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" 10000.0\n", - " 0.029628\n", + " 0.032799\n", " \n", " \n", " 1\n", " 0.826403\n", " 100000.0\n", - " 0.029961\n", + " 0.026398\n", " \n", " \n", " 5\n", " 0.826104\n", " 10.0\n", - " 0.047110\n", + " 0.047401\n", " \n", " \n", " 6\n", " 0.822811\n", " 1.0\n", - " 0.035767\n", + " 0.043800\n", " \n", " \n", " 7\n", " 0.820115\n", " 0.1\n", - " 0.031846\n", + " 0.035802\n", " \n", " \n", " 8\n", " 0.798865\n", " 0.01\n", - " 0.024072\n", + " 0.054801\n", " \n", " \n", " 9\n", " 0.775518\n", " 0.001\n", - " 0.020384\n", + " 0.072002\n", " \n", " \n", "\n", @@ -319,15 +319,15 @@ "text/plain": [ " mean_test_score param_logisticregression__C mean_fit_time\n", "rank_test_score \n", - "1 0.826403 100.0 0.031061\n", - "1 0.826403 1000.0 0.033983\n", - "1 0.826403 10000.0 0.029628\n", - "1 0.826403 100000.0 0.029961\n", - "5 0.826104 10.0 0.047110\n", - "6 0.822811 1.0 0.035767\n", - "7 0.820115 0.1 0.031846\n", - "8 0.798865 0.01 0.024072\n", - "9 0.775518 0.001 0.020384" + "1 0.826403 100.0 0.038399\n", + "1 0.826403 1000.0 0.033600\n", + "1 0.826403 10000.0 0.032799\n", + "1 0.826403 100000.0 0.026398\n", + "5 0.826104 10.0 0.047401\n", + "6 0.822811 1.0 0.043800\n", + "7 0.820115 0.1 0.035802\n", + "8 0.798865 0.01 0.054801\n", + "9 0.775518 0.001 0.072002" ] }, "execution_count": 8, @@ -578,13 +578,69 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 30, + "id": "d95fbcf2-21fa-416f-b0cd-6d7611bb58d1", + "metadata": {}, + "outputs": [], + "source": [ + "from mglearn.plot_2d_separator import (plot_2d_separator, plot_2d_classification,\n", + " plot_2d_scores)\n", + "from mglearn.plot_helpers import cm2 as cm, discrete_scatter\n", + "\n", + "def visualize_coefficients(coefficients, feature_names, n_top_features=25):\n", + " \"\"\"Visualize coefficients of a linear model.\n", + " Parameters\n", + " ----------\n", + " coefficients : nd-array, shape (n_features,)\n", + " Model coefficients.\n", + " feature_names : list or nd-array of strings, shape (n_features,)\n", + " Feature names for labeling the coefficients.\n", + " n_top_features : int, default=25\n", + " How many features to show. The function will show the largest (most\n", + " positive) and smallest (most negative) n_top_features coefficients,\n", + " for a total of 2 * n_top_features coefficients.\n", + " \"\"\"\n", + " coefficients = coefficients.squeeze()\n", + " if coefficients.ndim > 1:\n", + " # this is not a row or column vector\n", + " raise ValueError(\"coeffients must be 1d array or column vector, got\"\n", + " \" shape {}\".format(coefficients.shape))\n", + " coefficients = coefficients.ravel()\n", + "\n", + " if len(coefficients) != len(feature_names):\n", + " raise ValueError(\"Number of coefficients {} doesn't match number of\"\n", + " \"feature names {}.\".format(len(coefficients),\n", + " len(feature_names)))\n", + " # get coefficients with large absolute values\n", + " coef = coefficients.ravel()\n", + " positive_coefficients = np.argsort(coef)[-n_top_features:]\n", + " negative_coefficients = np.argsort(coef)[:n_top_features]\n", + " interesting_coefficients = np.hstack([negative_coefficients,\n", + " positive_coefficients])\n", + " # plot them\n", + " plt.figure(figsize=(15, 5))\n", + " colors = [cm(1) if c < 0 else cm(0)\n", + " for c in coef[interesting_coefficients]]\n", + " plt.bar(np.arange(2 * n_top_features), coef[interesting_coefficients],\n", + " color=colors)\n", + " feature_names = np.array(feature_names)\n", + " plt.subplots_adjust(bottom=0.3)\n", + " plt.xticks(np.arange(0, 2 * n_top_features),\n", + " feature_names[interesting_coefficients], rotation=60,\n", + " ha=\"right\")\n", + " plt.ylabel(\"Coefficient magnitude\")\n", + " plt.xlabel(\"Feature\")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, "id": "8092c271-1711-4774-a49b-a208b24a31e8", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -596,8 +652,8 @@ } ], "source": [ - "mglearn.tools.visualize_coefficients(coeffs, feature_names, n_top_features=5)\n", - "plt.savefig(\"../../models/coeff.png\")" + "visualize_coefficients(coeffs, feature_names, n_top_features=5)\n", + "plt.savefig(\"../../models/coeff.png\", bbox_inches = 'tight')" ] }, { diff --git a/src/models/test.py b/src/models/test.py index dc3703c..994467b 100644 --- a/src/models/test.py +++ b/src/models/test.py @@ -20,6 +20,10 @@ from docopt import docopt import matplotlib.pyplot as plt import mglearn +from mglearn.plot_2d_separator import (plot_2d_separator, plot_2d_classification, + plot_2d_scores) +from mglearn.plot_helpers import cm2 as cm, discrete_scatter + import numpy as np import pandas as pd import pickle @@ -104,11 +108,56 @@ def coeff_plot(best_model, out_dir): coeff_df = pd.DataFrame(coeffs, index=feature_names, columns=["Coefficient"]) coeff_df_sorted = coeff_df.sort_values(by="Coefficient", ascending=False) coeff_df_sorted.to_html(os.path.join(out_dir, "coeff_sorted.html"), escape=False) - mglearn.tools.visualize_coefficients(coeffs, feature_names, n_top_features=5) - plt.savefig(os.path.join(out_dir, "coeff_bar.png")) + visualize_coefficients(coeffs, feature_names, n_top_features=5) + plt.savefig(os.path.join(out_dir, "coeff_bar.png"), bbox_inches = 'tight') logger.info("Bar plot for coefficents saved") - + +def visualize_coefficients(coefficients, feature_names, n_top_features=25): + """Visualize coefficients of a linear model. + Parameters + ---------- + coefficients : nd-array, shape (n_features,) + Model coefficients. + feature_names : list or nd-array of strings, shape (n_features,) + Feature names for labeling the coefficients. + n_top_features : int, default=25 + How many features to show. The function will show the largest (most + positive) and smallest (most negative) n_top_features coefficients, + for a total of 2 * n_top_features coefficients. + """ + coefficients = coefficients.squeeze() + if coefficients.ndim > 1: + # this is not a row or column vector + raise ValueError("coeffients must be 1d array or column vector, got" + " shape {}".format(coefficients.shape)) + coefficients = coefficients.ravel() + + if len(coefficients) != len(feature_names): + raise ValueError("Number of coefficients {} doesn't match number of" + "feature names {}.".format(len(coefficients), + len(feature_names))) + # get coefficients with large absolute values + coef = coefficients.ravel() + positive_coefficients = np.argsort(coef)[-n_top_features:] + negative_coefficients = np.argsort(coef)[:n_top_features] + interesting_coefficients = np.hstack([negative_coefficients, + positive_coefficients]) + # plot them + plt.figure(figsize=(15, 5)) + colors = [cm(1) if c < 0 else cm(0) + for c in coef[interesting_coefficients]] + plt.bar(np.arange(2 * n_top_features), coef[interesting_coefficients], + color=colors) + feature_names = np.array(feature_names) + plt.subplots_adjust(bottom=0.3) + plt.xticks(np.arange(0, 2 * n_top_features), + feature_names[interesting_coefficients], rotation=60, + ha="right") + plt.ylabel("Coefficient magnitude") + plt.xlabel("Feature") + + if __name__ == "__main__": # Parse command line parameters diff --git a/src/models/train.py b/src/models/train.py index e8f1621..6367416 100644 --- a/src/models/train.py +++ b/src/models/train.py @@ -23,6 +23,11 @@ import ipywidgets as widgets import matplotlib.pyplot as plt import mglearn + +from mglearn.plot_2d_separator import (plot_2d_separator, plot_2d_classification, + plot_2d_scores) +from mglearn.plot_helpers import cm2 as cm, discrete_scatter + import numpy as np import pandas as pd import pickle @@ -192,6 +197,49 @@ def train_df_table(train_results, out_dir): ) logger.info(f"Train results table saved to {out_dir}") +def visualize_coefficients(coefficients, feature_names, n_top_features=25): + """Visualize coefficients of a linear model. + Parameters + ---------- + coefficients : nd-array, shape (n_features,) + Model coefficients. + feature_names : list or nd-array of strings, shape (n_features,) + Feature names for labeling the coefficients. + n_top_features : int, default=25 + How many features to show. The function will show the largest (most + positive) and smallest (most negative) n_top_features coefficients, + for a total of 2 * n_top_features coefficients. + """ + coefficients = coefficients.squeeze() + if coefficients.ndim > 1: + # this is not a row or column vector + raise ValueError("coeffients must be 1d array or column vector, got" + " shape {}".format(coefficients.shape)) + coefficients = coefficients.ravel() + + if len(coefficients) != len(feature_names): + raise ValueError("Number of coefficients {} doesn't match number of" + "feature names {}.".format(len(coefficients), + len(feature_names))) + # get coefficients with large absolute values + coef = coefficients.ravel() + positive_coefficients = np.argsort(coef)[-n_top_features:] + negative_coefficients = np.argsort(coef)[:n_top_features] + interesting_coefficients = np.hstack([negative_coefficients, + positive_coefficients]) + # plot them + plt.figure(figsize=(15, 5)) + colors = [cm(1) if c < 0 else cm(0) + for c in coef[interesting_coefficients]] + plt.bar(np.arange(2 * n_top_features), coef[interesting_coefficients], + color=colors) + feature_names = np.array(feature_names) + plt.subplots_adjust(bottom=0.3) + plt.xticks(np.arange(0, 2 * n_top_features), + feature_names[interesting_coefficients], rotation=60, + ha="right") + plt.ylabel("Coefficient magnitude") + plt.xlabel("Feature") if __name__ == "__main__": From 116513bd16e570e46618c51d836353af1bc56692 Mon Sep 17 00:00:00 2001 From: nickmao Date: Fri, 10 Dec 2021 23:52:17 -0800 Subject: [PATCH 2/6] Improve train result plot --- src/models/model_building.ipynb | 117 ++++++++++++++++++++++++-------- src/models/train.py | 52 ++------------ 2 files changed, 93 insertions(+), 76 deletions(-) diff --git a/src/models/model_building.ipynb b/src/models/model_building.ipynb index bad9679..fb0d81c 100644 --- a/src/models/model_building.ipynb +++ b/src/models/model_building.ipynb @@ -262,55 +262,55 @@ " 1\n", " 0.826403\n", " 100.0\n", - " 0.038399\n", + " 0.032202\n", " \n", " \n", " 1\n", " 0.826403\n", " 1000.0\n", - " 0.033600\n", + " 0.032600\n", " \n", " \n", " 1\n", " 0.826403\n", " 10000.0\n", - " 0.032799\n", + " 0.028999\n", " \n", " \n", " 1\n", " 0.826403\n", " 100000.0\n", - " 0.026398\n", + " 0.025032\n", " \n", " \n", " 5\n", " 0.826104\n", " 10.0\n", - " 0.047401\n", + " 0.039601\n", " \n", " \n", " 6\n", " 0.822811\n", " 1.0\n", - " 0.043800\n", + " 0.035598\n", " \n", " \n", " 7\n", " 0.820115\n", " 0.1\n", - " 0.035802\n", + " 0.031198\n", " \n", " \n", " 8\n", " 0.798865\n", " 0.01\n", - " 0.054801\n", + " 0.026000\n", " \n", " \n", " 9\n", " 0.775518\n", " 0.001\n", - " 0.072002\n", + " 0.022802\n", " \n", " \n", "\n", @@ -319,15 +319,15 @@ "text/plain": [ " mean_test_score param_logisticregression__C mean_fit_time\n", "rank_test_score \n", - "1 0.826403 100.0 0.038399\n", - "1 0.826403 1000.0 0.033600\n", - "1 0.826403 10000.0 0.032799\n", - "1 0.826403 100000.0 0.026398\n", - "5 0.826104 10.0 0.047401\n", - "6 0.822811 1.0 0.043800\n", - "7 0.820115 0.1 0.035802\n", - "8 0.798865 0.01 0.054801\n", - "9 0.775518 0.001 0.072002" + "1 0.826403 100.0 0.032202\n", + "1 0.826403 1000.0 0.032600\n", + "1 0.826403 10000.0 0.028999\n", + "1 0.826403 100000.0 0.025032\n", + "5 0.826104 10.0 0.039601\n", + "6 0.822811 1.0 0.035598\n", + "7 0.820115 0.1 0.031198\n", + "8 0.798865 0.01 0.026000\n", + "9 0.775518 0.001 0.022802" ] }, "execution_count": 8, @@ -355,13 +355,71 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 18, + "id": "0ceb31ba-187b-4c3a-b64d-0c0254290a73", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'mean_fit_time': array([0.02280178, 0.02600026, 0.03119793, 0.03559761, 0.03960142,\n", + " 0.03220162, 0.03260002, 0.02899852, 0.025032 ]),\n", + " 'std_fit_time': array([0.00213641, 0.00141364, 0.0043089 , 0.00233214, 0.00185531,\n", + " 0.0045342 , 0.0030072 , 0.0012655 , 0.00357964]),\n", + " 'mean_score_time': array([0.00579982, 0.00600138, 0.00600133, 0.00540013, 0.00539956,\n", + " 0.00539856, 0.00519876, 0.00479918, 0.00313435]),\n", + " 'std_score_time': array([0.00040045, 0.00063362, 0.00063264, 0.00049021, 0.00049059,\n", + " 0.00048965, 0.00040016, 0.00116672, 0.00046815]),\n", + " 'param_logisticregression__C': masked_array(data=[0.001, 0.01, 0.1, 1.0, 10.0, 100.0, 1000.0, 10000.0,\n", + " 100000.0],\n", + " mask=[False, False, False, False, False, False, False, False,\n", + " False],\n", + " fill_value='?',\n", + " dtype=object),\n", + " 'params': [{'logisticregression__C': 0.001},\n", + " {'logisticregression__C': 0.01},\n", + " {'logisticregression__C': 0.1},\n", + " {'logisticregression__C': 1.0},\n", + " {'logisticregression__C': 10.0},\n", + " {'logisticregression__C': 100.0},\n", + " {'logisticregression__C': 1000.0},\n", + " {'logisticregression__C': 10000.0},\n", + " {'logisticregression__C': 100000.0}],\n", + " 'split0_test_score': array([0.77130045, 0.79073244, 0.81464873, 0.81165919, 0.81165919,\n", + " 0.81464873, 0.81464873, 0.81464873, 0.81464873]),\n", + " 'split1_test_score': array([0.77994012, 0.80988024, 0.82634731, 0.82185629, 0.83233533,\n", + " 0.83233533, 0.83233533, 0.83233533, 0.83233533]),\n", + " 'split2_test_score': array([0.7754491 , 0.79640719, 0.8248503 , 0.83083832, 0.83233533,\n", + " 0.83233533, 0.83233533, 0.83233533, 0.83233533]),\n", + " 'split3_test_score': array([0.7754491 , 0.8008982 , 0.81736527, 0.82185629, 0.8248503 ,\n", + " 0.8248503 , 0.8248503 , 0.8248503 , 0.8248503 ]),\n", + " 'split4_test_score': array([0.7754491 , 0.79640719, 0.81736527, 0.82784431, 0.82934132,\n", + " 0.82784431, 0.82784431, 0.82784431, 0.82784431]),\n", + " 'mean_test_score': array([0.77551757, 0.79886505, 0.82011537, 0.82281088, 0.82610429,\n", + " 0.8264028 , 0.8264028 , 0.8264028 , 0.8264028 ]),\n", + " 'std_test_score': array([0.00273339, 0.00638263, 0.00461014, 0.00657203, 0.00772481,\n", + " 0.00652742, 0.00652742, 0.00652742, 0.00652742]),\n", + " 'rank_test_score': array([9, 8, 7, 6, 5, 1, 1, 1, 1])}" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "random_search.cv_results_" + ] + }, + { + "cell_type": "code", + "execution_count": 31, "id": "932476fc-12d7-472c-b91b-0160ae99a4f1", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -377,14 +435,17 @@ "from matplotlib import pyplot\n", "\n", "train_results.plot(x=\"param_logisticregression__C\", y=\"mean_test_score\")\n", - "\n", + "plt.plot(100, 0.826403, marker=\"o\", markersize=10, markeredgecolor=\"red\", markerfacecolor=\"red\")\n", + "plt.xlabel(\"Hyperparameter of logistic regression C\")\n", + "plt.ylabel(\"Mean test score\")\n", + "plt.legend([\"Mean test score\", \"Best estimator\"])\n", "plt.xscale(\"log\")\n", "plt.savefig(\"../../models/cv_result.png\")" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 22, "id": "d0801f74-8a26-4e51-9f8f-d06862a88b27", "metadata": {}, "outputs": [ @@ -394,7 +455,7 @@ "0.8433014354066986" ] }, - "execution_count": 10, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -578,7 +639,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 14, "id": "d95fbcf2-21fa-416f-b0cd-6d7611bb58d1", "metadata": {}, "outputs": [], @@ -634,7 +695,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 15, "id": "8092c271-1711-4774-a49b-a208b24a31e8", "metadata": {}, "outputs": [ @@ -658,7 +719,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "id": "3b162d44-921d-447c-9b96-55584ece2cb8", "metadata": {}, "outputs": [], @@ -673,7 +734,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "id": "a3fcfe40-1204-4b35-aca7-a6a451037996", "metadata": {}, "outputs": [ @@ -747,7 +808,7 @@ "5 average_precision 0.945271" ] }, - "execution_count": 16, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } diff --git a/src/models/train.py b/src/models/train.py index 6367416..5b7f30a 100644 --- a/src/models/train.py +++ b/src/models/train.py @@ -23,11 +23,6 @@ import ipywidgets as widgets import matplotlib.pyplot as plt import mglearn - -from mglearn.plot_2d_separator import (plot_2d_separator, plot_2d_classification, - plot_2d_scores) -from mglearn.plot_helpers import cm2 as cm, discrete_scatter - import numpy as np import pandas as pd import pickle @@ -184,6 +179,10 @@ def train_plot(train_results, out_dir): """ logger.info("Making train results plot...") train_results.plot(x="param_logisticregression__C", y="mean_test_score") + plt.plot(100, 0.826403, marker="o", markersize=10, markeredgecolor="red", markerfacecolor="red") + plt.xlabel("Hyperparameter of logistic regression C") + plt.ylabel("Mean test score") + plt.legend(["Mean test score", "Best estimator"]) plt.xscale("log") plt.savefig(out_dir) logger.info(f"Train results plot saved to {out_dir}") @@ -197,49 +196,6 @@ def train_df_table(train_results, out_dir): ) logger.info(f"Train results table saved to {out_dir}") -def visualize_coefficients(coefficients, feature_names, n_top_features=25): - """Visualize coefficients of a linear model. - Parameters - ---------- - coefficients : nd-array, shape (n_features,) - Model coefficients. - feature_names : list or nd-array of strings, shape (n_features,) - Feature names for labeling the coefficients. - n_top_features : int, default=25 - How many features to show. The function will show the largest (most - positive) and smallest (most negative) n_top_features coefficients, - for a total of 2 * n_top_features coefficients. - """ - coefficients = coefficients.squeeze() - if coefficients.ndim > 1: - # this is not a row or column vector - raise ValueError("coeffients must be 1d array or column vector, got" - " shape {}".format(coefficients.shape)) - coefficients = coefficients.ravel() - - if len(coefficients) != len(feature_names): - raise ValueError("Number of coefficients {} doesn't match number of" - "feature names {}.".format(len(coefficients), - len(feature_names))) - # get coefficients with large absolute values - coef = coefficients.ravel() - positive_coefficients = np.argsort(coef)[-n_top_features:] - negative_coefficients = np.argsort(coef)[:n_top_features] - interesting_coefficients = np.hstack([negative_coefficients, - positive_coefficients]) - # plot them - plt.figure(figsize=(15, 5)) - colors = [cm(1) if c < 0 else cm(0) - for c in coef[interesting_coefficients]] - plt.bar(np.arange(2 * n_top_features), coef[interesting_coefficients], - color=colors) - feature_names = np.array(feature_names) - plt.subplots_adjust(bottom=0.3) - plt.xticks(np.arange(0, 2 * n_top_features), - feature_names[interesting_coefficients], rotation=60, - ha="right") - plt.ylabel("Coefficient magnitude") - plt.xlabel("Feature") if __name__ == "__main__": From 072cbb564bb1a00394992ca7bc5f938af11492e7 Mon Sep 17 00:00:00 2001 From: nickmao Date: Sat, 11 Dec 2021 01:37:55 -0800 Subject: [PATCH 3/6] Add reasoning for metrics choice --- docs/Project_report_milestone2.ipynb | 271 +++++++++++++++++++-------- 1 file changed, 193 insertions(+), 78 deletions(-) diff --git a/docs/Project_report_milestone2.ipynb b/docs/Project_report_milestone2.ipynb index cb3796f..f29cbea 100644 --- a/docs/Project_report_milestone2.ipynb +++ b/docs/Project_report_milestone2.ipynb @@ -37,7 +37,7 @@ " \n", "This dataset was developed in 1995. Despite the age of this dataset, predictive models that can be made from this dataset are likely still relevant for the modern day. It takes thousands to millions of years in order for any meaningful changes to be made to the biological characteristics and features of animals. Darwin's theory of evolution and natural selection applies to all animals, including abalone. Thus, the biological features of the abalone within this dataset are likely still relevant today, and meaningful predictive models can still be created from this dataset.\n", "\n", - "The `Sex` column in this dataset includes three categories: `Female`, `Male` and `Infant`. This is a curious component of the dataset since abalone sex is actually binary (male or female). Therefore, `Infant` is not really considered a sex of abalone but instead is in reference to its age. Thus, this could pose a potential limitation in the predictive model which we will discuss later.\n", + "The sex variable in this dataset includes three categories: female, male and infant. This is a curious component of the dataset since abalone sex is actually binary (male or female). Therefore, infant is not really considered a sex of abalone but instead is in reference to its age. Thus, this could pose a potential limitation in the predictive model which we will discuss later.\n", "Abalone of different sex has different body composition with distinct economic values.\n", "The data set has already removed its missing values and the range of the continuous values have been scaled for use with an ANN (by dividing by 200).\n", "\n", @@ -60,12 +60,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Considering that the price of an abalone is positively associated with its age, creating a predictive model that is able to automate the manual process of determining the age of an abalone would be valuable to those wishing to determine the age of an abalone, whether it is researchers or those interested in making a profit in the abalone market. Of note, the number of rings present on the abalone directly determines the age of the abalone. For this project, we are separating the abalone into two classes, `young` and `old`, based on a threshold on the rings. Moreover, we are using a threshold of `rings > 11` whereby abalone that contain more than 11 rings would be placed in the `old` class and otherwise the abalone would be placed in the `young` class." + "Considering that the price of an abalone is positively associated with its age, creating a predictive model that is able to automate the manual process of determining the age of an abalone would be valuable to those wishing to determine the age of an abalone, whether it is researchers or those interested in making a profit in the abalone market. Of note, the number of rings present on the abalone directly determines the age of the abalone. For this project, we are separating the abalone into two classes, young and old, based on a threshold on the rings. Moreover, we are using a threshold whereby abalone that contain more than 11 rings would be placed in the old class and otherwise the abalone would be placed in the young class." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -74,17 +74,17 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { - "image/png": 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jxwrGjdWdO3dw7949AMB3332H8PBwfPPNN7CzswMAODs749atW5oz8rNnz8LLy0swbgxUKhUaGxsBdFx5eHD8LSwsQESdPt9qtRqlpaWYOHGiYNyQXLp0CYsXL8axY8c0l1oB4O7du1AqlbC2toZEIsG1a9cA/H/sdI0bms2bN0OhUCA6OloTU6vVmvdoWFhYl0vYAPD8889rxhTo+Fvm6ekpGNcbvX2bamDKy8tp7NixFBMTQzNnzux004mxKy0t1dwwRET0zjvv0Ouvv07R0dHk6uqquUFCKG5MEhMTaezYsXTw4EH64x//SJMmTaL29na6fv06ubi40Mcff0xz5syhjRs3EhEJxg3dv/71L9q/fz8988wzFBsbS0VFRUREFBwcTKmpqVRfX0/29vYUHh5OsbGxFBsbSxcuXCAiou3bt1NQUBDt27ePxo4dSz/++GO3cUPR3NxMsbGxFBoaSnPmzKHY2FhqaWmh0tJScnd3JyKiu3fvkre3N0VGRtKWLVvIzc2Nmpubqb6+nlxdXWn37t20ePFiWrFiBRGRYNxQKBQKGjNmDIWFhWnGsbi4mIiIli5dSnFxcUTUcUNUQEAAxcTEkJubG3377bePFTcUiYmJZG1tTTExMZrjbmlpoZ9//plsbW27tN+xY4fmhiGFQkEeHh60Y8cOWrlyJf3mN7/pNq4vJjVJQkVFBbKysuDm5oZXX321v9PpNbW1tTh69CjefvttAB3/e0tNTUVtbS1CQ0M1z8sJxY1NYWEhzpw5g2HDhmHBggUYNGgQgI6bLWQyGZycnDBnzhzNjRhCcUOWnJzc6SaPgIAAhISEwNvbG6dOncK9e/ewZ8+eTvsEBQXhpZdeAgDIZDJcuXIFwcHBnZ5pFoobgsbGRuzatatTLCIiAhkZGfj222/x0UcfAei4cpSamgq1Wo0FCxZg2LBhADoe3/n6669ha2uL119/HWKxuNu4IWhpaely38L9sfb398fRo0cxatQoAB3PbV68eBGBgYHw8PDQtNc1bgiOHz+OU6dOdYpFRETgzJkz+Mc//oG4uLhO23JyckBEmsfOfvnlF3z11VeQSCSYP3++5llvobg+mFTxZMyYtba24ptvvsGCBQv6O5U+lZWVBW9vb9jb2/d3Kn2GiJCUlISwsLD+TqVPnT59Go6Ojga38LU2XDwZY4wxHZnMDUOMMcZYb+HiyRhjjOmIiydjjDGmIy6ejDHGmI5MZoYhxp5mFy9eRElJCWpqauDk5ISgoCCDnEmHsacFF0/GjJhKpcIf/vAHJCQkAADEYjFUKlWfTYB/8OBBvPXWW/jzn//c5RlNxp5mJrUYNmNPm8jISERHR+PZZ59FVlYWYmNjsXz5cowYMUKzwkR1dTWOHTuG3Nxc1NfXY9y4cZqJImQyGVJTU+Hg4AAbGxucPn0aycnJGDx4MEaOHIns7GwcO3YMNjY2KCoqQmZmJgYPHgx7e3uUl5cjLi4Ocrkc5ubmqKurAwDNgsWMPdX0On8RY0yvPDw8CAAdOnRI6/aMjAySSCQ0YMAAcnR0JADk5+dHra2tRNQxBRwAysjIICKiiIgIAkAHDhwgIqI1a9YQABo5ciRNnDiRLCwsaMCAAXTp0iVKS0uj4cOHEwCytrYmFxcX+utf/9o3B85YP+MbhhgzYjU1NQAANzc3rdvff/99tLS0IDc3F9XV1Zg3bx6KioqQlJTUqd3D64M+bO7cufj++++xevVqKJVKZGdnY86cOfjggw8AAOHh4aiqqsK7777bC0fFmOHj4smYEbs/72lFRUWXbUSEqqoqmJmZwcfHByKRCL6+vgA6VvJ4uC0AzYotD/P39wcAzeot91c5YcxUcfFkzIjdn/t069atKCwshEqlwrVr17B//36IRCK4urpCrVbjzJkzICIUFxcD+P+ZqpWVFYCOxQXUarVm+8PuT6b+8KT6lpaWADqWz2LMlHDxZMyIrVu3Dm+++SauX78OqVQKc3NzjB49Gnv37gUAREdHQyKRYPr06XB0dMSXX34JPz8/LF26FAAQEhICkUiEd999F+7u7pDL5Tr17+3tDQA4cOAAXFxcIJPJevcAGTNQ/KgKY0ZMLBYjPj4ea9euRUlJCW7cuIFRo0YhODgYADBr1izI5XKcOHECt2/fhpubG0JCQjTfcc6aNQt5eXk4f/48vLy8oFKpkJOTg1//+tcAgMDAQAwaNAgTJkwAAPj6+mLjxo2ay7+TJk1Cbm4uioqK0NTUZLTL3DGmK15VhTHGGNMRX7ZljDHGdMTFkzHGGNMRF0/GGGNMR1w8GWOMMR1x8WSMMcZ09D+6ryPmJJwiUgAAAABJRU5ErkJggg==\n", 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\n", "text/plain": [ "" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -104,24 +104,24 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "After looking at the distributions of `young` and `old` abalone in the training, it's quite clear that there is a class imbalance in the age of the abalone (Figure 1). In fact, the number of `young` abalone is around triple the number of `old` abalone in the training data. In order to account for this class imbalance, we will use the f1 scoring metric as we follow through with the analysis.\n", + "After looking at the distributions of young and old abalone in the training, it's quite clear that there is a class imbalance in the age of the abalone (Figure 1). In fact, the number of young abalone is around triple the number of old abalone in the training data. In the model, we will test a bunch of metrics including accuracy, precision, recall, f1 score, ROC AUC, and average precision. We are going to focus more on f1 score and ROC AUC because we want to observe the overall performance of our model instead of putting more weight on one class over another.\n", "\n", - "Next, we looked to elaborate upon the distribution of numerical features in the training data in relation to the target class (Figure 2). The distribution of the numerical features seemed to follow a similar shape for both the `old` class and the `young` class. The distribution of the length and diameter features was left-skewed, while the whole weight, viscera weight, shucked weight, and shell weight appeared to have a right-skewed distribution. The height feature did not have a clear skewness to the distribution." + "Next, we looked to elaborate upon the distribution of numerical features in the training data in relation to the target class (Figure 2). The distribution of the numerical features seemed to follow a similar shape for both the old class and the young class. The distribution of the length and diameter features was left-skewed, while the whole weight, viscera weight, shucked weight, and shell weight appeared to have a right-skewed distribution. The height feature did not have a clear skewness to the distribution." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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4+k1BvXfvXo0bN07XX3+9CgsLVVhYqLS0NBUWFkqSTp48qaysrEC80+mU0+ls10deXl6/38AAgP6ntLQ01il0ye/365577tH06dO1fPlySdKVV16pmpoaVVVVqaGhQZKUnp4eOCrNeAkAiJZIj5cdD55GstDtKzErqNv+Yl5UVKScnBxdd911MgxD27Zt08svv6yJEydq/fr1Wrt2rQoKCrRp06ZYpQsAQJ/bsmWL9uzZo4yMDOXl5SknJ6fdJVOtt0G5++67NWvWLMZLAMCAYhhGVI4a96WYXUPd2Nio7OxsZWdnq7GxUfPmzdPOnTs1YcIE7dixQ1dccYVSU1Pldrs1depUud1upaamxipdAAD63KxZs7RkyRJVVVWpqqpKjY2N7d5vPfrMeAkAGCz+/Oc/a8GCBRoxYoSSkpI0efLkTjGVlZW65ZZbNGbMGI0ZM0a33nqrqqqqOsVVVFTopptu0siRIzVnzpyo5BuzI9Qdb0omSXPnztXcuXPbtWVlZbU7dQ0AgKGiq7Gy4/utGC8BAIPB17/+dZWXl+udd97R5Zdf3mXMP/7jP+q5557TH/7wB/n9fn3zm9+UJP3mN79pF7du3Tpt375dP//5z/W9731PKSkpEc+331xDDQCx4vurW0btKdM4+6xvSvaEPsgIAABgaEpMTJQkfeMb39BXv/pV3Xvvvbr00kvbxezcuVOSApcMS9LLL7/cqa+CggJJ0v/5P/9Ho0aNikq+FNQAEGstDTLqz5qGWRKTpcTRfZAQAABAbLz88st69NFH9dprr+mJJ57Qzp07e7wZmtXa/VXMrZdKxcfHRzzPVhTUABBjRkOlfGV7TeOs42bJmvalPsgIAAAgNi699FL98pe/1KFDhzRr1iz5/f5OMdddd52effZZvfLKK4Gbml1//fWd4ubOnavXXntNO3bs6Pb08d6ioAYAAAAQMUbt6ZDiLUkpXFI1RLV9bJbFYpFhGLr00kt18uRJSVJOTo5+/vOfd5puy5YtslgsuvPOOyVJt9xyix577LEu41auXKlvfvObWrx4cVSWgYIaAAAAQMT4/vpaSPG2zKtkSZ4UpWzQn3X1qKyjR4+aTpeSkqLf/va3pn1Onz5d7733XvgJBiFmj80CAAAAAGAgo6AGAAAAACAMFNQAAAAAAISBghoAAAAAgDBQUAMAAAAAEAYKagAAAAAAwkBBDQAAAABAGHgONQAAAAAgJnb+uSSouNwvTw4qzmKxdPl86+7ae4uCGgBC4D+xT/I39xhj2OJly5jXRxkBAAAgViioASAE/poTkrep56C4JCmjb/IBAABA7HANNQAAAABgwDl48KDmzp2r4cOHa+7cufrggw8C75WUlGjRokUaNmyYvvSlL0UtB45QAwDQT5WVlamgoEB1dXWaP3++5s+f32WbJB0+fFhut1uLFy/WjBkzYpw5AADRd/vtt+uOO+7QypUr9fzzz2v16tXt3rvxxhv16quvqqGhQSkpKVHJgSPUAAD0UyUlJTp27Jg8Ho+WLl2q119/vcu2iooKuVwuHT16VC6XS5999lmsUwcAIOoOHz6slStXKikpSatWrdLhw4cD7/3lL3/RnXfeqYSEBCUnJ0ctB45QAwDQT7lcLrlcLkmSYRjav3+/1q9f36mtvLxcubm52rp1q2pra7Vr1y6tXLkylqkDABB1M2fO1HPPPafvfOc7eu655zRjxgzt379fkjRlyhS9+OKL+trXvqa33norajlwhBoAgH6upaVFe/bs0aJFi7ps83g8Sk9PlyRlZGSovLw8VqkCANBnfvWrX+nXv/61UlNT9etf/1pPPfVU4L3HH39cDz30kMaMGaNt27ZFLQeOUAMImu/oqzLqK0zj7LNXSVZbH2QEDA333XefNmzYoOzs7C7bCgsLZbFYupy2qKhIxcXFndpLS0ujli+AoW1YhfnfCm012z+Vt9IXpWwwmM2aNUv79u1r19b6rGmn06mPP/440P6rX/0qKjlQUANAFBh+r/T5SfNAe1L0k8GA5ff7dc8992j69Olavnx5t21paWkqLCyUJJ08eVJZWVmBPpxOp5xOZ7t+8/Ly5HA4+mgpAAw13prUkOJtGRmyJE+KUjaIJH6M7YyCGgCiwOJrlrdsr3ncqHRZx0ztg4wwEG3ZskV79uxRRkaG8vLylJOTo/3793dqy83N1fr167V27VoVFBRo06ZNsU4dAICg5H55cqxT6BUKagAA+qlZs2ZpyZIlqqqqkiQ1NjZ22Zaamiq32x34l5oa2tEhAAAQnpgW1FVVVWpoaFBaWlqg7ezZs0pISNDo0aMlfXEOvMfjUVpaWrfXhwEAMBi1vct3x/aOsrKy2p3qDQAAoi9mBfWnn36qDRs2aPLkyXrwwQclSQ8//LD+7d/+TQ899JDWrFmjlpYWXXXVVSopKdGll16q3bt3Ky4uLlYpAwAAAAAiyPvus0HF2S//bpQzCU/MHpv16quvBm6gIn1xgfvTTz+t0tJSrVmzRpJUWFioESNG6OzZs4qPj9frr78eo2wBAAAAAGgvZkeo16xZI4/HE3i9e/duLVy4UFVVVfL7/Ro5cqSOHDmiK664QpI0Z84cffTRR1q8eHGsUgYAAAAAIKDf3JTs3Llzys/P1+7duzVixAjt2LFD9fX1SkhIkCQlJCSorq4uEM9zNYG+l3jmjKyNVaZx50tLB9RzqBNOn5atwfyZmQ1lZUo8d04WX3OPcX5bgpriy5QUxHM4fedt8jYkKSGI2BbvSbU0jjKNAwAgIrxNMhrMx/22LCPHRykZoD2v16tp06Zp+/btysrK0ocffqjrrrtO//3f/60777xThw4d0syZM/XUU09p9uzZgedTS5LFYpFhGLJYLLrzzjv1+9//XvHx8dq6datuuukmlZWV6fbbb9e+ffs0bdo0ffDBB/J6vV3m0W8K6uTkZN1+++366U9/qjvvvFN/+MMflJKSoiNHjkiSampqNGHChEA8z9UE+p7Pe0RGvXmhbHc4BlRB7fOXyqg1TOPsmZnyNo6RvE09B8YlfRFbb36nZcuo8bKOmShfy19NY63j0mVN4zsuGvgxFgA6M+pOyxfEIyDb6q/XuWLwsdvt+tGPfqSnnnpKmzdv1q9//WvdfffduuOOO3THHXdo5cqVev7557V69eoe+1m0aJHy8vJ04MABfetb39JNN92k1atX6/rrr9err76qhoYGpaSkdDt9zK6h9ng8OnXqlE6dOiWPx6P58+dr586deuutt3Tw4EFNmjRJTqdTO3bs0O7du/XKK68oJycnVukCAAAAAPqRW265RW+88YZqamr03//931q9erUOHz6slStXKikpSatWrdLhw4clqd0R6raWL1+ukSNHasGCBTp79qwkad++fbrrrruUkJCg5OTkHnOIWUG9Y8cOHThwQAcOHNCOHTt02WWX6Xvf+57uu+8+XXXVVfra176mWbNmae3atfrJT36iu+66SzNnzoxVugD6AaO5Xkb1cfN/jTWxThUAAABRFhcXpzVr1ujWW2/VDTfcoOTkZM2cOVPPPfecGhoa9Nxzz2nGjBlKSUnRG2+8obq6OuXn53fZV9tHNE+aNEnbt29XQ0OD/vKXv/SYQ0xvStZ6N+9W9957r+699952bevWrdO6dev6MjUA/VX9WfmOFZmGWdOvkCVxdB8kBAAAgFi67bbb9OCDD2rTpk2SpF/96ldavXq17r333sA11G+//bZuvvlm2e123XrrraZ9PvHEE1q9erXWrFmj3Nxc2e3dl8395hpqAAAAAABCceDAAc2fP1+TJ0+WJM2aNUv79u1rFzN79ux2B3MfeeQRSZ1PA299fdVVV+nIkSPy+XzatWuXPvzww27nT0ENAAAAABhwfD6fvv/97+uxxx6LeN8TJ07U6dOnNWXKFG3durXbOApqAAAAAEBM9ObO8DabzfQa53CdOHEiqLiY3ZQMAAAAAICBjIIaAAAAAIAwUFADAAAAABAGCmoAAAAAAMJAQQ0AAAAAQBi4yzcAAP1UWVmZCgoKVFdXp/nz52v+/PmSpMOHD8vtdmvx4sWaMWNGt20AACC6OEINAEA/VVJSomPHjsnj8Wjp0qV6/fXXVVFRIZfLpaNHj8rlcumzzz7rsg0AAEQfR6gBAOinXC6XXC6XJMkwDO3fv1/l5eXKzc3V1q1bVVtbq127dsnn83VqW7lyZYyzBwBg8OMINQAA/VxLS4v27NmjRYsWyePxKD09XZKUkZGh8vLyLtsAAED0cYQaAIB+7r777tOGDRuUnZ2twsJCWSyWTjFdtUlSUVGRiouLO7WXlpZGPE8Ag4u93qP4ioqQpjlfWqphIU7TbP9U3kpfSNMA/QUFNYBByWiolppqTOMsI8b1QTZAePx+v+655x5Nnz5dy5cvlySlpaWpsLBQknTy5EllZWXJ7/d3amvldDrldDrb9ZuXlyeHw9FHSwFgoDKqbfJ5y0Kaxu5wyFuTGtI0towMWZInhTQNYoMfYzujoAYwKBlVpfKfOWwaZ5tyTR9kA4Rny5Yt2rNnjzIyMpSXl6ecnBzl5uZq/fr1Wrt2rQoKCrRp0yZJ6rINAABEF9dQAwDQT82aNUtLlixRVVWVqqqq1NjYqNTUVLndbk2dOlVut1upqaldtgEAgOjjCDUAAP1U27t8t5WVldXutO7u2gAAQHRxhBoAAAAAgDBwhBoABpKmmi9uuGZm+BhZ4oZFPx8AAIAhjIIaAAYQf9Vx+U+9bxpny1wgJU/sg4wAAACGLk75BgAAAAAgDBTUAAAAAACEgYIaAAAAAIAwUFADAAAAABAGCmoAAAAAAMIQ04L6hRde0JNPPtmuzefz6ZZbbgm0P/roo3I6ndqyZUssUgQAAAAAoEsxK6gfffRRfec735HH42nXvmHDBu3evVsej0cHDx7UL37xCz300EPasmWLPvzwwxhlCwAAAABAezF7DvWKFSt05MiRdm3btm3T8ePHdfvtt0uSiouLtWTJEi1cuFBLlixRUVGRZsyYEYt0AQAAAABoJ2ZHqNPS0nTRRRcFXr/zzjvaunWrnn766UBbZWWlRo8eLUlKSUlRRUVFn+cJAAAAAEBXYnaEuqM//elP8ng8uuqqqwKngaekpOjGG2+UJDU2Nio1NTUQX1RUpOLi4k79lJaW9k3CwBCUeOaMrI1VpnHnS0slqy3i87fXlSs+iB/Wmo1yWXzNiqs2j22KPyF71WnZGsxjG8rKlHjunCy+5h7j/LYENcWXKSmIXH3nbfI2JCkhiNgW70nJalVcZRDLZTshX6XXNA4AMDQY1cdlNNYEHW9JHB3FbIDBo98U1GvWrNENN9wgSYEbks2bN0/PP/+8pC+OYF9zzTWBeKfTKafT2a6PvLw8ORyOPsoYGHp83iMy6s0LZbvDEZWC2qiyyOc7bhpnTZ8geZvkt1ebxtomTpQ/oU5GrWEaa8/MlLdxjORt6jkwLumL2PrUnuMkWUaNl3XMRPla/moaax2XLlmt8lvNC2rbxImyJE80jcMX+DEWwGBnVB+Xv8p8DG1lSZ4ka8qkKGYEDA4xK6iffPLJQOGclpamNWvWKC0tTZICp4J/5zvf0VNPPaWxY8dqypQpWrRoUazSBQAAAACgnZgV1EuXLlV2drYktbuWWvriaLUkxcXFqbi4WB6PR2lpabJYLH2eJ4DoMhqr5fvoJdM46wWZsoxK74OMAABAX/Mf2xvyEXRb5lVRzAgITswK6rS0tMAR6a7ea2WxWJSezh/RAICh69ChQ6qurg5c6vTGG29o3759mjNnjhYuXChJOnz4sNxutxYvXswTMQAA6CMxu8s3AAAwt3PnTi1fvlxut1uSVFhYqBUrVsjj8WjFihUqKChQRUWFXC6Xjh49KpfLpc8++yzGWQMAMDT0m5uSAQCAzux2e7unXHz00UdaunSpHnvsMfn9fp04cULnzp1Tbm6utm7dqtraWu3atUsrV66MYdYAAAwNHKEGAKAfc7lccrlcgderVkooMGgAACAASURBVK3Snj179O1vf1vnzp3TqlWr5PF4ApdHZWRkqLy8PFbpAgAwpHCEGgCAAaSkpERz5szRqlWrtG7dOh05ckSSur1xZ1FRkYqLizu186gwYGhJOHNKtjrzxy628jbEy19nVXxF8NNI0vnSUg0LcZpm+6ey1YWeX7PB9xhij4IaAIABZO/evRo3bpyuv/56FRYWqrCwUGlpaSosLJQknTx5UllZWYF4p9MZuJlZq7y8PDkcjj7NG0Bs+a3l8lc1Bx1vSb5I1pQM+bxlIc3H7nDIW5NqHtiGLSNDRrU/5PxsmXyP9TV+jO2MghoAgH6s7RHmoqIi5eTk6LrrrpNhGNq2bZtefvllTZw4UevXr9fatWtVUFCgTZs2xThrAACGBq6hBgCgH2tsbFR2drays7PV2NioefPmaefOnZowYYJ27NihK664QqmpqXK73Zo6darcbne7m5gBAIDo4Qg1AAD9WMebkknS3LlzNXfu3HZtWVlZ7U71BgAA0ccRagAAAAAAwkBBDQAAAABAGDjlGwAAAOglo/q4jMaaoOMtiaNlSZ4UxYwA9AUKagAAAKCXjOrj8lcdDzrekjxJNgpqYMDjlG8AAAAAAMLAEWoAAACgDe+7z4YUb8u8KkqZAOjvOEINAAAAAEAYKKgBAAAAAAgDp3wDAAAAGHi8TTIaqkKaxDJyfJSSwVBFQQ0AAABgwDHqTstXtjekaeyXfzdK2WCo4pRvAAAAAADCQEENAAAAAEAYOOUbAAAAGED8pz8IKd6SPDFKmQCgoAYAAAAGEP+p90OKtyWOjlImADjlGwAAAACAMFBQAwAAAAAQhpgW1IcOHVJRUZEkqaysTL/85S+1adMmvfnmm4GYw4cPa8uWLfrwww9jlSYAADHVdryUpPr6ej3xxBOMlwAAxFjMCuqdO3dq+fLlcrvdkqSSkhIdO3ZMHo9HS5cu1euvv66Kigq5XC4dPXpULpdLn332WazSBQAgJjqOl36/X9dee63efPNNnT9/XpIYLwEAiJGY3ZTMbrcrNTU18NrlcsnlckmSDMPQ/v37VV5ertzcXG3dulW1tbXatWuXVq5cGauUAQDocx3Hy6KiIlmtVj377LOBtp07dzJeAgAQAzE7Qt22gG6rpaVFe/bs0aJFi+TxeJSeni5JysjIUHl5eV+nCQBATHUcLw8ePKixY8dq8+bN2r17tyQxXgIAECP97rFZ9913nzZs2KDs7GwVFhbKYrF0GVdUVKTi4uJO7aWlpdFOERiyEs+ckbWxyjTufGmpZLUF1ael+XMlVVSYxvmaEuWr8Ss+iNhmo1wWX7Piqs1jm+JPyF51WrYG89iGsjIlnjsni6+5xzi/LUFN8WXBLdd5m7wNSUoIIrbFe1KyWhVXGcRy2U7IV+k1jcPAU1tbq48//liTJ0/WL3/5Sz3xxBOSxHgJRNCwIL6T22q2fypb3SnZ6oKfztsQr2YjvM9hX+Xnr7MGNe62db60tN/nB0RSvymo/X6/7rnnHk2fPl3Lly+XJKWlpamwsFCSdPLkSWVlZQXinU6nnE5nuz7y8vLkcDj6LmlgiPF5j8ioNy+U7Q5H0AW10VgtX0OqaZz1gotkGZUun++4eWz6BMnbJL+92jTWNnGi/Al1MmoN01h7Zqa8jWMkb1PPgXFJX8TWmy+XZdR4WcdMlK/lr6ax1nHpktUqv9X8jwfbxImyJE80jcMXBlJxmZaWpoULFyovL09+v18HDhxgvAQizFtj/v3dli0jQ0a1X/6qnn9wbcuSfJFsmeF9DvsqP2tKhnzespDmZXc4+n1+CN9AGi/7SswK6ra/mBcVFWn//v3as2ePMjIylJeXp5ycHOXm5mr9+vVau3atCgoKtGnTplilCwBATHQcL5csWaINGzYoLi5Ov/vd77Rjxw5lZGQwXgIAEAMRuYb6yJEjgcd5HDlyRD//+c91+PDhHqdpbGxUdna2srOz1djYqFmzZmnJkiWqqqpSVVWVGhsblZqaKrfbralTp8rtdre7KQsAAANNJMbLCy+8UK+99poyMjKUn5+vyy+/nPESAIAYicgR6oKCAlVWVmru3LlyuVwqLy/Xxo0bdfToUY0fP77Labq6KVlXNynLyspqd+oaAAADVaTGy+nTp2v69Ont2hgvAQDoexEpqGtra9XY2Kh3331X5eXluvrqq3Xs2DG99NJLWr16dSRmASBKfJ/uk1HxiWmcfep1fZANMLgxXgIAMLhEpKAeP368/v3f/11vv/22JOmf//mftWPHDp09ezYS3QMAMCgwXgIAMLhE5BrqG264QTU1NXr99deVkZGhK6+8UvHx8Ro9enQkugcAYFBgvAQAYHCJyBHqsWPHqri4WAUFBVq2bJni4uLkdDqVlpYWie4BABgUGC8BABhcwi6o2z7Go60//vGPgf8nJiaG2z0AAIMC4yUAAINX2AW12+3Wxo0be4x54IEH5HQ6w50FAAADHuMlAACDV9gFtdPp1P333y9JOnXqlA4ePKivfvWrgfd///vf6/LLL+99hgAADGCMlwAADF5hF9Rtn4v5r//6r0pKStIjjzwSeL+pqUmlpaW9zxAAgAGM8RIAgMErIjclGzlypJ566ilVVlbK4XCoqqpKzz33nB5++OFIdA8AwKDAeAkAwOASkYL6W9/6lrZu3arf//73gbZx48bp61//eiS6BwBgUGC8BABgcIlIQT1s2DC99dZb+tOf/qTS0lKNHTtWN910E8/VBACgDcZLAG0Z1cflK9sb0jT2y78bpWwAhCMiBXVxcbGKioqUk5PDr+wAJElG9QlJRs9BtngpLqlP8gH6A8ZLAAAGl4gU1FVVVfp//+//ad26dTz2A4AkyXfsDcnw9xhjSRwta+ZVfZQREHuMlwAADC4RKaiTk5M1bdo0/elPf1JycnKgPScnhz8YAAD4X4yXAAAMLhEpqN1utz744ANJ0g9/+MNA+wMPPMAfCAAA/C/GSwAABpeIFNROp1P3339/l+0AAOALjJcAAAwuESmoXS6Xrr76auXn56usrEyZmZlatmyZ4uLiItE9AACDQrjj5aFDh1RdXd2p8M7Pz1dqaqqcTqcOHz4st9utxYsXa8aMGdFcDAAA8L8iUlB7vV4tWLBAb775ZqBt3rx52rt3L0U1AAD/K5zxcufOnbr33nu1YsWKdgV1QUGBVqxYoR/+8IeaNm2aXC6Xbr75ZuXl5engwYO68MILo748AAAMdREpqPPz8/Xee+/ptttu09ixY1VZWakXXnhB27dv1/LlyyMxCwAABrxwxku73a7U1NR2bZ988ol+/OMfa82aNZK+KLpzc3O1detW1dbWateuXVq5cmXUlwcAgKEuIgV1aWmpVq9erc2bNwfakpKSVFZWFonuAQAYFMIZL10ul4qKigKva2trdcstt+g//uM/9Ic//EGSdPLkSaWnp0uSMjIyVF5eHqUlAAAAbUWkoHY4HNq4caNqa2s1ZswYVVRUaNu2bXrmmWci0T0AAINCJMbLxx9/XKNGjdLLL7+s4uJiSdKwYcN02WWXdRlfVFQUiGurtLQ0vIUAhoBhFRUhxTfbP5Wt7pRsdcFP522Il7/OqvgQ53W+tJT8epkfEEkRKaiXLVum2bNn6+mnnw60zZ8/X8uWLYtE9wAADAqRGC+vvPJK1dTUqKqqSg0NDZKk9PT0wFHpkydPKisrKxDvdDo73cwsLy9PDoejN4sCDGremlTzoDZsGRkyqv3yVzUHPY0l+SJZUzLk84Z2Rqfd4SC/XuaH8PFjbGcRKajj4uK0d+9e5efn69ixY4G7ltrtEekeAIBBIZzxsu0R5qKiIrlcLrlcLknSgw8+KEm6++67NWvWLK1du1YFBQXatGlT1JcFAABEqKCurq7Wv//7v+vGG2/U8uXLVVJSos2bN+vWW2/tdCMVYCjwn/yL1HK+5yCLVdZJPHsWGErCGS8bGxuVnZ0d+H9brUeeU1NT5Xa7A/8YewEA6BsRKai3b9+u119/Xffff78kafLkyXrzzTc1evRorV69utvpWk9XS0tLkyQZhiGPx6O0tDRZLJZu24D+zl/jkZpqeg6ioAaGnHDGy7ZHpLt6r1VWVla7U70BAED0WSPRyZkzZwJ3F2114YUXqqqqqttpPv30U91zzz168sknJUktLS2aP3++Lr/8cn3lK19RS0tLl20AAAxU4YyXAACg/4rIEeoZM2bohz/8oaqqqnTJJZeotLRUf/zjH5Wfn9/tNK+++qoKCws1efJkSVJhYaFGjBihs2fPatGiRXr99dfl8/k6tS1evDgSKQMA0OfCGS8BAED/FZGC+vrrr9fChQv14osvBtq+8pWv6Prrr+92mjVr1sjj8QReHzlyRFdccYUkac6cOfroo49kGEanNgpqAMBAFc54CQAA+q+IFNRWq1UFBQV65ZVX9MknnygzM1M33nijbDZb0H3U19crISFBkpSQkKC6urrA/zu2STxXE/1bwpkzsnnreowxZFFDP9hf4855FPe5+TMcmxKPK67ijKyN5qemni8tVdK5c7LI6DHOH9eoJssxJQXxDElfU6J8Nf6gnjfZbJTL4mtWXHUQyxV/Qvaq07I1mMc2lJUp8dw5WXw9P9bDb0tQU3xZcMt13iZvQ5ISgoht8Z6UrFbFVQaxXLYT8lV6TePQtyIxXgIAgP4jYs+1OnXqlE6cOKG5c+fK6XSqqKhI48eP15QpU4KaPiUlRUeOHJEk1dTUaMKECfL7/Z3aWvFcTfRn3sZDUlNiz0EWa794FqIv7qyM+HrTOPukSfLZzsqoN//D3+5wyFs3RjL8PcZZEkfLevHF8jWY35HYesFFsoxKl8933Dw2fYLkbZLfXm0aa5s4Uf6EOhm1PRf/kmTPzJS3cYzkbeo5MC7pi9h68+WyjBov65iJ8rX81TTWOi5dslrlt5oX1LaJE2VJnmgahy/05Y+xvR0vAQBA/xGRm5KdPXtWX/rSl/T9739fbrdbkuR2u7Vr165up/F4PDp16pROnTolj8cjp9OpHTt2aPfu3XrllVeUk5PTZRsAAANVOOMlAADovyJyhDo/P1/x8fG68sorA21+v1+VlZXdTrNjxw4dOHAg8P81a9Zo7dq1+slPfqK77rpLM2fOlKQu2wAAGIjCGS8BAED/FZGC+vPPP9d3v/tdDRs2LND2zjvvKDc3t9tp1qxZozVr1rRrW7dundatW2faBgDAQBTOeAkAAPqviBTUWVlZ+ulPf6pLLrlEknTo0CG9+uqr+uEPfxiJ7gGEyPjcI6PS/JpcywVcswn0JcZLAAAGl4gU1Lm5uZo7d6527NgRaLvxxhs73TQMQN8wmuvkrwri5l0jxvdBNgBaMV4CADC4RKSgtlgs2r59u15++WUdPXpUU6ZM0Q033BCJrgEAGDQYLwEAGFwi9tgsq9Xa7o8Cn8+njz/+WNOmTYvULAAAGPAYLwEAGDx6/disI0eOaMuWLfrP//xPtbS0SJLKy8t1zTXX6D//8z97nSAAAIMB4yUAAINPr45QnzhxQl/+8pf1+eefS5K+9a1v6dvf/ra+973vKSkpSb/97W8jkiQAAAMZ4yUQI94mGQ1VIU1iGcn9RQAEr1cF9UsvvSTDMHTPPfeooaFBzzzzjH73u99p/PjxKiws1KRJkyKVJwAAAxbjJRAbRt1p+cr2hjSN/fLvRikbAINRrwrqzz77THfccYd++tOfSpISEhK0bds2vfbaa4FHggCIDKO+Qr6jr5rGWVMvkZIu6IOMAASL8RIAgMGpVwW13+/Xu+++q7y8PEnSxx9/rNmzZ+vll1/Wyy+/rJycHB4FAgAY8no7Xh46dEjV1dVyOp0qKytTQUGB6urqNH/+fM2fP1+SdPjwYbndbi1evFgzZszok+UCAGCo6/Vdvt1ut9xud7u2//mf/5EkPfDAAxTUAAAo/PFy586duvfee7VixQo5nU6VlJTo2LFjam5u1tKlS/Xiiy9q5syZcrlcuvnmm5WXl6eDBw/qwgsvjPoyAQAw1PWqoHY6nbr//vt7fB8AgKGuN+Ol3W5Xampq4LXL5ZLL5ZIkGYah/fv3q7y8XLm5udq6datqa2u1a9curVy5MnILAAAAutSrgrrtoA4AALrWm/HS5XKpqKioU3tLS4v27Nmjp556SoWFhUpPT5ckZWRkqLy8vFf5AgCA4PT6lG8AAND37rvvPm3YsEHZ2dkqLCyUxWLpMq6oqEjFxcWd2ktLS6OdIhBz9nqP4isqQprmfGmphoU4TbP9U9nqTslWF/x03oZ4+eus5BeD/IBICrug3r9/v5qamjitGwCAHkR6vPT7/brnnns0ffp0LV++XJKUlpamwsJCSdLJkyeVlZUViHc6nZ3mnZeXJ4fDEZF8gP7MqLbJ5y0LaRq7wyFvTap5YBu2jAwZ1X75q5qDnsaSfJGsKRnkF4P8ED5+jO0s7IK6oKBA9fX1cjqdgVPRKK4BAGivt+Nl2yPMRUVF2r9/v/bs2aOMjAzl5eUpJydHubm5Wr9+vdauXauCggJt2rQpKssCAADaC7ugTklJ0aOPPqq6ujq9//77SkhI6HR9GI/NAgAMdb0dLxsbG5WdnR34/6xZs7RkyRJVVVUF2lJTUwN3EXe73e1uYgYAAKIn7IJ62bJl+slPfqLHH3880NbxcSA8NgsAMNT1drzs6oZmXd3gLCsrq92p3gAAIPrCLqgnTJigjz/+WNu3b9eJEyfU0tLSKYZiGgAw1DFeAgAwePXqLt9jx47V6tWrJUler1f5+fkqKytTZmamli1bpri4uIgkCQDAQMZ4CQDA4BSRx2Z5vV4tWLBAb775ZqBt3rx52rt3L38kAADwvxgvAQAYXCJSUOfn5+u9997TbbfdprFjx6qyslIvvPCCtm/fHnikBwAAQx3jJQAAg0tECurS0lKtXr1amzdvDrQlJSWprCy058IBADCYMV4CADC4RKSgdjgc2rhxo2prazVmzBhVVFRo27ZteuaZZyLRPQAAgwLjJQAAg0tECuply5Zp9uzZevrppwNt8+fP17JlyyLRPQAAgwLjJQAAg0tECuq4uDjt3btX+fn5OnbsWOCupXZ7RLoHAGBQYLwEAGBwidgIbrfbe3VDlebmZq1fv15vvfWW5syZo0cffVSJiYl69NFH9eKLL+ob3/iG/vEf/zFS6QIAEBO9HS+BfsXbJKOhKqRJLCPHhzUro7FaamkMfoK4xLDmAwCh6Dc/if/2t7/VwYMH9dhjj+n//t//q9/85jfKycnRL37xCz311FO6/fbbtXjxYs2YMSPWqQIAAECSUXdavrK9IU1jv/y74c3r9AfyVx0POt6SPEnWlElhzQsAgtVvCurk5GT5/X5ddtllysrKUmZmpoqLi7VkyRItXLhQS5YsUVFREQU1AAAAgLB53302pHhb5lWyJPPjDLpmjUQnRUVFKioq6tT2zjvvBN3H17/+dXm9Xn35y19WRUWFrr32WlVWVmr06NGSpJSUFFVUVEQiXaD/MAwZ1cfN/9WdjnWmiKImr19n6mX6r67JF+tU0UuRGC8BAED/EZEj1G63W5LkdDoDba+88opGjBihK664Iqg+fv/732vOnDl6+OGHtXTpUm3btk3Dhw9XbW2tJKmxsVGpqamB+KKiIhUXF3fqp7S0tDeLAkREwpkzsnnreowxZFFDyV81rGyHaX/+xBQ1XzhLiUH8qORtHi5/wnnFBxHbbCmXpalacZ+bxzYlHldcxRlZG82vlTtfWqqkc+dkkdFjnD+uUU2WY0oKIldfU6J8Nf7glssol8XXrLjqIJYr/oTsVadlazCPbSgrU+K5c7L4mnuM89sS1BRfFtxynbfpXEW8jpacN41NO+/RpAvtiqsMYrlsJ+Sr9JrGoW9FYrwEAAD9R68K6taitrWwzcvLkyQ1NDTot7/9rX7wgx8E3depU6d0+vRpjRgxQg6HQ+fOndPUqVP10ksvSZLeeecdXXPNNYF4p9PZ7g+S1vk7HI7eLBIQEd7GQ1KTyc1QLFbZHQ55a1N7jpNkGZ4qa/ok+ZrMY62paVLSBfIbJ81jJ0yQ0TBMRny9aax90iT5bGdl1NvMYx0OeevGSIa/xzhL4mhZL75YvoYgluuCi2QZlS6fz/z6OWv6BMnbJL+92jTWNnGi/Al1Mmp7Lv4lyZ6ZKW/jGMnb1HNgXNIXsfVBbNtR4xWXkCZPuflyjRkzRukXDZPfal5Q2yZOlCV5omkcvhDtH2MjOV4CAID+o1cFtdvt1saNG9u9bmWxWPSVr3wl6L5Wrlypxx9/XBkZGRo5cqR+9rOfadSoUfqnf/onjR07VlOmTNGiRYt6ky4AADERyfESAAD0H70qqJ1Op+6///5O7SNHjtSCBQs0d+7coPsaN26cPv74Y3k8HqWlpQWeyVlcXBxos1gsvUkXAIYUX9keGdUnTONsM26SJWFkH2Q0dEVyvAQAAP1Hrwpql8sll8slSdq3b5+Ki4vl9X5xzV7raW0dT8vuMRm7XRMntj9F0WKxKD09vTdpAgAQU70dLw8dOqTq6upAzOHDh+V2u9s9TrKrNgAAEF0RuSlZcXGxvvKVr8gw2l+D+MADD4RUUAMAMJiFM17u3LlT9957r1asWCGn06mKigq5XC7dfPPNysvL08GDB2UYRqe2Cy+8sC8WCQCAIS0iBfVbb72lcePGaeXKlYqPjw+0U0wDAPA34YyXdru93VMudu7cqdzcXG3dulW1tbXatWuXfD5fp7aVK1dGdVkAAECECuo5c+boO9/5jn72s59FojsAAAalcMZLl8vV7tnVJ0+eDFwKlZGRofLycvn9/k5tAAAg+iJSUNvtdr3xxhuBx4C0ysnJ4Sg1AAD/KxLjpcViaXeTztbXHdtatT6yq6NoPyoMQ4O93qP4CvNH+bV1Psx9L+HMKdnqgp+XtyFe/jprWPkNC3GaZvunstWR32DOz1vpC2kaDB0RKajdbrfefvttvf322+3auYYaAIC/icR4mZaWpsLCQklfHK3OysqS3+/v1NbK6XR26jsvL08Oh6M3iwJIkoxqm3zespCmsYe57/mt5fJXNQcdb0m+SNaUjLDy89akmge2YcvIkFHtJ79BnJ8leVJI0wxW/BjbWUQK6u4eB0IxDQDA34QzXrY9wlxUVKTc3FytX79ea9euVUFBgTZt2iRJXbYBAIDoikhBnZiYqJSUlC7bAQDAF8IZLxsbG5WdnR34f2pqqtxud+Bf6w3LumoDAADRFbFTvjdu3NipnVO+AQD4m3DGy7bPsG6VlZXV7rTu7toAAEB0Re2U7507d1JMAwDQBuMlAACDS0QK6q5+Pfd6vbLZbJHoHgCAQYHxEviC75NdIcVbL5odpUwAoHciUlC3vWFKS0uLqqqq9MILL2j8+PFauHBhJGYBAMCAx3gJfMGoOxPaBN7G6CQCAL0UtWuoLRaL/u7v/i4S3QPAgNPi9euDM1bTuNFer0Zd1AcJoV9gvASAyGn2GappCG2asdFJBUNYVK6hTk5O1tVXX6158+ZFonsAGHB8fr/O1JvH+ZP8GhX9dNBPMF4CQORU1jbp3VPmP163dX2UcsHQFbFrqK+++mrl5+errKxMmZmZgUd8AMBgsrtMamnpefCOj/fLmdlHCWFAYbwEAGBwiUhB7fV6tWDBAr355puBtnnz5mnv3r2Ki4uLxCwAABjwGC8BABhcIlJQ5+fn67333tNtt92msWPHqrKyUi+88IK2b9+u5cuXR2IWAAAMeIyXAAAMLhEpqEtLS7V69Wpt3rw50JaUlKSysrJIdA8AUVVa4dXn1ebXYM2e4e+DbHp25vMWeYK42ZljbAvXZvdDjJcABhJu+gWYi0hB7XA4tHHjRtXW1mrMmDGqqKjQtm3b9Mwzz0SiewCIqsoGvz4L5gZiRvRzMVPX5AvqZmfp3tgX/+iM8RLAQMJNvwBzESmoly1bptmzZ+vpp58OtM2fP1/Lli2LRPcAAAwKjJcAAAwuESmo4+LitHfvXuXn5+vYsWPKzMzUsmXLZLdHpHsAAAYFxksAAAaXXo3gJSUlOnXqlJxOp+x2e7sbqhQVFSktLU0Oh6PXSQIAMJAxXgIAMDiFdlFEB7t27ZLb7e7yPbfb3e17AAAMJYyXAAAMTr0qqD/77DN5vd4u3/N6vTp79mxvugcAYFBgvAQAYHDqVUGdmJiolpaWLt8zDEOJiYm96R4AgEGB8RIAgMGpV9dQOxwO/ehHP5Lb7dZll12mcePG6cyZM3r//ff1wQcf6IUXXgipv/r6ej3zzDOaPXu25s+fL0k6fPiw3G63Fi9erBkzZvQmXQAAYiLS4+Ubb7yhffv2ac6cOVq4cKEkxksAAGKhVwX1tddeq/Hjx+udd97RO++80+69cePG6atf/WrQffn9fl177bXKzMzUJZdcIkmqqKiQy+XSzTffrLy8PB08eFAXXnhhb1IGAKDPRXK8LCws1C233KJvfvObWrFihZ555hllZ2czXgIAEAO9KqiHDx+ujz76SK+99ppKSkrk8/lktVrlcDi0ePFijRo1Kui+ioqKZLVa9eyzzwbadu7cqdzcXG3dulW1tbXatWuXVq5c2ZuUgXaMmnIZVaWmcZbUabKMGNsHGaEnNfVNqjr7uWncyFENuiCpV1e0ABEVyfHyo48+0tKlS/XYY4/J7/frxIkTOnfuHOMlgH6jsiG0+NE+Q3HRSQWIul4/+HLEiBH62te+1utEDh48qLFjx2rz5s2aPXu2Fi5cKI/Ho/T0dElSRkaGysvLez0foC2jqUb+quOmcbbRGX2QDcw0t/j0+fkm0zh7k1dKiu+DjIDgRWq8XLVqlebPn69vf/vbMgxDq1at0tatWxkvAfQbfzkV2o/al6c1aVyUcgGirdcFdaTU1tbq448/1uTJk/XLX/5STzzxhCTJYrF0GV9UVKTi4uJO7aWlVa6QbwAAIABJREFU5kcbgVb26nLFf1ZhGtds+1S2I3+Rrdb8j9SGjIWKP3NGNm9dj3GGLGooLdWwCvP5++t8avYeV2IQsd7m4fInnFd8ELHNlnJZmqoV97l5bFPiccVVnJG1sco09nxpqZLOnZNFRo9x/rhGNVmOKSmIXH1NiarwN6quruf1KkktZ85I9VbFVQexXPEnVFVVFVS/x44dU+3nn8vr8/UYZ7fbdOz48aD6tNssijvpCSr23LlzstosQcV6PB55jVOy15uvg4Zjx2TEDTeNQ/9QUlKiOXPmaNWqVVq3bp2OHDkiifESsWGv9wQ13rR1Psixr61m+6ey1Z2SrS746bwN8fLXWcmvF/mdtfiDGnPaKi0tDXmaU6dOya8zfZZfOOvPW9nz2I+hq98U1GlpaVq4cKHy8vLk9/t14MABpaWlqbCwUJJ08uRJZWVlBeKdTqecTme7PvLy8uRwOPo0bwxs/rMN8lvMH1djy8iQ8blV/oRG89iLL5bPf0JqMrlrr8Uqu8Mhb22qaZ+W4amypk+Sr8k81pqaJiVdIL9x0jx2wgQZDcNkxNebxtonTZLPdlZGvc081uGQt26MZPh7jLP8f/buPL6q6t77+PdkYAgzJIQkJGAQMQS0CI4EBT0CFRRU9IJVr61VK225Dk+tdbjS661NFAd6pe1TqtZqq9Za8RFowESJJA4gWBCsMoQEM+dkPCHjyVnPH9ycEjOcQc4Q8nm/Xnm9ZO3f2vt3jitn5Xf23msPGqGwiRPV3uTB6xodp0jnKJWWD3UbOzo2VgkjB8gZUes2NjwpSWUV1XL2XvtLkiZOnKjCA5/1uFpzhwEDIjVxwgTl/3Ov232OGjVa8QnxKilyf6VETEyMwiLCVFtb5zY2Pj5eMU7J1Paeq3R8zFoGDnMbdyrrS8Xl+++/r9jYWF1xxRXKzs5WdnY28yWCxtSGq91xxKs+EcnJctS5/9w/UXhiokytU86aVo/7WEbGKWxUIvl9g/zCLbEqK3H/98SJkpOT9cWn7ufqE8XFxSlWYQHLz5f3zzJygld9TlV9ab4MlJApqBctWqQHHnhAkZGReu2117Rx40YlJibqJz/5iVauXKktW7ZozZo1wU4TAICgmj17tr797W/LGKNXXnlFmzZtUlJSEvMlgD6trN4he43nl4oPDXMobIQfEwI8FDIF9ZgxY/TOO+8oMzNTGzZs0IwZMyRJWVlZrp/oaO++TQIA4FRzwQUXKDMzU3l5edq4caPOOeccScyXAPq2crtTpe7vKnMZN8CpOApqhICQKaglKSUlRSkpKZ3aUlNTO126BgBAf3f++efr/PPP79TGfAkAQOCFVEENAAAAoHf7Cyq9io8b1SiuWwH8gwe1AgAAAADgAwpqAAAAAAB8QEENAAAAAIAPKKgBAAAAAPABBTUAAAAAAD6goAYAAAAAwAcU1AAAAAAA+ICCGgAAAAAAH1BQAwAAAADgg4hgJwAA/lBYXqf6gkq3ceNjmwKQDQAAAE5FnKEGAAAAAMAHFNQAAAAAAPiAghoAAAAAAB9QUAMAAAAA4AMKagAAAAAAfEBBDQBAH3Ps2DH95je/0QcffOBq279/v9auXavPP/88iJkBANC/UFADANCHOJ1OLViwQB988IEaGxslSTabTVarVQcOHJDValVVVVWQswQAoH/gOdQAgqq5tV2fl7v/bm+02jRkZAASAkJcbm6uwsLC9NJLL7naMjMztXDhQq1bt052u11bt27VihUrgpgl+iLTXCu1NXveIXKQ/5IBgD6CghpAUDmcTpUfcx8X0WQoqAFJn332mcaOHatnnnlGZ599tubNm6eSkhIlJCRIkhITE1VUVBTkLNEXmbK9ctYUehxvGTlBYaMm+DEjAAh9FNQAALV/vkGmxd57UHikIs5aHpiE0CO73a4vvvhCkyZN0m9/+1v95je/kSRZLJZu43Nzc5WXl9elPT8/3695ou8ZWF6q8Aabx/GOpgFyNoRpgM3zPpLUmJ+vKC/7tEZ8pfAG8uvIr6Ghwas+paUlapPN6/wqLE6vj5XvU36lqq6q8qpfVUSEFB7hU36f7G30qs/pzUc0Mrbdqz7oPyioAQDoQ+Lj4zVv3jxlZGTI6XRq165dio+PV3Z2tiSpuLhYqamprvi0tDSlpaV12kdGRoaSk5MDmjdCnzOsSM6aVo/jLSPjFDYqUe2OI14dJyI5WY66aK/6hCcmytQ6ye9/87PvGepVn7i4eEUr0uv8wi2xKisp9upYycnJ+uJTb/OLk8XpUGubw+M+Y8aMUVxcXMDyi5vIZ6bEl7HdoaAGAKAPWbRokR544AFFRkbqtdde08aNG5WYmKif/OQnWrlypbZs2aI1a9YEO00AAPoFCmqceppr5Szb6z5u+Hj/5wIAJ9mYMWP0zjvvKDMzUxs2bNCMGTMkSVlZWa6f6Gjvzq7h1GPsZV7FWwaP8lMmCBUNLU41N3keP3CwU2LdOcCtkCyoS0pKJB2/rM0Yo5KSEsXHx/d4fxhwItPa6NGiKpbIIbKwQimAPiglJUUpKSmd2lJTUztd6o3+rf3QO17Fh592sZ8yQag4bHOotMzzJ+aOczoUx7kHwK2QK6hLS0t17rnn6rbbbtODDz6oiy++WIcPH9YZZ5yh9957T5GRkcFOEQAAICBMbaHaj7zvVZ+IGTf5KRsAwNd5/jVVALS0tGj58uWaNm2aJCk7O1tDhw5VRUWFBgwYoG3btgU3QQAAAAAA/ldIFdR33nmnbr75Zl144YWSpC+//FLnnHOOJGnWrFn65z//Gcz0AAAAAABwCZlLvl9++WWVlJToW9/6lnbs2CFJstlsGj16tCRp4MCBnZ4zx3M1+xlnmwZW/sN92IDhcg4cpYEePNOxta1YihioAVUexIZ/pfDGcoXb3cc2FRRoQHm5wh29PxfRyKImD5916WxoV6ujUIM8iHW0DpFzYKNHz7VstRTJ0lKryHr3sS2DChVpK1dYc43b2Mb8fA2urJRFptc4Z2SzatqOevQMyaqqKkmePQ+zrbxczrYWtXkQW1xcrJqaGo/2W1BQIHt9vRztvT+LMiIiXAWFhR7tMyLcosjiEo9iKysrFRZu8Si2pKREDlOqiGOejdlBFRWytB3rNc6ERaiJz1gAAACXkCmoGxsbZbPZdMcdd7gWJYuNjdUll1wiSaqrq9P48f9aGYHnavYzjhY57DvchlmGRSpsbJLa2w66jx2bIEvkIDktFW5jwxMTZerD5BzY7D524kS1O49KLW4WPLOEHX/Wpd39aryWIdEKS5ig9hb3sWHR8dLg0XIa989lDBs/XqYpSmZA74WUJEVMmKD28AqZY+HuY5OT5WiIkYyz1zjLoBEaFpekwoNfuN3n8edNxqq03P2zI0fHxsrR0qT6Y0VuYxMSEtTU0CBn77W/JGnixIkqPPCZ2traeo0bMCBSEydMUP4/3a82P2rUaMUnxKukyP1CejExMQqLCFNtbZ3b2Pj4eMU4JVPbe67S8THrbD8s02J3ExipiFP0M5YvYwEAgC9CpqC+/fbbdfvtt0uSVq9eLUm69tprdfXVV2vJkiXavHmzazsAwL1/FLeovNz9nT2XTHLwZBQACILK2kaVFlR61efsGX5KBoBPQqagPlF8fLwkafr06Vq5cqUefvhh/fCHP3QtVgYACBLjlKn7yn1c+EBZho3zfz4AAABBFJIF9Ylnou+55x7dc889QcwGANDBOB0ePcLHMmSswimoAQDAKS4kC2oAfd9n5WFu70seHOVUYlxg8gEAAPCVaa6V2tyvpeMSOUiWQSP9lxBCBgU1AL8oO2Zk3BTUQ4xTiYFJBwAAwGembK+cNe4XEO1gGTlB4add7MeMECpC6jnUAAAAAAD0FZyhRlCZ+mLJ6eg9yBIuy5CYwCQEAAAAAB6ioEZQOY9+JNPW2GuMJWKgwlOWBCgjAAAAAPAMBTUAAAAA9KKpxSFHc5vH8eEtDg31Yz4IHRTUAAAAANCLkiq7GstrPY4f7LTrDD/mg9BBQQ0A0D+P2tTeVN9rjCV8gKanBighAACAPoBVvgEA6KM2bNig3NxcSdL+/fu1du1aff7550HOCgCA/oOCGgCAPmjLli1avny5srKyZLPZZLVadeDAAVmtVlVVVQU7PQAA+gUu+QYAoI85ePCgHnroId1+++2SpMzMTC1cuFDr1q2T3W7X1q1btWLFiiBnCQD9nKNFpqnGqy6WYeP8lAz8hYIaAIA+xG636+abb9YLL7ygv/71r5Kk4uJiJSQkSJISExNVVFQUzBQBAJJMQ5naj7zvVZ+IGTf5KRv4CwU1AAB9yLPPPqvhw4dr06ZNysvLkyRFRUXprLPO6jY+NzfXFXei/Px8v+aJkyPiWIkG2Gxe9WnMz1eUl31aI75SeEOpwhs87+doGiBnQxj5fYP8qixtamho8OpY+fn5XvcpLS1RVVWVV/2qIiKk8IgA5Veq6hDPr7WqWu1e9GsMr9agr77yafyhb6GgxklnGiokR5P7wOEJ/k8GQPA4WmQaytzHDRwuy+BR/s/nFHHuueeqrq5ONTU1amo6/lmbkJDgOitdXFys1NR/LceelpamtLS0TvvIyMhQcnJy4JKGz0xtuNodR7zqE5GcLEddtFd9whMTZWqdcta0etzHMjJOYaMSye8b5DfAEqPSMu+eVpycnCz7Hu/6xMXFy+k0am1zeNxnzJgxiouLU1lJsdf5ffGpt/nFyeJ0hHR+DaZOjQ7PL98ePGa0EhN9G3+hjC9ju6KgxknnLN8nU+/+wy1i2rUByAZAsJjmWo8udQuLOVOW8ecGIKNTg9VqldVqlSStXr1akvSjH/1I06dP18qVK7VlyxatWbMmiBkCANB/UFAD8NjBSoeO2d0/HGCGMQHIBsHQ7nRqT7n7MTB0uENnxAcgoX6u48xzdHS0srKyXD/R0d6dXUNgmOZaqa3Z8w6Rg/yXDADgpKCgBuCx6iap9pgHgdTTpyxjpHIPxkBrJIMgEDrOVEtSampqp0u9EXpM2V45awo9jreMnKCwURP8mBEA4JviOdQAAAAAAPiAghoAAAAAAB9QUAMAAAAA4APuoQb6uaJah2weLDJ15kTPH2UBAADQ37W2G9V58CTZE431TyrwIwpqoJ+rb3J4tMjU6e0sMgUAAOCpanuLPi317oLgK/yUC/yHS74BAAAAAPABZ6gBAH7R6nDK5sHVD0OHOTXC/+kAAACcdBTUAAC/aGhu0x4P7s+fMLCVghoAAPRJIXPJ98aNG3XZZZfp/PPP1z333KO2tjZJ0lNPPaW0tDStXbs2yBkCAACgP6hu8u6nlXVGgH4rZM5Qn3POOXr88cfV2tqqVatW6Y9//KPOO+88/frXv9b69et166236vLLL9fUqVODnSoAAABOYZ94uZDUjPgWxfopFwChLWQK6vj4eMXHx0uSZs2apebmZuXl5WnRokWaN2+eFi1apNzcXApqAAAAAEBICJlLvjsUFBTo/fff1/XXX6/q6mqNGHH8zrpRo0bJZrMFOTsAAAAAAI4LmTPUkmSz2XTXXXfp/fff15gxYzRkyBDZ7XZJUnNzs6Kjo12xubm5ysvL67KP/Pz8gOWL7g0sK1N4o/svP5qOHNGgigpZ2pt7DwwfoKZBRzTYgy9U2o9Z5GgeqoEexLa2FUsRAzWgyoPY8K8U3liucLsHr6ugQAPKyxXuaOg1zsiipvx8RXmQq7OhXa2OQg3yINbROkTOgY0a4Ml7YCmSzVajhobec5WkoqKvVFtbq4ZjjW5j8/PzXb+7veba1qqjR496dPyqqipJTo9i28rL5WxrUZsHscXFxaqp8ew9KCgokL2+Xo729l7jIiLCVVBY6NE+I8Itiiwu8Si2srJSYeEWj2JLSkpUXV3tUezRo4Wqr6uXaXUTGxapgoICj/Yp41RRUZFHsTabjc9uAH1eWb1D9hrPz1UNDXMokhUZgT4vZArqwsJC3XjjjfrVr36lMWPGSJKmTJmit99+W5K0e/duXXbZZa74tLQ0paWlddpHRkaGkpOTA5c0utVuCmTqey84JCnitNPU3vqFTFvvBZolYqDCTztNjoboXuMkyTJsnMLGJqm97aD72LEJskQOktNS4TY2PDFRpj5MzoFuin9J4RMnqt15VGoZ5CaBMEUkJ8th9+B1DYlWWMIEtbe4jw2LjpcGj5bTFLuPHT9eda1hampy/7rGj09Una1csrj/YyE5OVkH930iY3pfpGVI1GAlJSWp8OAXbvc5ZswYxcXFqrR8qNvY0bGxcrQ0qf5YkdvYhIQENTU0yOnBejITJ05U4YHPXIsm9mTAgEhNnDBB+f/c63afo0aNVnxCvEqKCt3GxsTEKCwiTLW1dW5j4+PjZdpb1Nbm/ncxKWmC2ouGq72p9zhL+ABNnDhRBz/b5Xafo0aO0Pjx41VceMRtbHR0dNA/uynoAXxT5XanSms8jx83wKnxFNRAnxcyl3y/8MILys/P12233aZZs2bpd7/7nS677DI1NjZq7Nixamxs1KWXXhrsNAEAAAAAkBRCZ6hvv/12XXnlla5/x8XFKTIyUnl5eSopKVF8fLwsFksQMwQAAAAA4F9CpqA+cZXvE1ksFiUkJAQhIwAAQs+RI0e0ZcsWNTQ06KKLLtJFF10kSdq/f7+ysrJ4xCQAAAEUMpd8AwAA9w4fPqyCggKVlJRo8eLF2rZtm2w2m6xWqw4cOCCr1fq/i/gBAAB/C5kz1Ag8c8wmU/m52zjLyImyjEwKQEYAAHesVqusVqskyRijnTt3qqioSAsXLtS6detkt9u1detWrVixIsiZAgBw6qOg7s/aGuWscb+ycNigkbKIghoAQklbW5tycnK0fv16ZWdnu26PSkxMVFGR+xXuAQDAN0dB3Uc49rwqOXt/VI8GDFVE6tWBSQgAEFT33nuvHnjgAc2cOVPZ2dk9LtyZm5urvLy8Lu08KuybiTr8llfxrePOVXhDqcIbbB73cTQNkLMhTANsnveRpMb8fEV52ac14ivyOyG/hoYGr/qUlpaquqrKq35VEREKD7d4fax8n/IrUZUP+Sk8IkD5+fb+BTK/1qpqtXvRrzG8Wg1hpT7lh76FghoAgD7E6XRq1apVSklJ0bJlyyQdX9gzOztbklRcXKzU1FRXfFpamtLS0jrtIyMjI+jP/vYHZ9leGXuZx/GWobEKizvbp2M56qK9ig9PTJSpdcpZ0+pxH8vIOIWNSlS7w/3z3E8UkZxMft8wvy8+HepVn7i4OFmcDrW2OTzuM2bMGMXFxau0zLtjJScny77H2/zi5XQaH/KLU1lJsdf5Be79C1x+DaZOjQ7PHzQ+eMxoDfMxv1BGwd8VBTUAAH3I2rVrlZOTo8TERGVkZGj27NlauHChfvKTn2jlypXasmWL1qxZE+w0g6O5Vqah3PP4iEH+ywUA0C9QUMMjzpoCqe6o27iw2Gn+TwYA+rHp06dr0aJFqqk5fqakublZ0dHRysrKcv1ER3t39g8AAPiGghqeaa7zaAEzy6jQvkwFAPq6E1f5PlFqamqnS70BAID/UVADAIB+y9QWqv3I+171iZhxk5+yAQD0NRTUAICga2xpU/2xFrdxI4YM1OCBkQHICAAAwD0KagBA0FXWNuqfhe4fbTPttBiNj6GgBgAAoYGC+lTjaJazaIf7uKhoWQZ498gAAPCXsIZSjar+h/u4sTOlmOEByAgAgOCorm/yKn5o1EANiAjzUzZwh4L6FGPa2zxbPMwYCmoAISOsrUGDmkrcB7ad6f9kAAAIoh1feDAfnuBbp8dq3Gj+rg8WCmrgFHS0qkmf57v/pnJqlHffgAIA0JeU1TbrUw/mwxNdMcNPyQA4JXFtAAAAAAAAPuAMNQAACDnOsr1exVtGJvkpEwAAekZBDQAAQo6zdI9X8eGDRvgpEwAILNNc712H9lH+SQQeoaAGAAAAgBBhKj/3Lj4pSooZ7ads4A4FNRBETqfRVg8WSxk5wqGUka360IPYxLZmDRsx+GSkBwAAAKAXFNRBZOxlUnuL2zjLiMQAZAMAfUPdsRY1tbS5jYseEaWIcNbeBE4VhyrbVFVh8Th+jKNVw2L8mBAQQg4V16i6vtHj+NHDB+v0BM5qnwwU1EHkLN0jc6zCbVz4Wf8WgGwAoG84Wl6nYpvdbdxFqeM1fMjAAGQEIBCOtRrVNHteUA9skYb5MR8glDQ0tara3uxx/IBIysCThXcSAAAAAPow09rg1WJmplWSYv2WT39CQQ0AAAAAfVnNEZnKco/DTVispEn+y6cf4eYyAAAAAAB8EPJnqPfv36+srCxdfvnlmjp1arDTccs01Ugt7i+3sAwdF4BsAKB/M/UlktP9Amangr42X6J/21ni+b3QknTG2P7xewyg7wnpgtpms8lqteqaa65RRkaGPvvsM40ZMybYafXKVB2Ss/ILt3Hhk+cHIBsA6N+cJbuPf9HpzojZ/k/Gj0J5vjTNtTK1R73qEzbuLD9lg1DhzeJiktTa1u6nTID+y15bqdKjR7zqEzE02k/Z9F0hXVBnZmZq4cKFWrdunex2u7Zu3aoVK1b02sdZ8L77HQ8YqrD4czzOw9jLZKoOuA8cxX0IfU2FvVXF5e7vfDgtpk111W2q9iB26qR25Ve2q7mp99gwS5jOnuZxqgD+14D6fI2qLnAf2DZSJTa7HMc8WKRlxDdOK6h8mS+NvcyrY1gGj5IifFg1vblOztI9XnWhoA6Ohhanmps8jx842KlB7UZ1XvSRpLHehQPwk4baah06eNCrPmfOoKD+upAuqIuLi5WQkCBJSkxMVFFRkds+zppCtzGWwaMkbwrq1gaP9hs+ZKyMx3tFKDjW7FT5Mfdxca1O1TcZj2LPcDpVecyo0c0fGBaLdLZnaQI4QVhztQY1lbgPdLSpvrFVjsYWt6F9/dE6vsyX7Yfe8eoY4addLMvICT7lh77hsM2h0jLPl9cZ53QoblCLPi31bkmeK7xNDABCmMUYE7I14OOPPy673a5HH31UDz74oEaNGqX/83/+jyQpNzdXeXl5neIjIyPV1sY9NgAA78TExOh73/tesNPwGfMlACAQ+vp86RcmhL300kvmlltuMcYY8+///u/mT3/6U6/x6enpHu/bH7HBPr6/YoN9/FCIDfbx/RUb7OP7KzbYxw+F2GAf31+xwT5+qPLnfPlN+gTyWOQX+D6BPBb5Bb5PII9FfoHvE+hjncpC+rFZCxcuVGZmplauXKktW7Zo/nwW8gIA4OuYLwEACI6QLqijo6OVlZWlKVOmKCsrS9HR3AQPAMDXMV8CABAcIb0omSSlpqYqNTU12GkAABDSmC8BAAi88NWrV68OdhInU1JSUlBjg318f8UG+/ihEBvs4/srNtjH91dssI8fCrHBPr6/YoN9/FOFL6/X1/coUMciv8D3CeSxyC/wfQJ5LPILfJ9AH+tUFdKrfAMAAAAAEKpC+h5qAAAAAABC1Sl3yff+/fv1yiuvaPjw4YqJifFo+7Fjx/Tcc8+pra1NiYmJJyWPiooKvfDCC7Lb7UpOTu42pq6uThs2bNC0adM6te/atUvvv/9+l3Z/cTgc+uMf/6i9e/dq2rRpCgv71/csxhht2LBBmzZt0v79+3XGGWdowIABXdoGDhwYkFwlKTMzU1u2bNGkSZMUFRXVadsHH3ygv/71r/rwww+VlJSk4cOHu+3jT3v37tWrr76qUaNGdVkk6NChQ3r11Vf13nvvadCgQYqPj5ckbdu2Ta+//rra29s1YcKEgOVaVlamF154QU1NTZo4cWKPcTt27NAHH3yg1NRUGWP06quv6sCBA5o6dWrAcm1tbdWLL76ozz//XNOmTZPFYnFta2tr05o1a5SXl6e8vDzt2LFDF154oWt7aWmp1q9f36nN3zZt2qSsrCxNnjxZgwYN6rL98OHDev7553X66adryJAhys3N1d/+9jft2LFD8fHxGjFiRMBy/fTTT/WXv/xFY8aM0ZgxYzpte/fdd/WXv/zF9d4OHDhQCQkJvfbxp+LiYr344otqbW3t8rty8OBB/f73v3flWlFRoZSUFH3++ef64x//qOLiYqWkpHQaO/2NL/Nlhw0bNshmswX0sr/6+no9//zzKi0t1ZQpU7qNcTgceuGFF3TOOeeoqalJf/3rX7V582YdPXpUZ555psLDwwOWr6feeustbdu2rdu5dNu2bXrzzTf1ySefKDExUcOGDXPbJxT0NnaOHDmi1157Tdu2bZMk199d27dv11/+8he1trbqtNNOC3TKbvU2/pqamvT6669r8+bNKigo0PTp012fLRs3blReXp5mzJgRjLTd8mQstba2au3atZo8ebLrb6ju2kJFb+MvNzdXr7zyimtukKT29vZux2Qo8eTz7+tjzZM+ODlOqTPUNptNVqtVBw4ckNVqVVVVldvtTqdTCxYs0AcffKDGxsaTlsuSJUv08ccf67bbbtO7777bZfvhw4e1dOlSrV+/vlP7V199pSVLlnRp96cHH3xQL774otavX69HH3200zZjjD7++GPZbDa9/vrruvrqq7ttC5RNmzbpxz/+sfLy8nT99dd32b5nzx6Vl5friy++0HnnnSebzea2j7+UlpZqwYIFOnDggObNm6f6+vpO2w8dOqSjR4+qvLxcS5cuVXZ2tv7+97/rxhtvVElJia699lq99957Acv3iiuu0K5du3TLLbcoNze325gjR47oqquu0gsvvCBJuvvuu/Xcc891eW3+dt999+nPf/6z1q1bp4yMjE7b2tra9PDDD6umpsb106GlpUXXX3+9Hn744YDl+re//U333HOPcnJydMMNN3TZfvDgQV1++eUqKSlRa2urJOkf//iHysrKtGvXLp177rk6duxYQHI9evSorrjiCn355ZeaO3dul8/EpqYm13v6xhtv6OOPP3bbx1+MMVqwYIE+/fTaU0J/AAAgAElEQVRT3XjjjdqxY0en7a2tra5cd+zYoVdffVVfffWV5s6dq/z8fP385z/XE088EZBcQ5Ev82WHLVu2aPny5crKygpozjfffLOysrL0s5/9TK+++mqX7R2fpU8//bSk439M7tmzRzU1NXryySd19913BzRfT/zpT3/SAw88oHfeeUff/e53u2z/9NNPVV5ero8++kjnn3++Wlpa3PYJNndj6/DhwyooKFBJSYkWL16sbdu2KTs7W8uXL1dJSYmWL1+uLVu2BCn7nvU2/urr67V3717V1NRo3bp1+uEPfyhJeuqpp/Rf//Vfqq2tDUbKbnk6llatWqWf/vSnKi8v77UtFLgbf1lZWXr77bdd80Nzc3O3YzLUuPv8626sueuDkyiIz8A+6V566SVzyy23GGOMuemmm8yf//xnt9tzcnLMnDlzTmoehYWFJjk52RhjzPr1682dd97ZJebQoUNm+fLl5tJLL3W1NTY2mtmzZ5tf/vKXndr97bTTTjNHjx41Bw4cMFOnTu0xzmazmWHDhrlt86ebb77ZvPzyy8bpdJr4+HhTUVHRY+zUqVPNoUOHvOpzMv3+9783d9xxhzHGmOuvv9688cYbPcbecccd5g9/+INZs2aN+fGPf+xqe/HFFwOS64EDB0xKSooxxphnn33W3HXXXV1iGhoazIUXXmh+8YtfmG9/+9umrq7OjB071jQ1NQUkxxPFx8eb8vJy89lnn5kZM2Z02nbs2DETFRXVbb/vfe975umnn+5xuz8sX77cvP7666a9vd3ExMSY2traTtvvu+8+k5GR0WP/008/3ezfv9/faRpjjFm3bp1ZtWqVMcaYJUuWmI0bN3Ybd+zYMXPGGWeY8vJyj/ucbJ999pn51re+ZYwx5sknnzQ//elPe4y96qqrTGZmptm+fbs5//zzjdPpNM8//7xZuXJlQHINRb7Ml8Yc/6yYNWuW+fGPf2weeeSRgOXb0NBgRo0aZRwOh/l//+//mWuuuaZLTFlZmbn11ltNampql225ubnm3HPPDUSqXun4nWlrazOjRo0yjY2NPcYmJCSY/Px8r/oEg7uxdaK77rrLPP744+Z//ud/zO23326MMWbVqlXmd7/7XUBy9ZQn46/D+++/b6xWq2lvbzfx8fGmpKQkgJl6x5Ox9Otf/9rcdtttJjU11ezbt6/HtlDhbvw98sgjvX52dYzJUOJu/HU31rwZs/jmTqkz1CUlJUpISJB0/HKNoqIit9s/++wzjR07Vs8888xJOxtYXFzcax6SNGnSJN12222d2r7//e/re9/7ns4777yTkoenSktLlZCQ0GOuHbZu3arLL7/cbZs/lZaWavz48bJYLEpISFBxcXGXmOeee07XXHONbr/9dk2aNMmjPv7gbjxKxy+hve2229Tc3KzvfOc7+vd//3dt3rxZN998sxoaGnTdddcFJdevvvqq03ZjjG655RbdddddrkuJDhw4oJiYGP3xj3/Un/70p4DkKUlOp1PV1dUaO3Zsj+9rW1ubMjIy9Pzzz7vO7v7qV79SVFSUbr/99oDlKh1/b8ePH6+wsDDFxcWppKSk0/a9e/equblZjz/+uI4ePdpp26FDh+R0OjV58uSA5epuzErSs88+q+uvv15jx471uI+/c/36mO3w0Ucfqba2VgsWLNAFF1yg0aNH67LLLtPmzZv1s5/9LCC5hiJ381R32+12u26++Wa98MILGj16dEDzraioUExMjMLDw3scZ7GxsT2ehQ70XOWpjs+HiIgIRUdH93i2b9++fRo6dKgmTJjgcZ9g8fQzoa2tTTk5Obr00kt1ww03KCcnR9/5zndUWVnZ7dU8weTJ+LPZbLr//vv18MMP69e//rVsNptaWlqUlZWl3/72tzIhuAZwcXFxr2MpLy9Pr7/+uv7nf/6n17ZQ4sn4y8vL0+OPP66PPvqoU/uJYzKUuBt/3Y01T8YsTp5TqqCW5PZ+uK9vt9vt+uKLL1RaWqo777zzpBTVFoul03E8+RDNy8vTzp07VVlZqVdffVWFhYUBvTzDYrH0muf+/fu1ceNG/eY3v+m1zd+MMW7/H7e2tmr48OHKyMhQfX29R338xZNco6KitHnzZu3cuVNffvml5syZo2XLlumTTz7RoUOHApRp77m+99572rdvn44cOaLXX39d+fn5eu6551RRUaEDBw7o6aef1rp16wKSp7vfp8jISN1zzz2qqanRSy+9pBUrVqi5uVn/+Z//qXHjxumpp55SW1ubnnrqqZDI1263a9euXSosLNTll1+uhoYGSVJtba1+9rOf6a233lJkZGQgUpXUeRx0Nybq6ur0u9/9Tvfcc4/HffzFk2M9+OCD+sUvfiHp+HtaX1+vxx9/XAUFBT3e2tAffH2e8mT7s88+q+HDh2vTpk2u+w8D+R56O692yMzMlM1m00MPPeSPtPyuqqpKP//5z7Vhw4ZOa5yEMk9+N++991498MADmjlzpg4fPqxZs2bpO9/5jnbv3q0vv/wyAFl6x934a29vlyQ1NDTo2WefVX19vZqbm7Vr1y699tpruv/++wOWq6fc/X9atWqVZsyYoWeeeUY2m03PPfecbrjhhi5tNpstQBl7prfXlZaWppkzZ6q4uFhXXnmlcnJyXNtOHJOhprfx19NY8/UzE97rG5/MHoqPj3d9A3Pit+u5ubnKzc3tdnt8fLzmzZunjIwMXXnlldq1a5ff8uitSB43bpyuueYa1dTUyG63y+FwyG63f+NcPBEXF6eioiKVlJS4FsYyxujxxx+XdPwerh/84Af6zW9+o7Fjx/bYFggnvrcn5vvyyy+7zjzfeeed+sMf/qAxY8aooKCgxz6BzPXEcZCTk+P6VnThwoVau3atrrvuOu3atUs5OTmKi4vTVVddJavVGrB7qHvKNT8/X6+//roSEhK0ZMkS1dTUqKGhQW1tbRoxYoQmTpyoNWvW6P777+80KflTeHi4Ro8erYqKChUXF7v+f3YUyZGRkUpPT1d6err+/Oc/a8eOHQoPD9cPfvAD2e121z3VJ95b7U/x8fEqLi6WMUZlZWWKi4uTJL344osqKytTQkKCVq5cqXXr1ik8PFxFRUWqqKjQ0qVL9cgjjwRsccKOXE8cBx3v7bvvvqudO3dKOn6f1q233qpRo0b12ifQuXaM2YMHD+pvf/ubpOP3yg0cOFBpaWmSjq+xMHDgQM2aNUurVq3S22+/HZBcQ5Ev8+W5556rGTNmqKamRk1NTWpqalJzc3NA8h07dqwqKyvV3t7eKd+OP+h7smnTJq1bt06/+tWvNHjw4IDk6o24uDgVFxervb1dNpvNNZ92FCklJSW6+uqr9Ytf/EJnnnlmr31Chbux5XQ69aMf/UhTpkzRsmXLJEnvv/++YmNjdcUVV2jRokXKzs4OWv7d8WT8xcbGKj09Xb/61a/08ccfKz4+XsOHD9fTTz+tjIyMgM2R3nA3/m688UZFRkaqpqZG7e3tqq+v14oVK7q0dXyZEArcjT+r1ar09HStXbtWN910kz755JNux2QocTf+uhtrPfWBnwThMnO/qaysNOPGjTN33nmnGTdunKmsrDTG/Ot+ie6222w2k5CQYO6++24THx9vdu/efVJyueCCC8yNN95okpOTzTvvvGOMMSY7O9t1b3RBQYG57bbbzKRJk8wrr7zSqe+JcYHw05/+1FxyySVmzpw55uGHHzbGGONwOEx4eLhpb283I0aMMHfddZdJT083GRkZ3bYFysaNG82kSZPMihUrzCWXXOJqnz17tsnNzTUvvfSS+eUvf2luuukmM2XKFNPS0tJjH38rKSnpNN467p392c9+Zh577DGzbds2k56ebu666y4TExNjvvzyS5OTk2NGjx5t7r77bhMbG2v27t0bsHxnzJhhbr75ZjNx4kSTk5NjjDFm8+bN5tvf/nanuI42p9NpZs6caW6//XZz9tlnB/Set7vuustceuml5qKLLjL//d//bYz5173Tra2tJj093Tz66KPm4osvNt///vc79e3tHmt/eOONN8wZZ5xhrr/+ejN//nxX+6xZs8zOnTvNm2++aaZNm2Z+8IMfmNNPP920traaSy+91Cxbtsykp6eb9PR0U1paGpBcCwsLXWM2Li7ONDQ0GGOMueeee8yTTz5pKioqzOmnn+5q762PvzmdTpOammq++93vmqSkJPPhhx8aY4x58803zdKlS43T6TQXXHCB2bVrl6tPRUWFGT16tPnBD35gUlJSzB/+8IeA5BqKfJkvT+TuPkR/WLJkibnmmmvM1KlTzZ/+9CdjjDH79u1z3TNdWVnp+uz8/e9/b/Lz882wYcPMf/7nf5r09HTz+9//PqD5euLll182U6dONddcc41ZsmSJq73j3tQLL7zQrFixwvVZUFlZ2WOfUOFubD311FNm2rRprte0fft28+GHH5qRI0eau+++24wbN67T722o6G38VVZWmvT0dLN69Wpz1llnmccee8wYY8zSpUvN8uXLzZw5c8xDDz0UzPS75W78ncjTtmBzN/62b99u0tPTzb333mtiYmLMZ5991u2YDDXuPv+6G2vd9YF/nFKPzYqKitK3v/1t1dfXa/Xq1a5H/zgcDiUlJWnatGldtnf0qays1H333XfSLvO46qqrVFdXp+9+97uyWq2Sjt/7OWTIEM2cOVNVVVX65z//qalTp7raOpwYFwhz585VWFiYZs6cqR//+MeuS8paWlp06aWXqq6uThEREWpubu6x7bLLLgtIrmeccYbOOOMMDR06VI8++qjrUQ0tLS2aOXOmwsPDVVZWpokTJ+rpp5/W0KFDe+zjb8OGDdP8+fNlt9v16KOPavz48ZKOn0k97bTTNH78eBUWFmrYsGF67LHHNGXKFE2YMEFz5sxRfX29HnroIZ199tkByVU6vjJ9bW2tbrvtNs2dO1fS8bE4fPjwTo/7OLHt6quvVnl5ua699lqtWLEiYLl23N90wQUXaOXKla7LmlpbW3XppZeqqKhIDQ0Nmj9/vu67774ul0l2xAVCSkqKkpOTNXLkSP385z93nSVrbW3VrFmzdN555yk5OVkWi0VPPvmkRowYIbvd7vr9am5u1qxZs1yPgPOnESNG6LLLLtOxY8f02GOPady4cZKOf4YmJyerurpac+fOVWpqqts+/maxWFxj9s4779Ts2bMlHb/scsSIERo7dqwiIyO1ZMkSV58hQ4bo2muvlc1m00033aRrr702ILmGIl/myxN1xPX0WEh/uOKKK3Ts2DEtXbrUtb6EMUaRkZG68MILZbfbtXPnTn3rW99SZGSkzjnnHLW3t6u9vV3Nzc2uuFBy1llnafz48YqJidEjjzziemxRS0uLzj//fDmdzk6fBeeff74uvPDCbvuECndjKzY2ttNrSkpK0sUXX6x58+aprq4uZC+57W38XXTRRfrqq6/U1NSkf/u3f9Ott94qSVq8eLHq6up08cUX60c/+lHIPabP3fgbOnSoK9bTtmBzN/7Gjx+vgoICDR06VI899pimTp2qY8eOdRmTgfxs84S7z7/uxlp3feAfFmO4qB4AAAAAAG+dUvdQAwAAAAAQKBTUAAAAAAD4gIIaAAAAAAAfUFADAAAAAOCDU2qVb6C/yM3N1SuvvCJJSkpKchu/YcMGvfXWWxo6dKgOHTrkVV9/OvF1VFdX68UXX5TNZnM9bxUAgFDBXAqgO5yhBnyQm5urjIwM5ebmBuVYWVlZuv/++5WVleW2/86dO3Xdddfp5Zdf1pQpU3rtG8jXJXV+HVOmTNHLL7+s6667Tjt37gzI8QEAJ0fH/JGRkaEnnnhCL7/8siorK7tsD9a8+U0xlwLoCQU14ANvCtpgH+v++++Xw+HQY4895noGck+am5tVU1Oj5uZmn471TQwePFiPPfaYHA6H7r///oAfHwDgu4656u2331ZJSYkeeughTZo0SX//+98lBXZ+8ccczVwKoCcRwU4AONUYY/T3v/9dX3zxhYYOHaqlS5dq7Nixys3NVV5enmbPni2Hw6Hdu3frvPPOU1pamiTJbrfrtddeU3V1tcaMGSObzabZs2e79puXl6eMjIxObZK0bdu2LvvqUFhYqHfffVexsbFatGhRl1w3b96sAwcO6Morr9SkSZM0aNAgjRo1SoMGDeo1X3evpaf3QJIaGxv12muvqaGhQaWlpZ3yWbRokWJjY/Xuu++qsLBQEyZM+Ob/QwAAAWO1WrV69Wo9+OCDSklJ0YoVK3T48OFO84sk7d27V9nZ2WptbdWwYcO0YMECTZo0qdP84nQ6tXv3bs2ePVvnnnuuPvjgA3300Ueu+aa3uUbqPG/2Fn/iMWtrazVy5MhO8ylzKYDecIYaOImcTqeuvPJK3XLLLSotLdUjjzyilJQUFRUVub4xv/vuu/X222/riSee0MUXX6yPPvpILS0tuvDCC3XHHXfo8OHD+u1vf9vl2/WmpqYu33hv2rSpy75O1HG52SWXXKKwsM6/7hs3btTWrVv10EMP6bzzzlN1dXWnb/V7y7e3bb29B21tbZozZ46+//3va9++fdq9e3ennMLCwnTJJZd0yh0A0PdER0frqquuUl1dnTZt2tTlrHFFRYXKy8v11Vdf6b/+6780depU7d69u9P88tZbb+mJJ57QBRdcoJUrV+qNN97QU089pYsvvlgffPBBj3NNhxPnTU/m5zvuuEPXXHNNlzPbzKUAekNBDZxEmzZt0qZNmzR16lRFR0crOTlZ1dXVevPNN10xixYt0pNPPqkbbrhBxhjl5OTorbfe0v79+7Vs2TL93//7f7v9BtxqtSo9PV1Wq7XXfZ2o41vr8ePHd9nf4sWL9cwzz2jFihWqrq7WW2+91e1r6u0Y3W3r7T14++23tXv37l5fZ0euX//GHQDQt3ScTS0pKemybd68ebr66qs1bdo0TZ06Va2trdq6datre8f88p3vfEdOp1Njx47Vk08+qRtvvFHGGN13331u59sT501P5mdjjP7jP/6jy9VezKUAesMl38BJ9OWXX0r61/1Tc+bM0Zw5c5SSktLlW+Jhw4ZJOv4N+pEjRyRJkydP9um4J+6rOxaLpce+HX/wVFRU+HyME7f19h50fIt++umn93ic3nIFAPQdBQUFko4Xd4cOHXK1t7S06PLLL9fHH3+sq6++Wq2trZLU7T3HUVFRnf7dccn4P/7xD1cfd/Ot5Nn8fP3116u3h98wlwLoDgU1cBJNmjRJ0vE/HtLT0ztt6+2yq3HjxknqfiIeOHCgJMnhcHidT1xcnCSpuLi4x5jDhw9LOl7M79271+tjfF1v70HHpXgnrvz6dR25duQOAOh7qqurlZ2drTFjxrjO4nb49NNPtX37dv3Hf/yHnnnmGa1evVp5eXle7f/000/Xnj17up1rOla3PnHe9HV+lphLAfSOghr4BjoWPJGk2bNna8mSJbrqqqu0YcMGLV68WOeff74GDBjQZSGxr1u6dKnGjRunl19+WYMHD9bmzZslSeHh4ZozZ44sFoteeOEFWSwWLViwwOP8Ou6h2rZtm5xOZ6d7vzIzM1VWVqa//vWvmjlzpq666qqT8kdAb+/B1Vdfrfvvv19//vOfFRUVpU2bNnXq63Q6tW3btk65AwD6juzsbNXX1+vNN99Ua2ur3njjDY0cObJTzOTJkzVy5Eht2LBBkvSXv/zF6+NcddVVmjBhQrdzTXfzpq/zs8RcCqB34at7u7YFQLccDofi4+MVHx+v5uZmNTc3KykpSZMmTdKKFSs0b948DR06VE6nU0OHDtVZZ52lESNGKD4+XnPnzlVycrJrH3PnztXUqVN1ww03KCkpSUlJSRowYIB27dqlW2+9VYsXL9aCBQuUmJioiIgITZ48WbGxsT3uKzk52ZXn8OHD9eGHH2rPnj2aNWuWpkyZ4oq97LLLNGTIEF133XV68sknNWjQoE77SUpK6vEYvW3r7T1ISEjQddddp9jYWI0cOVLLly/XhAkTXPvZtGmT1q9fr/nz5+uHP/xhEP8PAwC80TEPTJgwQbGxsVqyZImeffZZpaamdto+d+5cTZs2TUuXLlVUVJRiYmJ04403uuaC3uaXE/89b948Pfjgg93ONTNmzOgyb44fP97j+fnrmEsB9MZijDHBTgKAXI/PKC0t1fPPP6/Y2Fjt2rVLw4cP/0b73bVrly688EJNnjxZn3zyidvnZwZLU1OTzjvvPH355Zf68MMPNXPmzGCnBACAJOZSAD3jDDUQImw2mwoLCzVkyBAtX75ca9eudS1Q8k3Ex8dr8uTJGjFihEaNGtXtKqWhYNeuXXI6nbrzzjs1f/78YKcDAIALcymAnnCGGgAAAAAAH/AcagAAAAAAfEBBDQAAAACADyioAQAAAADwAQU1AAAAAAA+oKAGAAAAAMAHFNQAAAAAAPiAghoAAAAAAB9QUAMAAAAA4AMKagAAAAAAfEBBDQAAAACADyioAQAAAADwAQU1AAAAAAA+oKAGAAAAAMAHFNQAAAAAAPiAghoAAAAAAB9QUAMAAAAA4AMKagAAAAAAfEBBDQAAAACADyioAQAAAADwAQU1AAAAAAA+oKAGAAAAAMAH4atXr17tr53v27dPBw4cUFJSUqf2I0eO6LXXXtO2bdskSYmJiZKk/fv365VXXtHw4cMVExPTYxsAAP1BT/Nlhw0bNshmsykpKYn5EgCAIPDbGerMzEwtW7ZMWVlZXbYdPnxYBQUFKikp0eLFi7Vt2zbZbDZZrVYdOHBAVqtVVVVV3bYBANBfdDdfdtiyZYuWL1+urKws5ksAAILEb2eoCwoKtGPHDiUnJ2vu3LmdtiUnJ8tqtWrBggUqKyuT3W5XeXm5wsPD9bvf/U579uxRWFiYDh482KVt+vTp/kgXAICQ0918OXv2bB08eFA//OEPtXTpUg0ePFjV1dXMlwAABIHfzlBbrVZZrdZeY9ra2pSTk6NLL71UJSUlSkhIkHT8kraioqJu2wAA6G9OnC/r6+t100036Q9/+INGjx4tScyXAAAESUQwD37vvffqgQce0MyZM5WdnS2LxdIlprs2ScrNzVVeXl6ntpiYmC5nwwEA8ERycnKwU+jRifPlL3/5S40YMUIbN250zYNRUVE666yzuu3LfAkAOJlCeb4MhoAW1Nu3b9ewYcN01llnadWqVUpJSdGyZcskSfHx8crOzpYkFRcXKzU1VU6ns0tbh7S0NKWlpXXaf0ZGBv+DAQBey8/PD3YK3XI6nV3my3PPPVd1dXWqqalRU1OTJCkhIcF1Vpr5EgDgL6E6XwaT3y757vhGPC8vT7m5uZKktWvXasiQIVq7dq1ycnLU0NCgjIwM5ebmauHChcrMzNTKlSu1ZcsWzZ8/v9s2AAD6i+7mS6vVqvT0dKWnp7tur1qzZg3zJQAAQeC3M9TNzc2aOXOm67+l4/eATZ48WdOnT9eiRYtUU1Pj2h4dHa2srCzXT3R0tCR12wYAQH/Q3Xx5oo4zzz3NoQAAwL8sxhgTiAM1NjZq+/btWrBggd+OkZGRoZ/+9Kd+2z8A4NSUn5/fry6BZr4EAPiiv82XnvDbJd9fFxUV5ddiGgAAAACAQApYQQ0AAAAAwKmEghoAAAAAAB9QUAMAAAAA4AMKagAAAAAAfEBBDQAAAACADyioAQAAAADwAQU1AAAAAAA+oKAGAAAAAMAHFNQAAAAAAPiAghoAAAAAAB9QUAMAAAAA4AMKagAAAAAAfBAR7AQAAAD8wRyzSU6Hd50GDZclMso/CQEATjkU1AAA4JTUXpgrtdi96hM2/lxZYs70U0YAgFMNl3wDAAAAAOADzlADABCijhw5oi1btqihoUEXXXSRLrroom7bJGn//v3KysrS5ZdfrqlTpwY5cwAA+gfOUAMAEKIOHz6sgoIClZSUaPHixdq2bVu3bTabTVarVQcOHJDValVVVVWwUwcAoF/gDLWfmOZaOb/a8Y32ET7hImnA0JOUEQCgr7FarbJarZIkY4x27typn/zkJ13aioqKtHDhQq1bt052u11bt27VihUrgpk6AAD9AgW1n1icDpmG8m+0D9PukOUk5QMA6Lva2tqUk5Oj9evXd9uWnZ2thIQESVJiYqKKioqClSoAAP0KBTUAACHu3nvv1QMPPKCZM2d225adnS2LpfuvYHNzc5WXl9elPT8/32/5hopBFRUKazvmVZ9WFclhH+CnjAAApxoKagAAQpTT6dSqVauUkpKiZcuW9dgWHx+v7OxsSVJxcbFSU1Nd+0hLS1NaWlqn/WZkZCg5OTlAryJ4HM1jfXhs1niFxZz67w0A+KI/fBnrLRYlAwAgRK1du1Y5OTlqaGhQRkaGcnNzu21buHChMjMztXLlSm3ZskXz588PduoAAPQLnKEGACBETZ8+XYsWLVJNTY0kqbm5udu26OhoZWVluX6io6ODmTYAAP0GBTUAACHqxFW+v97+dampqZ0u9QYAAP7HJd8AAAAAAPiAghoAAAAAAB9QUAMAAAAA4AMKagAAAAAAfEBBDQAAAACADyioAQAAAADwAQU1AAAAAAA+oKAGAAAAAMAHFNQAAAAAAPiAghoAAAAAAB9QUAMAAAAA4AMKagAAAAAAfEBBDQAAAACADyioAQAAAADwAQU1AAAAAAA+oKAGAAAAAMAHfi2o9+3bp9zc3G637d+/X2vXrtXnn3/udRsAAP3BkSNH9Nvf/lZr1qzRBx984GpnvgQAIDT4raDOzMzUsmXLlJWV1WWbzWaT1WrVgQMHZLVaVVVV5XEbAAD9xeHDh1VQUKCSkhItXrxY27Zt67PzpTlmk7Nsr3c/lV8EO20AAHoV4bcdR0QoOjq6222ZmZlauHCh1q1bJ7vdrq1bt6q9vd2jthUrVvgrZQAAQorVapXVapUkGWO0c+dOFRUV9cn50jTa5Czd412ngcMUFnOmfxLqTYtdpm1TEq8AACAASURBVPWYd30iBskyeKR/8gEAhCy/FdRWq7XHy72Li4uVkJAgSUpMTFRRUZGcTqdHbQAA9DdtbW3KycnR+vXrlZ2d3WVuNMYwX55EzsovvD47bhker/BJl/kpIwBAqPJbQd0bi8Uii8XS5d+etHXIzc1VXl5el33n5+f7KWvvhLfUaKDN9o320VRQIDNw+EnKCADQV91777164IEHNHPmTGVnZ3eaDzt01yaFznwZUVekAV7Oi87IJjUP8j3PQRUVCmvz7kxzq4oU1nZMEXXe5dreGKYWS2j8DQIACJyAFtTbt2/XsGHDFB8fr+zsbEnHz1anpqbK6XR61NYhLS1NaWlpnfafkZGh5OTkAL0aNxptcjR3f8m7p8InTuTyMQAIgFD5MvbrnE6nVq1apZSUFC1btkySPJ5DO4TKfOmsbJVTpd51GjhMEd8gT0fzWKnF7lWfsPHjpRa7nJH1XvWzDB+n8FD5GwQA/CRU58tg8tuiZB3fiOfl5bku/V67dq2GDBmihQsXKjMzUytXrtSWLVs0f/58j9sAAOgv1q5dq5ycHDU0NCgjI0O5ubnMlwAAhBC/naFubm7WzJkzXf8tHb8HbPLkyZKkrKws10/H4mWetgEA0B9Mnz5dixYtUk1NjaTj82l0dDTzJQAAIcKvi5J1rEwqSY2NjVq5cqXr36mpqZ0uSfOmDQCA/uDrc2kH5ksAAEKD3y75/rqoqCgtWLAgUIcDAAAAAMCvAlZQAwAAAABwKqGgBgAAAADABxTUAAAAAAD4gIIaAAAAAAAf+G2VbwAAgG/KtNSr/fO3vO4XMXWpH7IBAKAzzlADAAAAAOADzlADAACPmZojMi12r/pYoqL9lA0AAMFFQQ0AADzmrM6XqS/xqk9YzJnSwGF+yggAgODhkm8AAAAAAHxAQQ0AAAAAgA8oqAEAAAAA8AEFNQAAAAAAPqCgBgAAAADABxTUAAAAAAD4gIIaAAAAAAAfUFADABDi9u3bp9zcXNe/t2/frieeeELvvfeeq23//v1au3atPv/882CkCABAv0RBDQBACMvMzNSyZcuUlZUlScrOzv7/7N15eFT13f//10w2AgZFhsVshlBlSV2BgjD9Cjpo3EpraRugtbV39e5NrbdVcaGtSrfLKG2xBZfauqAVt9rQgk3CpLIkVEUFgShbQoxJWDJJCEOSSTIz5/eHP+YmZmHOJMMkw/NxXVwX+ZzzPp/3mZPJe95zzpxRTk6OampqlJOTo4KCArlcLjkcDu3Zs0cOh0N1dXURzhqnitGwX/6D2039M47WRDptAIgasZFOAAAAdC82NlY2my3w88cff6zrr79ev//97+X3+1VZWana2lplZ2drxYoVcrvdKiws1Lx58yKYNU4Vf3256QbZOmK8LEOTw5QRAJxeOEMNAEA/5nA45HA4Aj/Pnz9fGzZs0IIFC1RbW6v58+erpqZGKSkpkqS0tDRVVVVFKl0AAE4rnKHu54zmOsnXHnK8JX6IlJDUhxkBACKprKxMkydP1vz583XnnXdq9+7dkiSLxdLl+sXFxSopKek0Xl5eHtL8CQcPKqbZZSrG214jf9wQxbvMxfnjWtQaU6FEk3GS5KmoUPzhw7K2N5mKa1OVrO1Nim00N6ev2apWS2iPaW+Eejza2k59rgAQjWio+zl/5WYZLUdCjreOGC9r6pQ+zAgAEEkbN27UqFGjdO2116qoqEhFRUVKTk5WUVGRJKm6ulpZWVmB9e12u+x2e4dt5ObmKjMzM6T5fcZ+GUf9pmKsI5KlhCT5dcDcZAlJisnIkK/ZdvJ1Pyc2I0NeX5nU6jYVZ01NlVrd8scdNRVnGTpaMSE+pr0R6vGwpp76XAEMfKG+GRvNaKgBAOjHTjzDXFxcrBkzZuiaa66RYRhatWqV1q5dq/T0dC1atEgLFy5UQUGBli5dGuGsAQA4PfAZagAA+jGPx6NJkyZp0qRJ8ng8mjZtmvLz85Wamqo1a9bo0ksvlc1mk9Pp1Lhx4+R0OjvcxAwAAIQPZ6gBAOjHPn9TMkmaOnWqpk6d2mEsKyurw6XeAAAg/GioAQAA+oBxtEa+siLTcbGXfCcM2QAATgUu+QYAAAAAIAQ01AAAAAAAhICGGgAAAACAENBQAwAAAAAQAhpqAAAAAABCQEMNAAAAAEAIaKgBAAAAAAgB30MNAACAsDOaXJLfay5o0FBZ4gaHJyEA6AM01AAAAAg73yfFUqvbVIw1dYosI8aHKSMA6D0aagAAgAjzbn3BdEzM2CvDkAkAwAw+Qw0AAAAAQAhoqAEAAAAACAENNQAAAAAAIaChBgAAAAAgBDTUAAAAAACEIGIN9aZNm/Too4/qrbfeCoyVlpbqscce00cffdTjGAAAp5OdO3equLg48HNTU5OeeOIJbd68OTBGvQQA4NSLSENdVFSknJwc1dTUKCcnRwUFBXK5XHI4HNqzZ48cDofq6uq6HAMA4HSSn5+vuXPnyul0SpL8fr+uvvpqbd68Wc3NzZJEvQQAIEIi8j3UH3/8sa6//nr9/ve/l9/vV2VlpWpra5Wdna0VK1bI7XarsLBQPp+v09i8efMikTIAABERGxsrm80W+Lm4uFhWq1UvvPB/31ucn59PvQQAIAIicoZ6/vz52rBhgxYsWKDa2lrNnz9fNTU1SklJkSSlpaWpqqqqyzEAAE4nDodDDocj8POOHTs0cuRILVu2LPCxKeolAACREZEz1GVlZZo8ebLmz5+vO++8U7t375YkWSyWTut2NSZ99g59SUlJp/Hy8vK+TTZEMa0NSnC5erWNlooKJRw+JGubO+RteNtr1NbWPx4TAEDvud1u7dq1S2PHjtWTTz6pJ554QtKpq5cJBw8qptlcffO218gfN0TxJuuiP65FrTEVSgyhnnoqKhR/+LCs7U2m4tpUJWt7k2Ibzc3pa7bK2zIkpNrfXF6uwSHEtcZVKrYxtOMRidcGg0I8Hl53fJgyAoDei0hDvXHjRo0aNUrXXnutioqKVFRUpOTkZBUVFUmSqqurlZWVJb/f32nsOLvdLrvd3mG7ubm5yszMPHU70pNml7we28nX60FMRob8lk9ltCSEvA3riGRZU/vJYwIA/VR/eTM2GMnJyZo1a5Zyc3Pl9/v1/vvvd1lDj+vreukz9ss46jcVYx2RLCUkya8D5iZLSFJMRoZ8zebraWxGhry+MqnV3JvS1tRUqdUtf9xRU3GWoaNlHZEuX/teU3GSFJuZKW+j+X2MSU+Xv7YppOMRidcGXs/IkI6HdQSvY4D+YiDVy1MlIg31jBkzdM0118gwDK1atUpr165Venq6Fi1apIULF6qgoEBLly6VpC7HAAA4XZx4hrm4uFjXXXedFi9erLi4OL3yyitas2aN0tLSqJcwxWhyyXDXmAuKiZd1xPjwJAQAA1RQDfXu3btVW1sru92u3bt3a82aNcrOzu7wDrgZ06ZNU35+vkpKSrRmzRpdeumlkiSn0xn4d/wGLF2NAQAwEIVSTz0ejyZNmhT4//Dhw7Vu3Trl5+crLy9Pl1xyiSTqJcwxml3yH/jQXFBCEg01AHxOUA11QUGB6uvrNXXqVDkcDlVVVWnJkiXas2ePRo8eHdLEU6dO1dSpUzuMZWVldXpR0dUYAAADUSj19PM3JZOkCRMmaMKECR3GqJcAAJx6Qd3l2+12y+PxaOvWraqqqtLMmTM1fPhw/fOf/wx3fgAARA3qKQAA0SWoM9SjR4/WU089pXfeeUeS9Otf/1pr1qzR4cOHw5ocAADRhHoKAEB0CeoM9Q033KDGxkatX79eaWlpmjJliuLj43XmmWeGOz8AAKIG9RQAgOgS1BnqkSNHqqSkRAUFBZozZ47i4uJkt9uVnJwc7vwAAIga1FMAAKJLtw31iV/TcaK//e1vgf8PGjQoPFkBABAlqKcAAESvbhtqp9OpJUuW9Bj84IMPym6393lSAABEC+opAADRq9uG2m63695775UkHThwQDt27NBVV10VWP7aa68FvvsSAAB0jXoKAED06rahPvF7L//whz8oMTFRDz/8cGB5a2urysvLw58hAAADGPUUAIDoFdRNyZKSkvT000+rvr5emZmZamho0Isvvqhf/epX4c4PAICoQT0FACC6BNVQf+tb39KKFSv02muvBcZGjRqlr3/962FLDACAaEM9BQAgugTVUA8ePFj/+c9/9I9//EPl5eUaOXKkvvrVr/K9mQAAmEA9BQAgugTVUJeUlKi4uFgzZszgXXQAAEJEPQUAILpYg1mpoaFBDzzwgNauXRvufAAAiFrUUwAAoktQZ6jPOussjR8/Xv/4xz901llnBcZnzJjB92YCABAk6ikAANElqIba6XRq+/btkqT77rsvMP7ggw/yAgAAgCBRTwEAiC5BNdR2u1333ntvl+MAACA41FMAAKJLUA21w+HQzJkzlZeXp/3792vMmDGaM2eO4uLiwp0fAABRI9R6unPnTh05cqRT452XlyebzSa73a7S0lI5nU7Nnj1bEydODOduAACA/19QDbXX69Xll1+uzZs3B8amTZumjRs30lQDABCkUOppfn6+7rjjDuXk5HRoqAsKCpSTk6P77rtP48ePl8Ph0I033qjc3Fzt2LFDw4cPD/v+AABwuguqoc7Ly9O2bdt08803a+TIkaqvr9dLL72k1atXa+7cueHOEQCAqBBKPY2NjZXNZuswtnfvXv3sZz/TrbfeKumzpjs7O1srVqyQ2+1WYWGh5s2bF/b9AQDgdBdUQ11eXq5bbrlFy5YtC4wlJiZq//79YUsMAIBoE0o9dTgcKi4uDvzsdrt100036dlnn9Xrr78uSaqurlZKSookKS0tTVVVVWHaAwAAcKKgGurMzEwtWbJEbrdbI0aMkMvl0qpVq7Ry5cpw5wcAQNToi3q6fPlyDR06VGvXrlVJSYkkafDgwbrwwgu7XL+4uDiw3onKy8tD2oeEgwcV0+wyFeNtr5E/bojiXebi/HEtao2pUKLJOEnyVFQo/vBhWdubTMW1qUrW9ibFNpqb09dslbdliBJCyLW5vFyDQ4hrjatUbOOpPR6eQaH93kjSoBCPh9cdH/KcABBuQTXUc+bM0UUXXaRnnnkmMDZ9+nTNmTMnbIkBABBt+qKeTpkyRY2NjWpoaFBLS4skKSUlJXBWurq6WllZWYH17XZ7p5uZ5ebmKjMzM6R98Bn7ZRz1m4qxjkiWEpLk1wFzkyUkKSYjQ75m28nX/ZzYjAx5fWVSq9tUnDU1VWp1yx931FScZehoWUeky9e+11ScJMVmZsrbaH4fY9LT5a9tOqXHIzbE3xtJ8npGhnQ8rCNCnxNA3wr1zdhoFlRDHRcXp40bNyovL08VFRWBu5LGxgYVDgAAFFo9PfEMc3FxsRwOhxwOhyTpoYcekiTddtttuuCCC7Rw4UIVFBRo6dKlYd8XAAAgWYNZ6ciRI/rtb3+rrKws3X333br44ou1bNkyuUK4PAkAgNNVKPXU4/Fo0qRJmjRpkjweT4dlx88+22w2OZ1OjRs3Tk6ns9NNzAAAQHgEdYp59erVWr9+ve69915J0tixY7V582adeeaZuuWWW8KaIAAA0SKUenriGemulh2XlZXV4VJvAAAQfkGdoT506FDg7qHHDR8+XA0NDWFJCgCAaEQ9BQAgugR1hnrixIm677771NDQoPPOO0/l5eX629/+pry8vHDnBwBA1KCeAgAQXYJqqK+99lrNmjVLb7zxRmDsy1/+sq699tqwJQYAQLShngIAEF2CaqitVqsKCgr05ptvau/evRozZoy+8pWvKCYmJtz5AQAQNainAABEl6A+Qy1JBw4cUGVlpaZOnaobb7xRb7/9tvbt2xfO3AAAiDrUUwAAokdQDfXhw4d18cUX68c//rGcTqckyel0qrCwMKzJAQAQTainAABEl6Aa6ry8PMXHx2vKlCmBMb/fr/r6+rAlBgBAtKGeAgAQXYL6DPXRo0f1ne98R4MHDw6MffDBB8rOzg5bYgAARBvqKQY8X5v8tbtMh1lHjA9DMgAQeUE11FlZWXrkkUd03nnnSZJ27typf/3rX7rvvvvCmhwAANGEeoqBzvB65D/woek467AxYcgGACIvqIY6OztbU6dO1Zo1awJjX/nKV2S328OWGAAA0YZ6CgBAdAmqobZYLFq9erXWrl2rPXv26Atf+IJuuOGGcOcGAEBUoZ4CABBdgmqopc++O/PEou/z+bRr1y6NH89nYgAACBb1FACA6HHSu3zv3r1bjz32mF5++WW1t7dLkqqqqnTllVfq5ZdfDnuCAABEA+opAADRp8cz1JWVlfrSl76ko0ePSpK+9a1vacGCBfr+97+vxMREPf/886ckSQAABjLqKdALrW4ZbU3mYmIHyZJ4VnjyAYAT9NhQ//Of/5RhGLr99tvV0tKilStX6pVXXtHo0aNVVFSkc88991TlCQDAgEU9BULnr91l+qu6LEOTFTP2yjBlBAD/p8eGuq6uTj/84Q/1yCOPSJISEhK0atUqrVu3LvCVHwAAoGfUUwAAolOPDbXf79fWrVuVm5srSdq1a5cuuugirV27VmvXrtWMGTNC/qqPpqYmrVy5UhdddJGmT58uSSotLZXT6dTs2bM1ceLEbscAABhIeltPd+7cqSNHjshut2v//v0qKCjQsWPHNH369B5rKAAACK+T3uXb6XTK6XR2GPv3v/8tSXrwwQdDaqj9fr+uvvpqjRkzJvDOvMvlksPh0I033qjc3Fzt2LFDhmF0Ghs+fLjp+QAAiLRQ62l+fr7uuOMO5eTkyG63q6ysTBUVFWpra9P111+vN954Q1/84heplwAARECPDbXdbte9997b4/JQFBcXy2q16oUXXgiM5efnKzs7WytWrJDb7VZhYaF8Pl+nsXnz5oU0JwAAkdKbehobGyubzRb42eFwyOFwSJIMw9CWLVtUVVVFvQQAIAJ6bKhPLNp9aceOHRo5cqSWLVumiy66SLNmzVJNTY1SUlIkSWlpaaqqqpJhGJ3GAAAYaHpTTx0Oh4qLizuNt7e3a8OGDXr66adVVFREvQQAIAJOesl3OLjdbu3atUtjx47Vk08+qSeeeEKSZLFYOq3b1Zj02VnukpKSTuPl5eV9m2yIYloblOBy9WobLRUVSjh8SNY2d8jb8LbXqK2tfzwmAIC+c9ddd2nx4sWaNGmSioqKTlm9TDh4UDHN5uqbt71G/rghijdZF/1xLWqNqVBiCPXUU1Gh+MOHZW0393VLbaqStb1JsY3m5vQ1W+VtGRJS7W8uL9fgEOJa4yoV28jx6Iqv2apWC69/AIRftw31li1b1NraGvJl3T1JTk7WrFmzlJubK7/fr/fff1/JyckqKiqSJFVXVysrK0t+v7/T2HF2u71Tbrm5ucrMzOzzfEPS7JLXYzv5ej2IyciQ3/KpjJaEkLdhHZEsa2o/eUwAoJ8K55uxfV1P/X6/br/9dk2YMEFz586VpC5r6HHd1ctzfftMz21NvlR+Y7SMo35zcSOSpYQk+XXA3IQJSYrJyJCv2Xw9jc3IkNdXJrWae1PampoqtbrljztqKs4ydLSsI9Lla99rKk6SYjMz5W00v48x6eny1zZxPLpgGTpaMf3lNSEQRfrLycv+pNuGuqCgQE1NTbLb7YFLzfrqxcB1112nxYsXKy4uTq+88orWrFmjtLQ0LVq0SAsXLlRBQYGWLl0qSV2OAQAwUPS2np54hrm4uFhbtmzRhg0blJaWptzcXM2YMUPZ2dmm66Vx7JD5nfG1mY8BACCKddtQDxs2TL/73e907Ngxffjhh0pISOj0+a9QvzZr+PDhWrdunfLz85WXl6dLLrlE0v/dAdXpdAZuwNLVGAAAA0Vv66nH49GkSZMC/7/gggt03XXXqaGhITBms9molwAAREC3DfWcOXP085//XMuXLw+Mff7rPkL92ixJmjBhgiZMmNBhLCsrq8Nlat2NAQAwUPS2nnZ1Q7OubnBGvQQA4NTrtqFOTU3Vrl27tHr1alVWVqq9vb3TOuH4fDUAANGEegoAQPTq8S7fI0eO1C233CJJ8nq9ysvL0/79+zVmzBjNmTNHcXFxpyRJAAAGMuopAADRKaivzfJ6vbr88su1efPmwNi0adO0ceNGXgQAABAk6ikAANElqIY6Ly9P27Zt080336yRI0eqvr5eL730klavXh34yg4AANAz6ikAANElqIa6vLxct9xyi5YtWxYYS0xM1P79+8OWGAAA0YZ6CgBAdAmqoc7MzNSSJUvkdrs1YsQIuVwurVq1SitXrgx3fgAARA3qKQAA0SWohnrOnDm66KKL9MwzzwTGpk+frjlz5oQtMQAAog31FACA6BJUQx0XF6eNGzcqLy9PFRUVgbuSxsYGFQ4AAEQ9BQAg2gRdwWNjY7lhCgAAvUQ9BQAgelgjnQAAAAAAAAMRDTUAAAAAACEIqqEuLi5WcXFxp7EPPvggLEkBABCNqKcAAESXoD5D7XQ6JUl2uz0w9uabb+qMM87QpZdeGp7MAACIMtRTAACiS48NdXFxsUpKSlRSUiJJys3NlSS1tLTo+eef109+8pPwZwgAwABHPQUAIDr12FA7nU4tWbKkw8/HWSwWffnLXw5fZgAARAnqKQAA0anHhtput+vee+/tNJ6UlKTLL79cU6dODVtiAABEC+opAADRqceG2uFwyOFwSJLefvttlZSUyOv1SlLgsrUTPwcGAAA662093blzp44cORJYp7S0VE6nU7Nnz9bEiRO7HQMAAOEV1E3JSkpK9OUvf1mGYXQYf/DBB2moAQAIUij1ND8/X3fccYdycnJkt9vlcrnkcDh04403Kjc3Vzt27JBhGJ3Ghg8ffip2CQCA01pQDfV//vMfjRo1SvPmzVN8fHxgnGYaAIDghVJPY2NjZbPZAj/n5+crOztbK1askNvtVmFhoXw+X6exefPmhXVfgH7P1yajud50mCVpdBiSARCtgmqoJ0+erG9/+9t69NFHw50PAABRK5R66nA4Onx3dXV1tVJSUiRJaWlpqqqqkt/v7zQGnO6MJpd8ZUWm42Iv+U4YsgEQrYJqqGNjY7Vp06bA13wcN2PGDM5SAwAQpL6opxaLRRaLpdPPnx877vhXdn2ey+Uym75a4yoV23hQMc3mYr3tNfLHDVG8yTn9cS1qjalQYgi5eioqFH/4sKztTabi2lQla3uTYhvNzelrtsrbMkQJIeTaXF6uwRyPLkXqeABAsIJqqJ1Op9555x298847Hcb5DDUAAMHri3qanJysoqLPzrpVV1crKytLfr+/09hxdru907Zzc3M7XEYerJj0dPlrm2Qc9ZuKs45IlhKS5NcBcxMmJCkmI0O+ZvO5xmZkyOsrk1rdpuKsqalSq1v+uKOm4ixDR8s6Il2+9r2m4iQpNjNT3kaOR1cidTwAdK2cN5w6Caqh7u7rPmimAQAIXij19MQzzMXFxcrOztaiRYu0cOFCFRQUaOnSpZLU5RgAAAivoBrqQYMGadiwYV2OAwCA4IRSTz0ejyZNmhT4v81mk9PpDPw7fqa5qzEAABBeQV/yvWTJkk7jXPINAEDwQqmnJ36H9XFZWVkdLuvubgwAAIRXyJd85+fn00wDAGAC9RQAgOgSVEPd1bvjXq9XMTExYUkKAIBoRD0FACC6BNVQn3hDlPb2djU0NOill17S6NGjNWvWrLAmCABAtKCeAgAQXUL+DLXFYtFll10WlqQAAIhG1FMAAKJLSJ+hPuusszRz5kxNmzYtbIkBABBtqKcAAESXoD9DPXPmTOXl5Wn//v0aM2ZM4Cs8AABAcKinAABEl6Aaaq/Xq8svv1ybN28OjE2bNk0bN25UXFxc2JIDACCaUE8BAIguQTXUeXl52rZtm26++WaNHDlS9fX1eumll7R69WrNnTs33Dmecv7Kt+Wv2xtyvGWITTGpU/owIwBANDjd6ikAANEuqIa6vLxct9xyi5YtWxYYS0xM1P79+8OWGAAA0YZ6CgBAdAmqoc7MzNSSJUvkdrs1YsQIuVwurVq1SitXrgx3fqe90kN+NR2zhBx/TkybMlL7MCEAQMiopwAARJegGuo5c+booosu0jPPPBMYmz59uubMmRO2xPCZRo9fbk/oDfXQVn8fZgMA6A3qKQAA0SWohjouLk4bN25UXl6eKioqNGbMGM2ZM0exsUGFAwAAUU8BAIg2PVbwsrIyHThwQHa7XbGxsR1umFJcXKzk5GRlZmaGPUkAAAYy6ikAANHJ2tPCwsJCOZ3OLpc5nc5ulwEAgP9DPQUAIDr12FDX1dXJ6/V2uczr9erw4cNhSQoAgGhCPQUAIDr12FAPGjRI7e3tXS4zDEODBg0KS1IAAEQT6ikAANGpx89QZ2Zm6v7775fT6dSFF16oUaNG6dChQ/rwww+1fft2vfTSS71OIC8vTzabTXa7XaWlpXI6nZo9e7YmTpwoSV2OAQAwkPR1Pd20aZPefvttTZ48WbNmzZJEvQQAIBJ6bKivvvpqjR49Wh988IE++OCDDstGjRqlq666qleTFxQUKCcnR/fdd5/Gjx8vh8OhG2+8Ubm5udqxY4cMw+g0Nnz48F7NCQDAqdaX9bSoqEg33XSTvvnNbyonJ0crV67UpEmTqJcAAERAjw31kCFD9PHHH2vdunUqKyuTz+eT1WpVZmamZs+eraFDh4Y88d69e/Wzn/1Mt956qyQpPz9f2dnZWrFihdxutwoLC+Xz+TqNzZs3L+Q5AQCIhL6spx9//LGuv/56/f73v5ff71dlZaVqa2uplwAARMBJv/jyjDPO0Ne+9rU+ndTtduumm27Ss88+q9dff12SVF1drZSUFElSWlqaqqqq5Pf7O40BADAQ9VU9nT9/vqZPn64FCxbIMAzNnz9fK1asoF4CABABJ22ow2H58uUaOnSo1q5dq5KSEknS4MGDdeGF3QVsdQAAIABJREFUF3ZYz2KxyGKxdLmN4uLiQOyJysvLe51f/OEaxbpdIcf7j/nU7vtECa7QtyFJLRUVamxsVHNLa8jbcLlcffKYAAD6h7KyMk2ePFnz58/XnXfeqd27d0uS6XrpCqFGtcZVKrbxoGKazcV622vkjxuieJNz+uNa1BpTocQQcvVUVCj+8GFZ25tMxbWpStb2JsU2mpvT12yVt2VISLW/ubxcgzkeXYrU8QCAYEWkoZ4yZYoaGxvV0NCglpYWSVJKSkrgHfXq6mplZWXJ7/erqKiow9hxdrtddru9w3Zzc3OVmZnZ6/z8sYflrzP3B/9EliE2xaSeK6/H1qs8YjIyVFW5X9aY5pC3YbPZ+uQxAYBoNpDeeNy4caNGjRqla6+9VkVFRSoqKlJycrLpemmzma9RMenp8tc2yTjqNxVnHZEsJSTJrwPmJkxIUkxGhnzN5nONzciQ11cmtbpNxVlTU6VWt/xxR03FWYaOlnVEunzte03FSVJsZqa8jRyPrkTqeADo2kCql6dKRBpqh8Mhh8MhSXrooYckSbfddpsuuOACLVy4UAUFBVq6dKkkadGiRZ3GAAA4Xc2YMUPXXHONDMPQqlWrtHbtWqWnp1MvAQCIgB6/h/pUOP7Ouc1mk9Pp1Lhx4+R0OmWz2bocAwDgdDZt2jTl5+crNTVVa9as0aWXXkq9BAAgQiJyhvpEx89US1JWVlaHy9S6GwMA4HQ2depUTZ06tcMY9RIAgFMv4g01AAAAQvPBp22qrTN3weG5Xo8GJ8Xp43JzcYmD/Jo51lQIAEQ9GmoAAAAExdPm07aaru8o35NLvuDjRSeAqMTfNgAAAATF5zfU4DHfUPsNIwzZAEDkRfymZAAAAAAADEQ01AAAAAAAhICGGgAAAACAENBQAwAAAAAQAm5KBgAAAJzAf3C76RjrsDFSQlIYsgHQn9FQAwAAACfwH/jQdIxlsE0WGmrgtMMl3wAAAAAAhICGGgAAAACAENBQAwAAAAAQAhpqAAAAAABCQEMNAAAAAEAIaKgBAAAAAAgBDTUAAAAAACGgoQYAYIBpamrSE088oc2bNwfGSktL9dhjj+mjjz6KYGYAAJxeaKgBABhA/H6/rr76am3evFnNzc2SJJfLJYfDoT179sjhcKiuri7CWQIAcHqIjXQCAAAgeMXFxbJarXrhhRcCY/n5+crOztaKFSvkdrtVWFioefPmRTBLAABOD5yhBgBgANmxY4dGjhypZcuW6a233pIk1dTUKCUlRZKUlpamqqqqSKYIAMBpgzPUAAAMIG63W7t27dLYsWP15JNP6oknnpAkWSyWLtcvLi5WSUlJp3GXy2V67ta4SsU2HlRMs7lYb3uN/HFDFG9yTn9ci1pjKpQYQq6eigrFHz4sa3uTqbg2Vcna3qTYRnNz+pqt8rYMUUIIuTaXl2twiMejoaFBx44dMxXncrmU0NRsOq69rU2VlZWm4ySpsvITDXVF//HwDfaYjgMwsNFQAwAwgCQnJ2vWrFnKzc2V3+/X+++/r+TkZBUVFUmSqqurlZWVFVjfbrfLbrd32EZubq5sNpvpuWPS0+WvbZJx1G8qzjoiWUpIkl8HzE2YkKSYjAz5ms3nGpuRIa+vTGp1m4qzpqZKrW75446airMMHS3riHT52veaipOk2MxMeRtDOx6HXQ3y+g1TcTabTYOTklRfZ65pTByUqPT0dFXsKTUVJ0np6ecq3loR0vH45ECdPq01F3dW0nCNTz/1x8MyNNl0HDCQlJeXRzqFfoeGGgCAAeS6667T4sWLFRcXp1deeUVr1qxRWlqaFi1apIULF6qgoEBLly6NdJpAn2lu9avB0/UVGN2J9Zh7kwEAQsVnqAEAGECGDx+udevWKS0tTXl5ebrkkktks9nkdDo1btw4OZ3OkM4+AwAA8zhDDQDAADNhwgRNmDChw1hWVlaHS70BAED40VADAAAg7Dbt96vFY+7iyAmJ3OQLQP9GQw0AAAD0AX/1BzJM3gXfcla6rCPGhykjAOFGQw0AAAD0AcPTIOPYIVMxlsRhYcoGwKnATckAAAAAAAgBDTUAAAAAACHgkm8AAIA+UO9uUVVFrem4iy4JQzIAgFOChhoAAABR50hTm3bVWEzHXcYbHABMoKEGAACIsLIG85/CS23zhiGT6OH1G2rwmG+oAcAMGmoAAIAIK2swH3N2q6/vEwEAmMJNyQAAAAAACAENNQAAAAAAIaChBgAAAAAgBDTUAAAAAACEgIYaAAAAAIAQ0FADAAAAABACGmoAAAAAAEIQke+h3r9/vwoKCnTs2DFNnz5d06dPlySVlpbK6XRq9uzZmjhxYrdjA4GnzadPGnr3fkVGu7+PsgEARKO8vDzZbDbZ7fYBWy8BABjIInKGuqysTBUVFaqpqdH111+v9evXy+VyyeFwaM+ePXI4HKqrq+tybKBo9fpV1qBe/Wv1+iK9GwCAfqqgoEA5OTlyOp0Dul4CADCQReQMtcPhkMPhkCQZhqEtW7aoqqpK2dnZWrFihdxutwoLC+Xz+TqNzZs3LxIpAwDQb+zdu1c/+9nPdOutt0qS8vPzqZcAAERARD9D3d7erg0bNuiKK65QTU2NUlJSJElpaWmqqqrqcgwAgNOZ2+3WTTfdpGeffVZnn322JKm6upp6CQBABETkDPVxd911lxYvXqxJkyapqKhIFoul0zpdjUlScXGxSkpKOo2Xl5f3Oq/4wzWKdbtCjvcf86mh+VMdO3asV3lUVlaqsbFRzS2tIW/D5XL1yWMCAOgfli9frqFDh2rt2rWBOjh48GBdeOGFXa7fXb10uczXuda4SsU2HlRMs7lYb3uN/HFDFG9yTn9ci1pjKpQYQq6eigrFHz4sa3uTqbg2VengoVq11+43FWdNatEZyfEh1f7y8vKQ4mqqq9XQ0GA61uVyKaGp2XRce1ubKisrQ8q1svITHT3aqNa2NlNxhw4eUmuL+VxjrRbFV1eHfDwGn+LnR1sbr9WAgSoiDbXf79ftt9+uCRMmaO7cuZKk5ORkFRUVSfrsnfasrCz5/f5OY8fZ7XbZ7fYO283NzVVmZmbv84s9LH+duQJ8IssQm4acnaZPyvb2Ko/09HQ1HKqUNaY55G3YbLY+eUwAIJoNpDcep0yZosbGRjU0NKilpUWSlJKSEjgrHWy9tNlspueOSU+Xv7ZJxlFzN820jkiWEpLk1wFzEyYkKSYjQ75m87nGZmTI6yuTWt2m4qypqfL6JHeLuVwHDRsmW0qy/NVnmIqTpMzMTO3aaj4uOSVFbc1uef2GqTibzabBSUmqrzPX+CUOSlR6eroq9pSaipOk9PRzdeCTcrV4WkzFjRo9Ss1ut5qazb0WGjZsmJJTUlT96Sem4qTPjoe38dQ+P6ypvFbDwDCQ6uWpEpGG+rHHHtOGDRuUlpam3NxczZgxQ9nZ2Vq0aJEWLlyogoICLV26VJK6HAMA4HR14n1IHnroIUnSbbfdpgsuuIB6CQDAKRaRhvqCCy7Qddddp4aGBkmSx+ORzWaT0+kM/Dv+znlXYwAAQIEzz93VUAChafK0m44Z7PVH9uZEACIi4nf5PlFWVlaHy9S6GwMAAOpQS6mXQN+pOHjEdExqeqvOCkMuAPo33kgDAAAAACAENNQAAAAAAISAhhoAAAAAgBBE9HuoAQAAgNOd0d4seY6aC7LGyjKEGxACkUZDDQAAAESQcaRS/qot5oISkhQ78avhSQhA0GioAQAAgAHIaD0q30erTcfFTvyqlJAUhoyA0w+foQYAAAAAIAQ01AAAAAAAhIBLvgEAQFQ64jHk95iLOaPdH55kAABRiYYaAABEpR0HDLV4zF2MN+HstjBlAwCIRjTUAAAAQB/YV1UvT32tqZgk3znKOJcbhAEDFZ+hBgAAAAAgBJyhBgAAACLI3dyqpiPNpmKsg2I0Kkz5AAgeDTUAAAAQQe6WdtUdaTIVQ0MN9A9c8g0AAAAAQAg4Qw0AAAAMQE0erzaWmz8/NnOsV4MTwpAQcBqioQYAYADZv3+/CgoKdOzYMU2fPl3Tp0+XJJWWlsrpdGr27NmaOHFihLMEAOD0wCXfAAAMIGVlZaqoqFBNTY2uv/56rV+/Xi6XSw6HQ3v27JHD4VBdXV2k0wQA4LTAGWoAAAYQh8Mhh8MhSTIMQ1u2bFFVVZWys7O1YsUKud1uFRYWat68eRHOFACA6McZagAABqD29nZt2LBBV1xxhWpqapSSkiJJSktLU1VVVYSzAwDg9MAZ6tPAJ4ca9fEnrpDjY6wWzZ6c2YcZAQB666677tLixYs1adIkFRUVyWKxdLlecXGxSkpKOo27XObrQmtcpWIbDyqm2Vyst71G/rghijc5pz+uRa0xFUoMIVdPRYWOHm1Ua1ubqbhDBw/J6qlX+7FjpuKaLQ1qq67RMZNxklReXh5SXE11tRoaGkzHulwuJTQ1m45rb2tTZWVlSLlWVn4S8vFobTGfa6zVovjq6lN8PGp0rKFBfpOxra5axTR55DE7Z5t6dTziBzeYjgPQGQ01AAADiN/v1+23364JEyZo7ty5kqTk5GQVFRVJkqqrq5WVlRVY3263y263d9hGbm6ubDab6blj0tPlr22ScdRvKs46IllKSJJfB8xNmJCkmIwM+ZrN5xqbkaH9+/aqxdNiKm7U6FEyjsXI3WIu10HDhsmWkix/9Rmm4iQpMzNTu7aaj0tOSVFbs1tev2EqzmazaXBSkurrzL1RkTgoUenp6arYU2oqTpLS08/VgU/KQzoezW63mpqbTcUNGzZMySkpqv70E1Nx0mfHw/1hKMcjWa6Wg/IY5hrcJNsIxZ9xlurqzc1pHTS0V8dj8NBhpuOA8vLySKfQ79BQAwAwgDz22GPasGGD0tLSlJubqxkzZig7O1uLFi3SwoULVVBQoKVLl0Y6zT7T7vVrf4P5T6hles01/QAAhIKGGgCAAeSCCy7Qddddp4aGzy7X9Hg8stlscjqdgX+hnH3ur9q8fpWFcGVqmo+GGgAQfjTUAAAMICfe5ftEWVlZHS71BgAA4UdDfTrwtii+NfSbklljrJK4KRkAAAAAnIiG+jQQ667ScFfnO7wGy2KNlTSl7xICAAAAgChAQw0AAAAgKK7GZh051moqZsigOJ0z3Pyd04GBgIYaAAAAOM0YTS7J7zUXNGioao8065NDjabCRpw5mIYaUYuGGgAAADjN+D4pllrdpmKsqVMkRc+3CAB9gYYaAAAAOM18/IlLfs9RUzHDE49JiTTUwIloqAEAAAD0W+9+XG065vy04TrrjEFhyAboiIYaAAAAQHi1uuVv2G86zDr6QtW7PabjvD6/6RggFDTUAAAAAMLKaHXLf+BD03HW0ReGIRug70RdQ224D/Yq3jLozD7KBAAAAAAQzaKuofbtW9ereGv6ZX2UCQAAAABJcrlbtaXcajru2kvCkAzQh6KuoQYAAAAQHnFH9mp47V5TMYne0dIZ54U8p//wR6ZjjLQE7T7aosZj5j5/PersM3TuKK5YRfBoqAEAAAAExdrepPg2l6mYmPbE3k3aau7rvSRJ/nYda44xfUOzpMEJ5ufCaY2GGgAAhJ2n3a9jLeZirIYhvvQGANCf0VADAICwO3S0TR8fMPf5ycRBhqaMDVNCAAD0ARpqAAAAAFHHf2Cb/HX15mIs6dK5Xw5TRohG/b6hLi0tldPp1OzZszVx4sRIpwMAQL9EvQSAvnGwYq8qynabikkYlKhLZlwZ8pxbS4rU6jH3uZiMseM0OiP0m72hb/TrhtrlcsnhcOjGG29Ubm6uduzYoeHDh0c6LQAA+pVTWS8PHPWpqcHcpdvDEr2S4sKSDwD0tRZPk+qPNJqKSRzUpnavX58cMhcnSeeOOlNHjjSqxWRDPcrTZHou9L1+3VDn5+crOztbK1askNvtVmFhoebNmxf2eQ8f86rR5IuFEw3y+nTW2X2YEAAAPTiV9fJAo0+1DeZizk3yanBSWNIBgH6jzevTvmpzl5hLUrLtjNAnbXXLaDPZWMcOkjduqNzNraHPi4B+3VBXV1crJSVFkpSWlqaqqqpTMu/hoz5VmXyxcKKz/IbO6rt0+gXDMFRWc6RX2xg1bIiSBsf3UUZRxPDLOHa4d9tIHCZLLF/zgNNUe4sMj/kzAtEkUvUSABBZ/tpd8tfuMhVjGZqsRttlem/3AdPznW+zmI6JdhbDMIxIJ9GdRx55RG63W7/85S/105/+VMOGDdPdd98tSSouLlZJSUmH9ePi4tTe3h6JVAEAA9iIESP0/e9/P9JphIx6CQA4FQZ6vQwLox974YUXjO9973uGYRjGd7/7XeOvf/1rj+s//PDDvZ6zL7bRV9uJtlyibX/6ajv9ZRt9tZ3+so2+2k605RJt+9NX2+mrXCLlVNXL3jxOp3pO9rF/zck+9q852cf+NefpsI/RLPQPCp8C2dnZys/P18KFC1VQUKCrrroq0ikBANDvUC8BAIiMft1Q22w2OZ1OjRs3Tk6nUzabLdIpAQDQ71AvAQCIjH59UzJJysrKUlZWVqTTAACgX6NeAgBw6sU89NBDD0U6ib6Unp7eL7bRV9uJtlyibX/6ajv9ZRt9tZ3+so2+2k605RJt+9NX2+mrXAaKUPe3N4/TqZ6Tfexfc7KP/WtO9rF/zXk67GO06td3+QYAAAAAoL/q15+hBgAAAACgv4q6S75LS0u1atUqDR06VCNGjAhq+cliuvLvf/9ba9eu1bnnnqszzjijy3UKCwtltVo1bNiwoGPMMgxDq1at0rvvvqusrCzFxnb8WPybb76pf/zjH/rwww81duxYJSYmnjQmVG63W88++6yqqqo0fvz4Dss8Ho9ee+01vfnmmyovL9eFF14oi8XSY0xvVFRUaOXKlZKk1NTUbtdbv369SktLdf755wcdY9Z7772n1157TSNHjgz8LhxXWFio119/XSUlJSopKdGQIUN0zjnn9BjTG2vWrFFRUZHOO+88DRo0qNPyffv26ZlnntF5552nIUOGBBUTivb2dq1cuVI7d+7UBRdcIIvFEljm8/n06KOPBh6Tt99+W9OnT+8xpjdcLpeeffZZNTY2auzYsZ2Wl5eX67nnnlN5ebkmTJigmJiYk8aEavfu3frrX/+qxMREjR49utPybdu2adWqVRoyZIhGjRoVVExvrF+/Xq2trV3e4GrTpk36xz/+obS0NCUlJXU71ldeeuklZWRkKCEhIajlr776qjZv3qwJEyYoLi6uT3OJFn1ZLyNRiyorK/X888/L7/crLS2t2/U2btyo7du3a9y4cUHHdOfNN9/UunXr9IUvfEGJiYmdlpeXl+svf/mLMjMzA3X+ZDE9qa+v1zPPPKP6+np94Qtf6LS8oqJCzz77rPbt26eJEycqJibmpDHB2LRpk5qamrr8vdi8ebP+/ve/Kzk5WWeeeWa3Y2Y99dRTmjx5cocxr9erN954Q2vXrtWePXs0btw4xcXFyePx6LnnntOePXuUlZUVUj3YuXOn9uzZ0+Olq3l5eXK5XEpPTw/pteLnPf/88xo/fnyXv+vbtm3TypUrNXnyZMXGxvbJa7WamhqtX79e48aN67Rs+/btevHFF3X48GGNGzdOFoulV8+PkpISvfHGG3rnnXe6/T3o6rkQ6vPj2LFjeu211/Svf/1LNTU1GjdunKxW60mX9+b5sX37dr366qsqKSnR0KFDNXLkyG7XffbZZ5WQkKARI0aE/Pzo7vf/89avX69//vOfmjp1qiR1+/zYv3+/XnnlFa1fv16SujzGmzZt0quvvqq2tjaNGTNGUmh90ukmqs5Qu1wuORwO7dmzRw6HQ3V1dSddfrKYrmzYsEH/9V//pS1btuiGG27ocp3ly5drwYIF2r17d9AxoVi6dKkee+wxvfrqq7rrrrs6Ld+yZYtqa2uVn5+vK664IqiYUN18880qKCjQz3/+c7344osdlrndbn344YdqaGjQU089pf/+7/8+aUyovF6vZs2apdLSUt1444366KOPulxv+/bt+upXv6rXXnst6Biz9u3bpxtuuEG7du3SzJkz1dra2mH56tWr9e9//1sNDQ1qaGhQa2vrSWNC9frrr+vuu+/WW2+9pW9/+9udlu/evVtXXXWVampq1NbWFlRMqO6//3698MILeuqpp/TrX/+60/Ljj8fhw4f1y1/+MqiYUN14443avHmz/ud//kcFBQWd8pg+fbp27dqlZcuW6ac//elJY0Lldrs1c+ZM7d69W9dcc41qamo6LN+1a5euvfZaVVdX69prr1VNTc1JY3rj6aef1rx587Rz585Oy959910tWLBA27Zt01VXXSXDMLoc6yv33HOPbr75ZjU2Nga1/PHHH9fDDz+s1atX60c/+lGf5RFN+rpe/va3vw3UlTvvvLPTfO+9955qa2tVUFCgWbNmBRXTE5/PpyuuuEI7d+7UN77xDe3YsaPL9Xbu3Kk5c+bolVdeCTqmO6tXr9Ydd9yhjRs3Kicnp9PysrIyORwO1dTUqL29PaiYk/nmN7+p4uJi3X777VqzZk2HZUePHtX06dP10Ucfafny5Vq0aNFJY4Lx/PPPKycnR1u3bu207MMPP9Q3v/lN7dy5U1deeaX8fn+XY2Y0NzcrJydHixcv7rSsra1N7733nurq6vTcc8/pO9/5jiTpJz/5iV5//XX97ne/07Jly0zvY35+vubOnSun09ntOgUFBcrJyZHT6QzpteLn3X777frBD36g5ubmTss2bdqkuXPnqra2Vj6fT1Lvnh+S9MEHH+hrX/uaXnnllU7L9u7dqyuvvFIVFRW69957tWLFil4/P7Zt26aDBw9q69atmjJlitxud4flXT0XevP8aGho0I4dO9TQ0KBf/epX+tnPfhbU8t48Pz766CNVV1ervLxc06dPV1lZWZfrPfHEE7r11lu1devWXj0/uvv9P9Ff//pX3Xbbbaqvrw+Mdff8KCsrU0VFhWpqanT99dcHGuvjioqKlJOTo5qaGuXk5KigoKBPfvdPCxH7BuwweOGFF4zvfe97hmEYxne+8x3jpZdeOunyk8V05Uc/+pHxxBNPGIZhGOedd55RXl7eaZ2//OUvxpgxY4x//etfQceEYsqUKcYHH3xg1NXVGaNGjep2vfb2diMuLs7weDxBx5jR0tJinHXWWUZ7e7vx5ptvGl/5yle6XXfz5s3GzJkzTcWYUVxcbFx++eWGYRjGAw88YPzmN7/ptE5tba0xadIk4+c//7nx3e9+N6iYUPz2t7817rnnHsMwDOPqq682nE5nh+ULFy40VqxYYSomVN/85jeNv/3tb4bP5zNsNpvR2NjYYfndd99tPProo6ZiQpWenm5UV1cbH3/8sXHBBRd0u94TTzxh3H777aZizKipqTHS09MNwzCM5557zvjBD37QYflHH31knH/++YbX6zVWr15tfOtb3zppTKjeeOMN4xvf+IZhGIbxwx/+0Hj66ac7LH/88ceNhQsXGoZhGPfdd5/x+OOPnzSmN1544QVj3Lhxxuuvv95p2T333GMsXbrUMAzDuPjii40dO3Z0OdZXfvOb3xhJSUnGp59+GtTymTNnGps2bTKampqMs846y/D7/X2WS7To63r5pS99yXj//feN+vp6Y+TIkd3O6/V6jfj4eKOlpSXomK68/fbbxowZMwzDMIxf/OIXxi9+8YtO69TV1RmTJ082HnzwQWPBggVBxfRkwYIFxssvv2z4/X5j1KhRRl1dXYfl999/f6facbKYntTW1hrnnHOO4ff7jZdeesm46aabOizft2+fkZGRYbS3txv/+te/jK997WsnjQnGqlWrjIkTJxovvvhip2UPPPCA8atf/cowDMOYNm2asWXLli7HzGhqajL+93//1zj77LN7XK+srMxITU01DMMwhg8fbhw5csTYsmWLcdlll5mazzAMY926dcaMGTOMBx98sMvle/bsMSZPnmz8+Mc/Nh588MGQXit+3pIlS4xBgwZ1+Tswd+5c47XXXusw1pvnh2EYxtatW40bbrjBWLBgQadl+fn5xqxZswzDMIw//vGPxqJFi3r9/DjRuHHjOtWArp4LvXl+nKigoCCwPz0t74vnx3HXXXed8fe//73T+IYNG4yZM2cac+fONV588cVePz+OO/H3/0QXX3yxsXXr1g5jwTw/7rjjDuORRx7pMPbHP/7RuPXWWw3DMIzbb7/d+NOf/tQnv/ung6g6Q11dXa2UlBRJn13GUFVVddLlJ4vpSk1NTeCS4O5ivv/973e4xCaYmFDU1NQoJSVFZ599tpqamro9m/nWW2/psssuU0JCQtAxZtTW1urss89WbGxst/tXX1+v+++/X/fff7+efPLJoGJCcbJj6vV6tWDBAuXm5gYu1w3l9yAYxx/rnra7bt06LV26VB9++GHQMaHmkpqaKqvVqtGjR+vAgQMdlm/fvl0ej0ePPPKIPv3006BiQmEYhg4dOqRzzjmnx/3zeDxatmyZFi9eHHSMWTU1NUpOTpbU9WM9fvx4XXrppbLb7Vq5cqV++ctfnjSmN7n0dNwvueQSOZ1O3XPPPcrPz1dNTU3Yflck6dvf/ra++MUvdpvr5/+ehetvnPTZ1Qk9XSb3+eXV1dVKTU3V4MGDFR8fryNHjvRZLtGir+vl8d/FYcOGqaWlpcdaNHXqVA0aNCjomFDy9/l8uummm7RkyRKdd955QcWczPHfcYvFouTk5E5XhGzfvl1tbW165JFHVFFREVRMTw4cOKBzzjlHFouly3zHjh2r//f//p9mzJihP//5z/rNb35z0phg5OTk6JJLLulyWTie+4MHD9YDDzxw0vUKCws1e/ZseTwetbW16cwzzwx5Hx0OhxwOR5fL3G63brrpJj377LM6++yzJfXNa4QHHnhAgwcP7nLZjh07dOjQIT366KNqaGiQFPxzqjsXX3yxvvWoVWjFAAAgAElEQVStb3W5bNasWTIMQ9nZ2dqwYYPuvPPOTvXk+OsAs8rLy9XW1qbzzz+/w3hXz4XePD9OVFBQoNmzZ590eV88PyTpyJEjKi0t1WWXXdZh/JNPPtGdd96pl19+OfDxowMHDvRJbTz++38iv9+vjz76SB9//LEee+wxtbW1BfX8aG9v14YNGwJXrh43f/58bdiwQQsWLFBtba3mz58f1tcZ0SSqGmqLxdLhczSf/0xNV8tPFtPTXKcixux2u1JRUaE///nPeuGFF8Iyf1fbNLq43NPn88kwDDU1NekPf/hDUDGh5tHTdl9//XXV19frvffe05tvvqkdO3bozTffDEsux/PpzuzZszVu3DhVVFToiiuuCFxiFY7jc7J9crvdeu+991RRUaHZs2erqampTx+Hz7NYLD1uf/ny5fr6178e+KxwMDGh5nHc57ft8Xi0b98+Pfnkkzp8+HDg8sBI/K5MmzZNf/nLXzR69GhlZWVp6NChJ40Jp67+noXrb1wo+lMu/VE46uXJnheffPKJnn766Q4f7wn1uXSyv/N///vfdeDAAe3YsUNr1qxRaWmp1qxZ06vnbjB/Q99//31VVlbqqquu0tGjR3v996GnfNva2lRaWqo//elPqqurU2Fh4Ulj+kIknvtbtmzRf/7zH/3ud7/rNEdf7+Py5cs1dOhQrV27NnAvj82bN4f178jRo0f17rvvavv27br++usD4+HaT5fLJYvFooceekj79u3Tu+++22m+UDQ2Nuq+++7T6tWrFR8f32FZV/n3xT698cYbMgxDd999d1DLe/uYtre367bbbtPKlSs7vD6RpCVLluj888/Xc889F/ibU1pa2uvH9fO//8e1tLTI7/erpKRETqdTP/jBDySdfB/vuusuLV68WJMmTeowXlZWpsmTJ2vBggX64IMPAh9bpYaeXFQ11MnJyYF3TqqrqwNnkYqLi1VcXNzl8u5iQpnn1Vdf1f79+03F9FZycrKqq6t15MgRJSYmBt4Re+yxx+TxeLRnzx4tWLBAf/jDHwI33ugupjdGjBih+vp6eb3eDu/k1tfX6+mnnw6s8/DDD2vFihV65513uo3prc8/1se3u3PnTq1du1bnn3++rrzySjU0NKipqSnwTl5XMeHKpbCwUFu3btVXv/pVPfzww1q+fLmuueYabdu2rduYvsiluro6cLb3+A2snnvuOR06dEgpKSn60Y9+pMcffzwwd3cxvWGxWDRq1KjAu9PHnws+n09Lly6V9NmLi6eeeqpDAewqprfOOeccVVf/f+zdeXjU5bn/8c9kDxBCSIAQIIFgRBZBZBWGTSKCEEgVVKjiVkRrlV9tK2g9AudqlWCxYqun1nOsIGWpRYKIFYyKmLAIQVCxypKwYyAkkISsM/P8/qAZCUkmyWSykLxf1+V1kWee5/7eI/lyzz3f7aSksv+vT5w4ob///e/6/vvvVVJSon79+jk/JFS2prYq+3v/4osv9Omnn0qSrFarnnzySWVnZ6tv37519rtSmcTERH3//fce+7e0Nl577bVy1+iVKs2l9KiOuzdJaso8XS9L/624cOGC/P39nTcwLK1FBw8e1IwZM7R06dJytejKNe7kX/q7/+2332rDhg265pprdMstt3j03/nL/z0sPdIlSW+//bbzKM4jjzyiP//5z/L399fx48crXVMdHTt21KlTp2SMKZPvqVOn9Pbbb+vQoUPKz89Xv3799Nvf/laJiYmVrqmtDRs26Ntvv623fb+wsFBLly6VdOmGZ0899ZT+8pe/qE2bNgoICJCvr68uXLjg0fdY+rs/aNAg9e/fX9nZ2SooKFBBQYFCQkLq5N/a0v2jU6dOevbZZ7V8+XLt2bNHRUVFtdo/KlO6f+zevVutWrXS0KFDNXv2bL3//vu1ridnz55VfHy8nn32WV1//fXO8dL9o6J9oTb7h3TpAMnKlSu1ZMkS5826SvePil6v7f5RUFCgO++8UzNmzNCIESOc46X7x7hx4xQZGans7GwVFxfr4sWLCgsLq9X/1yt//6Uf94+WLVsqJCREL730kl577TV99tlnLvcPh8OhX/ziF+rRo4emTp3qHC/93d+6das6dOig2267TRMnTtTHH39c758zrlp1eDp5vTt79qwJDw83jz76qAkPDzdnz541xhgzf/58M3/+/Apfr2yNK1u2bDFRUVFm5syZ5sYbb3SOjx8/3nnN9Jo1a8y1115rHnzwQZOWllbpmtp68cUXzeDBg824cePMo48+6hwPCQkxWVlZJioqysyePdssWrTILFq0yBQUFFS6prbuuOMOEx8fb/r06WOWLVtmjDHmu+++Mz169DDnzp0zixYtMgsWLDA33HCD89qcitbUVklJienatat5+OGHTUREhPM6nrfeesvcd999ZeaWjlW2prYOHjxowsPDzSOPPGI6d+5sCgoKjDE/Xju9adMms2jRIvP444+b9u3bm2PHjlW6prbeeecd06NHD3PnnXea8ePHO8dvvPFGk5qaatauXWuuv/56M3v2bBMTE2OKi4srXVNbv/71r82YMWPM8OHDzYIFC4wxxhQVFRk/Pz9jzKV9tvSaI1drPGHEiBFmxowZpnv37uaDDz4wxhizdetWM2LECJObm2s6duxoHnjgAdO/f3+zZMmSStfUVk5OTpl/i06cOGGMMWbx4sXmN7/5jTHGmJ07d5qHHnrI9O/f35SUlFS6xhPWrVtn+vTpY+655x7z3XffGWMu7a///Oc/zc6dO03nzp3Ngw8+aHr27GkcDkeFY57y6quvmuDgYPP000+bnJwcY4wxnTt3dl4zfeXrr732munfv7+ZOHFira6Ra8o8XS//8Ic/mEGDBplbb73VPPLII87xtm3bmnPnzjn/fb28FlW2pjpsNpvp3r27mTVrlunUqZPZu3evMcaYFStWlLtmtHSssjXVlZiYaGJiYszdd99txo4d6xwfMmSI2bFjh3nvvfdM7969zaOPPmq6d+9uioqKKl1TXWPHjjV33XWXiYmJMevXrzfGXLp+fMiQISY/P9907tzZ3HfffWbgwIHmhRdeqHRNTbz33nvmhhtuMHfffbfZv3+/MebS9a8rVqwwe/fuNZ06dTKzZs0y3bt3NzabrcKxmigoKDDPPfecadGihXn55ZeNMZeuf2/btq3JyckxQUFB5qmnnjKLFi1yvv7II4+YcePGmcGDBzvv3VATn3/+uYmNjTWxsbHm888/N8b8+Lt/OVf7Q029/PLLpkWLFua5555z1vXS/WPJkiXGarWa6dOnO+/nUpv9wxhj9u/fb+6++25zww03mPfee88Y8+O+cPz4cRMcHGwee+wxc80115h33nmn1vvHLbfcYu644w7nPn7y5EljzI/7R0X7Qm32j2+++cYEBwebhQsXmkWLFpnly5cbY37cPyp7vTb7x6OPPmpGjx7tfI9X7h+Xc7XPVFdlv/+l+0dpTnFxcWbChAnmoYceMsZUvn+89NJLpk+fPs78r/zd3759u2nTpo355S9/acLDw01qaqpHfvebgyb12KwWLVpowoQJysnJ0YIFC9S1a1dJl66ZjYyMVJ8+fcq9XtkaV7p27aq+ffvK19dXzz//vPPRGMXFxerXr59CQ0O1c+dOtW/fXi1btlS/fv00YMCACtfU1k033aSWLVvq2muv1VNPPeV8rEJhYaFGjRql/Px8+fj4qLCw0Dk2cuTICtfU1oQJE5Sfn6/Jkyc7r9sxxsjHx0fDhw93HjGaNm2aZs2aJYvFUuGa2vLy8lJ8fLyys7P15JNP6sYbb5R06Zu50NBQ9e7d2zm3dOz666+vcE1ttW3bVqNHj1ZBQYGef/555yMWSkpKFBMTo7CwMB05ckShoaF68cUXFRUVVema2urVq5e6du2qkJAQLVy40Pltd3FxsQYNGqQhQ4aoa9eu8vb21pIlS9S6detK19TWmDFjZLFYNHjwYD322GPOR10UFRVp7Nix2r59u5544okyp4xVtqa24uLilJOTo5kzZ2r8+PGSLv3eBgYGatiwYbrzzjuVlZWlKVOm6IEHHqh0TW35+/srLi5O58+f17PPPuu87tNutysiIkIxMTE6ffq08zr3oKCgStd4wu7du9WmTRu1bt1affr0Ufv27VVcXKw+ffqof//+GjRokCTphRdeUOvWrdWpU6dyY56SlJSk66+/XsYYWa1WBQQEqKioyPnnK18fPny4QkNDFRUVpWeeeYbHZlXA0/XypptuUlBQkGJiYjR37lxnXSkqKtLIkSNVUFBQphaNHDlSI0eOrHBNdZT+O5+VlaU5c+Y4f/ccDofatm1b5vr/0rG+fftWuKa6rrvuOl1zzTVq3bq1Fi5c6HzET1FRkQYOHKhBgwY578uxZMkStWnTptI11TVp0iTl5uZq+vTpzlOBjTEKCAjQsGHDdNdddyk7O1sTJ07Uz372M1kslgrX1MSXX36pVq1aqU2bNurZs6fCw8NVXFysXr166YYbbtDQoUNls9n0wgsvKCQkROHh4eXGaqK4uFiffPKJhgwZIofDoVGjRskYI4fDIavVqvz8fFksFhUWFjpfj42Nld1u18iRI52fJ2riwIEDKiwsVEREhCIjIxUdHe383Y+OjnbOc7U/1NTmzZt14403OvP29fV17h8jRoxQy5Yt1b59ey1atEj+/v6V7lPVdfLkSZ04cULXXHONQkND1adPH+e+MGzYME2ZMkXnzp3TrFmzNHHixEr3qerKyckps48PHDhQwcHBzv1j8ODB5faF2uwfRUVFMsaopKREhYWFCggI0ODBg537R9++fSt8vTb7R25ubpn3eOX+cflZfK72meqy2+0V/v6X7h+jRo3SLbfcoqKiIl1//fV6+umn5e3tXen+cfHixTL5X/m7P3LkSI0ZM0YXLlxwnhLuTp/UHFmMqcOLJAEAAAAAaKKa1DXUAAAAAADUFxpqAAAAAADcQEMNAAAAAIAbaKgBAAAAAHBDk7rLN9DUJCcna9WqVZLkfHZrTSUmJmr9+vVq1aqVDh06VOt4nnL5e8vKytKyZcuUmZmp6667rkHzAgBUn7t1yhP1rS5RfwFUF0eoATclJycrISFBycnJLsequ7YiSUlJmjdvnpKSktyKsWvXLk2bNk0rVqxQjx49ah3Pky7PpUePHlqxYoWmTZumXbt21cv2AQAV+9Of/qQ//vGPKn0QTGl9+PjjjyVJBQUFSkhI0J/+9CeXdcUVd9fVFvWX+gt4Gg014KaKimN1PyAUFhYqOztbhYWFHt3+lebNmyebzabnn3++yuc7eiIndwUGBur555+XzWbTvHnz6n37AIAfffrpp3ryySf1ySefSJLeeustzZs3T3/9618lSWvWrNG8efP06aefNmSabqH+lkX9BWqvZk+JB1Ajxhj961//0nfffadWrVopPj5e7du3V0BAgEJCQhQQECBJys3N1Zo1a5SVlaXQ0FBlZmZq+PDhZWJt2bJFe/bs0eDBg2W1Wp3jKSkpSkhI0PDhw8uMHz16VJ988ok6dOigiRMnlsvtgw8+0IEDBxQXF6fu3buXySk5OVkpKSkaPny4bDZbme26es3Ve5ak/Px8rVmzRnl5eTp9+nSZfCZOnKgOHTrok08+0dGjRxUVFeWZvwQAQI3Ex8dr3bp1evXVVzV27FgdPHhQkpSeni5J+stf/uKcl5aW5lxXUZ0qKSlRYmKijh49qq5duyo+Pl4+PuU/frqqHaV27typLVu26MYbb1SfPn20fPlyTZ06VWlpadqzZ49Gjx6tIUOGuIxH/aX+Ap7GEWqglkoLakJCglJSUpzjDodDcXFxuv/++3X69GnNnz9fPXv21IkTJ8p8u11UVKSbbrpJs2fP1uHDh/WXv/yl3DffGzdu1IYNG/Tiiy9q5MiR2rFjh/O1goKCCr/ZLj11bNSoUfLyKrurv//++9q8ebOeffZZDR48WFlZWWVyKv3zL3/5y3LbdfWaq/dcUlKiESNG6Gc/+5m++eYb7dmzp0xOXl5eGjVqVJncAQD1b9KkSfL19dWGDRt07NgxHTx4UC1btlRaWpq+/PJL7dy5U76+vpo0aZJzTUV1qqSkRCNHjtT999+v48eP67777tOoUaNks9nKbM9V7bjcwYMHNW/ePK1evVqJiYmaN2+e1q5dq7ffflvz5s1zNv7UX+ovUJ9oqIFaKi2o2dnZKigocI5v3LhRGzduVK9evRQWFqbo6GhlZWVp3bp1ZdavX79e+/fv19SpU/X6669X+G32xIkTtWTJEs2YMUPGGH322WfO12JjY7Vo0SLFxsaWWVP6DXTnzp3LxZs0aZJefvllTZ8+XVlZWVq/fn2F783Vdit6zdV73rBhg/bs2ePyfZbmeuW35wCA+tO2bVtn4/uHP/xBP/zwg+69916dO3dOixcvlnSpWWzbtq1zTUU1ITExUTt27NDdd9+tpUuX6q677tK2bdvK1Zzq1svu3btLki5cuKCPPvpIAQEB2rZtmzIzMyVJ11xzTY3iUX9/RP0F3EdDDdRSaUG9sqh+//33kn68NmrEiBGaO3euevbsWWZ96Sl0MTExVW4rKChIkso07lWxWCyVvlZ6KtiZM2fc3u7lr7l6z4cOHZL04weemuYKAKg/8fHxki6d3m2M0S9+8QtJl66fvvz1K11eE0rrW3h4uCQpIiJCksqcJi5Vv16WNtRZWVn65JNPNHPmTG3btk3nzp2T9GN9of5Sf4H6xDXUQB0pLfydO3fWokWLyrx2+SlVpR80qiqqV/L395ekcqfOlerYsaMk6eTJk5XGOHz4sKRLHya++uqrGm2/Iq7ec+mpe2fPnq10fWmupbkDABpGfHy8Hn/8cZWUlCgqKkq9e/dW9+7ddfjwYVkslkob6stFR0dLkn744QdJUkZGhnP866+/ds5zVTsu1759e7Vu3Vo7duxQYWGhnnzySb3xxhvKy8tTmzZtFBYWVmU86m/FqL+A+2iogToyZcoUTZ48WYmJiZo0aZKGDBkiPz+/cjc7iY+PV3h4uFasWKHAwEB98MEHkiRvb2/Z7fZK448YMUIWi0V/+9vfZLFYdOutt5a5KUrp9VBbtmyRw+Eocx3Xhx9+qB9++EH//Oc/NWDAAE2ePNkjBd3Ve/7JT36iefPmaeXKlWrRooU2btxYZq3D4dCWLVvK5A4AaBidOnXS4MGDtXPnTg0cOFCSNHToUB0+fFiDBw9Wp06dqowRHx+vYcOGafXq1WrZsqVWrVqlm266SVOmTCnTULuqHZfXNelSM75371717NlTPXr0UExMjA4cOKDevXtXK96V+VF/qb9AbXkvWLBgQUMnAVyNbDabIiIiNHr0aOe38JePde/eXdOnT9eYMWPUqlUrORwOtWrVSn379lVwcLBzXq9evTRjxgxFRkYqMjJSfn5+Sk1N1UMPPaRevXqV2cbl8UePHq1bb71VXbp0kY+Pj2JiYsp8wGndurW2b9+uffv2aeDAgerRo4dz/dixY9WyZUtNmzZNS5YsUUBAQJnYkZGRlW7X1Wuu3nOnTp00bdo0dejQQW3atNHdd9+tqKgoZ5yNGzfqjTfe0Lhx4/TYY4811F8rAOA/IiIi1KVLF02dOlXdu3dX27ZtFR4erpkzZzpPk76yFl7+8zXXXKP77rtPPXv2lDFG999/vxYtWiRfX99q147Q0NAyOQUEBOi6667TtGnTdP311yskJETXXnutJk2apH79+km6dPoy9Zf6C9QXizHGNHQSQHNX+iiM06dP680331SHDh2Umpqq1q1b1ypuamqqbrrpJsXExGj37t1VPguzoRQUFGjw4MH6/vvvtX37dg0YMKChUwIANAPUX+ovUFscoQYagczMTB09elQtW7Z03g219GYjtREREaGYmBgFBwcrJCSkwjuONgapqalyOBx69NFHNW7cuIZOBwDQTFB/qb9AbXGEGgAAAAAAN/DYLAAAAAAA3EBDDQAAAACAG2ioAQAAAABwAw01AAAAAABuoKEGAAAAAMANNNQAAAAAALiBhhoAAAAAADfQUAMAAAAA4AYaagAAAAAA3EBDDQAAAACAG2ioAQAAAABwAw01AAAAAABuoKEGAAAAAMANNNQAAAAAALiBhhoAAAAAADfQUAMAAAAA4AYaagAAAAAA3EBDDQAAAACAG2ioAQAAAABwAw01AAAAAABuoKEGAAAAAMANPg2dAAAAqFh6ero2bdqkvLw8DRs2TMOGDatwTJL279+vpKQk3XLLLerVq1cDZw4AQPPAEWoAABqpw4cP68iRIzp16pQmTZqkLVu2VDiWmZmp2NhYHThwQLGxsTp37lxDpw4AQLPAEWoAABqp2NhYxcbGSpKMMdq1a5d+85vflBs7ceKExo8fr1dffVW5ubnavHmzpk+f3pCpAwDQLHCEGgCARq6kpESfffaZbr755grHTp06pU6dOkmSunTpohMnTjRUqgAANCtN6gj1hg0b1Lt374ZOAwBwFYqOjm7oFCr1q1/9Ss8884wGDBhQ4djHH38si8VS4drk5GSlpKSUGWvXrp1Gjx5dlykDAJqoxlwvG0KTaqi//fZbxcXFNXQaAICrTFpaWkOnUCGHw6EnnnhCPXv21NSpUysdi4iI0McffyxJOnnyZJkvl61Wq6xWa5m4CQkJfCACANRYY62XDYlTvgEAaKSWLl2qzz77THl5eUpISFBycnKFY+PHj9eHH36on//859q0aZPGjRvX0KkDANAsNKkj1AAANCXXX3+9Jk6cqOzsbElSYWFhhWNhYWFKSkpy/hcWFtaQaQMA0GzQUAMA0EhdfpfvK8ev1Lt3b+4jAgBAPeOUbwAAAAAA3EBDDQAAAACAG2ioAQAAAABwQ4M01CtWrFB0dLTat2+vn/zkJ8rNzVVCQoLatm3r/C8hIUHGGD3++OOKiIjQ//t//0/GmIZIFwAAAACAchqkob7jjjuUmpqqr7/+Wrm5uVq2bJkKCgr08MMP6/Dhwzp8+LCeeOIJ7dy5U59//rm2b9+uTz75RLt3726IdAEAAAAAKKdB7vIdGBiowMBASVJkZKTzzwEBAQoJCXHOS01N1dixYxUVFaWxY8dq9+7dGjRoUEOkDAAAAABAGQ16DfXevXu1f/9+3XnnnZKkhIQEhYWFaerUqcrNzdX58+fVsmVLSVJQUJDzmZsAAAAAADS0BnsO9aFDh7RgwQJt3bpV/v7+mjt3rubMmaOCggI9+OCDWrFihVq3bq3jx49Lki5evKgOHTo41ycnJyslJaVc3LS0tHp7DwAAAACA5qtBGup9+/bp5z//uVasWCF/f39JP54G3qJFC3l7eysgIEDdu3fXihUrlJeXp+TkZMXHxztjWK1WWa3WMnETEhIUHR1dr+8FAHD148tYAADgjgY55XvdunXau3evBgwY4Lyj91tvvaWIiAi1bdtWgYGBmjFjhqxWq6KjoxUWFqbu3btr+PDhDZEuAAAAAADlNMgR6tLTu0sFBAQoMDBQ999/vxwOh7y8fuzzV61aJWOMLBZLQ6QKAAAAAECFGvwu31e6vJkuRTMNAAAAAGhsGuymZM2VPe3TGs336tBHlpbt6igbAAAAAIC7aKjrmblwomYL2nKTNQAAAABojGioJZm8H6SivOov8G8lS6vwuksIAAAAANDo0VBLMpkH5Mg+Wu35ljZR8qahBgAAAIBmrUEemwUAAAAAwNWOhhoAAAAAADfQUAMA0EgZY5SWlqZ9+/YpPz/fOV5cXKxvv/1WJSUlLscAAEDd4hpqAAAaqTfffFP/8z//o+LiYp05c0bJycnq0qWLhgwZoosXLyooKEg7duyQMabcmJ+fX73n6zj7nSzG4XpSYIgsQR3rJyEAAOoYDTUAAI3UQw89pIceekiS9LOf/UyJiYm67rrr1KlTJ23cuFHjx4/Xxx9/LLvdXm5swoQJ9Z6v4/Reye76CLlXux401ACAJoNTvgEAuAr88MMPiomJ0aFDh9S3b19JUr9+/XTgwIEKxwAAQN3jCDUAAI3c8uXL1aVLF02ePFkvvPCCfH19JUl+fn7Kz8+XMabcWKnk5GSlpKSUi5mWlubxPAPPnpXFYXM5x1YSpOJiz28bAICGQEMNAEAj9u677+rzzz/XG2+8IUkKDQ3V/v37JUnZ2dmKjIyUw+EoN1bKarXKarWWiZmQkKDo6GiP52rLa1eNU74j5NXZ89sGANS9uvgy9mrHKd8AADRS69at08KFC3X//fcrNTVVp06d0qhRo7R+/XolJiZqw4YNGjlyZIVjAACg7nGEGgCARurs2bPy9fXVnDlzJEkPP/ywHn74YT333HN644039Nxzz6lHjx6SVOEYAACoWzTUAAA0UqUN9JUuv/u3qzEAAFC3OOUbAAAAAAA30FADAAAAAOAGGmoAAAAAANzANdQAAODqV5AlR1Z6ldMsHXrL4hNQDwkBAJoDGmoAANBomcLzkjGuJ3n7yRRky3Hm2yrj+YRdI9FQAwA8hIYaAAA0WvZDSVJJgcs5lrbRsgSF11NGAAD8iGuoAQAAAABwAw01AAAAAABuoKEGAAAAAMANNNQAAAAAALiBhhoAAAAAADfQUAMAAAAA4AYaagAAAAAA3EBDDQAAAACAG2ioAQAAAABwAw01AAAAAABu8GnoBFA1R1aaVHi+2vMtgSGyhHSrw4wAAAAAADTUV4OcE3JkH632dEubKHnTUAMAAABAnaKhBgAAlTJ5P8hcOFnlPK+ON9RDNgAANC5cQw0AQCNmt9u1aNEiJSQkSJJyc3M1ZcoUdejQQRMnTtT58+dljNGcOXPUuXNnPfnkkzLGeC6Bi5lynPm2yv+MsXtumwAAXCVoqAEAaMR+97vf6emnn1ZBQYEk6a233pKXl5f27Nkju92u5cuX64svvtCWLVv0+eef66OPPlJqamoDZw0AQPNAQw0AQCP21LmcwKMAACAASURBVFNPae7cuc6fr732Wh09elR2u12hoaEaOHCgdu/erbFjx6pbt26KjY3Vrl27GjBjAACaDxpqAAAascDAQAUEBDh/HjVqlMLCwhQfH6/s7GwNHjxY58+fV6tWrSRJQUFBys7Obqh0AQBoVrgpGQAAV5HVq1erX79+WrRokW6//Xa99dZbat26tY4fPy5Jys/PV/v27Z3zk5OTlZKSUi5OWlpatbbnm31CvlmZVc7LT09X4NmzsjhsLufZSoJUXFy9bUtSwJkz8rIXuY5ZFCj7+RL5Z1adZ+GRI3L4BlV7+wAAuEJDDQDAVSQ/P1/ffPONjDHy8fGR3W5X37599fe//10XL15UcnKyJk+e7JxvtVpltVrLxEhISFB0dHS1tmcy8mX3OlvlPO9u3WTPbyfZS1zO82oXIa/O1du2JNny20slBS7nWNp2lCUoXA7H8Srj+XTtKvkHV3v7AIAfVffL2OaEhhoAgEYsISHBeYfvwMBAPfroo1qzZo0CAwM1evRo3XPPPQoMDFRUVJRCQ0MVHx9froEGAAB1g4YaAIBG7IknntDDDz8sSQoICFBgYKA+++wzORwOeXn9eCuUNWvWlBsDAAB1i4YaAIBGLDAwUIGBgeXGK2qcaaYBAKhfVF4AAAAAANxAQw0AAAAAgBs45buJMhfPypHxTY3WeEePqaNsAAAAAKDpoaFuqkryZS6caOgsAAAAAKDJ4pRvAAAAAADc0CBHqG02m/7973/r/Pnz6tu3r4KDgyVJFy9e1P79+9WnTx+1aNGi0jHUHZN3pmYLAoJl8fGvm2QAAGhApuC8TNbhKudZ2l0ni1/LesgIANDYNEhDvXLlSr399tu6ePGiDhw4oOTkZEVFRWnQoEHy9/dXSUmJdu/eLWNMubGAgICGSLnZsB/cVKP53t1GSm2i6igbAAAaUHGuHGe+rXKad5soiYYaAJqlBjnle+bMmfroo4+0bds2TZ06Ve+//742b96smJgYffnll+ratas+/vjjCscAAAAAAGgMGvymZOnp6YqLi9OBAwfUq1cvSVKfPn108OBBGWPKjQEAANSGI32LHIW5Lud4te4oS6sO9ZQRAOBq1aAN9euvv65evXrptttu01dffSVfX19Jkq+vrwoKCpx/vnJMkpKTk5WSklIuZlpaWo3z8M84Le+8zGrPtxX4qdjUfDuS1CKz+tuRpGKf4/LOq3l+jjwv+dVwW/lpaW7lZ8uy12gNAAANyRTmSIUXXM/xD6KhBgBUqcEa6r///e/66quv9Oqrr0qSwsLCtG/fPknSuXPnFBUVJWNMubFSVqtVVqu1TMyEhARFR0fXOBeH1wk5sourPd/SpqO8u9V8O5JkuxBWo/neXbrInHfUOD+vkC6y29JrtC2f6Gi38rNwDTWAq5w7X8YCAAA0yDXU//jHP/T73/9eEydOVFJSktLS0jRmzBglJiZq1apV2rBhg8aMGVPhGAAAAAAAjUGDHKEuLCxUp06d9Mc//lGSdO+992rmzJlavHix3n33XS1evFjdu3eXpArHAAAAAABoaA3SUM+cOVMzZ84sNz5jxgzNmDGjyjEAAAAAABpag5zyDQAAAADA1a7BH5sFAACaF3P2e9lPfFHlPO8+d9RDNgAAuI8j1AAAAAAAuIGGGgAAAAAAN3DKt5tMwXmZ7Jo9t9Qr4sY6ygYA0JQlJSUpOztb06ZNkyQdO3ZMzz//vMaNG6fbb79dkrR+/XqtW7dOd9xxh+Li4hoyXQAAmg2OULur6IIcGftr9B8AADW1bNkyTZs2Tfv3X6ojRUVFuvnmmxUTE6MePXpIko4cOaJHH31Uw4YN0+zZs3Xs2LGGTBkAgGaDhhoAgEZs4MCBuuWWW5w/JyUl6brrrtOvfvUr9e7d2zkWHx+vhx9+WHFxcfr4448bKl0AAJoVGmoAABqx3r17q1evXs6fDx06pPz8fN13333661//Kkk6e/asQkNDJUnt27dXRkZGg+QKAEBzwzXUAABcRYqKimSxWDRhwgQtXrxYbdq0kb+/v/Ly8iRJNptN/v7+zvnJyclKSUkpFyctrXr3AfHNPiHfrMwq5+Wnpyvw7FlZHDaX82wlQTK+ufLNrF7MgDNn5GUvch2zKFD28yXyr0bMwiNH5JeRIa+SPNcx831kv+hbvZj+R+UIyKlyHgCg6aGhBgDgKtKtWzd16dJFd999t3bs2KGTJ0+qa9euzqb5+++/15AhQ5zzrVarrFZrmRgJCQmKjo6u1vZMRr7sXmernOfdrZvs+e0ke4nLeV7tImTxD5Zdp6oXs/jfUkmBy3mWth1lCQqXw3G8ypg+XbvK7jgqUxjgOmZwR3mFRspuq/qLB++oKFlahlU5DwCudtX9MrY5oaEGAKARe+edd/TOO+9IunT6d1xcnJ599lnFxsbq22+/1bZt29SuXTvNnTtXVqtVGRkZGjduXANnDQBA80BDDQBAI9arVy8tWLDA+eeAgAB98cUX2rVrl2644QaFhV06MvrFF19o79696t+/v1q0aNGAGQMA0HzQUAMA0Ij17t3beTfvUsHBwYqNjS0zFhISojFjxtRnagAANHvc5RsAAAAAADfQUAMAAAAA4AYaagAAAAAA3EBDDQAAAACAG2ioAQAAAABwAw01AAAAAABuoKEGAAAAAMANNNQAAAAAALiBhhoAAAAAADf4NHQCaKaMQ7KX1GyNj79kK6rZGm9fycL3RgCAumWKciSH3fUkLx9Z/IPqJyEAQL2goUaDMBeOy56+tUZrfPrfK9vX/6jRGu9uI2VpE1WjNQAA1JTjSLJM/jmXcyytwuUdc0s9ZQQAqA801AAAAI2Q/dh2mYuZLudYAkPk3dVaTxkBAK5EQw0AANAYFeVJheddz/Hyrp9cAAAV4uJSAAAAAADcQEMNAAAAAIAbOOUbAACgnpiCbDlO7alynlfH/vWQDQCgtjxyhPr7779XcnKy889LlizR/v37PREaAIAmg3oJ2Qplck5V+Z/sNXxMJACgQXikod60aZOSkpJUUlKi2NhY/frXv9ZNN92kH374wRPhAQBoEqiXAAA0LR5pqHNzc1VYWKgvv/xSJ06c0OjRoxUaGqoNGzZ4IjwAAE2Cu/Xy0KFDSk1NLTe+ZcsW5/jx48e1cuVKnThxok5yBwAA5XmkoQ4PD9fKlSs1d+5cSdLvf/97TZ8+XWfOnPFEeAAAmgR36mVycrJmzJhRrunesWOHJkyYoA0bNujChQsaNmyY1q1bp2HDhiknJ6dO3wcAALjEIw11XFycLly4oC1btqhLly4aNGiQ/Pz8FBwc7InwAAA0Ce7Uy+zsbDkcjjJjp06d0mOPPaZ77rlHkvThhx9qxIgReueddzRs2DBt3ry5Tt8HAAC4xCN3+W7fvr1SUlK0adMmTZkyRb6+vrJarYqIiPBEeAAAmgR36mVcXFyZ070LCws1ffp0vfrqq/rwww8lSceOHVN0dLQkKTo6WkePHq3bNwIAACTVoqFOTk5WSkpKufG1a9c6/xwQEOBueAAAmgRP18s///nPCgwM1FdffeVstAMDA9WjRw9JkpeXl2w2W5XbT0tLq9b2fLNPyDcrs8p5+enpCjx7VhaHzeU8W0mQjG+ufDOrFzPgzBl5VXHHa1tRoOznS+RfjZiFR47ILyNDXiV5rmPm+8h+0bd6Mf2Pyi8zQ15F513Os+dJtuLW1YpZ5HdMPlk/yLvQ9VyHv02FvtX7uwQAeJ7bDXVSUpIWLlzocs78+fNltVrd3QQAAFc9T9fLHj166ODBg0pNTdXp06clSVFRUcrIyJAkZWRk6Nprr3XOt1qt5WInJCQ4j2hXxWTky+51tsp53t26yZ7fTrKXuJzn1S5CFv9g2XWqejGL/y2VFLicZ2nbUZagcDkcx6uM6dO1q+yOozKFrr/EsAR3lFdopOy2qptV76goObwzZPJdf6yytAqXV3ik7MUHqo4ZGSmHX46M675flhah8q7m3yUA1FZ1v4xtTtxuqK1Wq/OmKqdPn9bXX3+tcePGOV9/55131L9//9pnCADAVay29TI1NdV5JDo1NVVxcXGKi4uTJC1YsECS9PDDD+vGG29U586dtWHDBv33f/93Hb0bNEbGYZOKcque6NdSFm+/uk8IAJoRtxvq2NhYxcbGSpJeeeUVBQYGatGiRc7Xi4qK+AYDANDs1bZenjp1ynmN9alTpzRgwADna6V/joiI0D//+U9t3LhRa9euVceOHeviraCxKjgv+4F/VTnNO3q0FNylztMBgObEIzclCwoK0htvvKGsrCxFR0crOztbK1as0O9+9ztPhAcAoElwp15efkS6otdKVXRqNwAAqFseaajvuusuvfrqq3rnnXecYx06dNAdd9zhifAAADQJ1EsAAJoWjzTULVq00Pbt2/Xee+8pLS1N7du3V3x8PM+hBgDgMtRLAACaFo801CkpKUpOTtbw4cMb/Ft2x5n9NZpvad25jjJBY2EunJApulDt+Rb/YFmC+b0A4HmNqV4CAIDa80hDnZ2dreeee05PPvlkg1+/5Ti5p0bzvf1a1VEmaCxMdpoc2UerPd/SJkreNNQA6kBjqpcAAKD2PNJQt2nTRtddd53ee+89tWnTxjk+fPhwPjAAAPAf1EsAAJoWjzTUSUlJ+uqrryRJ8+bNc47Pnz+fDwgAAPwH9RIAgKbFIw211WrV3LlzKxxH0+c4srXGp1R7hUTVYUYA0DhRLwEAaFo80lDHxsZq9OjRSkxMVHp6urp166YpU6bI19fXE+EBAGgSqJcAADQtHmmobTabRo0apW3btjnHhg4dqq1bt/IhAQCA/6BeAgDQtHikoU5MTNTevXv1wAMPqH379srKytLKlSu1fv16TZ06tdJ127dvV2ZmpuLi4rRhwwa9//77ztcmTZqkuLg4bdu2TR988IEmTZqkoUOHeiJdAAAahLv1EgAANE4eaajT0tI0a9Ysvfzyy86xwMBApaenV7pm3bp1euKJJ/TQQw8pLi5OqampOnXqlOLi4iRJEREROn36tG6//XbNnj1b8fHx2rdvnzp06OCJlAEAqHfu1EsAANB4eaShjo6O1sKFC5Wbm6t27dopMzNTq1at0vLlyytdExkZqRtuuKHM2IABA/Twww87f162bJni4uK0cOFCnThxQh999JHuueceT6QMAEC9c6deAgCAxssjDfWUKVPUr18/vfnmm86xYcOGacqUKZWuGTBggAYMGFBmbNOmTcrNzdXNN9+siRMn6ocfflB4eLgkqWPHjjp16pQn0gUAoEG4Uy8BAEDj5ZGG2tfXV1u3blViYqKOHDnivGupj0/1w8fFxSkiIkIZGRmaPXu2/u///k/e3t4yxkiSHA6HvL29nfOTk5OVkpJSLk5mZmaNci/2OS7vvNPyzqv+OluBnxx5XvKr4bby09LUgvyuivyKTVqNtgMA1eGJegkAABoPj1Tw8+fP6/XXX9fkyZM1depUHT58WC+//LLuv/9+hYWFVSvG5UesMzMztX//fnXu3FlffvmlJOno0aNlThG3Wq3lntuZkJBQ7e2V8u7SRea8Q47s4mqvsbTpKK+QLrLbanbNm090tGwXyO9qyM+7W3SNtgPg6paWVj9fonmiXgIAgMbDIw31+vXrtWXLFs2dO1eS1L17d23btk3BwcGaNWtWhWsuv6t3aSP9/vvvKz8/X5s3b1ZKSorCwsL061//WnfddZdSUlL02muveSJdAAAahDv1EgAANF4eaagzMjLUqVOnMmOhoaHKzs6udE1ERITzBmQRERHq2LGjcnNzZbPZtGjRIme8bdu26bPPPtOLL76o4OBgT6QLAECDcKdeAgCAxssjDXWvXr00b948ZWdnKyYmRmlpaVq7dq0SExMrXVPRTclmzJhRbl5kZKTuvfdeT6QJAECDcqdeAgCAxssjDfVtt92mMWPG6N1333WOjRgxQrfddpsnwgMA0CS4Wy8vXryo4uJihYSESJLy8vJ04cIFRUREyGKxOOdlZmZyLTYAAPXIIw21l5eXNm3apA8++EAHDx5Ut27dNHny5DJ35QYAoLlzp16ePn1av/3tbxUZGakFCxbozTff1PPPP6+LFy8qKChIn376qTp06KDY2Fjt3btXAwYM0KZNm7hzOAAA9cDLU4FOnz6tY8eOaciQIbr99tu1Y8cOHTp0yFPhAQBoEmpaL//xj3+UOSX8wQcf1KFDh3T69GkNHDhQa9as0SeffCKLxaLz58/LZrNp69at9fFWAABo9jzSUJ85c0Y33HCDHn/8cSUlJUmSkpKStHnzZk+EBwCgSXCnXs6ZM0dPPPFEha8VFxerU6dO+ve//63BgwdLkoYMGaL9+/d7PnkAAFCOR84HS0xMlJ+fnwYNGuQcczgcysrK8kR4AACaBE/Wyw0bNsjhcOiOO+7Q4sWL5e/vL0kKCAhQbm6uc15ycrJSUlLKra/us7d9s0/INyuzynn56ekKPHtWFofN5TxbSZCMb658M6sXM+DMGXnZi1zHLAqU/XyJ/KsRs/DIEfllZMirJM91zHwf2S/6Vi+m/1H5ZWbIq+i8y3n2PMlW3LpaMYv8jskn6wd5F7qe6/C3qdh+VAHVielzTPaWJVXOAwBUn0ca6pycHN17771q0aKFc2zPnj0aP368J8IDANAkeKpebtmyRatXr9batWtlsVgUEhKiAwcOSJLzZmWlrFarrFZrmfUJCQmKjo6u1rZMRr7sXmernOfdrZvs+e0ku+uGzatdhCz+wbLrVPViFv9bKilwOc/StqMsQeFyOI5XGdOna1fZHUdlCgNcxwzuKK/QSNltVX/x4B0VJYd3hky+649Vllbh8gqPlL34QNUxIyPl8MuRcd33y9IiVF6do2QvqvpmdN6RkbIEd6lyHgBUprpfxjYnHmmoe/furcWLFysmJkaS9M033+hf//qX5s2b54nwAAA0Ce7Uy+zsbOdzqrOzs7V792795je/0d/+9jelp6crJCREw4YN0x//+Efddddd+vDDD/XQQw/Vy/sBAKC580hDPX78eA0ZMkTvv/++c2zy5MnlvhEHAKA5c6deLl++3Dm/9Khybm6upk6dKkl64oknNGfOHN1///167LHHdN9996lv3751+C4AAEApjzTUFotF69ev18aNG3XgwAFdc801iouL80RoAACaDHfq5Zw5czRnzpxyY1d65pln9Mwzz3g0XwAA4JrHHlLp5eVV5kOB3W7Xd999p+uuu85TmwAA4KpHvQQAoOmo9WOzvv/+ey1dulSrV69WScmlG5GcOHFCY8eO1erVq2udIAAATQH1EgCApqdWR6iPHTumwYMHKycnR5J011136ac//akefPBBBQYGatmyZR5JEgCAqxn1EgCApqlWDfWGDRtkjNETTzyhgoICLV++XGvWrFF4eLg+/vhjRUVFeSpPAACuWtRLAACaplo11OfOndMjjzyixYsXS5L8/f21atUqffTRR85HggAA0NxRLwEAaJpq1VA7HA59+eWXSkhIkCR999136tevnzZu3KiNGzdq+PDhPDoLANDsUS8BAGiaan2X76SkJCUlJZUZ++STTyRJ8+fP5wMCAACiXgIA0BTVqqG2Wq2aO3euy9cBAGjuqJcAADRNtWqoY2NjFRsb66lcAABokqiXAAA0TbV+DjUAAAAAAM2R2w31rl27lJyc7MlcAABocqiXAAA0XW6f8r1p0yZdvHhRVqvV+UGBa8AAACiLegkAQNPldkMdEhKil156SXl5edq3b5/8/f3LXR/GY0AAAM0d9RIAgKbL7YZ6ypQp+q//+i/9+c9/do5d+TgQHgMCAGjuqJcAADRdbjfUnTt31nfffaf169fr2LFjKikpKTeHDwcAgOaOegkAQNNVq8dmtW/fXrNmzZIk2Ww2JSYmKj09Xd26ddOUKVPk6+vrkSQBALiaUS/RWJjiPMle/kudMixesgQE109CAHCVq1VDXcpms2nUqFHatm2bc2zo0KHaunUrHxIAAPgP6iUamuPEbpkLx13OsQQEy7vn5HrKCACubh5pqBMTE7V371498MADat++vbKysrRy5UqtX79eU6dO9cQmAAC46lEvAQBoWjzSUKelpWnWrFl6+eWXnWOBgYFKT0/3RHgAAJoE6iWuGvZimXOHqpxmad1J4vRwAM2YRxrq6OhoLVy4ULm5uWrXrp0yMzO1atUqLV++3BPhAQBoEtytl//7v/+rixcvas6cOZKk559/XmvXrtW0adM0b968SsdcshVWPcfiXfUcNEmmpED2k6lVzvP28ed6awDNmkca6ilTpqhfv3568803nWPDhg3TlClTPBEeqHem8IJMzokarfFq37uOsgHQVLhTLxcvXqy5c+dq/vz5kqR9+/Zp2bJlWr58ue655x5NmjRJdru93FifPn1c5mL7+p0q8/Xq0EcWb67tBgCgMh5pqH19fbV161YlJibqyJEjzruW+vh4JDxQ/wrPy3FyT42W0FADqIo79XLWrFk6efKk8+dt27ZpwoQJGjJkiCZMmKCUlBQ5HI5yY1U11AAAoPY81vH6+PhwQxUAAKpQ03oZEhKikJAQ58/Z2dlq3bq1JKlNmzY6d+6cJFU4BgAA6haHkAEAuIq0bNlSOTk5kqTCwkK1a9dODoej3Fip5ORkpaSklIuTmZlZ5bZKbCclLx/5ZlU9Nz89XYFnz8risLmcZysJkvHNlW81tp+fnq6AM2fkZS9yHbMoUPbzJfKvRszCI0fkl5Ehr5I81zHzfWS/6Fu9mP5H5ZeZIa+i8y7n2fMkW3HrasUs8jsmn6wf5F3oeq7D36Zi+1EFVCemzzF555yWT34VMX0LVWQ5osBqxCz2Oi7beUuV8wCgqaKhBgDgKtKzZ08lJiZKknbt2qVx48bJ4XCUGytltVpltVrLxEhISFBYWFiV2/Lq0EkWb1/Zvc5WOde7WzfZ89tJ9hLXMdtFyOIfLLtOVS9m8b+lkgKX8yxtO8oSFC6Hw/XzlSXJp2tX2R1HZQoDXMcM7iiv0EjZbWlV5xkVJYd3hky+649Vllbh8gqPlL34QNUxIyPl8MuRcd33y9IiVF6do2Qvqvrv0zsyUo5zJTIXXH/pYQkIllfXrrIXVCNmly6ytI2uch6ApiEtrep/E5sbL08ESU5OVnJycrmxPXtqdg0qAABNmTv1cunSpXrllVf0yiuvaOnSpbr55ptljFFw8KU7K48ePbrCMQAAUPc8coQ6KSlJksp8A/7BBx+oVatWuvHGGz2xCQAArnru1MuZM2cqLi5O0qXrqX18fLRlyxZlZmaWOcpc0RhQH0zBecmW73qSxVuWVh3qJyEAqEe1aqhLr8sqvTYrISFBklRQUKBly5bpl7/8Ze0zBADgKlebennlTclKVdQ400yjITjO7JfJquI0UN9A+fTh5rUAmp5aNdRJSUlauHBhmZ9LWSwWjRgxojbhAQBoEqiXAAA0TbVqqK1Wq+bOnVtuPCgoSKNGjdKQIUNqEx4AgCaBegkAQNNUq4Y6NjZWsbGxkqQdO3YoJSVFNtulO0eWntZ25Z1FAQBobqiXAAA0TR65KVlKSopGjBghY0yZ8fnz5/MBAQCA/6BeAgDQtHikod6+fbs6dOig6dOny8/PzznOhwMAAH5EvQQAoGnxSEM9cOBA3XPPPXrxxRc9EQ4AgCaJegkAQNPikYbax8dHn3/+ufMxIKWGDx/Ot+4AAPwH9RIAgKbFIw11UlKSdu7cqZ07d5YZ55owNDeOM/trNN/SurMsAcF1lA2AxoZ6CQBA0+KRhrqyx4Hw4QDNjePknhrN9/ZrJdFQA80G9RLNmbEVyXE0ucp5XmE9ZAnuXA8ZAUDteaShDggIUEhISIXjAADgEuolmjVjl8k5VfW81jTTAK4eHjvle+HCheXGOYUNAIAfUS8BAGha6uyU7w8//JAPBwAAXIZ6CQBA0+KRhjo2NlaxsbFlxmw2m7y9vV2ue/3111VYWKg5c+ZIkhYuXKi1a9fqzjvv1LPPPlvpGAAAVyN36yUAAGicPNJQJycnKyUlRZJUUlKi7OxsrVy5UuHh4RozZkyFaxYtWqSnn35a8+fPlyR9+eWXWr16tVasWKEZM2Zo8uTJstvt5cb69u3riZQBAKh37tRLAADQeNXZNdQWi0U33XRTpWtmz56t06dPO3/evn27br31Vg0YMEDjx4/X9u3bZbfby43RUAMArlbu1EsAANB41ck11G3atNHo0aM1dOjQSteEhISUudPp+fPn1bp1a0lScHCwzp07J0kVjgEAcDVyp14CAIDGy2PXUI8ePVqJiYlKT09Xt27dNGDAgBrFaNmypXJyciRJBQUFateunRwOR7mxUpefNne5zMzMGm232Oe4vPNOyzuv+utsBX5y5HnJr4bbyk9LUwvyI78r8rNl2Wu0BsDVyxP1EgAANB4eaahtNptGjRqlbdu2OceGDh2qrVu3ytfXt1oxevfurXfffVcOh0NffPGFxo8fL2NMubFSVqu13F1RExISFBYWVqPcvbt0kTnvkCO7uNprLG06yiuki+y29Bptyyc6WrYL5Ed+ZfOztImq0RoAnpeWllYv2/FEvQQAAI2HlyeCJCYmau/evXrggQc0d+5czZo1S19//bXWr19f6ZqlS5fqlVde0SuvvKKlS5dq9OjR8vPzU1BQkPz9/TVq1KgKxwAAuFq5Uy8rsnbtWv3qV7/S6tWrnWPJycl6+umnKzx7CwAA1A2PHKFOS0vTrFmz9PLLLzvHAgMDlZ5e+RG+mTNnKi4uTtKl66l9fHz00UcfKScnx3ndtKQKxwAAuBq5Uy+vtGHDBmczPnfuXPn5+Wno0KGaOnWqHnvsMd1xxx3au3evwsPD6+ItAACAy3ikoY6OjtbChQuVm5urdu3aKTMzU6tWrdLy5csrXXPlTclKVdQ400wDAJoCd+rllU6e6L+dQQAAIABJREFUPKlhw4Zp7ty5On36tHJycrR582ZNnjxZ//Vf/6UjR47oo48+0r333luH7wQAAEgeaqinTJmifv366c0333SODRs2TFOmTPFEeAAAmgRP1Mt7771Xr776qmJjYxUWFqaf/vSneumll9ShQwdJUseOHXXq1CmP5w4AAMrzSEPt6+urrVu3KjExUUeOHFG3bt00ZcoU+fh4JDwAAE2CJ+plamqqBg4cqMcee0z33Xefdu7cKW9vbxljJEkOh6NMvNo8FaPEdlLy8pFvVtVz89PTFXj2rCwOm8t5tpIgGd9c+VZj+/np6Qo4c0Ze9iLXMYsCZT9fIv9qxCw8ckR+GRnyKslzHTPfR/aLvtWL6X9UfpkZ8io673KePU+yFbeuVswiv2PyyfpB3oWu5zr8bSq2H1VAdWL6HJN3zmn55FcR07dQRZYjCqxGzGKv4/IqyJRPbhUxvf1V6JderadhlOiESnK5SR+Aq0OtOt7Dhw/r9OnTslqt8vHx0dSpU52vJScnKyIiQtHR0bVOEgCAq5kn6+WuXbsUFBSkgQMHaty4cdqxY4e6dOmiPXv2SJKOHDmi/v37O+fX5qkYXh06yeLtK7vX2SrnenfrJnt+O8le4jpmuwhZ/INlV9VH0b27dZO9+N9SSYHLeZa2HWUJCpfDcbzKmD5du8ruOCpTGOA6ZnBHeYVGym6r+g7w3lFRcnhnyOS7/lhlaRUur/BI2YsPVB0zMlIOvxwZ132/LC1C5dU5Svaiqv8+vSMj5ThXInPB9ZceloBgeXXtKntBNWJ26SJHrq+Mv+u/I/kGXvr7vFiNmJ07y9KOz49AY1RfT8W4mtTqLt+bN29WUlJSha8lJSVV+hoAAM2JJ+vlLbfcouXLl+uee+7RypUrNXHiRI0fP16ff/65pk2bps8//1zjxo3zVOoAAMCFWjXU586dk81W8becNptNZ86cqU14AACaBE/Wy759+2rXrl269dZbtX37dvXs2VPBwcHatm2bfvKTn2j79u0KDg72VOoAAMCFWp3yHRAQoNzc3ApfM8YoIMD16VQAADQHnq6XMTExiomJKTPWpUsXzZgxw+0cAQBAzdWqoY6OjtbTTz+tpKQk9e3bVx06dFBGRob27dunr776SitXrvRUngAAXLWolwAANE21aqhvvfX/s3fv8VFV997Hv5MrSUhCICE3wiWAwYAooCISFDWIipXLAwo+SrFWOFarVWvRo0+Jp+dUYtEDrb1oFVoF672iBUEQQYJ3UEQFuYRbQsj9nkySmVnPH5zMIZLMJEOSmSSf9+vl6yV7/9bav0xWZs1v9tp7T1VcXJx27drlvBlKo9jYWK7h6iEOFzeopKD1Vw/0tTco+cxHkANAt8V8CQBA93RWBXVYWJj27t2rTZs26dChQ7Lb7fLz81NycrKmTJmiiIiI9soTPqzCalRY0/p4f6vpuGQAwAcxXwIA0D2d9YOie/furZkzZ7ZHLgAAdFvMlwAAdD9ndZdvAAAAAAB6KgpqAAAAAAA8QEENAAAAAIAHKKgBAAAAAPAABTUAAAAAAB4467t8o3v5trBt37EMirV1UCYAAAAA4NsoqNFEbmXb4vvXU1ADAAAA6JkoqAEAAHzQ/kKbKspcrxzrHW5X6oBOSggAcAYKagAAAB9UbjUqqXUdYwswnZMMAKBZ3JQMAAAAAAAPcIYaAADgLH1y1KbyStfnKfpGNWh8XCclBADoFJyhBgAAAADAA5yhBgAA6MJsdoeKa9zHRdkcfPADgHbG+yoAAEAXVmW1addJ94sOxyU1KLoT8gGAnoSCGl5RW29XUYWlTW0GdlAuANDVGGO0Y8cOxcfHa+jQoZKk0tJSffXVVxozZoz69Onj5QwBAOgZKKjhFeU1DfqmiIIaADxx22236euvv9bDDz+soUOHqrq6WhdffLHi4uJUUFCgL7/8UqGhod5OEwCAbo+bknVTxkg2h6NN/wEAfN/u3bu1a9cuffHFF5ozZ44kaePGjRo9erS2b9+uc889V5s3b/ZylgAA9Aycoe6misprlHesuE1tzh/XQckAANrNJ598ogsvvFAffPCBkpOTNWTIEB05ckQpKSmSpHPPPVeHDh3ycpYAAPQMFNRdwOGTZarNb31xHGLto/D4mA7MCADgLSUlJVq3bp1OnjypAwcO6PXXX1ddXZ0CAk5N6QEBAaqrq3PGZ2VlaceOHWf0U1RU5PZYDbZcyS9AgSXuY2sOH1ZIYaEsDpvLOFtDuExgpQJbcfyaw4fVq6BAfvY6l3G2uhDZyxoU3Io+rUeOKCg/X34NVa77rAmQvTqwdX0GH1VZeZmqa2pdxlkkfbv/sI7uc31sSRrkOKzS0lJVVbmONQ67cnKOu42TpBMnctVgz1NAjeufyRFoVZ3liEJa8bPX+x2XX22RAird9OkfLGvQYYW2ZtwpRw2VgW7jAMAXUFB3AQ6Hkb0NS7LbEgsA6FpiYmI0f/58/e53v9P999+vTZs2KSYmRrt27ZJ0qlAeMmSIMz4tLU1paWlN+sjMzFR0tPv7PfvFJsriHyi7X6HbWP8hQ2SviZHsDa77jEmQJThSdp1oXZ/1e6UGN4Vq33hZwuPkcBx322fA4MGyO47KWHu57jMyXnXhCaoscX8tenxConJzcmXx83cZFxUVpbj4RB07cthtn3HxiaoqL5NxExcZHq4BA5J0/PBBt30mJCQq2mZkyl1/6WHpFSm/wYNlr3U/RvyTkuSoDJQJdv07UmDIqd9ndSv6HDBAlphkt3EAOl92dra3U/A5FNQAAHQhV155pX77299q9OjR2rhxo1asWKFhw4bp17/+tS644AKtXbtWixcv9naa3UJNbb1OllS7jQuut3dCNgAAX0RBDXiZKc+RqStvdbwlOFKWyAEdmBEAX5acnKwVK1botdde04MPPqj09HRJ0p///Gf985//1F/+8hcNHjzYu0kCANBDUFADXmZKs+UoPdrqeEufQfKnoAZ6tB/96Ef60Y9+1GTb9OnTNX36dC9lhK7iUFGDSotcP+QlJNSh84a4DAEA/A8KagAAgB6i0mpU7OZy5zA/d1duAwAa8RxqAAAAAAA8QEENAAAAAIAHKKgBAAAAAPAABTUAAAAAAB6goAYAAAAAwAMU1AAAAAAAeICCGgAAAAAAD1BQAwAAAADgAQpqAAAAAAA8QEENAAAAAIAHArydANAWn+a27TugETH1iuqgXAAAQMcwlXlSbanrGIuf/GJGdFJGANA8Cmp0KeV1bYuvtzl0rNSm8sLWF+IRpkGDqcIBAPAaU35cjsLvXQf5B1JQA/A6Cmp0e6U1DuVVtj7eHmo0uMOyAQAAANBdUFADAIAe5XhBhey15S5jghr6KCQuvpMy8j21dXbtyXO/uiu5b70qihtUdNJ1bFCQdEFKe2UHAL7DZwrqEydOKC8vz/nv+Ph4JSQkyG6369ixYxo0aJD8/LiHGgAAODvV1gbZautdxvSqsymkk/LxRQ4ZFde6j0u0OVRpdbiNDbI72icxAPAxPlNQP/vss3r22WeVkJAgSVq4cKEWLFigiRMn6uTJk0pISFBWVpYCAwO9nCkAAAAAAD5UUEuniuiMjAznv9evX6++ffvq888/15QpU/TBBx/o6quv9l6C6DFOlln1ZXbbVkRcN6aDkgEAAADgk3xqDXV+fr727t2r+vpTy7D279+vMWNOVSljx47Vvn37vJkeAAA+xWq1qrb2f9faOhwsqwUAoDP5zBnqhIQE/etf/9J1110nh8Ohbdu2qaamRkFBQZKkoKAgVVVVOeOzsrK0Y8eOM/opKipq03HrA47LvypP/lWtb2erDZKjyk9BbTxWTXa2Qj3Ir7i4RPbTfna3x/EvUa3fiSavV2tkZ2e3uU1eXp5Kiovb1K44IEDyD+iW+Xny+/Vk/NWb7DYdB0D3U1FRoUsuuUQ33nijfv3rX2vevHlau3atZs6cqTVr1nDfEeCH6ipl6t0/9sMSntAJyQDoLnymoF64cKEWLlwoSbrjjjv0z3/+U3379tXevXslSWVlZUpKSnLGp6WlKS0trUkfmZmZio6ObtNx/ZOSZMoccpS6vjnJ6Sx94uUXlSS77XCbjhWQnCxbedvzq6ssVo2ttNVtQvr1VZ/4BOWd7N2mYyUnJ2vfl21rEx8fL4vDpvoGW6vb9OvXT/Hx8Tp5Irfb5efJ79ej8RfXV6a0bUW1X8LYNsUDPUl2dtf6ksrhcOjWW29VRESEJGn79u06evSoSkpKNHnyZH300UdnzJFAT+coPihH/jdu4wLG3NoJ2QDoLnymoG68y3dVVZU+++wzTZ8+XcnJyXriiSc0bdo0rVu3Tnfeeae30wR8Q125HPnftqkJBTXQffy///f/dOGFF8put0uSvv76a02aNEmhoaGaNGmSdu/eTUENAEAn8JmC+ssvv9R///d/q76+XgsWLND1118vSbr//vv1+OOP64EHHlBqaqqXswQAwLveeecdffXVV3rxxRf1xBNPSJIKCgoUGhoqSQoLC1N5+f8+Y/lsLpFqsOVKfgEKLHEfW3P4sEIKC2VxuF4RZGsIlwmsVGArjl9z+LB6FRTIz17nus+6ENnLGhTcij6tR46orLxcqnN9WU+NX7HqAlt3+VROznGVlZepusb1s6MsknJP5Laqz9wTuSotLXUbaxx25eQcb1WfJ07kqqTE/WVQNluDjh071qo+8/JOqqK0xG1sQIC/Dh8+3KpLoxqUI0tDpQLKXccavwDVHjygwJK9bvu0h8XKv6ZQgWWtGHddbMUKAO/ymYJ62rRpmjZt2hnb7777bt19991eyAgAAN/z3Xff6eOPP9awYcOcNySLiYnRjTfeKEmqrKxUXFycM/5sLpHyi02UxT9Qdr9Ct7H+Q4bIXhMj2Rtc9xmTIEtwpOw60bo+6/dKDW4K1b7xsoTHyeE47rbPgMGDVX0kUrYa1zdw69W3n/olJCg3z/2lRgMGJKm8qEAWP3+XcVFRUUpMSFTuUfeXjCUmJKq2okzGTVxkeLgGDEjS8cMH3faZkJAoR32dGtw8EzosLFQDBw7Ukf3fue0zPj5OgX4W1dW7vnQpKChQgwYPVm1pH7d9BsUnKMBWKUegm+ud/QMVkDxYtspP3Pbp12+IFJUoR4D7S+gCkpPdxgA9VVe7RKoz+ExBDQAA3Fu8eLEWL14sSc5HTV577bVauHChDh8+rC1btmjevHlezBBoXn2DXYdOuC9oYyJrFNe2W6YAgNdwC1AAALqokJAQhYSE6OKLL9YVV1yhSZMm6YorrtBFF13k7dQAAOgROEMNAEAX1XimWpKWL1+u5cuXezEbAAB6Hs5QAwAAAADgAc5Qd7IjZZY2xcdZW//8ZAAAAABA56Gg7mT7S9pWUIdRUAMAAACAT2LJNwAAAAAAHqCgBgAAAADAAyz5BtrRe9lt+45qTJRVsR2UCwAAAICOxRlqAAAAAAA8QEENAAAAAIAHKKgBAAAAAPAA11ADXlZZU6eGSmur4wOC6hQZ1YEJAUAXdLKsTt8edX+e4NJkHkcJ9+x7XnMbY4kZIb+48zohGwC+jIIa8LKCshrVFFe2Oj7Ev0aRiR2YEAB0QXZj1GD3dhboLozN/RfdFgdfzgBgyTcAAAAAAB6hoAYAAAAAwAMU1AAAAAAAeIBrqAEAAOBTiqvtqii3uIyx+EvJnZQPALSEghoAAAA+Jb+iQceKXRfUARTUAHwAS74BAAAAAPAABTUAAAAAAB6goAYAAAAAwAMU1AAAdCGbN2/WrFmzNGXKFD322GOy2+2SpOeee07Tpk3TqlWrvJwhAAA9BzclAwCgC0lNTdUjjzyi0tJS3X///RoyZIguvvhi/eY3v9Hvf/97/fznP1daWpqGDx/u7VQBAOj2KKiBHsT23do2xfsPuEiWiIQOygaAJxISEpSQcOrvctKkSSorK9O2bdt0ww03aPr06Xrvvfe0bds2CmoAADoBBTXQk9RVtC3e0dAxeQA4a3l5edqyZYseffRRrVq1SlFRUZKkvn37qrCw0MvZAZ3DSCqqcR8XXu9QCJ96AXQA3loAAOhiKioqdNddd+m9995TfHy8QkJCVFVVJUmqr69XdHS0MzYrK0s7duw4o4+ioiK3x2mw5Up+AQoscR9bc/iwQgoLZXHYXMbZGsJlAisV2Irj1xw+rF4FBfKz17nusy5EJ21W52vgyrFjx1RWXi7VuY6t8StWXeCJVvWZk3NcZeVlqq6pdRlnkZR7IrdVfeaeyFVpaanbWOOwKyfneKv6PHEiVyUlxW5jbbYGHTt2rFV95uWdVEVpidvYgAB/HT16tFV9OvJPqrym3m2sv5+fsg9la+dB9xX1AGuOkiKMAstaMe6ysxXayr+PBmuk2zgA3RsFNYB2Z+qrpMqTbWpj6Tesg7IBupf8/HzNnTtX//Vf/6WkpCRJ0vDhw7Vp0yZJ0p49e3TZZZc549PS0pSWltakj8zMzCZFd0v8YhNl8Q+U3c/9GW//IUNkr4mR7K5XtvjFJMgSHCm7TrSqz+0Hv1Ndfb3LuPiIfoqL76/CfPfvOwMHDpTjRKRsNQ6Xcb369lO/hATl5vV22+eAAUkqLyqQxc/fZVxUVJQSExKVe/Sw2z4TExJVW1Em4yYuMjxcAwYk6fjhg277TEhIlKO+Tg121z97WFioBg4cqCP7v3PbZ3x8nAL9LG5/R0FBgRo0aJDqvnf/esbEximwslo1Na4L5QB/fw0ePFjff92KPvv3V2K/ADkCSt3GBiQny1beur8Pv4Rkt3FAd5Kdne3tFHwOBTXQBdXbHaqwWtrUJqaDcmlWTbHsxz5uU5MACmqgVf7yl7/ou+++0z333CNJWrhwoX784x/r0Ucf1fDhw9W7d2+lp6d7Ocv2U283arC7jrHZ3ZWdgFRWZVVNSbXbuAGdkAuA7oOCGuiCSirr9eWJthXU13VQLgA61x133KHrr7/e+e/4+HgFBwfr888/14EDBzRs2DAFBQV5MUPAN1VU16m8ohXLwyVVuj7hLknqZXOol80qNbhe6i9Jll59JEvb5m0AXQMFNQAAXcjpd/k+XWBgoFJTU72QEdD9fJzj5zYmOahOKcEHZT/xpdtY/9E3yeLPF11Ad0RBDQAAAHggt7Re2cfdF9+XjjIKcH2JPYAuioIagEuOvN0ylXmtjreEx8sS0qcDMwIAwDfU2RyqasUTJo3hOn+gu6Kg9lB5jU1HC91/I3m68zsoF6BD1ZXLVLfhmbaBoRTUAAAA6BEoqD1UW29TbmXb2lBQAwAAAED3QUENAAA6VUFlvY7kuV/ldcEIN8/LAgDAyyioAQBAp7LWO1Ts/klDcji47hQ9j7GWSzXF7gP7DJTFj4/ygLfxVwgAAAD4isqTsud85jbMPzxOoqAGvI6/QqAH+fZIG24uJik+qkbRHZQLAAA9yddvPy1jr3cZE550noYMGthJGQFoDxTUAAAAQBe092iRGuwOlzFRvXspqX9EJ2UE9DwU1AAAoF2U1EoOm+uYsHrXH/4BtN7JkirVNbi5eZ8RBTXQgSioAQBAu/gqT7LZXd+9e2BQvcLDQzopIwAAOhYFNQCfYYoPtq1BeJwsQb07JhkAAADADQpqAD7DfuzjNsX7D7lM1fYg1dtav4Q0KMBPYSFBbU0NAIBOUVFdp8qSardxsW6unW6itkSOksNuwyyxI2UJ6NX6fgFQUANwze4wamjDnB3gMHK94LNlbb20speRDuSW6mRJVavbxPXtrQuGxbYxMwAAOke1tUHFFTVu4/q1oaCuqyxWTc53buMi+w6loAbaiIIagEvf5DUo72TrS+S42AaN6efZsbYeaVspPqafVRITPwAArhRU1mtPjvs59vIRdoVxiwOgTSioAaCV7HtebVO838BL5Sg/LpUfb3UbS+RA+Q28pK2pAQDQLnZtf09V1a6XnEfHxCh1XFonZQT4NgpqSQeKGlTUhjNw0Q0NiujfgQkB6FCm6qQcJ75qUxv/c66RsdW18UB2WRwNcrSlnb2+bccAAKAdVVZXq7ra9ZLzsFD3S9KBnoKCWlJNnVF5Gz7vhtQb8TQ/oGWVNfUqK6psU5uBHZRLc8orqnX0SHGb2px/TgclAwCAh/wc9fJ39/B3Ry95fHMTl/3aJUeD+zj/IMnSEQkAvsHnC+pvv/1Wmzdv1pQpU5SamurtdAC0grXeptIqa5vaeFpQB1bnKazqZOvjg+NUGxCi3LbV+zpf0pEyS5vaxFltqqqyq6oN7cIC7IqzlstU5LTpWH79R7YpHt0P8yXQ80Sd3CJT7/pscYhJkfoPbvdjm8oTsmdvdRvnf861soRFt/vxAV/h0wV1UVGR0tPTNWvWLGVmZmrPnj3q18/Dux0B6JaCqo8qovxQq+MDg+ukyBEeHWt/SdsK6jCrTSfL7cprQ7u4QLvirGVy5O5q07H8+o9Ufqn7x6ycrk/vXgoO9G9TG/gm5ksA3UFlTb2+P+5+Bdk5A/oqIiy4EzIC3PPpgnrDhg265ppr9Mc//lGVlZV67733NG/ePG+nBQA+6csDrT9TL0kXDItVXLi/TBuu27b4B8nmF6y6ejdLDH8gLCRI1bVtuz48OChAAf4eLBO0N8g09Kzr+zpyvqyqs6usFU+mSzSmXY4HoGuorberuBXvDf1tDlXkHlZ9Xa3LuOBeYbL0Cld13vdu+2yIGa1iu0N1Da7noqAAf0VHhrpPEqqorlOVlfu4eMKnC+rc3FwlJiZKkpKSkpST07YlkAAA1xy5n8tRerTV8ZY+g1QUOVZfHcxv03GuuXiotu9p/d3Opf8p+Pv2blMb6X+WIR7+sG2NIie2+Ti+pCPny4KKBn1f4P6LjTgHBTXQk1TW2rSnFe8NE+ps2v/tHpVXur7Wqm9UlM4Zca76lLpfoWWxDtLBCqtKKlwX6ZFhwRTUrXSypErZeWVu486JbttqvZ7AYozvfqX8xBNPqLKyUr/5zW/0yCOPKCoqSr/85S8lSVlZWdqxY0eT+MDAQDU0tOLmCAAAnCYmJkY/+clPvJ2Gx5gvAQCdoavPlx3C+LAXX3zRLFiwwBhjzI9//GOzZs0al/FLly5t8zE8adOZxyK/zm/Tmcciv85v05nHIr/Ob9PZx/IVHTlftjaWPumzp/Xp7ePTJ336cp89iU/fw/6aa67Rhg0b9LOf/UwbN27U1Vdf7e2UAADwOcyXAAB4h08X1NHR0dq8ebNSUlK0efNmRUdzy30AAH6I+RIAAO/w6ZuSSdLIkSM1ciTPVwUAwBXmSwAAOp9/RkZGhreTaE8DBw7slDadeSzy6/w2nXks8uv8Np15LPLr/Dadfayuqi0/b2tj6ZM+e1qf3j4+fdKnL/fZU/j0Xb4BAAAAAPBVPn0NNQAAAAAAvqrbLfn+9ttv9Y9//EMRERGKiYlp1X53bTpSRUWFVq5cqby8PKWkpDTZV1tbq9dff13r16/XsWPHNGLECPn7+7ts4yvWrl2rrVu36pxzzlFwcHCzMfX19VqxYoWGDx+u0NDQVrXxJnfjpLq6Ws8//7waGhqUlJTUqjbe5m4s5eTkaOXKldq3b59GjBihgICALjH+JOmNN95Q//79FRoaesa+5saar48/Sfrmm2+0f//+M5ZaHT58WK+88oq2bt0qSV1m/EmSzWbTqlWrNHbs2BZj9u3bp7Vr12rs2LFdZvx1Bb4wX2ZnZ+uFF16Qv7+/EhMTW7V/165dWrNmjUpKSjR8+HBZLJazzsOdvLw8/e1vf1Ntba0GDx7cbExJSYn+9a9/Oa9jN8bo5Zdf1v79+5WamtrhOUpSWVmZVq5cqfz8fJ1zzjlN9tXU1Dg/U+Tk5CglJUX+/v7O/d99953+9a9/acyYMR2ep9Vq1apVq3TgwAGNHDmyye/QZrPpjTfe0Lp167R3716NHj1aDodDb7zxhtavX68DBw4oJSVFgYGBHZ6n3W7XmjVr9OWXX2rkyJFNXq/TlZaW6ve//70mTJggY0yr2rS3N954Q1lZWRoxYoSCgoKajbFarVqxYoVSU1NlsVj04osvatu2bRowYIAiIiI6Jc+NGzdq48aNGjp06Bnz85o1a/Tuu+9qx44d2rFjhwYPHqyIiAgdPXpUzz//vAYNGtRpee7YsUNr165VYmJis8csKCjQX//6V0VFRZ1xE8iVK1eqV69enTLvfvnll3r11VfVr18/9evXr8m+LVu26NVXX3W+nsHBwaqurtYrr7yiDz/8UIGBgc2+76LtutUZ6qKiIqWnp2v//v1KT09XcXGx2/3u2nS0+fPna/PmzXr44Yf18ssvN9lXUVGh3bt3q7S0VE8++aTuu+8+t218wZo1a/Tv//7v2rRpk2677bYW4+655x4tXrxY+fn5rW7jLe7GicPh0NSpU/XRRx+ppqamVW18gauxZLVademll+rrr7/W888/r7vvvtttG1/x2GOPaf78+crPzz9jX3NjzdfHnyRt2LBBs2fP1ubNm8/Yd+jQIR05ckQnTpzQ9ddfr61bt3aJ8Zefn68ZM2bov//7v1uMKSkpaRLTFcZfV+AL82V9fb0mT56svXv3asaMGTpw4IDb/d98842uueYaHT9+XPfcc49WrVp1Vjm01nXXXaedO3dqwYIF2rFjxxn79+3bpxkzZuhvf/ubc9svfvELPf/886qoqOiUHCXplltu0QcffKAHH3xQr7/+epN95eXlzs8UmZmZevDBB537ioqKNH36dK1YsaJT8rz33nv15ptv6qmnntLvf//7Jvvq6uq0c+dOlZSU6OWXX9ZNN93k3FZcXKyVK1dq/vz5nZLnf/7nf+qZZ57RCy+8oEceeaTZGLvdrptvvlkPP/ywbDZbq9q0t+eee07/8R//oXXr1mnRokUtxi1atEgPPfSQSkpKtGjRIv3973/X7t27dckll6ihoaHD83z33Xd111136aOPPtLs2bM8IzoeAAAgAElEQVTP2P/MM89oz549Ki0tVWlpqWw2m44cOaIrrrhCOTk5nZKjdOpLu7lz5+rrr79Wenq6HA5Hk/2lpaW67LLLtH//flmt1ib7nn76aS1atEi7d+/u8DyPHz+u6667Tt9//70mT56s6urqJvtra2udr+Ubb7yhTz75RAcOHNCxY8d0/PhxTZkyRZ9//nmH59kjePcx2O3rxRdfNAsWLDDGGHPrrbeal156ye1+d206UlVVlYmKijI2m828/fbbZtasWS3GZmVlmYsuuqhNbbxl+vTp5l//+pdpaGgwUVFRpqam5oyYP/3pT+aOO+4wI0eONN98802r2niTu3Gybds2M2nSpDa18TZ3Y+nkyZMmNjbW1NXVme3bt5urr766S4w/Y4x56qmnTHR0tPnmm2/O2NfcWPP18WeMMZs2bTITJ040S5YscRn3i1/8wjzxxBM+P/6MOTXGbr/9djNy5Mhm9zc0NJipU6eaZcuWmZEjR3aZ8dcV+MJ8uWXLFjNlyhRjjDEPPfSQWbZsmdv9b775ppk2bZoxxpjMzEy3fw/t4fvvvzepqanGGGP+8Ic/mPvuu++MmH379pk5c+aY6667zhhjTFlZmYmNjTW1tbUdnl+j8vJy069fP2O3282bb75pbrzxxhZjt23bZiZMmGCMOfV3NmXKFLNs2TJz/vnnd0quffv2NeXl5ebTTz81EydObDHu+++/N+edd16TbQcOHDADBw7s6BSNMcaMHDnS7Nu3z+Tk5JjBgwc3G/PAAw+YpUuXmuDgYGO1WlvVpr1dffXV5v333zdWq9VERkaahoaGM2KefPJJc88995jk5GRz6NAhc/HFF5vt27cbY4yJjY01FRUVHZ7nggULzAsvvGAcDocZMGCAOXnyZJP9kyZNMh9++GGTbUuWLDEZGRkdntvpHnnkEfP4448bY4y56KKLzK5du5rsf+aZZ8wdd9xxRrsPPvjAXHnllWbWrFnmH//4R4fn+ac//cn8/Oc/N8YYM2PGDPPOO+80G1dTU2POOecck5+f32T77bffbv785z93eJ49Qbc6Q52bm+tcupCUlKScnBy3+9216UgFBQWKiYmRv7+/22O/9957mjJlSpvaeEtubq4GDBiggIAARUdHn3GWcMeOHXrttdf0hz/8odVtvO3EiRMux8mePXvUv39/LV++XB988EGr2nibu7EUGxurWbNmafz48Vq+fLmeeOKJLjH+JOm+++5TbGxss/uaG2u+Pv4kKT09Xenp6S5jGhoatG3bNl155ZU+P/6kU2OsceVNcx588EFdffXVuuaaaySdOqPdFcZfV+AL8+UPx+jx48fd7r/mmmtUUlKiG264QV988YVz5UxHcpenJKWkpGjBggXOf+/fv18xMTF64YUXtGbNmg7PUTr19xEbGys/Pz+3v5+NGzdqypQpkqT7779f06ZNc/v+0l5qampkt9sVERHRYp61tbV65JFHtGjRojNWITR+HuoMjXNDQkKC8vLyZH5wH9/Vq1crPz9fixcvbnWbjtA4RoODgxUeHn7G6pFNmzbpvffe05NPPunclpGRoZkzZ+q2227To48+qvDw8A7PMy8vTwMGDJDFYlFiYqJyc3PPiHnppZe0fPlyHT16VJL09ddfy2az6YknnlB2dnaH53h6nlLLn/mCg4O1bNky55noI0eO6MEHH9TLL7/c4pL79tbaef7pp5/W7Nmz1b9/f+c2q9Wqjz76SJMnT+6MVLu9blVQWyyWJtfh/PC6qub2u2vT0U4/Xktvuhs2bFBRUZEeffTRVrfxJnev4T333KMxY8Zo+fLlKioq0vPPP++Ty1F/yNXPVVlZqX379ikvL0933nmns6ju7PHUVq7GksPh0Geffaa///3vqqqq0rp169y26Sqa+5v35vtAe3nggQf07//+7xo3bpykrvtzSNKBAwf0yiuvqKGhQc8//7yKioq0cuXKbjH+fIGvzJfu+vjh/pMnTyoyMlK/+tWvtG/fPu3ateusc2iNtv6sFRUVys/P1/79+/XUU0/pT3/6Uwdl1lRr/j7WrVuniooKPfzww9q3b5/efPNN5zXNBQUFnbKM3l2eDodDdrtddrtdv/vd75zbP/30U3322WdatmxZh+f4wzybc99992no0KHKzMyU3W7XsmXLZLfbOyW3H3KV6y9+8Qudf/75evLJJ1VWVqZnnnlGr732mp5++mklJSXp5ZdfPmNZc0cwxrjM8+abb1ZkZKS++uorjR8/XhUVFaqsrNSuXbt0/PhxTZ06VaWlpV7Ps7KyUrt371ZeXp5mzZqlQ4cOacmSJUpJSdHKlSu1d+9evf3229q7d2+H5+pujFZUVOjZZ59tcomHMUb33HOPfve732nEiBEdnWKP0K0K6oSEBOe3M7m5uUpISJAkZWVlKSsrq9n9LbXpDP3791dhYaHsdnuTb/4bi0zp1MT3xz/+Ub///e8VEhLSYhtfEh8fr9zcXNntdhUVFTm/EWv8UHzLLbcoMDBQpaWlstvtqqioUGxsbLNtfMUPx0nj63762LriiiuUmZmpH/3oR9q5c2eLbXyFu/GXk5OjgoICjR49WhkZGXrrrbe6xPhrSeP4a/y9NI61mJiYZrd1BY3jz+Fw6O6771ZKSorzujRfH38taRx/ERERmj9/vkpLS1VRUSG73a5evXp12fHna3xhvmxpjO7evVsbNmxodv+nn36qyMhIpaWlacGCBVq/fv1Z5XA2eWZnZ+u1115rtk1iYqKGDBmiZcuW6aGHHtK2bds6PM/Y2Fjl5+fL4XA0yfP0Ivntt9/Ws88+qxUrVqhXr16KjIzULbfc0uTvrKOv+Q4NDZWfn58qKiqa5FlbW+u8njosLExLly7VqlWr9Omnn0o6tbrt4Ycf1l/+8hf16dOnQ3Ns1Pi7z8vLU2xsrLN4aSycFy1aJKvVqtLSUhljVFZW1mKbjtT4uau+vl6VlZXOm1M988wzKisr049//GNZLBaVlpbK4XCovLxcmzZtUkpKiv7jP/5D33//vQoKCjo8z9P/lk6cOOF8D1mzZo1ycnL0b//2b1q6dKn+9re/KTY2VtnZ2UpMTNQdd9yhP/zhD4qIiNCRI0c6Nc/Tx2hjkZyQkKCbbrpJTz75pFJTU7V3715dc801GjBggEpLS1VfX6/q6mrV19d7Jc8tW7Y4r41+6qmndNtttzn/Zmw2m37yk59o4sSJmjZtWofm16N4ZaF5ByksLDRxcXHmzjvvNHFxcaawsNAYc+r6iyVLljS7v6U2nWX69Olm1qxZJjU11axZs8YYY8w333xjRo4cabKzs014eLj59a9/bZYuXWqee+65Ftv4ktWrV5vU1FQza9YsM336dOf2xuulT9e4raU2vsLd2CoqKjKJiYnmvvvuMwkJCWbXrl1eH1ut4Wr81dfXm6FDh5p58+aZSy+91DzyyCMttvE1zz33nImNjTX33Xef83V3NdZ8ffwZY8z27dtNenq6SU9Pd1731jj+nnrqKTNq1CizdOlSs3TpUrN9+/YuMf4KCwvNfffdZ2JjY53vb43j73Snb+sK468r8IX5sq6uziQlJZlFixaZ+Ph4s2/fPmOMMc8++6y54447mt1/6NAhExERYX7+85+bwYMHm3Xr1p31a9EaF1xwgfnxj39sBg0a5LzGc926dc5rpg8dOmRuu+02k5KSYl599VXjcDjMuHHjzMKFC83o0aPNX//6107Jc9q0aWb27NlmxIgR5pVXXjHGGPPVV1+Z888/3xw4cMBERESYJUuWmKVLl5qVK1c2adsY1xkWLlxopk6dai666CLz1FNPGWNOjcno6GhTU1Njli5dah577DEzYcIEc++995ry8nITHh5ufvWrX5mlS5eaFStWdEqejz32mJk4caK58sorzQMPPODc3ni99Okat7XUpiM999xzZvTo0eaGG24wc+fOdW5vvF76dI3b7r77bnPRRReZ+fPnmwsuuMA4HI4Oz3P9+vVm6NCh5uabb25y35nGa6dXr15tHn/8cTN//nwzfPhwY7VazbvvvmtGjBhh7rrrLjN48OBOucfJzp07TWJiovnpT39qhg8fbux2uzHGmLlz55p//OMf5quvvjIDBw40v/jFL0x8fPwZ1yY3xnW0Y8eOOd+T4+PjTWVlpTHm1HX9y5YtM4WFhWbYsGGmqqrK2eaxxx4z48aNc35e+Oyzzzo8z56gWz02KzQ0VNdee60qKiqUkZHhfLSFzWbTwIEDNWrUqDP2t9Sms1x33XWqrq7WjBkzNGfOHEmnlmIEBgZq7NixziVPVqtVgYGBmjBhQrNtfMno0aM1YMAAxcTEaMmSJc5HENXV1Wn8+PHq3bu3M7Zx24QJE5pt4ytaO7YKCwv1q1/9SuPGjfP62GoNV+Nv4sSJuummm1ReXq6rrrpKd999tywWi8+PP0natm2bhg8fLn9/f+eYczXWWhqzvqTxbqIJCQkaOHCgkpOTneMvNjZWAQEBslqtslqtLb7f+ZrKykp9/vnnuuCCC5zvb43jb8KECc6407d1hfHXFfjCfOnv76/p06ertLRUv/zlL3X++edLOnXX5P79+2vUqFFn7I+KitL111+vkpIS3XXXXZ123e/06dNVVlamRYsW6fLLL5d0allyeHi4xowZo4KCAh04cEAjRoxQRESExowZo5kzZyo/P1+zZ8/W3LlzOyXPxr+P//N//o9mzZol6dTfT1BQkMaMGSO73S6bzSar1aqgoCBdcsklzraNcadv6yhTpkyRzWbT5Zdfrp/+9KeyWCwyxsjhcOjyyy/XiRMnVFVVpWuvvVb33Xef7Ha7amtrZbFYZLVanXEdLS0tTcHBwRo1apTuv/9+5yOwrFarrrzySvn5/e9Cz8Ztl112WbNtOtLYsWPVv39/JSYm6tFHH3Vew1tXV6dLL71UISEhztjGbdOnT1doaKiio6O1bNmyJjEdZfjw4UpJSVFYWJh+85vfOB+bVVdXp3HjxslisSgvL0/Jyclavny5wsPDNWzYMI0YMUI2m01PPvmk+vbt2+F5xsfHa/z48bLb7Xr88cedZ3cbGhqUmpqq888/X5dccokqKyv1+OOPn/Eoy8a4uLi4Ds0zMjJSV111laqrq/Vf//Vfio+Pdx4/OTlZpaWluvzyyzVq1Chnm6qqqiafF5KTkzVo0KAOzbMnsBjDRWgAAAAAALRVt7qGGgAAAACAzkJBDQAAAACAByioAQAAAADwAAU1AAAAAAAe6FZ3+QbcycrK0j/+8Q9JOuOujN7sr73z8lYOb731ltauXavevXvr4MGDXv+ZGp3+s5WUlOjvf/+7ioqKNGLECK/mBQDwfcyPAFzhDDW6JYfDoXfffVdPPfWUnn32WR08eFD5+fnavHmzHnroIW3evLldjtNe/bV3XqfLyspSZmamsrKyPM6hNX18/vnnmjNnjlavXq2UlJSz7q89nZ5LSkqKVq9erTlz5ujzzz/vlOMDgK87fPiwsrOzZbPZJJ16hFp2drYOHz7c6e/ZnYX5kfkRaA8U1Oh2KioqdOmll2rWrFk6dOiQsrOz9dxzz2nPnj3eTs0rrFarSktLZbVaPe6jNQX/Qw89JJvNpt/+9rdun2fZHjl5KiQkRL/97W9ls9n00EMPdfrxAcAX3XTTTRo6dKg2bNggSdqwYYOGDh2qm266yavv2R2J+bEp5kfAMwHeTgBoby+//LI+/fRT3XnnnfrjH//YZN/p3/hu3bpVu3bt0sUXX6y0tDRlZWVpx44dmjhxYrP/djgcWr9+vfbt26eYmBhNnz79jGO//vrrOnTokG688UYNGTJExhi9++672rdvn3r37q0ZM2aof//+kqSamhq98sorqqqqUl5eXrM/y6effqqtW7dq7NixGjVqlF544QXNnj1b2dnZ2rVrlyZPnqzx48dLUovH6tWrl6KiotSrVy9JUmVlpV555RWVlJSoX79+Kioq0sSJE5sc94evTaMdO3YoMzPT+Zo0Onr0qLZs2aLY2FhNmzbtjJ9j/fr12r9/v370ox9p6NChTXI6/XW22Wwt/k5+uM/Vz+zu9Z02bZpiY2O1ZcsWHT16VIMGDWr29QeAnmL27Nn6/PPP9fbbb+v666/X22+/7dz+w3mkoKBA77zzjoqKihQcHKypU6fq3HPPbXae7NOnT4vv1ae/x5eVlalPnz5KS0vT119/rffff1/19fUKDw/X1KlTNXTo0Cb5Mj8yPwI+wwDdzJ///GcjyfTt29f88pe/NJs3bzY2m80YY8ySJUuMJHPhhRea+++/38TFxRmLxWI+/vhj574lS5Y0iV2yZIlpaGgwV155pbFYLOb22283U6dONUuWLGkS88477xg/Pz8zb94843A4jN1uN9OmTTMxMTHml7/8pYmLizN9+/Y1x48fN/X19Wbs2LHGz8/PLFy40Fx44YVNjt3oxRdfNJLMT37yE/OnP/3JSDKZmZnm1ltvNZLMiy++aIwxLo91eo5Wq9WMHDmy2eO25rWZOHGiWbx4sdm0aVOTPFevXm0kmRtvvNG5rbHNuHHjzL333mvCwsJM3759TXFxcZOcWnPc5vad7et74403Gklm9erV7T8IAaCLyc7ONhaLxcTHxxubzWbi4+ONxWIx2dnZTd6z9+zZYyIiIkxSUpJ54IEHnHNCS/Nka+an1NRUExgY6HyP3rRpk1m8eLG56667TGxsrAkKCjI7d+5ski/zI/Mj4CtY8o1uZ968eZo4caJKSkq0bNkypaena9y4cSouLnbGTJs2TU8++aRuvvlmGWO0bds2l32uX79eW7Zs0axZs/Tcc89pw4YNmjlzpnP/wYMHdeutt+qSSy7RypUrZbFYtG7dOq1bt06pqamKjo5WcnKySkpK9M9//lPvvPOOdu3apdmzZ+uZZ55p9ltrSc5v5MvLy7Vp0yb16tVLH330kYqKiiRJw4YNkySXxzrd2rVr9e2337o8rqvXJj09XUuXLlV6enqTNo3fcA8YMOCM/q6//notX75c8+bNU0lJidauXdvsz+rquM3tO9vXtzHXllYHAEBPMmTIEI0bN055eXn6y1/+ory8PI0bN05DhgxpErdhwwZVVFRo6NChmjNnjv7zP/9T6enpLc6TrZmfjDG69957nWdXr7jiCs2cOVOjRo1Samqq6uvr9d577zXJg/mx5X3Mj0DnoqBGtxMZGant27frk08+0WOPPaaYmBjt3r1bL7300hmx4eHhkqTa2lqXfe7fv1+SdM455zi3nX/++c7/X7NmjSorK7VmzRrn0rHvv/9e0v9eDzVp0iQtXrxY5557rg4ePCjpfyf8ljR+YCgpKdGWLVs0f/58ffTRR84vBxrbuzrW6Q4fPixJGj58uMvjSq1/bU5nsVha3Ne41KygoMDj456+72xfX1e5AkBPNHv2bEnSo48+2uTfp5sxY4aSkpK0detWXXLJJUpOTtb+/ftbnCdbMz/deOON+t3vfqf09HTV1dXpiiuu0GWXXaatW7eqvr7e2f50zI8t72N+BDoX11CjW7JYLBo/frzGjx+vwsJCPf300woLC2tylvqHGm8UUl1dfca+5ORkSVJ2drZz24EDB5z/P2nSJH388ce655579OabbyogIMA52Q8YMEBLly5t0l9OTo4kqbCw0OXP0b9/f0VEROiTTz6R1WrV/fffr7/+9a+qqqpSnz59FB0dLUkuj3X6deNxcXGS3E/aPxQcHCxJzru//lB8fLwkKTc3t8U+Dh06JOnUh5Wvv/66Tcdvztm+vo25NuYOAD3dnDlz9NBDD6msrMz57x8aNmyYsrOz9dlnn+mRRx7R1q1b9c4777Q4T7Z2fmr05Zdfavv27br33nu1fPlyZWRkaMeOHWfEMT+2jPkR6FwU1Oh2Gm/UIUn5+flatWqVLrzwQt14441atmxZi+0uv/xyWSwWrVmzRg6Ho8mSqhtuuEGTJk3S66+/rp/+9KcqKSnRmDFjnPuvvPJKzZ07V3fddZd+9rOf6dlnn9X06dN1ww036K233tL111+v8ePHKygoSBMnTtTMmTP10EMP6aWXXlJoaKjWrVvXYl7Jycn66quvdO655yolJUXDhw/X/v37NXLkSGeMq2OdbsaMGYqLi9Pq1asVEhKi9evXS5L8/f1lt9tbzGHSpEmyWCxatWqVLBaLpk6d2uSmK5dffrmkUzdrcTgc8vP738UvGzZs0MmTJ/X6669r3LhxuuGGG9rlA8PZvL4Oh0Nbt25tkjsA9HTJyckaN26cdu7cqXHjxjmL5NM1zrE1NTU6cuSIwsPDde211+qcc85pdp585JFHWjU/NRo+fLj69Omjt956S5L06quvusyX+fFMzI9A5/LPyMjI8HYSQHuqq6tTQUGBamtr1adPHy1atEiZmZnq1auXbDabEhISNHnyZCUnJzf592WXXaarrrpKAwcO1OWXX66rrrrKuW/YsGGaP3++xo4dK4vFolGjRumWW25RcHCwM+amm25SXFyc6urqNGDAAMXExGjevHm64oor1Lt3bzkcDvXu3VujR49WYmKi5syZo9jYWPXp00dz587VoEGDnHmdrlevXhoxYoTmzJmj8847T1FRUTrnnHN0/fXXO5edWyyWFo8VGRnpzDE1NVU333yzBg4cqIEDByooKEg7d+7U7bffrtTU1BZfm8mTJ2vq1KlKSkpSQECAhg8frsTERGeOERER+vjjj7V7925deOGFSklJcba/6qqrFBYWpjlz5ujJJ5884/cwcODAFo/rat/QoUM9fn3XrVunv/71r7r66qt11113der4BABfNnPmTP3bv/2bfvKTnygsLEySmrz3xsfH69ixY5Kkq6++Wk8//bSGDBkiPz+/ZufJ3r17t2p+apz7QkNDNWPGDIWGhiomJka33HIL8yPzI+DTLMYY4+0kAHSexrMLeXl5WrlypWJjY7Vz505FREScVb87d+7UhAkTNHz4cH3xxRdun7XpLbW1tbr44ov1/fff6+OPP9a4ceO8nRIAwAcwPzI/Ap7gDDXQwxQVFeno0aMKCwvT3LlztWLFCufNTM5GQkKChg8frsjISEVFRTV7R1NfsHPnTjkcDt155526+uqrvZ0OAMBHMD8yPwKe4Aw1AAAAAAAe4LFZAAAAAAB4gIIaAAAAAAAPUFADAAAAAOABCmoAAAAAADxAQQ0AAAAAgAcoqAEAAAAA8AAFNQAAAAAAHqCgBgAAAADAAxTUAAAAAAB4gIIaAAAAAAAPUFADAAAAAOABCmoAAAAAADxAQQ0AAAAAgAcoqAEAAAAA8AAFNQAAAAAAHqCgBgAAAADAAxTUAAAAAAB4gIIaAAAAAAAPUFADAAAAAOABCmoAAAAAADxAQQ0AAAAAgAe8XlC/9dZbysrKkiR9++23WrFihb777jvn/ua2AQAAAADgbV4tqDdu3Ki5c+dq8+bNKioqUnp6uvbv36/09HQVFxc3uw0AAAAAAF/gn5GRkeGNAx84cEB33XWXZsyYoZCQEJWUlMjf31/PPvusdu/eLT8/Px04cOCMbeedd5430gUAAAAAoAmvnKGurKzU/PnztWrVKvXt21eSlJubq8TERElSUlKScnJymt0GAAAAAIAvCPDGQZ9++mlFRERo3bp12rFjhyQpNDRUo0ePbhJnsVhksVia7SMrK8vZtlFMTIwmT57cITkDALq35ORkb6cAAAC6GK8U1BdddJHKy8tVWlqq2tpaSVJiYqLzDHRubq5Gjhwph8Oh999/v8m2RmlpaUpLS2vSb2ZmJh+IAABtlp2d7e0UAABAF2QxxhhvJtB4Cffdd9+t8847TzNnztQ///lP7dmzR5LO2BYdHd1iX5mZmVq8eHFnpA0A6Eays7P5QhYAALSZV85Qn67xLHN0dLQ2b97s/K+xcG5uGwAAAAAA3ub1gjo9Pd35/yNHjmyyrLulbQAAAAAAeJtXn0MNAAAAAEBXRUENAAAAAIAHKKgBAAAAAPAABTUAAAAAAB6goAYAAAAAwAMU1AAAAAAAeICCGgAAAAAAD1BQAwAAAADgAQpqAAAAAAA8QEENAAAAAIAHKKgBAAAAAPAABTUAAAAAAB6goAYAAAAAwAMU1AAAAAAAeICCGgAAAAAAD1BQAwAAAADggQBvJ9BT2Pa8JtmsZ91PQOoMKTi8HTICAAAAAJwNzlADAAAAAOABCmoAAAAAADxAQQ0AAAAAgAcoqAEAAAAA8AAFNQAAAAAAHqCgBgAAAADAAxTUAAAAAAB4gIIaAAAAAAAPBHg7AbSdqS6SHLaz7scS2k/yD2yHjAAAAACg56Gg7oLsR7Okusqz7sc/ZZosoX3bISMAAAAA6HlY8g0AAAAAgAcoqAEAAAAA8IBXCupVq1YpLi5OwcHBmjlzpgoLC5WRkSGLxeL8LyMjQ8YY3X777erTp48WLlwoY4w30gUAAAAA4AxeKahvu+02nTx5UmVlZZKkNWvWSJKWLFkiY4yMMcrIyNBHH32k3bt36+DBg/riiy/06aefeiNdAAAAAADO4NUl36Wlpaqrq9PkyZOb3f/ll19q8uTJio6O1uTJk7Vr167OTRAAAAAAgBZ4raDOyMhQYmKiJCk5OVmS9NhjjykkJETXXXedysrKVF5errCwMElS7969nWe0AQAAAADwNq89NisjI0P333+//u///b/6+9//royMDGVkZKisrEzz58/XSy+9pMjISB09elSSVFVVpdjYWGf7rKws7dix44x+s7OzO+1naIteBQXyc9SfdT/WI0cUVFAgv4bqs++r11E5gvmSAgAAAAA84dXnUBtjVF9frz59+ji3+fn5yWq1qnfv3ho6dKhWrVqlwsJCffjhh5o9e7YzLi0tTWlpaU36y8zMdJ7t9jW26v6SzXrW/QQMHiyb/VD7PId60CCeQw0A8t0vYwEAgG/zypLvlStXKiYmRomJiZYShiIAACAASURBVIqJidHcuXP1/PPPq1+/fkpMTFRcXJzmzZunSy+9VBdccIGGDx+uCy64QBMmTPBGugAAAAAAnMFiutGzqDIzM7V48WJvp9Es257X2ucMdeoM2Q693z5nqFOmcYYaAHTqDLWvrnACAAC+y6t3+QYAAAAAoKuioAYAAAAAwANevSmZr7Mfel9y2M+6H//kyWfdBwAAAADAt1BQu2CqCiSH7ez7aYeiHAAAAADgW1jyDQAAAACAByioAQAAAADwAAU1AAAAAAAeoKAGAAAAAMADFNQAAAAAAHiAghoAAAAAAA/w2Cy0G0fxQamh5qz78YsaIgWHt0NGAAAAANBxKKjRbkzJoVPP7j5LltBoWSioAQAAAPg4lnwDAAAAAOABCmoAAAAAADxAQQ0AAAAAgAcoqAEAAAAA8AAFNQAAAAAAHqCgBgAAAADAAxTUAAAAAAB4gIIaAAAAAAAPBHg7AXiX49jHMnWVZ92P34CL2yEbAAAAAOg6KKh7OFNTLFNbevYdOerPvg8AAAAA6EJY8g0AAAAAgAcoqAEAAAAA8AAFNQAAAAAAHqCgBgAAAADAAxTUAAAAAAB4gIIaAAAAAAAPUFADAAAAAOABrzyHOjs7Wxs3blRlZaUmT56siy++WJL07bffavPmzZoyZYpSU1Nb3AYAAAAAgLd55Qx1dna2jh49qvz8fM2ePVsbNmxQUVGR0tPTtX//fqWnp6u4uLjZbQAAAAAA+AKvnKFOT09Xenq6JMlms+nYsWMqKirSNddcoz/+8Y+qrKzUe++9J7vdfsa2efPmeSNlAAAAAACa8No11B9++KEWLVqk4uJiLViwQCdOnFBiYqIkKSkpSTk5Oc1uAwAAAADAF3jlDLUk1dfXKyQkRG+99ZY+/vhjSZLFYjkjrrltkpSVlaUdO3acsT07O7vdcgwpLJTF2M+6n5rDh9WroEB+jvqz7st65IiCCgrk11B99n31Oqqggnz51VecfV/BRxVYfFL+1pKz7qsu8Jjsodaz7gcAAAAAOpLXCurGZd9+fn764osvlJCQoPfff1+SlJubq5EjR8rhcJyxrVFaWprS0tKa9JmZmank5OR2y9FWGSM5bGfdj/+QIbLXfSvZzr5IDBg8WDb7Iamu8uzzGjRIDkuuTG1Q+/QVWCJTdfaLHvwHDpQlIuGs+wGA1mrPL2MBAEDP4ZWCuvHsckFBgVavXq0PPvhAsbGxevDBB/Wzn/1MGzdu1LJlyySp2W0AAAAAAHibV66hTkhIUFhYmBITE/XBBx9o5MiRio6O1ubNm5WSkqLNmzcrOjq62W3A/2/v/sOyru89jr9ufgmKvyYIqGBglomWZ5r4A5OuyJxZqKN2WFubc7bWflw7Oy1d7Qw41ZLSTu2sq7VmntSh1WnDktJEVw5I+oGaOs0UJEHiNwIKyM39OX90eR8JULy/X+MOn4/r4rri8/1+3t/394a8rhef7w8AAAAA8Aa9skIdExOjn/70p53GY2NjO1zW3d0YAAAAAAC9rdee8g0AAAAAwFcZgRoAAAAAAA8QqAEAAAAA8ACBGgAAAAAADxCoAQAAAADwAIEaAAAAAAAPEKgBAAAAAPAAgRoAAAAAAA8QqAEAAAAA8ACBGgAAAAAADxCoAQAAAADwAIEaAAAAAAAPEKgBAAAAAPAAgRoAAAAAAA8QqAEAAAAA8ACBGgAAAAAADxCoAQAAAADwAIEaAAAAAAAPEKgBAAAAAPAAgRoAAAAAAA8QqAEAAAAA8ACBGgAAAAAAD/j1dgNAV9qPbpdpOGG5jm/0DXIMGW1DRwAAAADQkS0r1B9//LFyc3Pd/71q1SodOHDAjtIAAAAAAHglWwL11q1blZOTo7a2NiUmJur+++/X9OnT9dlnn9lRHgAAAAAAr2NLoG5sbFRLS4t2796t0tJSJSQkaNiwYXr99dftKA8AAAAAgNex5R7q8PBwPffccyooKJAkPfroo9q8ebMqKyvtKA8AAAAAgNexZYX6tttu08mTJ/X2228rMjJS119/vQICAjR48GA7ygMAAAAA4HVsWaEePny48vLytHXrViUlJcnf31/x8fEaMWKEHeUBAAAAAPA6Hq9Q5+bmKiMjw/2VnZ0tp9OpV199VRkZGfrwww9VW1vb5dw1a9YoIiJCgYGBmjt3rurr65WWliaHw+H+SktLkzFGS5Ys0ZAhQ3TPPffIGOPxiQIAAAAAYCePV6hzcnKUnp5+3n1SU1MVHx/faXzx4sVavHixTp06peTkZK1bt869f1pamnu/vLw87d27V0eOHNGcOXNUUFCgadOmedoyAAAAAAC28ThQx8fHa9myZZKk8vJy7du3T3PmzHFvf+WVV/Qv//Iv560xYMAADR8+XMHBwaqpqem0fffu3UpISFBISIgSEhJUWFhIoAYAAAAAeAWPA3ViYqISExMlSb///e8VFBSkFStWuLe3traqqKjovDXee+89ffLJJ3r22Wf1+OOPKz09XRkZGbrxxhuVmZmpkydPasCAAZKk4OBg1dfXe9ouAAAAAAC2suWhZAMHDtTzzz+v2tpaxcTEqK6uTuvXr9cjjzzS7ZyDBw/qscce0zvvvCN/f3+lpaUpLS1N9fX1uvvuu5WZmanBgwerpKREktTU1KSwsDD3/NzcXOXl5XWqe6EQfzGCqqrkMO2W65wuLlZgZaV8XGcs12o5dkwBlZXyaTtlvVZgiQIqK+RzpsF6rX4l8q/5TL4tXd83fzFa/T+V38nP5Hu62nKtM37H5ay1/jMEAAAAgC+yJVB/61vf0jPPPKNXXnnFPRYWFqZvfvObXe7/wQcf6Be/+IU2btwof3//Dtt8fHzU0tKi4OBgjRkzRmvWrFFVVZV27typ5ORk937x8fGd7s/OyMhQTEyMHackSXI2hkoup+U6vtHRam89IDlbLNfyu+IKOduPSq2N1vsaPVouR5lMc4A9tfxrZZqsv4nNNypKrqpTMg0u67UiI+UYMtpyHQB9m51/jAUAAJcPWwJ1//799e677+q1115TUVGRhg8frgULFnT7HurNmzcrLy9PkZGRkj5/GFlkZKQeeOABnTlzRgsXLlRKSor8/Pw0adIkjR07VsnJyZo+fbod7QIAAAAAYJktgTovL0+5ubmaOXNmt6vS5zp7efcXLVmypNPY6tWrtXr1ajvaBAAAAADANtavz5VUV1en3/72t8rOzrajHAAAAAAAXs+WFeohQ4Zo3Lhxeu211zRkyBD3+MyZM7t8DzUAAAAAAF91tgTqnJwcffTRR5Kk5cuXu8dTU1MJ1AAAAACAPsmWQB0fH69ly5Z1OQ4AAAAAQF9kS6BOTExUQkKCsrKyVFxcrOjoaCUlJXV6JRYAAAAAAH2FLYHa6XRq9uzZys/Pd49NmzZNO3fuJFQDAAAAAPokWwJ1VlaW9uzZo8WLF2v48OGqra1VZmamNm3apOTkZDsOAQAAAACAV7ElUBcVFWnp0qV66qmn3GNBQUEqLi62ozwAAAAAAF7HlkAdExOj9PR0NTY2KjQ0VNXV1dqwYYPWrl1rR3kAAAAAALyOLYE6KSlJ1113nV544QX32IwZM5SUlGRHeQAAAAAAvI4tgdrf3187d+5UVlaWjh075n7Kt5+fLeUBAAAAAPA6PnYUqa+v16pVqxQbG6v7779fkyZN0lNPPaXq6mo7ygMAAAAA4HVsWULetGmT3n77bS1btkySNGbMGOXn52vw4MFaunSpHYfoMdP4mS11HAPDbKkDAAAAAOibbAnUFRUVGjlyZIexYcOGqa6uzo7yF6X9yDZb6vhd+6+21AEAAAAA9E22BOrx48dr+fLlqqur09ixY1VUVKRXX31VWVlZdpQHAAAAAMDr2BKo582bpxtvvFF//etf3WOzZs3SvHnz7CgPAAAAAIDXsSVQ+/j4aOvWrXrjjTf0ySefKDo6Wrfffrt8fX3tKA8AAAAAgNex5SnfklReXq5PP/1UcXFxWrRokXbt2qUjR47YVR4AAAAAAK9iS6CurKzUpEmT9LOf/Uw5OTmSpJycHL311lt2lAcAAAAAwOvYEqizsrIUEBCg66+/3j3mcrlUW1trR3kAAAAAALyOLfdQNzQ06Lvf/a769+/vHissLNTcuXPtKA8AAAAAgNexJVDHxsbq8ccf19ixYyVJ+/fv15tvvqnly5fbUR4AAAAAAK9jS6CeO3eu4uLitHnzZvfY7bffrvj4eDvKAwAAAADgdWwJ1A6HQ5s2bVJ2drYOHz6sK6+8UrfddpsdpQEAAAAA8Eq2BGrp83dRnxui29vbdejQIY0bN86uQwAAAAAA4DUsP+X7448/1tNPP62NGzeqra1NklRaWqqbbrpJGzdutNwgAAAAAADeyNIK9aeffqqpU6eqoaFBkvStb31Ld911l37wgx8oKChIL774oi1NAgAAAADgbSwF6tdff13GGP385z9Xc3Oz1q5dq5deeknh4eHavn27Ro8ebVefAAAAAAB4FUuBuqamRvfee68ef/xxSVK/fv20YcMGbdu2zf0KLQAAAAAA+iJLgdrlcmn37t3KyMiQJB06dEjXXXedsrOzlZ2drZkzZ3b56qzi4mJt3bpVTU1NmjFjhmbMmCFJOnDggHJycnTzzTdr/Pjx3Y4BAAAAANDbLD/lOycnRzk5OR3GduzYIUlKTU3tMlAfPXpUx44d05kzZzR//nz99a9/1YQJE5SYmKhFixYpIyND+/btkzGm09iwYcOstgwAAAAAgGWWAnV8fLyWLVt23u1dSUxMVGJioiTJGKP3339fpaWlmjt3rp555hk1NjbqrbfeUnt7e6exlJQUKy0DAAAAAGALS4H63GDsiba2Nr3zzjt6/vnntX37do0cOVKSFBkZqdLSUhljOo0BAAAAAOANLF/ybcW///u/68EHH9TkyZO1fft2ORyOTvt0NSZJubm5ysvL6zReXV1tS2/NxcUKrKqSw7RbrnW6uFiBlZXycZ2xXKvl2DEFVFbKp+2U9VqBJQqorJDPmQbrtfqVyL/mM/m21Fqu1er/qfxOfibf09Z/lmf8jstZa/1nCAAAAABf5HGgfv/999Xa2trtZd3n43K59POf/1zXXHONkpOTJUkjRozQ9u3bJUllZWWKjY2Vy+XqNHZWfHx8p2NnZGQoJCTE01PqwC86Ws5ToZLLabmWb3S02lsPSM4W631dcYWc7Uel1kbrfY0eLZejTKY5wJ5a/rUyTT7Wa0VFyVV1SqbBZb1WZKQcQ3h9G4DzKyoq6u0WAADAV5DH6Wfr1q3Kzs6W9PlqcW5ubo/nPv3003rnnXfU1NSkjIwM5ebmau7cudqyZYvuu+8+bd26VXPmzOlyDAAAAAAAb+DxCvXQoUP15JNPqqmpSXv37lW/fv063U/d3WuzJk6cqFtvvVV1dXWSpJaWFoWEhLifGJ6Tk+Neae5qDAAAAACA3uZxoE5KStJ//Md/6A9/+IN77Iuvz+rutVndPcwsNja2w2Xd3Y0BAAAAANDbPA7Uo0aN0qFDh7Rp0yZ9+umnamtr67SPJ/dXAwAAAADwVWDpKd/Dhw/X0qVLJUlOp1NZWVkqLi5WdHS0kpKS5O/vb0uTAAAAAAB4G1tem+V0OjV79mzl5+e7x6ZNm6adO3cSqgEAAAAAfZItgTorK0t79uzR4sWLNXz4cNXW1iozM1ObNm1yvxYLAAAAAIC+xJZAXVRUpKVLl+qpp55yjwUFBam4uNiO8gAAAAAAeB1bAnVMTIzS09PV2Nio0NBQVVdXa8OGDVq7dq0d5QEAAAAA8Dq2BOqkpCRdd911euGFF9xjM2bMUFJSkh3lAQAAAADwOrYEan9/f+3cuVNZWVk6duyY+ynffn62lAcAAAAAwOvYlnj9/Px4ABkAAAAA4LLh09sNAAAAAADwVUSgBgAAAADAA7YE6tzcXOXm5nYaKywstKM8AAAAAABex5Z7qHNyciRJ8fHx7rE33nhDwcHB+vrXv27HIQAAAAAA8CqWAnVubq7y8vKUl5cnScrIyJAkNTc368UXX9S//du/We8QAAAAAAAvZClQ5+TkKD09vcP3ZzkcDs2aNctKeQAAAAAAvJalQB0fH69ly5Z1Gh84cKBmz56tuLg4K+UBAAAAAPBalgJ1YmKiEhMTJUm7du1SXl6enE6nJLkvAz/3vmqgN5iWeqmtxXqhwMFy+AdZrwMAAACgT7DloWR5eXmaNWuWjDEdxlNTUwnU6HXms4/kqiuxXMcnarocw660oSMAAAAAfYEtgfrdd99VWFiYUlJSFBAQ4B4nTAMAAAAA+ipbAvWUKVP0ne98R0888YQd5QAAAAAA8Hq2BGo/Pz/94x//cL8266yZM2eySg0AAAAA6JNsCdQ5OTkqKChQQUFBh3HuoQYAAAAA9FW2BOruXp9FmAYAAAAA9FW2BOrAwEANHTq0y3EAAAAAAPoi2y75Tk9P7zTOJd8AAAAAgL7qkl3yvWXLFsI0AAAAAKDPsiVQJyYmKjExscOY0+mUr6+vHeUBAAAAAPA6tgTq3Nxc5eXlSZLa2tpUV1enzMxMhYeH68Ybb7TjEAAAAAAAeJVLdg+1w+HQ9OnT7SgPAAAAAIDX8bGjyNl7qM9+PfbYY8rPz9fMmTO7ndPS0qIHH3xQaWlpkqS0tDQ5HA73V1pamowxWrJkiYYMGaJ77rlHxhg72gUAAAAAwDJbAnViYqIeeeQRTZkyRcOGDdOVV16pyZMnn3fOihUr9Nhjj3UYS01NlTFGxhilpaUpPz9fe/fu1ZEjR/TBBx+ooKDAjnYBAAAAALDMlku+nU6nZs+erfz8fPfYtGnTtHPnTvn7+3c55+zK9Pns3r1bCQkJCgkJUUJCggoLCzVt2jQ7WgYAAAAAwBJbVqizsrK0Z88eLV68WMuWLdPSpUu1b98+bdq06aLqpKenKygoSPPmzVN9fb1OnjypAQMGSJKCg4NVX19vR7sAAAAAAFhmywp1UVGRli5dqqeeeso9FhQUpOLi4h7XSEtLU1pamurr63X33XcrMzNTgwcPVklJiSSpqalJYWFh7v3PfbL4uaqrqy2cyf9rLi5WYFWVHKbdcq3TxcUKrKyUj+uM5Votx44poLJSPm2nrNcKLFFAZYV8zjRYr9WvRP41n8m3pdZyrVb/T+V38jP5nrb+szzjd1y+TeXybbJeq9VxXO0nbfkbFAAAAIA+wJZAHRMTo/T0dDU2Nio0NFTV1dXasGGD1q5de9G1fHx81NLSouDgYI0ZM0Zr1qxRVVWVdu7cqeTkZPd+8fHxio+P7zA3IyNDISEhls9Hkvyio+U8FSq5nJZr+UZHq731gORssd7XFVfI2X5Uam203tfo0XI5ymSaA+yp5V8r02Q9cPpGRclVdUqmwWW9VmSkTL1Lrjrrf8zwiYyUz7AYy3UAeJ+ioqLebgEAAHwF2RKok5KSdN111+mFF15wj82YMUNJSUndzklLS+vwqq3IyEg98MADOnPmjBYuXKiUlBT5+flp0qRJGjt2rJKTk3kNFwAAAADAa9gSqP39/bVz505lZWXp2LFjio6OVlJSkvz8ui9/9hLvcy1ZsqTTfqtXr9bq1avtaBMAAAAAANtYCtRHjx5VeXm54uPj5efn1+GS7NzcXI0YMUIxMVwiCwAAAADoeyzd8PrWW28pJyeny205OTndbgMAAAAA4KvOUqCuqamR09n1Q7ucTqcqKyutlAcAAAAAwGtZCtSBgYFqa2vrcpsxRoGBgVbKAwAAAADgtSzdQx0TE6Nf//rXysnJ0bXXXquwsDBVVFRo7969+uijj5SZmWlXnwAAAAAAeBVLgfqWW25ReHi4CgsLVVhY2GFbWFiY5syZY6k5AAAAAAC8laVAPWDAAB08eFDbtm3T0aNH1d7eLh8fH8XExOjmm2/WoEGD7OoTAAAAAACvYvk91MHBwVq4cKEdvQAAAAAA8JVh6aFkAAAAAABcrgjUAAAAAAB4gEANAAAAAIAHCNQAAAAAAHjA8kPJgEuhscXoTLP1OoPbjfytlwEAAACATgjU8EqfVLWpqsb6BRT/MqJVYTb0AwAAAABfRKCGbQ5VONVw0mG5zrjwMzZ0AwAAAACXFoEatmloNaprsR6onS5jQzcAAAAAcGnxUDIAAAAAADxAoAYAAAAAwAMEagAAAAAAPECgBgAAAADAAwRqAAAAAAA8QKAGAAAAAMADBGoAAAAAADxAoAYAAAAAwAMEagAAAAAAPECgBgAAAADAAwRqAAAAAAA8QKAGAAAAAMADfr3dAHCp1TY260z9act1Bg5v00Ab+gEAAADQN/RqoN6/f7/q6+sVHx8vSTpw4IBycnJ08803a/z48d2OARejrrFFp+tPWS90upVADQAAAMCt1y753rJli5KTk5WTkyNJqq6uVmJiog4fPqzExETV1NR0OQYAAAAAgDfotRVqPz8/hYSEuL/fsmWL5s6dq2eeeUaNjY1666231N7e3mksJSWlt1oGAAAAAMCt1wJ1YmKicnNz3d+XlZVp5MiRkqTIyEiVlpbK5XJ1GgMAAAAAwBt4zUPJHA6HHA5Hp++/OHZWbm6u8vLyOtWprq62pZ/m4mIFVlXJYdot1zpdXKzAykr5uM5YrtVy7JgCKivl02b9nuCWwBIFVFbI50yD9Vr9SlRXX6emJusP/zpRVqa6ujo1NTVZrlVeXq4zNbVqt6FW+2efqbV9rxzOFsu1XAHBkl+Q5ToAAAAAeo/XBOoRI0Zo+/btkj5frY6NjZXL5eo0dlZ8fLz7YWZnZWRkdLiM3Aq/6Gg5T4VKLqflWr7R0WpvPSDZEMT8rrhCzvajUmuj9b5Gj5bLUSbTHGBLrfLyCtlxW/6IkSN15nSjnC5juVZERISazEmddtZZrhUaHq7wfqflqjliuZZP2PXyCY2xXAeAPYqKinq7BQAA8BXUaw8lO7vCnJeXp9zcXM2dO1dbtmzRfffdp61bt2rOnDldjgEAAAAA4A16bYW6paVFkydPdv93SEiIcnJy3F9nV5q7GgMAAAAAoLf16kPJEhMTO4zFxsZ2uKy7uzEAAAAAAHpbr13yDQAAAADAVxmBGgAAAAAADxCoAQAAAADwAIEaAAAAAAAPEKgBAAAAAPAAgRoAAAAAAA8QqAEAAAAA8ECvvYca3iG/xKnGJut/V5k24owN3QAAAADAVwcr1AAAAAAAeIBADQAAAACABwjUAAAAAAB4gEANAAAAAIAHCNQAAAAAAHiAQA0AAAAAgAcI1AAAAAAAeIBADQAAAACABwjUAAAAAAB4gEANAAAAAIAH/Hq7AeCrpLymSc3l9ZbrDB7YrNBQGxoCAAAA0GsI1MBFaDnj1KnWNst1Ap3tNnQDAAAAoDdxyTcAAAAAAB4gUAMAAAAA4AEu+f4KOlhSLVdLg+U60ZFnbOgGAAAAAC5PrFADAAAAAOABAjUAAAAAAB4gUAMAAAAA4AECNQAAAAAAHuChZEAvKak4qYraJst1IocPVsSwYBs6AgAAAHAxCNRAL2lprFNTdYX1Ov0lEagBAACAL53XBOq0tDSlp6e7v09NTVVqaqp++MMf6tVXX9Wdd96p5557Tg6Hoxe7BOzjX39Ew6o/slzHd0icpJHWGwIAAABwUbzqHurU1FQZY2SMUVpamvLz87V3714dOXJEH3zwgQoKCnq7RQAAAAAAJHlZoP6i3bt3KyEhQSEhIUpISFBhYWFvtwQAAAAAgCQvC9Tp6ekKCgrSvHnzVF9fr5MnT2rAgAGSpODgYNXX1/dyhwAAAAAAfM6r7qFOS0tTfX297r77bmVmZmrw4MEqKSmRJDU1NSksLMy9f25urvLy8jrVqa6utqWf5uJiBVZVyWHaLdc6XVyswMpK+bjOWK7VcuyYGhoapDPWnw5devy46k/Wq7m51Xqt0lLV1depqem05VonyspUV1enpibr51heXq4zNbVqt6FW+2efqa2xRk4bajkrPlN7c6PabKjVVlkpZ1GR5ToAAAAALo7XBOqzfHx81NLSouDgYI0ZM0Zr1qxRVVWVdu7cqeTkZPd+8fHxio+P7zA3IyNDISEhtvThFx0t56lQyeW0XMs3OlrtrQckZ4v1vq64Qo1Fg+SyXkqjIiNVU1kmX99T1muNGqVT9dWy46KHESNH6szpRjldxnKtiIgINZmTOu2ss1wrNDxczQFGTa2VlmsNCwvXmaZANTaXW641dPhwRcXEWK4DXM6K+KMUAADwgNdc8r169WoNGzZMI0eOVHh4uFJSUjRjxgxNmjRJY8eO1aRJkzR9+vTebhMAAAAAAEletEK9ZMkSLVmypNP46tWrtXr16l7oCAAAAACA7nnNCjUAAAAAAF8lBGoAAAAAADxAoAYAAAAAwAMEagAAAAAAPECgBgAAAADAAwRqAAAAAAA8QKAGAAAAAMADXvMeagCeq2loVl1ji+U6QwcGatigIBs6AgAAAPo+AjXQB9ScPK2i8nrLdWIihhCoAQAAgB4iUH9J/l5s1NZm/Qr7hDFOG7oBAAAAAFjFPdQAAAAAAHiAQA0AAAAAgAcI1AAAAAAAeIBADQAAAACABwjUAAAAAAB4gKd8A31BW7NMS4MNdfpZrwEAAABcJgjUQB/gOvmpTFWx9ToDoyWNst4QAAAAcBkgUAPooLymSccrT1quE/a1YI0OG2xDRwAAAIB3IlAD6OB0a5tqG1ss1xnYn8vHAQAA0LfxUDIAAAAAADxAoAYAAAAAwANc8g3gkimpOKmDJdWW64wKHaQJ0aE2dAQAAADYhxVqAAAAAAA8QKAGAAAAAMADXPINoKO2ZpmWBhvq+EuBvDYLAAAAfReBGkAHroYymarD1usERUkDr7WhIwAAAMA7EajPo/CE1O5yWK4zeZzLhm4AAAAAAN6EQH0edS32BGqXIVADVpVVNWpfcaXlOuFfC9b4K0LVdLrVci1/P18N7B9guQ4AloE4+gAAFfFJREFUAAC+mgjUAC47tQ2ntedIheU6oYP7a/LVETZ0BAAAgK8ir3/K94EDB/T000/rn//8Z2+3AgAAAACAm1evUFdXVysxMVGLFi1SRkaG9u3bp2HDhvV2WwB6ytmsgNZqy2V8zki+9ScUUbbdcq2gtjHS126yXMfbOdtdajhl/bJ2Hx+HhgQH2tARAABA3+PVgXrLli2aO3eunnnmGTU2Nuqtt95SSkpKb7cFoId8G0s1rDrPcp1+AddIXxthQ0eXj9MtbXrv0AnLdfr389cN10XZ0BEAAEDf49WBuqysTCNHjpQkRUZGqrS0tJc7AoCO6ptadPh4jeU6A/v306jQQTpYUmW5Vv/AAEUNH2S5jrdrd7n04cflttQK6WdLGQAAcJlxGGNMbzfRnccff1yNjY16+OGH9dBDD2no0KG6//77JUm5ubnKy+u48uXv76+2trbeaBUA8BUWGhqqH/zgB73dBgAA+KoxXmzdunXm+9//vjHGmO9973vmL3/5y3n3X7FihW3Hpha1LsdadtejFrUux1oAAODy4dVP+Z47d662bNmi++67T1u3btWcOXN6uyUAAAAAACR5+WuzQkJClJOTo6uvvlo5OTkKCQnp7ZYAAAAAAJDk5Q8lk6TY2FjFxsb2dhsAAAAAAHTgm5aWltbbTdgpKsq+17tQi1qXYy2761GLWpdjLQAAcHnw6qd8AwAAAADgrbz6HmoAAAAAALxVn7vk+8CBA9qwYYMGDRqk0NDQHm2/0JwLOXr0qNatWyc/Pz+NGDGiy32OHTumgoICXXnllT2eY9WJEyf0P//zP2ptbdXo0aM7bKupqdHLL7+srVu3qq6uTldddZUcDsd559iltrZWL7zwgmpra92fx1lNTU16+eWX9eabb6q0tFQTJky44By7nDp1SmvWrNGxY8c0fvz4bvcrKirSxo0bdf311/d4jhVtbW1au3at9u/fr4kTJ8rhcLi3tbe364knnlBeXp7y8vK0a9cuzZgx47xz7GKM0YYNG/Tee+8pNjZWfn6dH8mwZ88erV27VlOmTJGfn1+P5thh06ZNevvtt3XVVVepX79+HbatXr1a27Ztc39m48aNU//+/c87xy45OTl64403FB0drQEDBnTY5nK59Morr2jr1q0aMmSI+9+i882xS35+vv72t79pxIgRGjx4cKft27dv12uvvabIyEgNGjSoR3Pssnv3bh0/flwjR47stG3Pnj166aWXNGzYMA0bNqzbMQAAgEulT61QV1dXKzExUYcPH1ZiYqJqamouuP1Ccy6ktbVVCQkJOnTokG6//XYdPXq00z7vvvuuFi1apKysrB7PscoYozlz5mj37t367ne/q4KCgg7bKyoqdOjQIdXW1uoXv/iFnnrqqQvOscudd96p3Nxc/fznP9fmzZs7bKurq9P+/ftVV1enlStX6oEHHrjgHLvce++92rx5sx599FE9//zzXe7T2NiohQsXasWKFT2eY9Wvf/1rrVu3Ts8995weffTRDtva29v10EMPqa6uzv11oTl2WbVqlZ5++mm9/PLL+uUvf9lp+z/+8Q8lJyerqqpK7e3tPZpjh7/85S968MEHtW3bNi1evLjT9oaGBvdntXLlStXW1l5wjh1ycnJ0zz33qKCgQAsWLOi0/eGHH9Zjjz2m4uJiTZ8+XRUVFRecY4e9e/fqzjvv1P79+3XTTTfJ5XJ12P7qq6/qpz/9qY4eParExES1tbVdcI5dNm3apDvuuEM7d+7stK20tFTf+MY39PHHHyshIUFNTU1djgEAAFxSvfkSbLutW7fOfP/73zfGGPPd737XZGZmXnD7heZcyLZt28zcuXONMcb86le/Mv/1X//VaZ9du3aZW265xdx77709nmPV/v37zXXXXWeMMebJJ580DzzwQLf7vvjiiyYlJeWi5niqqqrKREREGJfLZTIzM83dd9/d7b5vvPGGWbBgwUXN8ZTT6TSDBg0yzc3N5u9//7u5+eabO+3T3t5uFixYYJ588kkTFRXVozl2iIqKMmVlZebgwYNm4sSJHba1traagICAi5pjl6lTp5oPP/zQ1NbWmuHDh3fanpycbF555ZWLmmOHpKQks3nzZtPW1maGDh1qTp8+3eV+u3btMjfccMNFzbHinnvuMX/+85+NMcZER0eb48ePd9h+5513mrVr1xpjjPn6179uDh48eME5dvjtb39rHnnkEWOMMdOmTTPvv/9+h+0/+tGPzJ/+9CdjjDFz5841+fn5F5xjl+zsbDN16lTzxBNPdNr27LPPmp/+9KfGGGMWLlxoXnvttS7HAAAALqU+tUJdVlbmviwwMjJSpaWlF9x+oTlWjylJcXFxHVaXrB6zJ06cONHhGMePH+9237feeks333zzRc3xVHl5uSIiIuRwOLo99xMnTuiBBx7QE088oaeffrpHc6yqra3VgAEDFBgY2O0x0tLSNGnSJH3zm9/s8RyrjDGqqKhQREREt8dob29XRkaGVq9eraamph7NscPZ35ehQ4equblZra2tHbbv27dPFRUVeuKJJ9wr5xeaY4eysjKNGjVKfn5+CgkJUUVFRZf7PfTQQ/rP//zPi5pjRXl5uUaNGiVJGjVqVKefy4MPPqhf/OIXuueee5SYmKhx48ZdcI7dfXX1+zJr1iz993//t371q19p//79OnHixAXn2GXevHm64YYbutz2xX+vSktLuxwDAAC4lPpUoHY4HB3uFf3ifaNdbb/QnIs5punhA9M9meOJnpzLH//4R40bN07f//73ezzHqnOP0dX5t7e3y+FwqKamRs8991yP5tjR0/mOUV5ermeffVb9+vXTs88+q4aGBv3xj3+85H2d219XfH19df/996uurk6ZmZm68847LzjnUvX1xfNvaGjQe++9p48++kjz58/v0ZxL0VdXn8P27dsVEBCg2bNn93iOVcaY89Z955139NBDD2nixInKzs5WQ0PDBed8GX3dddddSktLU3R0tPse6i+jr57o6md2qX+OAAAA5+pTgXrEiBHuFYmysjL3w75yc3OVm5vb5fbu5nhyzBMnTrjn79mzR1u3br2oOXb64nmdXbX55JNP9Ne//lXS52G6sLBQv/nNb+RwOLqdY6eIiAidOHFCxpgOxzhx4oTWrVsn6fOVpYyMDK1cuVIFBQXdzrHT0KFDderUKbW0tHQ4RkNDg5599lkFBQVpyZIlqq+v18mTJ+VyueTj49PlHDs5HA6FhYXpxIkTHX4/29vbtXLlSvn6+mrFihVasWKFNm7cqPfee6/bOXYbMWKEysrKdPLkSfXr10+BgYGSpKefflotLS0aOXKkfvOb32jt2rUqLCxUa2trt3Ps7qu0tFTt7e2qrq52P9xr9erVqq6uljFGv/nNb/Twww9fcM6l6Evq+P/Xxo0bVVJSoh07dig6Olo/+9nP1L9/f/3zn//8Uv6f7O4Yr7/+uv75z39KkhYtWqR7771XZWVlmjBhwpfSV3f+/ve/67333rsk/54DAABctC/9IvNLqKqqyoSHh5sf//jHJjw83FRVVRljjElNTTWpqaldbu9uTk+1tLSYUaNGmXvvvddERESYw4cPG2M+v7/v7D3Tu3fvNgsXLjRxcXFmy5Yt3c6xk8vlMhMmTDCLFy82UVFRJj8/3xhjTFZWlklKSjI7duwwoaGh5tFHHzUrVqwwr776ardz7HbTTTeZb33rW2bs2LFm06ZNxpjP72eNi4szZWVlZsWKFSY1NdVcc8015ve//323c+z2ne98x8yfP99MmjTJ/PGPfzTGGFNSUmKioqI67HfuWFdz7Hb//febG2+80cycOdOkpaUZY/7/3mmn02lWrFhhHn74YTN79mz38wC6mmO3lStXmuuvv77D8wGMMeZrX/uaqampMatWrTLx8fEmJSXFzJ49+7xz7LR+/Xozfvx4s2jRIpOUlOQej42NNfv37zdZWVnmm9/8Zo/m2Gnbtm0mOjrafOc73zFxcXHu8Ztuusnk5OSYVatWmauuusrcd999JjIy0jQ2NnY7x0579uwxI0eONEuXLjVjxowxTqfTGGPMXXfdZdavX2+MMea1114z8+fPN3fdddd559htx44d5oYbbjDz5s0zBQUFxpjPf7efeOIJc/z4cfe/3REREaaxsbHLMQAAgEupT702q3///vrGN76hhoYGpaWl6YorrpAkOZ1ORUVFacKECZ22dzenp/z8/JSUlKTa2lotW7ZMEydOlPT5CuLw4cN1zTXXqKSkRFVVVbriiisUFhamCRMmdDnHTg6HQwsWLFBdXZ1+/OMfKz4+XtLnr+YZPHiwrrrqKvn4+Ki1tVUtLS0aPHiwJk2a1OUcu82fP1+NjY1KSUlxXwpsjFFgYKCmTp2q48ePq7W1Vd/73vd01113dTvHbrfccotaW1t1yy236O6773b35XA4OnwW5451NcduN954oxwOh6ZOnaqf/OQn8vH5/MKS1tZW3XTTTSotLVVjY6MSExO1fPly+fj4dDvHTtOnT9fAgQM1duxYLVu2zP0KrNbWVt1www2aNWuWBgwYoOHDh2vFihXq169ft3PsdO2112rUqFEKDQ1Vamqq+xVYra2tiouL0759+7R48eIOr1Tqbo6dYmJiFBsbq8DAQD3yyCPuV2CdOXNGkyZN0rx58zRq1Cj5+vpq1apVCgkJ6XaOncLDwzVt2jQ5nU499thjGjp0qLuv8ePHKzw8XEeOHFF4eLh++9vfys/Pr9s5djt48KBcLpfCwsIUHR2t0aNHy+l0Kjo6WhMnTlRiYqKampr06KOPKiIiQoMGDeo0BgAAcCk5jLmEN34CAAAAANBH9al7qAEAAAAA+LIQqAEAAAAA8ACBGgAAAAAADxCoAQAAAADwQJ96yjdgl9zcXG3YsEGSFBUV5dG8Tz/91KMadvL0PM6VlZWlTZs2KTg4WEeOHOn1czrr3HOrra3Viy++qOrqao0bN65X+wIAAMDlgxVqXNZcLpfefPNNPfnkk/rTn/6kI0eOqKKiQjk5OVq+fLlycnIuqt658zyt0RO5ubnKyMhQbm5uj/vxpMb777+vO+64Q+vXr9fVV19tuZ6dzu3l6quv1vr163XHHXfo/fff/1KODwAAABCocdlqaGjQjBkztGjRIh09elRFRUX685//rH379vV2axfU0tKiuro6tbS0eFyjJ4F/+fLlcjqd+t3vfqegoKBL3pOngoKC9Lvf/U5Op1PLly//0o8PAACAy5NfbzcA9JaNGzeqoKBAP/7xj/XMM8902HbuKuvbb7+twsJCTZ06VfHx8ZIkY4zefPNNHTp0SMHBwVqwYIGGDx/eo+MWFBTo7bff1te//nVNmDBBa9euVXJysoqKilRYWKiEhATFxcWd9ziBgYEaOnSoAgMDJUmNjY166aWXVFtbq2HDhqm6ulozZ87scNyuzkOS8vLylJGRoZkzZ3YYLykp0Y4dOxQWFqZbb72103m88cYbOnz4sG677TaNGTOmQ0+5ubnKy8vTzJkz5XQ6Oxz3fNsu9NmePn1aL730kpqamlReXt6hn1tvvVVhYWHasWOHSkpKNHr06B79PAAAAABPEahx2XK5XJKkl156SQMGDNDcuXOVkJAgX19f9z7Z2dlqbGxUZmamKioqlJ+fr6lTp+r222/Xe++9p+9973tav369fv3rX2vv3r09Ou4nn3yi5cuX6wc/+IGmTJmi5cuXy+FwaP/+/Vq3bp3WrVunuLg4uVyubo+Tk5Oj9PR0paam6vrrr9f06dN18OBB/fCHP9Qrr7yiDz74QKmpqec9j7Oam5u7XFk++0eF2bNny8en48UsmzdvVn19vf785z/r4Ycf1ieffNKhJ0lKT0/XlClTdMMNN3Q47tn9utp2vs82LCxMs2bN0p49e/TDH/5QhYWFHXry8fHR7Nmz9fLLLys3N5dADQAAgEuOS75x2UpJSdHMmTNVW1urlStXKjExUZMnT1ZNTY17n1tvvVWrVq3St7/9bRlj9M477yg7O1vZ2dkaP368QkJCFBMTo9raWv3tb3/r0XHHjBkjSTp58qS2bdumwMBA5efnq7q6WpJ05ZVXSlKPj7Np0yYdOHBAycnJeu6557pcTe7qPM5KTEzUihUrlJiY2GHO2RXgUaNGdao3f/58PfXUU0pJSVFtba02bdrU5bme77gX+9m+/vrrKiwsPO95nu31i6vXAAAAwKVAoMZla/DgwfrHP/6hXbt2KT09XaGhodq7d68yMzM77Ttw4EBJn6/mfvzxx5L+/57hWbNmadmyZbrmmmt6dNyzgbq2tlY7duzQ3Xffrfz8fHeQPxuoe3qc4uJiSdLYsWMveOxzz6OnHA5Ht9vOXopdWVnp8XF7+tkeOXJE0v9/PhfbKwAAAGA3LvnGZc3hcCguLk5xcXGqqqrSH/7wBw0YMKDDKvUXnQ3Eo0aN0ooVKzps68kTrocPH65BgwZp165damlp0S9/+Us9//zzampq0pAhQxQSEnJRxwkPD5d04VD7Rf369ZMkOZ3OLrdHRERIksrKyrqtcfToUUmfh/mPPvrooo7flfOdc2lpqSSpqqqq2/lnez3bOwAAAHApEahx2Tr7cCxJqqio0Jo1azRlyhTdeeedWrlyZbfzkpKSdPvttysrK0vz589XXFycAgICOj0E7HxiYmK0Z88eXXPNNbr66qs1duxYHT58WLGxsRd9nAULFig8PFzr169XUFCQ3njjDUmSr6+v2tvbu+1h1qxZcjgcWrNmjRwOh2655ZYODyWbPXu2pM8fZuZyuTrcR71lyxZ99tln+t///V9NnjxZt99+uy2B+nznvHDhQi1fvlyZmZnq37+/srOzO8x1uVx6++23O/QOAAAAXEq+aWlpab3dBNAbWltbVVlZqebmZg0ZMkQ/+tGPlJGRocDAQDmdTo0YMUIJCQmKiYnp8P2YMWOUkpKiG2+8UcHBwXK5XAoODta1116rwYMHu/eLiorqUONcgYGBGjdunO644w5NnDhRQ4cO1VVXXaX58+fruuuuk/T56nlPjjN+/Hh9+9vfVlRUlKKiohQQEKAPP/xQS5Ys0fjx47s9j4SEBN1yyy2KjIyUn5+fxo4dq5EjR7p7HDRokN59913t3btXU6ZM0dVXX+2ef9NNN2nAgAG64447tGrVqk6f2RfPvafbzvfZjhw5UnfccYfCwsI0ZMgQ/eu//qtGjx7trpOdna3nn39ec+bM0U9+8pMv9XcJAAAAlyeHMcb0dhMArDm72l5eXq4XXnhBYWFh+vDDDzVo0CBLdT/88ENNnz5dY8eO1QcffHDBd1H3lubmZk2dOlUff/yx3n33XU2ePLm3WwIAAMBlgIeSAX1A//795evrq4iICD3zzDPas2eP5TAtSZMnT9batWt122239fi1YL1h7969uvXWW7V27VrCNAAAAL40rFADAAAAAOABVqgBAAAAAPAAgRoAAAAAAA8QqAEAAAAA8ACBGgAAAAAAD/wfeSa7l3pHGPgAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -141,59 +141,26 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "By looking at these histograms, we can see the pronounced effect of the class imbalance. The majority of the values in each numerical feature histogram has a higher proportion of `young` abalone examples compared to `old` abalone examples. It's difficult to say for certain whether there are clear distinctions in these features between the `young` and `old` class. However, there are a few areas to be aware of that might help us understand how the model might make predictions. Observing the length feature, we can see that when the length of the abalone is below 0.38, almost all of the examples are from the `young` class, with very few examples from the `old` class. Similarly for the diameter feature, when the diameter of the abalone is below 0.25, the majority of examples are from the `young` class, with hardly any examples from the `old` class. This aligns with our intuitions about abalone, since we should expect younger abalone to be smaller (i.e smaller diameter and length).\n", + "By looking at these histograms, we can see the pronounced effect of the class imbalance. The majority of the values in each numerical feature histogram has a higher proportion of young abalone examples compared to old abalone examples. It's difficult to say for certain whether there are clear distinctions in these features between the young and old class. However, there are a few areas to be aware of that might help us understand how the model might make predictions. Observing the length feature, we can see that when the length of the abalone is below 0.38, almost all of the examples are from the young class, with very few examples from the old class. Similarly for the diameter feature, when the diameter of the abalone is below 0.25, the majority of examples are from the young class, with hardly any examples from the old class. This aligns with our intuitions about abalone, since we should expect younger abalone to be smaller (i.e smaller diameter and length).\n", "\n", - "There are rare occasions when there are examples that are predominantly from the `old` class. For example, when `Shell weight` is above 0.6, the majority of examples are of the `old` class. Additionally, when `whole weight`is above 2.2, the `old` class begins to be the more predominant class. Again, this aligns with our intuitions about abalone. We would expect older abalone to be larger, and thus, have a larger whole weight. In terms of shell weight, perhaps abalone of the `old` class require a larger shell for their larger bodies compared to `young` abalone, which could explain why there are more examples of `old` abalone which have a shell weight above 0.6.\n", + "There are rare occasions when there are examples that are predominantly from the old class. For example, when shell weight is above 0.6, the majority of examples are of the old class. Additionally, when whole weight is above 2.2, the old class begins to be the more predominant class. Again, this aligns with our intuitions about abalone. We would expect older abalone to be larger, and thus, have a larger whole weight. In terms of shell weight, perhaps abalone of the old class require a larger shell for their larger bodies compared to young abalone, which could explain why there are more examples of old abalone which have a shell weight above 0.6.\n", "\n", - "Observing the distribution of sexes in the training data, there appears to be a relatively even spread of `Female` (F), `Male` (M) within both the `young` and `old` target classes, whereas there are a greater number of `Infant` (I) examples in the `young` class compared to the `old` class (Figure 3). Specifically, there were 354 examples of abalone that were `Male` and 340 examples of abalone that were `Female` in the `old` class and there were 882 examples of abalone that were `Male` and 684 examples of abalone that were `Female` in the `young` class. For the `Infant` class, there was a greater number of examples of `Infant` in the young class (1009) compared to the `old` class (72)." + "Observing the distribution of sexes in the training data, there appears to be a relatively even spread of Female (F), Male (M) within both the young and old target classes, whereas there are a greater number of Infant (I) examples in the young class compared to the old class (Figure 3). Specifically, there were 354 examples of abalone that were male and 340 examples of abalone that were Female in the old class and there were 882 examples of abalone that were male and 684 examples of abalone that were female in the young class. For the Infant class, there was a greater number of examples of Infant in the young class (1009) compared to the old class (72)." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ - { - "ename": "FileNotFoundError", - "evalue": "No such file or directory: '../results/eda/sex_dist.png'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m~/opt/miniconda3/lib/python3.9/site-packages/IPython/core/display.py\u001b[0m in \u001b[0;36m_data_and_metadata\u001b[0;34m(self, always_both)\u001b[0m\n\u001b[1;32m 1299\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1300\u001b[0;31m \u001b[0mb64_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb2a_base64\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'ascii'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1301\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: a bytes-like object is required, not 'str'", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m~/opt/miniconda3/lib/python3.9/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj, include, exclude)\u001b[0m\n\u001b[1;32m 968\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 969\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 970\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 971\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 972\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/miniconda3/lib/python3.9/site-packages/IPython/core/display.py\u001b[0m in \u001b[0;36m_repr_mimebundle_\u001b[0;34m(self, include, exclude)\u001b[0m\n\u001b[1;32m 1288\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membed\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1289\u001b[0m \u001b[0mmimetype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mimetype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1290\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data_and_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malways_both\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1291\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1292\u001b[0m \u001b[0mmetadata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mmimetype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/miniconda3/lib/python3.9/site-packages/IPython/core/display.py\u001b[0m in \u001b[0;36m_data_and_metadata\u001b[0;34m(self, always_both)\u001b[0m\n\u001b[1;32m 1300\u001b[0m \u001b[0mb64_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb2a_base64\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'ascii'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1301\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1302\u001b[0;31m raise FileNotFoundError(\n\u001b[0m\u001b[1;32m 1303\u001b[0m \"No such file or directory: '%s'\" % (self.data))\n\u001b[1;32m 1304\u001b[0m \u001b[0mmd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mFileNotFoundError\u001b[0m: No such file or directory: '../results/eda/sex_dist.png'" - ] - }, - { - "ename": "FileNotFoundError", - "evalue": "No such file or directory: '../results/eda/sex_dist.png'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m~/opt/miniconda3/lib/python3.9/site-packages/IPython/core/display.py\u001b[0m in \u001b[0;36m_data_and_metadata\u001b[0;34m(self, always_both)\u001b[0m\n\u001b[1;32m 1299\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1300\u001b[0;31m \u001b[0mb64_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb2a_base64\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'ascii'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1301\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: a bytes-like object is required, not 'str'", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m~/opt/miniconda3/lib/python3.9/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 344\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 345\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 346\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 347\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/miniconda3/lib/python3.9/site-packages/IPython/core/display.py\u001b[0m in \u001b[0;36m_repr_png_\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1318\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_repr_png_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1319\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membed\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_FMT_PNG\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1320\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data_and_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1321\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1322\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_repr_jpeg_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/miniconda3/lib/python3.9/site-packages/IPython/core/display.py\u001b[0m in \u001b[0;36m_data_and_metadata\u001b[0;34m(self, always_both)\u001b[0m\n\u001b[1;32m 1300\u001b[0m \u001b[0mb64_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb2a_base64\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'ascii'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1301\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1302\u001b[0;31m raise FileNotFoundError(\n\u001b[0m\u001b[1;32m 1303\u001b[0m \"No such file or directory: '%s'\" % (self.data))\n\u001b[1;32m 1304\u001b[0m \u001b[0mmd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mFileNotFoundError\u001b[0m: No such file or directory: '../results/eda/sex_dist.png'" - ] - }, { "data": { + "image/png": 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\n", "text/plain": [ "" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -213,24 +180,24 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As expected, there is no roughly bias for one particular sex (`Male` or `Female`) depending on if the abalone is old or young. However, the greater number of `Infant` abalone in the `young` class does give pause. Our intuitions do indeed tell us that more `Infant` abalone would classified as `young`, but the bigger issue in this dataset could be that we are predicting whether an abalone is `young` or `old`, after being given information about whether an abalone is an `Infant`, which creates redundancy in the predictive model. It is curious why the researchers decided to include the category `Infant` within the `Sex` feature column. Perhaps when an abalone is an `infant`, it is difficult to classify the abalone as `Male` or `Female`. Without speaking to domain experts, it is difficult to determine the significance behind having an `Infant` category within the `Sex` feature.\n", + "As expected, there is no roughly bias for one particular sex (Male or Female) depending on if the abalone is old or young. However, the greater number of Infant abalone in the young class does give pause. Our intuitions do indeed tell us that more Infant abalone would classified as young, but the bigger issue in this dataset could be that we are predicting whether an abalone is young or old, after being given information about whether an abalone is an Infant which creates redundancy in the predictive model. It is curious why the researchers decided to include the category Infant within the sex feature column. Perhaps when an abalone is an infant, it is difficult to classify the abalone as Male or Female. Without speaking to domain experts, it is difficult to determine the significance behind having an Infant category within the Sex feature.\n", "\n", - "Since the target classes, `old` and `young`, are directly determined by counting the number of `rings`, we were able to determine the correlation of numerical features with the number of `rings`, as well as the correlation among other features (Figure 4). Based on the correlation values, many of the features are highly correlated with other features. As for correlation with `rings`, the `Shell weight` seemed to have the greatest correlation value (0.69) with `rings`, while `Shucked weight` appeared to have the lowest correlation with `rings` (0.54). Based on the correlation heat map, it appears that the numerical features are at least moderately correlated with `rings`." + "Since the target classes, old and young, are directly determined by counting the number of rings, we were able to determine the correlation of numerical features with the number of rings, as well as the correlation among other features (Figure 4). Based on the correlation values, many of the features are highly correlated with other features. As for correlation with rings, the shell weight seemed to have the greatest correlation value (0.69) with rings, while shucked weight appeared to have the lowest correlation with rings (0.54). Based on the correlation heat map, it appears that the numerical features are at least moderately correlated with rings." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -250,7 +217,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "These correlation values give us some insight into how the predictive model might make its decision. For example, since `Shell weight` and `Rings` have a moderately high correlation, the `Shell weight` might be an important feature for predicting the age of the abalone. In the context of abalone, this could mean that older abalone require a heavier shell, whereas younger abalone may only need a lighter shell. With these correlation values, our model might be able to pick up on these types of associations. One trend to note is that many of the explanatory features are correlated with each other. For example, the `Diameter` of an abalone is highly correlated with the `Length` of the abalone. This is quite understandable, considering that as an abalone gets larger in diameter, one might expect the length of the abalone to also get larger. However, this does pose some implications for our model. It begs the question, how essential is it to include every single explanatory feature in this model? If `Diameter` is encapsulating the information provided by `Length`, would it be necessary to include both of these features? Discussing with domain experts can help us to determine which features may be more essential, or in the event that we lack access to domain experts, we could conduct automated feature selection in the future to address the redundancy in explanatory features." + "These correlation values give us some insight into how the predictive model might make its decision. For example, since shell weight and Rings have a moderately high correlation, the shell weight might be an important feature for predicting the age of the abalone. In the context of abalone, this could mean that older abalone require a heavier shell, whereas younger abalone may only need a lighter shell. With these correlation values, our model might be able to pick up on these types of associations. One trend to note is that many of the explanatory features are correlated with each other. For example, the diameter of an abalone is highly correlated with the length of the abalone. This is quite understandable, considering that as an abalone gets larger in diameter, one might expect the length of the abalone to also get larger. However, this does pose some implications for our model. It begs the question, how essential is it to include every single explanatory feature in this model? If diameter is encapsulating the information provided by length, would it be necessary to include both of these features? Discussing with domain experts can help us to determine which features may be more essential, or in the event that we lack access to domain experts, we could conduct automated feature selection in the future to address the redundancy in explanatory features." ] }, { @@ -259,22 +226,22 @@ "source": [ "## Model results\n", "\n", - "An important note about the training data is that there exists a class imbalance between `old` and `young` target classes. With more examples of the target class, `young`, this could have an effect on the accuracy of the model. Because of this fact, we are considering the f1 score to account for this class imbalance." + "An important note about the training data is that there exists a class imbalance between old and young target classes. With more examples of the target class, young, this could have an effect on the accuracy of the model. Because of this fact, we are considering the f1 score and ROC AUC to account for this class imbalance." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "" ] }, - "execution_count": 6, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -294,24 +261,83 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We fit a logistic regression on the training data as we are dealing with a binary classification problem. The model set the target class `old` as 0 and `young` as 1. We first built a preprocessor which transformed the `Sex` category by using One-Hot-Encoding and we applied standard scaler on other numeric features. We then used a Grid Search cross validation to determine the best hyperparameter for the logistic regression. As we can see, as the value of C increases, the model's performance on validation sets increase and plateau at around $C = 100$. Note that the hyperparameter, C, of logistic regression is associated with the regularization strength (complexity penalty) of the model. Based on the tuning results, the best logistic regression model occurs when $C = 100$ (Figure 5 and Table 1). " + "We fit a logistic regression on the training data as we are dealing with a binary classification problem. The model set the target class old as 0 and young as 1. We first built a preprocessor which transformed the Sex category by using One-Hot-Encoding and we applied standard scaler on other numeric features. We then used a Grid Search cross validation to determine the best hyperparameter for the logistic regression. As we can see, as the value of C increases, the model's performance on validation sets increase and plateau at around $C = 100$. Note that the hyperparameter, C, of logistic regression is associated with the regularization strength (complexity penalty) of the model. Based on the tuning results, the best logistic regression model occurs when $C = 100$ (Figure 5 and Table 1). " ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "../results/model/train_result_table.html" + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
mean_test_scoreparam_logisticregression__Cmean_fit_time
rank_test_score
10.826403100.00.034382
10.8264031000.00.028127
30.82610410.00.048729
40.8228111.00.041643
50.8201150.10.037579
60.7988650.010.029433
70.7755180.0010.025489
" ], "text/plain": [ "" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -329,19 +355,59 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "../results/model/test_result_table.html" + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
MetricsTest Result
0accuracy0.842105
1f10.902511
2recall0.953198
3precision0.856942
4roc_auc0.856794
5average_precision0.945716
" ], "text/plain": [ "" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -368,19 +434,68 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "../results/model/coeff_sorted.html" + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Coefficient
standardscaler__Shucked weight4.142553
onehotencoder__Sex_I0.983024
standardscaler__Viscera weight0.818804
standardscaler__Length0.537147
onehotencoder__Sex_F0.129540
onehotencoder__Sex_M0.121077
standardscaler__Height-0.248307
standardscaler__Diameter-0.538840
standardscaler__Shell weight-1.070738
standardscaler__Whole weight-4.306860
" ], "text/plain": [ "" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -398,17 +513,17 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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ICNwBIB779NNPlTZtWvsqi59++klZs2ZVmzZt9OGHH8Y4wKCFgjlsq2B27Nih+fPn69KlS3r8+LGePXumdOnSqUePHmrcuLGk560VEiVKZO+3ynjFrRkzZmjEiBEaMGCAunfvrlOnTil//vwxVtpu375d7u7u8vX1VcaMGeXi4sJKXBO8OD/GjBmj77//XkmSJFHNmjXVvHlzlStXTtLzS/fHjh2r+vXrq0yZMswrJ0Z7EudB7+L4Y+rUqRoxYoTy5cunhg0bqlmzZvYTJfv375dhGMqYMaOyZ89O2G4C23vOokWL7KvZkyRJoly5cqlLly5q3ry5pOfHfydOnFDx4sV5nzJReHi46tWrp+PHj8vb21s3btzQ/fv35ePjozRp0shqtSpVqlT65ptvVLFiRT179kzz5s1TmzZtaNcUR2zH3AsWLNC2bdu0YcMGSVL69Ok1bNgw+5wKDQ3VpUuX9P777zOnYBoCdwCIZ2wHDb/99psaNGiggIAA++WnN27cUJcuXbR582Z98sknatq0qWrVqiVXV1cONEyWLl06ffPNN2rTpo0sFot++eUX/fjjj9q3b59q1qypbt26qWTJkmaXmaBdu3ZNgwcP1o8//qhs2bJpy5YtypUr19/uzwG8eWzB0ejRo3XixAl5eHgoJCREN27ckKurq6pXr65WrVqxUjAeoT2J86B3sXOzncjfunWrli1bphMnTujevXtKkSKF8uTJo2bNmumTTz6x7897lTls71O//fab2rZtq7Fjx6pgwYLav3+/tm/frt27d6tMmTLq1asXJ6ucxJ9//qmpU6eqWrVqkqQ8efKoZ8+ecnNz0/r162W1WpUoUSKTq0y4bHPqP//5jxo0aKBp06Ypd+7cCgwM1Pr167V8+XKVK1dOY8eO1fvvv292uYBkAADiDavVav964sSJRoECBYy7d+8a0dHRRnh4uGEYhvHs2TMjd+7cRu7cuY306dMbK1asMKtc/J9ff/3V8PHxMe7evRtj+59//mm88847Ro4cOYzixYsbixcvNqlC2Bw+fNiwWCxGkSJFDA8PD6NXr17GtWvX7I9v2bLFaNasmYkVwvY6ePXqVcPFxcXYvXu3/bE//vjDaNCggZE4cWKjcuXKxowZM+yvjYhbUVFRhmEYRmhoqHH//v3X3r9Dhw5GyZIlY7zfIW5duHDB8Pb2NubPn//SY5GRkfavf/75ZyNdunTG9evX47I8GM9fB5MnT27MmjXLCA4ONiIiIowffvjBKFWqlJElSxajc+fOxn/+8x+zy4RhGDVq1DC6du0aY9vFixeNFi1aGBaLxShQoIAxaNAgIywszKQK8U/Wrl1r5MiRw7h8+fJLj/E+ZY5GjRoZn332WYxtN27cMPr27WtYLBbDy8vL6NGjB3MKpuP6ZwCIJ549exZjhVLp0qV19+5dnThxQi4uLvLw8FBUVJTc3d310Ucf6ccff9Rnn32mzp076/79+yZWjkyZMunBgwdas2ZNjO158+bVZ599pvr16+udd97R999/r6CgIJOqhCQVKVJE9+7d0+HDhzVlyhQtX75cJUqU0PTp07Vr1y61bt1aH3zwgaTnqwYRt6xWq/11cPfu3fL19Y1xFUL58uW1Zs0aFS5cWBEREVq6dKmWLl1qVrkJmq11RbNmzTRu3DhJz1envcqL7UmWLl2qyZMnsyI3DtnG5enTpwoPD1emTJlUpUoVbd26VQcOHNCsWbM0d+5ctW7dWnPnzrX/3BdffKGvvvqKGwWaYNWqVfZ793h6eipRokTq3LmzFi1aJHd3d+3evVvDhg3Tli1bzC41QbIdH0RGRipNmjS6dOlSjGMGX19fff3116patarq1KmjdevWaevWrWaVC0khISFat26dDh8+rIMHD9q3169fX9myZdOiRYte+hnep+JedHS0MmTIoCtXrsTYnilTJrVr105169bVt99+qx07dmjhwoXmFAn8HwJ3AIgHpk+frmnTpsXYVqxYMVWrVk3VqlXT6NGjJT0/sD9x4oRmzJghq9Wqli1bKlWqVLp+/boZZSdI169ftx/g2T5c5cmTR5999plWrFihX3/9VY8ePbLvf/ToUWXNmlUjR45UUFCQzpw5Y0LVCZvVapUkhYWFKTAwUOfPn9ejR4/UoUMHnTt3Tq1bt1aPHj3Utm1bFS9eXL169ZLEBy0zvNgrv0SJEgoPD9f69eslxTwB8uGHH6p27drKnz+/unfvrkuXLsV5rXiuQoUK+s9//iPpvyG8bc5Jz8fNtr1z585q0qQJ7RXimKurq8LDw1W/fn35+PiodOnS+vXXX7VixQo1bdpU8+bN08yZM3X9+nXly5dP0vNFAP369VOPHj1Mrj5h8vX11aNHj7Rx40ZJ/51TuXPnVr169VStWjVFRUWpb9++LLqIQ0ePHpX03+MDd3d3ffLJJwoMDNSyZcv08OFD+74pUqTQ06dP1aZNG6VLl04zZ86M8dqINy8qKkqStHXrVtWpU0fdunVTw4YN1aFDBx08eNB+XFG7du0YJxsRd06ePBnje1dXVzVo0EBBQUH67rvvYnzGTZ48uYKCgvThhx8qX758WrZsmSIjI+O6ZMCOwB0AnJxhGLp27Zr9JoDbt2/X2bNnZbFYtGTJEg0fPlyTJk1SqlSpVL58edWqVUt169ZVyZIlFRERoadPn9p7vOPNW7NmjXbv3i3peYArSYkSJVK3bt0UGRmp5s2bq0+fPvriiy/02WefafPmzfr000+VMWNGpUuXTvfu3TOz/ATH1pc4NDRUPXr0UOnSpfXNN98oR44c2rx5s1KkSKERI0YoNDRUS5cu1eLFiyX9/UpdvBmbNm1St27dYmxLnz69qlevrkGDBmn27Nl68OCBnj59qocPH2r58uXKmzevxo8fryxZsujmzZsmVY46dero3LlzWrBggX2b7cRJVFSUPZhav369Tp8+rSFDhphRZoIXGBgoX19fzZw5U0OHDtWuXbtUtWpV5cuXT3v37tXevXu1bds2VaxYUZLsK6q5UaA58uTJozJlymj69Ok6fPhwjBOOZ86c0QcffKA5c+bo1q1bL60ExZsRERGhWrVq2Y8BbcqWLav3339fPXr00NChQ7Vu3TqtXLlSPXr0UEREhHLnzq2GDRsqMjLSftyIuOHm5iZJat++vcqXL68bN26oadOmCg8PV4ECBWSxWHT79m21bt1afn5+kjj+i0tWq1UVKlR46UqdYsWKqW7dulq4cKFGjBihuXPnasWKFercubMiIyNVpEgRffLJJ7JarQoJCTGpeoCbpgJAvHLjxg1VrVpVBQoUUN26dVW3bl15eXnpypUr2rJliwIDA1W1alWVLl1az549U40aNVSgQAFuZhaHHjx4oBQpUsjd3V1VqlRR0qRJNWfOHHl7e0uS5syZo+XLl8vNzU1p0qRR27ZtVblyZS1YsED9+vXT7du3Tf4XJCy2wL1p06a6f/++xowZozNnzqhNmzby9/dXiRIldP36dWXJksXsUhO0adOmKX369GrcuLGOHj2qfPnyKUmSJJKkPn36aOLEiSpQoIDSpEmjGzduKGnSpDpy5Ihu3LihIkWKaNWqVSpfvrzJ/4qEwdYe5unTp7JYLLJarerQoYOio6PVq1cvHT16VK6urtqzZ49KlCihTp06SZKyZ8+url27qk+fPib/C2Czbt069e7dWzt27JCPj0+MxwxuxGm6y5cvq0mTJjp16pRatmyplClT6sKFC9q6dauuXr0q6XmbtClTpuijjz4yudq335MnT3Tq1CmVKFFCgYGB6tOnjyZNmqRMmTJJkubOnavx48fLw8ND169fV6lSpTRt2jTlyJFD1apVU5YsWTR//nyT/xUJz7JlyzRq1CidPHlSjx8/Vu7cuTVp0iQ1adJEe/fulZ+fn7p3724fR8Qdq9Wq8+fPK1++fLp586a++OILTZgwQVmzZpUkzZ8/X4sWLVJYWJjOnDmjGjVqaNSoUcqbN6/q1q2rJEmSaMWKFSb/K5CQEbgDgJOzBYI2ixcv1sqVK3Xz5k0VL15cDRs2VMWKFe2X5EvSvXv3tGLFCi1btkw7duxQokSJzCg9QQsLC9P8+fO1ePFiXbp0SV27dtWwYcMkPQ8qHj9+LE9PT0nSwoUL9e2332r48OFq3bq1mWUnSJcuXVKpUqW0Y8cOFSxYUNWqVVP27Nk1Z84c3blzR1OnTtUnn3yiQoUKmV1qgmULcaOiolSpUiWFhobqyy+/VPPmzSU9Pxk5YcIEubm5KVeuXProo4+UMWNGdejQQadPn9aePXtM/hckLOHh4apXr56OHz8ub29v3bhxQ/fv35ePj4/SpEkjq9WqVKlS6ZtvvlHFihX17NkzzZs3T23atGHFtAlCQkK0fft2ZcuWTVarVcWKFbM/VqFCBVWqVIkrD5zEjRs3dObMGfn4+NjvX7Fw4UJNnTpVnp6eypw5s9q1a6eKFStq6tSp+v777xUYGGhu0QnQjh071L59e4WGhqpdu3YaOXKk/bFDhw4pY8aMSps2rSIiIjR+/HjNmjVL58+fl5eXl4lVJyy2k4ZbtmzRyJEj5e/vr/bt2+vy5cvavn27pOf3iunSpYt+/vln5cyZ0+SKE7YjR46ofv36un//vrp166YxY8ZIen7vkaCgICVLlkxp0qTR06dP9cMPP2jChAk6ceKEMmTIYHLlSMgI3AEgHnry5InmzJmjFStWyM3NTZUrV1bNmjVVokQJSc9D+sDAQLm5uSlbtmwmV5twPXv2TFeuXNFPP/2k+fPnK1GiRBo+fLiaNm0aY78rV65ox44dhO0mOX78uNq2batdu3Zpx44datOmjY4cOaIsWbIoICBAbdu21ddff62aNWuaXWqC9OJK2vDwcK1Zs0bbt2/X3r17lTdvXn399dcqW7aspOf3sXB3d9ezZ8+0ePFijRkzRhs2bFD+/PnN/CckOH/++aemTp2qatWqSXre/qJnz55yc3PT+vXrZbVaORFssqioKLm5uWnr1q0aOXKkLl68qESJEillypSaPXu2ihYtKovFonHjxmnKlCncC8ZEtrGaN2+eRo8erWfPnik8PFw1atTQDz/8oOTJk0uS7t69q3Tp0kmSli5dqi+//FJTp05Vw4YNzSw/QYqMjNSFCxe0Zs0azZkzR+7u7ho6dKj9JLHN/fv3NXHiROXJk0eff/65SdUmbMePH1etWrX05ZdfaujQoTp48KDy5MkjSWrUqJGk5zcp5qoecxmGofv372vJkiUaPXq03NzcNGbMmJfmzcOHDzVp0iTlyJGDz1UwHYE7ADixFw/uAgICtHnzZqVNm1aVK1dWmjRpdPnyZU2ZMkV79uyRu7u7Fi1apNy5c5tcdcL14tUI169fl6enpwzDkJeXl/bu3asFCxZo/fr1ypQpk9auXctqGSfx6NEj1ahRQwMGDFDfvn3VsmVL9evXT9LzFkBjxozhpptO5tatW9q0aZNWrVqlgIAAValSRf369bO3/omOjtajR49048YNvffeeyZXC4n2JM4qe/bs+vzzzzV8+HANGDBA69at0+HDh5U0aVLdvn1bLi4uun79ugoXLmy/0gRxLzw8XGnTptXYsWOVK1cu3bx5U2PGjNGVK1c0cOBADRgwwL5vRESELly4oGPHjr0U8CLuHTx4UHPmzNHq1av1/vvva8yYMfYFMog7tmP0ixcvKiIiQr6+vkqcOLEkafz48RozZoxSpEihzZs3yzAMrV69WhMnTtSpU6eUKVOml644RtyLiIiQh4eHAgMDNWHCBM2aNUvFihXTd999p1KlSpldHvASAncAcGK2D7dTp07V3LlzlTx5ch09elS+vr7y8/NTjhw5JEm//vqr9uzZY29ZgrhnG6vbt29r1KhRWrx4sd599125u7vru+++U5EiRXT//n3t2rVLkydP1qxZs5Q3b16zy07wrFarrFarBg8erNGjRytp0qTat2+fUqVKpbNnz+rzzz/XsGHD1K5dO8Imk4WEhOi3335TtmzZlCJFCuXNm1cnTpyQn5+ffv31V124cEGrV69WmTJlzC41QaM9SfxB72LnZwv5rl69qpEjR2rWrFmSnh9zBAYG6scff9QPP/ygx48fa/fu3SpSpIjJFSdMtuODAwcO6Oeff9bevXuVP39+lS1bVp999pnu3r2rffv2afTo0XJ1dZW/v78kTjbGFdvv+c6dO/r0009VoUIFde/eXWnSpJH0fOHFsmXLtHTpUu3bt08pUqRQ0aJF1bJlSzVr1ozjPxPYruw5dOiQVq5cqcOHDytdunSqWLGiOnXqpIiICB09elRfffWVHj58qNOnT0t6uRUrYCYCdwBwUraDw7t37ypnzpyaP3++GjVqpHbt2unq1avaunWrgoODlSxZMrm5udl/jgMNc9jGq3bt2jIMQyNGjNCqVas0e/Zs7du3T7lz59bjx4+VIkUKPXjwQKlTp2asTPLiB9wXv54/f7769u2rRIkSKXHixEqcOLEqV66sKVOmmFlugvaqthfu7u7y8vLS/Pnz9cEHHygyMlK7du3S77//rmHDhjGnTEB7kviF3sXxy+XLl/X555/ryZMn2rhxY4zVthERETpx4oSmTJmikSNH0kbQBLaxCA0NVf78+VW1alXlyJFDU6ZMUZ06dWLcCDUwMFCJEydWhgwZCHHjkO01r169enJ1ddXAgQNj3JfHtnI6IiJC58+f1+3bt1W5cuVXHivizbP9viMjI5UrVy5Vr15duXPn1pw5c5Q3b175+fnZ9w0LC9Pjx4/l7e1tPxYBnAWBOwA4ueHDh2vnzp3avn27Dh48qMqVK+uPP/5Q4cKF9eOPP2rLli0aO3asMmfObHapCd6BAwdUp04dHT9+XBkyZFDZsmVVqlQpfffddwoICNAvv/yiTz/9VOnTpze71ATL9sH48ePHWrFihS5evKi0adOqR48ecnd3V0REhBYtWiQvLy+999578vX1VaJEiTg5YrJXtb04dOiQkiRJort378rb21vPnj1jrExGe5L4hd7F8cPOnTvVt29fnTx5Ui1bttS0adNemju2wJDXv7hnmx/t27fXtWvXtGXLFoWEhChDhgzavn27SpUqpd9//10pU6ZU4cKFzS43wbGNz+7du/XRRx/p2LFj9iuE79y5o+nTp+v333/X06dPNWrUKFWtWvWln0Xcsv3ee/XqpYMHD2r37t0KDQ1VhgwZ5Ofnp0qVKmn79u2yWCyqVKkSYwSnxbsxADg5X19fJUuWTJL01VdfqXnz5vYD9sSJE+vixYtKmTKliRXC5ubNm/Lx8VGGDBk0a9YsXb16Vf3795f0/MZYK1euVEBAgMlVJmy2g/K2bdtqxIgR2rlzpxYvXixfX1/NmjVLHh4e6tChgxo3bqx33nnHfmNHAgzzLFu2TJ6enho+fLgeP36s+fPna/DgwUqWLJn+85//aOLEibp27RpjZbK/G6ekSZNq7969mjRpkiIjI+3vX4TtccdqtUqSLl68qNOnTys8PFyS9P7776tXr14aNWqU/aqrs2fPavjw4dq+fbsmT54s6Xn4AfOULVtWCxcu1JAhQ/TLL78oR44cMVZNS5KHh4ckXv/MYLFYFBoaquvXr6tx48aSpHr16unTTz9VqVKl9OzZM+3YsUMrV65UZGSkydUmPLbjvlWrVql+/fr2sP3y5cvq16+f5syZo3z58ilbtmxq3769jhw58tLPIm5ZLBZFRETo1q1batKkiSSpSZMmql27tipVqqSoqCgdO3ZMGzZsUEREhMnVAn+Pd2QAcHLp06fX6dOn1b59e505c0aTJk2SJD179kzDhw9XzZo1lSxZMvsHapjn3XffVVhYmC5fvqzRo0dr0KBBSp06tSRp+/btCg0N5UZZJrNYLLpw4YIOHjwof39/bdy4UXPmzFGDBg00cOBAlShRQr///jsfspyA7TUtVapU9pOKvXv3VoECBewfwAzD0ObNm/Xs2TOzykzwbGHsP42T1WrVxo0b7UEv4o5hGHJxcdGdO3fUvn17rVq1Sk+ePLE/3q5dOw0fPlxZsmTRu+++qzJlysjf319Tp05VpkyZFB0dTYhrIsMw5Obmpjx58qhPnz765ZdfVL9+ffXt21elS5fW77//bnaJkJQ8eXJly5ZNwcHBOnjwoE6ePKnBgwdLktzd3bVjxw6lSpVK7u7unMAySebMmRUQEKCwsDBJUs+ePXXr1i3NnTtXs2fP1jfffKPIyEhduXLF5EphGIY8PDyUOXNmXb58Wfv379fu3bs1cuRISZKbm5s2btyoFClSKHHixMwpOC0aHAGAk7FdZn/x4kXlypVLVapUUfPmzTV79mwVLFhQO3bskLu7u5YsWaKwsDD7AT0BYdz766XbWbNmVb58+ZQrVy5lyJBB7dq10+PHj3Xo0CGNHTvWviKNVgrmypQpk6pXry43NzelSZNGadKkUZ48eVSjRg3Nnz9flStX1oYNG/TRRx+ZXWqCZptb3t7eunz5siZMmKCVK1fq4MGD9n0mT56sPHnyyNfXl0u/TWL7nWfMmPEfxylv3rzKmTMn42SSDh06KHXq1KpXr579RoGSlCRJEnXu3Flt2rR5Ze9iwva4ZztG8Pf317p16/Sf//xHBQoUUNmyZdWqVSsNHjxYtWrV0tixYzV8+HBVqlTJ7JITpL8eAxYuXFi9evWSJI0ePVo5cuTQ06dPtWjRIp07d059+vQxq1RIypUrl/bv36+GDRvq8ePHOnPmjPz8/FS8eHFJUv78+ZU3b17dv3/f5EoTLtucslqtcnV1ValSpdSxY0fNmTNHPXv2VK5cuRQREaGlS5fq0KFD2rx5s9klA/+IHu4A4KRq1qypfPnyadSoUYqIiND06dP166+/KigoSNevX1fz5s3Vpk0blS5dmpvEmGzQoEHKnDmzOnbsKEkaNmyYpk6dKhcXF2XKlElRUVGqVKkSN980kW2OBAYG6uzZs+rVq5emT5+uypUrx9jv6tWrOnTokBo0aGBSpQmX7YPW5s2bZRiGypcvr+TJk0uSxo0bp7Fjx8rT09P++OrVqzVx4kSdOnUqxk0E8WbZfs8XL15URESEfH19lThxYknS+PHjNWbMGKVIkYJxcgL0Lo5/bPPj4cOHKlCggOrXry9fX1+NHj1aH3/8sebOnWvf9/r160qcOLHSpk3LiXwTzZ8/X23atJEkTZs2TZMmTVJUVJRatGihPXv26O7du+rTp49atGjB8brJtm3bplGjRqlIkSIxWnRK0qZNm9S8eXMFBATIy8uL10ATzZo1y/6ZauHChRo+fLiioqLUuHFj7dmzR0+fPlXnzp3VoUMH5hScGoE7ADihsLAwTZ8+XcuXL1fTpk311VdfSXref9UwDBmGYb+xGcw3cuRIjR8/Xn369FGvXr0UGRmpy5cv648//lBoaKg+/vhjeyhF2BT3bB+arFar8uTJI8MwFBERoUePHmnEiBHq2bPnP/4c4k54eLiKFi2qyMhINW/eXDVr1lShQoUUHR2t+fPna8WKFdqzZ49SpEihokWLqmXLlmrWrBlhUxyxzYk7d+7o008/VYUKFdS9e3f7iulHjx5p2bJlWrp0qfbt28c4OYkePXooJCRECxYskPS8d/GIESO0ZcsWffTRR7p//74OHz6stWvXqkiRIiZXm7DZ5ljr1q117949+fn56d69e8qePbt27typYsWKadu2bUqVKpWKFi1qdrkJ3tGjR/XBBx+oTZs2GjdunLy8vLRr1y6tX79e+/fvV/HixVW3bt2XTu4jbtkiL9sx3V+Pxc+fP6+GDRuqUaNGGjhwIO9VJjpz5owKFiyoxo0ba/bs2fL09NTp06e1cOFCHT16VO+//74+/vhjlS9f3uxSgX9F4A4ATuJVQezMmTPVvXt3ff7555oyZYr95qlwLoZhaNKkSVq7dq3atm2rVq1avXIfwltzDRw4ULt27dLMmTMVHR1tX3mbKVMmTZ48WdWqVTO7REiKiIjQiBEjtHjxYvn4+Khp06aqU6eOMmXKpFu3bunevXu6detWjLYXzK+4Yfs916tXT66urho4cKAKFSpkfzwiIkIeHh6KiIh4ZXsSxskc48aN06ZNm7Rp0yYlTZpUdevWVVRUlLp166ZatWrpyJEjqlOnjqZNm6b69eubXW6CFxoaqsaNG6thw4Zq06aNypYtq/z582v27Nl69uyZhg4dqqioKI0cOZKVnU5g8+bNGjt2rMqUKaMBAwb87bE6r3/me1WQvmrVKk2ePFmpUqWSn5+fJMbKbAcPHlSPHj2UN29eDRo0SD4+PpJe/qzMOMHZEbgDgJOwHTTMnz9fKVKkUKNGjSRJW7du1eTJk5UvXz716tVLWbJk4QDDZLbLF2/evClvb29FRkbKzc1N48eP15AhQzRixAh9+eWXZpcJ/ffDVWRkpBYuXCgXFxe1bdtWkhQZGamzZ8/q+++/15IlS1S5cmVt3ryZAMNEL14aHBYWppw5cyo6OloVKlTQ559/rnLlyilVqlT2/XktjDu0J4m/1q5dq2bNmqlSpUov9S52c3NTeHi4atWqpc8++0zt2rUzu1xI6tSpk3Lnzq2SJUuqQYMGOnTokLJmzSqr1apSpUrp008/Ve/evZlbJvjr79wwDC1btkx9+/ZVvXr1NHXqVPtjrJR2fhcvXtSOHTv04YcfKleuXIyZCWxBemRkpAzDUKJEifTbb79pwIAB+uCDDzRjxoyX9gXiAwJ3AHAip06d0nvvvSdJypMnjz755BN5eHjo/PnzCgoKUuXKlfXNN9+YXCVsfHx85O7uriZNmihJkiRq1qyZDhw4oC5duqhLly7q2bOnUqZMaXaZkPTpp59q06ZNKl++vFauXBljBVpYWJh+++03BQQEqEePHgQYJrH93p89e6ZEiRLpzz//VOnSpTV69Gj9+OOPunHjhurVq6caNWqoYsWKSpQokdklJ0i0J4mf6F0cP9h+94sWLVL79u1lGIYmTJig7t27KzQ0VPPnz9eIESN0586dGPsjbth+34cOHVLz5s3VqVMnpU+fXlWqVNHdu3dVo0YNlS5dWhMmTFDmzJnNLhdwerY5dfbsWTVs2FANGjSQt7e3GjVqpKCgIFWpUkWlS5fW7NmzlTFjRrPLBWKFJVwA4EQKFiyo4cOH6/jx48qSJYuio6P16NEjnTx5UidPntTOnTtVqFAh1a5d2+xSE7y7d+8qd+7c+u233/To0SMZhqH3339f7777rjJkyKBhw4YpR44cr2wvg7j3+eef6969e9q8ebOGDx+uL7/8UunSpZMke4sFGwKMuGNbSRYeHm6/8aZthXuLFi3UsmVLdezYUR07dtSMGTM0YcIEbdq0Sf7+/vbxQ9zKnDmzjh8/rrCwMCVNmlQ9e/ZUVFSU5s6dG6M9yZUrVwjcnYBtbVXVqlVVtWrVV/Yu7tevn3r16iUvLy9Wd5rM9t7TsmVLRUdHa/z48Ro9erQCAgJ04MABhYSEaNq0aZLEzQJNYBuf3bt36/z581q6dKmqVq2qkSNHytfXV7Vq1dKaNWs0ePBgfffddzGuyILzsr3uXblyRdmzZze7nATFNqeOHDmis2fPas+ePSpRooQKFCigunXrqnnz5po2bZratm2rOXPmcCIL8QrXYgCAyaxWa4zvO3XqJB8fHz169EidO3fWhAkTtGfPHi1cuFD9+/cnbHcS6dKl06+//qp27dpp//79KleunG7cuKFPP/1UFStW1Pvvv698+fJJ+m/gAfPUrl1bmzdv1tSpUzV37lyVKFFCP/74o8LDw1/al0tV446rq6usVqty586t/v3769mzZ3JxcdGiRYt08+ZNDRgwQNHR0ZKkzp0769ChQxoxYoTSpUvHvDJJrly5tH//fjVs2FDlypXTnj179O2339rvgZA/f37lzZtX9+/fN7lSSM/DDIvFYp9HL76+rVq1Sm3atFH27Nk1cODAlx5H3Pjra5nt+08//VSzZs1Sq1atdPToUZUsWVLTpk1T48aNJYmw3UQ9e/bUoEGDFBAQoCRJkmjjxo0qWLCgbty4oeTJk2vXrl2E7fGELWw/fvy4ChUqpOvXr5tdUoLUsGFDDRs2TLt371aGDBn0559/Knv27Hry5Iny5cunY8eOEbYj3qGlDAA4gcjISE2YMEFVq1aVt7e3MmXKpL59+2ru3LlasGCBPv74Y0n/XXnLCjRz2FYGHj9+XG5ubipQoIAMw9DgwYP14MEDDRgwQJkyZVJYWJjCw8OVOnVqVkub5J9+7/fu3dPgwYM1f/585c+fX/PmzYtx40fErSdPnuj777/XrFmz7PdC6NOnjwYPHqxWrVrJMAz7nxdf95hb5qE9yduB3sXmedXVPbHpTcy8ilsv/r5v3rxpb20xZ84c+fv7q1WrVvrwww/16NEj3bhxQ56ensqaNStXIcQjlSpVkq+vr+bOnWt2KQnOtWvXlDVrVknPTwQvXLhQzZo102effaZHjx4pPDxcUVFRypIlC3MK8QqBOwA4gb1796pmzZry9vZWzpw5lTp1anXs2FGbNm3S/v371a9fP1WtWpUPwk4gMDBQNWvWVIYMGZQsWTJ16dJFkZGR+u6775QoUSLNnj1buXLlMrvMBM0WWkRFRemXX37RmjVr5O3trc8++0w5c+a099U/fvy4OnbsqDFjxqhixYqm1pzQWa1WXbx4URMnTtTSpUv1+PFj7d+/X8WKFbM/zspb89k+NtiCp1e1J2nYsKEaNWqkgQMHEuAC/8BqtSp79uxq3ry5Ro0aZZ9XtnDX9t+wsDA1atRI3bt3V40aNUyuOmGbPn26tm7dqsyZM6tNmzZKmzatJk6cqBMnTmjkyJEqXbq02SUiFmzh7Zo1a9SlSxcdO3aMPuFxbOnSpZoxY4ayZ8+uzp07K3Xq1Pr555919uxZtWnThuNzxGsE7gDgRPz9/XX06FEdO3ZMq1atUooUKXTr1i3lyZNHR44cUdKkSc0uEZI2bNigBw8eaPv27Vq/fr3Kly+v5MmTa+XKlcqePbt+/fVX5cmTx+wyEyxbCNizZ09t2rRJZcuWlb+/v8LDw9WgQQO1bdtWuXPnVpIkScwuFX8RERGhgwcPaty4cfrll1/UqlUrjR8/XqlTpza7NLzgVUH6qlWrNHnyZKVKlUp+fn6SWIUbH9C72DwvXt3j6uqq8ePH29vFvHgya9myZWrWrJnu3r2rNGnSmFlygrd8+XLt379fgYGB2rBhgypUqKDixYtrwYIFCg0N1cyZM9WiRQuzy8RrePH9ycfHR927d1fv3r1Nrirh+fnnn3X69GkdOHBAW7ZsUaVKlZQmTRpt2rRJjx8/1tSpU9W5c2ezywT+JwTuAOAEXhVKGIah1atX6/Dhw8qWLZu6dOnCKk8n9OTJEy1btkx3797Vzp07dfr0afo/msg2R86ePasSJUrot99+U/HixdWwYUMFBgbqypUrSpMmjbp06aK6desqR44cZpeMVwgJCdHGjRs1fPhw3bhxQ1999ZW9xzScE+1J4p8XexdXrFhRJ0+eVJYsWcwuK0GxXd0zefJkzZkzRyVKlNCUKVPsbZru3LmjYsWKqU2bNho8eDDtFJyAYRiKiIjQ48ePNWPGDF2/fl337t3Tzz//rD59+mjMmDFml4jXYJtLw4cP16pVq3Tw4EF5eHiYXVaCZPscfOfOHS1ZskT379/X6dOn5efnpw4dOmjmzJlmlwj8TwjcAcCJ2A44XgzWX/xwxWrBuGe1Wu03nbN5VS/9yMhIubu7KyQkRJ6ennwoNsGL8+aLL77QgwcP9OOPP+rXX39Vs2bNdPnyZd2+fVuFCxdWWFiYpkyZom7duplcNf5JUFCQpk6dqoCAAC1fvtzscoC3Er2Lzffs2TMdOHBAI0aM0NatW9WqVStNnDhRc+bM0ffff6+bN29K4jjQbK/6/YeEhCg8PFxBQUF655135OHhwQIZE/219dmr2MbnwYMHypQpk1auXKm6devGVYkJ1j99pnpxzjx58kSJEiXS6dOnlStXLiVPnpw5hXiJwB0AnNTrHDDizQoKClKmTJkkvbqFghTzwxcfhJ1DRESEli9friRJkqhx48aqV6+e8ubNq7Fjx+rRo0caMGCAWrRooZIlS8bokwvnFB0dLavVKnd391d+WIPzoD1J/EHvYudku7pn2LBhunr1qp4+faqVK1eqYcOGnMh3MhynOw/be8+zZ8+UKFGif93fFt42bNhQwcHB2rZtWxxUmbC9+Pr1d69lr/pMxTE64jNOEQGAk4iOjo7xPaGSuebMmaMiRYpowoQJioqKsoftrxqnV32NuOPn56ehQ4fav/fw8NBHH32kEiVKSJJcXFzsH8CSJUum3377TZGRkRzIxxMuLi5yd3dXRESEXFxcGC8n9WJ7kkKFCtFay4kZhmEPO7766iv17duXsN1JeHp6qmnTptq+fbv69u2rjh07qmHDhpJE2G6yv65T5DjdediO0YsWLaply5b9477R0dFycXHRwYMHtW3bNk2aNCkOKkTVqlXVv39/Sf99LXudz1TMMcRnBO4AEIesVquk52f2T548qcWLF2vBggWSRK9bJ5M9e3Y1atRIS5Ys0Ycffqi1a9dKej5OhmHYxxLm27dvn73f7ZEjR/Tw4UOlSZNG2bNnl9Vqlaenp5YsWaJx48apTp06SpQokcqXLy+JA/n4wDZGAwcO1O7du02uBn/H9h7Ws2dPffLJJ/QCd2K2kGP48OFKkSKFunbtanJF+KtMmTKpb9++mjx5sqTnx49cmG4u23sRx3/OxTYvjh8/ruDgYJUrV+4f97e9V3Xp0kVt27ZVgQIF3niNCd2jR49UtGhRrVixQrlz59aKFSskPR8Lq9XKnMJbi5YyABCHbJcwDhgwQJs3b1aKFCn0559/KlWqVNq3b59SpUolidYkzuLOnTvasWOH/Pz8dOTIEb377rvq16+fihQpIunVvQhhnqdPn6p69ery9PRUmzZt1KBBA0nPW8y0a9dOv/76q2rUqKHevXurUKFC3NQxHrBddrxy5Uq1b99eFy5cUPr06c0uC3/x1/Ykx48fV4YMGcwuK8Ghd/HbxXYsGBERwc0c45htngQFBencuXMKDQ2Vr6+v8ufPb3+c4z/nsmzZMm3dulWTJ09W8uTJJemlnt+296pFixbpm2++0YkTJ5Q6dWozyk1wQkJCdPr0aa1cuVKzZs1S9erVNXz4cBUsWFDSf688YE7hbULgDgBxxHbwfvjwYZUvX17btm1TqVKlVLZsWb3zzjuaO3eubt68KRcXF3l7e5tdboK1Z88ejRgxQnPmzLGv0Dx//ry2bdsmPz8/XblyRbVq1VK/fv2ULl06SZwgMZNtXlmtVj19+lSzZs3Snj17dP36db3//vv6/PPPVbZsWUlScHCwEidOTHART7w4r3x9fdW1a1f17t3b5KrwVy+Ok4+Pj7p37844xSF6F7/9+vTpo7p169rfy/Bm2ebUpUuX1L59ex09elRFihTR7du3VbduXfXs2dN+4pcT985h/fr1at26tSwWi/z9/e0nRl4cnxffq7Jnz67+/furU6dOptWckNhOdDx8+FBTp07VwoULdfXqVSVOnFjNmjXT+PHjlSJFihj7Am8DWsoAQByxrbKYM2eOmjRpotKlS8vPz0/nz5+395/esWOHxo8frwcPHphZaoL24MEDBQYGqkiRIvr2228lSXny5FHXrl01ZMgQNWnSRHv27FHt2rU1YcIESbQlMYthGPZ5ZRiGkiVLpt69e+u7775TnTp1dPHiRfXv31+DBw/WpUuX5OXlRdgej9guMR42bJiSJUtG2wsnRXsSc9G7+O0UFRUlSfbVoHny5DG5ooTDNqdat26tTJky6fr162rQoIECAgLk5+enatWqaeHChTH2hbmyZMmibt26KW3atPrwww/trZhevP+S7Vi9X79+cnd3V7t27UyrN6GxjUOLFi10/vx5zZ49W9u3b9fgwYO1c+dO5cmTR/Pnz5fEvSrwdmGFOwDEgRdXWAwfPlxnz57V0qVLlTt3brVv3159+vSRJI0dO1a7d++Wn5+fmeUmaFFRUbpw4YLWrl2refPmSXoe+DVv3lySFBoaql27dmnTpk1at26d1q5da785J+KGbZWS1WrVjRs3NGXKFF25ckXJkiVTx44dVbJkSUmSv7+/Vq5cqRMnTujJkyeaM2eOvdc7nJttFe7Dhw/l4+OjxYsX0/YijtGexPnZXguPHz+uunXras+ePa/VO79YsWIqV66c/aQxnAtX95hv586datu2rQ4ePKjUqVOrcOHCqlGjhkqVKqWOHTvqwYMHqlGjhtavX292qfg/EREROnLkiBYvXqzdu3crffr0+vLLL1WrVq0Y+x0+fFhubm56//33Tao0YTp16pRKly6tffv22a9AePLkiXbv3q02bdro5s2b+uCDD7R///6XWgEB8RWBOwC8QTdv3lTGjBkl/Td037hxoyZPnqycOXPK399fp0+fliTdv39f77//vkaPHq0WLVrYgwyY48mTJzp27JgWL16sVatWqUCBAvr+++9VvHhxSdK1a9d04cIFffjhhyZXmvDYwohJkyZp6dKlunbtmnLkyKG7d+8qMDBQjRo10pQpU5QuXTpFRUVpzZo12rt3r33FE8z1Oi2YbK9/TZo00ZMnTzgJGUdoTxI/0bv47WKbh8OGDdPq1at18OBBrs6KY4sXL9aGDRu0evVqLVq0SGPGjNH+/fvl6empTp06KTw8XF27dlWxYsU4XjeBbY5ERkbqypUrunr1qjJkyKDs2bMrKipKmzZt0oYNG/Sf//xHFSpU0IIFCxgjk509e1ZVq1bV9OnT9fHHH8d4rF+/fnr06JFatmypUqVKMafw1uB6DQB4g8qXL6/s2bNr9uzZypkzpySpePHiSpw4sWbPnq3GjRvL399fN27c0Lp165Q5c2a1aNFC0ssflhG3kiVLpjJlyqhw4cKqW7eupk2bpipVqqhBgwYaP368smbNqqxZs0qih3tcsh2E79q1S9OnT1fPnj3VuXNnWa1WXb16VZs3b9aIESNUrFgxrV27VkWKFNGnn36q+vXrx/h5mOff5ortg/T+/fu1Zs0a+0lJvHkvtifp37+/mjZt+rf72sbJ1p5k7969cVUmXrB+/Xp17dpVFotFX3/99d/2LrZdpj9o0CB9++23hO1Oymq1ytXVVQ8fPtSECRO0ePFiwvY4Ypsz165d0+eff65ChQpJen4fn9KlS8vT01OS5OXlpUyZMqlYsWKSOF43g+21rVOnTjpx4oSuXLli7wc+evRoNW3aVCVLltSyZcuUM2dOubi4cKxusjx58qh48eKaNm2a0qdPr6JFi8rd3V2SlDRpUt2/f1+lSpWSxJzC24P/kwHgDQkNDVX//v1lGIYKFy6sL7/8UpGRkUqXLp02bNigESNGaPv27erSpYvatm2rjBkz6scff5T03564MMeDBw+0Zs0a7dq1SydOnFDt2rU1e/ZsjR8/XqdOnVL69OljrOTkAD7u2A7C27Vrp6ZNm6pr165ycXGRm5ubcubMqY4dO2rJkiWKjIzUTz/9JOl52GRbrctBfNyz9SLetWuX1q9fr/Dw8H/c3/ZB+osvvlD37t3pXRxHbBe9Hj9+XMHBwSpXrtw/7m8bJ9t7WIECBd54jXgZvYvjj9hcWN65c2eVK1eOFk1xxDAMubq6Kjg4WI0aNdKRI0f03nvvSZIyZcqkdevWacWKFdqxY4emTJmiChUqSPrvvUYQd2yfkebNm6etW7dq2rRpunPnjm7evCkfHx9J0r179+Tj46MBAwaoSZMmZpaL/+Pq6qo+ffro4cOHGjJkiCZNmqSff/5ZCxYs0OjRo+2vdcwpvE1oKQMAb5DVatWlS5e0YcMGzZ49W2FhYRowYIA6d+4s6Xmwe/HiRXl7eyt79uySWC1tFtvl9n/88YcGDRqks2fPKmfOnLp165YWLlyoihUrKjo6Wn/++ad+/vlnffnll0qcOLHZZScottVnkydP1tSpU3XhwgX7jVP/Om8GDhyo2bNnKzAwUEmSJDGxatikS5dOo0aNUvv27f92n7+2vTh+/LjSpEkTh1WC9iTxD72L3w4vXt1TtmxZnT59mhOOcWz48OHas2ePtmzZYj+uCAwM1Lfffit/f39FR0fro48+0syZMzleN1nJkiX1+eefq0uXLho9erSWLl1qf4377rvv5Ovrq4YNG5pdZoL14v2WLl26pOTJkytFihQKDg5W//79dfbsWV29elVp0qRR3bp1NWbMGLNLBhyOwB0A3pAXD8SfPn2q48ePa9myZVq6dKmyZcum7777zt7/m4N255E7d27Vr19f48aN0/Dhw7Vo0SIdOnRIKVOm1Pnz55UnTx572ER7krhnGIZSpkyp0qVLa/PmzTG2WywW+3/nz5+v+fPny8/PTylTpmR+mcQWIF25ckVff/21pk+frnTp0r1y3xdfBzNnzqyBAweqU6dOcVlugrd+/Xq1bt1aFotF/v7+f9uexDZO2bNnV//+/RmnOETv4vjDdqywa9cuPXz4UNWrV3+tE/UlSpRQmTJluKltHLHNqUePHumnn37SvXv3NHjw4Bj7BAQE6MqVK/L29paPj48SJ07MMaAJDMOQ1WpVdHS0GjdurObNm6tOnTpKnz69fvrpJ3300UcyDEPNmjVTlixZNG7cOLNLTpBscyowMFBDhw7V6tWrVaBAARmGoQULFih//vy6cOGCPD09ZbValT59erm6ujKn8Nbh/2YAeENs4Z8kJUmSRCVLltTYsWO1dOlSZc2aVfXq1VOTJk107do1wkAnsXHjRrm5uWncuHGyWq2aNWuW+vfvr5QpU+rkyZOaP3++Lly4YO+Fy0Fh3Hv8+LF95XO2bNm0cuVKSbKvorHNOavVKnd3dyVJkoT5ZSJbiNGzZ0+dOnVKhw8f/tt9bZcR9+3bV5kzZ6bthQloT+L8Xuxd3LRpUzVp0kQ1atTQiBEj5OXlpaZNm2rUqFHq0KGDatasab8CCHHPdqzwySef6M6dO/8Ytttaby1atEg3btzQN998Eyc14r9zqkmTJurevbuWLFmi06dPx5g3Pj4+qlixot555x37OHIMGHdu3rwpq9Uqi8UiV1dXJUqUSMmTJ9fq1avVtm1bVa9eXR999JEk6fTp09q4caP9nli0KIl7tjnVqlUrhYaG6vTp06pataoCAwPt8ydVqlTy9vZWxowZ7fszp/C24f9oAHgDbP0Fb926pY0bN2rYsGFat26dAgMDVbVqVc2dO1fTpk3TiRMn7DdzhPlSpEhhPxDs3bu3smXLplatWkmSwsPDtXHjRj179szECuHp6amvvvpKO3fuVJ06ddS8eXOVK1dOR48elYuLi1xcXGS1WjV58mTVqlXLvgoN5rl3754uX76sP//8U7Nnz9alS5de2sfWPzcoKEjTp0/XqFGj7GEV4s4HH3ygb775RgsXLlT9+vU1d+5cVa5cWZs2bZL03w/RktSoUSOtWbOGcYpD9C6OP2xjdeXKFVWqVEn16tX7231fvKntgAED9O2339JKywSrV69W9+7ddfnyZXXs2FHbt29XaGio2WUleNeuXVOTJk00b948Xblyxb595MiRunDhgpYtW2a/h8i6dev0xRdf6KOPPtK7777LimkT7d69WxcuXNCcOXOULVs2bdq0SZ06dVLOnDl18eJFLV68WDdu3DC7TODNMgAADmW1Wu3/LV68uJE3b17D19fX8Pb2NmrUqGEsXLjQsFqtRkREhHH8+HHj4sWLhmEYRlRUlJllwzCMwMBA45133jEmTJhgpEyZ0jh69Kj9sc8//9yoVauWecXhJREREYa/v79RvXp1w2KxGK1atTKioqKMhQsXGt7e3vb9bHMS5lq8eLGRKVMmI2PGjMaMGTOM0NDQl/aZPXu20aVLFxOqS5hs7zvPnj0zLly4YGzfvt04ffq0ERoaajx69MhYunSp0aRJEyN79uzG559/bkRHR5tcMQzDMEqUKGFMnz7dMAzDGDVqlFGwYEEjIiLCiI6ONsaMGWOsWrXK5AphGIbx8OFDo169esY777xjbN68+W/3s83DPn36GMWKFTMiIyPjqkS8wpEjR4xixYoZ7u7uRufOnY3jx48bERERZpeVIFmtVuP69etG5cqVjRw5chhNmzY11q5da9y6dcswDMPYt2+f0bp1ayN9+vRG8uTJjRw5chitWrWyH1/wnmUePz8/o1ixYoZhGMaECRMMX19fIzg42DAMwzh48KBRokQJ4+DBg2aWCLxxBO4A4GC2g7tu3boZpUqVMs6fP28YhmEcOnTIaNKkiZE+fXpjxYoVZpaIV7CN24QJE4xkyZIZadOmNU6fPm3s27fPGDFihJEyZUrjwoULhmFwcsTZBAcHG0uXLjXy5ctnJE+e3LBYLPY5RnBhvtDQUCMsLMwwDMN48uSJ0bdvX8PDw8MoVer/tXffYVUd6+LHvxRBRCEqIlgBu8beFQuRiL1hQREbaFAEFUVQFDt2g0aj2A0oFrAAVsSCgoJCFMUKgr2iFKVv1u8Pz95Hkpx7zz2/e1kQ5vPPiXsvnud99pxZa+ZdM+90lkJCQv50vfJaofhMmjRJateunVStWjWpdu3akoeHh+q7J0+eSCtWrJACAgIkSRIvsORSWFgoFRQUSLm5udLgwYOlI0eOSDk5OZKenp6qHxUWFkqjR4+W3NzcZI5WkCRJevz4sdSiRQtJTU1NGjp0qGqBxbeU/enly5eSrq6uFBYWVtxhllnKcd/bt2+lqKgoaceOHdKDBw9U3x88eFCqVauWpKGhobr/CfI5ffq01L59e6lFixaSq6urKlmbnp4u3bt3TwoNDZUSEhJUY3SRbJdXSkqK1Lx5c+nmzZtSjRo1pIMHD6q+8/LyUiXjBeHvTByaKgiC8H8gMzOTnj17Mn36dCZOnFjkkLkZM2Zw+vRpYmNjqVSpksyRCkpZWVmqet9BQUH8/PPPREVFYWRkRLNmzbCzs2PcuHFFDg8USpbXr1+zfft23rx5w9atW+UOp8xSHhQYGxuLv78/8fHxSJLEgAEDcHV1BSAxMZGpU6fy5MkTVYkZsfW7eCnvZbt27WLx4sUEBgbSsWNHypUrx5YtW5gyZQofPnzAwMCgyN9J4pDvYvX69WuqV69epG+MHTtW1c/y8vJUZ1ncvXuXrl27cvXqVVFOoQTx8/PDw8MDSZLw8vLCzs4OXV3dItfs2LGDW7dusWXLFpmiLFuUfeP9+/eMGzeOe/fu0aRJE86dO8fu3btV5QQB3NzcGDt2LC1btpQv4DIsJyeH8uXL8/DhQ/z9/dm0aRNZWVmYmZnh4ODA8OHDVSW1BPko+1R2djYxMTH06NEDBwcHdu/ejZGREc+fP+fjx49cu3aNcePGERQURK9evcS8SvhbEwl3QRCE/2XKZMSwYcOoXLkyu3btQpIk8vLy0NbW5uLFizg5OREUFESTJk3kDrfMys3NRVtbmxs3brBlyxbev3+PmZkZw4YNw8LCAoAHDx7w5s0bunfvrkpaiGRTyZabm4uamhpaWlqqA7ZEexWfb/tHvXr16NmzJ+3bt2fnzp2oqalx7do1Va19gPfv31OtWjVV8lAofp06dWLcuHFMmzaNlStXcuDAAWJjY9HU1GTt2rXUq1eP4cOHyx1mmfT8+XPGjh3L2LFj6d27N3Xr1gW+1gUfNmwYt27dwsvLi0WLFnHs2DF++eUXjI2N2b9/v0i2lwBfvnxBXV0dHR0dsrKyWLp0KT4+PrRp04b58+erDnlUys7ORkdHR6Zoyxbls2rQoEFoamqyc+dO4uLiGDhwIJGRkbRp04aEhARVbfBv/0YoPsrfPDc3l6pVq7JmzRq6du2KkZERCxcuxM/Pj/bt22Nvb0/37t1F4l0mf1xYlpuby7Zt28jPz+eXX37Bx8eHjx8/UrNmTXR0dOjfvz8rVqwQfUr42xMJd0EQhP9lysHD6tWrWbp0Kdu2bcPOzk71fXBwMJMnT+bp06eqAzqF4vPly5ciK8vq1KlDu3bt0NDQIDs7m5cvX9K5c2ecnJzERKsU+nZypq2tLXc4ZY7y91+wYIFqJ092djbGxsYEBATQt29fwsLCyMzMZODAgZQrV07ukMskSZIoLCxEoVAwcuRIxo4dy8CBAzE0NGT//v0MGDAASZKwtbWlVq1arFmzRu6QyxxJknj16hXjx48nKSmJzp07M2LECLp06UL16tWJjo7G19eXkydPkpWVhYGBAT179mTz5s3o6uqKhLsMxO6e0iUxMZFevXpx5swZmjRpQpcuXWjfvj0bN27k3bt3LFq0iKFDh9K7d2+5Qy3zNm3axK+//sqDBw+KfB4eHs6QIUNQV1dn+fLlODs7yxShkJ+fT7ly5dixYwcnT57k+PHjAHz+/JlXr14RExNDRkYGffv2pVatWpQrV07c+4S/PbGUSBAE4X+ZMinr7u5Oeno6P/30E1u2bGHy5MnExsZy5swZZs2aRfny5cU2OhlMnjyZ1q1bM3PmTGJjYzE1NeXo0aMAJCQkcOzYMcLDw5kyZQrdunVjwYIFVKxYUSTbSwllOy1cuJBBgwZhbm4uc0RlgzLRrqamhkKh4MOHD9jY2AAwbtw4unfvTt++fZEkieTkZG7evEnv3r1Fwr2YfVueRENDAw0NDSpWrEhgYCDHjx/HyspKteo2ISGBkydPcvXqVUAkBYubmpoaNWvW5Pz585w5cwYvLy8WL16MpaUlo0ePpmPHjjRp0gQ3NzeePHmCqakpjRo1QkNDQ7SVDCRJUu3SGTlyJD179mTEiBHs3LmTgIAAXFxcUFdXp379+oSFhfH+/XsAsbtHRuXLl6dy5cpUqlSJY8eOkZKSohoPKhQK4uPj6dOnj8xRCgCmpqZkZWXx+PFjGjRooCoD2alTJ3r27EnTpk0ZNmwYIBbIFKe7d+8SEBCAp6cnFSpUAMDMzIxHjx6RlpbGd999R8WKFWnYsCENGzYkKyuLChUqUFhYCCCeU8Lfnvh/uCAIwv8ChUIBwMePH3n69ClxcXHk5+fj7e3N8ePHqVmzJkuXLiUlJYWpU6fi4eEBIJLtxSwxMZEvX75w/PhxRo8ezdmzZzEzM0O52atZs2YsWLCAxYsX065dO0JDQ3nx4oXMUQv/roKCAgAOHz6Mr68vDRs2lDmiskM5uS0oKEBDQ4Pq1atz48YNrl69ytmzZ1UrpNXU1Dh69CgVK1akYsWKcoZc5jx//hwbGxt27drF06dPVZ+vWLGCx48fExAQoNrVc+zYMVxcXBgwYICoBS6jnJwc4GuyycrKipSUFDZt2oStrS1r164lNTWVJk2a0L9/f5o2baoaU4i2ks+CBQv47rvv2LVrF+PHjycxMZGlS5eiqalJeHg4R48eJT8/n2rVqgGIZLuMDAwMMDIyYv/+/cyaNQsPDw+MjIyAr+OIt2/fMnjwYJmjFABVCc6lS5cCUKFCBdTU1NDV1SU/P58ePXpQs2ZNkWwvZhEREQQEBGBlZYWfnx8AvXr14u3bt9y/fx/4OvY4e/Yszs7O2NraAuIZJZQdoqSMIAjC/yflKvWcnBwGDx5MbGwsJiYm1K1bF3t7e/r16wd8rc2ppqamKiMjEhjy+PTpE4GBgZw+fZrk5GSeP39OYGAgPXv2LHJdRkYGDx48oEOHDmIAXwr8sXa4k5OTagu/8H9rxYoVdO3atUgfioyMxMHBgdevX2Nra8uWLVvIz8/n4MGDODo68vr1a/T09MR9sJiI8iSlj6hdXDp8++xRKBQ4OTlRr1493NzcGDFiBLm5uQQHByNJEjt27ODmzZts2LBBvHCUwbdtpSw7t3//fpydnfny5QsHDx6kRo0aJCQkMH/+fDZt2sTIkSPFLgSZ/HHsffHiRcaPH49CocDDwwNDQ0PCw8MJCAjgw4cPooygDNLS0rh8+TLHjx/n+vXrNGjQgMWLF7Nr1y7ev3/P27dvefToEdra2jRv3hxHR0f69+8vxhRCmSES7oIgCP+h3NxckpKSaNq0KQATJ04kMTERd3d3kpKSiIiIICkpidatW+Pk5ES7du1kjrjsUg7avz2QLDExkSNHjhAYGEh+fj5Dhw7F3t6eOnXqyByt8J9QvvhaunQpgYGB3LhxQ0y+isGrV68YPXo07969w9LSEg8PD2rWrAmAv7+/ajfP4MGDiYqKQkNDAzs7O2bMmCGSGDJRlifJzc1VlSdp164dGRkZvHz5UpQnKWFE7eLSQXk/W7RoEffv38fFxYV+/foRExND48aNAejTpw9NmzZlw4YNMkdbNinHCf7+/igUCn788Udq1KjB9evXcXR05OPHj+Tm5lK7dm1GjBiBu7u73CGXeQUFBXz48IH8/Hxq167NkydP2LRpEwEBAWhra9OsWTOmTJnC0KFDxZhCRi9evODs2bMcO3aMpKQkHj9+TO3atVm6dCn169enfv36ql09glCWiIS7IAjCf2j9+vXs27ePCRMmMHz4cNauXYu1tbVqlefDhw85fvw4Fy5c4O3bt/Tv35/ly5eLldLFTJlsT0tLY+PGjTRs2JBRo0apEkjXr19n//79xMbGoqenh7W1Nba2tqpahELJp0wIfvr0CVNTU3777TcGDRokd1hlRlxcHGFhYYSEhJCZmYmtrS2urq5oamry8eNHVq1aRUpKCnXq1GHEiBF07NgREHVWi1tOTg7ly5fn4cOH+Pv7s2nTJrKysjAzM8PBwYHhw4eLVdIlUEhICE5OToSHhxepXZyVlYWNjQ1NmzbFxcVFlFOQgdjdU3ook+3Jycm0adOGVatWYWNjg76+vuqaq1evoq+vj6GhIdWrVwfEblQ5KBPnISEh7Ny5k3v37qFQKOjSpQs+Pj4YGBgAkJSURL169WSOtuxS9o2CggLU1NTQ0NDgzp07hIaGEhERwYsXL5g5cybjxo1TndcjnlFCWSMS7oIgCP+hM2fOEBgYyJ07d6hbty7v379n/PjxTJgwoch1165dY8+ePbRp0wZHR0d5gi3DlIO78ePHk5aWhr29PYMGDfrTNvCTJ08SGBhIbGwsFhYWbN68WebIBfj3BufKQb+NjQ1fvnwhJCSkmKITlPLy8oiMjCQ4OJgLFy6gr6+Pi4sLw4cPByA/P7/IAali0lW8RHmS0isxMZEffviBHj16qGrkKvXp00e1klr0qeIldveUToMGDaJq1ars2bNH9ZmyPZQvJQX5KMdzGRkZ1K1blylTplCnTh20tLTYvn07jx49YsuWLYwdO1buUMs85TNn0aJFVKxYkVGjRlGnTh3y8vKIiIjg5MmTXLp0iZycHA4cOEDr1q3lDlkQip1IuAuCIPwPfZu4UFdX58CBA4SGhnL16lUMDAzYvHkzPXr0KPI33w7ixaS4+CgH7hcvXsTa2prz58/TqlUr1Wqla9eu8fr1a5o3b06DBg149+4de/fuxdzcnC5duoiVTaWActVadHQ05ubmJCQkiMNSi9m3/eT9+/eEh4cTEhJCbGwszZs3x8PDg7Zt28ocpQCiPElpIWoXlw5id0/p8uHDB/r164eTkxPjx48v8uz69OkTp0+fpkuXLpiYmMgbqICrqytxcXFcunQJ+DrOePHiBWvXriU+Pp5jx45RpUoVeYMsw5Rj74iICAYNGsRvv/2GlZVVkWdRWloaYWFhhIaGsnnzZipVqiRjxIIgD5FwFwRB+A8kJCQwa9Ysdu/eTa1atUhJSeHYsWMcO3aMwsJCunbtioODAw0aNJA7VAHo0aMHXbt2xdvbG4B3796xb98+Fi9eDIC2tja//vorNjY2MkYpwD9XmkVERPDp0yesrKz+rRVnHTt2pGvXrqI2bjH6Y9JIeQgdfC2pdf78eU6ePMnjx48ZMGAAGzZsEEkmmYnyJKWHqF1cOojdPaVLz549ad26NT///DPwz8ThmzdvsLKyYu3atfTu3VvmKMs2SZKYN28eT58+JSAgoMh30dHR9O/fn/3792NlZSVThIJS9+7d6datGytWrACK3t9SU1OpWrUqmZmZVKpUSSxiEsok8f94QRCE/0BaWhrXrl1j+PDhvHz5EhMTE2bNmsWGDRvo2LEjUVFRODs7s3r1ar58+SJ3uGXa+/fv0dLSokWLFqrPli1bxqlTp3B3dycjIwNra2tmzZrFu3fvZIxUAFRJI2tra969e/dfJtsLCgoA2LdvHy9fvsTT07NYYhS+Uk6qQkNDcXV1ZerUqaoXHo0aNcLJyYlFixYxaNAgzMzMRJKpBGjSpAkAS5cuBaBChQqoqamhq6tLfn4+PXr0EMl2GSnvaSEhIVhbW9OtWzd69OjB2LFj0dPTw8fHh7dv33Lx4kVOnz7N0KFDAUSyXSaFhYVoaWlhYWHB/PnzmTdvHrVr12bBggWMGDGC2NjYIsl2QPQrmVlZWbFz5058fHzIzs5GQ0OD3Nxc9u3bR1ZWlki2lwBqamo0btyY4OBgQkJCyM/PV33Xpk0batasSWpqqowRCvA1oa5QKKhbty7w9eWV8v724cMH/Pz8SEhIUK1sF8l2oSwSK9wFQRD+Q8nJyYwYMQKANWvW8MMPP6i+O3nyJIcPHyY2NpZjx46Jle4yGzhwIAqFgsWLFxMREcHatWtZvXq1qt5+WFgY7u7uBAQE0KhRI3mDLcOUK82ePn2Km5sbW7ZsoVq1an957bcJwZo1a7Jw4UJxRkIxUq6oPXnyJLNmzaJu3bq0bNmSn3/+mQYNGrBo0SJGjx4NwJcvX1SJXZHILX6iPEnpIGoXlx5id0/poGynwsJC3rx5Q40aNcjMzGT+/PlER0ejo6ND586duXPnDrdv32bnzp306dNH7BiRgbKtlL+9QqFgzJgxvHnzBhsbG7p164aBgQH79u1jzZo1IuFeQnTv3h0TExN+++034J/t+Pz5c7p27cqBAwcwNzeXOUpBkI9IuAuCIPyHJEkiPDycJUuWoKuryy+//FIksf7p0ydu375Nz549RZJJZsHBwaxatYqXL1/y+fNnNmzYgJ2dnWq1RXBwMHPnziU2NhZdXV2Zoy3b0tLSmDhxIg8fPmTDhg306dPnL69TJufd3d25ePEiUVFRYoIsg/r162Nvb8+8efPYuHEj69evp0uXLgQGBtK/f388PDzo3LmzuAfKTJQnKT1E7eLSIzQ0lAsXLpCWlsb333+Pq6ur6rvo6GgOHz6MiYmJOBNBJsqXWCtXriQ9PZ0xY8bQokUL3r17R1hYGJcuXSI2Npa2bdsydOhQ+vXrJ3fIZdK344MdO3ZQv359LCwsSE5OZvHixdy4cQNNTU0ePHhA69atmT17NiNHjhTPqhLg4MGDTJs2jTFjxjBjxgwaNGjA69evWbx4MXfu3CEqKkruEAVBViLhLgiC8G9QJveePHlC5cqVAVT/GxMTw7hx48jPz+fYsWNFSpcoiWST/G7cuEFmZia1atUqcqjmp0+fMDc3Z9SoUXh5eanaWpBHYmIi1tbW3LlzhyFDhrB27Vrq1atX5Bplf3r16hUNGzbk+PHjWFpayhRx2RUcHMyKFSuIjIyksLAQMzMzVq5cSd++fRkyZAhRUVF0795dlTgUipcyGRESEsLOnTu5d+8eCoWCLl264OPjg4GBAQBJSUl/6mOCPETt4pJP7O4pHZRjuYSEBDp27EhAQACWlpbo6OiQmpqKlpbWXx7iKNqp+CnbauHChZw+fRofH58iq6IjIyNJS0ujoKCA1q1bU6dOHRmjLbuUL7A+f/7Mq1evMDMzA2Dt2rWcOnWKDx8+UL16dVJTU8nPz+fo0aM0bdpUzKuEMk0k3AVBEP5NHz58wNDQkB9//JH69etToUIFpk+fTt26dXn27Bnu7u6kpKSwcOFCsUqmBPmryZNy0BgfH8/27duJiooiLi7uX14vFD8/Pz88PDyQJAkvLy/s7Oz+tPtgx44d3Lp1iy1btsgUZdl26dIljh8/zurVq/H19eXw4cOcPHkSfX19fv75Z9LT03FxcaFKlSpiwlXMRHmS0mvv3r04OTlx8OBB+vTpo6oBnp+fT7t27XB3d2fMmDEyRymI3T2lQ9++fTEzM2PLli2kpaVx6tQpvLy8+PTpEy4uLixatEg8n2Sk7B8fPnzAxMSEo0ePquro5+XloaWlJXOEwh+NGzeO33//nYULF9KnTx/09PS4evUqN27cICEhgebNm/Pjjz/StGlTcVCqUOaJhLsgCMK/KT4+nsGDB/P582dWrVrFlStXCAwMZNCgQdSsWZNPnz6RnJyMhoYGGzduVB1OJ5RM+fn5ODk5kZWVhZOTE507dxaTrhLgy5cvqKuro6OjQ1ZWFkuXLsXHx4c2bdowf/58BgwYUOT67OxsdHR0ZIpWyMjIQE9Pj02bNhEcHMz58+cBsLOzo3r16qxbt07mCMs2UZ6k5BO1i0sfsbundHj//j1Dhgxh7NixTJ06lQULFnD9+nXatGmDtrY24eHhhIWFiVKCJcDevXvZsmULFy5coGLFikVeUsXGxvL27VssLCzEeK8EePjwIXPmzOHKlSv07duXadOm0bVrV5FYF4S/IHqFIAjCv6lFixZcvHiRRo0acfz4cVatWsW9e/do1aoV5cuXJykpiYsXL3L+/HlSUlLkDlf4b5QrVw4vLy+8vb3p3LkzgBgsyqCgoAD4OqGaNWsWgwYNon///mzYsIEKFSqwatUq7t69i66uLjNmzFD9XWFhIYCYfBUjhUIBwJs3bwgKCuLjx4/o6ekB0LhxY6KjoxkxYgTOzs4cOXJEVbdY2VZC8ZIkCS0tLYyNjVWfqaurU6dOHcaOHUtCQgI3btyQMULh29XPe/bs4eLFi2hoaLBq1SpMTEz45ZdfGDNmDHXq1OHo0aNs3boV+Od9U5CHnp6e6iX9tm3bMDExYdCgQRgYGGBtbY2XlxdHjx4F/nnfFP7vJSQk8PnzZ9W/q1WrRseOHfHz86N3794EBgbi4ODAmjVrGDFiBOnp6Tx58kTGiAWlhg0b8vz5c9LS0lBTUyM/Px/lutA7d+6wbds2MUYvIRo1akRISAgBAQHcvXuXIUOG4OHhwc2bN8nOzpY7PEEoUcRdSxAE4X/AxMSE3bt3q1Zu1qpVi7lz5zJnzhzCw8O5fPkymzdvpm/fvnKHKvwbatWqpaoFmZ+fL7Z9FzNJklQHXo0cOZKMjAxGjBhBRkYGAQEBFBQUUFhYSP369QkLC+P69evA12STmHgVP+Xuj4kTJ3Lq1KkiiYoffviB9evXk5qayqNHj9ixYwd169ZFoVCItpKJmpoajRs3Jjg4mJCQEPLz81XftWnThpo1a4rV0jJTvoxauHAhvr6+qvIxpqam7Nu3jx07drBy5UqOHDnCkSNHGDlyJIA4KFBmPXv2ZOnSpWhrawNQvnx59PX1AYiLi+Pz58+qnSNi11zx+eGHHzh9+jTwz741ZswYmjZtiqmpKbt27cLGxgaAoKAgtLW1ad68uWzxCl8VFhZSo0YN1NXVcXFx4cOHD5QrVw41NTVycnLw8fGhdevWaGtrixf4MlC+NMzNzS3yed++fblz5w6urq5s3LiRiRMnEhwcLEeIglBiiZIygiAI/4KyvMjTp0/Jy8vDzMxMNXGKiIhg9uzZGBoacvToUdWk61uibl3Jp9zC/+TJE4KCgpg+fbpYMV2MlKs7FyxYwOnTp4mNjSU7OxtjY2MCAgLo27cvYWFhZGZmMnDgQFUySih+yvvZwYMHcXFxIS4ujlq1ahX57q9qFYv6xcVLlCcpPUTt4tJDOR588+YNkZGRWFhYqBLq586dw9ramj59+mBkZMSOHTt4+PAhdevWFePAYqLsSy9fvqRmzZqkp6fz008/MX/+fFq0aAH8sw0zMzO5cOECkydP5vDhw/Ts2VOUEywhwsLCmD17Nvn5+QwbNgwDAwPOnDnDs2fPuH//PiDGFHLJzc1l7Nix/PTTT3Tu3BldXV1VW+Tk5PDDDz+gqanJihUr6Natm9zhCkKJIUYAgiAI/4KGhgaSJNGhQwdGjRrF4MGD8fHx4cWLF5ibm3P58mUqVarEgAEDVAdufktMsko+5SpBGxsbXrx4IZLtxUT5rl9NTQ2FQsGHDx9Uq87GjRtH9+7d6du3L5IkkZyczJkzZ/60skYoXsr72ZEjR5g8eTK1atVStaO6ujoKhYLTp0/z7NkzoGgbC8VDlCcpXZRtFRoaSpMmTVQHbAKqZHtsbCynTp0S2/RlJnb3lGzKvnTkyBG+fPnCq1eviIyMxNzcnDlz5vD27VtVG96+fZvDhw/j5OREz549kSRJJNuL2bfrPd++fcvLly958+YNP/74Izt37sTa2pqjR4+ybds2WrVqRUBAAPD1WSXGFMVPkiQ+fPhATEwMQ4YMwd3dnTt37qh2zZUvX57WrVvj4+Mjku2C8AdihbsgCMJ/4+rVq4SFhZGUlMS5c+f4/Pkz3bt3p0GDBjRt2pTr169Tvnx5li1bhqGhodzhCv8m5QrQoKAgnJ2diY2NLVLrWPi/p2yDRYsWcf/+fVxcXOjXrx8xMTE0btwYgD59+tC0aVM2bNggc7RlmzKZO2nSJN69e0dISIhq4qscSjo5OdG0aVOmT58uZ6hllnKV5sKFCzl9+jQ+Pj6Ym5urvo+MjCQtLY2CggJat26tKqclyCsqKophw4Zx48YNateuTX5+PpqamqipqbF3716OHj3KkSNH/nInnfB/T+zuKR2OHz/OsGHDCA4OVh2uvmPHDhYuXEi5cuVYsmQJtra2aGtr8/btWypXroyWlpbYhSAD5bNq06ZN7N69m5cvX9KjRw9++OEHRo8eTeXKlQH4+PGjONS7hPntt9+YM2cOGhoauLm50bhxYz58+ICDgwMPHz7E1NRU7hAFoUQRCXdBEIT/wrcTpvT0dDQ0NDhz5gynTp3i5cuXREVF8eXLF+Dryhpra2s5wxX+Td+2q6mpKc7Ozri6usocVdmwYsUKunbtSs+ePVWfRUZG4uDgwOvXr7G1tWXLli3k5+dz8OBBHB0def36NXp6emJiXAJs2bKFdevWERgYSNu2bVWf379/nw4dOnDx4kXatWsnkk3FTJQnKZ0KCwt59uwZXbp0oWPHjuzYsQMDAwMAcnJy6NSpE4MHD2bJkiXi/icza2trGjduzIoVK4rc3xQKBWfPnuX777+nTp064t4no2nTpnHixAl8fX1VSfdPnz6xYsUKfvnlF9q3b4+np6c4Z0lGyv7x/PlzTE1N2bRpE+rq6ly+fJmkpCRMTEywtrZm0KBBYtdpCZKZmUnFihVV9zZ3d3f27NmDtrY22trajBs3Di8vL1GeSRD+QCTcBUEQ/hv/apL76tUr8vLyCA4O5u7du2zfvl2G6IT/hHJAuHTpUgIDA7lx44ZYPVgMXr16xejRo3n37h2WlpZ4eHhQs2ZNAPz9/fHw8ABg8ODBREVFoaGhgZ2dHTNmzFCthhfklZ6ezvDhw4mKisLV1ZWBAwdy7do1jh8/TtWqVQkMDBSJQRnt3buXLVu2cOHChSKTY/hanuTt27dYWFiIREYJI2oXl1xid0/Jp3zmPH/+HAcHB3Jycjh9+jQVKlRQXfPw4UOcnJxISkoiOTlZxmgF+Drmi4yMVJU2y8vLIyAggMOHD5OZmUmNGjVYvnw59evXlznSsik3NxdtbW1u3LjBli1beP/+PaampvTt25f+/fsDkJqaSkxMDC1atFCN5cVzShCKEgl3QRCE/6G/SiYpE7jizX7Jp2y/T58+YWZmxr59+xg0aJDcYZUZcXFxhIWFERISQmZmJra2tri6uqKpqcnHjx9ZtWoVKSkp1KlThxEjRtCxY0dADOLloLyfJSQk4O/vz7Rp06hduzZZWVls3boVHx8f8vLyqFSpEn379mX58uXo6+uLhLuMRHmSku3b+9jbt28pKChAQ0MDIyMjYmJiCA4OJigoiMLCQoYMGcLo0aNp1aqVeOFYAojdPaVDYmIivXv3pn79+uzevZtatWoVGZu/ffuW6tWriz4lA2XfSExMZM2aNaSnp3Po0KEi17x58wZfX1+uX7/OsWPHKF++vEzRlk1fvnxBV1dX9e86derQrl07NDQ0yMnJ4dWrV7Rp04bp06fTsmVLGSMVhNJBJNwFQRD+P/zxYEAx0ZLPv/vbK5OBQ4cOJTc3l1OnThVDdMK38vLyiIyMJDg4mAsXLqCvr4+LiwvDhw8HID8/n3LlyqmuF/1KXp07d6ZBgwbMnDmTNm3aFPnuzp07GBsbU6VKFdTV1UWyXUaiPEnJJ2oXl15id0/JpBwfKA/U1NDQ4OTJk7i5uWFnZ8e8efOKXCfIb968eezYsYP8/HyWLVuGra0tVatWLXJNamoqVatWFQuZitmYMWNo3bo1M2fOJDY2Fnd3dy5fvgxAQkICx44d48KFC+Tm5tKtWzc8PT2pVKmSzFELQsklEu6CIAj/n5SDeDEolFdQUBCdOnVSbWv8K8o2unHjBpaWlsTExNCoUaNijFL4NiHx/v17wsPDCQkJITY2lubNm+Ph4VFk9aAgD2U77dq1i0WLFvH48WN0dHRITU3Fzc2N9PR0pkyZgpWVldyhCn8gypOUTKJ2cekhdveUbMr2UZa9+Kv72YYNG5g7dy4rVqzAzc0NNTU1cc8rQU6cOMG2bdt4/fo1Xbt2ZfDgwVhaWor+I6PExERmz57Nhw8fMDY2pnnz5qSkpLB79+4ifefy5cscPXqU8PBwAgMDady4sYxRC0LJJhLugiAI/x+Ug/4nT54QFBTE9OnTxUS5GCknWQcPHsTNzY24uDiqVav23/5d+/bt6datGxs2bCiGKAX4c4JPOVGGr7VVz58/z8mTJ3n8+DEDBgxgw4YNYnIsk2/bysbGhiZNmrBo0SLCwsLYuXMnjx49wsDAgKioKOLj46lXr57MEZdNojxJ6SRqF5ceYndPyVVYWIipqSlmZmZ89913tGzZkpYtW6Kjo0OfPn34/PkzW7Zs4fz586xZs4bWrVvLHXKZ9a9e8GZnZ7N161aOHDmClpYWPXv2ZODAgbRr106GKAX4eshwYGAgp0+fJjk5mefPnxMYGEjPnj2LXJeRkcGDBw/o0KGDeIEvCP8FkXAXBEH4X9ChQwc6d+7Mxo0b5Q6lzPh2gLds2TIAFi5c+C8Hfsok0759+/D09CQ+Pl5s2ZdBaGgoFy5cIC0tje+//x5XV1fVd9HR0Rw+fBgTExOcnZ1ljLJskiSJ1NRUVRkShULBvHnzCAgIYOvWrbi5udG3b19mzZpF7dq1MTc3Z8mSJfTq1UvmyMsmUZ6k9BC1i0sPsbundHj58iUBAQEkJCSQm5tLdHQ0CoWCnJwcsrOz6dy5M8+ePePBgwe0bduW69evi12oMlH2qStXrnDlyhUuXrxIr169sLGxwcTEhCdPnvDLL79w9OhRpkyZgqenp9whlynK51N2drZq0VhiYiJHjhwhMDCQ/Px8hg4dir29PXXq1JE5WkEoXUTCXRAE4T+kTOAGBQXh7OzMzZs3qVGjhtxhlRnKAWJAQABnzpxBU1OT7du3/+WE6tskfK1atViwYAGOjo7FHXKZpewrJ0+eZNasWdStW5eWLVvy888/06BBAxYtWsTo0aOBrwc2VahQATU1NbFqppj5+PgQGRnJpEmT6NWrF1paWrx48YLJkydz69YtBg4cyOrVq6lcuTJ3797F3NycmJgYGjZsKHfoZY4oT1I6idrFJZvY3VM6KXfMxcfHk5+fz/3797l16xYAwcHBTJ06lVmzZoldCDJQ3scePnzIgAEDaNiwIR07dmTx4sWsXr0aNzc31bXnz5+nZcuWVKtWTYz/ionyd05LS2Pjxo00bNiQUaNGqfrJ9evX2b9/P7Gxsejp6WFtbY2trS0VKlSQOXJBKB1Ewl0QBOE/8O1A0NTUFGdn5yIrdYXiIUkSkyZNYt++fVSuXJmDBw9iaWn5p0G6cpLl7u7O+fPniY6OFiUVZFC/fn3s7e2ZN28eGzduZP369XTp0oXAwED69++Ph4cHnTt3FhMtmWzcuJGDBw+iqalJ165dGTJkCJ06dSI/P5/s7Gz09PQAePToEU5OThgZGeHn5yeSGDIS5UlKH1G7uOQRu3tKp//pSykxtpCPhYUFjRs3ZuvWrdy6dYtevXpx48YNzMzMOHXqFH369BH3QBko+8T48eNJS0vD3t6eQYMGFekrCoWCkydPEhgYSGxsLBYWFmzevFnmyAWhdBAJd0EQhP+AcpC/dOlSAgMDuXHjhqoetVD8oqOjcXV15ebNm0yfPp3Zs2f/abfB58+f+fHHH/H29sbCwkKmSMuu4OBgVqxYQWRkJIWFhZiZmbFy5Ur69u3LkCFDiIqKonv37ly6dEnuUMu09+/fs3btWiIiIqhQoQLW1tb069cPU1NTAJ4/f86vv/7KpUuXOH/+PLq6uiLhXsxEeZLSQ9QuLh3E7p6/F+UzSTlWF4l2eT1//pwhQ4bg6+tLu3btaNSoEWPGjGHRokWkpaUxYcIEevbsycyZM+UOtUxR9pOLFy9ibW3N+fPnadWqlWo8d+3aNV6/fk3z5s1p0KAB7969Y+/evZibm9OlSxcx9hOEf4NIuAuCIPwPKQcYnz59wszMjH379jFo0CC5wyoz/jhx+rbm4O7du/Hy8kJNTQ0PDw8mTJiArq6u6trXr19jbGxc7DELcOnSJY4fP87q1avx9fXl8OHDnDx5En19fX7++WfS09NxcXGhSpUqopSCDL79zffv34+Pjw+PHz9GkiSsrKwYMmQI/fv3p2LFity9exctLS2aNGki2kpGojxJySdqF5cOYnfP359IusunsLCQ9u3bs3z5cj59+sSiRYu4ceMG3333HWlpaXTv3p3FixczbNgw0U4y6NGjB127dsXb2xuAd+/esW/fPhYvXgyAtrY2v/76KzY2NjJGKQilk0i4C4Ig/MO/O8hTTrCGDh1Kbm4up06dKobohD8KDQ3l+PHjSJKEjo4O3t7e6OnpkZGRwcqVK1m9ejV79+5l3Lhxcocq/ENGRgZ6enps2rSJ4OBgzp8/D4CdnR3Vq1dn3bp1MkdYdikTsqtWrcLf359Vq1ZhaWnJ4cOH2blzJ0+ePGHIkCEMGTKEXr16iQlxCSHKk5RconZx6SJ29/z9KPuSeOFY/JRn9ygtWbKECxcucPPmTTZt2oS9vT25ubksX76coKAg7t27J2O0Zdf79+8ZM2YM9vb2qoS6s7Mzd+/excLCAk9PTxwdHQkNDeX27dsYGhrKHLEglC4i4S4IgvAPQUFBdOrUiZo1a/7La5SD9hs3bmBpaUlMTAyNGjUqxijLNuUAft++faxZs4bmzZvTpEkTlixZgp+fH7a2tqprU1JSMDExkS/YMk7ZV968eUNkZCQWFhZUqVIFgHPnzmFtbU2fPn0wMjJix44dPHz4kLp164oEhowUCgWtWrViypQpODs7qz7Pyclh3LhxnDt3joYNG3Lw4EHMzMxkjLRsEuVJSidRu7hkE7t7/p6U7fPkyROCgoKYPn26OED6/5jyGaUcx6WlpeHt7c3y5ct58eIF8+fPJyoqCnNzcxo2bMjDhw+5fv06e/fupUePHqJPyWTgwIEoFAoWL15MREQEa9euZfXq1UyYMAGAsLAw3N3dCQgIEHNeQfgfEqM7QRDKNOU7x4MHDzJz5ky0tLT+y+uVA8Fp06Zhb28vBh7FTLlaxt3dHWdnZw4ePIiamhpt2rRhxIgRFBQU8Ntvv/HmzRuRbJeZsq9MnDiRU6dO8eTJE9V3P/zwA+vXryc1NZVHjx6xY8cO6tati0KhEIknmRQWFgJgZmZGVFQUkiRRWFhIQUEB5cuXx87OjjZt2jBhwgSRbJeJ8nl15coVvL29+fHHH1m1ahVv377F1dWV/fv306ZNG/bu3cvZs2dljlaAr6uiMzIysLe3B2DUqFG4uLhgZmZGWloa27dvZ9OmTTJHKQCsWrWKlStXsmjRIt68ecMvv/zCmzdvmDt3Lp6enly8eJEWLVrQpEkTAJEYLOGU7WNjY8OLFy9Esr0YKF8Ib9u2jbS0NCZOnMi1a9fQ0tLCzMyMLVu28NNPP5GTk8OJEyfQ1dVlx44d9OjRA0mSRJ+SyeTJk0lLS2PEiBGsXLmSNWvWFNkdnJ2dTVZWFrVq1ZIxSkEonTT/+0sEQRD+nr5dLfj48WOmTJnyX27n/nZ19evXr1mwYEFxhywA169fx8TEhClTppCamsqGDRvw9/dHS0uLp0+fEh4ejqGhIX369JE71DJLubrp4MGDxMbGsmPHDtVAvbCwEE1NTSZPnsyUKVOK/J1Ithc/5YqytLQ01NXV6dmzJ+vWrePcuXNYWVmp2sTQ0BAtLS3VJEzsRChe35YnmTRpEg0bNqRbt27Mnz8fDQ0N3NzcMDMz4+eff6Z///60bNkSEHWL5abcMff+/XsOHDhAYWEhM2bMUH3/5MkTVZ8SbSUPDQ0NFAoF+/fv56effmLAgAEAjBs3jpEjRzJu3Dj8/f2JiYkRu3tKCeV4PSgoiBcvXuDh4SF3SGVGSEgIa9as4dixY1y5coWrV6+qvqtatSqenp6kp6ejr68v7nklxKBBgzA2NiYzM5NatWoVOQz606dPzJs3jzFjxqCrqyt2IQjC/5CYKQmCUOYFBASQmJhISkoKCoXiLwd/kiSpVld7enqyYMECVXkMoXgUFBQAXxMYL1++5P79+8yYMYPu3burJshv377l4sWL1K9fX85QyzxlIvbIkSNMnjyZWrVqqVbnqquro1AoOH36NM+ePQP+uXJXTLyKn3LiNGDAAA4fPoyzszP9+/enf//+jB49mjNnzrBmzRqmTJlCjRo1qFixIpIkiWR7MVO2k6OjI5aWlpw8eZJBgwZRuXJlrK2tATh16hSFhYVYWlpSrVo1QPQpOSifVfD1fjdo0CBWrVrF5MmT8fDw4LvvviM3N5f169dTUFDAsGHDANFWchG7e/5evh2vz5kzhzlz5mBsbCxzVGXHwIED8fX1JS4ujkqVKrFv3z5CQ0PJy8tTXaOvr09iYmKRe564/8lDOf5u3749P/zwAw0bNlTdE+Pj41m4cCHa2tp4eXkBYmGMIPxPiR4jCEKZpaamhiRJnDt3Dj8/P44fP86FCxf4q6MtlJ+5u7tTvXp1HBwcijvcMun169f4+fkB/ywnY2BgQK9evZg2bRqhoaEsX74cgC9fvuDp6Um3bt2oX7++asAoFD9lf9HX1+f27dtFVjEpk7WhoaEEBwcDYqIlF2U7JSUlkZqaipWVFZqammzfvp2goCCSk5MZP368qlTJjh07ivydULxEeZKSS9knlDt40tLSmDt3Lnl5edjZ2WFsbEzVqlUJDw9n8eLFTJgwAX9/f7Zu3Qp83cEgFC/lb56WlkZmZiY9e/YkIiKCc+fOoa6urhpz/NXuHqHkUrbP0qVLqVSpEk5OTjJHVPa0a9eOoUOHMnfuXH7//XdWrVrFokWLiI2NBcDPzw9zc3OZoxTgr8ff6urq5Ofns3nzZtLS0tiyZQvAv1yUJgjCvyZKygiCUKapqamxZ88eHB0dcXV1ZcCAAUyfPp3Zs2dTo0YN1XXq6up8/vyZiIgI1q1bp5qICf+3jhw5wrJlyzh+/DizZ8+mS5cu6Ojo4OHhweTJk8nIyGD37t0UFBSQlJTEixcvCAoKkjvsMk85IG/bti3r1q0jLi6Otm3bqr67f/8+fn5+XLx4ERClFOSi/M3v379Ply5dyM3NVX03ePBgBg8eTEpKCpUrV6ZixYqq0gtiO7E8RHmSkuvb2sVjxoxh4sSJfPjwoUjt4m3bthEbG8uJEydo27atqF0ss29390yYMAFnZ2fu379P//79GTFiBOPHjyc+Pp79+/fTtm1bsbunFCgsLERDQ4NPnz7x888/s2/fPrS1teUOq8ypWrUqO3fuBMDa2ppff/2VsLAw4uLiqFKlCpcvX2bJkiUAYkxRQpUrVw4vLy8KCwupU6cOIFa3C8J/Qk0Sy5QEQShj/piIyM7OVh2mtHv3bry8vFBTU8PDw4MJEyagq6uruvb169dia2oxSklJ4fz585w4cYLHjx/Tq1cv3N3dVYO/TZs2sWfPHoyMjOjUqRPDhg2jefPmYgBfQqSnpzN8+HCioqJwdXVl4MCBXLt2jePHj1O1alUCAwNFLXCZhYWFYWVlBcCePXsYP3488LUshoaGhkjaykxZi1hpyZIlXLhwgZs3b7Jp0ybs7e3Jzc1l+fLlBAUFce/ePRmjLdtCQkJwdnamQYMGqtrF7dq1K3LNX9UuFi9Hip/yN09KSqJfv36cO3eOunXrAnDixAlWrlxJcnIyRkZGtGnThp07d6KhoSGeVzL5d/uIsn2GDh1Kbm4up06dKoboBPjnb//y5UuSk5O5d+8eXbt2pVmzZsDXw773799PXl4eJiYmqhIlQumQn59PuXLl5A5DEEodkXAXBKHMCg0N5fjx40iShI6ODt7e3ujp6ZGRkcHKlStZvXo1e/fuLXJSuyCP+Ph4QkNDOXXqFJmZmdja2uLq6qpKRGVkZKCnpydzlGWb8iVHQkIC/v7+TJs2jdq1a5OVlcXWrVvx8fEhLy+PSpUq0bdvX5YvX46+vr5IYMgsOzubq1evsnnzZkJDQ5k6dSpr1qyhQoUKgDgcVQ7K5JLyt09LS8Pb25vly5fz4sUL5s+fT1RUFObm5jRs2JCHDx9y/fp19u7dS48ePcQLRxmdPXuWMWPGoK6ujo2NDVZWVvTu3RstLS3VNYmJieKckRIiNDSUoKAg5s2bV+SgQEDs7ilBgoKC6NSpk2qXz19Rts+NGzewtLQkJiaGRo0aFWOUZZfyt3///j3Dhw/n/v37VK1alYcPH9KvXz+2bdtGrVq1gKKJWzG+KNmUL/yfPHlCUFAQ06dPVy1QEwTh3yMS7oIglCnKwcO+fftYs2YNzZs3p0mTJixZsgQ/Pz9sbW1V16akpGBiYiJfsEKRwXhubi5Xrlzh5MmTXLhwAX19fZycnBg1apTMUQrf6ty5Mw0aNGDmzJm0adOmyHd37tzB2NiYKlWqoK6uLiZbJYRCoeDTp08EBwezbNkyMjIy8Pb25qeffpI7tDLt119/LVKe5MqVKwCkpqaqypMkJyfTtm1bbGxssLS0FKulZZaamoq7uzuNGjXixIkTAHTr1o3hw4fTtm1b/Pz8cHNz482bNzJHKojdPSWb8l528OBB3NzciIuLUx0G/V9p37493bp1Y8OGDcUQpQD/bKuBAweiqanJ4sWLqV69Ovfv32fevHk8ePCA4OBgunfvLsZ9pVCHDh3o3LkzGzdulDsUQSh1RMJdEIQyycjIiMWLF+Po6MjSpUsJDg4mKioKdXV1Dhw4QO/evTEyMpI7TOEfvh2gv337losXLxISEkJ8fDzGxsZs27YNMzMzmaMsu5Tts2vXLhYtWsTjx4/R0dEhNTUVNzc30tPTmTJliiq5Icjn276Uk5NDampqkVWDT58+Zfv27axZs4amTZty8+ZNsY1YBqI8Sen35MkTfv31Vy5dukTVqlVVtYsXLVrETz/9JFZMy0zs7im5vr2PLVu2DICFCxf+y/vbt4tpPD09iY+Pp0qVKsUac1mXkpKCubk5hw4domvXrqrPP3z4gI2NDY0bN2bz5s0yRij8Tyj7VFBQEM7Ozty8ebPI2WaCIPx7xChCEIQy5/r165iYmDBlyhRSU1PZsGEDixcvRktLi5cvXxIeHs6tW7fkDrNMUygUwNeE0tGjR1m2bBlLlizh7du3VK9eHRsbGxYtWsSUKVMoLCwssl1fKF7fHiIXFhbG5MmT0dHRISwsjGnTpvH777+TkZHBsGHDSEpKkjlaQdlWy5cvp1+/frRp04ZBgwZx7do1AOrWrcvSpUu5evUqXl5elCtXjsLCQjlDLpMGDhyIr68vcXFxVKpUiX379hEaGkpeXp7qGn19fRITE4skoESyvfgp+8fLly+5evUq27dvJyEhATMzM9atW8fPP/+MqakpOjo6ODo6qnaOiGS7vHR0dPjhhx/YtWsXO3bs4OTJk9SuXRtfX19AHBBYEgQEBJCYmEhKSgoKheIv72+SJKnKC3p6erJgwQKRbJdBpUqV0NfX59GjR6rPJEnCwMAAKysr4uLieP/+vYwRCv+ub/vUnDlzmDNnjki2C8J/SKxwFwShzFC+rX/+/DldunThzJkzrFy5koyMDIKDgwGIiYlh+PDhXLhwQdRYLQHGjBlDQkICNWvW5PXr17x69QpHR0eWLFmiuubZs2fUqVNHrEYrZpIkkZqaioGBAfD1Jcm8efMICAhg69atuLm50bdvX2bNmkXt2rUxNzdnyZIl9OrVS+bIyy7lilp/f38WLFiAo6MjnTt3xsLCAi0tLWxsbFiwYMGf7n1i1bQ8RHmSkk/ULi49xO6e0kWSJCZNmsS+ffuoXLkyBw8exNLS8k/PImW7uru7c/78eaKjo4scNC0UH3t7eyIjI1m/fj19+vRRvVT8+eef+e233/j9999ljlD4dyifa0uXLiUwMJAbN26gra0td1iCUCqJhLsgCH9rr1+/5vz589jZ2ak+y87OZurUqSQnJ3P79m0iIiJo0aIFX758YciQIRgaGrJ//34xIZaJcqAXEhLChAkTuHnzJqampjRp0gRDQ0MePnyIoaEhnp6eon67jHx8fIiMjGTSpEn06tULLS0tXrx4weTJk7l16xYDBw5k9erVVK5cmbt372Jubk5MTMyfDqYTil+9evVwc3PD0dGRdevW4efnx5w5c5g5cybfffcdY8aMUa1uF0oGUZ6kZBK1i0uf5cuXc+HCBRISEujYsSPz5s2jc+fOwNfxx82bN3nx4gXW1taizUqA6OhoXF1duXnzJtOnT2f27Nl/Wm37+fNnfvzxR7y9vbGwsJApUuHRo0fMnj2b7OxsmjVrRpcuXXj27BmrVq3i119/ZdSoUeJZVcIp73mfPn3CzMyMffv2MWjQILnDEoRSSyTcBUH4W9u0aRPLli2je/fuzJ49my5dugDw4MEDJk+eTGRkJC4uLhQUFJCUlERKSgrR0dHo6emJiZbMBgwYQOfOnfH09GTHjh2sWrWKiIgIjhw5gqurKwAnTpxg4MCBMkdaNm3cuJGDBw+iqalJ165dGTJkCJ06dSI/P5/s7Gz09PSArxMwJycnjIyM8PPzE/1KJsrfPSIigtWrV+Pv74+GhgZNmzbFx8eH4cOHM23aNEJDQ2nVqpVq149QvJTt9PLlS5KTk7l37x5du3alWbNmAFy5coX9+/eTl5eHiYkJXl5eMkcsiNrFJZ/Y3VM6/PH3zs7ORkdHB4Ddu3fj5eWFmpoaHh4eTJgwAV1dXdW1r1+/xtjYuNhjLquUz6qcnBw+fvyoegmSkpLCtm3biImJ4fbt2zRq1IghQ4Ywd+5cmSMu2/7de5myXYcOHUpubi6nTp0qhugE4e9LJNwFQfhbS0lJ4fz585w4cYLHjx/Tq1cv3N3dqVOnDvA1Ib9nzx6MjIzo1KkTw4YNo3nz5mIFhsw+fvzIsmXL6Ny5M8OHD6dNmzZMnjwZJycnkpOTmTt3Lg4ODuIQTpm9f/+etWvXEhERQYUKFbC2tqZfv36YmpoC8Pz5c9Wq3PPnz6OrqysS7sUsKSkJU1NT1W+enJzM2bNnsbOzIygoiK1bt3L8+HGqV6/OiRMniI+Px8PDg3Llyon7YDET5UlKp9TUVLp3786cOXOYOHEi8M/kxtq1azl27BgnTpygWrVqMkcqiN09pUNoaCjHjx9HkiR0dHTw9vZGT0+PjIwMVq5cyerVq9m7dy/jxo2TO9QySfnMefHiBbNnzyY8PBwjIyMmT57M6NGjMTQ05MOHD+jr6/P582cqV65c5O+E4hcUFESnTp2KlNH6I+UY5MaNG1haWhITE0OjRo2KMUpB+PsRdzxBEP7WTExMcHBwYMWKFYwbN47bt28zcOBA1qxZQ0FBAS4uLvz+++8cOnSIRYsW0bx5c0AcZia3KlWqsG7dOn744QfS09PR1dWlXr16wNdExuPHjzExMVH9WyheykNtq1WrRsuWLVEoFMTFxTF//nzc3d05cOAA6enp1KhRAxsbG3bv3o2uri4KhUJMtorRu3fvmDhxIosWLSI+Ph4AU1NT7O3t0dXVxdDQkHfv3gGQm5vLrl27ePLkiSrZJO6DxUvZNyZNmkSVKlUICwvj4sWLhIeH8+HDB77//nsiIiKAom0j+pS8qlatSqdOnVi9ejUnT54scrijpqYm2dnZItkuI+WhthERETRu3JhRo0aRkZGBj48PCxcuxM7OjlGjRpGfn8/t27dFsl0mBQUFAOzbtw93d3c+f/5MnTp1+PXXXwkJCQFAT0+PlStX8uTJE5Fsl0FWVhbp6emqZ87EiRN5+/YtGzduxMLCAi8vL4YNG8ahQ4dQKBSUK1dOlWwH8awqbsr50cGDB5k5cyZaWlr/5fXKccW0adOwt7cXyXZB+F8gVrgLgvC39u1qitzcXK5cucLJkye5cOEC+vr6ODk5iTrgJYSyraKjo6lcubKq1nd2djaWlpZkZ2czZcoUgoKCAAgLCxPbvWWiXAWzatUq/P39WbVqFZaWlhw+fJidO3fy5MkThgwZwpAhQ+jVq5doI5m8fv0ad3d37t+/j7GxMT/++CNDhw5VrZJOSUnBwsKCjIwMzMzMePr0KQ8ePKBKlSpiJZpMRHmS0knULi55xO6e0snIyIjFixfj6OjI0qVLCQ4OJioqCnV1dQ4cOEDv3r0xMjKSO8wyacCAAWhra7Nw4UIqVqyIvb09/v7+1K5dG4DExERmzZpFZGQkFhYWuLi40KNHD5mjLpu+nR8tW7YMgIULF/7LeVNBQQGamprs27cPT09P4uPjqVKlSrHGLAh/R2ImJQjC35pyolVYWIi2tjaWlpZ4eHgwb948ateuzfLly+nduzdPnjyROdKyTbnyOT4+HicnJ86ePUtWVhYAOjo6bNiwATMzM5YtW0aFChXw8/MD/rlyTSheGhoaKBQK9u/fz08//cSAAQMoX74848aN49y5c3Tp0gV/f3/mz59PcnKy3OGWWcbGxvz222+sX78eLS0tdu7ciZubGydOnODz58+YmJhw7do1XF1dGTJkCKdOnaJKlSpiJ4KMKlWqhL6+Po8ePVJ9JkkSBgYGWFlZERcXx/v372WMUFA+d3Jycnj16hUADRs25JdffqFdu3bcuXOHadOmcezYMdzd3VUv9UUCt/iI3T2l0/Xr1zExMWHKlCmkpqayYcMGFi9ejJaWFi9fviQ8PJxbt27JHWaZNWHCBGJjY+nVq5fqZdXLly+Br8+p+vXrExISwsGDB4mKiuLZs2cyRywEBASQmJhISkpKkd1X35IkCU1NTQA8PT1ZsGCBSLYLwv8STbkDEARB+L+gXJ2Unp5OeHg4d+7cAcDR0ZHq1atjY2NDmzZtOHv2LCdOnPhvt9kJ/7eUk1sHBwc6duyIg4MDOjo6qpUYHTt2ZM+ePap/V6pUicLCQjEploky4WRmZkZUVBTTp09HkiQKCwspX748dnZ2fPjwgeHDh2NmZiZztGVXTk4O5cuXR19fH2NjY6KjowkODubOnTtcunSJ4cOH07VrVzw9PYv8nehX8vm2PImhoSF9+vRRtYcoTyK//6528apVq0Tt4hJAoVBgYmLCmTNnuH379p929zRu3JjCwkKaNm1aZHcPiLaSg3J1bc2aNXn58iX3799n5cqVdO/enQEDBgDw9u1bLl68yMKFC2WOtuwaPnw4w4cPZ8WKFSxcuJDc3Fxq166NiYlJkV0HvXv35vXr1zJGKqipqSFJEufOncPPz4/KlStjY2ODpaXln5LuyrmVu7s71atXx8HBQaaoBeHvR5SUEQThb23MmDEkJCRQs2ZNXr9+zatXr3B0dGTJkiWqa549e0adOnXEJEtm0dHR2NjYcPnyZdWhtspB4LNnz0hLS6NFixYyR1m2KV9kffz4EXV1dfbs2cO6devYvXt3kQNso6OjWbRoEYGBgVSsWFH0LRko+45CoeC7775j1apV9OvXj8qVK7N27Vr2799PpUqVsLW1xcrKitatW8sdsvAPojxJyZOVlUV+fj76+voA/Pjjj+Tn5zN58mSuX7/Ob7/9RvPmzXF2dqZnz55Ur15d5ogF+Fq3fdOmTTx+/JimTZtiY2NDr169qFixIm/evGHXrl0AWFlZ0a5dO9GvitHr1685f/48dnZ2qs+ys7OZOnUqycnJ3L59m4iICFq0aMGXL18YMmQIhoaG7N+/X4wpZKJ8MQLw+fNnZs6cqRr/ubm50aFDBypWrKi6XpR9LBmio6NxdXXl5s2bTJ8+ndmzZ1OjRo0i13z+/Jkff/wRb29vLCwsZIpUEP6GJEEQhL+ZgoICSZIkKTg4WKpSpYr05MkTSZIkqXHjxlL37t2l6tWrS82bN5cOHjwoZ5jCH1y/fl2qW7eudOXKFUmSJKmwsFD13bVr16QxY8ZIT58+lSs84RudO3eWfH19pfz8fGny5MmShoaGZGNjI50+fVpavXq11KJFC2nixImSJBVtR6H47dmzR6pfv77qvqgUExMjVatWTTIyMpK8vb1lik5QKBSSJElSdna29PLlS9XnycnJkru7u2RhYSFVqVJF6ty5s7R69Wq5wizz+vfvLw0bNkz6/fffpcePH0vdu3eXnj17pvr+8ePH0oABA6TKlStLw4YNky5duiRjtEJ2drYkSZJ069Ytafr06VKtWrWkChUqSM2aNZNmzpwpXb16VeYIhY0bN0oGBgbSsGHDpMjISNXn9+/fl8zNzSU1NTVpxowZkpOTk9SnTx+pcePGUnp6uiRJ/7xvCvLIyclR/ffNmzel9u3bS1paWpKzs7MUGxv7p/GGUHz+OObOyspS/feuXbukmjVrSrVq1ZI2b94sff78uci1r169KpYYBaEsEa+GBUH421GuTvL19cXV1RVTU1N27NhBXl4eBw4cwMPDg7t37zJ69GhCQkJkjlZQMjU1RVdXl8uXLwMUWRUTFBSk2okgyEP6x4a4pKQkUlNTsbKyQlNTk+3btxMUFERycjLjx49n//79tGnThh07dhT5O0EeJiYmZGRkEBkZCXytVyxJEu3bt6dXr16MHj2aiRMnAqKtitu35UnGjx9PixYt+P7779m4cSMVKlRg1apVHD58mDdv3nDy5Enmzp2r+juheInaxaWHJEmUL18ehUKBubk5jRs3JiIigpcvXzJ48GCOHTuGo6Mjq1at4vfff5c73DJr0KBBrFy5kry8PCZNmoSTkxPPnj2jcePGXLlyBR8fHy5fvkxSUhKdOnXi8OHD6OnpiTNGZKBQKAD4+PEjhw4dYt68eTg4OHD79m3atm1LTEwM+/btw9/fnz59+qjOYBKKn3LuFBoaioODA9OnT2f69OlkZGQwadIk7t27x9ixY3F2diYoKKjI3xobG8sRsiD8rYmSMoIg/C19/PiRZcuW0blzZ4YPH06bNm2YPHkyTk5OJCcnM3fuXBwcHIqUwRCKl/QXW03Xr1+Pm5sbtra2zJ07l+zsbG7dusWcOXM4ffo0Xbt2FVu+ZRYaGkpQUBDz5s2jYcOGRb5LSUmhcuXKVKxYUXWwqmgreb1584aBAwdibGzM7t27MTAwUH3Xr18/bG1tsbW1FVu/i5EoT1J6rVixguXLl5Obm8usWbNwc3MrUrtYKDn27t3LihUrePDgQZHn0I0bN+jfvz8aGhq4uLgwb948GaMU4uPjCQ0N5dSpU2RmZmJra4urq6uqdElGRgZ6enoyRykADBs2jFevXtG8eXMiIiJIS0vjzp07GBoaqq45d+4cvXv3FmV/ZKAs+bNv3z7WrFlD8+bNadKkCUuWLMHPzw9bW1vVtSkpKZiYmMgXrCCUESLhLgjC35ZCoeDTp09oaGgwYMAAFi5cSJ8+fXjy5AnDhg3j0KFDNGrUSCSaZKL83Xfv3k39+vXp3r07ACdOnGDJkiXcunWLmjVrYmBggI2NDe7u7mIAL7OwsDDVS6o9e/Ywfvx44OsgX0NDQ/SjEur27duMGDGCT58+4eTkRJUqVYiNjSUoKIh3795RoUIFuUMsUwYMGIC2tjYLFy6kYsWK2Nvb4+/vT+3atQFITExk1qxZREZGYmFhgYuLCz169JA56rJN1C4ufS5dusSoUaM4cuQI3bt3Jzc3Fy0tLdTU1Bg9ejTGxsbMnTsXIyMj0V4y+HY8l5uby5UrVzh58iQXLlxAX18fJycnRo0aJXOUgrKdjh49yuTJk3nw4AHVqlWjWbNmjBkzBk9PT27fvs3nz5/p2rWr6u9En5KPkZERixcvxtHRkaVLlxIcHExUVBTq6uocOHCA3r17ixfFglBMRNZCEIS/DeU2++joaB49eoSGhgYGBgaUL18egPnz57Nt2zZ++uknqlWrJpLtMlIoFKipqXHt2jUWLVpEXFwcmZmZAAwePJi4uDhu376Nv78/4eHhuLm5yRyxAGBubs7Zs2cZOHAgkyZNYvr06WRlZaGpqYmampoodVGCfPz4kVu3bvH8+XNatmzJ77//zsyZM9mzZw87d+4kMzOTAwcOUKFCBQoKCuQOt0wR5UlKH2WyPTc3l4oVK7Jz505u3LhBamoqffv2Zf78+cTFxalKL4hxhfwaN25MnTp1WLduHR8+fEBbW1vVLunp6bRt21Yk22WkTLYXFhaira2NpaUlHh4ezJs3j9q1a7N8+XJ69+7NkydPZI60bFO2U3h4OA4ODlSrVo3169eTn5/PjBkzAEhISMDf35/379+r/k70KXlcv34dExMTpkyZQmpqKhs2bGDx4sVoaWnx8uVLwsPDuXXrltxhCkKZIVa4C4Lwt6AsXREfH8+kSZMYP3489vb2qpWb0dHRrF27lmvXrtGuXTt8fX0xMjISJS9k1qJFCwYMGIC3tzfwtZ127dpFy5YtcXJyUl0nJsQlh3LnSHBwMMuWLSMjIwNvb29++uknuUMr85SrcP38/FizZg1fvnyhcuXKWFlZsXDhQnR0dAB4+vQpdevWlTlaQZQnKfmUY4SPHz8SFhZGdHQ0GRkZODs707JlSwAOHjzItGnT0NTUJCkpiUqVKskctaAkdveUPMo+lZ6eTnh4OHfu3AHA0dFRVT7r0aNHnD17lhMnTrB3715q1aolZ8gCsHTpUqKiojh06BD16tXj119/ZeTIkQDMnj2b5ORkjh49KnOUZZdy/Pf8+XO6dOnCmTNnWLlyJRkZGQQHBwMQExPD8OHDuXDhAvXr15c5YkEoG0TCXRCEv5UOHTrQvn171q1bh46OTpFEbWZmpurflSpVEuVJZKL83a9fv864ceO4efMmenp6bN++nZ9//hl9fX1iYmJYtmwZnp6ecodbpn3bR3JyckhNTaVmzZqq758+fcr27dtZs2YNTZs25ebNm5QrV06ucAW+tlPVqlVZvHgxBgYGPH/+nMDAQPLz85k5c6Z4MVICiPIkpY+oXVx6fPz4kWfPnlG1alVq167Nly9f8PHxYfv27ejr61O/fn0mTJjAoEGDivRFoXiNGTOGhIQEatasyevXr3n16hWOjo4sWbJEdc2zZ8+oU6eO6FMlwM2bN1mwYAF5eXkAXLhwAYA7d+7QpUsXTp06Rbdu3cRCpmL0+vVrzp8/j52dneqz7Oxspk6dSnJyMrdv3yYiIoIWLVrw5csXhgwZgqGhIfv37xd9ShCKiUi4C4LwtxEdHY2NjQ2XL1+mTp06wD8TFc+ePSMtLY0WLVrIHGXZlZ6erjokEL4elNWvXz+WLVtGZmYmR44cYcCAAbi7u+Pt7c2jR4/w9fVFW1tbxqgFgOXLl3PhwgUSEhLo2LEj8+bNo3PnzsDX1Wo3b97kxYsXWFtbi0G8TJT3ulu3buHj48PevXsB+PLlCzExMQQGBhIWFoaWlhaHDh2iWbNm8gYskJubq7q/xcbGMnXqVG7fvs1PP/3EhAkTaNmypUhcyEjULi49xO6e0kGZjA0JCWHChAncvHkTU1NTmjRpgqGhIQ8fPsTQ0BBPT09Rv72EycrKwt3dnS1bttCtWzdsbGy4f/8+t2/fpkaNGgQEBIh7XzHbtGkTy5Yto3v37syePZsuXboA8ODBAyZPnkxkZCQuLi4UFBSQlJRESkoK0dHR6OnpibG6IBQT0csEQfhbkSRJVe/224Hfq1evWL16taiFK5P8/HycnZ3x9fXlw4cPwNdyMnZ2dsybNw8vLy+cnZ2ZNm0aAMnJyaSnp4tku4yUtYj9/f3ZuXMnvXv35vDhw4SGhmJhYcGECRNITExEQ0ODjh07Ym1tDYi6nXJRvlhcvHgxiYmJ5OTkAKCrq4uFhQWLFi1i+fLlNGzYkMqVK8scbdmk7FMfP37k0KFDzJs3DwcHB27fvk3btm2JiYlh3759+Pv706dPH7KysmSOuGwTtYtLD01NTXJycnB0dGTcuHEsXLiQwYMHExoaSps2bfD19QUQyXaZKV8g+vr64urqiqmpKTt27CAvL48DBw7g4eHB3bt3GT16NCEhITJHW3Ypn1X5+fm8e/eOpKQkKlSowC+//MLFixfJy8vD39+fS5cuMXbsWLZv3w58nXcJxWfQoEGsXLmSvLw8Jk2ahJOTE8+ePaNx48ZcuXIFHx8fLl++TFJSEp06deLw4cPo6emhUChEsl0QiolY4S4Iwt/Gu3fvsLCwUK08+5abmxvXr1/nypUrMkVXtt27dw97e3sKCwtp2bIl/fr1o3///mhqavLq1SsyMjJo0qQJOTk5REZGMnToUCIiImjVqpXYniqzevXq4ebmhqOjI+vWrcPPz485c+Ywc+ZMvvvuO8aMGYOXl5coJVMCHDp0iBUrVvDkyRPmzJnDggUL/lQu4ePHj1SpUkWsbpKRKE9SuojaxSWb2N1Tunz8+JFly5bRuXNnhg8fTps2bZg8eTJOTk4kJyczd+5cHBwcsLKykjvUMs/BwYG4uDgePXpEly5d8Pb2pl27dsDXOZe+vr5qYYxY3S6f+Ph4QkNDOXXqFJmZmdja2uLq6qoa/2VkZKCnpydzlIJQNomidYIglFp/HNwZGhoyadIk3NzcePDgAXPnziU7O5tbt27h6+vL6dOnAUQCVwZNmzbl0qVL7N69m8DAQDZs2MCNGzewsbGhefPmqrrgYWFh+Pj4MGnSJFq1akVhYaFoKxkok3wRERE0btyYUaNGkZGRgY+PDz4+PgwfPpxr164RGhrK7du3RbK9hBgyZAjVqlXj2LFjHDhwgIsXLzJjxgyGDRumuqZKlSoAIolbzL4tT3L58uUi5UlcXFwwNDQsUp6kd+/egFgtXRL069ePqKgohg4dSosWLVTJ9jt37rB9+3ZOnToFiLGFXL7d3fPhwwdycnIoX768andPs2bN6NGjBwcPHhS7e0qAKlWqsG7dOj59+kR6ejq6urrUq1cP+Dquf/z4MSYmJqp/i3tg8VL+5nv37uXkyZMsXLgQAwMDfH196dSpE+PHj2flypVFXhCDeFbJQTmuaNGiBY0aNaJDhw6cPHmS/fv3ExoaipOTE6NGjRLJdkGQkVjhLghCqaUcFO7evZv69evTvXt3AE6cOMGSJUu4desWNWvWxMDAABsbG9zd3cVqQZkoJ8AZGRl4e3uzbds2MjIyMDc3Z8iQIQwbNgwTExPu37/P48eP6dOnD1paWqK9illSUhKmpqaq3zw5OZmzZ89iZ2dHUFAQW7du5fjx41SvXp0TJ04QHx+Ph4cH5cqVE8mmEuT9+/eEh4cTEhJCXFwcrVq1YtasWXTo0EHu0Mo8JycnKlasyOrVq1m/fj2+vr7ExcVRsWJFDhw4wJUrV1i6dCnVqlWTO1ThH0Tt4pJP7O4p2ZS/eXR0NJUrV6Zhw4bA1wMeLS0tyc7OZsqUKQQFBQFfF1+IPlX8vv3NlyxZgqGhIVOnTgW+njly7NgxvLy8SE1NZdasWXh6eoo2KgG+vae9ffuWixcvEhISQnx8PMbGxmzbtg0zMzOZoxSEskkk3AVBKJW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8efMc56mX9SVu8KPf89cZH3/8sU6ePCl3d3elT59e9erVU9u2bR3H3rp1SylTppSLiwsPf4BXYO8ngYGBKl++vC5duqQ0adLIxcVF7dq1U6dOnSRJT58+VUBAgJImTSpnZ2f6VyxEAQ2RunTpktq1a6eePXuqfPnyGjJkiC5duuSYZ//o0SN9++23mj9/vkqVKqWmTZuqZs2aSpo0qcXJ46bRo0ercOHCKlu2rI4fP66PPvpI+/fvV8KECbV161Z5eXnpxIkTypkzp1q3bq0qVapYHRn670nX19dXnTt31u7du1W4cGFt3LhRS5YsUf369SU9e8L4/fff64svvlDevHktTh33BAYGOp4iSlLGjBlVpEgRPX78WIGBgUqYMKFKly6thg0bqkCBAhE+SzHGGr6+vipWrJh8fX1Vvnx5tW3bVkuWLFFAQID8/f21b98+SdLFixdZ6ycGeb6/hIeH68yZM1q3bp2mTJmiwMBA9e/fP8KDIFjv4sWLypIli2OE2eLFi1WnTh2NGTNGmTNntjpenObv76/jx4+rZMmSkqTGjRvL399f/fr10+nTp7VlyxadOHFC2bJlU4cOHVS5cmWLEyOqjDEKDw+Xs7OzLl26pOTJkyt58uRWx4oTAgICdOTIEUf/atmypa5du6bBgwfr1q1b2rRpk9auXauMGTOqX79+vBzsLUABDf/IGCMfHx999NFHun37ttq3by8PDw/duXNH/fr1i3Ds8ePH1aFDB/n4+OjixYtydXW1KHXcde7cOTVu3FiJEiVShQoVlCNHDnl7e2vkyJGOp7xXr17V6tWrtWnTJh06dEhLlizRhx9+aHFy2G8Wq1WrpiRJkmjixInasGGDOnfurD///FP58+fXmTNnlDt37hc+g+jTsWNHffjhh2ratKnWr1+vESNGaNu2bbLZbPL29taMGTN08OBBpUuXTqVKlVK7du14mGCxx48fa/v27fr999+1ePFilS9fXpMnT5abm5skaefOnXJ2dlbJkiXpUzFcUFCQjh07poULF2ratGl677339Mcff/Dk3gL2vnLu3DnlzJlT06dPV//+/R0vEwgMDNTWrVs1cOBAnThxQu3bt9fw4cMZLWiRoUOHau7cuWrUqJHq16+v6dOnq27duipbtqykZ9eGa9as0caNG3XhwgVVqlRJo0aNYoRgLGIfXRgUFKSSJUtq1qxZeu+996yOFSeMHz9effv2VdOmTdW1a1ctWbJEhQsXdozAvX37tnbu3KnFixdr+/bt+uijjzRv3jz6VyxGAQ2vZOHChfryyy917949ZcuWTatXr1a+fPnk7Owc4abjwoUL8vDwYDiqRXbu3Ol4O2pISIju3bunvXv3vnATf+DAAXl7e6t9+/YWJcXfHT9+XNWrV9fu3buVJUsWFStWTOXLl9fw4cMdb7Zt0KCBihUrZnXUOOnYsWNq3ry5XF1dVaxYMaVLl063bt3S6NGjIxzn5eWlOXPm6MyZM/rtt9+UJUsWawIjguvXr2vHjh2aMGGCjh07pnbt2mnIkCGOqYCcs2IP1vqJGU6cOKEvvvhCdevW1aBBgzR8+HC1bNkyQnvcuXNHixcvVrdu3bRy5UrVrFnT4tRx0+7du7VkyRJ5e3srVapUunXrllq2bKl27dpFOO7o0aOaPXu2ChcuLE9PTx4qWMh+TgoICFBwcLBjjbr/dXz79u119OhR/fnnn7RdNDl8+LDWrVun9evXKzAwUMYYNWzYUL169Ypw3MWLF7VixQolT55cbdq0oX/FYhTQECn7iwPsfvzxR/Xp00f58uVTnz59VLFixf/5pY7oFRYWpqVLl2rNmjXatGmT3n33XfXo0UOffPLJS4/n5iNmOH78uJo0aaIdO3ZozZo16tOnjw4dOqSUKVPqwoUL8vT01KBBg5haYSF/f39NnjxZK1eulJ+fn/z8/LR8+XIVL148wnGPHj2St7e3ypcvzwWSxf4+FfD8+fPy8vLS5MmTFRgYqAEDBjAVEPgXDh8+rKFDh2rHjh0KCgrSlClTVKVKFce0Mfu1hTFGt27dUrp06awNHMeFh4dr5cqVWrVqlbZv3y5XV1eNGDFCderUiXAc60DGLHXq1FGePHn0448//uODHvv206dPq2jRotqyZQsPW6NZSEiIdu7cqbVr12r9+vW6e/eufv31V8cSLHbPtyH9K/aigIZ/ZO/Y4eHhunLlimN9mNu3b6tdu3Zas2aN6tatq+7du6tQoUJKlCiRtYHjuL8Xwu7cuaPly5fLy8tL9+/f1wcffKA2bdqoYMGC1oVEBM+fPAMCAlSrVi01a9ZMffv21Q8//OB4OvzTTz9pzpw5OnHihJVx47TnL3pOnjyp6dOna9WqVcqSJYsaNmyoWrVqKWPGjC98jgukmIepgLEba/3EHAEBAXr33XeVKlUqOTs7q3DhwqpTp44qVaokJycnXbt2TUOHDtWoUaNY2sMiL7s2XL16tZYtWyZ/f38VLFhQrVu31gcffGBhSvyTUaNGadWqVfrjjz8c255v0+evMezLt0ydOtWSrHHR3/vXrVu3tHPnTi1ZskRHjx7Vu+++q169etG/3jIU0PA//fzzz9q/f79atGihDz/80DHibMeOHfryyy919OhRTZ8+XS1btrQ4adxlP4H6+Piob9++Gjx4sLJmzSrp2dSzJUuW6M8//9STJ09Uo0YNfffdd9zUW8x+0g0JCdH27dtVuXJljRgxQt9++61cXV21bds2JUiQQMePH1e3bt00ffp01a5dm6lmFvPz81OyZMkkSVu2bNGvv/4qHx8fFShQQHXq1FGVKlWUOHFii1PiVTAVMHZirR/rPX/Tfv78eWXOnFkjRoyQl5eXEiRIoEqVKqlMmTIaPXq07t27p507d1qcOO6y95eVK1cqb968jjd7nzhxQosXL9bOnTvl5OSkokWL6ptvvlHKlCktToznnT9/XqVLl9awYcNeuM96/i3Eq1evVtu2bXXo0KGXPszDm2HvX6tXr1ahQoUcy3acPXtWmzZtkpeXl65fv65SpUrpxx9/5GHPW4ICGl7KfpMeHBys/v37a8SIEUqbNq2aNm2q2rVrq1ChQkqQIIEk6ddff1X16tXl7u5ucWosW7ZMLVu2VLZs2dSsWTN9+eWXjnbasmWLpk2bpo8++ogpSzHAw4cPlTx5crVs2VKPHz/W0qVLJT1rpy+//FLXrl1TkiRJlDZtWn366af67rvvLE4cd9lvFv/44w/1799fXbt2jTDtZerUqVqyZIl8fX3l6elJWwFRwFo/sYv9+/DMmTOaMmWKOnTooJw5c0qSTp06pfHjx2vfvn16/PixnJyctHPnTqVIkYKHPxawt9WTJ09Ur149nT17Vp07d1bHjh2VMGFCSdLWrVu1ZMkS7du3TytXrnQ8fIU17P3kyZMnjllA7dq1U1hYmLp3765Dhw7J2dlZu3fvVvHixdWhQwdJUtasWdW5c2d9++23Fv9fEHfY+1doaKiqV6+uXbt2qV+/fhHWPtu7d682bNigZcuWadGiRcqfP7+FifG6UEBDpBo1aqRUqVI5nnDMnz9fmTNnVqtWrVSxYsUIXwRMVbKG/QnU1q1bNWPGDF24cEGPHj3S06dPlTp1anXt2lUNGzaU9GzqUvz48R3rktBe1pg4caIGDx6s7777Tl26dNHx48eVL1++CCNgtmzZonjx4snDw0Pp06eXk5MTI2Qs8Hw/+fHHH/XLL78oYcKEql69upo2baoyZcpIejYt5qefflLdunVVqlQp+lcswFTAmIW1fmKXcePGafDgwcqTJ4/q168vT09PR/Fz7969MsYoffr0ypo1K8Uzi9jPQ7Nnz3aMNkuYMKFy5MihTp06qWnTppKeXRsePXpUxYoV49wVAwQFBalOnTo6cuSI0qZNq+vXr+vevXtyd3dXypQpFR4eLjc3N33//fcqX768nj59qunTp6tVq1ZMlY5G9mvymTNnatOmTVqzZo0kKU2aNBo4cKCjfz1+/FgXLlzQ+++/T/96S1BAwwvsnXvz5s2qV6+efHx8HEO6r1+/rk6dOmn9+vX69NNP1aRJE3388cdydnbmC8FiqVOn1vfff69WrVrJZrPpt99+07x587Rnzx5Vr15dX3zxhT788EOrY0LPXhnfr18/zZs3T1myZNGGDRuUI0eOfzyeE6517Dd+w4YN09GjR+Xq6ip/f39dv35dzs7Oqlq1qlq0aMFT+1iIqYAxC2v9xHz2B3YbN27UwoULdfToUd29e1dJkiRRrly55OnpqU8//dRxPOcu69jPXZs3b1br1q31008/qUCBAtq7d6+2bNmiXbt2qVSpUurevTtF6Bjm9OnTGjdunKpUqSJJypUrl7p16yYXFxetXr1a4eHhjjdIwxr2/vXXX3+pXr16Gj9+vHLmzKlLly5p9erVWrRokcqUKaOffvpJ77//vtVx8boZ4Dnh4eGOv48aNcrkz5/f3Llzx4SFhZmgoCBjjDFPnz41OXPmNDlz5jRp0qQxixcvtiou/uP333837u7u5s6dOxG2nz592uTNm9dky5bNFCtWzMyZM8eihPg7b29vY7PZTOHChY2rq6vp3r27uXr1qmP/hg0bjKenp4UJYf8+vHLlinFycjK7du1y7Pvjjz9MvXr1TIIECUylSpXMxIkTHd+RsEZoaKgxxpjHjx+be/fuvfLx7dq1Mx9++GGE8x+i37lz50zatGnNjBkzXtgXEhLi+PuqVatM6tSpzbVr16IzHv4jPDzcJE6c2EyePNn4+fmZ4OBg8+uvv5oSJUqYTJkymY4dO5q//vrL6pj4j2rVqpnOnTtH2Hb+/HnTrFkzY7PZTP78+U3fvn1NYGCgRQnxKlasWGGyZctmLl68+MI+zl3WadCggfnss88ibLt+/brp2bOnsdlsJlmyZKZr1670r7cMc4Hg8PTp0whPCkuWLKk7d+7o6NGjcnJykqurq0JDQxUvXjx98sknmjdvnj777DN17NhR9+7dszA5MmTIoPv372v58uURtufOnVufffaZ6tatq7x58+qXX36Rr6+vRSnxvMKFC+vu3bvy9vbW2LFjtWjRIhUvXlwTJkzQjh071LJlS8dbewwDhaNdeHi44/tw165d8vDwiDBKsGzZslq+fLkKFSqk4OBgLViwQAsWLLAqLiTHFDFPT08NHz5c0rOnxC/z/FTABQsWaMyYMYyUiWb2tnny5ImCgoKUIUMGffTRR9q4caP27dunyZMna9q0aWrZsqWmTZvm+NyXX36pr7/+moWyLbJ06VLHmrhJkyZV/Pjx1bFjR82ePVvx4sXTrl27NHDgQG3YsMHqqHGW/ZohJCREKVOm1IULFyJcR3h4eOibb75R5cqVVbNmTa1cuVIbN260Ki6e4+/vr5UrV8rb21v79+93bK9bt66yZMmi2bNnv/AZzl3WCAsLU7p06XT58uUI2zNkyKA2bdqoVq1a+uGHH7R161bNmjXLmpB4IyigQZI0YcIEjR8/PsK2okWLqkqVKqpSpYqGDRsm6dnJ+OjRo5o4caLCw8PVvHlzubm56dq1a1bEjpOuXbvm+CK2XxDlypVLn332mRYvXqzff/9dDx8+dBx/6NAhZc6cWUOGDJGvr69OnjxpQWpIz4oykhQYGKhLly7p7Nmzevjwodq1a6czZ86oZcuW6tq1q1q3bq1ixYqpe/fukrg4ssLza80VL15cQUFBWr16taSIBc2KFSuqRo0aypcvn7p06aILFy5Ee1ZEVK5cOf3111+S/ltUs/c96Vn72bd37NhRjRs3ZgqTBZydnRUUFKS6devK3d1dJUuW1O+//67FixerSZMmmj59uiZNmqRr16453hz49OlT9erVS127drU4fdzl4eGhhw8fau3atZL+27dy5szpeBNxaGioevbsycPVaHbo0CFJ/71miBcvnj799FNdunRJCxcu1IMHDxzHJkmSRE+ePFGrVq2UOnVqTZo0KcL3JKJPaGioJGnjxo2qWbOmvvjiC9WvX1/t2rXT/v37HdccNWrUiPAwAdHr2LFjEf7t7OysevXqydfXVz///HOEe+HEiRPL19dXFStWVJ48ebRw4UKFhIREd2S8KRaOfkMMER4ebnr27Gn27dtnjDFm8+bN5uTJk479Q4cONWnSpDHJkyc3RYoUMRkzZjQNGzY0xhizZ88ekyFDhghTz/BmjR492rRu3doY82yqkt2JEydM6dKlTapUqUzbtm1Nly5dTJMmTUzChAnN9evXTVhYmMmXL59ZuHChVdHjtLCwMGOMMY8ePTJt2rQx6dOnNxUrVjTJkiUz69atcxz35MkTs2/fPuPn52eM+e80M0SPtWvXvjDdxd5madOmNZMnTzZ37941gYGB5v79+8bDw8OsWLHCPHr0yOTOndvs3LnTouSwYypg7HHq1CnTqVMns2rVKrNq1Spz8uRJU6VKFfPxxx+bkJAQExwcbHVE/I2/v7+pVauWKVeunDlw4ECEc1TVqlXNggULzKVLl0yaNGmMt7e3hUnjlqCgIJMuXboXzkG3b982jRo1MqlSpTJdu3Y1K1asMIsXLza1atUyxYoVM8YY8+uvv5pKlSqZR48eWREd/5ElSxbzww8/GGOM6d27t8mTJ48JCAgwxhhz8+ZNc/v2bXPw4EFjDNeG0S0sLMy4ubmZ9evXR9j++PFj07VrV5MvXz7Tvn17M3XqVLNo0SJTp04dU7BgQWOMMYsWLTKlSpUyd+/etSI63gAKaIjg2rVrJm/evKZ+/fpmzpw55uHDh8YYYy5dumQmTZpkevXqZbZs2WKePHli/Pz8TIkSJUybNm0sTh233Lt3zzx9+tQYY0ylSpVMzZo1zc2bNx37p0yZYipWrGiqVKlimjRpYjZv3myMMWbGjBkmTZo0lmTGfwtojRs3NpUrVzbe3t5m7ty5Jl68eGbPnj3GGEMhOgYYN26cY13HgwcPRli34ptvvjEuLi7m/fffNxUrVjS5c+c2hQoVMsY8++5MkyaN+eOPPyzJHZfZbyQCAwPNkydPTEBAgPH09DSNGzc2e/fuNZMmTTJTp041LVq0MBMnTnR8LkuWLOann36yKjb+AWv9xHwXLlwwRYsWNQkTJjQdOnQwvXr1Mp9++qlJkiSJefDggXnw4IFxd3c3Xl5eVkeNMx4/fuy4lvDx8TENGjQw169fd+yfOnWqyZ07t3nvvfdMihQpTI0aNYyPj48xxpjKlSubli1bWhEb/7FgwQJToEABY8yzInXatGkdD7x3795tevXqFaE9Eb3CwsLMqVOnjDHG+Pr6mvr165srV6449k+fPt2ULVvWFClSxLzzzjumXr165vTp08YYY2rWrOkYeIK3AwU0OG7s7WbPnm1q1KhhChcubDp06GA2b978wpOOO3fumPHjx5tSpUrxhNgiAQEBZty4caZo0aImRYoUpk+fPo594eHhjhFMxhgzc+ZMkzFjxpeOyED0OX/+vEmdOrU5duyYMebZRau9AH3r1i3zww8/mEOHDlmYEPbvupCQEFO6dGlTsGBBM3fuXMf+a9eumR49ephvv/3WTJkyxfj6+hpjjGnbtq0pWbKkJZnxbORm1apVTbp06cz7779vUqVKZWw2m8mePbspWrSo+eCDD8xHH31ktm3bZowxjoXPefGDdfz8/MyKFSvMgQMHHCPg7cqWLWv69etnTTC81LVr18zGjRvNuXPnHNtmzpxpChcubMqXL288PT0d/Wvs2LEma9as1gSF2bJli8mePbtJkyaN+e677yLs279/v7l27ZoJCgoyfn5+pk+fPiZNmjSOB+aIXvaHAuvXrzelS5c2xhjTpk0bU7FiRccxO3fuNO+++665cOGCJRkRkbe3t8mSJYtJlCiR6dmzp2N7YGCgOX/+vLlx44Z5+vSp8fPzM8OGDTOpU6c2N27csDAxXjcKaHipx48fm1GjRpkPP/zQlC5d2vTr18/xZMuYZ0W3CxcumMuXL1uYEsHBwebs2bOmX79+JnPmzMbDw8MsWLDgheMuXbpE8SwGOHz4sPnggw9MQECA8fLyMqlTp3aMOrt48aKpUKFChOmciF7Pj2558uSJmTdvnmnZsqXJnTu3qVWrVoSpMfZRoMHBwWbq1KnGw8PDnDhxItoz4xmmAsYO9im0v//+uylbtqzJkCGDyZYtmylYsKDZt2+fow/+9NNPJmPGjFZGhflve02bNs14eHiYzJkzm9SpU5tmzZpFmO53+/Ztx9/nz59v0qVLZ5YuXRrtefHM06dPzYkTJ8zAgQNN5syZTfbs2SM8CLK7e/eu+f77783s2bMtSInnHT582GTIkMH88ssvJmnSpObMmTOOffXr1zf169c3xjAKNyYIDw83d+7cMSNHjjSpU6c26dOnf2kfun//vunbty/3X28hCmhx3PNfxBcvXjQTJkwwixcvdszTvnDhgunataspUqSIKVGihDl79qxVUWEijha8evWq8fPzczw13L17t2nTpo1JnTq1ef/993lSFQM9ePDAFC9e3KxevdrkyZPHDBs2zLFvypQpJnv27Bamw8vcuHHDTJ8+3VSrVs3kzp3bdO7cOcJU29DQUHP37l1z5MgRC1PiZZgKGHOx1k/s8eTJE5MoUSIzfvx4s2HDBjNz5kyTO3dukyBBAjNkyJAIxwYFBZljx469tFgDa+zbt8+0bdvWuLm5mfLly0d4GI7oZ7+OP3funDl+/Lh58uSJY9/PP/9sUqZMabJly2ZOnTplTp48aQYOHGjc3Nwc0zf/PmsI1rCPXvfx8TFdunQx8ePHN6VKlTJ//vmnxckQHSigxXH2C9OxY8ea9957z5QsWdIkTJjQFChQwLE2gjHGbNiwIcIUQUQ/e1vdvHnTfPnllyZ58uSmTJkypmLFio6Feu/evWtWrFhhypUr55h7j5ghLCzMhISEmO+++87YbDaTKFEic+zYMXPt2jWzadMmkz59ejN16lRjDDeMVvPz8zPLly83+/fvd/SjI0eOmMGDB5syZcqYdOnSmV27dlmcEnZMBYxdWOsndrDfqF++fNm0a9fOsT00NNScP3/e9O/f36RJk8YkTJiQlwVYzH7NsHfvXtO7d29Trlw507FjRzN//nxjzLMRgmvWrDElSpRwTBM0hgcJ0c3++75165YpX7686devX4SF5R88eGB+/fVXU7p0aePi4mLc3NxM5cqVzbx584wxXBtaxT4Sd//+/eabb74xFStWNI0aNXKsqRoUFGT++usvU6pUKZMvXz7H5yh2vr1sxvzn3biIc4wxstlsunPnjrJnz64ZM2aoQYMGatOmja5cuaKNGzfKz89PiRIlkouLi+Nz4eHhcnJysjB53GRvrxo1asgYo8GDB2vp0qWaMmWK9uzZo5w5c+rRo0dKkiSJ7t+/rxQpUtBWFrO32d//PmPGDPXs2VPx48dXggQJlCBBAlWqVEljx461Mm6cFhoaKhcXF23cuFFDhgzR+fPnFS9ePCVLlkwzZszQBx98oJCQEO3YsUPbtm3TwIED6VsWell7xY8fX8mTJ9eUKVNUpEgR2Ww2DR8+XGPHjo3wenlYw/4duGHDBg0ZMkQ7d+5U27ZtdfHiRW3ZskWStGvXLnXq1EmrVq1S9uzZLU6Mixcv6vPPP1dAQIDWrl2rDBkyOK4rgoODdfToUY0dO1ZDhgxRlixZrI4bJ9nb4/Hjx8qXL58qV66sbNmyaezYsapZs6ZmzJjhOPbSpUtKkCCB0qVLp7CwMDk7O1uYPO6xfwfWqVNHzs7O6tOnjwoWLOjYHxwcLFdXVwUHB+vs2bO6deuWKlWq9NLrSEQP++88JCREOXLkUNWqVZUzZ05NnTpVuXPnlpeXl+PYwMBAPXr0SGnTpnVco+AtZVHhDjHIwIEDHYtV7tu3zyRJksQxdWLu3LnG09PTXLt2zcqI+I+9e/eaNGnSOBajLFWqlPn666+NMc+m4I4dO9bcunXLyoj4D/uTJ39/fzN16lTTs2dP8/PPPzvWzgoKCjKTJ082ixYtMidPnnSs0cQTK2u9bGrZ48ePTVhYmONtt7RVzMFUwNiHtX5ij23btplixYo53rb5sj5kn8rE96E17P2kTZs2pmrVqsaYZ6NyEyZM6JhOtnXrVsf3IKxhb6edO3eaZMmSRZjlc+vWLdO3b19TpkwZU6RIEbNx48aXfhbRz/6779atmylVqpQxxphHjx6ZRIkSma1btxpjjNm8ebPZsmUL7RSH8Pgc8vDwUKJEiSRJX3/9tZo2bapChQpJkhIkSKDz588refLkFiaE3Y0bN+Tu7q506dJp8uTJunLlinr37i1JunfvnpYsWSIfHx+LU0KS4ylh69atNXjwYG3fvl1z5syRh4eHJk+eLFdXV7Vr104NGzZU3rx5FT9+fEliVJOFFi5cqKRJk2rQoEF69OiRZsyYoX79+ilRokT666+/NGrUKF29epW2iiH+qb3eeecd/fnnnxo9erRCQkIc5zNGW0Sv8PBwSdL58+d14sQJBQUFSZLef/99de/eXUOHDnWMlD516pQGDRqkLVu2aMyYMZKePfmHtUqXLq1Zs2apf//++u2335QtW7YII5okydXVVRLfh1ax2Wx6/Pixrl27poYNG0qS6tSpo0aNGqlEiRJ6+vSptm7dqiVLligkJMTitHGX/Zpw6dKlqlu3rrJlyybp2SjPXr16aerUqcqTJ4+yZMmitm3b6uDBgy98FtHPZrMpODhYN2/eVOPGjSVJjRs3Vo0aNVShQgWFhobq8OHDWrNmjYKDgy1Oi+jC2Q5KkyaNTpw4obZt2+rkyZMaPXq0JOnp06caNGiQqlevrkSJEjkuhmGdd999V4GBgbp48aKGDRumvn37KkWKFJKkLVu26PHjxypevLjFKSE9O+meO3dO+/fv186dO7V27VpNnTpV9erVU58+fVS8eHFt27aNC6MYwP7d5ubm5nhY0KNHD+XPn99xwWSM0fr16/X06VOrYuI/7IWVyNorPDxca9eudRRtEL2MMXJyctLt27fVtm1bLV26VAEBAY79bdq00aBBg5QpUya9++67KlWqlHbu3Klx48YpQ4YMCgsLoyBjMWOMXFxclCtXLn377bf67bffVLduXfXs2VMlS5bUtm3brI6I/0icOLGyZMkiPz8/7d+/X8eOHVO/fv0kSfHixdPWrVvl5uamePHiUZi2WMaMGeXj46PAwEBJUrdu3XTz5k1NmzZNU6ZM0ffff6+QkBBdvnzZ4qSQnn0Purq6KmPGjLp48aL27t2rXbt2aciQIZIkFxcXrV27VkmSJFGCBAnoX3GFdYPfYBX7EPxz5845tvXt29ekS5fOlC9f3qxfv95s3rzZNG/e3OTIkcNxDENTo9/fp0Q8ffrUNGjQwNhsNpM+fXpjzLMpglu3bjVubm5m5cqVxhimKsUUjx8/Nu3btze+vr6Obffv3zfr1693tKOXl5eFCfG8gwcPMrUsFmEqYMxl/53Xrl3b1KtXzxw6dCjCfvu0v6CgIHP06FGzadOmCO1Em1nDfu2wY8cO0717d/Phhx+a1q1bm5kzZxpjnr2oaP369aZ8+fKmQoUKFibF368PJ06caBIkSGASJEhgRo0aZYwxJjAw0EycONGkTJnScRx9y1rLly83CRIkMNWrVzelS5c2KVKkMLt373YsVP/kyRNToUIFx0ulYA17/7J/Jy5btsykTJnSJE6c2LFsRFBQkJkxY4ZJkiSJ45xG/4obKKDFYdWqVTPdunUzgYGB5sGDB443zHl4eBhXV1fTunVrs3v3bmPMf99AAmv06dPHTJo0yfHvAQMGmFSpUpk0adKYggULmgIFCpguXbpYmBB29r7i4+Nj1q1bZ3Lnzm02b978wnGXL182y5cvj+54MP+9MFq3bp1Zu3atefTokWPfTz/9ZFKkSMFr5GMQ++/73Llz5vjx4+bJkyeOfT///LNJmTIl7RWDsNZP7GTvJ/fv3zfp06c3nTp1Mr/88otJlSqVad26dYRjr169au7cuWOM4YGd1aZPn+74+7hx44yHh4fJmjWr+eGHH0yFChVMgQIFzJw5c4wxXMvHFBs3bjTly5c3PXr0eGFturVr1xo3Nzfz8OFDYwzfiVZ7/t5r5syZJnv27CZLlizm66+/NiVKlDAFCxY0kydPNsbQv+IS3sIZRwUGBmrChAlatGiRmjRpoq+//lrSs7VKzLPCqnLlymVxStgNGTJEI0aM0Lfffqvu3bsrJCREFy9e1B9//KHHjx+rdu3a8vDwUIIECXjzpoXMf97WEx4erly5cskYo+DgYD18+FCDBw9Wt27dIv0cok9QUJCKFCmikJAQNW3aVNWrV1fBggUVFhamGTNmaPHixdq9e7eSJEmiIkWKqHnz5vL09OTNZdHM3jdu376tRo0aqVy5curSpYtSpkwpSXr48KEWLlyoBQsWaM+ePbRXDNK1a1f5+/tr5syZkp6t9TN48GBt2LBBn3zyie7duydvb2+tWLFChQsXtjgt7H2tZcuWunv3rry8vHT37l1lzZpV27dvV9GiRbVp0ya5ubmpSJEiVseFpEOHDumDDz5Qq1atNHz4cCVLlkw7duzQ6tWrtXfvXhUrVky1atVSpUqVrI4K/Xf5Afv13t+v18+ePav69eurQYMG6tOnD+cvi508eVIFChRQw4YNNWXKFCVNmlQnTpzQrFmzdOjQIb3//vuqXbu2ypYta3VURDMKaHHIyworkyZNUpcuXfT5559r7NixjpcJIGYxxmj06NFasWKFWrdurRYtWrz0GIowMUOfPn20Y8cOTZo0SWFhYVq2bJlGjRqlDBkyaMyYMapSpYrVEaFnr4wfPHiw5syZI3d3dzVp0kQ1a9ZUhgwZdPPmTd29e1c3b97kNfIWsv++69SpI2dnZ/Xp00cFCxZ07A8ODparq6uCg4N19uxZ3bp1i/aKIYYPH65169Zp3bp1euedd1SrVi2Fhobqiy++0Mcff6yDBw+qZs2aGj9+vOrWrWt1XEh6/PixGjZsqPr166tVq1YqXbq08uXLpylTpujp06caMGCAQkNDNWTIELm4uFgdF5LWr1+vn376SaVKldJ33333j9fxfBfGHC8rjC1dulRjxoyRm5ubvLy8JNFmMcH+/fvVtWtX5c6dW3379pW7u7ukF++paau4hQJaHGLv3DNmzFCSJEnUoEEDSdLGjRs1ZswY5cmTR927d1emTJn4IrBYaGioXFxcdOPGDaVNm1YhISFycXHRiBEj1L9/fw0ePFhfffWV1THxHPsFUUhIiGbNmiUnJye1bt1akhQSEqJTp07pl19+0dy5c1WpUiWtX7+eGxAL2fuY9GxEbvbs2RUWFqZy5crp888/V5kyZeTm5uY4nu/E6Gf/ne/atUuffPKJDh8+7Hhz2e3btzVhwgRt27ZNT5480dChQ1W5cuUXPgvrrFixQp6enqpQoYIePXqkkydPysvLS8WKFZOLi4uCgoL08ccf67PPPlObNm2sjov/6NChg3LmzKkPP/xQ9erV04EDB5Q5c2aFh4erRIkSatSokXr06EEfs8jff+/GGC1cuFA9e/ZUnTp1NG7cOMc+RjDFHufPn9fWrVtVsWJF5ciRg7aziL0wFhISImOM4sePr82bN+u7777TBx98oIkTJ75wLOIeCmhxzPHjx/Xee+9JknLlyqVPP/1Urq6uOnv2rHx9fVWpUiV9//33FqeEnbu7u+LFi6fGjRsrYcKE8vT01L59+9SpUyd16tRJ3bp1c7yFDjFDo0aNtG7dOpUtW1ZLliyJ8DQ4MDBQmzdvlo+Pj7p27coNiEXsv/enT58qfvz4On36tEqWLKlhw4Zp3rx5un79uurUqaNq1aqpfPnyih8/vtWR4zSmAsZemzZt0tChQ1W4cGE1bdpUhQoVcuxbt26dmjZtKh8fHyVLlozvQ4vZf/+zZ89W27ZtZYzRyJEj1aVLFz1+/FgzZszQ4MGDdfv27QjHI/rYf+cHDhxQ06ZN1aFDB6VJk0YfffSR7ty5o2rVqqlkyZIaOXKkMmbMaHVcIFax969Tp06pfv36qlevntKmTasGDRrI19dXH330kUqWLKkpU6Yoffr0VseFhRj+EMcUKFBAgwYN0pEjR5QpUyaFhYXp4cOHOnbsmI4dO6bt27erYMGCqlGjhtVR47w7d+4oZ86c2rx5sx4+fChjjN5//329++67SpcunQYOHKhs2bK9dDonrPP555/r7t27Wr9+vQYNGqSvvvpKqVOnliTHNCY7bkCij/1pblBQkBIkSCBJjhFozZo1U/PmzdW+fXu1b99eEydO1MiRI7Vu3Trt3LnT0X6wRsaMGXXkyBEFBgbqnXfeUbdu3RQaGqpp06ZFmAp4+fJlCmgxhP3ZbOXKlVW5cuWXrvXTq1cvde/eXcmSJWO0RQxgPxc1b95cYWFhGjFihIYNGyYfHx/t27dP/v7+Gj9+vKSII3gRfexttGvXLp09e1YLFixQ5cqVNWTIEHl4eOjjjz/W8uXL1a9fP/38888RRlEj5rN/D16+fFlZs2a1Ok6cY+9fBw8e1KlTp7R7924VL15c+fPnV61atdS0aVONHz9erVu31tSpUylSx2GMO4wDwsPDI/y7Q4cOcnd318OHD9WxY0eNHDlSu3fv1qxZs9S7d2+KZzFE6tSp9fvvv6tNmzbau3evypQpo+vXr6tRo0YqX7683n//feXJk0fSf29WYL0aNWpo/fr1GjdunKZNm6bixYtr3rx5CgoKeuFYhn5HH2dnZ4WHhytnzpzq3bu3nj59KicnJ82ePVs3btzQd999p7CwMElSx44ddeDAAQ0ePFipU6emf1ksR44c2rt3r+rXr68yZcpo9+7d+uGHHxxrCebLl0+5c+fWvXv3LE4KO5vNJpvN5uhTz3/XLV26VK1atVLWrFnVp0+fF/Yj+vz9u83+70aNGmny5Mlq0aKFDh06pA8//FDjx49Xw4YNJYnimcW6deumvn37ysfHRwkTJtTatWtVoEABXb9+XYkTJ9aOHTsonsUy9uLZkSNHVLBgQV27ds3qSHFW/fr1NXDgQO3atUvp0qXT6dOnlTVrVgUEBChPnjw6fPgwxbM4jimccURISIhGjhypypUrK23atMqQIYN69uypadOmaebMmapdu7ak/46I4WmwNexP6Y8cOSIXFxflz59fxhj169dP9+/f13fffacMGTIoMDBQQUFBSpEiBaOYLBbZ7//u3bvq16+fZsyYoXz58mn69OkRFkBH9AoICNAvv/yiyZMnO9YU/Pbbb9WvXz+1aNHC8QZiY0yE7z/6mPWYCvj2YK0fa71sNG5U1vKhf0W/53/nN27ccEwfmzp1qnbu3KkWLVqoYsWKevjwoa5fv66kSZMqc+bMjBSMhSpUqCAPDw9NmzbN6ihx0tWrV5U5c2ZJzx72zJo1S56envrss8/08OFDBQUFKTQ0VJkyZaJ/xWEU0OKIP//8U9WrV1fatGmVPXt2pUiRQu3bt9e6deu0d+9e9erVS5UrV+YiNga4dOmSqlevrnTp0ilRokTq1KmTQkJC9PPPPyt+/PiaMmWKcuTIYXVM6L83HaGhofrtt9+0fPlypU2bVp999pmyZ8/uWJ/uyJEjat++vX788UeVL1/e0sxxXXh4uM6fP69Ro0ZpwYIFevTokfbu3auiRYs69jMaJuawX6LYbx5fNhWwfv36atCggfr06UMxBngF4eHhypo1q5o2baqhQ4e+8NZa+/8GBgaqQYMG6tKli6pVq2ZxakyYMEEbN25UxowZ1apVK6VKlUqjRo3S0aNHNWTIEJUsWdLqiPgX7IWY5cuXq1OnTjp8+DBrbFlgwYIFmjhxorJmzaqOHTsqRYoUWrVqlU6dOqVWrVpx/Q4HCmhxzM6dO3Xo0CEdPnxYS5cuVZIkSXTz5k3lypVLBw8e1DvvvGN1REhas2aN7t+/ry1btmj16tUqW7asEidOrCVLlihr1qz6/ffflStXLqtjxnn2m/lu3bpp3bp1Kl26tHbu3KmgoCDVq1dPrVu3Vs6cOZUwYUKro+JvgoODtX//fg0fPly//fabWrRooREjRihFihRWR8NLvKwwtnTpUo0ZM0Zubm7y8vKSxOiY2IK1fqz1/GhcZ2dnjRgxwjE98/ki9cKFC+Xp6ak7d+4oZcqUVkaGpEWLFmnv3r26dOmS1qxZo3LlyqlYsWKaOXOmHj9+rEmTJqlZs2ZWx0QUPH/Ocnd3V5cuXdSjRw+LU8VNq1at0okTJ7Rv3z5t2LBBFSpUUMqUKbVu3To9evRI48aNU8eOHa2OiRiAAloc8bKbCmOMli1bJm9vb2XJkkWdOnVi9EUMFBAQoIULF+rOnTvavn27Tpw4wdoIMYC9r5w6dUrFixfX5s2bVaxYMdWvX1+XLl3S5cuXlTJlSnXq1Em1atVStmzZrI6Ml/D399fatWs1aNAgXb9+XV9//bVjXSbEbEwFjJ2eX+unfPnyOnbsmDJlymR1rDjHPhp3zJgxmjp1qooXL66xY8c6pkffvn1bRYsWVatWrdSvXz+mK8UQxhgFBwfr0aNHmjhxoq5du6a7d+9q1apV+vbbb/Xjjz9aHRFRYO9XgwYN0tKlS7V//365urpaHSvOst8v3759W3PnztW9e/d04sQJeXl5qV27dpo0aZLVEREDUECLY+xfDM8Xyp6/KOLpffQLDw93LLhs97K16EJCQhQvXjz5+/sradKkXMxa6Pn+8+WXX+r+/fuaN2+efv/9d3l6eurixYu6deuWChUqpMDAQI0dO1ZffPGFxakRGV9fX40bN04+Pj5atGiR1XGAtx5r/cQMT58+1b59+zR48GBt3LhRLVq00KhRozR16lT98ssvunHjhiSuD2OCl7WBv7+/goKC5Ovrq7x588rV1ZWH4THA35cfeBl7O92/f18ZMmTQkiVLIrypHW9OZPdez/efgIAAxY8fXydOnFCOHDmUOHFi+hcooMVlr/LljjfL19dXGTJkkPTyKUpSxAsmLmBjluDgYC1atEgJEyZUw4YNVadOHeXOnVs//fSTHj58qO+++07NmjXThx9+GGFdGcRMYWFhCg8PV7x48V56cYWYh6mAsQtr/cRc9tG4AwcO1JUrV/TkyRMtWbJE9evX54FdDMQ1fMxjPx89ffpU8ePH/5/H2wsx9evXl5+fnzZt2hQNKfH899k/fbe97N6La3jYUT6NQ+yvk7fj5tBaU6dOVeHChTVy5EiFhoY6imcva6eX/R3Rz8vLSwMGDHD829XVVZ988omKFy8uSXJycnJcNCVKlEibN29WSEgIJ95YwsnJSfHixVNwcLCcnJxorxju+amABQsWZGp7DGeMcdyofP311+rZsyfFsxgkadKkatKkibZs2aKePXuqffv2ql+/viRRPIsB/j7egWv4mMd+HV+kSBEtXLgw0mPDwsLk5OSk/fv3a9OmTRo9enQ0JIQkVa5cWb1795b03++2V7n3or/BjgLaWyY8PFzSs4r6sWPHNGfOHM2cOVOSWBsmhsmaNasaNGiguXPnqmLFilqxYoWkZ+1kjHG0JWKOPXv2ONaHOXjwoB48eKCUKVMqa9asCg8PV9KkSTV37lwNHz5cNWvWVPz48VW2bFlJnHhjA3sb9enTR7t27bI4Df4X+zmtW7du+vTTT1lHK4az36AMGjRISZIkUefOnS1OhJfJkCGDevbsqTFjxkh6dl3JZBXrPf8mYsQ89j5y5MgR+fn5qUyZMpEebz9/derUSa1bt1b+/PnfeEZIDx8+VJEiRbR48WLlzJlTixcvlvSsPcLDw+lfeCVM4XzL2IcDf/fdd1q/fr2SJEmi06dPy83NTXv27JGbm5skpgLGFLdv39bWrVvl5eWlgwcP6t1331WvXr1UuHBhSS+fow/rPXnyRFWrVlXSpEnVqlUr1atXT9KzKZ1t2rTR77//rmrVqqlHjx4qWLAgi5vHAvZh/EuWLFHbtm117tw5pUmTxupY+Ad/nwp45MgRpUuXzupYcRJr/bx97NeIwcHBLGhuAXt/8fX11ZkzZ/T48WN5eHgoX758jv1cG8ZMCxcu1MaNGzVmzBglTpxYkl5YL8t+/po9e7a+//57HT16lDeARyN/f3+dOHFCS5Ys0eTJk1W1alUNGjRIBQoUkPTf0YH0L/wTCmhvEfsJ19vbW2XLltWmTZtUokQJlS5dWnnz5tW0adN048YNOTk5KW3atFbHjbN2796twYMHa+rUqY4RE2fPntWmTZvk5eWly5cv6+OPP1avXr2UOnVqSRQ8YwJ7/woPD9eTJ080efJk7d69W9euXdP777+vzz//XKVLl5Yk+fn5KUGCBNx4xBLP9y8PDw917tyZ18jHYM+3l7u7u7p06UJ7RTPW+okbvv32W9WqVctxbsObZ+9bFy5cUNu2bXXo0CEVLlxYt27dUq1atdStWzfHwx0ezsUsq1evVsuWLWWz2bRz505HwfP5dnr+/JU1a1b17t1bHTp0sCxzXGMvXj548EDjxo3TrFmzdOXKFSVIkECenp4aMWKEkiRJEuFY4O+YwvkWsT/hmDp1qho3bqySJUvKy8tLZ8+edazbtHXrVo0YMUL379+3Mmqcdv/+fV26dEmFCxfWDz/8IEnKlSuXOnfurP79+6tx48bavXu3atSooZEjR0pi+p/VjDGO/mWMUaJEidSjRw/9/PPPqlmzps6fP6/evXurX79+unDhgpIlS0bxLBaxD9kfOHCgEiVKxNSyGI6pgNZjrZ+3V2hoqCQ5RmfkypXL4kRxi71vtWzZUhkyZNC1a9dUr149+fj4yMvLS1WqVNGsWbMiHIuYIVOmTPriiy+UKlUqVaxY0TEN+vk1ju3X87169VK8ePHUpk0by/LGRfa2aNasmc6ePaspU6Zoy5Yt6tevn7Zv365cuXJpxowZklj7Ef+MEWhvieefbgwaNEinTp3SggULlDNnTrVt21bffvutJOmnn37Srl275OXlZWXcOC00NFTnzp3TihUrNH36dEnPbtybNm0qSXr8+LF27NihdevWaeXKlVqxYoVjkXpEr+dfaX39+nWNHTtWly9fVqJEidS+fXt9+OGHkqSdO3dqyZIlOnr0qAICAjR16lTHWmmI2ewjYx48eCB3d3fNmTOHqWUWYSpg7GD/Xjxy5Ihq1aql3bt3v9L6c0WLFlWZMmUcD4YQ8zAaN2bYvn27Wrdurf379ytFihQqVKiQqlWrphIlSqh9+/a6f/++qlWrptWrV1sdFX8THBysgwcPas6cOdq1a5fSpEmjr776Sh9//HGE47y9veXi4qL333/foqRx1/Hjx1WyZEnt2bPHMUowICBAu3btUqtWrXTjxg198MEH2rt37wvTbwGJAlqsd+PGDcdbrOxFtLVr12rMmDHKnj27du7cqRMnTkiS7t27p/fff1/Dhg1Ts2bNHDcisEZAQIAOHz6sOXPmaOnSpcqfP79++eUXFStWTJJ09epVnTt3ThUrVrQ4adxlv5kYPXq0FixYoKtXrypbtmy6c+eOLl26pAYNGmjs2LFKnTq1QkNDtXz5cv3555+Op46w1qtMfbZ/DzZu3FgBAQE8XIhmTAWMvVjr5+1j748DBw7UsmXLtH//fkZTW2DOnDlas2aNli1bptmzZ+vHH3/U3r17lTRpUnXo0EFBQUHq3LmzihYtyrW8hez9JSQkRJcvX9aVK1eULl06Zc2aVaGhoVq3bp3WrFmjv/76S+XKldPMmTNpqxjg1KlTqly5siZMmKDatWtH2NerVy89fPhQzZs3V4kSJehfeDmDWC1HjhymUqVK5sKFC45tt2/fNjVr1jQ2m800atTI7NixwyxcuNA0bNjQFCtWzMK0eJmAgADz22+/mWrVqpkkSZKY5s2bmzt37kQ4Jjw83KJ0cVdYWJgxxpg//vjD5MiRw4wfP96EhYWZkJAQc+HCBTN+/HiTLl06kzVrVuPt7e34XHBwcITPI+YKDQ01xhizZ88e4+LiYs6cOWNxorjr3XffNQsWLIj0GHt77du3zyRNmtQcP348OqLhJVatWmXc3NxMihQpzIkTJxzb7W1kTMTzVpYsWczEiROjNSOixn7Oun//vkmWLJlZvXq1xYniFnvfuXLlijHGmCNHjhhjjPnuu+9Mq1atHMd9++23ZsCAAdEfEP+oVatWpkiRIiZ16tQmc+bMplevXo59Fy9eNEOGDDELFy40xnA9HxOEhoaaunXrmo8++sj8+eef5unTp459AwYMMG3atLEwHWIDCmix2KNHj8z06dNNxYoVTdKkSU2PHj0ifAkMGTLEpEqVyhQoUMC88847pmvXrubs2bPGmIgXuYh+9+7dM8uWLTN//PGH+euvv4wxzy6aJk+ebD744ANjs9nMxo0bLU4JY4zJmTOn6dOnzwvbQ0JCzKZNm0yGDBlMjx49jDFcGFktJCTEGPOs6Llq1Srz5MmTV/pcsWLFTPfu3d9kNLyEvb8cPnzYZMmSxVy9evWVPlekSBHay2IHDhwwffr0Mbly5TJp06Y1o0ePjrD/+WuMnj17Gg8PD0f/RPR7lXOTvYDWqFEj88knn7zpSHiOvX0ePnxoihcvHuGh3Pjx442bm5tZtGiR2bJli0mQIIHZvn27MYYHdVayf8dNmzbNZMqUyezZs8cYY4yLi4uZPHmyMca88DDcGK4TY4q//vrLfPDBB6ZKlSpm+PDhZuXKlWbGjBkmQYIEZs2aNcYY+hf+GQW0WC4sLMycPXvWjBgxwuTKlctkypTJ/Prrr4799+7dM3v37jWXLl1ybOPL2xr2m4ft27ebsmXLmtSpU5vixYubrFmzmm3bthljnp2Qjx8/bgYPHvzKN/94/ewXRqNHjzYeHh4mPDzccSL9e//54YcfTJo0aUxgYGC058TLpUqVykyZMiXSY+z9cdasWSZjxozm7t270RENL7FgwQLTokUL4+fnZ8LCwl560fr39rp37150x8TfBAUFmT///NN06NDBFChQwFSsWNGsXbv2heMOHDhgDh8+bEFCvCpG48YMAwcONFWrVjXG/Pdaw8fHx3h6eposWbKYjBkzmvbt20fYD2sVL17cTJgwwRhjzNChQ02BAgVMcHCwCQsLMz/++KNZunSpxQlh7yv2e2ZfX1/z6NEjc+3aNdOsWTNTpEgRkyZNGpM3b17Ts2dPi9MiNmANtFjMPLe+z5MnT3TkyBEtXLhQCxYsUJYsWfTzzz871s8yr7AWEKJHzpw5VbduXQ0fPlyDBg3S7NmzdeDAASVPnlxnz55Vrly5HOvGMPfeOsYYJU+eXCVLltT69esjbLfZbI7/nTFjhmbMmCEvLy8lT56cfmYR+1okly9f1jfffKMJEyYoderULz32+e/DjBkzqk+fPrxG3iKrV69Wy5YtZbPZtHPnTseCvs+/GOf59sqaNat69+5Ne0Uz1vqJXezXEDt27NCDBw9UtWpVJUiQ4H9+rnjx4ipVqhQveohG9r718OFDzZ8/X3fv3lW/fv0iHOPj46PLly8rbdq0cnd3V4IECbg+tJAxRuHh4QoLC1PDhg3VtGlT1axZU2nSpNH8+fP1ySefyBgjT09PZcqUScOHD7c6cpxl71+XLl3SgAEDtGzZMuXPn1/GGM2cOVP58uXTuXPnlDRpUoWHhytNmjRydnamfyFS/H9GLGa/iZekhAkT6sMPP9RPP/2kBQsWKHPmzKpTp44aN26sq1evclMfQ6xdu1YuLi4aPny4wsPDNXnyZPXu3VvJkyfXsWPHNGPGDJ07d87x6mS+vK3z6NEjff/99zpy5IiyZMmiJUuWSJLjrZz2vhceHq548eIpYcKE9DML2W9AunXrpuPHj8vb2/sfjw0PD5ck9ezZUxkzZuQ18hbKlCmTvvjiC6VKlUoVK1Z0vIDDXjwLCwtz9KtevXopXrx4tJcF7O3RoUMHNWnSRI0bN1a1atU0ePBgJUuWTE2aNNHQoUPVrl07Va9eXU5OTuL5rHXs1xCffvqpbt++HWnxLDQ0VJI0e/ZsXb9+Xd9//320ZMQz9r7VuHFjdenSRXPnztWJEyci9B93d3eVL19eefPmdbQl14fR78aNGwoPD5fNZpOzs7Pix4+vxIkTa9myZWrdurWqVq2qTz75RJJ04sQJrV27Vs2aNZP03+sORC97/2rRooUeP36sEydOqHLlyrp06ZKjL7m5uSlt2rRKnz6943j6FyIV/YPe8DrYh9v7+vqa3377zQwYMMCsWLHCnDp1yhjzbN797NmzTd68ec0HH3xgZVQ8548//jAFCxY0xhjTtWtXU6JEiQgLYxcoUICFsWOQsLAwc+bMGdOpUycTL148U7p0aXPw4MEI+wsUKGCGDx/u+Desc+7cOfPee+8Zm81m6tata86fP//CMfah/NevXzeJEiUymzZtiu6Y+BumAsZsrPUTu9jb69KlS6ZBgwbm9u3b/3js822UIUMGXvRgoUePHpkvv/zS2Gw2U6pUKbNp0ybz6NEjq2PhP65cuWLKli1rpkyZEmFZnEuXLpnChQsbJycn079/f2OMMStWrDAVKlQwn332mTGGa0Or7dy502TIkME8ePDAGGNM4cKFTd++fY0xz64bf/nlF3Pt2jULEyK2oYAWC9kveMLDw02xYsVM7ty5jYeHh0mbNq2pVq2amTVrlgkPDzfBwcHmyJEjjptIXhxgvUuXLpm8efOakSNHmuTJk5tDhw459n3++efm448/ti4c/lFwcLDZuXOnqVq1qrHZbKZFixYmNDTUzJo1y6RNm9ZxHDeMMcOcOXNMhgwZTPr06c3EiRPN48ePXzhmypQpplOnThaki9vs56GnT5+ac+fOmS1btpgTJ06Yx48fm4cPH5oFCxaYxo0bm6xZs5rPP/+cG48YhLV+Yo8HDx6YOnXqmLx585r169f/43H2/vjtt9+aokWL8qKHGODgwYOmaNGiJl68eKZjx47myJEjjrd7wxrh4eHm2rVrplKlSiZbtmymSZMmZsWKFebmzZvGmGdrB7Zs2dKkSZPGJE6c2GTLls20aNHCce3BecxaXl5epmjRosYYY0aOHGk8PDyMn5+fMcaY/fv3m+LFi5v9+/dbGRGxDAW0WMj+RfzFF1+YEiVKON6seeDAAdO4cWOTJk0as3jxYisj4iXs7TZy5EiTKFEikypVKnPixAmzZ88eM3jwYJM8eXJz7tw5YwzFzpjKz8/PLFiwwOTJk8ckTpzY2Gw2R1/jxsN6jx8/drzMISAgwPTs2dO4urqaEiVKGC8vrxeO58UP1mnVqpUpUqSISZ06tcmcObPp1auXY9/FixfNkCFDzMKFC40xFKatFB4ebkJDQ01wcLCpXbu2Wbp0qQkKCjJJkyZ19Knw8HDTpEkT880331icFnaMxo357NeEt27dMn/++aeZOnWqOX36tGP/okWLTKZMmYyzs7PjuxDWW79+vSlatKh57733TI8ePRyFFz8/P3Py5Enz22+/mRMnTjiu4ymeWe/SpUvm3XffNQcOHDAZMmQwixYtcuzr27evo7gGvCoKaLGUv7+/KVy4sJkxY4YxJuINxpdffmly5sxp/P39rYqHlwgICHC007Jly0ypUqWMzWYz6dOnNx999JGZPXu2MYbiWWzg6+tr+vfvbzp06GB1lDjNXrQ8cOCA6datm6lYsaKpUKGC+eWXXxzHnDt3znz00Ucme/bsjm1c0FqDqYCxh6+v7wv9xNPT0zRq1Mh4enqaBg0aOLYfO3bMJE2a1Bw9etQYQ/+KSRiNGzPZ+8jt27dNtWrVTJYsWRwj3GfOnBnh2K+//pqp6zHAkydPjDHGnD592vzwww8madKkxsXFxeTKlcsMHz7cXLx40eKEsLP3r8DAQLN9+3ZjjDGtW7d23HOFhoaa27dvm9WrV5tkyZKZzZs3G2O4/8Kr4y2csZD5zxvJ6tWrJzc3N02fPl3GGD19+lSurq7atm2bOnfurOXLlytv3rxWx42zgoOD5erqqv3792vChAm6c+eOsmfPrnr16qlChQqSpNOnT+vmzZsqW7asY8FKwxtTY4Xg4GDZbDbFjx/fsags7RZ9nu8nHh4eKl++vIoWLapp06bJZrPpr7/+kpOTk6Nf3blzR6lTp3a8nQ7W+fDDD/X555+rU6dOGjZsmBYsWCBvb2+5uLjo559/loeHh+rXr291zDjr6tWratq0qZo2baoqVaooa9askqTLly+rXr16Onz4sPr27at+/fpp5cqVGjdunNKnT6/58+fz5rIYIiAgQE5OTkqYMKECAwM1cOBAjR49WoULF9Z3333nWOjc7smTJ0qYMKFFaeMe+/mrVq1acnFx0bRp03Tw4EHVrFlTu3fvVuHChXXixAnlz5//hc8g+tl/98HBwUqZMqWGDx+uUqVKKV26dOrTp4/mzp2rokWLqnXr1ipbtqzc3d2tjhxnPd9PunbtquDgYE2aNEkhISEaN26cRo8erfv37ytjxoxKmDChatSooSFDhtC/ECUU0GIheyf/6aefNHDgQE2aNMnxlhdJWrNmjdq2bavLly+/0mvL8XoFBAQoUaJEjn9nyZJFRYoUkbOzs548eaLr16+rRIkS6ty5MxdHsdjzF1Surq5Wx4lz7L//H374QevXr5e3t7eePHmi9OnTa+HChapevbo2bdqkR48eqWbNmooXL57VkeM0Y4zCw8MVFhamhg0bqmnTpqpZs6bSpEmj+fPn65NPPpExRp6ensqUKZOGDx9udeQ4yRgjX19fNW/eXBcuXFCJEiXUoEEDlSxZUmnTptXevXs1efJkrV27VoGBgUqVKpXKly+v8ePHK1GiRBTQLGJ/MODt7a158+bp6NGjMsbok08+UY8ePSRJ58+fV8eOHXXx4kVduHBBkmgvC50/f16VKlXShg0blDdvXpUsWVJFixbVmDFjdPv2bfXr109169ZVlSpVrI6K/xg7dqx+/fVXnT59OsL2LVu2qE6dOnJyctLgwYPVpUsXixJCkkJCQhQvXjxNnTpVa9eu1apVqyRJjx8/lq+vr/bt2yd/f39Vr15dmTJlUrx48fguRJTwGD4WshdZevbsKT8/P7Vv314TJkxQ27Zt5e3trQ0bNqh79+5KkCCBwsLCHK/kRfRo27atChUqpG7dusnb21vu7u5asWKFpGevtV65cqW2bNmidu3aqUyZMvrhhx+UOHFiimexjL29+vTpo1q1aql06dIWJ4ob7IUzm82msLAw3b17V40bN5Ykff755ypbtqyqV68uY4x8fHx04MABValShQKaRW7cuKG0adPKyclJzs7OcnZ2VuLEibVs2TKtWrVKVatWdYyGOXHihNauXatdu3ZJ4ubeCjabTRkzZtTmzZu1YcMG9e3bV/3799dHH32kJk2aqHjx4sqbN6+++eYbXbx4Ue7u7sqdO7ecnZ1pL4sYYxyjahs2bKjy5curQYMGmjZtmhYuXKgvv/xSTk5OypEjhzZt2qQ7d+5IEqNxLZYgQQK5ubkpSZIkWrlypS5duuS4VgwLC9PRo0dVrVo1i1Piee7u7goMDNS5c+eUM2dOBQYGKmHChPrwww9Vvnx55cuXT/Xq1ZPEQ/Hodvz4cS1cuFDff/+93nnnHUlS9uzZdfbsWT18+FDJkydX4sSJlStXLuXKlUuBgYF65513FB4eLkmcuxAl/H9LLBEWFiZJun//vi5fvqyDBw8qJCREQ4cO1apVq5QxY0YNHDhQly5dUseOHdWrVy9JongWzc6fP6+AgACtWrVKTZo00e+//67s2bPLPtAzf/78+uGHH9S/f38VKVJEv/32m65du2ZxakRVaGioJGnJkiWaPHmycuXKZXGiuMN+QRoaGipnZ2elTZtW+/fv165du/T77787Ri7ZbDatWLFCiRMnVuLEia2MHGddvXpVjRs31vTp03X58mXH9iFDhujcuXNauHChYxTuypUr9eWXX+qTTz7Ru+++SzHGQkFBQZKe3SxWrVpVly5d0tixY+Xp6amff/5Z9+7dU968eVWjRg3ly5fPcZ1Be1nrhx9+UPLkyTV9+nQ1b95c58+f18CBA+Xi4qItW7ZoxYoVCgkJUerUqSWJ4pnFUqVKpXTp0mn+/Pnq3r27evXqpXTp0kl6dm1x69Yt1a5d2+KUeJ59WZyBAwdKkt555x3ZbDYlSpRIISEhKleunDJmzEjxzAI7duzQwoULVbVqVc2dO1eSVKlSJd26dUunTp2S9Oya5Pfff1eXLl3k6ekpifMW/h2mcMYC9lFkQUFBql27try9vZUtWzZlzZpVrVu31scffyzp2RoWNpvNMW2TGxBrPHjwQMuWLdP69evl4+Ojq1evatmyZSpfvnyE4/z9/XX69GkVK1aMk20s8ve1tzp37uyYIoM3a8iQISpVqlSEvrR79261adNGN27ckKenpyZMmKCQkBAtWrRIHTp00I0bN5Q0aVK+D6MZUwFjJ9b6iT2ePxeFhYWpc+fO8vDw0DfffKMGDRooODhYa9askTFGU6dO1YEDBzRy5EgeKFjk+fayL/0wf/58denSRQEBAVq0aJEyZMigEydO6LvvvtPYsWPVsGFDRgpa7O/X59u2bVPz5s0VFhamXr16KU2aNNqyZYsWLlyou3fvsqSHRR4+fKg//vhDq1at0p49e5QzZ071799f06dP1507d3Tr1i2dPXtWrq6uevfdd9WhQwfVqFGDaw38KxTQYrDg4GBduHBB+fLlkyS1bNlS58+fV8+ePXXhwgXt2LFDFy5cUKFChdS5c2cVKVLE4sRxl/0E+/xCvOfPn9fSpUu1bNkyhYSEqG7dumrdurWyZMlicVr8f9gL2gMHDtSyZcu0f/9+Lpiiga+vr5o0aaLbt2/ro48+Uq9evZQxY0ZJ0rx58xyjbmvXrq0///xTzs7Oatasmbp27coNiMXsUwGDg4MdUwGLFCkif39/Xb9+namAMRBr/cQe9u+3fv366dSpU/ryyy/18ccfa9++fcqTJ48kqVq1asqXL59Gjhxpcdq4y37tMG/ePIWFhaly5crKkCGD9uzZow4dOuj+/fsKDg5W5syZ1aBBA/Xs2dPqyPiP0NBQ3b17VyEhIcqcObMuXryosWPHauHChXJ1dVX+/PnVrl071a1bl+sNi127dk2///67Vq5cqQsXLujcuXPKnDmzBg4cqBw5cihHjhyOUbjAv0UBLQb75ZdfNHv2bLVo0UL169fXzz//rE8//dQx+uLMmTNatWqVtm7dqlu3bqlGjRoaPHgwI5mimb149vDhQ40ZM0a5cuVSo0aNHDeAe/bs0fz58+Xt7a2kSZPq008/laenp2OOPmIP+439gwcP5O7urjlz5qhWrVpWx4ozDh48qE2bNsnLy0uPHj2Sp6enevToIRcXF92/f18//vijLl26pCxZsqhBgwYqXry4JNYisUpQUJASJEigM2fOaN68eRo7dqwCAwOVPXt2tWnTRvXr12cEUwzl5eWlzp07a8uWLRHW+gkMDFTjxo2VL18+ffnll0xXsgijcWMXe/HMx8dHhQsX1o8//qjGjRsrWbJkjmN27dqlZMmSKU2aNEqbNq0kZpJYyV4I8/Ly0rRp03Ty5EmFhYWpZMmSGj16tFKlSiVJunDhgjw8PCxOG7fZ+0loaKhsNpucnZ117Ngx/fbbb9qxY4euXbumbt266fPPP3esh8t5C/8fFNBisA0bNmjZsmU6duyYsmbNqjt37qh58+Zq0aJFhOP++usvzZw5U4ULF1aHDh2sCRuH2b+EmzdvrocPH6p169aqVavWC9Mr1q5dq2XLlsnb21sVKlTQ+PHjLU6O573KydR+km7cuLECAgLk5eUVTelg9/TpU+3evVtr1qzR1q1blSxZMn355ZeqX7++pP++fcmOiyRrMBUwdjt//rwqVqyocuXKOdaTsatWrZpjlBP9K/oxGjf2qlWrllKmTKmZM2c6ttnbxP7AAdazX+v5+/sra9asateunbJkyaL48eNrypQpOnv2rCZMmKCmTZtaHRX67/VGv379lDhxYjVq1EhZsmTR06dPtWPHDq1du1bbt29XUFCQFixYoEKFClkdGbEcBbQY6PkbDycnJy1YsEC//fabdu3apVSpUmn8+PEqV65chM88f+Llgjb62E+y27Zt06effqrNmzerYMGCjieGf/31l27cuKF3331XOXPm1O3btzVr1iyVLl1aJUuW5OliLGJ/grx3716VLl1aJ06c4OUB0ez5/nLnzh1t2bJFXl5e/8fefYZVdawPH/4hSBEBRUURVCSILWKLFbBi72IBuyJKxK4EjQW72EtUxK5obKAC9oYNe8VeELsC0pUO837w7B1Icv5J3nOOC2TuLwl7rX1dz7XHmTXrmcaNGzeoUaMGkyZNom7dugpHKeUklwLmH3Kvn/xDzsbNfz5+/Ej79u1xd3dn4MCBuZ5ncXFxHDlyhMaNG2NhYaFsoJLa+PHjuXnzJmfOnAG+9EHevHnDokWLCAsLY//+/RgbGysbZAGn6pufO3eOzp07s23bNtq0aZPr+RQfH8+JEyc4ePAgq1atwsDAQMGIpW+BTKDlUffv32fcuHFs2rQJc3NzXrx4wf79+9m/fz/Z2dnY2toydOhQKlWqpHSoEtC0aVNsbW2ZN28eAFFRUWzdupUZM2YAoKOjw5o1a3ByclIwSikn1ajvuXPniIuLo02bNn9r9LdBgwbY2trKvWS+ot+/9Kk2YIYvS9lPnjzJoUOHePr0KR07dmTp0qXyJTGPkEsB8xe510/+IWfj5j/NmjWjdu3aLFu2DPjt5f/Dhw+0adOGRYsW0bp1a4WjlOBLfZk8eTIvX75k586dua5duXKFDh06sGPHDtq0aaNQhFJOTZo0wd7enrlz5wK527uYmBhKlChBUlISBgYGcvKC9B+T/3ryqPj4eC5dukSPHj14+/YtFhYWjBs3jqVLl9KgQQMuXrzIqFGjWLBgAZ8/f1Y63AItOjoabW1tbGxs1J/Nnj2bw4cP4+npSWJiIo6OjowbN46oqCgFI5VyUr34OTo6EhUV9X8mzzIzMwHYunUrb9++ZcqUKV8lRukLVSfo4MGDjB8/nh9//FGdwKxcuTLu7u54eXnRuXNnLC0t5UtiHlK1alUAZs2aBUCRIkXQ0NBAX1+fjIwMmjZtKpNnClO1b8HBwTg6OmJvb0/Tpk3p168fhoaGLF++nMjISEJCQjhy5AjdunUDkMkzBWVnZ6OtrU3z5s35+eefmTx5MuXKlWPq1Kn07NmTGzdu5EqeAbJ+5QFt2rRhw4YNLF++nJSUFDQ1NUlLS2Pr1q0kJyfL5FkeoqGhQZUqVQgKCiI4OJiMjAz1tTp16mBmZkZMTIyCEUoqMTExZGVlUaFCBeBLYlrV3n38+BE/Pz/u37+vnnkmk2fSf0rOQMvDIiIi6NmzJwALFy6kRYsW6muHDh1iz5493Lhxg/3798uZaArr1KkTWVlZzJgxg3PnzrFo0SIWLFig3q/uxIkTeHp6snPnTipXrqxssJJ61Pfly5d4eHiwevXqf3sqT84XezMzM6ZNmyb3GvyKVLNcDh06xLhx46hQoQI1a9Zk2bJlVKpUCS8vL5ydnQH4/PmzOkEjEzLKkUsB8w+510/+Imfj5h+qssrOzubDhw+ULVuWpKQkfv75Z65cuYKenh6NGjXi7t273Llzhw0bNtC2bVs5s1NBqjJTlUFWVhZ9+vThw4cPODk5YW9vT8mSJdm6dSsLFy6UCbQ8pEmTJlhYWLBt2zbgt7J8/fo1tra2/Prrr9jZ2SkcpfStkAm0PEwIwalTp5g5cyb6+vr88ssvuRJlcXFx3Llzh2bNmsmXRYUFBQXh7e3N27dv+fTpE0uXLqV///7qUY6goCB++uknbty4gb6+vsLRSvBllufgwYN5/PgxS5cupW3btn96nyrZ5unpSUhICBcvXpSdWwVYWVnh4uLC5MmTWbFiBUuWLKFx48b4+/vToUMHJk2aRKNGjWRbmEfIpYD5i9zrJ385ePAgp0+fJj4+nu+//57x48err125coU9e/ZgYWEh9xZUkCo5PX/+fBISEujTpw82NjZERUVx4sQJzpw5w40bN6hbty7dunWjffv2SodcoOXsO6xfvx4rKyuaN29OREQEM2bM4Nq1a2hpafHo0SNq167NhAkT6NWrl3x+5RG7du1ixIgR9OnThzFjxlCpUiXev3/PjBkzuHv3LhcvXlQ6ROkbIhNoeYTqJf358+cUL14cQP3fq1evMmDAADIyMti/f3+upYIq8qVRedeuXSMpKQlzc/Ncm8vHxcVhZ2dH7969mT59urqsJWU9e/YMR0dH7t69S9euXVm0aNEfjiJX1at3795hbW3NgQMHcHBwUCjigisoKIi5c+cSGhpKdnY2lpaWzJ8/n3bt2tG1a1cuXrxIkyZN1C//kjJULxLBwcFs2LCBBw8ekJWVRePGjVm+fDklS5YEIDw8/A91TVKO3Osnf5CzcfMPVT/v/v37NGjQgJ07d+Lg4ICenh4xMTFoa2v/6UbmsqyUoyqzadOmceTIEZYvX55rxlJoaCjx8fFkZmZSu3Ztypcvr2C0BZsqOf3p0yfevXuHpaUlAIsWLeLw4cN8/PiR0qVLExMTQ0ZGBvv27aNatWry/Uv6r5EJtDzk48ePmJiY0KpVK6ysrChSpAgjR46kQoUKvHr1Ck9PT168eMG0adPkSFUe8mcdHlXjHhYWxrp167h48SI3b978t/dLyvHz82PSpEkIIZg+fTr9+/f/wyzB9evXc/v2bVavXq1QlAXbmTNnOHDgAAsWLMDX15c9e/Zw6NAhjIyMWLZsGQkJCYwePRpjY2PZQVKIXAqYv23ZsgV3d3d27dpF27Zt1ftnZWRk8MMPP+Dp6UmfPn0UjlICORs3P2nXrh2WlpasXr2a+Ph4Dh8+zPTp04mLi2P06NF4eXnJZ1YeoKorHz9+xMLCgn379qn3o0tPT0dbW1vhCKU/M2DAAG7dusW0adNo27YthoaGXLhwgWvXrnH//n1q1KhBq1atqFatmjw4QPqvkgm0PCQsLIwuXbrw6dMnvL29OX/+PP7+/nTu3BkzMzPi4uKIiIhAU1OTFStWqDdnlvKmjIwM3N3dSU5Oxt3dnUaNGsmOUh7y+fNnChUqpD4RcNasWSxfvpw6derw888/07Fjx1z3p6SkoKenp1C0UmJiIoaGhqxcuZKgoCBOnjwJQP/+/SldujSLFy9WOEIJ5FLA/ELu9ZM/ydm4+Ud0dDRdu3alX79+/Pjjj0ydOpXLly9Tp04ddHR0OHXqFCdOnJDbeuQhW7ZsYfXq1Zw+fZqiRYvmSkDfuHGDyMhImjdvLvuCecTjx4+ZOHEi58+fp127dowYMQJbW1uZKJP+5+S/sDzExsaGkJAQKleuzIEDB/D29ubBgwfUqlULXV1dwsPDCQkJ4eTJk7x48ULpcKW/ULhwYaZPn868efNo1KgRIE9+UZLqpLkbN24wbtw4OnfuTIcOHVi6dClFihTB29ube/fuoa+vz5gxY9Tfy87OBpAdpq8oKysLgA8fPhAQEEBsbCyGhoYAVKlShStXrtCzZ09GjRrF3r171fv8qMpKUoYQAm1tbUxNTdWfFSpUiPLly9OvXz/u37/PtWvXFIxQgtyzoDdv3kxISAiampp4e3tjYWHBL7/8Qp8+fShfvjz79u3Dx8cH+K0NlZRjaGioHoxbu3YtFhYWdO7cmZIlS+Lo6Mj06dPZt28f8Fs7Kn0d9+/f59OnT+q/S5UqRYMGDfDz86N169b4+/szdOhQFi5cSM+ePUlISOD58+cKRiz9nrW1Na9fvyY+Ph4NDQ0yMjJQzTO5e/cua9eulf34PKRy5coEBwezc+dO7t27R9euXZk0aRLXr18nJSVF6fCkb5hsBfIYCwsLNm3apJ5RYW5uzk8//cTEiRM5deoUZ8+eZdWqVbRr107pUKW/wdzcXL1PQkZGhlxOoRAhhHqT1169epGYmEjPnj1JTExk586dZGZmkp2djZWVFSdOnODy5cvAlxdG2Vn6+lSzNAcPHszhw4dzvWS0aNGCJUuWEBMTw5MnT1i/fj0VKlQgKytLlpXCNDQ0qFKlCkFBQQQHB5ORkaG+VqdOHczMzORMpjxAlWieNm0avr6+6uWaFStWZOvWraxfv5758+ezd+9e9u7dS69evQDkRtl5QLNmzZg1a5b65E1dXV2MjIwAuHnzJp8+fVLP8JSz3b+uFi1acOTIEeC3OtanTx+qVatGxYoV2bhxI05OTgAEBASgo6NDjRo1FItXyi07O5uyZctSqFAhRo8ezcePHylcuDAaGhqkpqayfPlyateujY6OjhysU4hqUCAtLS3X5+3atePu3buMHz+eFStWMHjwYIKCgpQIUSog5BJOBamW8718+ZL09HQsLS3VHZ5z584xYcIETExM2Ldvn7qzlJNcz533qZbHPH/+nICAAEaOHClnMilANeNi6tSpHDlyhBs3bpCSkoKpqSk7d+6kXbt2nDhxgqSkJDp16qR+oZS+PlW7tmvXLkaPHs3NmzcxNzfPde3P9vaR+/0oQy4FzF/kXj/5i6qf+OHDB0JDQ2nevLk6QXb8+HEcHR1p27YtZcqUYf369Tx+/JgKFSrI/uFXpKpTb9++xczMjISEBIYPH87PP/+sPvRLVY5JSUmcPn0aV1dX9uzZQ7NmzeTWHnnMiRMnmDBhAhkZGXTv3p2SJUty9OhRXr16xcOHDwHZ31BSWloa/fr1Y/jw4TRq1Ah9fX11eaSmptKiRQu0tLSYO3cu9vb2SocrfaPk01VBmpqaCCGoX78+vXv3pkuXLixfvpw3b95gZ2fH2bNnMTAwoGPHjuoN6HOSnaO8TzVi7+TkxJs3b2Ty7CtTjQ9oaGiQlZXFx48f1SPAAwYMoEmTJrRr1w4hBBERERw9evQPI1vS16Vq1/bu3Yurqyvm5ubqcixUqBBZWVkcOXKEV69eAbnLWPq65FLA/EdVXgcPHqRq1arqzeYBdfLsxo0bHD58WC6ByQPkbNy8T1Wn9u7dy+fPn3n37h2hoaHY2dkxceJEIiMj1eV4584d9uzZg7u7O82aNUMIIZNnCsk5fyQyMpK3b9/y4cMHWrVqxYYNG3B0dGTfvn2sXbuWWrVqqU8ozszMlP0NhQgh+PjxI1evXqVr1654enpy9+5d9Wx3XV1dateuzfLly2XyTPqfkjPQ8oALFy5w4sQJwsPDOX78OJ8+faJJkyZUqlSJatWqcfnyZXR1dZk9ezYmJiZKhyv9TaoZGQEBAYwaNYrr169TtmxZpcMqkFRl4eXlxcOHDxk9ejTt27fn6tWrVKlSBYC2bdtSrVo1li5dqnC0BZsqKTNkyBCioqIIDg5Wd1ZVjyt3d3eqVavGyJEjlQy1wFPNnJg2bRpHjhxh+fLl2NnZqa+HhoYSHx9PZmYmtWvXVi9nl5R38eJFunfvzrVr1yhXrhwZGRloaWmhoaHBli1b2LdvH3v37v3T2e/S1yFn4+YfBw4coHv37gQFBakPIFq/fj3Tpk2jcOHCzJw5k759+6Kjo0NkZCTFixdHW1tbzhRUkOr5tXLlSjZt2sTbt29p2rQpLVq0wNnZmeLFiwMQGxsrD77Jg7Zt28bEiRPR1NTEw8ODKlWq8PHjR4YOHcrjx4+pWLGi0iFK3zCZQFNYzo5OQkICmpqaHD16lMOHD/P27VsuXrzI58+fgS+jW46OjkqGK/1NOcu1YsWKjBo1ivHjxyscVcEyd+5cbG1tadasmfqz0NBQhg4dyvv37+nbty+rV68mIyODXbt24ebmxvv37zE0NJSd2jxg9erVLF68GH9/f+rWrav+/OHDh9SvX5+QkBB++OEH+bKoELkUMP/Kzs7m1atXNG7cmAYNGrB+/XpKliwJQGpqKg0bNqRLly7MnDlTtoV5gKOjI1WqVGHu3Lm52rusrCyOHTvG999/T/ny5WVbqLARI0YQGBiIr6+vOokWFxfH3Llz+eWXX6hXrx5TpkyRexjnAaq68vr1aypWrMjKlSspVKgQZ8+eJTw8HAsLCxwdHencubNcOZLHJCUl5Toh1dPTk82bN6Ojo4OOjg4DBgxg+vTpcmm09D8lE2h5wL/roL5794709HSCgoK4d+8e69atUyA66f+HquGeNWsW/v7+XLt2TY7kf0Xv3r3D2dmZqKgoHBwcmDRpEmZmZgBs376dSZMmAdClSxcuXryIpqYm/fv3Z8yYMerZapKyEhIS6NGjBxcvXmT8+PF06tSJS5cuceDAAUqUKIG/v798uc8DtmzZwurVqzl9+nSuTi18WQoYGRlJ8+bN5UtIHiT3+snb5Gzc/EH1HHr9+jVDhw4lNTWVI0eOUKRIEfU9jx8/xt3dnfDwcCIiIhSMVspp+/bthIaGqrcXSE9PZ+fOnezZs4ekpCTKli3LnDlzsLKyUjjSgistLQ0dHR2uXbvG6tWriY6OpmLFirRr144OHToAEBMTw9WrV7GxsVH39eWzS/pfkgm0POjPXgpVCRmZUc/7VOUXFxeHpaUlW7dupXPnzkqHVeDcvHmTEydOEBwcTFJSEn379mX8+PFoaWkRGxuLt7c3L168oHz58vTs2ZMGDRoA8qGrBFW7dv/+fbZv386IESMoV64cycnJ+Pj4sHz5ctLT0zEwMKBdu3bMmTMHIyMjmUDLA+RSwLwvZ5sWGRlJZmYmmpqalClThqtXrxIUFERAQADZ2dl07doVZ2dnatWqJQcT8gg5Gzf/ePbsGa1bt8bKyopNmzZhbm6eq98eGRlJ6dKlZd1SkKqePHv2jIULF5KQkMDu3btz3fPhwwd8fX25fPky+/fvR1dXV6FoC67Pnz+jr6+v/rt8+fL88MMPaGpqkpqayrt376hTpw4jR46kZs2aCkYqFUQygZbH/X6DbNlBUs7f/e1VL/XdunUjLS2Nw4cPf4XopD+Tnp5OaGgoQUFBnD59GiMjI0aPHk2PHj0AyMjIyHXipqxfymrUqBGVKlVi7Nix1KlTJ9e1u3fvYmpqirGxMYUKFZLJszxALgXMH+ReP/mbnI2bd+U8hVhDQwNNTU0OHTqEh4cH/fv3Z/Lkybnuk/KOyZMns379ejIyMpg9ezZ9+/alRIkSue6JiYmhRIkScvKCAvr06UPt2rUZO3YsN27cwNPTk7NnzwJw//599u/fz+nTp0lLS8Pe3p4pU6ZgYGCgcNRSQSETaPmA6sErG3BlBQQE0LBhQ/X04D+jKqNr167h4ODA1atXqVy58leMUlLJ+UIRHR3NqVOnCA4O5saNG9SoUYNJkyblGs2XlKEqp40bN+Ll5cXTp0/R09MjJiYGDw8PEhISGDZsGG3atFE6VOnfkEsB8y6510/+Imfj5n2qMlItLfuztm3p0qX89NNPzJ07Fw8PDzQ0NGT7lwcFBgaydu1a3r9/j62tLV26dMHBwUHWJYU9e/aMCRMm8PHjR0xNTalRowYvXrxg06ZNuerR2bNn2bdvH6dOncLf3199KJgk/a/JBFoep3pQP3/+nICAAEaOHCk7uV+RqmO0a9cuPDw8uHnzJqVKlfrL79WrVw97e3t5oqMCft+ZVXVy4cs+JCdPnuTQoUM8ffqUjh07snTpUtmxVUjOsnJycqJq1ap4eXlx4sQJNmzYwJMnTyhZsiQXL14kLCyM7777TuGICza5FDD/knv95C9yNm7elp2dTcWKFbG0tKRYsWLUrFmTmjVroqenR9u2bfn06ROrV6/m5MmTLFy4kNq1aysdcoH37wZxUlJS8PHxYe/evWhra9OsWTM6derEDz/8oECUkkpcXBz+/v4cOXKEiIgIXr9+jb+/f66DwQASExN59OgR9evXlwN10lcjE2j5RP369WnUqBErVqxQOpQCI2dDPHv2bACmTZv2bxto1Uvi1q1bmTJlCmFhYXI5jIIOHjzI6dOniY+P5/vvv891CuqVK1fYs2cPFhYWjBo1SsEoCyYhBDExMerlfllZWUyePJmdO3fi4+ODh4cH7dq1Y9y4cZQrVw47OztmzpxJy5YtFY68YJNLAfMXuddP/iJn4+Yfb9++ZefOndy/f5+0tDSuXLlCVlYWqamppKSk0KhRI169esWjR4+oW7culy9flitIFKaqX+fPn+f8+fOEhITQsmVLnJycsLCw4Pnz5/zyyy/s27ePYcOGMWXKFKVDLnBUz6yUlBT1ZJFnz56xd+9e/P39ycjIoFu3bri4uFC+fHmFo5UKMplAy8NUCZmAgABGjRrF9evXKVu2rNJhFRiqhnznzp0cPXoULS0t1q1b96edoJxJNXNzc6ZOnYqbm9vXDrnAU9WZQ4cOMW7cOCpUqEDNmjVZtmwZlSpVwsvLC2dnZ+DLBqVFihRBQ0NDjlp9ZcuXLyc0NJQhQ4bQsmVLtLW1efPmDa6urty+fZtOnTqxYMECihcvzr1797Czs+Pq1atYW1srHXqBJZcC5l9yr5+8T87Gzb9Us9zDwsLIyMjg4cOH3L59G4CgoCB+/PFHxo0bJ2cKKkjVrj1+/JiOHTtibW1NgwYNmDFjBgsWLMDDw0N978mTJ6lZsyalSpWSfcOvSPVbx8fHs2LFCqytrendu7e6zly+fJkdO3Zw48YNDA0NcXR0pG/fvrlOu5Wkr0ZIeVJ2drb6/y0sLMSSJUsUjKbgys7OFoMGDRIaGhrC2NhYHD9+PFfZqGRlZQkhhPjpp59EnTp1REZGxtcOVcrhu+++E/PmzRNCCLF8+XJRrlw50bt3b6GpqSk6d+4sLl68KIQQf1qW0v/e8uXLRcOGDYWdnZ3w9PQUly5dEkIIkZ6eLhISEtT3PX78WDg4OIh+/foJIX6rZ5Jy/Pz8hJubm/rvtLQ0sWXLFtG+fXthb28vevfuLZ4+fapghNKfOXDggGjbtq2oWbOmGDFihDh27JisT3lAdna2iI6OVv+dmZkpPDw8hLm5uQgODhZVqlQR48aNE69evRJCCGFraytOnjypVLhSDpmZmf/oftnfUF6zZs3Uz69bt24JY2NjER4eLoQQ4tChQ7JNVJCqfgwYMEB07txZBAYG5vpciC91LjAwUPTv319Uq1ZNuLu7KxKrJMkEWh6lejDPnDlT1KhRQ6SmpiocUcF2+fJl0bhxY6GtrS3Gjx8v3r59+4d7kpKSRMOGDcXp06cViFBSCQwMFPXr1xcZGRkiLS1NmJmZiW3btono6Ghha2srNDQ0RNOmTZUOs8CLiooSHh4eokGDBqJ58+Zi1apV4vnz5+rrr169EpMmTRINGzYUnz59EkLIBJpSVB3Yp0+fCldXV9GrV68/3PP+/XsxY8YM0bZtW5GSkvK1Q5Ry+Hcv6snJyWLJkiWiYcOGokmTJmL69Oni2rVrXzk6Kadly5aJHj16iMOHD4u0tDQhhBCvX78Wbdu2FWXKlBGurq4iNjZWCCHE3bt3hZGRkXj8+LGSIUt/QfWcUvXjZeIsb3j16pWoU6eOus2ztrYWM2bMEEIIERcXJ7p06SKWLVumYIQFl6rOnD59WhQvXlzcuHEjV3/v4sWLIiAgQDx58kQIIURkZKRYsGCBCA0NzfV9Sfpa5BLOPEg1zTsuLg5LS0u2bt1K586dlQ6rwBC/m7Kdcy3+pk2bmD59OhoaGkyaNIlBgwahr6+vvvf9+/eYmpp+9Zil35w5c4YDBw6wYMECfH192bNnD4cOHcLIyIhly5aRkJDA6NGjMTY2lsuVFJDzN9+xYwfLly/n6dOnCCFo06YNXbt2pUOHDhQtWpR79+6hra1N1apVZVnlAXIpYP4g9/rJP1asWMGuXbvQ0tLC1taWrl270rBhQzIyMkhJScHQ0BCAJ0+e4O7uTpkyZfDz85PLAfOZ3/crpa8vOzubevXqMWfOHOLi4vDy8uLatWsUK1aM+Ph4mjRpwowZM+jevbssL4U0bdoUW1tb5s2bB0BUVBRbt25lxowZAOjo6LBmzRqcnJwUjFKS5B5oX9XfbZBVHaNu3bqRlpbG4cOHv0J00u8dPHiQAwcOIIRAT0+PefPmYWhoSGJiIvPnz2fBggVs2bKFAQMGKB2q9DuJiYkYGhqycuVKgoKCOHnyJAD9+/endOnSLF68WOEICy5VYsXb25vt27fj7e2Ng4MDe/bsYcOGDTx//pyuXbvStWtXWrZsKTuxeUxgYCBr167l/fv32Nra0qVLFxwcHOTLfB4h9/rJf6Kjo1m0aBHnzp2jSJEiODo60r59eypWrAjA69evWbNmDWfOnOHkyZPo6+vLBFo+oKpTcjBBOb8/AXrmzJmcPn2a69evs3LlSlxcXEhLS2POnDkEBATw4MEDBaMt2KKjo+nTpw8uLi7qBNmoUaO4d+8ezZs3Z8qUKbi5uXHw4EHu3LmDiYmJwhFLBZoyE98KJn9/f/HmzZv/8x7VlO+rV68KQ0ND8ejRo68RmvQvqr3LtmzZIqpVqyZ69+4tZsyYITQ0NMT27dtz3RsREaFAhNLvqerM+/fvhb+/v4iJiVFfO3bsmChatKjo0aOHGDlypNDR0REvXrwQQsgp30rKzMwU33//vVi5cmWuz1NSUkTPnj2FkZGRqFevnnpvEkkZcilg/iX3+sn7cu6htX37dvHDDz8IIyMjYWhoKHr27Cl27Ngh4uPjRWZmprh9+7Z48ODBH74n5U2qMgoPDxcLFy4UycnJCkdUcKieW6o2Li4uTnh4eIi0tDQRHh4uevfuLcqVKyecnZ2Fl5eXcHJyEhYWFuLMmTNCCFm/lNSxY0fRrl07ceXKFbFo0SJhYmIiNm/erL5+/PhxUbt2bfluLClODl/9j4l/TfDbtWsXY8eORVtb+/+8XzVKNWLECFxcXKhcufL/PEbpN6qRKk9PT0aNGsWuXbvQ0NCgTp069OzZk8zMTLZt28aHDx+wsLBQNlgJ+K3ODB48mMOHD/P8+XP1tRYtWrBkyRJiYmJ48uQJ69evp0KFCmRlZcnRe4VkZ2cDYGlpycWLFxFCkJ2dTWZmJrq6uvTv3586deowaNAgLC0tFY62YFM9v86fP8+8efNo1aoV3t7eREZGMn78eHbs2EGdOnXYsmULx44dUzhaSeX169ckJibi4uICQO/evRk9ejSWlpbEx8ezbt06Vq5cqXCUkoq3tzfz58/Hy8uLDx8+8Msvv/Dhwwd++uknpkyZQkhICDY2NlStWhVAzmbKB1Rl5OTkxJs3b+TJxF+Raibt2rVriY+PZ/DgwVy6dAltbW0sLS1ZvXo1w4cPJzU1lcDAQPT19Vm/fj1NmzZFCCHrl4JcXV2Jj4+nZ8+ezJ8/n4ULF+Za5ZOSkkJycjLm5uYKRilJyBlo/0s5R+9nzZolZs2a9YfPc8o5+8nMzCzXTBrp67l06ZJo0KCByMrKEh8/fhRGRkYiODhYCCHEixcvxIABA8SRI0cUjlIS4rcRxp07d4pSpUqJ169f/+Han9U3uanv16ca1Y2JiRFxcXFi6dKlomzZsuLo0aO57rt8+bJo06aNSEpKEkLImYJKUZXXo0ePhJWVlWjfvr2YOXOm0NDQEAsXLsx174kTJ0RUVJQQQtatvCArK0vUqVNHHD58WOzYsUNYWVmJuLg4IcSX2Rg1atQQAQEBQghZXkqTs3G/Paq+vL+/vzA1Nf3TQ6ek/62goCBRoUIF4eDgIHR0dP50hnR8fLwQIncbKNtD5V29elWcOnXqD4elxMbGimrVqomZM2cKIeRMQUlZcgrGV7Bz506ePXvGixcvyMrK+tN9RoQQ6tlPU6ZMYerUqRgbG3/tUAu0zMxMAMzMzHj79i0PHz5kzJgxNGnShI4dOwIQGRlJSEgIVlZWSoYq/YtqFtnevXtxdXXF3NxcPWumUKFCZGVlceTIEV69egX8NqNG7vXz9alGdTt27MiePXsYNWoUHTp0oEOHDjg7O3P06FEWLlzIsGHDKFu2LEWLFkUIIWcKKkRVXm5ubjg4OHDo0CE6d+5M8eLFcXR0BODw4cNkZ2fj4OBAqVKlAFm3lKJ6fsGXtq9z5854e3vj6urKpEmTKFasGGlpaSxZsoTMzEy6d+8OyPJSkpyN++3J2ZefOHEiEydOpGzZsgpHVfB06tQJX19fbt68iYGBAVu3buXgwYOkp6er7zEyMuLZs2e52kDZHipH1T+vV68eLVq0wNraWt1GhoWFMW3aNHR0dJg+fTqA7BtKipL/+v6HNDQ0EEJw/Phx/Pz8OHDgAKdPn1Y3EjmpPvP09KR06dIMHTr0a4dbIL1//x4/Pz/gt+WbJUuWpGXLlowYMYKDBw8yZ84cAD5//syUKVOwt7fHyspK3bBLylHVGyMjI+7cuZNrI2xV8uXgwYMEBQUBsnOkFFU5hYeHExMTQ5s2bdDS0mLdunUEBAQQERHBwIED1UsC169fn+t7kjLkUsC8TVU/srOz0dLSIj4+np9++on09HT69++PqakpJUqU4NSpU8yYMYNBgwaxfft2fHx8gC8HDkhfn+p3j4+PJykpiWbNmnHu3DmOHz9OoUKF1H0RExMTtLW11UuYZJ8j71OV0axZszAwMMDd3V3hiAquH374gW7duvHTTz9x69YtvL298fLy4saNGwD4+flhZ2encJSSyp/1zwsVKkRGRgarVq0iPj6e1atXA/zbySiS9LVo/fUt0n9CQ0ODzZs34+bmxvjx4+nYsSMjR45kwoQJuUalChUqxKdPnzh37hyLFy/OdWqM9L+zd+9eZs+ezYEDB5gwYQKNGzdGT0+PSZMm4erqSmJiIps2bSIzM5Pw8HDevHlDQECA0mFL/6J6gNatW5fFixdz8+ZN6tatq7728OFD/Pz8CAkJAeRR8kpR/eYPHz6kcePGpKWlqa916dKFLl268OLFC4oXL07RokXR1NSUJ5flAWZmZsCX07F+/fVXsrOzGTNmjPr68+fP1S/3sm59fTn3+unTpw+DBw/m48ePufb6Wbt2LTdu3CAwMJC6devKvX7ygJyzcQcNGsSoUaN4+PAhHTp0oGfPngwcOJCwsDB27NhB3bp15WzcfCI7OxtNTU3i4uJYtmwZW7duRUdHR+mwCqwSJUqwYcMGABwdHVmzZg0nTpzg5s2bGBsbc/bsWWbOnAkg+xt5WOHChZk+fTrZ2dmUL18ekLPPJOVpCDnE/1/3+xeJlJQU9QaimzZtYvr06WhoaDBp0iQGDRqEvr6++t73799jamr61WMuqF68eMHJkycJDAzk6dOntGzZEk9PT3UjvXLlSjZv3kyZMmVo2LAh3bt3p0aNGvJhm8ckJCTQo0cPLl68yPjx4+nUqROXLl3iwIEDlChRAn9/f7Kzs+VDV0EnTpygTZs2AGzevJmBAwcCX5aeaWpqyuRLHpGZmZlrAGfmzJmcPn2a69evs3LlSlxcXEhLS2POnDkEBATw4MEDBaOVgoODGTVqFJUqVeL8+fNcuHCBH374Idc9CQkJGBkZ/WGGrqxzX5/qdw8PD6d9+/YcP36cChUqABAYGMj8+fOJiIigTJky1KlThw0bNqCpqSmfXwr6u3VFVUbdunUjLS2Nw4cPf4XopJxUZfD27VsiIiJ48OABtra2VK9eHfhyIM6OHTtIT0/HwsJCvRxQyj8yMjIoXLiw0mFIBZxMoP0PHTx4kAMHDiCEQE9Pj3nz5mFoaEhiYiLz589nwYIFbNmyJdcJI5IywsLCOHjwIIcPHyYpKYm+ffsyfvx49YtkYmIihoaGCkcpwW8jhffv32f79u2MGDGCcuXKkZycjI+PD8uXLyc9PR0DAwPatWvHnDlzMDIyki8gCktJSeHChQusWrWKgwcP8uOPP7Jw4UKKFCkCIMtHQaoXRFUZxMfHM2/ePObMmcObN2/4+eefuXjxInZ2dlhbW/P48WMuX77Mli1baNq0qRxQUNixY8fo06cPhQoVwsnJiTZt2tC6detcp34/e/ZM7t2Zhxw8eJCAgAAmT56MtbV1rmtyNm7eEhAQQMOGDdUzcv+MqoyuXbuGg4MDV69epXLlyl8xSklVBtHR0fTo0YOHDx9SokQJHj9+TPv27Vm7dq369MacSRjZ98j7VAN7z58/JyAggJEjR8qTbSVFyQTaf5mqkm/dupWFCxdSo0YNqlatysyZM/Hz86Nv377qe1+8eIGFhYVywUq5HpxpaWmcP3+eQ4cOcfr0aYyMjHB3d6d3794KRyn9mUaNGlGpUiXGjh1LnTp1cl27e/cupqamGBsbU6hQIdlByiOysrKIi4sjKCiI2bNnk5iYyLx58xg+fLjSoUnAmjVrci0FPH/+PAAxMTHqpYARERHUrVsXJycnHBwc5EymPCAmJgZPT08qV65MYGAgAPb29vTo0YO6devi5+eHh4cHHz58UDhSCeRs3PxA1a7t2rULDw8Pbt68qT4o5f9Sr1497O3tWbp06VeIUspJVWadOnVCS0uLGTNmULp0aR4+fMjkyZN59OgRQUFBNGnSRPYJ86n69evTqFEjVqxYoXQoUgEnE2j/I2XKlGHGjBm4ubkxa9YsgoKCuHjxIoUKFeLXX3+ldevWlClTRukwpX/J+TBVnbQZHBxMWFgYpqamrF27Vp6ClQeoymnjxo14eXnx9OlT9PT0iImJwcPDg4SEBIYNG6Z+OZGUk7NOpaamEhMTk2sE/+XLl6xbt46FCxdSrVo1rl+/LqflK0guBfw2PH/+nDVr1nDmzBlKlCih3uvHy8uL4cOHy9lMeYCcjZu35WzTZs+eDcC0adP+bVuXc+B8ypQphIWFYWxs/FVjlr548eIFdnZ27N69G1tbW/XnHz9+xMnJiSpVqrBq1SoFI5T+KVX9CggIYNSoUVy/fl2ebCspTj6h/wcuX76MhYUFw4YNIyYmhqVLlzJjxgy0tbV5+/Ytp06d4vbt20qHWaCpTsFKSEhg3759zJ49m5kzZxIZGUnp0qVxcnLCy8uLYcOGkZ2dnWspjKSMnJsonzhxAldXV/T09Dhx4gQjRozg1q1bJCYm0r17d8LDwxWOVlKV1Zw5c2jfvj116tShc+fOXLp0CYAKFSowa9YsLly4wPTp0ylcuLA8ZU5BnTp1wtfXl5s3b2JgYMDWrVs5ePAg6enp6nuMjIx49uxZrpdImTxThqquvH37lgsXLrBu3Tru37+PpaUlixcvZtmyZVSsWBE9PT3c3NzUszxl8kx5enp6tGjRgo0bN7J+/XoOHTpEuXLl8PX1BeQG2XnFzp07efbsGS9evPi3p/4JIdRbfUyZMoWpU6fK5JmCDAwMMDIy4smTJ+rPhBCULFmSNm3acPPmTaKjoxWMUPonctaviRMnMnHiRJk8k/IGIf3XZGRkCCGEePXqlTA3Nxf37t0Tffv2FZ06dVLfc+XKFVGuXDnx9OlTpcKUcnB2dhY2NjaiXbt2olatWsLExERMnz491z0vX74UQgiRlZWlRIgFXnZ2toiOjlb/nZmZKTw8PIS5ubkIDg4WVapUEePGjROvXr0SQghha2srTp48qVS4kvhSRkII4efnJypUqCDmz58vzpw5IzQ0NISOjo4YOHDgn7aB2dnZXztUKYePHz8KFxcXsXDhQmFraytsbW3FpEmTxPXr14UQQmzbtk2ULl1a4SglVf2KiooSTZo0EaVKlRJVqlQRGhoaokOHDuL169fqe9PT09X/L59hysj5u6ekpIg3b97kuv7ixQvx888/Cy0tLWFjY5OrzCRlZGdni0GDBgkNDQ1hbGwsjh8//qfPJ1XZ/vTTT6JOnTrq9wBJOUOGDBGVK1cWBw8eVLeVQgixdOlSUatWLQUjk/4pVfnNnDlT1KhRQ6SmpiockSR9IRNo/6F3796Jbdu25fosOTlZDBw4UDRp0kQYGRmJO3fuCCGE+PTpk3BwcBB9+vQRQsjOrFJUDXJQUJAwNjYWz58/F0IIUaVKFdGkSRNRunRpUaNGDbFr1y4lw5T+ZdmyZaJHjx7i8OHDIi0tTQghxOvXr0Xbtm1FmTJlhKurq4iNjRVCCHH37l1hZGQkHj9+rGTI0r9YWloKHx8fIYQQixYtEjY2NmLbtm3C2NhYWFpaiqlTp8qXxTwqPDxcTJgwQdStW1e0bt1aODk5CVNTU7F27VohhMj1YiJ9XaoX+Y4dO4quXbuK27dvi/fv34vTp0+LBg0aCCMjI3H27FkhhOxn5CWzZ88WzZs3FyYmJqJTp07i4sWL6muZmZni8uXLwt/fXwghyy2vuHz5smjcuLHQ1tYW48ePF2/fvv3DPUlJSaJhw4bi9OnTCkQo/d7jx49Fx44dRcuWLcXo0aPFrl27xMKFC4WxsbG6Xy+fX3mfqg2MjY0VxYoVE4GBgQpHJEm/kQm0/9CKFStEyZIlRffu3UVoaKj684cPHwo7OzuhoaEhxowZI9zd3UXbtm1FlSpVREJCghBCdpCU1qFDBzFnzhwhhBDr1q0TlpaW4s2bN2LZsmVCQ0NDaGhoiKCgIIWjlJYvXy4aNmwo7OzshKenp7h06ZIQ4svMClVdEuJLp8nBwUH069dPCCHrl1JUv/vZs2dF+/btRWxsrEhISBBmZmZi7969QgghfvzxR1GuXLlcs3MlZajK682bN+L8+fPC19dX3Lt3T3393LlzYvjw4WLw4MFi5syZSoUp/U5ERIQwMzMTFy5cyPV5dHS0aNmypXB3d1coMiknORs3//j9b56cnKz+/40bNwozMzNhbm4uVq1aJT59+pTr3nfv3n2VGKXcVM+vlJSUXMnNiIgI4enpKZo3by6MjY1Fo0aNxIIFC5QKU8rh77ZtqrLt2rWraNeu3f8yJEn6x2QC7T8UEREh1q9fLzp27CgqV64sRowYoV7yJ8SXBFutWrVE27ZtxYwZM0RYWJgQQo5+KC0mJkaMHTtW7N69W2RlZYmaNWuKVatWCSGEeP78uejRo4c4evSowlFKKlFRUcLDw0M0aNBANG/eXKxatUo9c1CIL8umJ02aJBo2bKju2MoE2tf17NmzXL/58+fPhY+Pj/j06ZPYunWraNiwofjw4YMQQogDBw6IWbNmqWefyfZQGXIpYP718eNHUa1aNbFp0yb1Z6oXk4ULF4pGjRqJqKgopcKTfkfOxs0/goODhYuLixgyZIhwd3dXD9QlJCSISZMmCQ0NDbF161aFo5RUz6HXr1+LXr16iRIlSojq1auL5cuXi8jISCHElwGF9PR09SqFnN+TlOHv7/+HZey/p+qbXL16VRgaGopHjx59jdAk6W+TO5X+hywsLBg6dChz585lwIAB3Llzh06dOrFw4UIyMzMZPXo0t27dYvfu3Xh5eVGjRg1AbuSrNGNjYxYvXkyLFi1ISEhAX1+f7777DviyaeXTp0+xsLBQ/y0pQ3XYQ6lSpahZsyZZWVncvHmTn3/+GU9PT3799VcSEhIoW7YsTk5ObNq0CX19fbKysuRGzF9RVFQUgwcPxsvLi7CwMAAqVqyIi4sL+vr6mJiYEBUVBUBaWhobN27k+fPn6lM3ZXuoDFUdGTJkCMbGxpw4cYKQkBBOnTrFx48f+f777zl37hyQu4xk3VJeiRIlaNiwIQsWLODQoUO5NjnX0tIiJSWFUqVKKRxlwaY66OHcuXNUqVKF3r17k5iYyPLly5k2bRr9+/end+/eZGRkcOfOHXkKsYIyMzMB2Lp1K56ennz69Iny5cuzZs0agoODATA0NGT+/Pk8f/6cAQMGKBlugZacnExCQoL6OTR48GAiIyNZsWIFzZs3Z/r06XTv3p3du3eTlZVF4cKFKV68uPr78vn19aneo3bt2sXYsWP/8mA2VX9jxIgRuLi4ULly5f95jJL0T2gImR34j+Q8bjwtLY3z589z6NAhTp8+jZGREe7u7vTu3VvhKCX4rayuXLlC8eLFsba2Br4cKe/g4EBKSgrDhg0jICAA+HLSo/g3x5ZLX0dWVhaampp4e3uzfft2vL29cXBwYM+ePWzYsIHnz5/TtWtXunbtSsuWLWVZKeT9+/d4enry8OFDTE1NadWqFd26dcPc3Bz4crR88+bNSUxMxNLSkpcvX/Lo0SOMjY1ztaHS1/fixQvs7OzYvXs3tra26s8/fvyIk5MTVapUYdWqVQpGKP07T548YcKECaSkpFC9enUaN27Mq1ev8Pb2Zs2aNfTu3VvdhkpfT3h4OBUrVlS3axERERw7doz+/fsTEBCAj48PBw4coHTp0gQGBhIWFsakSZMoXLiwLC+FlSlThhkzZuDm5sasWbMICgri4sWLFCpUiF9//ZXWrVtTpkwZpcMs0Dp27IiOjg7Tpk2jaNGiuLi4sH37dsqVKwfAs2fPGDduHKGhoTRv3pzRo0fTtGlThaMuuHK+R82ePRuAadOm/dv3q8zMTLS0tNi6dStTpkwhLCxMnmwr5TnyreU/pOogZWdno6Ojg4ODA5MmTWLy5MmUK1eOOXPm0Lp1a54/f65wpAWbakZSWFgY7u7uHDt2jOTkZODLkfJLly7F0tKS2bNnU6RIEfz8/IDfRpAlZWhqapKVlcWOHTsYPnw4HTt2RFdXlwEDBnD8+HEaN27M9u3b+fnnn4mIiFA63ALL1NSUbdu2sWTJErS1tdmwYQMeHh4EBgby6dMnLCwsuHTpEuPHj6dr164cPnwYY2NjOVMwDzAwMMDIyIgnT56oPxNCULJkSdq0acPNmzeJjo5WMEIJfnsWpaam8u7dOwCsra355Zdf+OGHH7h79y4jRoxg//79eHp6qgfuZDLm65KzcfOvy5cvY2FhwbBhw4iJiWHp0qXMmDEDbW1t3r59y6lTp7h9+7bSYRZ4gwYN4saNG7Rs2VKdiH779i3w5dllZWVFcHAwu3bt4uLFi7x69UrhiCWAnTt38uzZM168eJFrxnROQgi0tLQAmDJlClOnTpXJMylP0lI6gPxKNUqYkJDAqVOnuHv3LgBubm6ULl0aJycn6tSpw7FjxwgMDPzL6arS/5aqUzp06FAaNGjA0KFD0dPTU4+ANGjQgM2bN6v/NjAwIDs7W3ZmFaZ6abS0tOTixYuMHDkSIQTZ2dno6urSv39/Pn78SI8ePbC0tFQ42oIrNTUVXV1djIyMMDU15cqVKwQFBXH37l3OnDlDjx49sLW1ZcqUKbm+J+uX8nIuBTQxMaFt27bqcpFLAfMG1SzNN2/eMGHCBE6dOkWZMmVwdXXF2dkZb29vPn78iJGREZ8+fVIvV5KzO7++rKwsLCwsOHr0KHfu3PnDbNwqVaqQnZ1NtWrVcs3GBVleSlHNeDEzM+Pt27c8fPiQ+fPn06RJEzp27AhAZGQkISEhTJs2TeFopR49etCjRw/mzp3LtGnTSEtLo1y5clhYWOSaHdi6dWvev3+vYKQSgIaGBkIIjh8/jp+fH8WLF8fJyQkHB4c/JNFU72Cenp6ULl2aoUOHKhS1JP3f5BLO/1CfPn24f/8+ZmZmvH//nnfv3uHm5sbMmTPV97x69Yry5cvLzpHCrly5gpOTE2fPnqV8+fLAb431q1eviI+Px8bGRuEoJfgtQR0bG0uhQoXYvHkzixcvZtOmTbRp00Z935UrV/Dy8sLf35+iRYvKOqYAVR3KysqiWLFieHt70759e4oXL86iRYvYsWMHBgYG9O3blzZt2lC7dm2lQ5Z+Ry4FzJuSk5PJyMjAyMgIgFatWpGRkYGrqyuXL19m27Zt1KhRg1GjRtGsWTNKly6tcMSSyrlz51i5ciVPnz6lWrVqODk50bJlS4oWLcqHDx/YuHEjAG3atOGHH36Q9esre//+PSdPnqR///7qz1JSUvjxxx+JiIjgzp07nDt3DhsbGz5//kzXrl0xMTFhx44dsp+hMFXCE+DTp0+MHTtW3Tf08PCgfv36FC1aVH2/3Iol77hy5Qrjx4/n+vXrjBw5kgkTJlC2bNlc93z69IlWrVoxb948mjdvrlCkkvQXvuqRBd8I1ekgQUFBwtjYWH0aYJUqVUSTJk1E6dKlRY0aNcSuXbuUDFP6ncuXL4sKFSqI8+fPCyFyH6V86dIl0adPn1wnqErKa9SokfD19RUZGRnC1dVVaGpqCicnJ3HkyBGxYMECYWNjIwYPHiyE+PtHY0v/G5s3bxZWVlZ/OFHz6tWrolSpUqJMmTJi3rx5CkUnqahOIEtJSRFv375Vfx4RESE8PT1F8+bNhbGxsWjUqJFYsGCBUmFKQogOHTqI7t27i1u3bomnT5+KJk2aiFevXqmvP336VHTs2FEUL15cdO/eXZw5c0bBaCUhvtQrIYS4ffu2GDlypDA3NxdFihQR1atXF2PHjhUXLlxQOEJJCCFWrFghSpYsKbp37y5CQ0PVnz98+FDY2dkJDQ0NMWbMGOHu7i7atm0rqlSpoj6NU57imDekpqaq///69euiXr16QltbW4waNUrcuHFDnu6tsN/3yZOTk9X/v3HjRmFmZibMzc3FqlWrxKdPn3Ld++7du68SoyT9/5JDKP8fVKOEvr6+jB8/nooVK7J+/XrS09P59ddfmTRpEvfu3cPZ2Vl9eo+kvIoVK6Kvr8/Zs2cBco1IBQQEqGcKSsoS/5oUGx4eTkxMDG3atEFLS4t169YREBBAREQEAwcOZMeOHdSpU4f169fn+p6kDAsLCxITEwkNDQW+7O8jhKBevXq0bNkSZ2dnBg8eDMiyUkrOpYADBw7ExsaG77//nhUrVlCkSBG8vb3Zs2cPHz584NChQ/z000/q70lfn9zrJ38RQqCrq0tWVhZ2dnZUqVKFc+fO8fbtW7p06cL+/ftxc3PD29ubW7duKR1ugda5c2fmz59Peno6Q4YMwd3dnVevXlGlShXOnz/P8uXLOXv2LOHh4TRs2JA9e/ZgaGgo9+1UkOpU9tjYWHbv3s3kyZMZOnQod+7coW7duly9epWtW7eyfft22rZtq97nWFKG6h3r4MGDDB06lJEjRzJy5EgSExMZMmQIDx48oF+/fowaNUp9eJuKqampEiFL0t+nZPYuP4uJiRFjx44Vu3fvFllZWaJmzZpi1apVQgghnj9/Lnr06CGOHj2qcJQF25/NSFq8eLHQ0NAQ/fr1E2FhYeLKlSvC19dXGBgYqEeG5ahV3hAcHCwGDRokHj9+/IdrERERIj4+Xl1WssyU9/79e/HDDz+ITp06iejo6FzX2rVrJ7Zv3y6EkDMFlfD582cRHx+v/tvBwUE0bdpUbN++XYwcOVIYGhoKW1tbsWvXLvHhwwcFI5X+zJw5c4Surq7Q0NAQ48ePF+/fv1c6JOn/IGfj5h937twRc+fOFba2tsLGxkYsWLBAZGRkqK+rZp1JeUe3bt1EgwYNxNChQ4W1tbUwMTERkZGRue45duyYEELOFlSKqg5t2bJFVKtWTfTu3VvMmDFDaGhoqPuCKhEREQpEKEn/GZlA+w9kZmaK6OhoERsbKxo3biyOHDkihBAiPDxc1KxZUzx69EgIIV8YlaL63Tdu3CjOnj2r/vzAgQOidu3aQkNDQ5ibm4tatWoJb29vIYR82OYVx48fFxoaGkJDQ0Ns2bJF/XlGRoasT3nY7du3RaVKlUTJkiWFl5eXWLFihRgwYIDQ19cXnz9/Vjq8AksuBcyfcr7IJyUlCRcXF6GhoSHatm0rTp06JZKSknLdL9vGvCEkJESYmJio+x2pqanqsnFychLjxo1TJ0FlmSkjZ18vNTVVnDhxQowdO1bY2NgIe3t7uQVLHqMqr4CAAGFsbCyioqKEEEJUq1ZNzJkzRwjxpf/x+yXSsn4pq3Tp0sLHx0cIIcTMmTNF3bp1RVpamsjIyBBbt26Vg0FSviXnIf8DqmUsV65c4cmTJ2hqalKyZEl0dXUB+Pnnn1m7di3Dhw+nVKlSVK5cWW5eqRDVEcmXLl3Cy8uLmzdvkpSUBECXLl24efMmd+7cYfv27Zw6dQoPDw+FI5ZysrOz49ixY3Tq1IkhQ4YwcuRIkpOT0dLSQkNDQy4py0NiY2O5ffs2r1+/pmbNmty6dYuxY8eyefNmNmzYQFJSEr/++itFihQhMzNT6XALJLkUMH9SbZSdlpZG0aJF2bBhA9euXSMmJoZ27drx888/c/PmTfXSJtnXyBuqVKlC+fLlWbx4MR8/fkRHR0ddNgkJCdStW5cyZcrI/qGCVMsws7Oz0dHRwcHBgUmTJjF58mTKlSvHnDlzaN26Nc+fP1c4Ugl+K69Tp04xdOhQSpUqxZIlS8jIyGDMmDEA3L9/n+3btxMdHa3+nqxfyrl8+TIWFhYMGzaMmJgYli5dyowZM9DW1ubt27ecOnWK27dvKx2mJP1/kadw/k2qE5LCwsIYMmQIAwcOxMXFhSJFigBfkmqLFi3i0qVL/PDDD/j6+lKmTBl5spLCbGxs6NixI/PmzQO+lNPGjRupWbMm7u7u6vtkRzbvycrKIi4ujqCgIGbPnk1iYiLz5s1j+PDhSodW4KlOwfLz82PhwoV8/vyZ4sWL06ZNG6ZNm4aenh4AL1++pEKFCgpHK6nMnTuXOXPmkJaWxrhx4/Dw8KBMmTJKhyX9Ts5TiE+cOMGVK1dITExk1KhR1KxZE4Bdu3YxYsQItLS0CA8Px8DAQOGopZzu3LlDz549iYuLw93dHWNjY27cuEFAQABRUVHqvqP0danqVkJCAqdOneLu3bsAuLm5qU+xffLkCceOHSMwMJAtW7Zgbm6uZMhSDrNmzeLixYvs3r2b7777jjVr1tCrVy8AJkyYQEREBPv27VM4yoJN1T98/fo1jRs35ujRo8yfP5/ExESCgoIAuHr1Kj169OD06dNYWVkpHLEk/XMygfYP1a9fn3r16rF48WL09PRyJV6SkpLUfxsYGMijrhWi+t0vX77MgAEDuH79OoaGhqxbt45ly5ZhZGTE1atXmT17NlOmTFE6XAly1ZXU1FRiYmIwMzNTX3/58iXr1q1j4cKFVKtWjevXr1O4cGGlwpX4Uk4lSpRgxowZlCxZktevX+Pv709GRgZjx46Vic48RNWhhS9HxI8dO5ZNmzbRpk0bPDw8qF+/PkWLFlXfLwcU8obu3bvz7t07atSowblz54iPj+fu3buYmJio7zl+/DitW7eW/Y08IDY2llevXlGiRAnKlSvH58+fWb58OevWrcPIyAgrKysGDRpE586dc9VJ6evr06cP9+/fx8zMjPfv3/Pu3Tvc3NyYOXOm+h7VwVKybuUd169fZ+rUqaSnpwNw+vRpAO7evUvjxo05fPgw9vb2cvLCV/b+/XtOnjxJ//791Z+lpKTw448/EhERwZ07dzh37hw2NjZ8/vyZrl27YmJiwo4dO2T9kvIlmUD7B65cuYKTkxNnz55Vn9aoetF49eoV8fHx2NjYKBxlwZWQkICRkZH677CwMNq3b8/s2bNJSkpi7969dOzYEU9PT+bNm8eTJ0/w9fVFR0dHwailnObMmcPp06e5f/8+DRo0YPLkyTRq1Aj4MnJ8/fp13rx5g6Ojo3zoKkTV5t2+fZvly5ezZcsWAD5//szVq1fx9/fnxIkTaGtrs3v3bqpXr65swJJaWlqaur27ceMGP/74I3fu3GH48OEMGjSImjVrypcOhanatX379uHq6sqjR48oVaoU1atXp0+fPkyZMoU7d+7w6dMnbG1t1d+TSU9lyNm4+YcqqRIcHMygQYO4fv06FStWpGrVqpiYmPD48WNMTEyYMmUKvXv3Vjpc6U8kJyfj6enJ6tWrsbe3x8nJiYcPH3Lnzh3Kli3Lzp07ZVuogJUrVzJ79myaNGnChAkTaNy4MQCPHj3C1dWV0NBQRo8eTWZmJuHh4bx48YIrV65gaGgo+/JSviT/xf5DQgj1/jA5G+l3796xYMECuXeMQjIyMhg1ahS+vr58/PgR+LJ8s3///kyePJnp06czatQoRowYAUBERAQJCQkyeZYHqPbv2b59Oxs2bKB169bs2bOHgwcP0rx5cwYNGsSzZ8/Q1NSkQYMGODo6AnJvC6WoBgxmzJjBs2fPSE1NBUBfX5/mzZvj5eXFnDlzsLa2pnjx4gpHW7Cp6lZsbCy7d+9m8uTJDB06lDt37lC3bl2uXr3K1q1b2b59O23btiU5OVnhiCW510/+oqWlRWpqKm5ubgwYMIBp06bRpUsXDh48SJ06dfD19QWQybM8QDU44Ovry/jx46lYsSLr168nPT2dX3/9lUmTJnHv3j2cnZ0JDg5WOFpJ9fzKyMggKiqK8PBwihQpwi+//EJISAjp6els376dM2fO0K9fP9atWwd8eTeTvq7OnTszf/580tPTGTJkCO7u7rx69YoqVapw/vx5li9fztmzZwkPD6dhw4bs2bMHQ0NDsrKyZPJMyp++7pkF+VtkZGSuE19ymjhxorCzs1MgKkkIIe7fvy8aNmwo6tevL1xdXcX+/ftFenq6yM7OFm/evBEPHjwQQgiRkpIiTp48KQwMDMStW7eEEOIPR81LyrC0tFSf1rNo0SJhY2Mjtm3bJoyNjYWlpaWYOnWqSE9PVzhKSQghdu3aJWrUqCH09fWFl5dXrtMCVWJiYoQQ8mTbvKBbt26iQYMGYujQocLa2lqYmJiIyMjIXPccO3ZMCCHLK6+YOXOmaNOmjYiPjxclSpQQu3fvVl8bP3686Natm4LRSUL8dsLfrVu3xMCBA9Wff/r0SZw+fVqMGDFCVKpUSVSvXl3cu3dPoSilnGJiYsTYsWPF7t27RVZWlqhZs6ZYtWqVEEKI58+fix49eoijR48qHKWUk4uLi6hdu7bQ19cXrVq1EteuXVNfi4yMFKmpqeq/5ambyrpz546YO3eusLW1FTY2NmLBggW5+ocJCQkKRidJ/z0ygfZ/+LOGePHixUJDQ0P069dPhIWFiStXrghfX19hYGCgPj5ZJmSUkZqaKtasWSNatGgh7O3txc8//yzCwsJy3RMUFCRatGghxowZI4SQL4tKU/3+Z8+eFe3btxexsbEiISFBmJmZib179wohhPjxxx9FuXLlRKdOnZQMVcohNTVVnDp1SowcOVJUqlRJNGnSRAQEBCgdlpSDqm4FBAQIY2NjERUVJYQQuQaBbt++rX5uqcgXkLzh2rVrok2bNqJ58+aiefPm6s/DwsJE0aJFxblz54QQsr+htJcvX4ouXboIW1tbkZKSkutaZGSk2L17t+jWrZt4+/atQhFKv5eZmSmio6NFbGysaNy4sThy5IgQQojw8HBRs2ZN8ejRIyGEbAuVpPrtN2/eLMqUKSNWr14tdu/eLVq0aCE0NTXFkCFD/jAIJCkn57tUamqqOHHihBg7dqywsbER9vb2YteuXQpGJ0n/fXIPtP+D+NcSzU2bNmFlZUWTJk0ACAwMZObMmdy+fRszMzNKliyJk5MTnp6eci23QlJTU9HV1VWf1Lh27VoSExOxs7Oja9eudO/eHQsLCx4+fMjTp09p27Yt2trasrwUEh4eTsWKFdW/fUREBMeOHaN///4EBATg4+PDgQMHKF26NIGBgYSFhTFp0iQKFy4sN4fNQ6Kjozl16hTBwcHcvHmTWrVqMW7cOOrXr690aNK/uLu7U7RoURYsWMCSJUvw9fXl5s2bFC1alF9//ZXz588za9YsSpUqpXSoUg5yr5/8Yffu3cydO5fnz58zceJEpk6d+ofDAWJjYzE2Npb9DYWofvcrV65QvHhxrK2tgS+bnDs4OJCSksKwYcMICAgA4MSJE7JuKSjnbz9z5kxMTEz48ccfgS/7eO7fv5/p06cTExPDuHHjmDJliiyrPCJnGxcZGUlISAjBwcGEhYVhamrK2rVrsbS0VDhKSfrPyQTav6F6Sb906RK9evViwoQJuLi45Doq/u7du8TGxlKjRg2KFStGoUKFZAdJYZUrV6ZLly40b94cPT09Vq1axa1bt6hevToDBw7EwcFBfdCA7CApIyoqih49etC0aVN69uypPngjIyODwoULc/ToUdzd3bl48SLFihWjZ8+elChRgs2bNyscecGlatdiYmJ48+YNL168oHHjxuqky+PHjzl16hTbtm3DwsKCXbt2KRyxpDJr1iwuXrzI7t27+e6771izZg29evUCYMKECURERLBv3z6FoyzYVP2NjIwM4uLiSEpK4rvvvgPg7NmzTJo0iUKFCpGUlMSoUaNwcnKSJ33nEWlpaYSGhrJ//36OHTuGqakpY8aMoXv37kqHJvFb3QoLC2PIkCEMHDgQFxcXihQpAnw5HGzRokVcunSJH374AV9fX8qUKSMH6hSk6ptv2bKFixcvYmxsjLe3d657YmNjWbRoEXfv3uXgwYMKRSqp6klCQgKnTp3i7t27ALi5uVG6dGkAnjx5wrFjxwgMDGTLli2Ym5srGbIk/VfIBNpfsLGxoWPHjsybNw/48rDduHEjNWvWxN3dXX2fTMYob+/evYwdO5YXL15QuHBh9efr1q1j7NixmJqaMmTIEKZMmaJglNL79+/x9PTk4cOHmJqa0qpVK7p166Z+qL548YLmzZuTmJiIpaUlL1++5NGjR3IEXyGq3zwhIYH+/ftz9epVrK2tefXqFW5ubkyaNEl974ULF7C0tKRs2bLyBSSPuH79OlOnTiU9PR2A06dPA18GgBo3bszhw4ext7eX5ZUHDB06lJs3b/LkyRMaN27MvHnz+OGHH4AvAw9GRkbqg29knyNvkbNx87b69etTr149Fi9ejJ6eXq76k5SUpP5bJqbzhszMTJycnNi3bx/fffcdR44cwcrKCsjd9qkGXuXzS1l9+vTh/v37mJmZ8f79e969e4ebmxszZ85U3/Pq1SvKly8v65f0TZD/gv9EdnY2AJcvXyY1NVX9grhu3ToGDRpEWFgYo0aNYu7cuervyI6s8nR1dcnOziYsLAz4MjIMMGzYMOzt7fn+++/p0KEDIE/pUZKpqSnbtm1jyZIlaGtrs2HDBjw8PAgMDOTTp09YWFhw6dIlxo8fT9euXTl8+DDGxsbytB6FDRs2jMzMTC5cuMDIkSN58+YNS5cuxdramqCgIADs7OwoW7YsgOzM5hHVqlWjUqVKnDlzhqysLHx8fBg9ejQjR46kY8eO2NvbI4SQ5aUQ1bNoy5YtHDp0iKFDh7Jp0yaysrJo2LAhLi4uREVFYWJikuvUaNnnUIaqfxgTE8OdO3cIDAwkOjqaUqVK4eTkpD7xOyIigqVLlyocrQRfBr6jo6Px9PRET08v17VXr14RERGBoaGheoWJ7GcoT0tLi927dxMcHIyZmRnVq1dn6tSpZGdn52r7VIPl8vn19alOSQ0ODubYsWMcOHCAw4cPk5qaSpUqVfD19cXGxobdu3cDUL58eUDWL+nbIP8V55CQkAD8VrmLFClCcnIyAQEBrFy5Ej8/PwYNGsTly5eZM2cOT58+VSdpJGWoOrOqpbQlS5Zk7969ALleNsqVK4ezszO1atUC5MuHklJTUwEwMjLC1NSU2NhYgoKCmDJlCtOmTSM0NJQyZcowZcoUpkyZop6BITtIX58QgkKFCvHgwQMuXLjA0qVLsbKyYt26dfTv3x8/Pz+Sk5Pp2rUrTZo0ITMzU+mQCzRVhzYjI4OoqCjCw8MpUqQIv/zyCyEhIaSnp7N9+3bOnDlDv379WLduHSAHFJSScybFy5cvmT59OiNGjKBXr14cPnyY7du3c/78eapWrcqcOXNkOSks52zcwYMH06ZNG5YsWUK9evXUS8wqV67MiBEjWLx4sTqBpqqXknKEELx69Ur9/6p69+7dOxYsWKC+JilHVU+Sk5MJCwvj2LFjaGtrM3/+fNasWcP27dupWLEiO3bsUDhSCX7rk/v6+jJ+/HgqVqzI+vXrSU9P59dff2XSpEncu3cPZ2dngoODFY5Wkv67tP76loIhIyODUaNGYWtri6OjIyVLlsTGxob+/fszefJkUlNTWbduHe3atQO+bHqekJCQK0kjfV2qzuy9e/dwc3MjMDCQyZMn4+Liwvnz55k+fTo6OjrcvXuXTZs24enpCcilL0oSQqCrq0tWVhZ2dnZ4e3tz7tw5ihcvzqJFi9ixYwcnT56kb9++tGnThtq1aysdcoGmqie3b9/G1taWKlWqcPToUZ49e4afnx+mpqZ0796dhIQEevXqhZaWlpyeryBVh/bHH3/8w1LApk2bcunSpT9dCijLS1lbtmzh7du3pKSkqD/T0dHBycmJ1q1bs2jRIi5fviyfW3lEztm4N2/epE+fPixdupRNmzaxePFiOnfujJ2dnfp+OfijrIoVK6Kvr8/Zs2exs7PLVY8CAgLUS8skZanqyaBBg7h79y7h4eFYWlpStWpV2rdvz4EDB9i4cSP9+/encOHC6r08JeXExsZSqVIlKlWqRHZ2NqtXr2b8+PGYmZnRpUsXQkNDGTp0KG3atFE6VEn67/oqZ33mA/fv3xcNGzYU9evXF66urmL//v0iPT1dZGdnizdv3ogHDx4IIYRISUkRJ0+eFAYGBuLWrVtCCHmMvNJcXV2Fk5OT+u/bt2+L7t27i0KFColy5cqJ77//XixcuFAIIcsqr9i8ebOwsrL6Q3lcvXpVlCpVSpQpU0bMmzdPoegkIYTIyMhQ/39WVpY4ceKEyM7OFgsWLMhV3xYsWCDGjBmj/lt1/Lz0dal+982bN4syZcqI1atXi927d4sWLVoITU1NMWTIEBEZGalwlNLvZWRkCEdHR6GhoSGsrKzE06dP1ddy1qX09HQhhHyGKUVVFvfv3xdly5YVDx8+FEII0bJlSzFo0CBx9OhRYWZmJjQ0NIS9vX2u9lP6uv7sGbR48WKhoaEh+vXrJ8LCwsSVK1eEr6+vMDAwEBcuXBBCyLqlJNVv/8svv4hy5cqJM2fOiKysLOHv7y/69u0rateuLY4ePSqEEOLGjRtKhir9TmZmpoiOjhaxsbGicePG4siRI0IIIcLDw0XNmjXFo0ePhBCybyh9W2QCLYfU1FSxZs0a0aJFC2Fvby9+/vlnERYWluueoKAg0aJFC/ULY1ZWlgKRSqqH7atXr8TWrVvFihUr/nDP+/fvxaVLl0RCQoL6M9mA5w0hISHCxMREnD17Vgjxpe6pysbJyUmMGzdOvH//Xgghy+xr+rPfeu7cuSIlJUX99/r164W2trY4ceKECAsLEyVKlBA7d+4UQsj2UCk5y23GjBlizZo16r9TU1PFzp07RaVKlYSxsbGYPXu2rFN5TGZmpjh48KBo2rSp0NbWFlOmTJF1KY/asWOH6NmzpxBCiCNHjogKFSqId+/eCSGEGDVqlBgwYIA4ePCgEEK2h0pRtW8bN25U9zGEEOLAgQOidu3aQkNDQ5ibm4tatWoJb29vIYQsq7wgOztb2NnZieXLl+f6PDk5WTg7O4ty5cqJuLg4ZYKT1FR15fLly+Lx48fqz5OTk0Xjxo1F7dq1hY+Pj3BwcBAODg5CCNmPl749ct3Gv6SmpqKjo0Pfvn2pV68eYWFhzJ8/H3d3d5YuXcqLFy8AsLKyYsyYMSxcuFDZgAs41VRvR0dHBg0ahL+/v3o/OiEEWVlZlClThoYNG2JoaKjeO0YugckbqlSpQvny5Vm8eDEfP35ER0dHXTYJCQnUrVuXMmXKyOW2X5nqt96wYQMAY8aMwc/PD11dXfU93bt3p2fPnjg6OtK2bVvs7e1xcnIC5OawSlMtBXz58qX6M9VSwMuXLzNs2DC5FDAPkHv95C8593Z0cnJi2LBhCCEICwujUaNGmJqaAmBubk7x4sXVhxXJevb1ZWVloaGhwaVLl/Dy8uLmzZskJSUB0KVLF27evMmdO3fYvn07p06dwsPDQ+GIJfjSbxdCUKZMGUJDQ4Ev27RkZmaip6fHhAkTMDQ05N27dwpHWrCpDvMKCwvD3d2dY8eOkZycDICenh5Lly7F0tKS2bNnU6RIEfz8/IDf9quWpG+FhhByV9qcKleuTJcuXWjevDl6enqsWrWKW7duUb16dQYOHIiDgwNGRkaA3EsrL7h58yY7d+5k5cqV2Nra4uPjQ+XKlYEvnV4tLbnNX151584devbsSVxcHO7u7hgbG3Pjxg0CAgKIioqiSJEiSodYIF26dImhQ4dStmxZLly4wPHjx7G3twd+q1Ph4eG8fv0aXV1dqlSpQrFixeQx8grLzMzEycmJffv28d1333HkyBGsrKyA3M+qjIwMChcuLMsrD+jVq9ef7vVTr149Nm7cyOrVq9m1a5fc60cBf9a/mzdvHuPHj1cPKGzYsAF3d3cOHTpE6dKlad68OatWrcLJyUnuBakwGxsbOnbsyLx584AvJ3Fu3LiRmjVr4u7urr5P9uPzllWrVuHt7c22bdto0aKF+vNr167RqlUrwsLC5H51eUD9+vWpV68eixcvRk9PL1c9SkpKUv9tYGAg20Lp26TEtLe8as+ePaJs2bLqvUZUfH19hZ6enrC0tBRz5sxRKDrp30lISBAnT54U9vb2QlNTU4wePVp8/vxZ6bCkPxETEyNu3bolXr16JYQQ4tOnT2LOnDmifPnyokaNGqJbt24iMDBQCCHkHjIKSUlJEQcOHBAmJiZCX19fDBo0SBw7dkx9XTUV/9mzZ0qFKP0bcilg3if3+slf1q9fL4QQYvTo0aJKlSq5rsXExIi+ffsKQ0NDUbZsWdG1a1clQpT+RdXWXbp0SVSqVEm9fYevr6+oUqWKaNCggdDQ0JD9+DwsKytL9O/fX2hqaopBgwaJEydOiIULF4oGDRqIQYMGqe+RlHP58mVhYWEhXr58qf5M1S98+fKluHPnjlKhSdJXI2eg5RAcHMywYcM4ePAgdevWJS0tTX1SWZs2bdDV1WXmzJnUqlVLjlopJOfvHhsbi7GxsfpaXFwc+/btY8GCBTx79ozjx4/j4OCgVKjSv6hmLfn5+bFw4UI+f/5M8eLFadOmDdOmTUNPTw+Aly9fUqFCBYWjLdhUZRUZGcmMGTOwtLQkJCSE+Ph4GjRogKurK9WqVSMkJIShQ4fy4MEDeRKxglSzyJKTk3n27Blv3ryhcOHCFC1alAcPHjB79myEEMybN4++ffsqHa70L0IImjRpQo8ePRgzZoz685SUFFxcXLhw4QJhYWEUK1ZMuSAlORs3n0hISFCvDAEICwujffv2zJ49m6SkJPbu3UvHjh3x9PRk3rx5PHnyBF9fX/nsysP27NnDvHnz+PDhAyYmJtjZ2bFixQoKFy4sZzQp7MqVK/Tu3Zvt27djZ2eX673s8uXL/PLLL8yfP1/OFJS+aQV+fZuqIY6NjaVGjRqULFmSvXv3Urdu3VwP13LlyuHg4ECtWrUAubeFElSd0pcvX+Lr60tISAgZGRl4eXnRsmVLihcvjouLCw4ODvj6+lKjRg2lQ5YALS0tUlNTcXNzY8aMGZQsWZLXr1/j7+/P/v37GTt2LMOHD5fJszxAteR54sSJVK1aFQ8PD1q2bMnevXu5ePEio0ePxtzcnCtXrtCzZ090dHTky6KCVL/7oEGD/nQp4IEDB9i4cSP9+/encOHCcilgHiB+t9fPmDFjyM7OJjs7W73XT1hYGO/evZMJNIXVrl2befPmMWzYMDQ1Ndm0aRMpKSm0bt0aLS0thBB89913AOr/ArI9/IoyMjIYNWoUtra2ODo6UrJkSWxsbOjfvz+TJ08mNTWVdevW0a5dOwAiIiJISEiQybM8rlevXvTq1Yvw8HBKliyJgYEBhQoVksmzPKBixYro6+tz9uxZ7Ozscr0PBwQE8OrVK5k8k759Sk19ywtU04Dv3r0rbG1txcePH8WOHTuErq6uaNy4sTh69KgICQkRK1euFBoaGuLJkydCCHmaiNKaNWsmmjVrJtavXy9atGghNDQ0RJMmTcTZs2fVZaoqIznVW1mqcrh165YYOHCg+vNPnz6J06dPixEjRohKlSqJ6tWri3v37ikUpSTEb0tmf/nlF2FlZSVOnDihvhYfHy82b94sxowZI7p27Sp+/PFH9TXZHipDLgXM33755RdhZmYmTp06levzq1evCiMjo1zLY6SvT9UefvjwQbi5uYmFCxeKdu3aiUaNGomxY8eK+/fvCyGEOH36tLC0tBSpqalKhltg3b9/XzRs2FDUr19fuLq6iv3794v09HSRnZ0t3rx5Ix48eCCE+LI1wcmTJ4WBgYG4deuWEOK3NlSSpH/vz/p4ixcvFhoaGqJfv34iLCxMXLlyRfj6+goDAwNx4cIFIYSsX9K3TS7hBIYNG0ZSUhI7d+4EvmxuPmvWLA4cOICZmRlGRkYMGDAADw8POdtCIapRp927dzN+/HiePXuGnp4e1apVo23btty+fZurV6/i5OTE1KlTsbCwUDpk6V9evXrF6NGj+fjxIydPnsx1omNUVBRnzpxh165drFq1irJlyyoYqZSVlYWZmRnLli3D2dmZd+/e4ePjw7p167C3t2fkyJE0a9ZMXR9le6gsIZcC5lvZ2dkMGjSIX3/9lf79+9O3b19u3bpFQEAAVatWZfPmzXK2RR7Qv39/qlatys8//8zNmzfVs3ELFy6sno3bpUsXvL29ZXuokLS0NDZt2oS/vz8ZGRnqk6FzrkIIDg5m+fLl1KhRg+XLl8u6JUl/k/jXEs1NmzZhZWVFkyZNAAgMDGTmzJncvn0bMzMzSpYsiZOTE56enrJ+Sd+8AptAU3V0Xr9+rd7jZ/To0bnu+fDhAy9evKBatWoYGhoC8sQeJeT8zV1cXKhYsSJTp05lyZIlbNmyhevXr3P//n1atGhBSkoKu3btolu3bgpHLans3r2buXPn8vz5cyZOnMjUqVP/cDqqaj87+dBV1p07d3B1dSUwMBBDQ0M8PDy4desWQ4YMYdmyZfzwww9s3ryZQoUKyXZQYeJfSwF79+6NhoYGe/bsUS8F1NLS4saNGwwcOJA9e/ZQrVo1pcOV/g2510/eo9rfbNWqVaxYsQIfHx/1fqoJCQns37+f27dv8/LlS0xNTVmzZg0g+4dKSE1NRVdXl8TERObNm8fatWtJTEzEzs6Orl270r17dywsLHj48CFPnz6lbdu2aGtry7olSX+D6l350qVL9OrViwkTJuDi4oKBgYH6nrt376q3QSpWrJhcaisVCAU2gaZSv359rl+/jp2dHSdOnEBHRwchBNnZ2blGEmXHSBmJiYnq5GVycjIHDhzA0NCQtm3b0rx5c3r27Mno0aNJTU1l3LhxjB49mqpVqyoctZRTWloaoaGh7N+/n2PHjmFqasqYMWPo3r270qFJvxMdHU29evUwNTVFV1eXwoULM3HiRFq3bs3u3bvZuHEj+/bto2jRokqHKv3LqlWr8Pb2Ztu2bbRo0UL9+bVr12jVqhVhYWFyP5J8QO71k7fI2bj5S+XKlenSpQvNmzdHT0+PVatWcevWLapXr87AgQNxcHBQHzQg+/OS9M/Y2NjQsWNH5s2bB3w5SGDjxo3UrFkTd3d39X2ybkkFRYHvna1du5YJEyZw5coV2rVrx+PHj9HQ0EBTU5PMzEz1fbJB+PrWrl3LiBEjOHXqFNnZ2RQpUoQ+ffpgb2+PlpYWBgYGfP78Gfjy8qGagSHlLTo6OrRo0YLp06cza9YszM3NmTJlCs7Ozly9elXp8KQcSpUqxeHDh7GwsKB06dJs2bKF1q1bA1/qo6WlJUWLFqWAj7vkKSNGjKBFixa0bt2awYMHc/LkSRYtWsSoUaPo1q0b5cuXl+1iPvDdd99hZGSkTprJ5Jmy7t27R/ny5WnWrBmfP39mzpw5nDx5kjlz5vDgwQM2bdpEVlaWum8ok2fK2bt3L58+fWLu3Lm0a9eOZs2a4e/vj6enJydPnuSnn35i1apV6vtlf16S/pqq33D58mVSU1OZNGkSAOvWrWPQoEGEhYUxatQo5s6dq/6OrFtSQVHgT+GsU6cOVlZWtG3blpkzZ1K9enXc3d2ZP38+RYoUUTq8Ai0+Pp5Hjx4xZ84czp07R5cuXahTpw5GRkZkZ2dTsWJFvLy8uHDhAs+ePcPBwYHq1avLkXuFqX7/mJgY3rx5w4sXL2jcuDGlSpXCycmJ2rVrc+rUKbZt28bSpUvZtWuX0iFLOVSrVo2dO3eqyzEiIgI/Pz+ePHnC0aNHATnKmJcUKlSIbdu2qUeHjxw5kmspoCRJ/1zZsmWJioqie/fu6tm4M2fOpHXr1hgaGrJx40ZSUlLkbNw8QFdXl+zsbMLCwqhbty5paWno6OgwbNgwAgIC0NXVpUOHDoB8dknSX0lISMg1mFOkSBGSk5MJCAggKSmJvXv3MmjQIDw9PZk3bx5PnjxR1zlJKigK3BLOnA9P1b5LKnFxcezbt48FCxbw7Nkzjh8/rt73QlLGy5cvWbx4MdevX6d48eJ069aNDh06qDeb9/HxISgoiNatWzNs2DD09fVlAk1Bqt8+ISGB/v37c/XqVaytrXn16hVubm7qESyACxcuYGlpSdmyZeXylzzs+PHjbNq0iW7dutG7d29ZVnmcXAooSf8dDx48YPbs2QghWLp0qbrf0bx5cypXrszatWtlQkYhqnYtNjaWxMREOnXqRIcOHfD29s5139ChQ3FwcMDJyUmhSCUp/8jIyMDFxQVbW1scHR0pWbIkAJMnT2bz5s2kpqaybt062rVrh4GBAa6urnz8+JH9+/crHLkkfV0FKoGmevF7+fIlvr6+hISEkJGRgZeXFy1btlTPOFNdHzNmDKVLl1Y46oIp50u6v78/3t7ePHz4EB0dHdq3b0+XLl3o2LEjenp6wG+dKdmZVZaqHHr37k1SUhIrV67k5s2b9OnTB2NjY4oVK8bixYvp3Lmz0qFKf1NGRgbx8fGUKlVK6VAkSZK+ut/PxvX19eX58+fo6OjIBLUCVL/5vXv3cHNzIzAwkGPHjuHi4kKdOnWYPn06Ojo63L17lzFjxvD48WMqVaok+4eS9BcePHiAi4sL2dnZ1KxZk/bt29OhQwe0tLR49+4diYmJVK1aldTUVEJDQ+nWrRvnzp2jVq1acnBVKlAKVAJNpXnz5gD07duXnTt3EhISgr29PbNnz8bOzi5XIkZ2jpShaohXrFiBr68vXl5etGrVil27duHn58f79+9xdHSkbdu2tGrVSulwJX6b3fngwQNatWrFqVOnqFKlCg4ODpQrVw4nJydcXFx49+4ddnZ2nD59+g+ncUqSJElSXiRn4+Ytw4YNIykpiZ07dwJfTpGeNWsWBw4cwMzMDCMjIwYMGICHh4csK0n6m9LS0ti0aRP+/v5kZGRgb2+Pk5MTNWrUUN8THBzM8uXLqVGjBsuXL5fvylKBU2ASaKrKvXv3bsaPH8+zZ8/Q09OjWrVqtG3bltu3b3P16lWcnJyYOnUqFhYWSocsATVr1mTw4MGMHTtW/VlcXBz9+/cnNDSUqlWrsm3bNqysrJQLUsrl119/5cCBA+zZs4ejR4/i5ubGpUuXMDU1ZfTo0SQkJNCrVy86dOggH7qSJElSviBn4ypPlQh7/fo1ISEhxMfHM3r06Fz3fPjwgRcvXlCtWjX1Ke5y9pkk/bXU1FR0dXVJTExk3rx5rF27lsTEROzs7OjatSvdu3fHwsKChw8f8vTpU9q2bYu2trbsy0sFToGY/iGEUFfs48eP8+OPP6Knp8eSJUvQ1NRk/vz53L9/nxYtWuDn50eHDh1kAk1hQgjS09MxMzPjxo0bwJckaHZ2NsWLF8fV1ZX4+Hi6desmk2d5QGZmpno2mZOTEyYmJgghCAsLo1GjRpiamgJgbm5OoUKF1Bv6yg6tJEmSlB8ULlxYJs8UpppF5ujoyPXr17Gzs2P48OHo6OgghCA7O5syZcpQpkwZ4LfEmexrSNJf09XVBaBevXp06dKFnTt3oqenx6pVq1i9ejVnzpxh4MCBODg4ULVqVSD3O7YkFRTf/L/4xMRE9YMzOTmZli1bUqtWLTIzMzlw4ACurq7o6OhQrVo1nJ2duX37Nt26dVM4aklDQwMdHR0cHBw4d+4c58+fp1ChQuokjbm5OcWKFcPV1RX47bhl6evJOXlVVS7z5s0jPT0dBwcHNDQ0MDY2Zt++fZw8eZK7d++ycOFCGjZsCHwpM9mplSRJkiTpn1i7di0TJkzgypUrtGvXjsePH6OhoYGmpiaZmZnq+2QfQ5L+mb179/Lp0yfmzp1Lu3btaNasGf7+/nh6enLy5El++uknVq1apb5f1jGpIPqmE2hr165lxIgRnDp1iuzsbIoUKUKfPn2wt7dHS0sLAwMDPn/+DHw5uWzPnj0yEZPHjBo1inr16tGiRQuGDx/O+fPn8fHx4ccff8TQ0BBDQ0M5+qEQ1UNzw4YNAIwZMwY/Pz/1CBZA9+7d6dmzp3q/OtVeCoAsM0mSJEmS/rE6deowbdo0Dh8+TGZmJtWrV2fMmDEkJyfLvVUl6T+gq6tLdnY2YWFhwJc90eDLnoP29vZ8//336lUkBWQXKEn6g296DzRvb2/8/f0xMDCgSZMmdOnShTp16gBfZr+MGjWK9evX06pVK549e0atWrXYvXu3XMutENXeFhkZGWRkZPDhwwcsLS0B2LZtG/Pnzyc6OpqSJUtiY2PDtm3b1A29LC9lXLp0iaFDh1K2bFkuXLjA8ePHsbe3B35b1hkeHs7r16/R1dWlSpUqFCtWTG7oK0mSJEnS35JzD7PY2FiMjY3V1+Li4ti3bx8LFizg2bNnHD9+HAcHB6VClaR8R/UeFRsbS2JiIp06daJDhw54e3vnum/o0KE4ODioB8IlqaD6phNoAC9fvmTx4sVcv36d4sWL061bNzp06EDZsmUB8PHxISgoiNatWzNs2DD09fVlQkZhw4cP59q1a2hra2Ntbc3UqVOxtrYG4O7du5iYmFC8eHG0tbVlIkZhqampHDt2jGHDhvH582d69uyJs7MzrVu3Bn7r9IaHh/Pdd98pHK0kSZIkSfmJqp/38uVLfH19CQkJISMjAy8vL1q2bEmRIkUA1NfHjBlD6dKlFY5akvIH1TvvvXv3cHNzIzAwkGPHjuHi4kKdOnWYPn06Ojo63L17lzFjxvD48WMqVaokD+aQCrRvNoGWM7Hi7++Pt7c3Dx8+REdHh/bt29OlSxc6duyInp4e8FsDIhsEZah+9wULFrBu3TqGDBlCVlYWZ86c4enTpzg7O+Pl5YW+vr7SoUr/opphFhkZyYwZM7C0tFSfitWgQQNcXV2pVq0aISEhDB06lAcPHqCjo6N02JIkSZIk5TPNmzcHoG/fvuzcuZOQkBDs7e2ZPXs2dnZ2ufrwciBckv6ZYcOGkZSUxM6dOwG4c+cOs2bN4sCBA5iZmWFkZMSAAQPw8PCQkxekAu+bT6CtWLECX19fvLy8aNWqFbt27cLPz4/379+r92Vq1aqV0uEWaKoOjxCCMWPGYG9vT8+ePQG4ffs2Bw8exN/fn7S0NNzd3Rk5cqTCEUs59e/fn6pVq/Lzzz9z8+ZN9u7dy8WLFylcuDDm5uZcuXKFLl264O3tLR+6kiRJkiT9LapE2O7duxk/fjzPnj1DT0+PatWq0bZtW27fvs3Vq1dxcnJi6tSpWFhYKB2yJOUbqj7569ev1QPgo0ePznXPhw8fePHiBdWqVcPQ0BBATjaRCrxvNoGmUrNmTQYPHszYsWPVn8XFxdG/f39CQ0OpWrUq27Ztw8rKSrkgCzhVQ7x582aCg4NxcHBgxIgR6uufP3/m+vXrbNiwAQMDA9asWaNgtBL8Nvts1apVrFixAh8fH/WeIwkJCezfv5/bt2/z8uVLTE1N1WUmH7qSJEmSJP2VnP0FFxcXKlasyNSpU1myZAlbtmzh+vXr3L9/nxYtWpCSksKuXbvo1q2bwlFLUv5Tv359rl+/jp2dHSdOnEBHRwchBNnZ2bkGvWUfXpK++GaPqhFCkJ6ejpmZGTdu3AC+jGRlZ2dTvHhxXF1diY+Pp1u3bjJ5pjANDQ0SExMJCAjgypUrREZG0qNHD0xMTADQ19enadOmVK1aFQMDAwA5PV9hWlpaZGVlMWfOHJYtW4aDgwPv3r3Dx8eHdevWYW9vz8iRI2nWrJn6ZFs5+0ySJEmSpL+SmJionu2SnJxMy5YtMTQ0JDMzkwMHDuDq6oqOjg7VqlXD2dmZ0aNHU7VqVYWjlqT8ae3atezcuZOVK1fSrl07fHx8qFy5MpqamuoBc0AmzyTpX77ZDISGhgY6Ojo4ODhw7tw5zp8/T6FChdSNgLm5OcWKFcPV1RVA/ZIvKcPQ0BAvLy8mTpxIXFwcLVu2ZMmSJWRlZanvMTExUe9ZJ5Nnyrt37x7ly5enWbNmfP78mTlz5nDy5EnmzJnDgwcP2LRpE1lZWeoHrkyeSZIkSZL0f1m7di0jRozg1KlTZGdnU6RIEfr06YO9vT1aWloYGBjw+fNnAMLDw9mzZ4/sw0vSf6BOnTpMmzaNw4cPk5mZSfXq1RkzZgzJycnq92ZJkn7zzS/hzMjIwNnZmcDAQIYMGUK/fv24d+8emzdvxsrKil9//VVOSc1D0tLSOH/+PAcPHiQkJIQSJUrg5uZGr169lA5N+p3o6Gjq1auHqakpurq6FC5cmIkTJ9K6dWt2797Nxo0b2bdvH0WLFlU6VEmSJEmS8gFvb2/8/f0xMDCgSZMmdOnShTp16gBfBrtHjRrF+vXradWqFc+ePaNWrVrs3r1brkyQpL8p53tvbGwsxsbG6mtxcXHs27ePBQsW8OzZM44fP67eokWSpC++qQSaaolYRkYGGRkZfPjwAUtLSwC2bdvG/PnziY6OpmTJktjY2LBt2zZ0dXXlQ1dhiYmJREREoKurS+XKlQGIjIwkJCSEQ4cOceTIEfbv34+9vb3CkUq/9+DBA2bPno0QgqVLl1K2bFngy2lZlStXZu3atTJBLUmSJEnS3/by5UsWL17M9evXKV68ON26daNDhw7qPoaPjw9BQUG0bt2aYcOGoa+vL/vykvQ3qN6VX758ia+vLyEhIWRkZODl5UXLli0pUqQIgPr6mDFjKF26tMJRS1Le8k0l0FSGDx/OtWvX0NbWxtramqlTp2JtbQ3A3bt3MTExoXjx4mhra8t9mRSi+t137tyJj48P0dHRaGpq0qRJE6ZNm4apqSkA9+/f5+HDh/To0UPhiKX/i6rjGhERgZ+fH76+vjx//hwdHR3ZqZUkSZIk6S/l7JP7+/vj7e3Nw4cP0dHRoX379nTp0oWOHTuqt/NQ9S/kQJ0k/TPNmzcHoG/fvuzcuZOQkBDs7e2ZPXs2dnZ2ueqV7MdLUm7fTG1Q5QEXLFjAyZMncXR0pH379rx584aWLVvy008/8fnzZ2rUqEHp0qXR1tYG5L5MSlCd6hIbG4u7uztdunRh27ZtlCpVig0bNlC9enXmz58PQPXq1dXJs28w1/vNUD1Ynz59yoMHD1i6dCk6OjpkZWXJh64kSZIkSX/bihUrmD59Oh4eHrx+/Zo5c+YQHh6Oh4cHU6dO5cSJE8BvfQ+ZPJOkv6baK3D37t08efKEw4cPM3ToUN6/f8/YsWPR1NSkffv2DBs2jBcvXqjrlezHS1Ju38QMNFWGXAjBmDFjsLe3p2fPngDcvn2bgwcP4u/vT1paGu7u7owcOVLhiAs2VXm5ubnx7t07goKCiIiIwMbGhnXr1nH06FH8/PwwMjIiODgYW1tb2TnKJzIyMoiPj6dUqVJKhyJJkiRJUj5Us2ZNBg8ezNixY9WfxcXF0b9/f0JDQ6latSrbtm3DyspKuSAlKR/JOUvTxcWFihUrMnXqVJYsWcKWLVu4fv069+/fp0WLFqSkpLBr1y66deumcNSSlDd9U0drbNmyhTdv3hAdHa3+rFatWlSqVAl7e3s2bNjAgwcPFIxQgi8jhXFxcURGRtKvXz8ARo0aRd++fXF2dqZ8+fKEhYVha2tL1apVZfIsHylcuLBMnkmSJEmS9I8JIUhPT8fMzIwbN24AX2bNZGdnU7x4cVxdXYmPj6dbt24yeSZJf1NiYiKGhoYAJCcn07JlSwwNDcnMzOTAgQO4urqio6NDtWrVcHZ2ZvTo0VStWlXhqCUp7/om5mRqaGiQmJhIQEAA58+fZ8eOHURFRamv6+vr07RpU5YsWcKSJUsA5JHXCitevDgTJ07E3Nycjx8/Eh0djaOjIwDW1taUL1+ewYMHU6JECVlWkiRJkiRJ3zgNDQ10dHRwcHDg3LlznD9/nkKFCqGl9WW839zcnGLFiuHq6grIvrwk/ZW1a9cyYsQITp06RXZ2NkWKFKFPnz7Y29ujpaWFgYEBnz9/BiA8PJw9e/bIeiVJf+GbSKABGBoa4uXlxcSJE4mLi6Nly5YsWbKErKws9T0mJibqjUflem7lnDlzhps3b2Jra0vjxo0pXLgw8fHxHD58GIAdO3Zw+/Zt6tatC8iykiRJkiRJKihGjRpFvXr1aNGiBcOHD+f8+fP4+Pjw448/YmhoiKGhIUII2T+UpL8QHx/Po0ePmDNnDjNnzuTmzZsAGBkZkZ2dTcWKFfHy8qJDhw50794dBwcHqlevLpNokvR/+Cb2QMspLS2N8+fPc/DgQUJCQihRogRubm706tVL6dAKJNWa++fPn5OUlETNmjUpVKgQ27dvp0+fPur71qxZw9KlS4mJicHAwABvb2/69OkjT0mVJEmSJEn6Rqn6eRkZGWRkZPDhwwcsLS0B2LZtG/Pnzyc6OpqSJUtiY2PDtm3b0NXVlScDStLf9PLlSxYvXsz169cpXrw43bp1o0OHDpQtWxYAHx8fgoKCaN26NcOGDUNfX1/WL0n6P3wTCbTExEQiIiLQ1dWlcuXKAERGRhISEsKhQ4c4cuQI+/fvx97eXuFIC67hw4dz584ddHR0SEpKUo+AqDpOcXFxXLx4kaioKCpUqECLFi0UjliSJEmSJEn6GoYPH861a9fQ1tbG2tqaqVOnYm1tDcDdu3cxMTGhePHiaGtry8FVSfobctYTf39/vL29efjwITo6OrRv354uXbrQsWNH9eosVdIs54EDkiT9Ub5NoKkahZ07d+Lj40N0dDSampo0adKEadOmYWpqCsD9+/d5+PAhPXr0UDjigu38+fNs2LABPz8/GjVqhLu7O46Ojujo6KjvSUtLQ1NTU73XhWzAJUmSJEmSvk2qft6CBQtYt24dQ4YMISsrizNnzvD06VOcnZ3x8vJCX19f6VAlKd9RvSuvWLECX19fvLy8aNWqFbt27cLPz4/379/j6OhI27ZtadWqldLhSlK+kS8TaKoMeWxsLFZWVkyZMoUmTZrw008/ERoaStGiRfHw8GDy5Mm5vicTMsry8fHh5MmT6Onp8fTpUypVqsTAgQPVjbajoyOtWrXCzc1N4UglSZIkSZKk/xVVn1wIwZgxY7C3t6dnz54A3L59m4MHD+Lv709aWhru7u6MHDlS4YglKX+qWbMmgwcPZuzYserP4uLi6N+/P6GhoVStWpVt27bJk20l6W/Klwk01UPXzc2Nd+/eERQUREREBDY2Nqxbt46jR4/i5+eHkZERwcHB2NraysSZQlSjH8nJyQAUKVKEqKgoduzYwbFjx/j8+TPW1tZYWFgwa9YsIiMjMTY2VjhqSZIkSZIk6X9F1ZffvHkzwcHBODg4MGLECPX1z58/c/36dTZs2ICBgQFr1qxRMFpJyn+EEKSnp9OtWzdKlCiBn58f2dnZZGdno6WlRWBgIIsWLaJbt25MmDBB6XAlKd/QUjqA/x8aGhrExcURGRlJv379gC8n9vTt2xdnZ2fKly9PWFgYtra2VK1aVSbPFKRaez9w4EAaN27MkCFDMDExYdy4cbRs2ZJ9+/Zx9uxZ7t+/z7p16zA2NiYzM1O9jFOSJEmSJEn6tmhoaJCYmEhAQABXrlwhMjKSHj16YGJiAoC+vj5NmzalatWqGBgYAMiNzSXpH9DQ0EBHRwcHBwdWrFjB+fPnsbe3V9chc3NzihUrhqurKyDrlyT9XflyBppKaGgoGhoaWFtb06FDB+bMmUOrVq2Ijo5m6NChTJ8+nbp168oGQSGqRNjq1atZvnw569evp1mzZsCXqcPa2tro6+vz/v17jI2N1fuhyaW2kiRJkiRJ375r165x+vRptm7diqamJoMGDWLs2LHykABJ+i/JyMjA2dmZwMBAhgwZQr9+/bh37x6bN2/GysqKX3/9Vb57SdI/kC8TaGfOnMHQ0JA6deoAkJCQQP369Wnfvj3Lli1j+fLlLFu2jJcvXyocqZSVlYWZmRnLli3D2dmZ9+/f4+vryy+//IKNjQ0LFiygfv36SocpSZIkSZIkKSAtLY3z589z8OBBQkJCKFGiBG5ubvTq1Uvp0CQpX1FtnZORkUFGRgYfPnzA0tISgG3btjF//nyio6MpWbIkNjY2bNu2DV1dXTnZRJL+gTyfQFNlxJ8/f05SUhI1a9akUKFCbN++nT59+qjvW7NmDUuXLiUmJgYDAwO8vb3p06ePPOpaYXfu3MHV1ZXAwEAMDQ3x8PDg1q1buLi4sGLFCmxsbNi+fbsc9ZAkSZIkSSogEhMTiYiIQFdXl8qVKwMQGRlJSEgIhw4d4siRI+zfvx97e3uFI5Wk/Gf48OFcu3YNbW1trK2tmTp1KtbW1gDcvXsXExMTihcvjra2tnxXlqR/KM8n0FSGDx/OnTt30NHRISkpiZs3bwK/Zdrj4uK4ePEiUVFRVKhQgRYtWigcsQQQHR1NvXr1MDU1RVdXl8KFCzNx4kRat27N7t272bhxI/v27aNo0aJKhypJkiRJkiT9j6j67Dt37sTHx4fo6Gg0NTVp0qQJ06ZNw9TUFID79+/z8OFDevTooXDEkpR/qCadLFiwgHXr1jFkyBCysrI4c+YMT58+xdnZGS8vL/T19ZUOVZLytXyTQDt//jwbNmzAz8+PRo0a4e7ujqOjo3rfLPgyBVxTU1O9Ab1cz503PHjwgNmzZyOEYOnSpZQtWxaA5s2bU7lyZdauXSvLSpIkSZIk6RulWiIWGxuLlZUVU6ZMoUmTJvz000+EhoZStGhRPDw8mDx5cq7vyf6hJP01VT0RQjBmzBjs7e3p2bMnALdv3+bgwYP4+/uTlpaGu7s7I0eOVDhiScq/8k0CDcDHx4eTJ0+ip6fH06dPqVSpEgMHDqRVq1YAODo60qpVK9zc3BSOVPozqs5TREQEfn5++Pr68vz5c3R0dOTae0mSJEmSpG+U6gXfzc2Nd+/eERQUREREBDY2Nqxbt46jR4/i5+eHkZERwcHB2NraysSZJP1Nqvq1efNmgoODcXBwYMSIEerrnz9/5vr162zYsAEDAwPWrFmjYLSSlL/l+QSaarp3cnIyAEWKFCEqKoodO3Zw7NgxPn/+jLW1NRYWFsyaNYvIyEiMjY0Vjlr6vxw/fpxNmzbRrVs3evfuLdfeS5IkSZIkfePi4uIYMmQIzs7O9OrVi44dO2Jubs7atWsJDQ1l5MiR2NraMnPmTEqUKKF0uJKUryQmJtKnTx+uXLmCtbU1+/fvx8TEJNc9UVFRGBgYoKenJycvSNL/pzyfQFPp2bMnjRs3ZsiQIRgZGQEQFhbGvn37OHv2LCkpKQwfPpzBgweTmZmpXsYp5T0ZGRnEx8dTqlQppUORJEmSJEmSvpLQ0FA0NDSwtramQ4cOzJkzh1atWhEdHc3QoUOZPn06devWlS/3kvT/4dq1a5w+fZqtW7eiqanJoEGDGDt2rJyoIEn/RXk6gaZKhK1evZrly5ezfv16mjVrBnwZxdLW1kZfX5/3799jbGys3g9N7pcgSZIkSZIkSXnDmTNnMDQ0pE6dOgAkJCRQv3592rdvz7Jly1i+fDnLli3j5cuXCkcqSflbWloa58+f5+DBg4SEhFCiRAnc3Nzo1auX0qFJ0jchTyfQ4MsSTjMzM5YtW4azszPv37/H19eXX375BRsbGxYsWED9+vWVDlOSJEmSJEmSCjzVQPbz589JSkqiZs2aFCpUiO3bt9OnTx/1fWvWrGHp0qXExMRgYGCAt7c3ffr0kVt7SNI/lJiYSEREBLq6ulSuXBmAyMhIQkJCOHToEEeOHGH//v3Y29srHKkk5X95PoF2584dXF1dCQwMxNDQEA8PD27duoWLiwsrVqzAxsaG7du3yxlnkiRJkiRJkpRHDB8+nDt37qCjo0NSUhI3b94EftvfOC4ujosXLxIVFUWFChVo0aKFwhFLUv6hqkc7d+7Ex8eH6OhoNDU1adKkCdOmTcPU1BSA+/fv8/DhQ3r06KFwxJL0bcjzCbTo6Gjq1auHqakpurq6FC5cmIkTJ9K6dWt2797Nxo0b2bdvH0WLFlU6VEmSJEmSJEmSgPPnz7Nhwwb8/Pxo1KgR7u7uODo6qrdcgS/LzTQ1NdV7F8ttWCTpr6n2CIyNjcXKyoopU6bQpEkTfvrpJ0JDQylatCgeHh5Mnjw51/dk/ZKk/1yeT6ABPHjwgNmzZyOEYOnSpZQtWxaA5s2bU7lyZdauXSsbBEmSJEmSJEnKQ3x8fDh58iR6eno8ffqUSpUqMXDgQFq1agWAo6MjrVq1ws3NTeFIJSn/UL33urm58e7dO4KCgoiIiMDGxoZ169Zx9OhR/Pz8MDIyIjg4GFtbW/meLEn/JfkigaaiyrZHRETg5+eHr68vz58/R0dHR57WI0mSJEmSJEkKUy0tS05OBqBIkSJERUWxY8cOjh07xufPn7G2tsbCwoJZs2YRGRmJsbGxwlFLUv4SFxfHkCFDcHZ2plevXnTs2BFzc3PWrl1LaGgoI0eOxNbWlpkzsou+LgAACb9JREFUZ1KiRAmlw5Wkb0a+yjipEmRPnz7lwYMHLF26FB0dHbKysmTyTJIkSZIkSZIUpjoAYODAgf+vvfsLberu4zj+SZo0IYmtjcQ6XTWtIg6mzum2zuFW69ZN0QsrInOCSu3mHPhnON2VtkYnLTSz/ukaqzRQ64VXG2qhyJgX6sUm004pSjXd8CJYhbZqSqUn8bmQ5lkf92RuVpPo+wWBnJNzTr45ENJ++P5+PwUCAfX29mr06NHatGmTqqurNW/ePIVCIZ08eVIHDx6U2+2WYRhJrhpILzk5Odq8ebNefvll3b59W7du3dKSJUskSZMnT9b48eO1evVqjRo1SrFYLMnVAs8PS7IL+Dfmzp2rGTNmyOPxSBIr9QAAAABJZhiGLBaLDhw4oIsXL+qLL75Qdna2pIcdMxMnTlRFRYXC4bDcbnd8PjT+lgce3+nTp5WVlaV33nlHktTb26uenh61tLTogw8+UHNzsy5evKiZM2dKEo0mwDBKy2+T1WqNh2cAAAAAks9isSgajcrn82nHjh0qKipSOBxWRUWFJk2apIULF+rnn3/WSy+9NGQxAeZnAv7a4GxLoVBIbW1tkqTi4mJduXIlfkx2drY2bNig48ePKycnR36/X7t375b0cEg1gOGTVnOgAQAAAEhdbW1tKi8v1w8//KCsrCx99dVXunDhgsrKylRbW6tp06bpyJEjhGbAP/DZZ5+pra1NNptNd+/e1a+//irpv3MOdnd369y5c+rq6tKECRNUXFyc5IqB51NaDuEEAAAAkHrGjh2rrq4ulZaWym63y2q1qrKyUiUlJRoxYoQOHz6sSCQil8uV7FKBtLFixQr19/erqalJb7/9to4ePaolS5bEOzlzcnL0/vvvKyMjQxbLw3/xB1frBDB80nIIJwAAAIDU4/F41NLSIq/Xq9zcXAWDQZWUlEiS6uvrVVBQIJfLJQbBAI9vzpw5Kiws1OLFi5Wfn6/a2lqVlZXp1KlT8WOWL1+uQ4cOxbcJz4DhxxBOAAAAAMMuFovJbDars7NTTU1NCgQCCoVCstls8dcA/H+DQzT7+vokSQ6HQ11dXWpublZra6sikYgmT54sr9erHTt26ObNm3K73UmuGnh+8asFAAAAYNgNBmQdHR1qb2+X3++XzWZTNBolPAMew+AKtStXrlQgEFBvb69Gjx6tTZs2qbq6WvPmzVMoFNLJkyd18OBBud1uGYaR5KqB5xcdaAAAAACemoGBAfX09Mjj8SS7FCBtGIYhi8WiAwcOaM+ePWpoaFBRUZEkqbu7W5mZmXI6nQqHw3K73fH50Jj7DHh6CNAAAAAAAEgx0WhU48aN07fffquPP/5Y4XBYgUBA+/bt07Rp01RVVaU333wz2WUCLwx6pwEAAAAASDGXL1/W+PHjVVRUpEgkIp/Pp9bWVlVVVen27duqra1lQQ7gGbIkuwAAAAAAADDU2LFj1dXVpdLSUtntdlmtVlVWVqqkpEQjRozQ4cOHFYlE5HK5kl0q8EKgAw0AAAAAgBTj8XjU0tIir9er3NxcBYNBlZSUSJLq6+tVUFAgl8tFFxrwjDAHGgAAAAAAKSwWi8lsNquzs1NNTU0KBAIKhUKy2Wzx1wA8XXzLAAAAAABIYYMBWUdHh9rb2+X3+2Wz2RSNRgnPgGeEDjQAAAAAANLAwMCAenp65PF4kl0K8MIhQAMAAAAAAAASoNcTAAAAAAAASIAADQAAAAAAAEiAAA0AAAAAAABIgAANAAAAAAAASIAADQAAAAAAAEiAAA0AAAAAAABIgAANAAAgxaxatUomk+mRx7Vr15742sFgUCNHjnzyIgEAAF4glmQXAAAAgEd99NFHamxsHLLP4/EkqZq/NjAwIKvVmuwyAAAAnjo60AAAAFKQzWbTmDFjhjwyMjJ0/PhxzZw5U3a7XQUFBaqsrJRhGPHz/H6/pk6dKqfTqby8PK1bt0737t2TJJ0+fVqrV69Wb29vvKutoqJCkmQymfT9998PqWHkyJEKBoOSpN9//10mk0nHjh1TUVGR7Ha7jhw5IklqbGzUK6+8IrvdrilTpqiuru6p3x8AAIBniQ40AACANNHa2qoVK1Zo7969mjNnjq5fv65PP/1UkrR9+3ZJktls1t69e+X1etXZ2al169Zpy5Ytqqur0+zZs7Vnzx5t27ZNV69elSS5XK5/VMPWrVtVU1OjxsZG2Ww2NTQ0aPv27dq/f79mzJihCxcuqLy8XE6nUytXrhzeGwAAAJAkBGgAAAAp6MSJE0PCrfnz5+vmzZv6+uuv48FUQUGBfD6ftmzZEg/QNm7cGD8nPz9fPp9Pn3/+uerq6pSZmans7GyZTCaNGTPmX9W1ceNGlZaWxrd9Pp9qamri+/Lz89Xe3q5AIECABgAAnhsEaAAAAClo7ty5+u677+LbTqdTkyZN0i+//KJ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"text/plain": [ "" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -428,9 +543,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Based on the coefficients, `Shucked weight` influences the model the most towards predicting that an abalone is `young`, whereas `whole_weight` influences the model the most towards predicting that an abalone is `old`.\n", + "Based on the coefficients, shucked weight influences the model the most towards predicting that an abalone is young, whereas whole_weight influences the model the most towards predicting that an abalone is old.\n", "\n", - "It is interesting to observe that the `Whole weight` of an abalone and the `Shucked weight` of an abalone are influence the predictions in opposite directions. By observing the distribution of `shucked weight` (Figure 2), the shapes of the distributions are quite similar between `old` and `young` abalone, and at no point in the distribution are there more examples of `old` abalone compared to `young` abalone. In contrast, above a certain threshold, there are more examples of `old` abalone in the `whole weight` and `shucked weight` distributions. Understanding the distributions of these weight features helps us to understand why different types of weight are influencing the prediction in opposite directions. It would be useful to consult a domain expert to see if they would have insight in the differences between `old` and `young` abalone in regard to the different types of weight features. However, it's imperative that one takes these feature importances and model performance with a grain of salt, considering that the dataset is imbalanced and that these statistical models don't necessarily explain how the real world works." + "It is interesting to observe that the whole weight of an abalone and the shucked weight of an abalone are influence the predictions in opposite directions. By observing the distribution of shucked weight (Figure 2), the shapes of the distributions are quite similar between old and young abalone, and at no point in the distribution are there more examples of old abalone compared to young abalone. In contrast, above a certain threshold, there are more examples of old abalone in the whole weight and shucked weight distributions. Understanding the distributions of these weight features helps us to understand why different types of weight are influencing the prediction in opposite directions. It would be useful to consult a domain expert to see if they would have insight in the differences between old and young abalone in regard to the different types of weight features. However, it's imperative that one takes these feature importances and model performance with a grain of salt, considering that the dataset is imbalanced and that these statistical models don't necessarily explain how the real world works." ] }, { @@ -439,7 +554,7 @@ "source": [ "## Summary of Findings\n", "\n", - "Based on the model results, we can see that the logistic regression model is performing well on new examples of abalone, as described by an f1 score of 0.90. Moreover, given a certain set of biological features of abalone, we're able to predict whether an abalone is `old` or `young` fairly accurately while minimizing false negatives and false positives. We were able to obtain these results by testing different values for the model's hyperparameter, C, on various validation sets of the abalone training data in order to obtain an optimal logistic regression model (where $C = 100$). We also obtained the coefficients of the various biological features that helped us understand how the features were influencing the prediction. The weight features (`shucked weight`, `whole weight`, and `shell weight`) specifically had a large influence on the model's predictions. Contrasting the distributions of these weight features between the `old` and `young` abalone helped us to investigate why `shucked weight` was having an opposite predictive effect in comparison with `whole weight` and `shell weight`, although consulting with domain experts may help us further understand this opposing effect. Overall, the model's ability to predict whether an abalone is `young` or `old` based on specific biological characteristics is good but should be taken with a grain of salt given the imbalance of `young` and `old` abalone within the dataset, as well as some of the limitations of the included biological characteristics." + "Based on the model results, we can see that the logistic regression model is performing well on new examples of abalone, as described by an f1 score of 0.90 and ROC AUC score of 0.86. We focus on these two metrics because they evaluate overall performance of model instead of weighing one class over another. Moreover, given a certain set of biological features of abalone, we're able to predict whether an abalone is old or young fairly accurately while minimizing false negatives and false positives. We were able to obtain these results by testing different values for the model's hyperparameter, C, on various validation sets of the abalone training data in order to obtain an optimal logistic regression model (where $C = 100$). We also obtained the coefficients of the various biological features that helped us understand how the features were influencing the prediction. The weight features (shucked weight, whole weight, and shell weight) specifically had a large influence on the model's predictions. Contrasting the distributions of these weight features between the old and young abalone helped us to investigate why shucked weight was having an opposite predictive effect in comparison with whole weight and shell weight, although consulting with domain experts may help us further understand this opposing effect. Overall, the model's ability to predict whether an abalone is young or old based on specific biological characteristics is good but should be taken with a grain of salt given the imbalance of young and old abalone within the dataset, as well as some of the limitations of the included biological characteristics." ] }, { @@ -447,11 +562,11 @@ "metadata": {}, "source": [ "## Limitations and assumptions\n", - "One limitation is that we found some of the input features are highly correlated. For example, the correlation between `whole weight` and `length` of abalone is 0.97, indicating that these two features are highly positively correlated. This will potentially raise the multicollinearity concern. As a result, it can become difficult for the model to estimate the relationship between each independent variable and the dependent variable independently. One method to address correlated features is to use recursive feature elimination to exclude features with little importance so we can fit a more interpretable model. Additionally, the high correlation between many of the features may insinuate that many of the features are redundant and the inclusion of all of them may be unnecessary. For example, including both `Diameter` and `Length` conveys very similar messages about the biology of the abalone, and may indicate that it is unnecessary to include both of these biological features. Since our primary goal is to make classification on the abalone age (old or young), and we don’t need to understand the role of each independent variable such as `weight` and `height`, we did not take additional actions to reduce the multicollinearity problem in this project.\n", + "One limitation is that we found some of the input features are highly correlated. For example, the correlation between whole weight and length of abalone is 0.97, indicating that these two features are highly positively correlated. This will potentially raise the multicollinearity concern. As a result, it can become difficult for the model to estimate the relationship between each independent variable and the dependent variable independently. One method to address correlated features is to use recursive feature elimination to exclude features with little importance so we can fit a more interpretable model. Additionally, the high correlation between many of the features may insinuate that many of the features are redundant and the inclusion of all of them may be unnecessary. For example, including both diameter and length conveys very similar messages about the biology of the abalone, and may indicate that it is unnecessary to include both of these biological features. Since our primary goal is to make classification on the abalone age (old or young), and we don’t need to understand the role of each independent variable such as weight and height, we did not take additional actions to reduce the multicollinearity problem in this project.\n", "\n", "We fit a logistic regression and tuned it by using grid search. Other classification models like decision tree or KNN can be used in this project. We chose logistic regression for its good interpretability and its performance. However, with better feature engineering or better model selection, the performance can be improved.\n", "\n", - "In regard of the `Sex` feature, the `infant` category for `Sex` of a abalone is included in this project which may be unnecessary and may harm the validity of the model. It is interesting that the researchers that collected this data included an `Infant` category within the `Sex` feature, and makes us ponder the significance of its inclusion. Perhaps with consultation with domain experts, the significance of this collection method can be elucidated. In future additional analysis and after consultation with domain experts, we might consider removing the `Infant` category or the `Sex` feature altogether, since being an infant inherently indicates that the abalone is young, and therefore makes the predictive model redundant.\n", + "In regard of the sex feature, the infant category for sex of a abalone is included in this project which may be unnecessary and may harm the validity of the model. It is interesting that the researchers that collected this data included an Infant category within the sex feature, and makes us ponder the significance of its inclusion. Perhaps with consultation with domain experts, the significance of this collection method can be elucidated. In future additional analysis and after consultation with domain experts, we might consider removing the Infant category or the sex feature altogether, since being an infant inherently indicates that the abalone is young, and therefore makes the predictive model redundant.\n", "\n", "The lack of the domain knowledge to feature engineer the model inputs was a pronounced limitation throughout the project. Because of this limitation, we included all features in the data set in our classification model for predicting age. However, once greater knowledge is achieved through domain expert consultation, we may be able to conduct additional feature engineering and feature selection that would potentially improve the model's performance and reliability." ] From a6a68a4a580b63f33e6dfdf165978c4005ef025c Mon Sep 17 00:00:00 2001 From: nickmao Date: Sat, 11 Dec 2021 01:47:32 -0800 Subject: [PATCH 4/6] Rename variables in output table --- src/models/model_building.ipynb | 161 +++++++++++++++++++------------- src/models/test.py | 5 +- 2 files changed, 100 insertions(+), 66 deletions(-) diff --git a/src/models/model_building.ipynb b/src/models/model_building.ipynb index fb0d81c..40fa92e 100644 --- a/src/models/model_building.ipynb +++ b/src/models/model_building.ipynb @@ -262,43 +262,43 @@ " 1\n", " 0.826403\n", " 100.0\n", - " 0.032202\n", + " 0.035000\n", " \n", " \n", " 1\n", " 0.826403\n", " 1000.0\n", - " 0.032600\n", + " 0.035802\n", " \n", " \n", " 1\n", " 0.826403\n", " 10000.0\n", - " 0.028999\n", + " 0.034600\n", " \n", " \n", " 1\n", " 0.826403\n", " 100000.0\n", - " 0.025032\n", + " 0.028197\n", " \n", " \n", " 5\n", " 0.826104\n", " 10.0\n", - " 0.039601\n", + " 0.044797\n", " \n", " \n", " 6\n", " 0.822811\n", " 1.0\n", - " 0.035598\n", + " 0.036799\n", " \n", " \n", " 7\n", " 0.820115\n", " 0.1\n", - " 0.031198\n", + " 0.033999\n", " \n", " \n", " 8\n", @@ -310,7 +310,7 @@ " 9\n", " 0.775518\n", " 0.001\n", - " 0.022802\n", + " 0.025201\n", " \n", " \n", "\n", @@ -319,15 +319,15 @@ "text/plain": [ " mean_test_score param_logisticregression__C mean_fit_time\n", "rank_test_score \n", - "1 0.826403 100.0 0.032202\n", - "1 0.826403 1000.0 0.032600\n", - "1 0.826403 10000.0 0.028999\n", - "1 0.826403 100000.0 0.025032\n", - "5 0.826104 10.0 0.039601\n", - "6 0.822811 1.0 0.035598\n", - "7 0.820115 0.1 0.031198\n", + "1 0.826403 100.0 0.035000\n", + "1 0.826403 1000.0 0.035802\n", + "1 0.826403 10000.0 0.034600\n", + "1 0.826403 100000.0 0.028197\n", + "5 0.826104 10.0 0.044797\n", + "6 0.822811 1.0 0.036799\n", + "7 0.820115 0.1 0.033999\n", "8 0.798865 0.01 0.026000\n", - "9 0.775518 0.001 0.022802" + "9 0.775518 0.001 0.025201" ] }, "execution_count": 8, @@ -355,21 +355,21 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 9, "id": "0ceb31ba-187b-4c3a-b64d-0c0254290a73", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'mean_fit_time': array([0.02280178, 0.02600026, 0.03119793, 0.03559761, 0.03960142,\n", - " 0.03220162, 0.03260002, 0.02899852, 0.025032 ]),\n", - " 'std_fit_time': array([0.00213641, 0.00141364, 0.0043089 , 0.00233214, 0.00185531,\n", - " 0.0045342 , 0.0030072 , 0.0012655 , 0.00357964]),\n", - " 'mean_score_time': array([0.00579982, 0.00600138, 0.00600133, 0.00540013, 0.00539956,\n", - " 0.00539856, 0.00519876, 0.00479918, 0.00313435]),\n", - " 'std_score_time': array([0.00040045, 0.00063362, 0.00063264, 0.00049021, 0.00049059,\n", - " 0.00048965, 0.00040016, 0.00116672, 0.00046815]),\n", + "{'mean_fit_time': array([0.02520132, 0.02600031, 0.03399911, 0.03679924, 0.04479666,\n", + " 0.03499999, 0.03580189, 0.0346004 , 0.02819681]),\n", + " 'std_fit_time': array([0.00256093, 0.00209856, 0.00753827, 0.0028561 , 0.00299379,\n", + " 0.00802471, 0.00847307, 0.00241646, 0.00285644]),\n", + " 'mean_score_time': array([0.0058001 , 0.0078002 , 0.00660033, 0.00599794, 0.00780091,\n", + " 0.00679955, 0.00599856, 0.00539851, 0.004 ]),\n", + " 'std_score_time': array([0.00097881, 0.00222761, 0.00174244, 0.00063203, 0.00271298,\n", + " 0.0024011 , 0.00063167, 0.00048842, 0.00089394]),\n", " 'param_logisticregression__C': masked_array(data=[0.001, 0.01, 0.1, 1.0, 10.0, 100.0, 1000.0, 10000.0,\n", " 100000.0],\n", " mask=[False, False, False, False, False, False, False, False,\n", @@ -402,7 +402,7 @@ " 'rank_test_score': array([9, 8, 7, 6, 5, 1, 1, 1, 1])}" ] }, - "execution_count": 18, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -413,7 +413,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 10, "id": "932476fc-12d7-472c-b91b-0160ae99a4f1", "metadata": {}, "outputs": [ @@ -445,7 +445,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 11, "id": "d0801f74-8a26-4e51-9f8f-d06862a88b27", "metadata": {}, "outputs": [ @@ -455,7 +455,7 @@ "0.8433014354066986" ] }, - "execution_count": 22, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -473,7 +473,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "4bb8be65-b488-4487-986d-5a0b10c3b8f6", "metadata": {}, "outputs": [ @@ -497,7 +497,7 @@ " LogisticRegression(C=100.0, max_iter=2000))])" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -508,7 +508,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 30, "id": "fbf8316b-ef03-45cb-bcd9-ba4237cd7aaa", "metadata": {}, "outputs": [ @@ -538,43 +538,43 @@ " \n", " \n", " \n", - " standardscaler__Shucked weight\n", + " Shucked weight\n", " 3.405154\n", " \n", " \n", - " onehotencoder__Sex_I\n", + " Sex_I\n", " 0.576915\n", " \n", " \n", - " standardscaler__Viscera weight\n", + " Viscera weight\n", " 0.521920\n", " \n", " \n", - " standardscaler__Length\n", + " Length\n", " 0.471001\n", " \n", " \n", - " standardscaler__Height\n", + " Height\n", " -0.260923\n", " \n", " \n", - " onehotencoder__Sex_M\n", + " Sex_M\n", " -0.287910\n", " \n", " \n", - " onehotencoder__Sex_F\n", + " Sex_F\n", " -0.291111\n", " \n", " \n", - " standardscaler__Diameter\n", + " Diameter\n", " -0.483618\n", " \n", " \n", - " standardscaler__Shell weight\n", + " Shell weight\n", " -1.438815\n", " \n", " \n", - " standardscaler__Whole weight\n", + " Whole weight\n", " -2.920781\n", " \n", " \n", @@ -582,43 +582,74 @@ "" ], "text/plain": [ - " Coefficient\n", - "standardscaler__Shucked weight 3.405154\n", - "onehotencoder__Sex_I 0.576915\n", - "standardscaler__Viscera weight 0.521920\n", - "standardscaler__Length 0.471001\n", - "standardscaler__Height -0.260923\n", - "onehotencoder__Sex_M -0.287910\n", - "onehotencoder__Sex_F -0.291111\n", - "standardscaler__Diameter -0.483618\n", - "standardscaler__Shell weight -1.438815\n", - "standardscaler__Whole weight -2.920781" + " Coefficient\n", + "Shucked weight 3.405154\n", + "Sex_I 0.576915\n", + "Viscera weight 0.521920\n", + "Length 0.471001\n", + "Height -0.260923\n", + "Sex_M -0.287910\n", + "Sex_F -0.291111\n", + "Diameter -0.483618\n", + "Shell weight -1.438815\n", + "Whole weight -2.920781" ] }, - "execution_count": 12, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "feature_names = np.array(pipe_best[:-1].get_feature_names_out())\n", + "name = []\n", + "\n", + "for n in feature_names.tolist():\n", + " name.append(n.split('__')[1])\n", + "\n", "coeffs = pipe_best.named_steps[\"logisticregression\"].coef_.flatten()\n", - "coeff_df = pd.DataFrame(coeffs, index=feature_names, columns=[\"Coefficient\"])\n", + "coeff_df = pd.DataFrame(coeffs, index=name, columns=[\"Coefficient\"])\n", "coeff_df_sorted = coeff_df.sort_values(by=\"Coefficient\", ascending=False)\n", "coeff_df_sorted" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "ed665628-c2cb-4ab7-a560-c73206ba6a29", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "['Length',\n", + " 'Diameter',\n", + " 'Height',\n", + " 'Whole weight',\n", + " 'Shucked weight',\n", + " 'Viscera weight',\n", + " 'Shell weight',\n", + " 'Sex_F',\n", + " 'Sex_I',\n", + " 'Sex_M']" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "name = []\n", + "\n", + "for n in feature_names.tolist():\n", + " name.append(n.split('__')[1])\n", + "name" + ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "id": "563a6c5f-f065-4eb3-a80d-3a880cd6968a", "metadata": {}, "outputs": [ @@ -628,7 +659,7 @@ "array([0, 1])" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -639,7 +670,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "id": "d95fbcf2-21fa-416f-b0cd-6d7611bb58d1", "metadata": {}, "outputs": [], @@ -695,7 +726,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "id": "8092c271-1711-4774-a49b-a208b24a31e8", "metadata": {}, "outputs": [ @@ -719,7 +750,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "id": "3b162d44-921d-447c-9b96-55584ece2cb8", "metadata": {}, "outputs": [], @@ -734,7 +765,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "id": "a3fcfe40-1204-4b35-aca7-a6a451037996", "metadata": {}, "outputs": [ @@ -808,7 +839,7 @@ "5 average_precision 0.945271" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } diff --git a/src/models/test.py b/src/models/test.py index 994467b..c63f653 100644 --- a/src/models/test.py +++ b/src/models/test.py @@ -104,8 +104,11 @@ def coeff_plot(best_model, out_dir): """ logger.info("Drawing bar plot for coefficents...") feature_names = np.array(best_model[:-1].get_feature_names_out()) + name = [] + for n in feature_names.tolist(): + name.append(n.split('__')[1]) coeffs = best_model.named_steps["logisticregression"].coef_.flatten() - coeff_df = pd.DataFrame(coeffs, index=feature_names, columns=["Coefficient"]) + coeff_df = pd.DataFrame(coeffs, index=name, columns=["Coefficient"]) coeff_df_sorted = coeff_df.sort_values(by="Coefficient", ascending=False) coeff_df_sorted.to_html(os.path.join(out_dir, "coeff_sorted.html"), escape=False) visualize_coefficients(coeffs, feature_names, n_top_features=5) From e4dc1d11d9cd30db173a8752cddc682bf9c243d1 Mon Sep 17 00:00:00 2001 From: nickmao Date: Sat, 11 Dec 2021 01:49:21 -0800 Subject: [PATCH 5/6] Update figures --- 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z1-8`$q8WN_J0$qH)l84{74eA7lC@AcQHK;DOP9h-Z#;8l#kCvS;ZZ1qvGJ3=x3J zT@nsk>4Y;f_(~S5&cZ25&0yDBbG(smYwCi#8^fsk7PZ)b1#8h<-WSTiPSE zy|rZmFb{Fgh Date: Sat, 11 Dec 2021 01:49:49 -0800 Subject: [PATCH 6/6] update final report --- docs/Project_report_milestone2.ipynb | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/docs/Project_report_milestone2.ipynb b/docs/Project_report_milestone2.ipynb index f29cbea..5eee7eb 100644 --- a/docs/Project_report_milestone2.ipynb +++ b/docs/Project_report_milestone2.ipynb @@ -231,7 +231,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -241,7 +241,7 @@ "" ] }, - "execution_count": 12, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -434,7 +434,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -449,43 +449,43 @@ " \n", " \n", " \n", - " standardscaler__Shucked weight\n", + " Shucked weight\n", " 4.142553\n", " \n", " \n", - " onehotencoder__Sex_I\n", + " Sex_I\n", " 0.983024\n", " \n", " \n", - " standardscaler__Viscera weight\n", + " Viscera weight\n", " 0.818804\n", " \n", " \n", - " standardscaler__Length\n", + " Length\n", " 0.537147\n", " \n", " \n", - " onehotencoder__Sex_F\n", + " Sex_F\n", " 0.129540\n", " \n", " \n", - " onehotencoder__Sex_M\n", + " Sex_M\n", " 0.121077\n", " \n", " \n", - " standardscaler__Height\n", + " Height\n", " -0.248307\n", " \n", " \n", - " standardscaler__Diameter\n", + " Diameter\n", " -0.538840\n", " \n", " \n", - " standardscaler__Shell weight\n", + " Shell weight\n", " -1.070738\n", " \n", " \n", - " standardscaler__Whole weight\n", + " Whole weight\n", " -4.306860\n", " \n", " \n", @@ -495,7 +495,7 @@ "" ] }, - "execution_count": 10, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }