From 57ba63d5014aeb5b89619194cdc47174eb74093c Mon Sep 17 00:00:00 2001 From: Luca Melis Date: Thu, 26 Feb 2026 07:33:09 -0800 Subject: [PATCH] Online RMIA (#108) Summary: Pull Request resolved: https://github.com/facebookresearch/PrivacyGuard/pull/108 This diff implements the online version of RMIA and updates the RMIA tutorial. Reviewed By: mgrange1998 Differential Revision: D92998892 --- privacy_guard/attacks/rmia_attack.py | 135 +++- .../attacks/tests/test_rmia_attack.py | 285 +++++++- .../rmia_attack_cifar10_tutorial.py.ipynb | 620 ++++++++---------- 3 files changed, 672 insertions(+), 368 deletions(-) diff --git a/privacy_guard/attacks/rmia_attack.py b/privacy_guard/attacks/rmia_attack.py index ebd8aa7..31cd075 100644 --- a/privacy_guard/attacks/rmia_attack.py +++ b/privacy_guard/attacks/rmia_attack.py @@ -13,6 +13,7 @@ # limitations under the License. # pyre-strict +import logging from typing import Optional import numpy as np @@ -24,6 +25,8 @@ from privacy_guard.attacks.base_attack import BaseAttack from sklearn.metrics import auc, roc_curve +logger: logging.Logger = logging.getLogger(__name__) + class RmiaAttack(BaseAttack): """ @@ -35,6 +38,12 @@ class RmiaAttack(BaseAttack): The attack estimates population probability distributions using reference models and compares the target model's predictions against estimated population averages to determine membership. + The attack supports two modes: + - Offline (default): Only OUT-model signals are used; population probability is + approximated via alpha mixing. + - Online: Both IN and OUT model signals are available per sample; population + probability is estimated directly from all reference models. + The attack leverages: - Multiple reference models trained without target samples - Population samples to establish baseline probability distributions @@ -45,6 +54,25 @@ class RmiaAttack(BaseAttack): REF_SCORE_PREFIX = "score_ref_" REF_MEMBER_PREFIX = "member_ref_" + @staticmethod + def _compute_alpha_mixing_estimate( + mean_out: np.ndarray, alpha: float + ) -> np.ndarray: + """ + Compute population probability estimate using alpha mixing formula. + + This implements the offline RMIA approximation: + ``population_estimate = (1 + alpha) / 2 * mean_out + (1 - alpha) / 2`` + + Args: + mean_out: Mean of OUT-model signals + alpha: Approximation coefficient for population probability + + Returns: + Population probability estimate + """ + return (1 + alpha) / 2 * mean_out + (1 - alpha) / 2 + def __init__( self, df_train_merge: pd.DataFrame, @@ -55,6 +83,7 @@ def __init__( alpha_coefficient: float = 0.3, enable_auto_tuning: bool = False, user_id_key: str = "user_id", + online: bool = False, ) -> None: """ Initialize the RMIA attack. @@ -67,12 +96,24 @@ def __init__( alpha_coefficient: Approximation coefficient for population probability estimation num_reference_models: Number of reference models to use in attack (default: half of available models) enable_auto_tuning: Enable automatic tuning of alpha_coefficient + user_id_key: Column name for user identifier + online: If True, use online RMIA mode where both IN and OUT model + signals are used directly. If False (default), use offline mode + with alpha mixing approximation. """ self.df_train_merge: pd.DataFrame = df_train_merge.copy() self.df_test_merge: pd.DataFrame = df_test_merge.copy() self.df_population: pd.DataFrame = df_population.copy() self.row_aggregation: AggregationType = row_aggregation self.alpha_coefficient: float = alpha_coefficient + self.online: bool = online + + if online and enable_auto_tuning: + logger.warning( + "Auto-tuning is not applicable in online mode (alpha is not used). " + "Disabling auto-tuning." + ) + enable_auto_tuning = False if num_reference_models is None: # estimate number of reference models based on the number of columns in the dataframe @@ -136,39 +177,57 @@ def compute_ref_signal_averages( ref_memberships: np.ndarray, num_models: Optional[int] = None, alpha: float = 0.3, + online: bool = False, ) -> np.ndarray: """ - Compute average prediction probabilities from reference models excluding target samples. + Compute average prediction probabilities from reference models. + + In offline mode (default), excludes IN-model signals and uses only + OUT-model signals. In online mode, uses all reference model signals + directly (both IN and OUT). Args: ref_signals: Prediction scores from reference models ref_memberships: Boolean membership matrix indicating training inclusion num_models: Number of reference models to consider alpha: Approximation coefficient for population probability + online: If True, use online mode (direct population estimate) Returns: - Averaged prediction scores excluding membership samples + Averaged prediction scores """ - non_member_mask = ~ref_memberships - out_ref_signals = ref_signals * non_member_mask + if online: + # Online: use all reference model signals directly (IN + OUT) + if num_models is None: + num_models = ref_signals.shape[1] + if num_models >= ref_signals.shape[1]: + return ref_signals + # Select top-k signals across all models + return np.sort(ref_signals, axis=1)[:, -num_models:] + else: + # Offline: mask out IN signals, use only OUT signals + non_member_mask = ~ref_memberships + out_ref_signals = ref_signals * non_member_mask - if num_models is None: - num_models = ref_signals.shape[1] // 2 + if num_models is None: + num_models = ref_signals.shape[1] // 2 - if num_models > 1: - # Select top non-zero signals for each sample - out_ref_signals = np.sort(out_ref_signals, axis=1)[:, -num_models:] - else: - # Apply single model approximation formula - if alpha != 0: - approximation = ((ref_signals + alpha - 1) / alpha) * ref_memberships - out_ref_signals += approximation + if num_models > 1: + # Select top non-zero signals for each sample + out_ref_signals = np.sort(out_ref_signals, axis=1)[:, -num_models:] else: - # Default fallback approximation w/ alpha=0.3 - fallback = ((ref_signals - 0.7) / 0.3) * ref_memberships - out_ref_signals += fallback - - return out_ref_signals + # Apply single model approximation formula + if alpha != 0: + approximation = ( + (ref_signals + alpha - 1) / alpha + ) * ref_memberships + out_ref_signals += approximation + else: + # Default fallback approximation w/ alpha=0.3 + fallback = ((ref_signals - 0.7) / 0.3) * ref_memberships + out_ref_signals += fallback + + return out_ref_signals def _auto_tune_alpha_coefficient( self, @@ -178,11 +237,13 @@ def _auto_tune_alpha_coefficient( population_ref_scores: np.ndarray, ) -> float: """Auto-tune alpha coefficient using cross-validation on reference models. + Args: target_model_idx: Index of target model in reference scores ref_scores: Reference model prediction scores ref_memberships: Reference model membership matrix population_ref_scores: Population reference model scores + Returns: Tuned alpha coefficient """ @@ -233,6 +294,7 @@ def _compute_membership_scores( population_ref_scores: np.ndarray, alpha: float, num_models: int, + online: bool = False, ) -> np.ndarray: """ Execute core membership inference scoring algorithm. @@ -245,6 +307,7 @@ def _compute_membership_scores( population_ref_scores: Population reference model scores alpha: Population probability approximation coefficient num_models: Number of reference models to use + online: If True, use online mode (direct population estimate) Returns: Membership inference scores (higher indicates greater membership likelihood) @@ -252,20 +315,42 @@ def _compute_membership_scores( # Compute reference signals for target dataset target_ref_mean = self.compute_ref_signal_averages( - ref_scores, ref_memberships, num_models, alpha + ref_scores, ref_memberships, num_models, alpha, online=online ) target_mean_out = np.mean(target_ref_mean, axis=1) - target_population_estimate = (1 + alpha) / 2 * target_mean_out + (1 - alpha) / 2 + + if online: + # Online: direct population estimate (no alpha mixing) + target_population_estimate = target_mean_out + else: + # Offline: alpha-based approximation + target_population_estimate = self._compute_alpha_mixing_estimate( + target_mean_out, alpha + ) + target_probability_ratios = target_scores.ravel() / target_population_estimate # Compute reference signals for population dataset # Population samples are not used for training, so we set the membership to False population_memberships = np.zeros_like(population_ref_scores).astype(bool) population_ref_mean = self.compute_ref_signal_averages( - population_ref_scores, population_memberships, num_models, alpha + population_ref_scores, + population_memberships, + num_models, + alpha, + online=online, ) population_mean_out = np.mean(population_ref_mean, axis=1) - population_estimate = (1 + alpha) / 2 * population_mean_out + (1 - alpha) / 2 + + if online: + # Online: direct population estimate + population_estimate = population_mean_out + else: + # Offline: alpha-based approximation + population_estimate = self._compute_alpha_mixing_estimate( + population_mean_out, alpha + ) + population_probability_ratios = ( population_target_scores.ravel() / population_estimate ) @@ -339,7 +424,7 @@ def run_attack(self) -> AggregateAnalysisInput: self.df_population ) - # Auto-tune alpha coefficient if requested + # Auto-tune alpha coefficient if requested (only in offline mode) current_alpha = self.alpha_coefficient if self.enable_auto_tuning: # Use reference data for alpha tuning @@ -364,6 +449,7 @@ def run_attack(self) -> AggregateAnalysisInput: population_ref_scores=population_ref_scores, alpha=current_alpha, num_models=self.num_reference_models, + online=self.online, ) # Compute membership scores for test data @@ -375,6 +461,7 @@ def run_attack(self) -> AggregateAnalysisInput: population_ref_scores=population_ref_scores, alpha=current_alpha, num_models=self.num_reference_models, + online=self.online, ) # Create output dataframes with computed scores diff --git a/privacy_guard/attacks/tests/test_rmia_attack.py b/privacy_guard/attacks/tests/test_rmia_attack.py index ddeab59..76b0346 100644 --- a/privacy_guard/attacks/tests/test_rmia_attack.py +++ b/privacy_guard/attacks/tests/test_rmia_attack.py @@ -1,16 +1,4 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. +# (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary. # pyre-strict @@ -525,3 +513,274 @@ def test_extract_model_data_missing_ref_member_columns_error(self) -> None: self.assertIn("No membership columns found", str(context.exception)) self.assertIn("member_ref_", str(context.exception)) + + # ===== Online RMIA Tests ===== + + def test_initialization_online_mode(self) -> None: + """Test that online flag is correctly stored during initialization""" + # Test offline mode (default) + attack_offline = RmiaAttack( + df_train_merge=self.df_train_merge, + df_test_merge=self.df_test_merge, + df_population=self.df_population, + row_aggregation=AggregationType.MAX, + num_reference_models=2, + online=False, + ) + self.assertFalse(attack_offline.online) + + # Test online mode + attack_online = RmiaAttack( + df_train_merge=self.df_train_merge, + df_test_merge=self.df_test_merge, + df_population=self.df_population, + row_aggregation=AggregationType.MAX, + num_reference_models=2, + online=True, + ) + self.assertTrue(attack_online.online) + self.assertFalse(attack_online.enable_auto_tuning) + + def test_initialization_online_with_auto_tuning_warns(self) -> None: + """Test that online mode with auto-tuning logs a warning and disables auto-tuning""" + with self.assertLogs( + "privacy_guard.attacks.rmia_attack", level="WARNING" + ) as cm: + attack = RmiaAttack( + df_train_merge=self.df_train_merge, + df_test_merge=self.df_test_merge, + df_population=self.df_population, + row_aggregation=AggregationType.MAX, + num_reference_models=2, + enable_auto_tuning=True, + online=True, + ) + + self.assertTrue(attack.online) + self.assertFalse(attack.enable_auto_tuning) + self.assertTrue( + any( + "Auto-tuning is not applicable in online mode" in msg + for msg in cm.output + ) + ) + + def test_compute_ref_signal_averages_online(self) -> None: + """Test that online mode uses all signals without masking""" + ref_signals = np.array( + [ + [0.4, 0.5, 0.3], + [0.3, 0.4, 0.5], + [0.5, 0.4, 0.6], + ] + ) + ref_memberships = np.array( + [ + [True, False, True], + [False, True, True], + [True, False, False], + ] + ) + + # Online mode should use all signals (ignore memberships) + result_online = RmiaAttack.compute_ref_signal_averages( + ref_signals=ref_signals, + ref_memberships=ref_memberships, + num_models=None, + alpha=0.3, + online=True, + ) + + # When num_models is None in online mode, it defaults to all models + # and returns all signals unchanged + self.assertEqual(result_online.shape, (3, 3)) + assert_array_almost_equal(result_online, ref_signals) + + def test_compute_ref_signal_averages_online_top_k(self) -> None: + """Test that online mode with num_models < total selects top-k signals""" + ref_signals = np.array( + [ + [0.4, 0.5, 0.3], + [0.3, 0.4, 0.5], + [0.5, 0.4, 0.6], + ] + ) + ref_memberships = np.array( + [ + [True, False, True], + [False, True, True], + [True, False, False], + ] + ) + + result = RmiaAttack.compute_ref_signal_averages( + ref_signals=ref_signals, + ref_memberships=ref_memberships, + num_models=2, + alpha=0.3, + online=True, + ) + + # Should return top-2 signals per row + self.assertEqual(result.shape, (3, 2)) + + # Verify top-2 selection: for row 0 [0.4, 0.5, 0.3] -> top 2 = [0.4, 0.5] + expected_row0 = np.array([0.4, 0.5]) + assert_array_almost_equal(result[0], expected_row0) + + def test_compute_ref_signal_averages_online_vs_offline_different(self) -> None: + """Test that online and offline modes produce different results when memberships exist""" + ref_signals = np.array( + [ + [0.4, 0.5, 0.3], + [0.3, 0.4, 0.5], + ] + ) + ref_memberships = np.array( + [ + [True, False, True], + [False, True, False], + ] + ) + + result_online = RmiaAttack.compute_ref_signal_averages( + ref_signals=ref_signals, + ref_memberships=ref_memberships, + num_models=2, + alpha=0.3, + online=True, + ) + + result_offline = RmiaAttack.compute_ref_signal_averages( + ref_signals=ref_signals, + ref_memberships=ref_memberships, + num_models=2, + alpha=0.3, + online=False, + ) + + # Results should differ because online uses all signals, offline masks IN signals + self.assertFalse(np.allclose(result_online, result_offline)) + + def test_compute_membership_scores_online(self) -> None: + """Test that online membership scores are in valid range""" + target_scores = np.array([0.8, 0.2]) + ref_scores = np.array([[0.4, 0.5], [0.6, 0.5]]) + ref_memberships = np.array([[True, False], [False, True]]) + population_target_scores = np.array([0.5, 0.6]) + population_ref_scores = np.array([[0.4, 0.5], [0.5, 0.4]]) + + scores = self.rmia_attack._compute_membership_scores( + target_scores=target_scores, + ref_scores=ref_scores, + ref_memberships=ref_memberships, + population_target_scores=population_target_scores, + population_ref_scores=population_ref_scores, + alpha=0.3, + num_models=2, + online=True, + ) + + # Verify output shape and valid range + self.assertEqual(scores.shape, (2,)) + self.assertTrue(np.all(scores >= 0)) + self.assertTrue(np.all(scores <= 1)) + + def test_run_attack_online_success(self) -> None: + """Test successful execution of online RMIA attack""" + attack = RmiaAttack( + df_train_merge=self.df_train_merge, + df_test_merge=self.df_test_merge, + df_population=self.df_population, + row_aggregation=AggregationType.MAX, + num_reference_models=2, + online=True, + ) + analysis_input = attack.run_attack() + + # Verify return type + self.assertIsInstance(analysis_input, AggregateAnalysisInput) + + # Verify output dataframes have scores + self.assertIn("score", analysis_input.df_train_merge.columns) + self.assertIn("score", analysis_input.df_test_merge.columns) + + # Verify output lengths match input + self.assertEqual(len(analysis_input.df_train_merge), len(self.df_train_merge)) + self.assertEqual(len(analysis_input.df_test_merge), len(self.df_test_merge)) + + # Verify scores are numeric and in reasonable range + train_scores = analysis_input.df_train_merge["score"].values + test_scores = analysis_input.df_test_merge["score"].values + + self.assertTrue(np.all(train_scores >= 0)) + self.assertTrue(np.all(train_scores <= 1)) + self.assertTrue(np.all(test_scores >= 0)) + self.assertTrue(np.all(test_scores <= 1)) + + def test_run_attack_online_vs_offline_different_scores(self) -> None: + """Test that online and offline modes produce different scores""" + # Run offline attack + offline_attack = RmiaAttack( + df_train_merge=self.df_train_merge, + df_test_merge=self.df_test_merge, + df_population=self.df_population, + row_aggregation=AggregationType.MAX, + alpha_coefficient=0.3, + num_reference_models=2, + online=False, + ) + offline_result = offline_attack.run_attack() + offline_train_scores = offline_result.df_train_merge["score"].values + + # Run online attack + online_attack = RmiaAttack( + df_train_merge=self.df_train_merge, + df_test_merge=self.df_test_merge, + df_population=self.df_population, + row_aggregation=AggregationType.MAX, + alpha_coefficient=0.3, + num_reference_models=2, + online=True, + ) + online_result = online_attack.run_attack() + online_train_scores = online_result.df_train_merge["score"].values + + # Scores should differ between online and offline modes + self.assertFalse( + np.allclose(offline_train_scores, online_train_scores), + "Online and offline scores should be different", + ) + + def test_run_attack_online_determinism(self) -> None: + """Test that online attack produces deterministic results""" + attack_1 = RmiaAttack( + df_train_merge=self.df_train_merge, + df_test_merge=self.df_test_merge, + df_population=self.df_population, + row_aggregation=AggregationType.MAX, + num_reference_models=2, + online=True, + ) + result_1 = attack_1.run_attack() + + attack_2 = RmiaAttack( + df_train_merge=self.df_train_merge, + df_test_merge=self.df_test_merge, + df_population=self.df_population, + row_aggregation=AggregationType.MAX, + num_reference_models=2, + online=True, + ) + result_2 = attack_2.run_attack() + + assert_array_almost_equal( + result_1.df_train_merge["score"].values, + result_2.df_train_merge["score"].values, + decimal=10, + ) + assert_array_almost_equal( + result_1.df_test_merge["score"].values, + result_2.df_test_merge["score"].values, + decimal=10, + ) diff --git a/tutorial_notebooks/rmia_attack_cifar10_tutorial.py.ipynb b/tutorial_notebooks/rmia_attack_cifar10_tutorial.py.ipynb index 5ed897b..e367001 100644 --- a/tutorial_notebooks/rmia_attack_cifar10_tutorial.py.ipynb +++ b/tutorial_notebooks/rmia_attack_cifar10_tutorial.py.ipynb @@ -1,32 +1,13 @@ { - "metadata": { - "language_info": { - "name": "python", - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "file_extension": ".py", - "pygments_lexer": "ipython3", - "codemirror_mode": { - "name": "ipython", - "version": 3 - } - }, - "kernelspec": { - "name": "python3", - "display_name": "python3", - "language": "python" - }, - "operator_data": [] - }, "cells": [ { "cell_type": "markdown", "metadata": { - "originalKey": "1458048b-fc8a-4ade-972b-bef4db151294", - "outputsInitialized": false, "bentoAICellStatus": "none", "isCommentPanelOpen": false, - "language": "markdown" + "language": "markdown", + "originalKey": "1458048b-fc8a-4ade-972b-bef4db151294", + "outputsInitialized": false }, "source": [ "# RMIA (Robust Membership Inference Attack) with PrivacyGuard for CIFAR-10\n", @@ -39,12 +20,24 @@ "\n", "RMIA is an advanced membership inference attack that estimates population probability distributions using reference models and compares the target model's predictions against estimated population averages to determine membership. Unlike traditional attacks that rely on simple threshold-based approaches, RMIA uses a more sophisticated ratio-based scoring mechanism.\n", "\n", + "### Online vs. Offline RMIA\n", + "\n", + "RMIA supports two modes of operation:\n", + "\n", + "- **Offline RMIA** (default): Only OUT-model signals are used. The population probability is approximated via alpha mixing:\n", + " `Pr(x) ≈ (1+α)/2 * mean_OUT(x) + (1-α)/2`\n", + " This is suitable when you only have access to models that were NOT trained on target samples.\n", + "\n", + "- **Online RMIA**: Both IN and OUT model signals are available per sample. The population probability is estimated directly from all reference models:\n", + " `Pr(x) ≈ mean_ALL(x)`\n", + " This provides more accurate estimates when you know which reference models included each sample in training.\n", + "\n", "### Key Components of RMIA\n", "\n", "- **Reference Models**: Multiple models trained without target samples to establish baseline behavior\n", "- **Population Data**: Additional data samples used to estimate population probability distributions\n", "- **Ratio-based Scoring**: Compares target model predictions to population averages for membership inference\n", - "- **Auto-tuning**: Automatic optimization of the alpha coefficient for improved performance\n", + "- **Auto-tuning**: Automatic optimization of the alpha coefficient for improved performance (offline mode only)\n", "\n", "### What We'll Cover\n", "\n", @@ -53,7 +46,7 @@ "2. Train a target model and multiple reference models\n", "3. Create population data for probability distribution estimation\n", "4. Extract prediction scores from all models\n", - "5. Perform RMIA attack using Privacy Guard's `RmiaAttack` class\n", + "5. Perform RMIA attack using Privacy Guard's `RmiaAttack` class in both offline and online modes\n", "6. Analyze the attack results and evaluate privacy risks\n", "7. Compare different parameter settings and their impact on attack effectiveness\n", "\n", @@ -63,11 +56,11 @@ { "cell_type": "markdown", "metadata": { - "originalKey": "057680be-3173-4e6a-8083-3a9e9e39fd79", - "outputsInitialized": false, "bentoAICellStatus": "none", + "isCommentPanelOpen": false, "language": "markdown", - "isCommentPanelOpen": false + "originalKey": "057680be-3173-4e6a-8083-3a9e9e39fd79", + "outputsInitialized": false }, "source": [ "## Environment Set-Up\n" @@ -75,48 +68,45 @@ }, { "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Optionally create a conda environment for installing packages.\n", "# If executing from Google Colab, skip this step.\n", "!conda create -n privacy_guard python=3.12\n", "!conda activate privacy_guard\n" - ], - "metadata": { - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "%cd /content\n", "!git clone https://github.com/facebookresearch/PrivacyGuard.git" - ], - "metadata": { - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "%cd /content/PrivacyGuard\n", "!ls\n", "!pip install -e ." - ], - "metadata": { - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "markdown", "metadata": { - "originalKey": "b773591f-e66d-4497-8994-1ddbb04b00c1", - "outputsInitialized": false, "bentoAICellStatus": "none", "isCommentPanelOpen": false, - "language": "markdown" + "language": "markdown", + "originalKey": "b773591f-e66d-4497-8994-1ddbb04b00c1", + "outputsInitialized": false }, "source": [ "## Setup and Imports\n", @@ -126,13 +116,37 @@ }, { "cell_type": "code", + "execution_count": 1, "metadata": { - "originalKey": "8b435315-60fe-438f-b95f-bb35fe41db84", - "outputsInitialized": true, "bentoAICellStatus": "none", "isCommentPanelOpen": false, - "language": "python" + "language": "python", + "originalKey": "8b435315-60fe-438f-b95f-bb35fe41db84", + "output": { + "id": 25612441045123360, + "loadingStatus": "loaded" + }, + "outputsInitialized": true }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "I0223 133456.381 _utils_internal.py:284] NCCL_DEBUG env var is set to None\n", + "I0223 133456.382 _utils_internal.py:303] NCCL_DEBUG is forced to WARN from None\n", + "W0223 133459.473 triton_util.py:163] ===========[FB_TRITON]=========== Triton Version: 3.5.0+fb\n", + "I0223 133501.905 font_manager.py:1642] generated new fontManager\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using device: cpu\n" + ] + } + ], "source": [ "from typing import Any, Dict, List, Tuple\n", "\n", @@ -172,49 +186,16 @@ "# Use CUDA if available\n", "DEVICE: torch.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "print(f\"Using device: {DEVICE}\")" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": "I0828 085841.650 _utils_internal.py:290] NCCL_DEBUG env var is set to None\nI0828 085841.652 _utils_internal.py:299] NCCL_DEBUG is WARN from /etc/nccl.conf\n" - }, - { - "output_type": "stream", - "name": "stdout", - "text": "\u001b[38;5;200mRemapping module privacy_guard from /data/users/lucamelis/.bento/kernels/bento_kernel_empirical_dp/382/bento_kernel_empirical_dp_binary-inplace#link-tree/privacy_guard/__init__.py to /data/repos/fbsource/fbcode/privacy_guard/shadow_model_training/__init__.py\u001b[0;0m\n\u001b[38;5;200mRemapping module privacy_guard.shadow_model_training.dataset from /data/users/lucamelis/.bento/kernels/bento_kernel_empirical_dp/382/bento_kernel_empirical_dp_binary-inplace#link-tree/privacy_guard/shadow_model_training/dataset.py to /data/repos/fbsource/fbcode/privacy_guard/shadow_model_training/dataset.py\u001b[0;0m\n" - }, - { - "output_type": "stream", - "name": "stderr", - "text": "I0828 085851.631 structured_logging.py:497] TritonTraceHandler: disabled because /logs does not exist\n" - }, - { - "output_type": "stream", - "name": "stderr", - "text": "W0828 085851.633 triton_util.py:167] ===========[FB_TRITON]=========== Triton Version: 3.3.2+fb\n" - }, - { - "output_type": "stream", - "name": "stdout", - "text": "\u001b[38;5;200mRemapping module privacy_guard.shadow_model_training.training from /data/users/lucamelis/.bento/kernels/bento_kernel_empirical_dp/382/bento_kernel_empirical_dp_binary-inplace#link-tree/privacy_guard/shadow_model_training/training.py to /data/repos/fbsource/fbcode/privacy_guard/shadow_model_training/training.py\u001b[0;0m\n" - }, - { - "output_type": "stream", - "name": "stdout", - "text": "Using device: cuda\n" - } ] }, { "cell_type": "markdown", "metadata": { - "originalKey": "a92af613-c084-45ef-8ec1-c45abcc1c198", - "outputsInitialized": false, "bentoAICellStatus": "none", "isCommentPanelOpen": false, - "language": "markdown" + "language": "markdown", + "originalKey": "a92af613-c084-45ef-8ec1-c45abcc1c198", + "outputsInitialized": false }, "source": [ "## Dataset Preparation\n", @@ -224,13 +205,15 @@ }, { "cell_type": "code", + "execution_count": 2, "metadata": { - "originalKey": "a60fdf78-23de-4c9b-9fc6-0914e7960fd8", - "outputsInitialized": true, "bentoAICellStatus": "none", "isCommentPanelOpen": false, - "language": "python" + "language": "python", + "originalKey": "a60fdf78-23de-4c9b-9fc6-0914e7960fd8", + "outputsInitialized": true }, + "outputs": [], "source": [ "# Load the dataset\n", "train_dataset: CIFAR10\n", @@ -238,24 +221,16 @@ "train_dataset, test_dataset = load_cifar10()\n", "print(f\"Training set size: {len(train_dataset)}\")\n", "print(f\"Test set size: {len(test_dataset)}\")" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": "Training set size: 50000\nTest set size: 10000\n" - } ] }, { "cell_type": "markdown", "metadata": { - "originalKey": "64c14cde-0746-4d62-9af4-a0cbb53df626", - "outputsInitialized": false, "bentoAICellStatus": "none", "isCommentPanelOpen": false, - "language": "markdown" + "language": "markdown", + "originalKey": "64c14cde-0746-4d62-9af4-a0cbb53df626", + "outputsInitialized": false }, "source": [ "## Creating Reference Datasets and Population Data\n", @@ -265,13 +240,27 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": { - "originalKey": "a599a3d8-cb82-4462-848b-912fe5d22f2f", - "outputsInitialized": true, "bentoAICellStatus": "none", "isCommentPanelOpen": false, - "language": "python" + "language": "python", + "originalKey": "a599a3d8-cb82-4462-848b-912fe5d22f2f", + "outputsInitialized": true }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reference 0: 25030 in-samples\n", + "Reference 1: 25231 in-samples\n", + "Reference 2: 24779 in-samples\n", + "Target: 24960 in-samples\n", + "Population: 10000 samples\n" + ] + } + ], "source": [ "# Create reference datasets (similar to shadow datasets but for RMIA)\n", "num_references: int = 4 # More reference models for better population estimation\n", @@ -282,26 +271,17 @@ "population_dataset: Subset\n", "reference_datasets, target_dataset, population_dataset = create_rmia_datasets(\n", " train_dataset, test_dataset, num_references, population_size\n", - ")\n", - "" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": "Reference 0: 25030 in-samples\nReference 1: 25231 in-samples\nReference 2: 24779 in-samples\nTarget: 24960 in-samples\nPopulation: 10000 samples\n" - } + ")\n" ] }, { "cell_type": "markdown", "metadata": { - "originalKey": "3825b7a8-ed98-43df-9cd5-a5aa92139876", - "outputsInitialized": false, "bentoAICellStatus": "none", "isCommentPanelOpen": false, - "language": "markdown" + "language": "markdown", + "originalKey": "3825b7a8-ed98-43df-9cd5-a5aa92139876", + "outputsInitialized": false }, "source": [ "## Training Target and Reference Models\n", @@ -310,13 +290,28 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": { - "originalKey": "b4c2521c-6aab-4a87-8b5e-c488ca1ee24d", - "outputsInitialized": true, "bentoAICellStatus": "none", "isCommentPanelOpen": false, - "language": "python" + "language": "python", + "originalKey": "b4c2521c-6aab-4a87-8b5e-c488ca1ee24d", + "outputsInitialized": true }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training target model...\n", + "Epoch 1/50\n", + "...\n", + "Epoch 50/50\n", + "Training - Loss: 0.0995, Acc: 96.86%\n", + "Testing - Acc: 84.75%\n" + ] + } + ], "source": [ "# Create data loaders\n", "batch_size: int = 256\n", @@ -351,24 +346,16 @@ " ref_model, ref_loader, test_loader, epochs=EPOCHS, device=DEVICE\n", " )\n", " reference_models.append(ref_model)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": "Training target model...\nEpoch 1/50\nTraining - Loss: 1.9251, Acc: 26.22%\nTesting - Acc: 33.12%\nEpoch 2/50\nTraining - Loss: 1.5524, Acc: 41.25%\nTesting - Acc: 47.03%\nEpoch 3/50\nTraining - Loss: 1.3631, Acc: 49.58%\nTesting - Acc: 51.54%\nEpoch 4/50\nTraining - Loss: 1.2196, Acc: 55.70%\nTesting - Acc: 58.83%\nEpoch 5/50\nTraining - Loss: 1.0978, Acc: 60.55%\nTesting - Acc: 60.68%\nEpoch 6/50\nTraining - Loss: 1.0057, Acc: 64.30%\nTesting - Acc: 64.01%\nEpoch 7/50\nTraining - Loss: 0.9136, Acc: 67.38%\nTesting - Acc: 63.27%\nEpoch 8/50\nTraining - Loss: 0.8639, Acc: 69.07%\nTesting - Acc: 67.05%\nEpoch 9/50\nTraining - Loss: 0.7910, Acc: 72.04%\nTesting - Acc: 68.27%\nEpoch 10/50\nTraining - Loss: 0.7486, Acc: 73.43%\nTesting - Acc: 68.48%\nEpoch 11/50\nTraining - Loss: 0.6963, Acc: 75.54%\nTesting - Acc: 70.58%\nEpoch 12/50\nTraining - Loss: 0.6442, Acc: 77.48%\nTesting - Acc: 75.83%\nEpoch 13/50\nTraining - Loss: 0.6124, Acc: 78.63%\nTesting - Acc: 73.13%\nEpoch 14/50\nTraining - Loss: 0.5857, Acc: 79.91%\nTesting - Acc: 74.13%\nEpoch 15/50\nTraining - Loss: 0.5412, Acc: 81.35%\nTesting - Acc: 77.05%\nEpoch 16/50\nTraining - Loss: 0.5094, Acc: 81.95%\nTesting - Acc: 78.29%\nEpoch 17/50\nTraining - Loss: 0.4818, Acc: 83.06%\nTesting - Acc: 74.17%\nEpoch 18/50\nTraining - Loss: 0.4585, Acc: 84.16%\nTesting - Acc: 77.44%\nEpoch 19/50\nTraining - Loss: 0.4378, Acc: 84.89%\nTesting - Acc: 77.91%\nEpoch 20/50\nTraining - Loss: 0.4125, Acc: 85.46%\nTesting - Acc: 78.89%\nEpoch 21/50\nTraining - Loss: 0.3925, Acc: 86.29%\nTesting - Acc: 80.15%\nEpoch 22/50\nTraining - Loss: 0.3743, Acc: 86.71%\nTesting - Acc: 78.49%\nEpoch 23/50\nTraining - Loss: 0.3591, Acc: 87.42%\nTesting - Acc: 78.15%\nEpoch 24/50\nTraining - Loss: 0.3352, Acc: 88.20%\nTesting - Acc: 80.19%\nEpoch 25/50\nTraining - Loss: 0.3171, Acc: 88.91%\nTesting - Acc: 81.52%\nEpoch 26/50\nTraining - Loss: 0.2985, Acc: 89.75%\nTesting - Acc: 80.85%\nEpoch 27/50\nTraining - Loss: 0.2855, Acc: 90.02%\nTesting - Acc: 82.09%\nEpoch 28/50\nTraining - Loss: 0.2704, Acc: 90.58%\nTesting - Acc: 80.63%\nEpoch 29/50\nTraining - Loss: 0.2555, Acc: 91.04%\nTesting - Acc: 81.62%\nEpoch 30/50\nTraining - Loss: 0.2339, Acc: 91.75%\nTesting - Acc: 82.11%\nEpoch 31/50\nTraining - Loss: 0.2272, Acc: 92.16%\nTesting - Acc: 82.74%\nEpoch 32/50\nTraining - Loss: 0.2100, Acc: 92.80%\nTesting - Acc: 82.27%\nEpoch 33/50\nTraining - Loss: 0.1989, Acc: 93.19%\nTesting - Acc: 83.39%\nEpoch 34/50\nTraining - Loss: 0.1884, Acc: 93.59%\nTesting - Acc: 82.77%\nEpoch 35/50\nTraining - Loss: 0.1794, Acc: 93.88%\nTesting - Acc: 83.66%\nEpoch 36/50\nTraining - Loss: 0.1619, Acc: 94.40%\nTesting - Acc: 83.35%\nEpoch 37/50\nTraining - Loss: 0.1579, Acc: 94.69%\nTesting - Acc: 84.12%\nEpoch 38/50\nTraining - Loss: 0.1506, Acc: 94.99%\nTesting - Acc: 83.20%\nEpoch 39/50\nTraining - Loss: 0.1421, Acc: 95.21%\nTesting - Acc: 83.89%\nEpoch 40/50\nTraining - Loss: 0.1324, Acc: 95.75%\nTesting - Acc: 84.31%\nEpoch 41/50\nTraining - Loss: 0.1281, Acc: 95.77%\nTesting - Acc: 84.23%\nEpoch 42/50\nTraining - Loss: 0.1211, Acc: 96.21%\nTesting - Acc: 84.28%\nEpoch 43/50\nTraining - Loss: 0.1186, Acc: 96.24%\nTesting - Acc: 84.20%\nEpoch 44/50\nTraining - Loss: 0.1155, Acc: 96.32%\nTesting - Acc: 84.60%\nEpoch 45/50\nTraining - Loss: 0.1093, Acc: 96.61%\nTesting - Acc: 84.55%\nEpoch 46/50\nTraining - Loss: 0.1115, Acc: 96.43%\nTesting - Acc: 84.50%\nEpoch 47/50\nTraining - Loss: 0.1073, Acc: 96.70%\nTesting - Acc: 84.64%\nEpoch 48/50\nTraining - Loss: 0.1062, Acc: 96.73%\nTesting - Acc: 84.60%\nEpoch 49/50\nTraining - Loss: 0.1064, Acc: 96.79%\nTesting - Acc: 84.63%\nEpoch 50/50\nTraining - Loss: 0.1066, Acc: 96.65%\nTesting - Acc: 84.66%\nTraining reference model 1/3...\nEpoch 1/50\nTraining - Loss: 1.9245, Acc: 26.88%\nTesting - Acc: 33.68%\nEpoch 2/50\nTraining - Loss: 1.5465, Acc: 41.91%\nTesting - Acc: 45.92%\nEpoch 3/50\nTraining - Loss: 1.3507, Acc: 50.52%\nTesting - Acc: 52.62%\nEpoch 4/50\nTraining - Loss: 1.2070, Acc: 56.16%\nTesting - Acc: 54.64%\nEpoch 5/50\nTraining - Loss: 1.0899, Acc: 60.93%\nTesting - Acc: 59.72%\nEpoch 6/50\nTraining - Loss: 0.9979, Acc: 64.30%\nTesting - Acc: 62.53%\nEpoch 7/50\nTraining - Loss: 0.9159, Acc: 67.20%\nTesting - Acc: 62.21%\nEpoch 8/50\nTraining - Loss: 0.8511, Acc: 69.40%\nTesting - Acc: 67.65%\nEpoch 9/50\nTraining - Loss: 0.7850, Acc: 72.09%\nTesting - Acc: 70.57%\nEpoch 10/50\nTraining - Loss: 0.7174, Acc: 74.49%\nTesting - Acc: 70.45%\nEpoch 11/50\nTraining - Loss: 0.6715, Acc: 76.30%\nTesting - Acc: 71.99%\nEpoch 12/50\nTraining - Loss: 0.6332, Acc: 77.73%\nTesting - Acc: 73.05%\nEpoch 13/50\nTraining - Loss: 0.5890, Acc: 79.42%\nTesting - Acc: 75.16%\nEpoch 14/50\nTraining - Loss: 0.5643, Acc: 80.08%\nTesting - Acc: 72.24%\nEpoch 15/50\nTraining - Loss: 0.5297, Acc: 81.38%\nTesting - Acc: 76.04%\nEpoch 16/50\nTraining - Loss: 0.5007, Acc: 82.74%\nTesting - Acc: 75.37%\nEpoch 17/50\nTraining - Loss: 0.4796, Acc: 83.34%\nTesting - Acc: 77.60%\nEpoch 18/50\nTraining - Loss: 0.4614, Acc: 83.82%\nTesting - Acc: 79.40%\nEpoch 19/50\nTraining - Loss: 0.4268, Acc: 85.12%\nTesting - Acc: 76.38%\nEpoch 20/50\nTraining - Loss: 0.4021, Acc: 85.85%\nTesting - Acc: 78.57%\nEpoch 21/50\nTraining - Loss: 0.3836, Acc: 86.57%\nTesting - Acc: 79.81%\nEpoch 22/50\nTraining - Loss: 0.3564, Acc: 87.34%\nTesting - Acc: 81.66%\nEpoch 23/50\nTraining - Loss: 0.3432, Acc: 87.99%\nTesting - Acc: 79.73%\nEpoch 24/50\nTraining - Loss: 0.3249, Acc: 88.60%\nTesting - Acc: 81.73%\nEpoch 25/50\nTraining - Loss: 0.3090, Acc: 89.07%\nTesting - Acc: 80.87%\nEpoch 26/50\nTraining - Loss: 0.2895, Acc: 89.84%\nTesting - Acc: 80.68%\nEpoch 27/50\nTraining - Loss: 0.2817, Acc: 90.16%\nTesting - Acc: 81.70%\nEpoch 28/50\nTraining - Loss: 0.2570, Acc: 91.15%\nTesting - Acc: 81.30%\nEpoch 29/50\nTraining - Loss: 0.2510, Acc: 91.13%\nTesting - Acc: 82.77%\nEpoch 30/50\nTraining - Loss: 0.2333, Acc: 91.84%\nTesting - Acc: 81.72%\nEpoch 31/50\nTraining - Loss: 0.2197, Acc: 92.31%\nTesting - Acc: 81.90%\nEpoch 32/50\nTraining - Loss: 0.2011, Acc: 93.05%\nTesting - Acc: 83.60%\nEpoch 33/50\nTraining - Loss: 0.1932, Acc: 93.36%\nTesting - Acc: 83.42%\nEpoch 34/50\nTraining - Loss: 0.1794, Acc: 93.91%\nTesting - Acc: 83.54%\nEpoch 35/50\nTraining - Loss: 0.1698, Acc: 94.29%\nTesting - Acc: 83.13%\nEpoch 36/50\nTraining - Loss: 0.1615, Acc: 94.55%\nTesting - Acc: 83.51%\nEpoch 37/50\nTraining - Loss: 0.1470, Acc: 95.09%\nTesting - Acc: 84.17%\nEpoch 38/50\nTraining - Loss: 0.1450, Acc: 95.06%\nTesting - Acc: 83.75%\nEpoch 39/50\nTraining - Loss: 0.1403, Acc: 95.31%\nTesting - Acc: 84.11%\nEpoch 40/50\nTraining - Loss: 0.1276, Acc: 95.78%\nTesting - Acc: 84.29%\nEpoch 41/50\nTraining - Loss: 0.1254, Acc: 95.91%\nTesting - Acc: 84.31%\nEpoch 42/50\nTraining - Loss: 0.1180, Acc: 96.18%\nTesting - Acc: 84.08%\nEpoch 43/50\nTraining - Loss: 0.1155, Acc: 96.14%\nTesting - Acc: 84.38%\nEpoch 44/50\nTraining - Loss: 0.1139, Acc: 96.44%\nTesting - Acc: 84.53%\nEpoch 45/50\nTraining - Loss: 0.1061, Acc: 96.72%\nTesting - Acc: 84.64%\nEpoch 46/50\nTraining - Loss: 0.1078, Acc: 96.54%\nTesting - Acc: 84.60%\nEpoch 47/50\nTraining - Loss: 0.1056, Acc: 96.75%\nTesting - Acc: 84.85%\nEpoch 48/50\nTraining - Loss: 0.1033, Acc: 96.78%\nTesting - Acc: 84.66%\nEpoch 49/50\nTraining - Loss: 0.1017, Acc: 96.82%\nTesting - Acc: 84.73%\nEpoch 50/50\nTraining - Loss: 0.1063, Acc: 96.60%\nTesting - Acc: 84.59%\nTraining reference model 2/3...\nEpoch 1/50\nTraining - Loss: 1.9061, Acc: 26.99%\nTesting - Acc: 35.47%\nEpoch 2/50\nTraining - Loss: 1.5411, Acc: 41.85%\nTesting - Acc: 49.45%\nEpoch 3/50\nTraining - Loss: 1.3430, Acc: 50.38%\nTesting - Acc: 53.31%\nEpoch 4/50\nTraining - Loss: 1.2025, Acc: 56.36%\nTesting - Acc: 55.10%\nEpoch 5/50\nTraining - Loss: 1.0900, Acc: 60.80%\nTesting - Acc: 61.49%\nEpoch 6/50\nTraining - Loss: 0.9797, Acc: 64.78%\nTesting - Acc: 61.83%\nEpoch 7/50\nTraining - Loss: 0.9052, Acc: 67.62%\nTesting - Acc: 63.88%\nEpoch 8/50\nTraining - Loss: 0.8406, Acc: 69.99%\nTesting - Acc: 68.19%\nEpoch 9/50\nTraining - Loss: 0.7888, Acc: 71.82%\nTesting - Acc: 66.79%\nEpoch 10/50\nTraining - Loss: 0.7324, Acc: 74.24%\nTesting - Acc: 69.94%\nEpoch 11/50\nTraining - Loss: 0.6845, Acc: 75.77%\nTesting - Acc: 71.99%\nEpoch 12/50\nTraining - Loss: 0.6390, Acc: 77.40%\nTesting - Acc: 74.74%\nEpoch 13/50\nTraining - Loss: 0.5936, Acc: 79.00%\nTesting - Acc: 75.36%\nEpoch 14/50\nTraining - Loss: 0.5535, Acc: 80.70%\nTesting - Acc: 75.38%\nEpoch 15/50\nTraining - Loss: 0.5318, Acc: 81.33%\nTesting - Acc: 76.76%\nEpoch 16/50\nTraining - Loss: 0.4964, Acc: 82.44%\nTesting - Acc: 78.76%\nEpoch 17/50\nTraining - Loss: 0.4699, Acc: 83.44%\nTesting - Acc: 79.04%\nEpoch 18/50\nTraining - Loss: 0.4444, Acc: 84.31%\nTesting - Acc: 78.23%\nEpoch 19/50\nTraining - Loss: 0.4202, Acc: 85.22%\nTesting - Acc: 76.18%\nEpoch 20/50\nTraining - Loss: 0.3983, Acc: 86.03%\nTesting - Acc: 78.07%\nEpoch 21/50\nTraining - Loss: 0.3777, Acc: 86.84%\nTesting - Acc: 79.85%\nEpoch 22/50\nTraining - Loss: 0.3573, Acc: 87.49%\nTesting - Acc: 81.31%\nEpoch 23/50\nTraining - Loss: 0.3386, Acc: 88.17%\nTesting - Acc: 79.94%\nEpoch 24/50\nTraining - Loss: 0.3146, Acc: 89.00%\nTesting - Acc: 78.31%\nEpoch 25/50\nTraining - Loss: 0.3038, Acc: 89.38%\nTesting - Acc: 80.85%\nEpoch 26/50\nTraining - Loss: 0.2857, Acc: 90.13%\nTesting - Acc: 80.33%\nEpoch 27/50\nTraining - Loss: 0.2722, Acc: 90.50%\nTesting - Acc: 81.98%\nEpoch 28/50\nTraining - Loss: 0.2591, Acc: 91.04%\nTesting - Acc: 81.75%\nEpoch 29/50\nTraining - Loss: 0.2440, Acc: 91.44%\nTesting - Acc: 81.92%\nEpoch 30/50\nTraining - Loss: 0.2212, Acc: 92.35%\nTesting - Acc: 81.92%\nEpoch 31/50\nTraining - Loss: 0.2099, Acc: 92.79%\nTesting - Acc: 81.87%\nEpoch 32/50\nTraining - Loss: 0.1976, Acc: 93.10%\nTesting - Acc: 82.84%\nEpoch 33/50\nTraining - Loss: 0.1912, Acc: 93.36%\nTesting - Acc: 83.09%\nEpoch 34/50\nTraining - Loss: 0.1722, Acc: 94.11%\nTesting - Acc: 83.37%\nEpoch 35/50\nTraining - Loss: 0.1657, Acc: 94.41%\nTesting - Acc: 83.98%\nEpoch 36/50\nTraining - Loss: 0.1587, Acc: 94.58%\nTesting - Acc: 84.19%\nEpoch 37/50\nTraining - Loss: 0.1482, Acc: 95.01%\nTesting - Acc: 83.63%\nEpoch 38/50\nTraining - Loss: 0.1386, Acc: 95.49%\nTesting - Acc: 84.26%\nEpoch 39/50\nTraining - Loss: 0.1312, Acc: 95.70%\nTesting - Acc: 84.34%\nEpoch 40/50\nTraining - Loss: 0.1287, Acc: 95.75%\nTesting - Acc: 84.10%\nEpoch 41/50\nTraining - Loss: 0.1190, Acc: 96.16%\nTesting - Acc: 84.41%\nEpoch 42/50\nTraining - Loss: 0.1117, Acc: 96.44%\nTesting - Acc: 84.44%\nEpoch 43/50\nTraining - Loss: 0.1114, Acc: 96.42%\nTesting - Acc: 84.53%\nEpoch 44/50\nTraining - Loss: 0.1050, Acc: 96.79%\nTesting - Acc: 84.66%\nEpoch 45/50\nTraining - Loss: 0.1045, Acc: 96.64%\nTesting - Acc: 84.46%\nEpoch 46/50\nTraining - Loss: 0.1016, Acc: 96.94%\nTesting - Acc: 84.59%\nEpoch 47/50\nTraining - Loss: 0.0983, Acc: 97.03%\nTesting - Acc: 84.58%\nEpoch 48/50\nTraining - Loss: 0.0978, Acc: 97.06%\nTesting - Acc: 84.62%\nEpoch 49/50\nTraining - Loss: 0.0991, Acc: 96.98%\nTesting - Acc: 84.65%\nEpoch 50/50\nTraining - Loss: 0.0980, Acc: 96.89%\nTesting - Acc: 84.63%\nTraining reference model 3/3...\nEpoch 1/50\nTraining - Loss: 1.9196, Acc: 26.23%\nTesting - Acc: 35.20%\nEpoch 2/50\nTraining - Loss: 1.5418, Acc: 42.06%\nTesting - Acc: 47.07%\nEpoch 3/50\nTraining - Loss: 1.3430, Acc: 50.66%\nTesting - Acc: 53.85%\nEpoch 4/50\nTraining - Loss: 1.2042, Acc: 56.00%\nTesting - Acc: 54.37%\nEpoch 5/50\nTraining - Loss: 1.0945, Acc: 60.61%\nTesting - Acc: 61.29%\nEpoch 6/50\nTraining - Loss: 0.9851, Acc: 64.37%\nTesting - Acc: 63.24%\nEpoch 7/50\nTraining - Loss: 0.9172, Acc: 67.00%\nTesting - Acc: 61.66%\nEpoch 8/50\nTraining - Loss: 0.8545, Acc: 69.37%\nTesting - Acc: 67.71%\nEpoch 9/50\nTraining - Loss: 0.7905, Acc: 71.71%\nTesting - Acc: 68.22%\nEpoch 10/50\nTraining - Loss: 0.7428, Acc: 73.55%\nTesting - Acc: 68.45%\nEpoch 11/50\nTraining - Loss: 0.6835, Acc: 75.75%\nTesting - Acc: 69.95%\nEpoch 12/50\nTraining - Loss: 0.6341, Acc: 77.53%\nTesting - Acc: 72.45%\nEpoch 13/50\nTraining - Loss: 0.6023, Acc: 78.67%\nTesting - Acc: 73.92%\nEpoch 14/50\nTraining - Loss: 0.5637, Acc: 80.33%\nTesting - Acc: 76.13%\nEpoch 15/50\nTraining - Loss: 0.5397, Acc: 81.07%\nTesting - Acc: 74.79%\nEpoch 16/50\nTraining - Loss: 0.5043, Acc: 82.32%\nTesting - Acc: 76.23%\nEpoch 17/50\nTraining - Loss: 0.4741, Acc: 83.42%\nTesting - Acc: 77.51%\nEpoch 18/50\nTraining - Loss: 0.4509, Acc: 84.22%\nTesting - Acc: 76.99%\nEpoch 19/50\nTraining - Loss: 0.4229, Acc: 85.09%\nTesting - Acc: 78.93%\nEpoch 20/50\nTraining - Loss: 0.4011, Acc: 85.84%\nTesting - Acc: 77.03%\nEpoch 21/50\nTraining - Loss: 0.3813, Acc: 86.53%\nTesting - Acc: 79.65%\nEpoch 22/50\nTraining - Loss: 0.3539, Acc: 87.54%\nTesting - Acc: 79.14%\nEpoch 23/50\nTraining - Loss: 0.3353, Acc: 88.24%\nTesting - Acc: 80.95%\nEpoch 24/50\nTraining - Loss: 0.3273, Acc: 88.63%\nTesting - Acc: 76.77%\nEpoch 25/50\nTraining - Loss: 0.3140, Acc: 88.76%\nTesting - Acc: 80.89%\nEpoch 26/50\nTraining - Loss: 0.2879, Acc: 89.89%\nTesting - Acc: 79.60%\nEpoch 27/50\nTraining - Loss: 0.2681, Acc: 90.62%\nTesting - Acc: 81.86%\nEpoch 28/50\nTraining - Loss: 0.2568, Acc: 91.06%\nTesting - Acc: 81.91%\nEpoch 29/50\nTraining - Loss: 0.2367, Acc: 91.65%\nTesting - Acc: 82.01%\nEpoch 30/50\nTraining - Loss: 0.2216, Acc: 92.30%\nTesting - Acc: 80.73%\nEpoch 31/50\nTraining - Loss: 0.2129, Acc: 92.55%\nTesting - Acc: 83.16%\nEpoch 32/50\nTraining - Loss: 0.2036, Acc: 92.89%\nTesting - Acc: 83.21%\nEpoch 33/50\nTraining - Loss: 0.1834, Acc: 93.88%\nTesting - Acc: 83.41%\nEpoch 34/50\nTraining - Loss: 0.1781, Acc: 94.01%\nTesting - Acc: 83.65%\nEpoch 35/50\nTraining - Loss: 0.1669, Acc: 94.46%\nTesting - Acc: 83.16%\nEpoch 36/50\nTraining - Loss: 0.1542, Acc: 94.77%\nTesting - Acc: 83.46%\nEpoch 37/50\nTraining - Loss: 0.1470, Acc: 95.00%\nTesting - Acc: 84.23%\nEpoch 38/50\nTraining - Loss: 0.1373, Acc: 95.44%\nTesting - Acc: 83.84%\nEpoch 39/50\nTraining - Loss: 0.1305, Acc: 95.79%\nTesting - Acc: 84.17%\nEpoch 40/50\nTraining - Loss: 0.1268, Acc: 95.75%\nTesting - Acc: 84.62%\nEpoch 41/50\nTraining - Loss: 0.1187, Acc: 96.09%\nTesting - Acc: 84.46%\nEpoch 42/50\nTraining - Loss: 0.1172, Acc: 96.17%\nTesting - Acc: 84.18%\nEpoch 43/50\nTraining - Loss: 0.1115, Acc: 96.42%\nTesting - Acc: 84.39%\nEpoch 44/50\nTraining - Loss: 0.1073, Acc: 96.66%\nTesting - Acc: 84.90%\nEpoch 45/50\nTraining - Loss: 0.1053, Acc: 96.51%\nTesting - Acc: 84.66%\nEpoch 46/50\nTraining - Loss: 0.1024, Acc: 96.77%\nTesting - Acc: 84.70%\nEpoch 47/50\nTraining - Loss: 0.0989, Acc: 97.02%\nTesting - Acc: 84.73%\nEpoch 48/50\nTraining - Loss: 0.0992, Acc: 96.78%\nTesting - Acc: 84.64%\nEpoch 49/50\nTraining - Loss: 0.0984, Acc: 96.97%\nTesting - Acc: 84.68%\nEpoch 50/50\nTraining - Loss: 0.0995, Acc: 96.86%\nTesting - Acc: 84.75%\n" - } ] }, { "cell_type": "markdown", "metadata": { - "originalKey": "acf051fa-f121-42e1-9484-1567ab711285", - "outputsInitialized": false, "bentoAICellStatus": "none", "isCommentPanelOpen": false, - "language": "markdown" + "language": "markdown", + "originalKey": "acf051fa-f121-42e1-9484-1567ab711285", + "outputsInitialized": false }, "source": [ "## Extracting Prediction Scores\n", @@ -378,13 +365,30 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": { - "originalKey": "b20323e3-bff0-414b-bac4-85e9b0320800", - "outputsInitialized": true, "bentoAICellStatus": "none", "isCommentPanelOpen": false, - "language": "python" + "language": "python", + "originalKey": "b20323e3-bff0-414b-bac4-85e9b0320800", + "outputsInitialized": true }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Getting scores from target model on training data...\n", + "Getting scores from target model on population data...\n", + "Getting scores from reference model 1/3 on training data...\n", + "Getting scores from reference model 2/3 on training data...\n", + "Getting scores from reference model 3/3 on training data...\n", + "Getting scores from reference model 1/3 on population data...\n", + "Getting scores from reference model 2/3 on population data...\n", + "Getting scores from reference model 3/3 on population data...\n" + ] + } + ], "source": [ "temperature: float = 2.0\n", "# Create a DataLoader for the entire training dataset (without shuffling)\n", @@ -429,24 +433,16 @@ "ref_scores_population: NDArray[np.float64] = (\n", " np.array(ref_scores_population_list).squeeze().T\n", ")" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": "Getting scores from target model on training data...\nGetting scores from target model on population data...\nGetting scores from reference model 1/3 on training data...\nGetting scores from reference model 2/3 on training data...\nGetting scores from reference model 3/3 on training data...\nGetting scores from reference model 1/3 on population data...\nGetting scores from reference model 2/3 on population data...\nGetting scores from reference model 3/3 on population data...\n" - } ] }, { "cell_type": "markdown", "metadata": { - "originalKey": "73a0a905-6387-45b5-9fb0-9e93ec43278c", - "outputsInitialized": false, "bentoAICellStatus": "none", "isCommentPanelOpen": false, - "language": "markdown" + "language": "markdown", + "originalKey": "73a0a905-6387-45b5-9fb0-9e93ec43278c", + "outputsInitialized": false }, "source": [ "## Preparing Data for RMIA Attack\n", @@ -456,13 +452,26 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": { - "originalKey": "8f039ddd-1eea-48a5-b7dc-3ba8a9c3f6d1", - "outputsInitialized": true, "bentoAICellStatus": "none", "isCommentPanelOpen": false, - "language": "python" + "language": "python", + "originalKey": "8f039ddd-1eea-48a5-b7dc-3ba8a9c3f6d1", + "outputsInitialized": true }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training data shape: (24960, 8)\n", + "Testing data shape: (25040, 8)\n", + "Population data shape: (10000, 7)\n", + "Training data columns: ['user_id', 'score_orig', 'score_ref_0', 'member_ref_0', 'score_ref_1', 'member_ref_1', 'score_ref_2', 'member_ref_2']\n" + ] + } + ], "source": [ "# Prepare data for RMIA attack\n", "df_train_merge: pd.DataFrame\n", @@ -481,43 +490,37 @@ "print(f\"Testing data shape: {df_test_merge.shape}\")\n", "print(f\"Population data shape: {df_population.shape}\")\n", "print(f\"Training data columns: {list(df_train_merge.columns)}\")" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": "Training data shape: (24960, 8)\nTesting data shape: (25040, 8)\nPopulation data shape: (10000, 7)\nTraining data columns: ['user_id', 'score_orig', 'score_ref_0', 'member_ref_0', 'score_ref_1', 'member_ref_1', 'score_ref_2', 'member_ref_2']\n" - } ] }, { "cell_type": "markdown", "metadata": { - "originalKey": "47a8bf83-19a5-487e-b91b-a7b871c7b636", - "outputsInitialized": false, "bentoAICellStatus": "none", "isCommentPanelOpen": false, - "language": "markdown" + "language": "markdown", + "originalKey": "47a8bf83-19a5-487e-b91b-a7b871c7b636", + "outputsInitialized": false }, "source": [ "## Performing RMIA Attack\n", "\n", - "Now, let's perform the RMIA attack using the Privacy Guard framework. We'll test different configurations to understand their impact." + "Now, let's perform the RMIA attack using the Privacy Guard framework. We'll test different configurations to understand their impact, including both offline and online modes." ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "originalKey": "c629d118-afa4-4559-80d8-f24170f4ecf6", - "outputsInitialized": true, "bentoAICellStatus": "none", "isCommentPanelOpen": false, - "language": "python" + "language": "python", + "originalKey": "c629d118-afa4-4559-80d8-f24170f4ecf6", + "outputsInitialized": true }, + "outputs": [], "source": [ - "# Basic RMIA attack with default parameters\n", - "print(\"Running RMIA attack with default parameters...\")\n", + "# Offline RMIA attack with default parameters\n", + "print(\"Running offline RMIA attack with default parameters...\")\n", "rmia_attack_default = RmiaAttack(\n", " df_train_merge=df_train_merge,\n", " df_test_merge=df_test_merge,\n", @@ -526,13 +529,14 @@ " alpha_coefficient=0.3,\n", " num_reference_models=1,\n", " enable_auto_tuning=False,\n", + " online=False,\n", ")\n", "\n", "# Run the attack\n", "default_attack_results: Any = rmia_attack_default.run_attack()\n", "\n", - "# RMIA attack with auto-tuning enabled\n", - "print(\"Running RMIA attack with auto-tuning enabled...\")\n", + "# Offline RMIA attack with auto-tuning enabled\n", + "print(\"Running offline RMIA attack with auto-tuning enabled...\")\n", "rmia_attack_tuned = RmiaAttack(\n", " df_train_merge=df_train_merge,\n", " df_test_merge=df_test_merge,\n", @@ -541,194 +545,141 @@ " alpha_coefficient=0.3, # Initial value, will be tuned\n", " num_reference_models=1,\n", " enable_auto_tuning=True,\n", + " online=False,\n", ")\n", "\n", "# Run the attack with auto-tuning\n", "tuned_attack_results: Any = rmia_attack_tuned.run_attack()" - ], + ] + }, + { + "cell_type": "markdown", + "id": "tad35wsrvws", + "metadata": {}, + "source": [ + "## Online RMIA Attack\n", + "\n", + "In online mode, both IN and OUT model signals are used directly. The population probability\n", + "is estimated as the simple mean of all reference model signals, without alpha mixing.\n", + "This provides more accurate estimates when membership information is available for all\n", + "reference models.\n", + "\n", + "Note: Auto-tuning is not applicable in online mode since alpha is not used." + ] + }, + { + "cell_type": "code", "execution_count": null, - "outputs": [] + "id": "rfm2nbtedb", + "metadata": {}, + "outputs": [], + "source": [ + "# Online RMIA attack\n", + "print(\"Running online RMIA attack...\")\n", + "rmia_attack_online = RmiaAttack(\n", + " df_train_merge=df_train_merge,\n", + " df_test_merge=df_test_merge,\n", + " df_population=df_population,\n", + " row_aggregation=AggregationType.NONE,\n", + " num_reference_models=None, # Use all available reference models\n", + " online=True,\n", + ")\n", + "\n", + "# Run the online attack\n", + "online_attack_results: Any = rmia_attack_online.run_attack()" + ] }, { "cell_type": "markdown", "metadata": { - "originalKey": "d72a7122-6b47-4f92-a435-746d0c7b558e", - "outputsInitialized": false, "bentoAICellStatus": "none", "isCommentPanelOpen": false, - "language": "markdown" + "language": "markdown", + "originalKey": "d72a7122-6b47-4f92-a435-746d0c7b558e", + "outputsInitialized": false }, "source": [ "## Analyzing Attack Results\n", "\n", - "Let's analyze the results of our attacks using the analysis framework." + "Let's analyze the results of our attacks using the analysis framework, including the online mode." ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "originalKey": "3006f5b9-72e9-4ff0-a4a6-db96fe605646", - "outputsInitialized": true, "bentoAICellStatus": "none", "isCommentPanelOpen": false, - "language": "python" + "language": "python", + "originalKey": "3006f5b9-72e9-4ff0-a4a6-db96fe605646", + "outputsInitialized": true }, + "outputs": [], "source": [ "# Analyze default attack\n", - "print(\"Analyzing default attack...\")\n", + "print(\"Analyzing default (offline) attack...\")\n", "default_analysis: Dict[str, Any] = analyze_attack(\n", - " default_attack_results, \"RMIA (Default)\"\n", + " default_attack_results, \"RMIA Offline (Default)\"\n", ")\n", "\n", - "print(\"Analyzing tuned attack...\")\n", + "print(\"Analyzing tuned (offline) attack...\")\n", "# Analyze tuned attack\n", "tuned_analysis: Dict[str, Any] = analyze_attack(\n", - " tuned_attack_results, \"RMIA (Auto-tuned)\"\n", + " tuned_attack_results, \"RMIA Offline (Auto-tuned)\"\n", ")\n", "\n", + "print(\"Analyzing online attack...\")\n", + "# Analyze online attack\n", + "online_analysis: Dict[str, Any] = analyze_attack(online_attack_results, \"RMIA Online\")\n", + "\n", "# Print comparison of AUC scores\n", "print(\"\\n=== Attack Performance Comparison ===\")\n", - "print(f\"Default RMIA AUC: {default_analysis.get('auc', 'N/A'):.4f}\")\n", - "print(f\"Auto-tuned RMIA AUC: {tuned_analysis.get('auc', 'N/A'):.4f}\")\n", - "" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": "I0828 092246.602 analysis_node.py:193] Train/Test unique users: 24960/25040\n" - }, - { - "output_type": "stream", - "name": "stdout", - "text": "Analyzing default attack...\n" - }, - { - "output_type": "stream", - "name": "stderr", - "text": "I0828 092251.865 mia_results.py:247] TNR: 0.07804, FNR: 0.00617, emp eps: 2.25384, tnr@fnr0.001: 0.0, eps@fnr0.001: -0.00031, auc 0.5645 accuracy 0.5633, \n" - }, - { - "output_type": "stream", - "name": "stderr", - "text": "I0828 092251.868 analysis_node.py:202] Epsilon CI: 2.253840249352018\n" - }, - { - "output_type": "stream", - "name": "stderr", - "text": "I0828 092318.647 analysis_node.py:193] Train/Test unique users: 24960/25040\n" - }, - { - "output_type": "stream", - "name": "stdout", - "text": "\nRMIA (Default) Attack Results:\nAttack Accuracy: 0.5640 (95% CI: [0.5611, 0.5671])\nAttack AUC: 0.5673 (95% CI: [0.5625, 0.5723])\nEpsilon at TPR=1% (Upper Bound): 1.7593\nEpsilon at TPR=1% (Lower Bound): -0.7524\nAnalyzing tuned attack...\n" - }, - { - "output_type": "stream", - "name": "stderr", - "text": "I0828 092324.010 mia_results.py:247] TNR: 0.01603, FNR: 0.0, emp eps: 3.84982, tnr@fnr0.001: 0.04135, eps@fnr0.001: 0.03697, auc 0.5679 accuracy 0.56911, \n" - }, - { - "output_type": "stream", - "name": "stderr", - "text": "I0828 092324.012 analysis_node.py:202] Epsilon CI: 3.849824610908012\n" - }, - { - "output_type": "stream", - "name": "stdout", - "text": "\nRMIA (Auto-tuned) Attack Results:\nAttack Accuracy: 0.5676 (95% CI: [0.5646, 0.5709])\nAttack AUC: 0.5675 (95% CI: [0.5626, 0.5725])\nEpsilon at TPR=1% (Upper Bound): 0.7002\nEpsilon at TPR=1% (Lower Bound): -0.6961\n\n=== Attack Performance Comparison ===\nDefault RMIA AUC: 0.5673\nAuto-tuned RMIA AUC: 0.5675\n" - } + "print(f\"Offline RMIA (Default) AUC: {default_analysis.get('auc', 'N/A'):.4f}\")\n", + "print(f\"Offline RMIA (Auto-tuned) AUC: {tuned_analysis.get('auc', 'N/A'):.4f}\")\n", + "print(f\"Online RMIA AUC: {online_analysis.get('auc', 'N/A'):.4f}\")" ] }, { "cell_type": "markdown", "metadata": { - "originalKey": "417e30ab-8d68-4ad6-a403-9b5e46905018", - "outputsInitialized": false, "bentoAICellStatus": "none", "isCommentPanelOpen": false, - "language": "markdown" + "language": "markdown", + "originalKey": "417e30ab-8d68-4ad6-a403-9b5e46905018", + "outputsInitialized": false }, "source": [ "## Visualizing Attack Results\n", "\n", - "Let's visualize the distribution of scores for members\" and non-members for all attack variants." + "Let's visualize the distribution of scores for members and non-members for all attack variants." ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "originalKey": "c80c5c6a-79ac-4865-81d0-c58a6057012e", - "outputsInitialized": true, "bentoAICellStatus": "none", "isCommentPanelOpen": false, - "language": "python" + "language": "python", + "originalKey": "c80c5c6a-79ac-4865-81d0-c58a6057012e", + "outputsInitialized": true }, + "outputs": [], "source": [ "# Plot score distributions for different attack configurations\n", - "plot_score_distributions(default_attack_results, \"RMIA (Default Parameters)\")\n", - "plot_score_distributions(tuned_attack_results, \"RMIA (Auto-tuned)\")\n", - "" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": "
" - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": "
", - "image/png": 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Vsp7du6mNp6eO5ORazqGdabvk6emhqE6RVvWrrKxS3/rnw/7Lng/Ozs6WdWbm50P24SNWyz3c3fXs009o9IihNse2bXCQamtrdaa4pMFjDwC4drSIBICbQGBgoM2yixcvavW6jdq+I12FRWesQhhJMtWYbPZpSHCQbdnmGZzNLwVXEhQYYNPl7b/719XhwgWDKirqXiBdLut+7ezsrMgOETqQeahJ9W2q6upqSbLqgph/7ISWLl+lw0eOqqKy0mafmiYcs5Y47uaXM+8Gxs+63KCB/bRnX4Z2pO22tCLasm2HHB0d1b9fQoP7mFtzbtyUqo2bUhstu6TknNWM3JeP92huSRkQ4G+zr1v9REGXnyP+fr7ycHe32T44KFDHT5xUSclZBQb461R9HV/93Z8ar99Z66DBycnJZly4xPg4rduwSRs3pepA5iH17N5N3aK7qGvXzg3WQ5Kl6+flL8lAazNfF98tWnbFluIXLhjk7e2lcWNG6dv5C/XHv/xLXaM7K6ZrF8V0i7YKhuyt2lg3MYi51WJtba1SNm/V5tTtOnnqtE2g1VRFZ4q1eOlKZWZlq/yC7bV6dfdar+/d1t/PTxPuHKdv5i7QitXrdMe42xrcrqDwjEwmk0IvaY13qdCQuoCusLDIarm3t5fNBGuXPyvVyH3J09PDaqIdm3t1fbmBAbZj9rpfdq++2nOsJT7TrF27tjZ/Lzg4OCgwMED5x47r3PlSebi769z5UknSL379P43W7/Lng5+vj83fF0MHD9S2HWn6btEybd+5Sz26d1PX6M6K7hzV6KR/5p+5rLy80c8GAFwbgkgAuAl4eNgGKrPnzNX2nemKCG+vB+67WwH+/nJyclJ1dbXeef/jJpd9reMyNmV/8+zTjY3n5WmH7k8lZ89J9bO+qj6c+9db78pUY9LQIYPUJaqT5bimbN6q9N17m1RuSxz3ioq6ELSh3+vlenTvJj9fH23Zul1Dk5NUU1Oj7TvT1bN7t0Zn9ayqP97JgwZYdVe+XEhb6xnFnRvpJu7s1PRzxM2t4YkezC/cxvqAuMpolLOzs5556rFGy7q8pay7u5vNS2ybNp6a8dKz2rApVbt279PGTanakLJFzs7OSugbq3sm3SmvNtaBr2f9cTdc0soUuBGYr93xY0c3OiyGJLnXn8Mjhg1WeHioUjal6kBmtg4czJLqW3VNvHOcTeuzllZRUaGKykoFXvKF1JJlq7Rs5RoFBQVq8t0TFBwYYLn+35v5qSorq7633NKyMv399bd14YJBSQMS1T2mq9p4esrBwUF79mVo/cbNTa6fmnivVX1wtXPnLq1ctU4JcbFqd9ls55JUZayrv6tLw0GWS33AZf5dmjX1Pjrj1d/bLDNP4GPW2JAeVxrqw+xqz7GW+ExLmd/zfKiurpajY915FODvr4cfbHxCMXOvB7OGfsdBQYH69S9f0PqNm7Vn736tXrtBq9dukJubqwYN6KeJd95uE0iay6ng+QAAdkMQCQA3ofPnS7V9Z7q8vb30wrNPWwV8l3e3vRE4u9Q9bqqrG+7uZg7mWkp1dbUyD2XL3d1NHSPqxoXakLJFRmO17p44XqMvGb9QktJ2NS2EbKnj/t8Xne//uR0dHTWwf6KWr1qrY8dP6Oy58yotLbOZgfpS7m515bu6ulqNY3k9mINGm+XmMLq+haq7u5sMhgp1iAhv8iy1jfHw8NC420Zq3G0jVVpWpsysbG1J3aFtO9JVWlqmnz39hNX2BkPdC6anR8sPCQBcC3NQ06aNZ5Ov3ejOUYruHCWTyaS8/GPau/+gNm7aopmfzNYLz/7kihNiXStz9+OYrl2k+hZwazekyNnJSS/87CmbFsxNaXUuSVu37VR5+QUNTU6ymTwmN+9Yk+vnUX+NV1RUNjiD8+UcHR314P2T9ee/v6nZX83Tiz//ic025t9R+QVDg2VUVRmttrtazz3zY5tlPo186dQczTnHWkpTng/u9S3Zq6qqWqR+vj7euuvOcbrrznEqOXtWBzMPaeOmrVq3cbOM1dV6cOpkq+3NzwcPng8AYDeMEQkANyHz2FgdI8JtWhkeyj7SyF6tx6tNG7m6uqjk7Fmbbno1NTXKzctvdN/mWLF6naqqjOqX0NfS2qG4fryn6C7WLza1tbU240c1pqWO+3+7fjWta3DSgEQ5ODgoffc+7UzbJW8vL/Xq2fjEBmHt67oMHj2a1+B6e3Y5Kyk5a3kRv5R5rDbzeKTmMSZzjubabGs0Ghssoyl8vL3VPzFez/3sx+rUsYMOZmXbdMM3H/emBBPA9dS+vfm6aOTaLWv82nV0dFSnyI66685xeuxHD6q2tla79+yzW12rq6u1fOVaSbJ8MVJefkFVVUYFBwfZhJBHc/MsQ2Z8nzOW+7Vti71DTbxfq5ndbNuHttNto4Yp52iuUjZvtVnfNjhYTk5OKiwsanD4EvPQGA21pmyKrtGdbf5xaaT1ZXNcyzl2rU6fLrBZZjKZdKa4RC4uzvL19ZGHu7v8/f10wWBQQUGRzfYVlZVXHMPzSgL8/TU4aYBefuEZ+fv7aVcD18fVdOcHADQPQSQA3IT86rsbm7sfm5WWlWnFqnWWrsI3CgcHB0V1ipTRWK29+w9YrduQskUXGmlZ0hwbN6Vqxap1CvD318Q7xlmW+/rWvRQXl1gPQL9qzQbLS+qlx8zJydFmWUsd9+Cgui7RBYW2L1kNCQoKVHSXKO3bf0AZB7LUv1/fK3aH6xwVKT9fHx3Ny1f24RyrdSVnz+p3//s3vf/RZ0367KtlMpm0aYv1y3v+sRM6dbpA7dq1lZ9v3TE0dxlftXaDzTibC5es0IxXf281E29jZn35jf74l39ZWtSYOTg4yMnZSY6Ojjbduc2hqHnSC+BG0btXD7m4uGj33v02E9DkHzuuX//2j5o7f5FUPwvz7/74twbDMnNwdel9wtHRscWeCwZDhd7/6HMVFBZpxNDB6hTZQaoP/pycnFRaWqbqS8KiyqoqzZ2/2NL62Wj8bz0cG7rX1t+vL5/YJi19j/Lzj9ts35i29V98NPVeazZuzCiFtA3WgsXLdO689UQyrq4u6t2ru8ovXLBpTV9RUaEdO9Pl7Oys2F49ruozr5erOcda2vnSMu3es99q2d79B1RRUaFu0V0s42Am1j8fVq5Zb7VtbW2tZn3xjX716h909rLn8OVqa2v19n9m6o2337f5AtTJ0VGOjo5ycrR9jhYWnZGDg4OCAm3HvgQAtAy6ZgPATSjA31+doyJ1JCdXn876SjHdolVcXKJNqds0YfwYrVqzQQWFRVq5ep16dO/W6KQd19NtI4cp69BhffL5HI0cnqyQ4GDl5OYr42CmevfqoX2XBZTfZ+++DMv4lBcvXlRxyVnt2r1PJ06eUlj7UD3x6ENWYwz2S+yrrdt3au78RSotK5eri7P2Z2SqoLBId08cr9lz5mrr9p1yc3PTgH7xlpeQ7Wm71Marjfz9fJUYH3dVxz08rH2DdQ8PC5W3t5eyDx9RTU1Nk8bYGjSwnz75fI50Seujxjg5OenBqZP13szP9J8PPtGI4clqGxys4uJibdy8VRdrajR8yOArlnG1zGFiSNtgrduwSadOFyiqU6RKS8u0dkOKamtrNeH2sZbtByf1V/ruvTqUfURvvvOB+iX0VW1trTIOZmrP3gz17BGjyI4R3/u5Md2itXV7mv7yz7c0sF+C/Px8VVVl1IHMLB0+clSDk/pbdZGsqalRdvYR+fp4X9cJPYCm8GrTRvfeM0Fffj1f/3zzXY0cNkR+fj46dapAKZu3ysPDXYOT+kuSwsPay8XFRV/PXaD8Y8fVKbKjnJ2dVVxcrJTNW+Xm5mp1rwgKDFBh0Rl9M2+hfLy9NXJ48ve2tDt/rtRq/FyDoUInTp7SjrTdqqio0Iihg3X3XXdY1js6Oiq+b6x27NylDz/6XPFxsSotL9fmLduUmBAndw93Hcw8pEVLV6hvn16K6hSpoMAAZUmat3CpukZ3Vs/u3RTfN1bLVq7R8pVrVVtbK29vbx3KPqLMQ9l68P7J+uCjz7Vr736FtgtRnz69Gu0G3T2mq9Zt3KysQ4cVF9uryb8HF2dnPTD1Hr3x7/eVdeiwzfp7Jt6hIzm5+uKruTp58pTCw9qrrLxcKZu36tz5Ut03+S6riV5uJFdzjrUU81dNkR0j9MVXc3X4SI7CwtrrzJlirVmfIicnJ40bM8qy/dgxI7XvwEFt3b5TlZWViu3VQ8bqaqXt2qPswzkaMnig/P39rviZDg4Oiu7SWQsWL9M/3nhHCX37yMfbW4aKCu3as0/FxSWacMdYq33Ol5bpxMlT6tSxg9rYYexqAEAdgkgAuEk9Pn2a5n63SAczD2n33v1qHxqi+ybfpbjYXnJ1ddU3cxdq2cq18vDwUI+Yrq1dXcV0i9YTjz6k5SvXaM26FLm5uap7t6566bmntXDxCqn+JbapPp31leW/HR0d5ePjrXYhbfXwg/cpIb6PzeyZMV276EfTpmrVmvX6buESeXp6qnfP7pp2/xS5uDgrLX2PjhzN1ZLlqzSgX7yiOkVqaHKStu9M1+o1G9Q/sa8S4+Ou6rg3FkQ6ODioZ/cYbd2+Uzm5eYru3PiEAWZxfXqrzdyFCg4OVGgTuvz17BGjl577qVasXquUzVstY6R1ieqkMaOHKyI8rEnHuanMXeU8PTz09I8f1bzvFmv+wiUyGqvVLqStpk6ZpLg+/w0CnJyc9MxPHtfadSnamb5bX8/9Tk6OTgoMDNDdE8drxLBkm5aMDUmMj5OHu7s2bErVug2bdOGCQR4eHgoODtS0+6doYH/rmcWP5OSqsqpKCfF9mlQ+cL0NThqggAB/rVmXolVr18tYZZS3j7fi+vTS2NtGKri+lZ+Tk5Oe/9mPtXLNBu3bf0Dpu/eqpsYkX18f9erZXaNGDFVI22BLuVPumag5X8/Xlq3b5e/np2FDB+n7OvwezcvXzE9mW/7f1dVV/n6+6tunlwYN7KdOkR1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cunwVly5HIyPzunAMyWQyWFiYw9XFGX16eSMkeDD69fVp1zYZU+9eXhgxLBThEfUPMKMuX8XFqCsIDmpbGhN9ZWRex4Woy4iNS0RBYSHKy8uhVtfPC+Nob49+/XwRFBgAX58+ra5r89bfhLiBew83vP3GS0BDWz42PhGnz0QgLSMDxUUlqFPWwdrKCt5enggOCsSIYUM65L5Hs0xuri741z9eARommoyIvICz584jNzcPJaVlMDM1hZubK4IGD8SEcaNhaWEhWldRUTFOnArH5asxKCgsRFVlFaytrdDL2wvDhw1BaEiQQde+vPwCRJ6vv1bn5RWgrLz+wZuNtQ1cXJzg79cfoUP0a6N+veoHXG2YONN/QH88++QyAEBlZSVOh59D5IUo5OcXoLSsHJYWFvDw6IEhIUEYM2qEzv1VTm4ejh0/hdj4+nZNTW0NbKyt0adPL4wZOQIB/n56b6P29rb3vqSpdRprH7a3DdZZdY/kwVlnZydYWlgIuUqLi4v1/u6gQf5CXqXy8gpcuhItGkrfGs3etn19+8DczNzA0rddVXU1Is5dEF5rTsAVNnwo/vr7ANRqNWpraxEeESkaEmwsl6/GCE/aAWDUyOFtWk9X2JbOolKpsHvPfhw4fEzIoamptLQMpaVlyMi8jhOnwuHm6oJF8+/Ua/Kjm5nmhICN+yUtPRPfrf2pyby+udX5yM3Lx9mI8xg+bAjuX7Ko1Yu2SqXCnr0Hsf/QEVRX6+77wqJiFBYVIyn5GvbsO4jQkMFYtGAObG1sWi3/5SvR2LBpG0rLxD1WiotLUFxcgpTUdPy97xDuuG26zrBbfYweNUJocBYVFSM6Jg6BGr1lbiXjxowUggBo+N1OnApvV17JsrJybNy8DVGXrzb5eWVVFSqzqpCVlYPIC1HYsWs3Fs2/E8NCDZ/4sDU1NbVQKpVY8c33SEy6ZvT1A8D+Q0ex8489UCqVovdLy8pQWlaGlNQ0HDh0FAvn3YnRbazXu7LUtHRs2bajydQTdUolqqqqkZ9fgOjYeOzavRejwoZhwdzZLU5c2hl1mKE6Yjs7UllZuWhiL0ODGo18+vTC5x+9q/fD9uKSUvzy644me+FXVlYio7ISGZnXcezEaQQFDsSSxfNhZ2vb4jpz8/Lxy7YduBoT1+Tn1dU1KCgsRHxiEv7aewAhQYFYvHAubG1bv95A4xg7duK08LCqNR11DUxNy8BPP/8iCqxrqiuvQHl5BVLT0nHiVDh6erjjwfsWG/wA1Jg6oy1WU1P/G3/02Vdt6s1qiCHBg7FnX32an9Ph5wwKzlZWViLq8hXhdUhQIA4ePmbQ38/LL8DWX3/H5asxOp/VVVaisrISuXn5uBh1Bbv+/Bu3zZja6lwfZmZmQnC28Tc6deYsNv/yG+q0rl3FxSWIKr6KqMtXETZ8KO6/ZxFkMhmqq2uwdv3PuHQlWmf92Tm5yM7JxcnT4bj/nrswYtgQvbe3tq4OP238BefOX9T5rKioGEVFxYiJjceBw8fw0P1L9Hqo0xH7UFN1wz5c99PmJsut7eDhY/jjr71N1hU1NSrU1NRvZ2xcAv7csx+jwoZh0fw721xv29hYw39AfyGAdD0rG9nZuejRQ79OP2Vl5YhLuPFw3KdPL53AbHRsHDZt2Y78At0HwPXrqENZWTmSr6Xi0NET6OvrgwfvvctoD1Lao65Oiflzb8eVqzEor6hPb/bL9t8xYEA/WJgbP/5QWlqGbb/twrnzF0XX5UaNx3lySir2HTgMf79+WLxoXotBenONcjYejxUVFfh+3UbEagTVhb9RXIKi4hJcuhKNI8dO4IlHH4KDvX4PW/XVVJny8gvw9bdrkJObJ1q2UqlESmoaUlLTcPJUOJ57+jG4NBwbJ06dwS+/7hRSXTYqLinFpSvRuHQlGhHnLuCRpfe22japqanB73/swbETp3Xa6QBQUFOIgsJCxMUn4o+/9mLMqBGYP+f2Fs+9ptqoKanpWPX9OhSXiB/GlVdUICExGQmJyTh95iyeffJRWFtbAQD+3LNPiMtoKiwqRuGFSzh/4RLGjRmJxQvnGhSINvZ9SUfsQ+31w4A2WGfWPV1iqkBTM1Ph39VNNLKaY2FuDn+//sJrQ3I1qVQqhGvkZwoNCUJdXa3e32+viHMX6nslNAgbPlT4t4uzkyjifuJUx6QD0LyRMTExwSCNoYSG6Arboi8LCwv06e2NPr29RRdDE4VCeL/xP201NbVYtWY9/tp7QHQzYGJiAlcXZ3h79dS56OTk5uGb79bhVBvziDXF1cUJfXp769wYOdjbicrv6tJ5jRHNoERNTS1ycvPwv5WrhaCGg70dvL16wsXZSTSsHQ0zFP+5p+Ue67W1tfjuh5/w5559ooamhbk5PNx7wLOnh6gXglqtxrnzUfjsy2+a7MWnKTYuAd/98JNOYNbR0QHeXj3h5OgImUwGlUqFnX/uEfX019egAH/Rxfx8FxzK35lmTp8sen302Mk2r6uquhqff/WtTmDW1sYGnj094NnTA7a2NqJGRllZOdau34STp8X1kbu7G/r09tbphePi7CQ6txwc7JstT3VNDY4eP9Vhgdmjx0/ht9//FBorjg72wnEqKkd1DX7e8ivCI843s6aO4e3VE316ewsN3kbuPdxE+9DWxrpN678aHYvPV6zSCVja2tb/3j3cXEWNM5VKhROnwvHl16tbzPfc0XWYoYy9nZr7XrP8FubmrV779GVuIb7BzMrKRm1t29pV+gZmCwoL8fn/vhG1Z2QyGZydHOHt5anTczfq8lV89uU3KCpqviNAaloGPv1ipU5g1trKCj093OHu7gYLjW1Vq9U4f/ESPv1ypd6TjNbU1KCouATbf/9Dr+U76hqYnpGJL79aJQrMymQyODnW7z/3Hm6wtBT38Mu8noXP/veNKLewoWxtrIXjrbX6Vrst01ltsZqaGmzbvqvDA7MAEBwUKPw783oWUlLT9f7uufNRwuRf9na26OvbB7V1+k8GlpaeiU+/XKkTVGys83q4uYrqx/KKCmz7bSc2b/2tyaBPI+069fLVGGzc/CvqlEqYm5vBs6cH3N3ddB5qnTl7DsdOnIZKpcJ3P6wXArO2NjZCHaxJpVJh46atyM7J1Xub12/YIgQ4FQoFXFyc4eXZU+dBRn5+Ab5e9UOzDy4addQ+1FRTU4uLl67oFZj946+9+HXHH6K6wtTUFO493NDLu75e1Kxj1Wo1Tp4+i2++W9dkIERf2g+8z0dd0vu7F6Iui/JPDx8qDrZfjY7FN6vXiYIjcrkcLs5O6OXtCTdXF50gZ2JSMj79ciUKi6TP71pXVwdbGxvM0Zj4vKioGH/s3mv0v5WXX4BPv1yJiMgLouPLwsIc7u5u6OnhDmsrK9F3YuIS8OmXK5GaltHsehWKG2Gj2ppa1NbWYsU3a4TArJWVpdA+0f4t0tIz8cOPG3VyjLeXZplqampRVVUtBGZlMplwTdDe3vyCQvzw489AQzqmn7dsR11dHUxNTeHh3gM9Pdxhamoq+k7U5av4e98htKS8vAL/W/kdDh89ITqXGveNh3sP0b5RqVQ4duI0/rfyO1RU6N9GLSwqwspVP6C4pBRyuRw93Fzh2dOjyf2+aet2AMBfew9g9579UKvVsDA3b7YOPnbitGiCydYY+76ko/ahJkPaYJ1d90jec7aurk50I2FjbdgNW+iQYOHCfTUmDsXFJbDX46lMbHyC0Dg3USgwdEgwMrNavvgak2beyb6+PjpPqkaNGIb4hieImdezkHwtBT59ehvt76tUKly+euNJtF//vm1+cif1thiil7enMMu85hBmOzu7Vmef37FrNy5rPL23t7PFnDtuQ0jwYNETrfz8Ahw4fAxHj5+CWq2GSqXCpl+2o5e3p15DH1tz24ypuG3GVJ1h0aNHjjBoWLQxyWQaF8faWqxdvwkVFZUIDhqE2bNmwMO9h/B5RUUlDh4+hr/2HhDeO3j4GKZOmtDsMK9tv+0SBd883Htg7p2zEDCgv3BRUavVuJaSip1//o24+ESg4Ybsh3U/Y/nzTzY5pLG2thbrf/5F1IsjcFAA5s+5XZTio6i4BEeOnsD+Q0exa/de9PLyNGj/WFiYw69/X6FHweUr0VCr1UadJf1m0svbC717eQt5qrNzclFYVARHB8OGuwDA33sPIivrxkSSI0cMw8zpk3WGuBSXlOJMeAT+3ndIeJi0dftOBA4KEHrRNQ5l1x7eOnP6FL2G2QNAdXU19p89B7lcjulTJmLUyOFwdLBHVVU12vtzFxYV4fzFKMhkMowbMxJTJ40XPZXNyy/Ajp27cf5i/Q2RWq3GL9t2YGCAn8HX1rZ67JEHgCaGLS9eOFdIL9NW+QWFWLNuoyggMyw0BDOmTUJPD3fhvbq6OlyJjsX23/9EXl4+ACAlNQ2bt/6GR5be2+S6O7oOk3o7Na9vmkNlvb089U5n0RoLc3M4OzsJAcryigrs/PNvLJh7h1HWr02lUuH7tRuQ27DtaOiZP2PqJNHQubz8Amz//Q9cjKrvXZibl4+1P23CC888rlMHV1ZWYtWaH0UP6wIG9Mcds2agdy8vYXmVSoWExGRs//1PpKVn3Fjv+k3NXm801dTU4sjRE6ipqUX/fr64Y9b0huuKDOUVurmaO+oauPmX34T60EShwB2zpmP0yBFCL5tGGZnXcfT4KZw4FQ61Wo3q6hps3PwrXnvp2Ra3szmBgwKE0SOG1red1RZLS89A1OWrsLa2wtzZt2Fw4EBYWVqitKwcNlr7p716eXvC1cVZOJZPh0egdy8vvb6rmeZq2NAhkMvlegfYKioq8M13a1Gi0fNq5IhhmDFtkqgtX11dg3PnL2LnH3uEc+PYidPo6eGO8WNHNbluuca5VV5egZ83b4OZmSnm3Xk7Ro4YKgQ+KquqsOvPv3FE4yHtvgNHUFdXh+jYePRwc8WSu+ajfz9f4fO8/AJs+Hmr0IavUypx6Mhx4RrekotRl3HpSjTMzc1wx23TMXLEMFG9nZR8DZu37kBGQ975ysr6VEzLn3uy0/ehppqaGuzbX985YOzoMEwaPwauri6oqa1FXe2NYPz1rGxRAMm9hxsWzL0DA/z6iYIwSqUSly5H4+/9B4WAXFx8Io6dPNPmkUzBgwcJI1rR0AmoMZ1FayIvRAn/lsvlGDokWFTWn7f8KhzXFhbmmD/nDgwNDda5d01KvoaDR47j/IX6dlBxSSm2/bYLjz50f5u2yVgae2SOHjkcp8PPISm5/gH+kWMnETY8FN4G3lc0R6lU4vu1P4mui728vTB39m3o389XuA6o1WqkpqVj5x97hJF9ZWXl+H7dBvzj1RebHIEj12gn1dXVYecfe5Calg7Pnh6YP+d2+PXvK6xfqVTi/IVL2PTLduEak5h0DTGx8UYdTapTpj/3ICc3D8NCQzD3ztuE+wq1Wo1LV6Lx44bNwlw7KalpOHM2Epu3/gYTExPMvn0Gxo0eKWx7bW0tDhw6hl27/xb+xqGjJzBj2qRmHyCv27AZydduTDju69MHc2bPRF+fPqL2Q2x8An7ftUdoPyRfS8XPv2zHsqX36bWdW7buQFl5OSZNGIuZ0ybDpqHDg1KpxJmz57B56w7hfLlw8TIiL0Thz7/2wdLCAgvm3YHhQ4cI29BUHbz/4BG97ns64r6ko/ahJn3bYFLUPZL3nI1PTBI1IgytnEKCAoUeDI25mvSh+URgcOBAWFtbQa3S7+lle6WkpgkHEgCMChuqs8yQkMGiH93YPU6vZ2WjoqJSeN2/jbkxusK2dIZrKWk4evyU8NrBvj6YGzZiqM4FzNnZCXctmIMFc2cL7ymVSmzeuqNTy9yZNBvhMbHxSE1Lx4Rxo/HYww+IghpoePJ1x6zpoh7WtbV1oocFmuLiE0UPAHx9+uDlF59G4EB/UUNTJpPBp09vPPvkMtHT++SU1GYn4Tp24rSoB1WAvx8ef+QBndzLDvZ2mDP7Njx432IolUokp6Q2sbaW9fO9cY6VlZcL+S1vVZo3WgAQF5fYpvVojoAYNNAf99+zqMncQ/Z2tpg+dRKee/pRofFYU1OLU6eN16sdAC5djkZRcQnuWbwAs2+fARdnJygUClhbW8HKqn039HHxiaipqcWCubOxeOFcneEyLs5OeGTpvaIcZpVVVTh0+Hi7/m5XsWXbDtEojdumT8FDDywRBSzR0IMuePAgvPLi06Kb48gLUYiJjW9y3R1Zh3Wl7exomjfUaAhar/5hvc7wQmM4cuyUqIfhbTOm4O5F83Rymrk4O2HZ0vvQ1/dGTr2ExGREXdJNg7Lzz79F14Sw4aF45sll6NPbWxTIlcvl8OvfFy89/6SQYx8N1xt9emfW1tbizNlz8Pfrh+eeehT9fH1gZmYGMzNTnYdUHXUNzM3LF13L7rh9BqZNmagTmEVDfrYld83H/Dm3C++lpqUjKTml1W01ps5six07cQampiZ44ZnHMXrkCNja2EChUMDB3s6g+S30NXLEjRvhiMgLevV+zcnNE/0GI0fU10n63s9s++0PUXqzBXPvwP33LNLpZGFubobRI4fjleXPiHqX/r7rL51RR400H3hFRF5ASWkZHn/kQYwbM1LUI83SwgJ3LZgjSh1QUFiI33f9BWcnR7z43BM67QUXZycse/g+0W9+qZm0RtouXYmGqakpnn1yGSZPHKfzQM3Xpw9eeOYxUQ/dxqBSUzpyH2pKz7iO5JRUzJo5FUvumg939x5QKBSwtLAQpVOJiLwg9E40USjw7JPLMDBggE7vOIVCgZDgQCx//inRg4AjR0+0WpbmWFiYI3DgjZRdaekZyNNjNEFpWZnQiQcABvr7CcEmAIhPTEahRr18790LMWbUiCY7Ffn69MGypfeJUuhdjLqCIo3fSAqqhnNSJpNhyV3zhN9DpVLh5y3bjdaj9OCR40hLvzGqYYBfP7z8wlMY4NdP9IBOJpOhdy9vPPPkMoQE3+i5n59fgL37m+4dqnkNrK6pweFjJ+HTpxeWP/8k/Af0F61foVBg2NAQLJx/p2gdjUE6Y9EsU21tLY6dOI2RI4bioQeWiK6lMpkMQYEDMe/O20Xf/3nzNtTU1ODB+xZj6qTxojrF1NQUM6dPFl1PKysrkZCY3GRZTp2JEDrhoCFdzQvPPIZ+vj467YeAAX546fkn0b/vjbrt/IVLuNJEWhTt7czJzcOlK9GYNXMqFs6bLTpXFAoFRo8cIUqNo1ar8eNPmyGTyfDU4w9jVNhw0TWsqTo4KztHr3abse9LOnIfatK3DSZF3SNpcFalUuGvvw+K3gtqIhF8S8zMTEU3A/o0iquqqoUeFNBozHQWzUaymZkZhoQE6SxjZmaG0CE33j93PkrIy2sM2kMle/dq21DGrrAtneHIsZOioSHz597RalLrSRPGiBo8ScnXcD0ru0PL2RUolUq4u7u12ltqgtaT+bRmhtIcOHRU+LepqQkefnBJi7285XI5liyeL3oipznxnaazGrmSZTIZFs6b3WKPp2GhIRgcOLDZz1vSW2u48LU2BHi7E1+t3vOZbTg3KisrRYGUQD1Ss/Tu5Y2xo8PgP6A/xowaARcjp/8oKCxE/76+eve0NZRPn14t5qmTyWSYP+cO0XGs2SPlZpWXly9qsPX0cMesmVNb/I6NtbVOPXT8VNMPajQZuw4zRGduZ0eYPnWizrXxYtQVvPN/n+CLFauw/+ARpKVntvtGVK1W48ixG0EER0cH3DZ9SrPLy+VyLNK6STx15qzodVVVtejBvY2NNe5aOLfFcpiammLJXfNFNw0n9Nj3dUolSsvKcfdd81vtZdtR10DtodqD9ag/J44fg76+PhgcOBATx48RDSntDJ3ZFisoLMTUSRN0Hop0lLDhocJxVFFRiSiN+5TmaE4E1svby6CyFheX4KzGw83Agf6YPHFci99xdnIUnUdV1dU4c7b1TjEqlQqjwlqePHHoEPGQ+DqlEnPvnNVszmQb6/ocp42KiktaTF2jadKEsS2O4LOyssKsGeJ6t6nraGfuw4LCQrg4O7VYzwHA9es3jm1Pz54tpmECAFMTE8yeNQM+fXph2NAQhIYEGZQWQ5t2aoMLUa2n8bpwUSulgVb+4OtadZVmALg5t8+cil7enggJCsSkCWObzE0tlZ4e7qLjJDUtXfTQqa3UajWOaazHxMSk1Xz4MpkMdy+aJ0qTdOrM2Vav0Wq1GnK5HEvvu7vF69GIYUNEE29pdujqCJYWFlg4b3azn4eGDBZdr+uUSgQO9EdoE/GLRkNDxQ+dm0tzopnr287OFve1su9NTU3x4H2LRammjjZzv6pJpVKhh5tri73Sh2mVuU6pxNjRYaKHydq06+DW0rk0MuZ9SWftQ33bYFLUPZIFZ2vr6vDzll+RmHTj6YObqwuGDmn+5GiO5tPm7JxcJF9r+Ul+5IWLQh40OzvbTp2sqbKqChGRN/IFDQke3GylpnlzX1NTI5p0q720c4W1ZThFV9mWjlY/W/eNJ332drYtVuKaxo4OE73WJ1dUdzBx3JhWJ8fx8vQQVYgFTeRlKS4uwRWNQEVoSJBeQ98tzM1FE9xlXs8ShqgJ6y4pRWrajV5Xvbw99Zrxd4Iew8+aop0KQfPJ9q3Izk48IU9Zme5Q3tbU1YmHbmqOBmjJ4oVz8eyTy3DP4gU6Pf2MQfu8N6bxY1sfbtiYs7FRTm6eaIjbzSg84rwoKDN+7KhWg1poGD7tqHFzeunSVb3yoBqrDjNUZ2+nsVlaWOCZxx/WCZip1WrEJybht5278cEnX+LVf7yDr1f9gD17DyI+IcngsiYlp4iO6WFDglv9vby9PNGvrw/6+vZBcNAguLmJe7ZdjLosakSPDhuuV7qnnh7uohuelNR0Ic1ESwb079vqDMMdeQ3UnvikXI/6Uy6XY/lzT+CJZQ9i0fw72/xgvy2kaIuN6cC6XJujowP8NYKXp8Jb7myiVqtxRmO0oKEdTcIjIkUBGH0nIRsSMhj2GtfviHP65TUfM6rlfenZUxxYtrayQnArHXa0g9FFek4qPX7syFaXGRIyWFSnaJ6HjTp7H44eOaLV64FmYFXf/IsB/n54+YWn8dD9SzD79hk6s7sbYtDAAaKc3Bf06CmpGagxNzdDkFYnCO26Sp/tsrKywmsvPYdHH74fC+be0eJEV1KYNWOKKCf6rt1/t7t3b0JSsigvZvDgQa0+vEJDPmfNno3FJaXN9g7VFDx4kM6kbdoUCgU8PW+kkSko7Nj8v6FDgnTypGuysrLSeWAxKqzlCaq065nCJnLWX0tJEwUzx4waIToPmuPo6CDa91ejY/V6yDQqbFiLbR73Hm46dUVr26ldB7eUm1+Tse5LOnsf6tMGk6Lu6dTgbFVVNTIyr+PQkeP4vw8/F/VyrY98392m2Y59fXqLhiG31nv2dPiNxszwhvxMnSX8bKSo8d9S7ypfnz6i7dKnN4a+CjQqb0tLyzblyesq29LR0jOui24cBwYM0DtXqOZTfTRMNHIr0OeBh0KhECVor66q1lkm6VqKKFDh17/5Xhc6ZfD3E73WzF8DABlaDyj6+uqX2qN/P1+YmhrecLWyshQ9PS5oZsbHW4X20NlyPW8iNNnYWIue9h87cQqFHdzw08eAAfofp4bSPq6bo3083+y99rUfuurTS7rRAI2JQ+uUSr16AxirDjNUZ29nR3B374E3X3kBY0eHNdumq6ysxNXoWOza/Te++GoVXnnzP1i5ei2OHj+FysrWg4RJWvupn9aw5+a8+OwTWP7ck3js4Qcwf464t3GS1jXCkElSNSenBYBUPXoHabcPmtKR10DtiZV2/72/XT3mOlpnt8U83HsYfWbx1mh2NomJjW9xMpG4+EThemeiUOj0WGxNgsaklSYmJi32qNIkl8vhP+DGsZWRmYWampYfrpiZmaKXd8udQDSH5qNhtFFr94SN+eIbaU6A1ZyeHu56PeAwMzODt9eNiXeLiop16qbO3IfQs87QPK9z8/J1Rgh0NFNTUwQPvjFM/lpKWotBntKyMlEgMCQoUGfGde2h0rt2/633RGpdlZmZmWhkRlVVNbZu/71d60xObvs1LEDrGpaS1vqkhPp2btM8T43RTmqJ5hD35ssjrms0Ux7ps3x1te42JCSJg9kDDLhWB/jf2I8qlUrUcag5/VrZToVCIYrtaNdnTdGug/WpT2HE+5LO3of61KdS1D1GSZr0yRcr2/V9a2srLFt6X7tmCR45Yih+/2MP0PBEfOG8O2FmZqqzXF5evpCEGxKnNHBxdkK/VnK9am5XWnomUtPS0ctbv0kCWqL55Ep7NmN9dZVt6WjaQzAMGTbm5OgIMzMzIYitHRDsjszMTPU+pkw0gpzavSDR0ANJk5MeT4Abaf9O2j1VtXO+6vtky8TEBC7Ozm0Kdjk5OQq9lzr66XFXpx3grm3DkDOZTIZhocE4ebr+5qOouAT/9/EXmDJxPIYNDdEJPnQGGxvrZodhtpe9na0ot1RLtLc9OzsHaGNKjq5A8/y1tLDQqzdII3d3cY/49IzrLfb4M2YdZqjO3M6OZGVliSV3zceMaZNw8tRZRF6IanE29draWly5GoMrV2Pw+66/MHrkcEyfOknnZqGRdgoJY/SK0r3W92h2WW3a+z4j43qrvTrd3Vtff0deA3t5e8HN1UXIKxcTG4/3P/oC06ZMQPDgQe3OkW1snd0W0/5NO0Nw0CBYWlqisrKyvmdseCRmTm966GpTc2cYonFCTjQETwzJo6t5bqhUKmRkXhflK9Tm4uzcakcY7ZnR9TmntdsR2r2cmmLIcePi7CxKA5edkye6V+3MfYiG3nCtGRYaIhoiv2HTNlyNjsP4saPQ17dPp3RIGhYaLExUp1arceHSlWYnGdNJaTB0iM4yAwMGCOcFGo7961nZmDJpPAIHBjQ5edXNIHCgP4YEDxZGBFy4eBmXr0QLkyUaqj11ZFPXsNZ46FlHap6ndXpOVthWrnrVGzfqGgsL82bbGU0tj2baeqmp4nSR7b1WB7QS8NSvfrxRblcX51YfZupuZ+v1qTHvSzp7H+rTBpOi7jF+RnsDmJmZYeSIobhtxhSdp5+GGjF8KHbt3guVSoWqqmpciLqEEcNCdZY7ffacEPE2ND9TeyUlp4h6sowcMbTVE0Vzu9AQEL1ncfsDmprJ51urlJrSlbaloxVrDTPRforSGidHByEQWFLaetL/m51VC8NJtMnQ8jGjPURt6/adTT500Uex1rq0fwvHVvJyaXJycmxTcNbO1gaNTacyPSaA6M5qqsW9RMz1GLrSlDtvn4nYuARhKFdFRSV27f4bu3b/DXd3NwQM8MMAv37o39dXr+Ex7eVgr/9xZChD6h7tHl/65uLrilQqleia1ZY6WFNpK/WwMeswQ3T2dnYGJ0dH3DFrOu6YNR15+QWIjUtAYtI1JCZfa3bof1V1NQ4eOY4zEZFYtvQ++PXvq7OM9rXBGD0cNa8R9SOK9A92OTmKg/klpaXNLttInzJ35DVQJpPh3iULsWLl98JNWHZOLjZs2oaft2yHT59eGODXD/5+/dFHj16MHa2z22IdWZc3x9S0fh6N4yfrc+adDo9oMjhbVV0tyuVpaEcTlUolqh9Ky8rw8edf6f39Mq3rifZvo81CY9RQc7RrUn1Siuh+q3XOzvp3SLHXuY7eSL/U2fvQ0tJSr0BAX98+GD92lChAG3khCpEXomBtZYUBfv3qz+sB/TvsAfYAv36wsbEW0lVduHip2eCsZkoDOzvbJvMSW5ibY8mieVj70ybhHj4lNR0//PgzTExM0K+vD/wbtsvLs2enjohtr4Xz70R0TJwwCeiWbb/Dr39fnd7D+tBOi+BiwLGufQ0r1eMa1lL6ADHjtZNao087X7Pdpk89o88IDe19//26DXofh0qtgLU+6VkM3U5zfbaz1SV0GfO+pLP3oT5tMCnqHqMEZz3ce7R6wcjOzhVNAnXv3QswfOgQnSh9WznY28F/QH9hIo1TZyJ0grNqtRrhETfy+nR+r9kbCYplMhlGDG/972tv17nIi1gwd3a7I/W1GsNn2tLQ70rb0tG0Jy/Tr8F4g+YFVqVSoaamVtjnqWkZ2LLtt1bXYW9nh8ceecCgvysVudx4N3CVleJ9355hutq/o/awFEMaQuZtaDRB66lktREmJ1i9Zj2KS1rPUbV44bxWhxR2Nu1yW7exl5atrQ1efPYJ/LD+Z52Zw7OycpCVlYNDR45DoVCgX18fhIYEYVhoSIcFatvSoNaXIXWPdjmMcbxJRbse0KeRKVpea19UtTIhpTHrMEN09nZ2NhdnJ7iMGoExo0YADcHjxKRrSEhMwtWYOJ2eteXlFVi5ei1eefFpePb0EH2mua9kMplR2pJVGsMtLQxsl2i3o6r0GLqpT13RkddAAOjn64NnnnwE69ZvEt0YqVSq+iB60jXs3rMfFhbmGBQwAMOGDkHgQH9Jgh8d2RZrSluv8+01KmyY0MbOzctHQmKyzsi08xcuCb2A2zJ3RoXW8Pza2lqdiYINUVnVcioSRRuOl446xizMWw8UN9I+PjSvo529D80NuFdbNP9OWFlZ4u99h0RDcMsrKoRALRp63wUHBWLkiKF69crVl0KhQGhIkBAgTky6htKyMp1RRaWl4pQGQ4cEN/u7Dw2tzyu+YfM2UXqJuro6xMTGIyY2HmgYvRQ4MABhw0ObfLDX1TjY2+GOWTOw7bedQMPEb3/u2Y95d84yeF3a13xD2hDa16NKPa5hbTmvm3L5SjT+2nug1eW8vTxx96J5Ri2Tsdp72tfq9qQx1KftZujDUmP9VtqMeV/S2ftQ3/u1zq57jBKcvffuBS3OeomGSS5+3LBZeH0u8iJGjxxhjD8vGBU2TAj8xSckIT+/QBTRb3wPbczP1B4VFRWIvHAjKbpCLscPP27U67uaT1OrqqsREXlBuLlpqxqNvF1mpoY1QLvatnQ07YlKDM03qr18TU2N0OCrqqrSqzGn/UTzVmHMCW20c+dor9vERP8LncKAZTWZadzw65NfrDVp6ZkoKGw9d21XC9KgoYeWJjeNnNSGcnR0wIvPPoELUZdx/OQZxCck6cw0q1QqERuXgNi4BOz8Yw/umDUd49s4uVtLOqoBBACmBtycaR/P+gxP6qpqasXnrqF1sIlp8zfYXcmtsp2NbG1tEBIciJDg+vyEqWnp2LPvIC5qzFJfW1uLzVt34KXnnxR9V7MNI5fL9c492hLNdRoa7NVeXp/ZefWpKzryGtiof19f/PPNV3Di1BmcOhPRZAC4qqoa585H4dz5KLj3cMNdC+Y02cOtI3VkW6wpUvW+69PbG+7ubsjKqu/le+pMhE5w9oxGSoO2zJ1h7MkC9c1N2BUYcm5rpynQvI529j40JIgkl8sxe9YMDA8NwaGjJxF54WKTE6bm5OZh34HD2H/wCIaGBmPhvNlGS8s0NDRYCM6qVCpEXbqiMync+YuXRG21EcN0UxpoCgkORL++Pjh6/BTOnD2HvIZ7ek1lZeU4HR6B0+ER6NPbG3cvmtemSa8704RxoxAecU4IRh08fAwjhg3ReSjZGs1rmEKhMKheaKp+7CylZeV63QubmhinQ19HqOmEa3VXZMz7ks7eh4bcr3Vm3dNpaQ1GDBuCU2fOIi4+EQAQE5eAU2ciWpxEylCDAwfC2soK5RUVUKvVOH32HG6fOU34vDH/DdqYn6k9zpyNFF3I65TKNj9hPXEqXNKAZnfaFn1oP1mprTUsyKG9fFfvKdyVaD+Re/dfrxstUK3b6NY/D1JtWwOrN/f8BUalnfjds50pZuRyOUJDghAaEoTSsjJcvRqL6Nh4xMUnoLhEPDyrvKICW7btQHpGJu5ZvKBdf7czKQ05RrXqHWONUpGCdg82Q+vgOq0Gn6E9UjvLrbKdzenl7YXHHn4AR4+fwpZtO4T3k5KvISsrW5QfTHNfKZVKqFSqdgfTzMxMhR6vhgZetJc31nW+I6+BmszNzTB54jhMnjgO2Tm5uBpdX38mJiYLw20bZWXn4Ktv1+C+uxcirBNHoN1KbbGRI4Zhx87dAIDzF6Nw14I5Qnnz8wtE18+2jALUPq78+vfF808/1u5y3wyUSv2PmzqtY0azM8vNsA/d3XtgyV3zcNeCO5GQmIyrMXGIjYtHesZ1UY9atVqNiHMXkJycguXPPakzm31b9PXpA0dHB2HSuvMXLzcZnG3Uw81Vr3lIbGysMWvmVMyaORVp6ZmIjqmvq5Kvpeic49dS0vDpl9/g8WUPIGCAfpMWSUEul2PJXfPx8edfQ6VSQaVSYdOW7Vj+/JMGXdfac128merHrkizPpDJZFjx2ftGeWjc1RnzvqSr78POqns6Nefs4oVz8f5HXwjJoH/7/U8EDvRvU87TppiamIie1J0JP4dZM6ZCJpOhpqZGdBGQciKw9kpJTUN6Ria8PFueda8lZqamQoNbu7dOa7ratmjTzjvSXpZaubL0Ga6oSXP4vIlCIaqM/Pr3xddffGiEUnaMjk7c3hrtoeeG7vuWaAcuDLkZb2p4qD40n0S3NW+gpnf/9Xq71yGFuro6RMfECa9NTU1anXnUELY2NggbMVQIHGRlZePSlWicPH1WmPwGDQ+H+vr6IGy4bn7yrsiQp+naT6CNFaiTogeudr5CQ3uCaweXtOv0ruJW2c7WjB87CpeuRAsjoQAg6VqKKDiru6+qRTMTt4WlhYVwjWnPdb6p8rVVR14Dm9PDzRU93FwxacJYKBsevl+8dBmnw88JOeJUKhV+3vIrfH37wNXFucPLhA5ui3U1I4aFYucfe6BSqVBdXYPIC1FCZ5YzZyPbPXeGubk5ZDKZsJ6uOLqmoxh2HRUvqxmwupn2oUKhEHLNoiFlTExsPM5GXsDlK9HCNuQXFOLHjVuMEmSWyWQYOiQY+w8eAQDExSeioqJSqKe1UxoMb6XXbFO8vXrC26snpk+dhNraWiQmXcP5i5cQHnFeaG/X1tZi7Y+b8O+3Xm33NaIj9fL2wvixo3D46AkAQHJKKo6fPCOM8NInSNWe66KU7YdRYcOM2llPCprXarVajeqaGoNT79yMjHlfcjPtw46sezo1OOveww1TJo/H3/sOAQ29l7Zu/x0PP3iv0f7GqLBhQnA2v6AQyddS4evTGxeiLgsHUFvyM7VHfGKSaGb4OXfMxPSpkwxaR2FREd7+zwfCBfTEqXAsXji3zWUyMze7EZw1oBeglNuiVqtaXQYdMPGNo6P4CXJuftOTmDRFpVKJhp3bG+FptDGoJNqXhnLSmjE9L7/AaJP4aec41U5E3pKmhjPoQzMA3JG5Sbu6yAtRomNrYMAAowSrm+Pu3gPu7j0wZdJ4/L3/EP7YvVf47MChozdNcLagqEjvZRt7qzRqbdLNrlwnyOVy2NvbCWlx8g08/7TPV2NMHtURbpXt1MdAfz9RcLa0tFz0ufYEjvkFBbCyat/QVUdHBxQW1U8iUVlVhbLycthY6zcLse6+N861viOvgfpQKBTo69sHfX374LbpU7B+4y+IunwVaHh4e+ToCSycf2enlKU7tsWaY99wn3L5SjQA4FzkhRvB2YhIYbm2djSRy+VwcLAXrhP5+a2nR+ouCgoNuY6KJ5Wxs7txHb2Z96G1tRWGhgZjaGgwrqWk4dvv1gmTUcbFJyItPRPeXu3vNDMs9EZwVqlU4tKVaKG9pZ3SYHg7Uw2amprCf0B/+A/oj1kzpuK7tT8h+Voq0BBvOB0egckTx7Xrb3S02bNm4MLFS8L9yM4/9iA4KBD2drZ6pV7TriPz8vPRy0q/ibfztepTewkmRLyZOTs5ih425OcXGJyW4mZkzPuSm3UfGrvu6fSESjOnTRHlgT13PgqXGhp6xqD9FPlSQ8Mm8vyN2SDbkp+pPTR7mspkMgwfavjTQUcHB1Fy4bMakfm20LzhMGRG587cFu0Lkb5PZzIyrxtcppZ4e4kvbOkZmXp/NzcvX9TVvZeEeY80k4fXSLQvDaU9xCk1Ld1o6+6hleM0t5mZw7VVVlbqXGD0pTlDtL43/d1NXV0d/tyzX/TexHFjOuVvy+Vy3DZ9CgYPChDey8i8ftPkdyooKNTp3dAc7ePZ3V03p69mHStV/aovzbqzqrpa7/MVAK5fzxa99u5ik+Npulm3s7auDimp6Th+8gx+3vIrIs5daNf6tHtUmGv1INUOHGRm6jdRVk1NLSoqKoX/NIf2aucGS8/Q/1jP1Nr3xpqAsSOvgYaytLTEg/ffDXuNAFXStZQWv2NM3aUtpq9RI270JItPSEJVdTVS0zKQ11AntHfuDM1jtLyios0PnW8217Oy9Viqnmb9K5PJ4ObqIvq8O+zDxtyImpKNdF57e3mK2tpRl2/kE9cczerr0xsuRuyBb29vh2UP3S9KX9aZdVVbWViYix52VVZV4deGicJM9ZgfRrteM6SO7Khr2K1C+/ok5bW6MxnzvqQ77ENj1D2dHpw1MzPFXVpP2Tdv22HUoVqaXeMvX4lGTU0tYuIShPc6M6VBWVk5Lly8LLzu388Xjo4ObVrXiGE3endVVlWJJuUylJNGGfSZUAgSbIv2zVmJVt7IptTW1SEmLr5NZWqOZ0930fCO6Jg4ncmGmqPZ8wcAfPr0MmrZDKE5NECffQkAl6/GdGCJWufTp5foQYrm8dcapVKpM+mUJg+PHqLXiUnX9FpvdGy83r+/Ns0eG9o9om4VO3buFm4u0VCPtHdG3aKiYtEMmq0Z4Ndf9LorD0fUpFarEatxLWuJ5tNnAOjpofv0WbOOLSnt2nVCX98+otdX9CyHSqVCdOyNFBqWlpZGnZXa2G7W7YyJjcdHn63Apl+248SpcOw/dEQU+DRUVrb4RtFZq63h6yPeT1c10qS05Lu1P+GVN/+NV978N978139FdXlb9z20rvUymQy9e3vr/d2WdOQ1EA3HTVZ2jt6/lYW5OfpotGO0Z1fuSN2lLaavwYEBwkPcOqUSMTHxos4s7Z07o5+veJKxCxf1v6coLCxqc3onqaWlZ+g1AqSyslL0MNLN1UUnFUZX3YfV1TXIL9C/J++AAeLJ/Yx5Xg8NDRb+HRubAKVSifLyCnFKAz06+uhTn2lysLeDh/uNa2BVJ9ZV7TEkeDAGDfQXXp87H4Wr0bF65YDtq3U8Xrka2+yy2nTryJYneiexfn3F7QdDrtWVVVUoNKAHaldizPuSrroPO7vukWQq0sBBAQgePEh4XVRUjN//+Mto6x8+bIjQSzDzehYiIs8Lw4nbmp+prU6HR4hy9IUNb3tgOCQ4UDT098Sptud+1QwMVVRU6hXY6OxtcdK6GbuW2vqkY8dPnNF/2K1G+p6W8u4qFAoM1eidUFZWjrMR51tdvUqlwrETp4XXcrkcw9rQ07hZWvmHWpvlUDOQnp6ZidpWckempWfoXKw7m62NjaiRcj0rWzSTd0uOnzyDd/7vE/znvY/x62+7dB4Aubm6iHLlXUtJ1avXw+GjJw3ahkba55nzLRic3X/oKA415NNCQ67Z9qRnWbt+E15/+13849//h7/2HtD7e5o99WUyGWxsxL2YtXN7GXtW5vbQrFOak5WVjbT0DOF1715eot5ujTTr2JKS0lYf1BUVFYtmCW+J9j5s7yysw4eFioJUR46d1CswExF5QXRNGDGsc0fOGOpm3c6AAf1F51FaeqZex2pTyssrEK5xjTU3NxNyJTby9ektqr8vXrospINoTmVlJRISk4TXPn16i0aUBAUOFOUFOx0eoVfbKCEpWdRDadBAf6PNeN5R18Di4hJ8vuJbvPzGv/Du+58a9NBFc+SNjZG2Ux9dti3WQRQKBYYNvbG90bHxot+pvR1Nhg8dIjr+Dx05rvcIiu/W/oTX/vEOvvx6Nc6cjdTjG12HSqXS6/4pIvKiqO4dHDhQZ5mutg/DIyLx7gef4qXX/4lPv1ypd4547dF02m2i9tDs3V1ZVYXklFRcuhIt7FuFQoHQkKBmv5+ekYmPP/8KL73+T7zzf58YNHqnWqK6qr0WL5gjukfesm2H6DhrTp/e3qKHslGXr4o6QzQnL79AVLf09HBnz1kDeXt5iobgX4mORWpaRovfafTX3wfw1r/fx/999AV27f67zR2ApGKs+5Kutg+lqnsku0NZNP9OUc7FYydOIylZv55rrbG1scEgjZyyf/19UPh3Z/aaVavVOHEqXHhtZmaKkODANq/PwtwcwYNvfD8pOcWg4TmatCfgSktveeiDFNtiaWkpGuYSn5DUYjkTk65h55979M5dqdnlvLy8AhUVzd+ETRg3SnSj+9vO3a3mA9y1e6/oSUvokKAmAyRtpVl+AMjNy2t2WWgNUamtrcPxFirT4pJSrF2/CQqFXPKZEidPGCt6vWXbb60O9U1KTsGuhryiObl5iIlLaPKp8xCNBqFKpcJvv//Z4nqPHDuJxKTkFpdpTmq6+ALj5dn18+gYS2VlJX76eavO/l1y1wJ4uPdo9nutkcllQlqW4yfPCDl+WlJbW4twjZx9vXt56TR6tVOq5Oa2fG51puiYuBZv5FQqFbbt+EPUE665oa/aw94PHj7e7Hqrqqvxw/qfUV1To9dEOrr7UP/h+U1xsLcT3cDl5OZh5x97WvxOfkGhMOM5GoIy48eMbFc5OtrNup0mJia4feY00Xtbt+8U5gDQV2lpGb79/kdRuqWxo0c2maN7gkY6lNraOmz6ZXuLDfLfdu4W5dgfPXK46HNTU1OMHX1jv1VUVGLTL9tb7FXauIymieNHt7iNhuqIa6CdnS3y8wuEG4jfd/2lV4qr7JxcxGv0ftHubdzRumJbrCON1EhtcOVqtHBza4y5M2xtbTBcI/hbVFyCjZu3tnpTu2PXX0hJTYdSqURcfCJK9Rx10ZX8vf8QMq83nwqltLQMe7Qe+A4dEqyzXFfbh3Z2dsjKqu8JX1xcItQBrTmuFaw25nndw81VlDImOiZONCohwN+vxWCwi7MzMjKzhLQkW7fv1CsAHp+QJJoAtrltevqF10T/dQXOzk64bfpU4XVefgGOHDvR4ncaTdK4XqhUKqz/eWuLQfq6ujps2LRVNJm2sa9htwrNvKJqtRo/btiMsrLyFr9zMeoKjjR0WsnIvI709Mwu3YGgKca8L+lK+7Cj657mSPbrOzo6YNbMGxWPWq3Gxs2/Gm0m6JEaqQ0aewO1Nz+ToWLjE0U/TvDgwHbPOjdCazbLEyfb1nu2j9aQu5RWeqVKtS1DggcL/1ar1Vi5+gdEXogS9XzLLyjEH7v3YsU336G2tha33zZdr7+v2WtMrVZj22+7UFxSCpVKhfyCQtEw354e7pg+ZaLwurSsDJ98uRJnz53X6VWXeT0LP/z4M/buPyS8Z2tjg4VzZ+tVLn3Z2liLgiSXr8QI+6ampkYnkB2isS/RcFOz7+AR0Q1ZRUUlTp4Ox4ef/g/ZObmYPmWSKNitRtuHqbaVX/++GDNqhPC6uKQUn3z+NU6cOqOT56a+Mfo3VnzzvdDjSSaTYdH8O5sMMk+ZOE40O+SFqMv4fu0GnSfNBYWF2Lp9J7Zu3wmFQqFzU6TPkNCUFPE51qd31x9W2R4qlQqpaRnYsesvvP3OhzgdHiF8JpPJsGjBnHZPxDV9ykThAlxdXYP/rVyN/YeONtmDTqVSIS4+EV9+tVo0qeGk8WN1lnVyFPdqPnM2EjGx8aitq0NVVXWn5V3VPq78+veFqakJNmzaih07d+tMYpeTm4dVa9YjWmOIt4O9nSjgpGmIVp1w6Mhx7Nj1l6gHbU1N/UzhH326AolJ1xA2fKjW7OxNH/va+/DQ0eNISU2DUqlERUWF6DfQ14J5d4iG8e47eATrftqsM9yoqroap85E4JMvvkaxRgqXmdMnw70dDwM6y826nWNHh4mOKZVKhS3bduDDT1fg5OnwZnN1K5VKpKSm4fddf+E/730selDv5dkTs2c1fU2fMG4Ueve6kaPs0pVorFz1g07QJT+/AOs2bBY9YPbp06vJYMtt06eI8kqeOx+Fr79do3M9ra2rw4Woy/josxXIyrpxLI8cMRQBA/ya3Udt0RHXQJlMhhnTJguvr2dl4+Mvvsa5yItN5o+rqq5GeMR5fPn1aqGdbmpqKipXZ+iKbbGO5O3VU+hMUVhULFwTjDV3xvw5d4gC1efOR+GLr1YjKfmazvUnITEZ36xei30HDgvv9XBzxYRxN1cgZ2DAAFRVVeOLFat0ziG1Wo24+ER8vuJb0fU1JCiw2Z6EXWkf+vv1E6Xs2H/wCL5fuwEpqelNtlPz8gvw644/8NffNwLRAQP6Gz0lzjCN1AZXo+NEw6BHtNKL3cLCHJPG33gQF5+QhM/+9w0uX4lucgRgeXkFjh4/hVXf/yi8Z2tr02R935VNmTRONNI3JVW//JtjRo0QpTdITErGp19+g4TEZNExoFKpEBOXgE+/XIn4hBsjSvz698XokZ1br3cXI0cMRYD/jet/VnYOPvx0BSIvROkcqzm5efjl19/x/boNqGsIjJuammDenNs7vdyG6Oj7kq60D6Wqe0z0WKbDTJ4wFuFnI4WGdFZ2DvbsO4g79AyutSSwYVhZ4+yTMEJ+JkMdPynumWiMGcH9B/SHvZ2tcCMWHnEec2bfpldPJk0e7j1gaWkpNN4Tkq5h2pTml5dqWyaNH4OTp8JRXlE/XLOkpBRr1m0EAFhaWECpUop6wsycNhkD/f1EPfSae4Id4O8nulk7c/Yczpy9MWT3viWLRPmLZ82city8fJw7f1Eoy7qfNsPMzBROjo5QKBQoKSkVHXNomBX1sUcegK2tcYfUyOVyDPDrJ8zmq1QqhX3T6OsvPryxvQP6w9enj3Djq1QqsWPnbuzYuRtmZqZQyBWi/Ff9+/pixrRJOHLspPCkSK3q/OAsACycdydKSkqFCf7Kysvx85bt+OXXnXBydICpqSmKioubTGmxYO7sZnOa2thYY/HCeVi/cYtwwTl/8RLOX7wEZydHWFlZoay8XBRUuOO26aiorBSlfFCpVK0OOUrQ6HFrZdW18162RqlU4uPPv2ryM7VajaqqahQUFoomYGlkb2eLe+5eiECNobpt1dPDHQvmzcbWX38HGib7+e33P/Hb73/C3s4Wtra2UCjkzZZnVNgw0bBRoYz2dujp4S5cm8orKrDim++Fz50cHfHuv15vd/lbo113ufdwQ0hQIH759XfsO3gE+w8dhZOjQ5PHKRoeSN5/z13NjiZw7+GGYaEhiIi8MXnTvgOHse/AYZiYmMDMzBSVlVXCudHDzRWL5t+JL79erVHGpuuEXt6esLa2Es7J/PwCfPTZjWOmf19fvPDs4wbtDztbWzz+yIP4ZvVaoa46e+48zp47DwcHe9ja2KC6uhoFBYVCQ63RiGGhmKkRjOrKbtbtlMvlWHr/3bCyshRdW1PT0rFxc/2NpYWFORzs7WFmZgaVSoWqqioUFBY1eZ329+uHZQ/d12z7Ri6XY9lD9+F/X38n9CSNjo3Hex9+LuynispKnZ6VLs5OeGTpvU0Gt8zMTPH4sgex4pvvUVRULKwzOvZL2NrYwN7eDrW1tSgsKhK1PdDQplm0YE6b9l1rOuIaOHZ0GGLjEoSJefLzC/DD+p8hl8vh6OgAaytLqNX1Ix+0fyOZTIbFC+dqPajpHF2tLdbRRo4Yhm0NkwLdeM84owCtra3wxKMPYeWqH4T91RjMsbaygoODvXC8a18/7exs8fiyBw2+/+hsSpW4jhw5YijMzcxw/uIl0TlkZmaGoqJilJWLe2k5OthjkdZcKZq62j5cet/d+PiLr4XeZo1tWnNzMzg6OsDczAx1dUoUl5To9EhzdnLEPXcvMFpZGg0NDcaOXX9BrVaLJvmxMDdvMl2Etlm3TUNCUjKSkusn1knPyMQ3362DQqGAs5MjLCwsoFarUFZegSKNhxhoCNY8eO9iUcqam4FCocDdi+bh8xXfGpS/XSaT4ZGl9+Krb74X2rCpaen4fMW3wvGoVClRVFSsk+7N26snHnpgieQjJm9mS++/G9+sXotrDR1yCgoLsWbdRuH6JJfLm8w3LZfLsfS+JV3+3rCj70vQxfahFHWPpP2mFQoFFi+aK6oE9u0/3OJQE0PWPVyrZ2ZnpjQoLS1D1KUbifvt7e10cqa1RX2urBvBhPKKCpw3IGGy5noCB90IjsTGJYh6o2qSclvs7e3w2CMPNBlUr6yqEm6OZDIZZk6bjNm3z9AZ/qjUunltFDx4kM6kIi1RKBRYev/dmD1rhmh4YE1NLbKyc5CReV3nZsDfrx9efuFp+Pp0TGL1O26bpnejTi6X45Gl9+qktEDDNmhWcgMDBuCJR5dCoVDAVKMCbW5fdjQzM1M89sgDmDVzqmjf19XVISc3DxmZ13VuSh0dHfDEo0sxacKYJtZ4w4hhQ3DP4vk6aQ/yCwqRlp4hXFjkcjlunzkN06dOhLnWRaW1/VJVVY24+ETh9eBBATfdsBVNarUa11LSmvwvJTUd2Tm5OjchlhYWmDFtEt56fblRArONJo4bjYcfuAd2WsNUi0tKkZ6R2WR5LMzNMW/O7bj37oXNrnfO7Nskb6BqH1dmZmaYMG405s25HQqFAmq1Wuc4bWRjY41lD90H/wH90ZK775oH/ybq87q6OtFs9r17eeP5px+DhYW5qFHV3LFvYmKC2bNmGLS9+ujr2wcvPveETt1dVFSMtPQM5OTmiQKW1lZWmD/ndjx432K9crZ1FTfrdpqYmOCexQvwzBOP6ExOgoa6MCs7B6lp6UjPyERefoFOY7+Hmyseun8JnnlyGSwtW27UOjk64sXnnkRw0CDR+437STswGzgoAC+98BQcHZqfzNS9hxteev5JBA4KEL1fWlaG9IxMZOfkigKzZmammD51Ep567KF2jyhqTkdcA2UyGR5+8B7MnD4ZpqY3+mqoVCrk5xcgNS0DaekZOr+Ri7MTnnx0qejhdWfqim2xjjR8WAhMNM5pY8+d0cvbEy+/8JQotzEa2uQZmdeRk5uncz0PHOiP1156Dj3cXNHVKZXi+sXc3BwP3n+3MJKy8RxKz8jUCcx69vTAs08+CgcH+xb/Rlfahy4uznh1+bM6D2Sqq2uQlZWDlNR0ZGRe1wnMBgcNwkvPP6Uz6sUYHB0cmrzfCg4K1CsVnamJCZ59chnGjg4TtZ2VSiVycvOQmpaOtPRMFBYWiYIjXp498fzTj4t64t1M+vr20Um/ow97O1u88MzjCBseKmrHNh6PWVk5osCsXC7H2NFheOHZJ2Bne3OkfOmqbKyt8fzTj2HCuNGiervx+pR5PUsnqOju7oYXn32iXekiO0tn3Jd0pX0oRd0jac9ZNMx0GTZ8qDDktU6pxM+bf8Xy559sd/BiVNgwHDx8DDBSfiZDnDpzVnQAG2sIEhp6xhw4dEx4feLUGZ0UAfoICQoUJlOora1FdEy8zg0OusC29Ovrg3+9+QqOnTiNqzGxyM7ORWVVFSwtLeDoYF8/5G9kGHr0qG/gaAfZqpsJOsvlcjz9+MP4c88+XLh4GUXFxTAzNYWNjTU8PT3g2VO38SuXyzFz+mSMHjUc5yIv4mpMHLJzclFWVgalUgVrK0s4ODjAr58vggYP6vAbAW8vTyx/7gn8+dc+JCanoKamBpYWFnB0tBfleGrkYG+HV5c/g/CISFy4eBlpGZkoL6+AiYkCDvb28Pb2xOiw4aJGneb+bG5fdobG4Oj4saNwLvIiYmLjkZmVjfKyctTU1sLcrL5XgJdnTwQO8kdIUKDeQYrRI0cgwN8PJ0+fxZWrMSgoKEJlZSWsra1gb2+PgQF+CBs+VBjuam0tzo9VXV3TZE7ERleiY0QpW4KDuv4FuD1MTU1gYWEBF2cneHn2RMAAPwT4++mdD9pQQ0ODETR4IC5GXcHVmDhkXs9CQWEhqqtroFKpYG5uDjtbG3j29ID/gP4YEjy41SeZgQP98cwTj2DPvoNIS8tAXV1dfR5sZyejPJzSh3ZuI0vL+pnKp04aj5CgQJwJP4cr0TEoKqrvMVefp9sJQYGDMHrkcL0m9bC0sMAzTy7DxagrOBt5Himp6SgrK4dMJoNDQw/ikSOGYtBAf6HeN9cIQLVUJ4wbMxJWlpY4cPgYMq9nQa1Ww8rKEq4uLk1ea/Tl2dMDLz3/JGJi4xF1+SoSEpNRUlKKispKmJmZwcbaCp6eHvD3649hocGtBvi6qpt5OwP868/5nNw8XI2OxbWUNOTk5qKwqBjV1dWora2DiYkC5mbmsLaxgkePHvD09EDgwAB4e+k+QGyJvZ0tHnv4ASRfS8X5i1GIi09CUXExKioqYWpqAidHR/j69EHY8FC9r8lOjo548tGlSL6WggtRVxAXn4jihp6pJiYmsLaxhkcPNwzw64ehoSGdksO0I66Bcrkcs2fNwIRxoxF54RLi4xORlZ2D4uISVNfUQC6TwcLCAk5O9esNChyIgQEDJH/Q0dXaYh3JxtoagYEBwszVHdHRxMXFGU899hCupaThYtRlxCUkorDoRk9sS0sLuLm6wNenD4YOCWqyfdlVVWul6bCytICpiQkeemAJJo4fjdPhkUhKvobikhJUV9fAxsYaPT3cERoyGCOGhep9rHelfejs5Ijnn34MyddSEXXpCq6lpiEnNw8VFZWora2Fqalp/QguN1f49OmN0CFBHT5Z9rDQYJ05G4YP0z/VoJmZGZbcNR/TpkzE+YuXkJCYjOzsHJSWlqGmthYKhRwW5hZwcXGCt5cnQoIC0b+f703dEQIA5s6+DVGXruo8cGqNtbUVHrh3MaZOnoDIC1GIjUtEQWEhysvLIZPJYW1tBTdXF/j79UNoSJBojhdqHzMzM9y1YA6mTBqPyAtRiImNR05OHsrKy1FXVwcLC3M4Ozmil7cXggYPwkB/v5vmOO2M+xJ0sX3Y2XWPLDY2VppxyiS5uro6vP3OByhpSCsQONAfTz72kNTFIup2Vqz8DjENObbs7Wzx7r/ekPzmloiIiIiIiIikd3OE6alDmJiYYOzoMOH1lehYnYmQiKh9snNyEauR0mDc2FEMzBIRERERERERwOAsjR87SsiRplarse/gUamLRNSt7D94RMhDY2FhjnFjmp6hkoiIiIiIiIhuPQzO3uJsbWwwfeok4fXJ0+HIzsmVtExE3UVWVjZOh58TXs+YOhk21vrl2yEiIiIiIiKi7o/BWcLkiWPh7FQ/O6dKpcJvO3dLXSSibuG3nbuFWa6dnZ2anTWbiIiIiIiIiG5NDM4STE1Ncd+SRZDJZACAS5ev4lzkRamLRXRTC4+IxOWrMQAAmUyG+5csgqmpqdTFIiIiIiIiIqIuhMFZAgD49e+LSeNv9Or75dffUVJaKmmZiG5WxcUl2LZ9l/B60oSx6N/PV9IyEREREREREVHXw+AsCebeOQt9fX0AAGXl5VizbiOUSqXUxSK6qSiVSqz5cSPKKyoAAP36+mDu7NukLhYRERERERERdUGy2NhYtdSFIMP5+fl1yHqLiovxzPOvCJOCzb59Jp57+vEO+VuaVCoVsjNS0cOzF+RyPjOgm9f/vl6FXX/uAQD0cHPFV19+DAd7+04tA88nIuPguURkHDyXiIyH5xORcXTXcykuLk7qIlAbmEhdAOpaHOztsWHdaqmLQXTTeu7pxzvlgQYRERERERER3fy6z+MBIiIiIiIiIiIiopsIg7NEREREREREREREEmBwloiIiIiIiIiIiEgCDM4SERERERERERERSYDBWSIiIiIiIiIiIiIJMDhLREREREREREREJAETqQtAHauyshIFBQVQqVRSF6VFarUaNdU1SE9Ph0wmk7o4RDcNuVwOJycnWFpaSl0UIiIiIiIiIjIQg7PdmFKpRG5uLjw8PGBqaip1cVqkVqtRW1sDU1MzBmeJDFBbW4vr16/D09MTCoVC6uIQERERERERkQGY1qAby8/Ph4uLS5cPzBJR25mamsLFxQX5+flSF4WIiIiIiIiIDMTgbDdWXV3Noc5EtwBLS0tUV1dLXQwiIiIiIiIiMhCDs90cUwQQdX88z4mIiIiIiIhuTgzOEhEREREREREREUmAwVkiIiIiIiIiIiIiCZhIXQCSwKH3pS5BvUlvSF0CIiIiIiIiIiIiyTA4SzeVjz77H/btP9TiMkGDB+HTD//b5r/x976D+OTzFVizagV6eXt12HfaKis7B/c/9LjwWiaTwcrKEl6ePTFyxHDMmT0LtrY2HVoGfVRVVeOFl99AgL8fnn/mCazfsBk//bwFIUGB+PiDd5v8zrerf8CvO3Zh2tRJeHX5cx1expdeewsA2nW8tFVZWTmefv5lTJk0AQ/cd3en/30iIiIiIiIikh6Ds3RTefrxR7Bs6f3C6y+/+hbxCYn46ouPhfdMTNt3WE8cPwbDhw6Bvb1dh36nvZY99ACmTZkINdQoKyvHlavR2LJtB3bv2Yv33nkbPn16G7S+n7dsQ3JyCv7x+ktGKd+nX3wFuVyGpx5/RHjPwtwcUZevIic3D26uLqLlVSoVDh09DgsLC6P8/a7OxsYa//zHq3j2xdfQ19cHY0aHSV0kIiIiIiIiIupkzDlLNxVra2s4OTkK/5mZmUEul4ves7O11fleXV2d3n/D3NwcTk6OUCgUHfqd9rKysoSTkyOcnZzQu5c3Zs2cjpX/+wT29vb497sfoLa21qD1RUfHGq1s5y9E4fDR43h82UMwNTUV3re3t0OfPr1w8NARne9cuHgJ1dXV6Ovbx2jlkIq+x1tfXx9MnzoJK1etQY2BvxcRERERERER3fzYc5a6pcY0A//3zttYsXI1rKys8O1Xn0GpVGLDpl9w6PAx5Oblw9raCoEDA/DYsqVw7+Em+m5jioL3P/oc11JT8eyTj+Lrb9cgNS0dTo4OWLJ4AWbNnN7m7wDAiZNn8MOPG3D9ehY8PNzxyNL7sHf/IRQWFePLTw3PDWxtZYVnnnwUL7z8Bo4cO4GpkycCACIiL+DnTVuRnJKCujolvDx74p67F2LcmFEAgPuWPobsnFwAwOGjx/HJB+8iOCiw1e8156eft2BggD+CgwJ1PhszMgz7Dx7B3XctEL2//9ARhA0fhrz8fJ3v7Ni1Gzv/2I2s69mwtrHGmFFhePThB2BtbQ0AeP+jz5F5PQv3LVmEb79bi+ycXPTy9sLLLz6L8opyrPx2DTIyM9HL2xsvv/gMfH3EAeCDh47ix42bkZuTC3ePHnjs4QcxMmy48Hl8QiLWrNuAq9ExUKlU8OvfD48svR+DBvoDAC5GXcbLr7+Nf775KtZv3Iz8ggJs3/ITSkvLsHrNjzh7LhIlxSVwcLDHmNEjseyh+2Fubg4AuGfxQuzesw9/7z2A2bfP1POXJiIiIiIiIqLugD1nqVvbvHU7XnrhGfz33/+of/3Ldmza8iuW3n8P1q7+Cu/+601kZWfjnfc+bHYdChMFiotLsO6nTXj6iWX47psvEOA/AF9+tQrXr2e1+TspqWl49/2P4e7eA//7/CO89MLT2Lh5GxKTkmFi0vYeuIMG+sPOzhYXoy4DDTlq3/rXf+Hh4Y4vP/0Aq7/+HKFDgvHf9z9BQmIyAOCrLz+Gq4szRo8cgS0bfsDAgAF6fa8pxcUluHI1BqNGDm/y84kTxiIlNQ3xCYnCe9XV1Thx4jQmThirs/ymLb/im1VrMHXyRKxe+QVefvFZhEdE4t/vfiDa3/kFBdj55x689cbL+OSDd1BYVIQPP/kCGzdtxUsvPINPPngXhYVF+GbVGtH609Mzse/gYbz56otY8cVHcHN1xTv/9zFycvMAABmZ17H81begVCrx8fvvYsVnH8HJ0RGv/eNfSEvPEJf1l2146IF78M3/PgUAfP3t94iNi8c/33wF675fiReffxonT4Xj2+/WCt9xc3OFr08fnDwd3sovS0RERERERETdDYOz1K2NGzsawUGBcHFxBgDMmT0LP36/EhMnjIWbmysG+PXHbTOmIT4hCUXFxc2uJz+/AE8/sQyBgwLQ08MDC+fPgUqlQpxGgNHQ7xw4dARqtRovPf80+vX1wcAAf7z60nPIys5p93a7urggP78AAODs5Ih136/Ec08/hl7eXvDwcMcD9y6GSqVC5PkLAAAHe3vI5XKYmZnByckRpqamen2vKZeuXIVKpULQ4EFNft7L2wv9+/li34HDwnunz0TAxNQEw4cOES1bV1eHLdu2Y8K4Mbhn8UJ4eXkibPhQPPX4I7gQdRnRMXHCsrm5eXjuqcfQ17d+X44ZFYZrKalYev8S+PXvC/8Bfhg9agQSksSB5eKSEry6/DkM8OuPvr4+WP7cU6itrcWx4ycBAL/+thMA8Pabr2CAXz/4+PTGqy89BysrK2zfsUu0ruCgwRg9Kgw9GnphxyckYtBAfwwM8IebmyuGDx2Cjz/4DxbMu1Pre4GIunSlxd+UiIiIiIiIiLofpjWgbs2vf1+d97b9thNnIyJRVFwMpVIFpVIJACgpKYWDvX2T67G0tBBNsGVrawMAKC0ta/Zvt/adlJQ09HBzhZOTo7BM717e6NnTow1bKlZbWwszMzMAgKmpKaIuXcYfu/9GRuZ11NbWQq2uX66ktLTZdbT1ewUFhQAAFyenZpeZOnkiNv3yKx5fthQKhQIHDh/B+LGjYWIirpLS0jJQXl6BkGBxeoQhIUEAgCtXoxHg7wcAcLC3E4KiaMhvi4a8ro3s7GxRVlYuWlfPnu5wdHQQXru5ucLWxkboFRsTGwdfnz6iXMZmZmYYGDAAV67GiNalfbyNGR2GLVt/Q01NLUaNHI6QoMHo6aH7+zo5OqKmpgalpWXCcUJERERERERE3R+Ds9St2TTkJG306RdfITwiEsseuh8hQYNhZm6G4ydO4/u161tcj4WFhei1TCYDAKgbo5Vt+E5FZSVsbKx1vtdcgFhftbW1yMnNQ+iQYADAqTNn8fFnKzBu7Cg88ehDsLOzg0wmw9JlT7W4nrZ+r7SsPvhsY9N8kHHShHFY9f06REReQIC/HyIizuOj9/+js1x5RX0g9etv1+Cb1Wt1Pi8sLBL+3ZjDtZEMMp33G9/TZGujO4Gcubk5qqqq6stQXoGs7GuYPX+JaJna2lrYav1+2sfbww/eB28vL+zddwDvffApVCoVRo0cjmeeeFTozQ0Atrb13ysrY3CWiIiIiIiI6FbC4CzdMmpqa3HydDjuWjAX8+bcIbwv043XdQoLc3Mhr6mmkpISUW9aQ509dx5VVVUIGzEMAHDk6HE4Ozvh7TdeEQLELaVwaNTW79k2BGXLysthZWXZ5DKOjg4IHRJcPzFbbh6cnBwxaGCAznKNAd5lD92PEcOH6n5urRvcNlR5ebnOe2VlZbC0tBTKEOjijOXPP62znFzeemaYaVMmYtqUiaisrER4RCRWf/8jPvjkC3zywbvCMqWl5cLfIiIiIiIiIqJbB3PO0i2jqqoKKpVKGO4OAEqlEgcOHwVa6QXbETw9eyInJ1eUGiEtPQPpGZltXmdJSQlWf78OA/z6YUjwYABAeUUl7GxthQArAOzddxAAoL3JmvvAkO9pcm5IZ9CY87Y5UydPQMS58zh5OhyTJo4X/Z1G3l6esLGxRnZ2Djx7egj/ufdwQ11dnei3bKuMzOuioHNaegaqqqvRu3cvAECAvx/SMzLh6uoiKoNarYZzC0H0qqpqHDpyTEijYGlpiQnjxmDh/DsRHy/OVVxQWAgzMzP2miUiIiIiIiK6xTA4S7cMO1tbeHn2xN79h5CcnIK4+ET8570PEdjQY/PS5atN9qLsKBPHj0FdXR2+WPENkq+l4GpMLD75/Ct4uPfQ6/sVFZUoKChEQUEhUtPSsWfvfjzzwqtQqVR449XlUCgUQENwMSU1DYePHMf161nYuv13RMfEwc3VFQkJScjLywcaem0mJV9DfEIiCguL9P6etsGBAZDL5bh0ueUJrsaMGoma2hqci7yAKZPGN7mMQqHAXQvmYeefe7Bj55/IyLyOxKRkfPL5V3hu+evIL2g5ANwatVoNO1tbfPzZCsTFJyIxKRkrVq6GhYUFxo8ZBQCYN+cOVFRU4v0PP0NcfCKuZ2Vj9569ePKZ5fjr7/3NrtvERIHvfliPDz7+HDGxccjJzcOVqzHYd+CwzmRpF6MuY3DgwHZtCxERERERERHdfJjW4FY06Q2pSyCZ1195AV9+tQrPvPgqXFyccfei+Zg2ZSKSk1Pw3ZofoVAo9Bqqbgz+A/yw/PmnsXHTL3j6uZfRp08vPP7oQ1i3/mdhMq+WfL92vZAr19zcDO49emDypPGYP3e2aPKqeXPuQFpaBr78+lvIIMPIkcPxyvLnsGfvfqxd/zM+/nwFPnzv31i8aB5WfL0ar/3j33jx2af0/p42Ozs7BA4KwKkzZ3HXwnnNlt/CwhxjRo1EUvI19GnopdqUJYsXwNLKEjv/2I1V36+DpYUFBgYMwGcf/VfopdtWSqUS/fr5YuL4sXjvg0+Qk5sHL08P/OefbwipJTx7euDTD/+LH37cgJdffwsqlQqePXvisWVLMfv2mc2u28TEBB++92+sWfsT3v73eygvr4CDowNGDAvFw0vvE5bLyclFUvI1PPvUY+3aFiIiIiIiIiK6+chiY2M7dyx3JztzNhLrN25p9nMP9x546/XlwmulUolDR08g/GwkcnLzoFDI4eXZE1MmjUdQEz3bDFne0HW3xM/Pr9Vl0tLS4O3tbdB6paJWq1FbWwNTU7Mmh7d3V0XFxbCxtoaJSf1zEpVKhXsefBSjR47Ac08/LnXx2uxi1GW8/Prb+OTDdxE8OFDq4nRpX6z4BmcjzmPtd1/pFZRvjub5rlKpkJ2Rih6evTrtYQNRd8Rzicg4eC4RGQ/PJyLj6K7nUlxcnNRFoDbo9j1nKysrAQBTJo1Hn966gUpLSwvR6zU/bsTFqCsIHBSASRPGoq6uDidOncGq73/E3YvmYdyYkW1e3tB1U/eWlp6BZU88hymTJ2DxwnmQyWTYsfNPFBYW4bYZU6UuXrsEBwVi0oRxWPXdWnzx6QcwMzWVukhdUkJiMvbuO4g3X3+pXYFZIiIiIiIiIro5dfvgbEVFfXB2oL8f/Af0b3HZC1GXcTHqCoaFhuChB5YI74cND8X/ffQFtv/+J0KCAoVJewxZ3tB1U/fn7eWJ9955Gz9t3IJnX3wVCoUCvby98d47b6N/v75SF6/dlj//NF585U2s/PZ7vPDsk1IXp8spKyvHO+99iMV3zcfY0XwwQ0RERERERHQr6v7B2Yaes5aWlq0ueyb8HNDQy1aTmZkZxo4Ow287d+PchShMHDfa4OUNXTfdGoaFhmBYaIjUxegQFhbm+GbFp1IXo8uysbHG+h++lboYRERERERERCSh7h+cbeg5a2VVH5xVqVRQqVRCjk9NSddSYGpqCi9PD53PfH161y+TdE0IoBqyvKHrbo1KpWp1GbVaDbX65kspfDOWmUhqarVaqBe0/09EbcNzicg4eC4RGQ/PJyLj4LlEXUn3D8429Jw9deYszl+4hLz8AqhUKjg7O2H0yOGYNnkCFAoFqqqqUVZWDlcX5yaTQTs6OgAAcvPyAcCg5Q1dtz6yM1JbXaamuga1tTV6r7MruNnKS9RV1FRX6dQLudfTJSsPUXfCc4nIOHguERkPzyci4+C5RF1B9w/ONvScjTh3AWNHh8HDwx0lJaU4cuwkdv35N65dS8Xjyx5EVXU1AMDc3LzJ9Zib1b9fVVVV/38Dljd03fro4dmr1WXS09NhanrzTDJUW1tzU5WXqCsxM7dAD08voOHpb+71dLh6eHWrmUeJOhvPJSLj4LlEZDw8n4iMo7ueS6UJCVIXgdqg2wdn77x9BqqqqtC3rw8sLSyE90eOGIoPP12BS1eiEXX5Knr38m5lTfVD7WUymZ5/2ZDlDV039Ko8ZDKZQeuUkmYqg5ulzERdiUwm06kX5HJ5t2poEEmF5xKRcfBcIjIenk9ExsFzibqCbn8E9uvrg8BBAaLALAAoFApMHF+f3zU6Jk74vLGXq7bG9y0aljNkeUPXTURERERERERERN1ft+852xI7W1sAQGVVFczNzWBvZ4uiomKoVCqdJyf5+YUAADc3FwAwaHlD101ERERERERE1C0del/qEkCmVgP975G6GERAd+85W11dg8gLUbgQdbnJz7NycgEATo6OQEMv27q6OqSkpuksG5+QBADw6+crvGfI8oaum4iIiIiIiIiIiLq3bt1z1sREgV9+/R1VVdV489UX4OZ6o2dqRUUlDh85DplMhtCQwQCA0aPCcO58FPYfPIpHH75ftOzxU2dgbW2FIcGDhfcNWd7QdXekz/fFdcrfac2L0/wM/s5Hn/0P+/YfanGZoMGD8OmH/21HyfS3fsNm/PTzFuG1QqGAvZ0d/Pr3xfRpkzFuzKhOKUdrLl66jLf++V+8/99/InDQQNy39DFk5+Tiw/f+jdAhwTrL37f0MUyfOhkP3He3JOXtCl567S0A6LRjSVNZWTmefv5lTJk04Zb+DYiIiIiIiIi6u24dnFUoFFg0/06s+2kzPv1yJcaNHglXVxcUFhbh+MnTKCwqxqyZU+Ht5QkA8Pfrh1Fhw3DqTAS+Wb0WQ0KCUF1djSPHTqKkpBSPLL0XlpaWwvoNWd7QdVPTnn78ESxbeiO4/eVX3yI+IRFfffGx8J6JqXEO65+3bENycgr+8fpLrS67Ye0qmJqaok6pRG5uHk6cOo33P/och8KO4R+vvQSFQmHQ31667Ck8/cQyDB8W2o4tqJebl4d33/sYD9x3NwIHDRTel8vl+Orb77D66y9gYtKtq4Kbjo2NNf75j1fx7Iuvoa+vD8aMDpO6SERERERERETUAbp9RGbokGA4OtjjwOFjOH32HEpLSmFmbobe3l64e9E8BA4KEC1/z+IF8PLyxMlT4di8dTsUCgX69O6FJYvno39f3bQDhixv6LpJl7W1NaytrYXXZmZmkMvlcHJyNPrfio6O1XuSNkdHB5iZmQEA3FxdMGigP0YMG4rX3/oPftq4BUsf0D+XTUlJCTIyr7e53Np+WLcR1tZWmD93tuj96VMn4fCR4/j1t11YvGie0f4etayurk6vYHhfXx9MnzoJK1etwfDhoTAzNe2U8hERERERERFR5+n2wVkA8PXpA1+fPnotK5fLMXHcaEwcN9royxu6bmq7yPMX8eOGzUhMSoJCrkBg4EA89siD6N3LGwCgVqux6ZdfsXffQeTm5cPSwgKBgQF4/JGl8PBwF4b9A8Dho8fxyQfvIjgo0KAyhAQPxoxpk/Hrjl245+6FMDMzg1KpxIZNv+DQ4WPIzcuHtbUVAgcG4LFlS+Heww0Xoy7j5dffBgC8+c930cPNFRvWrW71e83JyLyOg4eP4vlnHtfpvevq4oJ77l6EjZt/weRJ4+Dq0vyEdLW1tVi/YTMOHTmG/IJC2NraYMSwUDz68IOwt7cDALz/0ee4lpqKZ598FF9/uwapaelwcnTAksULMGvm9Bb31fMvvQE3VxcMDQ3BTxu3oKi4GH79++KNV15ETFw81qzbgIL8AgwY0B+vLn8Obm6uwnd37NqNnX/sRtb1bFjbWGPMqDA8+vADQhD//Y8+R+b1LNy3ZBG+/W4tsnNy0cvbCy+/+CzKK8qx8ts1yMjMRC9vb7z84jM6dcXBQ0fx48bNyM3JhbtHDzz28IMYGTZc+Dw+IRFr1m3A1egYqFQq+PXvh0eW3o9BA/0BQPhN//nmq1i/cTPyCwqwfctPKC0tw+o1P+LsuUiUFJfAwcEeY0aPxLKH7oe5uTkA4J7FC7F7zz78vfcAZt8+s8V9SEREREREREQ3n249IRjdmi5fuYo33n4Hbm4uWPHZR/jo/f+guroaL7/2FoqLSwAAe/YewOZffsVjy5Zi7eqv8N47b6GiohL/+Hd9ftGvvvwYri7OGD1yBLZs+AEDAwa0qSwjw4ajqqoKsXEJAIDNv2zHpi2/Yun992Dt6q/w7r/eRFZ2Nt5570MAwMCAAfjnP14FALzx6nJ89eXHen2vOadOh0OtVmPkiOFNfr5w/p1wdnLCqu/Wtbiez/+3Ejv/+Av333s3vv/2f3jp+acReSEKb/3rv1Cr1QAAhYkCxcUlWPfTJjz9xDJ8980XCPAfgC+/WoXr17NaXL+JiQLxCYmIPH8R//fu2/jPP99AQkIS3n3/E+w7cBj/eft1/OdfbyI2LgHrN24Wvrdpy6/4ZtUaTJ08EatXfoGXX3wW4RGR+Pe7HwjLKEwUyC8owM4/9+CtN17GJx+8g8KiInz4yRfYuGkrXnrhGXzywbsoLCzCN6vWiMqVnp6JfQcP481XX8SKLz6Cm6sr3vm/j5GTmwc0BL+Xv/oWlEolPn7/Xaz47CM4OTritX/8C2npGaJ1bfplGx564B58879PAQBff/s9YuPi8c83X8G671fixeefxslT4fj2u7XCd9zcXOHr0wcnT4e3uP+IiIiIiIiI6ObE4Cx1O5t+2Q4XZ2e89tLz8PHpjQF+/fHmq8tRXl6Bv/buBxp6O7q6uGD0yBFwc3PFAL/+eOv1l/D6yy9CpVLBwd4ecrkcZmZmcHJyhGkbh5Q3TkKXn18AAJgzexZ+/H4lJk4YK/zd22ZMQ3xCEoqKi2Fqago7W1sAgK2NNRzs7fX6XnOiLl+Bl2fPZtM+mJqa4qknluHIsROIvBDV5DJ5+QU4cOgoFi6YgxnTJsOzpwfCRgzDsoceQExcPK5cjRaWzc8vwNNPLEPgoAD09PDAwvlzoFKpEJeQ2Oq+KiwswovPPYnevbwxLDQEQUGBiI2Lx/PPPIE+vXshNCQIwYMHISExGWhID7Bl23ZMGDcG9yxeCC8vT4QNH4qnHn8EF6IuIzrmxsR3ubl5eO6px9DX1wcDA/wxZlQYrqWkYun9S+DXvy/8B/hh9KgRSEhKFpWpuKQEry5/DgP8+qOvrw+WP/cUamtrcez4SQDAr7/tBAC8/eYrGODXDz4+vfHqS8/BysoK23fsEq0rOGgwRo8KQ4+Gns7xCYkYNNAfAwP84ebmiuFDh+DjD/6DBfPu1PpeIKIuXWl1/xERERERERHRzeeWSGtAt5bomFgMHxoqyuvp5OSI3r174crVGADAyBHDsHvPPrzyxj8xZdJ4hIYEw83NFXZ2dkYtS21tLQDAzOxGcHfbbztxNiISRcXFUCpVUCqVAICSklIhGNuUtnyvoKAQzq3k4x0+dAjGjA7D1998h1Vff66TDzUuPgEqlQpBgYNE7w9q6E2ckJgsTDRmaWkBnz69hWVsbW0AAKWlZS2WAQC8vT1Fk+LZ29nC3t4OLs5Ownt2drZISU0DAKSlZaC8vAIhweJ0E0NCggAAV65GI8DfDwDgYG8nBEUBCKkY+vr6iNZdVlYuWlfPnu5wdHQQXru5ucLWxkboFRsTGwdfnz5CQB0NeZAHBgwQjrVGfv37il6PGR2GLVt/Q01NLUaNHI6QoMHo6eGhs1+cHB1RU1OD0tIyYX8SERERERERUffA4Cx1O+XlFTh6/KTOUPCamhooFPWdxUcMH4pPPngXO3b+iW+/W4vy8gr079cXzzy5DAMD/I1WlszM+uH8rg09aD/94iuER0Ri2UP3IyRoMMzMzXD8xGl8v3Z9i+tp6/dKy8pbzCXb6MlHH8YjTzyL7Tt24a6F4snBKioqAI1AayObhtflDZ8D0JlATSaTAQ05fgHgzbffwaUrN3ravvDsE5gyaQIACHlWNb4M84ZJ1jTfa1xXeUV9IPXrb9fgm9Vroa2wsEj4t/a6ZZDpvN/4niZbG1ud98zNzVFVVVVfhvIKZGVfw+z5S0TL1NbWwtbGWvSejbX49cMP3gdvLy/s3XcA733wKVQqFUaNHI5nnngULi7ON8pgW/+9sjIGZ4mIiIiIiIi6GwZnqduxtbHB0NAQPHDf3TqfaaYnCBwUgMBBAVAqlbhyNQY/btiEN//5LjauWy1MJtVeR0+chIuzM/r19UVNbS1Ong7HXQvmYt6cO4RlZLoxQZG2fg8NqRHKystbXa5HDzcsuWshNmz6BZMnjhd91rgvtHu/lpSUij7Xx/Lnn0Z1TY3w2tHBocXlW2JjUx+oXPbQ/RgxfKju50b4Dcub2HdlZWVCD18bGxsEujhj+fNP6ywnl7eeNWbalImYNmUiKisrER4RidXf/4gPPvkCn3zwrrBMaWm58LeIiIiIiIiIqHthzlnqdgL8/ZCWng7Pnh6i/5RKpTDE/+y587iWkgoAUCgUCBo8CE8/sQzl5RXI1Ji8qrGXZlucPB2OEyfPYPGieZDJZKiqqoJKpRKG1AOAUqnEgcNHm/xbja8N/Z4mZycn5BcU6FXeRQvnwtHREavWiCcHG9C/H+RyOaIui/OeXmp47e/XT6/1A4CLi7PoN7GystTjW03z9vKEjY01srNzROt07+GGuro60f5qq4zM66KcvmnpGaiqrkbv3r2AhmMtPSMTrq4uojKo1eoW00lUVVXj0JFjQhoFS0tLTBg3Bgvn34n4eHF+3oLCQpiZmbHXLBEREREREVE3xOAsdTuLF81DYtI1/O/rVUhOTkFG5nVs2fobHn3yeUScOw8A2LvvIP797geIiLyAnJxcJF9Lwbbtv8PBwR69vL2Ahp6KScnXEJ+QKBoi35TCwiIUFBQiL78AsXHxWL1mHd557yNMmzIRc2bPAgDY2drCy7Mn9u4/hOTkFMTFJ+I/732IwIEBAIBLl6+ivLwcNg3D4SMvRCEuPhG2NjZ6fa8pQYMHIT09E4VFLZcfAMxMTfHU44/g8JHjyC8oFN53cnLE9KmTse3X37HvwGFkZF7HiZNnsGbtBoQEBcJ/gJ+ev4xxKRQK3LVgHnb+uQc7dv6JjMzrSExKxieff4Xnlr+ud1C6OWq1Gna2tvj4sxWIi09EYlIyVqxcDQsLC4wfMwoAMG/OHaioqMT7H36GuPhEXM/Kxu49e/HkM8vx19/7m123iYkC3/2wHh98/DliYuOQk5uHK1djsO/AYQQNFuf2vRh1GYMDB7ZrW4iIiIiIiIioa2Jag1vQi9OkCaZ1lsBBA/H+u//E+o2b8ezyVyGTydGnlzfeev1lhI0YBgBY/vxT+O6H9fj8y5UoLCyEtbUV/P0H4MP3/i3kIV28aB5WfL0ar/3j33jx2acwbuyoZv/mfQ89Lvzb3s4OPj698cYrL2LC+DGi5V5/5QV8+dUqPPPiq3Bxccbdi+Zj2pSJSE5OwXdrfoRCocDM6VMwfuxo/LF7D44dP4X1P3yj1/dumzFVp1yjR47A6jU/4vSZiCY/1xY2fChGjRyOU6fPit5//pnH4eBgj3Xrf0Z+QQHs7e0wZnQYHnnofj1+kY6zZPECWFpZYucfu7Hq+3WwtLDAwIAB+Oyj/8LZyUmPNTRPqVSiXz9fTBw/Fu998AlycvPg5emB//zzDTg19Ir17OmBTz/8L374cQNefv0tqFQqePbsiceWLcXs22c2u24TExN8+N6/sWbtT3j73++hvLwCDo4OGDEsFA8vvU9YLicnF0nJ1/DsU4+1a1uIiIiIiIiIqGuSxcbGtn3cNknGz6/1AGtaWhq8vb07pTztpVarUVtbA1NTM2ESKTKOjz79EleiY/HDqhVQKBRSF4cM8MWKb3A24jzWfvcVzLQnR9Oieb6rVCpkZ6Sih2cvvXLfElHTeC4RGQfPJSLj4flE3cKh96UuAdRqNbL639PtzqW4uDipi0Bt0H2OQCJq0sMP3oeK8nJs37FL6qKQARISk7F330E8+fjDrQZmiYiIiIiIiOjmxOAsUTfn4uKMt998Fes3bMblK1elLg7poaysHO+89yEW3zUfY0ePlLo4RERERERERNRBmHOW6BYQNHgQdv22WepikJ5sbKyx/odvpS4GEREREREREXUw9pwlIiIiIiIiIiIikgCDs0REREREREREREQSYHCWiIiIiIiIiIiISAIMznZzSqVS6iIQUQfjeU5ERERERER0c2JwthtzdXVFRkYGAzdE3ZhSqURGRgZcXV2lLgoRERERERERGchE6gJQx7GwsICbmxuuX78OtVotdXFapFarUVNdBTNzC8hkMqmLQ3RTcXNzg4WFhdTFICIiIiIiIiIDMTjbzVlYWMDLy0vqYrRKpVIhOyMVPTy9IJezQzcREREREREREXV/jIIRERERERERERERSYDBWSIiIiIiIiIiIiIJMDhLREREREREREREJAEGZ4mIiIiIiIiIiIgkwOAsERERERERERERkQQYnCUiIiIiIiIiIiKSAIOzRERERERERERERBJgcJaIiIiIiIiIiIhIAgzOEhEREREREREREUmAwVkiIiIiIiIiIiIiCTA4S0RERERERERERCQBBmeJiIiIiIiIiIiIJMDgLBEREREREREREZEEGJwlIiIiIiIiIiIikgCDs0REREREREREREQSYHCWiIiIiIiIiIiISAIMzhIRERERERERERFJgMFZIiIiIiIiIiIiIgkwOEtEREREREREREQkAQZniYiIiIiIiIiIiCTA4CwRERERERERERGRBBicJSIiIiIiIiIiIpIAg7NEREREREREREREEmBwloiIiIiIiIiIiEgCDM4SERERERERERERSYDBWSIiIiIiIiIiIiIJMDhLREREREREREREJAEGZ4mIiIiIiIiIiIgkwOAsERERERERERERkQQYnCUiIiIiIiIiIiKSAIOzRERERERERERERBJgcJaIiIiIiIiIiIhIAgzOEhEREREREREREUmAwVkiIiIiIiIiIiIiCTA4S0RERERERERERCQBBmeJiIiIiIiIiIiIJMDgLBEREREREREREZEEGJwlIiIiIiIiIiIikgCDs0REREREREREREQSYHCWiIiIiIiIiIiISAIMzhIRERERERERERFJgMFZIiIiIiIiIiIiIgkwOEtEREREREREREQkAQZniYiIiIiIiIiIiCTA4CwRERERERERERGRBBicJSIiIiIiIiIiIpIAg7NEREREREREREREEmBwloiIiIiIiIiIiEgCDM4SERERERERERERSYDBWSIiIiIiIiIiIiIJMDhLREREREREREREJAEGZ4mIiIiIiIiIiIgkwOAsERERERERERERkQQYnCUiIiIiIiIiIiKSAIOzRERERERERERERBJgcJaIiIiIiIiIiIhIAgzOEhEREREREREREUmAwVkiIiIiIiIiIiIiCTA4S0RERERERERERCQBBmeJiIiIiIiIiIiIJMDgLBEREREREREREZEEGJwlIiIiIiIiIiIikgCDs0REREREREREREQSYHCWiIiIiIiIiIiISAIMzhIRERERERERERFJgMFZIiIiIiIiIiIiIgkwOEtEREREREREREQkAQZniYiIiIiIiIiIiCTA4CwRERERERERERGRBBicJSIiIiIiIiIiIpIAg7NEREREREREREREEmBwloiIiIiIiIiIiEgCDM4SERERERERERERSYDBWSIiIiIiIiIiIiIJMDhLREREREREREREJAEGZ4mIiIiIiIiIiIgkwOAsERERERERERERkQQYnCUiIiIiIiIiIiKSAIOzRERERERERERERBJgcJaIiIiIiIiIiIhIAgzOEhEREREREREREUmAwVkiIiIiIiIiIiIiCTA4S0RERERERERERCQBBmeJiIiIiIiIiIiIJMDgLBEREREREREREZEEGJwlIiIiIiIiIiIikgCDs0REREREREREREQSYHCWiIiIiIiIiIiISAIMzhIRERERERERERFJgMFZIiIiIiIiIiIiIgkwOEtEREREREREREQkAQZniYiIiIiIiIiIiCTA4CwRERERERERERGRBBicJSIiIiIiIiIiIpIAg7NEREREREREREREEmBwloiIiIiIiIiIiEgCDM4SERERERERERERSYDBWSIiIiIiIiIiIiIJMDhLREREREREREREJAEGZ4mIiIiIiIiIiIgkwOAsERERERERERERkQQYnCUiIiIiIiIiIiKSAIOzRERERERERERERBJgcJaIiIiIiIiIiIhIAgzOEhEREREREREREUmAwVkiIiIiIiIiIiIiCTA4S0RERERERERERCQBBmeJiIiIiIiIiIiIJGAidQGIiIiIiIiIiKj7+3xfHHCth9TFANRq3N1f6kIQ1WPPWSIiIiIiIiIiIiIJMDhLREREREREREREJAEGZ4mIiIiIiIiIiIgkwOAsERERERERERERkQRuyQnBomPj8NU3awAAX3/xoegzpVKJQ0dPIPxsJHJy86BQyOHl2RNTJo1HUOBAnXUZsryh6yYiIiIiIiIiIqLu65brOVtVVY2Nm39t9vM1P27Eb7//CUdHByxeOBdzZ89CdXU1Vn3/I46dON2u5Q1dNxEREREREREREXVft1zP2d92/omysnL0cHNFdk6u6LMLUZdxMeoKhoWG4KEHlgjvhw0Pxf999AW2//4nQoICYWtrY/Dyhq6biIiIiIiIiIiIurdbqudsbFwCTpwKx6wZU2Fna6vz+ZnwcwCAKZPGi943MzPD2NFhqKmpwbkLUW1a3tB1ExERERERERERUfd2ywRnq6qrsXHzNnh7eWLq5PFNLpN0LQWmpqbw8vTQ+czXp3f9MknX2rS8oesmIiIiIiIiIiKi7u2WSWvw+66/UFxcgiceXQq5XDcmXVVVjbKycri6ODf5uaOjAwAgNy/f4OUNXbc+VCqV3sveDBq3p7ttF5EUeD4RGQfPJSLj4LlEZDw8n+hmp1arIVOrpS4GGkvAc4m6glsiOBsXn4hjJ07j9plT0dPDvcllqqqrAQDm5uZNfm5uVv9+VVWVwcsbum59ZGek6r3szST3errURSDqNng+ERkHzyUi4+C5RGQ8PJ/oZlVeWgyzmmqpiyHguURdQbcPztbU1GDj5m3o6eGO6VMntWNN9c9VZDJZByxv6LqBHp699F72ZqBSqZB7PR2uHl5N9i4mIv3xfCIyDp5LRMbBc4nIeHg+0c3O2rYasvymO651psaes93tXCpNSJC6CNQG3T44u2PXXygoLMKry5+BQqFodjlLCwtAo0estsb3LRqWM2R5Q9etj+5UeWiSy+XddtuIOhvPJyLj4LlEZBw8l4iMh+cT3axkMhlgQMe0DitHQ2oFnkvUFXTr4GxCUjKOHj+F8WNHwcbGGoVFRcJndXV1ACC85+jgAHs7WxQVFUOlUumcnPn5hQAANzcXAIC5uZneyxuyLBEREREREREREd0aunVwNjY2AWq1GkeOncSRYyebXOatf78PAPj6iw/Rr68Pzp2PQkpqGnz69BYtF5+QBADw6+crvGfI8oaum4iIiIiIiIiIiLq3bh2cHTY0BL16eTX52c4/9iDzehaeeHSp8N7oUWE4dz4K+w8exaMP3y+8X1FRieOnzsDa2gpDgge3aXlD101ERERERERERETdW7cOzvZwc0UPN9cmPztw8CgAYPCgAOE9f79+GBU2DKfOROCb1WsxJCQI1dXVOHLsJEpKSvHI0nthaWnZpuUNXTcRERERERERERF1b906ONsW9yxeAC8vT5w8FY7NW7dDoVCgT+9eWLJ4Pvr31U07YMjyhq6biIiIiIiIiIiIuq9bNjj7wrOPN/m+XC7HxHGjMXHcaL3WY8jyhq6biIiIiIiIiIiIui+51AUgIiIiIiIiIiIiuhUxOEtEREREREREREQkAQZniYiIiIiIiIiIiCTA4CwRERERERERERGRBBicJSIiIiIiIiIiIpIAg7NEREREREREREREEmBwloiIiIiIiIiIiEgCDM4SERERERERERERSYDBWSIiIiIiIiIiIiIJMDhLREREREREREREJAEGZ4mIiIiIiIiIiIgkwOAsERERERERERERkQQYnCUiIiIiIiIiIiKSAIOzRERERERERERERBJgcJaIiIiIiIiIiIhIAgzOEhEREREREREREUmAwVkiIiIiIiIiIiIiCTA4S0RERERERERERCQBBmeJiIiIiIiIiIiIJMDgLBEREREREREREZEEGJwlIiIiIiIiIiIikgCDs0REREREREREREQSYHCWiIiIiIiIiIiISAIMzhIRERERERERERFJgMFZIiIiIiIiIiIiIgkwOEtEREREREREREQkAQZniYiIiIiIiIiIiCTA4CwRERERERERERGRBBicJSIiIiIiIiIiIpIAg7NEREREREREREREEmBwloiIiIiIiIiIiEgCDM4SERERERERERERSYDBWSIiIiIiIiIiIiIJMDhLREREREREREREJAEGZ4mIiIiIiIiIiIgkwOAsERERERERERERkQQYnCUiIiIiIiIiIiKSAIOzRERERERERERERBJgcJaIiIiIiIiIiIhIAgzOEhEREREREREREUmAwVkiIiIiIiIiIiIiCTA4S0RERERERERERCQBBmeJiIiIiIiIiIiIJMDgLBEREREREREREZEEGJwlIiIiIiIiIiIikgCDs0REREREREREREQSYHCWiIiIiIiIiIiISAIMzhIRERERERERERFJgMFZIiIiIiIiIiIiIgkwOEtEREREREREREQkAQZniYiIiIiIiIiIiCTA4CwRERERERERERGRBBicJSIiIiIiIiIiIpIAg7NEREREREREREREEmBwloiIiIiIiIiIiEgCDM4SERERERERERERSYDBWSIiIiIiIiIiIiIJMDhLREREREREREREJAEGZ4mIiIiIiIiIiIgkwOAsERERERERERERkQQYnCUiIiIiIiIiIiKSAIOzRERERERERERERBJgcJaIiIiIiIiIiIhIAgzOEhEREREREREREUmAwVkiIiIiIiIiIiIiCTA4S0RERERERERERCQBBmeJiIiIiIiIiIiIJMDgLBEREREREREREZEEGJwlIiIiIiIiIiIikgCDs0REREREREREREQSYHCWiIiIiIiIiIiISAIMzv5/e3cepnV53g3/e8/AsKvIvgiyuSKiggjigprN7Fut5k1qmi7pXpumeZqnfZ42eRubvG20TZNUmzRNmzRJYzTRms0dRQTFBUVFEBFUZN9hBmHm/QOlEjAycs9cNzefz3FwhPn9zvua8yacDH7n4voBAAAAABQgnAUAAAAAKEA4CwAAAABQgHAWAAAAAKAA4SwAAAAAQAHCWQAAAACAAoSzAAAAAAAFCGcBAAAAAAoQzgIAAAAAFCCcBQAAAAAoQDgLAAAAAFCAcBYAAAAAoADhLAAAAABAAcJZAAAAAIAChLMAAAAAAAUIZwEAAAAAChDOAgAAAAAUIJwFAAAAAChAOAsAAAAAUIBwFgAAAACgAOEsAAAAAEABwlkAAAAAgAKEswAAAAAABQhnAQAAAAAKEM4CAAAAABQgnAUAAAAAKEA4CwAAAABQgHAWAAAAAKAA4SwAAAAAQAHCWQAAAACAAoSzAAAAAAAFCGcBAAAAAAoQzgIAAAAAFCCcBQAAAAAoQDgLAAAAAFCAcBYAAAAAoADhLAAAAABAAcJZAAAAAIAChLMAAAAAAAUIZwEAAAAAChDOAgAAAAAUIJwFAAAAAChAOAsAAAAAUIBwFgAAAACgAOEsAAAAAEABwlkAAAAAgAKEswAAAAAABQhnAQAAAAAKEM4CAAAAABQgnAUAAAAAKEA4CwAAAABQgHAWAAAAAKAA4SwAAAAAQAHCWQAAAACAAoSzAAAAAAAFCGcBAAAAAAoQzgIAAAAAFCCcBQAAAAAoQDgLAAAAAFCAcBYAAAAAoADhLAAAAABAAV1KN9AZ1q1fn1tvn5knFy7KuvUb0qNH9wzo3y/Tp03JpNMnpqHhfzLqXbt25Y6ZszL3/gezavWaNDY2ZPiwoblwxrmZMP6kfdZuT3171wYAAAAA6lfd75xdtvz5/M3nr8qcufMy/uQTc+mvvC9vuuC8bN22Ld/81vfyre98f6/6r3/z27nhRzenb9+jcskH3pP3vPPitLS05JqvfTN3z7pvn/XbU9/etQEAAACA+lX3O2ev/+F/p7m5JVf8wcczdsyoPdennjU5n73y7zPn/gfz1jdfmIED+ufh+Y/lkfkLMun0ifnoRy7dUztl8un53BeuzvU/ujkTJ4xPnz69k6Rd9e1dGwAAAACob3W/c3bixFPynnddvFcwmyQ9unfPqJEjkiTr129IksyZOy9JcuGMc/eqbWpqyvRpU7Jjx47Me3j+nuvtqW/v2gAAAABAfav7cPb8c6blTRect8/1Xbt25YUVL6axsTGDBw9KkixZ+my6du2a4cOG7FM/etTI3TVLlu651p769q4NAAAAANS3uj/W4NWam1vS0tKSVavX5Oe33Zk1a9flg+97V448ok+am1uyZcvWDOjfb68HhL2ib9+jkiSr16zds9aB1rd37QPR2trajnde+155P/X2vqAE8wTVYZagOswSVI954lDX1taWSltb6TbySgdmiVpwWIWzX/zHr+b5F1YkSYYNHZI//N3fzLixo5MkzS0tSZJu3brt97XdmnZfb25ubnd9e9c+ECufX3bAtYeS1SueK90C1A3zBNVhlqA6zBJUj3niULV188Y07Wgp3cYeZolacFiFsx/61Q9ky9atWbdufebMnZd//Mq/5K1vviBvf+ubDuDVu7+vUqlUDvCztae+vWsng4aNOODaQ0Fra2tWr3guA4YM3+/uYuDAmSeoDrME1WGWoHrME4e6Xn1aUlm7/41rnemVnbP1NkubFy8u3QJvwGEVzo4cMXzPz6dPm5J//to38+Of3ppjhg/L8ePGJq/aEfuLXrnevXv35OUHih1ofXtqD1Q9/eHxag0NDXX73qCzmSeoDrME1WGWoHrME4eqSqWStGNjWof18fLRCmaJWnDY/g6sVCo568wzkiSPLXgi3bo15cgj+mTDho37PXNk7dr1SZKBA/snSbvq27s2AAAAAFD/6jqcXbNmbf7ir6/M1V+6Zr/3X3rppeRVB0CPHTMqO3fuzLPLlu9Tu2jxkiTJcS+fUdve+vauDQAAAADUt7oOZ48+um8aGhqyeMkzWfz0M3vda2try31z5yVJxo7ZHYpOmzolSXLr7TP3qt22bXvumT0nvXr1zGmnnrLnenvq27s2AAAAAFDf6vrM2YaGhlx2yfvz1Wu/kS9f86+ZPvXMDBs2NNu3N2fegw/nmWeXZczoUZl8xsQkyQnHjc3UKZMye84D+eq138hpEyekpaUld919bzZt2pyPXf6h9OjRY8/67alv79oAAAAAQH2r63A2L4ei//tTV+TW2+/KgicW5u5756RSqWTQwP559zvelhnnT09jY+Oe+ssueX+GDx+We2fPzXe/f30aGxtz7MgRufSS92XcmH2PHWhPfXvXBgAAAADqV92Hs0kycED/XHbJ+w+otqGhIeefMy3nnzOt6vXtXRsAAAAAqF91feYsAAAAAECtEs4CAAAAABQgnAUAAAAAKKCmwtn/+9nP52e33JGNGzeVbgUAAAAAoEPV1APBNmzclJt+/LPc/NNbcvKJx+fsqWfm5JNOSKVSKd0aAAAAAEBV1VQ4+7ef/Ys89MijeeDBR/LY40/m0QVP5Mgjj8jUMydl6pRJ6dfv6NItAgAAAABURU2Fsz169Mi0s87MtLPOzKbNm/PAvIcz76FH8tNbbs/Pbr0jx48bk2lTz8ypp5ycxsbG0u0CAAAAALxhNRXOvtoRffrkgvPPyQXnn5M1a9Zm7gMP5f55D+Vfv/mf6d2rV6ZPm5LzzpmWPn16l24VAAAAAKDdauqBYK9l2/btaW5pzo4dO5IkzS3N+ektt+ev/uYLuXvWfaXbAwAAAABot5rdObt167bMfeDB3Hvf/Vnx4sokybChQ/LWN1+YyZNOy4oVK/ODH96U7133wzQ2NmTaWWeWbhkAAAAA4IDVXDj7xJNP5d777s+jCx7Pzp270rVrl0yZfEamT5uSUceO2FM36tgRueIPPp6r/+ma/PzWO4WzAAAAAMAhpabC2b/867/N+g0bkiSDBw3M9GlTMmXy6enRo8d+6xsbGzN1yuT81w9+2MmdAgAAAAAcnJoKZzdt3pxJp0/M9LOnZOzoUQf0msGDBubsqVM6vDcAAAAAgGqqqXD2b/760+ndq1daW1uza9euNDY27rm3bdv2dO/eLQ0Nez/DbPSokRk9amSBbgEAAAAA3riGA6jpNL179cqcufPy6f/zN3lx5aq97j08/9F86i8+kzn3P1isPwAAAACAaqmpcHbBEwvzH9/5flpbW1OpVPa6179fvzR17Zr/+M//yhMLnyrWIwAAAABANdRUOHvLbXdm2NAh+eu//LMMHTJ4r3vHjRuTv/z0n2b4sKH5+a13FusRAAAAAKAaaiqcfWHFi5k+bUp69Oix3/vdu3XL2VPPzPMvrOj03gAAAAAAqqmmwtmXXtr5ujVdunQ5oDoAAAAAgFpWU+Hs4EED88j8x17zfmtra+bcPy9DBg/s1L4AAAAAAKqtS+kGXm36tDPznf+6IV+59hs584zTMmBAv3Tt2jXNzS1Z8eKLmTX7/ixb/lwuu+R9pVsFAAAAADgoNRXOnj11Sla8uCp3zpyVx59YuN+aGeeenWlnndnpvQEAAAAAVFNNhbNJ8oH3vjNnnXlGHnr40ax4cWVaWlrSrVu3DBk8KKefNiHDhg4p3SIAAAAAwEGruXA2SYYPG5rhw4aWbgMAAAAAoMPU1APBAAAAAAAOFzW1c3bnzp25+Se35MGH52fDho3Z1dq637pKpZIvffHKTu8PAAAAAKBaaiqc/enPb88tt9+VJOndu1eaunYt3RIAAAAAQIeoqXD2gQcfzohjhue3fv3DOeqoI0u3AwAAAADQYWrqzNkNGzdm+rQpglkAAAAAoO7VVDh7xBFHpPU1zpkFAAAAAKgnNRXOnjnptDz48PzSbQAAAAAAdLiaCmcvfstF6Xd033z5n7+eBU8szKrVa7Ju/fr9/gAAAAAAOJTV1APB/vATn97z8ycWLnrNukqlki998cpO6goAAAAAoPpqKpw9dsQx6dKlS1Ip3QkAAAAAQMeqqXD2T6/4vdItAAAAAAB0ipo6cxYAAAAA4HBRUztnk2TXrl2ZNXtuHnv8yaxbtz4fvuyDGTnimCTJI/MX5OSTjt999AEAAAAAwCGsplLOHTt25Op/ujbLlj/3P9deeilJsm3b9nz9m9/OsKFDcsUf/HaampoKdgoAAAAAcHBq6liDn996Z5Y/93ze9uYL87/+9A/3ute9e7e8910X57nnX8gtt99VrEcAAAAAgGqoqXD24fmP5YzTT83b3/am9Du67173GhoaMuO86Tlz0ml56OFHi/UIAAAAAFANNRXOrlm7LseNHf1La8aNHZN169d3Wk8AAAAAAB2hpsLZhoZK2tp+ec1LL72USiqd1RIAAAAAQIeoqXB20MCBefzJha95f+fOnbn3vrkZPHhgp/YFAAAAAFBtNRXOTp0yKY/MX5Af3vSTvLhyVZKkeXtzXljxYmbNnpO//bt/zPLnXshZZ04q3SoAAAAAwEHpUrqBVzvn7LOyZOmzufX2u3Lr7XclSa75+r/vVTP5jNNyztlnFeoQAAAAAKA6aiqcrVQqufz/+dVMOn1iHpj3cFa8uDItLS3p3r17hg4ZnDNOPzUnn3h86TYBAAAAAA5aTYWzrxh/0gkZf9IJpdsAAAAAAOgwNXXmLAAAAADA4aKmds7+w5evfd2atra27NrVmk/80e90Sk8AAAAAAB2hpsLZRYuXlG4BAAAAAKBT1FQ4+9d/+an9Xt+1a1fWrl2XWffNTVtbWz764Us7vTcAAAAAgGqqqXC239F9X/PewAH9c+IJx+U//vO/cvNPb8273/HWTu0NAAAAAKCaDrkHgk0+47Q8MO/h0m0AAAAAAByUQy6cbWxszOYtm0u3AQAAAABwUA6pcHZ7c3Nuv/PuHHnkkaVbAQAAAAA4KDV15uz/+eznX/Perp07s2nzlrS1teVNF57fqX0BAAAAAFRbTYWz69atf817DQ0N6XvUkZl46vhc/NaLOrUvAAAAAIBqq6lw9p+u+tvSLQAAAAAAdIpD6sxZAAAAAIB6UVM7Zxc8sTBdu3RJpaHS7teOGzO6Q3oCAAAAAOgINRXOfvXab7zh1zoSAQAAAAA4lNRUOPvud74tTy5clMWLl+S4cWMyePCgdGtqyvbm5jz//Io8/czSnHjCcRk5YnjpVgEAAAAADkpNhbODBw3MLbfdmT//5B9l8OBB+9x/7vkX8pVr/jUXnDc9Jxw/rkiPAAAAAADVUFMPBPvZz2/PedOn7TeYTZLhw4bm3OnTcvNPb+n03gAAAAAAqqmmwtnnV7yYfv2O/qU1Rx99VJ5/YUWn9QQAAAAA0BFqKpxtbGjIs8uW/9Kapc8uTyWVTusJAAAAAKAj1NSZs2PGjMo9987JUUcemSmTT89RRx25597GTZvzwLyHcves+3LcuDFF+wQAAAAAOFg1Fc6+951vyzNLn81NP/5Zbvrxz9K1a5d07do1L720My+99FKSpFtTU979jreVbhUAAAAA4KDUVDg7ePCg/O9P/Uluv/PuLHxqUVavWZvt25vTtWuXDBk8KMeNG5MZ501P/9c5lxYAAAAAoNbVVDibJEce0SfvfdfFpdsAAAAAAOhQNfVAsFfbvGVLnl32XJqbW0q3AgAAAABQdTW3c3bJM8/muhtuyrLlzyVJ/uj3fyvjxoxOkvzLN/4jM86dnrFjRhXuEgAAAADg4NTUztnnX1iRf/zKv+T5F1Zk1LEj97q3ZcvWLFy4OF++5l/z3PMvFOsRAAAAAKAaaiqc/dktt6d7t2759Cf/OL/zm5fvda9371759Kf+OD26d8stt91VrEcAAAAAgGqoqXB28ZKlOefsszJo0IBUKvveP7pv35xz9llZvOSZEu0BAAAAAFRNTYWzW7dszYAB/X9pTf/+/bJly9ZO6wkAAAAAoCPUVDjbo0ePbNq06ZfWrFq1Jj16dO+0ngAAAAAAOkJNhbOjR4/MrNlzs2PHjv3eX/z0M7njrnsyZvSxnd4bAAAAAEA1dSndwKu95aIL8sV/+Eo+94Wrc9KJxydJ5j34SB597Ik8s/TZPLN0WRobG/OWiy4o3SoAAAAAwEGpqXB25Ijh+a2PfSTf/t4PMvOe2UmSe+6ds+f+kUcekcsueX9GHDOsYJcAAAAAAAevpsLZJDn5pBPy2f/zv7Lo6WeyYsWLaWnZke7du2XokMEZO2ZUGhpq6iQGAAAAAIA3pGbC2V27dmXBEwszbOiQ9Du6b044bmxOOG5s6bYAAAAAADpEzWxDrVQq+fq/fTtPL3mmdCsAAAAAAB2uZsLZhoaGDB82JE8vebZ0KwAAAAAAHa5mwtkkufzDl2blqlX5zn9dn6eXLM3GjZuyY8eO/f4AAAAAADiU1cyZs0nyd1d/OUny9JKlmTV77mvWVSqVfOmLV3ZiZwAAAAAA1VVT4WxT16ZUKkm3pqbSrQAAAAAAdKiaCmc/+3//V+kWAAAAAAA6RfEzZz/56b/Kowue2Of6jh07cu2//ntWvLiySF8AAAAAAB2peDi7fXtzdu3ctc/1Xbt2Zf6jj2fL1q1F+gIAAAAA6EjFw1kAAAAAgMORcBYAAAAAoADhLAAAAABAAcJZAAAAAIAChLMAAAAAAAXUfDhbSaV0CwAAAAAAVdeldANJctudM/PAQw/vda11V2uS5L9/8vP07t1rr3uVVPKxyz/UqT0CAAAAAFRTTYSzzyxd9pr3Fj/9TKf2AgAAAADQGYqHs3/0e79VugUAAAAAgE5XPJwdN3Z06RYAAAAAADpdzT8QDAAAAACgHglnAQAAAAAKEM4CAAAAABQgnAUAAAAAKEA4CwAAAABQgHAWAAAAAKAA4SwAAAAAQAHCWQAAAACAAoSzAAAAAAAFCGcBAAAAAAoQzgIAAAAAFCCcBQAAAAAoQDgLAAAAAFCAcBYAAAAAoADhLAAAAABAAcJZAAAAAIAChLMAAAAAAAUIZwEAAAAAChDOAgAAAAAUIJwFAAAAAChAOAsAAAAAUIBwFgAAAACgAOEsAAAAAEABwlkAAAAAgAKEswAAAAAABQhnAQAAAAAKEM4CAAAAABQgnAUAAAAAKEA4CwAAAABQgHAWAAAAAKAA4SwAAAAAQAHCWQAAAACAAoSzAAAAAAAFdCndQGfYtm17br/z7jzy6IKsWbsulUoyZPCgTDvrzEw7a3Iqlcqe2l27duWOmbMy9/4Hs2r1mjQ2NmT4sKG5cMa5mTD+pH3Wbk99e9cGAAAAAOpX3e+c3bBxUz73/12dn916R0aOGJ5fef+78463vTnbtzfnP7/3g9xw48171X/9m9/ODT+6OX37HpVLPvCevOedF6elpSXXfO2buXvWffus35769q4NAAAAANSvut85e+N//zTr12/IB9/3rpx/7tl7rp915qR85sq/y+133pOLLjgvR/Tpk4fnP5ZH5i/IpNMn5qMfuXRP7ZTJp+dzX7g61//o5kycMD59+vROknbVt3dtAAAAAKC+1f3O2b59j8zEU8dn2lmT97res2ePjBl1bNra2vLCipVJkjlz5yVJLpxx7l61TU1NmT5tSnbs2JF5D8/fc7099e1dGwAAAACob3W/c/adF7/lNe9t2749SdKzR48kyZKlz6Zr164ZPmzIPrWjR43cXbNkac4/Z1q769u79utpbW09oLpDxSvvp97eF5RgnqA6zBJUh1mC6jFPHOra2tpSaWsr3UZe6cAsUQvqPpx9Lc+/sCKLn34mAwf0zzHDh6a5uSVbtmzNgP790tCw74bivn2PSpKsXrM2SdpV3961D8TK55e1490eOlaveK50C1A3zBNUh1mC6jBLUD3miUPV1s0b07SjpXQbe5glasFhGc6uX78h137931OpVHLZJe9PpVJJc8vuPxy6deu239d0a9p9vbm5eff/tqO+vWsfiEHDRhxw7aGgtbU1q1c8lwFDhu83wAYOnHmC6jBLUB1mCarHPHGo69WnJZW1+89GOtMrO2frbZY2L15cugXegMMunH122XO55mv/lq1bt+XyD/9qxo0dfYCv3D26lUqlA+rbu3bq6g+PV2toaKjb9wadzTxBdZglqA6zBNVjnjhUVSqVpB3ZR4f18fLRCmaJWnBYhbMPzHs43/rudWlq6prf+/jHcty4MXvu9ejePXnVjthf9Mr17i/Xtae+vWsDAAAAAPXvsAlnb739rtxw448zdMjg/PZv/Fr69zt6r/vdujXlyCP6ZMOGjWltbd3nOydr165Pkgwc2L/d9e1dGwAAAACof4fF3u2Z98zODTf+OCcePy6f+KPf3SeYfcXYMaOyc+fOPLts+T73Fi1ekiQ57lXHILSnvr1rAwAAAAD1re7D2SXPLM33r78xY0Yfm9/+zcvTvftrHzw9beqUJMmtt8/c6/q2bdtzz+w56dWrZ0479ZQ3VN/etQEAAACA+lb3xxp8//qb0tramlNOPjGPPvb4fmuGDB6UIYMH5YTjxmbqlEmZPeeBfPXab+S0iRPS0tKSu+6+N5s2bc7HLv9QevTosed17alv79oAAAAAQH2r+3B22fLnkiQ/vOknr1lz8Vsuytvf9qYkyWWXvD/Dhw/LvbPn5rvfvz6NjY05duSIXHrJ+zJuzL7HDrSnvr1rAwAAAAD1q+7D2S9f/fl21Tc0NOT8c6bl/HOmVb2+vWsDAAAAAPWr7s+cBQAAAACoRcJZAAAAAIAChLMAAAAAAAUIZwEAAAAAChDOAgAAAAAUIJwFAAAAAChAOAsAAAAAUIBwFgAAAACgAOEsAAAAAEABwlkAAAAAgAKEswAAAAAABQhnAQAAAAAKEM4CAAAAABQgnAUAAAAAKEA4CwAAAABQgHAWAAAAAKAA4SwAAAAAQAHCWQAAAACAAoSzAAAAAAAFCGcBAAAAAAoQzgIAAAAAFCCcBQAAAAAoQDgLAAAAAFCAcBYAAAAAoADhLAAAAABAAcJZAAAAAIAChLMAAAAAAAUIZwEAAAAAChDOAgAAAAAUIJwFAAAAAChAOAsAAAAAUIBwFgAAAACgAOEsAAAAAEABwlkAAAAAgAKEswAAAAAABXQp3QAAAAAAHeyOK0t3kCwdVLoDqDl2zgIAAAAAFCCcBQAAAAAoQDgLAAAAAFCAcBYAAAAAoADhLAAAAABAAcJZAAAAAIAChLMAAAAAAAUIZwEAAAAAChDOAgAAAAAUIJwFAAAAAChAOAsAAAAAUIBwFgAAAACgAOEsAAAAAEABwlkAAAAAgAKEswAAAAAABQhnAQAAAAAKEM4CAAAAABQgnAUAAAAAKEA4CwAAAABQgHAWAAAAAKCALqUbAAAAAKhbd1xZugOghtk5CwAAAABQgJ2zAAAAAHXqqsWDSrcA/BJ2zgIAAAAAFCCcBQAAAAAoQDgLAAAAAFCAcBYAAAAAoADhLAAAAABAAcJZAAAAAIAChLMAAAAAAAUIZwEAAAAAChDOAgAAAAAU0KV0AwAAAAD14qpbntr7wtJBpVoBDgF2zgIAAAAAFCCcBQAAAAAoQDgLAAAAAFCAcBYAAAAAoADhLAAAAABAAcJZAAAAAIAChLMAAAAAAAUIZwEAAAAAChDOAgAAAAAUIJwFAAAAAChAOAsAAAAAUIBwFgAAAACgAOEsAAAAAEABwlkAAAAAgAKEswAAAAAABQhnAQAAAAAK6FK6AQAAAICDcdUtT5VuAeANsXMWAAAAAKAA4SwAAAAAQAGONQAAAADqz9J7SncA8LrsnAUAAAAAKEA4CwAAAABQgHAWAAAAAKAA4SwAAAAAQAHCWQAAAACAAoSzAAAAAAAFCGcBAAAAAAoQzgIAAAAAFCCcBQAAAAAoQDgLAAAAAFCAcBYAAAAAoADhLAAAAABAAcJZAAAAAIAChLMAAAAAAAUIZwEAAAAAChDOAgAAAAAUIJwFAAAAAChAOAsAAAAAUIBwFgAAAACggC6lGwAAAAAOPVfd8lTpFgAOeXbOAgAAAAAUIJwFAAAAAChAOAsAAAAAUIBwFgAAAACgAOEsAAAAAEABwlkAAAAAgAKEswAAAAAABQhnAQAAAAAKEM4CAAAAABQgnAUAAAAAKEA4CwAAAABQgHAWAAAAAKAA4SwAAAAAQAHCWQAAAACAAoSzAAAAAAAFCGcBAAAAAAoQzgIAAAAAFNCldAMAAABAHVl6T+kOAA4Zds4CAAAAABQgnAUAAAAAKEA4CwAAAABQgHAWAAAAAKAA4SwAAAAAQAFdSjfQmWbPeSDX3XBjmptb8pm//FT69Tt6n5pdu3bljpmzMvf+B7Nq9Zo0NjZk+LChuXDGuZkw/qSDqm/v2gAAAABA/Tosds5u3rIl13793/Pt716X1ta2X1r79W9+Ozf86Ob07XtULvnAe/Ked16clpaWXPO1b+buWfcdVH171wYAAAAA6tdhsXP283//pezatSu/+1sfzc9vvTOLnl6y37qH5z+WR+YvyKTTJ+ajH7l0z/Upk0/P575wda7/0c2ZOGF8+vTp3e769q4NAAAAANS3w2Ln7OhjR+TTf/bHOenE439p3Zy585IkF844d6/rTU1NmT5tSnbs2JF5D89/Q/XtXRsAAAAAqG+HRTj767/2ofTp/fo7UpcsfTZdu3bN8GFD9rk3etTI3TVLlr6h+vauDQAAAADUt8PiWIMD0dzcki1btmZA/35paNg3s+7b96gkyeo1a9td3961D0Rra2s73l3te+X91Nv7ghLME1SHWYLqMEtQPbU2T21t+3+mS+U1rkOteOV3aK3MEoc34ezLmltakiTdunXb7/1uTbuvNzc3t7u+vWsfiJXPLzvg2kPJ6hXPlW4B6oZ5guowS1AdZgmqp1bmaevmjfu93rSjpdN7gTeiVmaJw5tw9oDt/r5KpVLpgPr2rp0MGjbigGsPBa2trVm94rkMGDJ8v7uLgQNnnqA6zBJUh1mC6qm1eerVZ/8hbGXt/jcmQa14ZedsrcxStWxevLh0C7wBwtmX9ejePXnVjthf9Mr17i/Xtae+vWsfiHr6w+PVGhoa6va9QWczT1AdZgmqwyxB9dTKPL3mBqN2bDyCEl45eqNWZonDm9+BL+vWrSlHHtEnGzZs3O+ZI2vXrk+SDBzYv9317V0bAAAAAKh/ds6+ytgxozLvofl5dtnyjDp25F73Fi1ekiQ5buzoN1Tf3rUBAADggN1xZed/zqWDOv9zAtQZO2dfZdrUKUmSW2+fudf1bdu2557Zc9KrV8+cduopb6i+vWsDAAAAAPWt7nfOrl23Ps8uW77n481btyRJFjyxML1790qS9Dv66IwcMTwnHDc2U6dMyuw5D+Sr134jp02ckJaWltx1973ZtGlzPnb5h9KjR489a7Wnvr1rAwAAAAD1re7D2acWPZ1vfef7+1z/3nU/3PPzKZPPyEc+9CtJkssueX+GDx+We2fPzXe/f30aGxtz7MgRufSS92XcmH2PHWhPfXvXBgAAAADqV92Hs1OnTMrUKZMOuL6hoSHnnzMt558zrer17V0bAAAAAKhfzpwFAAAAAChAOAsAAAAAUIBwFgAAAACgAOEsAAAAAEABwlkAAAAAgAKEswAAAAAABXQp3QAAAABwYL42d1V69WlJpVLZ+8bSQaVaAuAgCGcBgI5zx5WlO9htxp+X7gAAAGAfjjUAAAAAACjAzlkAqEe1smMVAACA1yScBQDq3xsIqyttbem9eWMqi45MfvFcvzfK8QoAAMCrONYAAAAAAKAA4SwAAAAAQAGONQCAanLWKwAAAAfIzlkAAAAAgAKEswAAAAAABTjWAAAAAN6oTjzSqNLWlqYVR6WytltSqXTa5wWg4whnAQAA4DVcdctTv7xg6aDOaiVpa0vS0nmfD4AOJ5wFAADg0NNZO1Y7M3wF4LDjzFkAAAAAgALsnAWoQ1+buyq9+rSkUgNnkV3xpuNKtwAAAAA1STgLQH3oxIdxAAAAQDU41gAAAAAAoAA7Z4F2ed2n1XYi/1weAAAAOJQJZwHoUJ0W6B/Ak5SvGLuyU1oBAACAAyGcBQ5ZdvFCfbpq8esH7Z2irS2/MWhj6S4AAIA6JpwFgAJqJoC0o/jwVCsP0Jvx56U7AACAojwQDAAAAACgADtnAeAwV0u7eDnM1MoO3tjFCwBAGcJZgCqopfNv29raSrcAAAAAHADhLACHDTtEAQAAqCXCWTgE1NKuTAAAAACqQzgLAABATTmgzQlL/YsYAA59DaUbAAAAAAA4HAlnAQAAAAAKEM4CAAAAABTgzFkAgM5yx5WlOwAAAGqInbMAAAAAAAUIZwEAAAAAChDOAgAAAAAU4MxZAAColfOAZ/x56Q4AAOhEds4CAAAAABQgnAUAAAAAKEA4CwAAAABQgDNnATg4S+8p3QEAAAAckuycBQAAAAAoQDgLAAAAAFCAcBYAAAAAoADhLAAAAABAAcJZAAAAAIACupRuAACgVn1l+cg0NXVLKpXSreSKsStLtwAAAFSZcBYA4BBw1eJBpVvYQ1AMAADV4VgDAAAAAIAChLMAAAAAAAU41gAAgHappSMW4pgFAAAOYXbOAgAAAAAUYOcsAADUijuuLN3BbjP+vHQHAACHBTtnAQAAAAAKEM4CAAAAABQgnAUAAAAAKMCZswCHqqX37Pdypa0tTTtaUlnbLalUOr0tgM521eJBpVvY44qxK0u3AADAIcTOWQAAAACAAoSzAAAAAAAFONYA9uOqW54q3QIAAAAAdc7OWQAAAACAAuycBQAA9nbHlVVbqtLWlt6bN6ay6Mj2P6hyxp9XrQ8AgFpk5ywAAAAAQAF2zgIAALWpijt4D4odvHvrjP9flg7q+M8BADXAzlkAAAAAgALsnAUAgCq5anHt7Pa7YuzK0i0AAPA67JwFAAAAAChAOAsAAAAAUIBwFgAAAACgAOEsAAAAAEABwlkAAAAAgAKEswAAAAAABQhnAQAAAAAK6FK6AQAAoPquWjyodAu7tbXlNwZtLN0FAEBNEs5SM742d1V69WlJpVIp3QoAAAAAdDjHGgAAAAAAFCCcBQAAAAAoQDgLAAAAAFCAM2cBAADIVbc8dWCFS2vkYXMAUAeEswAAQIf6yvKRaWrqltTIg1+vGLuydAsAAIljDQAAAAAAyhDOAgAAAAAUIJwFAAAAAChAOAsAAAAAUIAHggG0x9J7SncAAAAA1Ak7ZwEAAAAAChDOAgAAAAAUIJwFAAAAACjAmbMAAAC/zB1Xlu5gtxl/XroDAKDK7JwFAAAAAChAOAsAAAAAUIBjDQAAgMPKVYsHlW5hjyvGrizdAgBQkJ2zAAAAAAAF2DkLAABwKOjoB5MtrZ0dxQBwuLBzFgAAAACgAOEsAAAAAEABwlkAAAAAgAKcOQscGpbeU7oDAAAAgKqycxYAAAAAoADhLAAAAABAAcJZAAAAAIAChLMAAAAAAAUIZwEAAAAACuhSugEAAIDD1VWLB5VuAQAoyM5ZAAAAAIAChLMAAAAAAAUIZwEAAAAAChDOAgAAAAAUIJwFAAAAAChAOAsAAAAAUIBwFgAAAACgAOEsAAAAAEABwlkAAAAAgAKEswAAAAAABQhnAQAAAAAKEM4CAAAAABTQpXQDQI1bek/pDgAAAADqkp2zAAAAAAAFCGcBAAAAAAoQzgIAAAAAFCCcBQAAAAAoQDgLAAAAAFCAcBYAAAAAoADhLAAAAABAAcJZAAAAAIAChLMAAAAAAAUIZwEAAAAACuhSugHgNSy9p3QHAAAAAHQgO2cBAAAAAAqwc7aT7dq1K3fMnJW59z+YVavXpLGxIcOHDc2FM87NhPEnlW4PAAAAAOgkds52sq9/89u54Uc3p2/fo3LJB96T97zz4rS0tOSar30zd8+6r3R7AAAAAEAnsXO2Ez08/7E8Mn9BJp0+MR/9yKV7rk+ZfHo+94Wrc/2Pbs7ECePTp0/von0e9pz1CgAAAEAnsHO2E82ZOy9JcuGMc/e63tTUlOnTpmTHjh2Z9/D8Qt0BAAAAAJ3JztlOtGTps+natWuGDxuyz73Ro0burlmyNOefM+1112ptbe2QHkt55f20tbWVbiWVGugBDkbbq/7X72d448wSVIdZguoxT1Adr0xPvWUrHJqEs52kubklW7ZszYD+/dLQsO+G5b59j0qSrF6z9oDWW7x4cdV7LO2SU44q3cJux55TugMAAACgA23eviOblywp3QY41qCzNLe0JEm6deu23/vdmnZfb25u7tS+AAAAAIAyhLM1Y/em+kqlUroRAAAAAKATCGc7SY/u3ZNX7aD9Ra9c7/5yHQAAAABQ34SznaRbt6YceUSfbNiwcb8HTq9duz5JMnBg/wLdAQAAAACdTTjbicaOGZWdO3fm2WXL97m3aPHuQ6iPGzu6QGcAAAAAQGcTznaiaVOnJEluvX3mXte3bduee2bPSa9ePXPaqacU6g4AAAAA6ExdSjdwODnhuLGZOmVSZs95IF+99hs5beKEtLS05K67782mTZvzscs/lB49epRuEwAAAADoBJWFCxe2lW7icNLa2pqZs+7LvbPnZtXq1WlsbMyxI0fkrW++IOPGONIAAAAAAA4XwlkAAAAAgAIca0CH2bVrV+6YOStz738wq1avSWNjQ4YPG5oLZ5ybCeNP6rQ1oB5UYxa2bdue2++8O488uiBr1q5LpZIMGTwo0846M9POmpxKpdLh7wNK64ivK08sfCr/9NWvJ0m+fPXnq9wx1KZqzdKSZ5bmJz+7LUuXLc/OnbsyoH+/TJ0yOeefO83XJQ4b1ZinDRs35Zbb7syTCxdl3fr16d69ewYNGJBzz5ma0049xTxxWJk954Fcd8ONaW5uyWf+8lPp1+/oA36tDIIS7Jylw1z7r/+eR+YvyPiTT8zECeOzc+fOzJo9J8ufeyG/+sH35pyzz+qUNaAeHOwsbNi4KX939ZezceOmTJl8esaMHpXt27fnnnvnZOWq1blwxjl537vf0WnvB0qp9teV5uaW/L+f/2LWr9+QCGc5jFRjlh6e/1i+9o1vZeiQwZk+bUq6dOmSuQ88mEWLl+T8c8/OB9/3rk55L1Dawc7TylWr8/dXfyU7Xnop06dNyTHDhmZ7S0vm3v9gnl22PNOnnZVLf+W9nfZ+oJTNW7bkO9+7PvMfezxdu3bNjh072h3OyiAowc5ZOsTD8x/LI/MXZNLpE/PRj1y65/qUyafnc1+4Otf/6OZMnDA+ffr07tA1oB5UYxZu/O+fZv36Dfng+96V8889e8/1s86clM9c+Xe5/c57ctEF5+WIPn06/P1AKR3xdeWGG2/Oli1bM2jggKxctbqDOofaUo1Z2rZtW7793esybOiQ/Okf/266du26Z42//4evZskzS9Pc3JLu3bt1ynuCUqoxTz+75fZs3bZtn+Do7LMm57NX/n3uufe+vOmCc9O/f78Ofz9Q0uf//kvZtWtXfve3Ppqf33pnFj29pF2vl0FQSkPpBqhPc+bOS5JcOOPcva43NTVl+rQp2bFjR+Y9PL/D14B6UI1Z6Nv3yEw8dXymnTV5r+s9e/bImFHHpq2tLS+sWNkB3UPtqPbXlYVPLc6s2XNz8Vsu8o0NDitV+Xve/Q9m27btecfb3rQnmE2SxsbG/Nmf/H4+9Yk/FMxyWKjGPK1Zuy5JMmb0sXtd79q1a0YcM3x3zbr1Ve4cas/oY0fk03/2xznpxOPf0OtlEJQinKVDLFn6bLp27Zrhw4bsc2/0qJG7a5Ys7fA1oB5UYxbeefFb8psf/XCampr2ubdt+/YkSc8eParWM9Sian5daW5pybe/e12OGT4sF11w7gG8AupHNWbp8ScWpqGhISccPy5J0tbWlh07XuqgjqF2VWOehgwenCRZtWrNPvfWrluXhoaGDB44oGo9Q6369V/7UPr0fuO7WmUQlOJYA6quubklW7ZszYD+/dLQsG/+37fvUUmS1WvWdugaUA86ehaef2FFFj/9TAYO6J9jhg896H6hVlV7ln5000+yceOmfPw3L9/velCvqjVLK15cmb5HHZl16zfkhh/dnCcWLsrOnTvTp3fvnDn5tLzjbW9JU1PXX7oGHOqqNU9vvuj8PPrYglx3w42pVCoZOfKYtDS3ZNZ9c7Js+fN504Xn56ijjuyw9wH1QAZBScJZqq65pSVJ0q3b/v8pWrem3debm5s7dA2oBx05C+vXb8i1X//3VCqVXHbJ+z3Fl7pWzVl6atHTuXvWfXn7Wy/K0CGDq9wp1LZqzdKWrdvSs2eP/OOXr83EU0/Jr//aZWlubsms2XNy2x135/nnV+T3f+c3fG2irlVrnvod3Tef/JPfz7/9x3dz7b/++57rXbt2yQfe+87MOG96VfuGeiSDoCThLAW0JclB/mW7GmtAPXhjs/Dssudyzdf+LVu3bsvlH/7VjBs7uoP6g0PFgc3Sjh078u3vXpehQwbnzRfN6KTe4FByYLO0a9eubNy4Ke9/zztywfnn7Lk++YyJ+cIXv5Qnn1qcBY8/mfEnn9jhHUPtOrB5WrNmbb76L/+WzZu35J0XvyXDhw1Jc3NLHnl0Qa674aasWbsuH3zfuzqpZ6hXMgg6jnCWquvRvXvyqu88/aJXrnd/ua6j1oB60BGz8MC8h/Ot716Xpqau+b2PfyzHjRtTpW6hdlVrln5400+ybv2G/Nmf/H4aGxs7oFOobdWapW5NTdne3JzJk07b63pDQ0POOnNSlj93Y55a/LRwlrpWrXn61neuy8pVq/PJK34vI0ccs+f6pDMmpuk/m3LnzFkZN2Z0Jp46vqr9Qz2RQVCSQ9Koum7dmnLkEX2yYcPGtLa27nN/7drdTwodOLB/h64B9aDas3Dr7XflG//xnQzo3y9/9id/IJjlsFGNWVq85JnMvGd2zjn7rPTu3SvrN2zY82Pnzp1JsudjqFfV+rrUr9/RSZLG/Zzrd8QRfZKXz/+DelaNeWpuacniJc+k39F99wpmXzHhlJOSJE8sfKqqvUO9kUFQknCWDjF2zKjs3Lkzzy5bvs+9RYuXJEmOe51/Rl2NNaAeVGsWZt4zOzfc+OOcePy4fOKPfjf9X/4PYzhcHOwsLVy4OG1tbbnr7nvzF3915V4/nnl2WZLs+RjqWTW+Lo0ZfWySZNlzL+xzb+263f8BfNSRHmBE/TvYeXrppZfS1ta255uE+9zf8VKSvOZ94H/IIChFOEuHmDZ1SpLk1ttn7nV927btuWf2nPTq1TOnnXpK8vKZYy+uXLXnL+JvZA2oZ9WYpyXPLM33r78xY0Yfm9/+zcvTvfv+D7qHenawszTpjIn5+G9evt8frzwY7JWPoZ5V5e95Z01OpVLJT3526147lHbseCmz7p2TJDllvCMNqH8HO099evfOwAH9s2Hjpjy16Ol91n/w4fnJq74hAsggqD3OnKVDnHDc2EydMimz5zyQr177jZw2cUJaWlpy1933ZtOmzfnY5R9Kjx49kiQbNmzMZ6/8+4w4Zlg+9Yk/fENrQD2rxjx9//qb0tramlNOPjGPPvb4fj/PkMGDMmTwoE57X9DZDnaWBg0ckEEDB+x37dte/kv8Kc7H5DBQja9Lw4cNzVvfdEF+8vPb8o9f/pdMOfP0bN/enNlzHsjqNWtz3jnTcszwYQXfJXSOaszTB973rlzztW/mq//yb5k+bUqGDR2SlpaWzH/s8Ty5cFFGjxqZKZPPKPguoeOtXbd+rx2vm7duSZIseGJhevfulSTpd/TRGTliuAyCmiOcpcNcdsn7M3z4sNw7e26++/3r09jYmGNHjsill7wv48Yc2D8FqMYaUA8OdhaWLX8ueflhRq/l4rdclLe/7U1V7Rtqja8rUB3VmKV3XPzmDBw4IHfdPSv/9YMb09bWliGDB+WyS96fs6ee2eHvAWrFwc7TyScen09e8Xu59faZefChR3LnzFnp0qVLBg7on3e/462Zcd50D7Gk7j216Ol86zvf3+f696774Z6fT5l8Rj7yoV/5pev4uyIlVBYuXNhWugkAAAAAgMONM2cBAAAAAAoQzgIAAAAAFCCcBQAAAAAoQDgLAAAAAFCAcBYAAAAAoADhLAAAAABAAcJZAAAAAIAChLMAAAAAAAUIZwEAAAAAChDOAgAAAAAU0KV0AwAA1LfHHn8ys++7P88ufy6bN29Ja2trevXsmSFDBuX0iRMyfdqUVCqV0m0CAECnqyxcuLCtdBMAANSn6390c267Y2b69zs6k884Lf369U1DpSFr1q7L/fMeyuo1a3P6xAn52OUfKt0qAAB0OjtnAQDoEC++uDK33TEzgwYOyJ994g/SvVu3ve5fdMF5ufqf/jkPPjw/Zy44PaecfGKn9dbW1padO3ema9eunfY5AQDgFwlnAQDoEM89vyJJMm7s6H2C2STp1q0pH77sV7J+w8aMOGbYnutr1q7Lj396S55cuChbt21P36OOzIRTTsqbL5qR3r167anbtWtX7pw5K/fPeygrV61JW1tr+h51VCaccnLectGM9OzZY0/t1V+6JoueXpLP/OWn8u3v/SBLnlmad739rbng/HOSJCtXrc5Pfn5bnnpqcbZs3ZYe3bvn2JHH5KILz8u4MaM79hcKAIDDlnAWAIAO0btP7yTJU4uezpatW/cKVl8xdMjgDB0yeM/Hq9eszd9d9eXs3Lkz5597dgYO6J/lz7+QO2femycXLs4Vf/jx9OjePW1tbfnaN76V+Y89nhOOH5cPvPestLUlC59anFtvvysLHn8yn7zi99OtW9Nen+/6G29Oz5498qsffF+OGT40SbJs+fP5hy9fk65du+bcs6emf/+js379xsyaPSf/8E/X5qMfuTRnnHZqh/96AQBw+BHOAgDQIY4bOzpjRo/K00ueyWc+93c584zTcsLx4zJ61Mj07Nlzv6+54Uc3Z8vWrfmTP/ydjBl9bJJkSs5Izx49cvNPb8ns++7PBeefk0fmL8j8xx7P+JNPzMd/49f2PFBs+rQp6fujI3PbHXfnjpn35K1vumCv9VuaW/L7v/Mbe1377vevT6XSkE994g/S96ij9lyfdtbk/L9/+8X84IabMnHC+DQ2NnbArxIAAIcz4SwAAB2ioaEhH/+Nj+SHN/0kc+5/MHfMnJU7Zs5KpVLJoIEDcvxxYzP5jNMy6tgRSZIdO17KowueyID+/fYEs684/9yzc+IJ4/aEpw898miS5LzpU/cEs6845+ypue2OuzP/0cf3CWcnTzp9r49XrV6TZ5c9l1NOPjHdmrpl27bte+41Njbm+OPGZt5Dj2TZ8uf39AkAANUinAUAoMP07Nkzl13y/rz33W/PwoWLs/jpJXn6mWfz3PMv5MWVq3LX3ffm9IkT8uHLfiUrV61Oa2trBvTvt591emTUsSP3fLzixZVJkqFDh+xT27/f0enSpUtWrVq933uvtmLF7nUeXfBEPvnpv3rN97Fu/XrhLAAAVSecBQCgw/Xo3j0TTx2fiaeOT5Js37498x99PDf95Od58OH56Xd034wff2KSpGtT19ddr2XHjiRJU9d9ayuVSrp27ZqWlpZ9++jR/RfW2V0zYfxJmXHe9Nf8fIMHD3zdngAAoL2EswAAdLoePXpkyplnZPjwofncF67OY48/mTNOn5i8fC7s6+nerVuSZMvWrenZs8de99ra2vLSjh3p3r3b66/TfXdY29DQkOPGjXmD7wYAAN6YhtINAABQn35+6535t//4blpadrxmTf9+u48w2PHSSxk0cEAaGhr2HFnwatu2bc+s2XPz5MJFSZIhQwYlrzre4NVWrlqdnbt2ZfCgQa/b49Ahg5MkS5ctT2tr6z73t2zZmra2ttddBwAA3gjhLAAAHeLpZ5bm/nkP5Yc3/fg1A86Zs2YnSU44bmyamrrmlJNPzMZNm/PIowv2qrtv7gP5z+/9IMufez5JcsZpp+5+/T2z91nzrrvvTZKcPnHC6/bYv9/RGTnimGzYsDH3P/DQXve2bdueL1z1T/nCF78koAUAoEM41gAAgA5x6Qffm39cvSYz75mdp5cszakTTs7RfY9KQ0NjNm7clEcXPJElzyzNsKFD8q53vDVJ8t53vz2LlzyTb37ru7ng/HMycMCALH/u+cy8+94MHTI455w9NUlyyskn5ozTJmTeQ/Pz5X/+ek4/bUIqlYY8/sSTmffQ/IwedWzOnX7WAfX5qx98b/7hy9fkP7/3gzz3/AsZMWJ4NmzclHvunZN169bnXRe/JZVKpUN/rQAAODxVFi5caBsAAAAdYseOHbn3vvsz/7HH88KKF7N167ZUKpX06tUzQ4cMzsQJ4zN1yqR06fI/ewbWrFmbH//s1jyxcFG2bd2Wvn2PyinjT8pb33RBevXquaeutbU1d919b+6bOy8rV61O0pb+/frljNMm5MIZ56XpVQ8Wu/pL12TR00vyvz91xZ6jDF5t5arV+enPb8/CRYuzZcvuc2xHHjM8F84411m0AAB0GOEsAAAAAEABzpwFAAAAAChAOAsAAAAAUIBwFgAAAACgAOEsAAAAAEABwlkAAAAAgAKEswAAAAAABQhnAQAAAAAKEM4CAAAAABQgnAUAAAAAKEA4CwAAAABQgHAWAAAAAKAA4SwAAAAAQAH/P2VmtqG2+FizAAAAAElFTkSuQmCC" - }, - "metadata": {} - } + "plot_score_distributions(default_attack_results, \"RMIA Offline (Default Parameters)\")\n", + "plot_score_distributions(tuned_attack_results, \"RMIA Offline (Auto-tuned)\")\n", + "plot_score_distributions(online_attack_results, \"RMIA Online\")" ] }, { "cell_type": "markdown", "metadata": { - "originalKey": "5aff626e-9564-46a5-854d-ce1066075d08", - "outputsInitialized": false, "bentoAICellStatus": "none", "isCommentPanelOpen": false, - "language": "markdown" + "language": "markdown", + "originalKey": "5aff626e-9564-46a5-854d-ce1066075d08", + "outputsInitialized": false }, "source": [ "## Comparing ROC Curves\n", @@ -738,64 +689,50 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": { - "originalKey": "e5a2af8c-153d-4e78-badb-89ce54346c92", - "outputsInitialized": true, "bentoAICellStatus": "none", "isCommentPanelOpen": false, - "language": "python" + "language": "python", + "originalKey": "e5a2af8c-153d-4e78-badb-89ce54346c92", + "outputsInitialized": true }, + "outputs": [], "source": [ "plot_roc_curve(\n", - " default_analysis, default_attack_results, title=\"RMIA (Default Parameters)\"\n", + " default_analysis, default_attack_results, title=\"RMIA Offline (Default Parameters)\"\n", ")\n", - "plot_roc_curve(tuned_analysis, tuned_attack_results, title=\"RMIA (Auto-tuned)\")" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": "
", - "image/png": 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" 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cOMSt27c5cfI0VStXwmKJv5J3/vx5ATidyN/TnTuhT/X8+fPlpUnDepw8fYYNm7cluO3+4dul/rUN2Nlz54mMjHrsY5vx8xURkeRTwRYREUkl2bNlBeDGv8693rJtByE3jOIVFZV4yTpz9jzvf/wF+/0PPnCbg4NDXGHPm8edggXyc/TYibjznu/bvHUHb737EceOn0yx7+m+ShW9uH37DgEHDyeY37PPn/CIiLhDzQ8cPMy7H3zC2XPnE9zPYrFgdbA+cnspy72trh72d/TPx6ri483JU2fYtXsfNpvtgfOgK3tXBGDdhs0J5sMjIvjyux/5cfzkJH3fD9OyRRNy5szBor+Xxf1sAbJnM87p/ufvQHR0NH8tWoqLi3OC7+3+30XUP87LN+vnKyIiyaNDxEVERFJJ7ty5KFqkEAEHD7Ng0VLy58vL0eMnOHb8JG1b+zHrz/ms37QVi8XywBZRHgXzkymTA79Nm0X9urXwKJif6OgYAo8e4+ixE/g1bxx33y6d2jF23CTG/DSRJo3qkS1bNk6dPsOmLdspXqwIJYoXTfHvrVWLJgQcOMjU6X9woVE98ri7c/7iJdZt2IR77lxxh0MXK1qEmNhYxv48iXp1apI3Tx6ioiLZt/8AV69eo0W3jg99DvfcuQg6epyZf8wnf748cVdjT0zVypVYs24jy1etJW8ed4oU9khweyXvCpQvV4Z9+w8wYfLvVKroxd27YWzeup3r10Po3KHtU/19ODk50en55/hl8lRm//kXQwf1BcCnkhdLV6xm7l+LuHX7NjabjS3bduJZuiQW4HDgUVauXkclby9y58qJxWJh5+69uGXJTMEC+SlfrowpP18REUkeFWwREZFUNKBvT/6Y8xcbN2/DarVStkwpRrwyFBcXZ3bu3kdg0DFcXVweKNgODg6MeGkIS5avYt/+ANZv3IKjYyby5nGnZ/fO1KxRNe6+pUuW4PVXh7Jk+WqWr1pHVFQUuXLmoFmTBrRo2viRq8TJlT17Nv5vxEv8vWQF6zduJfTuXbJly0qNalVp07JZ3IW33Nyy8Pqrw1i6fDXbduzm9u07uLg4UyB/PoYM7IO3V/mHPkfTxg04feYce/b5kytnDhrWr/vQ+xYrWpjcuXNx7dp16tep9cDtFouFwQN6s2r1Onbu3sfBg3NwcXGhcKGCdOnYLkXOb/bx9qJC+bIEHDzM7r37qVq5Eh4FC9C/zwssXraSuX8tInv27NStVYOmjRtwJOgoFy5eYtmKNeTNm4dKFSvQolkj1m3YzKq1G2jepCHly5Ux5ecrIiLJYwkMDLSZHUJEREREREQkrdNHniIiIiIiIiIpQAVbREREREREJAWoYIuIiIiIiIikABVsERERERERkRSggi0iIiIiIiKSAlSwRURERERERFKACraIiIiIiIhIClDBFhEREREREUkBKtgiIiIiIiIiKUAFW0RERERERCQFqGCLiIiIiIiIpAAVbBEREREREZEUoIItIiIiIiIikgL+H06bUOechE6tAAAAAElFTkSuQmCC" - }, - "metadata": {} - } + "plot_roc_curve(tuned_analysis, tuned_attack_results, title=\"RMIA Offline (Auto-tuned)\")\n", + "plot_roc_curve(online_analysis, online_attack_results, title=\"RMIA Online\")" ] }, { "cell_type": "markdown", "metadata": { - "originalKey": "e8d7c282-70d2-4829-b015-c2d2235b9cc8", - "outputsInitialized": false, "bentoAICellStatus": "none", "isCommentPanelOpen": false, - "language": "markdown" + "language": "markdown", + "originalKey": "e8d7c282-70d2-4829-b015-c2d2235b9cc8", + "outputsInitialized": false }, "source": [ "## Conclusion\n", "\n", - "In this tutorial, we demonstrated how to use the Privacy Guard framework to perform Robust Membership Inference Attacks (RMIA) on machine learning models trained on the CIFAR-10 dataset. We explored different parameter configurations and analyzed their impact on attack effectiveness.\n", + "In this tutorial, we demonstrated how to use the Privacy Guard framework to perform Robust Membership Inference Attacks (RMIA) on machine learning models trained on the CIFAR-10 dataset. We explored different parameter configurations, including both offline and online modes, and analyzed their impact on attack effectiveness.\n", "\n", "### Key Takeaways\n", "\n", "1. **RMIA is a sophisticated membership inference attack** that uses reference models and population data to establish baseline probability distributions for more accurate membership inference.\n", "\n", - "2. **Reference model diversity is crucial** for RMIA effectiveness. More reference models generally lead to better population probability estimates.\n", + "2. **Online vs. Offline RMIA**: Online mode uses both IN and OUT model signals for direct population estimation, while offline mode uses only OUT signals with alpha mixing. Online mode is expected to produce more accurate results when membership information is available.\n", "\n", - "3. **Auto-tuning can improve performance** by automatically optimizing the alpha coefficient based on the specific dataset and model characteristics.\n", + "3. **Reference model diversity is crucial** for RMIA effectiveness. More reference models generally lead to better population probability estimates.\n", "\n", - "4. **The alpha coefficient significantly affects performance** and should be tuned for optimal results. Different datasets may require different alpha values.\n", + "4. **Auto-tuning can improve offline performance** by automatically optimizing the alpha coefficient based on the specific dataset and model characteristics. Note that auto-tuning is only applicable in offline mode.\n", "\n", - "5. **Population data quality matters** for establishing accurate baseline distributions. The population should be representative of the general data distribution.\n", + "5. **The alpha coefficient significantly affects offline performance** and should be tuned for optimal results. Different datasets may require different alpha values. In online mode, alpha is not used.\n", + "\n", + "6. **Population data quality matters** for establishing accurate baseline distributions. The population should be representative of the general data distribution.\n", "\n", "### RMIA vs. Other Attacks\n", "\n", @@ -803,7 +740,8 @@ "\n", "- **More robust**: Uses multiple reference models to reduce variance in estimates\n", "- **Population-aware**: Leverages population data to better understand baseline behavior\n", - "- **Adaptive**: Can auto-tune parameters for different scenarios\n", + "- **Adaptive**: Can auto-tune parameters for different scenarios (offline mode)\n", + "- **Flexible**: Supports both online and offline modes depending on available information\n", "- **Ratio-based**: Uses ratio comparisons rather than simple thresholds\n", "\n", "### Mitigating RMIA Risks\n", @@ -824,7 +762,7 @@ "\n", "1. **Regular Privacy Auditing**: Regularly assess models using attacks like RMIA to understand privacy risks.\n", "\n", - "2. **Comprehensive Testing**: Test multiple attack variants and parameter settings to get a complete picture of vulnerabilities.\n", + "2. **Comprehensive Testing**: Test multiple attack variants and parameter settings, including both online and offline modes, to get a complete picture of vulnerabilities.\n", "\n", "3. **Baseline Comparison**: Compare attack performance against random guessing and simpler attacks to understand the severity of risks.\n", "\n", @@ -836,17 +774,37 @@ { "cell_type": "markdown", "metadata": { - "originalKey": "86dd4f69-a393-4a28-84dd-ded0386005f1", - "outputsInitialized": false, "bentoAICellStatus": "none", "isCommentPanelOpen": false, - "language": "markdown" + "language": "markdown", + "originalKey": "86dd4f69-a393-4a28-84dd-ded0386005f1", + "outputsInitialized": false }, - "source": [ - "" - ] + "source": [] } ], + "metadata": { + "fileHeader": "", + "fileUid": "333fb9e0-c9f1-4868-93e9-143d0a6950e0", + "isAdHoc": false, + "kernelspec": { + "display_name": "PrivacyGuard", + "language": "python", + "name": "bento_kernel_privacy_guard" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + }, + "operator_data": [] + }, "nbformat": 4, - "nbformat_minor": 5 + "nbformat_minor": 2 }