981 lines
920 KiB
Plaintext
981 lines
920 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "code",
|
|
"id": "initial_id",
|
|
"metadata": {
|
|
"collapsed": true,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-29T12:42:26.431121700Z",
|
|
"start_time": "2024-04-29T12:42:25.507044600Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"from data import *\n",
|
|
"from classifiers import *\n",
|
|
"from sklearn.ensemble import RandomForestClassifier\n",
|
|
"from sklearn.model_selection import GridSearchCV\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"import pandas as pd\n",
|
|
"import numpy as np\n",
|
|
"import os\n",
|
|
"%matplotlib inline"
|
|
],
|
|
"outputs": [],
|
|
"execution_count": 1
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [],
|
|
"source": [
|
|
"metadata_train, experiments_train = load_data(os.path.join(\"..\", \"data\", \"train\"), \"\")\n",
|
|
"truth_train, metadata_train = categorize_metadata(metadata_train)\n",
|
|
"metadata_test, experiments_test = load_data(os.path.join(\"..\", \"data\", \"test\"), \"\")\n",
|
|
"truth_test, metadata_test = categorize_metadata(metadata_test)"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-29T12:42:26.588454100Z",
|
|
"start_time": "2024-04-29T12:42:26.433343300Z"
|
|
}
|
|
},
|
|
"id": "572471b20c6dc13e",
|
|
"execution_count": 2
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"# Look for optimal classifier parameters for arbitrary processing parameters"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"id": "5e59ba986f3af3df"
|
|
},
|
|
{
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-29T12:42:27.680898100Z",
|
|
"start_time": "2024-04-29T12:42:26.589519600Z"
|
|
}
|
|
},
|
|
"cell_type": "code",
|
|
"source": [
|
|
"process_params = {\n",
|
|
" 'baseline_lam': 10,\n",
|
|
" 'baseline_p': 1e-2,\n",
|
|
" 'smooth_window_length': 7,\n",
|
|
" 'smooth_polyorder': 3\n",
|
|
"}\n",
|
|
"X_train, X_test = process_train_test(process_params, experiments_train, metadata_train, experiments_test, metadata_test, scale=True)"
|
|
],
|
|
"id": "8fb458c0b78c9aa7",
|
|
"outputs": [],
|
|
"execution_count": 3
|
|
},
|
|
{
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-29T12:43:46.201964400Z",
|
|
"start_time": "2024-04-29T12:42:27.682968Z"
|
|
}
|
|
},
|
|
"cell_type": "code",
|
|
"source": [
|
|
"param_grid = {\n",
|
|
" 'n_estimators': range(1, 501, 100),\n",
|
|
" 'max_depth': range(1, 21, 5)\n",
|
|
"}\n",
|
|
"\n",
|
|
"clf = RandomForestClassifier()\n",
|
|
"\n",
|
|
"grid_clf = GridSearchCV(clf, param_grid, cv=5)\n",
|
|
"_ = grid_clf.fit(X_train, truth_train.to_numpy().ravel())"
|
|
],
|
|
"id": "80a355d2740ebf4a",
|
|
"outputs": [],
|
|
"execution_count": 4
|
|
},
|
|
{
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-29T12:43:46.213707800Z",
|
|
"start_time": "2024-04-29T12:43:46.203065Z"
|
|
}
|
|
},
|
|
"cell_type": "code",
|
|
"source": [
|
|
"print(grid_clf.best_params_)\n",
|
|
"grid_clf.cv_results_"
|
|
],
|
|
"id": "790017144f8feaa6",
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"{'max_depth': 11, 'n_estimators': 301}\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": "{'mean_fit_time': array([0.00681539, 0.14610763, 0.27718945, 0.39620948, 0.52502027,\n 0.00736065, 0.43403726, 0.86569772, 1.2813745 , 1.70391393,\n 0.00718741, 0.48835073, 0.9537066 , 1.4265492 , 1.93588476,\n 0.00835772, 0.48221674, 0.94742947, 1.42890635, 1.93304458]),\n 'std_fit_time': array([0.00046738, 0.00867747, 0.01135155, 0.00112488, 0.00349504,\n 0.00088113, 0.00534944, 0.00746005, 0.00887032, 0.01338315,\n 0.00048124, 0.00671597, 0.01533272, 0.01902896, 0.03648975,\n 0.00067703, 0.00985057, 0.01882552, 0.0128699 , 0.01072727]),\n 'mean_score_time': array([0.00065007, 0.00185828, 0.00349832, 0.00409431, 0.00529351,\n 0.00024419, 0.00252962, 0.00395293, 0.00567269, 0.00726399,\n 0.0006793 , 0.00220752, 0.0038765 , 0.00551586, 0.00653486,\n 0.00045471, 0.00235896, 0.00373554, 0.00502024, 0.00725088]),\n 'std_score_time': array([0.00053118, 0.00048533, 0.00020484, 0.00057571, 0.00022786,\n 0.00048838, 0.00076014, 0.0004935 , 0.00059544, 0.00054311,\n 0.00055532, 0.0006391 , 0.00065416, 0.00067045, 0.00045184,\n 0.00055693, 0.00017962, 0.00043132, 0.00051096, 0.00048935]),\n 'param_max_depth': masked_array(data=[1, 1, 1, 1, 1, 6, 6, 6, 6, 6, 11, 11, 11, 11, 11, 16,\n 16, 16, 16, 16],\n mask=[False, False, False, False, False, False, False, False,\n False, False, False, False, False, False, False, False,\n False, False, False, False],\n fill_value='?',\n dtype=object),\n 'param_n_estimators': masked_array(data=[1, 101, 201, 301, 401, 1, 101, 201, 301, 401, 1, 101,\n 201, 301, 401, 1, 101, 201, 301, 401],\n mask=[False, False, False, False, False, False, False, False,\n False, False, False, False, False, False, False, False,\n False, False, False, False],\n fill_value='?',\n dtype=object),\n 'params': [{'max_depth': 1, 'n_estimators': 1},\n {'max_depth': 1, 'n_estimators': 101},\n {'max_depth': 1, 'n_estimators': 201},\n {'max_depth': 1, 'n_estimators': 301},\n {'max_depth': 1, 'n_estimators': 401},\n {'max_depth': 6, 'n_estimators': 1},\n {'max_depth': 6, 'n_estimators': 101},\n {'max_depth': 6, 'n_estimators': 201},\n {'max_depth': 6, 'n_estimators': 301},\n {'max_depth': 6, 'n_estimators': 401},\n {'max_depth': 11, 'n_estimators': 1},\n {'max_depth': 11, 'n_estimators': 101},\n {'max_depth': 11, 'n_estimators': 201},\n {'max_depth': 11, 'n_estimators': 301},\n {'max_depth': 11, 'n_estimators': 401},\n {'max_depth': 16, 'n_estimators': 1},\n {'max_depth': 16, 'n_estimators': 101},\n {'max_depth': 16, 'n_estimators': 201},\n {'max_depth': 16, 'n_estimators': 301},\n {'max_depth': 16, 'n_estimators': 401}],\n 'split0_test_score': array([0.51886792, 0.59433962, 0.59433962, 0.61320755, 0.59433962,\n 0.68867925, 0.94339623, 0.95283019, 0.97169811, 0.96226415,\n 0.66037736, 0.91509434, 0.94339623, 0.95283019, 0.96226415,\n 0.67924528, 0.9245283 , 0.95283019, 0.95283019, 0.93396226]),\n 'split1_test_score': array([0.50943396, 0.58490566, 0.58490566, 0.58490566, 0.58490566,\n 0.77358491, 0.94339623, 0.9245283 , 0.93396226, 0.93396226,\n 0.69811321, 0.95283019, 0.93396226, 0.95283019, 0.94339623,\n 0.73584906, 0.91509434, 0.94339623, 0.96226415, 0.95283019]),\n 'split2_test_score': array([0.43396226, 0.66037736, 0.67924528, 0.66981132, 0.66037736,\n 0.52830189, 0.91509434, 0.9245283 , 0.90566038, 0.93396226,\n 0.71698113, 0.9245283 , 0.93396226, 0.94339623, 0.90566038,\n 0.67924528, 0.90566038, 0.9245283 , 0.93396226, 0.91509434]),\n 'split3_test_score': array([0.5047619 , 0.58095238, 0.57142857, 0.58095238, 0.58095238,\n 0.73333333, 0.98095238, 0.97142857, 0.99047619, 0.97142857,\n 0.77142857, 0.98095238, 0.98095238, 1. , 0.99047619,\n 0.84761905, 0.97142857, 0.97142857, 0.99047619, 0.98095238]),\n 'split4_test_score': array([0.53333333, 0.65714286, 0.64761905, 0.65714286, 0.64761905,\n 0.75238095, 0.94285714, 0.92380952, 0.92380952, 0.94285714,\n 0.67619048, 0.94285714, 0.92380952, 0.91428571, 0.9047619 ,\n 0.78095238, 0.93333333, 0.93333333, 0.88571429, 0.93333333]),\n 'mean_test_score': array([0.50007188, 0.61554358, 0.61550764, 0.62120395, 0.61363881,\n 0.69525606, 0.94513926, 0.93942498, 0.94512129, 0.94889488,\n 0.70461815, 0.94325247, 0.94321653, 0.95266846, 0.94131177,\n 0.74458221, 0.93000898, 0.94510332, 0.94504942, 0.9432345 ]),\n 'std_test_score': array([0.03446663, 0.03556799, 0.0410394 , 0.03648556, 0.03348314,\n 0.08805168, 0.02095978, 0.01945042, 0.03130263, 0.01529003,\n 0.03853354, 0.02305058, 0.01985921, 0.02756642, 0.03306781,\n 0.0641147 , 0.02268027, 0.01623269, 0.03482546, 0.02231857]),\n 'rank_test_score': array([20, 17, 18, 16, 19, 15, 3, 11, 4, 2, 14, 7, 9, 1, 10, 13, 12,\n 5, 6, 8])}"
|
|
},
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"execution_count": 5
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "(1.0, 0.9426553672316386)"
|
|
},
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"evaluate_classifier_params(RandomForestClassifier, grid_clf.best_params_, X_train, truth_train, X_test, truth_test, iters=20)"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-29T12:44:23.130616300Z",
|
|
"start_time": "2024-04-29T12:43:46.210540Z"
|
|
}
|
|
},
|
|
"id": "3c0217cdaea05b55",
|
|
"execution_count": 6
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"# Look for optimal processing and classifier parameters"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"id": "17ce0afe5a7a7b70"
|
|
},
|
|
{
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-29T13:04:48.001960600Z",
|
|
"start_time": "2024-04-29T12:44:23.122083700Z"
|
|
}
|
|
},
|
|
"cell_type": "code",
|
|
"source": [
|
|
"param_grid = {\n",
|
|
" 'baseline_lam': range(1,21,5),\n",
|
|
" 'baseline_p': [1e-1, 1e-2, 1e-3, 1e-4, 1e-5],\n",
|
|
" 'smooth_window_length': [3,5,9,15,21],\n",
|
|
" 'smooth_polyorder': [3,5,9,15,21],\n",
|
|
" 'n_estimators': [100], #range(1, 101, 100),\n",
|
|
" 'max_depth': [10] #range(5, 16, 5)\n",
|
|
"}\n",
|
|
"import warnings\n",
|
|
"with warnings.catch_warnings():\n",
|
|
" warnings.filterwarnings('ignore')\n",
|
|
" results = param_grid_search(RandomForestClassifier, param_grid, experiments_train, metadata_train, truth_train, cv=5)"
|
|
],
|
|
"id": "e518d47d3a6aef5e",
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 5}, {'fit_time': array([0.5342443 , 0.5351305 , 0.51417208, 0.53326702, 0.52174711]), 'score_time': array([0.00225377, 0.00225544, 0.00217366, 0.0022366 , 0.00257421]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.8490566 , 0.83018868, 0.83018868, 0.91428571, 0.86666667])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 9}, {'fit_time': array([0.52323151, 0.51764464, 0.52236128, 0.52475786, 0.52849317]), 'score_time': array([0.00286651, 0.00227451, 0.00227356, 0.00338054, 0.00214696]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.85849057, 0.85849057, 0.83018868, 0.8952381 , 0.87619048])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 15}, {'fit_time': array([0.52853799, 0.54870653, 0.50620103, 0.52176952, 0.51584792]), 'score_time': array([0.00261402, 0.00228 , 0.0026679 , 0.0026679 , 0.00225568]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.86792453, 0.83962264, 0.8490566 , 0.91428571, 0.88571429])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 21}, {'fit_time': array([0.52009225, 0.52740884, 0.51096416, 0.522264 , 0.52387953]), 'score_time': array([0.00329041, 0.00225878, 0.00232506, 0.00244737, 0.00227976]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.85849057, 0.78301887, 0.8490566 , 0.8952381 , 0.87619048])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 9}, {'fit_time': array([0.53165364, 0.5209353 , 0.51807761, 0.53548908, 0.53231478]), 'score_time': array([0.00210094, 0.0034039 , 0.00237131, 0.00228 , 0.00382209]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.87735849, 0.87735849, 0.87735849, 0.91428571, 0.84761905])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 15}, {'fit_time': array([0.52925205, 0.52280021, 0.52203202, 0.52873111, 0.51950955]), 'score_time': array([0.00267816, 0.00243664, 0.00243306, 0.00225592, 0.00277925]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.85849057, 0.87735849, 0.8490566 , 0.94285714, 0.87619048])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 21}, {'fit_time': array([0.52440596, 0.52496266, 0.50395179, 0.52605247, 0.52150369]), 'score_time': array([0.00211096, 0.00221109, 0.00227976, 0.0022912 , 0.00228596]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.86792453, 0.8490566 , 0.87735849, 0.93333333, 0.86666667])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 15}, {'fit_time': array([0.53493118, 0.51223302, 0.52570391, 0.5328269 , 0.53306437]), 'score_time': array([0.00259519, 0.00254083, 0.00259185, 0.00213337, 0.00336051]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.8490566 , 0.86792453, 0.88679245, 0.91428571, 0.85714286])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 21}, {'fit_time': array([0.53201365, 0.53170753, 0.51821184, 0.52443767, 0.50972915]), 'score_time': array([0.0022738 , 0.00289297, 0.00343561, 0.00260139, 0.0021162 ]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.86792453, 0.86792453, 0.86792453, 0.93333333, 0.88571429])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 15, 'smooth_window_length': 21}, {'fit_time': array([0.54126811, 0.52926087, 0.52981567, 0.57177258, 0.53631258]), 'score_time': array([0.00342512, 0.00257134, 0.00252295, 0.00210452, 0.00229001]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.85849057, 0.83962264, 0.83018868, 0.9047619 , 0.84761905])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 5}, {'fit_time': array([0.50935388, 0.50514483, 0.48704553, 0.49426126, 0.50067449]), 'score_time': array([0.00212765, 0.00239921, 0.00226593, 0.00339222, 0.00233912]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.93396226, 0.93396226, 0.90566038, 0.92380952, 0.9047619 ])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 9}, {'fit_time': array([0.49256754, 0.50147533, 0.48508883, 0.50105143, 0.49664807]), 'score_time': array([0.0033226 , 0.00113726, 0.00223351, 0.00220418, 0.00339985]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.93396226, 0.93396226, 0.91509434, 0.94285714, 0.8952381 ])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 15}, {'fit_time': array([0.49202847, 0.48953962, 0.48896384, 0.50095057, 0.48813128]), 'score_time': array([0.00223804, 0.0025866 , 0.00243378, 0.00261283, 0.00129938]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.93396226, 0.93396226, 0.91509434, 0.97142857, 0.87619048])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 21}, {'fit_time': array([0.49978018, 0.47929621, 0.53254366, 0.5612855 , 0.54925299]), 'score_time': array([0.00210524, 0.00219774, 0.00327563, 0.00214982, 0.00323415]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.88679245, 0.91509434, 0.90566038, 0.96190476, 0.8952381 ])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 9}, {'fit_time': array([0.50226331, 0.50715232, 0.48324561, 0.49798346, 0.49212313]), 'score_time': array([0.00268722, 0.00227356, 0.00216365, 0.00330615, 0.00343966]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.9245283 , 0.93396226, 0.91509434, 0.97142857, 0.9047619 ])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 15}, {'fit_time': array([0.4775362 , 0.50976062, 0.49008322, 0.50908399, 0.50268173]), 'score_time': array([0.00226688, 0.00210452, 0.00209761, 0.00212145, 0.00229573]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.95283019, 0.94339623, 0.89622642, 0.95238095, 0.8952381 ])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 21}, {'fit_time': array([0.49819136, 0.5010674 , 0.49012852, 0.50509 , 0.49800611]), 'score_time': array([0.00207639, 0.00224137, 0.00236702, 0.0022912 , 0.00206327]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.93396226, 0.94339623, 0.93396226, 0.95238095, 0.88571429])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 15}, {'fit_time': array([0.53041911, 0.52354741, 0.56365085, 0.54665589, 0.54504132]), 'score_time': array([0.00246453, 0.00311065, 0.00216365, 0.00319195, 0.0031395 ]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.9245283 , 0.97169811, 0.93396226, 0.93333333, 0.88571429])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 21}, {'fit_time': array([0.54345965, 0.5710752 , 0.53151894, 0.5547967 , 0.54563665]), 'score_time': array([0.00338101, 0.00257635, 0.00265837, 0.00339198, 0.00287819]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.9245283 , 0.91509434, 0.91509434, 0.94285714, 0.91428571])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 15, 'smooth_window_length': 21}, {'fit_time': array([0.51513243, 0.49808645, 0.4871242 , 0.50093317, 0.49111056]), 'score_time': array([0.00211525, 0.00221825, 0.00228405, 0.00217891, 0.00215769]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.88679245, 0.91509434, 0.90566038, 0.95238095, 0.8952381 ])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 5}, {'fit_time': array([0.47366953, 0.48173428, 0.47270489, 0.4942553 , 0.47499871]), 'score_time': array([0.00249505, 0.00230169, 0.0022769 , 0.00340533, 0.00315237]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.9245283 , 0.9245283 , 0.95283019, 0.97142857, 0.91428571])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 9}, {'fit_time': array([0.47247076, 0.47326469, 0.45167184, 0.47404385, 0.47115397]), 'score_time': array([0.00217938, 0.00232434, 0.00205374, 0.0022459 , 0.00235701]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.9245283 , 0.99056604, 0.95283019, 0.98095238, 0.96190476])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 15}, {'fit_time': array([0.4731071 , 0.45436168, 0.45842218, 0.46580791, 0.47029614]), 'score_time': array([0.00247121, 0.00229073, 0.00232744, 0.00226855, 0.00149798]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.91509434, 0.97169811, 0.90566038, 0.99047619, 0.94285714])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 21}, {'fit_time': array([0.45389605, 0.47876334, 0.5043478 , 0.46282005, 0.46792889]), 'score_time': array([0.00226998, 0.00331497, 0.00230265, 0.00239778, 0.00229168]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.89622642, 0.98113208, 0.88679245, 0.98095238, 0.96190476])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 9}, {'fit_time': array([0.47568536, 0.46887088, 0.46370721, 0.53093672, 0.53053689]), 'score_time': array([0.00232863, 0.00248098, 0.00214243, 0.00233507, 0.00291467]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.94339623, 0.98113208, 0.88679245, 1. , 0.94285714])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 15}, {'fit_time': array([0.48566771, 0.48261166, 0.47351861, 0.48842788, 0.48898244]), 'score_time': array([0.00317669, 0.00240064, 0.00309992, 0.00228024, 0.00240636]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.9245283 , 0.97169811, 0.93396226, 0.97142857, 0.96190476])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 21}, {'fit_time': array([0.48634791, 0.48253107, 0.47012424, 0.48358154, 0.47641659]), 'score_time': array([0.00220323, 0.00229335, 0.0022676 , 0.00212193, 0.002424 ]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.96226415, 0.96226415, 0.9245283 , 0.99047619, 0.95238095])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 15}, {'fit_time': array([0.49733424, 0.49756742, 0.49118471, 0.49855351, 0.49789143]), 'score_time': array([0.00225568, 0.00314188, 0.00310278, 0.00223231, 0.00314546]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.94339623, 0.96226415, 0.90566038, 0.97142857, 0.95238095])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 21}, {'fit_time': array([0.49744034, 0.49362445, 0.49429512, 0.49678922, 0.49117804]), 'score_time': array([0.00327325, 0.002069 , 0.00314617, 0.00277448, 0.00218797]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.93396226, 0.97169811, 0.94339623, 0.97142857, 0.93333333])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 15, 'smooth_window_length': 21}, {'fit_time': array([0.47444606, 0.4727006 , 0.46757865, 0.47514653, 0.47866964]), 'score_time': array([0.00310111, 0.00261307, 0.00215721, 0.00333261, 0.00226951]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.9245283 , 0.97169811, 0.89622642, 0.96190476, 0.94285714])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 5}, {'fit_time': array([0.51227808, 0.51531672, 0.50120497, 0.52689099, 0.5266459 ]), 'score_time': array([0.00225925, 0.00373149, 0.00216746, 0.00212908, 0.00211453]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.77358491, 0.80188679, 0.75471698, 0.81904762, 0.74285714])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 9}, {'fit_time': array([0.51323128, 0.51217079, 0.5086112 , 0.5322268 , 0.53138471]), 'score_time': array([0.0021441 , 0.00225735, 0.00376391, 0.00239301, 0.00228 ]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.77358491, 0.83018868, 0.74528302, 0.8 , 0.7047619 ])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 15}, {'fit_time': array([0.5207026 , 0.51990771, 0.51134038, 0.52895665, 0.52014732]), 'score_time': array([0.00224566, 0.00245762, 0.00227308, 0.00228643, 0.00231242]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.76415094, 0.80188679, 0.74528302, 0.83809524, 0.76190476])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 21}, {'fit_time': array([0.51051259, 0.5196023 , 0.513273 , 0.52279258, 0.52046919]), 'score_time': array([0.00237513, 0.00230169, 0.00227833, 0.00227475, 0.00333738]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.76415094, 0.80188679, 0.76415094, 0.8 , 0.76190476])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 9}, {'fit_time': array([0.53223228, 0.53049326, 0.53013945, 0.53305793, 0.5401926 ]), 'score_time': array([0.00294662, 0.00229335, 0.00228357, 0.00214338, 0.0031426 ]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.77358491, 0.77358491, 0.71698113, 0.78095238, 0.78095238])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 15}, {'fit_time': array([0.53075218, 0.53423452, 0.53046703, 0.52614212, 0.51875687]), 'score_time': array([0.00242496, 0.00255966, 0.00257325, 0.00231743, 0.00220442]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.75471698, 0.85849057, 0.68867925, 0.81904762, 0.75238095])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 21}, {'fit_time': array([0.52797961, 0.52547741, 0.52353835, 0.53726435, 0.52791739]), 'score_time': array([0.00218892, 0.00263095, 0.00221729, 0.00229478, 0.00307393]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.76415094, 0.83962264, 0.73584906, 0.8 , 0.79047619])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 15}, {'fit_time': array([0.5265615 , 0.52378845, 0.51593733, 0.53247595, 0.52241349]), 'score_time': array([0.00232983, 0.00249195, 0.00228548, 0.002285 , 0.00338793]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.75471698, 0.78301887, 0.72641509, 0.80952381, 0.76190476])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 21}, {'fit_time': array([0.5257287 , 0.53342414, 0.52824521, 0.5431118 , 0.52576113]), 'score_time': array([0.00225759, 0.00337291, 0.00211501, 0.00341296, 0.0022738 ]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.78301887, 0.77358491, 0.74528302, 0.82857143, 0.73333333])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 15, 'smooth_window_length': 21}, {'fit_time': array([0.52428055, 0.52180696, 0.52739787, 0.5289731 , 0.52359915]), 'score_time': array([0.00229764, 0.00367498, 0.00223207, 0.00212193, 0.00220728]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.73584906, 0.81132075, 0.71698113, 0.82857143, 0.72380952])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 5}, {'fit_time': array([0.56409597, 0.5598557 , 0.55534053, 0.55481958, 0.5561595 ]), 'score_time': array([0.00248957, 0.0022943 , 0.00343466, 0.00260854, 0.00393772]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.5754717 , 0.76415094, 0.59433962, 0.7047619 , 0.61904762])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 9}, {'fit_time': array([0.55672193, 0.55272889, 0.55374837, 0.55499792, 0.55009174]), 'score_time': array([0.00339341, 0.0036087 , 0.00226736, 0.00342584, 0.00226927]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.67924528, 0.75471698, 0.60377358, 0.66666667, 0.66666667])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 15}, {'fit_time': array([0.56581378, 0.56014848, 0.53823638, 0.55674767, 0.54735756]), 'score_time': array([0.0022645 , 0.00323343, 0.00226927, 0.00341225, 0.00227141]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.67924528, 0.71698113, 0.5754717 , 0.60952381, 0.66666667])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 21}, {'fit_time': array([0.57030106, 0.56400418, 0.55455947, 0.56509399, 0.55015206]), 'score_time': array([0.00227475, 0.00340199, 0.00212526, 0.00223398, 0.00351357]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.58490566, 0.71698113, 0.63207547, 0.65714286, 0.68571429])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 9}, {'fit_time': array([0.55478668, 0.55389786, 0.55007958, 0.55707145, 0.55384517]), 'score_time': array([0.0026114 , 0.00262642, 0.00226665, 0.00225997, 0.00229502]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.64150943, 0.76415094, 0.61320755, 0.67619048, 0.65714286])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 15}, {'fit_time': array([0.5670023 , 0.5671556 , 0.56015897, 0.55959344, 0.55704761]), 'score_time': array([0.00234413, 0.0031817 , 0.00215316, 0.00227642, 0.0034914 ]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.62264151, 0.73584906, 0.63207547, 0.68571429, 0.6952381 ])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 21}, {'fit_time': array([0.56827235, 0.55964661, 0.55687356, 0.55062389, 0.54214597]), 'score_time': array([0.00231051, 0.00359416, 0.00217056, 0.00329423, 0.00248575]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.58490566, 0.74528302, 0.56603774, 0.67619048, 0.6952381 ])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 15}, {'fit_time': array([0.54354143, 0.55539918, 0.55702662, 0.55412507, 0.54333758]), 'score_time': array([0.00275302, 0.00216556, 0.00228047, 0.00361705, 0.00390697]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.63207547, 0.73584906, 0.64150943, 0.67619048, 0.68571429])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 21}, {'fit_time': array([0.56076694, 0.5613606 , 0.55203819, 0.56498146, 0.5558219 ]), 'score_time': array([0.0026443 , 0.00261855, 0.0022788 , 0.00229383, 0.00259995]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.66037736, 0.73584906, 0.61320755, 0.67619048, 0.67619048])}]\n",
|
|
"[{'baseline_lam': 1, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 15, 'smooth_window_length': 21}, {'fit_time': array([0.56740403, 0.55626607, 0.56446099, 0.55643892, 0.55169129]), 'score_time': array([0.00210857, 0.00235033, 0.00223637, 0.00361037, 0.00226927]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.66981132, 0.71698113, 0.60377358, 0.67619048, 0.62857143])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 5}, {'fit_time': array([0.52061915, 0.51152658, 0.50957489, 0.52790284, 0.52324867]), 'score_time': array([0.00229573, 0.00336552, 0.00228906, 0.00222111, 0.00340724]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.90566038, 0.90566038, 0.91509434, 0.91428571, 0.87619048])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 9}, {'fit_time': array([0.52195883, 0.51749372, 0.51738954, 0.53614998, 0.51970673]), 'score_time': array([0.00341439, 0.00216293, 0.0022049 , 0.00228119, 0.00248241]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.87735849, 0.89622642, 0.87735849, 0.94285714, 0.88571429])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 15}, {'fit_time': array([0.49940872, 0.49292684, 0.49373436, 0.50171399, 0.49784112]), 'score_time': array([0.00227714, 0.00239539, 0.00127339, 0.00327849, 0.00237775]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.87735849, 0.9245283 , 0.89622642, 0.92380952, 0.9047619 ])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 21}, {'fit_time': array([0.49311447, 0.49530363, 0.50242639, 0.50374699, 0.48844337]), 'score_time': array([0.00210524, 0.00227427, 0.00230646, 0.00248194, 0.00225258]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.85849057, 0.87735849, 0.87735849, 0.92380952, 0.88571429])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 9}, {'fit_time': array([0.52349186, 0.52419353, 0.51210427, 0.51848626, 0.50048161]), 'score_time': array([0.00227666, 0.00234747, 0.00227904, 0.00226688, 0.00224686]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.91509434, 0.87735849, 0.91509434, 0.96190476, 0.8952381 ])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 15}, {'fit_time': array([0.50278473, 0.50039053, 0.48616648, 0.49657583, 0.50275421]), 'score_time': array([0.00332022, 0.00231338, 0.00250387, 0.00237846, 0.00223088]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.9245283 , 0.9245283 , 0.91509434, 0.96190476, 0.91428571])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 21}, {'fit_time': array([0.50365567, 0.50197816, 0.49143338, 0.50564694, 0.49702072]), 'score_time': array([0.00226808, 0.00227332, 0.00216508, 0.00226951, 0.00261378]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.87735849, 0.91509434, 0.93396226, 0.96190476, 0.9047619 ])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 15}, {'fit_time': array([0.51666021, 0.51014495, 0.50729465, 0.51596737, 0.50769234]), 'score_time': array([0.00323272, 0.00228524, 0.00228357, 0.00225854, 0.00223923]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.91509434, 0.90566038, 0.9245283 , 0.91428571, 0.87619048])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 21}, {'fit_time': array([0.50071764, 0.49834776, 0.50756836, 0.52344823, 0.51575708]), 'score_time': array([0.00223637, 0.00216007, 0.00230598, 0.00328779, 0.00216269]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.91509434, 0.89622642, 0.9245283 , 0.95238095, 0.9047619 ])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 15, 'smooth_window_length': 21}, {'fit_time': array([0.50721741, 0.51364136, 0.50209165, 0.53215146, 0.51647329]), 'score_time': array([0.00339723, 0.00227237, 0.00238705, 0.00232291, 0.00250983]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.87735849, 0.86792453, 0.90566038, 0.94285714, 0.88571429])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 5}, {'fit_time': array([0.48526406, 0.49671245, 0.48247361, 0.48301291, 0.47120166]), 'score_time': array([0.0022645 , 0.00260997, 0.00328088, 0.00212646, 0.00227261]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.95283019, 0.91509434, 0.93396226, 0.95238095, 0.93333333])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 9}, {'fit_time': array([0.46888256, 0.46726489, 0.46408963, 0.48971224, 0.48371673]), 'score_time': array([0.00334978, 0.0022645 , 0.00232625, 0.00208354, 0.00344968]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.91509434, 0.95283019, 0.90566038, 0.97142857, 0.91428571])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 15}, {'fit_time': array([0.48879385, 0.48472404, 0.45557618, 0.48295045, 0.47043228]), 'score_time': array([0.00237942, 0.00238633, 0.00246263, 0.00211668, 0.00210261]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.93396226, 0.90566038, 0.95283019, 0.98095238, 0.93333333])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 21}, {'fit_time': array([0.46689487, 0.48011899, 0.47599649, 0.48214078, 0.47028351]), 'score_time': array([0.00337124, 0.00232744, 0.00235415, 0.0022676 , 0.00235581]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.90566038, 0.93396226, 0.89622642, 0.97142857, 0.92380952])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 9}, {'fit_time': array([0.49400997, 0.48201561, 0.48676777, 0.48609161, 0.50847507]), 'score_time': array([0.00225639, 0.00227046, 0.00314593, 0.00225186, 0.00244617]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.94339623, 0.9245283 , 0.9245283 , 0.98095238, 0.92380952])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 15}, {'fit_time': array([0.47364068, 0.48342514, 0.47739887, 0.473665 , 0.46449447]), 'score_time': array([0.00218511, 0.00345707, 0.00220561, 0.00326943, 0.00227737]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.95283019, 0.95283019, 0.90566038, 0.96190476, 0.94285714])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 21}, {'fit_time': array([0.48549652, 0.479316 , 0.46031189, 0.47426248, 0.47515106]), 'score_time': array([0.00218129, 0.00247383, 0.002285 , 0.00221896, 0.00310969]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.9245283 , 0.96226415, 0.90566038, 0.98095238, 0.93333333])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 15}, {'fit_time': array([0.49441051, 0.48597622, 0.47625542, 0.48234677, 0.48900628]), 'score_time': array([0.0022819 , 0.00261903, 0.00251818, 0.00368237, 0.00317478]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.90566038, 0.95283019, 0.93396226, 0.97142857, 0.9047619 ])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 21}, {'fit_time': array([0.48720574, 0.47825933, 0.48462534, 0.49466705, 0.49422121]), 'score_time': array([0.00207329, 0.00211525, 0.00199699, 0.00333238, 0.00212049]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.94339623, 0.95283019, 0.93396226, 0.96190476, 0.91428571])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 15, 'smooth_window_length': 21}, {'fit_time': array([0.4887538 , 0.4804337 , 0.4749074 , 0.48413968, 0.48529553]), 'score_time': array([0.00323796, 0.0021503 , 0.00220633, 0.00242734, 0.00263596]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.94339623, 0.97169811, 0.88679245, 0.95238095, 0.9047619 ])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 5}, {'fit_time': array([0.45719099, 0.46220469, 0.4557476 , 0.46049786, 0.45586419]), 'score_time': array([0.00224209, 0.00211287, 0.00235748, 0.00225639, 0.00227499]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.9245283 , 0.95283019, 0.94339623, 0.96190476, 0.95238095])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 9}, {'fit_time': array([0.45287442, 0.45522666, 0.44568849, 0.44901562, 0.45025301]), 'score_time': array([0.00227356, 0.0033443 , 0.00225854, 0.00221729, 0.00252581]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.95283019, 0.95283019, 0.9245283 , 0.98095238, 0.97142857])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 15}, {'fit_time': array([0.43669152, 0.45507693, 0.44093037, 0.45043564, 0.44390416]), 'score_time': array([0.00227046, 0.00244379, 0.00113726, 0.00220609, 0.00223207]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.93396226, 0.96226415, 0.9245283 , 0.96190476, 0.96190476])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 21}, {'fit_time': array([0.44327879, 0.45949364, 0.44188452, 0.45794272, 0.44698167]), 'score_time': array([0.00121617, 0.0028367 , 0.00113726, 0.00221419, 0.00227809]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.94339623, 0.90566038, 0.9245283 , 0.98095238, 0.97142857])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 9}, {'fit_time': array([0.45274258, 0.46499491, 0.45277071, 0.46756935, 0.46792078]), 'score_time': array([0.0022707 , 0.0022471 , 0.00227904, 0.00220251, 0.00211811]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.95283019, 0.97169811, 0.95283019, 0.99047619, 0.97142857])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 15}, {'fit_time': array([0.45097613, 0.46867728, 0.453444 , 0.45209336, 0.45299125]), 'score_time': array([0.00213289, 0.00346279, 0.00229478, 0.0012958 , 0.00239038]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.93396226, 0.95283019, 0.93396226, 0.98095238, 0.97142857])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 21}, {'fit_time': array([0.44421101, 0.45205998, 0.44093394, 0.45220208, 0.44731927]), 'score_time': array([0.00227356, 0.00214553, 0.00223112, 0.00330353, 0.00242162]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.94339623, 0.99056604, 0.96226415, 1. , 0.95238095])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 15}, {'fit_time': array([0.46312237, 0.46285439, 0.453475 , 0.46835375, 0.4692502 ]), 'score_time': array([0.00226521, 0.00228715, 0.00311017, 0.00229359, 0.00227284]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.9245283 , 0.98113208, 0.9245283 , 0.98095238, 0.95238095])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 21}, {'fit_time': array([0.45599484, 0.45897985, 0.43572402, 0.45453382, 0.45571566]), 'score_time': array([0.00228214, 0.00212002, 0.00227189, 0.00225711, 0.00227594]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.95283019, 0.95283019, 0.94339623, 1. , 0.98095238])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 15, 'smooth_window_length': 21}, {'fit_time': array([0.45795155, 0.46398449, 0.45544457, 0.46591353, 0.45860696]), 'score_time': array([0.00242996, 0.00211191, 0.00216746, 0.00219321, 0.00287008]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.95283019, 0.94339623, 0.9245283 , 0.96190476, 0.96190476])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 5}, {'fit_time': array([0.52195001, 0.52252936, 0.51658368, 0.51793051, 0.52460694]), 'score_time': array([0.00334454, 0.00227904, 0.00226212, 0.00226665, 0.00226164]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.74528302, 0.74528302, 0.73584906, 0.78095238, 0.81904762])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 9}, {'fit_time': array([0.518821 , 0.52246809, 0.51241899, 0.52281404, 0.5226953 ]), 'score_time': array([0.00227189, 0.00354338, 0.00227284, 0.00227427, 0.00229478]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.75471698, 0.78301887, 0.70754717, 0.72380952, 0.77142857])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 15}, {'fit_time': array([0.52820706, 0.5246706 , 0.5308404 , 0.5190413 , 0.52469015]), 'score_time': array([0.00235915, 0.00261354, 0.00264049, 0.0037303 , 0.00155282]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.70754717, 0.76415094, 0.66981132, 0.80952381, 0.8 ])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 21}, {'fit_time': array([0.5305419 , 0.52138948, 0.53023219, 0.5196631 , 0.53556585]), 'score_time': array([0.00256729, 0.00234437, 0.00244927, 0.00260997, 0.00231767]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.75471698, 0.76415094, 0.71698113, 0.76190476, 0.81904762])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 9}, {'fit_time': array([0.5246892 , 0.53090715, 0.53176951, 0.52641797, 0.53613639]), 'score_time': array([0.00323725, 0.00336695, 0.00233817, 0.00346565, 0.00326514]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.75471698, 0.74528302, 0.68867925, 0.8 , 0.78095238])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 15}, {'fit_time': array([0.52069569, 0.53294492, 0.52510023, 0.52978706, 0.52821207]), 'score_time': array([0.0033257 , 0.00222158, 0.00231791, 0.0023675 , 0.00360298]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.72641509, 0.71698113, 0.72641509, 0.77142857, 0.8 ])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 21}, {'fit_time': array([0.53049397, 0.52707386, 0.51842141, 0.52901602, 0.5435214 ]), 'score_time': array([0.00338149, 0.00362372, 0.00243402, 0.00228858, 0.00224495]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.73584906, 0.72641509, 0.68867925, 0.72380952, 0.80952381])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 15}, {'fit_time': array([0.52816248, 0.53465509, 0.53848958, 0.52376533, 0.53592563]), 'score_time': array([0.0014782 , 0.00259733, 0.00263309, 0.00234437, 0.00210238]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.71698113, 0.72641509, 0.67924528, 0.79047619, 0.81904762])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 21}, {'fit_time': array([0.5318296 , 0.5247817 , 0.52948022, 0.52814579, 0.53084922]), 'score_time': array([0.00238681, 0.00320983, 0.00340414, 0.00257063, 0.00345731]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.75471698, 0.72641509, 0.72641509, 0.76190476, 0.76190476])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 15, 'smooth_window_length': 21}, {'fit_time': array([0.53207016, 0.51821351, 0.53122616, 0.52291441, 0.54019475]), 'score_time': array([0.00344801, 0.0034759 , 0.00228953, 0.00330544, 0.00288773]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.73584906, 0.74528302, 0.72641509, 0.79047619, 0.8 ])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 5}, {'fit_time': array([0.56204987, 0.55101538, 0.5634048 , 0.56586266, 0.5553329 ]), 'score_time': array([0.00208759, 0.0022378 , 0.00228643, 0.00235224, 0.00224304]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.66981132, 0.67924528, 0.63207547, 0.7047619 , 0.66666667])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 9}, {'fit_time': array([0.5581944 , 0.55417705, 0.60715914, 0.56055617, 0.5569129 ]), 'score_time': array([0.00260329, 0.00334549, 0.00342441, 0.00230002, 0.00321794]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.66037736, 0.6509434 , 0.64150943, 0.6952381 , 0.63809524])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 15}, {'fit_time': array([0.56600404, 0.55868435, 0.56576586, 0.56628656, 0.55685139]), 'score_time': array([0.00232935, 0.00333858, 0.00319123, 0.00227785, 0.00311637]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.62264151, 0.66981132, 0.67924528, 0.68571429, 0.71428571])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 21}, {'fit_time': array([0.56067872, 0.55473042, 0.55279994, 0.56533504, 0.55190706]), 'score_time': array([0.00227499, 0.0033958 , 0.00270772, 0.00235057, 0.00245452]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.68867925, 0.63207547, 0.6509434 , 0.6952381 , 0.62857143])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 9}, {'fit_time': array([0.56762886, 0.55742455, 0.55198741, 0.55642414, 0.56015348]), 'score_time': array([0.00226808, 0.00259256, 0.00256824, 0.00338483, 0.00220084]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.68867925, 0.66981132, 0.64150943, 0.72380952, 0.67619048])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 15}, {'fit_time': array([0.56002927, 0.56833529, 0.55619431, 0.55603623, 0.552495 ]), 'score_time': array([0.00323963, 0.00224662, 0.00238323, 0.00341535, 0.00226402]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.66037736, 0.69811321, 0.66981132, 0.71428571, 0.68571429])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 21}, {'fit_time': array([0.56229305, 0.55177069, 0.56313944, 0.56208086, 0.56039214]), 'score_time': array([0.00242567, 0.00226998, 0.00226593, 0.00225258, 0.0023005 ]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.61320755, 0.68867925, 0.60377358, 0.6952381 , 0.63809524])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 15}, {'fit_time': array([0.55876374, 0.55805349, 0.55780935, 0.56607199, 0.55756974]), 'score_time': array([0.00224352, 0.00210786, 0.00227332, 0.00228477, 0.00358558]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.63207547, 0.6509434 , 0.6509434 , 0.7047619 , 0.68571429])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 21}, {'fit_time': array([0.55401468, 0.55132127, 0.55364656, 0.55743718, 0.54207182]), 'score_time': array([0.00259542, 0.00227237, 0.00343323, 0.00342464, 0.00326514]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.66981132, 0.66037736, 0.66981132, 0.72380952, 0.67619048])}]\n",
|
|
"[{'baseline_lam': 6, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 15, 'smooth_window_length': 21}, {'fit_time': array([0.54491472, 0.54399133, 0.54884505, 0.55733466, 0.54369164]), 'score_time': array([0.00233221, 0.00251341, 0.00235724, 0.00344133, 0.00383496]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.70754717, 0.66981132, 0.6509434 , 0.6952381 , 0.7047619 ])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 5}, {'fit_time': array([0.50828171, 0.49166298, 0.50226378, 0.504879 , 0.50460815]), 'score_time': array([0.00226212, 0.00235128, 0.00323462, 0.00228524, 0.0021596 ]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.91509434, 0.93396226, 0.89622642, 0.94285714, 0.87619048])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 9}, {'fit_time': array([0.50097942, 0.50395513, 0.50271416, 0.51265121, 0.4907732 ]), 'score_time': array([0.00321889, 0.00228119, 0.00341201, 0.00227308, 0.00226712]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.94339623, 0.89622642, 0.91509434, 0.95238095, 0.87619048])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 15}, {'fit_time': array([0.50128412, 0.50056863, 0.4884367 , 0.49789047, 0.48727727]), 'score_time': array([0.0025444 , 0.00221992, 0.00224924, 0.00132632, 0.00346303]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.91509434, 0.93396226, 0.91509434, 0.96190476, 0.9047619 ])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 21}, {'fit_time': array([0.49587011, 0.49024653, 0.48348761, 0.50247955, 0.4948554 ]), 'score_time': array([0.00226569, 0.00229073, 0.00331163, 0.0022738 , 0.00225711]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.90566038, 0.9245283 , 0.90566038, 0.94285714, 0.9047619 ])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 9}, {'fit_time': array([0.49626851, 0.51193953, 0.49616241, 0.50033188, 0.49750948]), 'score_time': array([0.00248957, 0.00241804, 0.00218344, 0.00337982, 0.00219655]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.93396226, 0.90566038, 0.93396226, 0.97142857, 0.8952381 ])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 15}, {'fit_time': array([0.50546122, 0.49189162, 0.48408079, 0.4985311 , 0.48513722]), 'score_time': array([0.00231743, 0.00147629, 0.00332665, 0.0023036 , 0.00226378]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.94339623, 0.9245283 , 0.93396226, 0.95238095, 0.88571429])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 21}, {'fit_time': array([0.49072504, 0.50097871, 0.48168039, 0.50466275, 0.51977992]), 'score_time': array([0.00323462, 0.00250363, 0.00342226, 0.00216317, 0.00337434]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.9245283 , 0.95283019, 0.93396226, 0.94285714, 0.8952381 ])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 15}, {'fit_time': array([0.50585604, 0.5085113 , 0.49396539, 0.50366092, 0.50346375]), 'score_time': array([0.00328565, 0.00245309, 0.00232196, 0.00222898, 0.00208497]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.93396226, 0.93396226, 0.91509434, 0.94285714, 0.9047619 ])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 21}, {'fit_time': array([0.51031828, 0.50753665, 0.50298333, 0.51662779, 0.50008798]), 'score_time': array([0.00221753, 0.00319552, 0.00228715, 0.00230074, 0.00331259]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.91509434, 0.94339623, 0.9245283 , 0.94285714, 0.87619048])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 15, 'smooth_window_length': 21}, {'fit_time': array([0.51810122, 0.51704049, 0.50313783, 0.52253985, 0.51418877]), 'score_time': array([0.00260448, 0.00324702, 0.00245667, 0.00307846, 0.00235844]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.91509434, 0.89622642, 0.91509434, 0.93333333, 0.9047619 ])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 5}, {'fit_time': array([0.47447562, 0.48451304, 0.48691106, 0.49023557, 0.48719382]), 'score_time': array([0.00253749, 0.00223517, 0.00226903, 0.00258231, 0.0022285 ]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.91509434, 0.93396226, 0.90566038, 0.98095238, 0.93333333])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 9}, {'fit_time': array([0.48347783, 0.48463058, 0.47871089, 0.48665452, 0.47365451]), 'score_time': array([0.00259352, 0.00228333, 0.00263357, 0.00244212, 0.00249243]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.93396226, 0.93396226, 0.93396226, 0.97142857, 0.93333333])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 15}, {'fit_time': array([0.47523761, 0.47368765, 0.45954967, 0.47869039, 0.47557831]), 'score_time': array([0.00243902, 0.00317121, 0.00226879, 0.00225663, 0.00226903]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.9245283 , 0.96226415, 0.91509434, 0.97142857, 0.93333333])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 21}, {'fit_time': array([0.48056197, 0.48318815, 0.46303344, 0.49125552, 0.47895741]), 'score_time': array([0.00220728, 0.00210476, 0.00375509, 0.00218558, 0.00258493]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.94339623, 0.89622642, 0.91509434, 0.98095238, 0.8952381 ])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 9}, {'fit_time': array([0.49964523, 0.47962904, 0.47322416, 0.49400496, 0.49751449]), 'score_time': array([0.00250435, 0.0023737 , 0.00231862, 0.00227809, 0.00228715]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.94339623, 0.95283019, 0.94339623, 0.95238095, 0.88571429])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 15}, {'fit_time': array([0.4812088 , 0.48008657, 0.48177743, 0.49181747, 0.49958992]), 'score_time': array([0.00260663, 0.00344777, 0.00227356, 0.00210047, 0.00223994]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.96226415, 0.96226415, 0.93396226, 0.95238095, 0.93333333])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 21}, {'fit_time': array([0.49019718, 0.49193144, 0.47173023, 0.47804785, 0.48441434]), 'score_time': array([0.00227833, 0.00233507, 0.00338435, 0.00213075, 0.0031383 ]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.91509434, 0.94339623, 0.88679245, 0.96190476, 0.92380952])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 15}, {'fit_time': array([0.47869301, 0.47999048, 0.47482777, 0.48056293, 0.47754765]), 'score_time': array([0.00354576, 0.00220203, 0.00235009, 0.00226879, 0.00220609]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.9245283 , 0.94339623, 0.94339623, 0.96190476, 0.91428571])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 21}, {'fit_time': array([0.4718926 , 0.46903205, 0.46352577, 0.48481059, 0.48493052]), 'score_time': array([0.00217295, 0.00224423, 0.00373888, 0.00227475, 0.00351357]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.94339623, 0.95283019, 0.91509434, 0.99047619, 0.91428571])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 15, 'smooth_window_length': 21}, {'fit_time': array([0.48784804, 0.48695302, 0.47358108, 0.48040581, 0.46847367]), 'score_time': array([0.00316954, 0.00329161, 0.00212193, 0.00163269, 0.00319529]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.9245283 , 0.95283019, 0.90566038, 0.95238095, 0.88571429])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 5}, {'fit_time': array([0.45665932, 0.45895219, 0.45276141, 0.46938109, 0.46160173]), 'score_time': array([0.00212646, 0.00230551, 0.00216484, 0.00225639, 0.00217366]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.95283019, 0.97169811, 0.94339623, 0.99047619, 0.95238095])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 9}, {'fit_time': array([0.4586153 , 0.45773578, 0.45393395, 0.46664548, 0.46051049]), 'score_time': array([0.00236511, 0.00226068, 0.0023191 , 0.0023458 , 0.00226331]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.93396226, 0.96226415, 0.9245283 , 0.98095238, 0.96190476])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 15}, {'fit_time': array([0.43990612, 0.45030618, 0.44197106, 0.44930267, 0.43949652]), 'score_time': array([0.00207067, 0.00218678, 0.0033772 , 0.00210667, 0.00247574]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.94339623, 0.96226415, 0.94339623, 1. , 0.96190476])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 21}, {'fit_time': array([0.44454265, 0.4467299 , 0.43884158, 0.44303846, 0.45175982]), 'score_time': array([0.00268245, 0.00261927, 0.00260425, 0.00260925, 0.00244141]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.95283019, 0.94339623, 0.94339623, 1. , 0.92380952])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 9}, {'fit_time': array([0.45207095, 0.4605968 , 0.45091629, 0.4665792 , 0.4667201 ]), 'score_time': array([0.002249 , 0.00245667, 0.00317907, 0.00250912, 0.00215507]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.95283019, 0.96226415, 0.94339623, 0.98095238, 0.95238095])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 15}, {'fit_time': array([0.45074248, 0.45023227, 0.4411962 , 0.46282148, 0.46212029]), 'score_time': array([0.0021708 , 0.00232601, 0.00260496, 0.0022645 , 0.00314498]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.96226415, 0.96226415, 0.95283019, 0.99047619, 0.94285714])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 21}, {'fit_time': array([0.44150329, 0.43916821, 0.42682171, 0.45918465, 0.4563868 ]), 'score_time': array([0.00105667, 0.00208855, 0.00228095, 0.00319266, 0.00274515]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.97169811, 0.96226415, 0.94339623, 0.99047619, 0.98095238])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 15}, {'fit_time': array([0.45696044, 0.46092534, 0.44196415, 0.46169424, 0.45578194]), 'score_time': array([0.00232053, 0.00229764, 0.0020771 , 0.00226855, 0.00342894]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.96226415, 0.99056604, 0.93396226, 1. , 0.95238095])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 21}, {'fit_time': array([0.4672308 , 0.46117926, 0.45530248, 0.46776748, 0.46559143]), 'score_time': array([0.00227714, 0.00341344, 0.00113153, 0.00225234, 0.00228143]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.95283019, 0.97169811, 0.95283019, 1. , 0.96190476])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 15, 'smooth_window_length': 21}, {'fit_time': array([0.47982359, 0.47303534, 0.46356606, 0.4660306 , 0.47123528]), 'score_time': array([0.00343776, 0.00342655, 0.00263095, 0.00247765, 0.00255156]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.96226415, 0.95283019, 0.94339623, 0.97142857, 0.96190476])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 5}, {'fit_time': array([0.54668093, 0.54282117, 0.53181887, 0.54105139, 0.54286218]), 'score_time': array([0.00272322, 0.00229239, 0.00270629, 0.00225329, 0.00240421]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.6509434 , 0.72641509, 0.74528302, 0.82857143, 0.8 ])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 9}, {'fit_time': array([0.53212833, 0.53872323, 0.53254223, 0.55069089, 0.55474234]), 'score_time': array([0.0027144 , 0.00263333, 0.00261664, 0.00273418, 0.00336099]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.72641509, 0.74528302, 0.74528302, 0.8 , 0.77142857])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 15}, {'fit_time': array([0.55919552, 0.53096938, 0.53127933, 0.53532147, 0.53558898]), 'score_time': array([0.00234699, 0.0022831 , 0.00226355, 0.00227213, 0.00232434]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.74528302, 0.73584906, 0.75471698, 0.78095238, 0.79047619])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 21}, {'fit_time': array([0.53691053, 0.53172064, 0.53290582, 0.53845906, 0.5352478 ]), 'score_time': array([0.00230956, 0.00329185, 0.0022614 , 0.00261593, 0.002527 ]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.71698113, 0.70754717, 0.75471698, 0.84761905, 0.78095238])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 9}, {'fit_time': array([0.52521658, 0.53031993, 0.52348661, 0.53169036, 0.53068686]), 'score_time': array([0.00228047, 0.00228238, 0.00226998, 0.00327826, 0.00226808]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.71698113, 0.67924528, 0.76415094, 0.82857143, 0.79047619])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 15}, {'fit_time': array([0.53397536, 0.53859305, 0.5397644 , 0.55444336, 0.55329061]), 'score_time': array([0.00227523, 0.00227118, 0.00212955, 0.00227857, 0.00229836]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.73584906, 0.73584906, 0.75471698, 0.79047619, 0.74285714])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 21}, {'fit_time': array([0.54749823, 0.52514005, 0.53389049, 0.54722929, 0.5434823 ]), 'score_time': array([0.00248837, 0.00226498, 0.00207901, 0.00227118, 0.00338268]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.73584906, 0.72641509, 0.73584906, 0.8 , 0.78095238])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 15}, {'fit_time': array([0.54616761, 0.54858279, 0.53478241, 0.54171419, 0.55090499]), 'score_time': array([0.00245786, 0.00228786, 0.00229406, 0.00223565, 0.00225902]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.71698113, 0.74528302, 0.73584906, 0.84761905, 0.80952381])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 21}, {'fit_time': array([0.55045462, 0.54140067, 0.54576015, 0.54585528, 0.55191827]), 'score_time': array([0.00226927, 0.00340533, 0.00230265, 0.00340104, 0.00228834]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.70754717, 0.73584906, 0.73584906, 0.8 , 0.76190476])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 15, 'smooth_window_length': 21}, {'fit_time': array([0.53381705, 0.52764773, 0.53143954, 0.53536868, 0.53302836]), 'score_time': array([0.00391912, 0.00227547, 0.00339246, 0.00252724, 0.00338125]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.67924528, 0.76415094, 0.73584906, 0.84761905, 0.80952381])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 5}, {'fit_time': array([0.57407665, 0.56062841, 0.56711388, 0.56980824, 0.56353426]), 'score_time': array([0.00241733, 0.00227427, 0.00374269, 0.00147295, 0.00278115]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.70754717, 0.68867925, 0.60377358, 0.64761905, 0.71428571])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 9}, {'fit_time': array([0.5711 , 0.56640577, 0.56304193, 0.57630968, 0.56921983]), 'score_time': array([0.00228715, 0.00228024, 0.00237179, 0.00228715, 0.00221324]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.67924528, 0.68867925, 0.60377358, 0.67619048, 0.71428571])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 15}, {'fit_time': array([0.57241178, 0.57676482, 0.58130813, 0.57594085, 0.57156372]), 'score_time': array([0.00307512, 0.00221062, 0.00215483, 0.00314856, 0.00318885]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.66981132, 0.73584906, 0.58490566, 0.66666667, 0.71428571])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 21}, {'fit_time': array([0.56665826, 0.56873727, 0.56466627, 0.57970691, 0.55550432]), 'score_time': array([0.00342274, 0.0035975 , 0.00215483, 0.00227118, 0.00348186]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.69811321, 0.70754717, 0.59433962, 0.64761905, 0.7047619 ])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 9}, {'fit_time': array([0.55617619, 0.5527544 , 0.55189753, 0.55361342, 0.54810214]), 'score_time': array([0.00248337, 0.00329185, 0.00242615, 0.00212002, 0.00319672]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.68867925, 0.69811321, 0.63207547, 0.68571429, 0.72380952])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 15}, {'fit_time': array([0.56917715, 0.56876302, 0.56666732, 0.56928492, 0.56667233]), 'score_time': array([0.00353742, 0.00312781, 0.00368071, 0.00360894, 0.00227737]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.67924528, 0.66981132, 0.58490566, 0.71428571, 0.71428571])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 21}, {'fit_time': array([0.56604695, 0.57118368, 0.56518555, 0.58722377, 0.57427859]), 'score_time': array([0.0021348 , 0.00227666, 0.00343537, 0.00209117, 0.00232816]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.73584906, 0.72641509, 0.62264151, 0.64761905, 0.71428571])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 15}, {'fit_time': array([0.56449771, 0.55512786, 0.55671573, 0.56564689, 0.57077265]), 'score_time': array([0.00230479, 0.00261068, 0.00251293, 0.00249052, 0.00350475]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.69811321, 0.70754717, 0.58490566, 0.63809524, 0.72380952])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 21}, {'fit_time': array([0.56026888, 0.56566048, 0.56324649, 0.56144285, 0.55441213]), 'score_time': array([0.00343561, 0.00226545, 0.0034349 , 0.00315619, 0.00209379]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.69811321, 0.73584906, 0.62264151, 0.63809524, 0.7047619 ])}]\n",
|
|
"[{'baseline_lam': 11, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 15, 'smooth_window_length': 21}, {'fit_time': array([0.55005145, 0.55055428, 0.54494357, 0.56557918, 0.56301284]), 'score_time': array([0.00229549, 0.00338531, 0.00323439, 0.00346756, 0.00235748]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.66981132, 0.71698113, 0.59433962, 0.66666667, 0.71428571])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 5}, {'fit_time': array([0.51540542, 0.51010036, 0.51462221, 0.51533556, 0.52641368]), 'score_time': array([0.00227404, 0.00235748, 0.00229096, 0.00338721, 0.00227165]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.9245283 , 0.90566038, 0.90566038, 0.94285714, 0.88571429])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 9}, {'fit_time': array([0.49365211, 0.4895587 , 0.47952247, 0.50170112, 0.49930692]), 'score_time': array([0.00227356, 0.00249982, 0.00226927, 0.00229216, 0.00230145]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.9245283 , 0.9245283 , 0.9245283 , 0.95238095, 0.9047619 ])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 15}, {'fit_time': array([0.49384761, 0.49342513, 0.47356343, 0.4989078 , 0.48935246]), 'score_time': array([0.00212312, 0.00319695, 0.00227094, 0.0021069 , 0.002321 ]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.93396226, 0.93396226, 0.91509434, 0.96190476, 0.88571429])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 21}, {'fit_time': array([0.48586154, 0.48942327, 0.48090482, 0.50256515, 0.4942739 ]), 'score_time': array([0.0021615 , 0.0024848 , 0.00237894, 0.00308704, 0.00227737]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.90566038, 0.9245283 , 0.89622642, 0.95238095, 0.88571429])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 9}, {'fit_time': array([0.52099919, 0.51628542, 0.50662017, 0.54044294, 0.53569365]), 'score_time': array([0.0022285 , 0.00225496, 0.00264239, 0.00229764, 0.00218272]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.9245283 , 0.93396226, 0.90566038, 0.96190476, 0.9047619 ])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 15}, {'fit_time': array([0.50067139, 0.48585176, 0.48639941, 0.5051074 , 0.50175858]), 'score_time': array([0.00228047, 0.00227952, 0.00227404, 0.00358629, 0.00224996]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.93396226, 0.97169811, 0.90566038, 0.94285714, 0.88571429])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 21}, {'fit_time': array([0.49412537, 0.48818755, 0.47483897, 0.49007058, 0.49225664]), 'score_time': array([0.0021174 , 0.00247025, 0.00222301, 0.0023849 , 0.00222325]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.9245283 , 0.95283019, 0.93396226, 0.95238095, 0.91428571])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 15}, {'fit_time': array([0.51284575, 0.55079985, 0.51460385, 0.53754854, 0.54817629]), 'score_time': array([0.00242925, 0.00314307, 0.00211334, 0.00352263, 0.00231695]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.9245283 , 0.91509434, 0.93396226, 0.96190476, 0.8952381 ])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 21}, {'fit_time': array([0.5273447 , 0.5226419 , 0.51405621, 0.51522923, 0.52114463]), 'score_time': array([0.00221896, 0.00244021, 0.00208306, 0.0033884 , 0.0024631 ]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.93396226, 0.97169811, 0.95283019, 0.95238095, 0.88571429])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 15, 'smooth_window_length': 21}, {'fit_time': array([0.51814365, 0.51869917, 0.50793982, 0.51120973, 0.52372456]), 'score_time': array([0.00251126, 0.00228357, 0.00212979, 0.0022893 , 0.00226784]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.94339623, 0.91509434, 0.91509434, 0.94285714, 0.91428571])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 5}, {'fit_time': array([0.4900167 , 0.49304438, 0.49261546, 0.48603773, 0.48247838]), 'score_time': array([0.00263524, 0.00230622, 0.00354958, 0.00340986, 0.00227785]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.96226415, 0.95283019, 0.95283019, 0.98095238, 0.93333333])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 9}, {'fit_time': array([0.48321533, 0.46908474, 0.48240757, 0.48759794, 0.49263716]), 'score_time': array([0.00339937, 0.00340819, 0.00243068, 0.00259829, 0.00230455]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.94339623, 0.95283019, 0.94339623, 0.97142857, 0.94285714])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 15}, {'fit_time': array([0.4872911 , 0.48742461, 0.48108935, 0.47488761, 0.47065997]), 'score_time': array([0.0019567 , 0.00206685, 0.00334048, 0.00252771, 0.00338697]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.95283019, 0.93396226, 0.91509434, 0.98095238, 0.9047619 ])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 21}, {'fit_time': array([0.49184346, 0.48093104, 0.4697032 , 0.47732615, 0.47942567]), 'score_time': array([0.00210905, 0.00223541, 0.00263691, 0.00228643, 0.00224662]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.91509434, 0.9245283 , 0.90566038, 0.99047619, 0.94285714])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 9}, {'fit_time': array([0.48352957, 0.48253822, 0.4808197 , 0.48718643, 0.48601651]), 'score_time': array([0.00248766, 0.0023632 , 0.00228715, 0.00242233, 0.00260997]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.96226415, 0.91509434, 0.88679245, 0.97142857, 0.92380952])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 15}, {'fit_time': array([0.48748875, 0.47895336, 0.4713788 , 0.48285723, 0.4950707 ]), 'score_time': array([0.00260115, 0.00229239, 0.00221562, 0.00227189, 0.00208926]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.94339623, 0.98113208, 0.9245283 , 0.97142857, 0.93333333])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 21}, {'fit_time': array([0.47275424, 0.47785044, 0.46582818, 0.47793698, 0.47523451]), 'score_time': array([0.00225353, 0.00211334, 0.00226617, 0.00248408, 0.00359845]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.93396226, 0.95283019, 0.98113208, 0.97142857, 0.9047619 ])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 15}, {'fit_time': array([0.48485923, 0.48503089, 0.47741699, 0.494807 , 0.499331 ]), 'score_time': array([0.00258994, 0.00258827, 0.00233507, 0.00229764, 0.00208592]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.94339623, 0.95283019, 0.90566038, 0.97142857, 0.92380952])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 21}, {'fit_time': array([0.49167562, 0.48811555, 0.48174906, 0.48370814, 0.48096013]), 'score_time': array([0.00225806, 0.00255728, 0.00231504, 0.00310373, 0.00234365]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.96226415, 0.98113208, 0.95283019, 0.97142857, 0.94285714])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 15, 'smooth_window_length': 21}, {'fit_time': array([0.50026202, 0.49894929, 0.4911499 , 0.48922229, 0.48670053]), 'score_time': array([0.00312638, 0.00228286, 0.00214195, 0.00331593, 0.00206494]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.93396226, 0.95283019, 0.94339623, 0.94285714, 0.91428571])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 5}, {'fit_time': array([0.4681325 , 0.4592433 , 0.45582581, 0.47429037, 0.4701221 ]), 'score_time': array([0.00221634, 0.00136209, 0.00339818, 0.00228763, 0.00256157]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.97169811, 0.98113208, 0.91509434, 0.99047619, 0.94285714])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 9}, {'fit_time': array([0.44659972, 0.44663382, 0.44378805, 0.46155405, 0.45759702]), 'score_time': array([0.00347829, 0.0024507 , 0.00226855, 0.00227547, 0.00225782]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.95283019, 0.98113208, 0.9245283 , 0.99047619, 0.96190476])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 15}, {'fit_time': array([0.44086647, 0.45173955, 0.42661619, 0.45281887, 0.45028496]), 'score_time': array([0.0022583 , 0.00259566, 0.00229025, 0.002285 , 0.00226378]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.95283019, 0.97169811, 0.94339623, 1. , 0.94285714])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 21}, {'fit_time': array([0.44525743, 0.44267344, 0.42724323, 0.43734932, 0.4408412 ]), 'score_time': array([0.00228333, 0.00226855, 0.0022707 , 0.00268865, 0.00226235]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.93396226, 0.97169811, 0.95283019, 1. , 0.93333333])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 9}, {'fit_time': array([0.46750927, 0.464885 , 0.44531012, 0.45607686, 0.45787239]), 'score_time': array([0.00227809, 0.00225472, 0.0022707 , 0.00367093, 0.00226784]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.94339623, 0.98113208, 0.91509434, 0.98095238, 0.92380952])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 15}, {'fit_time': array([0.45632339, 0.45309854, 0.43780947, 0.46855521, 0.45629954]), 'score_time': array([0.00321507, 0.00255775, 0.00333738, 0.00230789, 0.00243592]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.96226415, 0.99056604, 0.94339623, 0.99047619, 0.95238095])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 21}, {'fit_time': array([0.45484591, 0.44695902, 0.44532347, 0.45981574, 0.44977522]), 'score_time': array([0.00208044, 0.00224638, 0.00226688, 0.00234818, 0.00212407]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.96226415, 0.98113208, 0.95283019, 1. , 0.93333333])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 15}, {'fit_time': array([0.46205044, 0.46212721, 0.44969654, 0.46969318, 0.46046305]), 'score_time': array([0.00258851, 0.00261712, 0.00243878, 0.00254297, 0.00232196]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.97169811, 0.96226415, 0.93396226, 0.99047619, 0.94285714])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 21}, {'fit_time': array([0.46329165, 0.45592165, 0.45052028, 0.46651387, 0.46237373]), 'score_time': array([0.00113034, 0.00243163, 0.00314617, 0.00212002, 0.00288939]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.98113208, 0.93396226, 0.9245283 , 1. , 0.94285714])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 15, 'smooth_window_length': 21}, {'fit_time': array([0.46723199, 0.47632122, 0.45786452, 0.47063875, 0.47087026]), 'score_time': array([0.00234318, 0.00336742, 0.00228047, 0.00227594, 0.00226569]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.94339623, 0.99056604, 0.93396226, 0.99047619, 0.95238095])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 5}, {'fit_time': array([0.51915836, 0.52103424, 0.51841784, 0.52590275, 0.51030254]), 'score_time': array([0.00327587, 0.00251961, 0.00239968, 0.00291348, 0.00350404]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.79245283, 0.82075472, 0.73584906, 0.73333333, 0.81904762])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 9}, {'fit_time': array([0.50960755, 0.5137589 , 0.51381779, 0.52285814, 0.5180254 ]), 'score_time': array([0.0031774 , 0.00286651, 0.00226498, 0.00330877, 0.00322771]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.78301887, 0.73584906, 0.69811321, 0.77142857, 0.84761905])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 15}, {'fit_time': array([0.52965665, 0.52960134, 0.51937509, 0.51608419, 0.51778102]), 'score_time': array([0.00228667, 0.00259113, 0.00148344, 0.00226665, 0.00316811]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.77358491, 0.79245283, 0.70754717, 0.77142857, 0.79047619])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 21}, {'fit_time': array([0.51967621, 0.51517725, 0.51349258, 0.50998831, 0.52506137]), 'score_time': array([0.00226259, 0.00335312, 0.00234985, 0.00226212, 0.00227284]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.78301887, 0.75471698, 0.68867925, 0.7047619 , 0.82857143])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 9}, {'fit_time': array([0.51956034, 0.54727817, 0.53248835, 0.53390098, 0.50875902]), 'score_time': array([0.00350356, 0.00231886, 0.00236034, 0.00224447, 0.00315857]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.79245283, 0.75471698, 0.73584906, 0.71428571, 0.79047619])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 15}, {'fit_time': array([0.51466799, 0.51861477, 0.51041293, 0.52379298, 0.5107007 ]), 'score_time': array([0.0022769 , 0.00362325, 0.00226569, 0.00228786, 0.00236082]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.77358491, 0.76415094, 0.6509434 , 0.7047619 , 0.75238095])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 21}, {'fit_time': array([0.52684855, 0.52915597, 0.52124405, 0.52616858, 0.5323863 ]), 'score_time': array([0.0025053 , 0.00267386, 0.00367641, 0.00331354, 0.00222778]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.75471698, 0.77358491, 0.74528302, 0.73333333, 0.80952381])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 15}, {'fit_time': array([0.52670026, 0.51864815, 0.50877857, 0.51897407, 0.5189724 ]), 'score_time': array([0.00227356, 0.00391507, 0.00228024, 0.00316048, 0.0023191 ]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.71698113, 0.75471698, 0.73584906, 0.71428571, 0.81904762])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 21}, {'fit_time': array([0.51453567, 0.51653242, 0.51602554, 0.51407623, 0.51877642]), 'score_time': array([0.00323844, 0.00330853, 0.00218058, 0.00206852, 0.00225616]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.77358491, 0.79245283, 0.66981132, 0.75238095, 0.81904762])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 15, 'smooth_window_length': 21}, {'fit_time': array([0.53391051, 0.53223085, 0.52859545, 0.53306556, 0.53095341]), 'score_time': array([0.00332546, 0.00229716, 0.00217748, 0.00228 , 0.00209999]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.79245283, 0.76415094, 0.63207547, 0.77142857, 0.79047619])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 5}, {'fit_time': array([0.57309628, 0.58223128, 0.56289411, 0.57553363, 0.57948804]), 'score_time': array([0.00227642, 0.00229001, 0.00245333, 0.00212908, 0.00228977]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.73584906, 0.70754717, 0.6509434 , 0.67619048, 0.72380952])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 9}, {'fit_time': array([0.56888056, 0.56287861, 0.55723619, 0.55990505, 0.57036185]), 'score_time': array([0.00340629, 0.00229549, 0.00268698, 0.00242639, 0.00226712]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.77358491, 0.73584906, 0.64150943, 0.67619048, 0.73333333])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 15}, {'fit_time': array([0.56206536, 0.56974959, 0.55887055, 0.5618372 , 0.57203674]), 'score_time': array([0.00230193, 0.00228477, 0.00207949, 0.00350881, 0.00257897]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.73584906, 0.67924528, 0.67924528, 0.67619048, 0.68571429])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 21}, {'fit_time': array([0.57169175, 0.56945419, 0.55793834, 0.56491661, 0.56198812]), 'score_time': array([0.00243926, 0.00257421, 0.00259924, 0.00227785, 0.00344634]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.72641509, 0.69811321, 0.6509434 , 0.65714286, 0.73333333])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 9}, {'fit_time': array([0.56784749, 0.56324482, 0.55494237, 0.57094359, 0.56542826]), 'score_time': array([0.0022614 , 0.00241065, 0.0035069 , 0.00226021, 0.00257826]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.71698113, 0.70754717, 0.66981132, 0.68571429, 0.77142857])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 15}, {'fit_time': array([0.58082008, 0.57902908, 0.56728029, 0.58658981, 0.59594345]), 'score_time': array([0.00340176, 0.00230408, 0.00213933, 0.00227427, 0.00355196]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.72641509, 0.72641509, 0.66037736, 0.67619048, 0.68571429])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 21}, {'fit_time': array([0.56925416, 0.57333541, 0.56887221, 0.57036018, 0.5799315 ]), 'score_time': array([0.00339007, 0.0022862 , 0.00243115, 0.00240493, 0.00230575]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.73584906, 0.68867925, 0.66037736, 0.66666667, 0.72380952])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 15}, {'fit_time': array([0.56541538, 0.56747699, 0.56590462, 0.57279587, 0.57426763]), 'score_time': array([0.00224066, 0.00222659, 0.00254583, 0.00339532, 0.00391769]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.74528302, 0.73584906, 0.67924528, 0.66666667, 0.71428571])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 21}, {'fit_time': array([0.57916236, 0.57365298, 0.57374072, 0.57865381, 0.58455348]), 'score_time': array([0.00208807, 0.00359035, 0.00217104, 0.00320792, 0.00231767]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.70754717, 0.69811321, 0.64150943, 0.64761905, 0.73333333])}]\n",
|
|
"[{'baseline_lam': 16, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 15, 'smooth_window_length': 21}, {'fit_time': array([0.57128263, 0.57569408, 0.55591512, 0.5729866 , 0.56995869]), 'score_time': array([0.00372767, 0.00213623, 0.00222516, 0.00275683, 0.00361252]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.71698113, 0.73584906, 0.66981132, 0.68571429, 0.71428571])}]\n"
|
|
]
|
|
}
|
|
],
|
|
"execution_count": 7
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "(0.9697574123989219,\n {'baseline_lam': 11,\n 'baseline_p': 0.001,\n 'max_depth': 10,\n 'n_estimators': 100,\n 'smooth_polyorder': 5,\n 'smooth_window_length': 21})"
|
|
},
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"k = 0\n",
|
|
"best_params = dict()\n",
|
|
"for r in results:\n",
|
|
" mean = np.mean(r[1]['test_score'])\n",
|
|
" if mean > k:\n",
|
|
" k = mean\n",
|
|
" best_params = r[0]\n",
|
|
"k, best_params"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-29T13:04:48.007939600Z",
|
|
"start_time": "2024-04-29T13:04:48.002985100Z"
|
|
}
|
|
},
|
|
"id": "846605ecc9c07eb4",
|
|
"execution_count": 8
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "(1.0, 0.977966101694915)"
|
|
},
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"process_params = {key: best_params[key] for key in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}\n",
|
|
"classifier_params = {key: best_params[key] for key in best_params.keys() if key not in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}\n",
|
|
"X_train, X_test = process_train_test(process_params, experiments_train, metadata_train, experiments_test, metadata_test, scale=True)\n",
|
|
"evaluate_classifier_params(RandomForestClassifier, classifier_params, X_train, truth_train, X_test, truth_test, iters=20)"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-29T13:05:01.580495600Z",
|
|
"start_time": "2024-04-29T13:04:48.009042100Z"
|
|
}
|
|
},
|
|
"id": "f07d35308265f471",
|
|
"execution_count": 9
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAjkAAAGhCAYAAACDNqXeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOydd3wUZf7H37N903sjQOi914CgWEDF3j3beZbTEz3l1NP7efaznf3s3Tu754koiiIgKr330EJI72Wz2Trl98dsdrOkQBBIAs/79cqLnZlnZp7ZXXY+862SpmkaAoFAIBAIBMcYho6egEAgEAgEAsGRQIgcgUAgEAgExyRC5AgEAoFAIDgmESJHIBAIBALBMYkQOQKBQCAQCI5JhMgRCAQCgUBwTCJEjkAgEAgEgmMSIXIEAoFAIBAckwiRIxAIBAKB4JhEiByBQCAQCATHJO0SOVlZWUiS1OzvlltuAcDj8XDLLbeQmJhIVFQUF154IWVlZWHHyM/PZ+bMmURERJCSksJdd92FLMthY3766SdGjx6N1Wqlb9++vPfee83m8vLLL5OVlYXNZmPChAmsWrWqnZcuEAgEAoHgWKZdImf16tWUlJQE/xYsWADAxRdfDMAdd9zB119/zeeff86SJUsoLi7mggsuCO6vKAozZ87E5/OxbNky3n//fd577z3uv//+4Ji9e/cyc+ZMpk2bxoYNG7j99tu5/vrr+f7774NjPv30U2bPns0DDzzAunXrGDFiBDNmzKC8vPw3vRkCgUAgEAiOHaTf0qDz9ttv55tvvmHXrl04HA6Sk5P56KOPuOiiiwDIyclh0KBBLF++nIkTJ/Ldd99x1llnUVxcTGpqKgCvvfYaf/3rX6moqMBisfDXv/6VefPmsWXLluB5LrvsMmpra5k/fz4AEyZMYNy4cbz00ksAqKpK9+7dufXWW7nnnnsOev6qqlJcXEx0dDSSJB3q2yAQCAQCgeAoomka9fX1ZGRkYDC0Ya/RDhGv16slJiZq//jHPzRN07SFCxdqgFZTUxM2rkePHtqzzz6raZqm/f3vf9dGjBgRtj03N1cDtHXr1mmapmlTpkzR/vznP4eNeeedd7SYmJjgeY1Go/bll1+Gjbn66qu1c845p805ezwera6uLvi3bds2DRB/4k/8iT/xJ/7EXxf8KygoaPO+b+IQmTNnDrW1tfz+978HoLS0FIvFQlxcXNi41NRUSktLg2MaLThNtzdua2uMw+HA7XZTU1ODoigtjsnJyWlzzo8//jgPPfRQs/UFBQXExMS0fcECgUAgEAg6BQ6Hg+7duxMdHd3muEMWOW+//TZnnHEGGRkZh3qIo869997L7Nmzg8uNb1JMTIwQOQKBQCAQdDEOFGpySCJn3759/Pjjj/zvf/8LrktLS8Pn81FbWxtmzSkrKyMtLS04Zv8sqMbsq6Zj9s/IKisrIyYmBrvdjtFoxGg0tjim8RitYbVasVqt7btYgUAgEAgEXZJDqpPz7rvvkpKSwsyZM4PrxowZg9lsZuHChcF1O3bsID8/n+zsbACys7PZvHlzWBbUggULiImJYfDgwcExTY/ROKbxGBaLhTFjxoSNUVWVhQsXBscIBAKBQCAQtDvwWFEUrUePHtpf//rXZttuuukmrUePHtqiRYu0NWvWaNnZ2Vp2dnZwuyzL2tChQ7Xp06drGzZs0ObPn68lJydr9957b3BMbm6uFhERod11113a9u3btZdfflkzGo3a/Pnzg2M++eQTzWq1au+99562bds27cYbb9Ti4uK00tLSdl1LXV2dBmh1dXXtfRsEAoFAIBB0EAd7/263yPn+++81QNuxY0ezbW63W/vTn/6kxcfHaxEREdr555+vlZSUhI3Jy8vTzjjjDM1ut2tJSUnaX/7yF83v94eNWbx4sTZy5EjNYrFovXv31t59991m5/rXv/6l9ejRQ7NYLNr48eO1FStWtPdShMgRCAQCgaALcrD3799UJ6er43A4iI2Npa6uTgQeCwQCgUDQRTjY+7foXSUQCAQCgeCYRIgcgUAgEAgExyRC5AgEAoFAIDgmESJHIBAIBALBMYkQOQKBQCAQCI5JhMgRCAQCgUBwTCJEjkAgEAgEgmMSIXIEAoGgDXzOWnIWPYGzZE9HT0UgELQTIXIEAoGgDTYvuZ0i3mTjups6eioCgaCdCJEjEAgEbVBr/wUAjz23g2ciEAjaixA5AoFAIBAIjkmEyBEIBAKBQHBMIkSOQCAQCASCYxIhcgQCgaAtNKmjZyAQCA4RIXIEAoGgLTTxMykQdFXE/16BQCAQCATHJELkCAQCQRtICHeVQNBVESJHIBAI2qJJTI6qyB04EYFA0F6EyBEIBIKDRPV7O3oKAoGgHQiRIxAIBG0SsuQoQuQIBF0KIXIEAoHgIFH9no6egkAgaAdC5AgEAkEbaJISfK3IQuQIBF0JIXIEAoGgFVRFAUNI5Kh+XwfORiAQtBchcgQCgaAVNMWHqknsc2SiapJwVwkEXQxTR09AIBAIOiuK38u6shG8uukPjE7ZwOieIvBYIOhKCEuOQCAQtILq97GseDwA68pHoipC5AgEXQkhcgQCgaAVVNmH2jSFXBYiRyDoSgiRIxAIBK2gKj4U1Rhc1oTIEQi6FELkCAQCQSuosg9ZM4UtCwSCroMQOQKBQNAKqhxuyfH4RHaVQNCVECJHIBAIWkFVfEiSFlx2CZEjEHQphMgRCDoxqkdGk9WOnsZxi6b4gJDI8fhETI5A0JUQIkcg6KSoXpniB5dT8uTqjp7KcYui+FC10M+kS1Q8Fgi6FELkCASdFH9RAwBqvQ9N0w4wWnAkUGUvihaKyfHK/g6cjUAgaC9C5AgEnRTJ3OS/pyJETkewfwq52y9EjkDQlRAiRyDopDQVOapXaWOk4Eihyj5kNZRC7pHlDpyNQCBoL0LkCARdAM0nRE5HoKq+MHeVTxWfg0DQlRAiRyDopGhqyEUlRE7HoCpe5KZ1cmTxOQgEXQkhcgSCzkqYyBFp5B2Bpvj3s+SIz0Eg6EoIkSMQdFaaxBqLWjkdg6r6wiw5QuQIBF2LdoucoqIirrzyShITE7Hb7QwbNow1a9YEt2uaxv333096ejp2u51TTz2VXbt2hR2jurqaK664gpiYGOLi4rjuuutwOp1hYzZt2sSUKVOw2Wx0796dp556qtlcPv/8cwYOHIjNZmPYsGF8++237b0cgaDTEuauUsTNtSPQY3JCgcdekeUmEHQp2iVyampqmDx5Mmazme+++45t27bxzDPPEB8fHxzz1FNP8eKLL/Laa6+xcuVKIiMjmTFjBh5PqBz6FVdcwdatW1mwYAHffPMNP//8MzfeeGNwu8PhYPr06fTs2ZO1a9fyz3/+kwcffJA33ngjOGbZsmVcfvnlXHfddaxfv57zzjuP8847jy1btvyW90Mg6DxoTUWOuLl2BPtbcvziYxAIuhSS1o4qY/fccw9Lly7ll19+aXG7pmlkZGTwl7/8hTvvvBOAuro6UlNTee+997jsssvYvn07gwcPZvXq1YwdOxaA+fPnc+aZZ1JYWEhGRgavvvoq//d//0dpaSkWiyV47jlz5pCTkwPApZdeSkNDA998803w/BMnTmTkyJG89tprB3U9DoeD2NhY6urqiImJOdi3QSA4Knhza6l4YzMAiVcNxj4ksYNndPyxbcGjnLlwVHD5ql6/8Mgfn+jAGQkEAjj4+3e7LDlz585l7NixXHzxxaSkpDBq1CjefPPN4Pa9e/dSWlrKqaeeGlwXGxvLhAkTWL58OQDLly8nLi4uKHAATj31VAwGAytXrgyOmTp1alDgAMyYMYMdO3ZQU1MTHNP0PI1jGs/TEl6vF4fDEfYnEHRWtCYeKuGu6hh8SngbB2HJEQi6Fu0SObm5ubz66qv069eP77//nptvvpnbbruN999/H4DS0lIAUlNTw/ZLTU0NbistLSUlJSVsu8lkIiEhIWxMS8doeo7WxjRub4nHH3+c2NjY4F/37t3bc/kCwdGlqZFVuKs6BJ8SXuFYxH8LBF2LdokcVVUZPXo0jz32GKNGjeLGG2/khhtuOGj3UEdz7733UldXF/wrKCjo6CkJBK0jAo87HJ+6n8jpoHkIBIJDo10iJz09ncGDB4etGzRoEPn5+QCkpaUBUFZWFjamrKwsuC0tLY3y8vKw7bIsU11dHTampWM0PUdrYxq3t4TVaiUmJibsTyDorDQ15IjA447Bt1/xP1kVVTcEgq5Eu/7HTp48mR07doSt27lzJz179gSgV69epKWlsXDhwuB2h8PBypUryc7OBiA7O5va2lrWrl0bHLNo0SJUVWXChAnBMT///DP+Js3wFixYwIABA4KZXNnZ2WHnaRzTeB6BoMvTxJIj/CQdg1cNt934NamDZiIQCA6FdomcO+64gxUrVvDYY4+xe/duPvroI9544w1uueUWACRJ4vbbb+fRRx9l7ty5bN68mauvvpqMjAzOO+88QLf8nH766dxwww2sWrWKpUuXMmvWLC677DIyMjIA+N3vfofFYuG6665j69atfPrpp7zwwgvMnj07OJc///nPzJ8/n2eeeYacnBwefPBB1qxZw6xZsw7TWyMQdDCqSCHvaOT9elXJmrDkHEuUbvmeip2/dvQ0BEcQ04GHhBg3bhxffvkl9957Lw8//DC9evXi+eef54orrgiOufvuu2loaODGG2+ktraWE044gfnz52Oz2YJjPvzwQ2bNmsUpp5yCwWDgwgsv5MUXXwxuj42N5YcffuCWW25hzJgxJCUlcf/994fV0pk0aRIfffQR9913H3/729/o168fc+bMYejQob/l/RAIOg2aqJPT4ezfkFMWlpxjBmd5LltLZ4FBZULcQqJSsjp6SoIjQLvq5BxriDo5gs6Ma2MF1R/rdaFipvck5uQeHTyj44+PP7mBezecF1yenLaaD29/sMPmIzh87FvxH3a7HgRgUOLzZIw4u2MnJGgXR6ROjkAgOIqERR533DSOZ2RNuKuOVbzukuBrn7umA2ciOJKI/7ECQSelae8qjl+Da4fi3+99VzQDqmjSeUzg9VUGX/u91R04E8GRRIgcgaCz0rTisSpETkfga2bJMaHJvlZGC7oSfqUq9NovLDlHgtLN3+EoyunQBwMhcgSCTorsaQgtCI3TIfi08B9nWTWh+NwdNJtDo3TrAvb8+mpHT6PT4VdDwsYv13bcRLoQ+as+onTLDwc1VlUUtpfcxeodM6nNW3eEZ9Y67cquEggER4+GilzAqi8IkdMh+DWNOCSuwsI3+JFVI7KnAUtUfEdP7aDZWnYTADE5g0keeGIHz6bz4JdCIkdW6jpwJl0DZ8kedjn/Dk6IKltAVGrv0LbyPCp2/UBS75ORDAaq9y2nvOJ7VLsbSbEQ22NEh81biByBoLPStE6OiAPpEHwaXIOFi7FyKVZ+r5lQfA0H3rGT4HOFmhDXV24nGSFyGpGNtaHXmhA5B6KudHPwdfG2/9I/9W4AHEU55Ky/n/qIteRufzK0gz3wj7cvRpP5aE41DCFyBIJOSnhbB9E1qSPwA/0whpZVE4rP1XETaieuir3B1z6fCK5tRFVkFHN9cFmWHG2MPr5RFYXybT9QXvIdBMrdFWivE7m2L1Ep/Vmz61yIaH3/+IiO7UIgRI5A0FlpYslRZSFyjjay142MAbWJr1BWTchdSOQ01OwLvvb5KjpwJp0Lb105SE0+V0N9G6OPb3b/8jwF6itBgdNITt1dZDpubHmnABGugfQ99c9HcHYHRogcgaCTEl7xWIico42vvhK/YsJEqMqxrBqR/V1H5Lhq9wRf+xQhchpx15aELSsmIXJao9T1RTOB00ih9kbYcqRrKKNPep+ijZ+T0HMSsZlDjsIM20ZkVwkEnRStifVGFSLnqONzVuFXzWFPgrJqQvF3neyquoZQI2QfVW2MPL7wOssBMHtSQTWiGX3UF+04wF7HH6qqohpDMWgmbwLZw38izj2l2dhM6UYmnvUVlqg4ek2+oVMIHBAiRyDotDQVNpqitDFScCTwuXSRY2yyTlZNqF3IkuOlNPjabxAipxGvqwwAq5qG3dsLgLrSLR05pU6Jt7YUxewMLg/u/QwRSd2JjmzeI7L70MuP5tQOGuGuEgg6KZraxHojRM5Rx+Uowq+awn4kFcWEIned7Cq/KeSiks21qIqCwWhsY4/jA59Hr3ZsNsRj0Oy42Y3XVd7Bs+o8+Jw1bFwyCz9VYAeLJ43JM34Ofnd6jLyagnWh2ks9jLcRkdw5e+sJS45A0ElxeUOVdVUhco46noZ8/Gp46qummlHkrmHJ8bvrw57CMSh4HeJGDuALtHQwGxOxGBIB8HrFe9NI7spXcNhX4LbvAiAt8uIwcWyLS+GkE7Zg9EeBJpHcc2pHTfWACEuOQNBJqfOoNPbW9fhEK4GjjdtbiF+JC6vDqGkm/H5nq/t0JhrK9fRxgxyBpJpRLHW4a4qwx6d38Mw6Hp9cBSawWpLwy3qNHEURwceN+P3VYAktdxt2cbMxRoudMcM+R/G7ievZccX+DoQQOQJBJ8XY5PbqcAuRc7TxqiX41eSwdSaMuH1dQ+Q4K3cCYPGnARoKdXgc4VlFdYXbsMWmYY1O6IAZdhw+VY9VskVkoDr9ACiqpyOn1GmoL9lNuWVOcDnRezoRCd1aHBud0f8ozerQEe4qgaCTojSpcuzzi4rHRxufsRRZbZpADmagwd81YnJqKlcBECH1wkISAJ6G4uD2wrX/Zc3Os1nz86XHXWd1r1EPPLbH9cRo0ivZKWrXcEMeSeryt7BpXaj2Tax7MsNn/KsDZ/TbESJHIOikqE2KAcrK8XUT6mgUvxe/Rc+uMiNjM6xCogET4D5AdpWqqjjLcjtUOHgdlVQYvgEgLnYsFoMucrzusuCYyvLFAHjsuTSU7jr6k+wgFJ8H2aJXf45M7h0UOSpdpzTA4aZq13IqcpawZve5eOyhApJDsp/AYOjaMqFrz14gOIbRmoqc4+xJu6NxVewDScOnmOlp/Joky8Mkmv+hixzF2+a+2xc+wMqtp7Htx/uOzmRbmsPSB9CMXszeZHqMvwaLOQUAnz8UXOtR8oOv6yt2HvU5dhSuynyQNCTFjC0uPSRytOPPXeWqyGfhoj5sKLiSTcV/CNuWrl6NPSGjg2Z2+BAiRyDopKhNKh6L5KqjS0O1HrTrk22kGRcBYDNuwoyEW21d5Lhryyk1fgRAuTTniM+zJWSvm2rzjwDES5Mxmq1Y7akA+NTK4DiPOSRy6irWH91JdiBuh+6yM/kTMBgMmMzRAMhS14i1OlyUbPyG5Zuntbo9PmXcUZzNkUOIHIGgk6JpoWgQRdXaGCk43LjrdJO9R7ajNuk+aAK8WuvVp6t2Lwm+1iQZRfYfsTm2hrN0J5pBn2P/SfcAYI3UM6oaqx4rPk9YenmV78ejPMuOw+vUXXZmNR6A6NSB+npLAT5nbUdN64jidVSwbcH91BVuA3QhvL38zjb3aXxfujpC5AgEnZSmIkcTGueo4nIVAOCRbRgJZbaZAS+ti5yaqpWhBYOCq3xvq2OPFI1NOW3uXlhj9Owwe4zudvAbdZHjdVSG7eOzlB03wcdet14g0SzpIicqvT9mbxKa0U/FzsUdObUjxoqlZ1Ji/JB1Wy/F56xh9YJL0IzhAjxVvoSBcc9gkG0MTHiOqNTeHTTbw4tIIRcIOilhwkaInKOKy7cL7OBRrEiGkKgxIeGjdd+hQ16vK6EAzso9Rz3N1heo3GsmlBZuj+8GeaCYHSh+Lz6nfqOXFAua0YdmkJE9TiwRMS0d8pjC560Ag14IEMBgMGBVu+OnEr+3roNnd/ipyFmCbNUDrVWzi6XLpqJGNA+eHzDl/zDbo+jGeUd5hkcWYckRCDormvjv2VG4Jd0a4lXMSE2SyE1o+NBatHr4nDV4bI1WlCwAPM7iZuOOND5voJpvwFIBYI1JAdUIkoanthRPQ2CMPwlJ1Z91fc7K5gc7BvH7dWuWxZQYXGciEgDZ7+iQOR0pnOV5FOd9GbZONYULnAjXALJH/ozZHnU0p3bUEJYcgaCT0tRdJQlLzlFDVWT8lio0DXyaCZqInAj8+DVji1aP+pIckDRMvjgipQF4yMPrLuVo4/NVghnMTW7iBqMRkz8O2VqFp7YIv1t/sjepsSiyC8VSi89ZDSnHhouiLXyqfu0Wa6jQo9Gg3+BluWsFHyuyH6MpvPVI0Ya55BW9QKL9FIp4O6xycVP6RDxAj/FXdvkU8QMhRI5A0FnRpAOPERx2XJX5YFDw+CLRkDCEiRwZv2pGbqhtJnIaavT4G4ucjsWq16Xx+Q/eOuKpLccSnYDB+Nt+lv1KDZjBYkkMW29WE5Gpwu0owdcocqQYFMWJQi0+1/HRpdyPfu3WiKYiJ2DJkQ+ttYPf7SR/zfukDTwLc0Qslsi43zzPtqjL34KjbAu7q/+BZlDQjF4y1GuIiR9KTt1dYEcXOPvR2/Y3CmvfI940mayJVx/ROXYWhMgRCDorTUWOEDxHjT3rXwQLuBsym22z4cevmPG7m7s13A16SrbVkI7JHAsqyOrBuT9KN3/H1vJbSfafzfDTn/tN8/erNQBYbOEix0IibsDjKkH21YEEZikOWdVv7H53zW86b1dBNtQCYI1KDa4zm/X4Jb98aO/B+kXXUW9fQ97mZzH6o5g0eQmWqLjfOtUW8dZXs3bHRXrgcBMjTrHhfYrbCCnqG/EgPSdeRS+uOyLz6qwc23YqgaALozWxIAiJc3RQFZkaKVAJ2NMPAKlJoLFd8uFXzVQVLGXvsndQlVBQstutixybJROzJQ4AWTs4y8CugsdA0qiwzP3N1+CX9Bu1NSI1bL3VlAaAqyE3eDM3m+IwSXqdGJ/32Bc5qqoim2oBsMWEGpXaGusIaYfWibzevib4WjE7qdix6LCno/ucNRRtmMvKX85qlhnVGpISUkFxmaMP63y6CsKSIxB0VsICj4XMORrkr/4oWD9GTrwMqIYmIicCH/WKlb3epwBw/LCepPTTSB9+Fg3aDgCi4wYhSUbwgMyBRY7i8+CzhQKUa/auJ77XqEO+BtmoP85bo3R3zB9f/Y6dFR5emTYc/F/iVLZjk7rpcTvmeCyKbm3yekLxQ/VFO1D8LuKyDn0enRFPdbEuEDSJiKTuwfW2mExwg9O+kbrCrcRmDjm449WWs/HXP9KklBIAOXV3kbMKkrxn0GfMnUSlZLVrnnuXvUVN7UpGnPYSRrMVVVVZteR8vPYCsLW9b6L3DIZOexKTLTJwrHdQZOdBX9OxhhA5AkFnRQQeHxFkrxvZXY/id+N31VG+9wfKG+YxctzbVFR9D3ZIlS9lpcOPEZCkkLUmEi8VsjW4XGn9lsrqb1HWNuC1FgIQlzESd20B1IFyEFV06wo2hy2v23sRp/Tac0jXpioyilkXLbaYVGRZ4ft9KmBhzl474zPBZyzHKOt3ZbM1kIGlgduXHzzGqh1nAnBCwkqsMUmHNJfOSG2RXtnZ7E3GaA59jsn9piCVmNAMMmt2nsPA8mfoNvo8AEo3z6co/zOy+l9PYr9JqIrMpu9vI8KeharJOCM2BY/TmJLfSKX1O5yrt5F9xo/tCvDN9TwONshf/R/s0ZlsrbgF7C2PlVQTQ9Jfpnjff1E0J4NPfCQocAB6TfpDyzseJwiRIxB0UqQw683xa8mpy9+CIntI6D32Nx2ncM3n5JY/i9/WgkvCDuvWXo7frgcKJ6ZOojjPjZ5bFRI5UXhxy80fpXfVPQAGBUkxE5naG8WndypXDAchcko3HNL1tITXURlUxNbYFHYWhQKfyz36vBVLLU6Lfs6Y5MG4HSVQB16tCID6opzgPg0Ve48pkVNR8iNYIFYK/y4ZLTZ6Wm8nz/80ADk1d6Ku8eF1lbFPfh7skLNzHxN7zmPjj7dQY/uJqv0ePGLc4xk4+hFWbZ8Rtt5j38fOn55g4Ml/O6g5Fq39X/B1rusp8DSvyzQw7mmS+p2Iu7qQmMwhGIxGUoecelDHP94QMTkCQWelibvqeJU4it/Luu2/Y33epRStn/ObjrW3/PmWBU4AvzUkCKKS+lFe7ws8BYZEToTkwdOCyMGg34gsvnQMRhPmSD2QVTE1HLCSsKN+a7N1iu/QOmJ7HXrLAqM/CqPZyr7yUJxNhUvC4kkLLpu9ycT3nkBUYh99X3MxqqpSV7IhOMZdX3JI8+isNCi6SzEhaTIAFbVOtu3T3XRxaSNDAyWNnY57dYETwGPPY+OPt1Jj+6nZcVN85zHy5DeITu/LiZM2YfImhG0vVv5NXWHzz3l/vPXVenZUI4YWBE7sP+k2+nys0QnE9RyOwWg84HGPZ4TIEQg6K2HuquNT5tQX70A161aRvOJ//aZjqZLvwIMCRKRkUeWSMSKFWXIi8FDvCnVmHpr6OjZ3z+CynV4AWKICmU0GBdnVdhXdBkW3nGRKNwTXOcsOzV3lqQ+IHEVPby+rDRV+K3fKZMRcGVyO1kZgMBqJzhgIqhHV5KahLJfcmlB2l7eVYoYlm77F0cTi0xVwVRbiNevtOiITdGF35jMLOPPVtWzZW0J87/HY3f3aPEaNbVGzdanyJQw7/RnMdj2A22SLZMqMlQxJeZVJI3/B5u6JZvSzZeNtrQpexe+lvmQ3jqLNLW5vZGSPj+g25oIDXqsghBA5AkEn5XgVNorsZ9U3F7Pu2+twlIeefj3WgkNueKmqKqrRA+jCZEjyS3Q3/IlJo5fTTbs2bGw3bmDTvgr2NFgClpzw7CqnP4VEzwwGJ75A6pBTyUq/Vd+oSXTLuhwAozUCoy8WgLri1p/gFdmPx5IHQFq/c7C5dZFUW7SenIWPUrrlh3Zdp6suUKtH04OOKxwhi1C1RyNr/O8xe1JAk0jLPFefq8WGzasLtdwNL6JYaoP7eDxlzc5Rsuk7tlXeyuodM1k77w9doudVbd4Glm86EdWkfweiUvtRVuOgwq9Xypu7ehcGo5GJp3/D1OwN9DTdHrZ/tGtMi8eNdo9h6PTHm603GAykDZ2OPSGD4SNfBXRLUOnmeXgdFc3Gr/nhclZtn8Gm4ubxMyMy/820E3cyNXsDiX0ntOu6BSImRyDoxDR1Vx0/gqe+aBv1EesAqKn/KbTBoOCuKjikxoFVu5ehmlxIipmk/lMwmq2kcQYAUbEDIFDOpl/0o/QYdzlXvzAPIBCTExJWNry4ZYmRZ74SXNdt9PkYN9sx2+NI7DuRRRt2c9tnWzg540zOHfwxDVW7SOaEFueVs/jhYDpwdPoArBsz8LCX3a4HQYKi8ndJ4+CtOg3OXWCGCFNfACrrPcFtdbIRyWRl4tTvcFcXEttjaHBbpKE/HnKpMn8fdrxa79Jm5ygu+DQYBFtrX4KzZCcx3Tpnx2pF9lO07jOKyj8KZkCZvclYouLYtmF3cJys6AE2BqMJgz2avlNvpWzeXDz2XACy+v6JzcV6fRmjP4re8XdjMFpJHDnpgHOI7jYA+4Z+uO272F51O8aSGLIn/UB13mo89YXsq38VxR5eTynFfyGp3U8nrufoYGFBQ8BSJGgfQuQIBJ2Uppac40figKs2v9Vt3vqKQxI5+bvfBhvE+iaEZdUAJPY5AeOqKIxKDGkTZwJQ45YBE8lGL5IUslTYJV9LcaCkDTs9+Pre/23GqVqYW5jNuYM/xuNqvX9VqfEjAAxyBAajkZiIodRp4cLC764PukIOhFcuBTPY7Xp6dLUr5KLzY6SizklqfFyzQnXRUUOp8s9HCzQjNfqjUMxO3PY9FG/4moyRZwN6ynSdZUXYvvWl2zqdyNnzy8s0NOzGZIqjxPDvsBRvq9INgNyy2uC60rrmMVAWkvCgi5yUgSfRq+puKmrmM3Dko+1Ox442DcfNLgAUi4Nf10xseqLw83rSGHzqwxgtB8gVFxwUwl0lEHRSwqw3x5HrylNf1Oq2Q2k9ULp5PrW2nwFIST2z2XZ7fDpTpq1g0vRFwVYNFS5d2Fw5KjlsrFXy4lXb/tms9Ye2q5qE19fc5eNrqGXH4ieDywNTdZdH1vgbifNM1V1KATYsuiGs6GBb+NADq+2R+o28xhXu3ttZ2HKbibi0EWHLaebLg68rShYEX5fvWohm9GN1dyfBcxoAe0tfOKi5HSkUn4e9S9+gYscv7PnlVRYu6kOe/1kqLHN1gbMfmelXAFBQFcp8q3A2j9fqP+I+jL5Yukt/BKD35D8y4awvD6neTLd+lxxwjNEfxaisT5k0/SchcA4jwpIjEHRSjldLjsdTEnz8MnuTsKt9UTQnDRFbKC/6lvThzYVKa5Rs+pZt5bcHj5c6cHqL44yW8CIkjUKle2y41ceCH18b3eG9Pj8+LZTtUueNIV6rbTZu28/3UWX9DgCTN4H0EWfpx4+MY8yZ7wKw8psLcEZsxGFfzb6V/z5gvRO/W7e8ANjjdEtOjUcBQvPJLa1lyrDm+8b2GAX7DGDQxV103ED6+P7OHtcjeJSQZc1RsxlMEG0YTnrW+VSXLMBrL8RZsoeo9D5tzq8lWmow2V52/vxPig3vQevaOMhJU3OC5yuuDVlvqtzNzXOxmUOYmrHmsDSwTOg9lpHKB+za8UTg4UUK1tfpH/M4UYn9iEzudcRaQRzPCJEjEHRSpOMkJkeR/WiKjMmqCw2vrwRsegDwwDPuAWDrgr/TwBYqrd/hra/GGp3Q1iEBvb7OtorbwKDHWwxOfAFLVPwB96tyNODR9J/GbnHhpWzN+FAw4PL4iLA1b++8KbckrB1HpTuR+PilLJ13MlYyGTz+USKSewQFDkCE2nJGT68+t7K55HoAyqu/phdti5xNi0IF4+yJPckrrWa30xKYt4IfI3kVodiPH9ft4pbPt/GH0Qn89eIpYRUnE3tPwlGyFVzgNYYqITf4toMJYmKGkjJoGvZcPdakOn95qyJH9jTgc9ZgtEawc9ljxCaMAU3DYo9nW+lsMk3X0n/a3W1eW1vUeVY1qzjcEkZ/VJigKq7z0XgLrPO2vM/h7NCd2C+bxH5fBZcVnxvJaP7NDVkFbSPcVQJBJ8XQxGJgOEbdVYrfy7IfTmbZopNw1+g3U6+qu3dsEaFU7T7jbg2+zl35cpvHdBTlUJu3gTW7zw2/cfeZfFBzyinQXT5WSSbCFP4TaQlUP651tlzHZm1uadhytScO0AvC1dmXsnvts/icNaCGrCtWYwotkTJoGgPj9OJ0zohNlGz6ts1519p/Db42RMRzyStLgst9onULTXmTQOR/LdiGVzPx6loHHp+faLfewsHiSccWl4I9Vn///dYKti74O4VrvwhaH2LT9LF2g54N5nLmtTgnn7OGpYunsWL9aaxf8nvKzV+yq/4+djn/ztaKWWhGHwXa621eV1uoioLbktvq9lh3Nj1NtyMpVvolPxS2rbghFGtVLx/9WjNGi10InKOAeIcFgk6KFNag89h8Hqnc+Uuwb9PuVc8ybMZT+A163EhETLfgOFtcCr1tfyPX8xg13mWtHm/vsrf0kvgtcDBWHICcQj3uJ96s4nOH954yBTKtqutdZCTFNtt3S2F4k8sab1zYskPZwPZfHwSL7h4xe1PoPfJWWiMmbQjU6q+3Vd5K6bdfEWHvTY8RV1FXvJm0oTPY+dPTFPrfCXqlBie9xO9e+I5yn27FmZoOiZER5NT7w2J0Ii2h79T7P67nd6P/wd5Nr9F33O0AWGNCDT5LjR9RWvdRcDm2u+7zspp1gVYkvUu34kuIzugfNv/CjZ8hW/X3syGi9VR6VVUPyWriKNiEavIgKRa6ma6humEpg0c+QW3ROlIGzMAep8+vt/KnsKJ5xZV1VMshS5wPI7VON3FRrfROEHRZ2vWtevDBB5EkKexv4MBQVL3H4+GWW24hMTGRqKgoLrzwQsrKwoPu8vPzmTlzJhEREaSkpHDXXXchy+FBdT/99BOjR4/GarXSt29f3nvvvWZzefnll8nKysJmszFhwgRWrVrVnksRCDo1qqqGx+Qco5YcR+WW4Oty8xeUbf0Rv1m/Kdrj9dotfllBlhUSuus1QjyWfbiri/HU6haXpkG5hbXvhR3f6ulGd8OfGNn9g4Oe0/p9+vl7xJrwusJTe02BwoBV9a5m+wHsqdQtPEZ0K0FN3YiwoHGvvYBKyzcADIx/hqlntO7mAYjO6E+Sd2Zwudr2I4XaGyzbMIWt5X9i4aI+FKivohl1f4vVnYklayprq/Qb+p/GxvLvP88kKVoPZK1tEnvi9IYsGV9tLCY6oz/DT3+WiOQe+rUGgrD3J1O6IRgYa7WGrFA56x8IvnYU5bB63uXBRqYHwlN7aJWVy/MWAhDlHcqAafeQfdbXxGYOoeeEq4ICB2hWFXhHoV6rJsLgxxSog7S3tP1B7YLOT7stOUOGDOHHH38MHcAUOsQdd9zBvHnz+Pzzz4mNjWXWrFlccMEFLF2qp0QqisLMmTNJS0tj2bJllJSUcPXVV2M2m3nssccA2Lt3LzNnzuSmm27iww8/ZOHChVx//fWkp6czY4beE+TTTz9l9uzZvPbaa0yYMIHnn3+eGTNmsGPHDlJSWjb9CgRdCU1VMHDsBx67XLuhSWzvlrI/Bi0SEYmZqKrKlIfn4vAbeOlCPXZFM3pZtmEKZm8KcdIEqgwLGdX/38RljUKVQsEVkmpi0uk/BS0EucWVzHrvV3Y4jHSPkJmcFYvT6+eu88aTmaxbeWRZYW2RC7AwsnssSkN4G4hGkVPr9NASRXpxZnpGyuQ2WHDRk1NO2U19yW7Wr78avy300Jc+4pyDeo+GzXieZd9t0jtQH4BY0zh2BfpVmVG4+yK9Pk9KjB2ox+ELue/qmoicvBaapRsMBqJcw3BGNKnCq0kMOPme4GLGkPPJ2/AsAA3GneSv/pj88tf1ubZiFOlhvI185cWwdTX5a4lI6NbieHdNCe6aYkzWyLBU9fxVH1Kg6oX24qMPXK+mKaW1+gcVZdTwSQq1ipF95XWM6pvZruMIOj/ttg+aTCbS0tKCf0lJevO2uro63n77bZ599llOPvlkxowZw7vvvsuyZctYsUKvq/DDDz+wbds2PvjgA0aOHMkZZ5zBI488wssvv4zPp6fwvfbaa/Tq1YtnnnmGQYMGMWvWLC666CKeey5UavzZZ5/lhhtu4Nprr2Xw4MG89tprRERE8M477xyO90Qg6HA0RUbSVJLM/0ec6ZVjNvDYpexteYNqxGixsT2/jFKfBZdm4t65OzB7Q80i/dZyKixfo5pcbNk6mz2/vopsrQ5uT5bPCnOB3P/ZCrY5zCgYyHNZ+HCbm6/2yNzx/i/MW7kdgFfmraLEa0FC48zRffDv15Kh0V1V29A8UrWy1olD0QNbh6bpkbAufyBbKb0vfdLvxhDoe9XDOOug3TMGg4Exkz6hh3FWWCxPI/GeaXSXbiJduYL+k/7GvnJ9zrGmkNUmLV7vSl3fJKO8rknWtFs1IcvNM4zGnv45/WMex+LJwOiLY0jaq2Hb7QkZTBm/BlQjiqWWXfX3NRNjkmIh0TMDszeFSaOX0+/EP3PSlG2M7fsV0W69UWZO9R04S5oXPfQ5a1ixajrr8y5h9Y6Z1OxdH9xWUPF26Pr6n91s37YobxQ5ZogLiOyCSkcbewi6Ku0WObt27SIjI4PevXtzxRVXkJ+vpxeuXbsWv9/PqaeGOqEOHDiQHj16sHz5cgCWL1/OsGHDSE0N+XpnzJiBw+Fg69atwTFNj9E4pvEYPp+PtWvXho0xGAyceuqpwTGt4fV6cTgcYX8CQWdEVfxESnnYjBuJMn2LpLVQga6Lo6oqHrP++7F/z6B09Fom6/eEAnnLfWZ6ZD5JS3jt+eT5ng4unzB2BUOm/zNsTH5dyy0hVlcaueXLXPreM5dnl+si6ew+Zkb27Ya2X0yOIeDaqG3wcMkzX3PR03ODbQ1W7gj0RTL46R4QFS5/yHLSbdR5TJu+lVNO3kO/E+9oNo8f1+2iuLLlPlf2+DT6nXgHJ03dGLZ+YNzTjD7zLfpPu4vBpz2MNTqBssANPLpJ8ldGgl5M0KWERFJDk9caEpWOhmbnNZrMdB97CVPO/IWTTl9L2pDTmo2xRMUT4Wm951MqFzLyzFeYesbyoAvJaLYS22MoGWkXB8dtWHd98LWqKPhcDjYuuQXVHHIN5m57AcXvpWr3Sjz2fQCM6f0F0el9Wz1/S1Q6dZEaYzMQb9Pfh5Lall2Qgq5Nu0TOhAkTeO+995g/fz6vvvoqe/fuZcqUKdTX11NaWorFYiEuLi5sn9TUVEpL9R+q0tLSMIHTuL1xW1tjHA4HbrebyspKFEVpcUzjMVrj8ccfJzY2NvjXvXv39ly+QHDU0BQZU5P/nmZq2hjdNfFUF+s3ME1i2MgXglaKvhEPMvjUB1izs4DXf8kLjteQqLX348RJm0jytf7kHuUehTUmuZmlpKkVI8rg59rhkWHb5YCfzIDKX84Zp5/Tu7/I0d1Vz62oYVWFgTWVRlbmFODy+NiQp8d5dIuA6AhdYXhkjYPhxa+Wc/1nOznr+eYNIJtitNgZP/A7DP5Iknxn0230+c3GVAQyqGKsoetPT9Tja/wY8fj81Drd+Am3CpXVODlUYm3jw5ZT/Ocz7cSdTBr5C4NOebjV/TLHXETfyAcBXai6a0pQVZXV313ILytG4bCvDBtfa/+FPb++QMU+PWQiyj2KuKyR7Z5vVUDkxNlMJEbqIRcV9a3kkQu6NO2KyTnjjDOCr4cPH86ECRPo2bMnn332GXZ7549Kv/fee5k9e3Zw2eFwCKEj6JSoioxZatIzyVDdxuiuiaNEDzq2eNOI7jaAUZ4P8TZUkj78DP7+wWL+s0WPjWlKpcOFydaNYdOfZd+KYeTXvU6G/XL8ci1OzzYMkpXB2c2tPXVON7Wy7kr69uYxDO6ZBkB1w/d8tSc88eHEbgZ6pgbq8PjCn+6NNK88fPm/tzAwej3JkXqnq96JVmLsAZFzkAa4OZtKAQvVsuWAWT7RGf05MXV9s2DaRqoDrrQ4e+jnPTk2Kvi6rKY+GFNkQsFmUHGqZspqD13kxMQNpiRgGB+W8TYpA08CdHfWgeg54SoK5r2N117A9uX3UWtehhYR8qXFuifTs891weaV5a55REh99TYdtpGHNF89y8xAQmTj98uH03vsWUsPJ0u37uWGDzdy6dA4HvjdSa2O21VUgdloJCstgQuemkuU1cj9F42nb7fkVvc5kvymFPK4uDj69+/P7t27Oe200/D5fNTW1oZZc8rKykhL039Q0tLSmmVBNWZfNR2zf0ZWWVkZMTEx2O12jEYjRqOxxTGNx2gNq9WK1Wptc4xA0BnQVH/QNQJgobkroatTX70DAJumZ/Mk9BkX3PbjrlqaNfUBKhy66DAYDPSadB29uO6gzvXdmp1oSEQZ/AzsHkpOeOGGGVyZk8/F74WCax+8JDv4WpLDRY6hBZEDkFNvxuHzARYGZ8QRG6H/zhzsfbNp94WdhRWMH9ijzfGtCRxocgOPCBW+M5uMWCQFn2akvMZJeZ3+fYowqtiMGk4fVNQdursmZfAM9iz5JwbNQmKf7APvsB/d4n5HrvdJamw/Nds28tTXMVnt9K66h1zvE3jthXgpBMAe2fOQ5lvtVgADydFWfLIK+HD6On839Y7kns834FItvLupgamDd3Pi8N7NrKWLNuzmD5/saLJG/56a2vi+Hml+U/ENp9PJnj17SE9PZ8yYMZjNZhYuXBjcvmPHDvLz88nO1r/02dnZbN68mfLyUMbCggULiImJYfDgwcExTY/ROKbxGBaLhTFjxoSNUVWVhQsXBscIBF0dVfZjbCJyTFLL2TxdGZdLb1gYYQlPoV6+bR8lXl3gRBn8SGjYA0X4GgNG28Ntb37PPfP1m+LAeEOzH+ZxA3twzbAITsmU2PXo6SErDiDJ4S4MSWo5rgegODDnMX3TiAmIHN8B+lyB/vtV7QvdBF5dsLmN0QemNmA+SowKf6CzBVo2lNc1hLKLTBpRgUfdqvqWCxweDJaIGLJP/JGJ0+Y3a4B6MKQOaLlVR7+oR4KVsLuNuKjZ9oj4rHafC6DKo7sRe6XEEhdwLTb4hchpiypv6Lt87Sc7uPNd/R7c4PHxj09/5twnv9pP4ITokRJ3NKbYIu0SOXfeeSdLliwhLy+PZcuWcf7552M0Grn88suJjY3luuuuY/bs2SxevJi1a9dy7bXXkp2dzcSJesfV6dOnM3jwYK666io2btzI999/z3333cctt9wStLDcdNNN5Obmcvfdd5OTk8Mrr7zCZ599xh13hAL1Zs+ezZtvvsn777/P9u3bufnmm2loaODaa689jG+NQNBxaIqM1ETkGDkGRY6SB0BUTHgBubmrdwOQZvGx8v4zWXz7JEal6D9Vz61of2zS3CbuqIm9E1sc89AV03h71pmYTeFPnAZ1P5GjKfz6l0l0t/mINjQXPBIao/p0Iz7gbmqrz1Uj363ega9JfMyKYhl/C5lOB0u9V7+BJ8eEu7ySbfr6TfsqqHDogibGIhEdiN2paiUt/mCxRMRgtkcdeGALRCRl0t3wJ0Bvv9Av+lGmnbiTHuN/Fzp+VHywWWYjMRntb5apqip1/kD8V0YCCdH6+9TQun49rql3eRj+f3NwaeGOnzm7PPhlhSEPLuDN9fVsrGnZMWRGOaztMdpLu9xVhYWFXH755VRVVZGcnMwJJ5zAihUrSE7WfW3PPfccBoOBCy+8EK/Xy4wZM3jllVeC+xuNRr755htuvvlmsrOziYyM5JprruHhh0OBab169WLevHnccccdvPDCC2RmZvLWW28Fa+QAXHrppVRUVHD//fdTWlrKyJEjmT9/frNgZIGgq6KpcphrxMihP2V3RppmVtWZejD77QWM75PCZSeNYF+1CzAwLjOCSJuFyLQEzhyeybIFemXkH9ft4tTRrWfzNPL+gnU8sDC8yNzMse3LwjEqemyIqlkxSF4Mkkxmcjy/PHg+lbVOHvtiOYW1HlZV6D/iNkkhwmYhLkpPFVcw4PX5sVpab0K5cItuZeob6WdPgwm3ZmLJptyDusaWaAywTo8PFxz9k+3saVDYXFRHRqw+vxibEYtRL09Q42reifto0v+kv9BX1R9mW7sp9p92Nyl5p7Fl6x3EW044qB5m+1NaUx8Muh6QmYzb6weKcfg77kbcmXnnh3XB0ghNUTHw+Oe/trAHWFD4/IaxPDl3LXedPfpIT7FN2iVyPvnkkza322w2Xn75ZV5+ufXeMj179uTbb9vuwXLSSSexfv36NsfMmjWLWbNmtTlGIOiqaMp+Ikc6tkSOp7YE1dwAmsSd31Wx02nlf7sKiY+yUVwvAxb6pEQHx195yig+WLGPnHozX6zac1ACYH+B8/TMHgzq2b4HIWOgLo6GHfAiNflMkuKiePY6PaX6T6/N59s8hYkZ+k9qQnQkoAESOQUVjOjTegBuUa0HMDA8IxJ/oZN9bgvbCysPWeQ0poZ3Tw6vWDwsM57v8iopqJOxmfXrio8wYTJIgEKtq+V4o6PJwTzxx2WN4oSsnw75HDsL9WKJdkkmOsJGv2567SW3ZqLe5SE6wnbIxz4W+W5rOaCLnFcv7MOXq/fyQ77u2ntnY8vB6hPSDIzok8FHdxw48PxII6SrQNAJ2T/wWGol4LWr4ijWM6s8zp7scoYCjFfuKqXCo1sWBnYLf0o/fYgeMPxdnsLc5dtaPK6qqry/YB1/em1+s20XTRnW7nkaVP19VzXdpdHa5/DSjdP5/PfDePWP0wGIsFnItOlCYuGmVgoeBihv0I/ZIzGS5EhdoORXHVqgebWjIWil6JES3qsrLU5PmXfK8Guhfs7ESAsJkXqoQL03dG23v/UDV78wL1gD6Fhid4nu8oy36NeWEhcVbO2wM9DuQaDz5dIt5NTrAufjq4dyxriBvPGnM5ic2rw0wuAYPz/dns0fR0fzwh+mHe2ptooQOQJBJ0RV5LAbqoFj62ZTX5UDwNbKEWhNqjkXVDfQoOo/qsN7pYftc9HkQUjoP66PfLujxRvwPz79hQcWlvBtXkgg2iWZG0ZFNxt7MIQsOfrTvUTLgRsGg4FxA3tga+KW6hWnv95d1kLPhABLNu2hyKWLkj5pcaTH6IJjX/WhWe4KKvRigiaUYFxQI8mxehXmGtkSjAFKiraRFAhQrvHo72e9y8Oc3X5+LoGlW/MOaR6dmfwq/fNItOu3P4PBQEygOvSe0mOvHtWh4PL4+MNL33LH13rBxXiTj+zBoUy2W6YPDRsvofH69SeSlZbAvZdMJSEmvAZVRyJEjkDQCdk/Jkc6xkSOy6UHF+9whKdK/1qoiwibJDfr8p2ZHM+/r9CzMCv8Fkb9fS7rdxcGt3t9fj7fEn6TsqCw6ZGz+L9Lpx7SPI2a/hlomi4E2mNR6xani4zy+pZjXdbvLuSaj3KCgmNUn3S6J+g3h1UVBrLumceTn//SrvkWBiomRxibd/VO2u/Gk2L2cd6EAfRKjQOg2quLzS15ofIcjVaPY4kyhx5gnRwVEqQJAQ/VvnJRBR/g8f8uZVFhyFrzfzPCY9kmDclizvUjg8tf/3EM3fezHHYWflOdHIFAcGRQ1fAUcji4yrmdHdnTwMqF5+Cx5wGwp17Pdsq0+Sj0WHAHMjiGJrTcq2vykCziTRupkS3UKWbOf2sjfzuxBJfXz887K4MBkn2jfFyTncVlJw5vljHVHgyBdhpaoGaPxMEH5+qdvz1hjTCbsiE3JCaGxPrJTI6nd2osEGrt8OpaB3O2fMmP984k0ta8btD+5DX2rTI3/750Tw6Jxt4RPhbdr1dLlhUV2Eu9YsLr87OzKNSNe3vRsSdyKp1+wEhKkxT7lEgTu51QWHPs1aM6WGRZ4S/v/khWUjS5FQ00bQt89oSBzcaP7NuN2ybk0eCRGbqf1bUzIUSOQNAJ0VPIQzdHia5fjbV23ya2bpkdFDgAFR49A2hURgSFubqVJNLg57lrTmzxGAaDgXd+P44fN+Tx5ppqfBh5bElj3S1dzEhozL/nHEy/QdwEz0ejyNHFkyQpqIrSZjG+RpJj7UAtdd7mgmPjnmIeWhRqQ/PezXovvr7pCUB+2NgSr4VFG3Zz9sTBBzznjlJd5HSLaf7THhVho5vVR5HXwqMXjgqu75WeiAEVFQO5JdX8uLWYxhvc3qpjr59TjVsBjKTHhdx5aTE2KPNR6ujYDLOO5IulW/Xq33tqsKDS+P/ptglxrWYHzj5/8lGc4aEh3FUCQSdE2U/kHAuWnK1b/oLHHgrC9XmjaVB168Tk/qFq5TP7RbVp+h7VN5O7LjqBWye1XCb+zUsGHBaBAzQJ/g4dz+c9uHiZAd10K1W538ILc5aFbXty7trg61EJMslxutgb1COFNIt+o+1uC91w1+aWczDsrdJdMX2SWo6J+OqO0/jqhlFMGpIVXGc2GYkz6QLz1R828Etp6Am+qL51cb1ow27W7CxodXtnpTbwtnZLDMVpZSbo8UoVnSDD7GhT53TzxOe/8PHKJv83A9/3q4ZGcPu5XbvIrrDkCASdEMXvh7Dsqq4dk+Nz1uCx54atk6S/AnrczPmTBvP4gr04FSOXT2luGm+JW8/JZkL/fC5p0pJhcKz/kFOvW8IgKaCB1uR50OtxY4s4cNG78QO6k2ZZR6nPwhuryrFaVpEWF8l5k4YE0+QB/E10hNViZsXD5yPLCiaTkSue+4alZRLvb3JyxYkV9DtA/58Sp/49GdStZZGYFBdFUlzzuY9ItbC4CL7d46WpoKv0GlHV5vE9t7/1A3N2+zGgsmR226K0s+EMpNj3aOK+65EUA9RSc4g9Ouet3M79X+egAY+dO4jTxx3cd/ho0lK9Jr+scPEL37Oz3kxTOSCh8eJ5vQ/KetjZEZYcgaAT4veHVzyWuoglR1VVKnb8gs8VHsBZW7AhbLmHYRZlxgEAxFkUrBYz3/z5RL65eTyj+mYe9PnGD+xB3hMzuWpoBOlWH3eeMfTAO7UDg9b4ZC+habqFw+c+uLgNg8HAgnvOADQaVDNP/FzB7XPzuOzZr8lzheJrRnWPabZvoyXqlulDsUkyGhJPzlnd5vk+WLieSlk/7qg+bffx258R3XWR0tiJfVySgoSGDyO5JVVhY79esY05u/UAcRUDa3YVtetcHUm9y4NP06+xZyDgGqBnQPA0yO23AMqywn1f51Al6w1Wb/piD+8vWMeP63YdljkfDr5cuoXB98/noY9+otrRgByoqH3jq/MDAifE6ASFD64ackwIHBAiRyDolPj8PgiLw+kalpydPz3BpqLfk/PzA2HrHRV6XRybuzdZ5tn0nvwndpbWApAWof8MdU+Jb3exvkYeuXIayx86n5NHtq+i8YFodFdJqMHgY3/DwcepREfY6GkPTztfUR762b2gn4V7L2o9rmHSkCz+Ok0XfUsL/Tz+2c/MfntBi20f3l6aF3w9IDOl2fa26N3khg/QM8EeTO2/4z/hrrYfN4e7qL5cs69d5+pI9pZWA2BEDevMnpWm12TyYWTD7nDR5vX5ue3N7/lq2dYWj/mHl+dTI4cHhT+wsITrP9vJ29+vOZzTPyTqnG7u+HofCgbe3dTA6McWM/HBuZz9xFcsbkGf3nvOSCYP6XX0J3qEECJHIOiE+PwyktT1UsiLeBuACsvc4DpPbTn75OcBSLBOpc+UW9AkEx9t1cVCj4TOW2G2MY1fU2Qazfmyp301bG4+sXeL63+6PZtnrzuNiANkTV1+0nDskoxbM/H6unr+t8vHNf/6lp0F4XE6jbEm142IandMUu+0cHdTj8Qokkz6ATfXhtwYTpeH7/boPp3GAnpLSw7+u/nYZz9z/lNzKa6qO/DgI0BOoNpxtFEOc8ElxkQEY6Ee/2pd2D4zn5zH3D0yf56bF7a+rMbB5c9+zc+BwtpXDY2gb2S4oH1kcRn//eW3NVw9EC99vYJTH/2SwormmXCqqnL3f37ab61EpWwJ+1zTrT5GJSg8cXom4wb24FhCiByBoBMiyzL7W3I0rXO7rBQ5/Ade8Xtx15SwZ/WLwXUpPfUsote/Wx20FPRNObRCfUeDxsaolcv3osr6++8/SHdVI5edNILcx85gSGzo/blkgDVoPTgQNouZEzLDXQrLygxMf3l1sPLzi18tD1oTLsju3+wYB2JA93DLz5lj+/LY+XrzSwktWHjxi2Xb8GHEgsK7V+iuQQUDxZUHFi07C8p5Y10966uNTPrnr3y/ZgeLNuym1nn0Wpasy9UrGveIDr/1GQwGLh6pvwcrKwyc9PCX5JVW8+gnS9jdEHrvvb7QZ3jvh7+yvLyxZ5nMI1dO490/nsjQuPDg5c9W5R2JSwny9NIqdjstnPDMMh7/7Gee+d9S+t4zl89+3sSw++by/b7WRaiExmsX9mH5Q+fz5d3ncNlJI47oXDsCEXgsEHRC/LKyX0yOiiorGM2d97+suzrcjfHrwknIltpgHKvV3Z34PhMA2FgQeuqcMrj70ZpiuzEFGqMqPgOqxwtmUFytVzBuDYPBwKMXj+XBL9YyJD2KBy5rX3HCu84Zy4KXm8fk3PbVXh75dgcV/pA1qL2uKtAzrBprFY1NUujbLZm0+GhgFxoSpTX1ZCTGsnGfHp8zPAmmDOtNtGET9aqZtbuLmhVvbESWFZZv38ez322haVDzH/+rF4Qck7iNL+46p91zbi/frc7hkxxdtA5Kax58PXFABv9aVQtAnsvCSc8vbzbmxEe/YUw3O8nRtrBieXee2A3QXa7f3HMu4/7+ZfAzWVVhYM6yrZw3qf0d05ty1fPfUNEgM+eumby7YB1xkTYu30+UvL6u8btp5O5vC2jsOdUSz5+TRWyElWmH2cXb2ei8v5gCwXGMHhjYxJIjqcg+X6cWOTX54fEHsqU2+Nroi2H8tLlBF4Ff0Z8uR8TLjOnfeUVOoyVH9Uuosm55khvaL3JAT33/6q8HH1TdlP7dU1hx9xT+s2gTK/ZWs7YqJBaaCpyxScohp89/cNOJvDhvLXefrwvRqAgbFknBpxmZ/M9f+P6W8ewsdwEmBqXp1rfUCI16J2wtqOLsiS0f97pX5rOkGJoKnKY0vZYjyd1f5tB40z99ZM9m2ycP6cVNYwp5bW3zqsdGVBQMlPoszNurAOHWvMumhge8/2lKj7A6SLfPzSMtPoqJg5qf92BYlZMfSO03M+0f31Di1T/ze+cXtr1jE/4yKYFnlukxSQ+dkv6bRVdXQbirBIJOiKyo+1lyFHw+TwfO6MCUlv0PgCjX8GbbUoxnY4kIZRFVBJpSjure8tN/Z0BVFIwBS47qM6AqushRPR1T+j8tIYa7LjqBkweGLDWNPc36R/m5bkQUb9902iEfPystgWevO420hNDnNCzgUdOQmP7yKrbU6SJ7VJaeyp4Zo99s91a23I1aVdWAwAkxJrF50HRLgdSHk9e/XUV9oCfa4Bh/q9aLey6ewu5HT8faJB4u3uRjareWb5WTUzU++/0wovbrXH7NqaP4zxWDyIoI1Tq67P0t7cq4UlWVFdv3cckzX3PXp6G6So0C52CJMerfjRtPH8s/pnfjqqERXHXKyHYdoyvTeR8LBYLjGEXZz5KDiuzv3IXKGow7AejV+1Z27n0Yr113X0W6htJn6q1hY6vd+s25W3znaeS3P9UVxSShoWkgew1BS47kPjRLzuHi5pnjqfcsZUK/dE4c3rtZDZvDyWs3nMzUJxcG2m2EigROHKRb33omRkCxi+/3qVTUOoNFDRuZsyzULf7pmT24aMowqh0NjH7sp7Bxj376Mw9d8ds7V6uqyr6yGqrqXYzt352yGgc3vL6YTU2CbJ+/alKbxzCZjOx4/FzeX7COeZuKuPvsseSV1bK4KLwS9cBoPx/ecV6LxzAYDEwZ1puXo+3MfC0UyLxkW+EB6zg9+skS3trQVDQaaKypdCCGxclcNKY7WamxPDRnE04ZHj13CNPH6HFaV5w88qCOcywhRI5A0AmRFWW/ZpAqPm/nLTnvqixEsdSBJhHfazzZfeezbsG1WAxJjDjrX4CemfPk/5azfG8dxYGn0b7pnbeIXG3ZPpIAxWsETUKTdTGheTomM6gRg8HAPRdPOSrnSo6L4p6Tu/PAwpLguiGxfjISdQvc9JFZvL9ZFzIPfbaUl26cERz33182c+c8XRgMj5O5aMowABJiIrnvpFTKHS7eCMSQ/Lizlod+41xVVWXSg19R6tO/W70j1+DyE1wGXWj1735wMUvXnDaaa04bDcDovt34cs0+XH6V208fwmfLd3H3eQeuBDwkKz2YGQdQWKNnFHoCAcy2/Yrzrc7J30/gNCfF7GPO7SeTkRjLvrJq6l1ebv3PCsZkRvH0H2YGxy0a3uegrvNYR4gcgaATIisqSOGBx36fv409OpaaQv1p1erJxGzXn+bHzfw4bMzt7y7ixwKNxriIKIOfE4e3nF7dGXCU6IGxfpc+36Alx9uxlpyjzag+aRAQOR9fPZThvUPNGCcP6cXQuE1sqTWxqkC/gbs8PmY+NY+9TQoe/uXM8JiV608fC0CMfQVPL63C+xu8VQ0eH+c+PY/dTgtNLR65DeHWj+wUNSi02ovBYODDO84KLp/YDgHxwkWDuPFz3U21uEhPPT/5n4toUM1M6wav/nE6NouZ175dxRM/VzTbP83iY0qvaHZXNHDGsAwunTKU2Ci971bPVN2fuDjQbFXQHCFyBIJOiLxfdhUozVK0OxP1VZtAggip9UyNZUU+mmZ7RJm0I+pq+a14S7YD4KvXb5aNMTkGf8fE5HQUw3tncMvYXBKibWQPbh44+9xVkzjtX6uo8Jupdbr5ctm2MIFzSqbUqijIHtANllZRI5uodjSQENPcfVnv8nDJ8/OxmSQ+m31Ws67yj33+a0DgtM7kVI1/Xt2+jLbDxfQx/XmktoG/L9CDkyY8+QuN/w8WF8HA+39odd9HTsvgimkjOvX/k86OEDkCQSdEVRXYz13l78QxOfWe7WAHTIOYu3wbZ4wb0OxmFGPScDXxuI1M77xFAAGMdbqrxRewCDRacoxy++rkHAvcddEJrW7r1y2ZKIMfp2pmymPfBwN8QQ+MfuEPrQdDj+idQZxxDbWKhXGPLeL7WyfSN9Cfq7iyjkf/u5xv8xQaRcHXK7ZzwQnhVqGle+sAC1ZJxq8ZOCHdwIoShV7RKh/dehqqqjWLFTraXDJlaFDktIVNkvn0+nEM6Zl62JrMHu8IkSMQdEL07KpQEa/O7K5SVRWXMQeApzamsqJqL9PX7+ONP50RHFNW4wiLjUgx+7j/kpOP+lzbg92tu2j8Lv1nUguIHNNxKHIOxBn9ovh8hzdM4JycKfHOrLPb3M9kMnL9xHSeXlqFgoG3Fmziid+fwgVPzWVddfOb/LbCKi5osnzx03ODfcDeu2okY/tnYja13FS0I7FazNw2IY4XV9a2Oe7Da0czok/G0ZnUcYIQOQJBJ0TVVJpZcuTOaclxVebrNXFUAyuq9EDiH/JVPD4/FpORxz//lXfX19JYJ+U/VwxiyrDOG4vTSLyvDADZZQaUoCXHpBx876rjhUevOBHnOwspcXjZUK3fVu6/qJXCOfsx6+yJrM2bx+IiWJrnYG9JVZjAiZBkXIHA3byqkMCsd3lYXRkaN65/ZtD60ZkETiO3nT2RQZm7iLJZcHp8fL02j1tOH4HL42dU327CcnOEECJHIOiEqPvVydFjcjqnyCnZ9iUARld4PM5jn/9KXISFN9fX0yhwslPULiFwNFUlWa0ACfwuvbJtUOSonbteUUdgtZh59abTAfhlcy5un3zQbSsAbpkxnMXvbKLAY2HaCyuC6z++eijZg3vyyMdLeHujkx8LNKocDSTGRPL7l3+g8Xs1IVnt9CLBZDJyxriBweWmrwVHDiFyBIJOiKppNK2TI0kqcicMPHbXlJCvvgSAZjorbNu/N7uAkNVjUIyfj2efdxRnd+hUVxSTKPnRNAm/U3cbqoEUcrPm7ciptZvK4n001FXSc9CYo3K+QxGxI3tnAJvC1vWL8gcDnacOyeTtjbpL9J//W0FcpCVYKXl8ssrbNx96EUTBsU3ns+kJBAJd5KhNb6YqyhGuCtteZK+bZetDAan1MW0XWbtkbOdt37A/1cW5AKjEowXe9qAlR+u89YpawvHWOfT89GT2bl3Z0VNpFZPJyIvn9gpb98zvxgdfnzi8D9P09lB8kuMJa73w2GUTm1UcFggaESJHIOiEqCqgNu3OrAaqIHcOqnYtY8nSUJaL1d2djQX6jaeH3YeZ8Ln+fngk104/OpaEw4GjWK/eLKspEOjDqAVSyI10HZHjqK2it5oHQOmyjzp2MgfgnOzBXDU0AoBog5/hvcMDcK8/eXCzfQbF+IPZWAJBSwh3lUDQCVE19suuUpD9nUfk7NnxHESElkeOfYcH39wIWBibGcWk/mms3lNOWqydbolRXDK1eT+rzoyvaCMAfjUT0Cv6Bi05tO023LZiPo71c+hz7j0kZ2QdyWkekOJd62nsRGVylrY5tjPwyJXTOHHdLvp1S2y2bcKAHiSYNlAth7L0Pr5tRrNxAkFThMgRCDohmqYhNal4jKagqp0j8Li+ZDf1EXqFY5MvnmH9XoXoDPY4dTHwuykDGdu/Oxcdnc4DR4TksqUAeOUB7C9yjFrrIkdTVeLnz2IwFWz4II/ku+cf8bm2hSN/S/B1hLukjZGdh9Z6O5lMRn69byYfLt7IY0vKmJIuEReo/CsQtIYQOQJBJ0TTgP2yq2RFbWX00aOuYAtrdp0bXJ580hJMtkgWbdiNhoRVkhndt9thPeeGhZ+Q8cu95I/7G2Nn3nBYj90SmqqSIReCBF5/KE6kUeQYpNbdVSX5u8hAL82f5dp8ZCd6EKgVO4OvI+XqDpzJ4SHCZuGGM8ZxyRR3sLWBQNAWIiZHIOiEqJoU5q7qLCnkBds/CFs22fQy/Gt2666Qbvb2F2FT3TKujRWovpbdcRm/3EsK1QxZ9bdDmHHb1Nc1v/FXlRcRIXnRNAlZDjUQDVpy8KGpLQvO0pzlwddxOHE3dGyfK1vdnuDrOLWmA2dyeBECR3CwCJEjEHRCNI39GnQqKK3cWI8WpZu/o8z0eXC5u/TH4OutxXpn7j6J1nYft2bObqo/zqH2y91h6zVVxeWsIwVdiNglH1VlhYcy9eDxivfmoMgyZYV7WPnydUQ/14tVXzwfNm7vsv8BIBMLqhRcr3j1n0sDCg3OljuRewvWhy1Xl+Uf8nwPB4me0PnjcOL3da30d4HgtyLcVQJBJ0TTpP2KAaqoHWjJqc5dw9aKWcHlUT0/Ia6Xni2lqiqbyn2AhWGZ8a0coWU0TcO9UXfvuNaXE39Jf9A0Vr4xi4mlHzaNbQagYt92ElMzD+kaVn70MBN3PwdAauAPYPzmB+DC24Pj4re+D4Bf64HWJA5KUyRU1YjBoOCoLicqpvm1Wutyw5brK4qg95BDmu9vpbJ4H9218H5JNRXFpHTr1coeAsGxh7DkCASdEomwYoAoKB0Yk1O067Pg61FZn5LQZ1zQLVVQUUtNIOPl0qntu6ErdeHxLarTT/7OjUws/bDF8c7S3S2uPxiydv+n1W27HxlNXXUFVWWF9FV0F0+d/zpQmwYZS6iKfp0NNWUtHifaEx7c66ouOuT5/lZK3786+LpCiwWgruLQLWHHIis/eZzVz12MzyuqWB+rCJEjEHRCVE1CkpqKmo6tk+OR9Zt1mnwpCb3Hhm276wM9EynK4Cc1PqbZvm3hL3aGL5c24NyxpZXRoJTntrqtLVzOOpK0UPzNXkMWeYZQccK+yh62ffkke1fNAyBfykTR+sB+GW2NIsdZkd+i6yxR1sVPISkA+Go7JqNJU1WGejcAsFXtQZ2mx051pOjqbJTs28GEnCcYV/cDO1Z+39HTERwhhMgRCDohmmageXbV0XdXKbKfnYufwmHX+wklpIZXNd6UW8yqCv1nxChp7T6+vyS8o7dc44HiXc3GqZoeaBpbvrbd5wDYu2kpJkmljET8fysn67719LxvE+smvhAck13wJmPX3g2A26q3JvBr4fNTZb3L9qhltxD9ygj27dgQ3OZuqCcBvSBiqaJbTrT6jqlNU1qgv4d+zUgfqZgG9IrA3pqQ6NJUlRUfPcLKz57qkDl2NMVbfw2+9lTmddxEBEcUIXIEgk6Ibslp6q5S8XeAJWfH4kco0F4PLsd2HxW2/c6PVwVfp9ol2otS1xgIG+gPVe/H5tgHQL71MnYk30G9NpQ6+RoABvrWsmXp1+06h6aqOLYvBKAoaghmixXJYEAyGBh9+u9Z0ff2ZvvEG/R4G78Unh2lyKEwRoskY/v4guBy4U496LhOi8AfCHc0uiraNdfDRcl2Pctrn6Ebswfdw86UPgAojpDo2vLLHCbufJoJ2/7Bnk3LOmSeHYm/uiD4Wq4paGOkoCsjRI5A0AnRNANSmGXk6Mfk5P76GiXGUGxMvOdkIhL0GjiLNuzm3Ce/Yme9Obj93rOGNjvGgVAcXhLND5BpO4ck8/346xuI9OpuIF9kdwbc8iDRqRqqFgryrd+6oF3nWPnGLLIL3tKPmT622faJVz7Ein6zg8u7jX2wN+jNHxUcYWNlT3j2WCpVFOzSqyNX5eiWgUotlqGGPACs3qp2zfVwIefo7pcKUxr/Sz2Ngoh0AAwN5cExzp1Lgq8rNv94dCfYGXCG3gtjvXDjHasIkSMQdEY0CaSmbR1UZKX97qDfQmWtbv2IdA3mpKk5jJzxWnDbA3M2s7EmYK1AJfexM5g2sm+7z+GrysFu1F1QNuM6GvYuIErRXSpSYk9w10D5VhQtFp+qWyMs9Qf/1L3io0fDgpjj+k9ucdzEKx7A89di1ox5ivgb5yIruqhStdqwca6a5u0Gun84lW3Lv8OWr4uG7lI50ZLedyzCd3Aix+moYfPPXx2WFO+i3K2Mr/0WAEe0Pt9ySwIAFnfoxm6rDQVxW4uWc7xhaghZtezuzt/yQnBoCJEjEHQyVEVGUzWkpt4fSUU+ynVy3MY8APr0uwujyYzBqFs3vl+zgwJPqH/QoNj2FwBsxOQM74xtathNBHrwbrfdb8OTWQAYpToc8uUAxLr2tXq8ytICVv33WbweFyteuZGJO/8Z3LbT1J9+o05qdV+bPZKxZ/+RxIQkNFWPqZHU8Ho4/gYLLnVEs337zL+K4S79WixN3IyxysFVGd7679kMW3Q16167/qDGt0XZDt2FqGoSVVbd8lRs1QOhozyhm3mkNyR4uru2/ebzdjWi3aHA8Rh/eRsjBV0ZIXIEgk6G7HZi1PaPb1E5moYcr6MC2arfoGO7hZprrtlZwB//G7IAnN/XzEvXHnqTqkhZt+J4AoHF0f7VGCQZTTNiUkI1XoxSKbKmu8r6KntY++27LR6v8q2LGb/lIaxPpDOx/NOwbf3+thKjyQSuavC3kTJcuhmFOAAM6n4VizWZSt9DrMu6gV3nfs1G+wQArJIfg6Sh7ve5xWt1rVZHbkqvyp8AmFA9F6/HdcDxbeF36CJxQ+QkciL09yzfrrurUpVQ4HGcXBk2T7UTdbk/GiTLofciWak4qM9J0PUQIkcg6GTIHieSGn6zlNBQj5LKqdq1nF/XTATA7E3GEhUH6EX/bvlgTdjY566fTlZawiGdx11bh92gp4vvSDwPgGhJtyj4tSyMUugmbDHsQNbSgstjVt3Oqi+eY+Unj1P40EBWz3kZRZYZKG9v8Vzrok5EMhigaB38awz8IxUejIW174G63829ZAMekgAwKeEiR1NkDJgYNflc+o2ayoi//sDacc8Et9cT3m7ALCnUVrVcU6cRr8dFkhZqubDtxQvbHH8gtHrdKuE3R7MtUnfx7bNlABBDA3XVFSiyTEITV5xR0lpscXGs4nTUBDPhACIkL64GRxt7CLoqQuQIBJ0M2evEsL+ekTRU7cg+acqeBpwle9hQcGVwnV0NVcf9aVMuZT7dTWVG4R/Tf1sjzr0rfsAgeZG1JGyjzw/b5lKmIEkK6mmPoQEW0wqM1OBVBwfHjN/8IBNyniBTK2Hchr/heyS92Tk2n/xv6m7dydBbP4NVb8Kb08Dd5Gb+9Z/hn31BaVL0L38FMnpMjkXZ78YXqJuj1oTcPkNP+V3wtZHmn1FV8Z5m68Leh83LMDQJMh/l+m2ZTlIgo0v1OoMix220UW7Wr6l833ZqKosxSSqKJuHV9NgqZ+3x47KpKtLrLdUZo/BKevB8fU3HZMIdaxTlbGPtvDkUbN3Epw/eww9v/AtHRcd9t36TyHniiSeQJInbb789uM7j8XDLLbeQmJhIVFQUF154IWVl4U8y+fn5zJw5k4iICFJSUrjrrruQ9ytZ/9NPPzF69GisVit9+/blvffea3b+l19+maysLGw2GxMmTGDVqlXNxggEXQ3ZU99M5EiShuw/cpYcVVFYvuh0Vm6fHrY+whIKJv523V4AMm0+dj1xDlecPPI3ndNXqFtxGhhA73EnIau6SKn03Y9PHQojfods68X64THk9bRhM66jwvcouZnPtXg8+37dwXPO+h/Dpp5LbGIqFqsNVr2BqkWiaYZAHaIA7mp4JAlq9FgfNW85RlUvnmeWw4sVaoFaRWpVyNVhtUWwPONqVE3CJVm4o//dZE5ZGLx5Okr3tvk+1O5qHvT7WyrwWjy6BcznqaPaEhdcv9eut8NwlOyiplS/1mopmhqiAWioreR4wRmoWF1hiafGHBNYd/yIvEPF0+Bk9+oVKLK/xe31VZV88sDd/PTvt/js4b9RuH0Lmxd+j6Yd3aSJphyyyFm9ejWvv/46w4cPD1t/xx138PXXX/P555+zZMkSiouLueCCUC0JRVGYOXMmPp+PZcuW8f777/Pee+9x//33B8fs3buXmTNnMm3aNDZs2MDtt9/O9ddfz/ffh6pSfvrpp8yePZsHHniAdevWMWLECGbMmEF5ufiiCro2sr8Bo9rCj8IRtOTU7F2Nzxbe50hSTaT3Pie4vLJAv+GfMah5htGhYK7Tb7SylIR5zvVUxjxFsec9POp4ok1fQGIfnFu+oCbOQlWCBaNUBliI8o2k8qbNrR5X1SR2nfcNA8eeElpZV4i7LJZi70cUeedS4v0AWUtGb58RYMNH8NapaPUODIGfRpPiRkFiU2JvvAZTyJJTXgBFa6FgNQATr3+B+qkPsT5xMB+nz0Q2mFgfPRAAX2XbIsdQkQPA8sw/0KDpRftK8nIO+n3cnwifbqmqs+k3757uIiJlF/vsusvKX76H+lLdupQfnUm1PQ4AT93xY8nw1ukip8ocFxQ57rrjR+QdKp89eA9fPf0oz19xPm5nuCt30Xuv88afft9sn34TJhGbktps/dHikESO0+nkiiuu4M033yQ+PlS/oq6ujrfffptnn32Wk08+mTFjxvDuu++ybNkyVqzQK6b+8MMPbNu2jQ8++ICRI0dyxhln8Mgjj/Dyyy/j8+lPYq+99hq9evXimWeeYdCgQcyaNYuLLrqI554LPcE9++yz3HDDDVx77bUMHjyY1157jYiICN55551W5+31enE4HGF/AkFnQ/E6Mbb05HOEnobcteVs3fXn4LLJm8jk0csZP/w7EvqMw+vzc/o/5lDg1l1VZ49rf6p4S9i8+s3Yonhh+1wSHX8jamwCGRl/x25cDhs+or5Er9/ijDSiWQP1c/IbiHLG4P9bOcszrmHLaR+wMukCdpgGsiL5Ynae/QX9Ru4XDL3pUxzy1YCeIaYSQ5n3ZVStSQvQJU9A4WrUQJ8nhwkMqsqXfafy1yl/4uURF6IF4nfkPb/AmyfD26eCuwbJYCCq4Bd227OI86kkeFU2Rg/Qj1vbdifyGKfuOjE78ik26DeD6n2tt7Y4ENGBjK5VKSMB6O/aR4a3gp0RWfp5qrYFhdc+ezpVNv033OfsmJo+HUFjcHalJY5aky5yfE4hctrCXe+gIj8vuPzlEw9SlrubXz5+nzn/fIT134UX6ZQMBq584gXOmf23ozzTcA5J5Nxyyy3MnDmTU089NWz92rVr8fv9YesHDhxIjx49WL5cN8kuX76cYcOGkZoaUnYzZszA4XCwdevW4Jj9jz1jxozgMXw+H2vXrg0bYzAYOPXUU4NjWuLxxx8nNjY2+Ne9e/dWxwoEHYXsd2FpQdBIR0DkKD43a5ZdgN+q/8DHuMczcsg72OJSiErRWxs8+tmv5ASK/k1MURneO+OwnNsm6+nZUsBCZTYUEJO0HEOtbqUpqS/DnR5wY0sSdck7gvtWvrWZ6le3Muasxxg6+WwmzHqXAfetZOItb4VbcBop3gCEu7M0Iij2fopLmRq2XgkUHqwzq2iqxLuDzwRgQc9xaIEMJIPWJMD4hRF6xlbVLsrMPfnvry7+92sDHikQvOxpPaB3009fMNCvB1sn1m6mLtDo1FOwvtV92kJTVeICae8L0vSaQLH+etJ8FWyO6gdAinMHUk0eoAck15p0d5V8HIkctU4v/ldiTaY6YMk5nq6/PTirqyjZtYN9m8K/kyW7dvDBvbezas7n7FkTKgVhjYhk8NSTufyRf5Laq8/Rnm4zTAceEs4nn3zCunXrWL16dbNtpaWlWCwW4uLiwtanpqZSWloaHNNU4DRub9zW1hiHw4Hb7aampgZFUVock5PTupn33nvvZfbsUGVTh8MhhI6g06H4GzA3tjlQwGAMbDiMIkdVVcq3/UhZwTf4bHp8icmbyKhT3sJkiwyO27K3hP9sCaU0P3TxhMNyfkdtFd3UDYDeYT3Iju9AlfFLBrZNtNK0WFBVkszwkt9R6n0TjUj8JQ1Uvb+N9HvHt36iNe/CN7cDoGp6cHOM9Xkc3tuDQ6r9d2MzrMMg6e64ndGDSaiGerOMpoAa/ACAQJqxR4oL5VF56uDnp5FcFZh8g4gLxE71dEYBYPHXtjg1TVUZ/tMfgsvdpXIqA1ak7IK34MG3WJ51M9m/f6L169uPBmcdUZJeULDCoou1U6pXsjhhHIsS9Iy5bmoJDQ5dMObb0olS9M9Xc3RMM9GOwOTUr7XEkoxd0d8v1XX8ZJcdLJqq8tF9d1JfdXCuzD+99RH26PY16T3StMuSU1BQwJ///Gc+/PBDbDbbkZrTEcNqtRITExP2JxB0NmS5ARP6jVJTQjd5Q7PaOQeP4vdSkbOE4o1fs3BRHxb/1I+t5TdTadW7bpu9SUyZsSJM4AC8/L3esiDK4GfNPScyoHvKIc+hKdu/fzP4WpI8MPY6faFkAwAN0YagwFnKCdzOK/wQPxnN6CbG9Enouuq8yFXulk9Sm99E4EShkAzAnsHbSLNdGjZU1vTr0jBgkHVXV429Gk2VsMmhKsRaII2/wbBfXNKGj1AUN5KaHFyV4NFFjt0fXlAQ9KKFa5+/OLi8UhmASdIYadjNJsvo4PrsvFdZ/u5fD7qGS225bqFwGWy4jBFcX/hfzq1YRDzVVFriqTDGYZA0Bvr1VPudET0ptOoPixZnqJL0znU/seXXuQd1zq6I3aO7q4qtycGYHMktRM7+OCrLwwSOyWLl5jc+ICY53MDQbeBgZn/ydacTONBOkbN27VrKy8sZPXo0JpMJk8nEkiVLePHFFzGZTKSmpuLz+aitrQ3br6ysjLQ0vcZFWlpas2yrxuUDjYmJicFut5OUlITRaGxxTOMxBIKuiiI7MQWsG1rTejm/wZKTu+w1NhX/ge1Vt7e4fdzEr5pVLS6tdrAwT3fxzDohg6S4qEM+//6o9aH/u0apAnXHwrDtNXG6e0xF4hXpDiqkVBZJ0ymNtBFp+4WmHdpL/7mG6k9yUD0ynt21VLy0HNc38/A9ex6KFkeDchLF3vcB8EYWUp3sY8lUOyUD3gseo07+PZpm4OeYU4lzZAHgtO1EUyUMTQK+G7VGrS0p/II8NeRE9iTZG7L62Px6/aB+ym62/PJVaL4Fu0l4dRhjHXq8UYNmY6xhB9sje6MYTGTFGVgTc1pwfPa+11j56eMH87birNKDxyss8Qyr38mje/7FnujbuGTHCSR51GBKeSPbo/pQaNN/M6Pc+r4edwNZX13I0B+vYs/mFQd13q5GrKzfuCsNsdQEYnLwNhejxztlubvDloefejoRsXGccGmozIQ1IpLTbrgVSTr0h7AjSbtEzimnnMLmzZvZsGFD8G/s2LFcccUVwddms5mFC0M/WDt27CA/P5/s7GwAsrOz2bx5c1gW1IIFC4iJiWHw4MHBMU2P0Tim8RgWi4UxY8aEjVFVlYULFwbHCARdFb+/FlPgbto0ocr4G7xVFc7vWt1mdWdij2/+cHDvh7/gw4gZhcunDjv0k7eAxaHfUOv8V2OWCpDq89AMeguCkqgo9vbQA4JrCBUazKcnWqQfqU823awXhx3PtaGChpUl1H+1FG+hTPWvMZT7XqDE+wE1/jsB/dgV/f7LLvpzhfQFZ2fdyq8puljyqqOpVy7kw5SbASiyS/R1b0NTwvKvgpYcr5TJV+mTmZ84Kbhta1RfEryhD8mo9Ai+7vPj9SiBMhk1/746rC7OlvhTeDbr90wb+y539f8LMeVrGDv7v5Rdvw5foIZN9N5QZmlbOMv1gOISSxInV69k7cB47BWnEetJ4Nq9Pj5NOz04dntkLzSgICByEmVdeOZu+BmLpM+1fO2cgzpvV0JTVZJVPQatwWeiQdO/GyZfbQfOqnOhqSpr533Fd6+Ekn1skVGMPVt3+Q6aMo3bP/ySv3z6DbPe/ZTEzM4b9tGumJzo6GiGDg3vNBwZGUliYmJw/XXXXcfs2bNJSEggJiaGW2+9lezsbCZO1P3B06dPZ/DgwVx11VU89dRTlJaWct9993HLLbdgDfRZuemmm3jppZe4++67+cMf/sCiRYv47LPPmDdvXvC8s2fP5pprrmHs2LGMHz+e559/noaGBq699trf9IYIBB2NX67FEoj40FQJTQXJAAZVQ/G5MVrsBzhCG2gS4wfOw2iPwu+sYc+W50nvfkGLQ7dW+AAL03tZiI36Defcj9VzXmacYz4AqmbFY/eyZmQCw7fWEZH9BnsL7uMHw2lsc2VzUvd+EPAi1EiJ7E2Jp9vGrzFIkGB+gmr/PcHjOn/NQ6lv3Y2+e9QLfJXck4+lq4PrCmw2QK/50aBOZmyg8PCmOIkL6vNxq1JYwHejJadB68Mf+z8GwGcbZzO1di05kX04oTo0Nspv4vNup3Fx+QLsko+qR3vjvGwOg/xbQ+9F3BmMTYa/xJ0MwOdpp/PwnpeId5SQmtmHXefPod+cs8jw5aKpql61uQ3kIt29uC2qL3Gyg5poK42OhTifxq8JY8g/+xPWrPmaV9NPYmR9TtC6k0QtdTWVOJp0ebdWbt3/FF2euupy4gKfudtnwR0QklG+4yeF/kDsXr2Cn/4dcimfduMsBmRPwRoRcmcbTeaOmFq7aXfg8YF47rnnMBgMXHjhhXi9XmbMmMErr7wS3G40Gvnmm2+4+eabyc7OJjIykmuuuYaHH344OKZXr17MmzePO+64gxdeeIHMzEzeeustZsyYERxz6aWXUlFRwf33309paSkjR45k/vz5zYKRBYKuhqw5MEv691hTdS+VBJjQ8LnqsbdD5JRs/IacsntR7Xpw6ahenxLdLZDanNCN0T3earZPtaOBmc/8QHmguvHtZ435bRfUhG3Lv2PchiYppZJKbawZv8XAtoExjFn2OrUD7bwn3QiRsGq/MIktcb3JiPbS11lNhPFXIoxn4VWHUuF7Ar0Dg4XWeCVpMj9L08LW+QwhO42i9uVMPaSFWGkFEWopLlVCIiRcfJrujkpxRmFSNWSDxCUjnuXTTX9ha1Q/zvGFxkYq8H76BfRu8DGmYQmJ1JH4Sej8+y5fwrgBI9H+NRZPQmjeF4x4gcUlGyEmne79R6FoEvFSPTvW/cSAsSe3+f5G1eiiZHNUP8Y5tmAIWMcA7IpGhSWe9HgnBf1PYjP9uaT0O3ZE9KLQmkqmt4zC7avoVTgnuE+Mu6jN83VFqkryiAMqzXFY/X5qjbq7Klk5/kSO3+dl5f8+I3PgYLoNGsLquV9QnreXPWtCbsrknr0YOPlELLbD96BzNPnNIuenn34KW7bZbLz88su8/PLLre7Ts2dPvv322zaPe9JJJ7F+fdtplLNmzWLWrFkHPVeBoCsgaw5MgewqBQNmTQU0jIDsqoW4Awf/qqqKqzKP7eV3opkC1UlVI1Fp/Q6474vfrKbEG7rp9uuW3Mbog2fTT1+EZRP51W5IqHhsunDwWA0YnWvZa2u94WchPaiP3wGNhYhju2O58F/wSkNwjISbDOtl+LRByGoqtfJN1Mdt52dDSGCMzN/J4JI8NmYNhUDF30bmZZgorvdykVqNpsSGbfNpZrxGN1GynbHVCiuS9J/QeUlT2RrZjwRfuE/RI8XTZ8RUvEuXYZVCVWJriaLngJHgc1HjKKfQFmpJsT2qD1T+DANOxxYRxfrIbEa5ljHgm/PZF7WYngNH0xI71/3EUO8GQBc5U2pXYVJCT952RUOTDOTXlFPk18AM3bzlJPur2RbZm0xvGc51/2UIoVTqHvI+PC4ntojDF4/V0Tgr8gA96PhkaR1VVt0lmqg5cDpqiIqJb2PvY4vl//2Y1V/9l5WtbD/nL3+j79iJSAYDqqKwa/0Segwahz0yupU9oLayFMloIjY+qdUxRxPRu0og6GT4CYmcvKgM3EZdcJg0Db+n9QKWrop8VFUlZ9FjLP6pHyu3nIZmDN1Ye9lmY4loPfthVU4+s99ewCebQwGYcUZfq+NbQlNV1nz9OqvnhD/keNwNDFp8Q9i6av9szFIhbqv+M6QZJGoSLJQYmtfhaayls5c+GKMC2U7JA+GPvyD1GE3CecnEDi0m1vQmCeYnkSQFq2ELNm0xsTF/4JRxoVo4E3K3MGbfDmI8LtLyt3PhCZH8cayd+Wkmvuxm5tEhNoampmFARlVAaxKV45XMGM0bAHhprZvIQLr4f9LPRiEGU0DjKJIe6+OTYohryMX6UCVbTvuA7WY97jA3O5AWvuULvkxubp1xLnsVqvSqxAlnPxpc3/OTaWxa/N8W33vv9w8FX++I7EWMuQaDP1TsMM6nfxfuc8RSrOjCMt1bQZKvlk2BwoUTKr8AYLt5MBXEY5Fk9mz4ucXzFeVupbJ4X4vbOjOeKr2oZIk1md5yCVGqmyqzLmbL8lpu8HosomkauWtbb4VksUfQc/gofD4P6757l3UvXMqAb85n6+u/b3WfuppKlJfGY3p+COVFe1n3z7NZ9eKVlBbsbnWfI81hd1cJBILfhkdyEx3IHpIxIAeq9Bo0DbkVkbNj8ZMUam+0uM3q7sGIUa8TndG/zfP+5dO1garG+s9C7wgfz/xuXLvmvv6HfzN27d0A5PYdS++hel2djW/PYkLgxl9iug3FeRogYTK8zrLoMSwhm8v4gE1DoRTdqtG9qpSCRD0odlhlMZuSM1ksncaFMV8wVNuut0N4KguA4K18v180yazy69AklIBbamT+TkY1+cFNcNUzbcO3rB+cxT8H9qDOGsOIgl1kWvXxsmzEbwgd1Gc0Y5Rrg8sPlXzHnT3ORJMMpHkCn5lRRkPBqBgxqzZcdUVEAEMnnw2TzwYgaIvZ+DE7I/Q6PxfUzWNh5BTqTDHsNMQw+l+j4dIP6TnoLFYmnMOEaj2le/iS61i+62cMcZn0mHAu6T0HoKkqfTxbQYKne1yD32AmzlCNX+4dnGuyzw3EUeyHWqOeAt/PtY8kfw2rY8JjLesSRuBypZJcvxjHjiUw6czgtqLc7fg/uJgstQCnZqf21g3EJXWdrFY1UO25xJpMv4Z9lFiTybVnkuivo7ZgGwyfdIAjdD0UWSZn6RJ6jx6H1+XCUVFOXXkpVYUtV+M+4bKr6TNmPBabneXv3EV2fui3ZazjR3Y/MhrPyQ/p32l06832OU+SXRjoOCBB5JsjSQFogErzwWUHHgmEyBEIOhGqItMgyaQHLDkyRpSAwdWggsdZGjZe8bnZ8fMTlBg+aPF4kmJlYL9HDyhwvlq2Ndi2AeD5c7I4b9KQ9s9/W6i0e+WOZfQeOoHl7/+N7Mr/6et8D6J4xgbHWKRc/h6lBzgaNZWreScock6MiyI1KxW3ojG0rpC/O2upiorjZ+sUpibtIaWqdSuT3yixLyOSa3q+wB6j3obCrriYsHcbkydPZurUqcFEh02bNmH73//IJiR+egS2qbIBvzH0M+k1mjEYl6DJZwDQf8cgBmZuJccwhKwG/TMzqhqS5AKsRMsaG9QoWrxt+lxQuJrdQy4CYICynX3e7qw1DefM0a9TvOQkDJ9eAVPuZMKt/2bNsxcytl7PKM0ufh+KoXD7O/jv3ciaD/5OtuTFhZUXe+rpvdGGOkoqBgRPF+PXRdgOs+5+NKt+hjtzyPIU8nbGRWFTs8Z3w2e0QD1El4ankVd+fhsjVL2mTpTkZv3a7xk145pWP4vOhtmhW5+KLcmcVL2a3XIPdkX0ZJxjK75dS4HrO3aCR4BVX33Oss8+bHV7au9+lOXuAuCGl94hJjnkEh+c33y/vsoeWHAlFb02UrFvO4O/v4zW8prrNTtJaR2XfSXcVQJBJ8JTU4pbtQTdVU1FjhFw1Yc3eyxY92mYwDF5E4h2jWZ45vuccvIeTj5tG0n9J7d5zg27i/jz3Lzg8ruXDTgkgaOpKlmONaHl4g0A9Nn7MQB77OfjURsFjkai+R+8O+ik4PiVgZ/JUnR31eD4GO7qlc5dHgvjVscys0hPa86lL3t7RtBaRn1edzv/mnwKl/Z6LShwALIqShk3diynnXZaUOAApKeH4mHGjBnDZZddhsWvi0m/YkSVQj+TfoMJo1ZA1B900ZihJjGraAM2zUVPl+5Gs5CLRdNdIlGyxh7CCywGKVyFrChsj9StLWnGYlK1kIhdGjdKf/HL01C2lYE3vM1Ge3h150ytBPNjKWTnvw7AVtswfAYLCb5aVItKpDwqONagWcP2He7cidEo09eTh9MUycqoUJmA/jkvElusC6renu3Ifl1Qyn4ffVzhzVG9e5a2fH1tUFdVduBBRwh7IIuqzJJIpqeMWNnJjkBfr6jaQ2+M2lnxulxtCpxRZ5zNlY8/x42vvMdVT74YJnAAfG0E83vfPJ3B31/WbP2q+JlssY4EYGvaeYc078OFsOQIBJ0Id00BHtmGsdFdpYVusgY0XN684Fi/28ke1yPB5Sj3KCbMbDleA6DO6ebFr1cxuncKC7cUcv+lJxAXZefDX0JxCDN7GZk28tAacOblrKUXtcHlCVVz2PTEKQynGlkzEBV/M/4aH+aMSFITHoPdy5kXE2pZUCsl8J02k0JJry9zQlZ3NFml6oNNRBiWcXLNeP4NrJEm8HPURKJSNzKgspTdvSKIr/WTWuljaWZfnut1Fb9KJzWb3/jcHAZf3vwHOTk5mauuugqDwUCvXr3090opBAm8aniarNdoxuCTie6WgCPVgqHMx8Rt5/Ce9V/EuEcAk7EZ1uLXeoMGUX6NYsN+QZrf/x8sfxnQeCPzUqotcST6akg153O570O+ZToAF494ju/W/ZFR9TlQvI6o0UMZ8dcFbP75K4YtupqWKB2u1w9K9VXhNRuI9zWNwbLRy1XE3ohuAEyqXY+jwUaGUgz9Ydbg/+PF8iVUjziD62pjSfVU8OvKq4iW3OzZvoY+wyexffl3DJPc1BDNrhF/ZfzG+4ipOfg08/ydGyj/5lHGOhawovdtJAw9hW79RhIZHXfQx/itxKh6nQAZIyYUkvw17IjUP/dk+dBaW2iqSv7ODWT2HY7RdHRvq4os4/O4sUdFB+eye/UKqooKSMnqzZdPPtTiftkXXU5yVm/6jdMfLqITk4hObB4sbGxSePPx3rfgj0xjetlmJpZ/RqYWer9KSaJw9F0o+SvpfeFD5FuiWbZ5MSPHndbsmEcTIXIEgk6E21GMW7ZjDNgpZIzBmByjpuHRQoGehes/C75OV6+k16Sb2zz2fR/9zNe5Mm9v1FOT/vfoorDtUQY/z147/ZDnXrZhPr2AclJJQX9SH+7RLTv7TFlEBJo8x53VB77ehYKBDVbdemDVVLySgQ8kPfsqweemX0Ic/iInCeaniTAuJbvhPKzyLLwmM89I9/L2gGqeG3ALFnwUpdtRd9bzYO9b2SyNbDa3AVW5JJiM9OjRo9k2gD59QpWANb+CWSpC0wgW42vEZzQjySpqvY/YwanUl+lum8zdZ6JY6gEwSWUoWqCKsAxltjh461TIHA/jb4DlLwF6EPNTWXo7i0scX6Ekahhs9Tyz71H+0vM+AM4Y/TpXlHzDk8UbMI3Whc2wqeeywf86Cd0H0uOjE4Nz23fZYnYV7dSv15WHQ40mylEDhDLEHt/5Ks9nXUSKr5qbCj6j1mMj1VeBWfVTYE/Het7/8efVm8AEZbZkdtj6M9azkarFL9Fn+CQatupZsbvippDUbzxshEz/3oOq4bNrwy90+/JCegR6a03MfRFyX2Rl4nlMuPX9Nvc9XKiKQpKmixyrqlunBjTksSMyC4BkYy2716zAHhVDWt9+GIwmastKqCosoM+Y8a1W9V391cuM33gfy7tfT/Z1zxyVa2lk4duvsOWnHxl1+tnEpqSyZ80K8rdsanFs98HDKNi+hVOuvYmRM2Ye8Ng5q39kIHoc4Lu9/sSLmRehSQbG26Kh/LOwsRsv/5p33FaWRE+EnGqgmqiqhTD/F16ZdAPT0wf/5ms9FITIEQg6EZ6GYtyyDWMTd5WqGUDSLTkNln3IXjcmq52a2qVggwjXYAaf1fLTWlMW7vXQ1n/5z/44Eavl0At82Qt+AcDiPxOf8Wcshj2h60q9EGuuDyQwfToFvPsotKXRYIjCqMk8khrD3eV6LZ9kRw1PlkqUPr0GpcpDpk13h8Sa5tCn4Tq2xepzrJUSeEB7nHwpi4FsJX1AcTOBMyF3K/3L8onwebnz73/HaDQiV3twLNgHmkbkpAws3aPDbl5Kgx+TVI7qk8KCjgF8BhMGn4pS7yNqUgb1i3WR43UmowV6Yxml8kBMDsTICoXGSChcrf/t1TOVNkX1Y/oYvUZRlNzACVHfUlw0gIxuO8iM3hJ2zg/Tz2Ja0duc1WTdyFN0i9SWU/+Dd+W7ZF35L3qmZrJ6XwFYYFzdZmojUsj0ecKOdULtdiZtugejJmHERbGSillVGevYwvK4UdyxeQdOU8i99kPGSYzN3cj4mnms/ORx4qs2AGDoPZXMfiPwa0ZipAZKC/eQ1qP18gQbfvyYkb/eFF4+uvEzqprT6n6Hm6K92+iOD49kIVrRyw4k+WvwSmYcxkhilAZ+feFeqnzNXYwX/d+j9Bw+ssXjjtzwAEh6Y9Xlr/vod+7dRzQOJW/DWmS/n7K9e9i86AcA1n37VavjU3v35ZL7H8Nij8Draggr6tcamqqSMS9kMbyv56V6ZxlN5SbrYBZbMunl092yf+8zizeLvJi965DMWZj8BURVv4spYBlLM//xN1ztb0PE5AgEnQivtxS3bAv2S1KaxOSYUfBpBnb98jSqIlNn0gNCe2XdesDjfrVsK64mVonsFJUUcyhw9+OrhzK456FnyCiyTF+XXm3Xq46k0vcg+RG3k2/oRo55MKnR5wJgl37C6NWtUdvj9JtAor+OswrNnFlXw5i8HK5av47he40oVR6kYEEc0DQLD0QlkuB1EyXr6/OlLABypCEslkJmcbvfw/RtKxhVsItInxcJPXC75qvdlD61Gtf6clwbKqh4ZSN13+5FaQil2ssODyatBsVrwGcIF30+oxmDH2SHF2O0hfS/65XcY7VI4jT9xmEyFGNAv4Emer3MTTmZgriB+gHKNlNoTQkKHIA/FP8PLDKO+iQU2YQc7+eTrX/mD0X/C46Zb+oOn1wB/vBmpENPOIcxf/mSxNRMVE1jrVGPpxjr2ILD3B1J9YeN17QIJE1F0fTCbg2yhQbFzJUl3wCwQ9a/I5Nq9Rplb2ecyT6D/jlNyHmCAbIes5Iy6ASWNfjIM+nv/95Fb9MaedvX6AKnDbweV5vbDxdFy3Trw6bo/vT0lqABskligCtkzUmz17e473//cR9aK/3jaqWQtSy75N8kvTaUytKWM5cOFWdNNbnrV/PClRfwxeMP8NXTj7Lii48PuN+oM87md48+g8Wu5yAeSOBoqsq67/9D7cM9iAl8j//R6wYUDWzOxSQV3oDi3032xA94uddNvNHtAj6NspNUcA1x5U+SVHQzceWPBQWOhoHBCYfmAj8cCEuOQNCJ8PrL8cg2DAFLjr+JyDGh4lKsFJveo3r+YlS7B0kxk9S39cDibftK+dunK9lQHfqvvui2CfTOSEJVVWqcbhJjDvxUdyCK87bTXfKgamb8WhZgxCyfSfr9D+FYUoDjuzwAIow/AaAZLKxLzwKgu1fBtayQxzNiWdfPg9FrgzwADYthV5Oz+BlusLHt9CH8sDefq/Na7hp9xq6f6VlczcCBA7nk5hv59ddf6dGjB46FBTQsbx5z4fylCOcvRaTcMhJDjIWaVxaSblPx+cxhmVWgx+SY/BLuqnKiSMMYacaYaEOp0i0mEm6MVGEIWHISfLqQHDfidc6vWMx1hZ9z1qhQBXi74uEi5WMKJDtR8f1xeXKIjqpiqLqVk3ZvYFLteq4f8gj/TZ3O+Zt/5JQtX8CoK2mJnbtX4jDasStu+vr2ssl6IkYpPGhUJQIrXgi4jBpkM5FxCiPrwwNuby77hq2RfakzR1Pz+/9iePd8umvFwe3/MyXxxMZcHs46g357XmJg3of4vA9isYa31XA6aoj4NLzPWIGUQYMxFu/kv9BryW3E4KJo9+ZguYEjhez3MXrPyyDBooQJjHZsw2Mz4LYaGdCQx+L48YxzbGVSSj6GxEy25vpQ97MD7F2/ht6jQ2UV6uuq2fLJ38mm+Xcx6tXRyPcVYzK3HrjbGmW5u6ks2MfgqSejyDJ15aW8N7ttdzRAr5FjGH3GOexYsZTug4dittvpM3o8BqOx1X0a6mvZ8t2bdB9/DhlZA1j56RNM3PFkcPvr3a/gXz0CGXvVeop4XPkT+KwDeKT734iueh2b49NWjy+hYjJ0nNQQIkcg6ET41HIa/N0waHomkR9TMCbHhIJHsRJLPR67bg3pFXkXJlvLIqWgvIbzXl2Fr8l/8x52H70z9OBCg8FwWAQOQMWuNXQH5IDAAVAcPuQ6b9ClI+HDatgCI6/Et/E7cqMyAejj0OfnL25gxk3TaVhejDNvFZGRDxGjhGKQJEnDXVlEPH04Jas7J1c2kLc3l1O2r6HBYqc0Lom+lcWcc8bpxJwUQ3p6OgaDgalTp6JpGmX/W4vdsBRFi8OnDcE01Ya6pgzVpT+FuzaUo3oVjJIeT+RUTc3dVUYzqiLhWbqS0i1+Yk7rgSUtEndA5BgNRUgSSIEn4Fg5FLT5ZfI0vkwOVV2OlF1sXX4WSyfFAdCt2wjKK5YRTRWL7WO4mBWMc4RcV9cNfoRdc8/AvGsBXPQO1ORBfC8IxMJs2LoIYs9kZH0OigXsUjwGKbxSsaaFl+avN1qITGwgqyG8NMGpyQmMrM9hScI4Fu9azyknPkr3QLXqnxLO4Yk8/T16sNsFnL/nQ5KpYfV3bzHuvFAFek1V2fX6lYwKCIAVKZcy/Op/0j0qZPXYvuw5Yvxbqc5df8RFzs41CxkcqNX0adrpnFO2mjr3naimJQxu2MPrmZdw9753iTF5mW5aTLf+fVm+Nw6bUWZGt92UuSJZOedTtv68iAHZJ9B/wmS2fnA32RWft3g+m+Rn9ctXM+72T9o1T2dNNR/cezsA8xsbZLYQC9RvwiTGn3sxkXHxLP/vR2QOGsrgqXphyayRbbdiURUFTdOQJAn16YFMkNyw7VF2mfoxUQ49WMzP/D1PZJxAZO3nJHuW0NDkGBbvDhKLZmFQW7Z8dRaEyBEIOhFeYzEO30CM6GmuPs2ET9L/m5qRiZFuA+4HGoONr2v1WG/8sAEfoSc4myQz9y+ntzr+txC1VrdO+NVemKU9aJiQtZ6UPh6oqGrQSDdficEow2kPU7NjDqWSnrrd02EHAqKupIGGogLSbX+gSVJHEKVStyYYJYmPxg3CN6IP8lnTsNls1NbWYrFYiIpq3oLAs7UKKnaSaNWLkhVEZJK2qpA6UwxO3kHDRsOqUjS/SqJZdxPVaiZ8+1ly/AYTmixhMJUgl7uo/jCHyOxQCrrZqFtEGi056f46IJ2W2Ll0Jo4YAxgkfF4740dMYt7Pc4EdeGxGdqoD6e/L4cPNd3PFsKfwGK18nHYmV277CsPDge7spz0MSQOo37OE7/166v0Q5x4c0SZMdUYkYtEAA7WoxCFroQrIe+oTqIy2kJ7sx9gQanc/s2IJ7JrHWSkySxLGscQfyR2nnAMnXUhdXSXvLAgFnKqSkS3pZzKt5EPGbfg/llfsIr1kEVlqPhLQmMC+dfrHTGxSULCRurjBULEVZd9yoG2XVlu4G+qpr60kpVuvFrfvy1kXTHX+PHU6JdYUEp1j8PungncqQ6U72RPRg20RvRnsytXfR+NuhjTxsiRbnDRUfodaZWDZuvnkL+zDqb6QwNlUk8Z2ZRBjb/grfb7U/5+Nq/2Odd//h9Ezrjqo63A56nj95hZqDjVxk0UlJHLCZVcz5MRTguum//G2No/r93nZtnQuvUefgtlsofKfY3Ebo6juf6kucAL0ayJwPh35BH+OGUNi0Z8xqM4wgdNIU4FjNljoE9ubfY59TOsxjW/36kHqfx795wNd9hFFiByBoJPgczmQrdU4fNEYAqmZPsz4A/9NTchISaMZaH4Gn7uCntm6wKl3eaiodfL03DVcML4PEwd2550F6/l0az1g5Ly+Zq4/Rc9iijuM3cQbcTfU01/Ws3q82gBMUgFWw3Zq5ZB5PUqaq9/4+50J//097hgoQrfkZLpCP+D+IidqfngavMtmwKiC1aci1W5DdfkxROixMhaLBYtFdwckJCS0OkfP7lqshlCqc3eXHjAZKzswR1xDtetTNL+KSSrCblyNKkGO2d5i4LGqSBgMpSEN1iRMw4ouctx2D/ghQfZyvusrvow4N+w48f46jKhUJuruHYcjmYSEBCLjBoO2iKSkfLbVZ9C3Ak6pXsnde9/mqV7XcXf/O7m7/530a8jjrrx36bn8Ax7s8yeWx11KoHE9Axv2UhljwblsE1rUCQCYpGJ8WhzLKgYwPV0P5F5T3Q3LKA01Thc481dcxyMZ1/P3qg+Q/A2Mrd0A/8/ee4fHUZ3v35+Z2V7Vu2TLVbbce8c2YIrppocSaiB0EiCkQkJoCS2hJfQeTAcbG2zAvfcuyypW79qVtu+U949Z7UqWTCBfUt7rp/u6DNqZM2fOzOzOuc9T7gc4aEiNj/vVqmq+TO0pbfjqgAXMq9d1WKbXvtrr3h8wjaa4D4IDYB1xEjS/x4DWdd8pQ+t4OPjsJYzybWBj7o+YXvc6JYbhuK98m6S0bNoaaxjQrTDqkrQTsMghTKpEV8TScN9RRE3lZ8Pv4qFDL2GNdjJcLul1nqlp+vdmenoVRHYAoGrw3OFphFQjIFPz4B/JtY7h4oF6htOEjTdTmlHI0PGz/ul11JYc6EFousPidHHD86//Synq21+6lWmN/2D/xrF05J3AdK1eX1cceOC4x9xpG4jFtwZRTcTFGQQDmfZMan29C7cuX7SMDFtCY+eROY/0avPfQD/J6Uc//kfgObodAG/IjaDpr98IBiLoE7pRiNLmC5F74jnxYzYeOMrlr++Ou7TWVe9nVNohNjSKgIRTjHL3ubPISe1ZaPKHRM3hnXTl1QSU+Tilj7FLy/ErC4hqemq2TVoFORPQDn+JoMlsLZ5MvaDrtQwxLkXL/xSx8adEajJwhfVygRqwfmoyYZPIuL0dmCMqeZGnqPp4MgWXnvm9xhit9+MUN/a5z6b66RCqkbU8ko26e8DrMuCvN6D0EXisKQIW7TCKuIGQOhX/pkScj0UoQREhbA1ABwiKgx+HX2X+0X3Mbd9CWtTDu5mnMtW7F5/BTEW2EwkF2XASgiAwvmghR/Y/i8kcxFRUxkr3UBaUlnJDzbt8njaHfU79TpfaB3J9cd8ZdUX+csr86aRH2rtGjSi2gAIaNip8yYiCSoNqJy1XJGTQSeK48BFmff4Z/hwvuCE/pF+XV7Lzbn0rF2alUOILAHaKfaXsd+hj+cI8kDWDbmVO+V96jaUNF/bzem/vwtApp8EayKKFkgenMeyXm7430dn55ZtM8K/Vs5vqXgfQCcpLE+nUrOR0s1R8KU7ji9SZDPVVIpIIvDerNgYGatnpGsmPZj7F2qlFbP3sGSbv+S1b3QsYduWzuP/Sd/Ds2qZCPAYnhwcVM6jqMI5AJ7VBN88enspPh+nf5aGfLGTDoUewJOUw/tQzEQQBT0M9bXU1DJowmeajFXz+9GO0VFX26v/GF95i1xdLyS8e/Z0JzqHNX6J89Qd8gxeCpjGtUXeZFUd2Q/nuPo9ZnnUBpzbolqnFyUNJqb87vs9pcnHBsPO5Y+IdoGm0hto46f2TkFWZn4z5CTeP/98tlN1PcvrRj/8BqIpMafkDYAVvOAkx5r4JY4xrtRhQONrcs3bVku1lcYID0KEY2dBNTPad66b+WwkOQGeDniru0wYBRqQ0F4LfSAZ34pWvQBQ6MYmlaNJ0hFis0VLXSQDMbW9kkPI8ye0yQfMDtO4YhTMWb7RtnJuwOVah3JKY+Oyly9HUMxDEvjVLuqCpGr51tUSbAgjVX2E16SRyV7GL1lQTlpDCzC06Ecgy30hN0oOYY4q3HrcRRxM0S8fG5BhQZYFUeSuYthJSxtASfQAp2YpMGYZgPT6LhGLyIaCTnJDBwAVNX8b7uLhxOQDPp5zNUMN6fMEkLj7nFwAMyh7Guu2jyLXpVgAxywOlYFPDvLb/Xh4ZeA2Ls0771useqJVzpG06xSk/0++DwYMmBEEBq8HAh9V6nSpXQSeKoYBOrQFoAiDb2onDoAclO5QgmaEWGi1p3HaoGgWoipk+bjv6Jp0Zo/lZul4OQjzzbg4/szxu0QPYlHkJU3/yLCndSIuiaUjd4ktsDjfbXCcxqWMlw+USdnzxGhNOuyq+/2jJLhr2rEQNtFEw+3JyB43oca1713zE+A03HfdeOLsRnHJfMo8PPA8EgUkd+1G7KVErWiojfOWU2/Npjcrce7iGv513G00zL8BpcbBHESgUsnuI36mawKc1Iyjzp7H5+rvYIpjJQmHsqk85MnAE8zZ8Tl3ASY5Nd+lYNz7J141DcKamkT1kOG/84jYiwT6yygSB02/+GStffJYJp5+FzeVmxgWXsu3T56nd8g5TLv8Du758HaM9hdFzzubwjlWEvvwDimhCGXkuzqwhFC2LBXsf7Fsv51j8evhP2Zs3nkeNmzjN38m7ziDdk68fmPEH5ufMgGdngCub1EsXs2LRClqCLRSlFn2nc/y30E9y+tGP/wE07F1GyFqJGLXhDbsQBZ0M6JacrpicKFVtPV+KX5R44Tiy62cNNjCqsO94kB8Km97+A9MO/xkAQdPdGoa2NZA9CKHpAEnGV/SGQxcgl66gNd1Ee5KRvUZ9oj23sYTkDv1arVEfBqEWo6ZPCgFrgryVZSWT26AHx5qjTfi3NeCYol+b4o+itIcw5TmJVHfS/skRbKPT8G9tRG7RJ7lk4+p4X163fj9DFgmfTcIR0B1PeZ5fxttU51hJaoY607HuKiOqnJikLdIeTHIpkfbh2I1bQNLHHTQq2ABBtRExS/ix0Eg6BdRhQOEfSfMZOkp3G3nVEUiSRPhoB6LVwJDBN9NSdQd2ox9B0PAarLjlILnhZv5S8jD3VrzI+WOfoMxWQErEwyUNn/Ormaex5vAWnAc/JuCG7MqZEAtN8qXtx+wJQlQnOfGxJ4UZVV9L2CKiCCBpcHJ2KTZDIu384QN/4qoJutvhzkPVECMGBQVjGJdfxLN1VZTZCjjsDzHosjfZvOVTUg6/i6RGGX/V4wiiSFTVqA9HaI0qXLy7jEKrmWSjxG0DMpmW5KD4J69S8dgsCtVKbDv+DjGSs2/tJwxZeQ0DBH087RVvELm3tEcG1+ivf8w/Q4OSglUNsKmlgNK5Okk6vW0NqjY73kbVUhnfcYClmbq44idNHixiFe82tNFFAH95wp85YfNTSDNvIWvIONKyChi/dxcHIhJbOvTvUAMSDXPPBaBsYBGWjzUu19YwwO5hfEo9Zb5UPn3sweOOtWjmCZx07U2YbTZGzJrLzi/fZOuTFzPggoeYtOMevdEfnokXeN3ue5KJW25PdLBtwz+9HwBrp/6FjflTiO79mKhnKZ+GP0MrXwIGgdfdCZXsNGsaA10DmZs/F0q/hKb9+r+abaQVTCXN1lshOY66nfDGuXDqwzD8NLD8exdbx0M/yelHP/6LUBWZjup9NNYtBQsYwnMJawbE2Is9ohmJxgKPJUGhoSNhYt92uJoWWSc4fzt/CJOG5THzoZVENJG3rhjD9JED/q1jL9+3OU5wAFRV19kxikehLQyXvAt7F4Mgwd7FVA2w8bcBi1jPHOoEPR5nRqCnaqrV8AUAUYOA1m3FH3XJlA2wMfhoAIPQQMuHR/BvbsAxM4f2xbr1wDk/n86v9Uwub00ijkCkA5uoCxWWD7AhGxIr1APDnUzZ6ekxhn1FTsKSSFabRiT3WHeVAS3c04KUYf4ZDeFnMBp2gQY+q4mgpGAjChgxyS5WzLmDXWvq0Z1wAmMLl8WPN1iHEDrShvfFD4lSwNTfzEMYvotvVunuoG0z7MzZEMIo67Ea2ZEW1m+NBbKmDIIT7gElxNwt+uS5xZFKijE53n9j0YekbNPjaJwpGqJHQ5NhcH4T48tbAajONpNfHybV3FOHZ4F3M+/vvJXzx/d0OeUTAHsas9s3UGYr4JeltZw5s5ipF94F3NWj7RV7y/mmLRGguqtTJ+pd2/46ooBpl7wOb82hKHqA3Y+czNjgFkZBD/HAZDrY+uyVTL5DT1dWFaVHgvem9AsY0fw5h0beTmbxPDIWL8SnWflH6QgUTWTnyCn47S5s0QBz2nfQySnxYxUtlUvrl9LuyuOdvHNoiyoxgpPAq6ZCbr1nRfzzh43tfCkm8XGHh+Ph+bNux/RqkEUZ2xnsbOP8gn18VD2Scl8qoCHE/jtgzHhOvOZGkrNyaG2sofSp01AEI4WhgyThgxeW9dl/D4LzT1BpMBARBH6Uk0luxlS21NeRHP0Y0RagL5XGe6f8iktHdCuD8s5Fib/3fwQF3bLh6vfAyvug6HQoPg/WPwnrn9L3ffQTuGVHP8npRz/+X0PlxlcpC8ZqT8UWp+2SXkdGoLslR59oDcg0dMuCWbNfFxuzCjKnTNKrTb977WQC4ei/neAc2vxlwiSOXmMrqMxBwIeIRw9qdGbC+S+jrdFl7p/J/hFvCwmNl8JAA+khPZsjYhQwRTWCzrW4A7o1RDH0fPG2JpkYfDSAUSqFaJRorS9OcIA4wTkWyca/IMRSh70uA2gaggaSIhBxp3FgmMzIwwlS1JZsxNciYFAEwoaeJCcqGgnL+tQasIjYQvrzsEnfYNf0wpVNbitiWMIg1CNrBZj8WXhZy5i50+hsrMRlX43LlphAB+fPo/n9vyEwDYk2mv++h6w7eqYAr0wby2kNu3pfXFu5Pol0Q0VjCrPMyWiAlPobFFMniqS7oCxGI/nntxDRbKS3JwhNa4qJ3IYwYizmtVrJwCZ3kmoOklLbwAkDtrI6RdeH+Un1u6SWvwiV35CaltBhfqW2hbu7WQ5frGnm79XNVIWOXy0e4JaDVdTNGU1NzB00Nrglvm+/aSxD7lzOjnfuZ/rR5xngTezztjWR3K0f31m/59COU6muqeGjvz7HoM6xeE0Ols07mwPDxsXbzWzbhVmL4iGRaSZraTi1AHcd+iu/WXQXz9d3cF9ZHd1RF45SH46QbTbx69IaXqxp6bG/bM5oSvwhnqlqYmmzV+9XlOj87ZOUvfVLBqMrEp+bf4A3KsZzWl4pfqOLlok/o2ju+ZRt/4rOv/+GArWWVL4/1qSdg9FVgKl5H5oa4v6J9+AQBWZk5/D6llsIqYlg4bJ9F3zrOc4cdGaC4MgR2Pw8oL+PBDSMm5+D2u1wzZd6oPTfYlaxsq9g6c96d5gy6F+4oh8G/SSnH/34L8DXVJkgODGYg/kcEYYAzfHYlTCmeHaVhExz1EiLx4cgCvxlsweAMWmJ9ezYwTn/1nFrqsqWxY+QeThR1Xi7cx6Zw66H9U5MwiEQYvaKv8+F9CKE5kN0mM0sMSUmxHHtDdwTNGCJhtCAw/lZjCqvJyugp877bQlXVTgAbZsNBMcJRA0CRjkI6bugOSHK1hNhLJOyaDHuoj28m7wDm+J7PDYjCAKaALIIsuqhKc2KPaDgs0s0pZtRRYFgrX5Pg9aeOkIRyYBXMfPNzFRUEYYf8ZNXH0IyfYWoRpElAV+KiqVZxCA0IWsFGEOp5KWtAXUj7vREXzXByUwf91uybVlEOjYSxIBCBjTqFo5W8XpS1b8D0JkC2xpGM4meFcCPhd8iInqMaOhZLs05uqslKqpIgKhYcNtaiaoduKrl+HHWkIps0IkmwHZpLJMiW4AgTinCu3t/njiJM0Zk2soQTZ3xDPnD/kQJCVXT+HVp7wyc3HATteaMXts1UcR3+tOwdFF82y7rNIpu+QCzxcao8+5BffxvZAht7Fv3KSOnL+TQhw/G6tbD3UX38/qRJk7afYAdo6bRNnIup37zIcvnndfz/IS4tlLP3lOExLNVtVQMKLSSRJbnKDcUDOeS7BQerWjgzIwkflNay15fkPEbDvR5398eXYi97QgTUofw0qhCDvqCnL+rjNaozAs1LXx9yt041rcww6e7KC8v1BWl0/AxYPvPYPvPGNNnzwkomsAO11w6TVZsBjeBtKG4U/L4RaefEoNMamgdc1N9FE9YxGN73yXcsYaoeTBrKjtIUXs/i2PhsBdzzfDTmZo1ntHpiYr0bHgKvn4AP1aeFX6MyyJxXfAZxJot0LAHtveuO6YiEMXAV8zE4s5git/fp7TDfwL9JKcf/fgvoKXsm17bsl0X8MmRDr0EAd2yq7oFHmsInPSnFdwxN2GpmVvUe9L4d6F01xqmHnq4x7ah17xE8IUDRIFKh8C10x7mTwefYEhLI1LzITTgr4UX0yEkIWkyv6or4bqLLqThJT2TI2ISqHNYdPdEDPWZ5vjf6jMmJlbA7hoBzwQj6a0R8jrvZ9e5r5BTW0hz49NkVg8npE7HKS3GbXwd9sGx0QLbx7hQzb3N8opBo6rAScSQsJIpNTrJ8tldPdpGJAOSDKqk91OdayGvPoQ9Vtk6YhRoWGEkc3cEbUQNmCYR8U8H1vTopyY0l4sXPIvZaObwys9wqMXxfWZxB2p4NhfOvYeth2fQUfNjXO5mlnABTaRyOquO83TAY7OQ1GiA2O1rzoqgRs0ERQkHIMo2REHDLEWwhBLXawsqtCUZyWrWrS77Gc4wQwngJdPiZXHlKM4tOIBRVFFDvrib6NbG11icfxJHbbksafay3xfk5Zpm3qrvW436uQP3ExZNXDzmTxQFqthv11f4vymt5cHJJ+EddJije9YiGoyMm51Iu3e6U6iQBlCoVjJq5eVs3rWI6S0fALDHOILXM+cCsHL2WfFjjiU4ANs3LqI8kgTomWZdkElBADpxkLX1JZhyPe60IfxxmO5WvW1AJtfur+zVnwC8PWYQ845+Ap/eDCfdB7PuYITDyqopwzljeylHQxHKQjI/nvowr+36M9Nbj19jqgsV4kAK1UoUTaDmjkqSLRbuLW9kR+kT+DxfEXCdiTnwOVKNTmLd6MbTlX5YWbUSA2CI9B5vXxAQWHH+CjLtmX03OKiX/NhJMX7Ngj8IVeQwkFr425xezT04eZ1FtHXZ2bwwSVV7tftPoZ/k9KMf/wW0t28GCzgDEygYcA1KJED2uLMp37QUExJCLPB4imYlKSbNb4jF6XgUE7/7Ss/ycEtRfnLa8SwaPzza9vesXF57xQayrW469NAO3skZwxbRxK+GR3mv5TcA7C5M56+ZulrudOUQP70s5rLq1K0SIZMRzRmMWWk0mlNNeJL0a84RpkGFrkdSuFug8RQT6a36RDxu2VU0mVIpjrSimgTqZ36OuPmDLl3BHtg+xo0n6fjFR7sTHABrtQQo+Kx6HIFZChNWzEQkA2K3mpcBq4QsCRgU3QKiSAITP9YpgC+6DeOkcxgiTmW1dCMpynPx4y466RnMRp2JSPvqUUlkDYXVycjNAUx5TsYUTuGroybMxjDJyXVsbR/LCWzGTs/YmS5ssheTo3RdSxAEjaA3H69BxAEISkInyRxO6LFYQwoVBTaymiOEjAJFo76hymNnQKkRuyEKgkCFL5lBjlYM0U5UDfZ7Mxmd1Mjj637HogV6La4Tt/bWllnYvJrbqt7giG0Akzv2IQC1a3Qhu1PHP88u1wjWtuvWK3dqJmPmnd/7+agqrRNupnCbblGaGiM4AG9mn9yrfV+4f0AKrPZhigVPq5otHonSITrJAIJYYMvf4NASuG03SPp35oyMJEYdtbLPl7jvtxZk8Msuy+lfYynUK++DSVeDxU26yciLowZy2Z5yGiMyHbJKmzMHWo8/xkPGkQy4/Us2tAVYsuEVDmZN5cOdlaCpuJofwxzSs6VsHZ99p2vuDqMpgyTXBE7PyOaK4suwGWy0h9tB4/gEZ8cbUL+LDuysJBGo/SoXcj1vkRMLygbgtEfZEyngw6829+rG6XR+7/H+UOgnOf3ox38Y/uajeCTdbJ2XfwVZoxMqxNWdClYMCDFLzhysmGIqbwZk8q0RqoOJbKoZ+RbEf1FA7fuibM8GppXpwYQHjcUkX/E6uflDCFd6422W5uiTwjrDHF4etoCrD3/Ji/m6C0LQVB6aoE9IocPtGNQjAHjNVgySzK5iNxmtYary9OtNsY/HZrmeQEwJ16JoNKSbKTwaxB7UY2wyIvqMIaKRuz6RWl2X5qR6gAlFVAmZRbQ+0s2zss6hoeHjPq81o0nv32fVX842Q5CwYiZsNCJ0L+wtCNTkWBhYrU9++1McdIVXyh2dGAG5M8QFJ/ycd74OkMFr1IROwmJKZAhZ2kPd9QQBCNd3YMpzYjaaaVVPJ0f6mIzBpQjlo3my7RrOYgV1ZOLFybl8QTn5fMICcow7sAo6edIkXaO2oWY2ZlMDuQBq7LyahimSkJS2BlU6Lfpxm7IHk+RsBCe0H7BgN0QZndTA7qSLCbvrGd3+IQ0hJwe9GYxOamS0WMXMhi2sz5rS4xqGBKt5be8vGBzUBfTG+EohZwLU7Yi3+d2GP3HuqS9TGgjTFpVJMfackhRN4yf7K1nS7GWueRDHFkhYLMzi9UL9+zVrywoChUWEk1I4anbgUzUmumy8OWYQJf4Qk9t0F5GZiK63pyYsOWrsNxZ/Dh21ULIMRp4FJcvh8DIeH3cjT3ndnJWRRHtU4dLsmPhk5Bgt4IcLwJkDl77L6Owx7J45irtKqnmjrpUX3UUsPOYato5/CNvcy4loAsPsZu49sJn36xuQM04FOYTD8xqG8BGM0Uq+D4pTR1OYPILVVcvJd+Tx1sK3etWQcpi+xYW0+13dOgUsFU7h2C/p+5zOT3mdDzmNAwyDZfVA79pwAEIfZSn+U+gnOf3ox78ZqiKDICKKIq2lG9lVfRkYQYq4SR82N94uHInSGjHE3Cw6ydEwosV+piIKX9yzkEm//5yAasQuRrlm/j/z5P8waKg+Qu4HZ8WTMGznP0tWvi6OJjfqrok6WwsdpsSK7VfZv6KOPL5iAQBXZzkZmqqvGEOH25EkffJrs9qAKB1uA2QUEQnr5Kdxj42Ot26lyxlnCgtEAgJbxyUzoMZPYXXf1gyfTeLgyC53V9+FCSeMf5ukpCl0duzHHyjtsU9TIcmjZ0EFjPqq32oI0B5OIioZ0UI9X9hlhXaO5lmRjSJNeyXGxbarsm5SUgK6leKiub+msvESTshIlB5Q/FFMUYUwEE75HHObrgzsPXII52RdLHHexNso2fMxadYazv7JtdRV17F0aQbt7bqL7ADD4v1FmxQskv4MNIMPJWomvGMzppZ6tNHnIgj6ZG6MaqDZaYz8EaNYgUt4CtWo8rZwJoK1ngw8ADgygtAJI9zN7Cpfhy21GpxQ0ZnMKHcDqgYuY5iTl7zHiScepKH4XGyNe7ng4LNxcgNA0RlETUkY97zZ495lKW0IqoomiizaeYRvpvTUXNni9bMkFsS7ypDK+7nXMb/2XVLooF2x8+vpumDdODnA+9eeB6m6+CRHvsLrqcM14TIEQWCaTYAn9ZgwMxFUzAjdvxuxcheG7rVEFl8OZz8Dn94KmsKYpkO8dM0XPcZHZwM8Npxe6KyDpXfCtSsBuCo3jTfqWtmYNI7rR/yOWyoWMzp0kAeK7+OTjLlU7yiLjUMlpf53JMtNdKT+BFNwD5ZA3yKWAIroRlJjQc7J53NpdhbnDZrLsORhGLuI/czfHPf4PqFpcOATWPEb/Fj4EzfGCc6PfvQj1q5dS1VVFW0k8wC9SzYMHjyYBQsWUFlZye7duznjjDN6tflPop/k9KMf/0aEO1rYvP5MouamXvtGDforRmuCFOytbEBBxB4L2wMIIyBq+stYQMFmMfHe9VOpb/Mxf9zg/5gVp3LNW0yLucs25l/L9OHj4vuUBn31VmnXJ9CLO99nlWMGDUIOz2TrdXjMWoifD01E3UQb/Rhj9bm8Fhugv6iD/nIwQMOHBias3NprHKFWMBdA1cAkKgtsSKrGmP0dJMW0dnaMduG3H/+1ZjZlMnPm+vjKsrj4CbZs7fkSDgfAGNX3hyX9mmxGnVBFJCOoAtEQGLsV3JaN+nOIllghpqSrhqJomgaxSuSiKDIoeyjd0frafoRYzJVPa8UitKFpKWi1FfE2OSl57JItWA0hNq4fTW3kTG677UkURaGuro69e/disVgI08qhlzdjykpYcryVUzhpm+4+iCZtxjRwCkrgBuisJqjUE9WGEFWG4FTeQ9LCVBpzmJSeyGDqdBpwd8gIAlwyMKGUW+RoxeIoIhptwywpZFs7ETds4KdVL8fb+GUjRkGlNugk78DnGMXecRmFjnbm163nq7zZHPSHeKS8np8XZvFFi5fXa1tZ1d6z+OMDrrmcct4vWP2PP/NZQ4COmDvxk+an4a9LYdadMOwUePM83aKmhcGWBhuf1u8xNixEKDMkYwsn+hVVC0FBwKTJqEkDET2V+o5PugkN1myFcCeYY79ZJQrf/DG+e7fJxBKHnRxZJigKXF2zFcvBJTDiDEY6rBgEkDX4NGM+S9JPoCBUT6UlF7pln0lyPZKsvytcrX/rdb+6sPL8lWxub6FRTcHUsYIzh5yL1WjHKv0f3weV6+Hoevjmj0Qw8AGJGKfJkyczdOhQhg4dyn333dfn4VOmTGHBggUYDAYyMzOZOvXfW3T1u6Cf5PSjH/9GlG56rE+CYwsOJ23YzB7bdpTpYnfZRg1B1SftlaZD5GuNzAek2CqzeGA2xQP/rcPugWgkzLTSxwHYWPATpl/9aGLn/o+IbCkHplDq0Gf9wViQIxt535zIlLk2y01yzBURruogVNpGqtsDgN9iIEvIp12rJmpQ8VYITFjZ98taagIKQBaiZOecR33jR+wvcjK03E9lvo1Op36OFNs4Ojv2ED0m1sZhmxSvvgzgdI5g5sz1rF+feBaadyawFcms6BpFmu6ughjJAcI+AaNFX94aFAFZ0v9OKTHQRXJQgWgAQbD3OGf8PJqGWn0QIeYqKS814U5pxSCmkOr9mNq/Z5P941MRTRJtkSHkGvSK5Lmmz3hvdQGLZt9Ofn4++fn5APzj01mo0SQMok5yVEMAT3Vu4nwhL2BmMufSpq0jqiUqnCtaFsZoBVl5NUhiwprhtZnI7+Gf0yHaBiHkjMNUratIZ1s6WdtcyAFvKwPt7axrHsheTzY2Q5Qbh27qcezG5nympyfS/Z85+jBFeXq8xxNHG3niaCPHYvKutWwdO5OGjDweu+sWghY7b16gu1LGW42YDy/VG657XP/XhWPSmavJpogyaozObvYvsGhmNlpsFAUDtFy6nozSd2DFb+P7l9itjA5HGVC9BQbNI1K5hrfW3sdJ1bvBIHFGXg7qMc/3+SQ35k33cH/tWjryxlPU8Q37HNeAYEIVJCqteaDJmIL7QFOJmofgaHul17V3IcmcRJI5iZ+M/QmZ9kzOisfRXHHcY74zVv8JvknUsWomhWe4gi7TrdFo5LTTEu7ga6+9lhdffDH+edGiRYwe3S0jqwuHvwBbKmSO6rkq+A+in+T0ox//JkT8HpqE3pkUxlAGUxZ81Gv7gVrd/ZBvkxB8MVcHElFBn/DFvspy/wew7fVfxFN1jRnd6vdEAkTe/QMhRY/T+TxbX+EWFUxmSLSdrM7HCWlmcgvu5vohQ1D8UdreOki43Iua/Cm2YBQVwOEnM/1C2pv0wFxDYu7tBWO3oM30SCoj55dRs+0XVCbvISQ3k5dxOnZPByX+T3u93by1Avafr+Rr2zjq5z/CsInNtL/yPAV3/5JxY1/hyJGHGTDgBlau3gxsxWhTUGIv+YQlR4+HkvyFkKZXq5Zj4jKKLJDREu1xTjXcgWSy41lSTvKZgxPbQzLe5ZU4pTfpkK8FwBMJE5RbcZqGImvZpFY/TPuHg0m9uIihQ27DW3MjRlH/XqQoz/Duss847YQP2XFkNcnObKQ6P5CESbIia6AYAwRaEhOvENV1X4IVdQhSPbKW0LSRtUyM0XIG5PUMGm11WoCOmIRhAqYRpyOmFSDUvARAtlUvN7Ksrqe7aWxST62Zb9pGclgZitSiMSGlFoOokST7WLDpM76cdvx6ZIOqDlOTNYD6rAI+OvUywqZE9t2jSe3HPe5Y1BXfSNH+n7PNlMwwICB6sKlJABwwuZgU7OBwVRUZM26FQCusf4qnR53I3/ylJCsKy9c8ys0rf8JWiwUEgTeys2g+Tj0pTRAICQL31H4OtXpF7umWdDYaY6UwFB/5HS8S6Nx+3PGmWdNYes5Snt/zPFePupokS9J3vtYeUFWoWAW5E2HXO6BEoPhccOXqbrXtOrlSEBDR2MR4up64xWLhrrvuQqzbobvuIp3kXfAa9913H9XV1Xi9XkaOHJk4V2ejHrBtTYbFV4Acglt3/te0cvpJTj/68W9C7e730WIibEnB2Qwbfy+hjgbsaYORjOYebUORKCvKA4CRQrcVwa9PljISSix2QOwrbejfCE1V2fz2/UyvSbgghkyLma99TShfPkFLRC8SKRv3U+achk3zM2HwJJLtybBdwWFNZfZQ3UXT8mEp4XLdLWUwHoAg1CQl4T8is3v9XoJDICkfnLF5MXVEJ6JBQ1MFon4Jb6UNxx4zkZPDCCLsaX+R9GUf0mxuwxjVyDWOp/roG71EBLugLnHhDgRxB6LkLr4TFoMLqL/3V4xcvZvU1Dlomkbadn1Fa0mOEkuaSlhyYoGbWts0TrxqBbt2X0Nr6yoAmrcnkR/Wg1Als4ISljCGN6I6z8O/sa4HyelYWYV/Uz0GKRmFNFRNxhdtpz3SgdMEspaNy7CYPev/jui8iekL50NRCW8tPZksq06uMqxVbN8yCU2DLa/n46vVrTYm0YKsQBgJNZoQrJOietyH2liH1VaPrE2L71O0DIxywurVzI9J51WiDrVH9hiAgojl5GsQDAbU1SCKkG/vYKC9jUp/z0rwA+weAHa1ZzFs0a0IyghmZ2ax/fW/sa2uiSsGbschN3Lukc8ZXLKfjRPnsmvUNEzhELO3rGCIEMBvU/m56QMyXVP5C9CYnrBOJQsqo/8RC+U1OSASE3UcOBsqdZVrZv8MHJkw4UoCf72LZXYbey16erOEFxU7IkY+dGZwvbeR1754jdV1q1k481Ls067jhY90d2a7JHFR5CiV1kSG2vEIzvFwpP4TiizrcFtzKW/fQx+VqwD428l/40j7EWblzsJmsnHnpDu/+0kUGbzVkJKI/WLbS/D5z3u2W/EbXYU4pP8mwxh5hitIpw1Dt3fNhDEjkd46D8pXJY59YR7cW5OwJPqaYNNzkD4cPrkZ1KhuvZFDgACuvO8+/h8Y/SSnH/34N6G57QuwQS7XUbRQL8DozOkjSBG46e8r8Ku6K2R0TgpCvf6SkTHQJV4v/IctOZte/yXTK3XrykHjSJIvf52sNL10g/rGZTRXXY1KEiJtLBmgM5NctRb5NRvBqVFOm/yjeF9yW4jQ/oQZRhJ0F16DycHg51WskS00pJlRfx/G0aRfr9kt4x6ok4umPbqVKL1CY8cWiaxp+r1oNutBz1GjQCW74DgEB6CgtrfbBSC7MULl+o0MnDmdDR8up7DSi4qIqyBI17RvjZGcaIzkRA/sZuVLzzH6jMvjJCe83wb4MVgUjE6ZYLOEXX2XTs4DFYKH2rAWpRDY3YxvXQ1GoZKAqgdlH/XtJKKGqA0EKXCArOpWlhGuN/j76yXcOPFNbFnJ5OTfitpye3zswVYzzXtT8NXqWTIGQUGI6b8ENQNJ0YRbSA0HdEFAoRWHvxmvlpV4PlqGHowcw9TiS9i+4zOSza2U2QoY3nk0vq89mkljZx3b9z3AmJxMxjXo7qV5o2Bvxnl0trZQsmENVilCplUnHY0jr2Dd1yWcyMs48XGOyUvD2AtplIfi8DYyOqmR8ppUTl63hJPX6bosc8a6mBxZCp2AFS7e9yB/mfJ2j2e3/cCtiQ+Xfdiz1EDdLn0STykkEonw7j/eZXzHJt5Mt2KLpdI7NT9BIQCaG5tiYWJhHrAK2uHVJa/2+q5UmvqWITBoGm/XNfCJw4HqyGBLSjZl3vI+27aGWmkN9cwj//W0X7OicgWyJvPsSc9iM9iYkTOjz+MBiARgx2t6ZlfLYb10yhlPgGiAdU/orqf8afCjxSCIvQlOF0JeokisYzIdo6+hY+8BOkhoQ6WmpjKfjT0JThfeuhCu+hyW/yKuiNwDjTGTrD0dDH3X1/tPoJ/k9KMf/wYE2+vptOmpspmFp/TZRlVVrnp6GVvqZYKx4NPpGSpj87Nge8JdJf+XLDmZVXq9nB2OExh/58cIXUHOikxb9cnImr6ijpje4y8DrwdgRvAg0Zps2mtKMQ9yY0i1okVV2t7V9VPMg9xUDN3CuHV6YK2nVSQ/olOJrBaNkPEOTK16kOgrI07mufHXc/eXB0i1fMxwKgHIWG2HaT2rsf8zRP2ZOFrb6KtGD0D0liv4euF8/Kv3kCKLmJxRqlIyUBT9kDKjbo3qUp9OL62mqeIt3vrCjWvgADInNmKKuYaMDgXJpF+TGtGQaEQhk9ZX9+Ocl0/nN9XYxK9xG1+lPvwGALvbNjHzNgdVH+tB3BFtFIrmwmnsYKC9nb1vL2XybZdQqOVS5vktQePDhOrNVK/OQZUT8Us5MwahNugZYUHVQJo/MZmqEd2qKIn1CJoPSEzYipaJ3JkD6a20hjI5MXMIa7SRJLOWXcPnMnxbQtXWNucudu/7GbnWo7QOg3CLgFnWSOnYT/KUHHItNZw+Yj1ijCI2kIZSv4Wbu4sYiuBsfY1grLjsYGcb1w7eTElHBg0hJ8NdzQwLt/R4XIOCtVwrH+JFg+4SEwBbm56JxwWvxQlOMBikqqqKwYNHoWka33z5JY2NjVSUHWYRFWywZjCpU48PEYUAJi2Ighur+t1jRn428Wc8tl0vVzIwLPFhXQVGYERbO7S1E6k6wq+Hn8H5826m/dBS3ix5m12W3v1PypzEw7MfJtOeyUXDL+q1n2hQj2vJnwq73gJHBhSdAY8W9m67+22QzKDEIqqrN+np7MegEzs2ggSwsJ7JHCWXejJhb2815xtuuAHDH7vJal7wqi4OuO99qNoAf50IbWXffrO6rGv/JfSTnH704weGHPKzYees+Gd3Qd9p3juO1LK6Drr/DJ+//iS0vW2Iov6iCmOMu6sE7T9nyWlvrmeQWgnAwCueSxAcVUXb8DQhNebqEAIsTsqkQ3IhaTLntDTH+/B8XoEh1YLkMhM52gFo1Cd9ypTVf4q3Uat7EreaN5YxJCKAoFHmnszUPQLeuq1ofjNdNjCrqpKRfzXV1S/zbZgw/i2Sk6fR1PQFy3/zHAO0WOyGoIEm4BwTwKwotOx3IgcMFKz4iqhHn/hdg4N8mXQm0VZ9lt1jmoiJdqKChIpAWlOINEIYZZVSIQXv0UIGhfX+jXY9GwlAiYoYxGoUVQ8S7fymGlCwSavjBCeqVBJWA/hDB3FNcaFsVZAEibrQ62SYf8Wigv08s+0N1l3aXSVmMN1hzUgl5ewRFItFqA36c4tqAhmeBBlUY7o4Rs2LIPaceGQtA5tvBHsjbqaOuZ5oS5Bk63jQ1hKhknDAidnWSfuAG/lM20muNWHZ8TkkzB79OY5Z2XuiTqX9uCrN1liQtoCG2xRhSlpNzwauXBrH386+hghTS/7IH9b/hNSFn/KeksGzye0gB2nFzbrDCuH9i2ltbaWxUbcsFRYW4k/383zT82QGMjnFmMQ6o4F2ScIdIzQCAUQCKIDtW0hOtj+bentCA+YE+wmkTkqlZtdb3FjRu3imCYVflXxF07i7GTfjV5wS1bi67G22dnN1nTbwNB494dGeB6oqrP0zuPP0yt2PFcVcPt3w6S3HHWec4BwDDaghi7BzAG92fouFqBuuvvpqjJWrExsGzoaR5+ixPK2lUL+7b4Iz4kyY/1t4JiZSeupD3+l8/y70k5x+9OMHRsXmRNZBUmgOotS3VsvqfVU9PqcYIrgdVtr8PhyC7joIY07E5AjRPrN0fmgc3PwF3h0fMQ1oJJXMjFgMhKqiLbkb72YN0AtIvmbaQsiuTw5j2IWrM1E7q7t7CiA88GXGHEgEXIeRyN2qAiKiUUWNigxZo5v4zUlRxu0uIKi0EFYa8FogOtyCsSREWl2InOQrGFR4K/WHn0UzOyk9+li837y8K3G7xpGcrBOxNMMwpuzbj4aBjEkelktDaQjp7i95eh4X1q0k1G6KExyDVaE+fwrm5HYiXZdgSlhLoqKEOZb9NripnYp0N7JBwhrRtxntCqGIbk0JBI2kSV8TVicBIODHJT1JWzSR9VPaWUfONH1iNqZ0UJ31HgMbL0YQDLRHbybLfCMTkmvZ0DKwz+d1wuXXMO6UMzAYjRx492lUTXddBSNRspoViLk71YgAyEhqFIMYS3MXw4iqGRUn1oDKFaf+hUhNJ42PbWOkO4OjUyDJWI5443JClXvZlWIjt7VnfIjXbiLVc3wro/EYN2s4aqTUWMgoDh/nCMDkhF8cJSIr/OPBu5nCLp7jMu7kRe5YeRl3XPwWvHYmDaTxPJfD7kRNr5AYYmXeSmRVRmlWQIAGewOv2RsAvXjYsJiKuCj4ETTdFTlCMlGiqjzf0MTYcIQF+Tk0xGJuHmry81BKASXuKgo6C3jtNd2ydTn74+f14EJDr5YO4MaH/d05LB7yJOdPnMHf1z/Fi24X53X6yLBlQHQHlJ8Oc+7S09GX3K6LEH5PHCWHQwxmNCW46aCJNFYzDdCYxk4KqeYVLqSBDN311wfOOussPv300/jn888/nwIX8GQiQ1Ke+wjhp68BTxXWgVmIJCQFSB0Kt2zTLU8GPSib37YDGoh9v//+U+gnOf3oxw+MRt+HYAVLcCAjpv7+uO3e2dEEmCi06RPOg+dPACDS6QFA1QSigoFwzKQvEARF+9a4k/8r2pvrGbHswvjnDimZuOD7tpfwbpbxKfqLTxEPU5ds54NhelZMDjU4G2ciOk2onb0rTycHlsf/3jk8kyNhA6OCGoKokT3ZQ+2GRNCqeZhKUBGJBtfFt1WkOSmu9RH1GSh57h4m/uZt8kfeA0BG7jmAQDBYTVLSZDRNw9NSS1Lbfvx/ugytNRUEjVq7iwZPLOagaDTb5l9FccleBq/VrTCiQaXmomz2V95EnvlZwjFCqZkTJCeU7cZc26W0DMMbGziYnU1SUF9xm91RmtqykWikI2CmQFqDJDTRHPkzGna8yq963JfQ8NWkFrdR/5kBUiF7xnIqfHso3PAgspZPU/ghxiY/zq52GwGlZ6hqzrARjFuwEINRJ2jGlho0xgIQaDyIEBHjlis1KiKoHhQtDUXT9WVCJg+2UCZgxBLVrTueT8oQtACmmEvHZgwSzMzHVTiK6uW3kmuCmtAsTJahZPAKRx3pDEK37IQwUU8Gy5nHjbzRY6x+bRT2+9djBkYB/ncfxH7wkZ5fkvxpUHwOOLPwl65lU43C+XxOLo0Uc5gdjGJyeA+8pn/nPuB0DroPUpF0gNmBCNutVlqlf27xLNb0+yUQQBQCoMGJViP3VNSgqQKCCH9tbOaarAwu6+hkIlX8rm0477adhREJDTARYQA6KXmRiyiccS7TCixo7y6KW10NKBiPLOPBI8O5Z9od3LDpCX0Avgb9H8Ab6487znZcNJHGcMrxY6GZVCqyz6TZWcyBw+WMyxTY1agviDYyqdfxlfR2Vx2L008/nfHjx7N9+3ZaWlq4+eabcZpFeDCxYNHOfQn1uROxW3VCGDgyDFvqIGiLxR1NuU7/v9FK8MPHUEu+wfaz9wn9ZjSawY7h0mcxFX83C9IPjX6S049+/IDwHN1D2FoDqsSkOe9hdqb02e5wdRMtsk5eXr5uDoXZqfF9Sqee7RDCCghEYiRHJIgmqwiGH14A8PCO1QRX/JGxwZ4pxClKzP2kqrD5ecLqjQBohPnUeJT9hePibacGSgm5cvCP3U4e1xNelZiQQyNLyK7QX5B7xhQTdHiR90cBCaNDpjGpCEFsRFMFUob7qHa5CTf9vcdYjviczBp8hKbdbuxLt6H+So2LIVos+gtZUNPYs3ofNX+/lUE7GmBqO60HdVLTkWtiXXAQ1/zledA0HCmpXInI1LPuZ1nJjZgElV9cfTMbsudxd2UzgiQTjgWFaEYxnkbdPn04k5UP8FZZadyexICWEGa5FltM9E92uQl4U3HSiEHW8EQsJJkOYRZ3ElbH97imz5ufZOAJpVRtcDJzme5qqJ0GEUcdqhREVKxEtNEIwiCuGHSYJbXzmXLaIsK5CukDCknNy4/3paka7vY6ZE3X/ElpOgiAtTBIsFwPRhblRhRDFqqmZxf5TAGsIRUBEasawL+9kWh1E5mmWxGEEGVBB6o1xFc73uTcWTchyOVgguTk2WQkD8Zf9wr+5ERWVjRiwZY9ixtbEwQnHLQjXr8MW05PF5v9ol8S+MUb2Czd0syrN9Ew6S6SPvwxdqWTE7u1d+GnoVvZ1QBm9lqiHEjRY0m+dJjgOwTnz5bC2GWdwMmmIEJY/15Kqgk5Vj9N1QSKIlHWVdXGw4JGU0Indqayi3IKGBqLEfPiYN60uWRtfQpTwe8RftuKXL8Pw991l/UMtrOf4TyyCa6/dj85pW/A6oePHVYcDYMu4HCbgN1zgM/Qy6Ccm9PIqjoz7STplRPqdXLRRXC+D6ZMmcKpp55KTU0NaWlp2Gz6d+Pyyy9HlmUcNiu8nIgjVIrOR/roGkwJTxtm+QiRE5dizM0n/NpNKJs2YP74VxjMUeLNHsykyzsnuxLvt/80+klOP/rxA6GztoTtZecC4ApPOC7BAbjyxfWAiUxTpAfBAVD9ulUhhP4ijltyBJ3k/DsgfP4zxsqlvbaXDr+BVICyr5FbvEQ1PTJmTdIK/jj1kni7X2j3MzxgoHnEExRWtFIzpInxD3+EJqvIngAVS3+MpIEsiAw46VN2bp6G2KATFJNDYWf0FObPeZJIh4GkwX6+rh4Y71uyyGSMaaP1QBKbbaMYbDiK6pHY9qvzMc6+mJaPXidqcXHKk2/y3s+uZuI32+hS5KjfnBzvZ6cjE9fQ0SRlJrKK0oDtp85jUfI7NPk7aDal8qOoAbOrkWAgJa6Tg0EESQBFoxMHklkjeUiAziorgWYzWZ6E5arFOIywlIQTQFP5on4oFxTspdb3NoNdOxEI4tNSODB1EwPNHVisKRQtTcTIBIMiEyc+jGfvLlz1ukJRWC0mybiZ2Zknou5QySouxprX83vj21iHPdRMO3YErZX8ilYUJPYPFBhUrUJUhGgTsnkEQUEnCx3GCKlSJ5LiRsJP23uHcUqfYRBjwpRHR1FbdAit8xXC0WtxGfSYmdz0kRRmFbGzDqymIMGoGasxjNPUgbM1ETsUXvA6xpEzEZOOrQmvQ0sfB5111JNOdkwBO+ujRX22BTATpZJcBlJLJdlsyNrQZ7sJoRBPNrbwgdPBR047VUYjl3o7ETWNKVlWpKg++8qSgEnQybgxbCViFDHKCqKgoeBAomfs0gz0ZIIuggOw1z6HGet/iihphF65CN1BGCR62UcYPziXXBo5jW9Yxjz+/uKL/PznP8dRME0XLfTWgjWJButQPmoZTKsvglze5fpLpMp/VHecAprfglGjRlFWVkYwGEQQBG6++WaMRiMul076CwpiVh45DAc+wTJ4PsgB+PoVXd0ZwOQgvGcrtmOSoySjivTeaYRmPIWlPRa301MVowcM+X1nlf4n0E9y+tGPHwhVB16Pl0rKL9DLGRxtbOPNVfu4aNYIbGYTFY1tZCQ5qA/rb405hX1U5w3qadFBzQZCguSIBFD+DSRn68dPM7kbwTkq5pNy2xoqd61iyrjpsOUF2PsenXIiqPSeSQmXlkULMpo92IIzGVbdhLtTJr31G9on7Sc5uZiAsYL8Ov2aNgxaiHnDCSgmDWeTvgpVrVDWtI4Fw1QcWX4OBwqpC7rj/Q9e2IktrRVraojyL7KZODhAW4kD50cH4aPf0aXAcWh+ERMb+/b/KyL4TSaKs4YSCcoEOiK01fspHJtGktHAipmTOOALMsxuYeO726hKqsHj0ycWQYi5CEVAgU7BEdsOBfNaObQ4YdY3WBTq1eEEY/L6QlQg9bJ6ltTnIZi8lPu3USh1EEgR0GwSEuBpUBjamViRby8bwaxZedRPeRdtq4y7bjZRbSAARrGMsDqe1jcOkPvHmQjdZPw7P9tFlnk77bIRseZlFL8EJpUHxtl4cauMGAXCLciOdJrsI8jwNjCl7bdIopUWnkXUZARCOA0fxvvMqs+mdHAVbnM77636HdmmThRVZFjuKBxGB4GoFZsxSOXYnzDiwF963PPwgtcxzzi7z+fRBdPCO1Df+pxsqZl1TGQWfQvj1eRP4ouOEt51lpPeMZ5zMqfx++jWePZVpixTFJFpkkSetA4np+MAqCrXeju40tvBHrOZMeEwRmBFoRtJjtXwCuQhCHqgiiFi4UDxPYxZ8zRmWwcSPlRFDynp+n8PjL4A9fTHGPf4RYgxxWuLtS2+27/8GYwZxdC0n6nsYgMT8eLiz3/+M/fccw/+ha8giiJtbW18+eWXNHl6K6N/F0iSxIUXXkhhYSFvvfUWTU1N/PSnP41X/vZ4PCjRMKl138Dg+YkDoyHY/iosv6fvjkeeDRe8hvSLhMZN+LR3MC9LLG6Uja/1dWQP+J2nxeq+/3fQT3L60Y8fCO2RDWCFLOVSskbrEug/e2Md21oklhxYhy8q0Kn21Nn401Un9epHCOqWnGBM7yRhyVEI+/0Ykn44efRDW1YweZceI7LTNgPXab8jKSMPpzuF0SecB6+fDeWriKp5+BX9ZfjkUAm5WzD1j3gVgNQ2EXdnLPhW1qhYfy1JCzdQseMFxoVVQgYDweytRGOqvY5YynXYLKEi8lH1CCxmI+UeG6JRQTAPRbCNw5am1wdyFfgpV41scucwjN4p5OIxBMdol3GP8rOlvIAWpw0EgdLNEmXb18TbDJ6QwgmXjMTqNDHKqd/vo7ubsRRUU9+m24OGGOCejQFewch6IlQ7k1hkXcJs+z+4vepNrGkRgi36MzJYFRoiwzEa9QlLDINBTMKe49FPmBahrdtrVxAkysutDO0mC3f+30rxnL0TV9o4mnPX4K6bjazpRCrd9BuCyjRao7+g9lfrST5/GJHqDixDk3FankTTHBBpw7vzMCCwc4yGzywRsUexdIIabkbQJGyRZEz+pVR/7cTkknGesAVNs+E2vIgoJKwXJpqQI6eC8UOypfcAaAiNxlgl88hn99JRaOTE5CD1KSmMiB2jqRDIuBD7PyE4ATnAA80fc5FxCGPVIwxTAxwQhzCSIzSQxLYh93DGKSeyeNm9/EErhRTdKlefuoeDYWN8QVEcDvNGXWO3hPiGnt8DYGJYdwVqgGaJIMhdVcfNdEXjGqJm2tPyUMZcBUd0Fe8uYiNKoGkigtBtkTHpGkSrG4P3ICQKmsdh968kPOI3mJv04OTpbGc58wB45JFHeh8QQ2ZmZjxD7JxzzqGtrY01a9YwYsQI3G43BoOB7OxsiouLqaqqIjU1FbtdpxGXXXYZiixjMSbkHpIMUXiyWxELRxaoMgRajj11AuMvg7OeBkGI19ELjrgb69TTkT82YDDrv2G7tqPXoUpURNMERFElPPNP2BZcdfzz/AfQT3L60Y8fAJWbXiVsrQJNYPBkPcVTVVW2tehvyS7LTXecNrBvq4MU9QAQiq1/It1e30FPO/bc7+/f9nd62Lf8JcSaLTgC1YSMbgxKiKGhvSDALus0RtzyPhZrtzWXHIHyVaiahdboLwETBxwybxc64k3e1BYhAKk1v8Ya7VmqwtVayZ73BjPiiE7a9qfnYehKXVZV8lshhIQvJhRWF3RDUA/+HXlJGQbrYWBpvD9NFQCNI+F0IsON5Nb6MBdESE4KoO42EvXrrzODRSF/bismu8Kb1eNoztHHa7SPQ5ASriqAsh1tlO1YR2qeg1OvG4UzxUKnx0TSmGra6/RAzptUG0WdKo9gYRYRgoqZiZ4Wpp97B7u32mmStpD3Viw7yq3idykMmvgGbBVRIyJqFFLTZ9PavrbHuefPO0xl5XPsfP+DHtslDVqatjN2/MPUHlmsPwotA00zIQgRrNImkrQXsEkr8Xx4MwF1Hv7NDeRZthDVChA83+j3yqjy53lGptplNKcKDRAJ1WMErIEcIrWHUGWRUJuJ5MA6NJcdh0EPDlc1EVFQEWknPzwGr72bdSf7Eh5f+SjvpH8OPhjpELBrr9PpOBVRMmG/4w2sqoqq6jFTsiyzsuwLJmVP5bdfnkZlJMBs+yA8rmF8XvUlSwbAngpIUw9Re9pnXLH9BXY6djLatY6M1TX8QT0Mx2QURiV94j3V5+fXbR56Lh2Oj4YBVyMZPkVQbTrh0Sxxa5AkWwl7arFeeh+RXbMxfHgBoiFhYdNUFW36TShtzQjuLARLDtHlL2GxeQDwa6OxC3tRoiKSUSdD5m1/QJtwDcKOl5jGLgwoLOFE+tJrkiSJuXPnMn3iGI4eOYTJnUm+XUYuLiYrK4shQ4ZgMhr1e6Hp4yooKNAVi+t2Qc44jEYjxp2v6uJ/pz6i1986Nq3c19Dr3BRMh6pYtfNpN8GCB4jsW4e8ewU2axhNA+Ns3YKjXvAP+PT8Xl1EgjYMv9qH1C3+xtqr1X8e/SSnH/34AdDU8jnYwBoaiiUpA9B1cI4HtxTlmetP63OfUekAKeauAjREIpoJkxAhHAtK/r7Y8/5DTD/aTZW0q8RSl57L6At7EhyIv/QCyknIWgGtJo07JrnjhQjP1D5EAGpCcxhZMxKz4UkAQpIBiyIzpCIAMQtFh81A4wAFMzDYeTaG1nw6PXqV5Razi2TtRNqjXwFgSQljsPYOIBVEDbPbSdjro8qSRNXgJP1+BRRGTW5gslxH22E7mRO8VIluviwfStnpV3L7xDFYbSm89/AhAAzWNoz2FiKdWShhPT6htcbHW7/TC0lqmoA5qZqWcBJIkHTMBBtUrIzytDJUSkabchf3iCv5oO5WPOU2KEonc8rrqMiACTUq0BaUmD3qrxxcv4hm5QiSaCG/4BoEQaKp6QvS6vRSEGnFnbTs110MdU1rSKl5nWHjfk90vQ9JdtAqDidN01OlHQad/KWYHkOJpGIU9HRsFTv4KgHw58pEDSbOcoeQUjUoBa2b0q7cmbDYCP5S/K6pcYPEUcsFJPlF7OIqshoGUee0Yzf6CTvu4/OWVax2JKxhzzebuSuzg0Nn3cLkYbM40HqAi5dcjIbGyJSRVHkO4OvhZRV521MJsUrfGnBlRiavNTUy/LObaJmYCz7Y276Xa9jbi+B0x30tbdi17xZ8GxxxN/UT50PVpxBTPFZjwf0AomxBCBxFEEVME07G//lU7PIm5LABVTVgsoYIrX4n4ZLa/he6L1Nsv1sDoogEBN57GNt+XR8mUFKBzVWA0FHFJPZSNO8S/vxNU/y8WVlZLFy4UC+PoMjw97kMbkykxBtGnsNIixsW9+EaKjoDDi1JfE4uhPZYBfvjuaG6kDUGGvboaskXvAZlX0FLKcz/DcH3H8F64GG6lmbB6EBsObpl0zThZNShlQSfvgJ7WP8eBCc/guW06xN6Wv9D6Cc5/ejH/xGqqhIw6oq+w4clKhdvLa3r1Xb3r+fz9uo9nDimMJ4ZdCxMmj75hLrZwCOYMREh4vvuxQgBGqqP0Fp9mPyq3oVCuyN75KzeG0u/xCefgke+AYDXCi20xlKpH9FuI5cawoqJOcPvQVnTjMWlB45uLRjA7IqESNjRPCsH85Iwm4K4yWTg5MdZdes8MlUBo12mThxJ0DoWg68BVevAlpEPvNTnOO3Zs0AYwdqhDQw/+Dlunxe/wcrO9lxqLS6KxzVx56DfcGJLA+OuO4mb5+vuwPceWQ2CSuqIz0kflbgXLfvPpPXQKWhKImrSaO1AMvlol60gqZgRqDM2kSIngQYhxUxYjfDO/XrQa9LVYwnlWsnN9LCu8xzMriWoMvHU7c4KK5s3n8KsuRtQIz5Ek4Pm5pV89fVgVAQKG/Rz29LDGKw25KBEU62TSvvT2GxDSXGdjb1tFJXOBaR1JCa/LqSbfhn/W9VcqEEPAB4XXHXkGuz5f0WLLa6FYMItJvsjdPl8op1hqvOSSQf2GoaQ7L0cP7DPtZ/BdV7m/ngNomRgWdUyVh9MEBwAryLy6zob5vobmVZ9AqtrEgJyB9p6q+j2hR12M6MLCwANfDW99k8OBpnnD5KjKDQYJN5w6XozXQRHlQXC0x/BOP5ktEAHYko2UkommiLrZQ3kCFaThaZt7+hWHy1G6LUwaDrjF2UrRiXhwrH9/CP8L92OccYlRJf9CVNkfY+Ym+7wW+dj7/Z7tl3wC/yVW7H7V2L3f40SFYgVsMfxzS+5DziUcz4Dx8/FMukyfcem5/QSCcfiwMfHv3HdCQ4kCM7xYHbp5R9GnAkGM5R9DZqKZkuFMRfHSYq4/ekeZhjDpc/26EZ0JmO75xMC980GJYR1wdX/kwQH+klOP/rxvVC1+U0aW5Yx9oSnMTmSqdr8Jkc6HkAzRhFlC0kD9BThfRX1PLJWf2F2yds/cEo+boeVGxdOPW7/mqwiCb1JThgzDjqJ+r5fOQPfqxdSrCQIx6a8a5h27eMcLdmFOzWLQ+//HmypTBvYO/vBtzuMR9Zdb/vcIp/k6W/pX2q/Iw99Ilpwwl78X9fgI4Q1olskfBlhPjZNIDOokTztHNSOrRgDupWkcPSDAEgt+nVEUkW8wggMaBgcetqqyf5lfAxCRT6GjChRu25iNzkaCQgjmXMkm5Qf/Z6qNI0rhhUgKwoNHR1s80e5PzMlHl8DEA5EaaqQcQ/c2IPgAKQVf4Zk8dK4/fL4NktqOVF/Gp6YAWG7cxd/y3qJ0f6hUHUNIcWIZhQgqE+UV7YFeWTaE8z+Yjct9nQKAE0SMOVGiNSYcZQFCI9pQ1WjiCYHmqaxZ+9PANjlK+aMdt0KY06SMblk5KBEdW0OgWH5FAf2YC04iL1tFJnNs1k9fTkeQWHq3mayIq0ci6OWImxRXQZANtpJkfSLCGUImAEhAAIhNAwooYQFJOqX8IpOWgSRzaaZnBrbvt04niL2YPHMpsRcwe83HV/3KazRg+D0hQk2mR2BxLQz2ipTHRHxKMefIAUNHqjuIMeYcLv8qKNn1lP09New9hEDJEixc5n0OLYOXzVpsoiGDZNwgAzzL1E0F/7wQgTFgkFK3FPBYsN+ky5jICSlo740q4f7qguhYCr2+z7qtd1y9dPIfx6FwSwjGTVCai4WMWHdLap7H+reh/3vgr8Zmg/1fQO6Co8Omgfl3yS250yAut4xMT2w8HGYfI3+t6roooPGbjF9g+cTLduN8OtMFMWKbBuMoISxxWqO+ZPPQZpwFpZRM3t1LYgitt8fX+PnfwX9JKcf/fgeKPX/DqxQuvExik74NWXeh9EM+kownbORYi/TO9/eTFdtoCvGOPntxXOOa7npDjUoI8WCasNaYikViS2rlI7jBwtqqhpfTbXUHaX95UUM7UZwAPLn66JdA4aPA2DaDT1XaHG0ltHRNjf+8ZopNhRR4CcdL1Hs1AvvNQQH0/FJOYHtjUimfQhA2ChgNAUxZVYRAKL1fyMqxKT7NZHk1BksfeMt8st9gEjALjF80dsY7c/TXjaHxu2XY7Tr15hSfgZpRxZRMuBqNMWAKMlY047gKdODN9sWH8UBtFxoZsSMbAZkZzI+JFN72IM2yoogCjQd7eCDP20DBFwDN/Z5qcmD1xBoGEln7QQki5esiW8QbBmKN5Yxs92xE4C99lIMQFgxIpkSbsPQvloe/fkinnnfRXLWNzRs/xHeyhlkJP0OY00Hyc26SOA3q4rIyb6QltavAb3C/NaDxZzBYYx2mb3CNLJd+wg0mrHUKzwk/J5LtddYmLYEp30GZn8uruZJyIVL2T9NYINnLAVVIbxWB0J2K52SEduBfIZG9Ngfi5aLyaEHQEdSdbIjB0TqLZsYEByBHEo4W6KdEmHBwryCPG6vS6Quu+Q8bhnwBgdWvo0mJCZ4i6qy4WgNdQYDt+UXUXZMILhRgNPdEY6ERCbYFNLMZ5OmLSOsuDml+BbeLXmHaUIjU1xteGSBhxodhFXdRZkZlflTcwvDQsMxXvcqprwhqB2tyA8Nx2COcizkiPRPs7jizypUhyWs/5bMov49loQOzOI+wmoWgq0uHkvU43oKRxE+623kr54FTQFHJuaz7ia85AnM5/VhfQGk1Gwi570FS/WsRItYizz3YQyrjmlfubaPo4FxP4Jzjvl9RoOw7B7IGAHTboR1TwKgDT4JoX4nNO7Xy0CkDoZRi8CVyPxDlOKR1FrQR/C9hxDcWVh3/hrMYKATM7vo7oOz/fQFBON/r7jmD4HvZV967rnnGDNmDC6XC5fLxfTp01m2LFG3IxQKcdNNN5GamorD4WDRokXxKPEuVFVVsXDhQmw2GxkZGdx1113Ick9J8FWrVjFhwgTMZjNDhgzh1Vdf7TWWZ555hoEDB2KxWJg6dSpbtmz5PpfSj358b4Q7EnWZApFydq28ETVWnbpAupUR8++P76/yJeIIRuSmfCeCAzGSI+i/mQ4hKb7djx48q3Y29nUYWz95lubfD2brJ/pLsezD+xjaLS08ohkolYaQVTD02wfQWkb0lbOQ/7IAFT2N+8w5dhRR4JLGz5jl0H/vNaE5zM16isB2fTyCTf/9tTpsBMrdyCH9ersIDoDQMYdd48Yy6I8PYPTo+6VhYYx2fYJMHrwG0RjAmqYTM2/OWkpOvorKFb+hYvkfkMMO3AO2YMs42GPI6xaX8sLta6ja38qKlw/w+bN7ePan3/DMDV/z3kPbUKIq2ZNfxp5RgqZBxu5b0KI9M9RyZz5P1qTXGHrWXRgsPkKePDyiPqkr3Sd3NCKyCZMtsSKvLbXzwaNfAxJGezOesrloiolqRa9Z5uhMxBfV1S8mEmnhE87jSmExp2zWLR+WlCgvFpRidunvwnPXlXPDe/eztGMBG8WpRAfrlqz00gvIOnQhKSEn7qRavGNaYehRNIcPjO2YfXlokRgR0LKwZ++lduP1VOzUq1BHQxKfFco8NKwOTUl8RyM+A+mxzznR9Pj2NDmJ/RZ6EJwTfCY2HK3BCAyQZR49eDenOzUssTYGQeBPeQHmOWWuS48w3qZy1rS7mDNzLSfPWcalI3/EJ+cu4d5zttIczCPJoPHIqN8yMXMiOcZkXqkQGGZfiP3+dZjyhgAgulIJu3TNoJCcj2pMSAwoynfPNtSUeixBa2yc1WixWCGjUAaqFc3USWV9b70oAPOU07HfuwT7L5dhv/VVDANHYr/5BQzHiBx2h2nyqUTOSVgPwwf3wNVf6i607hBEPetp3q/gPq/+75xnCbz5WwK/nozSEvu+Ga1w1l90ggMw63b8a1agPj2biJAHpz1MtOgK/Gu+QonGhCxVlcALtxP66k38j55H8BdFCI/kYit/Wic4x0Fo9jP/vyc48D0tOXl5eTz88MMMHToUTdN47bXXOPvss9m5cyfFxcXccccdLF26lPfeew+3283NN9/Meeedx/r1uklLURQWLlxIVlYWGzZsoL6+niuuuAKj0ciDD+pm7IqKChYuXMgNN9zAW2+9xVdffcW1115LdnY2p5yim7Pfffdd7rzzTp5//nmmTp3Kk08+ySmnnEJJSQkZGRk/8C3qRz90VG5PxIl0WBPKwGmRMxl66m3xz79/ZzWhWFVxtxThjKlF3/kcsi+ERdAnNK+WAugkyq9LyyEFemppBP2d7PrHfUyv1utlZey8l7I9f2eqkvDNb8q4kCk/eZ6hx6mhBUC4Ez65GQ58jEETCKq6ebrKJlBvFckIt3Kh8CYBQaM+OJSLip6g5YVEfEjQVQIhqPUnoe01U2vOpuDckvi7fKBrIbue2kNWt2oPkllBLtZrV3XBVbAFS7Je00uxeFHDdsJeXdW3eff5ZE95lfwTnqDss0eRQ0k9LuGzv+6mLzhz9+Au1K04ggBNY//aZy3ypEEJ03tNUzFRASRBRdOgqL2Io86jKGKUaMRM+qgPsWV/TcO2e0Gx0RBTt9dIRNj6DHogjC0g82XoZHItRxhABT4cLBZ+BKrK6Eo9xsqVH2SXOZXaHB9STCrmoq8PMaPqTq6++Q+Md5bGijWAu+p07OdeSOqR5zgSXokmCpiDBg5VzWZiOI9QWAYEglIGJuc+OqsnI3Zl2KgCE9t3szOuLqRDDkokyypoMCCcHd+eFu15j2d1DuIvzWsRAIVkRNpxCx7maDOZlr2STzwmRh8TNN4qXEmaq+/3cphsoIa2jkpePfXVPtt0QRh2Auxdg8VQnQicBxQp/fgHHQO7WIk5qLsylY56Sr7Owj0wiHt8OcQEAg+VfMWg3O8vXicrMgap95RqGjcX//K52EOrsDe+jf/FnVhv3oWYMQA81bD9FRhzMaQP63GcpqrYjjwFBvA/fQnC0BMRjCasF97b85oi68AIoY9/jWn8eoSXT8Zulgk9OjWmli5iszZC7Sv6Af+EE6oKBN2nYD/xsu99D/4X8b0sOWeeeSann346Q4cOZdiwYfzxj3/E4XCwadMmvF4vL730Eo8//jjz589n4sSJvPLKK2zYsIFNm3R//JdffsmBAwd48803GTduHKeddhp/+MMfeOaZZ4jEJNGff/55CgsLeeyxxxgxYgQ333wz559/Pk888UR8HI8//jjXXXcdV111FSNHjuT555/HZrPx8svfXpW4H/34V6FEgtRF3u61PSNyDiNP+EP88+o9Zby8W/dnz87S2P3Hc7FbvvtqyFt3FEGQUTWBThJCgf5YOrk50tNdtWvxA3GC04XBMYJTI2TTdN0uJl33zHGLhAIgR9DW/Dke4NgYeZi2qG5SbzGLZEWb+JPpRgLpsi4E576ZtrcT8QOZ94wj1a8TE2tTlLPzDjLPeoBI6ULsYQPZ0TxScn/DkLoEQXMOCBKaBd4sfVyWdn3Fnj46lk4dMxzIIVf8mM7a8XpdIUEjc8I7x7+ebnBk7yZv1jMARDzfMfVeg4qAPinnONpI9idR7CnmxNoTMYshoqqIaFFwD2ggacirieMs7az1JJ5ZVNTFBM0BjWetN/Ar4c984zuPN7WrAThr9d9xBjQQNeoKFTokiZuLUrBlJGJP8o/AlO338bFahuG0hPvywMdv8VzrajYEDPy5wcJPW4zMqD0L0OKxNpE0lWhAJwCqaEITdWo3qr2EQa2x4rA2fVLWVAFXJMT1dgcuJSERkB/J4PaWIMvKs3mr5CEebUhFFBQO2c6iPvwGXvlqkox/p8A/D5cEl6dGGGdLkJzkgje5eP5vjnurjWZ9Yg91foWifHtJBtPE03ttCwecGK944VuP60JlUznJ5hZMsViqYH0HmiziOWJHDJcBZlAlgp29q4r/Myzd8jorvynmkw1/73O/8Yy743/bxYME34hlPiXlw4m/RZHchFa8hv++Ocj3puJ/+edEdyfib+zsxlb6ONYDDxO+Jw/ucyf+xWBV9qM0VMY1bCxWL1ZrM1Zr39bf7vAL41F+ug/1Z5UIv23G/vPFvdrsqdjGe59P4/01j3+ne/K/gn85HFpRFP7xj3/g9/uZPn0627dvJxqNctJJCXGzoqIiCgoK2LhRX0Vt3LiR0aNHk5mZkKg+5ZRT6OjoYP/+/fE23fvoatPVRyQSYfv27T3aiKLISSedFG9zPITDYTo6Onr860c/vgvq9nyGavRjCKcw1PkA7uAMRmX+jdGnPobRmpjYlu2ojP/9+I/nfu/z+Op0U7k/YiMsJSY7fywbxCInAiOjkTBDjyYk9EsNQ3v8bb5uORm5hRi+zeQc8cNTYxHWP0lAmUVNaAmyVhzf7TEK3GJ4BEOsJlC08zxylrhR/fpSOuOmcTQHt+AI6J/zLV68Ry3kG7xUrzvItNNKGDRzKWsuPhtTTNU3Ol3l3aRRrI4MRDBoaIoRovrxkkmPX/FWTaH0gyepXP472kWVdZYoXtlKxKeTD2feDhCOmRS17nnKGpnj3yZv9tPxLQaHHkfjbx5MoKnnqrk7VMVInaZnPGU727BGYjEcqhmTGCYsaDR2JrPdL5E+ZieDTr8bZ952fFnbEXxZqGh4RZWQVbeWqF4DObFChi85f8Q68QTOOPoOdyyOuaqSo/w91Y014qDVIPHM+QKOnFB8PH98Q6GkehMnV17FLpduPRtw9GxG77+RD9ps+INJWBQLZs0Iigc1qr/W3fM/IOqLWTkEAcWkx4jldTYxsV23eglpIYgF0wrhZqYHdatjjT2Mx+JDQOISzwjyhM1kas0YhUo0zYiz/XoAfMp5mISDpNbm0ipeHx9zY3AAI8evZcKQ6ce9zwBjhpxLVDWQYzvEsq2vf2tbw8CRhIKJMh3RkAnzozUYi6Z863FdWLdbJyDm2D2R/Ynfl9zRAkQRFQtJzgO8sfQawtFwX930CYvvfoyijCP0CF/t/BCv39Njv2nUTAIDb4x/tncuI7LtS4Lv/pFo6Q6iD0/Bsv5W7OzGYJaxV72A6ZPz+jyX2dp3KXHRoCE9P7bPfd0RHPs7/MnnoN5ehnp7GeEFr2P/3SqkjHxEZ3IvF5WqqpTVlbD/wF2kWJpJlp+hpGbfPz3P/wq+d+Dx3r17mT59OqFQCIfDwUcffcTIkSPZtWsXJpOJpKSkHu0zMzNpaNDN7w0NDT0ITtf+rn3f1qajo4NgMEh7ezuKovTZ5tCh40Snx/DQQw9x//33f2ubfvTjWPiaKilveRRMkGZYQMHkSyjgkl7tthyq4h+H9Mnp4iIL6UmOXm3+GZTYZNgZsSGYEnEQXlGfdC1qguRse+VOpuNB0QQqz1/OkOIpIIpoqsrQY2KAZFmmtLSUjGQHlk1PYi9fDqc/qgcpdtbhlc+mU76uxzElTpGPBrVyc7c6PaObe66moyktyG9fiqhBm2IhuMtCR6UNZ36QnAEdvH73LVjkDkY3JlLfv0ifRKRRIinDBpShymZCGUfj+zXZSNPuC1AUO5qg8ZYjRFCEDlFjhC8Ds0tfmRZdcAOdtaOIBC207rkSVbbgzN9C5vh3MFh6Zt8AiAYZQyiJ8bt+iaBJrMt9g5CrnmR3Aw5XIjU42pZDYyzoOMPZhtBtLWQUo/gEjc8aMjjg8PGBR+MPOe3kznie7aXFZNVfxsf2CGVGlZMlF7PsoPnh3afv5sx7X8ZjdCFGqvn9u+/gjVnnKuYGqVamc19OA5/XD2eZu4TyiyM89X6AjirdtTK/MsoHo00cMtYyjtEAzOmcyPRDYzF2V1CO6CRIEDUUu5AgOUDUZMUQikBAJN0r4wXUJBXBK4BXQog0YG/RQwIOOC0EbQHOLoegMh2btAbRtIewoRItMqbnd0DLx9BRwqLJd1LhPZuMpGxcNjffBaMGjmfPkUvI5A0Mvof5eH2IM6ddj3Qcy6Phtq8IrHgZrXYXxosSFqK6tlq2HPyUqBxE9n2KwXEGF5zwcxrb6znaXMqh0qfIte4CwBxKIkoUuRtXUEIiEl4s7QMIZBwgx7qK5Z/fy+nTH6CVdkRNICMzh77gD/l7bmi/i7XrrRSNep8hOQlXte3HD+P/0xHs/hUAmJZcoO84+CjGPhST4/07TsV44vWonW2YV17bK5zneNBUCIfTUCxZiCNPB7MDreUotnPv7NHunwVtL171R9J5lYxuKeVbD77J8LxEkdGoHGHlzsWAgMlgpbZxNZragds9hZMnXoHd8t8r7PC9Sc7w4cPZtWsXXq+X999/nyuvvJLVq789bfB/Bffeey933pl4wB0dHboAUz/68S0o2fo7ZGs75mA+Q+b87Ljt/rJ8D10CX4um/ZMA3+NA8lUD0BFxQDcJ+UaSAHBRRSQcwmS2kNasxwXtcs1j4uhp8bZ96VWUvP8OabsjCGIFiOV0apMwvX0/RqGKxsgLKFoiDkMF7phgZX26gWRNJ22toWxOznwbf7lORlQhinWmg0M7f8bEWr1NTYsbY6U+OXVWW8kb6KWyupQF5n20x1xvlhMj7Mj/CbM6ZGzpn8cG3NMiE2wbiBJKQkYjKEAwdjlHDYpuyYkYwaRbfpy5+orSmlzB0a8fIG3Ue30SHAAlYkPeeDsBWcQiaoyvuowmJUKDvRz/jCexm/WVe8XuS/GI+r1Ps3kJdgtiMAphIoJGNJgCjnICqsDbbSYm22W2CB4WRVIps+r3Y6dFYcmMLBauaECuN/LJndfx4QWFzPu8Eq838dI/kGbiwuQIZlcDC+wNVOyfQKc1hayJ78ZJzoxqmQ9Gm3gv9UvmeSeTKeuuN+Mxr/A2pRoDINpUECDiy+CoQcEraoxxZGHt8BJsMyHFAsODaSLWFhC8QKQZZ9NEAMK2cnY7nJwN+KXhpALpwst67S6tZzqxomVhEGqI1voYMuz48Weaqt/TUDiIWC9jynciGEWmFV9KxcE3MIoyxvCfWbrFyFnTryUUCfHeN78CVHIyZjN95KnYcgZjuPKPPfr1h/xs2HwuyebYAsAKKM/x1dfPxdvkdpugxagNSWgj6EvcOzksIgrtZJWeQnmGru3jcH7Cjq9q8WVuQ4w4sBStwDWwZ2xRIBxg+TdzSTqmOKXVEGTHrqvISFreg/BZb3oF9aH8eK2rfwa//SSsN/4d0R7rY84FhFYvRkzJRt63Cu3g50gL7kLrbEWt3IlQuxkGnoDl/F8iJqX9s/Cb74QU9fVePp9s6T1e+7yRy099CVEUeWflHeSalsf353YZg8JrWLP2KU6ev6/PeKX/BL73WU0mE0OG6P7ziRMnsnXrVp566ikuuugiIpEIHo+nhzWnsbGRrCxdSj0rK6tXFlRX9lX3NsdmZDU2NuJyubBarUiShCRJfbbp6uN4MJvNmM3fUiq1H//PIhrs5NDq+7DZBzF49k0AtBxez5HDj+K36RPpiOGPHLeyuKqq7G6SASO/OiGDyUUF/9I4jCH9e+2LWKFbEGuT4EaRzUhSmP0bllA04wwKo2UgQObZfzhObzr8Oxpx7xpIFIgqwwgopxy37ZVTbVTZRTqNOllz46UllM0C7TX87yWsLU1nvIA3tJmBR4LxbS1NdrJJuFkyq4NcO3QblSt0a4ItI8xafs+s/bHaVdn6fdWAjqopWFLLMNlb8bcNZLE9zFFjz2KkPgH83hxSTFEiHhumpISwnS21lREX3NijvRw0YLAmMjfb1/yMlrZcjtA9m1Ok06LydqOBs+Q8HIoboyePgFsnXk5zJ/5u5ZWNQpSwoGGPJMW3bQ8Y2B4wkBNKpcqQGHNYgA0DBjI7qQ6XR0SVRc555yjdJf2HL6rnM89F2At1C4xdguszQgzaeTGS/R9kT22nfnMyBfXQnn4vbZKdh7Q1PFl6DgCf2QWKERjkV1mSY6DwwA4KAMGuEmgpxNOcz2KHHu84LnU8c+tKCLYa4zWZoslgcmr6RBDxxEYV4Yrme7isETp5AaIZqKIFUYi5EuWEWwpA1rKRhCbC5R4sw5I5Fpqs4itfxfbya8jcW0CRbx8BZS5tRTeRfdl8BmUPY89uF06TbjILtb+MolzFpyvnk2OJvefbP+WDFS9x+RlLe/W/dNOzpJpbe23vC5kbf42gVSBqrcjhxMythEQkoR1zFKSGs1CyPgXAl7kNANXkY8emvzJ3YM/f2sdrHyHTrFsB/dZ7OXH8JSzf+gLO8DOkWppYved9zpx2Tby9aHPCHzwEP34KdffH2NQdREMWokmTsd78BlokROjpS8GZhTTpPOxzLuh1DZYT9OK4ptGzgePHO/0QkBVZL1AbQ014AXlmXccqz7KG9Qe/pMPX0IPgHAuTFGXjwZXMHnXqcdv8O/F/lihUVZVwOMzEiRMxGo189dVX8X0lJSVUVVUxfbrul50+fTp79+6lqSkRgLhixQpcLhcjR46Mt+neR1ebrj5MJhMTJ07s0UZVVb766qt4m3704/uibvfHNJk+pjL6ON6aAzTs+4LdNVfECY4hnELyoMnHPf7pzzbTqRoxoHDh7FH/8jgssv7b6JQdPTJ1FFEl4NOtjrmr7+TIn0/EIKh4cJCVOwxNVmneVU24UycdkUiEuro63nruVdoXH/5O535iuJmxwWUs23k5L+z/LdlqLT/hr4ysuxb/2oR6s+v0fOyVa5i/tpXBlTrRWOMZhDOWuqxY9XF7K21xggPwWeFEvIpu8rck78WWXkrEl07lF/dTt+k6qlfdgaYKlDcX9yI4AAhQVzMZOejClBSIx54cC7UhG8ORKT0ITvWas2lpy6fCoBASeq6inW3DmFt2CUd9Lva0ZxIUYsG4ooDFGEXrZlEziRHCAkytOoMz99/MwLbR8X2p0WQapG7ZVYKGLezm4bP6XksuudnNpdnjsbYtxORMvBMdWQcoMdRxxHU1lpRYGlGrgSn+AIppAOsG/og7hn3D2ZOPcP8sBxfOsjN5gYP7RlvJ9uqpxnKqhujPoFJJkPISt7449dVaCbTo8TlKskY0ZiRQw0EgjFnchz0awKkEkEy6hkuDoNfxktVEmIAckweQtSwEqYnAjiY0pedzU8MK9Y+tpfPlDiavH8sI/y4EQcZuWIm75DGiDX40WcWWnsgYSrU0smr1MFItPReyObZD7C7f2mPbF9vfJVXVy5XUBYqoCU7DZ7kHb7gn2QorJiTbI1i8g0AzISqNoCbIphySEIV2RLGNSYPvJiL3XgwraYupadEDtjcd+oZ3vrqfZO1dAEKO33HW9GuxW+wsmn079dGTAbAFHmTDgRW9+rKecxv2332DcL8X0yON2O9dguhMRkrNxv67b7Df+Q6WPgjOfxrvrX4QMfZ7qQmfylSbRG5yolxEpPEmLP6+F1myKlETnIIj9+X/GsGB72nJuffeeznttNMoKCigs7OTt99+m1WrVvHFF1/gdru55ppruPPOO0lJScHlcnHLLbcwffp0pk3TTekLFixg5MiRXH755Tz66KM0NDTw61//mptuuiluYbnhhht4+umnufvuu7n66qv5+uuvWbx4MUuXJhj8nXfeyZVXXsmkSZOYMmUKTz75JH6/n6uu+u9WO+3H/z+hKjJHAvfFP7dXb6a9fWOPVEuXNvFbtW4+29sIGJmaKeJ2/Otl6WxaMwjQGbXT3ZKjodLSNAqzUyFVrCA16kXWROoH/RLn/Yl09n2mPchzkti5ZQdCUGVhdEKvc8gCdBgFRA2SohoVdpEnh4vcVPdr5nv0vgxCMn8WbgXAeTRBVJLOHYI6oplhq3xxe4RPNbO9PpuTQuWAgKfQjtIWJaMhQTIOjXaBWV/RDjnyPm2n6Te3reRk5FgwadSfib9hFNXefDhOrLRXM9FeNpf0UZ8i9kWEADGrHpn6+OemuuFk1A7hDbvMRos+pgwiFJgPMa9RV58e3jyVulAq6f586mP6OJpFQtMMCFriuRvFCFFBwC9AbsdQcjuGwtAluEd9itTh5G/dSI4mgDWaxu5CkQt/YWREjZ1Z5V5O3qCwbYSbS05ehu2JndjSS3qslgGaPMmkXP9L9r+5CJP5WgxhkceXPcili56i3JbP2sKzerTXBIGkcAcZPi/tOIikQ7CxiO6F2avtiYlfjqkOR/LBWKE3UsIiBqEBSUrENqqmEoicQVPucMpa5pKSvAN3pb7vYCTEaKOJiDoUs/0LlI4Iwb0t2MZloMkqre/sIbS/E5u4ihRL74wcm7QO39PX0TTgMhZcdyFfbheQvL2F9TrNP6ezfSk5toPsPvggYwcl1IXbG54l3QpNwXxmTnyCQdldAeXX4w/52VG2lolD5mAxWgjuaaFN2IamOREiPeM35ZCIhAcJkWhTgCbDPPK6ipX6bsAWXUko+QirNt2BzT0Xd/RJMgRAgobgIC6Z2zPdekDu6SgtXwBQWvESM0ae3Ou6/tfR2tFCBol6WfOdCocjK6B9GeUtIxmU1rNkR2NwIFEhD4Mpn7On34UhWInRUYBk6W3d+0/ie5GcpqYmrrjiCurr63G73YwZM4YvvviCk0/WH+ATTzyBKIosWrSIcDjMKaecwrPPJhQbJUliyZIl3HjjjUyfPh273c6VV17J73+fkAovLCxk6dKl3HHHHTz11FPk5eXx4osvxjVyAC666CKam5v57W9/S0NDA+PGjWP58uW9gpH70Y/vgtod7/f47Os8TKeguw9cwSmEaaCw+IbjHh+ORDnq0yfCa+d9d02cY6GpKlZ0s3tH1BWrfZTYH9GitEZ+g9t8MzYhwM7cq8kq6RkEmhlxwUqVXMb12L4vuZ4PcwpZlWEgLEFEBIsCWSGVgeFtPHv4YTIjutl96YgzsKTHilWG3RiiiTTuxtR3CXz+Z0Z34xeHPamoiBj9KhoSPkc6h9Ovpkj+I/ktUdbNy2N38QOM2Ocnqf0wdn89na4kAEJtA9DQULQoBsFEYNUs6p0WQCEjuY3qEUUIHVEM1T5Eb5StZpmiIyfDqE+/w/0USa6ZR8aRM1gjOSg3JFxpTZhoCo9hXu4OqNWJYE6nbumoNcUUmm0iimBE7EZyDLHq122SikOOMYjSM7AnNePzpdMk9SRe6Voy1wZGcduN79AciXJXSTXb53/FqcXT+OQJXUnZWaC78DUFEHUtH5O7iXce2cuChSa27RvEuN0VBDeYWZV8Jc1pKVw78g/sdI3oca6nDj0er8QezJJoqZ5EoyUxnhZDT3UgOU2jdOvvyanajpPPkUMiJssGUrSEVEJIasMIJB9dQOu8m4lU6oHnNXKECkFgNBDVBpMSaSKAh7Z3S5A9YYJ7mlHq6rCK+0gxJQhOs2E0DvUwVlWPf3IYliJUBVA6TuTEcefx7vLnyLTqbtGa4HQuPPkFrCYru8sn0VJ5Memmg4QiISwmC9tK15Nu1cuLzJzyCgXphT2uz26xM7tYtyBoUZX2f5RgFFtRtWSkSH2Ptl3uKlFz4N9Yx7m/epB9VecxeehsjAYTe58xEUr+C9m2XRDd1fMZZ13fawE0d8wZfLapEVvgQfKsW3lr6UmcOe+D7xyQ/b+A5VueIit2WZWeUVitK+ny3FosPd2D5W1FTLIFkdlClsXK/tVT8MUWFO6QhaJRT+AoWPCfHH4c34vkvPRS30XzumCxWHjmmWd45plnjttmwIABfP7559/az9y5c9m5c+e3trn55pu5+eabv7VNP/rxXdDWsoFuYRc0GhaDAQTVwNgTnsPkSIrv+2TDflbsq+GBS+fgsJh48cvt/GlNIwoSZkFmxsgB//I4OjytuAX95e+V7T3cVQBRfChk0DLkFQLDREanTKLlJV16wWV4nU75QrQ+Qg03Zq/hljELETWFYf4K8kSJm0qfIJQ3Hbu9gCnb9dVzyCSycuKdWI2vxo+1+3T3knlIEqlXjmTL29cyrSKRllLSkcb65gG4tCBau/5GrLMtQCKddTP/yptznUQlgZ8v9wDgSR6GOradnMEvomlQHcjg1SSdfJwakBnNBJpiqfM1+YPRnEY0pxHVG0H0Rik3qmyNGBlUPQMtcy85u27BEE6iccTrCJqIP3UvjqaJOJsmYGsdRSjkZLUvgopGWx/Bnn/yj2CGu42ZXt2t0yF62Gw1gCaR4u4kqpqOITn6i7ve6mOY6iLgBxGBmh0XIksagViWjNEiEQ0pBKPJ5Eo2VFUl3WTk1dGDYPQgvnxsKzqlVHEP2ISqGGjccgZJw3dgTakic8Lb1Ky9lc1vHcGQcQt+653Yg1C2NJPCU5tYFr6BTWPzCbgC7BNGk6y1cYpnD2WdemDs0fJrUOwWGqUEsfOKGp7RySTt1TPdGhdkEa3LIhQT05MDErnaW6iyQMN2N6JRxT6lmggKkmzH3FmAuUOPNWsxqJjSdxBsm4hVcSArI8ixXEZj+Ek6loMkNJBlvgVRSMRsrclfwJxr3kORo3zz0tnMq9fFF83CITo3HSFpQTGXLvyaLYfX0N5Zz5XzL4ofW5w5luWldqxGP3uPbmPy0Fl4q6+I789L/fbfXdsHusvWKFQRVsdgiDT32N/lrpJUK6gQfuso06+eh2DQn32mZS5N/KXHMSHZQsT2U84ev4hQy27ajryBO+901KgXoz2fhVOu4ptVushtlrWClatP5LzTtn3rOP/b2F2+lTZfA2rL7XGCU9E2isKUfQS7tctxNBL0DmPI5D9RtvXHDEo5RFd+YkdkRQ8ruNcSwpz0ryVi/BDor13Vj/+noaoqHnSrRb74U6rVhOXRHZ7Wg+D4QxHu/uwIYc3AuoeW41G6fCr62+CaiamYY1ok/wpaakpxA4rmIoKZHmYcNCLEyEW9m6LLp9OwUn9xW8QtuAyLcUof0hR5HFVz4ja+SkiZwPs5g/lt8UIAzmn6mmcPPaD3poLSuC9eC6glxcjukW6s4qvxMxpCSaSVXoBpgIv0a0dztPRTsvaGiFWYYHNLHuuaCzE6IjhdEuwR0NwKIWUEgiCT6YWffeJBkUCKJVBZUsrImaaLF8rBJL4yGulyyy23RcmWRZpj1hDNmbiXaroFqvVU3VqDSu3qc5iTkQiAzd/x8x73UtM0SkIqJWEZEKk2KMRU7lHder+iV7/2DYKVDUlBLjPWsd3RSbR9OJpFYoi5lohqQtISPh8hlgm2RjKxRgoxyC1yrteEqNipERQggmYzgFmCkII34kAyd/LN699w4o9PjPdTXqo/S3NSNaIhQvPes/FWL6Sjfj7Dzr0VW1oZw869jbbS+TTtHELL6NM4cYsuUlexPIPh59cxbbeeiTdF2oVD8SAHRSKd+ivdYxpBp6DRKSa+Qx2iRu2MkQSHbqGzYDiywQZ14HfoRDbkMaJp0H7EhrdCZ2uDBjdxJGkN7uA8UsvPxOLVSxg0Z1SQkbcGj6cIq+Igqg3CylYyzbfjiV5PkrGnKF75VauYEyteKxmMzPvJ55SVbWPwGydiEOvpXLsf9/wRCAaRKcPm9DhWDURpfXk/aelD8WfsoqJ2I4WZCZ2jpsBgOpZWgCTgmJFLYFsDsidM8tlD0DSN9g9KCe7WSY1BqCTILAh79L4zFMQmCSUsItKOqOUjECRcDsF9uusNwJTlJK30PJoGrMTnuBZ/sIYzZtxNkiOZjrL32Ho05mY7nHClTS54EL/1XuzBhwBwm9tp7Wwl1fkdRSn/Qyip2cfm3fejiankmXvHD2VYdFkLQzCV3L1X05n7FW25O7C6D9O0/2ws/8Q4lRSyYXQVfnujfyP6SU4//p9G477lyOZWBMXMoBk3oG4IU8tLiLKFEVMf6NF22dYSwrFyDQmCo+NXJ2Rw3WnHD0z+Z1BkmcBSPVNC1nJQJRGtO8nRQNP01GihLUo0GKFjdQk2kjAIelCwIMhkmG4HRL5KnchTBYPZ6tYDYzPDLdxe9Ua8O0EkTnBCZpE9I10gQlg24+7MJ3vfdZiCmVT7D1GRtJoxlQbWPvAYlxV6APikfgQNdgtJg70MnNVKykv6WFszXFjC7SR5yqjPmQXoBEeUg1jzysif81R8DMHWQnzHxKJssUSICnoVb82u32tB8aGlGlEyLUiNIZoklXZTMvv2LcORUkhHsJMsm4OM7BF0KCp7lQ46iBDpFny6z6STEznPhlycDJqGWB/EtDeh3/NmNAfadWoZLU4ir7OJiNyT5IjHpLuXCyoNkkaOItAYI2eq24hRDRMFOhUTosnHodUCQyZVM2BUPrs+PYKChj17D/mzn0aVjbQfma8/ZtlK/ZaryZmqq7enDP2a5j3nMTDjJNaMPcqc3XocRMn7OQw/vw5BAoeiP5OG7fpsE8o1IRsd1Bn0sap2A6JfRhXAq2bimqMitPuJNOrWD78tC1UUICoS9Ut4yrpVcPcaMKTtBubhbNKDjxVNwzbkG4a7J1C9SyAbCBhy6HJqHktwtp78VybHCE53DB48iWpLNvmhetJ5nqNvljDwx4lsIcUXwbusMl4fzWocgj9jF2HfKpau3UpeLPRtRv29+Mr034BvTaKmmLnQTehwe5zgALqEgiKghjsBO9FcDXOTrvhM1IMq2Wk2byYtPJe2z7cSbnHiWxVGcthI9ZxFSsWZ5Nw7FcmdMP1WHn4ETGAIJyOb2+PJc1urfsnM9NtInlrKN6t0S0ZJ9Y7/WnxOa0cLSfZkJElCVVUicgRVUzly4EJyrX0LH4ryOdhtHyNGbQxe+xgAFk8xUeMjdGYcJHrMmm6ocR6RSBORqIeBYx6get8fyB/3780A+2foJzn9+H8aTbXLwAypyskYLHaGzf0FqUfm4MoajtnVsybOl3treh1vF6NcP+X/RnAA9j12GmPDuim7TVmEIjbT05IDKDJhyYdZcdB4/2ZsMe0ck3iQHc4iHi68jmtqP2B8xyEuG/1o/LB5bZt5a+89iGgEgplI5z7O1uadBDsOUmwezT73K0iigjeQy+T19yNoidfChqZPoAn2rv6CiwfoK7q9gWxs5zczRAKjIhFwPUFyyy0EMSMX2Mk782Fq1/+UkWvf4EDR5QAY1Chpoz5OXEvVcELePHxdRTAzrUiNQfbHBBDVZBM274fYOpeBpuJLvYXwsNFIjSEaJZWAoFGWF1M9t0EFgKeroJEdsKOh0SwqBEWBAzGSo2THJnBBQM2xEUozY17diKAm7nV0bApqmoVMTzM+JQ1Dt5RvQewd7PyWM8y1HWYaYunjglPE1dFOADdBQDIGEMQoS54uxWg4SFQ2kDz0KzLH65k5nbUTUKMJYtFxdHqc5ADkn/AEgXVzIPUG4Nb49pL3+xana5w1ABqh3tEGmgO7y4QcUIhqGp5ACgNNQQyWDjo7s1htiTImIhF0pWD3tNJeaifSmZi5Qh4jWcJGOrk9vq0irDJRbmDIN5+jSUdB/Sk+Uw5Eeo/lyOUrmDx4CqFQPSZTKtFoO52+g6SmnIAgCHgX/pX8D87HIm3H+v+xd9ZRllTn2v+VHNc+7e7j7gMMMDO4BAkOgRiEyCVy404S4kQhxIjhkOBuo4xrz/RMu3v36T6uJd8fdaZPNz0kublJ7rrfnWctFtNVu6p2ydn72a88b9sGYkeHEJCJHx0jdmi6S8k+YdSTKrFPDxoW208uHDjxxPSsQoEIVmGcCAHUjO8lVQBmq46QENCTQTS7kyXCDxkWKkiHaoi+DgJWtIBmuLAVAcUfR/JY0FWF9s1XMmqZwN2/juLGDxAo28TwvGyw7qHRn7KkUacvvoIy2z7iQ7fT5H6GOWX/eAbmP4IHX/sCxdLMUg0A1ndgAV7bRwjEf4GYdlC3/QfT9pUc+hwdZ3yWtN3IClxe+nW8s43fupZU0SIpBKtMZfXjiOr/LM04RXJO4f80QvoBAPIK1wNGiZD8WWfMaDceirKtN8XUn8xty1x88ZozZ7T9r6L9yC4WxzPBp7rMkLwERXzjbRRHR9DAb1EpycrDoMqt3LL0SrblGKvsrZn/T8Wlo5sR0Ymkz2TE9n66Bu5D9zYhOuA4uzkxRcxrumEawWkPG9L/PnOMS0uPk2eNkUKm6ywrdilNuesc6pb/gtfuuY5UwDhOWtGPZtYpWvEnUq3X4gp3o5rMeNc/g81nBJQ27j+T+cu3MvrmuyZdSGqBFWnYmHl0SUCpdeOM7WNOpJBzX++lcf4gj29YiuY2QSjNbluC9bGTZ7Ep6MgI7LMobLYp0/bNHz1KaPyPDJbeii46UCw1JM8tQeoII44nUcscaEU2Vh3ZRq41QEApmj5I6hq57hj+0HSJ2j+5EqQzZMhnDuMgAniIizqCAJUbv0P3ps+SVqyIcmKS4ACEuucTE3RaTSo1aQmXLiC0FaDXGROIPa+d8byNrD6thlfjl3Hezmd4J4z7TATzK0kN6xzRjcCILw7q3I9AFzqBTCabxTPIa9FK9loV9lgVznCX4Aj4GW+ertKdDJgoSI3zq8J2rh6qQdPBUfFj6hqM380c6UX6MqJORwABAABJREFU0+9FShbRW+hlnDmw5qOUVi0nz1dKHXC47yXGWu4AsQAlHUaWojTLZzK77EYY/jQnpvtC+Qf0PbiOd1I2OeEqmwqP+mUEBFLWIXrnPEh1wx2IWtbKKhDFI/+BuHY6DulFVD0HWRgilRFDjAkWLDYNc0JHT6bRbMYzK7TcQV/iaUDAIb1GSqslpdQhIND6+lcYrjshZAly3Edh53o88q+R+8/DEfkSJLwolgC9K35Ac9/Pkc1XA8Yipr/lMkYmfs6ZC2fW4jqB7pEOovEQPaPHGR7dwqXrvo2mKSRTSUrzDCmJZDrJ0e79LKpagUk2MzDeTzQeomPwMEMjL2LRu0hSTkXZNe9IcE6gP76Ia897lNf2P0x0/H7S6bmA4brPbb8MIW2fcUzN9u+T9ykfksWGyVNjPM9DI4w/2jy9oSSQ/4EFWGq8f7UP/yqcIjmn8H8WkcF2UtYh0ETy689+x3aJVJpbfvE6cV3GI6X52bUL2Xy0l89cefo7HvP3oLftCOrD11Or9U5uG079jCFvEkWWMCw5xsQpAIIu8VKxlQ+0G21F/Hx1fh7bck4uPHjt0Et40yGuHNrOeOIKBkprGZ7/6ZO2zW++DufYfEylTjwXVGGu8fDcR+7BLCpcVXEEl8lYpr9Wuhi7rRszdmqW/px7f303H+5/ndZkEbqgoxQb1MzsGiG4cTeFO2czOlvEXZ4pra0LzF++lcR4BX2BWnCmkMwSusdIuRZ0UGZ7sDhHuWqnh+phN5IjF6fZsJKoZQ7EYwH2meG4dZyLUwnMlije8Spsuom9lvQMYnMCSpkdS+Ig8ZwJvKPfR0dEMddS7V/MwfmXodYYqsyyEmXJ+DiOmjiqIjI1lV/QVM6u7+SJ1iWoJXaQBEyNAVKC8Z50oDQ5hjVhMNG4AOl4Dq6SBio3fJfuN76IJad7Wr8So/PZZEtzzKxSoijcGLFS1HkaE/qbJOoNgbyyM35NV1uQG+78Eo9/+zzU5Eus3/08U7QHUQUYXZ9LOpZLi0klgYxglTgzIfMUKbqAYMpwKuk6DCv2yRlgyD2fQrJV5UfmaxQ0ikQGLCQCMhsWfJ1XrO9lQdGfOf1w9nsFsImb0JMX015ZyKoNv8VmMybhlKbxwmgQd0smQUQbQs4w6tnKVujaCiIE3DLekPHOJGEEVc+Kuo5XvUjE1kcsp505O75H+Z4v0bvKUDzOr34M60tPAeXkid+goqmPvaVR1o7uZVfumbgTK8mJHcKpv4ETI54pqpyHJAyhxI2ODPe+B4v6BmZ6URIiKVOWYJVZLyeqnItDNuJUBhIPo+HG3XkFWjqHUKmhIVS16xt45Xtwyi9jlzYzGvwBbvl+kpEl+MfnEM1voCp6kD6xmDybkdWVHv0PDrb7WFpryKv0j/fSPnCUHGcBB479kjLrmwCYgDILPLcNrPpRXCY/O8wfRIo9jMcSAGBrz7TXgQyUTQb+9qP5d03br2gSI+oluHkDpynCaLyE1d55EGyhPvQIiqgz5Hpzsr17JOtqPHe9g89FTJyzN5B5pQWYZ+cQfLWLZEeQVNfMepCmYgfmCveM7f8unCI5p/B/FgPNRpCgPVk/LcD47bjzka0cyVgqbltTxFmLajlr0cxV5X/5+q/8hNVTCM6AchWaXkG7u5W0ZOLtRnhNM/GbmlzWTzzEGdEH2JEzlxeLfgLAksBxftb0Wx4uXc0Thedyd8sPuMD/Fn22K2nN+yCaKcpY/R/esS/uwTWYypy4bqhi/2vPs+vLj2GTUnywdj822ZiAXqhagrncGFHnzf45vzk+woXBP5EIGO6NcI6Mbk5PntNTuwdq91A89UKZGJx4/3LGM66qtFvCG99HcPFKhISKWmanqmMrzp4QieQEI4vXYlYVrtvzOo+u3IgUiCMOJIlqNh6XbaDmgEcBTk5unEUpAvZclDoXnQNHkFS4ZPxMtrr3E6KVoNRLbuA0olY75shjyKk+RBbhcvrRgm8jObrG4mAbhwqqaKw0glKFcBq5xwiK1p0ylrSG4DBBAOKCTmy4DFdJA1ZvPzUXfglRNuIfEn4LXVtvQFddHDMZWVADsk4anWDyXXT21WOOP07uIoMUSd59NPVcSNHszzAxMo9N69ay8ZY6ahfXY3XYSCZH6Xr2WpRG32QMUk6eDalPICdDlsOKsSJX4h6sWvbe9hWsZDHZYq/CRSrKqIA8IhDssrOgIEh45Z/IHUkiYJCk7YPVLIgNY67eRky7GId/PkPDz1FecRtoSd7f0EZp4Nf8rcThhnluztxl5OYotm0IsavR0ehZ9W3GzBNsONiDtStFwvRDCNxBzuCXcZ21mpKRXCZ6zscl/Rm3ZnyXa0cNscA1/q3A1hnXUslBFoZRMrVCUuY8UuZcyJAcBIkRoYQC3YjxOUFwAPLMX2ck9SNE3YSv91x8vdnYGptkWGIlIUSR5UOAoQNUfehumtb1knC0cpbzGnYrVTgThit5vPtGHmsuBtvF5OlGMP7IyFSCksXUgGCL+otp2aB/L/pTF5Cbs4qNNQuRzF4E5/fRVIVDry+hL/kofQ2PzmAEpmgRcsJw21+7LsGD7f+BYlfwu75ObriQ8YffuV7k0XOK6Qq8xpt5ZXw/NZcq+Z9RZOK/jlMk5xT+T6Jv/5/p1YzaNl7LTKVsRVH55Ut7efHoEMeCxiR+cbXERy9dM6PtP4J4NEzdqDFw7ch9N6ZUMSWjZ5MWNPodg2iyjJU0wpR4kLQgoYgCd89axBmHknyx7uMAXNV/jM8fLQe+zh2R1/h6x+UoWgFD6ftoPvMrvH2wD4/k42q7HndCQ6rswtu3DvHiMp578B6CH86qzJ5f3DpJcJ6cuxxPvjHZ1ggreHX3dj7ccBnhfgu924xsEbXQCsQJdK0BTcZbs33adXU9owEzmIO4r46efGMiNttTrGkY4aUNWffTxp2NyLrIvN5hRjKFlT3xCOZ0klSdF+vAdDXcd4Iy181YhWGhEbQ0pwfKuPD4RjrH9nCzaRZzKs/jY7O+hxD6HI6wCVGPsW5oHqBhdY2ijk8/n6DrCGYFf+qbFPUuZaj8U2heM2RIjpZnxZ1Oo6uG5Ssu6ISOL6NwqeHeMNkDxna/jYONl+BiAYOyNrXSA8fMKqaYwGphLptZSS7Gc7fntZNKgWfRp/AK4LCcSV7tVcgWI3Ol5fjXkawR+mL59GaEEks9VuhLkZtxAUWR6HztC2iKjcgUr9CQaTqlTlfrhM9XyXlAJjFuojClUTYQp2gkSXTYTM+mPPJIMoSX6oIWBHMMW6COjo676egwAlTfLs2awoTe+AU+PH8W3+VTFGC449JmkT2L3s2qhr+gWF6le9kutLiXQFphw7EBXJkAGpO0mZS6kryeOnLaXUy81ILP9F3s0lt/17dwLL4epbyUmsEG0gnjfpMWDymz8X0oCQlLSmHbrHlc3jUw43iz2IIReDQ96cAj/xZJGJ/RHsAiNpHfejWDC39Nz9hTXHLeUR58dS9l1k0A5FkHIUNw3gmKJiGL0wPeR+OleM3DmKSTE3sATRdQPd8kmQqxtP48VkWPcPzYZ3krmkZUdRYVf4pY4Chha3rGsb6EE6vow3XgTkAnYj3GW3uzSsf7F3+KQN+tePvPNq4lJUg6+7AGa2ksfpgidT/dWj71vmbqNZAij4N9+V+9z38VTpGcU/g/ifbR706OVYU1F87Yf/dTO7hvfwjDYGzgfevn/9Ouf/jZn7GGIH16Ho0jC7kwvQId2JGfwEyEkOTCytTBR0ARjOXbXvd8PjH7c7Q4qpE1lQ+0VnAiSDmmnktKrSMtVDC4wMhyURIS6ahM9xslpMJONEUBDIJVPbiIzsgf4G0LMoecpNZlDNwd9hJcecZEW+a8hN97PsU3tixC12DkUDZ/VFlg9MHq6ad/562MN5+Ht3YLvllGCRZBAOvRhXjva2frGhctGRdUjhKifKBr8jxXvvBb3JEgmiSza2n2mQu6TnX3bprrzyK5Jh+5JYQ0PjMrRKlwoFY50a2ScdEM6ju/w+LdVjpTxqo7kh6ntX8rv5K+wseqv0OOYuXTAx/CPqGxq7gRUVLQlLcFf+ugWHTyR80M5R2krO0D6O5vMDTbDaKAUGymdNcogYyCe0IAXfTQtHkNFfMasBfE0FSB481noOpWZMVOp2V6MPN+i8LilMzuqEpe4hz2vClTU3GIvLqmyeeoKRJRtnK00SCweXkbGerfi2yN0J7ygFVHzLFQG4sBMr4Mi4qKOunRYnQxRdSTvbdxSWfsvBzyXp0gcKMxcSolGatbhuTXd8ZQUwItm6aLrqZDIpaCI8iRd67Xlr/vLKpijaSEb9O9dYjf5N/IHys38Cn/j3Hk9dPvDhCRbFTHB6neN12oT0FCxpjkLeJx4sGzCL7UBSjTCM6wOY8xSxF55/6J4GNt9LsPMVu9H795Fm+1fQzzvEbWpkSE1CBoAjqQMntIm4w4JDUlYhH6yPWN8HznrVwi/Gby3DHVg10KYhZaSenzgTQm04uYdQ2X/DQAe9xLQJZYNb5/8jiv6Tckhu5BSn2S/qU/Z/PW2dx49k52dhwgNfLRafc5rN1IOtmJyVJLgW8hQuCzDKQvx+uZhxz79mS7kXg5V637Pbpg4ZFtd5Irb0fRTISV09GEEMX551KaOk5QtbG4qJCh5sfQGrewR9s9qV+jSQKHRn484z3ZkyJVuVdSePqdBF/sJRofwiZupowfTmu3/HCQ/Yt+g98cIXciitP6KAVxFYtVozyTsLhkRwvtlXZSZpFkkQcKZlzu34JTJOcU/k9B0zQaXv4YitX4JeYk1uOrWcHrB1q544ljFNs1HvjIBh465GcqwQFYUnPybJa/hmQixsGnfoK3ZgUF1fMJjvZRVDWPmmZj9dapXsCFSjZY+JvzndzWH8Evet8uk0NctFKWHqHPVMDjRQYxu3ygl/xkLrqg4K95DjmRg2fgDPzVzxEu3k14wE7HCxVGiizwdpdOZ6QBALOocGV5I13RHA7Eirix/gCkIGyX6VyRQgRcipvX8z/H8J5HAAh12yZ1WbauXE/tWa8iABZPL0UrHsJ/7BLs+S2TFhzNX8OhY3fAWbDLloLMxFUQGsETCXDliw8w4Taz+thxupauRjedWDHrWK1hEgkn5+94g47KZaQ9LtIr80grmnHyzO0JUQXdKU+Sm5q+VnQ08vz7uKqpgrFUGwCabEazWAlGB+jr3c1v1K/i1B1IaY0nx3+Hs96wKunqdJVgAVBNKlV+N7oLhi1jrG3cxrPrrwNg1fEDeKxjFLgCvDAxHwQIyTbiYj2jTfWUHjuClpKJWouQFBuCLk8K9qn5VqTRBH5JR0VHQiCqSlydOp9He2yYTB48lUbZDVFWURUzo4euwlW5B3iD6OhKnEWNTAgioJL2mlje1QrMJvcEyRF0FNmGJEUIi1mLxISoMba0HHXtMGoBJEOFCMXD6IIOSRElISJbNWIjM/0kqbCEo/hlEqGvEg6spcMbZ4RCGljK93fmkpNupZRvT37HALeOPsStow8ZfRqS2LXyMEeX3M6a/dMn3oDspq38A0i2N1l67DCydHzyEz4hnQAw7LWzZaGPbQN38dpYD/mrc7nzyBm0rL2axse7sPo6qVzwCyzbvo+eHAVkUiYHuihlSU5SRBb7kBM+9q8LMPHG13iP5U4ORi/DKw1QLe2lr+xZFoz8BLMwnYgdd1RzxZK7UTN1zx5p+DTrJwzXmVN6HsX/MYobbmdg8T307r6DdesfJa0cZ9vRF7FbPQyNHeeqpddgduShaxqp8UaU0scYbbqXsfG7cVb+iNaWuxhUXZyRW8/WfUZmYZkVFvtu5/D4L3GZXyY/6SM5to8hq2FJ3NXyR+OjPXkFlEnUDl1L8RmfxVLhJdEeYOBrezOvS0PJux8yoTbHat3Mazf+WHI0yOH5D1M2GGJKXd7p5+02YtM6Vr1Dg38DTpGcU/j/HvGJISSLHbPdTd/eh/FbjZoyomJlwdk/Ymg8xEefOEZSl2mPwu2/2Yz+tgW8WVCR5ZOnqv41HHjgi6zt/z00Q0qX8QnGCG0DxvRcqpQrp7UPmG3kJAOkpCqm+jAEICnZuGr8aX5SmBXBu7Ezj7injeYFv0YNh7CVJxkvf5WYNEK4xU3PptKZnRJ10ARsOTrxiQwZcI5Tag9Rag+xwNmPK2L0M6E6AHCKBSxd9Qz89hre33OU0QEnY0eNYMJti+Ygl1cjiDrh/kUoRy6kJO8gntV/QLEZ8u9K3I3/jQ9NduHE49XNIosHG0nmFlPT24I5lSbq9k4hOFBRcYTKqsN0dixFPCJQ2fxxbKbrODLnPJCnZ+JMFRBceORbjDmaUGQBs8PNRKyC8JzliJqGJhnvUlBVurqb6G27n3r3cnoix5FtFhxOw4qla9NnB0EHxaRx4dAq5FErCRI8VL+Hs/ZXUT7cT0EyyPLLjTp71tHLScTMjIoy+ZFy4s5e+sWFYBGwxPNwB+eiozNk0gABpdaFOJFEUHTGLHEKk0b8zCshhYI8Dz37r6EwWELBIiOWTJJTFK3IlmDwVOwlGSyZ1B5yCREKEwpYwJdxV8VEHU2Ecdk2tT4lE6JOKlKAmhEODnSchbt8L2p+H/IIRAMmPEVJUmHjubXUQ6LCwqI3kqTCMrnSXpzakyzdcxtHqy38YZaFWzqSeMNJ8szf/auloB1xFUHTidmfoOHyR1j09PUAbHJdR4G6iGD5j7AkDUJsEjoRCKPjwioaE3Gzq5K+RVFyGefy0g9wOZB2ynxn7ecpH2rlivWPYs9vRU74sMTt6PEgkEvSkkOBLJBfOAvaBNSkiEnox5RYyBniJl5cWsfIW48wvuwol7cnqAaWjb41zbUIsN81j8uW/BxVkDHrSQQ07qz9COv3GQ47m7SFoPJeXCMrsPsX0JG3l9EX57B4zZNsWHI5uppiT8ft7Nl2NzkUEdJHiE217lkhOPwp7B6oxc+g3jXt+ofHfzn571HLyd1mUzHfdT2S7ODo2G/QJIGcvpXIxy5ktOHI21qm6Cl7gjV+Y0F4YKGbYamcMWs5ZyZeRtJg2ZGZgcZTMZ4uJ6Ll4ra+s5XvX41TJOcU/r+FmorTs+9hOhLGKtIVX0HYlpVVn1/2M8x2N4+8smNS5A+gIZD9943zbDzTFOKuS/7rsuS6plE+8PLk32ZhuhUlkv4Q8hT//pCjFVhGQWQMRZpJqOKClVmB/VyTHCdka+GrB+fjd7/Bq91vkTjkA3xTWp9chrRs3SB58wJG/3QI9Tix2vOoGhyCQObISLaf26prKVbGWLnmMTof+ByLBxroeK3ASIUCRt0SCe9VFJW9jK7D4N73oqVcBMcK2ei/nmjJTmK+43RuuYgJ2UcKnYdcScYyJRa81QnMegEpybC+WAd7iHim972yykhlLy5uoTNvDWuPx3l5zQMsOvw6RxfciSlxmNq2e3jX3nz6i88AbYyXFm9nyAMgMDdSxZptIqnSYkMfZ8qz1SWJWM18nE0HaA0Zboa0rwyns9NooKlMI5u6QMqkMagOkYgaBO49I+fy7Tn3oEbseMxZVeNC5wjdsTLGJI3qSDWaFCdtiuAM12BJ5gEwLOkkEdAlgaLEKGMeC5I/SWrR00hHLkJNeAFwJ4oZ9jaT7pkPsp+CeTODagFCvcuIZgK67UIMi+wgnL8fUY7A4FJigo6sQUC1ASkEs4ie0kiIEBmeQ958I3Yo4a8m1L2akrLPIo9ItOz14T0nTnfaRimQLjFRkGsQj1SmXpZN2klEfTe3dKZYO6ZQF46Ta/oOFrEZXTcznPopil6EXdqEzzS9RII9phJ1JpA3HWOX/BOihZvQSo4zZHsdgKRFIuyQcEVVjtbfQ83wPMpSvweg05uHiSiWYBWKbQzVHMGEwuf5FpxI1NIFPH1nYRGbJjOrUq4yRCe4nOWIOdWoyWYkYQQpbsSYrRdeY/+aGBsKHyJaCN3bF1EpN0z2ucVewUu56/hTyWXUCG18PPUTvKZRUpi41/4Jys54nd17r6c0OUqp9Tr6E3+mpOEjdJ72RcLWANsPXYqg6eiiMOlGGmIIdAE5kTNpbf5HIao6S8q+hL1wLR2vfxXXgY+jW2KYF1QTOzjMbOcGtNBJxI0ySOX+kNPHdgCQlgQmvCbMwjDpVcMEd7rwpMPT2j8z8RUK5E5y5W5CaiEt8TOZUMupW1HA+eWnsqtO4RT+qVDTSXa/dhlxW/vkthMER0p5Of2sLZhshpn6haMjgIkLqyRe6poe4HfXzRu46x/sw8FX/8QyPWvWjutmBvVcCoVxXlSvYq12GoJJoXDhK0QbAtyw5DIACgOjaKJpWtAxCCRFK+aoyme6ZDzyPsaseRwOvkli4p0HENlsInfBAMMHjAyJnLrsyksQwFMZwZQKMe94cNpxvdZC9s3LxePsoY7zEH+0kFpgrN85SXBSEhxe+i1EwYan+jBKzIeWMoI4E2YX3Tt+RanTgxSLMjrHIGAvVb3KWCCrLVQijIBkDEOK003SJJN0GvezPFFGvdXDCQ1bs0lhrLiE+uNjhINLeCvnEL7+D5GjuBjNEbj/vDFywk8RsoNHcXJXzx3IKZ2RkaN0cJSY652fU2TOMuztR9HsLhJOFw5HZkWsK0x1Wwq6SFLSkKbUwerufYtzXMsJxUYw+bKTRpF9lG7KJrPI3MF5M67bkt8NiSK0XAvFw0/gd18Lfni09Qqunf8cFfuvASAZ96A64kTd3TSPVtP3/HpMjnYkRyvV9V1YPSNMtG5k/PhFJJzGN+xMhrDILgaW/hw96YLBpUQF0NAnRRgVtwlpzIhrCoRKiQwuQEl4iPtrAYGJdS5sB2I4owL7xmzkhIx37y40I5cEABOBhIlKQBL8gIIkRFkW24zL/CyyaASIR9VzUXQjtTymbsQpvYQojKPrDkxiD96AiahTZ3DxfZPP5oTxR1SsCIqVwcI4ro4oC0db8SrZxYpeOEbB8ZvI6T0HTUzyl1UvsMg9vYBrScNHcA2vxCz/gWSG5BytXMn3iJGHwIO2HNQJEUkIog/VQz1Yc3pYETLi4lQzHFlo4aWWr5Aq6SAhmrmn4gZUQeCa1JNcwuOTn4mZNJ/kB0REJ9+quY37jmdS3s2fZyT1E+q2/oT+RfeQdHeDLpJ2DCOmHBQfvRXFOoEtWoBlIhuLlsg5QlpOI2pmvLVeuqxfR0nlUdH5YdJBO8Pzf0cODmZd9lu2vrkOXVAoSy4kX/42kT/2oS0W8XR+Bo0UQtI+qSD9doKjCwoDi35JpGAvBZ1OFvZlpQ4apdUgtE3+fcg3n7OGs6npvx+5n5jmY5iVmOQ0joI0dlMRc1Y2UjM/H13XEYS3mcD+TThFck7h/yuoSprx9p00dn4C1Racsd8em0115X9MEpzfvryPtqgxOn1ww3wOPriPoZRhXamxv/Mq5++B3miItoWVSxnWlvOIPIIqWkDXWKYbKehWdQfysfto9i2gxWlUOi8MDKM5p1tyBF0gLVowRwVUc4hQ6hb68h4geMyYuCW7TsmyIL3bvdl7neej/oy3EARwl0dB17GhoElu5s//CcMjzzM09DT5/ux9vli+AkcyzXipgts5jA682iNzIxAZsjB62LhewCGwd9G7ONfpYcJ+iLglSnBo+iR+cPZ8vrjsYdY251GdFOmXE7RMITiaQ6ZACFCq+liiVLHP0k7Y2oPmsLFs+bPYHcFJggMgyklkOUnc4+Pi49V8yX0rOjqKnkaTBFJiihF5gtpkGd3R4wSSbfREjxNXwugeg+RJisKcPTuI2m2kcvIZqKyaPH+s1pClM5tjmM1JdE1E11XAhIaGiIikS6R1C4nKYlS1Ejk0gXWwi7pWnZCjBFNplkSWW/vYzVKGvBGIT7WyGYjICfYmjX6pFU4Wv5kgPK+fXgzBvsfaL+U9yx+laP+1RFSJmkgxHa5BEDSi7g5AgMQs/A11yKoFa7QKGyKJjMXQGQ1hcmZcV+YIIjqaIBATmLT26FYJQRLQVZ0IJvq2fXxaH8eFJdhv2Ir3YZklb2UnKcusAIpLRxd1zEGRREDG4vGTa7oLm7R3xr22LDpMSv8RXv+F2M4wsX1knJzu86jtTBskp8dFW1ka27QykCAoZsrf+gHWpIto7gFU/auUJEYm9/d4cikYW01OrxGjImoWrtx1Jc9qZ+PL30fcX8Pc3HFcfkOR3GTeSSRm0KdujxFAPYZOU045s4f2oetQbN7NBIa6tdWb/QJt+Z2YDuXw3dIPIpmjfD/yHfKkEQTbUObb0lAlYTIezEmEq/J/xSMTF3D90MuYxTZMQgdpvYbShhPFpXX0dc8jHDoLwicn4daJhZP1LtN+KCWbjWUGyvcbWU8D+w9Rx88n90UwFNrjh0fRpCSRogM4xhYhKYYLOm0dJVC+CW/PRl5ZtJP6nD9ji6usOhDCFc1WGY9YLIyvbGWqRVOZ3cYfR79KZCCroTN7TT75S+9jzG+EA9hslUzEe9h/UGfF8ifweJad9P7+1ThFck7h/xsoyTj7X7uBiL3h7VmeyCkva05/DYtr+mTz6L5ewMziHIXls8r5zpVJntvfidUkcscl6/7hvkyMDjIntAMEiKtnYtXnskTt5IjQQ0pQKMdwV+zzCfR63s0fSgwrzurQW1g1BYSZP82UYMIREYmXDmMfcHDkWA8g46kKUX2+MRhb8yZAdeEomS6J7yiKU9MVpXpXHBiHTZeTB5Q6TZPxN+32UsxVnSiCMFmHaO/E9dTEW9HSAn1bs89uz7JPsUDKY6joEdRlbyAA0eG5AEQsYziTeZjkNVzYUk1OshAVnccdRuzJCaQX+3D2p9iQXokFE6en5/BMQS8ubxCHYyZBBXA6x4l6PHS1b6NF6McUCZJOBllacCGzHIsIhwb5y+iPUPWsy00HFI8Pj2eIefM2ESqz499kx+nt4oz2I2yvvXTaNfI9XQAosSoEzbCKyIodTU4gaRJpxYLFkkJNWFG8eaihccLRcTSpBJcr+9zL7YYGUuBtAewAknWC/RVNaCNL0HLMLPC3kTs2ypqmBnqdZ022e7TzQu7w9CMGywhH67lU2sFz9rcVOxREFDmNKeVBkBMkBGMSd8XjyC7NqMUlmHCLaQKamcicLUwEqiFegGzSwSxCXCWmzexnZGApyQWbp21TnTqpXInIyCI8CxqwNQj4B22UesMnJTjNNR6GxUWcddGPaDn0FL2bjyPVzYKlGv6xUtxxKEx3srfpxyyccw85Gb9pT+h85jZchzVpfDMO/zI6Kk+jfnjH5Lk75+gUNi6a/myBK0QfW7o2UGFWqfTXATpm2+PYtH5GM4RzaErcV6urgNmagJYWyBHeuVJ43oJn+UH3TpwV+zE7s4S2aDjB/OYIzXUO+kqmK3EnZo2wI7GY0wKHKbTcQUw9k/H0ZzN7BYRt07+//w7SFj+qKYp1SqabYpmg/axPApAzkSJlFjlmXkqlyYjBiRY/x7pAmmhYYllDCFnNWim3R25msDiBTzTEAefO+R59/Q8QDh+l7PRf0/L03RQtfRF3lUFsxrLciHjcsARZLMW43Uv+aff4X8UpknMK/18g2NfIvpZ3wdvUx8W0nRLzjdSe/R/IVseM44ZixgB629lGVeP1S+pYv6Tuv92fpie/xVohQVyrIaUbNXeWqtUsWrgQuciB/rJhMv7cvAsYsmUmpXSCdX2vY9I1o4LmFAgIKKKMOmpm7+InsEaWkegyfr65cwOT7RyFCU6W6iALTqp7xmZs90YMc3ynrZimRTI2XUBWIV+oYYd6PjukQr4w/HNCg9bJDK2tSxawQvBgL+xnfPkbk7RleLyCYVmlJW+C2ZEElcEychLGavktq4KSWeFqHhOpVflYtCSeMZFUQQP+wr0UNN+Ay1SEw5WdKEVVxzqxAASI5Tbico4RcBSTlxIY8PdMJtkfHHmJw7yKxnR3IyYLKY+PlMNFbeleZJOCb1YI36zsBLXid69wtGQdCbud4lAPFSsOAZAaWw+68cxEzYxGAlmXSSsWXOY4iYRBBeMVs7AOdKI63DhdWUn7KrchUJeKy5QW98JYBal0krRnmKNzNrOn+SrAqKf1/kcfot8lMr/7CA/fciXiQAxpIE5aM3O3kMvHBBWbLrEndAPnm3/E5vRliJoJVYoDIqoUQ1btpIT4CW8iXl0D+xg/5IscYRE11g4CMTMj3iihpApxyBMVRi0CQhwyP4UpOtuQGK9GzQXNoiMmBZJ1GhMfUPC3Xor/2CUU1v4HtgYYClopZXqMBkBTtYMDO+3YzUk2P3gh1U1JyjL7+q/rQci9CS0CoqTzheGf8JWRH+Gx9VORHGVtdCVOQcAsHEHHRlqvIyrPpTfnGOUTAYZtHhSLiBgxSF/vsh/gHFtMTo8hP3iWS0YVjBuyi5vx6UaB2kTCIHOD4glVcRi0GdazdEzCau5m41bYu8SDNakxtzlM0ywnwwVWnMVHZ9wjwPxmo3ju7LYos9uiHK2pYLjMyCyqoAepRgGjEgZ2aSvDq27DcjAPLZYl45LXgm1eLlK5yyjDsn2AnrkifeEeVo1XobcGAEjmmmk/7Rn6bT7OfPMszP4oijmIKemjc+UPMEv9iC2n8UD1ahJqPudZfsfazihVvVkr2TznVoJuE7KiUzxy8uKc+6OXczhyGXPqDKtTScn76N65j0qpkmbHETRJYdYVHz/psVNRVPguBOGvRJ7/i3GK5JzC/3pomkbHkXtmqoBqEotqfkVu/WknPc4fihLJrF6X158kC+kfgKoo7Pndp1g7+CAAUeUmpqaWSIci6BgD4qglPUlwAH51cJDe6DCypp+U5KQEMxZRZeBZGTACIN2VYVzlUUqKryGZGsXvN0TGBEEmN/dsXM65CILE0W1PAV2T5+uz5FOWNKwODe5aBhbG8WkKq9YfQZLs6LrO3U/8lKeO3UEyKNO/w1j97p1Tgu7+IErlq4yvfXryfE8cOY36SCFPuVIo0TIGbeNsEBqxJdKkxXwOmL2AiFLtRJllBBZf8voLlNmsDCwxTOzh4l0Ubv4UNo+xHMxruQZf14UICIxXvkQst5GqmkP09c+jedlK6ve8hY6GO5EiZrdT1zfESGkZ+1asIGegl4JQjON1WReawzplmTkFtvePUH5wM+5XLHDlBLJdRQjlkfKvQ+AvAIiZ78Sw5Jip8+xF0gT2Rc9B1yUSRRWAgMORDRZ1mmOYXWlSYROfiOexoOQYP4rU8PuqPTySITgAZqeGlophds8irPRz8dGtPHvWeaCPIw0aE1OsrBFb7yI0TBwY+xzvFF0U0yxAEkGAEtlBzHOEQ8K1AOh5IvRA+0Q1gbjxDiQhjmyyopIlOVMjJ1TFxljzeVhufQ3LEZHIBSqaB8I7VgIikZxScujH3mWic70Vm1OjvcpBwma4W/0tIks3CcDxGX0tfVQHHqBzViW1y7qxCl3ckfDTk3JQq8dRBAWRIAWWLwAwkHgQS6SMtoUSj41ey6qc13COLMWeyFRfd3cQ97aR3zsHTfeg4UHSdVzSY3hMD2TvKS6iA31C1mLhNxuro3B4DVavIWK58lDWmrigKYKgw1DhTNVea1ydsW1BRw9q+jLGqg0dn4hT5uBCN0sz2UjV+2/iyLuex/eshJ5Qcd2+ELncxZFwnJ9tf46rep5At5t59/aXWIkR5PzUnI3MiXXiMvnx+HupH00yWPRHZpsGyZ1IgxXKJo1QL3DaxAsz+nUC7oiKOzKz30nNxkuBLwAa8eohFiz4I4qQRhQsyDv2srZnMwDC7DkcK5y5aAIoK7uZivL3s2v3+WgaRKNLSSQSWK2nFI9P4RT+yxg59gZHhm6bRnBExcq6M/egqWnMDu87Hrv9aBcAdjFNke+fE/2//7n7WDtgVCFu0WZj1965OvnnFhuD87ygysdakjgjnaQ67WiWrF9/KlRRxmeN0hkxCIdsVaja2I/VWoKqxbFaS5g370d4PSuw2UoJBg+x//DdLN26iUsVY5X9culyTLXdgM6+yDy0uA13XhcmQWB+7feQJDv7g1H+1DbE5zt/i6RpNO3NnywxYXdvoLrkAMnVTyMAfTErT3TWE7QMkCOYUAQjvmck4aNXSlKkzKErr4N0wofmMqHUG895QcP3mduvU3HedKuTt7AFc05GUn9s4WTwtSWSJaHFRS0MDM6ldc06FjY0cGCN4a4YmB0klMnM8pdXMZXSOJ1+rE7jWqZIEf4jeYS0UUrWjCBKOkVL/ZANLyC38TpaXDJCwpgITxR9lHWZqAqO6lGohqU7t3AgvQFECUlKYbUayseJ0Vys+X7qa3tpPGQULzwarOKXdc/wZFvWPaHUurhy32Y6lixFcXow5eZS034QzjqP9Dwv4lgCIa2z26Zyg2OcQHRmbM9UJE58NiaRypQVvytrXbHkpaDHTtuQYfnQAY+QIGAyowKBKRXWQ3OacbXUIWgSYw1XY183gP1aw4ox3rKRVNgo1hG01VCeiZxqftWH6UvG+3e7F6N0bcD6u/uYKtKi5OvEl2m4XsnGnKVa0pAJ1yi33oor/V685j8QV1eStgY4Ya4rsd5EMvhjNFFgaeEbpBGpaHy/8X6s2zhjXx8mRQfLRwCIqadhFjqQxaHJa43E50AyRNDqJTrlJzZuMgaQEbUSc7p6Gik6gfnNEYIuE3F7tu9yWmNey0wLFsDi3mc4bnUyUGxM7uM5ZjbnL+Hs0UMALHz2Eu6ueB+rgg14/hxmUaSFNcDJNNVnxXr4XNfvsxsyoULV9Jyk9T+OneGb6U8txFW+l9LFf0bBcHEtOzKWvShQ3NxEPG6js8qwjtfXfxlP0oXliY8T3fUIsvIL1p79n/x05yg5yb9g/9Ai6otPkZxTOIW/C717H2V09DWKSi/nuP8T0/bNL/gFzvz6k7qm3o6Hd3YAIrO8/5yo/+7mQ9Qf/h4ASV2mWb2JpQgkiwOYblpLiWom9HoPkeNHuHr1bLodIp9vauOq7kI0XePp/i0UyEk0RUAQMgPpiaqVgCZJ1MwZpn3Ig4BO3tJxRJNOPDrERHArdmuQ/v6HqKn+JCZTDnve+i1XHj8wrY+xXIXyhI2QNY7LOQJOyEvmsnDjm4hmFwPdIe7d/QfODOyjNtrL4GEP0pjRl7YVp1NdYydw5j0ci4s84rcR0XVwt1M3towu03RNmbfUYnCmIGE4KJQ6Fxdsu5e9Nce4bKvM+MIzmFP4l2nHmOc+D4ApWoglYhynoWELzJ5sU+waYWDQiP85sigbjxF6W+q5KxgkJUuYi+IsWZpJ5Z/Io2bvd6nJtDn06hPYLpy+4tUUAV94GSN5InpmlX7CkiMiklaysRyOFf3kbhvAL5fg8RguSFOsgJzYYiZ4jUvUTRxctBxzg2HhmUpwkqvzuHn7c6w5upvDa41ir2mrBdFp57O//hZH5p/OyytOx7JzlKaxOtwbv4bltW8RStiJv03H6QTimW9FkHXmxUtpmFKsVMkR8QgCwYwIlO41403HGDI5SQL+KRljc2f9ms6Rj2Eez7iCdnyY00u72NFTi4aEgAa6Tnh4Gcm6LVjaRCr74eiTeSy+7QsMHWom/9v3Tp4veJVCbJVGEiujXfMp/9Yhir48JSamuIT6QYPcek1/AMAm7cX2tooDOekjCIkcdOsEYtqGnLZQbLkBidCM0mV2ace0v5viZ9NGBbW8zpBveu05v2h841H7NsLqL0jps7C6fogrOT02bO4BkVdcl1Fn2cmKkWPT9u1Ir8eUdlEuN1FkbjHat0YYyzWTMhuWWXNlN0wJl/vPnt/z78L20HtxSaOUmhsZSs/iYPQK0roVn6mTVPEo7jETExVxzAMjlC3bbTjydH2aFk6vVka5aAQ01/TE8RSdj9zWiGfrJybbWDOWatObd/E5IIGZHss3/233+XacIjmn8L8GmqbR8dYv6E7/GKww4d88bX+t/SsULTj/7zrXVx7cxO5RY+C5aW3N32j998H/7JepJMwwPn7JjWzAsD780lXIAw19XDf4Iuf7d/DL5dfQ7RCxqArzOpI0RfYQSo2RTqu4bEm0mDBpwRB0EV0wJlpVkrEmZao2ZldUZs3J7H1BClJGqvzO3Pkc7X6aBSN9XBkf5O2Qwh6WX/oy4a5niPr3k1t3C5a8hfQcHWH781+mjE5+l9gCwGiji2CzQRaPr1iAa0WMwKJ7iGnwmzErUyWZc6OlNMqZfhbbJt0sJ6DUuji9/xBRjnH9qx5kmwebI4IgKYgpB3ntVzAy98HJ9vkt1yAgoKPzrjn/ybXB27l899cZWP117EU9LLM9zaEjl6BpxhAmxqPoJjO6bJARe/sRSld0k79wutaIq+dMYppOStPwyhJLpKsZe2kl/gu/nm2z6cMIiDQ6U/gCxswpTgnKVZQpmW8mqF+6k2jreeT4TligFiAnjRgPrxDk/buf4v5zr0VuCSKNZNWNz2new8LGl2mpWjitj8nCcszjw1zyxpN0lJTTaXNCHH7fdDUfvfTjlA0vpWv3e/En3haABoybDUtKkQY+W5JxMUv8wpKTD5kEvp/SJ99T7lgQj8NNCCsjFoW04md03Vvct/PzxDQH10saJYqArprZ3jNr8lw6ohFUPzaLsU+oFHxTwDQssOD1EOrrXyB/Sp80q058uUY0Xkrvli+haybaB7qJ3PwKdX8yNJDeitgptYrYE39dmlcWhnF3X0xw9oOYoyW4pGeQhL8uSAcwrpSy07yeMtUgu/4cQ/XQAUQBv2CUeTCNpBgp2UtBZCUB7U4aF3yXksN3YkqbKLTeSo7m57rg/TPOv0M5g1dNi8EkYImvoTQ2ws32nwDg3n8pY2sNIh23S7xxZh5FQwnmt0T+Zr8bZzvplaupiXeQECwsas+K/W0+zYes6Iy2n4Ozq4DBxCIightBEdGRcEp+dF3EK/dhEhL0W8pJhQtBlzA5RjHnD2N1D6G6QuTWGs+lAChY/NTkLzt/bHqGaXdkI0NijJXOJwDI3fP037yHpGhlVqIPpn0V/z6cIjmn8L8G/ta3DILzNnjia1lx8YMnOeLk6Boa54GjRlCgW0pz1bqFf+OIv6Nvw30sjBjZVI9KV6KrMiVyLqjQ7DbI1KPFF/Fo8UWTx/zwrUGO9Dwy7Twl1ePoh0UyGb4IuoSeCabVJBl7ZPoS3tpqoiCVncjX+huZ6qcZl13sWJ6H9uZVkLTRN7QU8RY7nvrr8dQbyrLxSIrnXvw1H038Ydq5w32GebmzzE595QV0Lfg2jVGJxyeyK3CLZuZ7zR/m4ISLN7xG3+Y7mmgsn4MwkUaMKOhWCaXKSeLAK5x9zEfFWJDR8nzsdmOVbImWII9lLW+O0cU4R5eRbH2Z0dnzmch7Pz+tXEGqNcEFw8uJFO7H4QlT5t5LOJpDOG2n0nOMuN+KxZui7PRhOJMZGH76NGbbLgVRIE9+g950CLdwBXlSFcqha4jntlHefjOy7gWg15bAl4niFTR50qomxFzoqsh4qwt3eQRTToz5czYhZEiew78QVTa+L5Mlgaa0cvHeB9k5/xJCmgXMIoXeOPPe+BXDnioUq0FWXPiJCF503SBR3fluPvSXP/CZaz6PeDzIgZElvNBxLudWbib34v/E9soPCMScpHUwC2BzD3C8QIWRPOboAv6aZ5kgd/L+Q3jYaOnhrvm16IqO3auxvE0mKgXopYiYJhJZ+gY9g6VE0obEQqtJpSyloUlZf3AmlncSQ/tvxHrlQ+TeNz07K1WtEbhRRbPrdOz9PInxrPUkGaikJ3AbRVVfwNkVYsHTUTZ/uZS5/WMosoBvPE1uwDDjHCpYy1ZxLncM/Q6n/AK54dsJAtZgFXbpzclz/nb4j0iCgo7IaufDHI9vJKZ5cZoGaSqz8V7fPtqOGmYUv9NQCawUJI7pKkkBYrIFW0uCsfN2UhBZiS1ch7v3NsR0KSqQ0uZgFRun3eOR+Dm8Ya4jKLpwhCpImwMkbaN0IPAKZ3I+WymLbaLnwHuwL8u6wIaKrEzkmKjtjDGSb2Ys13i+gq4bz3aKu3p010W0964CXWQbUGhqIa65CD1ZxAz55SnvJqwaxaIiqTxy6t6gZtnXCXSejjNXR3bvmHHcVJiTGnNbI+SNZ0nOQGoOLfGNJHQ71Za95Jm6TnrsixOfpyu5ghXOJ/BIg7wVfh+XKLX/U6WrTpGcU/jfA3/flmm/aVu8npqq/6Rgzoa/eezOY90c6R7hnu39hNTsYHzJbNc/pW+tb/yeNYJKo1bFBC6Kcwux9BuTVbNrpnrxhT1tjDQ/MW2bpzpEXl4YNWnKCmfp2QBkTZRwEkFT8zGZdSyai+XD0wfdMa2EPHGA/TnzGMp3Iuf1wGgJveFs8PUn/3SAiwq9lOXZqZrt49HHfsFHYz+Y3N8XrKRfqcEdNDKF9l2j8oj3hxzqn245WByew7f7PkbHoafpq78IXUiBTWLJsYP4lxXSO68WkiqIAr6JrZy9P0l+KMGo2864102F3bA0maPFvBQ/wJl9Z6KLCgVNN9JtHuRny1/AFnuGpMMgsI9WmHnf1tvo8H4a1RKmclEb/xWstd4yOXnEtPPIzbwWiTaKRi4iUxQbHZ1uh4RZHceUseAIujxpVVN1Df0vt9E7sQVrTpI513Rgd2fcGpqEfXwOcY9hWTObE3g0H6+XvM7vvvMG3cU1dJRVQKoFU7wKBBCtbnLzupk3byuRSA4HD1zCiqKraPa/iV8Z4bShI+yhAgHD3bV9YDWfXfFzaua9QqFjECntINlUT9eilxg48GUAVhdvJlSyg3Gu5eL+NHURlXvqHaStfpzeeQTNIv9x8DjzF77K4qSXLePXI6Q08t09PN553uQzGxczBEdLgGiQXklNoEjZ+IrY6GxSG3RUh44UFUiVa6g+CF6noHlg9Oil0wjOVBytu43VvT/AMSogPevD8b1XaOp/CpMgY0/4OFi8gTm9w+RuO4SiP4AspCkIN9OdklnS9QKSaFhD/uL/DkndPTnDbw59hKQYANHLE7Nz+OPIB0if9hC2ndsBkQmbYeGyWyRsCZU4MOYtp3KsDUE+SNB1HE94Lu7hVZN9jShXYjYfY9zkIeBeyuDwNexP64Tlfqr0HObUH6FtrJCJWCFC3EeL2cE59u3kmnq4NPJjetustNQ6Jr/BpEXi2Bxj/BnafwPRoQUULXkNR+mmac+oZM39uCt3MdF6Dkrcw1iiCHf1boo8rxAbmUXcX4fN10modwXoIsWrf4unck/mYxZIRX2YncbKx1t9kortuo5vIk3Aa0ITjb7VtyemEZwn/XehM5+Nbom0rvNG8ONclftpJCEbvLwp+GFaE+tI60YK/d7IddlLWA4D/7gkx38Hp0jOKfyvgKYqjCZfBitUSHdQu+4/EMW/npaoaRpfeXAzDx2b6jrJEpwr6kx8++aNMw/8B2Dp2wnAPt3I5rlg+QboH2HQKhAxqcyeGMAck7ki3Mq8aCNHD0ZIqFFMVo3Kc3sIdrsoWOzH0qmhpETEzGgtIEzm9GoZZeDweC4FZQqDfSJiJqjzxYnPMZKuJ655cJftJtZfhTCoomsSqdCJoF0dmxjkk8fvZLihnjcjN2AqOcBt2jeNEg/dNva35VM8lsaNQXD8LnjCokI8O1R4Y4V8vfd2aqMyE5s+S9uyz9CZsWIoeVbe/dR+8vwKP77pw6gWM46JB7hgTzM1I0HaC3NJ5RaiePNw21qNY2I+fj/3VeZ0foKF8XqGZT9frvg5fpOKJmQ1RybMKmgmKnd9nZ7V30SxBv7me3ENrCHsOYaj+3RM4szikgAq0yUDBAR25Up44kNYNOMYUTNNWtV0PUWhbwGyfyuJCQvRYWsmdR9sgTp6LXaanWVUAGZTHFU2c+Nr5XQU9qDTjm8oRthm4cSrVexmZs0yvh+ncwKPd5BW+xrWm67nzcGHuHrTM2z7zHewvma4w0ZiBXx66zcpdgxR7BimM1jB2vK9TAwa5EQXoKrWqG/VHCvlj0fjpFFpdUmMO8N8/vEHCHl8nF6dJphnkOSyvsvpT1l4tWcDoWQ2CL/NrBGP6xREhhguTGEyJSG0EElJkD+yg6GSDaQjBWhmgdEvpBE0UPOyzzIZLMZ/7F0nfzl6iqhSRSLfgW0ohmVgFO2Bx1h328fZ9cXfsqtjiBznn3hcmo0qF/O07z+5yvxdHNImluy4Akk07rE7sZyh9Bxks0i1dxzfsz9CMSl4wkESFgte53LeqrgZqfdLVO4XMt+SQdjNkk4OAnF0hnPrqBxrw9QnMLH4YVw770RERJXi9Fl7qIyuZijxIFrCjjVsohoIW3vp8C9lwex9FHa/ixO/NM2isy1SwE7rGs4QDatJ+UCCiEOeDESeiqLlD8/YNhXO4kacxY0ztk8lLSVr7kcULWjalGB+QZ8kOCcgKRq5E2lGc83ookBFf5z6jhiBHCeamsYbSk2OP4ouszn4EfzKPC70GGOAJAis8BTxp9HfkNatmIQ4MS0HQUpRcea9eBOFaIlZjDsOMHr0EkrtCfK87/ur9/evxCmScwr/44iOdtO895vkF5xH+YprZuxX00n2vXI9KfsgomKlctXNf5PgbDvSwXsempm2CmAVFGpcOnfddO5/u+/h4DhHH/8Ga2PGYDNGHmeccQbuoEYUOOKVuHX7/bgaDWG4EtcS2pJpkooRRzD72jZkq4qzxCBi5oRAMikiMtWSYzgINElC16FoUCGW18uSNmMyak6djk04jTwZulMagb7TZ/SzwryfC7w/xCQaA2CF5RA5ch91mjG5RgYsDOzKofht0ZtbF0w3h1/ScS1rA6uZbbMSO/JL9i39FFFrDsfMxnldnjideQ5WHTvMjU9+g+fWqcjpPup7agnZrCQLy0j7DO0ce6Z0QqPgJGFfy2erfsJXe26j1d7DsN1FzH0DmjQlm0gw0W2NUp3Ipfqt7xAue5zhureQI4XYouWESozJxDZRjy7oePvOwjMwffWoo7+tXMbJcdQjYU4OY1YN15yomQyXlZQCLQmCQGn+e+nz/5lwv2OS5Pi6LuSWJRLdrnJu1TdytvkNJBEUSWLAUz3jOkurrqfBvR1ZzkbYVlcd5IHhtZw9Xs2agktpGr6Xzz3wS+5eewNyZzaOYzBaxGDUcLu8OMX6klOnIksqYtrOuOpmq+kYreIQNWMr8PvimFUVZyyA4gpMHrPI0U9/sIb9Q0sA0LxmxICxkj9mUrG5q6gKNhEWy4nL4Akcoy2/AycbAJHmP/+KivXfx54/3boW7Dxj2t9JyyiqHMcerQDBjACMe+dSOrSfwuOwSXmUmt+9wp5Vd6AXKgwhYEs+iKA5GRt/N0qhA1mI4s0QHIBt4VuwsIeJ9D7a+0y0XFiL6oaqHROM5eYwZMvBKxyk/IExxJiIYjYzbnEAGnYpjUuQQFcZc2eqk7b6SJ7RS8+aO7H75xEs3Yqk2GH7D9DeVhNurlqGq34nhcPT71MUBM5yyTSHPssh95dYknFzlbSaGSjST5o9+d+HPp3gAKaUhiOmEvDIlMQLiKYHKBuIUzSaQhNkXln4Geo7vgOAd2J6jJCqS9w/8gAiVs5wTqcKFtWFr34zfc0XkNZtFK/6He7KXVRv/w4JbyvRvMO4ivaxuP16RCWf8QN7yV+79l9wz38bp0jOKfyPIhWZYP++a0hbx5gIbSJv4kxsOUVMdB4kOt5B8cKLadpyFxG7EaBYl/MVzM6ck55L0zR6RgL84Jk9vNA5UwMCIN+UYu83r/iH+hoOjtN4380IsoXZ770Xj6+A3nsuZW06KxC29MyL2LBqPsfu2Y8bDx2OBO7dfZP728OHJv+dUxZHtk7vpzkuoaYkRH2qJceIBVFlCV2F0wKH8O/ykJs2ikgOW2uo1yTimk53Khu4KUkaBbY4+UX3si6yc8b91FmNbYkJE33bsrEbfpfIyKxZHLc389Qqg0yWRMpZ2H8uS2L1LLVaONC0jf7a96BJZvySSloAXRaYpbfTm+uhN9fD4vYwrXMtnNe0hvqeQ+xdPH+S4DhdY0iOcTRd5DtV5xExO2BMp8PWx17LfoJ5n0MzGW1NiWZc/nuJ5NzM9yuPc1/z5YiqDU/3LXi6b8Et3U9cXU7q6K2kbaOY4nkIKCh6AEXYA/osZMELwAH7cb5cec/kva4OL+SrfR9CfFuZ7J25CRZ3j2LSjbW5oJkQMkVcBTWOINhY43Zz3PQhDrf8GKsnhSs8D2FiCd0uw/q0iXM4mzew29OcrEDI+aXvZ5ulG+fbVtout5+Fhzr5XBVcFCxBGamkqusoys1utFwL8rEgYkw5yRmNT2WFt8t4brF8vJqbVslwnYmRbsbKUrhNDhQz6PaszslqdysvDWQD8NUyO7pVQhqKM2hLQkpmxDwHgDQ6uwpDdJcInNOjZjIBBXo2fwbfrNeIjdajxL04Co8T7DImtrCrlaRtBASVKrWQSCIJqmElaym4DrerAdfeNLMGI7xxxodJ6w9hVULUL+qi/sfGb2TbtcNsDr2XczzZrK3fDT+KLnaweufDyEmDKCZrNXQTSF1mrI4u5ifjOH8vIYWMdzyau5BMCS5sYhqbJIMC43YjMNa7N0TqHIFkebdRXwrQzFFGa5/E3HYx3XGZiKazyCbhlASqR08j1fYa6a6tmFbdiBAKIdh8yLl1zLbKPBO+g5c8h/mUfj8eYZzIM1+jX5mPvaAZLW0lf/Gfsfm6SIxXEhmaj7O3Gl/JGPFFj1LovZwFIyLReBfJ+ADxeC/NVSJ2cxmzZ3+Did6nCHY9idVailSwCK3tVUYLHdRaziZeXEPNCw8hjJ+o3zf9OxN1hQsbvnPS72goVc/B6BUoupW5njAuwVhsDC74NcVHbwOgKGXCfOaPScRysEejCIJOc+kvyO3fSMA6gaVpLb32TeSGl1K69CRBcv8mnCI5p/A/iuGm10hbsoPtvp1XY9drCdi2AdC8/bOcEGmpd32L8pXXnew0aJrGlx7YxCPHZ6r9fvnsQq4/exE/fnonFy77xzOp9jz1czYmjH713rseRY8xjwCaLrBLn0+vXsi5VbkM/ehzuBO3AhAL7idH06l1jjGc8BJRjJ9cnjVKwfr+aecXNDM21Y+SdGWnXF3MZBkZMTmRpBe3HCA3nU1tdaXOQxltpr96LqUpjf6ExmqHxFguXJC6CaYs0O4tu469rnnc1fgzSoQx0qrEK80VzMrUDNozS+CH7xaBNk48+KvjC/Ad+QACIktcMi1Nb9JbnB20DjkigBnNbaa4rRfF7kSKRdAFgXXbZOZ2HWDn8kUkiyonj1nqMywk/vHziOXaqQ2r9FuX8UjevSSscyYJDoB35FsAeMZ+ynH3Rn5W9DB3DN0wuT+kfgCT0Ipd2Eo6UYWCCJixiAoOqZWQko2reMa3edoz3+06wsVzP8bv275BUdrws/w5dy9BywY0NQiUImgyoi4jnsjk0pIkZDMoOnNtEsHYWrpeV1mdv47j5dn4q4jmM3QgS3QuTt/OlsHHiChGkPhF5R+m0TrEsBhkttP4/of251G4dAxBNOpnLRzq4rDdT/C821jw0vdZfuwQ++cvJbXahHnvGLpdJr3Yh9QfQ+qOgCSglto5PXgEckCMeskn6+6TlCQTFo08sxuLSSflyGbfVTk7Sc/xIHdF0LxmquNddBTWIA3F8duiEMwGhm+zpthvXQ3h1Wg5E5wfyJv8Vm3tHiKKCwUfwS7DoqijkbSNUKy7WZispggvugOOpaN0xy3oop3m2VeytOVR7EFYmLybqk0Z8v9G9l2te6yVTZcVMTd5IaWWl5hIn0/+XInyx34zSXAALO0nfj0KFYeAQ9OnuaC7lmAm1b4sPURMKAEkJmxeNEFG1BWcvzAR+nwKzQOBtll4HW2M1z4Ltc8SPHwVui7iF204h9ajpxMkjxrxdck3fzR5HftZX0TKqWKV1cmeoXNpzGlkmWULl/nupCW1hniymr7oBkJbb8JbpuO2JVkpHqbQ8VUIAtsAfg0YWWAn3kBpH4Af3ryM7HIvxAnBxTktE8Dfn4gB8HrgDjRk+lILiWveye059a9THKuDqI9gyTZCJTswR4vJ7byUotBZ6O0Wwmv+hCjpCIqVqsiF2NW5lHaeNe388YEJXDUzswH/HThFck7hfwyqkqZz9G6wGAJ+mpwgZR0gxcCMts7YYio2XH/S8/w119R1c6x88IIVAHz5urNO2ubvga5pFHY/P/l3uZ7t46v6anaJa8nPzyfS1ISYITi7fOBp3EedS+KyMqN/40kbNjlNS52bMbsxGC9c+EvGxl6nrXUzgjCAmhIRMhlVhmslUw5BkOiOzgdcFJpb6E0uZX/0WspmvYDi28d36u6gNGLnGw05HHOLvFi9kwsyVQbScZHWpjyWPL+NDZGthDBzvKAKW0xhViQbs3TvxTPdgPmNN6IhstohERlrpWkKwQmIKg2iEedkcmoUBkTilXMQ0kkGO48jqgqHKwpQnF4kKUVZ2TFssSKofA2AI+pKPt6c5MbuNK8VzOZun0jKZqjyXTy8jxcKphf1U0xlvJTzRy4fX09Fqnhye1qvJ63XT2ur6iWElBsn/+6w9LHXeZS0uQ5dMGNOZnVO7ir9DV/uu40H81/gheIksAFNNewvkmIQBSGT9aTrKYImgeKMUOAq3xpCyT58lhK2eLIkZ0zMQUPE7gjQYD7OhuIb6Iw0UGqfxfP2BqJiEkFQ8ToNfZ3EgJ1UlRlLbgqnZYTxVDW+WJjBRDvFiz/Gmj2/o66vC1Uy8+dzLs7eZ7kDtdyYApc3HSEno7qciHnwidk8KFWPEzaD1epDsSRQrNl0ZNHiR/U5UCuNrKrK7Tvw1+cRB8ZTNlRBQ8oEwbe4wpA2rteAgzMFHbcukAb6lZlSdgl3G3W2UmI9VRzQQULhDKfMfJOd7owOUbB3JRMrXiPvjfEswTkJavXDPBn+AksT7+J42oU98hUs/pOXJZgKXdbRdJkhXwkjxfMZz8ScnC+9iKKfxxtUE5Zl9i/9JAuP/BB7UCfyYwvBjyiYnummpF0mdIlC5CKNgsV/Nu5LFxj2DCC+1cTJZO5iW76NdfVHKCxehCyoHAtdy/y8XViEJLPMu4BdLLZksitPXo3ln45XA5/EbOmhLXQZs21bAJ3G2Pmoby/2B9Rd9klyDr8LV9T4XUWEbbi+aaanah+5hZciqTb0OXuwJHMpaHoPomLFPkXPaiqGJnbi4qqT7vtX4xTJOYX/MQwdeWHSijMr7y66B39F3NYyrY2o2MjTL6B2zSdOeo7u4fEZBMclpnng/Ss53DnMezYu+af0tXnfmyzQjb49oF7ANeIb9OgFbNJXMSCW4Xa7ueKqq/jT063clBlADw8+gzcWwX56D6OCmZxBFWvtDWj+TvqK2rESRUPiyBGj+rjeeha63kQ6KSIIGbeTnnFXAaooIAtB3gx/erJfkZI0twUNheVXDnyIsGRn45n3kxTNHN6VNUVvOVhGaY+Cb0ryb+lI1omiiHDbf0jErdNjBc626xSbBByWdsRDO9hZ957sTl1jX2E/xA0z/8JUJ4LZcEHoJgup/BIsQz1ESqtRnR6qK/ZTVn4MODJ5ipfcJfzhmOF6OXfExLMFd7DTvYK8hMbF3Q7O6m7kRwUSFtVCQk6QcG7AHnqer1X8it+1fe3viq8BOG7r4Gtlv2G8+C5Us1G8cG5HE935KSzhB2mz9fLe+q8YjaVb8MYDWDLkRlaMif+Eu0rX0gTM2euKgsR5JdeioXHMkyWJiiAxoedgt4U4Zo7ii91FwvUuXnIcJJqx1VVUHMHsSKAkRXzRpVjDjei5bdTX7eLwNgsJZwnzB7vYUVbGu3tW09/bxJJDO0iZzCDAcE4ex6vrCDlduKIRPvD8E1huMWbLYdWHLxmd7E+KNEHBjNviRneGJ0UmARSbn/OPHuaVhUsoHOnnpkEbF6UjfFG0omgym9aMsm57D/NSzxBJf2Hasx0TNWzqzAzCqLOLhG2A0vHTCEyRslGBLZmisCfS0QXBRq/pYvKYqTQ8mlsEFhP5A73kt4XYs3APfdaL6Eu8ylUvBwGBVJXGjoq7KYi/Qqi4ltzirRR1HUUahzHzUtrMt+JyBohMCEREBylBRUBneFUTqe48aK0mIuiE3VUMn/dpvJt/SMGITsHXs/WtXC9KRM7XQAJzi4DmhHjkdXK3zyxsegKJ3b/Atu6zXJxbx+ZIAY+M3st1+R/BKpzMgZl5broTh2CYXxOanTeSV1BnPsjhxAbmWLcxXzyKJKh0JZfSkF5DWslhrnk3Ajpz7W9OO1dXYjnbw+8jpvkQ0Ejpjkky1RC7ZLKdLa8V36zX8DddgK6ZKJ71Orn7r8PqUdHHFdKdW3E/3WU8h8FBxq9+FV/6PHK6z8c5tmTGPeipCCCghgfR53ipX/4/Q3DgFMk5hX8zxjv2I5ltmB0+mib+EwBTMo/SZZdTmDiXvkN/wV0wH11NkVOzClGaOXiC4Z462NbPu3/XMG37t84t4YLl9eR5nSyp++fUo4pHw8x58d0AbNEW0y7N5Tv6bMgEP69cuZILL7yQv+x9kWX+HEDnMW8n3oNN5FZFEOZGaZDcxGfbUfTtiOVpHCZj8hEzFpt02sz6wFbUtEhCMk9uF6a5qwRsyjiFJoG4plNmEXmpaj90ZPvqUmPs2TPd4vXD6Gwu7slKz4+5IGWCksxC/q25ApsWCXg8Oh/yJUhqAhVmjf60SKVZI5o8Qt7Wv7Bzzbcmz9EnqRwtaORI3FjlpZblsnDHK+DNBgqncwpQrQ40m7Hq9+ZMFye0D64lkptDXBngL+ZWliiVVKUX0ZBWWdP+M75WdAB0OL/vfByKg935u4knxlCspzOkPsv7FrWwJD6HdkeCC3uHudBfjoDAa94GHNSwNOTgDW8D9xb+CoCEY90kwQE4XmPEmMRc3ydvZBNa+gUC+Z9ApoA5/r04MtYKSTHM7ELGXYWu8cXKP5Kbe5Bbh9/NecHTMu9S5IhnOukaoZAyq2Gp2e49B4hxopZZt8XHOo8RkOrfWcuZvisIhgsZog2zS2HlRW/Q2bmUvt4F+JVefAWrmO1ZyZuecm578k84Utm4nKDDiTMWZceqNVTbjHN2Czk4EzHSU/qjx3Nw2dJo9ggpwDySSypvAl1UOLv/KDc0DtMdbWRh2a2QAos9RiqmUGfeRGPFbA7bTkMfE9EcMrpNQhpL0idrlKsSEqCiM+47gGqKIqoWak2LCLyzYQYdQ+MnpUOCavas+jQr9v2EtMlO/MaNlF9yDfX183jz42fDANiOwbqrH2M0sZWqo2cjB43n3VpxKe8787eYltzC4MTLNEYa8NdIOJQyZu+7FWvufmLtJYTlUkKCBqjkiSmsAyspyQSXhwUdAehO5+M543bCW3+Ja4q0tKAJpBtMWPIUcn8qI+jZdz3iBksaolbjP1GH2kw1CWXgAHJuHafnpHlxNJdHRu9jrutpOqQ8rjA9gk3ImnACagGPjf4MSUijI6DoZjRMdGWsIKPhjYYX620YShjlY7aGb2WN80EmlHIa4+dxwgosSCl0Neu6NLsGKVj8BP7jFxH311FjArXsIFbfYYqabyY6UIHdKeHuP4NUx2uT7rhJPP0X1NPn4mTJ9Pep63R03klBwwCxE+KO2+B4yQhzr//QO38I/0KcIjmn8G/DaPNWGno/AFNq5ACUe4yPX7Y6qFpz8988z2d+/zp/bk6gv20V/61zS7hp49J3OOofR/Pe1yd/yvEFN1EwpjAyMoLNZuP222/H4/HQO9bPlhaRz4c0FAHSrZuptNdQcN4Lk9o+NjmGMclNh64L2N9ag1t6kuCEk7hsQRJOTGBZd5UqgC0aYU2Z8bPdmZfgP/r/+Ff73jdo4+ItWYLTUCXwretEEATWH9aoGNF5cIPIaR6F9+cY06GasoIC1ZY4we5V5B0+xM6130bPSN+3ySpPOVOQITiaQ+a9m3+P2elCkFIsq2wmKIdobV07SXByfH04nYYbpejoBzDF8xhKzaPGJrBLbmFCjLLJ3EhRvIwVw22YInZWxFdw1HcUZ8aSsmh8EYm8Pvqd6yD6LMPpn/KKDCTh5wXww+rzKQvsYdSUEUfMerPQAVk8C8/I90nZFhN3nU+RP03UPELYWcJYwXrs8dVoZjspXSeWfA6HYpjeJdVwRognLDm6DkIuIRl+XvwIG4KrkZHwmwVGbDLW8Jt4gluZ8L2LgC2HuZ5jFBc1MziUNeWHHXBD2X3g1dAUgTOUzwDgHlxLk7oJ7xIjvb66+iDDQ7XMHermx+U+vt3mY4P7bJQLTuebvh784utsOBLi3L2tqKLE1mUrqbUamX6Plq3hpsMd00iOGHMhuwZx26KMAULAiWTXUJ0TuN0CTd27qXdnXYTlskg78GLLudy8+DGePG6s/tVyB+g60liSXY4Uy5IyTkHgEo8ZtDV0R+McisgEMueRgEVWlZ2DP8XkuBjJnH0WGQFm4vECLvvSbBpfraZwTglrz52DklL44+efQtbfTTm/BaDwTjN8c5Cq5OOARKJMJpnv5Pc73sPla1aSt+xCfFt3YJU9eHdGEWQRZXQlHZnrRDLjzzlyjG3tczAj40UlKBgK20rKgSl3CY6Nd9G77xuUDyWIWMGZANejIr7wTMuNqMvE3MsoGGtAnjBIy/EymNsH6kQruq5jSrtYlROgPZzL/qBRc+sR8Uw0XUMURBTdgqKb0ZFQ9JPLHZyA2T2A7uohNbAcQc/2RzWl2R7+IAAmxxjlZ/4Ys2uEyJANyawiWx2YbAFI2fC0n8c8rZruyq2oqx8gNeSj3P8uQlXbcY4twd1pBI0nO16acX1TWie2+Zs4L70XdJVk45NoMT/q8JFJ0b8T6tU6UHvpyWMp/x04RXJO4d+C8GAbR7s/DPJ0guOIzaPijBve4aiZeGZHI080J5mqClhsSbG63MEN6xf/s7o7ieG+duK7jfoyu/UFnH/1rfj9fjo7O1m2bBlSxtL000MNfKzZEPbqSXRgiozgrbOfTJAUAFU3IQlp1NDZpHqu4yzBeAZDiTzisgVRyFpyTrirNASEqM6h4Gvk2hfTkbedq7uGCOl5fMU5m+F4N2tdccrUFL2yiDMosHaLfTKIWRHhu1eLnOtRKDdp/G5xdiC90JMmOjSX/p0fQks7kCxhHEVH8HaLNM5dOklwmkwKzzmy06ZuFrHNk6nd3UHv0tWsmr8Fi3cAKxAO5zE4OJt81wRzFhgCZ66hVXgG1pEUUtwzayezAqczIWZdKrnRCOXxYzgTRuBxZTQbrGxX7fjCnTQVLMPJAhSyWW0AlugrjL6D58CbOochfSe2xBHMiSPkDz9L3BzCGgVFv4G4+0JiNsNiI6X7CAr9ONPLjb8zJEfQjGcg6RLhnAsh9yr+s+PndFj7mJWoNFxVuo4z8DiaHsXtv4dg7oVgg9ryfezpXIpdijMr1cq603ZOjr7xruLJAqACAsWB93Po8QeY9e5jCBLMrt3K0abzqOw/zM1LT+feI2Zciok7A7XEhTKeWvosT5aU0DD7CkqCe0BSGNXzGXLkISrTn5EctxEu2IwuGe/wcHyA+ZFCcE7gcCkoeop8W/aZ3x5N8mmrTDRu575dWa0TLd/KNdte4GnrapQEdJs0ZqUlngmkkQH1bVNLjSnAwdG/0G0tJT/+JvbYG6AnMDnfjWSqxCJAEoFnftzC1Z+fhyPHScMrB9nzUjvJRC6QS+Pitcw/bGQE5v7UhJRJFuoqcxHwG3FiT/6wBTjh8jaC801opKdkz0UyNbryxSghVBRUlohxdqjOE5JU7PGnOMedy7w1P8Nc7aZv6BGcjzyDLzyzaFhLMWiuqxkoOYPWdJTTm+9iTKwjL7jfULEa7yYy/AdcRe+jWPdS7IRD8STdSWEyyFfQodwsEFB06uwKObl9tJa/AOYw9twOQ/og6WKibxlm9yCC2I0lXoi09vfo6KSjBch2P6KkoGug98wmP7QabWw5I4EGnOVGkkPOkVsYtb2FXNNEvHI3E64GSifOx/3qH1DlGJJix9t/9uS96WoaJR0x3qksIinTx2//5i9gCf/1shqqCBan56+2+VfiFMk5hf82NE1DFEVaN/+YcKyRxefcg2S2oqaTDB5+lkiwhX7hd9O/Nl1gjvf7lG648u++zr6WXj77XBsgU25LsabCxfmLKzlnWf3fPPZvIRIK8tqDP0DLqeeyq29EFAQa7r6YJbGdnMjzGfAsRRAE8vLyyMvLKp7puk5g1I5TgaiQ4MDgU+Rby/Es2U4c0DOyGC7XCkpLriAW6yAUamCwt5s1e/eRZ/ozCDCWrkBPKMRk66S7CsTJuBNV1EhFZB4/s4CtuSXs3vs0yaBM/0tmbsdIJ9+0yMq4YuX8Y9nBOCnDAzfkkyge5QcV8UmJjm9YYjw5YWauVWX8rY8RGTRIomyKIpuieBrcDNoq0TJxNqmUn+e82QyJ9Cw3arWL9+z5PkmrBbfPj8WbDciuq9/D/OGNpBY9min2J5Dfci0Aj+S9xL4cJ9e17p72HnKCbcQ8ozinmmGmvqf0XtAuYaL043zw8a9R09dL0AE/v6wAUR1HF2ykbAs4e/9BZDWFQ9/Agxe/n1HAO5wtEhg3ZwdmZ+BhUrbFqKYSAKzRHZg0E6bMCllSMpacjPqxWTXjSI0Rsbn4Yc0d3Bj9BVVD1/N8YRRRCSPoBmkT9DSR4aVQ9SiCTeOGc3+FGAXtbSLb2uFVMGVbWaqQHNeHOPTq8+Sf9xo5BSO42wcJpUtY3bKT9yxdxZWjbm7sSpOSLVzhv5pvLLaypdjEfx4wXIJ96cVg1klm+pISU5g1M3LcRLyoZbIeWqsiMCcsQBFY7XF02UyeNevmXak4UE53YNqarSqpu2X+fOfH8IRD/OUjZyL3xxixhJmV9gLT62QKwGKbRPv4m/yh6Cx80TSXdj6MzzLBl2tux2Qv5vz49LXAE9+dWvgy6/4cc10FGCRHHjOOaK8Fk2MR/BWX2FSCEzULBDIkxySokwrJPhRSglHwPGYX8cZgtzzCFR/eyMvPbUFV51HGM5PnGf7Pazm71gRbv0+dIvLrMUMnRzE52LLg2wDMaTJz3LuT+b3Arp3EKwSsS25CEGWW2CxU5h8jIKUYmyhmpZjNJgQTBOtZFL6dcfd+HMfeR3vlfSg2P67St1CSIlZPGkEdITxkwSnmIcVNJN0B9GgOgpzEWTSMv+IhEiEJNWrBBJgjJaQK9zL/0GenubhP4IRb9gRsqwrY13oPFekT+2fWFXsngiOsPw1l9wGkWILopf8zSscncIrknMI/jFQ0QO+Bh+hKZ9MmscLgkRcoW/5uWrfdTT/3TxvB6p3fJK/6DBBE7Hllk9vTiopJPnn8DUAilebLT+wnqZsosaR45pPn4XP/7Urjfy923ftBrki+Su9wHo2Ni4kceY7TY1ltmZ3aPKrOfi8vjgaIqhpXFeYgCAKKpnP+vmbOn7AAaSZix/CY87mgws8Rn+EmOkEqwuF9NDXvA0DXRIp2riHP9uzkNRocFzEr+ljGXTXVkpMZpHWdVFjmvtZvMdHlojA9Tlfv9BXS+oaZK03/pWVctGbmqOaW4L15KWKjdfRkCE7Y24jGAO5QiFHzmaTNxuzrnWjmlQVDEDXSsZVqJ2qlk883fhr1sI2Iy0VprqEHZIoWknYYcSjJM7LBz8VHbsOUyOWYS6BX7SXm+QIh9X7yyJ2chNvMo3CSINYTcKRtzB7+A83FH+Ke679HoX+M4VyDcNqDT6HK+SQdZ/Dc2+q0CmoYU9JY4S+M6xyxTTex+QY/R/XExXTmGIUUqyIZ4qxJCEjIJEllrC0W1YInPkrEVg2CwEOLPsor3g8wUfANaibamVq32m+KEB204SiOgzid4HS+Ukq4z8l8bwmKAB9d7Kc+auHTrU7SZjhduZ6j7buwzApTlrufo/2FWIBLjuzghbkr+ENNKSGTgDmdQhVFlnc14XQYVx9RavDqKsmMs2rcMk5RvAhTChRLyAj4TQu8UXE9jugYG+jFao1Q5FuMSTCRQkFCRBZkzuhs5a2acuSOMGqehVnOYUI2OFhRjZswMSRGywYpfukpegorkeVSVDWIEz/nF7+LtMXPn+eNcW7fPjZebOSEB4Cvv/JzBgZr6Ld9lgQCLhHCf6U+pypbaV+3htptuya3WaoLaVEMAdE8CayiQF965m+gpv1pOmovx5HSGbMaF5GmMCOPmAINoqJOTszYHxr38fufv4QYKgLcDBZUUjzSTcvqZaxzX482Ryfw+m94ZOwnMDMpkaY5N3Hm5t0M5mgUT4DSs4OJ+XF8lg8DkBOZRw5Q/Q66ppJmJT9gpOEvbL5rcrsuKAi6jCYlGFz4a8wRg5Rq0RrQwdW/AvvROdNPNlMseRp0NQ2iPFlOJhXrZOxrt00SnBMYdts5Vj6L9Y2HZpzD8aH3E33oMXIuv4LCz30WRBElEsHk+Z+z4sApknMK/wVoqkp4sInjhz5P1H7sHds1Bz9L7/P3E7M3T9u+/qxmRGnmJ/fx37zC8+0pzq8y8YvbL5ix/1j3EJfetxc1U5LhZzeu+KcSnLaGnZyTfBWAcmGM9he+ytmpLZP779WuIWitZl5RFbcf7QLAKYpszHPzicYO2kJxfjRkrF+7Qh2clr+R7nlfe0dXFUB4eDbrrcaEelxazpB8MUWxKogYJEeYQnKEDMkR0EkGZayJBGVaHCUhEmq2Txaq6F5qovJgdlRKmyHwiTRUZQnO8MHrmGjdiGwbxzfrdURTgtEjlwNQOLyDZQeewxmJM1h8Oq31RtZUSk/T5djL1qgRfK25TdTZRrjgT99CTViRVIVITQ25HiOzw9x5PqaCBmIFhyavW7b/U5j8c/mP8m/R5hwg4Tgds6LiSBurx8acRpb6lxLWk+RkKni3J3wcFwpxCCnWiS3IskRlpJIJ227QNRDESYIDEPOcRORRVzDHD+AZ+7nx3hQr3+++FYe+g22+Pfw4F8Yy5PoEwQEj/gcA0XgPdmmChGYoTFs0C87UKFMRLfgiqqmUysCLNEzJJx6XAsR3ria18ABuPYFZVxmI5jC0Lw9dMz4Qt6eMl4tk9hdWsR+4vi9CadwDAhQkLiTI4+TOClDvf4MD0uV4EjHOPb6PTbNUcmUT5x/bM3k9Z42xst4rLWZOwLDiqIJK0BykKF6ENZ0kggsXYXbEVzJa8C7uBxbrO7HZ/JzpuBVNTNGx4B6sqo3Kxg9xWUMTW6+Zi1LtBFnkzEef5FiR8dxdqTAxvIzFfeSU7WOXaZDCCStpSeO1RX6OShrr6rdwsWuCozlz+cCrP+XED+MLa35EnaeN6PMHCcaWEdM0CiSRkQz3sAhwhlPGIUJ7UqMxodEt3oQ9bxfFGWmtltTHQTQ4hl+F+SaBaotINNHG4VAj8bQfu/ViOmovn3xGI5IGuoCmZ21Otky2U1TQyQE0jHMaBMdAW93tKPN2sdRzIbGDI+hiAU/FvgditgyGIkWR1ezYtOP071F//E5UIYKkg+mlgwxe/mmKtO8hiO9M5v8aTmT5iaqV0kN3/EPnAOByjcTxMUy7E4Te+h4Jt4NERSmyKuJtOMLJooL6Kq8h7ZB4dWGSdW3t2OIpNAHS93yNuRuvI/Xhj5FOJBBMGVmJ/2GCA6dIzin8nYiN9bKz4Wzjj7dpOkkpL/nShQRSO0nYuoz2byM4vsQ5JyU4WxraeaZdAURe7FLZfLidsxfX8tyuY9z5fDO3n1HOPdt6J3UcVudrrJhV/k+7r7GhXuqenE6sphKc72gfJCm6uO7qq7m1Z3hy+/sauwCjavCNPWlyUzqKFqTQWkSp4y56CtTJxZ2gFZNXuJRotIlYrANNlbG1z0IStjGql+CK3okLOGz/I/MiEnGXY7Im1YnsKgABY3BuebKY8rP8HAg4yE8LBLww8ZUUFluKib0iobZK7MsllPI2MrXy0NIWhg9dNymzr8R9jBw2VsA6GnWtj9FftoHRvDvRpKxmRlzQucerAO+e3FafN8Ynf/M9morLKBsP0V1SiGTVMDmNiX+75qdmaA7FYhrFEsQzeBp2/0I+Ufk92uyGO0sxVXLaYBe2TMZH7Vg5qqgiZ1xEGjq79ArSmBjTwS2UsZxBvCkvzmQutxx+hGhBIS49xG7PIo45p9efKg0PIUc6UBNPk5SyWV0XBNaBPpcoc1k2/gF+PdLMJ2b/kh5zVmvlq/1VkwtfS9j41hziOAHFUIU2a2YIb+LidjM7589i3FNB3FoLus640IakSThUKyFTlKAUwJOup/GN7LczFYW2GsyilSfLss/8jiUp/rLTeA7e6PxJy1DJ2mHcA49wuOMiNM3E+paDM87nsgcZoZDj1iL+Y8gIOg6bwiQl4/6kdJRxfLgI85a0fvK4TuootLUiIjJRsh2hqIEkkOg9n/rkHBa3NHBozhKKxkbwjvehZD7uovggw3gJBN00Lqlii7mX2BQZgshwCntwPg/tmlmu5Tu7P4W5GG4++1m8LyxGFSTMosA6m4hPFtk8/CJvpUpQ5XKsogzYQRAYq15F/vgemufNBdGT+V4MHD0R7KrmkkqPYrWuR7FMIcKCTkw38hXTenpyIWLKBPqPFvVT1l9xMsMMabObMdN6toRVCk06wS0DJBVjIaCjGhY/dfriSzHZGSm9iZjpl9RmPgHn0yEGL/gQyhl12MfnktdxGRMVrzEy+yEQQE2LqNEcagY/icdbj1xpJfFSANSZFqq/F2lnGNu8XAgIyLkOBt3HKSxbhOIyE/ve+5EBpz+A0x846fH7F3+MiKsaVbYiA5pczPalSW75/JmkHQLu3BJa9xzgxXsfBqEAybIMUdTRtQC+4ijrb95IUc0/b9z+r+AUyTmFvwtHdn+cKeKpADhji3BZl1C37uOYHV6CfY3sa8kW45NSHhbV/4rA0AGKl1x60vM+/NZ0XZxbH2mk4tkGuqMSCma+tXkYMgTn8joTP3jvP6eg5gl07H6eE0PgqO4hX8g6G8Z0NwnByXtvuYWhnHxa+9rISWrURjT25cqg69y3N86KCWPp2RPpYrGvmd3zBcSMBkl0eA69Wz5JRW0Ol35mObFQihe+9Duuzv0cAMels8h//g5SFStxn9tNOioTz3MahAZQpDhaxqwu6llbfu+WXPIz/1avS2PJvJv4Sg3Tys5pGTVjjRcz1ng5APbYMPWtTxByV2JNjBP01JI31kBv2XoStnymQkfnsRw/aMbArVtEUsvzuOCpB2gqKUcA+nLdqA43HveoUQwwWsSrjqOkhBQ/bvgs+YphlXk092Va7D0opjJs6hwSznMo7DHIZFpIM5CsweFrpDxqDISSJpEWTLwHM3+p/Anj/nmgGP2YG5jLayWvs7z/DPbmvkVs/Cne3V3G4crbMIXa0IJbiJnHiJmik2rZJ7A0sJAhIYAdM27djk2ezX3td/Pzkh+w2dXDR8YKWBb6JI3WrQA4Y8YzsUsTCGl5ssRGTAqyp/xPSCGYO3Y1x+veRWEsxLgY4KLeizDpEi+VvUJMCGB1zIOJmd+ew7qQlcXr0bBxxJudVrtdbj5R+U1+0v1prOFKctuuwF9n1GpyloSoH9vM8YlzEd9mKfREu3F6BmliBXZFp3h8iB4Jhm3DxKWM4KMa42i4mgpNp1vOxrL16JWssO4h7u5gvPz1ye1xXxO5wXM4861fUBtSWf3WC4zaNHRRRHF6mTvQxIHaJUijSX7Y/zHOcn6D3PlBTBNVVAWqCJb188uGD8y8+QxSg/Br+Vo+n9qEbllPX1qnL62iqX4E89kIgkH2pnqgRuw34V0Pw+otAFgFSLxt/hckNxbP+3g7LRic8yYMnka9mCZGltgKGUvOsDfNaGwn9thpOJIzScVE2uhPKJn9LWpqEFF6Z4uFP3ch1thVDCX/TFHA2OZ6WeRQMMywYMVd+jwO9RAjj7+PHPcYRQe3Eq4uolF8DN/xTkxJlbLf30/TMw9gHQgz572fwtxjp6XxEWINu1j8o/uQxgQEpwm9M4Up34Z9SQHNe18hOj5K1eJ17PjaR6l9sJ2gU8QT0ZAxijwkTZzUYjMV7aWLCObMnbZNlIvR9TTPPjCCr8hC6677EaQ8ZNtFk210HRByGR/Kxd8/corknMK/H5GRLvwd2yhecClmp3fG/kDXQZqPfpM8z7lErIYeTW7yQuzWSqpWfwiz3T2tvadsPhvL2kmGRkmG/bhLDb+wr3blSa//5PajvNJtDBa3LXPxZIOfMcVMe3SmGff0Qp2ffPC8Gdv/u4h17Z/8d8uCT5E4ei/lwggAv9av48abbqSqqor/3H0QdIHHd/jJSVn5zvxxKmIaKyYMiqTrOjXuxQQrfwH5xuCpA+6j5dxWeD17Bq/nmS/tID/RwpU+o+JwSM3HM7AYsXKAb77rPH7f91H6Uj6iVrfhrtIhZcuWvBDRJlNZJ/tfqZFcOHMw1nUBVIHx169kLJQJUNHT6AzTUn8etuReSo8dwxts49icWwh662acI6weYVQzJsL0Ai9qiZ2v/fIzzO6a4GB1MbogEC+rQ3V6WFhuKBhrgVp6bUNI6hgfqbmLWfFKzg6t5I8FzxJ3rifie78x3+sqreKbrGIVCAr7tdncOCJgtvpJCzK70hV8HAsXizoHTO3MlSTGEyvwyRoOxUFhvIhdhZsn+7rV1QfjXzX+eJsnc3lkHhdPrENQzByQujkgGfWICvUkOWo1i9UqPj7wOT6eae8XjFiqtC4i6obvySGOIyAgaiY0KTUpTggwZn6CisGl1IW6SCbLDEsPUBuq5YAzgMmWg0W1k5Sy8gEOOZeLiy5E0AXe8krogkBOKEHEJpE2mWh0x/hR8Z/41ODN5HVcRqTTR3jtHzE7FQoWDWHufgZFdVI8ohAMmkjnglQ/jiBpBJO1rAok6BcNy1qffQBXyiCbupbizXgOs9rOJLwmOzF3xReC/QkGF/0SxT6S/b5ymvB1XYRbFZnT0o7JlseQZQKpajlL1ArG08McijfRhFF8dPnsXGocEQRHNw+E17CrYebi5vzoAV5xTFGy9qfwlm1mYjRrWRIl74zjTkAQJFozBAeyBCflCtCQ8xaoEiv6Z7q9N5erlGnGu51nipEWsjE5amZZEIh6WKa9zl3L57CuLZ/qkZPXCDsBCSBDcCwC5EgCQ8rM32N/+XqSFjt+1wPM7zX2L9k5CvyFLUsXUfHmAIv8D022L+1rnXZ87Mr3ckLhqWX/TeSNK7gBNzB8TvZeu+b5UCuL8d3WRE5IxQYMA7WZ/Z7I9KAny9tibqZi/KKPcjBSC4LppJ53QTDh736d8V47Jsc573wioGLBvL+6/1+JUyTn/yiOvvI5hk2GPHnflj9Qnv8BCmadg9VrqBz0H3iKpsCnwQ6R9GEQwBs/gyUX3/PXTguAxZ2PxZ3/V9tEYgm+8VIrYKbGnuKzV57OJ96l8vH7X+e1zCCwrkhnNKpw6aIiPnrpTLn4fwaKAgcAeMV0Lue9+w725lSydcujtIqzWLBoMbNmzWLf6Dg74iJzQio5KWPS+8wRHVnMmsEFQUAgQFdFdnWYjjk5x2TItp/uzujZZAJogkoR+9WPUlP6XcRK+MrEpzCFjAF13O7DO2WVeQIiGsMf8yHdN44tk3Ud/KhCzF9LqHs1sdFZSOYokjlGZGDJtGN1NATBRNxuxJrE7bPYfvqNiGpqmnvqBGY3Pcjd5y6GMCgldjb0HeD0J18ibyJOyG5FFyVSuUWoTg8mUxybx1A++5nrXCY8C5FT3WgjP+CAdJwDzuMkrYuJ+N4/ef660a240kYUrqxrpAX4gzSLDx9+kofqLiBqtnOV8wc0CKv4wcgYZckAi5QPcbOwDVGysm5sNU/ah0mK7zxKbwys5j1jl1CYzmVAHOdF83T3zrBgYVgeoEke4F3JFRToHnR02iXDrxBXTZwYIu1iwHjP/4+9t47To7rf/t8zc7uve1aSTbJx9xAIIcECwUuLQ1soUkpbKlRpi5bitIWWQnHXYCEkIe62m3XNut3uI78/ZrObZROkLd/v8zy/Xn0V2LnPnDmj5zofuT6qEaQE8wPTmMMafp9uQhWA8B3EEwWkxoeygdJj6RiUFoIWG6cU30B1oIWOZA9uIcSsjHmDbsj9Hp3U5/WF8NkdtGYaiWb/hh3x27hHeIpb268gW51N/5/eRr4shK0oiqcwAARIlozwHhMKT6a0bT+KoBEyhOiVPRhU/blVNYWE0cEmWw2KJDK2bRcBWyotZp2kJAcIjtjjQs0IEPHomVi57sm0+8sxGEzEcopYIZeRp6aCWEBTx2scGjUO0ZvgT7uvP+a9yMyMcOnHb5Ma9DOjp5ZT8rZx402/wby1GzGisGHaXK6978dsnnEngqBfc5cpQQ9RDEkHzSnllPSPlIiwCRDR4ED2J2wpHsp+2l3wIZdsXoJJWszTpxRjSKpkVD9CqzQWgFxTjMhRj06MOCagL2wlzdbI0i2r+XTWqXw6xsqFa99gTH8hbXkjs4SOTuqa0/kJnoLZxFwp7A4r9H/GvdSbOZe8ZILuKZvIPDBUtHfJ3uGCpl+E9P7jE6+iQ/1wqP+4v38R/DmTSWCmt2wFHZFcVKUByXj8en9G26Lj/nYEmqbh7YziTLF+YduvA/8lOf8/w+GdL9LYe/+wopgxaxO1oV9Su+eXlNp/i9mRrROco2CMZzBp8X3/9vH/+t4O3jvYwX6vATBhFWReuXk5BoOEwSDxxPWn8/qmcgrSXcweP+oL+/t30NvZwli1AQSQZlyKIIrMWXYOc5adw6cdPVxb30mivY89uz4BZymXN7fBQEk8g5g2oj858xkGFvEIghWxphRoGtEuorjZk7yUvVmPsjTggySMSoLPq38E+qypeI5Rv0tEQQ55CN3QRc9aO6YzQmgO6N52EbH+4uOepyLEkY4jLnY0wQkLGiccuI2OVBt/WnIWlUHdujNOPMwp618jzxuiPcVJl8dBaOxUEHQXy3iH/tGNYWGrUxd5k02FRDwX4Oz/G6poJ5A59Dw9Wf4L3hRSURM6yVGTJs4Qt7Janc+fJ+iSAlZiXJ7cgUEYyKQRknxs+hGnJ+7im+JuEoic2XwWdkzMTo5ml6OCNzPW4DeGMKtGHm78GXmJzMHq4jVHxeUcCx+YtrE0OYtOsY8DhsMAGEMGBooOYBIjGIQoompEAU7vP4Gx6tncoV7DT7NcRE0hakyVnOY/bbBPq2xFkr30WD1MDCnMSCkECkcce116CFNkP13iI1jCo4FfExPt1BU/QG/PX7mpI44FC7GSRbR+VEF2qRfXlADWtJFE2F49BZ8zHyG5mVRSqXPVEe85Hb97m661goBFK2RnXiPzmw8xtakVlRaenXsSCUyYBuqlP9sR53w3mEwxIilVjFNn0ejdQdBtJ8uUT15yiMyNElNITVXwHcMlpwmQlh7m6cd/PWz7xLYGXr7j+1x36o8JxU2sOXgCF85ZwwHLr8gwfJOmjAgpHc9w8XoR2WgkOi5G3PV7zEkPIkMxOJEBHnEwR9cBdig2QlKEok6FlRvX8P1b5tORamBidS3X5B/gju5TAHBKCSJJMAhmZC1OUlDIQKYtaeDgNDcXf7qZy9YPZVcmDfvoyJmHKh5biCmvbSPG2tcIde7CPuMqyjb+iQOTryPoHO6iactbzKhtawjMHke3LYm5pZuCxqEKuooANVddS2bh6ex/925UDEyr2kh64NhpZzGjiEHWMGj/erxOwuikNW8JTYWnDqaCaooCJEcQnI3Fr7C48YKv1L9mjJPUQhwtB/A/if+SnP8fQFVVGjY9QizapltvBuY7S7SI3JSLaYjcM5hFUhv+NQxpsyElXLjUGYyecgtmV/oxev/y+MlTa3mpKsbRj90fzhhN2mcypc5dNOnfOs6XRdvT15AuaBxQSxhVNo3f17ezPM3FHI+D+1u66U8q3FJ9GMExGkHTOKH72K+LSTiE5lxD9TTdUqBpEIkbOCmwfUTbjy3fxnpoG2L1i1x2Zvew34KtOsnpt7gYTeuIfQU04iE3ySkahqtCqEC4s4xYfzExSxdBdzXWSC7WcAGaoKBIMQyyDUk9VvnA4egV+/mn28RjiwfqNw3Mn0q2lZtee5pOp5Gq3HRiJgPx7FGDBCcnuwbPWP08t/vOQ00ZIk0xxxJEoYiEYWjJfOXhV/iwrZ7t4w5zWr9OCFpVF981vM7qxPzBdmdLWzAM1O/yN1vpLXeQPaOP8QX1VMWyGG/QXTFhEqw3VkJc5PyWc1kil3FY7COhJjhoeg40D35tNHWSbmlyRw/zYeZuAlaZcf1FuITRpCRTSAgGPjDtGzy+pmiM9crErAMkR4hiEqKDon1RIYGGhan9z3K/dhk/yHZgkS3YlCG7ikWxgBqgyi1yUs/QJOWTgngUJ+3Gbh4rrKfRvRx3119A0IhJdQiKD23AXRNJu5SX01ZzWe+pzE4/lfaWeqStRuorCzFmJcia0kek34JLiJG5O4380bfgn9JDyUCG2uHgdMRoNgarg5gUw6pYsSnpGLRSprTpLhERkZz+arqaJjFqxh4cLxvYvVhkgl9iWoaCL2czef0TyUydQoNHY6YnTvX0KzAHCsnd/z08ai4/3rmFH59yOiRVDPVBhKhMriPAOdVvc+IHQ0kIz5x2Lhd/vBpDMk6638v1h97gzsnfhAjcNf073PfCQxjb/jx0HwwKfd+OskCEdRVrMHMBKgyUkNDR5jzEeHo4V7CSvjOAIa6w1yHxxFUlBMaYuFx7guVjPyCStNBbq18XkzRQfFW0oslJFEFlob2fl8OZ3L3zJv5+9Q/IultAjAsoLg1RjTDx0JPUF59NxJ5Nad2ruL01iGjYIt2IA5lagreZyNpfYwZm776L/TN/TJ+zaNi7tnXe7czb9mumTCqi8P3VbH7+T/gO7Gbpr/6MnIgxacCaXjrnjzTsqWfS3N/T+dd/IL/8pH6+6Wlk9/mRNBlL8nNy7o+BpMFGyJ5HV+ZMotZ0/O7RKKIRTenTi8doMqrSg2TI5bMBbdtGvUNF1iaaUyq4ZM9vPvc4nY5GwmYftoSbT0Y/w0lpj36lcf4n8V+S8/84EiEfG3foyq18ZhEyaerDuPMnkB/9BoHWA1TV/IKYtRk0AXdsPiVl3ye1ZNYXHkOWFTQ4rs6NLCv89J+f8GrN8KJ0r101hZn/wUypr4LOllqmRrejagLviycTDyd5rNvLIy3dtMzMpdnvB6M+YWmCyLnttZiV7GP2lWm+lR2lqRwRyxAEEDoycNBIf7KAPYGTSc3exo56hZ1Z79MxWSB/fozHu1S8osj3sjO4uiZCXrtORnwm8zF94CIqfb1jSUcnFapspKd8FbKtA1mMkdY9H0WKIWgSkmrGoHzWkQFxVNZaE0QFgXkxiRxFpJ5DvOkazdFRmppBQCmwc1LGFnptELIOFN6UJJKeDEymCJlZDRQXD7mA3rCMA03m27Vv0eIu48OsCUTsQ5aL71XtZou4jbYyA4XBAiyqBVBxEMBqbmSVvIk31UXMt37E95veoM9owZMR50DNGMpnTmJyewW50x5ja+81xOI5TDMMt874xDBvmXYdtSV34N9D7eZsKOdU3xELSD19hRIbRzcTz5w22CahCFz4xutsWfRDBDTCxgB/y+kgjTokRZ+AWsU+pij6uc0InMdUz9v0KPrxRD0JDlEQMStGKi31HG3B8Si6BevO/H9ycNStCGoUU3wozd/ue4VQml7JPiHaeHj6WSzd0E5+Ip3T8q7mE/UppjQ1kl4RRVqroYgJokYDnjkXDTwX7QgIqKj0BedwmrSDvqhM1BTFqlhxJUW8xvGQPMiR9KJx3e2sNs/gt7c18aflERRJYKssMQ2FYMZ+NFSyXOPwGXuIjHtNf5ZczbRPfYyUHTfQF9vJc3/6MZf/5o/Ma6nDpsa49Z9/G3yOt02aTsHEKygTK3h/2YlMUvOZEHIzd9ejyGdfi6ncS21dEbuuzmDuPb2IMYGEwcCuM8ZSNHaffu/6PqW+WbciHCE4fnsL1lHPcFqBlbpQDpbzKxEEKEGhhCrO4NbB6/p+9wpAYBQC6kD6PqINoxBHIUqBJQjhTDRE/t57MVfd//yw5yv97n3M23ls15Jq0QicrWCuErHuHwoin7T3AfZP+R4+z9hh7bfN+y0lDW8i3ns/C26+EeGb+sc51tTKwWUr6bMVErHkktu5jdaEf9i+eb19xxzD56ElZw6Noy8intyLonQhGeyIxhxEyaQXjjEcWcCKAwRnOPbkrmFf3seMjxQRFqPsKFjNnMNnDGuztfBNuh0tZIRGUZ79Kaqg6HX3BI0x7qKvPOb/FL4Sybnzzjt5/fXXqaqqwmq1smDBAu6++27GjRuqQxKLxfjhD3/Iiy++SDweZ8WKFTz22GNkZQ0pOra0tHDdddexbt06HA4Hl19+OXfeeScGw9Bw1q9fzy233EJFRQUFBQX84he/4Iorrhg2nkcffZR7772Xzs5Opk6dysMPP8ycOXP+xUvx/x4S4aMIzhFoAlMLniZ97MLBTUarg7TSBcwr/pDOA+/izpuKI+v4fliAzv4AggDPry/nke19KIgUWhP8/IwJLJhQiNOmT9g//+danj80FClb5kpy37fmYTUZKc4Z6fL5n0A0HCT49DfIBg5oJcRHT+evnT4Q9efvR++8RnuublX4Xd1DjIp1MKNzAnFWIdKDx/g43uSNaLhINd6DHDfiTTFjIElSc2EUAuT16UGmO5NL+PPEDkJxha4TYiCIeBSFu1r1D1VFfBRXVF1E3nt69WXf6BR8oghoICfBMMRMRRR647mYPvw1jtz9BNumEYnZ0RQLjiP1leTjV0ROaippPECF+TpAo5sQ92x/hPsWD00EmklEybYij3Pj1AIsfHEHPoduYRKtLvxF+sd66qT1WBz6OQiKib7qVfRMGEdR53O8afmYaX2VnCf38lqeLre/sHcfm8TnKO0vY0Zy7mBwbo9sZVKinNpeF/dnP8bZjn/g6RXwbfUAdrqA7afPI2ksZkNKBqOiz7Ap/0WS9d/h9fgkpolt5Pd1YMoeaa0q5DD9WgpBQa99Nbr6EClHl8MG0pprWNUMPncnMasFn9tDcWMjZjmJIqYjqbA/dx37U2KQ8hxX7tEtXe2Sl2fFT7kgPh+Us7mr3sl6cyZ1UhdexUCOJBMRdJdVk72Wz7qptjsPI6efTchoxxp4HxhaANhCmzhvWy6bpk6kPr8ITTRzb/FBHqw+CbvRzcqS79OZ08Sm3veJyPr5mEUbZ6WO060cCd1CqIoyIHCGtI3N8SJ6bTIkIC0coC/FNayCe25AY01JHg2uENvLDMSdZ7HNsYiz438g19xB3NHKhFAJisk/GLcDOtHxZB+kszfBTF8fH37/ihH34cmVF+DMW0aht41GNYDq9rBDC1KeGmb5ASdPP/ZTrjn5pwhJjTvKf8Rdv/8NvX252AuCpMTraQ9lUeMdw/yZOzgceJwC73cG+95V+Geun9jL73fcQqOviBSzlx/PeoTuSDoT06vojGTwRvNZLMreyvtNywCNFcY4sjygIq4ZMQoSaGA3DX2nNrfPoySlmRPzNg9uq7/BwesVV/Dp9LmcvvkTbnr5aQR0gbz+b6m4JweILFFpTVoRe8D1MWRsSzK5/HEqx19Kb/pUBFWmpOFtujNn0lR4OuHNe6j+6Ho686chJXrJaakiKxQgK3QQODjiWh5B2JaNOe5DNlmwRH0AeLPHUTvlfOZePIme7Zs5sN8NgglECQwDbnbD/K9s2VAEmcqMDdy8aTEr0vTiv4eEerY4msgIFeGztTPBFWeFVsTUzuXsN1dyWd31TFT0xJPN1h2o5bUw6z9fV/DL4Cud74YNG7j++uuZPXs2sizz85//nOXLl3Po0CHsdt3l8IMf/IDVq1fzyiuv4Ha7ueGGGzj33HPZvFl/WBRF4YwzziA7O5stW7bQ0dHBZZddhtFo5I47dDnsxsZGzjjjDK699lqee+451q5dyzXXXENOTg4rVuiZIi+99BK33HILf/nLX5g7dy4PPPAAK1asoLq6mszMzGOfwP/lCLRVUbn3p2go2I1lWK355E06H8niwGRz0V+/k1BfLZFQE5Jko0V5eNj+JZaf4c6aetxsJ8lgJG/GMQTVjoIsK+yta+MbT+1nSAlmoKpy1MR3X60D6vj10mwOtPTxRt3wwNArFpYwofDYFpH/CdTt30T+66soFfRx7VYnsFYUUcShV+Hl3CG3ybfbXkNVTbQrOhGwSx9jk7Zik3R/fSJqo/6ESzCI7wAgagHkhkKmh3ejAW3Zq3mz08uTbieNISO7LBZebevArmkkQhKu9xOkKc8MHq8rq4gIeqFAUVWHjCuqgiCphEw24v5c4n5dLToqJnCqIwOHP4tKY5J37TJw3eC2kMnG944iOPE56WgpZqyxKE/9+mbS+3vYPiYfrCZkq4NIYSmioDA5r3GQ4ADk7vkBN+WvASQSsQ3kxHLYZ6/miu4+mut/y4MZY3nGHqI4UUxGfHhA+gG1gKVbNjCh30jvJAcnTQrRXeekDzg4aRKHJk3E4S/F5c8hYnOxaoeNnUujeIue55u+bxA0F9KRs4PucAFTIzaMgkqz6EH0+6hJb6UzZQfT6xcz72Abk5qGyxUAyJKEQVHw+P3g95PdqQcdN06bhKha0dBo8QwJXxrjQxlJMSHJOuMmTkkuQRKX0ibqcSFdMYkSS4KIKGJVrHSKPRx0vMk6R5ibOr9FRIJbZ48mbNStY2nefcQFuK4nnTV2iTpbFzGLgXlVMRpyI2iijT0ZWSRrkoOlJrKtRZyQdT4bOl9hTsbpZFuLANhnb8cS9wEeDMBJhg2cIW6nU0ilXdKPlxHsJDaQQdZqayUvkodBkxAkC9vGi2jG0QRSdIvJe4nTuIYnCeRuxlxTQH5KL92AudkBoRjxiTKx/C0ITZOoGZVPWfPhwevT73TxvZ/8nnGKh3kVn/CpEMHg78Ps60VMxlGcaTRkpTGqpR5jiRm5OoasGqn0j2NBwS4+ajmRl6qGyr48U3kRV+T9lT3WNcxoP4Xa9J2ssMXYGp9Ho08/f288hZ9v1onoBFcNLck8QlE7e1qnABpm4BRHJ9vjui2o0r4JW1IiL5JLUhF5Bycr0a/NMxUXUR8soqZnDJn2Hi4b/xJhV5KEycSbJ53K20uW851n78FVGqF88kya46NoShSjbA2T6ejhlvMeQ6oUSPUHmFz+BLtm/Jigq5D6MeeS17aBWXvuRRUlJFUm77D+7IRtWVSXfoOILZP8tg2keKtoy12MbLCS2b0HS9xHzOxh5+yfj3iWAUjA2qf7gbIvzg3/Aihigu0Fq/FaDnPl5lJmTssjFm/CEixigjaaCQbAA1AIMfT/AydFhieJLIzOoUqMMoP/HQia9q9HLPX09JCZmcmGDRs44YQT8Pv9ZGRk8Pzzz3P++Xpp+KqqKsrKyti6dSvz5s3j/fff58wzz6S9vX3QuvOXv/yFn/zkJ/T09GAymfjJT37C6tWrKS8fKi73jW98A5/PxwcffADA3LlzmT17No88omf7qKpKQUEBN954Iz/96U+/1PgDgQButxu/34/L5friHb4kEmEfJrvn3+5HjkfRVAXJbCPUUcO+A5cPCxg+AinhYnTaT6gJ3nbcvhZM34w15d8jF3e/spE/7/78Ymyfh29NsPK7S05EFI+jY/414MC6V8nd8EPS8bHTvYJJvnWD6qZb1In8M/8q3izVXXIPVt3BLeNuRRnI7ji951OePPRLeuK/I65NBxIcXnAVikFg1l4fsrSEj+aOxq0NZXX4e0dx7qE9dEoSv0pN5/GeY4vAAVTtzEarH7oW0ekq63Kv5mHjRC63bkdMyGjGAQIjJ5GNNrz+8ZRFPIPuCPGYsmXD4bBs5m/ZWRz25R23jeo0skSs4tw1r9OQlYY74MeeTNKW5iGRmU8iRScn0wqrcBbuHNwv58B3eUneztNjF5A0l3HOnvdIj6cTlaJ8mP8hykCsFxosa1uGOzlEEpoVD41+Bw+vuZ+ExYwjGSZ3no/Dm1LZNXMWHbknIKombOEhl+b46t+jjqvnlvkj39eSwDiW+U+hwr2Tza7toGmceFDje6uHxy28s2IaSZOBdnMuWZqV8dUViKpK0OVk3rbthG1Wqk86G19Yz6Z5cdrv8Vn1GKDLd/4egwa+jOGZWufF5/KaWXcjvif4uSpqodNiZn/qfqrS43hz7uDqqtc4s/8gP5zyXRocOaBp2ALvYPe/AsCf63/BR54tvJH2yWC/6eGxVJbpk/b85tX8vnYGbsVxzHuoaTK3jH4PU9RHqTcPk5BkpvkFwrHZrFGm059aSZm/GFkS6LeZyQzG6La3446lYlYsrBmdRUB5DJfzEupT9cXk+NhufmnWF58pTSsI5GxDMftxvWokxXozzWfcC5pA8+bvEKrfz7Lte+gunMy+uaezKpBOxOGmpuM9OgKHCKdlEsoeegbNXYcx9XeREo7SXDKbv084DUNT6JjndgRnjv+ILYl1OOVsPH1+LpsZ59att6Mkv5x68K1YWJy/iTW9NgKE2Zy1maxoFmMCY8iwuzi7bzbXTzGzr6oPITH8uclN6eR6nmBP1zRWn3khE6hgvu9TXpYvoKveg+gfvqBLzEjj9ujPCH1s4aTOVKJ+L7tnDE/okJQ446qfJ2LLIuTIozf9SxQZHlD7/jrR5WhkU+lzrPDOYmX3AuIT1tNfoi/kUhtWkl53DsJxvj2+vPVEUg+RXXE1mqjQ2F9N6umFTDnz7P/oGL/s/P1vxeT4/bqvMDVVj5revXs3yWSSZcuGcubHjx/PqFGjBknO1q1bmTx58jD31YoVK7juuuuoqKhg+vTpbN26dVgfR9rcfPPNACQSCXbv3s3Pfvazwd9FUWTZsmVs3bqV4yEejxOPD2UkBAL/+oT9edi77gqixkbMch7jyn5HasnMY7bzHy7H27aHcLCa/LJvIkkWTK40THYPVWv/oBe1PBpHM/MBUTIAxRQ4LsHxRBczecn9mBwp/9Y5/eq5dfzzYGTE9hxzgk9/eRbhWAKTQeLBt7fxVnkfnQkTEiqnlZj49YULCUZilOT+e4HLXwU1ezYQ6W9n2qZrB7fN9n84qHD6J+USTrnwWnY1DJGQpYen8lL4dso9WURFM9/sXE3CcSIJeSLIoFjriTj0V2Zr2Wyizmbc2vAqz2k9Ks+6nNydlsKq4PE/2H3BacgN3UhAfLxKbKJK+GSV3ncGiKg2+A8ABFUFQSWu2YiYOzBoJsyJdDRNQRAkVDQqTAlSFQN5ikSzQeGgOcri1P3cG5yhFwsCEtNTUTMsCGEZQ00AIa4gRGQmqw0s2/YR9lgEa8yK32HBJ1gJF5ehmSyYzGEmTfwEu0PvyN16Aul1q/ib+DhvFrcRc/6Sgu6PSI/r99iqWFncuZjylHL6LH3kRHJwJ90YNRlve5RX05YCcFXDu2xdMJ/2/Hym7tuHuqma7owcfJ6LcQRHuqC6sgqZvb2VU/LjHLSbGN2hEXXYaEpN0myvoquikkvfVBk7TmBcq0bJURxTliTePn8lWjIDQ9JOiutNqlKCCMlvodj0ht0ZmfRY2ygODLmXYsbg4H+HTX7SI9k4zRuJ+2eTsOhj3GSsAnRR2psSbYS0mUAUm2zDkKzDGKvk7+PP4+9HlKM1jTEt2/ELOsEpjeSznSbMgTRsLhsRo/6u9dprMMRrkM1j2Vp4BhdK15KSEBgfLeb6jgvIUPTrnUx0c03ZQ/Rb5zM5pE+2dlnAY0yyKzGX7HgGzS79HA2KRlooiq6hLelBRAq44v3k+c5nT9ZiFtZVIAgCFfklg5W5vUUfAiDGLfgjc/nl9Bf4mTcVOaWfPE8fuzwelOW/ZLQlh9FxkSpLM9W+NfRl29EKRroq4lkFSNEQXuDsT95lT85EDh4jA0cosqB0JRGjCvtjU7g8uof1IT+XFUZ5oPL6QYKTLHMj9seRumIj+lBdRsYZk6zsM9JpbSeujQIBIoYIMUlvr8iginFO6VLYMTUV887hC8p2bzYtc0Zx+uw1nM4aVE3g/sbr6OmxIzJSysC0p48X5l3KtYsf5uO1EnPz5jGh7SWqss9GlfTnRpHMHJpw5Yh9Pxf/YYKTkCL0Zu/BHMoiXXZRk7sR1Rjjn42/BUA1J+ks/IBypvB89JssKN7Mcv9LjA0vwRTJRjVG6B39Bp7WE6nvaESa+BQAVfWHSZ3Qh4xIa+e5TOE/S3K+LP5lkqOqKjfffDMLFy5k0iQ9G6azsxOTyYTH4xnWNisri87OzsE2RxOcI78f+e3z2gQCAaLRKF6vF0VRjtmmqqrquGO+8847+e1vf/vVT/YrIBHyETZXo0kJIsZq9jZdyHReIrVkFk3bnqLJ9yAudSYG0UmPaaA4owSdNS8O9mGJlhCzjiyoKCZtlOXeQ/ZkPSulu3IdTQ1/IWjdNaxdifmnmCxppBbPw5o6MojsCO56ZSMfVfYyq8DJOXNLmT9hZHqrqqqcfc87HPQNPSpnFEtcOL+UTLeDUVkpGA0SnoG4jZ9deAI/uxB6fCFSHFYMA8HIGZ5jrz7/0zi44XUmr7uSscf5vV9z8qR2LhMXnMbECRPwtulE/YHKe4mrVzO6byxzA7/BILTil6+mOzDkvgumD90T1dMwwhoc96biCVRyd24WaBrfChw1OSon4ReXkhCaMccWsK39acZr3cTHqvTdpGdmJELp+LQUjqQtD5Nr1TRAJSiI2OND91QQJBQ01uXVsC+czxmeD3mhfykL7TuZUXiYx6uG0j2VbCvjI9Wc8ve32Tr7FLqy8xl7uImpjVsR5AC9Nui1Dbl6ZacHzWTBrEnMnvIJgtU3+Ftm9cU84n6W97NaSFh1Ep/lrwGGiq6mxdNY0rmEWlctxaEiAOzxfp5IGwpYLIz24LVeSlq3lfqSfEpraqgfsxBJOXZGWHvOFOIHdvDtZ1WGwk9DyAYDsqBgGZhrTts93EDdl5pC49IyTMEybBF9jM7gj7Gmb6IpdS8TYl1IndfTbwyR7vgQNZGNiB6PcET0DyBiCkAkH5um8OqME5jdWEFqNEyXqD9HPs1KIHABqjkO1OFI6s+9zf8mAdMoNNGOoEZIbf8JfmHoep7XuZJaUY91OaNjOa+MenPwt5Su39FT8A8QDJhZjN/wAdudB9nuOMioRDYX7E7hvvmVaIJI1DULY8f7ALjiAgajE5d3IqJmJKc/id9xEHfSjaQJKChEcZI5MGlmhsIczjibCV2HmTyQedXlSuMZcS6XpQ5lCpr227j2JN2a5+9yY08Bt7sb1ZbL1urncUtWRjW3ELOIRKfPGrJGopKbW00s5qS/X78HkaIyzF2H6XV187vn/8iVF9+FdyBdWpRUvlf3Dme8uZFH55/Pu1nzONyUTcYJCa70hwmnOWg+pPejGQRefeAHeKIhtudOALPIQ1PPpx8H8/3bmN24j1WjbkKQBCKmXuKCXuU+JsWGSI6iEXce5qzOYmrbXuXNlGUQUUjMycB4sB8xJPNa3UqMM1XWHD6J6qqiYc+YXOgASUDJsGDerlv+6rZlsffkORRk1rG9ewdLii8kkb+d3u5RZIeGZB9UITlQwkWiI3UfpngKaWH9e/zi1DvIChZxUsM3B9snDf2ogoh5oPL7ZyELSdo8VWSG87AmUokYg2wc8yyNrqrBYqIFsRy6jH1MjJZwx+GbdCZrBHouHtaXP38Dh6RS7u65FdPefl7znEHNzHpukX5OYPtErAV9GHM78Y1aO5iPpSCSt6iDvcwgXetggud/L0ThXyY5119/PeXl5WzatOk/OZ6vFT/72c+45ZZbBv8OBAIUFPxns3tMDg8L521m34arCdn0SPy9TRch1llRDVEwgZd1n9vHZwlOSmwpFlMu45b+HMk4NLVmlp2Ep2Aa2z89C1WIMWnMgxisbtz5Ez+3/x5fiF+9uIn3mxTAREN1nLdq9nHGmBouP3EiU0frk+hf3tvBX7e04ZWHYj5OzIVHvztSTfSz+J8iNUfjwPrXmLL+qhHbK9QiarUCukknc8opXHPKGajRMLfe/D3C516HqCksactDwYiiZRNUzsMufUxIGR6fFEqvHtE3QFSYRo+/kb1d/azL1Yn3nFic8YkkUb+Z9ZVFJLLs5KXacRiWkMRITk0dAOHFKnLASeuOG4h5RxE2aXiIDQSFDmM5CGj0icNN6G2SwvPOBIR1TaF3faeACJui89hUNeQbl/NsyBM9nPT0nzHJCZZsXY2oqqSEY/Q5h7KwTEmZuNEACCTd6djsXuaM3Y1yFMHJPvhtvlN4N43uTCKu20haxmOIVQ8EQOaTosXxCkPPaWlAV00WNZnHGF4eXEgvwSjr2UbWaAmHJiwn4CrDFIcDRRqfTPLws/eaiKq6JdKYHENr/igKW5qG9WOQ5WEfs1C6Ay0GjUUT6MlKpS/djsM/EVts+MLI6Z1JkeJmtJhgR9FTGKJjMXpPHEgVl4kYh1t7wyadzNhlI4WdLgLpi8hoeBdloCBhXrIHTZ4NA+KBo6J5bAFM8UOkt15LunonfcJv0IQhi/IfDi+iTgsOWhlVReLv1b9jc+p7PJmhW6aLm1+mseibtOd/C1dwOXbv48hCFS3mTu5boC8OI66VJIwKVllfcBgSSfzxiTRk2fA6RAqa82nMXIvbr7sMWxwtJOITMBBBBdIi8OFUM1duH9JomtXewZr8OVwaO4AUjeJ53sAbo12QE0MVXeyPBVgAJFPqKDbMpTm/n06rnUa3BdmZgpriJC2lBZerh9y8KsSB5zeZMFFRsZRQKJV4Zi77S7yceLCO5/95y0AVqKFnXwNOL9/Iu1n68/ynhu/z3YK/8YdPfjjY5obmN0nanGjREPN8XWiJEHfJL/FGfj7FkSbG22ZgGrCehFT92VRRSYiJQZIjKzJxZwdW/xgujo8j9+BjmMjkoJBFjymNg4yltz+VhzdcPcKVpUz3sOjwAbZNmo5sNpGYmoJpvxcBeLrjMu45+UdEXzazrW81y43f5P78t+kKNFGftoekFMdn1QmuQTWQlJKgCozrnU27q46gpR+frYvqzB3kBkbjs3YTMQUQVRGzbCdmDGFNOpFUAzFjGEVIkJPMoM3UPfhMzQiV8djhm3gx9h7bXRXc3/xjvggJWyetZX9lc1oWD6q3Y6oZEIr0JdjfUEao1IFzbgXvKSvpbPQwNaeCGZa93MFvqFQnYqjxo6aYUTNN/DX3f34+OIJ/ieTccMMNvPvuu3z66afk5w+t3LKzs0kkEvh8vmHWnK6uLrKzswfb7NixY1h/XV1dg78d+feRbUe3cblcWK1WJElCkqRjtjnSx7FgNpsxm//NaKwvAbMzldmnvUrztidpCj+IaojqBOfI79F8QCTHfSGHw39BMYYY774XR8ZYdtUNmfSy5YuwO8dStPSK4x7L5EhhwfJPQJWRTJ+vKBlLJPnhP9ayulEZ8VtcM/B6bYJtLdt57toTuOzxTzkcNXGkbhTAW9+ePkiA/k9Da105+etvHrH9GeVUGgwTOP300zltypTB+//CQ/fyxmmXALC0dx+KMmRdiCtjSarDrVqKIUg4rQJN0+nGkdpUqakn0t//KWt7TOy362ThSl+AW7w+AGr2pFLUFYSmPcAeNEAG3ICcqhEsdVL/4T0wUGk8JibIOCI3rw1lvwgDlpwmSSWBhgkBBY0PbZ+jyz4Aa6nA7MMbsHwUITUQwJxUdL0bo2GQ4JR0ezEa7RyYPJGkzY4mGcjOrGfMuC0ogj4WQTFh9hfzR8MuGl2p+DN/MngMj28NJQE9I69DzsAhtiFLzqFBaBpbkkUkMGD0bCfNl8H5+/YSLBpOmFvzF2Ia0HgxGoIs62wkqh7J9FMxyHb2TjuLcOFa0uq6yeoYXg2cLIExCzp0jX1BpZccKrViBNWAOabHFTmlLoKKTnYkxYrHO5nDTMYdbUUTk1gGiZA4SGqOIDLguspM2KhwX0cyOAGTfTa5Cd01OVpN0g2oA0RD1jTMsoG4QbfW9Yo/G9bfeQ0aFvkEAsZqREVDjQM2gQ3GSkp88/hpPIO78t8mJL6Ps99GMHUVAWcGAedtFDa/SshYTYY/zryWMM8uOxdjaLWuzwMoso9N1hN5YYEDBIFRBSbGtwy5jOtddRRUzMeWAiGiOBNGTHISazw4KLaXFvJiEUrZULuQq+sXEHYc5q2ylwh5LiLqOpOnPbuZr9xJwtFOoSNMnaRfu1i+A0FQmT//RSRp5PfGaEowbboeX9neNJ3DPansKMllSVUzsiRSk+Eh2x8iYZA4mJ+JgEa6FKJXcdDb6uYPrUMEx2OOcubOjUOPGoA9i/LUJGMiDeSQwth0PZlAE2QCsu4SixliIDBIchJakjZrNR4Wk2MbgxkBRetgYm0HPmcK+0snIXoTwwmOSWCivYPvPf4wJW3NdGfk8+2f30kg24YcTGJoCGE85OPWiX/kJ1ffTXZDNe+tfZwz1RO5beo/OSrBDVNSJGlIcuKedAq6bHw8+wBBy1GuN0Gj3a0vjiRNZH5gChX2Bk7vO5mzvCfyy1GP0i2Euf3wTUyL6BnPu+wV5CYyyE3qFtpveE/nG96h+lLHw57wm5iXvI3BrPJcy41YqoaLkxoaQ/zCdjuL87byds2pGA6H2dQ+n8TCTKSGIJa6gfYtYbQ0A4uu+7+krIOmadx444288cYbrF+/nuLi4SqrM2fOxGg0snbtWs47T/c9V1dX09LSwvz5+kM2f/58/vCHP9Dd3T2YBbVmzRpcLhcTJkwYbPPee+8N63vNmjWDfZhMJmbOnMnatWtZtWoVoLtV1q5dyw033PAVL8F/HqqqIkoSxQu/TaFyJR3736Gj4zXSUpYyas6lSEelBWd1rwBEHJlFACzybKO9/C2yxp+GLfX4gaJHQ+/v+KnDR3DXq5sHCY6IyqIckYSisqdHI6Hphsb2uImTHtzG0eRmZYmB+69aNuh6+j8FiXiM3f/4IfM7nx10koQ0Cw9ql5MUzJi0GGMmzebWs1ZiteqTTtOBvWx87ineSC8kbNUn4V9U74CjHFwKhQMFf1WO2Hb7ilejCAqveo0UmVSmWW3IgsRtB7cRVi2Dl8stq9zg1SdGf9iFqetYijegiRr+i2U6910+SHBUTSMsQeqAG0avlXxkB/2/ZAH2e+qY5BtFixH6JH27mmVGLnBg2tWHJoI81o3qMaE5jPzw8d+R7uujsNePz24jyx/CkkiypTSfgNXEmC4vLeMm0pd75HnTAI3RRQcRBDCFs0mrX8W2aJxHsl8jklqMP+tXg+cy3XcQa3c/NiUfA/CKMhpNGcNCtYaCSA999hS2ysUEcDCGSm7b8RLZh0Q6M8dyiHQ0VHaP0ZhVJ2GO67ICVrGfsrpU9Oo8R6BfJ3M8nfKcaWi5MmYhTFmsgexIN1neLtzFUf4sXkY/KaCBqJhxBouxxPRvTcIWwj9hA9e0vkZIS+PVrodQj5RuiAwt2ALuSlz+MjRL47D7FjUGAI1IdCqzOzLpNgfYmvs0o2OXUNS/D2+/PoGIqgmDJiELCqf0jefdrKHYLVHVmF+psWr3VMbNHcsaSSdqWd1BHCEftaWjCAtxDhpaSImnMDlUyEFHM5bQa8hGK1Gnbg1rLtSTO/pyoWqgfqI9Vo1V0b/LKUoLb6WfN6hi25JpJFQzg/aM50FUEBOjCCa7sIbcdHi6ccpOphyuR9WGiHNS9pGUbLzj3sO5rtPZk9NJ1DaVqOtMAEKOmdTXTmPMmL1oY9+lwJtCd1cJZnOE7JzaYQRHjFjJaLyIroGYjSPILdpLffM3iPh6+XDK6MHtjZmeYe3Oan6HJ/OHu1JGpfq595k/0uV2UlVYQPgoC9kRS6hky8Ai6dm/O+yvEI1mARphg656GjPo+ySR6Xe0E5WDWA1OUs3Z9MR0UU5P0EuJ1EKzmEWuIHGJYmLJpoexTb0EemxE2/R6aJk9rbz1g0vpLpnJNddeR6JdQogpGCt8POS9lkcn3Yx1X4za9g18p3sySYfEizPKmdxVxISK4QuWlbuKKHJMRJSs1Lv9zBAm0aF28WrBx/yh9WYswnCX7l8bfslnMSs8ceBKaEQ9dVgChYOClrLJj7fwQ6ze8bRYP8bbLZMyrxrJrAyWf/s0sphQ1RBNOLt+M2+N1uVHghUG3vatwNCmX0cxLGP5aKRSu9An01DrI236/wVlHa6//nqef/553nrrLZxO52AMjdvtxmq14na7ufrqq7nllltITU3F5XJx4403Mn/+fObN002Ny5cvZ8KECVx66aXcc889dHZ28otf/ILrr79+cJV97bXX8sgjj3Drrbdy1VVX8cknn/Dyyy+zevXqwbHccsstXH755cyaNYs5c+bwwAMPEA6HufLKrxjE9TXgR/9YS28owf1Xnkiay07ejHPI49ip2Y7M4Xo0ZlcGxQuuOW7fr28q52BLL7esmjeoRfN52F1zmF++tptD/uEk6CcnZPHd04c0hQ41d3LV37fSmRgiN3Yxybs3Lv5f07M5Hnramqjesx6lbh1L/G8O++1J9RzmnbiC2bNnc2jvbkRfH53Vh/h4/ToqmppwhAP0pGaxa/ICAM7v+oiUyOxjhA0CR2UPbEndwt9arSgIbA3DC14ZPSZkqM3MaIwz+pZj4lkA1ndMYCwtg7/HS1U0E0j94LtEoUddRLhzSN1ZFAR6RZX8wcKBwwnSkb/Wk09v7kHKI/oHTMmw8L3Kt1j5xFqu+O2dtLryyfG2M233Nk7dupEJ7T0YNIHD42ZTnXsS5XIFilHD2FtLWjBAY+lkfLl6jMIoV4BRU97B1DqfpCWIoBgo3Ho7L6Su4dnc1SQsk/FnDqWdr2rdjLtCxmDVZ9l0WUEZ+KxsVMrAXKabroBlLTv54Z6XBq9ZU5Ge1ea3R2jIbmdW3RDRjKnHz5Ywx9Mwdy8gau1EMYSpZhz73V1oqUkEVcIY82CQ4pijmcOIC8Cm0lQ2j76WP4zWA9Jz+2S+s7YbRRt6l9zGBkKSniKv2epJiaewqHMRB1IPEDEFAY2O8InMHFAGX1fyHJWp73Ci10AikYsAyNYWLIqNkCHISf6ZaNWtTO7swxHTKOqC98su4eHZWTysZtNh1IvEloXNZGWvopY9g2PxiiHG9syix9hFpzmGw/ssDu+z9OXej2r4TCC/puEONSNpejkOvyONLrdzWBOrLY/6zksQzZ0o4VImmz+k36vRWNDAFO9UZrXoMY0hQ4i4FCctnkZpTzvlqd/iI882Dlnbh5XoAHiPGdyk7SPpbqHI3UJR0f5hvwuymTEbHkJU9O+7vXcyfaPfwp//6WAbhzNALLcE6+GRaf5HYE/6OL11M+/lL0QzCCTLPPzugdtxaBq7iwuJatER+1hEOwuz9G+vHN9FYFw7yf4xQICoFEUVbIQsuYOZiiZDjE7BSzFOShxT6Ym1MsmziNHGIhx7VxOQfaQY0/Ene1hbYOFEk4bho7tGHDezYTdv33oNPRPP4R9jT2SdlkRu13gm9xKuPO9pmj/JxVunIYY0LvokDwa+QB5TBiflfIuEEkEUDNgM+v07knNVJBYwv23WZz8NX4hA7kY6Jw0lskR7LFgzBqxExe8hAelHFRr3Jxzcuvl25OQQRbihYjWnt+xhUfcBfjxfl6IwtI1MRvksbsbCrMlZX9ju68JXSiEXhGNf2X/84x+DQn1HxABfeOGFYWKAR7uRmpubue6661i/fj12u53LL7+cu+66a4QY4A9+8AMOHTpEfn4+v/zlL0eIAT7yyCODYoDTpk3joYceYu7cuV/65L+OFPKG9l6WPbQVdeBDfuMcDz88d+EX7DUSzV39WEwGslJcPPjmFl7a24XLJFAVPEJWNCZ5FF64aQUC0OkNsq+hk8IM92DNp7+8t4O7Ph1uzvdICTb/8gzslpHaKg3tvdz09GZkVeP0SVncdPb8EW3+N6EqCtsfupQZvg8xC0NF6lq0TD5S59MrZHDuJd+ltLSUPe+9zdpn/86m2cuIm8zsnzjyuZgaqGL1nu/TGX8JkPDmryWl9WQghkHoQNaKUVF5dNaNvBf+/NdkRizGde2pzBP0SaI+uRTvR1XYg+C9QiZeqqIeleDmrV9M1+5LOfpr5RdUHnfHuUD0YjfVIcaTqGb9fovRMLLNyj+jI8/jIrOR7/kTiEYbwfoPec8TYNH+XbjDIQRbOqYFN5GwO3EIdqIkeM+0B68wsPrSBFRRw6IZOUUtxXfScHeK0TuG64PdRFzLyQjm0DTqRP0HTWNiz17yaytJ2A8zzq8Lf70VG8/cpgqCRhub8/RioCWRem7cuZpS7+HBWIukwcAnS3+CMZGLUfJiJUZIs6GqbuT0PRh6j62qoRjDSEn7iO2aoBCxtWILjxomcnc0Op0xeuwVWDQ3RkUhv38MBTaJtZPspDXGKeztIpnZRzYW2vsEtEQBdeP/QDReSnZU/35tTtvLqoqbht03DY39o1/m1LYsOmMnIht8yFIU1RwibOtgspzPx4oXz2EzZ9auoTprFMbJGguVC3jTtIOAGMXl9zM/VoTTMw5baxXt9a+wfulS5AHr6UJZ5dbit5ENysAxBYzCdLLDi9g/ppBRrR8RFj/ElXBxStspmDWJgJjG04vmo4oCZZ0+KrM9FDf30VGlT26CprLSUE48aqWrdA2Tu2cgDVh0D6YcJGKIMLdnLorRwStTTsLcexUO9VTqS3RrytTGOPuLzZjjUZ7tuY9E3vC0egDDoVGMbr1dH7OSYK3wDk8Ufcy1TQtYYriQ1hl/IppaTbJpIdtaSshu6SAcbhvcf6xrFqMcE6gP7KUxdJAxUQtJk4sHzzyTb73/BosPldNaNo9yQdfnGWUvoyVcCYDTkEKRYzITUuajaRrrjAewTX+Brppv0BPvo9pdzd6sAAgiK+vGY1fsTJn8Mb0HVnF6fOR71hquZnP3m8O2FZqKmLhnIygJrPNvRLB4iGy8F+QhN5Nt6W/4Tk4a1WHdYlS6uJMfWe/BgIyQNBN8fxlxOU6xYwruFDPBrB0YYqmIqpFIShUoZlIPL0VQLMTcDZiDBaiGKElrD47eqUgJJxHNS5dnI/GMKlRLAEWKYPDEkEzDVeaPBQ0oT05ECiZwOqI4jQEeb7ya6jrdIqiJcE9PPbP6u3nxlJU4uxsJlx/g49GLqdeGXHezkRiHxDxZwWWwIAKFiKhynIMnKpx55hfHcn4VfNn5+9/Syfm/HV+XTs4L6/Zx97uH8BucaIKIAYUHzynltNnjuOHxj3i/Seb0YgO/unABiqLxq5c2U94VRxLgtLJULlo8gTMf2YII/GpFET/7YGQdo8/DqYUizd44lYHh1pvZ6Qp3f3PB/2gq938SW575LQvq/zRs2w61jNXCClJSUphXUoASjbAlprKrrYOI1c7OaSMrBwMURVt56cAPSQ3NwC9/D9nop37JDyjc9hssoaHCoHfk/Y2Nrj3D9j01FGaL1cqMWIzl4QhnhiMIQFjxYJd87LBNon17knEVQRSXRvvNIuYOA7ZNYFRi+Kancqj312iKGRUN8Ug1apPMR7YkV1p60WgcQXIUq40D5rns9Q+9sitFEz9UzRiOmnQ73rsea1JGtKZiO/k3GKShODRFiuLP+xQVDVfrEkyqFRmF3gn/xJ+/YcR1quos5k+WmYRThrsJMjqegOSnFAeKmdGnE5I0zcR98anc3vR3Zu+rJGC0YVJlLMrQh9ZdEqH1BBt7ffOQfZeOOF7N6EZmd/XiD80G96cYhTwcwXyS5mpCkSlkG6t4dcwkTqgNoMojy1YcC93jqvnrtPmcu+k1JradNOL3UrPIaLPIoVQDnqTKWuMHZDTpKe57pv0Yg38WOVHd0vVx1gYu3v9LRCGITQriV9OQVDNxcy8G1YCU9GARfcRUDzFLJ0FPDVmqm4uVw6xSJ2AnzqNCF6p6Eh8bD9Au6VUtS+rq6cw+CYOocIE4jvjBV+jp3cmHp+kTg1u1kWnbyF05TZ97rjnhHBZ0L8ClWrjnJH3fBbX9LKnr4cGTSjEqCeKbdCvVssg+piZnI2oG+gt2UWeuoDhUTFyMsyljPwmjlxWtpyAi8tycUyirfpPW3MW0Z44i3S9zw5o+/nRaOgG7xK8OHKRs/D2oA6nvUr8J50Y7afFrMXjG0abU8XH6PgRDgBZzK7vsnTzZ2o+cuRJv6RoA2spPp6k3lVXh6VR6N9EarkLJKiGSkgKCgKWtAWcoyuJOMHSUE8kaQ1QJsjPPScLpxpw1HqNkxaAKSIk4hH2c7FiOSTCx1niQZmMb8+a/yoEdNxBK9rE7bTdNLv16zuiZQXGomJIxVShylPzaiylThocLyMkwr7c+inZUULRJtLK820J/ejqh9FHIjhQqjO0s2bgFR1c9APXzL0TNOpGbGLJ6JKakcEf2zxHQKAyKqLYOwpKBT1hOBZPpIJeT+JiQ5uBD+VRuMD6EnRAhnMzl+DIpx0MEKyaSGJCJYyKGhTgW/sZ1HAqOx7x1aDGs2g1gEAZ1f+7DyMzew1SNHs8V8/QFxrOf9uFe/WsaSiZgd88jq78Fd/te1HgAQ/pY1HAvhvzZoCkkatcg/PVhxs2a/JXH/Xn4L8n5Evi6SM6GZ59k1zuv02Ar5P3MFajCfy6WJcec4N0fLuebD6+hOvjFcTgZxgTfWZDPt087tsrx/wk4XFdOyxu/xTjpLOacNnLiA9j8+A9Y2K6bWxu0HOSLXiTUUYclayx52Tk07t7Bh08/QVd6Di+edQ2qdOxrfnPzP5kUqmWxdw9OWaY9/gogEErfR9uMB0irW0V6w6rB9leN/jUdJv0DsDxm566OyhHRT6om0pMsIctUR4/mYtdWF0Ut+gqn/2qZmsDNRHrK+CKsdoQ5ZBD5rq2DuNqKmJBRTbp1U4yFUS120rOm8CdDOmp9ELfdyDthyyBJGhwPKsLA/45AE2S8BWvpK3kb1TRUgTWr4kqS1m76S4ZcwU0BJ81EkRQLz0nnEHAPXQ9PuANn550kpQCl/lIm+oYy+fJlE7+Xp7I1eT3W3hgd24drM2XN9vH6UjN1rgnYdy4l1TfSWjPavIXG+GxUjIxLrWC8Og2AGtNbVHWvQEMiw7afH582j6mH/fy+5q9kWg/yUf9tBON6sHh69gcI6c08k3UOB11jSYgq31/fgaNvpAXoaJS5QuyzNGHu1l2IaYZGXp32ECn9c8iL6BPee/nvcdmuOyjOup8zhE08mbyKSN+ZR13rI6oyIEsRvBm7EDSBS+MnYBV6ULQBsmQ8QNNALE5mVxcGqZSkrJ9rcUo3U7Q8Ynv/SaRtG2+fdz6yoLEyPounTe/zqcuHkn7sGkpXNC8nqDoxSrk8vEh3R//89U6kpInGTAPPnuQif+/zeLwRTopNJ2AuJbdfRjDIrJvwIGrSQMIQ5XDblRgzPmBB1EFGLIM9o8azo3j8wClqXFX+LDtM+1gUXMwTM85m+uFyHqvOQxiwBB0tFKepCshxdqU08qf0V0kYouT5S+lyV/P0plk0Xz4QfxlJZeOu0zH6esnqj5FpH02qZxzbLBWESaJpEvbaAyzoUnFqVujYz9rpZcScbqxlBqzWAB3tYwGRcXIui2X9neuw17IjZQPjJ+iZwOu2XY8hEWBt7lp8Zh8AJYESpvdNJzvXR+mYd+g5PJkFVTchakaqhcMUq9mYBCO92x+AnmrCZiO7xxSSRGZuxpnsTeyjLztDL6EApMQNjKmuoeTQThTJwN233E92E7ySHIoZSkxOQU0xUSQ1Mj5azmbniQTjTqSeGKI/gWozIoaSiN1RkjPSUdP0xcrvtR/zChdTwWR+yJ28oZ1PQjGRa2gngJtyQXduLdM+oJMccpKtrC+fCxokp6aCKGCo8iN1RVGdRqS+kdXsj+DacD0XdvRiGHMKy5dI+IwSDCya1q0Nwps3YZl+OUrCzx8nrafQMJXF0XnUWFpYHphPh6EXp2zBdOVsSsb9ZxfX/yU5XwJfB8kJ9ffx1+suH/w7ZkvlqYxzSIrHl94XUZmSqrK/X0I7jql9eqrCdSePZ/lMPW6hob2X7z65idrQsYlOrjnBlfP+zyY3R7D7twuYqVUAsG32g8w74wo0VaWrrYGsvBL2rHudmRuvHmxfef6nlE2aipxIULt9M+89ch9Bu5PnV32HgHP4xDo1UMXlHW9xXtcazNrwyJtg2k/xty0CoKvobbaPehNvxMm5FT/DGc3m2fTVPJehT/52QWVDYyvmz7wtrfHJvOO9jbMKbiYv2ckHHSUUbtBN1dGZCr2XGah96wFQPz/8LaoF+XO6iKKIfM/WSETtRUgoaCb9oylGw6hWO2L+BMoOdvFmmsTvI5MwDjwvCipJFCzHCECPkqC75A1iY97/3DEArAsaeMs3UGnbcQqh1MsGfzttXzddwt847KlkQdeCQeuGXTNyjraRbyQuIyUR5JG0X1KcTGJCRVNAVQREo8a7dgdt8gSS/mVowcnDqqM7zGsIxU8Z/NsltbPEmY84oLyhkaBKeYOaoC7zH8ls576ThuKZJEVjVI/MeG0Xh1xzGRNUmNOn0KLWIfe0MLZHjwmUSHBW6m9IMzTjV7J5pe++wT4Mhh5keajsxOK0X/PDEi9jehaTNZBx9VHeR5x18BbMqZ8QT1vKyp5qtgfdRKN60oRN6iIykLmlodGfsQNVilOkZLAsqbvv/EKEV8xDq/GZFc00ZJ6LQdFJWNTWxhmmLOxJgXj5y2wtcdOQ5SBNdXKGuoMztTNh1NPIoQmMDU0gIPWTtLdwfbCZOlV3s7SlzuedyVlY4iqXrzlA3BAhNzCGOy7IoKSnjROqd/DJ+BW0ZNiZ0hjnjB0hQmk+UlyHaPeN41nNjmDsZ0LWi8zunU3caOUfC/SA59MPfMgorx7/EhNjbBlbSo8llxf/+ABpi36hn3siDEYraBoNYi8b5TCKCumR4ZId/dYOlvr2Erv0DQBGbb8Nq1+XHJBNPmKuZjon/h1FitNaeTJt1VEK+mLM3L+bfcV51E2dgS09yvQZQ4kqUtyFvW8SUtyNs3sWLXN/N/hbUpPYvPkyJFXmnVHvkBioRp4ZzWRx52LSPCITpjwNgM+bQ/mBZWgCLE6WMW6gAGtk0x+Rpn2LHqJs7nkF2ebEMUvCYo3g8+ZgNMZIJi0EA6mUVNcza99eAnYH59z7OPltMXoq+vlXoEkCgjL0AUqWulCKHBj39yP2xFCKnShZFjSH/g0Q/Pq5Gav8iIEvzsL8LCbHeniou5vn5rp40fMXhIGEiOLuPKoKMpmePI/7KlJ537OJcmsdn3iGZ00f4ftm1cTD8//M/PH/2bqS/yOKx//FSDhS07jy/r/w5j2/w9/diSXSz9LoXra6ZnDuRA8NPWG+vbSMRz6q4GCvysQ0ge8uLWPZjFJUVeXxD3Zxz6ddLMkT6Y/ocSeXLijhzLnjsZiGJrCS3HTW/GIVoNeTMhgk/KEoO2taWTpt9P9o6YR/Bwd3bRwkOADzdn6f+oKxdFVuYkHlHwA4Wi9690nPINdW8+if7iQaDgECB8pm8dGSVcP6Xd67mTN7N3BW9zos2rH80iIRbxG9xj7Sk2n8LdnFph4LkOTFotsHW3kkgfNSZNw9mZg13W2YUC2YxBh7QqvYGroMW+E68uKdJAUJa5O+KlIcGj1n26l7827QRr5mmqYhCAL9xijSgqc42DcBpXUOmZKGoulmbU08SitkIB4uaJCZZ5nK3D4volVA0zQ2WjZQIyhkqC5OSE4gRRuyWNRaavGNfw5XZtPgtke6zUy2KixxDsU1ve41siGaTsI0BtHkQ5U8hFL0FHsp2cmP9r7DO45GehyHmdM9Z5DgAFyovcBFiVvpxc079tt4L3o2rziauSZaSbYUQJI0VKA5cjpKzyoMypEsCz17zWzo4iTLabwtNiJE9UlQdTUTEzLZ6jhATjKDJ9Pf5LKusxkoK4StO5ebdz1PXV4+3vgMXAaJj3JNnFs+jQu3+8k0dNOueDHExjJUjRwmpj/GJ8Zidhtn4HRWc57tEhzByaz130jyKILzrfTruCMzgVe0DxYTBTCqRnzWbgqiGXysvMI/xyW4r6WR+rbfoCDiELyDJEdAwOkvxZ9aTpPUw3PCJyAYiQ6UFUnt62PO9h2sWbCc1JidXqeIUQZnNIPn3BvYnbeBn2+NMmaviablK+gTg/xTLOMvCYWC1ihPqxrPKLkoFDA1bCfVkAkSFMgpfJipp0lfVnsfr0/RrT55vrHY47fRkJnP4dRMkgb9vA4UmVhcIfGcbGGCdzb7BiYzLZlKSFJQUTEno+T3dxMXg4MEB8CiWnD2r6a55DZuP0vij219IEg0B8vJT52JKBk44HOTQipJCd6dZWPvaAspQYXzt4QwJfP41CmxqGcjiYxeWub+YcS7cgT5k98jGF9Cd5+PD6aMRjVIqBYbOel1w9op5gCB3C0AeIuHE/v4gUIkVSYiRQYJDkDQoD9YSmio7IgnpYNRRfuwO/oh7RlCe7+Po2c6tkV64HU+IKijMJUqjC/7WN/pqKDrykMn0CCMJc3vo6Sxkfe+fzmnP/AU2NMx7xhZnueLcDTBATDWBjDWDuk4GRqCGBqCn93tS+GUwFYWZCwgaLIwtmo7Fb5WlhYvJThmDC96bh4kOACNmW2Y421UsJezx9tICMcJPh5Yr8eFBK3t1fAfJjlfFv+15HwN7qojqNy8gfceuhdBFDn3Z78lq2QMXXU1HNq0nslLl1Mw4dg+ykgsge0YgcH/L2LL7SezQN1FUpMwDmQVBTUrTmFkpsSbuT8GSw5rDlbg8ffxyplXjLDc/LjpSaKimava3iA30TOijyOIjvkFPeVzkAUZk2bi6tG/pt00vP0VaXGm2RTC0VTm7e0nU+5F0wQEQaMxNou3xctY5nqUiTFdJLDG6kF+yoqgCbT8WKSp4pfIkSEF4Q5JIUcZcqP1iCr/dMdQj9LD+WZOBIu3EhVV15mXBghSPAxmO72Fpfy0eihmaK/UyG5jAwZDnKzsOvr7CkhGPCgoGEwxJk/5CLtd/xA2UsJ6eRpbQyKGRBOFajnneRJsiBWwxXA6McfSEZLxGYG94PsTZtnMzN6Z2GU7rqT+rsyQM0mNPs/tpkuo0Qr4lvA+U5Pp9IROQEPjo/EPslypwk2YyuipZHWuwiDb0VAJOCOMV/bTEVmI01bLUtMEEppCq/opGVInTs7nJ6Me5YC9BkHTSV5eNJOSUBET677JZ5FqaOYMz128FP4liehILaccYwW5aS/yrHkaG3I+HfwAJ43FXHcoznfFCioip7A3fA4eQxsW2x38etpSEok9nNZyGjZFj//ZlLWJ0q5FTOrUq6vbLZU8NvlJToyE+W6PgY+670NGRLMdQoro73fYuYuIffhEIMkyp37wAYdywrSW/oSoqZi/rdAn2Bve9YFlF7IxxJa0dTz4aDeHi0vZMnfIvZerpHK5eB8GYJeaQ4kY5a+chyyopEUyuPfkeVj8j2CODldDz48/wN7SgWxJTcbhfQ3FYCcjuhxxTxudogk1xYStxEAyIWNt3ckMQxUlwRJiBoVGRw1lvuGu10ZnIx9Pvpi5VV1c3y8yLlaEX+yi1djHTwsf55od9wKwdZyFDeMbsQY/ImkuJeo8HbQEM+s1bn3/TuQbK/gixLvHU/6eDSkeRXa4iRcWs2DBywii8oX7pvxd4qMpFxGLSlR4KqhKGa6Of3LbyeQnSzj91UfpeFA+Zh9j1j2ClBwStvvYuJ+0uX/DZhtJLjo7R9PYMBPBF2fVu+8jAj3uFC686zHEnhiGxiCid4hoeRJhzq1dR1/OFN5K1d/xS+QE05I9/Mj65eREjoUsIlhkmWaD/t5m2/tI7+siI9BDUTLK0ootpEciIBoQjDY0VeYvV53H2tRXABUB9fMPcBzMqlFRRRPTGl3M/uHDLJo+5V8+h2Phv+6qL4Gvm+Romsa7D9xNzbZjq0LnjZ/A/PO/yf6P3mPMnPlMWDwyKPJ4CPu8iAYDVofzixv/H4TezsP0tzcQ6Kij++AnnB57F1kTeb5lFs50M+fYNw5rL2siz6sr8Mw4D+lwE7/MGEd79qhj9r2yex0PV90x0nJjsAxlO2SMJ1p2N30fSbztWc9ZvhMJihEuGvtjyqxJgopApyxyY2acUSb95baV5zK//wCqJiIKx37hvTYrtTUunJskomNU9mbdRsyYf8y2ACoazznidBqGXj+3zcg5xe1Q34SGpterGvDxa/EwgtlOW/5Yfl2nWzuCQoTnzVs4MCMPgyNKGr3MYRumcBd59vDAcQSaKaaWsTyvXUpS/HzZgVS/l7jRRNimW4PyWh6gxBseFnsDUKBA4YEGrh13HrJoYLm0mwcMD/P33r8jDhSQTBq9vFf6NKfWXol5oDCnIEZ45NQs0tU6LvpAt3gUOXwUmS24laFn+c68vzM/OJXiWB4P5jxLpU3Xq7mi4xziWgfu5uGB0J+H5e77CNo0vp9nwWesPWabGdEYfiGNLjHO9GSITTbzoPVsVePZSANG7+0Z2/EEi1nUdP7gvlZjM++WvMPCiBm18yJen/wQYZOPExrOo6x7PqrQRVtGIxZRfy7NsRhztu9AS8R4YlU+09qv48Gzhsi6M6Ly/Xd8dGQ24ndEGFO7nlM2VdI2dhxbpg8v4CgNaCwpRz2X2wvnUp7dj6dbT202ymaSUgIEjeXV17Ju1kTaUzxMrPmAbstzAPRn30FJu0iwMUrnSWMG+xIT/WS238LK5pVIDBH0QtJ4N5HBZFMVcTHOa5Nz8CTms/zgenJiAoSGCFl7isQHkw4R1V5AUryD27UBJaiw6yxu+vhkTqy+nr6fD727QgwMbQL2jSJWYTYdl28DVaJi2zfw+4IoDjdTpnyI26Mr8WY/ezKK2Ia0s4HQcplklozzYxOaBMYWgadOmc2hyWcwu7mKdTnr6Lf0M6flDLoc7TSn7mWsbyxlwRlc/vpTGJwazT+SUezDY/taD53MIu/JdE78O4ZYCkGjFyFNDzBObluJV9iLIsbImT1kqTlwYBnivgjLtu1GROOtxSfzwDcHZEKSKlJ7hCK7RH36UToyqsYPqmPkRUERwd0X521CLLS6Wdznp7fjIE95rGTF/UR8QWb0VVCZm06bPYfUuJfUmIPi7gZMNg0h6Wd00QVEMyYTinkpPryHLreLPUWFzGloQs4o5e4ZHqZ17yNqHUe9tI1a6XU+ixVWiRVpQWJ+K567ZZ46WWLHOBGjrHHSfo0pTRqzazSCVnB9Zo3a/L2bOfWm747o89/Bf0nOl8DXTXJAJyNP/eh6YsEvLgbqTM+goGwSS6+6DqPZjJxMYDRb6G6sJ9jfh83l4qO/Pkwk4CcWDOJIS+OqBx7HYPziAOSvE3Iyga+ng57DVYyffQrCMVxl7U3VtO5azZzykbXDdvTm825sOq6gD3eJhzFCC3GLh4pgNhHNzgnLV9C9fSO7Orp4+oLhYo9WLcnynk95uOoOTNoxVl8zLiPo/gmxWi+elSX43m0gWtvPC2kf4DcE+F7XN2hyVdA/+gnGHFbot0v0l/iwSXpNKFU1smRrP7ajNDhkQcKgDa0ct8VX0VO0hdEPSgiqQO1pMzkcHSovERBUbJowLAOq3F7H+8Y8NIPAWLuZqD/BbVh4d2IjGfUtJIQkJlUatKxoiQiCyUZH5lgu6LAyOunhA9sW3lyQw35paEJJ0fq4jV+zjxksYCP38AuahCGBtc/Dd2pe4uctT9ASn83dYy/nowKFlfs3kxkbskZlqm5O4xW+kbiOroFiirmij3XGG/kkeB0NkZOP278mJvnr8hSu8D7LispONgR0rZqC9HbuK36WJ+p/SFKQ+U3+n6m2NvF87V14FCe3jP0Vv+3p5r70XLYbOnEzm8uaphMNqPhMMmVCF97AdGRZJ005xkpmOF5ljzKdgxYPYWc6Va46Gi16Fo8GWBOlFLULtGe7CVh2jhirIqUjKb2Iqsg5zUMaV3vT9hK2erl873cAkYAyJI2hGcJsy/uY/bl6FXFRlbhk9+3YZAe26KtMOLQT2WDEHgqxbtF5mLTFhMwCL57gpCPVgCFehybaUYw5w8aycu9O8noPcNqavcTNAp8sO/41jopmnl50Cp6uOzAmavFEMjmj8jo2Fb9Oc+pBPJEszqy5ms60GLvSnsM3UJg0bplCIPPYUv+pLas5saNt2HPw5MIzkEWJqza9hUET2FDQQn3Bjdz6undYVGHcAH896RCm0F+PO2aARa2PcHHL/WRsqEYTNBBAUPWe0sqC9Jgt+C8oJZpSg9A5lU9rppDj9DNmul7/L63mLMyPrB+Wun0E5SWlNBbl8acLvsuZ+zeRHmhldaEea7ey4gYiJj9rS5/BE/dwUvvJTG+tZG3WFK6wvom7oJV+fw5P55/NDM8mcuQmJESMhqFvTViz4KxYSn+gjKnhcXSY/Xw0/ncsyghgGLgYFftOoLSqlKJPnwU0okYTG5ecxZ0DorlfBc9uCfPAODO70gz8qDLGyrYkNU6JVzIjnNWWpEB18m6ukZmdVWzKMPNu8VhCSiXu3oeJuFcSdZyCMVaOMV6NKqXg9D33hce8Ki3OFNvQN09ptZF3bxIhKQzUitYpq2rV6FtoJDDaTNonSbSSJIeL03Ff9Dqn5vw38Ph/HF8nyfF7e3Gn6Dc15O3H39XJ4UMHMVlt5JdN5IM/P0BP08ginABZJaX0t7eSjI102XwW88//JiXTZ5E95nglKf+zaGuopLt2B1lj55KWM4pDn75OyZZbcaNbDrZP/CVzLxguFrbr7T8za89PR/R1UC2mM+Dkj6WXsn/CHObvXseinWsBfSLq96ST6utDQGPnlAWsX6CryVpFgbtjW1ix4/e4leNU+l70AxLmuWg5c/j9278lKsZpsLQxM1TGG2mfEBcT3Nl8E9Mi4+koeYninrdJT+pKxa+Pn4lgi7JsfyNORb8HcdWKWYzS4k6ncYKK7WAONn8K7ZN8iBmdOP9owNkg0l9qZ1/ePfo5aBpVJoV37UmyZYGL+tajRg5h9sR5ouBq/CEny+wCvwkPWTB+Pa6eguYmQmIMx1GBuUdITshVQMjeT3ZHmN5x8Gr2qq90/yb2NNNhyyBiMBEzG7DEYyzbsYkLO9awgD3sP7yMLcUXkZBifFr6LPN7Zg3ua9BEjD0BXndNHSQ4BhSuMW3gR8I/+GvXq/q1MsaI2NpJ8Q8JXZptFYzNfQM0Pz0tV9GZ1F0eormHupKdRD0H2aWGUYkSFxNM6z+Tn/UtotPSxhjpXkbF9dX6pKICBEEgZptD3HEBU4Ieqj1mZtRUsqBWQ7BVsCH/UxrTViHGd6KoNcji0IRkSpoBjZhJRJXcSHI/SXMJCesMbP43EbQ4Edcq7AF9JXtRN8jhoYmoPKWcak81o22XcVLrqwi1tyGpA5YrMc4zM39N4qgSLifXXEZpnx5V5k3bQ0ndLuoKx5AWWEpjpoGXFjtJSpDe9RBCYhcWzcThUU+McBteuWEL1RkmvvfCM0hmExsWD2lvOTU7jWkp7EnLoSE3D0toPc7+vyOpIhfu/zlJNYND7v0cGqNnJ66suB6DauSNyQ8cdQSB3ryH0STd4nb+7nVETGbeGxDNXFLfTFmrroPT6Urlzem6u27l3g/IC8TYm7aX3WOv4MItadTmmZhTEyNuEPjbKWYyWr897FyCKZchaAqOoyZXq/Rz5lY3sSpsYFrv07hyuxFMKQhCEknoI9JrpN5xAd3LP+KzsPaPJeUvGfT7a3jikotZP3YuD9/3GzQE6goKuecynUyLqsqVm1ezIftD/GY/kmLiyp13kDBE+ecsXS34lNZT2Dn6RGqzRpHj7+Ib7e9zf9kVALg1L3/iBqJYsWp+9sXTyCKPe8OZmCK6tT5pHouUbENUw3ikDM7PsTFJ0HV7DlUsYcpqP5lNhwA9Im31mVfSM2YOuz0Ch7LcfB7mlO8jJejnw/lL9A2qDKIBZ88jmKN78Wb/Cg0Bc3Qf5ugejAndwnTEYvZVYE5oXP2RijMKTec6mV80MoZIC0p4HjHT9G037tQu5LgJk3UkyQToPnwmF1/+4FcawxfhvyTnS+DrIDn+vi7qnvouJcHdGG85gMOVcty2nfW11O7YQt64CXiyc/nkH3+h+cBIQa2j4UhJJWfseJr27SEZH/5AXXbPw2QUFh9nz5EIB3201x+k9DhaMp/FwQ2vM/6TawZjZ46FqGbikGshwoSzkcx2op3VzKv547A2b8sL2CtMw9JxmF6LjWfO+97gb5e8/meyu9vYP2E2a044mwU715LT08Zrpw9l+TxRfQcrOz/U/xCNoA5lDtRLadxZNBe3OZ2ixjReTfuYfuPw+kMANsXCyzX3IiHRM+kWptcNKa2uy5iNImks69TjGVRNQNYsmMQoB8ucdGcMr3+W2C9Q9Fcjmqix/cQfE1GH7sFLzigtR1m8Uz0+XNYQTR26K+t9nDgH1r5hIcofC7rI667HK4ZJUYcCiLVkFMFoRTWn8umsdE7ce5CnZ61ES0qsqNzGpLZqvGPcPDb2omFjE1SVJR076FLTyOoKcsWH75Dq7cSYiKKKApLDgT9/PHWu0whYIG7p4/3ilzEjsKB7wWA/BaqNtlgfT4r65JYlBMgWg6w0bOca4VW2xk9hj1e/jwtT7qLXFuJV+9kUdrkIWWQWuV9jdp+Xl/ruP2p0MmPT+/lL7haqU0zYtA6SiXLyIzMYLfXxQIeu26MCuyxmipNJLstfSKvYPOwc49aZCEqI9FAHYUMYVVB1a8Bn4Imk0edUEJUQcdtMVCkFQUuiCQbitjnI5rFIiWZSO38xuM+jVWY+MZ85+PdhezU7ModKNGhiGoLaR0nvNLJChWwtegswoZhKkBJVTO5YwqSu83BHB8pvCDKCZmDreAtrp+pxPi7/J5j9/xjsM1cxsb/ob4PlGI5Ghr+PG198ir70dKREksUV63jowu+wbsqSwTaF9bcRMbYwu+UMZrYt52NrAiMqYvY7VGRvIs83FqvsoC59D2N6Z9CUUo4sJciOn0tL3oksaNpMv7KLuJSgPe9Gut26ZSk15Gf64Vp2F47DZ3OS17KetESQBW0KrbZW1o3xEB4IVkdTEbQ4ltBaHL6XAEiaSpBNxYRSLtfPTUvi7rkfU+wgcetsFjZdxYTWbsalZ3DbTI2ivo2sajPQbdmChUaWvWfHd6ORhGNILDCOieBrJ/DI3BXUFgxXj/8sMgP9LCt/lzX5ukVvdvPpzGzXM8ZennIP/fY2XAkXWcoqtpYuQBMH3j81hsP3IgnLJBK2WQiKD2f/0yPinY6H5W43uYZuUiSNzg9OJr+2lmnNnRiV4a7vI3/12pwoRgObps3i7YX5zDy4hooiH9/5IExaQOPBi2+lzf5XBDUMiP9yzMzRcEQ0Fh3SGN2hoQqwoFJDzRHp/b6CZEuiAXEs/Cj6IF5bOrO1rdzIn5A+c+wYZmooI4ydeWzmdS7A6BdY1ZfJSdf84N8e59H4L8n5Evg6SE4yEafrzqnkax36MW5uwOX5cmUR5GSSx66+eJC8jJo0BbsnlZ6WJk759g3kjBk76Aryd3fx1C3XISeHx5+Mnb8Yo8lM2aITSc3Px5mqW5M0TWPHW6/SVV+LxxHH5LYzruohUgbSVbbmXML87z56jDElEASRtoZyRj2/ZMTvAJ8oM2glm8uk9475+xF8nJyO4A2yvy97MFX+9VMvob5o/LB25niUuHlknROzpvD+7muYED62BeyN9CX8ytl4zN+OhqRp/CZ8IrMOX0TC1onoupUiXz8yIoaBl9YnufAoAZKqEa+ST6axkbBFYtsMD0ds0JoKnn9K2HfoLMY7Oo+9BT8fPI6MxkMpMZTjvGGTkPjLQJWYqBDnzpJn8QQX4Qq00iuFSVeOJjkxBKMFEPnbotORRJm4YGFZ7TbGtHcOtmtKyWDL2CmcsfsTukwZnL35Y2omT8AWFxEsLgKSbvmyB4vJbfVjjQVozZ9BZ+ZWTNqxtYWuSO7gPOUS+tBXmulCiDNMlczTtnGaqKdCPxH4A4nIBNxSK35FJ3CjMh+kIjud53LG8fGep3ir916S2tB9zUpr5428N6i0NJOU9Ky0+U2rqMzayIv9h8lL9NIhSfzd4+IllxOPojBfHsUagx9ZOo4F77MYkq0hYRqLJdmKqn2xFD3A8lCY6V3TqBOHAiZlGnmreM/n7AUOaxEWWaA3qT+L2ck7WFLpJGwRaMo0sm7KkIhhaefD+BI7RvQhaRpGDfIUN3uzvkHcvuiYpOezKGrfT1j+I6Iqcenu2/GZHTxniDI+KbFQ6+elaXcOa7+y4gY6nQ3sHKW/u6c1n0aHrYt9GUPnWBS4mJ2TjirqqMgs3PtrajJaQNADdj0JD+Up5RzIsQMSktyJIXl4cJdA6neJOxaNGK8h3kBK16/1Nmk3MKVtKjvHHlViI9DM4v37yfUX02fejiPvbE7MeA5T+kaSGHgo/Ah7HANZcQNFbDlal0xTMcZrEMVMlu9/gfLUckLGEGgC395+H2ZNRkPgQOYONo5+ZdjYZEM2Brlz2LaEZTKm2MEvvA/HgwicXDOTmYcCpPRXUpMncEK5RtgMry4SKerS2DFOoLzo682O/dYnCnNqNOpyBeZXahg+w5M0UaPtPpUW8yju5hcEBM+IPmZr27iMvyOh0EIh1ZTxhnDhMY/3prubeTOW/0fP4b8k50vg63JX7VvzPNM267U9to//KXO/8bMv2GMItTu20N3UwOgZc8gaXXrcUhoAvS1NVHz6CbveGRkkdgQmq42UnDzi4RD+rnZOza1hgrv7mG332hZimPdtJp+gxyAkE3Ga757PGKV+sI1XtfPPhhkIskKWNYjJqNEQSEUVJeJ5RaRYQ5wnrCVbGK4F8X77WA75h9cvaSqbwStLzkXQVN7adyNXTL6LfsOxA6mLEj18uOOKEe6pN+x2fp2ZhkEwkFTlY9Z0SZUVTg5HmBxP0Jy1kCvOvo/eRzZgCRYRzPmQMu/DR8+Fg9C04fPKvnQn5kf08fm+IWPbIWJq0D9G0Qwj+0p+RtQ6dI6HrBFWmwVSEJiSZWND15AIn8Ms8se4hTGCymOZL7PJuYcZjjNJb/RgiHvpFaOkq0OEQJPjxE0CFtXEmrJZ1GfmU9zTzopDIydIT78XX2oKlmiUpMmMIh37g2lQTSCbkU3HTzvNEiX+ESmmG90iWSL2cUqynEsOvUzBbC8a0JSYxHv9R7RIhoqaZhpquSD9ViojJ/FJ4AZARBOSFBnNlNgTBIwBflJ4Pz8OSazwlfN45kz+bO1gsZxHRqyGToPEVutwsjtKNhCO5ZMS8VCXue+44z4aGhIgIfDFEvdH8NP+EBf4+/m7toouYcgylxLpYKnnLb6Zmw3HsWje68uiLLiPMwt0C4gjMZ7GMbcNNVDjmKP7cAfXQLxqMMD5nIM/4J0JjyJLxxin5Wp6Mk887niz/X1Mbq6gzvIaIUs/pT0zObnuMh4uVjlN3MCsZDfelgt5Y/K9dA9YQhYfXszE1vPpt7bz8rS7j9u3K+7i1J2z+ahsHsZEJT05w7839qSdEzpOQBblQSvJ0UiaxjBVnc8k/8uMCVmwqRlkxs8gbC+nxTyO+12PDbbtyX8CviA43pqQ+VFbNb8r1oPhpWQbdt8rmKO7B1wzVhhQF9YEO4IWHt6BBoZIAdcc+BFphkY0JNpJ45kZv0KRjp1V9UXI8Y9hYdM5HPZUogpGQuYolVkf/Et9fVlkeTW6UoZ/tX7xgoImgDOi0esSeG6pSEeq3mZepUrQCj9+XUU12hFVFbMSoe9UIxXzihjd0oqWqaAYRNZLJ/FMyuXHOuxXhiUps6/AhafsPxtS8V+S8yXwdcbkbPvzd5nX9SIAe+wnYJh1OeMXnImmqex94360cC+zLrsLo8n8BT0NR29nC1a7C5tdH68ginQ3NXBg7Yfs/2j15+67JLOBWWltw7Z1Rh1kW4eIg6IJ7BhzM3hbmdv3KuJRZn9Vg+ebptEV+/yMLs1sIcMZJRQU0JIy8aOE8OoKx+GxWikcP4HfZOsCape3vcnddffTaMmjYtLl9AlWPrGN55yW1yDUzZhICyXRw9jUODEBOPHndHUfINT4Kd/I8hxzDCcHVVrMMt/v9zH/rLcwlelicIFQO61/fgdX3wQUY4hgzq+Y1D28KGBCNRNTXbgMQynlzTk2OhsduN4cqXmjWEQ2zH1wWBxFyBriSQ/EoxIXYeJGLPT27WOf9TABDc61rwRgn6GRbcYaZEFh3dy5nLZ1P6DRQ4KMoyrBa3KCbmuErKR+vh9NmMn8ugqciRjj5VwWyuP50Lif1oHCksdC0BgkYAwMqvd+FvXOeupd9cztm8XCcCnpGuxQ+3hM1QUlCwQvPyh/haldFRSf2oMqCLwfvZKWgH4ukhBF0axYjB3Ekjkj+jcZ+6kqXE+1s4Zmi27pvDmUxxn9O7CrKjFR4NGcUzhIDTXGzxIIkSMG/RTDZPrlgyjGfAQliKj6EYDJ7UuozWilP2U0ktKPMVaJQBLhGDpJV/gCTIvHGZdI8IGnmIediQF6pnGFMYUf1Byg1+jm5cByuq1DWXK2aB+3Wv/JTXOv4x3zWeT2/wlVTdKreTDH9lCiZvB28240YErx8CxAVbCStEzAHN09bPt4fw6Tmi5BMaiY4xY2l7zGYc/w9ObBa6hkkZD0YOGYfRGOaC+uRArz2rLYl7KfdpduPVp18GbsSQ9/cxrYZLiFoObko64/EjVEmJzzE5ToOPy+s+iXiwB4t+xRWj3D3wOzbCNuiCBoAgvbZiFHDrJt7JCL3B134zcPuYJL/aXUumoHVwsqRjTJSabioo+mY57PZ+FP/z6CYQZxkwhqDGOinqS5DHNkG5poI2GdNthWlL2okgt3zx8xxcqP3+lnoUHW4Qs4p20Ro81bUBFpjM/jqal3YxWjeC1enEknQeMQ+T+x7mL25X5C2BxiSnMajphEfvgMLOooWjyV5PvHkZTiWGQbGhpG1UzcEKR9zEY+yQrRlbYKm/9NLJHNX36cQDoGvMjMaRQ4kAths8YJ5QKp8Ql0p0xlcs0marKbCFlhUaPK3EMaEcHGzglTyUp003J6Cp1eA8WF+6hNPRF3pYgjHmXU+N3ssMyhmAaqKeNF4dhK859FQXeUc979I/WF41l98gVfap+L33yCyxaewPQLL/rixl8B/yU5XwJfF8kJyQrbWg8z/ulTB91Wx0JCk9ibvpLx37yHqk+eQWzdDsUnMuus62go3wZAyaR57HjsGub2vkYvHtLxAbqWTIexgNRrXic9W08pbty3m73vv83JV38PV0YmG/75N6Q9TzDG0oYIZA2QmW1jf0QiIlBd00WnIuHoqGOau41J7k4yLMc35T9ZPxNvwkZrdiE1JRMJu1LITEunXhNQYjE6M/PJ7m7lorefxCQPn1jCVjt10xby0dQThm3PU4Ns2nwuVvWLV9kNBokL8nNJHMe65VCsXNGXQ3GyjxNP+QViVhFabgmvbrgdLbYbQXJS0i+Qd0DP0Gqb/BBTaj/Fiv7h7ndaSA3GSGp6cKpRSNBgLcFvN9E/pof0+42YmkdaRXafcB1+cUh9VxY0yud+ypqqOViBN3DiOI6S9QvOSqocSbaPHY85qrDywBbiYpygYiL96PNUktTbvYw+KsMFQNDg4vgizJqJuJBgl6GeGsPwZ85sUngj60NiUgxN0BA0gfRYOvO65xGVokQNUabHc7k4+SGNwU6se23s0Iq5d8G38OPAnQzzzAe/xWpOYJukkFfSxwvyN+nxrkRShlbdZiFAXHNR4P6UtshU1ORQIKXBGGLDuL9z0DnkatQ0jdu8MnekHjs70GqZTZdtOghGzrRUsrl1HcoA6VaMJfgyrgdErME1mKO7UKQMjPFqBI69Gr/e6+PiQJBdFguyezwrOobi3/ySjfVFEzFFFRZ3VeJQonyYN5OK8kkEUlIRkgk0owljLMhtlr/xyqRppKYO1ZTbwIn0y3BCdT8X9unZVTuEYq4uOn782uyIwjndGTR3/AoBAV/KQQTFiDugu29VVEJmL/sm/JJDli+fQTnv8DKmta6k0qPQ5SpnUaIZLx4yAvkQKeGslN+Qayrn8a4XUAcUsjU0atN3saXoTXICJZzXNoFQeD5vTXyIDlf9yGN0zSM3kkulu4rK1ENfemzHg0EDWYCkaTS+zJ9hDX6A3f/6iHiTQNp1xO0LBoOrvwxSEh4CR8T+BAVNNbL44C+YFPWQYagjoGQR15y8NuYFgin7OO3waYiIiK4NvJ7aw9hEks5kARcc1JMnREZ+A9aVPEd11oBVVYPFDRcysXshVgFOdopEjRI/niizK1MkteNniKo+nph9MaboXiRTHqIhFb9hFFHnGYCCgwhOgvSRThEN1AjHLg0zSmukRSjGrgWZpexgg+H42Xf/CgraGphRvo1YylIWVYZJqC3E4msJW+wcLJtFRl8HRa11KKIBk5xAFUR2Tl2IUU4yvu4A9liCRdd8hzmnnP7FB/sK+C/J+RL4OkhOZzzJqr21dMaTvDomjcmPT8MsfDVJ7cNCLgVa+5duvz1tFao1ldLW10nHxx77YhK2bOb1vDKi7S7nUsTlt3NnRT1dmoioqaSEA5Q2VZHfUcMVOZ/iMurxEZX+DLb0FpLvDFIZK6Alq5Tnl3/jS41pRaCTtY5MZFHk5lGZPN3eh1ce/sE3qwnW7L6GsZHm4/QyAEEkiMqpBaMIHCNsxCXbeab2TtKkV3Ebn0X+zh4MuaNpaPwzjY1DQc+u9gXklH8HgO6ij2nO+JhzDuorwKhoYft8Owt29GNKDkykImyam4psFHG9KuH4REKVRJoKTie3YzOdWXNoKF45zKelofFKdpTmmL5tBUZ+phoQRcOIGlMv5ca5b1IKkqKiiiInVe1hTE8bDc4GPL4SUo8+V1WhxuplvJyKqgx9ZMco2WSERrODbpyyg2UWDw5Jo1kN4TcEeKToCTrMwzMjzuqO40bmmUw95ufcxhirNth5Kn0Fb40eTkIB/rbmTubOr8WZHkPTBD5OnEWV7xLEASVnRYyR6nkHf/8FSEKUp2b8Fk+0gIWN55IazaI/pY43xzw5mHWUFsqj39bBfGEysfh29lhGuidyFSM3Zwq0k4fXZmaCv5MKWxaVTYfYafnyteBcqsR13l4WRKOUJGU2ZkwhUdaOvWcK/e46tM4ipnXWkBcbHnvRas5g3zQ75R+eTMzjQgr5URxuxESMX5n+zCujTsCRX4/JMFT3R5aN2A9msiioq94qmoEfqD9gY/EbXNxnQjF38Lxbt4J+qyMLZ9NQ/FbSEKIr9SBGRcIZycMWHbK2pbneZXvRW3xs//yCpGbZxvyWU5nXOxqfks8bORoX2P9JR0h3t6XHRQTvIjpzXuG7yc281fsAcUlFMPgxxVMwEEXGikiSC9N+yLv+n9AhWtkw+kVaPdUDxzAzt2cuWeF8MqI2vAYBIgd4ZfruYVlsx0OxPwdDPExbSoyIMYEn7hmsH/XvIGGZQjD1ar0gcrwZTbJiCW1g/uEQJf4iNDTKU8qp9dSS9M3k0trz8MjDa5ntSqlk1/i/MKVvCqWBUmRJIS2ll2olgyyvgF8wUtqxYNg+Giofj/4b9ZmfFTI0c822OzBoBjINAnOtGqJkZG1qD7fNyEaWvlxx2f8tpAaDXP7ifUQtVmzRCJJmZEK4GC19GhFvE20uA3J843H3N1gWoCQq0FQ/Ruc5nH/bCnKLj68b9q/gvyTnS+DrIDmqpvGtfbWs80WwSSKPOoIUhHtxpueT8eJp2IQ420p/SMH882jb+xEzD/wWaZhLSBjmIjoa5eZphFylGOI+BE1mZnDdlx5XuXka5jPuoicEv2zooCJvZCaCIxrirE0vM6NtP82uMuJp2bw+YwkJg5GTQ728kVF0zL6XpDixSyLv9Y7MYvospodqOL93PSXBeuZ792DREviVUlZbT+LExIdoUgvdBiPT47plRwXWW638IjOdoHgUSdDgV4e/TViK863YU4CKUWwnMv13JJeeTWXVbYT9O8g9cB3O7lnDxpAw+WmeeQeFVX5KovpqvCnfSn2JnRRfguIaBYsWo67YRnemmWQvjLrdiCALHJx4Jb3pk9GEITejoCTQJN21VG6L875paPX5PHbey3iZA/Y6bm+9kVTZRbuxh4/Me3hm/qnIsokLdq3DdlThvrW5a5nTsgynQQNFAUkCVaXBEkRxtnNb+9Xsl5qwYMJv9PNY7otETbqVTtDgrIbL8BNh/ehXh1/7DpnrXhLYlDWPPVnjuPHgq8geBXtPkvcLF/K3SStH3K9vt37AnJRalqVtZQ2LqfWvgujwZ8eUs5UxvhCHoqcguDby54nDj6s/zQYEHESc8zBHthN2ncvk3hdplvR4iRKzSkN8iLxdmx7FuuVWEqFMDO4WDJJCweJH6OgrprarntedDj4PJZqV0+Imru0YHiT69pwx2C2+YduCoQxO21uHWUvSas5iX/YojPmtyP4c9m2bSdLhwNjXSTItG2SZ28RHMYpD9/iV6VNJdbbR0TGGU5pqyEr202TNoSjawWOBB9AieuHQMusaMm2bsKoSm/zfI6QO6YYcyv2ICe16YGbA3gSGKC7/0Mq9ecztrFIqyZZlCpIy96SmEPe7WNabSaXmpto1mkndWZyS+garfb9AwcRfXTEeS7mbj+O6yGiaEkXsWUGrq4b6jG0sqb+MljQJ1RCkqMvGLPvLHFJHc6bteTKMDXQmxvFavy4o2JjzMQljiJRECpZoJi7/OI7EXvlSd6MlQmwYvYWkmEQRFApDhZQESkiKSWRRxmfpp7jbQsKWgTGRYMbuPYxqaWHT6RfS6I6xNWsr/ZavXtOpwFtGR1oa/a7R2IOrcYVjBK0CGVETZsXM/O75g23fK3iPqCFKsuFabuwq5bNVjeJilIfT+jgh81OyfKmDKtdHIAsKu9M3s7j2u7hi6Xw49u/0uRoIGeNox7EeXrjvd6RGXeQZBWbZ9eOpqPwm71EWFfrodqTxd+06guLx55+Szmoas0rRjnKHT9L2cw1/5gm+RysF+AU9Zi473klcMpOQjISFke+IWYsRFyzk+3q4eduzHEgvoNY2muk7PwQ0rLEIfZ4M0r3dQ0sywc0Y6wKmZE7GKAj0ySrbwwrRZD1y8K2BNlZEQxGisQiDeaTFacklKpMWLTvuOf4r+C/J+RL4utxVPS9ew5Wu09llLSHbZOStGWMotJpprtxNNOxj/Kwhc2LF5tUED67GHGjEvuwnxEN+IgffRiqciz2jEJs7A6szhfTsUbQnFX5R20p9OM7NHhN5+15mXtVdnzuWfYv+wpi5Z/Lsu6t5P6Kwr6CUuPHzS0Z8Z8MbWMwWds05iS3HCAK8Kc9DugTrfBHuiW+lwJMBkpFQ2Mv1kQI+TI6M2RllMfHq4YcpOPT8MHtGQLCz9P9j7y8D67iu9X/8M3SYxbLQliyDzBBDmLHhNg0Ub1NIc0u3vTdtU7ptU26KaVNKCknbYJOG0XbimBklixmODuPA/r84imRFcuJ7e/P//l7k8RvrzMyemT0ze6+91rOeVVNBTs6jCBmBiQXcEonxgtvJUZttgpgJ4DVcNOfgq+EWKg0JMEmL+Tj/6zmiI9vZfuwzqFJhwvcOrKFy/8emXUvHui8SC9u5tGtSBG7XYh+RwHEcGAvkGIgABO5RcG1XiBXPYufC2wqeGysH8lQ+VY4MP61UIF3wWP0WN0Gb4LPNowTjKRoGD7CzeCcO084l9Xa+6fw6Z7bsZN7AZBaKXZK5r/YBLmy9GrcGkp5DaIXzdMsZ9ldu4N6j38WnyAwqYT7c8FUseapL/41Ysq+K0oFyHq65Ztq2pkgXR4O1U35zk+N0WzsVUpSrpGdopoWXjXPYlXgP9txkfSeHlqO48vucmT3CX4d/gyGcPLpqP0dnzULOd+GO/h1LDZH2XU7OvWa8Y03c0ftR8z3YcoUwR5Ps4ZPWAnJVLwOF5DUz52L3yzdjaVF8o+tBsmi47D9QHQliw7XYe9tISQZ77TZOy2Sp03V2OuzcHfDTkNf53eB0cv0/Zq/CU1XgrJg5N7lEOa7iQigmkSpGWDI+7+RxIwfeRcuAHcvuwNHXTnZ8YfDJ/K8ptk2GdXvsZexYGCTesoL3J/+ChcSTtSu4tGsHTzovpqNjqk7MGxH1HyKJRNVxRk3G2U/G1U8oXDDQB73tjNR/ly+PhakyThwCS5pF3DvyWyws/lgywr3KD/g7BeNVEzr+obMRQFugjcZoA9sb7BiyYG1LniWuxzjV94cp7f0jchu9udVgG2Ak2IotH8QXnY90XD22tKsXf2ITcxynsKmolzfCITQUIZOSp1a7VgyDyoEhequqMCSDnJJD97awIXAM1VK5pP88FNPDcquWTns/95b+g7Q22e+XHfwMzyyvZMyxDW/0QSrTlaTVNCtGVuA/LlQK8HLFywxZXrJ972VlTufMRDUyeSyOHwtNfu5NcpHiosj7Cro+3WPYFxplW4WKlX6R1833ib4P3ICpluJMPjfBERLIXL3/K5Qmg6z3KBSrk4bKbcFv0xy5EmdsFoaawaKItF3i6eU21h/rJbpwgLp2HefObSTtEnY9R0/9Amb1tGAz0hiOAAQdNC7bja76ONZ+CgynUGNhJMMA02QkVELp6ADy+DQvgM6qBiqHerDrU5/HNEg2NNf5LLQHafJPLZXSnbfYmzYxrSSWlUJWSmdMklGArDrIGRcaLLv0fdO2/yt4x8g5CbwdRo6I9WH+aBVpFVasfZDEuFvyY9Ul1Dnt7Imn6ckWvBRNbgc2WcISUG7XWOBxENJUFnmc/Kx7GCGgwW3nm239hZczcxxvRQhWWjmuIUPQZSfu81A90oZt+z0UXf5tigMBRpI5DrS1cUdS0BecyuWo0+BURrmmppacYuc9LSe3itqz0Ef53WtnVBadwLv/RLbpUuKGyfP9fcQSo7xvx9dxd28sbLf7IVfw+lxT0cBRx0lkvQh4/8i7uCG8nHL7v5G2rwNnCOdHf4ns8mIYWV7YsAwVHXuiGsmyUbXrsyhGof+zvg7C9Y8TCR0mHSvhqkOTKbKGDDszpTg3K2QXW8QulWC3QtU9gswKE8cuGUlI7Fj2OeL+E2txPFs5yt60G0kVPBBJUu6dxWfW9bHJW+BYnN6ym6bBHrzOMQ6uLKF1dCnnHdo+xeiTPAkeLHmWi45egcumIGfTWI7CPQxgsbn+EdZ3Xc65Y6fy9WUFhVpvNkRVz9VsFyV45n670MW6TGlMZSy5nsHMeTADj+CNKJJSrNU6KZbTSAhu4iE8Ismj6VvQE5NlUnP2MH2lW7lRPMgyI8KWxPXsTF3LqBfuuih4wlRnNddKcOgbU347M5Xh7OI6BrsXMXf5YxO/dzz3X4yEtjCmCOpaxguF2hPMvujLKLY0Od1F7PDpyIpAshqo5yFWRQ6Qk0ATk3e739PI4LLohHe0b/PHcAQ7CR+9ECvvxl2xl9LFD2H3T+UxxbtX0rvjfYSDW0BRcLUdID17IUgSje7t3JCaWqqlxVGL28owKz/MppLFDJeqXH1wF12OCm4u+yEX7FSwG1MXF5aUJxY8SLe3gyVdV83YZ4fnbqepdTnyeHr/w80/5HOZXVycmpk7tze3nlci/0FCyfPJsg+wzVrKRtZMbC+KzUXOTKo0h4oU7q9WuWBPjirbXi4PfQ2ADxY38JWRBPnsPJ6PfRoA3X0ILVVIFqix7WKe8yWejX0OQ0kTKdnBaRs2YlTN4rU5kwrbAVPDkGSScg7ZcBAML0cWKml3DxnnAN54A6azk5QjiZj5tZmCR2sfxbKfwUc3Xs79awcYdjxN48gYq0ZXnfCYLbKXdncn+tipCNPDu41hapO1yEoaa9xbI8bzsvqdW3jBque7znI2Bp9hg28HMVuMZdFFVCZqiNgiHAkcoSnWRMQWYU/xHgAi7m8iIkGQJYRbxa3cgyO9ebxtjQ9u/zoOw02jXWaBc9J4etXaxCOV21nX9kFGS/ZTNLIIVZ/qgRFCIIxekBzIkoKZb0VIGg57EyGrhe74y7zR4Pqfwu61k0sUjB5JLUexLSHpL8FuKVznmnxf9phbWaoUKt1nLcELcQNz/Ow2CfTxb69YlYibgiUuBVWCg8aLXH/ndLX7fwXvGDkngbdFDPBYP0/8+CWuKfosrxU188XGz9L2LxRXeyMUy8SVy5Jwut965xPgJ3VFvOeRCyA+nmkla3Re+BPucS7l7zGLMX1ypXitD24LZHjZPps10d3MfnAGhrxqB+O4VYEjAMVzIdoNycFpu7erKn/ye3nS4yY9rvuzMDWHg662aXncilD48NAV1OUqWZVxUWL7EuY1f8a26DT6RnvYduAnKPmXcauFmjihjosoaZ28RkOLs3fhz/BGMzj1PGvDkyTJ12tRDXtthH8zGTqIvtfAflTCuWtyMIp5q9m5Yrpq8+s4VjTEw8KPZAlu1Pv5mDaPtJblq1VdFMXjPLloLelx7Z9ia5jASI6zj+xCRhCw3PiEk9VGA9+t+BN7/Pu4sOUy3JoNJRXHHM+kS5genm24F4CyRB1D3k4Asv1Xo8cKg7xsG0Z29HNmXrA/N4chMfW9XqgMsETtZ0A4CZsBhiwPiiQol+PcrPyTRjGAR45Sxijtopp/RL+CLTfZN6nAUYrKH6NBymOP+jgYv5xsqpBF9OgpbtyOv9OZexZFKaWv8lsT6cCyGaWo79Zp/faduIshbyV/08N8tjyJ6orTu/vd/MG1k7C7n6poE5cenhSL3FX7CO9esgnVMTXtXQhBOhvEMjUczhi5vBsz68IX6J1YYfZv/RBPug7iiM+iOFVFMFOONxciXbKPpWv+jOqME+9excCO9yEMB2O+BKarQE72HNlFak4zQrNRF9hOiS9HLphFteU4f0cHDlHg3aVkBxtWVhDtWMf1IwVF3zvXnMOvbJ+kPGxRNuBicWcef9okWrQLQ0uRs+xUhpdSVPcYTWIvr41+Aik+uXKO2yP4cpOioi2lG3i/4y7GVJmn3S4ysoxNCBblcvT0f5HixDzq3Bu4xHsnv+fddDM5/tSLPMmhQtjA0KB+wV0kVTuRnR9CIc+Hy25iVJH44+jduHUn14U+z57c6RxNThphbnmUUypu4W/eYkqOFLS1so5BkHZw2qZNiIW12MOCzVUlDJeXo+Z9eOONqMaJxyzZvomh4FtPRfuD+2kJtKDYzsbMFwje5/aeO81zA+C3vGwzKnnNmvwGLnC8zJx4M+50NbpioY3z2143ciKBA1z2+G9onX0mqxdew0bfThak5zCghtlO97RzPF31Iv0lH0DeVsp/bfkzHfNK2Foxn458FerSTbgSk2nkH976PTTLzmy7zCLnVC+REIJ+0YeCg3K5mPaszqhpMZQPYxLCkHVUYUeaIYFBCIGwYggrgmX0I6sVWPl2kCQczkYkOU0u70DR6hDCQnq9VIwwQeQAAZILyxxCkn2YioKQBTbTwQKHTOM4B+63vke4T5X59dgZzMGHjEx/3qLSNnUBZQmBfNxCxzIN9psbueRHX33TZ/s/xTtGzkng7TByop0tvPaL32PkijnH/zPQMpy66s/0OwqelLMThzk9UMS2nI2nFP+UUIxsFQioM0ExTa7Z+RIlmShr5lXyu7Sbl+qXzLjvifCNhkqWy0lW7vklbP/NjPuYF/2AjkU38fJYAvJpbvrradjzb+TaSPxPVw4xWeagpvHJilL0N6z056Xrubt3MYeVxXTY++i099Pu6OUL/R/EadmRkXEpz+GU/8DBVWWkgiqmmUU30qjHVR9W8h4aXv75lLbHAvexOHvftOsxhIY6Tgjfmw9he3gyLKeXCSyfwN46+Sxa5lxNb/XZUwYJgGN2g8dPcWKls1jH8hTbdR7JhZCQ+NW8PaidhZTu3kAJ/1xcIC2u6jzMiu5Cum6tWcK5+iIkJLKWzg2Nt5O2xbmw5SLcmgslEcF0+0GWkcxq/l77B+TjFJzNbBnpjk/xVp6akJTiMmkHX9V+gyxZDOYb6ZWLeE1twk2Gc60t1MiFQXyUIEMU83T6AzjiCzAli6gvwj/n3MVa51I2uK/n/S/EsR0XNdm4wMGrc0coHfzS5POVg7hLrsUhYoSHCxWNX0eRYXJ9PEHxrAZ+nQ/TbwoUBF4UoljMGV3KwsHTqEw0cDxerXuYM/BTu/A5NPeJ0+Un+ifnYqz1HBK9K9jh309ewMreC6fvKBkgpnI0RssOIqQwGAbe1j2k6uZjOd2USp00nTZJutTbarmwr5AWvqlkMaPeUqJdq7lAvZvK3CgfbfoSK8ueZzZt9OZqObrzLGzjIadDvl7WtF3Jytk/5fREIaPymKOGh0c/jTs5qc/TFeqhdqx6yvVtrn0UK/g8XVIp57S9B693A56+DyFQuaH4E7jUEb7FrUiALx0j7vKz1L4fz/Ct9FsCW+3TXBf/FYaQ+WX0zyg5J2uLvsWD2ipmDRb4QQtdT3C69/c8F/8YQ/lFJM0SGkK/5nxbQQ/nochXGMwtAyAa3If+RhKxgOLh9UgnEJp8HdHQ8yzesI3WRStJlbowZQvyOnNjx8hrLjoDhZBqWknzVPVTE4uhQC7AOf1Ts4k8RpQ9RhnZiEZ5ZIyMzU5PdTWLbAMUS2PYI6dgP85wPx5pdze1nf9k0cGDPHDm55kVqOVYej9NqQ4i1RXEmOrBrpKCfEObx3c2/4qa5hjOoRT+jJd/WM38pPlCpGXH8I1N1u26fvvH8BnzUYBzfCpO+c3dV0lToAvB3oyJIQoyHhkBAUUiJ+XYHtpKXWQRDt2Nyokz8Pp8hxnwd1AbXkRRuhKQiDtG6Szaw5yxxXiP8+5BIcy0wCkz2154bv+w/syvFm6e2F6dLeOnnbfhENOpDxYWFgIFGdPMsmH0Ho7OXci3b/vStH3/Fbxj5JwE3hbicSbF1i8+hk2u5GBmiAsC38fl7CWpuCjPj06xw2OKh7ysIWGRVNzMyg1xwNnIp2q/SJEZw1IgLVw0jx7jq5n7UIw83tykW32vtojvlH2Abl8FbSVTvUW+fIa03ckqv5sfNFZR53agHHse7n/vlDIIAGgu0Mfd37IKi98DQwdgYO9J3bMuz0M2B1Gk6IzbX3I5+ExpCebxBh0StdlKbhm4jtk2ieFF36C6T0br/A4mpahSN055K4Yowas+ArkB+j78n7T3/uyE11F24EME+k9HSCZDpd9iRXQXkvTmGR87agO4fuRE0iU66pdT07UPxZo85sjSZgxzHiPB0xGywuY6lU6XwXmtFq8utJEq72dIKkffn0NOGnw6l+EaexkWgk8s7WTlkcmUaUXWMa3JgajCDHCevoScGWfE8LND6ucPS+5AshTObzsXj+pBjoex3H5QVLDK+KMicFX/HknJYel+Mt0fYo6u83vte8iS4Nv69TxhTZItT6GdO2w/Z7Zc8KjlUbGdgCS521zOU/rV5O1hnOlKXMk5bJ3rYNN8i5y94EIPJE0+9Hwcd25y2HhojYueshZ8wz8k/yYS837T5OPRGOdmbZTlx9hYspjDngB9+6/EkwuStsU5XPoaiwbOIJidKhxpyjkUq8BNumflF7kk0ciFgRyq5CbYehWdS38CReNq19Fa9I5T0NUw+tgC7qj+G7Kl8J69t2E3pytpz4TR0tdQdRndnkPOpnF3HCJd3YDpCaCk06y78CFSQ/PRUyH89a+Q6ZmNN5MnWR/j2OFLSKXrON/zO1ZFjvCLouu5q/ZaVoX3UdMd5fW8gv2llTS3y5TZ4RPa56ac/9v2j2MmiwiFJ8OE+6oVFvecmI/zOrJams+GbuBF5ylsyq7DkU7jjY8wUl7LMmk/tZlP0mMP4w3dxZmRQmmCX5jfh5EGcqqJ3Zg0SDStj4+EbmWXGqJWz5NX0lgKE7ygqFHJX0YnldJHyjYV2O8CXEkVb6wBS50MlQ+HdmDJFmqmBF+uGJtR4O+NlryGaqa48KmncWWm1+zLlTfz+BmLMCWLF2v7GdVaUMwwV/RdjJIvPNNr//Z3wkVFFIXDExwUgGcuOJ/RkJcuTxdz4nMoHl6LJDQsySDlOYquqASjjUjImFqUsaJ9lA4NsfDAQX5VfzH/sf/vaLrOs6feypIiL17hpF0eokUdwCMcnJsPcUSBlJRmoVmHX7jokgfo7drENxa9G63xBVyJgqK0atq4Yt8HKc4Wwn5VmkRIlShWZbzKScTrAEMIVEnCEoKjWQtZKgiXtuctJMAlS6gSxE2BT5HGDZZC2ChrCVqyFj6lkJzfnrNY5JSptyuEDYukCX5FIqBOXssvPH/ln9Ubp13H4lgz3+n/ON22Af6z9k5iakFJmjckzsiGk+9VvI8LLv7ktDb+Fbxj5JwE3g4jJ9GTIPzz3aiSRE/eYlfa5Fz/XTQ5pxeW+7+CXncW0eA8NjmqeSh0Gl/2xmmqX4p8/3UwsAdyiYLxYk2d3HSrhpy1ELfy1MkoxgMghMyo/k1MESA4dy9W4zWMPJHhNfc+Xqh4lNv791BtFs7zstPBrWUl0zgay5Lz+ErPx3CMk/4613yOnK+wKj9jYwJLL0IWQximH5k0evGpyB/5Hpt3Xwti0oshGQ48w8tIlu7CO7iaikMfBsCn/hmf+te3vJeBUjsdcT/Bewor+MPn34bz6EbqugqCXaassPOMb5EUhYE4j+BnJXks3aI+1EfP/EZyR0yU8GSo7lE8hGSDn9Xt4phczpLe6RojAM1GNacYjUQtgw1xQWdwP8/NvQdLNnGmmzm9rxyP6keKDYEriNBsWIaPP8rzIW8BJqAg2eEB5zcYdPvZFlhJQ7qbi4Y2IgnY5F9Gv6ecHf5mbup/jGcdp7LPPo96o5trRv7BqblDKFj81X8xB9UmHqu+GEuSOXdPmrYKjR2NU0nnlWGD970U53WdPkO2+OvaUZKhAdZnfs2rqYIBd008gSFJPHpcBlSxYfLHgSF2zFmKr6xz4veN266itPOiGftoNDCCJMVRLIshP8zpbUS14HDJFjY03M+NI5dQkg/Rl4cDvgNclVmBzRHnl4GnichZgoYXNV3Mmq7LKT5OANGUs2RdA+TsYUw1jS0XwlQzmEq28H8lw5hfo7JviFTQgZKI4u1qIVVVjx4owUzkmaXORaRKsQwHvpqtVK75LQBdB67jywuvYk4izZdGvsaFfTt4InA226NTva411gix6NnY8kFmz/0lF8WfY1PJYuyGzurIYXZ657HbX8/QYD2B3vMm+2telAW9ToqTJxYRNaqf4VP6r/il63qG02Wo0VGUbJpceQ3zOEbAtRydalYZ/0l5vvDdHcqdxkuRz060EVAkouP1SBa7H+Y075/YJZfwaMjkG6MF/t7zahXnGr3soY5XBws1yXpKNxNKzcKdmkpmH/W28mDzz0EI5ud1OjQVV8LNZS234TQKoaaR0ldAtph3+DCyZeHMptm5YhWSMCkfGEKUNDGoJVhgKpT7jtIeX0unUhDsXHDwCKHwCJX9/ZiKwlgoRLioiNrOTu699lTStgRVqTrUvJfg2DIsySTj7iHtKXgvvdEmHOOGdbhkC9YMqtMNR1tgVoprxasMCS9/9n6I/JvU8VtplFAlXuBq7SbsDT9CMaMAqEIgG05u2nw2odx8Et4a5rQ9QsiIo6k23IEqJMBevQYjE8bmrz7hOd5OmHqK20q/w/7ySY9peaaZQTkJ9s6Tbscy3Nxa/ik+dsl7/0+v7x0j5yTwtokB7uklcn87kiQxrBsczgosBlnhilBrf5Yx/VycShxTVFJi+yISWXLWEmzyIWTpzRnvQsjkrQZscvtbeinevB2VvvxDIBRcJccIxj+H9CYfLAClC0j6PsHYgRpkZAwMvjPr97zq3TOFS2OTbXgUG2P6pJJyUAtwlvNUqo76uCg6WcMm6+2ia+1krLbZ8xnKVk9a/EJYjI29wp69H+b1cEeo/VKCXeej6tOfmUd5kIB2z5veRm+Fg7xNpqfcgfvvGu7XFNrqTqWr7r1oQlCeewlH9w5GSq4gGpyUIn+qOM+BN8lsuRqNTzqSPLv+D3xd/jrXb30OTz7DWfmFFAsfcSlNvxxBRWaJUUevfYjPV/2UpC1+3P3KmP5vc8muZ/EoQYj2ITmLEHYHhm7HqMjzcO9i8thQi2XSy8pmDHG6jTQp9c21OJp7emgcO8YjS8560/2+1P5rlh5R2JeY5GX8ecXPGChdgz/+MIpIMy4thM80uT1XTHEuy4roUXY67LRpGpclUzw7eyXeWQVvS6rjVB5x7mPZpu+gCI2cPYw9N1njTVeTRIt2gQRdoTIqo6McLpvLObsL2V2tZRvZUPsPGkZXcmb7e7GweLT5Toa9Bc2lUKqCppFTWDIw9d7i/iPknFMzr/Kygs0qPNe0asNl5JEdzUj9L2GGStDGhilvb2Gobg754gqIp4iWVFPfOxt1nLjqLDmKYkvyk3nrae5PM//oPmprurk++weOuOr4a7pQKkURFhcIeEayExpai1q5k49a3wTgi4GPISy4I/6riWvb7Wvi3uynaOyeqiAd9qbw5NMT2W4CQR7B3z15/rv8x5yT3MY31E+BIePo70AydDI1cykhTKhslLsrr+el3TdO9oHl4DfD90/8/cTpXi5ozaIO6Jhyho+V3IBtfIWet5z8PvptEnoVq5xPc4b3dzwe/zQ9mZlr25lSnoeWfYnbYv2cns7gOm7Kuc+4jLHR9yOhYCpZxoq3zViaBSBk+hhTCt+KRzhISlkkIVEXMekInVytJ1eyBneyjphbIGz70McVmx2pSpzpelRTIeMcJOlvmfH4uo4ODEXFm0zQO28ZCfnNx+s1+lzCVjefX346dtvLeKKTfYwQrD4CslyGsIaIuQVIbo5VaszvTrKqReee8xTO3W3j6u0yRxdX46o5i/WJpVi5OLL9X5uz+rURng1s5pLI6ZQYQdK5IX5V9wxV+TLmZer5eunvSTsL/a1n5uL3LaOr/GKQZEItv0dxzCxhkhs5FzNdh61oE2a2AjMzi387u5bb1v2/MXKma9S/g38ZnqVV9O6J4TwcplRTKVYFLblKOnKVHM0uYLZdZiQr8LgyZDI/xS6rmBQDgpD2A1zKhintpc0zSBoXYogqLF4nICYp0b6KXTn6P7o2ISAfuIhw4uMgCgNDeqSBnOcfeLK/wa08iyxNuotF5WrEouswqy+l87ljfCD3OeLzUydqHoC8lWfsOAXjynwJPzt8Gy6megZc8kuEq6aWojiQ+h1mfynpvMA0YvR2/4CC16IAZ6SRkmPTU6El0gS1n+JSpma9ZOwy4ZCN9loXpaM5km6VmH8yZGRvKfRBwlsowqhLEkn/KXTNOxXZmow3ZyXBAWt6KEZVDD7etYGVVe+iVlhsqHqaXyi30DTYjSefQRMKs6wQDmz4hYvqcW0UgeD2irunGDgA2fDl+DyToRrJMkGM37+V59XKpTw8/BX+UXwWP1v0gWnX8zreysABOFBdzYHqN18l3rX9Jzj7Z7Mvux6AhG2Mvy77FqZs4I0ew2KSaaMIwd/6B9nWUE6kJMxTZhX0lDM7meTF1Vm89g7E4FKybefz3dL7uX7jDyf6Ih44iKb7cSfqkYRSmGSkQtu1Y4UyBuWJAUYDguJoKY1Dp9M4NClcKCNz1YGCJ8KQ84W6XMfBlLMk/FIfXiMAAQAASURBVEcnJrWJ3yWJttIqekKlnNq6DyFBb6gK29E2ygKFgVNJx/FmcgwbhTCvLFmMeRwESnaAJeNJ1sNIE4dnqTg7Rpm3+x5sVp7WdAnUwbx0JxfiZciSSAgnL6cdBNOzkIqO8R75TrBgY2Apfx1ajyUkFpZ0cuM4YXVZ/CjL+ARfbr6VkiOrUY2Cd6wo4SbrSJJ0tyMJDTlTxc/9hQl3Yb6dPirAkFEMAzUeQaiFoX4MP+fEX6aypI8cGi9I62iw2pkr93Bs3jBzjpSyYbmLXRUae8pUvvRgBMVy8i3PCr6eKoS2Hhn7b0yjDhdwMH0pTY5X0IueQulbhSlez1aySHk7GHF3QPHf2NA/VZDydVyvPs5zRRlawregmA60fBDdHplx36icKETCJEhKBW5MZdpGR+gtUqFff9a6G3mcJJ+XZSRtchwz1Qxhr0pZVODOBsk6XFQOHKKnpgZnOk3aU+j3zvr641rMTVITxw2zpT0tKJkEexuXYUgyW7QWVunVhI6GGV1xAaqrHUd66/gxEtvmAwyPNyABGSDD/nrYX18IGz67Is+zKwBagVYkC967AR5dC4uHg1y1083Gpizn9sxmVrqIlN5HfsHZPOJ/moZeHSVvUJVw8KeFR+kok/FIxTRH6nipbDe6ovO34mdQM14M58w17AyjiujcLxE9jos41vgBQq1pJOUwwvAhqXEE0JAqpSEnkZaGeM2S8Lv3sTwnWD1Wd1LP6O3AO56ct6l2lbAE+x/Yj33HKG7l5CTZbdJmctbaiWwQTWpDoGCIusl2hUCSJEwhMKQ8ler3sMkHUeS3rspsuJYxkv8GZvLEsSnFIyi9VkHu3kg62UTsYCVGMsdtNT9hn7t1hhsFt2WjTNc4P1HCQ/44XstJTa4Cu2XnA8OXEbCO71uToHYnDutl0vICtp0+BsrJKULLups5G36APF64UiKLV70PRRrFrUyNGY8FNA7P9ZC1yxPhMhGXsFQJySaQVYEyDGVfs2FJMpvWfx9TLRhhFhbS+L/CgToPz8nTNiYT1Ez+PfEY36l8F4FYgm/sfYXGmtOw+arplEe479RdPKVcytW7NuDJZVhs1rDDe4hFsUWcZhbSz3vzFi+rx/jLwjsLPZKtxIgvJh8+DVuVjbJAiDUtf8EtB9GjfdjsPoTTSy5n8Ifzpxp4DZEI87sPktbsRN1eEs4AGVeIYYdMUTJCaSxHS3kF6XEC4Tl705RGTTYudNJXPLnGqR7NUdX/EhH3fdwSHaNGrqM50cJz4S/Rl19ETknzzwW/Y8RzbNpzUbGzKqfwobEOgmoZv610srRojHLtuKFlbDbPRF08pRU4Sst7z2N1z6UAxJwj7LL70eVeXBVRZo+lac0Xs1SZzQIrxx519/hzkRj0lNLU68eWK55QXDaUNBISygx8G0syiBTvxFJyVFhFOMw5bCnWEa5i2lWL6iTsLbcxkNGxgnY0Cc7dEaO0/0EcVeVgmnha91AZzdIzq5RMdSNyJsV878VklTT7lA7cqVF0SeWV6lXUbnmJJfHJOkofXLCL0HiByF9wO5gOznH/nnnZyTBmj6OUi5L/TXKcnyIQrPa1st5+mE8n/jax3/tLv4MrVoU67EQ3LWbrCmHF4rBmss9eMIQDWpI9ys08wMUcpAn/8ABWuA8BJOctB0nm3/k9L5auomg4yQbWoll5vij/gq/OuYXfVl6LJUv4jASqMHnvi1lC0YLBGJj9MZbHSngx/N8cj2bXU5zmu5sfOpvJxy6j1d9F2NlLUa6ImOcgfx9qmUKJNYSNI5kzaXBsxjE+bn0/+U1cyULBzXDgIN7EbGymk5yaYMQ1gCfvwyk0TF8HiAwpdfq0ZQqQssWEPV3UjC1F1b084k0RtRwst/KckhjXeLLFSEkO0sGtE8fKhgNDX0tpJoqUHy/C6xqgp6yNYe0A9fF6irMSuRnUuefuPshY+UdAkpEtC1s+jjfRQVfjKGmHE1lIvFv38xGrgvamYrTivXjH/jCtnf+vQZgOjMR8pNobiPgLC6/lXUfZVdsEQCgZ44s7fkM3s8hLTgxJ5mh5DWNuH20ls1jbfoDSeISGTDdL113Aaedf8n96fe+Eq04CbwvxOJVi2x+/Tyb7NGfespH0/g4iD8xc9fv/EkXru9H3H0DL7MCpbCMTuIlI+GokO8gOFX3mhRSyGCFHMZokIZFEMMmjeCj0PH8veoa4Ot1zowgZr+nmS73/xoJsHbLQcMrPAU4y1qnT9nfKG3EpG8jPOgv3JVeR0Qx27/4our1ApE4NNeIum8GIggKJcXgJriNXUaV3oskdeJTHkKXpcfOsTWbbigC69ubuaykDoV/YsLfDWHAee5bcirBySG8Q+Cvq+Dk/+tgNDO73IKcN/gMHV2Ajven7SO5SnMsLlXoNCf5zZTdH1HKu3F3IvHEIlYFQJy8GCoNpUaqSNV2XU5as4/er/xMAPbaUbP91CIeC5VJRlniZ1yNY0v1XnHKAsXycoAmS00c6m+GP5183YbSdMRijsWUTmikhCQlXsg7dFiHnGMUTn4MkVOzZYkxZ5uVFTooSFss7w2xp+gWrWj6BiZeDNTbcqT5cRd/gy9E+XEJgCI2YUcED4e9zLHiUlxr+MlGS4Y3w5b1cPnImX8gXyKf31V7AyI73kfC3I6/8LctEMdnOs/id61Xi5Fk4vI45Y0twjoemDCVLSdDJ6YbKBh+8mNlBb1EJdZQwp60VRBfp4snU4382rmVt/0HGbBYLe0OoQpB29SEh407U4cwUUq91LUbS246l5FFkA59wEhBL+aqeexNqdAGzU+2cIx1CD5Vhi4ZxDnQzV5lDpzTASGMTkqHzQHYRs1Q/pchcIKkUqQk+Z9m5qusvOIoDmC4P7o7D1C8QXG1ueNPzfdv7fu4euYBSJE7JZnj8uIn0wtAufpUulCbZ4mrmPWO3zZhG/DpuqNrEl0b/wPf4GCBRdHgP+XGieap+IZbDyXt4jPm08WuuZ4DC5HUmmxkq8/OpeV+iPDzIo8c+g8dMc/G8e3j/sxZIEim7RFaBonRhynh8pYvLdhQSFi4Lfo3WQCv/Ia6Hom0YtiiVusGvhoap1w1G9NlsTb6Hw/kVuF7PtJJjXBH4PiVqG72yh6cGZ874PB5pVx+p8VpakmkiFAXFcOKJNWIbL177VjjW3EF1i5946AB2kSMn2UFAcPhUXlmR48wdJyCnm6PUH72bu5deQcbj4kNbHmFx/zFaGq6lt+rMabtbkk647DUASiwf11gH+aB0LrvWz0VSkwSGvoVqFMY/I3o6RlBHTpoYI8tRXB1ISgo9sQjVcwjF1YWZno29+MWTuseTgaV7sXJlIFmYySYkNQHIWLoPK1PNJbY+9lev4HBVYXF28+7DXDdcwjO+Lr67tkCIX9Z9FEuS6fcXM+ILznie+bE2PrXjKa745m//z64d3jFyTgpvh5HTO7iflv2XE4jrPOu+kc+d9y1ST7Sij8TxXzAP7Aojh45y5KkhQgoUGy4MQHsT5u/rugM5S7A7bSIBp3hmjjTmLYEjYMeKzyywJ4SgI2fRYTvIL5vupTpTSXX/aZxl1TLfCvFKYAdPBV+h1dGNIU/ln2iWyle7P8EC2UVe5MhUbyNWW0glrdn6JWTdjS3rJCT/Gpt8FJkMSDKKFEcIyJx+D+FX/CBkjq25FWtcNbbs4A14e0+lzfcaYuUDoB03oZoag699jIXxUqpsgxTbTiwoFfGr7F7kR8gSpq6gvKGStZlzE+taQ7pjAc3PPYQrXbD8Dsz/ID1VzfzubBdX7B2jcjCAgeC5MomdS9zI0TxaSxw3hWKbrhkmmT/M7eOXdU1ctetlSpKFkMgavZEvN/yInHxiscOI43sYpVP5FpdsT1KafBi77GNtvol2qY8hLUlCj2N6V9NRYrB6KI6ajWKLzsWWD00+XwSGFkebQTekN9SOVfx7sA3RZzSQMss5FtrFncMjrEp4uCd5GyJXNS48J3ih8U+0Fe+e1o4kbFzTewEflf5KmT5JStwZaKJVOo3w4cKKLavFGfW14sn7CbwhFfx1tJYlKdVztEoxTrHK+PWaCpr27GLeCWq3dUUU9px1PpfufQZFWOQUBZCwmUbB4W+paMhUiSCDchQFlVONZRw0ZX6f7eSM0ZfQPA6SvjLas1586TAHvAsZtRfCiHYzx/t7/kS+aRFIMq7uFubK9awsvpDRTB+PBgvVwQ8NSWSx0+OsImIrDO4luRGuTr44oYysxsL06OVcuOYlrjy2fdq9fM97AyomvwpfiGzZ+KalsUqy0XvgIdwdL9PZcCV7atYTqXuFnyR/OnHchx3/xab4PPLjITmz2I4VsuMiQ2v3tbRQz31cgSuRQOk5MmEUZ8tr0IOlrGY3Z/IaPxYfQZcKPhYNnSsdj/P38Lk0uw5xjVSYnD879/MUvVZJQK+bOL/AYuXuHzLi1ehu+DQAC53PcKb/V+jA3QE/Psvipngh/HEgfQEb4tOVx19Ho2Mj5wd+zA/Nm3CMTBVFzDqGSNpiKIaXYLoChCAWOkB+PKTlDzdg0ytnanYaFHK4Zz3En2tv5Ny9naS8nSzkKEfFbAxJIziyit+dW4Ys4NLtKSoi0/l3XSWv0aelaRheR6kx1RgyMBiRdCrE5O+SNMxwWeGdqTZDXMnLfNS6mE3nHldqRoipyRm6hZQ1kcM55FgeYZNRegoLTUlOYAvsACSEZcdIzgUhIwwfWnArsn0YM12LveQZjFQjVnYWWmgzsjZGpvdGzNRcFM8RrEwNwpwqOrhIbafMPohXyuDBx30rzic1LkRaOZZl9fYYp6HSh8WrjRpbZwdOqt8BzjrWwv0fefdJ738yeIeT8/8IXnclp20ZQzUF6dX387Ut9Xz90o+gHqeHMOuMZcwa5+hZhkU2o7Ptb63kexPEwlnCusCvSMyySZgCDmetCWWa1y3SnrxJtW269oRNlk5o4OxIGXTpOWLOEZ6a+yfSaoaj3jaONrXxvABJyIgTlAgI5YN8puWTKKt/TXdR57Tt3ad8CwAtMQux9avIx6m75tx96M5RYiN/IXnabjDsMF7Y0JaopGLAQdDxQYKZc/jzI3fiLDuCJBtkRhtwYDBbHaTYXo1H+eWUc6adMnlNZqTIRm+lE+u4FMzjDZzhfVcx1nIekiGo7n2ZWeHnJwyc/vI1DJUu5zezLDJbU/yx2Mct8jO8XHYGRzIC+7ZJg+sWHNMMnEExRLstzEuz3JzSfnDCwDk710y3t/tNDZyU/eppBo4zr9PUEycWLDzbkHAzpLiBJIacZkGfSSB7DB2Q8qEpBg6AhDTFwDElg/uWfYMywvy+S+Xh1m/jU4a41fcbDvr3onXXEBv9It92qTyx7M4TXqvDcHB6pJELxuYzaNUzO/QnytKTBo4uKXTV2oi+fNrkMbqPquNSoI+HIZscaMhgC0fI2zqoBbqtfuKvwVxbH5xAP2Ru+hiHX65k2LuOxGyVnDnMtdFZbHNb9IhjnNmfY49RzxFgNSr7MfkZOUL5MNeEnyZf3wSKggdY7MoguyWaIhvIxEHN5bDbBJl5hUwo1bCQk3EqygoKvsXOWWAeAEVlZeoASi6DANKKiz2+xbjMNPmiSb0R0+UhO5Tgt4PX01FZR5ERQ87J6JbGX1Jn0TZSzqxsjH93+GiK7WZ3aiuDsptz512N6HqVhZleqrf/hE3O93Jv0zm8v/cFAH6X/Q7Y4EnPqWwpmk9PlZ/npIu5a/8dAAwz7iUzcijHTZ5KKo4eLOVwroFgPoru1bBlc+QddnQ0QtkUa85/ktAeH5mUHROZMyPbufnKr3Hj863Uh0MgySzd+wtk00QmRMbcilM5hYOZC1jifoyg2s8t0UnekxASzyffNxGuymoSDn3quro1ezpnWnfxOeVPvKc2x3BiHf2KhZEvokSLMxY4gpUv4dLBII15ByWDAVKeDpzpBRjauOcOwagiSEuCrCSoNmR845zDkNrFef47+XPNImpyw3gSdoxx73QmaWCzZzA0DVPJUBo1OVBn54H1Hq7YkqR61GDUKyhJFNqqHVnL1Lyx11/onZyy+x5GvAHuXfoezso2A2CJEnxjrcRDJj3KGD9lMR+ynMx9eTtPLmpioMg3XSFckxGajOnVJtiI3nKTuM+PlgiQaStGOBSUoSwYhfFaAvLRdYUJQoI8q1mcPMp+azZ6dA24wd7sZN5AN7YxN4laiebcPnKajWNaNd1lFRzQVtGTSTHoL5pyOb6sybd2RfHISTZrLegY/HvbIj4oK+jlToRj3IzImmhHYgiPihTJYzT6EAEb6qEoF9bP4f8V3vHk/F9zcmJ9mHc2owiLlFNmx4Ii7vb+O19cdDEh1eSXw26uKw8yz6Uiy7Zp9T5yGYNjO4cZ60/Sc2iMXDYKAvSsEz03fWUhActcCtW26eGZqCHo1S3crhiHExrP1jzE0dITZy+8EZpp5+zWGwmmK6gIDFJ7xk/f+qBxVL38fRLkyYdaySy+Z+ad8gpLXjmDkPIEslQYdJLGOcTMND7lGILlpIz34lUfwKNOEpRfWR0kb5MRr0+E0XryIootOJ2wmHxgDbnufub0DuPKTTXgeipX0jr3g2x15tloP47cXG2SiylYx3GC10g638VDa91TdJoa8/rm01a+hahlULLgAH/rvoVlPQW+yvp8E41WBTfP/m8G7MPkRs7BSDZh+ewope2oZhvZojPJuQvy6OdFOymNViJS3VT2jxHPKwhXG4ps48rcag4qPbSoA4yILhYM3YAtuJOECurYQpTx7J54yeN0e3swcnUUJ+dgN1yYShJv6e85V+9hfsbG74f/SEqL4dJ9/HPBLxhzDnLp4Y8z7Olmw5yZU+4lIfHl9ovpNn04IsuZV38356ZemLLPHn8jnQ0Kwxs+zQFPkJeqd3P1oQWEMhqKNRl6CXssts8+RkJsJuavp/ToOpbrm7A7Zl5rBdIWHi1ILH6YrKSghwqhFVf7Qe4uvwFDPjHXTRIWEoK5yWOU5EdoSLUhV1RiegMnPOaNUCMxnIOtnF32HkpcdQDcwxMYDgfOnlbU5BtIzHZnofTD6xCCWOcAj1RcPmP71+QG+ZS9kVh+hKeG7yPVsAiAoliOK+0XF9qM9TK27TfsW3IdxvxuPpw6sU7U6/i7dTGH5CZsw73Yw4MgBC7spBSTZOMSOC4bzzbci+n2Ybp9XMSLFNlHKcnEuFu+HhOF98t/5/xTfsOIrYhlR1v54Z1f4ZGzruJn774WgDXHujhvZ4HD4rK3cE7xlyk3LPZZ9USsMmzZORxIXYkhw4/fFeDDj/6ZUDLDndd9mLWHhzilreD1WOZ+mHXePwHwrMvJfxeHuCGe4GPROIOKgsey2CvNYk9/4f4NM4mqFDwRXarOzpKXWTp0mOVmhBG5juahdtZXH0YEdGQJHICBwpzTnuKyzTnKclsw1TTO7hb0QAmGL4gnPoehUg9PLAsyZgtMPEMkCdUQfPTpKKHU9OlSS93HaXs3kV2dJt7mxD+iIOHkiXVL8cuFDDZv6nna50wu/E7PL+Cw2svf5jXQUlEgQ5fHwtMMjJOBquvICPInqEnoTyeIuabXE3xLCIFj5wh3huMktBHalKGJTbKQULILuFtygSaBKZDGh9czLYX3IPO0EOxTEoxaNv6wqp6V1y76n1/Dm+CdcNVJ4O0wcsxslK5fnsrs+GTRxb4yO0fmeqZZ7Cm5kjHnGdTX3UxMT7K2uJYSxyT/wDSzbNlyHqaVpab6w8Tj+0ilWsjnx0j0LaZ/1wXoyTJkRaJ5XTkdu4dIJgtvWlAp1A7ZWfsMr1U+OeO1XubPM8dmMWhI2OWCZm7ago1d87j46IcprtmNzTNEYM4GZDWPdILq6P9b2F+4jFXKK9jl6VwcU/hRpOlVzfvK7RysL0FW8kgzeJ30VIiB7R8gNdyEnPwTZ+3YNuO5x3x+9i79b8ZUid/63jw7o04a4ddiNu0L/skXS85iTAlyi/pj8tjZyJnsZynv3fY8vmyaUr0I1ZPgZ5WFVFHL9BDl66C5Maumy9rPHw1z9f49jMoF1351uI7dwV6KpDySJHNddj371S4Oqr2M0E7T0E0oQkFIeiGrhjw3lHwSm5TgkMNiga4TlWVcwsJnCVLCxg+9jWSH38+2mn9OlIJ4K9hMDYdQOLf7MgJjzWiWkw+WfQC3Vchq6XBWsruuAndRH4qkM7z33TyfW8+LKBATCBXcFTnmJdwoUpaddYeRo17KI9X0RgteqlpziLPcBZ0SJRGdYoBIho67de+EPW7Z7KTq5oOiIuk5Ej3DbAqtI624COkRGlNtdLhqCegxXGaapmQrTiuLAEy3H8MXQA8UiKfF+VGKS/oZ7q3A0OzkZDt5RUUok57R+UMpWjJ9uDJxLq+8GYe9EJK6z/gnaY8TzciwYusuioaG2Dh/PGMnWEquvIacrKCZBrIk4Wo7wC8rPwCSxCwk+hBUSTI/FC5mjatU7xvbyB5bF/niybBLrVFETNFZac0jiBf3QCu63ccBzyhi5V9p6o6yMNEx7bkZyHxd3IoiyROGWEkSDMdsImo7uVAZ+bJCRp1kmrjaD2D4QuTKqpnFAB/hr7zAOjZRMMDP4RXaZpXxXw2FOmmlYxGGQ0EuHXkZj5lms38Zs1u9nHa44LHUyndTErfoT0/14D2z1IUvso1T4w6q2/cz69AG7LrO0fqz6Ku9BlXKcHnwq5TbTsDLG8ePI7/AlpsantpXdweXKgcIWRabnQ5sQlBqmNTrOmuyhW/7JzU3ckf9R3DmLP79sVFiJZtBAnfrXvRgKfniCpypSoJGmpv9X+W3lVfx5cZPTTlPzbDONa9GmTWwj+robjyM4uzpYqTY4vRzC2Kbe+w2PlNagiO5kAt2gle6hqyz8N7F/YfJOUcm2rs4v4xKK8SIqtOudOPjIfZJTcTxEHO6eWbhKTQNdpPTbOypbkRIEjZDJ6+eXCLLvwJ5JIujLc4FEcF8x37GxgnisqUQwsWonKDI8hDILmUwBRVIGIFWOrXJMj4hy0NSymJgscBTyjWfP3HI8n+Dd4yck8DbYeR097zGxzf+Gz8aGKYpORmqiHlVemY58CRNAnGd9loXUb9WKEp3vEvZUYe/5HKy6V7S4Yfe9FzCdBE5ehGxnmZysSosLEbc3ShqjpJkHa3FO3h+9t+nHfcufx63IljlMmeMCgzvuZbg3OfQXNE3nHBq7DjeuZKy0TxL09uI+GwM1xqMBbQTFmgEMA0NaaSYWYlB5gxI2KVhhFDZtjTAqj1JZGnmwp8Jl5Mxj0pbVQDhmZkEGz58Eb173Sw8/BQ1IzNnbEU9El2N64m4L8dSXbzq1tmsFYiZ7+v5C3+svmHK/t/fegfLg2egLFzHN1buZ9aeHGmbgwdXnEVOs+HI53jX3lcIpROoQma25ue7Vb/DGjcIE/6byPrPn3YddjPH2QO9BPva8GUL3CTZcFA0upKX5vyF5lQtCPhQ7iz2Kl3s1NqJiF5mRU/HnauaaOdU72/JO3r4QoWTUXeBxPiJSJT9Nge77V6S6klkrgkJRcgUp8s4s+cynLoHd6YCw5ZA5HxISJxW/xWWpAt1v14oXwZzC0b88L6riHedwo5ZTp4xbciRkyi2CshY3GjfiSyBGo/gGOgkXTMXy+lGi46gjQ2j5KY+Z9PhIl2/YPyaBXImhRYfQw8UI5kGzt5C7TPD7UdJJzG8AXRfCMt1HPdAwPuzpyPHh0CYKOPlAvqa72Ioq9EfLabBDrt3RZDTw8goXFP3uQmP6+OZJxkKTiWnq/k89u4j5EqrMHwhttQvYM5wHyWpGI7eY8yxrcTpW8LSGdgB4dwAzw7fT2pOM8gzlz7YMHcVPzniJ5QvvFPZgw/TERpA8mbJVJawSHqVulwfBjKXLv0Fa3YfBUnC3boX2dBxuz+IoXnIRX8OCCR3EMPpRmTiqKk4lqqRalwCQvBB6e/cyzVYFK7FRo6Py3/k9FV/ZMRR8DKcHd7CfQcKxPmk4uTcFb/lun/a0KyZPXJtZSoHZw2yt7GgOVWZHeG7R3/FZs813F03j1ueCOPPyEhKlg8XfRi7PLX46BG5knlWgaP1jDSfnUNfwz/ORzrD+Vma/dONveMeN38vu4BPzfsiAAu7cly2Y5Bo8e4JQ1oPFJOrqMOWC+KPzKNlwX/yk7EOHiw9j0/O/zIAH++5n7uqCzovkrD4TNc9ZCRYPfAK6/Id+IVFWrbjsPLICPbYbdxSVoIzrvKhzR8hElo4zpcbIxE8gjnOdwxabiJywYvdaIboksMsNoPUGHXIkpOAcNFDjnu0Y2w1QgTkLE3eMGHDzaurVyAUCdUSVEdidJSEpnfAONa0HWDE8NFpD2IbTGElLcwKJ3JARYzoqEMZqhsDDBfbkbqSnNeVY4k0is+R4aA6WVl+8VAWn9PkVa8HIQnKrQAe4eCYPPimEYKyGg8f/9B/nHiH/wXeMXJOAm+HkdN68BXaB9+PKkHn4TwfHomfcF8BGKpEX7kdXZPxJAv1XA/N804zFOxZk5xdRh0zMJNgVqnkJIl9GYViRUB0BT/TD/Bmb9q/Fedodo6nmkbzNB1L4ciZJNwqfZVOhottkyGg469TSDhzBsv3xTAUiYxDJhS2UJkePtvpW8K+2cXU+KaXhNDaizm998i034edK9i/qouijkU0dWZxKq8RVxeScUgki9rprXSQTFUhLBVnqLDyj3asI965FkdRO8n+JaSjOlUdv2BBz9QBMumQGVpzO3G5lIgWJ2EqeMcLBZqY/LjSQqQNzrI28N49B9nj9fHLxoKL+V3xHj49Ooxt9lk8tGgfz+jFrO48DEB3qAzZMqmKFrg9spBw2+GeWQ9Onjt4IxnPeXCcvsS3D73EGUMag2YVz6t7EULClg+CGsedqsDMl/F48484Y+g0bKbE+/SzOaz08qp2lKQ1jE2vpTbSjIGTxe6HGPQf4hs14WlS6ieCNxfAo3tQLQW37iGY91MVb8CeD+JJTq2wLhcfY5XvL6xM75n47aXSJbzgquOV/jWY6RBDlpu8Vbg/CcEKtYeFyhAjVpBn9NmYx9XUcgBZoEKOsUQZpVwpKOe62w+h5fOYYqq45fqWHvZXlRJ1uwglU0TdDrKlVejH8V6Oh6TnENrMSsCqnsfQbKw5Fsd3ZAOjbjuWJNPoWUJ2/vnsK/NzzqhCStL5tW03pe2bsedzzEk7WLlwckW/K/ISewIJrDcpkDuWSeEXAsXlwTY6QCCSZLX/bIq1ElRnwSOUMZLsT+6nI7Jx4p4GvUEy2RT1ueyUkBLAhsYlDBbVcu6QweW9OlUZiyHNRCgax7wyP5mXpkiPkdADXLZ348QEXpt0Mlz9cQBysXsR1sz1vtI1cycKwQJIhoFqmuh2O8vZT0Wgnz8qtxP0prlx6FvMzUyWK3mq6FR+L32ZMw9MNUr7A3mqoq9y7/kX0BmcNDQf2fPvrI3tJak4aTj1aZw5i089GUPLCx5f6WKWbytLE0f4R8lZHPQ0gJD4/JG/8dmRuzCAr1DKhU/X8c9z0nxPO8BMOGL34RMq36v9AH+tnAwXfmhjH6FolKS/FSUVx9XdguHykqltQjM0AqNrOdv3U1ye1zAUg2LLgcdMo0syq0/5O8P2qeEkr5Hk+Z3/xrNF6/jqnFtoSHdz96Gv0ZTuZIPTwW2lxVQO+nnP1rNI+prJuMoQmESL9mBob6439jrsQmWFMYcyy49L2HFiIyYytEtJXhMSlxKkSpL5B3leVLKsMi3qcaHJCXYUa3jiDs7JqFRLCv0ijSRpuIWCLCSOWCZLVIhKaUalOENKjCqziLiUZpc2aTyqwmTdplco7x/EWaSz64zr2G+b2aiVhMQCvYZDWjdCEjRbYTweGxd+4Ycndb8ni3eMnJPA22Hk9LT18MqLt1I6Zz/DusTebos7+sInS4MBQFckNFMQDqgMldgJDeYYy0n4LGuiZsxjHhc/CQYYVt+aO/6BohxLnCaSBJIQVPZnmdc2/QPL2GX2LfSRPC5zS9UtVEOwZmcE5a1yb8eRMIt52n0hwbot1GUGUFIaVeEk2hsmMYCctZCdi7xkigp8Flu0Dt0ziFBn9ugAdO1eR6ztPAx5DCP9ElgOysaOsrZlathpqKiC7tobSfjqprUhhMH9C1P09duQZME90T7qA3PoTrXBxrvR686hoelSJCQMJcPVZ+VZs6d1glj8RuS9Hh4vvnfi79GqXyFkNxcd2UD1cJKAqVJphSi3AhxUexiR40iWSmmkAUufrO1ztGQbRypeZN3wOny6yrvNM+iQh3nBtp+cGWVPcR+njsxhmfdefucvocU58+c7O7yU2eGldIb2kdYSNI6uIJAppSJxYgKggSAqmxSHOgh6jnJ99ndTtm93LeC9kf/CEDO/c+VynAttk+KU8/PzabWKGMNiJQpupZ/d2tQyF3ImSW1HH0sO7eeVphpyamFy92dhpGw2W5qX8a27vk/KrvFKUzVClslUNUyZkN8KK7xJaloCbBzbRn6GEGd3ZT2bVp9HOFhCQ1cLq3dvoDgyjE03OTVZRMnyj07seyy+i52jzzEyazFVo0PEKkrQXZO8o5hqY9b+zeiBEnIVtSjJGK6eQghmYWA9c1wL6c31sitcCCGbDjfpunkgSRwom4VmJHCNxph3bD9IkKluxBqvXv/wstMZ9p14tQ7wn88/R0xLocYj+Ho7mJVuZrCqUHVcT7+ImduDItnw2IqIHVcHz1JtpBoXT/xtH+jCq5uM1szGQZYvcBevei+h3DhCY6YNHYU/By/hqsgL+Elx3tLfMCRmUxozWT8yTFdNgmHdya5Zk+J5Z4e38KX237EwPakm/M36j/JE8elUdhZx9v4MpgRDAYXKiElOBVmAZhYWhCv8v2et83E6NJWPl5XycN/AhHpymHJunfc9Low9wI9r3seAY/KbAqiLm/znK2H6EyoJ7zGy7n608CCuMRWDPlKNS5CEoGjoNCRk3l/yNdzyXiQJonIQYZQz6hrj9sb/4OXQ6mn9rpiCBT15+kMqYV/BC/bR3r/z7v6H+U7AYq/dwX/+zcRhv4HB8jVYcp6sY5iUr31aW2+FoOUmLeXIjSven51vxoGNgOUiI+UZkeNoQqFbGaVNGaLU8jPXrOAVbXKB6bWcOLEhAUPyzGPa8bg48izeZ47jPBYXMXDGMjZqk0bfKt3Deu7FFNdhWGX4HF+ki1lUmxa/Xf4pPnXlh/7H9/pmeMfIOQm8HUZO7FArD73QRlnDbTjscQwBu7pVzgjbWZgZ5u6qd2NJCmujuzgldgD7DBP//wQjiswjHg//9Ljp1FSaJYPzzQwvCjufMJK4FYFDA3/cwJ02cWfevHSDAOJelZEiG76ETklYn9FAS0lBZLOUtHEZGeYTVL+HW35r9WUhJMLG58ia67HLh0lpRbSd9hWQ3/y6Xoe600bmnzK1QzNbXO11lzBcspy0e+bVPoCw0rxW38kmqQk5muccR4wvuBK0LfsFJUOL8TwyC72uDn3xLgI9Z/FqXRd/kOdzRsseBKCIIFgpTEnBb7roqG3mmcAfsWcKirC6fS7Rstv5yr6fEhmrRZ/JyyIgFF6GYkwSAhO2Mf6y4us0ROawJLqUsryTy6x1DEgRnrDvwjTTPNrw1LSmKqNzOe/IR3AIDUsyMeQ89nFCckIW9PlkiuMmG506BhCTBQ26Qp9qMj+v0mDIHHUl+FjZD1mb2j+t/Vtdn2Zneg792emkSMutImVNZguZa+ReRo5zbQMTCrWKkDGlNzwzy8TdfpDn1l7KvgXLec+zj3HTC8/QEfTxqc9/E0tROHXPdv771z8CoDfkZ191IdXbdLhxFZUh2Z3Q00epdy5pp8qo08SQLIpFgPmihly0m13h5074LrwZ6oejlFSeRn31xRO/9aeOsWn4Ie69+hMMlxS4IXOGeznv8A56AyXU7n0NVzpaMF7q5yMsE+/R3TN+Q6bdSapuPpIsM+Zwc8ljD5K2qwwUl1MUCSMJk+7SEMm5Syc8u0erZvPy7EWIN3h6T8nv4ab0zzmwtyCw6Og9xvyeMQaqb8FUneTRUc0U+fg9HF8NPiD7iVsJLCx0fxHZynrU6CjuwT5AkGhchKUorGIPl1CQ8R9Wgvwtcj5hXyVBM8Ityp94svgsbl54+/SbFIILw69wz8Evn7CfNwZW8OHGH/Dxp2PY9Tefjs4L/jdz7bum/Narr2NX/AvENQlTk/jeOs+EoQFw49409UcmF02x4E7y9hSOvnYc+UbM/FESDXWgqARGl6EZXiSRYZ27jwWeX9GRugFTzKbadTdedTMmMl+Z/TGGlCaeL11MWdjkgt1pymKFMSzpkOgq0dhbb2O0OMc/dt7EjwMqm11ObnpRpcz4FElvgcdlSTpZ5xCOTBmWkgMkoqF9CPnkBFLfTjgsk4+KZxk8God9IKsW+YtjNJl5+l4Lkok4GL7iI6QVwRFbF6cmV1Ia0Yn3byDuNalSa3GYm7GVpnjw/E9z8zu1q/7/j7fDyNl+qJ/NL3ZxylieY0u+SIV3koiVsyBrSciWxOORlRywzeVPrQ9Sl+0lK0v02oqwmQka9BMTYX/n97I0m2NFbjr3Ia9K2IyTe5wGMndW3oqqL+TmkdtwMbMbe8oxVoDh/A8BBxbTdVgkMtjk/YS0O1GkqWE6S9gIKxeTS//bxG9CMuhYfxtJXeXIpo+w4Jw70ZxTjxNCItJ6DsN9NYjETs55+vAJr6+97iI66y6d9rtljmLqHSDy6GKIDeub2Kc2I7emQRLc5+1n05qX+AWfZh4H+eVmJ/FZGzhYe5AwxTwk3s2KHb0EMim21NZwqFSQdy4rhKGEgTPxHJ7ofQCkzcugfQEPqj/isFhJq6hGywfI2yNo+cI7ptuilOctzMiZZNUUS51P8kjlITaECuGb1QNLqc7OYXYuxNliGREpxUP2LUiWzovl2xlzT67A17Zfw8bsCrKShFNVqDZlvAbkDIt2h0X6uM/7tMAhLtW2IJC4P3smNkmnK1fKxd4dfC35+xn79D32r7I1VlA4NeZ4CDhizLa3U5PeTCK2ko8Oz6fGDBCT0jxgL2ir2Eb6yJfMmrE9KJCM7cO9yKZOOFDBH66+mfqBXoZDxVQNDTAaCHL7735G7WAfgeRUqflPfO52Ln3+vhO2fTwEEhJTv4fO6iZOObyPBd2DaKZJb3GIQxWBGY8/90AH0dNvZo5/ORHDIqjKxPJhnu77LRmHi4zdSSAe4cX1F7O7eQ1V/R2897HfIQmB3TAZaV4FskIuN0Zx++SKXcgK+WAJ+dJJbtXyl1+ktazwftSOxljYN4opSRytCNFeWTaRefU6LEUhPd/HqsyfGKUYr27R27qYvN0DloWndQ9NI266Gz5CQhI85s5zQ9JOPvk4lj5J7l3WK9EWyhJ32ZElhYCtjKyRJG0WvsNceQ35YMEr8knuQc/KPJK6gOGiydIjp7GVU6XtXNn8CLtCU7N4/r3rT3yxc1IErosKtqSWYrptnM5WUriopp/3LP0BrcoCzt+dpqm/MMG3Vmh4Mha95Rr1QzrFEROfOsKNRTdPRPOHjct4YHSqh8CUYdivIAlBeXSqYS2wiJRuwpQlXO0HcajnYOYPkyyXMT1+Koc1dGvtxP4eq4O4YYASZIVLY44rhi68GCJExJDZnjHR38TD/fwSJ+mKLv67/Wc8qw3xN5+Tq1saWL95jPLhOJasEC5aRMJTTV33k8imgSVraHqSdKgJl2Yn7fPyxLJZSBIoVsHT35hNkzAWMziDAvnrkIVEqYBBefIbcFgWWVnGJXI0WEMMmnU4LI1a22Y2yQWyeJmI02QmcBuzkJ99GF8+h39pAlcT/LrqWlxmlsuOvkT6CRlhSQz7bWw49VzO2voaxaNTM1wdRXn0pEL3jddw7ue/feKO+l/gHSPnJPB2GDnt246QeagbPRdmf001xc0fxfaG0IshYNSQeDmhIYRgX0YlIyZXZvNzedyWRVyRsVuCQ3YbJVIFfitPrQ1qDB8pq4FT4vs5JzJdZOxEMJBJKG4kBH8L/QcX9y5FGg89HC79E6tTj+M30zMeK4RMn/4bsAqDm2GLouYDJzyXZN+Lx34Xedcwg4El+Fs/j2xO1lPKufvoX/JzxuLFWP8oRRt5jWNL5uMurcXt1kFApOViMo5Rkr5WvD2vcvHmyYy1iM9HpLgS3VuLc6yfhGcpQ2Vrpl1HzB3mVxdVo8azlCb7GbDNwurRJyqHr5LGuHHdX/mk+yvoUoHP8YPux7GKDnCb6z8wJY2Ffe2cdmwfWVXlyaonEVICS3KR8V2MI7URxSgoWguzDnfLtdxn+zaDciXPidPxRRdOKTx5PA6Ub+SV+unk8ovaz8ElBViRrWUZDWTR+bOjULaiwjXEWO+5jHq6iMomW/MLSL6puLPF5aHt/CT9kzfbaQL7nI1s1+ZRJwZ4TF1Jdpbg8eC7+ZB+F5X5bRzpOoMP9l5GiTT5LAWCp7Td9CsREBaelr3ki8oLxSzHoSSiOPs7CrW4xmGqdp454xo+8vjfWdJ6hP6iUr5286e54xffoygePeE1fv+699FwbAd2PYclSSQ8fvyJE+9//Pku2XmIN3ZXTlU4UhHClGVK42n6gh6ae0foD3qpWv4ZSu3ltGRN5joUDMvg4a4fId5gPPWXVVM5VHg/y6NJbIZJy8Jm9FAZEZeHV2vmIizBqoPbKNemZseUdneRSY1M+c2Z0/Fm89SNxpBNi9fmVpNqWIR4Q5qwnE2DJE2EtKAQarJHwtiCtyJJCrtsBi+4dN4fVyjK9aInHwVAtS9HdZ0JqZ1k8wVVZgkZcZynR0gSyXmFya96pIeow0/C60MxDLREjGywCFUYfFa6m1cDpzFv8NP8dK5JRjU4P3I/74oUOGo6Cs8MnsrO0qWIN/CNFnOYRY5DnL/iD2RUFX/KJGOTMRSwxjmCrqzFrU9EsRnQOtfihuwm/lGyjra0l1MPT46vw36F0tjMXmFPZAeyEqNtroak53EfO0A2cCmezABpby/5klk0RNuo2ZFnf/NHpx0vmTEqpQ7SRoSYsgBLmTT0sOLY9CPkbctBmhrOfXiNm4O1dorzY8wa+Dp90hiOvOCm3TWcurULV+okeQDA06e4WOf/ANK+x3AND9F5XjNtgdkYws28PNhsduaYSSLCyUGnjENtRYpXktb9rBBlOBwRvGI38cgiXq1MsCG4E7cZIZbMMMdYRSDnozvUgatrD6fvsti88hKGVpTR46tgh795yrW8p+NJ5r10lDvedwt5dep7eXH/BnaEmhl2FPHR7r+xzhJc8MHvnPR9ngzeMXJOAm+HkfPgUw8y8Jv7sZFjfd2H2FFdwuw5t6F7BogYEv+IahzIKBhvdGCPizi9EQ5J8KGiHHOd0z+EtoF6Wq21fKzvIZxWDp+ZwmNOJf+96l9Cm7MaQ+R5ObiEszp3clru3TgyVdPaM9UEEdcGWgLtlOWGUYWCYV+H25TxJ2fjjM9BYNG59nby3r7x6y7IFHqHVuEKLyTQd+bU25JMJDHpOo5Uv8DwvD+Ti5fTs+lTOAdjrNpVIKSNeSSeu/ByUOwILBzDB/Ek8ti8Fms2tmMbH7teO+VrZMbTMmdC1Hgery1JR9GlPN7kISME2sHotP0a7W18VdP4xjoH+6RlhJIxUnYnK9XX2CuWUzUUZs5IH9WRwgR0KDTAYf/mGc8pRZYSH3wPL9r/gw3SqXRRhS1bhD1ZR1fgIHWRRdhNJ1HHEHsrXyJli9EdPDStnVXdlzAnL6Erds7PLqaGEgSCP9pfRpcsVjp38F/hj6MJiYgisMl5rnCNEFRVjrpy7IwHCRhZfGoG2Wdys/E0l8U3TbSfkzQ0YSAz/bM/5Kmjd2kaSRIcM5cgUjV091RzysZjNOQ0/DWnInsLYUATi6SUZbN6lL5xAjGAq/MwSiZVEK7UbMh6fkoNw3zFEuTwMVpq5nBo7nJ+8cNv4szP7Lk8uGABnlQaVR4imXHSNDh5nnsvuoJNixbhy+RorWlg5f5XaD6yi1dXnYOQJNION95kjKVH9xHMQuNQK3M6BlAEoAoOVxQh6eDN5ilKZnC8obr8kM/FvvpqLq/7DIok82AyzxVuDVWSOLT7JwzKYUZ808nHrpxOc2aIHstPT00VmerGQr+rGi81LePCg5OSBmpsjIrOdrzxGD3F0z2jr0O2LBoGI7SXBsh6/WSr5iBOkEbs6D2Glogia43YPJdhYPFHb56IIqjRZa5N2bH0XpAUZLVghAphoScewDL7jmvJBhiARb6oglzpVM+cq+MQcjZNun4BlsPFaWIr50ibyUsqtjeE4F+U1rBRrOXN8FV+zN36efRGSojMXoM5eog5HYcorruS+5bNYV+ZnzP3pzntUMGgeWmRk6XtOYLjBsL+WhsjylZW7nsNl7oaWWtEVsb7NLGJ+oFD1PXv46VzzmKkpBQtPIQSzjIq1VClusjJe8jUNuGS0pyxYQtJq4G2OVegax4cmZGJNPCZYJmj5BMPgphcIEpKMXbf+wrbEXz/qhB5rTDAz8r0Uj7yK7rpojgmWNUi2FcvsbhD8MHnC/eT0zTs+smFrForwJuxYyg5+oolukok9GAF7lGD83cO48oLdEXhpRVrWXtgN950gY+pKwpCkjAUle/d9FFseh6hSuRVGxGvj/1z5k87ly+bI+6Ymdz/ZvhSz4vc+r7P/o+PezO8Y+ScBN4OI+fRp56n7MtfxpdOYEkStopl5FbfRNKZRdF1+mzD5OU8MSVFm6OHhJLi6ugZVCsKvct+xH4zR09eolITzLab2GSBy3LgG1iLqaZxReYxVvckumty5WcaEooqCIeDHM4tIGsGOOxewAJ9F03GISrLB5EVAQIq9n8U32BhwEmUbWdg4W+o2/xtbNkC18GSdMZqnsaZqMc91jzt/kYa/06f/yCj+68gNdQMkoXqjKA64jiCnaxPrJ7Rw2MqWdqaf87Q4DxG29aRdPeQc4yxcPtjNLdPkqD316oMlPmpiMRZ1KoT8cn44xYyYMoKO1b8Fyn3dBl3YWUx7D1sWlLD5vISEALbjlGk3MyrpBqth093t3Pgahf3y2dz3qHthNIJsqrG40tOZVZkmHXtByf2H7VH2FjxEnYhUz68HJu3hTAqg7m5ZEfPBdPNLeo/sKkuIgSQTRtGLsgjC3854/lfh0N3M394LcF0OY2jKxkLtmPZC5PNjZnTcYzL7j+ibSGspLhCPMlN5udJGC5UyeDxwDeYnzmxy/p4HPTW0780iSwJ4slSatpNVkSP8vCC5djdSYwN1zHPmE+3kqEr+AraQIi1u3eizlpJZvYqYlIaE4u8ZLBJmx42VKOjOAc6GS6exWigiIrhPoLxMLrDx9MXfIAjFaUsO3KAlNNFV8Usvv2L77G85RB5p419669m4SuP4swWjPTdS5tpnVOPMxND6Sv0x5yhMZoGZ65QDRB3uXlpxVpOPbCdSEkAey5PddfAlH36/W721JZNy16ULYFiWejqpEHeFLqCpf4mUqbgjnyGf9cclKoyR3pepmLnfRMeobjDRneRD3dOpyKW5NBpt+KMHabDOEZq3Ah4IzyRMRbs3MaY28FQoJB5FHAuwhkLM6L0YijT3XMVkQTNvaOolsX+mnKOrFo74dlRUnEcg13I4waj5rkGSQnyw6DKtyUnzQI+LmVZEU3SROFbD0RbKR3aQWvjNVgS5GK/BVHof5v3RiRJIxf/E0IyyZVVk/WXoMgStpF+7KOFlG7dGyRbVSCzf4678TL5LWdlG91qGfflJ4vKFvf3kU2MYLe5SJfOIuMuGIrv4TEUu85jB+uRThD/GS6eQ405VVgxo0lEHGO4B/6KXZ+6wHMH51NnO0ztswWy+2hRES+cd25BHLH9IK96VrLXv4hLxzqpSzxDcu4ykCQWLn4Rpa8e/0MylYNbAIj4G9i97DPTrsnIbsfIvMLrWvTuCgM9GSKfiCMpldh91wEgSSl+cZmfsHNynpk79iy9chzdsRCbESVnn4OSb8MnXIx4llA9uB+DMTLepTR0d5HTbFSMDnPTk4/w4DkXkbY7CCbibFu4hM7KgvbRu5/7J68uWUHfcUrqmp5HP84DeNOTDxOMR/npdSdPBK5P97Jil495fQY5FX50ZQBTflMX8gQuaT3KucVO3nvVu076fCeDd4yck8DbYeTcdd/PcT25l6ESP5c8/SSyEGTmn09p0zVvfTCFtGaFyYE2J2ewW9MLxumOUdLBowwu/P1bk3YtGdfYQoqPXYEzXhiQDAy2eLcR7lmJXZKoLemlwSpGM7zTDje1JInSnXRnJTraCgaSqSTJ2PtAUVFMB4rhJuPqo8LyUGwVI4oPQjaEOx8knbfRnbIR93WQcwyDZWAf6UNNdHDxziFkoK3+UuZ0/POEt5C1udm+8nZ02+T1CSuBkdnCoCdKidLJr+feQr5/cvKaiADKEpJhUi0N4E+MsSh/BLfsonTpOn5aX8ZFu3bizk8NKWZUG05jkvf0UsVLjDnGqO65jEPJ9VP2na/0cK/6He6TriSKH92CFLCl6nlS9hNnLtREFrCi9wL0TDXbHAZjsuAzxX+lPTMbSc/zYfOiiX1fVPfTrg6z3tjCE42n8tDQKdymvsgn4m9uRAG8WLYUagdwtAcoeXIZhiOKWRcg0rgBJWrhHTsd+0GQTQm1YilCWIhsFFv9mQAT2V0zwjSwhYeQTAMtPoZkGpxzsJMjs+fylY98BkcuzXBxOZUjQ9z+u58yr2t6NsneOY30+/zIZhrF7UY3JURuZo5YeTTJ8q6hGbe9FXKqzMammimGDABqMRijyHYNlHIUaTluWwUXBAqegNasyXfkLJ+Q7KzQVLoyMXb1/YGg6aMyOoB3rB9PTkcAB5rew0jF6SAEvq7vMuS3YXkCZGbNRsvnwNApHxwiLNLIxtSVut3/CSTZgS0XxRvZQUYaonS0hfZSL/nxrDNFSBTHkyiWRdRpx1AVHHmTXlc5PgqeLkVrQvNcwnYtxX63wj8ofDOPkef7VobvdG9GSDaa9z6MbOmMBZrYu/TfscwYZm4XsjoLxVbQtDH1LvTkZEhVSDKSeN0IkRGIgoqyqlJvdvB+5VFa85V0u8sYPqYhMsW0zCtwuhw9x9CS0Sn3nKmag+ENUmKN8gn5T0jALsdcjhiVLFG7KTXGeEVZyFjGSaQtD7ZlZEJnEEpJpG2QTfwNV7oPyxdEdknU1R3GtJsEG+NoMYuyLxe8DklXEc9cdDGGlkeNhnEOdHBv1Q3ENR8LMjnOGbyXVH0TlsPFfGcrxau2EO1YT661gbJz7kUdlHD9uIa4rxZnZpQgneCU2Vl8CuVFuyk9EsdfmieJHSMqSK6Ew6KR5MB6NOcpE/dr1DzPHWuufVMtsf8vYEHyGJ/suZ9Vsf2E0nkeCH+PlFU8sd3IHUFVRhAlVaQ9SdydSTxaMcIuMI0gJVIfPfml6GSRZQfyBR4+efn0rLR/Be8YOSeBt8PIeX7bE7zwxG7sksGibXtZ0H4EAeSKa3AUNeGuPRtJcyPMLLIjABRyHU7OJp4Zu+bdha1iF5pacBPLph0hG9iSlai5IKGWa3GlJsNTx7ImB7OFgcqnCLJWEsk1ipar5HSvHU0UvAcmFq/pYcIp38QV6lqMaGjvRGhNyo4w+8h2RgJeErNWI1Q7jkwFlqyTt4+i6l4sJYcl50FYaNFRvIlByuYHqfvba/hSgsHSlRxa8EHqOp9kdmehfENWU+iefSlZrRjZEowFmzA0D5Aj472bfUsWUus+xmuZ0zk6PA95MINVbMesdCFcKsKugGNyMpNNE0uZLra2uv0Qy3taSCt5Dgb3siy8DHWcp5SVc2wv3YZAMOIc4ZN953FvsplzbPt4IHs2RdoQv5F/xAKpn8c5h50sZsg2zCuzNk05R2WskYyWwGHa8GXKmTe8ZiKV+xWHzmsOA7nITq7Yzs87fsheeSm2VIr3KZMrnz1KJzu0NqqMbq52PMvO+NUs9j3KXqOZV1lFwD9AqV3C5hjAJZI48oK8quDSV1DR1ojaP4IRquFIwMAmVDw4KGs7hK2sGdlVhIHJfuMI5VIxDtVNuzKMJOCA0k1+BiNaTidRk1G0WJjhQDHlowWPSXE8zeqOqd6T9gXVlLWP4M5OlwVoL/FzpKJoxkHfrQbImkk02YGERMYskJAlIWjuHaF6LMGox8mwz8WC/qlGUXfIh2pZyELQE/KSdNjIKwqWbRY271VIkh0hDECZVloF4Cyvgm/cm/JYIs+PPVk+ati5TrMzZuhsSh63s7Coif6DrGsWKcdqzvUXvp+W6CD7R3+PoSgIoDSeIu60k9OmcjbccimG73okafoo4EoNUNX7Ar2ubsY8jmnb3whJ9mPz3Ygk2bnXk+UK1cb7KEz07ULnfVKG9x16ijP69nDzOV/AlBXO6d7BZYOd9M29CYCcIviNM8b1WS8hQ8EyhsgnH5kIx0hyCJv3OhTZjmEmSMqPkquoA2DNxg3U9A8iAb1Vs3j11FMLx+SzeNqma9oIWSHZuBhkhfOsDayXd03b53V02it4qGUeGVOjpWE1szsPUpk3KT2lB+/CSb6ebqoEnrTjfTGH3WXx6tJ1dJQVUtllQ8bZsZuo5OGxquu4PZfkIZufZQObEO5BcmXVICSa8ilc617G7RxDjszFkazC8cJGnDve8K7IAn9Nhso10YmfYl1OUgN2dl87C19xko5nf0jCnBx7LOMw7cvh/vmFxZLHSOE1UwzYCwTv5kQrRXqUDaFVJ+yL6swgS5JHSClOYqqHfnspuqQSts1cCfx12Kw8eXl6+YeP99zPPk8Tl45u4NqhZ3CaOXaPzeZg1EdcvQrZVihXIqwkoCLJDvKJBxEij7CiSLIHYY6h2BaCEhifHiQMaxBT8zDvA+fxrtNOm3befwXvGDkngbfDyPnLIw+xYWeYSrUfQygs2HqQZZ3ThfEsJPY2rOHpM85ioHoO1UkDp6GxeMDg9EgMzDxpR4icnOefRc+TzdtZObwKI63jcQfxuBMsYLpmhoWJzPTJ3MQih8FBWw/3BF5gSe8SGjoepCiaZqyyjPLq69mVh1CmFlXLYOoOQCKpxtC1BJKQyToH8ZJASg3gTyVxDHXR2DWGc9zhsbfOwZHlZ4GVQ9iLJgZtbWwIbagDn1tn1c5+XMdRMHTVxWunfBVD8yBZJhWDW4gEGsg6ihFvVIAVvbResIdHdp8LsoQwLKxqD0KTET4Nq/itJ4Gp7Qlu3PosnlyGLaVb6HP3MTs+m/mRheSVJFtLdxK3FbJMarNl9Ld/mLNsLXgVwXt5lCY6iOLlfi7nsKbxUtXUmk6BnJtlPVfQNDLzCqZHNfnzMjv2uI43HeYK7w4q+0fod9TiG4vzbteV5CyBXZbol8d40laoCN5IO5fyAlH8/IGplX1VC9Yb85ltlROWEryiHZmQZJ8JFTkXuqYyKp9YtBJAymWxRYaQTJO4ywPZNHGPH4REfd9kuGx1Wz/FyZkVqV+HKUvIQtBRWsKRch922clc/0o0yc7+yEY02c5ZFdfj0QJYwkSWJt+D/ZFNHIluxcKcosCtmBZVkQQJh42oy4414UrXUGzzkGQvZc5aVvkUTOEhZXmxSSYmGSK6E1WW6chazHHIeBRBcFx/akvS4Cf2LJ3lGouHdX49Xi/pWNbEJUvsz5jkxWRS9nq3QrFWOLclBAMvfZlX6mceWzy2UvKOi5GVwne8wFE47lB25nCNM36UlDKKJsKk9Mk+l5BwqcUYWiNCm4esBNjk0NnhMPiH5cR/XI2v95Kkh+ntu/IZfrD/Re5feDm9Zg9dzlIu7tnG3MDpKONGv2UMYBlDKLYmJHnSu2wa/YxVHRcyFQIlncR0FzxIkqHjbjuKqjag2OYjqVVY+cMY2a0IK0I+VFYwLoD36o/SdJwI3RuxMbCYXVt9BF2luBbphBa/Rs5woHaVsbSnjUxUozZUCOVnLSf3mdfTrY33vwBn9xHUdJId/mWs8a3karXQ91/J9bGg/y9kqhswPX4kSyE0shpVS1OnbmfQYRByl1MaeBTvUyG2z16IL3yYqxtfwEDhj1zNqAhxpfkyHfmVVClHWWDfTlj2cWBWNQe2f3PavTTZfkFTyV6qzcL1Hk7MJaiNogs7nenZlHqG6A4WUTGsYysN4zcSxBUPTxWfxo0DjxMypn6zBgqPlZ7FivgBdEmjJjswwY/aFFjOmObn8pGX0CWFdqkcNZbhxfB8grOKqE8e4kDEh4wgZWqkDQeKYw2qs+C5F1YKI7sDM7cLULH5rkeS7BjZ3aiO5UjyVH7aGxcQ2tz93PzZqWUy/lW8Y+ScBN4OI6f3+cf5xZOtqN4EiiQwhEwmUocUHsIT7+TdrS/OqJkhkIgGZvPykqVcsOUJXLks++c08bcL3kf1sA5mBx99pCA2t23efF5rXsDF8ZUsd715QbcRNcJRZwf3lDxGn30YxRS87wXBRTunD3S7Vs7GV/pv7He2klcyVBtl1Eh+RqQ4Gipa+AhNL504pHQ8XphXwuN113Klsg33UDfzDgzjecNCPuKrpbXxOpLeGhKnbMS79fQZ2xLCIuY5wgsr3BxNzUG4VOSkjlnmRPhnLkr3Ok49dAS/3c9AsJgel8KYTeLSxCFyvnaOjq7ggkPbyMs6T1T/E+sEFdgvjtbTOFrGfmaBM8KIY4TaZC2zrVG2uaDN18aIc2p2zJ09Bof7fogkHOz0WayIT67SDQRHnYJXFzl579HHWZTtJxBRKG/Yzp/Mq0CSqeqLcmHR1cRMgVcuzOV/yT5MNhAAwEEWExmd8fu3rGkquf9XcPQeoztYhmIZxD0ByiNxSke6wZpKGG7uGaZmLEFOVbAb070/I34nbeV1jDkmw4BOxcPZFTfg0Qr3NZjppMxRO6N35XWMGRavRIfI5w9i5nYCoDrPpmBqWEhyADN/EMU2n6A9QJU9T6XNjkuqeNN2E6ZAAjzj1ex3pAyetqd4WpUwZ2tII/BUzIH/DargeUuwJWXikU2Wu6eSMrsjw3S3/5y+UGESqEkkMIoWM2pfjaRUTCwEijUIuwQpBBcKDQNwCImOnElHzpyimg0ghA6oSJKEEGLKfR3Nt9NTWsUdwolHksljMCzHqLKKeNRK8YNxz5wduBobg1i8iEF1YghN1ml3T3p9y3NR7LKXa5PuKefwa/1UKgfpyDWTFRVk5X0kSqMz9qur8ygu2w1I8nRukpHdhZ55mVx5LXqwBMmEFQf7MBSD/uA65oy8ij23l7jm58zmg/i0PI/PnY+rvPC9xaIVXLi/BV0PMKjPY1ivpi29iqBtjMMOF1lXQcJDMvQCWXo8RHh/5bX8TimmSC2M+1uFztHuu7BZGdKzF07JVjseVYkihlwxdKVgPFRkE0SFh4xz+nsV7J9HSLOo8PyV9LIY4d2fwor7yR6XSSusDLKkI6QTzz9CWMj51zDkCspsg9Q4u3FLfRyOWKA1UeM4xlLvDpxqBkvIdKZreWkwRDTvRJJ9FGsDhXnI9BN01zCUkZEkG4KCyqLqWIoQOpLkQJijCETBkB3nA1rGIPnEdNkGWWtEc1+CMIcw861IShBhxTFzB0CkkJRSFNs8VKuP0uYY137hnhPe4/8Gb5uRs3HjRr7//e+zc+dOBgYGeOSRR7jiiismtgsh+OpXv8pvfvMbotEo69ev56677qKxsXFin7GxMW699VYef/xxZFnm6quv5ic/+Qkez6T09759+7jlllvYvn07JSUl3HrrrXzhC1+Yci0PPPAAt99+O52dnTQ2NvLd736Xiy++mJPF22HktG3dwRM/vJNtjadTooTxyzmiloOX9AaSws7cVBaPnuGckT7mjRykZGQPsjg5IbzjkVcd3H3lzYDg1LEiDNnNpponORY8gCpUFqZnc8jeTv/45GvXFeaOBvj4wzGK4ydWE35ipcSfz5Y5tc3L3O48VQNpFEvCn9Yoi86gzaN52brqyxSH9zH/6F+mbDtS6WZe/yQRMafKKCZknSUMlq+lre5cOstsPLrQRjZkQ0rr1JgjqHGFWfEULVVBVqc3kXXaec57/rTU09cRiIbBEqzd8zI3Ho0yt+5qoplBeudmKfNsI1W8j7x7EAwHumrQSzVfln7AeYe2MWekn6P+o/T4OvlV++08UPQsDxcVPDJLUk3cPaATl/bwbdt6XqtoJfsmSswAs5IlfKOjiu2p60HYGLUJjp7ipVeCuEMm7pSp6c+xuFenOb2PFc77aKcWGYv9FLIZlFSc2TEfZ4TOYVi38CsSdlni2e576Kv0YXqOy8QRVmHwzuUYqKrHpzkQbyC6SrkssqEj57PYRvuRLJNM5ewpBTG1yAi20QHSxZVo+SyW04XhCyGHB3nqnA+iGHk+c99vkSPHaC+ddIl7MnnKY0kahiIFcjiwYX4NnqxO9VgcV06nq8jHYMCD8QYuTK27mdUlFyHPEKYBaM+ZqJKEZ3xzSH3jfnHSZpS8ZWfAKKZvvK9qbGOUaaXT2psJlhDIbzB8DCHYkzZpyY1yV9CJkCQcTXGS2Qo+3KrzEdubewwH82G6hMQp9hAZS/Bc3MAaH2aPNxRkoNYusUMx8WkS10gFg/X7ZPgHOsWmxCczDtY6FXYns6TzSTKqD6TpnloAT7IXxnbzl8Xnc5dwYx8/12b1KFEpxcX6cvJC511WlNnRTm61OZjvLejv3EaaTcwsTDrbGKFcQMispE6AMyeh58LstWWop5JKESEj+Uloj2M6Hch6DtMVwFIVnD1tOMRiVMcqhN5OSnRQo/hQZY3h5EEMdQ6SWkY+/Q/SNU0zlstQklEkw8Ax1MtZJe2sCHWxObiQBYOd5OQK9icv5GBUxiCGocbQ/UEMX2BKG662A9gMwVFHJcfcDczWTW4PToaDkgi+HHuVtWOvFso8VDf+y4sGOZvBPzIXVaundu73cTe2snPbR/COrDpBT//vIYSFMMPI6vQsMMsMAzKy8uahrOnHRbD0TozMSxO/+YoksjGNvHFyNepex+LLLuG8Gz/+PzrmrfC2GTlPPfUUr776KitWrOCqq66aZuR897vf5Y477uDee++lvr6e22+/nf3793Po0CEcjsLgcNFFFzEwMMCvf/1rdF3ngx/8IKtWreK+++6buPi5c+dy7rnnctttt7F//34+9KEPceedd3LzzTcDsHnzZk4//XTuuOMOLr30Uu677z6++93vsmvXLpqbp2cF/Sud9D/BkYODvPjjDeRVP9tKBqlT+rAxSTA8apRwzCxCIOFz56jNZ6gfjWCP+VjZ/gxaZoiibIKMsxh3ZnhK2zmbj87ai5jVvwlPqn/auYf9KjnNYsyj0h1yU5pbRijrp2JgE65MZIoxNRJaSHf1cqKBlfiS/SzZ+3NsxlvXUtm8+DxGS0/Do4dwJg/yp/OW4U0Pc/6+IFpumPmtj1IyOj0811+2nD3NN/HbC0uIeGYepN8SGQMcCsHoKMWRYXzJKMsObqN8dJhQMs1iz2qcy5czNO9PZEIF9WUBbGMtGZyEKWIr64nki1jW3cqi/oJb/MXKF/l23y08bx7kA9KTHLBfRFQOUpso4ovBl2hWOtkajKMrM6d0ypZCTXQBTdFqrkyk2ZO+cmJbIKhwhpDJCcGraZOMKTAssMsmzdV38HRuuveqqL2Fcu9a1nqX0qWbhBQZryyxse8ZOjmCVVSO6fGjpBJosTBHauYT85cxVD6LzuISFrbs4czDO5D0HElFJRQurGaLE1lWhWX2LFxOS80cNpXJrNu/hbjDRQ1lXLzn/8fee8fJddXn/+9z6/SZndletKu26s2S3LvBNtimhWJ6QocQQighBPIlpAEhIUACIU5CCCRgMMXGxr0X2bJl9baSdrW9zs5On7n1/P6Y1a5WxZYJTn5/8Lxeftneuffcc8s55zmf8nz2oxQnKS0+n6eDM4yGJa/esRfpWWxf2rKgj425EitHp4nYDodakqwaqwW97mtvYCh19rFk6EsIGItYGgjTHauN07IvebLosi6g0GLUvo0By2f3KercQQGXRjVCZ6oqew6o+JLnSx5JTVB2C4yXdqMoEToDUbpCnfjCoOpV2Z+5h2GnyvdaX4WlarhtITpD2zgevhpzV4Y3luG1WgSJpFVXME/pz+9QYEZK7pJRQorg+ZLL8ClKvnUqbAmrpBXJolPcyy6SB3G4HoMePO72LSqu4Hxb44BisTjdg2YuQgDxfD9tI4+jODm+vvZVbG9Zw58T5BXoTHj93BM6jit81EKWt+g3EsJk+9SvyFUnuLrtnWizrqwZ3+YtSpUysHxmiPf2/JS+WBu3rK65Qxt8m0v8YVrKLfQUj/Bw64a5/t5YVrnBr9Jra0hpzS2mEh+kQBFwdVTlE5Q5pMi5hIBWT7DcmaErd4y4PYEnpyh3LUW+AIkMH9vL5fUVpLqY49b5TFXALT9MuSWBe4aSF3pmglBmmorRwfdTF1FVgyjS4++n9rG18XLGxAwRGSRKgH+UFdSRH5ByZvANk2pzJ1IoKHZtkzCn++T7aMUcbmyeNOilCkYFSvULLUDCdYiPNKDpyzEVj+jSRzHad9C444tM2lD1i3iUKXl1eLPfgSnytBv7yLidBJUsLcZheipXUTwp8Pc3Cd+bxi0/imIsQ9UX4zmDKM4hbGdowXGL6jup71hCdKaFfWOPkLHGztiegsri6DomKgOUvTwNyS7U1Cbe+pe/+xvt9/+Ku0oIsYDkSClpbW3lk5/8JJ/6VK3iaC6Xo6mpie9973vcfPPNHDp0iNWrV/Pcc8+xZcsWAO69915e/epXMzw8TGtrK//8z//M5z73OcbHxzGM2u7mT/7kT7j99ts5fLhWf+Mtb3kLpVKJu+6ad59ceOGFbNy4ke985zvn1P+Xg+Q8cvt9fK4Uoq81yrrjFc7f/QhmUuBzuhbItB9ip9vGiJ+Y+5siPd6Q8xFamGnh8XDYIyItzo/kMXyHQTeC4cA7n7mT1eO7XnL/SsEG7r/6AxSu2sadR68m5lSp9zUuGo6z9cDPaRt78qRjG5lKdjHQvgXDU3libQv3XFAL4OsqH2Baa6JgLBx4kYrFjc+WaR87wqLJSZ5d3c39W5YyFVPPWPwTAF8icjYyosNsPAO+BMuDoAaOj9ZbYPnYAV515B5UKUnZCpGqx3q9G8X38RuX463xGN30TSTgYPANPsVusZm2mSlKZgBH1dh6/BArJwbn34E5TTo2zKLRK3EC/exVQlxYqqIZOj9b/KsF3TTcAFf2vpXp8CiHGp+mNb+MNeOX0lro4qrYt0l7TewtzcfIlCIKi8M/JlMukYoppAIVfLmGrplLeCbwLH1+Ce8k4TDhOugzU6zbuw+x+cNsDCxmr+3Soio0qAqPTu/Bn7qfx1evoXVikKH25fzhT3+G6c3vC/OhGN9+88fY3HOYK7ffjlQVVNdBSHnW4HapGgjv9J1ZydA51pRgJLlwbIQsB9Xz8QV0TefpnK7FBhSCQVqu/TpCCDzpUXHzqELn+elHWFt3MTE9eZrVBOChvEPRB03AIl1h3PUp+yC9QYTaQiYxxP1OG28oQlCYRFQwhGCxqdCiv/Bue9zxGXNq7c04BSrFB3C9KVwsMkYdeS1GwKswHmjGFRoZvY6gX+VgtGZVk2GwLmllzcF/4dDSd6A/kUf4kg5HISIcXlXYxxvqLp+zCP0TVW6l9izf7xu8Wwngy5obbNorkXF92owoKBA+xXF9pHiQztAyzDMFhlJi30kFcZOeoCgkId9nddXlubCBIyCFmMumesC4nQElilaYwSpW2NRwEee7yxa+Y20aPAjLFAemHmHqyA40s0x86VUYSoivihiHY6eXSFmLShOCvXhMIbmpovNuVaWveBhXhLFFnCa1SjjQzJ2ayy7P5Zh2ZnewLuHCzJN0F45i4qOaIfISnFCMqKbj6QpuvGa9FHaVSP84mrkRzzqAJ9I4dY0LhCdPQCkXcUdG6Qt18WRqPiPyyqkePhFtIxloZbt2FBuXy9xVDONzM7X4tTeM/oI2axyUOo5E15PXk2y0n8eJ6oSmi4SFStmeRjStpUPrZDBzjEFZobnUy4xRT0OyhUJdjUAq1TLh4wdRAxegBS5GCEHbBd8moQZJ9d5AnVXTIHKkxPJhxpO4UrK34qPqJRo6nsNNx6Acp0k/SkIbY8JezqSzlJg2Sb3WR1DJM2KvI+MuouAlaVCewxGtmEqVVmMvWUtyPJshpJuYcoDxauS053U2RPQUMrSS65IPEhBpSn4ze8QNmDmXmWoPi4JraQt1I4RKwRfoQkERKlOOR4suCao621Y9yJvf/cVzvua54P+E5PT19bF06VJ27drFxo0b54674oor2LhxI9/4xjf47ne/yyc/+UlmZub1LlzXJRAIcNttt/H617+ed73rXeTzeW6//fa5Yx555BGuvvpqMpkMdXV1LFq0iE984hN8/OMfnzvmC1/4Arfffjt79pxuSQCwLAvLmicb+Xyejo6O32zg8W0/5ZOppXMBkV0TDpuO9NM0sx0/XoeOj3dK9e6SYlLUA+iWg+r7POgsJ2+Ga4u8JsCRc9OhH68F2IqcjZJ3+N3QXi5/6Blah2rkz1MMbCNKsDqNrUd4YtMV3HvhVoqhBCnP52BLhJng6WniImNxgbsbZybFdDRKtGrSktV5bF0Q60UWkpcCVbrUy0mmnRQcKaOOlQHJewZ+wHiyiadiFzNTV48ou6hlGy9soFZsrrC2sW5kPxvFYpovakd1IuRbnsIOTWDF+2vPkRAPcy13cxN5kUD1PC44fpD1I71n7EtPvIeeeA8bK608FxmYfxazW015Us2pkB3jlUd+lwusLBdEf8TO4htQhcPG8B006MfZV76Gx/MfBWA8oXLfBSG+vGMPT4g+pHb2uCHFqiBVDXyf4PBRuocm0V2b4PmfpVtP8qjt0KEqLFVVniwMseGhv2Rw+Wp+dcG1fPi/vj7XTlVTTxO0eynwBeSCBpGqy1QsyFQ0zEQ8hHtSRprmecQDVRJeiFBa0Drch3rS9FFYfinNq9+xwP1U8SSGAuoZyE3Bk2wrulT8HIWWg5gTbZiyVtPHUgqosQjrjH7eY88vsh8YfYyItgwhQNGX0WUoxFTot300BAVf4soTqiW1OmVO5Ql8p5dhM8lzic0MB08Xwjwb7M0J/PowG3f+JUdXvofqVBz98OmyAK1elVG1ZoFQfZeEm6NgpPgXGWL5KQq4Z8KoNcATo7eiKyZXt7ydhHG622EIj9143I/DAD45JB0orEFlHx4fxOQKagtr0c1xa2g7KCpyrI+eZIpVRpzX2lupl/Nz3d36TqIyyGVujdRZXhVfugS1+UVw4PjDfC1Sx/MNtYzAzxHgVbOxYJ6U/L2o8kscltsK3ULlRl0niOCw53EfDrvU+e/SkBDyBVn13Jadtw3fSsqZwa5rwGruBCAw0oeez+CGY1RbFy8QRtTGh5iuqIwmlrAjsBrvJNdegzXFddlh3kaEWFNNxfknxjbKwuad1uWoKLydIgOzgdlC+siTvuWYk+eKwhhpVdBQOYJEkNeiTBsp9sfWnNJzyVt5GjNQ65s+PU5gchhFX4EWvBBFTVFbem2EMNHUCUy9j454nMpMPz0ThwnGXBo7dHQ3Qbi9yNixZsLBOqySx1TaxAipIE2qpSyaUmVR0Ga0YlKqzJftUDQN3/31nGMd8UtQjC1MuL+m5R0Q0qVOPc66VyZZ+/o3/drtnAnnSnJefPS9BIyP18ziTU1NC/7e1NQ099v4+DiNjQv95ZqmkUwmFxyzePHi09o48VtdXR3j4+MveJ0z4Utf+hJf/OJvlk2eCtUos0bu5YComXP7m3T6m5YDy0kWPDLR2gfTkk2zaqyf5ZPDhH2L8AnypcBrggcZrmsgbFcZi6XY2bkCteihKS4pO09/KoWv1F7qv47F+dd3XYpwfIyqheJKjHaDZelJDrS2Ug6f7uM+E2TS5BkugJNezRmVUU4IdfkSTBUsDyVtIXyJKLm4y6JwWuwEhKp5qmkVkbbITUg00rXrAmvsI6zK2MScYyyd6mPKqCfhZDGkiy10fCFIOR5bzHU4r9vPeOpuHDRUPBQkz7OFr4nPgpQ05zN0FDMsn9xLQ2FmwSJ8MoZDw+xP7mdJpX0BwYF5cmO6QTqyq1gyvZHFmfUs0neyJnQ/x9UQ19d9FYDHzDi7R29Bc2qLkqXBf10R5pdPZHiY55H6wm/0ZCjlIm5xil0rLub1j9zL+T09qFKyv62extkgzREhiZzojxZBAouOHuTDR+fVknd2NjEeD7NybJolU2fW5anoGs8uaaEUMAhZDmHLZt3QFKrvMxMOsr+9nqpxZhXdFs9hcyNM5zcT2Hc3Qas2xlzVpGAEmFn+amRyLUti9adZaoKzQbxVX9Jn+XhAQEDRl4zYkoz7MD/f8hzT8lVoRpH3zBTZ4+s8r2k4VMFeaEW4tfkirpy8n/bKCCoKPVorUlpIbwJFSSFlZTbVWWMk1M3hUCNxDMYTV3I8vHBOORuiToWry49x11UJ/PrfIV4e5wf557ixehMDi1oJ9I/gnbITPkFwAN429FOCts2/LnsH7xNl3oDO1eisRkWd3a78EIsDeES8Kq1sJzC6FwDHt7h/5D8IqBEqXoGuyFouaLgBgA5UOlC5iRcOti+7eR7XDoKiohZz3LdmhqnGMmJoBL2ocblsoN5u4X7jCBmliCmLcyTHVE93FXUuvppvAJW+R5hOLaM9Pr+gqELwxwS5Qep8zChzFJ9fnXDPq7AMhd/FYBxJWVq8b2I3pdwYD0Yb6E+u5neG97OzpZ3bQ+24ZzD0/rD9Zpa7VRYLjfrK8xhBhWrbEqptSxYcN+WHedbpoJLYyl8lQhzFY/sp1vM6zeC6Ro2YqBGcfeogh32VJuEwqszQ4af4SsXiQ4ZPSSg4ysLxkNdj3HnCqhnvfsF3AIJbuYhXuQdo1Co4qdp3HJjswXZ6EEoc6S8cq0Vg+iQPUCWvMXBAAjPQAzA0+08NJ2sfesDRMyQ1np3gCEwjhuILfALI4NVz6eAgQFaYFFvRPUG3qZDUBIaAkCLwgT7L5/jseI6pNQtsTBVkPEmdWnNRljyYcAWu7Gbn5CHOLYjkN4/fKMn5/zs++9nP8olPzEtLn7Dk/CaxIlzhWyN/wefbP82T4soFv50gOABjiXrGEvVsX7KGhkKW7olBQrZFcz6D7nssno2jaCxk2XAGS4QnFH654VImWub90JVZM3UJeK7uDDLxFRd0hUDfDNdPjrPCWMSRJoNhVbIjriKjpyxyro86VkHkbWTMQB0qoRRqE5hXb6I1qnQdP8xYuQlDdcj7EdSBIn7KBCnx6wMoI6Wa+6LkYpzCNwKyyg3pe7hhfQRx3VFaj17I0IMZoqqF5UsaZ/JIBKlgB01blpPbfDd2MM0RVvAlvoAjTBJyhqyo+cY3D/SwdeDwwltWKwyHh5kITpAzclTVKlEnSkEvcHluM4/Ha9k5m4ev47zha8kFJulr3E5dNcWSiUsRJzl5lgS2c0/uswDc0lUmEy6xarSeNuekrBMq3Lbvr3hQW0dOaQIpsawZArkSCgIiUZRqBcWxCIUtLn3mAG99+NkFfZ6OBFk3m9lwXPeJCQES4kqInZ1NbBqoCSiWDJ0D3d1s6ngLrdYofqrK3ZknCKOwKFrPpOODL1hcsehVpigJA1WoyFCEXNDl4dmyBJrQceXp8UatwWW0TE6hZauUj47ih3vYed6fsSgcZXUkyJBdC/RtVefvv+hJtpVckqpgS1jDkZJjVZ8+28eV4FmHUIwlgEre3sEtTRdBfy1N1QH+RaVWhfAkSAFeVwTteJG8YvDL5loRVs13cE9aiJaVelGlS09kxWn3cgJRJ0dTwz1ENJfX7T3GstESozLErpYO1g1W6MylURIuH3h7K6WGWkHB9w7/kgaR4zzrKANiI2L9INfdNYImXfZF12D6FmkjhQCunXqQO1qvpcvZy9bs8zxbt5XbcLgNhwDwFwSZRrJXO8oSZQJFukSPHMABgq7DlvQIx8wUVbNM0Id+9jNtjdIaWkZKTdEcXYGunF1WfzS7h0erT1DuWgWeywGZpn/5O3ADK9ip/ZwV+10e9IdQ3R68QAw9O4Vjhtij9bPB6wIgXR3hgOxl2MzzFq6bi9sJLrmKEzawo1OPs7v4LBc23kRHeAVrhMZDzJOfKpLAqXmkIgjNV0IzdFUnMSp5jKWv4BKnyEXyaR7wmzjsL6Nf99nku8woMILGsBbgIgymuJCQv5uostC12ue2cYm7hPejUT87XregkULhSjRKno8nBfVaDKjN9zu9/ewMTDCsBMCzOMYYHX6K9mCKO/IjTOz9Ef+45Hz6Ey283YErI618T/jcOlvs8go0Yr7DgKKxD4+lUvBBEeBJ6XJEeHyREAfweNLdSEbZQ1Ip46SaMWamUBzrNILz66NWVmchdDgpDlTRFqGa6xBKHVIWEUpqruTFiWpCJ96ULhbNKTNcEFZInSqcOYs1QZWlhkfBV2nQT7KWnbSE1GvQadbex1OZwqlN/K/hN0pymptrbHViYoKWlnkf6cTExJz7qrm5mcnJhQG1ruuSyWTmzm9ubmZiYqGq6Yn/f7FjTvx+JpimiWm+9LobLwXjR3xeMazwF/4tfGfRCD8Rb5+/vuMSEgVCXoWijLF0tEAsZ7N0MkGitJzdSwL0d42yauI4AUvDtIOgTcIZdC1U6fP63Y9zLNnBI8vW4QXPsLtzfJTJKlpfAVF1ET68TkreoHjkFZ997mNs7I3wQXc5Rz2DL+q1asSi5NQuKWDeY1NisT3Oe2SU9qBg0bjAmAwinS6EFiA9sYsfhXL8JHb+XPFLNbNwMjJ1m01Tu8iFEzQXx1ibPcS6i5by0LoK2/k07139HZraVdr3xQiVLkRvk9hGFl0GyC66k4FAiL+X/0jZipOyCrTm+lmUmSBWKRE6qXJ7SS0xGB0kq2cZC4/VLDNSEPR0mpwkOa0AgjmC055dyZahVxFScqiVVpIDr0fO1rBWsYiqaZYGnuKR8jvmKM95/SFgYRbT5pBCsWkn20urySk14mXkpnjjfY9iBU1GOht4/KIt1NllbvjhdnR/4eSUCxrs6WjEi7QTmdUI2huQaFJykwVJVWd/XRPPaiphy6HQ1s3lje9g3IWIESJd7eW61vcQM1KApCf3HDk7zW5vAl8sQzc34jlHqdg9gARhgrTmCI6uL6Uzfg0lZ5q4GifQfzd5ZTFD669GAGuCCpdqylyKdYex0GI35VjcnT/KYVVlS6nKQL6Aa+0BFBA6OcUl5hZoNa9iUprc0rSwsvYJKJFRqkvXIiMayrSFnzSRUR2vLYS5bRj82rfunrLTPhZeesb2TkCoebyu/2QiOMoE8PfXAKiABRzjLqDVb2JczlBMzQeEX5uvSfuvK43wC8A2k0x23sMVByp0F47gqvNj77H1ZfSWrzMgXC7ct5SIu4qiFkH3bRr9LHcoGdaUj7CxUiVZAb88Rc5UUFSXhy6PMBRezSsW1b5L84Ag+Aud4yJHT30tsDs8egfnjxQItV2EUprGs3Lge4wGffbXm7jSxp21GijlPAfWjeAGaqSvELsU6T2GUHW8SAyk5PElB2iYasVXwzyvHGPRVJmhOHjBMBDknuLDLMsnSBgNNAe7wLXYPfgTjii1Tdj2qbtIGA1E9YVBv6cRnFMQDTRCoGY2Vo0YF3AdFzKDoR9FJUuFi5CcalUK8LSzge3qABFtEkcqBOw1fEbWEz9DxNm1s6474xQRUNurssccoyd+lKHkfoakIDx8DW2VAN3aMtRYG62XfoovndLeR4GPSElOZqlTYgsVXGdv9yIx/022ovBKdGac9fzMrH1DpWXrGKsYtA8+T8ivzp0Y1SVhVaHOjKKqnXhqC57nM1zOkDLraTA0dHWUo+UIni9oVHspyiC6sZyKb6LIBJoSJespJNTjGGontm9iS4eCp7AsoGFLSYPmoIkEIUUhpir0VGwkKiuDL+6ScrUCOeFRUY+TKHQR0etO1lsFIG9PE9WTC7IIbYqojS+e1PJy4WUJPP7Upz7FJz/5SaBmLWlsbDwt8HjHjh1s3lwzG95///1cf/31pwUeT0xMoM9W7P3TP/1Tfv7zny8IPC6Xy9x5551z/bn44otZv379/2ng8Y+//y0u3BZBBFJokVt47Pw8fxf5BCNi0QueJ6TE8DyuOzRO80iE5lIZ6Wm4ikM1NIrmRNCdGJg5cqFBvFNSmUNEaBJLGLJDrJZ5JtQwDf4MFVw6ZYSgNBhXsvRooxTF6WnQdX4YUxqMOkn6gwH6LRfH0WlzLN5WmmGlB6FQM1qk5n4piAqqVAgxTxod12aweJifWIfoiy5GwSdRmcGQDktK/YT8CogAieD5hGSQpoYmctf+HZ/0vklDYYZFyUN8SHxrrj0XjYOsZSUHuJcbucN/A9fs30VHduq0/gPk9Tx7knuYDE6esdjpqWiy6jCqKa449k5WyAG6g0/weP79eLP3FFIyvL3+o0i1wjeLf0moUDO4BkWtdKFwLeKaQpNp0qzDgdSvGCha5GYFxvTpca58ajvJ8tnTzqfDASqGzqHWFI6moguTzrob2RxfxoTn87p6myVS4b+zBo6UPJ1+issbLqXo5DlsBxk5xQAj/RJCCeNa+wEPRa1H0dpOOaYICIQSxvfSSG8aoTagqEmCAqqy9vg0AWuD6mlk5gRyrk3FzTJVHSTtznCvKniqbl748PrxAzQpGR6Ir2LUPHtmiNcSxF0eq+0qXTlnUUwVsoTtKqOJely1th8Tbp5Q30OI6QBuYTVIDYQFqLX/BoSaw6h/DC26H6+0HNQKqjmGMGawQhfimCuRSgiz/CxOYCWB4jNozjGEL4i4EfKGy3T7N5BKkGVDR3lo8AOYrs9DyQt4+7q/RbUHSY59DtMzcRSHSFHwhifbmYxb3H3JvLtc81Te8mArHRmLmaCGpWuYjktHtsKxxpPmGyl5ZpNkpb0ZVao8rod53/qf4vsqzZEJyCmMP9TIRPoMKcAnCSKeQKFrGQQTpN0e7t2Yoph639xvaw7+GReOb0RVBDsadjAUHQIf3nDoKkTo9OwkfI/U1HPYmdlnO1ulXBUqq1py9A6FqaiCLXIJSzpfh1Br785NH8XPD6OEkuQKgwwkg0gp6Svspi28gi2pa5FIRspH6YqsWSD4eC6wcPDw5+YflzGswH706qUo2kEKWpq66nVzx3u5HnBVqoEQzzi7ON4cmCvVArAst4zzhpawJmNwXmIrmn72gpy/DnqVCR4x5hWfg24bndUW6qqjNOspUua5SR6cirJbQBEKAfXsYQk5J0t8VofqpWKgeJBp7UnWa7/Dc1qUL1CijMI/CEGQB5GZBohkMTfsRdhRvMU7OXj4FQznN/B2L4ZM7aOSOIZtXMgVv/PpX6sPZ8PLFnhcLBY5dqymbrlp0ya+9rWvcdVVV5FMJlm0aBFf+cpX+PKXv7wghXzv3r2npZBPTEzwne98Zy6FfMuWLXMp5LlcjhUrVnDttdfymc98hv379/Oe97yHf/iHf1iQQn7FFVfw5S9/mRtuuIFbb72Vv/mbv/k/TyHf+6sHid+TQ4004hXG0Yt/xY7XmdwVvw4PjQQzPMPF9Iqz+3TVWZXKV3IPi7MZnIDNgL2SlXYPq1Lb0KRLevermEl3UQj8enV8zEAJNV4ES6WcXbgAvat6xdx/G7PGvimRZ6/Wz6Q+iSXBnQ3O1YTE0GzaApKtxY2EKm1MTh7FjzcQdCW2EafkObWsgdI2oolu2kQrodg4xy/9LD8svIeGfRBwHY41tJFZGqTd6GEte7mNt9Iru2nJpZmKJLio7wCLp3oYD45TZ9UxHhqnL9pHWS+ftYr72dBSbuaS0auon7oQgDXBezlQuR5dlLks+q+U/SSdgWf4udpBIbOegFPTXzraZVJfp/Hu4zbe7PUeas4g7Z8zlmvDmw0yNsf6WXuoh2WTWaorItgZD8O20Is+wgdfCI40J+lvSBDREjSHlnBe8hU8Mv4rLm66gYAi+Iln8Q8tAkVXeGxYRROCB/IOIcpM+6EFRmoNaDMUMq5PYdbwFxA1g5whoNNQaDMUdAGaEDhSMuFIxhyfig/LAy+eqQSwr3Kcg9LmR5pJRo8TdQvkfs0JFCTW+Q2Egx437N3GPWu3kg/VzOiG6/DWZx8gOFvF3NJ0frnhYjKR+YU+NHUrUjEIWNtR3VH8ShtoHl60Hs3uR/HnlZ59JUK+/mM4gdMrKyN9EpN/ja8k0Ow+8qkP4QZWEC/lePejt/Knkdq8dCzYwaXn/xfCt0iN/D5CWiduA8UHhIJldlCJ3YjiZQkUH+aa5wWLR3SqRpWqIUgUF1qSq7rLcytzrPAvI+jPWwarik3AN+iJ9xCOTbK0fhS1N4L2bCNSnvlD1xSV/PISnrgEhOCp8D6OrHolVvjCuWMS419Et4+dNl4iFZ3rjl8Bwdl6XdEeOgptBIigFrJExnvwTwpA9QMuwtJr1t5Zi+TmwQypUgVfsZA6BE2XyozOEysXYc+6PSJxSevGNNUdgolsHEvXiev1XFH3aoLhE1XRXezCEeyj2wg0bQDpYx34KWLdqwm3XXXafStyjPSRr2BlbfSISzDmkD8exKyA7qjg2uSCOpamsmNJK9XGduxUE7d33o6nAKjovuTG/huIHTuAUa2yPm3RsfoyVCuEXncffqiO0h1D6C0bUaJNuON78TJ9FDbcCE3diIFthI/vo9K8FLd+CfvCOVrqL8QkzEoviUTSp0wuIDoAN1lbaJJnr0D/vwFf+hScDHGjHl/67Mw+jaOY3BdvYRtRfBw2aKNEhUWrksNDYcyPcdBZyjUywF3CIXeay2wev4vLh25cSvOlv9monJeN5Dz66KNcddXpH9q73/1uvve9782JAd5yyy1ks1kuvfRSvv3tb9PdPb+oZzIZPvrRjy4QA/zmN795VjHA+vp6/uAP/oDPfOYzC65522238fnPf35ODPBv//Zv/8/FAHt2Ps9fPPglAkGV35m5ljWjJqKwjaFLdpGMD6I7Pkv7y1QDCjs7W7ml7l08YVxKhAJlQhRfQPnyVLzCv5fL9g8wk69DER4VJ7QgI8jUHMJOjKwoI1SHYKBALJzFXzTDn4fnZcavz95H156zS/ELKRa0+0LoiBTZrCepG7sINIty8iDV2ACeXiTX8WitPU9Hqg53ezfBjhaCdoGskSVlpRAI9rYtoWQGiVXLrJnVsilpJQYjgxysO/gCV4fV5U7iXhwfycbSClZVljBs/JKKtZnx4AyqFaAkLIxSOw25FSioJJRhiiRw/dr3d1HkP1kceJYfzvw1eIm5tqsxwa76Z1nsjfLEmsvIiBh/tevv0CbLPFv/irnjkmO9XPDMHmKWw2giwu5FjfO77ZOHmxB0J65gWfR8RhxJSDhk3TLrI0kqUvJaCuQ7QkhD4Xu9PquExtGqx+Gqjw8064KUYhFRStTrCbQzpB7/T2FLj124NAiN72Cx7UVkzIQ2g97yOPbQjXBqeRGlAn4QtCLasqcopd6JjOhccmwv60b68IRgMNlEX0Mbq4eP0jY+gV6YQS2lsRINVBrbuGfdxYwlTrcKCb+EXj2MayzG1+YtEpHCXorR9QuOjVZKFM4gOncqXv3wz1gbyPFx+/s8469ig9LLJVv/m/FwA4nsM2j5by3g1VLoZFr+Fl+r9U+zjrHu0De55pko4gwMfGf3DPuXFtkwvYGWcgu9sV6SVpLWcutpx2bMDE81PYX0XSJllbXHY1RMj/6WMrqrUAq4BElw6fjFoOoMGD1sXzRBpu1rAOiujaMZJKZuRSvfgxA+bmkZF+fXcdRIM5N6AiTU2XW4wqVgFKiz6rh69GqgJlBpTPWhVlx8I4DU9Lng32D/IVTHJmg5XHZkCNWXeArsb48yWjdvoSgsy6KMhQmXdCRQTVWwQy7xoSiq53PRsRFi1ROmyTPNN4Lq0g7WbDpKXtuML1NMHtqNeqACZ5BAOIF0JMizS2vPVCoqpaVrSYfzPNr6KPn6P8QOrKZu7E+5angNbUPOXIX1gO3QWC2TD5h4imC5k6HxaJmZCNQVat6qsg5BB2wNzDMMjb43tbM/vhU1W8f5laV06QEe0o8zoM5bo4UUJGSIGaXm0vGcpWR8kxFRJSXjeKLEPj/OSjS2oBJEcD8OQ/i0oLDUKVDR49yEzhO4qL7DCjQiis6TOLS6FR7RTGIIqtYUOVmlL1AjlFtQyONzBDCoRfOc7FhS8NmqDbFKWxhiAjDoJXjG6UQisFA5TxvBB/q9JDo+pnDxEYSFzVXLdf7kd99x1nf06+C3ZR3OAS8Hydn5g1/yB4XPkZ+dQ29OX0+jk+SazAaC/kFiwacIKk/hCwdb6IRmd4NVQ8FRVL605L08nLiYjJqgUxznOEtQ8FlEPwNyMQVlIes3pMXr+Ckb2EWbN0S5WEe1EkXXbMKpSXThMuQv4pBYzbSo5zkuZEqcnvETqZa5/NhOFk2nX/D+ikaAY43tFAM10auOzCSdmdOtSau6DtDY2IsfqAXYzZTrqYoAB6fOJzQhGKxvxC6F6cpMsK1xG2PhMdpKbXRnu6lqVWJ2jHQgjaM4uIrL4cRhfLEwNinoaayttOBisrK6nNdkriThRdHFEYLandyqrKJgtWBLHZBoTgTF19GtJKqc950booAt59PqVeHgSY2Tt7quIskGbkPOaoYoroVSLCANEy80f+5Fjz3KorHa85iKBHl+cfNcHaWGQAf1ZhsRvY6E0YirNHCkKsl5EpeaNeaGRK1f/+xX+UGdRIY0/JDKTf0Wn/fmhcZyniSuvgTT1Uk4oeky7lepFzrarKvgMRz+DYstaFyKxjSSH2Jx7JSYMMOzsdWFhMpIPYpe9zTV+AZK9e9Hs3qJDvwLQrgIJNKwKTV+DovGWuHUWdJnOjZvevJeIsLDKZTQAzqeVKiWqoST0XlS6HuE+w9RWryaHYtXs6PrDBaZc8ArnrmPlRNDWOEYB9qWEC/meGLNBRRDC7OllvYf4rqd21CX5whMGPyj93ouV/awvHOCb3X/3txxmlsgMvW36E4/5eirKdW9db4R6ZMa/jDLhlUu21OHQFAIuvS3lOhLrCRgd7DMdwhoJR5of2DutGUTDWwdXI6daKh9grOuOin9uTIQY8ExDtUdwsenq9BFnZUgZdfIleVMc9fSp8i0/yWe3obpeKwZmmHnknrC+Yfpkh7HtaVsHP0eR83jbBi9kt26QDTUlG3d0hKc3HmYDQ+yvtDOilwtpkdY1ZqacWThHKRYFULHD81VJ68aHuPJKl3j80RSCgXfDOJGE7iRGL4ewJiZQLGqyEoWYzZrM2g7bByYoK58ktRHsEYi6mdjVy1dQS6SuOMqkZK74DjdhYAzH447FQ2yY8k8aazOlo/YldrFkWSJmda/AyCc/TGbB3pZM7WMcO/+kyqtL8REcprjrUWu3x6gNSPozBSoGBq5oEmsYpENBTBcj/piZW726LvI4sqWDG5M47vWzaybaWd7vITzEl10M36QYT/OHreVxWqGbnWKtB/GRqUidXq8RuRJc1abkgUgJ4MEcLhc72OX28aQn8BF4eT5TcdFw8cQHs1KgWYlz2J1hlMxoZVpck8vz/FC8JHEEy6f/Phfv6TzXgy/JTnngJeD5DzXe5hbv/JH7F49xGTd/Ee0tbiGj4y/he81/JJxI81nRt5DcmSUmHofifpn0AyrZvo9qa0TL0YAngBVQlkxeTqxkbev/cppvnhVunhC45XyHobpoIdV+C8wkEJ2hc2DPexp6yYfrH24Ldk0DcUs6UictfnDpMYqHGlaxOGWDkpG6LRrKu4URnE/un4el/fup2PmpB1KyKKghQkUfTS/Nmn4+OSNPGWtTEEvMB4cJx18YWJ1Km6eup7rc5fQ5Jxet0tVnudxfQ+9bjeh/Ep05+ym4FXBBwkrGXbMCvjNNGkoRZ94qdZXW4OfXxShdarCpoO3YDV0IvWzW0u69+1l04FDAGxf0sJ0NIRA4dLmd6BoTTiypuESU2DShYQmaNEVyr6k7EvqZ1PvXSl5jSiSXhtHHasgIzpq1ePbE4INZ8gV2I7L97E4iscthOlCZTcuHrUsl2kk/zmbTusAmZN2yQkEDQiOniG4/QS6iv0sLvZh6z7VJU8z0FzEOlXYUYKrt5Jt/mukUutjpDxCrDyFp0aZqDs9KDhSLfPaHY8SdqooLyCh7/g6OWlQrxTR06PIQIhKvJ6SGSBeyOIaASZjdUzEkhiuQ3Nump7mTjxFoWgGGU40UJfP0Dk5xAV9B5GnEBqAsVgSX1HwqxXi1TIxJE87nXyo/bv8/eB7GJY1i8R9TR/jmrU/xtXOnrNxzd5d7FmygnQkRGzq65iV52k49gFGqi04wTzdZh91oV6W5ZdxJH5kzjrpaq1IJYBmD7D1WAN10z6xsolvmigNq+AF9JZOQC1kuaMxR26pSSH1IQBiu2do9ODY5jpUe5CPH/8JRnyIf7Xz2MFN6NZhGtJdDGavRroxpAxz09K7eGzkSir6IVpDPVw1c/aMNQDh2IQGelBOSgA4AV83KHd0I80zKxkLq0q4b/9ptq7BxiJ7luVYNhLF0Sr8yc/PXGj2mRWCn1ymMNxQa0HxIJk3WDEYYfnI/AbEMwMUF69CESp3tN9PtuE1VGLXA2BUdtHd/0MunayJBprjg+gzkwhA8X2cQBDFdRD+wnGSLJSpGDoVc2EQvOr5XHJ0mIjlkA/CLy5WeGiDIK541KPQ75u0lrrZOH4RgeAIY8Ep4pXOsz7fc4EjBSPCJCU9ouLM6uwn4EtIyzBJpYSQ8GL7JTN7nGsf34fnV/ji23Ua2Uxn6ez9lfhzmal+eYBNTfW8/pNffcn39EL4Lck5B7wcJGfk6V/wbw88SXgyy6NdexloOHvQacQL4QgXazYlsrES5I+faWNzVx+Wv5TjwSO0elm6HIkiagP8iJYgq1bYalncnbqUv1j6EQaCbWe9xgkovk9TLs2yieO8r3o7lxSep0+V3B8JMamEKWuv4qcr3/mCbUQrOV41sI2VuW08GmriSKyCJ/bO/e57bTTpN9Oes1iUmZgjNgC2YjMQGWBvau+ZmqbRgaJQKWunC9opnsHbZiRvTH+VPaEjbC6tQp11hdhimINqiS4fStYQz5U34hNaYKk5Gd2Bx2g1DtBp7kQCP0h/GykNitoQofJPmWxdxJHmd5DKu5SaR3iuuYHzn32MJeUyfiiC5lRJTI+SbugEVSVUzdN+fIS2sTEaJ6fIhALs7mxEBOtYn7qRpNlOWFVwZS2Qt+rXlE0TZ9ASOoHvY/GdlIu9uQnj6Un8qIHfGiSyY5qVqLwKnSUoDOPzbSyunryPwx0dWOhUchEmAs28UGWZVckeOvVBRvta2B+qEeFGb4qoXWGlf5xeo42yNFiWH2J80x6GIoOntaH44J90CxKVXNPf4Jinu1pORn0hy+aBHhZlxqlYKhHDBanQU+pmRegYKLWd+YST4gm/jarU8CVIIVitTrJZHUd1iwjpExjuRaparc6QqqI4OtKvqS9LwwQEeB6+AEWdJyUHnWZU4XHMS5H1I2zxKrR5kDTHsQPTpO0UD8gWbGly77IP85Zjf0d2Vp7hruinuU2+jrFQC7sXLWcwtTCbM5Wf4aaHZ3hmYx2Hu+rQqweIT34FgUT1VUJuiIJRM0mccANLdMrJD1OO1GopqfYQdRNfnI/5AVb117FpbCl2qhmpnzlDVFQrPFtKkT1vJ72dnwah0nqoQGYwj6YplK5uQgqFlqHP0VJQONhxJdXoNSjOBImJv+R1w1dxZ2IvHzcCPFZUqASn2dP/AWw/yCZtmA1aTcTlkNvAdrcTATSLHNeZNfE5pVom1H8IqWoI10EAvmFS7lw5J9hXkjojXpxubeHGRstnCIz0vWBYXV/zDFE3y7sfmp9X/v71CttXKsSLGh3jIaJljRXDC8VOnWgd1fZ5kj0SmODplifJtH4dX5vdKEmXxoE/4KbBq9Fn5w7FqiAce85ypVTLNYsVEl/VEL5/VosPgESyeqSfxen5Y361VfDTSxRKZyjoaXomEomt2ESdKA3VBtpL7WTMDIZv0FBpIOKeu1LxbwJlMcNr7ttBKpNZ8HdPwN4lBiOtLSwbzJAqGtTNzCCAicZGGtJppBAUIxHqZmbg859l9Tve9Rvt229Jzjng5SA5zz9xK7988BBCCHTb5poHH+LO84rcu0Vga6eYas6CNquRMWMKfzYOJuZGCHkmU3oWT5knAe/J5nhNsUSTK5g26/nDFX/CUKCFluoka/K9nJ/bQ8SaIuZKuipjJMQM/x2PcH8kxCFz4a7Q9CXX5up5rOV99DbWAsTqc/2sGdpFSBQZNwaZsKv4oSE89cWVdYU0CLsJJDYlPXvGY6SEhBPgk+PvYWtpLYIiivl39Gk5GpV+FKlje5cQr74ZX85LErhUeXJ6GEeNUiKBbWRRvSDaSe4cAIHH6uADbAjfRdlP0KofmDNE3ea/ksnJjwBgiQNY2n6cYBRjepzeTStpVnMke6axyh5OLIkXiaP5Nq+85wHCxSLj8QiJik3YqtEJT0BuxWVUmrvxkKRCm1GFSsOLBPMW3CJHpIWrx7kLm2dwqSCxLmxERnXMh8bwkwbO5nr0niEivVUqas3qdv7kHmKVKo91nIcjNBCCVy56hNcsvYecFcW3HWKhPFVVsq0QYtTS0YSCg4Or2NieyquiPhYez1YUjlQVkpqk4gtK/okYopO+Ec/A0ubpk5ACRaqEvVYKqQ8wmehEs2xet+Mxnlq2nrFZsc5F/X044Qj1uSIr+ntBlaREee5d7LGb2Xz8QX7S/iYUTaEqdVwpuSLzCDNmkGZvjMGmKvur7yaMxQ3mIUIvslM9E3Y6bezzWhaY9Gs3UkHoOXQnhq9Y6NQqNr9+2R38fuVeXjHyz3OHftv4B/41eg2ZbCOLXIPW0Bg9ra0I36VqBunq97jHihKJW2S2LsVXBOGZHxAq3H/GPvkiALE/YDq+/rTfjPIOzPJ2dOswqpdlWXYpHfl21EyettEZstEmjpsBJkIRxuQmqopJV8vP6e9spVR3M7Gyi/H4JFogS8zIM9G9nlx9iGDuToKF+8i0fWOu2Geg8BAt47dz8+7PcbhhO08u+SkASyYuZU/mBgTQpuSY8sNYqGixfdQlniJb2EAqt45XGj2nRmAtQME32OYuZsKP4KMAkjA27WqOi/SaGKdWmCE4PK8J5gXCCM9BcU6i7KZBuNyHL2x+eYHg2v4NyKn8addzgxGspo4zFvy8NzFAtjFNtuUvMV2JIiUVXSEx/gWah9u4snT2TCe1VMDX9JpVSkqM6XGUahm7oRVfN1BsC62YQ8tnUK1anOMFfSOkCgs3u0daoWoIfnFRbU1YOSSpz0M6Cpv6JN2jMF4n+NnFELAgH6rFAKUqIUqxRrYezqA5OvuXN+JXj1KnrKAQSuCKKlYwQkQ24eMzHhonp89gVLKURY46t5mWcgrXDJI1KvTEj5Aqh9h6xKc+a1GIhCmbKmuPl2lOF4mU/uep364q6PiPPyNx/ltf/OCXgN+SnHPAy0Fy9v30AZ54ZhfpUAVfkQjfZ9mRI5y3ew/+rO5MIQjpOAw2KBzdtIYnm/uoqKebeYNegIr6wlWvT6DJdbmgUiWnqqyxLFKez62xCEcNgzbHJeF7HDiDRlCzlSKtT+OetBZfkHfRfY9tMQP/1yyG+ILwdJryTXylcJiO8mew5amS6BBQnsH2V+Bzctqsx5g1yo5KE/5Z2OKywBOsC92LKYqk9JoFoiiC2F4zo94ahvSlPBu8mvrjtd2V64+Tq9uJH5zdIUlJYPQ4XjCMk1wYu3Tt8YcojAZwGlYSGdqJIsooiQj52BYa41uwjDZ0Aa6ElKYQVsWCKtfTjo3jTtEQaKYqXf5B8bn3JNEu3XMwpUW1q5fimlcjcjbmM1P4YQ370iaEX+aVj/0Lx9JXEHMrHKlr4LrSfbQGapXJ/dIM084YO5cNgVJFF0EaJyUR0UAuVCVWDtCSiRCwfHLaDPsXTzMZt1g8HqWpWIcd0BhPlqkoJRzNIx8on/6AJSjqckTgGtZO+DzRfQHWCReelLzi4A6WpUfwhMJ4PElTLoN2lt1uxQ3zuNtGp3UfdqqHoUgdjX0XEJB5Cst3cmHnGJOOYJHhszHkcSTfzLef/ySOZ7BBG2WjNkqP28DzbjvL1DRB4RASNkVpkvWDlKRBWobxUWgKTfLO1T/m4PQKslacrtgQA/kO+vMdlJwQWSsOCMJaiQtadrAyeZTNTXsJb49xc24+SP/T2q24Swr8w5H3kUo9QrtS4qgbYXl2Daodxm7p44/O+1cA/rP8Ee4PXwNAV//PWDJ+kJ7GSYrhVlpLcba613JnZyczs66O9cctVF+ya+npbp260U+huQtj33RPX1A0VvcMHNUm2/DHOMF1dO/NMzM9yhcv+hJxs8h3xt/PEy3XI3wHzT6KE1g935hvUT/yYaLVCGU9j6f4SCWK4udZM34pzSM3cnsIhJYj0XwbbvTY3KleaQnJ4TfyCuPM5VNKnsavnNX4aFzgenQEBD8ygnjlmtXuYu34nGVHLRdQqhWkruNGZ7WmpkbwdRNzahTFtQHB+sB6euwjWH7lxGeJHwghVQ27rhEvmlj4rUmNo36SWCzDgbo7ySSvolR3M8vTeRxV0F8XJTL9XepLTzJ++PM0C4vztUGSSoW0H6JOVFDPMfniZKiFLMGRPtYGV9C48x6C1q9feuWlYLbmPeIFsp5eCibrN7J/zXtJ5Hrp6r8HVwvSmN4997svFCYat6K5ZaYaNlIKtZCcOUwsf5ye5W+i+bLHuOm9//Qb6csJ/JbknANeDpKz/e4fsuuXSSzVpRA/gmvkERISM9Os37MP07aomgFS09MYzvwEVQyA5sHj568h19RJb9nlBq5komUvT0Z3U6koRIsdWNUVnBdQGUw+QVrP0hPsf2kdlNBhNzOup3GUX6+mydbCWvJakZQT503T1zJgjvFkbCedVgMPJB4jP7ulUx0TqbpIqZCqmLxmLM4VokijkqYFmxnnD6j6W/ClT7/ls9hUF4hIQa1Kr/BmOFYN0evFThmyHiltkGm3JtefMI5jdN/DcWM5ruZzSFtL98EWktkzW1Ok9MiFf4UTS6B6NqniDJPx04Oyk36GrplB4tOLyKVejwBMRdCqK0y5Ppas1WG6KLIwTsOTkkcn7uVp1SYR3sDduonEo6Cf/q3d3PMAl089w3+8JsmOFZdSib0adbCIfiiHVMB6Zc0luabni1xxfCmWpqCrJr4iGIgMMBgZxFZtGioNrJhsIxOpYGseUkgmA5MEvSAFvcBEcGLOmhh0g/j4OIqDr5zd7A5gugGSzgqWZpZxz+ZXUTUWEmbV83j1/qdZlC4yVW0nZUwijfkdtpQ1OfjjfpJRL8a4H6WMwXUrfsDVdc8RUSSEFP4jbdJm+LwmMT82TMtDSKjOKo/dsvcd7Mk0I/0YlpuYO07g0xoZZ2XyKE8MX4SpWaxNHcJULd65+rYXvL8zQXF8Sk8u56POH8797Y3qY7xz4w9Jp+bvf1e6m8dnkiTsTt695sdzf09Tzyflt3GVFw8wXT8xxauq/wTpFTwdeA0H24Ok4wvPM8rPEU9/8wXbkQRJL7oFgDePf5fXNv3qpN/gIzP/ST457/L4Q++r/Jfye0yLemJTX8Os7MLVF1FM/QGO0Uw4+xNC+TuJVVMsnt7AnraHz3hdL7+KwOgbuFQfIKnUiMdBt5F9bgsOGpeXDTba2lxZC4mkIqAiJHeHq6wI9J4x0PUEfHxcxSU2OIRemJm7Hy8Sx4klGWvyyRpZinqRgcgASwpL6M51Y3omD7ttrFJ8WvUJtiV2MRbrJZ/6A6zw+dwsv0+RKHeJ15PIPYGeu4XVXhJRuIKBfCclJ0RzeJLLW7dROL6K4OwwMVJjqJUglXICFJdoJIOhV3FdA8sKU63Ou8yE6xA+upeglmSV3kTw+BMEsmBa2ZeievF/gr6uV1OILCKdWovv9FHSpxmvy9JZuIyGzBjJmUMMLLoOISX+rEaSlBIfF0ezCXg1S9r6a3u47A0f/o327bck5xzwcpCc0ewQz/3ZB5HiWkadpcwkenHMLADCr9Vb8oSsybnnBCsO9tA2dgj9LDVGqpEE43GFULGArwrG4w6PbAoz0VrP+6feTItXx0/q7+PZyH7Sp7iFgp6JikpRPcOO/BwR9AJ0VxehS42Ab/K6zNWsqdT822ndw1UUmq2FQ9XFQzf+lVZxF6BR9i9HYOPJOgruW/CJcbJc6NGqx8FqbfaIOTkaFJuIHmRSjTLhyAUhsQKXX50XY+9ikx/u/RTPJtdwL28nVfAYSmm87YkzByeeiooO6cj9BAwThODaw/cR35Pjl6+9CWs2cyxUMAmVzmdx9gCLGpZhGWGadQVfSrKeRFKz2JwJx8ojPGdP861E11n7sKzYR0KU6DBL1KlVKmqZe5Y/Rbr1z3GNLvS9GcyxHC4G1SuaIaCSGP9zumaqtJZaKepFDiUOvSR9oJcC0zPZlN6EUlnMtlWbmWo4nQDGywU6ZqZYM9JHcybEI147RzQVXUoMKSkLCHpVylqQkztqqBZvXf9trqrr5ZJnZ1A8yaEVESYaau+jdazKqqNFSkGVYLVGco4tDjHYHjwt+P2JbJSlAYfWwLzVM+cqmEIhoJ6dyDuegq6emdxVK0EW71R4zFrPV9y3wmz+22bRw1e7v8rxzrNnmOQqMQJ6FVOz2Wlt4OnMm3i2aQWuevq3okuHL7h/xmLt6IK/Tw+cz+TU+Xxj3aUUAvPxZSuyOSa8e7H0KKHcL7ggv4zRxHqy8iibC83cv/QNFAyNuJzhW7wPAQRKPoGKJJtS6Ct18e/8Cf3RRjrkAH/DJ/lv3s294iZSVpWbeu7k7mXXMRmanw8XDX2Jilwo3fCRqU8wFW7Ds7bz87qfAOCPv5pyYQ2KmUH6BtILcV4lSLnt52hSJeiGKATHKWsVdDdELjBFY7GTTYOv5k49QkCxWaVNEhEWHgpNSpG+aB+9sV4qagVXcdkyuYVl/QpqtYwXjGA1tHAkfoT9yYX6My8EiUqp9VtUtDB/Kr9AjgTfEn/EktI0palPzBH+KyMOa4Me7YbP9pJGGJUuO8mgOUWz4dGoSSqlFKFQDlV1qc4sIlA3iJQwNrqCwcF1OM68C10pFwkOH8ORQZzojTRQTyw/QCVYj+ZWCVgZJAqlcDOeGsC0Zmia3EEh2knzxLMkM4fIJpZRDjZh2lnq03tRfYeqmaAaSJHILbSkVcwkQStD1ayjGG7FUw2qgXqqgSSK72AbMZomdgCS3iWvpWlyJ4HqNIdXvA3Vs5BCoRQQ7Gi7h2zwEK7iMB7PIYUkVNVI5RME6KKkTpCOjqH6Go3FCEW9QMVw6RwPMRNX2WBfx6e2voPmq7vO+R2dC35Lcs4BLwfJOfDoQ/zsF3tB6qjsZ6kS4bi5BCt4us4AgHADZPVJHl30CG25FFftS7Ly2CF+fLnkQKfAPnP87BySlQCXly/maHCAtJYFYFLPvKCuTdyOcV76POKVDiQlFD3MJmUNa0sR7kk8wrHAIMsrnaypLKW7Oh9B/1S9SltF0lXy2Z5U+cPzAsTKklcdrHBdFVZXF16zpsBw9ufqSsmg7XOo4lPSfUznxcXo/uX6MNmQQV3RQ/VB8ySaD6/dXiRyyvUlgJRIWWEmMM1UIkJy7CHqKFINhajW1YJG64pjvGKokdCiqxnwhtlv9+Bo61gpm/CkZImpEjuHdG1PetyRe5Z/Cy8mry9cBEOyQJkICMGSwiCbK7sZXpVmIDGGrdi4iouneHhqikzb1xHSx3hkgoQ/SdGLU9ncjF8fIJL5T4LFB894/YgXpKRUT3v3USdMgxUn5ATZVFzHZKTAnohPU3oEwwmDbbFYX8VMajPCSNBrFjmaTDIdPnOAa12pwMW9B0gUfRpnDEQlzbDfznPhJGOaRIvuwystRvphTiTzLo0fRxjTXL3sJ0jhsizg0yw9ztubI1qaN+FPJXXGmwKsO3TmWjdZ02Dfxgi2+eLfysmQEg7sv5pcrpFYfIpctgkpFVpajlCXHMEMFIiE83iewsEDV7M6O8K1PMFnnffyI+8atMhB3OJqkuS5u/5T7NkQZibXSENyDM9XUGcXxt3j68gfWMmUGuDqC39J3ayEQkYmmBaNbOdijrhr2aBt5xKeIMk0Bi8cXzRKK3/K13DEi0wGJ+Hd8t/YVNxBcaKdt4w8SZQKe7sSTC3SqGLyLBexgV0cGltGKp7lb0P/j7KYt/AEZAWBT0WE0X2fi45+h+PGTjrtTpoj7+WnbfMB5peN9HDY+6szdeMlIVBux5IGAg8ZGEeewYUP0FBpYM3MGgYjg/TF+l7ydVb6N/NE1w3E5Qxfcf+QXcWN/EvdJzB9m8t2/wt76p998UZmMZvcj/QMFFmzU+l6hUahszhooZbrCPZdgX5SUdXg4BFkpUJGb8SQEtQ6Ip5NSeYpaiFyRgthN0NbZQDdr5DXm4hpK5kKCzQXGlnDaHQX0VKYaEWimOsQQuAoNrqngxD0Jfeg+TqL00vwVANP8RGz/bPJI7GwTEHFKNBbt5u+uqcw/CSGDNFY6ET4eXylzKHmYwviQE+F6ehYmvOCGy3FV7jY+gD//KHfP+fnei74Lck5B7wsOjmP384TPwqizUboTwdH2dd6H2OxAVoqTazIrUCTZ04/zek5BiODDEWGqGhnFufTPANXfaHcmRpMT0fxYXmmkfNHO+gNpdG9ICEnRXs5gi6XUBHzGQdC7UEEhknELiCuBAlIlfaqRsgXeEj+qcOhmBW0ZX2aUdgT06nPe9QX5nfCSVXQaApWGGc2z48LSd6VuLak6kvGXYkjYbBe4yeXROiacnnjtpolZtdiAxD4Ap7r1rF0FdWD5aMO1+0+u2XKExbVYC8NhUNk7DSuomCnmlEVUFwHz/Nx4imYdSEIx2ZTfitrgymM2fijGddHE4LoKcSm6Pv8rDpJn3B5XVBnWVkilAD3ulmGI808iEP2ZIealBiBYdTmu9BCAzSWWlB8F0+V5Mw89hneoxW+lnzqnSzJDTD6jEa9PkDZTZHvasbtjtOZHaSc/xwAmm9SH/5TcsFmLDWA4cMX9uUxnCnKRj29EclX1yRpK3v8zlBtIf3vLoPpWYLQUvH54DGLu1t1nk29eBm7G3YdoPvYKFphBEeJ0BPUGQgl6TNqonCpQIa3rPkem1P9c+dkbRPXN6gPnE5aVhwt0j42b305m2h11dMoOAYNszFCkyQ5HmgiTIVD9U2klBkq1SB7gibhSoroTJBiROBLnWy2iVAgx3JrGN/X2CiOkpRpYiKPhU6JJL/iKgZoQiBp8LPUiwlexwPowuMy768ZchbTkHyYqUxNGO9544P8u/9GyiKKZrq4roEZzlK2w6iOwcXVJ2hVpvik8T7etflHNIfPvME5GXum1nDvsVeype15ViaP0RYZW/C7i8p2LuJebqRPLD9rO4vlMd7Mj6gvjvHortdzkTzG+7gVgCEaefb8CIlAzY1YdU0+99TniJt53nnhz/kbvjBHdD4sv0GKNP/AH1MSNdfLlYVeHo2eLgUgpM+b+v+JR9TnXvAeFSlo82NMqHmkr567u1xCxI4jzSIlzrzgXht16DI80qUIw4UUz6kTyFkerPgKql+bjzdWO5he9lq2Gxu5Qd5O5TkTQ7V48rxrKIoon+l7iNuM71LwXxqJPhdcNnYZCTuBL3wCXi3uSrgOSIk5MYQTjVPUSxxpGiNeDbMk10kh6CLKGeJZH1WqYJfJRyS6r5JNGZQNm/iMQ1Ev0jplMlYnCZWj2MkwmtpOxp2gvpSjtz2DFfCpGjYd2UYWH5f0N7v0N02RiRTmntOLua03uUm0mUbGgyWGIkMveOzJuLp0I9/4yKnVwP5n+C3JOQe8HCTn9gduZ/sDw+hehKbSwnpV6dAwFb3IdGiEXHCCqJ0g4etEfGOBIqpE4giXquKQNnIEvCBFrcSi6XW0FJei+DpCKgzUHWAs1ktFLxIuO2huDtuI0VLczIbjCo7RQDncDNIHca6D1sd3p5GyhKY1I4RAoiHPkjtRo0ALf9OAsAohRRAUgpJfE7w72dAylNJ4cEOQoDrDq/emiU00c7DD4GcXhuAUzRTVk9y0d5h1R8JI6SO9cYRaD9LBc0eRfhpdbcC3DzO9ODxHYF4MqqWw2VnPeu10vZ1TcdSa4uOqR057ESEsKWmuTnJ5aRtPbJqkGDn+gocrvkLCTpAJZFhc7mSg8/8xETR4Ve+DPHJsFU2Bw1SdemZCzdgX19xFy/MeV4zkEZrCvy5fqAOk+hJf1FKuf22cVA/JtH3e9ngR29lDjkdwCk08X7eOoj9v4QuoVV677G6u7Xy0dk7VQ/MkpZC60LUkJc0TFoVsI6Giz/pyPwDHKg30FpOkRIHNqVF8BMcKKR6f7CKgukQ0h2uajzJeidIdmz5rt4dopo48EWpkKEuMIBXMF7GUnA3H/GZeYX8N8Dk/eRfP5S9HugluM75IUq3wGBcyLJvxpZgbX0u8Xt6l/hKA7f4a3mJ/jsWxAa7seJLm4AQ7+jdgRF1sz+DhoctmRSehURS4yjiGj8IxL8UetwVTs2gNT3DjkvtY3zDvLioS4fu8h6e5lCbGWcYRuulhNftpZJx9U6v5wYG38CrRxxb5JI2yyDHZyjuUB7jZ+Cyp+gmmK0mGCq2sc3Lsdlt5x6YfIOp1fs6buIb72ew+S0Cz2M15fFV87rRns0j283H+lq/yOcZEG12ylw/Yn6XggWubeJpFq+ETViRRpebsU4XCXbyWp7mELvq41r8Dzc8zUK3QU1E5ZilYkrmkgoiAV5sBNgYEGipGdJL/SpvsqMyObwmviTtsDLvUKQJx0gLtuwaFyWUQmcTwTfxqDD2cJheFT/JPeEJj8a7dfD17C11ilA9v/hzbolu5Vt7Nu/l3XCuMapSYdAVpV9Bl+Iw4CseqKgUf2nSfshRsL2pMewqNWu3ahgBVSDKuQsE/+xhMWAmqapXGaiNIGA2P4v6aMZIvJ0zX5PqB5Wzu3k+kqYKiShzHYO/uaxlzFbLGDLpSZbnjY7tBKnVl1KnVCATJ+j5cKakW0jQvuYb33Pyp32jffktyzgEvS4HOn32HH+9XOVpu5kMzFVDOXpjwBHzhUgkP4WolXL2I/yKWGuELdCeBbscJnUKkzgVGNUO0OEr7yCPko4vIxZeSSZ17XRHh2zRah1jMNhqCA0zIOqa0GE2BGY65byTjduDPpnN7io86uyuq6pKBxQVu8P6LejFMIpflYP46Bq3XzrWtygxRvZ+gXqZQaSSvRlC9FBITKR3swk9xzCJutA43Vjenv6EWsvhmcFYfZSFU20SrCLyAQ5AgXc5i6r0kS81568U+O8fjuV2EdYUV8ZUsEykGkHx+dsE8l0ifG0bvpUGkObS6xMHmGrlRfZV3Db2RLFUOxg9zLFYz/3blu1jstHBj8QYSbhKpCr68OsDT9RqaJ/nQ0//Kv5VuoDm8C9euJ+100HqBS1/izAJchitrEgVnQLji4ymQKvp0Tjj4KiRzFjuXRxiv0whbHjc82Uds5gCpcgxVa8Wr7qC3/ihPrRvDyG8lPbOR9ck+ms0Kk+UGNMVha/NONjQcBCSKANX1WXa8TNtYFQFMh0Jsi69gyO1gdWmQK8u7T+vb/mwjPwu8gu1iLSudPprGjmIgaWxpYNxU0IXLN9w3sEbr40/Ej+npD3ND2wHqzTL/08Q/X4q5vp8KCfx+5P3cnb4KPXyEj7c8ybfGL6JcXMX/077Pe7R7Aah6KkIR7BRrGPNSXC8fJ6S5c+1fbn+DYflCc4DkVcZhmpTTv7Cq1NjrtjDoJyjKAIrwuLbzEbrjvUwOLiU+FqajtQcpYmQDOpmgyw96b8SVOlu1Qc4z9vG71v1c632JAiE+q/0QVRh813slCpIVssTGbCe7o1M8rMSpC2RZnuhlb3otFTeIpji8YtFjXNK9gy/xBTKidh/dhUOsOXiQg7lOlndPcFfXDQCslzv5KF8nS4IoBY6zhFt5B6O0457F1aZKhw/yLS7miZro6Wxx2LO9WykhV47RR4k6TbLYfGHLw8koEeaf+Ri7xBbqyjO8fdtP+Lxeq0v2tbq38bfrP0hSZvgGH0R5AXHMc4UrYcIR/ChjMnwOrvhfBy+l5M6pCLgBqrOFnlsrdei2QjpYYsv0Cpp8nUWdvRiFPE46yPShOhwtTKxLx82WUUWeaHsZr6LULG1tJUafbsIuzLvl/NlyrlJRaHr963nnm3/vbF35tfBbknMOeDlIzmOP/Io/uW+KMRoIiQqbZZnubBN1aGesX3MqJBLHyGIbWaqhMeQJdi8VEGceeOH8YsLVOGWziOoFCNkhFFnBEVEkCsxWDgaBo+dQvQAIcPQ8wq/1K5YtoXk+nmpSCdUsBrqVI1IaxbDz+IrOTN0KXL0WLa/b+Vp7xnwWgeHmUe1+wo5NMOWzKPwITdo4CWWKveUbGbQ2E9XGmXaWYMsgZT+BL12qxgH86jH0io4evAJFraWOSmmD9JB+Ed/pxfb3U21qOk1W/mQIKemyk9SJCL7waPBTNPopTJS5VO5T8QgOX6DyotPa+TPPsa54ENVzmTQbMH2LvBZlUWWYYlTn2U0ZxsPzab5CCq4ufIIjrWt524DDBeM2A7ZLWFEJh1Ru6zT4jyUG7ikz+s37DmJO7OQX9qX88/YvMRWs4082fpSPRn/BnZ0fpKe9NpEovuTiQ1Wu3F9h1MzRbIfIh0xmIgqtUyWGEtOUtAMUdIuGnMu2ZbvwnEaWzKxkILGXQLGLiDnNlO6huSZR14WwSVmUqdoqorKe93c/RHddH1XXIKCdnXz7PqzfVaWpdG6B3wAVX+cr3pv5nnfDOR2vCof/jPwNlzo92H6Ag8WNJNQ+wCOqVUgaZSqeiUsQKQU5N0VUy6EIgSMNKh1TDIfiLJ3KE3Vs0kmD6bhOtOySmnHQHYlpe/iK4EdtW/jC4T8C4PrV/8yb2g/xX4dv4JHB67hGf5p/V/9xQd8cX0FXTihlCzxNEKz6PFa5nJ+ZlzLg1bHHq2XIGbhcqA/QqBQJYaMI0CtZLrd20GCmeUReyESoa/7ZSjjkNbHDbZ8dzy+MZor8hfsYF0d+yXZvGe91atWfl4hR/sv4BreINwKwoqiyYeUPmNIa+LMjv8+gPz+ulippQsJmnzdb80kXtEWnKeZ1cm50biaTCnRsrHKs4XQ31kvFlfJBXsF9TJNimnpyJKgQRPo6x1jJ6+z72RK454znTgxdRsf4RUQiQ+QDadTQJLISRnEjGIEKjzYpfF3/CJ7QEFKib5vkHvszPOGvYae/nC/o/8nFl/wMS1f4/NEhLi4eZGbjLXPt2zt/F7XrcRzFwi0n8ewwvhvALjRhZTvw7BBqoQ3Lh8at3yO++Gkmnns3lVwbwfpjNKy/jWFXsK+iklJ9Cr7gSFUl7dae5CrVoNl0WBWyiFoG01XIVBTqm6tQUfnVlIkISTxVEhQe58ccmoVGvpjk0VKJainAlpRgaV2avmwU6XocFy6lssnSQgBDKLR5FqXJIBg+ZUziaoVqLoqdEQQSNsFUlfxg5CSiUnMgC12haeM4zefNW1Hdikrv/cupjM/PXVoygqZV0INlQm0N2OmVuNUZqtXNXH9Dku7rz72u5LngtyTnHPBykJxdB/ay4+8+xahVx2ighYFgB1k9gROM0ejbeLZK2A+RsKoMBIOY6Gy1NBp9QZVakbST0yxPhq9WkcLH0fMU40dPv/gJzAY3KJ6BkEpNQ0J7cb0d3UoQKrVj2LUCh75iI5GovjnbrF8T+vEVHDOLY+RwtRKGVUeg0jwn430ukNLBtQ9QDh/Drp8X39JnJjHSE3jhCG40gXBdfF3HiyTO2I4pVbrdJgbVPHEZ5BJnBWHOHDB7Mgr4fBOLB3DmSk4uKg8yEWjEURR8DJqr47xm4ldIQPddyjGdRzdPU9EqbB5YDG6VvZ3j2JpHRV/4fDVf461jN/Pvm19Jzqg9F9OTvHrUoSem0hNV8E4hN51TDlfsq3BT6K/4duEVWOMaf72tprny4as+yU3Jbewvv5rVSp79bZ0sSs/wjBXgqFYTYLqifRua4vLQ4OWYoT5sJ450UnTGhpgq15Mwc4T0Ml2xIQ5Mr2RRdIjGUJqm0BRHs0t4/bJfETXOTfyrVIpTrUTxfUFltIXz8r1soAdfCnbOtDAuG1jRWma5Nf+dTnkN9FW7WRU4QFTN8lXjJr6drwmEabFduPlNp19IWCDP/D7/JPE9IlQ4ai/i0sAeLqscZKA5gh2RiKKOhU6faCYULdCnNfPkyIU8N7GJoFZlSbwf2zPozy/Cl4I/v+grNIenGCq08pOe13EwsxIAPbGdfzjvRwQ1n57MMv52x8dQhcWP2j9PfTBDIucQK7jMeiuYlHE+Yn4ExZN8y/02DSLPt603M2m2MVusu2alkFBnaxRDOV7pbGejvgdPU/AVsD2V3c5ievXN5O0oEkmBCK4U3G2vIiPDmDhYzFtHFHyalCKrRJqPhX7KRmc3AJ92PsBt3pVzx90d+hT91kb67Es4/6Kv0ZgpUw6pTNDCd/d9gKMiSjd51lgGTiDNThFjp9t+2rNvFAVU4TM2S4zOS/ayf9UW8pHT3bmmY9HgzfD2/l+yeryH/3au4QL9MJdFD/LlrvfxcOrC0845Gz5zZJKLnMfJxI4S1nyiQ5dzwFrGXd31lDRBX0QhYyo0Vn3WZT1yuuD5pLrAfasfmKF1eJiv6t/hXc6fAvAX2n/wnY6b6eteRsyR/GFPlavyA0xu/hKxgetg9DX4ApqrkorngqbieDZZ28JQdKRQGW4KsmHGQ5e1KdgVID3JMctnKNiPFpwhmDxO/ZpfnXZfnhVENSv4roaiueQHt6JoFqqZZ+bYFbjOMMIogKNQGBa4lgLGeqiMIf1xfFmBSJxQXYbSZD2KK8AeR2oJhBYDaWOEC9jVEL5TQnHKoEaRVBGegx9MoeiLUGQQLTBOuHWccKqKEYBYx4Gzvg/PDqIaZy/uDCAdjdbw+1l96W/dVf/reFlIztN7eOTrnz+NoACMm43sim1gfX4/Ab/KAw3XMG0ka/WlZl+DBqyzNSxvhoQHm7wUKioVRRLxBYd1l35dEvVhrZhGN2pk48VcXC8VmhPB1YsIXyFUaME2szhm6axR9MJ1US0PxVUI5qOosg6hxJF+FtQIvnUITyljmzPoFROfInYqiRt/8XiYkxGSBpc7q2n1k1SxCZ1CaNKz9piDeEzg04DCQTwexCGIII7gMB4CsIRLI4e4ZLSPturo/O6U02uI9Xa6PLl65EXTtTVP59reN3GddwlfPj/M3rqzxwfFyh6beqdZMnmIqqaRSHdxRPf4TsN7uT7/Vbb0HuZD++4A4JsbfofYsipqXYmfTF+HJ1UUIVmTOsRrlt7L4vjppRcy1Ti2Z9Acnjrtt3OF6+q4rkE+10gm00Yhl2K1PcBW9iBnzdHNzLf/qLKGb6s34vsq31a/gZCSmGtRDqg8o3XzM+Uy9hW6mSjPp6Mb9Q9iNjyIPXMB1sSNIHX0xHbMpjsRilvT2LEaKR//GKdVNn8ZoQQHSLR/l690ZVGExJeCjz31R1TKXQCsTR1ka/NOloX7WVMaJZhR+f3sJzhWqek2rRXHuc34IkFhM+l0MuEtoohOPWO0J3oYaAuQrjdpnrDoGqqgebU5oGoqTKUMNEdSDCvESh79Q1fSqzYh9BwZGcMWJgNeE7aTwlYrxNUC9UqBD2r/Rb7ZoWu4gi1Vttj/TF5GQDggdT6t3corux9kuDXI5j054oUaxR9uCXC4pZm+vW8hYUF918NUVZP+o9fSK1zucZdRns0lWq+Ms0EbQfV0nvXbOChrY1gKuHbZTlRNwTVChO0yr+57hKfdVdzhXcIMUU6FFLCha5SZ5nqOxmrPTTgeiuPhKwpCSJLFKYpGjGq0FhTdWvZ524DNa0YcPrUxyLP1Lx40D3DTyMNM9wh2Ot18Wf8uD3nrecDfAkATGV6jPcl3tvweTnxejLGj5DMUnt+8XTPuUFUFz6RUIi68ZsRhTc7jsxsCSCFI2D7Z2U1NxJH8/a4Km2c8phyfZ0oePmBEx+i8+iuo5v9cTVhKgZh1V7mVGFahBd8JoAWzlCdWI30VMz5CpHUv4qQsqZPC7iinuwjV97/gdVQ7Smn3Krz+LsrhQYwrDhKPLFSa9qSCegZvQ8ULUpp6I29525//j+71VPyW5JwDXg6S03t4J//2/buIp3vRc2cPkjwBV6j4KCj4CCmZMBuxFJPFlZrU+YjZQkUNUtLCJO1pOqqjANhCZyzQzITZREBbha7GSCuSFaJEwJyk7MSwlCplUaWuMoZaLmCYkrA1U/MtSAmqilrMgaJSaV+KFwjNVTz+n0KxKiAlfqC2sxOuMxc/cyo6vXoudLs5pI5wQB3Cmx0oAamzyGvAEx4+kiY/zkqvjTQCA4ggeAiHbUqhVqspOMiznorUi6jhHrziSoSeQw31IdQKip4BBFJqSKeOaw4N0jXVWIsfSUh2dE9y2Z56QlZtIXViSTKtKY7Ee+hN1TIJInaYolFC9dW51MqYHSNux7l04hpeXd3K3iaNv1wXpKALTMdn/ZGHiFWbGG5K4akSWw2iWnsITB5mOHcRKV3SFR8kYeZ4e+g+lvVWuMj6J967/5e88djjANzafTXH17bx113f4MiyX6N+jZSYlo9lKguCgX0pUGYnyYoVIj3RyXRmEa4TwHV1fF+l0Zvheh6jlQl0zhwcaQmVz2vv4rbCK+f+ZigWW5p3I5A8NXrm3boW3ceW4B4WlTp4oP2BueDL+koDF0yej4LCZHCSZDXJjsRhJjSfavpqvNJyeAmWw7k+pR5BDfXhOwmkF8KvtuEWzlBSIfUIRuoxrq528NqVe+b+/u/HV7Pt6IfO2HZQK1M5pULzZ7Qf8WHtzgV/c1UBUs5ZfwBK0uSv3HeyWIzxAW3hTn+k2eTw8gjxvEs871CIaEghiJRcjPF6yCQpdWRwGmfIJA227pwhm0/xLvvT9NOGUIsY9Q9iTbyO9aKX2wN/hqsJDEfyjL8SW+pcru7jmc0JSmGNWN5hw4HaArZrXZyZw69g7/Sl7PNjrC5FWJd6mHX1txFyLSbNFP81/kF+5Had0/PX8HDPQlR1YbPB6KeLYX5hXY6HRlLkWK/1ciX7eHr5Zu7ouO6sbQvHpTU3SBhJQFhE/SLnpw9wa+kypq0oStGd7YPDvZFPc13x7/FO6svntB/wV8q74eI6rLMUE/11sX7G448PVtDLOY4lYlTHXKj4aEv2syQ0zkhjD0Z0GttOUg47FIiwwd+Prli1QnH/B6g4AapOgLr9m1mRfSfThuDpepWAB+snsgxrt5EJHMPKKJhqADO9AiR0RlcT1xuY4CCFcpkCo4x3rOJTn/vMb7R/vyU554CXg+Tc/exunvnlz1C02uCRviQwdBSjfHp9ld8kykoATbrk9XhNSMs+8+5dIEiaLQTVKCGzAUdPstRoYdTNMD7zFGMRid3YVpuELRtP1xBAuxVhud+JrgbpVSdoFFGWey1IKSgqFXrUEYaVDFnl3HYnihREZIAL3OVM+HU8icO7MEkhmMIio+Rp8pN4KFSBB3B4FpdhpYjV+R2E8BDCRgoQWoFE2WfrEUl7WtI9IrF0QTEAS8YkvS31ZMImQpbompCMJeJUjXnCNRP1uOPSYRCQKOg0Z0IQbWYyNEUmMF+YbvPkFi4//nbS5hiuVqDebiOHTcyNoCZD3L0uzLaGeZJYV6qgHHmeJqfKuCMolxYjtCLdjc9wfXMvBSvJougwHdHRuXPWHCrwxNgFfNr9EF/Y9S9cOFBz99zbeT4/2vRKtgV+n+3n182p/2quT1uPwEInXJDklTCTiQCaS61AqgBzRmOrXxNLq2JwiGWMqfWkmOGQt4IQJUzhsEr20s1xckRJU0cHoxhnIDUSKIZUbEOhGFaZaAzwvfHXc/fxV5527Gnfn5YDJHJWqbih4xZeOb0GwzewFRtPeGi+Nlck8VTYis2QkeZ43X5mFB3phVACo/jVNqQbQXphfC+EdOLoiecQaglQav8WLkJAxI6CVCiaNQ0b6ev41RZ8J4n0TfT48+jAFWNXsDJWYGXqUcJPqpQv8tmrNvOPwxfglReDYuGVVi68Pz1DsOM/8CsdVMfeTEQtsF37GGFxZs0XqO03PuL9Ife4FwBwvnmQHyhfRvEluqgR6amkwWS9geH4lMIa08nTK5InMzab9uc56rfxBvvPKVCLnwu03kpKTTMy9GFA5cfGX3CBcpjvu6/k/7m1YNC/1v6NNwUfYSau05ReaBUeazQ51B1BImiYrtI8ZdN40jED7UF+qF/Ovxx69xnvr9WYoCGc5qqux1nXcAjXV/jJ/t/hofHLuLT5GSqFOg6WllLhhTdYGh5Xte7hznU3Lfh7x2Q/9iGXXDVIlAoFghg42Jz+DcWlyzc7/5J9o6v5O/ct6MHjRPUxMvmLebP6CHd4l1AVBl5nBHdFzRXXUMyxuWeESqtHwYyQLFVQCxa+IXlg2XkANGVn0MdzaL5ABkNEMyWynSGG60+vg/XaYZs/6rGoqIIGa375PeqPoQARVyB8h7hej6nWEjgeU49zPCLpbywz7Sp0EKHsTbAykeWC8SVMB2zSDUdp0Q4SH7yaamyYfOvjSCSGbUPJJxYNky6GGJ9O0u6HaKi0USVM1VdokD5quIo33oziBZguTNMS3sihVITJkMH3FxsMhhduKtrKPl99PofjlOjy48wYgp91GEQdyYqCR14XhF1JR1mSqTzEa//m/73g+32p+C3JOQe8HCTnrkcepOffbqG77nxGAxXGwh5BDKp+BSklQtUwpELA06hOHqbJGkFRJJYXpCvcju0OMF4VJIx2FJFipHwUH0jGmlkVXk5CXUpQTeD6++mrTjJVLGJ5BbL2FE2BTlzpkLHG8KRLS3ApATVM2csT1uJEYxtpV0MElbOnQdu+S0XaeEIjqZw+kb4YSlSZUUr0KhMMqlNYwmWJ10REBmjx6xB+mD1UmBHT7I8cxQpNcyj+LEJ18XLraSmuZrpuG54xiZ3diu/FaLQn6SiMUA5VaCg4XH0oh6tCe1oSroKnQHI23jW9eB39oTKuEqSk6tgy/4LVW3wBd1w2Qi7i0lpsIR2cPk2/Rvd0XjHyKn6vdB2/6DJotCSvGHc5GlX498UGjzafPpl2Vp5m6bFR3tx9x4I4l5wVJW7WdCmiBYdQxSMX1dE8Sddgmaa0ze/bf8Cv/Iu4Zftf0zFWk7Df3ryKP7/wvTxpfox2kSYf0CmoYVpKOZTfUH2as+GE+y4X1cjrJncEt3JcaeZnR19z2rFqsJ9g5y34lXY8qwWv0oF0oyjmFEbd0yjGNPFqHdJXqas2syG/hGClmaedTs4PHkLO6kOZlQZ2+o1UgdUuJEQVK7ZQ0dWVPhNGCdMNUTLTjIZHGY/0szy/HN0Nkg5ME3ZDRN0gcTtOwSjQUmoh4AeQSDzhUdbKWKrFsVgvGSOLKhWaqg1smt6EZsdYtnQ7K361m8BBBbdBcvTj9fxwuJmeRA8AvhvBtxpxZi5ECYygJ57jvGw3vdHjjA29H+nUMpI+HLid88KH2FruZcRr4DF/PTN+nH1eFztZjn1K9lHAr2IJgzaR5ve1O3ij+vgc4TkVuaiG4ss5UcVP2B/m5/5lCC1HsP0H1Kl5VmRX8ITXhpOtWdTeKB7lp/LKuTZ0HJ4yP0ajyPGkt5YfeK9Awec69Xlepz5FKagy2mzSPGnNXackzTnyNt5gsqc7xni5mb3pNUxX6riwZQft0VGC2tkJ3tw3JuHpsa18d//bkShcF99OIpjhnulL0FQXU7EZK9fEO5tdQcuSModTzdiDJbzpF0+9XmRM8NbFd7I18DzLhsvclPkqQ7KRQMttKGqW8vD7qaPAOxvu4JtT76j1SRXgLSznquDPFhid7bcmQBEI+3RriwT8epPYEoPpujOvLxelXYaDCqYvmQgoFHRByvJJWZLugs9oUFBWBe/qt7lkysVSBXldcDyssDLv8VijxvqsR9SVJGzJeEAh7Ep+ukintSK5MO3SXpE8k5D8+/IQF6c9XjvioPmSsFtz/j6XVHm8UeM1ww4dZZ/nUir9YYWfdRgMh34zWWE39x7i6+/7bYHO/3W8HCTn8EAfR/9lD5v82uRm+xaGYlLGQiAw0VFmh00VG382eudEsKwvfZRz1rT59eCKKtPaDL3mOBXF5pr8Bed03qHwPdwfP4wjExwLDLOhvIJhdZhRMcZk0KbNTfKO6Su5sHgBljpIyZdopFDVHRTNI/xzYoTgcIF40SMTFVzQI2nJSOqKEC/BZALGkgJrdr7fcFyi+BCYlTnxhGCouZm2Ra8mm97HRGWQbDKBq3joqk/e50WXe1/V8MJRfCEZTZbY1zZAJuawJn0en594F7vMPv676WfMBKfpnFnNmtFrWOG0srzO5K/XBhdYak5F3CnQ5jzAh8xfkVKyADRMWXQOV1B8ia8I0kmDyQaDyJjKupHT3ZlDfj1X2l/DQ+Pnj36GYLa2oPTWN/HRSz/N1/Rv8wb1yTNe35EGFS9OTJsiJ5P4QhAng6cKdK82CZ8gLGVTIWQtnJjLAQVbV7B9DcX0qARUJhtMHiicz52D15EKZNibfmGpgUj3n3FRejMTwQlKeolpc5qQEyXuRFiZXUnQC2L4C8lzfnoLnU6QbaaDrZVZ7kkMO0ajr6AiEKoFkTGcUgOFyHHck6xrp0Iiz5rFKHwV1Q3haVXAR76AkitA62CK+rd8k5V/pCJmNU+G/xy2HX4zLj5joTGmglNMm9PkzBxI6M51s25mHWkzzUPxYSqD71/QpulZWOoZAqmFi173FE72fPCDp/28hBGuErv5qHEHMUpzxSKrUkfHRRWScVnHd9yb+J53PQChrn+kBYdNE+cTKLdw+/IfU+z7BPjzrhg9/hxOYS34QaKUEECe+crdQkr+UPspH9F+iSE8pIS/c9/Et7zXA3CFspu/0r5Lh5ImH1Hp7Qozk9DRXYmtC5IzDouGKxQiGr6jEs5JfF8h3ayj+z6VhE+mbnbAC0G6UkdYrxDUqiAlqifxVEF92uaJzFb+buB9Z3xXq+KHWWYMM6akaApN0hCaRkpByQlxVccTBFSL1IxDKmPz/NBmPu78PkKpsKL5BwjFZWzsTWS9Bj6r/TfdFz7LF/Z8isFCxwt+HycjFZjGlwpxM0/BjjBdXRhrKAUE16gUW+pxld9MSMD/JhbJfurzY5QGVZZGjvNg12uxxdlderq0cYQxe+4Af9jzNG//8Jd/o336Lck5B7wcJGfvof04P+ylyUn+Rto7GVNyhmPKCG2ygQYZBwlB5ewfmk0Z6XloShBV1AbW0+o2HvMeo20IDFfBVX0mmir019uMpywuqnSAUGlxBbgJhinhBwP0cowNY+20Z0IIaxxVayKWnSZgOyztG6BieJgOpGNwuF3B0iUrhiWJEgzXCxwNNh+b/9Q8RaEaCDCVrGckEcQ2JS0lweLjwwi3gmcECFbKVONNeL7LRF2YgZRGxRdnVcadazsYRgoF3wzgaxrZqMtgYwl8D0f1GY2MUdLnrSuXTV7I5eL3uGWZyeKiz007SuRtnw0hBUdTONAo+NI6nax25mfdICd4PbdxOY8s6FfzRJXVPcVzLi113F/MJ+33sJOl6IF+7vjJP84trrlYkJuv/kvWGgf4jvwPQsIj4we4N7SGA3o7dlCQrqRYoo5QJwoE63PszKyjNTyGREFXHY7NLKHsBmkMpYkbefJ2lM7wMKZeZd/kGlyhMFFuZKTYiidfPMBXaDm0SA9qqBev2kFr4AgrK/W0VFrmjjkT6TgR1A4QSZ+HUKtkl95D07HXIuzaOPQa9nIocoznkzvxjDxLAj6Rsc1s6b+R6qwcAtQsAI5qI7UyKA52YBp8hXB+OVKtgpC4WhmzmiJYjCJ8F8OzqBoxKB9CNSMUEgEUX8c2pxFSQ7dDtI4dJf+6H9OtezR/fp6Uzbzb5ZDzASbKJvYs2ZJI8nqeoBfEdEOkxldSCY9w56qfMVFcizX5AinywkYNDmM2345qTiIleOVlIBx8qxFr/HdOOUGiSIl/ykZoBQMck214s+NcDR9ha2Q7qzMbqLea0IN5Hko8w97wFNb4a/Eqi9AiBwm03oZvtVAe+CCcosQu9PScJSoiy6xR+lHx2SYXEt2ILPMF/fu8UX381LJiZ8W0jJKksOD4oeYgQx0BbF2w6kjxNLcZwMz/196bh9tV1Qf/n7X2eOZz5zG5mROSQJiRgAqCgBNWXsU64NiKVl+1vi+vrf4snRzatw61Ylt8W6xDFVpFEQcEBBQZAwmEAJmnO0/nnvnsaa3fHye55JIEghKGsD/Pcx/C3muvs9baa+/13d/1Hcwk76x/ik3RIpbmt7Oq7VHOEpt4y+DDT9Sdt/BtSS1hAJquCR8z1Li+Yrfq5GL/bymSxmq9g4tqkrpdYl2iwszYW+lmipvtK3g028VP7VM5M9qMVgatfYOMyyx7vD6mySKE5mT3MSpemo6oxEBlBlXIUjDSJLPTNLo91qll/HLPq3hwfM3cO2hLpFS0HtegmGmjksjTO7GD6qSNzEvaxBTYAj+ZIDAduowxHuGEw8YaOlqsje5gZfUhtowu4r6dTc/HFHWquIDAteocv3wbqbBE5Dgssnaxsz7A1l39aC2piiTzF44zMLGXN9e38apPXf+sti8Wco6AoyHk3PnAJr79oz9jgd/JTUs20xm2ofwGrxhfTVApIIoNumknaWZJmBkqYYmiP4EtHRr5VvxsjpR28QkZlTP0qBYsDIbkNL54klpWQ04nMZBkdYLRYJgorNNRlXjCo6ZqaBWibAfpe6AU2rRQtkOUSCNUhFGvYFTLGLUyTYsdjdMVohJJpO8RBhYyGUCjaSwZliXa16A00tKgQIUSXIGWBulGREol6JouUEsmCQ2TdKVCPZFgOuUSKcX0olPosOdj7votk/VdT3RHCMS+6SiFROnDG9xpIYiSGUQUYTSq+Ok0jbTL5t5JdrZNkA7SlO0ykYgOmT5hPxcMvY4V7Sfx14uXEOyLlPzy6lbWeHu4Pd/Fo2Ll7ILSp/fyYb5MP3vZxAnsZR7ncCspnkgzMTLZS8e4T2Wyk4u5C4uQx+svZ0+4grWpa0nLpm3WjGqlFLbwTV5DXZissge5zlzJw+UVQMTprdfwV//++Gy9kQWvf90/AE2D3jaryIh38H7/0cbMrsdwR5DuIEZyJ21eC931brrqXbR6rYjIwphZhTKLqNwT0Z5lkMJsdJBudGBECZQI0TKg0r2Opad/Byk068oJtpey9GWnuSDnY8tmQLVQgyuhEsG3phwao6tZWzyRTmlQqXeQ8gLq1VZM2giMCkJLkok7CVSKiaRmuztKqxqhuvBx6vvk1GWuYiIQaA2ViSSv/0mecutrkWKS3Sfcyqo1BfKmxtkkaLvqicWl+vKInaclMH/yJqqpLOgE413tBHYRoQ3y2iO36ivYRpJHJ87kxpYHqVgVtLJBhET1+egwg5HYhTBriH1bUIYyePXQqwlkwJ1dd+Lt2+LRyiSYORV/+uXo4Mg8EaUzRKL3Wi7ZcyGr6jPkX3MtlgE7tryMbxq7KNmlpncNsHZsLSPJEbaIBN7oG9BRCrRNcuE/Ip0x/KmXE0yfjY7mekYZiZ0Y6S34E69mvwH4Uvbyf61/ZakcIiU8HlYLaWibtKjz7ejVfC86j3cZN1HH4b+icxgQowyIMV4mH+W9xk04BLNG8ACBNpgiS6ANIgxaRIUsVYSA4U4Ho2LQVas1E2LSSg/TDNFOoE16xBQeNg4+j+oBNqglfCc6nx26GfdHuntp6/8mb3/gCqqJMb6/+mqC7f8bL2rhUuM2XiXXs1ZuIivmukeHWhJqA1cGTDSSDNWbdjstdo1aaFNRCVZlhkmaISN+Hs8w2d7dws8rZ3FTeS3l8MicBlw8Gvu0+5YI8G0HlbfJW0VanWkKwymKUZ4luR0sat3NTYULSKkyxyc3kuxWpLwiQUkSZhz26gW4jSrBJExGbSTTHk4mxE8m8EYFjbKF17Cw2qAvPYJbrfDg6BoEsEzsZa3cxMvlRvrEJCvkXrarHu5VK/ilOpXb1UkkaNAtCphE9IlJGtgMiDF+q1ZxqtjCOHlO8If5s3/4xyPq+5ESCzlHwFERcv7rS9x33a1oKbCjAMNUbFx9Ag/1r6KY7SEyXZbtXE850cqUncQe91k5+Ci+Mukr7aXDDjFqZWQUEqTzFLM9GKbErRUQhoEMfKpWGuG6GMmD1drPCK2RjRpGo0aQb5sNTe+M7AJpEDkJomQaqzCJ4ddRtkuYSCGiCKEitGEgvQb2zCTaMAgTKYxGDaEjTNMn8oyme6NBU8BSJloaiCgCrY5Yw3EgtdY8g90hU+k6E+kZNBpLWYwnD58fyAkT9M0sp+oUKLqTnDh0Hhmvjda23YwvWcV12dMBsLWHLw7eSpA6Yi2/4cLyj9kwejx5p0h/ZpjtMwu5be/ZzM8M0WoVOKUwxLvUr0iLptATasknog/xs+h0EsJndWYzZ6Qf5kR7K6Myx98P/hHT3sEaP7f3e3ygMMFrvrcH5WpkozlS7//gKkanX4vyD8wGrjASexD2JNIsE5ZXPun8AeVSW5D2JDpoJawtRNpToCU6SiHMEoYzitY2KsiBsgCBlXsQM/dg814ZHonIor/aT3+1n7yfb+bT2YfpZ8kWl2Psi3b9RBgFfVAMJW3UWL/0Wi49/i4OE6j5kCgNY6GgFAkeqJpYUtNharKGZrhhEYiIRY5C01x6Oy1Ft/XUrzilYa8vkUIzz55btv6THhb/fAotNUIJgj7FxKebHxvVCCY9iTMhmPfbboJSJ7xvPftzMapIsuGhC9kehexJ76Fu1CnZzZxlUknavXbSQZpkmGSg0ssS4WAIzW7PpbyvnC99Nuc3M+VOoaMEkdcN2sCfPJeo1gzAJ6wpzNQ2kA3slvsw7EkuFK28KlfAztQQPhBCZJvc8dA5rEvuRgnFqSLD2fN3MFPJcbU3zdQBS0F3rZslxSXc03UPgVD4k+cRzpyKmdyJ0XIvZrIpwGplUR98x0EG2L8Ly8Revmp9jXGd5/+Gb2WjXnRQmTViG1fbX6JLzNDQFnepVXwxfAub9MIj/h1pzpBY8M+cNL2CswbfiCLip8uvZodqoTH09tlyvUzyaeu7fD86l9+oEzhe7OD/Wv/KDCnWq6UsEKNcJO8nQlLDOUggOhR7ow626j5+oV/GJjWfgk7TJyb5c+s/WSV2sznqJyk9BsQYN6qXcUt0ChlRY7Oax4lyG3UcqtplQIyxRu5gULezXfdS1w79YoINegn3qOPoEVOkqbND91LDpY0ia+UmKjSfzQY2gTbQCE6WW2kTZRx8KiSY1DnOlht5tfHg3LarDrbrHjKizhqxHVMoxnQeF5+cOHw+QYCrez7PBy7/kyO+R0dCLOQcAUclC/kPvsOeL/0TW3raCI0js60R+5T6+3lw0cmkqjUyjTJdpRGMfRqN/WGyx+wOymaGqVQ37f4EpmngWQnabR/HBGVaTVdwzwOr+RUqAg8fE8urYhYmcOulWa0JQJjMELR0oE0L4XvIKGhuNKiIKJHGLBeQYYBRqyDUU9syRE4Cw6s3haREGqs4Oee3DkSZJvevbrC9Y5TTtnbRNe1ghBohLfykDdLGjVyG8zNs7B+mkCg+7Xi2+S3MD1oZdSZ5md/HmT17MYyIeqkbyyli2g08S/KQs4SviU8AcKG6kXkjW3iw52welE2hJ6dneMf0v2N6df5ryxspexk+IX5GqyjxNX0hJ7Cby42b8HWSxcY2WmVhtg3jKZdPRe/jlpmzn7a9+5HOMHb7r+i1xvjwdpuVP9mNv0BhDglkIPjp207mxs4RxnSGqLoEYc1g5dch0fRV+yjZpX1f6bIppEgPtEnOy6GiFJX0HrpqXYQyZNo5fKb6jJ/FMxq4kUs6SNPeaMeX/pzksiKy0EZAZmYejjcfjUJqC1IjTK/5D3rcBsXh45kOJaViN0MdD9LTvYU+12Oxo2i3NEqDNQ35/zARU4LgeEXxLREIKN5u0hhK0LY7opTNkCjbyIEyzttnfhfP8YMYDySVqBlFfNEhUgPcXcvy4M538dmrmlFvK+dFpG810AJG/8FHP833RV1JElIRBQ4P3vVOGqKpTdwv+AktQEAmUaG3YyedCzbMXqsqDqXSfKbGVlINDBooHrFHmXKmGEoPPeXvtngtvNnOsGr+Pi1gBOkv2KRHoPiOkMklAzz2wCXoxDRrXnYttrMvbUkjwZdHUkyZNVZPr+aEoVchkQy1PsRvu397kDa0v9LPovIidqd3syu9F3/qlYRTr0Adwp7oQCxrjCDKgkpgWBNEQcdTD+QhSFNjqRhkvV52xNe45iTSmcQ36jidN5HTEe/aezEnev+OMSb51XEn8e2uR/DG3kA4czr6MN59TyZJgxpPbGNLIhL42IQcL3fwCvkwA2KMU+RWWsXBSWoPJNAG4+TpE08feuRoEWqJgUIIqO0LwvkLdRrfDc/jAb18ttwKsYdlYi8awWXmzYT7tG6ny8f57+iVrFeL6RBFljDEpM5SkKv5wt9+7FltayzkHAFHQ8ipjA3x2L+dw+TtOWqR28y1knSZST27cRcOJBA2hg6QaHxhYeuAhrQxdYShI0JhYulnN/mb2GcYI7RCSxMFaCmaWp7DXBPZDpHjogxJKa3Y0VdjKDNG2X7qh3/O76JZ7Ch6/Da68+MoDUHg0Oo2MAUsdyOsAxpQIM/tnI+DR5IqD3MS6zlljsamr7CL4L4Gp8ktbGSAcbOd4zNbebS4kChsLuqvE/fyZeeqQ7pUH8h15ln8Rf39NKIDjDtb7iSsrGx6GVkFlN8JwsPMbMLpuBVhFonqAxjJnbR7Lbxs7GWcP3E7fTcXqZ+ksHYLzGnB/W9aw3ZnOWOJMYaTw7hhgq5GZ3ObaN+o14waRbspCEotSYdpUuEThqQHGjRpNEW7iKlMfOnT4rccPvWIFmSKy0EoLK9lNgo2gJABQoa0rvwJbctvPqRdxoHBx/a3I/c9g9Sdc21/GpZkqN1l8cihvwy3LJrPo6dJ7P5R5ndXCKSmFBoszRy8lavUE7la7/L6+G71Vbzr1i0ox+Dal1+IMNpQMkHG2sK8+n/jUmbGmMeYdQoLH81y5f/76mx1M/+7h9S3h7HGBI+8tpfwNSNM6zYSwmPAeGJRetTP8zXrs5gYfJGPYO3bYlYNG8ouajKFnFdAphvoUCLMw2/JHkhpfAnjM21MTvcx5Uwi620kwyRj6b1sTe/GEpqBRicrRYrjV98xe934jd2c+LOm7ZCyNeN/G6CetGNSigRZQ6M1rL/r7XQmPdoW/Iba2PHs2fpyyrltlK0yW3NbqZk1Tpg6gdZaL4ZQ6ESBPWaB+zrvI5Qhym8jmDkZmRhCmkV0mEW6w0jriY8TrQxAImSAVjZB8USiygpUmEE1msa+0hkh2XYLZB8DBCibqN6PN/Q2lJrrHSqFh0ju3xoVWPl1oA2U14WwClj5B2a3Bffzxm1v5XWV75P/efN44zjNP164kAcSgwD402cQjV9EuE+aFdY0+lmws1wshqhph7PkIyyTQ6SoM0Ge74bnUSUxR2BK0qBHTJGlyquNBzhZbiVLjXvVcdykTiVDnQViFB+LEd3KHxq3kRZ1fhGdxphuYZ6YYI/uokWUKekkG/QSepjCIOLuA+yqzpEbGNZtbNHNse9hiqyoslk/87yIh+NT2R/zgU9d/fQFnwGxkHMEHA0hZ3zLLfzbNx7CDDpxvC60KpOr1rAbg3SN3ktkGAR2JzU5ieVPIaMGKd+n7Drsbs/SUm2mB8jWfSIpmEwnsJQiX/WouCbpekA5FVCz0ihh07CfuaV+W7mEIarUbcXDCxWhtOkoJEl4FgnPQu1bGVINn7qjUMIlkgEjbQ0GO3w2z6+gn/Q1vT9BZKZq8Pp781hhksA0abgajIhNA2W2dM8csj2O0Ly5xef+qskW79AGrxLNKzMh52cCUocoohBsZA236wvJRB6b5HGEmEwa+cOOQyIICEc9Fm7Zwo/N/48WqpRx+UPvL9ikF+Dgc6a5ka/aV5FVzftS1EkcAlwR4FuCqm2wxerjDu8kvlu5iGLwxDwSRgWn+wZWKw+pJUW7iI/PuKVwCTlh+jh6a70YwJ7MHtzIpbveTbLRyqt2fp3UQ4LSH4Q4myTOVsmWCxawIb92jleQUAa5QidK2kSGxHenEcoABL47hQwdLD+LFYBbT+M2bBpuDt+pEhl1IqOBQCNDl9CqIhBYfp7IaGAGGew5go9GGD52ZhSsGkZ6jPyyW0mkx7GM5mJt7RS4GySyDsF8jXIhatfIEqghQWPEIrNXkJhROAd4FhfyefIzMweJWOV0mpnjXTJTVfJbDr8d8MjihfzqpDUsmCgxMDLMmi2PEwnBaGcX2UqZeiKB4/vkSk2bqFoiQT2ZwK03mG5rRUmJ5fmgNb1jT+Qf06Zm4s8DOsf/hGLxm+S+3xRavvTOP2LTwqWU0hm0AQPWPVRFhsUPhvzBHb/Edjw2vWwhJ55+22HbfCBbo3n8o/4LWuUeVpW/TococnxC0WEdWgg6MNLtodgd9lO+djkX/PY3c44XL4qoXtycP7XI4rcbLiFoD3jtwA8PWU8YGZR2nMnY9lcQpEbIZgqk3AJtS36NNJu/v+2h17G1arOxdSNFu4jUEiUUoQyJRERbo428n8eNXGpmjYpZIRtkGUoNoVA0zAZqXwBQrSxAg4jm9C/tp6nYFVSQIyqcShi2gDZwOn6JsGaeciygGQZiYXkhvbVeWsIUZ7T8loVXTTQ1avvY/q4FfM9tZVemabcEoCMHtIEwm7Y/OswhzDJCKJTfQlA6CSE9hKyjgjaE8FBhC8pvJaotAtRh05K8mDBzD2Dl1iPtMXSYQxdPwCuejHqyxAyYRgnDKCOVhTLLmLLBBzIP87E//c6z2qZYyDkCjoaQs3PzZt57+weZcidnj/XNLKO3tATPrFFxZlAiwlAmFbuA0BWUcPapRgxWTPSzuW0LVafGwkI3BgV0ZDLj1NjdUSMZWNSsAKHhzMdsEBZdMwJXKRI1l5oUSKVJeR675oW0uYp8OeShNsmmDkEpFT5taoL9bpsAaOiYsZnOBkTGszdVLNHUyHSbilNSmrK9nAXsQEV1JkJJzmjaWez2Ja2GxpEaWxr8igu4LzoHL8rT53lstzsYdp86nk9HJaRqCGoJAyuIcCdqNCYC5Gid48Qe/tu5kjRPrLhVHLbKHk5UuwCItOC66Bz+yzib9d5yhFC0udNMN1qIDlJrK6QziuEO0Z5Zx0mV+XQ25hoIhyJEaok8cN9FgxEmyU+3k5q3ndN/+AOMimDyEwHuA5L0HQaTrzR4yP8jXJUEAVo0qEYOKteCFebQQjNJQC4ycQ9hW/R0OPk9pHs3UBtfQX1qMYZToX3lT0j0bsBwqljGobVYog7JeyTZHzzhan2kPL5qGaPnpUllCshNCfK7y6QyM8g1RWRDU15q46TroEE+6GL8OoszHZCd+v1D4j8djeMU0x8KSRVOoOc3b2Ps9B+S+MYGzIkn+jjd2sJITw+e7dA3NETX+BO2YZEluOPtpxOeOgS6jhWOs8eX7I5a6XccQuGwyzyZe61LeMOGu5k/OUK6WOLOVSezp9VBRMOU27r4A64Dfw9rnDpthxF6DqQ6k6L1q2naRpvbp1OvcAgWRnT/R0hgGQz+RcSmzHyW/rPF8s3bCQ2D4cVdVD48TN7yCTWM7M2w9LEa/hkRKv/0YzW8+VzGRhfjOwVsu46UEan0NGbk0Na5A9upEgVJQBP6KZK5IaLIJqi2MVXNsKEm2UqNmlnDVCaLS4tJRAlMbZIJMsjIZjQ5zH2d98waZe/HiixWFlbSVe9CC81wcpi+ah/DqWHcyGWgMoDl5TEiB0ObLDvjO8y7bpTEw5LRhe2UkimWbdpNfY3i7hPexkSgGUwOMZQaompWyQU5TGWyLbdt9jczfgZfBnhPkxNw/+qqg1a88degGj1omPVagwjXHiUUEsseJfS7sFJbiYRC2BMgFI3hP2wW3Z/HTQRY9ghCJfCDDhAh0qiiIheQcIitNosaAQdowGRjNpSAkdwBIkLV5mO33Q5mhaBwJsKoYrfehXQHkdahte1RvR+tHIzkDnSYRgVtGO7eOSkkDGXwjkffyBX/96+ecqyeKbGQcwQclSzkD9/Kp7f+N2NK4dTuRhzlQG2/K3lDYQlY6UZMhpLxUGAKGAnmqmhcoUlKTSEStJmaC7IBkYYBW9FhaQqhYH3NoN9WDNiK70w7bG1IHAntpqLV0PhaMM9WdEUplIBObdMlLJxEBbc0wJedP+HOfAcD9Qqfnfw+gzLHiOwjUC20JB9mNFqM73dzS0svI4mnNsjoqJepTgXUUymk0IihOsZQDbEvaV5ziw1Ac57xIF9xvkY68rjJOpHrk6ezoFLg8ugXtFCmQIZ1ahnXRa/kln05bg6NQrrDzUi52Q0sqfSxtLykuU2kBT3DmomOJKF9wBaMkriNdlKlOnZjlNbSXsb7l0F4Bstzn6bveo+wRVP6xDzkY7vI/6eJt1yx7pTL8UdXoZWF4ZRwWrdTSE+jIpt0+zZsZZAodxD6SYxkgWzfeqLIph66iMw4thFgyoB6mGBoZgEdZplseoxSkKY9NTEryITKwJQRhGANCoJ+TWKdxJwUyGlIPCCb2rxI8GTZp5G0GRroJT9Wom16bkybcm+CnYsWEJoGRleDlrO2YprBU97TQxFOJ7C8AH9LnloqRc/DU9hVn0iaRKGJkfLxExauriPqEuVISAdUz47QNtg7m4JK8m4De6cgbNVELeAvU5jjgrBNUz1PYTU6GLjnL/Hu+Abqgpcz0fY12r5mPqUw5y9UyKrAHG+WeXT5ch487nhuOellBJZFd2mafLlE3+goK3dsoXt6Ateu0b5nGtNTRFIy0tuD0JpSNsdIeyd3H38SU61ZsrqEpX1yLY+yx2whRQ1tdtLQClNrEiMZ/vjffkjnxARawsxlIfUzFCho+6yDM/LU76NtK/twKwHdQ1OYUXOh2vnHBomF9VlhR46Cd0ue/OMhyUSDwnsiwr7f/z23e/tp1AObtvwoKBvdyKIr3ZjJGWjdjVHLMTLRz46GwebcFjzD4/TJU+hwG3Tnp1CNPFE9j9u6EykV9UoHCMjOX4eQERO1PO3ZIbpv8cj+1EQLuPV15yKJeNWNzfQpU38S4K3WzAyvYtvjZyPSE/TOexjtpxgZXsqYXcD1clhBCiEjykaNRHoKNztGSls4boWR8QUYkUvWqbPBj9ibGsSXfjOW0u+A1oC2EdJv2tvts5mZPcfBWr39mr7928QHbRcfIUIL3MglESboqnexpLSEaWeadR3r8AwPK7IIZYgWmpyXY2F5IZayUEJRcApoNAOVAY6XOT7wyb/9nfp/OGIh5wg4GkLOrx6+g8snTMoyRU6N4uoSZjRNSTm0hlsZTZyHowosD35Nn5xiZ93DiCYZkYsoyl4SwQ5kfSNLnIjhwCFv+KxKCEr28aw1HmIisimok9DGEGPVZj4lT4MrYLsnqShBVmoCDWkDjNBFy4AWK2SFSLHYMnBmTuWmlrV0Vh1agojp7AQLxBZWlgKmxTilxCQ7RIlkkOWk6lLcWg9OeT73dLawOdFKYChW1id5LNnC9V2dJEPFceWA+fUGry+tJ1uvUreK6EYrVmoMSgNMRqfgmZKSI0kpzZaswf1tJlvTksYzcK9JBD7OjiIVZWG2gCx6hJPN2BMYEjnRmKOoWi72YBJRIUFFOJhK89XEVzlDbwFgq+rjM+G7uUcdHOROaIU+IB6JtMewO36J4Y4SNbqRdgFHVnGVgW9V6Kx3smZ6De4+exzLS7Jw2wz1VS6mG0CgCAoexWQFoY7DLS5huGMXg1ic1rKFnF2ip+02uv/WQPqC4ltCrP6XU03cT8fnmpLE1LtChtfkKDZyhEiWJHZh1RTKBmtYELVqiCBqB3NY4D4mMMcElXObkRLdxwTmkCCYr/GXarQAndC46yU6Cca4QBZBegJ7p8AoNUdTyadOoROYJloKGidEVN/WQLmCiYkBkpTIBwUqVha3pcghIu1jVbtxy/OpdKxHG02BJzNyOvnBc4msKk55ABm6jK7+fzSyu4h+x8XimSDrrbQOnkvrrtfiP34Tkzt/Rsey/0HhtYPM9N1K8k6JymmsXRJzEmRZgAR/gcZ7WRf2SIbMN3bM2Q4BUEKgpcCIDrMNZWlEcOjnITBNptra6N63nTba1UWhpYWFO3cy0dFBotEgUyrh+D7K0kx/OMRf9sTr3RwUtP6riTn1RP3l10YQQeamI0t8WmjNkJ2pYKi5y8b4qRJxnkcwv+mb3lAGg5Uk81NVSlGCvG6wvb6AmakcC0qj9PUMY9zlQF+Af4J6SkNucwRkSTT7IqA200utMI9s96PYicPb8xmTYI4JzJHmvbG3SxLrn3iet5wzn+SbdyClwr66hfYNVZSjqZ2l8Bdq/KUK7YIxKbD2CrylCvUUZjnGJEStzDGMb9TTKC2Znu5joppl3ChTqWeohhZFq8ikO0kqTGFLRVuUJO+1kBACLUOmfZtCYhxPA6lJpkNJOsxQMSskwyQCwXCqmRLGiizavDYSYYLR5CihCAlkAAJSQYpMkEFogUSSCBNUzSojqZHZdrY3WumvzCdpecgggQxS2HadNj+HYyj6e7bhWj4SgeFUmRw8npJnEckQITQyWaA1VaTRSOO6FXwvOWvULvw0wxsNrvjSdYcfvN+BWMg5Ao6GkHPz9T/mf45nqS1O4xu/W/AmoTV6n9i9slJi1Eoz7ciDzrmRossL6dZjnFvewZI9x7O5fQrDCBhMh4hGB6laK76E4SR4UrI1YzxlqG4n0pw6HaGB3SnJUFLSVVdoAePukbm19NUU7Z4iG8CMLZh0xFNrYJRGjtZR7S7YkkQQkGsETFkWgdscw8xkkcZYhBxtIMKDp6yLhys83mf9nGXWHqTSLLEHWeRNHlS2+ZOC70bn8fnwbdRIACFm9mHC8qon7aFHSGcMt/tHZM1pVs2sxI1cSlaJnloPyehJRpCRRaJmoLVNlK3h5Aq4ic2ku2ZIW2UmGm1UvDx5u0xfag/tP9AYM4L6KQpzTJC8U2JUBWGHYPzTHh13Xkjp5E1krh7F3tUcw7BT4y1VmFMCa5eYdTF/LghMk/HedpJejVqPjWl7FFclUUsDkqkCpvX0IfzRApQJ2qBz2yXk95yPQKKkj5YBIrKR2kJHTYFHPOk5qrRvoNT7W2otm7EabRiNFtKTJ1BtewQ/PUxiZim11kex6h0kCstJFBchQxe0gVue37SxQBCZVWptm0hNnkCtZQuRXcRPjpKeOJlEcTFaRYSD91Lc+B1+uPJELtkzSusrPk2jZQdCGUhlU+58gGrbIzTy20Ab5AdfSfu2NyHDFMPL/hHnhkcwRwRG8dD3SLmaxvGKsBOC9izeaQWsQUHyDknqbgPlaFSaOYLJ0xFlNYX3huh57SRmltKy6yKMMMXeU/6eIDWGvVmQvEsSdewTcgSkbpfk/stEWVA7XdE4PUL4zIkRdKQUWnI0HJee0TF82yaSkkSjgWfbOP6hY1YFeQGJCH+JhhB0CoKSib3HIDX6hKqwtlwRHK+on6awdgmyPzFQCQgQiKLEQCMdhfAF1tjhx+zxtYvIvOPxWe2GqEDyC63kpitH3E9/gSLs1AQZkLtMMts1jdUKf0A1NX0aopymfopCRKByB9dRKXVgO1Usu97UxjwR57L53xBk9Ylra9UcjlslCFykUAgZUatlyWSmkFIxNdXfNLKOHKRVR6lmGI8oMrEsD5SB0pKW1iGisHlvjSdpUqPIJAxsHKsZfiBzg4GsgzkqkDWBrIK/UFN5TUTYoTGHBeZkU3Mp6hAMNPuhE02h3V+g2TH5et7/gThOznPO0RByHr3te/zTHVv4WXQ6KmuBKYi6E9hJC9MQVFJNQ+HspEeYMvdF5YSWmk8heWS5orJ1n7ptERi/3+LWXgqYzO6b6FFEZDz915xVaxAYFrISgClRORtRDhDlAN1ioxOHN4TO1huUEk0th1toEI42kDM+q71tzA/G+JU4iYblIhpNDy0tgLyFOVMn0iarxQ7+0v4WS+QggSHZG3RhaM18xmmRT7yctIbduguBJitqXBedw6huoaAzGPu0Ojep02fLG4mduL3/RV/gMIJDvXg6QvgYqW2YmU2kwgQnT51MZ72zaYT7pJDLlpdFyQhPuGzP1jhl3l30JkZpWR/Qd3sNa0zMxrtRribs1pgjAukd5os9ISn87wZht8C95nysV4xR7XiYlq9mcUae2gbgcOxvcsNxcD2PSEoM9YQ2YbKtDakUypBUWxKY0kc0BPWFFtbSGmYtIuoOMBbPPOXvSD9F//o/JUiMU+5ah58awU+PkJhejum10Lb9YiyvBRHtEySjCO+xHxPsuA3n+EsxWpegykNE0zsIdtyGcHPI/ADR+KOYvSdhdq/B7FqFsA6ff+2Q/VcRqjpOOHgfwk4hEq0IaWB2rzm4bBQQDj+Iv/UmBkWB2xcv57/63sT/euQaztX9OGvejnhSxOHIrIKWGFGCaHonyiti9q5mauGNTC35EaLR1AiYk01TCJXXhB2ABLe0kO5H/gin2ocyGlTaH0LLkGLfb6i3Pg6qucDYWwXmhMB5TGBMCzCbAq+9UxK2acqvjVB5jbdU07brTbTveCOqNk11688Iy0Ok176H0dO+Ra1tEyIySE6vonvT+6jndjB+3LcI3ZmD5jYRGDMgJk2s32bwXRt5+iTBkn02FwqMR234VQtdjxd4IXFgV6rpJApJaJpsP6ufrldtxkhUyQ6djTIbVLrW4ZeTTNxwPD27xukZGT5IW/VsoRxNsGBf0NMZAYbGX6yx9gqsPWJ2K1SlNLK679+WbmqJLI01KPEXKcLWZh0qB7JIU+uqmxpcnQRRA6MqCPoV/kBTC6btZr3aAXeDJGrRGNPNeWWUBPUTFCqvSf36yDR7R0I9ZbLuvefxgY985VmrE2Ih54g4GkLOb266k5/e9nN2m72kqGHiU9BJlBaA5j6Wsc+RAIkiTFpgSmSpKU1rW6ITBlG7i6yFaMdA1EKINJgCWfARvkJLiPpSYArslEUjZ4Eh0JbEqEeoetRc7bVGhBoj1LSGmqlagKgEiEo4a6fSbBlgCKKeBHQn0NUQAtVs24wPoUI0ImRlrgFGmhodosiUzlIkBY5EJEysrE0tYyJqIaIcICshotHUEOFI8BR9TPJu85dcbt4IwO6ok+/rc9mte1jNDjplgZfLjeSoUtIpOuUMMzpFhQQNbfO4nseobqNVlBjVLezQvWxW/WxV/TSO0PDW7vwZmex9nDF5Gp2NTspmmb3pvWT9LAKBZ3jNfWYBfR2DzBgGjwdZltsTuIkyVUOTdiq4hsdAY5j0XZLEBjlrj3EkRIYgtExMP2K6vZXG+6oY84ukR0/m5z/u4g09dWYuvh0ik8KPl9L6eAWlDMY7O/D60+QkdOzdQnGlw6TOI5MBbkngtaZw0x5OfhIjFLiZCYSUzdB8xS7klk7qLVMo20f2ljDdIkJZYD19UDMAp7AIr2UHVq0Tp9JHZuRM0pNrkNHhx17VpommtmC0LATDpnT/v1CpD1NM2KQbAdmGR8WxGWrNMJJPY4cRM06KqUQLr9q2iflTJXzTwAkVWAlkphvt1yDykeluhJ1ERwG6MYN2sxDU0IXdoEIqjkUx4RBJidSaqmOR9JqG2ipskPdBmA7oiBnpce+iXqbcPNf2voXPXLKSL//3vXz27q8z0LCQpotMdaD9Cs7KS5Cti1CFnXiP3UC1sAUlBfmFF2EvvYgo2UBLHz85SiO7h+zImZh+BmU2iKwyVr0TVZogmtwMWmH1n4awm14rGkXoThEkphCRvU9jJCgM/JIgOYFV68RqtEEkiZwyTnmA/OArcafns3fdl9iU0Hxv0Wto86d5z9DDrF76HqIWH9PLI3zN+GPfI1ktkTr9Q5T776GR2Y3pZ8nveTXabDC68ptUOx465L2UQRJlPWFnZu0WuA9KtKuxNxs426B+mqJ+kiLsbX7xh91N2ydrryCYpxEhmCMC8+E89q6AhnKoixQJVSfq1EQGRB2Kye42uu4roso2mZE62XJzmyo0DBCw9fhFmFFIslHFDnwqawxqK0HYmmqhlZmwk5b8CG6iTGtrc4vHrLex4O6/Qhkee0/7AkFyAoBioYfaUDupdSGhspjoa6OUz9C9ZZL+3cMUWlrwXQvSES17ykgjJFFt0D4+xfTCDEHCgIaJ0/BIjTUOq716MRGYJp7jsHtgPpVMhmVbttBSmDlk2VoiQSWdJj8zgx0E+I7LDz96OX/1/g8+q22KhZwj4KgIOf/9Ve797X0sd0ZZzh4e9BYxIrrBstlLFxGSukyS0RXKMoWpAhYbQ5REivvUCookmS+mOEFsY0al2EYfPWIaLS0SQQU3rGNaiseZx166iRBorZggR4IQD5OU8FEaGljkRIOcaGAQohBkRYN2NcWrjXUsYw8pGjwQLqXLmGZaZHlArWAdK0jgk6GO0JqkqJOgzuuM+xhgFENHZEWVhAxI74vyWdMOUypHWSQZ0S2sV83Im8vlCEprTpJbSUd1+o0J8qJKqyiSlh7TOsN10St5TA2wXO6hR0xznNhDXlRoaBsDxd1qJdNk+I/wQkY4stD2ByHrSGsaI7UNISKEUUPaU/SYk7y8vpBl2QqTfisZd5JCoYNaNc+83q0k3SpmskRLNEJyncR5XGLtEggPvJW6uV1U55CGqJEh2XLiInbNW4hZ1WSLJRbs2oWhIsa6O0jmC1TWGEyKXiy3QT47SWv7HgCMRp6OGy/mf2XzvG3HDpb84WM08nOzcIuGhXafudHuMxq2IElq8niS0ytRRgMjTJCcWoXlzb0PUWEX4cxuxifuZzoYY8FEEYnA719DiKZRH0cFVUJCZpIuWghmUg7r25bxUO4ERtxu2v0pOrxJakaCwUQfDenS441SsFoIhMk5U7+hw59AI5hfGifn1Sm5NiXHJRn6ZOseQsN4IoclPSIpQAii0KZsJ5hw26iYGVqDaUwVUrRyGDokG5YRaDKNOiOpdgJhoYFpp41HMiu5aE2er779HD72vRvZ++uHuGD0N6QbPg3LJFv3SPghA1NFDKV5pL+d+7qXMWO38Npd97B6ooG99EJkywLM9uXosEE0vQNVGQcUqjxKqfA4Q3aDiUyyGUp/ZJrWWoPR/iWEQtGbWUmm92XI9FwvPa0ihJz7xa3qBUId8NuJW/lKz8uYspv3SaBZO303Z01s4fxyEqKAX6cb/KLvRI6b2cK5QYqezApS2YVgupS2/xypNen5r0CkOyh330el80GU9MmOnkF67FSktlDSZ2rRDUwv/Ol+i/7Dowxk5KKlP2t79TsRgr1d0LAT0CuRzpFvMe0nPXYKnfdfzK+3/if1VDfnL38Ve8/6POqpvKU0hDNtCCEw8s1tcFmxUemnEGJCCHflqZTyJDeHBNqme3CCUlsaNeVSSaaRKFyrgpofMOLMI5OaIlFqUJ9owTcdpI7o3zyKEArfsZhJtoGhSZZqJII6ph9Sak+TKtWpukkK+aZW9sQNTeG04TiMdnchNLh+ncC0MAhxQo+KnaVrfIxyJkPNTRLakkytQut4gR2n91PszEBrQGrpOI7dwHYr1Go56vUsznhAXafwwhRu+wzCbmqLbLtOsdgJEXQwTLHURWX7Wi7/2qee8X16KmIh5wg4KttV6x7iff/9EKO0sVQMsmLfgm0S0S8mmNEZ7lUryIsKC8QYaVHHIKKDIh1ihq2qjy5RQIlmjhSAIimquHRRYJwWKjpBBwXO5UGWWGP42mCb340XCOq4PKiXcqq5FVcEbKcPw9Ccaz7MvdFy7tUrOV7u5F51HOvUMnJUaRFlXmvcS6BN0qLOMjFIXlR4TM0nIXwsQiZ0ngYWW9Q8Nut+BsQ4Q7qNOg47dQ8LxQjLxV5c4dNNgQE5ShvNxeNxPY8kHpM6x71qBTeoswgwWSIG2al7iPgdVaPCBxECEsMdRJgVpDWNmdmEdMaI6vMRIqTHnqIFG8fymA4MEvU2FrshXVGKvuwM80d2kfmxiVGAqAW81YqgX2NOCKydAm2Du1EckXu0FlDNJxjv7aB4kkXX2seaTZ1uxa/nwJkhDBzs/DRG4tAvRxE69N/5EX7++P2od7yZm+7ewlfrMHHOz2jkdswtG9mYfpbAncIIUmgRoaw6MnQxgjRWtQvTz2H4WYQyiawKdq2LyC5Tz2/D9HIIZWNXu3DK84msGk65H6cyDy3C5kJWn6ExuYlQhVjCRMzspT71OMVcC6TaqZd2QW2aXe05NrUu4retZ6KBE4qPklRVJu02JIpIGGxJLSUUJtmwhK189uTmc+ZCj3lZ+O3OkL3TGTSCbEJy/nHd3LdzmtMGsrxqRTc/fuC33Lo1PSc6uNQRShi0+VOYKqBuJClZWbJBiZqRQKLw5VzNkhTNVA62ofCjQ9uKSaE4qd/i0284g5PntwAwU/N5/ZdvoDpVp2G4RKIZFr/VL7Cgvhtf2ozbHQy5vSAEJxYf4szJ+1kxPoaSgqpjowS4QYQdRhQTDkOZNrZlFrA9tZCSmcWTDj3eCCDo8sZpCQp0ehOkgwotOse07TBvdDcOAZlaA5XpgkwfOlJUKruYTMFdnSfxQO5kTl9mcMHJp3PS/Fb6cw7/59++w88G21H73isCxXH5aXwN1bGAVeVH0cqgYTmUzAwKyRsGf8VZLKJl5R+iVUSw+06qM9sYFAXKYYGFdNG59H+gOpOE7hSN3A4MP41d68GqdqMND8PPocwqxr68TV51FDvVhRYh2vQYX/Z9qh0PER0QFNQpzSc1eTyhU8RLD2J5LURWhUZ2J/owoQz2Y9U6CZ6U5kVEJnalH4QiNX4iiXsW8cNoB//e/UqSYZ0vj21i0ZJTqOe3ESTGqXQ8jJ8aQh2hVhMAZcDTZLY/EB3KfWlvntkSrCMTYYRoJRGH8AbQSqCips1bo5zHND3sdBFCE+H4h/W00pFEGEcWnPKZsnX0XXzw7Vc+q3XGQs4RcDSEnHu//+98bscIG0snoJ6N+PPPEJOQkGceIPD5RMga+dwjFGv9KK/3kGWkM4Qw6tjJPazu+RUqssklSkz4BhkzYoUtkEaIG6TotzUdqTKVWoZ08gDviwhkpSkXmeMCc1DgbpQ4247sPlVaXaYXZ7EKisRUQGlBgujkatNI71GJnZOol1eg85nZJhheFqvRjhYBRpAlu/4MfrtzJ/9v4cu5868v4wvXfZv0D37NxZ3nYud7Kc67HT85SmbsNNziIvACQr9CbWwDifxi7EwfOvJpFHdhpTqJwgYzxc00HJdsso/66HqS1RJG4KNyvSS7TyIoDVIdvItyJguJFjAsjHQvw1P3skf6TCRbCIVJxUxTN5p2VZN2O7606fTG2ZpawoTTQVs2YElnB2Fjmu1T+16oMgJtMFN3ybiaVy4W9Lf2sLCznQtWdtOSmmuLVvFCbENimwffl23jFbwwYt2uApVGlb68ScrNs2loipGZAvNakvS2tmObBtNVn3IjZFVvllMXtDJV8ZjX0rTlibTGMiSR0gwWaiRtk5maT7Ee4JgGS7vSuNbBwvdE2eO79+wil7Tpybls2LUdX1kMzzTww4h3nrmEVy6fx+OjJT753+t4ZLgBWtPpTxAKE1/aREIitEYJg8a+sezJCv7k3JW0phyuf3ArjwyVGSvDknxAqAU7i3OfaTeqkwuK+NKhYOXnrFptToX/ePfJrF609KD237lpNzfe9zgDbSnefPZqOlrzAPzt93/G7ZtHEDKg4OdYnjXYXhVMNiw6GpMEwsRUIR3BFHUjwZjbTllmSag6pxfWMdCY4g1hFwIYrmzDliZm21Im9DQz7nxGiPi1nUEbGcywQFYpPDNDmzB4WVDl1EDTJtKo7CQk0tjFHkLlY0iTSAVEKiCZ7EbbisiqEjoz1PNND8nsyFqEMgjL25D5eXO0jMorIZw0CE1UnqQwei8bgp1c33cqjdY2Pv/OV+M16nzqu3fwxzvuY4XVQyq7ADfTj2mliYw6UtmgBaE7TbVtU/OZbaSxplM0WgZxg6VoGeIWF80GzvSr4xi5JNX2R4jsMuWu+wjcKbQMiZwSMkjMFaC0mKsJU80M6k/p0vh7IiLzaQVGEbo41V7SY6dg1ztRhketdVMzf1pqFGXWUUaD5PQKhLKp57fip0bIjJ+MMjxMrwXpJxl9dAGv/dL/flbbHws5R8DREHK++/1v0ln7expb0/ym+0QqbhKBwo9sHpk6rpmWIL8L2/DwQ5t64FANbRQWQ5V+KkGa7tQQDSXImmVMCRKBZXiUwxQGkHMnmap1MFwZQD+NIOUaVRrRE2H9TemRdKYQQpG1y/RmBikHCUYr/ShlUPPa8aO5/pyGrOM6kyRMD8PwSBgBUkRkE+OkzDpBmCaIHCa9JJOhSdToI1KSKEo3vWgwscwyWXeSpDuGkRjCkgG2dkibdd7a/yA5t6nV2FZuw7GL1EIXH49Hi/PoTIywIunTZils2TQkMqabhpz+0n25gGpgFATmVNOYzygI7F0CWQMigQie2kNl5kSL8tIM1CD5aEBmrI7fKvHnSQwjIDyxgbfyGTwqqunBk5o8nty2swkykwTZSexKH1qG2LVOEjPLkJHLdHELFgZ1XWd3Yy8/MkI2tJ/IVe9/BWcv6yeMFGs/+336R3ZyamUHOT+kUyYJ3CRbidhotzJlt2LoCFNHpIIqC+s78Q2HQJhsTi+jzZ9GC8GQ20sgrKaKPGpg6ghTNzUg01YLfY0h9ib6qcsD5sC+BbQzWSWVsGlxFAnLIGFLTMPi9WvmoUWSvtb8rNYjpskDuwvcsWUCP1Qc15Nhw94ZkrbBsq4MZy1px5KSRhjRlrIxD5PrTmtN1Y+wDclvtk4wWKiTcU1OnJfntsdGeHSkzNtftoC+fBIhoD3tYMhnx+Ou4oXc9sheHtiyg7HiBClD8ZoTl7N29UqEafPLhzaz7pHfsnEkYGctTylIY8oIkwAVSKQl6DVL9DkerpWk3QUrkaUWaiYmJxiPLDY1OkFrFtR3M68+iCcddiUHcKMGFTNNKExyYYmFtSEWVSc5vaHpazmOCUoMVrYx5ZeYdPJUrBQZNUOL08+UFgwbEUUrjwZMq4UhaVMx0ziO4oKFSf7m3a/B2pfbb/OWR/ib797CZr8VM/TxtMN8r0JC+8xIlxlpMOBN42iFDKsEAnLBNBUzTSa1jK1mikEzyeLKDmytKAtYUdvDielVLPB8UmGAC6RxEaGPbF9C0D6JkgFWowUZJdBVTT0YxJUdGGYKbUt8bzfCF+hMBVevIdTj+C0F3Mo8IqvedAuPXOr5rfu0txmCxAQyTKAMj8guYvhZTC9PZJeRYQIROZheHhnZeJm9yDBBI7urOddkSHb0ZfjJUcxGK7Iq0LVpGmGZsdG7ibwiebuDlJVDtCzAzPajIx+8Cl5YxjAcIq+EBpx0X/Mjq7CbH582xMc/GGtynnOOhpDzo6/8C0v+31cx9rk5K0ujchptgTaabnXNkJf7tAoaojyEeY1O7AtCaTeN9DCb1vCi0fSiMKcEUaZpZa+SGs+ymKnliHJQ7zPIJUuM1jrpSw3jTyRJTAe4C6rohmAibCWXKJOo+Oh9H87Wbom9XaBdjb9IIyLwBzRBrvmlKZWmGKXJyCpmvekRZBQEUVtT0NA2mBMCldD73AVB7WtfGBnUfIeUU8eQc6eYqIOIwJgW2NsFqVuMJ9oQNF0Qg3kaWWkaJQpPoB2NOS6wt0lkWSD37fQoRx/WS+lQ7A8IqGxQefBWKeqnR834Hr8j+9Xj7sxi3OIiWvacjzftM1HdwV4d8UNL4kZ1WlSEKS1MHZEOiowaSR7KriAXlADNuNOJ5yY4q7PM377nLXTmnwiZPjJT48PX3MHOgg8qJG/U6c6UOWnRAgKVpRjadKc8Gn6ViVqKWiOkM5fkxL4U7UmDXw96VKs1Tl/ajxc2qAdgG1Ct+eQSNulUknqg6GtJIBBIAZvHypx3XBe5hEXaeXFpB2OeW7TWFItFpJQEQUCj0cDNtdMoTdHV2YlpHn7+VKtVRsfGGJsssHPPHrwghMgi19FJpHweHAvobG+DwijbxndQrnvUQ4OycnHtGjkrxLW7qEcmjcBACcVkVZNzPOa3pjlloJd1e6fpCKY5bcUCzn/FmTjOwcbxWmu2bdtGo9GgXiiwa2SSYsODsM42P09YnsHwykxpm3xW0q4NDCdBq/BQnQN0pB0m9g5T0AphOmwcLDARBBR1AiEiMlGJnrFBLBUxkuinRUesiDykmeE2w6aOwFUNRp0ucmEZR3nUpUsumGHN1Fb25OaRCSbo9aqMJTpwhIEfluirFzHsNjwMOjHxLEUi9MkrkHaOmahOEckuy2CBN8V8I08tqPCQYZETcIrTwYzfYFs0wYSZIWlYDHgF6gRscDqYtDvYlZrPUms7FSfLaL0dMwzoC0cwgxBfOJQSaYLIIqvLjJpNz1Y3atDjjTGgJvjAhy/hpNVnPKtzLhZyjoCjIeRsuP56+N7ncR4+8qSTzwYajXaBQCDU09sAPmVdpm5qPzRooQ8KaPZ0KGefUGfTNMoNm/vEiOa/j1ZUF+WAdgQqB0G/QguNapGErSE6qfEXNmOOzIlDAZiNFjKjp6OFopHfPmv3YtU6yYydip8aQQQO0bZWaiPdWEKQTHUzaW4nV+0HYVBITHLfjMdmaRFJg4KRYCrdQW+qyvG9bdSUwYNj4HsRC5I1urMOqUyeN5yxivkdOTrSNiPlgI6MQ/J3yEcWE/NSIgxDlFLY9pGF3Xg+mZmZwbZtHnnkEXbt2s243QWNANP0EV6Jmi9Y2ttCV0cHViKNEBKpQ36xYTf9OZv+7naUV2XbaJGO9gx1f5pt4wXq9QQZSzNRMqnUZkhERVwnwKuWsFpXsG3KJ2t4zGuZpBwKogp4MkHOrVIuu8x4Dp4pWNSlSdituKGFm/Ro71nM7gJ02AHz2yyS2TRnr1iBaZoEQYBlWdQaHqYU1Ot1oigilUoxNjZKGEa0tbczOjKCspLYyQwDHVks89lzSd9PLOQcAUdDyBncvoPtj70FMVLEHNu3XSJB1kQzsFOlqcVQ+3aQRL0Z38DaIwj6NJjNbRid1AT9Gi2b1xhFgUo2gytJrxl0SVaa9csqiGiu6HA44URb+4Jtmc3onP5yhbVXNNWShsYcO7yBbdim0QmNOSSI2kAEoNL7BKIQZJlnpFVRtiZq12gDgv6mOaksN/tmjQiiFo3at2sStWvCjuaYRO2asO0J25oor1FJ4GnedzJ0kUESu9pLI7cTULTufA21TcdxezDBTsNgvpFjgaVpzU2ydaadLV4Dz3KYxKKSCTkhM4gyLHaV2pifGQI/wXiQx0rkOLM9x3FrVrCgO0cu30LWNQ+7BRETExMT87tzpOt3/Mn4LJNtb2PTxpPoo5uc2SDoVKRkmsAuo7WBpyRWYppskEMZPsrwEAjqZoPIqmAEKQLDRxk+WvqgJTJy8Q2PyKrMxiAJ3enZuA5EYA8msbcEBH0hKg1BT1NgktXm1sx+rYWQNjKw0TLECHJYjXYaZhUtQ/zMYDNo2YRA27qZ623fFpt2AAmGn0aHCmEac7wh9mOONm1ihA8YEGVBO826REMgGzSFksy+XHORiV3tacYKsWp4qSG0DOYY5RleFqvejhYKbQS41W7UjI+y6jh6EQ1/D1rXMLYuJ6xliFQbk/Use0KPdKKAmOlhu0zghBYLhc24MHhE+2ScaYakSW93wMvPOJf3rJzH+o33s/mxx3AyffScOsAr+tvQjTpOvpt53XkymQzid0kCExMTExPznBNrcp5lTQ5AUCqzdc9uCtPTeBMTFEfHUFFIRSky1SpOS56yNCkXiySEQIaaauBjC0WtVsWKbCSKvNvC+PhWNJoomcB0clQsSdpOkGg41BrjJMI6SmeJzG4kDXTrDI1aloQIsRouuDV8w0f4kqKXQBDRgscEEEZQMxtokcdWFtIxWNS7GbPQj7BqVFSCcq2NlAm+VSCKfKyZTrQrcUSIDkNmZIWSXEivMUVKePiOIpcZxrGrqFqanEziNvKUQgtfesxYk+jQxbaq6FqaYqmddbJOj1HBFBH1IIMUbWTdiEwUUdeSehDSMCYpWhUyCYu6bsdVHqZvYjkRjmMT6Ih2FZHv6CNtgZnNYiaTZByHtpYWAtNmulKmo62VZDKJZVlk0mlsx0HKWNsSExMT82Ii1uQ8j1jZDCtXH5zw8blienoa27ZJp9P4vo8QAtM08X0f27YPq4lQSrFt2zYsyyLtOGgpQUoSieaeUaVSIZPJkEgkMA6RAqJer9Oo1xncto1ofBzlOFSAVEsLi/J5ZD6PNE3KpRJGsUhy3jwSiQTvM83Z+ur1Ort27cJxHPr6+lBKYRgGQohZT4iYmJiYmJgjIdbkHAVNTkxMTExMTMzR40jX71hPHxMTExMTE3NM8qIXcq666ioWLFiA67qcccYZ3Hfffc93k2JiYmJiYmJeALyohZxrr72WT3ziE1x55ZU8+OCDrFmzhgsvvJDx8fGnvzgmJiYmJibmmOZFbZNzxhlncNppp/G1r30NaBrOzps3j//5P/8nf/Znf3ZQec/z8Dxv9v9LpRLz5s2LbXJiYmJiYmJeRBzzNjm+7/PAAw9w/vnnzx6TUnL++edz9913H/Kaz3/+8+Ryudm/efPmPVfNjYmJiYmJiXmOedEKOZOTk0RRRFdX15zjXV1djI6OHvKaP//zP6dYLM7+7d2797loakxMTExMTMzzwEsqTo7jOIdMzBYTExMTExNz7PGi1eS0t7djGAZjY2Nzjo+NjdHd3f08tSomJiYmJibmhcKLVsixbZtTTjmFW2+9dfaYUopbb72VM88883lsWUxMTExMTMwLgRf1dtUnPvEJ3v3ud3Pqqady+umn85WvfIVqtcp73/ve57tpMTExMTExMc8zL2oh561vfSsTExP8xV/8BaOjo5x44on84he/OMgYOSYmJiYmJualx4s6Ts7vS5y7KiYmJiYm5sXHMR8nJyYmJiYmJibmqXhRb1f9vuxXYpVKpee5JTExMTExMTFHyv51++k2o17SQk65XAaIIx/HxMTExMS8CCmXy+RyucOef0nb5CilGB4eJpPJIIR41urdnxNr7969L1lbn5f6GLzU+w/xGEA8Bi/1/kM8Bker/1pryuUyvb29SHl4y5uXtCZHSkl/f/9Rqz+bzb4kJ/WBvNTH4KXef4jHAOIxeKn3H+IxOBr9fyoNzn5iw+OYmJiYmJiYY5JYyImJiYmJiYk5JomFnKOA4zhceeWVL+lkoC/1MXip9x/iMYB4DF7pYWiiAAAMNklEQVTq/Yd4DJ7v/r+kDY9jYmJiYmJijl1iTU5MTExMTEzMMUks5MTExMTExMQck8RCTkxMTExMTMwxSSzkxMTExMTExByTxEJOTExMTExMzDFJLOQcIb/+9a95wxveQG9vL0IIfvSjH805X6lU+MhHPkJ/fz+JRIKVK1fyL//yL3PKXH311Zxzzjlks1mEEMzMzDx3HXiW+cIXvoAQgo9//OOzxy6//HIWL15MIpGgo6ODN77xjTz++ONzrvvoRz/KKaecguM4nHjiic9to58FhoaGeOc730lbWxuJRILjjz+edevWzZ5/KcyDcrnMxz/+cQYGBkgkEqxdu5b7779/9vxf/uVfsmLFClKpFC0tLZx//vnce++9c+r47Gc/y9q1a0kmk+Tz+ee4B0fO0z33P/zhD7ngggtoa2tDCMGGDRsOqmN0dJTLLruM7u5uUqkUJ598Mj/4wQ/mlHkhj8fTjcGBfPCDH0QIwVe+8pU5xy+++GLmz5+P67r09PRw2WWXMTw8PHu+0Wjwnve8h+OPPx7TNPmDP/iDo9OZ35EjGYPHHnuMiy++mFwuRyqV4rTTTmPPnj2z51/s8+DFSizkHCHVapU1a9Zw1VVXHfL8Jz7xCX7xi1/wne98h8cee4yPf/zjfOQjH+GGG26YLVOr1bjooov41Kc+9Vw1+6hw//3386//+q+ccMIJc46fcsopXHPNNTz22GPcdNNNaK254IILiKJoTrn3ve99vPWtb30um/ysUCgUOOuss7Asi5///Oc8+uijfPGLX6SlpWW2zEthHvzRH/0RN998M9/+9rfZuHEjF1xwAeeffz5DQ0MALFu2jK997Wts3LiRO++8kwULFnDBBRcwMTExW4fv+7zlLW/hQx/60PPVjSPi6Z77arXK2Wefzd/93d8dto53vetdbN68mRtuuIGNGzdyySWXcOmll7J+/frZMi/k8Xi6MdjP9ddfzz333ENvb+9B584991yuu+46Nm/ezA9+8AO2b9/Om9/85tnzURSRSCT46Ec/yvnnn/+s9+H35enGYPv27Zx99tmsWLGC22+/nYcffpjPfOYzuK47W+bFPg9etOiYZwygr7/++jnHVq1apf/6r/96zrGTTz5Zf/rTnz7o+ttuu00DulAoHMVWHh3K5bJeunSpvvnmm/UrX/lK/bGPfeywZR966CEN6G3bth107sorr9Rr1qw5eg09Cnzyk5/UZ5999lOWOdbnQa1W04Zh6BtvvHHO8cP1UWuti8WiBvQtt9xy0LlrrrlG53K5o9HUZ51DPff72blzpwb0+vXrDzqXSqX0t771rTnHWltb9Te+8Y2Dyr7Qx+NwYzA4OKj7+vr0I488ogcGBvSXv/zlp6znxz/+sRZCaN/3Dzr37ne/W7/xjW98dhp8FDjUGLz1rW/V73znO5/yumNpHryYiDU5zxJr167lhhtuYGhoCK01t912G1u2bOGCCy54vpv2rPLhD3+Y173udU/7tVWtVrnmmmtYuHAh8+bNe45ad3S54YYbOPXUU3nLW95CZ2cnJ510Et/4xjfmlDnW50EYhkRRNOcLFSCRSHDnnXceVN73fa6++mpyuRxr1qx5rpr5gmLt2rVce+21TE9Po5Ti+9//Po1Gg3POOef5btqzglKKyy67jCuuuIJVq1Y9bfnp6Wm++93vsnbtWizLeg5aeHRRSvHTn/6UZcuWceGFF9LZ2ckZZ5xx0JbWsT4PXqjEQs6zxD/90z+xcuVK+vv7sW2biy66iKuuuopXvOIVz3fTnjW+//3v8+CDD/L5z3/+sGW+/vWvk06nSafT/PznP+fmm2/Gtu3nsJVHjx07dvDP//zPLF26lJtuuokPfehDfPSjH+U//uM/Zssc6/Mgk8lw5pln8jd/8zcMDw8TRRHf+c53uPvuuxkZGZktd+ONN5JOp3Fdly9/+cvcfPPNtLe3P48tf/647rrrCIKAtrY2HMfh8ssv5/rrr2fJkiXPd9OeFf7u7/4O0zT56Ec/+pTlPvnJT5JKpWhra2PPnj38+Mc/fo5aeHQZHx+nUqnwhS98gYsuuohf/vKXvOlNb+KSSy7hjjvumC13rM+DFyqxkPMs8U//9E/cc8893HDDDTzwwAN88Ytf5MMf/jC33HLL8920Z4W9e/fysY99jO9+97sHfcUfyDve8Q7Wr1/PHXfcwbJly7j00ktpNBrPYUuPHkopTj75ZD73uc9x0kkn8YEPfIA//uM/nmNYfKzPA4Bvf/vbaK3p6+vDcRy++tWv8ra3vQ0pn3idnHvuuWzYsIG77rqLiy66iEsvvZTx8fHnsdXPH5/5zGeYmZnhlltuYd26dXziE5/g0ksvZePGjc93035vHnjgAf7xH/+Rb37zmwghnrLsFVdcwfr16/nlL3+JYRi8613vQh8DWYWUUgC88Y1v5E//9E858cQT+bM/+zNe//rXz3k3HMvz4AXN87tb9uKEJ+3J1mo1bVnWQXYK73//+/WFF1540PUvRluM66+/XgPaMIzZP0ALIbRhGDoMw4Ou8TxPJ5NJ/Z//+Z8HnXsx2uTMnz9fv//9759z7Otf/7ru7e3VWr805sGBVCoVPTw8rLXW+tJLL9Wvfe1rD1t2yZIl+nOf+9xBx19MtgdPfu4P5HA2Odu2bdOAfuSRR+YcP++88/Tll19+UD0v9PF48hh8+ctfnn0HHPhekFLqgYGBw9azd+9eDei77rrroHMvNpscz/O0aZr6b/7mb+aU+z//5//otWvXaq2PvXnwYsJ8XiSrY4wgCAiCYM6XLIBhGLNS/oud884776Avjve+972sWLGCT37ykxiGcdA1Wmu01nie91w186hy1llnsXnz5jnHtmzZwsDAAPDSmAcHkkqlSKVSFAoFbrrpJv7+7//+sGWVUsfMPHgm1Go1gGN2Tlx22WUH2eddeOGFXHbZZbz3ve897HX7+34szAnbtjnttNOe8t1wrM+DFzKxkHOEVCoVtm3bNvv/O3fuZMOGDbS2tjJ//nxe+cpXcsUVV5BIJBgYGOCOO+7gW9/6Fl/60pdmrxkdHWV0dHS2no0bN5LJZJg/fz6tra3PeZ+eCZlMhtWrV885tn9/ffXq1ezYsYNrr72WCy64gI6ODgYHB/nCF75AIpHgta997ew127Zto1KpMDo6Sr1en40rsnLlyhe87c6f/umfsnbtWj73uc9x6aWXct9993H11Vdz9dVXA5DNZo/5eQDMhgdYvnw527Zt44orrmDFihW8973vpVqt8tnPfpaLL76Ynp4eJicnueqqqxgaGuItb3nLbB179uxhenqaPXv2EEXR7DxYsmQJ6XT6eerZwTzdc7+/D/tjvuxf6Lq7u+nu7mbFihUsWbKEyy+/nH/4h3+gra2NH/3oR9x8883ceOONs/W+kMfj6cagra1tTnnLsuju7mb58uUA3Hvvvdx///2cffbZtLS0sH37dj7zmc+wePFizjzzzNnrHn30UXzfZ3p6mnK5PDsGL4R4Wk83BldccQVvfetbecUrXsG5557LL37xC37yk59w++23AxwT8+BFy/OsSXrRsH9r4cl/7373u7XWWo+MjOj3vOc9ure3V7uuq5cvX66/+MUvaqXUbB1XXnnlIeu45pprnp9O/Z4c6EI+NDSkX/Oa1+jOzk5tWZbu7+/Xb3/72/Xjjz9+0DWHGoOdO3c+9x34HfjJT36iV69erR3H0StWrNBXX331nPMvhXlw7bXX6kWLFmnbtnV3d7f+8Ic/rGdmZrTWWtfrdf2mN71J9/b2atu2dU9Pj7744ov1fffdN6eOd7/73Yccg9tuu+156NHhebrn/pprrjnk+SuvvHK2ji1btuhLLrlEd3Z26mQyqU844YSDXIlfyOPxdGPwZJ7sQv7www/rc889V7e2tmrHcfSCBQv0Bz/4QT04OHjQdYf6nRcCRzIG//Zv/6aXLFmiXdfVa9as0T/60Y/m1PFinwcvVoTWx4DlV0xMTExMTEzMk4i9q2JiYmJiYmKOSWIhJyYmJiYmJuaYJBZyYmJiYmJiYo5JYiEnJiYmJiYm5pgkFnJiYmJiYmJijkliIScmJiYmJibmmCQWcmJiYmJiYmKOSWIhJyYmJiYmJuaYJBZyYmJiYmJiYo5JYiEnJiYmJiYm5pgkFnJiYmJiYmJijkn+f4pL84La9xnFAAAAAElFTkSuQmCC"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"clf = RandomForestClassifier(**classifier_params)\n",
|
|
"clf.fit(X_train, truth_train.to_numpy().ravel())\n",
|
|
"_ = experiments_train.transpose().plot(legend=False)"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-29T13:05:02.987612100Z",
|
|
"start_time": "2024-04-29T13:05:01.567576100Z"
|
|
}
|
|
},
|
|
"id": "fd7b893d195e56a2",
|
|
"execution_count": 10
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
|
"image/png": "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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"_ = plt.plot(clf.feature_importances_)\n",
|
|
"_ = plt.title(\"Feature Importance\")"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-29T13:05:03.073580500Z",
|
|
"start_time": "2024-04-29T13:05:02.986578100Z"
|
|
}
|
|
},
|
|
"id": "22c926f0aff2cf9e",
|
|
"execution_count": 11
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
|
"image/png": "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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"to_plot = []\n",
|
|
"params = best_params.copy()\n",
|
|
"for baseline_lam in range(2, 21):\n",
|
|
" params['baseline_lam'] = baseline_lam\n",
|
|
" process_params = {key: params[key] for key in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}\n",
|
|
" classifier_params = {key: params[key] for key in best_params.keys() if key not in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}\n",
|
|
" X_train, X_test = process_train_test(process_params, experiments_train, metadata_train, experiments_test, metadata_test, scale=True)\n",
|
|
" to_plot.append(evaluate_classifier_params(RandomForestClassifier, classifier_params, X_train, truth_train, X_test, truth_test, iters=10)[1])\n",
|
|
"_ = plt.plot(range(2, 21), to_plot)\n",
|
|
"_ = plt.title(\"Impact of varying baseline lambdas\")"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-29T13:07:28.464220800Z",
|
|
"start_time": "2024-04-29T13:05:03.075672100Z"
|
|
}
|
|
},
|
|
"id": "5e1397e0d62bdae5",
|
|
"execution_count": 13
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
|
"image/png": "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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"to_plot = []\n",
|
|
"params = best_params.copy()\n",
|
|
"for baseline_p in [1e-1, 1e-2, 1e-3, 1e-4, 1e-5]:\n",
|
|
" params['baseline_p'] = baseline_p\n",
|
|
" process_params = {key: params[key] for key in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}\n",
|
|
" classifier_params = {key: params[key] for key in best_params.keys() if key not in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}\n",
|
|
" X_train, X_test = process_train_test(process_params, experiments_train, metadata_train, experiments_test, metadata_test, scale=True)\n",
|
|
" to_plot.append(evaluate_classifier_params(RandomForestClassifier, classifier_params, X_train, truth_train, X_test, truth_test, iters=10)[1])\n",
|
|
"_ = plt.plot([1, 2, 3, 4, 5], to_plot)\n",
|
|
"_ = plt.title(\"Impact of varying baseline ps [1e-x]\")"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-29T13:08:10.487807400Z",
|
|
"start_time": "2024-04-29T13:07:28.454989200Z"
|
|
}
|
|
},
|
|
"id": "5562f44317c1d060",
|
|
"execution_count": 14
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
|
"image/png": "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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"to_plot = []\n",
|
|
"params = best_params.copy()\n",
|
|
"for smooth_polyorder in range(3, 15):\n",
|
|
" params['smooth_polyorder'] = smooth_polyorder\n",
|
|
" process_params = {key: params[key] for key in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}\n",
|
|
" classifier_params = {key: params[key] for key in best_params.keys() if key not in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}\n",
|
|
" X_train, X_test = process_train_test(process_params, experiments_train, metadata_train, experiments_test, metadata_test, scale=True)\n",
|
|
" to_plot.append(evaluate_classifier_params(RandomForestClassifier, classifier_params, X_train, truth_train, X_test, truth_test, iters=10)[1])\n",
|
|
"_ = plt.plot(range(3, 15), to_plot)\n",
|
|
"_ = plt.title(\"Impact of varying smoothing polynomial order\")"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-29T13:09:41.590715700Z",
|
|
"start_time": "2024-04-29T13:08:10.478203400Z"
|
|
}
|
|
},
|
|
"id": "e763853b27eb8b33",
|
|
"execution_count": 15
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAj4AAAGzCAYAAAAv9B03AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAACBIklEQVR4nO3deVhUZfvA8e8MMAyyKjuIoGjijoqimdlC4lK5ZdbrnlmZZmqrb2Zmv7LVLDNN3zLTFjOX1EpDc88V3BfcQZHFjUWQZWbO7w9kcgIEFDgD3J/rmuuSc55zzn3O4MzNs2oURVEQQgghhKgBtGoHIIQQQghRWSTxEUIIIUSNIYmPEEIIIWoMSXyEEEIIUWNI4iOEEEKIGkMSHyGEEELUGJL4CCGEEKLGkMRHCCGEEDWGJD5CCCGEqDEk8RGiEixcuJCQkBDs7Oxwc3NTO5wyue+++7jvvvvUDqPK27hxIxqNhl9++aXEssOGDSMoKKjigyonZ8+eRaPR8O2331r1OSuK/B+pWiTxEXfk22+/RaPRsGfPHrVDuWO///47U6ZMKffzHjt2jGHDhhEcHMy8efOYO3duuV9DWI8ffviBGTNmqB2GEKIYtmoHIIS1+P3335k1a1a5Jz8bN27EZDLx2Wef0bBhw3I9d2X4888/1Q6hSvnhhx84dOgQ48aNu+1zzJs3D5PJVH5BVbDAwECuX7+OnZ2d2qGoQv6PVC2S+AhRwVJSUgCspokrMzMTR0fHUpfX6XQVGI0oSlVLIDQaDXq9Xu0wVCP/R6oWaeoS5W7YsGE4OTkRHx/Pww8/jJOTE/7+/syaNQuAgwcP8sADD+Do6EhgYCA//PCDxfEFzWebN2/m2Wefxd3dHRcXF4YMGcLVq1ctyv7666/07NkTPz8/7O3tCQ4O5p133sFoNBaKa+fOnfTo0YPatWvj6OhIy5Yt+eyzz8wxF8Sn0WjMr5J8+eWXNGvWDHt7e/z8/Bg9ejSpqanm/UFBQbz11lsAeHp6otFoiq1R+vjjj9FoNMTFxRXaN3HiRHQ6nfn+t2zZQv/+/alXrx729vYEBAQwfvx4rl+/bnFcwXtx6tQpevTogbOzMwMHDuStt97Czs6OixcvFrrWM888g5ubG9nZ2UDh/gsFfVV+/vln3n33XerWrYter+fBBx/k5MmThc43a9YsGjRogIODA+3bt2fLli2l7hMRFRXFPffcg5ubG05OTjRu3Jj//ve/Rcby9ttv4+/vj7OzM4899hhpaWnk5OQwbtw4vLy8cHJyYvjw4eTk5Fhcw2Aw8M477xAcHIy9vT1BQUH897//LVQOSn6/77vvPn777Tfi4uLMv0P/7qtjMplKfG7/7uNT0N/l448/Zu7cueZY27Vrx+7duwvFuWTJEpo2bYper6d58+YsX768VP2GJkyYgLu7O4qimLe98MILaDQaPv/8c/O25ORkNBoNs2fPtojv5v44Bb97CQkJ9O7dGycnJzw9PXn55ZcL/f9MTU1l2LBhuLq64ubmxtChQy2e683++usvOnfujKOjI25ubvTq1YujR4+a9x84cACNRsPKlSvN26Kjo9FoNLRp08biXN27dyc8PPyWzyQpKYnhw4dTt25d7O3t8fX1pVevXpw9e9Zc5t+/z0FBQRafIze/Nm7caC6XkJDAU089hbe3N/b29jRr1oxvvvnmlvGIOyc1PqJCGI1Gunfvzr333suHH37I999/z5gxY3B0dOSNN95g4MCB9O3blzlz5jBkyBA6duxI/fr1Lc4xZswY3NzcmDJlCrGxscyePZu4uDjzlx3kJ0lOTk5MmDABJycn/vrrLyZPnkx6ejofffSR+VxRUVE8/PDD+Pr68uKLL+Lj48PRo0dZvXo1L774Is8++ywXLlwgKiqKhQsXluoep0yZwttvv01ERASjRo0yx7h79262bduGnZ0dM2bM4LvvvmP58uXMnj0bJycnWrZsWeT5Hn/8cV599VV+/vlnXnnlFYt9P//8M127dqV27dpA/hdbVlYWo0aNwt3dnV27djFz5kzOnz/PkiVLLI41GAxERkZyzz338PHHH1OrVi06duzI1KlTWbx4MWPGjDGXzc3N5ZdffqFfv34l/gX//vvvo9Vqefnll0lLS+PDDz9k4MCB7Ny501xm9uzZjBkzhs6dOzN+/HjOnj1L7969qV27NnXr1r3l+Q8fPszDDz9My5YtmTp1Kvb29pw8eZJt27YVKjtt2jQcHBx4/fXXOXnyJDNnzsTOzg6tVsvVq1eZMmUKO3bs4Ntvv6V+/fpMnjzZfOzTTz/NggULeOyxx3jppZfYuXMn06ZN4+jRoyxfvtxcrjTv9xtvvEFaWhrnz5/n008/BcDJyanMz604P/zwAxkZGTz77LNoNBo+/PBD+vbty+nTp821RL/99hsDBgygRYsWTJs2jatXrzJixAj8/f1LPH/nzp359NNPOXz4MM2bNwfyk2ytVsuWLVsYO3aseRvAvffee8vzGY1GIiMjCQ8P5+OPP2bdunV88sknBAcHM2rUKAAURaFXr15s3bqV5557jiZNmrB8+XKGDh1a6Hzr1q2je/fuNGjQgClTpnD9+nVmzpxJp06diImJISgoiObNm+Pm5sbmzZt59NFHLe5h//79pKen4+Ligslk4u+//+aZZ5655T3069ePw4cP88ILLxAUFERKSgpRUVHEx8cXm0jOmDGDa9euWWz79NNP2bdvH+7u7kB+8tihQwc0Gg1jxozB09OTP/74gxEjRpCenn5HTaWiBIoQd2D+/PkKoOzevdu8bejQoQqgvPfee+ZtV69eVRwcHBSNRqP89NNP5u3Hjh1TAOWtt94qdM62bdsqubm55u0ffvihAii//vqreVtWVlahmJ599lmlVq1aSnZ2tqIoimIwGJT69esrgYGBytWrVy3Kmkwm879Hjx6tlPa/REpKiqLT6ZSuXbsqRqPRvP2LL75QAOWbb74xb3vrrbcUQLl48WKJ5+3YsaPStm1bi227du1SAOW7774zbyvqvqdNm6ZoNBolLi7OvK3gvXj99deLvFZ4eLjFtmXLlimAsmHDBvO2Ll26KF26dDH/vGHDBgVQmjRpouTk5Ji3f/bZZwqgHDx4UFEURcnJyVHc3d2Vdu3aKXl5eeZy3377rQJYnLMon376aYnPrSCW5s2bW/yuPPnkk4pGo1G6d+9e6J4DAwPNP+/bt08BlKefftqi3Msvv6wAyl9//aUoStne7549e1pc49+xlvTcFCX/fbv5HGfOnFEAxd3dXbly5Yp5+6+//qoAyqpVq8zbWrRoodStW1fJyMgwb9u4caMCFBnXzVJSUhRA+fLLLxVFUZTU1FRFq9Uq/fv3V7y9vc3lxo4dq9SpU8f8/6cgvvnz51vcA6BMnTrV4hqtW7e2+B1fsWKFAigffviheZvBYFA6d+5c6JyhoaGKl5eXcvnyZfO2/fv3K1qtVhkyZIh5W8+ePZX27dubf+7bt6/St29fxcbGRvnjjz8URVGUmJiYQp8n/3b16lUFUD766KNbPrd//x/5t59//rnQsxgxYoTi6+urXLp0yaLsE088obi6uhb5f1yUD2nqEhXm6aefNv/bzc2Nxo0b4+joyOOPP27e3rhxY9zc3Dh9+nSh45955hmLvg6jRo3C1taW33//3bzNwcHB/O+MjAwuXbpE586dycrK4tixYwDs3buXM2fOMG7cuEL9bErTnFWUdevWkZuby7hx49Bq//lvNHLkSFxcXPjtt99u67wDBgwgOjqaU6dOmbctXrwYe3t7evXqZd52831nZmZy6dIl7r77bhRFYe/evYXOW/DX9c2GDBnCzp07La71/fffExAQQJcuXUqMdfjw4RZ9Gzp37gxgfi/37NnD5cuXGTlyJLa2/1QuDxw40FxzdSsF79Wvv/5aYkffIUOGWPyuhIeHoygKTz31lEW58PBwzp07h8FgADD/Lk2YMMGi3EsvvQRgfh/L8/0u6bndyoABAyye3b+PvXDhAgcPHmTIkCEWNU1dunShRYsWJZ7f09OTkJAQNm/eDMC2bduwsbHhlVdeITk5mRMnTgD5NSj33HNPqf7/PPfccxY/d+7c2eJef//9d2xtbS1+R21sbHjhhRcsjktMTGTfvn0MGzaMOnXqmLe3bNmShx56yOJzoXPnzsTExJCZmQnA1q1b6dGjB6Ghoebaqi1btqDRaLjnnnuKjd3BwQGdTsfGjRsLNbOX1pEjR3jqqafo1asXkyZNAvJruZYuXcojjzyCoihcunTJ/IqMjCQtLY2YmJjbup4omSQ+okLo9Xo8PT0ttrm6ulK3bt1CH5aurq5Ffqg0atTI4mcnJyd8fX0t2tYPHz5Mnz59cHV1xcXFBU9PTwYNGgRAWloagPmLvaDqvjwU9MNp3LixxXadTkeDBg2K7KdTGv3790er1bJ48WIg/wNyyZIldO/eHRcXF3O5+Ph48xdAQd+JgmSl4L4L2NraFtmsNGDAAOzt7fn+++/Nx61evZqBAweW6gutXr16Fj8XfCEXvJcFz+DfI9lsbW1LNUfNgAED6NSpE08//TTe3t488cQT/Pzzz0UmQf+OxdXVFYCAgIBC200mk/kZxcXFodVqC8Xo4+ODm5ub+R7K8/0u6bndybHFPfPithWlc+fOFslBWFgYYWFh1KlThy1btpCens7+/fvNSdetFPU5ULt2bYt7jYuLw9fXt1CT4L+fdXHvAUCTJk24dOmSOdHp3LkzBoOB7du3ExsbS0pKCp07d+bee++1uLemTZtaJFH/Zm9vzwcffMAff/yBt7e3uek+KSmpxHsHSE9Pp2/fvvj7+/Pdd9+Z/19dvHiR1NRU5s6di6enp8Vr+PDhwD+DIkT5k8RHVAgbG5sybVdu6kxZWqmpqXTp0oX9+/czdepUVq1aRVRUFB988AFAlRoOXMDPz4/OnTvz888/A7Bjxw7i4+MZMGCAuYzRaOShhx7it99+47XXXmPFihVERUWZO5b++77t7e0taikK1K5dm4cfftic+Pzyyy/k5OSYE8eSlOd7WRQHBwc2b97MunXrGDx4MAcOHGDAgAE89NBDhTrH3unv2+3W/N2OO3luFf3MAe655x4SEhI4ffo0W7ZsoXPnzuaakS1btvD3339jMplKlfgUF29FCwsLQ6/Xs3nzZrZs2YKXlxd33XUXnTt3ZteuXeTk5JjvrSTjxo3j+PHjTJs2Db1ez5tvvkmTJk2KrFn9t2HDhnHhwgVWrFhh8YdLwf/RQYMGERUVVeSrU6dOt/8AxC1J4iOsVkG1eoFr166RmJhori3YuHEjly9f5ttvv+XFF1/k4YcfJiIiolAzSnBwMACHDh265fXK8uUXGBgIQGxsrMX23Nxczpw5Y95/OwYMGMD+/fuJjY1l8eLF1KpVi0ceecS8/+DBgxw/fpxPPvmE1157jV69ehEREYGfn1+ZrzVkyBCOHz/O7t27+f7772ndujXNmjW77dhvVvAM/j1iyWAwWNTa3YpWq+XBBx9k+vTpHDlyhHfffZe//vqLDRs2lFuMJpOp0O9acnIyqamp5nsoy/tdmUnUvxX3zIvbVpSCZCAqKordu3ebfy6oLdmyZQuOjo60bdu23GJOTEws1Bn438+6uPcA8icJ9fDwME/ToNPpzCMIb05wOnfuTE5ODt9//z3Jycklds4uEBwczEsvvcSff/7JoUOHyM3N5ZNPPrnlMe+//z4rVqzgu+++IyQkxGKfp6cnzs7OGI1GIiIiinx5eXmVKjZRdpL4CKs1d+5c8vLyzD/Pnj0bg8FA9+7dgX/+mrz5r93c3Fy+/PJLi/O0adOG+vXrM2PGjEJDZG8+tuBDs7hhtDeLiIhAp9Px+eefW5zj66+/Ji0tjZ49e5buJovQr18/bGxs+PHHH1myZAkPP/ywxbw7Rd23oijmofll0b17dzw8PPjggw/YtGlTqWt7SiMsLAx3d3fmzZtn7lMD+f2IStOsc+XKlULbQkNDAYocan47evToAVBopuXp06cDmN/Hsrzfjo6OhZobK4ufnx/Nmzfnu+++s0gkNm3axMGDB0t1jvr16+Pv78+nn35KXl6eueahc+fOnDp1il9++YUOHTpY9Nu6Ez169MBgMJiHxkN+rebMmTMtyvn6+hIaGsqCBQss/o8eOnSIP//80/xeFujcuTM7d+5kw4YN5sTHw8ODJk2amGuFS6rxycrKMk/rUCA4OBhnZ+db/g6uW7eOSZMm8cYbb9C7d+9C+21sbOjXrx9Lly4t8g+yoqaZEOVHhrMLq5Wbm8uDDz7I448/TmxsLF9++SX33HOPeYjq3XffTe3atRk6dChjx45Fo9GwcOHCQtX+Wq2W2bNn88gjjxAaGsrw4cPx9fXl2LFjHD58mLVr1wKY/4IdO3YskZGR2NjY8MQTTxQZm6enJxMnTuTtt9+mW7duPProo+YY27Vrd0cJhJeXF/fffz/Tp08nIyPDopkLICQkhODgYF5++WUSEhJwcXFh6dKlt9X50s7OjieeeIIvvvgCGxsbnnzyyduO+990Oh1TpkzhhRde4IEHHuDxxx/n7NmzfPvttwQHB5dYMzJ16lQ2b95Mz549CQwMJCUlhS+//JK6deveskNqWbRq1YqhQ4cyd+5cc9Pprl27WLBgAb179+b+++8HyvZ+t23blsWLFzNhwgTatWuHk5OTRY1dRXvvvffo1asXnTp1Yvjw4Vy9epUvvviC5s2bF6pVKU7nzp356aefaNGihbkGtU2bNjg6OnL8+HH+85//lFu8jzzyCJ06deL111/n7NmzNG3alGXLlhWZPH700Ud0796djh07MmLECPNwdldX10LzY3Xu3Jl3332Xc+fOWSQ49957L1999RVBQUElTqlw/Phx82dQ06ZNsbW1Zfny5SQnJxf72QDw5JNP4unpSaNGjVi0aJHFvoceeghvb2/ef/99NmzYQHh4OCNHjqRp06ZcuXKFmJgY1q1bV2TiL8pJpY8jE9VKccPZHR0dC5Xt0qWL0qxZs0LbAwMDlZ49exY656ZNm5RnnnlGqV27tuLk5KQMHDjQYhiroijKtm3blA4dOigODg6Kn5+f8uqrrypr164tNCRbURRl69atykMPPaQ4Ozsrjo6OSsuWLZWZM2ea9xsMBuWFF15QPD09FY1GU6qh7V988YUSEhKi2NnZKd7e3sqoUaMKDZkvy3D2AvPmzVMAxdnZWbl+/Xqh/UeOHFEiIiIUJycnxcPDQxk5cqSyf//+IocUF/Ve3KxguHzXrl2L3F/ccPYlS5ZYlCtqSLOiKMrnn3+uBAYGKvb29kr79u2Vbdu2KW3btlW6det2y7jWr1+v9OrVS/Hz81N0Op3i5+enPPnkk8rx48dLjKWo30tFKfq9yMvLU95++22lfv36ip2dnRIQEKBMnDjRPB3CzUrzfl+7dk35z3/+o7i5uVkMIS/LcytuOHtRw6r513QQiqIoP/30kxISEqLY29srzZs3V1auXKn069dPCQkJKXR8UWbNmqUAyqhRoyy2R0REKICyfv36Ut1DUb97Be/BzS5fvqwMHjxYcXFxUVxdXZXBgwcre/fuLfL3ad26dUqnTp0UBwcHxcXFRXnkkUeUI0eOFLpOenq6YmNjozg7OysGg8G8fdGiRQqgDB48uMTncOnSJWX06NFKSEiI4ujoqLi6uirh4eHKzz//bFHu3/9HgGJfN38uJScnK6NHj1YCAgIUOzs7xcfHR3nwwQeVuXPnlhibuH0aRSnHXnFClINvv/2W4cOHs3v3bsLCwtQOp9rbv38/oaGhfPfddwwePLjCr2cymfD09KRv377Mmzevwq8n8oWGhuLp6UlUVJTaoQihKunjI0QNN2/ePJycnOjbt2+5nzs7O7tQ0+N3333HlStXSrVkhSi7vLw8iz5VkD8QYP/+/fLMhUD6+AhRY61atYojR44wd+5c83Ii5W3Hjh2MHz+e/v374+7uTkxMDF9//TXNmzenf//+5X49kb/+U0REBIMGDcLPz49jx44xZ84cfHx8Ck0mKERNJImPEDXUCy+8QHJyMj169ODtt9+ukGsEBQUREBDA559/zpUrV6hTpw5Dhgzh/ffflxWtK0jt2rVp27Yt//vf/7h48SKOjo707NmT999/37xOlBA1mfTxEUIIIUSNIX18hBBCCFFjSOIjhBBCiBpD+vjcxGQyceHCBZydnVWddl4IIYQQpacoChkZGfj5+RW5NuHNJPG5yYULFwqt5iyEEEKIquHcuXMlzsgtic9NnJ2dgfwHd/NKukIIIYSwXunp6QQEBJi/x29FEp+bFDRvubi4SOIjhBBCVDGl6aYinZuFEEIIUWNI4iOEEEKIGkMSHyGEEELUGJL4CCGEEKLGkMRHCCGEEDWGJD5CCCGEqDEk8RFCCCFEjSGJjxBCCCFqDEl8hBBCCFFjSOIjhBBCiBpDEh8hhBBC1BiS+AghhBCixpDERwghhChnMfFX+WlXPIqiqB2K+BdZnV0IIYQoR4qiMOb7GC6kZeNWS0e35j5qhyRuIjU+QgghRDk6d+U6F9KyAVi0I07laMS/SeIjhBBClKPdZ6+Y/7315CVOX7ymYjTi3yTxEUIIIcrRzYkPwPc741WKRBRFEh8hhBCiHBUkPoM61ANgyZ5zXM81qhmSuIkkPkIIIUQ5uXwth1MXMwGY8FBjAuo4kJ5tYNWBCypHJgpI4iOEEEKUkz1xVwG4y9uJOo46BoYHAtLJ2ZpI4iOEEEKUkz03mrnCguoA8HhYADpbLQfOp7H/XKqKkYkCkvgIIYQQ5WTX2fwan3ZBtQGo46ijZwtfQGp9rIUkPkIIIUQ5yMo1cDghDYB2N2p8AAZ1yG/uWrn/AqlZuarEJv4hiY8QQghRDvbFp2IwKfi66vF3czBvb1PPjaa+LuQYTPwSfV7FCAVI4iOEEEKUi93mZq46aDQa83aNRsPgjv90cjaZZP0uNUniI4QQQpSDPXH5HZsL+vfcrFeoH872tpy9nMW2U5cqOzRxE0l8hBBCiDtkMJqIuTGUPeym/j0Fauls6de2LgALt0snZzVJ4iOEEELcoaOJGWTmGnHW29LY27nIMgUzOa87msyF1OuVGZ64iSQ+QgghxB3aVTB/T2BttFpNkWUaejnToUEdTAr8tEvW71LLbSU+s2bNIigoCL1eT3h4OLt27Sq2bF5eHlOnTiU4OBi9Xk+rVq1Ys2aNRZmgoCA0Gk2h1+jRowE4e/Zskfs1Gg1Lliwxn6eo/T/99NPt3KIQQghRagUTF7arX7iZ62aDOwQB8OPuc+QaTBUdlihCmROfxYsXM2HCBN566y1iYmJo1aoVkZGRpKSkFFl+0qRJfPXVV8ycOZMjR47w3HPP0adPH/bu3Wsus3v3bhITE82vqKgoAPr37w9AQECAxf7ExETefvttnJyc6N69u8X15s+fb1Gud+/eZb1FIYQQotQURbEY0XUrXZt54+lsz8WMHP48klQZ4Yl/KXPiM336dEaOHMnw4cNp2rQpc+bMoVatWnzzzTdFll+4cCH//e9/6dGjBw0aNGDUqFH06NGDTz75xFzG09MTHx8f82v16tUEBwfTpUsXAGxsbCz2+/j4sHz5ch5//HGcnJwsrufm5mZRTq/Xl/UWhRBCiFI7ezmLS9dy0NloaeHvesuydjZanmwXAMhMzmopU+KTm5tLdHQ0ERER/5xAqyUiIoLt27cXeUxOTk6h5MPBwYGtW7cWe41Fixbx1FNPWcyDcLPo6Gj27dvHiBEjCu0bPXo0Hh4etG/fnm+++QZFKX6+hJycHNLT0y1eQgghRFnsvtHM1SrAFb2dTYnlnwyvh41Ww47TVziRnFHR4Yl/KVPic+nSJYxGI97e3hbbvb29SUoqusouMjKS6dOnc+LECUwmE1FRUSxbtozExMQiy69YsYLU1FSGDRtWbBxff/01TZo04e6777bYPnXqVH7++WeioqLo168fzz//PDNnziz2PNOmTcPV1dX8CggIKLasEEIIUZTdZywXJi2Jr6sDEU28AKn1UUOFj+r67LPPaNSoESEhIeh0OsaMGcPw4cPRaou+9Ndff0337t3x8/Mrcv/169f54YcfiqztefPNN+nUqROtW7fmtdde49VXX+Wjjz4qNraJEyeSlpZmfp07d+72blIIIUSNtefG/D3tS5n4wD+dnJfGJJCZY6iIsEQxypT4eHh4YGNjQ3JyssX25ORkfHx8ijzG09OTFStWkJmZSVxcHMeOHcPJyYkGDRoUKhsXF8e6det4+umni43hl19+ISsriyFDhpQYb3h4OOfPnycnJ6fI/fb29ri4uFi8hBBCiNK6mJHDmUuZaDTQJrDwjM3FuTvYnfoejlzLMfDrvgsVGKH4tzIlPjqdjrZt27J+/XrzNpPJxPr16+nYseMtj9Xr9fj7+2MwGFi6dCm9evUqVGb+/Pl4eXnRs2fPYs/z9ddf8+ijj+Lp6VlivPv27aN27drY29uXWFYIIYQoq4Jh7I29nXF1sCv1cVqthoHh+RMafrf97C37o4ryZVvWAyZMmMDQoUMJCwujffv2zJgxg8zMTIYPHw7AkCFD8Pf3Z9q0aQDs3LmThIQEQkNDSUhIYMqUKZhMJl599VWL85pMJubPn8/QoUOxtS06rJMnT7J582Z+//33QvtWrVpFcnIyHTp0QK/XExUVxXvvvcfLL79c1lsUQgghSqW0w9iL0r9tAB//GcuxpAxi4q/SNrDs5xBlV+bEZ8CAAVy8eJHJkyeTlJREaGgoa9asMXd4jo+Pt+i/k52dzaRJkzh9+jROTk706NGDhQsX4ubmZnHedevWER8fz1NPPVXstb/55hvq1q1L165dC+2zs7Nj1qxZjB8/HkVRaNiwoXnovRBCCFERCkZ0hRWxMGlJXGvZ8UhLP5ZEn2fRjnhJfCqJRpH6NbP09HRcXV1JS0uT/j5CCCFu6VqOgZZT1mJSYPvEB/B1dSjzOQ6cT+XRL7ahs9GyfeIDuDtJ14zbUZbvb1mrSwghhLgN++JTMSng7+ZwW0kPQMu6brSq60qu0cTPe86Xc4SiKJL4CCGEELehYGHS9iWsz1WSgR0CAfhhVxxGkzTCVDRJfIQQQojbsOcO+vfc7JGWfrg62HHuynU2H79YHqGJW5DERwghhCijPKOJvfGpwO2N6LqZg86G/m3rArCwGs/knL+Y6xW1w5DERwghhCirwxfSuZ5nxK2WHQ09nUo+oAQFzV0bYlM4dyXrjs9njZbsOU//Odt59Zf9qsYhiY8QQghRRub1uQJro9UWvaB2WdT3cKRzIw8UBX7YFX/H57M2SWnZvPPbEQCCyyFRvBOS+AghhBBlVNBkc6fNXDcbdKPWZ/Huc+QYjOV2XrUpisJ/lx8kI9tAqwA3nu5ceMmqyiSJjxBCCFEGiqKYFyYt7YrspfFgiBe+rnquZObyx8Gkcjuv2pbFJPDXsRR0Nlo+fqwlNuVQQ3YnJPERQgghyuDUxUyuZOZib6ulhb9ruZ3X1kbLk+3z1+9aVE06OaekZ/P2qsMAvBjRiEbezipHJImPEEIIUSYFw9hDA9zQ2Zbv1+gT7QKw1WrYE3eVIxfSy/XclU1RFN5YcYj0bAMt/F159l51m7gKSOIjhBBClMGuCujfU8DLRU9kcx8AFu2s2rU+K/dfIOpIMnY2Gj7q3xJbG+tIOawjCiGEEKKK2FOwIvsdzthcnEHh+Z2cV+xNICM7r0KuUdEuZuQwZWV+E9eY+xsR4mM9619K4iOEEEKUUnJ6NvFXstBqoE09twq5RocGdWjo5URWrpHlexMq5BoV7a2Vh7ialUcTXxeevz9Y7XAsSOIjhBBClFLBMPYQHxec9XYVcg2NRsPgG0PbF26PQ1Gq1vpdvx1I5PeDSdhqNXz0WEvsrKSJq4B1RSOEEEJYsYJmrjtdmLQkfdr4U0tnw4mUa+w8o/4yD6V1+VoOk389BMDz9wXTvBxHvZUXSXyEEEKIUtp1pnwWJi2Ji96OXqH+QNUa2j5l1REuZ+bS2NuZMQ80UjucIkniI4QQQpRCenYex5Lyh5hXxIiufxvUIX9OnzWHkkjJyK7w692ptYeTWLX/Ajba/FFc5T3Uv7xYZ1RCCCGEldkbn4pJgXp1auHtoq/w6zXzc6VtYG0MJoXFu85V+PXuRGpWLm8sz2/ieubeBrSs66ZuQLcgiY8QQghRCrsrqZnrZgW1Pj/uisdgNFXadctq6qojXLqWQ0MvJ1580DqbuApI4iOEEEKUQsGIrvaV0MxVoHtzX+o46riQls1fx1Iq7bpl8dexZJbtTUCrgQ8fa4nezkbtkG5JEh8hhBCiBDkGI/vOpQLluzBpSfR2NjweFgDAQivs5Jx2PY+Jyw4CMOKe+rSpV3m1YbdLEh8hhBCiBIcS0skxmKjjqCPY07FSrz0wvB4aDWw5cYkzlzIr9dol+b/VR0hOz6GBhyMvdW2sdjilIomPEEIIUYKChUnDAmuj0Wgq9doBdWpx312eAPxgRet3bYxNYUn0eTRVpImrgCQ+QgghRAnM/XsqeOLC4gzumD+T8897zpOdZ1QlhptlZP/TxDXs7qBKbf67U5L4CCGEELdgMinsicufsVmtL/gud3lRt7YDadfzWLX/giox3Oy934+RmJZNvTq1eCWyajRxFZDERwghhLiFkxevkZqVh4OdDc381Fll3Ear4T/h+UPbF+2MVyWGAttOXuLHXfkxfNCvJbV0tqrGU1aS+AghhBC3UNDM1bqem6oLbj4eFoDORsv+c6kcOJ+qSgyZOQZeW3oAgMEdAukY7K5KHHdCEh8hhBDiFgoWJlW7H4uHkz09WvgA6q3f9cGaY5y/eh1/Nwde7x6iSgx3ShIfIYQQ4hYKFiatzIkLizOoQ34n55X7L5CWlVep195+6jLfbc9PuD58rCWO9lWriauAJD5CCCFEMS6kXich9To2Wg2h9dzUDoe2gbUJ8XEmO8/ELzHnK+26Wbn/NHE92b4enRp6VNq1y5skPkIIIUQxCvr3NPV1wckKajg0Go15aPuiHXGYTEqlXPejtbHEX8nCz1XPf3tUzSauApL4CCGEEMUo6N/TzgqauQr0DvXHyd6WM5cy+fvU5Qq/3p6zV/j277MATOvXEme9XYVfsyJJ4iOEEEIUo6DGp10lrsheEkd7W/q28QcqvpNzdp6RV385gKJA/7Z16XJjBumqTBIfIYQQoghp1/OITc4A1B/R9W8FnZyjjiaTmHa9wq4zPeo4py9l4u1iz6SHm1bYdSqTJD5CCCFEEWLirqIoUN/DEU9ne7XDsXCXtzPh9etgNCn8uOtchVwjJv4q/9tyGoD3+rTA1aFqN3EVuK3EZ9asWQQFBaHX6wkPD2fXrl3Fls3Ly2Pq1KkEBwej1+tp1aoVa9assSgTFBSERqMp9Bo9erS5zH333Vdo/3PPPWdxnvj4eHr27EmtWrXw8vLilVdewWAw3M4tCiGEqOF23bQwqTUqqPX5aVc8eUZTuZ47O8/IK0v2Y1Kgb2t/HmziXa7nV1OZE5/FixczYcIE3nrrLWJiYmjVqhWRkZGkpKQUWX7SpEl89dVXzJw5kyNHjvDcc8/Rp08f9u7day6ze/duEhMTza+oqCgA+vfvb3GukSNHWpT78MMPzfuMRiM9e/YkNzeXv//+mwULFvDtt98yefLkst6iEEIIYV6RvZ1KC5OWJLKZDx5O9qRk5BB1JLlcz/3Z+hOcupiJp7M9kx+pHk1cBcqc+EyfPp2RI0cyfPhwmjZtypw5c6hVqxbffPNNkeUXLlzIf//7X3r06EGDBg0YNWoUPXr04JNPPjGX8fT0xMfHx/xavXo1wcHBdOnSxeJctWrVsijn4vLPmil//vknR44cYdGiRYSGhtK9e3feeecdZs2aRW5ubllvUwghRA2WnWdk/7k0wLpGdN1MZ6vlyfYBACzcXn6dnA+cT2Xu5vwmrv/r3Ry3WrpyO7c1KFPik5ubS3R0NBEREf+cQKslIiKC7du3F3lMTk4Oer3eYpuDgwNbt24t9hqLFi3iqaeeQqPRWOz7/vvv8fDwoHnz5kycOJGsrCzzvu3bt9OiRQu8vf+pjouMjCQ9PZ3Dhw8XG1t6errFSwghhDiYkEau0YSHkz1B7rXUDqdYT7avh1YD209f5mRKxh2fL8dg5JUlBzCaFB5p5UdkM59yiNK6lCnxuXTpEkaj0SK5APD29iYpKanIYyIjI5k+fTonTpzAZDIRFRXFsmXLSExMLLL8ihUrSE1NZdiwYRbb//Of/7Bo0SI2bNjAxIkTWbhwIYMGDTLvT0pKKjKugn1FmTZtGq6uruZXQEDALe9fCCFEzXDzMPZ//xFuTfzcHMz9bxbtuPNV22f9dZLY5AzcHXW8/WizOz6fNarwUV2fffYZjRo1IiQkBJ1Ox5gxYxg+fDhabdGX/vrrr+nevTt+fn4W25955hkiIyNp0aIFAwcO5LvvvmP58uWcOnXqtmObOHEiaWlp5te5cxXTM14IIUTVsvvG+lzWNoy9KINvdHJeGn2erNzbH9BzKCGNLzfmf6dO7dWcOo7Vq4mrQJkSHw8PD2xsbEhOtuxElZycjI9P0dVhnp6erFixgszMTOLi4jh27BhOTk40aNCgUNm4uDjWrVvH008/XWIs4eHhAJw8eRIAHx+fIuMq2FcUe3t7XFxcLF5CCCFqNpNJYU9c/ozN1rAwaUnuaehBkHstMnIM/Lrvwm2dI9dg4pVfDmAwKfRo4UPPlr7lHKX1KFPio9PpaNu2LevXrzdvM5lMrF+/no4dO97yWL1ej7+/PwaDgaVLl9KrV69CZebPn4+Xlxc9e/YsMZZ9+/YB4Oub/+Z07NiRgwcPWowui4qKwsXFhaZNq1ePdCGEEBUnNjmDjGwDjjobmvg6qx1OibRaDQPD82t9Fm6PQ1HKvn7X7I2nOJqYTu1adkzt1by8Q7QqZW7qmjBhAvPmzWPBggUcPXqUUaNGkZmZyfDhwwEYMmQIEydONJffuXMny5Yt4/Tp02zZsoVu3bphMpl49dVXLc5rMpmYP38+Q4cOxdbWciG4U6dO8c477xAdHc3Zs2dZuXIlQ4YM4d5776Vly5YAdO3alaZNmzJ48GD279/P2rVrmTRpEqNHj8be3romnhJCCGG9Coaxtwmsja1N1Zjn97G2dbG31XIkMZ2951LLdOyxpHS+2HACgCmPNsPDqXp/Z5Z5qdkBAwZw8eJFJk+eTFJSEqGhoaxZs8bckTg+Pt6i/052djaTJk3i9OnTODk50aNHDxYuXIibm5vFedetW0d8fDxPPfVUoWvqdDrWrVvHjBkzyMzMJCAggH79+jFp0iRzGRsbG1avXs2oUaPo2LEjjo6ODB06lKlTp5b1FoUQQtRgu28sTBoWaP3NXAVqO+p4pJUfv0SfZ9H2ONrUK92kiwajiVeWHCDPqPBQU28ebeVX8kFVnEa5nTqxaio9PR1XV1fS0tKkv48QQtRAiqJw9/t/kZiWzQ9Ph3N3Qw+1Qyq1fedS6T1rGzpbLTsmPliqzsmzNpzko7WxuDrYETX+Xrxc9CUeY43K8v1dNerwhBA11umL12jzThSTfz2kdiiiBkhIvU5iWja2Wg2h9dzUDqdMWtV1pYW/K7kGE0v2lDxK+URyBp+ty2/imvxw0yqb9JSVJD5CCKv29dYzXMnM5Yed8VzJlFnYRcUqmL+nmb8rtXRl7g2iKo1GYx7avmhnHCZT8Q06BqOJl385QK7RxP2NPenbxr+ywlSdJD5CCKuVkZ3Hir0JABhMCqv2395QXSFKq6B/T/sg61yYtCSPtPLDRW/LuSvX2XTiYrHlvt56hv3nUnHW2zKtb0urnqSxvEniI4SwWiv2JpCZa6TgM3lZzHl1AxLVXsGIrqowcWFRHHQ2PNY2fxWC73cUvX7XqYvX+CTqOABv9myKj2vNaOIqIImPEMIqKYrCwhsf3GPub4itVsP+82nlsh6REEW5mpnL8eRrAIQFVs0aH4CBHeoBsP5YCueuZFnsM5oUXv3lALkGE/fe5Un/sLpqhKgqSXyEEFZp15krHE++hoOdDSPvbcB9jT0BWBaToHJkorqKvjFbc7CnI+5VeC6bYE8n7mnogaLAj7ss1+/69u+zRMddxcnelml9W9SoJq4CkvgIIazSop35H9i9W/vhorejb5v8v0yX7024ZadNIW7XPwuTVs1mrpsNulHr8/Oec+QYjACcvZTJR2uPATCxRwj+bg6qxacmSXyEEFYnJSObNYcSARh0Y5TKAyFeuOhtSUzLZsfpy2qGJ6qp6pT4RDTxxtvFnkvXcllzKAmTSeHVpQfIzjNxd7A7/2lfT+0QVSOJjxDC6vy8+xx5RoU29dxo5ucKgN7OhodvzCq7VJq7RDnLzjNyMCENqB6Jj62Nlv+0vzG0fUcci3bGsevMFWrpbPigX80axfVvkvgIIayK0aTww41mroLangL9bsw18sehRLJyDZUem6i+9p1LJc+o4O1iT0Cd6tEE9ET7AGy0Gnafvcq7vx0F4PXuIQTUqaVyZOqSxEcIYVX+OpbChbRsateyo0cLX4t9berVJtC9Flm5RtYeTlIpQlEd3TyMvbrUhni76Ilslr+OZo7BRHj9OgwKDyzhqOpPEh8hhFUpGML+eLsA9HY2Fvs0Gg19W+d3cpbRXaI87boxcWG7KjyMvSiDOwQBoLfT8kG/lmi11SOpuxOS+AghrMbZS5lsPn4RjQYGti/6L9M+rfObu7aevERSWnZlhieqKaNJIebGUPZ29at+/56bdQx25/MnW7NoRDhBHo5qh2MVJPERQliNH27MOdLlLk/quRfdD6Geey3aB9VBUWDFPqn1EXfuWFI613IMONvbEuJz65W9q6JHW/lV2ZmoK4IkPkIIq5CdZ+TnGytKD+5w634IBQsqLo0+j6LInD7izuw+k9+/p01gbWykKajak8RHCGEVVh9IJDUrD383B+5r7HXLsj1a+qKz1XIi5RqHL6RXUoSiutpd0MxVRRcmFWUjiY8QwiosutGp+T/h9Ur8q9tFb0fXpvmjVZbKwqXiDiiKYq7xkeagmkESHyGE6g6eT2PfuVTsbDQMaBdQqmP63VjCYuW+C+QZTRUZnqjGzl25TkpGDnY2GkID3NQOR1QCSXyEEKorqO3p0cIXj1IuDtm5kQceTjouZ+ay+fjFigxPVGMFy1S08HctNH2CqJ4k8RFCqCotK49f9+ePzvr3TM23Ymuj5dFW+Z2cZU4fcbvM63NVs2HsoniS+AghVLU05jzZeSZCfJwJK+PkcQWju6KOJpOWlVcR4Ylqzpz4BEriU1NI4iOEUI2iKOZmrkEdAsu8VEAzPxcaezuTazDx28HEighRVGOXr+Vw6mImAG2r2YzNoniS+AghVPP3qcucvpSJo86G3jdmZC4LjUZjrvVZJqO7RBntuTGM/S5vJ2o76lSORlQWSXyEEKopqO3p26YuTva2t3WO3q390Wryv8TiLmeWZ3iimpNh7DWTJD5CCFUkpWXz55FkoGydmv/N20VPp4YegHRyFmVTMHFhe0l8ahRJfIQQqvhxVzxGk0L7+nVo7ON8R+cqmNNn+d4EWcJClEpWroHDCWkAhMmMzTWKJD5CiEqXZzTx440FSe+ktqdA12beOOpsiL+SRfSNv+KFuJV98akYTAp+rnrq1i56QVxRPUniI4SodOuOJJOSkYOHk45uzXzu+Hy1dLZ0b+ELwFJp7hKlsPtsfoIs/XtqHkl8hBCVbuGNTs1PtKuHzrZ8PoYKRnetPnCB7DxjuZxTVF/m+XukmavGkcRHCFGpTqZc4+9Tl9Fq4MnweuV23g713fFz1ZORbWD90ZRyO6+ofgxGEzHxN1ZklxmbaxxJfIQQler7nfm1PQ+EeOPv5lBu59VqNfSROX1EKRxNzCAr14iL3pa7vO6sY72oeiTxEUJUmqxcA79E5yclgzveeafmf+vTOn9018bjF7l0Lafczy+qh11n/5m/R6st22zhouqTxEcIUWlW7rtARraBQPdadL4x9055aujlRKsAN4wmhZX7LpT7+UX1sMec+Ej/nppIEh8hRKVQFMXcqXlgeL0K+0u7X0Fz115p7hKFKYpyU8dm6d9TE0niI4SoFPvOpXL4Qjo6Wy392wZU2HUebumHnY2GQwnpxCZlVNh1RNV09nIWl67lorPV0rKuq9rhCBVI4iOEqBQFtT2PtPSr0AUh6zjquL+xFyC1PqKwgtqeVnVdsbe1UTkaoYbbSnxmzZpFUFAQer2e8PBwdu3aVWzZvLw8pk6dSnBwMHq9nlatWrFmzRqLMkFBQWg0mkKv0aNHA3DlyhVeeOEFGjdujIODA/Xq1WPs2LGkpaVZnKeoc/z000+3c4tCiHJ0NTOX1QcSARjUofyGsBen740lLFbsTcBokiUsxD8KFiaVZq6aq8yJz+LFi5kwYQJvvfUWMTExtGrVisjISFJSip43Y9KkSXz11VfMnDmTI0eO8Nxzz9GnTx/27t1rLrN7924SExPNr6ioKAD69+8PwIULF7hw4QIff/wxhw4d4ttvv2XNmjWMGDGi0PXmz59vca7evXuX9RaFEOVsSfQ5cg0mmvu7EBrgVuHXuz/EE7dadiSn5/D3qUsVfj1Rdey5saSJJD41l0Yp44p+4eHhtGvXji+++AIAk8lEQEAAL7zwAq+//nqh8n5+frzxxhvm2huAfv364eDgwKJFi4q8xrhx41i9ejUnTpxAoym6A+SSJUsYNGgQmZmZ2Nra5t+MRsPy5ctvO9lJT0/H1dWVtLQ0XFxcbuscQghLJpPCfR9vJP5KFh/0a8GAdhVf4wPw5opDLNwRR5/W/nw6ILRSrimsW0pGNu3fXY9GA/smd8XVwU7tkEQ5Kcv3d5lqfHJzc4mOjiYiIuKfE2i1REREsH379iKPycnJQa/XW2xzcHBg69atxV5j0aJFPPXUU8UmPYD55gqSngKjR4/Gw8OD9u3b880339xypeacnBzS09MtXkKI8rX5xEXir2ThrLfl0Vb+lXbdgiUs1hxK4lqOodKuK6xX9I31uRp7O0vSU4OVKfG5dOkSRqMRb29vi+3e3t4kJSUVeUxkZCTTp0/nxIkTmEwmoqKiWLZsGYmJiUWWX7FiBampqQwbNuyWcbzzzjs888wzFtunTp3Kzz//TFRUFP369eP5559n5syZxZ5n2rRpuLq6ml8BARU30kSImmrRjvxV2B9rWxcHXeV1Jg0NcKO+hyPX84ysOVT055OoWQoWJpVmrpqtwkd1ffbZZzRq1IiQkBB0Oh1jxoxh+PDhaLVFX/rrr7+me/fu+Pn5Fbk/PT2dnj170rRpU6ZMmWKx780336RTp060bt2a1157jVdffZWPPvqo2NgmTpxIWlqa+XXu3Lnbvk8hRGHnr2bx17FkAAZ1KP+Zmm9Fo9HQt7UsYSH+YZ6/R9bnqtHKlPh4eHhgY2NDcnKyxfbk5GR8fHyKPMbT05MVK1aQmZlJXFwcx44dw8nJiQYNGhQqGxcXx7p163j66aeLPFdGRgbdunXD2dmZ5cuXY2d366rK8PBwzp8/T05O0VPX29vb4+LiYvESQpSfH3fFY1KgU0N3gj2dKv36vW8kPttPXyYh9XqlX19Yj2s5Bg5fyB8JLCuy12xlSnx0Oh1t27Zl/fr15m0mk4n169fTsWPHWx6r1+vx9/fHYDCwdOlSevXqVajM/Pnz8fLyomfPnoX2paen07VrV3Q6HStXrizUb6go+/bto3bt2tjb25fi7oQQ5SnXYGLx7vxa1EHhlVvbUyCgTi3C69dBUfKHtouaa2/8VUwK+Ls54OtafovjiqrHtuQiliZMmMDQoUMJCwujffv2zJgxg8zMTIYPHw7AkCFD8Pf3Z9q0aQDs3LmThIQEQkNDSUhIYMqUKZhMJl599VWL85pMJubPn8/QoUMLdVguSHqysrJYtGiRRUdkT09PbGxsWLVqFcnJyXTo0AG9Xk9UVBTvvfceL7/88m09GCHEnVlzOIlL13LxdrEnoql3yQdUkH5t6rLzzBWWxZzn+fuCbzloQlRfBf172kszV41X5sRnwIABXLx4kcmTJ5OUlERoaChr1qwxd3iOj4+36L+TnZ3NpEmTOH36NE5OTvTo0YOFCxfi5uZmcd5169YRHx/PU089VeiaMTEx7Ny5E4CGDRta7Dtz5gxBQUHY2dkxa9Ysxo8fj6IoNGzYkOnTpzNy5Miy3qIQohws2p4/U/OT7ethZ6PeJPHdW/jw5q+HOHUxkwPn02hVCfMICesjC5OKAmWex6c6k3l8hCgfx5LS6TZjCzZaDdteewAf15KbpivS2B/3snL/BYZ2DOTtXs1VjUVUvjyjiZZT/uR6npGo8ffSyNtZ7ZBEOauweXyEEKI0vr8xhL1rU2/Vkx74Z06flfsvkGswqRyNqGyHL6RzPc+IWy07VTrZC+siiY8QolxdyzGYh48PruQh7MW5p6EHns72XM3KY9Pxi2qHIypZwfpcYYG10Wqlj1dNJ4mPEKJcLd+bQGaukQaejnQMdlc7HABsbbT0Ds2fG0zm9Kl5zPP3yMSFAkl8hBDlSFEUc6fmQeGBVjWCqmDF9vVHU0jNylU5GlFZFEUxL0waJomPQBIfIUQ52hN3ldjkDPR2Wvq1rat2OBaa+LrQxNeFXKOJ1QeKXjJHVD+nLmZyJTMXe1stLfxd1Q5HWAFJfIQQ5Wbhjdqe3qH+VrkIZL821WcJi+2nLvPznnO3XIhZ/DOMPTTADZ2tfOUJSXyEEOXkYkYOfxzKr0mp7HW5SuvRUD+0GoiJT+XMpUy1w7ltW09cYtDXO3n1lwN8vv6k2uFYtV03Eh+ZuFAUkMRHCFEuft5zjjyjQmiAG82ttEnBy1nPvXd5ArC8itb6nLmUyfPfR2M05df0fLruOH8clKa74uw5K/17hCVJfIQQd8xoUvhhZ/7cPdYyhL04BZ2cl+1NwGSqWs1EadfzGLFgN+nZBlrXc2NIx/xnPeHn/RxKSFM5OuuTnJ5N/JUstBpoU89N7XCElZDERwhxxzYcSyEh9Tputezo2dJX7XBuqWtTb5ztbTl/9bp5mHNVYDQpjP1xL6cvZuLrquerwW2Z/HBT7r3Lk+t5Rp75bg8XM3LUDtOqFLy/TXxdcNZbX58zoQ5JfIQQd2zhjvxOzY+HBaC3s1E5mlvT29nQo0V+crYspuqs2D7t96NsOn4RvZ2WeUPC8HLWY2ujZeaTrWng4ciFtGyeXbiHHINR7VCtRkEzl8zfI24miY8Q4o7EXc5k84n82ZAHhtdTOZrSKVjC4reDiWTnWX+i8POec/xv6xkAPukfatGHytXBjv8NDcNFb0tMfCpvLD8kI71u2HVGJi4UhUniI4S4Iz/sjEdRoMtdngS6O6odTqm0C6pD3doOXMsx8OeRZLXDuaU9Z6/wxvKDALz4YKMimxIbeDoxa2AbbLQafok+z/+2nKnsMK1OenYex5LSAVmRXViSxEcIcduy84z8vOccYL1D2Iui1Wro29r65/Q5fzWL5xZFk2dU6N7chxcfbFRs2c6NPJnUswkA0/44yoZjKZUVplWKibuKSYF6dWrh7aL+QrnCekjiI4S4bb8fTORqVh7+bg48EOKldjhl0ufG6K7Nxy+SkpGtcjSFZeYYGPldNJeu5dLU14VPHm9V4gKbw+4O4ol2AZgUGPvjXk6mZFRStNZH+veI4kjiI4S4bQWdmv8TXg+bKrbqdX0PR1rXc8OkwMp9F9QOx4LJpPDSz/s5mpiOh5OOeUPDqKWzLfE4jUbD1F7NaR9Uh4wcAyMW7Kmx65L9szCpNHMJS5L4CCFuy6GENPbGp2Jno+HxsAC1w7ktBXP6LLWy0V0z1p9gzeEkdDZavhrcFn83h1Ifq7PVMntQG+rWdiDuchajf4ghz2iqwGitT47ByL5zqQC0kxmbxb9I4iOEuC2LbtT2dGvui6ezvcrR3J5HWvpiZ6PhaGI6Ry6kqx0OAKsPXODz9ScAeLdPc9oGlv2L293Jnv8NDcNRZ8O2k5d5Z/WR8g7Tqh1KSCfHYKKOo44GHlWjw72oPJL4CCHKLO16Hr/eaB6y9pmab8Wtlo4HQ7wBWL5X/U7OB8+n8fKS/QCM7Fyf/ndQkxbi48KnA0LRaOC77XHmRLUmKGjmCgusjUZTtZpgRcWTxEcIUWbLYs5zPc9IY2/nKt+HomBOnxX7LmBQsUkoJT2bkd/tITvPxH2NPXm9e5M7PmfXZj683LUxAFNWHmb7qct3fM6qYI8sTCpuQRIfIUSZKIpirj0Y1KFelf+L+r7GXtSuZcfFjBy2nrykSgzZeUaeWRhNUno2Db2c+PzJ1uXWWfz5+4LpFeqHwaQw6vto4i9nlct5rZXJpLAnThYmFcWTxEcIUSbbT1/m1MVMHHU29L4xF05VprPV8mgrP0CdJSwURWHisoPsO5eaPwvzkDBcynFdKY1Gwwf9WtKqriupWXk8/d1uMrLzyu381ubkxWukZuXhYGdDMz8XtcMRVkgSHyFEmRTU9vRp419tFn4sGN219nBSpScFX20+zfK9CdhoNXw5sA1BFdAZV29nw9whYXi72HM8+RrjftqHsYqtTF9aBf17Wtdzw85GvuJEYfJbIYQoteT0bNYezl/ioSrN1FySlnVdCfZ0JMdg4o+DSZV23fVHk/lgzTEA3nqkKZ0aelTYtbxd9MwdHIa9rZb1x1L4aG1shV1LTbtvrM8lzVyiOJL4CCFK7cdd8RhNCu2CahPiU32aETQazU1z+lTO6K7jyRmM/XEvipK/uGtljI5rFeDGh4+1BGDOplNWvVzH7dp9Y8bm9pL4iGJI4iOEKJU8o4kfd8UD1au2p0Dv1v5oNLDzzBXOX63YDsBXMnN5esEeMnONdGhQhymPNqu0TuK9Qv0ZfX8wAK8vO8je+KuVct3KcCH1Ogmp17HRamhdz03tcISVksRHCFEq648mk5yeg4eTjm7NfdQOp9z5uznQsYE7ACv2Vlwn5zyjiee/jyb+Shb16tRi9sC2ld4X5aWHGvNQU29yDSaeWRhNYtr1Sr1+Rfn7xnD9Zn4uONqXvMSHqJkk8RFClErBulyPhwVgb2ujcjQVo6C5a1lMAopSMZ1/p6w8zI7TV3Cyt+V/Q8Oo7airkOvcilarYcaAUEJ8nLmYkcPI7/ZwPddY6XGUl8wcA9N+P8rrSw8AmBNYIYoiiY8QokSnLl5j28nLaDT5C5JWV92a++BgZ8PpS5nmtZ7K08LtZ/l+ZzwaDXz2RCh3eTuX+zVKy9HelnlDwqjjqONQQjov/7K/wpK9iqIoCn8cTCRi+ia+2nwag0khook3z9/fUO3QhBWTxEcIUaLvd+T37XkwxIu6tWupHE3FcbK3NTfjlfecPttOXmLKqvw1s17rFsKDTbzL9fy3I6BOLWYPbIOdjYbfDiQy86+TaodUamcuZTJ0/m5GfR9DYlo2AXUc+HpoGP8bGoarQ/WYZkFUDEl8hBC3lJVrYEn0OQAGVsNOzf9WsITFqgMXyDGUT/PP2UuZPP99DEaTQt/W/jx7b4NyOW95CG/gzju9mgMwPeo4aw4lqhzRrWXnGZn+ZyyRn25m8/GL6Gy0jH2wEVHju1hFMimsn/T+EkLc0qr9F8jINhBQx4EujTzVDqfC3R3sgbeLPcnpOWw4dvGOO3KnZ+cxYsFu0q7nERrgxnt9W1jdMh9PtK9HbHIG87edZfzi/QTUqUUzP1e1wypk3ZFkpqw6zPmr+Z2x773Lk7cfbUZ9WYFdlIHU+AghiqUoirlT86DwQLTltH6UNbPRasxLcdzpPDdGk8LYH/dy6mImPi565g5ui97OOjuGv9GjCZ0beXA9z8gz30Vz6VqO2iGZnbuSxdMLdvP0d3s4f/U6vq56Zg9sw4Lh7STpEWUmiY8Qolj7z6dxKCEdna2W/mEBaodTafq2zh/dtSE2hSuZubd9nvf/OMrG2Ivo7bTMGxKGl4u+vEIsd7Y2Wr54sg0NPBxJSL3Ocwujy62p73blGIzMXH+CiOmbWHc0BVuthme7NGDdhC50b+FrdTVnomqQxEcIUayF2/Nrex5u4UsdFYZdq6WxjzPN/V3IMyqsPnDhts7xS/R55m05A8DH/VvRoq71NR39m2stO+YNDcNZb8ueuKtMWn5ItZFem49fpNuMLXwSdZwcg4kODerwx4udmdi9iczRI+7IbSU+s2bNIigoCL1eT3h4OLt27Sq2bF5eHlOnTiU4OBi9Xk+rVq1Ys2aNRZmgoCA0Gk2h1+jRo81lsrOzGT16NO7u7jg5OdGvXz+Sk5MtzhMfH0/Pnj2pVasWXl5evPLKKxgMhtu5RSFqvKuZueYv/UEdq3+n5n8rqPVZehuju6LjrvDfZQcBGPtAQx5u6VeusVWkYE8nZv2nDVoNLIk+z9dbz1Tq9RPTrjP6+xiGfLOLM5cy8XS257MnQvlxZAcaqTj8X1QfZU58Fi9ezIQJE3jrrbeIiYmhVatWREZGkpKSUmT5SZMm8dVXXzFz5kyOHDnCc889R58+fdi7d6+5zO7du0lMTDS/oqKiAOjfv7+5zPjx41m1ahVLlixh06ZNXLhwgb59+5r3G41GevbsSW5uLn///TcLFizg22+/ZfLkyWW9RSEE+TUWOQYTzfxcaB3gpnY4le7RUD9stBr2n0vlZMq1Uh+XkHqdZxdGk2s00a2ZD+Mi7qrAKCvGvXd5MqlnUwDe+/0oG2OL/nwvT3lGE19tOsWDn2zit4OJ2Gg1PNWpPn+91IVeof7SrCXKj1JG7du3V0aPHm3+2Wg0Kn5+fsq0adOKLO/r66t88cUXFtv69u2rDBw4sNhrvPjii0pwcLBiMpkURVGU1NRUxc7OTlmyZIm5zNGjRxVA2b59u6IoivL7778rWq1WSUpKMpeZPXu24uLiouTk5JTq3tLS0hRASUtLK1V5Iaorg9Gk3PvhX0rga6uVH3bGqR2OaobP36UEvrZa+XDN0VKVz8zJU7rP2KwEvrZa6TZjs5KZk1fBEVYck8mkvLpkvxL42mql+eQ1yonkjAq71vZTl5SITzYqga+tVgJfW630/XKbcjhBPodF6ZXl+7tMNT65ublER0cTERFh3qbVaomIiGD79u1FHpOTk4Neb9mhz8HBga1btxZ7jUWLFvHUU0+ZM/zo6Gjy8vIsrhsSEkK9evXM192+fTstWrTA2/ufeRwiIyNJT0/n8OHDxcaWnp5u8RJCwJpDScRdzsLVwY5eoVWnmaa8FczpszwmAZPp1n1dTCaFl37ez5HEdDycdMwb0pZauqrbF0Wj0fBO7+a0D6pDRo6BpxfsJjXr9jt6FyUlI5txP+3libk7OJFyjTqOOj58rCVLnu1IUz+Xcr2WEAXKlPhcunQJo9FokVwAeHt7k5SUVOQxkZGRTJ8+nRMnTmAymYiKimLZsmUkJhY9SdaKFStITU1l2LBh5m1JSUnodDrc3NyKvW5SUlKRcRXsK8q0adNwdXU1vwICas6oFSGKoygKX27Mn8F36N1BVfrL+05FNPHGWW/LhbRsdpy5fMuyn60/wR+HkrCz0TBnUNtqMcO1zlbL7EFt8Hdz4OzlLMb8sBeD0XTH5zUYTczfdoYHP97Ein0X0GhgYHg9/nqpC4+HBdSIaROEeip8VNdnn31Go0aNCAkJQafTMWbMGIYPH45WW/Slv/76a7p3746fX8X/lTlx4kTS0tLMr3PnzlX4NYWwdptPXOLwhXQc7GwYfneQ2uGoSm9nw8MtfYFbL2Hx24FEPlt/AoB3+7QgLKhOpcRXGdyd7Pnf0DBq6WzYevIS//fb0Ts6X3TcVR79YhtvrzpCRo6BlnVdWfF8J97t0wK3WjVn5KBQT5kSHw8PD2xsbAqNpkpOTsbHp+jZTT09PVmxYgWZmZnExcVx7NgxnJycaNCg8JTtcXFxrFu3jqefftpiu4+PD7m5uaSmphZ7XR8fnyLjKthXFHt7e1xcXCxeQtR0X27Ir+15sn09VVYOtzYFK7b/cTCRrNzCo0QPJaTx0pJ9ADx9T30er4bzHTXxdeHTAaEAfPv3WX7YGV/mc1zJzOXVX/bTb/bfHElMx9XBjnf7NGf5851oVQM7zwv1lCnx0el0tG3blvXr15u3mUwm1q9fT8eOHW95rF6vx9/fH4PBwNKlS+nVq1ehMvPnz8fLy4uePXtabG/bti12dnYW142NjSU+Pt583Y4dO3Lw4EGL0WVRUVG4uLjQtGnTstymEDVWdNxVdp65gp2NhpH31lc7HKsQFlibgDoOZOYa+fOw5R9XKRnZjPxuD9l5Jrrc5cnEHk1UirLiRTbz4eWu+SPUJv96iB2nb930V8BkUvh+Zxz3f7yRn/fkz4Tdv21d/nqpCwPDA7GRZi1Rycrc1DVhwgTmzZvHggULOHr0KKNGjSIzM5Phw4cDMGTIECZOnGguv3PnTpYtW8bp06fZsmUL3bp1w2Qy8eqrr1qc12QyMX/+fIYOHYqtrWWfAldXV0aMGMGECRPYsGED0dHRDB8+nI4dO9KhQwcAunbtStOmTRk8eDD79+9n7dq1TJo0idGjR2Nvb1/mByNETTR74ykAeof64+vqoHI01kGj0dw0p88/S1hk5xl5dmE0iWnZBHs6MvM/rav9l/jo+xvySCs/DCaFUYuiOXcl65blD55Po8+X23hj+SHSrucR4uPML8915KP+rXB3ks9loY4y91ocMGAAFy9eZPLkySQlJREaGsqaNWvMHYnj4+Mt+u9kZ2czadIkTp8+jZOTEz169GDhwoWFOiqvW7eO+Ph4nnrqqSKv++mnn6LVaunXrx85OTlERkby5Zdfmvfb2NiwevVqRo0aRceOHXF0dGTo0KFMnTq1rLcoRI0Um5TBuqPJaDTw3H3BaodjVfq28eez9SfYdvISSWnZeLvY89/lB9kbn4qrgx3/G9oOF72d2mFWOI1Gw0ePtSTuciYHzqfx9II9LH3+bpz+NZNyWlYeH/15jO93xqMo4GRvy4SH7mJIx0BsbWTBAKEujaKoNB+5FUpPT8fV1ZW0tDTp7yNqnPGL97F8bwLdm/swe1BbtcOxOo/N/ps9cVeZ2D0EgGl/HMNGq2HB8Pbc08hD5egqV1JaNo9+sZWUjBwimngzd3BbtFoNiqKwNCaBab8f5fKNNc56hfrxRo8mVr1Omaj6yvL9XXPHqQohzM5dyWLl/vzlKZ6/r6HK0Vinvm3qsifuKvO2nDZ/qU9+uGmNS3oAfFz1zB0SxuNfbWfd0WQ+/jOWR1r5MfnXQ+w+exWAhl5OTO3VjLuDa97zEdZN6hyFEMzbchqjSaFzI48qsZimGnq28EVnq+XStVwUJX/U25AauIZZgdAANz56rCUAX248xcMzt7L77FUc7Gx4vXsIv4/tLEmPsEqS+AhRw13MyGHx7vw5rEZJ355iuday46Gm+X0Zw+vX4e1Hm9X49aN6hfrz/I3fGaNJoXtzH9a/1IXnugSjs5WvF2GdpKlLiBpu/rYz5BhMtApwo2MDd7XDsWpvPdKUsMDa9G1TV77Yb3i5a2PqezjiX9tBanhElSCJjxA1WHp2Hgu3xwHw/H3BNb4GoyReznqGd5L5jW6m1WroXw0nbRTVl/zJIkQNtmhHHBk5Bhp5OfFQE++SDxBCiCpOEh8haqjsPCPfbD0DwHNdgmVhSCFEjSCJjxA11JLo81y6lou/mwOPhlb8osBCCGENJPERogYyGE3M3Zy/PMXIzvWxk9l0hRA1hHzaCVEDrT6QyLkr13F31DGgXT21wxFCiEojiY8QNYzJpJgXIx3eKQgHnY3KEQkhROWRxEeIGuavYynEJmfgZG/L4I5BaocjhBCVShIfIWoQRVH4cuNJAAZ2qIerQ/VfUVwIIW4miY8QNciuM1eIiU9FZ6tlhEzEJ4SogSTxEaIG+fJG357H2tbFy0WvcjRCCFH5JPERooY4lJDGpuMX0Wrg2XsbqB2OEEKoQhIfIWqI2Zvya3sebulHoLujytEIIYQ6JPERogY4cymTPw4mAjDqvmCVoxFCCPVI4iNEDTB38ylMCjwQ4kUTXxe1wxFCCNVI4iNENZecns3S6ARAanuEEEISHyGquf9tOU2u0US7oNq0C6qjdjhCCKEqSXyEqMZSs3L5fmc8AM/f11DlaIQQQn2S+AhRjS34O46sXCNNfF24r7Gn2uEIIYTqJPERoprKyjXw7d9ngPy+PRqNRuWIhBBCfZL4CFFN/bTrHFez8gh0r0WP5j5qhyOEEFZBEh8hqqFcg4l5W04D8My9DbC1kf/qQggBkvgIUS2t2JdAYlo2ns729GtTV+1whBDCakjiI0Q1YzQpzLmxPMXT99RHb2ejckRCCGE9JPERopqJOpLE6YuZuOhtGdghUO1whBDCqkjiI0Q1oigKX27Mr+0ZencQTva2KkckhBDWRRIfIaqRbScvc+B8Gno7LcPuDlI7HCGEsDqS+AhRjXy58SQAT7Srh7uTvcrRCCGE9ZHER4hqYt+5VP4+dRlbrYaR9zZQOxwhhLBKkvgIUU3MvlHb0yvUH383B5WjEUII6ySJjxDVwMmUDNYeTkajgVH3SW2PEEIURxIfIaqB2RvzZ2nu2tSbhl7OKkcjhBDW67YSn1mzZhEUFIReryc8PJxdu3YVWzYvL4+pU6cSHByMXq+nVatWrFmzplC5hIQEBg0ahLu7Ow4ODrRo0YI9e/aY92s0miJfH330kblMUFBQof3vv//+7dyiEFVGQup1ft2XAMCo+xqqHI0QQli3Mk/ysXjxYiZMmMCcOXMIDw9nxowZREZGEhsbi5eXV6HykyZNYtGiRcybN4+QkBDWrl1Lnz59+Pvvv2ndujUAV69epVOnTtx///388ccfeHp6cuLECWrXrm0+T2JiosV5//jjD0aMGEG/fv0stk+dOpWRI0eaf3Z2lr9+RfU2b/NpDCaFu4PdCQ1wUzscIYSwahpFUZSyHBAeHk67du344osvADCZTAQEBPDCCy/w+uuvFyrv5+fHG2+8wejRo83b+vXrh4ODA4sWLQLg9ddfZ9u2bWzZsqXUcfTu3ZuMjAzWr19v3hYUFMS4ceMYN25cqc6Rk5NDTk6O+ef09HQCAgJIS0vDxcWl1LEIoZbL13Lo9MFfZOeZWDQinHsaeagdkhBCVLr09HRcXV1L9f1dpqau3NxcoqOjiYiI+OcEWi0RERFs3769yGNycnLQ6/UW2xwcHNi6dav555UrVxIWFkb//v3x8vKidevWzJs3r9g4kpOT+e233xgxYkShfe+//z7u7u60bt2ajz76CIPBUOx5pk2bhqurq/kVEBBQbFkhrNG3f58lO89Ey7qudGrornY4Qghh9cqU+Fy6dAmj0Yi3t7fFdm9vb5KSkoo8JjIykunTp3PixAlMJhNRUVEsW7bMounq9OnTzJ49m0aNGrF27VpGjRrF2LFjWbBgQZHnXLBgAc7OzvTt29di+9ixY/npp5/YsGEDzz77LO+99x6vvvpqsfczceJE0tLSzK9z586V9lEIobqM7DwW/H0WgOfvC0aj0agbkBBCVAEVvpDPZ599xsiRIwkJCUGj0RAcHMzw4cP55ptvzGVMJhNhYWG89957ALRu3ZpDhw4xZ84chg4dWuic33zzDQMHDixUkzRhwgTzv1u2bIlOp+PZZ59l2rRp2NsXnsXW3t6+yO1CVAU/7IwnPdtAA09Hujb1UTscIYSoEspU4+Ph4YGNjQ3JyckW25OTk/HxKfqD19PTkxUrVpCZmUlcXBzHjh3DycmJBg3+mWvE19eXpk2bWhzXpEkT4uPjC51vy5YtxMbG8vTTT5cYb3h4OAaDgbNnz5bi7oSoOrLzjPxv6xkAnusSjFYrtT1CCFEaZUp8dDodbdu2tehQbDKZWL9+PR07drzlsXq9Hn9/fwwGA0uXLqVXr17mfZ06dSI2Ntai/PHjxwkMDCx0nq+//pq2bdvSqlWrEuPdt28fWq22yNFmQlRlS2POczEjB19XPb1D/dUORwghqowyN3VNmDCBoUOHEhYWRvv27ZkxYwaZmZkMHz4cgCFDhuDv78+0adMA2LlzJwkJCYSGhpKQkMCUKVMwmUwWfW/Gjx/P3XffzXvvvcfjjz/Orl27mDt3LnPnzrW4dnp6OkuWLOGTTz4pFNf27dvZuXMn999/P87Ozmzfvp3x48czaNAgi2HxQlR1BqOJrzblT1g4snMDdLYyD6kQQpRWmROfAQMGcPHiRSZPnkxSUhKhoaGsWbPG3OE5Pj4erfafD+Ls7GwmTZrE6dOncXJyokePHixcuBA3NzdzmXbt2rF8+XImTpzI1KlTqV+/PjNmzGDgwIEW1/7pp59QFIUnn3yyUFz29vb89NNPTJkyhZycHOrXr8/48eMt+v0IUR38fiiJ+CtZ1K5lxxPtZSSiEEKURZnn8anOyjIPgBBqUBSFHp9v5WhiOhMeuouxDzZSOyQhhFBdhc3jI4RQ18bYixxNTMdRZ8OQjoX7wAkhhLg1SXyEqEK+3HgSgP+E18Otlk7laIQQouqRxEeIKmL32SvsPnsVnY2Wpzs3KPkAIYQQhUjiI0QVMXvjKQD6tfXH20VfQmkhhBBFkcRHiCrgaGI6fx1LQauBZ+8NVjscIYSosiTxEaIKKKjt6d7ClyAPR5WjEUKIqksSHyGsXNzlTFYfuADAqC5S2yOEEHdCEh8hrNxXm09jUqDLXZ4093dVOxwhhKjSJPERwoqlpGfzy57zADx/n9T2CCHEnZLERwgr9vW2M+QaTbQNrE37+nXUDkcIIao8SXyEsFJp1/P4fkc8kF/bo9FoVI5ICCGqPkl8hLBSC7ef5VqOgcbeztzf2EvtcIQQolqQxEcIK3Q918g3284CMOq+YLRaqe0RQojyIImPEFbo5z3nuJKZS0AdBx5u6at2OEIIUW1I4iOElckzmpi7+TQAz9wbjK2N/DcVQojyIp+oQliZlfsukJB6HQ8ne/q3rat2OEIIUa1I4iOEFTGZFGZvyl+e4ql7gtDb2agckRBCVC+S+AjV/HYgkcW748kzmtQOxWqsOnCBkynXcLa3ZVCHQLXDEUKIasdW7QBEzbR873nGL94PwNdbz/BOr+aEN3BXOSr1XM3M5YM1x/hp9zkABncMxEVvp3JUQghR/UjiIyrd3virvLb0IAA6Wy3Hk68xYO4O+rb2Z2KPJng626scYeUxmRQW7znHB2uOkZqVB8Bjbesy9sFGKkcmhBDVkyQ+olIlpl3nmYXR5BpMPNTUmw/6teSjtbH8tDueZXsTiDqazMtdGzOoQyA21XzumkMJaUxacYh951IBCPFx5p3ezWkXJEtTCCFERdEoiqKoHYS1SE9Px9XVlbS0NFxcXNQOp9q5nmuk/1d/cyghnRAfZ5aOuhtH+/zce9+5VCatOMihhHQAmvm58E7v5rSpV1vNkCtE2vU8PvkzlkU74jAp4GRvy/iH7mJox0AZui6EELehLN/fkvjcRBKfiqMoCi/8uJfVBxKp46jj19GdCKhTy6KM0aTww844PlobS3q2AYAn2gXwWrcQajvq1Ai7XCmKwrKYBKb9cZRL13IBeLSVH2/0bIK3i17l6IQQouqSxOc2SeJTcWauP8EnUcexs9GwaET4LTsyX7qWw7Tfj7E05jwAbrXseK1bCAPCAqrs0g2xSRm8ueIQu85eASDY05F3ejXn7oYeKkcmhBBVnyQ+t0kSn4qx5lAizy2KAeD9vi14on29Uh23++wV3lxxiGNJGQCEBrjxf72b09zftcJiLW/XcgzMiDrO/L/PYjQpONjZMPbBRoy4pz46W2nWEkKI8iCJz22SxKf8Hb6QxmOzt3M9z8jwTkG89UizMh2fZzSx4O+zfBp1nMxcI1oNDOoQyEtdG+PqYL3DvRVF4beDibyz+gjJ6TkAdGvmw5uPNMXfzUHl6IQQonqRxOc2SeJTvi5dy6HXF9tISL1O50YezB/W7rY77yanZ/N/vx1l1f4LAHg46ZjYvQl92/ij0VhX89epi9d469fDbD15CYBA91q8/Wgz7mvspXJkQghRPUnic5sk8Sk/OQYjA+ftZE/cVRp4OLL8+U641rrzGpptJy8x+ddDnLqYCUD7oDq807s5jX2c7/jcd+p6rpGZf51g3pbT5BkV7G21PH9fQ57t0kCWnhBCiAokic9tksSnfCiKwqu/HGBJ9Hmc9basGN2JYE+ncjt/rsHE/7aeZub6k1zPM2Kj1TD87iDGPXQXTvaVPzWVoij8eSSZqauOkJB6HYAHQryY8kgz6rnXKuFoIYQQd0oSn9skiU/5+N+W0/zfb0fRauDb4e259y7PCrlOQup1pq46zNrDyQB4u9gzqWdTHm7pW2nNX/GXs3hr5SE2xF4EwN/NgbceacpDTb2trglOCCGqK0l8bpMkPnduY2wKT327G5MCkx9uylP31K/wa26ITWHKysPEXc4C4J6GHrzdq1m51jL9W3aeka82nWbWxpPkGkzY2Wh45t4GjLm/EQ46adYSQojKJInPbZLE586cTLlGn1nbyMgxMCAsgPf7tai0Wo+iEpGRnRvwwgPln4hsjE3hrUpOtIQQQhRPEp/bJInP7UvNyqX3rG2cvZxFu6DafP90B1XmqYm/nMWUVYf561gKkN/0NPmRpnQth6anhNTrvLPqCGsOJwH5TWtvPtyUni0qr2lNCCFEYZL43CZJfG6PwWhi2PzdbD15CX83B34d0wkPJ/VWWFcUhagjybx9U2fj+xt78vajzW+rs3FRnamf6hTEixHqdKYWQghhSRKf2ySJz+2ZsvIw3/59llo6G5aOupsmvtbx7K7nGvliwwnmbs4fXq6z1fL8fcE81yW41MPL/z55iTetdPi8EEKIfGX5/r6ttohZs2YRFBSEXq8nPDycXbt2FVs2Ly+PqVOnEhwcjF6vp1WrVqxZs6ZQuYSEBAYNGoS7uzsODg60aNGCPXv2mPcPGzYMjUZj8erWrZvFOa5cucLAgQNxcXHBzc2NESNGcO3atdu5RVFKP+yM59u/zwLw6YBQq0l6ABx0NrwSGcKacfdyT0MPcg0mZqw7QeSMzWyITbnlsSnp2Yz9cS//+d9OTl3MxMNJx/THW7H42Q6S9AghRBVW5nr6xYsXM2HCBObMmUN4eDgzZswgMjKS2NhYvLwKz0w7adIkFi1axLx58wgJCWHt2rX06dOHv//+m9atWwNw9epVOnXqxP33388ff/yBp6cnJ06coHbt2hbn6tatG/Pnzzf/bG9v2ZwycOBAEhMTiYqKIi8vj+HDh/PMM8/www8/lPU2RSnsOH2Zyb8eAuDlrncR2cxH5YiKFuzpxMIR7c1LSMRdzmL4/N1FLiFhMJpYsD2OT6OOcy3HgFYDgzsEMsHKl8gQQghROmVu6goPD6ddu3Z88cUXAJhMJgICAnjhhRd4/fXXC5X38/PjjTfeYPTo0eZt/fr1w8HBgUWLFgHw+uuvs23bNrZs2VLsdYcNG0ZqaiorVqwocv/Ro0dp2rQpu3fvJiwsDIA1a9bQo0cPzp8/j5+fX4n3Jk1dpXfuShaPfrGVq1l5PNLKj8+fCK0SHXyv5Rj4bN1xvtn2z6KhLzzYkKfvacCB86lMquKLogohRE1UYU1dubm5REdHExER8c8JtFoiIiLYvn17kcfk5OSg1+sttjk4OLB161bzzytXriQsLIz+/fvj5eVF69atmTdvXqFzbdy4ES8vLxo3bsyoUaO4fPmyed/27dtxc3MzJz0AERERaLVadu7cWWxs6enpFi9Rsms5Bp5esIerWXm0rOvKR4+1rBJJD4CTvS1v9GzK72M70z6oDtfzjHy4JpbOH/7FY3O2cywpA7dadrzftwXLRt0tSY8QQlQzZUp8Ll26hNFoxNvb22K7t7c3SUlJRR4TGRnJ9OnTOXHiBCaTiaioKJYtW0ZiYqK5zOnTp5k9ezaNGjVi7dq1jBo1irFjx7JgwQJzmW7duvHdd9+xfv16PvjgAzZt2kT37t0xGo0AJCUlFWpqs7W1pU6dOsXGNm3aNFxdXc2vgICAsjyOGsloUhj3015ikzPwcrZn7uCwKrkOVWMfZxY/24Hpj7fCw0lnXkH9yfYBbHjpPp5oXw+ttmokc0IIIUqvwsfifvbZZ4wcOZKQkBA0Gg3BwcEMHz6cb775xlzGZDIRFhbGe++9B0Dr1q05dOgQc+bMYejQoQA88cQT5vItWrSgZcuWBAcHs3HjRh588MHbim3ixIlMmDDB/HN6erokPyX4+M9Y1h1NQWerZe6QMHxc9SUfZKU0Gg1929TlwSbeLI0+T9vA2rQKcFM7LCGEEBWoTDU+Hh4e2NjYkJycbLE9OTkZH5+iO7Z6enqyYsUKMjMziYuL49ixYzg5OdGgQQNzGV9fX5o2bWpxXJMmTYiPjy82lgYNGuDh4cHJkycB8PHxISXFcqSOwWDgypUrxcZmb2+Pi4uLxUsUb8XeBGZvPAXAR4+1JLSaJAmuDnY8dU99SXqEEKIGKFPio9PpaNu2LevXrzdvM5lMrF+/no4dO97yWL1ej7+/PwaDgaVLl9KrVy/zvk6dOhEbG2tR/vjx4wQGBhZ7vvPnz3P58mV8fX0B6NixI6mpqURHR5vL/PXXX5hMJsLDw8tym6IIe+Ov8urSAwCMui+YXqH+KkckhBBClF2Z5/GZMGEC8+bNY8GCBRw9epRRo0aRmZnJ8OHDARgyZAgTJ040l9+5cyfLli3j9OnTbNmyhW7dumEymXj11VfNZcaPH8+OHTt47733OHnyJD/88ANz5841jwS7du0ar7zyCjt27ODs2bOsX7+eXr160bBhQyIjI4H8GqJu3boxcuRIdu3axbZt2xgzZgxPPPFEqUZ0ieIlpWXz7MJocg0mIpp48UrXxmqHJIQQQtyWMvfxGTBgABcvXmTy5MkkJSURGhrKmjVrzB2e4+Pj0Wr/yaeys7OZNGkSp0+fxsnJiR49erBw4ULc3NzMZdq1a8fy5cuZOHEiU6dOpX79+syYMYOBAwcCYGNjw4EDB1iwYAGpqan4+fnRtWtX3nnnHYu5fL7//nvGjBnDgw8+iFarpV+/fnz++ee3+2wE+bMfP7NwDykZOTT2dmbGE62l068QQogqS5asuInM42NJURTG/rSPVfsvULuWHSvH3ENAnbKvdSWEEEJUpApfskLUDLM2nGTV/gvYajXMHtRWkh4hhBBVniQ+okhrDiXx8Z/HAZjaqzkdGrirHJEQQghx5yTxEYUcuZDOhJ/3ATDs7iD+E15P3YCEEEKIciKJj7Bw6VoOI7/bQ1aukXsaejCpZxO1QxJCCCHKjSQ+wizXYGLUomgSUq8T5F6LL/7TGlsb+RURQghRfci3mgDyR3BNWnGQ3Wev4qy35X9D2+FWS6d2WEIIIUS5ksRHAPDNtrP8vOc8Wg3MfLI1Db2c1A5JCCGEKHeS+Ag2Hb/Iu78dAeC/PZpwX2OvEo4QQgghqiZJfGq4UxevMeaHGEwK9G9blxH31Fc7JCGEEKLCSOJTg6Vl5fH0gj1kZBtoG1ib/+vTHI1GlqMQQghRfUniU0MZjCZG/xDDmUuZ+Ls5MGdQW+xtbdQOSwghhKhQkvjUUP/321G2nryEg50Nc4e0xdPZvuSDhBBCiCpOEp8a6Mdd8Xz791kAPh3QimZ+ruoGJIQQQlQSSXxqmF1nrvDmikMATHjoLro191U5IiGEEKLySOJTg+QaTLzyy34MJoWeLX154YGGaockhBBCVCpJfGqQ73fGEXc5Cw8nHR/0aykjuIQQQtQ4kvjUEGnX8/h8/QkAxkXchZO9rcoRCSGEEJVPEp8aYvbGU1zNyiPY05En2gWoHY4QQgihCkl8aoCE1Ot8s+0MAK93byIrrgshhKix5BuwBvjkz1hyDSba169DRBNZh0sIIUTNJYlPNXf4QhrL9yYA8EaPJtKhWQghRI0miU81pigK034/hqLAI638aBXgpnZIQgghhKok8anGNh2/yNaTl9DZaHk1srHa4QghhBCqk8SnmjKa8mt7AIZ0DCSgTi2VIxJCCCHUJ4lPNbU0+jyxyRm46G0ZIzM0CyGEEIAkPtVSVq6BT6JiAXjhgUa41dKpHJEQQghhHSTxqYa+3nKG5PQc/N0cGNwxUO1whBBCCKshiU81czEjhzmbTgHwarfG6O1sVI5ICCGEsB6S+FQzn60/TmaukRb+rjzS0k/tcIQQQgirIolPNXIy5Ro/7joHwH97NEGrlckKhRBCiJtJ4lONfLDmGEaTwoMhXnQMdlc7HCGEEMLqSOJTTew6c4WoI8loNfB69xC1wxFCCCGskiQ+1YCiKLz7+1EABrSrRyNvZ5UjEkIIIayTJD7VwOoDiew/l0otnQ3jH2qkdjhCCCGE1ZLEp4rLMRj5cG3+0hTP3NsAL2e9yhEJIYQQ1ksSnypu4fY4zl25jqezPSM7N1A7HCGEEMKq3VbiM2vWLIKCgtDr9YSHh7Nr165iy+bl5TF16lSCg4PR6/W0atWKNWvWFCqXkJDAoEGDcHd3x8HBgRYtWrBnzx7zOV577TVatGiBo6Mjfn5+DBkyhAsXLlicIygoCI1GY/F6//33b+cWq4S0rDxm/nUSgAkP3YWjva3KEQkhhBDWrcyJz+LFi5kwYQJvvfUWMTExtGrVisjISFJSUoosP2nSJL766itmzpzJkSNHeO655+jTpw979+41l7l69SqdOnXCzs6OP/74gyNHjvDJJ59Qu3ZtALKysoiJieHNN98kJiaGZcuWERsby6OPPlroelOnTiUxMdH8euGFF8p6i1XGrI0nSbueRyMvJ/q3rat2OEIIIYTV0yiKopTlgPDwcNq1a8cXX3wBgMlkIiAggBdeeIHXX3+9UHk/Pz/eeOMNRo8ebd7Wr18/HBwcWLRoEQCvv/4627ZtY8uWLaWOY/fu3bRv3564uDjq1asH5Nf4jBs3jnHjxpXqHDk5OeTk5Jh/Tk9PJyAggLS0NFxcXEodixrOXcniwU82kWs08c2wMB4I8VY7JCGEEEIV6enpuLq6lur7u0w1Prm5uURHRxMREfHPCbRaIiIi2L59e5HH5OTkoNdbdrh1cHBg69at5p9XrlxJWFgY/fv3x8vLi9atWzNv3rxbxpKWloZGo8HNzc1i+/vvv4+7uzutW7fmo48+wmAwFHuOadOm4erqan4FBATc8prW5JM/Y8k1mujYwJ37G3upHY4QQghRJZQp8bl06RJGoxFvb8vaBW9vb5KSkoo8JjIykunTp3PixAlMJhNRUVEsW7aMxMREc5nTp08ze/ZsGjVqxNq1axk1ahRjx45lwYIFRZ4zOzub1157jSeffNIisxs7diw//fQTGzZs4Nlnn+W9997j1VdfLfZ+Jk6cSFpamvl17ty5sjwO1Rw8n8aKffn9m/7bowkajSxNIYQQQpRGhfeG/eyzzxg5ciQhISFoNBqCg4MZPnw433zzjbmMyWQiLCyM9957D4DWrVtz6NAh5syZw9ChQy3Ol5eXx+OPP46iKMyePdti34QJE8z/btmyJTqdjmeffZZp06Zhb29fKDZ7e/sit1szRVF478Zkhb1D/WhR11XliIQQQoiqo0w1Ph4eHtjY2JCcnGyxPTk5GR8fnyKP8fT0ZMWKFWRmZhIXF8exY8dwcnKiQYN/hl77+vrStGlTi+OaNGlCfHy8xbaCpCcuLo6oqKgS2/HCw8MxGAycPXu2DHdp3TbEprD99GV0NlpejmysdjhCCCFElVKmxEen09G2bVvWr19v3mYymVi/fj0dO3a85bF6vR5/f38MBgNLly6lV69e5n2dOnUiNjbWovzx48cJDAw0/1yQ9Jw4cYJ169bh7l7yIpz79u1Dq9Xi5VU9+sAYjCam/Z4/WeGwTkHUrV1L5YiEEEKIqqXMTV0TJkxg6NChhIWF0b59e2bMmEFmZibDhw8HYMiQIfj7+zNt2jQAdu7cSUJCAqGhoSQkJDBlyhRMJpNF35vx48dz991389577/H444+za9cu5s6dy9y5c4H8pOexxx4jJiaG1atXYzQazX2K6tSpg06nY/v27ezcuZP7778fZ2dntm/fzvjx4xk0aJB5WHxVtyT6PCdSruHqYMfo+xqqHY4QQghR5ZQ58RkwYAAXL15k8uTJJCUlERoaypo1a8wdnuPj49Fq/6lIys7OZtKkSZw+fRonJyd69OjBwoULLUZjtWvXjuXLlzNx4kSmTp1K/fr1mTFjBgMHDgTyJzdcuXIlAKGhoRbxbNiwgfvuuw97e3t++uknpkyZQk5ODvXr12f8+PEW/X6qsswcA9OjjgPwwgMNca1lp3JEQgghRNVT5nl8qrOyzANQ2WasO86MdScIqOPAugldsLe1UTskIYQQwipU2Dw+Qh0pGdnM3XwagFcjQyTpEUIIIW6TJD5VwKdRJ8jKNdIqwI2HW/qqHY4QQghRZUniY+VOJGeweHf+sP43ZLJCIYQQ4o5I4mPl3v/jGCYFHmrqTfv6ddQORwghhKjSJPGxYttPXWb9sRRstBpe7x6idjhCCCFElSeJj5Uymf5ZmuLJ9gEEezqpHJEQQghR9UniY6VWHbjAwYQ0HHU2vPjgXWqHI4QQQlQLkvhYoew8Ix+uyV/C47kuwXg6V62FVIUQQghrJYmPFfpu+1kSUq/j7WLP050blHyAEEIIIUpFEh8rk5qVyxd/nQTgpYca46CTyQqFEEKI8iKJj5WZ+ddJ0rMNhPg4069tXbXDEUIIIaoVSXysyLkrWXy3/SwAr3cPwUYrkxUKIYQQ5UkSHyvy4dpY8owK9zT0oMtdnmqHI4QQQlQ7kvhYif3nUlm1/wIaTX5tjyxNIYQQQpQ/SXysgKIovHtjssI+of4093dVOSIhhBCiepLExwqsO5rCrjNX0NlqeSmysdrhCCGEENWWJD4qMxhNvP9Hfm3PU53q4+/moHJEQgghRPUliY/Kftp9jlMXM6ldy47n7w9WOxwhhBCiWpPER0XXcgzMWHccgLEPNsJFb6dyREIIIUT1JomPiuZuOsWla7kEuddiYHig2uEIIYQQ1Z4kPipJTs9m3pYzALzaLQSdrbwVQgghREWTb1uVTP/zONfzjLSp50b35j5qhyOEEELUCJL4qCA2KYMl0ecAeKNnE5msUAghhKgkkvioYNofRzEp0K2ZD20D66gdjhBCCFFjSOJTybadvMTG2IvYajW81j1E7XCEEEKIGkUSn0pkMim8d2NpioHh9ajv4ahyREIIIUTNIolPJVqxL4HDF9Jxtrdl7ION1A5HCCGEqHEk8akk2XlGPl4bC8Bz9wXj7mSvckRCCCFEzSOJTyWZv+0sF9Ky8XXVM+Ke+mqHI4QQQtRIkvhUgiuZuXy54SQAL3VtjN7ORuWIhBBCiJpJEp9K8OWGk2TkGAjxcaZPa3+1wxFCCCFqLFu1A6gJnu0SzPU8I92a+2CjlckKhRBCCLVI4lMJPJ3tebdPC7XDEEIIIWo8aeoSQgghRI0hiY8QQgghaozbSnxmzZpFUFAQer2e8PBwdu3aVWzZvLw8pk6dSnBwMHq9nlatWrFmzZpC5RISEhg0aBDu7u44ODjQokUL9uzZY96vKAqTJ0/G19cXBwcHIiIiOHHihMU5rly5wsCBA3FxccHNzY0RI0Zw7dq127lFIYQQQlRDZU58Fi9ezIQJE3jrrbeIiYmhVatWREZGkpKSUmT5SZMm8dVXXzFz5kyOHDnCc889R58+fdi7d6+5zNWrV+nUqRN2dnb88ccfHDlyhE8++YTatWuby3z44Yd8/vnnzJkzh507d+Lo6EhkZCTZ2dnmMgMHDuTw4cNERUWxevVqNm/ezDPPPFPWWxRCCCFEdaWUUfv27ZXRo0ebfzYajYqfn58ybdq0Isv7+voqX3zxhcW2vn37KgMHDjT//Nprryn33HNPsdc0mUyKj4+P8tFHH5m3paamKvb29sqPP/6oKIqiHDlyRAGU3bt3m8v88ccfikajURISEkp1b2lpaQqgpKWllaq8EEIIIdRXlu/vMtX45ObmEh0dTUREhHmbVqslIiKC7du3F3lMTk4Oer3eYpuDgwNbt241/7xy5UrCwsLo378/Xl5etG7dmnnz5pn3nzlzhqSkJIvrurq6Eh4ebr7u9u3bcXNzIywszFwmIiICrVbLzp07i40tPT3d4iWEEEKI6qtMic+lS5cwGo14e3tbbPf29iYpKanIYyIjI5k+fTonTpzAZDIRFRXFsmXLSExMNJc5ffo0s2fPplGjRqxdu5ZRo0YxduxYFixYAGA+962um5SUhJeXl8V+W1tb6tSpU2xs06ZNw9XV1fwKCAgow9MQQgghRFVT4aO6PvvsMxo1akRISAg6nY4xY8YwfPhwtNp/Lm0ymWjTpg3vvfcerVu35plnnmHkyJHMmTOnQmObOHEiaWlp5te5c+cq9HpCCCGEUFeZEh8PDw9sbGxITk622J6cnIyPj0+Rx3h6erJixQoyMzOJi4vj2LFjODk50aBBA3MZX19fmjZtanFckyZNiI+PBzCf+1bX9fHxKdTB2mAwcOXKlWJjs7e3x8XFxeIlhBBCiOqrTImPTqejbdu2rF+/3rzNZDKxfv16OnbseMtj9Xo9/v7+GAwGli5dSq9evcz7OnXqRGxsrEX548ePExgYCED9+vXx8fGxuG56ejo7d+40X7djx46kpqYSHR1tLvPXX39hMpkIDw8vy20KIYQQoroqa8/pn376SbG3t1e+/fZb5ciRI8ozzzyjuLm5KUlJSYqiKMrgwYOV119/3Vx+x44dytKlS5VTp04pmzdvVh544AGlfv36ytWrV81ldu3apdja2irvvvuucuLECeX7779XatWqpSxatMhc5v3331fc3NyUX3/9VTlw4IDSq1cvpX79+sr169fNZbp166a0bt1a2blzp7J161alUaNGypNPPlnqe5NRXUIIIUTVU5bv7zInPoqiKDNnzlTq1aun6HQ6pX379sqOHTvM+7p06aIMHTrU/PPGjRuVJk2aKPb29oq7u7syePDgIoeXr1q1SmnevLlib2+vhISEKHPnzrXYbzKZlDfffFPx9vZW7O3tlQcffFCJjY21KHP58mXlySefVJycnBQXFxdl+PDhSkZGRqnvSxIfIYQQouopy/e3RlEURd06J+uRnp6Oq6sraWlp0t9HCCGEqCLK8v0tq7PfpCAHlPl8hBBCiKqj4Hu7NHU5kvjcJCMjA0Dm8xFCCCGqoIyMDFxdXW9ZRpq6bmIymbhw4QLOzs5oNBq1w6kQ6enpBAQEcO7cOWnOu4k8l8LkmRQmz6Ro8lwKk2dStIp6LoqikJGRgZ+fn8U8gUWRGp+baLVa6tatq3YYlULmLSqaPJfC5JkUJs+kaPJcCpNnUrSKeC4l1fQUqPCZm4UQQgghrIUkPkIIIYSoMSTxqWHs7e156623sLe3VzsUqyLPpTB5JoXJMymaPJfC5JkUzRqei3RuFkIIIUSNITU+QgghhKgxJPERQgghRI0hiY8QQgghagxJfIQQQghRY0jiI4QQQogaQxKfGsJoNPLmm29Sv359HBwcCA4O5p133inVgm7VxebNm3nkkUfw8/NDo9GwYsUKi/2KojB58mR8fX1xcHAgIiKCEydOqBNsJbrVc8nLy+O1116jRYsWODo64ufnx5AhQ7hw4YJ6AVeCkn5Xbvbcc8+h0WiYMWNGpcWnhtI8k6NHj/Loo4/i6uqKo6Mj7dq1Iz4+vvKDrUQlPZdr164xZswY6tati4ODA02bNmXOnDnqBFtJpk2bRrt27XB2dsbLy4vevXsTGxtrUSY7O5vRo0fj7u6Ok5MT/fr1Izk5uVLik8Snhvjggw+YPXs2X3zxBUePHuWDDz7gww8/ZObMmWqHVmkyMzNp1aoVs2bNKnL/hx9+yOeff86cOXPYuXMnjo6OREZGkp2dXcmRVq5bPZesrCxiYmJ48803iYmJYdmyZcTGxvLoo4+qEGnlKel3pcDy5cvZsWMHfn5+lRSZekp6JqdOneKee+4hJCSEjRs3cuDAAd588030en0lR1q5SnouEyZMYM2aNSxatIijR48ybtw4xowZw8qVKys50sqzadMmRo8ezY4dO4iKiiIvL4+uXbuSmZlpLjN+/HhWrVrFkiVL2LRpExcuXKBv376VE6AiaoSePXsqTz31lMW2vn37KgMHDlQpInUByvLly80/m0wmxcfHR/noo4/M21JTUxV7e3vlxx9/VCFCdfz7uRRl165dCqDExcVVTlAqK+6ZnD9/XvH391cOHTqkBAYGKp9++mmlx6aWop7JgAEDlEGDBqkTkJUo6rk0a9ZMmTp1qsW2Nm3aKG+88UYlRqaulJQUBVA2bdqkKEr+Z6udnZ2yZMkSc5mjR48qgLJ9+/YKj0dqfGqIu+++m/Xr13P8+HEA9u/fz9atW+nevbvKkVmHM2fOkJSUREREhHmbq6sr4eHhbN++XcXIrE9aWhoajQY3Nze1Q1GNyWRi8ODBvPLKKzRr1kztcFRnMpn47bffuOuuu4iMjMTLy4vw8PBbNhHWFHfffTcrV64kISEBRVHYsGEDx48fp2vXrmqHVmnS0tIAqFOnDgDR0dHk5eVZfN6GhIRQr169Svm8lcSnhnj99dd54oknCAkJwc7OjtatWzNu3DgGDhyodmhWISkpCQBvb2+L7d7e3uZ9Ir9d/rXXXuPJJ5+s0StOf/DBB9ja2jJ27Fi1Q7EKKSkpXLt2jffff59u3brx559/0qdPH/r27cumTZvUDk9VM2fOpGnTptStWxedTke3bt2YNWsW9957r9qhVQqTycS4cePo1KkTzZs3B/I/b3U6XaE/nirr89a2wq8grMLPP//M999/zw8//ECzZs3Yt28f48aNw8/Pj6FDh6odnqgC8vLyePzxx1EUhdmzZ6sdjmqio6P57LPPiImJQaPRqB2OVTCZTAD06tWL8ePHAxAaGsrff//NnDlz6NKli5rhqWrmzJns2LGDlStXEhgYyObNmxk9ejR+fn4WNR7V1ejRozl06BBbt25VOxQzSXxqiFdeecVc6wPQokUL4uLimDZtmiQ+gI+PDwDJycn4+vqatycnJxMaGqpSVNajIOmJi4vjr7/+qtG1PVu2bCElJYV69eqZtxmNRl566SVmzJjB2bNn1QtOJR4eHtja2tK0aVOL7U2aNLGqL7zKdv36df773/+yfPlyevbsCUDLli3Zt28fH3/8cbVPfMaMGcPq1avZvHkzdevWNW/38fEhNzeX1NRUi1qf5ORk82dxRZKmrhoiKysLrdby7baxsTH/pVbT1a9fHx8fH9avX2/elp6ezs6dO+nYsaOKkamvIOk5ceIE69atw93dXe2QVDV48GAOHDjAvn37zC8/Pz9eeeUV1q5dq3Z4qtDpdLRr167QkOXjx48TGBioUlTqy8vLIy8vr8Z99iqKwpgxY1i+fDl//fUX9evXt9jftm1b7OzsLD5vY2NjiY+Pr5TPW6nxqSEeeeQR3n33XerVq0ezZs3Yu3cv06dP56mnnlI7tEpz7do1Tp48af75zJkz7Nu3jzp16lCvXj3GjRvH//3f/9GoUSPq16/Pm2++iZ+fH71791Yv6Epwq+fi6+vLY489RkxMDKtXr8ZoNJrb4OvUqYNOp1Mr7ApV0u/Kv5M/Ozs7fHx8aNy4cWWHWmlKeiavvPIKAwYM4N577+X+++9nzZo1rFq1io0bN6oXdCUo6bl06dKFV155BQcHBwIDA9m0aRPfffcd06dPVzHqijV69Gh++OEHfv31V5ydnc2fGa6urjg4OODq6sqIESOYMGECderUwcXFhRdeeIGOHTvSoUOHig+wwseNCauQnp6uvPjii0q9evUUvV6vNGjQQHnjjTeUnJwctUOrNBs2bFCAQq+hQ4cqipI/pP3NN99UvL29FXt7e+XBBx9UYmNj1Q26EtzquZw5c6bIfYCyYcMGtUOvMCX9rvxbTRjOXppn8vXXXysNGzZU9Hq90qpVK2XFihXqBVxJSnouiYmJyrBhwxQ/Pz9Fr9crjRs3Vj755BPFZDKpG3gFKu4zY/78+eYy169fV55//nmldu3aSq1atZQ+ffooiYmJlRKf5kaQQgghhBDVnvTxEUIIIUSNIYmPEEIIIWoMSXyEEEIIUWNI4iOEEEKIGkMSHyGEEELUGJL4CCGEEKLGkMRHCCGEEDWGJD5CCCGEqDEk8RFCCCFEjSGJjxBCCCFqDEl8hBBCCFFj/D9pmrC21+SvPwAAAABJRU5ErkJggg=="
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"to_plot = []\n",
|
|
"params = best_params.copy()\n",
|
|
"for smooth_window_length in range(7, 21):\n",
|
|
" params['smooth_window_length'] = smooth_window_length\n",
|
|
" process_params = {key: params[key] for key in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}\n",
|
|
" classifier_params = {key: params[key] for key in best_params.keys() if key not in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}\n",
|
|
" X_train, X_test = process_train_test(process_params, experiments_train, metadata_train, experiments_test, metadata_test, scale=True)\n",
|
|
" to_plot.append(evaluate_classifier_params(RandomForestClassifier, classifier_params, X_train, truth_train, X_test, truth_test, iters=10)[1])\n",
|
|
"_ = plt.plot(range(7, 21), to_plot)\n",
|
|
"_ = plt.title(\"Impact of varying smoothing window size\")"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-29T13:11:25.914335900Z",
|
|
"start_time": "2024-04-29T13:09:41.581935700Z"
|
|
}
|
|
},
|
|
"id": "710747d57aa84b92",
|
|
"execution_count": 16
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
|
"image/png": "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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"to_plot = []\n",
|
|
"params = best_params.copy()\n",
|
|
"for max_depth in range(1, 21):\n",
|
|
" params['max_depth'] = max_depth\n",
|
|
" process_params = {key: params[key] for key in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}\n",
|
|
" classifier_params = {key: params[key] for key in best_params.keys() if key not in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}\n",
|
|
" X_train, X_test = process_train_test(process_params, experiments_train, metadata_train, experiments_test, metadata_test, scale=True)\n",
|
|
" to_plot.append(evaluate_classifier_params(RandomForestClassifier, classifier_params, X_train, truth_train, X_test, truth_test, iters=10))\n",
|
|
"_ = plt.plot(range(1, 21), to_plot)\n",
|
|
"_ = plt.title(\"Impact of varying tree depth\")\n",
|
|
"_ = plt.legend([\"training loss\", \"validation loss\"])"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-29T13:13:40.774246600Z",
|
|
"start_time": "2024-04-29T13:11:25.903010900Z"
|
|
}
|
|
},
|
|
"id": "8b4cbe0b798df349",
|
|
"execution_count": 17
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
|
"image/png": "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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"to_plot = []\n",
|
|
"params = best_params.copy()\n",
|
|
"for n_estimators in range(1, 201, 10):\n",
|
|
" params['n_estimators'] = n_estimators\n",
|
|
" process_params = {key: params[key] for key in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}\n",
|
|
" classifier_params = {key: params[key] for key in best_params.keys() if key not in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}\n",
|
|
" X_train, X_test = process_train_test(process_params, experiments_train, metadata_train, experiments_test, metadata_test, scale=True)\n",
|
|
" to_plot.append(evaluate_classifier_params(RandomForestClassifier, classifier_params, X_train, truth_train, X_test, truth_test, iters=10))\n",
|
|
"_ = plt.plot(range(1, 201, 10), to_plot)\n",
|
|
"_ = plt.title(\"Impact of varying number of trees\")\n",
|
|
"_ = plt.legend([\"training loss\", \"validation loss\"])"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-29T13:16:03.256973700Z",
|
|
"start_time": "2024-04-29T13:13:40.758410700Z"
|
|
}
|
|
},
|
|
"id": "a12b68fb6514154c",
|
|
"execution_count": 18
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"# Removing data"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"id": "cb73da091b1f3d9c"
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [],
|
|
"source": [
|
|
"process_params = {key: best_params[key] for key in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}\n",
|
|
"X_train, X_test = process_train_test(process_params, experiments_train, metadata_train, experiments_test, metadata_test, scale=False)"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-29T13:16:05.098757100Z",
|
|
"start_time": "2024-04-29T13:16:03.242224700Z"
|
|
}
|
|
},
|
|
"id": "ea25ffb654d57354",
|
|
"execution_count": 19
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"0 1748 0.9809829059829058\n",
|
|
"0.05 1748 0.9810541310541309\n",
|
|
"0.1 1009 0.9829059829059827\n",
|
|
"0.15 455 0.9886752136752135\n",
|
|
"0.2 298 0.9849002849002849\n",
|
|
"0.25 202 0.9849002849002849\n",
|
|
"0.3 134 0.9867521367521366\n",
|
|
"0.35 62 0.9621082621082617\n",
|
|
"0.4 42 0.9544159544159543\n",
|
|
"0.45 31 0.9542735042735042\n",
|
|
"0.5 19 0.9467236467236466\n",
|
|
"0.55 9 0.9335470085470087\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"param_grid = {\n",
|
|
" 'n_estimators': range(1, 201, 100),\n",
|
|
" 'max_depth': range(5, 21, 5)\n",
|
|
"}\n",
|
|
"results = []\n",
|
|
"X_size = []\n",
|
|
"for min_std in [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55]:\n",
|
|
" to_drop = X_train.std() > min_std\n",
|
|
" X_train_small = X_train.loc[:, to_drop]\n",
|
|
" X_size.append(len(X_train_small.columns))\n",
|
|
"\n",
|
|
" scaler = StandardScaler()\n",
|
|
" scaler.fit(X_train_small)\n",
|
|
" X_train_small = scaler.transform(X_train_small)\n",
|
|
" \n",
|
|
" hyper_results = []\n",
|
|
" for params in ParameterGrid(param_grid):\n",
|
|
" try:\n",
|
|
" clf = RandomForestClassifier(**params)\n",
|
|
" hyper_results.append([params, cross_validate(clf, X_train_small, truth_train.to_numpy().ravel(), cv=20, return_estimator=True)])\n",
|
|
" except Exception as e:\n",
|
|
" pass # print(params, e)\n",
|
|
" \n",
|
|
" crossval_res = 0\n",
|
|
" best_params = dict()\n",
|
|
" for r in hyper_results:\n",
|
|
" mean = np.mean(r[1]['test_score'])\n",
|
|
" if mean > crossval_res:\n",
|
|
" crossval_res = mean\n",
|
|
" best_params = r[0]\n",
|
|
" \n",
|
|
" print(min_std, X_size[-1], crossval_res)\n",
|
|
" results.append(crossval_res)"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-29T13:26:42.086233700Z",
|
|
"start_time": "2024-04-29T13:22:45.050103200Z"
|
|
}
|
|
},
|
|
"id": "8e64395e456294ff",
|
|
"execution_count": 21
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
|
"image/png": "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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plt.plot([0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55], X_size)\n",
|
|
"_ = plt.title(\"Amount of features\")\n",
|
|
"_ = plt.xlabel(\"minimum standard deviation\")"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-29T13:26:54.764759600Z",
|
|
"start_time": "2024-04-29T13:26:54.647378500Z"
|
|
}
|
|
},
|
|
"id": "6c47122fe8c7be5a",
|
|
"execution_count": 23
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
|
"image/png": "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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plt.plot([0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55], results)\n",
|
|
"_ = plt.title(\"Mean validation score\")\n",
|
|
"_ = plt.xlabel(\"minimum standard deviation\")"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-29T13:26:55.661004700Z",
|
|
"start_time": "2024-04-29T13:26:55.589740400Z"
|
|
}
|
|
},
|
|
"id": "b620fcf2b4b9f6f4",
|
|
"execution_count": 24
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
|
"image/png": "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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"to_drop = X_train.std() > .15\n",
|
|
"X_train_small = X_train.loc[:, to_drop]\n",
|
|
"X_test_small = X_test.loc[:, to_drop]\n",
|
|
"_ = X_train.iloc[:,10:].transpose().plot(legend=False)"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-29T13:28:49.137639600Z",
|
|
"start_time": "2024-04-29T13:28:48.167781600Z"
|
|
}
|
|
},
|
|
"id": "975356d69cc20c53",
|
|
"execution_count": 31
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
|
"image/png": "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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"_ = X_train_small.transpose().plot(legend=False)"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-29T13:28:51.983978100Z",
|
|
"start_time": "2024-04-29T13:28:51.289055Z"
|
|
}
|
|
},
|
|
"id": "fdf20b15d00a8430",
|
|
"execution_count": 32
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "(1.0, 0.9838983050847455)"
|
|
},
|
|
"execution_count": 33,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"scaler = StandardScaler()\n",
|
|
"scaler.fit(X_train_small)\n",
|
|
"X_train_small = scaler.transform(X_train_small)\n",
|
|
"X_test_small = scaler.transform(X_test_small)\n",
|
|
"evaluate_classifier_params(RandomForestClassifier, best_params, X_train_small, truth_train, X_test_small, truth_test, iters=20)"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"ExecuteTime": {
|
|
"end_time": "2024-04-29T13:29:05.925694400Z",
|
|
"start_time": "2024-04-29T13:28:59.691080400Z"
|
|
}
|
|
},
|
|
"id": "7661e709fa106edd",
|
|
"execution_count": 33
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"outputs": [],
|
|
"source": [],
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"id": "9737bff20459b9ff"
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 2
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython2",
|
|
"version": "2.7.6"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|