": "6120c30f-72ed-44a9-9233-729b6912af68" - }, - "outputs": [ + ] + } + }, + "c01546199ab748e09c326a91126464ec": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + 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- "output_type": "stream", - "name": "stdout", - "text": [ - "blurred+noise error / error desenfoque+ruido: 0.1891\n", - "RL 20 iterations / iteraciones: 0.1309\n" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "iterations/iteraciones=31 | before/antes=0.1891 | after/después=0.1241\n", + "EN: recovery improved the interior error.\n", + "ES: la recuperación redujo el error interior.\n" + ] }, { - "output_type": "display_data", - "data": { - "text/plain": [ - "VBox(children=(IntSlider(value=20, continuous_update=False, description='Iterations / Iteraciones:', max=50, m…" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "312a87d3c33b44379702ae4610625b9d" - } - }, - "metadata": { - "application/vnd.jupyter.widget-view+json": { - "colab": { - "custom_widget_manager": { - "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" - } - } - } - } - } - ], - "source": [ - "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", - "psf = np.ones((9, 9), dtype=float)\n", - "psf /= psf.sum()\n", - "\n", - "blurred = signal.fftconvolve(work, psf, mode=\"same\")\n", - "noise = 0.002 * np.random.default_rng(0).standard_normal(work.shape)\n", - "noisy = np.clip(blurred + noise, 0, 1)\n", - "\n", - "border = 20\n", - "\n", - "def interior_relative_error(candidate):\n", - " ref = work[border:-border, border:-border]\n", - " cand = candidate[border:-border, border:-border]\n", - " return np.linalg.norm(cand - ref) / np.linalg.norm(ref)\n", - "\n", - "recovered_20 = richardson_lucy(noisy, psf, num_iter=20, clip=False)\n", - "\n", - "print(\n", - " \"blurred+noise error / error desenfoque+ruido:\",\n", - " f\"{interior_relative_error(noisy):.4f}\",\n", - ")\n", - "print(\n", - " \"RL 20 iterations / iteraciones:\",\n", - " f\"{interior_relative_error(recovered_20):.4f}\",\n", - ")\n", - "\n", - "iterations_slider = widgets.IntSlider(\n", - " value=20,\n", - " min=1,\n", - " max=50,\n", - " step=3,\n", - " description=\"Iterations / Iteraciones:\",\n", - " continuous_update=False,\n", - " style={\"description_width\": \"145px\"},\n", - ")\n", - "\n", - "def explore_deconvolution(iterations):\n", - " recovered = richardson_lucy(\n", - " noisy,\n", - " psf,\n", - " num_iter=iterations,\n", - " clip=False,\n", - " )\n", - "\n", - " err_before = interior_relative_error(noisy)\n", - " err_after = interior_relative_error(recovered)\n", - "\n", - " print(\n", - " f\"iterations/iteraciones={iterations} | \"\n", - " f\"before/antes={err_before:.4f} | after/después={err_after:.4f}\"\n", - " )\n", - "\n", - " if err_after < err_before:\n", - " print(\"EN: recovery improved the interior error.\")\n", - " print(\"ES: la recuperación redujo el error interior.\")\n", - " else:\n", - " print(\"EN: at this iteration count the recovery no longer improves the metric.\")\n", - " print(\"ES: con este número de iteraciones la recuperación ya no mejora la métrica.\")\n", - "\n", - " fig, axes = plt.subplots(1, 3, figsize=(8.6, 3.0))\n", - " images = [work, noisy, np.clip(recovered, 0, 1)]\n", - " titles = [\n", - " \"original real / original\",\n", - " \"blurred + noise / desenfoque\",\n", - " f\"Richardson–Lucy ({iterations})\",\n", - " ]\n", - "\n", - " for ax, im, title in zip(axes, images, titles):\n", - " ax.imshow(im, cmap=\"gray\", vmin=0, vmax=1)\n", - " ax.set_title(title, fontsize=9)\n", - " ax.axis(\"off\")\n", - "\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "deconv_output = widgets.interactive_output(\n", - " explore_deconvolution,\n", - " {\"iterations\": iterations_slider},\n", - ")\n", - "\n", - "display(widgets.VBox([iterations_slider, deconv_output]))" - ], - "id": "fevJ1to2fdAX" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "n9x9uzxpfdAX" - }, - "source": [ - "## What just happened\n", - "\n", - "You followed one operator from forward use to inverse use.\n", - "\n", - "1. **Correlation vs convolution:** deep-learning libraries usually slide the learned kernel without flipping it, which is cross-correlation. True convolution gives the same result when the kernel is flipped first.\n", - "2. **Toeplitz view:** a convolution of real pixel measurements became an ordinary matrix product `C @ x`. The special part was the repeated structure of `C`, not a new algebra.\n", - "3. **Transposed convolution:** overlap-add changed the spatial shape using the same local weights. It behaved like the transpose/adjoint partner of a convolutional operator, not like a true inverse.\n", - "4. **True deconvolution:** Richardson–Lucy used the known blur kernel plus an iterative model to recover some detail from a noisy real image. The result had to be measured away from unreliable boundaries.\n", - "\n", - "### The sentence to remember\n", - "\n", - "> **Convolution applies a structured operator; transposed convolution applies its shape-changing partner; deconvolution tries to solve the inverse problem.**\n", - "\n", - "In microscopy, astronomy, and medical imaging, the blur kernel is often described by a **point-spread function (PSF)**. Deconvolution is useful precisely because imaging systems spread information before we ever see the pixels.\n", - "\n", - "> 🇪🇸 Seguiste un mismo operador desde el problema directo hasta el inverso. La correlación y la convolución se diferencian por el volteo del kernel; Toeplitz muestra que la operación sigue siendo multiplicación matricial; la convolución transpuesta cambia la forma pero no recupera automáticamente lo perdido; y Richardson–Lucy intenta resolver un problema inverso real bajo ruido.\n", - ">\n", - "> **Frase para recordar:** la convolución aplica un operador estructurado; la convolución transpuesta aplica su compañero que cambia la forma; la deconvolución intenta resolver el problema inverso." - ], - "id": "n9x9uzxpfdAX" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "MSfwCollfdAX" - }, - "source": [ - "---\n", - "\n", - "## Done with this section\n", - "\n", - "Next up: **10 · Tucker decomposition on real data** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/10-tucker-decomposition.ipynb).\n", - "\n", - "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)\n", - "\n", - "> 🇪🇸 **Fin de esta sección.** A continuación: **10 · Descomposición Tucker con datos reales**." - ], - "id": "MSfwCollfdAX" - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3" - }, - "colab": { - "provenance": [] - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "version_major": 2, - "version_minor": 0, - "state": { - "6919773a5f424929a218e688c37fd897": { - "model_module": "@jupyter-widgets/controls", - "model_name": "VBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "VBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "VBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_0c73ae284a094f0dbeb5f9d8a4a28b53", - "IPY_MODEL_f986b5345f574416abe533a93ae3ac55" - ], - "layout": "IPY_MODEL_00f17bafcda144e0af306d4ad3464b05" - } - }, - "0c73ae284a094f0dbeb5f9d8a4a28b53": { - "model_module": "@jupyter-widgets/controls", - "model_name": "ToggleButtonsModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ToggleButtonsModel", - "_options_labels": [ - "valid", - "same", - "full" - ], - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ToggleButtonsView", - "button_style": "", - "description": "Mode / Modo:", - "description_tooltip": null, - "disabled": false, - "icons": [], - "index": 1, - "layout": "IPY_MODEL_8aded483293f49269adf02c8125e6ce7", - "style": "IPY_MODEL_2cd870b8a3734d628fd39b3ba099c70f", - "tooltips": [] - } - }, - "f986b5345f574416abe533a93ae3ac55": { - "model_module": "@jupyter-widgets/output", - "model_name": "OutputModel", - "model_module_version": "1.0.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/output", - "_model_module_version": "1.0.0", - "_model_name": "OutputModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/output", - "_view_module_version": "1.0.0", - "_view_name": "OutputView", - "layout": "IPY_MODEL_48f38061dc4e414ba31ec1b4ce6e57d4", - "msg_id": "", - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "mode/modo=same | input/entrada=(160, 160) | output/salida=(160, 160)\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": "
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null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + } } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/scripts/content.py b/scripts/content.py index 5cc1c74..bbaf3e2 100644 --- a/scripts/content.py +++ b/scripts/content.py @@ -382,10 +382,42 @@ def unfold(T, axis=0): from scipy.linalg import toeplitz from skimage import data from skimage.restoration import richardson_lucy +import matplotlib.pyplot as plt +import ipywidgets as widgets +from IPython.display import display + +# Enable ipywidgets in Google Colab when available. +try: + from google.colab import output + output.enable_custom_widget_manager() +except ImportError: + pass -img = data.camera().astype(float) / 255. # real photograph, 512x512 -sobel = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], float) -print(img.shape, img.min(), img.max())""", +img = data.camera().astype(float) / 255.0 +patch = img[176:336, 176:336] +work = img[128:384, 128:384] +scanline = img[256, 220:252].copy() + +sobel_x = np.array([ + [-1., 0., 1.], + [-2., 0., 2.], + [-1., 0., 1.], +]) + +kernel_1d = np.array([1., 0., -1.]) + +def convmtx_full_1d(kernel, n): + m = len(kernel) + col = np.zeros(n + m - 1) + col[:m] = kernel + row = np.zeros(n) + row[0] = kernel[0] + return toeplitz(col, row) + +print("full image / imagen completa:", img.shape) +print("real patch / recorte real:", patch.shape) +print("deconvolution crop / recorte deconvolución:", work.shape) +print("real scanline / fila real:", scanline.shape)""", } # ───────────────────────────────────────────────────────────────────────────── From bd739c3991d8b46bf192ea79bc1713f7421a18a5 Mon Sep 17 00:00:00 2001 From: Sebas Laverde Chunza <106851243+Laverde97@users.noreply.github.com> Date: Fri, 28 Aug 2026 22:36:20 -0500 Subject: [PATCH 21/29] Improve notebook 10 pedagogy with real data and interactive Tucker decomposition for issue #44 --- notebooks/10-tucker-decomposition.ipynb | 2509 ++++++++++++++++++----- 1 file changed, 2013 insertions(+), 496 deletions(-) diff --git a/notebooks/10-tucker-decomposition.ipynb b/notebooks/10-tucker-decomposition.ipynb index d06fd49..0216db6 100644 --- a/notebooks/10-tucker-decomposition.ipynb +++ b/notebooks/10-tucker-decomposition.ipynb @@ -1,501 +1,2018 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 10 · Tucker decomposition on real data\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/10-tucker-decomposition.ipynb)\n", - "\n", - "*Part IV · exercise · 15 min*\n", - "\n", - "> 🇪🇸 **Descomposición de Tucker con datos reales** — PCA generalizado a todos los ejes, sobre un tensor real de viajes en taxi de Nueva York.\n", - "\n", - "PCA generalized to every axis, on a real tensor of New York taxi trips.\n", - "\n", - "## What you will be able to do\n", - "\n", - "- Build a genuine order-3 tensor out of a flat table of real trips.\n", - "- Compute a Tucker decomposition by HOSVD, using only unfolding, SVD and einsum.\n", - "- Contract three axes at once with a single `einsum` string.\n", - "- Measure reconstruction error against compression ratio.\n", - "- Read a factor matrix and recognise a real pattern the decomposition found by itself." - ], - "id": "s10-00" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "Run this first. It installs and imports everything this notebook needs, and nothing else.\n", - "\n", - "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." - ], - "id": "s10-01" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "from skimage import data\n", - "\n", - "TAXIS = \"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/taxis.csv\"\n", - "taxis = pd.read_csv(TAXIS)\n", - "\n", - "def unfold(T, axis):\n", - " return np.moveaxis(T, axis, 0).reshape(T.shape[axis], -1)\n", - "\n", - "print(taxis.shape) # (6433, 14) — 6,433 real NYC taxi trips" - ], - "id": "s10-02" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Rank you can see\n", - "\n", - "> 🇪🇸 Antes de generalizar a tensores, comprimamos una sola matriz: una\n", - "> imagen real. La SVD truncada de rango k conserva las k direcciones\n", - "> singulares más fuertes y descarta el resto — por Eckart–Young, es la mejor\n", - "> aproximación de rango k posible en norma de Frobenius.\n", - "\n", - "Before generalizing to tensors, let's compress a single matrix — a real\n", - "image. The rank-`k` truncated SVD keeps only the `k` strongest singular\n", - "directions and drops the rest. By the **Eckart–Young theorem**, that\n", - "truncation is the *optimal* rank-`k` approximation to the original matrix in\n", - "Frobenius norm — no other rank-`k` matrix is closer.\n", - "\n", - "We'll reconstruct a 512×512 grayscale photograph (`skimage.data.camera()`) at\n", - "`k = 1, 5, 20, 50` and full rank, and compare three things side by side: how\n", - "much storage each reconstruction needs, how much of the image's Frobenius\n", - "energy it retains, and how it actually looks." - ], - "id": "s10-03" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "img = data.camera().astype(float)\n", - "m, n = img.shape # (512, 512)\n", - "\n", - "U, s, Vt = np.linalg.svd(img, full_matrices=False)\n", - "\n", - "ks = [1, 5, 20, 50, min(m, n)]\n", - "total_energy = np.sum(s**2)\n", - "\n", - "fig, axes = plt.subplots(1, len(ks), figsize=(15, 3.5))\n", - "for ax, k in zip(axes, ks):\n", - " recon = (U[:, :k] * s[:k]) @ Vt[:k, :]\n", - " stored = k * (m + n + 1) # mk + k + nk\n", - " storage_pct = 100 * stored / (m * n)\n", - " factor = (m * n) / stored\n", - " energy_pct = 100 * np.sum(s[:k]**2) / total_energy\n", - " label = \"full rank\" if k == min(m, n) else f\"k={k}\"\n", - " ax.imshow(recon, cmap=\"gray\", vmin=0, vmax=255)\n", - " ax.set_title(f\"{label}\\n{storage_pct:.1f}% storage, {factor:.1f}x\\n{energy_pct:.1f}% energy\",\n", - " fontsize=9)\n", - " ax.axis(\"off\")\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "for k in ks:\n", - " stored = k * (m + n + 1)\n", - " print(f\"k={k:>3} storage={100*stored/(m*n):6.2f}% \"\n", - " f\"{(m*n)/stored:6.2f}x energy={100*np.sum(s[:k]**2)/total_energy:6.2f}%\")\n", - "# k= 1 storage= 0.39% 255.75x energy= 87.01%\n", - "# k= 5 storage= 1.96% 51.15x energy= 97.04%\n", - "# k= 20 storage= 7.82% 12.79x energy= 98.98%\n", - "# k= 50 storage= 19.55% 5.12x energy= 99.60%\n", - "# k=512 storage=200.20% 0.50x energy=100.00%" - ], - "id": "s10-04" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Storage, energy, and what your eyes see\n", - "\n", - "> 🇪🇸 El almacenamiento, la energía retenida y la calidad perceptual no son\n", - "> la misma curva. Con muy pocos componentes ya se retiene casi toda la\n", - "> energía, y la imagen es reconocible con una fracción minúscula del\n", - "> almacenamiento original. La misma idea — quedarse con las direcciones más\n", - "> fuertes y descartar el resto — es exactamente lo que Tucker/HOSVD hace a\n", - "> continuación, un eje del tensor a la vez.\n", - "\n", - "At `k = 1`, under 0.4% of the storage already recovers 87% of the energy —\n", - "but the picture is barely recognisable. By `k = 20`, storage is still under\n", - "8% of the original and the picture is already unmistakably the photograph,\n", - "while the energy curve hasn't yet reached its final digit. At full rank, the\n", - "factorized `U`, `s`, `Vt` together need *more* numbers than the dense image\n", - "itself (about 200% of its storage) — factorizing only pays off once you\n", - "truncate. The same idea — keep the strongest singular directions, drop the\n", - "rest — is what Tucker/HOSVD does next, one tensor axis at a time.\n", - "\n", - "**The picture is recognisable at `k = 20` — under 8% of the storage — long\n", - "before the numbers claim it should be. Energy retained and perceptual\n", - "quality are not the same curve.**" - ], - "id": "s10-05" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The theory\n", - "\n", - "> 🇪🇸 PCA comprime una **matriz**: dos ejes. La descomposición de Tucker\n", - "> generaliza PCA a un tensor de cualquier orden: una **matriz de factores por\n", - "> eje**, más un **tensor núcleo** pequeño.\n", - "\n", - "PCA compresses a **matrix** — two axes. Real data often has more. **Tucker\n", - "decomposition** generalizes PCA to a tensor of any order: one **factor matrix\n", - "per axis**, plus a small **core tensor** describing how the factors combine.\n", - "\n", - "The way to compute it, called **HOSVD**, uses only tools you already have:\n", - "\n", - "1. **Unfold** the tensor along each axis (section 01).\n", - "2. Run **SVD** on each unfolding; keep the top components. These are the factor\n", - " matrices.\n", - "3. **Contract** the original tensor against all factor matrices to get the core\n", - " (section 06).\n", - "\n", - "The related **CP decomposition** instead writes the tensor as a sum of simple\n", - "rank-1 pieces. Tucker is usually more accurate at the same size; CP is often\n", - "easier to interpret." - ], - "id": "s10-06" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Our real tensor\n", - "\n", - "From 6,433 real New York taxi trips we build a genuine order-3 tensor:\n", - "**pickup borough × dropoff borough × hour of day.**\n", - "\n", - "> 🇪🇸 Un tensor real de orden 3: barrio de origen × barrio de destino × hora." - ], - "id": "s10-07" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "taxis['hour'] = pd.to_datetime(taxis['pickup']).dt.hour\n", - "sub = taxis.dropna(subset=['pickup_borough', 'dropoff_borough'])\n", - "pb = sorted(sub['pickup_borough'].unique())\n", - "db = sorted(sub['dropoff_borough'].unique())\n", - "\n", - "T = np.zeros((len(pb), len(db), 24))\n", - "for (p, d, h), v in sub.groupby(['pickup_borough', 'dropoff_borough', 'hour']).size().items():\n", - " T[pb.index(p), db.index(d), h] = v\n", - "\n", - "print(T.shape, pb, db)" - ], - "id": "s10-08" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 1 — read the tensor before you decompose it\n", - "\n", - "> 🇪🇸 Entiende el tensor antes de descomponerlo." - ], - "id": "s10-09" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO 1: Print T.shape and T.sum(). What does the entry T[i, j, k] mean?\n", - "\n", - "# TODO 2: Which hour has the most trips overall? (Sum over the first two axes.)\n", - "\n", - "# TODO 3: Unfold T along each axis and print the three shapes. Confirm the total\n", - "# number of entries is the same each time — unfolding loses nothing." - ], - "id": "s10-10" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "wHEhVIsEk_Yv" + }, + "source": [ + "# 10 · Tucker decomposition on real data\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/10-tucker-decomposition.ipynb)\n", + "\n", + "*Part IV · exercise · 15 min*\n", + "\n", + "> 🇪🇸 **Descomposición de Tucker con datos reales** — Construir un tensor real de viajes en taxi, comprimir cada modo con HOSVD y explorar el compromiso entre tamaño, error e interpretación.\n", + "\n", + "Build a real tensor from New York taxi trips, compress each mode with HOSVD, and explore the trade-off between size, reconstruction error, and interpretable structure.\n", + "\n", + "## What you will be able to do\n", + "\n", + "- Build and interpret a genuine order-3 tensor from a flat table of real trips.\n", + "- Unfold the tensor along each mode and explain what information each matricization exposes.\n", + "- Compute Tucker/HOSVD using only unfolding, SVD, and `einsum`.\n", + "- Change the rank of each mode independently and measure reconstruction error versus compression.\n", + "- Read the temporal factor matrix and connect a learned component back to real hourly taxi activity.\n", + "\n", + "> 🇪🇸 **Al terminar podrás:** construir e interpretar un tensor real de orden 3; desplegarlo por cada modo; calcular Tucker/HOSVD con SVD y `einsum`; variar el rango de cada eje y medir error frente a compresión; e interpretar un factor temporal comparándolo con la actividad horaria real." + ], + "id": "wHEhVIsEk_Yv" }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "print(T.shape, T.sum())\n", - "# T[i, j, k] = how many trips started in borough pb[i], ended in borough db[j],\n", - "# and were picked up during hour k.\n", - "\n", - "by_hour = T.sum(axis=(0, 1))\n", - "print(by_hour.argmax()) # 18 — evening rush hour\n", - "\n", - "for ax in range(3):\n", - " M = unfold(T, ax)\n", - " print(ax, M.shape, M.size == T.size) # True every time" - ], - "id": "s10-11" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 2 — HOSVD, in two einsum calls\n", - "\n", - "> 🇪🇸 HOSVD en dos llamadas a einsum.\n", - "\n", - "Look at the einsum strings you are about to write: `'ijk,ia,jb,kc->abc'`\n", - "contracts three axes in one expression. **That is why einsum came first.**" - ], - "id": "s10-12" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO 4: Run SVD on each unfolding, keep the top (2, 2, 3) components, and\n", - "# build the core tensor with ONE einsum call.\n", - "\n", - "# TODO 5: Reconstruct T from the core and factors, again with one einsum.\n", - "# Compute the relative error and the compression ratio." - ], - "id": "s10-13" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true + { + "cell_type": "markdown", + "metadata": { + "id": "_XnbMyuJk_Yy" + }, + "source": [ + "## Setup\n", + "\n", + "Run this first. We use 6,433 real New York taxi trips and build a tensor indexed by pickup borough, dropoff borough, and hour of day.\n", + "\n", + "> 🇪🇸 Ejecuta primero esta celda. Usaremos 6.433 viajes reales en taxi de Nueva York y construiremos un tensor indexado por distrito de origen, distrito de destino y hora del día." + ], + "id": "_XnbMyuJk_Yy" }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "Us = [np.linalg.svd(unfold(T, ax), full_matrices=False)[0] for ax in range(3)]\n", - "r = (2, 2, 3)\n", - "Us = [Us[i][:, :r[i]] for i in range(3)]\n", - "print([u.shape for u in Us])\n", - "\n", - "core = np.einsum('ijk,ia,jb,kc->abc', T, Us[0], Us[1], Us[2]) # (2, 2, 3)\n", - "recon = np.einsum('abc,ia,jb,kc->ijk', core, Us[0], Us[1], Us[2])\n", - "\n", - "error = np.linalg.norm(T - recon) / np.linalg.norm(T) # 0.067\n", - "ratio = T.size / (core.size + sum(u.size for u in Us)) # 4.71\n", - "print(core.shape, round(error, 3), round(ratio, 2))" - ], - "id": "s10-14" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The exercise above fixed one rank, (2, 2, 3). Move the slider to see the\n", - "whole error/compression trade-off, not just that one point on it.\n", - "\n", - "> 🇪🇸 El ejercicio anterior fijó un solo rango, (2, 2, 3). Mueve el\n", - "> deslizador para ver toda la curva de compensación, no solo ese punto." - ], - "id": "s10-15" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " from google.colab import output\n", - " output.enable_custom_widget_manager()\n", - "except ImportError:\n", - " pass\n", - "\n", - "import ipywidgets as widgets\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Precompute the full SVD basis for each axis once; the slider only re-slices\n", - "# and re-contracts these small matrices, which is what keeps it responsive.\n", - "bases = [np.linalg.svd(unfold(T, ax), full_matrices=False)[0] for ax in range(3)]\n", - "max_rank = min(u.shape[1] for u in bases)\n", - "\n", - "ks, errors, ratios = list(range(1, max_rank + 1)), [], []\n", - "for kk in ks:\n", - " Uk = [bases[ax][:, :kk] for ax in range(3)]\n", - " core_k = np.einsum('ijk,ia,jb,kc->abc', T, *Uk)\n", - " recon_k = np.einsum('abc,ia,jb,kc->ijk', core_k, *Uk)\n", - " errors.append(np.linalg.norm(T - recon_k) / np.linalg.norm(T))\n", - " ratios.append(T.size / (core_k.size + sum(u.size for u in Uk)))\n", - "\n", - "def show_rank(k):\n", - " i = k - 1\n", - " plt.close('all')\n", - " fig, ax1 = plt.subplots(figsize=(6, 3.2))\n", - " ax1.plot(ks, errors, color='#C44E52')\n", - " ax1.scatter([k], [errors[i]], color='#C44E52', zorder=5)\n", - " ax1.set_xlabel('rank k (shared across all three axes)')\n", - " ax1.set_ylabel('relative error', color='#C44E52')\n", - " ax2 = ax1.twinx()\n", - " ax2.plot(ks, ratios, color='#4C72B0')\n", - " ax2.scatter([k], [ratios[i]], color='#4C72B0', zorder=5)\n", - " ax2.set_ylabel('compression ratio (x)', color='#4C72B0')\n", - " plt.tight_layout()\n", - " plt.show()\n", - " print(f\"rank k={k}: error={errors[i]:.3f}, compression={ratios[i]:.2f}x\")\n", - "\n", - "widgets.interact(show_rank,\n", - " k=widgets.IntSlider(min=1, max=max_rank, step=1, value=2,\n", - " description='rank k'));" - ], - "id": "s10-16" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 3 — what did it find?\n", - "\n", - "> 🇪🇸 ¿Qué encontró la descomposición por sí sola?\n", - "\n", - "This is the important one." - ], - "id": "s10-17" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO 6: Look at the first column of the hour factor matrix. At which hour is\n", - "# it largest? Does that match what you found in TODO 2?" - ], - "id": "s10-18" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "o_qdhKfXk_Yy", + "outputId": "e80f8b89-784d-4f1d-bc86-7bd423f7eb2b" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "taxi rows / filas: 6433\n", + "usable trips / viajes utilizables: 6383\n", + "tensor shape / forma: (4, 5, 24)\n", + "pickup boroughs / origen: ['Bronx', 'Brooklyn', 'Manhattan', 'Queens']\n", + "dropoff boroughs / destino: ['Bronx', 'Brooklyn', 'Manhattan', 'Queens', 'Staten Island']\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import ipywidgets as widgets\n", + "from IPython.display import display\n", + "\n", + "try:\n", + " from google.colab import output\n", + " output.enable_custom_widget_manager()\n", + "except ImportError:\n", + " pass\n", + "\n", + "TAXIS = \"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/taxis.csv\"\n", + "taxis = pd.read_csv(TAXIS)\n", + "\n", + "def unfold(T, axis):\n", + " return np.moveaxis(T, axis, 0).reshape(T.shape[axis], -1)\n", + "\n", + "def hosvd_bases(T):\n", + " return [\n", + " np.linalg.svd(unfold(T, axis), full_matrices=False)[0]\n", + " for axis in range(T.ndim)\n", + " ]\n", + "\n", + "taxis[\"pickup_dt\"] = pd.to_datetime(taxis[\"pickup\"], errors=\"coerce\")\n", + "taxis[\"hour\"] = taxis[\"pickup_dt\"].dt.hour\n", + "\n", + "sub = taxis.dropna(\n", + " subset=[\"pickup_borough\", \"dropoff_borough\", \"hour\"]\n", + ").copy()\n", + "sub[\"hour\"] = sub[\"hour\"].astype(int)\n", + "\n", + "pickup_names = sorted(sub[\"pickup_borough\"].unique())\n", + "dropoff_names = sorted(sub[\"dropoff_borough\"].unique())\n", + "\n", + "pickup_index = {name: i for i, name in enumerate(pickup_names)}\n", + "dropoff_index = {name: i for i, name in enumerate(dropoff_names)}\n", + "\n", + "T = np.zeros((len(pickup_names), len(dropoff_names), 24), dtype=float)\n", + "\n", + "for (p, d, h), count in sub.groupby(\n", + " [\"pickup_borough\", \"dropoff_borough\", \"hour\"]\n", + ").size().items():\n", + " T[pickup_index[p], dropoff_index[d], int(h)] = float(count)\n", + "\n", + "print(\"taxi rows / filas:\", len(taxis))\n", + "print(\"usable trips / viajes utilizables:\", int(T.sum()))\n", + "print(\"tensor shape / forma:\", T.shape)\n", + "print(\"pickup boroughs / origen:\", pickup_names)\n", + "print(\"dropoff boroughs / destino:\", dropoff_names)" + ], + "id": "o_qdhKfXk_Yy" }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "hour_factor = Us[2] # (24, 3) — one row per hour\n", - "peak = np.abs(hour_factor[:, 0]).argmax()\n", - "print(peak) # 18\n", - "\n", - "print(T.sum(axis=(0, 1)).argmax()) # 18 — the same hour, from raw counts\n", - "\n", - "# THE DECOMPOSITION DISCOVERED EVENING RUSH HOUR BY ITSELF. Nobody told it about\n", - "# time, traffic or commuting; it found the dominant pattern along that axis\n", - "# because that is what a decomposition does.\n", - "#\n", - "# (Take the absolute value: singular vectors are only defined up to sign, so the\n", - "# strongest component may come out negative.)" - ], - "id": "s10-19" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What just happened\n", - "\n", - "**4.7× fewer numbers, 6.7% error.** But the important part is TODO 6. The\n", - "strongest pattern in the hour factor peaks at **hour 18** — and that is also the\n", - "busiest hour in the raw data. The decomposition found rush hour on its own.\n", - "\n", - "**Where this is used.** In tech, Tucker and CP compress the large weight tensors\n", - "inside neural networks so models run on phones instead of servers. In biotech,\n", - "applied to data such as (genes × samples × conditions), they find structure\n", - "ordinary PCA cannot reach, because **PCA can only ever see two axes**.\n", - "\n", - "For real projects use [`tensorly`](https://tensorly.org), which implements both\n", - "properly. Take-home C in section 11 compares CP against what you just built." - ], - "id": "s10-20" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Time for Kahoot 🎯\n", - "\n", - "**Kahoot 3 — Convolution & Tensor Decompositions** · 6 questions, about 5 minutes.\n", - "\n", - "> 🇪🇸 **Convolución y descomposiciones tensoriales** — 6 preguntas, unos 5 minutos.\n", - "\n", - "Join at **kahoot.it** with the PIN on the facilitator's screen.\n", - "\n", - "- [Quiz details and facilitator notes](https://project-delphi.github.io/tensors-workshop/kahoot.html#quiz-3)\n", - "- [Import file (`.xlsx`)](https://github.com/project-delphi/tensors-workshop/blob/main/kahoot/kahoot_quiz_3_convolution_decompositions.xlsx)\n", - "\n", - "Next up: **11 · Wrap-up and take-homes** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/11-wrap-up-and-take-homes.ipynb).\n", - "\n", - "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" - ], - "id": "s10-21" - } - ], - "metadata": { - "colab": { - "name": "10-tucker-decomposition.ipynb", - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" + { + "cell_type": "markdown", + "metadata": { + "id": "9zF6ZTFxk_Yz" + }, + "source": [ + "## Why this matters\n", + "\n", + "A matrix has two axes. A tensor can have three or more, and each axis can carry a different kind of meaning.\n", + "\n", + "For our taxi tensor,\n", + "\n", + "`T[pickup, dropoff, hour]`\n", + "\n", + "stores a real trip count. The three modes answer different questions:\n", + "\n", + "- **pickup mode:** which origins behave similarly?\n", + "- **dropoff mode:** which destinations behave similarly?\n", + "- **hour mode:** which times of day share similar traffic structure?\n", + "\n", + "**HOSVD** applies an SVD to each unfolding and keeps the strongest directions for each mode. Tucker then combines those directions through a small **core tensor**.\n", + "\n", + "This is better described as a multilinear extension of truncated SVD ideas across several tensor modes — not as “PCA itself becoming a tensor factorization.”\n", + "\n", + "### How to use the folded solutions / Cómo usar las soluciones plegadas\n", + "\n", + "Each exercise has a **Solution / Solución** cell that is intentionally closed. Try the `TODO` first; then open the solution.\n", + "\n", + "> 🇪🇸 Un tensor conserva varios ejes con significados distintos. HOSVD aplica SVD a cada despliegue y conserva las direcciones dominantes de cada modo. Tucker combina esas direcciones mediante un tensor núcleo pequeño.\n", + ">\n", + "> Es más preciso entenderlo como una extensión multilineal de las ideas de SVD truncada a varios modos del tensor, no como “PCA convertido en una factorización tensorial”.\n", + ">\n", + "> Cada ejercicio tiene una celda **Solution / Solución** cerrada a propósito. Primero intenta el `TODO`; después abre la solución.\n", + "\n", + "### Learning cycle: Predict → Run → Explain\n", + "\n", + "Before each exercise, predict the shape, rank, or dominant pattern. Then run the computation and explain what the result means in the original taxi data.\n", + "\n", + "> 🇪🇸 **Predice → Ejecuta → Explica:** anticipa la forma, el rango o el patrón dominante; luego ejecuta y traduce el resultado de vuelta al contexto de los viajes reales." + ], + "id": "9zF6ZTFxk_Yz" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pPuQfB9Bk_Yz" + }, + "source": [ + "## Exercise 1 — read the tensor before decomposing it\n", + "\n", + "Before compressing anything, understand what the entries mean.\n", + "\n", + "For example, `T[i, j, h]` is the number of usable real trips that started in pickup borough `i`, ended in dropoff borough `j`, and were picked up during hour `h`.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Print `T.shape` and `T.sum()`.\n", + "2. Find the busiest hour overall.\n", + "3. Unfold along each of the three modes and confirm that every unfolding contains the same total number of entries.\n", + "4. Move **Hour / Hora** to inspect the real origin→destination count matrix at different times.\n", + "\n", + "> 🇪🇸 Antes de descomponer, entiende el tensor. Busca la hora con más viajes, revisa las tres formas de unfolding y usa el slider **Hour / Hora** para inspeccionar la matriz real origen→destino a distintas horas." + ], + "id": "pPuQfB9Bk_Yz" + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "WnOH_23Hk_Yz" + }, + "outputs": [], + "source": [ + "# TODO\n", + "# 1. Print T.shape and T.sum().\n", + "# 2. Compute T.sum(axis=(0, 1)) and find the busiest hour.\n", + "# 3. Print unfold(T, axis).shape for axis=0,1,2.\n", + "# 4. Predict which hours should show the most concentrated traffic." + ], + "id": "WnOH_23Hk_Yz" + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "colab": { + "base_uri": "https://localhost:8080/", + "height": 578, + "referenced_widgets": [ + "5e1b1ca6f0794da5bf0d2bc913dff7b3", + "e8108f1a0791441cafce0b894f6dcd1d", + "0d9b3eeee98a4071b1dbb283ad6768cc", + "8ffc4ee34e4c4d8a9a4af276f54859a1", + "c54c897dbdd6446b804d7d94b7915328", + "e903bde3d1c74f88b9e51520415e75f7", + "6f4afe3ad61e4a69b7da015e7f123eaf" + ] + }, + "id": "sQdcOqdXk_Y0", + "outputId": "26a047b7-0a86-4dbc-8907-921f4dbe2253" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "T.shape: (4, 5, 24)\n", + "total usable trips / viajes utilizables: 6383\n", + "busiest hour / hora más ocupada: 18\n", + "trips at busiest hour / viajes: 417\n", + "axis/eje 0: shape/forma=(4, 120) | same entries/mismas entradas=True\n", + "axis/eje 1: shape/forma=(5, 96) | same entries/mismas entradas=True\n", + "axis/eje 2: shape/forma=(24, 20) | same entries/mismas entradas=True\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(IntSlider(value=18, continuous_update=False, description='Hour / Hora:', max=23, style=SliderSt…" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "5e1b1ca6f0794da5bf0d2bc913dff7b3" + } + }, + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "colab": { + "custom_widget_manager": { + "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" + } + } + } + } + } + ], + "source": [ + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "print(\"T.shape:\", T.shape)\n", + "print(\"total usable trips / viajes utilizables:\", int(T.sum()))\n", + "\n", + "by_hour = T.sum(axis=(0, 1))\n", + "busiest_hour = int(np.argmax(by_hour))\n", + "\n", + "print(\"busiest hour / hora más ocupada:\", busiest_hour)\n", + "print(\"trips at busiest hour / viajes:\", int(by_hour[busiest_hour]))\n", + "\n", + "for axis in range(3):\n", + " M = unfold(T, axis)\n", + " print(\n", + " f\"axis/eje {axis}: shape/forma={M.shape} | \"\n", + " f\"same entries/mismas entradas={M.size == T.size}\"\n", + " )\n", + "\n", + "hour_slider = widgets.IntSlider(\n", + " value=busiest_hour,\n", + " min=0,\n", + " max=23,\n", + " step=1,\n", + " description=\"Hour / Hora:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"90px\"},\n", + ")\n", + "\n", + "def show_hour(hour):\n", + " matrix = T[:, :, hour]\n", + "\n", + " print(\n", + " f\"hour/hora={hour} | total trips/viajes={int(matrix.sum())}\"\n", + " )\n", + "\n", + " fig, ax = plt.subplots(figsize=(5.4, 4.0))\n", + " im = ax.imshow(matrix, cmap=\"viridis\", aspect=\"auto\")\n", + " ax.set_xticks(range(len(dropoff_names)))\n", + " ax.set_xticklabels(dropoff_names, rotation=45, ha=\"right\", fontsize=8)\n", + " ax.set_yticks(range(len(pickup_names)))\n", + " ax.set_yticklabels(pickup_names, fontsize=8)\n", + " ax.set_xlabel(\"dropoff borough / destino\")\n", + " ax.set_ylabel(\"pickup borough / origen\")\n", + " ax.set_title(f\"Real taxi counts at hour {hour} / Viajes reales a la hora {hour}\")\n", + " fig.colorbar(im, ax=ax, label=\"trip count / viajes\")\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "hour_output = widgets.interactive_output(\n", + " show_hour,\n", + " {\"hour\": hour_slider},\n", + ")\n", + "\n", + "display(widgets.VBox([hour_slider, hour_output]))" + ], + "id": "sQdcOqdXk_Y0" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WSeYDtwmk_Y0" + }, + "source": [ + "## Exercise 2 — HOSVD: compress each mode separately\n", + "\n", + "Now compute one SVD basis per unfolding.\n", + "\n", + "If the retained ranks are `(r₁, r₂, r₃)`, then:\n", + "\n", + "- `U₁` summarizes pickup patterns;\n", + "- `U₂` summarizes dropoff patterns;\n", + "- `U₃` summarizes hourly patterns;\n", + "- the core has shape `(r₁, r₂, r₃)`.\n", + "\n", + "The core is produced by contracting all three modes at once:\n", + "\n", + "`core = einsum('ijk,ia,jb,kc->abc', T, U1, U2, U3)`\n", + "\n", + "and reconstruction reverses that contraction.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Compute the full SVD basis of each unfolding.\n", + "2. Start with ranks `(2, 2, 3)`.\n", + "3. Build the core with one `einsum`.\n", + "4. Reconstruct with one `einsum`.\n", + "5. Compute relative Frobenius error and compression ratio.\n", + "6. Change the three rank sliders independently and inspect how error and storage trade off.\n", + "\n", + "> 🇪🇸 Ahora cada modo obtiene su propia base SVD. El tensor núcleo resume cómo interactúan esos factores. Cambia los tres rangos de manera independiente y observa que reducir almacenamiento siempre tiene un costo de reconstrucción." + ], + "id": "WSeYDtwmk_Y0" + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "gytR3GhUk_Y0" + }, + "outputs": [], + "source": [ + "# TODO\n", + "# 1. Compute one SVD basis for each unfolding.\n", + "# 2. Keep ranks (2, 2, 3).\n", + "# 3. Build the Tucker core with one np.einsum call.\n", + "# 4. Reconstruct T with one np.einsum call.\n", + "# 5. Compute relative error and compression ratio." + ], + "id": "gytR3GhUk_Y0" + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "colab": { + "base_uri": "https://localhost:8080/", + "height": 601, + "referenced_widgets": [ + "f94a9b7f473b44c39538893f83abb25c", + "786f08f45f4541d498f8f338b05f3e18", + "fd8508644aff41798849a8ec7ed9ba21", + "5e3f2bb2e9b44a6fbefb764fbd40d431", + "5033b44c99924232906527bd334bb87d", + "0ee65819c47f46bab896c8d2a08a5f41", + "839be48c53474c5db3ce983478424cbf", + "89b87fd3621a4d849c9bec6ce96feb92", + "e1679f5d060e4377996e78391e329207", + "3f50d0c8ff6b416fada101ffe7f6c11c", + "5d657c6a842845bd9063ba09ae1f87db", + "77d96d7f90e443f2a7d59f7e01e7c689", + "3b06629abdb4499d880522748f4a8df1", + "9d01c609fb6748b786df7ddd57e9b1fe", + "94dcb420e7c44100bcc830e2ba10a612", + "1935d3bcb797451da1805f63bd785e82", + "929a64760bf94328a7ccd6f2320ede01", + "57215af16e304ccf966125c674814602", + "a13c1664d32f46f49c4a58af638d66ba" + ] + }, + "id": "v7iXv9bDk_Y0", + "outputId": "9d3209de-ad77-48b3-b3c3-b87e32920ce5" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "default ranks / rangos: (2, 2, 3)\n", + "factor shapes / formas: [(4, 2), (5, 2), (24, 3)]\n", + "core shape / forma núcleo: (2, 2, 3)\n", + "relative error / error relativo: 0.0670\n", + "compression / compresión: 4.71x\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(HTML(value='Tucker rank explorer / Explorador de rangos Tucker: change each mode indepen…" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "f94a9b7f473b44c39538893f83abb25c" + } + }, + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "colab": { + "custom_widget_manager": { + "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" + } + } + } + } + } + ], + "source": [ + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "bases = hosvd_bases(T)\n", + "\n", + "def tucker_from_ranks(T, bases, ranks):\n", + " factors = [\n", + " bases[axis][:, :ranks[axis]]\n", + " for axis in range(3)\n", + " ]\n", + "\n", + " core = np.einsum(\n", + " \"ijk,ia,jb,kc->abc\",\n", + " T,\n", + " factors[0],\n", + " factors[1],\n", + " factors[2],\n", + " )\n", + "\n", + " recon = np.einsum(\n", + " \"abc,ia,jb,kc->ijk\",\n", + " core,\n", + " factors[0],\n", + " factors[1],\n", + " factors[2],\n", + " )\n", + "\n", + " error = np.linalg.norm(T - recon) / np.linalg.norm(T)\n", + "\n", + " compressed_numbers = (\n", + " core.size\n", + " + sum(factor.size for factor in factors)\n", + " )\n", + " compression = T.size / compressed_numbers\n", + "\n", + " return core, factors, recon, error, compression\n", + "\n", + "default_ranks = (\n", + " min(2, T.shape[0]),\n", + " min(2, T.shape[1]),\n", + " min(3, T.shape[2]),\n", + ")\n", + "\n", + "core, factors, recon, error, compression = tucker_from_ranks(\n", + " T,\n", + " bases,\n", + " default_ranks,\n", + ")\n", + "\n", + "print(\"default ranks / rangos:\", default_ranks)\n", + "print(\"factor shapes / formas:\", [u.shape for u in factors])\n", + "print(\"core shape / forma núcleo:\", core.shape)\n", + "print(\"relative error / error relativo:\", f\"{error:.4f}\")\n", + "print(\"compression / compresión:\", f\"{compression:.2f}x\")\n", + "\n", + "pickup_rank = widgets.IntSlider(\n", + " value=default_ranks[0],\n", + " min=1,\n", + " max=T.shape[0],\n", + " step=1,\n", + " description=\"Pickup rank:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"100px\"},\n", + ")\n", + "\n", + "dropoff_rank = widgets.IntSlider(\n", + " value=default_ranks[1],\n", + " min=1,\n", + " max=T.shape[1],\n", + " step=1,\n", + " description=\"Dropoff rank:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"110px\"},\n", + ")\n", + "\n", + "hour_rank = widgets.IntSlider(\n", + " value=default_ranks[2],\n", + " min=1,\n", + " max=min(12, T.shape[2]),\n", + " step=1,\n", + " description=\"Hour rank:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"95px\"},\n", + ")\n", + "\n", + "compare_hour = widgets.IntSlider(\n", + " value=busiest_hour,\n", + " min=0,\n", + " max=23,\n", + " step=1,\n", + " description=\"Hour / Hora:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"90px\"},\n", + ")\n", + "\n", + "def explore_tucker(r_pickup, r_dropoff, r_hour, hour):\n", + " ranks = (r_pickup, r_dropoff, r_hour)\n", + "\n", + " core, factors, recon, error, compression = tucker_from_ranks(\n", + " T,\n", + " bases,\n", + " ranks,\n", + " )\n", + "\n", + " print(\n", + " f\"ranks/rangos={ranks} | core/núcleo={core.shape} | \"\n", + " f\"error={error:.4f} | compression/compresión={compression:.2f}x\"\n", + " )\n", + "\n", + " vmax = max(T[:, :, hour].max(), recon[:, :, hour].max())\n", + "\n", + " fig, axes = plt.subplots(1, 2, figsize=(7.6, 3.3))\n", + "\n", + " axes[0].imshow(\n", + " T[:, :, hour],\n", + " cmap=\"viridis\",\n", + " aspect=\"auto\",\n", + " vmin=0,\n", + " vmax=vmax,\n", + " )\n", + " axes[0].set_title(f\"real / real — hour {hour}\")\n", + "\n", + " axes[1].imshow(\n", + " recon[:, :, hour],\n", + " cmap=\"viridis\",\n", + " aspect=\"auto\",\n", + " vmin=0,\n", + " vmax=vmax,\n", + " )\n", + " axes[1].set_title(f\"Tucker reconstruction / reconstrucción\")\n", + "\n", + " for ax in axes:\n", + " ax.set_xticks(range(len(dropoff_names)))\n", + " ax.set_xticklabels(dropoff_names, rotation=45, ha=\"right\", fontsize=7)\n", + " ax.set_yticks(range(len(pickup_names)))\n", + " ax.set_yticklabels(pickup_names, fontsize=7)\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "tucker_output = widgets.interactive_output(\n", + " explore_tucker,\n", + " {\n", + " \"r_pickup\": pickup_rank,\n", + " \"r_dropoff\": dropoff_rank,\n", + " \"r_hour\": hour_rank,\n", + " \"hour\": compare_hour,\n", + " },\n", + ")\n", + "\n", + "display(\n", + " widgets.VBox([\n", + " widgets.HTML(\n", + " \"Tucker rank explorer / Explorador de rangos Tucker: \"\n", + " \"change each mode independently. / \"\n", + " \"cambia cada modo de manera independiente.\"\n", + " ),\n", + " pickup_rank,\n", + " dropoff_rank,\n", + " hour_rank,\n", + " compare_hour,\n", + " tucker_output,\n", + " ])\n", + ")" + ], + "id": "v7iXv9bDk_Y0" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "v4OEefzbk_Y1" + }, + "source": [ + "## Exercise 3 — what did the temporal factor learn?\n", + "\n", + "Compression is useful, but interpretation is where Tucker becomes more than a storage trick.\n", + "\n", + "The hour factor matrix has one row for each hour and one column for each retained temporal component. Singular-vector signs are arbitrary, so we compare magnitudes when asking where a component is strongest.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Inspect the first temporal factor.\n", + "2. Find the hour where its absolute loading is largest.\n", + "3. Compare that hour with the busiest hour in the raw taxi counts.\n", + "4. Move **Component / Componente** to inspect other temporal patterns.\n", + "5. Explain why a factor can capture a pattern even though nobody explicitly labeled “rush hour.”\n", + "\n", + "> 🇪🇸 La matriz de factores temporales tiene una fila por hora. Busca dónde alcanza mayor magnitud cada componente y compáralo con los conteos horarios reales. El signo de un vector singular es arbitrario, así que interpreta principalmente la forma y la magnitud del patrón." + ], + "id": "v4OEefzbk_Y1" + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "pAmVh-Vuk_Y1" + }, + "outputs": [], + "source": [ + "# TODO\n", + "# 1. Use the hour-mode SVD basis.\n", + "# 2. Inspect the first temporal component.\n", + "# 3. Find its peak absolute loading.\n", + "# 4. Compare it with the busiest hour in T.sum(axis=(0,1)).\n", + "# 5. Repeat for another component and describe the pattern." + ], + "id": "pAmVh-Vuk_Y1" + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "colab": { + "base_uri": "https://localhost:8080/", + "height": 427, + "referenced_widgets": [ + "a4e6589192fa4b0e8e2b6c16f7752075", + "0472c1031b014ab5884f17cc26d39cf3", + "8aa3d5bfdce64e1cbf373fbcb55a409a", + "5c98eedf41414f48af186670bd6b9e4d", + "27740e8fadfb4b1c8d72637e2f6122a2", + "af2345a66f11416e8b4a2feb44093062", + "fedce70a3fa34c6db0713db0c9a30d68" + ] + }, + "id": "AHQe7C-Ak_Y1", + "outputId": "5b92472a-3a41-4c84-d458-cadd52d40d06" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "first component peak / pico primer componente: 18\n", + "raw busiest hour / hora real más ocupada: 18\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(IntSlider(value=1, continuous_update=False, description='Component / Componente:', max=6, min=1…" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "a4e6589192fa4b0e8e2b6c16f7752075" + } + }, + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "colab": { + "custom_widget_manager": { + "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" + } + } + } + } + } + ], + "source": [ + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "hour_basis = bases[2]\n", + "raw_hour_counts = T.sum(axis=(0, 1))\n", + "\n", + "first_component = hour_basis[:, 0]\n", + "first_peak = int(np.argmax(np.abs(first_component)))\n", + "\n", + "print(\"first component peak / pico primer componente:\", first_peak)\n", + "print(\"raw busiest hour / hora real más ocupada:\", busiest_hour)\n", + "\n", + "component_slider = widgets.IntSlider(\n", + " value=1,\n", + " min=1,\n", + " max=min(6, hour_basis.shape[1]),\n", + " step=1,\n", + " description=\"Component / Componente:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"145px\"},\n", + ")\n", + "\n", + "def explore_hour_factor(component):\n", + " idx = component - 1\n", + " factor = hour_basis[:, idx]\n", + "\n", + " peak = int(np.argmax(np.abs(factor)))\n", + "\n", + " raw_scaled = raw_hour_counts / raw_hour_counts.max()\n", + " factor_scaled = np.abs(factor)\n", + " factor_scaled = factor_scaled / factor_scaled.max()\n", + "\n", + " print(\n", + " f\"component/componente={component} | \"\n", + " f\"peak absolute loading / pico absoluto={peak}\"\n", + " )\n", + " print(\n", + " f\"raw busiest hour / hora real más ocupada={busiest_hour}\"\n", + " )\n", + "\n", + " fig, ax = plt.subplots(figsize=(6.6, 3.2))\n", + " ax.plot(\n", + " range(24),\n", + " raw_scaled,\n", + " marker=\"o\",\n", + " label=\"raw hourly trips / viajes reales\",\n", + " )\n", + " ax.plot(\n", + " range(24),\n", + " factor_scaled,\n", + " marker=\"o\",\n", + " label=f\"|temporal factor {component}| / |factor temporal|\",\n", + " )\n", + " ax.axvline(\n", + " peak,\n", + " linestyle=\"--\",\n", + " linewidth=1,\n", + " label=f\"factor peak / pico={peak}\",\n", + " )\n", + " ax.set_xticks(range(0, 24, 2))\n", + " ax.set_xlabel(\"hour / hora\")\n", + " ax.set_ylabel(\"scaled magnitude / magnitud escalada\")\n", + " ax.set_title(\"Raw activity vs learned temporal factor / Actividad real vs factor\")\n", + " ax.legend(fontsize=8)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "factor_output = widgets.interactive_output(\n", + " explore_hour_factor,\n", + " {\"component\": component_slider},\n", + ")\n", + "\n", + "display(widgets.VBox([component_slider, factor_output]))" + ], + "id": "AHQe7C-Ak_Y1" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "I5XWJQZLk_Y1" + }, + "source": [ + "## What just happened\n", + "\n", + "You built a complete Tucker/HOSVD pipeline from real observations.\n", + "\n", + "1. **Real tensor construction:** a flat taxi table became an order-3 tensor `pickup × dropoff × hour`.\n", + "2. **Unfolding:** each mode exposed a different matrix view without losing any entries.\n", + "3. **HOSVD:** SVD supplied one low-dimensional basis per mode.\n", + "4. **Tucker core:** one `einsum` contracted all three axes into a smaller core, and another reconstructed the tensor.\n", + "5. **Rank trade-off:** changing pickup, dropoff, and hour ranks independently changed both storage and reconstruction error.\n", + "6. **Interpretation:** the hour factor exposed temporal structure that could be compared directly with real hourly trip counts.\n", + "\n", + "### The sentence to remember\n", + "\n", + "> **Tucker compresses a tensor by learning a basis for each mode and a small core that tells those mode-specific patterns how to interact.**\n", + "\n", + "This is why tensor decompositions are useful in domains such as imaging, recommender systems, neuroscience, and multi-condition biological measurements: the axes represent genuinely different kinds of structure.\n", + "\n", + "> 🇪🇸 Construiste Tucker/HOSVD de extremo a extremo con datos reales. Cada modo obtuvo su propia base, el núcleo resumió sus interacciones y los sliders mostraron que la compresión y el error dependen de cuánto rango conservas en cada eje.\n", + ">\n", + "> **Frase para recordar:** Tucker comprime un tensor aprendiendo una base para cada modo y un núcleo pequeño que describe cómo interactúan esos patrones específicos de cada eje." + ], + "id": "I5XWJQZLk_Y1" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Mx5SdJaBk_Y1" + }, + "source": [ + "---\n", + "\n", + "## Time for Kahoot 🎯\n", + "\n", + "**Kahoot 3 — Convolution & Tensor Decompositions** · 6 questions, about 5 minutes.\n", + "\n", + "> 🇪🇸 **Convolución y descomposiciones tensoriales** — 6 preguntas, unos 5 minutos.\n", + "\n", + "Join at **kahoot.it** with the PIN on the facilitator's screen.\n", + "\n", + "- [Quiz details and facilitator notes](https://project-delphi.github.io/tensors-workshop/kahoot.html#quiz-3)\n", + "- [Import file (`.xlsx`)](https://github.com/project-delphi/tensors-workshop/blob/main/kahoot/kahoot_quiz_3_convolution_decompositions.xlsx)\n", + "\n", + "Next up: **11 · Wrap-up and take-homes** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/11-wrap-up-and-take-homes.ipynb).\n", + "\n", + "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" + ], + "id": "Mx5SdJaBk_Y1" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3" + }, + "colab": { + "provenance": [] + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "version_major": 2, + "version_minor": 0, + "state": { + "5e1b1ca6f0794da5bf0d2bc913dff7b3": { + "model_module": "@jupyter-widgets/controls", + "model_name": "VBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "VBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "VBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_e8108f1a0791441cafce0b894f6dcd1d", + "IPY_MODEL_0d9b3eeee98a4071b1dbb283ad6768cc" + ], + "layout": "IPY_MODEL_8ffc4ee34e4c4d8a9a4af276f54859a1" + } + }, + "e8108f1a0791441cafce0b894f6dcd1d": { + "model_module": "@jupyter-widgets/controls", + "model_name": "IntSliderModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "IntSliderModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "IntSliderView", + "continuous_update": false, + "description": "Hour / Hora:", + "description_tooltip": null, + "disabled": false, + "layout": "IPY_MODEL_c54c897dbdd6446b804d7d94b7915328", + "max": 23, + "min": 0, + "orientation": "horizontal", + "readout": true, + "readout_format": "d", + "step": 1, + "style": "IPY_MODEL_e903bde3d1c74f88b9e51520415e75f7", + "value": 16 + } + }, + "0d9b3eeee98a4071b1dbb283ad6768cc": { + "model_module": "@jupyter-widgets/output", + "model_name": "OutputModel", + "model_module_version": "1.0.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/output", + "_model_module_version": "1.0.0", + "_model_name": "OutputModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/output", + "_view_module_version": "1.0.0", + "_view_name": "OutputView", + "layout": "IPY_MODEL_6f4afe3ad61e4a69b7da015e7f123eaf", + "msg_id": "", + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "hour/hora=16 | total trips/viajes=332\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
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VzMcWRPQG6ebmJrFdVGUfFhb20+MjIyPx8eNHqRIIVVVV9O3bF9euXRMn8aJk4mfNFyKy/F4/fvyIpKQkbN68Oc/7weDBgwH8//3gd15zQN7fjyj58vT0zHPurVu3IjMzUxz7t2/fMGvWLFhYWEi8FyUlJf30+ricw8/PDw8ePICFhQUaNGiAOXPmSJXMyxqbPMuT9e8QyG0qFiVZ38vv7ysxMRETJkxAuXLloKWlBRMTE/F+hfkZUdDfoZubG759+4aoqKhCO5c8lJg+EA0aNBB/G+/evTuaNm0Kd3d3PHnyBLq6uuJse8qUKWjfvn2+ZVStWhUAsHDhQsycORNDhgzBvHnzYGxsDD6fj4kTJ3LuSPYzSUlJaNGiBfT19TF37lxYW1tDU1MTt2/fxtSpU/M9l6iT47t37/D582eYmZkVWjyyKqi9TyAQQEVFJc/2/LYBkKpzUM2aNfHkyROcPn0agYGBOHLkCNavX49Zs2aJh1/mZ8+ePRg0aBC6d+8Ob29vmJqaQkVFBb6+vnk62srid67pR1zvZ2HXPgBAhQoV8PDhQ5QrV05iu6mpKYBfJ3tnz56FpaXlT5PU7/Xv3x9r167F/v37MWXKFOzfvx82NjY/7cMAyP57Ff1t9e/fH56envnuI+r7IutrTuTH34/o3EuWLCnw+kR9fsaNG4cdO3Zg4sSJaNSoEQwMDMDj8dCvX7+fvhdxOUefPn3QrFkzHDt2DBcuXMCSJUuwePFiHD16FC4uLgWeQ9bY5FleYf4diuT399WnTx9cv34d3t7eqF27tvgzpkOHDoX6GVGhQgU8e/ZM5r9DRSsxCcT3RG8grVq1wtq1azFt2jRUqVIFAKCmpgZnZ+efHh8QEIBWrVph27ZtEtuTkpJQtmxZzvEU9IFw+fJlfP78GUePHkXz5s3F22NjY/Pdf+PGjQgKCsKCBQvg6+uLkSNH4sSJE5zjsba2hlAoRHR0dIFvLpUrVwYAPHnyRHzvgNxe77GxsRL30MjIKM9IAiA32//+WC5+1glJR0cHffv2Rd++fZGVlYWePXtiwYIFmD59eoGd+wICAlClShUcPXpUomxR7ZM05/0dir6fXNWrVw9BQUF4+/atxLc1UTW3aLRKQc6cOYOOHTtKfT4nJydYW1tj3759aNu2LR4+fIgFCxb88jhpf68/MjExgZ6eHgQCwS/fDwDZXnMFsba2BgDo6+tL9V7k6emJZcuWibdlZGTk+/qQ9RwAUL58eYwZMwZjxoxBQkIC6tatiwULFvw0gZA1tqIqLz/f/x3+6PHjxyhbtuwvh2l++fIFFy9ehI+PD2bNmiXe/qtmPVnUq1dP3Lz+/d++tH+HilZimjB+1LJlSzRo0AArV65ERkYGTE1N0bJlS2zatCnfiTu+H3KnoqKSJ2M9fPiwzDNbil6QP/4hiLLl78+VlZWF9evX5ykjNjYW3t7ecHV1xd9//42lS5fi5MmT2LVrF+d4unfvDj6fj7lz5+bJlkWxODs7Q11dHatXr5aIb9u2bUhOTpaomra2tkZ4eDiysrLE206fPv1bE53o6OjkWxX44zApdXV12NjYgDGG7OzsAsvL715HRETkqYoX9fQuzDctQPH3kyvR3Ck/JtFbt26FqqrqT4cKf/jwAbdv35aq+eJ7Hh4eiIqKwuzZs8Hj8aQa4SDt7zW/41xdXXHkyBE8ePAgz/Pfvx/I+porSL169WBtbY2lS5ciLS3tp+fO771ozZo1EsO8f+ccAoEgz9+ZqakpKlSogMzMzJ+eQ9bYiqq8/JQvXx61a9fGzp07Jf7GHzx4gAsXLkiV9Ob3mgMgl5lz+/btC0Dy71AoFGLHjh0wNjZGvXr1Cv2chalE1kCIeHt7o3fv3vD398eoUaOwbt06NG3aFPb29hg+fDiqVKmCDx8+ICwsDG/evBHP89C5c2fMnTsXgwcPRuPGjXH//n3s3btX5m9/tWvXhoqKChYvXozk5GRoaGigdevWaNy4MYyMjODp6Ynx48eDx+Nh9+7deV6YjDEMGTIEWlpa2LBhAwBg5MiROHLkCCZMmABnZ2dOQ0urVq2KGTNmYN68eWjWrBl69uwJDQ0NREZGokKFCvD19YWJiQmmT58OHx8fdOjQAV27dsWTJ0+wfv161K9fX2L64WHDhiEgIAAdOnRAnz59EBMTgz179oi/BclCNFXrpEmTUL9+fejq6qJLly5o164dzMzM0KRJE5QrVw6PHj3C2rVr0alTJ+jp6RVYXufOnXH06FH06NEDnTp1QmxsLDZu3AgbGxuJN1gtLS3Y2Njg4MGD+OOPP2BsbAw7O7ufTrokDUXfT5GrV6/i6tWrAHI/RL5+/Yr58+cDyB1yLKoJq1OnDoYMGYLt27cjJycHLVq0wOXLl3H48GFMnz79p6+3s2fPQlNTE61ateIUW//+/TF37lycOHECTZo0kWrWVml/r/lZtGgRLl26BCcnJwwfPhw2NjZITEzE7du3ERwcjMTERACQ+TVXED6fj61bt8LFxQW2trYYPHgwzM3N8fbtW1y6dAn6+vo4deqU+Pp2794NAwMD2NjYICwsDMHBwShTpkyhnCM1NRUVK1ZEr1694ODgAF1dXQQHByMyMlKiJiA/ssZWVOUVZMmSJXBxcUGjRo0wdOhQ8TBOAwMDqeZ80dfXR/PmzeHn54fs7GyYm5vjwoULBdYc5+fUqVPiz5vs7Gzcu3dP/HfYtWtXcfNZt27d0KZNG/j6+uLTp09wcHDA8ePH8e+//2LTpk0S/fyKpSId8yED0TC6H4czMpY79NHa2ppZW1uznJwcxhhjMTExbODAgczMzIypqakxc3Nz1rlzZxYQECA+LiMjg02ePJmVL1+eaWlpsSZNmrCwsLA8Q8ikHcbJGGNbtmxhVapUYSoqKhLDrUJDQ1nDhg2ZlpYWq1ChAvvrr7/Y+fPnJfYRDXM8cuSIRJmvX79m+vr6rGPHjuJt0gzjFNm+fTurU6cO09DQYEZGRqxFixYsKChIYp+1a9eyGjVqMDU1NVauXDk2evRo9uXLlzxlLVu2jJmbmzMNDQ3WpEkTdvPmzQKHHR4+fFji2PzuY1paGnN3d2eGhoYMgHhI56ZNm1jz5s1ZmTJlmIaGBrO2tmbe3t4sOTn5p9cqFArZwoULWeXKlZmGhgarU6cOO336NPP09MwzXPT69eusXr16TF1d/ZdDOgt6/f04rE6kKO7nz4iGpuX3+PE6s7Ky2Jw5c1jlypWZmpoaq1q1KluxYsUvz9GrVy+J1yQX9evXZwDY+vXr833+x3vA5fea3zV++PCBjR07lllYWDA1NTVmZmbG2rRpwzZv3izeR9bX3K9+P1FRUaxnz57icitXrsz69OnDLl68KN7ny5cvbPDgwaxs2bJMV1eXtW/fnj1+/DjPUN+CXm+/OkdmZibz9vZmDg4OTE9Pj+no6DAHB4cC7//3pI2tID/+Pn6nPNF7yJIlS355HsYYCw4OZk2aNGFaWlpMX1+fdenShUVHR0vsI/pb+fjxY54y37x5w3r06MEMDQ2ZgYEB6927N3v37p3UQ8A9PT0L/Dv88fMkNTWVTZgwgZmZmTF1dXVmb2/P9uzZ88tzFAc8xkrQtFeEEIXKyclBmTJl4OvrizFjxig6HDGBQABVVVXMmzcP//zzj6LDIaRUKLF9IAghRS8xMRF//vmneFbX4kLU70mWTtCEENlQDQQhpEQLCAjArl27cPr0aTx69CjfOQAIIYWvRHeiJISQv/76CzweD9u2baPkgZAiRDUQhBBCCOGM+kAQQgghhDNKIAghhBDCGfWBKEaEQiHevXsHPT09uU25TAghJRVjDKmpqahQocJPV3HlKiMjQ2JmWGmoq6tznuZc2VACUYy8e/cOFhYWig6DEEKKtbi4OFSsWLFQysrIyIBVZV28T+A2pbaZmRliY2NLdRJBCUQxIpo2t7mWK1R5agqORrGE6d8UHULxQDVRuaivNwGQg2z8i7MyTTFekKysLLxPECD2VmXo60lXq5GSKoRVvVfIysqiBIIUD6JmC1WeGlR56gqORrGEvBxFh1A8UALxH0ogCMQvA3k08ero5j6kIaCXIwBKIAghhBAIwSCUMlGVdj9lRwkEIYSQUk8IIYQc9iWUQBBCCCEQMAaBlH1tpN1P2VECQQghpNSjJgzuKIEghBBS6gnBIKAEghNKIAghhJR6VAPBHSUQhBBCSj3qA8EdJRCEEEJKPeF/D2n3JZRAEEIIIRBw6AMh7X7KjhIIQgghpZ6AST/DJM1EmYuW8yaEEEIIZ1QDQQghpNSjPhDcUQJBCCGk1BOCBwGkW6RLKOV+yo4SCEIIIaWekOU+pN2XUAJBCCGEQMChBkLa/ZQdJRCEEEJKPUoguKMEghBCSKknZDwImZR9IKTcT9lRAkEIIaTUoxoI7iiBIIQQUuoJwIdAyqmRBHKOpaSgBIIQQkipxzg0YTBqwgBACQQhhBBCTRgyoASCEEJIqSdgfAiYlE0YNA8EAEogCCGEEAjBg1DKPhBCWo0TACUQhBBCCDVhyIASCEIIIaUetyYMqoEAKIEghBBC/mvCoMW0uChVCYSlpSU0NDSgpaWFzMxM1KlTB1u2bIGOjo6iQyOEEKJAQg7zQFAfiFzS3S0lcvDgQdy5cwcPHz5EcnIy/P398+wjENA0IYQQUpqImjCkfZBSmECIZGVlIT09HUZGRvD390erVq3g6uoKe3t73LhxA+fPn0fdunVRq1YttGjRAtHR0QCAy5cvw87ODmPGjIGDgwNsbW1x8+ZNAMDVq1dhbW2NxMREAICXlxeGDx+usGskhBAiHSH4nB6kFCYQffv2Re3atWFmZgY+n48+ffoAACIiIrBw4ULcv38f1tbWcHd3x86dO3Hv3j2MGDECvXr1Avuv48zjx4/h6emJu3fvYty4cZgxYwYAoHnz5hg2bBgGDRqEQ4cO4d9//8WaNWsKjCUzMxMpKSkSD0IIIUVPwHicHqQUJhCiJoxPnz7B0tISU6dOBQA0btwY1atXB5CbTNjb28Pe3h4A4OHhgXfv3uHt27cAgKpVq8LJyQkA0KhRI8TExIjLnzZtGrKysjBixAgcOnQImpqaBcbi6+sLAwMD8cPCwkIu10wIIeTnRGthSPsgpTCBEFFVVYWrqysCAwMBALq6ulIf+31SoKKigpycHPHPqampePHiBXR0dPDx48efljN9+nQkJyeLH3FxcRyvghBCSGEQMj6nBynFCQQAhISEiGsdvtewYUPcv38fDx48AAAcOHAA5ubmMDc3/2WZQ4cOhYeHBw4dOoQBAwbg8+fPBe6roaEBfX19iQchhJCiRzUQ3JWqYZxAbh8ILS0t5OTkoHLlyti4cSMuXrwosY+JiQn27t2LgQMHIicnB0ZGRjh8+DB4vJ+3e61duxaJiYmYOXMm+Hw+Ro8ejYEDB+L06dO/PJYQQojiCAGp+zYI5RtKicFjjKbUKi5SUlJgYGCA1tr9oMpTV3Q4CiVMT1d0CMUDJZ656G2KAMhh2biME0hOTi60GlvR++6G2/WhpSvdd+pvaTkYXTeyUOMoiUpdDQQhhBDyI25TWVMTBkAJBCGEEEJTWcuA0ihCCCGlnjxnovT19UX9+vWhp6cHU1NTdO/eHU+ePJHYJyMjA2PHjkWZMmWgq6sLV1dXfPjwQWKf169fo1OnTtDW1oapqSm8vb0lRgEWNUogCCGElHryHIVx5coVjB07FuHh4QgKCkJ2djbatWuHr1+/ivf5888/cerUKRw+fBhXrlzBu3fv0LNnz//HJxCgU6dOyMrKwvXr17Fz5074+/tj1qxZhXYPuKJOlMUIdaL8P+pE+R/qRJmL3qYI5NuJ0i+yGadOlH/VvyZzHB8/foSpqSmuXLmC5s2bIzk5GSYmJti3bx969eoFIHfG45o1ayIsLAwNGzbEuXPn0LlzZ7x79w7lypUDAGzcuBFTp07Fx48foa5e9J8ZVANBCCGk1BNyqH0QrYXx41IEmZmZUp0rOTkZAGBsbAwAuHXrFrKzs+Hs7Czep0aNGqhUqRLCwsIAAGFhYbC3txcnDwDQvn17pKSk4OHDh4VyD7iiBIIQQkipJ8tMlBYWFhLLEfj6+v76PEIhJk6ciCZNmsDOzg4A8P79e6irq8PQ0FBi33LlyuH9+/fifb5PHkTPi55TBBqFQQghpNQTgAeBlKMrRPvFxcVJNGFoaGj88tixY8fiwYMH+Pfff2ULtBihBIIQQkipx2WNC9F+XJcg8PLywunTp3H16lVUrFhRvN3MzAxZWVlISkqSqIX48OEDzMzMxPvcuHFDojzRKA3RPkWNmjAIIYSUegL8vxbi1w9uGGPw8vLCsWPHEBISAisrK4nn69WrBzU1NYllFZ48eYLXr1+jUaNGAHJXfr5//z4SEhLE+wQFBUFfXx82NjayXvZvoRoIQgghpZ4sNRDSGjt2LPbt24cTJ05AT09P3GfBwMAAWlpaMDAwwNChQzFp0iQYGxtDX18f48aNQ6NGjdCwYUMAQLt27WBjY4MBAwbAz88P79+/xz///IOxY8dK1XQiDzIlEElJSbhx4wYSEhIgFEouKzJw4MBCCYwQQggpKvKcynrDhg0AgJYtW0ps37FjBwYNGgQAWLFiBfh8PlxdXZGZmYn27dtj/fr14n1VVFRw+vRpjB49Go0aNYKOjg48PT0xd+5cTrEUJs4JxKlTp+Dh4YG0tDTo6+tLrDLJ4/EogSCEEFLiMA5TWTOOU1lLM92SpqYm1q1bh3Xr1hW4T+XKlXH27FlO55Ynzn0gJk+ejCFDhiAtLQ1JSUn48uWL+JGYmCiPGAkhhBC5kudU1sqKcw3E27dvMX78eGhra8sjHkIIIaTICRkPQiblYlpS7qfsOKdR7du3x82bN+URCyGEEKIQ8lwLQ1lxroHo1KkTvL29ER0dDXt7e6ipqUk837Vr10ILjhBCCCkKVAPBHecEYvjw4QCQb89PHo8HgYDrCFlCCCFEsYTfrXEhzb5EhgTix2GbhBBCSEknYDwIpKxZkHY/ZfdbE0llZGRAU1OzsGIhhBBCFIKaMLjjXA8jEAgwb948mJubQ1dXFy9evAAAzJw5E9u2bSv0AAkhhBB5YxxW4mQ0jBOADAnEggUL4O/vDz8/P6irq4u329nZYevWrYUaHCGEEFIUpF8HQ/pVO5Ud5wRi165d2Lx5Mzw8PKCioiLe7uDggMePHxdqcIQQQkhRELL/N2P8+qHoaIsHmSaSqlq1ap7tQqEQ2dnZhRIUIYQQUpTkuZiWsuJ8F2xsbHDt2rU82wMCAlCnTp1CCYoQQggpSsL/1sKQ9kFkqIGYNWsWPD098fbtWwiFQhw9ehRPnjzBrl27cPr0aXnESAghhMgVDePkjnMNRLdu3XDq1CkEBwdDR0cHs2bNwqNHj3Dq1Cm0bdtWHjESQgghciXtCAwuTR3KTqZ5IJo1a4agoKDCjoX8R5j+DUJejqLDUChV8wqKDqFYECR8UnQIxQKjGW5zCek+yIsQHOaBoCYMAL85kRQhhBCiDBiHvg2MEggAMiQQRkZG4PHy3jwejwdNTU1UrVoVgwYNwuDBgwslQEIIIUTeaCZK7mTqRLlgwQK4uLigQYMGAIAbN24gMDAQY8eORWxsLEaPHo2cnBzxwluEEEJIcUbDOLnjnED8+++/mD9/PkaNGiWxfdOmTbhw4QKOHDmCWrVqYfXq1ZRAEEIIKRGoBoI7zmnU+fPn4ezsnGd7mzZtcP78eQBAx44dxWtkEEIIIcUdzQPBHecEwtjYGKdOncqz/dSpUzA2NgYAfP36FXp6er8fHSGEEFIEpJ/GWvqaiuIkLi4Ob968Ef9848YNTJw4EZs3b5a5TM5NGDNnzsTo0aNx6dIlcR+IyMhInD17Fhs3bgQABAUFoUWLFjIHRQghhBQlZW/CcHd3x4gRIzBgwAC8f/8ebdu2ha2tLfbu3Yv3799j1qxZnMvknEAMHz4cNjY2WLt2LY4ePQoAqF69Oq5cuYLGjRsDACZPnsw5EEIIIURRlD2BePDggfhL/6FDh2BnZ4fQ0FBcuHABo0aNKpoEAgCaNGmCJk2ayHIoIYQQUuwoewKRnZ0NDQ0NAEBwcDC6du0KAKhRowbi4+NlKlOqBCIlJQX6+vri//+MaD9CCCGkpGCQfobJkriat62tLTZu3IhOnTohKCgI8+bNAwC8e/cOZcqUkalMqRIIIyMjxMfHw9TUFIaGhvlOJMUYA4/Hg4CmnCWEEFLCKHsNxOLFi9GjRw8sWbIEnp6ecHBwAACcPHlS3LTBlVQJREhIiHiExaVLl2Q6ESGEEFJcKXsC0bJlS3z69AkpKSkwMjISbx8xYgS0tbVlKlOqBEI0oiInJwdXrlzBkCFDULFiRZlOSAghhBQ3yp5AALktBbdu3UJMTAzc3d2hp6cHdXV1mRMITvNAqKqqYsmSJcjJKd0rRRJCCFEuyj4PxKtXr2Bvb49u3bph7Nix+PjxI4Dcpo0pU6bIVCbniaRat26NK1euyHQyQgghpDhijMfpUdJMmDABjo6O+PLlC7S0tMTbe/TogYsXL8pUJudhnC4uLpg2bRru37+PevXqQUdHR+J50dAQQgghpKTgMkV1SZzK+tq1a7h+/TrU1dUltltaWuLt27cylck5gRgzZgwAYPny5Xmeo1EYhBBCSiJl7wMhFArz/Xx+8+aNzEtPcG7CEAqFBT4oeSCEEFISKXsTRrt27bBy5UrxzzweD2lpaZg9ezY6duwoU5kyzURJCCGEKBNlr4FYtmwZ2rdvDxsbG2RkZMDd3R3Pnj1D2bJlsX//fpnKlCmBuHLlCpYuXYpHjx4BAGxsbODt7Y1mzZrJFAQhhBCiSFxqFkpiDUTFihVx9+5dHDhwAPfu3UNaWhqGDh0KDw8PiU6VXHBOIPbs2YPBgwejZ8+eGD9+PAAgNDQUbdq0gb+/P9zd3WUKhBBCCFEUxqEGoiQmEEDuVAz9+/cvvPK4HrBgwQL4+fnhzz//FG8bP348li9fjnnz5lECQQghpMRhAJiUi1yUlLUwTp48CRcXF6ipqeHkyZM/3VdXVxc1atRAhQoVpC6fcwLx4sULdOnSJc/2rl274u+//+ZaHCGEEKJwQvDAU7JhnN27d8f79+9hamqK7t27/3J/FRWVPBUEP8N5FIaFhUW+k04EBwfDwsKCa3GEEEKIwinjKAyhUAhTU1Px/3/2yMjIwJYtW+Dn5yd1+ZxrICZPnozx48fjzp07aNy4MYDcPhD+/v5YtWoV1+IIIYQQhRMyHnhKPArjV9TV1eHq6op79+5JfQznBGL06NEwMzPDsmXLcOjQIQBAzZo1cfDgQXTr1o1rcYQQQggpItHR0Xj9+jWysrIktnft2hV6enr5ThJZEJmGcfbo0QM9evSQ5VBCCCGk2GGMQyfKktKL8jsvXrxAjx49cP/+ffB4PLD/LoLHy61NkWUiSM59IAghhBBlo4x9IL43YcIEWFlZISEhAdra2nj48CGuXr0KR0dHXL58WaYyS0wCYWlpierVq6N27dqoWbMm3N3d8fXr10Irf86cOZg4cWK+z/F4PCQlJRXauQghhBQv8kwgrl69ii5duqBChQrg8Xg4fvy4xPODBg0Cj8eTeHTo0EFin8TERHh4eEBfXx+GhoYYOnQo0tLSpI4hLCwMc+fORdmyZcHn88Hn89G0aVP4+vqK53TiqsQkEABw8OBB3LlzBw8fPkRycjL8/f3z7EPrcRBCCOFKNJW1tA8uvn79CgcHB6xbt67AfTp06ID4+Hjx48fppT08PPDw4UMEBQXh9OnTuHr1KkaMGCF1DAKBQLxoVtmyZfHu3TsAQOXKlfHkyRNO1yNSohIIkaysLKSnp8PIyAj+/v5o1aoVXF1dYW9vjxs3buD8+fOoW7cuatWqhRYtWiA6Olp87JIlS2Brawt7e3t4eHggOTk5T/nR0dGws7PDuXPnJLYHBASgXbt24p8FAgEqV66M6OhoXL58GXZ2dhgzZgwcHBxga2uLmzdvyu8mEEIIKTSiPhDSPrhwcXHB/Pnzf9p3UENDA2ZmZuKHkZGR+LlHjx4hMDAQW7duhZOTE5o2bYo1a9bgwIED4kTgV+zs7HD37l0AgJOTE/z8/BAaGoq5c+eiSpUq3C7oP1InEM2aNcPSpUvx9OlTmU5UGPr27YvatWvDzMwMfD4fffr0AQBERERg4cKFuH//PqytreHu7o6dO3fi3r17GDFiBHr16gXGGM6dO4ft27cjNDQU9+/fh46ODqZNmyZxjsuXL6NXr17YtWsXXFxcJJ7r0aMHnj59Ks7WTp48iapVq8LGxgYA8PjxY3h6euLu3bsYN24cZsyY8dPryczMREpKisSDEEJI0ctNDKRtwsg95sf378zMTJnPf/nyZZiamqJ69eoYPXo0Pn/+LH4uLCwMhoaGcHR0FG9zdnYGn89HRESEVOX/888/EAqFAIC5c+ciNjYWzZo1w9mzZ7F69WqZYpY6gRg+fDjCwsJQr1491KxZE1OnTkVoaKi4J2dREDVhfPr0CZaWlpg6dSoAoHHjxqhevTqA3GTC3t4e9vb2AHKrfd69e4e3b98iODgYffv2haGhIYDcIalBQUHi8kNCQjBq1CgEBgaibt26ec6voqKCMWPGiKuh1q1bBy8vL/HzVatWhZOTEwCgUaNGiImJ+en1+Pr6wsDAQPygibgIIUQxZOkDYWFhIfEe7uvrK9O5O3TogF27duHixYtYvHgxrly5AhcXF3GTvGg2ye+pqqrC2NgY79+/l+oc7du3R8+ePQHkflY9fvwYnz59QkJCAlq3bi1T3FIP4xw4cCAGDhyIzMxMXLx4ESdOnEDv3r0hEAjQqVMndO3aFe3bt5d5VS8uVFVV4erqCm9vb9jb20NXV1emckTDV0RENzU8PByVKlXK95jhw4fDxsYGAwcOxPPnz9G1a1fxc5qamuL/q6ioICcn56fnnz59OiZNmiT+OSUlhZIIQghRAAbp17gQ7RcXFwd9fX3xdg0NDZnO3a9fP/H/7e3tUatWLVhbW+Py5cto06aNTGVKw9jY+LeO59wHQkNDAx07dsSmTZvw7t07nDx5EuXLl8fMmTNRpkwZdO7cGaGhob8VlDRCQkLEtQ7fa9iwIe7fv48HDx4AAA4cOABzc3OYm5vD2dkZhw4dEjcVbNq0SaJPQ6VKlXDx4kXMnz8fO3bsyPe8RkZG6NatG3r06IGRI0dCRUVF5mvQ0NCAvr6+xIMQQkjRk6UG4sf3b1kTiB9VqVIFZcuWxfPnzwEAZmZmSEhIkNgnJycHiYmJMDMzK7Ccnj17ij/vevbs+dOHLGSaSOp7Tk5OcHJywoIFCxATE4OTJ08iPj7+d4vNV9++faGlpYWcnBxUrlwZGzduzLMuh4mJCfbu3YuBAwciJycHRkZGOHz4MHg8HlxcXPDgwQM0atQIfD4ftWrVwvr16yWOL1++PEJCQtChQwekpqbmO7xl+PDh8Pf3x/Dhw+VynYQQQoqYLFUQcvLmzRt8/vwZ5cuXB5DbJJ6UlIRbt26hXr16AHK/RAuFQnGzeX4MDAzENe0GBgaFHiePFWUnBiWxdOlSPHr0CNu2bSvUclNSUmBgYICW6AZVnlqhll3SqJpLv6SsMhMkfFJ0CMUCo+HZuYSl+z7ksGxcxgkkJycXWo2t6H23iv8M8LU1f30AAGF6Bl4MWiB1HGlpaeLahDp16mD58uVo1aoVjI2NYWxsDB8fH7i6usLMzAwxMTH466+/kJqaivv374trNVxcXPDhwwds3LgR2dnZGDx4MBwdHbFv3z7ZL/43/XYNRGlja2sLHo+HwMBARYdCCCGkkMhzKuubN2+iVatW4p9Ffd88PT2xYcMG3Lt3Dzt37kRSUhIqVKiAdu3aYd68eRJNInv37oWXlxfatGkDPp8PV1dXTqMn5s+fDw8PD1hZWXEL/icogeDo4cOHig6BEEJIIeMywyTXmShbtmz50xGL58+f/2UZxsbGv1XbcPjwYcyePRtOTk7o378/+vTpg7Jly8pcHlBCJ5IihBBCChXjcXuUMHfv3sW9e/fQsmVLLF26FBUqVECnTp2wb98+pKeny1QmJRCEEEJKPXnORFlc2NraYuHChXjx4gUuXboES0tLTJw48acjOX6GcxOGQCCAv78/Ll68iISEBPHMViIhISEyBUIIIYQoTDEahVEUdHR0oKWlBXV1daSmpspUBucEYsKECfD390enTp1gZ2eXZzImQgghpKSRZx+I4iI2Nhb79u3Dvn378OTJE7Ro0QI+Pj7o1auXTOVxTiAOHDiAQ4cOoWPHjjKdkBBCCCmWlKBmoSANGzZEZGQkatWqhcGDB8PNzQ3m5ua/VSbnBEJdXR1Vq1b9rZMSQgghxYmy10C0adMG27dvFy/+WBg4d6KcPHkyVq1aVaSLaBFCCCFyxTg+SpgFCxYUavIASFkD8eM82SEhITh37hxsbW2hpiY5Y+LRo0cLLzpCCCGkSPD+e0i7L5EqgfhxDu0ePXrIJRhCCCFEIUrZKIzCIFUCUdDKlIQQQohSoASCM5pIihBCCFHSmSi3b9+OT5/ksygf51EYderUyXfuBx6PB01NTVStWhWDBg2SWDiEEEIIKc7kuZiWIu3ZswdjxoxB3bp10a1bN3Tt2hU1a9YslLI510B06NABL168gI6ODlq1aoVWrVpBV1cXMTExqF+/PuLj4+Hs7IwTJ04USoCEEEKI3CnpKIyQkBDEx8djzJgxuHXrFpycnFCtWjVMnjwZV69ezTObNBecayA+ffqEyZMnY+bMmRLb58+fj1evXuHChQuYPXs25s2bh27duskcGCGEEFJkuDRNlKAmDAAwMjJC//790b9/f2RlZSEkJAQnT56Eh4cHvn37ho4dO6Jr165wcXGBjo6O1OVyroE4dOgQ3Nzc8mzv168fDh06BABwc3PDkydPuBZNCCGEKASPcXuUVOrq6ujQoQPWr1+PuLg4BAYGwtLSEvPmzcPy5cs5lcW5BkJTUxPXr1/PMxvl9evXoampCQAQCoXi/xNCCCHFXikdheHo6AhHR0fMnTsX2dnZnI7lnECMGzcOo0aNwq1bt1C/fn0AQGRkJLZu3Yq///4bAHD+/HnUrl2ba9GEEEKIYihxE4a0fpwY8lc4JxD//PMPrKyssHbtWuzevRsAUL16dWzZsgXu7u4AgFGjRmH06NFciyaEEEIUo5TWQPwOzgkEAHh4eMDDw6PA57W0tGQOiBBCCClylEBwRhNJEUIIIUo6jFPk9evX+S6CyRjD69evZSqTcwLB5/OhoqJS4IMQQggpcZR0JkoRKysrfPz4Mc/2xMREWFlZyVQm5yaMY8eOSfycnZ2NqKgo7Ny5Ez4+PjIFQQghhCgSl+GZJXEYJ2Ms31mk09LSZB41yTmByG9yqF69esHW1hYHDx7E0KFDZQqEEEIIURgl7QMxadIkALnLTcycORPa2tri5wQCASIiImQeNSlTJ8r8NGzYECNGjCis4gghhBDym6KiogDk1kDcv38f6urq4ufU1dXh4OCAKVOmyFR2oSQQ3759w+rVq2Fubl4YxRFCCCFFigcOTRhyjaRwXbp0CQAwePBgrFq1Cvr6+oVWNucEwsjISKIdhTGG1NRUaGtrY8+ePYUWWKnGVwF4pbtDquDTZ0WHUCwEvrqh6BCKBZeqjRUdQrEgTE9XdAjKS8knktqxY0ehl8k5gVi5cqXEz3w+HyYmJnBycoKRkVFhxUUIIYQUHSXtAyHy9etXLFq0CBcvXkRCQkKeVThfvHjBuUzOCYSnpyfnkxBCCCHFmpInEMOGDcOVK1cwYMAAlC9fPt8RGVzJ1AciKSkJ27Ztw6NHjwAAtra2GDJkCAwMDH47IEIIIaSoKfswznPnzuHMmTNo0qRJoZXJeSKpmzdvwtraGitWrEBiYiISExOxfPlyWFtb4/bt24UWGCGEEFJklHwmSiMjIxgbGxdqmZwTiD///BNdu3bFy5cvcfToURw9ehSxsbHo3LkzJk6cWKjBEUIIIUVCyROIefPmYdasWUgvxI64nJswbt68iS1btkBV9f+Hqqqq4q+//oKjo2OhBUYIIYQUFWVvwli2bBliYmJQrlw5WFpa5lm6W5YWBM4JhL6+Pl6/fo0aNWpIbI+Li4Oenh7nAAghhBCFU/JhnN27dy/0MjknEH379sXQoUOxdOlSNG6cOzY7NDQU3t7ecHNzK/QACSGEELlT8lEYs2fPLvQyOScQS5cuBY/Hw8CBA5GTkwMAUFNTw+jRo7Fo0aJCD5AQQgiRN2VvwpAHTgmEQCBAeHg45syZA19fX8TExAAArK2tJRboIIQQQkoUJa+B4PP5P537QSAQcC6TUwKhoqKCdu3a4dGjR7CysoK9vT3nExJCCCHFDocaiJKYQBw7dkzi5+zsbERFRWHnzp3w8fGRqUzOTRh2dnZ48eIFrKysZDohIYQQUuwoeQ1Et27d8mzr1asXbG1tcfDgQQwdOpRzmZzngZg/fz6mTJmC06dPIz4+HikpKRIPQgghpMRR8nkgCtKwYUNcvHhRpmM510B07NgRANC1a9c8q3LyeDyZ2lEIIYQQRSqNnSi/ffuG1atXw9zcXKbjOScQorXFCSGEEFIyGBkZ5fnSn5qaCm1tbezZs0emMjknEC1atJDpRIQQQkixpeR9IFauXCnxM5/Ph4mJCZycnGBkZCRTmbQaJyGEkFJP2ZswPD09C71MWo2TEEIIAeTWgfLq1avo0qULKlSoAB6Ph+PHj0ueljHMmjUL5cuXh5aWFpydnfHs2TOJfRITE+Hh4QF9fX0YGhpi6NChSEtL4xRHUlISli1bhmHDhmHYsGFYsWIFkpOTuV/Qf2g1TkIIIUSOozC+fv0KBwcHrFu3Lt/n/fz8sHr1amzcuBERERHQ0dFB+/btkZGRId7Hw8MDDx8+RFBQEE6fPo2rV69ixIgRUscgjy//PMYYp1uhpaWFqKioPItpRUdHw9HRsVCXCi1tUlJSYGBggJb8nlDlqf36ACXGU5OpdU3pBMZGKDqEYsGlamNFh1AsCEv5+2sOy8ZlnEBycjL09fULpUzR+261vxZCRUNTqmMEmRl45ve3THHweDwcO3ZMvLgVYwwVKlTA5MmTMWXKFABAcnIyypUrB39/f/Tr1w+PHj2CjY0NIiMjxateBwYGomPHjnjz5g0qVKjwy/M2a9YMVatWlVhNOycnB8OGDcOLFy9w9epVTtcByFADIVqN80e0GichhJASS4YaiB/nQcrMzOR82tjYWLx//x7Ozs7ibQYGBnByckJYWBgAICwsDIaGhuLkAQCcnZ3B5/MRESHdl4ybN29i6tSp4uQBAFRVVfHXX3/h5s2bnOMGZEggRKtxHjx4EHFxcYiLi8OBAwcwbNgwWo2TEEJIiSTqRCntAwAsLCxgYGAgfvj6+nI+7/v37wEA5cqVk9herlw58XPv37+HqampxPOqqqowNjYW7/Mr8vjyT6txEkIIITIM44yLi5NowtDQ0Cj0sAqL6Mv/0qVL0bhxbpNgaGgovL29Zf7yzzmBUFdXx6pVq2g1TkIIIcpDhgRCX1//t/timJmZAQA+fPiA8uXLi7d/+PABtWvXFu+TkJAgcVxOTg4SExPFx/+KPL78c27CENHW1oahoSEMDQ0peSCEEFKiydKEURisrKxgZmYmsR5FSkoKIiIi0KhRIwBAo0aNkJSUhFu3bon3CQkJgVAohJOTk1TnEX35//LlC+7cuYM7d+4gMTERK1askLnmhHMCkZOTg5kzZ8LAwACWlpawtLSEgYEB/vnnH2RnZ8sUBCGEEKJQchzGmZaWJv7QBnI7Tt65cwevX78Gj8fDxIkTMX/+fJw8eRL379/HwIEDUaFCBfFIjZo1a6JDhw4YPnw4bty4gdDQUHh5eaFfv35SjcAAckd2JCYmQltbG/b29rC3t4e2tjYSExNlXgiTcwIxbtw4bN68GX5+foiKikJUVBT8/Pywbds2jB8/XqYgCCGEEIWSYwJx8+ZN1KlTB3Xq1AEATJo0CXXq1MGsWbMAAH/99RfGjRuHESNGoH79+khLS0NgYCA0Nf8/rHTv3r2oUaMG2rRpg44dO6Jp06bYvHmz1DH069cPBw4cyLP90KFD6NevH7cL+g/neSAMDAxw4MABuLi4SGw/e/Ys3NzcOM1qZWlpifT0dLx9+xZqarnzHly6dAmtW7fGhAkT8szdLS1/f38cP348z2xfv3Lnzh08fvxY4mbOmTMH06ZNk/hFygvNA/F/NA9ELpoHIhfNA5GL5oGQ3zwQNcZzmwfi8WrZ5oFQFGNjY4SGhqJmzZoS2x8/fowmTZrg8+fPnMvkXAOhoaEBS0vLPNutrKygrq7OOYBKlSrh5MmT4p+3bdsmMda1KN25cydPhubj4yMxGxghhBAlJMcaiOIgMzNT3Hnye9nZ2fj27ZtMZXJOILy8vDBv3jyJCTMyMzOxYMECeHl5cQ5g8ODB2L59O4DcNprw8HB06NABAHD//n00bdoUdevWhY2NDebPny8+bs6cOejbty+6dOkCGxsbtG7dGomJieLn09LS4ObmBnt7ezg6OuLFixcAcsfTtmrVCvXq1YOtrS28vLwgFAqRkJCAWbNm4dKlS6hduzZGjRqFUaNGAcidwat27dpISEjAvn374OTkhDp16sDBwQGnTp0Sn7Nly5aYMmUKmjVrBmtra/HxhBBCijdFdaIsKg0aNMi3yWPjxo2oV6+eTGVKVU/cs2dPiZ+Dg4NRsWJFODg4AADu3r2LrKwstGnThnMATZo0wfr16/Hu3TucPHkSvXv3hoqKCoDcJo6LFy9CQ0MD3759Q+PGjeHs7IyGDRsCACIiInDr1i2UKVMG/fr1w6ZNmzB9+nQAQGRkJO7cuQMrKytMmzYNixcvxqZNm2BoaIhTp05BV1cXAoEA3bp1E7cBzZ07N0/Tx6ZNm3Dt2jUYGhoCANq3bw83NzfweDy8fPkSDRs2xKtXr8S9WGNiYnDp0iVkZ2fDxsYGYWFh4p60P8rMzJRIxGTtyEIIIeQ3Kfly3vPnz4ezszPu3r0r/qy+ePEiIiMjceHCBZnKlCqB+HGZbldXV4mfLSwsZDq5yIABA8T9Fvbu3Yu9e/cCAL59+4YxY8bgzp074PP5iIuLw507d8QJRIcOHVCmTBkAucNc7t+/Ly6zUaNGsLKyEv9/zZo1AAChUIipU6fi33//BWMMCQkJsLOzk7oTSWxsLDw8PPDmzRuoqqoiMTERsbGx4rVB+vbtC1VVVaiqqqJ27dqIiYkpMIHw9fWFj4+PDHeMEEJIoVLyBKJJkyYICwvDkiVLcOjQIWhpaaFWrVrYtm0bqlWrJlOZUiUQO3bskKlwaQ0cOBB169bFH3/8IXEhf//9N8qWLYuoqCioqqqiZ8+eEv0Rvu/YqKKiItG+U9Bzy5cvR0JCAiIiIqCpqYlJkyZx6uPQr18/LFq0CL169QKQ2zFF2ph+NH36dEyaNEn8c0pKym8nY4QQQrjj/feQdt+SqHbt2uIv6IVB5omkClOFChXg6+uLxYsXS2z/8uULKlasCFVVVTx58gRBQUG/fa4vX77AzMwMmpqaeP/+PQ4fPix+Tl9fP88oEj09PYltX758Edds7NmzB1++fJE5Fg0NDfFMZoUxoxkhhBAZKXknSnkoFgkEkNuZ8seq/n/++Qc7duxArVq1MG3aNLRu3fq3zzNhwgRERETA1tYWAwYMkFgBrU2bNsjMzEStWrXEHSAnT56Mtm3bijtRrlq1Cr169UKdOnUQFRWFSpUq/XZMhBBCFEvZO1HKA+d5IIj80DwQ/0fzQOSieSBy0TwQuWgeCPnNA2E7kts8EA83lax5IOSB3qUJIYQQgJomOCo2TRiEEEIIkb+4uDjExcX9djkyJRAXL15E586dYW1tDWtra3Tu3BnBwcG/HQwhhBCiCMreB0IeC2FyTiDWr1+PDh06QE9PDxMmTMCECROgr6+Pjh07Yt26dTIFQQghhCiUko/CkMdCmJz7QCxcuBArVqyQmLZ6/PjxaNKkCRYuXIixY8fKFAghhBCiKFxqFkpiDcS+ffvyLIRZq1YtWFhYwM3NDRs2bOBcJucaiKSkJPFaFd9r164dp5U4CSGEkGJDyWsgCnshTECGBKJr1644duxYnu0nTpxA586dZQqCEEIIUSRl7wNR2AthAjI0YdjY2GDBggW4fPmyeOKn8PBwhIaGYvLkyVi9erV4X1nbVQghhJAipeRrYURFReHixYsFLoT5/aKZR48elapMzgnEtm3bYGRkhOjoaERHR4u3GxoaYtu2beKfeTweJRCEEEJKBiVPIAwNDQt9IUzOCURsbOxvnZAQQggpbpS9E6U8FsWkmSgJIYQQJa+BkAfOCcSQIUN++vz27dtlDoYQQghRBB5j4Em5NJS0+yla3bp1cfHiRRgZGaFOnTrg8QpeiPz27ducy+ecQPy4fHV2djYePHiApKSkQlktkxBCCClySlgD0a1bN2hoaAAAunfvXujlc04g8hvCKRQKMXr0aFhbWxdKUIQQQkhRUsY+ELNnzwYACAQCtGrVCrVq1YKhoWGhlV8oi2nx+XxMmjQJK1asKIziCCGEkKKlxBNJqaiooF27dnlaEH5Xoa3GGRMTg5ycnMIqjhBCCCkyyj6RlJ2dHV68eFGoZXJuwpg0aZLEz4wxxMfH48yZM/D09Cy0wAghhJAio4R9IL43f/58TJkyBfPmzUO9evWgo6Mj8by+vj7nMjknEFFRURI/8/l8mJiYYNmyZb8coUEIIYQUR8rYB+J7HTt2BJC7HMX3ozEYY+DxeBAIBJzL5JxAnD9/vsCFNz59+oSyZctyDoIQQghRKCWvgbh06VKhl8k5gXBzc0NAQECe8aQfPnxAmzZt8ODBg0ILjhBCCCkqJbFmQVpWVlawsLDI89nNGENcXJxMZXLuRPn69WsMGzZMYlt8fDxatmyJGjVqyBQEIYQQolCMcXuUMFZWVvj48WOe7YmJibCyspKpTM4JxNmzZ3H9+nVxZ8p3796hZcuWsLe3x6FDh2QKghBCCFEkZR+FIerr8KO0tDRoamrKVCbnJgwTExNcuHABTZs2BQCcPn0adevWxd69e8HnF9qoUEIIIaToKGkfCNGXfR6Ph5kzZ0JbW1v8nEAgQEREBGrXri1T2TItpmVhYYGgoCA0a9YMbdu2xe7du386xzYhhBBSnPGEuQ9p9y0pRCMnGWO4f/++xCAIdXV1ODg4YMqUKTKVLVUCYWRklG+CkJ6ejlOnTqFMmTLibYmJiTIFQgghhCiMktZAiEZfDB48GKtWrZJpvoeCSJVArFy5stBOSAghhBQ3yj4PxI4dOwq9TKkSCJphkhBCiFLjMrqiBI7CkAeZRmGcP38+z/YLFy7g3LlzhRIUIYQQUpSUfRSGPHDuRDlt2jQsWrQoz3ahUIhp06bBxcWlUAIrzXh8XqnvlFrar1+kY602ig6hWOCb6vx6p1JA+PK1okNQXkraB0KeOCcQz549g42NTZ7tNWrUwPPnzwslKEIIIaQoKXsfCHng3IRhYGCQ75Kgz58/z7O6FyGEEFIiKPlMlPLAOYHo1q0bJk6ciJiYGPG258+fY/LkyejatWuhBkcIIYQUBeoDwR3nBMLPzw86OjqoUaMGrKysYGVlhZo1a6JMmTJYunSpPGIkhBBC5ItxfBDufSAMDAxw/fp1BAUF4e7du9DS0kKtWrXQvHlzecRHCCGEyB31geBOpsUreDwe2rVrB29vb3h5eVHyQAghpGQTMm4PDubMmQMejyfx+H716oyMDIwdOxZlypSBrq4uXF1d8eHDh8K+wkInVQ3E6tWrMWLECGhqamL16tU/3Xf8+PGFEhghhBBSZOQ8jNPW1hbBwcHin1VV///x++eff+LMmTM4fPgwDAwM4OXlhZ49eyI0NJT7iYqQVAnEihUr4OHhAU1NTaxYsaLA/Xg8HiUQhBBCShweODRhyFC+qqoqzMzM8mxPTk7Gtm3bsG/fPrRu3RpA7rTTNWvWRHh4OBo2bCjD2YqGVAlEbGxsvv8nhBBClIKcp7J+9uwZKlSoAE1NTTRq1Ai+vr6oVKkSbt26hezsbDg7O4v3rVGjBipVqoSwsLCSn0AUhP13E2nWQEIIISWZLJ0oU1JSJLZraGhAQ0Mjz/5OTk7w9/dH9erVER8fDx8fHzRr1gwPHjzA+/fvoa6uDkNDQ4ljypUrh/fv38tyKUVGpk6U27Ztg52dHTQ1NaGpqQk7Ozts3bq1sGMjhBBCioYMwzgtLCxgYGAgfvj6+uZbtIuLC3r37o1atWqhffv2OHv2LJKSknDo0CF5X5Vcca6BmDVrFpYvX45x48ahUaNGAICwsDD8+eefeP36NebOnVvoQRJCCCHyxGMMPCmbJkT7xcXFQV9fX7w9v9qH/BgaGuKPP/7A8+fP0bZtW2RlZSEpKUmiFuLDhw/59pkoTjgnEBs2bMCWLVvg5uYm3ta1a1fUqlUL48aNowSCEEJIySP87yHtvgD09fUlEghppaWlISYmBgMGDEC9evWgpqaGixcvwtXVFQDw5MkTvH79WvwlvbjinEBkZ2fD0dExz/Z69eohJyenUIIihBBCipIsNRDSmjJlCrp06YLKlSvj3bt3mD17NlRUVODm5gYDAwMMHToUkyZNgrGxMfT19cU1/MW5AyUgQx+IAQMGYMOGDXm2b968GR4eHoUSFCGEEFKk5DiV9Zs3b+Dm5obq1aujT58+KFOmDMLDw2FiYgIgd6qEzp07w9XVFc2bN4eZmRmOHj1aWFcmNzKNwti2bRsuXLggzo4iIiLw+vVrDBw4EJMmTRLvt3z58sKJkhBCCJEnOQ7jPHDgwE+f19TUxLp167Bu3TpO5Soa5wTiwYMHqFu3LgCIV+QsW7YsypYtiwcPHoj3o6GdhBBCSgpaC4M7zgnEpUuX5BEHIYQQojhynkhKGf3WRFKEEEKIMuAJcx/S7ksogSCEEEKoBkIGlEAQQgghcl6NUxlRAkEIIaTUk+c8EMqKEghCCCGEmjA4owSCEEIIYZB+KmvKHwBQAkEIIYRQE4YMKIEghBBCGDg0Ycg1khKDEghCCCGE+kBwxnkxreIoKysLU6dORdWqVVGzZk3Y2dlh27Ztig6LEEJISSHk+CDKUQMxaNAgZGZm4u7du9DR0cHLly/h4uKCrKwsjB49WtHhEUIIKeaoDwR3Jb4G4tmzZzh+/Dg2b94MHR0dAIClpSWWLVuGefPm4fLly6hdu7Z4/wcPHsDS0lL88/nz59G0aVPUq1cPDRo0kFjrY/fu3XByckLdunXRvHlz3L17FwDg7+8PZ2dnuLm5wd7eHo6Ojnjx4oU4niZNmsDBwQH29vb4559/5H8TCCGE/B5RE4a0D1LyayCioqJQrVo1lClTRmJ7o0aNEB8fjw8fPhR47IsXLzBnzhycP38e+vr6eP78OZo1a4aXL1/i5s2b2L9/P65evQoNDQ1cu3YN7u7uePjwIQAgMjISd+7cgZWVFaZNm4bFixdj06ZNWLt2LTp37ozp06cDABITEws8f2ZmJjIzM8U/p6Sk/M6tIIQQIivqA8FZiU8gfkVLS6vA5wIDA/H8+XM0b95cvI3P5+P169c4ceIE7t69CycnJ/FziYmJ+PbtG4DcBMXKykr8/zVr1gAAmjdvDm9vb6SlpaFFixZwdnYu8Py+vr7w8fH5resjhBBSCCiB4KzEN2HUqVMHz549w+fPnyW2h4WFwdbWFsbGxhAIBOLtGRkZ4v8zxtC2bVvcuXNH/Hj79i2qVasGxhg8PT0lnouPjxcnJJqamuJyVFRUkJOTAwBwdXVFaGgoqlevLq6NKMj06dORnJwsfsTFxRXKPSGEEMIRdaLkrMQnENWqVUOXLl0wYsQIpKenAwBevnyJqVOnYtmyZahSpQpevXqFjx8/Asjt1yDSvn17BAcH4969e+JtN27cAAB07doVe/bswevXrwEAQqEQN2/e/GU8z549Q7ly5TBw4ED4+fkhPDy8wH01NDSgr68v8SCEEFL0RJ0opX0QJWnC2LVrF2bOnAl7e3vw+XzExsbi9OnTaN++PQDgr7/+QoMGDVCuXDm4uLiIj6tatSr27duHkSNHIj09HVlZWahTpw727duHZs2awc/PDz169EBOTg6ysrLQqVMnODo6/jSWgIAA7NmzB+rq6hAKhdi4caNcr50QQkghoCYMzniMKdedEAqFmDZtGoKCgnDx4kUYGxsrOiSppaSkwMDAAK1UXaHKU1N0OArFU1WK3Pa38XR1FB1CsUD3IVfOy9eKDkGhclg2LuMEkpOTC63GVvS+62w9EaoqGtLFIchEcMzKQo2jJFK6d2k+nw8/Pz9Fh0EIIaQkoRoIzpQugSCEEEK44zK/AyUQACUQhBBCCNVAyIASCEIIIUTIIHXNgpASCIASCEIIIQRgwtyHtPsSSiAIIYQQasLgjhIIQgghhJowOKMEghBCCKEaCM4ogSCEEEIYOCQQco2kxKAEghBCCKEaCM4ogSCEEEKEHJbZFNIoDIASCEIIIYRqIGRACQQhhBBCCQRnlEAQQgghNIyTM76iAyCEEEJIyUM1EIQQQko9xoRgUk5RLe1+yo4SCEIIIYQx6ZsmqA8EAEogCCGEkP+SAkoguKAEghBCCBEKAR6txskFJRCEEEII1UBwRgkEIYSQUo8JhWBS1kBQJ8pclEAQQgghVAPBGSUQhBBCiJABPEoguKCJpAghhBDGcjtHSvXgnkCsW7cOlpaW0NTUhJOTE27cuCGHiyhalEAQQggp9ZiQcXpwcfDgQUyaNAmzZ8/G7du34eDggPbt2yMhIUFOV1M0KIEghBBCpK59EHIexrl8+XIMHz4cgwcPho2NDTZu3AhtbW1s375dThdTNCiBIIQQUurJqwYiKysLt27dgrOzs3gbn8+Hs7MzwsLC5HEpRYY6URYj7L92tRyWreBIFI9Hw6QAADyhmqJDKBZ4QnqrAui9IQe518/k0Ikxh2VKXbMgiiMlJUViu4aGBjQ0NCS2ffr0CQKBAOXKlZPYXq5cOTx+/Pg3IlY8+qssRlJTUwEA1wQnFRxJMZCj6ACKiQxFB1BMfFZ0AKQ4SU1NhYGBQaGUpa6uDjMzM/z7/iyn43R1dWFhYSGxbfbs2ZgzZ06hxFUSUAJRjFSoUAFxcXHQ09MDj8dTSAwpKSmwsLBAXFwc9PX1FRJDcUD3ge6BCN2HXMXhPjDGkJqaigoVKhRamZqamoiNjUVWVhbnWH58n/6x9gEAypYtCxUVFXz48EFi+4cPH2BmZsY94GKEEohihM/no2LFiooOAwCgr69fqt8sReg+0D0QofuQS9H3obBqHr6nqakJTU3NQi8XyK3hqFevHi5evIju3bsDAIRCIS5evAgvLy+5nLOoUAJBCCGEyNGkSZPg6ekJR0dHNGjQACtXrsTXr18xePBgRYf2WyiBIIQQQuSob9+++PjxI2bNmoX379+jdu3aCAwMzNOxsqShBIJI0NDQwOzZs/NtyytN6D7QPRCh+5CL7sPv8fLyKvFNFj/iMXmMhyGEEEKIUqOJpAghhBDCGSUQhBBCCOGMEghCCCGEcEYJBCGEEDx8+BABAQGKDoOUIDQKgxBSqgmFQvD5pfu7VEZGBvbv34/Y2Fjw+Xz07NlT0SGREqB0/9WUUjk5tNAEIQAgEAjA5/PBGMP9+/cVHY5CCIVCaGpqYubMmahYsSJOnz6NwMBARYdFSgCqgShlTp48icePH+Ovv/4qld+8RPPXf/nyBerq6tDR0ZHYXhp8f62l8TUgIhAIoKKiAqFQiO7du6N58+aws7MDAPB4vFLzmhD9/vfv348HDx7g/v37SEhIQHp6OtVEkJ8qne8cpYh4ifD/ah3+/fdfvHv3DgBK5QcHj8fDqVOn0KpVKwwZMgQ+Pj7i7aVlShTRh+KOHTswZMgQ7NmzB+/fv1dwVEVPRUUFjDF07twZ9erVw5QpU8Dj8fDs2TMAKBXJg8ihQ4ewfPlyHD58GJcuXYKDgwNOnDiB8+fPKzo0UoyVvk+QUkb0JihKGtq0aQMtLa08+5WWD8979+5h27Zt+PvvvzFo0CCcOHECU6dOBaD8ScT317Zz506sXr0aNWvWxOrVq7F161bxB2dpEhUVBR0dHXh6euLMmTMYPnw4WrdujRkzZig6tCKVmpqK9u3bQ1tbG9bW1hgzZgwePHiABQsW4OTJk4oOjxRT1IRRCty4cQOurq6oW7cu4uPj8fDhQzg4OODbt2+wsrKCo6MjdHV1FR2m3D158gTu7u4YN24c+vTpA4FAAFNTU4wcORITJkzAqlWrlPpbp+jagoODER4ejkOHDqFatWpwcnLC6tWrwePx4Orqiho1aig4UvkRNVuI6OvrQ1VVFW3btkX37t3RoUMHeHp6wt/fH9nZ2VBTU1NgtPKRX9OMUChEWFiYuEnL3NwcXbp0QXx8PJycnBQUKSnuKIFQQj+2a9euXRsXLlyAmpoaTpw4AW9vb7x58wYXL16EsbExrKyslD6B+PbtG/h8PkxNTbFmzRoMHDgQWlpaqFu3LtatW4ehQ4fi8ePHqF69ulInEZmZmVi5ciUePXqEFi1awMrKCi1btgSfz4ePjw/U1dVhbW2tlB+cQqFQ3Odhz549sLS0hJOTE1auXImXL1+KPygHDRoEQ0NDpbwH3ycPISEhyMrKQuvWrTF8+HAcOHAAzs7OmDx5MmJiYhAUFIQ9e/aU+AWfiPzQWhhK5vs3iGvXrkFHRwcVK1aEqakpgNy+EO7u7ti0aRP09fWRnZ0NTU1NRYYsd1FRUZg5cyZOnz6Nt2/fYvLkyfj69Sv2798vTpxSUlKgr6+v4EgLX37fNj9//owJEyZAS0sLY8aMQe3atcHj8RAaGorKlSujYsWKCopWfkRJtajPQ2JiIszNzaGmpoZVq1bB1NQUr169wuTJk2FgYIBt27YBUK7Otd9fy8qVK7FlyxYAQJ06dTB16lTY29tj3LhxyMzMxOvXr7Fs2TLY2toqMmRSzFEfCCXy/RvE+vXr0a9fPyxevBidO3fGy5cvAQDp6el49uwZnj59ChUVFaVPHoDcN8gvX77g2LFjMDc3h4+PD4yNjdG1a1d8/foVAJQ+eThy5Ah27NiBwMBAlClTBqtXr0ZSUhI2bNiAyMhIMMbQpEkTpUweAIiTh+nTp6NNmzYICwvDjBkzUKZMGYwZMwafP39GdnY2mjZtKk4ehEKh0iQPwP+bsCIjI3H9+nU8ePAAN2/ehK6uLlasWIG7d+9izZo12Lx5M44dO0bJA/klSiCUiOgN4syZM3j48CHCwsKwceNGtGzZEl26dMHz58+hr6+Pli1bwszMTMHRyo9AIJD4VygUokOHDuJx/tWqVcP06dNRrlw5PHr0SGFxypvo9bB27Vr4+fkhKysLHTt2xJo1a2BsbIxNmzbh1atX2L17N7KyshQcrfwdOHAA/v7+4uY9e3t7jBo1CqampujduzfMzc0xceJEAMo7vDUyMhLDhg1DZmYm0tLSoKWlhXnz5kFdXR2+vr64fv06AJSKLxakEDCiVJ4+fcosLS2Zm5sbY4yxnJwclpaWxv766y9mbm7O4uPjWVZWloKjlI/Y2Fj27ds3xhhjd+7cYb169WK3bt1i2dnZ7OnTp8zCwoLdvn1bvP/Xr18VFapcCYVC8f9PnTrFWrRowVJTU9mqVauYo6MjK1OmDFu8eDFjjLEvX76wN2/eKCpUucrJyZH4+d27d8zb25s1bdqUBQcHi7ffvn2bbd++vajDKxLfvxZE5s6dyxo2bMjOnTvHUlNTGWOMffjwgY0fP57Fx8cXdYikBKM+ECUc+6GN9tu3b9i+fTvmz5+PZcuWwd3dHQCQlpYGX19fDB06FFWqVFFUuHLVq1cvhIeH49mzZ3jz5g02btyI27dvo0yZMmjVqhVu376NNm3awN3dXWm/Yebk5EBVNbdvdEZGBu7evYuKFSvi3Llz2LNnDy5fvoxly5bB29sbmzdvxrBhwxQcsXx8P0nU6tWroaGhgZYtW8LCwgIrV67EjRs3MG7cOLRt21biuB//nkqy768lJiYGGRkZ4maJ2bNnIyIiAuPHj0ezZs2gp6entH8TRH4ogSjBvn+DuH79OlRVVWFsbIyqVatix44dWLVqFf766y9xEqFMb47f+/66OnXqhLdv3yI8PByampp49uwZEhISMG/ePMTExEBdXR33799XyjfKpKQkbNq0CX/++Se2bduG2NhYLFq0CAAwefJkdOvWDS1btsT27dsRHh6OqVOnwtraWsFRy49QKETXrl3h4OCAT58+4ciRI3j58iU+fPiA/fv348yZM9i6datStvV//zexYsUKHD9+HBkZGahevTo2btwIbW1tzJs3D2fOnIGPjw/atWsHoHRNnkUKgYJqPkghWrt2LXNwcGDu7u7M0NCQHT58mDHG2Pbt21mlSpXYwYMHFRyhfAkEAol/27Zty2xsbMTNGYzlNlfExcWxHj16SFRfK5vp06czPp/P6tWrxz5//swYy63G7tSpE2vXrh1bsmQJs7OzY3FxcQqOVP5mzZrFVqxYwdLT01nbtm3ZihUrGGO5r5O3b9+yY8eOKTS+orBx40bWokULlpmZySZOnMg0NTVZ165dxc13ixYtYq9evVJwlKSkUr6vYaVMWFgYNm3ahDNnzmDv3r3YunUrRowYgdDQUAwePBi+vr5wdHRUdJhykZ2dDSC3h/21a9fQr18/AMCFCxdgbm6O+vXr49u3bwAALS0tVKxYETweD2/fvlVYzPKQmZkp/n/nzp3h4OCAV69eQUNDA0Dut8qAgABYWVnhxYsX2LNnj9KOtviepqYmVFVV0aNHD7Rr1w4TJ07E58+f4evrC2NjY3Tv3h2Acs3CeufOHVy5cgXZ2dn4+vUrUlJScPz4caxZswavXr3Cu3fvcPPmTfTq1QspKSmYOnUqKlWqpOiwSQlFCUQJ8/2b3cePH5GUlAQbGxuYm5sjOzsbrq6uGDlyJAICAgAAbm5uStnnISEhAbNmzcK9e/cA5CYR31fHi5IIGxsbfPv2DTweD69fv8aNGzeUKqFKTk7G0aNH8ebNGwQEBODYsWO4ffs2+vbtC2tra7x69QoAEB0djUWLFmHdunVwcHBQcNSFTzTi5nvm5ubw8/MTr3MBAMOGDUNCQoLEKANlqrbfuHEjFi1ahIiICOjo6GDMmDFITU1FcHAw1q9fDyMjI/Tq1Uu8WBYhv4NmoixBvu/ktHbtWjx58gRjxozBrVu3sH//fri5uQHI/bYtelNUpjfH7z19+hRv3rzB9u3bMXLkSLx//17imzgABAYGomPHjoiMjETz5s1RqVIl3L17F8bGxgqKuvBlZ2cjMTERzs7OUFFRQXh4OIDc10dWVhYaNWqEMWPGYO3atYiMjIShoaFiA5aD7ztMzpgxA4wxNG7cGE5OTmjZsiVevXolTjZNTU2xatUqAMrVJ0j03rBx40ZMmDABS5cuBWMMDRs2RHZ2Nl69eoWkpCScO3cOnz59wunTp5V6KDcpGtSJsgQ6efIk9uzZg6VLl6JSpUrYunUr9u3bhzp16qBq1arYsmUL9u7di5o1ayo6VLm6dOkSDh06BF1dXTDGkJycjLFjxyI+Ph4pKSkwNTVFq1atAPz/Q0aZPjREdu7cibFjx6JJkybYsGGDRI3T6tWr8fLlSwwbNgw2NjYKjFK+hEIhunXrhoYNG0JVVRWzZs1CXFwcPn36hHv37uHp06cwMzPDiBEjxPsrS0fa71/TmZmZ0NDQgJeXF+Lj4/Hnn3+iadOmGDFiBB48eIDU1FTs2bNHKWuhSNGjBKIECA8Px9OnT2FpaYmyZcvin3/+QXR0NKKjo8Hn8/Hp0yc8evQImzZtQsWKFTFgwACl7Fmen7CwMGzfvh03b97E27dvMWjQINy5cweqqqrw9vYWJxDK5MckiDGG27dvIzAwEJGRkZgxYwbq16+P8PBw2NnZQUdHR+mSph9t3LgR6enpGDt2LHr06IG2bdvizz//xNevX6GjoyOxr7ImD6tWrUJcXBwWLlwIdXV1jBkzBnFxcZg5cyYaNGiAhIQEqKurK2UtFFEMSiCKuXPnzmHKlCmoVasWEhMTMWPGDHz79g2LFi1C7dq1sWLFCkWHWGREb5bh4eG4e/cu9PT00LFjR3z8+BHLly8Hj8fDzJkzUb58eUWHKjfff2CcPHkSKSkpqFGjBurWrYtXr15h06ZNuHfvHmrVqoV///0XBw8ehLm5uYKjLnw/rqq5fv16PH/+HA8ePECHDh0wadIkpKWl4c8//8ScOXOU8h58b+XKlTh8+DD2798v0Sly+vTpuH79OhYtWoRGjRopMEKijJQjDVdSgYGBmDlzJrZv3479+/fDwMAAL168gLGxMQYNGoScnBx4e3srOswiw+PxcOrUKYwcORJRUVEICAhAy5YtoaenhzFjxiAtLQ3z589HcnJyvp3qlIEoeVi9ejX8/PwQEREBLy8vrFixAuXLl8e4cePQsWNH3L9/H5s3b1baD05Rc1RISAgAwNHRESdOnICVlRUmTZoEABgyZAh4PJ5S3oPY2FhxB9mEhAScPXsWBw8ehIqKCjZs2ABXV1csXrwYvr6+qF+/PiwsLBQcMVFGVANRTCUnJ8Pc3Bxz5szBlClT8OnTJ9SqVQt169aFhoYG4uLiMGrUKJw5cwb29vaYM2eOokOWu8+fP8Pd3R1+fn5wcHBAZmYmZs6ciZiYGAQEBCAkJARmZmZK33yzZ88ebNq0CdeuXcOiRYtw8OBBVK1aFY0bN8aIESOgo6ODrKwsqKurKzpUubpw4QK6d++O3bt3w9XVFT4+Prh27RpMTU2RmpqK8uXLY/PmzQCUq8Pkhw8f4OXlBXt7ewwaNAhlypSBs7MzLC0tkZWVBVtbWxgaGuL+/fvYsWOHosMlyqwI55wgHIWEhLD69euzgwcPsubNm7O1a9cyxhiLjo5mPXv2ZOvXr2fnz58vFZMCMcZYcnIya9CgAQsMDGSM5a51EBoaylxdXcWTSJUGkZGR7PXr12zDhg2sffv27Nu3b2zEiBGsSpUqbPHixUwgEOS7BkJJl52dnefn3bt3MysrK3b69GnGWO66Fps3b2bHjx8X76eMr419+/YxNzc35uvryzIyMlhERARbv349e/78OWOMsYMHD7JGjRqxpKQkBUdKlBkN4yzGWrVqhSVLlqBbt27w9PTE2LFjAQA1a9aElpYWdHV1xVPQKiP23bdGoVAoXkn0+vXrsLCwgI2NDRhj+Pz5Mz5+/AhTU1Ol+ZYpwvL55uzo6IjMzEyEhobCy8sLmpqaqFWrFtTU1ODp6ak0HQR/JFrjY9WqVZgwYQJUVVXh5uYGVVVVeHl5gTGGzp07o06dOuJjlKnDJPD/14ObmxvU1NRw8OBBLF++HEOHDkWDBg0A5HYo3bJlC3bt2gUDAwMFR0yUGTVhlAD//vuveCx/8+bNceTIEcyfPx+HDx9G1apVFR2eXIjeKIOCgnD69Gl8/vwZY8aMAY/Hw9atWxETEwNHR0ecPHkSK1asQKdOnRQdcqF7//69eKz+unXrcPv2bVSuXBkeHh6wtraGl5cXMjMzYWpqiuDgYOzdu1cpXw+hoaFISUmBpqYmypYti1GjRsHJyQnLly8HkLsGiLu7O4KDgxEaGor69esrOGL5+j6pDAgIwKFDh1CvXj24u7tDXV0do0ePxrx585S+KY8oHiUQJcTly5cxadIktG3bFpcvX8b27duV/g3i7NmzmD59Ory8vBAXF4f169djz549cHJyQkhICBISEuDg4IDGjRsrVRs3ALx69QqjRo0SJ00LFy6Eq6srnj9/jlevXmHLli2Ij4/HyZMnERUVBV9fX9jZ2Sk67EI3ePBgxMfHIykpCSoqKvj8+TPmzp2LvXv3okqVKuJRSH/99Rfq1KkjnkxN2f2YRBw9ehRVq1bF1KlToaqqKp7GnBB5ogSiBAkJCYGnpyfOnz+v1JMCAblVzxMnTkT79u3FtQsHDhyAl5cXbt26hcqVKys4Qvl68+YNdu/ejaioKHz9+hXbt29HuXLl8PTpU2zatAnPnz/HqlWrYGlpiezsbKipqSk65EI3ePBg8VTdWVlZyMzMxIABAxAVFYWtW7fC19cXmpqaEAqFKF++vLjDoDI1WxR0LaK3bVESsXfvXgQFBWHZsmUoU6ZMkcZISi/l+CsrJVq3bo2nT58qffIQHh6Ohw8f4unTpwgNDQWQ+0bar18/tGvXLs+U1cpE9MFQsWJFuLu7o3HjxoiIiBCvbfLHH39g1KhRKF++PKZMmYKsrCxx3wBl8vz5c/H6HkBu/wc9PT0cP34ctWrVQnBwMPbt24dGjRqhU6dO4uSBMaY0ycP313Ly5EkEBwcjMjISwP8TB9HrxcPDA2vWrKHkgRQp5XvnUXJaWlqKDkEuRN+0wsPDMXToUJw5cwbTp0/HggULsGfPHvTv3x/h4eG4f/8+hEKhosOVix+bYSpXroyhQ4ciIyMDZ8+eRbly5dCrVy9Uq1YN3t7e0NPTU+qhmmlpaUhPT4eWlhb4fL74NdKtWzdERETAzMwMM2fOFO+vTDUP378Wtm7divnz56Np06b48OEDhg4din79+oHH44ExJt5XT09PwVGT0oYSCKJQ6enp0NbWBp/Px6tXrzB16lTMmTMHlpaW0NDQQL9+/fDPP//gxIkTePDgAZYsWYIaNWooOuxC9/0HxpYtWxAREQFHR0d07twZ48ePh5qaGnbt2oWMjAz0799fYuVRZaSlpYUXL17gxIkT8PDwAABkZWVBQ0MD9vb2SExMxOPHjyVeC8qSPAD/r2E4c+YMrl69igcPHiA7OxvHjx/Hhg0bwOfz0adPH6Xq90NKHuoDQRTm8ePH8Pb2RsWKFVG/fn3o6elh1qxZsLS0xLlz58T7vXnzBu/evYOenh5q1qypdB0mv7d7926sX78e7dq1w8uXL6GmpoYZM2bAzMwMy5Ytw71797Bt2zbo6uoq7T0QWb9+PbZu3YrZs2ejW7du4u2tWrXCvXv3UKlSJQQFBaFs2bIKjFJ+YmJi4OXlhY8fP+LmzZsAgI8fP+LUqVNYtWoVZs2aBVdXVwVHSUozSiCIQjx58gTu7u4YOnQoXr9+jbdv32Lnzp04f/48Dh8+DCMjIyxdulTpPyS/d/XqVfj5+cHf3x9ly5bF9evXcfjwYXz9+hV//fUXzM3N8e3bN6VajvxnkpOTsXr1aqxevRoeHh6wsLDApUuXUL58eUydOhUGBgYwMTFRdJiF5sfEODMzE4GBgfD19UX79u3h4+MDIHcmygsXLqB58+ZK35mYFG+UQJAi9/TpU7i6umLixIkYOnQovnz5gsaNG8PT0xPx8fGwtbVFdHQ0BAIB1qxZo+hw5Y4xhqSkJMybNw/79+/H3LlzMXz4cAD/X21UU1MTK1asUMoOkz+Tk5ODkJAQ7N27F5UqVYKhoSEmT56s6LAK3ffJw/79+5GdnY2cnBwMGTIEx48fx549e2Bvb4/Zs2cDUK7+HqTkKl3vRqRYSElJwYsXL9CuXTtkZ2ejb9++qFOnDvT19fH8+XPs2rULI0aMwKlTp/DkyRNUr15d0SEXuu8/MHg8HoyMjDBx4kRoaWkhLCwM5cuXR+fOndGoUSOoqKigcuXKpS55AHJHX7Rr1y7PjKvK9gH6/ZLcAQEBmDRpElxdXWFgYAAXFxcwxrBu3Tr4+vpi+vTpSnXtpOSiGgiiEJcuXcLYsWOhr6+P5s2bw8/PD0DuEM6VK1dixYoVUFNTU8r27e8//I4dO4a3b9/C0dERDRo0wLt377B+/Xp8+PABHTt2pDbu/yhzvxeRW7duYcaMGQgMDMSyZctw6dIlHDt2DGpqahAKhTh37hwcHBxQsWJFRYdKCACaB4IoSKtWrbBt2zbExsaib9++4u1ZWVl49uwZeDyeUiYPwP9HC6xZswZLlizBx48f4ebmhqVLl0JPTw9jx46Frq4uQkJCkJaWpuBoiwdlTB5ycnIkflZXV0e1atWwYMECBAcHIyAgAGpqali2bBmioqLQqVMnSh5IsVL66kRJsdGoUSMcOHAAAwcOxMGDB5GcnIyJEydi7ty54jUglMmVK1fw+vVrDBgwAOfPn8fevXtx5coVbN68GXp6erh8+TKEQiHGjh2LadOmQVVVFbq6uooOm8hBYmIinj17BicnJ3EtQ61atXD37l1ERkbiypUr0NDQwK5du7B792706tVL0SETkgc1YRCFu3z5Mvr27QtdXV2sWbMGHTt2VLoq648fP8LKygrW1tYYPXo0mjVrBgsLC+zfvx8BAQEICgqCj48P1qxZg1mzZsHLy4vauZXY7du3sWvXLrx+/RrPnz9HaGgo9PT0MG/ePDx79gxCoRB2dnY4cOAA9u7dq/Tr3pCSid6hiMK1bNkSx48fx7p169CxY0cAyldlrauri169eqF69eqIjY3FhQsXoK+vj9jYWAwYMAAAYG1tjQ4dOqBPnz6UPCi5unXrgjGG8+fPw9XVFdra2gCACRMmYMiQIahWrRp0dHRw8OBBSh5IsUU1EKRYUbaah+/t3LkTvr6+cHd3R2xsLGrXro3Hjx9DXV0dKioquH79Ovbu3av0s0yWVj++tm/evIl///0XUVFRqFOnDvr06YMKFSogJiYGVlZWlESSYo/6QJBiRZmSh6CgICQnJ6NFixYwMTGBp6cnXrx4AScnJxgbG4vnujA0NMSnT5+wZcsWSh6U1PfJQ1RUFIRCIRwdHeHo6Ah/f3+cOXMGampqSEtLw927d7F+/XoYGhoqNmhCfoESCELkIDk5Ge3bt4eOjg6GDRsGgUCAhQsXIikpCbdv38a0adOwbt06REREoFKlSpgzZ45SJU9Ekuh3u2LFChw5ckS8uuiCBQswaNAg8Pl8REREIDIyEtu2baPkgZQI1IRBiJxcv34dffv2hY+PDy5dugRDQ0MkJibi/PnziIiIQNmyZbF//3506dIF5ubmig6XyNnWrVuxa9cuXLlyBTNnzsTWrVvh5OSEhQsXwtbWFtnZ2cjIyKBVNUmJQTUQhMhJ48aNsWfPHkyZMgWbNm0CkLuAmGj1UQMDA4wcOZJqHpTU9xOGpaamQkNDA8eOHcPKlStx7949PHv2DE2bNsXw4cOxatUq1K9fH2pqagqOmhDpUS8dQuSoRYsWWLx4MQYNGoTU1FS4u7tjxYoVsLKyAqBcfT7I/3348AEREREAgO3btyMiIgJdu3YFkDsL65IlS6Cnp4cePXrAyMgIlSpVUmS4hMiEaiAIkbPWrVtj1apVGDduHJYvXw5nZ2dFh0TkLC0tDZMmTYKJiQliY2MRGBgIAwMDvHv3DikpKXj8+DEuXLiA27dvY+PGjShXrpyiQyaEM6qBIKQItGrVCsuWLcPMmTPx7ds3RYdD5OTmzZv4+PEjrK2t0bVrV1y8eBHdu3eHubk5BAIBKlSogObNm2PXrl3YuXMn5s+fDwsLC0WHTYhMqBMlIUUoPT1dPGkQUS7nzp2Dl5cX5s6dCw8PD4SHh+P9+/fw9vbG8OHDMW7cOGhpaSExMRECgQBaWlo0VTkp0agJg5AiRMmDcjp79iz++ecf7Ny5E02bNgUANGzYEABgaGiIIUOGQFNTE+np6QgKCsKxY8coeSAlHtVAEELIb8jIyMCgQYMwfPhwtGnTBklJSXj+/DkOHTqE+vXro0uXLrhz5w78/PyQnZ2NefPmoXbt2ooOm5DfRjUQhBDym96+fYv3798jNTUV3t7eSExMxIsXLxAREYHo6GjMnj0b27dvh5qaGnR0dBQdLiGFgjpREkLIb9DU1MTIkSPx999/o1q1ahAIBBg+fDiioqLg5eWF69evIzs7G4aGhpQ8EKVCNRCEEPKb+vfvjwYNGuD9+/do3rw5hEIhAODbt2/Q1dVFdnY2TRJFlA71gSCEEDnYt28fVqxYgR07dsDOzk7R4RBS6KgGghBCClFCQgK2bduGvXv34sCBA5Q8EKVFNRCEEFKIBAIBbt68iXLlysHS0lLR4RAiN5RAEEIIIYQzGoVBCCGEEM4ogSCEEEIIZ5RAEEIIIYQzSiAIIYQQwhklEIQQQgjhjBIIQgghhHBGCQRRSi1btsTEiRMVHYbY5s2bYWFhAT6fj5UrVxa47XsvX74Ej8fDnTt3ijTWwlTU18Dj8XD8+PESfw5CSgJKIAiRs5SUFHh5eWHq1Kl4+/YtRowYke82kpeVlRWCg4MVcu45c+bku+x2fHw8XFxcij4gQooZmsqalEpZWVlQV1cvknO9fv0a2dnZ6NSpE8qXLw8AePDgQZ5tRaUor/133Lt3D1++fEGLFi0UHYoEMzMzRYdASLFANRCkxPv69SsGDhwIXV1dlC9fHsuWLcuzj6WlJebNm4eBAwdCX19f/I3/yJEjsLW1hYaGBiwtLfMcKzrOzc0NOjo6MDc3x7p16yT2ef36Nbp16wZdXV3o6+ujT58++PDhAwDA398f9vb2AIAqVaqAx+Plu+3ly5cFXt/jx4/RuHFjaGpqws7ODleuXJF4/sqVK2jQoAE0NDRQvnx5TJs2DTk5OeLnW7ZsCS8vL0ycOBFly5ZF+/btpTrO0tIyT9NK7dq1MWfOHInYmjZtCk1NTdjY2CA4ODjfKv4XL16gVatW0NbWhoODA8LCwgq8XpETJ06gQ4cOBa5i+ezZMzRv3lx87qCgoDz7xMXFoU+fPjA0NISxsTG6desmca8vX76MBg0aQEdHB4aGhmjSpAlevXoFf39/+Pj44O7du+DxeOLfGyDZhCFqojl69OhPr+9XrzNCSiRGSAk3evRoVqlSJRYcHMzu3bvHOnfuzPT09NiECRPE+1SuXJnp6+uzpUuXsufPn7Pnz5+zmzdvMj6fz+bOncuePHnCduzYwbS0tNiOHTskjtPT02O+vr7syZMnbPX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Tp0+15v/8888z9KFOExMTFCtWLMU6IyMjUahQoRTrfPXqlWadydcNV6xYMc3l37t3DwDw5ZdfpritXLlymtuT5c2bN8Xrv0CBAlqfBfDy8kLZsmXRsmVLFCtWDH379tW6PCg9SpYsmaH53xUeHo6oqKgPbndG3bt3D46OjjAy0m6nki/vef9xen80lQIFCgBAqp+ZeJ+/v3+6j3tAyscqvfvkp+4f5cuXx7Nnz1J8fu9j296tWze4uLjA09MThQsXxtdff43NmzenuIT2QzL6OnrfnTt3Prp/ZCQjkoWHhyMmJibNx0utVqe41PlT9pVkHF2H0uVDYZGad6+3vn//Pn777Tfs3r07xc4ZGRmZ4VrOnz+P8PDwDDX577t16xYuX76cZlP67gF47969mDBhAoKDgxEXF6eZnpkx/cPDw1N8EC297O3tYWxs/MF5PhYKHztwnzhxIlO1AZ928CXKbmm9XpOSkj76ukpL8+bNERQUhKlTp8LV1VXrD2e1Wg2VSoXAwMBUl29hYaH1e0ZHiDEzM0vRWKrVahQqVAjr1q1L9T7Z+Ud4eh7DQoUKITg4GPv27UNgYCACAwOxYsUK9OrVK8UHVdOS2uP0oec2t0nrcXp74jdtd+/exfXr17Fw4cJ0r+v9xyqj+2RW+9i258uXD8ePH8eRI0fg7++PoKAgbNq0CY0bN8b+/fvTtY9l9HWUG/eRZJndV97FJp/SJbNhkZSUhKZNm+LFixcYOXIkypUrB3Nzczx8+BAeHh4Z+gs9WUBAABwcHFChQoVM1Z5cf9OmTTFixIhU71O2bFkAbz803K5dOzRo0AALFizAZ599hjx58mDFihUpRqFID2dn5xRndtLr7t27H/0QalaEQjKVSpXq/dIKxZwcxo4oowoUKJDiw6LA2z98S5Uqpfm9dOnSOHv2LBISEj764fDatWtj4MCBaNOmDbp27YodO3Zozu6VLl0aIoKSJUtq8iS7lS5dGgcPHoSLi8sHX4+lS5cGAPz9998oU6ZMqvOUKFECwNsPur4/XOONGzc0t2eUqakp2rZti7Zt20KtVsPLywuLFy/G2LFjUaZMmUydPEk+mfH+8/t+1trb28PKyuqDH44EMnYCp0SJErh8+TLUarXWH13Xr1/X3J4V/P39YW1trTXwRUald598d/9wc3NLdZ5394/3Xb9+HXZ2dpkastPIyAhNmjRBkyZNMHPmTEyaNAljxozBkSNH4ObmlukvzEzt9R8fH49Hjx5pTStduvRH94+MZEQye3t75M+fP83Hy8jICMWLF0/XsjKCl+tQprwbFm5ubil+ateuDeDtF1bdvHkTM2bMwMiRI9G+fXu4ubl9dGz0D/H390/36AIfqv/Vq1ep1u7m5qY5I75t2zbkzZsX+/btQ9++fdGyZcs0Qy891q1bhwMHDmTqp0iRIp+0zcCHg/n9A/eHmiIifVO6dGmcOXNGa2jXvXv3pniLvHPnznj27BnmzZuXYhmp/dHr5uaGjRs3IigoCD179tScuOjUqROMjY3h4+OT4n4igufPn2fFZmlxd3dHUlISfv/99xS3JSYmal7PzZo1g6WlJXx9ffHmzZsUtQFAjRo1UKhQISxatEjrHczAwED8888/GbpsJNn722xkZITKlSsDgGYdyY1hatmTFisrK9jZ2WldZgkACxYsSLG+Dh06YM+ePbhw4UKK5SRve0ZqaNWqFR4/foxNmzZppiUmJmLu3LmwsLBAw4YN070dHxIQEIBmzZpl6BKR96V3n6xWrRpKliwJPz+/FI9B8v0+++wzODk5YdWqVVrz/P3339i/f3+mjtEvXrxIMS159KRP2T+At6//9/ePJUuWpDhp1blzZ4SEhGDHjh0plpG87RnNCODtCbhmzZph165dWiMPPXnyBOvXr0e9evVgZWWVoW1KD57Jp0zp1KkTRo0aBR8fH6xdu1brr2sRwYsXL2Bra6s5s/zuji8iqQ4tlh5PnjzBxYsXP/nb/tzd3TFu3Djs27cvxfX6ERERsLCwgImJCYyNjaFSqbSCIDQ0FDt37szUel1cXD6l7E/27oG7b9++MDMzA/C/A/dvv/2mmbd06dIICAhAeHi45m3+kJAQnDx5MlvOOBBlJ09PT2zduhUtWrSAu7s77ty5g7Vr12rOWibr1asXVq9ejeHDh+PcuXOoX78+Xr9+jYMHD8LLywvt27dPsewOHTpoLjuxsrLC4sWLUbp0aUyYMAGjRo1CaGgoOnToAEtLS9y9exc7duxA//798fPPP2fpNjZs2BADBgyAr68vgoOD0axZM+TJkwe3bt3Cli1bMHv2bHTp0gVWVlaYNWsWPD094ezsrBkeOSQkBDExMVi1ahXy5MmDKVOmoE+fPmjYsCG6d++OJ0+eYPbs2XBwcMCwYcMyXJ+npydevHiBxo0bo1ixYrh37x7mzp0LJycnzTXsTk5OMDY2xpQpUxAZGQkzMzM0btwYhQoV+uiyJ0+eDE9PT9SoUQPHjx/HzZs3U8w3adIk7N+/Hw0bNkT//v1Rvnx5PHr0CFu2bMGJEydgY2OToRr69++PxYsXw8PDA3/99RccHBywdetWnDx5En5+frC0tMzw4/S+2NhYHDlyBIsWLfqk5aR3nzQyMsLChQvRtm1bODk5oU+fPvjss89w/fp1XL16VfOlhNOmTUPLli1Rp04d9OvXD7GxsZg7dy6sra0z9a3k48ePx/Hjx9G6dWuUKFECT58+xYIFC1CsWDHNOxilS5eGjY0NFi1aBEtLS5ibm6NWrVofvVzU09MTAwcOROfOndG0aVOEhIRg3759sLOz05rvl19+wdatW9G1a1f07dsX1atXx4sXL7B7924sWrQIVapUyVRGAMCECRM03wPg5eUFExMTLF68GHFxcZg6dWqGH690SfdHdMkgJX8qPrUhrXx9fQWA1K1bV6ZOnSoLFy6UESNGiKOjo0ybNk1E3o7mUrp0abGzs5OJEyfK3LlzxdXVVapUqZLiE/LpGV1n+fLlki9fvlRH7ElNiRIlpHXr1immv379WqpVqyYmJibi6ekpCxculOnTp0vv3r3F3Nxcs73JQ5bVr19fFi5cKD4+PlKoUCGpXLlyukfvyAnJo+ts2bJFa3pqo04kf3K/Vq1a4ufnJ6NGjZL8+fOnGELz2rVrYmRkJFWrVpV58+bJb7/9JoUKFZJKlSqlOrpO8nOeHiEhIfL777/L77//Ll9++aXY2Nhoft+9e7fWvKtXr5bff/9dRo0aJQCkUaNGmnlDQ0Mz9DiRYUhtdB0RkRkzZsjnn38uZmZm4uLiIhcuXEh1FKmYmBgZM2aMlCxZUvLkySNFihSRLl26aIa+S2ufX7BggQCQn3/+WTNt27ZtUq9ePTE3Nxdzc3MpV66cDBo0SG7cuKGZp2HDhvLVV1+le/uScyotS5YskerVq0u+fPnE0tJSKlWqJCNGjJCwsDCt+Xbv3i1169aVfPnyiZWVldSsWVM2bNigNc+mTZukatWqYmZmJgULFpQePXrIgwcP0lXP+5m+detWadasmRQqVEhMTU3liy++kAEDBsijR4+07rd06VIpVaqUGBsba41yk1aei7x9zvr16yfW1tZiaWkp7u7u8vTp01RHVbl375706tVL7O3txczMTEqVKiWDBg3SGpUlrRpS21+ePHkiffr0ETs7OzE1NZVKlSqlGP3lQzmZWo3v2rt3r6hUKnny5Ema87zrQ8dtkfTtkyIiJ06ckKZNm4qlpaWYm5tL5cqVZe7cuVrzHDx4UFxcXDT7UNu2beXatWvpqif5WJQ8JOihQ4ekffv2UrRoUTE1NZWiRYtK9+7dU4yCt2vXLqlQoYKYmJhoHd8+9DpKSkqSkSNHip2dneTPn1+aN28ut2/fTvW4/fz5cxk8eLB8/vnnYmpqKsWKFZPevXvLs2fPNPN8LCNEUn9eL168KM2bNxcLCwvJnz+/NGrUSE6dOpXq4/L+MK/Jx/nURn1KC5t8+qCsCItr166Jm5ubWFhYiJ2dnXz33XcSEhKSqSa/S5cu0qpVq3TX/6GDQnR0tIwaNUrKlCkjpqamYmdnJ3Xr1pXp06dLfHy8Zr4//vhDHB0dxczMTMqVKycrVqxItVZ9afJF0nfgFhFZu3atlCpVSkxNTcXJyUn27duX5hCaGWnyk0MstZ/3H8PksY9T+8lI2BER6aPvv/9enJ2ddV0G6SGVSCY+kUekA4mJibC1tYWvry+8vLx0XQ4REVG2W7JkCYoXL46WLVvquhTSM2zySW8kX583YMAAfPbZZ7ouh4iIiCjXYpNPRERERKQwHEKTiIiIiEhh2OQTERERESkMm3wiIiIiIoXhl2F9hFqtRlhYGCwtLTP9dcpERFlNRBAdHY2iRYvCyIjna9KDeU5EuVF25Tmb/I8ICwvjt3sSUa7133//oVixYrouQy8wz4koN8vqPGeT/xHJX0ld36QDTFR5dFxN9pOEeF2XkHOMjHVdQc4Rta4ryDkGMmBYIhJwAgGajKKPS36s6qEVTKD8PDcohvTOjIFknCHJrjxnk/8RyW/pmqjyGEaTrzKg8FAZUJMPA2ryYSD78P/fTF52kn6aPIdh5LlBMajXgYFknCHJpjznhZxERERERArDJp+IiIiISGHY5BMRERERKQybfCIiIiIihWGTT0RERESkMGzyiYiIiIgUhk0+EREREZHCsMknIiIiIlIYNvlERERERArDJp+IiIiISGHY5BMRERERKQybfCIiIiIihWGTT0RERESkMGzyiYiIiIgUhk0+EREREZHCsMknIiIiIlIYNvlERERERArDJp+IiIiISGHY5BMRERERKQybfCIiIiIihcl1Tb6JiQmcnJzg5OQEZ2dnBAcH67okIiLKBOY5EZHu5Lom38bGBsHBwQgODsavv/6K8ePHp5gnKSlJB5UREVFGMM+JiHQn1zX574qKioKNjQ0AYOXKlejUqRNcXV3RtWtX/Pvvv3B1dUXlypXRrl07vHjxAgDg6uqKkSNHokaNGqhYsSKuXr0KAPj+++8xa9YsAMCyZcvQvXt3nWwTEZEhYp4TEeUsE10X8L6IiAg4OTkhJiYGz58/x6lTpzS3hYSE4NKlS7CyskKbNm3g5eUFd3d3TJkyBePGjcOcOXMAAHny5MGFCxewfPlyzJw5E3/88QemTZuGWrVqoWrVqpg6dSpOnz6d6vrj4uIQFxen+T0qKip7N5iISKGY50REupPrzuQnv7178+ZNLF68GIMGDdLc1rx5c1hZWQEAzp8/j65duwIAevbsiT///FMzX8eOHQEA1atXR2hoKADAwsICs2fPhpubGyZNmgRbW9tU1+/r6wtra2vNT/HixbNjM4mIFI95TkSkO7muyX9XmzZttM785M+fX/N/lUqV5v3MzMwAAMbGxlrXe165cgUFCxbEo0eP0rzvqFGjEBkZqfn577//PmUTiIgIzHMiopyWq5v8U6dOoVSpUqneVqNGDWzbtg0AsG7dOjRo0OCDy7p9+zaWLVuGS5cuYf78+bh7926q85mZmcHKykrrh4iIPg3znIgoZ+Xaa/JFBCYmJliyZEmq882ZMwd9+vTB+PHjUaJECaxatSrNZYoIPD09MXv2bHz++eeYNWsWPD09cfDgwQ+eQSIiosxjnhMR6Y5KRETXReRmUVFRsLa2RqM8XWGiyqPrcrKdJMTruoScY2Ss6wpyjqh1XUHOMZBIS5QEHMUuREZG8gx1OiXnuSvaG0SeGxRD+gPPQDLOkGRXnufqy3WIiIiIiCjj2OQTERERESkMm3wiIiIiIoVhk09EREREpDBs8omIiIiIFIZNPhERERGRwrDJJyIiIiJSGDb5REREREQKwyafiIiIiEhh2OQTERERESkMm3wiIiIiIoVhk09EREREpDBs8omIiIiIFIZNPhERERGRwrDJJyIiIiJSGDb5REREREQKwyafiIiIiEhh2OQTERERESkMm3wiIiIiIoVhk09EREREpDBs8omIiIiIFIZNPhERERGRwpjougB9IQnxEJXouoxsZ2xnq+sScow6MlrXJeQYSUjSdQlElMOY58okCfG6LoH0BM/kExEREREpDJt8IiIiIiKFYZNPRERERKQwbPKJiIiIiBSGTT4RERERkcKwySciIiIiUhg2+URERERECsMmn4iIiIhIYdjkExEREREpDJt8IiIiIiKFYZNPRERERKQwbPKJiIiIiBSGTT4RERERkcKwySciIiIiUhg2+URERERECsMmn4iIiIhIYdjkExEREREpDJt8IiIiIiKFYZNPRERERKQwbPKJiIiIiBSGTT4RERERkcKwySciIiIiUphsafJNTEzg5OQEJycnODs7Izg4OEuW6+DggFevXmlNO3r0KLp06ZIlyyciopSY6URE+idbmnwbGxsEBwcjODgYv/76K8aPH59inqSkpOxYNRERZTFmOhGR/sn2y3WioqJgY2MDAFi5ciU6deoEV1dXdO3aFf/++y9cXV1RuXJltGvXDi9evAAAXLx4ETVr1kSlSpXQq1cvvHnzJsUyXV1dsWfPHs00tVqNcuXKITIyEgAQHR2NUqVKITExEa6urhg5ciRq1KiBihUr4urVq9m92UREisRMJyLSD9nS5EdERMDJyQlly5bFzz//jJEjR2puCwkJwe7du7F9+3YMGTIEXl5euHz5MlxcXDBu3DgAQO/evTF37lxcuXIF5ubmWLBggeb+kZGRaNOmDX7++We0bdv2fxtiZAR3d3ds3rwZALB161Z07NgRJiYmAIA8efLgwoULGD58OGbOnJlm7XFxcYiKitL6ISIyZPqa6cxzIjJk2Xq5zs2bN7F48WIMGjRIc1vz5s1hZWUFADh//jy6du0KAOjZsyf+/PNPREREIC4uDrVq1dKanqxVq1b45Zdf0KZNmxTr9fDwwOrVqwEAa9asQe/evTW3dezYEQBQvXp1hIaGplm7r68vrK2tNT/FixfP5KNARKQM+prpzHMiMmTZfrlOmzZtcOrUKc3v+fPn1/xfpVJleHl169ZFYGBgqreVKlUKJiYmOHz4MCIjI1G5cmXNbWZmZgAAY2PjD147OmrUKERGRmp+/vvvvwzXSESkVPqU6cxzIjJk2d7knzp1CqVKlUr1tho1amDbtm0AgHXr1qFBgwawsbGBmZkZzp8/rzU92dSpU/HmzRuMHTs21WV6eHjg22+/Rc+ePTNVr5mZGaysrLR+iIjoLX3KdOY5ERmybL0mv0qVKvjll1+wZMmSVOebM2cO5s6di8qVK+P48ePw9vYG8PbDXIMGDULlypURHR2N77//XnMflUqFpUuX4u+//4afn1+KZXbp0gUvX77EN998kx2bRkRkcJjpRET6RyUiousistLRo0cxe/Zs7NixI0uWFxUVBWtra7iiPUxUebJkmbmZsZ2trkvIMerIaF2XkGMkIV7XJVAWS5QEHMUuREZGKvoMdVZmOvNcuZjnpM+yK89NsmxJuYCPjw9WrlyJnTt36roUIiL6RMx0IqLMU9yZ/KzGMz/KxTM/pM8M5Ux+VmKeKxfznPRZduV5tn/wloiIiIiIchabfCIiIiIihWGTT0RERESkMGzyiYiIiIgUhk0+EREREZHCsMknIiIiIlIYNvlERERERArDJp+IiIiISGHY5BMRERERKQybfCIiIiIihWGTT0RERESkMGzyiYiIiIgUhk0+EREREZHCsMknIiIiIlIYNvlERERERArDJp+IiIiISGHY5BMRERERKQybfCIiIiIihWGTT0RERESkMGzyiYiIiIgUxkTXBegNI2NAZazrKrKdOjJa1yXkmKB753RdQo5pWaaurkvIMeqYGF2XQLkd81xxmOfKxDz/NDyTT0RERESkMGzyiYiIiIgUhk0+EREREZHCsMknIiIiIlIYNvlERERERArDJp+IiIiISGHY5BMRERERKQybfCIiIiIihWGTT0RERESkMGzyiYiIiIgUhk0+EREREZHCsMknIiIiIlIYNvlERERERArDJp+IiIiISGHY5BMRERERKQybfCIiIiIihWGTT0RERESkMGzyiYiIiIgUhk0+EREREZHCsMknIiIiIlIYNvlERERERArDJp+IiIiISGEy1OSrVCp4eXlpfn/06BGMjY0xbty4TK185cqV+Pnnn9M9f0REBJYsWaL5PTQ0FJs3b87UuomIDB0znYhIuTLU5BcsWBBnzpxBUlISAGDr1q346quvsqWw1PCAQESUdZjpRETKleEz+fXr18exY8cAADt27ECnTp00t+/cuRM1a9ZE1apV0bp1a0RERAAAPDw8MHToUNSuXRuOjo6a+wPA/fv30bRpU5QpUwYzZszQTG/Tpg2qV6+OihUrYt26dQCAMWPG4Nq1a3BycsL48eMxZswYHDx4EE5OTli2bBnu3LmD+vXro1q1aqhZsyaCg4MBvD275O7unup6iIgMFTOdiEi5TDJ6B3d3d6xZswblypWDqakp7Ozs8OzZMwBAw4YN0b59e6hUKsyZMwfz58/HmDFjAAAvXrzAmTNncPjwYYwfPx6HDh0CAFy+fBkXLlxAYmIivvzyS/zwww8wNTXF6tWrUbBgQbx+/RrOzs7o0qULJk6ciBs3buDChQsAgAYNGmDevHnYunUrACAmJgYHDx6EmZkZLl++jJ9++gkHDhz44HreFxcXh7i4OM3vUVFRGX2IiIj0hpIznXlORIYsw01+3bp18cMPP2Djxo3o0qUL3rx5o7nt/v376Nq1K548eYLY2FjUqlVLc1uHDh0AANWrV0doaKhmetOmTWFhYQEAKFq0KJ48eYLixYtj1qxZ2L17t2a59+/fR548eT5YW1xcHAYPHozLly/D2NgY4eHhH13P+3x9feHj45OxB4WISE8pOdOZ50RkyDI8uo5KpUKDBg0wefJkdOzYUeu2IUOGYMSIEbhy5Qr8/Py0zqCYmZkBAIyNjTXXf747/d3bjhw5gpMnT+Ls2bMICQlBuXLltJaVFj8/Pzg4OODy5cs4ceJEqutPrYZ3jRo1CpGRkZqf//7776PrJSLSV0rOdOY5ERmyDJ/JB4BBgwahSpUqsLW11ZoeFRWFzz//HCKC1atXZ7qoqKgo2NraIm/evAgODkZISAgAwNLSEtHR0Zr53v89KioKZcqUgUqlwsqVKzO1bjMzM62DBxGR0ik105nnRGTIMjVOvqOjI/r06ZNiure3N9q2bQtnZ+dUL4VJrxYtWiA6OhoVKlTAxIkTUb16dQCAra0tqlWrhkqVKmH8+PGoXLkyEhISNB/S8vLywuLFi+Hk5ITnz59nev1ERIaEmU5EpDwqERFdF5GbRUVFwdraGq5GnWCi+vD1o0qgMjbWdQk5JujeOV2XkGNalqmr6xJyjDomRtcl5IhEScBR7EJkZCSsrKx0XY5eYJ4rF/NcmZjnn4bfeEtEREREpDBs8omIiIiIFIZNPhERERGRwrDJJyIiIiJSGDb5REREREQKwyafiIiIiEhh2OQTERERESkMm3wiIiIiIoVhk09EREREpDBs8omIiIiIFIZNPhERERGRwrDJJyIiIiJSGDb5REREREQKwyafiIiIiEhh2OQTERERESkMm3wiIiIiIoVhk09EREREpDBs8omIiIiIFIZNPhERERGRwrDJJyIiIiJSGDb5REREREQKwyafiIiIiEhhTHRdgN5QJwEq5f9NJOokXZeQY1o6uui6hByjMjPVdQk5JyZG1xVQbsc8VxzmuUIxzz+J8lOOiIiIiMjAsMknIiIiIlIYNvlERERERArDJp+IiIiISGHY5BMRERERKQybfCIiIiIihWGTT0RERESkMGzyiYiIiIgUhk0+EREREZHCsMknIiIiIlIYNvlERERERArDJp+IiIiISGHY5BMRERERKQybfCIiIiIihWGTT0RERESkMGzyiYiIiIgUhk0+EREREZHCsMknIiIiIlIYNvlERERERArDJp+IiIiISGHY5BMRERERKQybfCIiIiIihcnRJv/+/fto3bo1HB0dUbp0aYwZMwZqtTonSyAioizCTCciyr1yrMkXEXTs2BHffPMNbt26hatXr+Ly5cuYOXNmTpVARERZhJlORJS75ViTf+jQIVhYWKBHjx4AgLx582LOnDmYPn06xo0bh3nz5mnmtbOz0/x/ypQpcHZ2RuXKlTF9+vQPTj969CiaNm2KDh06oGzZshg+fDgAICkpCd9++y0qVKiASpUqYcWKFTmxyUREisVMJyLK3UxyakXXrl1DtWrVtKaVLFkScXFxiIiI0DoIJNu/fz8ePHiAc+fOQa1Wo2nTpmjRogXCwsJSnQ4AFy9exLVr11CgQAF89dVX+PHHHxEeHo67d+/i2rVrAIDIyMg064yLi0NcXJzm96ioqKzYfCIiRdGHTGeeE5Ehy9UfvN2/fz/8/f1RtWpVVK9eHffu3cPNmzfTnA4AdevWReHChWFqaoqKFSvi3r17KFWqFMLCwjBo0CDs378f1tbWaa7T19cX1tbWmp/ixYvn1OYSESlaTmc685yIDFmONfkVKlTApUuXtKbdvXsXtra2sLOz0/qwVvKZF7VaDW9vbwQHByM4OBh37txBp06d0pwOAGZmZprlGBsbIykpCQUKFMCVK1fQsGFDzJo1Cz///HOadY4aNQqRkZGan//++y8rHwYiIkXQh0xnnhORIcuxJr9JkyaIjIzEhg0bALwN/R9//BHe3t4oUaIEgoODAQAHDhzAq1evAADNmjXDsmXLEBMTAwAIDQ1FZGRkmtPT8uzZM6jVari7u2PcuHGadaXGzMwMVlZWWj9ERKRNHzKdeU5EhizHrslXqVTYsWMHvLy88Ntvv+Hx48f46aef0LNnT7x+/RorV65EpUqV0KRJE9ja2gIAWrRogWvXrqF27dpQq9WwsbHBtm3b0pyelocPH8LDwwNqtRomJibw8/PLoa0mIlImZjoRUe6mEhHRxYqDgoLg5eWFw4cPw8HBQRclpEtUVBSsra3hivYwUeXRdTmUhYzMzXVdQo5RmZrquoQck/Typa5LyBGJkoCj2IXIyMhccYZaHzKdea5czHNlYp5/mhw7k/++Fi1a4N9//9XV6omIKAsx04mIcpdcPboOERERERFlHJt8IiIiIiKFYZNPRERERKQwbPKJiIiIiBSGTT4RERERkcKwySciIiIiUhg2+URERERECsMmn4iIiIhIYdjkExEREREpDJt8IiIiIiKFYZNPRERERKQwbPKJiIiIiBSGTT4RERERkcKwySciIiIiUhg2+URERERECsMmn4iIiIhIYdjkExEREREpDJt8IiIiIiKFYZNPRERERKQwbPKJiIiIiBTGRNcF5HYiAgBIRAIgOi6GspSRxOu6hByjMqB9N0kSdF1CjkjE2+1Mzij6OOa5cjHPlYl5/mnY5H9EdHQ0AOAEAnRcCWW517ouIAcZ0rYamOjoaFhbW+u6DL3APFcwQ8o4Q9pWA5PVea4Sngb6ILVajbCwMFhaWkKlUuXIOqOiolC8eHH8999/sLKyypF16gq3VZm4rdlPRBAdHY2iRYvCyIhXXqaHLvIc4OtBqbityqSLbc2uPOeZ/I8wMjJCsWLFdLJuKysrxb+YknFblYnbmr14Bj9jdJnnAF8PSsVtVaac3tbsyHOe/iEiIiIiUhg2+URERERECsMmPxcyMzODt7c3zMzMdF1KtuO2KhO3leh/DGkf4bYqE7dVP/GDt0RERERECsMz+URERERECsMmn4iIiIhIYdjkExEREREpDJt8IqIP+PfffxEREaHrMoiIKAsYUqazySeiDEtKStJ1CTni+fPnWLx4MaZPn47IyEhdl0NElOUMJc8Bw8t0Nvk6tGfPHixcuFDXZWQrQxm8KbXtVOq2JyUlwdjYGGq1GitXrsSjR490XVK2sbW1Rbt27ZCYmIgFCxbg1atXui6JcilDyHNAubn2PkPJdEPKc8DwMp1Nfg56PyBu3LiB2NhYHVWTM1QqFU6cOIHAwECcOnVK1+VkCxGBSqUCAJw4cQJBQUEAoJmmNMkHhIYNG+LmzZsoUqSIrkvKVlevXsWdO3ewfft2zJs3z2De5qUPM8Q8B5jpSmNoeQ4YVqZznHwduHLlClQqFR4/fozNmzdj0aJFMDL6399b7waMvkrehlOnTqFbt25o3749QkND0bZtWwwYMEDX5WWLRYsWYd68ecifPz8cHBywdOlSWFtb67qsLJX8vE6YMAFhYWFYsGAB7t27h3PnzsHa2hrNmjXTdYlZyt/fH8OGDYO/vz8CAwPx6NEjWFhYYOjQobCwsNB1eZQLGEKeA8x0JWa6oeU5YHiZbqLrAgxB8gtJRHD37l3MmjULFy9eRLly5bB161ZUrFgRZcuWhbW1NerUqaOIA4JKpcL58+exc+dOLF++HE2bNsWff/6JCRMmQKVSoX///rouMUsdP34cu3btwsWLF2FqaopvvvkGw4YNg5+fH6ysrHRd3idLfks3ed8sX748du3ahW7dusHc3Bxv3rzB06dPUbJkSTg6Ouq42sx797WqUqkQGxuL9u3bw9HRESVKlMC+ffswe/ZsJCQkYPjw4Yp4biljDDHPAWa6kjLdUPIcYKbzcp1s9u5ZnISEBJQqVQrLly/HhQsXMG3aNFSvXh3GxsZYsmQJ/Pz88PDhQx1XnDUSEhJw9epV7Ny5Ew8ePAAA1K9fH7/99htWr16NBQsW6LjCT/PuG2ARERE4ceIEbty4gcOHDwMA1q9fj/j4ePTr1w9RUVG6KjNLqNVqGBsbQ0QQEBCAp0+fol27dhg2bBjq1q2LmTNnYvXq1ShUqJBeX7P67ms1MTERAFCxYkUEBQVhz549MDU1Rdu2bWFubo7IyEjEx8frslzSAUPNc4CZrpRMN5Q8B5jpAM/kZ6t3d7BZs2bhyJEjKFu2LLp06YLatWvD3t4eVlZWqFq1Kry8vDQvPn2WfHara9euOHnyJF6/fo0dO3bA2dkZFStWhIuLC3x9fWFmZqbrUjPt/eCwsbHBsGHDAABBQUEwNzdH/fr1sXbtWvTr1w+vX7/W27MDarUaRkZGEBG0bNkSsbGxiI6Ohq+vL77++msYGRkhPj4e33zzDfLly4eyZcvquuRMefc5Xbx4Mfbv34/27dujefPm8PX1xcKFC/HgwQNYWVnh+fPnWLhwIezs7HRcNeUkQ8xzgJmupEw3lDwHmOnJeCY/GyXvYCdPnkRQUBAGDRqEL774AmPGjMGpU6eQN29e1KpVC2FhYVCpVHp9QFCr1ZoXValSpVCvXj2cPHkSgwYNQrVq1eDt7Y3g4GAAb8/+1KxZU7cFf4Lk59XPzw8DBgzAt99+i9DQUAwdOhTFihXDtm3bNGd//vjjD3z22We6LPeTJF9bPHz4cLi4uODYsWPw8vLCypUrERQUhCdPnmDPnj0oUKAAVq1aBUA/R6BIfk737t2L3bt3o1WrVggMDMS6detQrFgxeHt74/jx49i3bx/mz5+PokWL6rhiymmGlOcAM12JmW4oeQ4w0zWEstXBgwelfv36MmPGDBERiYuLk6VLl0rTpk3lwoULcuTIEbl586aOq8y8qKgozf/v3bun+f+SJUukT58+mt/HjBkjrVu3lujo6BytL7vMmTNHGjduLI8ePZJmzZqJg4OD/PXXXxIbGys+Pj4yYsQIiYmJEbVaretSM+Xduv/++2+pXbu2dOzYUTNtxYoV0q1bN9m6datERkZqpiclJeVonVnp3Llz8vnnn0tAQICIiJw5c0aGDRsmU6dOlYcPH4qIfm8ffTql57kIM12JmW6IeS7CTBcRYZOfxd4PgNjYWPnuu++kU6dO8uzZMxERiY+Pl9mzZ0u7du0kISFBF2VmidevX0vfvn1lwYIFIiJSpUoVGTNmjCxfvlySkpKkVatWsnbtWs38t27d0lWpn+zNmzea/0dFRcn8+fMlPDxcpk+fLt26dZP58+fLF198IZcuXZKYmBh5/vy5Dqv9NImJiZr/h4eHi1qtljNnzki3bt1k0qRJmtvmzp0r8+bN00WJWeL912pMTIy0b99enJ2dJS4uTkREzp8/L56enuLn56eZRobDkPJchJmuxEw3lDwXYaanhkNoZiF55xqwHTt2IC4uDoULF0ajRo0wYMAAGBsbw8fHB/b29khMTMSrV69gY2Oj26I/QWxsLIKCgrBlyxb06tULderUwaVLl7BkyRKYmZlBrVajcOHCmDp1KgD9HUru9evX2Lt3L1xcXBASEoK4uDg0bdoUT548Qb9+/bBnzx5YWVmhVq1aiI6OxqVLl/T2+tTk50itVqNLly6wtraGvb09OnfujKSkJCxduhSOjo4YPXq0rkv9JO/ui3/99RcSExNRq1YtqNVqDBkyBPfv38fmzZuRN29eXLp0CUWLFkXhwoV1XDXlJEPLc4CZrrRMN5Q8B5jpadLVXxdKNnfuXHF2dpb/+7//k3bt2skPP/wgIiIDBgyQ3r17a84A6bPkv5ifPHkiAQEB0rlzZ9mwYYPm9rVr18rIkSMlb968curUKV2VmSVevXol27ZtkwoVKkjp0qU1f/0/evRIPD095fTp0/LHH3+It7e35i1AfdenTx+ZO3eu3LhxQ2xsbOTPP/+UpKQkOXnypLRr10527dql6xIz5f0zPbNmzRIXFxfp1KmTdOnSRZ48eSIxMTEydOhQadCggdbZPjJMhpDnIsx0EeVmulLzXISZ/jFs8rPAuztZUlKSdOvWTS5evCgiIgkJCdKpUyeZO3euxMXFiaenpzx+/FhXpX6yd9/eOnHihIwbN04iIiJk79690rVrV/njjz+05l+1apX4+vrmdJlZ4t1rE9esWSOVKlWSVq1aye3bt0Xk7Vvbo0aNksGDB0vJkiXlypUruio1S0VGRsro0aPl1KlT0q5dO5k2bZqIiDx//lxevXql2X599O5b7tu2bZMGDRqIiMjEiRPF1tZWevToIU+fPpWYmBgZMWKE3L9/X1elko4YUp6LMNOVnulKznMRZvrHcHSdTyTvDdO0aNEimJiY4NmzZwAAExMTDBgwAA8ePICpqSmWLl2qt28RRUREoGHDhrh06RIAID4+HiqVCtbW1qhfvz48PDxw4MABLFmyRHOfJ0+e4K+//tK7T+gnJiZi586dmD9/PjZt2oT79+9j/fr1+PrrrzFy5EicPn0a+fPnR/fu3eHt7Y0LFy6gYsWKui47U5KSkjT/FxFYWVnBxsYGPXr0QJ06dfDzzz8DAHr06IHTp0+jdOnSmnn1hYjgyZMnqFatGvbt2wcAqFu3LlauXIk//vgDJ06cwOPHj3H37l18/fXXiIiIwOTJk1G8eHEdV045yZDyHGCmKzHTDSHPAWZ6enGc/E+UfEDw9/fH9u3bERQUBGtra/Tp0wf79+9HhQoVEBoaihs3biAuLg558uTR+spzfWJjY4OOHTuiZ8+eWLduHRISEvD69WsAgJWVFVq1aoXExESsX78eLVq0QJEiRZA3b174+Pjo3XWbJiYmaNu2LZydnREREYHQ0FBYWFjAxsYGr169wpQpU1CmTBncu3cPS5cu1dtrcZPH8lar1fjll19gZmaGxo0bo1ixYnBxcYG1tTVOnjyJ2bNno2jRonBzc9PcV5+eU5VKhcKFC2PixIkYMmQI5syZg+bNm0NEcOXKFXTp0gUmJibo3Lkz/P39YWJiolfbR1nDkPIcYKYrLdMNJc8BZnp68YO3mfTy5UuYmprC3NwcDx48wIgRIxAWFoajR48CAGbPno0VK1agVq1aOH36NDZu3IgKFSrotuhPkPwlGsDbsYT/+OMPdOvWDREREahQoQKKFi0KIyMjqNVqODs7w9bWNsX99MG7Z/IiIiLwww8/4N69e2jZsiVGjRoFAIiMjMTx48excuVKjB8/Hl999ZUuS/5kIoK2bduiSZMmiI+Px6pVq7B3715cu3YNwcHB+Pfff1GkSBFMmjQJgH4/pwCwZ88eDB06FIsXL0bTpk2xdu1abNiwAY6Ojrh48SJWr14NBwcH3RVMOc7Q8hxgpis105We5wAzPUNy9uogZQgICJDatWtL3759NeMl7969W9q3by9z5szRzBccHCw3btzQ+2vAkq9RvXfvnub6zc2bN4ulpaW4ubnJlClTpH///tKxY0e5cOGCLkv9JO9ei3vnzh15+fKliIg8ffpUmjdvLqNHjxYRkZMnT8rDhw/1enxdtVqt2d6LFy/KL7/8IgkJCdK6dWuZPn26iIhm/Ov4+HjN/fRtm999TpctW6YZGnDjxo1SsmRJOXz4sLx580Z27Ngh3333nVy9elVXpZKOGFqeizDTlZbphpLnIsz0jGKTn0GBgYHi4uIiW7dulaNHj8qAAQM0twUEBMigQYP0fqzZ1Pj7+0u9evXk66+/lsGDB8t///0ngYGBUq5cOc0XpuhjYKRm2rRp0qRJE3FxcZGpU6dKYmKi3Lp1S+rVqyfNmjUTZ2dnrS+J0XdRUVHSt29fKVOmjKbJiYqKkoEDB2qNLKGPXwKTbP78+eLk5KT1IbOAgABxdHSUwMBAEdHv7aPMMdQ8F2GmKzXTDSHPRZjp6cUmPwOeP38uKpVKduzYISJvRyIoXry4jBo1Sry9vUVEJCgoSPr06SPLli3TXaFZ7N69e1K+fHk5ffq0BAcHy+LFi6Vdu3YSHR0tfn5+UqpUKXn58qXWGQJ9tWLFCmnUqJGIiAwcOFBsbW1l4sSJIvJ2lII5c+bo9RfAiPwv+Lp37y6DBw8WEZHJkydLjx495MyZMyIi0rVrV/Hw8NBZjZ/q4sWLEhISImq1Wl6+fKn5RtKoqChZvny5DBkyRC5fvix79uyRKlWqyKtXr3hAMDCGmucizHQlZboh5LkIMz2z2ORnUEBAgNSpU0dCQkKkcePGMmLECDl79qw4OjrK0KFDReTtGRJ9H1ZN5H/h8eDBA+nWrZtm+osXL+THH3+U7du3i4jI3bt3dVFelnj/TNXx48fl9u3bMmfOHGnXrp2EhIRI0aJF5aefftL7s1rvfxvngwcPpGzZsuLr6ytJSUkyZcoUadmypbRt21b69eunmU8fg9LHx0eaNm0qly9fFpG3B70iRYpIly5dZPDgwTJ58mT57rvvROTtWS4yTIaU5yLMdCVluiHluQgzPbPY5GfCvn37RKVSaY0VfOfOHenSpYteh0ay5BBIvoZPrVZLgwYNZOTIkZp5xo4dq/lK7He/NltfPX36VPPcRUdHy9dffy3nzp0TEREvLy9xc3PTXM+p77Zv3645cxUWFiYODg4yYcIEEXk7Zva7b1vr8/48adIkadu2rVy+fFmSkpIkKChInj59KiIimzZtko4dO0p8fLzeHvQoayg9z0WY6UrOdEPJcxFmemZwCM1MaNasGfbv34+ffvoJAwcOhI2NDYKCghAeHo43b94gf/78ui7xk6hUKuzbtw8zZ87El19+CQcHB2zfvh3ffvst+vbtizZt2mDHjh2YP38+AMDY2FjHFWfcP//8g6tXr6JLly6YM2cO9u7di4SEBKxbtw5FixZFuXLlMGfOHFSsWBF3797FsmXL9HJINQAYN24cwsPDYWVlhZcvX8LOzg6PHj2CiYkJHBwccODAAVSoUAGRkZGYOnUqvvjiCwBvRzDQt1EXgP+NFjFq1Cj8/vvvGDt2LMaPH4/mzZsDABYsWIAlS5ZgzZo1yJMnj46rJV1Tep4DzHQlZbqh5TnATP8U+vmM5wJubm6YOnUq3NzcsHDhQmzYsAHz5s1TxAHh8uXL+OGHHzB06FC4u7vj9OnT8Pb2xrZt21CwYEFcuXIFkydPRoMGDXRdaqYkJCTgzJkzCAoKwsSJExEYGIg1a9agatWq6Nu3L27fvo1u3bqhWrVqOHbsGKZOnYoSJUrouuxM6dOnD/766y80bNgQjo6OCAoKQlBQEJ49e4agoCA8fvwYZcqUQe/evTXjYyfT1zGFk4f9A4CxY8eiRo0aGDt2LP7++2+8fPkSALBu3TpUqlRJl2VSLqLkPAeY6UrJdEPMc4CZ/kl0/VaCvvP395c8efLI33//retSPkny21t3796V69evy7BhwzS3xcbGSvv27eX8+fOp3kcfPX36VFatWiU9evQQT09PzfTRo0dL27Zt5Z9//hERkTdv3uiqxE8WGBgoTZs21ZoWGhoqjo6OUqdOHZk2bZp4enpK/fr1Zdy4cZp59O15Tavedy85mDhxojRu3FiuXr2qd9tHOUcpeS7CTE+mlEw3lDwXYaZnJZ7J/0StWrVCRESE3n95hkqlwtGjR9GzZ0+EhIQgMDAQN2/eBADkzZsXDg4OCA8P17qfvp0ZSD4TAAD29vbo0KED3NzccO/ePWzatAkAMHHiRJQpUwa//vorEhISYGpqqqtys0Ty2arExEQkJCSgRIkSCAwMRP78+VG+fHm0atUKrVq1gre3N4CUXzKS271b76lTp3Du3DnNbcbGxpqveB89ejRatGgBKysrvdo+yllKyHOAma7UTFd6ngPM9KzGa/KzgL6/patSqXD9+nX4+vpi7NixaNasGR48eAA3NzcsWrQIarUaR44cgbu7u65L/STJ1yOuW7cO8fHxKFmyJDw8PAAAx44dg0qlgru7O2bOnInw8HC9v7avRIkSOHfuHM6cOYPatWsDAGJiYlC4cGGYm5ujatWqKFq0qGZ+ffzmw+RwX7x4MebNmwe1Wo1u3bqhT58+KF68OIyNjZGYmAgTExP88ssvOq6W9IG+5znATAeUl+mGkOcAMz2r6d8eQFni33//RUBAAA4cOAAAuH37NqKiorBjxw6ICIYPHw5vb2/s2bMHq1evxsSJE1G3bl0dV/3pNmzYAB8fHzx9+hReXl5YvXo1PDw8ULt2bezduxfbt28HANjZ2em40k/35Zdfonv37ti0aRMuXboE4G0DY2FhARGBr68vzp49q5lfHw8IABAQEAB/f39cuXIFhw8fxp07d7BmzRo8ePAAAGBiwnMZpHzMdGVnuqHkOcBMz0p8pAzQzZs30aVLF7i6uuLGjRt49eoV2rRpAxMTE+zZswfz58/H4MGD0a9fP3h4eEBEYGJiopdv/b3r0KFDOHjwIDZs2IDq1aujSZMm6N69O4yMjNCrVy/kyZNHc9DT5+1MZmRkhN69e2P+/Pnw9fVFq1atULt2bYwaNQpPnjzBF198oZejS7y7H8bGxuLs2bM4deoUrl+/jnLlysHb2xs+Pj5YtGgRvv/+e3z++ec6rpgoezHTlZ/pSs1zgJmerXL+YwCkS1evXhUnJyfZs2ePiIgsWrRIVq5cqfm668DAQBkyZIjm67D1WfKHcZL/nT59uri4uMjcuXMlMjJSRETOnz8vBQsWlA0bNuiszuz24sUL2bBhg9SsWVP69+8vP/74o65LyrR3P2D16NEjSUhIkPDwcBk7dqz06NFDM170zZs35bvvvpNnz57pqlSiHMFMN6xMV1KeizDTs5tKRETXf2hQzjlx4gQaNGig+cBS5cqVUbBgQcTFxaFq1apYsGABdu7ciUOHDuGXX37RjLGrb+SdMwO3b99GmTJlAABr1qzBsWPH8PXXX6NOnTowNzfHpUuXYGlpqZlHqeLj47U+dKav12wCwIwZM3DmzBnky5cP7du3R4UKFbBz5078888/GDNmDL788kskJCTo9TW4ROnBTDfMTFdSngPM9OzCy3UMTL169RAUFITy5cujWLFi6Nq1K8aOHYuIiAjUqFEDa9euhbu7O+rUqYPChQvrutxMSz4YzJ8/H5s3b0a9evVgb2+PH3/8EQkJCdi8eTPi4+Ph6uqKqlWr6rjanPFuOIoefzHKnj174O/vj8OHD6NOnTo4evQoOnfuDAsLCyxZsgQzZszA/Pnzed0mGQRmumFmulLyHGCmZyeeyTdQhw8fRsuWLREbG6sJhylTpsDW1haenp46ri7zYmNjkS9fPgDA+vXrsWDBAuzatQuDBg1CaGgoXF1dMXnyZMybNw83btzAlClTFDG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4sQCEs7OziIuL0yyfOHGi1jEohPSfAOKHH37QLEtJSRF+fn7C0dFRpKamCiGEmDdvngDEihUrNO1SU1NFs2bNhJWVlWYctVJgY2MjIiIitOTX91xT/67cbu7qduqHGDXJyckiIyNDa1lISIgwMzPTelj6448/BCDmzJmTpV/1fx8ZGZmtLOp9FBUVpVl2/vx5oVQqxeDBgzXL1IrigAEDcv09avbu3Zvlv8qO06dPC0Ds3r1bI7ubm5sYM2aMVrsvv/xSAGLDhg1Z+lD/3lOnTmVR6tRkVhR37dql88G0S5cuWuelLkXRz89PuLi4iJiYGM2yf//9VwBZzonM535qaqrw8fER7dq10ywLCgoSQBbFauDAgQYpilWrVtUaz5Drga5r1LFjxwQg/vzzzyxj5absDBgwQDg6Oor09HTNsrCwMKFUKjXH8ZMnT7IYFvQlOyOOrt8xY8aMLPelVq1aCWtra61lQmjfD2fNmqXzOFb/X2+//bbW8o8//lgAGoObENlf04sTg6eeJ0+erPFjmD59OiEhIQQEBLBjxw7mz59vaHdllsDAQBwcHKhcuTJ9+vShXLlybNmyBTc3N00bIyMjTE1NAWmaNzo6mvT0dBo2bMjZs2c17dTT1gcPHgQkU3mjRo3o0KEDhw4dAqQplkuXLmnaZoe+Y/7zzz8oFAqmTJmSpY+8+B2qp8PWrl2rCYACabq8adOmVKlSxeA+lUolgwYNYsuWLcTHx2uWr1y5kubNm+Pp6ZnttgqFgtdff50dO3bw9OlTLXlcXV01U2Vqf5Zt27bpnOI2hHXr1lG7dm1q1arF48ePNS+1T9aLU5IvTr/Fxsby+PFjWrduze3bt4mNjdXq19PTk06dOukcc9iwYZr/G54fS7dv3wYgKCiIGzduMHDgQKKiojQyJSQk0L59ew4ePIhKpSIjI4Ndu3bRq1cvrf+qdu3a2Y5dGPsFoH379lrT1uqo1969e2NtbZ1lufq3qjE2Nubdd9/VfDc1NeXdd98lIiKCM2fOALBjxw6cnZ0ZMGCApp2JiQkffvghT58+zTJF1rt3b820rRp9z7X8YmZmpgn+ysjIICoqCisrK2rWrJnlnLa3t+eDDz7I0kdu53RYWBhBQUEMHToUOzs7zfK6devSoUMHduzYkWWbkSNH6v0bduzYgbe3t17BaitXrsTJyYm2bdtqZO/Xrx+rV6/WCqb8559/qFevHq+++mqWPvJyDWvXrh329vasWbNGs+zJkyfs3r2bfv36Zbudet8NGTIEW1tbzfIOHTrg7e2dpf2L5/6TJ0+IjY0lICBA679U7+/M7l6GBooNGTJEazx9rweZ5UxLSyMqKgovLy/Kly+fp+O7X79+REREaMU6rF+/HpVKpdm/FhYWmJqacuDAAZ48eWLwGLp48XckJCTw+PFjmjdvjhCCc+fOARAZGcnBgwcZPnx4lnuVPseS+v8aN26c1vLx48cDktvFi+R0TS8ODFYUO3XqpPGX8/Ly4urVqzx+/JiIiAi9nJBfFn7++Wd2797N+vXr6dKlC48fP9bpGL18+XLq1q2Lubk5FStWxMHBge3bt2spA05OTlSvXl2jFB46dIiAgABatWrFw4cPuX37NkeOHEGlUuWqKOo75q1bt6hUqZLWTSG/9OvXj3v37nHs2DHNGGfOnMnxIpsbgwcPJikpSePvc+3aNc6cOcObb76plzxJSUmaAKOnT5+yY8cOXn/9dc3J37p1a3r37s20adOwt7enZ8+eLF26NItfiT7cuHGDy5cv4+DgoPWqUaMG8NzZGuDIkSMEBgZqfMEcHByYNGkSgE5FMTsyX9QqVKgAoLnI3rhxA5BuGJnlWrJkCSkpKcTGxhIZGUlSUhLVq1fPMkZOvqD6YMh+0fWb1DffypUr61ye+YZSqVKlLGkx1GOp/Ynu3LlD9erVtaLvQVKM1etfJLv/QJ9zLb+oVCrmzp1L9erVMTMzw97eHgcHBy5cuJDlnK5ZsybGxgYnu9D8Xl3/de3atTXKxIvkdFxmZvv27XTt2jXXdhkZGaxevZq2bdsSEhLCzZs3uXnzJk2aNOHRo0fs3btX0/bWrVv4+PjoLUNuGBsb07t3bzZv3qw5/zds2EBaWlqO1zD1vtP33Nm2bRtNmzbF3NwcOzs7HBwc+OWXX7T+yzt37qBUKqlWrVqu/eVE5v9I3+sBQFJSEl9++SWVK1fWOu5iYmLydHyrfSJfVMTXrFmDn5+f5vw0MzPju+++43//+x9OTk60atWK77//nvDwcIPHU3P37l3NA5CVlRUODg60bt0aeH6tVT9s5vV4Uv9fmSPznZ2dKV++vN7Xk+LC8CuGDgpSmSgrNG7cWBOg0atXL1q2bMnAgQO5du0aVlZWAKxYsYKhQ4fSq1cvPvnkExwdHTEyMmLGjBlaQS8ALVu2ZO/evSQlJXHmzBm+/PJLfHx8KF++PIcOHeLKlStYWVlRv379HOUyZMyCpnv37lhaWrJ27VqaN2/O2rVrUSqVmmCVvODt7Y2/vz8rVqxg8ODBrFixAlNTU/r27Zvrtk2bNsXDw4O1a9cycOBAtm7dSlJSktZFX6FQsH79eo4fP87WrVvZtWsXw4cP54cffuD48eOa/1IfVCoVvr6+zJkzR+d6taJz69Yt2rdvT61atZgzZw6VK1fG1NSUHTt2MHfu3CyRiTk5/2cXeam26qr7mjVrVpb0IGqsrKzypBjri777RU12vym331qY6PoPiupc+/bbb/niiy8YPnw4X331FXZ2diiVSsaOHVusUaz6BKWAFMF89epVncFamdm3bx9hYWGsXr2a1atXZ1m/cuVKOnbsaLCs+tK/f38WL17M//73P3r16sXatWupVasW9erVK5D+Dx06RI8ePWjVqhULFy7ExcUFExMTli5dyqpVqwpkjBfJ/B/pez0A+OCDD1i6dCljx46lWbNm2NraolAo6N+/f56OOzMzM3r16sXGjRtZuHAhjx494siRI3z77bda7caOHUv37t3ZtGkTu3bt4osvvmDGjBns27cv1/tfZjIyMujQoQPR0dFMmDCBWrVqUa5cOR48eMDQoUML/PzR15Kt77lTVOilKGaOuM2JDRs25FmYsor65tC2bVsWLFjAZ599Bkhm9apVq7JhwwatA0jXdG9AQABLly7VTK80b95cE3GuVhSbN2+ea0oGfcesVq0au3btIjo6OscHAUOmcMqVK0e3bt1Yt24dc+bMYc2aNQQEBFCpUqUct8ttjMGDBzNu3DjCwsJYtWoVXbt21VjOcqNv3778+OOPxMXFsWbNGjw8PGjatGmWdk2bNqVp06Z88803rFq1ikGDBrF69WrefvttvcYBaZ+eP3+e9u3b5/ibtm7dSkpKClu2bNGynuUlWlYfmUByDcgpt5g6yk9tcXiRa9eu6TVWdr9Z3/1SUDx8+JCEhAQtq+L169cBNFOf7u7uXLhwAZVKpWVVvHr1qmZ9bhhyfueH9evX07ZtW37//Xet5TExMdjb22u+V6tWjRMnTpCWlpZt+qns9r/69+r6r69evYq9vX2ekxdv374dW1tbvSKjV65ciaOjIz///HOWdRs2bGDjxo0sWrQICwsLqlWrpomUzQ5Dj7dWrVrh4uLCmjVraNmyJfv27dNEvWaHet/pc+78888/mJubs2vXLq0ZqKVLl2bpU6VSaazE2fVnKPpeD0A67oYMGcIPP/ygWZacnJyvZNP9+vVj+fLl7N27lytXriCE0GmtrVatGuPHj2f8+PHcuHEDPz8/fvjhB1asWGHQeBcvXuT69essX76cwYMHa5bv3r1bq506Y0dejyf1/3Xjxg3NrARIlZZiYmL0up4UJ3pNPdva2mpeNjY27N27V5MiBeDMmTPs3btXy/9CRps2bdrQuHFj5s2bR3JyMvDcAvKixePEiROaqdkXUU8pf/fdd9StW1ezrwMCAjT/hz7TzvqO2bt3b4QQmuS7L/LituXKlTPowtCvXz8ePnzIkiVLOH/+vF7TzuobUHbjDBgwAIVCwZgxY7h9+zZvvPGGQfKkpKSwfPlydu7cmcUS+eTJkywWKfWTtqFWtr59+/LgwQN+++23LOuSkpI0U3e6/qPY2NgsN4uCwN/fn2rVqjF79mwtX001kZGRGpk6derEpk2buHv3rmb9lStX2LVrl15jlStXTueUlL77paBIT0/XSjeRmprK4sWLcXBwwN/fH4AuXboQHh6uNQ2Wnp7OTz/9hJWVlWZqKicMOb/zg5GRUZZjdN26dVkqQfXu3ZvHjx+zYMGCLH2ot7e0tASynmsuLi74+fmxfPlyrXWXLl3i33//pUuXLnmWf8eOHXTs2DHXKfGkpCQ2bNhAt27d6NOnT5bX6NGjiY+P17iS9O7dm/Pnz+tMQ6P+vbldWzKjVCrp06cPW7du5a+//iI9PT3Xa9iL++7F43/37t0EBwdrtTUyMkKhUGj5WoaGhmapfvXKK68AZIkLyG/lI32vB2pZMx93P/30U76KbgQGBmJnZ8eaNWtYs2YNjRs31pqGTUxM1Nw/1VSrVg1ra+s8zXroOkeFEPz4449a7RwcHGjVqhV//PGH1vUv87bZHU/q8yPz/6OeRdHH7aI40cui+OINasKECfTt25dFixZpdnJGRgajRo3CxsamcKQsI3zyySe8/vrrLFu2jJEjR9KtWzc2bNjAq6++SteuXQkJCWHRokV4e3tnOUm9vLxwdnbm2rVrWs7orVq1YsKECQB6KYr6jtm2bVvefPNN5s+fz40bN+jcuTMqlYpDhw7Rtm1bRo8eDUgXlj179jBnzhwqVaqEp6dnjiW1unTpgrW1NR9//DFGRkb07t07V5n9/PwwMjLiu+++IzY2FjMzM02OQUCT33HdunWUL1/eoJOuQYMGeHl58fnnn5OSkpLlor98+XIWLlzIq6++SrVq1YiPj+e3337DxsbG4Jvjm2++ydq1axk5ciT79++nRYsWZGRkcPXqVdauXavJm9WxY0dMTU3p3r077777Lk+fPuW3337D0dGRsLAwg8bMDaVSyZIlS3jllVeoU6cOw4YNw9XVlQcPHrB//35sbGzYunUrIOXW27lzJwEBAYwaNUqjONWpU0crn2d2+Pv7s2bNGsaNG0ejRo2wsrKie/fueu+XgqJSpUp89913hIaGUqNGDdasWUNQUBC//vqrxtL2zjvvsHjxYoYOHcqZM2fw8PBg/fr1HDlyhHnz5mkFzWSHIed3fujWrRvTp09n2LBhNG/enIsXL7Jy5coseUsHDx7Mn3/+ybhx4zh58iQBAQEkJCSwZ88eRo0aRc+ePbGwsMDb25s1a9ZQo0YN7Ozs8PHxwcfHh1mzZvHKK6/QrFkz3nrrLZKSkvjpp5+wtbXNc23hpKQk9u/frzOPZWbUQWs9evTQub5p06aa5Nv9+vXjk08+Yf369bz++usMHz4cf39/oqOj2bJlC4sWLaJevXpUq1aN8uXLs2jRIqytrSlXrhxNmjTJ0UesX79+/PTTT0yZMgVfX18tC1F2zJgxg65du9KyZUuGDx9OdHS05tx58Vjo2rUrc+bMoXPnzgwcOJCIiAh+/vlnvLy8tM4xPz8/BgwYwMKFC4mNjaV58+bs3btXZ45UQzDketCtWzf++usvbG1t8fb25tixY+zZs4eKFSvmeXwTExNee+01Vq9eTUJCArNnz9Zaf/36ddq3b0/fvn3x9vbG2NiYjRs38ujRI/r372/weLVq1aJatWp8/PHHPHjwABsbG/755x+dgTLz58+nZcuWNGjQgHfeeQdPT09CQ0PZvn27pgSk+kHz888/p3///piYmNC9e3fq1avHkCFD+PXXX4mJiaF169acPHmS5cuX06tXL01gVonF0DBpe3t7cfXq1SzLr169Kuzs7AyPuy5jZBeCL4SUzqVatWqiWrVqIj09XahUKvHtt98Kd3d3YWZmJurXry+2bduWJS2Dmtdff10AYs2aNZplqampwtLSUpiamoqkpKRc5TNkzPT0dDFr1ixRq1YtYWpqKhwcHMQrr7wizpw5o2lz9epV0apVK2FhYSEATVqbzOlxXmTQoEECEIGBgTplzJweRwghfvvtN1G1alVNKpbMqRbUaW3eeeedXPdBZj7//HMBCC8vryzrzp49KwYMGCCqVKkizMzMhKOjo+jWrZs4ffp0rv1mTo8jhPR/fffdd6JOnTrCzMxMVKhQQfj7+4tp06aJ2NhYTbstW7aIunXrCnNzc+Hh4SG+++47TXqTF/epu7u76Nq1a5ax1Skp1q1bp7VcV+oOIYQ4d+6ceO2110TFihWFmZmZcHd3F3379hV79+7Vavfff/8Jf39/YWpqKqpWrSoWLVqkSYeSG0+fPhUDBw4U5cuXz5IWRN/9go70MOrflDllhq590Lp1a1GnTh1x+vRp0axZM2Fubi7c3d115u979OiRGDZsmLC3txempqbC19c3y37LbmwhDDvXyGd6nPHjxwsXFxdhYWEhWrRoIY4dO6bz+EtMTBSff/658PT0FCYmJsLZ2Vn06dNHkztQCCGOHj2q+Y8zy7Vnzx7RokULYWFhIWxsbET37t01abvUqI+HyMjIXH/Ptm3bhEKhEI8ePcq1bffu3YW5ublWjr7MDB06VJiYmIjHjx8LIaScs6NHjxaurq7C1NRUuLm5iSFDhmjWCyHE5s2bhbe3tzA2NtY6N7K7DqtUKlG5cmUBiK+//jrL+uzOsX/++UfUrl1bmJmZCW9vb7FhwwadY/z++++ievXqwszMTNSqVUssXbpU5zmWlJQkPvzwQ1GxYkVRrlw50b17d3Hv3j2D0uNkvj6o0ed68OTJE835YWVlJTp16iSuXr2a5fqtb3ocNer8uAqFQiudnBBCPH78WLz//vuiVq1aoly5csLW1lY0adIkSz5cXWR3bw4ODhaBgYHCyspK2NvbixEjRojz58/r/A8vXbokXn31VVG+fHlhbm4uatasKb744gutNl999ZVwdXUVSqVS63qdlpYmpk2bpjn3KleuLCZOnCiSk5O1ts/uml6cKIQwzNO7QoUKLFu2jJ49e2ot37x5M0OHDi2wkHUZGUPYvHkzvXr14uDBg3pZVmVeTtq0acPjx49z9TWSKRpGjRrF6dOnOXnyZHGLIiMjkw0GRz0PGzaMt956i1u3btG4cWNA8ruZOXOmzvJGMjJFwW+//UbVqlX1coiXkZEpGfj5+dG9e/fiFkNGRiYHDFYUZ8+ejbOzMz/88IPGX8rFxYVPPvlEkzxSRqaoWL16NRcuXGD79u38+OOPRRI1KyMjUzC88847xS2CjIxMLhg89fwi6gLichCLTHGhUCiwsrKiX79+LFq0KE/JhGVeHuSpZxkZGRnDyJeiKCMjIyMjIyMjU3bJk/ll/fr1rF27lrt375Kamqq1riBrmMrIyMjIyMjIyBQfBiuK8+fP5/PPP2fo0KFs3ryZYcOGcevWLU6dOsX7779fGDIWKCqViocPH2JtbS37s8nIyMjIyMi8dAghiI+Pp1KlSllq2mfG4KnnWrVqMWXKFAYMGIC1tTXnz5+natWqfPnll0RHR+vM+l+SuH//fpbasTIyMjIyMjIyLxv37t3Dzc0txzYGWxTv3r1L8+bNAalwdXx8PCBVnWjatGmJVxTVFRXu3bsnB+HIyMjIlEKCH8bSd/Fx1r7bFO9KculYGRlDiYuLo3LlynpVmTJYUXR2diY6Ohp3d3eqVKnC8ePHqVevHiEhIVnqPpZE1NPNNjY2sqIoIyMjUwqxihcozSyxspav4zIy+UEfF7ycJ6Z10K5dO03R9WHDhvHRRx/RoUMH+vXrx6uvvmq4lDIyMjIyMjIyMiUSgy2Kv/76KyqVCoD333+fihUrcvToUXr06MG7775b4ALKyMjIyMi8iI25CV18nbExNyluUWRkyjwvXR7FuLg4bG1tiY2N1TllkZGRQVpaWjFIJiMjU5iYmJhgZGRU3GLIyMjIFDu56UIvopdF8cKFC3oPXrduXb3bljSePn3K/fv3S4WvpYyMjGEoFArc3NywsrIqblFk8oMqg9TbR4iKiqBiRUdMq7YA5cv7AJChEpwMiSYiPhlHa3Mae9phpJRTv8kUHHopin5+figUilwVKIVCQUZGht6DHzx4kFmzZnHmzBnCwsLYuHEjvXr1ynGbAwcOMG7cOC5fvkzlypWZPHkyQ4cO1XvM7MjIyOD+/ftYWlri4OAg51iUkSlDCCGIjIzk/v37VK9eXbYsllaCt8DOCVyPMaVb6rdsM/0An/Kp0Pk78O5R3NIVOTsvhTFtazBhscmaZS625kzp7k1nH5dilEymLKGXohgSElIogyckJFCvXj2GDx/Oa6+9ppccXbt2ZeTIkaxcuZK9e/fy9ttv4+LiQqdOnfIlS1paGkIIHBwcsLCwyFdfMjIyJQ8HBwdCQ0NJS0uTFcXSSPAWWDsYEIDH8+VxYdLyvn++VMrizkthvLfiLJnNN+Gxyby34iy/vNFAVhZlCgS9FEV3d/dCGfyVV17hlVde0bv9okWL8PT05IcffgCgdu3aHD58mLlz5+ZbUVRjqCVRNvvLyJQO5FmCUowqA3ZOgCxqEc+WKWDnZ1Cr60sxDZ2hEkzbGpzT3mDa1mA6eDvL9yOZfJOnWs8AwcHBOms99+hReE90x44dIzAwUGtZp06dGDt2bLbbpKSkkJKSovkeFxdXYPLIZn8ZGRmZIuDOUYh7mEMDAXEPpHaeAUUmVnFxMiRa676TGQGExSZz/HYULbzsi04wmTKJwXkUb9++Tb169fDx8aFr16706tWLXr168eqrrxZ6HsXw8HCcnJy0ljk5OREXF0dSUpLObWbMmIGtra3mVVDl+9Rm/8wnq9rsv/NSWIGMU5h4eHgQFBRUqGMcOHAAPz8/g7ebOnUqycnZXwhB8p1VVwYqCNLS0vDw8Mjy8JMTb7/9Nvv37y+wdkXJsmXLcvUJlpEpETx9VLDtSjkR8TlfG9WM/Os0EzdcYP+1CFLS9Y8fkJF5EYMVxTFjxuDp6UlERASWlpZcvnyZgwcP0rBhQw4cOFAIIuaPiRMnEhsbq3ndu3cv122EECSmpmf7ik9OY8qWy9ma/QGmbgkmPjktx34Mia5OT0/Xu21JIT8yT5s2LVtFUd1vUFCQXuWH9OXAgQM0a9YMU1NTvbdZsmQJbdu2LbB2hqJSqTR5TWVkyixW2gYCb8UdrpkNxltxJ8d2ZZWIuJTcGwHxKRn8ffIew5aeouFXe/jw73NsvxBGQkrpu5/IFB8GK4rHjh1j+vTp2Nvbo1QqUSqVtGzZkhkzZvDhhx8WhowanJ2defRI+4nx0aNH2NjYZBuAYmZmpinXp2/ZvqS0DLy/3JXty3fqvzzK4UQVQHhcMr5T/82xn6S0nJ/wFAoFU6ZMoVGjRkycOJGLFy/SsmVLGjRogLe3N19//bWmraurKw8fSlMzffv21dTjTklJoWLFilrT7y+yYcMGmjVrhqenp1Z/N2/eJDAwkLp16+Ln58emTZu05IqJidF8t7e3JzQ0FJCslBMmTKBx48YMGTJEa6zRo0fz7bffar5fu3aNypUrZ1EoR44cCUBAQAB+fn5EREQwdOhQhg8fTqtWrfDx8ckih4eHB5988gn+/v54eXkxa9YsQFKkRo8eTe3atalXrx7+/v7ZKqCbNm3SaRWvUaMGp0+f1nxftmyZpl2bNm00+2bVqlU0adKE+vXrU69ePbZu3arZ5sV28fHxjBgxgsaNG1O3bl3eeecdjRXz66+/pnbt2vj5+eHn58edO5luhEjW1t69e9OpUyd8fHwICwtj165dtGzZEn9/fxo3bqyxXoaHh9O2bVv8/f2pU6cOo0ePzlax/Ouvv2jSpAkNGjSgVatWnD9/HoDjx4/j7++Pn58fPj4+/PLLLzq3l5EpNNybg00lzVelQmCmSEepUD9sK8DGVWpXhknLUDHzf1f5ZseVHNspkNyglg9rxBtNq+BgbUZ8Sjpbzj/k/VVnqf/Vbt5efoq1p+8RnZD9DEqGSnDsVhSbgx5w7FYUGSo5ddzLiME+ihkZGRorjr29PQ8fPqRmzZq4u7tz7dq1AhfwRZo1a8aOHTu0lu3evZtmzZoV6rjFiZGREadOnQIkBWPv3r2YmZmRlJRE8+bNCQwMpGnTprRv3549e/bwxhtvcP78eYyNjYmLi+PUqVP4+/tjZmams/+YmBiOHTvG48ePqVatGsOGDcPV1ZVBgwYxfPhw3n33XW7cuEHTpk2pX7++XoFNUVFRnDhxAoVCoWVl/uCDD+jUqRMTJkzAyMiIhQsX8s4772BsrH0YLlq0iMWLF3Po0CHKly+vWX7mzBkOHz6crRXx0aNHnD59mqioKBo0aECLFi2wsLBg7969XL58GaVSSWxsrE6LoRCCnTt3MnPmzCzrhg4dyrJly2jYsCEAS5cu5eOPP87SrlOnTgwYMACFQkFoaChNmzblzp07Wfb9+PHjCQgI4LfffkMIwYgRI/jxxx95++23mT17NmFhYVhYWJCYmIhSqftZ7tixY5w7dw4nJydu377N1KlT2bVrFzY2Nty8eZOAgABCQ0MpX748W7duxcrKioyMDHr27MnatWvp37+/Vn9Hjhzh77//5uDBg5iZmXHo0CEGDhzI5cuXmTFjBh9//DEDBgwA4MmTJzplkpEpNJRGUgqctW8CcFvlzMS0t5lhsoSqymfGg84zy3Qgy52oBD5cHcT5ezEABFS35/CNx4B2iI86dGVKd29a13SkdU1Hpvfw4dy9GP69HM7Oy+HciUpkz5UI9lyJwEipoLGHHZ3qONGxjjOVyktGF9kHX0aNwYqij48P58+fx9PTkyZNmvD9999jamrKr7/+StWqVQ3q6+nTp9y8eVPzPSQkhKCgIOzs7KhSpQoTJ07kwYMH/Pnnn4BkaVqwYAGffvopw4cPZ9++faxdu5bt27cb+jNyxMLEiODp2UdRnwyJZujSU7n2s2xYIxp72uU4Tm4MHz5c8zkpKYlRo0YRFBSEUqnk3r17BAUF0bRpUwIDA9mzZw916tShXr16ODk5ceDAAY4dO0b79u2z7X/gwIGApPRXrVqVkJAQbGxsOHv2LEeOHAGgevXqtGzZkkOHDumlKA4dOlRnhGnNmjXx9vZm8+bNdOrUib///puLFy/m2p+a119/Pcep5rfeeguFQoG9vT2vvfYae/bsYcyYMaSnpzN8+HDatm1L165ddSpfp06dolatWjr7Hzx4MPXr1+eHH37gwYMHXL9+XWe0fkhICIMGDeL+/fsYGxsTHR1NSEgItWrV0mq3adMmjh07xpw5cwDpfzUyMsLGxobq1avzxhtv0LFjR7p27Yqbm5vO39qlSxeNv+7OnTu5efMmrVq10qxXKpXcvXsXV1dXJkyYwOHDhxFCEBERgY+PTxZFcfPmzZw/f54mTZpolkVHR5OUlETbtm356quvuHHjBu3ataNly5bZ/QUyMoWHWyPNx0TMOSG8ScRcsjR2nlmmU+NsDnrA5xsv8TQlHRtzY77vU5fOPi46lTlnHcqcUqnA370C/u4V+OyVWlx7FM+uS4/YeTmcK2FxHLsdxbHbUUzdGkw9N1s87MuxOShr8JCceuflxGBFcfLkySQkJAAwffp0unXrRkBAABUrVmTNmjUG9XX69Gktv61x48YBMGTIEJYtW0ZYWBh3797VrPf09GT79u189NFH/Pjjj7i5ubFkyZICS42jRqFQYGma/a4JqO6Ai6054bHJOv0UFUgna0B1h3ynJnixisSkSZOwt7fn3LlzGBsb89prr2mmUQMDA5k4cSLe3t4EBgbi5OTEnj17OHbsWI5Thebm5prPRkZG2foVvqj4GRkZaSVWzzyVm1PlizFjxvDdd98RGRlJhw4dsgQn5YShFTUUCgW2trZcunSJ//77j/379zNx4kQOHjyIl5eXVtuckr27ubnRsGFDNm/ezOXLl3njjTeyWEEB+vfvz8yZM+nTpw8AdnZ2Oqe5hRD8888/1KhRI8u648ePc/ToUQ4cOEDTpk35+++/CQjIGsX54r4QQtChQwdWrVqVpd3XX39NREQEJ06cwNzcnHHjxmUr05AhQ7RcA9SMHTuWnj17smfPHiZNmoSPjw8LFy7M0k5GplAJ3iy9uzaEaiPgX0BpCh+eAmP9/YpLE09T0pmy+TL/nL0PQGMPO+b298P1mdWvs48LHbydDUrRplAoqOVsQy1nG8YEVuduVCL/Boez63I4p+884fz9WM7fj9W5rZx65+XEYB/FTp06aZJje3l5cfXqVR4/fkxERATt2rUzqK82bdoghMjyWrZsGSD5gmUOkGnTpg3nzp0jJSWFW7duFUhVFkMxUiqY0t0beG7mV/Oi2b+gT6InT57g5uaGsbEx165dY/fu3Zp1lSpVwtbWlkWLFhEYGEjbtm3Ztm0boaGhNGjQwKBxrK2tadCgAUuXLgUkf8XDhw9rLFZeXl6cOHECkHwc1Q8O+tCxY0fCw8P5+uuvGT16dI4yxMbqvlhlh/q4iY6OZuPGjbRv357IyEgSEhLo2LEj3377LR4eHgQHB2fZdsuWLfTs2TPbvocNG8Yff/zBn3/+qWXlfZEnT57g6ekJwIoVK7Kdou3VqxffffedRil/8uQJN2/eJD4+nkePHhEQEMAXX3xBy5YtOXfuXK6/u1OnTuzZs0er1ObJkyc1fTs7O2Nubk54eDjr1q3T2UePHj1YsWKF5sFMpVJp/DKvXbuGp6cnI0aMYNKkSRw/fjxXmWRkCpzLG6V3n95QvaP0WZUK0beLT6ZC5OL9WLrNP8Q/Z++jVMDYwOqsGtFEoySqMVIqaFatIj39XGlWraLB950qFS15O6Aq60Y258Sk9rzd0jPH9urUOydDog39STKlFIMtirGxsWRkZGBn93xK1c7OjujoaIyNjfUKFikLdPZx4Zc3Guhl9i8oJk+ezJtvvsny5cupVq1aFsU8MDCQbdu2aVwAnJ2dqV+/frZ+bjmxcuVKzVS/QqFgyZIlVKlSBYC5c+fy4YcfMnnyZLp27UrFihX17lehUPDWW2+xatWqHH1Lx48fT4cOHbC0tOTff//Vq28HBwf8/f2JjY1l9OjRNG/enLNnzzJixAjS0tLIyMigRYsWWaaNr169SoUKFXB0dMy27549e/Lee+9RvXp1ateurbPNjz/+SJ8+fShfvjzt2rXT7K8XfztI+++zzz7Dz88PpVKJsbEx33//Pebm5vTp04eEhAQUCgXVq1fPEhCkCy8vL1atWsW7775LYmIiqamp1K9fn1WrVjFmzBj69OlDnTp1qFSpUpY8pGoCAgL4/vvvefXVV0lPTyc1NZWuXbvSsGFDFixYwL59+zA1NcXIyEiT8F5GpsiIvQ/3nj2g1OkF8S+47Tw8B461dG5WGlGpBL8fDuH7XVdJyxBUsjXnxwH1aeSRvRtTQeFobY6vm61ebfVN0SNT+lEIQ3K0IFVT6d69O6NGjdJavmjRIrZs2ZIl2KSkERcXh62tLbGxsVpKbXJyMiEhIXh6empNx+aGXJnFcLp160a/fv148803C6xPDw8PNm3alKecjTNnzsTY2FhngEpBUadOHX799VdatGhRaGPI5Exez3GZEsDRBfDv51ClGQzfSXRCKv+u+YWOd2Zh12QgdPm+uCUsECLjUxi/7jwHr0cC8IqPMzNfq4utpUmRyXDsVhQDfst91uDvEU1pVk1/I4FMySI7XUgXBlsUT5w4oXHCf5E2bdrw+eefG9pdqUdt9pfJndOnT9O/f3+8vb01QTQlgc8++6xQ+69Zsya1a9emadOmhTqOjEyZ5fIG6b2O5PZkV86U/g0rwd14CAsqPrkKkP+uRzJ+bRCPn6ZiZqxkSvc6DGhcuchLTzb2tMvRBx/AWKnArYLulHQyZQ+DFcWUlBSdAQ9paWnZVkeRkQFo2LChVpR7QaLO41gSKey0UTIyZZonofDgDCiU4C35EUcnpPLvY086Cmvswi5ARjoY5bkibbGSmq5i9r/X+PWg5GtZy9ma+QPqU8Op4IoJGILaB/+9FWdRoLu6drpK0GfRUX4f0ggfV/2mqmVKLwY7rzVu3Jhff/01y/JFixbh7+9fIELJyMjIyMgAcHmT9O7eAqylLAkPY5L47N9HPDSuDOlJ8LhkP4xll7g65HECvX85qlESBzdzZ9P7LYpNSVSj9sF3ttV20XCxNeebXj7UcLLiUVwKfRcfY9/Vl6Ns4suMwY9gX3/9NYGBgZw/f16Tn2/v3r2cOnVK76ADGRkZGRkZvVBPO/u8lnWdQ00ID4aHQeBUp0jF0pfsEld39HZi3Zn7JKZmUN7ShO9716VjHedilFSbnFLvdPerxKgVZzl88zFvLz/N1B51GNzMo7hFlikkDLYotmjRgmPHjlG5cmXWrl3L1q1b8fLy4sKFCzrzvcnIyMjIyOSJqFsQdh4URlBbR0Jt+2e5SB/mnkaqONh5KYz3VpzVUhJBSi+z/NgdElMzaFrVjv+NCShRSqKa7FLv2JibsHRYI/o1rIxKwJebL/PVtmC5xF8ZJU9OHX5+fqxcubKgZSmdqDLgzlF4+kgqSO/evEyXkZKRkZEpMtTWRM9WUM4+63qHmtJ7CVQUM1SCaVuDsw0IAbA2M+bP4U0wNTY8hVlxY2KkZGZvX6pUtGTWrmv8fjiEe9GJzOvvl2PBCpnSh8FH59mzZ7XKrm3evJlevXoxadIkUlOzLy5eJgneAvN8YHk3+Oct6X2ej7RcRkZGRiZ/qP0TM007W5oa0cTTDstKUuEDHl2CjLSilS0XToZEZ7EkZiY+JZ0zd0pv7XSFQsH7bb34aUB9TI2V/Bv8iP6/HpdzLJYxDFYU3333Xa5fvw7A7du36devH5aWlqxbt45PP/20wAUssQRvgbWDIS5TPcy4MGl5ASiLQ4cO1VSmCQoKYvXq1fnus7hQKBTExMToXPfWW2/h7e3Nq6++mqe+C3rfKBQKfH198fPzw8/Pj0OHDmnWtWnTJscI6/Xr1/Pee+9lWb5s2TKmTp2q+b548WJq1aqFn58fUVFRBssYExPDzJkzDd4uO+bNm0d4eHiB9VeUTJ06lbFjx2q+e3h4FJssMgVI5HVJAVQaQ61uWquqOlix5t1mVPWqBWa2kJ4MkVeLSVDd6KsslQWlqnu9Sqx6uwkVLE24cD+WV38+yvVH8cUtlkwBYbCieP36dU1S43Xr1tG6dWtWrVrFsmXL+OeffwpavuJBCEhNyP6VHAf/+xTdiQOeLds5QWqXUz8G5DovyYqiSqVCpVLladtHjx6xevVqLl68yMaNG/PUR372TXa1rQ8dOkRQUBBBQUEG+d7mVDP6RebNm8fSpUsJCgoyqLKNmvwoirp+c0lWFLP7j2TKOOpp56ptwVK7KolKJUhJz0AlgEr1pIUlbPrZ0Vq/pO76tivpNPSwY+OoFnjal+NBTBK9Fx7l8I3HxS2WTAFgsKIohNAoBXv27KFLly4AVK5cmcePy8hBkZYI31bK/jWzMsSH5dCBkCyNMyvn3E9aol7iRERE8OWXX7J//378/PwYOXIkAKdOnaJdu3Y0bNiQ+vXra+r4hoaGUr58eb744gsaNGhA9erVOXLkCB999BF+fn74+Phw6dIlAA4cOICPjw+DBw/Gx8cHf39/goKCNGPPmjWLOnXq4Ovry6BBgzT1l6dOnUrv3r3p1KkTPj4+hIWF8fHHH9OoUSP8/Pxo1apVrvkDY2JiaNu2LcnJyfj7+zNz5kwuXrxIy5YtadCgAd7e3nz99dea9qmpqXzyySf4+PhQr149OnfunO2+2bVrFw0aNKBu3bq0bt1aU9/5wIED1KlTh7feegs/P788K6e6SEtL48iRI7nWPO/Tp4+mTnmfPn1IT0+nU6dONGzYkDp16jBw4ECt+tlLly7Fz8+PevXq0bBhQ0JDQxk5ciTx8fH4+fnRsGFDQKrJHRgYSN26dfHz82PTpk2aPhQKBVOmTKFRo0ZMnDhRS57p06fz8OFD+vXrh5+fH0FBQaSlpfHZZ5/RuHFj/Pz86Nu3r6Z29dChQ3nnnXcIDAzE09OT4cOHc/LkSdq0aUPVqlUZN26cpu82bdrwwQcf0KhRI7y8vBg/fjzqYlCGyJvTcSFTBhECLmUf7RwcFkfNyTsJDouDSvWlhQ+Dik4+PWjsaYeTjVm26xVI0c+NPQu/NF9R4WFfjg3vNaexhx3xKekMXXqStafuFbdYMvlFGEjbtm3F4MGDxZ9//ilMTEzEjRs3hBBCHDhwQLi7uxvaXZETGxsrABEbG6u1PCkpSQQHB4ukpCQhUp4KMcWm8F8pT3OUdciQIWL//v1CCCGWLl0qevbsqVn35MkT4efnJx4+fCiEECIyMlJUrlxZ3L9/X4SEhAhAbNy4UQghxJIlS0S5cuXEvn37hBBCfP/996JPnz5CCCH2798vALFnzx4hhBBr1qwRNWvWFCqVSuzYsUPUqlVLPHnyRAghxIgRI8TIkSOFEEJMmTJFuLi4iPDwcI1MERERms9///236NSpk+Y7oOnnRUJCQoStra3me1xcnEhOThZCCJGYmCj8/PzEsWPHhBBCTJ06VfTo0UOzXj1e5n3z6NEjYWdnJy5cuCCEEGLFihWidu3aQqVSif379wuFQiEOHDiQ7X4HRIMGDUTdunXFRx99JJ4+ff4/tW7dWoSEhOjcbteuXWLAgAE61y1dulRMmTJF893d3V2cO3dOCCGESqUSjx8/1nweOXKkmDFjhhBC+n88PDw0/3NCQoJISEjIst+EEKJx48Zi0aJFQgghrl+/Luzs7ERoaKjmN02bNi3b3/yiPEII8c0334jp06drvk+fPl2MGjVKCCEdl02bNhVJSUkiJSVFVKtWTfTq1UukpqaKp0+fCkdHR3Hp0iXN/mrXrp1ITU0VCQkJwt/fX6xcudJgeXM6LqZMmSLGjBmj9Vt0oXWOy5Rswi9J18jp9kIkxWRZffF+jHCfsE1cvB8jxMV/pLaL2xSDoNmTnqES3ecfFO4TtmV5eTx7/e/iw+IWs1BITksXH/59VvN7v/vfFZGRoSpusWReIDtdSBcGhybNmzePQYMGsWnTJj7//HO8vLwAyTerefPmBabAFismljDpYfbr7xyFlX1y72fQeikKOqdx8sjRo0e5ffs2r7zyitbya9euUbVqVczNzTVToA0bNsTKyoq2bdsCUtL0F6PWPTw8NDkx+/btyzvvvMO9e/fYs2cP/fr1o3z58gC89957vP7665rtunTpgpOTk+b77t27+emnn4iPj0elUhEdHW3w70pKSmLUqFEEBQWhVCq5d+8eQUFBNG3alG3btvHdd99hZiY9pTs4OOjs48SJE/j6+uLr6wvAoEGDeP/993nw4AEAVatWpXXr1tnKcOfOHapUqUJCQgIjR47kk08+YeHChbnKvmnTpjz5WQohmDt3Ltu3byc9PZ3Y2FjNubR9+3befPNNXFxcALC01H3MxMfHc/bsWY4cOQJA9erVadmyJYcOHcLd3R2A4cOH6y3Tpk2biI2N1biTpKamavn+9ezZU1Mv2dfXl06dOmFiYoKJiQne3t7cuHGDOnWkvHaDBw/WrHvjjTfYs2cP3bt3N0jenI4LmTLI5WeWfq9AMM+l8ofaovjoEqSngrFp4cqmJ7N2XePCgziMlQpsLUyISnge7Olsa86U7t509nEpRgkLDzNjI+b188PdzpL5+26y8MAt7kYnMvv1epibyFlBShsGK4p169bVinpWM2vWLIyMysgBoFCAabns11drBzaVpMAVnX6KCml9tXaFlipHCEGdOnU4evRolnWhoaEaZQrAyMhIc1NXf8/J70uhUOisL5p5mZWVlebz3bt3GT16NKdOnaJatWpcuHCBVq1aGfSbACZNmoS9vT3nzp3D2NiY1157jeTkgnX2flFuXVSpUgWAcuXKMWrUKN55551c+xRCsGvXLr7//nuD5Vm1ahX79u3jv//+w8bGhvnz57Nv3z6D+8lMTv9Xbggh+Omnn+jYsaPO9ZmPJ0OPL0PlLYrjQqaE8OK0cx0dSbYzU8EDzMtDcgxEXgGXeoUonH78c+Y+i/67BcAPfevRrW4lnYmryzIKhYJxHWtS2c6SiRsusu1CGOGxyfw6uCF25UqGMi+jH3lK3hQTE8OSJUuYOHGixmoUHBxMREREgQpXYlEaQefvnn3JfLI/+955ZoEqiTY2Nhr/QIDmzZsTEhLCnj17NMuCgoLylKIoNDSU/fv3A5Jl2MnJCTc3NwIDA1m7di1xcXGAFKmbneIQGxuLiYkJLi4uCCFYsGCBwXIAPHnyBDc3N4yNjbl27Rq7d+/WrOvRowc//vgjKSkpAERGRgJZ903Tpk25ePGixg9z9erVuLq64urqqtf4iYmS76hKpWLNmjXUr18/1+1OnjxJ7dq1DVLGXhzT3t4eGxsb4uPjWbZsmWZd9+7dWbFiBWFhkk9sYmIiiYmJ2NjYkJSUpPm/ra2tadCgAUuXLgUk/7/Dhw/rraxn3oe9evVi7ty5mn2RmJjI5cuXDf5tACtWrNDUgl+1ahWBgYEGy5vTcSFTxgi/ANG3wNgcanbOvb1CAZX8pM8lIKDlzJ1oJm6QjCkftPOip59rtomrXwZeb1iZP4c3xtrcmNN3nvDawiOEPE7ItqyhTMnDYIvihQsXaN++PeXLlyc0NJQRI0ZgZ2fHhg0buHv3Ln/++WdhyFny8O4Bff+UoptfTJFjU0lSEr11VBHIB+3bt2f27NnUrVuX5s2bs2jRIrZv387HH3/M+PHjSUtLo0qVKloBAfpSp04dli1bxocffoipqSl///03CoWCV155hUuXLtGsWTOUSiV169bNdgrW19eX/v37U6dOHSpWrKhX5K8uJk+ezJtvvsny5cupVq2aVmDIhAkT+Pzzz2nQoAEmJiZUqlSJHTt26Nw3K1euZPDgwaSnp1OhQgXWrVuXrSXrRa5evcq7776LQqEgPT2dBg0a8OOPP+a6nb7RzroYPHgwmzdvpmbNmjg4OBAQEMCdO3cAaNWqFVOmTKFTp04oFApMTU1Zv3497u7uDB48mLp162JlZcXp06dZuXIlI0eOZMGCBSgUCpYsWaKxjubGhx9+yIgRI7C0tGTZsmVMmDCBlJQUmjRpotlvEyZM0EwnG0Lt2rVp0aIF0dHR9OzZk/79+wMYJG9Ox4VMGUNtTazeAcx01zyu4WTNsYntqFju2cxJpfpw+4CkKPoPLRIxdXH/SSLv/nWG1AwVneo48VFgjWKTpSTR3MueDe81Z9iyU4RGJdJ1/iEsTIy0puNdyvh0fGlGIYQBOVqAwMBAGjRowPfff4+1tTXnz5+natWqHD16lIEDB+aYY64kEBcXh62tLbGxsdjY2GiWJycnExISgqenp9Y0Wq4UYmWWoUOHMnToUNq0aVMg/eniwIEDjB07VivSWSZ72rRpw7Jly7Lk6qtTpw779+/H0dFR53bLli0jNDRUK5fiy0CbNm0YO3ZsnpXovODh4aHzOpTnc1ym6BACfqwHMXegz1Ld9Z11cXkTrBsCLn7w7n+FKWG2JKSk0/uXo1wNj8fbxYb17zWTK5RkIjI+hT6LjnInKmvGD/Vj/C9vNJCVxSIgO11IFwZPPZ86dYp33303y3JXV9cSm4etUFEagWcA+PaR3uXyfS8lly9fzlZJlJGR0ZOHZyUl0cQSanTKttndqERGrTzDXbXCoQlouQzpKUUgqDYqleCjNUFcDY/H3sqMJUMaykqiDuzKmZKSlqFzndpiNW2rXDO6pGGwomhmZqbxWXuR69evZxuFKpM3evXqVehVJtq0aSNbEw1g6NChmihwQ/Dz8ytUy3BJ5cCBA0VqTQS0qrTIFA959j9TTzvX6JxjQGFccho7LoYTl/ysbF/5KmBhB6o0SVksYmb/e41/gx9haqzk18H+VCpvUeQylAZOhkQTHpe9Ii+AsNhk1py8KyuLJQiDH3l69OjB9OnTWbt2LSBFNt29e5cJEybQu3fvAhfwZaaob7AyuTN06NA8baeuZiRT+MiKYvGy81IY07YGa9U51sv/TKV6Xtu5joFpptQBLbf2QVgQuDYwVOw8s/HcfRYekCKcv+9dlwZVKhTZ2KUNfcsVTtp0iRn/u0pjTzuaVq1I06oV8a5ko3cQUIZKvHRR5oWJwYriDz/8QJ8+fXB0dCQpKYnWrVsTHh5Os2bN+OabbwpDRhkZGRmZUsDOS2G8t+JslqRh4bHJvLfibM7+Zw9OQ9x9MLWSAlkMpVJ9SVEswsjns3efMOEfKcJ5VJtq9Kqfe2aFlxl9yxVamCiJT0ln79UI9l6VsqlYmxnTyNOOplUl5dHbxQZjo6yTonl+UJHJFoMVRVtbW3bv3s2RI0c4f/48T58+pUGDBgQGBhaGfDIyMjIypYAMlWDa1mCdmWUFUrDCtK3BdPB21m3dUU871+wCJnmYutWU8isaRfFBTBLv/HmG1HQVHbyd+LhjzSIZtzTT2NMOF1tzwmOTs8tAjLOtOf990pbrj+I5fjuK47ejOBESTXxyOvuuRrAvB8Vxz5VHeX9QkcmWPHvbtmjRghYtWhSkLDIyMjIypZSTIdFaVpzMqP3PToZE06xaRe2VKhUEb5I+6xHp7GhjxiedauL4Yi1lFz/pPeIKpCWDSeFFtiempjNi+WkeP02hlrM18/r5oZSnNnPFSKlgSndv3ltxFgXa5SrUe29Kd29MjZX4uNri42rL2wFVyVAJroTF5ag4ljM1Il0l8v6gIpMtcliWjIyMjEy+0df/TGe7u8cgPgzMbKWKVrngaG3O+229tBfauoGlPSQ+lgJa3Pz1ksdQVCrBuDXnCQ6Lw97KlCVDGlLOTL6V6ktnHxd+eaNBlunhnMoaGikVOSiO0ZwIiSI+OftqUJDLg4pMjuSpMotM0bB582Zq166Nn5+fzrKJ+jB16tQSXepMoVAQExOjV9v169fz3nvvGdT/22+/rak6U1iEhobSpk0bbG1tswStHDhwAAsLC/z8/DSvpKSkQpVHRqY40Nf/TGe7y8+mnWt1BWOzrOszEZuUxu7gR8QmpT1fqFC8MP18Vi9Z8sLcPdfZeTkcUyMli9/0x62C7vrrMtnT2ceFwxPa8feIpvzY34+/RzTl8IR2ek8LqxXHtwOqsmRIQ4K+7MgnnfSb+tf3gUbmObKiWIJZtGgRX375JUFBQfj6+uapj2nTpuVJUcypVm9xkZfqJ0uWLKFt27aFI9AzbGxs+Prrr1m1apXO9TVr1iQoKEjzsrCQU2fIlD2EEFkKmmbGSKnA2ChTK1UGBG+WPuuZYPtedCIj/jzNvehMiZvVpfzCgvTqx1A2Bz3gp303AZjxmi/+7naFMs7LQEGWNTRSKvSONtf3gUbmObKimAMRcclcehCr9VJfmJLTMrKsu/Tgea3cW5FPs6yLSZTKFUU9TSEiLmfl7cMPP+TQoUNMmjSJ5s2bAzBo0CAaNmxI3bp16dq1q1aC8+3bt9OoUSPq1auHn58fJ06cYOTIkQAEBATg5+dHREQEERERvPbaa/j6+uLj48PixYs1fXh4eDBhwgQaN27MkCFDssjUpk0bPvjgAxo1aoSXlxfjx49HXdgnPDycvn370rhxY3x9fZk8ebJmu48//phGjRrh5+dHq1atuHbtWpa+hRBMmDCBHj16aOoLv0haWhpHjhzRWbotJ7natGmjKWsYGxvL22+/jY+PD/Xq1WP48OEAPH36lOHDh+Pj44OPjw/Tpk3L8b/JjJ2dHS1btqRcuezzvsnIlGX2XnnEsGWnNP5h2d3yM1SCfouP8f3Oq6SkP0u8HHoYEiLBogJUbZM/QTQWxaD89aODoHsxfLL+AgDvtq5Kb3+3Ah9DJu+oA2VyUjdtLUxo7Ckr94ail2OFrgTb2ZFbKZjSxMoTd/lx7w2tZb38KjGvf33CY5Pp9tPhLNuEzuwKwMfrznPubozWurn96vFqfTe2Xwwj6mkqH3XIvg7o/PnzuXDhglb5s3nz5mmSms+cOZOpU6eyaNEirl+/zrBhwzh48CC1atUiLS2NxMREFi1axOLFizl06JAmSXS/fv2oWbMmGzZsICIiAn9/f+rVq0fTpk0BiIqK4sSJE9nWRQ4ODubo0aOkpaXRqlUr/v77bwYOHMiQIUOYNGkSrVu3Jj09nW7durFu3Tpef/11JkyYwOzZswFYvXo1Y8aMYefOnZo+U1JSGDBgABUrVmTjxo0YGWWtbrN//36aN2+OiYmJQXK9yNixY7GwsODChQsolUoiIyMB+Oqrr0hJSeHChQskJSXRsmVLatWqRb9+/Zg1axYrV67UOeaUKVN49dXc873dunWLBg0aYGRkxLBhwxg1alSu28jIlBY2nXvA+HXnyVAJAms70qNeJWb872qW9CQfd6zJ4ZuP2XjuAQsP3GLf1Qjm9PXDWz3tXLs7GOk+v/VGrShGXIG0pLxFT+sgLDaJEX+eJjVdRWBtRz7tVKtA+pUpOHIKlFETm5TGtK2X+aKbNyY6UuvI6EYvRbF8+fLZKg6ZycjQXZ6nNDKoSRU6eDtpLbO1kC5kzrbmbPugZbbbzn69Hkmp2vvCrYJ00erq65KnrPOrVq3ir7/+Ijk5meTkZOzt7QHYvXs3nTt3plYt6eJlYmKCra2tzj727NnDmTNnAHB0dOS1115jz549GkVx6NChOf7XgwcPxsTEBBMTE9544w327NlDz5492bt3L48ePdK0e/r0qcZyuHv3bn766Sfi4+NRqVRER0dr9dm1a1d69uzJF198ke24mzZtylEp0yVXZkVx27ZtnDhxAqVSukCole49e/bwww8/oFQqKVeuHIMHD2b37t3069ePTz75hE8++STbcXOjQYMG3L9/H1tbW+7fv0+XLl2wt7enb9++ee5TRqaksPxoKFO2SJVQXqvvynd96mJipKRr3Uo6Ex739nejUx0nPt94iavh8fT++QBnLTZiAVBHz7rOOWHtAuUcISECwi9B5Ub57jIxNZ0Rf54mMj6Fmk7WzOtfX46aLaFkFyjjYisdg5uDHvLnsTvcePSUnwc1wK6caTFKW3rQS1F8MRggNDSUzz77jKFDh9KsWTMAjh07xvLly5kxY0bhSFlMONqY42ij25/B3MQIH1fdyhhANQerbNdVtMrdWTszhw8fZv78+Rw7dgxHR0e2bNnCl19+aXA/mcmsFFpZZS93dturp3mPHz+Oubn2/rp79y6jR4/m1KlTVKtWjQsXLtCqVSutNu3atWP37t2MGTNGp0VaCMGuXbv4/vvvDZIrr7y4bX4tii/+Hjc3NwYMGMChQ4dkRVGmVCOE4Kd9N5mz+zoAQ5t78GU3b02KGLX/mS46+7jQ0MOOSRsuknR1NxbpscQobHlcrj5eOrfIipmxkuqOVpgZZ7IKqQNabuyS8inmU1FUqQQfrzvPpQdx2JWTIpyt5AjnEk1nHxc6eDvrfFDpVrcSY1ef49jtKHr+fJjfBjeklnPZmQUtLPSyvbZu3Vrz+vPPP5kzZw4zZsygR48e9OjRgxkzZjB79myWLl1a2PK+tDx58gRra2sqVqxIamqqlm9hp06d2LVrF1evXgUkf77YWMlf0traWvMZIDAwkN9++w2AyMhINmzYQIcO+ldBWLFiBWlpaSQlJbFq1SoCAwOxsrKibdu2zJw5U9Pu4cOH3L9/n9jYWExMTHBxcUEIwYIFC7L0OWnSJF577TUCAwOJiorKsv7kyZPUrl07RyVWl1yZ6dGjB7Nnz0alUml+v3qf/P777wghSEhI4K+//qJjx44AfPLJJ1qBKC++9Jl2DgsL04wXHx/Ptm3bqF+/fq7byciUVFQqwVfbrmiUxLGB1ZnS3dugPIL2VmYsftOfr7ykPralNaTrgmMsOXQblR6zLdWdrNk9rjXVnayzrizAxNvz9t5gx8VwTIwULH7Tn8p2coRzaSC7QJkO3k5sfL8F7hUtuRedxGsLj7LzUnguvckYPEl/7NgxGjZsmGV5w4YNOXnypMEC/Pzzz3h4eGBubk6TJk1y7WPevHnUrFkTCwsLKleuzEcffVSi078UFJ07d6ZmzZrUrFlTE5yixsvLi6VLl/LGG29Qr149mjRpopn2HT9+PB06dNAEs8yfP58rV67g6+tL27Zt+fzzz2nSpInectSuXZsWLVrg6+tLQEAA/fv3B2DlypXcvHkTHx8ffH19ee2114iKisLX15f+/ftTp04dGjVqRJUqVXT2O3bsWEaMGEG7du20gnRAv2jn7OR6kblz55KSkoKvry9+fn5MmjQJgC+++AITExN8fX1p0qQJPXr0MMjil5iYiJubG6+//jrBwcG4ubkxceJEAP755x98fX01fqAdOnRg2LBhevct83KToRIcuxXF5qAHHLsVlSeXlYIkPUPFp/9c4I8jIQB82c2bsYE18mTBV2Sk4RGxF4C7lV4hJV3F19uv0P+341mjmQ0hH5HPL+7vOf9eY/4zH/VvXvWlkYccBFEWqOFkzeb3W9DCqyKJqRmMXHGGH/fc0OsB5WVFIdTzhnpSs2ZNevbsmWUa8NNPP2Xz5s06I1qzY82aNQwePJhFixbRpEkT5s2bx7p167h27RqOjo5Z2q9atYrhw4fzxx9/0Lx5c65fv87QoUPp378/c+bM0WvMuLg4bG1tiY2N1ZoWTE5OJiQkBE9PzyzTpzISbdq00QquKSrq1KnD/v37dR4TxSmXTOmitJ3jJa1mbXJaBh/+fY5/gx9hpFTwfe+6+Yv8vb4LVvUFKyfER8H8ffohX28PJjE1A0tTIyZ39WZA48o6ldDLD2Ppt/g4a95tSp1KmVyA4sJgTi1QKGHifTDVLxuBrv0NEFjbkSVD8u/rKFOySM9Q8c2OKyw9EgrAKz7OzH693kuTPD07XUgXBlsU586dy08//YSvry9vv/02b7/9NnXr1uWnn35i7ty5BvU1Z84cRowYwbBhw/D29mbRokVYWlryxx9/6Gx/9OhRWrRowcCBA/Hw8KBjx44MGDAgT5ZMmdLD5cuXs1USZWTKIjsvhfHeirNZlBZ1zdqdl8KKVJ6nKekMX3aKf4MfYWqsZNEb/vlPD6Ou7ezdE4WRMQObVGHnmFY09rAjMTWDSRsvMnTpKcJ1lAUUQpJJp5nDxkUKahEqCNevUEF2+xtg75WIIt/fMoWPsZGSKd3r8H3vupgYKfjfpXB6/3I0f9bsMorBimKXLl24fv063bt3Jzo6mujoaLp3787169fp0qWL3v2kpqZy5swZLV8ypVJJYGAgx44d07lN8+bNOXPmjEYxvH37Njt27Mhx3JSUFOLi4rReMnnjwIEDJdJqV1LlkpHJCxkqwbStwdnWrAWpZm1RTUM/SUhl0G/HOXorinKmRiwb1ihLNgiDSUuGazukzy9EO1epaMnqd5oyuWttTI2V/Hc9ko5z/2PTuQeaoLkMleDCfcnv+sL9WN37QV33WY98ihkqwdQtuve3mqLc3zJFS99GlVn9TlPsrcy4Gh5Pz5+PcPx2Vl/5l5k82VgrV67Mt99+m6+BHz9+TEZGBk5O2hccJycnTVBGZgYOHMjjx49p2bIlQgjS09MZOXKkxtdMFzNmzDAogbKBM/EyMjKlACEECclpJKamc+5uDI29nEpsipOTIdE6LVtqirJmbXhsMm/+foIbEU+pYGnC8uGNqetWPv8d39oLKXFgXQkqa/tIK5UK3g6oSpuaDoxbe54L92MZuyaInZfCaV/bkTm7r2v2z6SNF/lp342s0/GV6sP1/2kFtCSlZnA3OpE7UQncjU589jmRa+HxhOdQAEGuEVz28Xe3Y8voFrz71xkuPojljSUnmNKjDm82dS9u0UoEBiuKBw8ezHF95tQnBcmBAwf49ttvWbhwIU2aNOHmzZuMGTOGr776KtscfBMnTmTcuHGa73FxcVSuXDlLOxMTExQKBZGRkTg4OOQrvYqMjEzJIT45lYi4FJLjY7gTlcSkvWepYGVWbL5+uaFvLdot5x9Q18220HyqQh8nMGjJCR7EJOFsY86Ktxvj5agjyjgvqKed67wKSt0TW16O1mx4rzm/HLjFj3tvsPNyODsvZ41QVU/HLxzUgMaedtyJTiQluTLNgPCrx/hg0VHuRCUSEZ+SL5HlGsFlm0rlLVg3shmfrr/AlvMP+WLTJa6GxTGlex1MM6dheskwOJhFqeOkflGp0jfhdmpqKpaWlqxfv15r2nDIkCHExMSwefPmLNsEBATQtGlTZs2apVm2YsUK3nnnHZ4+fapTtszk5MD59OlT7t+/L1sVZWTKCEmpGUQnpCIQxCdn8MvpGG5Ep2nKfP3yRoMSpywevB7B4D9O6dW2nKkR3etV4vWGlWlQRf/CCLkR/DCOwX+c5PHTFDwqWrLi7Sa4VSig1DCpiTDLC9IS4O294JY1i0Zmzt+LofcvR0nPYfr3xWoc9sRy2vw9VEKBT8rvJCIFL1mbG+Ne0RJ3u3JUqWiJu50lT5PT+XrHlVxl+HtEU9mi+BIghGDRf7f5ftdVhJBKA/4yqEGe8h+XZAwJZjH4UfTJkyda39PS0jh37hxffPEF33zzjd79mJqa4u/vz969ezWKokqlYu/evYwePVrnNomJiVmUQXW5t4JQ7qysrKhevTppaWn57ktGRsYwJN+zGKITUrErZ0pdt/L5mh7OUAkG/XacyKcpqAQ8SVKRnCFdJwSSYjFtazAdvJ1LzDT0k4RUTUqWnLA2N8bO0oQ70UmsPnWP1afu4eVoRb+GlXm1gSv2+bipnQ6NZtiyU8Qnp+PtYsPy4Y1xsC7Am+TN3ZKSaFsFXP312iQxNSNHJRGeK4nONuZUqWhHTKQD5dMj+bWDCVY1WuBuZ0l5S5MsynSGSvD7kRDCY5N1+ikqkCpxyTWCXw4UCgXvtalGTWcrxvwdxMmQaHosOMJvgxviXcmGDJXQmcy7LGOwoqirNFyHDh0wNTVl3LhxmvJw+jBu3DiGDBlCw4YNady4MfPmzSMhIUGTZ27w4MG4urpqKr50796dOXPmUL9+fc3U8xdffEH37t111gfOC0ZGRgXWl4yMjH4URCqYpynpkv9ZVCJ3ohM5GRJNUFj2EYwlzfcs9HECw5adIuRxAubGSpLTVVlq1qpvR7P61KVTHWdOhESz9vQ9dlwM42bEU77ZcYXvdl6lfW1H+jWqTKvqDhhnU9NW1w3v0I1IRq44Q3KaikYeFVgypJGmbGmBoZl27iVVUtEDfad9Z79elz7+z1yLVjeBq9toaXkPKpfPdpucagSrpZvS3bvMKwMy2rSr5cTG95vz9vLThEYl0vuXo7zRtArbLoSVmJRVRUWBObc4OTkZlEMRoF+/fkRGRvLll18SHh6On58fO3fu1AS43L17V8uCOHnyZBQKBZMnT+bBgwc4ODjQvXt3gyyZMjIyJQt1apLM1hy175l6elgIQWR8CneeBSHcjUrQfL4XnUhUQmqexi8JvmenQ6MZ8edpniSm4VregqXDGnE78mkW5dk5002padWKNK1akak96rD1/EPWnrrH+fux7Lr8iF2XH+FkY0Yffzde96+Mh/3zfIK6FPPyFibEp6SRoYI2NR34ZZA/FqYF/NCc8lTKnwjgo39tZ0dr/fJeupZ/YXrcxQ+ubtMr8jm7GsGZ97fMy4WXozWb32/J6L/PcujGY347FJKlTebrVFnEYB/FCxcuaH0XQhAWFsbMmTNJT0/n8OHDBSpgQWPIvLyMjExWCnLqJUMlaPndvhyjfM2MlVSxs+DekySS01Q59mdXzpQqdpa4V7TESAEbzj3MVYbfhzSkfe18pnvJB1vPP2T8uvOkpquo62bLkiENNYpRXvb11fA41p66z8Zz93mS+NyNpomnHf0aVcZIqWDs6qBs08H4u5fn7xHNCseB/+J6+OctqOAJH57T26KoPk5ymx4+PKHd8/1zYw+s7A0Vq8MHp/Ue52WbVpTJnZS0DBp8tZuEVN0xGDqPvxJOofoo+vn5oVAosvgENm3aNNtE2TIyMmWDgpgijktO426UlJ7k0PXIHJVEgJR0FTciEgBQKqToRPeKllSxK/fs3VKjHFqbP58mzVAJjt2Ozla5UDN540XMTYxo4WWvl/wFhRCChQduMWuXNBPTwduJH/v7YWn6/LKsrllrCLWcbfiyuzcTXqnJnuAI1p6+x8EbkZwIieZESHSW6dXMPIxJLryb3eWN0rvPa3oriZDH6WF1Kb+oG5AcB+a5Gwbysr9lyj5n78ZkqyRCyXNjKWgMVhRDQrRNr0qlEgcHh1JREktGRibv6DtFrFIJIuJTuPNsavjes+nhO9HSdPGLVi59Gdm6Kv0aVcG1vIXelq7clAsB2FuZEhaXwqAlJxjczJ3PXqmlpagVFmkZKiZvvMSa0/cAGN7Ck8+71i5QBc3M2IiudV3oWteFhzFJrD9znz+PhfL4ac5T9IV2w0uOgxu7pc91XjV4c4Onh8vZg21liL0H4RfAo2V+pJd5idHXPaUkuLEUBgZfEf/77z/69euHmZl2FFxqaiqrV69m8ODBBSacjIxMyUCfaiFjVgdRucI17j1JIiU95yniiuVMqVLREksTI47cyr0KQusajnja61ez90VyUy4Cqjsw839X+ev4Hf48doeD1yP5oW89/N0LL8I1NimNUSvPcORmFEoFTOlehyHNPQptPJCssB+2r04VO0vGrgnKtX2h3PCu/Q8yUqSpYCefPHXR2ceFDt7O/O9iGOvP3qdPAzde8XXJXsGu5Ccpig/PyYqiTJ7R10fWWI8UfaURgxXFYcOG0blz5yy1d+Pj4xk2bJisKMrIlEFyqxYC0hTxzcjnU8SuFSw0+eqq2Ek569Sf1VPE+vqe5Sc1iVq5yM737KtePnSs48Sn6y8QGpXI64uOMaJVVT4KrIG5ScEGc9x/ksjwZae4/ugplqZG/DSgfpH6RzrZ6HfD0/fGaBCXn0U7GzjtnBkjpYJu9SrRrV6l3BtXqg9XtmpVaJGRMZTGnna42Jrn6sYyfm0QNyOe8k6rqgUfCFaMGKwoCiF0JnW9f/++ztQ5MjIypR99LUwjW1ejf6PKuFawwCSbtCwvUlSpSXLzPQuo7sDOsa2YvjWYf87eZ/F/t9l/NYI5ff3wcS2Y69qF+zG8tfw0kfEpOFqb8cfQRgXWt77kdsMrtJyBSU/g5l7pcx39o52zIyElnavh8dRyts65Mo0BNZ9lZLJDHzeWqg7luB2ZwNw911l96i6fdq5Jz3quKEtJcEtO6G0nrV+/Pg0aNEChUNC+fXsaNGigedWrV4+AgAACAwMLU1YZGZliQl8LU+saDnjYl9NLSVSjnh52ttUew9nWvEhTTthamPBD33r8+qY/9lamXH/0lF4/H+HHPTdIy8h5Kj03/r0cTt/Fx4iMT6GWszWb3m9R5EoiPL/hwXNFXE2h5gy8ugNUaeDoDY618t1dyOMEev9ylJDHCTk3rFRfeo++BUkx+R5X5uUlp+vUojcasHdcaxYMrI9reQvCYpP5aM15Xv3lKGfuRBeTxAWH3hZFdfWUoKAgOnXqhJWVlWadqakpHh4e9O7du8AFlJGRKX7Ulqjspp/za4nKbXq4KOlYx5mGHnZM3nSRHRfDmbvnOnuuPGJO33pUdzKs1rEQgj+OhPL19mCEkBTpBQPra0VnFzXFkjPw8gu1nYsSSzso7w4xdyDsPFRtXbTjy5QpcrtOdatbicDaTvx+OISF+28+Kz15jG51XfjslVoFVwaziDE4j+Ly5cvp169fqY1ylvMoysjkjfl7bzBn9/Usy0ty3eT8IIRgy/mHfLn5MrFJaZgaK/m4Yw3eallVLwU2PUPFV9uCWX7sDgADm1Rheo862VZKKWqKLGdgYjTMrg6qdBh9Buy98t3lpQexdPvpMNs+aJm7ZXbtYAjeDB2mQ4sx+R5bRkYfIuKTmfPvddacvocQYGqs5O2Wnoxq64VVTu4SRYQhupDBV6whQ4aUWiVRRkYm75y+I9V5t8zkpF3UU8RFhUKhoKefK/9+1Io2NR1ITVfx7Y6r9P/1GHeink95ZqgEx25FsTnoAcduRZGhEiSkpPPuX2c0SuKkLrX4ppdPiVES4bnfZk8/V5pVq1h41tsrWyQl0dm3QJREg1FPP8sBLTJFiKO1OTN712X7BwE0q1qR1HQVCw/cos2sA6w+eZeMXGqXlyT0Umvt7Oy4fv069vb2VKhQQWcwi5ro6NI/Hy8jI6PNhfsxHLweiZFSwY4PAwiLTS72KeKiwsnGnKVDG7Hm1D2+2hbMqdAndJ53iElda2NfzpTp27SncB2tzTAzVnLvSRJmxkrm9fPjFd+ypUQbhKa2c/6DWNQYKRXYlTPV77jTBLTIiqJM0eNdyYZVI5qwO/gR3+64QmhUIp9tuMjyY3f4olttmld7nui/pFYG0ktRnDt3LtbWkm/OvHnzClMeGRmZEsjP+28C0LNeJTzsy2nVDX4ZUCgU9G9chRZe9nyy/jzHb0fzxaZLOttGxKcAYGVmzJ9vNaZBlQpFKWrJ4mkkhB6SPhegf2JtFxvOftFBv8bqCi1PQqXoa4uX+P+QKRYUCgUd6zjTpqYjfx4LZf7eG1wJi2Pgbyfo4O3EpC61uRYel++qV4WFwT6KpR3ZR1FGxjBuPIqnw9yDKBSw+6NWeDkaFtBR1lCpBH8cCeHr7VdybOdobcaxie1LhEWg2Di1BLaPl6Z/3zlQfHL86AdPQuDNTVCtbfHJISMDRCek8uOe66w4IU1BGylBV2KFwvT/LlQfRQCVSsX169c5fPgwBw8e1HrJyMiULRYeuAVAJ2/nl15JBFAqFdSplHtqm4j4FE6GvKSuOKoMCDkEJ36Vvnv3KtDurz+Kp/Ws/Vx/FK/fBmqrojz9LFMCsCtnyrSePuwcE0DrGvY6lUR4nq9x2tbgYvVpNDj05vjx4wwcOJA7d+6Q2RipUCjIyMi+cLaMjEzp4m5UIlvOPwTg/bbFEIhQQnnZa7/mSPAW2DkB4h4+X3Z8IdhVBe8eBTJEarqKO1GJpOZSKlJDpfpweSOEBRXI+DIyBUF1J2tGtvbiv+uPs20jKMT663pisEVx5MiRNGzYkEuXLhEdHc2TJ080LzmQRUambLHo4C0yVILWNRzwdZMrL6nRNwF5oZTCK8kEb5HS0byoJAI8jXiWpmZL8cglRz7LlFBKw0OnwRbFGzdusH79ery8ZOuCjExZJjw2mfWn7wOyNTEzxVYKrySjypAsiTr3iAAUsPMzqNUVlEVcB9elnvQec1fK62j5Ev0vMiWa0vDQabBFsUmTJty8ebMwZJGRkSlB/HboNqkZKhp72L1cCo8eFFspvJLMnaNZLYlaCIh7ILUrasxtwa6a9Fm2KsqUINQPndldKRRI0c/FeQ02WFH84IMPGD9+PMuWLePMmTNcuHBB6yUjI1P6iU5IZdWJuwC83062JuqipNSoLjE8fVSw7XLAvaIly4c3xr2iASXR5OlnmRJIaXjoNHjqWV3Pefjw4ZplCoUCIYQczCIjU0ZYeiSEpLQMfF1taVXdPvcNXlJKUo3qYsfKqWDb5YC1uQmtazgYtlElP7i0XlYUZUocxVJ/3QAMVhRDQkIKQw4ZGZkSQlxyGsuOhgLwfttqOVZiknleCu+lx7052FSCuDB0+ykqpPXuzfM9VERcMitP3GVQkyo42ujpu6W2KIadz/f4MjIFTUl+6DRYUXR3dy8MOWRkZEoIK47fIT45HS9HKzp6Oxe3ODKlBaURdP5Oim7OwrObXeeZBRLIEhGfwo97b9DB20l/RdG5riRH7D2pYoyVgRZJGZlCpqQ+dBqsKG7Zoju9gUKhwNzcHC8vLzw9PfMtmIyMTNGTlJrB74ekWYNRbaqhLAFPszKlCO8ekjK4c4L2cptK0vICyqOYJ8xtoKIXRN2Q8ilW17MEoIzMS47BimKvXr00Pokv8qKfYsuWLdm0aRMVKsg1NWVkShOrT90lKiEVtwoW9KhXqbjFkSmNWJSX3h29IWC85JPo3rzoU+LoolJ9SVF8GCQrijIyemJw1PPu3btp1KgRu3fvJjY2ltjYWHbv3k2TJk3Ytm0bBw8eJCoqio8//rgw5JWRkSkkUtNV/HrwNgAjW1fD2ChPFT5lXnYenJXeq7YB3z7gGVAylESQI59lZPKAwRbFMWPG8Ouvv9K8+XOH5Pbt22Nubs4777zD5cuXmTdvnlZUtIyMTMln47n7hMUm42htRh9/t+IWR6a0olbC1EpZIWBrYUIvv0rYWpgYtqFc81lGxmAMVhRv3bqFjY1NluU2Njbcvi1ZI6pXr87jx9nXLpSRkSlZZKgEvxy4BcA7rapiblJCLEAypYuMdAh/lk+3UoNCG6aynSXz+udBEVUHtMQ/hPhHYJ3/VD1lGlWGlCD96aOS5UIgU6QYPLfk7+/PJ598QmRkpGZZZGQkn376KY0aNQKkMn+VK1cuOCllZGQKle0XwwiNSqS8pQkDGlcpbnFkSiuRVyA9GcxswK5qoQ2TnJZB6OMEktMMzNtrZgUONaXPYUEFLleZIngLzPOB5d3gn7ek93k+xVevW6bYMFhR/P333wkJCcHNzQ0vLy+8vLxwc3MjNDSUJUuWAPD06VMmT55c4MLKyMgUPCqVYOF+qSzn8BaelDMzeKJBRkZCM+3sB8rC83G9GfGUNrMPcDPiqeEbu/hJ7/L0c/YEb5HSHGUuyRgXJi2XlcWXCoPvCDVr1iQ4OJh///2X69eva5Z16NAB5bMLQ69evQpUSBkZmcJj39UIrobHY2VmzJBmHsUtjkxppgj8E/NNpfpwYbUU+SyTFVXGs/RGupKmC0ABOz+DWl3laeiXhDyZDpRKJZ07d6Zz584FLY+MjEwRIoRgwTNr4htN3bG1NDA4QEbmRdQRzyVdUQTZopgdd45mtSRqISDugdTOM6DIxJIpPvKkKCYkJPDff/9x9+5dUlNTtdZ9+OGHBSKYjIxM4XP0VhRB92IwM1byVks5Ub5MPkhPgUeXpc+FGMiSb5x9QaGEp+HSVKpN8dbRLXE8fVSw7WRKPQYriufOnaNLly4kJiaSkJCAnZ0djx8/xtLSEkdHR1lRlJEpRfz8zJrYv1FlHKzNilkamVLNo0ugSgMLOyhfggOiTC3BoRZEBEsBLbKiqI2VnpHg+raTKfUY7G380Ucf0b17d548eYKFhQXHjx/nzp07+Pv7M3v27MKQUUZGphA4e/cJR29FYaxU8E7rasUtjkxp50X/REXhln70cbUldGZXfFxt89aBPP2cPe7NpZKLZPcfKsDGVWon81JgsKIYFBTE+PHjUSqVGBkZkZKSQuXKlfn++++ZNGmSwQL8/PPPeHh4YG5uTpMmTTh58mSO7WNiYnj//fdxcXHBzMyMGjVqsGPHDoPHlZF52fl5n2RNfK2BK67lLYpZGplSj1rpci3B085q5Mjn7FEaQefvcm7TeaYcyPISYbCiaGJiooludnR05O7duwDY2tpy7949g/pas2YN48aNY8qUKZw9e5Z69erRqVMnIiIidLZPTU2lQ4cOhIaGsn79eq5du8Zvv/2Gq6uroT9DRualJvhhHHuvRqBUSOX6ZGTyzYOii3i+FfmUVxce4VZkHtLjwAsWxSAQuqJ7X3K8e8Dry8liVTQvD33/lNbLvDQY7KNYv359Tp06RfXq1WndujVffvkljx8/5q+//sLHx8egvubMmcOIESMYNmwYAIsWLWL79u388ccffPbZZ1na//HHH0RHR3P06FFMTKToTA8PjxzHSElJISUlRfM9Li7OIBllZMoiCw9I1sQuvi5UdbAqZmlkSj2piVKybSiSQJak1AzO3Y0hKdXAhNtqnH1AYQQJEVKEr61sbMiCvRcgwNgcqneCK5vBo6WsJL6EGGxR/Pbbb3FxkZx/v/nmGypUqMB7771HZGQkv/76q979pKamcubMGQIDA58Lo1QSGBjIsWPHdG6zZcsWmjVrxvvvv4+TkxM+Pj58++23ZGRkf7GYMWMGtra2mpdcMUbmZed25FO2XwwD4P22XsUsjUyZIPwiCBVYOZeO4BATC3CsLX2Wp591c/fZfbhKM2j2vvT5zhFQqYpPJpliwWCLYsOGDTWfHR0d2blzZ54Gfvz4MRkZGTg5aUdOOTk5cfXqVZ3b3L59m3379jFo0CB27NjBzZs3GTVqFGlpaUyZMkXnNhMnTmTcuHGa73FxcUWjLMo1MmVKKIv+u4UQ0L6WI7VdstZtl5ExmIelIH9iZir5SZHaYUFQu1txS1PyuHtceq/STPI7NSkHSU+kaHFnw2YPZUo3papWl0qlwtHRkV9//RUjIyP8/f158OABs2bNylZRNDMzw8ysiNN+BG+RMtu/mLTUppLkICyb7WWKkQcxSWw4+wCA99vJ1kSZAqI0BbKoqVQfzq2QLYrZoVEUm4KRifR+ay+EHpIVxZcMg6eeo6KieP/99/H29sbe3h47Ozutl77Y29tjZGTEo0faSTsfPXqEs7Ozzm1cXFyoUaMGRkbPLXO1a9cmPDw8S+LvYkOukSlTgvn1v1ukqwTNq1WkQZUKxS2OTFmhiCuyuFWwYG6/erhVyEe0vssLKXLkgBZtYu5B7D3Jj9Pt2SyiugpLyKHik0umWDDYovjmm29y8+ZN3nrrLZycnFDkMV+Wqakp/v7+7N27V1MbWqVSsXfvXkaPHq1zmxYtWrBq1SpUKpUm8vr69eu4uLhgamqaJzkKFLlGpkwJJjI+hdWnpMwEsm+iTIGRHAdRN6TPRaQolrc05dX6bvnrxKkOKI0hMQpi70N52X9dg9qa6FIPTMtJnz1aSe93Dkv3Ovke9tJgsKJ46NAhDh8+TL169fI9+Lhx4xgyZAgNGzakcePGzJs3j4SEBE0U9ODBg3F1dWXGjBkAvPfeeyxYsIAxY8bwwQcfcOPGDb799tuSUw1GrpEpU4L5/XAIKekq/CqXp3m1isUtjkxZIey89G5bBcrZF8mQUU9T2H4xjK6+LlS0yqNrkYm5FNASflGyKsqK4nNeDGRR41IPTK0hOVbaZ5X8ikU0maLHYEWxVq1aJCUlFcjg/fr1IzIyki+//JLw8HD8/PzYuXOnJsDl7t27GsshQOXKldm1axcfffQRdevWxdXVlTFjxjBhwoQCkSffyDUyZUoosYlprDh+B5CsiXmdCZCRyYImkMWvyIYMi03my82XaVClQt4VRZAsoGpFUfYff86L/olqjIzBvRnc+BdCD8uK4kuEwYriwoUL+eyzz/jyyy/x8fHR5DNUY2NjWBTl6NGjs51qPnDgQJZlzZo14/jx4waNUWTINTJlSijLj4XyNCWdWs7WtK/lWNziyJQlHhZdou0Cp1J9OPunFPksI6GObAZtRRHAI+CZongImuu+b8uUPQxWFMuXL09cXBzt2rXTWi6EQKFQ5JjTsMyjrpEZF4ZuP0WFtF6ukSlThCSkpPPHkRAARrX1QqmUrYkyBUhpjHhW82IpPyEKvUZ1qeDeKUCAXTWwyvRQqXaZunMUMtIlK6NMmcfgf3nQoEGYmJiwatWqfAWzlEnUNTLXDkYqffSisvhsP8k1MmWKmL9P3iUmMQ2PipZ09S0FyZBlSg+J0fAkVPqsVrpKE051QGkiWdFi7kAFj+KWqPjR5Z+oxrkumNlCSiyEnwdX/6KVTaZYMFhRvHTpEufOnaNmzZqFIU/px7uHVAszcx5Fayd4ZZbsByNTpCSnZfDrwdsAvNemGkayNVGmIFFbE+2qgUX5Ihu2nJkxAdXtKWeWT4uWsZmkLIYFSXWfZUXxuX+iuw5FUWkkzYhd/5+UJkdWFF8KDM6j2LBhQ+7du1cYspQdvHvA2EswZJt0AQVoOU5WEmWKjAyV4NitKL7YfImI+BScbczyn05ERiYzxVSRxdO+HH+91QRP+3L570wdlCEn3ob0FHhwRvqsy6IIz6efQw8XjUwyxY7Bj2MffPABY8aM4ZNPPsHX1zdLMEvdunULTLhSjdJIOqH8h8LuL+DqdmjybnFLJfMSsPNSGNO2BhMWm6xZlpiawb6rj+jsI089yxQgD4Ok9yJWFDNUgsTUdCxNjfNvJa9UH84skxVFkP7PjBQo5wB2VXW38XimKN49BhlpUtUWmTKNwYpiv379ABg+fLhmmUKhkINZsqN2N0lRDD0s+fNY6l+9RkbGUHZeCuO9FWezhFLFJ6fz3oqz/PJGg5dTWZRrrxcOxRTIciUsjm4/HWbbBy3xcbXNX2dqJTcsSA5ouXtUeq/SNPv94OQDFhUkv86HQVC5UZGJJ1M8GKwohoSEFIYcZRe7quDkC48uwrX/Qf1BxS2RTBklQyWYtjU4p7pATNsaTAdv55fLV1GuvV44xD+SCgigkIIcSisOtcHIVEok/SQke0vay4Amf2I2084ASiW4t4Cr2yD0oKwovgQY7KPo7u6e40tGB7W7Se9XtxWvHDJlmpMh0VrTzZkRSImKT4ZEF51QxY1ce73wUFsTHWqCmVXxypIfjE0lKxm83NPPKpXuRNu68JDrPr9MGKwoyuSB2t2l95t7IeVp8coiU2aJiH+uJCpR0VQZTA/lUZoqg1Gi0tmuTJNr7XWk2usq2V0mT2gSbZfC/ImZUU8/q30uX0YeX4PkGDCxzN1CrA5ouXcC0lMLXTSZ4kXOllkUOHpDBU9pWuPmHqjTq7glkimDOFqbA9BJeZIpJn9SSfHccvhQ2DEtbTC7VI017co8ZaH2ekn2rSymiOdCQY58fp4/0a1h7gEqDrXBsiIkRknHQW4WSJlSjWxRLAoUiudWxStbi1cWmTJLY087+lud4xeTeTijPb3sTDS/mMyjv1UQjT1fkoCq0l57PXgLzPOB5d3gn7ek93k+JWO6XIhirchS09maM5MDqelsXTAdagJazktTsC8j+vgnqlEqwaOl9Fmefi7z6K0o3r59uzDlKPuoFcXru6RcVTIyBYwRKj5jGQCZY1XU36eY/IkRL8mNsDTXXi/pvpWx9yEhEpTGUsLqIsbESElFKzNMjArI1uFQC4zNISVOmvkpaFQZkkJ1cb30XhLdHTQVWfS0Dqr9FEMPFo48MiUGvc+yunXr4uPjw6RJkzhx4kRhylQ2cW0IVs6QGg8h8oklU/A8vXGQ8umRWZRENUoFWCSFS1OZLwPq2uvZogAb15JXe700+FaqrYmOtcHEosiHvxOVwNvLT3EnKqFgOjQyKbyAlpJsGVYT+wBi7oLCCNz0jGJWK4r3TsrGjzKO3ori48ePmTFjBhEREfTs2RMXFxdGjBjB1q1bSU5+SZzjDUBdGWNz0AOO3YoiA8Xz6OcrJegCIVNmOHD6on4NS+pUa0GjNIJWn+TcpiTWXjfEt7K4KOZAlvjkdPZciSA+Ob3gOtUEtBSgoljSLcNq7j2bdnb2BTM9p/MdakI5R0hPhvunC082mWJHb0XR3Nyc7t27s2TJEsLCwvjnn3+oWLEiEyZMwN7enl69evHHH38QGRlZmPKWCnZeCqPld/sY8NtxxqwOYsBvx2n53T5OmT+zXFzdUTKnHmRKLRHxyay9mqZf45I41VpY3D4gvRuZZl3n07tk5lEsDb6VZSmQRY36t9w+UDBTxKXBMqzmjnraWQ//RDUKxXM/xVDZT7EskycHD4VCQfPmzZk5cybBwcGcO3eOgIAAli1bhpubGz///HNBy1lqUFfGyJzPLjw2mYG7jUk1sYXEx88dh2VkCoAF+25yOK0Gj5X2CHJIpl0Sp1oLi1v7IXgzKJTw1h6p9nrv36W66yBZ5DL0VK6LkpLuW/liIEtZUhSTY6T3R5cMnyJOTYRHl6VgxcPzYMsHsLh1ybcMq9E3f2JmPOV8ii8DBZIep3r16owfP57x48cTFRVFdPRLlND3BXKrjJGOMXsy6tOFA1LybY8WRSyhTFnkblQiq07cRYWSqIDp2P83KvvG7b4oeVOthUF6KvzvU+lzoxFQqd7zdbW7w7m/IP6hdGP3ea14ZMwOtW9ltkqGQlpfXAr/kxCpiomRmZT6qywQvAV2fZ51uXqKuO+fUL0DRIdA9C2IugXRt5+/4h7kfezidgVJjpWUYzBcUfRoJb3fPwlpScXirypT+BR4HsWKFStSsWLFgu62VKBPZYyNyQ3oYnpAukF1+vblrisqUyDM2X2NdJWgVQ0HarbtCrHHIOgv7UYKIxAZcHEt+L4ORmU8herJxfD4OljaQ9tJ2uuMzaDhcPjvOzixuOQpikojqbzg2jezb1OcvpUPnk07O/tIVU2KAScbcyZ3rY2TTQHkBNVninjdUOn8yQkzW6hYFeyqSWUAVelweE7u4xe3K8i9U4CQcv1aOxu2bcVqUpDm03ApqKVq60IRUaZ4KeN3i6JFn4oXB1V1STeywDj2nlSEvixN3cgUOcEP49h8XrI8fdqpprQwNU56r/8mVG0j3YhMrWBZF7i1D/79HF75rngELgriw+HATOlz4FSwKJ+1TcPhcGiO5MT/8FzJOw+tXXQvN7GAV38tXt/KElCRxcHajLcDCqgmc67BQzxXEjMrgxWfvdtVA0s77Qd/VQZcWC1ZJXUqocVsGVZzNw/+iWoUCmn6+eI6CD0sK4plFDnhdgGiT8WLFEyJdX12Ml2Raz/L5I/Z/15DCOhW1wUfV1vJf0ztmO43EHz7SBdy1/rw6mJp+YlFcHpp8Qld2Oz+ElKfgqs/+A3S3cbaGeq8Kn0+8WvRyaYvR+ZJ734DJd/KtuppUSVU71hcUkmUAP/E2MQ0tl8IIzaxAHxM9Z367TYPPrsD7xyAPr9Du8+hXn+o3BjKVcw6O6S2DANk5zdcEqLu8+qfqEaTT1H2UyyryIpiAdLY0w4XW/NsQwkUgIutORX8e0sL5CotMvngVGg0+65GYKRUML7jM2ti9G1IiJCifDNbfLx7QLvJ0ucdH0sWgLLGnaNwYQ2ggC6zpAoS2dFkpPR+aT08LUHZGiKvST7MKKDFR5KiH/AxWFeCtITnkdzFgSpDql4Cxaoo3nuSyPurznLvSWL+O9N36reil+GuQt49JP9GGx0W4g7Tiz/qPj0VHjxLbZMXiyI8D2i5f1oK6pEpc+RJUYyJiWHJkiVMnDhRE7hy9uxZHjzIh0NvGcBIqWBKd8m5O7vLyZTu3ihrdgKliVSEPfJ60QkoU2YQQvDd/64C0LdhZTzty0kr7hyR3l39wUSHhTvgYyktjCod1rwpOeeXFTLSYcezvIkNBkv7ICfc/KVE+BmpcGZZoYunN0fmS++1uoJDDemzUvlCGdBizL0XdVOy1ppYSnn0ygKaxOw5POLnJ1uAdw8Ye+l51L37syDGxMd5668gCTsv5UG0rAj21fPWRwVPsHEDVdrzfIwyZQqDFcULFy5Qo0YNvvvuO2bPnk1MTAwAGzZsYOLEiQUtX6mjs48Lv7zRAGdb7Zu0sVLBzwMb0NnHBcxtn/tyXJWtijKGs/9aBKfvPMHMWMmY9i9c4HPLh6ZQQM+fJWtQUjT83R+S4wpf4KLgzFIpetO8PLSfot82aqviqSWSdaW4iX3wzCIKtBirvU6tKF7dXnxpfdSBLC71in/KtKDIcYr42ff8ThErjSTLm28faPyOtOzyRslVpDh50T8xr4GVaj9FkNPklFEMVhTHjRvH0KFDuXHjBubmz5WhLl26cPCgXJoOJGXx8IR2/D2iKd/19sXSREm6Smifh7XUVVpkP0UZw1CpBN/vvAbA0BYe2g8ld5/lZMvJ+mFiAf3/lgImIq/CP2+XjKS/+SHhMez7SvrcbrLkM6YP3j2lqcen4SWjYtLxhZJlxr0lVM5USs29uRTFnRxTfG4DJcA/sVDIborYppK0vCCniKt3BJNyUsk8teJdXOTXP1GNJvF2GXRnkTFcUTx16hTvvvtuluWurq6Eh4cXiFBlASOlgmbVKtKvURXeblUNgJ/23USonyBrdQUUUoWDmHvFJ6hMqWPL+YdcDY/H2tyY91pXe74iLgyehEoJpis3zrkTGxfovwqMzeHGLtijpwWupLJ3mpQPztlXimjWF2NTaPiW9PnEosKRTV+SnjyfAm85Nut6pdGz6wbFp9SWgIhnAHMTJXUq2WBuUoBu9pmniIdsg7EXC96P0NQSanaWPl/eULB9G4JKlb+I5xdRB7Q8PAspT/PXl0yJw+CzzMzMjLi4rFNV169fx8HBoUCEKmsMa+5BOVMjgsPi2H8tQlpo5fj8Ke7q9uITTqZUkZqu4ofdkjVxZOtqlLd8IY+d2pro5CO5N+SGawPotVD6fPQnOLeygKUtIh6cgbPP8kZ2mW34FGHDYZLP8P1TcP9MwcunL6eWSP5/Tj7gFai7Te1nSsuVbUVvBc5Ig/AL0uditih6OVqz/cMAvBz1rEusLy9OEXsGFN70ep1nuTsvbyq+6eeoG5L7ibEFONfNX18V3KF8Fcn3Wa46VuYwWFHs0aMH06dPJy1N8pFRKBTcvXuXCRMm0Lt37wIXsCxQoZwpbzRzB2D+3hesihqfI3n6WUY/Vp+6y73oJByszRjWwkN7pdo/0RCne5/e0OpZBZNtY0vfRV6lehbAIqBu/7xNoVk5SvsBpETdxUFaEhx/ZtFsMTZ7fzHPVlIuv4QIKcFxURJ5VQp8MLOVcgfK5B2vQCm3adx96QGlOFBbE90aFkzidHWVllDZBa2sYbCi+MMPP/D06VMcHR1JSkqidevWeHl5YW1tzTfffFMYMpYJ3m5ZFTNjJUH3Yjh6K0paqPZTvHNE8rGSkcmBhJR05u+9CcCH7atjaZopX35ep5HaTJQsVRmpsOYNyXeqtBC0UrIomlpDh2l576fJM3eaSxsgvhhKqp1bIUXBlq/yPL+jLoxNn09bFvX0s9qfrlK9nNMOFQGXHsRS4/P/celBbLHKkWdMzKFmF+nz5Y3FI0NB+SeqkQNayiwGn+22trbs3r2brVu3Mn/+fEaPHs2OHTv477//KFeuXGHIWCZwsDZjQOMqAPy074a0sIK7ZPIXKrj2v2KUTqY0sPRICI+fpuBe0ZL+jSprr0x6Ao8uS58NTeOhVMKriyT/voRI+HtA6fAzSnoCe6ZKn9t8Znj5sRdxbQCVm0iBJGeKOBl5Rro09Q/Q7IPcyytqpp+3Fu20ZQkLZEnNUBW3CPnD54XpZ1Ux/BbNg2UBKYrqgJawoLKTSUEGyEfC7ZYtWzJq1Cg+/fRTAgOz8aeR0eLd1lUxMVJw/HY0p0Kl/JNaF30ZmWx4kpDK4v9uAzCuQw1MjDKdundPAEIqJWblaPgApuVgwGoo5yilmNn4bvHcvAxh/wzJCmdf87lFMD+o+zj1e9GmygneBDF3pFx29d/Ivb1XeymPYey958pbUVBCAlnKDNXagZkNxD+EeyeKduwXA9/ccgl80xdbNymnonghSEamTKBXref58+fr3eGHH36YZ2HKOi62FvTxd+Pvk/dYsO8my4c3htrdYP/XcHs/pMSDWQE7Z8uUCX757xbxKenUdrGhe91KWRto0uLkI3rR1k2KhF7WVfKb3f8NtP8i7/0VJuGX4NRv0ucu34ORSf77rN1DShkUHyYpb3X75r/P3BDiebm+JiOliNjcMLGA6h0geLP0gOlaBIpbespzi3UJsSiWeozNpCj2839L08/5OXcNRZ0Y26kOmNsUXL+eAfAkBEIOQo1OBdevTLGil6I4d+5cre+RkZEkJiZSvnx5QKrUYmlpiaOjo6wo5sJ7rb1Ye/o+/12P5ML9GOq61pJKQ0XdhBv/Pneql5F5RlhsEsuOhgLwaeeaKJU6Ah00gSwt8jdY5UbQY75kUTw0GxxqQd3X89dnQSOEFMAiVODdC6q2KZh+jUyg0Vuw72spVU5RKIq39kL4RSmvXqO39d+udo9niuIWaP9l3pMl68ujS9K0vIWd5EcpUzDUeU1SFIM3QecZRZfEXOOfWMDKqUcrOPunXPe5jKHX1HNISIjm9c033+Dn58eVK1eIjo4mOjqaK1eu0KBBA7766qs8CfHzzz/j4eGBubk5TZo04eRJ/aL5Vq9ejUKhoFevXnkatzioUtGSnvUki9CCfTelC7wm+bY8/SyTlfl7b5CarqKxpx1tauhIQZWW9HxasCAu/PX6P68Ksvn94k0Zo4uL6yULqrEFdPy6YPv2HwZGZlKAzP3TBdu3Lg7PezbuELC003+76h2let5RNyHiSqGIpoX6+HJtUPhKqR54OVrx70et8HK0Km5R8kfVNlIloaePina6tqDyJ2ZG46d4QfIhlikTGOyj+MUXX/DTTz9Rs+bzOp81a9Zk7ty5TJ482WAB1qxZw7hx45gyZQpnz56lXr16dOrUiYiIiBy3Cw0N5eOPPyYgIMDgMYubUW2roVDAv8GPuBoe99xP8cZuSEsuXuFkShS3Ip+y9vR9ACZ0rolC1036/mnJ2mPtAhU8Cmbg9lOgxiuQkQKrB0il5UoCKfHw77PrTKvxUL5yzu0NpZy9lEMP4PgvBdt3Zu6fkSwvSmNo9r5h25rbSD5uUDQPmA9KViCLuYkRNZysMTcp5WUEjU0l9yMouujn5DjJig0FF8iixsZFmiFDPJ/lkCn1GKwohoWFkZ6enmV5RkYGjx4ZnlZizpw5jBgxgmHDhuHt7c2iRYuwtLTkjz/+yHabjIwMBg0axLRp06haNed8XikpKcTFxWm9ihsvR2u6+Eilon7ef0u6+Nq4Ssl2bx8oXuFkShRz/r1OhkoQWNsJf/dsLE4FUa81M0ol9P4NHL0la8fqgZCaWDB954f/vpfK7VXwlCKECwN1Ld7gTZLTf2Fx5JlLj29fyT/UUDSBcEWQJqeEBbLcf5LIhPUXuP+kBByT+UWdDil4sxQBX9jcPyW5bZR3l0oUFjTqKi3y9HOZwWBFsX379rz77rucPfu8RuWZM2d47733DI5+Tk1N5cyZM1rbKZVKAgMDOXYs+6eR6dOn4+joyFtvvZXrGDNmzMDW1lbzqly5gC0QeeT9tl4AbLvwkFtRic9Lc12Vp59lJC7cj2H7xTAUCvikU83sG97Ro75zXjCzhgF/S9G4YUGweVTxVZEAiLwu1UIGeOU7KRddYVDJT1K6VelwOvsH1nzx+MbzOu8txuStj5qvgMJI8h+MulVwsmUmNQEin01vlxCLYkxiGmtO3yMmMa24Rck/nq0l38+ESCmnbmFTWP6JauR8imUOgxXFP/74A2dnZxo2bIiZmRlmZmY0btwYJycnlixZYlBfjx8/JiMjAycnJ63lTk5O2daNPnz4ML///ju//fabXmNMnDiR2NhYzevevZJRV9m7kg2BtR0RAn45cOuFKi07iuapUkY/VBnSBe/ieum9CMumzdollep71c+Vms7ZRMNnpD+v0FEYF/4KHtBvhVTi7vJGyaJXHPtECPjfp5LyVqNz4UdUqlPlnP5DivgtaI7OB4Q0ve9YK299WNo9vykX5vRz+EXJAmXlLE0tyhQsRibPr/9FUfu5oPMnZkZtUXx0ERKjC2cMmSLFYEXRwcGBHTt2cPXqVdatW8e6deu4cuUKO3bswNExD/nbDCA+Pp4333yT3377DXt7e722MTMzw8bGRutVUlBbFTeee8A9az/pqTIpWs5BVVII3gLzfGB5N/jnLel9no+0vJA5cvMxh248xsRIwUcdamTfMPw8pCVItZ0dvQtHGPfm0G2O9PnAtzCrWtHvk6vbpBRSRqZSdGhhU6u75A6S+Fiq1lKQxIXB+dXS55Yf5a8vtYJRmNPPLwayyBQOmunnLYVrKMhIex6kVVgWRStHKVsCQOjhwhlDpkjJc8LtGjVq0KNHD3r06EGNGjncyHLA3t4eIyOjLL6Njx49wtk5a5WFW7duERoaSvfu3TE2NsbY2Jg///yTLVu2YGxszK1bhTj9UgjUr1KBgOr2ZKgEiw7deV7SSY5+Ln6Ct8DawRD3UHt5XJi0vBAVIyEE3++8CsCgJu5Utssht57aYbxy08Itq9ZgsGTJg6zRjIW9T1ITYeck6XOLMUVTZ9jI+Hm6mhOLCnbK/fhCqVxilWZQpUn++qrVHVBIUdqx9wtEvCxoSveVjGnnMolHAFjaS4aCwqyVHHYB0pPAogLY5+2+rRfq6GdZUSwTGHxnGT58eI4vQzA1NcXf35+9e/dqlqlUKvbu3UuzZlmfdmrVqsXFixcJCgrSvHr06EHbtm0JCgoqMf6HhjD6mVVx3en7PKnSUVp4dVvx+oK97KgyYOcEQNd/8GzZzs8Kbcp11//bO+/4pur1j7+T7g0tlLZAy5TSsvcSEFkOhqigskQvKjJE7lUcP0DccBVQURAUERegV6CgVDayV0EolFVKy+iglG46c35/fJu0pStpkyZpv+/XK6+cnJyc8yRtcp7zjM9zNo5/rqfgbG+jizqXiTb6bOz6xHvR5IuTTKmY+DM5sARSYsCjMfSZZfz9l0WniWDrKOozr+kn2VUhd5PheMGIQK0EUVVwa1CYQtTWPBobCxvdB1DP1YEp/ZtTz9XB3KYYBxtbCCpoTjJ2BLsoMdV0YSkbWmoUBv+n3Llzp9gtISGBXbt28fvvv5OcnGywAbNmzWLlypV8//33REREMGXKFDIyMpg0aRIAEyZM4M033wTA0dGRNm3aFLvVqVMHNzc32rRpg729vcHHNzfdm3nRrYknOfkavrrmL4R3U2/AzbCKXywxDdEHS0YSi6GIv5G2icSI5OVrdLWJ/+rTlPpu5ZwIFaX6HMXog2LUWNnGmOYzSYoq1Boc8oF+k0uMhYsXtC0QGz+y3Dj7PL4KctKgfmuhhWgMdOlnE2QislLhdsFsegtyFH08HJk9NBAfDxM1NJkDbfo5YrNIEZsCU9cnatE6ignnICPRtMeSmBy9JrMUZcOGklpPGo2GKVOm0Lx5c4MNGDNmDLdu3WLu3LnExcXRoUMHQkNDdQ0uMTExqE155WMBTBvQggmrjvLD8Tj+EzQQhwva0VydzW1a7SRdT5knfbczgN/DbhB5K4O6znb8q28FKdbEi5B5WwhP+3Ywui3F0Pe9nlgtJnfUDTDOcf96S2g5NutfKAdTnXR/EU7+IKRLUm6AR8PK7ys3q1Cbsc9M40V0Wg8Tn1PMQUi/Ba6liLJXlthT4t7DX2hMWgjp2XmcuZ5C20YeuDoYfBqzTAJ6i1nrGQlwZS+0NExFpEIUxfQdz1pcvMA7GBLOiqii1gmWWCVG+aVSq9XMmjWrxKg/fZk2bRrR0dFkZ2dz5MgRuncvrNvZs2cPq1evLvO1q1evZuPGjZU6rqVwf8t6tG/kQVauhr/yu4iVEZtl+tlcuDaoeBtDttOTrNx8Fu+4CIhGJ3fHCuYXa6N3jboI4V5Tou97Df8NPmsHKwfAwS8guQoqAxe3wYU/hSD1QwvNMxHEpy0E9AElv+pSOf/8LJwAj8bGHdVZx19cKCgaUbZiTHSNLJYTTQS4mpjB0ysPczUxw9ymGA+1DQSNEMum6H6+HSmas2wdhQSUqZEyOTUGo4XqIiMjSxXillSMSqVi2oCWALx/sRGKdjTXrQtmtqyW4t9TlACUiUp0xBo53fvj4WhiU7Lw9XBkXA89InJaR9HU0QEQ79XdDyjLWVOJUWRN7geVWjRXbPs/0RH9zSA49FXF012Kyu5c3iXkcAB6TIH65ehImhqtVM6J7yo/OUmTLxxnEFNYbCq4CDAUbX2bsdPPspGlemkzStxHbIG8HOPuO6bg96JhZ7CthtpO2dBSYzA4Zj9rVvFickVRiI2N5Y8//mDixIlGM6y28WCgN4E+bpyPg6teXWl654D40a+sxpqk8ux+X0jOlMfQj0UEwEikZeXy5e7LALw68D79RpPp6hOrwVFU28DQBaK7GRXFG30KnMfhXwiHJT1BpGrPbhQCwtePittfb4oi+jajRBq5qCbfuRDRQHRvbahjHej7uknfWoW0elhEAVOuiYhpx3GG7yMiBJKuiG7TThOMb2PrEbDzXYjaK7rSneoaZ78W2MhSo2ncQ+hVpsdB5C5oNdR4+9alnU1cn6gloDeggsQLkBYvGq8kVonBEcWTJ08Wu50+LTohP/30U5YsWWJs+2oNarWKaQNEh+v3d9qKlXJKS/Wz71PYX1BC0XlSyRFXDu4wek1hBMdIrNwXxZ3MXJrXd2FUJz3q4JKvCcdFZQONuhnVljIJGi7e+72iy+5+xT8TV2/oNhkm/QH/Pg8P/bcw6nntsIgULmoN3z0MR1dC2A+lSxEBZCWbf6xlVaVyFKXwf6rbi2BfXrS6ktRrIRpkNHlw8S/j7DMzCZKjxbKpa2AlArUagkeKZWPPfi466rM6cPYEnzZiWXY/WzUGRxR3795tCjskwENtfGlW/yIhtzow11GNOvYfuBNtvMYASfkcWSGiMgCD34de00XKMPoghP9PpB7dGxrNSczXKByNSiLyVjpf7xUaoK8NaYWtjR7Xb9offd/24OBqFHv0Imi4GDcZfVA0uLg2EGnpsqKrbj7Q/QVxS71ZEGncANeOiGhjhSPLVEJ2J/ARo0ZwDabTBNjzsZhSEnPIsLKDK3sg9h/RdKSdI20KgobD3ggRnW3/VNX3p1Ve8GwOTnWqvj8jYmujwsfdEVsbM9StmprgUeKC5MKfotTBGKMq0+JFRBsVNOpa9f3pS5O+4jtzdR+0faL6jisxKgZHFAcMGFCqDE5qaioDBgwwhk21Fhu1iqn9W5CEO2EUpJzP/2Feo2oLp36Gra+J5X6zhZMIwjlpej8MnCfG2N2KgITzVT5caHgsfRbs4umVh/m/jeFk52mws1HpH6wy1XxnfdB+Jm2fEPf6OnDufqLe8PltMDMcBn+gh+iv6aSIDMLZE9qPEcuGSuUcWCLuO00Q3aCmQiuTE7kTstOrvj8LTjsH+rhz+K0HCfSxnElbRqNRV3FBmp0q/pbG4FpB2rlBcPU6/bKhxTDMODK2PAx2FPfs2UNOTski26ysLPbtk/8MVWV4Bz8aezqxJbdI97PEtJzbBJumiuUeL0P/N0tu41QXmhdcCFUxJRQaHsuUH8OITSneGJGbr/DyT2GEhsdWvJPqTiMZmzqNodc04ZTrgwmkiAymW0FTS8QW/bu5b54UEUWVjXi/pqRBG6jbFPKy4PL2qu/v5ilxL0f3VS9qNQSNFMvGSj9Xd32iFv+eorktKbICbVqJOUfGVoTejuLp06d19Yjnzp3TPT59+jQnT57k22+/pWHDKmiMSQCws1EzpV8LthXI5Cgxh0RzgMQ0XN4Bvz0vpEU6jochH5Ytw6LtSDz7e6Wli/I1CvM3nyt15ouW+ZvPka8pZ4uM23CrIKpprY6iFjNJEVWKBkHQtG+BVM63+r1GKxbe9gkhY2NKVKrCqKIxTi4W3PF8Pi6VHh/u5HxcqrlNMQ3a35oLWyH3btX3Z64LS6c64NNOLMvu57Ix48hYfdDbUezQoQMdO3ZEpVIxYMAAOnTooLt17tyZ999/n7lz55rS1lrD450bonFvxD+aZqhQRK2KxPhEH4S140CTKwRhh31WvlZfq4fBxkEIXSecq9Qhj0YllYgkFkUBYlOyOBqVVPZOtD/69VqZNpVZHegju2MCKaJK0/0lcX9idcUn8NuRotsZxIzq6kCrw3dpW+WlfADS4sQkHpW68ERvQeTlK8SlZpGXX0O1Zht2FiLnOelwqYrR4ez0wvGb1R1RhCLpZxPOsLZmzDwyVh/0dhSjoqKIjIxEURSOHj1KVFSU7nbjxg1SU1MNnvUsKR0HWxte7NdMJ76tsYDQc43jRhj8NBry7opRao+tqLjWztEdWhRMS6jkPNaENP1O3uVuV52yOKZGK7sDlHQWCx4bWYqoStw3VEQG796BM7+Wv+3BL0SkuuVgURtWHfh1Eo51TjpcqULjobY+sV6r6m2WkghUKuN1P984LqLgHo3Bo1GVTTOYJn3Fvex8Lh0zjozVF70dxYCAAJo0aYJGo6FLly4EBATobr6+vtjYWMgPeQ3hqa7+HHHsLR5c2QtZKeY1qCaREAE/Pi5m7gb0EdIu+k42qWL62dtNvw7GcrfTCW1bSJStqugru2MJqG0KO5ePfF32/0BavGiQAujzavXYBqK+LfBRsVyV+mYLbmSpNWjH3l0MhZwqTKCJNnM9s38PUaN752rVJjXVVMw4MlZf9JLHCQkJ4aGHHsLOzo6QkPKjW8OHW9CPuhXjZG/DoL73c3mnHy3UN9Fc+At1+9HmNsv6SboCa0bC3SSR3nlmLdg56f/6+4aKEVhJVyDutJCnMYBuTT3x9XAsM/2sAnw8HOnW1LP0HWSnC6kVsJx0rDEwVHbHnHQcB7s/hPhwIe+jnUBRlCPLxIzqRt2q/wQdNByOfi0UE/JzKzcFRje6TzaymA2/jlC3iXCwLm2r/LxkXX2iGdLOIDIxfh3EtKar+6HD0+axw1LRN6VsxjptvRzFkSNHEhcXh7e3NyNHjixzO5VKRX6+ZbRz1wTG9Qjgl93dacEG4o78hp90FKtGyg1YM0JMPfAOgrG/gYObYftwcBWpxIgQkX420FG0UauY1KsJH24tKbGjTbzOGxaEjbqMmr3rxwrTSHUaG2a7paOV3bF0nOoKncLjq+DwspKOYlYqHCuYC91nZvXPqPbvCc71xFzfq/sKu/X1RVEsupEFoEk9F36Z3IMm9UwgXm4pqFTCOdy/WKSfK+Mo5ufC9eNi2ZyNb03uL3AU90lHsSgRm+GPf1ewkUpkV8wYGNAr9azRaPD29tYtl3WTTqJxcXWwxbWDSHV63tyDJjvTzBZZMRmJ8MNISI4Bz2YwfqPQxqsMVUw/H716BwBHu+JfPx8PR5aN68TQNr6lvUxg7bI4NQWtVM6FP4UoflFOfAfZKaK+776Hqt82tY2IzkLl0s8p14WTqbYVkjsWiKuDLT2be+HqYPDMCOtCl37eVjltzLgzYhypowfUN+M4WKmnWJz8PNj2f7BunCiBqncfIlRgmXXaBusoSqqXhwcN5aZSD0eyOf33RnObY53cTYYfHhPdyu6NYMKmqs0dbTkE7JyF06mdXqEnp68nsyMiHrUKQqb14ZfJPfjsqQ78MrkH+2cPKN9JhCJC29JRNCvegdDsAdGscuybwvV52XDoK7Hc+xVRM2gOtHWdEVsM75bUpp29g4wzFcQExKVksSD0PHHlKAjUCHzaick4eXdFraKhaPUTG/cw3/+i7vi2kBIjUum1mbQ4WDNcNLsB9JwGUw5adJ12pS7Hdu7cyc6dO0lISECj0RR7btWqVUYxTCLwcLHnku+D+MWt4/bx31AGPo2qulNZ1kxOBvw8WtQTutQXTmJV9ezsnUWt4tnfRfq5YWe9X7pkxyUARnZoyH0N3MAQfzUvp0gaqQbVJ1or3V8SncVh30P/N8QM53/WitIG94bQ9knz2dakLzh4QEYCXDtq2IXFTctOOwMkpmezbE8kj7T1xcfDMp1Zo6BNP+/7RKSfDR2DZ+76RC0OruJ38toREVWs28S89piLqwfgt0miDtveDUZ+WShpZcF12gZfYsyfP5/Bgwezc+dOEhMTuXPnTrGbxPi07CdqOjpnHWb/BT2mdkgEuVmw9hnx4+ToAeM3QL0Wxtm3Lv28Ee65WCqLkzF32HU+ARu1iukPtjT8mLGnRGTByRPqtzL89RLj0nKwmISSlQK7PoDT62Hvx+K5Hi/r30lvCmztoVVB2jui/AbEEshGFstCm36+tF3Uv+qLohSZyGIBGYgmBenn2iiToyhw4HP4fphwAr2D4IU9hU6ilsqORzUxBkcUly9fzurVqxk/frwp7JGUgkervmTY1qVO3h12/7WR+wNfNrdJlk9+Lvz2nBifZucCY/8HPm2Nt/8Wg8DeFVKvC52yxt0qfMnigmjiqI4NaVqZInydLE7P6m+QkJRErRZX/Hei4PCXRZ5QgWt9s5mlo/UwOL1W1CmWN3GoKIoipXEsjQbBooYt8aJIP7fTs6kx6YqIKNvYW8bfskkfERm9ul/8n9WW37CsFDEiVlsv3G4MPLpYZCCsBIMjijk5OfTqJdNe1YraBlXgwwA0ubWLVfuvsOnUDQ5F3i5/1Ftt4d5B6nm5sPFluPCHmKTy9C/QuKtxj2nnKCa1gF7i2yeik/j74i1s1SqmD6hENBFqltB2TeBcSKFWYjEU+P1Fs4/dosWD4iIp5Vqh81cRSVfEic3GQUQ9JOZHm34Gw4T+tdFEv06WUWvauDuo7YR4dNIVc1tTPcSfhRUPCCdRbQePfAqPfW1VTiJUwlH817/+xc8/l/bjKDElzu1GAjDY5jjvbTnLK2tP8fTKw/RZsIvQ8Fqcji5tkPoCfzizXhRPj/4emvUzzbG16edzGytMPy/eLqKJT3RuhL+Xs+HH0mgKf/hrkn6itVLu2K0CzDx2CzsnaDlILOubftY6lD5tK6e/WE3UcbZjTJfG1HG2XBuNitZRjNwpmvP0IUabgTBzfaIWe2doVHDBXhvSz/+shZUPQlKkkDN77i/o+i+rjKQanHrOyspixYoV7Nixg3bt2mFnV/yLumjRIqMZJynkr7ut6KU44aO6QwdVJCcVEZWKS8liyo9hFcuq1ES0g9TvPVnnFsgIdXuhsE7LFDQfIBoG0mLh2uEyHbijUUnsv5yInY2KqQ9UskbyVgRkJYsIkY9h2o0SE2DI2C1zakO2HiYuZM6FwIPzKj5JWUnauVFdZxY8YXkzqE2Gd2uo31r8DlzYqp8WoSVeWDa9XziwUfug87PmtsY05GWLi8TjBY29zQfAqG/Axcu8dlUBgyOKp0+fpkOHDqjVasLDwzl58qTudurUKROYKMnXKLzz52V2azoAMMTmuO45rYs0f/O52pWG1ieic26TaSM6tg6FenXlpIQWb78IwJNdGtPYsxLRRCisT2zcFWxquHacNWAFY7cAuG+IqFFLihSjKyvCShzFrNx8LsankZVbi7R7tVHFs3qkn9Nvwe3LYlmP+ulqo2hDSyU0aC2e5BhYNbTASVRBvzfEYAcrdhKhEo7i7t27y7zt2rXLFDbWeo5GJRGbkkVovgjbD1EfpaiDpACxKVkcjUoyj4HmoMKIDtUzSF2Xfi7dKT0YmcihK7ext1FXPpoIRWQuLCg6UJvRd5yWGcduAWLykHYyS0XpZ01+4XhIC+94vpyQzuDFf3M5oRIi1NaKLv28C+5WoDByrSCa6B0kJglZCo26ivrX9PhCR7amcGkHfN1XyEs51RUO4gNvWkznclWQgttWQEKaEJXdo+lAtmJLU3U8L9pspof6HGoKa+P+b+MZ5m0KZ+3RGE5dS+ZujmFX2/kahUORt62jUcZSIjrN+oNjHdFdGH2g2FOKorCkoDbxqW6NaVjHgJnSxXckhbYtjYBeQgy3xCQFLSqhpWgJab/WWvHtCqa0JF6CnHRR3lDvPtPbJTGM+veJSTmaPCGkXh46WRwLqU/UYudYGOGM+tu8tlSW0pon93wMPz0hHHi/jvDi39ByoLktNRoG57Aee+yxUgWfVSoVjo6OtGjRgmeeeYZWraTOm7HwdhMda/erT6MUnJjetFsLwE3Fk/m5E/hL043IWxlE3srQvU6lgqZeLrT2dSfQx03c+7rRsI5Tib9haHgs8zefI7bIpANfD0fmDQuyzNpHS4no2NiJOrCTP4j0c9O+uqcOXL7N0atJ2Nuqebl/FaKJd66KOki1HTTsUnWbJVVHbQNDFxTUyKooXgJhGWO3dLR6CFQ2EB8OtyPBq3np22nTzr7tLcNuSUmCR4q/49kN0KkciTpLHvXZ5H6Rer66D7o+b25rDONciCh5KprNsnGA/Gyx3OU58b23dTCPfSbC4Iiih4cHu3btIiwsDJVKhUql4uTJk+zatYu8vDzWrVtH+/btOXDgQMU7k+hFt6aePOV6imV2S3Agt9hzPiSxzG4JTzqf5L9PtGPy/U3p06IeXi72KApcSczgjzOxfLr9Iv9ac5w+C3bTbv42Ri8/xLxN4fxyNIZleyKZ8mNYMScRChtlLLKrWhfRKYtqjOho088RIWKGJyKauHiHqE18ppt/1aZHaH/0/TqIzkGJZRA03KLHbulw9ixsqCkvqmgFE1lqPcEFvzVX9kBmGaVGORmFJQSWFlGEwv9FrZ6itaBtnry35EnrJHZ7Uegj1jAnESoRUfTx8eGZZ55h6dKlqAtmR2o0Gl555RXc3NxYu3YtL730ErNnz2b//v1GN7g2YoOGeXZrILdk06JaBRoF3nX4AadOxeshEtKyOB+bxvm4VCJi04iITeVyQjppWXkcvZrE0avl1zQqiNjI/M3nGBTkg43agtr6dRGd0q6qqzmi06QvOHtB5m24+jc0H8DflxI5EX0HB1s1L/cvI4KjL7q0swWkMSXFseCxW8VoPVw4FxEh0Gdm6dtYSSOLFnubWlg55dVczH+OOy3+lqV1Dt84IdLT7g2FLIul0bAz2DpBxi24dV50dFs6+jRPnt8CQz+yvO++ETDYUfz22285cOCAzkkEUKvVTJ8+nV69evHhhx8ybdo07r/fjJIQNY3ogzjdjSuzHEqtQjx/jxSHt5sj3m6O9L2vcEpETp6GyFvpRMSmcj4ujUORiZy5UfZYqKKNMj2bW1jnVlnzQt39hJNYXREdG1txIj7xHYT/jtLsARYVdDqP7xGAt3sVxW5lI4tlox27ZckEPgp//Fs4ESnXwaNR8efzcyHujFi28EYWgDYNPbj4gQmlryyZNqOEo3h2Q+mOYtH6REvU7LN1EHWKUXtFVNEaHEVDmict/begEhh8SZaXl8f58+dLrD9//jz5+aJ5wtHRsdQ6RkklMWLjhr2tmta+7ozq1Ii3Hm7Nv+5vpteutQ01FsXuD8R98CiYuAUe/1bczzxT/Wk/Xfp5M3vP3eCfa8k42ql5sV8Vo4npCQXdgSrw715lMyW1FLcGhWnI0hohEiIgL0vogtZtWr22SQwjaKS4j/obMhJLPm/J9YlatM6UtTS0WErzpJkw2FEcP348zz//PIsXL2b//v3s37+fxYsX8/zzzzNhwgQA9u7dS3BwsNGNrbWYsHFD2yhTEfVdLazu4toxMfdUZQMPvG3+QeoBvcHFG7KS2RP6GwATezahvlsVPzftj76lyVxIrA9d93MpMjm6tHN7McPawrmckMYjn+/jckKauU2pfjybivIARSNkuYqSnwfXjoplS6xP1NKkoOkvcjecXi+6h805xagiLKV50kwYnHpevHgxDRo0YOHChcTHC++5QYMGvPrqq8yePRuAwYMHM3ToUONaWpvRNm6kxlJmjYSzV6Vq2Lo19cTXw5G4lKzyqi/47kAUbRt54OZoISOzdr0r7js8DfWq0FFsLNQ2EDQCjq2kTfJOnO0DeaGvftHacpGyOBJj0fpR+OvNgnrKBHD1LnxO5yhaftoZICtXw9mbqWTllj86s8YSPEr8zc5uKN45HB8uJI4cPCx7VnfqTUAFOWnw+2Sxzt1P1J1bShNYUQJ6CRm0rOQyNlAJ+2toHbnBl442Nja8/fbbxMbGkpycTHJyMrGxsbz11lvY2IhIjr+/P40aNapgTxK90TZuAGUWKuZkFNYYGYCNWsW8YUGl7ln72FatYntEAiO/PEDkLQsQuL2yV6Qs1HbQb7a5rdGhFAjiDlYf57kefngZIwqrdRQtOY0ksQ7q+Bc0qihw/o/iz8mOZ+sieKS4jz4AaUXSndr6xMbdLLep4lwI/DaJEkGP1FjRVXxOz7nk1UlaLOTllPGkhclhmYAq5Rjc3d1xd3c3li2S8ihPisM7SNQX/fQEJEUZvOuhbXxZNq5TCQkXHw9Hlo/rxK8v9cTH3ZHIWxmMWHqAbWfjqvJOqoaiwK73xHKXSeLkZyH8ldaUOKUu7qpMXmoYXfUdZqWKCAHU2CtVSTXTepi4LyqTk5sF8efEshU0skgQv3uNuor0c9FSAl19ooWmncvtHi5YF/qGZaWhFQU2TYO8TPBsDm73yLJZmhyWCajU0NjffvuN9evXExMTQ05OcS87LCzM4P19+eWX/Pe//yUuLo727dvzxRdf0K1b6fMpV65cyZo1awgPFyfQzp078+GHH5a5fY2iLCmOnAz47mGIPwM/Pg7PbwOXegbtemgbXwYF+XA0KomEtCy83Rzp1tRTJ4mzeXofpv4UxtGrSbzwwwlmDGjBzIH3oa5uyZyLf8H1Y0Je4f7/VO+xy0GjUViy8zKj87vznG0orpdDoP2wqu302lFxIqjbpALNSIlET1qPgJ3vio7Tu3dE3Wv8WdDkivIVS5RTkZRO8GPit/DsBug2WTg0uo5nC81AVNg9rFhe9/DxVXBlN9g6wjPrwLOZ5cthGRmDI4qff/45kyZNokGDBpw8eZJu3brh5eXFlStXeOghw+UK1q1bx6xZs5g3bx5hYWG0b9+eIUOGkJCQUOr2e/bs4emnn2b37t0cOnSIxo0bM3jwYG7cuGHwsa0SrRRH0cYNR3cY+yt4+ENSJPw8WjiPBmKjVtGzuRcjOjSkZ3OvYrqJ9d0c+Glyd57t1QSAz3dd5vnvj5FyN7eMvZkAjQZ2vS+Wu78gOjkthNCzcZyPS2OXTR+x4sKfkHu3ajuN0aadZTRRYiTqtRAZCE0eXAgV64qmna1EraJxXWe+fKYTjevWYgH6oBHiPvqgSNveuQrpcQUTnCw0Mmxt3cNJV2DbHLE88B2o17L0c3ANx2BH8auvvmLFihV88cUX2Nvb8/rrr7N9+3ZmzJhBSkqKwQYsWrSIyZMnM2nSJIKCgli+fDnOzs6sWrWq1O1/+uknXn75ZTp06EBgYCDffPMNGo2GnTt3GnzsGoW7L4z7n4gQ3DgBv07STQkxFnY2at4ZHsyi0e1xsFWz+8ItRizdz4W4auo8PLdRRE0d3KH3zOo5ph7kaxQWF+gmdu49WERlctLh8o6q7Ti6II0kG1kkxuTe9PPNU+LeShpZADyc7XiknS8ezhbSXGcOPBpB4x6AIrqftdFEv45gV8m58qbGmrqHNfmwcSrkZkBAHzF5pZZisKMYExNDr14iwuHk5ERamnASxo8fzy+//GLQvnJycjhx4gQDBxYOz1ar1QwcOJBDhw7ptY/MzExyc3Px9PQs9fns7GxSU1OL3Wos9e+Dp9eJEPmlv2DLTJOMSBrVqRH/m9KLhnWcuHo7k8e+OsCW0xWIkVaV/DzY/aFY7jlVjCWzEP44E8ulhHTcHW157v5mhVf64b9Xfqd52cLhBxlRlBgXrUxO5E7ITrfKRpZbadl8s+8Kt9KyzW2KeSlooOPEajj1k1hubMFlWLrRq+VErqtr9GpFHF4msjr2rjDyS6uQjTIVBr9zHx8fkpLE6Dd/f38OHxZXMVFRUSgGOiWJiYnk5+fToEHxq4cGDRoQF6dfw8Ts2bPx8/Mr5mwW5aOPPsLDw0N3a9y4htfg+HeHJ1aBSg0nf4A9H5vkMG0aerB5eh/6tKhHZk4+034+yUdbI8jLN5Fcxel1cPsSOHlCj5dNc4xKkK9R+KxgpvPk+5vh4WRXKL59MRRyMiu34xthYoaoS30xtksiMRYNgoWodl6WiETdKhigYEWOYnxqFu//EUF8qgUOAqhOtJHDWxFwdZ9YPvWzZXYOg34KHj1eNn8699YFUcsLMPj9sqeA1RIMdhQHDBhASIj4J5w0aRKvvvoqgwYNYsyYMTz22GNGN7A8Pv74Y9auXcuGDRtwdCxdOPrNN98kJSVFd7t27Vq12mgWAh+BRz4Vy3s/huPfmeQwni72rJ7UlRf7Cb3Ar/de4dnvjnEnoywZgUqSlyPeB4g5tY6W02m/+Z+bRN7KoI6zHc/2biJW+nWCOgGQmykiu5Uh+oC49+9pNXVjEitBpSrs0NwxXzRMOXkW11WUWD7nQmDzKyXX371juTIzULaCh02BnNjhZcUlf6qb/DzY8KK4UG/+YOljEmsZBnc9r1ixAo1GRI2mTp2Kl5cXBw8eZPjw4bz4omE5/Hr16mFjY6MT7tYSHx+Pj49Pua/95JNP+Pjjj9mxYwft2rUrczsHBwccHCxsqkh10OU50V3293/hj1mi5iPwYaMfxtZGzZsPtaZtQw9e+/U0+y8n8ugX+/l6fGfaNPQwzkHCvofkGPEeuk42zj6NQF6+hs92XgJENFEnRq5SiZTQgSUi/RxciQsorcyFJaRgJDUPx4IpPxkFv713k2BJG8sVPJYUp0KZGZWQmQl8xPzRudIoTcHDuzWsGiJGlq59Gp79wzy1lvsXCzFzRw8YsVReqFOJiKJarcbWttC/fOqpp/j888+ZPn069vb2Bu3L3t6ezp07F2tE0Tam9OxZdgH/woULee+99wgNDaVLly6GvoXawwNvQ8dxImLw23OFo51MwKPt/Ng4tTcBXs7cSL7L48sO8nvY9arvOCcT/v5ELPd9Dewtp8tx46mbRCVm4Oliz8SCbnAd2vTzpW2iDswQNPlFxnDJRhaJkTkXAjvnl1xvyYLHkuIYIjNjqdzbPexSD55ZX9iQueEloXRRncSehr0FqfGH/itlyQqoVHVmVlYWR48eZcuWLYSEhBS7GcqsWbNYuXIl33//PREREUyZMoWMjAwmTZoEwIQJE3jzzTd12y9YsIA5c+awatUqmjRpQlxcHHFxcaSnW8DEEEtDpYJHl0DLwZB3F34eA4mXTHa4Vj5uhEztwwOt6pOdp2HW+n94J+Qsufka8jUKhyJvs+nUDQ5F3iZfo2c967FvhOSDhz90mmgy2w0lN1/DF7vEZ/lC32a4OtwTnPdpJ/S28rJEraIhxIdDdirYu4FPWyNZLJFgnYLHpeDmaMvA1t64OVZKCtj6sTaZGX3xag5jfhISP+c2wp4Pq+/YedmwcYrQFA18FNqNrr5jWzgGf8tCQ0OZMGECiYmJJZ5TqVTk5xv2AzNmzBhu3brF3LlziYuLo0OHDoSGhuoaXGJiYlAX6TZatmwZOTk5PPHEE8X2M2/ePN555x1D307Nx8YOnlwNqx8V3Y0/jIJ/bQe38lP7lcXD2Y5vJ3ZlyY6LfL7rMqsPXmX/5URS7+aSUKRD0dfDkXnDghjaxrfsnWWlijQAQP/ZYGtYxNqUbAi7QfTtTLxc7JnQM6DkBiqVmMe67xORfm77RMltykIri+Pf3TLTRhLrxRoFj0shwMuFbyZ2NbcZ5sOaZGYMpUlvGPYZbHpZlE55tYD2T5n+uHsXiIt0Zy8RYJEpZx0GRxSnT5/Ok08+SWxsLBqNptjNUCdRy7Rp04iOjiY7O5sjR47QvXt33XN79uxh9erVusdXr15FUZQSN+kkloO9ixDk9mwGKTFi1F+W6WSC1GoVswa3YsX4zjjaqrmckF7MSQSIS8liyo9hhIbHlr2jw8tE7ZRXS2hXDT8UepKTp+HzgmjilP7NcbYv43pLm36+vN2wz1sntC3TzhIjU0MiUbn5Gm6nZ5NrKpUFS6dCmRmV5cjMVIaOY6HPq2I5ZHrhxbOpuH68MCjx6BJwrW/a41kZBjuK8fHxzJo1q4SkjcTCcaknBLld6kPcGVg3rpwh58bhwdYNcC0jNaRNfM3ffK70NHRmEhxaKpYfeBNsLCfF9L+w61y/c5d6rg6M7V5KNFGLdxDUuw/yc8SkFn1QlCJC21b6Iy+xXGpIJOpCXBqd399RfWL/lka5MjMFj4d+bN0ZiQFzheZnfg6sfUZMSTEFOZmiy1nRQNvRspmrFAx2FJ944gn27NljAlMkJsezmSgWtnMRs143vWzSYuGjUUkkppftjCpAbEoWG05eR3Ovs3jgM1Gn16AtBFWv7FJ5ZOfls3TXZQBe7t8cJ/tyfoi16WfQX3z7diRkJICNvVVNypBYCTU9ElWbKEtmxt1PrLd2h0ethse+Fvqed5NEjf3dZOMfZ9d7otPazRceXmj8/dcADA7TLF26lCeffJJ9+/bRtm1b7OyKj1CaMWOG0YyTmICGnWDMGvGlO/Or+HIMfs8kh0pI008M9z+/nua9LRF08q9DlyaedK+fR+cjX4tT2YC3K1TEz9coHI1KIiEtC283R7o19Sw2p9qYrD9+nRvJd2ng7sAz3f0rfkHwY0IDMnKX0Ddzqlv+9tq0c8POYFe6NqhEUmm0kaj1ExDOYtELtBoSiapNlCYzE9Cr5vz97J3h6bWwcgAkXoRfJ8LY30TtvTGI2geHvxLLw7+o+Pe5lmKwo/jLL7+wbds2HB0d2bNnD6oiBZ8qlUo6itZAi4EwfClsfAkOfi6uQHtMMfphvN30c3TsbFSk3M1l94Vb7L5wi3m239PF9i7nbVux9nxjOmXdpHNAXfw8HIv9vwGEhscyf/M5YlMKnVK9GmUqQVZuPl8WRBOnPtACRzs9foy9A0UKOuEcnP9DyBWVh66RRdYnSkyENhIVOrt4Y4u7n3ASrT0SVdvQyszUVNx8hLO4aihc2QN/vgaPLq56s0l2msiqAXSaAC0HVdnUmorBjuLbb7/N/PnzeeONN4p1I0usjA5PQ9pNMaYo9E1xJRo0wqhXpt2aeuLr4UhcSlapYhwqwMfDkd3/6c+FuDRORN/hSuR5xkYJXc13Mx/n4KFoVh+KBsDH3ZHOAXXpFFCXLgF1uXYnk+k/nyyxb22jzLJxnYzqLK47do241Cx8PRwZ09WAUZDBo4SjGP57xY6iNqIY0LvyhkokFVHTI1GSmoVvO3jiW/jlaTjxHdRrCT2nVm2f2+aIQQ4e/jD4A+PYWUNRKQYOaPb09OTYsWM0b26d82dTU1Px8PAgJSUFd3fLGQVnFhRFXJ0dWwlqW3CsA5lFZI/c/ao8qSE0PJYpP4aJwxVZr70WLOHMbZoGJ38gu1FvtnX9hhPRdwiLucPZm6n6ay9S6ITunz2gSmlobVr7RnIm72+JIPluLu+PbMO4HuU0sdxL4mVY2hlUNvDaZXD2LH271FhYFCjmdM+OtqhRhRKJJZGvUcjMycPZ3tZkZSYSC+TgUtj2NqCCp3+BVg9Vbj+Xd8CPj4vliZuhaV+jmWgtGOILGRwSnDhxIuvWrau0cRILQqWChxZAwy6gySvuJIJRJjUMbePLsnGd8PEonob28XAs6STejhQD7QGHIe8wrL0f7wwPJmRaH868M5i1L/TgtSGteDDQGxeH8iMf2kaZD/44x54LCUQlZpCTZ1jjTmh4LH0W7OLplYf5z6+nSb6bi1oFHk4G1sfUayGEs5V8iCjns9RGExu0kU6iRFIONmoVbo520kmsbfScWjB7WYHfnhcKHoZy9w5smi6Wu79UK51EQzE49Zyfn8/ChQv566+/aNeuXYlmlkWLFhnNOEk1kXqjjCeMMzN0aBtfBgX5VNxwsvtD4Uy1HAKNuxV7ytnelh7NvOjRzAuATSdv8Mq6UxUee9WBq6w6cBUAtQr86jgR4OWMv6dLwb24BXg5F85qpjASem8MU6PAjF9OYmejMiytHTxK/KiF/172kHkpiyOR6EVUYgZzN4Xz7og2NK3nYm5zJNWFSgUPfwJJUUK54+cxMHmXYQMktr4hyq48m8OD80xnaw3CYEfxzJkzdOzYEYDw8PBiz93baCCxAqIPQlo5otdGmtRgo1bRs7lX2RvEn4Xw/4nlAf9X4f683fVrlOkcUJf0rDyikzLIytVw/c5drt+5ywFul9jW08W+wHF0Ytf5hFLrKrXM33yOQUE++kc0gh8T83Wv7oP0W6ULukZLoW2JRB8ysvPYdymRjOw8c5siqW5s7GD09/DNILh9CX55Cp79U3RIV0TEZji9VpT3PLZcv9dIDHcUd+/ebQo7JObCUiY17PoAUCBopChcrgB9G2XWv9gTG7UKRVG4lZZNdFIm0bczibmdQUxSJtFJmcTczuR2Rg5JBbdT15LLPbY2rX00Kql857conk2FHtjNkxCxCbr+q/jzd++IhheQEUWJRCIpD6e6MHY9rHxQ/KZueBGe/L58KbWMRNg8Uyz3fqVE1kpSNpYz7kJiHixhUsP1E3DhD3GV98Bber3ERq1i3rAgpvwYVpYaHPOGBekifiqVCm93R7zdHenapGQzSVpWLjEFTuPW8DhC/ilvHq5AX51IHcGjxI/a2Y0lHcWYI+JdeDYHV2/D9iuRSCS1Dc9m8NRP8P1wUfu96z0YWEYqWVFgy0xRh+8dBP3frFZTrR2pb1PbqXBSQwH//AwZJdO1RmFXgeB3uzFQv5XeLzOoUaYC3BztCPbz4KG2vjzdTQ8hbfTXidQRPFLcX90PaXHFn9PJ4shookQikehFQC8hlA2wf5GuGbIEZ34TaWe1rUg52zpUn401ABlRrO1UOKmh4PGpn+FCqJji0v6ZCqel6M3V/XBlt/gC95tt8Mv1bpQxAH3T2t2aliFzUxZ1/KFRV7h+DM5tgu4vFj4nG1kkEr3x9XDk3RHB+HrI6UW1ng5Pi1rFfZ9CyAyoEwD+PQo1QtW28McssW2/2eDb3rz2WiEG6yhaO1JHsQzOhZQyqaGhmNTg6g1bZkHCWbHevxc8ugi8W1ftmIoi1PavHYYuzwm1fQvBYP1HfTn0Ffz1pmhYeS5UrMu9Cx81Bk0uzDgl6hklEolEoh8aDfz2rLgAt3cRt/SE4tvUaQLTjxtv/J+VY1IdRUkNJWg4zAyHiVvg8W/F/cwzYr1/D3hxLwx6D+ycRZp0eR/YPg9yMip/zMs7hJNo6wh9XzPeezECxkxrF0Obfo45BCkFskTXjwsn0c0X6japtM0SSW0hOTOHDSevk5yZY25TJJaAWg0jl0PdpuKcdK+TCJB8FS5srXbTagJ6pZ5DQvQXXB4+XM4JtVrKmxlqYwe9ZwiZl9A34PwWOLBE6AI+/F9oNdSwYylKYW1i138V1ElaFqZIa+PuJ6KJMYfg3EYhIBtTZL6zlJiSSCrk+p27vLruH7ZM70MdZ3tzmyOxBGwdRHamTKquCVxb0ctRHDlyZLHHKpWQGyn6WEt+fr5xLJNYJnUai06z83/C1tchJQZ+GQOBj4o0dR09ZyBHhEDsP2DvCn1eNa3NVaBC/cfKEDxKOIdnNwhHMfqAWC/rEyUSiaRyRB+E9LhyNjCOJnBtRK/Us0aj0d22bdtGhw4d2Lp1K8nJySQnJ/Pnn3/SqVMnQkNDTW2vxFIIfBimHoHeM0Wx8Pkt8GV3OPgF5OeW/1pNfoFuItBjCrjUM7m5FkXQCEAlmlqSrsC1Y2K9FNqWSCSSymEpmsA1EINrFGfOnMlnn33GkCFDcHd3x93dnSFDhrBo0SJmzJhhChslloq9CwyaDy/uE05ObgZs+z/4ul+BLmAZnPkVEi+Aowf0nFZ99loKbg2gSR+xHDJdfG52LlBPf2kgiUQikRTBEjSBaygGO4qRkZHUqVOnxHoPDw+uXr1qBJMkVkeDIDFCafhSoZifcBZWDRZSBZlJYhtNPkTtg3/Wwva5Yl3vV8CpjtnMNiuezcX91f3iPjcDPm8nus8lEkm5ONnb0NG/Dk72stZMUkCFmsAqoeQhS3wMxmB5nL59++Lo6MgPP/xAgwbCM4+Pj2fChAlkZWWxd+9ekxhqLKQ8jonJuA075sLJH8VjZy9o87hITReV3lGp4bEV0O5J89hpTs6FFOhW3vvVK/iBG71GdJtLJBKJRH90v61QqrCZ/G3VYVJ5nFWrVhEbG4u/vz8tWrSgRYsW+Pv7c+PGDb799ttKGy2pIbh4wYgvYdJWqN8aMm/D0RXFnUQARQO/T659ETRNvtCrLFXKu2Bd6BtiO4lEIpHoT9Bw4Qy63yNf5u4nncQqUCnBbUVR2L59O+fPnwegdevWDBw4sFj3s6UiI4rVSM5d+PQ+yE4tYwOV+ALPPFN75Aqi9sH3j1a83cQtsjNPIimD8BspPPrFfrZM70Obhh7mNkdiaWjyCyezuDYQ6ebaco7RE0N8oUqN8FOpVAwePJi+ffvi4OBgFQ6ixAzcOF6Okwi1Uq5AduZJJBKJaSlPE1hiMAannjUaDe+99x4NGzbE1dWVqKgoAObMmSNTz5LiSKeoJLIzTyKRSCRWhMGO4vvvv8/q1atZuHAh9vaFivht2rThm2++MapxEitHOkUlkZ15EolEIrEiDHYU16xZw4oVKxg7diw2NoU5//bt2+tqFiUSQDpFpaG2gaELCh7c+7kUPB76saynkUgkEolFYLCjeOPGDVq0aFFivUajITe3gokcktqFdIpKR3bmSSRVooW3K3v+058W3q7mNkUiqfEY3MwSFBTEvn37CAgIKLb+t99+o2PHjkYzTFJD0DpFobOLS+S4+wknsbY6RUHDxXB62ZknkRiMo50NTeq5mNsMiaRWYLCjOHfuXCZOnMiNGzfQaDT8/vvvXLhwgTVr1rBlyxZT2CixdqRTVDqyM08iqRTXkjL5dNsF/j24FY09nc1tjkRSozE49TxixAg2b97Mjh07cHFxYe7cuURERLB582YGDRpkChslNQGtU9T2CXFf251EiURSaVLu5rLx1E1S7spyJ4nE1FRKR/H+++9n+/btxrZFIpFIJBKJRGJBGBxRlEgkEolEIpHUDvSKKNatW1fv6StJSUlVMsjUaCcWpqaWNzFEIpFIJJZKeloqmuxM0tNSSU2Vk8EkEkPR+kD6THHWy1FcsmRJlQyyJNLS0gBo3LixmS2RSCQSSVXoucTcFkgk1k1aWhoeHuXPS1cp+riTNQiNRsPNmzdxc3Mz+Yzq1NRUGjduzLVr1yocum0pWKPNIO2ubqzRbmu0GaTd1Y012m2NNoO025woikJaWhp+fn6o1eVXIVaqmUVLVlYWOTk5xdZZ+oemVqtp1KhRtR7T3d3d4j+Xe7FGm0HaXd1Yo93WaDNIu6sba7TbGm0Gabe5qCiSqMXgZpaMjAymTZuGt7c3Li4u1K1bt9hNIpFIJBKJRFIzMNhRfP3119m1axfLli3DwcGBb775hvnz5+Pn58eaNWtMYaNEIpFIJBKJxAwYnHrevHkza9asoX///kyaNIn777+fFi1aEBAQwE8//cTYsWNNYadV4uDgwLx583BwcDC3KXpjjTaDtLu6sUa7rdFmkHZXN9ZotzXaDNJua8HgZhZXV1fOnTuHv78/jRo14vfff6dbt25ERUXRtm1b0tPTTWWrRCKRSCQSiaQaMTj13KxZM6KiogAIDAxk/fr1gIg01qlTx6jGSSQSiUQikUjMh8ERxcWLF2NjY8OMGTPYsWMHw4YNQ1EUcnNzWbRoEa+88oqpbJVIJBKJRCKRVCNV1lGMjo7mxIkTtGjRgnbt2hnLLolEIpFIJBKJmal1gtsSiUQikUgkEv0wuEZxxowZfP755yXWL126lJkzZxrDphrBl19+SZMmTXB0dKR79+4cPXrU3CaVy0cffUTXrl1xc3PD29ubkSNHcuHCBXObZRAff/wxKpXKKv4Pb9y4wbhx4/Dy8sLJyYm2bdty/Phxc5tVLvn5+cyZM4emTZvi5ORE8+bNee+99/SaFVqd/P333wwbNgw/Pz9UKhUbN24s9ryiKMydOxdfX1+cnJwYOHAgly5dMo+xRSjP7tzcXGbPnk3btm1xcXHBz8+PCRMmcPPmTfMZXEBFn3dRXnrpJVQqldnHwupjc0REBMOHD8fDwwMXFxe6du1KTExM9RtbhIrsTk9PZ9q0aTRq1AgnJyeCgoJYvny5eYwtQJ9zS1ZWFlOnTsXLywtXV1cef/xx4uPjzWSxoCK7k5KSmD59Oq1atcLJyQl/f39mzJhBSkqKGa02DQY7iv/73//o3bt3ifW9evXit99+M4pR1s66deuYNWsW8+bNIywsjPbt2zNkyBASEhLMbVqZ7N27l6lTp3L48GG2b99Obm4ugwcPJiMjw9ym6cWxY8f4+uuvraL84c6dO/Tu3Rs7Ozu2bt3KuXPn+PTTTy1esH7BggUsW7aMpUuXEhERwYIFC1i4cCFffPGFuU0rRkZGBu3bt+fLL78s9fmFCxfy+eefs3z5co4cOYKLiwtDhgwhKyurmi0tTnl2Z2ZmEhYWxpw5cwgLC+P333/nwoULDB8+3AyWFqeiz1vLhg0bOHz4MH5+ftVkWdlUZHNkZCR9+vQhMDCQPXv2cPr0aebMmYOjo2M1W1qciuyeNWsWoaGh/Pjjj0RERDBz5kymTZtGSEhINVtaiD7nlldffZXNmzfz66+/snfvXm7evMmoUaPMZjNUbPfNmze5efMmn3zyCeHh4axevZrQ0FCef/55s9ptEhQDcXBwUC5dulRi/aVLlxQHBwdDd1cj6datmzJ16lTd4/z8fMXPz0/56KOPzGiVYSQkJCiAsnfvXnObUiFpaWlKy5Ytle3btyv9+vVTXnnlFXObVC6zZ89W+vTpY24zDOaRRx5RnnvuuWLrRo0apYwdO9ZMFlUMoGzYsEH3WKPRKD4+Psp///tf3brk5GTFwcFB+eWXX8xgYenca3dpHD16VAGU6Ojo6jFKD8qy+/r160rDhg2V8PBwJSAgQFm8eHG121YWpdk8ZswYZdy4ceYxSE9Kszs4OFh59913i63r1KmT8vbbb1ejZeVz77klOTlZsbOzU3799VfdNhEREQqgHDp0yFxmlkCfc+L69esVe3t7JTc3txotMz0GRxRbtGhBaGhoifVbt26lWbNmVXBZawY5OTmcOHGCgQMH6tap1WoGDhzIoUOHzGiZYWjD556enma2pGKmTp3KI488Uuwzt2RCQkLo0qULTz75JN7e3nTs2JGVK1ea26wK6dWrFzt37uTixYsA/PPPP+zfv5+HHnrIzJbpT1RUFHFxccX+Vzw8POjevbtVfT9BfEdVKpXFy5JpNBrGjx/Pa6+9RnBwsLnNqRCNRsMff/zBfffdx5AhQ/D29qZ79+7lptQthV69ehESEsKNGzdQFIXdu3dz8eJFBg8ebG7TdNx7bjlx4gS5ubnFvpOBgYH4+/tb1HdSn3NiSkoK7u7u2NoaPMvEojH43cyaNYtp06Zx69YtBgwYAMDOnTv59NNPzV53YgkkJiaSn59PgwYNiq1v0KAB58+fN5NVhqHRaJg5cya9e/emTZs25janXNauXUtYWBjHjh0ztyl6c+XKFZYtW8asWbN46623OHbsGDNmzMDe3p6JEyea27wyeeONN0hNTSUwMBAbGxvy8/P54IMPrGoaU1xcHECp30/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b/_variables.yml @@ -392,20 +392,20 @@ sections: format_es: "ejercicio" title_en: "Tucker decomposition on real data" title_es: "Descomposición de Tucker con datos reales" - summary_en: "PCA generalized to every axis, on a real tensor of New York taxi trips." - summary_es: "PCA generalizado a todos los ejes, sobre un tensor real de viajes en taxi de Nueva York." + summary_en: "Build a real tensor from New York taxi trips, compress each mode with HOSVD, and explore the trade-off between size, reconstruction error, and interpretable structure." + summary_es: "Construir un tensor real de viajes en taxi, comprimir cada modo con HOSVD y explorar el compromiso entre tamaño, error e interpretación." objectives_en: - - "Build a genuine order-3 tensor out of a flat table of real trips." - - "Compute a Tucker decomposition by HOSVD, using only unfolding, SVD and einsum." - - "Contract three axes at once with a single `einsum` string." - - "Measure reconstruction error against compression ratio." - - "Read a factor matrix and recognise a real pattern the decomposition found by itself." + - "Build and interpret a genuine order-3 tensor from a flat table of real trips." + - "Unfold the tensor along each mode and explain what information each matricization exposes." + - "Compute Tucker/HOSVD using only unfolding, SVD, and `einsum`." + - "Change the rank of each mode independently and measure reconstruction error versus compression." + - "Read the temporal factor matrix and connect a learned component back to real hourly taxi activity." objectives_es: - - "Construir un tensor real de orden 3 a partir de una tabla plana de viajes reales." - - "Calcular una descomposición de Tucker mediante HOSVD usando únicamente unfolding, SVD y einsum." - - "Contraer tres ejes simultáneamente con una sola expresión `einsum`." - - "Comparar el error de reconstrucción con la razón de compresión." - - "Interpretar una matriz de factores y reconocer un patrón real encontrado automáticamente por la descomposición." + - "Construir e interpretar un tensor real de orden 3 a partir de una tabla plana de viajes reales." + - "Desplegar el tensor a lo largo de cada modo y explicar qué información expone cada matricización." + - "Calcular Tucker/HOSVD usando únicamente unfolding, SVD y `einsum`." + - "Cambiar el rango de cada modo de manera independiente y medir el error de reconstrucción frente a la compresión." + - "Interpretar la matriz de factores temporales y relacionar un componente aprendido con la actividad horaria real de los taxis." s11: n: "11" slug: "wrap-up-and-take-homes" diff --git a/docs/notebooks/10-tucker-decomposition.ipynb b/docs/notebooks/10-tucker-decomposition.ipynb index d06fd49..f193d78 100644 --- a/docs/notebooks/10-tucker-decomposition.ipynb +++ b/docs/notebooks/10-tucker-decomposition.ipynb @@ -10,19 +10,19 @@ "\n", "*Part IV · exercise · 15 min*\n", "\n", - "> 🇪🇸 **Descomposición de Tucker con datos reales** — PCA generalizado a todos los ejes, sobre un tensor real de viajes en taxi de Nueva York.\n", + "> 🇪🇸 **Descomposición de Tucker con datos reales** — Construir un tensor real de viajes en taxi, comprimir cada modo con HOSVD y explorar el compromiso entre tamaño, error e interpretación.\n", "\n", - "PCA generalized to every axis, on a real tensor of New York taxi trips.\n", + "Build a real tensor from New York taxi trips, compress each mode with HOSVD, and explore the trade-off between size, reconstruction error, and interpretable structure.\n", "\n", "## What you will be able to do\n", "\n", - "- Build a genuine order-3 tensor out of a flat table of real trips.\n", - "- Compute a Tucker decomposition by HOSVD, using only unfolding, SVD and einsum.\n", - "- Contract three axes at once with a single `einsum` string.\n", - "- Measure reconstruction error against compression ratio.\n", - "- Read a factor matrix and recognise a real pattern the decomposition found by itself." + "- Build and interpret a genuine order-3 tensor from a flat table of real trips.\n", + "- Unfold the tensor along each mode and explain what information each matricization exposes.\n", + "- Compute Tucker/HOSVD using only unfolding, SVD, and `einsum`.\n", + "- Change the rank of each mode independently and measure reconstruction error versus compression.\n", + "- Read the temporal factor matrix and connect a learned component back to real hourly taxi activity." ], - "id": "s10-00" + "id": "wHEhVIsEk_Yv" }, { "cell_type": "markdown", @@ -34,7 +34,7 @@ "\n", "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." ], - "id": "s10-01" + "id": "_XnbMyuJk_Yy" }, { "cell_type": "code", @@ -45,7 +45,15 @@ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", - "from skimage import data\n", + "import ipywidgets as widgets\n", + "from IPython.display import display\n", + "\n", + "# Enable ipywidgets in Google Colab when available.\n", + "try:\n", + " from google.colab import output\n", + " output.enable_custom_widget_manager()\n", + "except ImportError:\n", + " pass\n", "\n", "TAXIS = \"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/taxis.csv\"\n", "taxis = pd.read_csv(TAXIS)\n", @@ -53,172 +61,104 @@ "def unfold(T, axis):\n", " return np.moveaxis(T, axis, 0).reshape(T.shape[axis], -1)\n", "\n", - "print(taxis.shape) # (6433, 14) — 6,433 real NYC taxi trips" - ], - "id": "s10-02" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Rank you can see\n", - "\n", - "> 🇪🇸 Antes de generalizar a tensores, comprimamos una sola matriz: una\n", - "> imagen real. La SVD truncada de rango k conserva las k direcciones\n", - "> singulares más fuertes y descarta el resto — por Eckart–Young, es la mejor\n", - "> aproximación de rango k posible en norma de Frobenius.\n", - "\n", - "Before generalizing to tensors, let's compress a single matrix — a real\n", - "image. The rank-`k` truncated SVD keeps only the `k` strongest singular\n", - "directions and drops the rest. By the **Eckart–Young theorem**, that\n", - "truncation is the *optimal* rank-`k` approximation to the original matrix in\n", - "Frobenius norm — no other rank-`k` matrix is closer.\n", - "\n", - "We'll reconstruct a 512×512 grayscale photograph (`skimage.data.camera()`) at\n", - "`k = 1, 5, 20, 50` and full rank, and compare three things side by side: how\n", - "much storage each reconstruction needs, how much of the image's Frobenius\n", - "energy it retains, and how it actually looks." - ], - "id": "s10-03" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "img = data.camera().astype(float)\n", - "m, n = img.shape # (512, 512)\n", - "\n", - "U, s, Vt = np.linalg.svd(img, full_matrices=False)\n", - "\n", - "ks = [1, 5, 20, 50, min(m, n)]\n", - "total_energy = np.sum(s**2)\n", - "\n", - "fig, axes = plt.subplots(1, len(ks), figsize=(15, 3.5))\n", - "for ax, k in zip(axes, ks):\n", - " recon = (U[:, :k] * s[:k]) @ Vt[:k, :]\n", - " stored = k * (m + n + 1) # mk + k + nk\n", - " storage_pct = 100 * stored / (m * n)\n", - " factor = (m * n) / stored\n", - " energy_pct = 100 * np.sum(s[:k]**2) / total_energy\n", - " label = \"full rank\" if k == min(m, n) else f\"k={k}\"\n", - " ax.imshow(recon, cmap=\"gray\", vmin=0, vmax=255)\n", - " ax.set_title(f\"{label}\\n{storage_pct:.1f}% storage, {factor:.1f}x\\n{energy_pct:.1f}% energy\",\n", - " fontsize=9)\n", - " ax.axis(\"off\")\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "for k in ks:\n", - " stored = k * (m + n + 1)\n", - " print(f\"k={k:>3} storage={100*stored/(m*n):6.2f}% \"\n", - " f\"{(m*n)/stored:6.2f}x energy={100*np.sum(s[:k]**2)/total_energy:6.2f}%\")\n", - "# k= 1 storage= 0.39% 255.75x energy= 87.01%\n", - "# k= 5 storage= 1.96% 51.15x energy= 97.04%\n", - "# k= 20 storage= 7.82% 12.79x energy= 98.98%\n", - "# k= 50 storage= 19.55% 5.12x energy= 99.60%\n", - "# k=512 storage=200.20% 0.50x energy=100.00%" - ], - "id": "s10-04" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Storage, energy, and what your eyes see\n", - "\n", - "> 🇪🇸 El almacenamiento, la energía retenida y la calidad perceptual no son\n", - "> la misma curva. Con muy pocos componentes ya se retiene casi toda la\n", - "> energía, y la imagen es reconocible con una fracción minúscula del\n", - "> almacenamiento original. La misma idea — quedarse con las direcciones más\n", - "> fuertes y descartar el resto — es exactamente lo que Tucker/HOSVD hace a\n", - "> continuación, un eje del tensor a la vez.\n", - "\n", - "At `k = 1`, under 0.4% of the storage already recovers 87% of the energy —\n", - "but the picture is barely recognisable. By `k = 20`, storage is still under\n", - "8% of the original and the picture is already unmistakably the photograph,\n", - "while the energy curve hasn't yet reached its final digit. At full rank, the\n", - "factorized `U`, `s`, `Vt` together need *more* numbers than the dense image\n", - "itself (about 200% of its storage) — factorizing only pays off once you\n", - "truncate. The same idea — keep the strongest singular directions, drop the\n", - "rest — is what Tucker/HOSVD does next, one tensor axis at a time.\n", - "\n", - "**The picture is recognisable at `k = 20` — under 8% of the storage — long\n", - "before the numbers claim it should be. Energy retained and perceptual\n", - "quality are not the same curve.**" - ], - "id": "s10-05" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The theory\n", + "def hosvd_bases(T):\n", + " return [\n", + " np.linalg.svd(unfold(T, axis), full_matrices=False)[0]\n", + " for axis in range(T.ndim)\n", + " ]\n", + "\n", + "taxis[\"pickup_dt\"] = pd.to_datetime(taxis[\"pickup\"], errors=\"coerce\")\n", + "taxis[\"hour\"] = taxis[\"pickup_dt\"].dt.hour\n", + "\n", + "sub = taxis.dropna(\n", + " subset=[\"pickup_borough\", \"dropoff_borough\", \"hour\"]\n", + ").copy()\n", + "sub[\"hour\"] = sub[\"hour\"].astype(int)\n", "\n", - "> 🇪🇸 PCA comprime una **matriz**: dos ejes. La descomposición de Tucker\n", - "> generaliza PCA a un tensor de cualquier orden: una **matriz de factores por\n", - "> eje**, más un **tensor núcleo** pequeño.\n", + "pickup_names = sorted(sub[\"pickup_borough\"].unique())\n", + "dropoff_names = sorted(sub[\"dropoff_borough\"].unique())\n", "\n", - "PCA compresses a **matrix** — two axes. Real data often has more. **Tucker\n", - "decomposition** generalizes PCA to a tensor of any order: one **factor matrix\n", - "per axis**, plus a small **core tensor** describing how the factors combine.\n", + "pickup_index = {name: i for i, name in enumerate(pickup_names)}\n", + "dropoff_index = {name: i for i, name in enumerate(dropoff_names)}\n", "\n", - "The way to compute it, called **HOSVD**, uses only tools you already have:\n", + "T = np.zeros(\n", + " (len(pickup_names), len(dropoff_names), 24),\n", + " dtype=float,\n", + ")\n", "\n", - "1. **Unfold** the tensor along each axis (section 01).\n", - "2. Run **SVD** on each unfolding; keep the top components. These are the factor\n", - " matrices.\n", - "3. **Contract** the original tensor against all factor matrices to get the core\n", - " (section 06).\n", + "for (p, d, h), count in sub.groupby(\n", + " [\"pickup_borough\", \"dropoff_borough\", \"hour\"]\n", + ").size().items():\n", + " T[pickup_index[p], dropoff_index[d], int(h)] = float(count)\n", "\n", - "The related **CP decomposition** instead writes the tensor as a sum of simple\n", - "rank-1 pieces. Tucker is usually more accurate at the same size; CP is often\n", - "easier to interpret." + "print(\"taxi rows / filas:\", len(taxis))\n", + "print(\"usable trips / viajes utilizables:\", int(T.sum()))\n", + "print(\"tensor shape / forma:\", T.shape)\n", + "print(\"pickup boroughs / origen:\", pickup_names)\n", + "print(\"dropoff boroughs / destino:\", dropoff_names)" ], - "id": "s10-06" + "id": "o_qdhKfXk_Yy" }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Our real tensor\n", + "## Why this matters\n", "\n", - "From 6,433 real New York taxi trips we build a genuine order-3 tensor:\n", - "**pickup borough × dropoff borough × hour of day.**\n", + "A matrix has two axes. A tensor can have three or more, and each axis can carry a different kind of meaning.\n", "\n", - "> 🇪🇸 Un tensor real de orden 3: barrio de origen × barrio de destino × hora." - ], - "id": "s10-07" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "taxis['hour'] = pd.to_datetime(taxis['pickup']).dt.hour\n", - "sub = taxis.dropna(subset=['pickup_borough', 'dropoff_borough'])\n", - "pb = sorted(sub['pickup_borough'].unique())\n", - "db = sorted(sub['dropoff_borough'].unique())\n", + "For our taxi tensor,\n", + "\n", + "`T[pickup, dropoff, hour]`\n", "\n", - "T = np.zeros((len(pb), len(db), 24))\n", - "for (p, d, h), v in sub.groupby(['pickup_borough', 'dropoff_borough', 'hour']).size().items():\n", - " T[pb.index(p), db.index(d), h] = v\n", + "stores a real trip count. The three modes answer different questions:\n", "\n", - "print(T.shape, pb, db)" + "- **pickup mode:** which origins behave similarly?\n", + "- **dropoff mode:** which destinations behave similarly?\n", + "- **hour mode:** which times of day share similar traffic structure?\n", + "\n", + "**HOSVD** applies an SVD to each unfolding and keeps the strongest directions for each mode. Tucker then combines those directions through a small **core tensor**.\n", + "\n", + "This is better described as a multilinear extension of truncated SVD ideas across several tensor modes — not as “PCA itself becoming a tensor factorization.”\n", + "\n", + "### How to use the folded solutions / Cómo usar las soluciones plegadas\n", + "\n", + "Each exercise has a **Solution / Solución** cell that is intentionally closed. Try the `TODO` first; then open the solution.\n", + "\n", + "> 🇪🇸 Un tensor conserva varios ejes con significados distintos. HOSVD aplica SVD a cada despliegue y conserva las direcciones dominantes de cada modo. Tucker combina esas direcciones mediante un tensor núcleo pequeño.\n", + ">\n", + "> Es más preciso entenderlo como una extensión multilineal de las ideas de SVD truncada a varios modos del tensor, no como “PCA convertido en una factorización tensorial”.\n", + ">\n", + "> Cada ejercicio tiene una celda **Solution / Solución** cerrada a propósito. Primero intenta el `TODO`; después abre la solución.\n", + "\n", + "### Learning cycle: Predict → Run → Explain\n", + "\n", + "Before each exercise, predict the shape, rank, or dominant pattern. Then run the computation and explain what the result means in the original taxi data.\n", + "\n", + "> 🇪🇸 **Predice → Ejecuta → Explica:** anticipa la forma, el rango o el patrón dominante; luego ejecuta y traduce el resultado de vuelta al contexto de los viajes reales." ], - "id": "s10-08" + "id": "9zF6ZTFxk_Yz" }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercise 1 — read the tensor before you decompose it\n", + "## Exercise 1 — read the tensor before decomposing it\n", + "\n", + "Before compressing anything, understand what the entries mean.\n", "\n", - "> 🇪🇸 Entiende el tensor antes de descomponerlo." + "For example, `T[i, j, h]` is the number of usable real trips that started in pickup borough `i`, ended in dropoff borough `j`, and were picked up during hour `h`.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Print `T.shape` and `T.sum()`.\n", + "2. Find the busiest hour overall.\n", + "3. Unfold along each of the three modes and confirm that every unfolding contains the same total number of entries.\n", + "4. Move **Hour / Hora** to inspect the real origin→destination count matrix at different times.\n", + "\n", + "> 🇪🇸 Antes de descomponer, entiende el tensor. Busca la hora con más viajes, revisa las tres formas de unfolding y usa el slider **Hour / Hora** para inspeccionar la matriz real origen→destino a distintas horas." ], - "id": "s10-09" + "id": "pPuQfB9Bk_Yz" }, { "cell_type": "code", @@ -226,56 +166,118 @@ "metadata": {}, "outputs": [], "source": [ - "# TODO 1: Print T.shape and T.sum(). What does the entry T[i, j, k] mean?\n", - "\n", - "# TODO 2: Which hour has the most trips overall? (Sum over the first two axes.)\n", - "\n", - "# TODO 3: Unfold T along each axis and print the three shapes. Confirm the total\n", - "# number of entries is the same each time — unfolding loses nothing." + "# TODO\n", + "# 1. Print T.shape and T.sum().\n", + "# 2. Compute T.sum(axis=(0, 1)) and find the busiest hour.\n", + "# 3. Print unfold(T, axis).shape for axis=0,1,2.\n", + "# 4. Predict which hours should show the most concentrated traffic." ], - "id": "s10-10" + "id": "WnOH_23Hk_Yz" }, { "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "print(T.shape, T.sum())\n", - "# T[i, j, k] = how many trips started in borough pb[i], ended in borough db[j],\n", - "# and were picked up during hour k.\n", + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "print(\"T.shape:\", T.shape)\n", + "print(\"total usable trips / viajes utilizables:\", int(T.sum()))\n", "\n", "by_hour = T.sum(axis=(0, 1))\n", - "print(by_hour.argmax()) # 18 — evening rush hour\n", + "busiest_hour = int(np.argmax(by_hour))\n", + "\n", + "print(\"busiest hour / hora más ocupada:\", busiest_hour)\n", + "print(\"trips at busiest hour / viajes:\", int(by_hour[busiest_hour]))\n", + "\n", + "for axis in range(3):\n", + " M = unfold(T, axis)\n", + " print(\n", + " f\"axis/eje {axis}: shape/forma={M.shape} | \"\n", + " f\"same entries/mismas entradas={M.size == T.size}\"\n", + " )\n", + "\n", + "hour_slider = widgets.IntSlider(\n", + " value=busiest_hour,\n", + " min=0,\n", + " max=23,\n", + " step=1,\n", + " description=\"Hour / Hora:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"90px\"},\n", + ")\n", + "\n", + "def show_hour(hour):\n", + " matrix = T[:, :, hour]\n", "\n", - "for ax in range(3):\n", - " M = unfold(T, ax)\n", - " print(ax, M.shape, M.size == T.size) # True every time" + " print(\n", + " f\"hour/hora={hour} | total trips/viajes={int(matrix.sum())}\"\n", + " )\n", + "\n", + " fig, ax = plt.subplots(figsize=(5.4, 4.0))\n", + " im = ax.imshow(matrix, cmap=\"viridis\", aspect=\"auto\")\n", + " ax.set_xticks(range(len(dropoff_names)))\n", + " ax.set_xticklabels(dropoff_names, rotation=45, ha=\"right\", fontsize=8)\n", + " ax.set_yticks(range(len(pickup_names)))\n", + " ax.set_yticklabels(pickup_names, fontsize=8)\n", + " ax.set_xlabel(\"dropoff borough / destino\")\n", + " ax.set_ylabel(\"pickup borough / origen\")\n", + " ax.set_title(f\"Real taxi counts at hour {hour} / Viajes reales a la hora {hour}\")\n", + " fig.colorbar(im, ax=ax, label=\"trip count / viajes\")\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "hour_output = widgets.interactive_output(\n", + " show_hour,\n", + " {\"hour\": hour_slider},\n", + ")\n", + "\n", + "display(widgets.VBox([hour_slider, hour_output]))" ], - "id": "s10-11" + "id": "sQdcOqdXk_Y0" }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercise 2 — HOSVD, in two einsum calls\n", + "## Exercise 2 — HOSVD: compress each mode separately\n", + "\n", + "Now compute one SVD basis per unfolding.\n", + "\n", + "If the retained ranks are `(r₁, r₂, r₃)`, then:\n", + "\n", + "- `U₁` summarizes pickup patterns;\n", + "- `U₂` summarizes dropoff patterns;\n", + "- `U₃` summarizes hourly patterns;\n", + "- the core has shape `(r₁, r₂, r₃)`.\n", "\n", - "> 🇪🇸 HOSVD en dos llamadas a einsum.\n", + "The core is produced by contracting all three modes at once:\n", "\n", - "Look at the einsum strings you are about to write: `'ijk,ia,jb,kc->abc'`\n", - "contracts three axes in one expression. **That is why einsum came first.**" + "`core = einsum('ijk,ia,jb,kc->abc', T, U1, U2, U3)`\n", + "\n", + "and reconstruction reverses that contraction.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Compute the full SVD basis of each unfolding.\n", + "2. Start with ranks `(2, 2, 3)`.\n", + "3. Build the core with one `einsum`.\n", + "4. Reconstruct with one `einsum`.\n", + "5. Compute relative Frobenius error and compression ratio.\n", + "6. Change the three rank sliders independently and inspect how error and storage trade off.\n", + "\n", + "> 🇪🇸 Ahora cada modo obtiene su propia base SVD. El tensor núcleo resume cómo interactúan esos factores. Cambia los tres rangos de manera independiente y observa que reducir almacenamiento siempre tiene un costo de reconstrucción." ], - "id": "s10-12" + "id": "WSeYDtwmk_Y0" }, { "cell_type": "code", @@ -283,117 +285,216 @@ "metadata": {}, "outputs": [], "source": [ - "# TODO 4: Run SVD on each unfolding, keep the top (2, 2, 3) components, and\n", - "# build the core tensor with ONE einsum call.\n", - "\n", - "# TODO 5: Reconstruct T from the core and factors, again with one einsum.\n", - "# Compute the relative error and the compression ratio." + "# TODO\n", + "# 1. Compute one SVD basis for each unfolding.\n", + "# 2. Keep ranks (2, 2, 3).\n", + "# 3. Build the Tucker core with one np.einsum call.\n", + "# 4. Reconstruct T with one np.einsum call.\n", + "# 5. Compute relative error and compression ratio." ], - "id": "s10-13" + "id": "gytR3GhUk_Y0" }, { "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "Us = [np.linalg.svd(unfold(T, ax), full_matrices=False)[0] for ax in range(3)]\n", - "r = (2, 2, 3)\n", - "Us = [Us[i][:, :r[i]] for i in range(3)]\n", - "print([u.shape for u in Us])\n", - "\n", - "core = np.einsum('ijk,ia,jb,kc->abc', T, Us[0], Us[1], Us[2]) # (2, 2, 3)\n", - "recon = np.einsum('abc,ia,jb,kc->ijk', core, Us[0], Us[1], Us[2])\n", - "\n", - "error = np.linalg.norm(T - recon) / np.linalg.norm(T) # 0.067\n", - "ratio = T.size / (core.size + sum(u.size for u in Us)) # 4.71\n", - "print(core.shape, round(error, 3), round(ratio, 2))" - ], - "id": "s10-14" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The exercise above fixed one rank, (2, 2, 3). Move the slider to see the\n", - "whole error/compression trade-off, not just that one point on it.\n", + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "bases = hosvd_bases(T)\n", "\n", - "> 🇪🇸 El ejercicio anterior fijó un solo rango, (2, 2, 3). Mueve el\n", - "> deslizador para ver toda la curva de compensación, no solo ese punto." - ], - "id": "s10-15" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " from google.colab import output\n", - " output.enable_custom_widget_manager()\n", - "except ImportError:\n", - " pass\n", + "def tucker_from_ranks(T, bases, ranks):\n", + " factors = [\n", + " bases[axis][:, :ranks[axis]]\n", + " for axis in range(3)\n", + " ]\n", "\n", - "import ipywidgets as widgets\n", - "import matplotlib.pyplot as plt\n", + " core = np.einsum(\n", + " \"ijk,ia,jb,kc->abc\",\n", + " T,\n", + " factors[0],\n", + " factors[1],\n", + " factors[2],\n", + " )\n", + "\n", + " recon = np.einsum(\n", + " \"abc,ia,jb,kc->ijk\",\n", + " core,\n", + " factors[0],\n", + " factors[1],\n", + " factors[2],\n", + " )\n", + "\n", + " error = np.linalg.norm(T - recon) / np.linalg.norm(T)\n", + "\n", + " compressed_numbers = (\n", + " core.size\n", + " + sum(factor.size for factor in factors)\n", + " )\n", + " compression = T.size / compressed_numbers\n", + "\n", + " return core, factors, recon, error, compression\n", + "\n", + "default_ranks = (\n", + " min(2, T.shape[0]),\n", + " min(2, T.shape[1]),\n", + " min(3, T.shape[2]),\n", + ")\n", + "\n", + "core, factors, recon, error, compression = tucker_from_ranks(\n", + " T,\n", + " bases,\n", + " default_ranks,\n", + ")\n", + "\n", + "print(\"default ranks / rangos:\", default_ranks)\n", + "print(\"factor shapes / formas:\", [u.shape for u in factors])\n", + "print(\"core shape / forma núcleo:\", core.shape)\n", + "print(\"relative error / error relativo:\", f\"{error:.4f}\")\n", + "print(\"compression / compresión:\", f\"{compression:.2f}x\")\n", + "\n", + "pickup_rank = widgets.IntSlider(\n", + " value=default_ranks[0],\n", + " min=1,\n", + " max=T.shape[0],\n", + " step=1,\n", + " description=\"Pickup rank:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"100px\"},\n", + ")\n", + "\n", + "dropoff_rank = widgets.IntSlider(\n", + " value=default_ranks[1],\n", + " min=1,\n", + " max=T.shape[1],\n", + " step=1,\n", + " description=\"Dropoff rank:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"110px\"},\n", + ")\n", + "\n", + "hour_rank = widgets.IntSlider(\n", + " value=default_ranks[2],\n", + " min=1,\n", + " max=min(12, T.shape[2]),\n", + " step=1,\n", + " description=\"Hour rank:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"95px\"},\n", + ")\n", + "\n", + "compare_hour = widgets.IntSlider(\n", + " value=busiest_hour,\n", + " min=0,\n", + " max=23,\n", + " step=1,\n", + " description=\"Hour / Hora:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"90px\"},\n", + ")\n", + "\n", + "def explore_tucker(r_pickup, r_dropoff, r_hour, hour):\n", + " ranks = (r_pickup, r_dropoff, r_hour)\n", + "\n", + " core, factors, recon, error, compression = tucker_from_ranks(\n", + " T,\n", + " bases,\n", + " ranks,\n", + " )\n", + "\n", + " print(\n", + " f\"ranks/rangos={ranks} | core/núcleo={core.shape} | \"\n", + " f\"error={error:.4f} | compression/compresión={compression:.2f}x\"\n", + " )\n", + "\n", + " vmax = max(T[:, :, hour].max(), recon[:, :, hour].max())\n", + "\n", + " fig, axes = plt.subplots(1, 2, figsize=(7.6, 3.3))\n", + "\n", + " axes[0].imshow(\n", + " T[:, :, hour],\n", + " cmap=\"viridis\",\n", + " aspect=\"auto\",\n", + " vmin=0,\n", + " vmax=vmax,\n", + " )\n", + " axes[0].set_title(f\"real / real — hour {hour}\")\n", + "\n", + " axes[1].imshow(\n", + " recon[:, :, hour],\n", + " cmap=\"viridis\",\n", + " aspect=\"auto\",\n", + " vmin=0,\n", + " vmax=vmax,\n", + " )\n", + " axes[1].set_title(f\"Tucker reconstruction / reconstrucción\")\n", + "\n", + " for ax in axes:\n", + " ax.set_xticks(range(len(dropoff_names)))\n", + " ax.set_xticklabels(dropoff_names, rotation=45, ha=\"right\", fontsize=7)\n", + " ax.set_yticks(range(len(pickup_names)))\n", + " ax.set_yticklabels(pickup_names, fontsize=7)\n", "\n", - "# Precompute the full SVD basis for each axis once; the slider only re-slices\n", - "# and re-contracts these small matrices, which is what keeps it responsive.\n", - "bases = [np.linalg.svd(unfold(T, ax), full_matrices=False)[0] for ax in range(3)]\n", - "max_rank = min(u.shape[1] for u in bases)\n", - "\n", - "ks, errors, ratios = list(range(1, max_rank + 1)), [], []\n", - "for kk in ks:\n", - " Uk = [bases[ax][:, :kk] for ax in range(3)]\n", - " core_k = np.einsum('ijk,ia,jb,kc->abc', T, *Uk)\n", - " recon_k = np.einsum('abc,ia,jb,kc->ijk', core_k, *Uk)\n", - " errors.append(np.linalg.norm(T - recon_k) / np.linalg.norm(T))\n", - " ratios.append(T.size / (core_k.size + sum(u.size for u in Uk)))\n", - "\n", - "def show_rank(k):\n", - " i = k - 1\n", - " plt.close('all')\n", - " fig, ax1 = plt.subplots(figsize=(6, 3.2))\n", - " ax1.plot(ks, errors, color='#C44E52')\n", - " ax1.scatter([k], [errors[i]], color='#C44E52', zorder=5)\n", - " ax1.set_xlabel('rank k (shared across all three axes)')\n", - " ax1.set_ylabel('relative error', color='#C44E52')\n", - " ax2 = ax1.twinx()\n", - " ax2.plot(ks, ratios, color='#4C72B0')\n", - " ax2.scatter([k], [ratios[i]], color='#4C72B0', zorder=5)\n", - " ax2.set_ylabel('compression ratio (x)', color='#4C72B0')\n", " plt.tight_layout()\n", " plt.show()\n", - " print(f\"rank k={k}: error={errors[i]:.3f}, compression={ratios[i]:.2f}x\")\n", "\n", - "widgets.interact(show_rank,\n", - " k=widgets.IntSlider(min=1, max=max_rank, step=1, value=2,\n", - " description='rank k'));" + "tucker_output = widgets.interactive_output(\n", + " explore_tucker,\n", + " {\n", + " \"r_pickup\": pickup_rank,\n", + " \"r_dropoff\": dropoff_rank,\n", + " \"r_hour\": hour_rank,\n", + " \"hour\": compare_hour,\n", + " },\n", + ")\n", + "\n", + "display(\n", + " widgets.VBox([\n", + " widgets.HTML(\n", + " \"Tucker rank explorer / Explorador de rangos Tucker: \"\n", + " \"change each mode independently. / \"\n", + " \"cambia cada modo de manera independiente.\"\n", + " ),\n", + " pickup_rank,\n", + " dropoff_rank,\n", + " hour_rank,\n", + " compare_hour,\n", + " tucker_output,\n", + " ])\n", + ")" ], - "id": "s10-16" + "id": "v7iXv9bDk_Y0" }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercise 3 — what did it find?\n", + "## Exercise 3 — what did the temporal factor learn?\n", + "\n", + "Compression is useful, but interpretation is where Tucker becomes more than a storage trick.\n", + "\n", + "The hour factor matrix has one row for each hour and one column for each retained temporal component. Singular-vector signs are arbitrary, so we compare magnitudes when asking where a component is strongest.\n", "\n", - "> 🇪🇸 ¿Qué encontró la descomposición por sí sola?\n", + "### What should you try?\n", "\n", - "This is the important one." + "1. Inspect the first temporal factor.\n", + "2. Find the hour where its absolute loading is largest.\n", + "3. Compare that hour with the busiest hour in the raw taxi counts.\n", + "4. Move **Component / Componente** to inspect other temporal patterns.\n", + "5. Explain why a factor can capture a pattern even though nobody explicitly labeled “rush hour.”\n", + "\n", + "> 🇪🇸 La matriz de factores temporales tiene una fila por hora. Busca dónde alcanza mayor magnitud cada componente y compáralo con los conteos horarios reales. El signo de un vector singular es arbitrario, así que interpreta principalmente la forma y la magnitud del patrón." ], - "id": "s10-17" + "id": "v4OEefzbk_Y1" }, { "cell_type": "code", @@ -401,41 +502,103 @@ "metadata": {}, "outputs": [], "source": [ - "# TODO 6: Look at the first column of the hour factor matrix. At which hour is\n", - "# it largest? Does that match what you found in TODO 2?" + "# TODO\n", + "# 1. Use the hour-mode SVD basis.\n", + "# 2. Inspect the first temporal component.\n", + "# 3. Find its peak absolute loading.\n", + "# 4. Compare it with the busiest hour in T.sum(axis=(0,1)).\n", + "# 5. Repeat for another component and describe the pattern." ], - "id": "s10-18" + "id": "pAmVh-Vuk_Y1" }, { "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "hour_factor = Us[2] # (24, 3) — one row per hour\n", - "peak = np.abs(hour_factor[:, 0]).argmax()\n", - "print(peak) # 18\n", - "\n", - "print(T.sum(axis=(0, 1)).argmax()) # 18 — the same hour, from raw counts\n", - "\n", - "# THE DECOMPOSITION DISCOVERED EVENING RUSH HOUR BY ITSELF. Nobody told it about\n", - "# time, traffic or commuting; it found the dominant pattern along that axis\n", - "# because that is what a decomposition does.\n", - "#\n", - "# (Take the absolute value: singular vectors are only defined up to sign, so the\n", - "# strongest component may come out negative.)" + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "hour_basis = bases[2]\n", + "raw_hour_counts = T.sum(axis=(0, 1))\n", + "\n", + "first_component = hour_basis[:, 0]\n", + "first_peak = int(np.argmax(np.abs(first_component)))\n", + "\n", + "print(\"first component peak / pico primer componente:\", first_peak)\n", + "print(\"raw busiest hour / hora real más ocupada:\", busiest_hour)\n", + "\n", + "component_slider = widgets.IntSlider(\n", + " value=1,\n", + " min=1,\n", + " max=min(6, hour_basis.shape[1]),\n", + " step=1,\n", + " description=\"Component / Componente:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"145px\"},\n", + ")\n", + "\n", + "def explore_hour_factor(component):\n", + " idx = component - 1\n", + " factor = hour_basis[:, idx]\n", + "\n", + " peak = int(np.argmax(np.abs(factor)))\n", + "\n", + " raw_scaled = raw_hour_counts / raw_hour_counts.max()\n", + " factor_scaled = np.abs(factor)\n", + " factor_scaled = factor_scaled / factor_scaled.max()\n", + "\n", + " print(\n", + " f\"component/componente={component} | \"\n", + " f\"peak absolute loading / pico absoluto={peak}\"\n", + " )\n", + " print(\n", + " f\"raw busiest hour / hora real más ocupada={busiest_hour}\"\n", + " )\n", + "\n", + " fig, ax = plt.subplots(figsize=(6.6, 3.2))\n", + " ax.plot(\n", + " range(24),\n", + " raw_scaled,\n", + " marker=\"o\",\n", + " label=\"raw hourly trips / viajes reales\",\n", + " )\n", + " ax.plot(\n", + " range(24),\n", + " factor_scaled,\n", + " marker=\"o\",\n", + " label=f\"|temporal factor {component}| / |factor temporal|\",\n", + " )\n", + " ax.axvline(\n", + " peak,\n", + " linestyle=\"--\",\n", + " linewidth=1,\n", + " label=f\"factor peak / pico={peak}\",\n", + " )\n", + " ax.set_xticks(range(0, 24, 2))\n", + " ax.set_xlabel(\"hour / hora\")\n", + " ax.set_ylabel(\"scaled magnitude / magnitud escalada\")\n", + " ax.set_title(\"Raw activity vs learned temporal factor / Actividad real vs factor\")\n", + " ax.legend(fontsize=8)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "factor_output = widgets.interactive_output(\n", + " explore_hour_factor,\n", + " {\"component\": component_slider},\n", + ")\n", + "\n", + "display(widgets.VBox([component_slider, factor_output]))" ], - "id": "s10-19" + "id": "AHQe7C-Ak_Y1" }, { "cell_type": "markdown", @@ -443,19 +606,26 @@ "source": [ "## What just happened\n", "\n", - "**4.7× fewer numbers, 6.7% error.** But the important part is TODO 6. The\n", - "strongest pattern in the hour factor peaks at **hour 18** — and that is also the\n", - "busiest hour in the raw data. The decomposition found rush hour on its own.\n", + "You built a complete Tucker/HOSVD pipeline from real observations.\n", "\n", - "**Where this is used.** In tech, Tucker and CP compress the large weight tensors\n", - "inside neural networks so models run on phones instead of servers. In biotech,\n", - "applied to data such as (genes × samples × conditions), they find structure\n", - "ordinary PCA cannot reach, because **PCA can only ever see two axes**.\n", + "1. **Real tensor construction:** a flat taxi table became an order-3 tensor `pickup × dropoff × hour`.\n", + "2. **Unfolding:** each mode exposed a different matrix view without losing any entries.\n", + "3. **HOSVD:** SVD supplied one low-dimensional basis per mode.\n", + "4. **Tucker core:** one `einsum` contracted all three axes into a smaller core, and another reconstructed the tensor.\n", + "5. **Rank trade-off:** changing pickup, dropoff, and hour ranks independently changed both storage and reconstruction error.\n", + "6. **Interpretation:** the hour factor exposed temporal structure that could be compared directly with real hourly trip counts.\n", "\n", - "For real projects use [`tensorly`](https://tensorly.org), which implements both\n", - "properly. Take-home C in section 11 compares CP against what you just built." + "### The sentence to remember\n", + "\n", + "> **Tucker compresses a tensor by learning a basis for each mode and a small core that tells those mode-specific patterns how to interact.**\n", + "\n", + "This is why tensor decompositions are useful in domains such as imaging, recommender systems, neuroscience, and multi-condition biological measurements: the axes represent genuinely different kinds of structure.\n", + "\n", + "> 🇪🇸 Construiste Tucker/HOSVD de extremo a extremo con datos reales. Cada modo obtuvo su propia base, el núcleo resumió sus interacciones y los sliders mostraron que la compresión y el error dependen de cuánto rango conservas en cada eje.\n", + ">\n", + "> **Frase para recordar:** Tucker comprime un tensor aprendiendo una base para cada modo y un núcleo pequeño que describe cómo interactúan esos patrones específicos de cada eje." ], - "id": "s10-20" + "id": "I5XWJQZLk_Y1" }, { "cell_type": "markdown", @@ -478,22 +648,1175 @@ "\n", "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" ], - "id": "s10-21" + "id": "Mx5SdJaBk_Y1" } ], "metadata": { - "colab": { - "name": "10-tucker-decomposition.ipynb", - "provenance": [], - "toc_visible": true - }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { - "name": "python" + "name": "python", + "version": "3" + }, + "colab": { + "provenance": [] + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "version_major": 2, + "version_minor": 0, + "state": { + "5e1b1ca6f0794da5bf0d2bc913dff7b3": { + "model_module": "@jupyter-widgets/controls", + "model_name": "VBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "VBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "VBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_e8108f1a0791441cafce0b894f6dcd1d", + "IPY_MODEL_0d9b3eeee98a4071b1dbb283ad6768cc" + ], + "layout": "IPY_MODEL_8ffc4ee34e4c4d8a9a4af276f54859a1" + } + }, + "e8108f1a0791441cafce0b894f6dcd1d": { + "model_module": "@jupyter-widgets/controls", + "model_name": "IntSliderModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "IntSliderModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "IntSliderView", + "continuous_update": false, + "description": "Hour / Hora:", + "description_tooltip": null, + "disabled": false, + "layout": "IPY_MODEL_c54c897dbdd6446b804d7d94b7915328", + "max": 23, + "min": 0, + "orientation": "horizontal", + "readout": true, + "readout_format": "d", + "step": 1, + "style": "IPY_MODEL_e903bde3d1c74f88b9e51520415e75f7", + "value": 16 + } + }, + "0d9b3eeee98a4071b1dbb283ad6768cc": { + "model_module": "@jupyter-widgets/output", + "model_name": "OutputModel", + "model_module_version": "1.0.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/output", + "_model_module_version": "1.0.0", + "_model_name": "OutputModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/output", + "_view_module_version": "1.0.0", + "_view_name": "OutputView", + "layout": "IPY_MODEL_6f4afe3ad61e4a69b7da015e7f123eaf", + "msg_id": "", + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "hour/hora=16 | total trips/viajes=332\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
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4sQCEs7OziIuL0yyfOHGi1jEohPSfAOKHH37QLEtJSRF+fn7C0dFRpKamCiGEmDdvngDEihUrNO1SU1NFs2bNhJWVlWYctVJgY2MjIiIitOTX91xT/67cbu7qduqHGDXJyckiIyNDa1lISIgwMzPTelj6448/BCDmzJmTpV/1fx8ZGZmtLOp9FBUVpVl2/vx5oVQqxeDBgzXL1IrigAEDcv09avbu3Zvlv8qO06dPC0Ds3r1bI7ubm5sYM2aMVrsvv/xSAGLDhg1Z+lD/3lOnTmVR6tRkVhR37dql88G0S5cuWuelLkXRz89PuLi4iJiYGM2yf//9VwBZzonM535qaqrw8fER7dq10ywLCgoSQBbFauDAgQYpilWrVtUaz5Drga5r1LFjxwQg/vzzzyxj5absDBgwQDg6Oor09HTNsrCwMKFUKjXH8ZMnT7IYFvQlOyOOrt8xY8aMLPelVq1aCWtra61lQmjfD2fNmqXzOFb/X2+//bbW8o8//lgAGoObENlf04sTg6eeJ0+erPFjmD59OiEhIQQEBLBjxw7mz59vaHdllsDAQBwcHKhcuTJ9+vShXLlybNmyBTc3N00bIyMjTE1NAWmaNzo6mvT0dBo2bMjZs2c17dTT1gcPHgQkU3mjRo3o0KEDhw4dAqQplkuXLmnaZoe+Y/7zzz8oFAqmTJmSpY+8+B2qp8PWrl2rCYACabq8adOmVKlSxeA+lUolgwYNYsuWLcTHx2uWr1y5kubNm+Pp6ZnttgqFgtdff50dO3bw9OlTLXlcXV01U2Vqf5Zt27bpnOI2hHXr1lG7dm1q1arF48ePNS+1T9aLU5IvTr/Fxsby+PFjWrduze3bt4mNjdXq19PTk06dOukcc9iwYZr/G54fS7dv3wYgKCiIGzduMHDgQKKiojQyJSQk0L59ew4ePIhKpSIjI4Ndu3bRq1cvrf+qdu3a2Y5dGPsFoH379lrT1uqo1969e2NtbZ1lufq3qjE2Nubdd9/VfDc1NeXdd98lIiKCM2fOALBjxw6cnZ0ZMGCApp2JiQkffvghT58+zTJF1rt3b820rRp9z7X8YmZmpgn+ysjIICoqCisrK2rWrJnlnLa3t+eDDz7I0kdu53RYWBhBQUEMHToUOzs7zfK6devSoUMHduzYkWWbkSNH6v0bduzYgbe3t17BaitXrsTJyYm2bdtqZO/Xrx+rV6/WCqb8559/qFevHq+++mqWPvJyDWvXrh329vasWbNGs+zJkyfs3r2bfv36Zbudet8NGTIEW1tbzfIOHTrg7e2dpf2L5/6TJ0+IjY0lICBA679U7+/M7l6GBooNGTJEazx9rweZ5UxLSyMqKgovLy/Kly+fp+O7X79+REREaMU6rF+/HpVKpdm/FhYWmJqacuDAAZ48eWLwGLp48XckJCTw+PFjmjdvjhCCc+fOARAZGcnBgwcZPnx4lnuVPseS+v8aN26c1vLx48cDktvFi+R0TS8ODFYUO3XqpPGX8/Ly4urVqzx+/JiIiAi9nJBfFn7++Wd2797N+vXr6dKlC48fP9bpGL18+XLq1q2Lubk5FStWxMHBge3bt2spA05OTlSvXl2jFB46dIiAgABatWrFw4cPuX37NkeOHEGlUuWqKOo75q1bt6hUqZLWTSG/9OvXj3v37nHs2DHNGGfOnMnxIpsbgwcPJikpSePvc+3aNc6cOcObb76plzxJSUmaAKOnT5+yY8cOXn/9dc3J37p1a3r37s20adOwt7enZ8+eLF26NItfiT7cuHGDy5cv4+DgoPWqUaMG8NzZGuDIkSMEBgZqfMEcHByYNGkSgE5FMTsyX9QqVKgAoLnI3rhxA5BuGJnlWrJkCSkpKcTGxhIZGUlSUhLVq1fPMkZOvqD6YMh+0fWb1DffypUr61ye+YZSqVKlLGkx1GOp/Ynu3LlD9erVtaLvQVKM1etfJLv/QJ9zLb+oVCrmzp1L9erVMTMzw97eHgcHBy5cuJDlnK5ZsybGxgYnu9D8Xl3/de3atTXKxIvkdFxmZvv27XTt2jXXdhkZGaxevZq2bdsSEhLCzZs3uXnzJk2aNOHRo0fs3btX0/bWrVv4+PjoLUNuGBsb07t3bzZv3qw5/zds2EBaWlqO1zD1vtP33Nm2bRtNmzbF3NwcOzs7HBwc+OWXX7T+yzt37qBUKqlWrVqu/eVE5v9I3+sBQFJSEl9++SWVK1fWOu5iYmLydHyrfSJfVMTXrFmDn5+f5vw0MzPju+++43//+x9OTk60atWK77//nvDwcIPHU3P37l3NA5CVlRUODg60bt0aeH6tVT9s5vV4Uv9fmSPznZ2dKV++vN7Xk+LC8CuGDgpSmSgrNG7cWBOg0atXL1q2bMnAgQO5du0aVlZWAKxYsYKhQ4fSq1cvPvnkExwdHTEyMmLGjBlaQS8ALVu2ZO/evSQlJXHmzBm+/PJLfHx8KF++PIcOHeLKlStYWVlRv379HOUyZMyCpnv37lhaWrJ27VqaN2/O2rVrUSqVmmCVvODt7Y2/vz8rVqxg8ODBrFixAlNTU/r27Zvrtk2bNsXDw4O1a9cycOBAtm7dSlJSktZFX6FQsH79eo4fP87WrVvZtWsXw4cP54cffuD48eOa/1IfVCoVvr6+zJkzR+d6taJz69Yt2rdvT61atZgzZw6VK1fG1NSUHTt2MHfu3CyRiTk5/2cXeam26qr7mjVrVpb0IGqsrKzypBjri777RU12vym331qY6PoPiupc+/bbb/niiy8YPnw4X331FXZ2diiVSsaOHVusUaz6BKWAFMF89epVncFamdm3bx9hYWGsXr2a1atXZ1m/cuVKOnbsaLCs+tK/f38WL17M//73P3r16sXatWupVasW9erVK5D+Dx06RI8ePWjVqhULFy7ExcUFExMTli5dyqpVqwpkjBfJ/B/pez0A+OCDD1i6dCljx46lWbNm2NraolAo6N+/f56OOzMzM3r16sXGjRtZuHAhjx494siRI3z77bda7caOHUv37t3ZtGkTu3bt4osvvmDGjBns27cv1/tfZjIyMujQoQPR0dFMmDCBWrVqUa5cOR48eMDQoUML/PzR15Kt77lTVOilKGaOuM2JDRs25FmYsor65tC2bVsWLFjAZ599Bkhm9apVq7JhwwatA0jXdG9AQABLly7VTK80b95cE3GuVhSbN2+ea0oGfcesVq0au3btIjo6OscHAUOmcMqVK0e3bt1Yt24dc+bMYc2aNQQEBFCpUqUct8ttjMGDBzNu3DjCwsJYtWoVXbt21VjOcqNv3778+OOPxMXFsWbNGjw8PGjatGmWdk2bNqVp06Z88803rFq1ikGDBrF69WrefvttvcYBaZ+eP3+e9u3b5/ibtm7dSkpKClu2bNGynuUlWlYfmUByDcgpt5g6yk9tcXiRa9eu6TVWdr9Z3/1SUDx8+JCEhAQtq+L169cBNFOf7u7uXLhwAZVKpWVVvHr1qmZ9bhhyfueH9evX07ZtW37//Xet5TExMdjb22u+V6tWjRMnTpCWlpZt+qns9r/69+r6r69evYq9vX2ekxdv374dW1tbvSKjV65ciaOjIz///HOWdRs2bGDjxo0sWrQICwsLqlWrpomUzQ5Dj7dWrVrh4uLCmjVraNmyJfv27dNEvWaHet/pc+78888/mJubs2vXLq0ZqKVLl2bpU6VSaazE2fVnKPpeD0A67oYMGcIPP/ygWZacnJyvZNP9+vVj+fLl7N27lytXriCE0GmtrVatGuPHj2f8+PHcuHEDPz8/fvjhB1asWGHQeBcvXuT69essX76cwYMHa5bv3r1bq506Y0dejyf1/3Xjxg3NrARIlZZiYmL0up4UJ3pNPdva2mpeNjY27N27V5MiBeDMmTPs3btXy/9CRps2bdrQuHFj5s2bR3JyMvDcAvKixePEiROaqdkXUU8pf/fdd9StW1ezrwMCAjT/hz7TzvqO2bt3b4QQmuS7L/LituXKlTPowtCvXz8ePnzIkiVLOH/+vF7TzuobUHbjDBgwAIVCwZgxY7h9+zZvvPGGQfKkpKSwfPlydu7cmcUS+eTJkywWKfWTtqFWtr59+/LgwQN+++23LOuSkpI0U3e6/qPY2NgsN4uCwN/fn2rVqjF79mwtX001kZGRGpk6derEpk2buHv3rmb9lStX2LVrl15jlStXTueUlL77paBIT0/XSjeRmprK4sWLcXBwwN/fH4AuXboQHh6uNQ2Wnp7OTz/9hJWVlWZqKicMOb/zg5GRUZZjdN26dVkqQfXu3ZvHjx+zYMGCLH2ot7e0tASynmsuLi74+fmxfPlyrXWXLl3i33//pUuXLnmWf8eOHXTs2DHXKfGkpCQ2bNhAt27d6NOnT5bX6NGjiY+P17iS9O7dm/Pnz+tMQ6P+vbldWzKjVCrp06cPW7du5a+//iI9PT3Xa9iL++7F43/37t0EBwdrtTUyMkKhUGj5WoaGhmapfvXKK68AZIkLyG/lI32vB2pZMx93P/30U76KbgQGBmJnZ8eaNWtYs2YNjRs31pqGTUxM1Nw/1VSrVg1ra+s8zXroOkeFEPz4449a7RwcHGjVqhV//PGH1vUv87bZHU/q8yPz/6OeRdHH7aI40cui+OINasKECfTt25dFixZpdnJGRgajRo3CxsamcKQsI3zyySe8/vrrLFu2jJEjR9KtWzc2bNjAq6++SteuXQkJCWHRokV4e3tnOUm9vLxwdnbm2rVrWs7orVq1YsKECQB6KYr6jtm2bVvefPNN5s+fz40bN+jcuTMqlYpDhw7Rtm1bRo8eDUgXlj179jBnzhwqVaqEp6dnjiW1unTpgrW1NR9//DFGRkb07t07V5n9/PwwMjLiu+++IzY2FjMzM02OQUCT33HdunWUL1/eoJOuQYMGeHl58fnnn5OSkpLlor98+XIWLlzIq6++SrVq1YiPj+e3337DxsbG4Jvjm2++ydq1axk5ciT79++nRYsWZGRkcPXqVdauXavJm9WxY0dMTU3p3r077777Lk+fPuW3337D0dGRsLAwg8bMDaVSyZIlS3jllVeoU6cOw4YNw9XVlQcPHrB//35sbGzYunUrIOXW27lzJwEBAYwaNUqjONWpU0crn2d2+Pv7s2bNGsaNG0ejRo2wsrKie/fueu+XgqJSpUp89913hIaGUqNGDdasWUNQUBC//vqrxtL2zjvvsHjxYoYOHcqZM2fw8PBg/fr1HDlyhHnz5mkFzWSHIed3fujWrRvTp09n2LBhNG/enIsXL7Jy5coseUsHDx7Mn3/+ybhx4zh58iQBAQEkJCSwZ88eRo0aRc+ePbGwsMDb25s1a9ZQo0YN7Ozs8PHxwcfHh1mzZvHKK6/QrFkz3nrrLZKSkvjpp5+wtbXNc23hpKQk9u/frzOPZWbUQWs9evTQub5p06aa5Nv9+vXjk08+Yf369bz++usMHz4cf39/oqOj2bJlC4sWLaJevXpUq1aN8uXLs2jRIqytrSlXrhxNmjTJ0UesX79+/PTTT0yZMgVfX18tC1F2zJgxg65du9KyZUuGDx9OdHS05tx58Vjo2rUrc+bMoXPnzgwcOJCIiAh+/vlnvLy8tM4xPz8/BgwYwMKFC4mNjaV58+bs3btXZ45UQzDketCtWzf++usvbG1t8fb25tixY+zZs4eKFSvmeXwTExNee+01Vq9eTUJCArNnz9Zaf/36ddq3b0/fvn3x9vbG2NiYjRs38ujRI/r372/weLVq1aJatWp8/PHHPHjwABsbG/755x+dgTLz58+nZcuWNGjQgHfeeQdPT09CQ0PZvn27pgSk+kHz888/p3///piYmNC9e3fq1avHkCFD+PXXX4mJiaF169acPHmS5cuX06tXL01gVonF0DBpe3t7cfXq1SzLr169Kuzs7AyPuy5jZBeCL4SUzqVatWqiWrVqIj09XahUKvHtt98Kd3d3YWZmJurXry+2bduWJS2Dmtdff10AYs2aNZplqampwtLSUpiamoqkpKRc5TNkzPT0dDFr1ixRq1YtYWpqKhwcHMQrr7wizpw5o2lz9epV0apVK2FhYSEATVqbzOlxXmTQoEECEIGBgTplzJweRwghfvvtN1G1alVNKpbMqRbUaW3eeeedXPdBZj7//HMBCC8vryzrzp49KwYMGCCqVKkizMzMhKOjo+jWrZs4ffp0rv1mTo8jhPR/fffdd6JOnTrCzMxMVKhQQfj7+4tp06aJ2NhYTbstW7aIunXrCnNzc+Hh4SG+++47TXqTF/epu7u76Nq1a5ax1Skp1q1bp7VcV+oOIYQ4d+6ceO2110TFihWFmZmZcHd3F3379hV79+7Vavfff/8Jf39/YWpqKqpWrSoWLVqkSYeSG0+fPhUDBw4U5cuXz5IWRN/9go70MOrflDllhq590Lp1a1GnTh1x+vRp0axZM2Fubi7c3d115u979OiRGDZsmLC3txempqbC19c3y37LbmwhDDvXyGd6nPHjxwsXFxdhYWEhWrRoIY4dO6bz+EtMTBSff/658PT0FCYmJsLZ2Vn06dNHkztQCCGOHj2q+Y8zy7Vnzx7RokULYWFhIWxsbET37t01abvUqI+HyMjIXH/Ptm3bhEKhEI8ePcq1bffu3YW5ublWjr7MDB06VJiYmIjHjx8LIaScs6NHjxaurq7C1NRUuLm5iSFDhmjWCyHE5s2bhbe3tzA2NtY6N7K7DqtUKlG5cmUBiK+//jrL+uzOsX/++UfUrl1bmJmZCW9vb7FhwwadY/z++++ievXqwszMTNSqVUssXbpU5zmWlJQkPvzwQ1GxYkVRrlw50b17d3Hv3j2D0uNkvj6o0ed68OTJE835YWVlJTp16iSuXr2a5fqtb3ocNer8uAqFQiudnBBCPH78WLz//vuiVq1aoly5csLW1lY0adIkSz5cXWR3bw4ODhaBgYHCyspK2NvbixEjRojz58/r/A8vXbokXn31VVG+fHlhbm4uatasKb744gutNl999ZVwdXUVSqVS63qdlpYmpk2bpjn3KleuLCZOnCiSk5O1ts/uml6cKIQwzNO7QoUKLFu2jJ49e2ot37x5M0OHDi2wkHUZGUPYvHkzvXr14uDBg3pZVmVeTtq0acPjx49z9TWSKRpGjRrF6dOnOXnyZHGLIiMjkw0GRz0PGzaMt956i1u3btG4cWNA8ruZOXOmzvJGMjJFwW+//UbVqlX1coiXkZEpGfj5+dG9e/fiFkNGRiYHDFYUZ8+ejbOzMz/88IPGX8rFxYVPPvlEkzxSRqaoWL16NRcuXGD79u38+OOPRRI1KyMjUzC88847xS2CjIxMLhg89fwi6gLichCLTHGhUCiwsrKiX79+LFq0KE/JhGVeHuSpZxkZGRnDyJeiKCMjIyMjIyMjU3bJk/ll/fr1rF27lrt375Kamqq1riBrmMrIyMjIyMjIyBQfBiuK8+fP5/PPP2fo0KFs3ryZYcOGcevWLU6dOsX7779fGDIWKCqViocPH2JtbS37s8nIyMjIyMi8dAghiI+Pp1KlSllq2mfG4KnnWrVqMWXKFAYMGIC1tTXnz5+natWqfPnll0RHR+vM+l+SuH//fpbasTIyMjIyMjIyLxv37t3Dzc0txzYGWxTv3r1L8+bNAalwdXx8PCBVnWjatGmJVxTVFRXu3bsnB+HIyMjIlEKCH8bSd/Fx1r7bFO9KculYGRlDiYuLo3LlynpVmTJYUXR2diY6Ohp3d3eqVKnC8ePHqVevHiEhIVnqPpZE1NPNNjY2sqIoIyMjUwqxihcozSyxspav4zIy+UEfF7ycJ6Z10K5dO03R9WHDhvHRRx/RoUMH+vXrx6uvvmq4lDIyMjIyMjIyMiUSgy2Kv/76KyqVCoD333+fihUrcvToUXr06MG7775b4ALKyMjIyMi8iI25CV18nbExNyluUWRkyjwvXR7FuLg4bG1tiY2N1TllkZGRQVpaWjFIJiMjU5iYmJhgZGRU3GLIyMjIFDu56UIvopdF8cKFC3oPXrduXb3bljSePn3K/fv3S4WvpYyMjGEoFArc3NywsrIqblFk8oMqg9TbR4iKiqBiRUdMq7YA5cv7AJChEpwMiSYiPhlHa3Mae9phpJRTv8kUHHopin5+figUilwVKIVCQUZGht6DHzx4kFmzZnHmzBnCwsLYuHEjvXr1ynGbAwcOMG7cOC5fvkzlypWZPHkyQ4cO1XvM7MjIyOD+/ftYWlri4OAg51iUkSlDCCGIjIzk/v37VK9eXbYsllaCt8DOCVyPMaVb6rdsM/0An/Kp0Pk78O5R3NIVOTsvhTFtazBhscmaZS625kzp7k1nH5dilEymLKGXohgSElIogyckJFCvXj2GDx/Oa6+9ppccXbt2ZeTIkaxcuZK9e/fy9ttv4+LiQqdOnfIlS1paGkIIHBwcsLCwyFdfMjIyJQ8HBwdCQ0NJS0uTFcXSSPAWWDsYEIDH8+VxYdLyvn++VMrizkthvLfiLJnNN+Gxyby34iy/vNFAVhZlCgS9FEV3d/dCGfyVV17hlVde0bv9okWL8PT05IcffgCgdu3aHD58mLlz5+ZbUVRjqCVRNvvLyJQO5FmCUowqA3ZOgCxqEc+WKWDnZ1Cr60sxDZ2hEkzbGpzT3mDa1mA6eDvL9yOZfJOnWs8AwcHBOms99+hReE90x44dIzAwUGtZp06dGDt2bLbbpKSkkJKSovkeFxdXYPLIZn8ZGRmZIuDOUYh7mEMDAXEPpHaeAUUmVnFxMiRa676TGQGExSZz/HYULbzsi04wmTKJwXkUb9++Tb169fDx8aFr16706tWLXr168eqrrxZ6HsXw8HCcnJy0ljk5OREXF0dSUpLObWbMmIGtra3mVVDl+9Rm/8wnq9rsv/NSWIGMU5h4eHgQFBRUqGMcOHAAPz8/g7ebOnUqycnZXwhB8p1VVwYqCNLS0vDw8Mjy8JMTb7/9Nvv37y+wdkXJsmXLcvUJlpEpETx9VLDtSjkR8TlfG9WM/Os0EzdcYP+1CFLS9Y8fkJF5EYMVxTFjxuDp6UlERASWlpZcvnyZgwcP0rBhQw4cOFAIIuaPiRMnEhsbq3ndu3cv122EECSmpmf7ik9OY8qWy9ma/QGmbgkmPjktx34Mia5OT0/Xu21JIT8yT5s2LVtFUd1vUFCQXuWH9OXAgQM0a9YMU1NTvbdZsmQJbdu2LbB2hqJSqTR5TWVkyixW2gYCb8UdrpkNxltxJ8d2ZZWIuJTcGwHxKRn8ffIew5aeouFXe/jw73NsvxBGQkrpu5/IFB8GK4rHjh1j+vTp2Nvbo1QqUSqVtGzZkhkzZvDhhx8WhowanJ2defRI+4nx0aNH2NjYZBuAYmZmpinXp2/ZvqS0DLy/3JXty3fqvzzK4UQVQHhcMr5T/82xn6S0nJ/wFAoFU6ZMoVGjRkycOJGLFy/SsmVLGjRogLe3N19//bWmraurKw8fSlMzffv21dTjTklJoWLFilrT7y+yYcMGmjVrhqenp1Z/N2/eJDAwkLp16+Ln58emTZu05IqJidF8t7e3JzQ0FJCslBMmTKBx48YMGTJEa6zRo0fz7bffar5fu3aNypUrZ1EoR44cCUBAQAB+fn5EREQwdOhQhg8fTqtWrfDx8ckih4eHB5988gn+/v54eXkxa9YsQFKkRo8eTe3atalXrx7+/v7ZKqCbNm3SaRWvUaMGp0+f1nxftmyZpl2bNm00+2bVqlU0adKE+vXrU69ePbZu3arZ5sV28fHxjBgxgsaNG1O3bl3eeecdjRXz66+/pnbt2vj5+eHn58edO5luhEjW1t69e9OpUyd8fHwICwtj165dtGzZEn9/fxo3bqyxXoaHh9O2bVv8/f2pU6cOo0ePzlax/Ouvv2jSpAkNGjSgVatWnD9/HoDjx4/j7++Pn58fPj4+/PLLLzq3l5EpNNybg00lzVelQmCmSEepUD9sK8DGVWpXhknLUDHzf1f5ZseVHNspkNyglg9rxBtNq+BgbUZ8Sjpbzj/k/VVnqf/Vbt5efoq1p+8RnZD9DEqGSnDsVhSbgx5w7FYUGSo5ddzLiME+ihkZGRorjr29PQ8fPqRmzZq4u7tz7dq1AhfwRZo1a8aOHTu0lu3evZtmzZoV6rjFiZGREadOnQIkBWPv3r2YmZmRlJRE8+bNCQwMpGnTprRv3549e/bwxhtvcP78eYyNjYmLi+PUqVP4+/tjZmams/+YmBiOHTvG48ePqVatGsOGDcPV1ZVBgwYxfPhw3n33XW7cuEHTpk2pX7++XoFNUVFRnDhxAoVCoWVl/uCDD+jUqRMTJkzAyMiIhQsX8s4772BsrH0YLlq0iMWLF3Po0CHKly+vWX7mzBkOHz6crRXx0aNHnD59mqioKBo0aECLFi2wsLBg7969XL58GaVSSWxsrE6LoRCCnTt3MnPmzCzrhg4dyrJly2jYsCEAS5cu5eOPP87SrlOnTgwYMACFQkFoaChNmzblzp07Wfb9+PHjCQgI4LfffkMIwYgRI/jxxx95++23mT17NmFhYVhYWJCYmIhSqftZ7tixY5w7dw4nJydu377N1KlT2bVrFzY2Nty8eZOAgABCQ0MpX748W7duxcrKioyMDHr27MnatWvp37+/Vn9Hjhzh77//5uDBg5iZmXHo0CEGDhzI5cuXmTFjBh9//DEDBgwA4MmTJzplkpEpNJRGUgqctW8CcFvlzMS0t5lhsoSqymfGg84zy3Qgy52oBD5cHcT5ezEABFS35/CNx4B2iI86dGVKd29a13SkdU1Hpvfw4dy9GP69HM7Oy+HciUpkz5UI9lyJwEipoLGHHZ3qONGxjjOVyktGF9kHX0aNwYqij48P58+fx9PTkyZNmvD9999jamrKr7/+StWqVQ3q6+nTp9y8eVPzPSQkhKCgIOzs7KhSpQoTJ07kwYMH/Pnnn4BkaVqwYAGffvopw4cPZ9++faxdu5bt27cb+jNyxMLEiODp2UdRnwyJZujSU7n2s2xYIxp72uU4Tm4MHz5c8zkpKYlRo0YRFBSEUqnk3r17BAUF0bRpUwIDA9mzZw916tShXr16ODk5ceDAAY4dO0b79u2z7X/gwIGApPRXrVqVkJAQbGxsOHv2LEeOHAGgevXqtGzZkkOHDumlKA4dOlRnhGnNmjXx9vZm8+bNdOrUib///puLFy/m2p+a119/Pcep5rfeeguFQoG9vT2vvfYae/bsYcyYMaSnpzN8+HDatm1L165ddSpfp06dolatWjr7Hzx4MPXr1+eHH37gwYMHXL9+XWe0fkhICIMGDeL+/fsYGxsTHR1NSEgItWrV0mq3adMmjh07xpw5cwDpfzUyMsLGxobq1avzxhtv0LFjR7p27Yqbm5vO39qlSxeNv+7OnTu5efMmrVq10qxXKpXcvXsXV1dXJkyYwOHDhxFCEBERgY+PTxZFcfPmzZw/f54mTZpolkVHR5OUlETbtm356quvuHHjBu3ataNly5bZ/QUyMoWHWyPNx0TMOSG8ScRcsjR2nlmmU+NsDnrA5xsv8TQlHRtzY77vU5fOPi46lTlnHcqcUqnA370C/u4V+OyVWlx7FM+uS4/YeTmcK2FxHLsdxbHbUUzdGkw9N1s87MuxOShr8JCceuflxGBFcfLkySQkJAAwffp0unXrRkBAABUrVmTNmjUG9XX69Gktv61x48YBMGTIEJYtW0ZYWBh3797VrPf09GT79u189NFH/Pjjj7i5ubFkyZICS42jRqFQYGma/a4JqO6Ai6054bHJOv0UFUgna0B1h3ynJnixisSkSZOwt7fn3LlzGBsb89prr2mmUQMDA5k4cSLe3t4EBgbi5OTEnj17OHbsWI5Thebm5prPRkZG2foVvqj4GRkZaSVWzzyVm1PlizFjxvDdd98RGRlJhw4dsgQn5YShFTUUCgW2trZcunSJ//77j/379zNx4kQOHjyIl5eXVtuckr27ubnRsGFDNm/ezOXLl3njjTeyWEEB+vfvz8yZM+nTpw8AdnZ2Oqe5hRD8888/1KhRI8u648ePc/ToUQ4cOEDTpk35+++/CQjIGsX54r4QQtChQwdWrVqVpd3XX39NREQEJ06cwNzcnHHjxmUr05AhQ7RcA9SMHTuWnj17smfPHiZNmoSPjw8LFy7M0k5GplAJ3iy9uzaEaiPgX0BpCh+eAmP9/YpLE09T0pmy+TL/nL0PQGMPO+b298P1mdWvs48LHbydDUrRplAoqOVsQy1nG8YEVuduVCL/Boez63I4p+884fz9WM7fj9W5rZx65+XEYB/FTp06aZJje3l5cfXqVR4/fkxERATt2rUzqK82bdoghMjyWrZsGSD5gmUOkGnTpg3nzp0jJSWFW7duFUhVFkMxUiqY0t0beG7mV/Oi2b+gT6InT57g5uaGsbEx165dY/fu3Zp1lSpVwtbWlkWLFhEYGEjbtm3Ztm0boaGhNGjQwKBxrK2tadCgAUuXLgUkf8XDhw9rLFZeXl6cOHECkHwc1Q8O+tCxY0fCw8P5+uuvGT16dI4yxMbqvlhlh/q4iY6OZuPGjbRv357IyEgSEhLo2LEj3377LR4eHgQHB2fZdsuWLfTs2TPbvocNG8Yff/zBn3/+qWXlfZEnT57g6ekJwIoVK7Kdou3VqxffffedRil/8uQJN2/eJD4+nkePHhEQEMAXX3xBy5YtOXfuXK6/u1OnTuzZs0er1ObJkyc1fTs7O2Nubk54eDjr1q3T2UePHj1YsWKF5sFMpVJp/DKvXbuGp6cnI0aMYNKkSRw/fjxXmWRkCpzLG6V3n95QvaP0WZUK0beLT6ZC5OL9WLrNP8Q/Z++jVMDYwOqsGtFEoySqMVIqaFatIj39XGlWraLB950qFS15O6Aq60Y258Sk9rzd0jPH9urUOydDog39STKlFIMtirGxsWRkZGBn93xK1c7OjujoaIyNjfUKFikLdPZx4Zc3Guhl9i8oJk+ezJtvvsny5cupVq1aFsU8MDCQbdu2aVwAnJ2dqV+/frZ+bjmxcuVKzVS/QqFgyZIlVKlSBYC5c+fy4YcfMnnyZLp27UrFihX17lehUPDWW2+xatWqHH1Lx48fT4cOHbC0tOTff//Vq28HBwf8/f2JjY1l9OjRNG/enLNnzzJixAjS0tLIyMigRYsWWaaNr169SoUKFXB0dMy27549e/Lee+9RvXp1ateurbPNjz/+SJ8+fShfvjzt2rXT7K8XfztI+++zzz7Dz88PpVKJsbEx33//Pebm5vTp04eEhAQUCgXVq1fPEhCkCy8vL1atWsW7775LYmIiqamp1K9fn1WrVjFmzBj69OlDnTp1qFSpUpY8pGoCAgL4/vvvefXVV0lPTyc1NZWuXbvSsGFDFixYwL59+zA1NcXIyEiT8F5GpsiIvQ/3nj2g1OkF8S+47Tw8B461dG5WGlGpBL8fDuH7XVdJyxBUsjXnxwH1aeSRvRtTQeFobY6vm61ebfVN0SNT+lEIQ3K0IFVT6d69O6NGjdJavmjRIrZs2ZIl2KSkERcXh62tLbGxsVpKbXJyMiEhIXh6empNx+aGXJnFcLp160a/fv148803C6xPDw8PNm3alKecjTNnzsTY2FhngEpBUadOHX799VdatGhRaGPI5Exez3GZEsDRBfDv51ClGQzfSXRCKv+u+YWOd2Zh12QgdPm+uCUsECLjUxi/7jwHr0cC8IqPMzNfq4utpUmRyXDsVhQDfst91uDvEU1pVk1/I4FMySI7XUgXBlsUT5w4oXHCf5E2bdrw+eefG9pdqUdt9pfJndOnT9O/f3+8vb01QTQlgc8++6xQ+69Zsya1a9emadOmhTqOjEyZ5fIG6b2O5PZkV86U/g0rwd14CAsqPrkKkP+uRzJ+bRCPn6ZiZqxkSvc6DGhcuchLTzb2tMvRBx/AWKnArYLulHQyZQ+DFcWUlBSdAQ9paWnZVkeRkQFo2LChVpR7QaLO41gSKey0UTIyZZonofDgDCiU4C35EUcnpPLvY086Cmvswi5ARjoY5bkibbGSmq5i9r/X+PWg5GtZy9ma+QPqU8Op4IoJGILaB/+9FWdRoLu6drpK0GfRUX4f0ggfV/2mqmVKLwY7rzVu3Jhff/01y/JFixbh7+9fIELJyMjIyMgAcHmT9O7eAqylLAkPY5L47N9HPDSuDOlJ8LhkP4xll7g65HECvX85qlESBzdzZ9P7LYpNSVSj9sF3ttV20XCxNeebXj7UcLLiUVwKfRcfY9/Vl6Ns4suMwY9gX3/9NYGBgZw/f16Tn2/v3r2cOnVK76ADGRkZGRkZvVBPO/u8lnWdQ00ID4aHQeBUp0jF0pfsEld39HZi3Zn7JKZmUN7ShO9716VjHedilFSbnFLvdPerxKgVZzl88zFvLz/N1B51GNzMo7hFlikkDLYotmjRgmPHjlG5cmXWrl3L1q1b8fLy4sKFCzrzvcnIyMjIyOSJqFsQdh4URlBbR0Jt+2e5SB/mnkaqONh5KYz3VpzVUhJBSi+z/NgdElMzaFrVjv+NCShRSqKa7FLv2JibsHRYI/o1rIxKwJebL/PVtmC5xF8ZJU9OHX5+fqxcubKgZSmdqDLgzlF4+kgqSO/evEyXkZKRkZEpMtTWRM9WUM4+63qHmtJ7CVQUM1SCaVuDsw0IAbA2M+bP4U0wNTY8hVlxY2KkZGZvX6pUtGTWrmv8fjiEe9GJzOvvl2PBCpnSh8FH59mzZ7XKrm3evJlevXoxadIkUlOzLy5eJgneAvN8YHk3+Oct6X2ej7RcRkZGRiZ/qP0TM007W5oa0cTTDstKUuEDHl2CjLSilS0XToZEZ7EkZiY+JZ0zd0pv7XSFQsH7bb34aUB9TI2V/Bv8iP6/HpdzLJYxDFYU3333Xa5fvw7A7du36devH5aWlqxbt45PP/20wAUssQRvgbWDIS5TPcy4MGl5ASiLQ4cO1VSmCQoKYvXq1fnus7hQKBTExMToXPfWW2/h7e3Nq6++mqe+C3rfKBQKfH198fPzw8/Pj0OHDmnWtWnTJscI6/Xr1/Pee+9lWb5s2TKmTp2q+b548WJq1aqFn58fUVFRBssYExPDzJkzDd4uO+bNm0d4eHiB9VeUTJ06lbFjx2q+e3h4FJssMgVI5HVJAVQaQ61uWquqOlix5t1mVPWqBWa2kJ4MkVeLSVDd6KsslQWlqnu9Sqx6uwkVLE24cD+WV38+yvVH8cUtlkwBYbCieP36dU1S43Xr1tG6dWtWrVrFsmXL+OeffwpavuJBCEhNyP6VHAf/+xTdiQOeLds5QWqXUz8G5DovyYqiSqVCpVLladtHjx6xevVqLl68yMaNG/PUR372TXa1rQ8dOkRQUBBBQUEG+d7mVDP6RebNm8fSpUsJCgoyqLKNmvwoirp+c0lWFLP7j2TKOOpp56ptwVK7KolKJUhJz0AlgEr1pIUlbPrZ0Vq/pO76tivpNPSwY+OoFnjal+NBTBK9Fx7l8I3HxS2WTAFgsKIohNAoBXv27KFLly4AVK5cmcePy8hBkZYI31bK/jWzMsSH5dCBkCyNMyvn3E9aol7iRERE8OWXX7J//378/PwYOXIkAKdOnaJdu3Y0bNiQ+vXra+r4hoaGUr58eb744gsaNGhA9erVOXLkCB999BF+fn74+Phw6dIlAA4cOICPjw+DBw/Gx8cHf39/goKCNGPPmjWLOnXq4Ovry6BBgzT1l6dOnUrv3r3p1KkTPj4+hIWF8fHHH9OoUSP8/Pxo1apVrvkDY2JiaNu2LcnJyfj7+zNz5kwuXrxIy5YtadCgAd7e3nz99dea9qmpqXzyySf4+PhQr149OnfunO2+2bVrFw0aNKBu3bq0bt1aU9/5wIED1KlTh7feegs/P788K6e6SEtL48iRI7nWPO/Tp4+mTnmfPn1IT0+nU6dONGzYkDp16jBw4ECt+tlLly7Fz8+PevXq0bBhQ0JDQxk5ciTx8fH4+fnRsGFDQKrJHRgYSN26dfHz82PTpk2aPhQKBVOmTKFRo0ZMnDhRS57p06fz8OFD+vXrh5+fH0FBQaSlpfHZZ5/RuHFj/Pz86Nu3r6Z29dChQ3nnnXcIDAzE09OT4cOHc/LkSdq0aUPVqlUZN26cpu82bdrwwQcf0KhRI7y8vBg/fjzqYlCGyJvTcSFTBhECLmUf7RwcFkfNyTsJDouDSvWlhQ+Dik4+PWjsaYeTjVm26xVI0c+NPQu/NF9R4WFfjg3vNaexhx3xKekMXXqStafuFbdYMvlFGEjbtm3F4MGDxZ9//ilMTEzEjRs3hBBCHDhwQLi7uxvaXZETGxsrABEbG6u1PCkpSQQHB4ukpCQhUp4KMcWm8F8pT3OUdciQIWL//v1CCCGWLl0qevbsqVn35MkT4efnJx4+fCiEECIyMlJUrlxZ3L9/X4SEhAhAbNy4UQghxJIlS0S5cuXEvn37hBBCfP/996JPnz5CCCH2798vALFnzx4hhBBr1qwRNWvWFCqVSuzYsUPUqlVLPHnyRAghxIgRI8TIkSOFEEJMmTJFuLi4iPDwcI1MERERms9///236NSpk+Y7oOnnRUJCQoStra3me1xcnEhOThZCCJGYmCj8/PzEsWPHhBBCTJ06VfTo0UOzXj1e5n3z6NEjYWdnJy5cuCCEEGLFihWidu3aQqVSif379wuFQiEOHDiQ7X4HRIMGDUTdunXFRx99JJ4+ff4/tW7dWoSEhOjcbteuXWLAgAE61y1dulRMmTJF893d3V2cO3dOCCGESqUSjx8/1nweOXKkmDFjhhBC+n88PDw0/3NCQoJISEjIst+EEKJx48Zi0aJFQgghrl+/Luzs7ERoaKjmN02bNi3b3/yiPEII8c0334jp06drvk+fPl2MGjVKCCEdl02bNhVJSUkiJSVFVKtWTfTq1UukpqaKp0+fCkdHR3Hp0iXN/mrXrp1ITU0VCQkJwt/fX6xcudJgeXM6LqZMmSLGjBmj9Vt0oXWOy5Rswi9J18jp9kIkxWRZffF+jHCfsE1cvB8jxMV/pLaL2xSDoNmTnqES3ecfFO4TtmV5eTx7/e/iw+IWs1BITksXH/59VvN7v/vfFZGRoSpusWReIDtdSBcGhybNmzePQYMGsWnTJj7//HO8vLwAyTerefPmBabAFismljDpYfbr7xyFlX1y72fQeikKOqdx8sjRo0e5ffs2r7zyitbya9euUbVqVczNzTVToA0bNsTKyoq2bdsCUtL0F6PWPTw8NDkx+/btyzvvvMO9e/fYs2cP/fr1o3z58gC89957vP7665rtunTpgpOTk+b77t27+emnn4iPj0elUhEdHW3w70pKSmLUqFEEBQWhVCq5d+8eQUFBNG3alG3btvHdd99hZiY9pTs4OOjs48SJE/j6+uLr6wvAoEGDeP/993nw4AEAVatWpXXr1tnKcOfOHapUqUJCQgIjR47kk08+YeHChbnKvmnTpjz5WQohmDt3Ltu3byc9PZ3Y2FjNubR9+3befPNNXFxcALC01H3MxMfHc/bsWY4cOQJA9erVadmyJYcOHcLd3R2A4cOH6y3Tpk2biI2N1biTpKamavn+9ezZU1Mv2dfXl06dOmFiYoKJiQne3t7cuHGDOnWkvHaDBw/WrHvjjTfYs2cP3bt3N0jenI4LmTLI5WeWfq9AMM+l8ofaovjoEqSngrFp4cqmJ7N2XePCgziMlQpsLUyISnge7Olsa86U7t509nEpRgkLDzNjI+b188PdzpL5+26y8MAt7kYnMvv1epibyFlBShsGK4p169bVinpWM2vWLIyMysgBoFCAabns11drBzaVpMAVnX6KCml9tXaFlipHCEGdOnU4evRolnWhoaEaZQrAyMhIc1NXf8/J70uhUOisL5p5mZWVlebz3bt3GT16NKdOnaJatWpcuHCBVq1aGfSbACZNmoS9vT3nzp3D2NiY1157jeTkgnX2flFuXVSpUgWAcuXKMWrUKN55551c+xRCsGvXLr7//nuD5Vm1ahX79u3jv//+w8bGhvnz57Nv3z6D+8lMTv9Xbggh+Omnn+jYsaPO9ZmPJ0OPL0PlLYrjQqaE8OK0cx0dSbYzU8EDzMtDcgxEXgGXeoUonH78c+Y+i/67BcAPfevRrW4lnYmryzIKhYJxHWtS2c6SiRsusu1CGOGxyfw6uCF25UqGMi+jH3lK3hQTE8OSJUuYOHGixmoUHBxMREREgQpXYlEaQefvnn3JfLI/+955ZoEqiTY2Nhr/QIDmzZsTEhLCnj17NMuCgoLylKIoNDSU/fv3A5Jl2MnJCTc3NwIDA1m7di1xcXGAFKmbneIQGxuLiYkJLi4uCCFYsGCBwXIAPHnyBDc3N4yNjbl27Rq7d+/WrOvRowc//vgjKSkpAERGRgJZ903Tpk25ePGixg9z9erVuLq64urqqtf4iYmS76hKpWLNmjXUr18/1+1OnjxJ7dq1DVLGXhzT3t4eGxsb4uPjWbZsmWZd9+7dWbFiBWFhkk9sYmIiiYmJ2NjYkJSUpPm/ra2tadCgAUuXLgUk/7/Dhw/rraxn3oe9evVi7ty5mn2RmJjI5cuXDf5tACtWrNDUgl+1ahWBgYEGy5vTcSFTxgi/ANG3wNgcanbOvb1CAZX8pM8lIKDlzJ1oJm6QjCkftPOip59rtomrXwZeb1iZP4c3xtrcmNN3nvDawiOEPE7ItqyhTMnDYIvihQsXaN++PeXLlyc0NJQRI0ZgZ2fHhg0buHv3Ln/++WdhyFny8O4Bff+UoptfTJFjU0lSEr11VBHIB+3bt2f27NnUrVuX5s2bs2jRIrZv387HH3/M+PHjSUtLo0qVKloBAfpSp04dli1bxocffoipqSl///03CoWCV155hUuXLtGsWTOUSiV169bNdgrW19eX/v37U6dOHSpWrKhX5K8uJk+ezJtvvsny5cupVq2aVmDIhAkT+Pzzz2nQoAEmJiZUqlSJHTt26Nw3K1euZPDgwaSnp1OhQgXWrVuXrSXrRa5evcq7776LQqEgPT2dBg0a8OOPP+a6nb7RzroYPHgwmzdvpmbNmjg4OBAQEMCdO3cAaNWqFVOmTKFTp04oFApMTU1Zv3497u7uDB48mLp162JlZcXp06dZuXIlI0eOZMGCBSgUCpYsWaKxjubGhx9+yIgRI7C0tGTZsmVMmDCBlJQUmjRpotlvEyZM0EwnG0Lt2rVp0aIF0dHR9OzZk/79+wMYJG9Ox4VMGUNtTazeAcx01zyu4WTNsYntqFju2cxJpfpw+4CkKPoPLRIxdXH/SSLv/nWG1AwVneo48VFgjWKTpSTR3MueDe81Z9iyU4RGJdJ1/iEsTIy0puNdyvh0fGlGIYQBOVqAwMBAGjRowPfff4+1tTXnz5+natWqHD16lIEDB+aYY64kEBcXh62tLbGxsdjY2GiWJycnExISgqenp9Y0Wq4UYmWWoUOHMnToUNq0aVMg/eniwIEDjB07VivSWSZ72rRpw7Jly7Lk6qtTpw779+/H0dFR53bLli0jNDRUK5fiy0CbNm0YO3ZsnpXovODh4aHzOpTnc1ym6BACfqwHMXegz1Ld9Z11cXkTrBsCLn7w7n+FKWG2JKSk0/uXo1wNj8fbxYb17zWTK5RkIjI+hT6LjnInKmvGD/Vj/C9vNJCVxSIgO11IFwZPPZ86dYp33303y3JXV9cSm4etUFEagWcA+PaR3uXyfS8lly9fzlZJlJGR0ZOHZyUl0cQSanTKttndqERGrTzDXbXCoQlouQzpKUUgqDYqleCjNUFcDY/H3sqMJUMaykqiDuzKmZKSlqFzndpiNW2rXDO6pGGwomhmZqbxWXuR69evZxuFKpM3evXqVehVJtq0aSNbEw1g6NChmihwQ/Dz8ytUy3BJ5cCBA0VqTQS0qrTIFA959j9TTzvX6JxjQGFccho7LoYTl/ysbF/5KmBhB6o0SVksYmb/e41/gx9haqzk18H+VCpvUeQylAZOhkQTHpe9Ii+AsNhk1py8KyuLJQiDH3l69OjB9OnTWbt2LSBFNt29e5cJEybQu3fvAhfwZaaob7AyuTN06NA8baeuZiRT+MiKYvGy81IY07YGa9U51sv/TKV6Xtu5joFpptQBLbf2QVgQuDYwVOw8s/HcfRYekCKcv+9dlwZVKhTZ2KUNfcsVTtp0iRn/u0pjTzuaVq1I06oV8a5ko3cQUIZKvHRR5oWJwYriDz/8QJ8+fXB0dCQpKYnWrVsTHh5Os2bN+OabbwpDRhkZGRmZUsDOS2G8t+JslqRh4bHJvLfibM7+Zw9OQ9x9MLWSAlkMpVJ9SVEswsjns3efMOEfKcJ5VJtq9Kqfe2aFlxl9yxVamCiJT0ln79UI9l6VsqlYmxnTyNOOplUl5dHbxQZjo6yTonl+UJHJFoMVRVtbW3bv3s2RI0c4f/48T58+pUGDBgQGBhaGfDIyMjIypYAMlWDa1mCdmWUFUrDCtK3BdPB21m3dUU871+wCJnmYutWU8isaRfFBTBLv/HmG1HQVHbyd+LhjzSIZtzTT2NMOF1tzwmOTs8tAjLOtOf990pbrj+I5fjuK47ejOBESTXxyOvuuRrAvB8Vxz5VHeX9QkcmWPHvbtmjRghYtWhSkLDIyMjIypZSTIdFaVpzMqP3PToZE06xaRe2VKhUEb5I+6xHp7GhjxiedauL4Yi1lFz/pPeIKpCWDSeFFtiempjNi+WkeP02hlrM18/r5oZSnNnPFSKlgSndv3ltxFgXa5SrUe29Kd29MjZX4uNri42rL2wFVyVAJroTF5ag4ljM1Il0l8v6gIpMtcliWjIyMjEy+0df/TGe7u8cgPgzMbKWKVrngaG3O+229tBfauoGlPSQ+lgJa3Pz1ksdQVCrBuDXnCQ6Lw97KlCVDGlLOTL6V6ktnHxd+eaNBlunhnMoaGikVOSiO0ZwIiSI+OftqUJDLg4pMjuSpMotM0bB582Zq166Nn5+fzrKJ+jB16tQSXepMoVAQExOjV9v169fz3nvvGdT/22+/rak6U1iEhobSpk0bbG1tswStHDhwAAsLC/z8/DSvpKSkQpVHRqY40Nf/TGe7y8+mnWt1BWOzrOszEZuUxu7gR8QmpT1fqFC8MP18Vi9Z8sLcPdfZeTkcUyMli9/0x62C7vrrMtnT2ceFwxPa8feIpvzY34+/RzTl8IR2ek8LqxXHtwOqsmRIQ4K+7MgnnfSb+tf3gUbmObKiWIJZtGgRX375JUFBQfj6+uapj2nTpuVJUcypVm9xkZfqJ0uWLKFt27aFI9AzbGxs+Prrr1m1apXO9TVr1iQoKEjzsrCQU2fIlD2EEFkKmmbGSKnA2ChTK1UGBG+WPuuZYPtedCIj/jzNvehMiZvVpfzCgvTqx1A2Bz3gp303AZjxmi/+7naFMs7LQEGWNTRSKvSONtf3gUbmObKimAMRcclcehCr9VJfmJLTMrKsu/Tgea3cW5FPs6yLSZTKFUU9TSEiLmfl7cMPP+TQoUNMmjSJ5s2bAzBo0CAaNmxI3bp16dq1q1aC8+3bt9OoUSPq1auHn58fJ06cYOTIkQAEBATg5+dHREQEERERvPbaa/j6+uLj48PixYs1fXh4eDBhwgQaN27MkCFDssjUpk0bPvjgAxo1aoSXlxfjx49HXdgnPDycvn370rhxY3x9fZk8ebJmu48//phGjRrh5+dHq1atuHbtWpa+hRBMmDCBHj16aOoLv0haWhpHjhzRWbotJ7natGmjKWsYGxvL22+/jY+PD/Xq1WP48OEAPH36lOHDh+Pj44OPjw/Tpk3L8b/JjJ2dHS1btqRcuezzvsnIlGX2XnnEsGWnNP5h2d3yM1SCfouP8f3Oq6SkP0u8HHoYEiLBogJUbZM/QTQWxaD89aODoHsxfLL+AgDvtq5Kb3+3Ah9DJu+oA2VyUjdtLUxo7Ckr94ail2OFrgTb2ZFbKZjSxMoTd/lx7w2tZb38KjGvf33CY5Pp9tPhLNuEzuwKwMfrznPubozWurn96vFqfTe2Xwwj6mkqH3XIvg7o/PnzuXDhglb5s3nz5mmSms+cOZOpU6eyaNEirl+/zrBhwzh48CC1atUiLS2NxMREFi1axOLFizl06JAmSXS/fv2oWbMmGzZsICIiAn9/f+rVq0fTpk0BiIqK4sSJE9nWRQ4ODubo0aOkpaXRqlUr/v77bwYOHMiQIUOYNGkSrVu3Jj09nW7durFu3Tpef/11JkyYwOzZswFYvXo1Y8aMYefOnZo+U1JSGDBgABUrVmTjxo0YGWWtbrN//36aN2+OiYmJQXK9yNixY7GwsODChQsolUoiIyMB+Oqrr0hJSeHChQskJSXRsmVLatWqRb9+/Zg1axYrV67UOeaUKVN49dXc873dunWLBg0aYGRkxLBhwxg1alSu28jIlBY2nXvA+HXnyVAJAms70qNeJWb872qW9CQfd6zJ4ZuP2XjuAQsP3GLf1Qjm9PXDWz3tXLs7GOk+v/VGrShGXIG0pLxFT+sgLDaJEX+eJjVdRWBtRz7tVKtA+pUpOHIKlFETm5TGtK2X+aKbNyY6UuvI6EYvRbF8+fLZKg6ZycjQXZ6nNDKoSRU6eDtpLbO1kC5kzrbmbPugZbbbzn69Hkmp2vvCrYJ00erq65KnrPOrVq3ir7/+Ijk5meTkZOzt7QHYvXs3nTt3plYt6eJlYmKCra2tzj727NnDmTNnAHB0dOS1115jz549GkVx6NChOf7XgwcPxsTEBBMTE9544w327NlDz5492bt3L48ePdK0e/r0qcZyuHv3bn766Sfi4+NRqVRER0dr9dm1a1d69uzJF198ke24mzZtylEp0yVXZkVx27ZtnDhxAqVSukCole49e/bwww8/oFQqKVeuHIMHD2b37t3069ePTz75hE8++STbcXOjQYMG3L9/H1tbW+7fv0+XLl2wt7enb9++ee5TRqaksPxoKFO2SJVQXqvvynd96mJipKRr3Uo6Ex739nejUx0nPt94iavh8fT++QBnLTZiAVBHz7rOOWHtAuUcISECwi9B5Ub57jIxNZ0Rf54mMj6Fmk7WzOtfX46aLaFkFyjjYisdg5uDHvLnsTvcePSUnwc1wK6caTFKW3rQS1F8MRggNDSUzz77jKFDh9KsWTMAjh07xvLly5kxY0bhSFlMONqY42ij25/B3MQIH1fdyhhANQerbNdVtMrdWTszhw8fZv78+Rw7dgxHR0e2bNnCl19+aXA/mcmsFFpZZS93dturp3mPHz+Oubn2/rp79y6jR4/m1KlTVKtWjQsXLtCqVSutNu3atWP37t2MGTNGp0VaCMGuXbv4/vvvDZIrr7y4bX4tii/+Hjc3NwYMGMChQ4dkRVGmVCOE4Kd9N5mz+zoAQ5t78GU3b02KGLX/mS46+7jQ0MOOSRsuknR1NxbpscQobHlcrj5eOrfIipmxkuqOVpgZZ7IKqQNabuyS8inmU1FUqQQfrzvPpQdx2JWTIpyt5AjnEk1nHxc6eDvrfFDpVrcSY1ef49jtKHr+fJjfBjeklnPZmQUtLPSyvbZu3Vrz+vPPP5kzZw4zZsygR48e9OjRgxkzZjB79myWLl1a2PK+tDx58gRra2sqVqxIamqqlm9hp06d2LVrF1evXgUkf77YWMlf0traWvMZIDAwkN9++w2AyMhINmzYQIcO+ldBWLFiBWlpaSQlJbFq1SoCAwOxsrKibdu2zJw5U9Pu4cOH3L9/n9jYWExMTHBxcUEIwYIFC7L0OWnSJF577TUCAwOJiorKsv7kyZPUrl07RyVWl1yZ6dGjB7Nnz0alUml+v3qf/P777wghSEhI4K+//qJjx44AfPLJJ1qBKC++9Jl2DgsL04wXHx/Ptm3bqF+/fq7byciUVFQqwVfbrmiUxLGB1ZnS3dugPIL2VmYsftOfr7ykPralNaTrgmMsOXQblR6zLdWdrNk9rjXVnayzrizAxNvz9t5gx8VwTIwULH7Tn8p2coRzaSC7QJkO3k5sfL8F7hUtuRedxGsLj7LzUnguvckYPEl/7NgxGjZsmGV5w4YNOXnypMEC/Pzzz3h4eGBubk6TJk1y7WPevHnUrFkTCwsLKleuzEcffVSi078UFJ07d6ZmzZrUrFlTE5yixsvLi6VLl/LGG29Qr149mjRpopn2HT9+PB06dNAEs8yfP58rV67g6+tL27Zt+fzzz2nSpInectSuXZsWLVrg6+tLQEAA/fv3B2DlypXcvHkTHx8ffH19ee2114iKisLX15f+/ftTp04dGjVqRJUqVXT2O3bsWEaMGEG7du20gnRAv2jn7OR6kblz55KSkoKvry9+fn5MmjQJgC+++AITExN8fX1p0qQJPXr0MMjil5iYiJubG6+//jrBwcG4ubkxceJEAP755x98fX01fqAdOnRg2LBhevct83KToRIcuxXF5qAHHLsVlSeXlYIkPUPFp/9c4I8jIQB82c2bsYE18mTBV2Sk4RGxF4C7lV4hJV3F19uv0P+341mjmQ0hH5HPL+7vOf9eY/4zH/VvXvWlkYccBFEWqOFkzeb3W9DCqyKJqRmMXHGGH/fc0OsB5WVFIdTzhnpSs2ZNevbsmWUa8NNPP2Xz5s06I1qzY82aNQwePJhFixbRpEkT5s2bx7p167h27RqOjo5Z2q9atYrhw4fzxx9/0Lx5c65fv87QoUPp378/c+bM0WvMuLg4bG1tiY2N1ZoWTE5OJiQkBE9PzyzTpzISbdq00QquKSrq1KnD/v37dR4TxSmXTOmitJ3jJa1mbXJaBh/+fY5/gx9hpFTwfe+6+Yv8vb4LVvUFKyfER8H8ffohX28PJjE1A0tTIyZ39WZA48o6ldDLD2Ppt/g4a95tSp1KmVyA4sJgTi1QKGHifTDVLxuBrv0NEFjbkSVD8u/rKFOySM9Q8c2OKyw9EgrAKz7OzH693kuTPD07XUgXBlsU586dy08//YSvry9vv/02b7/9NnXr1uWnn35i7ty5BvU1Z84cRowYwbBhw/D29mbRokVYWlryxx9/6Gx/9OhRWrRowcCBA/Hw8KBjx44MGDAgT5ZMmdLD5cuXs1USZWTKIjsvhfHeirNZlBZ1zdqdl8KKVJ6nKekMX3aKf4MfYWqsZNEb/vlPD6Ou7ezdE4WRMQObVGHnmFY09rAjMTWDSRsvMnTpKcJ1lAUUQpJJp5nDxkUKahEqCNevUEF2+xtg75WIIt/fMoWPsZGSKd3r8H3vupgYKfjfpXB6/3I0f9bsMorBimKXLl24fv063bt3Jzo6mujoaLp3787169fp0qWL3v2kpqZy5swZLV8ypVJJYGAgx44d07lN8+bNOXPmjEYxvH37Njt27Mhx3JSUFOLi4rReMnnjwIEDJdJqV1LlkpHJCxkqwbStwdnWrAWpZm1RTUM/SUhl0G/HOXorinKmRiwb1ihLNgiDSUuGazukzy9EO1epaMnqd5oyuWttTI2V/Hc9ko5z/2PTuQeaoLkMleDCfcnv+sL9WN37QV33WY98ihkqwdQtuve3mqLc3zJFS99GlVn9TlPsrcy4Gh5Pz5+PcPx2Vl/5l5k82VgrV67Mt99+m6+BHz9+TEZGBk5O2hccJycnTVBGZgYOHMjjx49p2bIlQgjS09MZOXKkxtdMFzNmzDAogbKBM/EyMjKlACEECclpJKamc+5uDI29nEpsipOTIdE6LVtqirJmbXhsMm/+foIbEU+pYGnC8uGNqetWPv8d39oLKXFgXQkqa/tIK5UK3g6oSpuaDoxbe54L92MZuyaInZfCaV/bkTm7r2v2z6SNF/lp342s0/GV6sP1/2kFtCSlZnA3OpE7UQncjU589jmRa+HxhOdQAEGuEVz28Xe3Y8voFrz71xkuPojljSUnmNKjDm82dS9u0UoEBiuKBw8ezHF95tQnBcmBAwf49ttvWbhwIU2aNOHmzZuMGTOGr776KtscfBMnTmTcuHGa73FxcVSuXDlLOxMTExQKBZGRkTg4OOQrvYqMjEzJIT45lYi4FJLjY7gTlcSkvWepYGVWbL5+uaFvLdot5x9Q18220HyqQh8nMGjJCR7EJOFsY86Ktxvj5agjyjgvqKed67wKSt0TW16O1mx4rzm/HLjFj3tvsPNyODsvZ41QVU/HLxzUgMaedtyJTiQluTLNgPCrx/hg0VHuRCUSEZ+SL5HlGsFlm0rlLVg3shmfrr/AlvMP+WLTJa6GxTGlex1MM6dheskwOJhFqeOkflGp0jfhdmpqKpaWlqxfv15r2nDIkCHExMSwefPmLNsEBATQtGlTZs2apVm2YsUK3nnnHZ4+fapTtszk5MD59OlT7t+/L1sVZWTKCEmpGUQnpCIQxCdn8MvpGG5Ep2nKfP3yRoMSpywevB7B4D9O6dW2nKkR3etV4vWGlWlQRf/CCLkR/DCOwX+c5PHTFDwqWrLi7Sa4VSig1DCpiTDLC9IS4O294JY1i0Zmzt+LofcvR0nPYfr3xWoc9sRy2vw9VEKBT8rvJCIFL1mbG+Ne0RJ3u3JUqWiJu50lT5PT+XrHlVxl+HtEU9mi+BIghGDRf7f5ftdVhJBKA/4yqEGe8h+XZAwJZjH4UfTJkyda39PS0jh37hxffPEF33zzjd79mJqa4u/vz969ezWKokqlYu/evYwePVrnNomJiVmUQXW5t4JQ7qysrKhevTppaWn57ktGRsYwJN+zGKITUrErZ0pdt/L5mh7OUAkG/XacyKcpqAQ8SVKRnCFdJwSSYjFtazAdvJ1LzDT0k4RUTUqWnLA2N8bO0oQ70UmsPnWP1afu4eVoRb+GlXm1gSv2+bipnQ6NZtiyU8Qnp+PtYsPy4Y1xsC7Am+TN3ZKSaFsFXP312iQxNSNHJRGeK4nONuZUqWhHTKQD5dMj+bWDCVY1WuBuZ0l5S5MsynSGSvD7kRDCY5N1+ikqkCpxyTWCXw4UCgXvtalGTWcrxvwdxMmQaHosOMJvgxviXcmGDJXQmcy7LGOwoqirNFyHDh0wNTVl3LhxmvJw+jBu3DiGDBlCw4YNady4MfPmzSMhIUGTZ27w4MG4urpqKr50796dOXPmUL9+fc3U8xdffEH37t111gfOC0ZGRgXWl4yMjH4URCqYpynpkv9ZVCJ3ohM5GRJNUFj2EYwlzfcs9HECw5adIuRxAubGSpLTVVlq1qpvR7P61KVTHWdOhESz9vQ9dlwM42bEU77ZcYXvdl6lfW1H+jWqTKvqDhhnU9NW1w3v0I1IRq44Q3KaikYeFVgypJGmbGmBoZl27iVVUtEDfad9Z79elz7+z1yLVjeBq9toaXkPKpfPdpucagSrpZvS3bvMKwMy2rSr5cTG95vz9vLThEYl0vuXo7zRtArbLoSVmJRVRUWBObc4OTkZlEMRoF+/fkRGRvLll18SHh6On58fO3fu1AS43L17V8uCOHnyZBQKBZMnT+bBgwc4ODjQvXt3gyyZMjIyJQt1apLM1hy175l6elgIQWR8CneeBSHcjUrQfL4XnUhUQmqexi8JvmenQ6MZ8edpniSm4VregqXDGnE78mkW5dk5002padWKNK1akak96rD1/EPWnrrH+fux7Lr8iF2XH+FkY0Yffzde96+Mh/3zfIK6FPPyFibEp6SRoYI2NR34ZZA/FqYF/NCc8lTKnwjgo39tZ0dr/fJeupZ/YXrcxQ+ubtMr8jm7GsGZ97fMy4WXozWb32/J6L/PcujGY347FJKlTebrVFnEYB/FCxcuaH0XQhAWFsbMmTNJT0/n8OHDBSpgQWPIvLyMjExWCnLqJUMlaPndvhyjfM2MlVSxs+DekySS01Q59mdXzpQqdpa4V7TESAEbzj3MVYbfhzSkfe18pnvJB1vPP2T8uvOkpquo62bLkiENNYpRXvb11fA41p66z8Zz93mS+NyNpomnHf0aVcZIqWDs6qBs08H4u5fn7xHNCseB/+J6+OctqOAJH57T26KoPk5ymx4+PKHd8/1zYw+s7A0Vq8MHp/Ue52WbVpTJnZS0DBp8tZuEVN0xGDqPvxJOofoo+vn5oVAosvgENm3aNNtE2TIyMmWDgpgijktO426UlJ7k0PXIHJVEgJR0FTciEgBQKqToRPeKllSxK/fs3VKjHFqbP58mzVAJjt2Ozla5UDN540XMTYxo4WWvl/wFhRCChQduMWuXNBPTwduJH/v7YWn6/LKsrllrCLWcbfiyuzcTXqnJnuAI1p6+x8EbkZwIieZESHSW6dXMPIxJLryb3eWN0rvPa3oriZDH6WF1Kb+oG5AcB+a5Gwbysr9lyj5n78ZkqyRCyXNjKWgMVhRDQrRNr0qlEgcHh1JREktGRibv6DtFrFIJIuJTuPNsavjes+nhO9HSdPGLVi59Gdm6Kv0aVcG1vIXelq7clAsB2FuZEhaXwqAlJxjczJ3PXqmlpagVFmkZKiZvvMSa0/cAGN7Ck8+71i5QBc3M2IiudV3oWteFhzFJrD9znz+PhfL4ac5T9IV2w0uOgxu7pc91XjV4c4Onh8vZg21liL0H4RfAo2V+pJd5idHXPaUkuLEUBgZfEf/77z/69euHmZl2FFxqaiqrV69m8ODBBSacjIxMyUCfaiFjVgdRucI17j1JIiU95yniiuVMqVLREksTI47cyr0KQusajnja61ez90VyUy4Cqjsw839X+ev4Hf48doeD1yP5oW89/N0LL8I1NimNUSvPcORmFEoFTOlehyHNPQptPJCssB+2r04VO0vGrgnKtX2h3PCu/Q8yUqSpYCefPHXR2ceFDt7O/O9iGOvP3qdPAzde8XXJXsGu5Ccpig/PyYqiTJ7R10fWWI8UfaURgxXFYcOG0blz5yy1d+Pj4xk2bJisKMrIlEFyqxYC0hTxzcjnU8SuFSw0+eqq2Ek569Sf1VPE+vqe5Sc1iVq5yM737KtePnSs48Sn6y8QGpXI64uOMaJVVT4KrIG5ScEGc9x/ksjwZae4/ugplqZG/DSgfpH6RzrZ6HfD0/fGaBCXn0U7GzjtnBkjpYJu9SrRrV6l3BtXqg9XtmpVaJGRMZTGnna42Jrn6sYyfm0QNyOe8k6rqgUfCFaMGKwoCiF0JnW9f/++ztQ5MjIypR99LUwjW1ejf6PKuFawwCSbtCwvUlSpSXLzPQuo7sDOsa2YvjWYf87eZ/F/t9l/NYI5ff3wcS2Y69qF+zG8tfw0kfEpOFqb8cfQRgXWt77kdsMrtJyBSU/g5l7pcx39o52zIyElnavh8dRyts65Mo0BNZ9lZLJDHzeWqg7luB2ZwNw911l96i6fdq5Jz3quKEtJcEtO6G0nrV+/Pg0aNEChUNC+fXsaNGigedWrV4+AgAACAwMLU1YZGZliQl8LU+saDnjYl9NLSVSjnh52ttUew9nWvEhTTthamPBD33r8+qY/9lamXH/0lF4/H+HHPTdIy8h5Kj03/r0cTt/Fx4iMT6GWszWb3m9R5EoiPL/hwXNFXE2h5gy8ugNUaeDoDY618t1dyOMEev9ylJDHCTk3rFRfeo++BUkx+R5X5uUlp+vUojcasHdcaxYMrI9reQvCYpP5aM15Xv3lKGfuRBeTxAWH3hZFdfWUoKAgOnXqhJWVlWadqakpHh4e9O7du8AFlJGRKX7Ulqjspp/za4nKbXq4KOlYx5mGHnZM3nSRHRfDmbvnOnuuPGJO33pUdzKs1rEQgj+OhPL19mCEkBTpBQPra0VnFzXFkjPw8gu1nYsSSzso7w4xdyDsPFRtXbTjy5QpcrtOdatbicDaTvx+OISF+28+Kz15jG51XfjslVoFVwaziDE4j+Ly5cvp169fqY1ylvMoysjkjfl7bzBn9/Usy0ty3eT8IIRgy/mHfLn5MrFJaZgaK/m4Yw3eallVLwU2PUPFV9uCWX7sDgADm1Rheo862VZKKWqKLGdgYjTMrg6qdBh9Buy98t3lpQexdPvpMNs+aJm7ZXbtYAjeDB2mQ4sx+R5bRkYfIuKTmfPvddacvocQYGqs5O2Wnoxq64VVTu4SRYQhupDBV6whQ4aUWiVRRkYm75y+I9V5t8zkpF3UU8RFhUKhoKefK/9+1Io2NR1ITVfx7Y6r9P/1GHeink95ZqgEx25FsTnoAcduRZGhEiSkpPPuX2c0SuKkLrX4ppdPiVES4bnfZk8/V5pVq1h41tsrWyQl0dm3QJREg1FPP8sBLTJFiKO1OTN712X7BwE0q1qR1HQVCw/cos2sA6w+eZeMXGqXlyT0Umvt7Oy4fv069vb2VKhQQWcwi5ro6NI/Hy8jI6PNhfsxHLweiZFSwY4PAwiLTS72KeKiwsnGnKVDG7Hm1D2+2hbMqdAndJ53iElda2NfzpTp27SncB2tzTAzVnLvSRJmxkrm9fPjFd+ypUQbhKa2c/6DWNQYKRXYlTPV77jTBLTIiqJM0eNdyYZVI5qwO/gR3+64QmhUIp9tuMjyY3f4olttmld7nui/pFYG0ktRnDt3LtbWkm/OvHnzClMeGRmZEsjP+28C0LNeJTzsy2nVDX4ZUCgU9G9chRZe9nyy/jzHb0fzxaZLOttGxKcAYGVmzJ9vNaZBlQpFKWrJ4mkkhB6SPhegf2JtFxvOftFBv8bqCi1PQqXoa4uX+P+QKRYUCgUd6zjTpqYjfx4LZf7eG1wJi2Pgbyfo4O3EpC61uRYel++qV4WFwT6KpR3ZR1FGxjBuPIqnw9yDKBSw+6NWeDkaFtBR1lCpBH8cCeHr7VdybOdobcaxie1LhEWg2Di1BLaPl6Z/3zlQfHL86AdPQuDNTVCtbfHJISMDRCek8uOe66w4IU1BGylBV2KFwvT/LlQfRQCVSsX169c5fPgwBw8e1HrJyMiULRYeuAVAJ2/nl15JBFAqFdSplHtqm4j4FE6GvKSuOKoMCDkEJ36Vvnv3KtDurz+Kp/Ws/Vx/FK/fBmqrojz9LFMCsCtnyrSePuwcE0DrGvY6lUR4nq9x2tbgYvVpNDj05vjx4wwcOJA7d+6Q2RipUCjIyMi+cLaMjEzp4m5UIlvOPwTg/bbFEIhQQnnZa7/mSPAW2DkB4h4+X3Z8IdhVBe8eBTJEarqKO1GJpOZSKlJDpfpweSOEBRXI+DIyBUF1J2tGtvbiv+uPs20jKMT663pisEVx5MiRNGzYkEuXLhEdHc2TJ080LzmQRUambLHo4C0yVILWNRzwdZMrL6nRNwF5oZTCK8kEb5HS0byoJAI8jXiWpmZL8cglRz7LlFBKw0OnwRbFGzdusH79ery8ZOuCjExZJjw2mfWn7wOyNTEzxVYKrySjypAsiTr3iAAUsPMzqNUVlEVcB9elnvQec1fK62j5Ev0vMiWa0vDQabBFsUmTJty8ebMwZJGRkSlB/HboNqkZKhp72L1cCo8eFFspvJLMnaNZLYlaCIh7ILUrasxtwa6a9Fm2KsqUINQPndldKRRI0c/FeQ02WFH84IMPGD9+PMuWLePMmTNcuHBB6yUjI1P6iU5IZdWJuwC83062JuqipNSoLjE8fVSw7XLAvaIly4c3xr2iASXR5OlnmRJIaXjoNHjqWV3Pefjw4ZplCoUCIYQczCIjU0ZYeiSEpLQMfF1taVXdPvcNXlJKUo3qYsfKqWDb5YC1uQmtazgYtlElP7i0XlYUZUocxVJ/3QAMVhRDQkIKQw4ZGZkSQlxyGsuOhgLwfttqOVZiknleCu+lx7052FSCuDB0+ykqpPXuzfM9VERcMitP3GVQkyo42ujpu6W2KIadz/f4MjIFTUl+6DRYUXR3dy8MOWRkZEoIK47fIT45HS9HKzp6Oxe3ODKlBaURdP5Oim7OwrObXeeZBRLIEhGfwo97b9DB20l/RdG5riRH7D2pYoyVgRZJGZlCpqQ+dBqsKG7Zoju9gUKhwNzcHC8vLzw9PfMtmIyMTNGTlJrB74ekWYNRbaqhLAFPszKlCO8ekjK4c4L2cptK0vICyqOYJ8xtoKIXRN2Q8ilW17MEoIzMS47BimKvXr00Pokv8qKfYsuWLdm0aRMVKsg1NWVkShOrT90lKiEVtwoW9KhXqbjFkSmNWJSX3h29IWC85JPo3rzoU+LoolJ9SVF8GCQrijIyemJw1PPu3btp1KgRu3fvJjY2ltjYWHbv3k2TJk3Ytm0bBw8eJCoqio8//rgw5JWRkSkkUtNV/HrwNgAjW1fD2ChPFT5lXnYenJXeq7YB3z7gGVAylESQI59lZPKAwRbFMWPG8Ouvv9K8+XOH5Pbt22Nubs4777zD5cuXmTdvnlZUtIyMTMln47n7hMUm42htRh9/t+IWR6a0olbC1EpZIWBrYUIvv0rYWpgYtqFc81lGxmAMVhRv3bqFjY1NluU2Njbcvi1ZI6pXr87jx9nXLpSRkSlZZKgEvxy4BcA7rapiblJCLEAypYuMdAh/lk+3UoNCG6aynSXz+udBEVUHtMQ/hPhHYJ3/VD1lGlWGlCD96aOS5UIgU6QYPLfk7+/PJ598QmRkpGZZZGQkn376KY0aNQKkMn+VK1cuOCllZGQKle0XwwiNSqS8pQkDGlcpbnFkSiuRVyA9GcxswK5qoQ2TnJZB6OMEktMMzNtrZgUONaXPYUEFLleZIngLzPOB5d3gn7ek93k+xVevW6bYMFhR/P333wkJCcHNzQ0vLy+8vLxwc3MjNDSUJUuWAPD06VMmT55c4MLKyMgUPCqVYOF+qSzn8BaelDMzeKJBRkZCM+3sB8rC83G9GfGUNrMPcDPiqeEbu/hJ7/L0c/YEb5HSHGUuyRgXJi2XlcWXCoPvCDVr1iQ4OJh///2X69eva5Z16NAB5bMLQ69evQpUSBkZmcJj39UIrobHY2VmzJBmHsUtjkxppgj8E/NNpfpwYbUU+SyTFVXGs/RGupKmC0ABOz+DWl3laeiXhDyZDpRKJZ07d6Zz584FLY+MjEwRIoRgwTNr4htN3bG1NDA4QEbmRdQRzyVdUQTZopgdd45mtSRqISDugdTOM6DIxJIpPvKkKCYkJPDff/9x9+5dUlNTtdZ9+OGHBSKYjIxM4XP0VhRB92IwM1byVks5Ub5MPkhPgUeXpc+FGMiSb5x9QaGEp+HSVKpN8dbRLXE8fVSw7WRKPQYriufOnaNLly4kJiaSkJCAnZ0djx8/xtLSEkdHR1lRlJEpRfz8zJrYv1FlHKzNilkamVLNo0ugSgMLOyhfggOiTC3BoRZEBEsBLbKiqI2VnpHg+raTKfUY7G380Ucf0b17d548eYKFhQXHjx/nzp07+Pv7M3v27MKQUUZGphA4e/cJR29FYaxU8E7rasUtjkxp50X/REXhln70cbUldGZXfFxt89aBPP2cPe7NpZKLZPcfKsDGVWon81JgsKIYFBTE+PHjUSqVGBkZkZKSQuXKlfn++++ZNGmSwQL8/PPPeHh4YG5uTpMmTTh58mSO7WNiYnj//fdxcXHBzMyMGjVqsGPHDoPHlZF52fl5n2RNfK2BK67lLYpZGplSj1rpci3B085q5Mjn7FEaQefvcm7TeaYcyPISYbCiaGJiooludnR05O7duwDY2tpy7949g/pas2YN48aNY8qUKZw9e5Z69erRqVMnIiIidLZPTU2lQ4cOhIaGsn79eq5du8Zvv/2Gq6uroT9DRualJvhhHHuvRqBUSOX6ZGTyzYOii3i+FfmUVxce4VZkHtLjwAsWxSAQuqJ7X3K8e8Dry8liVTQvD33/lNbLvDQY7KNYv359Tp06RfXq1WndujVffvkljx8/5q+//sLHx8egvubMmcOIESMYNmwYAIsWLWL79u388ccffPbZZ1na//HHH0RHR3P06FFMTKToTA8PjxzHSElJISUlRfM9Li7OIBllZMoiCw9I1sQuvi5UdbAqZmlkSj2piVKybSiSQJak1AzO3Y0hKdXAhNtqnH1AYQQJEVKEr61sbMiCvRcgwNgcqneCK5vBo6WsJL6EGGxR/Pbbb3FxkZx/v/nmGypUqMB7771HZGQkv/76q979pKamcubMGQIDA58Lo1QSGBjIsWPHdG6zZcsWmjVrxvvvv4+TkxM+Pj58++23ZGRkf7GYMWMGtra2mpdcMUbmZed25FO2XwwD4P22XsUsjUyZIPwiCBVYOZeO4BATC3CsLX2Wp591c/fZfbhKM2j2vvT5zhFQqYpPJpliwWCLYsOGDTWfHR0d2blzZ54Gfvz4MRkZGTg5aUdOOTk5cfXqVZ3b3L59m3379jFo0CB27NjBzZs3GTVqFGlpaUyZMkXnNhMnTmTcuHGa73FxcUWjLMo1MmVKKIv+u4UQ0L6WI7VdstZtl5ExmIelIH9iZir5SZHaYUFQu1txS1PyuHtceq/STPI7NSkHSU+kaHFnw2YPZUo3papWl0qlwtHRkV9//RUjIyP8/f158OABs2bNylZRNDMzw8ysiNN+BG+RMtu/mLTUppLkICyb7WWKkQcxSWw4+wCA99vJ1kSZAqI0BbKoqVQfzq2QLYrZoVEUm4KRifR+ay+EHpIVxZcMg6eeo6KieP/99/H29sbe3h47Ozutl77Y29tjZGTEo0faSTsfPXqEs7Ozzm1cXFyoUaMGRkbPLXO1a9cmPDw8S+LvYkOukSlTgvn1v1ukqwTNq1WkQZUKxS2OTFmhiCuyuFWwYG6/erhVyEe0vssLKXLkgBZtYu5B7D3Jj9Pt2SyiugpLyKHik0umWDDYovjmm29y8+ZN3nrrLZycnFDkMV+Wqakp/v7+7N27V1MbWqVSsXfvXkaPHq1zmxYtWrBq1SpUKpUm8vr69eu4uLhgamqaJzkKFLlGpkwJJjI+hdWnpMwEsm+iTIGRHAdRN6TPRaQolrc05dX6bvnrxKkOKI0hMQpi70N52X9dg9qa6FIPTMtJnz1aSe93Dkv3Ovke9tJgsKJ46NAhDh8+TL169fI9+Lhx4xgyZAgNGzakcePGzJs3j4SEBE0U9ODBg3F1dWXGjBkAvPfeeyxYsIAxY8bwwQcfcOPGDb799tuSUw1GrpEpU4L5/XAIKekq/CqXp3m1isUtjkxZIey89G5bBcrZF8mQUU9T2H4xjK6+LlS0yqNrkYm5FNASflGyKsqK4nNeDGRR41IPTK0hOVbaZ5X8ikU0maLHYEWxVq1aJCUlFcjg/fr1IzIyki+//JLw8HD8/PzYuXOnJsDl7t27GsshQOXKldm1axcfffQRdevWxdXVlTFjxjBhwoQCkSffyDUyZUoosYlprDh+B5CsiXmdCZCRyYImkMWvyIYMi03my82XaVClQt4VRZAsoGpFUfYff86L/olqjIzBvRnc+BdCD8uK4kuEwYriwoUL+eyzz/jyyy/x8fHR5DNUY2NjWBTl6NGjs51qPnDgQJZlzZo14/jx4waNUWTINTJlSijLj4XyNCWdWs7WtK/lWNziyJQlHhZdou0Cp1J9OPunFPksI6GObAZtRRHAI+CZongImuu+b8uUPQxWFMuXL09cXBzt2rXTWi6EQKFQ5JjTsMyjrpEZF4ZuP0WFtF6ukSlThCSkpPPHkRAARrX1QqmUrYkyBUhpjHhW82IpPyEKvUZ1qeDeKUCAXTWwyvRQqXaZunMUMtIlK6NMmcfgf3nQoEGYmJiwatWqfAWzlEnUNTLXDkYqffSisvhsP8k1MmWKmL9P3iUmMQ2PipZ09S0FyZBlSg+J0fAkVPqsVrpKE051QGkiWdFi7kAFj+KWqPjR5Z+oxrkumNlCSiyEnwdX/6KVTaZYMFhRvHTpEufOnaNmzZqFIU/px7uHVAszcx5Fayd4ZZbsByNTpCSnZfDrwdsAvNemGkayNVGmIFFbE+2qgUX5Ihu2nJkxAdXtKWeWT4uWsZmkLIYFSXWfZUXxuX+iuw5FUWkkzYhd/5+UJkdWFF8KDM6j2LBhQ+7du1cYspQdvHvA2EswZJt0AQVoOU5WEmWKjAyV4NitKL7YfImI+BScbczyn05ERiYzxVSRxdO+HH+91QRP+3L570wdlCEn3ob0FHhwRvqsy6IIz6efQw8XjUwyxY7Bj2MffPABY8aM4ZNPPsHX1zdLMEvdunULTLhSjdJIOqH8h8LuL+DqdmjybnFLJfMSsPNSGNO2BhMWm6xZlpiawb6rj+jsI089yxQgD4Ok9yJWFDNUgsTUdCxNjfNvJa9UH84skxVFkP7PjBQo5wB2VXW38XimKN49BhlpUtUWmTKNwYpiv379ABg+fLhmmUKhkINZsqN2N0lRDD0s+fNY6l+9RkbGUHZeCuO9FWezhFLFJ6fz3oqz/PJGg5dTWZRrrxcOxRTIciUsjm4/HWbbBy3xcbXNX2dqJTcsSA5ouXtUeq/SNPv94OQDFhUkv86HQVC5UZGJJ1M8GKwohoSEFIYcZRe7quDkC48uwrX/Qf1BxS2RTBklQyWYtjU4p7pATNsaTAdv55fLV1GuvV44xD+SCgigkIIcSisOtcHIVEok/SQke0vay4Amf2I2084ASiW4t4Cr2yD0oKwovgQY7KPo7u6e40tGB7W7Se9XtxWvHDJlmpMh0VrTzZkRSImKT4ZEF51QxY1ce73wUFsTHWqCmVXxypIfjE0lKxm83NPPKpXuRNu68JDrPr9MGKwoyuSB2t2l95t7IeVp8coiU2aJiH+uJCpR0VQZTA/lUZoqg1Gi0tmuTJNr7XWk2usq2V0mT2gSbZfC/ImZUU8/q30uX0YeX4PkGDCxzN1CrA5ouXcC0lMLXTSZ4kXOllkUOHpDBU9pWuPmHqjTq7glkimDOFqbA9BJeZIpJn9SSfHccvhQ2DEtbTC7VI017co8ZaH2ekn2rSymiOdCQY58fp4/0a1h7gEqDrXBsiIkRknHQW4WSJlSjWxRLAoUiudWxStbi1cWmTJLY087+lud4xeTeTijPb3sTDS/mMyjv1UQjT1fkoCq0l57PXgLzPOB5d3gn7ek93k+JWO6XIhirchS09maM5MDqelsXTAdagJazktTsC8j+vgnqlEqwaOl9Fmefi7z6K0o3r59uzDlKPuoFcXru6RcVTIyBYwRKj5jGQCZY1XU36eY/IkRL8mNsDTXXi/pvpWx9yEhEpTGUsLqIsbESElFKzNMjArI1uFQC4zNISVOmvkpaFQZkkJ1cb30XhLdHTQVWfS0Dqr9FEMPFo48MiUGvc+yunXr4uPjw6RJkzhx4kRhylQ2cW0IVs6QGg8h8oklU/A8vXGQ8umRWZRENUoFWCSFS1OZLwPq2uvZogAb15JXe700+FaqrYmOtcHEosiHvxOVwNvLT3EnKqFgOjQyKbyAlpJsGVYT+wBi7oLCCNz0jGJWK4r3TsrGjzKO3ori48ePmTFjBhEREfTs2RMXFxdGjBjB1q1bSU5+SZzjDUBdGWNz0AOO3YoiA8Xz6OcrJegCIVNmOHD6on4NS+pUa0GjNIJWn+TcpiTWXjfEt7K4KOZAlvjkdPZciSA+Ob3gOtUEtBSgoljSLcNq7j2bdnb2BTM9p/MdakI5R0hPhvunC082mWJHb0XR3Nyc7t27s2TJEsLCwvjnn3+oWLEiEyZMwN7enl69evHHH38QGRlZmPKWCnZeCqPld/sY8NtxxqwOYsBvx2n53T5OmT+zXFzdUTKnHmRKLRHxyay9mqZf45I41VpY3D4gvRuZZl3n07tk5lEsDb6VZSmQRY36t9w+UDBTxKXBMqzmjnraWQ//RDUKxXM/xVDZT7EskycHD4VCQfPmzZk5cybBwcGcO3eOgIAAli1bhpubGz///HNBy1lqUFfGyJzPLjw2mYG7jUk1sYXEx88dh2VkCoAF+25yOK0Gj5X2CHJIpl0Sp1oLi1v7IXgzKJTw1h6p9nrv36W66yBZ5DL0VK6LkpLuW/liIEtZUhSTY6T3R5cMnyJOTYRHl6VgxcPzYMsHsLh1ybcMq9E3f2JmPOV8ii8DBZIep3r16owfP57x48cTFRVFdPRLlND3BXKrjJGOMXsy6tOFA1LybY8WRSyhTFnkblQiq07cRYWSqIDp2P83KvvG7b4oeVOthUF6KvzvU+lzoxFQqd7zdbW7w7m/IP6hdGP3ea14ZMwOtW9ltkqGQlpfXAr/kxCpiomRmZT6qywQvAV2fZ51uXqKuO+fUL0DRIdA9C2IugXRt5+/4h7kfezidgVJjpWUYzBcUfRoJb3fPwlpScXirypT+BR4HsWKFStSsWLFgu62VKBPZYyNyQ3oYnpAukF1+vblrisqUyDM2X2NdJWgVQ0HarbtCrHHIOgv7UYKIxAZcHEt+L4ORmU8herJxfD4OljaQ9tJ2uuMzaDhcPjvOzixuOQpikojqbzg2jezb1OcvpUPnk07O/tIVU2KAScbcyZ3rY2TTQHkBNVninjdUOn8yQkzW6hYFeyqSWUAVelweE7u4xe3K8i9U4CQcv1aOxu2bcVqUpDm03ApqKVq60IRUaZ4KeN3i6JFn4oXB1V1STeywDj2nlSEvixN3cgUOcEP49h8XrI8fdqpprQwNU56r/8mVG0j3YhMrWBZF7i1D/79HF75rngELgriw+HATOlz4FSwKJ+1TcPhcGiO5MT/8FzJOw+tXXQvN7GAV38tXt/KElCRxcHajLcDCqgmc67BQzxXEjMrgxWfvdtVA0s77Qd/VQZcWC1ZJXUqocVsGVZzNw/+iWoUCmn6+eI6CD0sK4plFDnhdgGiT8WLFEyJdX12Ml2Raz/L5I/Z/15DCOhW1wUfV1vJf0ztmO43EHz7SBdy1/rw6mJp+YlFcHpp8Qld2Oz+ElKfgqs/+A3S3cbaGeq8Kn0+8WvRyaYvR+ZJ734DJd/KtuppUSVU71hcUkmUAP/E2MQ0tl8IIzaxAHxM9Z367TYPPrsD7xyAPr9Du8+hXn+o3BjKVcw6O6S2DANk5zdcEqLu8+qfqEaTT1H2UyyryIpiAdLY0w4XW/NsQwkUgIutORX8e0sL5CotMvngVGg0+65GYKRUML7jM2ti9G1IiJCifDNbfLx7QLvJ0ucdH0sWgLLGnaNwYQ2ggC6zpAoS2dFkpPR+aT08LUHZGiKvST7MKKDFR5KiH/AxWFeCtITnkdzFgSpDql4Cxaoo3nuSyPurznLvSWL+O9N36reil+GuQt49JP9GGx0W4g7Tiz/qPj0VHjxLbZMXiyI8D2i5f1oK6pEpc+RJUYyJiWHJkiVMnDhRE7hy9uxZHjzIh0NvGcBIqWBKd8m5O7vLyZTu3ihrdgKliVSEPfJ60QkoU2YQQvDd/64C0LdhZTzty0kr7hyR3l39wUSHhTvgYyktjCod1rwpOeeXFTLSYcezvIkNBkv7ICfc/KVE+BmpcGZZoYunN0fmS++1uoJDDemzUvlCGdBizL0XdVOy1ppYSnn0ygKaxOw5POLnJ1uAdw8Ye+l51L37syDGxMd5668gCTsv5UG0rAj21fPWRwVPsHEDVdrzfIwyZQqDFcULFy5Qo0YNvvvuO2bPnk1MTAwAGzZsYOLEiQUtX6mjs48Lv7zRAGdb7Zu0sVLBzwMb0NnHBcxtn/tyXJWtijKGs/9aBKfvPMHMWMmY9i9c4HPLh6ZQQM+fJWtQUjT83R+S4wpf4KLgzFIpetO8PLSfot82aqviqSWSdaW4iX3wzCIKtBirvU6tKF7dXnxpfdSBLC71in/KtKDIcYr42ff8ThErjSTLm28faPyOtOzyRslVpDh50T8xr4GVaj9FkNPklFEMVhTHjRvH0KFDuXHjBubmz5WhLl26cPCgXJoOJGXx8IR2/D2iKd/19sXSREm6Smifh7XUVVpkP0UZw1CpBN/vvAbA0BYe2g8ld5/lZMvJ+mFiAf3/lgImIq/CP2+XjKS/+SHhMez7SvrcbrLkM6YP3j2lqcen4SWjYtLxhZJlxr0lVM5USs29uRTFnRxTfG4DJcA/sVDIborYppK0vCCniKt3BJNyUsk8teJdXOTXP1GNJvF2GXRnkTFcUTx16hTvvvtuluWurq6Eh4cXiFBlASOlgmbVKtKvURXeblUNgJ/23USonyBrdQUUUoWDmHvFJ6hMqWPL+YdcDY/H2tyY91pXe74iLgyehEoJpis3zrkTGxfovwqMzeHGLtijpwWupLJ3mpQPztlXimjWF2NTaPiW9PnEosKRTV+SnjyfAm85Nut6pdGz6wbFp9SWgIhnAHMTJXUq2WBuUoBu9pmniIdsg7EXC96P0NQSanaWPl/eULB9G4JKlb+I5xdRB7Q8PAspT/PXl0yJw+CzzMzMjLi4rFNV169fx8HBoUCEKmsMa+5BOVMjgsPi2H8tQlpo5fj8Ke7q9uITTqZUkZqu4ofdkjVxZOtqlLd8IY+d2pro5CO5N+SGawPotVD6fPQnOLeygKUtIh6cgbPP8kZ2mW34FGHDYZLP8P1TcP9MwcunL6eWSP5/Tj7gFai7Te1nSsuVbUVvBc5Ig/AL0uditih6OVqz/cMAvBz1rEusLy9OEXsGFN70ep1nuTsvbyq+6eeoG5L7ibEFONfNX18V3KF8Fcn3Wa46VuYwWFHs0aMH06dPJy1N8pFRKBTcvXuXCRMm0Lt37wIXsCxQoZwpbzRzB2D+3hesihqfI3n6WUY/Vp+6y73oJByszRjWwkN7pdo/0RCne5/e0OpZBZNtY0vfRV6lehbAIqBu/7xNoVk5SvsBpETdxUFaEhx/ZtFsMTZ7fzHPVlIuv4QIKcFxURJ5VQp8MLOVcgfK5B2vQCm3adx96QGlOFBbE90aFkzidHWVllDZBa2sYbCi+MMPP/D06VMcHR1JSkqidevWeHl5YW1tzTfffFMYMpYJ3m5ZFTNjJUH3Yjh6K0paqPZTvHNE8rGSkcmBhJR05u+9CcCH7atjaZopX35ep5HaTJQsVRmpsOYNyXeqtBC0UrIomlpDh2l576fJM3eaSxsgvhhKqp1bIUXBlq/yPL+jLoxNn09bFvX0s9qfrlK9nNMOFQGXHsRS4/P/celBbLHKkWdMzKFmF+nz5Y3FI0NB+SeqkQNayiwGn+22trbs3r2brVu3Mn/+fEaPHs2OHTv477//KFeuXGHIWCZwsDZjQOMqAPy074a0sIK7ZPIXKrj2v2KUTqY0sPRICI+fpuBe0ZL+jSprr0x6Ao8uS58NTeOhVMKriyT/voRI+HtA6fAzSnoCe6ZKn9t8Znj5sRdxbQCVm0iBJGeKOBl5Rro09Q/Q7IPcyytqpp+3Fu20ZQkLZEnNUBW3CPnD54XpZ1Ux/BbNg2UBKYrqgJawoLKTSUEGyEfC7ZYtWzJq1Cg+/fRTAgOz8aeR0eLd1lUxMVJw/HY0p0Kl/JNaF30ZmWx4kpDK4v9uAzCuQw1MjDKdundPAEIqJWblaPgApuVgwGoo5yilmNn4bvHcvAxh/wzJCmdf87lFMD+o+zj1e9GmygneBDF3pFx29d/Ivb1XeymPYey958pbUVBCAlnKDNXagZkNxD+EeyeKduwXA9/ccgl80xdbNymnonghSEamTKBXref58+fr3eGHH36YZ2HKOi62FvTxd+Pvk/dYsO8my4c3htrdYP/XcHs/pMSDWQE7Z8uUCX757xbxKenUdrGhe91KWRto0uLkI3rR1k2KhF7WVfKb3f8NtP8i7/0VJuGX4NRv0ucu34ORSf77rN1DShkUHyYpb3X75r/P3BDiebm+JiOliNjcMLGA6h0geLP0gOlaBIpbespzi3UJsSiWeozNpCj2839L08/5OXcNRZ0Y26kOmNsUXL+eAfAkBEIOQo1OBdevTLGil6I4d+5cre+RkZEkJiZSvnx5QKrUYmlpiaOjo6wo5sJ7rb1Ye/o+/12P5ML9GOq61pJKQ0XdhBv/Pneql5F5RlhsEsuOhgLwaeeaKJU6Ah00gSwt8jdY5UbQY75kUTw0GxxqQd3X89dnQSOEFMAiVODdC6q2KZh+jUyg0Vuw72spVU5RKIq39kL4RSmvXqO39d+udo9niuIWaP9l3pMl68ujS9K0vIWd5EcpUzDUeU1SFIM3QecZRZfEXOOfWMDKqUcrOPunXPe5jKHX1HNISIjm9c033+Dn58eVK1eIjo4mOjqaK1eu0KBBA7766qs8CfHzzz/j4eGBubk5TZo04eRJ/aL5Vq9ejUKhoFevXnkatzioUtGSnvUki9CCfTelC7wm+bY8/SyTlfl7b5CarqKxpx1tauhIQZWW9HxasCAu/PX6P68Ksvn94k0Zo4uL6yULqrEFdPy6YPv2HwZGZlKAzP3TBdu3Lg7PezbuELC003+76h2let5RNyHiSqGIpoX6+HJtUPhKqR54OVrx70et8HK0Km5R8kfVNlIloaePina6tqDyJ2ZG46d4QfIhlikTGOyj+MUXX/DTTz9Rs+bzOp81a9Zk7ty5TJ482WAB1qxZw7hx45gyZQpnz56lXr16dOrUiYiIiBy3Cw0N5eOPPyYgIMDgMYubUW2roVDAv8GPuBoe99xP8cZuSEsuXuFkShS3Ip+y9vR9ACZ0rolC1036/mnJ2mPtAhU8Cmbg9lOgxiuQkQKrB0il5UoCKfHw77PrTKvxUL5yzu0NpZy9lEMP4PgvBdt3Zu6fkSwvSmNo9r5h25rbSD5uUDQPmA9KViCLuYkRNZysMTcp5WUEjU0l9yMouujn5DjJig0FF8iixsZFmiFDPJ/lkCn1GKwohoWFkZ6enmV5RkYGjx4ZnlZizpw5jBgxgmHDhuHt7c2iRYuwtLTkjz/+yHabjIwMBg0axLRp06haNed8XikpKcTFxWm9ihsvR2u6+Eilon7ef0u6+Nq4Ssl2bx8oXuFkShRz/r1OhkoQWNsJf/dsLE4FUa81M0ol9P4NHL0la8fqgZCaWDB954f/vpfK7VXwlCKECwN1Ld7gTZLTf2Fx5JlLj29fyT/UUDSBcEWQJqeEBbLcf5LIhPUXuP+kBByT+UWdDil4sxQBX9jcPyW5bZR3l0oUFjTqKi3y9HOZwWBFsX379rz77rucPfu8RuWZM2d47733DI5+Tk1N5cyZM1rbKZVKAgMDOXYs+6eR6dOn4+joyFtvvZXrGDNmzMDW1lbzqly5gC0QeeT9tl4AbLvwkFtRic9Lc12Vp59lJC7cj2H7xTAUCvikU83sG97Ro75zXjCzhgF/S9G4YUGweVTxVZEAiLwu1UIGeOU7KRddYVDJT1K6VelwOvsH1nzx+MbzOu8txuStj5qvgMJI8h+MulVwsmUmNQEin01vlxCLYkxiGmtO3yMmMa24Rck/nq0l38+ESCmnbmFTWP6JauR8imUOgxXFP/74A2dnZxo2bIiZmRlmZmY0btwYJycnlixZYlBfjx8/JiMjAycnJ63lTk5O2daNPnz4ML///ju//fabXmNMnDiR2NhYzevevZJRV9m7kg2BtR0RAn45cOuFKi07iuapUkY/VBnSBe/ieum9CMumzdollep71c+Vms7ZRMNnpD+v0FEYF/4KHtBvhVTi7vJGyaJXHPtECPjfp5LyVqNz4UdUqlPlnP5DivgtaI7OB4Q0ve9YK299WNo9vykX5vRz+EXJAmXlLE0tyhQsRibPr/9FUfu5oPMnZkZtUXx0ERKjC2cMmSLFYEXRwcGBHTt2cPXqVdatW8e6deu4cuUKO3bswNExD/nbDCA+Pp4333yT3377DXt7e722MTMzw8bGRutVUlBbFTeee8A9az/pqTIpWs5BVVII3gLzfGB5N/jnLel9no+0vJA5cvMxh248xsRIwUcdamTfMPw8pCVItZ0dvQtHGPfm0G2O9PnAtzCrWtHvk6vbpBRSRqZSdGhhU6u75A6S+Fiq1lKQxIXB+dXS55Yf5a8vtYJRmNPPLwayyBQOmunnLYVrKMhIex6kVVgWRStHKVsCQOjhwhlDpkjJc8LtGjVq0KNHD3r06EGNGjncyHLA3t4eIyOjLL6Njx49wtk5a5WFW7duERoaSvfu3TE2NsbY2Jg///yTLVu2YGxszK1bhTj9UgjUr1KBgOr2ZKgEiw7deV7SSY5+Ln6Ct8DawRD3UHt5XJi0vBAVIyEE3++8CsCgJu5Utssht57aYbxy08Itq9ZgsGTJg6zRjIW9T1ITYeck6XOLMUVTZ9jI+Hm6mhOLCnbK/fhCqVxilWZQpUn++qrVHVBIUdqx9wtEvCxoSveVjGnnMolHAFjaS4aCwqyVHHYB0pPAogLY5+2+rRfq6GdZUSwTGHxnGT58eI4vQzA1NcXf35+9e/dqlqlUKvbu3UuzZlmfdmrVqsXFixcJCgrSvHr06EHbtm0JCgoqMf6HhjD6mVVx3en7PKnSUVp4dVvx+oK97KgyYOcEQNd/8GzZzs8Kbcp11//bO+/4pur1j7+T7g0tlLZAy5TSsvcSEFkOhqigskQvKjJE7lUcP0DccBVQURAUERegV6CgVDayV0EolFVKy+iglG46c35/fJu0pStpkyZpv+/XK6+cnJyc8yRtcp7zjM9zNo5/rqfgbG+jizqXiTb6bOz6xHvR5IuTTKmY+DM5sARSYsCjMfSZZfz9l0WniWDrKOozr+kn2VUhd5PheMGIQK0EUVVwa1CYQtTWPBobCxvdB1DP1YEp/ZtTz9XB3KYYBxtbCCpoTjJ2BLsoMdV0YSkbWmoUBv+n3Llzp9gtISGBXbt28fvvv5OcnGywAbNmzWLlypV8//33REREMGXKFDIyMpg0aRIAEyZM4M033wTA0dGRNm3aFLvVqVMHNzc32rRpg729vcHHNzfdm3nRrYknOfkavrrmL4R3U2/AzbCKXywxDdEHS0YSi6GIv5G2icSI5OVrdLWJ/+rTlPpu5ZwIFaX6HMXog2LUWNnGmOYzSYoq1Boc8oF+k0uMhYsXtC0QGz+y3Dj7PL4KctKgfmuhhWgMdOlnE2QislLhdsFsegtyFH08HJk9NBAfDxM1NJkDbfo5YrNIEZsCU9cnatE6ignnICPRtMeSmBy9JrMUZcOGklpPGo2GKVOm0Lx5c4MNGDNmDLdu3WLu3LnExcXRoUMHQkNDdQ0uMTExqE155WMBTBvQggmrjvLD8Tj+EzQQhwva0VydzW1a7SRdT5knfbczgN/DbhB5K4O6znb8q28FKdbEi5B5WwhP+3Ywui3F0Pe9nlgtJnfUDTDOcf96S2g5NutfKAdTnXR/EU7+IKRLUm6AR8PK7ys3q1Cbsc9M40V0Wg8Tn1PMQUi/Ba6liLJXlthT4t7DX2hMWgjp2XmcuZ5C20YeuDoYfBqzTAJ6i1nrGQlwZS+0NExFpEIUxfQdz1pcvMA7GBLOiqii1gmWWCVG+aVSq9XMmjWrxKg/fZk2bRrR0dFkZ2dz5MgRuncvrNvZs2cPq1evLvO1q1evZuPGjZU6rqVwf8t6tG/kQVauhr/yu4iVEZtl+tlcuDaoeBtDttOTrNx8Fu+4CIhGJ3fHCuYXa6N3jboI4V5Tou97Df8NPmsHKwfAwS8guQoqAxe3wYU/hSD1QwvNMxHEpy0E9AElv+pSOf/8LJwAj8bGHdVZx19cKCgaUbZiTHSNLJYTTQS4mpjB0ysPczUxw9ymGA+1DQSNEMum6H6+HSmas2wdhQSUqZEyOTUGo4XqIiMjSxXillSMSqVi2oCWALx/sRGKdjTXrQtmtqyW4t9TlACUiUp0xBo53fvj4WhiU7Lw9XBkXA89InJaR9HU0QEQ79XdDyjLWVOJUWRN7geVWjRXbPs/0RH9zSA49FXF012Kyu5c3iXkcAB6TIH65ehImhqtVM6J7yo/OUmTLxxnEFNYbCq4CDAUbX2bsdPPspGlemkzStxHbIG8HOPuO6bg96JhZ7CthtpO2dBSYzA4Zj9rVvFickVRiI2N5Y8//mDixIlGM6y28WCgN4E+bpyPg6teXWl654D40a+sxpqk8ux+X0jOlMfQj0UEwEikZeXy5e7LALw68D79RpPp6hOrwVFU28DQBaK7GRXFG30KnMfhXwiHJT1BpGrPbhQCwtePittfb4oi+jajRBq5qCbfuRDRQHRvbahjHej7uknfWoW0elhEAVOuiYhpx3GG7yMiBJKuiG7TThOMb2PrEbDzXYjaK7rSneoaZ78W2MhSo2ncQ+hVpsdB5C5oNdR4+9alnU1cn6gloDeggsQLkBYvGq8kVonBEcWTJ08Wu50+LTohP/30U5YsWWJs+2oNarWKaQNEh+v3d9qKlXJKS/Wz71PYX1BC0XlSyRFXDu4wek1hBMdIrNwXxZ3MXJrXd2FUJz3q4JKvCcdFZQONuhnVljIJGi7e+72iy+5+xT8TV2/oNhkm/QH/Pg8P/bcw6nntsIgULmoN3z0MR1dC2A+lSxEBZCWbf6xlVaVyFKXwf6rbi2BfXrS6ktRrIRpkNHlw8S/j7DMzCZKjxbKpa2AlArUagkeKZWPPfi466rM6cPYEnzZiWXY/WzUGRxR3795tCjskwENtfGlW/yIhtzow11GNOvYfuBNtvMYASfkcWSGiMgCD34de00XKMPoghP9PpB7dGxrNSczXKByNSiLyVjpf7xUaoK8NaYWtjR7Xb9offd/24OBqFHv0Imi4GDcZfVA0uLg2EGnpsqKrbj7Q/QVxS71ZEGncANeOiGhjhSPLVEJ2J/ARo0ZwDabTBNjzsZhSEnPIsLKDK3sg9h/RdKSdI20KgobD3ggRnW3/VNX3p1Ve8GwOTnWqvj8jYmujwsfdEVsbM9StmprgUeKC5MKfotTBGKMq0+JFRBsVNOpa9f3pS5O+4jtzdR+0faL6jisxKgZHFAcMGFCqDE5qaioDBgwwhk21Fhu1iqn9W5CEO2EUpJzP/2Feo2oLp36Gra+J5X6zhZMIwjlpej8MnCfG2N2KgITzVT5caHgsfRbs4umVh/m/jeFk52mws1HpH6wy1XxnfdB+Jm2fEPf6OnDufqLe8PltMDMcBn+gh+iv6aSIDMLZE9qPEcuGSuUcWCLuO00Q3aCmQiuTE7kTstOrvj8LTjsH+rhz+K0HCfSxnElbRqNRV3FBmp0q/pbG4FpB2rlBcPU6/bKhxTDMODK2PAx2FPfs2UNOTski26ysLPbtk/8MVWV4Bz8aezqxJbdI97PEtJzbBJumiuUeL0P/N0tu41QXmhdcCFUxJRQaHsuUH8OITSneGJGbr/DyT2GEhsdWvJPqTiMZmzqNodc04ZTrgwmkiAymW0FTS8QW/bu5b54UEUWVjXi/pqRBG6jbFPKy4PL2qu/v5ilxL0f3VS9qNQSNFMvGSj9Xd32iFv+eorktKbICbVqJOUfGVoTejuLp06d19Yjnzp3TPT59+jQnT57k22+/pWHDKmiMSQCws1EzpV8LthXI5Cgxh0RzgMQ0XN4Bvz0vpEU6jochH5Ytw6LtSDz7e6Wli/I1CvM3nyt15ouW+ZvPka8pZ4uM23CrIKpprY6iFjNJEVWKBkHQtG+BVM63+r1GKxbe9gkhY2NKVKrCqKIxTi4W3PF8Pi6VHh/u5HxcqrlNMQ3a35oLWyH3btX3Z64LS6c64NNOLMvu57Ix48hYfdDbUezQoQMdO3ZEpVIxYMAAOnTooLt17tyZ999/n7lz55rS1lrD450bonFvxD+aZqhQRK2KxPhEH4S140CTKwRhh31WvlZfq4fBxkEIXSecq9Qhj0YllYgkFkUBYlOyOBqVVPZOtD/69VqZNpVZHegju2MCKaJK0/0lcX9idcUn8NuRotsZxIzq6kCrw3dpW+WlfADS4sQkHpW68ERvQeTlK8SlZpGXX0O1Zht2FiLnOelwqYrR4ez0wvGb1R1RhCLpZxPOsLZmzDwyVh/0dhSjoqKIjIxEURSOHj1KVFSU7nbjxg1SU1MNnvUsKR0HWxte7NdMJ76tsYDQc43jRhj8NBry7opRao+tqLjWztEdWhRMS6jkPNaENP1O3uVuV52yOKZGK7sDlHQWCx4bWYqoStw3VEQG796BM7+Wv+3BL0SkuuVgURtWHfh1Eo51TjpcqULjobY+sV6r6m2WkghUKuN1P984LqLgHo3Bo1GVTTOYJn3Fvex8Lh0zjozVF70dxYCAAJo0aYJGo6FLly4EBATobr6+vtjYWMgPeQ3hqa7+HHHsLR5c2QtZKeY1qCaREAE/Pi5m7gb0EdIu+k42qWL62dtNvw7GcrfTCW1bSJStqugru2MJqG0KO5ePfF32/0BavGiQAujzavXYBqK+LfBRsVyV+mYLbmSpNWjH3l0MhZwqTKCJNnM9s38PUaN752rVJjXVVMw4MlZf9JLHCQkJ4aGHHsLOzo6QkPKjW8OHW9CPuhXjZG/DoL73c3mnHy3UN9Fc+At1+9HmNsv6SboCa0bC3SSR3nlmLdg56f/6+4aKEVhJVyDutJCnMYBuTT3x9XAsM/2sAnw8HOnW1LP0HWSnC6kVsJx0rDEwVHbHnHQcB7s/hPhwIe+jnUBRlCPLxIzqRt2q/wQdNByOfi0UE/JzKzcFRje6TzaymA2/jlC3iXCwLm2r/LxkXX2iGdLOIDIxfh3EtKar+6HD0+axw1LRN6VsxjptvRzFkSNHEhcXh7e3NyNHjixzO5VKRX6+ZbRz1wTG9Qjgl93dacEG4o78hp90FKtGyg1YM0JMPfAOgrG/gYObYftwcBWpxIgQkX420FG0UauY1KsJH24tKbGjTbzOGxaEjbqMmr3rxwrTSHUaG2a7paOV3bF0nOoKncLjq+DwspKOYlYqHCuYC91nZvXPqPbvCc71xFzfq/sKu/X1RVEsupEFoEk9F36Z3IMm9UwgXm4pqFTCOdy/WKSfK+Mo5ufC9eNi2ZyNb03uL3AU90lHsSgRm+GPf1ewkUpkV8wYGNAr9azRaPD29tYtl3WTTqJxcXWwxbWDSHV63tyDJjvTzBZZMRmJ8MNISI4Bz2YwfqPQxqsMVUw/H716BwBHu+JfPx8PR5aN68TQNr6lvUxg7bI4NQWtVM6FP4UoflFOfAfZKaK+776Hqt82tY2IzkLl0s8p14WTqbYVkjsWiKuDLT2be+HqYPDMCOtCl37eVjltzLgzYhypowfUN+M4WKmnWJz8PNj2f7BunCiBqncfIlRgmXXaBusoSqqXhwcN5aZSD0eyOf33RnObY53cTYYfHhPdyu6NYMKmqs0dbTkE7JyF06mdXqEnp68nsyMiHrUKQqb14ZfJPfjsqQ78MrkH+2cPKN9JhCJC29JRNCvegdDsAdGscuybwvV52XDoK7Hc+xVRM2gOtHWdEVsM75bUpp29g4wzFcQExKVksSD0PHHlKAjUCHzaick4eXdFraKhaPUTG/cw3/+i7vi2kBIjUum1mbQ4WDNcNLsB9JwGUw5adJ12pS7Hdu7cyc6dO0lISECj0RR7btWqVUYxTCLwcLHnku+D+MWt4/bx31AGPo2qulNZ1kxOBvw8WtQTutQXTmJV9ezsnUWt4tnfRfq5YWe9X7pkxyUARnZoyH0N3MAQfzUvp0gaqQbVJ1or3V8SncVh30P/N8QM53/WitIG94bQ9knz2dakLzh4QEYCXDtq2IXFTctOOwMkpmezbE8kj7T1xcfDMp1Zo6BNP+/7RKSfDR2DZ+76RC0OruJ38toREVWs28S89piLqwfgt0miDtveDUZ+WShpZcF12gZfYsyfP5/Bgwezc+dOEhMTuXPnTrGbxPi07CdqOjpnHWb/BT2mdkgEuVmw9hnx4+ToAeM3QL0Wxtm3Lv28Ee65WCqLkzF32HU+ARu1iukPtjT8mLGnRGTByRPqtzL89RLj0nKwmISSlQK7PoDT62Hvx+K5Hi/r30lvCmztoVVB2jui/AbEEshGFstCm36+tF3Uv+qLohSZyGIBGYgmBenn2iiToyhw4HP4fphwAr2D4IU9hU6ilsqORzUxBkcUly9fzurVqxk/frwp7JGUgkervmTY1qVO3h12/7WR+wNfNrdJlk9+Lvz2nBifZucCY/8HPm2Nt/8Wg8DeFVKvC52yxt0qfMnigmjiqI4NaVqZInydLE7P6m+QkJRErRZX/Hei4PCXRZ5QgWt9s5mlo/UwOL1W1CmWN3GoKIoipXEsjQbBooYt8aJIP7fTs6kx6YqIKNvYW8bfskkfERm9ul/8n9WW37CsFDEiVlsv3G4MPLpYZCCsBIMjijk5OfTqJdNe1YraBlXgwwA0ubWLVfuvsOnUDQ5F3i5/1Ftt4d5B6nm5sPFluPCHmKTy9C/QuKtxj2nnKCa1gF7i2yeik/j74i1s1SqmD6hENBFqltB2TeBcSKFWYjEU+P1Fs4/dosWD4iIp5Vqh81cRSVfEic3GQUQ9JOZHm34Gw4T+tdFEv06WUWvauDuo7YR4dNIVc1tTPcSfhRUPCCdRbQePfAqPfW1VTiJUwlH817/+xc8/l/bjKDElzu1GAjDY5jjvbTnLK2tP8fTKw/RZsIvQ8Fqcji5tkPoCfzizXhRPj/4emvUzzbG16edzGytMPy/eLqKJT3RuhL+Xs+HH0mgKf/hrkn6itVLu2K0CzDx2CzsnaDlILOubftY6lD5tK6e/WE3UcbZjTJfG1HG2XBuNitZRjNwpmvP0IUabgTBzfaIWe2doVHDBXhvSz/+shZUPQlKkkDN77i/o+i+rjKQanHrOyspixYoV7Nixg3bt2mFnV/yLumjRIqMZJynkr7ut6KU44aO6QwdVJCcVEZWKS8liyo9hFcuq1ES0g9TvPVnnFsgIdXuhsE7LFDQfIBoG0mLh2uEyHbijUUnsv5yInY2KqQ9UskbyVgRkJYsIkY9h2o0SE2DI2C1zakO2HiYuZM6FwIPzKj5JWUnauVFdZxY8YXkzqE2Gd2uo31r8DlzYqp8WoSVeWDa9XziwUfug87PmtsY05GWLi8TjBY29zQfAqG/Axcu8dlUBgyOKp0+fpkOHDqjVasLDwzl58qTudurUKROYKMnXKLzz52V2azoAMMTmuO45rYs0f/O52pWG1ieic26TaSM6tg6FenXlpIQWb78IwJNdGtPYsxLRRCisT2zcFWxquHacNWAFY7cAuG+IqFFLihSjKyvCShzFrNx8LsankZVbi7R7tVHFs3qkn9Nvwe3LYlmP+ulqo2hDSyU0aC2e5BhYNbTASVRBvzfEYAcrdhKhEo7i7t27y7zt2rXLFDbWeo5GJRGbkkVovgjbD1EfpaiDpACxKVkcjUoyj4HmoMKIDtUzSF2Xfi7dKT0YmcihK7ext1FXPpoIRWQuLCg6UJvRd5yWGcduAWLykHYyS0XpZ01+4XhIC+94vpyQzuDFf3M5oRIi1NaKLv28C+5WoDByrSCa6B0kJglZCo26ivrX9PhCR7amcGkHfN1XyEs51RUO4gNvWkznclWQgttWQEKaEJXdo+lAtmJLU3U8L9pspof6HGoKa+P+b+MZ5m0KZ+3RGE5dS+ZujmFX2/kahUORt62jUcZSIjrN+oNjHdFdGH2g2FOKorCkoDbxqW6NaVjHgJnSxXckhbYtjYBeQgy3xCQFLSqhpWgJab/WWvHtCqa0JF6CnHRR3lDvPtPbJTGM+veJSTmaPCGkXh46WRwLqU/UYudYGOGM+tu8tlSW0pon93wMPz0hHHi/jvDi39ByoLktNRoG57Aee+yxUgWfVSoVjo6OtGjRgmeeeYZWraTOm7HwdhMda/erT6MUnJjetFsLwE3Fk/m5E/hL043IWxlE3srQvU6lgqZeLrT2dSfQx03c+7rRsI5Tib9haHgs8zefI7bIpANfD0fmDQuyzNpHS4no2NiJOrCTP4j0c9O+uqcOXL7N0atJ2Nuqebl/FaKJd66KOki1HTTsUnWbJVVHbQNDFxTUyKooXgJhGWO3dLR6CFQ2EB8OtyPBq3np22nTzr7tLcNuSUmCR4q/49kN0KkciTpLHvXZ5H6Rer66D7o+b25rDONciCh5KprNsnGA/Gyx3OU58b23dTCPfSbC4Iiih4cHu3btIiwsDJVKhUql4uTJk+zatYu8vDzWrVtH+/btOXDgQMU7k+hFt6aePOV6imV2S3Agt9hzPiSxzG4JTzqf5L9PtGPy/U3p06IeXi72KApcSczgjzOxfLr9Iv9ac5w+C3bTbv42Ri8/xLxN4fxyNIZleyKZ8mNYMScRChtlLLKrWhfRKYtqjOho088RIWKGJyKauHiHqE18ppt/1aZHaH/0/TqIzkGJZRA03KLHbulw9ixsqCkvqmgFE1lqPcEFvzVX9kBmGaVGORmFJQSWFlGEwv9FrZ6itaBtnry35EnrJHZ7Uegj1jAnESoRUfTx8eGZZ55h6dKlqAtmR2o0Gl555RXc3NxYu3YtL730ErNnz2b//v1GN7g2YoOGeXZrILdk06JaBRoF3nX4AadOxeshEtKyOB+bxvm4VCJi04iITeVyQjppWXkcvZrE0avl1zQqiNjI/M3nGBTkg43agtr6dRGd0q6qqzmi06QvOHtB5m24+jc0H8DflxI5EX0HB1s1L/cvI4KjL7q0swWkMSXFseCxW8VoPVw4FxEh0Gdm6dtYSSOLFnubWlg55dVczH+OOy3+lqV1Dt84IdLT7g2FLIul0bAz2DpBxi24dV50dFs6+jRPnt8CQz+yvO++ETDYUfz22285cOCAzkkEUKvVTJ8+nV69evHhhx8ybdo07r/fjJIQNY3ogzjdjSuzHEqtQjx/jxSHt5sj3m6O9L2vcEpETp6GyFvpRMSmcj4ujUORiZy5UfZYqKKNMj2bW1jnVlnzQt39hJNYXREdG1txIj7xHYT/jtLsARYVdDqP7xGAt3sVxW5lI4tlox27ZckEPgp//Fs4ESnXwaNR8efzcyHujFi28EYWgDYNPbj4gQmlryyZNqOEo3h2Q+mOYtH6REvU7LN1EHWKUXtFVNEaHEVDmict/begEhh8SZaXl8f58+dLrD9//jz5+aJ5wtHRsdQ6RkklMWLjhr2tmta+7ozq1Ii3Hm7Nv+5vpteutQ01FsXuD8R98CiYuAUe/1bczzxT/Wk/Xfp5M3vP3eCfa8k42ql5sV8Vo4npCQXdgSrw715lMyW1FLcGhWnI0hohEiIgL0vogtZtWr22SQwjaKS4j/obMhJLPm/J9YlatM6UtTS0WErzpJkw2FEcP348zz//PIsXL2b//v3s37+fxYsX8/zzzzNhwgQA9u7dS3BwsNGNrbWYsHFD2yhTEfVdLazu4toxMfdUZQMPvG3+QeoBvcHFG7KS2RP6GwATezahvlsVPzftj76lyVxIrA9d93MpMjm6tHN7McPawrmckMYjn+/jckKauU2pfjybivIARSNkuYqSnwfXjoplS6xP1NKkoOkvcjecXi+6h805xagiLKV50kwYnHpevHgxDRo0YOHChcTHC++5QYMGvPrqq8yePRuAwYMHM3ToUONaWpvRNm6kxlJmjYSzV6Vq2Lo19cTXw5G4lKzyqi/47kAUbRt54OZoISOzdr0r7js8DfWq0FFsLNQ2EDQCjq2kTfJOnO0DeaGvftHacpGyOBJj0fpR+OvNgnrKBHD1LnxO5yhaftoZICtXw9mbqWTllj86s8YSPEr8zc5uKN45HB8uJI4cPCx7VnfqTUAFOWnw+2Sxzt1P1J1bShNYUQJ6CRm0rOQyNlAJ+2toHbnBl442Nja8/fbbxMbGkpycTHJyMrGxsbz11lvY2IhIjr+/P40aNapgTxK90TZuAGUWKuZkFNYYGYCNWsW8YUGl7ln72FatYntEAiO/PEDkLQsQuL2yV6Qs1HbQb7a5rdGhFAjiDlYf57kefngZIwqrdRQtOY0ksQ7q+Bc0qihw/o/iz8mOZ+sieKS4jz4AaUXSndr6xMbdLLep4lwI/DaJEkGP1FjRVXxOz7nk1UlaLOTllPGkhclhmYAq5Rjc3d1xd3c3li2S8ihPisM7SNQX/fQEJEUZvOuhbXxZNq5TCQkXHw9Hlo/rxK8v9cTH3ZHIWxmMWHqAbWfjqvJOqoaiwK73xHKXSeLkZyH8ldaUOKUu7qpMXmoYXfUdZqWKCAHU2CtVSTXTepi4LyqTk5sF8efEshU0skgQv3uNuor0c9FSAl19ooWmncvtHi5YF/qGZaWhFQU2TYO8TPBsDm73yLJZmhyWCajU0NjffvuN9evXExMTQ05OcS87LCzM4P19+eWX/Pe//yUuLo727dvzxRdf0K1b6fMpV65cyZo1awgPFyfQzp078+GHH5a5fY2iLCmOnAz47mGIPwM/Pg7PbwOXegbtemgbXwYF+XA0KomEtCy83Rzp1tRTJ4mzeXofpv4UxtGrSbzwwwlmDGjBzIH3oa5uyZyLf8H1Y0Je4f7/VO+xy0GjUViy8zKj87vznG0orpdDoP2wqu302lFxIqjbpALNSIlET1qPgJ3vio7Tu3dE3Wv8WdDkivIVS5RTkZRO8GPit/DsBug2WTg0uo5nC81AVNg9rFhe9/DxVXBlN9g6wjPrwLOZ5cthGRmDI4qff/45kyZNokGDBpw8eZJu3brh5eXFlStXeOghw+UK1q1bx6xZs5g3bx5hYWG0b9+eIUOGkJCQUOr2e/bs4emnn2b37t0cOnSIxo0bM3jwYG7cuGHwsa0SrRRH0cYNR3cY+yt4+ENSJPw8WjiPBmKjVtGzuRcjOjSkZ3OvYrqJ9d0c+Glyd57t1QSAz3dd5vnvj5FyN7eMvZkAjQZ2vS+Wu78gOjkthNCzcZyPS2OXTR+x4sKfkHu3ajuN0aadZTRRYiTqtRAZCE0eXAgV64qmna1EraJxXWe+fKYTjevWYgH6oBHiPvqgSNveuQrpcQUTnCw0Mmxt3cNJV2DbHLE88B2o17L0c3ANx2BH8auvvmLFihV88cUX2Nvb8/rrr7N9+3ZmzJhBSkqKwQYsWrSIyZMnM2nSJIKCgli+fDnOzs6sWrWq1O1/+uknXn75ZTp06EBgYCDffPMNGo2GnTt3GnzsGoW7L4z7n4gQ3DgBv07STQkxFnY2at4ZHsyi0e1xsFWz+8ItRizdz4W4auo8PLdRRE0d3KH3zOo5ph7kaxQWF+gmdu49WERlctLh8o6q7Ti6II0kG1kkxuTe9PPNU+LeShpZADyc7XiknS8ezhbSXGcOPBpB4x6AIrqftdFEv45gV8m58qbGmrqHNfmwcSrkZkBAHzF5pZZisKMYExNDr14iwuHk5ERamnASxo8fzy+//GLQvnJycjhx4gQDBxYOz1ar1QwcOJBDhw7ptY/MzExyc3Px9PQs9fns7GxSU1OL3Wos9e+Dp9eJEPmlv2DLTJOMSBrVqRH/m9KLhnWcuHo7k8e+OsCW0xWIkVaV/DzY/aFY7jlVjCWzEP44E8ulhHTcHW157v5mhVf64b9Xfqd52cLhBxlRlBgXrUxO5E7ITrfKRpZbadl8s+8Kt9KyzW2KeSlooOPEajj1k1hubMFlWLrRq+VErqtr9GpFHF4msjr2rjDyS6uQjTIVBr9zHx8fkpLE6Dd/f38OHxZXMVFRUSgGOiWJiYnk5+fToEHxq4cGDRoQF6dfw8Ts2bPx8/Mr5mwW5aOPPsLDw0N3a9y4htfg+HeHJ1aBSg0nf4A9H5vkMG0aerB5eh/6tKhHZk4+034+yUdbI8jLN5Fcxel1cPsSOHlCj5dNc4xKkK9R+KxgpvPk+5vh4WRXKL59MRRyMiu34xthYoaoS30xtksiMRYNgoWodl6WiETdKhigYEWOYnxqFu//EUF8qgUOAqhOtJHDWxFwdZ9YPvWzZXYOg34KHj1eNn8699YFUcsLMPj9sqeA1RIMdhQHDBhASIj4J5w0aRKvvvoqgwYNYsyYMTz22GNGN7A8Pv74Y9auXcuGDRtwdCxdOPrNN98kJSVFd7t27Vq12mgWAh+BRz4Vy3s/huPfmeQwni72rJ7UlRf7Cb3Ar/de4dnvjnEnoywZgUqSlyPeB4g5tY6W02m/+Z+bRN7KoI6zHc/2biJW+nWCOgGQmykiu5Uh+oC49+9pNXVjEitBpSrs0NwxXzRMOXkW11WUWD7nQmDzKyXX371juTIzULaCh02BnNjhZcUlf6qb/DzY8KK4UG/+YOljEmsZBnc9r1ixAo1GRI2mTp2Kl5cXBw8eZPjw4bz4omE5/Hr16mFjY6MT7tYSHx+Pj49Pua/95JNP+Pjjj9mxYwft2rUrczsHBwccHCxsqkh10OU50V3293/hj1mi5iPwYaMfxtZGzZsPtaZtQw9e+/U0+y8n8ugX+/l6fGfaNPQwzkHCvofkGPEeuk42zj6NQF6+hs92XgJENFEnRq5SiZTQgSUi/RxciQsorcyFJaRgJDUPx4IpPxkFv713k2BJG8sVPJYUp0KZGZWQmQl8xPzRudIoTcHDuzWsGiJGlq59Gp79wzy1lvsXCzFzRw8YsVReqFOJiKJarcbWttC/fOqpp/j888+ZPn069vb2Bu3L3t6ezp07F2tE0Tam9OxZdgH/woULee+99wgNDaVLly6GvoXawwNvQ8dxImLw23OFo51MwKPt/Ng4tTcBXs7cSL7L48sO8nvY9arvOCcT/v5ELPd9Dewtp8tx46mbRCVm4Oliz8SCbnAd2vTzpW2iDswQNPlFxnDJRhaJkTkXAjvnl1xvyYLHkuIYIjNjqdzbPexSD55ZX9iQueEloXRRncSehr0FqfGH/itlyQqoVHVmVlYWR48eZcuWLYSEhBS7GcqsWbNYuXIl33//PREREUyZMoWMjAwmTZoEwIQJE3jzzTd12y9YsIA5c+awatUqmjRpQlxcHHFxcaSnW8DEEEtDpYJHl0DLwZB3F34eA4mXTHa4Vj5uhEztwwOt6pOdp2HW+n94J+Qsufka8jUKhyJvs+nUDQ5F3iZfo2c967FvhOSDhz90mmgy2w0lN1/DF7vEZ/lC32a4OtwTnPdpJ/S28rJEraIhxIdDdirYu4FPWyNZLJFgnYLHpeDmaMvA1t64OVZKCtj6sTaZGX3xag5jfhISP+c2wp4Pq+/YedmwcYrQFA18FNqNrr5jWzgGf8tCQ0OZMGECiYmJJZ5TqVTk5xv2AzNmzBhu3brF3LlziYuLo0OHDoSGhuoaXGJiYlAX6TZatmwZOTk5PPHEE8X2M2/ePN555x1D307Nx8YOnlwNqx8V3Y0/jIJ/bQe38lP7lcXD2Y5vJ3ZlyY6LfL7rMqsPXmX/5URS7+aSUKRD0dfDkXnDghjaxrfsnWWlijQAQP/ZYGtYxNqUbAi7QfTtTLxc7JnQM6DkBiqVmMe67xORfm77RMltykIri+Pf3TLTRhLrxRoFj0shwMuFbyZ2NbcZ5sOaZGYMpUlvGPYZbHpZlE55tYD2T5n+uHsXiIt0Zy8RYJEpZx0GRxSnT5/Ok08+SWxsLBqNptjNUCdRy7Rp04iOjiY7O5sjR47QvXt33XN79uxh9erVusdXr15FUZQSN+kkloO9ixDk9mwGKTFi1F+W6WSC1GoVswa3YsX4zjjaqrmckF7MSQSIS8liyo9hhIbHlr2jw8tE7ZRXS2hXDT8UepKTp+HzgmjilP7NcbYv43pLm36+vN2wz1sntC3TzhIjU0MiUbn5Gm6nZ5NrKpUFS6dCmRmV5cjMVIaOY6HPq2I5ZHrhxbOpuH68MCjx6BJwrW/a41kZBjuK8fHxzJo1q4SkjcTCcaknBLld6kPcGVg3rpwh58bhwdYNcC0jNaRNfM3ffK70NHRmEhxaKpYfeBNsLCfF9L+w61y/c5d6rg6M7V5KNFGLdxDUuw/yc8SkFn1QlCJC21b6Iy+xXGpIJOpCXBqd399RfWL/lka5MjMFj4d+bN0ZiQFzheZnfg6sfUZMSTEFOZmiy1nRQNvRspmrFAx2FJ944gn27NljAlMkJsezmSgWtnMRs143vWzSYuGjUUkkppftjCpAbEoWG05eR3Ovs3jgM1Gn16AtBFWv7FJ5ZOfls3TXZQBe7t8cJ/tyfoi16WfQX3z7diRkJICNvVVNypBYCTU9ElWbKEtmxt1PrLd2h0ethse+Fvqed5NEjf3dZOMfZ9d7otPazRceXmj8/dcADA7TLF26lCeffJJ9+/bRtm1b7OyKj1CaMWOG0YyTmICGnWDMGvGlO/Or+HIMfs8kh0pI008M9z+/nua9LRF08q9DlyaedK+fR+cjX4tT2YC3K1TEz9coHI1KIiEtC283R7o19Sw2p9qYrD9+nRvJd2ng7sAz3f0rfkHwY0IDMnKX0Ddzqlv+9tq0c8POYFe6NqhEUmm0kaj1ExDOYtELtBoSiapNlCYzE9Cr5vz97J3h6bWwcgAkXoRfJ8LY30TtvTGI2geHvxLLw7+o+Pe5lmKwo/jLL7+wbds2HB0d2bNnD6oiBZ8qlUo6itZAi4EwfClsfAkOfi6uQHtMMfphvN30c3TsbFSk3M1l94Vb7L5wi3m239PF9i7nbVux9nxjOmXdpHNAXfw8HIv9vwGEhscyf/M5YlMKnVK9GmUqQVZuPl8WRBOnPtACRzs9foy9A0UKOuEcnP9DyBWVh66RRdYnSkyENhIVOrt4Y4u7n3ASrT0SVdvQyszUVNx8hLO4aihc2QN/vgaPLq56s0l2msiqAXSaAC0HVdnUmorBjuLbb7/N/PnzeeONN4p1I0usjA5PQ9pNMaYo9E1xJRo0wqhXpt2aeuLr4UhcSlapYhwqwMfDkd3/6c+FuDRORN/hSuR5xkYJXc13Mx/n4KFoVh+KBsDH3ZHOAXXpFFCXLgF1uXYnk+k/nyyxb22jzLJxnYzqLK47do241Cx8PRwZ09WAUZDBo4SjGP57xY6iNqIY0LvyhkokFVHTI1GSmoVvO3jiW/jlaTjxHdRrCT2nVm2f2+aIQQ4e/jD4A+PYWUNRKQYOaPb09OTYsWM0b26d82dTU1Px8PAgJSUFd3fLGQVnFhRFXJ0dWwlqW3CsA5lFZI/c/ao8qSE0PJYpP4aJwxVZr70WLOHMbZoGJ38gu1FvtnX9hhPRdwiLucPZm6n6ay9S6ITunz2gSmlobVr7RnIm72+JIPluLu+PbMO4HuU0sdxL4mVY2hlUNvDaZXD2LH271FhYFCjmdM+OtqhRhRKJJZGvUcjMycPZ3tZkZSYSC+TgUtj2NqCCp3+BVg9Vbj+Xd8CPj4vliZuhaV+jmWgtGOILGRwSnDhxIuvWrau0cRILQqWChxZAwy6gySvuJIJRJjUMbePLsnGd8PEonob28XAs6STejhQD7QGHIe8wrL0f7wwPJmRaH868M5i1L/TgtSGteDDQGxeH8iMf2kaZD/44x54LCUQlZpCTZ1jjTmh4LH0W7OLplYf5z6+nSb6bi1oFHk4G1sfUayGEs5V8iCjns9RGExu0kU6iRFIONmoVbo520kmsbfScWjB7WYHfnhcKHoZy9w5smi6Wu79UK51EQzE49Zyfn8/ChQv566+/aNeuXYlmlkWLFhnNOEk1kXqjjCeMMzN0aBtfBgX5VNxwsvtD4Uy1HAKNuxV7ytnelh7NvOjRzAuATSdv8Mq6UxUee9WBq6w6cBUAtQr86jgR4OWMv6dLwb24BXg5F85qpjASem8MU6PAjF9OYmejMiytHTxK/KiF/172kHkpiyOR6EVUYgZzN4Xz7og2NK3nYm5zJNWFSgUPfwJJUUK54+cxMHmXYQMktr4hyq48m8OD80xnaw3CYEfxzJkzdOzYEYDw8PBiz93baCCxAqIPQlo5otdGmtRgo1bRs7lX2RvEn4Xw/4nlAf9X4f683fVrlOkcUJf0rDyikzLIytVw/c5drt+5ywFul9jW08W+wHF0Ytf5hFLrKrXM33yOQUE++kc0gh8T83Wv7oP0W6ULukZLoW2JRB8ysvPYdymRjOw8c5siqW5s7GD09/DNILh9CX55Cp79U3RIV0TEZji9VpT3PLZcv9dIDHcUd+/ebQo7JObCUiY17PoAUCBopChcrgB9G2XWv9gTG7UKRVG4lZZNdFIm0bczibmdQUxSJtFJmcTczuR2Rg5JBbdT15LLPbY2rX00Kql857conk2FHtjNkxCxCbr+q/jzd++IhheQEUWJRCIpD6e6MHY9rHxQ/KZueBGe/L58KbWMRNg8Uyz3fqVE1kpSNpYz7kJiHixhUsP1E3DhD3GV98Bber3ERq1i3rAgpvwYVpYaHPOGBekifiqVCm93R7zdHenapGQzSVpWLjEFTuPW8DhC/ilvHq5AX51IHcGjxI/a2Y0lHcWYI+JdeDYHV2/D9iuRSCS1Dc9m8NRP8P1wUfu96z0YWEYqWVFgy0xRh+8dBP3frFZTrR2pb1PbqXBSQwH//AwZJdO1RmFXgeB3uzFQv5XeLzOoUaYC3BztCPbz4KG2vjzdTQ8hbfTXidQRPFLcX90PaXHFn9PJ4shookQikehFQC8hlA2wf5GuGbIEZ34TaWe1rUg52zpUn401ABlRrO1UOKmh4PGpn+FCqJji0v6ZCqel6M3V/XBlt/gC95tt8Mv1bpQxAH3T2t2aliFzUxZ1/KFRV7h+DM5tgu4vFj4nG1kkEr3x9XDk3RHB+HrI6UW1ng5Pi1rFfZ9CyAyoEwD+PQo1QtW28McssW2/2eDb3rz2WiEG6yhaO1JHsQzOhZQyqaGhmNTg6g1bZkHCWbHevxc8ugi8W1ftmIoi1PavHYYuzwm1fQvBYP1HfTn0Ffz1pmhYeS5UrMu9Cx81Bk0uzDgl6hklEolEoh8aDfz2rLgAt3cRt/SE4tvUaQLTjxtv/J+VY1IdRUkNJWg4zAyHiVvg8W/F/cwzYr1/D3hxLwx6D+ycRZp0eR/YPg9yMip/zMs7hJNo6wh9XzPeezECxkxrF0Obfo45BCkFskTXjwsn0c0X6japtM0SSW0hOTOHDSevk5yZY25TJJaAWg0jl0PdpuKcdK+TCJB8FS5srXbTagJ6pZ5DQvQXXB4+XM4JtVrKmxlqYwe9ZwiZl9A34PwWOLBE6AI+/F9oNdSwYylKYW1i138V1ElaFqZIa+PuJ6KJMYfg3EYhIBtTZL6zlJiSSCrk+p27vLruH7ZM70MdZ3tzmyOxBGwdRHamTKquCVxb0ctRHDlyZLHHKpWQGyn6WEt+fr5xLJNYJnUai06z83/C1tchJQZ+GQOBj4o0dR09ZyBHhEDsP2DvCn1eNa3NVaBC/cfKEDxKOIdnNwhHMfqAWC/rEyUSiaRyRB+E9LhyNjCOJnBtRK/Us0aj0d22bdtGhw4d2Lp1K8nJySQnJ/Pnn3/SqVMnQkNDTW2vxFIIfBimHoHeM0Wx8Pkt8GV3OPgF5OeW/1pNfoFuItBjCrjUM7m5FkXQCEAlmlqSrsC1Y2K9FNqWSCSSymEpmsA1EINrFGfOnMlnn33GkCFDcHd3x93dnSFDhrBo0SJmzJhhChslloq9CwyaDy/uE05ObgZs+z/4ul+BLmAZnPkVEi+Aowf0nFZ99loKbg2gSR+xHDJdfG52LlBPf2kgiUQikRTBEjSBaygGO4qRkZHUqVOnxHoPDw+uXr1qBJMkVkeDIDFCafhSoZifcBZWDRZSBZlJYhtNPkTtg3/Wwva5Yl3vV8CpjtnMNiuezcX91f3iPjcDPm8nus8lEkm5ONnb0NG/Dk72stZMUkCFmsAqoeQhS3wMxmB5nL59++Lo6MgPP/xAgwbCM4+Pj2fChAlkZWWxd+9ekxhqLKQ8jonJuA075sLJH8VjZy9o87hITReV3lGp4bEV0O5J89hpTs6FFOhW3vvVK/iBG71GdJtLJBKJRH90v61QqrCZ/G3VYVJ5nFWrVhEbG4u/vz8tWrSgRYsW+Pv7c+PGDb799ttKGy2pIbh4wYgvYdJWqN8aMm/D0RXFnUQARQO/T659ETRNvtCrLFXKu2Bd6BtiO4lEIpHoT9Bw4Qy63yNf5u4nncQqUCnBbUVR2L59O+fPnwegdevWDBw4sFj3s6UiI4rVSM5d+PQ+yE4tYwOV+ALPPFN75Aqi9sH3j1a83cQtsjNPIimD8BspPPrFfrZM70Obhh7mNkdiaWjyCyezuDYQ6ebaco7RE0N8oUqN8FOpVAwePJi+ffvi4OBgFQ6ixAzcOF6Okwi1Uq5AduZJJBKJaSlPE1hiMAannjUaDe+99x4NGzbE1dWVqKgoAObMmSNTz5LiSKeoJLIzTyKRSCRWhMGO4vvvv8/q1atZuHAh9vaFivht2rThm2++MapxEitHOkUlkZ15EolEIrEiDHYU16xZw4oVKxg7diw2NoU5//bt2+tqFiUSQDpFpaG2gaELCh7c+7kUPB76saynkUgkEolFYLCjeOPGDVq0aFFivUajITe3gokcktqFdIpKR3bmSSRVooW3K3v+058W3q7mNkUiqfEY3MwSFBTEvn37CAgIKLb+t99+o2PHjkYzTFJD0DpFobOLS+S4+wknsbY6RUHDxXB62ZknkRiMo50NTeq5mNsMiaRWYLCjOHfuXCZOnMiNGzfQaDT8/vvvXLhwgTVr1rBlyxZT2CixdqRTVDqyM08iqRTXkjL5dNsF/j24FY09nc1tjkRSozE49TxixAg2b97Mjh07cHFxYe7cuURERLB582YGDRpkChslNQGtU9T2CXFf251EiURSaVLu5rLx1E1S7spyJ4nE1FRKR/H+++9n+/btxrZFIpFIJBKJRGJBGBxRlEgkEolEIpHUDvSKKNatW1fv6StJSUlVMsjUaCcWpqaWNzFEIpFIJJZKeloqmuxM0tNSSU2Vk8EkEkPR+kD6THHWy1FcsmRJlQyyJNLS0gBo3LixmS2RSCQSSVXoucTcFkgk1k1aWhoeHuXPS1cp+riTNQiNRsPNmzdxc3Mz+Yzq1NRUGjduzLVr1yocum0pWKPNIO2ubqzRbmu0GaTd1Y012m2NNoO025woikJaWhp+fn6o1eVXIVaqmUVLVlYWOTk5xdZZ+oemVqtp1KhRtR7T3d3d4j+Xe7FGm0HaXd1Yo93WaDNIu6sba7TbGm0Gabe5qCiSqMXgZpaMjAymTZuGt7c3Li4u1K1bt9hNIpFIJBKJRFIzMNhRfP3119m1axfLli3DwcGBb775hvnz5+Pn58eaNWtMYaNEIpFIJBKJxAwYnHrevHkza9asoX///kyaNIn777+fFi1aEBAQwE8//cTYsWNNYadV4uDgwLx583BwcDC3KXpjjTaDtLu6sUa7rdFmkHZXN9ZotzXaDNJua8HgZhZXV1fOnTuHv78/jRo14vfff6dbt25ERUXRtm1b0tPTTWWrRCKRSCQSiaQaMTj13KxZM6KiogAIDAxk/fr1gIg01qlTx6jGSSQSiUQikUjMh8ERxcWLF2NjY8OMGTPYsWMHw4YNQ1EUcnNzWbRoEa+88oqpbJVIJBKJRCKRVCNV1lGMjo7mxIkTtGjRgnbt2hnLLolEIpFIJBKJmal1gtsSiUQikUgkEv0wuEZxxowZfP755yXWL126lJkzZxrDphrBl19+SZMmTXB0dKR79+4cPXrU3CaVy0cffUTXrl1xc3PD29ubkSNHcuHCBXObZRAff/wxKpXKKv4Pb9y4wbhx4/Dy8sLJyYm2bdty/Phxc5tVLvn5+cyZM4emTZvi5ORE8+bNee+99/SaFVqd/P333wwbNgw/Pz9UKhUbN24s9ryiKMydOxdfX1+cnJwYOHAgly5dMo+xRSjP7tzcXGbPnk3btm1xcXHBz8+PCRMmcPPmTfMZXEBFn3dRXnrpJVQqldnHwupjc0REBMOHD8fDwwMXFxe6du1KTExM9RtbhIrsTk9PZ9q0aTRq1AgnJyeCgoJYvny5eYwtQJ9zS1ZWFlOnTsXLywtXV1cef/xx4uPjzWSxoCK7k5KSmD59Oq1atcLJyQl/f39mzJhBSkqKGa02DQY7iv/73//o3bt3ifW9evXit99+M4pR1s66deuYNWsW8+bNIywsjPbt2zNkyBASEhLMbVqZ7N27l6lTp3L48GG2b99Obm4ugwcPJiMjw9ym6cWxY8f4+uuvraL84c6dO/Tu3Rs7Ozu2bt3KuXPn+PTTTy1esH7BggUsW7aMpUuXEhERwYIFC1i4cCFffPGFuU0rRkZGBu3bt+fLL78s9fmFCxfy+eefs3z5co4cOYKLiwtDhgwhKyurmi0tTnl2Z2ZmEhYWxpw5cwgLC+P333/nwoULDB8+3AyWFqeiz1vLhg0bOHz4MH5+ftVkWdlUZHNkZCR9+vQhMDCQPXv2cPr0aebMmYOjo2M1W1qciuyeNWsWoaGh/Pjjj0RERDBz5kymTZtGSEhINVtaiD7nlldffZXNmzfz66+/snfvXm7evMmoUaPMZjNUbPfNmze5efMmn3zyCeHh4axevZrQ0FCef/55s9ptEhQDcXBwUC5dulRi/aVLlxQHBwdDd1cj6datmzJ16lTd4/z8fMXPz0/56KOPzGiVYSQkJCiAsnfvXnObUiFpaWlKy5Ytle3btyv9+vVTXnnlFXObVC6zZ89W+vTpY24zDOaRRx5RnnvuuWLrRo0apYwdO9ZMFlUMoGzYsEH3WKPRKD4+Psp///tf3brk5GTFwcFB+eWXX8xgYenca3dpHD16VAGU6Ojo6jFKD8qy+/r160rDhg2V8PBwJSAgQFm8eHG121YWpdk8ZswYZdy4ceYxSE9Kszs4OFh59913i63r1KmT8vbbb1ejZeVz77klOTlZsbOzU3799VfdNhEREQqgHDp0yFxmlkCfc+L69esVe3t7JTc3txotMz0GRxRbtGhBaGhoifVbt26lWbNmVXBZawY5OTmcOHGCgQMH6tap1WoGDhzIoUOHzGiZYWjD556enma2pGKmTp3KI488Uuwzt2RCQkLo0qULTz75JN7e3nTs2JGVK1ea26wK6dWrFzt37uTixYsA/PPPP+zfv5+HHnrIzJbpT1RUFHFxccX+Vzw8POjevbtVfT9BfEdVKpXFy5JpNBrGjx/Pa6+9RnBwsLnNqRCNRsMff/zBfffdx5AhQ/D29qZ79+7lptQthV69ehESEsKNGzdQFIXdu3dz8eJFBg8ebG7TdNx7bjlx4gS5ubnFvpOBgYH4+/tb1HdSn3NiSkoK7u7u2NoaPMvEojH43cyaNYtp06Zx69YtBgwYAMDOnTv59NNPzV53YgkkJiaSn59PgwYNiq1v0KAB58+fN5NVhqHRaJg5cya9e/emTZs25janXNauXUtYWBjHjh0ztyl6c+XKFZYtW8asWbN46623OHbsGDNmzMDe3p6JEyea27wyeeONN0hNTSUwMBAbGxvy8/P54IMPrGoaU1xcHECp30/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null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + } + } + } } }, "nbformat": 4, diff --git a/notebooks/10-tucker-decomposition.ipynb b/notebooks/10-tucker-decomposition.ipynb index 0216db6..f193d78 100644 --- a/notebooks/10-tucker-decomposition.ipynb +++ b/notebooks/10-tucker-decomposition.ipynb @@ -1,2018 +1,1824 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "wHEhVIsEk_Yv" - }, - "source": [ - "# 10 · Tucker decomposition on real data\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/10-tucker-decomposition.ipynb)\n", - "\n", - "*Part IV · exercise · 15 min*\n", - "\n", - "> 🇪🇸 **Descomposición de Tucker con datos reales** — Construir un tensor real de viajes en taxi, comprimir cada modo con HOSVD y explorar el compromiso entre tamaño, error e interpretación.\n", - "\n", - "Build a real tensor from New York taxi trips, compress each mode with HOSVD, and explore the trade-off between size, reconstruction error, and interpretable structure.\n", - "\n", - "## What you will be able to do\n", - "\n", - "- Build and interpret a genuine order-3 tensor from a flat table of real trips.\n", - "- Unfold the tensor along each mode and explain what information each matricization exposes.\n", - "- Compute Tucker/HOSVD using only unfolding, SVD, and `einsum`.\n", - "- Change the rank of each mode independently and measure reconstruction error versus compression.\n", - "- Read the temporal factor matrix and connect a learned component back to real hourly taxi activity.\n", - "\n", - "> 🇪🇸 **Al terminar podrás:** construir e interpretar un tensor real de orden 3; desplegarlo por cada modo; calcular Tucker/HOSVD con SVD y `einsum`; variar el rango de cada eje y medir error frente a compresión; e interpretar un factor temporal comparándolo con la actividad horaria real." - ], - "id": "wHEhVIsEk_Yv" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_XnbMyuJk_Yy" - }, - "source": [ - "## Setup\n", - "\n", - "Run this first. We use 6,433 real New York taxi trips and build a tensor indexed by pickup borough, dropoff borough, and hour of day.\n", - "\n", - "> 🇪🇸 Ejecuta primero esta celda. Usaremos 6.433 viajes reales en taxi de Nueva York y construiremos un tensor indexado por distrito de origen, distrito de destino y hora del día." - ], - "id": "_XnbMyuJk_Yy" - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "o_qdhKfXk_Yy", - "outputId": "e80f8b89-784d-4f1d-bc86-7bd423f7eb2b" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "taxi rows / filas: 6433\n", - "usable trips / viajes utilizables: 6383\n", - "tensor shape / forma: (4, 5, 24)\n", - "pickup boroughs / origen: ['Bronx', 'Brooklyn', 'Manhattan', 'Queens']\n", - "dropoff boroughs / destino: ['Bronx', 'Brooklyn', 'Manhattan', 'Queens', 'Staten Island']\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import ipywidgets as widgets\n", - "from IPython.display import display\n", - "\n", - "try:\n", - " from google.colab import output\n", - " output.enable_custom_widget_manager()\n", - "except ImportError:\n", - " pass\n", - "\n", - "TAXIS = \"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/taxis.csv\"\n", - "taxis = pd.read_csv(TAXIS)\n", - "\n", - "def unfold(T, axis):\n", - " return np.moveaxis(T, axis, 0).reshape(T.shape[axis], -1)\n", - "\n", - "def hosvd_bases(T):\n", - " return [\n", - " np.linalg.svd(unfold(T, axis), full_matrices=False)[0]\n", - " for axis in range(T.ndim)\n", - " ]\n", - "\n", - "taxis[\"pickup_dt\"] = pd.to_datetime(taxis[\"pickup\"], errors=\"coerce\")\n", - "taxis[\"hour\"] = taxis[\"pickup_dt\"].dt.hour\n", - "\n", - "sub = taxis.dropna(\n", - " subset=[\"pickup_borough\", \"dropoff_borough\", \"hour\"]\n", - ").copy()\n", - "sub[\"hour\"] = sub[\"hour\"].astype(int)\n", - "\n", - "pickup_names = sorted(sub[\"pickup_borough\"].unique())\n", - "dropoff_names = sorted(sub[\"dropoff_borough\"].unique())\n", - "\n", - "pickup_index = {name: i for i, name in enumerate(pickup_names)}\n", - "dropoff_index = {name: i for i, name in enumerate(dropoff_names)}\n", - "\n", - "T = np.zeros((len(pickup_names), len(dropoff_names), 24), dtype=float)\n", - "\n", - "for (p, d, h), count in sub.groupby(\n", - " [\"pickup_borough\", \"dropoff_borough\", \"hour\"]\n", - ").size().items():\n", - " T[pickup_index[p], dropoff_index[d], int(h)] = float(count)\n", - "\n", - "print(\"taxi rows / filas:\", len(taxis))\n", - "print(\"usable trips / viajes utilizables:\", int(T.sum()))\n", - "print(\"tensor shape / forma:\", T.shape)\n", - "print(\"pickup boroughs / origen:\", pickup_names)\n", - "print(\"dropoff boroughs / destino:\", dropoff_names)" - ], - "id": "o_qdhKfXk_Yy" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9zF6ZTFxk_Yz" - }, - "source": [ - "## Why this matters\n", - "\n", - "A matrix has two axes. A tensor can have three or more, and each axis can carry a different kind of meaning.\n", - "\n", - "For our taxi tensor,\n", - "\n", - "`T[pickup, dropoff, hour]`\n", - "\n", - "stores a real trip count. The three modes answer different questions:\n", - "\n", - "- **pickup mode:** which origins behave similarly?\n", - "- **dropoff mode:** which destinations behave similarly?\n", - "- **hour mode:** which times of day share similar traffic structure?\n", - "\n", - "**HOSVD** applies an SVD to each unfolding and keeps the strongest directions for each mode. Tucker then combines those directions through a small **core tensor**.\n", - "\n", - "This is better described as a multilinear extension of truncated SVD ideas across several tensor modes — not as “PCA itself becoming a tensor factorization.”\n", - "\n", - "### How to use the folded solutions / Cómo usar las soluciones plegadas\n", - "\n", - "Each exercise has a **Solution / Solución** cell that is intentionally closed. Try the `TODO` first; then open the solution.\n", - "\n", - "> 🇪🇸 Un tensor conserva varios ejes con significados distintos. HOSVD aplica SVD a cada despliegue y conserva las direcciones dominantes de cada modo. Tucker combina esas direcciones mediante un tensor núcleo pequeño.\n", - ">\n", - "> Es más preciso entenderlo como una extensión multilineal de las ideas de SVD truncada a varios modos del tensor, no como “PCA convertido en una factorización tensorial”.\n", - ">\n", - "> Cada ejercicio tiene una celda **Solution / Solución** cerrada a propósito. Primero intenta el `TODO`; después abre la solución.\n", - "\n", - "### Learning cycle: Predict → Run → Explain\n", - "\n", - "Before each exercise, predict the shape, rank, or dominant pattern. Then run the computation and explain what the result means in the original taxi data.\n", - "\n", - "> 🇪🇸 **Predice → Ejecuta → Explica:** anticipa la forma, el rango o el patrón dominante; luego ejecuta y traduce el resultado de vuelta al contexto de los viajes reales." - ], - "id": "9zF6ZTFxk_Yz" + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 10 · Tucker decomposition on real data\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/10-tucker-decomposition.ipynb)\n", + "\n", + "*Part IV · exercise · 15 min*\n", + "\n", + "> 🇪🇸 **Descomposición de Tucker con datos reales** — Construir un tensor real de viajes en taxi, comprimir cada modo con HOSVD y explorar el compromiso entre tamaño, error e interpretación.\n", + "\n", + "Build a real tensor from New York taxi trips, compress each mode with HOSVD, and explore the trade-off between size, reconstruction error, and interpretable structure.\n", + "\n", + "## What you will be able to do\n", + "\n", + "- Build and interpret a genuine order-3 tensor from a flat table of real trips.\n", + "- Unfold the tensor along each mode and explain what information each matricization exposes.\n", + "- Compute Tucker/HOSVD using only unfolding, SVD, and `einsum`.\n", + "- Change the rank of each mode independently and measure reconstruction error versus compression.\n", + "- Read the temporal factor matrix and connect a learned component back to real hourly taxi activity." + ], + "id": "wHEhVIsEk_Yv" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "Run this first. It installs and imports everything this notebook needs, and nothing else.\n", + "\n", + "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." + ], + "id": "_XnbMyuJk_Yy" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import ipywidgets as widgets\n", + "from IPython.display import display\n", + "\n", + "# Enable ipywidgets in Google Colab when available.\n", + "try:\n", + " from google.colab import output\n", + " output.enable_custom_widget_manager()\n", + "except ImportError:\n", + " pass\n", + "\n", + "TAXIS = \"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/taxis.csv\"\n", + "taxis = pd.read_csv(TAXIS)\n", + "\n", + "def unfold(T, axis):\n", + " return np.moveaxis(T, axis, 0).reshape(T.shape[axis], -1)\n", + "\n", + "def hosvd_bases(T):\n", + " return [\n", + " np.linalg.svd(unfold(T, axis), full_matrices=False)[0]\n", + " for axis in range(T.ndim)\n", + " ]\n", + "\n", + "taxis[\"pickup_dt\"] = pd.to_datetime(taxis[\"pickup\"], errors=\"coerce\")\n", + "taxis[\"hour\"] = taxis[\"pickup_dt\"].dt.hour\n", + "\n", + "sub = taxis.dropna(\n", + " subset=[\"pickup_borough\", \"dropoff_borough\", \"hour\"]\n", + ").copy()\n", + "sub[\"hour\"] = sub[\"hour\"].astype(int)\n", + "\n", + "pickup_names = sorted(sub[\"pickup_borough\"].unique())\n", + "dropoff_names = sorted(sub[\"dropoff_borough\"].unique())\n", + "\n", + "pickup_index = {name: i for i, name in enumerate(pickup_names)}\n", + "dropoff_index = {name: i for i, name in enumerate(dropoff_names)}\n", + "\n", + "T = np.zeros(\n", + " (len(pickup_names), len(dropoff_names), 24),\n", + " dtype=float,\n", + ")\n", + "\n", + "for (p, d, h), count in sub.groupby(\n", + " [\"pickup_borough\", \"dropoff_borough\", \"hour\"]\n", + ").size().items():\n", + " T[pickup_index[p], dropoff_index[d], int(h)] = float(count)\n", + "\n", + "print(\"taxi rows / filas:\", len(taxis))\n", + "print(\"usable trips / viajes utilizables:\", int(T.sum()))\n", + "print(\"tensor shape / forma:\", T.shape)\n", + "print(\"pickup boroughs / origen:\", pickup_names)\n", + "print(\"dropoff boroughs / destino:\", dropoff_names)" + ], + "id": "o_qdhKfXk_Yy" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Why this matters\n", + "\n", + "A matrix has two axes. A tensor can have three or more, and each axis can carry a different kind of meaning.\n", + "\n", + "For our taxi tensor,\n", + "\n", + "`T[pickup, dropoff, hour]`\n", + "\n", + "stores a real trip count. The three modes answer different questions:\n", + "\n", + "- **pickup mode:** which origins behave similarly?\n", + "- **dropoff mode:** which destinations behave similarly?\n", + "- **hour mode:** which times of day share similar traffic structure?\n", + "\n", + "**HOSVD** applies an SVD to each unfolding and keeps the strongest directions for each mode. Tucker then combines those directions through a small **core tensor**.\n", + "\n", + "This is better described as a multilinear extension of truncated SVD ideas across several tensor modes — not as “PCA itself becoming a tensor factorization.”\n", + "\n", + "### How to use the folded solutions / Cómo usar las soluciones plegadas\n", + "\n", + "Each exercise has a **Solution / Solución** cell that is intentionally closed. Try the `TODO` first; then open the solution.\n", + "\n", + "> 🇪🇸 Un tensor conserva varios ejes con significados distintos. HOSVD aplica SVD a cada despliegue y conserva las direcciones dominantes de cada modo. Tucker combina esas direcciones mediante un tensor núcleo pequeño.\n", + ">\n", + "> Es más preciso entenderlo como una extensión multilineal de las ideas de SVD truncada a varios modos del tensor, no como “PCA convertido en una factorización tensorial”.\n", + ">\n", + "> Cada ejercicio tiene una celda **Solution / Solución** cerrada a propósito. Primero intenta el `TODO`; después abre la solución.\n", + "\n", + "### Learning cycle: Predict → Run → Explain\n", + "\n", + "Before each exercise, predict the shape, rank, or dominant pattern. Then run the computation and explain what the result means in the original taxi data.\n", + "\n", + "> 🇪🇸 **Predice → Ejecuta → Explica:** anticipa la forma, el rango o el patrón dominante; luego ejecuta y traduce el resultado de vuelta al contexto de los viajes reales." + ], + "id": "9zF6ZTFxk_Yz" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 1 — read the tensor before decomposing it\n", + "\n", + "Before compressing anything, understand what the entries mean.\n", + "\n", + "For example, `T[i, j, h]` is the number of usable real trips that started in pickup borough `i`, ended in dropoff borough `j`, and were picked up during hour `h`.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Print `T.shape` and `T.sum()`.\n", + "2. Find the busiest hour overall.\n", + "3. Unfold along each of the three modes and confirm that every unfolding contains the same total number of entries.\n", + "4. Move **Hour / Hora** to inspect the real origin→destination count matrix at different times.\n", + "\n", + "> 🇪🇸 Antes de descomponer, entiende el tensor. Busca la hora con más viajes, revisa las tres formas de unfolding y usa el slider **Hour / Hora** para inspeccionar la matriz real origen→destino a distintas horas." + ], + "id": "pPuQfB9Bk_Yz" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO\n", + "# 1. Print T.shape and T.sum().\n", + "# 2. Compute T.sum(axis=(0, 1)) and find the busiest hour.\n", + "# 3. Print unfold(T, axis).shape for axis=0,1,2.\n", + "# 4. Predict which hours should show the most concentrated traffic." + ], + "id": "WnOH_23Hk_Yz" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true }, - { - "cell_type": "markdown", - "metadata": { - "id": "pPuQfB9Bk_Yz" - }, - "source": [ - "## Exercise 1 — read the tensor before decomposing it\n", - "\n", - "Before compressing anything, understand what the entries mean.\n", - "\n", - "For example, `T[i, j, h]` is the number of usable real trips that started in pickup borough `i`, ended in dropoff borough `j`, and were picked up during hour `h`.\n", - "\n", - "### What should you try?\n", - "\n", - "1. Print `T.shape` and `T.sum()`.\n", - "2. Find the busiest hour overall.\n", - "3. Unfold along each of the three modes and confirm that every unfolding contains the same total number of entries.\n", - "4. Move **Hour / Hora** to inspect the real origin→destination count matrix at different times.\n", - "\n", - "> 🇪🇸 Antes de descomponer, entiende el tensor. Busca la hora con más viajes, revisa las tres formas de unfolding y usa el slider **Hour / Hora** para inspeccionar la matriz real origen→destino a distintas horas." - ], - "id": "pPuQfB9Bk_Yz" + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "print(\"T.shape:\", T.shape)\n", + "print(\"total usable trips / viajes utilizables:\", int(T.sum()))\n", + "\n", + "by_hour = T.sum(axis=(0, 1))\n", + "busiest_hour = int(np.argmax(by_hour))\n", + "\n", + "print(\"busiest hour / hora más ocupada:\", busiest_hour)\n", + "print(\"trips at busiest hour / viajes:\", int(by_hour[busiest_hour]))\n", + "\n", + "for axis in range(3):\n", + " M = unfold(T, axis)\n", + " print(\n", + " f\"axis/eje {axis}: shape/forma={M.shape} | \"\n", + " f\"same entries/mismas entradas={M.size == T.size}\"\n", + " )\n", + "\n", + "hour_slider = widgets.IntSlider(\n", + " value=busiest_hour,\n", + " min=0,\n", + " max=23,\n", + " step=1,\n", + " description=\"Hour / Hora:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"90px\"},\n", + ")\n", + "\n", + "def show_hour(hour):\n", + " matrix = T[:, :, hour]\n", + "\n", + " print(\n", + " f\"hour/hora={hour} | total trips/viajes={int(matrix.sum())}\"\n", + " )\n", + "\n", + " fig, ax = plt.subplots(figsize=(5.4, 4.0))\n", + " im = ax.imshow(matrix, cmap=\"viridis\", aspect=\"auto\")\n", + " ax.set_xticks(range(len(dropoff_names)))\n", + " ax.set_xticklabels(dropoff_names, rotation=45, ha=\"right\", fontsize=8)\n", + " ax.set_yticks(range(len(pickup_names)))\n", + " ax.set_yticklabels(pickup_names, fontsize=8)\n", + " ax.set_xlabel(\"dropoff borough / destino\")\n", + " ax.set_ylabel(\"pickup borough / origen\")\n", + " ax.set_title(f\"Real taxi counts at hour {hour} / Viajes reales a la hora {hour}\")\n", + " fig.colorbar(im, ax=ax, label=\"trip count / viajes\")\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "hour_output = widgets.interactive_output(\n", + " show_hour,\n", + " {\"hour\": hour_slider},\n", + ")\n", + "\n", + "display(widgets.VBox([hour_slider, hour_output]))" + ], + "id": "sQdcOqdXk_Y0" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 2 — HOSVD: compress each mode separately\n", + "\n", + "Now compute one SVD basis per unfolding.\n", + "\n", + "If the retained ranks are `(r₁, r₂, r₃)`, then:\n", + "\n", + "- `U₁` summarizes pickup patterns;\n", + "- `U₂` summarizes dropoff patterns;\n", + "- `U₃` summarizes hourly patterns;\n", + "- the core has shape `(r₁, r₂, r₃)`.\n", + "\n", + "The core is produced by contracting all three modes at once:\n", + "\n", + "`core = einsum('ijk,ia,jb,kc->abc', T, U1, U2, U3)`\n", + "\n", + "and reconstruction reverses that contraction.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Compute the full SVD basis of each unfolding.\n", + "2. Start with ranks `(2, 2, 3)`.\n", + "3. Build the core with one `einsum`.\n", + "4. Reconstruct with one `einsum`.\n", + "5. Compute relative Frobenius error and compression ratio.\n", + "6. Change the three rank sliders independently and inspect how error and storage trade off.\n", + "\n", + "> 🇪🇸 Ahora cada modo obtiene su propia base SVD. El tensor núcleo resume cómo interactúan esos factores. Cambia los tres rangos de manera independiente y observa que reducir almacenamiento siempre tiene un costo de reconstrucción." + ], + "id": "WSeYDtwmk_Y0" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO\n", + "# 1. Compute one SVD basis for each unfolding.\n", + "# 2. Keep ranks (2, 2, 3).\n", + "# 3. Build the Tucker core with one np.einsum call.\n", + "# 4. Reconstruct T with one np.einsum call.\n", + "# 5. Compute relative error and compression ratio." + ], + "id": "gytR3GhUk_Y0" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "WnOH_23Hk_Yz" - }, - "outputs": [], - "source": [ - "# TODO\n", - "# 1. Print T.shape and T.sum().\n", - "# 2. Compute T.sum(axis=(0, 1)) and find the busiest hour.\n", - "# 3. Print unfold(T, axis).shape for axis=0,1,2.\n", - "# 4. Predict which hours should show the most concentrated traffic." - ], - "id": "WnOH_23Hk_Yz" + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "bases = hosvd_bases(T)\n", + "\n", + "def tucker_from_ranks(T, bases, ranks):\n", + " factors = [\n", + " bases[axis][:, :ranks[axis]]\n", + " for axis in range(3)\n", + " ]\n", + "\n", + " core = np.einsum(\n", + " \"ijk,ia,jb,kc->abc\",\n", + " T,\n", + " factors[0],\n", + " factors[1],\n", + " factors[2],\n", + " )\n", + "\n", + " recon = np.einsum(\n", + " \"abc,ia,jb,kc->ijk\",\n", + " core,\n", + " factors[0],\n", + " factors[1],\n", + " factors[2],\n", + " )\n", + "\n", + " error = np.linalg.norm(T - recon) / np.linalg.norm(T)\n", + "\n", + " compressed_numbers = (\n", + " core.size\n", + " + sum(factor.size for factor in factors)\n", + " )\n", + " compression = T.size / compressed_numbers\n", + "\n", + " return core, factors, recon, error, compression\n", + "\n", + "default_ranks = (\n", + " min(2, T.shape[0]),\n", + " min(2, T.shape[1]),\n", + " min(3, T.shape[2]),\n", + ")\n", + "\n", + "core, factors, recon, error, compression = tucker_from_ranks(\n", + " T,\n", + " bases,\n", + " default_ranks,\n", + ")\n", + "\n", + "print(\"default ranks / rangos:\", default_ranks)\n", + "print(\"factor shapes / formas:\", [u.shape for u in factors])\n", + "print(\"core shape / forma núcleo:\", core.shape)\n", + "print(\"relative error / error relativo:\", f\"{error:.4f}\")\n", + "print(\"compression / compresión:\", f\"{compression:.2f}x\")\n", + "\n", + "pickup_rank = widgets.IntSlider(\n", + " value=default_ranks[0],\n", + " min=1,\n", + " max=T.shape[0],\n", + " step=1,\n", + " description=\"Pickup rank:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"100px\"},\n", + ")\n", + "\n", + "dropoff_rank = widgets.IntSlider(\n", + " value=default_ranks[1],\n", + " min=1,\n", + " max=T.shape[1],\n", + " step=1,\n", + " description=\"Dropoff rank:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"110px\"},\n", + ")\n", + "\n", + "hour_rank = widgets.IntSlider(\n", + " value=default_ranks[2],\n", + " min=1,\n", + " max=min(12, T.shape[2]),\n", + " step=1,\n", + " description=\"Hour rank:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"95px\"},\n", + ")\n", + "\n", + "compare_hour = widgets.IntSlider(\n", + " value=busiest_hour,\n", + " min=0,\n", + " max=23,\n", + " step=1,\n", + " description=\"Hour / Hora:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"90px\"},\n", + ")\n", + "\n", + "def explore_tucker(r_pickup, r_dropoff, r_hour, hour):\n", + " ranks = (r_pickup, r_dropoff, r_hour)\n", + "\n", + " core, factors, recon, error, compression = tucker_from_ranks(\n", + " T,\n", + " bases,\n", + " ranks,\n", + " )\n", + "\n", + " print(\n", + " f\"ranks/rangos={ranks} | core/núcleo={core.shape} | \"\n", + " f\"error={error:.4f} | compression/compresión={compression:.2f}x\"\n", + " )\n", + "\n", + " vmax = max(T[:, :, hour].max(), recon[:, :, hour].max())\n", + "\n", + " fig, axes = plt.subplots(1, 2, figsize=(7.6, 3.3))\n", + "\n", + " axes[0].imshow(\n", + " T[:, :, hour],\n", + " cmap=\"viridis\",\n", + " aspect=\"auto\",\n", + " vmin=0,\n", + " vmax=vmax,\n", + " )\n", + " axes[0].set_title(f\"real / real — hour {hour}\")\n", + "\n", + " axes[1].imshow(\n", + " recon[:, :, hour],\n", + " cmap=\"viridis\",\n", + " aspect=\"auto\",\n", + " vmin=0,\n", + " vmax=vmax,\n", + " )\n", + " axes[1].set_title(f\"Tucker reconstruction / reconstrucción\")\n", + "\n", + " for ax in axes:\n", + " ax.set_xticks(range(len(dropoff_names)))\n", + " ax.set_xticklabels(dropoff_names, rotation=45, ha=\"right\", fontsize=7)\n", + " ax.set_yticks(range(len(pickup_names)))\n", + " ax.set_yticklabels(pickup_names, fontsize=7)\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "tucker_output = widgets.interactive_output(\n", + " explore_tucker,\n", + " {\n", + " \"r_pickup\": pickup_rank,\n", + " \"r_dropoff\": dropoff_rank,\n", + " \"r_hour\": hour_rank,\n", + " \"hour\": compare_hour,\n", + " },\n", + ")\n", + "\n", + "display(\n", + " widgets.VBox([\n", + " widgets.HTML(\n", + " \"Tucker rank explorer / Explorador de rangos Tucker: \"\n", + " \"change each mode independently. / \"\n", + " \"cambia cada modo de manera independiente.\"\n", + " ),\n", + " pickup_rank,\n", + " dropoff_rank,\n", + " hour_rank,\n", + " compare_hour,\n", + " tucker_output,\n", + " ])\n", + ")" + ], + "id": "v7iXv9bDk_Y0" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 3 — what did the temporal factor learn?\n", + "\n", + "Compression is useful, but interpretation is where Tucker becomes more than a storage trick.\n", + "\n", + "The hour factor matrix has one row for each hour and one column for each retained temporal component. Singular-vector signs are arbitrary, so we compare magnitudes when asking where a component is strongest.\n", + "\n", + "### What should you try?\n", + "\n", + "1. Inspect the first temporal factor.\n", + "2. Find the hour where its absolute loading is largest.\n", + "3. Compare that hour with the busiest hour in the raw taxi counts.\n", + "4. Move **Component / Componente** to inspect other temporal patterns.\n", + "5. Explain why a factor can capture a pattern even though nobody explicitly labeled “rush hour.”\n", + "\n", + "> 🇪🇸 La matriz de factores temporales tiene una fila por hora. Busca dónde alcanza mayor magnitud cada componente y compáralo con los conteos horarios reales. El signo de un vector singular es arbitrario, así que interpreta principalmente la forma y la magnitud del patrón." + ], + "id": "v4OEefzbk_Y1" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO\n", + "# 1. Use the hour-mode SVD basis.\n", + "# 2. Inspect the first temporal component.\n", + "# 3. Find its peak absolute loading.\n", + "# 4. Compare it with the busiest hour in T.sum(axis=(0,1)).\n", + "# 5. Repeat for another component and describe the pattern." + ], + "id": "pAmVh-Vuk_Y1" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ], - "colab": { - "base_uri": "https://localhost:8080/", - "height": 578, - "referenced_widgets": [ - "5e1b1ca6f0794da5bf0d2bc913dff7b3", - "e8108f1a0791441cafce0b894f6dcd1d", - "0d9b3eeee98a4071b1dbb283ad6768cc", - "8ffc4ee34e4c4d8a9a4af276f54859a1", - "c54c897dbdd6446b804d7d94b7915328", - "e903bde3d1c74f88b9e51520415e75f7", - "6f4afe3ad61e4a69b7da015e7f123eaf" - ] - }, - "id": "sQdcOqdXk_Y0", - "outputId": "26a047b7-0a86-4dbc-8907-921f4dbe2253" - }, - "outputs": [ + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "hour_basis = bases[2]\n", + "raw_hour_counts = T.sum(axis=(0, 1))\n", + "\n", + "first_component = hour_basis[:, 0]\n", + "first_peak = int(np.argmax(np.abs(first_component)))\n", + "\n", + "print(\"first component peak / pico primer componente:\", first_peak)\n", + "print(\"raw busiest hour / hora real más ocupada:\", busiest_hour)\n", + "\n", + "component_slider = widgets.IntSlider(\n", + " value=1,\n", + " min=1,\n", + " max=min(6, hour_basis.shape[1]),\n", + " step=1,\n", + " description=\"Component / Componente:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"145px\"},\n", + ")\n", + "\n", + "def explore_hour_factor(component):\n", + " idx = component - 1\n", + " factor = hour_basis[:, idx]\n", + "\n", + " peak = int(np.argmax(np.abs(factor)))\n", + "\n", + " raw_scaled = raw_hour_counts / raw_hour_counts.max()\n", + " factor_scaled = np.abs(factor)\n", + " factor_scaled = factor_scaled / factor_scaled.max()\n", + "\n", + " print(\n", + " f\"component/componente={component} | \"\n", + " f\"peak absolute loading / pico absoluto={peak}\"\n", + " )\n", + " print(\n", + " f\"raw busiest hour / hora real más ocupada={busiest_hour}\"\n", + " )\n", + "\n", + " fig, ax = plt.subplots(figsize=(6.6, 3.2))\n", + " ax.plot(\n", + " range(24),\n", + " raw_scaled,\n", + " marker=\"o\",\n", + " label=\"raw hourly trips / viajes reales\",\n", + " )\n", + " ax.plot(\n", + " range(24),\n", + " factor_scaled,\n", + " marker=\"o\",\n", + " label=f\"|temporal factor {component}| / |factor temporal|\",\n", + " )\n", + " ax.axvline(\n", + " peak,\n", + " linestyle=\"--\",\n", + " linewidth=1,\n", + " label=f\"factor peak / pico={peak}\",\n", + " )\n", + " ax.set_xticks(range(0, 24, 2))\n", + " ax.set_xlabel(\"hour / hora\")\n", + " ax.set_ylabel(\"scaled magnitude / magnitud escalada\")\n", + " ax.set_title(\"Raw activity vs learned temporal factor / Actividad real vs factor\")\n", + " ax.legend(fontsize=8)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "factor_output = widgets.interactive_output(\n", + " explore_hour_factor,\n", + " {\"component\": component_slider},\n", + ")\n", + "\n", + "display(widgets.VBox([component_slider, factor_output]))" + ], + "id": "AHQe7C-Ak_Y1" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What just happened\n", + "\n", + "You built a complete Tucker/HOSVD pipeline from real observations.\n", + "\n", + "1. **Real tensor construction:** a flat taxi table became an order-3 tensor `pickup × dropoff × hour`.\n", + "2. **Unfolding:** each mode exposed a different matrix view without losing any entries.\n", + "3. **HOSVD:** SVD supplied one low-dimensional basis per mode.\n", + "4. **Tucker core:** one `einsum` contracted all three axes into a smaller core, and another reconstructed the tensor.\n", + "5. **Rank trade-off:** changing pickup, dropoff, and hour ranks independently changed both storage and reconstruction error.\n", + "6. **Interpretation:** the hour factor exposed temporal structure that could be compared directly with real hourly trip counts.\n", + "\n", + "### The sentence to remember\n", + "\n", + "> **Tucker compresses a tensor by learning a basis for each mode and a small core that tells those mode-specific patterns how to interact.**\n", + "\n", + "This is why tensor decompositions are useful in domains such as imaging, recommender systems, neuroscience, and multi-condition biological measurements: the axes represent genuinely different kinds of structure.\n", + "\n", + "> 🇪🇸 Construiste Tucker/HOSVD de extremo a extremo con datos reales. Cada modo obtuvo su propia base, el núcleo resumió sus interacciones y los sliders mostraron que la compresión y el error dependen de cuánto rango conservas en cada eje.\n", + ">\n", + "> **Frase para recordar:** Tucker comprime un tensor aprendiendo una base para cada modo y un núcleo pequeño que describe cómo interactúan esos patrones específicos de cada eje." + ], + "id": "I5XWJQZLk_Y1" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Time for Kahoot 🎯\n", + "\n", + "**Kahoot 3 — Convolution & Tensor Decompositions** · 6 questions, about 5 minutes.\n", + "\n", + "> 🇪🇸 **Convolución y descomposiciones tensoriales** — 6 preguntas, unos 5 minutos.\n", + "\n", + "Join at **kahoot.it** with the PIN on the facilitator's screen.\n", + "\n", + "- [Quiz details and facilitator notes](https://project-delphi.github.io/tensors-workshop/kahoot.html#quiz-3)\n", + "- [Import file (`.xlsx`)](https://github.com/project-delphi/tensors-workshop/blob/main/kahoot/kahoot_quiz_3_convolution_decompositions.xlsx)\n", + "\n", + "Next up: **11 · Wrap-up and take-homes** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/11-wrap-up-and-take-homes.ipynb).\n", + "\n", + "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" + ], + "id": "Mx5SdJaBk_Y1" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3" + }, + "colab": { + "provenance": [] + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "version_major": 2, + "version_minor": 0, + "state": { + "5e1b1ca6f0794da5bf0d2bc913dff7b3": { + "model_module": "@jupyter-widgets/controls", + "model_name": "VBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "VBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "VBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_e8108f1a0791441cafce0b894f6dcd1d", + "IPY_MODEL_0d9b3eeee98a4071b1dbb283ad6768cc" + ], + "layout": "IPY_MODEL_8ffc4ee34e4c4d8a9a4af276f54859a1" + } + }, + "e8108f1a0791441cafce0b894f6dcd1d": { + "model_module": "@jupyter-widgets/controls", + "model_name": "IntSliderModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "IntSliderModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "IntSliderView", + "continuous_update": false, + "description": "Hour / Hora:", + "description_tooltip": null, + "disabled": false, + "layout": "IPY_MODEL_c54c897dbdd6446b804d7d94b7915328", + "max": 23, + "min": 0, + "orientation": "horizontal", + "readout": true, + "readout_format": "d", + "step": 1, + "style": "IPY_MODEL_e903bde3d1c74f88b9e51520415e75f7", + "value": 16 + } + }, + "0d9b3eeee98a4071b1dbb283ad6768cc": { + "model_module": "@jupyter-widgets/output", + "model_name": "OutputModel", + "model_module_version": "1.0.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/output", + "_model_module_version": "1.0.0", + "_model_name": "OutputModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/output", + "_view_module_version": "1.0.0", + "_view_name": "OutputView", + "layout": "IPY_MODEL_6f4afe3ad61e4a69b7da015e7f123eaf", + "msg_id": "", + "outputs": [ { - 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\n" + }, + "metadata": {} } - ], - "source": [ - "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", - "print(\"T.shape:\", T.shape)\n", - "print(\"total usable trips / viajes utilizables:\", int(T.sum()))\n", - "\n", - "by_hour = T.sum(axis=(0, 1))\n", - "busiest_hour = int(np.argmax(by_hour))\n", - "\n", - "print(\"busiest hour / hora más ocupada:\", busiest_hour)\n", - "print(\"trips at busiest hour / viajes:\", int(by_hour[busiest_hour]))\n", - "\n", - "for axis in range(3):\n", - " M = unfold(T, axis)\n", - " print(\n", - " f\"axis/eje {axis}: shape/forma={M.shape} | \"\n", - " f\"same entries/mismas entradas={M.size == T.size}\"\n", - " )\n", - "\n", - "hour_slider = widgets.IntSlider(\n", - " value=busiest_hour,\n", - " min=0,\n", - " max=23,\n", - " step=1,\n", - " description=\"Hour / Hora:\",\n", - " continuous_update=False,\n", - " style={\"description_width\": \"90px\"},\n", - ")\n", - "\n", - "def show_hour(hour):\n", - " matrix = T[:, :, hour]\n", - "\n", - " print(\n", - " f\"hour/hora={hour} | total trips/viajes={int(matrix.sum())}\"\n", - " )\n", - "\n", - " fig, ax = plt.subplots(figsize=(5.4, 4.0))\n", - " im = ax.imshow(matrix, cmap=\"viridis\", aspect=\"auto\")\n", - " ax.set_xticks(range(len(dropoff_names)))\n", - " ax.set_xticklabels(dropoff_names, rotation=45, ha=\"right\", fontsize=8)\n", - " ax.set_yticks(range(len(pickup_names)))\n", - " ax.set_yticklabels(pickup_names, fontsize=8)\n", - " ax.set_xlabel(\"dropoff borough / destino\")\n", - " ax.set_ylabel(\"pickup borough / origen\")\n", - " ax.set_title(f\"Real taxi counts at hour {hour} / Viajes reales a la hora {hour}\")\n", - " fig.colorbar(im, ax=ax, label=\"trip count / viajes\")\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "hour_output = widgets.interactive_output(\n", - " show_hour,\n", - " {\"hour\": hour_slider},\n", - ")\n", - "\n", - "display(widgets.VBox([hour_slider, hour_output]))" - ], - "id": "sQdcOqdXk_Y0" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WSeYDtwmk_Y0" - }, - "source": [ - "## Exercise 2 — HOSVD: compress each mode separately\n", - "\n", - "Now compute one SVD basis per unfolding.\n", - "\n", - "If the retained ranks are `(r₁, r₂, r₃)`, then:\n", - "\n", - "- `U₁` summarizes pickup patterns;\n", - "- `U₂` summarizes dropoff patterns;\n", - "- `U₃` summarizes hourly patterns;\n", - "- the core has shape `(r₁, r₂, r₃)`.\n", - "\n", - "The core is produced by contracting all three modes at once:\n", - "\n", - "`core = einsum('ijk,ia,jb,kc->abc', T, U1, U2, U3)`\n", - "\n", - "and reconstruction reverses that contraction.\n", - "\n", - "### What should you try?\n", - "\n", - "1. Compute the full SVD basis of each unfolding.\n", - "2. Start with ranks `(2, 2, 3)`.\n", - "3. Build the core with one `einsum`.\n", - "4. Reconstruct with one `einsum`.\n", - "5. Compute relative Frobenius error and compression ratio.\n", - "6. Change the three rank sliders independently and inspect how error and storage trade off.\n", - "\n", - "> 🇪🇸 Ahora cada modo obtiene su propia base SVD. El tensor núcleo resume cómo interactúan esos factores. Cambia los tres rangos de manera independiente y observa que reducir almacenamiento siempre tiene un costo de reconstrucción." - ], - "id": "WSeYDtwmk_Y0" - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "gytR3GhUk_Y0" - }, - "outputs": [], - "source": [ - "# TODO\n", - "# 1. Compute one SVD basis for each unfolding.\n", - "# 2. Keep ranks (2, 2, 3).\n", - "# 3. Build the Tucker core with one np.einsum call.\n", - "# 4. Reconstruct T with one np.einsum call.\n", - "# 5. 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Tp0+15v/8888z9KFOExMTFCtWLMU6IyMjUahQoRTrfPXqlWadydcNV6xYMc3l37t3DwDw5ZdfpritXLlymtuT5c2bN8Xrv0CBAlqfBfDy8kLZsmXRsmVLFCtWDH379tW6PCg9SpYsmaH53xUeHo6oqKgPbndG3bt3D46OjjAy0m6nki/vef9xen80lQIFCgBAqp+ZeJ+/v3+6j3tAyscqvfvkp+4f5cuXx7Nnz1J8fu9j296tWze4uLjA09MThQsXxtdff43NmzenuIT2QzL6OnrfnTt3Prp/ZCQjkoWHhyMmJibNx0utVqe41PlT9pVkHF2H0uVDYZGad6+3vn//Pn777Tfs3r07xc4ZGRmZ4VrOnz+P8PDwDDX577t16xYuX76cZlP67gF47969mDBhAoKDgxEXF6eZnpkx/cPDw1N8EC297O3tYWxs/MF5PhYKHztwnzhxIlO1AZ928CXKbmm9XpOSkj76ukpL8+bNERQUhKlTp8LV1VXrD2e1Wg2VSoXAwMBUl29hYaH1e0ZHiDEzM0vRWKrVahQqVAjr1q1L9T7Z+Ud4eh7DQoUKITg4GPv27UNgYCACAwOxYsUK9OrVK8UHVdOS2uP0oec2t0nrcXp74jdtd+/exfXr17Fw4cJ0r+v9xyqj+2RW+9i258uXD8ePH8eRI0fg7++PoKAgbNq0CY0bN8b+/fvTtY9l9HWUG/eRZJndV97FJp/SJbNhkZSUhKZNm+LFixcYOXIkypUrB3Nzczx8+BAeHh4Z+gs9WUBAABwcHFChQoVM1Z5cf9OmTTFixIhU71O2bFkAbz803K5dOzRo0AALFizAZ599hjx58mDFihUpRqFID2dn5xRndtLr7t27H/0QalaEQjKVSpXq/dIKxZwcxo4oowoUKJDiw6LA2z98S5Uqpfm9dOnSOHv2LBISEj764fDatWtj4MCBaNOmDbp27YodO3Zozu6VLl0aIoKSJUtq8iS7lS5dGgcPHoSLi8sHX4+lS5cGAPz9998oU6ZMqvOUKFECwNsPur4/XOONGzc0t2eUqakp2rZti7Zt20KtVsPLywuLFy/G2LFjUaZMmUydPEk+mfH+8/t+1trb28PKyuqDH44EMnYCp0SJErh8+TLUarXWH13Xr1/X3J4V/P39YW1trTXwRUald598d/9wc3NLdZ5394/3Xb9+HXZ2dpkastPIyAhNmjRBkyZNMHPmTEyaNAljxozBkSNH4ObmlukvzEzt9R8fH49Hjx5pTStduvRH94+MZEQye3t75M+fP83Hy8jICMWLF0/XsjKCl+tQprwbFm5ubil+ateuDeDtF1bdvHkTM2bMwMiRI9G+fXu4ubl9dGz0D/H390/36AIfqv/Vq1ep1u7m5qY5I75t2zbkzZsX+/btQ9++fdGyZcs0Qy891q1bhwMHDmTqp0iRIp+0zcCHg/n9A/eHmiIifVO6dGmcOXNGa2jXvXv3pniLvHPnznj27BnmzZuXYhmp/dHr5uaGjRs3IigoCD179tScuOjUqROMjY3h4+OT4n4igufPn2fFZmlxd3dHUlISfv/99xS3JSYmal7PzZo1g6WlJXx9ffHmzZsUtQFAjRo1UKhQISxatEjrHczAwED8888/GbpsJNn722xkZITKlSsDgGYdyY1hatmTFisrK9jZ2WldZgkACxYsSLG+Dh06YM+ePbhw4UKK5SRve0ZqaNWqFR4/foxNmzZppiUmJmLu3LmwsLBAw4YN070dHxIQEIBmzZpl6BKR96V3n6xWrRpKliwJPz+/FI9B8v0+++wzODk5YdWqVVrz/P3339i/f3+mjtEvXrxIMS159KRP2T+At6//9/ePJUuWpDhp1blzZ4SEhGDHjh0plpG87RnNCODtCbhmzZph165dWiMPPXnyBOvXr0e9evVgZWWVoW1KD57Jp0zp1KkTRo0aBR8fH6xdu1brr2sRwYsXL2Bra6s5s/zuji8iqQ4tlh5PnjzBxYsXP/nb/tzd3TFu3Djs27cvxfX6ERERsLCwgImJCYyNjaFSqbSCIDQ0FDt37szUel1cXD6l7E/27oG7b9++MDMzA/C/A/dvv/2mmbd06dIICAhAeHi45m3+kJAQnDx5MlvOOBBlJ09PT2zduhUtWrSAu7s77ty5g7Vr12rOWibr1asXVq9ejeHDh+PcuXOoX78+Xr9+jYMHD8LLywvt27dPsewOHTpoLjuxsrLC4sWLUbp0aUyYMAGjRo1CaGgoOnToAEtLS9y9exc7duxA//798fPPP2fpNjZs2BADBgyAr68vgoOD0axZM+TJkwe3bt3Cli1bMHv2bHTp0gVWVlaYNWsWPD094ezsrBkeOSQkBDExMVi1ahXy5MmDKVOmoE+fPmjYsCG6d++OJ0+eYPbs2XBwcMCwYcMyXJ+npydevHiBxo0bo1ixYrh37x7mzp0LJycnzTXsTk5OMDY2xpQpUxAZGQkzMzM0btwYhQoV+uiyJ0+eDE9PT9SoUQPHjx/HzZs3U8w3adIk7N+/Hw0bNkT//v1Rvnx5PHr0CFu2bMGJEydgY2OToRr69++PxYsXw8PDA3/99RccHBywdetWnDx5En5+frC0tMzw4/S+2NhYHDlyBIsWLfqk5aR3nzQyMsLChQvRtm1bODk5oU+fPvjss89w/fp1XL16VfOlhNOmTUPLli1Rp04d9OvXD7GxsZg7dy6sra0z9a3k48ePx/Hjx9G6dWuUKFECT58+xYIFC1CsWDHNOxilS5eGjY0NFi1aBEtLS5ibm6NWrVofvVzU09MTAwcOROfOndG0aVOEhIRg3759sLOz05rvl19+wdatW9G1a1f07dsX1atXx4sXL7B7924sWrQIVapUyVRGAMCECRM03wPg5eUFExMTLF68GHFxcZg6dWqGH690SfdHdMkgJX8qPrUhrXx9fQWA1K1bV6ZOnSoLFy6UESNGiKOjo0ybNk1E3o7mUrp0abGzs5OJEyfK3LlzxdXVVapUqZLiE/LpGV1n+fLlki9fvlRH7ElNiRIlpHXr1immv379WqpVqyYmJibi6ekpCxculOnTp0vv3r3F3Nxcs73JQ5bVr19fFi5cKD4+PlKoUCGpXLlyukfvyAnJo+ts2bJFa3pqo04kf3K/Vq1a4ufnJ6NGjZL8+fOnGELz2rVrYmRkJFWrVpV58+bJb7/9JoUKFZJKlSqlOrpO8nOeHiEhIfL777/L77//Ll9++aXY2Nhoft+9e7fWvKtXr5bff/9dRo0aJQCkUaNGmnlDQ0Mz9DiRYUhtdB0RkRkzZsjnn38uZmZm4uLiIhcuXEh1FKmYmBgZM2aMlCxZUvLkySNFihSRLl26aIa+S2ufX7BggQCQn3/+WTNt27ZtUq9ePTE3Nxdzc3MpV66cDBo0SG7cuKGZp2HDhvLVV1+le/uScyotS5YskerVq0u+fPnE0tJSKlWqJCNGjJCwsDCt+Xbv3i1169aVfPnyiZWVldSsWVM2bNigNc+mTZukatWqYmZmJgULFpQePXrIgwcP0lXP+5m+detWadasmRQqVEhMTU3liy++kAEDBsijR4+07rd06VIpVaqUGBsba41yk1aei7x9zvr16yfW1tZiaWkp7u7u8vTp01RHVbl375706tVL7O3txczMTEqVKiWDBg3SGpUlrRpS21+ePHkiffr0ETs7OzE1NZVKlSqlGP3lQzmZWo3v2rt3r6hUKnny5Ema87zrQ8dtkfTtkyIiJ06ckKZNm4qlpaWYm5tL5cqVZe7cuVrzHDx4UFxcXDT7UNu2beXatWvpqif5WJQ8JOihQ4ekffv2UrRoUTE1NZWiRYtK9+7dU4yCt2vXLqlQoYKYmJhoHd8+9DpKSkqSkSNHip2dneTPn1+aN28ut2/fTvW4/fz5cxk8eLB8/vnnYmpqKsWKFZPevXvLs2fPNPN8LCNEUn9eL168KM2bNxcLCwvJnz+/NGrUSE6dOpXq4/L+MK/Jx/nURn1KC5t8+qCsCItr166Jm5ubWFhYiJ2dnXz33XcSEhKSqSa/S5cu0qpVq3TX/6GDQnR0tIwaNUrKlCkjpqamYmdnJ3Xr1pXp06dLfHy8Zr4//vhDHB0dxczMTMqVKycrVqxItVZ9afJF0nfgFhFZu3atlCpVSkxNTcXJyUn27duX5hCaGWnyk0MstZ/3H8PksY9T+8lI2BER6aPvv/9enJ2ddV0G6SGVSCY+kUekA4mJibC1tYWvry+8vLx0XQ4REVG2W7JkCYoXL46WLVvquhTSM2zySW8kX583YMAAfPbZZ7ouh4iIiCjXYpNPRERERKQwHEKTiIiIiEhh2OQTERERESkMm3wiIiIiIoXhl2F9hFqtRlhYGCwtLTP9dcpERFlNRBAdHY2iRYvCyIjna9KDeU5EuVF25Tmb/I8ICwvjt3sSUa7133//oVixYrouQy8wz4koN8vqPGeT/xHJX0ld36QDTFR5dFxN9pOEeF2XkHOMjHVdQc4Rta4ryDkGMmBYIhJwAgGajKKPS36s6qEVTKD8PDcohvTOjIFknCHJrjxnk/8RyW/pmqjyGEaTrzKg8FAZUJMPA2ryYSD78P/fTF52kn6aPIdh5LlBMajXgYFknCHJpjznhZxERERERArDJp+IiIiISGHY5BMRERERKQybfCIiIiIihWGTT0RERESkMGzyiYiIiIgUhk0+EREREZHCsMknIiIiIlIYNvlERERERArDJp+IiIiISGHY5BMRERERKQybfCIiIiIihWGTT0RERESkMGzyiYiIiIgUhk0+EREREZHCsMknIiIiIlIYNvlERERERArDJp+IiIiISGHY5BMRERERKQybfCIiIiIihcl1Tb6JiQmcnJzg5OQEZ2dnBAcH67okIiLKBOY5EZHu5Lom38bGBsHBwQgODsavv/6K8ePHp5gnKSlJB5UREVFGMM+JiHQn1zX574qKioKNjQ0AYOXKlejUqRNcXV3RtWtX/Pvvv3B1dUXlypXRrl07vHjxAgDg6uqKkSNHokaNGqhYsSKuXr0KAPj+++8xa9YsAMCyZcvQvXt3nWwTEZEhYp4TEeUsE10X8L6IiAg4OTkhJiYGz58/x6lTpzS3hYSE4NKlS7CyskKbNm3g5eUFd3d3TJkyBePGjcOcOXMAAHny5MGFCxewfPlyzJw5E3/88QemTZuGWrVqoWrVqpg6dSpOnz6d6vrj4uIQFxen+T0qKip7N5iISKGY50REupPrzuQnv7178+ZNLF68GIMGDdLc1rx5c1hZWQEAzp8/j65duwIAevbsiT///FMzX8eOHQEA1atXR2hoKADAwsICs2fPhpubGyZNmgRbW9tU1+/r6wtra2vNT/HixbNjM4mIFI95TkSkO7muyX9XmzZttM785M+fX/N/lUqV5v3MzMwAAMbGxlrXe165cgUFCxbEo0eP0rzvqFGjEBkZqfn577//PmUTiIgIzHMiopyWq5v8U6dOoVSpUqneVqNGDWzbtg0AsG7dOjRo0OCDy7p9+zaWLVuGS5cuYf78+bh7926q85mZmcHKykrrh4iIPg3znIgoZ+Xaa/JFBCYmJliyZEmq882ZMwd9+vTB+PHjUaJECaxatSrNZYoIPD09MXv2bHz++eeYNWsWPD09cfDgwQ+eQSIiosxjnhMR6Y5KRETXReRmUVFRsLa2RqM8XWGiyqPrcrKdJMTruoScY2Ss6wpyjqh1XUHOMZBIS5QEHMUuREZG8gx1OiXnuSvaG0SeGxRD+gPPQDLOkGRXnufqy3WIiIiIiCjj2OQTERERESkMm3wiIiIiIoVhk09EREREpDBs8omIiIiIFIZNPhERERGRwrDJJyIiIiJSGDb5REREREQKwyafiIiIiEhh2OQTERERESkMm3wiIiIiIoVhk09EREREpDBs8omIiIiIFIZNPhERERGRwrDJJyIiIiJSGDb5REREREQKwyafiIiIiEhh2OQTERERESkMm3wiIiIiIoVhk09EREREpDBs8omIiIiIFIZNPhERERGRwpjougB9IQnxEJXouoxsZ2xnq+sScow6MlrXJeQYSUjSdQlElMOY58okCfG6LoH0BM/kExEREREpDJt8IiIiIiKFYZNPRERERKQwbPKJiIiIiBSGTT4RERERkcKwySciIiIiUhg2+URERERECsMmn4iIiIhIYdjkExEREREpDJt8IiIiIiKFYZNPRERERKQwbPKJiIiIiBSGTT4RERERkcKwySciIiIiUhg2+URERERECsMmn4iIiIhIYdjkExEREREpDJt8IiIiIiKFYZNPRERERKQwbPKJiIiIiBSGTT4RERERkcKwySciIiIiUphsafJNTEzg5OQEJycnODs7Izg4OEuW6+DggFevXmlNO3r0KLp06ZIlyyciopSY6URE+idbmnwbGxsEBwcjODgYv/76K8aPH59inqSkpOxYNRERZTFmOhGR/sn2y3WioqJgY2MDAFi5ciU6deoEV1dXdO3aFf/++y9cXV1RuXJltGvXDi9evAAAXLx4ETVr1kSlSpXQq1cvvHnzJsUyXV1dsWfPHs00tVqNcuXKITIyEgAQHR2NUqVKITExEa6urhg5ciRq1KiBihUr4urVq9m92UREisRMJyLSD9nS5EdERMDJyQlly5bFzz//jJEjR2puCwkJwe7du7F9+3YMGTIEXl5euHz5MlxcXDBu3DgAQO/evTF37lxcuXIF5ubmWLBggeb+kZGRaNOmDX7++We0bdv2fxtiZAR3d3ds3rwZALB161Z07NgRJiYmAIA8efLgwoULGD58OGbOnJlm7XFxcYiKitL6ISIyZPqa6cxzIjJk2Xq5zs2bN7F48WIMGjRIc1vz5s1hZWUFADh//jy6du0KAOjZsyf+/PNPREREIC4uDrVq1dKanqxVq1b45Zdf0KZNmxTr9fDwwOrVqwEAa9asQe/evTW3dezYEQBQvXp1hIaGplm7r68vrK2tNT/FixfP5KNARKQM+prpzHMiMmTZfrlOmzZtcOrUKc3v+fPn1/xfpVJleHl169ZFYGBgqreVKlUKJiYmOHz4MCIjI1G5cmXNbWZmZgAAY2PjD147OmrUKERGRmp+/vvvvwzXSESkVPqU6cxzIjJk2d7knzp1CqVKlUr1tho1amDbtm0AgHXr1qFBgwawsbGBmZkZzp8/rzU92dSpU/HmzRuMHTs21WV6eHjg22+/Rc+ePTNVr5mZGaysrLR+iIjoLX3KdOY5ERmybL0mv0qVKvjll1+wZMmSVOebM2cO5s6di8qVK+P48ePw9vYG8PbDXIMGDULlypURHR2N77//XnMflUqFpUuX4u+//4afn1+KZXbp0gUvX77EN998kx2bRkRkcJjpRET6RyUiousistLRo0cxe/Zs7NixI0uWFxUVBWtra7iiPUxUebJkmbmZsZ2trkvIMerIaF2XkGMkIV7XJVAWS5QEHMUuREZGKvoMdVZmOvNcuZjnpM+yK89NsmxJuYCPjw9WrlyJnTt36roUIiL6RMx0IqLMU9yZ/KzGMz/KxTM/pM8M5Ux+VmKeKxfznPRZduV5tn/wloiIiIiIchabfCIiIiIihWGTT0RERESkMGzyiYiIiIgUhk0+EREREZHCsMknIiIiIlIYNvlERERERArDJp+IiIiISGHY5BMRERERKQybfCIiIiIihWGTT0RERESkMGzyiYiIiIgUhk0+EREREZHCsMknIiIiIlIYNvlERERERArDJp+IiIiISGHY5BMRERERKQybfCIiIiIihWGTT0RERESkMGzyiYiIiIgUxkTXBegNI2NAZazrKrKdOjJa1yXkmKB753RdQo5pWaaurkvIMeqYGF2XQLkd81xxmOfKxDz/NDyTT0RERESkMGzyiYiIiIgUhk0+EREREZHCsMknIiIiIlIYNvlERERERArDJp+IiIiISGHY5BMRERERKQybfCIiIiIihWGTT0RERESkMGzyiYiIiIgUhk0+EREREZHCsMknIiIiIlIYNvlERERERArDJp+IiIiISGHY5BMRERERKQybfCIiIiIihWGTT0RERESkMGzyiYiIiIgUhk0+EREREZHCsMknIiIiIlIYNvlERERERArDJp+IiIiISGEy1OSrVCp4eXlpfn/06BGMjY0xbty4TK185cqV+Pnnn9M9f0REBJYsWaL5PTQ0FJs3b87UuomIDB0znYhIuTLU5BcsWBBnzpxBUlISAGDr1q346quvsqWw1PCAQESUdZjpRETKleEz+fXr18exY8cAADt27ECnTp00t+/cuRM1a9ZE1apV0bp1a0RERAAAPDw8MHToUNSuXRuOjo6a+wPA/fv30bRpU5QpUwYzZszQTG/Tpg2qV6+OihUrYt26dQCAMWPG4Nq1a3BycsL48eMxZswYHDx4EE5OTli2bBnu3LmD+vXro1q1aqhZsyaCg4MBvD275O7unup6iIgMFTOdiEi5TDJ6B3d3d6xZswblypWDqakp7Ozs8OzZMwBAw4YN0b59e6hUKsyZMwfz58/HmDFjAAAvXrzAmTNncPjwYYwfPx6HDh0CAFy+fBkXLlxAYmIivvzyS/zwww8wNTXF6tWrUbBgQbx+/RrOzs7o0qULJk6ciBs3buDChQsAgAYNGmDevHnYunUrACAmJgYHDx6EmZkZLl++jJ9++gkHDhz44HreFxcXh7i4OM3vUVFRGX2IiIj0hpIznXlORIYsw01+3bp18cMPP2Djxo3o0qUL3rx5o7nt/v376Nq1K548eYLY2FjUqlVLc1uHDh0AANWrV0doaKhmetOmTWFhYQEAKFq0KJ48eYLixYtj1qxZ2L17t2a59+/fR548eT5YW1xcHAYPHozLly/D2NgY4eHhH13P+3x9feHj45OxB4WISE8pOdOZ50RkyDI8uo5KpUKDBg0wefJkdOzYUeu2IUOGYMSIEbhy5Qr8/Py0zqCYmZkBAIyNjTXXf747/d3bjhw5gpMnT+Ls2bMICQlBuXLltJaVFj8/Pzg4OODy5cs4ceJEqutPrYZ3jRo1CpGRkZqf//7776PrJSLSV0rOdOY5ERmyDJ/JB4BBgwahSpUqsLW11ZoeFRWFzz//HCKC1atXZ7qoqKgo2NraIm/evAgODkZISAgAwNLSEtHR0Zr53v89KioKZcqUgUqlwsqVKzO1bjMzM62DBxGR0ik105nnRGTIMjVOvqOjI/r06ZNiure3N9q2bQtnZ+dUL4VJrxYtWiA6OhoVKlTAxIkTUb16dQCAra0tqlWrhkqVKmH8+PGoXLkyEhISNB/S8vLywuLFi+Hk5ITnz59nev1ERIaEmU5EpDwqERFdF5GbRUVFwdraGq5GnWCi+vD1o0qgMjbWdQk5JujeOV2XkGNalqmr6xJyjDomRtcl5IhEScBR7EJkZCSsrKx0XY5eYJ4rF/NcmZjnn4bfeEtEREREpDBs8omIiIiIFIZNPhERERGRwrDJJyIiIiJSGDb5REREREQKwyafiIiIiEhh2OQTERERESkMm3wiIiIiIoVhk09EREREpDBs8omIiIiIFIZNPhERERGRwrDJJyIiIiJSGDb5REREREQKwyafiIiIiEhh2OQTERERESkMm3wiIiIiIoVhk09EREREpDBs8omIiIiIFIZNPhERERGRwrDJJyIiIiJSGDb5REREREQKwyafiIiIiEhhTHRdgN5QJwEq5f9NJOokXZeQY1o6uui6hByjMjPVdQk5JyZG1xVQbsc8VxzmuUIxzz+J8lOOiIiIiMjAsMknIiIiIlIYNvlERERERArDJp+IiIiISGHY5BMRERERKQybfCIiIiIihWGTT0RERESkMGzyiYiIiIgUhk0+EREREZHCsMknIiIiIlIYNvlERERERArDJp+IiIiISGHY5BMRERERKQybfCIiIiIihWGTT0RERESkMGzyiYiIiIgUhk0+EREREZHCsMknIiIiIlIYNvlERERERArDJp+IiIiISGHY5BMRERERKQybfCIiIiIihcnRJv/+/fto3bo1HB0dUbp0aYwZMwZqtTonSyAioizCTCciyr1yrMkXEXTs2BHffPMNbt26hatXr+Ly5cuYOXNmTpVARERZhJlORJS75ViTf+jQIVhYWKBHjx4AgLx582LOnDmYPn06xo0bh3nz5mnmtbOz0/x/ypQpcHZ2RuXKlTF9+vQPTj969CiaNm2KDh06oGzZshg+fDgAICkpCd9++y0qVKiASpUqYcWKFTmxyUREisVMJyLK3UxyakXXrl1DtWrVtKaVLFkScXFxiIiI0DoIJNu/fz8ePHiAc+fOQa1Wo2nTpmjRogXCwsJSnQ4AFy9exLVr11CgQAF89dVX+PHHHxEeHo67d+/i2rVrAIDIyMg064yLi0NcXJzm96ioqKzYfCIiRdGHTGeeE5Ehy9UfvN2/fz/8/f1RtWpVVK9eHffu3cPNmzfTnA4AdevWReHChWFqaoqKFSvi3r17KFWqFMLCwjBo0CDs378f1tbWaa7T19cX1tbWmp/ixYvn1OYSESlaTmc685yIDFmONfkVKlTApUuXtKbdvXsXtra2sLOz0/qwVvKZF7VaDW9vbwQHByM4OBh37txBp06d0pwOAGZmZprlGBsbIykpCQUKFMCVK1fQsGFDzJo1Cz///HOadY4aNQqRkZGan//++y8rHwYiIkXQh0xnnhORIcuxJr9JkyaIjIzEhg0bALwN/R9//BHe3t4oUaIEgoODAQAHDhzAq1evAADNmjXDsmXLEBMTAwAIDQ1FZGRkmtPT8uzZM6jVari7u2PcuHGadaXGzMwMVlZWWj9ERKRNHzKdeU5EhizHrslXqVTYsWMHvLy88Ntvv+Hx48f46aef0LNnT7x+/RorV65EpUqV0KRJE9ja2gIAWrRogWvXrqF27dpQq9WwsbHBtm3b0pyelocPH8LDwwNqtRomJibw8/PLoa0mIlImZjoRUe6mEhHRxYqDgoLg5eWFw4cPw8HBQRclpEtUVBSsra3hivYwUeXRdTmUhYzMzXVdQo5RmZrquoQck/Typa5LyBGJkoCj2IXIyMhccYZaHzKdea5czHNlYp5/mhw7k/++Fi1a4N9//9XV6omIKAsx04mIcpdcPboOERERERFlHJt8IiIiIiKFYZNPRERERKQwbPKJiIiIiBSGTT4RERERkcKwySciIiIiUhg2+URERERECsMmn4iIiIhIYdjkExEREREpDJt8IiIiIiKFYZNPRERERKQwbPKJiIiIiBSGTT4RERERkcKwySciIiIiUhg2+URERERECsMmn4iIiIhIYdjkExEREREpDJt8IiIiIiKFYZNPRERERKQwbPKJiIiIiBTGRNcF5HYiAgBIRAIgOi6GspSRxOu6hByjMqB9N0kSdF1CjkjE2+1Mzij6OOa5cjHPlYl5/mnY5H9EdHQ0AOAEAnRcCWW517ouIAcZ0rYamOjoaFhbW+u6DL3APFcwQ8o4Q9pWA5PVea4Sngb6ILVajbCwMFhaWkKlUuXIOqOiolC8eHH8999/sLKyypF16gq3VZm4rdlPRBAdHY2iRYvCyIhXXqaHLvIc4OtBqbityqSLbc2uPOeZ/I8wMjJCsWLFdLJuKysrxb+YknFblYnbmr14Bj9jdJnnAF8PSsVtVaac3tbsyHOe/iEiIiIiUhg2+URERERECsMmPxcyMzODt7c3zMzMdF1KtuO2KhO3leh/DGkf4bYqE7dVP/GDt0RERERECsMz+URERERECsMmn4iIiIhIYdjkExEREREpDJt8IqIP+PfffxEREaHrMoiIKAsYUqazySeiDEtKStJ1CTni+fPnWLx4MaZPn47IyEhdl0NElOUMJc8Bw8t0Nvk6tGfPHixcuFDXZWQrQxm8KbXtVOq2JyUlwdjYGGq1GitXrsSjR490XVK2sbW1Rbt27ZCYmIgFCxbg1atXui6JcilDyHNAubn2PkPJdEPKc8DwMp1Nfg56PyBu3LiB2NhYHVWTM1QqFU6cOIHAwECcOnVK1+VkCxGBSqUCAJw4cQJBQUEAoJmmNMkHhIYNG+LmzZsoUqSIrkvKVlevXsWdO3ewfft2zJs3z2De5qUPM8Q8B5jpSmNoeQ4YVqZznHwduHLlClQqFR4/fozNmzdj0aJFMDL6399b7waMvkrehlOnTqFbt25o3749QkND0bZtWwwYMEDX5WWLRYsWYd68ecifPz8cHBywdOlSWFtb67qsLJX8vE6YMAFhYWFYsGAB7t27h3PnzsHa2hrNmjXTdYlZyt/fH8OGDYO/vz8CAwPx6NEjWFhYYOjQobCwsNB1eZQLGEKeA8x0JWa6oeU5YHiZbqLrAgxB8gtJRHD37l3MmjULFy9eRLly5bB161ZUrFgRZcuWhbW1NerUqaOIA4JKpcL58+exc+dOLF++HE2bNsWff/6JCRMmQKVSoX///rouMUsdP34cu3btwsWLF2FqaopvvvkGw4YNg5+fH6ysrHRd3idLfks3ed8sX748du3ahW7dusHc3Bxv3rzB06dPUbJkSTg6Ouq42sx797WqUqkQGxuL9u3bw9HRESVKlMC+ffswe/ZsJCQkYPjw4Yp4biljDDHPAWa6kjLdUPIcYKbzcp1s9u5ZnISEBJQqVQrLly/HhQsXMG3aNFSvXh3GxsZYsmQJ/Pz88PDhQx1XnDUSEhJw9epV7Ny5Ew8ePAAA1K9fH7/99htWr16NBQsW6LjCT/PuG2ARERE4ceIEbty4gcOHDwMA1q9fj/j4ePTr1w9RUVG6KjNLqNVqGBsbQ0QQEBCAp0+fol27dhg2bBjq1q2LmTNnYvXq1ShUqJBeX7P67ms1MTERAFCxYkUEBQVhz549MDU1Rdu2bWFubo7IyEjEx8frslzSAUPNc4CZrpRMN5Q8B5jpAM/kZ6t3d7BZs2bhyJEjKFu2LLp06YLatWvD3t4eVlZWqFq1Kry8vDQvPn2WfHara9euOHnyJF6/fo0dO3bA2dkZFStWhIuLC3x9fWFmZqbrUjPt/eCwsbHBsGHDAABBQUEwNzdH/fr1sXbtWvTr1w+vX7/W27MDarUaRkZGEBG0bNkSsbGxiI6Ohq+vL77++msYGRkhPj4e33zzDfLly4eyZcvquuRMefc5Xbx4Mfbv34/27dujefPm8PX1xcKFC/HgwQNYWVnh+fPnWLhwIezs7HRcNeUkQ8xzgJmupEw3lDwHmOnJeCY/GyXvYCdPnkRQUBAGDRqEL774AmPGjMGpU6eQN29e1KpVC2FhYVCpVHp9QFCr1ZoXValSpVCvXj2cPHkSgwYNQrVq1eDt7Y3g4GAAb8/+1KxZU7cFf4Lk59XPzw8DBgzAt99+i9DQUAwdOhTFihXDtm3bNGd//vjjD3z22We6LPeTJF9bPHz4cLi4uODYsWPw8vLCypUrERQUhCdPnmDPnj0oUKAAVq1aBUA/R6BIfk737t2L3bt3o1WrVggMDMS6detQrFgxeHt74/jx49i3bx/mz5+PokWL6rhiymmGlOcAM12JmW4oeQ4w0zWEstXBgwelfv36MmPGDBERiYuLk6VLl0rTpk3lwoULcuTIEbl586aOq8y8qKgozf/v3bun+f+SJUukT58+mt/HjBkjrVu3lujo6BytL7vMmTNHGjduLI8ePZJmzZqJg4OD/PXXXxIbGys+Pj4yYsQIiYmJEbVaretSM+Xduv/++2+pXbu2dOzYUTNtxYoV0q1bN9m6datERkZqpiclJeVonVnp3Llz8vnnn0tAQICIiJw5c0aGDRsmU6dOlYcPH4qIfm8ffTql57kIM12JmW6IeS7CTBcRYZOfxd4PgNjYWPnuu++kU6dO8uzZMxERiY+Pl9mzZ0u7du0kISFBF2VmidevX0vfvn1lwYIFIiJSpUoVGTNmjCxfvlySkpKkVatWsnbtWs38t27d0lWpn+zNmzea/0dFRcn8+fMlPDxcpk+fLt26dZP58+fLF198IZcuXZKYmBh5/vy5Dqv9NImJiZr/h4eHi1qtljNnzki3bt1k0qRJmtvmzp0r8+bN00WJWeL912pMTIy0b99enJ2dJS4uTkREzp8/L56enuLn56eZRobDkPJchJmuxEw3lDwXYaanhkNoZiF55xqwHTt2IC4uDoULF0ajRo0wYMAAGBsbw8fHB/b29khMTMSrV69gY2Oj26I/QWxsLIKCgrBlyxb06tULderUwaVLl7BkyRKYmZlBrVajcOHCmDp1KgD9HUru9evX2Lt3L1xcXBASEoK4uDg0bdoUT548Qb9+/bBnzx5YWVmhVq1aiI6OxqVLl/T2+tTk50itVqNLly6wtraGvb09OnfujKSkJCxduhSOjo4YPXq0rkv9JO/ui3/99RcSExNRq1YtqNVqDBkyBPfv38fmzZuRN29eXLp0CUWLFkXhwoV1XDXlJEPLc4CZrrRMN5Q8B5jpadLVXxdKNnfuXHF2dpb/+7//k3bt2skPP/wgIiIDBgyQ3r17a84A6bPkv5ifPHkiAQEB0rlzZ9mwYYPm9rVr18rIkSMlb968curUKV2VmSVevXol27ZtkwoVKkjp0qU1f/0/evRIPD095fTp0/LHH3+It7e35i1AfdenTx+ZO3eu3LhxQ2xsbOTPP/+UpKQkOXnypLRr10527dql6xIz5f0zPbNmzRIXFxfp1KmTdOnSRZ48eSIxMTEydOhQadCggdbZPjJMhpDnIsx0EeVmulLzXISZ/jFs8rPAuztZUlKSdOvWTS5evCgiIgkJCdKpUyeZO3euxMXFiaenpzx+/FhXpX6yd9/eOnHihIwbN04iIiJk79690rVrV/njjz+05l+1apX4+vrmdJlZ4t1rE9esWSOVKlWSVq1aye3bt0Xk7Vvbo0aNksGDB0vJkiXlypUruio1S0VGRsro0aPl1KlT0q5dO5k2bZqIiDx//lxevXql2X599O5b7tu2bZMGDRqIiMjEiRPF1tZWevToIU+fPpWYmBgZMWKE3L9/X1elko4YUp6LMNOVnulKznMRZvrHcHSdTyTvDdO0aNEimJiY4NmzZwAAExMTDBgwAA8ePICpqSmWLl2qt28RRUREoGHDhrh06RIAID4+HiqVCtbW1qhfvz48PDxw4MABLFmyRHOfJ0+e4K+//tK7T+gnJiZi586dmD9/PjZt2oT79+9j/fr1+PrrrzFy5EicPn0a+fPnR/fu3eHt7Y0LFy6gYsWKui47U5KSkjT/FxFYWVnBxsYGPXr0QJ06dfDzzz8DAHr06IHTp0+jdOnSmnn1hYjgyZMnqFatGvbt2wcAqFu3LlauXIk//vgDJ06cwOPHj3H37l18/fXXiIiIwOTJk1G8eHEdV045yZDyHGCmKzHTDSHPAWZ6enGc/E+UfEDw9/fH9u3bERQUBGtra/Tp0wf79+9HhQoVEBoaihs3biAuLg558uTR+spzfWJjY4OOHTuiZ8+eWLduHRISEvD69WsAgJWVFVq1aoXExESsX78eLVq0QJEiRZA3b174+Pjo3XWbJiYmaNu2LZydnREREYHQ0FBYWFjAxsYGr169wpQpU1CmTBncu3cPS5cu1dtrcZPH8lar1fjll19gZmaGxo0bo1ixYnBxcYG1tTVOnjyJ2bNno2jRonBzc9PcV5+eU5VKhcKFC2PixIkYMmQI5syZg+bNm0NEcOXKFXTp0gUmJibo3Lkz/P39YWJiolfbR1nDkPIcYKYrLdMNJc8BZnp68YO3mfTy5UuYmprC3NwcDx48wIgRIxAWFoajR48CAGbPno0VK1agVq1aOH36NDZu3IgKFSrotuhPkPwlGsDbsYT/+OMPdOvWDREREahQoQKKFi0KIyMjqNVqODs7w9bWNsX99MG7Z/IiIiLwww8/4N69e2jZsiVGjRoFAIiMjMTx48excuVKjB8/Hl999ZUuS/5kIoK2bduiSZMmiI+Px6pVq7B3715cu3YNwcHB+Pfff1GkSBFMmjQJgH4/pwCwZ88eDB06FIsXL0bTpk2xdu1abNiwAY6Ojrh48SJWr14NBwcH3RVMOc7Q8hxgpis105We5wAzPUNy9uogZQgICJDatWtL3759NeMl7969W9q3by9z5szRzBccHCw3btzQ+2vAkq9RvXfvnub6zc2bN4ulpaW4ubnJlClTpH///tKxY0e5cOGCLkv9JO9ei3vnzh15+fKliIg8ffpUmjdvLqNHjxYRkZMnT8rDhw/1enxdtVqt2d6LFy/KL7/8IgkJCdK6dWuZPn26iIhm/Ov4+HjN/fRtm999TpctW6YZGnDjxo1SsmRJOXz4sLx580Z27Ngh3333nVy9elVXpZKOGFqeizDTlZbphpLnIsz0jGKTn0GBgYHi4uIiW7dulaNHj8qAAQM0twUEBMigQYP0fqzZ1Pj7+0u9evXk66+/lsGDB8t///0ngYGBUq5cOc0XpuhjYKRm2rRp0qRJE3FxcZGpU6dKYmKi3Lp1S+rVqyfNmjUTZ2dnrS+J0XdRUVHSt29fKVOmjKbJiYqKkoEDB2qNLKGPXwKTbP78+eLk5KT1IbOAgABxdHSUwMBAEdHv7aPMMdQ8F2GmKzXTDSHPRZjp6cUmPwOeP38uKpVKduzYISJvRyIoXry4jBo1Sry9vUVEJCgoSPr06SPLli3TXaFZ7N69e1K+fHk5ffq0BAcHy+LFi6Vdu3YSHR0tfn5+UqpUKXn58qXWGQJ9tWLFCmnUqJGIiAwcOFBsbW1l4sSJIvJ2lII5c+bo9RfAiPwv+Lp37y6DBw8WEZHJkydLjx495MyZMyIi0rVrV/Hw8NBZjZ/q4sWLEhISImq1Wl6+fKn5RtKoqChZvny5DBkyRC5fvix79uyRKlWqyKtXr3hAMDCGmucizHQlZboh5LkIMz2z2ORnUEBAgNSpU0dCQkKkcePGMmLECDl79qw4OjrK0KFDReTtGRJ9H1ZN5H/h8eDBA+nWrZtm+osXL+THH3+U7du3i4jI3bt3dVFelnj/TNXx48fl9u3bMmfOHGnXrp2EhIRI0aJF5aefftL7s1rvfxvngwcPpGzZsuLr6ytJSUkyZcoUadmypbRt21b69eunmU8fg9LHx0eaNm0qly9fFpG3B70iRYpIly5dZPDgwTJ58mT57rvvROTtWS4yTIaU5yLMdCVluiHluQgzPbPY5GfCvn37RKVSaY0VfOfOHenSpYteh0ay5BBIvoZPrVZLgwYNZOTIkZp5xo4dq/lK7He/NltfPX36VPPcRUdHy9dffy3nzp0TEREvLy9xc3PTXM+p77Zv3645cxUWFiYODg4yYcIEEXk7Zva7b1vr8/48adIkadu2rVy+fFmSkpIkKChInj59KiIimzZtko4dO0p8fLzeHvQoayg9z0WY6UrOdEPJcxFmemZwCM1MaNasGfbv34+ffvoJAwcOhI2NDYKCghAeHo43b94gf/78ui7xk6hUKuzbtw8zZ87El19+CQcHB2zfvh3ffvst+vbtizZt2mDHjh2YP38+AMDY2FjHFWfcP//8g6tXr6JLly6YM2cO9u7di4SEBKxbtw5FixZFuXLlMGfOHFSsWBF3797FsmXL9HJINQAYN24cwsPDYWVlhZcvX8LOzg6PHj2CiYkJHBwccODAAVSoUAGRkZGYOnUqvvjiCwBvRzDQt1EXgP+NFjFq1Cj8/vvvGDt2LMaPH4/mzZsDABYsWIAlS5ZgzZo1yJMnj46rJV1Tep4DzHQlZbqh5TnATP8U+vmM5wJubm6YOnUq3NzcsHDhQmzYsAHz5s1TxAHh8uXL+OGHHzB06FC4u7vj9OnT8Pb2xrZt21CwYEFcuXIFkydPRoMGDXRdaqYkJCTgzJkzCAoKwsSJExEYGIg1a9agatWq6Nu3L27fvo1u3bqhWrVqOHbsGKZOnYoSJUrouuxM6dOnD/766y80bNgQjo6OCAoKQlBQEJ49e4agoCA8fvwYZcqUQe/evTXjYyfT1zGFk4f9A4CxY8eiRo0aGDt2LP7++2+8fPkSALBu3TpUqlRJl2VSLqLkPAeY6UrJdEPMc4CZ/kl0/VaCvvP395c8efLI33//retSPkny21t3796V69evy7BhwzS3xcbGSvv27eX8+fOp3kcfPX36VFatWiU9evQQT09PzfTRo0dL27Zt5Z9//hERkTdv3uiqxE8WGBgoTZs21ZoWGhoqjo6OUqdOHZk2bZp4enpK/fr1Zdy4cZp59O15Tavedy85mDhxojRu3FiuXr2qd9tHOUcpeS7CTE+mlEw3lDwXYaZnJZ7J/0StWrVCRESE3n95hkqlwtGjR9GzZ0+EhIQgMDAQN2/eBADkzZsXDg4OCA8P17qfvp0ZSD4TAAD29vbo0KED3NzccO/ePWzatAkAMHHiRJQpUwa//vorEhISYGpqqqtys0Ty2arExEQkJCSgRIkSCAwMRP78+VG+fHm0atUKrVq1gre3N4CUXzKS271b76lTp3Du3DnNbcbGxpqveB89ejRatGgBKysrvdo+yllKyHOAma7UTFd6ngPM9KzGa/KzgL6/patSqXD9+nX4+vpi7NixaNasGR48eAA3NzcsWrQIarUaR44cgbu7u65L/STJ1yOuW7cO8fHxKFmyJDw8PAAAx44dg0qlgru7O2bOnInw8HC9v7avRIkSOHfuHM6cOYPatWsDAGJiYlC4cGGYm5ujatWqKFq0qGZ+ffzmw+RwX7x4MebNmwe1Wo1u3bqhT58+KF68OIyNjZGYmAgTExP88ssvOq6W9IG+5znATAeUl+mGkOcAMz2r6d8eQFni33//RUBAAA4cOAAAuH37NqKiorBjxw6ICIYPHw5vb2/s2bMHq1evxsSJE1G3bl0dV/3pNmzYAB8fHzx9+hReXl5YvXo1PDw8ULt2bezduxfbt28HANjZ2em40k/35Zdfonv37ti0aRMuXboE4G0DY2FhARGBr68vzp49q5lfHw8IABAQEAB/f39cuXIFhw8fxp07d7BmzRo8ePAAAGBiwnMZpHzMdGVnuqHkOcBMz0p8pAzQzZs30aVLF7i6uuLGjRt49eoV2rRpAxMTE+zZswfz58/H4MGD0a9fP3h4eEBEYGJiopdv/b3r0KFDOHjwIDZs2IDq1aujSZMm6N69O4yMjNCrVy/kyZNHc9DT5+1MZmRkhN69e2P+/Pnw9fVFq1atULt2bYwaNQpPnjzBF198oZejS7y7H8bGxuLs2bM4deoUrl+/jnLlysHb2xs+Pj5YtGgRvv/+e3z++ec6rpgoezHTlZ/pSs1zgJmerXL+YwCkS1evXhUnJyfZs2ePiIgsWrRIVq5cqfm668DAQBkyZIjm67D1WfKHcZL/nT59uri4uMjcuXMlMjJSRETOnz8vBQsWlA0bNuiszuz24sUL2bBhg9SsWVP69+8vP/74o65LyrR3P2D16NEjSUhIkPDwcBk7dqz06NFDM170zZs35bvvvpNnz57pqlSiHMFMN6xMV1KeizDTs5tKRETXf2hQzjlx4gQaNGig+cBS5cqVUbBgQcTFxaFq1apYsGABdu7ciUOHDuGXX37RjLGrb+SdMwO3b99GmTJlAABr1qzBsWPH8PXXX6NOnTowNzfHpUuXYGlpqZlHqeLj47U+dKav12wCwIwZM3DmzBnky5cP7du3R4UKFbBz5078888/GDNmDL788kskJCTo9TW4ROnBTDfMTFdSngPM9OzCy3UMTL169RAUFITy5cujWLFi6Nq1K8aOHYuIiAjUqFEDa9euhbu7O+rUqYPChQvrutxMSz4YzJ8/H5s3b0a9evVgb2+PH3/8EQkJCdi8eTPi4+Ph6uqKqlWr6rjanPFuOIoefzHKnj174O/vj8OHD6NOnTo4evQoOnfuDAsLCyxZsgQzZszA/Pnzed0mGQRmumFmulLyHGCmZyeeyTdQhw8fRsuWLREbG6sJhylTpsDW1haenp46ri7zYmNjkS9fPgDA+vXrsWDBAuzatQuDBg1CaGgoXF1dMXnyZMybNw83btzAlClTFDGahpIlJSVpfQPn1q1bER0djTdv3mDXrl3YtWsXzMzM8PjxYxgbG8PIyAi2trY6rJgo5zHTmen6gpmec/T3Tz/6JI0bN8bu3bs13xB39+5dLF++HKVKldJxZZkXEBCAIUOG4OrVqwAAa2trbNu2DZs2bcLz588xZ84c/PXXXxg5ciQGDx6MCRMm8GCQy0VFRWHdunUAAH9/fwQEBKBYsWJYtmwZtmzZgqCgIJiZmWHq1KkYP348ChYsyIMBGSRmOjNdHzDTcxbf+zBgzZs3x6xZs2BtbY1ixYrBz88PjRs31nVZmXbkyBFs3LgRtra26N69O1q3bo3Y2FicPn0aq1atQtGiRVGoUCE8e/YM4eHhsLe313XJ9BGWlpZ4+fIlHBwcULBgQVy4cAERERGoWrUqihQpgi1btiAmJgbr16/H2rVrtc4OERkaZjozPbdjpucsNvkGrlmzZti+fTuioqLQsmVLXZfzSXr06IE7d+7A1tYW27ZtQ1xcHGrWrInHjx8jKCgIKpUKjx8/xoYNG3gw0BMqlQpVqlSBqakpzM3NYWRkhIIFC2LgwIE4e/Ys9u3bBwBYu3YtKlasqONqiXSPmU65GTM9Z/GafNIQPRwz+Z9//gEAlC9fHmq1GgMGDEBUVBTq1auHsLAweHl54fHjx5gzZw5evnyJCRMmwMnJSbdF0we9ux8mJSXByMgIr1+/xsyZM3H8+HGsX78ehQoVws2bN1G2bFmOuECUBmY65QbMdN1hk0966/nz57C3t0epUqUwY8YMVKxYETY2Nvjjjz9QoUIFBAcH49WrV+jXrx8cHR1TDDlGuZufnx+uX78OlUqFadOmwdTUFOPHj8fx48fRtWtXHDt2DMuXL4eVlZWuSyWiLMBMVzZmes7jB29Jb9na2uLQoUN48OABjh07ht27d8PLywuhoaGwtraGp6cnjI2NsX79esTFxfFgoEfmzZuHXbt2YcqUKfD390ePHj3w8OFDTJgwAe3bt8fhw4fh4+PDgwGRgjDTlYuZrhs8k0967+jRo+jbty9OnDiB7du3Y+nSpXBxccGCBQtw/fp12Nvb89P5udy7b+eq1Wr8+uuvGDJkCLZu3YpDhw6hSJEi+Pvvv7Fu3TqUKlUKb968Qd68eXVcNRFlB2a6/mOm5w5s8kkR9u3bhzFjxuD48eMwMjLCo0ePULJkSV2XRenw8uVLbNmyBf3798f27dthaWmJJk2a4Pr16xg8eDAOHz4MAPjss8/QrVs3TJs2jddrEikcM11/MdNzD46uQ4rQvHlziAhcXFxw+PBhHgz0hFqtRoECBXDr1i0UKlQIpUuXxqFDhzQjLpiYmODw4cOIjIxEkyZN8OOPP/JgQGQAmOn6iZmeu7DJJ8Vo0aIFEhIS0LJlS5w+fRoA9G5kCUMSEBCA06dPw9vbG99++y0CAgIQFxen+TKbfPnyoXPnzpg/fz6uXbuGbdu2wcHBQbdFE1GOYabrF2Z67sPLdUhxXr9+DXNzc12XQR+wd+9ejB49Gv/3f/8Hd3d3zfWYnp6euHz5Ms6cOQMjIyOEhISgXLlyiIiIQOHChXVdNhHpADM992Om505s8okoR4WFheGbb77B9OnTUaNGDcTHx0NEYGJiAmNjYwwePBjnz5/HwIEDsX79es01nURElPsw03MvXq5DRDkqb968MDc3h4WFBd68eYOZM2fi6NGjSExMRIsWLTBv3jyMGzcOx48fh5+fHw8GRES5GDM99+KZfCLKUSICPz8/HD58GCEhIWjUqBFq1qwJe3t7bN26FWPGjEGVKlU4pBoRkR5gpudebPKJKMe9evUKly9fxr1799CuXTvN9bbu7u7o3r07OnbsqOMKiYgovZjpuRMv1yGiHGdhYYG6deuibt26mmlbt27FrVu3ULVqVR1WRkREGcVMz53Y5BORTj1+/BibNm3C0qVLsXHjRg6pRkSkx5jpuQcv1yEinYqNjcXhw4dRtmxZODo66rocIiL6BMz03INNPhERERGRwhjpugAiIiIiIspabPKJiIiIiBSGTT4RERERkcKwySciIiIiUhg2+URERERECsMmn4iIiIhIYdjkExEREREpDJt8IiIiIiKFYZNPRERERKQwbPKJiIiIiBTm/wEaqjh/wS+ZggAAAABJRU5ErkJggg==\n" + }, + "metadata": {} } - ], - "source": [ - "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", - "bases = hosvd_bases(T)\n", - "\n", - "def tucker_from_ranks(T, bases, ranks):\n", - " factors = [\n", - " bases[axis][:, :ranks[axis]]\n", - " for axis in range(3)\n", - " ]\n", - "\n", - " core = np.einsum(\n", - " \"ijk,ia,jb,kc->abc\",\n", - " T,\n", - " factors[0],\n", - " factors[1],\n", - " factors[2],\n", - " )\n", - "\n", - " recon = np.einsum(\n", - " \"abc,ia,jb,kc->ijk\",\n", - " core,\n", - " factors[0],\n", - " factors[1],\n", - " factors[2],\n", - " )\n", - "\n", - " error = np.linalg.norm(T - recon) / np.linalg.norm(T)\n", - "\n", - " compressed_numbers = (\n", - " core.size\n", - " + sum(factor.size for factor in factors)\n", - " )\n", - " compression = T.size / compressed_numbers\n", - "\n", - " return core, factors, recon, error, compression\n", - "\n", - "default_ranks = (\n", - " min(2, T.shape[0]),\n", - " min(2, T.shape[1]),\n", - " min(3, T.shape[2]),\n", - ")\n", - "\n", - "core, factors, recon, error, compression = tucker_from_ranks(\n", - " T,\n", - " bases,\n", - " default_ranks,\n", - ")\n", - "\n", - "print(\"default ranks / rangos:\", default_ranks)\n", - "print(\"factor shapes / formas:\", [u.shape for u in factors])\n", - "print(\"core shape / forma núcleo:\", core.shape)\n", - "print(\"relative error / error relativo:\", f\"{error:.4f}\")\n", - "print(\"compression / compresión:\", f\"{compression:.2f}x\")\n", - "\n", - "pickup_rank = widgets.IntSlider(\n", - " value=default_ranks[0],\n", - " min=1,\n", - " max=T.shape[0],\n", - " step=1,\n", - " description=\"Pickup rank:\",\n", - " continuous_update=False,\n", - " style={\"description_width\": \"100px\"},\n", - ")\n", - "\n", - "dropoff_rank = widgets.IntSlider(\n", - " value=default_ranks[1],\n", - " min=1,\n", - " max=T.shape[1],\n", - " step=1,\n", - " description=\"Dropoff rank:\",\n", - " continuous_update=False,\n", - " style={\"description_width\": \"110px\"},\n", - ")\n", - "\n", - "hour_rank = widgets.IntSlider(\n", - " value=default_ranks[2],\n", - " min=1,\n", - " max=min(12, T.shape[2]),\n", - " step=1,\n", - " description=\"Hour rank:\",\n", - " continuous_update=False,\n", - " style={\"description_width\": \"95px\"},\n", - ")\n", - "\n", - "compare_hour = widgets.IntSlider(\n", - " value=busiest_hour,\n", - " min=0,\n", - " max=23,\n", - " step=1,\n", - " description=\"Hour / Hora:\",\n", - " continuous_update=False,\n", - " style={\"description_width\": \"90px\"},\n", - ")\n", - "\n", - "def explore_tucker(r_pickup, r_dropoff, r_hour, hour):\n", - " ranks = (r_pickup, r_dropoff, r_hour)\n", - "\n", - " core, factors, recon, error, compression = tucker_from_ranks(\n", - " T,\n", - " bases,\n", - " ranks,\n", - " )\n", - "\n", - " print(\n", - " f\"ranks/rangos={ranks} | core/núcleo={core.shape} | \"\n", - " f\"error={error:.4f} | compression/compresión={compression:.2f}x\"\n", - " )\n", - "\n", - " vmax = max(T[:, :, hour].max(), recon[:, :, hour].max())\n", - "\n", - " fig, axes = plt.subplots(1, 2, figsize=(7.6, 3.3))\n", - "\n", - " axes[0].imshow(\n", - " T[:, :, hour],\n", - " cmap=\"viridis\",\n", - " aspect=\"auto\",\n", - " vmin=0,\n", - " vmax=vmax,\n", - " )\n", - " axes[0].set_title(f\"real / real — hour {hour}\")\n", - "\n", - " axes[1].imshow(\n", - " recon[:, :, hour],\n", - " cmap=\"viridis\",\n", - " aspect=\"auto\",\n", - " vmin=0,\n", - " vmax=vmax,\n", - " )\n", - " axes[1].set_title(f\"Tucker reconstruction / reconstrucción\")\n", - "\n", - " for ax in axes:\n", - " ax.set_xticks(range(len(dropoff_names)))\n", - " ax.set_xticklabels(dropoff_names, rotation=45, ha=\"right\", fontsize=7)\n", - " ax.set_yticks(range(len(pickup_names)))\n", - " ax.set_yticklabels(pickup_names, fontsize=7)\n", - "\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "tucker_output = widgets.interactive_output(\n", - " explore_tucker,\n", - " {\n", - " \"r_pickup\": pickup_rank,\n", - " \"r_dropoff\": dropoff_rank,\n", - " \"r_hour\": hour_rank,\n", - " \"hour\": compare_hour,\n", - " },\n", - ")\n", - "\n", - "display(\n", - " widgets.VBox([\n", - " widgets.HTML(\n", - " \"Tucker rank explorer / Explorador de rangos Tucker: \"\n", - " \"change each mode independently. / \"\n", - " \"cambia cada modo de manera independiente.\"\n", - " ),\n", - " pickup_rank,\n", - " dropoff_rank,\n", - " hour_rank,\n", - " compare_hour,\n", - " tucker_output,\n", - " ])\n", - ")" - ], - "id": "v7iXv9bDk_Y0" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "v4OEefzbk_Y1" - }, - "source": [ - "## Exercise 3 — what did the temporal factor learn?\n", - "\n", - "Compression is useful, but interpretation is where Tucker becomes more than a storage trick.\n", - "\n", - "The hour factor matrix has one row for each hour and one column for each retained temporal component. Singular-vector signs are arbitrary, so we compare magnitudes when asking where a component is strongest.\n", - "\n", - "### What should you try?\n", - "\n", - "1. Inspect the first temporal factor.\n", - "2. Find the hour where its absolute loading is largest.\n", - "3. Compare that hour with the busiest hour in the raw taxi counts.\n", - "4. Move **Component / Componente** to inspect other temporal patterns.\n", - "5. Explain why a factor can capture a pattern even though nobody explicitly labeled “rush hour.”\n", - "\n", - "> 🇪🇸 La matriz de factores temporales tiene una fila por hora. Busca dónde alcanza mayor magnitud cada componente y compáralo con los conteos horarios reales. El signo de un vector singular es arbitrario, así que interpreta principalmente la forma y la magnitud del patrón." - ], - "id": "v4OEefzbk_Y1" - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "id": "pAmVh-Vuk_Y1" - }, - "outputs": [], - "source": [ - "# TODO\n", - "# 1. Use the hour-mode SVD basis.\n", - "# 2. Inspect the first temporal component.\n", - "# 3. Find its peak absolute loading.\n", - "# 4. Compare it with the busiest hour in T.sum(axis=(0,1)).\n", - "# 5. Repeat for another component and describe the pattern." - ], - "id": "pAmVh-Vuk_Y1" - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ], - "colab": { - "base_uri": "https://localhost:8080/", - "height": 427, - "referenced_widgets": [ - "a4e6589192fa4b0e8e2b6c16f7752075", - "0472c1031b014ab5884f17cc26d39cf3", - "8aa3d5bfdce64e1cbf373fbcb55a409a", - "5c98eedf41414f48af186670bd6b9e4d", - "27740e8fadfb4b1c8d72637e2f6122a2", - "af2345a66f11416e8b4a2feb44093062", - "fedce70a3fa34c6db0713db0c9a30d68" - ] - }, - "id": "AHQe7C-Ak_Y1", - "outputId": "5b92472a-3a41-4c84-d458-cadd52d40d06" - }, - "outputs": [ + ] + } + }, + "89b87fd3621a4d849c9bec6ce96feb92": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", 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"output_type": "stream", - "name": "stdout", - "text": [ - "first component peak / pico primer componente: 18\n", - "raw busiest hour / hora real más ocupada: 18\n" - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "component/componente=5 | peak absolute loading / pico absoluto=15\n", + "raw busiest hour / hora real más ocupada=18\n" + ] }, { - "output_type": "display_data", - "data": { - "text/plain": [ - "VBox(children=(IntSlider(value=1, continuous_update=False, description='Component / Componente:', max=6, min=1…" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "a4e6589192fa4b0e8e2b6c16f7752075" - } - }, - "metadata": { - "application/vnd.jupyter.widget-view+json": { - "colab": { - "custom_widget_manager": { - "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" - } - } - } - } - } - ], - "source": [ - "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", - "hour_basis = bases[2]\n", - "raw_hour_counts = T.sum(axis=(0, 1))\n", - "\n", - "first_component = hour_basis[:, 0]\n", - "first_peak = int(np.argmax(np.abs(first_component)))\n", - "\n", - "print(\"first component peak / pico primer componente:\", first_peak)\n", - "print(\"raw busiest hour / hora real más ocupada:\", busiest_hour)\n", - "\n", - "component_slider = widgets.IntSlider(\n", - " value=1,\n", - " min=1,\n", - " max=min(6, hour_basis.shape[1]),\n", - " step=1,\n", - " description=\"Component / Componente:\",\n", - " continuous_update=False,\n", - " style={\"description_width\": \"145px\"},\n", - ")\n", - "\n", - "def explore_hour_factor(component):\n", - " idx = component - 1\n", - " factor = hour_basis[:, idx]\n", - "\n", - " peak = int(np.argmax(np.abs(factor)))\n", - "\n", - " raw_scaled = raw_hour_counts / raw_hour_counts.max()\n", - " factor_scaled = np.abs(factor)\n", - " factor_scaled = factor_scaled / factor_scaled.max()\n", - "\n", - " print(\n", - " f\"component/componente={component} | \"\n", - " f\"peak absolute loading / pico absoluto={peak}\"\n", - " )\n", - " print(\n", - " f\"raw busiest hour / hora real más ocupada={busiest_hour}\"\n", - " )\n", - "\n", - " fig, ax = plt.subplots(figsize=(6.6, 3.2))\n", - " ax.plot(\n", - " range(24),\n", - " raw_scaled,\n", - " marker=\"o\",\n", - " label=\"raw hourly trips / viajes reales\",\n", - " )\n", - " ax.plot(\n", - " range(24),\n", - " factor_scaled,\n", - " marker=\"o\",\n", - " label=f\"|temporal factor {component}| / |factor temporal|\",\n", - " )\n", - " ax.axvline(\n", - " peak,\n", - " linestyle=\"--\",\n", - " linewidth=1,\n", - " label=f\"factor peak / pico={peak}\",\n", - " )\n", - " ax.set_xticks(range(0, 24, 2))\n", - " ax.set_xlabel(\"hour / hora\")\n", - " ax.set_ylabel(\"scaled magnitude / magnitud escalada\")\n", - " ax.set_title(\"Raw activity vs learned temporal factor / Actividad real vs factor\")\n", - " ax.legend(fontsize=8)\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "factor_output = widgets.interactive_output(\n", - " explore_hour_factor,\n", - " {\"component\": component_slider},\n", - ")\n", - "\n", - "display(widgets.VBox([component_slider, factor_output]))" - ], - "id": "AHQe7C-Ak_Y1" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "I5XWJQZLk_Y1" - }, - "source": [ - "## What just happened\n", - "\n", - "You built a complete Tucker/HOSVD pipeline from real observations.\n", - "\n", - "1. **Real tensor construction:** a flat taxi table became an order-3 tensor `pickup × dropoff × hour`.\n", - "2. **Unfolding:** each mode exposed a different matrix view without losing any entries.\n", - "3. **HOSVD:** SVD supplied one low-dimensional basis per mode.\n", - "4. **Tucker core:** one `einsum` contracted all three axes into a smaller core, and another reconstructed the tensor.\n", - "5. **Rank trade-off:** changing pickup, dropoff, and hour ranks independently changed both storage and reconstruction error.\n", - "6. **Interpretation:** the hour factor exposed temporal structure that could be compared directly with real hourly trip counts.\n", - "\n", - "### The sentence to remember\n", - "\n", - "> **Tucker compresses a tensor by learning a basis for each mode and a small core that tells those mode-specific patterns how to interact.**\n", - "\n", - "This is why tensor decompositions are useful in domains such as imaging, recommender systems, neuroscience, and multi-condition biological measurements: the axes represent genuinely different kinds of structure.\n", - "\n", - "> 🇪🇸 Construiste Tucker/HOSVD de extremo a extremo con datos reales. Cada modo obtuvo su propia base, el núcleo resumió sus interacciones y los sliders mostraron que la compresión y el error dependen de cuánto rango conservas en cada eje.\n", - ">\n", - "> **Frase para recordar:** Tucker comprime un tensor aprendiendo una base para cada modo y un núcleo pequeño que describe cómo interactúan esos patrones específicos de cada eje." - ], - "id": "I5XWJQZLk_Y1" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Mx5SdJaBk_Y1" - }, - "source": [ - "---\n", - "\n", - "## Time for Kahoot 🎯\n", - "\n", - "**Kahoot 3 — Convolution & Tensor Decompositions** · 6 questions, about 5 minutes.\n", - "\n", - "> 🇪🇸 **Convolución y descomposiciones tensoriales** — 6 preguntas, unos 5 minutos.\n", - "\n", - "Join at **kahoot.it** with the PIN on the facilitator's screen.\n", - "\n", - "- [Quiz details and facilitator notes](https://project-delphi.github.io/tensors-workshop/kahoot.html#quiz-3)\n", - "- [Import file (`.xlsx`)](https://github.com/project-delphi/tensors-workshop/blob/main/kahoot/kahoot_quiz_3_convolution_decompositions.xlsx)\n", - "\n", - "Next up: **11 · Wrap-up and take-homes** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/11-wrap-up-and-take-homes.ipynb).\n", - "\n", - "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" - ], - "id": "Mx5SdJaBk_Y1" - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3" - }, - "colab": { - "provenance": [] - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "version_major": 2, - "version_minor": 0, - "state": { - "5e1b1ca6f0794da5bf0d2bc913dff7b3": { - "model_module": "@jupyter-widgets/controls", - "model_name": "VBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "VBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "VBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_e8108f1a0791441cafce0b894f6dcd1d", - "IPY_MODEL_0d9b3eeee98a4071b1dbb283ad6768cc" - ], - "layout": "IPY_MODEL_8ffc4ee34e4c4d8a9a4af276f54859a1" - } - }, - "e8108f1a0791441cafce0b894f6dcd1d": { - "model_module": "@jupyter-widgets/controls", - "model_name": "IntSliderModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "IntSliderModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "IntSliderView", - "continuous_update": false, - "description": "Hour / Hora:", - "description_tooltip": null, - "disabled": false, - "layout": "IPY_MODEL_c54c897dbdd6446b804d7d94b7915328", - "max": 23, - "min": 0, - "orientation": "horizontal", - "readout": true, - "readout_format": "d", - "step": 1, - "style": "IPY_MODEL_e903bde3d1c74f88b9e51520415e75f7", - "value": 16 - } - }, - "0d9b3eeee98a4071b1dbb283ad6768cc": { - "model_module": "@jupyter-widgets/output", - "model_name": "OutputModel", - "model_module_version": "1.0.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/output", - "_model_module_version": "1.0.0", - "_model_name": "OutputModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/output", - "_view_module_version": "1.0.0", - "_view_name": "OutputView", - "layout": "IPY_MODEL_6f4afe3ad61e4a69b7da015e7f123eaf", - "msg_id": "", - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "hour/hora=16 | total trips/viajes=332\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": "
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null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + } } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/scripts/content.py b/scripts/content.py index bbaf3e2..aa789b6 100644 --- a/scripts/content.py +++ b/scripts/content.py @@ -425,7 +425,15 @@ def convmtx_full_1d(kernel, n): "setup": """import numpy as np import pandas as pd import matplotlib.pyplot as plt -from skimage import data +import ipywidgets as widgets +from IPython.display import display + +# Enable ipywidgets in Google Colab when available. +try: + from google.colab import output + output.enable_custom_widget_manager() +except ImportError: + pass TAXIS = "https://raw.githubusercontent.com/mwaskom/seaborn-data/master/taxis.csv" taxis = pd.read_csv(TAXIS) @@ -433,7 +441,41 @@ def convmtx_full_1d(kernel, n): def unfold(T, axis): return np.moveaxis(T, axis, 0).reshape(T.shape[axis], -1) -print(taxis.shape) # (6433, 14) — 6,433 real NYC taxi trips""", +def hosvd_bases(T): + return [ + np.linalg.svd(unfold(T, axis), full_matrices=False)[0] + for axis in range(T.ndim) + ] + +taxis["pickup_dt"] = pd.to_datetime(taxis["pickup"], errors="coerce") +taxis["hour"] = taxis["pickup_dt"].dt.hour + +sub = taxis.dropna( + subset=["pickup_borough", "dropoff_borough", "hour"] +).copy() +sub["hour"] = sub["hour"].astype(int) + +pickup_names = sorted(sub["pickup_borough"].unique()) +dropoff_names = sorted(sub["dropoff_borough"].unique()) + +pickup_index = {name: i for i, name in enumerate(pickup_names)} +dropoff_index = {name: i for i, name in enumerate(dropoff_names)} + +T = np.zeros( + (len(pickup_names), len(dropoff_names), 24), + dtype=float, +) + +for (p, d, h), count in sub.groupby( + ["pickup_borough", "dropoff_borough", "hour"] +).size().items(): + T[pickup_index[p], dropoff_index[d], int(h)] = float(count) + +print("taxi rows / filas:", len(taxis)) +print("usable trips / viajes utilizables:", int(T.sum())) +print("tensor shape / forma:", T.shape) +print("pickup boroughs / origen:", pickup_names) +print("dropoff boroughs / destino:", dropoff_names)""", } # ───────────────────────────────────────────────────────────────────────────── From bcd510a7f0a219062e2de439ea5e7e358a3a4860 Mon Sep 17 00:00:00 2001 From: Sebas Laverde Chunza <106851243+Laverde97@users.noreply.github.com> Date: Sat, 29 Aug 2026 00:10:58 -0500 Subject: [PATCH 23/29] Improve notebook 11 wrap-up and take-homes for issue #44 --- notebooks/11-wrap-up-and-take-homes.ipynb | 2976 +++++++++++++-------- 1 file changed, 1794 insertions(+), 1182 deletions(-) diff --git a/notebooks/11-wrap-up-and-take-homes.ipynb b/notebooks/11-wrap-up-and-take-homes.ipynb index 76e6e7c..895fdd2 100644 --- a/notebooks/11-wrap-up-and-take-homes.ipynb +++ b/notebooks/11-wrap-up-and-take-homes.ipynb @@ -1,1190 +1,1802 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 11 · Wrap-up and take-homes\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/11-wrap-up-and-take-homes.ipynb)\n", - "\n", - "*wrap-up · 5 min*\n", - "\n", - "> 🇪🇸 **Cierre y ejercicios para casa** — Qué conecta los bloques 4, 5 y 6, más cinco ejercicios para casa.\n", - "\n", - "What connects Blocks 4, 5 and 6, plus five take-home exercises.\n", - "\n", - "## What you will be able to do\n", - "\n", - "- State the one idea that connects the pseudoinverse, deconvolution and Tucker.\n", - "- Find the scaling trap in PCA on real, unstandardized data (take-home A).\n", - "- Build attention out of two contractions, and mask padded positions (take-home B).\n", - "- Run a real CP decomposition and read its components as trip types nobody labelled (take-home C).\n", - "- Build correlated data from independent noise with Cholesky, and see why ignoring covariance understates portfolio risk (take-home D).\n", - "- Denoise a real voice recording by truncating the SVD of its STFT, and measure the result in SNR rather than by ear (take-home E).\n", - "- Trade parameter count against reconstruction error with a rank slider, on a real dense tensor (optional appendix)." - ], - "id": "s11-00" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "Run this first. It installs and imports everything this notebook needs, and nothing else.\n", - "\n", - "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." - ], - "id": "s11-01" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "from sklearn.datasets import load_breast_cancer\n", - "from scipy import signal\n", - "\n", - "rng = np.random.default_rng(0)" - ], - "id": "s11-02" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What you did today\n", - "\n", - "> 🇪🇸 Lo que hiciste hoy.\n", - "\n", - "1. **Section 01** — learned the vocabulary of tensors (axis, order, shape, slice,\n", - " fiber, unfolding, contraction, decomposition), and that unfolding turns any\n", - " tensor into a matrix without losing anything.\n", - "2. **Sections 02 and 05** — argued about what axes *mean*, and found that a batch\n", - " axis and a time axis behave differently even when the shapes look identical.\n", - "3. **Sections 03 and 04** — indexed, broadcast, reshaped and transposed real\n", - " tumour data and real medical images, and hit real problems: zero-variance\n", - " pixels, and reshape silently destroying an image.\n", - "4. **Sections 06–10** — wrote contractions with `einsum`; solved an unsolvable\n", - " 20,433-equation system with the pseudoinverse; used recursion to forecast real\n", - " airline traffic and to find an eigenvector; convolved and deconvolved a real\n", - " photograph; and compressed a real taxi tensor 4.7× with Tucker, which found\n", - " rush hour on its own.\n", - "\n", - "### One idea connects sections 07, 09 and 10\n", - "\n", - "**When a problem has no exact answer or no true inverse, you do not give up —\n", - "you find the best stable approximation.** The pseudoinverse does this for linear\n", - "systems, Richardson-Lucy for blurred images, and Tucker for tensors that are too\n", - "large to keep in full.\n", - "\n", - "> 🇪🇸 Cuando un problema no tiene respuesta exacta ni inversa verdadera, no te\n", - "> rindes: buscas la mejor aproximación estable." - ], - "id": "s11-03" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Where to go next\n", - "\n", - "- `torch.einsum` / `tf.einsum` / `jnp.einsum` — **identical syntax** to what you\n", - " used today.\n", - "- [`tensorly`](https://tensorly.org) — proper Tucker and CP decompositions.\n", - "- `np.linalg` — the rest of Chapter 2: eigendecomposition, `lstsq`, `pinv`, `qr`,\n", - " `cholesky`.\n", - "- `scipy.signal` and `skimage.restoration` — convolution and deconvolution\n", - " beyond today.\n", - "- The five take-homes below." - ], - "id": "s11-04" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Optional: the same contraction in PyTorch\n", - "\n", - "Everything today was NumPy, because that is what the workshop's real datasets\n", - "and verified numbers are built on. The einsum string does not change when you\n", - "move to a deep learning framework — only the array type does." - ], - "id": "s11-05" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Optional. Colab has torch pre-installed; skip this cell if you prefer.\n", - "try:\n", - " import torch\n", - " photo = rng.standard_normal((8, 8, 3))\n", - " w = np.array([0.2125, 0.7154, 0.0721])\n", - "\n", - " np_gray = np.einsum('hwc,c->hw', photo, w)\n", - " pt_gray = torch.einsum('hwc,c->hw', torch.tensor(photo), torch.tensor(w))\n", - "\n", - " print(np.allclose(np_gray, pt_gray.numpy())) # True — same string, same answer\n", - "except ImportError:\n", - " print(\"torch not installed — nothing here you need\")" - ], - "id": "s11-06" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Take-home A — How many principal components are enough?\n", - "\n", - "> 🇪🇸 Ejercicio para casa A: ¿cuántas componentes principales bastan?\n", - "\n", - "**Real data contains a trap here. Find it.**" - ], - "id": "s11-07" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "bc = load_breast_cancer(); X, y = bc.data, bc.target\n", - "\n", - "# TODO 1: Center X, run np.linalg.svd, and compute the fraction of variance each\n", - "# component explains (variance is proportional to S**2).\n", - "\n", - "# TODO 2: How many components explain 95% of the variance? The answer will look\n", - "# TOO GOOD. Do not trust it yet.\n", - "\n", - "# TODO 3: Print X.var(axis=0). The 30 measurements use different units — some are\n", - "# areas in the thousands, some are ratios below 1. What is that doing?\n", - "\n", - "# TODO 4: Redo everything on standardized data: (X - mean) / std. How many now?\n", - "\n", - "# TODO 5: Scatter-plot the first 2 components, coloured by y. Do the two groups\n", - "# separate?" - ], - "id": "s11-08" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "A9S7jpWPpbCq" + }, + "source": [ + "# 11 · Wrap-up and take-homes\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/11-wrap-up-and-take-homes.ipynb)\n", + "\n", + "*wrap-up · 5 min + take-homes after the workshop*\n", + "\n", + "> 🇪🇸 **Cierre y ejercicios para casa** — Resume la idea que conecta el taller y luego elige entre cinco extensiones: PCA, atención, CP, Cholesky y audio.\n", + "\n", + "Wrap up the workshop around one connecting idea, then choose among five extensions: PCA, attention, CP, Cholesky, and audio.\n", + "\n", + "## What you will be able to do\n", + "\n", + "- State the approximation idea connecting pseudoinverse, deconvolution, and Tucker.\n", + "- Diagnose the scaling trap in PCA on real breast-cancer measurements.\n", + "- Build masked attention from two `einsum` contractions.\n", + "- Compare CP with Tucker on the same real New York taxi tensor.\n", + "- Use Cholesky to turn independent noise into correlated draws and quantify the portfolio consequence.\n", + "- Denoise a real voice recording with STFT → truncated SVD → ISTFT and measure the SNR trade-off.\n", + "\n", + "> 🇪🇸 **Al terminar podrás:** conectar pseudoinversa, deconvolución y Tucker mediante la idea de aproximación estable; detectar el problema de escala en PCA; construir atención enmascarada; comparar CP y Tucker; simular correlación con Cholesky; y medir una reducción de ruido de audio basada en SVD." + ], + "id": "A9S7jpWPpbCq" }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "Xc = X - X.mean(axis=0)\n", - "S = np.linalg.svd(Xc, full_matrices=False)[1]\n", - "frac = S**2 / (S**2).sum()\n", - "n95 = np.argmax(np.cumsum(frac) >= 0.95) + 1 # 1 (!)\n", - "print(n95, round(frac[0], 3)) # 1 0.982\n", - "\n", - "print(np.sort(X.var(axis=0))[[0, -1]]) # ~0.0000075 up to ~324000\n", - "\n", - "Xs = (X - X.mean(axis=0)) / X.std(axis=0)\n", - "S2 = np.linalg.svd(Xs, full_matrices=False)[1]\n", - "frac_scaled = S2**2 / (S2**2).sum()\n", - "n95_scaled = np.argmax(np.cumsum(frac_scaled) >= 0.95) + 1 # 10\n", - "print(n95_scaled)\n", - "\n", - "# Without standardizing, the first component appears to explain 98.2% of the\n", - "# variance. IT IS AN ILLUSION: `worst area` has a variance around 323,000 while\n", - "# smoothness values sit below 1, so PCA reports the largest UNIT, not the\n", - "# largest PATTERN. After standardizing, the first component explains 44% and\n", - "# TEN components are needed.\n", - "#\n", - "# PCA KNOWS NOTHING ABOUT UNITS. Features on different scales must be\n", - "# standardized first.\n", - "\n", - "import matplotlib.pyplot as plt\n", - "fig, ax = plt.subplots(figsize=(6.5, 3.5))\n", - "n_show = 15\n", - "ax.plot(range(1, n_show + 1), np.cumsum(frac[:n_show]), marker=\"o\",\n", - " label=\"unstandardized\", color=\"#C44E52\")\n", - "ax.plot(range(1, n_show + 1), np.cumsum(frac_scaled[:n_show]), marker=\"o\",\n", - " label=\"standardized\", color=\"#4C72B0\")\n", - "ax.axhline(0.95, color=\"gray\", linestyle=\"--\", linewidth=1, label=\"95% threshold\")\n", - "ax.set_xlabel(\"number of components\"); ax.set_ylabel(\"cumulative variance explained\")\n", - "ax.set_title(\"The scree plot IS the standardisation trap\")\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "# TODO 5 — the two groups do separate, on standardized data, in 2 of 30 columns.\n", - "Z = Xs @ np.linalg.svd(Xs, full_matrices=False)[2][:2].T\n", - "fig, ax = plt.subplots(figsize=(5, 4))\n", - "ax.scatter(Z[:, 0], Z[:, 1], c=y, s=8, cmap=\"coolwarm\")\n", - "ax.set_xlabel(\"component 1\"); ax.set_ylabel(\"component 2\")\n", - "ax.set_title(\"standardized data — malignant/benign in 2 components\")\n", - "plt.tight_layout()\n", - "plt.show()" - ], - "id": "s11-09" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Take-home B — Attention is two contractions\n", - "\n", - "> 🇪🇸 Ejercicio para casa B: la atención son dos contracciones.\n", - "\n", - "Attention is the mechanism that answers question 5 from section 05: *which parts\n", - "of a sequence matter most?* Protein language models use it so every amino acid\n", - "can look at every other one; recommenders use it to weight a user's past\n", - "interactions." - ], - "id": "s11-10" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.random.seed(6)\n", - "batch, seq_len, dim = 4, 12, 16\n", - "Q, K, V = (np.random.randn(batch, seq_len, dim) for _ in range(3))\n", - "\n", - "def softmax(x, axis=-1):\n", - " x = x - x.max(axis=axis, keepdims=True)\n", - " e = np.exp(x); return e / e.sum(axis=axis, keepdims=True)\n", - "\n", - "# TODO 1: With einsum, compute scores[b,i,j] = how much position i attends to\n", - "# position j. Shape (4, 12, 12). Scale by 1/sqrt(dim).\n", - "\n", - "# TODO 2: Apply softmax on the correct axis so each row of weights sums to 1.\n", - "\n", - "# TODO 3: With einsum, combine V using those weights -> (4, 12, 16).\n", - "\n", - "# TODO 4: Suppose the last 3 positions are padding, not real data. Build a mask,\n", - "# set those scores to -np.inf BEFORE the softmax, and verify the padded\n", - "# positions receive exactly zero weight." - ], - "id": "s11-11" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true + { + "cell_type": "markdown", + "metadata": { + "id": "DI1X2khopbCs" + }, + "source": [ + "## Setup\n", + "\n", + "Run this once. The live five-minute wrap-up only needs the summary below; the code supports the take-homes you can explore afterward.\n", + "\n", + "> 🇪🇸 Ejecuta esta celda una vez. El cierre en vivo dura solo cinco minutos; el código prepara los ejercicios para casa que puedes explorar después." + ], + "id": "DI1X2khopbCs" }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "scores = np.einsum('bid,bjd->bij', Q, K) / np.sqrt(dim)\n", - "weights = softmax(scores, axis=-1)\n", - "output = np.einsum('bij,bjd->bid', weights, V)\n", - "print(scores.shape, weights.shape, output.shape)\n", - "print(np.allclose(weights.sum(axis=-1), 1.0)) # True\n", - "\n", - "mask = np.zeros((seq_len, seq_len)); mask[:, -3:] = -np.inf\n", - "weights_masked = softmax(scores + mask, axis=-1)\n", - "print(weights_masked[..., -3:].max()) # 0.0 — exactly zero weight\n", - "\n", - "# `scores` is Chapter 2's dot product (eq. 2.8); `output` is Chapter 2's linear\n", - "# combination (eq. 2.28). ATTENTION IS TWO CONTRACTIONS built from ideas you had\n", - "# already read.\n", - "#\n", - "# TODO 4 solves the variable-length problem from section 02: THE MASK IS HOW\n", - "# REAL MODELS HANDLE SEQUENCES AND VIDEOS OF DIFFERENT LENGTHS." - ], - "id": "s11-12" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Take-home C — CP decomposition, compared to Tucker\n", - "\n", - "> 🇪🇸 Ejercicio para casa C: CP comparado con Tucker." - ], - "id": "s11-13" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO 1: Build one rank-1 tensor with einsum from three random vectors of\n", - "# length 4, 5 and 24. What shape is it? How many numbers define it?\n", - "\n", - "# TODO 2: Compare that against 4*5*24. What is the compression of ONE rank-1 piece?" - ], - "id": "s11-14" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "kb1BmNE4pbCt", + "outputId": "8bdfa787-6cc3-464a-b06b-b8a9b5241f43" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Setup ready / Preparación lista\n" + ] + } + ], + "source": [ + "import hashlib\n", + "import io\n", + "import subprocess\n", + "import sys\n", + "import urllib.request\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import ipywidgets as widgets\n", + "\n", + "from IPython.display import Audio, display\n", + "from scipy import signal\n", + "from sklearn.datasets import load_breast_cancer\n", + "\n", + "try:\n", + " from google.colab import output\n", + " output.enable_custom_widget_manager()\n", + "except ImportError:\n", + " pass\n", + "\n", + "rng = np.random.default_rng(0)\n", + "\n", + "def softmax(x, axis=-1):\n", + " x = x - np.max(x, axis=axis, keepdims=True)\n", + " e = np.exp(x)\n", + " return e / np.sum(e, axis=axis, keepdims=True)\n", + "\n", + "def snr_db(reference, estimate):\n", + " reference = np.asarray(reference)\n", + " estimate = np.asarray(estimate)\n", + " return 10 * np.log10(\n", + " np.sum(reference**2) /\n", + " np.sum((estimate - reference)**2)\n", + " )\n", + "\n", + "print(\"Setup ready / Preparación lista\")" + ], + "id": "kb1BmNE4pbCt" }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "a, b, c = rng.standard_normal(4), rng.standard_normal(5), rng.standard_normal(24)\n", - "rank1 = np.einsum('i,j,k->ijk', a, b, c) # (4, 5, 24) from only 33 numbers\n", - "print(rank1.shape, len(a) + len(b) + len(c), 4 * 5 * 24) # (4,5,24) 33 480\n", - "print(round(480 / 33, 1)) # 14.5x for one piece\n", - "\n", - "# A full CP decomposition is a SUM of R pieces like this one, not just a single\n", - "# rank-1 term. The cells below build a real rank-3 CP model on real data — no\n", - "# more commented-out pseudocode." - ], - "id": "s11-15" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Now decompose a real tensor with CP\n", - "\n", - "> 🇪🇸 Ahora sí: una descomposición CP real sobre un tensor real.\n", - "\n", - "This take-home is separate from section 10's notebook, so it rebuilds the same\n", - "real taxi tensor here rather than assuming section 10 already ran." - ], - "id": "s11-16" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "TAXIS = \"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/taxis.csv\"\n", - "taxis = pd.read_csv(TAXIS)\n", - "taxis['hour'] = pd.to_datetime(taxis['pickup']).dt.hour\n", - "sub = taxis.dropna(subset=['pickup_borough', 'dropoff_borough'])\n", - "pb = sorted(sub['pickup_borough'].unique())\n", - "db = sorted(sub['dropoff_borough'].unique())\n", - "\n", - "T = np.zeros((len(pb), len(db), 24))\n", - "for (p, d, h), v in sub.groupby(['pickup_borough', 'dropoff_borough', 'hour']).size().items():\n", - " T[pb.index(p), db.index(d), h] = v\n", - "\n", - "print(T.shape, pb, db) # (4, 5, 24) — the same real taxi tensor as section 10,\n", - " # rebuilt here so this notebook stands on its own" - ], - "id": "s11-17" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "CP needs a library here rather than the by-hand HOSVD from section 10: an ALS\n", - "loop short enough to read is also too short to be a reliable optimizer, and\n", - "getting that wrong would teach the wrong lesson. [`tensorly`](https://tensorly.org)\n", - "is not part of Colab's default image, so the install is explicit, the same way\n", - "section 10 tells you it borrowed the idea from a real library rather than\n", - "hiding it.\n", - "\n", - "> 🇪🇸 CP necesita aquí una librería en vez del HOSVD hecho a mano de la sección\n", - "> 10: un bucle ALS lo bastante corto para leerse también es demasiado corto\n", - "> para ser un optimizador confiable. `tensorly` no viene instalado por defecto\n", - "> en Colab, así que la instalación es explícita." - ], - "id": "s11-18" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%pip install -q tensorly\n", - "\n", - "import tensorly as tl\n", - "from tensorly.decomposition import parafac\n", - "\n", - "R = 3 # three real, checkable trip patterns fit this tensor's size\n", - "cp_weights, cp_factors = parafac(tl.tensor(T), rank=R, init='svd',\n", - " random_state=0, n_iter_max=500, tol=1e-9)\n", - "Fpb, Fdb, Fhr = cp_factors # (4, 3), (5, 3), (24, 3)\n", - "\n", - "cp_recon = tl.cp_to_tensor((cp_weights, cp_factors))\n", - "cp_error = np.linalg.norm(cp_recon - T) / np.linalg.norm(T)\n", - "print(f\"CP rank {R}: relative reconstruction error = {cp_error:.3f}\")\n", - "print(\"Section 10's Tucker, rank (2, 2, 3), measured 0.067 on this same tensor.\")" - ], - "id": "s11-19" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What CP's uniqueness buys you, and what it does not\n", - "\n", - "> 🇪🇸 Lo que la unicidad de CP te da, y lo que no te da.\n", - "\n", - "PCA and Tucker's factor matrices are only defined up to an arbitrary rotation\n", - "within each subspace of similar size — ask for the \"second principal\n", - "component\" of near-equal-variance data and the answer is unstable. **CP has no\n", - "such freedom**, under a condition on the factor matrices called the Kruskal\n", - "condition, which this tensor satisfies. A CP component is only free to move in\n", - "three limited ways: the three components can be listed in any **order**; a\n", - "scalar can move between the three factor vectors of one component as long as\n", - "their **product** is unchanged; and because these are real (not just\n", - "positive) numbers, an even number of those factors can flip **sign** together.\n", - "None of that changes what one component *looks like* — it is still one\n", - "coherent pattern per axis, not a rotated mixture of several. That is why the\n", - "components below are worth reading individually, and why the code below uses\n", - "`abs()` before asking which entry is strongest — the strongest entry does not\n", - "move, only its sign might.\n", - "\n", - "**Analysts benefit because CP exposes one interpretable pattern per axis —\n", - "pickup, dropoff and hour together — that can be read as a coherent trip type,\n", - "the way PCA's freely-rotating components cannot be.**" - ], - "id": "s11-20" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Colab renders ipywidgets through its own widget manager rather than the\n", - "# classic Jupyter one; this call is a no-op outside Colab, which is why it is\n", - "# guarded rather than assumed.\n", - "try:\n", - " from google.colab import output\n", - " output.enable_custom_widget_manager()\n", - "except ImportError:\n", - " pass\n", - "\n", - "import ipywidgets as widgets\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def show_component(component):\n", - " r = component - 1 # the slider shows 1..R for students; factors are 0-indexed\n", - " plt.close('all')\n", - " fig, axes = plt.subplots(1, 3, figsize=(12, 3.2))\n", - " axes[0].bar(pb, Fpb[:, r], color='#4C72B0')\n", - " axes[0].set_title('Pickup borough'); axes[0].tick_params(axis='x', rotation=40)\n", - " axes[1].bar(db, Fdb[:, r], color='#DD8452')\n", - " axes[1].set_title('Dropoff borough'); axes[1].tick_params(axis='x', rotation=40)\n", - " axes[2].bar(range(24), Fhr[:, r], color='#55A868')\n", - " axes[2].set_title('Hour of day'); axes[2].set_xlabel('hour')\n", - " fig.suptitle(f'CP component {component} of {R}')\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - " print(f\"Strongest pickup borough: {pb[np.argmax(np.abs(Fpb[:, r]))]}\")\n", - " print(f\"Strongest dropoff borough: {db[np.argmax(np.abs(Fdb[:, r]))]}\")\n", - " print(f\"Peak hour: {int(np.argmax(np.abs(Fhr[:, r])))}\")\n", - "\n", - "# TODO 3: Flip through all three components (1, 2, 3). Does each one read as\n", - "# a different, nameable kind of trip? Which hour is each one busiest?\n", - "widgets.interact(show_component,\n", - " component=widgets.IntSlider(min=1, max=R, step=1, value=1,\n", - " description='Component'));" - ], - "id": "s11-21" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Take-home D — Cholesky: the factorization that builds\n", - "\n", - "> 🇪🇸 Ejercicio para casa D: Cholesky, la factorización que construye.\n", - "\n", - "Every factorization used today — LU, QR, eigendecomposition, SVD — takes an\n", - "existing object **apart**. Cholesky is the one exception: you use it to\n", - "**build**. Given a covariance matrix `Sigma` that is symmetric and\n", - "positive-definite, `np.linalg.cholesky` finds a lower-triangular `L` with\n", - "`L @ L.T == Sigma`. Feed `L` independent Gaussian noise and it hands back\n", - "correlated draws with *exactly* that covariance.\n", - "\n", - "`Sigma[i, j]` is the **covariance** between asset `i` and asset `j` — how much\n", - "they move together, in the assets' own units. Its diagonal `Sigma[i, i]` is\n", - "each asset's own variance. **Correlation** (`corr`) is the same relationship\n", - "rescaled to sit between -1 and 1, so it is comparable between assets of\n", - "different volatility; `Sigma = outer(vol, vol) * corr` puts the original scale\n", - "back in.\n", - "\n", - "If `z` is independent noise (`Cov(z) = I`) and `x = L @ z`, then\n", - "`Cov(x) = L Cov(z) L.T = L L.T = Sigma` — which is exactly why `L` turns\n", - "independent draws into correlated ones.\n", - "\n", - "> 🇪🇸 `Sigma[i, j]` es la covarianza entre el activo `i` y el `j`: cuánto se\n", - "> mueven juntos. La diagonal es la varianza de cada activo. `corr` es la misma\n", - "> relación reescalada entre -1 y 1. Si `z` es ruido independiente\n", - "> (`Cov(z) = I`) y `x = L @ z`, entonces `Cov(x) = L Cov(z) L.T = L L.T =\n", - "> Sigma`: por eso `L` convierte ruido independiente en ruido correlacionado." - ], - "id": "s11-22" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vol = np.array([0.012, 0.015, 0.010])\n", - "corr = np.array([[1.00, 0.85, 0.20],\n", - " [0.85, 1.00, 0.20],\n", - " [0.20, 0.20, 1.00]])\n", - "Sigma = np.outer(vol, vol) * corr\n", - "\n", - "weights = np.array([0.4, 0.4, 0.2])\n", - "mu = np.array([0.00030, 0.00035, 0.00020])\n", - "n_days, n_paths, initial_value = 252, 20_000, 100.0\n", - "\n", - "rng = np.random.default_rng(5)\n", - "sample_sizes = [100, 1_000, 100_000]\n", - "\n", - "# TODO 1: L = np.linalg.cholesky(Sigma). Verify np.allclose(L @ L.T, Sigma) is\n", - "# True, and print L and the reconstruction L @ L.T, both rounded.\n", - "\n", - "# TODO 2: For each n in sample_sizes, draw z = rng.standard_normal((3, n)),\n", - "# build x = L @ z, and compute the Frobenius error between np.cov(x)\n", - "# and Sigma. Confirm it shrinks as n grows. For the LARGEST n, also\n", - "# print np.cov(z) (should look like the identity) and np.cov(x)\n", - "# (should look like Sigma) — that is the whole trick, made visible.\n", - "\n", - "# TODO 3: Simulate a CORRECT correlated portfolio. Draw\n", - "# z_paths = rng.standard_normal((3, n_days * n_paths)), build\n", - "# correlated_asset_returns = mu[:, None] + L @ z_paths, reshape to\n", - "# (3, n_paths, n_days), combine with `weights` into one daily\n", - "# portfolio return per path per day, and compound each path into\n", - "# terminal_correlated = initial_value * prod(1 + daily_returns).\n", - "\n", - "# TODO 4: Simulate the SAME portfolio again but WRONG: replace L with\n", - "# independent_scale = np.diag(np.sqrt(np.diag(Sigma))) — same\n", - "# individual volatilities, zero cross-asset correlation — and reuse\n", - "# the SAME z_paths. Produce terminal_independent the same way.\n", - "\n", - "# TODO 5: Plot terminal_correlated and terminal_independent as overlaid\n", - "# histograms (density=True) on the same axes, labelled and legended.\n", - "\n", - "# TODO 6: Compare std, and the 5th and 1st percentiles, of both. Which\n", - "# distribution has the fatter left tail — and why, given that no\n", - "# individual asset's volatility ever changed?" - ], - "id": "s11-23" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true + { + "cell_type": "markdown", + "metadata": { + "id": "aRbA3maRpbCu" + }, + "source": [ + "## Why this matters — one idea connects the workshop\n", + "\n", + "Sections 07, 09, and 10 looked different:\n", + "\n", + "- the pseudoinverse handled an overdetermined linear system;\n", + "- deconvolution tried to recover an image after blur;\n", + "- Tucker compressed a tensor into lower-dimensional mode-specific factors.\n", + "\n", + "But the same decision appears in all three:\n", + "\n", + "> **When an exact inverse or exact representation is unavailable, unstable, or unnecessarily expensive, choose a controlled approximation and measure what you lose.**\n", + "\n", + "That sentence is the bridge from linear algebra to modern machine learning. The five take-homes below reuse it in different settings.\n", + "\n", + "### How to use this notebook\n", + "\n", + "Each take-home has a `TODO` cell and a folded **Solution / Solución**. Try the task first, then open the solution.\n", + "\n", + "> 🇪🇸 **Una idea conecta el taller:** cuando una inversa exacta o una representación exacta no existe, es inestable o cuesta demasiado, construye una aproximación controlada y mide qué pierdes.\n", + ">\n", + "> Cada ejercicio tiene un `TODO` y una **Solution / Solución** plegada. Intenta primero; abre la solución después." + ], + "id": "aRbA3maRpbCu" }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "L = np.linalg.cholesky(Sigma)\n", - "print(np.allclose(L @ L.T, Sigma)) # True\n", - "print(np.round(L, 4))\n", - "print(np.round(L @ L.T, 6)) # matches Sigma\n", - "\n", - "errors = []\n", - "for n in sample_sizes:\n", - " z = rng.standard_normal((3, n))\n", - " x = L @ z\n", - " err = np.linalg.norm(np.cov(x) - Sigma)\n", - " errors.append(err)\n", - " print(n, err)\n", - "print(errors[0] > errors[1] > errors[2]) # True — error shrinks as n grows\n", - "\n", - "print(np.round(np.cov(z), 3)) # close to the identity\n", - "print(np.round(np.cov(x), 6)) # close to Sigma\n", - "# Cov(x) = Cov(Lz) = L Cov(z) L.T ~ L I L.T = L L.T = Sigma. Independent noise\n", - "# in, correlated noise out — Cholesky is the \"square root\" that makes it work.\n", - "\n", - "z_paths = rng.standard_normal((3, n_days * n_paths))\n", - "\n", - "correlated_asset_returns = (mu[:, None] + L @ z_paths).reshape(3, n_paths, n_days)\n", - "portfolio_returns_correlated = np.einsum('a,apd->pd', weights, correlated_asset_returns)\n", - "terminal_correlated = initial_value * np.prod(1 + portfolio_returns_correlated, axis=1)\n", - "\n", - "independent_scale = np.diag(np.sqrt(np.diag(Sigma)))\n", - "independent_asset_returns = (mu[:, None] + independent_scale @ z_paths).reshape(3, n_paths, n_days)\n", - "portfolio_returns_independent = np.einsum('a,apd->pd', weights, independent_asset_returns)\n", - "terminal_independent = initial_value * np.prod(1 + portfolio_returns_independent, axis=1)\n", - "\n", - "import matplotlib.pyplot as plt\n", - "plt.hist(terminal_independent, bins=80, density=True, alpha=0.6,\n", - " label=\"Assets simulated independently\")\n", - "plt.hist(terminal_correlated, bins=80, density=True, alpha=0.6,\n", - " label=\"Correct correlated simulation\")\n", - "plt.xlabel(\"Terminal portfolio value\")\n", - "plt.ylabel(\"Density\")\n", - "plt.legend()\n", - "plt.show()\n", - "\n", - "print(terminal_correlated.std(), terminal_independent.std()) # ~18.8 ~13.6\n", - "print(np.percentile(terminal_correlated, [1, 5])) # ~70.7 ~79.7\n", - "print(np.percentile(terminal_independent, [1, 5])) # ~79.9 ~86.9\n", - "\n", - "# EVERY asset kept its own individual volatility in BOTH simulations —\n", - "# independent_scale used the SAME diagonal as Sigma. The only thing that\n", - "# changed is whether the simulation lets the three assets fall together.\n", - "# Ignoring the positive covariance did not touch any single asset's risk; it\n", - "# erased real cross-asset comovement and manufactured DIVERSIFICATION THAT\n", - "# ISN'T THERE — the correlated portfolio's distribution is wider and its\n", - "# lower tail is worse.\n", - "#\n", - "# This is NOT \"correlation always increases risk.\" It is specific to THIS\n", - "# positively-correlated book: a negatively correlated pair would do the\n", - "# opposite, and ignoring it would UNDERSTATE diversification, not overstate\n", - "# it. What generalizes is only this: assuming independence when assets are\n", - "# not independent gets the TAILS of the distribution wrong." - ], - "id": "s11-24" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### What the comparison shows\n", - "\n", - "**Every individual asset kept the same volatility in both simulations.** The\n", - "only thing that changed is whether the simulation lets the three assets move\n", - "together. Ignoring the positive covariance did not touch any single asset's\n", - "risk; it erased real cross-asset comovement and manufactured diversification\n", - "that was never there — the correlated portfolio's terminal-value distribution\n", - "is wider, and its bad days are worse, than the (wrong) independent one.\n", - "\n", - "**This is not \"correlation always increases risk.\"** It is specific to this\n", - "book, where every pair is positively correlated. A negatively correlated pair\n", - "would do the opposite: ignoring it would make the simulation *understate*\n", - "diversification, not overstate it. What is general is only this: **assuming\n", - "independence when assets are not independent gets the tails of the\n", - "distribution wrong.**\n", - "\n", - "> 🇪🇸 Cada activo conservó su propia volatilidad en ambas simulaciones — lo\n", - "> único que cambió es si la simulación permite que los tres se muevan juntos.\n", - "> Ignorar la covarianza positiva no tocó el riesgo individual: borró el\n", - "> comovimiento real y fabricó una diversificación que no existía. Esto **no**\n", - "> significa que \"la correlación siempre aumenta el riesgo\" — es específico de\n", - "> esta cartera, donde todo está correlacionado positivamente. Con correlación\n", - "> negativa ocurriría lo contrario. Lo único general es que **asumir\n", - "> independencia cuando los activos no lo son distorsiona las colas de la\n", - "> distribución.**" - ], - "id": "s11-25" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Take-home E — Audio denoising by rank reduction\n", - "\n", - "> 🇪🇸 Ejercicio para casa E: eliminar ruido de audio reduciendo el rango.\n", - "\n", - "Section 10 used truncated SVDs of matrix unfoldings to build a Tucker\n", - "approximation of a real taxi tensor. This take-home applies the same\n", - "low-rank idea to the frequency × time matrix produced from sound.\n", - "\n", - "**The recording is real**: a five-second CC0 voice sample by Bart Massey, from\n", - "[`pdx-cs-sound/wavs`](https://github.com/pdx-cs-sound/wavs), pinned to commit\n", - "`ed5ebcbbbc2d11f0adddc9b50b78d581c29f738c` so the file this notebook fetches\n", - "cannot silently change under you. It downloads at runtime and is checked\n", - "against a known SHA-256 — if the download is corrupted or does not match the\n", - "pinned file, `fetch_verified_wav` below raises instead of quietly handing you\n", - "something else. **The noise is not real** — it is added on purpose, with a\n", - "fixed seed and a target signal-to-noise ratio, precisely so there is a known\n", - "clean reference to measure against. Do not confuse the two: the recording is\n", - "real data, exactly like every other dataset today; the noise is the\n", - "controlled experiment.\n", - "\n", - "### Why a waveform becomes a matrix\n", - "\n", - "A recording is one axis: amplitude over time. The **short-time Fourier\n", - "transform** (STFT) slices it into overlapping windows and Fourier-transforms\n", - "each one, producing a matrix `Z` with two axes — **frequency × time**. Row `i`\n", - "is \"how much of frequency `f_i` is present\"; column `j` is \"during time window\n", - "`t_j`.\" Nothing earlier today paired frequency against time this way.\n", - "\n", - "Because `Z` is a matrix, the SVD from sections 07 and 10 applies unchanged —\n", - "except `Z` is **complex**, and truncating its SVD keeps both magnitude and\n", - "phase. Reconstructing from magnitude alone would throw phase away and produce\n", - "audible distortion, so the truncated matrix goes straight into the inverse\n", - "STFT.\n", - "\n", - "Speech energy concentrates in a handful of dominant frequency-time patterns —\n", - "a few singular vectors carry most of the signal. Broadband, unstructured noise\n", - "has no such structure: it tends to spread its energy across many singular\n", - "directions, including many smaller ones. Keeping only the largest `k`\n", - "singular values keeps most of the speech and discards a disproportionate\n", - "share of the noise.\n", - "\n", - "> 🇪🇸 La STFT convierte una onda de una dimensión (amplitud en el tiempo) en\n", - "> una matriz de dos ejes: frecuencia × tiempo. La voz concentra su energía en\n", - "> pocas direcciones singulares dominantes; el ruido de banda ancha tiende a\n", - "> repartir su energía entre muchas direcciones singulares, incluidas muchas\n", - "> pequeñas. Por eso conservar solo las `k` mayores retiene la voz y descarta\n", - "> una parte desproporcionada del ruido — pero **esto no es un eliminador de\n", - "> ruido universal**: la comprobación real es el SNR medido, no cómo suena.\n", - "\n", - "**This is not a universal denoiser.** It only works to the extent that the\n", - "noise really is broadband relative to a structured signal — narrowband noise,\n", - "or noise correlated with the signal, is not separated this way. The proof\n", - "either way is the measured SNR below, not how it sounds." - ], - "id": "s11-26" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "VOICE_URL = \"https://raw.githubusercontent.com/pdx-cs-sound/wavs/ed5ebcbbbc2d11f0adddc9b50b78d581c29f738c/voice.wav\"\n", - "VOICE_SHA256 = \"2c4b4d9d5f90715fdbf599869a465d521638f40ca978b186df96f1543a4d67dc\"\n", - "\n", - "def fetch_verified_wav(url, expected_sha256):\n", - " \"\"\"Download a WAV and refuse to proceed if it does not match the pinned\n", - " checksum. No silent fallback to synthetic data on failure.\"\"\"\n", - " import hashlib\n", - " import io\n", - " import urllib.request\n", - " from scipy.io import wavfile\n", - " raw = urllib.request.urlopen(url, timeout=30).read()\n", - " got = hashlib.sha256(raw).hexdigest()\n", - " if got != expected_sha256:\n", - " raise ValueError(\n", - " f\"checksum mismatch for {url}: expected {expected_sha256}, got \"\n", - " f\"{got}. Refusing to use unverified audio data.\")\n", - " return wavfile.read(io.BytesIO(raw))\n", - "\n", - "def snr_db(reference, estimate):\n", - " \"\"\"Energy-based SNR in dB. `reference` is always the real clean signal.\"\"\"\n", - " return 10 * np.log10(np.sum(reference**2) / np.sum((estimate - reference)**2))\n", - "\n", - "# TODO 1: fs, clean_i16 = fetch_verified_wav(VOICE_URL, VOICE_SHA256).\n", - "# Convert to float in [-1, 1] (divide by 32768), and average channels\n", - "# to mono if clean.ndim > 1. Print fs, duration in seconds, and shape.\n", - "\n", - "# TODO 2: With rng = np.random.default_rng(42) and TARGET_SNR_DB = 5.0, build\n", - "# additive noise scaled from the CLEAN SIGNAL'S OWN MEAN POWER (not an\n", - "# arbitrary standard deviation) so that clean + noise lands at the\n", - "# target SNR. Verify with snr_db(clean, noisy)." - ], - "id": "s11-27" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true + { + "cell_type": "markdown", + "metadata": { + "id": "ZjR0Aq8NpbCu" + }, + "source": [ + "## Take-home A — PCA: the scaling trap\n", + "\n", + "The Wisconsin Diagnostic Breast Cancer dataset has 30 real measurements per tumour sample, but those features use very different numerical scales.\n", + "\n", + "Your goal is not merely to run PCA. It is to discover why unstandardized PCA can give a technically correct but scientifically misleading answer.\n", + "\n", + "> 🇪🇸 El conjunto Wisconsin Diagnostic Breast Cancer tiene 30 mediciones reales por muestra, pero en escalas numéricas muy distintas. El reto es descubrir por qué PCA sin estandarizar puede producir una respuesta matemáticamente válida pero científicamente engañosa." + ], + "id": "ZjR0Aq8NpbCu" }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "fs, clean_i16 = fetch_verified_wav(VOICE_URL, VOICE_SHA256)\n", - "clean = clean_i16.astype(np.float64) / 32768.0\n", - "if clean.ndim > 1:\n", - " clean = clean.mean(axis=1)\n", - "print(fs, round(len(clean) / fs, 3), clean.shape) # 48000 4.949 (237568,)\n", - "\n", - "rng = np.random.default_rng(42)\n", - "TARGET_SNR_DB = 5.0\n", - "noise = rng.standard_normal(clean.shape)\n", - "scale = np.sqrt(np.mean(clean**2) / (np.mean(noise**2) * 10**(TARGET_SNR_DB / 10)))\n", - "noisy = clean + scale * noise\n", - "print(round(snr_db(clean, noisy), 2)) # 5.0 -- exactly the target, by construction\n", - "\n", - "# fetch_verified_wav is not decorative: it raises ValueError instead of\n", - "# silently returning something else if the download is corrupted or does not\n", - "# match the pinned file. voice.wav ITSELF is real -- a five-second CC0\n", - "# recording. The noise added here is the controlled, synthetic part of the\n", - "# experiment: it exists only so `clean` is a known reference an SNR can be\n", - "# measured against." - ], - "id": "s11-28" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO 3: f, t, Z = signal.stft(noisy, fs=fs, nperseg=1024, noverlap=512).\n", - "# Z is COMPLEX -- frequency bins x time frames. Print Z.shape and the\n", - "# full possible rank, min(Z.shape).\n", - "\n", - "# TODO 4: U, s, Vh = np.linalg.svd(Z, full_matrices=False), on the COMPLEX\n", - "# matrix directly so phase survives truncation, not magnitude alone.\n", - "# For k in [2, 5, 10, 20, 40, 80, len(s)]: build\n", - "# Z_k = (U[:, :k] * s[:k]) @ Vh[:k, :], run\n", - "# signal.istft(Z_k, fs=fs, nperseg=1024, noverlap=512), align its\n", - "# length to `clean`, and print k, the retained singular-value energy\n", - "# sum(s[:k]**2) / sum(s**2), and snr_db(clean, reconstruction).\n", - "\n", - "# TODO 5: Pick the k with the best SNR among the candidates above. Report its\n", - "# retained energy, its SNR, and the improvement over the noisy SNR\n", - "# from TODO 2.\n", - "\n", - "# TODO 6: Build ONE common peak-scale factor from\n", - "# max(|noisy|, |denoised|, |clean|) and make playback-only copies\n", - "# scaled by it -- SNR itself is computed on the unscaled signals\n", - "# above, never on these copies. Then display Audio players for the\n", - "# noisy (\"before\") and denoised (\"after\") copies. You may run this\n", - "# cell to listen, but do not save its Audio output into the tracked\n", - "# notebook: Audio() output contains embedded base64 data and must\n", - "# not be committed." - ], - "id": "s11-29" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "FiWwed7DpbCu" + }, + "outputs": [], + "source": [ + "# TODO\n", + "# 1. Load the breast-cancer data.\n", + "# 2. Center X and compute SVD.\n", + "# 3. How many components explain 95% of variance?\n", + "# 4. Inspect feature variances. Why is the answer suspicious?\n", + "# 5. Standardize every feature and repeat.\n", + "# 6. Compare the two cumulative-variance curves." + ], + "id": "FiWwed7DpbCu" }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "f, t, Z = signal.stft(noisy, fs=fs, nperseg=1024, noverlap=512)\n", - "full_rank = min(Z.shape)\n", - "print(Z.shape, full_rank) # (513, 465) 465\n", - "\n", - "U, s, Vh = np.linalg.svd(Z, full_matrices=False)\n", - "for k in [2, 5, 10, 20, 40, 80, len(s)]:\n", - " Zk = (U[:, :k] * s[:k]) @ Vh[:k, :]\n", - " _, x_rec = signal.istft(Zk, fs=fs, nperseg=1024, noverlap=512)\n", - " n = min(len(x_rec), len(clean))\n", - " energy = np.sum(s[:k]**2) / np.sum(s**2)\n", - " print(k, round(energy * 100, 1), round(snr_db(clean[:n], x_rec[:n]), 2))\n", - "# k energy% SNR dB\n", - "# 2 33.2 2.29\n", - "# 5 51.1 4.76\n", - "# 10 61.9 6.78\n", - "# 20 70.1 8.48\n", - "# 40 78.3 9.08 <- best of these candidates\n", - "# 80 87.1 7.68 <- WORSE than k=40: noise has leaked back in\n", - "# 465 100.0 5.00 <- full rank matches `noisy` to numerical precision\n", - "\n", - "k = 40\n", - "Zk = (U[:, :k] * s[:k]) @ Vh[:k, :]\n", - "_, x_rec = signal.istft(Zk, fs=fs, nperseg=1024, noverlap=512)\n", - "n = min(len(x_rec), len(clean))\n", - "denoised, clean_a, noisy_a = x_rec[:n], clean[:n], noisy[:n]\n", - "\n", - "snr_before = snr_db(clean_a, noisy_a)\n", - "snr_after = snr_db(clean_a, denoised)\n", - "print(round(snr_before, 2), round(snr_after, 2), round(snr_after - snr_before, 2))\n", - "# 5.0 9.08 4.08\n", - "\n", - "peak = max(np.abs(clean_a).max(), np.abs(noisy_a).max(), np.abs(denoised).max())\n", - "noisy_play = noisy_a / peak\n", - "denoised_play = denoised / peak\n", - "\n", - "from IPython.display import Audio, display\n", - "display(Audio(noisy_play, rate=fs)) # \"before\"\n", - "display(Audio(denoised_play, rate=fs)) # \"after\"\n", - "\n", - "# k=40 keeps 40 of 465 possible components -- 8.6% of full rank -- and\n", - "# recovers 4.08 dB of SNR: real, but modest, not a miracle. k=2 and k=5 keep\n", - "# too little of the SPEECH itself to beat the noisy baseline by much. k=80\n", - "# already lets enough noise back into smaller-but-still-significant singular\n", - "# directions that SNR gets WORSE than at k=40 -- more components is not\n", - "# always better. At the full rank of 465 the reconstruction matches `noisy`\n", - "# to numerical precision: proof that whatever denoising happened at k=40\n", - "# came specifically from truncating, not from the STFT -> SVD -> ISTFT round\n", - "# trip itself." - ], - "id": "s11-30" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### What the numbers say\n", - "\n", - "Keeping 40 of 465 possible singular directions (8.6% of full rank, 78.3% of\n", - "the singular-value energy) raised the SNR from 5.00 dB to 9.08 dB — a real\n", - "**+4.08 dB** improvement, not a dramatic one. Fewer components (`k=2`, `k=5`)\n", - "discard too much of the speech itself; more (`k=80`) already lets noise back\n", - "in, and SNR gets worse again. At the full rank the reconstruction matches the\n", - "noisy signal to numerical precision, which is the honest control: the\n", - "denoising is entirely a property of truncating, not of the STFT/SVD/ISTFT\n", - "machinery itself.\n", - "\n", - "**Do not generalize this to \"truncated SVD removes noise.\"** It suppresses\n", - "noise that is broadband and unstructured relative to a signal that\n", - "concentrates in a few dominant directions — the same low-rank argument\n", - "section 10 used on the taxi tensor, applied here to sound instead of trip\n", - "counts. Narrowband noise, or noise correlated with the speech itself, would\n", - "not separate out this way, and the only way to know which situation you are\n", - "in is to measure the SNR, the way this take-home just did.\n", - "\n", - "> 🇪🇸 Conservar 40 de 465 direcciones singulares posibles (8.6% del rango\n", - "> completo, 78.3% de la energía de los valores singulares) subió el SNR de\n", - "> 5.00 dB a 9.08 dB — una mejora real de **+4.08 dB**, no espectacular. Menos\n", - "> componentes descartan demasiada voz; más vuelven a dejar entrar ruido y el\n", - "> SNR empeora. En el rango completo la reconstrucción coincide con la señal\n", - "> ruidosa hasta la precisión numérica, lo cual es el control honesto: la\n", - "> reducción de ruido es una propiedad de truncar, no del mecanismo\n", - "> STFT/SVD/ISTFT en sí. **No generalices esto a \"la SVD truncada siempre\n", - "> elimina el ruido.\"** Solo funciona cuando el ruido es de banda ancha y no\n", - "> estructurado frente a una señal que se concentra en pocas direcciones\n", - "> dominantes — el mismo argumento de bajo rango que la sección 10 usó con el\n", - "> tensor de taxis, aplicado aquí al sonido. La única forma de saberlo es\n", - "> medir el SNR, como se acaba de hacer." - ], - "id": "s11-31" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## After the workshop — Tucker compression for deployment\n", - "\n", - "> 🇪🇸 Después del taller — compresión de Tucker para producción.\n", - "\n", - "**Optional — run this after the workshop.** Section 10 ran one fixed Tucker\n", - "rank. Here a **rank slider** drives the trade-off live, on a real dense array,\n", - "so you can feel the curve instead of reading one number on it.\n", - "\n", - "One honest note before the code: this is **not** a neural network's weights.\n", - "A small, stable, seconds-to-download real conv-weight file that both fits a\n", - "free Colab CPU and is not already engineered to be maximally compact turned\n", - "out not to exist — the two real options checked while building this notebook\n", - "(a modern efficient architecture, and a small classifier trained from scratch\n", - "on this workshop's own data) were **already so parameter-efficient that Tucker\n", - "found almost nothing left to compress**, which is itself real and worth\n", - "knowing, just not the point of this appendix. So instead this is a\n", - "**comparable dense tensor**: real NYC taxi trips again, but counted over\n", - "**pickup borough × dropoff borough × hour × weekday** — a genuine order-4\n", - "array, the same shape of thing an on-device cache or a recommender's usage\n", - "table has to fit in memory. The Tucker math, the slider, and the trade-off it\n", - "shows are identical to compressing a weight tensor; only the source of the\n", - "numbers differs, and it seemed better to say that plainly than to relabel taxi\n", - "trips as something they are not.\n", - "\n", - "**Deployment engineers benefit because Tucker lets them choose a point on this\n", - "curve explicitly** — cut most of an array's storage and pay only a measured,\n", - "bounded increase in error, rather than guessing at a fixed compression\n", - "level." - ], - "id": "s11-32" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "TAXIS = \"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/taxis.csv\"\n", - "taxis = pd.read_csv(TAXIS)\n", - "taxis['hour'] = pd.to_datetime(taxis['pickup']).dt.hour\n", - "taxis['weekday'] = pd.to_datetime(taxis['pickup']).dt.weekday\n", - "sub = taxis.dropna(subset=['pickup_borough', 'dropoff_borough'])\n", - "pb2 = sorted(sub['pickup_borough'].unique())\n", - "db2 = sorted(sub['dropoff_borough'].unique())\n", - "\n", - "demand = np.zeros((len(pb2), len(db2), 24, 7))\n", - "for (p, d, h, wd), v in sub.groupby(\n", - " ['pickup_borough', 'dropoff_borough', 'hour', 'weekday']).size().items():\n", - " demand[pb2.index(p), db2.index(d), h, wd] = v\n", - "\n", - "print(demand.shape, int(demand.sum())) # (4, 5, 24, 7), same trips as above" - ], - "id": "s11-33" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def unfold(T, axis):\n", - " return np.moveaxis(T, axis, 0).reshape(T.shape[axis], -1)\n", - "\n", - "# Precompute BOTH SVD bases once. The slider below only re-slices and\n", - "# re-contracts these small matrices — it never redoes an SVD, which is what\n", - "# keeps it responsive. Only the two time axes are compressed; pickup and\n", - "# dropoff borough stay exact, the way section 10's kernel spatial dims would\n", - "# stay exact in a channel-mode Tucker compression of a real conv layer.\n", - "basis_hour = np.linalg.svd(unfold(demand, 2), full_matrices=False)[0] # (24, 24)\n", - "basis_weekday = np.linalg.svd(unfold(demand, 3), full_matrices=False)[0] # (7, 7)\n", - "\n", - "n_pb, n_db, n_hour, n_weekday = demand.shape\n", - "original_params = demand.size\n", - "\n", - "# Sweep every achievable rank once, up front, so the widget only ever looks\n", - "# values up rather than recomputing them.\n", - "ranks = list(range(1, n_hour + 1))\n", - "compressed_list, ratio_list, error_list, madds_list = [], [], [], []\n", - "for k in ranks:\n", - " r_hour, r_weekday = k, min(k, n_weekday)\n", - " Uh, Uw = basis_hour[:, :r_hour], basis_weekday[:, :r_weekday]\n", - " core = np.einsum('ijhw,hc,wd->ijcd', demand, Uh, Uw)\n", - " recon = np.einsum('ijcd,hc,wd->ijhw', core, Uh, Uw)\n", - " compressed = core.size + Uh.size + Uw.size\n", - " compressed_list.append(compressed)\n", - " ratio_list.append(original_params / compressed)\n", - " error_list.append(np.linalg.norm(demand - recon) / np.linalg.norm(demand))\n", - " # Multiply-adds to RE-EXPAND the compressed factors back to the full\n", - " # array — the cost a deployed system pays each time it reads the cache.\n", - " # This is not a network FLOP count; it is specifically that one contraction.\n", - " madds_list.append(n_pb * n_db * n_hour * r_weekday * (r_hour + n_weekday))\n", - "\n", - "print(f\"original parameters: {original_params} \"\n", - " f\"(pickup {n_pb} x dropoff {n_db} x hour {n_hour} x weekday {n_weekday})\")" - ], - "id": "s11-34" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " from google.colab import output\n", - " output.enable_custom_widget_manager()\n", - "except ImportError:\n", - " pass\n", - "\n", - "import ipywidgets as widgets\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def tucker_tradeoff(k):\n", - " i = k - 1\n", - " plt.close('all')\n", - " fig, ax1 = plt.subplots(figsize=(6.5, 3.2))\n", - " ax1.plot(ranks, error_list, color='#C44E52')\n", - " ax1.scatter([k], [error_list[i]], color='#C44E52', zorder=5)\n", - " ax1.set_xlabel('rank k (shared by the hour and weekday axes)')\n", - " ax1.set_ylabel('relative error', color='#C44E52')\n", - " ax2 = ax1.twinx()\n", - " ax2.plot(ranks, ratio_list, color='#4C72B0')\n", - " ax2.scatter([k], [ratio_list[i]], color='#4C72B0', zorder=5)\n", - " ax2.set_ylabel('compression ratio (x)', color='#4C72B0')\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - " print(f\"rank k = {k}\")\n", - " print(f\"compressed parameters: {compressed_list[i]} (of {original_params} original)\")\n", - " print(f\"compression ratio: {ratio_list[i]:.2f}x\")\n", - " print(f\"relative error: {error_list[i]:.3f}\")\n", - " print(f\"reconstruction MAdds: {madds_list[i]} \"\n", - " f\"(multiply-adds to re-expand the factors back to the full array)\")\n", - "\n", - "# Move the slider from 1 to n_hour. Both ends are worth visiting: rank 1 is\n", - "# the cheapest possible model, and the top end (hour AND weekday both at\n", - "# their true dimension) should reconstruct the tensor exactly — a check on\n", - "# the implementation, not just on the trade-off.\n", - "widgets.interact(tucker_tradeoff,\n", - " k=widgets.IntSlider(min=1, max=n_hour, step=1, value=4,\n", - " description='rank k'));" - ], - "id": "s11-35" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Thank you\n", - "\n", - "> 🇪🇸 Gracias por venir. Pregunta en Discord en español o en inglés — lo que te\n", - "> permita preguntar más rápido.\n", - "\n", - "Questions stay welcome in Discord, in Spanish or English. The\n", - "[handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)\n", - "has everything from today, including the facilitator notes." - ], - "id": "s11-36" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Done with this section\n", - "\n", - "That is the whole workshop. Thank you for coming.\n", - "\n", - "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" - ], - "id": "s11-37" - } - ], - "metadata": { - "colab": { - "name": "11-wrap-up-and-take-homes.ipynb", - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "colab": { + "base_uri": "https://localhost:8080/", + "height": 785 + }, + "id": "N_mhphqqpbCu", + "outputId": "3e0725a5-91c4-4e53-a6f4-a37000d6ecaf" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "95% components — raw / sin estandarizar: 1\n", + "95% components — standardized / estandarizado: 10\n", + "feature variance range / rango de varianzas: 6.989e-06 to / a 3.236e+05\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Take-away / Idea: PCA optimizes variance, not scientific relevance; scale the features when units differ.\n" + ] + } + ], + "source": [ + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "bc = load_breast_cancer()\n", + "X, y = bc.data, bc.target\n", + "\n", + "X_centered = X - X.mean(axis=0)\n", + "_, s_raw, Vt_raw = np.linalg.svd(X_centered, full_matrices=False)\n", + "frac_raw = s_raw**2 / np.sum(s_raw**2)\n", + "n95_raw = int(np.argmax(np.cumsum(frac_raw) >= 0.95) + 1)\n", + "\n", + "feature_var = X.var(axis=0)\n", + "\n", + "X_std = (X - X.mean(axis=0)) / X.std(axis=0)\n", + "_, s_std, Vt_std = np.linalg.svd(X_std, full_matrices=False)\n", + "frac_std = s_std**2 / np.sum(s_std**2)\n", + "n95_std = int(np.argmax(np.cumsum(frac_std) >= 0.95) + 1)\n", + "\n", + "print(\"95% components — raw / sin estandarizar:\", n95_raw)\n", + "print(\"95% components — standardized / estandarizado:\", n95_std)\n", + "print(\n", + " \"feature variance range / rango de varianzas:\",\n", + " f\"{feature_var.min():.3e}\",\n", + " \"to / a\",\n", + " f\"{feature_var.max():.3e}\",\n", + ")\n", + "\n", + "n_show = 15\n", + "fig, ax = plt.subplots(figsize=(6.4, 3.4))\n", + "ax.plot(\n", + " range(1, n_show + 1),\n", + " np.cumsum(frac_raw[:n_show]),\n", + " marker=\"o\",\n", + " label=\"raw / sin estandarizar\",\n", + ")\n", + "ax.plot(\n", + " range(1, n_show + 1),\n", + " np.cumsum(frac_std[:n_show]),\n", + " marker=\"o\",\n", + " label=\"standardized / estandarizado\",\n", + ")\n", + "ax.axhline(0.95, linestyle=\"--\", linewidth=1, label=\"95%\")\n", + "ax.set_xlabel(\"number of components / número de componentes\")\n", + "ax.set_ylabel(\"cumulative variance / varianza acumulada\")\n", + "ax.set_title(\"PCA changes when units dominate / PCA cambia cuando dominan las unidades\")\n", + "ax.legend(fontsize=8)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "Z = X_std @ Vt_std[:2].T\n", + "fig, ax = plt.subplots(figsize=(5.2, 3.8))\n", + "scatter = ax.scatter(Z[:, 0], Z[:, 1], c=y, s=12)\n", + "ax.set_xlabel(\"component 1\")\n", + "ax.set_ylabel(\"component 2\")\n", + "ax.set_title(\"Standardized PCA / PCA estandarizado\")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(\n", + " \"Take-away / Idea:\",\n", + " \"PCA optimizes variance, not scientific relevance; scale the features when units differ.\"\n", + ")" + ], + "id": "N_mhphqqpbCu" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Io0ukHyUpbCv" + }, + "source": [ + "## Take-home B — Attention is two contractions\n", + "\n", + "For this exercise we deliberately use **synthetic** `Q`, `K`, and `V` arrays. That is appropriate here because the goal is to isolate the tensor mechanics of attention — shapes, contraction axes, normalization, and masking — without mixing in a tokenizer or a trained model.\n", + "\n", + "Build:\n", + "\n", + "1. `scores[b, i, j] = Q[b, i, :] · K[b, j, :]`\n", + "2. row-wise softmax weights\n", + "3. `output[b, i, :] = Σ_j weights[b, i, j] V[b, j, :]`\n", + "4. a padding mask that forces the last three key positions to receive zero weight.\n", + "\n", + "> 🇪🇸 Aquí usamos `Q`, `K` y `V` **sintéticos a propósito**: queremos aislar la mecánica tensorial de la atención sin confundirla con tokenización o entrenamiento. Construye puntajes, softmax, salida y una máscara para padding." + ], + "id": "Io0ukHyUpbCv" + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "0iJy1TlwpbCv" + }, + "outputs": [], + "source": [ + "# TODO\n", + "# 1. Create Q, K, V with shape (batch=4, seq=12, dim=16).\n", + "# 2. Compute scaled dot-product scores with einsum.\n", + "# 3. Softmax over the key-position axis.\n", + "# 4. Contract weights with V.\n", + "# 5. Mask the final 3 key positions BEFORE softmax.\n", + "# 6. Verify masked positions receive zero weight." + ], + "id": "0iJy1TlwpbCv" + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "colab": { + "base_uri": "https://localhost:8080/", + "height": 413 + }, + "id": "4RpwHWPDpbCv", + "outputId": "1c85fed3-288c-4993-9a08-5dc9ffda46dc" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "scores: (4, 12, 12)\n", + "weights: (4, 12, 12)\n", + "output: (4, 12, 16)\n", + "rows sum to 1 / filas suman 1: True\n", + "largest padded weight / mayor peso en padding: 0.0\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ], + "source": [ + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "rng_attention = np.random.default_rng(6)\n", + "\n", + "batch, seq_len, dim = 4, 12, 16\n", + "Q = rng_attention.standard_normal((batch, seq_len, dim))\n", + "K = rng_attention.standard_normal((batch, seq_len, dim))\n", + "V = rng_attention.standard_normal((batch, seq_len, dim))\n", + "\n", + "scores = np.einsum(\"bid,bjd->bij\", Q, K) / np.sqrt(dim)\n", + "weights = softmax(scores, axis=-1)\n", + "attention_output = np.einsum(\"bij,bjd->bid\", weights, V)\n", + "\n", + "mask = np.zeros((seq_len, seq_len))\n", + "mask[:, -3:] = -np.inf\n", + "weights_masked = softmax(scores + mask, axis=-1)\n", + "output_masked = np.einsum(\"bij,bjd->bid\", weights_masked, V)\n", + "\n", + "print(\"scores:\", scores.shape)\n", + "print(\"weights:\", weights.shape)\n", + "print(\"output:\", attention_output.shape)\n", + "print(\n", + " \"rows sum to 1 / filas suman 1:\",\n", + " np.allclose(weights_masked.sum(axis=-1), 1.0),\n", + ")\n", + "print(\n", + " \"largest padded weight / mayor peso en padding:\",\n", + " weights_masked[..., -3:].max(),\n", + ")\n", + "\n", + "example = 0\n", + "query = 0\n", + "\n", + "fig, ax = plt.subplots(figsize=(6.2, 3.2))\n", + "ax.bar(\n", + " range(seq_len),\n", + " weights_masked[example, query],\n", + ")\n", + "ax.set_xlabel(\"key position / posición key\")\n", + "ax.set_ylabel(\"attention weight / peso\")\n", + "ax.set_title(\"Padding disappears after masking / El padding desaparece con la máscara\")\n", + "plt.tight_layout()\n", + "plt.show()" + ], + "id": "4RpwHWPDpbCv" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0W5EWbWApbCv" + }, + "source": [ + "## Take-home C — CP versus Tucker on the same real taxi tensor\n", + "\n", + "Section 10 used Tucker/HOSVD. Now use **CP decomposition** on the same real `pickup borough × dropoff borough × hour` tensor.\n", + "\n", + "CP represents a tensor as a sum of rank-1 outer products. Unlike Tucker, it has no separate core tensor. CP components can be individually interpretable, but uniqueness is only guaranteed under mathematical conditions — so treat patterns in this small dataset as exploratory, not as ground truth.\n", + "\n", + "> 🇪🇸 La sección 10 usó Tucker/HOSVD. Ahora aplica **CP** al mismo tensor real de taxis. CP expresa el tensor como suma de productos externos de rango 1 y no usa un núcleo separado. Sus componentes pueden ser interpretables, pero la unicidad requiere condiciones matemáticas; en este conjunto pequeño, interprétalos con cautela." + ], + "id": "0W5EWbWApbCv" + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "3KCacE6ipbCv" + }, + "outputs": [], + "source": [ + "# TODO\n", + "# 1. Rebuild the real NYC taxi tensor: pickup × dropoff × hour.\n", + "# 2. Fit a rank-3 CP decomposition with TensorLy.\n", + "# 3. Reconstruct the tensor and compute relative error.\n", + "# 4. Compare its parameter count with Tucker rank (2,2,3).\n", + "# 5. Inspect pickup, dropoff, and hour factors for each CP component." + ], + "id": "3KCacE6ipbCv" + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "colab": { + "base_uri": "https://localhost:8080/", + "height": 496, + "referenced_widgets": [ + "27879fcad4af4e0cbd4940b1a120c39f", + "68c2f2688c854983adea71cfaa880d47", + "7a6f89b309f8428db11c73ebbeadaf43", + "0a570d84b9c04beea75661ee7971694f", + "be805ac7d7dd4bf5b8cd48a77e24378f", + "916f2343e79e4158b8cdf569e574f1d3", + "82e9c57171424918aeaaeae685d24a81" + ] + }, + "id": "o4UNpJMUpbCw", + "outputId": "163fb309-dcfa-4d79-d66a-62c7f42e1714" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "taxi tensor / tensor taxis: (4, 5, 24)\n", + "CP rank: 3\n", + "CP relative error / error relativo: 0.0349\n", + "CP parameters / parámetros: 102\n", + "Tucker (2,2,3) parameters / parámetros: 102\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(IntSlider(value=1, continuous_update=False, description='Component / Componente:', max=3, min=1…" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "27879fcad4af4e0cbd4940b1a120c39f" + } + }, + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "colab": { + "custom_widget_manager": { + "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" + } + } + } + } + } + ], + "source": [ + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "try:\n", + " import tensorly as tl\n", + " from tensorly.decomposition import parafac\n", + "except ImportError:\n", + " subprocess.run(\n", + " [sys.executable, \"-m\", \"pip\", \"install\", \"-q\", \"tensorly\"],\n", + " check=True,\n", + " )\n", + " import tensorly as tl\n", + " from tensorly.decomposition import parafac\n", + "\n", + "TAXIS = \"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/taxis.csv\"\n", + "taxis = pd.read_csv(TAXIS)\n", + "\n", + "taxis[\"pickup_dt\"] = pd.to_datetime(taxis[\"pickup\"], errors=\"coerce\")\n", + "taxis[\"hour\"] = taxis[\"pickup_dt\"].dt.hour\n", + "\n", + "sub = taxis.dropna(\n", + " subset=[\"pickup_borough\", \"dropoff_borough\", \"hour\"]\n", + ").copy()\n", + "sub[\"hour\"] = sub[\"hour\"].astype(int)\n", + "\n", + "pb = sorted(sub[\"pickup_borough\"].unique())\n", + "db = sorted(sub[\"dropoff_borough\"].unique())\n", + "\n", + "p_idx = {name: i for i, name in enumerate(pb)}\n", + "d_idx = {name: i for i, name in enumerate(db)}\n", + "\n", + "T_taxi = np.zeros((len(pb), len(db), 24), dtype=float)\n", + "\n", + "for (p, d, h), count in sub.groupby(\n", + " [\"pickup_borough\", \"dropoff_borough\", \"hour\"]\n", + ").size().items():\n", + " T_taxi[p_idx[p], d_idx[d], int(h)] = float(count)\n", + "\n", + "rank_cp = 3\n", + "\n", + "cp_weights, cp_factors = parafac(\n", + " tl.tensor(T_taxi),\n", + " rank=rank_cp,\n", + " init=\"svd\",\n", + " random_state=0,\n", + " n_iter_max=500,\n", + " tol=1e-9,\n", + ")\n", + "\n", + "F_pickup, F_dropoff, F_hour = cp_factors\n", + "cp_recon = tl.cp_to_tensor((cp_weights, cp_factors))\n", + "cp_error = np.linalg.norm(cp_recon - T_taxi) / np.linalg.norm(T_taxi)\n", + "\n", + "cp_params = (\n", + " len(cp_weights)\n", + " + sum(f.size for f in cp_factors)\n", + ")\n", + "\n", + "tucker_ranks = (2, 2, 3)\n", + "tucker_params = (\n", + " np.prod(tucker_ranks)\n", + " + T_taxi.shape[0] * tucker_ranks[0]\n", + " + T_taxi.shape[1] * tucker_ranks[1]\n", + " + T_taxi.shape[2] * tucker_ranks[2]\n", + ")\n", + "\n", + "print(\"taxi tensor / tensor taxis:\", T_taxi.shape)\n", + "print(\"CP rank:\", rank_cp)\n", + "print(\"CP relative error / error relativo:\", f\"{cp_error:.4f}\")\n", + "print(\"CP parameters / parámetros:\", int(cp_params))\n", + "print(\"Tucker (2,2,3) parameters / parámetros:\", int(tucker_params))\n", + "\n", + "component_slider = widgets.IntSlider(\n", + " value=1,\n", + " min=1,\n", + " max=rank_cp,\n", + " step=1,\n", + " description=\"Component / Componente:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"145px\"},\n", + ")\n", + "\n", + "def show_cp_component(component):\n", + " r = component - 1\n", + "\n", + " fig, axes = plt.subplots(1, 3, figsize=(11.5, 3.1))\n", + "\n", + " axes[0].bar(pb, F_pickup[:, r])\n", + " axes[0].set_title(\"pickup / origen\")\n", + " axes[0].tick_params(axis=\"x\", rotation=40)\n", + "\n", + " axes[1].bar(db, F_dropoff[:, r])\n", + " axes[1].set_title(\"dropoff / destino\")\n", + " axes[1].tick_params(axis=\"x\", rotation=40)\n", + "\n", + " axes[2].bar(range(24), F_hour[:, r])\n", + " axes[2].set_title(\"hour / hora\")\n", + " axes[2].set_xlabel(\"hour / hora\")\n", + "\n", + " fig.suptitle(f\"CP component / componente {component}\")\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " print(\n", + " \"strongest pickup / origen dominante:\",\n", + " pb[int(np.argmax(np.abs(F_pickup[:, r])))],\n", + " )\n", + " print(\n", + " \"strongest dropoff / destino dominante:\",\n", + " db[int(np.argmax(np.abs(F_dropoff[:, r])))],\n", + " )\n", + " print(\n", + " \"peak hour / hora pico:\",\n", + " int(np.argmax(np.abs(F_hour[:, r]))),\n", + " )\n", + "\n", + "cp_output = widgets.interactive_output(\n", + " show_cp_component,\n", + " {\"component\": component_slider},\n", + ")\n", + "\n", + "display(widgets.VBox([component_slider, cp_output]))" + ], + "id": "o4UNpJMUpbCw" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mECc8EZCpbCw" + }, + "source": [ + "## Take-home D — Cholesky builds correlation\n", + "\n", + "This exercise is **synthetic by design**. We choose the covariance matrix ourselves so there is a known causal truth: the individual asset volatilities remain fixed, while only cross-asset dependence changes.\n", + "\n", + "Use Cholesky `Σ = L Lᵀ` to transform independent Gaussian noise into correlated draws, then compare a correctly correlated portfolio simulation with the incorrect assumption of independence.\n", + "\n", + "> 🇪🇸 Este experimento es **sintético a propósito**. Elegimos la matriz de covarianza para conocer la verdad causal: las volatilidades individuales permanecen iguales y solo cambia la dependencia entre activos. Usa Cholesky para convertir ruido independiente en muestras correlacionadas y compara ambas simulaciones." + ], + "id": "mECc8EZCpbCw" + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "qJr9eFnPpbCw" + }, + "outputs": [], + "source": [ + "# TODO\n", + "# 1. Build Sigma from volatilities and correlations.\n", + "# 2. Compute L = cholesky(Sigma) and verify L @ L.T == Sigma.\n", + "# 3. Transform independent noise z into correlated noise L @ z.\n", + "# 4. Simulate the same portfolio twice:\n", + "# - with the correct covariance;\n", + "# - with the same asset volatilities but zero cross-correlation.\n", + "# 5. Compare standard deviation and lower-tail percentiles." + ], + "id": "qJr9eFnPpbCw" + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "colab": { + "base_uri": "https://localhost:8080/", + "height": 461 + }, + "id": "bABSrLD9pbCw", + "outputId": "9b28a641-9fdf-4aee-af47-ec95c802a5c7" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "L @ L.T == Sigma: True\n", + "covariance error / error de covarianza: 1.118966e-06\n", + "std correlated / correlacionado: 18.89\n", + "std independent / independiente: 13.61\n", + "1%, 5% correlated: [70.1 79.59]\n", + "1%, 5% independent: [79.24 86.86]\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ], + "source": [ + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "vol = np.array([0.012, 0.015, 0.010])\n", + "\n", + "corr = np.array([\n", + " [1.00, 0.85, 0.20],\n", + " [0.85, 1.00, 0.20],\n", + " [0.20, 0.20, 1.00],\n", + "])\n", + "\n", + "Sigma = np.outer(vol, vol) * corr\n", + "L = np.linalg.cholesky(Sigma)\n", + "\n", + "print(\"L @ L.T == Sigma:\", np.allclose(L @ L.T, Sigma))\n", + "\n", + "rng_chol = np.random.default_rng(5)\n", + "\n", + "z = rng_chol.standard_normal((3, 100_000))\n", + "x = L @ z\n", + "\n", + "print(\n", + " \"covariance error / error de covarianza:\",\n", + " f\"{np.linalg.norm(np.cov(x) - Sigma):.6e}\",\n", + ")\n", + "\n", + "weights_portfolio = np.array([0.4, 0.4, 0.2])\n", + "mu = np.array([0.00030, 0.00035, 0.00020])\n", + "\n", + "n_days = 252\n", + "n_paths = 10_000\n", + "initial_value = 100.0\n", + "\n", + "z_paths = rng_chol.standard_normal((3, n_days * n_paths))\n", + "\n", + "correlated_returns = (\n", + " mu[:, None] + L @ z_paths\n", + ").reshape(3, n_paths, n_days)\n", + "\n", + "daily_corr = np.einsum(\n", + " \"a,apd->pd\",\n", + " weights_portfolio,\n", + " correlated_returns,\n", + ")\n", + "\n", + "terminal_corr = initial_value * np.prod(1 + daily_corr, axis=1)\n", + "\n", + "independent_scale = np.diag(np.sqrt(np.diag(Sigma)))\n", + "\n", + "independent_returns = (\n", + " mu[:, None] + independent_scale @ z_paths\n", + ").reshape(3, n_paths, n_days)\n", + "\n", + "daily_ind = np.einsum(\n", + " \"a,apd->pd\",\n", + " weights_portfolio,\n", + " independent_returns,\n", + ")\n", + "\n", + "terminal_ind = initial_value * np.prod(1 + daily_ind, axis=1)\n", + "\n", + "print(\n", + " \"std correlated / correlacionado:\",\n", + " f\"{terminal_corr.std():.2f}\",\n", + ")\n", + "print(\n", + " \"std independent / independiente:\",\n", + " f\"{terminal_ind.std():.2f}\",\n", + ")\n", + "print(\n", + " \"1%, 5% correlated:\",\n", + " np.round(np.percentile(terminal_corr, [1, 5]), 2),\n", + ")\n", + "print(\n", + " \"1%, 5% independent:\",\n", + " np.round(np.percentile(terminal_ind, [1, 5]), 2),\n", + ")\n", + "\n", + "fig, ax = plt.subplots(figsize=(6.3, 3.5))\n", + "ax.hist(\n", + " terminal_ind,\n", + " bins=70,\n", + " density=True,\n", + " alpha=0.55,\n", + " label=\"independent / independiente\",\n", + ")\n", + "ax.hist(\n", + " terminal_corr,\n", + " bins=70,\n", + " density=True,\n", + " alpha=0.55,\n", + " label=\"correlated / correlacionado\",\n", + ")\n", + "ax.set_xlabel(\"terminal portfolio value / valor final\")\n", + "ax.set_ylabel(\"density / densidad\")\n", + "ax.set_title(\"Dependence changes portfolio tails / La dependencia cambia las colas\")\n", + "ax.legend(fontsize=8)\n", + "plt.tight_layout()\n", + "plt.show()" + ], + "id": "bABSrLD9pbCw" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Moxsvg9OpbCw" + }, + "source": [ + "## Take-home E — Audio denoising by low-rank STFT\n", + "\n", + "The recording is **real**: a pinned CC0 voice sample. The added noise is **synthetic by design**, because a known clean reference lets us measure SNR objectively.\n", + "\n", + "Pipeline:\n", + "\n", + "**real voice → controlled noise → STFT matrix → SVD truncation → ISTFT → SNR**\n", + "\n", + "The key lesson is not “SVD always removes noise.” It is that a low-rank approximation can help when the structured signal concentrates more strongly than the noise in dominant singular directions.\n", + "\n", + "> 🇪🇸 La grabación es **real** y el ruido agregado es **sintético a propósito**, porque necesitamos una referencia limpia conocida para medir SNR. La lección no es que “SVD siempre elimina ruido”, sino que la aproximación de bajo rango puede ayudar cuando la señal está más concentrada que el ruido en las direcciones singulares dominantes." + ], + "id": "Moxsvg9OpbCw" + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "v4VJoIUMpbCw" + }, + "outputs": [], + "source": [ + "# TODO\n", + "# 1. Download and verify the pinned voice.wav.\n", + "# 2. Add controlled Gaussian noise at 5 dB target SNR.\n", + "# 3. Compute STFT(noisy) and its complex SVD.\n", + "# 4. Try several truncation ranks k.\n", + "# 5. Reconstruct with ISTFT and measure SNR for each k.\n", + "# 6. Find the best tested k and explain why full rank returns to the noisy signal." + ], + "id": "v4VJoIUMpbCw" + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "solution", + "hide-input" + ], + "colab": { + "base_uri": "https://localhost:8080/", + "height": 700, + "referenced_widgets": [ + "7041f21962494d23b5470b00f0e8c009", + "c879058c81504f51982b33ef21336a51", + "97730e2f5a6e40928acfadb441ef360e", + "ce86a3f4caac434f9eb6bed6326362a9", + "94b0813765d149c7ba13db1f15eb83c7", + "c8c287281e2f4efb87b39a729aca4a03", + "49685f60e0c04369a3f065ee04995688", + "f35a303bd29d4abc96460d291d97f418", + "a29d44b4318b44ad95f067bd57616dde", + "e771896785554b99ae7e73b1941d8143" + ] + }, + "id": "Du6gTydypbCw", + "outputId": "fada88c1-3076-4918-cad8-66d470395f65" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "duration / duración: 4.949 s\n", + "measured noisy SNR / SNR ruidoso: 5.00 dB\n", + "STFT shape / forma: (513, 465)\n", + "full possible rank / rango completo: 465\n", + "best tested k / mejor k probado: 40\n", + "best SNR / mejor SNR: 9.08 dB\n", + "improvement / mejora: 4.08 dB\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(Dropdown(description='Rank k:', index=4, options=(2, 5, 10, 20, 40, 80, 465), value=40), Checkb…" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "7041f21962494d23b5470b00f0e8c009" + } + }, + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "colab": { + "custom_widget_manager": { + "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" + } + } + } + } + } + ], + "source": [ + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "VOICE_URL = (\n", + " \"https://raw.githubusercontent.com/pdx-cs-sound/wavs/\"\n", + " \"ed5ebcbbbc2d11f0adddc9b50b78d581c29f738c/voice.wav\"\n", + ")\n", + "VOICE_SHA256 = \"2c4b4d9d5f90715fdbf599869a465d521638f40ca978b186df96f1543a4d67dc\"\n", + "\n", + "def fetch_verified_wav(url, expected_sha256):\n", + " from scipy.io import wavfile\n", + "\n", + " raw = urllib.request.urlopen(url, timeout=30).read()\n", + " got = hashlib.sha256(raw).hexdigest()\n", + "\n", + " if got != expected_sha256:\n", + " raise ValueError(\n", + " \"checksum mismatch: refusing to use unverified audio\"\n", + " )\n", + "\n", + " return wavfile.read(io.BytesIO(raw))\n", + "\n", + "fs, clean_i16 = fetch_verified_wav(\n", + " VOICE_URL,\n", + " VOICE_SHA256,\n", + ")\n", + "\n", + "clean = clean_i16.astype(np.float64) / 32768.0\n", + "\n", + "if clean.ndim > 1:\n", + " clean = clean.mean(axis=1)\n", + "\n", + "rng_audio = np.random.default_rng(42)\n", + "\n", + "TARGET_SNR_DB = 5.0\n", + "noise = rng_audio.standard_normal(clean.shape)\n", + "\n", + "noise_scale = np.sqrt(\n", + " np.mean(clean**2) /\n", + " (\n", + " np.mean(noise**2)\n", + " * 10 ** (TARGET_SNR_DB / 10)\n", + " )\n", + ")\n", + "\n", + "noisy = clean + noise_scale * noise\n", + "\n", + "print(\n", + " \"duration / duración:\",\n", + " f\"{len(clean) / fs:.3f} s\",\n", + ")\n", + "print(\n", + " \"measured noisy SNR / SNR ruidoso:\",\n", + " f\"{snr_db(clean, noisy):.2f} dB\",\n", + ")\n", + "\n", + "f, t, Z = signal.stft(\n", + " noisy,\n", + " fs=fs,\n", + " nperseg=1024,\n", + " noverlap=512,\n", + ")\n", + "\n", + "U, s, Vh = np.linalg.svd(\n", + " Z,\n", + " full_matrices=False,\n", + ")\n", + "\n", + "candidates = [2, 5, 10, 20, 40, 80, len(s)]\n", + "\n", + "snrs = []\n", + "energies = []\n", + "reconstructions = {}\n", + "\n", + "for k in candidates:\n", + " Zk = (U[:, :k] * s[:k]) @ Vh[:k, :]\n", + "\n", + " _, x_rec = signal.istft(\n", + " Zk,\n", + " fs=fs,\n", + " nperseg=1024,\n", + " noverlap=512,\n", + " )\n", + "\n", + " n = min(len(clean), len(x_rec))\n", + "\n", + " rec = x_rec[:n]\n", + " ref = clean[:n]\n", + "\n", + " reconstructions[k] = rec\n", + "\n", + " snrs.append(snr_db(ref, rec))\n", + " energies.append(\n", + " np.sum(s[:k]**2) /\n", + " np.sum(s**2)\n", + " )\n", + "\n", + "best_i = int(np.argmax(snrs))\n", + "best_k = candidates[best_i]\n", + "\n", + "print(\"STFT shape / forma:\", Z.shape)\n", + "print(\"full possible rank / rango completo:\", len(s))\n", + "print(\"best tested k / mejor k probado:\", best_k)\n", + "print(\"best SNR / mejor SNR:\", f\"{snrs[best_i]:.2f} dB\")\n", + "print(\n", + " \"improvement / mejora:\",\n", + " f\"{snrs[best_i] - snr_db(clean, noisy):.2f} dB\",\n", + ")\n", + "\n", + "fig, ax = plt.subplots(figsize=(6.4, 3.4))\n", + "ax.plot(\n", + " candidates,\n", + " snrs,\n", + " marker=\"o\",\n", + ")\n", + "ax.axhline(\n", + " snr_db(clean, noisy),\n", + " linestyle=\"--\",\n", + " linewidth=1,\n", + " label=\"noisy baseline / línea base\",\n", + ")\n", + "ax.set_xlabel(\"truncation rank k / rango k\")\n", + "ax.set_ylabel(\"SNR (dB)\")\n", + "ax.set_title(\"More rank is not always better / Más rango no siempre es mejor\")\n", + "ax.legend(fontsize=8)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "rank_dropdown = widgets.Dropdown(\n", + " options=candidates,\n", + " value=best_k,\n", + " description=\"Rank k:\",\n", + ")\n", + "\n", + "play_audio = widgets.Checkbox(\n", + " value=False,\n", + " description=\"Play / Reproducir\",\n", + ")\n", + "\n", + "audio_output = widgets.Output()\n", + "\n", + "def update_audio(*_):\n", + " with audio_output:\n", + " audio_output.clear_output()\n", + "\n", + " k = rank_dropdown.value\n", + "\n", + " print(\n", + " f\"k={k} | retained energy / energía=\"\n", + " f\"{100 * energies[candidates.index(k)]:.1f}% | \"\n", + " f\"SNR={snrs[candidates.index(k)]:.2f} dB\"\n", + " )\n", + "\n", + " if play_audio.value:\n", + " rec = reconstructions[k]\n", + " n = len(rec)\n", + " reference = clean[:n]\n", + " noisy_local = noisy[:n]\n", + "\n", + " peak = max(\n", + " np.abs(reference).max(),\n", + " np.abs(noisy_local).max(),\n", + " np.abs(rec).max(),\n", + " )\n", + "\n", + " print(\"Before / Antes\")\n", + " display(Audio(noisy_local / peak, rate=fs))\n", + "\n", + " print(\"After / Después\")\n", + " display(Audio(rec / peak, rate=fs))\n", + "\n", + "rank_dropdown.observe(update_audio, names=\"value\")\n", + "play_audio.observe(update_audio, names=\"value\")\n", + "\n", + "display(\n", + " widgets.VBox([\n", + " rank_dropdown,\n", + " play_audio,\n", + " audio_output,\n", + " ])\n", + ")\n", + "\n", + "update_audio()" + ], + "id": "Du6gTydypbCw" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ipGw84X-pbCw" + }, + "source": [ + "## What just happened\n", + "\n", + "The five take-homes reuse the workshop rather than introducing five unrelated tricks:\n", + "\n", + "1. **PCA:** approximation can be mathematically optimal and still answer the wrong scientific question if feature scales dominate.\n", + "2. **Attention:** two tensor contractions plus normalization and masking turn pairwise similarity into a weighted combination.\n", + "3. **CP:** another tensor factorization changes the representation and interpretability trade-off relative to Tucker.\n", + "4. **Cholesky:** a factorization can be used constructively to impose a known covariance structure.\n", + "5. **Audio:** low-rank truncation is useful only when the measured approximation improves the signal criterion you care about.\n", + "\n", + "### The sentence to leave with\n", + "\n", + "> **Represent the structure you actually have, approximate only when you can measure the loss, and never let shape manipulation hide what the axes mean.**\n", + "\n", + "The NumPy ideas transfer directly: `torch.einsum`, `tf.einsum`, and `jnp.einsum` use the same index notation, while libraries such as TensorLy provide production implementations of tensor decompositions.\n", + "\n", + "> 🇪🇸 Los cinco ejercicios reutilizan la misma idea del taller. **Representa la estructura que realmente tienes, aproxima solo cuando puedes medir la pérdida y nunca permitas que una manipulación de formas oculte el significado de los ejes.**" + ], + "id": "ipGw84X-pbCw" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "M29ikzGTpbCx" + }, + "source": [ + "---\n", + "\n", + "## Done with this section\n", + "\n", + "That is the whole workshop. Thank you for coming.\n", + "\n", + "> 🇪🇸 Ese es todo el taller. Gracias por participar. Las preguntas pueden continuar en español o en inglés.\n", + "\n", + "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" + ], + "id": "M29ikzGTpbCx" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3" + }, + "colab": { + "provenance": [] + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "version_major": 2, + "version_minor": 0, + "state": { + "27879fcad4af4e0cbd4940b1a120c39f": { + "model_module": "@jupyter-widgets/controls", + "model_name": "VBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "VBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "VBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_68c2f2688c854983adea71cfaa880d47", + "IPY_MODEL_7a6f89b309f8428db11c73ebbeadaf43" + ], + "layout": "IPY_MODEL_0a570d84b9c04beea75661ee7971694f" + } + }, + "68c2f2688c854983adea71cfaa880d47": { + "model_module": "@jupyter-widgets/controls", + "model_name": "IntSliderModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "IntSliderModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "IntSliderView", + "continuous_update": false, + "description": "Component / Componente:", + "description_tooltip": null, + "disabled": false, + "layout": "IPY_MODEL_be805ac7d7dd4bf5b8cd48a77e24378f", + "max": 3, + "min": 1, + "orientation": "horizontal", + "readout": true, + "readout_format": "d", + "step": 1, + "style": "IPY_MODEL_916f2343e79e4158b8cdf569e574f1d3", + "value": 2 + } + }, + "7a6f89b309f8428db11c73ebbeadaf43": { + "model_module": "@jupyter-widgets/output", + "model_name": "OutputModel", + "model_module_version": "1.0.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/output", + "_model_module_version": "1.0.0", + "_model_name": "OutputModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/output", + "_view_module_version": "1.0.0", + "_view_name": "OutputView", + "layout": "IPY_MODEL_82e9c57171424918aeaaeae685d24a81", + "msg_id": "", + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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#44 --- _variables.yml | 30 +- .../notebooks/11-wrap-up-and-take-homes.ipynb | 2183 ++++++----- notebooks/11-wrap-up-and-take-homes.ipynb | 3307 ++++++++--------- scripts/content.py | 37 +- 4 files changed, 2842 insertions(+), 2715 deletions(-) diff --git a/_variables.yml b/_variables.yml index ba6a66b..f1ce4f5 100644 --- a/_variables.yml +++ b/_variables.yml @@ -417,21 +417,19 @@ sections: format_es: "cierre" title_en: "Wrap-up and take-homes" title_es: "Cierre y ejercicios para casa" - summary_en: "What connects Blocks 4, 5 and 6, plus five take-home exercises." - summary_es: "Qué conecta los bloques 4, 5 y 6, más cinco ejercicios para casa." + summary_en: "Wrap up the workshop around one connecting idea, then choose among five extensions: PCA, attention, CP, Cholesky, and audio." + summary_es: "Resume la idea que conecta el taller y luego elige entre cinco extensiones: PCA, atención, CP, Cholesky y audio." objectives_en: - - "State the one idea that connects the pseudoinverse, deconvolution and Tucker." - - "Find the scaling trap in PCA on real, unstandardized data (take-home A)." - - "Build attention out of two contractions, and mask padded positions (take-home B)." - - "Run a real CP decomposition and read its components as trip types nobody labelled (take-home C)." - - "Build correlated data from independent noise with Cholesky, and see why ignoring covariance understates portfolio risk (take-home D)." - - "Denoise a real voice recording by truncating the SVD of its STFT, and measure the result in SNR rather than by ear (take-home E)." - - "Trade parameter count against reconstruction error with a rank slider, on a real dense tensor (optional appendix)." + - "State the approximation idea connecting pseudoinverse, deconvolution, and Tucker." + - "Diagnose the scaling trap in PCA on real breast-cancer measurements." + - "Build masked attention from two `einsum` contractions." + - "Compare CP with Tucker on the same real New York taxi tensor." + - "Use Cholesky to turn independent noise into correlated draws and quantify the portfolio consequence." + - "Denoise a real voice recording with STFT → truncated SVD → ISTFT and measure the SNR trade-off." objectives_es: - - "Enunciar la idea común que conecta la pseudoinversa, la deconvolución y Tucker." - - "Identificar la trampa de escala de PCA sobre datos reales sin estandarizar (take-home A)." - - "Construir atención mediante dos contracciones y enmascarar las posiciones de padding (take-home B)." - - "Ejecutar una descomposición CP real e interpretar sus componentes como tipos de viajes que nadie etiquetó previamente (take-home C)." - - "Construir datos correlacionados a partir de ruido independiente con Cholesky y observar por qué ignorar la covarianza subestima el riesgo de portafolio (take-home D)." - - "Eliminar ruido de una grabación de voz real truncando la SVD de su STFT y medir el resultado con SNR en lugar de evaluarlo solo de oído (take-home E)." - - "Comparar número de parámetros y error de reconstrucción mediante un slider de rango sobre un tensor denso real (apéndice opcional)." + - "Explicar la idea de aproximación que conecta pseudoinversa, deconvolución y Tucker." + - "Diagnosticar la trampa de escala de PCA sobre mediciones reales de cáncer de mama." + - "Construir atención enmascarada mediante dos contracciones `einsum`." + - "Comparar CP con Tucker sobre el mismo tensor real de taxis de Nueva York." + - "Usar Cholesky para convertir ruido independiente en muestras correlacionadas y cuantificar la consecuencia sobre un portafolio." + - "Reducir ruido de una grabación de voz real con STFT → SVD truncada → ISTFT y medir el compromiso mediante SNR." diff --git a/docs/notebooks/11-wrap-up-and-take-homes.ipynb b/docs/notebooks/11-wrap-up-and-take-homes.ipynb index 76e6e7c..e64de01 100644 --- a/docs/notebooks/11-wrap-up-and-take-homes.ipynb +++ b/docs/notebooks/11-wrap-up-and-take-homes.ipynb @@ -10,21 +10,20 @@ "\n", "*wrap-up · 5 min*\n", "\n", - "> 🇪🇸 **Cierre y ejercicios para casa** — Qué conecta los bloques 4, 5 y 6, más cinco ejercicios para casa.\n", + "> 🇪🇸 **Cierre y ejercicios para casa** — Resume la idea que conecta el taller y luego elige entre cinco extensiones: PCA, atención, CP, Cholesky y audio.\n", "\n", - "What connects Blocks 4, 5 and 6, plus five take-home exercises.\n", + "Wrap up the workshop around one connecting idea, then choose among five extensions: PCA, attention, CP, Cholesky, and audio.\n", "\n", "## What you will be able to do\n", "\n", - "- State the one idea that connects the pseudoinverse, deconvolution and Tucker.\n", - "- Find the scaling trap in PCA on real, unstandardized data (take-home A).\n", - "- Build attention out of two contractions, and mask padded positions (take-home B).\n", - "- Run a real CP decomposition and read its components as trip types nobody labelled (take-home C).\n", - "- Build correlated data from independent noise with Cholesky, and see why ignoring covariance understates portfolio risk (take-home D).\n", - "- Denoise a real voice recording by truncating the SVD of its STFT, and measure the result in SNR rather than by ear (take-home E).\n", - "- Trade parameter count against reconstruction error with a rank slider, on a real dense tensor (optional appendix)." + "- State the approximation idea connecting pseudoinverse, deconvolution, and Tucker.\n", + "- Diagnose the scaling trap in PCA on real breast-cancer measurements.\n", + "- Build masked attention from two `einsum` contractions.\n", + "- Compare CP with Tucker on the same real New York taxi tensor.\n", + "- Use Cholesky to turn independent noise into correlated draws and quantify the portfolio consequence.\n", + "- Denoise a real voice recording with STFT → truncated SVD → ISTFT and measure the SNR trade-off." ], - "id": "s11-00" + "id": "A9S7jpWPpbCq" }, { "cell_type": "markdown", @@ -36,7 +35,7 @@ "\n", "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." ], - "id": "s11-01" + "id": "DI1X2khopbCs" }, { "cell_type": "code", @@ -44,112 +43,87 @@ "metadata": {}, "outputs": [], "source": [ + "import hashlib\n", + "import io\n", + "import subprocess\n", + "import sys\n", + "import urllib.request\n", + "\n", "import numpy as np\n", "import pandas as pd\n", - "from sklearn.datasets import load_breast_cancer\n", + "import matplotlib.pyplot as plt\n", + "import ipywidgets as widgets\n", + "\n", + "from IPython.display import Audio, display\n", "from scipy import signal\n", + "from sklearn.datasets import load_breast_cancer\n", "\n", - "rng = np.random.default_rng(0)" - ], - "id": "s11-02" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What you did today\n", - "\n", - "> 🇪🇸 Lo que hiciste hoy.\n", - "\n", - "1. **Section 01** — learned the vocabulary of tensors (axis, order, shape, slice,\n", - " fiber, unfolding, contraction, decomposition), and that unfolding turns any\n", - " tensor into a matrix without losing anything.\n", - "2. **Sections 02 and 05** — argued about what axes *mean*, and found that a batch\n", - " axis and a time axis behave differently even when the shapes look identical.\n", - "3. **Sections 03 and 04** — indexed, broadcast, reshaped and transposed real\n", - " tumour data and real medical images, and hit real problems: zero-variance\n", - " pixels, and reshape silently destroying an image.\n", - "4. **Sections 06–10** — wrote contractions with `einsum`; solved an unsolvable\n", - " 20,433-equation system with the pseudoinverse; used recursion to forecast real\n", - " airline traffic and to find an eigenvector; convolved and deconvolved a real\n", - " photograph; and compressed a real taxi tensor 4.7× with Tucker, which found\n", - " rush hour on its own.\n", - "\n", - "### One idea connects sections 07, 09 and 10\n", - "\n", - "**When a problem has no exact answer or no true inverse, you do not give up —\n", - "you find the best stable approximation.** The pseudoinverse does this for linear\n", - "systems, Richardson-Lucy for blurred images, and Tucker for tensors that are too\n", - "large to keep in full.\n", - "\n", - "> 🇪🇸 Cuando un problema no tiene respuesta exacta ni inversa verdadera, no te\n", - "> rindes: buscas la mejor aproximación estable." - ], - "id": "s11-03" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Where to go next\n", - "\n", - "- `torch.einsum` / `tf.einsum` / `jnp.einsum` — **identical syntax** to what you\n", - " used today.\n", - "- [`tensorly`](https://tensorly.org) — proper Tucker and CP decompositions.\n", - "- `np.linalg` — the rest of Chapter 2: eigendecomposition, `lstsq`, `pinv`, `qr`,\n", - " `cholesky`.\n", - "- `scipy.signal` and `skimage.restoration` — convolution and deconvolution\n", - " beyond today.\n", - "- The five take-homes below." + "try:\n", + " from google.colab import output\n", + " output.enable_custom_widget_manager()\n", + "except ImportError:\n", + " pass\n", + "\n", + "rng = np.random.default_rng(0)\n", + "\n", + "def softmax(x, axis=-1):\n", + " x = x - np.max(x, axis=axis, keepdims=True)\n", + " e = np.exp(x)\n", + " return e / np.sum(e, axis=axis, keepdims=True)\n", + "\n", + "def snr_db(reference, estimate):\n", + " reference = np.asarray(reference)\n", + " estimate = np.asarray(estimate)\n", + " return 10 * np.log10(\n", + " np.sum(reference**2) /\n", + " np.sum((estimate - reference)**2)\n", + " )\n", + "\n", + "print(\"Setup ready / Preparación lista\")" ], - "id": "s11-04" + "id": "kb1BmNE4pbCt" }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Optional: the same contraction in PyTorch\n", + "## Why this matters — one idea connects the workshop\n", "\n", - "Everything today was NumPy, because that is what the workshop's real datasets\n", - "and verified numbers are built on. The einsum string does not change when you\n", - "move to a deep learning framework — only the array type does." - ], - "id": "s11-05" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Optional. Colab has torch pre-installed; skip this cell if you prefer.\n", - "try:\n", - " import torch\n", - " photo = rng.standard_normal((8, 8, 3))\n", - " w = np.array([0.2125, 0.7154, 0.0721])\n", + "Sections 07, 09, and 10 looked different:\n", "\n", - " np_gray = np.einsum('hwc,c->hw', photo, w)\n", - " pt_gray = torch.einsum('hwc,c->hw', torch.tensor(photo), torch.tensor(w))\n", + "- the pseudoinverse handled an overdetermined linear system;\n", + "- deconvolution tried to recover an image after blur;\n", + "- Tucker compressed a tensor into lower-dimensional mode-specific factors.\n", "\n", - " print(np.allclose(np_gray, pt_gray.numpy())) # True — same string, same answer\n", - "except ImportError:\n", - " print(\"torch not installed — nothing here you need\")" + "But the same decision appears in all three:\n", + "\n", + "> **When an exact inverse or exact representation is unavailable, unstable, or unnecessarily expensive, choose a controlled approximation and measure what you lose.**\n", + "\n", + "That sentence is the bridge from linear algebra to modern machine learning. The five take-homes below reuse it in different settings.\n", + "\n", + "### How to use this notebook\n", + "\n", + "Each take-home has a `TODO` cell and a folded **Solution / Solución**. Try the task first, then open the solution.\n", + "\n", + "> 🇪🇸 **Una idea conecta el taller:** cuando una inversa exacta o una representación exacta no existe, es inestable o cuesta demasiado, construye una aproximación controlada y mide qué pierdes.\n", + ">\n", + "> Cada ejercicio tiene un `TODO` y una **Solution / Solución** plegada. Intenta primero; abre la solución después." ], - "id": "s11-06" + "id": "aRbA3maRpbCu" }, { "cell_type": "markdown", "metadata": {}, "source": [ - "---\n", + "## Take-home A — PCA: the scaling trap\n", "\n", - "## Take-home A — How many principal components are enough?\n", + "The Wisconsin Diagnostic Breast Cancer dataset has 30 real measurements per tumour sample, but those features use very different numerical scales.\n", "\n", - "> 🇪🇸 Ejercicio para casa A: ¿cuántas componentes principales bastan?\n", + "Your goal is not merely to run PCA. It is to discover why unstandardized PCA can give a technically correct but scientifically misleading answer.\n", "\n", - "**Real data contains a trap here. Find it.**" + "> 🇪🇸 El conjunto Wisconsin Diagnostic Breast Cancer tiene 30 mediciones reales por muestra, pero en escalas numéricas muy distintas. El reto es descubrir por qué PCA sin estandarizar puede producir una respuesta matemáticamente válida pero científicamente engañosa." ], - "id": "s11-07" + "id": "ZjR0Aq8NpbCu" }, { "cell_type": "code", @@ -157,104 +131,112 @@ "metadata": {}, "outputs": [], "source": [ - "bc = load_breast_cancer(); X, y = bc.data, bc.target\n", - "\n", - "# TODO 1: Center X, run np.linalg.svd, and compute the fraction of variance each\n", - "# component explains (variance is proportional to S**2).\n", - "\n", - "# TODO 2: How many components explain 95% of the variance? The answer will look\n", - "# TOO GOOD. Do not trust it yet.\n", - "\n", - "# TODO 3: Print X.var(axis=0). The 30 measurements use different units — some are\n", - "# areas in the thousands, some are ratios below 1. What is that doing?\n", - "\n", - "# TODO 4: Redo everything on standardized data: (X - mean) / std. How many now?\n", - "\n", - "# TODO 5: Scatter-plot the first 2 components, coloured by y. Do the two groups\n", - "# separate?" + "# TODO\n", + "# 1. Load the breast-cancer data.\n", + "# 2. Center X and compute SVD.\n", + "# 3. How many components explain 95% of variance?\n", + "# 4. Inspect feature variances. Why is the answer suspicious?\n", + "# 5. Standardize every feature and repeat.\n", + "# 6. Compare the two cumulative-variance curves." ], - "id": "s11-08" + "id": "FiWwed7DpbCu" }, { "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "Xc = X - X.mean(axis=0)\n", - "S = np.linalg.svd(Xc, full_matrices=False)[1]\n", - "frac = S**2 / (S**2).sum()\n", - "n95 = np.argmax(np.cumsum(frac) >= 0.95) + 1 # 1 (!)\n", - "print(n95, round(frac[0], 3)) # 1 0.982\n", - "\n", - "print(np.sort(X.var(axis=0))[[0, -1]]) # ~0.0000075 up to ~324000\n", - "\n", - "Xs = (X - X.mean(axis=0)) / X.std(axis=0)\n", - "S2 = np.linalg.svd(Xs, full_matrices=False)[1]\n", - "frac_scaled = S2**2 / (S2**2).sum()\n", - "n95_scaled = np.argmax(np.cumsum(frac_scaled) >= 0.95) + 1 # 10\n", - "print(n95_scaled)\n", - "\n", - "# Without standardizing, the first component appears to explain 98.2% of the\n", - "# variance. IT IS AN ILLUSION: `worst area` has a variance around 323,000 while\n", - "# smoothness values sit below 1, so PCA reports the largest UNIT, not the\n", - "# largest PATTERN. After standardizing, the first component explains 44% and\n", - "# TEN components are needed.\n", - "#\n", - "# PCA KNOWS NOTHING ABOUT UNITS. Features on different scales must be\n", - "# standardized first.\n", + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "bc = load_breast_cancer()\n", + "X, y = bc.data, bc.target\n", + "\n", + "X_centered = X - X.mean(axis=0)\n", + "_, s_raw, Vt_raw = np.linalg.svd(X_centered, full_matrices=False)\n", + "frac_raw = s_raw**2 / np.sum(s_raw**2)\n", + "n95_raw = int(np.argmax(np.cumsum(frac_raw) >= 0.95) + 1)\n", + "\n", + "feature_var = X.var(axis=0)\n", + "\n", + "X_std = (X - X.mean(axis=0)) / X.std(axis=0)\n", + "_, s_std, Vt_std = np.linalg.svd(X_std, full_matrices=False)\n", + "frac_std = s_std**2 / np.sum(s_std**2)\n", + "n95_std = int(np.argmax(np.cumsum(frac_std) >= 0.95) + 1)\n", + "\n", + "print(\"95% components — raw / sin estandarizar:\", n95_raw)\n", + "print(\"95% components — standardized / estandarizado:\", n95_std)\n", + "print(\n", + " \"feature variance range / rango de varianzas:\",\n", + " f\"{feature_var.min():.3e}\",\n", + " \"to / a\",\n", + " f\"{feature_var.max():.3e}\",\n", + ")\n", "\n", - "import matplotlib.pyplot as plt\n", - "fig, ax = plt.subplots(figsize=(6.5, 3.5))\n", "n_show = 15\n", - "ax.plot(range(1, n_show + 1), np.cumsum(frac[:n_show]), marker=\"o\",\n", - " label=\"unstandardized\", color=\"#C44E52\")\n", - "ax.plot(range(1, n_show + 1), np.cumsum(frac_scaled[:n_show]), marker=\"o\",\n", - " label=\"standardized\", color=\"#4C72B0\")\n", - "ax.axhline(0.95, color=\"gray\", linestyle=\"--\", linewidth=1, label=\"95% threshold\")\n", - "ax.set_xlabel(\"number of components\"); ax.set_ylabel(\"cumulative variance explained\")\n", - "ax.set_title(\"The scree plot IS the standardisation trap\")\n", - "ax.legend()\n", + "fig, ax = plt.subplots(figsize=(6.4, 3.4))\n", + "ax.plot(\n", + " range(1, n_show + 1),\n", + " np.cumsum(frac_raw[:n_show]),\n", + " marker=\"o\",\n", + " label=\"raw / sin estandarizar\",\n", + ")\n", + "ax.plot(\n", + " range(1, n_show + 1),\n", + " np.cumsum(frac_std[:n_show]),\n", + " marker=\"o\",\n", + " label=\"standardized / estandarizado\",\n", + ")\n", + "ax.axhline(0.95, linestyle=\"--\", linewidth=1, label=\"95%\")\n", + "ax.set_xlabel(\"number of components / número de componentes\")\n", + "ax.set_ylabel(\"cumulative variance / varianza acumulada\")\n", + "ax.set_title(\"PCA changes when units dominate / PCA cambia cuando dominan las unidades\")\n", + "ax.legend(fontsize=8)\n", "plt.tight_layout()\n", "plt.show()\n", "\n", - "# TODO 5 — the two groups do separate, on standardized data, in 2 of 30 columns.\n", - "Z = Xs @ np.linalg.svd(Xs, full_matrices=False)[2][:2].T\n", - "fig, ax = plt.subplots(figsize=(5, 4))\n", - "ax.scatter(Z[:, 0], Z[:, 1], c=y, s=8, cmap=\"coolwarm\")\n", - "ax.set_xlabel(\"component 1\"); ax.set_ylabel(\"component 2\")\n", - "ax.set_title(\"standardized data — malignant/benign in 2 components\")\n", + "Z = X_std @ Vt_std[:2].T\n", + "fig, ax = plt.subplots(figsize=(5.2, 3.8))\n", + "scatter = ax.scatter(Z[:, 0], Z[:, 1], c=y, s=12)\n", + "ax.set_xlabel(\"component 1\")\n", + "ax.set_ylabel(\"component 2\")\n", + "ax.set_title(\"Standardized PCA / PCA estandarizado\")\n", "plt.tight_layout()\n", - "plt.show()" + "plt.show()\n", + "\n", + "print(\n", + " \"Take-away / Idea:\",\n", + " \"PCA optimizes variance, not scientific relevance; scale the features when units differ.\"\n", + ")" ], - "id": "s11-09" + "id": "N_mhphqqpbCu" }, { "cell_type": "markdown", "metadata": {}, "source": [ - "---\n", - "\n", "## Take-home B — Attention is two contractions\n", "\n", - "> 🇪🇸 Ejercicio para casa B: la atención son dos contracciones.\n", + "For this exercise we deliberately use **synthetic** `Q`, `K`, and `V` arrays. That is appropriate here because the goal is to isolate the tensor mechanics of attention — shapes, contraction axes, normalization, and masking — without mixing in a tokenizer or a trained model.\n", + "\n", + "Build:\n", + "\n", + "1. `scores[b, i, j] = Q[b, i, :] · K[b, j, :]`\n", + "2. row-wise softmax weights\n", + "3. `output[b, i, :] = Σ_j weights[b, i, j] V[b, j, :]`\n", + "4. a padding mask that forces the last three key positions to receive zero weight.\n", "\n", - "Attention is the mechanism that answers question 5 from section 05: *which parts\n", - "of a sequence matter most?* Protein language models use it so every amino acid\n", - "can look at every other one; recommenders use it to weight a user's past\n", - "interactions." + "> 🇪🇸 Aquí usamos `Q`, `K` y `V` **sintéticos a propósito**: queremos aislar la mecánica tensorial de la atención sin confundirla con tokenización o entrenamiento. Construye puntajes, softmax, salida y una máscara para padding." ], - "id": "s11-10" + "id": "Io0ukHyUpbCv" }, { "cell_type": "code", @@ -262,73 +244,89 @@ "metadata": {}, "outputs": [], "source": [ - "np.random.seed(6)\n", - "batch, seq_len, dim = 4, 12, 16\n", - "Q, K, V = (np.random.randn(batch, seq_len, dim) for _ in range(3))\n", - "\n", - "def softmax(x, axis=-1):\n", - " x = x - x.max(axis=axis, keepdims=True)\n", - " e = np.exp(x); return e / e.sum(axis=axis, keepdims=True)\n", - "\n", - "# TODO 1: With einsum, compute scores[b,i,j] = how much position i attends to\n", - "# position j. Shape (4, 12, 12). Scale by 1/sqrt(dim).\n", - "\n", - "# TODO 2: Apply softmax on the correct axis so each row of weights sums to 1.\n", - "\n", - "# TODO 3: With einsum, combine V using those weights -> (4, 12, 16).\n", - "\n", - "# TODO 4: Suppose the last 3 positions are padding, not real data. Build a mask,\n", - "# set those scores to -np.inf BEFORE the softmax, and verify the padded\n", - "# positions receive exactly zero weight." + "# TODO\n", + "# 1. Create Q, K, V with shape (batch=4, seq=12, dim=16).\n", + "# 2. Compute scaled dot-product scores with einsum.\n", + "# 3. Softmax over the key-position axis.\n", + "# 4. Contract weights with V.\n", + "# 5. Mask the final 3 key positions BEFORE softmax.\n", + "# 6. Verify masked positions receive zero weight." ], - "id": "s11-11" + "id": "0iJy1TlwpbCv" }, { "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "scores = np.einsum('bid,bjd->bij', Q, K) / np.sqrt(dim)\n", + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "rng_attention = np.random.default_rng(6)\n", + "\n", + "batch, seq_len, dim = 4, 12, 16\n", + "Q = rng_attention.standard_normal((batch, seq_len, dim))\n", + "K = rng_attention.standard_normal((batch, seq_len, dim))\n", + "V = rng_attention.standard_normal((batch, seq_len, dim))\n", + "\n", + "scores = np.einsum(\"bid,bjd->bij\", Q, K) / np.sqrt(dim)\n", "weights = softmax(scores, axis=-1)\n", - "output = np.einsum('bij,bjd->bid', weights, V)\n", - "print(scores.shape, weights.shape, output.shape)\n", - "print(np.allclose(weights.sum(axis=-1), 1.0)) # True\n", + "attention_output = np.einsum(\"bij,bjd->bid\", weights, V)\n", "\n", - "mask = np.zeros((seq_len, seq_len)); mask[:, -3:] = -np.inf\n", + "mask = np.zeros((seq_len, seq_len))\n", + "mask[:, -3:] = -np.inf\n", "weights_masked = softmax(scores + mask, axis=-1)\n", - "print(weights_masked[..., -3:].max()) # 0.0 — exactly zero weight\n", - "\n", - "# `scores` is Chapter 2's dot product (eq. 2.8); `output` is Chapter 2's linear\n", - "# combination (eq. 2.28). ATTENTION IS TWO CONTRACTIONS built from ideas you had\n", - "# already read.\n", - "#\n", - "# TODO 4 solves the variable-length problem from section 02: THE MASK IS HOW\n", - "# REAL MODELS HANDLE SEQUENCES AND VIDEOS OF DIFFERENT LENGTHS." + "output_masked = np.einsum(\"bij,bjd->bid\", weights_masked, V)\n", + "\n", + "print(\"scores:\", scores.shape)\n", + "print(\"weights:\", weights.shape)\n", + "print(\"output:\", attention_output.shape)\n", + "print(\n", + " \"rows sum to 1 / filas suman 1:\",\n", + " np.allclose(weights_masked.sum(axis=-1), 1.0),\n", + ")\n", + "print(\n", + " \"largest padded weight / mayor peso en padding:\",\n", + " weights_masked[..., -3:].max(),\n", + ")\n", + "\n", + "example = 0\n", + "query = 0\n", + "\n", + "fig, ax = plt.subplots(figsize=(6.2, 3.2))\n", + "ax.bar(\n", + " range(seq_len),\n", + " weights_masked[example, query],\n", + ")\n", + "ax.set_xlabel(\"key position / posición key\")\n", + "ax.set_ylabel(\"attention weight / peso\")\n", + "ax.set_title(\"Padding disappears after masking / El padding desaparece con la máscara\")\n", + "plt.tight_layout()\n", + "plt.show()" ], - "id": "s11-12" + "id": "4RpwHWPDpbCv" }, { "cell_type": "markdown", "metadata": {}, "source": [ - "---\n", + "## Take-home C — CP versus Tucker on the same real taxi tensor\n", + "\n", + "Section 10 used Tucker/HOSVD. Now use **CP decomposition** on the same real `pickup borough × dropoff borough × hour` tensor.\n", "\n", - "## Take-home C — CP decomposition, compared to Tucker\n", + "CP represents a tensor as a sum of rank-1 outer products. Unlike Tucker, it has no separate core tensor. CP components can be individually interpretable, but uniqueness is only guaranteed under mathematical conditions — so treat patterns in this small dataset as exploratory, not as ground truth.\n", "\n", - "> 🇪🇸 Ejercicio para casa C: CP comparado con Tucker." + "> 🇪🇸 La sección 10 usó Tucker/HOSVD. Ahora aplica **CP** al mismo tensor real de taxis. CP expresa el tensor como suma de productos externos de rango 1 y no usa un núcleo separado. Sus componentes pueden ser interpretables, pero la unicidad requiere condiciones matemáticas; en este conjunto pequeño, interprétalos con cautela." ], - "id": "s11-13" + "id": "0W5EWbWApbCv" }, { "cell_type": "code", @@ -336,224 +334,166 @@ "metadata": {}, "outputs": [], "source": [ - "# TODO 1: Build one rank-1 tensor with einsum from three random vectors of\n", - "# length 4, 5 and 24. What shape is it? How many numbers define it?\n", - "\n", - "# TODO 2: Compare that against 4*5*24. What is the compression of ONE rank-1 piece?" + "# TODO\n", + "# 1. Rebuild the real NYC taxi tensor: pickup × dropoff × hour.\n", + "# 2. Fit a rank-3 CP decomposition with TensorLy.\n", + "# 3. Reconstruct the tensor and compute relative error.\n", + "# 4. Compare its parameter count with Tucker rank (2,2,3).\n", + "# 5. Inspect pickup, dropoff, and hour factors for each CP component." ], - "id": "s11-14" + "id": "3KCacE6ipbCv" }, { "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "a, b, c = rng.standard_normal(4), rng.standard_normal(5), rng.standard_normal(24)\n", - "rank1 = np.einsum('i,j,k->ijk', a, b, c) # (4, 5, 24) from only 33 numbers\n", - "print(rank1.shape, len(a) + len(b) + len(c), 4 * 5 * 24) # (4,5,24) 33 480\n", - "print(round(480 / 33, 1)) # 14.5x for one piece\n", - "\n", - "# A full CP decomposition is a SUM of R pieces like this one, not just a single\n", - "# rank-1 term. The cells below build a real rank-3 CP model on real data — no\n", - "# more commented-out pseudocode." - ], - "id": "s11-15" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Now decompose a real tensor with CP\n", - "\n", - "> 🇪🇸 Ahora sí: una descomposición CP real sobre un tensor real.\n", + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "try:\n", + " import tensorly as tl\n", + " from tensorly.decomposition import parafac\n", + "except ImportError:\n", + " subprocess.run(\n", + " [sys.executable, \"-m\", \"pip\", \"install\", \"-q\", \"tensorly\"],\n", + " check=True,\n", + " )\n", + " import tensorly as tl\n", + " from tensorly.decomposition import parafac\n", "\n", - "This take-home is separate from section 10's notebook, so it rebuilds the same\n", - "real taxi tensor here rather than assuming section 10 already ran." - ], - "id": "s11-16" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ "TAXIS = \"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/taxis.csv\"\n", "taxis = pd.read_csv(TAXIS)\n", - "taxis['hour'] = pd.to_datetime(taxis['pickup']).dt.hour\n", - "sub = taxis.dropna(subset=['pickup_borough', 'dropoff_borough'])\n", - "pb = sorted(sub['pickup_borough'].unique())\n", - "db = sorted(sub['dropoff_borough'].unique())\n", "\n", - "T = np.zeros((len(pb), len(db), 24))\n", - "for (p, d, h), v in sub.groupby(['pickup_borough', 'dropoff_borough', 'hour']).size().items():\n", - " T[pb.index(p), db.index(d), h] = v\n", + "taxis[\"pickup_dt\"] = pd.to_datetime(taxis[\"pickup\"], errors=\"coerce\")\n", + "taxis[\"hour\"] = taxis[\"pickup_dt\"].dt.hour\n", "\n", - "print(T.shape, pb, db) # (4, 5, 24) — the same real taxi tensor as section 10,\n", - " # rebuilt here so this notebook stands on its own" - ], - "id": "s11-17" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "CP needs a library here rather than the by-hand HOSVD from section 10: an ALS\n", - "loop short enough to read is also too short to be a reliable optimizer, and\n", - "getting that wrong would teach the wrong lesson. [`tensorly`](https://tensorly.org)\n", - "is not part of Colab's default image, so the install is explicit, the same way\n", - "section 10 tells you it borrowed the idea from a real library rather than\n", - "hiding it.\n", - "\n", - "> 🇪🇸 CP necesita aquí una librería en vez del HOSVD hecho a mano de la sección\n", - "> 10: un bucle ALS lo bastante corto para leerse también es demasiado corto\n", - "> para ser un optimizador confiable. `tensorly` no viene instalado por defecto\n", - "> en Colab, así que la instalación es explícita." - ], - "id": "s11-18" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%pip install -q tensorly\n", + "sub = taxis.dropna(\n", + " subset=[\"pickup_borough\", \"dropoff_borough\", \"hour\"]\n", + ").copy()\n", + "sub[\"hour\"] = sub[\"hour\"].astype(int)\n", "\n", - "import tensorly as tl\n", - "from tensorly.decomposition import parafac\n", + "pb = sorted(sub[\"pickup_borough\"].unique())\n", + "db = sorted(sub[\"dropoff_borough\"].unique())\n", "\n", - "R = 3 # three real, checkable trip patterns fit this tensor's size\n", - "cp_weights, cp_factors = parafac(tl.tensor(T), rank=R, init='svd',\n", - " random_state=0, n_iter_max=500, tol=1e-9)\n", - "Fpb, Fdb, Fhr = cp_factors # (4, 3), (5, 3), (24, 3)\n", + "p_idx = {name: i for i, name in enumerate(pb)}\n", + "d_idx = {name: i for i, name in enumerate(db)}\n", "\n", - "cp_recon = tl.cp_to_tensor((cp_weights, cp_factors))\n", - "cp_error = np.linalg.norm(cp_recon - T) / np.linalg.norm(T)\n", - "print(f\"CP rank {R}: relative reconstruction error = {cp_error:.3f}\")\n", - "print(\"Section 10's Tucker, rank (2, 2, 3), measured 0.067 on this same tensor.\")" - ], - "id": "s11-19" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What CP's uniqueness buys you, and what it does not\n", - "\n", - "> 🇪🇸 Lo que la unicidad de CP te da, y lo que no te da.\n", - "\n", - "PCA and Tucker's factor matrices are only defined up to an arbitrary rotation\n", - "within each subspace of similar size — ask for the \"second principal\n", - "component\" of near-equal-variance data and the answer is unstable. **CP has no\n", - "such freedom**, under a condition on the factor matrices called the Kruskal\n", - "condition, which this tensor satisfies. A CP component is only free to move in\n", - "three limited ways: the three components can be listed in any **order**; a\n", - "scalar can move between the three factor vectors of one component as long as\n", - "their **product** is unchanged; and because these are real (not just\n", - "positive) numbers, an even number of those factors can flip **sign** together.\n", - "None of that changes what one component *looks like* — it is still one\n", - "coherent pattern per axis, not a rotated mixture of several. That is why the\n", - "components below are worth reading individually, and why the code below uses\n", - "`abs()` before asking which entry is strongest — the strongest entry does not\n", - "move, only its sign might.\n", - "\n", - "**Analysts benefit because CP exposes one interpretable pattern per axis —\n", - "pickup, dropoff and hour together — that can be read as a coherent trip type,\n", - "the way PCA's freely-rotating components cannot be.**" - ], - "id": "s11-20" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Colab renders ipywidgets through its own widget manager rather than the\n", - "# classic Jupyter one; this call is a no-op outside Colab, which is why it is\n", - "# guarded rather than assumed.\n", - "try:\n", - " from google.colab import output\n", - " output.enable_custom_widget_manager()\n", - "except ImportError:\n", - " pass\n", + "T_taxi = np.zeros((len(pb), len(db), 24), dtype=float)\n", "\n", - "import ipywidgets as widgets\n", - "import matplotlib.pyplot as plt\n", + "for (p, d, h), count in sub.groupby(\n", + " [\"pickup_borough\", \"dropoff_borough\", \"hour\"]\n", + ").size().items():\n", + " T_taxi[p_idx[p], d_idx[d], int(h)] = float(count)\n", + "\n", + "rank_cp = 3\n", "\n", - "def show_component(component):\n", - " r = component - 1 # the slider shows 1..R for students; factors are 0-indexed\n", - " plt.close('all')\n", - " fig, axes = plt.subplots(1, 3, figsize=(12, 3.2))\n", - " axes[0].bar(pb, Fpb[:, r], color='#4C72B0')\n", - " axes[0].set_title('Pickup borough'); axes[0].tick_params(axis='x', rotation=40)\n", - " axes[1].bar(db, Fdb[:, r], color='#DD8452')\n", - " axes[1].set_title('Dropoff borough'); axes[1].tick_params(axis='x', rotation=40)\n", - " axes[2].bar(range(24), Fhr[:, r], color='#55A868')\n", - " axes[2].set_title('Hour of day'); axes[2].set_xlabel('hour')\n", - " fig.suptitle(f'CP component {component} of {R}')\n", + "cp_weights, cp_factors = parafac(\n", + " tl.tensor(T_taxi),\n", + " rank=rank_cp,\n", + " init=\"svd\",\n", + " random_state=0,\n", + " n_iter_max=500,\n", + " tol=1e-9,\n", + ")\n", + "\n", + "F_pickup, F_dropoff, F_hour = cp_factors\n", + "cp_recon = tl.cp_to_tensor((cp_weights, cp_factors))\n", + "cp_error = np.linalg.norm(cp_recon - T_taxi) / np.linalg.norm(T_taxi)\n", + "\n", + "cp_params = (\n", + " len(cp_weights)\n", + " + sum(f.size for f in cp_factors)\n", + ")\n", + "\n", + "tucker_ranks = (2, 2, 3)\n", + "tucker_params = (\n", + " np.prod(tucker_ranks)\n", + " + T_taxi.shape[0] * tucker_ranks[0]\n", + " + T_taxi.shape[1] * tucker_ranks[1]\n", + " + T_taxi.shape[2] * tucker_ranks[2]\n", + ")\n", + "\n", + "print(\"taxi tensor / tensor taxis:\", T_taxi.shape)\n", + "print(\"CP rank:\", rank_cp)\n", + "print(\"CP relative error / error relativo:\", f\"{cp_error:.4f}\")\n", + "print(\"CP parameters / parámetros:\", int(cp_params))\n", + "print(\"Tucker (2,2,3) parameters / parámetros:\", int(tucker_params))\n", + "\n", + "component_slider = widgets.IntSlider(\n", + " value=1,\n", + " min=1,\n", + " max=rank_cp,\n", + " step=1,\n", + " description=\"Component / Componente:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"145px\"},\n", + ")\n", + "\n", + "def show_cp_component(component):\n", + " r = component - 1\n", + "\n", + " fig, axes = plt.subplots(1, 3, figsize=(11.5, 3.1))\n", + "\n", + " axes[0].bar(pb, F_pickup[:, r])\n", + " axes[0].set_title(\"pickup / origen\")\n", + " axes[0].tick_params(axis=\"x\", rotation=40)\n", + "\n", + " axes[1].bar(db, F_dropoff[:, r])\n", + " axes[1].set_title(\"dropoff / destino\")\n", + " axes[1].tick_params(axis=\"x\", rotation=40)\n", + "\n", + " axes[2].bar(range(24), F_hour[:, r])\n", + " axes[2].set_title(\"hour / hora\")\n", + " axes[2].set_xlabel(\"hour / hora\")\n", + "\n", + " fig.suptitle(f\"CP component / componente {component}\")\n", " plt.tight_layout()\n", " plt.show()\n", "\n", - " print(f\"Strongest pickup borough: {pb[np.argmax(np.abs(Fpb[:, r]))]}\")\n", - " print(f\"Strongest dropoff borough: {db[np.argmax(np.abs(Fdb[:, r]))]}\")\n", - " print(f\"Peak hour: {int(np.argmax(np.abs(Fhr[:, r])))}\")\n", - "\n", - "# TODO 3: Flip through all three components (1, 2, 3). Does each one read as\n", - "# a different, nameable kind of trip? Which hour is each one busiest?\n", - "widgets.interact(show_component,\n", - " component=widgets.IntSlider(min=1, max=R, step=1, value=1,\n", - " description='Component'));" + " print(\n", + " \"strongest pickup / origen dominante:\",\n", + " pb[int(np.argmax(np.abs(F_pickup[:, r])))],\n", + " )\n", + " print(\n", + " \"strongest dropoff / destino dominante:\",\n", + " db[int(np.argmax(np.abs(F_dropoff[:, r])))],\n", + " )\n", + " print(\n", + " \"peak hour / hora pico:\",\n", + " int(np.argmax(np.abs(F_hour[:, r]))),\n", + " )\n", + "\n", + "cp_output = widgets.interactive_output(\n", + " show_cp_component,\n", + " {\"component\": component_slider},\n", + ")\n", + "\n", + "display(widgets.VBox([component_slider, cp_output]))" ], - "id": "s11-21" + "id": "o4UNpJMUpbCw" }, { "cell_type": "markdown", "metadata": {}, "source": [ - "---\n", + "## Take-home D — Cholesky builds correlation\n", "\n", - "## Take-home D — Cholesky: the factorization that builds\n", - "\n", - "> 🇪🇸 Ejercicio para casa D: Cholesky, la factorización que construye.\n", - "\n", - "Every factorization used today — LU, QR, eigendecomposition, SVD — takes an\n", - "existing object **apart**. Cholesky is the one exception: you use it to\n", - "**build**. Given a covariance matrix `Sigma` that is symmetric and\n", - "positive-definite, `np.linalg.cholesky` finds a lower-triangular `L` with\n", - "`L @ L.T == Sigma`. Feed `L` independent Gaussian noise and it hands back\n", - "correlated draws with *exactly* that covariance.\n", - "\n", - "`Sigma[i, j]` is the **covariance** between asset `i` and asset `j` — how much\n", - "they move together, in the assets' own units. Its diagonal `Sigma[i, i]` is\n", - "each asset's own variance. **Correlation** (`corr`) is the same relationship\n", - "rescaled to sit between -1 and 1, so it is comparable between assets of\n", - "different volatility; `Sigma = outer(vol, vol) * corr` puts the original scale\n", - "back in.\n", - "\n", - "If `z` is independent noise (`Cov(z) = I`) and `x = L @ z`, then\n", - "`Cov(x) = L Cov(z) L.T = L L.T = Sigma` — which is exactly why `L` turns\n", - "independent draws into correlated ones.\n", - "\n", - "> 🇪🇸 `Sigma[i, j]` es la covarianza entre el activo `i` y el `j`: cuánto se\n", - "> mueven juntos. La diagonal es la varianza de cada activo. `corr` es la misma\n", - "> relación reescalada entre -1 y 1. Si `z` es ruido independiente\n", - "> (`Cov(z) = I`) y `x = L @ z`, entonces `Cov(x) = L Cov(z) L.T = L L.T =\n", - "> Sigma`: por eso `L` convierte ruido independiente en ruido correlacionado." + "This exercise is **synthetic by design**. We choose the covariance matrix ourselves so there is a known causal truth: the individual asset volatilities remain fixed, while only cross-asset dependence changes.\n", + "\n", + "Use Cholesky `Σ = L Lᵀ` to transform independent Gaussian noise into correlated draws, then compare a correctly correlated portfolio simulation with the incorrect assumption of independence.\n", + "\n", + "> 🇪🇸 Este experimento es **sintético a propósito**. Elegimos la matriz de covarianza para conocer la verdad causal: las volatilidades individuales permanecen iguales y solo cambia la dependencia entre activos. Usa Cholesky para convertir ruido independiente en muestras correlacionadas y compara ambas simulaciones." ], - "id": "s11-22" + "id": "mECc8EZCpbCw" }, { "cell_type": "code", @@ -561,295 +501,149 @@ "metadata": {}, "outputs": [], "source": [ - "vol = np.array([0.012, 0.015, 0.010])\n", - "corr = np.array([[1.00, 0.85, 0.20],\n", - " [0.85, 1.00, 0.20],\n", - " [0.20, 0.20, 1.00]])\n", - "Sigma = np.outer(vol, vol) * corr\n", - "\n", - "weights = np.array([0.4, 0.4, 0.2])\n", - "mu = np.array([0.00030, 0.00035, 0.00020])\n", - "n_days, n_paths, initial_value = 252, 20_000, 100.0\n", - "\n", - "rng = np.random.default_rng(5)\n", - "sample_sizes = [100, 1_000, 100_000]\n", - "\n", - "# TODO 1: L = np.linalg.cholesky(Sigma). Verify np.allclose(L @ L.T, Sigma) is\n", - "# True, and print L and the reconstruction L @ L.T, both rounded.\n", - "\n", - "# TODO 2: For each n in sample_sizes, draw z = rng.standard_normal((3, n)),\n", - "# build x = L @ z, and compute the Frobenius error between np.cov(x)\n", - "# and Sigma. Confirm it shrinks as n grows. For the LARGEST n, also\n", - "# print np.cov(z) (should look like the identity) and np.cov(x)\n", - "# (should look like Sigma) — that is the whole trick, made visible.\n", - "\n", - "# TODO 3: Simulate a CORRECT correlated portfolio. Draw\n", - "# z_paths = rng.standard_normal((3, n_days * n_paths)), build\n", - "# correlated_asset_returns = mu[:, None] + L @ z_paths, reshape to\n", - "# (3, n_paths, n_days), combine with `weights` into one daily\n", - "# portfolio return per path per day, and compound each path into\n", - "# terminal_correlated = initial_value * prod(1 + daily_returns).\n", - "\n", - "# TODO 4: Simulate the SAME portfolio again but WRONG: replace L with\n", - "# independent_scale = np.diag(np.sqrt(np.diag(Sigma))) — same\n", - "# individual volatilities, zero cross-asset correlation — and reuse\n", - "# the SAME z_paths. Produce terminal_independent the same way.\n", - "\n", - "# TODO 5: Plot terminal_correlated and terminal_independent as overlaid\n", - "# histograms (density=True) on the same axes, labelled and legended.\n", - "\n", - "# TODO 6: Compare std, and the 5th and 1st percentiles, of both. Which\n", - "# distribution has the fatter left tail — and why, given that no\n", - "# individual asset's volatility ever changed?" + "# TODO\n", + "# 1. Build Sigma from volatilities and correlations.\n", + "# 2. Compute L = cholesky(Sigma) and verify L @ L.T == Sigma.\n", + "# 3. Transform independent noise z into correlated noise L @ z.\n", + "# 4. Simulate the same portfolio twice:\n", + "# - with the correct covariance;\n", + "# - with the same asset volatilities but zero cross-correlation.\n", + "# 5. Compare standard deviation and lower-tail percentiles." ], - "id": "s11-23" + "id": "qJr9eFnPpbCw" }, { "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "vol = np.array([0.012, 0.015, 0.010])\n", + "\n", + "corr = np.array([\n", + " [1.00, 0.85, 0.20],\n", + " [0.85, 1.00, 0.20],\n", + " [0.20, 0.20, 1.00],\n", + "])\n", + "\n", + "Sigma = np.outer(vol, vol) * corr\n", "L = np.linalg.cholesky(Sigma)\n", - "print(np.allclose(L @ L.T, Sigma)) # True\n", - "print(np.round(L, 4))\n", - "print(np.round(L @ L.T, 6)) # matches Sigma\n", - "\n", - "errors = []\n", - "for n in sample_sizes:\n", - " z = rng.standard_normal((3, n))\n", - " x = L @ z\n", - " err = np.linalg.norm(np.cov(x) - Sigma)\n", - " errors.append(err)\n", - " print(n, err)\n", - "print(errors[0] > errors[1] > errors[2]) # True — error shrinks as n grows\n", - "\n", - "print(np.round(np.cov(z), 3)) # close to the identity\n", - "print(np.round(np.cov(x), 6)) # close to Sigma\n", - "# Cov(x) = Cov(Lz) = L Cov(z) L.T ~ L I L.T = L L.T = Sigma. Independent noise\n", - "# in, correlated noise out — Cholesky is the \"square root\" that makes it work.\n", - "\n", - "z_paths = rng.standard_normal((3, n_days * n_paths))\n", - "\n", - "correlated_asset_returns = (mu[:, None] + L @ z_paths).reshape(3, n_paths, n_days)\n", - "portfolio_returns_correlated = np.einsum('a,apd->pd', weights, correlated_asset_returns)\n", - "terminal_correlated = initial_value * np.prod(1 + portfolio_returns_correlated, axis=1)\n", "\n", - "independent_scale = np.diag(np.sqrt(np.diag(Sigma)))\n", - "independent_asset_returns = (mu[:, None] + independent_scale @ z_paths).reshape(3, n_paths, n_days)\n", - "portfolio_returns_independent = np.einsum('a,apd->pd', weights, independent_asset_returns)\n", - "terminal_independent = initial_value * np.prod(1 + portfolio_returns_independent, axis=1)\n", + "print(\"L @ L.T == Sigma:\", np.allclose(L @ L.T, Sigma))\n", "\n", - "import matplotlib.pyplot as plt\n", - "plt.hist(terminal_independent, bins=80, density=True, alpha=0.6,\n", - " label=\"Assets simulated independently\")\n", - "plt.hist(terminal_correlated, bins=80, density=True, alpha=0.6,\n", - " label=\"Correct correlated simulation\")\n", - "plt.xlabel(\"Terminal portfolio value\")\n", - "plt.ylabel(\"Density\")\n", - "plt.legend()\n", - "plt.show()\n", + "rng_chol = np.random.default_rng(5)\n", "\n", - "print(terminal_correlated.std(), terminal_independent.std()) # ~18.8 ~13.6\n", - "print(np.percentile(terminal_correlated, [1, 5])) # ~70.7 ~79.7\n", - "print(np.percentile(terminal_independent, [1, 5])) # ~79.9 ~86.9\n", - "\n", - "# EVERY asset kept its own individual volatility in BOTH simulations —\n", - "# independent_scale used the SAME diagonal as Sigma. The only thing that\n", - "# changed is whether the simulation lets the three assets fall together.\n", - "# Ignoring the positive covariance did not touch any single asset's risk; it\n", - "# erased real cross-asset comovement and manufactured DIVERSIFICATION THAT\n", - "# ISN'T THERE — the correlated portfolio's distribution is wider and its\n", - "# lower tail is worse.\n", - "#\n", - "# This is NOT \"correlation always increases risk.\" It is specific to THIS\n", - "# positively-correlated book: a negatively correlated pair would do the\n", - "# opposite, and ignoring it would UNDERSTATE diversification, not overstate\n", - "# it. What generalizes is only this: assuming independence when assets are\n", - "# not independent gets the TAILS of the distribution wrong." - ], - "id": "s11-24" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### What the comparison shows\n", - "\n", - "**Every individual asset kept the same volatility in both simulations.** The\n", - "only thing that changed is whether the simulation lets the three assets move\n", - "together. Ignoring the positive covariance did not touch any single asset's\n", - "risk; it erased real cross-asset comovement and manufactured diversification\n", - "that was never there — the correlated portfolio's terminal-value distribution\n", - "is wider, and its bad days are worse, than the (wrong) independent one.\n", - "\n", - "**This is not \"correlation always increases risk.\"** It is specific to this\n", - "book, where every pair is positively correlated. A negatively correlated pair\n", - "would do the opposite: ignoring it would make the simulation *understate*\n", - "diversification, not overstate it. What is general is only this: **assuming\n", - "independence when assets are not independent gets the tails of the\n", - "distribution wrong.**\n", - "\n", - "> 🇪🇸 Cada activo conservó su propia volatilidad en ambas simulaciones — lo\n", - "> único que cambió es si la simulación permite que los tres se muevan juntos.\n", - "> Ignorar la covarianza positiva no tocó el riesgo individual: borró el\n", - "> comovimiento real y fabricó una diversificación que no existía. Esto **no**\n", - "> significa que \"la correlación siempre aumenta el riesgo\" — es específico de\n", - "> esta cartera, donde todo está correlacionado positivamente. Con correlación\n", - "> negativa ocurriría lo contrario. Lo único general es que **asumir\n", - "> independencia cuando los activos no lo son distorsiona las colas de la\n", - "> distribución.**" - ], - "id": "s11-25" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", + "z = rng_chol.standard_normal((3, 100_000))\n", + "x = L @ z\n", "\n", - "## Take-home E — Audio denoising by rank reduction\n", - "\n", - "> 🇪🇸 Ejercicio para casa E: eliminar ruido de audio reduciendo el rango.\n", - "\n", - "Section 10 used truncated SVDs of matrix unfoldings to build a Tucker\n", - "approximation of a real taxi tensor. This take-home applies the same\n", - "low-rank idea to the frequency × time matrix produced from sound.\n", - "\n", - "**The recording is real**: a five-second CC0 voice sample by Bart Massey, from\n", - "[`pdx-cs-sound/wavs`](https://github.com/pdx-cs-sound/wavs), pinned to commit\n", - "`ed5ebcbbbc2d11f0adddc9b50b78d581c29f738c` so the file this notebook fetches\n", - "cannot silently change under you. It downloads at runtime and is checked\n", - "against a known SHA-256 — if the download is corrupted or does not match the\n", - "pinned file, `fetch_verified_wav` below raises instead of quietly handing you\n", - "something else. **The noise is not real** — it is added on purpose, with a\n", - "fixed seed and a target signal-to-noise ratio, precisely so there is a known\n", - "clean reference to measure against. Do not confuse the two: the recording is\n", - "real data, exactly like every other dataset today; the noise is the\n", - "controlled experiment.\n", - "\n", - "### Why a waveform becomes a matrix\n", - "\n", - "A recording is one axis: amplitude over time. The **short-time Fourier\n", - "transform** (STFT) slices it into overlapping windows and Fourier-transforms\n", - "each one, producing a matrix `Z` with two axes — **frequency × time**. Row `i`\n", - "is \"how much of frequency `f_i` is present\"; column `j` is \"during time window\n", - "`t_j`.\" Nothing earlier today paired frequency against time this way.\n", - "\n", - "Because `Z` is a matrix, the SVD from sections 07 and 10 applies unchanged —\n", - "except `Z` is **complex**, and truncating its SVD keeps both magnitude and\n", - "phase. Reconstructing from magnitude alone would throw phase away and produce\n", - "audible distortion, so the truncated matrix goes straight into the inverse\n", - "STFT.\n", - "\n", - "Speech energy concentrates in a handful of dominant frequency-time patterns —\n", - "a few singular vectors carry most of the signal. Broadband, unstructured noise\n", - "has no such structure: it tends to spread its energy across many singular\n", - "directions, including many smaller ones. Keeping only the largest `k`\n", - "singular values keeps most of the speech and discards a disproportionate\n", - "share of the noise.\n", - "\n", - "> 🇪🇸 La STFT convierte una onda de una dimensión (amplitud en el tiempo) en\n", - "> una matriz de dos ejes: frecuencia × tiempo. La voz concentra su energía en\n", - "> pocas direcciones singulares dominantes; el ruido de banda ancha tiende a\n", - "> repartir su energía entre muchas direcciones singulares, incluidas muchas\n", - "> pequeñas. Por eso conservar solo las `k` mayores retiene la voz y descarta\n", - "> una parte desproporcionada del ruido — pero **esto no es un eliminador de\n", - "> ruido universal**: la comprobación real es el SNR medido, no cómo suena.\n", - "\n", - "**This is not a universal denoiser.** It only works to the extent that the\n", - "noise really is broadband relative to a structured signal — narrowband noise,\n", - "or noise correlated with the signal, is not separated this way. The proof\n", - "either way is the measured SNR below, not how it sounds." + "print(\n", + " \"covariance error / error de covarianza:\",\n", + " f\"{np.linalg.norm(np.cov(x) - Sigma):.6e}\",\n", + ")\n", + "\n", + "weights_portfolio = np.array([0.4, 0.4, 0.2])\n", + "mu = np.array([0.00030, 0.00035, 0.00020])\n", + "\n", + "n_days = 252\n", + "n_paths = 10_000\n", + "initial_value = 100.0\n", + "\n", + "z_paths = rng_chol.standard_normal((3, n_days * n_paths))\n", + "\n", + "correlated_returns = (\n", + " mu[:, None] + L @ z_paths\n", + ").reshape(3, n_paths, n_days)\n", + "\n", + "daily_corr = np.einsum(\n", + " \"a,apd->pd\",\n", + " weights_portfolio,\n", + " correlated_returns,\n", + ")\n", + "\n", + "terminal_corr = initial_value * np.prod(1 + daily_corr, axis=1)\n", + "\n", + "independent_scale = np.diag(np.sqrt(np.diag(Sigma)))\n", + "\n", + "independent_returns = (\n", + " mu[:, None] + independent_scale @ z_paths\n", + ").reshape(3, n_paths, n_days)\n", + "\n", + "daily_ind = np.einsum(\n", + " \"a,apd->pd\",\n", + " weights_portfolio,\n", + " independent_returns,\n", + ")\n", + "\n", + "terminal_ind = initial_value * np.prod(1 + daily_ind, axis=1)\n", + "\n", + "print(\n", + " \"std correlated / correlacionado:\",\n", + " f\"{terminal_corr.std():.2f}\",\n", + ")\n", + "print(\n", + " \"std independent / independiente:\",\n", + " f\"{terminal_ind.std():.2f}\",\n", + ")\n", + "print(\n", + " \"1%, 5% correlated:\",\n", + " np.round(np.percentile(terminal_corr, [1, 5]), 2),\n", + ")\n", + "print(\n", + " \"1%, 5% independent:\",\n", + " np.round(np.percentile(terminal_ind, [1, 5]), 2),\n", + ")\n", + "\n", + "fig, ax = plt.subplots(figsize=(6.3, 3.5))\n", + "ax.hist(\n", + " terminal_ind,\n", + " bins=70,\n", + " density=True,\n", + " alpha=0.55,\n", + " label=\"independent / independiente\",\n", + ")\n", + "ax.hist(\n", + " terminal_corr,\n", + " bins=70,\n", + " density=True,\n", + " alpha=0.55,\n", + " label=\"correlated / correlacionado\",\n", + ")\n", + "ax.set_xlabel(\"terminal portfolio value / valor final\")\n", + "ax.set_ylabel(\"density / densidad\")\n", + "ax.set_title(\"Dependence changes portfolio tails / La dependencia cambia las colas\")\n", + "ax.legend(fontsize=8)\n", + "plt.tight_layout()\n", + "plt.show()" ], - "id": "s11-26" + "id": "bABSrLD9pbCw" }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "VOICE_URL = \"https://raw.githubusercontent.com/pdx-cs-sound/wavs/ed5ebcbbbc2d11f0adddc9b50b78d581c29f738c/voice.wav\"\n", - "VOICE_SHA256 = \"2c4b4d9d5f90715fdbf599869a465d521638f40ca978b186df96f1543a4d67dc\"\n", + "## Take-home E — Audio denoising by low-rank STFT\n", "\n", - "def fetch_verified_wav(url, expected_sha256):\n", - " \"\"\"Download a WAV and refuse to proceed if it does not match the pinned\n", - " checksum. No silent fallback to synthetic data on failure.\"\"\"\n", - " import hashlib\n", - " import io\n", - " import urllib.request\n", - " from scipy.io import wavfile\n", - " raw = urllib.request.urlopen(url, timeout=30).read()\n", - " got = hashlib.sha256(raw).hexdigest()\n", - " if got != expected_sha256:\n", - " raise ValueError(\n", - " f\"checksum mismatch for {url}: expected {expected_sha256}, got \"\n", - " f\"{got}. Refusing to use unverified audio data.\")\n", - " return wavfile.read(io.BytesIO(raw))\n", + "The recording is **real**: a pinned CC0 voice sample. The added noise is **synthetic by design**, because a known clean reference lets us measure SNR objectively.\n", "\n", - "def snr_db(reference, estimate):\n", - " \"\"\"Energy-based SNR in dB. `reference` is always the real clean signal.\"\"\"\n", - " return 10 * np.log10(np.sum(reference**2) / np.sum((estimate - reference)**2))\n", + "Pipeline:\n", "\n", - "# TODO 1: fs, clean_i16 = fetch_verified_wav(VOICE_URL, VOICE_SHA256).\n", - "# Convert to float in [-1, 1] (divide by 32768), and average channels\n", - "# to mono if clean.ndim > 1. Print fs, duration in seconds, and shape.\n", + "**real voice → controlled noise → STFT matrix → SVD truncation → ISTFT → SNR**\n", "\n", - "# TODO 2: With rng = np.random.default_rng(42) and TARGET_SNR_DB = 5.0, build\n", - "# additive noise scaled from the CLEAN SIGNAL'S OWN MEAN POWER (not an\n", - "# arbitrary standard deviation) so that clean + noise lands at the\n", - "# target SNR. Verify with snr_db(clean, noisy)." - ], - "id": "s11-27" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ] - }, - "outputs": [], - "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "fs, clean_i16 = fetch_verified_wav(VOICE_URL, VOICE_SHA256)\n", - "clean = clean_i16.astype(np.float64) / 32768.0\n", - "if clean.ndim > 1:\n", - " clean = clean.mean(axis=1)\n", - "print(fs, round(len(clean) / fs, 3), clean.shape) # 48000 4.949 (237568,)\n", + "The key lesson is not “SVD always removes noise.” It is that a low-rank approximation can help when the structured signal concentrates more strongly than the noise in dominant singular directions.\n", "\n", - "rng = np.random.default_rng(42)\n", - "TARGET_SNR_DB = 5.0\n", - "noise = rng.standard_normal(clean.shape)\n", - "scale = np.sqrt(np.mean(clean**2) / (np.mean(noise**2) * 10**(TARGET_SNR_DB / 10)))\n", - "noisy = clean + scale * noise\n", - "print(round(snr_db(clean, noisy), 2)) # 5.0 -- exactly the target, by construction\n", - "\n", - "# fetch_verified_wav is not decorative: it raises ValueError instead of\n", - "# silently returning something else if the download is corrupted or does not\n", - "# match the pinned file. voice.wav ITSELF is real -- a five-second CC0\n", - "# recording. The noise added here is the controlled, synthetic part of the\n", - "# experiment: it exists only so `clean` is a known reference an SNR can be\n", - "# measured against." + "> 🇪🇸 La grabación es **real** y el ruido agregado es **sintético a propósito**, porque necesitamos una referencia limpia conocida para medir SNR. La lección no es que “SVD siempre elimina ruido”, sino que la aproximación de bajo rango puede ayudar cuando la señal está más concentrada que el ruido en las direcciones singulares dominantes." ], - "id": "s11-28" + "id": "Moxsvg9OpbCw" }, { "cell_type": "code", @@ -857,303 +651,238 @@ "metadata": {}, "outputs": [], "source": [ - "# TODO 3: f, t, Z = signal.stft(noisy, fs=fs, nperseg=1024, noverlap=512).\n", - "# Z is COMPLEX -- frequency bins x time frames. Print Z.shape and the\n", - "# full possible rank, min(Z.shape).\n", - "\n", - "# TODO 4: U, s, Vh = np.linalg.svd(Z, full_matrices=False), on the COMPLEX\n", - "# matrix directly so phase survives truncation, not magnitude alone.\n", - "# For k in [2, 5, 10, 20, 40, 80, len(s)]: build\n", - "# Z_k = (U[:, :k] * s[:k]) @ Vh[:k, :], run\n", - "# signal.istft(Z_k, fs=fs, nperseg=1024, noverlap=512), align its\n", - "# length to `clean`, and print k, the retained singular-value energy\n", - "# sum(s[:k]**2) / sum(s**2), and snr_db(clean, reconstruction).\n", - "\n", - "# TODO 5: Pick the k with the best SNR among the candidates above. Report its\n", - "# retained energy, its SNR, and the improvement over the noisy SNR\n", - "# from TODO 2.\n", - "\n", - "# TODO 6: Build ONE common peak-scale factor from\n", - "# max(|noisy|, |denoised|, |clean|) and make playback-only copies\n", - "# scaled by it -- SNR itself is computed on the unscaled signals\n", - "# above, never on these copies. Then display Audio players for the\n", - "# noisy (\"before\") and denoised (\"after\") copies. You may run this\n", - "# cell to listen, but do not save its Audio output into the tracked\n", - "# notebook: Audio() output contains embedded base64 data and must\n", - "# not be committed." + "# TODO\n", + "# 1. Download and verify the pinned voice.wav.\n", + "# 2. Add controlled Gaussian noise at 5 dB target SNR.\n", + "# 3. Compute STFT(noisy) and its complex SVD.\n", + "# 4. Try several truncation ranks k.\n", + "# 5. Reconstruct with ISTFT and measure SNR for each k.\n", + "# 6. Find the best tested k and explain why full rank returns to the noisy signal." ], - "id": "s11-29" + "id": "v4VJoIUMpbCw" }, { "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form", "jupyter": { "source_hidden": true }, "tags": [ "solution", "hide-input" - ] + ], + "cellView": "form" }, "outputs": [], "source": [ - "#@title Solution — try it yourself first { display-mode: 'form' }\n", - "f, t, Z = signal.stft(noisy, fs=fs, nperseg=1024, noverlap=512)\n", - "full_rank = min(Z.shape)\n", - "print(Z.shape, full_rank) # (513, 465) 465\n", + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "VOICE_URL = (\n", + " \"https://raw.githubusercontent.com/pdx-cs-sound/wavs/\"\n", + " \"ed5ebcbbbc2d11f0adddc9b50b78d581c29f738c/voice.wav\"\n", + ")\n", + "VOICE_SHA256 = \"2c4b4d9d5f90715fdbf599869a465d521638f40ca978b186df96f1543a4d67dc\"\n", + "\n", + "def fetch_verified_wav(url, expected_sha256):\n", + " from scipy.io import wavfile\n", "\n", - "U, s, Vh = np.linalg.svd(Z, full_matrices=False)\n", - "for k in [2, 5, 10, 20, 40, 80, len(s)]:\n", + " raw = urllib.request.urlopen(url, timeout=30).read()\n", + " got = hashlib.sha256(raw).hexdigest()\n", + "\n", + " if got != expected_sha256:\n", + " raise ValueError(\n", + " \"checksum mismatch: refusing to use unverified audio\"\n", + " )\n", + "\n", + " return wavfile.read(io.BytesIO(raw))\n", + "\n", + "fs, clean_i16 = fetch_verified_wav(\n", + " VOICE_URL,\n", + " VOICE_SHA256,\n", + ")\n", + "\n", + "clean = clean_i16.astype(np.float64) / 32768.0\n", + "\n", + "if clean.ndim > 1:\n", + " clean = clean.mean(axis=1)\n", + "\n", + "rng_audio = np.random.default_rng(42)\n", + "\n", + "TARGET_SNR_DB = 5.0\n", + "noise = rng_audio.standard_normal(clean.shape)\n", + "\n", + "noise_scale = np.sqrt(\n", + " np.mean(clean**2) /\n", + " (\n", + " np.mean(noise**2)\n", + " * 10 ** (TARGET_SNR_DB / 10)\n", + " )\n", + ")\n", + "\n", + "noisy = clean + noise_scale * noise\n", + "\n", + "print(\n", + " \"duration / duración:\",\n", + " f\"{len(clean) / fs:.3f} s\",\n", + ")\n", + "print(\n", + " \"measured noisy SNR / SNR ruidoso:\",\n", + " f\"{snr_db(clean, noisy):.2f} dB\",\n", + ")\n", + "\n", + "f, t, Z = signal.stft(\n", + " noisy,\n", + " fs=fs,\n", + " nperseg=1024,\n", + " noverlap=512,\n", + ")\n", + "\n", + "U, s, Vh = np.linalg.svd(\n", + " Z,\n", + " full_matrices=False,\n", + ")\n", + "\n", + "candidates = [2, 5, 10, 20, 40, 80, len(s)]\n", + "\n", + "snrs = []\n", + "energies = []\n", + "reconstructions = {}\n", + "\n", + "for k in candidates:\n", " Zk = (U[:, :k] * s[:k]) @ Vh[:k, :]\n", - " _, x_rec = signal.istft(Zk, fs=fs, nperseg=1024, noverlap=512)\n", - " n = min(len(x_rec), len(clean))\n", - " energy = np.sum(s[:k]**2) / np.sum(s**2)\n", - " print(k, round(energy * 100, 1), round(snr_db(clean[:n], x_rec[:n]), 2))\n", - "# k energy% SNR dB\n", - "# 2 33.2 2.29\n", - "# 5 51.1 4.76\n", - "# 10 61.9 6.78\n", - "# 20 70.1 8.48\n", - "# 40 78.3 9.08 <- best of these candidates\n", - "# 80 87.1 7.68 <- WORSE than k=40: noise has leaked back in\n", - "# 465 100.0 5.00 <- full rank matches `noisy` to numerical precision\n", - "\n", - "k = 40\n", - "Zk = (U[:, :k] * s[:k]) @ Vh[:k, :]\n", - "_, x_rec = signal.istft(Zk, fs=fs, nperseg=1024, noverlap=512)\n", - "n = min(len(x_rec), len(clean))\n", - "denoised, clean_a, noisy_a = x_rec[:n], clean[:n], noisy[:n]\n", - "\n", - "snr_before = snr_db(clean_a, noisy_a)\n", - "snr_after = snr_db(clean_a, denoised)\n", - "print(round(snr_before, 2), round(snr_after, 2), round(snr_after - snr_before, 2))\n", - "# 5.0 9.08 4.08\n", - "\n", - "peak = max(np.abs(clean_a).max(), np.abs(noisy_a).max(), np.abs(denoised).max())\n", - "noisy_play = noisy_a / peak\n", - "denoised_play = denoised / peak\n", "\n", - "from IPython.display import Audio, display\n", - "display(Audio(noisy_play, rate=fs)) # \"before\"\n", - "display(Audio(denoised_play, rate=fs)) # \"after\"\n", - "\n", - "# k=40 keeps 40 of 465 possible components -- 8.6% of full rank -- and\n", - "# recovers 4.08 dB of SNR: real, but modest, not a miracle. k=2 and k=5 keep\n", - "# too little of the SPEECH itself to beat the noisy baseline by much. k=80\n", - "# already lets enough noise back into smaller-but-still-significant singular\n", - "# directions that SNR gets WORSE than at k=40 -- more components is not\n", - "# always better. At the full rank of 465 the reconstruction matches `noisy`\n", - "# to numerical precision: proof that whatever denoising happened at k=40\n", - "# came specifically from truncating, not from the STFT -> SVD -> ISTFT round\n", - "# trip itself." - ], - "id": "s11-30" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### What the numbers say\n", - "\n", - "Keeping 40 of 465 possible singular directions (8.6% of full rank, 78.3% of\n", - "the singular-value energy) raised the SNR from 5.00 dB to 9.08 dB — a real\n", - "**+4.08 dB** improvement, not a dramatic one. Fewer components (`k=2`, `k=5`)\n", - "discard too much of the speech itself; more (`k=80`) already lets noise back\n", - "in, and SNR gets worse again. At the full rank the reconstruction matches the\n", - "noisy signal to numerical precision, which is the honest control: the\n", - "denoising is entirely a property of truncating, not of the STFT/SVD/ISTFT\n", - "machinery itself.\n", - "\n", - "**Do not generalize this to \"truncated SVD removes noise.\"** It suppresses\n", - "noise that is broadband and unstructured relative to a signal that\n", - "concentrates in a few dominant directions — the same low-rank argument\n", - "section 10 used on the taxi tensor, applied here to sound instead of trip\n", - "counts. Narrowband noise, or noise correlated with the speech itself, would\n", - "not separate out this way, and the only way to know which situation you are\n", - "in is to measure the SNR, the way this take-home just did.\n", - "\n", - "> 🇪🇸 Conservar 40 de 465 direcciones singulares posibles (8.6% del rango\n", - "> completo, 78.3% de la energía de los valores singulares) subió el SNR de\n", - "> 5.00 dB a 9.08 dB — una mejora real de **+4.08 dB**, no espectacular. Menos\n", - "> componentes descartan demasiada voz; más vuelven a dejar entrar ruido y el\n", - "> SNR empeora. En el rango completo la reconstrucción coincide con la señal\n", - "> ruidosa hasta la precisión numérica, lo cual es el control honesto: la\n", - "> reducción de ruido es una propiedad de truncar, no del mecanismo\n", - "> STFT/SVD/ISTFT en sí. **No generalices esto a \"la SVD truncada siempre\n", - "> elimina el ruido.\"** Solo funciona cuando el ruido es de banda ancha y no\n", - "> estructurado frente a una señal que se concentra en pocas direcciones\n", - "> dominantes — el mismo argumento de bajo rango que la sección 10 usó con el\n", - "> tensor de taxis, aplicado aquí al sonido. La única forma de saberlo es\n", - "> medir el SNR, como se acaba de hacer." + " _, x_rec = signal.istft(\n", + " Zk,\n", + " fs=fs,\n", + " nperseg=1024,\n", + " noverlap=512,\n", + " )\n", + "\n", + " n = min(len(clean), len(x_rec))\n", + "\n", + " rec = x_rec[:n]\n", + " ref = clean[:n]\n", + "\n", + " reconstructions[k] = rec\n", + "\n", + " snrs.append(snr_db(ref, rec))\n", + " energies.append(\n", + " np.sum(s[:k]**2) /\n", + " np.sum(s**2)\n", + " )\n", + "\n", + "best_i = int(np.argmax(snrs))\n", + "best_k = candidates[best_i]\n", + "\n", + "print(\"STFT shape / forma:\", Z.shape)\n", + "print(\"full possible rank / rango completo:\", len(s))\n", + "print(\"best tested k / mejor k probado:\", best_k)\n", + "print(\"best SNR / mejor SNR:\", f\"{snrs[best_i]:.2f} dB\")\n", + "print(\n", + " \"improvement / mejora:\",\n", + " f\"{snrs[best_i] - snr_db(clean, noisy):.2f} dB\",\n", + ")\n", + "\n", + "fig, ax = plt.subplots(figsize=(6.4, 3.4))\n", + "ax.plot(\n", + " candidates,\n", + " snrs,\n", + " marker=\"o\",\n", + ")\n", + "ax.axhline(\n", + " snr_db(clean, noisy),\n", + " linestyle=\"--\",\n", + " linewidth=1,\n", + " label=\"noisy baseline / línea base\",\n", + ")\n", + "ax.set_xlabel(\"truncation rank k / rango k\")\n", + "ax.set_ylabel(\"SNR (dB)\")\n", + "ax.set_title(\"More rank is not always better / Más rango no siempre es mejor\")\n", + "ax.legend(fontsize=8)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "rank_dropdown = widgets.Dropdown(\n", + " options=candidates,\n", + " value=best_k,\n", + " description=\"Rank k:\",\n", + ")\n", + "\n", + "play_audio = widgets.Checkbox(\n", + " value=False,\n", + " description=\"Play / Reproducir\",\n", + ")\n", + "\n", + "audio_output = widgets.Output()\n", + "\n", + "def update_audio(*_):\n", + " with audio_output:\n", + " audio_output.clear_output()\n", + "\n", + " k = rank_dropdown.value\n", + "\n", + " print(\n", + " f\"k={k} | retained energy / energía=\"\n", + " f\"{100 * energies[candidates.index(k)]:.1f}% | \"\n", + " f\"SNR={snrs[candidates.index(k)]:.2f} dB\"\n", + " )\n", + "\n", + " if play_audio.value:\n", + " rec = reconstructions[k]\n", + " n = len(rec)\n", + " reference = clean[:n]\n", + " noisy_local = noisy[:n]\n", + "\n", + " peak = max(\n", + " np.abs(reference).max(),\n", + " np.abs(noisy_local).max(),\n", + " np.abs(rec).max(),\n", + " )\n", + "\n", + " print(\"Before / Antes\")\n", + " display(Audio(noisy_local / peak, rate=fs))\n", + "\n", + " print(\"After / Después\")\n", + " display(Audio(rec / peak, rate=fs))\n", + "\n", + "rank_dropdown.observe(update_audio, names=\"value\")\n", + "play_audio.observe(update_audio, names=\"value\")\n", + "\n", + "display(\n", + " widgets.VBox([\n", + " rank_dropdown,\n", + " play_audio,\n", + " audio_output,\n", + " ])\n", + ")\n", + "\n", + "update_audio()" ], - "id": "s11-31" + "id": "Du6gTydypbCw" }, { "cell_type": "markdown", "metadata": {}, "source": [ - "---\n", + "## What just happened\n", "\n", - "## After the workshop — Tucker compression for deployment\n", - "\n", - "> 🇪🇸 Después del taller — compresión de Tucker para producción.\n", - "\n", - "**Optional — run this after the workshop.** Section 10 ran one fixed Tucker\n", - "rank. Here a **rank slider** drives the trade-off live, on a real dense array,\n", - "so you can feel the curve instead of reading one number on it.\n", - "\n", - "One honest note before the code: this is **not** a neural network's weights.\n", - "A small, stable, seconds-to-download real conv-weight file that both fits a\n", - "free Colab CPU and is not already engineered to be maximally compact turned\n", - "out not to exist — the two real options checked while building this notebook\n", - "(a modern efficient architecture, and a small classifier trained from scratch\n", - "on this workshop's own data) were **already so parameter-efficient that Tucker\n", - "found almost nothing left to compress**, which is itself real and worth\n", - "knowing, just not the point of this appendix. So instead this is a\n", - "**comparable dense tensor**: real NYC taxi trips again, but counted over\n", - "**pickup borough × dropoff borough × hour × weekday** — a genuine order-4\n", - "array, the same shape of thing an on-device cache or a recommender's usage\n", - "table has to fit in memory. The Tucker math, the slider, and the trade-off it\n", - "shows are identical to compressing a weight tensor; only the source of the\n", - "numbers differs, and it seemed better to say that plainly than to relabel taxi\n", - "trips as something they are not.\n", - "\n", - "**Deployment engineers benefit because Tucker lets them choose a point on this\n", - "curve explicitly** — cut most of an array's storage and pay only a measured,\n", - "bounded increase in error, rather than guessing at a fixed compression\n", - "level." - ], - "id": "s11-32" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "TAXIS = \"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/taxis.csv\"\n", - "taxis = pd.read_csv(TAXIS)\n", - "taxis['hour'] = pd.to_datetime(taxis['pickup']).dt.hour\n", - "taxis['weekday'] = pd.to_datetime(taxis['pickup']).dt.weekday\n", - "sub = taxis.dropna(subset=['pickup_borough', 'dropoff_borough'])\n", - "pb2 = sorted(sub['pickup_borough'].unique())\n", - "db2 = sorted(sub['dropoff_borough'].unique())\n", - "\n", - "demand = np.zeros((len(pb2), len(db2), 24, 7))\n", - "for (p, d, h, wd), v in sub.groupby(\n", - " ['pickup_borough', 'dropoff_borough', 'hour', 'weekday']).size().items():\n", - " demand[pb2.index(p), db2.index(d), h, wd] = v\n", - "\n", - "print(demand.shape, int(demand.sum())) # (4, 5, 24, 7), same trips as above" - ], - "id": "s11-33" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def unfold(T, axis):\n", - " return np.moveaxis(T, axis, 0).reshape(T.shape[axis], -1)\n", - "\n", - "# Precompute BOTH SVD bases once. The slider below only re-slices and\n", - "# re-contracts these small matrices — it never redoes an SVD, which is what\n", - "# keeps it responsive. Only the two time axes are compressed; pickup and\n", - "# dropoff borough stay exact, the way section 10's kernel spatial dims would\n", - "# stay exact in a channel-mode Tucker compression of a real conv layer.\n", - "basis_hour = np.linalg.svd(unfold(demand, 2), full_matrices=False)[0] # (24, 24)\n", - "basis_weekday = np.linalg.svd(unfold(demand, 3), full_matrices=False)[0] # (7, 7)\n", - "\n", - "n_pb, n_db, n_hour, n_weekday = demand.shape\n", - "original_params = demand.size\n", - "\n", - "# Sweep every achievable rank once, up front, so the widget only ever looks\n", - "# values up rather than recomputing them.\n", - "ranks = list(range(1, n_hour + 1))\n", - "compressed_list, ratio_list, error_list, madds_list = [], [], [], []\n", - "for k in ranks:\n", - " r_hour, r_weekday = k, min(k, n_weekday)\n", - " Uh, Uw = basis_hour[:, :r_hour], basis_weekday[:, :r_weekday]\n", - " core = np.einsum('ijhw,hc,wd->ijcd', demand, Uh, Uw)\n", - " recon = np.einsum('ijcd,hc,wd->ijhw', core, Uh, Uw)\n", - " compressed = core.size + Uh.size + Uw.size\n", - " compressed_list.append(compressed)\n", - " ratio_list.append(original_params / compressed)\n", - " error_list.append(np.linalg.norm(demand - recon) / np.linalg.norm(demand))\n", - " # Multiply-adds to RE-EXPAND the compressed factors back to the full\n", - " # array — the cost a deployed system pays each time it reads the cache.\n", - " # This is not a network FLOP count; it is specifically that one contraction.\n", - " madds_list.append(n_pb * n_db * n_hour * r_weekday * (r_hour + n_weekday))\n", - "\n", - "print(f\"original parameters: {original_params} \"\n", - " f\"(pickup {n_pb} x dropoff {n_db} x hour {n_hour} x weekday {n_weekday})\")" - ], - "id": "s11-34" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " from google.colab import output\n", - " output.enable_custom_widget_manager()\n", - "except ImportError:\n", - " pass\n", + "The five take-homes reuse the workshop rather than introducing five unrelated tricks:\n", "\n", - "import ipywidgets as widgets\n", - "import matplotlib.pyplot as plt\n", + "1. **PCA:** approximation can be mathematically optimal and still answer the wrong scientific question if feature scales dominate.\n", + "2. **Attention:** two tensor contractions plus normalization and masking turn pairwise similarity into a weighted combination.\n", + "3. **CP:** another tensor factorization changes the representation and interpretability trade-off relative to Tucker.\n", + "4. **Cholesky:** a factorization can be used constructively to impose a known covariance structure.\n", + "5. **Audio:** low-rank truncation is useful only when the measured approximation improves the signal criterion you care about.\n", "\n", - "def tucker_tradeoff(k):\n", - " i = k - 1\n", - " plt.close('all')\n", - " fig, ax1 = plt.subplots(figsize=(6.5, 3.2))\n", - " ax1.plot(ranks, error_list, color='#C44E52')\n", - " ax1.scatter([k], [error_list[i]], color='#C44E52', zorder=5)\n", - " ax1.set_xlabel('rank k (shared by the hour and weekday axes)')\n", - " ax1.set_ylabel('relative error', color='#C44E52')\n", - " ax2 = ax1.twinx()\n", - " ax2.plot(ranks, ratio_list, color='#4C72B0')\n", - " ax2.scatter([k], [ratio_list[i]], color='#4C72B0', zorder=5)\n", - " ax2.set_ylabel('compression ratio (x)', color='#4C72B0')\n", - " plt.tight_layout()\n", - " plt.show()\n", + "### The sentence to leave with\n", "\n", - " print(f\"rank k = {k}\")\n", - " print(f\"compressed parameters: {compressed_list[i]} (of {original_params} original)\")\n", - " print(f\"compression ratio: {ratio_list[i]:.2f}x\")\n", - " print(f\"relative error: {error_list[i]:.3f}\")\n", - " print(f\"reconstruction MAdds: {madds_list[i]} \"\n", - " f\"(multiply-adds to re-expand the factors back to the full array)\")\n", - "\n", - "# Move the slider from 1 to n_hour. Both ends are worth visiting: rank 1 is\n", - "# the cheapest possible model, and the top end (hour AND weekday both at\n", - "# their true dimension) should reconstruct the tensor exactly — a check on\n", - "# the implementation, not just on the trade-off.\n", - "widgets.interact(tucker_tradeoff,\n", - " k=widgets.IntSlider(min=1, max=n_hour, step=1, value=4,\n", - " description='rank k'));" - ], - "id": "s11-35" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Thank you\n", + "> **Represent the structure you actually have, approximate only when you can measure the loss, and never let shape manipulation hide what the axes mean.**\n", "\n", - "> 🇪🇸 Gracias por venir. Pregunta en Discord en español o en inglés — lo que te\n", - "> permita preguntar más rápido.\n", + "The NumPy ideas transfer directly: `torch.einsum`, `tf.einsum`, and `jnp.einsum` use the same index notation, while libraries such as TensorLy provide production implementations of tensor decompositions.\n", "\n", - "Questions stay welcome in Discord, in Spanish or English. The\n", - "[handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)\n", - "has everything from today, including the facilitator notes." + "> 🇪🇸 Los cinco ejercicios reutilizan la misma idea del taller. **Representa la estructura que realmente tienes, aproxima solo cuando puedes medir la pérdida y nunca permitas que una manipulación de formas oculte el significado de los ejes.**" ], - "id": "s11-36" + "id": "ipGw84X-pbCw" }, { "cell_type": "markdown", @@ -1167,22 +896,648 @@ "\n", "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" ], - "id": "s11-37" + "id": "M29ikzGTpbCx" } ], "metadata": { - "colab": { - "name": "11-wrap-up-and-take-homes.ipynb", - "provenance": [], - "toc_visible": true - }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { - "name": "python" + "name": "python", + "version": "3" + }, + "colab": { + "provenance": [] + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "version_major": 2, + "version_minor": 0, + "state": { + "27879fcad4af4e0cbd4940b1a120c39f": { + "model_module": "@jupyter-widgets/controls", + "model_name": "VBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "VBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "VBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_68c2f2688c854983adea71cfaa880d47", + "IPY_MODEL_7a6f89b309f8428db11c73ebbeadaf43" + ], + "layout": "IPY_MODEL_0a570d84b9c04beea75661ee7971694f" + } + }, + "68c2f2688c854983adea71cfaa880d47": { + "model_module": "@jupyter-widgets/controls", + "model_name": "IntSliderModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "IntSliderModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "IntSliderView", + "continuous_update": false, + "description": "Component / Componente:", + "description_tooltip": null, + "disabled": false, + "layout": "IPY_MODEL_be805ac7d7dd4bf5b8cd48a77e24378f", + "max": 3, + "min": 1, + "orientation": "horizontal", + "readout": true, + "readout_format": "d", + "step": 1, + "style": "IPY_MODEL_916f2343e79e4158b8cdf569e574f1d3", + "value": 2 + } + }, + "7a6f89b309f8428db11c73ebbeadaf43": { + "model_module": "@jupyter-widgets/output", + "model_name": "OutputModel", + "model_module_version": "1.0.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/output", + "_model_module_version": "1.0.0", + "_model_name": "OutputModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/output", + "_view_module_version": "1.0.0", + "_view_name": "OutputView", + "layout": "IPY_MODEL_82e9c57171424918aeaaeae685d24a81", + "msg_id": "", + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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[ - "# 11 · Wrap-up and take-homes\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/11-wrap-up-and-take-homes.ipynb)\n", - "\n", - "*wrap-up · 5 min + take-homes after the workshop*\n", - "\n", - "> 🇪🇸 **Cierre y ejercicios para casa** — Resume la idea que conecta el taller y luego elige entre cinco extensiones: PCA, atención, CP, Cholesky y audio.\n", - "\n", - "Wrap up the workshop around one connecting idea, then choose among five extensions: PCA, attention, CP, Cholesky, and audio.\n", - "\n", - "## What you will be able to do\n", - "\n", - "- State the approximation idea connecting pseudoinverse, deconvolution, and Tucker.\n", - "- Diagnose the scaling trap in PCA on real breast-cancer measurements.\n", - "- Build masked attention from two `einsum` contractions.\n", - "- Compare CP with Tucker on the same real New York taxi tensor.\n", - "- Use Cholesky to turn independent noise into correlated draws and quantify the portfolio consequence.\n", - "- Denoise a real voice recording with STFT → truncated SVD → ISTFT and measure the SNR trade-off.\n", - "\n", - "> 🇪🇸 **Al terminar podrás:** conectar pseudoinversa, deconvolución y Tucker mediante la idea de aproximación estable; detectar el problema de escala en PCA; construir atención enmascarada; comparar CP y Tucker; simular correlación con Cholesky; y medir una reducción de ruido de audio basada en SVD." - ], - "id": "A9S7jpWPpbCq" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "DI1X2khopbCs" - }, - "source": [ - "## Setup\n", - "\n", - "Run this once. The live five-minute wrap-up only needs the summary below; the code supports the take-homes you can explore afterward.\n", - "\n", - "> 🇪🇸 Ejecuta esta celda una vez. El cierre en vivo dura solo cinco minutos; el código prepara los ejercicios para casa que puedes explorar después." - ], - "id": "DI1X2khopbCs" - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "kb1BmNE4pbCt", - "outputId": "8bdfa787-6cc3-464a-b06b-b8a9b5241f43" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Setup ready / Preparación lista\n" - ] - } - ], - "source": [ - "import hashlib\n", - "import io\n", - "import subprocess\n", - "import sys\n", - "import urllib.request\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import ipywidgets as widgets\n", - "\n", - "from IPython.display import Audio, display\n", - "from scipy import signal\n", - "from sklearn.datasets import load_breast_cancer\n", - "\n", - "try:\n", - " from google.colab import output\n", - " output.enable_custom_widget_manager()\n", - "except ImportError:\n", - " pass\n", - "\n", - "rng = np.random.default_rng(0)\n", - "\n", - "def softmax(x, axis=-1):\n", - " x = x - np.max(x, axis=axis, keepdims=True)\n", - " e = np.exp(x)\n", - " return e / np.sum(e, axis=axis, keepdims=True)\n", - "\n", - "def snr_db(reference, estimate):\n", - " reference = np.asarray(reference)\n", - " estimate = np.asarray(estimate)\n", - " return 10 * np.log10(\n", - " np.sum(reference**2) /\n", - " np.sum((estimate - reference)**2)\n", - " )\n", - "\n", - "print(\"Setup ready / Preparación lista\")" - ], - "id": "kb1BmNE4pbCt" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "aRbA3maRpbCu" - }, - "source": [ - "## Why this matters — one idea connects the workshop\n", - "\n", - "Sections 07, 09, and 10 looked different:\n", - "\n", - "- the pseudoinverse handled an overdetermined linear system;\n", - "- deconvolution tried to recover an image after blur;\n", - "- Tucker compressed a tensor into lower-dimensional mode-specific factors.\n", - "\n", - "But the same decision appears in all three:\n", - "\n", - "> **When an exact inverse or exact representation is unavailable, unstable, or unnecessarily expensive, choose a controlled approximation and measure what you lose.**\n", - "\n", - "That sentence is the bridge from linear algebra to modern machine learning. The five take-homes below reuse it in different settings.\n", - "\n", - "### How to use this notebook\n", - "\n", - "Each take-home has a `TODO` cell and a folded **Solution / Solución**. Try the task first, then open the solution.\n", - "\n", - "> 🇪🇸 **Una idea conecta el taller:** cuando una inversa exacta o una representación exacta no existe, es inestable o cuesta demasiado, construye una aproximación controlada y mide qué pierdes.\n", - ">\n", - "> Cada ejercicio tiene un `TODO` y una **Solution / Solución** plegada. Intenta primero; abre la solución después." - ], - "id": "aRbA3maRpbCu" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ZjR0Aq8NpbCu" - }, - "source": [ - "## Take-home A — PCA: the scaling trap\n", - "\n", - "The Wisconsin Diagnostic Breast Cancer dataset has 30 real measurements per tumour sample, but those features use very different numerical scales.\n", - "\n", - "Your goal is not merely to run PCA. It is to discover why unstandardized PCA can give a technically correct but scientifically misleading answer.\n", - "\n", - "> 🇪🇸 El conjunto Wisconsin Diagnostic Breast Cancer tiene 30 mediciones reales por muestra, pero en escalas numéricas muy distintas. El reto es descubrir por qué PCA sin estandarizar puede producir una respuesta matemáticamente válida pero científicamente engañosa." - ], - "id": "ZjR0Aq8NpbCu" - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "FiWwed7DpbCu" - }, - "outputs": [], - "source": [ - "# TODO\n", - "# 1. Load the breast-cancer data.\n", - "# 2. Center X and compute SVD.\n", - "# 3. How many components explain 95% of variance?\n", - "# 4. Inspect feature variances. Why is the answer suspicious?\n", - "# 5. Standardize every feature and repeat.\n", - "# 6. Compare the two cumulative-variance curves." - ], - "id": "FiWwed7DpbCu" - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ], - "colab": { - "base_uri": "https://localhost:8080/", - "height": 785 - }, - "id": "N_mhphqqpbCu", - "outputId": "3e0725a5-91c4-4e53-a6f4-a37000d6ecaf" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "95% components — raw / sin estandarizar: 1\n", - "95% components — standardized / estandarizado: 10\n", - "feature variance range / rango de varianzas: 6.989e-06 to / a 3.236e+05\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" 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\n" - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Take-away / Idea: PCA optimizes variance, not scientific relevance; scale the features when units differ.\n" - ] - } - ], - "source": [ - "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", - "bc = load_breast_cancer()\n", - "X, y = bc.data, bc.target\n", - "\n", - "X_centered = X - X.mean(axis=0)\n", - "_, s_raw, Vt_raw = np.linalg.svd(X_centered, full_matrices=False)\n", - "frac_raw = s_raw**2 / np.sum(s_raw**2)\n", - "n95_raw = int(np.argmax(np.cumsum(frac_raw) >= 0.95) + 1)\n", - "\n", - "feature_var = X.var(axis=0)\n", - "\n", - "X_std = (X - X.mean(axis=0)) / X.std(axis=0)\n", - "_, s_std, Vt_std = np.linalg.svd(X_std, full_matrices=False)\n", - "frac_std = s_std**2 / np.sum(s_std**2)\n", - "n95_std = int(np.argmax(np.cumsum(frac_std) >= 0.95) + 1)\n", - "\n", - "print(\"95% components — raw / sin estandarizar:\", n95_raw)\n", - "print(\"95% components — standardized / estandarizado:\", n95_std)\n", - "print(\n", - " \"feature variance range / rango de varianzas:\",\n", - " f\"{feature_var.min():.3e}\",\n", - " \"to / a\",\n", - " f\"{feature_var.max():.3e}\",\n", - ")\n", - "\n", - "n_show = 15\n", - "fig, ax = plt.subplots(figsize=(6.4, 3.4))\n", - "ax.plot(\n", - " range(1, n_show + 1),\n", - " np.cumsum(frac_raw[:n_show]),\n", - " marker=\"o\",\n", - " label=\"raw / sin estandarizar\",\n", - ")\n", - "ax.plot(\n", - " range(1, n_show + 1),\n", - " np.cumsum(frac_std[:n_show]),\n", - " marker=\"o\",\n", - " label=\"standardized / estandarizado\",\n", - ")\n", - "ax.axhline(0.95, linestyle=\"--\", linewidth=1, label=\"95%\")\n", - "ax.set_xlabel(\"number of components / número de componentes\")\n", - "ax.set_ylabel(\"cumulative variance / varianza acumulada\")\n", - "ax.set_title(\"PCA changes when units dominate / PCA cambia cuando dominan las unidades\")\n", - "ax.legend(fontsize=8)\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "Z = X_std @ Vt_std[:2].T\n", - "fig, ax = plt.subplots(figsize=(5.2, 3.8))\n", - "scatter = ax.scatter(Z[:, 0], Z[:, 1], c=y, s=12)\n", - "ax.set_xlabel(\"component 1\")\n", - "ax.set_ylabel(\"component 2\")\n", - "ax.set_title(\"Standardized PCA / PCA estandarizado\")\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "print(\n", - " \"Take-away / Idea:\",\n", - " \"PCA optimizes variance, not scientific relevance; scale the features when units differ.\"\n", - ")" - ], - "id": "N_mhphqqpbCu" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Io0ukHyUpbCv" - }, - "source": [ - "## Take-home B — Attention is two contractions\n", - "\n", - "For this exercise we deliberately use **synthetic** `Q`, `K`, and `V` arrays. That is appropriate here because the goal is to isolate the tensor mechanics of attention — shapes, contraction axes, normalization, and masking — without mixing in a tokenizer or a trained model.\n", - "\n", - "Build:\n", - "\n", - "1. `scores[b, i, j] = Q[b, i, :] · K[b, j, :]`\n", - "2. row-wise softmax weights\n", - "3. `output[b, i, :] = Σ_j weights[b, i, j] V[b, j, :]`\n", - "4. a padding mask that forces the last three key positions to receive zero weight.\n", - "\n", - "> 🇪🇸 Aquí usamos `Q`, `K` y `V` **sintéticos a propósito**: queremos aislar la mecánica tensorial de la atención sin confundirla con tokenización o entrenamiento. Construye puntajes, softmax, salida y una máscara para padding." - ], - "id": "Io0ukHyUpbCv" + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 11 · Wrap-up and take-homes\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/11-wrap-up-and-take-homes.ipynb)\n", + "\n", + "*wrap-up · 5 min*\n", + "\n", + "> 🇪🇸 **Cierre y ejercicios para casa** — Resume la idea que conecta el taller y luego elige entre cinco extensiones: PCA, atención, CP, Cholesky y audio.\n", + "\n", + "Wrap up the workshop around one connecting idea, then choose among five extensions: PCA, attention, CP, Cholesky, and audio.\n", + "\n", + "## What you will be able to do\n", + "\n", + "- State the approximation idea connecting pseudoinverse, deconvolution, and Tucker.\n", + "- Diagnose the scaling trap in PCA on real breast-cancer measurements.\n", + "- Build masked attention from two `einsum` contractions.\n", + "- Compare CP with Tucker on the same real New York taxi tensor.\n", + "- Use Cholesky to turn independent noise into correlated draws and quantify the portfolio consequence.\n", + "- Denoise a real voice recording with STFT → truncated SVD → ISTFT and measure the SNR trade-off." + ], + "id": "A9S7jpWPpbCq" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "Run this first. It installs and imports everything this notebook needs, and nothing else.\n", + "\n", + "> 🇪🇸 Ejecuta esto primero: instala e importa todo lo que este cuaderno necesita." + ], + "id": "DI1X2khopbCs" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import hashlib\n", + "import io\n", + "import subprocess\n", + "import sys\n", + "import urllib.request\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import ipywidgets as widgets\n", + "\n", + "from IPython.display import Audio, display\n", + "from scipy import signal\n", + "from sklearn.datasets import load_breast_cancer\n", + "\n", + "try:\n", + " from google.colab import output\n", + " output.enable_custom_widget_manager()\n", + "except ImportError:\n", + " pass\n", + "\n", + "rng = np.random.default_rng(0)\n", + "\n", + "def softmax(x, axis=-1):\n", + " x = x - np.max(x, axis=axis, keepdims=True)\n", + " e = np.exp(x)\n", + " return e / np.sum(e, axis=axis, keepdims=True)\n", + "\n", + "def snr_db(reference, estimate):\n", + " reference = np.asarray(reference)\n", + " estimate = np.asarray(estimate)\n", + " return 10 * np.log10(\n", + " np.sum(reference**2) /\n", + " np.sum((estimate - reference)**2)\n", + " )\n", + "\n", + "print(\"Setup ready / Preparación lista\")" + ], + "id": "kb1BmNE4pbCt" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Why this matters — one idea connects the workshop\n", + "\n", + "Sections 07, 09, and 10 looked different:\n", + "\n", + "- the pseudoinverse handled an overdetermined linear system;\n", + "- deconvolution tried to recover an image after blur;\n", + "- Tucker compressed a tensor into lower-dimensional mode-specific factors.\n", + "\n", + "But the same decision appears in all three:\n", + "\n", + "> **When an exact inverse or exact representation is unavailable, unstable, or unnecessarily expensive, choose a controlled approximation and measure what you lose.**\n", + "\n", + "That sentence is the bridge from linear algebra to modern machine learning. The five take-homes below reuse it in different settings.\n", + "\n", + "### How to use this notebook\n", + "\n", + "Each take-home has a `TODO` cell and a folded **Solution / Solución**. Try the task first, then open the solution.\n", + "\n", + "> 🇪🇸 **Una idea conecta el taller:** cuando una inversa exacta o una representación exacta no existe, es inestable o cuesta demasiado, construye una aproximación controlada y mide qué pierdes.\n", + ">\n", + "> Cada ejercicio tiene un `TODO` y una **Solution / Solución** plegada. Intenta primero; abre la solución después." + ], + "id": "aRbA3maRpbCu" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Take-home A — PCA: the scaling trap\n", + "\n", + "The Wisconsin Diagnostic Breast Cancer dataset has 30 real measurements per tumour sample, but those features use very different numerical scales.\n", + "\n", + "Your goal is not merely to run PCA. It is to discover why unstandardized PCA can give a technically correct but scientifically misleading answer.\n", + "\n", + "> 🇪🇸 El conjunto Wisconsin Diagnostic Breast Cancer tiene 30 mediciones reales por muestra, pero en escalas numéricas muy distintas. El reto es descubrir por qué PCA sin estandarizar puede producir una respuesta matemáticamente válida pero científicamente engañosa." + ], + "id": "ZjR0Aq8NpbCu" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO\n", + "# 1. Load the breast-cancer data.\n", + "# 2. Center X and compute SVD.\n", + "# 3. How many components explain 95% of variance?\n", + "# 4. Inspect feature variances. Why is the answer suspicious?\n", + "# 5. Standardize every feature and repeat.\n", + "# 6. Compare the two cumulative-variance curves." + ], + "id": "FiWwed7DpbCu" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "0iJy1TlwpbCv" - }, - "outputs": [], - "source": [ - "# TODO\n", - "# 1. Create Q, K, V with shape (batch=4, seq=12, dim=16).\n", - "# 2. Compute scaled dot-product scores with einsum.\n", - "# 3. Softmax over the key-position axis.\n", - "# 4. Contract weights with V.\n", - "# 5. Mask the final 3 key positions BEFORE softmax.\n", - "# 6. Verify masked positions receive zero weight." - ], - "id": "0iJy1TlwpbCv" + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "bc = load_breast_cancer()\n", + "X, y = bc.data, bc.target\n", + "\n", + "X_centered = X - X.mean(axis=0)\n", + "_, s_raw, Vt_raw = np.linalg.svd(X_centered, full_matrices=False)\n", + "frac_raw = s_raw**2 / np.sum(s_raw**2)\n", + "n95_raw = int(np.argmax(np.cumsum(frac_raw) >= 0.95) + 1)\n", + "\n", + "feature_var = X.var(axis=0)\n", + "\n", + "X_std = (X - X.mean(axis=0)) / X.std(axis=0)\n", + "_, s_std, Vt_std = np.linalg.svd(X_std, full_matrices=False)\n", + "frac_std = s_std**2 / np.sum(s_std**2)\n", + "n95_std = int(np.argmax(np.cumsum(frac_std) >= 0.95) + 1)\n", + "\n", + "print(\"95% components — raw / sin estandarizar:\", n95_raw)\n", + "print(\"95% components — standardized / estandarizado:\", n95_std)\n", + "print(\n", + " \"feature variance range / rango de varianzas:\",\n", + " f\"{feature_var.min():.3e}\",\n", + " \"to / a\",\n", + " f\"{feature_var.max():.3e}\",\n", + ")\n", + "\n", + "n_show = 15\n", + "fig, ax = plt.subplots(figsize=(6.4, 3.4))\n", + "ax.plot(\n", + " range(1, n_show + 1),\n", + " np.cumsum(frac_raw[:n_show]),\n", + " marker=\"o\",\n", + " label=\"raw / sin estandarizar\",\n", + ")\n", + "ax.plot(\n", + " range(1, n_show + 1),\n", + " np.cumsum(frac_std[:n_show]),\n", + " marker=\"o\",\n", + " label=\"standardized / estandarizado\",\n", + ")\n", + "ax.axhline(0.95, linestyle=\"--\", linewidth=1, label=\"95%\")\n", + "ax.set_xlabel(\"number of components / número de componentes\")\n", + "ax.set_ylabel(\"cumulative variance / varianza acumulada\")\n", + "ax.set_title(\"PCA changes when units dominate / PCA cambia cuando dominan las unidades\")\n", + "ax.legend(fontsize=8)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "Z = X_std @ Vt_std[:2].T\n", + "fig, ax = plt.subplots(figsize=(5.2, 3.8))\n", + "scatter = ax.scatter(Z[:, 0], Z[:, 1], c=y, s=12)\n", + "ax.set_xlabel(\"component 1\")\n", + "ax.set_ylabel(\"component 2\")\n", + "ax.set_title(\"Standardized PCA / PCA estandarizado\")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(\n", + " \"Take-away / Idea:\",\n", + " \"PCA optimizes variance, not scientific relevance; scale the features when units differ.\"\n", + ")" + ], + "id": "N_mhphqqpbCu" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Take-home B — Attention is two contractions\n", + "\n", + "For this exercise we deliberately use **synthetic** `Q`, `K`, and `V` arrays. That is appropriate here because the goal is to isolate the tensor mechanics of attention — shapes, contraction axes, normalization, and masking — without mixing in a tokenizer or a trained model.\n", + "\n", + "Build:\n", + "\n", + "1. `scores[b, i, j] = Q[b, i, :] · K[b, j, :]`\n", + "2. row-wise softmax weights\n", + "3. `output[b, i, :] = Σ_j weights[b, i, j] V[b, j, :]`\n", + "4. a padding mask that forces the last three key positions to receive zero weight.\n", + "\n", + "> 🇪🇸 Aquí usamos `Q`, `K` y `V` **sintéticos a propósito**: queremos aislar la mecánica tensorial de la atención sin confundirla con tokenización o entrenamiento. Construye puntajes, softmax, salida y una máscara para padding." + ], + "id": "Io0ukHyUpbCv" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO\n", + "# 1. Create Q, K, V with shape (batch=4, seq=12, dim=16).\n", + "# 2. Compute scaled dot-product scores with einsum.\n", + "# 3. Softmax over the key-position axis.\n", + "# 4. Contract weights with V.\n", + "# 5. Mask the final 3 key positions BEFORE softmax.\n", + "# 6. Verify masked positions receive zero weight." + ], + "id": "0iJy1TlwpbCv" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ], - "colab": { - "base_uri": "https://localhost:8080/", - "height": 413 - }, - "id": "4RpwHWPDpbCv", - "outputId": "1c85fed3-288c-4993-9a08-5dc9ffda46dc" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "scores: (4, 12, 12)\n", - "weights: (4, 12, 12)\n", - "output: (4, 12, 16)\n", - "rows sum to 1 / filas suman 1: True\n", - "largest padded weight / mayor peso en padding: 0.0\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} - } - ], - "source": [ - "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", - "rng_attention = np.random.default_rng(6)\n", - "\n", - "batch, seq_len, dim = 4, 12, 16\n", - "Q = rng_attention.standard_normal((batch, seq_len, dim))\n", - "K = rng_attention.standard_normal((batch, seq_len, dim))\n", - "V = rng_attention.standard_normal((batch, seq_len, dim))\n", - "\n", - "scores = np.einsum(\"bid,bjd->bij\", Q, K) / np.sqrt(dim)\n", - "weights = softmax(scores, axis=-1)\n", - "attention_output = np.einsum(\"bij,bjd->bid\", weights, V)\n", - "\n", - "mask = np.zeros((seq_len, seq_len))\n", - "mask[:, -3:] = -np.inf\n", - "weights_masked = softmax(scores + mask, axis=-1)\n", - "output_masked = np.einsum(\"bij,bjd->bid\", weights_masked, V)\n", - "\n", - "print(\"scores:\", scores.shape)\n", - "print(\"weights:\", weights.shape)\n", - "print(\"output:\", attention_output.shape)\n", - "print(\n", - " \"rows sum to 1 / filas suman 1:\",\n", - " np.allclose(weights_masked.sum(axis=-1), 1.0),\n", - ")\n", - "print(\n", - " \"largest padded weight / mayor peso en padding:\",\n", - " weights_masked[..., -3:].max(),\n", - ")\n", - "\n", - "example = 0\n", - "query = 0\n", - "\n", - "fig, ax = plt.subplots(figsize=(6.2, 3.2))\n", - "ax.bar(\n", - " range(seq_len),\n", - " weights_masked[example, query],\n", - ")\n", - "ax.set_xlabel(\"key position / posición key\")\n", - "ax.set_ylabel(\"attention weight / peso\")\n", - "ax.set_title(\"Padding disappears after masking / El padding desaparece con la máscara\")\n", - "plt.tight_layout()\n", - "plt.show()" - ], - "id": "4RpwHWPDpbCv" + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "rng_attention = np.random.default_rng(6)\n", + "\n", + "batch, seq_len, dim = 4, 12, 16\n", + "Q = rng_attention.standard_normal((batch, seq_len, dim))\n", + "K = rng_attention.standard_normal((batch, seq_len, dim))\n", + "V = rng_attention.standard_normal((batch, seq_len, dim))\n", + "\n", + "scores = np.einsum(\"bid,bjd->bij\", Q, K) / np.sqrt(dim)\n", + "weights = softmax(scores, axis=-1)\n", + "attention_output = np.einsum(\"bij,bjd->bid\", weights, V)\n", + "\n", + "mask = np.zeros((seq_len, seq_len))\n", + "mask[:, -3:] = -np.inf\n", + "weights_masked = softmax(scores + mask, axis=-1)\n", + "output_masked = np.einsum(\"bij,bjd->bid\", weights_masked, V)\n", + "\n", + "print(\"scores:\", scores.shape)\n", + "print(\"weights:\", weights.shape)\n", + "print(\"output:\", attention_output.shape)\n", + "print(\n", + " \"rows sum to 1 / filas suman 1:\",\n", + " np.allclose(weights_masked.sum(axis=-1), 1.0),\n", + ")\n", + "print(\n", + " \"largest padded weight / mayor peso en padding:\",\n", + " weights_masked[..., -3:].max(),\n", + ")\n", + "\n", + "example = 0\n", + "query = 0\n", + "\n", + "fig, ax = plt.subplots(figsize=(6.2, 3.2))\n", + "ax.bar(\n", + " range(seq_len),\n", + " weights_masked[example, query],\n", + ")\n", + "ax.set_xlabel(\"key position / posición key\")\n", + "ax.set_ylabel(\"attention weight / peso\")\n", + "ax.set_title(\"Padding disappears after masking / El padding desaparece con la máscara\")\n", + "plt.tight_layout()\n", + "plt.show()" + ], + "id": "4RpwHWPDpbCv" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Take-home C — CP versus Tucker on the same real taxi tensor\n", + "\n", + "Section 10 used Tucker/HOSVD. Now use **CP decomposition** on the same real `pickup borough × dropoff borough × hour` tensor.\n", + "\n", + "CP represents a tensor as a sum of rank-1 outer products. Unlike Tucker, it has no separate core tensor. CP components can be individually interpretable, but uniqueness is only guaranteed under mathematical conditions — so treat patterns in this small dataset as exploratory, not as ground truth.\n", + "\n", + "> 🇪🇸 La sección 10 usó Tucker/HOSVD. Ahora aplica **CP** al mismo tensor real de taxis. CP expresa el tensor como suma de productos externos de rango 1 y no usa un núcleo separado. Sus componentes pueden ser interpretables, pero la unicidad requiere condiciones matemáticas; en este conjunto pequeño, interprétalos con cautela." + ], + "id": "0W5EWbWApbCv" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO\n", + "# 1. Rebuild the real NYC taxi tensor: pickup × dropoff × hour.\n", + "# 2. Fit a rank-3 CP decomposition with TensorLy.\n", + "# 3. Reconstruct the tensor and compute relative error.\n", + "# 4. Compare its parameter count with Tucker rank (2,2,3).\n", + "# 5. Inspect pickup, dropoff, and hour factors for each CP component." + ], + "id": "3KCacE6ipbCv" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true }, - { - "cell_type": "markdown", - "metadata": { - "id": "0W5EWbWApbCv" - }, - "source": [ - "## Take-home C — CP versus Tucker on the same real taxi tensor\n", - "\n", - "Section 10 used Tucker/HOSVD. Now use **CP decomposition** on the same real `pickup borough × dropoff borough × hour` tensor.\n", - "\n", - "CP represents a tensor as a sum of rank-1 outer products. Unlike Tucker, it has no separate core tensor. CP components can be individually interpretable, but uniqueness is only guaranteed under mathematical conditions — so treat patterns in this small dataset as exploratory, not as ground truth.\n", - "\n", - "> 🇪🇸 La sección 10 usó Tucker/HOSVD. Ahora aplica **CP** al mismo tensor real de taxis. CP expresa el tensor como suma de productos externos de rango 1 y no usa un núcleo separado. Sus componentes pueden ser interpretables, pero la unicidad requiere condiciones matemáticas; en este conjunto pequeño, interprétalos con cautela." - ], - "id": "0W5EWbWApbCv" + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "try:\n", + " import tensorly as tl\n", + " from tensorly.decomposition import parafac\n", + "except ImportError:\n", + " subprocess.run(\n", + " [sys.executable, \"-m\", \"pip\", \"install\", \"-q\", \"tensorly\"],\n", + " check=True,\n", + " )\n", + " import tensorly as tl\n", + " from tensorly.decomposition import parafac\n", + "\n", + "TAXIS = \"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/taxis.csv\"\n", + "taxis = pd.read_csv(TAXIS)\n", + "\n", + "taxis[\"pickup_dt\"] = pd.to_datetime(taxis[\"pickup\"], errors=\"coerce\")\n", + "taxis[\"hour\"] = taxis[\"pickup_dt\"].dt.hour\n", + "\n", + "sub = taxis.dropna(\n", + " subset=[\"pickup_borough\", \"dropoff_borough\", \"hour\"]\n", + ").copy()\n", + "sub[\"hour\"] = sub[\"hour\"].astype(int)\n", + "\n", + "pb = sorted(sub[\"pickup_borough\"].unique())\n", + "db = sorted(sub[\"dropoff_borough\"].unique())\n", + "\n", + "p_idx = {name: i for i, name in enumerate(pb)}\n", + "d_idx = {name: i for i, name in enumerate(db)}\n", + "\n", + "T_taxi = np.zeros((len(pb), len(db), 24), dtype=float)\n", + "\n", + "for (p, d, h), count in sub.groupby(\n", + " [\"pickup_borough\", \"dropoff_borough\", \"hour\"]\n", + ").size().items():\n", + " T_taxi[p_idx[p], d_idx[d], int(h)] = float(count)\n", + "\n", + "rank_cp = 3\n", + "\n", + "cp_weights, cp_factors = parafac(\n", + " tl.tensor(T_taxi),\n", + " rank=rank_cp,\n", + " init=\"svd\",\n", + " random_state=0,\n", + " n_iter_max=500,\n", + " tol=1e-9,\n", + ")\n", + "\n", + "F_pickup, F_dropoff, F_hour = cp_factors\n", + "cp_recon = tl.cp_to_tensor((cp_weights, cp_factors))\n", + "cp_error = np.linalg.norm(cp_recon - T_taxi) / np.linalg.norm(T_taxi)\n", + "\n", + "cp_params = (\n", + " len(cp_weights)\n", + " + sum(f.size for f in cp_factors)\n", + ")\n", + "\n", + "tucker_ranks = (2, 2, 3)\n", + "tucker_params = (\n", + " np.prod(tucker_ranks)\n", + " + T_taxi.shape[0] * tucker_ranks[0]\n", + " + T_taxi.shape[1] * tucker_ranks[1]\n", + " + T_taxi.shape[2] * tucker_ranks[2]\n", + ")\n", + "\n", + "print(\"taxi tensor / tensor taxis:\", T_taxi.shape)\n", + "print(\"CP rank:\", rank_cp)\n", + "print(\"CP relative error / error relativo:\", f\"{cp_error:.4f}\")\n", + "print(\"CP parameters / parámetros:\", int(cp_params))\n", + "print(\"Tucker (2,2,3) parameters / parámetros:\", int(tucker_params))\n", + "\n", + "component_slider = widgets.IntSlider(\n", + " value=1,\n", + " min=1,\n", + " max=rank_cp,\n", + " step=1,\n", + " description=\"Component / Componente:\",\n", + " continuous_update=False,\n", + " style={\"description_width\": \"145px\"},\n", + ")\n", + "\n", + "def show_cp_component(component):\n", + " r = component - 1\n", + "\n", + " fig, axes = plt.subplots(1, 3, figsize=(11.5, 3.1))\n", + "\n", + " axes[0].bar(pb, F_pickup[:, r])\n", + " axes[0].set_title(\"pickup / origen\")\n", + " axes[0].tick_params(axis=\"x\", rotation=40)\n", + "\n", + " axes[1].bar(db, F_dropoff[:, r])\n", + " axes[1].set_title(\"dropoff / destino\")\n", + " axes[1].tick_params(axis=\"x\", rotation=40)\n", + "\n", + " axes[2].bar(range(24), F_hour[:, r])\n", + " axes[2].set_title(\"hour / hora\")\n", + " axes[2].set_xlabel(\"hour / hora\")\n", + "\n", + " fig.suptitle(f\"CP component / componente {component}\")\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " print(\n", + " \"strongest pickup / origen dominante:\",\n", + " pb[int(np.argmax(np.abs(F_pickup[:, r])))],\n", + " )\n", + " print(\n", + " \"strongest dropoff / destino dominante:\",\n", + " db[int(np.argmax(np.abs(F_dropoff[:, r])))],\n", + " )\n", + " print(\n", + " \"peak hour / hora pico:\",\n", + " int(np.argmax(np.abs(F_hour[:, r]))),\n", + " )\n", + "\n", + "cp_output = widgets.interactive_output(\n", + " show_cp_component,\n", + " {\"component\": component_slider},\n", + ")\n", + "\n", + "display(widgets.VBox([component_slider, cp_output]))" + ], + "id": "o4UNpJMUpbCw" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Take-home D — Cholesky builds correlation\n", + "\n", + "This exercise is **synthetic by design**. We choose the covariance matrix ourselves so there is a known causal truth: the individual asset volatilities remain fixed, while only cross-asset dependence changes.\n", + "\n", + "Use Cholesky `Σ = L Lᵀ` to transform independent Gaussian noise into correlated draws, then compare a correctly correlated portfolio simulation with the incorrect assumption of independence.\n", + "\n", + "> 🇪🇸 Este experimento es **sintético a propósito**. Elegimos la matriz de covarianza para conocer la verdad causal: las volatilidades individuales permanecen iguales y solo cambia la dependencia entre activos. Usa Cholesky para convertir ruido independiente en muestras correlacionadas y compara ambas simulaciones." + ], + "id": "mECc8EZCpbCw" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO\n", + "# 1. Build Sigma from volatilities and correlations.\n", + "# 2. Compute L = cholesky(Sigma) and verify L @ L.T == Sigma.\n", + "# 3. Transform independent noise z into correlated noise L @ z.\n", + "# 4. Simulate the same portfolio twice:\n", + "# - with the correct covariance;\n", + "# - with the same asset volatilities but zero cross-correlation.\n", + "# 5. Compare standard deviation and lower-tail percentiles." + ], + "id": "qJr9eFnPpbCw" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "id": "3KCacE6ipbCv" - }, - "outputs": [], - "source": [ - "# TODO\n", - "# 1. Rebuild the real NYC taxi tensor: pickup × dropoff × hour.\n", - "# 2. Fit a rank-3 CP decomposition with TensorLy.\n", - "# 3. Reconstruct the tensor and compute relative error.\n", - "# 4. Compare its parameter count with Tucker rank (2,2,3).\n", - "# 5. Inspect pickup, dropoff, and hour factors for each CP component." - ], - "id": "3KCacE6ipbCv" + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "vol = np.array([0.012, 0.015, 0.010])\n", + "\n", + "corr = np.array([\n", + " [1.00, 0.85, 0.20],\n", + " [0.85, 1.00, 0.20],\n", + " [0.20, 0.20, 1.00],\n", + "])\n", + "\n", + "Sigma = np.outer(vol, vol) * corr\n", + "L = np.linalg.cholesky(Sigma)\n", + "\n", + "print(\"L @ L.T == Sigma:\", np.allclose(L @ L.T, Sigma))\n", + "\n", + "rng_chol = np.random.default_rng(5)\n", + "\n", + "z = rng_chol.standard_normal((3, 100_000))\n", + "x = L @ z\n", + "\n", + "print(\n", + " \"covariance error / error de covarianza:\",\n", + " f\"{np.linalg.norm(np.cov(x) - Sigma):.6e}\",\n", + ")\n", + "\n", + "weights_portfolio = np.array([0.4, 0.4, 0.2])\n", + "mu = np.array([0.00030, 0.00035, 0.00020])\n", + "\n", + "n_days = 252\n", + "n_paths = 10_000\n", + "initial_value = 100.0\n", + "\n", + "z_paths = rng_chol.standard_normal((3, n_days * n_paths))\n", + "\n", + "correlated_returns = (\n", + " mu[:, None] + L @ z_paths\n", + ").reshape(3, n_paths, n_days)\n", + "\n", + "daily_corr = np.einsum(\n", + " \"a,apd->pd\",\n", + " weights_portfolio,\n", + " correlated_returns,\n", + ")\n", + "\n", + "terminal_corr = initial_value * np.prod(1 + daily_corr, axis=1)\n", + "\n", + "independent_scale = np.diag(np.sqrt(np.diag(Sigma)))\n", + "\n", + "independent_returns = (\n", + " mu[:, None] + independent_scale @ z_paths\n", + ").reshape(3, n_paths, n_days)\n", + "\n", + "daily_ind = np.einsum(\n", + " \"a,apd->pd\",\n", + " weights_portfolio,\n", + " independent_returns,\n", + ")\n", + "\n", + "terminal_ind = initial_value * np.prod(1 + daily_ind, axis=1)\n", + "\n", + "print(\n", + " \"std correlated / correlacionado:\",\n", + " f\"{terminal_corr.std():.2f}\",\n", + ")\n", + "print(\n", + " \"std independent / independiente:\",\n", + " f\"{terminal_ind.std():.2f}\",\n", + ")\n", + "print(\n", + " \"1%, 5% correlated:\",\n", + " np.round(np.percentile(terminal_corr, [1, 5]), 2),\n", + ")\n", + "print(\n", + " \"1%, 5% independent:\",\n", + " np.round(np.percentile(terminal_ind, [1, 5]), 2),\n", + ")\n", + "\n", + "fig, ax = plt.subplots(figsize=(6.3, 3.5))\n", + "ax.hist(\n", + " terminal_ind,\n", + " bins=70,\n", + " density=True,\n", + " alpha=0.55,\n", + " label=\"independent / independiente\",\n", + ")\n", + "ax.hist(\n", + " terminal_corr,\n", + " bins=70,\n", + " density=True,\n", + " alpha=0.55,\n", + " label=\"correlated / correlacionado\",\n", + ")\n", + "ax.set_xlabel(\"terminal portfolio value / valor final\")\n", + "ax.set_ylabel(\"density / densidad\")\n", + "ax.set_title(\"Dependence changes portfolio tails / La dependencia cambia las colas\")\n", + "ax.legend(fontsize=8)\n", + "plt.tight_layout()\n", + "plt.show()" + ], + "id": "bABSrLD9pbCw" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Take-home E — Audio denoising by low-rank STFT\n", + "\n", + "The recording is **real**: a pinned CC0 voice sample. The added noise is **synthetic by design**, because a known clean reference lets us measure SNR objectively.\n", + "\n", + "Pipeline:\n", + "\n", + "**real voice → controlled noise → STFT matrix → SVD truncation → ISTFT → SNR**\n", + "\n", + "The key lesson is not “SVD always removes noise.” It is that a low-rank approximation can help when the structured signal concentrates more strongly than the noise in dominant singular directions.\n", + "\n", + "> 🇪🇸 La grabación es **real** y el ruido agregado es **sintético a propósito**, porque necesitamos una referencia limpia conocida para medir SNR. La lección no es que “SVD siempre elimina ruido”, sino que la aproximación de bajo rango puede ayudar cuando la señal está más concentrada que el ruido en las direcciones singulares dominantes." + ], + "id": "Moxsvg9OpbCw" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO\n", + "# 1. Download and verify the pinned voice.wav.\n", + "# 2. Add controlled Gaussian noise at 5 dB target SNR.\n", + "# 3. Compute STFT(noisy) and its complex SVD.\n", + "# 4. Try several truncation ranks k.\n", + "# 5. Reconstruct with ISTFT and measure SNR for each k.\n", + "# 6. Find the best tested k and explain why full rank returns to the noisy signal." + ], + "id": "v4VJoIUMpbCw" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ], - "colab": { - "base_uri": "https://localhost:8080/", - "height": 496, - "referenced_widgets": [ - "27879fcad4af4e0cbd4940b1a120c39f", - "68c2f2688c854983adea71cfaa880d47", - "7a6f89b309f8428db11c73ebbeadaf43", - "0a570d84b9c04beea75661ee7971694f", - "be805ac7d7dd4bf5b8cd48a77e24378f", - "916f2343e79e4158b8cdf569e574f1d3", - "82e9c57171424918aeaaeae685d24a81" - ] - }, - "id": "o4UNpJMUpbCw", - "outputId": "163fb309-dcfa-4d79-d66a-62c7f42e1714" - }, - "outputs": [ + "tags": [ + "solution", + "hide-input" + ], + "cellView": "form" + }, + "outputs": [], + "source": [ + "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", + "VOICE_URL = (\n", + " \"https://raw.githubusercontent.com/pdx-cs-sound/wavs/\"\n", + " \"ed5ebcbbbc2d11f0adddc9b50b78d581c29f738c/voice.wav\"\n", + ")\n", + "VOICE_SHA256 = \"2c4b4d9d5f90715fdbf599869a465d521638f40ca978b186df96f1543a4d67dc\"\n", + "\n", + "def fetch_verified_wav(url, expected_sha256):\n", + " from scipy.io import wavfile\n", + "\n", + " raw = urllib.request.urlopen(url, timeout=30).read()\n", + " got = hashlib.sha256(raw).hexdigest()\n", + "\n", + " if got != expected_sha256:\n", + " raise ValueError(\n", + " \"checksum mismatch: refusing to use unverified audio\"\n", + " )\n", + "\n", + " return wavfile.read(io.BytesIO(raw))\n", + "\n", + "fs, clean_i16 = fetch_verified_wav(\n", + " VOICE_URL,\n", + " VOICE_SHA256,\n", + ")\n", + "\n", + "clean = clean_i16.astype(np.float64) / 32768.0\n", + "\n", + "if clean.ndim > 1:\n", + " clean = clean.mean(axis=1)\n", + "\n", + "rng_audio = np.random.default_rng(42)\n", + "\n", + "TARGET_SNR_DB = 5.0\n", + "noise = rng_audio.standard_normal(clean.shape)\n", + "\n", + "noise_scale = np.sqrt(\n", + " np.mean(clean**2) /\n", + " (\n", + " np.mean(noise**2)\n", + " * 10 ** (TARGET_SNR_DB / 10)\n", + " )\n", + ")\n", + "\n", + "noisy = clean + noise_scale * noise\n", + "\n", + "print(\n", + " \"duration / duración:\",\n", + " f\"{len(clean) / fs:.3f} s\",\n", + ")\n", + "print(\n", + " \"measured noisy SNR / SNR ruidoso:\",\n", + " f\"{snr_db(clean, noisy):.2f} dB\",\n", + ")\n", + "\n", + "f, t, Z = signal.stft(\n", + " noisy,\n", + " fs=fs,\n", + " nperseg=1024,\n", + " noverlap=512,\n", + ")\n", + "\n", + "U, s, Vh = np.linalg.svd(\n", + " Z,\n", + " full_matrices=False,\n", + ")\n", + "\n", + "candidates = [2, 5, 10, 20, 40, 80, len(s)]\n", + "\n", + "snrs = []\n", + "energies = []\n", + "reconstructions = {}\n", + "\n", + "for k in candidates:\n", + " Zk = (U[:, :k] * s[:k]) @ Vh[:k, :]\n", + "\n", + " _, x_rec = signal.istft(\n", + " Zk,\n", + " fs=fs,\n", + " nperseg=1024,\n", + " noverlap=512,\n", + " )\n", + "\n", + " n = min(len(clean), len(x_rec))\n", + "\n", + " rec = x_rec[:n]\n", + " ref = clean[:n]\n", + "\n", + " reconstructions[k] = rec\n", + "\n", + " snrs.append(snr_db(ref, rec))\n", + " energies.append(\n", + " np.sum(s[:k]**2) /\n", + " np.sum(s**2)\n", + " )\n", + "\n", + "best_i = int(np.argmax(snrs))\n", + "best_k = candidates[best_i]\n", + "\n", + "print(\"STFT shape / forma:\", Z.shape)\n", + "print(\"full possible rank / rango completo:\", len(s))\n", + "print(\"best tested k / mejor k probado:\", best_k)\n", + "print(\"best SNR / mejor SNR:\", f\"{snrs[best_i]:.2f} dB\")\n", + "print(\n", + " \"improvement / mejora:\",\n", + " f\"{snrs[best_i] - snr_db(clean, noisy):.2f} dB\",\n", + ")\n", + "\n", + "fig, ax = plt.subplots(figsize=(6.4, 3.4))\n", + "ax.plot(\n", + " candidates,\n", + " snrs,\n", + " marker=\"o\",\n", + ")\n", + "ax.axhline(\n", + " snr_db(clean, noisy),\n", + " linestyle=\"--\",\n", + " linewidth=1,\n", + " label=\"noisy baseline / línea base\",\n", + ")\n", + "ax.set_xlabel(\"truncation rank k / rango k\")\n", + "ax.set_ylabel(\"SNR (dB)\")\n", + "ax.set_title(\"More rank is not always better / Más rango no siempre es mejor\")\n", + "ax.legend(fontsize=8)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "rank_dropdown = widgets.Dropdown(\n", + " options=candidates,\n", + " value=best_k,\n", + " description=\"Rank k:\",\n", + ")\n", + "\n", + "play_audio = widgets.Checkbox(\n", + " value=False,\n", + " description=\"Play / Reproducir\",\n", + ")\n", + "\n", + "audio_output = widgets.Output()\n", + "\n", + "def update_audio(*_):\n", + " with audio_output:\n", + " audio_output.clear_output()\n", + "\n", + " k = rank_dropdown.value\n", + "\n", + " print(\n", + " f\"k={k} | retained energy / energía=\"\n", + " f\"{100 * energies[candidates.index(k)]:.1f}% | \"\n", + " f\"SNR={snrs[candidates.index(k)]:.2f} dB\"\n", + " )\n", + "\n", + " if play_audio.value:\n", + " rec = reconstructions[k]\n", + " n = len(rec)\n", + " reference = clean[:n]\n", + " noisy_local = noisy[:n]\n", + "\n", + " peak = max(\n", + " np.abs(reference).max(),\n", + " np.abs(noisy_local).max(),\n", + " np.abs(rec).max(),\n", + " )\n", + "\n", + " print(\"Before / Antes\")\n", + " display(Audio(noisy_local / peak, rate=fs))\n", + "\n", + " print(\"After / Después\")\n", + " display(Audio(rec / peak, rate=fs))\n", + "\n", + "rank_dropdown.observe(update_audio, names=\"value\")\n", + "play_audio.observe(update_audio, names=\"value\")\n", + "\n", + "display(\n", + " widgets.VBox([\n", + " rank_dropdown,\n", + " play_audio,\n", + " audio_output,\n", + " ])\n", + ")\n", + "\n", + "update_audio()" + ], + "id": "Du6gTydypbCw" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What just happened\n", + "\n", + "The five take-homes reuse the workshop rather than introducing five unrelated tricks:\n", + "\n", + "1. **PCA:** approximation can be mathematically optimal and still answer the wrong scientific question if feature scales dominate.\n", + "2. **Attention:** two tensor contractions plus normalization and masking turn pairwise similarity into a weighted combination.\n", + "3. **CP:** another tensor factorization changes the representation and interpretability trade-off relative to Tucker.\n", + "4. **Cholesky:** a factorization can be used constructively to impose a known covariance structure.\n", + "5. **Audio:** low-rank truncation is useful only when the measured approximation improves the signal criterion you care about.\n", + "\n", + "### The sentence to leave with\n", + "\n", + "> **Represent the structure you actually have, approximate only when you can measure the loss, and never let shape manipulation hide what the axes mean.**\n", + "\n", + "The NumPy ideas transfer directly: `torch.einsum`, `tf.einsum`, and `jnp.einsum` use the same index notation, while libraries such as TensorLy provide production implementations of tensor decompositions.\n", + "\n", + "> 🇪🇸 Los cinco ejercicios reutilizan la misma idea del taller. **Representa la estructura que realmente tienes, aproxima solo cuando puedes medir la pérdida y nunca permitas que una manipulación de formas oculte el significado de los ejes.**" + ], + "id": "ipGw84X-pbCw" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Done with this section\n", + "\n", + "That is the whole workshop. Thank you for coming.\n", + "\n", + "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" + ], + "id": "M29ikzGTpbCx" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3" + }, + "colab": { + "provenance": [] + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "version_major": 2, + "version_minor": 0, + "state": { + "27879fcad4af4e0cbd4940b1a120c39f": { + "model_module": "@jupyter-widgets/controls", + "model_name": "VBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "VBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "VBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_68c2f2688c854983adea71cfaa880d47", + "IPY_MODEL_7a6f89b309f8428db11c73ebbeadaf43" + ], + "layout": "IPY_MODEL_0a570d84b9c04beea75661ee7971694f" + } + }, + "68c2f2688c854983adea71cfaa880d47": { + "model_module": "@jupyter-widgets/controls", + "model_name": "IntSliderModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "IntSliderModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "IntSliderView", + "continuous_update": false, + "description": "Component / Componente:", + "description_tooltip": null, + "disabled": false, + "layout": "IPY_MODEL_be805ac7d7dd4bf5b8cd48a77e24378f", + "max": 3, + "min": 1, + "orientation": "horizontal", + "readout": true, + "readout_format": "d", + "step": 1, + "style": "IPY_MODEL_916f2343e79e4158b8cdf569e574f1d3", + "value": 2 + } + }, + "7a6f89b309f8428db11c73ebbeadaf43": { + "model_module": "@jupyter-widgets/output", + "model_name": "OutputModel", + "model_module_version": "1.0.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/output", + "_model_module_version": "1.0.0", + "_model_name": "OutputModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/output", + "_view_module_version": "1.0.0", + "_view_name": "OutputView", + "layout": "IPY_MODEL_82e9c57171424918aeaaeae685d24a81", + "msg_id": "", + "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "taxi tensor / tensor taxis: (4, 5, 24)\n", - "CP rank: 3\n", - "CP relative error / error relativo: 0.0349\n", - "CP parameters / parámetros: 102\n", - "Tucker (2,2,3) parameters / parámetros: 102\n" - ] + "output_type": "display_data", + "data": { + "text/plain": "
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\n" + }, + "metadata": {} }, { - "output_type": "display_data", - "data": { - "text/plain": [ - "VBox(children=(IntSlider(value=1, continuous_update=False, description='Component / Componente:', max=3, min=1…" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "27879fcad4af4e0cbd4940b1a120c39f" - } - }, - "metadata": { - "application/vnd.jupyter.widget-view+json": { - "colab": { - "custom_widget_manager": { - "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" - } - } - } - } + "output_type": "stream", + "name": "stdout", + "text": [ + "strongest pickup / origen dominante: Queens\n", + "strongest dropoff / destino dominante: Queens\n", + "peak hour / hora pico: 19\n" + ] } - ], - "source": [ - "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", - "try:\n", - " import tensorly as tl\n", - " from tensorly.decomposition import parafac\n", - "except ImportError:\n", - " subprocess.run(\n", - " [sys.executable, \"-m\", \"pip\", \"install\", \"-q\", \"tensorly\"],\n", - " check=True,\n", - " )\n", - " import tensorly as tl\n", - " from tensorly.decomposition import parafac\n", - "\n", - "TAXIS = \"https://raw.githubusercontent.com/mwaskom/seaborn-data/master/taxis.csv\"\n", - "taxis = pd.read_csv(TAXIS)\n", - "\n", - "taxis[\"pickup_dt\"] = pd.to_datetime(taxis[\"pickup\"], errors=\"coerce\")\n", - "taxis[\"hour\"] = taxis[\"pickup_dt\"].dt.hour\n", - "\n", - "sub = taxis.dropna(\n", - " subset=[\"pickup_borough\", \"dropoff_borough\", \"hour\"]\n", - ").copy()\n", - "sub[\"hour\"] = sub[\"hour\"].astype(int)\n", - "\n", - "pb = sorted(sub[\"pickup_borough\"].unique())\n", - "db = sorted(sub[\"dropoff_borough\"].unique())\n", - "\n", - "p_idx = {name: i for i, name in enumerate(pb)}\n", - "d_idx = {name: i for i, name in enumerate(db)}\n", - "\n", - "T_taxi = np.zeros((len(pb), len(db), 24), dtype=float)\n", - "\n", - "for (p, d, h), count in sub.groupby(\n", - " [\"pickup_borough\", \"dropoff_borough\", \"hour\"]\n", - ").size().items():\n", - " T_taxi[p_idx[p], d_idx[d], int(h)] = float(count)\n", - "\n", - "rank_cp = 3\n", - "\n", - "cp_weights, cp_factors = parafac(\n", - " tl.tensor(T_taxi),\n", - " rank=rank_cp,\n", - " init=\"svd\",\n", - " random_state=0,\n", - " n_iter_max=500,\n", - " tol=1e-9,\n", - ")\n", - "\n", - "F_pickup, F_dropoff, F_hour = cp_factors\n", - "cp_recon = tl.cp_to_tensor((cp_weights, cp_factors))\n", - "cp_error = np.linalg.norm(cp_recon - T_taxi) / np.linalg.norm(T_taxi)\n", - "\n", - "cp_params = (\n", - " len(cp_weights)\n", - " + sum(f.size for f in cp_factors)\n", - ")\n", - "\n", - "tucker_ranks = (2, 2, 3)\n", - "tucker_params = (\n", - " np.prod(tucker_ranks)\n", - " + T_taxi.shape[0] * tucker_ranks[0]\n", - " + T_taxi.shape[1] * tucker_ranks[1]\n", - " + T_taxi.shape[2] * tucker_ranks[2]\n", - ")\n", - "\n", - "print(\"taxi tensor / tensor taxis:\", T_taxi.shape)\n", - "print(\"CP rank:\", rank_cp)\n", - "print(\"CP relative error / error relativo:\", f\"{cp_error:.4f}\")\n", - "print(\"CP parameters / parámetros:\", int(cp_params))\n", - "print(\"Tucker (2,2,3) parameters / parámetros:\", int(tucker_params))\n", - "\n", - "component_slider = widgets.IntSlider(\n", - " value=1,\n", - " min=1,\n", - " max=rank_cp,\n", - " step=1,\n", - " description=\"Component / Componente:\",\n", - " continuous_update=False,\n", - " style={\"description_width\": \"145px\"},\n", - ")\n", - "\n", - "def show_cp_component(component):\n", - " r = component - 1\n", - "\n", - " fig, axes = plt.subplots(1, 3, figsize=(11.5, 3.1))\n", - "\n", - " axes[0].bar(pb, F_pickup[:, r])\n", - " axes[0].set_title(\"pickup / origen\")\n", - " axes[0].tick_params(axis=\"x\", rotation=40)\n", - "\n", - " axes[1].bar(db, F_dropoff[:, r])\n", - " axes[1].set_title(\"dropoff / destino\")\n", - " axes[1].tick_params(axis=\"x\", rotation=40)\n", - "\n", - " axes[2].bar(range(24), F_hour[:, r])\n", - " axes[2].set_title(\"hour / hora\")\n", - " axes[2].set_xlabel(\"hour / hora\")\n", - "\n", - " fig.suptitle(f\"CP component / componente {component}\")\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - " print(\n", - " \"strongest pickup / origen dominante:\",\n", - " pb[int(np.argmax(np.abs(F_pickup[:, r])))],\n", - " )\n", - " print(\n", - " \"strongest dropoff / destino dominante:\",\n", - " db[int(np.argmax(np.abs(F_dropoff[:, r])))],\n", - " )\n", - " print(\n", - " \"peak hour / hora pico:\",\n", - " int(np.argmax(np.abs(F_hour[:, r]))),\n", - " )\n", - "\n", - "cp_output = widgets.interactive_output(\n", - " show_cp_component,\n", - " {\"component\": component_slider},\n", - ")\n", - "\n", - "display(widgets.VBox([component_slider, cp_output]))" - ], - "id": "o4UNpJMUpbCw" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "mECc8EZCpbCw" - }, - "source": [ - "## Take-home D — Cholesky builds correlation\n", - "\n", - "This exercise is **synthetic by design**. We choose the covariance matrix ourselves so there is a known causal truth: the individual asset volatilities remain fixed, while only cross-asset dependence changes.\n", - "\n", - "Use Cholesky `Σ = L Lᵀ` to transform independent Gaussian noise into correlated draws, then compare a correctly correlated portfolio simulation with the incorrect assumption of independence.\n", - "\n", - "> 🇪🇸 Este experimento es **sintético a propósito**. Elegimos la matriz de covarianza para conocer la verdad causal: las volatilidades individuales permanecen iguales y solo cambia la dependencia entre activos. Usa Cholesky para convertir ruido independiente en muestras correlacionadas y compara ambas simulaciones." - ], - "id": "mECc8EZCpbCw" - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "id": "qJr9eFnPpbCw" - }, - "outputs": [], - "source": [ - "# TODO\n", - "# 1. Build Sigma from volatilities and correlations.\n", - "# 2. Compute L = cholesky(Sigma) and verify L @ L.T == Sigma.\n", - "# 3. Transform independent noise z into correlated noise L @ z.\n", - "# 4. Simulate the same portfolio twice:\n", - "# - with the correct covariance;\n", - "# - with the same asset volatilities but zero cross-correlation.\n", - "# 5. Compare standard deviation and lower-tail percentiles." - ], - "id": "qJr9eFnPpbCw" - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "solution", - "hide-input" - ], - "colab": { - "base_uri": "https://localhost:8080/", - "height": 461 - }, - "id": "bABSrLD9pbCw", - "outputId": "9b28a641-9fdf-4aee-af47-ec95c802a5c7" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "L @ L.T == Sigma: True\n", - "covariance error / error de covarianza: 1.118966e-06\n", - "std correlated / correlacionado: 18.89\n", - "std independent / independiente: 13.61\n", - "1%, 5% correlated: [70.1 79.59]\n", - "1%, 5% independent: [79.24 86.86]\n" - ] - }, + ] + } + }, + "0a570d84b9c04beea75661ee7971694f": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": 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\n" - }, - "metadata": {} - } - ], - "source": [ - "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", - "vol = np.array([0.012, 0.015, 0.010])\n", - "\n", - "corr = np.array([\n", - " [1.00, 0.85, 0.20],\n", - " [0.85, 1.00, 0.20],\n", - " [0.20, 0.20, 1.00],\n", - "])\n", - "\n", - "Sigma = np.outer(vol, vol) * corr\n", - "L = np.linalg.cholesky(Sigma)\n", - "\n", - "print(\"L @ L.T == Sigma:\", np.allclose(L @ L.T, Sigma))\n", - "\n", - "rng_chol = np.random.default_rng(5)\n", - "\n", - "z = rng_chol.standard_normal((3, 100_000))\n", - "x = L @ z\n", - "\n", - "print(\n", - " \"covariance error / error de covarianza:\",\n", - " f\"{np.linalg.norm(np.cov(x) - Sigma):.6e}\",\n", - ")\n", - "\n", - "weights_portfolio = np.array([0.4, 0.4, 0.2])\n", - "mu = np.array([0.00030, 0.00035, 0.00020])\n", - "\n", - "n_days = 252\n", - "n_paths = 10_000\n", - "initial_value = 100.0\n", - "\n", - "z_paths = rng_chol.standard_normal((3, n_days * n_paths))\n", - "\n", - "correlated_returns = (\n", - " mu[:, None] + L @ z_paths\n", - ").reshape(3, n_paths, n_days)\n", - "\n", - "daily_corr = np.einsum(\n", - " \"a,apd->pd\",\n", - " weights_portfolio,\n", - " correlated_returns,\n", - ")\n", - "\n", - "terminal_corr = initial_value * np.prod(1 + daily_corr, axis=1)\n", - "\n", - "independent_scale = np.diag(np.sqrt(np.diag(Sigma)))\n", - "\n", - "independent_returns = (\n", - " mu[:, None] + independent_scale @ z_paths\n", - ").reshape(3, n_paths, n_days)\n", - "\n", - "daily_ind = np.einsum(\n", - " \"a,apd->pd\",\n", - " weights_portfolio,\n", - " independent_returns,\n", - ")\n", - "\n", - "terminal_ind = initial_value * np.prod(1 + daily_ind, axis=1)\n", - "\n", - "print(\n", - " \"std correlated / correlacionado:\",\n", - " f\"{terminal_corr.std():.2f}\",\n", - ")\n", - "print(\n", - " \"std independent / independiente:\",\n", - " f\"{terminal_ind.std():.2f}\",\n", - ")\n", - "print(\n", - " \"1%, 5% correlated:\",\n", - " np.round(np.percentile(terminal_corr, [1, 5]), 2),\n", - ")\n", - "print(\n", - " \"1%, 5% independent:\",\n", - " np.round(np.percentile(terminal_ind, [1, 5]), 2),\n", - ")\n", - "\n", - "fig, ax = plt.subplots(figsize=(6.3, 3.5))\n", - "ax.hist(\n", - " terminal_ind,\n", - " bins=70,\n", - " density=True,\n", - " alpha=0.55,\n", - " label=\"independent / independiente\",\n", - ")\n", - "ax.hist(\n", - " terminal_corr,\n", - " bins=70,\n", - " density=True,\n", - " alpha=0.55,\n", - " label=\"correlated / correlacionado\",\n", - ")\n", - "ax.set_xlabel(\"terminal portfolio value / valor final\")\n", - "ax.set_ylabel(\"density / densidad\")\n", - "ax.set_title(\"Dependence changes portfolio tails / La dependencia cambia las colas\")\n", - "ax.legend(fontsize=8)\n", - "plt.tight_layout()\n", - "plt.show()" - ], - "id": "bABSrLD9pbCw" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Moxsvg9OpbCw" - }, - "source": [ - "## Take-home E — Audio denoising by low-rank STFT\n", - "\n", - "The recording is **real**: a pinned CC0 voice sample. The added noise is **synthetic by design**, because a known clean reference lets us measure SNR objectively.\n", - "\n", - "Pipeline:\n", - "\n", - "**real voice → controlled noise → STFT matrix → SVD truncation → ISTFT → SNR**\n", - "\n", - "The key lesson is not “SVD always removes noise.” It is that a low-rank approximation can help when the structured signal concentrates more strongly than the noise in dominant singular directions.\n", - "\n", - "> 🇪🇸 La grabación es **real** y el ruido agregado es **sintético a propósito**, porque necesitamos una referencia limpia conocida para medir SNR. La lección no es que “SVD siempre elimina ruido”, sino que la aproximación de bajo rango puede ayudar cuando la señal está más concentrada que el ruido en las direcciones singulares dominantes." - ], - "id": "Moxsvg9OpbCw" - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "id": "v4VJoIUMpbCw" - }, - "outputs": [], - "source": [ - "# TODO\n", - "# 1. Download and verify the pinned voice.wav.\n", - "# 2. Add controlled Gaussian noise at 5 dB target SNR.\n", - "# 3. Compute STFT(noisy) and its complex SVD.\n", - "# 4. Try several truncation ranks k.\n", - "# 5. Reconstruct with ISTFT and measure SNR for each k.\n", - "# 6. Find the best tested k and explain why full rank returns to the noisy signal." - ], - "id": "v4VJoIUMpbCw" - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "jupyter": { - "source_hidden": true + "output_type": "stream", + "name": "stdout", + "text": [ + "k=465 | retained energy / energía=100.0% | SNR=5.00 dB\n", + "Before / Antes\n" + ] }, - "tags": [ - "solution", - "hide-input" - ], - "colab": { - "base_uri": "https://localhost:8080/", - "height": 700, - "referenced_widgets": [ - "7041f21962494d23b5470b00f0e8c009", - "c879058c81504f51982b33ef21336a51", - "97730e2f5a6e40928acfadb441ef360e", - "ce86a3f4caac434f9eb6bed6326362a9", - "94b0813765d149c7ba13db1f15eb83c7", - "c8c287281e2f4efb87b39a729aca4a03", - "49685f60e0c04369a3f065ee04995688", - "f35a303bd29d4abc96460d291d97f418", - "a29d44b4318b44ad95f067bd57616dde", - "e771896785554b99ae7e73b1941d8143" - ] - }, - "id": "Du6gTydypbCw", - "outputId": "fada88c1-3076-4918-cad8-66d470395f65" - }, - "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "duration / duración: 4.949 s\n", - "measured noisy SNR / SNR ruidoso: 5.00 dB\n", - "STFT shape / forma: (513, 465)\n", - "full possible rank / rango completo: 465\n", - "best tested k / mejor k probado: 40\n", - "best SNR / mejor SNR: 9.08 dB\n", - "improvement / mejora: 4.08 dB\n" - ] + "output_type": "display_data", + "data": { + "text/plain": "", + "text/html": "\n \n " + }, + "metadata": {} }, { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} + "output_type": "stream", + "name": "stdout", + "text": [ + "After / Después\n" + ] }, { - "output_type": "display_data", - "data": { - "text/plain": [ - "VBox(children=(Dropdown(description='Rank k:', index=4, options=(2, 5, 10, 20, 40, 80, 465), value=40), Checkb…" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "7041f21962494d23b5470b00f0e8c009" - } - }, - "metadata": { - "application/vnd.jupyter.widget-view+json": { - "colab": { - "custom_widget_manager": { - "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" - } - } - } - } - } - ], - "source": [ - "#@title Solution / Solución — open after trying / abre después de intentar { display-mode: 'form' }\n", - "VOICE_URL = (\n", - " \"https://raw.githubusercontent.com/pdx-cs-sound/wavs/\"\n", - " \"ed5ebcbbbc2d11f0adddc9b50b78d581c29f738c/voice.wav\"\n", - ")\n", - "VOICE_SHA256 = \"2c4b4d9d5f90715fdbf599869a465d521638f40ca978b186df96f1543a4d67dc\"\n", - "\n", - "def fetch_verified_wav(url, expected_sha256):\n", - " from scipy.io import wavfile\n", - "\n", - " raw = urllib.request.urlopen(url, timeout=30).read()\n", - " got = hashlib.sha256(raw).hexdigest()\n", - "\n", - " if got != expected_sha256:\n", - " raise ValueError(\n", - " \"checksum mismatch: refusing to use unverified audio\"\n", - " )\n", - "\n", - " return wavfile.read(io.BytesIO(raw))\n", - "\n", - "fs, clean_i16 = fetch_verified_wav(\n", - " VOICE_URL,\n", - " VOICE_SHA256,\n", - ")\n", - "\n", - "clean = clean_i16.astype(np.float64) / 32768.0\n", - "\n", - "if clean.ndim > 1:\n", - " clean = clean.mean(axis=1)\n", - "\n", - "rng_audio = np.random.default_rng(42)\n", - "\n", - "TARGET_SNR_DB = 5.0\n", - "noise = rng_audio.standard_normal(clean.shape)\n", - "\n", - "noise_scale = np.sqrt(\n", - " np.mean(clean**2) /\n", - " (\n", - " np.mean(noise**2)\n", - " * 10 ** (TARGET_SNR_DB / 10)\n", - " )\n", - ")\n", - "\n", - "noisy = clean + noise_scale * noise\n", - "\n", - "print(\n", - " \"duration / duración:\",\n", - " f\"{len(clean) / fs:.3f} s\",\n", - ")\n", - "print(\n", - " \"measured noisy SNR / SNR ruidoso:\",\n", - " f\"{snr_db(clean, noisy):.2f} dB\",\n", - ")\n", - "\n", - "f, t, Z = signal.stft(\n", - " noisy,\n", - " fs=fs,\n", - " nperseg=1024,\n", - " noverlap=512,\n", - ")\n", - "\n", - "U, s, Vh = np.linalg.svd(\n", - " Z,\n", - " full_matrices=False,\n", - ")\n", - "\n", - "candidates = [2, 5, 10, 20, 40, 80, len(s)]\n", - "\n", - "snrs = []\n", - "energies = []\n", - "reconstructions = {}\n", - "\n", - "for k in candidates:\n", - " Zk = (U[:, :k] * s[:k]) @ Vh[:k, :]\n", - "\n", - " _, x_rec = signal.istft(\n", - " Zk,\n", - " fs=fs,\n", - " nperseg=1024,\n", - " noverlap=512,\n", - " )\n", - "\n", - " n = min(len(clean), len(x_rec))\n", - "\n", - " rec = x_rec[:n]\n", - " ref = clean[:n]\n", - "\n", - " reconstructions[k] = rec\n", - "\n", - " snrs.append(snr_db(ref, rec))\n", - " energies.append(\n", - " np.sum(s[:k]**2) /\n", - " np.sum(s**2)\n", - " )\n", - "\n", - "best_i = int(np.argmax(snrs))\n", - "best_k = candidates[best_i]\n", - "\n", - "print(\"STFT shape / forma:\", Z.shape)\n", - "print(\"full possible rank / rango completo:\", len(s))\n", - "print(\"best tested k / mejor k probado:\", best_k)\n", - "print(\"best SNR / mejor SNR:\", f\"{snrs[best_i]:.2f} dB\")\n", - "print(\n", - " \"improvement / mejora:\",\n", - " f\"{snrs[best_i] - snr_db(clean, noisy):.2f} dB\",\n", - ")\n", - "\n", - "fig, ax = plt.subplots(figsize=(6.4, 3.4))\n", - "ax.plot(\n", - " candidates,\n", - " snrs,\n", - " marker=\"o\",\n", - ")\n", - "ax.axhline(\n", - " snr_db(clean, noisy),\n", - " linestyle=\"--\",\n", - " linewidth=1,\n", - " label=\"noisy baseline / línea base\",\n", - ")\n", - "ax.set_xlabel(\"truncation rank k / rango k\")\n", - "ax.set_ylabel(\"SNR (dB)\")\n", - "ax.set_title(\"More rank is not always better / Más rango no siempre es mejor\")\n", - "ax.legend(fontsize=8)\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "rank_dropdown = widgets.Dropdown(\n", - " options=candidates,\n", - " value=best_k,\n", - " description=\"Rank k:\",\n", - ")\n", - "\n", - "play_audio = widgets.Checkbox(\n", - " value=False,\n", - " description=\"Play / Reproducir\",\n", - ")\n", - "\n", - "audio_output = widgets.Output()\n", - "\n", - "def update_audio(*_):\n", - " with audio_output:\n", - " audio_output.clear_output()\n", - "\n", - " k = rank_dropdown.value\n", - "\n", - " print(\n", - " f\"k={k} | retained energy / energía=\"\n", - " f\"{100 * energies[candidates.index(k)]:.1f}% | \"\n", - " f\"SNR={snrs[candidates.index(k)]:.2f} dB\"\n", - " )\n", - "\n", - " if play_audio.value:\n", - " rec = reconstructions[k]\n", - " n = len(rec)\n", - " reference = clean[:n]\n", - " noisy_local = noisy[:n]\n", - "\n", - " peak = max(\n", - " np.abs(reference).max(),\n", - " np.abs(noisy_local).max(),\n", - " np.abs(rec).max(),\n", - " )\n", - "\n", - " print(\"Before / Antes\")\n", - " display(Audio(noisy_local / peak, rate=fs))\n", - "\n", - " print(\"After / Después\")\n", - " display(Audio(rec / peak, rate=fs))\n", - "\n", - "rank_dropdown.observe(update_audio, names=\"value\")\n", - "play_audio.observe(update_audio, names=\"value\")\n", - "\n", - "display(\n", - " widgets.VBox([\n", - " rank_dropdown,\n", - " play_audio,\n", - " audio_output,\n", - " ])\n", - ")\n", - "\n", - "update_audio()" - ], - "id": "Du6gTydypbCw" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ipGw84X-pbCw" - }, - "source": [ - "## What just happened\n", - "\n", - "The five take-homes reuse the workshop rather than introducing five unrelated tricks:\n", - "\n", - "1. **PCA:** approximation can be mathematically optimal and still answer the wrong scientific question if feature scales dominate.\n", - "2. **Attention:** two tensor contractions plus normalization and masking turn pairwise similarity into a weighted combination.\n", - "3. **CP:** another tensor factorization changes the representation and interpretability trade-off relative to Tucker.\n", - "4. **Cholesky:** a factorization can be used constructively to impose a known covariance structure.\n", - "5. **Audio:** low-rank truncation is useful only when the measured approximation improves the signal criterion you care about.\n", - "\n", - "### The sentence to leave with\n", - "\n", - "> **Represent the structure you actually have, approximate only when you can measure the loss, and never let shape manipulation hide what the axes mean.**\n", - "\n", - "The NumPy ideas transfer directly: `torch.einsum`, `tf.einsum`, and `jnp.einsum` use the same index notation, while libraries such as TensorLy provide production implementations of tensor decompositions.\n", - "\n", - "> 🇪🇸 Los cinco ejercicios reutilizan la misma idea del taller. **Representa la estructura que realmente tienes, aproxima solo cuando puedes medir la pérdida y nunca permitas que una manipulación de formas oculte el significado de los ejes.**" - ], - "id": "ipGw84X-pbCw" - }, - { - "cell_type": "markdown", - "metadata": { - "id": "M29ikzGTpbCx" - }, - "source": [ - "---\n", - "\n", - "## Done with this section\n", - "\n", - "That is the whole workshop. Thank you for coming.\n", - "\n", - "> 🇪🇸 Ese es todo el taller. Gracias por participar. Las preguntas pueden continuar en español o en inglés.\n", - "\n", - "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" - ], - "id": "M29ikzGTpbCx" - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3" - }, - "colab": { - "provenance": [] - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "version_major": 2, - "version_minor": 0, - "state": { - "27879fcad4af4e0cbd4940b1a120c39f": { - "model_module": "@jupyter-widgets/controls", - "model_name": "VBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "VBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "VBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_68c2f2688c854983adea71cfaa880d47", - "IPY_MODEL_7a6f89b309f8428db11c73ebbeadaf43" - ], - "layout": "IPY_MODEL_0a570d84b9c04beea75661ee7971694f" - } - }, - "68c2f2688c854983adea71cfaa880d47": { - "model_module": "@jupyter-widgets/controls", - "model_name": "IntSliderModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "IntSliderModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "IntSliderView", - "continuous_update": false, - "description": "Component / Componente:", - "description_tooltip": null, - "disabled": false, - "layout": "IPY_MODEL_be805ac7d7dd4bf5b8cd48a77e24378f", - "max": 3, - "min": 1, - "orientation": "horizontal", - "readout": true, - "readout_format": "d", - "step": 1, - "style": "IPY_MODEL_916f2343e79e4158b8cdf569e574f1d3", - "value": 2 - } - }, - "7a6f89b309f8428db11c73ebbeadaf43": { - "model_module": "@jupyter-widgets/output", - "model_name": "OutputModel", - "model_module_version": "1.0.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/output", - "_model_module_version": "1.0.0", - "_model_name": "OutputModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/output", - "_view_module_version": "1.0.0", - "_view_name": "OutputView", - "layout": "IPY_MODEL_82e9c57171424918aeaaeae685d24a81", - "msg_id": "", - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": "
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def hosvd_bases(T): # ───────────────────────────────────────────────────────────────────────────── CONTENT["11"] = { - "setup": """import numpy as np + "setup": """import hashlib +import io +import subprocess +import sys +import urllib.request + +import numpy as np import pandas as pd -from sklearn.datasets import load_breast_cancer +import matplotlib.pyplot as plt +import ipywidgets as widgets + +from IPython.display import Audio, display from scipy import signal +from sklearn.datasets import load_breast_cancer -rng = np.random.default_rng(0)""", +try: + from google.colab import output + output.enable_custom_widget_manager() +except ImportError: + pass + +rng = np.random.default_rng(0) + +def softmax(x, axis=-1): + x = x - np.max(x, axis=axis, keepdims=True) + e = np.exp(x) + return e / np.sum(e, axis=axis, keepdims=True) + +def snr_db(reference, estimate): + reference = np.asarray(reference) + estimate = np.asarray(estimate) + return 10 * np.log10( + np.sum(reference**2) / + np.sum((estimate - reference)**2) + ) + +print("Setup ready / Preparación lista")""", } From b8d93a52a8a6717eacfd940b0db5f652153af32e Mon Sep 17 00:00:00 2001 From: Sebas Laverde Chunza Date: Sat, 29 Aug 2026 00:47:13 -0500 Subject: [PATCH 25/29] Regenerate derived tables and site for issue #44 --- _includes/notebooks-en.md | 22 ++++++++--------- _includes/sections-en.md | 22 ++++++++--------- _includes/sections-es.md | 22 ++++++++--------- docs/es/index.html | 24 +++++++++---------- docs/index.html | 24 +++++++++---------- docs/kahoot.html | 2 +- docs/notebooks.html | 24 +++++++++---------- docs/search.json | 6 ++--- ...-157ffcf8d215a6a9d1ca7ce373f62f76.min.css} | 4 ++-- ...ting-549806ee2085284f45b00abea8c6df48.css} | 6 ++++- docs/sitemap.xml | 14 +++++------ docs/slides/en/index.html | 2 +- docs/slides/es/index.html | 2 +- docs/tensors_workshop_plan_with_quizzes.html | 2 +- notebooks/README.md | 22 ++++++++--------- 15 files changed, 101 insertions(+), 97 deletions(-) rename docs/site_libs/bootstrap/{bootstrap-bc5beceb84f29304406e66ba9199029e.min.css => bootstrap-157ffcf8d215a6a9d1ca7ce373f62f76.min.css} (85%) rename docs/site_libs/quarto-html/{quarto-syntax-highlighting-97af773cfbe0d0670e77fe96929c7f4c.css => quarto-syntax-highlighting-549806ee2085284f45b00abea8c6df48.css} (94%) diff --git a/_includes/notebooks-en.md b/_includes/notebooks-en.md index bf8266d..18fa3e0 100644 --- a/_includes/notebooks-en.md +++ b/_includes/notebooks-en.md @@ -2,14 +2,14 @@ | # | Notebook | Covers | Colab | |---|---|---|---| | 00 | [`00-setup-and-data.ipynb`](https://github.com/project-delphi/tensors-workshop/blob/main/notebooks/00-setup-and-data.ipynb) | Setup and welcome — Load every dataset and confirm your runtime works before anything else. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/00-setup-and-data.ipynb) | -| 01 | [`01-what-a-tensor-is.ipynb`](https://github.com/project-delphi/tensors-workshop/blob/main/notebooks/01-what-a-tensor-is.ipynb) | What a tensor is — The vocabulary, shape in NumPy, and the three operations that matter. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/01-what-a-tensor-is.ipynb) | -| 02 | [`02-thinking-in-n-dimensions.ipynb`](https://github.com/project-delphi/tensors-workshop/blob/main/notebooks/02-thinking-in-n-dimensions.ipynb) | Thinking in N dimensions — Argue about what each axis means, and why a batch axis differs from a time axis. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/02-thinking-in-n-dimensions.ipynb) | -| 03 | [`03-indexing-and-broadcasting.ipynb`](https://github.com/project-delphi/tensors-workshop/blob/main/notebooks/03-indexing-and-broadcasting.ipynb) | Indexing and broadcasting real data — Select the right column of real tumour data, then meet zero-variance pixels. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/03-indexing-and-broadcasting.ipynb) | -| 04 | [`04-reshape-and-transpose.ipynb`](https://github.com/project-delphi/tensors-workshop/blob/main/notebooks/04-reshape-and-transpose.ipynb) | Reshape and transpose real images — HWC to CHW, NHWC to NCHW, and why reshape silently destroys an image. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/04-reshape-and-transpose.ipynb) | -| 05 | [`05-video-pipeline-design.ipynb`](https://github.com/project-delphi/tensors-workshop/blob/main/notebooks/05-video-pipeline-design.ipynb) | Video pipeline design — Design the tensor shape at every stage of two real video systems. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/05-video-pipeline-design.ipynb) | -| 06 | [`06-contraction-with-einsum.ipynb`](https://github.com/project-delphi/tensors-workshop/blob/main/notebooks/06-contraction-with-einsum.ipynb) | Contraction with einsum — One notation for the dot product, the matrix product, and a batch of images. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/06-contraction-with-einsum.ipynb) | -| 07 | [`07-inverses-and-pseudoinverse.ipynb`](https://github.com/project-delphi/tensors-workshop/blob/main/notebooks/07-inverses-and-pseudoinverse.ipynb) | Inverses and the pseudoinverse — Solve a 20,433-equation system that has no exact solution. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/07-inverses-and-pseudoinverse.ipynb) | -| 08 | [`08-recursion-with-matrices.ipynb`](https://github.com/project-delphi/tensors-workshop/blob/main/notebooks/08-recursion-with-matrices.ipynb) | Recursion with matrices and vectors — Apply one matrix again and again: Fibonacci, eigenvectors, and a real forecast. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/08-recursion-with-matrices.ipynb) | -| 09 | [`09-convolution-and-deconvolution.ipynb`](https://github.com/project-delphi/tensors-workshop/blob/main/notebooks/09-convolution-and-deconvolution.ipynb) | Convolution and deconvolution — Convolution is a structured matrix product, and blur can be partly undone. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/09-convolution-and-deconvolution.ipynb) | -| 10 | [`10-tucker-decomposition.ipynb`](https://github.com/project-delphi/tensors-workshop/blob/main/notebooks/10-tucker-decomposition.ipynb) | Tucker decomposition on real data — PCA generalized to every axis, on a real tensor of New York taxi trips. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/10-tucker-decomposition.ipynb) | -| 11 | [`11-wrap-up-and-take-homes.ipynb`](https://github.com/project-delphi/tensors-workshop/blob/main/notebooks/11-wrap-up-and-take-homes.ipynb) | Wrap-up and take-homes — What connects Blocks 4, 5 and 6, plus five take-home exercises. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/11-wrap-up-and-take-homes.ipynb) | +| 01 | [`01-what-a-tensor-is.ipynb`](https://github.com/project-delphi/tensors-workshop/blob/main/notebooks/01-what-a-tensor-is.ipynb) | What a tensor is — Learn to read a tensor's structure and track what its axes mean as you fix, rearrange, or contract them. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/01-what-a-tensor-is.ipynb) | +| 02 | [`02-thinking-in-n-dimensions.ipynb`](https://github.com/project-delphi/tensors-workshop/blob/main/notebooks/02-thinking-in-n-dimensions.ipynb) | Thinking in N dimensions — Learn to read real tensors by asking what every axis counts and why a batch axis is not the same thing as a time axis. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/02-thinking-in-n-dimensions.ipynb) | +| 03 | [`03-indexing-and-broadcasting.ipynb`](https://github.com/project-delphi/tensors-workshop/blob/main/notebooks/03-indexing-and-broadcasting.ipynb) | Indexing and broadcasting real data — Select named measurements from real tumour-sample data, compare meaningful subsets, then standardize real image data safely. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/03-indexing-and-broadcasting.ipynb) | +| 04 | [`04-reshape-and-transpose.ipynb`](https://github.com/project-delphi/tensors-workshop/blob/main/notebooks/04-reshape-and-transpose.ipynb) | Reshape and transpose real images — Use real images to move between HWC↔CHW and NHWC↔NCHW, then show why matching shapes do not guarantee matching axis semantics. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/04-reshape-and-transpose.ipynb) | +| 05 | [`05-video-pipeline-design.ipynb`](https://github.com/project-delphi/tensors-workshop/blob/main/notebooks/05-video-pipeline-design.ipynb) | Video pipeline design — Process one pinned real video end to end, then use what its axes actually mean to design two downstream video pipelines. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/05-video-pipeline-design.ipynb) | +| 06 | [`06-contraction-with-einsum.ipynb`](https://github.com/project-delphi/tensors-workshop/blob/main/notebooks/06-contraction-with-einsum.ipynb) | Contraction with einsum — Use one index rule on real data, then change the inputs interactively to test which index disappears. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/06-contraction-with-einsum.ipynb) | +| 07 | [`07-inverses-and-pseudoinverse.ipynb`](https://github.com/project-delphi/tensors-workshop/blob/main/notebooks/07-inverses-and-pseudoinverse.ipynb) | Inverses and the pseudoinverse — Use the pseudoinverse on real singular, tall, and wide systems, then inspect the geometry interactively. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/07-inverses-and-pseudoinverse.ipynb) | +| 08 | [`08-recursion-with-matrices.ipynb`](https://github.com/project-delphi/tensors-workshop/blob/main/notebooks/08-recursion-with-matrices.ipynb) | Recursion with matrices and vectors — Treat recursion as repeated state updates, connect repeated multiplication with dominant eigen-directions, and test recursive forecasting on real airline-passenger data. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/08-recursion-with-matrices.ipynb) | +| 09 | [`09-convolution-and-deconvolution.ipynb`](https://github.com/project-delphi/tensors-workshop/blob/main/notebooks/09-convolution-and-deconvolution.ipynb) | Convolution and deconvolution — Treat convolution as a structured linear operator, separate it from correlation, understand transposed convolution, and partially recover a real blurred image. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/09-convolution-and-deconvolution.ipynb) | +| 10 | [`10-tucker-decomposition.ipynb`](https://github.com/project-delphi/tensors-workshop/blob/main/notebooks/10-tucker-decomposition.ipynb) | Tucker decomposition on real data — Build a real tensor from New York taxi trips, compress each mode with HOSVD, and explore the trade-off between size, reconstruction error, and interpretable structure. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/10-tucker-decomposition.ipynb) | +| 11 | [`11-wrap-up-and-take-homes.ipynb`](https://github.com/project-delphi/tensors-workshop/blob/main/notebooks/11-wrap-up-and-take-homes.ipynb) | Wrap-up and take-homes — Wrap up the workshop around one connecting idea, then choose among five extensions: PCA, attention, CP, Cholesky, and audio. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/11-wrap-up-and-take-homes.ipynb) | diff --git a/_includes/sections-en.md b/_includes/sections-en.md index 3c08d3a..c770df1 100644 --- a/_includes/sections-en.md +++ b/_includes/sections-en.md @@ -15,7 +15,7 @@ 01 -What a tensor is
The vocabulary, shape in NumPy, and the three operations that matter. +What a tensor is
Learn to read a tensor's structure and track what its axes mean as you fix, rearrange, or contract them. demo 20 EN @@ -25,7 +25,7 @@ 02 -Thinking in N dimensions
Argue about what each axis means, and why a batch axis differs from a time axis. +Thinking in N dimensions
Learn to read real tensors by asking what every axis counts and why a batch axis is not the same thing as a time axis. group 20 EN @@ -35,7 +35,7 @@ 03 -Indexing and broadcasting real data
Select the right column of real tumour data, then meet zero-variance pixels. +Indexing and broadcasting real data
Select named measurements from real tumour-sample data, compare meaningful subsets, then standardize real image data safely. exercise 15 EN @@ -45,7 +45,7 @@ 04 -Reshape and transpose real images
HWC to CHW, NHWC to NCHW, and why reshape silently destroys an image. +Reshape and transpose real images
Use real images to move between HWC↔CHW and NHWC↔NCHW, then show why matching shapes do not guarantee matching axis semantics. exercise 15 EN @@ -58,7 +58,7 @@ 05 -Video pipeline design
Design the tensor shape at every stage of two real video systems. +Video pipeline design
Process one pinned real video end to end, then use what its axes actually mean to design two downstream video pipelines. group 15 EN @@ -68,7 +68,7 @@ 06 -Contraction with einsum
One notation for the dot product, the matrix product, and a batch of images. +Contraction with einsum
Use one index rule on real data, then change the inputs interactively to test which index disappears. exercise 15 EN @@ -78,7 +78,7 @@ 07 -Inverses and the pseudoinverse
Solve a 20,433-equation system that has no exact solution. +Inverses and the pseudoinverse
Use the pseudoinverse on real singular, tall, and wide systems, then inspect the geometry interactively. exercise 15 EN @@ -91,7 +91,7 @@ 08 -Recursion with matrices and vectors
Apply one matrix again and again: Fibonacci, eigenvectors, and a real forecast. +Recursion with matrices and vectors
Treat recursion as repeated state updates, connect repeated multiplication with dominant eigen-directions, and test recursive forecasting on real airline-passenger data. demo 10 EN @@ -101,7 +101,7 @@ 09 -Convolution and deconvolution
Convolution is a structured matrix product, and blur can be partly undone. +Convolution and deconvolution
Treat convolution as a structured linear operator, separate it from correlation, understand transposed convolution, and partially recover a real blurred image. exercise 15 EN @@ -111,7 +111,7 @@ 10 -Tucker decomposition on real data
PCA generalized to every axis, on a real tensor of New York taxi trips. +Tucker decomposition on real data
Build a real tensor from New York taxi trips, compress each mode with HOSVD, and explore the trade-off between size, reconstruction error, and interpretable structure. exercise 15 EN @@ -124,7 +124,7 @@ 11 -Wrap-up and take-homes
What connects Blocks 4, 5 and 6, plus five take-home exercises. +Wrap-up and take-homes
Wrap up the workshop around one connecting idea, then choose among five extensions: PCA, attention, CP, Cholesky, and audio. wrap-up 5 EN diff --git a/_includes/sections-es.md b/_includes/sections-es.md index dda6bef..8ebdcba 100644 --- a/_includes/sections-es.md +++ b/_includes/sections-es.md @@ -15,7 +15,7 @@ 01 -Qué es un tensor
El vocabulario, la forma en NumPy y las tres operaciones que importan. +Qué es un tensor
Aprender a leer la estructura de un tensor y seguir el significado de sus ejes al fijarlos, reorganizarlos o contraerlos. demostración 20 EN @@ -25,7 +25,7 @@ 02 -Pensar en N dimensiones
Discutir qué significa cada eje y por qué un eje de lote difiere de un eje temporal. +Pensar en N dimensiones
Aprender a leer tensores reales preguntando qué cuenta cada eje y por qué un eje de lote no significa lo mismo que un eje temporal. grupo 20 EN @@ -35,7 +35,7 @@ 03 -Indexación y broadcasting con datos reales
Seleccionar la columna correcta de datos reales de tumores y encontrar píxeles de varianza cero. +Indexación y broadcasting con datos reales
Seleccionar medidas reales por nombre, comparar subconjuntos con máscaras booleanas y detectar píxeles de varianza cero. ejercicio 15 EN @@ -45,7 +45,7 @@ 04 -Reshape y transposición de imágenes reales
De HWC a CHW, de NHWC a NCHW, y por qué reshape destruye una imagen en silencio. +Reshape y transposición de imágenes reales
Reordenar ejes correctamente en imágenes y lotes reales, y comprobar por qué `reshape` puede conservar la forma mientras destruye el significado. ejercicio 15 EN @@ -58,7 +58,7 @@ 05 -Diseño de un pipeline de vídeo
Diseñar la forma del tensor en cada etapa de dos sistemas de vídeo reales. +Diseño de un pipeline de vídeo
Convertir un archivo de vídeo real en un tensor, observar qué se pierde al muestrear fotogramas y diseñar formas tensoriales que respeten el significado del tiempo. grupo 15 EN @@ -68,7 +68,7 @@ 06 -Contracción con einsum
Una sola notación para el producto punto, el producto matricial y un lote de imágenes. +Contracción con einsum
Aprender una sola regla de índices, comprobarla con datos reales y cambiar las entradas de forma interactiva para identificar qué índice desaparece. ejercicio 15 EN @@ -78,7 +78,7 @@ 07 -Inversas y la pseudoinversa
Resolver un sistema de 20.433 ecuaciones que no tiene solución exacta. +Inversas y la pseudoinversa
Usar la pseudoinversa en sistemas reales singulares, altos y anchos, e inspeccionar su geometría de forma interactiva. ejercicio 15 EN @@ -91,7 +91,7 @@ 08 -Recursión con matrices y vectores
Aplicar una misma matriz una y otra vez: Fibonacci, autovectores y un pronóstico real. +Recursión con matrices y vectores
Entender una recurrencia como una actualización de estado repetida, observar cuándo domina una dirección propia y comprobar qué ocurre cuando un pronóstico reutiliza sus propias predicciones. demostración 10 EN @@ -101,7 +101,7 @@ 09 -Convolución y deconvolución
La convolución es un producto matricial estructurado, y el desenfoque se puede deshacer en parte. +Convolución y deconvolución
Ver la convolución como un operador lineal estructurado, distinguir correlación de convolución, entender la convolución transpuesta y recuperar parcialmente una imagen real desenfocada. ejercicio 15 EN @@ -111,7 +111,7 @@ 10 -Descomposición de Tucker con datos reales
PCA generalizado a todos los ejes, sobre un tensor real de viajes en taxi de Nueva York. +Descomposición de Tucker con datos reales
Construir un tensor real de viajes en taxi, comprimir cada modo con HOSVD y explorar el compromiso entre tamaño, error e interpretación. ejercicio 15 EN @@ -124,7 +124,7 @@ 11 -Cierre y ejercicios para casa
Qué conecta los bloques 4, 5 y 6, más cinco ejercicios para casa. +Cierre y ejercicios para casa
Resume la idea que conecta el taller y luego elige entre cinco extensiones: PCA, atención, CP, Cholesky y audio. cierre 5 EN diff --git a/docs/es/index.html b/docs/es/index.html index d2534f6..854cc59 100644 --- a/docs/es/index.html +++ b/docs/es/index.html @@ -39,7 +39,7 @@ - + - + - + - + - + Tensors for Machine Learning diff --git a/docs/slides/es/index.html b/docs/slides/es/index.html index 92f2b07..58ca183 100644 --- a/docs/slides/es/index.html +++ b/docs/slides/es/index.html @@ -7,7 +7,7 @@ - + Tensors for Machine Learning – Tensores para Aprendizaje Automático diff --git a/docs/tensors_workshop_plan_with_quizzes.html b/docs/tensors_workshop_plan_with_quizzes.html index 85d8ec5..fd47a68 100644 --- a/docs/tensors_workshop_plan_with_quizzes.html +++ b/docs/tensors_workshop_plan_with_quizzes.html @@ -73,7 +73,7 @@ - + \n
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"- Diagnose why recursive forecast error can compound with horizon." + "- Diagnose why recursive forecast error can compound with horizon.\n", + "\n", + "> 🇪🇸 **Lo que podrás hacer:**\n", + "\n", + "> - Escribir una recurrencia como una actualización repetida del estado `x[t+1] = A @ x[t]`.\n", + "> - Explicar por qué las multiplicaciones repetidas pueden alinear un estado con un autovector dominante.\n", + "> - Usar una matriz sintética controlada para observar cómo la razón entre autovalores controla la velocidad de convergencia.\n", + "> - Ajustar un modelo autorregresivo real con la pseudoinversa y reutilizar sus propias predicciones como entradas.\n", + "> - Diagnosticar por qué el error de un pronóstico recursivo puede acumularse con el horizonte." ], "id": "c2SUj_xUZhNB" }, @@ -632,6 +640,8 @@ "\n", "## Done with this section\n", "\n", + "> 🇪🇸 **Fin de esta sección.**\n", + "\n", "Next up: **09 · Convolution and deconvolution** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/09-convolution-and-deconvolution.ipynb).\n", "\n", "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" @@ -651,1057 +661,6 @@ }, "colab": { "provenance": [] - 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Predecir los tamaños de salida `full`, `same` y `valid` para una imagen real y verificarlos de forma interactiva.\n", + "> - Explicar por qué la convolución en deep learning suele ser correlación cruzada y mostrar la relación mediante el volteo del kernel.\n", + "> - Escribir una convolución 1D de una fila real de píxeles como multiplicación por una matriz Toeplitz.\n", + "> - Explicar la convolución transpuesta como un operador lineal de superposición y suma que cambia la forma pero no es una inversa verdadera.\n", + "> - Recuperar una imagen real desenfocada con Richardson-Lucy y medir la mejora lejos de los artefactos de borde." ], "id": "m_jYVv4wfdAQ" }, @@ -531,6 +539,8 @@ "\n", "## Done with this section\n", "\n", + "> 🇪🇸 **Fin de esta sección.**\n", + "\n", "Next up: **10 · Tucker decomposition on real data** — [open in Colab](https://colab.research.google.com/github/project-delphi/tensors-workshop/blob/main/notebooks/10-tucker-decomposition.ipynb).\n", "\n", "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" @@ -550,785 +560,6 @@ }, "colab": { "provenance": [] - 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Wi3jhC1/onvHud78br3zlKyPLKhaLqFQq3v/i8Tie/OQnI5vN4hd+4Rf2VMdKpRIZiexRZ+oZUnukQ4cOYXV1NaTMT506hUOHDrnv8Xjnbp2cnAwp2DNnzlxwff7rf/2vuPvuu/HZz34WlUrFrbkI9pB+pvU9fPgwbrzxRpTLZfeqVCq47777AABvfOMb0Wg08OUvfxmVSgWf/vSnd/28crmMu+++G895znO8/38r9m2PvrWJa2SArfU1a2trmJqa2nFdJpNBo9Fw38+fP7/jGh9v27kZj8cxMzMTmp+rq6uYmprC5ORkqD5Ad/7/93//d1fOG97wBrzhDW8IlX2hdOjQodDaydnZWaytrYXmMukJT3gC3vOe92B2dhbvfOc78Qu/8Au4++67cfjwYYyPj4fqs7Kyglqt5n3mfffdhyAIcPXVV3v/n5ycxMzMTGgjD+2fbs/LZDJ461vfigcffBBf+MIX8LGPfQx/9Ed/hJGREaRSKfzrv/5r6N5ms4mnPe1pF9J9HfuvWzsymQwARPJbNxnfo29Pmpqawp/+6Z/il3/5lyPXeh49ehSPPPLInsteX1/H937v9+InfuInMD09jZWVFbzmNa/ZoUtV3nWTZ4lEAr/yK7+Ce++9Fw888ADOnDmD3/zN3wSwN/lj6Z3vfCdqtVro9fKXv7zjPZlMBs1mM9QenXPd+u1Vr3oV3vOe9+Dzn/88lpaWQutELV133XX4+te/3rE+GxsboTVS3WhjYwOPPPIIrrvuul3f06Nt6hlSe6SpqSk861nPwi/8wi+gXq/jzJkz+O3f/u2uE03pJS95CX7v934Ps7OzWFlZwW/91m9dcH0qlQqSySSKxSJqtRp+5Vd+5YLLAoDnP//5mJubwx/90R9hdXUVm5ubePDBB92i0UqlglQqhUKhgKWlJSe4dkP//M//jFtuuSVyweO3et/26FuP/viP/xgPPvggms0mfvmXfxnPfOYzvcr6hhtuwEc+8hGcP38e1Wp1T/OGdODAAbzoRS/Ca1/7Wud5nZ2ddesA77jjDkxPT+NP//RP0Wq18H/+z//Bxz/+8Ytr4AXSy172MrzlLW/B2bNnUavV8PrXvx7Pec5zMDk5uePa97znPZibm0MsFkOhUEA8HkdfXx+e8pSn4PDhw/i1X/s1VKtVBEGA06dP48Mf/rD3mf/4j//YEYA89alPxfDwMH7rt34L6+vr+Nd//dfQBhndnvehD30IDz30ENrtNnK5HPr7+5FIJBCPx/Ga17wGP//zP++cOEtLS6Gy97P/urVjdHQUR44cwbvf/W6022184hOfwD/90z+5/7vJ+B59+9INN9yAW2+9FW95y1u8///ET/wEfvM3fxP33HMPgiDAmTNndrVBwdraGlZXVzEyMoLBwUH867/+6471UZa6ybOPf/zjuOeee9BqtZBOp5FMJpFIJADsTf7slVqtFlZXV91rbW0Nl112Gfr7+/G+970Pm5ubeP/734+vfOUr7p4f+qEfwhe/+EW84x3vwNraGhqNBj7zmc+4/2+77TYEQYCf+qmfwste9rJQtNnSnXfeiU984hPu++nTp/G///f/Rq1WQ7vdxuc//3m8/e1vx/Oe9zx3zfr6OlZXV9Fut139dRfYz3/+85iamsKVV1550f3z7Ug9Q+oC6H3vex+azSaOHj2Kpz/96bjjjjvwS7/0S7u+/9d+7ddw7bXX4qqrrsJ1112H7/7u7wYAt+vKXuj1r389+vr6MDExgauvvho333zznstQymQy+OhHP4qPfexjOHbsGEZGRvDSl77UpX385m/+Jh555BEUi0U8/elPx3d913ftuuzdbEv8rdy3PfrWo1e+8pV4yUtegomJCUxPT7uNSiy97GUvwy233IIrrrgC1113He64444Let5f/MVfuJS+XC6HZzzjGbj77rsBAMPDw/j7v/97/Lf/9t9QKBTwzne+M3KDh8eb3vjGN+J5z3sebr75Zhw7dgwbGxuRG1989KMfxbXXXotMJoMXvvCF+M//+T/juuuuQ19fHz70oQ9henoaV155JfL5PO64445Iz243+dLf349/+Id/wEc+8hEMDw/jDW94QyiFptvzHnnkEdx+++3IZrO46qqrcPPNN+Mnf/InAWylR91888149rOfjWw2ixtvvBF33XXXhXZfx/7r1g5gK13yz//8z5HP5/HHf/zHoY0vusn4Hn1706/+6q/ine98Zyizg/SzP/uz+Mmf/Em8+MUvRjabxXOe85xdZX1ks1n84R/+IX78x38cuVwOv/3bv40f+IEf6HhPN3k2NzeHl7zkJSgUCrjkkkuQz+fxpje9CcDe5M9e6Rd/8RcxNDTkXpdffjlyuRz+9E//FG94wxswMjKCz33ucyFD5tChQ/jYxz6G973vfZiYmMCxY8fwv/7X/3L/x2Ix/OiP/ijuvfde/OiP/mjH53/3d383FhcX8bWvfc399ra3vQ2HDh1CoVDAK1/5SvzMz/wM3vCGN7j/n/vc52JoaAif+cxnXP110573vOc9+Omf/un96J5vS4oFvTylbzp94QtfwK233orV1VXEYrFvdnUeF2q1Wjhw4ADuu+8+745ajxd9O/Rtj3r07U4LCwu44oorMDs729Gb+61Kb3vb2/DBD36wF1XqUY/+g9J73vMevP3tbw8diBxF73//+/HBD37woqLepNOnT+P222/HPffcc0EO5x71IlLfFJqfn8cnPvEJbG5uYmZmBm984xvxfd/3fd/SQL9UKuFNb3rT425EfTv2bY969O1Oy8vLeNvb3vZtaUT1qEc9+o9NtVoNb3/7212Euxu95CUv2RcjCthav/XAAw/0jKiLoJ4h9U2gzc1N/H//3/+HfD6P6667DlNTU/jv//2/f7Or9bjS+Pi4O4Du8aRvx77tUY++3emyyy7DD//wD3+zq9GjHvWoR3uiv/zLv8TExASmpqb2tB68R//vUC+1r0c96lGPetSjHvWoRz3qUY/2SL2IVI961KMe9ahHPepRj3rUox7tkXqGVI961KMe9ahHPepRj3rUox7tkXqGVI961KMe9ahHPepRj3rUox7tkXqGVI961KMe9ahHPepRj3rUox7tkRK7vfCSSy5Bs9nExsYGCoUCBgYGEAQBgiBAu91GLBZDEASIx7dss3a7jc3NTQBAPB5320/39fW5/wGETldut9tYXV1FpVJBEAQYHBxEPB7H0NAQ0uk0AKDRaLjT2Pl8lhuLxRCLxbCxseGezXrptQMDA2i32wiCALFYDENDQ3jiE5+Iq666CiMjI0gkEu7aeDyOZDKJdDqNIAiwsLCA+++/H/fccw9mZmZC9QcA3btDt9xm3WKxGOLx+I56x+Nx9PX1ob+/HwMDA0gkEkilUujv70csFnP91t/fj3Q6jUwmA2DrcMVsNotUKoVMJoOBgQG0Wi3U63WUy2XMzc1hdnYWjUYDm5ubaLVarm/Yvs3NzVAdWDc+t9Vqufs43gCQSCSQzWYxMjKCoaEhbG5uor+/HwsLC0ilUojFYjh+/Diq1SpKpRL6+vqwsLCAK6+8Ei960Ytw/Phxxy/aX532P+F4fjNI++VC7ut0r69sXzuj+sj3u/ZVVN11ftjf99LXWv4111yzq3t8NDQ05PguHo872cFXp+f6SPuFfGvlgf6m9+l1vmttn2sddS7rO1+JRMLJAfsMnj6/sbHhZBk/r62tuXlMGaZ10Db75A/neDwed3Xo7+939e3r63OfE4mEe/X19bl3jgmfz/q0Wi2sr69jY2PD1X9zczMkq+142T7kGJEHtA6JRAIDAwOuLxOJROheto16o7+/H4ODgxgYGEAymcTAwICrv48/fLyzm7nL+7vJhQuRW3Z+7lb2aJ/s5bm2vZ2e75MvJMUBUfXi993IxHa7jVqthkqlglqthlqtho9//OO7bpdS7yiMHvWoR7uh3crOXRtSVJgUrgTjFJTtdtsJbVXwlhTEA9vCmve3Wi0H7uPxODKZDNrtNqrVqlOQqvwoiNWYUwGshp4q73g8jna7jYGBAVx//fW46qqr0NfXh5MnT2J2dhblchmrq6tot9sYGhrC1NQUrrjiCoyPj+OKK65Af38/7rvvPjz88MM72qR9pvXgb6pcrCIhEEkmkwC2DKdkMokgCBwgC4IArVbLlZtIJDA0NOSMnna7jb6+PiSTSRSLRcRiMSwvL2N1dRUbGxtYX1/H2tpaSKnFYrGQcUzDjQCGQE7HVq8ZGxvDxsYGZmdnkUwmcezYMdx4441ot9u45557kEgkMDMzg0KhgCuvvPIbdt7LbowzH3UyPC6mHrt97l7/9/2+lzZfbD/tJynQtw6IqGd3A33W8NH5aL/7jC8t2xqZPmBOY2RwcDAE5vl5YGAgJJ/4opzlPI3H49jY2Ai1f319HcCWE8oadUr8TQ1Sa5jQILHvfX19zqHD32lkqQymoaTG3vr6OtbX150xRZnEl2/89Df+TsOJcm9gYCDUn3Qyscx4PB4yWBOJBJLJZKjf2QYfWb7xjbfPeOZ3NYwt/0URy+xmsF3MXI4q2/JNt3r7HAaqd6PqYD/7+q5Tv/I9mUw6PuxRj3rUo/9XaNeGFICQR1cjOmqgWA8Ur/cJUwJ2VVQDAwMOsNNAaDab2NzcdIBehTG9qgQVrAfBg0aM6OVMp9NoNBoYGBjAiRMncPnllyOVSuFrX/sa5ubmAMApYoKEM2fOYGFhAcePH8exY8dw6NAhBEGA5eVlLC4uetvN9vLdKi6rVAiU6NnlPYxODQ0NIZlMOmDDSN3AwMD2gP7/wae+p1IppFIpLC0toVqtujrYaBqVYjKZdMYyvb4EIJubm27s2Z5Wq4XZ2VnEYjFcccUVmJiYwJOe9CS0223MzMxgaGgIGxsbGB0dRaPRwLFjx1AoFCL7Svun2zW+a7sBgguNfD2eRtRu629B0W4BVidw5AM09lndyt2NR343xHlLuUCwzP9s3ewzO9VDwbyNOPFeK5+0j1X+6butP+cdgT/n7dDQkPtMpxCdIpQz/LyxsRGKyvGlc6+b04p1oVGqER6N7PClhpM1rtTAUkOKmQesO40oOoRoWNGY0uiUNaqsMcs60XiiUcQX68RyNArItrGv1ZDS+kcZyN3mVqf/dzMv9f+oqFGnudntN+1L64DodP1uHSo+WaGGZCeDjc/xPcPWXflscHBwR0ZLj3rUox51om4yTR21FhPvlvYUkVIFSIWkArib4mE5jD7Z+2OxrTQ7Nqavr88p33g87pQzhTYVIv8nMXLW39/vylJvLIFFOp3G0aNHMT4+jvn5eeTzeRw8eBDDw8PIZDJYXV3F2bNncfLkSVQqFVSrVTzwwAPo7+/H5OQkDhw4gCuuuAL/8i//go2NjRDQ0XarQojyHLM/CQSYipJMJpFKpVAoFNx/NKBoaKqHVj2ufN/c3EQ6nUYsFkM6nUatVkO9Xg/1J7BtSLGPUqmUGy8CIfb35uYmBgcHcfz4cTztaU/D5OQkyuUyxsbGsL6+jmq1ivX1ddRqNZTLZUxNTeGxxx7DTTfdhOHhYZfCdbEU1Z+7MbgupOxvBPnqv1fjcC9GaDfQtFuDbS9e8yhSQ4rGlDpbOrWrUx9QZmhURMG8flZArS+fIcfPOr8pXxiRGhoacs4MvggKaUjpi8aHtpmyzlcnNUJsP7IvrQFFA8WmH/JaNbD0ZVMuKZ81tU8NqbW1NWdI0UjkPRqFU6OQ7aIhlEqlnCHK/lOnEtvOiJS2h4YU5alGObvxkR1fq+ssn3STF2oU7IbsvNttNGe3pPd1ky/dylHnxG4cQN3qa8sg5giC7YjtfhEdfORJrbPO6071jnJCWZli77FyJKp83zO0XMoc6m6LN7TudGYA20a8phNbh9FeycpqK8Psd3331Z1yQR1h9jdb192Mm8We6nDyZRnYMlTeaLTelznhk9EXShdSTqf5rb91mv/7Vf9OZPvayg3LW8q72kZ1ZCYSCTQaDVSrVZcar9kLg4ODOHToEO688068/vWvx9ra2p7rvaeIlG0YU8mswPExNiuvE4dl8j5XqUQC6+vrTnCyYxiZ4kTv7+9HEAQuTU0NLABO2FK45PN5NJtN96xCoYDh4WFsbm5idHQUExMTTkFT8R44cACDg4O4++67Ua/Xsbq6igcffNB5RCcnJzE8PIzFxUXEYjEUi0XUarVQ5Eb7zjIqJyDXYfG52WwWhUIBmUzGgQddrwDAAQNNd9D2E4Dkcjk0m02Uy2VUq1XHYJrmx2gfo1CMBBDosS817YrpSevr6xgYGMDRo0cBAM1mE0GwlYrRaDRw/fXXY2lpCTfddBMuueQS5PN5B2i68dvFgIRO90R5Qy/kWXuh3QAun0fW/tfNoOr2nw/Y2N87/eYrezdt60YajWKEIQiCHcotqo4+gAlsAxrKIc5HOmt8azkoy2y5tkyr+GmMaEpfKpVCOp126xmTyaSL8HIeMoLD9qv88BmS1vjQaykHNLrEea+OGmtMaSSK8oXXqPxRw67dbofWRdF40neNTGkEyweO2AafEZrJZJBOp52sHBwcdH2hdbURKb6rUe6jKBkQxd/WyPKV5SvP/qZG2V7IGnf2ub66Atup+J3qF0Xd6ukzOrWffH3cyUiMxWLOObq5uYn19XWsrq7uur6dqFAo4Atf+ALOnj2Lr371qyiVSqhWqyiXyy4Klk6nMTY2huHhYbRaLTSbTaytrTnZ0Ww2XQYN11+vrq5iZWUFc3NzqNfrGBoaQjabdf83m03U63WnK7mcgXOFchDYSuWt1+totVqh6DGzUsbGxnDdddfh+77v+5DNZi+oH9rtNh566CF84AMfwAMPPIB6vY56vY5Go+HmsOKuwcFBN04qa+jwKBaLOHjwICYnJ3H8+HEcP34cBw8eRCqVAtDdYO82d0ibm5tYWlrC/Pw81tbWsLa2hlgs5vqbWGNtbQ3nz59HrVZz5XF5RCqVwuTkJCYmJva05GC3BkiPvnnUyTDT/9XW+Nmf/dk9R6Z2bUhRoFHhUYhoZXweNMCfkmMFOYE1FWIQbK8J0rx7kkZ/KHDUcKKiVkOFz+WzU6mUW5ysRgkVbyKRQC6Xw+HDhzE9PY3V1VWsr6+jUqng3LlzGB8fRzqdxsTEBJLJJM6fP++Er3p36K2zQJBAMZVKIZfLYXh4GBMTE0in08jlcl7gwlQWfqbw1v/1+lgs5kBEOp3G2tqaE+Krq6tuU4rV1VXnBeY40HPFNMP+/n7X5xScjUYDzWYT+XweALC6uurKqNfrOH78OEqlEi677DIcPnw4ZAheKHUCCvp/lBGy2/L0vwsxrqIAw27viyprP4T24yH4OwHUvZDP+RAFEn2/+wyO3Rp69hpbji6S18/22daYYnqZptCRKKtojNiNJAC4+UbHBuUAjRLbTt00gi+bImfXG6kRpQ4aTe3j/FXDtt1uu+g/wd7GxgYGBwcd6OW7bqLR19fnnDeUj+oQswYgQRo326FBqm22m2TYaL32u4+HOs33TvMyan76yuvE03s1pqJ4NcowIVkZvNd5uxcg7APFvnZ26ksL2vdLfjWbTXz0ox/FF77wBafj6QwgFhkYGHCGu0aOWUc6kzWKTmOKmR/kTf7HOcBy7PpydZwQ/7AfNPI0MDCA+fl51Ot1JBIJXHnllZiYmMDg4GAoWry5uYl6vY75+XnMzs6iVCphY2PDzZF2u43Z2Vl8/etfx8LCwo65Sk++zk2bPcA11n19fVhdXUW1WsX8/Dza7TYWFxeRTqedAUasofOUDieNjisWYX2IX+r1OlZWVtxrdXXVGaKsJ8toNpuoVCrO0FI5PTg4iFwuh/HxcZw4cQJXXHEFhoeHQ2vHKd/YL+vr68hkMhgdHe0ZUf9BaHl5GefOnUO5XEYul3NOfVK5XMYHP/jBC0ob3lNECoj2wqqRoEaONR40nU93geO1Nt2DQoihdxUyfL5uQkHhAYQVFr0PDAMC27v3aeoLwYUaigcOHMANN9yA1dVVLC4uIggCVKtVtz6J9ebOdZqaSKGhQIH9MDAw4NL2RkdHMTw8jEKh4MpVocy2cmc8H2gluFGgo2l+7XYbmUzGgZqNjQ2srq669VO6Sxiw5QnzpWGqAtzc3HSpgvl8PpTOxJ0W+/v7MT4+jqGhIRfFIl90M1SilK3+18lwihJyUc/tpOijyoiqj6/8iyX7zAsV4t28177n7rU/9oO6GVC7vTdqnC3ABXbKOFKn72qoESRwHtqoEA0Tyr6otD2Wq/M4CAInt1Ru0hjhfb6d7miI6LotuzmD1lt38rPGlQW0CtpoSA0MDGBtbQ39/f1YXV11nmG2h553yhyNDGpKYlSKZDqddinCBHA2lVFTEqOM6b3O1yi5Y3WTgmIfWb7cq6zoJjuVpy5mbu7GmbRbA7CbkdnJMOS1+ylTNzc38eCDD+LUqVNYXFx0zk+CcfISMzk0zV1xDOtoDSDqWdV5vEfTkXwpbWwvDS4tl3xOA2tlZQXnzp1DKpVydWX0joZYrVbDwsKC20yL6cPJZBLxeNztiqjGiE2j87WZ9VeDiBk7sVgMa2trKJfLqNVqLgqvbbYb89j1mJYHNCrZaDScU3h1dRXNZtM5bjhONIBqtZpztqshRRxWLpddX4+Ojjq5Qad6u93G2tqaKy+ZTGJubs4ts9Bxs6nLKsfVCFVZEbUsRMmnH9iX+v9u5pbqQGJLS4pfbR20fN7vswf4f1TKqJ0b7A8a0KqPdSfWvr4+rK2tYWVlBbVaLRSNzOfzmJiYcPsHLC4uus3hKpUKstks5ufnMTU1hcnJSRSLRWxubuL8+fMXJF/2tEaqE1C1gx4FMvidC7t4j6a0AFsDsbq6GmI0ACHvpYIQTgpbR0ZvWN94PO46fHBw0G0ZrgqbqYXcZr2vrw9jY2M4duwYFhYWXJibhkcisbUNeKvVcmX7wBANOgAuQlQsFjE6OopsNotcLodsNrtjd0IKT7vjlE5QNdYomNT4oRLQ9U0MxTPSNDc3F0rjUwBHI5bRPgWd6+vrodRNpuAQJGWzWaTT6R2GIdsQRRagRJH9P8qgiSrD50WOEkad6tDpN50fUaBjN9dcDPk8vBdCvrm+X3W1QGm3IDdq/LsBSasQrJKIAnZRdVAgbT22atgMDg6GHD1aT750cwYL0O224pZ3rGOIRpTd9EKjUVpPluFzzmi7tF5UfLqDn41ysUyCQQWHagxSntuolG0LI1JsN+tlQYuO326cCLtxwlge8n33yTCfAdWpjE6GkuVPn5Hom1N7NXp8Ze6Woq63fM3fop5t59d+UBBsHWlSr9edAaHzToGcXd/nc8YA2yCS88Cu36BRYo8hUcPCygVbZ5ULjDgxW4YRYhpyTHmrVqtYWlpy6YvcFXRoaAj9/f0O19AoUoe2yhrraOHcZOpyLpdDLpfbsY5RI1wKjnWpgG54o7JGZQ6xH2UUM4DYN+xb3eym2Wy643vUeKUsCoKtjJtSqeSWadDxy+UOWibLo0GlslnloMpnXYNlZac1uqL0ns+AsXJFr7fp0iQffymuY7kaffQZU1ofdYL5nqeGlJVHqguUx3WeMPhAfiOWp5FOe4G7Va+uruLAgQNIp9NYXFzE6dOnce7cORfJDILtZUHFYhHAVkbV42pIdVIenTpJO1gBhgoM7TD1vKr30gpRLZdegyAIsLq66qIv9NQynYMCrdVqOaXMbcNV0TG/eXFxEUtLS+7sitnZWVSrVefFiMVi7js3r2AqIoUSPc+JRMLVjbnNnKg0aNSoo7AAttMe+Zuuh9KIk/Ua65ixjewXRps44UdGRlzUj6l8Op52InEsgiBwYfPx8fFQqiTrxvUgfHYnpd4N7EQZA92MhL0oXx8w9f2m19v7o57tA1M+AWiv2StFGZfd7rH19AlufY+6f79ov4y/qLKjlEE3IzqKP/hdFQO/2ygLSaM56mRQZUxZxvmjKX3W4GJETNOUKW/44oYXdKiojNH66m/aH9ouazyqQaYGmMp8yiMdC+0P+xvHhi8FKXZMLXhgvxB8WV3i+6xyqtM86jZHfe3i790MNd/6Mb1Px8iOma88rY9Pdvr6slP7lCd22wcXO5/3WxYEQYCVlRU0m80dOpH/s/6+sfC1W40lpgHasbPAWY0U218+TEV+VsOu1Wqh0Wi4CNja2po7d7PZbLqNnyqViltvTrlC/KTAWZ3QKmt0bvvSb+3GLjon6YBmncm7XKNN54vKDpUn7F/KOBLLomxUh7D2k41wsC9pcDEdk5tzAXBRNDqNie8AuI21rGzyPcvnkLK8yGt3a0jZ6y1ui9LpfKmstPpHnfLWmPI9x5eWauWvrbvW34fvtQ+twU1DihlRHJPBwUG3tq/VaqFYLGJ2dhZzc3MolUpujSOjVYODgzh8+DCCIAgtH9oL7Sm1j51BZveF79gJPgHDieqzWPm/how1VEjSEKbtYHpVfAND44EMTmbWCEt/fz+azSZWVlZw/vx5nD17FnNzc07INhoNxOPx0MG33LyCub9cYE2hmEgk3KQOgiC08Fm342XKjfaNKkkKDTUMeZ0KaRU6agTxXk07UvDGBbXsV1rmsVjMneVFQcfyaCj19/cjl8s5gUrB2G63vWuiovgmipeiDJjdlNEN3EeV1014daNObfIZZr7n7xYs+MrpBKb2Wudu7X48DKio56hwjqpHlBEU9d0nr1TJ67M71WM3vKbGCRBOYbNreLjmIBaLufP1NLJNWalykjJAd63TSJTueGcPqfUpcFWAtu8o43xOFspWa6SyPLaBANAqaV6nG1gwVZBpgmr8+QxA1pHyXkGNfdkMgCgdpfWzRri20YIUvaYTn6h+UzBmUzktMNNsBV/WQieyxkk3no6SNZ0Mrm7y25YVRT5evFhaXl520QpfX3NMfClu1kCyv9uD7H1gVu9T3ajX+GQXMRLnCbELf2fqG9PeGo1GaMdeGkU691muyifrFLLzxr5YP64lskaOrgXVdqszWD9rtpA9Q4732jRedSTzudYBo21gfShfms3mDhlAJzOxoBqXeryDfvcZUj6Hh/KBymGdOz5+iZq7UYYU9Yc1flhnu6MjdQkznKKMKZuaaPG35Wmtk15vr7NzUTMcmPHUaDScAwHY0psMftTrdRQKBZw+fdoZUpwLAJzTYWJiAlNTUxe8G+ieU/uUGUkaafANtFW69IJYIKKT2SewgO31V9ZzbMPkQPgwWb7b65hnzHS3er2O6elpzMzMoFKpIJlMIp/PO8HUbrdd9Ki/v99FpGiIqeESi8Wc8QFsby7BdDoaNZlMxkXGNC+Yn1lmu90OHeTJfqGBpN4QLccqZxo6/J2h8Vwu50LkZE4dQ+1vlnHo0CFcc801mJiY2GE08Rr9bhW75RO99htBPoCs/zE3XckKut0+h9db5avldAMxu71uN/f56tbp3qh7fON5seQDDj5QaPvQx0M+AysKnPi+W4PKPt+W5QNaUbytYEQjzZZP6Pig7FVDSpWbKj8CD98GE7rBjgVAWm91VhFwWPAepWBtFEVT+jTToK+vb8cuSarc19fXnSeSm9n45KRNPaQjh5+tsRRlTKk33hrMalhaoGsjFfpd29WNH2z7+c57tN8IKhQA6zyJmi+dZIvvPnuPb251kuH2v73SXuXdXsol2LcvAKF3jr0P59h3ft4NL+hn9ep3a6/eQ6codzPU9VF2TZfleXXgqGOZuEOvBXYaPRqNsnLF9h3rSQeJTSdT2aAOIeKmjY0NtxbdYhw1pjRd2cpvawD45rFGl2hg09BiqqCVxcRXfPcZUtYo9dUpyshSfozS2T69o2VqxJP/sa4aKdRx1v5UZ54+80INKd+1eo3PkFInGqOwzWbT6RBi3IGBAbcur1aruUgUo04aqa1UKpicnLxgR80FpfZZz54aRdoJtiPVK2EFrnpi7LMoWKwBodY0o0A6sazyswzA53It1NDQEOr1OjY3NzE5OYnLL7/chf/K5TKWl5dRr9cRBAEmJyeRyWSwsLCA06dPY3NzE9lsFvV6PWTckTF5/hONKAKdXC6HQqHg0t8UBNi+4GcaZ1z3pNdYIWijUz5BTSG1vr7udgtkuFrzxjkZWQbDp3YnMjWYaP1bgawRQQWRu1HgOq7alk7gwPIlf7P38je2j2BMd1cipdNpnD171oEcKpV4PB5aCO9rlwJfX72tY+IbRZ36ci/37xd1q4fl5aj/uikeHxC05ey2T6wS84FnlWk6T32ykP9ryggj4rZOnFN6lpJue65rojoBDZXRfLdeamu4RMlo3elMZaIF/BY8aIoOt2D2GX82DUiBGL3u1jDq9PJ54C1FGU36eyeDOoof+TwCCO0zHQe2T3+zhqst1/Ief/cBGx/tZW775tlujINu5UWNx8WQRjGUVIcqaLc6V+WDnedqlPvG3PKHr736m69fFStoChbnPefe6uoqhoaGUK1WHeZJJLZ2Js5kMgiCwG15TgcG20vdbsE1I92ZTMa9uJum3TDCYjC+a8TOGjPaB5SRmhWj/W+Nr2Qy6QwhyjybvmbHQOWVruOkoaFnb6qsUMNR5afOR+vw8vFMlCGlhkUUluH/tg46bhxH7WfKmFgstmNnbN6jGVSqM+x4WcMxypDyyX4f7rdl+iJSrCcDFtz7IJ/Po1gsolAooFwuu503AYSOJCEGJw9dCO0pIqUTQn+3k94nZNSStaBVP/M/Bf9BELjBY2fpJOSCMYZ7KSCUiewhWxpZ0zNQ+vv7cfjwYYyNjSGbzWJtbc2F/tvt7TMjjhw5gomJCWSzWZTLZbRaLbcjnoKh/v5+bG5uuoWYHKjBwUE3kHpGlO0LMjjTX2g00rJWpiaTa44yBYsadz6hrUJocHDQ7bbVaDRc31qvDCcgD1HmBhMKCufm5rC0tOTal8vlQnVST4lG7PSgYSukdAIrL/h4ttPvPtDDV6vVQr1eRy6XQyqVcgJG65FKpXD33Xe7hY4c0/7+fpw4cQKXXHKJm7hRtBtAYUGuD3Tqdb5yOxkcFwJM9hvMkHxgxP7P5/vaGEVUhvys79qnnYwpLcsaRfbdB7A7ee1oALGe+rs6V9QoYfqsknpm1ZhST61NgbHts3WPcnz5jCifIaWeaJUb1knmK8Pey3WmlHvtdju0TTt/p7yh51oBXTfjyX5W0Gb7yDfG+p99sU3Kf5YnVfaz7Xaxv3qNeZ3qvG68ap9p9W8neWHL7/S8Tvft5rqo5+wXxWIx5PN5t/hcDX4AIb3EfrfzQfvKOiA4nhwjC0CtgaVOCvs73y2Q5u8EkFz3CIQjalzLvLKygnq97vBOsVh02SgrKyuoVqtuMwXyvkaMNQqVSqVCG0xkMpkdKcOck+RpNUiiIlPEBHaTGv5nIyg2ZY/r0DkOvN7OVz6PdWPaXqVScc7qoaEhxxu69ERT3vr6+pyc1X5XDBolVyye1v6y2EQjXVbvWDmkfao6QftEZTLXGenSFLafONUaVCRrkNk26nyJcqL5jDPVE2rA02HNDVZqtZrDaMlkEoVCARMTExgbG3O7R5fLZQBb2VZDQ0M4ePAgJiYmcOTIERw6dMhlpV0I7WmNFBW9po9pJ2lHWEGpgpDC3wds+BsFAZnfWtIKeNnJnLicoMr0XDvF7/TQMEeSkzKTyWB4eNil8zHPkgs1l5aWEI/HUSwWMTY2hkajgcnJSQDbzBSPx12YmxGvfD7vDA7+T4ETj2+vWyIDcdKxDWSS/v5+Z0hVq9XQ+Cgg08M2VQjpIloVdHyGnmNDY4cTmFuIaj5qu93GPffcg5mZGRfNSqfTzsLnGReclJdffrmrn3r2KDi13uwDCkVGeXSXHLbFflevkr70dwWlFgCRx0dGRpySsRM/kUjg1KlTqFarWFlZcSlT3J6Z54vZOdAJqPMaX4h5P8CELWMvxkgn8hlzF1pONwPZ958P2OwWCKrM6nZP1HOt8afKzkZhtK8IzgiCGXlS0KBgjPOBIIPlUnbonPaluvg2gKDctWBfUz7s/wrstI6+ftGybHTKGlI2k0DvJwDTlOfNzc0dW7ezj9R7afvT1lv7Qw1EBWe+8bbAzPe7NaSsvrT8GMV3LFNlhDXebFndjKjdft8L+e61BoO9Lmqud5KZ+0GxWAxHjhxxKUAK8Ik76FzkWUI2EhwEgeMV3VgCgAOqNIiVxzXCDOzc8p9lUCaogcWXgvhsNouJiQkXFdKsD2ALQC4vL6NUKmFlZcU5SycmJpDP57G+vo6FhQUsLS2hXC679VZA+CBz3cSGnv9CoeAMKRpyagyp0cC5zH7Wtes61zQaqEaVGjUqW2KxmCtLncg0cOLxuIvk0yGt/UoZ22w23VhTfyvPMrLPw8G5gU86nQ5hJp+R7JM/Pl5Sg0Lnvo1UWxnA/lCdA2wfoswsGb2O28UzGsn0UOKubDbrjuXxOeFYb5bJsfPJNyun9H+93heR0vmhqX3cPZFzj9vZ5/N5t1s065rP593255dccgnGx8cxMjKCYrGIUqmEbDa7Y0x2Q3s+RwrAjsnPxtpwqyW10q0Ct0oN2DZ4CFqB8M4i/KzGHeugW2xq2gOwvYMdmSadTqNSqTglTQOH33kg39DQEOLxOAqFgtsWs6+vDwcOHEBfXx9mZ2ddqDGRSLgIFZU5+0xT+3hmlVrvylAUGgpC6DWhB4H9Z8OqLF/Ls/2sAIIebk5S9gWNp1qt5rZNZb/E43G3hz93LRwbG0MsFnMTlO3q6+tDoVBw5fI3uyOM5r1qPbLZrEuN5IF5HFP1cnFxOr0rXEDK+vBlDW7lW/bhqVOnQkrNGm306LVaLSew0+m0ex7Hpdvk9DkefALHZzRGAZeo59h7LxSkqLDfb9Kybdt9oNaS7zc7Byyw83kIfeXtxvgkUFJFpwpPZZqNOlEhWWXLetpUNjXybcqdBSFqTPlS2HyRWV9Knvaj9q8PGPiMDQJL7WvtA8unakgp0CBo0kXpNKRURqtjRg0nPpdGaJRB4eM59fZ3MqS0HyxPK99FgQp+V/2pIMOW7ZvX1jj1GV2dvtty7HXdZECUnOr0X1RbLsbIsxSLxTA6Oup0tTWiyUfcItyuOWJd6BDx7bxJQ4hOW+IPRkAUx/iMfMsLrBvvUYdjNpvF8PCwO0aFKW6xWAyNRgNLS0vIZDJYXl5Go9HA4OAgJicnkc/n3SYurB91J9eYa2SAMkUjFjQs1KGhTmKOI/uJfcln6Bp2H0Zkf7FfrdGrOI84IZFIuB0JKVMpz7g0gfcCcA52tpcOZQXxfX197vzPQqGAbDaLbDaLfD4fyipgO5TXdIx90RqSlSH6Ii9Zg0llgkayge3lG6lUyuFlXksjhJiLuIXGeS6Xc5Gd/Zx7+02Mmvrk+MjICC655BK0220sLy+jUCjgsssuc1iVlEqlLujZe1oj5UvP438q9KxCBnaudWJ5NioVZVQoUGBEi8SJyXsVuNIY02cAW4zK07GHh4cd8KfXl8bQ0NAQRkZG3I5R5XIZx48fRxAE7gA3bkjByAsHkkKSgCQIAhelouFJo80nMIMgcBOXhiENBBoJaonbtAIKMX2+enNVEcTjcZeWxv6NxWLuNPdqtRraIIOKxUb+mJZHjw8VDNvKceG5WKoQqGS4S6KmQ1LAlUollMtlHDx40E3sRCLhUhG4IxFTLFVIAttGbDqd3sHHFlgrz9uUSL7n8/mQgI3H48jlcq79PlJwYD3wnQwme3830rZHGYJ87oUYQ7a/9oOiANXjQTrOUbygnxUY+0iv83kL+T9lC+eTGka8nqCL84POJc4lXSfkMwzV0PK99F7Wie/kG8oAtk2Boq+/tL62X3xAWGU42+6Tg3wu5yPLJJjSyJM6rdiXLFvBnaagEIRqGyibtI+iAE8Uv9j2++63RrItUz3HlpcUlFvDyj6j03ff2PjmhM+o3wv5jDs71r577Of9lAdBELg1Q9ZpoPOA/KfnIEWBff1P54I1jKxuUp7m3NDIg70X2JY1zWYTpVIJy8vLTlZwYwZge9dizl+2kWdN0UhU7KT8p5EB9odmc7B9Ov+1X1R+EY9QT+tZVywvykGgcogGmI4Vn8v1MMRbtp6sG3GRjlMQBG49ZjKZdMfwUO4ODg66zQsqlQrS6bTLwqGDmJjTOvDp1GHdOOa+OWnnvTq52WYdF50XNtqZSCQcZmTWFuvGo3o4Bs1m05XBdg8NDe2I7PsCJ7aPffMtSj75+sAGX9QBRtKzvQCEdsBWXVOv17G8vOzSeJeXl93YaVkXIl/2FJEisFeGB7aVu3YerV7bIRwAn/DmNVQOdjCo8BS4c/LacDsH3U4QLY+5sADcuiACcp7xlMvlHNOXy2WnrKvVqmNygnYqddaLOcb0vmg7mY+pp2JbUEPGYXtYhs0n5qRj2SqM2G6+1ENGw5HP5ATnQlHmOwNbDHb99dfj5MmTKJVKbmtm9ikFDD1S9HQwVEpDjUSwyIgZN/ngGQCcxPPz82g0Gujr63M54BsbGzhz5owLN9P7xBQDACHeUx70GUPKH/quUT3rjVcjiJ45jk+xWHT8ofxtedYKhN1SFDDqBPSi2mmFmA8kRQHAqP8uhqLGxv5nAYW9JsoI5X/qxNHr9T99ngXDVn5ZOaOeQQtuOa8po3TDFVV+FsgA2+e4aDqK1l2Nf/tSA0qdVcB21F5TBPl8n8GmjgULfGzfWUeJdZT5lKPKOF+kh/JDI1G66Q1lENuioEDlKj+r0WV5wsdD3Wg311seZvs0MqlpyeowVN2mBrIPVPnqFTV/uxlhPtkR1Tafjt8tRZW/n04bPocH8jLVn/qa//O5mpKmPE4eohyw891nANv+tzJDU83UkOLztB/UkJqfnwewpa/T6bSL2tBByaUKzDDhvGFmC7eB920QozhOf1dZECUrbcREN5DRw201DVINCGs42Hmh/dLX1+c21iCOC4LAu0kE66fGIfuC5es4EydVKhVUKhWkUim3HCGTybgNLXRNGee0boKhm1FxbK2MtQYeecliGZ8eIybWiBQxMrEqn8uonDrn2SfqxGLWjeotW1cuoWF7LZ/rWGqf2jHhdZoCrpteqENtZWUF5XLZ7TLNo3wymYybR/V6HQsLC1hYWEClUsHa2pr7/8iRI45HviHnSAHbli072eexs152Dp52uP3dZ8lS+XNSMQ2OjMprNeqj5foEm3p1Go0GVlZWsLq6ikKhgMXFRVSrVTdB9MBKYJsZp6enQ6l5NJZotbNO7JMg2Ip+6eJHhk3T6XTI6KHHgikrFDRsO5/L7R7J/JwQKlgs+OBnXTtEBiVwoocqm81iaGgI6+vrKJfLyOVyeMYznhGaeJxYsVgMqVQKw8PDLk86FouhXC6HeIG76DDCNjs7i8OHDwMAFhYWMDs7i9HRUeTz+dDugX19fcjlchgZGcHU1BSWl5edd0jHWYWtgkH1jFkwpqCKpF5spizQUGK/6uQnX+iZBKdPnw4JGvIeyWcEWAdDN4oCPToPo66Nom7gJaqcx8uwYpm7Me728r/PSaPvUY4I/qfPUcCgIFg9v76oAnlPyyBf8V4aClxb6TOUrDFlU4OijCq9RvtB20OZQ8Wq11pj0XqTtZ3AtmzT3QnV6NH0GXtUhS2r3d5OuaG8pOJVZxvHRJ9p+0Z389LIlBpWUfO4G9/55oUFDrbPCR4oozXrgIYUQQr1oRrsezVg7DUXYqxE3WPbv9t+VH7uZrRdLDFFXg0IBXvsc/a//Z+8onPfGlJWTijoV0eJxUgWeJKfVWfRmVupVLC8vBza8IDP0YiEprsT+Oq7GpTq2NHnWdlmDSVbX46j8rGtCzNRNM1Pn6NylfrWF60jXgPCTmn2MddZEffYa1WW0QGj8pmG2traGur1upNhXPrBvuNzOc5cV0YDl9EhNaQUE/swto9PfEaJxTgsh0tPdCdm4kD2qR4HwHJsVJ9tUl1IPgS2nTvaLl5DfrZyWHWRHXfqIhqtdMBvbGxgeXnZHTIdBAHS6TQ2NjYwNTXljKWVlRXMz8+jVCqhVqs5RwNTU6empgDg8T9HSpkQQGjyq5GkaVAccJ+iplLmPeoFViYkSGV0gF5IqyxVUPmErxpnfK2vr7sdbCYmJjA8POyME113wEnCsPvMzAz6+/sxPj6OZrPpcnB1ELkjDoURw6fMr+WmCXYS2QnEnFaWral57XbbCR8qWxU2OhHYdt2VhSCExhknB428ZDKJRqOBhYUF3HTTTY6xycA0CJkvnE6nXTkcF322eooqlQoWFxeRy+UwNTWFJz3pSTh79iyOHj2KAwcOuF1YnvCEJ6BQKKBer2NgYADDw8Nu50T1SqjHVietKh6dtCQFNZp+wH7OZrPY3Nx0OeTKb3qv7r44NTWFRqMREujdgAbLtb93AhF7ARd2TthnRf3X7bn7AcIs+UCH/X2vhpWtoyosyxdqhOucBLbPqFBDyz5fAVCUQaXeQc47VfZ0OJDPFRCocWSVpzWooowqH5Bnm339a73qqtCt99B6qH3GHkEMFePm5qZzVlDGqzyzGQesk+ocjSyxTgQOBESqc4DtSLydZ5QHOka+uWh/Vznucw6yDbpGje1Q4KCghp9Vb1rHkOXDbjKjU3s60YXOb999nYysqDIeD4PKZ0Qp2NPotV0vqPVWvWyNLNsGH+C19+p3/Y0v7T+ucalWq6ENENRZaDdeID9x8yjqdRoJNGqsA0h51bfpgzU+SLY9PrlrZaRNIVR5oAatyjWOk/6vcpZ6mZkAds6wHnymlYs0fnkd14DZ9E8dZzqHOB667krrqP2kOFD5xfKB7UvNJlA9x/TNWCzm8GI8HncGCI3aer3unEr8zcpNH28HQeB2x9YUcq23dSSqTvKNgRpS8Xg8dJQH+7pcLrudk4kFY7HtTUGIYyuVilvOEwSBczwUCgVnSGkkcS+05wN5SdbrYpU4f9OBp/fPpl2pQCKj6T2xWMwpWoZPgyBwwFYFHZmW92h5Cij4Ys7k8PAwhoeH3e48ABzob7VamJ+fx+zsLAYHB9Fut0NnKK2trbl2aWiX9WL0iMpeBZpV6hbc9fX1IZPJuHt53sPY2JgzziqVSmjLUoY5bQ4z683zkQCEvDJcrFooFDA6OgoAoRPQV1ZWQoKzv3/rMOFisehSFDU/m8+kMKhWq5iZmcHQ0BCOHz+OO++8E6lUygmzwcFBnDt3DsD2TkXVahW1Wg0rKyvuwLRarebqr4uB9Swrn3CM4lPlDYIuTsRCoeCAXa1Wc8LSpkTWajXn4VhcXMT09DTGxsZ2PKfTvPIZUtag6gaUfMpL22fba4FYN/IZUXu5fz+oG5iyfRVVhhXm+q5lUSmpF9Q3HlHGh4IB9bZyHqnS0d8oU3xRf7vmiXPMtw7KRq+0PNtelq3OBDWEFPTYdqpc1b61QE630KWXVg0fXzobnT92ow37TIJK1RuaOsh6K8Bhm7nAXJW79o81iHQdlfJLN9IxoJyjnGZb2R416BWoUgdaY8r2R7d67Nfc1fngk6++51oj1WKMTkbifhpU1vloU2n5mzV8FHPYvvC1R+eQXeMIYMdGFj7Z7APP5HmCYKbncQ7TUcpMEK4hIVBWjMA5qBEZa9x1MgC1zhYcAwgtVbDbmtNBrpEzzgUAO5wpUZ+1j3WrbLaFaVyaiaP19EVjtE2c83TS8B7+Z53VlMtMWyS2UJlj5Yga8+wzNfpUL9n66/iovlAHHh3VrVbLbYXPNVI0NPQgd/ICI+HsY58hxb5VBzb7iG3T/rSONpal8o7XarQ+FtvefZupoWw/l4isr69jcHDQHaKsxwY1m83QLoXsswuhPRtSbDgfai1V9bKxUcrk6om1IJKKhd85GWjNA1uL+5PJJOLxuNuek2VqCFrTRaxSZxsY7i6VShgdHXU7w2UyGWdMra+vY2BgAKOjo7jxxhtx5MgRzM3NoVwuO8bhADNEHYttb9KgntvNzU2Xl2xDiGQ4nRSa0sh1OBq9I5MxkraxseEsc+aBAttnbS0sLLg+03O1+vv73XaRBw8exMGDB1EoFFz5l19+OW677bZQZI1RNW53asEmmZVUr9dx9uxZXHXVVThw4AAGBgYwPT0dSrnZ3NzEyZMnsbGxgVwu59attVot5PN5HD58GIlEAgcPHtyxroKASfua/ajv/KwC0OdZVAFIofbwww+HPMMcq4GBAdx///1YXFx0G5T09fVheHi427TaQRcLEOz93cqz8zPKsOv0HCsAL5bs87WOPoCi1+h1SlFGqpanAMcCPDUsyANR4FEVh3pWaRwQIFApWNmk3y3/6nW6wQLbqFudRxlU1ijyRYzUwOMzuIZBjSPNKbdgg59ZhqZlaGq0NaS0b/hutyq2XkxrLOm4WUcdieMNILRLl/ID+5Iy12csWrKg09c/+lJDW8eC3lE1uskvrVYrtKlGFPCOoqi6K3UztHxyotNvvnt3Q/stX7rVxWeIdjLeLL4A/Aen6ovRWACOxzi/rLNGn2PrqXiM0SSCSupAri1hFs/a2hoajYbDLhpBBvw7f6pzWuWUdSJoNMoaUnynXOH8JJbQuusmFHzXLdMVSLNM1pWbSXEnPZ5vyTHhFujEFnQWK/Bn3bR97Bs7ljRkga25yvprmZpOqIaBOn4Vv2hkRMfC6kGOn46bXdKg4xUEW5EnOqgbjQbK5bLrezVIeJRLLpdzcoabjJFnOa7WsI6apz7nCPtJ50+UHtffouaG1d1R5JvTF4q/9rRrH7DtudPIhE+AAGHQox2kVjLJdx+f29fX59ZlDQ4OunOe5ubmsLi46HKcyTia0mEjUpbZ1tfXce7cObd1J0/l5m56QRC4VLJisYhsNouxsTE8+uijWF1dxcrKChqNhltHwzpms1lUq1W02+3Q7n0AXOREARq9wNYwpXUdi22Hg3XyMUTLa9lfyWTSCZuRkRHX19x5kB4qHl42MjKCsbExTExMYGJiAqlUChsbG1haWsJzn/tcXHrppVheXsbw8DBqtZrL9eXJ6Zx8pI2NjVCkaHl5GYcPH8a5c+cwMzOzw3sOAMvLy1hYWMDMzIwDL8ViEVNTUxgeHnaROZsyp7ypPMPokQoRBUnKE+xPC6S0zLNnz6JUKu3wlPT39+Ohhx5yY0TD2wq+bpPWCobdghL970KARpThpOXpNd3qcDGkANUHWDsJ6Ciy90f1J5WVgmcFqOokiMfjoXRaawyRNE2F9/Kdh8sCCClRnxGl9VTepIzi/zRYtA0+j5/tFwVN7XY7JJ9Yd8p+9bCqzNE0FVumGk5cS8qUad29Sg1OKmbuyERDQ+WjflbvNpW9jUSxjqyzggLWkQYfNxlSQ1XHRA3pTrynpDxix0Z1KO+1KVQ25cymoEY9N6pudj772uMDLtof+yEPbL/67u32rAslm/bNfvfV0TeXfHPf14c6ZtzYiVkpPNKE4JYU5SH38RBBPM+9ZFlra2tO3zMVP5VKuewWyiQgPGcpSxSwK2jXg77Zh9YJqX3GeuuOpWxjKpVyR4bwRWDPZ+j6KWaJcC7rBgjJZBLFYhHDw8PusGHKtSDYirToDs2U4zp+nHN0jFLGM5LEOul6NDp8GNlhv6lM5XgqD7B/VAfoGnZ1iCk/adRM+5aym+NFecj1/Pyd0adqteoyldRZRqOYBxJzzCkj7WYSutOsOvP0Gq2TzgnrGKPe0egwdbOukUokEs4xSaLs1t37VLZzkzN96Ty+ENqTIaUCTj197KQoC5H32P/0flUIej0ZkEJiY2PDbWwwMjKC+fl5B85tqNUKNgXvauGXSiWcOXPGbXDATtezRzY2NlzuaK1Wc+FCTg7WLQgCt2NINpt1z+bhuYVCAcVi0S2CI7OqZ1onHIWtelxUMBHgEXCw7zhJK5UKzp0755g+n8+78cvn825zh8nJSbdL4fDwsBNsT33qU3HppZc6jzSNTIKgWCyGkydP4rHHHnOpl2NjY5icnHQHFY+Pj+PRRx/F9PS0Eww+DwTTESnEyBfFYtEJtKWlpZBQ1rG139VjQsFgPR0UYL7IFJ8PwAlQjr+epA1sbZbBtg0PD2NqaspN0G7Gh4+soo4ibUeU9+ZiwMfjAVx2Q+rZsrJjt23qBiytTCPP6O5vKk8I8unJjcVizsnCZyiwiTLCuHmMbryg9YmSnfqugEwj7xpBoiLT+rCedODY8jQix+uY0mFfPNDTnsnG8VFHCfPV6XyhTFNFpmlF6pWnnNWUN5/xwHYT8DCCb/lA57YFBr6IngIcki03inx6j32jAFidBtoXNLg1UmG92nrIqM6PvdbPttFnKEUZWd3KtWVG0W6M0v2WSeQTNaT4buUCwaAFsIotVB6oY47jrk4EZnRQr1C/cd2SjTjq81gvdfrQ6cAzH6vVqtt9l7vx8iBdTTWzUU06PGiw6IYEbA8Bq+7kpv3oM/bUqWLxFWXj6uqqA/bq5GAEya6bUvlCQ3F0dBQjIyMYGRlxm19RfiSTSZf+GI/HnZNGx5/gnMsdyBO66RflFw0oymHtS3V40fmcy+UcDmT2CseSY0E9A4TPDSRf8aUBCOVTym119tJhVa/XUS6XXWTSGmnkMdZbD+XN5/POOa5GJAMGepivGthslzqDLC+rDNRrrSGl29ozhTIIAtd/qVTK1ZcYkDzPvQ44FsxAI9l+2C3tKbWPRKZWZmGnKCCNAjJkNqZjaWTKKhRg23pn6DqRSLhBZVoIga16kVmODiY/k3koGBcWFtxOa2RcKi0F6HpAHZUYt+tOJBIYHx93beNGEa1WC8ViEfH41uYFTCOkl1f7iJOY4W72qdbbCirNfSXgYy5wOp12giIIApfCl81mcemll2JqasoxMi11biNeqVR2CEEVptyH/4lPfCKuvPJKF4bf3NzEiRMncO+992J2dhbPetazdni5ySNqTKvxxHZToahRqkawj9Q7qEqX9+nzybs2pZTE8SfYYnSPgqNSqSAItiKDxWIx5KnxAWIFs7sFBN1AkZ2Hvv9386woUOUz6ux/uwFtuyVfW3zGFH/vZEB2M6Bs2XRkUFjbdFHKIP2dshAIb16i96m3kl5WBUCUhQTWvjQzNaQ07U7z+FXJW6eLb6y0bHXW+DyJzDcnYNEDr3Vdhc4/giUCLu6Gqt5jAlRNgVR5tra2hr6+7bP01GjQucx2a1SO4+TTSdqPCvh8INDqOWv8Wt7zfdd7fHONbdF1AJpuw7RqbmLEOhGkKa9E1c1Hu73O3uNrq49U/nQysDr9r322n7ImFouF1veyL/gsG6FR/rPjafnFOpvtcxOJBNLpNIaHh5FMJlGv151RBcCBfbtOic9Thwn11MbGhtt0YmhoyEWfGPngPalUasc5l+oE0SiM7qCpGyYQX3ALcJUZrKM1MgnoFYyzX1U+MqpGY42barGOWlemoDECQUc7M4iYNcNzhtLptHvOwMCAwyzsnyAI3BlRfX19bpc9PkvlJceLc47/UaaxvwnkR0dHMTk56ZZPpFKpkCEFbB8NQ4edGt92HqjeYRnErpSnNB4ZXaxWq2i1WqEzTTVix/Lo5FLjj052GsLKO3QQAgiNrdUj5DmVVxqR4n++iBRlnc+QomGXTqdRLBaRz+fdbzQGuUs2d2PmDtEkzXbaC+36LgU23DHD573S7wpm1Mtjy1RwSWJZClhWV1fdOQhcyzM5OYl4PI5SqYSVlRXH1Jq+oZEcndRKa2trbs2OGlk8R4kKjGl78/PzKJfLAOAGi5Oeg88ITjqddmkpvEZzjgnEtK4ELurFUsNO25JOp0OMSGFPJawLH3O5HA4fPowjR444T5UKMmDbG0pjhiHufD6PZz/72bj33ntRKpUwODiIq666Ctdee63bkv2xxx7D7Ows2u2tTTAIZp73vOd5z+pQTzbzden9Um94JpNBvV4P8aF+1lQMnzGu3iZr0PhSKi1fkmcYaRoYGECxWMQ999wT2gGHee+sXydgEEW+udXp2m5lXQh1AzaPJ9nxiTKqLJjtBmh95dv2EfxTYGtUgwqOhgDL0DRPBWD8rClrdARZI0o9tFQinLeqTJSPNXWPSpCyQt/VOGA/qXy2fWkNCmA7T7/V2j7HSBW83YqYsonKVNMtCLoIRNSD6tula3V11SlNmyqtBlcymQzVi3pKeYHXsk1RESj9bg0nHx/uFdxr/7PftO3UC0y/oZMKgOsb9dCqIduJ9/dCe3H0dHqmTx5fyPP204BSok7VeWGf6ZsXnHdKbGtfX59zDFospNf292+dfzg2NoZWq+Xmki4FIJ6JioBpmhzT7Gu1mnN4sH3r6+tui262m/eqjFMDgRiAnzWiwCwRrptReaEOFe0XxS2MBhM/JZNJ56zRLBzKD+ICEssg0KfBqKCZKWDNZtP1Vz6fd/KyWCyGIltcR1YqlTA3N4cg2IrOcQMGRpDYFqZHsm7r6+vuEF+fIVUoFNwRMawv26mpctQzHCfKJNUp1vBRvEu9ye8qC1OplHOw09DmhmnWocT6UycyQsV1++pkU/nP/1ln8r5uwsY6sp7WQCTf67o81kF3h+ZvnG/8X3GDRkFZL41i6py8ENqz+aWNVM+MAlIL7vQaq8B5nyo6tcw5kdkpDPnm83k86UlPQiaTwcjICJaWlrCysoJSqeQYSp/PSaL1s4NXrVZx9uxZx3xMI6R1TQaiIFHwYD2fFJL0HHECUihpeNOm2Kh3jIPMPmNf8FoCm2QyuQPAWM9qOp1GPp/HgQMHXNiTDEkhrl4CniNFz01fXx8uu+wyXHbZZe46VQ6nT5/GH//xH+PQoUO47rrrkM1mkc1mkUql8JSnPCU0IdhufR6VgC7mp/DKZDJoNBqh+1U5+YCOgnCfUWVTU/V/vnPi0yM+PDyMoaEhJ4i+/vWvuzD5ysqKWytHA9XOnW5zazdknRZ7pQsBflHlKO0n0LERQwU0Kg+int2tjZ2MVc5dTWOlwiDAVacFwb7OCcoXlmtTtdSIotLkHGO5aliojGS/0LnCsqxisjnqNn2H7bZttzLcAiBGly2wU/mvKZJ27ZHuWEXARD2ghgT7lvOIa3NpVLHtlIEKGm0Ov7aVL5sSpOCN7dJ+2S2pTPIBaOU31QcKUhW8qhON+jHqZefFburpmye7aa/y0G6v7VSX3d6/384d9r/FIT6d4XOOWQyjelg3DbD3cL6m02kcPnwY2WzWbVCk657tIbVAGDhrJI1LIGxd+TsjNHyGttFmExFHkIcVv7DunMt8Nn+3IF9TCPmf9jnLV7lBpzTlsWascO5yg7BisRiKlNBho89k1J+bT2xubiKXy7m6sL8Z/aVDW+eiyoxkMomRkRH3DGIEjR7rUhEeEzMyMoJ8Ph+S9zQyWBbHWw0QdW5r2p6PP9VBTZnAHag1uqdjoLJXdRN/t2v9VZbSycjx4nMtxtL+JM+RbERKy7Y8qktibOSK9bW8rc/Wl/bjhWKYC9r+XBUqFzJ2EuA+TwyVoiphBR0qBHg9GbtcLrsDbi+55BLkcjm37Xe1Wt2hAMmAQHj7TbZFQUOlUsHp06exsbGByclJx/TZbNaBBx7ky5S5drvtwpoaESPjsG84YAq4VDgogxIccIJyK3Y1DjhRCS6YKsP2pdPpEKP19/c7j4imn7EvWAeGdOkpr9fryOVykcBChXgQBBgdHXW7F3JLeWtA6zvbSaDFNioQsZ5h9rMqL2uga5/zGp2U6gDQe9gXm5tb26+fOnXKCRWGt7PZLJaXl5FKpdwizSAI3OHOJ06ccMDHNwd8tFuAYK/bq2G0m2u71cX3/34aaFHOmSgjUvmiWz2i/lMeVU8o5Rt5nEqWyoP3qTJWoKOKxhphVJCMfimQp6DX6Iz2D5+j9VaF6ossUXFYuesDkSpr7DMJekjsD/1PjRR9sT81WqbGDeWs1oHjytQiK0t13BTsaB9Z0uijvvsiUT7jvRtvWRDg4zXKejuGwPbGIXSSsS30qjLFxRqBu52b3Qyovc7lqHnX6dlRcsbK8cfTaaMAEgg7HCzgUkPAzhk7P+1Y2HvV257NZnHs2DEUCoXQuZCcE1y2oMaKz7ijnGk2myG9zMhmLpdDOp12kRfdJU+BsPaHGmwqB+0z7fzXsVOjnbqUhgdlrT6LG3epwaZZRryG0SeNQNmsIgvKNaquRiWfp4aLptmyXyhXOAeJKxhZHxgYCB0bw4iOOuUYbbayK0pOW1mkhoP2Ma/R8WRQgBE/bpJWrVZdlpBuOa/OKqZbciMUbYMu1WB7FGurnLYGoHU68KVzSZ1LwDZ2V92qG5Awg4t6VvuHhqA1DNlu0uNuSAHbBg4Hng1UZWsnjwolW5YCJb1G7+V3PqfdbqNarWJxcRGVSgWxWAxTU1O4/fbb0Wg08Nhjj7lNFwhidKGyDYFq+azD8vIyms0mlpaWMDo6igMHDmBsbMwdzrqysoJTp065CdtsNp0AYZs02sL2KjikYUPhSKWp9VIhyclM7ySfoUBBASDD7WQqGlaFQgFDQ0MA4Dw+OoYskwstWUem90UpPh1Lhsu5FiSXy4UWqZJYro65joWOF8sm+SIW6t3yKW8LatTT5POeBEHgQvzNZhMHDx7E+fPnEYvFcOzYMbfejcqBhrVu86zlXQj5+nu3xtnFGDadgE6UMRN1/V5IDewo76+VFfq71j2KrFPHlq0eV00JAOAUJQ1tGjlMbyCYsI4glk3goCCNc1s3uFDQrtfaduvct32n9bCgUF8W/NuXyiN+V+Cjc4fP1FQQNVQ0199GpIFtg0wNJCpQOpR03qqRao1o9iVBE8tiP2nqoX620R1rYPrki0/HRfGiAkrVB9pu7UdmM9jtsplWZVOhrBztRHuREXauKAjsRLu5xndPp+dfrJxR0kgLsNOQUmekz5jiPQp+fW2xAJGgljzNBfLNZhOlUgmlUsltOKDGGZ9hnRvKU2o0MKuEGRPpdNqtmdJNcNg2NUCA8Hb9vrFRA1Szazjn9KXAXgG3plfqWHAOEAPofKZs1rmqeEvlMkE+n83y7Roe3exKM450YzEfTvBF3PR/XsPdGTVNUcdQo4vqROR1Vo5b+cn/rWHMY3yY8cPDaXXnWZ8usDzUaDSc8WjXdNEI03oqvrN6R9ttdZUaUayX6hzOWz1vjDyl35mar4axrsW1O/59QwwpNsLX4CijRAW1jTTp9Xqd7179vrq6ipmZGUxPT+OJT3yi26Xl5ptvxl133YWHHnrIlW/rRWWjE16Jz6anZmlpCdPT08hmsy4XncxUKBQcU7Ev+ExlbgVm2mexWCx0v7aTL0a6NDqlk8wKVfXwciLpbjY2r5UAUb0z6kkh8NEddHykBuSRI0fwxCc+0e0WmEqldowtn6OeXssfqqiYJmGFDRWHglUf/6jh7xtvK0QIlCuVClZWVpBKpdBqtZwXaXp6GkePHkU+n3dheyooppumUqkdfG7r1Y06XWcNgijw0clwiKJOQMX3334BG59hY/uu071RwDUqKmGfpcIbQEhhk09pEFCBUEBbHrPRHD6DgluVDZWVgnid06yPHUuraFU+q3L1KXYfCNTnap1ZfpQRpf3FOarrj3TNkc5xVaD6TG0X5yJJt0fnvZrLH4/HQ2mZCgK1f1gfjYwpuFOvvBq2Vn9Zz7B12FhwYsGepqZoXzMiSm83gSDbZA0pm8oZVQf7v0//RP3H3zrJG/u8vTy/kwG4n8aTrZuCYatD9Bp7D0nnkGILvsgTlC8EccxuYXr4xMQEDh8+jOPHj+P8+fOo1+uh9EDqQCC8EYbOQ/IU5zidqbVaDZVKJbTGRevPOmtEVg0XzZqx/WR5nm1mf6gBoClsdNAqpqRBw3lu54Y6otg24hjKem2XNYrUiKEhpdET31hrP6gsU8e2DQ4A2CFj+Cwarlq+lUFRWEH7kv9xvPi7Rl94D9fMcwkFx4GRNW4wwvpwsxIeV0H+ZXRUn6mGihriKnfV+aAy3hrqHDeri3kteYZ8YiNX/KzpfbY8vUbnl+Xj3dKeDCkFIwzLWmVulYoyg04U3z1RAE0VJjtyaWkJMzMzWF5edqlqR44cwXd+53fikUcecXVUJakhXassLHjni6G/crnsQAR3bqNio+Bk+SpYdFG5FdAatVFvBxllc3Nr5z+dcJpWxN98Ey8IgpA3XVNdGFaPxbbXYBGs6ITXUHqz2UQ2m90xsS1AW1lZwV133YXNzU0Ui0WcP38eN9xwQ+g6TmD2WRAErh4UQjqptG2WhyxZQ813n+8/NcgUELdaLVQqFRw4cCB0Pz00xWIR3/d934dDhw4hnU67fh0ZGelodO6VOhlf9nfb1qhruxl02sfd+n0/SeeJry4XUg9VboDfwFRApakejAYwpYGfrcIKgsBFw63xZmWd3VCFEeooo4NAqZMjyvahRmj43coiznmVeWq0WHmt4Ipy1cp8/q4Lja2B7APT1qBSEKrpOlZeA+EzRhit0c1CrGGqwMgaoj55YbMxfG2wBoeVx77xIbAjr1Gpax9yRz7qF/YrjUU1pKL4w84fH8/Y8Ykqx3e9pW6yxd5ry+/Uv/tJ5AcdI6urffKI96oe1nmmvGbboBEpbrA0Pz+PUqmEsbExjIyM4PDhw5iamsLy8rLbuIC6m7JG+Zh4gc/gPFZHDdOv9PgFO5fVWCKusW22AF8dohq1JqjWKJ6mdZH3dQ7qfNO1LvaZdryA7RRN33X8Lx7f3unNyhxex41d9FBjpooxskO8ovKQGE75QnUH56fiPI28EWv59JTVhcQvVs+wj9XBp3qj0Wi4awcGBpDNZp0BNTQ0FNqUI5lMhnZApONGjRjLa1o/65zWOmv/2990Hul8svd3us7yWtR96gS8GNrTGikVsBq29V3rA9ydAICPUTSlQ6nd3krvO3PmDGZmZjA5OYlEIoFcLoenPvWp+OAHP4jFxcVQrq6mRfgUos/I04HjIEWFkrV9nHAEAJrfS8bXtqiHhmttGIK2AECNN04UFX4+8KfGHD25/F3vZyiURh7HQNOY7AJuK1DX1tbw4IMP4tSpU26HshMnTrj0FHo5mDOsfal51bZ8C2hshIH9z4mhESyfkmR9+QyNEmrZbIN679m/icTWTobpdBqXXHJJaGccH4C05OP93QIQpSgw4nue7bNudev0DF/b9tPI6iRb7P97aUvU7wQ5Gv7XiITOI84Jzc8Ogu3Du219tc4+haOpu7qNt4JjfbaVX/quRqNvPqk8UfljX0o2IsM6WEOK/6t8ZL+yHFXsPnBvjS6rkPU6jZBRpmk6IfvStks9qj7Ay2erTrBpKlZ32DH3ASCSKnpND9XIRSwWC8lbppTyN90K2G4/bSlKZmt9o2i3BpW9ptO13eTEfsqRbmRT0KLq02l+WGCmxokFizrn19bWUK1WsbCwgLm5ORw6dAiFQgEHDx7EoUOHMDc3586vZD1onKgRpEsCLG6JApN02NJJoevGFYNpWb5ytN9UBtm2atSJ9fQ5YvQZykvqEKFMse+aMmjHh0so1KHOOaPOcEa5NO2Nji+VK1Y36LmRrLviMK079QX7if3DPvH1BT/bMVY8ayMvlg8UP2q96LDhxkeUo4ODg26HQY2Ga5nKG7vBPTqe+r5XspjElhGFYXx1uVja0/bnmgvLh1uLF/Cn5Oy20jqx7OCwLFrW09PTOHPmDK644gq3S8uxY8fwnOc8B3/7t3/r9ejanFifIteoiP5OpcXf1GtNIcRJwWeTyTg5rDLV56ixsL6+7oyBRCLhIlc8UIzPVk+UKlwFCixbw53xeNx5urhBRSy25fHiZLHjqiekW+IzhoaGMDk5iY2NDZw/fx7NZhNf+9rX3GYdBAHxeNx5PEZGRjA1NeVSJTWFUNuq3gMFMZreqJ4QfragRv/XsVBwRKG9vr7utq/ndUyX5LUaSehGuzE+fMbUbg0FH+CMeu5u67oXILRXI7BTuZ2eESVfourTSaja/mOaneaZ83mUH4ziqNLldRqV0rraua//k891hzZ1XmgExWcoWWOKc1yvscrP91LDypJP4Sk48PW1Po/GiE95+sqw7fE9Qw0gTe/jGkUaUvocNZ58xpwFLlHfd6OofSCIbaJTS40olVfAdsqORqhoXOsW/TZip+311c13jU/u+H7bTbm+/tmN7Ov0nIuVLVGkDpIoEG75wEf2OjWkoub7+vq6O4h3dnYWpVIJw8PDGBsbw+HDhzEzM4Nyuewcs+wT8r0PjCqOUCxFsE5DQueBzhErCyzA97VXDShrOGqfqQ5W8hmq1rHK31W36/oe9qltm/K3tpdHMvAaRqN0HllDgX3OeciUQhoZlv/5HN3cy2Yj2HH1GVK2vyye4fW+SJAth8/RHRL1UPR0Oh2SKXpEBfvFPkcdWtrH2oc++8AGCkhWb/iMsyjcFHWt/q59bZ2EF0J7Su1TRlYG0ygBEE4B9Akfq1BsA2yH6CCwAzY3N7G0tITTp09jYWEBxWIRiUQC2WwWz3jGM3DXXXeh2Ww6hldPUZRwts9Q4M61ProTFMlGR8hoZCZlwG7CWvuZgCwWi4UO/STQUm+RhuatIFDPp+4EBIRT66zSZttYbrPZdOlrUfVPJpM4fPgwgK0dEHlg8YEDB/DsZz/bPbPRaOD+++/H+vo65ufnUSwWXR2BcG4xsJ1KR8HD+jEyoBPFJ3xJ1pOsddf+j8e3Dk/O5/NYXl4OCQ01xOxkZ3ndJmSUQu50326BxG5BXrcyutXnm0kWnO72nk7GGectAQ7TO9bW1kLeR10voCkzVJAsxwckrJLj/LSKyfKjOmasUrJGB/9TUE45pnLN9yIfE5BYsspI321b1QDls9kWVei+e339aPvIGkWU9bqTnZ6xY9vgM6L0+RZEdQOHWm9fObZ8zdfXd10TxnaR57RNek6QlftR1In/+b/vc9T1nf6LAnNR+s/WzyeTuz37Qknr3AmwWd7xRWS1fooBbCSawJ+7qC0sLGBpaQmHDx92DsmpqSksLi7ukEGUNTbyZeuq60T4bNVd2j6fTtTPqmd9/cYy7DN07tprFFCrbI3iZZal6yR1bQyxgcVCLIuRFysfmFqtGIiyKhaLuR0CeRYUtzEfGxtDu912u7HaNTgAQhF/Rs1UZyhfqGGh88/2neVHzWqyslLHz6Zts626DpgObToN+a5OeNbJYh/bb3bc7LhbY0rrFcVrlse1H7Q8a0TbvrXXXAxdcGqf7UxtqJIFsT5FzDJ8qV1WwOlAVCoVnDlzBrOzszh69KgLR544cQI33XQTPvWpTyEWi4W231Yw4suh9A1mPB53kRid4JZZGEHypT3qJFFm1merZ2hzczO07XIikXCebx7yyzpSmdrFxjqhdEG7prH19fVhbW0N2WzWTSh6w5kOSYXdbDbdwk6d6Po89hXr9dznPheLi4u48sorcc0117jrVldX3UGTPB1d+YqhZ7aHqYBBELhUKhqaavSRr6IUos/zaCco+61er6NYLGJ1ddX1N8Ez+1SNXR/PdwIj9lrf73sBI77/Ol0b9Zwo6laWlnextJs27KY+e6mbygWNNHGdJOWFKmam59iolKYFqldY5Safybr5DAYfb0b1g77bMrVsTSPRl8olq6B4r84xW77tR/6vfUWw4fMUq3Hlq7vWX198NkGMVZYWHCoYsHVW4GIdJnqdHT/7WcfBpx+jDFld58J66qY/mo7D7+qBj6JO/9l51O3abmX7ZOtuyrb1iZKd+yVjSFEGWpRM9IExX72oX6lPfZEajUotLi5ibm4OlUoFk5OTGB8fx+TkpEvv07U9Ot/VcNBnsA2+NY+2np0AvJVDPtms89fOV5VpUWVoPTTDw8oZ6l2dJ7a/dUmDRmqBnbiC/aM7udkoEedeJpNBPp/H8PAwRkdH3Y7OdPLqJg6sm8+gI56j7LJ9rO3qxpNRckn5UfUaM51UNgNwESfirFQqtSPt0WInzYywxrwvYMHvdj26ldOWfL/77lV90m39q88OiXrWbmhPESkOmi8kFnUtK8eOU2VsFb6934ZT+TsZoNFo4OzZszh9+jSuvPJKd2p3oVDAc57zHHzxi18MeSgUENCQ0AnOulgwomlbGtVipEvXLOjaAcusPrChTKY7kATBduoHmSKRSLh1FKy3bhtqBahOUjWkdEv0gYEBtNtbZ2NpOp32BwVTs9l0J6YzHdC2h4CJ280/7WlPw/LyMp70pCchn8+769vtNp7+9KejXq+j0Wi4SaqecXpjWaYKbB58Z7dD1vpopFAnjR0Hn/Lh9Y1GIwTQgPB2uepR8pFPaVnAq9f6lLEFytoeW4ZV1nqfLTtKWEeRrcNu2nsh5Cu/k6zQa/YqCH39qVEpGkjc2pV8QBnAQw4ZwaKnmB5KBRfkXR9vKiCz9bF9YJWulau+flF5YOeaAnjWQ40/kq7FtP1tDQ5tg9bRF83lc3z95aunXcPG/qTs1DVHUdkRtr86yU59570+QGrHy/ebBbg+z7GvH3UtDD/bbdqVOs2TqGujAIv9b7ft7mQM+a7t9tnOl/2kKNnIzzrencCf6mL9TfEAyySPrq+vo16vo1QqYXZ2FgsLCxgfH0c6ncbBgwcxOzuLlZUVp+tYpkao1Auv7bE6Larvon736QgfP2j51lHDDBqfjrb9yraoIaX9ZcdKn6tGgB4lQUPA9p3KGjrLdAMInXf9/f3IZrPI5XIoFosoFovI5XLIZrOhMyRVtvp4gP2iBx375Ivt+066Te/1ySX+Twe0GpWUm319fW49uMVevqwBHWPlb2uQk7SvtY0We1le60SqNy3vR81N+/qGR6SUOKg+0Mb/LYDlZ71eIwkKhu2E8z0f2BrMxcVFPPLII5ibm8PIyIjL57zmmmtw/fXX48tf/nLIGFLlS+NF62yNKHY0mZ1MR2DPe33WujWg1AjTqJAOun0Gv2sKhy1TvSiWORSI0OPCumld2Q+rq6sYGBjYEXmiIddoNEIpjlY5sK8WFxcRj8dx4MABTE5O4siRIzvG8dixY1hfX0elUgl5mnVNFAC35bzyBXfmisW21suxHZZHfN99vOkDUgDQbDZDgor3sS6a0qXUCXhYcKFgPgqQ+N6jnrNX8NJNkPhA/eNNUUDVXtPJ4AB2F4XSaznWakjZ1D0gvFnB5uZmKA1QZZl12KjMtNET1iEK1Fke9YElbbNPJuuzLeDReqps0HrZsrU863VmP1lPtTUcgLCXUp/vMzDs87QMlSGqYPV+HxD2AWcLcFhHn6zwlWNlox3TKENK9U3US2k38zeKN3YrJ7SNPr7rBPT2Uj+tW6cy9lMWdQJTto2d5JLyip0LvvQl1cua3jc3N4cjR46gUChgbGwMBw8exNLSEmq1WshxpxEq+xytgw+PWXnjA6V2ruj1Uf1k+0LnkMUdne7XPte2+BwpCqCZ/qobP+m9QRA4g5TzjH2pRhQQPjePqW/cuY7r5XVTIItvtc91mYJGyRRf2vttP3bCFNpP9ndmWHDdr/YX29jf3x/qgyi5FsUPLEujV/yf79a4jOJJn3yz7eokU+xcs/X0/XextGtDisxgf/Olx+n/yhwWeAM7OzxKSEU1vNFo4NSpUzh37hyOHz/uJlCxWMRzn/tc3HfffYjFYs47EY9vb39Jg8WCAvscZWZez4mp5zAp+drLSdlJSVhGIxDRAyd1AlCYMLKkHlggfG6F3YlPD40ls+qW7JlMJiRIaNwxX1tTMRV4tFotzM7Out1eeG6O9Wyw3fTOaL+q4OM9jHKxrFhsK1rGOtuJGWWQR4FR7TdVAsrnCraBLcOThpxvPH1khZKPuin2qOttO7TO9nl7BSO+ObifgMb3LFtvC7IskLiY+mhf2Vx267FUmbK5uYlMJuOus2dTaP2torZtjurXTmPqA54+JahzTCNA1pBSJ5F6vXmvL/XOGlJajyjjwOdh1siYlb/6rmXws0aidMMMHVPta0218fGCDwz65ISVO1HjpqTyzdcfmhZq62DL9QEP32ffNT5d1A3s+J53MXWIelYU7bfM2a1c9MnWqOvUUWF1ttWZjEppet/y8jKKxSKy2SwmJiawsLDgolK+7fKVCGh1t9mo6JW+NB3KyiYbLe805nbuEINopNtGg3kdMYbODZ0D7Cu+9EgA4gYrL1QmsJ6qVyjDo+QkZQfL1zRAdbatrq6i2Wy6rB/FjBpdi5Jlu3lF8R6/W5nKusdi2zsxq4zmmOuZXSo/mX2jst+Om8X5tn4+40V/t/xjcaKOVaeXluGTIfthMEXRnjebsI0C/IdY2Q7ygVd+70QKFrRc/s7d4U6dOoVrr70W6XTaeQhuuOEGXHPNNbj//vtdqJKT2TKl/c0+ywIFTijmlvJ6Bd8aPbIAXAUrr7Ee1SAIdoAWZTI1FHxMwvoSEHLnH+YP8xA2Thb12AwODoaiUjqBCBb1EDzWp9VqYXl52Z279fDDD7vDBg8cOIBEIoFarYZarYZYLIaVlZUdnirLOxod0MnG/tOcX8trPpDJ//Rdf9fx5hooC2B4HdMdfaAwChh0AvxRRoLvu62/BdUq2DqBFt/cstd24rNubdoL2bH3GVCd6uNrz16IZZOXddMJVZDAthIioFGjy55/omPjMzi0vpY3VYmp59L2hbbBtoefrSzT76ybL4qk8sY6KNT5YZWf7QP2GZW71lWBpua3a33tSyPxBDaJRMLJOrZHgRL72i5o9/W/7SMfWOgkZ+x4aJ/5gJT2sfatri9jtE35j/W3Olp5IKqeFzNftD+jfu9mVFkQpnWK0sn7BYp8fbEbQ243uEWvU4PKjjedNs1m050ptbS0hEOHDmFgYAAjIyOYmJhAqVRy64oph4gbFNBSXxPAazt1DtqXGlnKO/xd+8zKINtuNSTJtza9Ued3LBYLGTtans4BXkNDig5dLnvQ86J8xk8sFgtt886dUvVZije4nXqr1UK5XHbrE5mOSQxQr9dRq9XQaDRChi7Xtq+vr7s174xA6rpb5T3rRLG40cd/ii8tZvCVoXiGL41c0bmtOxHTKNSlDfo8/d7JKNLrrKMhysawfBvlEFD+jApaaJn7JUd2bUhxMHRCWetUG68d1+k67VA7CD4Q6ZuE5XIZJ0+exPz8PIaHh5FOpxGLbUWl7rjjDjz44IOhDtczAMgsFvgo8Ts9Jqq4yXBWaHXyVGobLUPrzl8A3OJARpx0G00yNicf+5q/USgouCPQ4HqyRCLhNnpgtIm74zECZFPuWJalzc1N1Go1nDt3Duvr61hcXMSHPvQhDA0NuZPVE4kEyuUySqUSYrEYzpw5gwMHDoQWhOpE0AmiPMD/LS8o7/gMAr3e979VBIy+aRqmCqVms+ndcML3nKj/fW2Lqruv3vYZnZR8tz7xka9Pta6+9lwoRQG9bmVf7LNtu3TuaNqebzt0yi7OHVWmLAdAyLvnyw331ckHrtlWjabbNvjapmWqQmP9bZof51iUEWUVqBpT2pcAXB+oslQPp8pQq2t8xpOm+FCW69bFlPM8N8UaG9YJ41OqFijuRqZcCFlDTdOaFWgr4NGxYp9q2+nEu1iwYOf3buenT49bgO4rm/9F1WW/gI+tP987GZp6jf7uwyv2Wotx9H+VNdVqFYuLi1hYWEClUsHY2Bjy+TzGx8exuLiIlZUV1Ot1F5nSpQrqnOmkPzvxe5T+YZk+jGfBvc+YUkNKywJ2Oil5Hz/rnGdfaQRFMZwaUrrmXA0pYiiVNdag4AYSzF7iOiguMyiVSshms6jVas7Z1mg0HF5gv3Bt+erqqjvMlrrCrq9WTMy2a5/wGqs3LD/ZMfWll5M0Csl+Juakkapl6RbvFg+pXrMyl23w6THlU9VtNjNCjS691/Klj7/tbz4+vBja0zlSupWmBYF7BYuWVFn7gK4VcCoU1tbWMDMzg5mZGRw+fNgdHtbX14cnPelJOHz4MGZnZ3ecx8JBtcJOJ5VlFnowKByazaZLL6Ni1omgdeYEIwBSb4RNGWS91OPNVDpfao0ympbBaBSfxZO5tb81dc8qYj2Qjh55nZwqPFutlvOYtdtbhxLfd999eMITnoDl5WV87WtfC02iRCKBRx991G1mYfmG+c48IE75yU4KTduxPOkTLjouKsz0Po43+1InugoF7m7oe5bvuVovOx/s9yjg5qOoe5WvuQmCeuN9QidqTkeBn/2ibqDMR51kSjfqVK6m90UZU+xHRndTqZRLZ6XHkWUpWQVuSb20qmDtu09hRX22vE0ZpiDPprrpbzp3LR+r7NL7FTxqyq7dTYt9yS2JtW+1fL40vYYyijKEdePuXDpvbSTHgktL2m47XipD9Xpfv/vKVE+xRhW0XB+gZJ9RJrM9epCz9p9SlA6Noqg2+MpTnugEZvSzT/ZZ3mL7LQjaL/LpDqXdPM+HhyxgtPiC1yqAbTQaKJfLLpVvbGwMyWQSo6OjGBsbw9LSUsiYorNTn+mLHtn6WSPH5xiNan/UOKjsVhnFZ7EePucMy7PprhqJ4n+Uy5TDAELpaYxGE5MRv9CQ4jmb+myb3sg28BmMTNGYYlYNy6UxZXfsGxwcDDlIKNtUbuk8VHmp//kMcDu+Ks9UxrCP1BnD67jhmG40xqUYegSGlkv8SzmjckzLt23zyUZr+FijX6+Nmpe8T8uw0Sj7nE7y/kJo14ZUtx2bLDjUina6Tz/baJcaJEC4w5RarRYWFhZw5swZXHXVVSgWiw7c5vN5POc5z8G73/3uUP59f3//DpDMemgurWVSC7rtPfxfd2Vh23weIo2EKZMyyqGhVBVAutU5QYR6cCkcuSUzvbMU1lwsyWvVW07vDs+uCoIgFOrW+rOflA+UHyqVCk6fPo3V1VXkcjkAW+vacrkcEokEzp0755QFyyIYoAFFIUSyvEVlYHnNjpPlOa1zFFllaMtst9tunZkCSd9k9lGU4eCru486XavgkXxSq9XcolmNXipoBcKHObJsC24eD1Jh2ukarddeyt7t9ZxnmuZBz6IqcPYdAKRSKbdVvp4/FWU0RckSBdTqlfMBJAUC2jc6Ny1g9hlkBFWqgFmuOrjs87T+VoEC4TVPTIlharEaOVTMem0n0EkjSlOWVQ7QkaMLwTVyQ/msbbPy3o6Lb6zsf7ZPou5RnlCjirLDAslYLBYCu0EQhJxMXGBPpxR1gjUkffXqZCx1a5vVAT6gYj/bdwuw7DPZJ/ttQJF0/UoURQEvew1J+dZmWvj0JWXN6uoqKpUKlpaWUCqVnOMzn89jbGwMi4uLWF5eRrVadceRqKzuVEcLom399JoogK5t1XJ8cyHKiNL+8DlqtC5qRFnng921U9cr0yhj2RqporPGbuvNOUR+UFmj85ZpmMRH6mhmn1C3BsF2pF31q8+QUge5r18t3/iMD9t/VrZoe2noJZNJ54Rqt9suEmc3NyNWSCaTLktKx1kNV+V3Owf03RcU0LrruzWWlJ8sltFMAz4nKiVwP+TKntZIMSqlg81GK+kgR12nDeQ11iOn5BPOvL/dbqNSqeCxxx5DqVTCxMREaG3P05/+dPz93/896vV66D52bru9nSKnytonPGlUANtGDsEB28CFj9brqQPM+7WtNo9YlSjTEdWoIPiwnltOfqbgMX2vXq+7NUzqfdnc3Fosz1AzvStcx8Q1B0wRTKVSoRRD9otPEHK3vdXVVeepn5ubC/XDwYMHkc/nnXdkaGjIbWXqM6SUbzjBrcDx8Z1VDD7ygTblC13/QuHNc8pU+aXTaeRyOaTTaXeuluXdTuRrQ5QhYJWclsEXeWR0dNT1q4JVFS4+UEye0qiA73kXS1GAK6q93Z4ZBQD0fy3HAgGNStkd/MiTNACSyaSLTDGdlQpcec4aNcpzHAv1QCqQ1nnD8VJjQMuIAs/6LFW2arSp4aROLSsT7WftR43kULZpyogeJKueYB8Y1zJ9Bq463fg8e+imynzKVs4D+0wfz+j4dZIhUWTHOUp26neCM32uRvV4+PDAwIDbPCiZTIb6XrdI12cqsLNzwva7D8TpfbuZ+9YZqzxr62DJAqj9kDVaL1IUiPXNXd888MkWILymt5PREJXeNzg4iOHhYYyMjGBhYQHlchmNRsPtQGc3t1G5YMeZhoWVG0B4nYsCTX1143sfn+vcVePJUtS4qh6zeI0yenV11UWm2EYF+8RCNBp0kys7tuqs0QgXf6NDjWXZ9H+b8aR9YA0Hi1269a293sos9q06sTiemj6tmRRsgx73Ynl2YGAAGxsb6OvrQyqVck4c9o3iAl+bLD/ZNqkuVCNW8ZbyiOIaNZLs1vkAXLuj1gZeLO15+3NVqsB2R0VZxQB2NMiCFZahTMb72IFqYCiRYer1Oubm5tzhvMxHBYDR0VHcdttt+Pu//3vHCFoXlsnIQr1eDzGEBZj0bPgGl58VmPgiOCqsbPkqaHRi9/VtH/ir+cJsB5+jEz4WiyGdTjuhkc1m8fDDD4fWOw0NDTmjiWt+uAueTygcOnTIGQ9KOhH0+sOHD2NyctJ5SkdHR3H//fe7OtfrdXfYXTabdR6SWGw7HUiFBp+lYM8HbDp99wEAqwB0DBOJBJaWlnD69Gn09/fj8OHD6O/vx9TUFNLpNEqlEv7v//2/ePDBB7G2toZisYijR4/iyiuvxKWXXopcLhcCwd3I1r2TEaXX2HtsFJTpTrxXd3JU5abCjzxAB4E1qh5P6qRc9hNMRZVJb6MvvY99EI/HXXovjShGp7izlOVPna9AOL1IlYoFeSRGjTXiQNCs1/J3fWe7VMEqyLGA3YJHXu8DQxa4sUxGsn2Go8oYn1KzIN4auFzTqc/jWDDqTsVqnVtaZ6vk7bvVU3ZMosiOu4JKzkE6KBUEqYONz1SAR68wDfioNRe6NbOtlwVm2jZb/ygDWsvS/oy6VuvGzz4e7dSfu+n33ZJvgwNbVwWVPkM7qo4WzPqMQPY7nQP1eh3Ly8uYn59HqVTCyMgIYrEYcrkcRkZGMDw8jFKphFqtFuJ91tviJWu8WWNG55Q6Zn3HrVBu7UWPWSOqk86w9VKMpMZTLBYLbf7DDB7OI+opOg051+h00B0NFddppFvPEKSO1DYQA7Gv1FHDyJceDtzJWaTtUx5ROcp3O8/s+nLKWWI8lWu8VlPTY7EYVldXUS6XXVvpwFFZnkwmMT4+jlarhZGRERQKBdcu8roP21tcYdunL+VZjUbq7z4dYr9bslhI+0117IXSnlP7VNBZocLPPmFjlYlea++1Qlmvs8qO13Jzg8ceewxXXXUVstlsCLg+4xnPwD//8z+7aBIVIhmeqW71ej0kNHktiUrNHl7LCUympGIDtk+N9gkgnQAW6BAstdttB9TUsFpbW/MKBUaAGGni2VrcsOKxxx7D6dOnsbS0hEKhgHQ6jWQy6ereaDRQq9Wcx0LDv5dccglGRkZCO/bpuFqBe+zYMVx33XWoVquuT9vtNorFomPk48ePY3x8PHQ2lXrAlQ/YX/w/6qA/pU7/+cjy4PDwMCYmJnDffffh3nvvxZ133olnPetZrr82NjbwgQ98AA899JAzXpvNJmZmZnD//ffj+uuvx9Oe9jRMTU1FOgTs+HcykKLq3I2Ut3yK0TpIgJ3pZOr1odLR8vaTfONm574PlET9v9ux1/tt9MMe0Ks5+owK0IiyqTe+uWLBpK2D9WBqO3QhMOtgDRz7DCVftIm8oE4gAiy93kbC7Njo/zY1R0mfb4GE1sd+VvBHAMQzUliOTR+0CtMqb+vAiuIvfbefLfn+Y701ekbnHH+zu7JxPGjIs62UzTwYmsT20iOvstPHG6yXpnjbl20Py4vH4yH9R0ef1aNRWR5K1qiyfenTyftB1jDy6Z1OdffNY9ZZdb/OC18fM72P66RKpRKazSbS6TRSqRSKxSKGh4dRKBTcWinlHa2Lym7KeYJdHw8y60WXE+gRJz68puXbsVHgS/1in9sN+Oqz1GBgtJ9revgCtte2ttttd+YleZHynP2hv7N9umkF113xPtaHzgniKpah0V8b8aDBR7xKbMex4vxTfrN9q/1t+VXnKJ+lZx9qdIdr6+iQYTpptVoNnYeo8jCZTKLZbLpnUt+pEalYjDxk+UR1mzWWrMHE/xSjaLqlbijCMvi/7S+fLNsvObJrQyoItr2dbIRP+NjJzHtJUR4vvc+WoffoZ2W0zc1NlMtlnD17FouLixgeHnYHpgHA4cOHcfPNN+NTn/qUA7tkXHqRY7GYNxplvbc0YvTAWgUDrKd6UPibeqhUkfO7Mo2ShmW1L2k0qWCjoCKo0/VU+Xwe1157LT784Q9jdnbWGZIUPq1WCzfddBOq1aoz4CjAL7/8clx99dXIZrNe/rCgjXVeWVnB3NwcBgYGcNNNN+HYsWNYXV1FvV5HtVrF8PBwKEfXtl37XnnDRhT1+iie8xkqep22R1MkNzY2UK1WMTY2huc///lot9toNBqYnZ3FAw88gJmZGcTjcaRSqdDkXlxcxOc//3msra3hjjvuQCaTcf1iJ7SN9qpnztcuX3t910cpOb3frkHzgSx7D/lfBfTFkh2nqHd+jhrPbnXpBsgUzJEPmPJKY0p35uN6FV5HY0oVvjUiokAEEI4YBYE/MqUyWWUQQQJ/53NUKapiUhlG+WMVGxDOrWfZ1tBWGangiYpN1x/wel3DpOBDwacCWR1zBWeUYSxjY2PDpXnY/vYBwU68oH2pz/eBmE7Uje90vBQo0imnu4MxEppMJp0nmf3FFCaWoWtoSTaypnKAfK8eeCuvfEaG8gav1Yi49mFUX/iMEWCn03G/iOtFlXxGTqf52km/WGPKxzO8RnfvW1pacul9qVQKwNaa7+HhYRSLRSwvL7sND4Cdu9+pM8TnhNBrWQcaUhrFYbvsmPnmkCWfPgfCY2kBu/Yp+Zhp/+RjGpAqmzlP+K5l2XJSqRQymQzS6TQGBgZCadoqt9Two8OAxujAwAAymYw7QkZlo3VOsW9VZqkxyT6x8oXXR+l32/fWaNJdoDWLYm1tDQDcOq9yuYxyuYzl5WUnX3SexmIxN09SqZTLuuBSDD5T8a3lE207+0jr5GuT9pPqKNUvPgPJzlWf7Lf8a5+/F9qTIeUTNvazhi6jBp3XRwknG0JXYQ3s3ACAHdpoNLCwsIDZ2VmXcqXK+Pbbb8dnP/vZ0HO48J4Ky3pZFfwQvOtgrq2tucmpXhyCLBoImrNpB1QNLG2b7jLTarXc6dOctPxd07I4Sfr7+936HHqVWNahQ4fwXd/1XfjYxz6G+fl5twMQz2l63vOeh6NHj+LrX/86pqencfz4cRw/ftwJ8yj+UOOR43/u3DkkEglks1m3Vmh8fByDg4NYXV0FAKc8dOxZngJ8DUfbKIryp8944PdOYMlORPVMbW5uolqtIpPJAIDbtOPBBx/E4uKii2oSvHH84/E4Go0GTp8+jQcffBBXXnmlAzpsmyo8riNj/rV6VhSgqpfdF4GwHlD2m4bJde4o0NY+sEJd+xNAyFlgQ/v7TT6wYp+3m2s6kU+ZUeDroYtRa6UIau16KrtWiv3ti4L45CrHL6otFmSyXP7mA6c+BcQ5p/LX1sX2tdUL9hqWaT8z0sVn6bopgj+d6z4wZ4EJedkXVWFdO/G0dbpYXrBAr1O7bX/5xjXqWvaTrsdotVouW4CymgY8dZeWSb3ETAQFdewLO/YE8zZ91xpTGvmzsiaqb9RjbR0FnfrI6s1OcvxCaK9yyydTOJ+j5ovKU+sw0/+DYHuL7XK57DaXGB8fd/qhWCyiWCwin89jZWXFgV6N9HD+c4w1Lc1GhNR49gF8Ncb0O++JGguVZyxbU1htJNPqHj3viTiNRgvPZ9JDZDVSwfuSyaRbLjA4OIhsNovx8XGMjIygWCwinU4DgNtxj2PoGzvbf8BWthHXjPOZPgPMyhBiAMsXPie61kPHxmeskRiN0ogUo2BsL7C18df6+jqq1Sqq1ao7C8sXkdrc3MTQ0BBWVlZQqVRQq9WQTqdDSz907C2vANtzTeWb3bxJ5T37knqAY6Q6xGZ8dNL5nTDDxdCe1kjZjvJNFCB6Ryfep++83goWazX6gLAK41hsO89zenoal156KQqFQmhr6qNHj+KJT3yiO6CXk4seHd0WU70I2g59NgeWdVdjaXNz001SnVR2wugEoOBgffifAjAqxUQi4Xao0cibRtnshhAacj58+DDuvPNOnDx5Eg899BDOnDnjdmphusgNN9yAG2+80cuUvt+4CFFD641GAw8//DAOHTqEYrHoBOfGxgaSyaQT8tYAswLDx0OWD6JIPRrdFCb/55hVKhX09/ej0WigUqlgamoKwJbxx8MTaTzRS5xOp0N1arfbWFxcxKOPPoobb7wRyWTSCYPNzU2XhtnX14dMJoOVlRU0Go0di10Z5SARKLHeGrXgWFtvsb5sX1pBTkON5XdTmlzDd7FkDQrfuFmQHzW2FrDqbwqUddzt7+oppjHVaDTcekK2malUQ0NDLnqVTqdDaYAKSjUdwtbZByIJ1qzxw+t0rHT81Siy5fmUsjV67PWaQtipv/W79q+9j/OfRpR6j6281Dao/Fe+9i0mtpEurYPtb+V/+z2q3eQVleeW3+yzFJxZGcZ5v7a25sAOo+JcG0NjKZlM7kgTUg8+3xWk+OpAuc1t+y1IscCNdVZnjoIdn7ywfWpBZhTvWAC0F8OnG9kNR1hPWxeri1QmquztpGeUX7VPgZ277TK9b3FxEQcPHnTp8Pl8HqOjo24rdBpSOm7WCNKtqumQ0Wdr2zoZUip/7FjaNtqy+Z+mXlm5a3UPNzigHIrH4yGjhbKCWEJl1MDAAHK5HIaHh916yWw264xQLntQA4rkkznEg5ryp+tkWR8dS8pS63TQbBo7j6yMZp/ZOauGhuVPjdDxHj2+ho7rZrOJeDzuZAp1nAYVOK/j8bhbk8prdXdpy/eq21TOaKokn2XTLRXPAttLOJTH2PeaAaSGrCXtI/u6WNqTIaUNVXBqrW0rZNS65HWdSO+xCk8npAIeMlupVML58+dRKpUwPj7uDueNxba8d9/zPd+DM2fOuAnJ/xhOZ0hYn2utXT5fmZSMSQYYHBxEtVpFNpvd4XmwQkafx23KGcImc3CxtjIusL3onIzIw3a53kiBLQ+sBLYMgVQqhcsvvxxHjx7Fl770JXzhC19AX19f6NA1ttU3RtofbMfY2Bimpqbw1a9+FZVKBUGwdZgddwVcWlpCLBZz4WGdbKrkOXHYfj6H3y0PWIXkIx9IjjIOgmDLgP3kJz8ZOnT1iU98ovMAP/zww07AEzQTjGj92+2tLUWXlpZQr9fxhCc8IQSc7HwoFAqhCa9Krd3eSuW8//77cfbsWSeEbOoD68yx5O9cmO7z/FNRWHCk/aR961vMqo6HiyEfiO10Ld998zTq+m6kPMc5pptOcK0UFQP7mZFJKgld18I+s8pSn2kBpLZNZajeZ/spSgarrOH/Nq3TN44WPOqc9Hld+a6y046BGk3cfY4RdJVDjOzzxeuSyWQoxVqNJ5/80j7UdloQp/2g97LfVAZpudp233hqn6hsUCObZREIMQJKjzkPfOZOhZT/AFyf2T5i+XQU2rHhZ5saZVNSVVdRXvhAtJWzFgCpkdXNCWbHT+uxn9Rt3Ozv1riLul7L9hlRPuzE7Jh6vY5SqeQO4i0UCgCAbDaL0dFRlEolVCoV54gDttcOKV/p83V5QJSsJm+yrtQvGj2yetTHB9p+K5d0DqgTgM+njKF84BxoNBoYHBzE5uamO7NSM1V0Pfrg4CDS6TSy2azDRExH07VMBOHATuPMnknFdvA3niOq/aoOa3WYW3lisaxeq32o6dbsM/aPyh72KQ09HXufHCbGARDSTUyVVD1lDal6ve52+KO8UfmgBi+x8ubmphtHls866DiqEcs5QnyjdgdlkE9OWf3nm7c+/dTNNomiPR3Iy4fwswVi7DSSftbJoRXWcq3A4W/WALNKT59Rq9UwNzeHM2fO4OjRo9jY2HBGBQBcccUVePKTn4xHHnkE9Xrd1dt686MsWvt8vW59fd2BbAAu2kUlpgalrkFQABAEgTtTSaNeGnVSo8kKAIIIZSy11qlM9YyVVCqFo0eP4uTJkyiXy87bqWPjG1MfANTthtmGWCyGxx57DAMDA7jqqqswPT2NYrHowvTqydGJy3crhPgcFYT2fxVIDFMrn3QyoPjcqakpvO51r3NpNABw7tw5PProo2g0Grj77rtDRlOj0QgJVQJrCrjTp0/jU5/6FC699FI3xtYo9PWr0ubmJs6fP4+HHnoI586dQzKZRC6Xc+vWdF5q39hD9xRgU3lbg0jTMHif1tV68Wxe+sWSL2pmKeq/Tn0ZJaPsPVYG2U0nqFQ4x9knNKTS6XTo/CndJGA3/WQNKb7Ic/q7enh1sbh6ki0Aone6W2SJclsXDyug5vxVmWi92yrrKCO4hpNrFbheQaNHlHNcC8Ron/UKM/VZo1jkawVKrBP53faP9qntd+tVjxpHO5d9ytsaUzrWCoh0rYOe5UfwqYCTIDOZTLqzAoeGhrC6uuoMKwv8CUR0nYl6s31zXr9rpMPXfzqP9LP2azcQrqCRPLRfciYWizndased73acfYZUlDGu/W31VFQbOCbNZjO06cT4+LhLIxseHsb4+DgqlQrq9boDppQDPgMFCO+arJEpawhaj72dL534vtvYWENK+Yr9RJmhc4DzgICaSxJsZIiygzzPlEA9P4rz2UaWgO1UQj1KQDMPOK5MiQuCwNVLI+sahVL+sv3DevOddbCyR69hHXz4iDKW5dCo0Q0uaLjkcrkdZ0apgat4KB6Po9lsolqtolQqIZfLIZ/PI5fLuXnJtjJSrvKCOwFyHG10S7GsznFtK9fAaoRK+YN4WWU+26S/22UbF0t7jkixUtZbro2mULFA3AoZVexWECmAUM+4kg/oNxoNLC0t4cyZM26wNb2vr68Pz3ve83DmzJlQR+ruLKowFURaJWSVtRqUej+ZV1Ms+AwrzNgutci5tkX7lcocQGhnHSCcC8u2AXBCgYqXkQke+nf77bdjbm4Oc3NzOHHiRKgd3Sx1CpbZ2VnMzMy4NDf2S6vVQrFYxOLiIpLJJObn591EswLIKlsVSuwnepg4BgoG7cL8/v5+FzHU8pQoLHUcGo0GYrGY22Qin8+jVCq5dhWLRScgWD8abVyfx75ut9uoVqu499578YlPfAJXXnmlS0dg2sHp06dxySWX4NSpU7j88suxuLiIvr4+J2zS6TQeeugh3HvvvahUKshms8hms8hkMm7XRespt3OMYIzb4bPPNUVQeV2Fq/Xw8D1qjn8jSOuxG+pmeEWRevpUAeh26O329sHY3HRE11VpBMsqVGs0cQysQaMyxdbPAjyfgWS9nsrv/F0VlCprjcRYRW8BrvX40WlDcMKIdCaTcS9G7+140BGlERbrdaWzgy/KPdbd5/jzfbagRPvQGhGqJywfaT/odQQp9mWNLNVN+ptNDyVPcA6rwW7X5lljh3OdRhSNNTWkrM6xfaL9Yeeh/m4NVfvdlqf83gk0XiwRBFsM46u/bz756u1rk+9a22fWuG02m1hZWcHi4qKLSnGdMqNS3CSgXq+7NC01jhUk8j+LzVQ/qOFleVKNKZX9tr94r/KLzgEtV39TnvSNhx0vNaIAhHACACdjhoaG3K7Dihl4n84X4iNGr9h/OpfYf5ub2+cuMduHc5HOH01Xto4bks4xXSdq1z/rnNF7bFqdOlfUmNO0Q/IGI5/NZjNkXGh/6BiqwcWoHXcMpbOLhi6fvbGxgUqlgkajgZWVFbczICPuxKvkH91zwKcjyTs2PZJ8o4YS+cjKWusouFja02YTJDZAmcJ6jXSi6KQgWaUd5eWgx8IqLZ9ioNW9vLyM5eVlnD9/HmNjY+4MH1535MgRXHnllfjqV7/qDAA9aFXrb4WFz4Dk9cxtZpiTbeKmASzThpUV9NhNCPid4MkKKkY8eD2tftZHjS1eQ08CjYS5uTmUy2UMDw/jwIED7r8oPvD9xz7QXbM4rqlUCrFYDAcOHHCLFOkdAuAWRPf19SGXy+HYsWMYGhpCtVp1E5D9xHsajYYLwdMoGBwcDPEkJyI91XZMLfikx8MqtC9/+ctIJpO45ZZb8PDDD2NhYQETExNoNBqIx+POG8jJzbVCOjfYd0tLS/jMZz6Dqakpd8YNnzM9PY3Dhw/j7NmzOHHihDtg8LHHHsPXvvY1rK6uum3k0+k0MpmMS1Ww60LolWKfWI8Wd3Pk2ClfqxGvHkOWqwKJCsVnnO4XKc/zeyfwRbLGlQ/kdgI8vvlNWcHUPnrTNBWCvMY1balUykWn+PI5gfhM+/K1i/JA/48yzjQyxeuUH5i2w+s1FVnL9QE/9qc10BQs8D9GoIaGhtx2zgQ6TOmzW7qznhrBYr3Ua8pomUaeOJcZrbIGk/K0/meNQwWf2kYFpD4wafuI7bHGlAWUqtw1smd1j+pYOsTUu26fYYEu/9NolzXCyGsqF+w87PSy8075whoeUfNSeVDHaL/Ipr/pWGj9VKdEyR/f7xbsWYNMn6P8obv3LS4uolwuY2xszM2RYrHoUvxWVlZQq9XcrsMWTGob7DO1jvyPfa73W17U1DX70nHWSIvKEB8fKyDW65SXdZ2P8qmmCvf19YWMKEakOkVeON+5lmp4eNiteaWjnZETTSNUxzXnGY02Xe/pc9iwbewbNSI0Vd83d1hf1deK+/Q7HcX8nUYMU/QqlYrrT3WmWCeQ8vfg4CByuRzGxsbczn1qvPG5TFGtVqtYXl52z+JYslwbSVPjU8dV+9HaIT79Z79b3rdy6EJo14aU5ktagdZJQNpGADtPXlaApxNIy9Ln6H866ckk1WoVKysrWFhYwMLCAkZHR93k4bW33norHn30UayurjpQomDCdrglFQg6MVWZ237yARsCUW2bGk2aeqPgh95vVcT0wNLjQHBHoTI4OIh8Pu/SY3iAMe9bWlpCIpHA7bffHkqHtGNhxxXYOmvptttuw7Fjx9zZF+vr65ifn8cXvvAFDA4OolAooF6vu1cikXDr0vr6+pBMJnH11Ve7k9sBuMX68XgcmUwGpVLJPVPTMgcGBlwEiROOAomCrNUK73CoY8it2NnGhx9+OATEKBROnz6NL33pSwC2PF5HjhxBq9XC+fPnQ7zEcDn5kjzARcQbGxuYmJgIgbTv+I7vQDqdxo033ohEIoGxsTFsbGxgcXER58+fd+eGpVKpHZ5ZFcIEpIw6WiVPT5Ke7K7GvHq4tJ9spE9BOcfkYsPknQB71LV7KbvTd/3d92yNSlERabSJ85i8zMgkNwzg5jY0kC1wjmqveszY3wQyth8s0FF+13LVQKDxxPqrU8A6q7qNC//3KT9N0UulUkilUs6DqUDDyh1dR2UVO9vAtaV6bhdBUl9fn5MhBMN8jnU++IwAXhel94CwI9BnSPE+BaO+9BPVH1qW8oj1pnKs1GuuaSxRoFkjX9YbrU44bYPtH5I1oC0uiLpXjYooY0SB5F7n/G5IDSTf831GguUR2wbAbxDasfCR8i69+EtLSy7tPpvNIhaLIZ1OY3x8HOVyOWRI2R2CbSq39rnygvXQqyPX/k+Z4Rtb39zwOR+0D3wyT/9j/XXXYrsOiA4XyplcLodCoeAwD2URryGpoZPNZp0jfGBgILSLHQ2ORqPhcB/ni45bPL59LIE6S5WXFNOp0cNsB9aTETSNVHFMdXkD5aEa0Cy/Xq+HdscjptEt0On41Y1XLL+ybNZRt0nXbAOmyg4MDDhZwpRARqR0jRSxi66NpV5QZ5eu/eR4+oxhjqfOA/a5TT+0zrvH3ZBSjxQf6PPOKFOTLCPZa9WI0gnP//SZPiOKxPuYW7y8vIxms4larebWpJAOHjyIW2+9FXfddRcWFxcdUNa68vl8p2LxtUdT9DRNkEyixiIVHO9VQc6D0hS4kjnooSA4oLLTycSolG7nqSkxc3NzWFlZQalUcmst2I8jIyN4/vOfj0KhsEM5qFfJ8gXHbGhoCAcOHAj9vri4iM9+9rOIxbZCvJlMxi0azWQy6O/vR7VaRaPRcH04MTHhhAujZ0NDQ1hbW0MqlUI2m0Wj0UA2m8WDDz6IdDrtztIC4NLUOKF1dzvlZb6azSZmZ2extraGK6+8EpdeeinOnz/vruPJ8o8++iguv/xyFItFxONbUZ2VlRX09fWhWCzi/vvvx+rqagig6nOY4sfnlctlBMHWmri+vj6cPHkShUIBuVwOc3Nzjp+Wl5cBbHt71GuWz+eRTqed5009++Q7ClUKdfaHzjONKqnXUo0s5QOdn9qn+0ndDJ9OBn6n+7qRL/rN3+kpZgqVfuaGI1QAmp+fTqfRaDTcOjqNkFDI22iDyjcrKzkW2hec/3ovQbZV4MqbajhRSasRZeW+Tx7bvmP5fPdtgsBoKjeaUN6zz7KOOJV5fX19bkvw/v5+ly5CeU4jitdb8KepNz5AqNepJ1TrG3WPPlNTUdhXCrZVp1ne0/G0/Ei9oRElAhibBsn7aHQSFOm7Rtg5drbv2GfWQ+zrQ0tWx+tcU+r0237JmlgsFjqwVfvY1+fWQLSRviiZ5XM+WP4m8bnMcOGalHK5jFqt5tbDxmJbmzvVajW3myMjDATHunbSJ9coR3wRT5X3Cvj5UsekjQiorLHzxMcntr/VmNNnqyGmjngCeUb/M5kMRkZGcOjQIYyPjzsdq84I9jH5mHiBR7XkcjmHH7kd/fz8PEqlkkslbrfbLp1f54ymztEJNDg4GNo0h+22/MZ+0n7QSJ1GmRUfUzdxKQfnLSM/6uDXtdt9fVu7LefzecTj8R3yQ8fN1tnKdrs+ns4Abq+uaYQcO82e4flUPLpHeYvPoO7QQILWUyOQigkZTOBeBrxObYNviCHFQVWPvgof6+lgxaiElDF4jRVAdnIRHJCxfBPPDjINqenpaSeAhoeHQ4IrHo/jyU9+Mh599FG3u5ztSAtkrOFmicYN10DQS6iHJ6q3UZ9J8MLvKsiYTkWQTmbnM9TzWK/XUavVHLCgd5wRmY2NDTSbTWxubiKZTGJ8fByHDh3CiRMncOONN+LAgQM7xoD9ZftH68rP1qM8OjqKZz7zmfjc5z6HxcVFd07S6uqqMwSWl5ddP01PT7vdDsngsVjMef8plOv1OsbGxpDJZNz5TpVKBSMjIw4gKO9ZMKPtU3DMdD329+joqBOMfD43MWm32zhw4ABqtRrOnj3rlBf7S8EXnwtsGTTHjh1zu/xxwheLRQcoFxcXnfeNgHNzc+sch/HxcYyOjiKXyzmBo14Y32JX9TZZL7oqtk6eWfK4zluds/tBUQY7+UnJB0KiyF7rMwL0Wt9/6sEnD3MHNUZg19fXndKmctaFyzQadJzIbxa0+YwqlYNqJAFwylKdI+rA0XmtIEeVozp76NHTe3xGQhTg5T0aUdJ+0J361LDsNB4sz/I31wfRUGWUkDJTvb4W7Kkjy7ZPZTI/R80VNRy1rwgC1Fmo/c3vKuPVIUbD0J5JRk8sn++LLinoInAk/2p6j+5EqeCbMoPvGr1TZ43+55MrVu7onNyNUaW8up/yBvBHP6IcGlpfa/SrMcbffAaSL5psy1ceqNVqWF5eduukDh486K6Nx+MYHx8PnQNEg5jOYQA71v9ag97yopU1Kld4rcowq/st1lL9x/91uYM6gMjXGqG1mz1o+/v7+90OwJlMBqOjoy7tcXJyErlcLjRWumsc54auFabMTCQSWF1ddYYqna66nkid1qxnLBbboYdpbNhUP8V61lHEJQ86/2Kx7U0bdM09x4p9SplKpwnvUQcIALc+nvgcAGq1Wkj2s052zrGOusyAmE2jV3QgUkcy00eXZtDZqIaUGkKqR9gvKpN0uYzNLgDgDG32GfURdZHy5YXQng0pHXg2Qg0kC6yVVNn4SCehehysB0sVov7GzxoSP3/+PA4dOoRqtYpcLhe6L5VK4Tu/8ztx5swZzM3NeUGi1k0Fi16n9aEXgMCbipXnIajRaRUrmYuGI4UfjSFa4NYrQSVbr9fdwlMKCmD7vCEqak76TCaDG264Aa985StD62l0zH18sBvidYlEAi984Qtx7733YmlpKbSgE4BrG/uj1do6n2l1ddV5EJaXl0OecwqHs2fPYnh4GK1WC+Pj41hZWUEymQxFCNrtdmg3L05EnXxra2tYWVlxQIPh6lwu53a6YuSvUCi4nbDm5uYAwG3Dahc4KpCl4BscHMTo6CgOHToEAKGDNTXV55FHHsHc3Bza7TYWFhZc+qPmXncyOFRh6zyxzgEfL/uUoZ0TygdUsvtNFjBZUGLrZfujk7HUibR/LFFZ6jop69mnMaUgmp40NaZ4oLbmtPsAnXoeVc5yvgAIgXw7/moo8BotTx1jagjws8pta2xEAVu91qZRsO1qROlaAB+4UKNMjVOWl0ql0Gw23Y51GmXQs5EI0LR/bf/oGCjo9xmg9n+f4akpjsDOHdH4G3lJAaVGmPTwUQJMyguVjT6wrPorCIJQeqpuiELApfVXsGKBjBpPGp2wRqd13ljZ4sMKdo5rv17IvI4irRPJ6mXtV19dbHuicITPGaQ8b5+5sbHhsEypVMLy8jLq9bpbdwxspb9zrdTy8jKq1aoD+xqBiTKYbJTG1++++mqbrAyx91B/a+qZGgf6bK0jjQM9E0l5k5iNuIu72ObzeZemp0aAOic0M4gyEdjCLENDQwiCwOEuOsUrlQqq1WoohZgOatZTZajKQM4P64CwzkNtG40J/s+5G4vF3IZbOu+IJVhnRpY0tZllq5wjDuPyBDsPgJ0bo8ViMWdIaQRJ1z4xHZJr47nLrcoFdbRRP6q89Ol9O4fU+PddYx069nWxtCdDygpY/k6GtINlyU5MMpGmDgA7TxrvJOh8ZQfBVmSmUqlgenoaV1xxhVNCHBxee/DgQdx0002Ym5tz6SB2jZPNZfcJV+0fRomCYPt8KfXKqGdFB1e9LpyMZKh2ux3yMtCA0jTCVCqFqakpDA8PI5FIYHl5GQsLCy5HttlsAtjepnpgYABLS0vIZDKh1Dfbl3YMfb9FfQ+CAE94whPwspe9DO9617swNDTkIoDsQ4Zq6/W6Wxum/aUHDmrYGQCmp6eRSqXcDo1zc3Po7+/HzMyMG096mri7nYI3Ahimz3GTCxqevJ+biHAcyKfsTwUy+pv1aA8MDODgwYMYHR11YEa9b3wG1/dRmVLI5HI55/WxiodKQY0ijeIy1ZNkhT0VmjW+fIAnSrj55v1eSQGs7xkkbZsFXlaZR333GZWd6qWKkwYUPW70ujEipcKbRgM3oNDognpjFVhbUKEvRr8tQObYMe2G9VajyIJrymHLq1qeBViqhBRIav2AbYNDvbJqWGokNUqhUf5ppG1zczNkkGk0SlMn6VSyskO94DZSxTYCcHJGf7eRCCsvdU5RhjNtyIJaEh1vlHUsS+trAa4l9jPHxUbueB/5Trd+tn2h3l4dM/XoqoFsn604wWIGnyHla1cnUL8fckb7PgrIc26RX6xBFVWXTjpUsUKUTGI9aExzoX6pVEKtVnO79wFbcpwHzRaLRWdsaYomMYymd/LdyhZbT35WvrW6xl7r6w/ym53zKq+tbFGdpJsjKYbUyL7yKeUgZSGxl91MRYMB5H06d3UNZxAEoTmqqfPsn1gsFjLQ7FirYaXGkToH1NhRh41G5+LxuDOk1FHT39/vzs4CtgxH6ibiUo47Zab2gRqFuk4S2D7PSucAMY06wlS2NBoNtyaeKafMwmFb1YjS5QnsEw2o2BfrbJ1KWu+oe3VOXKwjeM93q7IgE6+urrpOtaFD3qP3KqlHD9iZQmeBoS1TSQeX4dhSqeTOlMpkMqF6bWxsYHl5Gddccw2mp6fx2c9+1q2XYl2jgIwOmtZTDSAaOKurq26gKBR0cqnQZsoe22I9qwTawPb2xFRs+XweIyMjmJiYQCaTcUaIes7pGaB3h6l2nHh2rG3fdvvNRwMDA/iO7/gOLC8vY3V1FY899hhKpRIAuHpT8DQaDdcfnLjKIzQgfZ7YRqOBUqmEwcFBtyhU84U5SVOplIsKxGIxFwWMx7fWPc3OzjqvXrlcRjqddl5Aptcxwtlut52gUIOK9VWAxgjEysoKzp07h4mJCQwPD7toIncJIr/U63V3v273TmFE754CHc5JvrMOyvccO+sl9s1ZX7qVDwT5nB0XQj6j3Dor7H+2PlGffQreB6B9AI7fqcw0PUoPKazX66F1auxnKjk6DWhIKThToKJywQdSOF/4mQ4fNWTUgLJGs8/ZpddHfVdZbMu0pG2x6WCa4uIzomy/2/99qRvW68jruMOnlsm6WSPFGkJWD0W1VcdJn0OgoetRKR/0Wi7Ipvxn/S2Y4T3WAWJTiez4Up7qrly6Q59NfVRPsa5/sOPGMeUYWzDMsbNyQvs5aszt3PT18cUSwbXv+dpvNt1st9EbLYekEQifMaUvyhlmm3CpAh1q2q9DQ0MuXZ76yGcYaTv0d9YnSk6yL3zylW1S+WmfTaNDzwJS/aI8x0gzATrvoyy19Uwmky7FjNERzjdfSqDW3xeVYFo262DT6JLJpIu0MJWYOoBZP2p8Wx5QJxT52RpSbKvyjW4MQSNPo8XMoOEcJ8/U63Xn0CNOoOOP7dvc3HT119RH1p11smu01HDUZRLENUw5bTQaLmIHILTZFeWH3QBCnaU6hhaLU06yDsTGJP5GA8/KQDrHfby/G9q1IWW9D2wghSjXARGUajoVmUM7wnaGggKSKnAFp1FGl1r1zG2tVCpYWFjA/Py8W4MCANVqFV/72tdw7tw5TE1N4clPfjJGRkbw2c9+Fg899BDq9TqAsMeGDMP66Dvrph5a9UAy2qLhWNseNSAUSGmuqG4cwP7mIr1EYutQxsXFRQBAPp9HJpNxTEnDQHNm2edR3mCrXHZrSFnhMDY2hjvuuAPr6+s4f/487rnnHkxPTzuAwXOX+vv7QxE0RjnVi63hcx0bgoJarRY6Y4KCheufNL2IPHzgwAE85SlPQT6fxxe/+EV89KMfdbvx6PbiQbB9YHKxWESrtbVjn25YogCf9eSzkskkDh48iEQigXPnzmFoaAijo6MoFApuUjebTZw/f97xoAqv9fV1x0MbGxtYXV1148+oB731rLsVyvQ+KWCzKQTKmxZQ6hjTsGW/Xqggsjzl++4DWvpuwa4ClihwYN+jQJySppswJYqeN0YzmSqhkY9YLBZaVGujkdYrru2ynnD7WSNSKhfpuOHv1ijSPvX1r84zq9A6GVJq2GlZPgBHgBQ1Pj6Ay3HQazT1TJWrRpbJy2pU2GiytsUCXh/w1TGwoKmvb2vHVI1Oq86zhpSusVCd44uGqEFlDSu2k7yqG04Q6GiETnnURqJ0TYdvgwn7fBt90vrafvWNrzUmAOzQuxcrZ5TUAWHnDvlfDWE7HpYfdmPk6fhzrvjazvnRbDZRqVRQKpWwsLDgjg4g0TFDfuNuoVZOqL60v0XV38fjxAxR+M6Wa6NZ1EFqxBCnqKNZjSpGpWkI6BgxEkNDinqPcsA3R6yhz/JsRhL1Nw2XgYEBFAoFVKtVlMtlLC0tOcd9uVxGpVJx+EPPY7L8q1FB7Wc+m/XnNeQDjjOdqBqN051hK5UKZmZmcO7cOYcl1MlCGcBIECP4NHjUOPHVT5dHKG+w3TQsabDRCKQ+0/ljjVqfo8/njCPfaGScekDv1+iebz5cLO151z4Lumm5E1BR4BDMWY+2najWOLMWKBD2bNo62U5QBc5dbOghYNrNuXPncPr0aedBPnPmDAYGBjAyMoLnPe95mJycxJe+9CXMzMx4+8EqNwXQvr3/gyDYYSHr4mMLTtSgUnBEBiNDUKjQcwLATWBOOt0ZKwi2t6kkgOCW2joePtDsUw4+EOYrh20YHR1FEGztDjg+Po75+XnMzc3h/PnzmJ2dRbPZxMjICFZWVlCpVJzQIG/EYjG3jXqtVgspM3pymTrAflLhpaFx7vLH/3K5HA4dOoS+vj6cOXMGZ8+eDW0aojuxUbBWKhXE43EnjFgP1ssKmCAIkM/nce211yKdTiORSGBjYwPnzp1DKpVCJpPB/Pw8hoaGUCgUsLCw4OYYeSOdTqNYLLq8ZF1rwrlIoUoeIS8mEgmnjDg2rKMKWAVUUWNOgampqLsFEXshH1CJMigskNS2+PiT7z5BGsXbCk71/B0qDd2BjkqISknBNcfMRjFtHXxpIj6QwrGyoFCdDQpQVJ5Q1mhKMv8n/1uFp/Xx9SXLsgBK56DdScqOZyfis21qpDWo1AhQ5cw2q7Gl4JLv1kCw/OAzgNXg0XREdfSxzXxtbGw4Z5vKeI2AWoPPlqFzk/con3L9g3qMdd2VD9QQgNhUVR1za2D7DE4faX19vK1gU9uuv+8H+WSK/kZ+pSGs/WXbzLnCcnzlW4xjr7Ptp56u1WpuN2I6TjUSxDXZXOOTyWQAbINIH3bx1S+qj3QsonS+xQ8qg/VZlo9Zro148Ded32yP1ov8qdlQlA++sdE6Kz9bR75GtpPJJEZGRlwWDbf05rr0SqWC2dlZLCwsuIwWzjPyELGiNd6Ut+18U/6gA0/Hk+0j7uYuxu12G0tLSyiVSi6FT/U6dxvk4fH9/f2urnYMFXsSQzK7wkb/NOqjEXXFI7prHjeYoHOA5XEMyCfUm7pGk8Y215gTg9v1/sTKqgvUGaT8cCG05zVS+p0DTZBOIMnB4IJCu9WvVcbqCWTHKQjVZ+q7rZ9OqFgs5sKJ8/Pz6OvrQ6lUwunTp92W2Tx8tlQqOQMklUrhyU9+MkZHR/G5z30ODzzwgAPUOtlUsanXgG3XAVNjiuuUCGjViqbiJFP29W0vFtS0EBv6VWO13W477zi3StVdstgGtrder7sQqwWVUQAzqv99ZIEFBf3k5CTGxsZw9OhRt5CW52GcOXMG8/PzoZRRtpcTzAoqXqs7wLC/dTc93e1K86VrtZrr7/Pnz4eAr3p76F2mwGc0jZEyRpUowNWDMjExgWc961k4duwY+vq2duOjwPnyl7/swN+JEydw8OBBnDp1yvX54OCgM6KKxaJb76VCxa5XUANJBaEvhU+9gz7jQz9TsXP3Rz6Dz7sYsorD987Plud0vLT+9nsnYel7viXOeU0PYL65GlKUR+Qz8pRGRQcHB71rYPgisFYAGeXdtGBFwbt61zUFSwEK66syVz3HUf1k+1+fp6BeDU+uObQRMh/5/uOzWL4qf19ExeofC1S1DH35jHOfIWUNRt7PZ6shpqQguFarhcZIx86m01g+0bG3USj2OUEMPdE2pU/HVsFTVNs7yQjLK1Hj6jNArXEKhLdwtvW9WIqKqmod+XwgvBW13u+bI77y+O5zDvuup7yt1+tOV+rW1qrb6eSjniLopY4nX9j+tca5frbjRB7V9uh9Poxn9Yqvz+2cpNxU/qcxQj3H8vv7+0NrlTg+ajASO2imCmUr5wyAkO4nKc5i9IZplNwpuFarIZPJYHBw0KXTca2arqnluCsupGOS9VRnvOoadfhQZmsdg2D7oOJ2u+0CCVyrT2yg7VQDVQ0V5VHqKxpPulMfdYTiAou7KI+p+7iOixFE7thH/ekzbjQlnA4yYPscMMo3/u+TVZa/tD8tP++F9rRGSj0YFpgwqhGLxUJgkh3oM6i0s6jEWb4NdyqYUCVuO1wZi3vWLy4u4ty5c4jH485jcPz4cQwNDWF4eBj9/f1u/c7s7CySySSOHj3qLPwHH3zQ1cWCUvU4av1YNwVHQRC4ycKIHftNy+Wz2F8UiGrI0fpmH3M8Njc33eLC5eVl5PN55PN5F5nSDRaq1Srm5+dd/uxurXGfsRWlNDQ87AO/iUTCbTRBg3x2dhZnzpxxbVPjQHOCOQFpwAPbKWp86XawDHfreSsUYhR26+vrmJ6edvWkd0/PaaJwt4CNCoxlclzJ87lcDrlczq1ZI62vr6NarWJubg6HDx/G8vIyYrGYW7cWBIGLUtGAinJO6Fj4AJH+HkVRHhryLw1Y3VxGAc/FkM5vrW8nAeczfKwi17Z0cxZ0E6ZU0KrgmOKnW9FzDit/sX/IN1w350tzYJ0p+7RvrRGl99LQJ+9RtqqhqwZTFED28ZO+sy90rCz4VQ8lwYQaOJ0MqU7jbgGpBRid0tCsMaXg3IJ5H/DzGRU+o4z9S0BoZaCWwfNqODa6bsxnbGgZOret4ao7SpIfdKOTThGu3ZJPrvh4KMro02ilzxhlv9lr9pPsOCtZcK6fVVZxnkYZl3ae2Gfzdx9vc+F+pVLBysqKi0Ztbm4dY0JQyzlO0MssBd0K284T7Wuts3VWaL8rH+o4agqozjkrJywG1Dar4UrDQPGk7lrLcqj36TzWzQzI8wTetVrN4QVNF7MRem0zoyiKc9kP7GOmKFPe04BRQ4q7BdqosxqodNoS97Af1BCwxHGPxWIhg0uNKrbX8qXyC7Nk6AgmdtMNIYiFEomEW/MNbMl7yjE9soF1I2bhOnVGoCye0TbZuanGlOo2TfWzUSbWTZ1KdCgRG14s7cmQsqBEmZ+dzf/IMJyEfX19oR1brNfNKk1rtGkd+K4T3k5+dhS3pr7vvvuQz+fRaDTQarVw9uxZ9Pf3Y2pqCkEQYGFhwR26Bmx1/JEjR/DUpz4Vy8vLqNVqaLVazsomSCJgUi+HejdpOHGSEHyp8laPgBoCBBs6kViW3XCAfUmgy7VQy8vL7nRvRjBisa1NHaanp7G8vIxHH30U6XR6B/DU8VCwbA0iO0Z6Heto79dyT58+ja985SvudPaTJ0+iXC7vADMKjqwHI5fLubVV9IIkEgkXeaMQY84uFQonaKvVQr1eR7u9fQAuvXvqHbJ9oMqAv+vCReXzWq2Gf/mXf8Ha2hqy2WwIcPLaSqWCRx55BNVqFYVCwZWdSCSct0s9MrroW5WT73MUQNoNeGK0lfnfKpgpqKmU95O6ATofT2pE20a4fUDG973b89SLqalTjUYj5C2jZ4/XqYGt63l8IEbBmqYP+4CoD8BR3lDh+AC8NZpVaakxpgBTed9Gm/lOpWd3d9M5bL3KPnneaWzs2Csg8Rl9tn/VcLKg0EYc+O4zKhVE+8ZFjWCf4RIEgXN+BUHgndO8V9utPO4zpDT6RxlGWeeL7kTpVu0vnwGgBgj7qtNnq+vt+Pkib3aM9pN0TqrzwWfUaP10HOx807ZFfe7Ep3odQSD1V6VScSC21Wo5EE+9p8aSRmRpSFnwb6OBCu6Vd+0cU/yhAFfX5bI/eb/PsNa+UGNBIwZ03sVisRB+5HMoRxcWFlw9LK5iPYgTeK9N8WJ/21Q8riVSg0v5oVqt4vTp0zh9+jSWlpZcNIh9pGl+lI8sh/3JDAY62qlfrfPXyhvWm/ezDD3uRueqNcgUY9Fo0iiddUrRYb+wsIB0Oo1KpQIArp7kK75bp7NiGM4ffR7rQ96zTqUoWRCl39UBzBRY3U2QdKEOmj0ZUj6Fqe9kaHa2DqKmU1nFSlCh5agiI6mAt5NQhbGCRxpA9FRwcLl48/Tp0ygUCm7781arhVQqhZWVFRSLRRw6dAhPfOIT8eijjyIIApeapwwZBEHo4ER6H9XSVcYlsFLBBGyvm+KkV0GhqYQ0mBTIsU5sKxf40YPFFD/2c71ex+LiIuLxOB577DEXDfIpUZbt4wXfdZbJWa6CL36Px+P40pe+hE9+8pPOs5HL5dyGDJqGwr4iOKBQHRwcxKFDhzA9PR06D0U9+eqFUz5Tgcw1WZzMnOxsA3nVRgC0fTrWFI4cs2q1ivvvvx/NZhOXX355CFwQQHHjAvIB5wn706YlqJHkmyM+skCVAtSCHH5utbbO9pqbm3OH9dGgo9KM4pP9pG5GkP6uxhP/i+JnX3n2f/vdAglf2pqOm00hIN/pQmoL5G2kSp+vnmGNfujYkTc0cq5EGWPvsQaUgiE+02cs6O/a92yvKmsLJOyYWIoac2sYWcBr6+m7zqec7e8kO+ej5p6tq46rjh/lIeWcNZJ985L9SRmghgp5jYYUDXwarbxPU7y0bdZwVnluqZMhpcDUZ6yzrlqGb05a3n88SOuh/B4F8JVvSATH6pzr9CylqD7QewjuqR9oBNHA0sgJ5ZDNCKL+CIItZx+vUcNV08yIh/gbP1P/cC7zxXUqapjayJFPPykPKu+x3Uybs+t8lF+phxYWFhCPx0N4htdwTBuNhhsnxVn6TD27TQ2poaEhd72mh7XbbdRqNZw9exbnz5/HysqKA+xarjpwqQ9UjxDbra6uuqgP+0A30NI+tXqPfKqGF5+pPEWco2NLmUKdYQ0L3sezN2dnZxGPxzE8POz6V51+nEOcD77xpf5U2cRx0XqrDUE+Zn8oztJ+IHHzC77a7bZb/kJMz/ZdCO15jZSGlIHtyaBCUycXFQQnKpWFzcdkJ3QCOPo8Fe4+RUMG5lbf9O5ryHZtbQ2zs7OYnZ0NCVIyLA94PXbsmFtnlUqlQvXUNDsKJh1cACHBpELaCmRlYtZHPds6qTkJtJ9UGGrahh4+x9/IuExV0z6z/RylyKyStQonCiDZ9pZKJeepYbSJ29Vz18Xl5eWQkFZPBQ8nHB4edql1TOvUPuFuNNZ7T+8csLX2Sg0bBZG2fZpzrV5MC5o11SoIAkxPT7v1eVqu8rX2O79rlNf2uypuO46+MfQBQR9tbm5icXERX//61zEzM4NkMolisYhsNhsC6SxTAfeFUJRRo+1Qb5z2jy3DZ4AAnQ1M+9yoa3ScFLjqTnA2ZcwCps3NzdDGPGr008trvYkW7PueA4QNF593nf1go8X8XQ0qC6pZpgIJrbcqUS2T9bIODHtNFCko8f2n7aN81wNt9fwYTTm0c8oaGeQ3ym0LCjsZXiQ1eK2S5/++tihZ2eybvz7DXhfg69oaTemy+tvqYq2XnQO++tl5Z78rD0cZ5L7+tDx8scT5a3WYGp2djGP9rPXX/rD17cQnvuv53abJ6roZjRgQ6PJ3GjSDg4PIZDJotVoYGBhwc4N8qTzDOcA5pOmg1pAizyigZft17YovykpZQmKZamSxHnTM6zhRltDIqVarbu2xXsc6tNtthwv4u6aUdTKkOCacSyrrg2D77FKCc7s0QbMDtA9Uh6gxznQ7yh6md6ohyT4Cwo5hGtS6mQz7mvOLGJDtYL+rnrVzgHzOXZCXl5dduqOuwdd+soYvyyA/WyOWPMNsDr1H8S3HwBpSqgdJ5CFNN9RjgaLm3W5p14aUWnt8oBWcChjI3BwM3Q1EjTL1aKviV7DkA0AqiHQSkDghmR+ezWYdENWNHfQeNXI4KXTbbc3rJwNyw4ZarYZSqeSAOsvlpGSKYV9fnytL26FticfjbmtTneg0Etg3bIMKtFgsFgrLEtwGQRAyaBXY+LzVeo3t/26Axo5FN6pWq6EFnuolB4CVlRWcPn3atVMFLSfd5uYmCoUCJiYmcOjQIbRaLczOzjqDhptILC8vY21tzQm2zc2trfpzuRwGBgZcWh/7gC8KQMvDqohZZ+1jnRcAnDCcn59HoVDwghFrWJH4HKY61Ot1ZLNZZLNZt3tOlOFkyRpffJat88rKCh599FG38UWxWAzNBTWcorywe6FOIE15TuVDt7Zq23ZzfRRwssaMgmMCb5uWpFFHbYcFjpRL9OpSyVhjAwiDcp8hpXXVCKrPAPcZBDqmUX2m7dc0GCpuNTbUGLPRKFvuboypqO9W9ujZXtyAhx59vqgT7HxWZwjrb1PS+bIOlE66qZOBoPKDgIDgy9dH9j3KiFIPsO5mZfWrBXcKXqyxbIGR3qNzhJ+1DhYUadmWn1mu6mufIXox5JszbJc1GBTj7LUOUcaUb45ZY5T1tH1j/6PBpc4ALYdrfm00Q/EKU5TtYdZROzyqAWedENqnxDJ6NqZ14mg/KOgnjiKAts+PxWKhjAAgfOC8Rgx1jbOVeWrUKG/yXp0z2i9BEOzY7psOMss7Pryg8iYIArdcIZfLIR6Po9lshtYv6RzWcSampiGlbbC6h4aUrhsiFlQ5x+/2mdwgh+ucKKtsMIDRSvYz68B1WCoTWq1W6ExMnQfWmPM5X6JkhE9X+uyK3WAJH+3pHClg52JyBWRaMTWWuLMHPQEA3FkqTDWzykyFtBXUPgDI52qnE2CTMbmYV8938Xm/YrGYCwHS08ztpYHtHORyuYxarYbFxUWUSiXnBWE/kdmYakYht7m56VKjNIVAhXR/fz/W19fdLnRkcp0QanBQQPX392NkZMTds7KygiAIQnnT6n3kNqqsu0+w6fj7DCX9HHW/jp+dPLVaLTQOCj4VxNBIJM+wrwgYKBAqlYobN40GjYyMIJFIuEN/GTJPJpOYnJxEJpPB0tJSqG4cb7aLu+5YYWSVve07bXOz2USpVPIaS/xuAa7WSRULz68oFAooFArIZrNOQJE/rAOi21jy/42NDbf70PDwsBPsOndtxOJiI1JA+NBqSxZ4qNLdC12IENVxsWOipGOnYFv/t142NYy4LlKVEduo16mS9CkQa0xp5NDHowo69LlUrmpsqWIi6NBziWzkST/TYPSBRUs+wGr1jP6m1+rZXjwUknnynDecz3Y8O3nWbXsUEKlS1zHQ9lhSMGQ9tioLdd4qj7GeOgZ02umL40snnq9P+dnWwb7Ue277QdtpZY0PJHcCQ1EGy34aUsrvnQwpBdu2rXrf40HKnwBCcpbzifNb56nVp8C2gUH9x5R2NaDoaKCjxGa56HhyvjAVS51JKq/Yl3atpO1HtlOjKBpF1ueT9xixIf9rOrPynJahz1b5Ztvgwy2ahUDDrFarhZZV8FpNk+PLOpGs0ajto3ONuzNq2riODwC3EywxncXV+jxGzJj+y/tVplgZq/JReWZ1ddWNgwYq+BvnstoKmlpodQqwvRufjrHyjU+WRmEqYkhdy8fNM5iNxOsuhPac2kcBY8OvFqxpQxiFofULAJlMZkduqgXYQNiDy+8kvV6vVQbmFpWsO5/JnV04+Wk4cVJsbGy4DQ8AYGhoCO12G+VyGfPz86hWq+6sI13LwHspoOyheMC2IUbvASeZ5n6SUVkud91j/a1iI3NwfA4fPoy+vq0zkRYWFtyBbJra0dfXh3w+HwIN3Xgg6nfb97sti8LcB+yAbe8s+5/Cz3rm2OeVSsUJVabpsV4UdNpffBYNg9nZWXeeAgUlQQi9TOR3Rjw1rcIaPOqtU++y3SnGGjzaB6oAldrttku5qFar2NjYcJFXXq9KXsuzHkk1BtleRvmuv/76ELAIgmDHRgEsdz8MKUtavvaBBW9RAMd+tvf4nqOk8snWS59p5ySFvioPvZ7GjRpGPieRygZ9J0+p/PH1gR07n2Gi11lSEGSBr0ZBdF0F5yB/4+6Eeq0vWnchADkKaGtaElM47GG0lLdK7G8FNJTH1iixYE1Bh4JznYeq31RW2O/kIQu8dP7qPOb8s33O79wiOsrTq3qcBrXyBt/ZfjpsbPuU1GlnjUrrUdZ7tM1Rr/0iO39I2iabwqzzwSdf98rHnWSTlV3Uh8QJjDIxEyUWi4XapM5EHSPu3EuHAlOddDcz3u/z/mtbVe/5jGTyEseZ3zViautngbeNKvAa3XiD85q6XVP62u22+1/nmcWztr22fTQCNFWMSwcY9eb6KMUKNFiI/6i/iF1oWNB4isVibr1tq9Vy6/3VALU8p+u62U4f2TZRtvH5rJdmCtmonMpQNebUwcLxU9yqDhJ17utyFcVmnHtqBCnf+OSBbbceVsw00HQ6jVwu53ZH1v7cK+3JkPJVUMlWwnrT1Pqz6WRWUPgiX/pSJrWTjUTgXa1WMTAwENq17wlPeAKGh4dx4MABAMC5c+dw8uRJZ3RwgnADBG58cP78ebcjC5UNhRaFkR4kq31mBSLvYzSJURUKNs3hJ9NzbQqfy+iMer3J7Pl8HuPj4yiVSu6cpFgs5sagr68PExMTXiPVN66qQCxfqLFLJaik46LjTuHGMoIgcAI+Ftta41apVNyOelGKnmmYZ8+eDeX6cvLRkAfg1qOwnFZrazOF8+fPY3p6GsPDw1hcXHRRHwou9V4DcLnm3I1Szz9g+Uzl43PJrxRQUQakBRydriHv2UWT2q86T1Spcs6Qj1S5qWePSoq8Zj3nKtwuhtTIUEDlu4Zts4aKJZ+Q7SYwfUZV1H0WBJBfyBPWk61KmlFmBWL2Grv+SuWC9RJH8ZM+P8qgAsIHyGp9tX7qIe4EsvQZvnr5DLpufW3/98kiXx+yLuohj/I423ZRRljnAfWU1kN5Vb2gJO1LTVNS2anl8HoFJQrg+T8NLu1/33dLFsDrGLONBDPkb31Z2aLjqjxjr2fd+RyfkWiNFBsJ3A+ymMFnrKlHXPnKtlWNbC1f+1V/61Qny5NaDrEM+UpBIjekSCQSbnc6n7zc3NzadY2GlJ7HaJ0MPkPCtiXKkaNjrKmbyWQSGxsbIVmpYJl9qTvmWhzC/lHjig4PzQ4iz6ihBYQPL9d56ZMNdk5p9IuZLyqf1QDhu93+nOXxXmLetbU1t/6IGTisb6vVcql0nAMqMzQCpGvyWQ/lJ+sAoCzjtboezuplPk8de2o867vVj76xZD+QtDzlXSsfLG/xXp+DTHlBnekqn78hhpR9CJlOBYcKFxWWVkCpRWqFt0/o6EDqM3xGit7bbrdx9uxZJJNJDA8Pu51fnvzkJyOfzzsFMTk5iUsuuQQPPvggHnnkkVA0SplB8zr1/BiGwn0UNThsPxlWN96waw40cqPlMUTJNEICXHpMNKpFIatAmOt01MNhhaHt607/2fTBKGCrCkhBUavVwvT0NGZmZkI8ot5hkk68WCzmIjFWMff392N0dDSUnql8R+LkS6fT2Nzc3LHBBTfmYD05VrFYzEUyGeHU9AHNk+ak9Qk3H/8SzADRhybyWgJc5jvzZHvOQ03pUWOTdSdvJxIJVCoVzM3NucWtWp+hoSGMjo66vmi32+5ZPrB2oaS8ZeWKDxx0AijaX90cBr56+GSfVbKqyDiXufMSy1EjRJWA8rrOfcoYPZNEU+p84MXWPcoQJ6nHmLns6klWAAtsb1SgQETL1n6wOfP6OYq6GVQ++WPJ8qx+1v5TsMhrbPuUt9ifCuQ1hVbrqtEbBQEs25ar76o3FeQpiNDnsk4+Ptd3n2NAf6O843Osk03HlvdS5tkx0jK0L6zTgOXzP9W72r+2P/eLtC4WvKnhZh0iNgXL17f2Odov+rsdWx+AZLnxeDy0foYYgCnBejA4M2ZYPnmKZykRu+i5P1E7vWkbLCnfAgg5CVTu0Tm5urrqUtAIaOl40kNZbR8rdlGyBgOfyfZyztnIidZfMYsaU7ZMmwVgnV3qiKE+pp6lM9LynOIMdaAxZY7zSHlB1yhzfHUnXcVi6oRWnUGsyHG1zh2WYfvb53zoJIutc0L1ks/RxShcPB53RjGvoWMXgIv+EetynjItlX3NqJ+Oj2ZI+Nq1F7rg7c99SpRkrUUVpvq/r2wF1laY2Gfofb7/9DPBNA+EXFpawsbGRihlbnh42B3Uu7i4iPPnz7tzpcjMxWLRbbPNPHvmw/qEu4Jk9pdVcMD2phRqUDEMzDzUwcFB1Go1pNNpJ3goDCmUdFJXq1XMzs6i1WphcnISy8vLOxaDFwoFAOEFzp2MUo6RHR9OVBs9iDISFJRZZW29650mqAoIW19f3/PZmivNtWW6CDWVSqHdbqNUKrk+VcEJbE1iHoKogJgCgGdd6cYiFHbc/dFOXB8wVhCjc0r7RxeM0kPJ+8gHesg0n9FsNl3UaW5uDqlUCiMjIxgYGMDBgwd3ePVpDPLcDraX8+PUqVO49NJLveO1W/LJlShZoHPJ3mONL3vPbg0vW49OPE1Fxj5iXrtV5gquozyUHFNNu/F5S/nsqDb5lDa/b25uhownq2A0J90CPQUafI56+3RdEQG3DwhHyc7d/m+v0TFXJW+BkE0TsteocWDbyzbYg7HVyLBGFD3FOkbWoNJ6aL9qKpAPhFteV5lhjRc7Z6zctXNJy2WasjUs2BYL1ihr2UZdL6HPIRBW+Wrl3uNhRAFwG2H9/3j709/YsiytH1/hOcKzfee8mZU1ZGZR1WpmmkG0viAkGt7ygj+KfwIJCZBAvECo1aAGUa2upqDppubqqszKzJs3806eHQ477GtH/F7492x/4vHaJ8K+t3pLliNOnLOHtdde61nD3qcWVeHYOQbe6yDR6cZ6slKTYxkIjYhyeiqPtWcqMUGnHF6MylxcXIw4gAUyhUG0BjyKGHF9H3QNK9DRw3Wn1DvJSfVb8lLva1IWkJwdNJak+yOunMntdnskA8DnJEsLpDyU7qfOGzdvHDP/NH7VS7nqRht5nn1ln2iE04DSNf3Nzc2VI9rFq762+J9Ynqnl7BflBGkqnvK9ozU5T/7mPJBf1U/Ris4VRTA15sFgUPa+drvd4ixWn5gqqOgm/7jn7E3LjQypiNGNmSQMCw0pFU1QFpLPBDcn1AUrF0emVByEt1qt+Pjjj+Ojjz4q4c5erxf7+/sxNzdXXlg7Pz8fm5ub0el0ysvUqBQHg0ExapTe531jX/xzzdDi9cFgUPJlFXYcDAZF+FxcXESv14upqakidCQwJSj13gWlp21sbESn04nl5eXY3d0thy3Mz8/H6upqOodZP2kIe/9pGHEOJaxcQdcMSy0qLjTVV5vzWn/5WW3xmM+IKHTUvigqSRlT+/v7I/sBWC/TBHQaoE7Q03X3zrfb7Xj8+PHIWsqUUXadYxF9pfCY4yzv5OHhYfzyl7+ML7/8Mp4/fx737t2Lbrdb+P/DDz+M7373u9FqtWJvby8WFxcLX/laYr9kkEVEodtwOEw3st+ksC06XzR29mEcrfgMaZe158AuA+8OHqiEuQ6Y1qe3uGuelCJG40TGDJUjFS/z8D0K2yR3vM+kAdckPX6DwaAoIRpRvpdB8+OAgwYFwR3H69FYn4es75mR4L+5bNHnmnJ34EMD1b3RKjQKJG9Vr/Yg+Z5JHyt5hesrA+P8rD7RKPE5cT7k725MqU+cP/IF23ewrvUvekhuunNIgFLXPcLp/FkDX84bb7M4CPY/0i8DpOq36qqVJj2rNhzDuH64uLgo741iBgvvl6NO88GUdPEQT7TU3nBGyjPPfIav/HenKfGAeEEOGn9Zt2Rmu90uh2DxuGqtMfJbq9Uqx7rrsKWFhYViUInXaLz7muDa4FrifKp/TN2UvtNY+DJ2GgDkl0kLDRyPJmndeTRKB7vx/AHRN6uH9Xm0i7/7WlVbcthqvhhhzGQtdQ9lrfAE6cSIHnlL+krGlgIZyjTTqaTT09PlPtUvw0l9Fq0WFhYK3T0QMGm5cWofBTh/c8OHk0LGpeIiaPYJ5KRnBpS+q0+qi4YNF/ZPf/rT+Ef/6B+V9LelpaVYXl4uh0r0er1yItn+/n7s7e0VCzfiao+U3jul6IUXtunKLeuXxuPGn4SaBKOY8+TkJFqtyzxpnYaocWuBKdQ5Pz8f6+vrJdTbbrdjfX29vI/hnXfeKWf114C8j81BCvtPgeQAgfNWowtD2DXvYFaH/jvQVZE3Q8KVYOfi4iLu3r0b3/3ud4uComDRRsSDg4PUS0WP8vT05TuwFhcXR06WEQBWHvvy8nLcu3fvWj+dzvzuoE+RVR55zrVzcXFRPDXr6+uxvr5eDsuYnp6ODz/8ML766qtyguXr16/jiy++iAcPHozsBckMNzc46NF5+PDhtfHctPg8ZsJ4UuOpVn+Nxk11ZGuEPEHlzJOB5CEUnQUmZIBeXFyUlzDTQGO+vwBObU3WvteMEf1XnVTaUmA0qHhCY0Rck/e+Lgg83JHQFFUYBzYyg8vnjvcQoPFPMjXzmjsw9T5JLkfENbAmeSyQxXrUJ9LOjaqI0fR1ts3rPk43Fvk9M2AzelPu6rfhcDgCDKWXaECSb/WfYxOY5sZ/OlQlOzMnK412puV4KuGbFvKxA1+PYnCd69nM8NRnPZfpVJYMxPpcnZ+fl4MgaLwzjUkHFXBdUo5oDw4jUcpUyYwoNzQy/mN/vc9uTNHxRz5VpODk5KS8YkNOZEZmuI9zevry3Z537tyJe/fuxebmZtk/xH6zDx5BoZPIeUHjm5q6ep0PnaMRUd7JdXJyUgxA4YzMcCCPECM7rm0qNAj1J0NqZWVlhMbSPc5bHKu3m8lZ6jUZvMLRS0tL0W63C47LnAJqh6mjNHQ0/+LdGi3cIJM8iogR+eRpoaKFop8yqGhIjaN7rdzo+HP9ZQYEB6jPrnQjRoUDDS4HMQ7evH4V9aVmbavtJ0+exMOHD8v7oRRJUAhZEZz5+fl4/vx5HB0dlWiPJkHvIso2cPLz9PR0sXgFdF3YMFeYRpkzOgVPRIwIgcPDw1IvF/78/Hysra2VdyOJsaXcV1dXY3d3N95///0qXZtAZW2xuQLJ6uVc8Y+ezHHKRjTmIqEQz8AkowGkFQ1rN45F506nE7Ozs3FwcDDynjAXijScNE55aOQFXFhYiHfffbeArBroJe18vW1vb0ev14u1tbUizB1czMzMxN27d+PBgweFdz7++OP4P//n/8S3vvWtOD4+jnfeeafw5/HxcVGooo/6wD0d4j9GYrzvb1rcCHbw6e02gfImXsr6PAn/ev+4n4AKzqNT4gduso6IYhRT1rHeLEoQMeqgygwq1cOxOtDRNVfyUr6KPGj+CSCdDmqLCss3j7uR7jTPaO3Xa/PihZEiRQf7/f5IfxxAsY0m76QDEelFySUqcR9vxOjc9fv9EWNK/wkAWY/30flMEXb3jlPXOi/ReCfw4fi07uUIIH2yPXN8RvPhe0RFQ8kW4gvvs7/P6DdR3KASbahbSHM9w3XIQh7PcE6TbuU8SD9qn3Ov1yv6XHMnHUTdwvnQHOl57hnxMao4H2Vrhf10R4TGRCDtBpUb6eonD1mgIcX+zczMlGjUyspKcS5HXK1/Yie+C9RlkvMA17D4wOWyy3lFPfx9opkMq+mmTP+xb77+uE4VJVKEhjqJc8CS8YqXzEHIyI6cU+q/y07HvTyQQ4aU+qb5oO4g7tOZAOQf8qWMb+5Tz5x9TevvpuXGqX1UGu6h9Ilw4ytT4FnHnXHck5aBz6weRjjkof/ggw/i888/L4Io4lJYLC4ulnxLeYe1v2VjYyM2NjZicXExtra2qkbGzMxMbGxslKPVeTKhC0UJRhpUfIO4aO0LkRvjyAhazCsrK3Hnzp1iINLToUWo7/fu3btmvFDYjwOiPmd+r8+NAwnNEd97IA+91+GghPVQSWUCS0Kap9jQmJNwkJdVi08CnZ7do6Oj9CWeEZfeEHn5CNbk/RDIuXPnzjWAy3l0BURFuLu7G1tbW+VZHXlODxnz0CW8Zmdn4913341+vz9y4s/U1FTZAKv0RI1PtOCLrOnJv7i4KDnGmSfzNsXXCa9nfMTfmkBKtmad1/2+SftLT64UbbbeOR6BZL+eRaNcmVJ5+P+sf+Qh5zunIR1TvhYkT90TqvslqzMjylM+vO2blprx5XxA0OtRmiz1jc9lPFSTR6QDDaFMXrEu1SPaRoweN++0EpjwsRAk6ruPk/1lEb8RIHI9U1aSB9g3eeDdwKEhRScgwdFwOBw5kEXPEagSwDrAfdNCnejFjUpeq+kkLw5mvR6Xa1yrHp2Ujun1eiN7SMSvSiWenr565YwAcMTVwR3usVfxORVg5qtX2Hd9ltzyvYceWfD1RD1KXc3Irp53PEJgzT3ipDXb1P2SU5nM9Llmu5x3tcEo99nZWayvr5dXzah+0oHPkh84zxn/sf3Xr1+PGKcat+bb0+tJDxqXkunenhcai9leNueNjKY0ohQ84H4xN6Rq+klzSPpSjtCopGNcZw3w1FvuP1ZWzW3lyo1S+5xJ1WkqBwGEi4urtzpHXBI3y3f0QsZ35a4JyaJUvsjcGDg9PY0f/ehH8e1vfzseP34cn3322chLL2dnZ2N5eTn29/djcXEx7t+/H/fu3Svpfq9evYr//J//87VFpmeXlpZic3OzgNeIUQWkvkiJkDHEmIxkqB1NNE/fc8taUaa1tbUCkjlfUnIM33/7298uB02wrmze9cd6fa7YFvlA97lgo8L8R//oH8XLly+j2+2W/WdffvlliRpmPJO1zRRRN0LUf6ahibc6nU6sr6/HixcviiBXvXQUKP92aWmpnAojHpenUHSmoI2Ikmb53e9+N5aWllJjiUaUF73n7NmzZ7G/v1+Oj1X+u04alJJU/rDqa7fbERGxsbFRwvKHh4fFwSCD8vXr13F0dDRiMOk0RNFWPK59fMvLyyVy8baMqZphzLnjms9ADa/5c7V7vR/jigAe0444B55/zoiF2pC8pLefiobjlEKjctP1bEyURS6ra8qKcpdrn8ZVJoNJYwI8TzkbN0dZn2rFdQX740aUR9P453R1TydpWONLl23sW2ZIZzpL61B8lBlSbkQxakCjyo3FjNela7gnj4cNRMSIB5/OKK5R/Ub9oOuSp9xjwkMSBA7dmHLAKSfj25IzPhdq2+eY9ImIqqHgTjAWr5Pf6ShxHvI6FFE6ODiIXq8Xm5ubRa4Ih+g0Yh5KJT10cHAwwnvEI9TRmqNOp1MOcRAfubHBvnlUifNUw2gOhCVTaUiK54gbqJtlGCjlUetAMouRN43DI1KcDxqZ6oeAOZ1jjHDNzc3F7u5uOTBsdna26NjMYKzNdSZj+J26wuW1aKI9teRPrVkaUPPz89f4knKOMpQRb385cDaObA0otdQNKco7YeGMD9QPl1dam+q/DCeN//j4OA4PD8tx/4PB5UFir169ioWFhVhZWSm0vU2Z2JAiGCAzi2AE+ZxgPevCUd/p7aQBwYll8ckZB6Q0eYPBIP7v//2/8S/+xb8oIcmIqxfttlqtWFpaim984xvFGyzA+fz58/gv/+W/xL/5N/9mxBiYnr58oa1S6Gj0iWFpWGWLR589hUOeFuW7kr5iIiklCUwKGtKGOdCKfH3nO98pNKJnpKZws3n3eaAwolc1m0de+9rXvhZf+9rXyrOvXr2K//pf/2v8/Oc/L/cJYCwuLsb09OVeMdGCglQ8xcWpdrTgpFS0kDudTnQ6neKxUCFdXGgvLy9fE+7Ly8sjhu/U1GVe9dLSUrzzzjvxzW9+MxYXF+Pw8LAcQFDz8nnqy8HBQWxvb8fR0VG5LuOaAOXi4iK63W7s7e0Vwa+DL7S/bmVlpRjcCwsL0e/3Y319Pf7qX/2rZa8UhT8BgyJTrVarGIx37tyJTqcTR0dHMTMzE9/+9revzfWkJQOWTeBikuIKaZyRVutLxHXedwBBY4pyj8Az4uoFr/KKyVD2dBu1SQ8+lZvq5n/2neDAwVtNxmb0k5NCa4H6IHNyOGjQdTdOmoykWl+y53yuMh3iIIzGiPeb9HY6elSPgIPXVacbew6a+Zvfl9VFY4mRP27apwFVk72aV/Ei0+YkWyJG32VVi+QxEkteGQ6HI4eZ8DmNR2DXDRHRU/PDFyszM+NNS8aT7D/nnrTLwG42n6ozA8waO8fsbfNe7ZHa29uLbrdbsJOMHr7SZW5uLhYXF6Pf75d3Y2rfLJ1jzNyhESXDjK8N8TGTVoyU0hhhtMXXk883U7l8rZF/+JzG7zyZYRnHYN5POrUYmdUakNzk/uSZmZmyp+v+/ftl72vE1XsyuR2EfeTY2C831NU/d/BwLmSEZBkiXEvio5mZmYKDiS+Jw1utqyin9kJpHzi3rrjxyX5S1kjnaZ+++IaHV+k8Ar2qR4aR5l845uTkpGQI6V45y1utVuzv78fdu3djbm4uDg8P49WrV7Gzs1Oyzugg+vrXvx4RV8f237TcaI8UFwUXHhdgq3X1Vm0JVPfE8b8zCq/Jo0BAy8VHBc3nMkV9fn4eP/vZz+Lg4CDu3LlTwKA2m8mzoH4o9/iTTz6Jf/fv/l3863/9r6Pb7ZZ+LS4uxubm5rXQqNqi9U/AwWgegZL67wYAjQOlUqlNGRE0KOjdUh3c3H5wcBCrq6sjL+JVioDPM0s2h2qD//2zFwoN95JfXFzE4eFh7OzslBPwKGCWl5fjww8/jMXFxfjlL38ZOzs78fDhwxIR+fLLL0c8nhcXl5v4ZXQpukKgtLa2Fnfv3o1Wq1VevqtFqXGLr9lfF2SaO4W7T09P4/79+/HOO+/EyspKMXYvLi6i0+nEyclJWcikqdNWc3dwcBBHR0flZYuaZxkzCwsLRfgeHx/H9vZ27OzsRMTVC4iV7qej3qenp0sqwu/8zu/Ez372s8Jrfkw+laHajIjodDpxenoaS0tLZf/JmxTyg9rNjBd+zhRmk/E1iUHmypr1sS335lMZ03ihzKTRxfdE+bui3BPnINSvsa8+3ppB5WAmA0kZ8M9onrVHGcbfPa2kNlc1gMt2+Z9/vseMG6W1Xqampq4d+az54jqXXmN9Edc9mG401niXctadh6rHwRUNqSwa5fvRGGFT2wTunB/ypBvy3iYjdjR+XO9HXMkS0sqf0/1M5yL91F/JP53k9rYKwZOv+4y3uDZreo9zxvr8Pn0nPmBWh5fBYFAMqd3d3Tg9PS36T6ld5Nmzs7M4PDyMra2tePbsWTx79iy2trai1+uN0J3yRGBUB+UwUkhAzzHpP/e5OWb0cWSGlEe9GLnXf+rmVqs14kT2tvS8nJt6LotSa05ddusz+zI1dbVdQDiPa4Zrh8ZlFsnS2JTxMhwOi0ySLtDaVOTL06Q1fzo9z53ILrtFOz+siuOXrJAzeHV1NZaWlooBxYMhfG+1MBhpqyiRDB/KA+E19UnGVsRVWp7u1/4vvYJI8kC8oLWj/XNnZ2extbUVu7u7cXh4WJwK2t+1vLwc29vbI/xy0zIx6qHHX5PB/86MmhBPY9OEeRSERoUrYBpTEdc9P674uJB138XFRezv78fHH38cGxsbMT09HUtLS7G0tDSymChQ9vb24nvf+178x//4H4sRpSPDV1ZWRvogJmeYU+P3vmucNJqo+CKuTjbR28pVj2jh9WoRc3FpAYrRdFz7/fv3R17Wyjl1oa/igJV84UZX9jzr8fsGg0EcHh7Gn/3Zn8Uvf/nL4jFgPVzQCwsL8Vf+yl+JH/3oR8WY1Yl4SlPjSTIyQrSY2eeFhYVYW1srBogiOJpTLVDNq0f9NO9uLM/Pz8fGxkasr6+X+uRF1D42nnSjsRJADYfD6Ha7sbOzE3t7e3F4eFiO5FdaoYQ4j69Vut329na02+1YXFyMqampojzFQ71eL9bX18vLp4+Pj2NtbS3W1tbi/Py8HLMvECn+VuREQntjYyMePXoUFxcXZf/WmxQHlTWgn13LAMwk9/s9tX7pvytrrTO+MJdgi04neeakUHgMsVIymAZN/nNjyh0c2RrlmOhVdXpna5/0zIw21tEElkgLZiBQXjuds7rHzR3b0z00AmhM8dACrSkHAxwvPe2adzq1RF/dy6iW60v2lZ5c3uNRaYJVj0B5eh/1gNON68O98DIWmUrO8XhKH+UQwZTmXy/WpP5wHhXtWK9oScDb6/VGXh77NspwOBzhg0zu1AzeTL74s34te97lv/iM93Ou9I7I/f39OD09La83oS6JiOj1erG9vR3Pnj2LJ0+exJdffhnPnj2L3d3d4lxUezSmeMoledDlmcsBfqcxTaNKhWl/fN75lr87DiS9WCflrPqqCAjxHffs0WBy/UMZyO+vX78ur8kZDC5P0t3Z2Yn9/f1yLDcjKTJItc/IT13WoVYRUXhcdfBAGhmOvp4YJdNaJE1chrqxo9+ov2S46Yhz7b3XHOuwLspCx8HCD3Ia6iW6NUNqYWGh6NJWqzWiV8lLOvZczkf1Q+mdfP+inEP6k4NHdZ+dnZUTrG9TJjakxIQC6xwQPVycLHqnMmDOaJQbQF6yATqAp3KIuFJ0qvf09DR+/vOfx9/+23+7WKuMCrDe58+fx+///u/Hv//3/z5evXoVU1NTsbGxEaurqyMGEA0/efO0t0S/EWzTcHShStpx0WrzneqlYKWAosCXUtSbzpUGoBNlSFdvNwMuGUgi3fk8PaD87PMob/CTJ0/iBz/4QTx79qy8HNGNcIEGlU6nE++//37hx08//TT29/dLOqUWpgxlHWsvpSnAofQH7SlS2Jhef3lBp6YuX9TLsZFvuQ4IPATStMiPj4+L4avnNaeK9lC56Dj+w8PDInD5jgQJm16vVwxGRZykXJXSqH1T8iY9e/asKJmIKC9tlldLvEK6STEJcOkF1jIY37SQV2r1ZevG15evaz5X+167xutuaIjeSqHlEedSHtpjIKfG8fFxSbOR44BpS+KXiOuRd/KdrruirBlDej4D7qK3yxRPPWKbpItACftDOSd+0TPugMiMsWxOnD9Ud8SV8caUNe6ryYA6AR2jzoz4RYymxYi/JG/pnHHjzYEKx0hw6oDa6azPNAj552263nV949/VFz/UQd53gSE+r/sE3jgX+kzjNOMbykKfH/KUnA1yOLyt4nImkxHZH9dJjW+z4kYF26fuydqUrDk6Oor9/f3o9/sj7/3Tc/1+P7a2tuLp06fx5MmTePLkSTx//jy2trai2+2OvJtJzwnQ6o8OWTrI2W+XM1wrXNOZfCLfc9zO7yxcY+RBydvp6elrURYaBrrOfYjZn68/GmzCdIxkn52dlf3dh4eHcXx8XPYMar2KJjpZT4YUo02MsgkPRIw6yt344dzJQFDURbpI96jvisZzrfl8Sa65o4ZyT9iM/6kvhNmY0ic9x+AJI2BKd9calw7VXKp+6ltmyWi+lfonB7vkhuZFh7ZID29ubqZBgUnKjfJwRHSCFDI2BYQEMj10DnhYMg9HTQnweTGfhEwGEFQuLi7ihz/8YfzLf/kvi9fF+/T69ev4sz/7s/gP/+E/xPe+972yGU3RA9VDg8ULPXA0Fh041LywFEJME9R4KRw0BnqwFXlRKpsiLaqn2+3G9vZ2oenGxkbxhFCQUFmw/+qH05vChmOih5LC8cWLF/HLX/4ytre34+zsrGz4E421x0egod1uF6DZal1GnYbDYTx//jx6vV60Wq2Rd0Vp/8/MzEx58bDyw5XmJkFxfn5eFpvmgCBDAEIRHxlhGpf4T8LDhafzLXOoHdhSaWruRINM+agNeRGpKEVHGZMU/Epl5DjUntIQa156viR2eno6+v1+2W/4NkpmRGUGTmYE8PrbLFmdoosUK71c2vvU6/UKP+hl2d1uNw4ODmJvb694MMV/8kIyzczByE3H6ffVnnc5zmvZX8T1KHvNo+xz5fJF9OQ9KjVDSvQXrfSfwF+RZc0H9wI5kKBX1VMnVZdkrHQc+YDykdGUbAxuBNaAKf8EmHiIgAMd7hdhX92QVL+z6Cm/C6RmAF+6RmuA6U3D4dU75lx3k8c8IuQ8L4CmNG0BsbdVPF3TjUz11fXdJCXDIzU5xvnKsI/6IIfc0dFRcWQxc0BZDE+fPo1PP/00Pvvss/jqq69GQL4AOh0fzvME3Wqb/c1oRPnhhhN/ozOaa4b3uG709qWfZMRonVEnkn7ed/J7JpOy9UfDTcaAgLyAugw1pVoKK2jcwjJ6f5F4mfKDznPNE1N56cCXjNH6l/O8Zkjpmhwkzn+kuTttmLop+cdD21gHZQjTE/WZcy9nDCPiygZSfyOuO100v+INphcr9W96enrEWSnHsYyog4OD2N/fj83Nzd98al9m6DCMSiYQkBWj87maYMkWX42puTAyReRCiPd9/PHHsb29He+8887I5EdcTsKf/umfxr/9t/82/viP/zgODw9jcXGxePVdwGZjodHAhaP2MwEpwE3AypxavfBNDCJArT6TWXWPBOXs7GwxUBjG/v73v18WyTe+8Y346KOPrs2Pg9Ns3Cr0ptBDz/9SggpTay/S/fv347333ht5B4ra0di63W48ffp0pA8KX3/wwQfx+PHjcuLcxx9/HK9fvy71SJCtrq7GzMxMMTQiLiNbi4uLpX+KiFHg09sbcXUEvSI05AGmDfjL5SjgqRidV8mP2hO1uLhYIj6KNmlsauPk5CRevHgRz58/H3mZIdMouC4pRDUegXiNS/c7yCSA7ff7Ja3AT4K8aXGQlRUXoq7AVVwu1eh8237SmSRww1QBbfLW+p+dnY3T09NyGMjOzk7s7u7G/v5+yd0m0Hej2ceS0Yn3TDq+msHi9dAoyOQsZQBlhmSUDBs5dZiezHVS6wc/+zyrfiprGbP643fNERUzjSZGd0hrl4nucMkMKddtHAu94tmc+NhFY0/tY0qf05/GkV+j3vA/RqQY4WOhg47vhvE/OlydBpJR2dgF9uipFnB9W0V9rhmbpBn7rf8ZPhknw7xe8hUdluoXwalkjVIdOd/Hx8extbUVX331VTx58iSePn0az58/j+3t7Tg4OBjRceq/r1sC5Yi4xqPkfX13Ps3okUU9fM1kmTsR9ZdFy1l8dHRUfpPTkPLKo7QZDmP9XNdOEzrJfO5kPOkU3cFgMLLvtdW6SsHTtg3No7CG+iwHgw6v0Fr3Ax5kYOkoctGEezpFQ5/Ppowx0ZJOYc6P8y95iTKUuoG/ZTJOcyo8Jrmi9S5cJ7mg35hlJbqdnJxEt9uNVqtVDqXgXCg1UO+WJY1uWm4UkWI4sGYUkXHdGvYwcqY4KXTdk6c2m4Sy3yuGU3t7e3vx9OnTePz48Yhlqz06/+k//af40z/907i4uIi1tbWRo57plWP/3EKmkKVnhGkQNLwuLi5GTg+UgJIB5SFoWugM12phTE9fHn1K5idIIBBeWFiI58+fx4cffjgi3Kh0uRAJVjyfXvPOIzJlTOgEII1HoJJ7uBQ987C56Kq6KNimpi5PcXnw4EHMzc3FxcVFfPLJJ3F2dlb6ILrrfWGi0czMTKytrUW73S5eLY++8BQsARfShutB9Sq9QAtedS0uLpaNwRng8TI1dZlKOBhc7iEjf9HIbrUuj/d/9uxZUSicGwnrbrdbBJJ74xxUMR3KnQLiEaZFiVaK2r5JcYPdjflxAGWcAeERQLZVez4T+vQcSz5IUNOImpqaKgqg3+/HwcFB7OzslL1vPEjEj61lhHfSMWagnc9lPFczLrneMiCSASjxE/lHXkspUP1x/dSKK1q2EzF6BD0dN4xCaV6YCkKQQSVPuelp63QWNRmxrI/0zgBLRg+tLwdz0g9ZGp+35fX7d0ac+Z/ygAYGdRfnQHX7YRyUETWnRqt1FclyXiStxUPUO2+rZHOZ0cwjIvxcWxNZaVp/fg+xDp1XOkRid3c3+v1+SU/f3t6Op0+fxhdffFGMKG2y134SRoKkhwiQSQ+uEfIv7/WxuzNlkkKAzcgS6cO+cB2Ih7TulcaeGWasj7qNn73flAM+dj5P+U/5QseYGxHS4cQkcmJK33JvFx20dHDOzs6W04dlfNT0ZCYL3FCmzGe/6Ajm3PMed/AQF0m+Cq+SpqqXjgKNRfJB0e+ZmZlrjjHKId1/fHwc09PTIw5KzQ33bOkAi9+4IUVmJBFrkyRiKYQoI8wZOzPKIvKczYhImT0TgtmimZq63ID2F3/xF/F3/s7fienpy3df/PjHP47vf//78eMf/zg+/fTTGA6HBcBSMbigoUEiGlGZiXkyw5BRAYJwMSvBCKMk3GOg/6Lv3NxcOYyB+ayu/LhA7ty5E7u7u7Gzs1MEEvPjVb8Oc9BRmCoE4EztuLi4KJGfg4OD4okWPeVR4EvRtCnQX0y8sLAQm5ubsba2NuJJE5303PLycqHJ6elp2SSpfsroUKRKGz5nZ2fj6Ogozs/Pi7EnL6in6XFDIvnAlaiOIaXRp4U/NzcXa2tr1wSJK9apqalYW1uL5eXlmJubiy+//LL0S+0zh1opA0tLS3F4eFjmWLzDFE9d0zoVT9AYJ39SkdFjxu9KR3iTkinrTD7o3qywT6pnkueyPkx6v4xWGlHclHtyclLkjU7cohElkEOv/jhjieNzecpxZMDN6UIae9sZ+K1dp9xi2odHeMYVbyMDQ2yPDhn9KZVDSjRLLYkYBWzuOa3RziMXDkT8+XFzmdFeY3MQRxnuAJR/vN/bciPJ//x+N6T8N+kOrYWIq4i9rhEssTBlkrR2w9EdeG+rNBk0fh/7FjF6OFZmPPA3Ped18F43ykhf/pch9eLFi3j16lXcvXs3dnd349e//nX8+te/jmfPnsWLFy9KJEqHIvDkOhrgbCvi+nHYNaznv7GfLoM5LjeKSB+BdeEX6iWPGvEUY8odOv0zQ8ENocwopAEpPCSHq95JJGyWGVHetrCHMpy4p4hOVzoOuOeWffVom/Z66xRd8hJlQc0Y5lzQkFXqPw0p1z2SP75fk+8e03/qAZd55A3pUWJS0V94jPhRESlGqoQN6bQntvU9Vpzzm5YbRaRcKdSUPYkrxnZPAxlb1zQQLh5GrmpCLuuPTxIX2Z//+Z/Hv/gX/yIWFxfLi3b//M//PA4PD0deKujeGEa22G8aBhHXw8iehxxxFZ0SMNdv6iMFBieZbUhx6WhKHuWuRZoV0ZWpffJotdvtmJ2dLcKAJzFpv4cAI1MVRWPPkde1VqsV/X4/fv7zn494a9yLSbpPT09Hp9MZAQcaP42ZVqtVXlL48uXLsmgUytUJXfLk0JDQwpdiXl9fLyBM9ZCOVByaZ12TMJyamhqJxqm/p6ensbu7GwsLC7G8vJyCQ92rueW4RRNFxQRA1JfhcFh+E3/pVB0a4eIpbrhl25o/zQOVIPkyWxtNgPGmhWs44npkYtyzNQNM128DcPweyhcJdkVWNSevX78uSkWOBe2NYu42jaiMDpPQqyYLM5DXVIcDHwfmTcAq4irnXVF89zarTgdyWakZVQI4NKK0x0xzwL0CVKIO4rI1wMilGx4uqxyouaFT40OfK353Gedzw6hZFqFygMpxeCSKY3CgxHH7ngr2jwYU6UNDKhujgA/bYxty7NCbzAjW2yw1mUzajbvmGKRWau2wDt4bcWUs6OS+58+fx5MnT6Lf78eLFy/ik08+ic8//7wc9yxHjXShDIyI6xk/HI9HobI/l/dej/ghM0B5zaNilBUC7xExAqjVL3c6k/doeGYGomMP0YQ6lmM6Pz+P4+Pj2N/fL9kExEMuY1if6lTqn/YrM8tGa0HyTA45OZlFKxkt5NXp6elygIWMiCySxnnlHKmv5A0aUjKmJMs47y7rXA6R1uwX6Uu+cEOKc6R93cycoSEkDK1xSbY4xhQv6O9NHTM3fulLDUhkIETEVFSKSqUmbDyVIrvHhYzflynGiKv3O3388cdxeHgYMzMz8cd//Mfx05/+NA4ODkYWhCsJ1cMFKgGfgQ4aR1RmtN5daNGaZhRMn2lR610oShdTKl8m4Nh3X+S9Xi+Wl5fj4OCgHDnOgwaY/uF/amtmZiZWVlZifX09dnd3R+jAv1/+8pfx6tWrEePMlbf+6yQiHgnqY2J06uTkJJ49exZffPFF8U7woA0VvVNJ9JBw0wKWsNAx3/RmM13APb80eugpy4CYjyVTuNq8KkG6s7MT5+fn5X1UBDX6rE2mOkxDY2q326mHjEYv+Z1zzkIA67w1KdiftLCfDm58bbv8Ic21fgjgawZTrdTkENuWUCegl1DXgScyLHTQRLfbvWZEufKepM8ZbbL7asA9q4fPUH5lRpbThsq6plx1TZ8n4Z9s/Qt4yPmhNA191p4/9+iqZNEe1u/AjzogM0JIi8yYYdsOWpyO5K/MSBOAciOKhlQ2LvbfZYIbUew3U8JcJ7Kfbmj6dS+ZYUTaZfrvbUakNA7/7zLFZZLTKeu/X8vkGdvL5jrrqzbLv3r1Kr744os4Pj4uRtXz58/LqzZqke7MyM74ODOcJjGk9F11+nw5HZ3GEdeNGjktXYf5KyMUBdXBU6yTjh7KBMdmCwsL16JV4mc5bXd3d8vpeMzG0TM0RrS3SU5c16/SDXzPkvCLR1wYZKDs1OEzGqfG5PPDMdHwZKqk/njQhNrjXGa6wHGD5LCwFDFoJqPEL8LhlBuUuXTS6DMP5KJs9v7y+9twAN/akPJwMCNKKmJMMRkPVWDnHQxlAE6lBkRrYEfXCKqUyra9vR3/83/+z5F3DHn/M6HHsbow0HVG3WgosVCBSilqMeh4afVZdUoAaGP/ysrKyPuEyKAci/qhxa6FJkE0NXV1KIYbCgzb0rtCo0MHRnzyyScRESM5uoPBIHZ2duLjjz8uQs/nhnPXarVKWp6EKCM0vpAjIvb39+PTTz+Ng4ODEcUvr8bMzEz5LA8FQ9HD4XCkzzrgQcYmc8vlpdH8i09kiNGDw37KaJufnx9Ju/QigXp6ehpfffVVCdXzhX0y1obDq5f3yVvGKKfGy/cseFoTBTJ/l5DhWs+UPAW28/htyyRA3/kmu1911YyHcW3WSg0Q0pCKiOJBE2C/uLgopwXR6M3AoSuAGojLwC/ruInBWKMlecGBuveXck9ONMkMpfrJq8iIbtbvrC2CgGyPpTYQc2+UGw0qjOrIgSTZprWtNt0oYFSnBn4dMJDOHF+2xrI58HmoGarjQLo7TDLgkdGcz0rHCHxl97E+Gtj8r344jfSdnu0mg+xNiuuhbK35b+onx8Liv2WGhv7TGM3u9bm4uLhM+dee716vFy9evIgXL17E7u5ueZWH77kk/5BfqC/YvvNczZCqyWuPBtWK00I8RqwlY5A8OzU1Vd43pPWrvsjhrO/kU8deU1NX70vSXiO2RWNNx2rzcCACfM4ZnUUeAZFs5PgUhdLc8XCVVqtV9HhEXEu1Y+q0yxfS1mnt/Ebe4LaG2vyzDZeLg8FgRB5ri4djfvIa6ZFFwPx+/80xSM0R4GN5k3KrPVIRV95GFyxckALZWsgyptwYcgJm7ejzJH2sGVNi5pOTk/jBD35Q3vZNZRtxPeWE/dWfCz7dJ48DmVSLm15pLTL2O+LqZYsK/Qq4K7Uh4urkktnZ2ZEUO1dg7i11T2qrdfVujgcPHsTDhw9HTn/KPJ2ZEomIYiBoYdPgOT09jZ/85CfxxRdfjICOJiAsI0jCdDgclg2VTreLi4t4+fJleWky54hAdjgclrmmoJBByQ2Log+PlpWnSIYXwRaNWUXTNN+6Z3l5uaT0Me2PvD4cXnrZFNkTDTRuGfuqwxVKr9crPKyx8YV1og2NWReCtflhaJ5zT754U4HUtHZrvzc9x/8Z6MlAb/bbJEVzp1OWGOGkLGHKhgz0ScAh5YR/Jiio9d3Bkv+Wtaf/DmodRDlId8DO/bL0fHrf2J4bGA4I5LVkSp/ez6WIFE/ny/iBYEGpNlpXzDiQ/vK/zPgg6CdI9nki2KHn3HWYg1nKZTq0CHa9LtJMfOp/bli5Xs8AMeW97nM+yOS81+uGC3nVHZM1Hf82yiT1ug5k32qGVfY76SRsQvlZW6c0pPb39+PLL78sR55vb2/H4eHhtUis6iO/US7p90y/Z+udfWv6Tqetzz9p4mCc0R3RhqmdHh2VI7jVunLCyvGo9aF1LT0tUC9s5hk+ijZJfpycnMTh4WHZc5adsJqtW2EYT9uLiGIECqvIKeSpguy35KjLGpcj/C6a09B0+ZvxNR3Dvo8rIkbkj+aOdV1cXBRZ7Ea9F41H8ini+smQlE/C0Nxi4YV41/vmhu2blBvvkSJj+3V95nV5+uR5ZZ58ZkiQmPyfWbE1YMTv9HaSkX7/938/NjY2YnNzM3Z2dkY26Olet9Zril/FI0iceO8vhQ2FsRadjriUISChILqpbo2RnhnV4/mhblxFXIHg999/PzY3N0fGl425Ccj6/GgRffzxx/GTn/wkBTOuYFSkCMQvSlPY3NwsL1ImSNvb2xtJ9VP9ElyiqTw8NHJEQ+6HooCWUUQv32AwKKkDEjCKUi0sLES73R4Js2veNE8ZQFSZnr5874EbfRQiNM4V+eL7h7T2xEMcl6IBHhp3BaW+09NGZat7JKxrIP1NCkGal5osYN/p0XI50dRmVrfLNvWN90tBT01NFf6lYTAcDkeUpQPXcf104OPGjF/PvjfRMnuOf37diysryUOtQ42BfMR1q/90BGRtOCjhyxV1nLC/4NjHKyDpMjHikge0TijHJVN94zLrdXnLMXOcGZ19nbGfAjY8AIffHRgTMEj2qk884UwGqUCO04pjoF5U8fXpRsJNZILzYKZv3rYRVQPBvp4oY3x8zvO1Pvv/7D5fB05DzYFOY93a2op+vx+Hh4fl9E+eeutj8LGwTl8H7sWvjUttNLXlz6lN6ioVd06QZ2vrTgbGwsLCtRQyOoIzHCf9re0EnU6nODFlQOmk1f39/ZKl4vu2XGdo7WVzp8wUGSJ0OLkc9bWsTC/JGwYB1I5kqPqj590hw/5y3nlMOw0p0TEiRvA85YZkoLYoEDdlvO9zInkl+at2NVZGyJp4SzoiS+0mflTfbytbbpza54qb/32BigDOKDwrP1MgDiwIwCdZ0H4t82htbW3FP/yH/zB2dnbil7/85chYxIScuMxbpPsJJClY9Tzv1aIiM6uPHnpWxENe7Igoz/vLFyMugbQAPtPA1K5vzFOfp6enSwTEgZxHoTI+IL2pZE9OTuIXv/hFeSeXK2AHLq6oBLzULx3hvbGxESsrK8UDpXcmqL8+93zhHevLQJ8ElFJQdf/09HRJo9zf3y+LU4aUhPjy8nKsr6+XTaUc39HRUWxtbcX09HR8/etfH+EXjnt6+vKQjcPDwzg9PY2lpaXSDo/9VPSLfCZeFdBSfWqLAle/uUeaazfiKvXGT5SkN9oPAbltqUWe6NVn4ZrlHJPuNYCTGS5ZHV4yoOCKUy8TzPZQ0rPqCvgm9Mto4WPz7xlIdZmdyfMaXfmZCiwDFkxzcdBEukZEiej5+KRcpSD5zi4ZTv6eKN97VovaeJ8k87V26N30sbJ/EVdAIhtDBmJdn/r9NJz8s77XMilUD2WEAwxf/84vLttddksO+G/UKZkDVmNzYOzF+eltFgfx2fr3tU4nq+OLppLJIf7W1Gamv2RMRcTIfiiPcpMvMv7j/HK+uF4d+3lfOTY9R9p425TpWWpq7c/7y754REd6Wc8wOqRIEmUNX3eiAz329vbi1atXI6cg+nuanB7EH6SznBY6TY4v1aaztIl/tF4do7rcFobUZ+rtwWAwknLOeeNWDkajiCM4HuEgGiPqD88dEEbI1pLrBedHb5cYXXyTYQDVkznKar/dpkxsSGnCImIk5UGFBHCBqgllGgEXtSs2Dlb/ueic4H4/++OefxFtZmYmdnd34xe/+EX5Tg8OJ4UgyWmiawK0jDYwtCpFx8iaaKnJFPClcOY96gOBc0SMAGYZnVoEej+VNqdqP5iUqKI929vbIy969VIDmwqHaz8Rj5188uRJ/K//9b+i1+ulHqRJlA7LxcXl/hKNZ21trZzINz09etyme1hlTMnTwsUvemfRw8yDMxwOo9frjXinB4NBLC4uxjvvvBMbGxvX9kepDeX9sn7yhMasNMl2ux3n5+dxcHAQnU6nGMkXFxflEA4ayBKEFMp68V+/3x8RnDSMyA/eJxecEaOHwkhA+obx25aaUnIajQMkpLHGwdQHb8tBjn92meLAxj1zBK+8z72YrMvHyuL9oeKsKZEajTL6eWQxqytT8hkgy0BOZryIpzK5TnlIHRFx5U3kkfM0ovjOIVeUDu7UN8lOtu10cBBQy85Q/wguJKOdrt4Pes65nvU/M6YYtWLd7oTU+N3I5J/rPjeofL5dZ/Fe588sKpDRmLR0PuI9b6NkWKKpLY5Zv2fO2nHySc9m68z5gLqHc6yUs+FweG2/DuuvYbVsPMQv6gfXX4bV3Ajz+n1srl9r/XSQ7DQX35EHmY2j9D0aHfovrEKDgvx7fHwce3t78fLly/JS4/39/Wt0zvgmwy5Z/7RvVKctc3xa83TGUL4qIkUnuadEMg2Y0azBYFAcs+yvjCi+C1SZNz4PTBNm/zgn7Fsm551Hs7Xm+pPrwY9l98g86+Tzma3wJmViQ8oZI7Mi/Td2mO9U0WSOM4ZUlwvkmnAV4+jeLOLQarVifn4++v1+/OEf/mGZDKXSEfDTyGF7HLeYSyCUL9bVHz2BGUiUwtX4Mi8DhaMEgBiKIVOCOQpEHY2+tLQUZ2dn5dQw/T4cDuP58+cjBkDmueXYW61WfPTRR/HBBx+U/j1//jwuLi7i4OAgvv/978fh4WHjy+FEz9r1bGH1+/3Y29srxqsEkQMBgi6BB24kZ/hfi11CQONnmpyM5E6nEzMzMyPev6Wlpfjoo4/ivffei4WFhSpYEr9FRIlc0HOmOdNLW+U9W1hYGDkUROvJxythKZ7UZ/KMaKJ9XTrUgxFS3aP1Ip7SvJBO9PpRyd+muODkPLrxwN/4POuh4eTyI+M7ts/xuLDXOlDhM1yLXIdso0n5TiLYM5nZBNw4dqcv5XQGuDguzgHbdh7MaKD/2icrUEGQo3odlBKIeEofT+mTQUUjIaMFjWvvN/8k6+gtp5fXU+YiRlPF1A5pKrpMYhxoTfKl4PzPaBSdhg4sOXYCmxoYdb1e4zHXDc5X6pPvAXOaeN+dN2t8+bZKpjcchPm4M8cqeYvFwZuXmmwRnzDDQO2rLel/T+fLHM6UBfruMrFmSNXGk2Esp1k29ky/e1/1meuAuMszhtQvnWBLA5fpcBFXWzHkkOTBDsPhMPb29uLFixfx/PnzePXqVRweHo6k9HHN+Hh9HBGjRqOCEcR5dPRRzrjjh+ta65lReeKaTG+pr9r/xD5OTV1ugdChGwsLCyNHn1NeEMsrukd5qPnRQUDCCbWDttxuoJx0PuUY6GSg04m8S152vsvW/k3LxIYUGyO4oNDgxBF0R1xtShNo5Pn5jL6QSNmCdELzHuTbH30AAQAASURBVL3sjBaq9izQO6/xqG0tLgFceSvcy+Tj1J4YKVQtEC5oMlnmyfYJJrOcnJyUFDGNRX1UhIWLdDgcjoxVDMaFq7Sx2dnZWFpaKgthOBzGD3/4w5H5pVeWNOD7mGTIdbvdePfdd+NHP/pR2ZD5/PnzsuBcGWr8NSCcAWUCeG20bbVaxcDQHijyIYWKPCdKGeQR9DqWWoUHOaheggKFvkX7+/fvx+PHj0eMKF/4OiZ+cXExWq1WOdZcaY+krQ7PeP/99+Ps7Cza7Xb0+/04Ojoq7XJ/lBSAom068EBzf3JyEu12uygTzX/ElSE0Pz8fi4uLIylTMzMzsbq6GhERe3t717x79Jj5SYW/yZKBLRbySsToendDyXmM9Tq4oUxzftZ1rT1vW/e7wZ+N7SalCVg6cOIzTkOPrGSynePMgBKVKK9LzkZc5dW7l9uVve6lnpBs5gETPGiCb6/PaOy0oFxT/TT0PG1OnymPmcYnPaDfM0OKOoWefBX+Ljo17YlyuulZgkzSgnu8KKOyzzVwmPGWrxPne5eHdL5kkSq2V+OPt1Hc4ZrxjMZVG5M/6zLFr3m9nEca7aqTJx47eJf8zYworh3xWzYfvi6czk6XSWVU7T62x3WQ9YmymxjU15A+y4hSFk7ElRHjTnxlspyfn5c0Nt37/Pnz+Oqrr+Lly5ext7d37fAal5c+vnF04frTfMmh78Yr3wFGvpBc9deleOq4aMfraofG2/T0dDGkdICY9DplteQdo2Xcc0nnjQw96lsfX+ZszHC/ryXiUNJRa0hpiUrZlPwkLTOj7qblVqjHjQoNSN9JMC4WGVIKaco6JWO4Zypr14WWvPSdTmdEsAgcynhS/QLSYkAaRRoL+6HCyZRik0Gj53mSmwSvwKuAJ+lFQ06/aZGKNhFRPJLso5hUoNYVqOrn2PWshJMMtVarVd59I6bj2Ck05AmRMfDixYvY29uLhw8fRsTl+5tevXqVKmrSMrs+SZFgODg4iNPT09JfeVEUaen1emVjqEAcX+gofhH9lGOuBajfxCOKeglQcfFubm6OGGYcF8GqvEAXFxdxdHQUH3/8cTFQZOjLSLt//34cHByMCISIGBHkc3NzJUJGQ0zrTIadXrKslMGFhYVYWFgoqZ5TU1NlL5a8boPBINrtdjx69KgctSueFk/oqPXT09Oyzt60ZODd59/5h3Khdm/maW9ShrrmxlREc3pfLVKV9a02br/mgNX766AnM3Iyme286rR1+hDwOsilPHZjin+MvlIWuTKNiBEZyJQYbV6mISVvLDcPZ/Tld/VTvzHK5CCU+xR1P+WJ9p7S602g58X1mPMa9YzviXLwLbmvQqDDyFOrlaeWkh+c3wlgHKA74OZ99AxnRoo77LL+qJ/jnBhvWmhscs4yPo/Ijc6Mj7lW3SnrMoOfXY64IaX/qpPzmxnXWpM10Mh7fU59nH6dxceW0cDb59wza0drj7TweXK+UR18Ka34iIYUAbfWtiLcalsvTtcpiDUDKlsH47Cry0z1Swdb8b1Ts7OzIyfvql3NuzCkToOVQ0l9pZzlCaZ0CtBRlO2/9zHqee6xpJGZySLZABExMgekQ8SVkUze8PXDPvn6kcyhUUjDkMEcvtLnTcqNjz9nce8M78sWGSdUAt4NC9Wr4guXhBXBxHRSEnqexKGhJg8E98gwta7VuvQMyLtBAck6PfojAMuoEReqxql2aGzRA0rh6AYd81EVDZKg0EJw75boojmgocV0LVfamQCkYGa6hgC8vLk6+IIgZVKDKQM8/Eyh2+/34/79+8XwWFpaivX19Tg/P48XL16UvUHkC/emcBMlI0MSKIyg8n4ZyPRyZKlcKqenp/Hy5cs4ODiI9957r6QoyhjXvqfBYBBLS0uxvLxcQOPe3l4RBtxvODc3V45UPzw8vDbfFCjKF19dXY2ZmZk4PDy8pjwPDw8LTwvwLiwsRESMeHIY6he9mJp120JeIcjm3Dvg0/21kskjr5PXSI+asB4HJNmu95GyKAMmNRpOStsMqGS/1+Q5+13rX23s/rxfJxD0tUIjVWuROkIGi/ZCCVxwXxQ3Nasun4PsM2nA52hEzczMlFQX7i2Unuj3+9dolxl0KoxkZeCX+xuYzice1Bg86kVwRWOKxrFnCeh/ltrt80gdQADmBp6ececon9c4PdKi4kDqN1FIm0yOuBzIAKVk1Th+y4xJOpTZRhalUj0OcPW5iUaZ/KjpVjdom9aMissvl9XZ/TRafQ70LCMmtXF4fYx+R8SI3KGc0boShhTYZgTHjyKvAXnODyMyTh/ShniMOGp5eTlWV1fLoVMykiLimkzhmD3lkPSlLFBx3UZHDeW0dHvmcKDx2pRS7TTwQno6jTVffs15nuP2+dXeL8k44TbuFbtteaOnXSFTAWT3aZJEdHmz/V6mS7hCp/HWarUK84v48to7CNBzOpiAgFOMwIXWarVGGFzeAUa0WL/6ywMANA4VCQWCCvWVoFHtiyHo4VTdeocUNwvyOUak9KZuAW9FG9xT4F4C7wsNjohLxu50OvHee+/F69evS4RHUY5xhdGNbM5IN/KRg9/V1dV4/fp1LC4uxr1790pkZW5uLg4PD8vpd64ofNHxZBp6NjSnFOY0fDudzojCz4Cz+upeFRlH6psMlOXl5WsHs/AofEV/pIAVmdS60nWBPvGjDuhQJG96+uo0QkX4VJ9OhDo+Po7V1dV49OhRfPnll9eUn9YxPVhvUpyOLLVrGQDL7iHtazIiMw6oLDNAlfWtpjRosIwDKBnQyYyV7PkmfnRQnCk6RpXUvnsxs7nimnYQTcNBjoja2PyPkR95QLWvwU+GyuaD7TQpdPWTukBrSR5O8bycaXz1RGbYeB/cwHHeZK4/TxIjoK7Nk2glejEqpbYp92oAdRwQ9j5Q72d10vHC+8hLHgkij6iOcX25aRGAVF8cc/g4WYhLOH81GePPRYy+FN1ljOqoRbkmmZcamPdnm4wd57kmWcc14PKCzjbfH8TnKXP9OufJ8aD0vjI4MmxFrOO4R8aTIjxy2DCK63RQu+RhznuTvJGM0Typ35ubm/H48ePY3NyMvb292NvbKzTzo91rtPG2JXsp1+gUlbyhQ0TP0QDleGmAcStIhm21nnUomPMU+d9lgUecuK2B80de5PgYZdNzMq5oSN1WttwoIsWOZsKVzJ8xlECpjBedEkePlLeXPS9Fxj5pMunBVPHTmKjIZ2Zm4uTkZGRBcJFpHIq0RFyeqOZCUwcC9Pv9EYUVMboXhwxJQ5FjoVHYal3uAWKqnQt9KXGNR3WrLe4ZEDjnKW/acMhwrk6Lcy8AhWqr1YrFxcV49913r+XKii7jeIppHYwYTaIk9Pv6+nrs7e3F4uJirK2txeLiYqlrfX09vvrqq6oS1wJ0g040Zeqp7lUU6uLiokTANFfcJ0GPuurReJUG9PDhw+JY0LyenJxERJSQtAwhCVMerS1+0H3b29sRESMvBBbIuXPnTrx+/ToODw8Lf3c6nTg6Oop+v1/Wl4CYnj89PY3Dw8M4Pz+P9fX1ODg4GKEVBeWbena4HhzIUQ7w/nH1+ffaM5Rvzu8uzDNA59894qS6qTj5bDa+cWXS8bv89ntq19zwVKGX2OU0FSDXMj2jGUCWTM7GJvkg+SVgo/9aa010rNGqxlcEgJKN8/Pz0el0ykE3fNebxsaTOZtoov4rO4K01b2iCf9zLh3Aq34aUP7C0MywU5uqhwYwi8tQro8seuwGBY0CRqQywE596fx7W8CTlZpHm4W84Dzvf4xMZcWNWPJXBjC9H+y3y8qabCRmy3CW7vH6srYdr7gRxPaycat+RjfIn+SRjHbsh48zWyf+p2cpx9knOWmEkzwKk82D2qzJ9SbZzvHLWbO2thaPHz+Or33ta/Hq1av49NNPy4ESpJnPo3Q3HeUqTLkT/9FJ4zJYdYkOLiN8Pag/wsq6ru+aOxq4pAENOcoCXvdgA/lKY6aTl7iYxpSMOkar3qTc6NQ+X4TOHE2CmkaAPBPM4aTC0aS4gIsYZVqPaMj4IDNTWZOIKnpZsIwlGmhipKmpqZE9TiS8ADHDhWKEiKu9U5lSdaGtZ5l2pz0wU1NT0e/3y5g9zKxx8QQ1Ro78qEweVnH37t2IiFheXo7BYFBeTMfTadybqXnY39+PH/zgB3F+fh7f+c53IiKKIeB848JncXGxRI+2t7fj+Ph4IpDL79PT0/Hbv/3b8f3vfz/a7Xa8++678Y//8T+O4+Pj+B//43/E5uZmtNvt6Ha7VQUxGFyl5kxNTZU9TKenp4UfqCDIaysrK2VDJvdYiVYySFSPcnRfvHgRw+EwVldXo9vtlqPqW61WLC0tlYMgOMfLy8ulb5pPCcHl5eWYnZ2NbrdbDCfuDVxfX4+ZmZlyWMWdO3fi4OCg7KN6/fp1LC0tjdBIOdvr6+sxHA7L/jMJNEU4eVhLUyrTbUo2XxlPZUo1A0MU8HyeStyFPH/PIlGTlIyHav305yYt4wBmJq/H3Z/12enIOl3JM0XYDVB9594fKVntFdR/plB7FIWgiPs3vb/83kRzp5XqVmRfezF1jencipK5l1t1sh+Z4qdCd16kvpAu8r0Iupf6lZkH2Tipf90JmhXOPXWdGxikY7YWaYA5UPV2avX+JgplgV/zcfrvrtO9XmITjpn0n3QNe9RA17O2fT34OJy244wo3le7l/1lH0kLpwHrJID2NZwVPcMUrkwOME3Wo1LCUDzV0mUH69Lzyirh/qwsetlE16mpqZJauLKyEu+880789m//djx//jx+/vOfx/HxcXS73ZFTeWl4iheyvZG6nym9ig4p24pGR0QUg1J00XVFxeSMp9OFzhHNn5/6yzRWZk5xvskvmlNhXEUcmQnDFGgaYMJefKmw6mFq31+aISVCuTLmZy1GEoBFTCwFMBgMiuc/YjQ/O1N+2hPjIUi1ydAiF+NwOIxut1sYVX1T/3R6Hcfmi3o4vAr5utWu/wQRUugXFxexsLBQGDniygNHo0ggQApyauoyhVARAhpPMrBkDMl4zASw2hXtpYR1os3Z2Vl89NFHsbS0VICwgMHy8nLZe5Z5Aaanp2N9fb2MY2FhIVZXV0f2XTmPaPzLy8uxtrYWrVYrdnZ2yv4C3esCu6looWpeZAg+fvw4pqen48svv4y9vb1i9LJPogfpK1pdXIyedhcRxXCIuIr6DIfDOD4+junp6Tg6OopW6/JIcQIeRelEGxlpEroCXzJS5ufni7Gj40OVxqp5JEDSPrAHDx4Uo4z7qWRM623qXAPD4TCePHlSPKL0ZJP2p6encXx8XDzzPIo24lLwKiL2NkrNqMnu4+cmIEMZ1cRfkj1Z5GUc0Gmq96YAMJO3k/x2m1LrO2WcgyeX/QRIdODwHn2WguSpSlS+fI+J5+jL+SE5KIfCOEO6aU44dvdocx+FDCmtV57YenJyMhJZaDLaSC83RDkfNWPKHZE1XcTxZ0DO23D9l/VZhQ5Pri8Wj8ZFXO3V1X8HnVmkYhyQvm2pGT/Zdacz/2s8pJ2vHY6DGICAuGaIkh4ZT0xiaPh36vZs7w0/Typnsudq687Hx+edHho7aSyjR3hEv9MgkJGgyAijE1rD0pnKNpJDmQ4Rx7Nu7EWMZj+JXuqvrz//LHyq7QJra2vx6NGjmJmZibt378bW1lYMBoNyeJb6KVmjOdSpfTScHN9oTMJKzHKiQaasJdFXDlRhEv0eMWr4uOwkn3O/ufon3iMuF81o8MiQEo7x8RPTZMZ/5rx4G+VGqX3ZQnXvgzOLK4NsIWRKkM9L0fBYSy6WbGMfvQsCsRExknYhhewAkpa+Jt0VgNoiiIwYPeY34nIRK0JDwy4zFMjwDBUzyiTwQeZTtEOLgQuMhoKY6OzsLI6Ojopxub29PUJ/gZb5+fnodruxsrJSjkxX/eq7APXU1NUJidq/5fsVOG4ZD71erxz/TX5qYnJX+gQiWkjiq06nU6JemeDiwRqaV6Xu6Zo85QRY7XY71tbWRrw2mn/1SXWoTwrNk44yiim0xIe7u7vR7XbLdZ2Ko3Y0Ty44NBdMRZIXSf3r9XqFl3u9XszOzsbx8XHxsB8dHZWU16WlpZiamip73wQmaaTJKH9Tz46Kg9vMWMrWT1N9NSOL12qA0iPnTW3U+jSp4B4HWrL+u8xVqRlGvO7POmhzxVMDA7qHwJgvySYQkbxyr6C+61AHOQNUn+SYoqH+nKd1ZADTPeM1WlNGcO+FFDlP0ZRTUN5wjtcBmNrUOLQm/T7KLM6X1j6/k/5+Xc+7weTjzArro+xW+0zL83qZyaBnHFhTL+m786CP9W2DIOkv6vYMiGUGJ+lU65/PifMjI7O+z5QyiM87DiENWWqyTdcJeinfav33emrGwSRGV61NPkuDmi91JX7k60wE7kVHAnmtVwJxRqUUxZLuJrbJ8Cn3e6mfftiCG5EZXTgep6Wcp0tLS7G4uFh0NV/HwigccSyxrBtSdBR6RIY4kPVQZgnLCE97VFvYNeLqRMIMN9OJre/E56St/hhgYHRM7fCAOGEmHoTB1HCmV79JuVFEipaiBhiRe44zhhGBxAiaUBIvM8QiYmTA/X4/Tk5ORg6A0ATruwhDReW5mZpEtquJ0T2cNC1IplTMzMyMGGoyIjza5NEyjY3GEUGCnlNdYliluYiZVafAOPPrxSiM2p2dncXh4WEcHR2VZ3d2dmJ9fb08PxgM4vj4eGS+l5aWRk5GFH01P2p3dXU1VldXy/Mao/bxsO/Hx8cjistLDSDSII/IjeOzs7MRQOOePglY8QQNYRrDGpfePD49PV3S+TSXMnQYGVRKn3ig3W7H7Oxs9Hq90id6hwg0er1eTE9Px7179+Lp06dF2Knf4kEab1pfdDp0Op2yn+Ply5fx+vXrWF5eLu+bmJubi6Ojo3j9+nX803/6T+MHP/hBHB4elsMnlGLQ6XTK+64WFhbi7t27sba2FkdHR3FwcFBC7MPhMFZWVq7N401KBhzdCOfvfE7z70aTGxPuRdf/rA1X9JlB4Xw5ifGUjY9lnDGV0SB7zpV4re8cvwNyd1Q5QOfzBMaqS2CEEVQ9SxDAzb8ypOh5FFDRYTsCnjLICHjcoGL/M0O8Bgb1maCMKYdc7+wTeacGEAlUal5Utcu5IHhzEMN5z2gwTk9T3zbdT9lDGeaf6VjKoikEfqrP+cjryIyRNylZJMbXBcdPnUIDhmtgnJFFniAAZGQgM8A49pp88Od8vWX8WCtOC/KiG9+ZMV4zOv13/kZcqKJUe2WV0JDSffrt5OTkWsYSHe98P5L0u6Lg2i8+NXV54NPi4uLIu0f9v/qgMTElkOMbpwc059yXL+eq5B1fjOsGsOQS++CGk8tfjVPPi85OV65b9qt2AAd5TthVhrDqdvnBiJQ7kLTOfBzCxzxVkXtmiYtYJ2WLaP2XZkg587uwyO7lwnIQMzc3F/1+fySVLFPkEVGMJ02aIir0FMoAcEEu4oroMjg0DhorUvjqE6+pP6pL/c9AEJWuA8HsXjGZlLAMGi0UjU1MqL4LrMv403Vu2KMyOj8/j263GycnJyOphzpIgHPG79PT07G2thZLS0vXoltMvXn16lU8ePAg3nvvvdjf3y/9HQwG5YAQtacX59aAXRP/ZSCTyseNcxlAXtrtdiwvLxf6UjGSX0U7ClTxifohIaZUHylHKUql5il1gMa41pOK+GZ3d7cIHyrviChpgVIe7IvofnR0FO+//368fv06Dg4OotPpxF/5K38l/t//+39xdHQUx8fHZa/H0tJSnJ2dRbfbLUDx8ePHsby8HIeHhxERBdz2+/04ODiIFy9ejHjhIy73cb1JqRlB+o1ALJM/DkK8bq9Lz9SMBKaUsV03xDK5xfHUSq2vNbpk68TXEPuajb92nZ+5jjIQmI2zZkhJJmWeXckqyT5GfPQnGSfZr3uzv+wUwGzsTbLH+YsghxuUmebrETKNzSMqpJeDLW+Pf9RrHsWqGUa1ueIc1Iy8DKxTBmldCIBla7Jm5LHQoPT2nfd+E0ZUxNWejYxWKho7++njHQ6v9lRHXH9prBs2GivxkuaTkT6uRQeJTcXnWJ+9/WwcPvZxxldNHrvc8uuZsch79F17mJlu7vwgWXJ6elq2UwjDLSwsRKfTKWm5PDBGfLy4uFi2fpyfn8fDhw/jq6++KoczkS/533nTaTyJwaoiWedGi8ZBQ4p9l8x0DOM85AYEozIaNyM26pNoGTG6b5uGnObM5a7WhSL37oTgWqcBTIcx8T3nnwenae35Zx5MpEwp/Z2dnUWv14tut1vqvE25UUTKhWRmdUsIqDiQoHFAgoigvrjOz8/j5OSkAEb9rv0aSv0QU8mrI28hc/QjouTTq11fjFNTUwXsRsSIQcKQrVKydJ9OIyRY0IIQuB4Oh2Wh0muhvsug054aLRyGPbVHTB4TRRQYoRDt9F991wlTPpeKwOmFsxqPDLXl5eXy0lfO0+zsbDx8+DAePXpUvA4nJydxfHwcT548iXa7HREROzs71xY3gf84I8r5LxPMjDgx3Y6L0Z/ju5r0Ij7NOcGP+O38/LycDFjbU+fRL47XnxHtGXUcDofl1J6IGDnaX+tKQG4wGIy8MVzCdzAYRL/fj42NjWi32/HZZ59FRMS7775b3kWhqK3m4fT0NPb398sb3FdWVkpUUcppMBiUdEye9Dc/Px/f+c534vnz5zdSGpPMNb9nxkQT79TAYgaivQ6fO7anZ2hMsS0H8bX6a/1l3ZOUJiOudm9WMoCisTP3PwPoDhid9gSIBMg0CPiuD0akuKd2fn6+HN3vf8xOIPBhfzKwJro4L9XAkD5T4fNP69SjTDVas14a6t4fjYX1ZvXXjG3dTzDC52mI08nDOeT9AitMKc/G6DzDdigbeS9lpXhEoC/j9TcpNL71V6s/M3K9uJPZ14au8X7RgM5gFgfrogf7xf75dfZjEnnluq2JhzMaZb+7Qcb5lcGg+eXeGwJx8gXHJN0nXUWZTZylU59lNGkN632M2gZwfn4e+/v78fTp07L1QfNDMJ4ZUm4skxY1maz/xHvSt8ImjmOIpeWAEh0m0cOkKyNRMk5UD9MdOW9a93K8qs/kKeISrnO2T4NUhpT+hNXpwBG+m56eLtlpCrQMBpd7yGRAKYq3u7sbBwcHcXR0FL1er2Bb/a4TkW8rV260R4oE4DUHjj6RZGQ9T2+VQL5Sv5xoFOoqYoCzs7ORDfQChTJyBBjJrFqonGR6keR15OTK0NHEk4m4iNlPeemZqucKwwG36KX6ZQSRloPB1buhlKZFweMAzw1VB6KivzwaNNwEXlzQqw8zMzPR6/Xi888/L3ThHrP5+fnY2NiInZ2dkUXbxGeTCAJXBC68mSdNAcR2tH9K70tiHaSh+EFHkuqFfeI93UcB6ik4ojNB3nB4dUqXnpPHXUYyhSWjXUpzkMBT5FS5y4o+ffXVVwV4rq2txaeffhoXFxdFiTC9UfucxENKKdBJg5q7fr9f9rVxfb18+fKNDSkHKDUF3gQWRN9xRkNTX90zxSi3gC4dSQ4aa21m1xzYe7+zfhLwZc9kIMbrrwFu/dGIcg/sOEPK23ZDiu8R5P00SBgB0rME7qSBywAaTN7fJtDvxnZmDPC+GrDRb5Rl/E99Q+cLaZWBYH7O1gij1s6X+p008roiRlNffJ3RAepeepfdGcBU/Q6SOVeZd196ibrsbZVsL5obJi6T+EdZ4MZKTX5l8yIZq/7QmKq1I7pnBpSvET7HevS7Z21wbXEs7kB3umTP+3ioJ/09cC4P1V8a0rW2pB9dNpGfHaxLxjhOoIHCbRRcX+L5JrnvJZO9mTwSXfhqEnfeaDzS4dxH7TzgPKxxMDjAqA11qOhEelNOS0bT4UbaMyrkOoYRaeJvyn2XB3ye6Xzqv4ILZ2dnxdDiS9yF32SoysE86Rx6ufGpfQTjmSL2a77YdF2Tz9PMBNxYBNI0iSQiU/yUoqQ9S/QyycqnMeNKyoGCjCC2HREj1qwMhyyFQWlsFFBa6BRsFDL07Il5xJy6V20PBoNydHWv1yteGAdY2by4gL+4uIjt7e1YXl6+pswVrTk6Oop33323jIsL+eTkpKSa7e/vR7fbLS8AbrVa5X1I+/v7I6fzZYrarzn4yUAQhaRo5oZtZkjpKOOTk5MSNaOS0HMLCwtx586dWF1djZmZmWKEazHqPs8zdmOdXh+uJR4mokMg5Fnx1FT1T4afK229Y+rhw4extrYWn3/+efm+u7sbvV4vhsNhyRWPiDJXBK0yRBmdozeO5eLioqSLvq0i8EQemKQ4f2T8wjZq8knzo3lxZw55Kqu/1ocagPe2m8bj4+VYJhlr7V7yktYU36Piab0OZl3JsY9aH1J2NM4yWejR5IhRo2XSiJ33l/M3jq8chPpn3sdxOtDKADgNcgckAi0OFtU2HW38Ux3cC0y5qDUl3cl58kiHG8SkuerI1g/7qXodvJM+XjcjTm5c6X9mrL9JYf2+Bp0GTXPK+mrrnXXxHsl5vq+n5gDMcJd+8++ibbb+M2M0a8vHQr2T6eVsjbBdp5NkAvf2eF1ujDst9Fk40gG7G/TZXEZcGRMC3oyes8/sXzbHPnaOmf+dFsSiwgrCfDxp0A0p4UVlzkh2iubqj8au6D8xp8auuZC8oMPEdbLrQdVDnSknCCNfPieOlTO+dbmR6UXH4bqPETHvAw2528qVW789070SLA7mnVkjrlLo3NupugmGdfz0cDgsFiUZXkfOKg1KxhJT7FQ0WQKTMobI4JpEMhnBJw0J0kDCmAxMpePpYjUh7Aub+dIK38oAPTo6KgDbQVkGALyImba2tspx4VoYGqNoPjMzU6KGDk548uFgMIgHDx7EZ599NrK/6+TkJAVfGSjx/vN+v88jA+KdDIBR8C4uLsbi4mIcHh7Gl19+eW1hyiN17969WF1dHYkIaV51bDnBinu/1E+PcjGsrcMrBHCOj4/j+Ph4ZLOp6tAzTGXiHK+ursbGxka8fPkyVldXY29vLzY3N+P58+exubkZZ2dnsby8HN/61rdiY2OjKB1FnzLQozZkbIk3ZXhFRNy9e/etARxfjzctDiQnfSZi9D1vVLpUJqQPZYSPgaDZx5YBHx93Bj4yeZoBGi/j5obKjbzJz1R6PmYHFwI2NEYdWLtCltx1L7HLS7bPurI/HyNpSnplc8R7s7G6XCGA4dzoPw0g7gvLgFLNONF1RuhYl+ph/zVvEVfRcmZYZBEh0s+NKJZMtrLPDlLIp7U5IuDNAObbLP5aDO8L+83iIN75nzJf/O2ymu3QoPBo1CRr2turyYpsLL4GXCdnUaJa/2q/eZuMgHCfS2YoZ3ySjWk4vIpKSfaIx9mGO3C45i4uLsrx3sSnBOK1fmb0yPpZu+501jrggTw0pNSe1rOyk+RUIS6hQ5SGlK55MMHlKsdGeUdji3KeNJYhlTlHavKaWQka/3A4LPvttSdW++E0Lr0XU6+A0UFhlKlZJoDGd5tyY0PKBakrQieE38PnRCAdPKAJUD3T09Nlb45bpdqzodAdiTUzMxP9fn/EuqVCYLSCdRIYSqi58GfaBpUimcmFR7bg3GDQs86UZaIQKRDT7+/vl4VDoVkDODUmGQ4vjZzDw8PyPikpem72ZiqbBIzooblj//QS3F6vF/v7+2mKJvuX0aep3yo0vHU/o5ueuqG/paWlWFlZKS+p5e8y4O/du1feGUMvsqJtpDdBoxtvFCqq+86dO/Hq1as4PDwsv3EMPBTEhSxpTjrOzMzEyspKzMzMxPPnz+O3fuu3SvTq9PQ07t+/Hz/+8Y/j6OgoHj16FHfu3InFxcXo9/vx+PHjsvFSJx85P+qlvYqWMWLw9a9/vRxMcdtCIS06eMn4JzMssnXkQlP3qR7WzzXKdZ1FNhxM+fO1sWYlM7xUN/uaGQP8ntWf0YrtUgHWjCkaUqw3U7bOuzWFWdtvREPVjTyCG+5XyNpxOc7SJF9qsiqjLR1QGd/5GCVfI64yNGhEuuHFdmQ4yWjyQzqox0Q/Akr1RcBdY6LjoKaz9Jl/DuwIxLI6anR0PiKmeBuAJytZdK/WJy/ZM/6ZPEzHqgNJes0dW4hvXVZl8qZJz/sz2fds/p2nHeM4nsnoxLa0lmXc0EBpkl0uc0h70fD169cl9Z08rrQ3Py6bY1ZU5+joKA4PD0u2jyI9Wj9ucDjdJ52DTD7qWfVFzlqeBcD1Jl5RYEEGBR0i4i1hET+1MJsvtaO2dT/7OD19ddoho4CUyW58Oi1IL/731Evu1cr0RcToQW/aa0vboJYBQMfFbcqN9kjVrFInjAu+GhiSx9/zHHmUo+5V4QtqZeGenp7GycnJtROTeL9ALvsvIKg+sx15SiT8MgaiYBTQViEYYsodBRLpRAOSQrMGCC4uLsqeFgItF1hNC5rPXFxc7n1ZXFwcUaiiT7/fLy9j1UJhkfem2+3GV199FZ988knZACgPj/NJrdT4q3ZvNt5+vx/tdvsagGEE7eLi8l1NCwsL8ejRozKPU1NTsbKyEpubm9HpdEbmQgswO7RD3/Xn0b2IS4N4c3MzvvnNb8Zf/+t/PT755JP44z/+48LHfMktIx9UGrxGg077oPRS5NnZ2VhcXIx2ux2Hh4cjofWzs7PykuLBYBCbm5uFTxcXF69FAYbDy4jV48eP486dO/GrX/2qKCIdJ6uj1d9Gqc39pDyRySBGKSNGI5UsmbNIf3QGTMKn43id/c2AAn/3cdXayOTFuD4RzGUKmAaMA2QHVKpL/JvJO0afPBLlxlNEFCAkYNHv98ufQBN1iBtVNy3j6JsZNwQdvrfXxxhxqSP0WgSPxBEY6L/alHdWf4ok6/1ufFk2aSAaaR/O9PR0eR+N1ofWiANF71/tsA/SjzqXhZ7hmkFAWa17PfXxbRU6amvF12ZmSOp3B7vkZzcws89aZ6I526v1h9eb+u+yoYYZaphN/13ujVtrtXEScLthWRtbZsyRvqxXUQw3phiZEnbT98PDw3j16lW8fPkydnd3yzsV3XnDNUJ9mRWnXfZbJv+ZAePOWcfXxFja705+Y4SOxkUmm1Xk7NF+ch4PL5ze6XTKi9EPDw/j+Pi4BDi01cCDEjU952uKOoIOcRqFnAfNjfon5xKjeZKvjN7TkX6bcuPDJtR5EoEWNO+VUHaBzuc0WN3LEB69LxRUSrFjKFzvxSHxFTEhKNU1KQ5OCAWEJiLiSjBqkz8FpgwqeT8U1SJoEz0coHEB0bgjbRlt0bPn5+cjb7fOBGy2YGuFc8L+iyYq9JZlYzs/P4+tra34xS9+UfYcMf/0JsWF+7i+O40VkRTvsL+tVisePXoUCwsL8emnn5Yoi45D14k+5Cf2yyMSHjWSEfXee+/FBx98EKenp/GrX/0qpqam4qOPPooPP/wwvv71r8fa2lo8ePAgOp1OHBwcxN7eXvzwhz8svCM+FV/RsFVb9AIJREVc8kin04lHjx7FkydPotVqxd7eXjx8+LDMSat1FVmSR0l00Li0NofDy3dEfe1rXyv8rn1Uc3NzsbS0FMPhsGzafNPSpJAzYefXM8XlhpMDYQc5Xr+vRwp9ORi8b00GUDZWl5VuRE0C+Ca57v1xI8r/PJ03U4S19gmQRC8aTQTnDjzVLo0nbR4+OTkpn7lBWvOXAbPbKkqNrQYA3NAQzWq/sx/SSaSHnqNcUz3ytAogyIjSnk8ZZ+zjcHh18qhkoxtBkiUOTihD5fwUCFGKjWSlCueY+4Id/DlGoBHna0xZBnztw9sozs8sXPdNTgG/RjDI1HxfB01RKcp9d8hlssrpNalxqP64XlPJDKiaUcfPHj3jmGnsZOlkXm6ybhk1z6INHAP5Wnqt2+3G1tZWvHr1Kvb29uL4+LhEelweuqPTZbnTSbzCMXJ+3ajSOLTOnLf0vGSk6pVs4DpTJJoRKfGY5osn82mtLy4uxsbGRqyvr0e73S73Sd7IwOr1erG9vR2Hh4dxcnJSTsnzoII7ushfLpf4bkFF2dyJ4ryne5TmpxQ/1qP+C+fxTITblFsdf54xTEYUv4/3cAGLSXhSR40hxYzywjHicXx8XNIaKJRkEOlkOxfoDCcPh8ORXHUuEBlj2pck0Kp6dGQ4QQAFoK7RkCM4cRAl44SG3PT0dHS73Tg+Ph5hUNI+my+nv777Qheja+yLi4tlMWnhaawUAHNzc/Hq1av4i7/4i3jx4sW1zZ5NxQUtrzsveP/5GwW3jhR3gamw9+bmZkREfPHFF9Fut6Pf78f7778fjx49isFgUDxQEsR8N4GicQJMPGmJvPCNb3wj/sk/+ScxGFzuGZueno6/+lf/ajx69ChWVlai2+1Gu90u7zCYmpqK/f39+PLLL0udAixHR0dl7DxsRGPlnOlFu++++27pz/z8fPT7/Yi4OhlQvNput8t8SZAJJHU6nZLqICPzF7/4RaFpu90uqYRnZ2exvr7eONeTlkym+HyPE3rZenAec+DB+l2hkx8JWpj2Oa4PNbnpv7uira2FprFTlvgY9DlTQh59yoD1uH54G644PYLDSAPXsaIoNKK0f1B/2sPJ/Qwu82q0abpWey4zznwsjBRHXI9Y6bvAge+Nch7VdRktMpj4vi1doyFFvaLIndJ2PT2QL8z0PSDic+5X8NTCTN+o39w35vqcfRTQU9scu94D+bYNqXHFAX42/5ke9ghC7RmnAdefdJBjFhqavo4dZOt7LYqseRKgpozI6uIYXbawLzKS3WDjCW4E1rUocg13kr8yI2Rqamok8iLjQc4HOSMUqRgMBnF8fBz7+/uxtbUVW1tb0e12y7pxGVmT3aQbjTgaUl5cD6nUDCkftxtSjHSrbZ1mrWwVnT3AuaITR3JmdXU1Hjx4EPfv3y9bP4QJ9QqZ2dnZ6Ha78ezZs9jb24ujo6PY2tqK/f39ciK0XmOkuc8MZka6JNdo/BDneOSXfMt6GHmicUYHlA7eGocpauVGL+StGUIOzGuAwheoiEEFKuJ6qNzbZvRKaWVK8yDg1zG7qlcLJus7rzMflIyvfjJNSodcEHhQ2Kkvep7CsiY8SHcaWxcXF3F0dDTi7WyaJ17n50zwn5ycxL1794olr3crTE9Px9LSUjkQQW0TMPT7/fjVr34Vv/rVr0aidpmXq9bHrG9eaiDY25PRKQNHwEHj6Pf7sb29Hfv7+2Wuv/GNb8Tdu3fLHqHXr18XY/3w8LDUERFFyGhemL88M3N5fPn9+/fj4cOHsby8HOvr63F2dhZLS0vFg7O8vFyM1Xv37kW73Y7f/d3fjT/8wz+M2dnZWFlZKUAr4ko4qN1er1eOIZeTQM89f/48VlZW4uXLlyPeKII55V5PT0+XlDzRgqHxwWAQd+7cKQdWvHr1Ku7evRsRUYxC1be0tFSdu0lKxpdNANcNg5r8Ef28DfeiOwh08ME/Rp2bxuMKw8EPx+igowb4awZaDexn9/IzZYwDuVokalzhmNww1JrMDCrJeylbpW73er0RA6rX66Wpfdm4nV7jjKmMhxyw6j6NgeNwx0pmNDJ9XeMWkBHvkVZ6jpEngUFe02Zsn1+lNfV6vdL+7Oxs9Pv94ghRlNtTODlGblRnaowb/Oo/UxUdjPscSKe4IRURIy88lhx+GyUDsLVSA/g1Q8qzIfQ8jZYMkPsazPCT61Pn3XGyyWWZy1HHXbW2s/r8Gtd/ZkTdRK74GGoYaHp6euQluwsLC7G0tFQOmaLDQSmvu7u78fLly3jx4kU55dZPm6vR2+mhkhmgWXZSFpXSvMigoMNFa0C8wcMc9NJhGlNKtxM26nQ65TmuJ0aclpeXY3NzMx4/fhyPHj2K5eXlgguWl5eLc3VqaipOTk6i0+nE3t5edLvdWFxcjK2trXLqs2jJV6k43Sjr6BTS2hfWYhRfMkN9Fy3p6HHHkaL6iqitrKxExNWWoJuWGx9/7nuBmpQPhWrGYExf4v2sV0AkW7zK39TC5METAv1MsfBQc0SkQIkAgkJdfwKbNUGgSSbQojBh/d4+lZD6TYNMb/e+SamBCp+vvb29+Af/4B/E+vp6YT6dVsd0L3lnxND9fj8+++yz+N//+3/H/v7+NXo0gZhJBGjNMNd1p3NEFMNB983Ozsbq6mo8fPgwhsOrwzW08DY2NmJtbS3m5uZic3OzRKHEX7u7u8Uwv7i4GMkXXl5eLoJxbW2tCJ8PP/wwlpaWotPpxP379+Po6CjOz88LsJAgk7EVEfG3//bfjidPnsTr169jfX09hsNhAZIaj7xCR0dHsb+/X3K4Z2dn4+7du7G8vBwrKyslmiSP0f3794vRpvRZglcJz8XFxXJdL0SV0Hzy5EncuXMn1tbWYmrqci+ZHBd638ZvskxqZLO4ATUJACKQY3El6oZPdi+/Z4aV31uTl6wjIq7JxdqzmeHkcllyjwAuc/bUiq939jMDHjQ8/LPGJgeAjCi9CFp/x8fHJbWPG8Cb+jaujAPTNVlKpe6ZCAQHVO6kg296Fg1JG+mddrtdgBINqU6nM2Jcsb8CTP1+v4AyeeePj4/LoU9Kt+IrGtQfAhRPC8wySfiaEY2rZlDpWdGAa0q/0YAbZyTctNwUyGfA2A0y/07wnAFyN2xoSNHjXnOmRFw/qMb1YmYM0NhWv7lWGVnyupx2TQaGjyuLfDp9SUeWpvlSW1NTU7G6uhorKyslTb3T6cTy8nLZ1xMRZV0cHBzEs2fP4unTp/HixYuSoibZko05mwvSc5yxq880ujNDSuuaKbXMIomIkUyZhYWFWFlZGYlMKdggw1zRKUXz6fCZn5+PxcXFWF1djTt37sTjx4/jwYMHsbi4WPCUZBBP04uIWFxcLAd4KeWv2+2Wgzu019UzB8jfknPa66Xo0XA4LNt6lDYsJ7LGoOh8p9OJxcXFYuzJMFNfhZPW19fj3r17I06bm5Yb7ZFy760IMO47wQMFjgtNX6wZSOBnTShT/KRUuXlM6X1My+JvqlfGERe8LGcfu6JQVNwMO2o8NIyoWKhkJUgYVnXDTEzHaE+NTrcpSmdbXV2N7373uyMeGM0VT2GR4Xp+fh6ff/55fP/7349f//rXafpABvjcI3PbMWkOOd8yPujV7XQ65VhwRWIkyJUuIvC2vr4eKysrI3Ooo721eVILstW63G8Vcck/3/72t0uUZnNzsxzeIcHV6XSKcaKx6khyva/qww8/jN3d3cK3PBKXxo/4U4JpMBgUYfHNb36ztLO5uVn4dX19vdQjfuW7wQRUuBduOBwW8PXgwYMyp6pD/5kq8KblTfnZn28ypvhfc0UAkhlLDnpq4KYGMmpyMwM4meFXc8CMAzvZf42BhpM7iTLApz5xjNm8kU84Do/kuDzU6VsyoLrdbnS73REjytP6xvWFpfZ7dt3nnTRkVgUNKQKjLPKmiI7WIEGU2uQzAlHtdnvEq54ZUgKJ5BM5GpmWxyiPdKcfi8410Wq1RtIwGeXOIrh8zudexfWk16PfSLe3aUhlDlb2P/vNn+cce8nkgu73tcVnaHSQl2rGVM1AymTWJOvEx19b2xl9sn7SOe2Hw2Q0yOobDkf3kWf9GQ6v0lPb7XZsbGwUjKColF5If3Z2FsfHx7G3txevXr2KL774Ir788svY2dkprx+pOWi8b2y/9tllYEariCtHEo0D/slRrPVCnS7DQ0aEtrvIMaU2hC203hmllsG5trYWm5ub8eDBg9jc3Bw5z0D1agwRUTKYdCR5xOUretrt9kgattr0sVOmSjYNh1dOlIgoBpWcN4PB5XYNYXsZSaurq7G+vh7r6+uxurpa5KXuWVpaKpjwzp07EfGXEJESIzkwcMHhi1fXSDD3PI4DzU3Kmh4y5U7r9DMKKnoHXHAyYuWCg/u2tBdLC98PI5AxxPCjFn7mddG99ATpPxeGBIOiPzVa1MBNE11dIP/qV7+K73znO9foJGCjexVm3dvbi5/+9Kfxi1/8YsQo9ZIJ76Z+sk+ZEvJ7WedgcJnrTMUj78zJyUk5YU6RnPPz87Jgj46OYm1trQAW9bvT6Yy0L36it3hpaSk++OCDePjwYUSMHqu7sLAwsi+J/Cj+WlhYiFarVQ500Mt5eeAD+yDFkCkh9Z1r7c6dO9FqteLOnTupUaBCZwDXj6JipDXpkcmD25ZMiTcB3Ox7ZizVDCg+k62jmqIkHzSNpQYUMgDln2koEYjyWlPdGdihwqacofE0CcDJfstkkurjPTQuXJlKjisaJQNKKSLaAF57L0wTr2T0qt1fG4u3pbUmue8HZ9Dg8H2I+u/OOqbwyJDSCVran0BDivumJM/YZzkYs83c+sx3+rhxmhmFNK50j9plH2rAMQPcHpFS/90IfVvlTdLLsjGpvyqZLsscNewD16IMKc9y8ZJhHcqmJhnENcH++n3eZ65rHydlFbGW+Ct7b9Q4eaLvmSwj3WQgycEXEcXpLiMq4jJz5ejoKHZ2duLFixclpc+NqAy78HPmGOZc6DujMJkBxqPCFSmTs5d7emSMqj6tP80B9zJqn5j6w/RhRp+FeYRlVlZWyinAm5ubJfNGNHE+bLVaJTo1PT098g5MyS+lYisNkfLE514yVEaexiiDSg5gnVYofMp9TzxQQrJOfVJEXk6ojNcmLbc6bKKmhBx0+D0Uvi4ovS6V2sIicyoFSUxxcnIS7Xa7AFRNmiZMC59vEY8YTRHT5Mmzx3J8fFwYXqFH1csFJIZzZZyBHy66zHvLPWAZPTJ6OX0zervg/tnPfhb/7J/9s5LKl6UjqBweHsbPfvaz+MlPfjJiYPp9BG0OTtyQ877VGNuFESNKOsWLhtXKyko5rUqLSC+tffLkSQyHl2l5Onih3++PLC4aZaKV5n11dTXu3r0bq6ursby8XIS4jCc9Q+cBeUBpDsPhpXH++PHjuLi4KC80Fm0zI4HfXTDzHqaG+Dw44GLfMl50p4oUPH/7TZQaP2SKjePWdd7v17w+by8Dzv7Hg2S835PSpbZ+GK3hmqyt/aze2n0ur/zP++M0qLVF2rlsyOZA9+l0VO2D6na7cXh4GIeHhyUaJe+mwEQTAKvpGOf12vpykJm1w8iRwIp7jGmI0JiSohcAUp2+V1H3KhqlCBSBQ/ZC3ojrB6dQJqlfSu/jPgbxQA08an2p3w4cMxpnESoVYgPOw2AwKJE00fZtlQwAe3H5mvXfjRbKTneWki41Has6+Mf++Bh4f02fsn6ub/WjybHtfcz61dQ/6bksGtVUMrzE/vj6VORFeygjoqwNOhnOzs7i8PAwtra24vnz5/Hs2bPyXke+jDfjR33Wnzsfs3nJZCj5RUaB8J6cSYq48IAMZYwwnc/XWzaHwj+UQwo6KB2OaW/Ly8vFYUPZRPp7kVNIWMjlBXktIn8dgq6Thpxj4hnHZuSJ2tpp0l+3KTc+/lwD4TUKP09P4PPqPDeGjSs0UOiFpeKT197TnPSbmEyAWwzIqI/yLLUoPGrGE3Rk1U5NTZXPTNUicCI9CEBVH8ep57gwW63LSJjyS30+eG8t5J0JAb+ufmujoDZoOujXM91uN/7f//t/8X/+z/+J3d3dsd5r/uYLkvdknh0fb02J6N6Tk5MyNwKfnU6n1L23txfz8/Px6NGjWF9fj263WwDI48ePy9xKqBHUqD7O87179+KDDz6IVqt17fAHnVijvokXxVtHR0cjc6j9SB999FGJPn722WfXcrSdPk4jCm9fNzUFRP7j2vQ2qKgdJNWMk0lLTXb49xqQqd2T/T6uH5nyjrhuYIlumVct42U+N46Xnc5elxsBGSCrlcxookGV9Z3tZsovG5Ou1zzP+k3tuhF1cHAQh4eHJc+eRlS2f8GBukpGv6zPGWjOAC7v9dQ+RU7c60oAI93EiJQMIN87xpPG/LQxOidliGZ8SJ5jGo+AGYEJ04drfEQnBXVxJmuc1gRCTaBdv9ErnTkr3qQ0yQ0HatlzGc/7+vB143RxnuNzmYMy0+nEFZrjrK/8o45wR26mp/lcFsnL5C7Xt4wpHmjidKjJXO+DjyfiCmPJoa7jt7V+pH9PTk6i2+3Gixcv4unTp/H06dN4+fJl7O/vj+yLIgiv8Rz5mQbCpIVzT30qHEE5IRkgGaM9yVqr7rghzRgoYJ95uIP2dGtPtIyo7HCXTIdpHLOzs7G0tHRNFvn5A7V51VwOh8OR1zKo/4pUySjn+8j4zkGd9urp3+THbF3dtNzqhbw+cA8lk8Ba0L6QXWjwORfQTQqQz2uvi96zpIhARIxsqFNbYnopbjGsDqrQ2GSgqd/tdrtsltOLX8WQqt9PJCE49UUfMXqaH5WSGM73dqlMTV3mqvopKBmNfD69f+rTixcvYm1tbWReOHenp6fxox/9KH7wgx/Es2fPqkK+1h4Xu/ez5k3z+8hPCumKnjpwhClzum9+fj52dnbKApLSOTs7i263G51OJ/b392N5ebl4szS3miPNhcDIxsZGoZOMOOVhq11ukqZA03sZJLCnp6dLJOxv/I2/EU+fPo1PP/207I3LALULejo2MoXb5HXkPU5zLw6k3wa40bqs1ZkBHf/ucqEG7scVKpuMx6n0mLLp3vKazIq4viHZ/7Pv2TjYh6xkffY2qFzcSZCB4ayvk/RByou0pJda60sv/+a+KKX2ycvMl2pmdHH5wqJ5zbzpNbAcMWp4+h5S/efeIaajeIqfp8fx0IaIGDnAQQYPI040zMh/Wj+ZASldQuNT9QiouYHYFDHg3DsP+9zfdl3qGY43m6c3KRmPZ8YUjVX2bdK6M9DoNHE6Zmuz1k4tQuTYw9d3ZihkTqGs75mOcXnDtT9pFIptcRy1efJ2FdXudrvlfYda8/1+vxxx7kaUXiab7bn0Qv1DQ6rJmMqMDsoF0kz7lxStptNFBhQd3Zx/GhfCvTroqqZfMrmU8STnVM9xbPqvk415cI2yqhSt9z1yGS8Jb+n3TL5lxr7SI5m54DJ8ktTSScqNzxB1xc+JyISPjAsnkBsVNaFM4lKA+aKnwtFJT0rlEuDliWIyujTBOglvOBwWho24OhZT952fnxevgK7LW6Y+ei6zGwdcMBqTFCEX4nA4LItGdfhEq680tCYpGcOq77/+9a/jgw8+KFa/e0h+/vOfx5/8yZ/E06dPxxo+TWV5ebnsUWJfmorTUf/JK8fHx+XkueFwGKurq7G3t1eMbaWIqCh9U+mBOr3Ko3F+WEhExL1796LT6cRf/MVfxPz8fCwtLY14kWVAkc4EeDpdKzvtTodGfPbZZ/Hs2bMR0FhT5q4YMxDO6GkNADf9VgP+7NeblgwI+u+ZERVxPdKdGXwuQ2pj4v3ejgMhrmnvQwacXDFldfq9GS18rjKgU+srlY4bOU6HpjLuPiovKnilrxB8MBqlv16vVw6XkGJ0Y5+84MCbERD1pwb+a1kVBIQ0pkR7fXZjSX3KQAmvs6/UoQTC7Aflkb57m7X+MzrgjlAHiHq2Nse619dM9tmfy2jcdK+v27dRmmSWYxn1ocnJkBU946DP+dNxgfqna02gL5MhrdaVU5d86hElyaxs/UtnZXLEx5AZNmzL6eZz6bot+32c3pI8Oz09jf39/ej1eqVOpfPpYImnT5/G8+fP4+DgYMSIynjZ1wXlDLeANOlMfnc5xXVLQ4AZVZQrXBNcz0zNFQbh3LN9fibtXEZLrgkD+kERdAbpmvid+zv1R4zizjvSwHUW1yLToxX9Hwyu9qeqZLxOGXiTdVwrt3oZAxeZiOVeWOZe+rNcBAwpZ4KBi5TANlNMMzMz0el04ujoqESZdBRkxJVQ4eTJAIvIjcSpqctjqnkqIDf/kfkirt7cLJqoLU95EDPUFq2YQRvq+LuDhppQuU0ZDAbl+G0KtVbr0qPxy1/+Mr73ve/FF198kdKrqbgR9M477xShRq9D03P6rkVLcKbnd3Z2yiks5+fn0W63Y3d3t7RLegmUraysxGAwKMK02+1ee18Jaa/Pm5ub8cUXX8Qf/MEfxLe+9a34O3/n75S9VtPT06Vv9Gb6Qle//SV14g3lLfOkRPbDQRbnQoL05OTkGp9x3Y0D6k4D3utr4E2Lr21e99+z+1wBZ332+2rA2tebP5uBjgzo8Dfvg9+n/riso7LJFG/Wl6wd3suX2DYBxIw2kxY9I2DAUz9lRIl/BoNBSbvR3ijti+I7o9Tn2pxEjJ6mR4+xZJl7JF3fELBovbgyZpsEub4uMz7N5sl5mnNPWefRKHqS/XnyNsfg78fJ5pc8mF33OWa7N+WV2v2+ft+WrlNx73tGf+7L4hrVd67BbJ65fmqgOqtX/wn+6LSNuHIcaV5rmSmqx//8HvapJoddttSMKPa/KWLGNn3srKe2jrwuyTbtrVSa/vHxcezu7pZjzp8/f15eIKv1kBl8rN//lBXkEdOazPd6KKf4rJxMPOo8oxP1rzurPOLSFNmVfNZ9EVcpkpIrNEBYh8bgh+zIoGOUT/fzEDeOw+feo31OM/5xrXpEzYvmpSl7YdLyRm+18+iJn6rDCSMwUMkUGD0jDhCz4otfhpNy6RcXF8vGvPn5+eh2uxFxGX1Q6pVyZ5V3StBLZaUF1u/3Sz8JimlAUbGqDjcGnQ4+runp6djd3Y3t7e1rylZFjM9jLccVCiRXAjKkut1ueYeU5vrJkyfxR3/0RyUS5fS/SZmeni7vI9rf30+9ZiwEOEyBcWHf7/dja2sr/ubf/JtlPHxh8sXFRTkdbzi8PKlmcXGxbEBVZFLRTIIYtie6ff7553FychJ37tyJk5OT+L//9/+WkLZO26MhFRElKsb1o82tU1NXR/QrPWFvb2/kZEHRwQGcC0cJKfGtgzIKYIJF1qHi43dw87YAjivVGrDJPrvgzcD/JEAn+y2rT9cUyfV29N8Bfk1huDLhWnCZVKM357BWb6Z4nSecvqqHnulxxcE1Pab6U/os5Wq/3792uIQyDAh22E4NqLgxRaUpxc5xq8/SF0r1Zkqd7pOzSSXTZ00AqgbK/Jls3gSspNs4vqa2WY8bk043/fd55Nw6PzhfZgaE/57VPw54vg1Zo0Lj02VHRotMBup/Fo32cWTjy8bJumUsE8R6PyWDdJ+eJb7gmuec16L4rjPcmVCTc35fdrqm06FGE9XvvDKOByRLjo6OSnpfr9eL7e3tePbsWbx8+bK8dFdbNTJDr1ZII+1fIo+4EcN6CeC590/00oFXvV4v5ubmSpZMRIysd3fWZw5Bzm9NtjDzSvuK5EymA8HxhOoU9ibuV39k1OlVFk5rX1NqyzEmacVUaB5Aw/XahCHYb9dztyk32iOVKelWqzWSwqRrInqtLgJ3DcLDxzVgwnr4WREA7RmSQtYZ8tPTl2+6Vo7m6enpSN65E1Z9UM6+UuyGw2E5Yp1HsTKFUM/L2IqIEm5mbj/BugvzXq9XDnLweSBd+EbrpvnzUgOVw+Ewtre3Y2Njo9C02+3Gn/zJn8SzZ8/StpoAaHZd8/7+++/Hzs5OPHv2bIS+rpz5n3zjkT6Ftf/8z/+8HCNO74kMJEUpOT+dTqfscZLgzcaqazMzM+VwCd17dHRU7slSezjPNTqpz1w/fIGn18MTezQmV2aMunpaAKNuNYXlc+Hzqufe1LPjdKkJu0z4OWir8c+kfaDMqQE+FZdlWvvZPJN2bgST92k00cGUycVs7GzP6yWYcoXvvJqNtan4cw6wJAePj4+LEu/3+2V82iTOd0YxElXzaPO79AGVr3QUT9Jkfa5bpJyVCsw0NwJRzZPn4HP82Xrxkil7zhnTz2W8Z97XbA36fIjuTQA3yxbRf+61yMBZxPWDokirjD8yXsvWfI0vb1NarVY5uEPOVJaaV9t5h32tYRQ3Nmp8Sz7z9vTfsRKBec3QJEhnNJV01X3sSzb/2RjVF0+b8jTSbA5UqLO8Hb+3iQfE371eLw4ODsoBZHt7e7G9vR3b29vVPVE1/iJNuMYkI/Q+SL3kmu9jE81ZF9PhJJdIQxlSCgDoEAxiH/IncajzresY8hAdaXIO8bU/ei4zoJ0uvJ+RU8l9Ocsc47kDiGuADgLKO95LWmSRKvbN15XG9CblVodNUEnJABHBHORqABSo2SKUYtBnGhSu4EkEn2gxwdLSUhwdHZVjrKXotJ9oMBiUzbXD4XDkXT2aeE6WUrUElC8uLgro1oJg2mCn04nh8NJrKebJGEihetJItN7f3792Up/60mq1yqlVt2GCJkEYEfHFF1/ERx99FNPTl+8D+MM//MP45JNPrr0QOKu3plAcUHW73Zieno7/7//7/+Kzzz6Ln/zkJ3F0dDQCVDIvOYUslfVweGl8KkTdal16inZ2dmJmZqZsPm2327GyshKHh4dxcHAQEVHendDr9WJlZaWE67l/izzKcXIPlPqnxc/71E+lJKqQzykMCIQZ8XB66D/XHNNAKKy8bV+TGQhrAkyu8N4GwKFQHudhH1dqoL52z00UdfadRnsGAmjk8lkX6OQdPud119rK+knZ5sYU+1CjB2V7VneNXuJjpkfrnSJS3gRsp6en6Ut3J9mo7vzIPbJymHkaTjYPWnOqQ9EpGgT0EEvWE6gSPHFdufLPomWezaFC0EPaOahiWzXauDFdM6b8+aa6Hai4vucWAM5RVrK+6H/tmdsUHqveBKycP7zU1ozTRIVGZVa3G1yZnPD2meWga/585oxgX2hAax8eeUX3Z7TRfTr0i+05f7mTkbJFdWbFx+PYgr9rT5SwxqtXr+Lly5ext7cXvV5vxIjKHDSOezP5K8y5uLgYKysrMTc3F91ud+RQnOy9bIxESTapbkak5ufn4+DgoLz3Un0mPdUP/6vNkfMcI500RkhvnxefP6cXjSGNiXjVed1ln/Mc+09ZlukEjj+jhQplypuUG6f2SdjwrcY+GIJlMbYDB/0nWFBqEz1DfHZS8BARZb/U4eFh2R+jk9R00ISKDEIega42pqamynGT6gcFuRTZcDgs/b64uDyuXH3VYhGwVZiTxgIBsI5tZB9Jf0VT+Lbqm5YaY2lR/fznP49/+A//YURE/Mmf/En8+Mc/jsPDwzeq1397/vx5PHr0KGZnZ+Pb3/52fPOb34yDg4PY2tqKZ8+exd7eXqEDlb9oury8HI8ePSrtypOtFJyDg4NYWFiIp0+fRsTlXO3u7sa9e/ei3W7H/v5+mdPd3d3yRm+9D6rVal17j5gAlgsJGSh+PaMJBZmElgMtV9ZyBHA/hD/DdpSSpL5kSortU9HVDGHxvAQujQIHaW+jTCoM/Zns89voWwaIMgVOb1+tf1kaBnmc15jyQZA0LuLl4IaGCJVUFpGpjTcD97zeVKRMBRIk87V/QeXs7CxOTk4KaGDUhP10fs6Mo4hRY2pxcbG8oJIGjEoNMMqIkj7yE2Ajrl7srkIjyTdGZ2kpWUqKxqY5k46UHPD7yXfjDCpP6fRoAI1G8vc4/ZuBTS8ZAM766s86zd9G0TwwmuOOC19DTqvMiKqVJrnAe7L1rH64PKTRT2eZrwnXKy4r/E985uvdAajWtvCIGw6UMZSBdELqfjpHmwrpk/H3YHB5cNT29nZ89dVX8fr163j69Gl89dVXxUmdvYfOaVvTmyqak9nZ2fICW0Xcta+TR3BTz4qO7XY7FhcXR16VItmoNDs5l1gPsYT6ysgNjTXhJud15wmNk/rJ6cG+ZzTLCufb+a4m+7w99t/b5Rg0PjrQ+LoIvpRch8f5OG9SJjakKLx5Coh7mDJjx/8cAPKzFJV7COixpPHlbXOCtQeq1+vF4eFhYQ55GGUhq23WS6XJM+llDGkvFvus/jDCRGGjNubm5opBxT+lI3rUh8yq47JFE74/4CbFjV5n7pcvX8bFxUX85Cc/if/+3/977O3t3Vh5sY1MUezs7MT29naJErbb7bh79248fvw4/tbf+lvl9EUJPOXwanF0Op2Ynp6O7e3tEa+1PDs//OEPy8EPx8fHI2OcmZmJ+/fvR7/fj5OTk5iZmSnvl5qamorHjx+PnKRH/qPnUiUTvE2LknymuZOCnJ2dTRWYG5PiMaczFS2BBwGZ6uPv2fvdKFC11kVfAvzaPN+2ZEK1xkd8pgb8/ZkmsDauX5nidtDj/WnqG8Gsz4/uI6h1z7mDy3HgSIDcQU4GlNmuj3dSmZMBUjmtePKc2tYma+2j8qh7RksCOv5X0clR2r/IlG4ZMQKOkqfcN+DzwPXiHl61J4eO9IXaV7uMhGh9ZTyvIp1Y+91p4XPgtHOQ7t855gxskU9qxs249dI0Dv8tA1lvo+hVGTJAxPdchzSc/M+xzrjxZ5iH92Y4Sr9p/dSij8RAGd0dW9VwGg0xH0/myKMRJVzFAxJ8PFwXHFtEHu3KCq9n98iQ2tnZiU8++STOzs7KviimyWXzxr6y/uz/+fl5HB0dFcft0tJS2V+uYIOfUio5I1yjSPnMzEz0+/2R587Ozsqef99iwsLsKL4jTjKn1WqNyCVGL339+l4n0oP4uLYWszoznOk2AetkaqjzXRM2EG9JxooG7rgijWTA3la2TGxIabBOQDUuJcAUAQd4rmx8YlRUHz1hbkGzX/zuQEMMpJSv6enp8jb4iCipawLIJLYiS71eb+TIR/Xp4mL0cIqzs7MRASMPJTcEi44EImKWbrebhmv1Wf122mdRkJsWp+H5+Xn8h//wH+KTTz4p0bWm5xw08n+tvH79Ora2tkr0R4bk/fv3491334319fUipOTd4Uv2xGtbW1sj9Ii4XExnZ2extbUVs7Oz8eTJkxKR7Ha78eTJk3IajrzjivC9//77hY9UaEhpbLXx1aJLGdh2L7YMd9K2ZqgQ5DpP6H6mm3r7Xq/3VfVRyfm8UhhKKb5JcYGYCfGseB91bw0Q8Dner2tc5zWQ6P31Oc5o7m1nsqx23SNd7qjJ7if4oqGQpcllwDVb3010JC1Yr/pEp1FEjIAt3afUXJ4ql9Gd/7O+OFiUA21hYSEWFxeLPFWUSG1L1kse+Z5W3Ueec4AgeeKphXz9QhaNcqebj9HXZ8afzObIaDNuLr19pzlpXGuHdWRGFJ9rWlss7gF/W8VPQa0ZOrX16cXHPAm9vVAGuhzyCKHAMDfbZ044ppuSb11+MB2cRfT3DAqmh9KIyvbIaxyMLkSMf38kiwPxpnvOzs5ie3u7yBzut6zt2WI/vU6fS+mYs7Oz2N/fL4dIdTqdWF9fj83Nzeh0OiMZR0pZPjk5KYfqaPzEmHIyKbolI4oGIPuh+WG0hQdAiOaSORoj8ToxJfnJaUJ+dJr4OnH9WKNrttak89gnfic/ed9qGC37XXNzW7lyo4gUFRSNKu+kjCD3lpPYTAtiKLrVahXDQ8LAvY26zvxz9yDpb2pqqhhN2njIPVLySB4fH4/0m0zC4ye1cF6/fl2OJh8Oh2W/VUYPZyzVNRxe5tXrJZO1d0GpD4oEqKhdMUGTh8WFO+mZlYuLi/jRj35UTfuplUwJEdB6+1tbW7GxsTHiof3GN74RH3zwwbX3Y/V6vXj27Fn85Cc/iYgrcKz9UFlfut1uTE1dviB3eno6dnZ2YmpqKg4PD6Pf78fs7GwRdhsbG/E7v/M7sbKycm3Ra7E2pUFxjTgtfNzkJecbpjdoPUiIUMAQKIvXOfdcAw74xDNc07querQeNAcUNL5+JZBv+j6zWsn4KANl/M/PTbzugr2pZPd4X7J+TQK4/F4fE+eOMk/XM0Xh8oZ8QhCVKeKaIeX8WpPpTfzO37jxPJsnycUs7SZrp2YwiF7ifUaGOp1OLC0tRafTKal+EVceYBlR8hDTkab6mW7o+pDf3YiS3mGan89nk3GS0WtSmowrnNPMGPDfXKdnzgy276DdeaAmxx1s3RbwZMX3uGZtZOvZ16r/RlrW5oll0rXkNKanPktXjchfJq1nVeckctFpwwgzX23QtG6FEanTPI2MMv+mRc9oberVHzJ4smPBm8aqOnk//2vf58HBQZEvGxsbsbS0FPfv34/Nzc2Yn58vzvmjo6MSweL2jOFwWPbEM/LIPWfEoe58kYHU6XRicXGxyDZGviUD3RhRm1xnmeOipnP9u2PoGl1V3GGapa9mdYxbTxkOpl7kmniTMrEhpVQ0F6b03DMSo4mgJ1TgjCVT2how29DnTIBTuLAeLkZFcpQ6d35+Xo5GV2iPOacRMRKZIoCWwtDYJQzIBAxT843x+q/Fp2N9fSJJF6UR8qCDiChgQ7TW3q+aF9dLjQmbwt1NdfhnCnh6qAjm+/1+7OzsxMLCQszPz8eHH34Y77//fty5c2dEQOjes7OzWFxcLMKRmyKZsqDvCoNfXFzE7/zO78T8/Hy8evWqHLk+Pz8fa2tr8eDBg7J3yveOqA+iPcfpgkhFPOlRIwJS0YLP6X45E5xvXKDrWvYbFQodFjSAVDfvFf9IuLvREHF1apcr9rcFcBzAs26nGfvV9L0mP7y4sdhkTGX1ZL9NAmxrSopy1qMsEdcjL3rOFYauO29nRmsNmBN4cayTGqQEcpQDPuZJwNi49hh90zOSk4pKraysxOLiYslcUOqNjurVM5I9GR0pu2kQCbAwxYQGFB15Hn2tGRRqm9cyHm3ivWw9N5VM/9aK1zuOP9xJmvXVf3+bRhTr9FQnv37TdmvjbqL/pMaUCvGEG1BukDjWqhmNqldriJk0wnRaswL5SsUV/sgM6ho9M7nnz/p3N1RrRX3UqX1+4ENTyQy6TPdKXul05+Pj43j9+nXMzs7GxsZG3L9/v7xi5fz8PLrdbuzv78f09HTZduKZAhFRHKiU1zz4hrJE/ZievjyZenV1NVZXVwtupD5lXZwTjkdjrek5zhk/N+k+N9Bch7nRSl3h9gB1c5PBlBlN3nfKz0n4IisTG1JSFk5cFzw0pnhvxPVTPHzCRCgvaqcG2r048+te7e26uLiIw8PDODs7KyfgUckTYHJvlAOx8/PzEppW/6RseaKTTvyLiLKnSQq93+83Tp4OzXAgPxgMRqJYBPO1+mqgL7svu3cSwKQ+c99BJnw0P0rvW1lZKVEh7V8TENG8tFqtuHfvXmxsbJQXArt3ydvRHofh8PJY5YcPH8b9+/eLMKEnj2mejBSJZ3jkL3lLykX1SXhLsHmkSPPHNcQ1wqhDbYFnBpN7qJz2jBZl8ysB5f0THdhPHsVM+rgn9E1LtuZrfOy0yoR5Ew9nv03yXFaPt98kA7M/dwiouDFFxcS2OH/63gQianXxdwezN1U65EM61HhgTw2weF+yfvn41ZanGrVarQJIOp1OkT00pGRE+eEC4nteo15wPci9UUwj9JQbBxfOuzeVybzeBIRq9MtKZrD7czVAM67U6mzCBW+rOF9z/ni99mzTWsjoMc64VJv83ARkGb2g7OZ8EahrHbBOAvaa/HEQ7PU6UPXx6Hnyvvrh66yJbyalIfuegeesuKFXy3bK5K1SB4+OjqLX6xV9KxkgZ/3r16/Lvh0evuAOrwwzsa52u13eV8ksEkXbFxcXS995MiENEcoWyjP/zenHz24012haw+X67IYVnbo+79k8ZnNcu5fP3FSPZeVGe6TcG8cFFXHdm+mgRhMdccXgAga+IJxhM+Zt6mv2nIyXiCjHnZ+cnFwS4v8fYmXf5S2kx1HjY6RKERcaUlyIpJenU2XeG7UzNTU1EonieJjKp/trdMkYt/abM1ZTvVmfuXmaBmom9FRnv9+P3d3deO+996LX68XTp0/Lu7/cizs1dZl/LPp5njMBq9rT/D158iQePnxYjGeO3Y2+4fC6p5z8T2XExe0HMDgdVXisLPvClCfWQccEU1DZL/I5x6Q+0sijIFX7FHa+R9EFHeeWbXr66ZsW5zuXLZPyZxMQqv2erYOmOsfJMF2j4nQl5p81F+QpRi7ckcV2qYw8D5599jSPTHaSBzLeopwnLWrySAaJ+s++ka+ajIRMz6jQU690PaXGODAUOJGXV/L19PT02ilRqtsBCEEhaeQHTfjeKALKcQZKDbD7NZ+L7Fn234FTrTTNcQaQsn5lesD7xFJzvr4N8KPC95Nl8qY2DvbnJrTIaF7j5RqWyvrB/bCuc112+xpyTMJ6XZ+4bh+XJud6xfneaT8pP05aXNY2yXWuXTpZqSe9DspaRaW63W55+Syd3aybdWZ0dDlOPCCstbCwMIIRZ2Zmot1uR6fTKQcoMCtH9fKAm5r8YJ9dVlBOZ3zhRePN1nAmE7IIErFkVlxXudHf9PemZWJDyk8o8sXhnaHBxEXnxornnfvAOGFOcHpgMoXvnhfWLe8AN0pyYgeDQdkrs729fY3wYgh5U/mmebZFBvbogN4LoMIxaJHI8KNA0+LgMzctTQotu7fpuurJDtRwoZOVi4uL2NnZiY2NjVhfXy+n1mkfmupR1OPu3bvxF3/xFwXsuOLyPxktX331VRwdHcXGxsbIPAooMeVIdWVpRzK63ZiiQIkYTY9QHaQLjSECBnoHCeyyNJhMWZLm9EBRKfB59os09LZ0j8ZNJwPlwpuWDMz490nBm99/k7YnuT+juffF58uVkitP8kWmfMindNr4/a6cvV79pwzPABe/ZzIwo52DZh+fy2jXA16n9yvjCX+GhiSNKU+hkbzVWlM03Pcv+Thq/dS9NNKUPi5Zk0WjxslJdxhl/JnV4TxXo/Eksjp7ln33drwPt+27isu6t1GOj4/T/YM13m66xuuTypuafMvqqMk3lVp0IKNj05ohz9CBR10XcZUKXssKEW+L37mumsZTo11t3TfxrfNejbYOrCfFL6pXcsbTHDV+Oea1T5IHlelZT3uWs5g4VffS6Sr68rhvvkbIZX8mb7KxutwlXfTZ682wg/Oa//lcuM5yvUPnFdvL+Mbn1Pv5NsrEhpR76sd53HWNqSgZKGX9Xk9tYvhb1na2EDj5nBidNMZ0Kik6nd+fCRx91rMMnSrdL+LqBByBeTHByclJHB8fp5vcmAebgWTe44C5aV9BjUa10nSPCyPRk55ggu+sULi9fv06vvrqq5ifn4/79++PvOtA9UVc8tDi4mIxtPyQjoxX9Ozx8XHs7e3FgwcPSlRKRfxNL5l+51i8L87bNEwcSLNep40AnDslCDBVVw3sZ8KBwoZtabyMrCp1MVtb/t+VpkfW3qRMApSbFG0tAjrJ82zrJoDIwWT2XGYsuae0ptzEB5RlGa9S5tUiXOwz+VCfszlk5Is59+xjjW7sXw3MZ9/Zz4yeTTTmdwIP7X/SH99RJXlLmmQGpkekHJxJbsk489P6srVfGz/rdf5qWms1EDhpG+N+m4Tfa/3K6ho3Hq//bYEg8Yc7VL1/mlPKR6cB18Ek8qX2203pmY3Jx5elwPua96wMnydiPo/4uj4gjYSn6DzIZIXLn0ynuBzJ6FUzJsfRsYY3J11D4p/T09OC7xR5lyEVcRkBlZNcBmVGR13jQR661mq1ivwSnfykPtXtsop0dvnlc0ja1MY+rjhOyNpke9lzmQ5wPVf7oxGZ1a/f/fyGm5QbHTbBzecRoy+epLJUcYHigocT6oqq5tnmAudzTmDvh0AjAQgXIxlS9x8cHMT+/n4KvNS+Toxj/j0jCSr0DMhjkZ1wRiPKQQ371mpdplFxLhQVY19q9GOdTrPbKinRxI2ISRbfYDCIn/70p/GTn/wk7t+/HxsbG6UvEsQRox4wbfDkguAzal/Xh8NhPH/+PL72ta+NvIAtW9iZl0W/M6UzIj/u3IWV6tV/NzAlhMlLqk+/NaVeuSGY9Un3sYgHh8PR90hlQCFb46T3mwojtVO73gSkbwPqvI5J+qH7a7+Pa5eCnwogM3oo60jjbM1m3jkHRZk88ahIBnRVn8ufGj2c13wdNJWsfo6rZlTVwI3k4cXFRXkfoI4e1l+/3x/Zx9rEQ7UxZJEMB2et1vWjo5va87U6DtBlxo3fS9qSv7J+OA96fZPMgfevyRiqrW/JwKyPb1pcV9Talw6i7J60D5kOz37T92yufF3U6szApXifzl5fz64Ds7VOp7hKdkgC+0wjyo/dJn9l+rJJzvr4xxlPzjdN8oTyS3XX9Knfrz2WBwcH8fLly3jx4kXcvXu3vAhchg63K0RcHRyWHcrle9CE9XiyaURco7Xzhc+RZ8dkhjbnJ6NRxq+cWy+ZIUW68j/vU79JA2Elv0Yez5yVXrfu+0sxpHR8oxsc6hSJQUbzBVJjyEwoOxP7hPmi12/uUeHv2aLLBJRegqbjKH3SObEcj8AODaeZmZmRUK9volPbSgPRkebeR9KM4+GiaLWu9qm4wOdC8o3nTcBwUuXlNOf1ceBkMBjE3t5e/M//+T/j/fffj7/5N/9mSevj3OiUwtnZ2fLC3kyoMT1OZTAYxLNnz+Lw8DDW19dTZZHR2RcY+diBrvdB7XLRSuAxp9151GnlkSXnAeaxq35GnXg/U2LVBx7swTZoNJKfdI300SlDb6O40HNaZ/fdtP4aP7vBOAn/ZyC06b4MvGRj5pryyFNWmkAaf3eZUVOWNXmT9dHbzGRH7fem4m1T0XvJgKmAh440l7eYf9pnIOXcRLsMbGqt+JrNQKb323VSDVBk48vGXqN5DbiMq6dprYzrC5/LPjfNoWOJpj6/ScmijizUsZSDzEiYpF/Z3GVzk/GH/mdzUQOezFLx1EXdn/U7W+Nsm31lOzV6aZ+gp8kSf1B/6rcaDcfJzHF873TL6Ji1SRlZu0fG1OHhYTx//jw+//zzWFtbK/h5aWnpWrbOcHj1fj3hMj9pj3TV/XIM+V550p86I3PgOXai4cEgh+Mdf6aJ7s4vNZ53PeJ86PdneCnTY01lnD6dtExsSLVarXI6CEOuzlgcCEEZFY4TzMGX1+ngTqUGsDLQwXto2Ok6j5BUKLXf76e0cKVJOmgRyACLuFSo2bGg7J8iUcqj5T3OVKQHaSeBlTEWGX44HJbUlkk9OV4yBnRB5l7EpjZE088++yy+//3vx7179+Jb3/rWtSO6FxYWYmVlJdrtdvEuc15dWTtP7O/vx+7u7sihE+MAnxtSjI65dy+jhZ/Mw+uqj33wAxsyYz0TYNwDJRCnvriRxH7qOR3T7+ktbqTpOV2jJ/9tRqQyWTEO/NWU4U3bvunvtX41RWOysWYKziM7ao+ys2b8ZX102eERyBq4Yns+L7zmjqysnqyPmUyp9SVrt6a4tRbkLT4+Po5erxeLi4vR6/Wi0+lEu90u3uKa59Z1lrfJ/bFcH7X31vh81oBdVmq86Otgkvr8elMd/ozPRW39TTKnXgcxg5fbrO9aoRHNuh2sZWPI5mwcKMtAuK/drC814JnNH0GxRzRqdVIPE8/o3kzGZPNMXZFFo/gs+0L97WvOn6OOclzlNPK+sb7aPDbpoaxu3nd+fh4nJyexs7MTX375Zayvr0e73S58pmiUTu6j05dz6s5LjyAJp+rF5TVjljgwi5o7bWo08PXYtAbH4Ua/5ljVsyeEq+nMyDIpMt7LTkjNaFvr3yTlRsefs4ManAoJ7QuuBjAzJq8ZR7U66F3PJrhJQTlY1Dn/inQov9XbzfrfxGA1ZSBaTk1dbUrOLP9s0fvJeByHj9PpIKZ0hXWTQtq7B5eftcAnUS469eZP//RP4969e9HpdOLBgwdlXDI0T05OiiGl9zVkAJCLRW28fv06tre34+TkJDqdTlWJNSl73kP6e11aHzRuRK8a8KXRw/74PbX+eH85V/LMOx85X/Mz941wXFrnAoru/XqT0gQ0xq1t3n8bw2fcb5OUGuhS3/y//3lqhz7TgZUZULzeNK5aJKrJiNJzaqc2JvIiQVhNBnobLs+cfixsb5zspSGliNTR0VF0Op0RQ0ovWVeaNPvkdMrWBDeXa93xJaWUvW5Uef8zYOO6IAM7me6olQwIk6aTroUm2TNOt0yyFidZ07ctWbql2vS/25SMZ5zGN52niCuecRDINjy1qVanY7cmeZthMl8j3BPlRlTE9T22vv6zOiVTJFeyyHFGp6y+jF56Pqszc8y7o5iy5vT0NLrdbjx//jyWlpai3W6X/6urqzE7O1tO1+OBWqq/BvY110rX1HtRz87OYn5+/ppBqblwJ7PPW0Rcw0pOg0n4/6YyI/tjXfqf6axaHcTVbsh7PU6D25YbGVJM53GCZwSgUPdFI4vTFwMBQ5Nwyz77tQwgZABH9ylflZsEa6WJYVy50fhke7KWM+9LTWlkY/GwbpMwkVLXfjfv7ySFfaewViGfZMJSn934ED+8ePEivve978Xi4mL87u/+bmxubhavgry+m5ubcXJyMiJcVK8vFu/Lixcv4uDgINbX1yPiuveLxrmMD46VdCO4zXhCYxJNavzqQjIDpcPhVZqe8xjb0hgyIMt0QqbzOTh3PmPfBArdOHsbqX3ZmpoEVNcA103AVybTJgWktbr4nX0bB2z4menN2e/eRq39pj9/JqMh7/N1q++ZE602rqaS9anpvqbfmdonQ6rX68X8/Hw5Plj7UmVAMTLrhYo6S5ehLJTM8qjUTfippltZMr6/yfNeT3aP19M0z/5cU121Z7Ln3wbo8ZJFNCYBkk7vSfAAn53kPr+fclqF32uYYZy8Yd2+ttV+Nic1UOsGlPNO1ifnO+pufnfeq9EtMwL9efatplNrGVEZz+v+169fx9HRUWxtbRU5s7KyEisrK+UdUPqvrRxsQ851vk5G9csppMOpdCKoIl08/Y/11DJ2RJfMyPD5GMdDTbTJ5EdNF40rNZ7Oxlbj0du0Wys3Ov5cTOjAtUmIR1wxlz/Dex0sZIrbQWgtIsZrUnZMT6Kh5u0wNS4byySFfZiamho5rUl1knlrzJkJDO0RqgkAXcsMGS1CvdtgXFofC+ulAciDETgHbjg00YjfBXo+/fTT+KM/+qO4c+dO/IN/8A9Kys1wOIz5+fm4d+9e2duQKWl663gox3B4eZz9zs5OPHr0qBwvz/kgnwtY0YAR7X2MPLqUQpFGDGmpImFHAMc5JG31Wd85z+qT1+MnZ+o+GlC+dng/i0Ch2iNAdH64TeE8cZwZ+BwnxDPFPCmQ9/oz2ca51m8ZLzYZZq4Mxn3O6JApeH6uKZPa+sz66HV7G86TBB+e0lYDQU2KbRJgWysEN/1+/1p6jVJsZEipbW7QV/+0VngYkMbq6UYRMXLSlq8Vp4XTMqN5BnKz37zUfs/qqj3v38etJ+e3Wv9uo2PfFPiwKMXdwVm2NseB/xoGyorLuKZS+90BuK7pr0nH8xnHT94nygz+52/+l0U4tF74P6NLTUboudp8+f3e/6zPpEUts2kcxvX+CWcdHh7G1tZWdDqd2NjYiI2NjVhcXCwHTcgAImZptVojMkpOG8qx09PT8l1Hqstg0gmhcpaqLr22JeKKRxgdHw6HIydO6z7HMz4vGU3caVzTd7o3m5Ms4leb36Y5aSqTYopxZWJD6vXr1yP7iKiIvTOuKGqg3w0h3/Ohz1TMPim87vXzeTFnBq58gYgp/UCIGhhoKnofVPauLZYas/G6ju+VUZbtR8nq0Z+MFM/l53iydkVDepk095kyVxnnfc0WpnhAxtQPfvCDePToUXz7298uQuX8/DxmZ2ej3+/H/fv34+TkpAoeJVTY1vn5eezu7kav14u5ublrNJKx6rm13ufaKV+ki1/jd32WYSoQx1RFGmduHDcpK6ezK1quSQrUrLi3nXVxjbzp/iiVGv/7PTVgWQObEXVPvd/TJFBvApZq9blcy/rYJHsyxV4DNt7XWtQwU5Lep4x+GRjLxjSOP73v2fP+bBN/8FmBG6b3ZUeT83RM3U/vrtaJZBFlRsRoNDkiSvSfG8kn4Z/ab1kKoNOoVtdtgMa4vmVyt6YnJ1l73o7P+W2BTq0Mh8Po9/vpsedN/eL/t9GHpnb43dcKIwnUY+JVYbEmw2rcmF2eMBUy6xP77XNHY4X6hOPNZIDraO7/cgNoXHG9OW4eM4PBn8lwjPZKyZj68ssv4+7du7G+vh6dTqec3sdXvahuGkTcv6x5Zvrx3Nxc9Hq9WFhYiHa7PXKSH+fLdTzbyvRJRi/XzcQirmOy+WiiJefdbQy2Mame4/Wsz96vN1nPExtSZ2dnMRwOR06Ec+JTQTMKxEE5oNQ1RgF4H9MnVEicbKFSwFDxNIFZtinjR4o0a7epcEJ0WotPcMZQtTpEW98PIaXPsWRjc/rwe9aWF0VMPMfUn/U2PWXMS22haWwnJyfx85//PO7duxfr6+vxzjvvFI/uyclJnJ+fx9ra2jXekPBYXFyMqampkgJIIby1tRV7e3uxurpanuHhDAJHPFiCbei/5kTCjv3nHJMGityoTzx0QhEyet20ltxQoYNAfXTwzYMr1G8qMo2BRiH5hNExCnsVPxL4TYEO26fidHlTA+yT1J8pQv4eUY/GeJu1ejJwkD1LReFKyuvLxu/ZAfrzder3qDRFjUmTbJy1Prqc4dry4sowA1FsK+OxrH+1++SMkvOCxpQMKQJFPxWLe1q13vmyeoE7OpEYjaIxRZ2Y0SUbu483m4Ps+qRl3Bq+zXrzujPAlumlmm733960CJDW5iHjyXH0z/g3A3ZZXbW2anX7dbbVlLJV0781fEQdUDNEaus2A7K+D3gcJvI+Z7KmxheUlTUZSf3fVFfTHPNZGVPHx8exv78fL168iKdPn8bGxkYsLS3F0tLSNRqoT3NzcyXtWDKKeEQYIyKi3++XvVKSVewL6yU+8SwxyUR91/Me9czo7vRwXq+tad7ruJJ8XOM//55h04wHJ5nnm5Qbpfap4yxuPEWMEtFThNRpj1rJUHDANEn6GQkuRiPjMM0rq8+vzc7Olpe+ZnnyTcLQ7/NxC7jW6nMhwnrJ5B4Rcqbzuii8mkBJNh7RNQPSGehx2jQJRv+dfVXk6M/+7M9idXU1fu/3fi9WV1fLYSDOLxIEy8vL8fjx43j06FHs7u7Gq1evrgmBra2t2N/fL+1qnhlti7j+fqepqakSim+1rrw5mh+CJAotF2riRU8lEI1koJDuoomfGMn3P9G7xGjW3NzcSJqSXubMe2Xws01P//M1xU31TCe8bRGP0nGgz/KwurD10gQ4a98n7VtWWGfNaaDihosrJY6/pnxc8biyyUCC93XS8XLOm9ZvDYg6gOR4sr650vdnmvrqxesX7ygqxXe6yPN7dnZWrul1H/T+ao3JQSa+lHOEHnJG17m30I2Ccf3W+PwZH3Otzqa22OYkgKKm8xwM1+p1XsrWQlNfm3jttsXnZVz9tTXhOjuTQ7w3k/v8nunZm9AmA5qU6RnQZL+y9h2wZmPO5rtGxxo/Na2BN5n/mrzkfE66FnwMfIZrXwdPbG1txRdffFH2SW1ubsbR0VH0+/0R+SKHqowtyZrj4+ORd4yqHe3/pMPH54l6SfhBfJ8dfqOx6X9TRMppkfFqlopJmum5LPCR3Vubi2xexj07bl1NUiY2pIbDy/05WURCk5eBHC5YgkYHFJpcAlhXqG48+G9ZBEp1S9FlC54CRX9iZob9b1q4wc+tf9KVAqwmQDxCkTF9U2qV16FnxoGkJuHJOtjOJAKxCZSxv+fn5/HixYv4/ve/H+vr6/H3/t7fi8FgEAcHBzE9PR1fffVVRFy9OXx1dTV+67d+K373d383Li4u4vvf/375TfMwHF6mc3S73bi4uCin3XAs4qPsBDW9hDniyrgdDocjClnX/DPzliOu3ueltSXBFnGVuz81NRULCwvX5t/XInlN0dThcFhOItN1AsCIKG1zXjTezDkyHF5FQ12RvmlxZc71Udtb4oAlkw+T9i8DqjUg1FRH05qmUnNlIaXmBqnL1hogcO/cuDIpyJnkeZ8r1uMpGvqtNh4HNFlmwm1KkzE1NTUVZ2dnxbDivkX1gdEopn9LZ3D9aWx8R0ztqOZxJQMJ40DqTequgd+a/K49W+tvk7wfpy94/TchbwRAxzkZx4H4TD82FQeetXqIa2ryif91HzMe5Jhj1GXcWLwuyhjpjkn0uF+/Sdv+jO+Pyp5xOVNrg3RtGks2ptr64LpUX8/Pz6Pf75cUv88//zymp6ej2+3G+fl5HB4elj1PcsrMzc3F8vJybG5uRqfTiVarFXt7e7Gzs3Nt35R0vfhY8yOcIflLPE2ZREetp0xS71LPOh1IC9G2Rr9JackUzojr+66a5injM//M+RqHg8eViQ2pubm5YvWKUL65PmIUnLPzTak/Dt4IDpzIPmAP1+p+AmO/n95u9sGV/NzcXMzPz8fJycm1Po8DaK3W6NHR3PDvzxEsOIgS7U5PT0eEIFNQfJFwPLpfC7p24EGtZGDN++3tjat/0rYpJL788sv4gz/4g1heXo6///f/fkREHB4exu7ubvytv/W3YmZmJtrtdqytrcX9+/djbm6u7J1i3yUkZmZm4uDgIPb29mJhYaH0PTtUQnSWYaK5JTjU/Mow0txzf4Xqk3DQM27E8buvAxVXAJwb3b+wsFCEsh/LzJQCpiRmiiBTTGpPnjRFxZSa+KZF7RMA0FHg61t08Dr033/LAFkmmyYB7jX55feI7j63XgdTjSgbvK5amoPL46ZxqM81MOa8mdEmU1bkRXdg1fpDuvD+2r6OWh1N8x5x5cX1FD+tcRlScr5wj4EMKJ6QJV6dnZ0tThYCAEajtGdKm8GdHk3ydVy56T1OP/alpqt5X6bz+dn1rLdd46usvAnIGVf0Hp4aP2suHWw26fMMdL7JWP2a4xz2wx3OfJ46ySNTrINzJrniR0r7HGZr0vuQ3dMkL70uXxPevq43FZedpM9NHDYu9/1axFVGy+vXr+P4+Dh2dnaKjjw+Po7Z2dk4ODiI09PToqsjLh20nU4n1tfXY3l5uRhjklMqrJ97ozLdwC0mdMp6cSOktn6b5po0kjz09tyI0u/kX/7uDv3sem2dsT43xHWtSeaNKxMbUnrnjqxYKSAfVLYg2NGaYnQhHnHJDAJoHKAfAU1A4hPsRPZUqtrzAsftdnvkOnNQvb8simjVwJ0LP7aR0ZMnsNWMJ69Pv/Ho34gYOUEwYyzWQyCRjSNrl/dkwr8m6FwBCfAoYvL06dP4b//tv8XXv/71ePbsWezv78d7771X0jB9fiOuv2ODdN7Z2Skv5/Uxav4FCHRNAIoKdXp6esRQyebHw/EUeOfn5yXVTumBUu6tVmvkWOZWq3XNWHEhMTV1lX7Io+4pzORNVx9EMxoupGOmsCOiGDg6BGTcawNuWhT5Iohxg550yEqmCLJ7uP5qvOpyKpNbTQqG4JJyh/fpnqa+utFUo8kkZRLQ3PRcEwDX+uHYmsCQ81imyJv66v2pySDx+NnZWZycnJR1q1cd6DQ/rb/Z2dnimJAMEKih55nrR3MokCNHpFIABW6c15qUuY+nBiInkc9NpQaedC2rK+tXbe1NogN47215e5Ki+ciwgdq/aaRXdWR6lfVT7rA9v3dcu1x7lAdaf3xezo2sj74eqcs8vT8zJDiGGjaq/VYD6jRcM+de5hjP2tS9Po5JI8RN/VXdNQNDp/h1u93iQD05OYm5ubnY29uLbrdbouDCF9qmsLGxUV7wq/r0n6l93CPlBkaGSZpwn5ea3OHvEVc4wWnK3/W864lx8o+8SvzFKFrE1esMtK59fetZXas5FG5SJjak9AKxo6OjkRdw8qx7DrK20MhwNQWu52REefHJ0J/qdfDsRg/BWK3PKvISCMjyJWhZ0fiUy5qNnX3yz1kZDoflmG+f9CavEhewPKU6blP1Kq2M75Viu7V6RSs3NDIgdJPiC0reFtHul7/8Zfyrf/WvSmrf6urqCIhkmxSwWb3b29uxvb09wg8cl+qIuDp9UbzPqKEWqtp3Ad1qtQrIarVaxUBqta4ilaenpzE3N1c8U61Wq5wo6HuiSCfvJw8EoXeH1/TiZ45BzxMA1gwCzrcAoU4mexvFlTsNKP+cRUnHgTxecwCSARoHqZMK3Iz/2T/xpxtTUngEdm6MOL+6vPU2Mzr49UyZTTLmJlk2qVJmO+608T7ys3/P+pDxhBQpI1M0pM7OzuLs7Ky8p2U4HD1oSYYU5SydMQI4EVevDGBESu000bE2V7V1OU72Ns1p1lbt/kn4fxyoremFJrDKa28Cerwoi4C6lDQgYKeHvxaRqvVtUl2Y4afaOpI8ZP1NAFpOP57eyzpcnujZ7LApxwEcv8vVbGz+THafA33XQbqH86exOJjPxsU6fI6a+DRbI033ymnT6/ViOByWQyik85XGL5wnnT8/Px/Ly8ulz24YyOFJXOpYjlhDc0haeWQ8i9g0OfecFj5nzi+1kv1ek+kst5VPGd67bZnYkIqIcpKIXljrwCaiGUxnSpaCyg0dFXr9fNGzHvfyZQzgi0yEzFJYCHR1Xcr07OzsGn30vLyZTTSoGSkETLqHXn7SsBbZ46KQ0IyIcqQmae2gPxM+ojkPu/Cx8Lr38zaFUQgZL+fn53F0dBS//vWvYzgclmNE1WeCGM4pUzMiYsSQ6Xa70e12Y2VlpRg7UjaikQAV9xRFxEjKHL0ivnmTSot9VJRK9c7Pz5eTedSXiCjtUwlwXhkdlsGv/vEagRuNTKbM8Wh8d1ho/XFt8uCNt3H8+ThB6zKC13hf9myt/kzpNwG6DAyMA6JZ3X6Nf6RlBmi5dt2oysZdu+ZKLyuZotJzrjA5J7W0kUx+uGNrnDJsms/a/RnY1To6Ozsr60qHTsjY0UFLilDxgBUdS0yHouqVo0LR3+zFvBntJxlT9uwkYMPvy9qige9zNSm9JwFONyk1/n4bhdjF+boGspr0uJ7LissMN9yanq/d69EYjoFGlOrleLMURTc2pPuo13x9TyJHJi1NwNjHQQxKne2gPpOz/O5zrmezout0cOq5TM7ou5wrcpYOh5fvxOR2AI4jImJhYSGWl5cLrSlHiCsiLlMFu91uHB0dlYMplEJcc7jXjJ4aDTI5UlsjTaWmXzmnw+FVVpB+m0T3uS7NZAd1ONfEbeXLjQwpKZN+v18ALo9j5P8mpejM688Q5DF65REGLhoCD6Z5tVqtksteK1nf3SvqKSkZ40ix6thtGn5NQpOFXmhudiaAHSeA+bvy+gUKRCspc9UpEOFz5UK0NncZTSdRMJngoeCXABHYkcDRqTenp6fXlEPmrdcYnU7dbjd2d3fLYQ5MwSNY4qZMFRk4Dg79hEsHiu4hZAqjAB352o8wb7Wu9mh5GJ8gb21tLc7Pz+Pg4KDMtbztXDPeZ7UxOzs74j0X/8ho0wZZ8cmbntjXVKi8IkbXo6+DJiVQuzYpcKoJ2ux59iNTQFQYXK8qmjN3HDl/cw1PogB9zLX+Z2OsKcAmg6A2N1R8GdjJDAWfq3EpPbXiQJDyhodI0HHGaK3oXjvASGtK9Wl9648p8jU+YckcbK4DbkuHbE5d/2X849cl2yYB1U6r2m9/GYXOrRqOqTkF9J/3O628jPs9K5m+b1onWZuMdHN/LuW2g1SPRnENkl5sNwO1PoYabiFNanxQA8m1eaoVb0999HE1laY1y3v0P3PcqA9OV+ngpaWlklHEiDYzibTv+/DwMA4ODmJtba0coqXf1Y7v13ecXuOrbI58fK6rsuK4zK85xlRhdNV51J0F/lstgyPry23KjQypiEsPdKfTGUnx46DGARopPxYOwgc8CUP7hOjYWl3v9/vXvE6s36NfqkeMoWvyJgpAcuJ0ksrCwsJIOzUBOIkAGg6HI2HaLILA47edJq3W6PHcmWdK9SkKUquLtJhEETTd0zR+77+KPLwyXF6/fh2Hh4dxcnISX/va166BIkaddE3jZCrE3t5evHz5Mu7du1eec48s9wK6cTQ7O1vSD0VjjlEGizx7Ald6L4Tq0HPz8/Mj+60E6JSOx1P+yMPqmxTj1NRUbG1tFUHKlA71Xf3SYSrOezxJU6mH6jePUp+amipr4k0Pm6jxk6+lJlDv328C8jO+5G81AJnVRznj9dTAAuVRNk7KRlcgTaW2xmpGXlYmaac2B01e0ZqCZh+za25Q1PqR6SQvWlOMLum6+Fs8T0dHVq/WoNauv5RX3wV0qPNq/c14kU7GpvHV9GhG13GfqUuydm5THDdk7ftaelPgw7qls92Qot7j2vfPnI9a/2r8yrlp4s2s3iYDLsu4YTS0tj/EASr3E6lNOojH9V3PkL5NeIV1Ud96xhD7SJ7MaOt4kn/s321L03hIO4+gyaHput6j2e12e+T9ktnen8HgcnvA8fFxHB8fx8nJSUlZVh+p932PHMdBenE8tVLjYZ9Px95ZPawv68Mk/WD/NQc3Gc9tyo0jUoPBoBgNnFR2LBOytcKFwtLkaWyaBDEoBUgTwOLkciFmXglGDjjpc3Nzsbi4OHIUtjOW10nGZvTK+8pUKwqWTPE2CWI+48DNBQvpxHbcc+fCtKmwj+PAWjYuGU8U8MorlvBh+NuBBw0KGgn7+/uxtbVVgA0NMIIrKkseGzocDovHTgY7I36iG9P+Iq6OHGcan74zbZZGXcRVmlAmHCKirEvnwdnZ2ej3+2X+eMIYFbzqFh3VJ/G+xivhHxFlP6DuedMyzjhwfp10HXgdk4LOTMDXSra+/Hr2fBbx5lxnUSnngXHysgZixpUmYzNrR2uHz49Tgl5HVk/Wn5uOJzMcydtMdVKUlgYQ7yEwY1+0jpiSXPvzA5uaxnpT5T9unvSZ/JF9rj3rbUleTVom4aXs+235uFb88KWIUeBOg0N98f6QFzgu7+tN6DrJGCfV+bVoG8E9ZTr1ER0HpEWTATEJP2f3O75xgzWTfzU6NbWVyfIaDhpXt6+nmkFCHuO+PJ6k6zTWnMghnhlQnrUjp4+fRMn5dh07Tq95qfFwZvRn+snXm1/PeCgzgIlB/bpjBK+TWPJNM2omNqTIIPKi9/v9kUnngGiJk3HJIL5QVAhEPXWKOehUsh6edoEmJiIYV7u1hcDx6jOZs9VqFY8BD3DQ7wS1NXqSwZ0O/J4xHvuXCfeIqygaBWI21uxZMZkKhTMZlXM6Dsy6ss7azkCy2he4Ec3Ef/T0ZLyhRc450bVerxe9Xq/sgaByUfqeTtJRkdGsPU2Zwc6+MYVH8yCjg31Ve+qjolU8vY6nBHKv1HB49f4zpROpXxcXF2U/h/aVubCWgUWDiDxDA0rOFO4r0+mDb1Jc0GlcLM4jLryzNX2Tdie9LzOSmu5tMnQI3CTzXP5xXXr9NYcU2/Z+NgGGzAPMusaN3Z9puua/Zf2rycVaqSno7DvlmMsVRnF5z7g2KBe5NyrbK+XR9FqfJxk7+ayJ3zJ+zNr2eRevZr81lXFroel56ptx9960tFqtkorvAIy87rw4bg6aihsFfCZbV03ypUYTrnfvL3k8w2lZKhTpUSsck99bo4n3y+VLRusMFDfVy2vECBojDZJxc+f1ZXPj/XM+0rqXXmaaPp3BlD2qp9ZXjssNLce6nFs3qmr4kPWoLq4Z1u/1NGHDJr71PtTo63xKuo/jH9LgL8WQUkNiQHm0xRiymlkyAmsg/N2fkbHiE+eT5qUGohzws34CF7blRsfU1NX7eJhmprdOez98/OxLdh8jUyo6GVAg3NPSVBf3twjsumJw742YiEdjZyVjfo4ri/7UlEFWrz6rzypMeeH99PJyvCo0WNiuBOZweJXmJrqcnZ3F0dFR3Llzp9BiamqqzK32YRHotlqtQnc6EGhkSFCyXV3XPkOOVYbPcHj5wuBW6yqtUKl9mVBV3RLILqgpdAaDQXl/hZ8GSDBO45RHr2c8KFAYcbNodFYyIZnxSwYOWBzsZfc5nzYp/to6yIR3puAnUfpUBm5AOa/UgERtDE4rjxjV6qmNMVNO/I38NgkNXNZm7fC7j+GmxefTZZpkhBtVvDerL7tG/ZP9ZTxV63P2+7jvWXGwOg6YZvxd44FxALpJ/4/rs9f/pqXVao3sQeVciUY1gF3Tm5mjIyuZHOM6937ynhoYrRkZrr+8j2qXEQt9r83nJO36GsjqIL29Pn/W26jJBy/knSwic5My7rkaLbL+Roy+zofyVlsGdPANncRNxlTtUBsaatTTxF7SPayP8oHGh+7J5tixbtM1/0+6EWvUaF2rL5sHv/62ZMqNIlJahCK2QBs3zTqQGkdYV9JORBfyThRONhc/21ZdWT9coNDgoKDU9U6nU0BoTallgoG/eyEjqj+MJBGUcKGxTgJ3tqM6M4DLxZsphKwdf5Z9yu5jHU0KvyaEM2BGz8twOLqPjPVwTEynYfRqOBzG0dFRbG9vx/vvvx+DwaCE0RVxlcF2enoas7Oz5X+r1Rox6PRf0RnRnGkSEZfeJr3wU31SxEt91zGoOuFHyp4pdJ6WQUNLv4v+BIQy9jySrGsRMQIu6GhgH7kGaFS+aWlSlrzmsqW2tiYF8OS1pjr92XFA2GWYrwf95sZNpqxqCiIzDGqfKUMc3Ph/9atJbtVkoPcnu4fP1rIEslKTS1mbNd2h/7yfHmECFwKRDAjyPyPHEVcH9tTSMcfVx9+aZHKt1NZSk/ydxMjJdPrbKDW9+Ztqi85QN25qmCVbgyquo2sYaNJ58bqz/tT65/fSgGBUSvW68zJbv+N4rrbmvThdvA2XWby/yWHn93u/SJ9JZE3TmGr4psYbqoMGAqPVfp/wdZbOl41Rz/BQGx4skj1Dh05tHvyP0Tzen9HC14PL40wW6zkGF5wnMt3NPvCZWpT1bZUbRaS0f0OeOp5uRJCWKbJap5m+wmsqnFz3yPMeCUJNrlvcBCqeX6vnaykLnICZmZm4e/dufPXVVynAyDxJFFIZ43t7Wgx8D5UDILXFwwPm5uZGjijV/h6euMN21SceHODAxqNt4wASx+2/NzGu+ujRDvGWp1f6eLyvbhCzTl0XUDo5OYlutztimDG62mq14uTkJAaDqz1TJycnxeBSTrL6pIMnxDM8GEKCjS/EVb0yzkg/GjTan6ex0SDU/cz11clAjFqpiC8YfXLQ7GtINGRqBGmo9128SXH+q/GZz6XLiabnxvVR90wCYsY9w/tqSob3UfC788fbqCnxWvvjSrY+a0rSr3ufMpnfBAJ1f9O65X1NyjsbRxM41TNM96FOcxCSRShcFsmhIt2iNc+ItY/Nx9DEf5MCgdrc12hQu+8moKO2TsbV4Xzuz7+pbKkVHvaT8V0WRbwNCJtk/E2fm65lUTAHjS5D5AgnsPeskGxde9s1uZGB9owWxDPeb+dz4rGsrSYwnT2rdrmux8nBWnE5mI3T++k85bhGjlZGk5wWEVf4J5vzzPlc65Pu8376nOhazQHv/FJb2xmNnV99Tv15yVjRjplmdGJ53yeNHE9SbmRIeaNM78uES6ZUM2bhgh0OhyMg04vfS8Mjm+is8B6NiwzG/pHg6vva2lp8+eWXI8rfi4N6CqtM8XMcPl722RlLjK9IhYNIp38GgrL/YkTSbBwwairkhey3GjDIQJn27yiCwpS0iOv755weEVcGBz09igAyyqY25ETQPiDxveo9Pz8vqZ+ucMgfTKuIuDp0Qv0XD/jeQKbOMeWOe6e0NhilYqSY/aWhrn6Jloy2Ma1Pf7xPETO96JnvPLtNobLLlFJt7fB5v1YTwP671+HPsU9N/We944Bsxvcux5qifLddl5kMyOrVGMRbrjwzsD8pyPc6snnOFH1TGxnwygrryHSRAxw/5Yz1sMiImp2dLe9xU/RZ1xS95hyzPw7CnL+9Xb9W4/dMH7DtNy1ZPU31Nq1l6mJ+/8soDvgyUDdJyWRQ1s4kddb0ZsT19LubzGVThDTjtax+l1fOmx5pbxozaZ+lvHk/s/pqcsHluOPSm/JYEx3GZQBl8o0y1h06w+FwxCjgQUQyovT+0tnZ2XLyKNdPFvUiLbM+1mSDyxr/rSZ7slKTZe7AymRBTWfUxlL7LevLTcrEhhQNFW1k18TKS88OZoBC/3kymOomQzhwcEOkCYSwfSc8GT1TYFSi/I396ff78fTp09InTTDb8IlnH1QywefAKWNWTzVk3/30xJq3gFEKzosLMXpN1SZpnHlJsvayzyyZ8Vejre5R+Jpg3wWuz7uKxjU1NRV3796NDz/8ML75zW+O9EftKKqjY78Hg0E5Clx00n4n0pIGE40c/XE9cb0olK91JiOOhprGINpzHtgPj1y1Wq1ot9vX2tG6U91MlXSlxjWo5y8uLsoRwm9qSKmIHuxXDbT7Z33PrvO3GhhvEqZcC6y7Bn6dL8a1wUiU7uMa4LpVcUU4ru/jxuy/+xofB6wy5eQyOzN4amCx1u8mBZ/Vk333+dI1X1M+Nh+j+q/IE/cg6rUBekGmv5cnG0NT/7L+1669aWkCRVl73rbr+nFteXvUyTXav2nJohRNxXk1o1FNFnnJ8EtWR1N7rvv4TJMhSjrW5ElWXOZlJQOs3mYmZ+gM1PqZZE03yYgspbbWv0nLOFxTk3FZe+6gYT+F6+S8nJmZKY4ZFRlN8/PzsbS0FEtLS9HpdGJhYaE4gpuibd43v16Tyz5e1sPrrsMmKZLBvuYzXqrJZ0bhfL+Y3zcOx44rExtSHuYVg/J0MKYJZQLCAa6u83NT2sQkgpv38R6PqDH6UFusbtjoPp3PLyBZ648r4owu2eImHTOL3GnSxFyeF0p68DQ7jleAQM87Ld9Umfl8Oth3Gqq/bmydnZ2VjZiZgGQdfhDKo0eP4oMPPoivfe1rsba2NnJIB3lYaX40UnVSnx9V7sZRFiJ3bz7XEI9B12f1gUfukxeYyunzRYNYh1eor9zHdXFxMUJHpvxRqUkhqXD96J63UVxRe8onSwYg+FtTqYGWpmtZ/5rqzgAQjeomIOMK1a/VZFa2jpoAW+1aplxcVvjYMvDQ1H4GQCeR737PbeYv648+M93HFbC3ybWn1L25ublyz/z8fCwsLBRDSh7jbNN/rXBOxxlMGc9lOnQSPm6iE783zcdt+IHruoYL3kaR7HId2XQ/8Q95Yhwvs/g8NgFzX/PjaOjfs354v/1zEybKeMf7NM4YczpkerKGd7w/WXGs2mRQ+Tq/6Zg5Ducj0oG4IuNvd4SrXuFrncg7NzcXCwsLRYbISbOwsBDr6+uxvr4eKysrZT+/8ANP+BU9anPSxLe8x/VvJrtJY9dhNb3nbXhxWrqcpgHl+8u87zSkbiILWSY2pDQYbqAdDAZlorIOZsLJCRlxPXyfgaOMwfw7ASaLM7ULf5/8JkXA37PFTqVKI8ULBYMvNu6d8VRGH2NN0ThjuCIWyOdJjFzIGe3VDj+PE+ZelxeGsLNCIOnA8uLioqSWNbWvMWtPU6vViq9//evxne98J1ZXVwsP6zdG4mRg8YhxLuJ2u10MEd8fyNPzJMSZl17jaV0/OzsboUNmMCrFTsaX+ife05+MP7Wdba5WnzM6qx3VL+OSaZS+Hm5TxgnZcWWc3Jj0uZuUcc/WwIWPpwksZeMfB3aydlz+TULTWj1Z37OxZG25/M90wLh2J+17jSdr9KNso17LrrENeYVPT09jYWGhjGt+fr54iGlMEdhlfajpzowOTYaLz7lfcwDj9dZ0resul1+169lYve5JfntbRXvWPMujVprWXAYCm+hQWzfjDISmvjmeqM1r09iyfvOeSTCS81g2lppRmF2rGSi1+2vP1ZzDtcKxjpufrN6Mp/17LSoyGFy9SFkYRoaU9HC73Y52ux2dTidWV1eLEaWTfokLVE8Nc2cyumY4Zs+7oeiyU/dkhm5NpmR9JM3c6eXXMyNKtK3x+E3LjQypDMTJKnZQXRMWXOi+kLjXw9tsmsTMYGsSVgTCVCqTLA5OsveN0S15EGqMkdFzOLzyPkwCHJ0m2WIdt98kY+4mL2nNcLtJcRCl/vB/Nj6/xsgd+0b+ZF0Kh6+srMSDBw9idXW1RKpqbQ2HV0dRa9yMBk1NTZXj0WV0yQDKgBJ5jvVQGOgUPz92nWl9WX+y8HXElUE2HA5H9tI5DfWbj59RQYJeGus3SeFpKjVFVQN7k9R1mzKuLedHl0U1AZ0Bh5rhM4kxlbVRq7fpetZ+rZ0m4N30DNvP7nOQlPVpUsXnvHgT/uHa0neuT++TOyXa7XZxMtKQarfbsbCwMJLe5xHlGn0mAdD+OaNjdu8kvNdUh0fZHfxQ3nlbTXLX28tk29so3E8yDijX+uD6/G3IqcxoyGhUkyWkfVNb4wCyt+Hjz3CIrxVii+zZSWSOj3ESeVOLNk4ynknlGb9n+Iolw7aklzvOaQAIw8zPzxeH6Pz8fCwuLhb5sry8PGJEsW7vDw2qjE+cbrU5Il71te9/vF/F153TgzSnkaTvWbtsn3Pnf5Trk2DuWrnRYRO+18PThLzzGRDgoMjsTGXTvZlAVnHjoAkIqB5nUAqaJgGTCS+On/dmCymruyllrkbHTMjrPtbHSEgTaKktsKac8SZDqiZkMoU5bpHWeCebxwz8s4h3ZTS9++67sbGxUYQNBb6+S7EqUsW9Oopsyehh2l3E9Xf9OIBQiJ3pcHpG13jYxGAwGBGMHONgMChROUaPdB+jjfR6cc9T9vqCbP2Rzoyc8lCS2wqirLhRks1txuNaDzftC3nL2216xu9pUsBeJjWObjqWcX2uKfqmZ/xzTbZlz9ykn5ki5v1N89Kkc8YZfjW9kQEB1wOsW84wrdn5+fniNdZmcI+A1Hi79r1J6bt8yMZdo6n/z4BvE1Cp0dGvN9Ge94/Tk2+jSO5mjs+I6zp9Uifi21qzHqlwetZkoJ51mZbhKMrLzDBuGpM7F/wvwy01LOdtZPc5bhiHIWqlSQ6Om7tM9t0UQ3o/aGS57o24ClzMzc0VvNBut2Npaak4aRYXF8veKG4NYX803zVH76R0bNJbkzxTk1HiRTciPTuAhlUmk2pjdJor4ufbP25SbvxkbVIyBs8WsdfhdWdC3tuuMTA97V5Plurm9U3KBBw3md/vcUvd6VZri0LNGc9pTKZhWy7AaoCF944TVP5sFopmaVL27HNEvh9jkoXq/eCC03f9TU9Px507d+Kjjz6KlZWVMofc/yPa62AHGgw8ZEVtnJ2djaTDZR5relCotPW8fp+fn4+I6wcsCFBkoIjGU0R++qTeVzUcXp0KKZDHk3yyF2qLbpmgciE3TnFNUlR/k3c444EMZGbAISs1RdzUR+/XOOVzU4NjEnmZ3Xub39VGJqvH3XuT53x9NMnAmvxz2dZUanIvGwvby4yFJqAlXvV0bjlEtH+B0aibrpVJgFvWx3Frh8V1ZK3OrG+131Rv036McfVn5W3IGrbpWCZrowmbZHVmvNskk5rmq9ZG03XJ0Vr9+s3xRsb/tTZqNOJzTTgse87lSGaAZdgm66vLAL83cyTrvgx7ZTTIxpP1u8br6gcPgOK7o6jPFZHSoTadTqcYUopOaf8UHbXqA/EH92hm85b106+58afP2Xofp89VVOe41Lusba+/hmn9z3H8TcuNUvt8sfHzuBDqJMrSoyh8tiZ8aous1teMGbL6aguLDNJkSGVjrgkk3ZcdnZ3RksKGgokpVwSjtTlxmjCSQRpm8+IGQwZaaoKIhk02V5MCOad1Nrdqc2ZmJr72ta/Ft771rfjWt75VjvhWP7QBXPXMzMxEv98vRgQNGfK8F84hFYPoxSju6enpSHRKhlq2wGWs8ThyzjXH7/uc/KQ/CWkqEtWlOWEInb+rX3zvF42xN03vy9ad2uZ33l8zqGprvVYHaXgTEF4r4+RV1sdxMiPj+5pDw2VSDRQ1FV/b3vY4udKkCClP3xYwrsmi2r3+P5MjTfQjOOFxwxGjhpQfMlHrQ2YssV+eCp3d2zTeJkB3k+Ly3FOWsvGMWy+8n/VkoPk3USZZr7qW8YH3s0bXpjGMm5sajmoyTG7y3DgeyvqRyYas7UzWTdqvbAxcO01RQgfT1K3UcbeRj+wX/7serIF+fZcO1SnBfH+UMIr2RnU6nRgOhyVleGlpqUS6FZnSNoYM3/H9Shk+JG2dzsQ0mWxskp/67sbbJLYB28vazXRNTf9nz91m3lkmNqRcYTvTOLPquiuprF4V3VMzUHzCM/BTU3b+OQuX15iJ1wmIxeCejpiV2m8uDLJ+uzLRNTIiPWlN7fjnLLWsprj0HFPEnD6TFu61aWL4WskWM/tF2rRarXjw4EE8fvw4Hj16FMvLy0VQtVqtEp1h5EVjIkBqtVrl3Wnz8/PlumjHCBbT/7g+BoPLgx8U8VL6oI5TV4qsjCumzM7PzxdDiOtrOBxeOwyDYI0vGtXz/X7/Wiqt7mPan1Ib+TxpL6OtFtW6abkpL02yZifhqSbAwbYygdwErJqAyiR8Pwk4brrmCsc/jysZHScBw02KrgbCMtmW9XdSMO7/m/qZjTUDBD4OrUN6eLV2JAd00hbT+bLxk5f4n3puHF/78zW6+Fy4jsn0dtMzTbR1Y7mmtzmu2+iU2xbJTpba2s1opOL9pnymPmF9eo51eJ1O91q/JgHDb6PcVEay/Wxus/XQdI/X539NONPx1tsC0xlmugkeox7nSdjCmK1Wq6QILy0txfT0dDGqdDofnTY8WIoYMSKuvaiW6zPjI+83acfijmYfK3GaX8+wpj/nc0Y84n/Cgn5iX2Y013TTTcqtIlJNwrMGHMYtIL/WBEwyRe2f2TbBYrbQnQEyhvJ6GaVpUozZWLMxZKmHWd3+rEBzZuXXQAoL39XA57LPusffjl0rTYKE/WU7NxFmGfAiLdXOcDiMhw8fxqNHj2J+fr6cMsn3Nalov1P2niZ5k09PT0faokHFY9TVR6YNinYK0w+Hw/LeGQk/9UdGlubYj+xX/ZwjnqpJ2jPMzxTF4fDqMI1Wq1WOSSVd/fRB8b+ElAQ3UxLfpLhSrAFDv/9NwZevd197NWDqv2dK1a/fBOQ0jX3c701tZYoqo6H/nskmtpc9n7V7k3uzzz5fTdFQl4WZor4J//AZRbS1BqjE+d4X3xdVG/ckYDLjNx9fBmqy+jLZ6/op60NT32rtRFx/4X1W2G/XyePA3k3KcHiVXl3rj+uxSdbjJNczfNHEF039kzGvZ1xHsA3WWdsjU9PFXmf2bIZvxtEsG1tt/jkWH5c/X1v3WUpfk+yr/e79zJ51evpaZxsO9rl3Tzih3W6X6JQ+S7Z4RgmxmmORzKlDmumzO7w948XHTeNMY6rpzgxziya17QJZH2syoYmXs/puW278Hil52DPBr8HTK6fiaWOq01PlVHgyWU0gsF8kfKYE/NrU1FRhxuHw8l092u/StPA5DpVMYNDb7yW7nwCUfa4JJAoDHrvNNK4akKsJy4zWLK1WayRv9zbKJCJfaJOUJrr7NeeVwWAwcpw4jQO9K2pmZibOzs5iOByWNBwfLyM2fCeDhEun07nGQzTIIqJEs2ZmZuL09LSclhcRIwdgDIfDWFhYKF4qzjf5iylFjD6xz7pfgI4v1FbfWq1WiYSRhqyP/C/DcmFhoQhc0vg2ZZI5rpUm3vV7mtrP7quByqxNV1gux5rqdHmVybzaM2y/iW5Nz9aeq/W/CUCPA+Y+Rn+Wf5kydnk1Tn7dtowD+4paM5Ir5wfThglemoCFj63GP5MA8Vr9GfgZJ4cz8NN0bzYnNSBeA7yZDnsbwIfFs0yaQKL6WAPYTbzC4uvBdX72rPO6+iweYzoX07d1jXTl2D1K5vyZzZ+PdRz/NdGsdr3WNsdUiypNIudr+6PGPV+TezVe8XYyXmi1WiM4jte5jjjfNKDUjjJtIi6dwopssb6Mz2vz7VitSb7wNxr1Gd/pGY4rkwX6TxxDOcpMKjmxGHETznL5y3qIg25bbnX8ecSVVTrpMyQYwaUIxKLBKmLg7dQsVT6bMS4FxPT0dKytrcXXv/71mJqailevXsWLFy/i+Pj42oSzPWdKfSYwr4GDJtpkizkbJ5/THHhEKlM2mYL2dDDOD58jTXnE9rjxsUyibGrPjrsvU7g06Dnv7DsjMbpHe6dI24WFhfJsRJQTcbT4ZIhFXBk1jCydnp6WyFKrdZlKGBHlcAl6eBYWFuLs7KykBjLd0I0evbWcoENtq++KYlHgEvjJqNL4FSkTLRU107y1Wq0StWNd+r3T6Uw0d+PmtPY9Ky5jxt132+d5r/5nURCmTIhO2Vp2MMXSpID8vnG/ZXX4Gs8AVKZcJ5kPBzaTghW/t0YbPTtJnRn4a5JNfMZlvH92wKG1KsVNpe6p19QT/r/Wp0lo4DolqzsrLidrbU5KQ7+e/Z6ldtcAqqcgvc3i7Wd9dkBMo9N5dhI5MkkfMv6mc0Z00QEE0lMXFxfFMaw+N+0Dyvqb8UKNNj4G3pPxlbfZhCe8b05njc23G2T9avqtJt8cp3mhEZDhOY4tWzP68/Rgfycl/8tAcCNK7Qon8MAq6WnixabTdl0Gjptvv4f73xllm+R58o3LA5cFmXFFI0prg1kBbmTJCXGbdaty49S+TNm5AnDGdyCb1e0gWHX5727d87nMCFDbii4QhM7NzcXp6WlERHl3j9Ke9DyLAx+m1fneHIFMF1yZUPGc0XFF92cvT9VCGqcEqQyycXKfDQ3ESdP6vIgeql/XtDAmjUrxefaJSjnbv6UokIyPLKWBUSFFpuT9mZqain6/H4PBoNR1dnZWTsRTtIfzztxcLfBW6yrqw37qhbnkHdUtIcF9U4PBIJaXlyMiot/vFx6XV0b1K/IaESN1UVnPz89Hq9UqETL2QfXod/K96pufny/HwMsr+iZlHOhnyYT4TQRiBtxq92SAbxJh70q2VrLfKOdcuTSBj9r1SYwPXq8B5pqideXogLOpzQxwNNFrEmPjtoVgg04HfabzgCBIa06y01NomniziSbZvZleGQcMa/1o0nWT9LWm47Ix1dZqzaBxnqrVe9vCg3fGlQz7eBk35mzN1NYa7xMvSQ7rOYJGPUfnGPU9x5mtnxro5/hrhlFtnmrPMMvJcSTr8QhKZqjUDKBsvbnhwevjPnvf9EesRHrQkMiKR0WcL7L5EDaQYTQYDIqjNuIqK0VjpD5SXymjmDHjmWPELRw35yvjd48wZWvc+dDr8aAL++RZMpkjS2vCjagsMiU8c9tyo8Mm3BjyzWzjiJwJf79O4JYxFEutbRZtwNvY2IhutxvdbreA1pmZmTg6OoqTk5ORt81PAtyy64oGNKVv6D79xs1wfq/a9z5p8Zyfn5dDCobDYTWCV1PQTGlg//iZ/Zg0GtXUNj0VqteFcE0oZ3U3/fE+GRTtdrt47BgW5mI9OzuLVqtVoktKXWu1WuVgiIgoRxmfnJwUgPX69eviAaFHUHQW2BKfyAiZnp6OxcXFwkM6xrTb7Za61L6EhAQHI1OkMQW81oGMQ87N3NxcSQnQZxlLeqbf75f1roiV5okRsJsYxFnhumji5Ul5sAlw1UCl35MpAnrBeF28ljkzyPOMUHEt1oyNJkDtdev6OLmZXc/qHwfwfK2OA2Nsz8FOBh6ya+PmqzaOGnjSMzSe5Knkn9Yd5STXW5YmVRv7JDRqKjXjh//dYaTnXPfWaMb/TrdJ1pbX39Rupuek/1yeN7V9kyLZJn1wU/nF8XhkImuLz0mPE9gTyHohgJbcjYgRgMg+OZB3/m9qz9c9n80wXabH/fdMt3u0IusH15jTXfqySUZ5H7Ix1WRiU/H1lWEaj0iRPwjiBfhlHLGfjrvccGZ2mOshZqHov2jp6019yurIosHUhU5bzpc7jWngOT2zenVNfecYGMxw7OrRKo9c+TUf303KjY7YInNwwGQQGkHOyCxOfC401u3PM8rj92cKcmlpKf7aX/trcXp6Gp9//nkMBoPodrsFAPd6vXIamoMDts1cYl8gYhSegEdGYlSCY9IzmfBuSl+MiBIx8YWcCfNMuGUgkoIhA4m3jUaxEGiw7iZgkQlA9XdmZqZEVJwXXBhRILVaV6f1iW4yts7OzuL09LQYEvPz83F2dhbLy8txfHwcU1NT0ev1Sl3cZxcRJTIj4/z169elPqX8OI+0Wq04PT2N09PTWF5ejl6vF71eb0RYzMzMRLvdLgaL0lDlHefajIgilBVt1X/RSfeqnuFwOOJV15ikwKW8qfRl0ItOR0dHt2WNkZKBt+x33pOtF//NZQ7vy5SRt+drheuP6bH8XwMxGVCZtNSAj4OVWruZ7OXYa32pgTwvTWk2PndNIC4DSF5qCjkrTXzDqJM2cis1RH8CO3SW1dKmMrpzbDXQPY6fMyOkBmoyWZi1RfqwfT6f8Uut1NZOdp3FgVwNhL6tIhk3zkFILJMBUN7nn50PanSk0cCIpp5V+p5OfOVBRNKrwhR+UpkbbOP67v2nHMvSGm9CD3+OOjmrp9Yvzkd2T/bHUluzNR7L5srrdXq7vNW9jC4KK+hdUBnWJE4UTqEuZ7YJDahMrvMz685krtPH8TjngvdyDDWem0Ruk//dEGIf2Vfne/IXeYaGmePSm5YbHTbBTkeMKm8HFrWB6j4vtAh9InVd37OFzMiCnpGlf3R0FK9fv46FhYU4OjoaCYmqbnk22AcHJrX0PX/pmeiVCV6OnR4wtkMlXQM+fIks97m4ZV6by8yAU72kj8agZ5oAlrfp7TMalYElLsRa30U/RYoePXoUS0tLaSRRfb+4uCgpaycnJ9fmnWM8Pj4ufKPvR0dHMTs7WyKXOjpcNPfUkH6/X6JGenme6le0R2M9OjoqtJ2eno5+vx/r6+txeHg40i/3mvgeRdJVJ4UdHR3FcHhpaG1vb8dgMIhOpxNnZ2extLRUxn92dlaiae12u6QKMKWJyp2RKdLgptHKrJAvNO6aguUzmczJFGemBPiZisL5g4VrT3V7upcrGfajJvSztetrpgaw/XvtWk1hNj1ba6sm6ylfs/qbgL0DviaZ4zrI+5C1n41RvE5QI0NKRwwLxBK41sbH7w6MvG8ZoKjNlcvIjMdrY/b2st/Hyfem0sSfXk92T8b7DggdsL2NUgOL/rlWakBddXs7Pq8+FnrceXARo6QRoxlBlCG19hzgR1w5fdVuJmt9HXHd1cBqRpcmx5LXnY0ho6v/nhkr3o8mWvmc1fjUgTjr8Cib06LVujKWZ2dnyzHmy8vL0W63i+4VjWUY+wt61aZ0L1/mq/3R5BGXl97vSeR6Jp8zrEq6Exs5HWv6zu/zfnp/iD9Ef72jUwev1PhukjU+rtw4ta8GUDJrkQurJkBJeP0ur7hPFheI17+wsBD37t2Lfr8fe3t7Jd9xdnY2nj17FlNTU3F8fBztdruE8peXl0c283PSfZwSAGJYnaS2sLAwktoUkZ+T7+MdDq9O1mmijyvgiNET53gP004cWGjh+EL0MWZKlmPnmGqKw9tVH6kU+awLdy++2GZmZmJxcTG+/e1vx+/93u9Ft9uNn/3sZ9cENudyMLg8tY8nzLFOCaGIy8MhFGXRvA6Hw/L+KM2/5k9Rp+np6ZFT6waDQZycnIzwOKNTw+GwpAJqXAsLC7G7uxvD4aWRtbKyEoPBoBgtMnZUdFw591OJ9jKSNK6FhYXo9Xoj0SWNr9W6TGlUeh/nU2Nwo6Lf7xfvqMZbi0RMWlzY61pWMj6btH7WmwEhXnOA4UqRCtrb0nV/0TLfOeZC/ibjqtGmpnAcdHg7lF3jQFUTeMzG4XKwBphqIKs2Tho1mb6ogTACIr7vSUaUjhbmZ+bbZ3K7BvBq/SctHDD4c1kdTfWyX+PartXdpLd5vVYH28j63HS9Vt+beo+9+KleWfZIBvia5oP9r4HPbG7Ii15P7eASgkde42f+Zdk0NeCcyQjvtxtolJlOO+IAGokcvwrvd9DPtjMe9XZr/FuTM7V143ohM0Zqck7PaB4VYVxaWorNzc148OBBLC0txdHR0ch7Kn2OvX7qEc1nLTJG3ZoZKFm/eX8WwarJPv3WJEdqKXUZ37IutkXDKSJGvgszCSeRLjS03mR/VMQNDClfcC7ouXDopdUfmc6VNYnn76GZBCDdu3cvvvOd78Tp6Wl8/PHHI3s1BGDk7V9cXCwe+k6nE++9916srq7G7u5ufPrpp+VAAY1Rzws00/jRKTlOE32mkSYaiB7+5upaPU6nVuvKS0WhQyPFgQzp7nPgdHaAyIXaNC9NoIWGiz+TRfP4nAvHmZmZWF9fj7//9/9+/N2/+3djbW0ter3eCM20ULxN5gY7AJaAE13lqfMoUq/XK/Ovg0nEF4x0KcI0HA5HDpJglEWeo7m5uZI+2G63Rwwa0b7b7ZYo2enp6chmU59jGY0ag/bRzc/PF2NaRpD4u9PpFKNNY5VwVpEgkqIXD8uQZITmTcok4FMl4xm/Xqurdq/fU7vPFSnbYsSQRpMrxUzRTTpefpccyQxZ79s4QO6OoFqpARiXQ97XGrAe1y8vGa1cdnnhGtUaFj8vLCyMvOSy0+mU39rtdnQ6nQJwqOP0X3OZpZ408WNGq+wzdanG0sTDToNMNjtNmu6r/d70jIr31UFd1g7v9XUzCS6YtNSAYdbGuHHqWg04EoTT8aoxKUIhQ0ogUPPu0dBMllDe8H8WkWI/pfe8cD35HHIeSUPpOcd+vjY5lzUg722yZM7ZTGZzzOMct7Xic+fGSCbP+SxT+ebn52NxcTFWVlZic3Mz7t+/H3fv3i1zwNODZQwQ8LvzzY2nzKlXw2acE/IFdQufYRtOA5+nmhzmb1kdtT5731VPVh+xHuvL5Ilwzk15QuXGEakaU7tQdmWi33yinID+O4WNK5SIq43yn376aVmwq6ursbe3V4h0586dkdxT5cF//vnn8fnnn48o1IgYsWzFwP5iM276ZH8d0Kt4Sp/qbCpuhEhI0rhjFEJ9J13dwzMJo3DuFEVRm5MqW35WuzxdT9drp6X4fa3W5Z6mhw8fxj//5/88fuu3fqvUyfuyiFurdXlIw/HxcekDgRCNJtWnaI74QjnpEVGMctU1HA5HjivnISAREZ1OpxhS4gulUJ6fn5colughXtd+KvHAxcVFScu7uLgo+7QoODk2/T88PCwpfUpJjIjY398vqXzidRmAqkvtK1WAwlUpk6qPUb23UVzm8Dr/+/WIZo+q826tzzWBrFJzDvl/rtlMiGfKg2u4qY/6jWmFqpvj5jWngQObJqCcZRz4HDUp0to4mgyHWl1Zu7Xf/B7pABlK8/PzxXhaWlqKxcXF6HQ6MTU1NbJPSlEp9sXH57yQ8SD76dcdoDrAcXo4vb1PWXvZ/dl4+JzT2K9nfMZCByXplc1hjbYO+N5G0V5ZrncH5YykqGR8PWnfvJ2sXuoDYRfX9ZLZ/X6/6BgCb0/tqvFbU8nmgE5hn0PHgrwvi+B4NIt6kjo9W3NNffd+qU/jeN2fd17gNY0xk5vEUJQjCwsLsby8HGtra3H37t148OBB3L9/P1ZXV6PX68XR0dHIXJ6ensbZ2VlxsBJDMBrF04DHOdR8ntwoq8kV/dYUDOH9GT2aaJzNjfMtjXTqUfbN/1zueb1vKk9u/EJeDZ7EzhjMCeWdZ71ZlCQTMFNTU3Hv3r2Yn5+P/f396Ha7hYFOTk5GXqY6NzdX9ploz8q3vvWt2N3djd3d3ZGXmKpPiipIEHnomWHZhYWFa3utOMZM4Ygxa0aUC5LMAHEmPj09LVECX/R8ttYfL9kc1g7EuEnxhalr/z/i3utJsiPJ7vabWTp1lm4FdDdUAxgAMxi5a2NLru3yhbT9R8kX0vhAabQ10rifzc7sYBTEQDWAbnRVl8hKXTLzeyj7RZ30jriZ1d27DLO0VPfGDelxjruHx7TEYrKysmJvvPGG/fVf/7W98sortrCwYP1+387Pz4Orm2oXeJYuPoAnv9gg5FTbR/9gcUFYsRgsLCyEKJALCwtWLpfDdRA32hLLE2MFt1Be3W53wpJDGcjj+Pg4BJlQQqYaP9pS9zKxCBUKl6HbiS6obU9ex8fH4ZmQPOYTSgglvDxXfflftqaYOuR91+QX9ZSAnAbIZ3megkbeFbRo3uqrnrL2zCrIY2WNub/oYkrfaT+lnu3bxvcpecfcHXWOP+/CFCMhs8oJ/x67V2U4BGpxcTFYnMrlcnjhBowWmT1T3K9aTGRwbFFPyTztS/85rw1jAD72X+y+2PobA4CpeaPPmAWMzyITYm0Sq1eK5L9oirV1TEbEwCYpT0nhiSJ7YxQ/gD2YxzHw7surINrsKqCSf8XKRd46H1Kkzs9pr4hU5a0fx/zn8SO/eWLjZZOCZl+PaUA4Nd6nWaR8f8Xy9Ir91HxXxT3ypVqt2tramq2trdnGxoatrq7a2tpaMAroXmR/qG4sqbxXL5FUG6Ty8GsUv9MO2n+xSKXaNnqvxxqzpFn6W5/n1zvFgupRlHrWi8qVa0Xt86yU37xgpWAK9vhfiYeySr3fPw/Q9vbbb1u1WrVHjx6F85+UnaulBq3hd999Z7dv37bV1VXr9/v2+eefTzxTyZMyfJJGclKrFYBW60ryE5/kQRW/qYnUD2IvoKijXqOWkhhp8m1Lv2Dl0L4xswmrBFqQvJTqP803Va5UUiHfbDbtgw8+sL/8y7+0crls/X4/nOGkpIWyxAQv17EfiXZWwqkWy0LhMjIfFiY2+UI02BsEAMuyLITPPzk5sSzLwrPUHTTLLg+thayMRiMrlUo2HA7DM7rdro3H4+BHjSUKbfhoNAoaKm1/DZ+q7oMq+DRy38nJScgfd0H+032AjInx+CrUOm1IWyETXsY5Ut4aOW2c+M/at7HkBbyXYTGiEgMy+p15qQJdZVNsgUotcgqeYkBKy65AyIMhXkqM9X4/B2L10nctX0pu+zbNW6Biz8q75rrJA1oFNZAodeUrlUpWLpetUqkENz7u1Y3+yE7kogcZMUCaKlveuPJ5xNaaaetnqg9jIEf/07wUBHugnFeHGIBKrYu+3P77dSwJ1015a6bv21jy12m7qCxTTwHGU5ZdWVxSfaHYhuegGNP1mf5OKXV8WVkXVHakFLyeHKhCzrtGpoB0Hjn1faG4IeZCnZKdmndMLk2bY3kEVv9X3ObxsG8nou3W6/VAmjY2NqzZbFqtVrNKpWKlUmniftrUbxlIrQkxYqG/x3BlrC25NzZO/G+xLRKMB33X9vWEMNXOqb6Jrfd592m9UrLpZciSaxEpLUCeMNXGAdiaPav9ojNSDJjPbKb/+uuvQ3Qx/Q+AXCqV7OzsLAzKVqtl8/Pz9uTJE9vd3Z3Q+hM6VK1PWiYmgZr9lTxpSoEc6kyeWCxU0On9fnB78zn1jS2ueohwrI9UCMcWhtiABsBr26T6KJW8QNPyxBYOnaQLCwv25ptv2i9/+Uu7e/duONMIa2K9XrdutzuhHfd582yslKqNRghynZrLKQcEaHl52Xq9XiBQECDd56bnQakgU4A7Go0mzjHjBHraaTgchvIqwcctD6LmybdqJwmSkWVZCEIBMMTylGVZ2PsxGAyeCTCBrz7X4eoIuRqPx7awsGDD4TAAdQjiiyYdq7E0i9DVfGj3PKHN/PYkzi8WfoyphtMrSXiu117mlT21UMTahPIhp2Kb0VXG8Z/Ku5h2dhoYRuGg3gRq+Va5oLJmmrXJL/ixdkgB8llALlYACBTufKVSKZAo3PoILqGuvt5qrcCVdokBnZgM9PWPydK8tSb2u39Oykqi3/PGY4xo+zJPWwPywE7q+X498HPuZYEfUgyopsoaK4NiHPLRpPOGd1VwKdBTWc61yH/m8Xg8DkdlgGOmyUtPEHS/cGy/T6xfuQ8lhJafpDhE3/nslch+rdYx7BXtqZQah15G81sMh8by822ibaNy05eRdZq2WlpaslqtZtvb23br1i3b2NiwtbU1K5VKwV14aWlp4hBlXc91fCJjIWh8xkKuzza72uqh9fbfvSGC56WslL59dXyn5mdsTYm1e15fx3Cs4sW85Psztq7PgmdT6Vp7pPSBMeHjJ4UHMTR0zE/WP8eDCD0LR59xfn5ux8fHVqlUbHV11e7cuWNzc3P293//92b27N4kTKWAVxILpEbwQVBoWVPC1gtL7TDe/+qv/soajYZ99NFH9sknn1i3231mcGtSoKJl4LMmJoD2TUyTw/9MQG/V8G0+y2KcagtfL9oyRfp4zc3NWblctvfee89+/vOf240bN0I+kCnyQbMXW+T02YDHZrNpWZZZr9cLhIS2Aoxythj+6WiU9vf3g8WFcajnQ+GCd3x8PGHF07qhCCiVSsG33ezyjBAi9iDYIFqUazy+jOTHONW5wiJF+XFFYi8XhGxpaSksvlh1sehh7WLBxuJEtEK1AI5Go4nw6rQ31tEXSX7xjYHE6+Tl780DlXqtLsT85rWv/jlKrPMWFpKXJ7Hv+u5/YyzogY4avZP+1P1+ADDmhN9HoW2SZyH3Lki+Pb1Mi10b66dZwLJvr1TySjE28mNJVgKlpEoDs3hg4dc6bZtZQb4vc0qua96x/2Pfp4GK1Hrj5XnqGj8/UnXRazTfaWnaOPHPeNGU97zY82PjM7bm+PIyjphznpQrUPZWT2QL687p6elEJDK9NyZLYr95N0Itt8d3zKHFxUWrVCpWqVSsUCiE40EGg4EdHx/bycnJhLI4pajhGbHftUypsaxrQmoseFevFN6MjXsF56ny++cqZsSbhCBOa2tr9sorr9grr7xiq6urYf+lliGmdPPtr+ND9zLHXl4m+zZQ3KCkjf889kzNj5TcTsky7f/YGPVtrONC546/Pjb3tF1j5Ff/f5FAWdciUt5cqA/Wyuo1scaHIPjgA5qPflc3KJK6tAwGA/v++++t3+/bzs6OtdvtCXcaBZIe4AKC1VxN/jpZPSGhM71Q0MFvdkUebt26ZW+//bZtbm7a/fv37Y9//KP9wz/8g3399dcTG9EpV4yM5C3StJOW3fdfrJ9ii6ACsFhKLeo+j1TePsCEkoy5uTm7e/eu/eIXv7DXX3897H+CdGhwDQC97lXwSftxYWEh7I9T0gapgFCopuf09NSazWYo28XFhVUqlbCYaYRIxrVqh5ik6jZH2bESZNmlQuDg4GAibL9aEvRAXcicjgt1WUSQUmfv+gk5zLIsLMgLCwvP9BvWMgidWlJ5DocPz6o9nDWRV2qcxq6PXecXpVj5GO9+jps969YEoPH5eODg85kGJv2CkiIK+hkriRIEXroHFGUThAvSn2XZhB9+3jyK1UPHAW3g5bmXYzGLU56y5kUSc5E5M80KhSUKLbFaYf3al9ePPvl7PJBL1Ts2n/xY9NfH8koRm1Teqd+m/e/LEPsv9vusBMs/42Wl1Lzz10y7X797wMrvnljofSo/WAcIkuQVvzFrhZKPlNzQtVbJl9mz5yvpPRzNUa1WbX193TY3N8NxGoeHh9ZqtazT6QQFpSd6Kfzix6bKCAX1nmTyu/eW0bxi2Ez7J9Z3er+f81pe37aKRzUqX6PRsNXVVdve3rZXXnnFbty4YaVSyYrFop2cnARFKuuHV0Rq3r6/fBvGCLL/T9siJe89LuY75fCeB57oUB/Fz9q/qTXYl9ff48lVTI7GCJmOa++9o6T/X4RImeUHg0gtCFRM96BwnQcsmg+JDqdB0IqUSiU7OTmxg4ODYKXo9/shAIESqFTEOUBIbPFPkYA8QeuFptmVtuCNN96wpaUlG41GtrS0ZO+++65tbGzYH/7wB/voo4/CgamxZyp588/0bR7TAMXqppMyRq4AzrG6zpr8gPZCUIEyZvAf//jH9rOf/cxu3rwZzujCZY6xQNnQLCN8/OLlBacH5ggKf/4SrguQEELic54MFh+uQ+MPIWNCQszG43FwyUAozc/Ph8WGc5rY2wehxxJFubgPAQAZgrQhbBnTaAwRErgLQmTRiuFOCGkbjUYTkQQ1/C5jjHrEfLlfNDFOdNz4MZWaf9zzPCk1/3SeUVf/LNpBiVRKo+fliLfsxIiUbwe1nut+H8gCbmiUDaLFXjzu5ZwxSIN3K9Lyp9o6JsdTaZZ+yrOC+bxSY0P3tBJQYmVlxZaXlwNpgkjxmbbjrChdhPMIuCYtb2pxT4GJVHvNkhQAe/epWNk0b/97qpx5BNI/S58RA58+zVrP512L8lJqLM/6/BhB1LXItwuymvEZs3gjT09PT0NkVA8qvWI5trbHyu2tFirXfX9zL+szZx7duHHDKpWKHR8fW71et8PDQzs4OLD9/X3rdDo2HA6D+6Hit9Rc8u3Gc7Nscp9vzOLC2uiV2XnPSVm6fP2nySrtTz3Mu1QqWb1et42NDdva2rKtrS27efNmUMqCYTS4kyoqtf7qrqceTp7AgY9RmIFtvEz37aRtwjM1Dy2L1ps+jeEtfel6GFtTYvKS/72MTfWPlknHml+XY/Nyln6elmYmUjFfS31wDAzwWSvuwXlMACkJQcicnZ1ZuVy227dv2wcffGCdTsf+v//v/wvlMrMJqxPgz2uU/TNiAMYPGn9fbNHx5dZOvX37tt27dy+YwyFTt27dsmazaa+//rr99re/td/85jd2cnIy0ZkIEe6LLUq+fmY2MXn8dZAZtVCoYMmzRuWlacIrBoqZtIuLi/bTn/7U/uIv/sK2t7cD6Mc/XJOf5D7wR0qYck4TIIn7cfFbWlqasBDRTpARdaFAaHGdtmmxWAznQTE+VcPDobxKClXzo2WmHchLyZmOFbWmUjcInpJQ+tfs8kBdrBqEz6VeEDbqitVK3cg4O0rHz8vYI6Upb57Frp0FDOm1CGq9NyYn+E47ehnof/cWd42kqEAnJYemlR0ihQVKgyawv4cxwThQixXki0iOjBONZplqS35nzOu80LaNtbfmoe9eSefl3KxgnLmoig6i8sXIE2HOlUThGsm4989W0Ktl4z//UvnA9SlZmVd3kgIDBQgpwKB5qTXeA2lNsf6LpVi/+N9ehrU6RkpeVsL9+bpgSusZA6b8563quncGRRzjDCWZ2dVWBN3TqPnG5pP+n0ox+TPtWixSbKHY2Niwer1uFxcXwepSq9WsVCrZ3t6etVqt4IYes3j7tvNJcZvWUS0jlJ81i/ZLye1U2+S1Y+o3s0m3YdoHVz61RK2vr1uz2bRqtRqUw/StEs1YOVN95Meq71MlnDFLzHh8tdecNlV8qySf+2hrnqNjVa05qtTxFs9Y2X29tf9ixCklN2NtFMObnpjySuUzS7oWkVJGqUDEA5hZBqouJgo+YuwQzXer1bLT01Pb29uz8/Nz63Q6E0EjYmbkFKj25cxbCHxdYr/HiCMd9YMf/MBWV1cn2qlQKEyE3WV/169+9St7+PDhM4I5ZnKPTSb97rWlCB/NG5DtySv5pYTItMUmNoDpay3P/Py8VSoV+/GPf2wffvihbW1tTWjpcKcDwOuhdGjR9Rn++dSVeqp1i/8RxJAg9l5BXtS1bTAYhI2hCBUVTmj4NSKTby+i5QHciPLH3oxOp2OFwmVQiVKp9Ez/ojE3u3JvZCFhbFGupaWlABQU2PM+GAxCXwwGgwDO6S91neDFQdTaL3z2pPe6yc/ZvOu0TXwbpxZE6qWKCRWwKeGu49crKZRI+bmncs1r965LnvRa1YAuLS0FosBeH/b4oRQgKiMkyn9Ge4xFVV3aUppk2oPF0itkfNvFUowk6O+zLGy0B/KCOi0vL4e28Xug+I4rH3PRRz+MlVXrHPs/Bgx8eWN1mCUpGNJ+UVeVPCKl4dt9YBL/ruVGHk8jVHz268w0EuTLqfgihgledtLn63rl52kMzOm4iI0JLysUSLO2YMFRdz0zm/CooQz6npdS48y/KFdsnKq72tLS0oQColQqWaFQsJWVlRB5TufYwcGBtdvtEF0QDwzaapYx4a0Rur5pGfXaPIwZ65/UXE21MWtFrG3q9XoIbb6+vm7r6+tWr9etUqnY8vLyhOVJCaZXtvjyxV4ppYneq15Z+jxdB5VQ6fqSZdnEQcC+ncgTuevjCaAwiO0tVUxm9qw7ql7rlZa0n3rEqDXPE0ffp55MsWa8iGyZmUilJpq+5w1ChEyMDMSEpAoyFfa476UWlOsC/xjr92XXslKevDxVAN+9e9fu3btn5XJ5wvx8cXERwHahULBqtWoPHjywSqViH3/8sf3xj38MgQfySJS2lT5bBYuCRE+uVCvJferWNy2l2sr/pmWCaHC21w9+8AO7e/euVSqVCfciBjhaVOqoE1Y1rL4NfNsQ6htLjBdEkCCNlGdmgewOBoPg24xbAwRGNWI6eRFUWZaFuvFcyMvx8XEQOOqmCAAmH4JB+DqPRqPQTrpIHx8f28rKSrh+ZWVlQgOGEMHq6fcI0keqeaNNVZCRprltXDfNSi5mAWj+uxLcFAGIgXx1F/DX0K5eu8VvKsCvQ6I8iKLfWMDRhCrA4Zw7yqUWKbXWHB8fh/fhcGjHx8fBnQiXIuaDB4SUSee1zr08gOLv98mDvNj/Ko+9FYo20aASMSsULo8apCNWxlh/x4B1DPCYPXsOY2ytnDZ3aGf1vNB10O+b0fKYXe1h0PGD3MJKnwcep6VUf6fmYOwZun7FxsasbXWdpP3swVzqet/nqtGP5e1BIZYm1htVXGjdpuEaBYZqrdE10tdFQauOT98O5IW7WqVSsWq1auVyecJy66OGqiV4YWEhBKMYDoc2HA5DnXlWXh0Vs8TaXOvrPVNifer7RN9jz45hQ9pZD9gtl8vWaDQCgVpdXQ3hzVWxRVv7+ZpSVKhsjREnr+gib9Z0xpkSIsaA7ufiu5d54AgNzIUMYq0ws4kogvTNwsLCBIFUghaTjZ5MgVG4RsdxbI6m5EXeS9fjPPk3LV07/HmMPHkNnRY0NvhjEyMFyHUA0YF+8OQt2HnJaxVjA1nrwP9qtowBAoDU3Nycvf322wHA6p4FXbTpzKWlJbt3756tra3ZjRs37NGjR/bZZ59Zq9WaqJvXfPnFR3/XCaeDlQT41rbOI1Gp/vJtFvvM92LxMkT4Bx98YG+//bbduHEjCF2EC6QDIK/WD+rpw3WS/KJAG6CNHY1GE0IXQqtCgP1KgMn5+Xkrl8tmdkXk2CyKWV/dTHVOaL/t7u7an/70J2s0GlYul8N/uAQ0m80wrpaXl0PdCfoAwaJuvHD/0IiUo9Eo7O9CgKnFzcxCFD/6VseCapJ0nClh4EV4diVjLyP58RZbEPWz/y1vsYwBltQ1Oo+85UllQqwMeZYonzzA8f/5BVBdhHhBELBaqrzRPVWLi4sh9L0SK16Mf904HrP6+/ZS+RTrjzzQFEspWQN48nuhIE96PpQSKKxQkCgNa+7Hel7SdSFWzxi5nAYA8siIgiQFRv63mPxGltJO2ge4D3t5FStf7LtvkzwCMss9sTZ9XoAzS/IyTsuRB8z0ej+2dWzwv+IW1i/kKf0Ya+fUXNH1Kqao0TaMrck6j9XKriATOVMul61Wq1mtVgvrEmOG6/Ce4DtW8na7be122zqdTtg/hUyBTCpIj6WYtUnBv5bZ36NWWp+HvqcwKEkViiixOGC32Wza5uamra+vW6PRsGq1GmSNdztUYuPL5vtY8Q2f/VhK/UbdVWbTTqqEW1paCs/XPXonJyfW7Xat0+kEHEE9Tk5ObDAYhO0FBPShD+bmLo9DYa1S5W8Mo9JfMYwaaxPtoxiG98Qshu01v5RsmjVdO/y5FiAGamICxk8OvYYJ4QWPDhA/4HRA5aXYoqRlzvOLzAMD3BMTdNShWCza/fv37c6dOxOEi0FGQACEKYPQzMLG6Ndee81effVV+/bbb+3rr7+2nZ2dqEDx7erbAIHnQY4XPh5I+z6ZJcUENs9hUr3yyiv2zjvv2DvvvGO1Wi1os7COoD1XbYS3yqng1wN5Sb6diEDH5NZADQg6JRwAR6Lhcd1wOJwIUEK7Qvw4j4n+wmwPgWm1WvbNN98EYKfgeGlpyTY3N+3999+3LMtsbW3Njo+PrdvthrLiBjgejwMI5D/OdKK/IY2c+QEppEy0m19wNQCBd/PThZ9raCs9rPdlJC1TDFz5a2PzVMdB7H4vF1IAic8xBU4KcMXKl1q0ZymTLlLq1gehUi2wWnIZ14x7vYYAFGqd4oWVSsmUAoCYlUZlTao9Y3WddSFTWYI8Uc2wP2B3ZWUluPF5KxT3ehJF0oXYWxKvU4cUOeJ7bJ3x1yPvcJHS/vBaZ5WT5D8ajcKY0XxVGeJdi/LmTB7Z1/zz6qb/T/svNSdedpoVWPm5H5vvilE86CXRjikFZt540Tx0jHrNPtfEyuaVIlyn1ihc9+r1etjnE/NwYW8weKZSqVij0bBut2vtdtsODg7s8PDQjo6OrNfrBQu4t5ak2l4JKLhRlXp+jOj/eURqWlKZg/zkbCisUJubm7a5uWnNZtNKpdKExc7s6rxT1ssYcdb+4cV94AfuQf561zbWZ93TRL+CWYrFYlA4QfRof/VG6Pf7dnR0ZO12246PjyfKpAdCQ6Q0yNXCwoKdn5+HZ8Qi+/pxP41EafuoAnOa1VbbU/mDtiGY6HkJ1XOdI8V3Fah+gueBC+3gFDGIMW7Ne5YyzlIfzTOlAfICKQ8UMOlWVlbsgw8+sFqtZmaX5IjOZ8CpJkoB9fn5efC3bTabdvfuXbt//759/vnn9vDhQ3v69GlS8+jbie9KkMwmz5hSl8MU6U09Jwb29F7qhYbq7bfftp/85Ce2vb1tzWYzlA0Nl0bS85MEgG92NT7IPwVqtTw6cag3fWB2dS4VZTg5OQkCQa8ZjUbBmkQdyROT9yeffGL7+/vWaDSCoMenfHNz0zqdzjOLWb/ft16vZw8ePLDFxcVAdFRTzCICkdG+oD2wCvm9YAg8Qq6Ox1fufYwJFcT0B+3ohTIbpGm3WZQbs6QU4YgBuNhnLzR9vnlAMFYG/1ue9crny/iIETUPdlL5kZREefKkJEr3+3mApRpCDVah1iwWSfLTCFyQKk+mfJv5ek5r62kpRqDUusaCrdYo3bPBfikiGkKgNMSvf55aaVIAfpaxqG0T6++YhwHvqgVmczrgU88DQ1FEn2j7kx+KDr8GxNx6fPljYzVvLsa+56XYupF6xj8XiZolxQCZyl9tN2+59vfG+tw/K5VUrngipeWcpR5enikYZn9hrVYLe30gUroekCf4JssuD7GvVqs2HA6t1+tZs9m0Vqtl+/v71mq1gpUKy4YeMJxXBy0z7378+uumtUcq6RyBLGCFUgK1tbVljUYjhDbX5ytBxH1/msKR8aP4RC1LSgA8jvTEQBVw4/F4wnqPohgChRsm54MdHR1Zp9MJClzyPz09teFwGLxZwEjISzAB68Xi4uLEPm7qoBjPKwxpB98uZlcyM+YhEbPQxeatb0O1Zl43XcsPJzYQ1VWP5AFXTCupDeC1mzEgFHs2HZsnzGetl29EFVA+z9gixe/k89Zbb9krr7xiS0tLEwSA+uqgU9OvCsNCoRBAAcEovvnmG/vmm2/siy++sN3d3QnCkVc/3yexNruuNSEFLrTtADuvvPKKvfbaa/bgwQN75ZVXwrMgTYCzpaWlCS0d+QGgIAFKqFZWVqJ19uU0s+ACQ9L9Qjp5lRhkWRZc65aXl63b7U5oh9SyeX5+bru7u/b999/bYDCw5eVlG4/HoQ8RLEqeIdVZlgVz+sLCgh0cHNjKykqwRKlARogoSPLaZtw2qS++0Fl2dXCuEqJCoRCu0fZh/PrFlnd1uaQML5o8kErJgRRg9XMzdZ3+Nu1Z/r5YOXzZU/Xy9+UpLBQoqVsJ5MCH7YZIxcCW7ieIWXSOj49Dfrj3pcKm+3NiYhaqmKIt1r5cp+/8rkomtcYB9GgLXIl4x7WGa3z7kGesv7TdpwFefo+NzbzxlCJPvLPQq7aYfSZYpXQjOZpu3+4oeZAx1F2jE1533UzNw2ntFKu7/qbtNY2Av4zEGqUy3PeZtmPMlSpWXv/fNLnyonXwc4vPimFiijetn5cTWKMgDuVyOUS29XuB/XOYqyg2sGitrq7a6uqqtVqtEDL98PAwnEE1HA6f2b4RS1pmxTeURa/xFos8uaxt6WUOdWk2m8GV7+bNm7a2tmarq6u2vLwcFOHqaq/rNuujynOzySAwMSKYIoSeMPhxp8pyCJ7ujwQLQHa73W4gU/odvIq8UJdildGq3GaN0DZg3PGOjAN/pKxCOn41H98+qRTjFdp2L5qea0NDbMHxnc1k8gzaCyQvuDRf3lMgRQXXdYVuTMOWd63vSD8xdTDV6/XgnqUbSmkLdedjQGNpwIzLYodWdG5uzra3t217e9veeecd+/jjj+3jjz+2r776yo6OjibC9Po+Mpv0P40BHgVCee2Q+u77hcm6vr5ud+7csXfffdcePHgQ2g2Bo/754/HkIbej0WgC7LFnQ0H82dlZ0KrkARa/n4pnqeZGXQm9cBiNRuHwWTMLe97QhJHPycmJffXVV3ZwcBA22BYKBatUKhMLD2dH0c+6kGEux0+ZjZ4830eY0Y2ebCYnL42qw/ji7C3GIdoj6qtBBfwipHvq+I72EteFF02xeRbr2zzg6q/LA7r63Nh1HoDkKVdSdfFAIzV3Uvl5lz6Ij1qSNDhI7Bn8rlpWtUx5AuVdANUdTjXIqjXNA4upfoi1mZYzRvrUlc9bojQMvJJMjSDlrVAxUpMiWKn6xeo5K3jWa1VTCig7PT0NlkI0wQpmdL+U2ZXLsVrgUVppkApVVsXKxH8pEBpbc7yFTVOekmGWuR3770VTzHKvz6ONkZ+qQEiBdL/W5JV7VhwyLeXNr1i/xdZ81grkQalUsmq1GoIGMIY8CPVyhXyYt2rdwprT6XTs4ODAdnd3bX9/3/b29kKUPx8tLlWvPJnvr8lrE69w8tZ/2qHRaNjm5qatrq7a2tqabW1tWbVaDd4mjA212qPAwA0fzKMkBCuf9yjw+0DVqsRndU32dVCZr+SKOc2Bz+yFarfbgdD2+33rdrvh+BfKBTZRDKFtyHO1/n7LSKzvYuuxlxfT5opXBmgbTLvnRdIL7QxXAImwUcCu16jg8ezRaxA0f09W/PWzphQJ8ARJn63Xxq7XTiMVi0X74IMPrNFoTAQqQPAAOLB4IIgAoUwMyJWCb0BwvV63n/3sZ3bv3j379a9/bZ9++qk9fvzYBoNBUkNG8m4fWl8P8vLaNyWECoXLoBnr6+u2tbVlr7/+ur322mu2urpqi4uL1u/3J6LY6T1MPtzpIAW6X4rQ25AKyIOWxU9UrYsKkfH4Kpoa49gLbsY2QkRN2ZQFs3in07EnT57YZ599Zp1OJ4z9ubk5a7fb1mq1QhsD/M7PzydcAXAhyLLMFhcXJ0KwM4a0/AhUgBH3mVnYCMqiQNkBpASIwBKFphEh5Oey9rO2K8L+ZRApLwtigMT3rf8t1u86vmMLbh7ImUXO5BGkafWdJuS9ZtQTKCU53rIam6fj8VXgFcY1c06tPUqcfMS/4+PjQKhie6d8P6RIaqotvNVMy0T91drEfNJ32kgVVjo+YwRZ+9P3bez7tDGYAgixe9VDQwkUbnyQqOPj44m2jrnI6Bql89fX0z87y/IjFPr7vSyItV/eGE+t/bF5iiyYNo6um7x7lCdICsyUsPry+LVzmmyJYY9ZsEgqL81zlvHmr/EWbwIg1Wq1Z1z6FCSzxqjSQ4E96xtrU7FYtJWVlbDHqNFo2NOnT61UKtnu7m4gU0SLmxaUIdUOsXpq8uPYK23Yq85eL8jTxsaG1Wo1q1arVq/Xg9saY0itxep6CLnkuay1EKksyyb2tiqJQoZBwnTd5zq/RUQVw77tRqOrsyk7nY61Wq3gxkck4eFwGD778exJolqjkGExt7q8/kj1y7T1Ua/T+vvxqITKy8cXxS3PHbWPRKPBxJnI/BbT2sRSzDTnBXeK9KTM8qly+w6dJpz0mljHqeBaX1+3Dz74IICTubm54I7mB3iWXWkpFNSaWfBJ1Q3javIeDoe2ublpf/VXf2Xb29v2+9//3r777js7ODgIEVZi9Ui1b6rNfBupZkMHLmC80WjYjRs37J133rEHDx5Yo9EIhAjCg7ujEkkVyKr50L4fjUbhDCcmsJlNaMP9izLyLMqvJm8zCxY9wnky+XQ/lYJN6n96emqtVst2dnbsq6++sq+++sra7Xa459atW3bnzh3rdDr29OlT293dtbOzMyuVSqHe9O14PA7hyiHfWlb1P6bdVXCZ2USkNiVF1Gs0GoWQ6oTI9lpriBiAjYVF3QTH43HQ9CNAIcEvkvzc9C6//ppZgGwsXwUc/reYjNN5HwPSs5CmaXWO5UH/aXAJdedTsqAufbF8/GdddBgDSlrUkqOfCbiiUf00AEIKkKaStjmyhPrELHDeEqWfvRVKoxV6t5nY81P94PtKP+eNN/0/NmZ0nCuBUmu0bgBHW6/AUgGrAgjypQ9oSw8s8kifL6//7n/z98bW7tj8SrVzLP8XmWs+6fqg2nNVzvFcdWmb5s6aVzf/fF+WWH55JIjvsT5IEbNYPzFP1HpUr9et0Wg8Q6Q0H08mSbq3h2cw7rxcQfGxvLxs+/v71m63g1uZVxzMQoxidc7DcN4CBYFqNpuBPK2trVm1Wg0BN5C9jAsUH97tWZUZ1B8C5F3ZUjLF481Y/TR/dVtWRYvuEyc669HRke3v7wf3SsKaUx9wE+NDiSBJFXh5ZY2R/FS/pdaxFAbX8ZUiUan1Pe+Zs6SZiZS6H5ldRWdSH22SXzzzhIkKo1mSFzw6YHRxyfOt9XnpYpY3AGJ10UG+tLRkP/7xj8OGQ84rMrMAwhFYsYWb87E0Uhr3IeAA/KPRZWjrYrFor776qm1vb9vDhw/td7/7nT169Mg6nU6YBNNS3uIUAxk6kIvFywgw9XrdNjc37a233rJ33nnH6vV6EI5qrURIKRigLmhatJ0A/gB83d+g2h/qEdMQqiaCyY8GCQLLvYwnrCsXFxdWKpVsMBhYp9MJGzV7vZ71+31rtVr2xRdf2CeffBLO/YIgLy0t2euvv24//elP7fT01B4/fmz/8A//YPv7+2Hfk9nVQcmLi4v27rvvhnD5lIU2hFir8GX+KQAGbOEOuLKyMhE+HeKmC6NqlLz7Bu2prpDqSnhychIsXi8L5Gh/xIBeCrDynkeiSAoIPPhJEQ/eWaDylBDPW29PKmJESomF7v1JLWJKHLUOOkfUhUQ1oBom3Uf1Q2scI1TeE8GDU8oTczVUAhVz5fOfY258Sp5UBqSA+zQCmtdnsbzyxqp+V2uItiHtq8SKOUjZFCj433T+mtlEu6aiFWp5pwGOafNr1pSaQ6lrY2V70eSBpq4htDftmacg8DIolfx89O64irN8yiP9njD4fFNEN0YkCHfOWUgo6bS8+lyPu/jO83WuA3IJ3sC8X1paskqlYq1Wyw4ODqzValm3252wTnkSG6u7r6Mf0wq6kXtY4QiuAYHiYF3IpD8uhbmrFmRvrdO+0LZjLPmAEio/tKz6u2+HGCFUayvjW6Ow9vv9ZyIpxs67nJ+fD5b+QuFy/9P8/HzAo0qkzGxCzoBZtP6UV7GHH1Nav9TaTj5+rKfIk8cQOu89qb1OurZFSgekmjKvI0xnFTJ5bJYFA8tFDPykhJwf1HqPThB+8/WKCbK5uTm7ffu23bt3L4R9VJ91PcNISSkTkYGuvq+Esza7Yvzqg0sfMGk++OADu3Xrln3yySf28ccf287OjvX7/Wj/eCHo2ywlcHWgcuDa9va2/eAHP7D79++H8xP6/X4gIrr4nJ2dWafTMbNLcjkYDEKAA1zRxuNxAGe6/4eJCVGgPSFSmvzkY7yQL+E4tR0gsPzGtcfHxxNtg2/3N998Y3/84x+t1WqFiUi7zM3N2d27d+3OnTthnN68edP+9b/+17azsxOIo0Ysmp+ftx/84AcTZFvdtNT9EeE1Ho8DgZmfn7fz83PrdruBKNLm9AWLAG1KZMJmsxn8pblPN6MzhtTqrO3ebrdtcXHRhsPhM31xneSJkSdzHpimCJXv+2nyKbYI55EpzdfLiJjiIQ/45QEjtc5AJtT6AsnQxSpVlhTpNLvaJ8K4U1LFvPMWKk+kPKGCAHhS5V1xvQtLikDFXqrJpowaeTPmsqHtoP/H2ofPqbab9XOMdDC2NVgE7Ue7KjBTDw+/f0LXMw+6VHmkChfdi+HXwdg8yRvD0+ZV3tzKSzrHdG1+XsATK1+/30+6IimQ9b/nyZS8MnrC5MGf2bMHkJLntHaLEQXfn75sqqxhLum5USsrKxPKXZ+/1iMG9n35tS3JY2lpyZrNps3PX57X2Gw2rV6v297enh0eHlq73bZ+vz+hWNA9fnnjz7e7EjnkKgcO42q4trZma2tr1mw2rVKpWLlcDphB8QyWX+Sbt0R5Wap50E7IxFT7cq8q4FE2+4A/2pes9WqBIogELnu9Xs86nY51u90JBay2E0FDCDiCslvDn9PvtMvc3JxVKpVwGDFBNdSiGRu39Jl6Kqnnko7fGKnSfs5LMTKVN46mpeeO2geAm2b1yJvAqeQBCr/p55iAoAE1Qoje6wdqLH9veTO7sr75cijjLZVK9vOf/zy4QAF2ObB1MBiYmT1jBaA8sf0ydDAuNGqNATxrZLjxeGzNZtN+8Ytf2BtvvGGfffaZ/elPf7Ld3d2JoAWpNo8NPv97oVAIUbFu3bpl77zzjt29e9fK5XIgO0yufr9v5XI5WCuxwPR6veBSpoIJN0gd1OpK1uv1AlhfXFwM9fGWARXYvi3RgulnSCxCazweB+ufmQVXvGKxaO122z7//HP76KOP7MmTJyFvdalZXl621157zd5++20rlUrWarWCEKxUKgHIcMAvYwwyRBQd2oh0fn4e3O4UTJldufSdnJwEkElAC+qjEZH4bnYZze/8/NwGg0EgqsxrngFQPTg4COVQIVcqlYKgfpGkC2wM1MSE5/OAmpQc0N/yCE4KbPKfJ+mpfH1Z9DpIDJao2B4gDTIRc69ItYn/nfFndmUJ03KoVUznIJH+vBuaurkAMFRhpHNUtdNK1LzlLUagfBsAVJgXsf5LEWsFzt7NjjUFNx4dh54gqnsYCgx9rt7nXYKUnPqzoXRuaOQt7XtPpBQkKMBSkuz7O9ZO08hQirDH/s9LsyonXmby+1k8iaIceXKEz57oxUDeLHWKyRffxjElAdfpOPBl1Heu1QATBIRAKYq7trfUMc8oh847fU5sjrFuavk4e4oDgClHvV6fsE6hfEy5EafaTJU2emwCz1ldXQ0H6urhwygdwCM6v7W+ivs8oY25FyMzNIKzt3R6soW8wN0RS7XiJK904EwoCBOklD1QkColURrcgv6o1+tWq9XCUT48V5+p9SmXy6E/PQGlfroWKNHVMaT9qPWKjSeVeWph9nM7Nh5fJM1MpLxw0EbktxRAyAM5ZmmrT4xM8ZsfkKrpSAkrvnttTazMqfKNx1eWAJ41Pz9vP/zhD+3OnTs2Ho/DvhmN0w94x1pFfqo5xTWMFyCByQpBwbUMYKwLbKFw6QK3tbVllUrFXn/9dXvy5Il9+umn9vXXX4czYZRspogm77QTguett96y999/327duhWErG6mPD8/t3K5HPYKZVlmvV7PqtWqDQaDiWg8KysrlmVZcGWjPSCVCCgEIOXodrvBP1n3kTGhtL/G46soM4QTx6Kl/X9xcRHIh2rbisWidbtdOzw8tL//+7+3hw8fTggADf29vb1t7733nr3yyishkg8AkxCqfM6yLGzap4wKvJRockgx44vxQDuNRqMQphSSpBZb+sbMbDAYhMOJsywLLqX0M2ARsIb2KcsyazQa1m63w5wj6AfWU+++dd3k5y+/xYRv7N7Y51hKKQ38f7G54e/xYDNFmFSOTAOmul/Bh/mGVMVIVF698haMmIxWq6iCAXUzVKutEil/gC+fVb5RXl20lTgqoYq5NOo+qBSB0vEU60Ott3dFRKHBngGAgtmVa7CfN2ZXlmx1F1EypYs8JIo21P0Jqm3mXua0Ri/U9qMNPFgzu3LfYqxoyGXuSa2XfmzE1mc/3nz7xn5PkaLY9S8KdlIpzxrl56rKolh7+DrMkrzMmIZB9Huqv7TMCsZ1fTR71hqFJQqLULlcnrBGKYny5dDfdZ7Hyu3Xae6H6ENwCPJweHhoh4eH1mq1AiGADIBnVL7E2sRH4atUKqGuhGXnLChkDNhC5YPuVVfw7tsnRqJYj70lGsyheI78wQXqQoh8wSNMvU64Zjy+9Go6OjoKL/aeYd1DgaMWLfV+YK0hMEi9XrelpaWAQ3RLj+IsM3sGv2q9YoQmtU7NsmbpPdo3edamGHl73jQzkVJho0A4lmJCzzNsUkqQ6n9eKOj3lJkwL18zmwCYvhy6SKTy0Q5YX1+3H/7wh2Z2CW41mIISJQ11rpY8SJaZBWC9sLAQCAQWg0qlYkdHR5ZlWdhHs7GxEUKGY4XgPsyrtVrNHjx4YJ1Ox/785z/bRx99FM6gwkTM85WM8gLQPHjwwH7wgx/YvXv3bHFxMQguFmTGSL/fDz61TCIsGll2aZWhnAp+IGUKWnBHKxaLIV+0MCpUzCY1yn4MQTbUHG92tSGWiYQAp53Pz8/tyZMn9pvf/Mb+9Kc/TYxd9REul8v2s5/9zH70ox8Fsoa5vlQqWa1WC/XDusiZPXNzc7aysmLtdjvkrxoqHa8IZH+ulAI1DYZxdHQUygOJR+DqwYDcg2aK+y8uLgJYpe1UEUA95+bmghbxRZIHXjHhGhOy0+b7dcswDSCmkmr2dUHNI1meYKk7h0bQUpc+H1UvDwhPA3Qp8qrkwLuZKOHRYAjeKqXR53TfAACE+qpPvbr2xSIU+kh8nkTG1p68vlPipNGocHVlTiiRQvlhZgFs6b5L3U/I/FBAyzPUjY9IWd5yZ2YTJBYwCNCJuenFiBRlVICngM+Pw7xx46+ZBnzySNR15nZKifK8aTweW6vVeiYwVuy6PM8bX868/7180D7Q66a1o88jz73Tg1i91weYICKdBphA1nvspnXyipxUv8baVMukViO8X2q1mq2vr1un0wlEqtVqBStVzO3Pz52FhQUrlUoTBK3ZbFqj0QgWuOXl5TCXzJ4NZKXjIw+Ia31UyUE7YlVCqYkiCEUzxEmJFnJB8Y+6E/Jc5ArbKDivCyIVC+ChbU7Y+3K5/AzhrFarAa96EqZEE9ygroy+ffw4ipH/FPnxhpDYWPP36lqsc4T+8fPrOmlmIqUMWhtFCztN0JBmWdxjyWtDvFCINab/3Q/8FJmK5eHrYHYJ/v/tv/23IRQ2EwASwZ4T3XfCoj0ejydCRivwhmwBTEqlkvV6vWDhYGKySVKDLuikACCPRqMQQe4Xv/iFPXr0yD799FP7/e9/b91uN/SvAhyAyv379+3999+3Bw8eBCKI9oJy9/v9CRdDtcZxmOdgMAjuAoVCIfjoYhnBZZHJqO2MMOx0OpZlWdCKoJU2s1B+7SPtf9qMtsetT90JK5VK6LPT01P7P//n/9hHH300IVDpQ87befDggT148MDu3r0bysW1CEl1j1CBiICsVCohtGmz2bSnT58GMk17YZYHACkxVKDW7/dD30NsRqNRCLGKGybjSgVpsVi0UqkU3PtoL40iSeQvACLEirH/Iik2V/X3FGDz96Xy9mkWwZlHqrxMMrMJUhNbIPxi4fNVLbGSCtz6VFvqA0yklFR58jYm/7R+kHhIAUoJlBpqVdF3ZJi6yHkipRpbde1TFz/dm6XEMUagPBHXdvb1U1nhXfmQp94iRTvovbQPHgNKHGkzVVipS49a8lS77kGgbx+1zqWIVAxQaHvpmI2B99TcUJmaN3/0f/o7do3mGStLjBi/rITiz68d/nkpWaO/xRQZPsWUKP63WP5c68d1ikhpG3rtvN6PnMGlT0OSqzXKE/s8GavKgphFijIyl3Q8ejKo1m/c7yAg7XY7WKmwVKnbn7qpodilfuvr67a6uhqsbsgYZFvKs8KTSe0bs7jsV2UxsoEzmggKxjXIAJS0yCK1hKnnh+6Pot3BgWdnZ3Z4eGg7Ozt2cHDwzD4zVZahiF9ZWQkkir1hfFcXPfCL3+8KxmJ9gKDQv77tdB2IrWNewaBj3Y97PyZnmVP+uudNMxMpGLC6GZBmEao+TQM7MeDhU8wcl2VZICSx/xiwamYl0alMphij1bLNz8/bX/zFX9jNmzdtaWnJBoOBzc3NBXB6enoagGy9Xg9gmH0ktCWTg3Zst9s2Pz9vpVIpbIRlccVaglZ2PB4HooI7IIIPUKuLLJrVV155xW7fvm0//elPbTAY2P7+vn3xxRe2u7tr4/HYNjY27MGDB3br1q3gK8zhshoKW/3uzS4nNlostBL0CcJkf3/fzCwQL+quVhiEgQKRs7Mze/r0qbVarYk+08AWg8HAjo6O7ODgwE5OTuzo6MguLi7PSvjyyy8DoUFAs4GS/sby9fDhQ/uf//N/hvsREpDGpaUlazQa9pOf/MTefvvtQCYoN+2hABICpoFFGDecO2V26XpXqVRsbW3NDg4OwgG6SopxT9CFKMuyQHrK5XIYx/xGX1DHQuHSnRRSVa1WJ5QlOn70ObhhYulSAfeiIMcDuWkEKvY9laYpcGYFQvrZg1FdPD3QiSlqvJzT+aRnJfl9UQBq8k6VM6/dUnVKlY/EuEKpoi54jDUAgydRCgJUHhcKhQmy5AmUd1/zhDSWYmPJ/8788C6JWIoUmKnyAk0r+UFoza7mPG5/ECqep/JM90b5PQexvWnq4qgWPJ7v98ml+jk2HlLgMba2z+oKkyLzqfU59n0WJcmLJLTzKRcgfrvusz0g9KQpRqBS+cSuV5mR556pZIr7lKRwXhQEY21tLQSZYM3T+qTmHuWPuVX5pO6m5Kf4y49l1mbkIQEhtre3J0gJZ60xr7LsyqOGelYqlRAIAQuUlkuxmJejigu1LWKEVtvl/Pw8YLnBYGAHBwe2s7Nj7XY7RHcGn7FWz83N2eHhYSjj4eGhdTqdoNhRLxtkkOKy4+NjOzg4sL29PWu1WoFEqSIc+UK4d/aL4V2iLuV4FOmaTFsoWVRFm7ZfbLx78q9KoNgYS1ldY3xEx2Aekcr7b9Y0M5FSLZkXcDEtoE8vKgBjjRoT7jGNuA52NOgK/FT755/Du2fMxWLRtra27Mc//vGEKwvnAw0GgwBsi8WidTqdCSKzvLw8sfBeXFyESFgEAUDDUq1WJwgVA5X68g6JIsocARMYTFgqcA3Cclar1ez8/Nz+zb/5N7a3txc2fV5cXFiv1wtCDCsX1iQd0PPz8yHqntnl4nRxcRFM8+zxwaTN/gomcq/XC5oxP0kQAHt7e/af//N/DkQMkEUegCBCkq+urgbAX61W7enTp3Z0dGT9fj8Alnfeecd+9KMfTeyzGg6H9t/+23+z/f39kPd4PA51rlQq9sYbb9hf/dVfWblcDi5NtKua8AuFQhBGtOfZ2VkAycVi0crlchBwjLVqtWpHR0fPmNBLpZKdnZ0FENvpdML5XWZXc5DxRv4QbcZfs9m0o6MjG4/HwYqGNpxxubi4GPoTEsg+vkKhEMgf4ezL5fJL2SPFvPNa7GkEyn/PI04xQOK/xwBMHoD0YEkXhthzfMLC7EOdr6yshJdufvYWKV8v3y4pMBqz3lCelDIJUqGLOfIJQoVVl3e9TttMZYtanfLc9150PWGhReMLkUEjrHuWUCJihUVWUx4URrSNusxkWRZktNmV5csTKZ3jrEMoWtQiqQTTB5vw4zbWTv4/D4bMJj1LYmOHe3Qu+LVUf9PnzkJIUgQk9v1FE3J6Woq1a0ze+P+nlTuPIPq2iskoPzdSgNK3J5ZOJRgEEyAgEnPbe1B4uaZli5GLWFv6ccWaglzx+SmxUdyxuLho1WrV1tfXAwHS8ioxQ66qlZe8aaMU8GZOeCW9l/P6PNawi4uLIFP6/b4dHR3Z06dP7enTp9bpdMLWBE2Li4uBCHJ0Cffy3e8HxWqNon44HAaLHSHk1dNHSRR9T/+z3qisUWJLG5KX9jNtp3ujaLdZ13CvAPRjyo+rWD5+rPq5oesROP1503NF7ZsFnDxPvrNeG1vcWbBUe0IjslDrZlyzyQUjD7Dp71y3vLxsf/mXfzlBEtA+MpmZUBcXF8FqxHcmKwNbg1KUy2Xr9XqBhOhA5BnHx8fBCgJ5uri4PPMIVzkGC4SOBR3ihWUBzTFlXllZsfF4PDEhcQMbj8eBiPgNlAg2QDuCDFP2aDSyo6OjQCIpx/b2tq2trdnh4eGEKZu8schQZk9oAZbqcoarzNzc3ERUGxITCOGC4Op0Ovb48WN7/PhxeD7jCqHzN3/zN/buu++Gtjw5ObFarWYbGxt2dHQU9jlo2XDjYezgCoqVD9AEyTazQDbNrkz4aMjVHI/bpAI2Jdjffvut1ev1MCaWl5dtNBpZvV63brdr4/E4BAc5Pz+fcJ3yFrRKpRL6dTAYBILOnjnfztdN0wTty0xeuKq2i/9j13kw7wW95jUL8FMyoRt9ATk+3DkEI4/UeYDmwXFMQxjLQwGytg0Lpb4jb9QNRUENY1iBK3NILXFKDlIEIdaGef/x0jVMXfggUf6l+52QBdQBcEY9lVjR3gBD3nUfAfMF0ulJpRJp+h7turaPKgBTbTKNgOo880Qm9n/sOdPKoM+aFSsokPbk5GUlv13Bp1lIlJaVz/4/vT92vW9HcEpK/tD34IoUUPREypMoXPrUzY0odRrMANkes3jGxgSyI4/k6XxU10PFad61ywNqdW1n/ukeZcqh908D4kqmVLal+gOSoQRD2wUFcqfTCXuVOACXNd6Tz+PjY+v3+9ZutwPO0H1g4Fnupf1Q/AyHw7B/TPdUa/+XSiWr1+u2vr4eQr1DoFhjVDGse7F8e6TmCX3q5YonSz4pwaX/vEXQjyGdT16JkOrvaeWYJV1rj1SsEfwEyhO4qZQSTD4f3zG6cPE7A1o1mQoIYnnTQalyeQG3vLxsP/nJT2xjY8P29vbs/Pzc6vV6AJpo+hcWFkKEMzSYDHzKhmVkfn4+aECxmvE7z2XxbTabdn5+eV4QgHxlZSVYL6grddIABQACtBqFQsEODg6CNWE8vtx3dHR0FLTLuO/oRmXvooPbGBYPhCD7r7BiFYvFYOkAYKEFxjVSx83x8bF1u107Ozuzo6Mjq9VqwYIC4IdAQbpoK93wztjgf8jceDy2L7/8Mliiut2uff/996ENj4+PrVKpWKlUsq2tLftX/+pf2dbW1sS4297etvPzc9vf358I9kF76MHMjNnBYBD2u5jZBJiCLOIrrYSMupXL5dAOumGUMWJ2RcS2t7cnoojt7u7aN998YwsLC/buu+8GiyeuehCzs7Oz4EZGnQC5Ktxo+9XVVTs4OEjO4+uklHxJ/R8DWKn57vPVRUDnaB5gSJVLiZTmnVcOnqn7YPBVV0uURqnziyhjedpCoP3mP2s7arn9Z7PJPS9+IcKqxtjknYWc32KASAFJzEVGyzDLYqjET/9XMgNAGQwG1u/3gwVcI+hRLwWnKG08kfIKOmQBMg8NKM/3G+OJWoYiTQm03w8VI4laTz8/YmuhroGxNX7WeeSvi5Uplm9s3vr+8mDqZSbvxZIq9yxzbNo4zEsea+j80OerVSCmnTeLu9cpQcF9FmtUrVYLrlxgJiUkfr2fhdD6ua1yRHEc8yJGrKiLf26MlGnyJI7ftEwedHuArq6eHpDH7k3NE+o3GAyCCyJRl3En5lr6hrzBMWBFJRZeWYPHCfII7yYU5YwZH+Z+dXXVNjc3rdlsBndOyo2sUkLu5Y0nsypzUR6l3Os8eYyty15eaD4xZYHvJ+/hRJm13Kq0eJ507QN5fZoGeK6Th88nxXLNroidLsAqWNSsqPepMFQB5IGFLwMTc3l52R48eGA/+9nPzOwSFKDVN7NglTk6OgqhIhH+SjAYYGphAgDjinV6empHR0dWKpXs5OQkWJpGo9GE+wzp4uLCyuVysMZgrsQigoaBRdnsklAA4DUCFz62uK0gSMkPN7y5ubkAuufn5+3o6GjCRQdiRl2xchEwo1arBa3M+flVaHj67unTp/YP//AP9uc//3lC+CqAZ4Lw/EKhYI8ePQq/ESZdyRYkZ39/3548eTJhgTGzUC4sVn/xF39hH374YbDMUIazszPb3d0Nmml1s1GNPP2EVQ8Lji6SWKxw8dR7lZhh4VNSDOnRvSoqxCBskPSlpSVrt9tWrVYD0VtYWAghZWnX0WgUBC/aLlwcmTP4dA8Gg/Cc5015ipjYQvW8GiSzSTc1T3RjZMKXKY8g8dm7P/lrVZj7A3f13Ch/VpKXZ6kUA8aeiOjvKeKXyjvVBuqexrUKSHwZVDamQGcMzMaAa2yx1/+Zl34vFIdVMgd8SH/mOHnRF8g2/0ztX9XqMqd9UAmuhUDpHgUfZMOPrVn6Rdc8P89ifaL/+8/T5t20uevLOOs89u6+LyPljTdSrPyx36c9x8sUBXcx4qT9rJaV2P0xgOnnlydQzWbT1tbWbHV1deLcJNYvxr+2e0xGxIivVyioPND6edng3eu8rFNQrHkoaFYZoPfF8GQegSWp1Z3/leBRb2SL1unk5MRarZbt7u6G7QWEIQdjUT5w2dzcXFjn6XuN4EdfKgnwFm8NXEN96Hsi8dXr9XD4cLVaDVhNFT5KpFPWJXUx9WPSkx0doyTdR6pJsbdiPuqs38Gx2r+eRHlipa8XSdciUp5s5DHwWLoO6aLSaOFilY01kD4nteBy76yLAuUpFotWqVTshz/8YSA7HGIK6CSSlZlZu92e2Pivbly62ZKJsbi4GBZzovThnqabtbEgraysBBCcZVeHoaEdxUTPfiiuYY/OeHwZYaperwdtxsrKipmZra+vBzcyMwtRCIlwR1lVwELAIIaFwtW5CLqHAOLFYb3qFnZ4eBiExMnJiX3yySf28ccfByuNB6Zq4cPcTZ2bzWYQ3ApolOhhxel0Ora7u2u9Xs9OT0/DgnJxcWE3b960X/7yl4HMqk8tz8eSw3hCKEKKBoNBeCbRvbQOlG9packODw8DAS+VSnZ4eGi1Wi3kSxmwWCkYW1hYCBY8QBgAbjAYhHHKnqq9vb1gzcQlEi34eDyeiCiEICWqJGOs0+lM7Pd4mem6MkPvybuOa2OgxS8GsWekyhQjXzEhrVplxiv7YLBE8a7BJbwf96xALgZ8YqDaLA5WPaj0Vo3Uc1LgS9eJvDpcF8zGyGGsfsxh3Q8Feer3+0H2KfHzeZKPajO9Jtwv1MxFVYApGCBEM33PRnNImwJFbZ88haG2Yaxvuc6v7XlreYqo6edU36VAeIrMaV0UcL+s5NtEcYM+P1YXn/w4MZvUfnt3PR0n+rsnCB68+ms95olZonyYc4gU0etKpdIzAQJ8W6SSv5Zxrv+Tr1qntN4K1P089uOJa3xUOOYGL/JXBUhMHiiJU2zmZbmWDzKlss0TB9bddrttrVbLjo6OrNPpTJzjxLO9my6YDE8ev3WFMmpAL3AebUOf0zaEkq/X6+Hg4Xq9HkK/az4oaNUlW+WCygu2MsSIi/ajptS8yht/eeQ5Noc8b/Drs75eJD23RSpWaT/oZk0p4Ymvpnf34n9tTH+/2dVE8JNK700JihgAqNVq9uGHH1q5XA5udRcXl1HcGLDff/+9LS0tWbPZnGDzuslP/XfRKujicH5+bgcHB2HglkqlMOk06pwObh92ksnFMzmNGmLT6XQCODOzACRKpZJ1Op3g1sfEpbwXFxehrpCudrsdNkGurKyE8mdZFg6g1XDGWG50rw39jGvgcDi0J0+e2M7OTjgMlj72kwHiBSi9ceOGLSwsBGCvG/PVJQqhVywWg3/w7u6uffvtt1apVOy1116zs7Mz29zctCzLwj4jzPRYELVMy8vLwUWR/VlYcKgfqVKpWKFQCBY5Fh8OXEYALiwshLL2er1gVdKxBHkdj8fBsgfZZR6USqXg4jkYDMJ3wr1jVdT9c4yn8fgqOqS6D3S7XTO7CkbB95edYsBmFoDjF2BNXuB6d7KUPPPgRb/Pqtli4VTCrdYoH6FPtY8xmZtakPxc8YDLt6dPHmRO+5wCxXng2f/u53jsPl+H2P+penmLlIZt18h96nLnibUCxdRC7zWelEdlvd8bx74FXDrVbTk2NmMpNi98fzC38+ZGqg9jBC3W/qk0K1mLlUnb8WWnVJ2vC7T8vGCO63lvZpOKYJ7n7/fliOXPNSmSydqj1igsEpwZtbKy8swebsUL+izfJjGZothKAa+uuX6e6PzwfazWDOYeZfWyWMvEZ80zJXP8fb6uvh/0u5f72h9nZ5cHe6u1m20H3toHltGtFNRdA34oSVYlC+3qMRFtjisf52epsg73QQ1+o9EseR59pWMtpXgkKcH1fZqXtK6eJKfmps4Dta6pVQ3sDGEkWNjzypVrE6mUEJtF0KSu8YM4y7LgNqb+5WZXGzD1ep9HbDHOWxB8+fSdSV6v1+3DDz+0Bw8ehDJxeC6WDw5wpOMJYsC+JzML1iGuwXKj1gwsPyysWDPoeCUFaDvq9fpEW2FBwEqEdhOLyHA4DAEKmNjFYtF6vZ4Vi0XrdrthcUerwuDLsstIemhOcdEzs0C0CEqAmyMbJxESuB4uLCyECIFmV+HO5+fnwxkICAYlwtrfuJ7Nz8/bjRs3rNFohPtGo1HQ+lQqlWCiLxaL1mg0Avhnobt586aZmT19+jSUcXNzM2ivS6VSqCMEEW2RChesToVCIQipSqUSSEyhUAj9BmFiU6mZBTcLxoq6gmbZ5F40tVRQV/Z4MH5OTk6CK5+eeUYZIWFYArEAnp2d2draWgj4wXM1+pGOiRd17dM0jQTFFr2UINS8dDHwCznt7xfGFIiJkaxpiefpniglUhqlT/dFKSjPE/gpwBNri5j882XNawOfp4KVWYjQtDqkSFgecMpLMRKFssOfg4VMUkWer6N3WfGaUSXoXrHHNbhJa5ART5712akxR54xlyNtN5VTsfb1hHFWEhHrg9gcTf3/PHPpZaQ8EO2/zzp+za7ApVocdSwpqaJPUmXLw05+DPrxpVZvSBQWCQ0wgWs7ANTvidF2iI1Db/2JzRPWMeSZx2j6Xa1I+l3bST+rXNR2UCuTdy/zddLvMauKlok1n77z84YXuJDDvZWgaDnAgSjSIEcodFh/8QYBE1IvT27Ab/QDOA13Tlz5INBeJupZXIrB1JXQz18/P3z/+nVYr4u1nR/buhcVjyfaR/tet1VooCPv/qivF0nPZZHKAyyplBIIKVKWmmSxe7zg8APKP8t3nAJKZb1qDn3zzTftxz/+sRUKhWCBWlpaCpv5sBpkWWaNRsMODw+DJQI/eoAqIErLwARV7QEDhbIAYBnsHE67vLwcLAhq1QEwjEajsHeJfAh/jc9tuVwOlhaAG+XXIBj1en3CBUYj+bDfijbWU9EJZqALOe2r5FM3Nu7t7QVy4TUe+hkysbi4aHfv3rUvv/zSbty4Yb1ezx49emStVsvOz8/tq6++Cudz3bhxw6rVqp2dndmTJ0+s3W7b8vKy3bx507a2tgJxGg6HVqlUguDHOgaRoN7sbyIwBwKQ76VSKdQb4cYzaHfup20gyliM1KLHxIcU0gdYs7CKURZC/mORQmNF2xK1kAN/yZN9ZGgtNYITc41w+S8asc+nPPAwDaRrHilZpQu3ypzraP6nldeXlWephhjypJYIPXg3ps2eVj8PSmaR2bPI9FmJS0xD6xfLvOfGFuQYAIqVLVZXBWPetQ8S5QkVcpj5SD4pjTnP9iQ9tn+A/1AsqbuxBpSIjUMPRmJAxbdVrKw+X81jFoWEv9+Pjbz5658Zq4MH1N5d7WWn1BxJyZtZcVCebJkFD037T0Gntj3jTYMLAKRXV1efceljXqiniwfKMbctT2BQVrLeeZIXcxOLyauUFUPrHMNxei3rsBIOyhNTesSSPlPr4q1cWg+VMd5dWC1Ram3B0wQFWpZlE6BfreVgSfbWK9ZifVGrFus0Lp21Wi2cQwlx5hmeRKkMU/dDHS+eyKi89X2r4yk2llWO+HEde860l5L46/KWWdMLB5swmy44Z02xhVbJjdeQ5C3Oek3sOamF2j9jeXnZXnnlFXv77beDixNn+WBtAZASJa3b7U5YUBhMDGoOU1tYWJgQKmjzGfRmFrQXHKDKviIsWWgu2BOjzN3MwoHAej4V7luLi4s2HA7D+U5qClYyxoJP+zCxzCxcx6TGquX7iPpBRNiXgzYECw7BEyAQuAHqZk8dG+RfLBbt1q1b4SBe2l3DGvO88/Pz4HqJaXcwGNh4fBWl7+bNm/bkyRPb2toKkYzURKzjVTVTCDVIR6fTCeQJIaTtSFh49qch0HCjY0wgcNFY+YP1CGlOWwC+za72j2HlpQ0Zw7gCZtllpEDcyaiXWkOo78LCQhiDhMKnji+SUqAldp1PeSA/Bki965V3w7qOPMsja/oZMguJ8pYodbXQABOzlMnLYV3cvWyMgeyXJcdjZdL3aeQuVp7rkKgYuNc8UDBp0AcfilzBVgrw6pqhwFAVG+rCpASK/yDUGvpd55p/XqxeWr/YHPDt+Twptt6mxklM9vu6+DxSBDCVXiYg8gRkWt6zEiiu9SRKrd55/eWf463DKVDJtSlLVLPZtGazGQ7dZZ1gHjCmYyklK9RqquX17lRaTlW6epJGXv7lyYwnMXhHaL48y1+rn7Vd9dm+zgrOqWPKS4A1ln3vMZc+fS7Hq6C49fuT1O1O3S4LhasjFpSAqstflmUT3g/gLvIBg6hbM/2qlnElUupeqRYdP4cpY+x73lqj/U1bp/C+b/dYX8Ze2n8vkp6bSD0vyJgGlFILv3/3jZyaEExm/ww6MwW6AJB37tyxn//857axsREiqKClJPIc4BirC+53aqVigdVFFEJCGZeWlqzf7wdypLH/R6PRxHlK1IP9RZQHgaFnjfBMDm9lbxMTh708uvgRTAECYXa1LwtC6Cc5k079UQHxAHNIAKSQYAbspSK0N/uGZhnkWXa5B2xtbc0++uijoJ3xvrD0OS6PHC6LYNI6LS4u2ubm5oRL3enpabBoQToggRq9S7VAuFhqBEW03mzQHA6HgcRRX6xR5XI5lJkxaWYhwqKeK4FLJGOfz2imBoPBxKZ2ypVl2cQ+Lvpb2x+iDfknMR4gwhp18XlTHthOgZg8cBMjUbFrYi8tU15585LKIl2Y0BSrOx8EisUuti9q2nP0ulnkdArsotiY9qzYfbHf9DmxxTZ1fWrN8NaJ2Prg7/MuRirD9DPWqBgQNpsMq655armQQQoUtV+QB/49Rua1PrH66ruCK73X912M3MbGTaqffLlmWdun5eMJqv7uX/8cKUUUfDljKW9OmD27Jyr13Fh7xtbAGLHgd5U1S0tLViqVwqG7jUYjBBcAu7AW+D1RlFd/80RFyzhtbHoSRD5KBhRk+/lHfZUwKUnz1gs/ljTfmMU31v66D8uXXYG4f4YSKe/W5y1+3iKl81dlDFhGFT2s87QT9fKWbdpVg1L4PUTkzfW6jSTmohzD1r4d/fjx/av36Wftf2/Z8mu0J7h+fPn89OW3jTxPeq4DeWMTK7bgTWOMec/xjRBbaHk+v2lj81telKuYcNDGvnXrlv3whz+0mzdvBoEDSRoOh3Z8fBysObiJEGUpFY2FKE9ogtTkTB7UA00/E3lhYSEEISCPw8PDAGYZ2JSVckGGWKSVSFK2er0+EQEQlzA14/KiHnzGyqP+u2aXob7ZY0U7UF+uVQvV0tKSDQYDy7LM2u229fv9qKZLE4Lj5s2bYS8WJNLMgoZF9zAh3I6Ojmw0GgVrFf8zLlZWVqzX61mv1wvm9l6vF56tWiWNAIh1x8zCb0q6CHtOu2GFYx5p/x0eHoaIifV6PZxzQ1khRWZXGkXGDH2hpB33S8aZmvZxQaRu1Mm7P+iZYZCui4vLiH8vc49ULD2vTCF5gpT6HgNQqefk/UdSguyDS6g7n0bp0+AoqfLEynIdkBm7XuW4ytTrENgYqI7Jcv97Ss7zLP08ra4q03VeK1lKfVZg4hd/rR/rju571cXfE6kYKIyVN1UX32YpJdMsbezB+nVJVOo3/zz/XH22vz5Vd0+aX1SDrEn7edp4imEgUmrMpwhg6trYc30fKWlQEk99fIS+Wq1mjUYjGqHP7xlRGZiSPbF+jfVhTJam6qxtFMOYmjdjQrFIytWOddLf70liigT4+/TdK3rpB3WVAyuy79K7w+k8pJxqgVJFqb+fABEqX8bj8cRRDYrjwBUoYGPlMZsMgsPLt9k0jB57z1u3uE7bQ5VPahFTq79XOuk89KTLy/T/pxap6yzUmmZZ5HUBV3YZu8Y3mF6njD7vObH7C4WCbW9v23vvvWc3b96csDSYXQWRIPgAA1ddsiA/uJPhRqdAt1AoBNey0Wg0ETQAgYZwOzk5Ca5T+M8qGCcvBcBMXCxouI8sLS3Z0dHRhKVFJ2CWZVYuly3LskAW9VwB3d+lAgCyptoSLGmEzB4MBuHsItXgYvHAQrS3txdcBHUM+ImIkMAadX5+bu1227788stwwCaWOu7t9/thXxTm7mq1asVi0TqdjhUKBavVasHU/vjxY7t161Ygq+w/uri4sEqlEoQT7asLk4ZnVesW3xlLWOVwoYSs93q9YKXDBZF2082k1I98IG1Y1wijTxkIfDEej8O7vrAmQuoA9PQ/z0cwQZBfdNNmbGF7kTQNKF4nH703BkhTiX6JkSgCthByXg9f9fui8haLGAmJlcN/V8CQR4jySNQ00D4NTMVSClR6cOXzjn3WfFIWKSU93JtapPXliZiuX35NUTc/5EDs2an2iLVh6nfqEAPwsXvzxre2dx7Qj43N68zhPACu/fA88zcveUCtKda+Suxi13ONnwe6LvqkdYsRCM3bjz+Pe9QSxXlRuPQRXII9s2b2zDzwSgPvVu9JAPeo0kUVtlp2zTc1nnUexbyGtF10/fHtpxjEP4My+vb080Dro9fyu+4zNpvcfzgejyfCiKfmuPY9+IczpAgIRj5qzaLvUMyCHX1d+A/cBk6NKXPUSuMtUb6PfFukZG+qnWNzLtXXqvSPGSauo2zMS8+LOa59jlTsYakF3F/v/5tW6NgCEJss/nmxZ8TK7oUUhKDZbNqDBw/s1VdfDQQBYAphIXACAAnCMBwOJwIOZNlV6HEmwPn5eQhjDQhX9w4/4Ii0xwACHGPNIJ8syyY2HY5Go2fc4wD3TEwsaABntVxkWRZ8emkHSA/9oEExcGukPRGwaN339/etXC6H8lInnqngotPphD1dvk+1HwuFQjgHq9vt2sXFhQ2HQzs8PAz7TXDhI9H+w+EwhALFvXFhYcFqtVogp4VCIYQtpZ9pD6Lq6YLE9WY2YdHBZQJ/dIQf4wjwDLFFSBPxL8uyEMxBtWsa4IHxSBvhvsd5Z8wHNp1SJp5Fnuo6oNZENqJyxhVWNj0v60WJlCcI09KscsTP9dgCPYsQva6g1UUA10g9L8qfGaRR+mILWExm8ZxpxMOX67qAN0+Wpq5NpVn67bp5xq7Xeipp8S5GMVDqQZSZTYBGM5sANVzrQR5lYNH3rkKxslB+X4e8NvHE1gPDWYmC5pf6Hmsnzc8/L3avb1stZ+zZqee/aIrhjNR1100xcuhBet685Tq+e9c1bUvWY3UZJsw5EfoqlUpY81WZAEAneW29jtOY0kFfMVAfm0uxNtK6pQhriox4y0KMSMXmhNbJ5+fHohJExT9+zdI5HlPQ+Pmibr14dxBcQiP3ahlU0RObU76cSqQIOJXqbyVRHm9rm+n9Wh9t2xjBMnuWdMaSX3MlHDQbAACsh0lEQVR8P89CnlIyUftd9+s9T3ruqH3Pc32MFOnvfoD7hSGWUpMqNgF1gfO/00Hlctnu3btnr7zyysQ5SpSD7+xpYiEl2AMTi/1MAG+sA9xLB6JtIIIK16uVBnLE5ARoIww5dBWC4iO/YXHA2kHYdk/UsIop8KctcR8zs2AqJ38FCbQVwPv4+Di4pHE4MRtacS+AeOJe1u12gxVJ+yoGGguFSxfMJ0+emNll2PDd3V0bDAYT53kR9EHzWl5eDm4OnU7Hut2uLS8v29ramu3v7wdr3NzcnHU6HVtfXw9WM/oEUslkVCuN16wxdlRQZdnV/iQzC1ZLJd64j1J+DVqhY6tQKIQgEAiJ+fn5QEiXlpZCHt1u13q9npXL5XCdCkmENeVEUzYcDkNfQ6QuLi6CVS3PCvyiKUYKngew6/cU2Etd7/Pm/xRwVO1eyqWPg5M17LVaL1LPTdU1BcL8vV7+puS0Xwyntdm08qVIXay8sT7333Xx9ilWl1nGTCw/dSXyfa6uJpqHdx3xJC5Vrlg/purE/+rBEVtrU/el+sQ/L1YebasYaEqRu1R/zfLsl0mizCbJ8L9Eio1JHRO6bsQwUqo/NLiEJ1G1Wm0izLnZlUcEZCpGjFjnY+PTu1tqOWKE0SuvUmPUkyk/12LffT76WUG+tqG2tx8DMayYR3z1f73fkxltT71PD1xXN0t1uwM3UCa+q9cCz4SMaTAbxSUaTMLv3VeLD/nFlD36irV9rG/47pVWPq8Y4Yo9k7J5mZoaG7HneRL9PGlmIjVtsZ31vuskb67zwncasEp1otcq8FupVLJ79+7Za6+9Zo1GY4IknJ+fB7CpWprhcBi0zePxOIAhD1r1uT5E9ng8DnkQPY4JRCAD/oOsQaYuLi5CaHL2I6n/LJYrSAEBMADDaCiwJACG9RlYp7zWCrOy2VWEPDML1iVAPtEEi8XL86myLAuBOtSaRrvivxvTOnhBhVvfZ599FuqNpQ6Av7GxEYJ3MAbm5+fDImNmwY1ubm7Ovv3227CYIdw4+wkLUq1Ws1arFfYLEVUxy67C3WsAB9qLsrOYnZ6empmF/WyQID1vbDweB1KqGijtf4g9RBuig/WRfVaUCauZLl66h40yEjkO9wQ9l4JFmf6OAbLnSX6hfN48Y+Vh/k4TsLExx//6rv/HyplHovzhu0qivDXDpxSBmRVwxuo4a3tfp0z+fw+6YzI6RgTy2lvfZ1k4/TqC7KGf1HVWQYYu2MgH1RIrgCRP/R4DnJq/km6/9vn6p9onb1yk1k4PYGL/xeaSr9cs2EDzSsmLFNbwQPhlJfaZxMhC7PO0eqb6LQb2YmDV/6d5xV5KesAeBJdoNBqBSLFnGa+WlBXGj0tfjpQrqpZF5Wxs/Gs+HjRrm6QwH8mTvNhY9eDdg2h1bdS5y71+Hvs8KYdP4CzFnShAaW9eBBaCAPnzprgOvKEyw8tVtWDRFowNJQ7IK+QcZdR6Ur+YC3LeHKQsvn917vv55cei5pOSE74vfZ7an7E2o09i/XeddG2L1PMKsNh9XoMdE9ReqPpJ5idOCkyw6Ol1+jwi9L355pu2ubkZCAvX4RpFtDTAMdYJopnRSd5krmZU7qNskComDuHMsQzxPFzYIGB8ZtMgYEAHPBNIo/GQJxYMDUSh4U8hVwQo0KANBDbAzRDCBsDWaHyUYzQaWa/XC4fEnp2dhQN96ZPz83PrdDrBfc6PBx07WZZZvV43Mwv98+jRo7CfDCGrmjiIm7orYC0zu9w/9cc//tHW1tZse3t7Yr/Z3t6e3bp1K/S3JztY7dibpvVmjBKIBLJFWc2uiNZoNAp775TkEsxBARz/FwqF4B6ogpv9VcvLy9Zut0MY9OFwGIg34wnr23g8nmh/noWmE7LG2F9cXLR+vx/GxstMMbCfAlov8uxZ5FreAhKTUwqM1aUvdlYUfee1gSlCN43M6EKfAiapBcosvjch5U4xS4rVR8vpUwzY8z2WlwcAKYKm+ShoxO1SZbVuaja72uPogZvX5vqFm3u1rH4jN8/XvXEpMpUH4FNzIdbvsWtjwF9/T/V9ioBNK1sK9PI57/Wykl9nYsm3WSrlAT4+z/J/jECk6g5QRpnqSVS9Xo+GOmecat084OTZqkBIlSPWd6Q8oJpXV/9/3pjyYykPEypB8BY5tfj4vEh4B6G0VdkKgfIETYmUyggzC/0CgSJUuiq41WUTQqVeTerxxG9gELyWYsYJLYfvOyUpMfId6z9SzHVvWj9qflxLmZX05MkhL3+9sir2v26XeJ40M5FKLVDPk6hIXn7enKfPj7FZnUh5C4rmpx21vr5u7777rm1sbITBrlHPAOKQP41yBmBlwJ2cnFilUgmDe3FxcUIIoeHXYARE7FNz7eLiYrD4LCws2Pr6un333XchOuBoNJqw6OBTq65/tIO6XkHcCMPN735SFwqFEN0tJsipi7o0QsSwXGgfa0ACjSwH2Md3t9PpTJBV33d8LhQKtrGxYfv7+6HsCB8sglh8Njc37fbt27a8vGyDwcDa7bZ99dVX9vjxYzMzq9frtrGxEQ7vVbJK32FhMzPrdDoT7otYbjQcOec2kSCrWB0ZH2jB2XPVbrcDmNLzwk5PT0PoeupPe2bZlfVM/d+5FsGcZVkg3pRTtevsyTK7spaRn9YjyzI7Ojqa0ObWajXrdDrPTuZrpJhyRMeQHw8pIU5eqd89GE8pamKffRlSZVKAHIvQpy59PtR5HoHyoOE64Cz2PQZCZmlDzX+W63zZUoA6BYhS+fu8fYot0rqA0k8oKTRwi5YJbbG3zGt+uNAoqWIuqmZZCZSe3+eDjMTWvmlpGlmeRor8szyois2VWFtPIw/6OUVAeFc5nMrzeVMeOSBpnWPzJW8O5KVpBGlaUksqwSU4LwoiRahzwHfMjc0TKb7nWSS0DrHvsbGin1VWQui8hSmVYnI/j8Sm2taDdp33/kV+zGf1KGJN1HaDSJldeSZQFi8jdNsFuMwfiovX1OrqqlWrVTOzicN+dUsAfaXrO4pQTy58ualnjFx5q2qqH2LzRH9PKeWUZPKfyk3NJ7a++XGt/6fGxaxyNS9d27Vv2v/TwI9ek+eXyAIU8//0FffaUx0Q+p+aVXl+sVi0arVq7777rt2+fXtin5HezyDc398PpEoHGxYNSAoDemlpKUSr0+AKWXbl2qGaA8gFWgStx9HRkY3H43DoK9YMAgqQdP8SIHdpaSlYuTjrB0sLJAEXMM/Wx+OxdbvdoFlX4YGWnbZkbxNCm3akjgsLC4G8AcIhfqenp7awsGCVSsWazWaw0g0Gg/BMNDMQgW63a0dHR0GIFAqFQEwGg4EdHh7a+vq6vfPOOyFEOkLw+PjYut2u1Wo1u3v3rm1tbdnvf/97Ozo6CmCJ/j07O7MbN26E+9RdDrcsIuwAqGhviLRa9LDYMf76/X5o09FoZDs7O2EcQaCILIj1hwW00WiE/qIv/dwh+h/mfT3Al3e0YQSOoD3VSoWLJES51+tNBC3Jm9PXSXlAMA/4z5qv/0yKAaQY8Oe/1DPU0oHFCTc+PXRXw5yntG1a3xQwmbaozNImMYIVu27WNsi7z5M48poFQPGeWlDz8lBQ4i2GZlfyPuZBgCzjsy769LVamdR1WsvliZxapyDeGv005bLiv+eR0bz1OY/sxtK0ueA/p/pHy6zvsXtnITvPm2J7pGLf/djz18XqRVJwmNfW3JOndfd56jpMqPN6vW7VanXCnY+xpPtvYs+cZj3S91Sb5fWVl2cxsuKv13bTa5mnKqPVZZD7YjLGkzhtU5XhvHSMqvWHfLV/yU/7BzyAfEFpwrMIlKXKWJUxZpf7nDc2Nmxzc9PG47G1Wi07ODgI12l0Yn3+8vKyVatVazab0TYym5RrSrLy+pn7VV562R4jOCk55MeC5se1ntT7scZntQqmXp7wPq9see5gE7HBPuu9ZmlBrROMa9RaErsv1oixfHwZAeVvvvmmra+vB0ICkdIoKmjrWQDVjalUKgXrAfdzPdpFLEEAUr9HSkEugBWLAAOm1+sFsHV8fBwOa2WhprwAcvJQNz7+R+CenZ1Zu92209PTcCaV2ZXFxexSWA2HQ2s0GhMaEiYu5zBBPLAmjcfjiYiFgHvKgnZ3PB4Hy9fp6amtrq7ahx9+aOPx2B4+fGh/+MMfgoUH0jMYDOz7778PYUIPDw9DfpCNQqFg3W7XfvCDH9grr7wycQiuuiGWy2UzM3v69Gno02azaT/96U/trbfesvF4bF999VVYkLIsC2HIsRiqhkWFC21EO2XZ5ZlZuDTqSeKcU8VYoM4XFxchIiL7vCCtJycnExH3GAf0BYQHy5e6cJpZIHwQburCS10L6TvaEOE/NzdnvV4v7CN70TSNsFyHRD0P2YqBoFmBpYLklEufBpbw+6J8uX3eqefrtTFA5xenWPLPyWvnPJLlFVtaxhRYnpX4TVtoY8/U7ykihVxTYGN2JXvVgkv9GPueGCmZ8vXUNqUMyGpvpQLYIENSZCXWpjGCiRzw988KHlJ9BPDSvGLrcao//PiNPS9vvP1zpBj4S6XUWPftMq2dp4HHWN6qrMGlr1arWbVaDS7dukfXBzAwmyRtjEmzSUW3zgklEwrC/ZiKKdW8bFAySJ38ePEkS2WAltW7knF/rGx+nYrJSpURKCm1XxTzeKLMd45QUVd95AzrAXm32+0JLOUDgOhWi1qtNrE/nrKCDSgDBoB6vW6bm5u2ublpZhYsX7qVA5nmrWDqJeHJlbesxWS7/8x3/V37zz9H5UTq5ZPmqxY6JWDazv9iRCommK+bvGYhL6np0WzS1z+Wh07Q1CKhhIzO2t7etjfffNOq1aplWRYGFgCSA0bZR6PMG0LUbretXq8Hly0lN7rI0nmAKyaKBrHAkqJ7JyBCx8fHtr6+bu1220ajka2trVmn0wlmfaxiuJRR33K5bCsrK8H39vj42MyuwnVTTgA52lR1PYRMcf4QAJ42I49KpTIxEbBQzc/PW7fbtWq1GrSuBFfAMkWbsik/yzJ7+vRpCEleq9Xs3r179u/+3b+zw8PDYCX7L//lv9ijR4+CVYdxsr6+bsPh0F577TUbDofBslUoFKzdboczre7cuWONRiMEmSgWi/baa6/Z3/7t39obb7xh4/HYXnvtNfuv//W/WrPZtPF4HKyFPK/f7wdtkEbLGY/Hof/YRwaR5B6CgeCqub6+HvqOcd/tdidIve694zOLke6TUhJPu/T7fRuNRlYul63T6QQAh8uoClQl6dTFzIJFbjweBwudd8V8GWkWQJNS7MR+8//HZJEKcH1PXacJmQUYxq3Pv/KCSyhw0O+p8sTknQcGeeXPq1/sGl+22D0KiPQ/rxRLlW9a//ny5wFdv3YoQACIqvyjP3SxRfGioFT3U2l7m01uLFfipmUBQCqB0ndd9H0/vCiR8EB9Wnt6kq0y3pMon6/PO5Y8qdJrY4DpeTFI6tmUcRbylAcOvYzRdvJzOTaf/fxPAUYlIIuLixPnRdVqNatUKraysjLhzqeeJyQtm3729dW5ytzwdVUgnMJ6sXrn1Vn7ITb2fV4elOtvsT70/aSWGFWKoDT0gFvLFFMizs3N2dLSklUqlZAHx4+srq5OBML66quvJsiNmU0AfX5HQXd6ejpBgrRvKDvunvV63ba2tuzmzZs2Hl8q5YlUrOdc6ZpPHkomtW9ibRbray1jjGjF1uHYmqfyWJVZqfnox0Pqxdx4kfTykc+UFBv4MeHDgqUTOCWo84S0Tm4zm1jMlpaW7Be/+IWtrq4GzaJGKQNUm1lg3LxjNcJcy+G8x8fHYX8UoBl3qUKhEIIiUEeEHIMSy0SpVApubYXC5dlE8/Pz1ul0rFQq2Wg0soODA6tWqxMTKcuyYBnD4oG1ALDAczRIBaSR8kLMmPyQM4SF+vUTaANtioIKnlUqlQLRwU8XMgLBoCyAG/Z8ZVkWAGmj0bDbt2/b5uamPXjwwI6Ojuy///f/PiHIADAIlCzLgtWNz+1228bjsb3xxht2584de/LkSdDwHBwc2O9+9zv7y7/8S3vllVdscXHRbt++bVtbW3ZwcBDC1Y/HV66K+/v7EwAIQoNgmpubC33K2CZEvpmFaH06Rhm7kBwmPQf3QmCKxWKwNiLwlpeXA4nlmYPBYKKvSqXShHUUIQ3hpE2xFDJW8bvf3d0NizP9/DJSCizmffeLmAdmHgjy2QOHWQBqDHiRj1ollDipNUr3RaXc+VJAMSXwZwG/eo3O0Twyqd898UktfvrdE6o8UhdbC2L3KeBLlT32ewy4qUseeeveJuYvQIN9hl7TqZ/NrrTVvj5+47OSbcg340IBIARU55j2n7Z9nlU41nezzrPY7x7UpkBRar1PEYpYygNeL5KmEb6YnIgl305KTjzYTLVTTI7FiAXjVElUs9m0ZrMZ5LMGl4gFP4iRO/L3z9V+S40dJVixtophP95VwZDqY98GqbmfJ0P4HpO51MGTA+2T2FhVue9lLHMchfx4fOkFs7q6ardv37b79+9btVq1brdr/X7fvv/+exsOhxNtADZSd0xIBERCy4msiLkNLy4uTuzLJ+Kvelj5uaqWc/JPjYEUUYophXx/Kf6h7dUS68mUliVFptT65K1R/rcXSdc+kHfaQE7dp2nafSw26u4ybTH2moLYNXQMA/+DDz4I+17YjMlBaH6zMIMbQMy+lizLQgADzKiDwcAODg4C+19YWLDhcBgGtA4SJgcBGwhZ3e/3bWlpyXq9XiBotVotkDsmCCSDsNaDwSAIT8A8Gw4hRzxPz5Q4Pz+3/f19q1ardnx8HP6HjGFFOTs7s7m5OTs+Pg6nY7Npnn0GaGoRHmZmOzs7NhqNwv4sNDPb29vBTU4PoCsUCtbpdKzf7wfN/fLysjUajQnT9uHhoX3xxRdmZsHSVyqV7PDw0NrtdhBStNvR0ZF9/PHHtre3Z5ubm7a2tmb9ft+63W7Yr3V+fm6PHj2y3/72t/b666/bq6++anNzc/bhhx/af/pP/8lKpVKw/rGviP1Seh6TEsAsy4JVk++Qa4JLLC0tWbvdttXVVTs/Pw9nNiHoGo3GhFsiZJ25yLgg4p4KU51L3W43WDpxF2TcqfuSmYW9VOzv6/f7tre3Z3t7e3Z2dmYrKyvBB/v8/NwqlUru3J4l+bmr1uYUqJuWYkRDF0j+Y5zMItt8mVgAGIep86IAyyoLYvnmEZ2Ugsm3VQrUxvL2C2KqvVJAOwXgPdjyLkOxsuv/eeWIEcJUu5G/t0hxvWqfzWxi7mXZ1fl9ZlfRMnVfawxQaJ3o61i0Ph+xT/eiepcizd+3YV5K5ZEixvqfBz/+t1lxQYpIaBvFyjJLWV9m0nGlwHoamYuBRE+geI+RNU0K+mLzAiWmhjvXfVGqqFUgzvrsQayfNwpWFah78u6Jl15Dyuuv2FhI3att6suSut8T4RjZS+FFfb66gnnLsrr/cS39xv+63aFcLtva2prdvn3bVldX7enTpxORor1VnP4AowwGAxsMBoEI+bZScscYYL1nzKDMQ66lCDB1VPmuVhwl5t5wwf8xUp4iVF7poHmmiJrOk9j3vM9KzJ43XdsidR1B5gc9LDqWn/8NoOjZfUyAeYtMXpkZYGtra/baa69NsHo0jxAC3VysB6UqYMSaQ1hoDbbQ7XbDYMZKpBuJzSw8i7Iz2crlcrBOUXY6fG5uLjzz6dOnVqlUwv4aJi15siibXQ1+rGkXFxe2urpqnU4ngGcOmMV1jyAVBLjADQ3NOpY7hDPCBGAPOcX6wqTWM50QBhALQMbXX39trVYrAI56vW63b9+209PT4P728OFDGwwGEyGk6aPx+HK/GQEmlpeX7Q9/+IPt7OzY4uJi2LC5s7MTAmC8/fbb9vTpU/vyyy/tN7/5jb333nu2vb1ti4uLduPGDbt9+3Y4MBhgTNALBAnhzTHF0/8QdCxJvV4vkGCzy31pCJDxeGwbGxu2t7cXXPMgorRRtVq1TqcTDtxVlwQIV7vdtmazGUje2dmZlctlKxaLwTVgcXHRTk5OrN/vm5mFPWMXFxfBDVDdSIbDYXCHGo/HIdJfoVCwWq2WLxRmTF6w63tK2Oct5KnrPZGJLcxcy3tMBiKfFBxDpmJ7oq5jhcp7Ls/O+z5Lnvo9TxkVe04eQPH5xsCb2bMEi3yn1TX1vz7TE2YFPrq/QKMnmlmw2OPOikLJ7ApUEW3VL8wKovTZqgRT10K0xT78uT5Lrf2+LWLtngcUkcOxsa/tGpsLeXNQU6ycMQLox3bsebr2z/r8WRMKJF92lUFahuu6Ael48G3iASTP1nJoebiHcbO0tGTlctkqlUpw4Sd6q7qlaoTWmCzRevGfJ1KafPkZO3qtEqxY8mA71qcqH2Jt5j8z57QtvZwymwx5r2PLW6L83tUYSVOlZaqvdQzRbxyYXKvVrN1uTwSYACtrGHM8mg4PD61QuAqWpa6APCPLsiBTUK5i9QKDovxGiar1iK0Nvl+0nr7vZlnfYuRN50JsbfJWQu0vHW8xQuVdPrWsyPznlSvPfSAvKQUqdNFAaMdMez5p4yqL10bSRTk2sGMTl8lCmf7Vv/pXNj8/P2HxwCKEVQeAiNseGnnIgZYV7ZCeA0AdtO0Q2voZAlSpVCYi0hFcApJD+2kUqPF4bP1+PwB0LAj8XywWQ7Q39nONx1fR4XD7woKEBQzXOsqGtoQ9ThC6QuHq/CusXgsLC7azsxOAOs+DKEEgGo2GnZ2dBcsWbn5Zduk6eXh4aL1ez2q1mi0uLgYiBdnt9/shEAVtfHJyEojFeHwZ9vv777+3W7du2cOHD21nZ8f6/b7duXPHzMx2d3et3+8HCyBaPcKF/+Y3v7E333zT7t69a8Vi0T788EP7j//xP1qWZWGv2ng8nrCaEYoUYc1Y4RwwPYyYwBCMMwgnVslKpRKIVpZlIQw6JBZ3seFwGAg+FkwIGAQJF0EzC6R4dXXVBoOBlctlazab4TDA0ejyLKssy0II9rm5ORsOh4EoYolCGI9GI3vy5MkUSZKfYotkTNDGgADXxoSlCl+/sMWeoykGLrUMZpNhiCH1GqlPQ51rRDfymyYX9bpY2SiPB0keSMTuzwPgeQTOX2v27H4kf5+X3zGrYF7++lten8XqGmsXtRKpix1z2u891E3VLM6QLWSz+u17y3AsQqMCNm/BQmmGnPBjINb/0whxarzlEXlSyiXKr8X+nc/T+sXnp//pnElZP58nsY55oko5/DjNIweegHk3sRi4TvWDB+WaJ+tpqVQKYLxcLofxy7NiQQt0vCmh8GNHiZSXk7H+9eVPkU4/9vLkb2wtyGtLP+ZT49B7OsXmhP8PDEu7aP/SjsgArvP9hscCYerBlYPBIHisQJ7UQ4Z27Ha7AbOg6PHlR9aocnk8HttgMLBOpxM8JVivvIxi3MXmJe2m9Y0pwNTiyTW+f/yY0jHAfyh76DNPpNTjRq15KmdV1qbWixeVJ8+9Ryq2+Ook9wsOACdPCJMHGyRTk9YPcJIXvKkyvv766/bBBx8EEymDv1C4jNNvZs9MVgQ5B5vhGsVmfcCuWm8Asnp4KwOCZw4GgxARj01/lJVBvbKyErQTDH4zCwAY9xJ8ovv9vvV6PRuPx2GBr9VqwXo0NzcXQqljfSDyGhYwwLSZ2crKig0Gg7BPiz1NZhY0KxcXF9btdsNerK2trdCOx8fHYeLjz91qtQIhxUxdqVQCwD88PLTBYBAmwcLCglWr1WCJGo0uD/f97W9/O0HYNGw3wuzp06e2uLhoDx8+tG63a2YWrIUQOVwnb9++bRcXF/bkyRPr9/v20Ucf2Q9/+EPb2tqylZWVYJV68uSJnZ6ehrowYXHxI/oe43BxcTH0wfz8vA2HQ1teXg79VCgUghUSYsm40rkAwdNw+5BkiOr5+eWhzu1227LsMgDI/v5+cKdcXFy0VqsVFlyspaVSacLtA+sqY5+w64wR9mmZWZg/HJD8oiklYPU/5uUsYI37GJMpQKj5xN41LwXjyDx/bhRWKWSAgmivZdX3lNY7BZxjefg2iSXfrv76VL3zypaSwbFy6m+qAPKyP1beacDfj5tYX2r/ASq8RlbXArV2q8YfkIW1ERmlli/vxkedyVMVVP5eDfbigwX4dkkBc9+eeSDYt6MnEn6s5fWTvydVrrz5mMr3ZSTWD/LXdgJgMjYgzTpWU+XzJCoG5GLAXQG7txJh0WBvVKPRsNXVVVtdXbVarRaiwfq+1j4EWyiZ8FZPnY95sljLHgPgvgypNMv4IbG+63z2QFj/9/frfPbY05MeX1+VywrYeb53o/RWELAMLt8oa3zgByzdGlF3PB4HIoUSl7UfucN41Wcoput0OkFhj1WKdR58CTZVeefbQ614sb5TouRJqioKYspNfy/tyr0xxcK0MerH4cuWI9c+kNcnZbIKEMziISljSX9XZpllWVhY1PTuFw2d8H4xptFp7MXFRfvbv/3bABRV24J7F3kpqNG9U1hrAOFmFvYv0bGNRiPUp1Kp2Hh8GSkFNzM0oOydYPFlAlUqlQltxMbGRpg8aCJ0wqsQhMRh4cFFC+sFgP/o6CiUo9/vB61nqVSynZ0dOzs7s3q9HupNcAKsVhARJqGZBRMpC0+73ba5ublA8tC4FAoFGw6HgYgRqpU+fPLkiR0eHgbLR7lctvX1dSsUClatVu38/Nz29vbs4cOHE5oZHQsQ0cePH1uxWLRWqxVICnXPsiy4u73++uu2trZmR0dH9sYbb9ivfvUrOzg4sN/85jf2+uuv2+uvv25ZltnPfvYz+w//4T8EULyxsRFM5+1222q1WtibxtlYo9HI9vb2ArFBC4UGkdD3EE/aijGsWiUsVisrK8HCxX4Nokey4I7HYzs4OAgWN+qMEGeuYEUlmAbEDnKGWyD7fcyu9on0ej1rNBrP7BN5nqTzbxYByfz0Qj0FWGYhZpqmAU/KTBtBaNWtT10sYq4ifqFOlcXMJtyxZiEusbxSwDoGxvPAb6qNfTliAJ7fFMxp38fqlkp55Dn1fB0rXkMfA7doiNESq7zB5Q/ZgxVJo7FiQWAO6nM0L/XZp40BbAAnxnus3WNtNQuBirUnSddwTzRmae+862L3xIhV6vPLSAQOio0jXWPNJsnBtDaIgXLuS/WZEhq1fJhdWSgBydVqdeLQXdZ2yBjrcmq8aH388/x8hDCkSFKsLrFx6BVYqbESkz/anvrMaeNNyxOru/9fSZgf73ynPXDZpt1TLzML2BFLInin0+nYzs6OtVot6/f7QbHLC/lQKFwe5zIej0OAKPbHlUqlif23/nezS4VBt9sNVikUfuAn9RpTayhkSvG9J+HaznlkJtbmMcyt/+vnaQqM2LjU/2PzUuvzvLLlufZIMZBi55/Ers0roP4OGCH5Cc71sUY2mwQZMcE8Pz9vP/7xj21xcTEEIMDqwSY8BiL3UE/KQZADQG61Wg2H7WLVgRgRUj3LsgCAsUwRTIAEYK3VahORoYjQpgSGeh0dHQVXoUajYfPz89br9QKBAhxrpD7cBhcWFmx9fT3UHWvU4uJisHQR3Q5hQSS4LMuCFg+wgCvbeDwOgIEJy+nblL1QKARyhraFsp2engYSSQCPubm5sDHz+PjYtra2bDgc2hdffGGDwSD0MePET4rj42N79OjRRJTCo6Oj4IpXKpXs7bfftgcPHpjZpcDb3t6227dv2//9v//X/vEf/9Hee+89u3HjRtgkev/+ffv2229tPB7b4eHhhM+xum8ypgBcun8NAUxo+Dt37tjBwUHot2azad9+++3EmWGQf4S+WqHMzPb29qzX69na2podHx8HC1mj0QjRHnd3d211dTWMYx37W1tboY85mJc2ZX5SBtxJUQzg1vqiCfAYI1I+MZ4QsCyCMU1uipSlCFgeYNJFV7XEemYUVincKHQ/ToqgxJ6XB4Ly8oldk7fIpL7PklILG/+l6pBK066Jka1pxFDBqicH3gXEA0xkk577htziOtYLJfZeQ6xBANRyoARKXbkZNx7gqFXKr3Ox9ou1RWpcpOaZJwJKPnwfpPJI9VOs3LGxnpIDL5KwoqsWXvuPPjR7NqhVLKUIlFp7tF76TCVSPEPBnipgK5VKCHMOboI46bjVFLOOpcCnr4svc+zaFAHR3/RaL/d07Ph3nb/++rz1IfZOPtrutA+fFU+qlUn7RQmmWqA8GeF6Ak3g1pdll4fat1qtsJZ6Ekx56FNdf8FcyBgwiK5ByA+8hVDw1Wq1iUBRSrrVNVHHU8yCRJulyG9sPKTWViVW1Dk2TjRPbyljvtI3MU8Q7xKY4jCzppmJlC4IAAKEiwK7GNP0GhFN3IdPp2oGFRQpq2cBS7m/mD3bgcVi0dbX1+2nP/1psNgoqwbYsg+E8qNlBiDiEnZycmIrKyvW6/XChs+Tk5NgWXn06FEws3c6nZDfysqKlcvlcGBsrVazfr8fwqYzeCE/WIKKxaLduHHDer1e0ICOx+Ng9eBgNtUa8R+TYDgchgNyB4OB1ev1QHaY5FhPID0QTjObOAyWCH7kCfnBasZ5UlhMNjc3bWVlxb799tugTVNSx2RG63J0dGSj0SgIh3q9bhsbG6EdT09P7Q9/+MNEqNC8/of40Z687t+/b/fu3bNyuRwsOd1u15aXl+3+/fv261//2vb39+0f//Ef7Y033rAHDx5YoVCwH/3oR/bdd9+FA3MJwgGgVtceSBSCCoEGWWVf1+7ubgg9Xq1W7cmTJ1YsFu3+/ft2dnZme3t7gchDXpmbLKRYEbMsC26q7Kna3d0Ne6toj1KpZJ1Ox87OzqzZbAarbJZlwTqqbZpll5Zb+mxtbc3a7badn59bvV6fAHjPk/xC70mHF3jIBw/gVHB78hQDEz5Ps0lNfEx+qesVLlsaoY89C/7gXd+emjxo0N9UzvryekAbq0/s9+uSqNT/qd9jczNvEY3dr+9KPK5bZgiIWn5YG2IEl7JDotQapWXyJF7L6scHwIb/kZF6/ANyXcev7qWkrHnrn9bbg2bfZn4ceCVE6ro8YBSTx3o99fSWHk/WUnm9rJRlmTUajXB0R2zux+oVIwnUi3Xbh9VP1UfJkwff5K3KGvZFNZvN4M6HQo12jM07xVWxcR5L/KeRXH07xH5Tqw6/qaeRbwclC9peXqalyu3lus+f/poGmPPkq45RMBCYkbIrCdIgH8xtdcM3u9ye0Wq1QsAvv+bpvFFCrWOC7RK1Wi2Q6oWFBSuVSkFxz5rPfm2MBZVKJSiUPZHynj7UIUasdcxqv8TGgSeuvn11Pdf5oONK9+95j5TY/Im9/p9YpADzsFs/uL2pzw/o1ESFwADmzZ6NXqMNo4eQxfLSRlehMTc3Z3/3d39nxWLRdnZ2wj4s6kNYbvZBmVn4bzQahQEKsUAzQFAALDOj0WWEPyYLblAKUNnHg3sgAGxxcTGQE4A3YdOr1Wow5XY6HVteXg6BAggIgbWAukHc2OOElYRJ0+l0LMuyCSsazxsOh1av18MBuWqd0I2GGjABwF4oXLqn9Xq9cB1lLJfL4RDedrttGxsboR84J+vRo0chIAVtU61WJ4JxtNtt++ijjyai/6VANxMFgvqzn/3M3n//fatWq8GCMB6Pwx6g7e3tMOZ/8pOf2K9+9Sv73e9+Zx9++KHdunXLqtWqra2t2b179+zRo0eBRHKYntd0MG/wRUbTzFg5Pj4OpJb2ZPMpZ08pKSOoyWAweGZvB33H/41GI7gNInQ3NzctyzJrtVohlDxnWKDMyLKrsLmcg4RwpU+Pjo7CnjP65EU1O7qQxIS29qv2u87/GCj0efsF2pMRr/XU56lsUXc+rE+lUulaZ0aRZ+x7bHFKtdusi0BqvvBfHoBFxvk+iZU3di+f80hADKTxewro6PWx8nOfLs60gWoolXQh5wFDfnN3rM88CNL5zzhhHwJjVZ9TKBTCHGO+K2HHxcYDiBSB9spCTwB832j/5JFzbd88okwZPDnT+eX7LfbMvDn+IinLshBcCTnr2zMPlGm56SPkA1pwj2P8HMsjUbqGQaTK5bLV63Wr1+vBRZs2VpBJ2RQsequr1/jrZ21n3y55RIrrY22tYDhFyv09/vOs8tOXMUW0/Bqh1ygx0kNr6Vvetf+8RYr8lUTNzV0eK7O3t2ePHj2yvb29CW8TXxZdayBvCwsLVqlUbG1tzba3t63ZbIagYOACcA2YEiU8wY94N7MJi5uOIW+R8konT25o2xhPSCXfB54YaXuoLPBjWmVczJoVm79a9rw1Ni/NTKRS5q/YRPKauVjiPkC6Chyfn5+g/nevmdAJUihcbu77m7/5G1tZWQkT2LsBnp6eWrlctm63a+fn51ar1cLCCdgfj68isuASR9kBySyI/X4/RHCDCOHGxkTUKH9YFLIss/Pz84lzicwuLWG4Cm5tbQUyyMQZj8fhIGDOF0D7iTZJz5QiQtzKyoodHBxYo9EI/rKdTieUsdlsTrhulUqlQPCUiEIkuNbsUghB0KgjA/js7CwQGMo5HA6tUqlYu90Oe7ZwSwP8V6tVOz09tUePHtnh4eEzEXQIhEF/6bhlPBQKhXCAIdoaCD0TcjQaWb1etx/96Ef2xz/+0Vqtlv3TP/2Tvfnmm8Eq9f7779vDhw9tOBwGjWaxWLROpzNxzhZnLRHMgTFBoBNc/whVDqlfXV0N+TF2W61WAOaALsrPQl4oFAKh5hm65+L4+DgsyK1WK4TDr1Qq1u/3JwiaRlG8uLgIlk/aEhL+/fff2/r6eogQ+LxJF37tuxjxUcAHkIiBm9RCqnn5z9OUP7pY4cqq1iiIlFrw/TNSICT2PfbfrCAjVv4UQE6RKC9j+c0D9ZSlJK9dU8A+dU8e4IoBKgWDMbcpvy/X1xEFAi9d2DUfb43we+ZSGmxkPmMKyxMAhnFLvloH77oVa/dUn1KHFOiN5aPa4lh/6H8eTKVAVR7B02enyvoiKcuutPJqJfN18mVVQMfvXnnm20JfJA+4Y6Cefl9YWLByuWy1Wi3si8Lq4PPiu69raozHXr6tY4RYU0w+cF/s2XnyS9cBn6bJbw/EvZz1VpBY0vqPx+OJveDkqXJDyYa+lICydwnye35+bkdHR7a3txe8Qjy25TlKpLB0omQul8tWrVatXq9PBBsBd4EPKCPBv9iSQuRd8tZjHmgHLze1f/LWE/o/1q5m9sxnJflcz2dVdMXm4LSk9/gxEeMa10nXcu3TAsUeqgPUT1KvJcmyLOz/8Sk2Wb2mRlm/Th7uY8IsLCzYgwcP7P333w+DkTJhLRqNRsGti0WPfHDNM7MQjW9pacl2dnaClv7i4iIcqoZARriZWSAIgEzdJ6OWOCw4LKyLi4shsALBDHAzbDQawe2qUCiExZf8CI6hm54BxPyGBtTMgquhAlIi2bXb7aC14x7CrGPCNrNAACAQWGpqtZrt7u6GCHXD4TCcvE7ZsyyzWq1me3t7tru7G0jb/Py8ra2t2dbWVmiTdrttX3755YTgod0ALFgulRgxiSCpkF8V2NSHZy0tLdlPf/pT+9//+3/b73//e/vRj35kN27csHq9bo1Gw9544w378ssvwyG7uP7QN1mWhQh9KysrIW+shnweDAb2xhtvWKvVCoSKACbn5+fWarVCm/r9GZRbhYG6ka2srNje3l6I0EOe/X4/zAXaAG15uVwO1lH8qIvFYhijzHXCtWMd1UOYnyd5YReTO7pQco0H8nlWK36PgbeUQPWAWbWLbOxVIqVnAkGk8ohTXlJAGrsvBZhUHnqZPSsJ82DDt59va65Vi48vY6oOKaDt79M6aLvEFlWujWn9vXZVLdcqE/xijmVX64FrOhpefwizkinmmGpBUXZBpviu5dL/AWqzEOdYn6fa2KfUmPN5+2tT4JbkCXiqPPSFD670MlPK5czXTWWTki1+Y2x48u7zVPDoiVQMqCJr8MwgSh/7Xj25pq19eX39wC7TSEWq3GbPulqn+lDbT8vnx6YHyjGZGSOnKge4j7p5cqb3xORbjGDqVhLIjY9OrcoWcJZGatb9UeCnXq9nnU5nAi9ov5G/WrTZW61BjSBCYGodn+ohQ11Q9IIHyJuIwN57QuuuXjU6d3Qd9uOPz54s5c2TGMGJyWSfYnPVy3r/etH0QlH7YuQlxiQ1AdbUhYjr9V016Drx0dRpbP0YoYPM3Lp1y375y1/aaDQKe6Pq9XrYz8KiRlhztMxo+gGj8/PzVi6Xw0BEOwRp4KypXq8XNP4QJYI5aOQT3MCIqoJrHBYdgDaWHu7B3Qt3LQgh4TMBvJQV6wztyMQzu3ST7Pf7AfQdHByEMNjj8TiEJafdcWWEqEEE0GCoUCZgASbsUqlkR0dHVigUQiAEysV5WVmW2ZMnTybCf8/Pz4fFA0LY6/Xs448/nhDsCpTL5bJlWWYHBwcTwoDymF36Jnst8Pn5uXW73UBGESqvv/66/fa3v7WjoyP7p3/6J3v99deDq+WDBw/sm2++sYuLi2DxY2x7YaN++ICo09NTazabtra2Zp1OJ1g4CAcPIaa/WawLhUIInY/fPMSG+jEexuNxcJ/t9XrBGka/sS+OBQACoEFX6G82yKLZYm8g85B9Wc+b8oRcSgbpGJjm9uP/SwG/VLkUMLGQQaKIxKTufH6x0bL6NA3MxK73ANUTihjQSQHr1HWx52rKa3MlvLOkWcun189CEBQMKeD0C2wsX60Dew+8RwNKLPoetxkIlbdQIpspF+/MP+YYgI/yaeAjr0xMtf91kidDsb5L9U/sd7+++2flPV+vmQWwP2/imd4tKwbyvcKJsR/btO7zUQDvr4m5dCmJIrrt6uqqra2tWbPZnIiqqsTB1y1mNdB+9URHQbgvk7ZXTD77Z/h6krxVmP9iCqMYmdLfU5jTP1P7xre5ylL97C1NyFclOeBRMKMnUxAVPTyZsxe73W7Ahowjzd+PAY36qaRK25R7lChqv4A1wXd6lihbCzQ4g5IgDYbj56S2Jf3ox4ifU17BqSRLuYDej6xkzPvIlH7M6rzNI1l+rF0nPTeR8iQnD3zwIrKZsng6mYbXBUqJEp0TC+cZe97CwoKtrq7a3/7t34Zob0yK4XAY9jzhjsazAf46WBAsAF0WOzT2ECEALucFFYtFGw6HYQJCQtAgUWYmH9dg3cBFkGeVy+XgznZ0dDQx2cmXstF2uPrRJlg0NEDC4uKiDYfD4G5HW1DGQuFy71SxeHmOhlor6BttMx3gaF7YS3bjxo0wQdmzZWbBKnN4eBjCe0Oi6vV62H/CGVPffPNNctwtLCxYs9kMJJDfxuNLSw0H02GZwk0TsgDZKhYvo9KdnJzYW2+9Zb/5zW/sj3/8o/3whz+0GzduWLPZtHq9bvfu3bNPP/00EEA9CwxCSZ/SZ4Q2XVhYsKOjI1tYuDzEmJC2ZhYIdLFYDGeJUT7GmwJ2yJqZBRBHwAisZASXMLOw/6pQKARBfnFxeR4ZZYck+ag3zNfR6OpQZwTty04pwBb7zwMBL6c0edeVPA2X2dU+BXXZwuqgFikAs49qmgf4U0ROAY8vL+3vr/Pg1y9Msbz12hjAug4ZmvZM/T8GumLkKJYH16Z+922n7jZm9gxg8YA5VjZvceA65LZanjQEvobCRzNMHhpOHYKFrI2BbGSjr49vD/0eG9upcRZLefMo71kxi6SO21hfx+b7dQnhdZInjDEywjpBot09OCMv/9Ln+Hb3vzH+UDizj3Vtbc3W19cDiULZ6nGRB6sxjMZnVSoouOR+8vPWWi0r734u+vHigbZiPyWwfu5qPfJk/7QUI7LTiGDKWqhGAN1DxT4jfmM9JKADR72w3x7Fu5lNkBjmOIpO8A+/gSlQ2GuACw1a4+cibQuR6vV6ITpylmUTXhQohsEbnlSqkl7bSImO9rP2gY43HS+UMWYt1P7z8pyXXhe7V8dhbAw9r5yZmUh55q6/qUZBB79OQpg0DZcCMX6C66TLm0h0Auy9Wq3aL3/5S3vw4IGdnJxYrVazw8PDENGMvSOj0Wgi4hl7QMyuDqWFKDHgzCxo8ZUUoZEm4ADkKMuysMGQemvkQy/EmBhovs/Pz8P+LfYcYbUi2AIAnvLg+4o7Gb79JycnwcQM8cFfF1CMxW08HgcNqk6MLMtCKO3BYBAmHxpWNBuQ1VarZRcXF0GTC4nBlIxVrFAo2O7ubgjWMTc3ZxsbGyFa38rKip2cnNiTJ0/s5OTElpaW7M6dOyEMOeMHv+Dl5WU7Pj4O7TM3N2erq6vWbDbDfihAD+Tt/PzclpaWQnRGynHv3j37/PPP7fDw0P7pn/7JXnvtNatWqzY3N2dvvPGGPXz4MFjuFDxDpGiflZWVEJJeoywSjr3dbgciwxlU1WrVisViiBxJPzDv6BvIsAYJ0RPQcTE9PT0NApi+ZdGmvMxrngFZmpubC9o2PnOtRql63uTntP8vBZZnBVupa6aRMx8wAPetUqlk5XJ5whqlxygwFvLATR6I89fmpRgJmvXevPbQcueBab9I8lvK+nbdFCNffl2K1Rk5HyNSqhzwgNg/B3mAFlhlNhYp5LyCkZhVUu/VPVO+nMhoH7VP3XU8iMlr97x+o1za3nl9kfo/9nuKsPt8UuDfk9vrguhU0rXZK4n0ueoaFcM9JI9fYpry1HynLdQCsbKyYvV63VZXV21jY8PW19etVqsFzxOsmP7gVj/W9Hc/Trz1QOV/THmjRMu3k7YDmIJ+Uzmo16XAbEqm+3t8nXwdtL9ilk2/3qkMoQ00f69QVNKkc1hJBErser0ecIMqOZEfKN5pNzyfcAfU9kGBpxEbVbbpnPHufWDBfr8f9vOjlGWfuR+/iv+0f7Rv6XdfFk++FfPGSA/v6sFTKBQm3vk9pkzSeeuta/8c6dqoRyseE6YxwaAdqoRI81GrEPkokeK3mAZBGwz/9R//+Mf21ltvmZkFYYNWHiuNuuAUCpcR5QgvTsOrpgdNIZYLovwQSABixSQAxKsPMy4BXsNFHdmDxLOoN89i8gLAKR9A3W9o1n1QEAeschClXq8XyFG73Z7QTqi1K8sy6/f7AVgXi5eHDR8fHwfrE+5/aFmazWZos0Lhyl2OSanBEdrtdgh0wWKyvr5ua2trNh5fWuuOjo7sq6++stFoZLdu3bK7d+/av//3/z74kDcaDXvw4IG99dZbwWq2t7dnn3/+uWVZZq+//rqtrq6GxZPnQ6woy/n5uR0cHAQf9H6/b6+//rr95je/sY8//tj+/Oc/282bN211dTVYpT7//PMgRAE/fIfcqSCq1WoTxLlQuAzeANlljNNmuImi2fb7JEajUSCGBMAg3DlEKcsug5VQFjMLVlisk+PxVWh1XF7NJk9yV20Z0Q514/PzpjxBl0dGYnnEFuTUwu1JWQxIaQhrSBQvLFM6//zm2Lx6ahm9UkrHTKq9YoQiL8X+9+TL550iUzFw5Oujz0uRqDyQnOoz8o/9rv8r0PGAyxMp1jgP0DyRMrtSiAFANGKq30ehbcM1rAfaf5RTQYO6hQOMVFNsNukCEwPqKVAa+xwbB7F+yCM+Wld/b4oU+c/eQjHL2L5umkYKtS9ZDxWXxCwc9IUnIqSYzGIcMpZWVlasVqtNkKhGoxGCF/EcBfIeb/lneusKfaSWAf+fJ5b+u7ZhjDR6ueLbymNKvS8lW/T+WFJip/PXl1P7XhWgXn7568nDW6JUEQLYxw3P74/CbT/LsqA8HY/H4QxQM7NGo2G3bt2yzc1Nm5+fDxYkFKUct6MyibqY2YTCFjyn3kqc8cl+dWScWtbU4qPjmpeOXcaSN36k+j7Wt37tZc55BZKXC548+utVxmv+XDdtTOWlmYmUVibms6hJCxtblP0A1cmqgzYlhLRhVKtYLBZteXnZXnvtNXvw4EEQNuVy2TqdzoQblC6WZleaB1wPcZ1CqDGgKpWKdbvd0A60i9llZD2sOUp6zGzCjY32YGDyjPF4PBEyHHcuNBVmFq7DirK4uBiIEPXCksaizMJL/riyKSEDGBOAgzJrUASsiljomHRZloWgBErusuxy/1OlUgnkrVQqBaJCWZeXl4OlCusIEW6q1apVq9WwP6rb7doXX3wR+oK9U41Gw1ZXV+3mzZv285//3H784x/b8vKydbtd+93vfhf2EzUajRClqVQqTRAnXCgODg6CVgYyvLS0ZK+++qp9+eWXdnh4aL///e/DXqmFhQW7f/++PX78OLgTZlkW9sHRLizERCbEtU5d9mhPyD8Ejz1lquVBOaCLG2OvWLwML59lmdXr9RA1EYIE+eZQZx1zWC9RAkC+cd9EiGGdIWgFY+hFUx6wSREhD+D0v9h1eXnpYqnWXyVR5XI5vDTUeYxEXSfx/JiGLUX0YkAzBnRS7ZgCu2ZpGaz5xMrp85r2LF+fGPDy/RUjeHod/3uAo22VcuuLAYFCoTDhomdmEwoFlDNaNu8S6+vM7zxTNdysc7rOUA61UimgmQXAxvov1ubTxpCmafPO5xm7P/Zdga1vr5eZPODz75RJx5iCyRjOmTXpWMRtGBLF/lkUdrj0qZLXa/69YkDx1TRs5dtDxxbPiMlQJZbaPqnxpP3J/NDtBL4tfd/ELIc+X35XhaGWj/9VOcF9Wm99Bn3Emq7z1RMOb1li3UAxS7AHMwtueyjDwVt37tyxDz74wF599VU7Ozuzx48f2/7+foisizVK+4Y2YL1XDwmUSpSdY2o4NB6SzjEzvl6xPlfrPG2n81kVg9qXXl75/vP/IfPoA80ntvbRJjoXfH/Sp/9iRIo0TThrQb1JVCdn7J1rYhoerXCKZS4sLNjt27ftgw8+CCHLiZh2fHxs1Wo1bI5XrQEdz+KFpgGyMB5fRWRh4AC+cdMajUYTIBogDAkDUOtZFRAf6ntychIAL5oMwDfWCIRBqVQKQF3PqNI290QPMsdhuZiXsW7gesfijVaMviHCi9aLslEX3Msgtd1uNxAj3CYhg5AYAMje3l5op7m5OavX61ar1YIQOjs7s4ODA3vy5ImNx2NrNBqhPTTMMIKg0WiE+tOPBJJQ8oKwweTe7/ctyy4tdERaXF9ft1KpZB9++KH9r//1v+zTTz8NVqm1tTVrNBp2+/Zt++qrr0K7YIXSPV+McRZD2lvDqmZZFvoKoQdA1zHCHNCw7fzGIcmAP4S0uoDQbrTDeDwOpEg1QTo3GYOMGw2tjzXynzt5eYJ8iAHv2LsK+hg5UY2WuvPhKos7H/7uui9K3b58OWapEykm0GNkQ//TlweaeXLb56vXe/Djy5oiNikypW2teae03Kk6+Gfooqpl8cCG31OgM9Z+CkwAGzpvcMthHirwUK2obyt9lrYH64USMN33QPm9Vcqvo9ouvt98P8VSbFzEUiwPXw7/OXa9Hyv6X4wcvKzkLZUkVYYyhjxY5t0T0rwUmxP0JXKGvbJra2thfcE7gjJ7K6sSEx3DXl7G5grfU1jMz7XYvVqvlPVXr1MFuMrcFCnNk4laNm0D/zztW67VdVMJmO4B0jyY8+SjOFLJp85VIruyNQO3OhS1fv/83NzlkTlbW1t27949u3v3rrXb7WCR8vgQgoR1zMyCEpRjbZCxRDSmjpApjTSLQhYipfJT93PG+l8VQzqGYmuLXw+07zSBu8E9asn3ctzLU00qD3Vc6th4nnStYBNMNL6nBrhOPv9SweNZrr5r0sHr2SONPjc3Z9vb2/aLX/zCbt68GVg9Yc3J9/T0NERJGY/H1mw2w7k/aEO8Cfjk5MR6vZ6NRqPg2qZnFZldBQZQjSQA2mzSIqWdqSB6MBiEZ+s7Zz6pwGTScI+e06TP1AWY9uZA3Pn5ywNwC4XLCHDNZjNMTCahmYVzqdCCcSixanoQAgBxotoVi1cBN9SdjL6gDS4uLmxvby8c6js/P28bGxvWaDSsULgMt97pdOzx48fW7/etWCza+vp6GG/avxqNhmARuhkTYaBtRzsjZObm5oLQy7JL03u327V33nnHfv3rX9ve3p794Q9/CFapxcVFu3v3ru3s7ATCqW43PGNhYcFarVYgHqenp2Hfkp73oAoA+oRxA2DXPtB9avQf7qaMPaIBEtjDzEIdcc9kj5WZBe0Y5deNrP5ZhEx9WSlv8YzJihhpiv0fyzNGUPJIFAQKtz7dG6WLbAoQpOrF7ynSk/rNl90TkTzZGiMv+hxvuSefvLJoWWPgOVZWT/xi+fuyxNrVP8+DXv6f5u6hwJJnY41nfyfkSokUshl5Y2bJZ/G8YrEYyki9VdHHy4NOZLgHLIALbY9ZrCapvkr9F0s671L9Hrsnb577z7OU4zopZn1UvKJ9o+tIjMAqgdGypkiAkiiCCnDGIfuD2ReFtwzPV+VurA1TBIp6peZPSk7E5m3qWTFrlJ/vtJVal1JEKiXDUmuAB+c+P70nJfti5dftAMzTlBscbYB8IEKz2SWm6vf7AROpqxplQmlDdEbWcj1yxuxK8cI8R9FJvuAF7mEcm12OfUgdMkwVtPo83eaiSgTtx9i4UNKaNx78y/crvyMDvVXVz7WUskyvZ+6BZ55XtrxQ+HP9TycQiQbwg8vvg9JrNU9dLDRaSazBGo2Gvffee3b//v3ge8qzsGjwO1aQ8Xg8ce4PixWafPLmd8yomGMRbMvLy9ZqtaxUKoWDfNUsXK1WA1EDpJM37UObYOlhwqHl94Px4uLC+v2+XVxchGh2SlT9YKMtsaJRLyZXoVCwTqcTIgMS4ID7CXhBH6jFjol8enoaDuotlUo2GAyC6yLmbQQQkQIBnb1eL5BV+pyzMpiovV7Pvvrqq4mw8ViM/MLnF0EdX4Af1biMRqPg/gk5wEoHgURL/ODBA/vHf/zHYJW6ceNG8F/f3t4OdWF/Gs9CEEJiaP92uz0hCCF3lHl5eTlEb0QrrqFVFahhKWJcFQqFCWFdLBZDGRhXCwsLwXWQdkWo8CwNVw9pos1LpVIgfpDjF00euPvPMXDgx4EH/H6BjhEPFdbqzsfG3pRLn25Yj5GAaXXU3yh/bLHQdvDk47rJg68USPJl1fbKIza+nVMp1dex8sY+57W3JyW0m1qKFGgxBryCj3v0sGWIFASLeadHQjDvFBz4tlDrEvcjt7D4qvuTflerlGqBY+0W6x+dI3lkJtXXep0fF/6ZPo/rkKMYCHqZKSZDPH7xxNaTqFiZ/Wf/GwQdywEBCdbW1mxjY8PW1tasVquFg1ZZs/B0YN3XcY3syJsvHsTGyqzX+jHqr4/J39h11yXmecnL99izZiFpPk+vlNC8dH+w2eT+S71XZQ3bFHCbIxpzv98POJX1VF2P8SxRrHpychIU5KqAUZKEgpaxQbl1z7YqD1A+65lUyDPwqCoPUuMfeeoxGOVScuv7K0bSY33rcZ23BqbWe0/K9Vm0z78IkdJKUyD9rB1D8gTKN27eM9QChT+qv0aJyoMHD+y9994LQJEG4r75+fmwz4gBenx8bI8fPw4bxSFADGwG8Gg0snK5bGZXrlAMZDbY4w6GBeb8/PLQ27W1teCGNxwOw8Z/9VnFJdCTHogCZkwmLWG7IXe46tG+MRcr6mR2tR+MaIYEGdBwmlgj5ufnrVKpBNLImUbqGqfCw+ySYOJ2CJFlIzauYRApLCutVitoU7AG1Wq1sJ/p4uJiItAE5xnpKdzUzS+KOlbK5XIoH3WgDErcC4VCGC9EfFxZWbGnT5/avXv37OOPP7a9vT374x//aPfv3w8aQ6xS7XY79CUT9eLiIhxKfHJyElw4O51OCFiiglUtioxjoi1C/EkXF5eH7DG+1N0T9wEzC2eOoQEjKAjkkfHLGIEQQrjQ3BwfH4eojMViMYxJ8nve5Bd/v0DreNbf814+KYDwYF8XSyVR6tLnD1vFEuXBcmqB1+fp7wrcWFhjmjnfXrOQoWlkw5dlFuIUA0MeqOhnD+5SZYmR0Tygm8qH95TVwGs+tax+TeMePc/FzELfAzwoP/cjU2LP0qQAhOezsKMAQ2mlY1UJoQcxsXaLtY8nsrOQab7Hxp+/1j8/Nm5U8Zj33BSRe5GklvSY/GC9N7NnAGRsjsfmYOy7av49idrc3LS1tTWr1+tBzjImUFqqhSBWfp6j41eV0TEShNzxdQKHgSti/UcfxsZbrB1i8jdmOfX3+3r5z+AS3xfMD+1X32c672MyQy1SMWuUlxv0L+sFivh+vx+CRnjFL0QYKyV9n7KqMy7V00u9YVReaJuSF0pbCB/P5Plq/VTFk58L3tXQzyEdX2r1T/W3EqaUgcZbhpmrajGLEb+8tfl50rXDn+skoDKeHPlO91YofddER0MydACkGrtUKtn9+/ftgw8+sGazOdHZmCn7/X74vrKyEtzPeBUKhUAiiOuvZOvs7CzsFZqfn7etra3gOlatVu309NRqtVo4pRowy74btPREuDOzEGkF64SeOYDFq1arhXoCcLWdEbAaYID2UcuOmo0Hg0EIIw4R49DWarUaiCmWrvF4HPYQYdlQP1ztZ8hAsVgM7n+lUikI/qWlJWu321Yul8PkRbjs7e0F32AIKPujlpaW7OTkxHZ3d21/fz9YIFdWVqzVaj0jnGNAi/pzlpKSAxYohBxujES+0/O8AE1vvfWW/fa3v7VPP/3UHjx4YDdv3gyuiDdv3rRerxdcEAFd3W43lJGNoBBLBfDMD6IgMj4pI5pxbX+EiJkFd0bcQXGt3N7eDnMRKxvfsWoSBAPhrSefM6bn5uZCSHsCVjAGX9YeKRWyvHtwHPus75piiyayhd9pW/rBu/Tpy4c6597YmNMy5Qls7k8RjhgATdVNn59Hovz9St5iIDtVB1/vVN6p3zSPWYhSLE//nTw82KSv/T4KDy69AlCBKMl/zytr3u9aJoiTzlNIlJIrBYgoaiBamq4zBrhe2zQFvH2a9hxdb6f1rS+T/22W+2dNum7qmEGmAthi2vxY2XybeXKqIBjwWi6Xw1lRm5ubtrGxYaurq88oN/H+SO3r8mRV57CSKCX2itNSVk0tu9Yx9mx/j7/ePz82xvjdz9tYnWLj1c9tfa6S9hgJjQFwn7+SWvrDy5ssy4LSmMiuSqRYW2Mkzswmgk95d1Kd92Y24eqndWD8+n7SdZNrKSsvdTll6wh19uUF/+WtRT6lZIUSJT/n9N6YoUbLRf95shtzm/YY4rrpWqgntpB7AhUTNinBo0l9FVXroc/0z15ZWbF79+7Z+++/HwgLpIdocEtLS2EvDOQCFygsSqVSyXq93oS/ORvyC4VLVz81q1MOgkfA6nu9ntXr9WBROTs7C5HTsEBgySJ/JiCEiImmBHBubi64GxYKl/uZKpVK2PTc6XSCuyLgjrpSbwVJ2k/7+/u2ublpnU5nYvJTNt1/NRwOJw6Pw22MwQqQ1qh8CvBxUVPLFsTk8ePHoe/m5ubsxo0bVqvVLMsuLUb7+/v2/fffh+Aea2trViwWrVwuTyxUXuur445+IPQ7ZFkFFX0MaRyNRmGfXavVChsy3333Xfv0009tb2/P/vSnP9n9+/etWq1aqVSyV155xQ4ODizLsqCp9uSTfUWDwcC2trbs4ODAisVicAM9Pj4OCyzWQyyf6noE+GIO4WZnZsFVs1QqWavVClY4BPh4PLatra1geWIhRVDi2kZbcC9zgP2C7XY7EMAXDX9On6W++8UmpvkipQC9jhUPLHRfVMylD3kRO3Q3tTBruVJkKFZvrtOF3Cdfh9jilSd3fdm0XWMpVY+8BTP2HF+uFKHScqWuj5WP5LXECmK13/R++lEXWW1fX69Y+fy49HLIvzOPASQK5mP7LwBJ6uKnJCxVhlifxNZYrat+jpGC1JiP/e+fm+pDD5pSdXgZyVsEvHKKZyJ3ecWSJ+hmzxJTdZMjoFOlUgnBJYjQhzufAnddp3yKzWUv97z3ht7r79c5w/PUoqBzYdo493I2VrYYkfKffbt6WUGfsUZhOdL+Y24r+dA6859a/Xi2kk/wpp+bSlLANyjOUWyioKVcvq5YslBoesLGfWBlH5FQlSp6vW9jxaAoeFEgsv6zHureTx2HqnBQ+cA8iCk6aSOVP74/vezT//R5sbGg92vbKbmij1Gep9auWdJzqY9jldbB6UmUNqIvLEJF90FpUrO7CvaFhQV79dVX7Uc/+pFtbm4GsAlINbvaIA/DBqxCaMjv7OzMyuVy2F8CiFQLhIYU39vbMzMLIbPH43E4VNbMQoCApaWlAGZpm+XlZcuyLFgJmExYco6Pj21+fj5sMISksafF+8J3Oh1rt9vheZhiIYXU8/DwMITexHI0GAwsyzLb39+38XgcgnDUajVrt9t2eHhozWYztDfCnDITshPCNR6Prd1uh7LVajU7OTmxarUaxgdtzllHZhai1yiQXV9fDxayLMus0+nYw4cPQ3CG1dVVOz4+DkSK8aGAxy+6apaGYEJgh8Nh2OeCBZHxS9/gvknf37lzx/785z/bJ598Ym+++abdvHnTFhcXrV6v2/r6urVarUA+cevEMletVm1/fz8spggLfKhZAKgTfvKEccdFkHC4KAbm5+eDu6OelYVGG+FVqVSCFqrb7Yb7VPjha811ungzzrCoYZ1NAYxZ03WEWQqQx2QMefsXYyVmicIahUWKz7FQ5778swB/Be362eeTIiCxeqXAfqxc/nkqx/1vWuaXBWb9Ahqru19jUuPDAyzNV8ekAlnfXnxWMKVAKtW2qXrlkUrK4kmFd7/ymlXvvsf96qqo5UxpWmPl82TKf/bftX88Yc1rH5+H/hbDCbF5/jJTDAjrS+emH5PaZkpo84gU1yBnyuWy1et1azab1mg0rFqthuiylEfduT2YJqXGXEzu+T72/ejBLuNIPYt8HX37ad4eC6K0ow5KYLkvZoXw7a7Jjz+uUXnmrTB81noy79V9XrdF6P1+75Dul8qyK4sU++tRuOPW54kD96mih3bRgE96nZk9Ew5cxx3l1q0yfj8QSlJwLERK8VJM8aTlhozpdaq49+/aR+Tl2yM2prROqTGgZJh9ZZ70+qAtYNznSc8VbEInBJOcd53k0/ID+AH2UkKVDtL7Njc37c0337T19fVAHLB0MEBHo1EIe66b+TnX6Pj4OFiQOp1OiKzCJAeMAkKHw2GwkmRZFoD10tJSIA0HBwdhsuAOqNYaTiTXKIFYIAC4WAjMLFigAPRYmjhQWCOO4KLV7/fDwcJEb+N8qOPjY9va2gpuj0dHR6Et9vf37fz88mBVBMnBwUHon3q9bhcXl9H3aBdcHwuFwsS5SRC5LMus0WhMkCUsQrTz7373uwDI5+fnrVqthv1RAPb9/X375ptv7OLiMgDE6upqOLxW/bpVcHvQB0mmP7EaQvb8Ylkul+3JkycTIdlPTk5sY2PDjo+P7d1337VHjx7Z4eGhffzxx2GvVKVSsVdffTUcBDwejwOpubi4CMRnMBjY+fm5PXr0KBAonqMBItjzhgVIrZiM6aWlpWAd4twtiHKpVLJms2ndbtfa7bbdu3fPTk9PrdVqWbfbDeeQnZ6eWqPRsOPj4xBilbnEwk5+WGparVaILsg+upeR/IKfB6YUkPr7mUO8c613/UAWseipJapSqUxYovx5dFoGTepmmgJVMdcUrvPj0Y9rBXEe0JFmBaGx62KAcZb8YkDcX+8XxhhA9SRq2rP4HgN3Cohie5b8gu7dPnScpEiIJ2Se3E5rCz8edcFXAORJE8/0YMf3VarMnizkkUCfPOiO1Y1nkbe39MSu0zIq8Io940UTgDmvLqxVniTwniKympRs4bJOFNBarWb1et2q1Wpwd9f+xz2espjZRP/H2lK/x2RjHnn15U7lp+Mm1a+j0VXkWJV5KjvNbIIgpMaGn6e+LmpxIoFP+V+v9fVEua1KFOajzmO1yig4V5mBco794ePxOChBWftZ473lRdvp5OQkvLMlQZOOO08UqRPXgS90LxP3sPe51+tNBFACZ6QsWkqOKLPu39Jr/RYf3/6pPqb8Otd8v+jc9RYnFMLTXGKfN12LSOlk4eHK/lTg+Xu90MESoP7JOpHMnjVFcm+tVrMHDx7YvXv3wiDlWlz5suwy3HO73bajo6Nw4Fiv17NmsxlAP4AUkItFxMyCZavT6QRrTavVCuB2aWnJjo6O7MaNG7a3t2eNRsOKxaLV63VbWloKe6XMLByui9afTobsLC8v29HRUdB2A2wJT05ACA5PU7c5nUTdbjcMIogWbmEIs93dXTOzYKHCPbFWq9ne3p4VCgUrl8t2584d29vbC6bPbrdr4/FllDYzC651GvFwOByG9oUQHB4ehvFSqVRC2y0sLNjJyYk9fPjQRqNR6MsbN26E/V/Ly8s2GAzs6dOn1mq1rFAohOAdlAGhrJNJxyxpaWnJ1tbWrFAo2NHRUWh7LH8QWkjq4eFhMGtzsPDDhw+t3W4Hi+U777xjv/3tb+2zzz6zN954w27evBlC2G5vb9vh4WFwu1TXm5OTEyuXyzYYDELY+IWFheBC1uv1bG1tLbhtDofDYMmCoHOyOZoqCE2r1QpWKVwI5ubm7OjoKOxfa7fbtrq6ak+ePLH79+9bv9+3brcb3A0Z/wjcQqEwYYWhbfv9vt2+fdvm5ubs+++/D3u3XiTFFnTGt2pp865XoazkQhcEfmNhwY0Ba1RsXxTuwliivHyKgSxPRPxioHUkkY+CN33F6hrT0GlZYvMiRpBipNCDMZ9fKnlg7smR9mnq/uuCRLMrIOEJaIxwxNrJg4wsu9rA7p+btyjHAGfqvhgwV4AGodJIfgAxQAXjUl2o9Fme8PGun7U8Ol/yCO2sxNqD8Nj48CRQ/0uNz+dN4/GVIji2dqQAn77H3Ctj99NHYJ/l5eUQYKLZbIYDd1krKJsCdj+etI+1jWL10N/VIuTHiObvwW3sd98f/ndvffDyQq08er/Wyecd+12vV3mi81iVIjw7plTR9lNvBe1btaDpc8Aj6tanmI6gUNoHgH/tYzAJyu5utxvuLRSu9jj7flcyYXaFqyHwStbU/Q85AwZiPz3eYouLi4HQs2Z6IqkulX7M+BRbo33/aj2UtHFdqv76W+y5ed+fJ117j5TZs77CqQVeExXXEM5ec+a1u7r48fvy8rJ98MEH9t5774V9QxcXFwH80uEaHQ6LCxqd3d1d6/f7Vq/Xg1WDvSmAUsgLixeWISYFrn5nZ2cBuB4eHoaFDWKEq2G9XrejoyMzs4nDWBGEWBu63e4ziyTR3Pr9vh0dHdnBwYHdvn07kJXz83Or1+uh3dSFEEsXLoiFwmWggouLC3vy5InduHEjBN84PT21fr9vOzs7YfAS0nw0ugw4UKvVAssHzOP2xmKPhQrCdHp6GkgTmhn6rN/v29OnTwOhXVhYsFu3blm5XA7jpd1u26NHj0I+pVLJnj59GkB7DJQyflRI9no9a7fbwarImEILhSXCzMLZThcXFyEyIiR9c3PTdnZ2rFgs2uuvv26fffaZtVot+/jjj+3evXvWbDatUqnYnTt3bH9/3z7//HM7Pj62drsdCBSWD7NLTdDa2lpwPe31egEwjceX+5g6nc6EoIVw024rKyvPaIbog/F4bDs7O9ZsNm0wGFir1bJarRbcXr/66qtg7QSI0dZY6pToHh4e2uLioj158sTm5+ft8ePHQfnAGH+R5Bdu+jMlmFP/ednhQaECaz0vSkkUrnz0F4upuomq/FIgHwMOqQVFP8dIiydUedfmtasmnxfl1jp50BtLeaQ2ti5MW/xi7abrQazesTZQcMA1nkj5+5FNqhxU4u3LFgMJvlyz9A9lQYvLnFMNu3fv8yRRN4fr/zE3mlg/+TL7ss9SD+0bT5Ji98csKpoP/2k9ZvV6mTVpOT2pj5EIfbYnUCkSxbUAckhUrVazZrNpzWbTarVaAK7gA08KKIu6pirAjGGx2H/qNq5trIn8dX2NgV8vK8jPv88qP/zYi8mXlNxgzmg/xSxSHnvqc7wrGf3mlYipupA/+9+IFKxECmxH/mBBXrrVJeXeFptbnoRoWXV8Mm7ZlpFl2cTeSpS3KPDVkuWt+kpqVMmpCgYliPynkRW9YkstarE+n7bu6dj15fVjmvngx9p10sxEShcjjRxEwxISGz9EEhXRsJG6WPjKx4Qvv83Pz9tbb71lb775ZrDwYFWCMOE2hnUIiwtufJCqcrk8car00dFRyIfByzlItVotkKMsu9RMAsbH43Fw+bm4uLBut/vMIbnsQ2F/D3um6Mx2ux2sPew5qtfrwY+a/TUM6OPjYzs4OJgAChABDlNVUGx2KUyGw6EdHh5O7C0C+HIQa5Zltr6+bk+fPrV2ux0mORaZdrsdJh8RZcbjcegPfjezibOHmKRnZ2e2vr4ezMW//e1vQ1lZZJrNZjjHajwe28HBgX311VchXPvt27fDPVipSCoMvSBh4szNzYU9cQQBIZDD4uJi8FEnAmO9XrdOpxOINNpE7nn//fftV7/6lf35z3+2Tz/91G7fvh1832/cuGE7OzuBLFJHiPrOzo5tb29bsVi0VqsVLJhra2sBxHU6HatUKsEloNFohEiCCHkspkTcg9xi9To9PbV2uz1RbjMLLoO0EVZLNGnkhVY0y7IQ+hxyzhjjLIqXlRTYqLBW8JXSOPnF2IMhFbK6oZbzPvS1vLw84c6nVvRZyu5/90kXhdjCHqubyskY2M0jJP7Zen1ssY6955U1j+xo/ikXixhg9dfwUvcRb4FREsX/ulin1hp1AUmRkNhC7ts8Nua0/Km2RO7h7g4Q9OXid2Snjmv97LXwWv8YmdTy+PHl+0D7KY8w5fVnKvnypNr9RROKEtYtPwcUw+h/tI0Hl2aTeIm+AIyzNkCiCC7BflfknAYMYEwoOFUio+5kngRoGZgv4A8FrjFvI/Lkeazb1I36auAGnXMpeZI3dvwrNgf9tXzX/Hg2GMbfG7Mi8jzNB+UtSjU8l8CXinHoHw1prwfqElGXflViRnn03CnWHRSZWIUgaqy3arn27m46FtVVL8uyCYID4UHmEExjNBqFtW88Hk+E3/dWOW1f5gzlA6szjnUMx5ThKseUoGq/e3lHPTCo0I46x7U+EFa8z543Xcu1z0/wcrkc9qrgQqSaFIQHBMovXKRpmgo65f3337ef/exntrq6OnG+kdml5eS7776bWHwGg0EIJY61SQUS5cBCRGQ6HWQrKytWr9cDyQJ4Y6ECdCvQZO8UYcZbrZatra0FV8DhcBhcCYvFojUajTDRCPU9Gl2eXUWUu0ajMeGySFvjgnbnzh17+vSpHRwcBBe1Wq0WiBjtAJmE1AGMFxcXQ4hvH6YbITk3NzcRgr3b7QZhgal5a2vLFhcX7ejoKAQ0wEVxfX09HPibZZf7wfjMpCOsOREJj4+P7cmTJ/bo0aNAnlhssiwLAQ50UnktGN/ZL8YiMR6Pw/lPWM/oY7QxkE3cLhcXF+3rr78OhwLXajW7f/++ffTRR9Zut4NVqtFoWKPRsBs3btj+/r7t7e2F8cb+I0g4+7VeffXVoARYWVmxo6Mj6/f7tra2Zt9//32o58nJiVUqlQk3Oo10idWI8dNoNKzT6Vi/3w/nnjWbTdvZ2QlWXKLzMS+wHA4Gg7DPsNFo2N7e3kTUIfa/cd5Zp9OZVaTkznnqYnYFLJm3uuDHwLgHh/7FNd7NRsOcE6EPIawnvuuC7YGAT54gxRb6FMiMAQffTp60xIiPv9ZrN5XY6D26OPm8uM8TsVTdfTv4esR+SwHzPHBPGdVjQjWbHjjFnqtrHHnGXAF9PUn+ulh5Y6RGv1M+b5FSyxTPoZ6UEWWcd0/0z9bxl2pPfY/9llq7Y2M6VVffRqn3VL4vkliHiPQbawvkhO7j1vvBN0qUvcwpFosTe6JWV1dtc3MznBeFzGcdVSsK+ak13INl3Lw8kfLgU8volQoxIqVzR4G/7xvkqW7x0PXXy4IY2YvJHP6PJbVqaF29MsWvKeBSVfCzfiq4p19xqS+Xy0GRHZMjfKefyuWyVavVgBHH43EgIGY2Ad5RiKDQ0z25YFy8oSBsrFnUC8yKAh7jhgaAYyxSZnV/LhQKE2epomgG1/oIx9rv6smlZzBSR8amEnb6NUZoNSKg7zuUnxA/ykN+etafKkP1HExIlJIpHc/XTTMTKQpJZUqlkv3whz+0r7/+OpAM9sTo5POLSmwhVa2ZJhVCb775pr3zzjtWrVbNzILbHQe2sg/n1q1btrKyYu12OwDBer0e3LOOjo7szp07wcWJ/SbsF9LzoXB3ws2Jsg6HQ1tZWbHV1VU7OTmxTqdjCwsLVq/XbWNjw/b29ibOh6rVanZ2dmbLy8shbDdR+7AmqVtGo9EIhAiXuV6vZ7VaLYBv2oA9M2wUZCDj6kU0EhZc6jscDoMPLIMWzfxgMLButxsCYzQajRD6G01IlmUhep/2Ke5zw+HQqtVqEETkvbOzE6LgZVlmX3zxRQAB8/Pzdu/evaCZW1xctL29PdvZ2QlRC1dWVuz09NSazaa1Wq3QL4Bsxqq6D6mQxkqoVgizK0sMrphYorC60BalUsl2dnbCeFtfX7fxeGyvv/66/fGPf7QvvvjCPv74Y7t9+3YA47du3bLvv//eHj16FLRBp6engdThZknAkk6nE4DQ5uZmsLLeu3fPTk5OgqWQ+mE1nZ+ft48//jgsrOwHRMNVr9ft5OTEvv/++xA8gsWmUCgE4qvumSwe3W43HKC8trYWLJAQtUePHlmz2bTbt2/PKlJykwcAKhtUIGs7cJ/KDp+nyhTVECN0lURhicKlw++R8SDUk6KYTCN5QpW6Jk+wx8hU6jp9efc9/5k8Y7+n7vOkStcL32Yxshyrj/8cuyd1DSBQF2LVOvsX5VXyRf8xVvKsNx6k8t2TmNj6FxtDlFWVCJQPIMU6y3OYLwoQqUuMvHhLfopMeeDL7/7l65i37vtypIhl6vPzAp5Y4pBt1myzyUAErKl6xp+Ww7vzKUEws7DeEOY8RqLAMTpHlSwhq9W9StvA75fhXZWLfpx592RPANXqqffEiK3OoxiR8mNEU4zs6bVKhnw+fk57FzKdT0oYdM+TV/TzPEA7oFy9LVTppG2qlkesjgTNQvlI3youM7OwjQDXcpTJag0Et2D10q0SrN1KJAicgXygfOSnkQOVlEP6MEAw9lBkKhmiDVHW6xj1AZnUcqp9qHs7aVMd0zrXtO/A50pK1Rqle9QgUhrUi7Zi/qdI+7R07ah9sMbV1VX74osvAsgzu/Il1cVaBX1swdBrYs8sFov26quv2ttvv23r6+t2dnYWIt6dnp7awcGBVavVAIq//vrrMMCy7FLbRF6A8cPDQ2s0GmZ2GQSCM5QYhFh8iGaGuxRWopOTkwBoEY50mJmFQ18Hg4GVSiXb3t4OUfTMLLhoqRDCZGx2GdxiOByGoAVZdhmiHEvJzZs3A9FptVp27949Gw6HtrGxEcpEAAPCei8sLFi32w1WiUqlYt99913YJ0b/sWhAIFqtViCiZ2dnIToce2/CQBKtDsS03+9P7N9qt9vB1N3tdu3bb7+1g4ODicPfCNbAmDg8PLRHjx6Fvmk0GrawsBCsaXomlE5YXVT4nUg5w+EwkFGshwgU3WOwtLRke3t7Vi6XbXNz0w4ODkI+jC+sOO+8847t7OzYzs6Offrpp3b37l1bW1uztbU1W19ft7t379rTp08nTP/FYjEcuFuv1+3zzz8Pke+wWh4cHNj5+XmIPKmuo4T9JyAJ5If9foVCIRzqy/62drttb775ZiBrEG72ZbGILC8vB0smAhzhCzAjYAcBLoi4+KLJL9R5pMjsaqH1C1pefuyfZIFUS5QPcY7MI+mirvl7gJWqWwxMx/7TcewJo39uXn4qj2MWvFgbaVvFrks9kxQDGh4g+XzziKAvo/9fn8NiHYu6FyPE+lnv0/LHwL4vny/HrPX044mELDa7IngofNQdJqa49JaLWfo8RZpjxHuWNMtciM3vWPsqME+N4edNADsAGsCUBOjFa4Ry+D0clFXlgLpqlcvlcFYUBAq3LzMLoFddqhizZpNBQfLkIeXwuCpFhklK2pB3uiZ4AOzlXYoEKRj2hF2JKJ/Bi16BwP3+e6yu2kZ5csOPLbX6cq+2O4pvDAZq9aVsrC2QIfaxqycHa6mZBYsPskkPxKVcOp8hJ0pslCSpZQtcwDNVybK4uBgwaaxNiCzc7/eDwh43R40EqO3DmNW2TMkV3r2RRsupykglbeARMwt4XMeol4fT1pYXTTMTKb9YtFqtMBjm5+cDudGOxhexUCiEc29imgX8dUlUvFAoWKPRsF/+8pfWbDbN7MrNh4GHGZPzdUgQEYD/3Nycra+v26NHj0KUtqWlJWu329br9QLoPz8/D2Z2GDQdREQzwq3X6/XgHrW6umpra2v2xRdfhE2GAO12ux02GDJByuXyM25KhC7//vvvJ8Kw40J4cnJiy8vL1mw27fDwMERP+f77761arYb2Z9JibtUgDzqhMTfX6/XgQkhwh1KpFCLGsWCwz4t8aEe9l8Xm6OgoWCnZ1zMcDoNZ9ezsLJyjxAKGGyUHIJ+fn9v3339v33zzjZ2enlq5XLb19fXgZ0yfqCaF8cRvCGZejJeVlZWwcKlmir1ThMPHb77T6djZ2Zk9efIkWGnq9brV63VrNBq2v79vb775prXbbfvqq6/s448/tldffdXK5bItLy/bzZs37e7du/b48eOgQUEwQuTv3LkzEdDiyZMnYZziesLeucFgYPv7+zYajYJ1lmvPzs6sUqlYsVi0/f39iUWm2WwGzZWZ2XfffRfypzy4VK6srFi1Wg39ViwWA1nDMnV6emrLy8u2sbFhT58+tZ2dnecQQ1fJK1cAEfqf2aRVSrWI/Be7B+GKzNIDd707n98Xxb0xEuUBRh6Qjv3vQboHJ3l5phRQeYtX6v/YM1LESX+bthj5xTKVYoB/GmD0v3G/HsWhc18X2VhdmBu6P8WDKQVvvu9T7aHjJ0WmYiTZg0stowJQ33aADdYur92NtW2s/L5drzOulMh7cq5tlhrDmr/vh5cFgMbj8URIaQWj/KbuPwBE3ZjvyZPWAcAKidrY2LD19XVbXV21arU6EXCItRqQTnnALzwPa6SSCSUsWg5vLdM+UuuSH2/8p/iHZ3s56EmP/hYjGl4mxxQB1G8aiQbz0FaxOiug53uMDKs3hrrOQp4KhcvgTSgzcXvDKkJ4cm/xwjuE40bALvQnz1PyNh5fnR/K3mM9B4m6q9LIt7HOXT9e1OKtSmjKpPKTuphdnnfq56JX6ugc0Wepm6GOE9qI9mLMQwA996C+yAG1xiGP6UdeOgb9fKUcfu5cJ107/DkVHo1GtrGxEdyKCGKgixAVSrk3xBYz1UwUi0X7u7/7O/vwww9tPL4MOgCRAQCNRqMQm79QKIQBZ2bBTWpjYyMQOlygIAlZdrnnpNfrBXKE+U87QP1EfUQ+3J7wKWXzPq6Ch4eHVqvVgpYfdzolIBDE4XBoW1tbdnFxYYeHh1YsFm1tbW1Cq9Dv921ra8seP35s4/Gl1YjY/7gAMuDRapZKpVAuBAS+txqOfGFhwY6Ojmx9fd3MLGjMcDVsNpu2uLgYzg9i3xf1HY1GwWLDvisi9lH3drtt6+vr9j/+x/+Y8D+HSKD56Pf7tru7awcHB2E8sSdrNBoFH2DGpgpexpcKZ8gZZnZIFeSOPWD7+/tWKpVCABDqvba2Zp9//nmIKHh2dmYPHz4MY/WNN96whw8f2jfffGOffvqpvfrqq7a+vm5LS0vWbDbtzp07trOzE/q3Xq/bp59+auVy2TY2NoJlkP1z/X4/jJW9vb3gXogFlrCojGd88MkHSxZkgz2Ed+7cCQQX66b6GWPFhGirCxxtqFog3GizLAth6Z83qSDzoNeDK5K3dmuKaT/n5uaCxQnrLJZSLFJoBVEKxQiZAkT/vFkIQF7SZ3hLqydbPuWVSz/HgB/P8/n7Z3qFWCqvvHr7/2IgYNp3fS7l0DUopjlNuW944Bd7dqwOZpNunDHNeKquMULFu96j4JkXIJLPXA/xhwyQl7ppxZLWOVaeFBCOtYkfL7F7puXDM9Xtiv67zlyalvQcKe0rBeMesAHUfH+ZXcktZA3WCRRvYAHd44GHDUSK/NQKYXblEWQ2Gb3Qz0G9H7mhyddRkz7DW37UwqkvHWf+P33FxoJaDfhPyY8napQHbKPrO+UnX0gQY588FKMqflDcRz54QXE+aLvdDopVyshRNuALnoMXSbfbtf39fTs8PAyBoegX7lNrOBGUUeaBpVCCo8zkevbFj8fjCQsnz8fQwVp+dnYW8Nrx8XHYE049aQPWeGQaQc1UFimZ0zkBqWTMqExWcqdWMjXC0C5K/NVlENnX6/Ws1+uFMlO3lZUV6/f74QgdiC5zl4jUHP1CezxPuhaRglAwUNrtti0uLoaN8JzvA5HxZj0PDDSptg8B8Itf/MJu3bplrVYruOVhmTk/Pw+b8ZkQACENW43Gv9ls2uPHj61QuDqTiT1SS0tLdvfu3QDqe72eXVxcBOuE18jTMbhhMbB2d3etUqnY2dmZHR0dhcAOtVot7MHqdDr28OHDCZc0gPPFxYVtbm5OLPa43rFhkcAFW1tb1mg0rNfrhShu4/GldQnNiUbKw/ozHA6DNp5zhdjPxMZFwrBCMJaWluzWrVthPwxBKVgEms1m0JoRjIJFHFdIovH1+/3QPgB7rCB3796diIZzeHhoT548CQfSErEPwI6rn07KmDAHZGhEurm5uRD4o1gshlDyvDY3N63Vatnu7q5tb2/bd999ZysrK/bqq6/aYDCwra2tcNguQmRpack++OAD63Q69ujRo2CVYr/d1taW3b592x49emSFQsG63a7Nzc3Z/v5+sFAS7KLT6YTDGff394OPfaFQsN3dXWu326EeLCjUBevSycmJ7e/v2+rqanD16/V69t1339nJyUmInlir1YKQ1rF4enoarDbMg2KxGMY/+++2tras1+vZ3t5eIOAvmpA3MUCPnOB7ypLhwbBaTXUjKlaoWKjzaZamlDzz98RISCpPf48CjNS72bMHTGoe/toU2dKXtyLFNIM651JljT0vZqGK1TeWYsSN+rOQevcorVOKlCmQ8cA9z0XK1zHW5v4etTD5a/31vswAllh/0QYAGKwo9JPWzY/JlKUnRZZj5U4R+BiIzlMG8LuWUxUrL5NIcaYPZcobW0qkFJzTfkp8AffqOoyr18XFRVgrAXZ64KpiAyU2jEWvYU959HBfirirnGScqEXKX68WQb/uqjJB2zLmuqXkGCWylketLd4iRd7UT+U7ba6Ez1tj/HjVuijpAgvgbnlxcblXnXNFfWAGcC8AH5JCvvv7+xPeSSg3sFCNx1fR8nDfp82Xl5fD1gw9Q5Ny4aUDqQPvYA3DyJFll4Gkzs/Pwz5pCBReTMhOlFBqwSMycKFQCNeqAkDJpVoI6WPtVxQIqjDQftOgFrSDzgnmQb/fDwYWM5sgUt1uNyjlUf6ylwslf6fTsU6nY6urq//8RMrsyuRHA0Ga5ubm7LXXXrPhcGg7OztR8sRnfY8JUibZT37yE/vRj34UKkwnEEVMJzAA9PT01FqtVnC9Un/j7e1t++yzz4JFBPPr9vZ28BNFwDWbzeDGVygUAojE6kV0NdqDSCC4OjFA1YqABYr9U3NzVyGrGRQIApg4EWLYd1KpVGxzc9P+9Kc/hX1ZzWbTlpaW7OHDhyGaGwspIdqJqLawsBDCmLNHq1qthn0uBIIgwsnm5qbNz88HywdaEQYnboe4GLIwZFlm9Xo91J/6ZFlmtVrNjo+P7ZNPPgljB+3K5uZmIBRml+d9PX78OEz6Wq0WAh4Qwr3T6UxMLsap12QpeECzpFohrJu42f32t78Ne84gGWtrayEMOQJDtUIEwVhdXQ0ufp988oltb28HN7lXX33Vvv7666A1JrLP8fGxbW5uBjdT2letb1l26VJ7fHwc+omgJZwN1uv1Qrvg0jk/Px8i7pEvi3an07Esu7TKrq6uBmvdeDwO/tFquUSbxRlT7Xbbdnd3bWVlJZDPF0kp4EnyBIo+9sBUgRvX4qIAkSLqkb7rocMxly0FHqr80WsUJKTK5OsTA+Ekzc8DAW2P1P3kodasFBDVZ6VIm5In3waavwKrGOFNlXkaifLftT7eNU/Lqa5MPilIVgVgngUktc7pu47jWUi0bwcFmx6MaNAJBW9adt2ETX4xy1OsbX1d9J7YGq6glO9+DPh8YwQ8r0y+v180sR7EIuXFrmXN13HiCTLvyBqs3ijU6BN1PVLy70mtV1goQeAa1j5tT6wAvj5+DHoSCcaLKYjUDUyto3nWTP97qn21DJqvziNd171SgHKpZYNy6pjx84C8tF91/I7HVyG/cbWDmCiRQqFPGSAz3Hd0dBTwEuWBiNAelAPFJ15Win0hAqz9XK/7nSBb/AdWH4/HgbixjvtxCAHT+qmsx0ihssi/tG3BZ9ru2t5Knj2R0vWTMU6fMr6VxJE/wdQweLDfm+vAQFzX7/f/ZYiU1yLw2+LiYgj3/Nlnn4UQorFJ5EGDJn6bn5+31dXVYI0ajy9Nlbi5sbEeiwghFiE8aMqZAHqI6Hh8uUF+bW3NRqNROB+I/VTj8TgcnEZ0O/al4P6GNWd1ddU6nY69+uqrtru7G7R/tAnEo1gsWqVSCWSAgVqpVKzZbIaOx7pGlMFSqTQxoCFU33zzTdBAIKSfPn068VwIJPt7zCwE16hUKlYqlQKYXFhYsIODA7t37549efLEjo+Pg8WBPup2u8GdrdPphGAQ7I/BYqGTjihxBB8h3/F4HKwsBDHAWsWCQ9S8x48f287OTrBwQdbMLifzo0ePgnaFCQLoYNzpOCPEtwIRJh8WuV6vF/Y/ZVlmm5ubgbisrKzYzs6ObWxshPDiZhbOfyKwyf37921/f992dnbss88+s/v374ezsdbW1uy1114L1qwsy2xra8sePnxojx8/tuXl5eCSt7+/b0dHR+FA5mKxGIJkmFk4W4xAJRqRUTdlVioV29/fD0Idi+Pe3t7E3jpONCdoCpYZ3PpOT0/t66+/tuFwGEg6kRm73a6VSqWXZpGaBmhiKQXuGBNYVzXUOfuiIKbMcRX8/nmA1lS5lbCkromVNXU9YIiEHFbyqOPcW+K0Dh7A+nL5dpsGjFJ1oZz+Obp2+Pxjz0mlWHupe4kq2tSqFCtPDLx5SxblSq1hfjx4EuXrFRtXqYQ8w/VEgaSCXl66LzTLrlz81KoSGxMxQutJdSx5AsVv/hreYwR9GjFKEbkXTbRPrO55Y1MBvZYHbILFm7Oi1tfXw0Ht9CcWDH2mrl2qeVeZo88i+TVPx72fK34eK9lSYqb157MCXa8k0et0TGjZvEWKeuh8099UUaNEkv+0flpvbYdUHWKKBV7e+gHY1/1MXmFLn2mkuCzLglJevYTUqqhERcmKuqHpWPNj0HvjkD/tjhJG3Qex2nhLpx+PZpeyDWU7WBm3dzAVSnFvPfTKLB0rSsB9+1MmnV/a37GxGZM5efepvMQt9HnlyrWCTVAYPceGyHHffPONDQaDiUHpF4vYwPYNVy6X7a//+q/DYaBmFoAsAJG9DQgXZa8cGkvggsPDQ7u4uAhuiNrog8HA5ufngzsgLnx+8ymWFoJqHBwcBFe00WgUXLD0DB2sWJQN4epBEQOuVCrZ2tqanZycBG1Ev98Pe7bwYQXoDQYD29nZCa5WCESiwLFXDJeBnZ2dMHBu3bpljx49Cu10fn4ezjZaW1sL5eWA32+//dbK5fJE2G32zwDCVVNFPQHYWHROTk7sxo0b9t1339nDhw9D+6pbH/V7+vSp7e7uWq/Xs9FoZMvLy7a6uho0TbhYol2BBGjykwIhhuURUsHeH0LkczbX9vb2xL4pSNfBwYHNzc0FTabOg5WVFWs0Gra6umrdbtcePnxon3zyid28eTMA97t379qf//zncP7S9va2zc/P22uvvRbainF7dHQU2p0gHLQxmhdCqaP5MrPgGri9vW3D4dB2d3etVqsFYUcExlqtFhQBCB3m3dLSUgiWUi6Xg/BHYaDtytx6WUSK/vNAPSXoUkSBxY3xTKARyBMEWd35YtYoLVPsPVUeFuDYwp767PNRgOABsJKDGLHgd98uqsHVcqpG3ZOoVN1TFg5NHnxp8otbirBo+1Fef61qSVMAKda33iKlC34s+f7MGwdKrv1/WpcYKNTnKaDWfQmxvVNKrJjzarnyZXgehcUsyc9hf7/2R+x52g4KpmclobMkLH1+n4onxzqOFNCr3MSliD1R6+vrtrW1ZZubm9ZsNoP7PJp4nq9nf5G/utgVCoWwfnls5QmYtnlqzGmdVPGjuEf7RTGa/u5JuScxeYRLSZvKMu9ipzLLk4bYHjC8Lnw7xaIfkqfuQWfOaNhzcJsSGk3kuby8bJVKJQTNyrIs7F3KsquzwLLsKrw3ZJPyaUQ6vK3wCmF/1Hg8Dm6iWq8suzQCsNeX/LkO8s5YZT+VynvwFfOA/tJ+ybIsKFkp+/z8fCCL4KvT09PQnqrggZxp2RVH6jzUMa55MCcYE+AzDSSlLvus7RrgjJgHKMXV6nWddK1zpMbjS2sCPp7sW2LA6qD2i6u+xyY6DfmTn/zE7t+/Hzb1kRcEAeKA+xxRVCAR7CVhrxNnRwF+zS7DVxPjX/cfkT/3ZtmlKxpsnnCSECpCidMeWJ1wodPDei8uLuzg4CAMIA5GNbMAUgF9T58+DRMY/1OIJXWq1WohSACk6fbt23Z6ehpIE8Ej8BXFqtHtdm1lZSVsXMQ9RAUT7Z1lWXBZPDk5CVa2Vqtlo9EoRD40s0Ao2Px3cHAQ8lpYWAjhz/v9vvV6veD2ODc3Z6+88spE1BaI1NnZWbDENBoNW1paCv1N+zFRGYsq3PXF2MRqaWYh4AhRIVlQ9bwmhACWRfIiciVmbjUdb25u2uHhoe3t7dmnn35q9+7ds7W1NavX67a6umpvvvmm/frXvw5Wnvn5efvyyy8D0Gfin52dhQ2THGyMOZ75yDWE3IeY4+JnZsFqS1h8rF4QQfqaIC7dbteePn0aiCoBKBDO7P9jTtMXL8u1j77Qdz7rwurBqOah2iY9L8LvifLufC8C1GYBmbNcE9M+esWU/p+6P5afJ14Kbjzg9W3tQZISPP887RsFwLH31HN9nto/qpnmWaolJi9dpGPA3hMwLbeSRSWmvo1T9acssb7zyWvgNSkJ1DKpdV01vl4L6wEI8t7XNfbcWJo27nzfx8adB8D/rxKgVl2ZtIyelNLm2r4o4hRIr66u2vb2tm1ubtr6+npY88EA9Is+UxUcqpw0u1IEmj1rVVLLgv6un/285Xedi/SH7svS62gHnTOeSHlrj5YJ0qSEHosg9VW3L1VAxJQkrPl5RIo8NPoqLyVRAP/RaBS8MIi6y9oOzsClX9tsfn7eKpWKra+v28bGRvBkYr8Tx8rQX5AMPR+K/MfjccButVptIuoxazLbIMAw/3975/bcxpWd+9UAL+IFVwIEb5JsiZRVLo815apUnHlK5SV/Yt7yryQPeZq4EidTisaSVR6aliheQNxIkKII9Hlg/RY/bO+GRMmTOnVOryoUQaDR3Xv32nt/37ptQtbAh8vLyzYejz3aBGM20VKU5C8UCp6SovtdqWefOZR+wwExMzPjuA+vGwZmvFiQmzDfLwxl1WuERArdAcdwHPiQ/sTQS8pEtVq1er3uaSZEbSkWKJfLVqvVJkIzbyu3ypECNKr1QxcsnRDCRRgJF3RdHL/66iv7+uuvbTAY+DVIgKfD8KBASuhQlEA9IYA9BikdzACipDSLEIAUdyUlzM/Pz30gnp6eeulwrUqSJInvSdTv9ydKbCM8RLMbixSWATbChRiRI0V1mM3NTTs9PfX42HK57JYGqvVRMdDsBkQy6RFSACmFnNEW9gfQpEOsClgZ6Ee8XGpFRYGpgsLAwJrD316vZycnJ24RwILXbDa9mMJoNPKwPoqb0Jf8lgmAvDWAslrUFMSY3ZRKpfw4BPTk5MQGg4E1Gg0vIILHk+dAaJ6Zeazy+vq65yJVKpWJaoCrq6s2GAzs5cuXtre3Z8+ePbP79++7J/fhw4f24sULe/fundVqNSc/kNR+v+8knhxBFpbhcGiVSsV1AL3Dw3R5eWm1Ws0r0dD/hB9eXV1Zq9Xyin70ARY1iDH6wyRIGCb7oLH4MJ4LhYKtra3dZkr5lUwDaFkEJwQGPH+Aj1qeCOGLlTkPPasxUKlzmlpyp0kM3OhvQi++HqfHZrU963d6TSUbMeOWArjwPrOITkheFCyHoSgxQhYSmvC+soxxYdt0HQpJixIpBVDhmqQALSSYer6wTbHnH4LVGKAMf4cuxXRBQWLolWIdCa20CmgVwDCnva//36dPHyrTSGfYf1nX1P5831i7rSRJ4jnL+nwYL2pcxZAa6rBiC0L2G42GtVotW1tbs0aj4dVuIW3kVStR0LmLv3o9QGf4fMAB4fhTCQ1EStrUGMH3CngB/rRVCZmOGf2e8+q51TuqZJ6QVa3Ehm5rf8S8xhjHFXATMcG40+vQNsUHSqLUQEnxMrYkwXCfpqnjI+0zolHu3btnd+/e9erOFHtI03SiEiwFszDAsxZBECimtbq6aqurq44thsOhjUYjx7KcB2JWqVSs0Wg4PiBHiPwqMOb8/LxVKhXHZufn5x5BpEZhsB9EkPWTFBRSTSiEQf41WFCJWOiRU8N0SKRwTqDTxWJxYl9HSCR5+2BScsLX1tZsc3PTqtWqtdttq9VqnlO/sLDgBLVarVq5XLZOp/PX90jp4oJVhUGmQBXRSZKOiVmk6MD19XX727/9WwfDgBzcrVpymkQ6LAyFQsGJDVZ8PDoHBweWJNchR71ez66urjxMjQEJ+YDR8ldr95uZFyjQzUn7/b7nE0E80jT1Ut8UegDMUtqb5ECOhyCi+GbmleQ2Njac/DAoqEDYarUsSRInYgwCwhNRTF0ozs7OrNls+gCHIFar1YkcLogzFgY8L0pE1BJAztri4qKNRiMbDAZWqVQm3LlmZru7uxMgd3193a0jMzPXZblfv35tJycnNh5fh/VRjZBJB88IkwfCpK7AgpeZuTdH20bS5sXFhVe5InesWCw6SUrT1Mvko5vst0WpcuKi79y5Y/fv37dut2uvX7+258+f26NHj2x1ddVD/7744gt7+vSpA59isegl/s1uSqdCoiD/o9HIFyT2c7q4uLB+v+/tx6DA/m0spEoaRqOR54RRKEJJL4QSKxq6Apkul8teHIPJnE2LP1ZCshKCcP0bvldAopa+0M1PXhQLl1oCFXBkgUzms9ATE5KfLODOsTHgGAP50whE+Lusvgnb8CGi9xcjNe8D2+HnITlT0ZCmDwXx2mZAkYa9mdnEohvmfWg71QLK/QEeY22PEcssiR1vNhnuGAPo3Ft4nnCMUHRCSRLzif4eAKlglXOEwDzW3yGpjumFHhv2QZbEiGg45v9akiSJF2zS8Dp9LhhhiApgjgDoE0pUq9U8lG9zc9PW19dtbW3NvQP0F+s+/a9hT2H/8hs8J6ofSvT1WetYUNwWzquhQSMkUjofKpHS56RhpXwfEkD1sLG+spYD0PkLTlAipeCa1ItpRCrm1Qi9GdwTnhMlUoXC9ab2lUrFSqWSzc3N2WAw8HQGzQtSAkko/fb2tjUaDX+u/X7f8ZOZOVli7RyPx567To40OfCtVsvu3btnrVbL895PT099X80kSZxIXFxcWLFYtGq1aq1Wy6rVqpNCvFEUnWB7GnLT0WdKu0O8MKTTv1o8BQKCvl1d3exRSkEvKmCjgxpeF4sAUMO8zlE8V4ioEql+v+955DyfRqNh9+/ft1qt5nUX1tfX7fT01A3zq6urtra2Zmtra64zWpznNnKrYhPaoCwAM23xjoGKQqFg9Xrdvv32W69QhtKoJQaiAlkCJCVJMlFGVGMg6VhAIBZ4M7Nms+mejTRNPdyJ65mZb9aLGxQihQWIa7Onz/LysgNKBspoNPLByH47LH60kcmVe4YoUNo8SRIvlQ1QhzhR3IMQRjObUDYGebFY9N/iGQLAY8G5urryCn1XV1dWKpUmwgdZaFBEihJgFeC+8BxWKhUbj8ce5kgO0cHBgROpubk5e/DgwcR9ExJH3hYky8zcg0VooNmvdz1XHVPLrBKQcBPbQuE6F45cOsI3t7a2fJ8xys1T1RBiQnlxEi4xNNTrdVtfX7d2u22vXr2yp0+f2r1796xUKtn8/Lw9fPjQfvzxxwmvzsnJiRsOCJeDZDNZMA7oWyZmJiWKrlBpslarudcrTVMP/6N8OpX5IM9Y6fBQEfvMM8aQgedUF7SPtehMmy/Uuhou/LHfh1Zkcir1pRX6wrCv95Go8F4Q/V2MREwjU1mkMUv0eyUh00KlstoXI2D6fTi+9Hwx4pMFxMPfhf2ZdZ6sc4QAkHlL+0CfbRaJYkGPFahQIhXeZ9hvH0KmwjZ9CKkNr4F+KxBhvPJSAAnpB7xoGGBWW8J7DElU1v3xf2z8xP7P0vPYOcPXbyFJktjKyorP6RpSZnZDPjH6sXbrvMHcuba2Zvfu3bP79+/b/fv3PaQP8AgJAKDyfAjrUj0Nx7cSKSU3+myUOIUFEfS8Ol6UlOlzU+KhRArPlxrs9P/QcGl2Q+IgpZxHc1rwJHAd9baytihuUhLK5xqNousY58bwq+FznA/jMxihXq/7nqOFQmGi+FiSJBN7EoGhVlZW7N69e7a6uurGRwx1arCj6JaSulqt5lgUQkSO+urqqq9ZECzSQ+gDSACGX/Ys47lgQEb30DuqSaLbGBk1r131VckQOFzH6J07d7wSNGHzWpWQ5xBuyxBiN9UvhLGmKSA4McAl9DkRO2BbqibjQSOkj33dEHTttnLrfaRoKBM539GRWQAiDGvgNTc3Z3/4wx/s888/d4VVyzbXUUsOG5Ki0JQ5pCMAqgA/jX1koNbrdbfG8z0PXa17OsC5X+6HGvQoOaGCuKlxnyLsvwSAS5LElYzBxqBm13NYPpMPA8zsWil7vZ7Nzs56JT6ekVpfVTmZQHq9ng/oi4sLW1xc9PwbymUmyXWYIqBerU2cH49UsVj0fbcoj44lpFQqWaFQsNXVVXvz5o1PApCOu3fvToQS7O/v29HRkRNajsMTQgimDuQQbNB2tcRhBWMgY/XQUAC8bVh/ZmdnvWw4FhDaw2DH9c9vKak5Pz9va2tr9vr1a9vf37cff/zRXr58aWtra7aysmL1et0r+EFoaCs5TuiTVjiECEF+FRgVi0Wf+CDYMzMzTsJYnEajke9fNTc355UHkyTx8Eb6FGLGc4Z0qgfP7KboxG8lWaBJwR/Pm79KougzrMmaF0VfhqF800DgtPuIkTsFObH7DduYRaamETsVBfwxoDkNtIbfaQRB7B6ySGOsj2Ihgx9yXx8qOr+FVvjQAh1en9/FvFGcL4vs6DwTPutpbczSaV7aX6Gu63W5FgRSQ1P5LAxrBEhqjtQ0YvI+Pcp6H/bTNLIdvo8dTztD4PWpkqbXxiuMkmGltCRJ/D3GNy1GBdgLx97FxcVE1Mt4PPZwKUC4mTkgBWRr/9BGDXOiT0K95rjwnnQ8hEaHMKxQdQ/ArNcD9+kz0PNxLMeHcxb4SYkUfcAap3MxRBNcQB/w4j4hRNyLHs+YxgitnjZ+p9WRwWKnp6denClNr42Ph4eH9ubNG+v1eh4OyBgDu3H8/Py8Rw79/PPPdnp66uQFnKZepNPTUzcy49Whr2ZnZ61ardrV1ZUdHh769isYksk5Jwe+2+3aYDBwx0S/33fd1erGMzPX+7IuLy87yby4uJjYr0q9WOD92dlZOzg4sNevX3sBFYy+6De1C87OzpyEgT8xHOgcpjpkZhMEXXVT93dMksTDEfHSQSgPDw+t0+lYrVazpaUl29vbs+fPn9vu7q5HD11dXXlEGc8NI/Ft5dY5UjR62oKY9Z0OWhq8vb1tOzs7E25kEuroNB4i5yCpD6XRxYIqY7VazWZmZtySwH45hK8R08liG3o3lpeX3QpEhTYGCJ6uMOYWiz4TBKCV4xkcWAhRPiZTvGKAauJWSVLFqsBeR1QbhPBglSQ0kPYArCGGl5eX7m1S9g9ZZpKh7YAMyKGZeX8A7KnXT2ws1WV6vZ4tLS1Zt9u1ra0t++Mf/+iTGc+xVqt5ftTV1ZXt7e3Z0dGRjUYjP6Zardr8/LxX8SuVSv69hkTo+3CRgBxT2hzCCOGFUPLcl5aWfKItlUqe7ElfaDjGaDTyPmDywRLSbDbt4ODA9vf37dmzZ/bgwQM3Bjx69Mh2d3d9slcLGS53vFtqoeFZMiGpZQxvC7H4ZjdFNdB5nh0l/NlkkMWnUqn4AsKCAxDj+hQPgTxy/KdISEamzSfhMcwDmhelG+9mkajQCqb6o/+/7x5i32t7YqCY/6cRRu2XGNGKHT/tODPzsF/eZ/0uRjz0fmIkMUZwsyTm7QlBWCh6Xv29gjklUWoA0nPQn1i/w/CksA9i80oosWfCeRXUxn7Hd1rIIGy3tk1DphmjSpQwQjJ2lUgxX4R5Hu+T2HO+jWSNhZh+h8f9tYjU3t6e9Xo9NxaFhQ4wHKk3hs+ZX8kd7fV6dnh4aJeXl3Z4eGiFQsEJFCBWSZpWEFNvPm3V96rfMZ0Ox7+GTYVtDnUxJFJcIzbetf/D8R8zWOix9KHqnkbOhHOvtjEcq3pf6rngeNUXyKpGqOj5lAyzRyJrG2kK7XbbOp2O57Jr+sT8/LwdHR1Zp9Nxw2u327VffvnF9QESwfikqAORUfPz89Zut53AcI5Op2PNZtMuLy/t6OjIcQj4kf2QGP/kL1E9WcmMEkZwL95YsLbuVwVh1BLshULBi3/V63UnK8Vi0Y3IFxcXXk9At5yBTOv6q2GgoX6G+qtODbC1GkHQW/LVms2mVSoV293dtadPn9rBwYFzi+PjY6tUKvb69WtLksQeP37saT63lY8iUjQ2HFQq0wYdjf3888/t7//+763RaLiVHaLB5qsAdtiohvOZmbvpIGIk/5+envpeTI1GwxPosThArMbj6+onDBgzm7iP8P5RiiS5CbfDUkKIGAMtTW8KAdAGBi0WFTwdp6en1ul0rFC4ThaEGGk+GrG6FxcXDnQhV7SPwaULK5M57SW+Fg8K+2KNx2N79erVxPPDssLgp58gC4B9BqCCexQcd+pwOLSXL1/6s5qZmbHNzU1P1C0WrysK7u/vexgkMbmEEpKnw7MmrC2cIFUPeeF5UrL+9u1bd7djbVOPHBMgEwT9+u7dO08+ZmCfn5+7Z0dJcqvVsqOjI8+V+uKLL2x9fd0nofv379uzZ8+8LVS76Xa7HsqHzuBFRZjEyfHCKwRpxLN2dnbmzxJggIeQ3CuAl1rCsNKQa6Qe20Kh4CX5sTDqpoCfIlnkhe/COUU9UVj91AuF/uiiARkOdeV91/qQ7953PNeLSYwQfchnWd/HCFHs8xBYhcdOO0+sfe9rf/h5SMz0vFnnCtupv1E91XPoPShIVYCs4C3Wlqw+eN8xWQA0pnsh2dC+oG3qhVDPWhjax3uzG0/d+whrrG2xPoy9V0L8oZ7SaQQpNqf/FpKm19EZYXK9AmsMZazZfKbeP4xv5IYAsMfjmy0q1PPBOSBSoVFHPaQh8YkRqdCwYfbrwivajzEiZTZJhEKAy/gIvU/hOcKxpmObc6oBQD/LIlLaD6HXWc+v19ZjWRvCe9OQXgR8iHH33bt3dnp6av1+3/r9vmNAQhMJOcOAzt9Op2MHBwde4VgJBPgDoywYq9PpuC4SNZOmqXuhOp2Or/PqBdJQUSonE9Gke6ui2/SfhtpxPc6n4Yvq4QQXdjodL+JA+go551p8Qo0TZjcbJqvuq26GREr1VEO16Wv6C1yOM+H09NT3t9zf37eDgwM7OTnxvtVIm0ajYY8fP54oDncb+aiAwHDBiy2Q4QStk/bs7Ky1Wi376quvbGtry5O+j4+PXXH5H3c3FjQz8+Q6AGCz2fRqdqoMDAAtFtDtdq1SqTjA5/zcGyxePQ887LCIw/n5uQ8MlJbKd2Y3CkP/YHmhPRAT3JMk+lNwg8Gm3qnLy+tNWMvlsu83pQAa8gYpJC+KZwVxpNodA4jzafUfvtdBkKY3u3xr/D2WFirXYfUkQbdYvC6kcHBw4LlQs7Oz9vDhQxuPx+52Pzw8tKOjI3+eFA2BvEAueU+bdSIOJ38GGGTJzNyTBxHG9U91Hkp58lt+wyCGpOjiQl6VVnci/nlzc9OOjo5sf3/fnj59ag8ePPAY7C+++MJ+/PFHS9PU8/hKpdLE+TudjvcB5FWtmYVCwT2MZuYTIJ4tdblDbCGRo9HI89DUmMBCg6dTF0rCSTFaqGfwU+VDzqHHqCdKSZRW59MKfaFFLDZhIyE4ySJP04BdDGxMOz6LjIRAYRoxCf+f1qex+wt/lzXPv+++Y30VA1yx+w7PO+17BVkcy6IbglQkJB4KxtSq/aF9F95PVttjhCkEyhrSosdp34UeKR2vmkOlc6COFQxt00hc2P/vGyOxcylIVZAc66uYDoVz+m9FohAt8xy+9J5jLwyCGGaYTzAAAyx1fx0lATEd5dqa4xR6bpVIZY2j2Lymn8X6mvUxiwyZ3awNOk7C86jEjs/CjLG2cKwaDXSsImog4Lrq9VKvlX4fEjMz8+1fwJIUhgg9LPSZRpNAXDqdjns6uW/wEmszbUE/VFcg4ex3qsUclNhrAQ70Qr3RSqT4jPYSbcXamaap472YYYHfhf+n6XVRCzWy49VSbyzPNPTEqj6EnmfVPR0r2nawkhJvnst4PP5VRUH6FgcN28SEOvKhcisiFU6Y+nloEQg/0wfXaDTs66+/tkqlYkdHR1Yul736BxVOtOS27l2FRwQghyVdJxxNwAXAa8lyM3OvUaPR8OpwLF646wHYeCm4PmUouS778JA/Q5gT+VN4T4rFoufX4NouFApeCQhXK14oCi9g5WdAsAeUPnQsHml6k6yYJIl7ibhWsVi0tbU1e/XqlRPAQqFgR0dHnudE0i3hbqVSyffh0vKgEBzIHUl+g8HAxuOx56F1u127c+eOvXr1ytI0dSA7Pz9vW1tbE1aJvb09a7fbPkksLCw4uaDEJaSnVCr5vkWhrumLCZj+0gkN/VErIYNSdTBJEvdw9vt9Jzo8RwgqeVyU4kySa1d2vV63u3fv2osXL+z58+f24sUL29zc9FypBw8e2O7urnub+Ht2duZ5aOgEz5tJD73QqpUQasL7RqORT2aEu/X7/QmyzHXRbwwREDC8WiwAuMGZOLW8/6fIh5ANPRYwQ/hrWKUvDOvTMOLYZJ1FREICEAMCH0qowv9DYKnCooAoGJkGtmNtiB0zjcxktfF94Dq8pp47dt8f0p8xMsdYjnkR0IsYwde5QUGBko3wuuG9he/1/rLAagwAhm3lvvR4Pg/vSc9FP+icBOADXJjd7LunQCMGuGPXDN+H+hD2b0iAwrYrTgj7NUuXf2siFYb5hv2q5FP1g/xLyp6TzI5RkFAjROcciL4+hzAkLSR3KpxLrfOhfr3vuWhfcm01LGfpsnqSwnklJvp71i/V91DPwz7Ta+p1eBaKLbU/6EOzyXLuihU1pJfnilFRN+JlbdES9vod+5JWKhWv1qxkG6xFtI7eJ3qELoI5wEisX6SkcN942JSAax4WoYxgXjCPGvTRPTXIonfkioeGBu0r2sh1dW5RwgRhQ481BF/7Aj3RHCnVPc4V6n+SJBMYF4dEuE8kBDNst+YDfozcqvx5qOixCXLawlkoFKxardrXX39t9+7d80mefWnMrgcOyXJMSLhl8UBxLkgKwB2rEC5PlAmPDYAUJnp5eeneLVh5kiRehhu2izAQSa7r9/sT4U6qzHgzhsOhg24zm9hBGUUB2JKc2m63J0LDCK1qNps2GAy89LZOBFrpj1C1wWBg7969c08Dx9FOTcg/OjqySqVi9XrdkiSxo6MjW1pacvCO4jKA8XQB2gHRJycn7tmiEk2v17PFxUXb39+fyF+hugwhkZeXl7a7u2vtdtstqJTaRNcgb4RAsvmwSmjB4n+sQxqOAXGleIXZDWFNkus8qoWFBTs5ObG1tTUbDofuzev3+7azs+MlNSkHniSJ9w8en+XlZdva2rLd3V07ODiwp0+f2vb2tu8V9eWXX9pPP/1k+/v7niy5uLjoCaRaXEXvlXL3CBNJzJXOxIPH6+zszIkhnk3CAtBRvI2hoYINiwlppd8UgH6MhMBzmoQLAQsIY0HLnOPZjE3cIUgJ57MYuOL6tyWO4RwaLiAhWIBEZc2rer+x64THT+vfaYQq6zoxoBaKjk/9PvQGhufPAoLaDo37V4u/WvtjnmqzX+9Lo0SKOeI2Et67toHz6pwU65OQOGWRs3Bt5XjagxER67d6LzS0GlCh4yA8/4eSqvA5xYhxDHhnkcoY4fytJUkSK5fLvq1IDKwDzDA6MidizGM/GvaLWlxcNDPzEG3Aeaj76slSK3vMM6a6mDU/6X3rmMuaY0IyYxYnUhp1YXZjkNSxoucPRT9XTxrnU6+q3jvn1z4JSZe2MwzVVq8N3ylIx3urZAtSQ8EyXW95XupdxBhbr9et1Wp52fN2u21nZ2dOqObn5/2c4CYMsYB9jJ2A/YWFBVtZWfHzvn371mZnZx3XgQnUI1MsFn3tI18bjKrkXAmQrp/0EfqrG+yqYUbHBWOAPHZwLzlSGGK1UnKo/+F6HOo99xRWuCQnH7zNnA++JOxwPB5bt9u1q6srL5IF8W00Gr7/5cfil48qf07Dshbw8HM6vVKp2FdffWVPnjzxB7KwsOCdRU4GypmmNwUVEIBbmqZeCQVlBNCjQHNzc1apVKzX6zloxM149+5dD3Pi78zMTcl0raanFfUUUFLMApfr2tqaHR0dTRSdSNPUge54PLZ2u22Li4tuaUjT1EP02GSW0DydhPF4sH9Sml7vPXV8fOyEiLAvwg4hA3irsGzQrpOTEyuVSk4uACNnZ2fW7/c9TJJ422Kx6MBdQ9ggw3jneDavXr2ycrnsiv78+fOJgfvZZ5+ZmbmXrt1u2/7+vu99RLUadghP09QqlYrnXmEJ0kVGJ1udmJXM86KUpk6yYTImoIPvIcKEF0KA1bJodlOWlb5Gf3Z2duzp06f2ww8/2PPnz21ra8vq9bqtrKzYZ599Zt99990EGWKCRQ8JN8U1bXZDLhlPhCY2m00Pjy2Xy3Z0dOQe1V6v5/HUkD6I2uzsrJdhR58Io2S/C47FQMGGg5q/9bGSZdlU4RmGhSXwRMVC+sJchKxrhXNXjETFQH+sHTHAO+3a4X3EyA0EBD1XIMP3t5HYfB27F47Nuu9w3MWAsIIkvb6+wvZMI2uAIUJQssKxwt9mLdiMPdWRENi/DyzGyGoWYY/1U6gfMdKi5AzjSGhpZ2xCpDgeIASRAiApWA6f5bS/tyFSWf9nfad9H9Od30KokIuxNtQHIiAwsGKoXFpamtjUk5LZGJ6I1MDLoG3IIlI8Ax0P+nxCPdJ1zezTiVQIJEMdCNdVJYBKurKuqd43+hniEI5/bZcaOrTd2gY8Kkr4IFL0M2v8eDz2eUOJFFtlkDdP1IXqBOMGjFKpVHzvsHq97nMS4eUY/dmvLE1Tn69Yg4n+ITd8PB77b9bW1qzVannKyNzcnBdMCInUzMyM6yWRXAsLCx4ho4YmXuqsUIMrpENLw2u4MMexpQj7pRGdNRgMPEWCghicJyRxIRnnGSnBhpBieE+SxJ0hRGhxTt3XrVKp2Nu3b+3o6Mhzxtm2plar2fr6um1tbbmefIzcuvw5jeUzlBu3Yex3kISdnR178uTJRB4SA4nzJsn1vg5JkngOEnlHuu8SysxkRYejwISnYXEHMPJ7HgiKoOFjgFWtsAK4npmZ8dLhtVrNBoOBlctlGw6HXmYSkgbxgGCYmZ2fn/sGs9x3t9udsIyxG3atVvP4zZWVFS8lPhqNfLdmlOLdu3fuRcJSwWTOMVhhUDzC2tI09X4wMzs5ObH19fWJUAb1WEAc8cwApEulkt2/f9/29/dtb2/PlZxQzePjYyuXy27R++KLLzyEMEkS29/ft06n415ALD2FQsFOTk4mNsRjTy8Wvxhg5VnrBMwkQl4aFWeSJHGSRNUdBmu5XLYkSZwkJkniIXdv3rxx4q5V/siXKhavN+pjsv7888/t+fPndnR0ZH/6059sZ2fHy6x+/fXX9sMPP9jbt2+tXq/bYDDw516pVOzs7Mw9lUzC6FaSJF6shKo9b9++tU6nM2EV0/3S0jS1ra0tS5KbCoZXV9f7h5ndhEBgCNAJHrc/lQ5J7kSHPlZi1tWsY5j8w8ISeKIgUUqY1Tuh19BrMZeFoON99/w+UhYTBej8n3UePS4ESuj1NDCv77lOSO6ySJT2Qwjk9Hwx8B3rHwWMoeg6o8Aqdk21kupYD71RIZlSUMBvlWzo8THPlBJZfR+TEPTF+jpGOqdJCHwVdNO+q6urX4WzYEXXPqKfYgYpPXfsfdjuGDbQY2P9Eo6B2Lk+tp/eJ4XCdQ4r4T9KRtGFO3fuuOUazxShXBj6eC0uLk5gE4yO4JzQM6gkKvQ8h1EAjO+QdOgarQRQCYnqh4Zn6fHgAO1zJdYhqA3DuJTwhWNWyYoWXKBdWhVOnzXn0cIOOp5U//BUcC31noDl0jR1DwvPR8nW4uKilUolLzTR6/XciIkX8uLiwu97YWHBVldX7bPPPrONjQ1P7bi4uPDonKurK6vVatZqtRxDkGdFld35+Xnr9Xpu4DQzq1ardvfuXdve3rZms+l4mMp+GkIKkSIyBs8oRau4DyVSGmaJDrBGkiaiXilIUJgzxfhYWVmxlZWVCa8U0T1EgmkFQLObitVh1ILmZqlOEG3C8VquHdwFZ2g2m7a6umrVatXMzLrdrqXpdWXhhYUFW19ft9XVVbt3755tb29bmqYfvX3LrUL7kNByZXYTqhZOftzc1taWPXnyxF2UTFqh6xSl7/f7/rDwfNCRTECUeAZcmt14tUioH41GDgwvLy+9LGK/37dareb5OAy4lZUVV3Izs8FgYEmSuMenWq36ooS3ZWlpycPGFhYW3KrQaDQmLFEUiWDjNSoHmtnEhNJsNr1IA4NBwxMhEFi6UEaIZpIkPpHTrwwyFBwSVavVPCSLSRovGx4fXKJanU2LVeiCfX5+7iQPgjUzM+PHqxt5Y2NjArRoWF+hULBGo+GVCokHxnuyuLhoh4eHbgFR3Qz1VAEWz5ocOf7iXVxeXrZqtWozMzNOdA8PD200GtnPP/9stVrNK/rhVibUsdvtTkwguJj7/f6EF/HBgwf25z//2Z49e+ZeqUajYbVazXZ2duzPf/6zPyvGFB4/YqXxEFJNCO/rxcWFHR8fuw5gtWE8kcc1MzNj3W7X9vf3J6yCjGWsr4whdEy3IsArqeGnmrf3MRIjAqGE1qwwL0o33cUizLFZ18qydqpOxcBwaAnmfQyIxAB9CDxj76eBxywiNA3Yh7/PMkLErhmCZ/5X4vkhbQ2t7rE+DH8X3ocSohAgxkhUeH48N7pYK9AN+yh89rH7iRELbWusD7lu2NYYGVGyrASaV9gvrBlEHKgFmvlbLdUx8hK2MRwvKiG5VFFyFiP3WedSfQnD3D5VZmZm7Pe//7399NNPHn1BQn+aXhvtqtWqbW9v2/b2ti0vL9vCwoKTKKzwpVLJK5exjmvCvtkNiQhJiHrX1WNFO3k+rL1qPOB4JTP8PtR/9b5CSuhT3utv9HwY7fTZqC6Z/TqUTkXvCzwHzuK9hu9pGwD0SqTUc4YRBLIE6VJjghIpPCwabsZ9LS8ve574aDSyXq9nR0dH1uv1bDAYOEnCmF8qlRyI6z2hB7VazdI0tVarZRsbG45LibKCGBSLRY8Y6fV6liSJNZtNe/z4sX355Zf+nLe2tuzw8NCN2uga/Tc/P+/VgCE0VHpGF9M09fWQsYoHGz3U7WjCipa88OqBhdfW1qzRaETHGRFWGF/VMxWG2+MA4KVrCgZS7h0vHtUUwW3lctnW19c9FahUKtnCwoL99NNPbvC9e/eura+v+3M5OTmxZrPp29HcRm4dhxNaA8x+DXp0spyfn7e7d+/akydPfMdk9fLMzc050QGAJknild/IB2LQAVBRQPU8Qc7YhBSPDYoPwSKsjbC48XjshSiwFDGJMDjG47Gtra1ZkiRWr9f9XhcWFjx0yuza4/SXv/zFw/HW1tas0+k4OWEg4eY8Pz93gnB8fGzz8/NeIAKiyT2Qe4VXS601jUbDlpaW/HwQLQYRFVU0b4u4cOJL0zT1sK+TkxMPVSiVSvbzzz+7F4/JkiIKkBrIE4rKgDAze/XqlYP/2dlZazab7knA2/Xy5UuvTgdZgjhR6KHX6zk5ZoJV4KNheLoI67PE8sQihldFS6ujB+SQEVpoZraxsWEzMzN2dHTkBOX4+HjCWggJ1vwhvDkbGxv28uVLa7fb9v3339vDhw+tVqtZsVi03//+9/bixQt/xpqkiVfTzDz0Eo8MEx7PGE9kklx71NjkGQsbIZ5U+sNLPBqN3B1OtSIm3uFw6PluxWLRVlZW7OLiwotTkAf3KZJljAnnFZ4zz0fzo8IEUw0lybqeXgtwgT7FyEPs8/C8oQWXvyG4iREYBaShtQ6dDi2yYZ8pKAnvTf8qGQzPqSCW/7OeURYJ1n7XPtPXtJAy2qG/5b7QexZ5wBbtCkEk52P9CfOjwmcReijCcBNAXOjFVNCqlvqsPtPfxtZT7YOwj2P3TXiRejoYN5wn/JyXEsuwPfocVQdjhoCwHeH3sfsPx7yOAe7zt5RSqWTffPONvX371r777js7ODhwYJamqVWrVfvd735n//iP/2hPnjyZOp5UlEhkkc5QppHEJLlJpldvwm1EvVS3+Y1ZvA3cb6xkdFYZafQyJiFB4hpmvw65ihkcCCEL7388Hkc3WsW7GJ5nMBhMXAdPS5YkSeJRRfymVCrZ3/zN30x99uHnGxsbtrGx8avjOp3OxJhpNBqZY4nPTk9PPdfZzCYKKmRJzIgS/u59BKPdbk/cR+x41mvOF+sfPGKh6Hxudv0Mm82mNZvNXx1HWCLXefToke3s7ESPw0v5d3/3d/Y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component back to real hourly taxi activity.\n", + "\n", + "> 🇪🇸 **Lo que podrás hacer:**\n", + "\n", + "> - Construir e interpretar un tensor real de orden 3 a partir de una tabla plana de viajes reales.\n", + "> - Desplegar el tensor a lo largo de cada modo y explicar qué información expone cada matricización.\n", + "> - Calcular Tucker/HOSVD usando únicamente unfolding, SVD y `einsum`.\n", + "> - Cambiar el rango de cada modo de manera independiente y medir el error de reconstrucción frente a la compresión.\n", + "> - Interpretar la matriz de factores temporales y relacionar un componente aprendido con la actividad horaria real de los taxis." ], "id": "wHEhVIsEk_Yv" }, @@ -663,1160 +671,6 @@ }, "colab": { "provenance": [] - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "version_major": 2, - "version_minor": 0, - "state": { - "5e1b1ca6f0794da5bf0d2bc913dff7b3": { - "model_module": "@jupyter-widgets/controls", - "model_name": "VBoxModel", - 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null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - } - } - } } }, "nbformat": 4, diff --git a/docs/notebooks/11-wrap-up-and-take-homes.ipynb b/docs/notebooks/11-wrap-up-and-take-homes.ipynb index e64de01..98843a3 100644 --- a/docs/notebooks/11-wrap-up-and-take-homes.ipynb +++ b/docs/notebooks/11-wrap-up-and-take-homes.ipynb @@ -21,7 +21,16 @@ "- Build masked attention from two `einsum` contractions.\n", "- Compare CP with Tucker on the same real New York taxi tensor.\n", "- Use Cholesky to turn independent noise into correlated draws and quantify the portfolio consequence.\n", - "- Denoise a real voice recording with STFT → truncated SVD → ISTFT and measure the SNR trade-off." + "- Denoise a real voice recording with STFT → truncated SVD → ISTFT and measure the SNR trade-off.\n", + "\n", + "> 🇪🇸 **Lo que podrás hacer:**\n", + "\n", + "> - Explicar la idea de aproximación que conecta pseudoinversa, deconvolución y Tucker.\n", + "> - Diagnosticar la trampa de escala de PCA sobre mediciones reales de cáncer de mama.\n", + "> - Construir atención enmascarada mediante dos contracciones `einsum`.\n", + "> - Comparar CP con Tucker sobre el mismo tensor real de taxis de Nueva York.\n", + "> - Usar Cholesky para convertir ruido independiente en muestras correlacionadas y cuantificar la consecuencia sobre un portafolio.\n", + "> - Reducir ruido de una grabación de voz real con STFT → SVD truncada → ISTFT y medir el compromiso mediante SNR." ], "id": "A9S7jpWPpbCq" }, @@ -892,6 +901,8 @@ "\n", "## Done with this section\n", "\n", + "> 🇪🇸 **Fin de esta sección.**\n", + "\n", "That is the whole workshop. Thank you for coming.\n", "\n", "[← Back to the workshop site](https://project-delphi.github.io/tensors-workshop/) · [All notebooks](https://project-delphi.github.io/tensors-workshop/notebooks.html) · [Handbook](https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html)" @@ -911,633 +922,6 @@ }, "colab": { "provenance": [] - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "version_major": 2, - "version_minor": 0, - "state": { - "27879fcad4af4e0cbd4940b1a120c39f": { - "model_module": "@jupyter-widgets/controls", - "model_name": "VBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "VBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "VBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_68c2f2688c854983adea71cfaa880d47", - "IPY_MODEL_7a6f89b309f8428db11c73ebbeadaf43" - ], - "layout": "IPY_MODEL_0a570d84b9c04beea75661ee7971694f" - } - }, - "68c2f2688c854983adea71cfaa880d47": { - "model_module": "@jupyter-widgets/controls", - "model_name": "IntSliderModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "IntSliderModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "IntSliderView", - "continuous_update": false, - "description": "Component / Componente:", - "description_tooltip": null, - "disabled": false, - "layout": "IPY_MODEL_be805ac7d7dd4bf5b8cd48a77e24378f", - "max": 3, - "min": 1, - "orientation": "horizontal", - "readout": true, - "readout_format": "d", - "step": 1, - "style": "IPY_MODEL_916f2343e79e4158b8cdf569e574f1d3", - "value": 2 - } - }, - "7a6f89b309f8428db11c73ebbeadaf43": { - "model_module": "@jupyter-widgets/output", - "model_name": "OutputModel", - "model_module_version": "1.0.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/output", - "_model_module_version": "1.0.0", - "_model_name": "OutputModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/output", - "_view_module_version": "1.0.0", - "_view_name": "OutputView", - "layout": "IPY_MODEL_82e9c57171424918aeaaeae685d24a81", - "msg_id": "", - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": "
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Three 6-question Kahoot quizzes have been inserted as knowledge checks after Block 2, after Block 4, and after Block 6 (see the schedule and each insertion point below, marked 🆕). Running time increases from 180 to 195 minutes (3h15). If you need to hold the line at 180 minutes, see the cutting order in Appendix E, which now also covers the quizzes.\nBefore you arrive: you have read Deep Learning (Goodfellow, Bengio & Courville), Chapter 2 — Linear Algebra. You know matrices. This workshop assumes no previous knowledge of tensor theory.\nWhat you will learn: what a tensor is, the vocabulary used to talk about them, how to manipulate them in NumPy, how to solve systems that have no exact solution, what convolution and deconvolution really are, how recursion works with matrices, and how to factorize tensors.\nHow we work: Notebooks on GitHub, run in Colab, discussion in Discord. Two rhythms: - Exercise blocks — 10 minutes coding, then 5 minutes explanation. - Group blocks — 10 minutes discussion in your breakout channel, then share-back.\nA note on language. This workshop is taught in English, but many terms are nearly identical in Spanish: tensor/tensor, matrix/matriz, axis/eje, dimension/dimensión, decomposition/descomposición, factorization/factorización, contraction/contracción, convolution/convolución, recursion/recursión. Every new term is defined when it first appears. Ask questions in Spanish or English in the Discord threads — whichever lets you ask faster." + "text": "🆕 Facilitator note on this edit. Three 6-question Kahoot quizzes have been inserted as knowledge checks after Block 2, after Block 4, and after Block 6 (see the schedule and each insertion point below, marked 🆕). Running time increases from 180 to 195 minutes (3h15). If you need to hold the line at 180 minutes, see the cutting order in Appendix E, which now also covers the quizzes.\nBefore you arrive: you have read Deep Learning (Goodfellow, Bengio & Courville), Chapter 2 — Linear Algebra. You know matrices. This workshop assumes no previous knowledge of tensor theory.\nWhat you will learn: what a tensor is, the vocabulary used to talk about them, how to manipulate them in NumPy, how to solve systems that have no exact solution, what convolution and deconvolution really are, how recursion works with matrices, and how to factorize tensors.\nHow we work: Notebooks on GitHub, run in Colab, discussion in Discord. Two rhythms: - Exercise blocks — 10 minutes coding, then 5 minutes explanation. - Group block (video pipeline design, Part III) — 10 minutes discussion in your breakout channel, then share-back.\nA note on language. This workshop is taught in English, but many terms are nearly identical in Spanish: tensor/tensor, matrix/matriz, axis/eje, dimension/dimensión, decomposition/descomposición, factorization/factorización, contraction/contracción, convolution/convolución, recursion/recursión. Every new term is defined when it first appears. Ask questions in Spanish or English in the Discord threads — whichever lets you ask faster." }, { "objectID": "tensors_workshop_plan_with_quizzes.html#the-data-we-use", @@ -18,7 +18,7 @@ "href": "tensors_workshop_plan_with_quizzes.html#schedule-195-minutes-incl.-3-kahoot-checks", "title": "Tensors for Machine Learning", "section": "Schedule (195 minutes, incl. 3 Kahoot checks)", - "text": "Schedule (195 minutes, incl. 3 Kahoot checks)\n\n\n\n\n\n\n\n\n\nPart\nSegment\nFormat\nTime\n\n\n\n\n—\nSetup and welcome\n—\n5 min\n\n\nI\nWhat a tensor is: theory and NumPy\ndemo\n20 min\n\n\nII\nGroup discussion — Thinking in N dimensions\ngroup\n20 min\n\n\nIII\nBlock 1 — Indexing and broadcasting real data\nexercise\n15 min\n\n\nIII\nBlock 2 — Reshape and transpose real images\nexercise\n15 min\n\n\n🆕 —\nKahoot Quiz 1 — Tensor Vocabulary & Shapes\nquiz\n5 min\n\n\n—\nBreak\n—\n5 min\n\n\nIII\nGroup exercise — Video pipeline design\ngroup\n15 min\n\n\nIV\nBlock 3 — Contraction with einsum\nexercise\n15 min\n\n\n—\nBreak\n—\n5 min\n\n\nIV\nBlock 4 — Inverses and the pseudoinverse\nexercise\n15 min\n\n\n🆕 —\nKahoot Quiz 2 — Einsum, Distance & the Pseudoinverse\nquiz\n5 min\n\n\nIV\nRecursion with matrices and vectors\ndemo\n10 min\n\n\nIV\nBlock 5 — Convolution and deconvolution\nexercise\n15 min\n\n\n—\nBreak\n—\n5 min\n\n\nIV\nBlock 6 — Tucker decomposition on real data\nexercise\n15 min\n\n\n🆕 —\nKahoot Quiz 3 — Convolution & Tensor Decompositions\nquiz\n5 min\n\n\n—\nWrap-up\n—\n5 min\n\n\n\nWhy these three spots. Each quiz sits right after the block(s) that supply its content, while the material is still fresh, and before the next context switch (a break or a new Part) — so it reinforces rather than interrupts. Quiz 1 closes out Part III’s shape/vocabulary work; Quiz 2 closes out the einsum/pseudoinverse stretch of Part IV; Quiz 3 closes out the decomposition stretch of Part IV, right before the Wrap-up recap." + "text": "Schedule (195 minutes, incl. 3 Kahoot checks)\n\n\n\n\n\n\n\n\n\nPart\nSegment\nFormat\nTime\n\n\n\n\n—\nSetup and welcome\n—\n5 min\n\n\nI\nWhat a tensor is: theory and NumPy\ndemo\n20 min\n\n\nII\nThinking in N dimensions\ndemo\n20 min\n\n\nIII\nBlock 1 — Indexing and broadcasting real data\nexercise\n15 min\n\n\nIII\nBlock 2 — Reshape and transpose real images\nexercise\n15 min\n\n\n🆕 —\nKahoot Quiz 1 — Tensor Vocabulary & Shapes\nquiz\n5 min\n\n\n—\nBreak\n—\n5 min\n\n\nIII\nGroup exercise — Video pipeline design\ngroup\n15 min\n\n\nIV\nBlock 3 — Contraction with einsum\nexercise\n15 min\n\n\n—\nBreak\n—\n5 min\n\n\nIV\nBlock 4 — Inverses and the pseudoinverse\nexercise\n15 min\n\n\n🆕 —\nKahoot Quiz 2 — Einsum, Distance & the Pseudoinverse\nquiz\n5 min\n\n\nIV\nRecursion with matrices and vectors\ndemo\n10 min\n\n\nIV\nBlock 5 — Convolution and deconvolution\nexercise\n15 min\n\n\n—\nBreak\n—\n5 min\n\n\nIV\nBlock 6 — Tucker decomposition on real data\nexercise\n15 min\n\n\n🆕 —\nKahoot Quiz 3 — Convolution & Tensor Decompositions\nquiz\n5 min\n\n\n—\nWrap-up\n—\n5 min\n\n\n\nWhy these three spots. Each quiz sits right after the block(s) that supply its content, while the material is still fresh, and before the next context switch (a break or a new Part) — so it reinforces rather than interrupts. Quiz 1 closes out Part III’s shape/vocabulary work; Quiz 2 closes out the einsum/pseudoinverse stretch of Part IV; Quiz 3 closes out the decomposition stretch of Part IV, right before the Wrap-up recap." }, { "objectID": "tensors_workshop_plan_with_quizzes.html#vocabulary", @@ -81,7 +81,7 @@ "href": "tensors_workshop_plan_with_quizzes.html#group-exercise-video-pipeline-design-15-min", "title": "Tensors for Machine Learning", "section": "Group Exercise — Video Pipeline Design (15 min)", - "text": "Group Exercise — Video Pipeline Design (15 min)\nBack to your breakout channel. 10 minutes design, 5 minutes share-back. There is no single correct answer.\n\nDesign the tensor shape at each stage — raw file → decoded frames → preprocessed batch → model input → model output — for both systems: - Tech: a short-video app computing one embedding per video from sampled frames, to choose what to play next. - Biotech: a surgical-video model that labels the current phase of an operation from an operating-room camera.\n\n\nBoth panes hold the same eight frames, the same shape and the same sum. Only the order of axis 0 differs, and no arithmetic in this workshop can tell you which one is the video.\n\nSketch the shape at each of the five stages, for both. Where are they the same, and where must they differ?\nClips have different lengths — 30 seconds against 4 hours. Take one strategy your group proposed in Part II and give the exact shape of the preprocessed batch. What does an invented or wasted value in that tensor represent?\nThe surgical system adds three camera angles recording at once. Where does that axis go, and why does its position change how easy the rest of the pipeline is to write?\nThe recommender samples 8 frames out of 900. Which operation from Block 1 does that, and what is lost?\nBoth systems must decide which frames matter most. What kind of mechanism could learn that weighting?" + "text": "Group Exercise — Video Pipeline Design (15 min)\nBack to your breakout channel. 10 minutes design, 5 minutes share-back. There is no single correct answer.\n\nDesign the tensor shape at each stage — raw file → decoded frames → preprocessed batch → model input → model output — for both systems: - Tech: a short-video app computing one embedding per video from sampled frames, to choose what to play next. - Biotech: a surgical-video model that labels the current phase of an operation from an operating-room camera.\n\n\nBoth panes hold the same eight frames, the same shape and the same sum. Only the order of axis 0 differs, and no arithmetic in this workshop can tell you which one is the video.\n\nSketch the shape at each of the five stages, for both. Where are they the same, and where must they differ?\nClips have different lengths — 30 seconds against 4 hours. Take the padding-and-mask strategy from Part II and give the exact shape of the preprocessed batch. What does an invented or wasted value in that tensor represent?\nThe surgical system adds three camera angles recording at once. Where does that axis go, and why does its position change how easy the rest of the pipeline is to write?\nThe recommender samples 8 frames out of 900. Which operation from Block 1 does that, and what is lost?\nBoth systems must decide which frames matter most. What kind of mechanism could learn that weighting?" }, { "objectID": "tensors_workshop_plan_with_quizzes.html#block-3-contraction-with-einsum-15-min", @@ -144,7 +144,7 @@ "href": "tensors_workshop_plan_with_quizzes.html#wrap-up-5-min", "title": "Tensors for Machine Learning", "section": "Wrap-Up (5 min)", - "text": "Wrap-Up (5 min)\nWhat you did today:\n\nPart I — learned the vocabulary of tensors (axis, order, shape, slice, fiber, unfolding, contraction, decomposition), and that unfolding turns any tensor into a matrix without losing anything.\nPart II — argued about what axes mean, and found that a batch axis and a time axis behave differently even when the shapes look identical.\nPart III — indexed, broadcast, reshaped and transposed real tumour data and real medical images, and hit real problems: zero-variance pixels, and reshape silently destroying an image.\nPart IV — wrote contractions with einsum; solved an unsolvable 20,433-equation system with the pseudoinverse; used recursion to forecast real airline traffic and to find an eigenvector; convolved and deconvolved a real photograph; and compressed a real taxi tensor 4.7× with Tucker, which found rush hour on its own.\n\nOne idea connects Blocks 4, 5 and 6: when a problem has no exact answer or no true inverse, you do not give up — you find the best stable approximation. The pseudoinverse does this for linear systems, Richardson-Lucy for blurred images, and Tucker for tensors that are too large to keep in full.\nWhere to go next - torch.einsum / tf.einsum / jnp.einsum — identical syntax to what you used today. - np.linalg — the rest of Chapter 2: eigendecomposition, lstsq, pinv, qr, cholesky. - scipy.signal and skimage.restoration — convolution and deconvolution beyond today. - The take-home notebooks below. - Further Reading — books, the seminal Tucker/CP/SVD papers, and tensorly, for going deeper than today’s 195 minutes." + "text": "Wrap-Up (5 min)\nWhat you did today:\n\nPart I — learned the vocabulary of tensors (axis, order, shape, slice, fiber, unfolding, contraction, decomposition), and that unfolding turns any tensor into a matrix without losing anything.\nPart II — worked through what axes mean and why batch and time axes are semantically different.\nPart III — indexed, broadcast, reshaped and transposed real tumour data and real medical images, and hit real problems: zero-variance pixels, and reshape silently destroying an image.\nPart IV — wrote contractions with einsum; solved an unsolvable 20,433-equation system with the pseudoinverse; used recursion to forecast real airline traffic and to find an eigenvector; convolved and deconvolved a real photograph; and compressed a real taxi tensor 4.7× with Tucker, which found rush hour on its own.\n\nOne idea connects Blocks 4, 5 and 6: when a problem has no exact answer or no true inverse, you do not give up — you find the best stable approximation. The pseudoinverse does this for linear systems, Richardson-Lucy for blurred images, and Tucker for tensors that are too large to keep in full.\nWhere to go next - torch.einsum / tf.einsum / jnp.einsum — identical syntax to what you used today. - np.linalg — the rest of Chapter 2: eigendecomposition, lstsq, pinv, qr, cholesky. - scipy.signal and skimage.restoration — convolution and deconvolution beyond today. - The take-home notebooks below. - Further Reading — books, the seminal Tucker/CP/SVD papers, and tensorly, for going deeper than today’s 195 minutes." }, { "objectID": "tensors_workshop_plan_with_quizzes.html#further-reading", @@ -186,7 +186,7 @@ "href": "tensors_workshop_plan_with_quizzes.html#appendix-e-facilitator-notes", "title": "Tensors for Machine Learning", "section": "Appendix E — Facilitator Notes", - "text": "Appendix E — Facilitator Notes\n(Students may ignore this section.)\nStructure. Four parts that build on each other: understand what a tensor is → reason about why axes exist → manipulate axes → compute with and factorize tensors. Blocks 4, 5 and 6 share one theme — no exact inverse exists, so find the best stable approximation — and stating that connection explicitly at the wrap-up is what makes the second half feel like one lesson rather than four.\nDo not rush Part I. It is the students’ first contact with tensor theory and every later block uses its vocabulary. If running late, cut Appendix material, not Part I.\nLanguage. Students are ESL (Colombia). Speak slowly, avoid idiom, and define terms on first use. Name the Spanish cognates aloud early — eje, descomposición, contracción, convolución — it removes friction immediately. Invite questions in either language. Warn about the two meanings of “rank” at the start of Part I.\nVerified numbers. Every output quoted in this document was executed and checked: malignant vs benign mean radius 17.5/12.1; 3 zero-variance digit pixels; 207 missing values in the housing data; housing RMSE ≈ 75,980; deconvolution error 0.1157 → 0.0815 (25-pixel border excluded); taxi Tucker 4.71× compression at 6.7% error with the hour factor peaking at 18. If a student gets something different, it is worth investigating rather than dismissing.\nThe downloads. Three CSVs from GitHub raw URLs. They are small and fast, but confirm in the first 5 minutes that everyone’s download succeeded — a student who silently fails will be stuck at Blocks 4 and 6. Have the three CSVs mirrored in the workshop repo as a fallback.\nPre-assign breakout groups before the session; assigning them live costs 3–5 minutes, twice.\nGroup blocks need firmer facilitation than exercise blocks. If a group is still on question 1 with 5 minutes left, join their channel and tell them to sketch anything, even a wrong shape. The share-back matters more than a correct sketch.\n🆕 The three Kahoot quizzes. Each is 6 questions in kahoot_quiz_1_vocabulary_shapes.xlsx, kahoot_quiz_2_distance_pseudoinverse.xlsx, and kahoot_quiz_3_convolution_decompositions.xlsx, sitting after Blocks 2, 4, and 6 respectively. Import each into a kahoot ahead of time (Create → Add question → Import → Import spreadsheet) — don’t do this live. Budget 5 minutes per quiz including the podium; groups tend to want to see the leaderboard, and that’s fine, it’s the payoff. These add 15 minutes total, taking the workshop from 180 to 195 minutes.\nCutting for time. In order: drop Kahoot Quiz 2 (the least novel of the three — pseudoinverse and distance get re-covered narratively in the Wrap-up), then TODO 4 of Block 5 (true deconvolution — the most technically demanding), then the RNN snippet in the recursion demo, then question 5 of either group block, then Kahoot Quiz 1. Never cut Part I §1.3, Block 6, or Kahoot Quiz 3 — the last one is the cheapest way to check whether Tucker/CP actually landed before students leave.\nKnown rough edges. Block 5 TODO 4 is the hardest thing in the workshop; students who skip the border crop will conclude deconvolution failed, so flag the 25-pixel crop clearly before the exercise starts, not after. Block 4 TODO 3 asks students to trigger an error deliberately — some will think they did something wrong, so say in advance that the error is the expected result." + "text": "Appendix E — Facilitator Notes\n(Students may ignore this section.)\nStructure. Four parts that build on each other: understand what a tensor is → reason about why axes exist → manipulate axes → compute with and factorize tensors. Blocks 4, 5 and 6 share one theme — no exact inverse exists, so find the best stable approximation — and stating that connection explicitly at the wrap-up is what makes the second half feel like one lesson rather than four.\nDo not rush Part I. It is the students’ first contact with tensor theory and every later block uses its vocabulary. If running late, cut Appendix material, not Part I.\nLanguage. Students are ESL (Colombia). Speak slowly, avoid idiom, and define terms on first use. Name the Spanish cognates aloud early — eje, descomposición, contracción, convolución — it removes friction immediately. Invite questions in either language. Warn about the two meanings of “rank” at the start of Part I.\nVerified numbers. Every output quoted in this document was executed and checked: malignant vs benign mean radius 17.5/12.1; 3 zero-variance digit pixels; 207 missing values in the housing data; housing RMSE ≈ 75,980; deconvolution error 0.1157 → 0.0815 (25-pixel border excluded); taxi Tucker 4.71× compression at 6.7% error with the hour factor peaking at 18. If a student gets something different, it is worth investigating rather than dismissing.\nThe downloads. Three CSVs from GitHub raw URLs. They are small and fast, but confirm in the first 5 minutes that everyone’s download succeeded — a student who silently fails will be stuck at Blocks 4 and 6. Have the three CSVs mirrored in the workshop repo as a fallback.\nPre-assign the breakout groups for the video-pipeline block before the session; assigning them live costs 3–5 minutes.\nThe group block needs firmer facilitation than exercise blocks. If a group is still on the first design question with 5 minutes left, join their channel and tell them to sketch anything, even a wrong shape. The share-back matters more than a correct sketch.\n🆕 The three Kahoot quizzes. Each is 6 questions in kahoot_quiz_1_vocabulary_shapes.xlsx, kahoot_quiz_2_distance_pseudoinverse.xlsx, and kahoot_quiz_3_convolution_decompositions.xlsx, sitting after Blocks 2, 4, and 6 respectively. Import each into a kahoot ahead of time (Create → Add question → Import → Import spreadsheet) — don’t do this live. Budget 5 minutes per quiz including the podium; groups tend to want to see the leaderboard, and that’s fine, it’s the payoff. These add 15 minutes total, taking the workshop from 180 to 195 minutes.\nCutting for time. In order: drop Kahoot Quiz 2 (the least novel of the three — pseudoinverse and distance get re-covered narratively in the Wrap-up), then TODO 4 of Block 5 (true deconvolution — the most technically demanding), then the RNN snippet in the recursion demo, then question 5 of the video-pipeline group block, then Kahoot Quiz 1. Never cut Part I §1.3, Block 6, or Kahoot Quiz 3 — the last one is the cheapest way to check whether Tucker/CP actually landed before students leave.\nKnown rough edges. Block 5 TODO 4 is the hardest thing in the workshop; students who skip the border crop will conclude deconvolution failed, so flag the 25-pixel crop clearly before the exercise starts, not after. Block 4 TODO 3 asks students to trigger an error deliberately — some will think they did something wrong, so say in advance that the error is the expected result." }, { "objectID": "slides/en/index.html#all-the-data-here-is-real", @@ -200,14 +200,14 @@ "href": "slides/en/index.html#agenda-minutes", "title": "Tensors for Machine Learning", "section": "Agenda — 195 minutes", - "text": "Agenda — 195 minutes\n\n\n\n\nStart Time\nDuration (min)\nPart\nSegment Name\n\n\n\n\n00:00\n5\n—\nSetup and welcome\n\n\n00:05\n20\nI\nWhat a tensor is\n\n\n00:25\n20\nII\nThinking in N dimensions (group)\n\n\n00:45\n30\nIII\nIndexing & broadcasting · Reshape & transpose\n\n\n01:15\n10\n🎯\nKahoot 1 + break\n\n\n01:25\n15\nIII\nVideo pipeline design (group)\n\n\n01:40\n15\nIV\nContraction with einsum\n\n\n01:55\n5\n—\nBreak\n\n\n02:00\n15\nIV\nInverses and the pseudoinverse\n\n\n02:15\n5\n🎯\nKahoot 2\n\n\n02:20\n25\nIV\nRecursion · Convolution\n\n\n02:45\n5\n—\nBreak\n\n\n02:50\n15\nIV\nTucker decomposition\n\n\n03:05\n10\n🎯\nKahoot 3 + wrap-up" + "text": "Agenda — 195 minutes\n\n\n\n\nStart Time\nDuration (min)\nPart\nSegment Name\n\n\n\n\n00:00\n5\n—\nSetup and welcome\n\n\n00:05\n20\nI\nWhat a tensor is\n\n\n00:25\n20\nII\nThinking in N dimensions\n\n\n00:45\n30\nIII\nIndexing & broadcasting · Reshape & transpose\n\n\n01:15\n10\n🎯\nKahoot 1 + break\n\n\n01:25\n15\nIII\nVideo pipeline design (group)\n\n\n01:40\n15\nIV\nContraction with einsum\n\n\n01:55\n5\n—\nBreak\n\n\n02:00\n15\nIV\nInverses and the pseudoinverse\n\n\n02:15\n5\n🎯\nKahoot 2\n\n\n02:20\n25\nIV\nRecursion · Convolution\n\n\n02:45\n5\n—\nBreak\n\n\n02:50\n15\nIV\nTucker decomposition\n\n\n03:05\n10\n🎯\nKahoot 3 + wrap-up" }, { "objectID": "slides/en/index.html#two-rhythms-and-one-warning", "href": "slides/en/index.html#two-rhythms-and-one-warning", "title": "Tensors for Machine Learning", "section": "Two rhythms, and one warning", - "text": "Two rhythms, and one warning\nExercise blocks — 10 min coding, 5 min explanation.\nGroup blocks — 10 min discussion, then share-back.\n\n\n\n\n\n\n\nThe word “rank”\n\n\nChapter 2: rank = number of independent columns. Tensor theory: rank often = number of axes.\nToday: “order” for the number of axes. “Rank” only in Chapter 2’s sense.\n\n\n\n\nGive this warning at the start of Part I, not later. It is the single most reliable source of confusion in the whole workshop." + "text": "Two rhythms, and one warning\nExercise blocks — 10 min coding, 5 min explanation.\nGroup block (§05) — 10 min discussion, then share-back.\n\n\n\n\n\n\n\nThe word “rank”\n\n\nChapter 2: rank = number of independent columns. Tensor theory: rank often = number of axes.\nToday: “order” for the number of axes. “Rank” only in Chapter 2’s sense.\n\n\n\n\nGive this warning at the start of Part I, not later. It is the single most reliable source of confusion in the whole workshop." }, { "objectID": "slides/en/index.html#three-downloads-one-first-cell", @@ -273,32 +273,25 @@ "text": "To the notebook\n01 · What a tensor is\nOpen in Colab\n\nDo not rush Part I. It is the students’ first contact with tensor theory and every later block uses its vocabulary. If running late, cut Appendix material, never Part I." }, { - "objectID": "slides/en/index.html#your-task", - "href": "slides/en/index.html#your-task", + "objectID": "slides/en/index.html#same-shape-different-axes", + "href": "slides/en/index.html#same-shape-different-axes", "title": "Tensors for Machine Learning", - "section": "Your task", - "text": "Your task\n\nA grayscale image is a matrix. Almost nothing in ML is a single grayscale image. Each thing you add — colour, many examples, time — adds an axis, and each axis means something different.\n\nArgue about which axis goes where, and why.\n10 minutes in your breakout channel, then share-back." + "section": "Same shape, different axes", + "text": "Same shape, different axes\ndigits.images is (1797, 8, 8) and a photo is (512, 512, 3) — both order 3, but axis 0 counts images in one and rows of pixels in the other. In the notebook, digit_batch and video_patch are both (8, 8, 8): one stacks independent examples, the other stacks ordered moments.\n\nShuffling axis 0 is harmless for a batch — the examples are independent — and destroys a video, where order is the information. Same code, opposite meaning. Chapter 2’s notation has no concept of “order matters between elements”; that is genuinely new today." }, { - "objectID": "slides/en/index.html#the-five-questions", - "href": "slides/en/index.html#the-five-questions", - "title": "Tensors for Machine Learning", - "section": "The five questions", - "text": "The five questions\n\n\n(H, W) → colour image → batch → video → batch of videos. What does each new axis count? Do not say “we add a dimension.”\nA batch axis and a time axis look identical in code. What differs in meaning? What happens if you shuffle each?\nReal videos have different lengths. Two ways to batch them — what does each lose or invent?\nCells photographed every 10 min for 48 h: frame interval → ? field of view → ? number of dishes → ?\nIs there a limit on how many axes a tensor can have?" - }, - { - "objectID": "slides/en/index.html#share-back", - "href": "slides/en/index.html#share-back", + "objectID": "slides/en/index.html#ragged-clips-need-a-mask", + "href": "slides/en/index.html#ragged-clips-need-a-mask", "title": "Tensors for Machine Learning", - "section": "Share-back", - "text": "Share-back\n\ngray_image = np.zeros((28, 28)) # (H, W)\ncolor_image = np.zeros((28, 28, 3)) # (H, W, C) + colour\nbatch_of_images = np.zeros((32, 28, 28, 3)) # (N, H, W, C) + many examples\nvideo = np.zeros((16, 28, 28, 3)) # (T, H, W, C) + ordered time\nbatch_of_videos = np.zeros((8, 16, 28, 28, 3)) # (N, T, H, W, C)\n\n\nQuestion 2 is the point. Shuffling axis 0 is harmless for a batch and destroys a video.\nChapter 2’s notation has no concept of “order matters between elements.” That is genuinely new today." + "section": "Ragged clips need a mask", + "text": "Ragged clips need a mask\nReal videos have different lengths, but a batch tensor is rectangular. The notebook takes three real clips of length 4, 7 and 5, pads them into one (3, 7, 135, 240, 3) order-5 batch, and carries a Boolean (3, 7) validity mask — so valid.sum() == 16 and the padding stays visible instead of being averaged into the data." }, { "objectID": "slides/en/index.html#to-the-notebook-2", "href": "slides/en/index.html#to-the-notebook-2", "title": "Tensors for Machine Learning", "section": "To the notebook", - "text": "To the notebook\n02 · Thinking in N dimensions\nOpen in Colab\n\nGroup blocks need firmer facilitation than exercise blocks. If a group is still on question 1 with 5 minutes left, join their channel and tell them to sketch anything, even a wrong shape. The share-back matters more than a correct sketch. Pre-assign breakout groups before the session." + "text": "To the notebook\n02 · Thinking in N dimensions\nOpen in Colab\n\nThis is a live coding demo, not a discussion block. Open the notebook and run Setup first; it downloads and checksums the real video used in the exercises. Emphasize the contrast between shuffling a batch and shuffling time, then the padded batch with its validity mask." }, { "objectID": "slides/en/index.html#why-this-matters", @@ -371,8 +364,8 @@ "text": "Design both pipelines\nraw file → decoded frames → preprocessed batch → model input → model output\nTech — a short-video app computing one embedding per video from sampled frames.\nBiotech — a surgical-video model labelling the current phase of an operation.\n\nThere is no single correct answer." }, { - "objectID": "slides/en/index.html#the-five-questions-1", - "href": "slides/en/index.html#the-five-questions-1", + "objectID": "slides/en/index.html#the-five-questions", + "href": "slides/en/index.html#the-five-questions", "title": "Tensors for Machine Learning", "section": "The five questions", "text": "The five questions\n\n\nSketch the shape at each of the five stages, for both. Where must they differ?\n30 seconds against 4 hours. Give the exact preprocessed batch shape. What does an invented or wasted value represent?\nThree camera angles at once. Where does that axis go?\n8 frames sampled from 900 — which operation from §03, and what is lost?\nWhich frames matter most? What could learn that weighting?" @@ -725,7 +718,7 @@ "href": "index.html#the-sections", "title": "Tensors for Machine Learning", "section": "The sections", - "text": "The sections\nRead across a row: the slides in either language, the notebook to run, and the Kahoot check that covers it. The 🎯 rows are the three quizzes, shown in the order they actually run.\n\n\n\n\n\n\n#\n\n\nSection\n\n\nFormat\n\n\nMin\n\n\nSlides EN\n\n\nSlides ES\n\n\nNotebook\n\n\nKahoot\n\n\n\n\n\n\n00\n\n\nSetup and welcomeLoad every dataset and confirm your runtime works before anything else.\n\n\nsetup\n\n\n5\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\n—\n\n\n\n\n01\n\n\nWhat a tensor isLearn to read a tensor's structure and track what its axes mean as you fix, rearrange, or contract them.\n\n\ndemo\n\n\n20\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\nQ1\n\n\n\n\n02\n\n\nThinking in N dimensionsLearn to read real tensors by asking what every axis counts and why a batch axis is not the same thing as a time axis.\n\n\ngroup\n\n\n20\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\n—\n\n\n\n\n03\n\n\nIndexing and broadcasting real dataSelect named measurements from real tumour-sample data, compare meaningful subsets, then standardize real image data safely.\n\n\nexercise\n\n\n15\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\nQ1\n\n\n\n\n04\n\n\nReshape and transpose real imagesUse real images to move between HWC↔︎CHW and NHWC↔︎NCHW, then show why matching shapes do not guarantee matching axis semantics.\n\n\nexercise\n\n\n15\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\nQ1\n\n\n\n\n🎯\n\n\nKahoot 1 — Tensor Vocabulary & Shapes · 6 questions · 5 min — covers sections 01, 03, 04\n\n\n\n\n05\n\n\nVideo pipeline designProcess one pinned real video end to end, then use what its axes actually mean to design two downstream video pipelines.\n\n\ngroup\n\n\n15\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\n—\n\n\n\n\n06\n\n\nContraction with einsumUse one index rule on real data, then change the inputs interactively to test which index disappears.\n\n\nexercise\n\n\n15\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\nQ2\n\n\n\n\n07\n\n\nInverses and the pseudoinverseUse the pseudoinverse on real singular, tall, and wide systems, then inspect the geometry interactively.\n\n\nexercise\n\n\n15\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\nQ2\n\n\n\n\n🎯\n\n\nKahoot 2 — Einsum, Distance & the Pseudoinverse · 6 questions · 5 min — covers sections 06, 07\n\n\n\n\n08\n\n\nRecursion with matrices and vectorsTreat recursion as repeated state updates, connect repeated multiplication with dominant eigen-directions, and test recursive forecasting on real airline-passenger data.\n\n\ndemo\n\n\n10\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\n—\n\n\n\n\n09\n\n\nConvolution and deconvolutionTreat convolution as a structured linear operator, separate it from correlation, understand transposed convolution, and partially recover a real blurred image.\n\n\nexercise\n\n\n15\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\nQ3\n\n\n\n\n10\n\n\nTucker decomposition on real dataBuild a real tensor from New York taxi trips, compress each mode with HOSVD, and explore the trade-off between size, reconstruction error, and interpretable structure.\n\n\nexercise\n\n\n15\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\nQ3\n\n\n\n\n🎯\n\n\nKahoot 3 — Convolution & Tensor Decompositions · 6 questions · 5 min — covers sections 09, 10\n\n\n\n\n11\n\n\nWrap-up and take-homesWrap up the workshop around one connecting idea, then choose among five extensions: PCA, attention, CP, Cholesky, and audio.\n\n\nwrap-up\n\n\n5\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\n—\n\n\n\n\n\nNot shown: the three 5-minute breaks. The twelve sections come to 165 minutes; the three quizzes add 15 and the breaks another 15, which is how the session reaches 195 minutes." + "text": "The sections\nRead across a row: the slides in either language, the notebook to run, and the Kahoot check that covers it. The 🎯 rows are the three quizzes, shown in the order they actually run.\n\n\n\n\n\n\n#\n\n\nSection\n\n\nFormat\n\n\nMin\n\n\nSlides EN\n\n\nSlides ES\n\n\nNotebook\n\n\nKahoot\n\n\n\n\n\n\n00\n\n\nSetup and welcomeLoad every dataset and confirm your runtime works before anything else.\n\n\nsetup\n\n\n5\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\n—\n\n\n\n\n01\n\n\nWhat a tensor isLearn to read a tensor's structure and track what its axes mean as you fix, rearrange, or contract them.\n\n\ndemo\n\n\n20\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\nQ1\n\n\n\n\n02\n\n\nThinking in N dimensionsLearn to read real tensors by asking what every axis counts and why a batch axis is not the same thing as a time axis.\n\n\ndemo\n\n\n20\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\n—\n\n\n\n\n03\n\n\nIndexing and broadcasting real dataSelect named measurements from real tumour-sample data, compare meaningful subsets, then standardize real image data safely.\n\n\nexercise\n\n\n15\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\nQ1\n\n\n\n\n04\n\n\nReshape and transpose real imagesUse real images to move between HWC↔︎CHW and NHWC↔︎NCHW, then show why matching shapes do not guarantee matching axis semantics.\n\n\nexercise\n\n\n15\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\nQ1\n\n\n\n\n🎯\n\n\nKahoot 1 — Tensor Vocabulary & Shapes · 6 questions · 5 min — covers sections 01, 03, 04\n\n\n\n\n05\n\n\nVideo pipeline designProcess one pinned real video end to end, then use what its axes actually mean to design two downstream video pipelines.\n\n\ngroup\n\n\n15\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\n—\n\n\n\n\n06\n\n\nContraction with einsumUse one index rule on real data, then change the inputs interactively to test which index disappears.\n\n\nexercise\n\n\n15\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\nQ2\n\n\n\n\n07\n\n\nInverses and the pseudoinverseUse the pseudoinverse on real singular, tall, and wide systems, then inspect the geometry interactively.\n\n\nexercise\n\n\n15\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\nQ2\n\n\n\n\n🎯\n\n\nKahoot 2 — Einsum, Distance & the Pseudoinverse · 6 questions · 5 min — covers sections 06, 07\n\n\n\n\n08\n\n\nRecursion with matrices and vectorsTreat recursion as repeated state updates, connect repeated multiplication with dominant eigen-directions, and test recursive forecasting on real airline-passenger data.\n\n\ndemo\n\n\n10\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\n—\n\n\n\n\n09\n\n\nConvolution and deconvolutionTreat convolution as a structured linear operator, separate it from correlation, understand transposed convolution, and partially recover a real blurred image.\n\n\nexercise\n\n\n15\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\nQ3\n\n\n\n\n10\n\n\nTucker decomposition on real dataBuild a real tensor from New York taxi trips, compress each mode with HOSVD, and explore the trade-off between size, reconstruction error, and interpretable structure.\n\n\nexercise\n\n\n15\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\nQ3\n\n\n\n\n🎯\n\n\nKahoot 3 — Convolution & Tensor Decompositions · 6 questions · 5 min — covers sections 09, 10\n\n\n\n\n11\n\n\nWrap-up and take-homesWrap up the workshop around one connecting idea, then choose among five extensions: PCA, attention, CP, Cholesky, and audio.\n\n\nwrap-up\n\n\n5\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\n—\n\n\n\n\n\nNot shown: the three 5-minute breaks. The twelve sections come to 165 minutes; the three quizzes add 15 and the breaks another 15, which is how the session reaches 195 minutes." }, { "objectID": "index.html#a-note-on-language", @@ -830,7 +823,7 @@ "href": "es/index.html#las-secciones", "title": "Tensores para Aprendizaje Automático", "section": "Las secciones", - "text": "Las secciones\nLee una fila de izquierda a derecha: las diapositivas en cualquiera de los dos idiomas, el cuaderno que hay que ejecutar y el control de Kahoot que lo cubre. Las filas 🎯 son los tres cuestionarios, en el orden real en que se ejecutan.\n\n\n\n\n\n\n#\n\n\nSección\n\n\nFormato\n\n\nMin\n\n\nDiapos EN\n\n\nDiapos ES\n\n\nCuaderno\n\n\nKahoot\n\n\n\n\n\n\n00\n\n\nPreparación y bienvenidaCarga todos los conjuntos de datos y confirma que tu entorno funciona antes de empezar.\n\n\npreparación\n\n\n5\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\n—\n\n\n\n\n01\n\n\nQué es un tensorAprender a leer la estructura de un tensor y seguir el significado de sus ejes al fijarlos, reorganizarlos o contraerlos.\n\n\ndemostración\n\n\n20\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\nQ1\n\n\n\n\n02\n\n\nPensar en N dimensionesAprender a leer tensores reales preguntando qué cuenta cada eje y por qué un eje de lote no significa lo mismo que un eje temporal.\n\n\ngrupo\n\n\n20\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\n—\n\n\n\n\n03\n\n\nIndexación y broadcasting con datos realesSeleccionar medidas reales por nombre, comparar subconjuntos con máscaras booleanas y detectar píxeles de varianza cero.\n\n\nejercicio\n\n\n15\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\nQ1\n\n\n\n\n04\n\n\nReshape y transposición de imágenes realesReordenar ejes correctamente en imágenes y lotes reales, y comprobar por qué reshape puede conservar la forma mientras destruye el significado.\n\n\nejercicio\n\n\n15\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\nQ1\n\n\n\n\n🎯\n\n\nKahoot 1 — Vocabulario de tensores y formas · 6 preguntas · 5 min — cubre las secciones 01, 03, 04\n\n\n\n\n05\n\n\nDiseño de un pipeline de vídeoConvertir un archivo de vídeo real en un tensor, observar qué se pierde al muestrear fotogramas y diseñar formas tensoriales que respeten el significado del tiempo.\n\n\ngrupo\n\n\n15\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\n—\n\n\n\n\n06\n\n\nContracción con einsumAprender una sola regla de índices, comprobarla con datos reales y cambiar las entradas de forma interactiva para identificar qué índice desaparece.\n\n\nejercicio\n\n\n15\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\nQ2\n\n\n\n\n07\n\n\nInversas y la pseudoinversaUsar la pseudoinversa en sistemas reales singulares, altos y anchos, e inspeccionar su geometría de forma interactiva.\n\n\nejercicio\n\n\n15\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\nQ2\n\n\n\n\n🎯\n\n\nKahoot 2 — Einsum, distancia y la pseudoinversa · 6 preguntas · 5 min — cubre las secciones 06, 07\n\n\n\n\n08\n\n\nRecursión con matrices y vectoresEntender una recurrencia como una actualización de estado repetida, observar cuándo domina una dirección propia y comprobar qué ocurre cuando un pronóstico reutiliza sus propias predicciones.\n\n\ndemostración\n\n\n10\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\n—\n\n\n\n\n09\n\n\nConvolución y deconvoluciónVer la convolución como un operador lineal estructurado, distinguir correlación de convolución, entender la convolución transpuesta y recuperar parcialmente una imagen real desenfocada.\n\n\nejercicio\n\n\n15\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\nQ3\n\n\n\n\n10\n\n\nDescomposición de Tucker con datos realesConstruir un tensor real de viajes en taxi, comprimir cada modo con HOSVD y explorar el compromiso entre tamaño, error e interpretación.\n\n\nejercicio\n\n\n15\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\nQ3\n\n\n\n\n🎯\n\n\nKahoot 3 — Convolución y descomposiciones tensoriales · 6 preguntas · 5 min — cubre las secciones 09, 10\n\n\n\n\n11\n\n\nCierre y ejercicios para casaResume la idea que conecta el taller y luego elige entre cinco extensiones: PCA, atención, CP, Cholesky y audio.\n\n\ncierre\n\n\n5\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\n—\n\n\n\n\n\nNo aparecen en la tabla: las tres pausas de 5 minutos. Las doce secciones suman 165 minutos; los tres cuestionarios añaden 15 y las pausas otros 15, que es como la sesión llega a 195 minutos." + "text": "Las secciones\nLee una fila de izquierda a derecha: las diapositivas en cualquiera de los dos idiomas, el cuaderno que hay que ejecutar y el control de Kahoot que lo cubre. Las filas 🎯 son los tres cuestionarios, en el orden real en que se ejecutan.\n\n\n\n\n\n\n#\n\n\nSección\n\n\nFormato\n\n\nMin\n\n\nDiapos EN\n\n\nDiapos ES\n\n\nCuaderno\n\n\nKahoot\n\n\n\n\n\n\n00\n\n\nPreparación y bienvenidaCarga todos los conjuntos de datos y confirma que tu entorno funciona antes de empezar.\n\n\npreparación\n\n\n5\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\n—\n\n\n\n\n01\n\n\nQué es un tensorAprender a leer la estructura de un tensor y seguir el significado de sus ejes al fijarlos, reorganizarlos o contraerlos.\n\n\ndemostración\n\n\n20\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\nQ1\n\n\n\n\n02\n\n\nPensar en N dimensionesAprender a leer tensores reales preguntando qué cuenta cada eje y por qué un eje de lote no significa lo mismo que un eje temporal.\n\n\ndemostración\n\n\n20\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\n—\n\n\n\n\n03\n\n\nIndexación y broadcasting con datos realesSeleccionar medidas reales por nombre, comparar subconjuntos con máscaras booleanas y detectar píxeles de varianza cero.\n\n\nejercicio\n\n\n15\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\nQ1\n\n\n\n\n04\n\n\nReshape y transposición de imágenes realesReordenar ejes correctamente en imágenes y lotes reales, y comprobar por qué reshape puede conservar la forma mientras destruye el significado.\n\n\nejercicio\n\n\n15\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\nQ1\n\n\n\n\n🎯\n\n\nKahoot 1 — Vocabulario de tensores y formas · 6 preguntas · 5 min — cubre las secciones 01, 03, 04\n\n\n\n\n05\n\n\nDiseño de un pipeline de vídeoConvertir un archivo de vídeo real en un tensor, observar qué se pierde al muestrear fotogramas y diseñar formas tensoriales que respeten el significado del tiempo.\n\n\ngrupo\n\n\n15\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\n—\n\n\n\n\n06\n\n\nContracción con einsumAprender una sola regla de índices, comprobarla con datos reales y cambiar las entradas de forma interactiva para identificar qué índice desaparece.\n\n\nejercicio\n\n\n15\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\nQ2\n\n\n\n\n07\n\n\nInversas y la pseudoinversaUsar la pseudoinversa en sistemas reales singulares, altos y anchos, e inspeccionar su geometría de forma interactiva.\n\n\nejercicio\n\n\n15\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\nQ2\n\n\n\n\n🎯\n\n\nKahoot 2 — Einsum, distancia y la pseudoinversa · 6 preguntas · 5 min — cubre las secciones 06, 07\n\n\n\n\n08\n\n\nRecursión con matrices y vectoresEntender una recurrencia como una actualización de estado repetida, observar cuándo domina una dirección propia y comprobar qué ocurre cuando un pronóstico reutiliza sus propias predicciones.\n\n\ndemostración\n\n\n10\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\n—\n\n\n\n\n09\n\n\nConvolución y deconvoluciónVer la convolución como un operador lineal estructurado, distinguir correlación de convolución, entender la convolución transpuesta y recuperar parcialmente una imagen real desenfocada.\n\n\nejercicio\n\n\n15\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\nQ3\n\n\n\n\n10\n\n\nDescomposición de Tucker con datos realesConstruir un tensor real de viajes en taxi, comprimir cada modo con HOSVD y explorar el compromiso entre tamaño, error e interpretación.\n\n\nejercicio\n\n\n15\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\nQ3\n\n\n\n\n🎯\n\n\nKahoot 3 — Convolución y descomposiciones tensoriales · 6 preguntas · 5 min — cubre las secciones 09, 10\n\n\n\n\n11\n\n\nCierre y ejercicios para casaResume la idea que conecta el taller y luego elige entre cinco extensiones: PCA, atención, CP, Cholesky y audio.\n\n\ncierre\n\n\n5\n\n\nEN\n\n\nES\n\n\nColab · src\n\n\n—\n\n\n\n\n\nNo aparecen en la tabla: las tres pausas de 5 minutos. Las doce secciones suman 165 minutos; los tres cuestionarios añaden 15 y las pausas otros 15, que es como la sesión llega a 195 minutos." }, { "objectID": "es/index.html#una-nota-sobre-el-idioma", @@ -858,14 +851,14 @@ "href": "slides/es/index.html#programa-minutos", "title": "Tensores para Aprendizaje Automático", "section": "Programa — 195 minutos", - "text": "Programa — 195 minutos\n\n\n\n\n\n\n\n\n\n\nHora de inicio\nDuración (min)\nParte\nSegmento\n\n\n\n\n00:00\n5\n—\nPreparación y bienvenida\n\n\n00:05\n20\nI\nQué es un tensor\n\n\n00:25\n20\nII\nPensar en N dimensiones (grupo)\n\n\n00:45\n30\nIII\nIndexación y broadcasting · Reshape y transposición\n\n\n01:15\n10\n🎯\nKahoot 1 + pausa\n\n\n01:25\n15\nIII\nDiseño de un pipeline de vídeo (grupo)\n\n\n01:40\n15\nIV\nContracción con einsum\n\n\n01:55\n5\n—\nPausa\n\n\n02:00\n15\nIV\nInversas y la pseudoinversa\n\n\n02:15\n5\n🎯\nKahoot 2\n\n\n02:20\n25\nIV\nRecursión · Convolución\n\n\n02:45\n5\n—\nPausa\n\n\n02:50\n15\nIV\nDescomposición de Tucker\n\n\n03:05\n10\n🎯\nKahoot 3 + cierre" + "text": "Programa — 195 minutos\n\n\n\n\n\n\n\n\n\n\nHora de inicio\nDuración (min)\nParte\nSegmento\n\n\n\n\n00:00\n5\n—\nPreparación y bienvenida\n\n\n00:05\n20\nI\nQué es un tensor\n\n\n00:25\n20\nII\nPensar en N dimensiones\n\n\n00:45\n30\nIII\nIndexación y broadcasting · Reshape y transposición\n\n\n01:15\n10\n🎯\nKahoot 1 + pausa\n\n\n01:25\n15\nIII\nDiseño de un pipeline de vídeo (grupo)\n\n\n01:40\n15\nIV\nContracción con einsum\n\n\n01:55\n5\n—\nPausa\n\n\n02:00\n15\nIV\nInversas y la pseudoinversa\n\n\n02:15\n5\n🎯\nKahoot 2\n\n\n02:20\n25\nIV\nRecursión · Convolución\n\n\n02:45\n5\n—\nPausa\n\n\n02:50\n15\nIV\nDescomposición de Tucker\n\n\n03:05\n10\n🎯\nKahoot 3 + cierre" }, { "objectID": "slides/es/index.html#dos-ritmos-y-una-advertencia", "href": "slides/es/index.html#dos-ritmos-y-una-advertencia", "title": "Tensores para Aprendizaje Automático", "section": "Dos ritmos, y una advertencia", - "text": "Dos ritmos, y una advertencia\nBloques de ejercicios — 10 min programando, 5 de explicación.\nBloques de grupo — 10 min de discusión y puesta en común.\n\n\n\n\n\n\n\nLa palabra «rango»\n\n\nCapítulo 2: rango = número de columnas independientes. Teoría de tensores: rango suele significar el número de ejes.\nHoy: «orden» para el número de ejes. «Rango» solo en el sentido del capítulo 2.\n\n\n\n\nDa esta advertencia al principio de la parte I, no después. Es la fuente de confusión más fiable de todo el taller." + "text": "Dos ritmos, y una advertencia\nBloques de ejercicios — 10 min programando, 5 de explicación.\nBloque de grupo (§05) — 10 min de discusión y puesta en común.\n\n\n\n\n\n\n\nLa palabra «rango»\n\n\nCapítulo 2: rango = número de columnas independientes. Teoría de tensores: rango suele significar el número de ejes.\nHoy: «orden» para el número de ejes. «Rango» solo en el sentido del capítulo 2.\n\n\n\n\nDa esta advertencia al principio de la parte I, no después. Es la fuente de confusión más fiable de todo el taller." }, { "objectID": "slides/es/index.html#tres-descargas-una-primera-celda", @@ -931,32 +924,25 @@ "text": "Al cuaderno\n01 · Qué es un tensor\nAbrir en Colab\n\nNo corras con la parte I. Es el primer contacto con la teoría de tensores y todos los bloques posteriores usan su vocabulario. Si vas con retraso, recorta los apéndices, nunca la parte I." }, { - "objectID": "slides/es/index.html#vuestra-tarea", - "href": "slides/es/index.html#vuestra-tarea", + "objectID": "slides/es/index.html#la-misma-forma-ejes-distintos", + "href": "slides/es/index.html#la-misma-forma-ejes-distintos", "title": "Tensores para Aprendizaje Automático", - "section": "Vuestra tarea", - "text": "Vuestra tarea\n\nUna imagen en escala de grises es una matriz. Casi nada en aprendizaje automático es una sola imagen en gris. Cada cosa que añades — color, muchos ejemplos, tiempo — añade un eje, y cada eje significa algo distinto.\n\nDiscutid qué eje va dónde, y por qué.\n10 minutos en vuestro canal y luego puesta en común." + "section": "La misma forma, ejes distintos", + "text": "La misma forma, ejes distintos\ndigits.images es (1797, 8, 8) y una foto es (512, 512, 3) — ambas de orden 3, pero el eje 0 cuenta imágenes en una y filas de píxeles en la otra. En el cuaderno, digit_batch y video_patch son ambos (8, 8, 8): uno apila ejemplos independientes, el otro apila instantes ordenados.\n\nBarajar el eje 0 es inofensivo en un lote — los ejemplos son independientes — y destruye un vídeo, donde el orden es la información. La misma operación, un significado completamente distinto. La notación del capítulo 2 no tiene ningún concepto de «el orden entre elementos importa»; eso sí es nuevo hoy." }, { - "objectID": "slides/es/index.html#las-cinco-preguntas", - "href": "slides/es/index.html#las-cinco-preguntas", - "title": "Tensores para Aprendizaje Automático", - "section": "Las cinco preguntas", - "text": "Las cinco preguntas\n\n\n(H, W) → imagen en color → lote → vídeo → lote de vídeos. ¿Qué cuenta cada eje nuevo? No digáis «añadimos una dimensión».\nUn eje de lote y un eje temporal son idénticos en el código. ¿Qué cambia en su significado? ¿Qué pasa si barajas cada uno?\nLos vídeos reales tienen longitudes distintas. Dos formas de agruparlos en lote: ¿qué pierde o inventa cada una?\nCélulas fotografiadas cada 10 min durante 48 h: ¿intervalo entre fotogramas → ? ¿campo de visión → ? ¿número de placas → ?\n¿Hay un límite al número de ejes de un tensor?" - }, - { - "objectID": "slides/es/index.html#puesta-en-común", - "href": "slides/es/index.html#puesta-en-común", + "objectID": "slides/es/index.html#los-clips-de-distinta-duración-necesitan-una-máscara", + "href": "slides/es/index.html#los-clips-de-distinta-duración-necesitan-una-máscara", "title": "Tensores para Aprendizaje Automático", - "section": "Puesta en común", - "text": "Puesta en común\n\ngray_image = np.zeros((28, 28)) # (H, W)\ncolor_image = np.zeros((28, 28, 3)) # (H, W, C) + color\nbatch_of_images = np.zeros((32, 28, 28, 3)) # (N, H, W, C) + muchos ejemplos\nvideo = np.zeros((16, 28, 28, 3)) # (T, H, W, C) + tiempo ordenado\nbatch_of_videos = np.zeros((8, 16, 28, 28, 3)) # (N, T, H, W, C)\n\n\nLa pregunta 2 es la clave. Barajar el eje 0 es inofensivo en un lote y destruye un vídeo.\nLa notación del capítulo 2 no tiene ningún concepto de «el orden entre elementos importa». Eso sí es nuevo hoy." + "section": "Los clips de distinta duración necesitan una máscara", + "text": "Los clips de distinta duración necesitan una máscara\nLos vídeos reales tienen longitudes distintas, pero un tensor de lote es rectangular. El cuaderno toma tres clips reales de longitud 4, 7 y 5, los rellena en un lote de orden 5 (3, 7, 135, 240, 3) y lleva una máscara de validez booleana (3, 7) — así valid.sum() == 16 y el relleno queda visible en vez de promediarse en los datos." }, { "objectID": "slides/es/index.html#al-cuaderno-2", "href": "slides/es/index.html#al-cuaderno-2", "title": "Tensores para Aprendizaje Automático", "section": "Al cuaderno", - "text": "Al cuaderno\n02 · Pensar en N dimensiones\nAbrir en Colab\n\nLos bloques de grupo necesitan más mano firme que los de ejercicios. Si un grupo sigue en la pregunta 1 a falta de 5 minutos, entra en su canal y diles que dibujen cualquier cosa, aunque la forma esté mal. La puesta en común importa más que un dibujo correcto. Asigna los grupos antes de la sesión." + "text": "Al cuaderno\n02 · Pensar en N dimensiones\nAbrir en Colab\n\nEsto es una demostración de código en vivo, no un bloque de discusión. Abre el cuaderno y ejecuta primero la celda de preparación; descarga y verifica por checksum el vídeo real que usan los ejercicios. Recalca el contraste entre barajar un lote y barajar el tiempo, y luego el lote rellenado con su máscara de validez." }, { "objectID": "slides/es/index.html#por-qué-importa", @@ -1029,8 +1015,8 @@ "text": "Diseñad las dos tuberías\narchivo → fotogramas decodificados → lote preprocesado → entrada del modelo → salida del modelo\nTecnología — una app de vídeos cortos que calcula un embedding por vídeo a partir de fotogramas muestreados.\nBiotecnología — un modelo que etiqueta la fase actual de una operación quirúrgica.\n\nNo hay una única respuesta correcta." }, { - "objectID": "slides/es/index.html#las-cinco-preguntas-1", - "href": "slides/es/index.html#las-cinco-preguntas-1", + "objectID": "slides/es/index.html#las-cinco-preguntas", + "href": "slides/es/index.html#las-cinco-preguntas", "title": "Tensores para Aprendizaje Automático", "section": "Las cinco preguntas", "text": "Las cinco preguntas\n\n\nDibujad la forma en cada una de las cinco etapas, para los dos sistemas. ¿Dónde tienen que diferir?\n30 segundos frente a 4 horas. Dad la forma exacta del lote preprocesado. ¿Qué representa un valor inventado o desperdiciado?\nTres ángulos de cámara a la vez. ¿Dónde va ese eje?\n8 fotogramas muestreados de 900: ¿qué operación de la §03 es esa, y qué se pierde?\n¿Qué fotogramas importan más? ¿Qué mecanismo podría aprender ese peso?" diff --git a/docs/sitemap.xml b/docs/sitemap.xml index 38306e4..5c3b9be 100644 --- a/docs/sitemap.xml +++ b/docs/sitemap.xml @@ -2,11 +2,11 @@ https://project-delphi.github.io/tensors-workshop/tensors_workshop_plan_with_quizzes.html - 2026-08-27T20:32:37.773Z + 2026-08-31T11:03:24.417Z https://project-delphi.github.io/tensors-workshop/slides/en/index.html - 2026-08-27T20:32:37.630Z + 2026-08-31T10:12:05.957Z https://project-delphi.github.io/tensors-workshop/kahoot.html @@ -26,6 +26,6 @@ https://project-delphi.github.io/tensors-workshop/slides/es/index.html - 2026-08-27T20:32:37.721Z + 2026-08-31T10:28:08.556Z diff --git a/docs/slides/en/index.html b/docs/slides/en/index.html index 591ee97..2b4635c 100644 --- a/docs/slides/en/index.html +++ b/docs/slides/en/index.html @@ -317,7 +317,7 @@

Agenda — 195 minutes

00:25 20 II -Thinking in N dimensions (group) +Thinking in N dimensions 00:45 @@ -391,7 +391,7 @@

Agenda — 195 minutes

Two rhythms, and one warning

Exercise blocks — 10 min coding, 5 min explanation.

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Group blocks — 10 min discussion, then share-back.

+

Group block (§05) — 10 min discussion, then share-back.

@@ -689,52 +689,29 @@

02 · Thinking in N dimensions

II

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Part II · group discussion · no code

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Part II · demo · code

⏱️20 min allocated

Start: +00:25 · End: +00:45

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Your task

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A grayscale image is a matrix. Almost nothing in ML is a single grayscale image. Each thing you add — colour, many examples, time — adds an axis, and each axis means something different.

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Argue about which axis goes where, and why.

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10 minutes in your breakout channel, then share-back.

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The five questions

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  1. (H, W) → colour image → batch → video → batch of videos. What does each new axis count? Do not say “we add a dimension.”
    2. -
  2. A batch axis and a time axis look identical in code. What differs in meaning? What happens if you shuffle each?
    3. -
  3. Real videos have different lengths. Two ways to batch them — what does each lose or invent?
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  4. Cells photographed every 10 min for 48 h: frame interval → ? field of view → ? number of dishes → ?
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  5. Is there a limit on how many axes a tensor can have?
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Share-back

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gray_image      = np.zeros((28, 28))            # (H, W)
-color_image     = np.zeros((28, 28, 3))         # (H, W, C)      + colour
-batch_of_images = np.zeros((32, 28, 28, 3))     # (N, H, W, C)   + many examples
-video           = np.zeros((16, 28, 28, 3))     # (T, H, W, C)   + ordered time
-batch_of_videos = np.zeros((8, 16, 28, 28, 3))  # (N, T, H, W, C)
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Same shape, different axes

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digits.images is (1797, 8, 8) and a photo is (512, 512, 3) — both order 3, but axis 0 counts images in one and rows of pixels in the other. In the notebook, digit_batch and video_patch are both (8, 8, 8): one stacks independent examples, the other stacks ordered moments.

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Question 2 is the point. Shuffling axis 0 is harmless for a batch and destroys a video.

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Chapter 2’s notation has no concept of “order matters between elements.” That is genuinely new today.

+

Shuffling axis 0 is harmless for a batch — the examples are independent — and destroys a video, where order is the information. Same code, opposite meaning. Chapter 2’s notation has no concept of “order matters between elements”; that is genuinely new today.

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Ragged clips need a mask

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Real videos have different lengths, but a batch tensor is rectangular. The notebook takes three real clips of length 4, 7 and 5, pads them into one (3, 7, 135, 240, 3) order-5 batch, and carries a Boolean (3, 7) validity mask — so valid.sum() == 16 and the padding stays visible instead of being averaged into the data.

+

To the notebook

02 · Thinking in N dimensions

Open in Colab