{ "cells": [ { "cell_type": "markdown", "id": "01f78ac4", "metadata": {}, "source": [ "# Clustering Sampling Techniques Comparison\n", "\n", "This notebook compares the performance of different clustering sampling techniques on the nlbse24 dataset.

\n", "\n", "Given the number of samples to draw K, the clustering sampling methods compared are the following:\n", "- K-means with K clusters (it plans to draw one sample from each cluster)(the drawn sample is the centroid of the cluster)\n", "- K-medoids with K clusters (it plans to draw one sample from each cluster)(the drawn sample is the medoid of the cluster)\n", "- K-means with D clusters (it plans to draw S samples from each clusters, so that S*D=K)(the drawn samples are those closer to the centroid of the cluster)\n", "- K-medoids with D clusters (it plans to draw S samples from each clusters, so that S*D=K)(the drawn samples are those closer to the medoid of the cluster)

\n", "\n", "First, a Grid Search on the number of clusters and the number of samples to draw and on the clustering method (means or medoid) is performed to find the best hyperparameters for the clustering sampling techniques.

\n", "\n", "Then, the best hyperparameters are used to train and the clustering sampling techniques on the nlbse24 dataset. The plot of the clustering and of the sample drawn is stored in the apposite folders.

\n", "\n", "For further information, please check the MLflow experiments for name \"Clustering_Sampling\"." ] }, { "cell_type": "code", "execution_count": null, "id": "8b229320", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "c:\\Users\\utente\\Desktop\\uni\\anno 2 magistrale\\semestre 1\\software engineering for ai\\Capibara\\.venv\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import os\n", "import matplotlib.pyplot as plt\n", "from syntetic_issue_report_data_generation.config import RAW_DATA_DIR, DATASET_CONFIGs, DATA_DIR, EMBEDDING_DIR, FIGURES_DIR, SAMPLES_DIR\n", "from syntetic_issue_report_data_generation.utils.clustering_sampling import ClusteringSampling\n", "from syntetic_issue_report_data_generation.utils.text_encoder import Encoder\n", "\n", "dataset_name = \"nlbse24_train\"\n", "dataset_folder = \"nlbse24\"" ] }, { "cell_type": "markdown", "id": "24ee7f59", "metadata": {}, "source": [ "Load the dataset, get all the bodies and extract embeddings for them. The embeddings are exracted using gemmma encoder and then reduced to 2 dimensional vectors using UMAP.
\n", "The embeddings are saved in the embeddings folder" ] }, { "cell_type": "code", "execution_count": 3, "id": "2329463d", "metadata": {}, "outputs": [], "source": [ "# Load the dataset\n", "df = pd.read_csv(\n", " RAW_DATA_DIR / f\"{DATASET_CONFIGs[dataset_name]['data_path']}\",\n", " sep=DATASET_CONFIGs[dataset_name].get('sep', ','),\n", " encoding=\"utf-8\",\n", " )\n", "\n", "# Encode the bodies of the dataset, obtain the embeddings\n", "bodies = [str(x) for x in df[DATASET_CONFIGs[dataset_name][\"body_col\"]].tolist()]\n", "\n", "embeddings_path = EMBEDDING_DIR / \"issue-report-classification\" / dataset_folder / f\"{dataset_name}_embeddings.npy\"\n", "\n", "if not os.path.exists(embeddings_path):\n", " encoder = Encoder()\n", " embeddings = encoder.encode(bodies, save=True, save_path=embeddings_path)\n", "else:\n", " embeddings = np.load(embeddings_path)" ] }, { "cell_type": "code", "execution_count": 4, "id": "4670b914", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1500, 2)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "embeddings.shape" ] }, { "cell_type": "markdown", "id": "538f3c8b", "metadata": {}, "source": [ "To tune the hyperparameters of number of clusters, number of samples to draw and the clustering method (medoid or means), we can use a Grid Search.
\n", "In the followig code we evaluate the clustering sampling performance using differen parameters for the hperparameters.

\n", "\n", "The evaluation metrics used are:
\n", "- Sampling metrics:
\n", " - Distributions mean norm similarity: the norm of the difference between the mean of the sample and the mean the original dataset
\n", " - Distributions std norm similarity: the norm of the difference between the std of the sample and the std the original dataset
\n", " - Kolmogorov-Smirnov Test: Compare the KS-test between the sample and the original dataset. Used to test if accept, with a confidence level c, the hypothesis that the sample comes from the same distribution of the original dataset.
\n", "It is False if we can accept the hypothesis that the sample comes from the same distribution of the original dataset.
\n", "- Clustering metrics:
\n", " - Silhouette score: Estimates the average distance among the clusters
\n", " - Mean squared error: Calculates the for each cluster, the squared distances between each sample and the centroid/medoid and then make the mean of these values
" ] }, { "cell_type": "code", "execution_count": null, "id": "f1b54a74", "metadata": {}, "outputs": [], "source": [ "# n_clusters, n_samples hyperparameter tuning\n", "n_clusters_values = [2, 5, 10, 20]\n", "n_samples_values = [20, 30, 40, 50, 60, 70, 80, 90, 100]\n", "\n", "# Create a dictionary to store the results\n", "hp_results = {}\n", "\n", "for n_clusters in n_clusters_values:\n", " for n_samples in n_samples_values:\n", " hp_results[(n_clusters, n_samples)] = {\n", " \"means\": {\"samples\": {}, \"evaluation\": {}},\n", " \"medoid\": {\"samples\": {}, \"evaluation\": {}},\n", " }\n", "\n", " print(\"evaluation for n_clusters = \", n_clusters, \"and n_samples = \", n_samples)\n", "\n", " # define the clustering models\n", " clustering_models = {\n", " \"means\": ClusteringSampling(n_clusters=n_clusters, n_samples=n_samples, method=\"means\"),\n", " \"medoid\": ClusteringSampling(n_clusters=n_clusters, n_samples=n_samples, method=\"medoid\"),\n", " }\n", "\n", " # run the models\n", " for model_name, model in clustering_models.items():\n", " hp_results[(n_clusters, n_samples)][model_name][\"samples\"], hp_results[(n_clusters, n_samples)][model_name][\"evaluation\"] = model.train_and_eval(embeddings, dataset_name, save=False, save_figures_folder=None, save_samples_folder=None)" ] }, { "cell_type": "markdown", "id": "29275dc1", "metadata": {}, "source": [ "Here we plot the results of the hyper parameter tuning of the clustering sampling techniques." ] }, { "cell_type": "code", "execution_count": null, "id": "8c602f41", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For the K-means\n", "The best number of clusters for mse is 20\n", "With a mse of 0.20207472737630208\n", "For the K-medoid\n", "The best number of clusters for mse is 20\n", "With a mse of 0.369772705078125\n" ] }, { "data": { "image/png": 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S8EgAJL0xO3fuVIFOXocPH1bH6M9Tvnx59O7dW/XqSEAjvS/Ozs5o2bKl4Tn169dXQ1d6cox89+qDH9GwYUN1jDyWV0JCggpw5LtYT16jVatWhtvNmjVDjx49VNAzZMgQzJ49Gzdu3EBZM2sPkHS/ybigRINycSS4kQ/j9OnTKqLN67fffsNrr72motsOHTrgzJkzqrtOstxnzJhRonOS5Tm3+280d9Dhkltt6Bo9hucOeuAb1y/hdOhXwMUD6PepVMIydzOJyIp4uDip3hhzvG5xVatWTQ0/devWTQ1ZSa/MypUrDZWP9ctBGJOgaPz48RgzZozK4ckvCVmCqQkTJqi8n7wkYVq+U81BlrOQobQdO3Zg7dq1+Prrr/H6669j9+7dKg/JJnuAJGgZN26cSu6SCFKCFilxbZzMZUwuzv3334/HHntMRce9evVS44bGPTzFPSdZ3vCX+/nNaj89pBue71EHGx3aYHL6v6GDA7D3B2DdmzKgb+6mEpEVkT+UZSjK1FtJp6HLUJDk2MiirhIE+fj4IDQ0VG0SIOUlvSojRoxQ+TP5DX8JyQk6ceKE4TyhRpurq6vq7ZGhqP379xueI50Hsq6angyHSY6QbHpyTjlGvnPzqlSpklq0VIIZvbyvIeQ6yff722+/jYMHD6r25J3ZZjMBUHp6uroAkultaIyjo7ot3Xf5kV4feY4+4JGuPomK+/XrV+JzirS0NCQmJubayDwOXbiBVlmH1H61Vg8iyMcTj7YKwrLsjvi+Us5fLTu+ArZ8zI+IiGyaDDNJQCNJyDKScbfvJkmajo2NVccWlFgtHQmS9Hzo0CGEhYWpXCN9ErSkl0iwJb1EErDI9+nYsWNz9TjJ96kMVckQ24EDB9T3sQRekkdkPKxl7IUXXsCHH36Iv/76C6dOncLTTz+dK6iS1/rggw+wb98+lYe0ZMkS9T6Mc49sKgCKi4tTU+MCAgJy3S+3JeLNj/T8vPPOO+jYsSNcXFxQu3ZtdO3aFf/5z39KfE59YphEqfrNeGyTTGvf7m3wd4hHmoM7XGt2UPc92z0Urs6OmB7dFuda/p924OYPgB1f8+MhIptWvXp1FQTJ99vdgiDpNfH19S2w10lyi6RXSYa6OnXqhBYtWqik6apVqxqO+emnn9RtCWgkKVmG1YzTR+TcEjRJ8cXOnTurgKhWrVoq/aQgUr9IZqrJtPz27durukXGM9tkKv3WrVtVZ0bdunXxf//3f2rmWH65TqXJQVfW88wKcPnyZdWNJ9GoXBA9qQ8gH5Bxd5me/BAMGzYM7733nsrvCQ8PV5GlDHm98cYbJTqnvgdINj35AZMgSJK35IMh05AfxW/ffw5PZ/6MmMCu8P/3MsNjby0/jrk7ItE8yAtLG++Aw6b3tAf6fwa0HsuPiIgMZGKMJPVK/ohMhiH7+XwTExNVR0ZRvr/NlgQtUaokPhkXTxJyOzAwMN/nSJAjUaR0yQnphpNCSRKhSsJUSc4p3Nzc1EbmdfhiApqn7QecAO+mfXI99nS32li4NwqHLsRjU48R6N4xFdg+A1jxEuBSDmg+3GztJiIi62O2ITDpqpOpdsbrhchUPLlt3HtjLDU1VeX0GJOAR997UJJzkuVYd+gsWjmeVvsu9Xrlesy/gjtGtA9R+zPWnYGu+xtA239rDy57Gjj+l+kbTEREVsuss8BkurrM9583b56qHzBx4kTVo6Mv+S2JVVI8SU+m9kmxJinxLd1fMm1OeoXkfn0gdLdzkmWSADb26Hq4OmQh1bM64FPrjmMmdK6Fcq5OOHYpEWtOxAC9pwMtngR02cCfY4Azpi9yRkRE1smsdYCkdLZkeksSliQpy9ohq1evNiQxSza4cY+PJEZJApb8/9KlS/Dz81PBz/vvv1/kc5JlOnopAQ1T96mfSNd6D+Rb56dyeTeMur8m/rspHJ+vO4NeDQPg+NCXQMZN4NgfwKIngcd/B2p1Nct7ICIi62G2JGhLVpwkKiod01edxNCdg1DL8Sow9FegwYP5HpeQmoGOH29E0q1MfD28BR5qVlVbJ+z3p4DTKwAXT+DJpUCNdvxoiOwUk6Bt261SSoI2+1IYRBKDHzp8WAU/2Q7OQM3OBV6USp4uGNtRGx77fP0ZZGZlA04uwJCfgNrdgYxU4NchwOWDvLBERFQgBkBkdscvJyI0SStRoKveGnAvPGof3TEEXp4uOBebgmWHLmt3OrtpPUfB9wNpicDPDwPRJ0zRfCIiskIMgMjsVhy9gs6OR9S+U50edz2+grsLJnSurfa/3BCGDOkFEq6ewGOLgGotgZs3gPkDgWtny7bxRERklRgAkdmHv9YeuYAOjse1O2rfPQAST3UIhm95V0RdT8Wf+y/efsCtAvDEn0BAEyAlBpg3AIiPKqPWExGRtWIARGZ14koiKt84jAoON6HzqAxUaV6k58kig//uovUCfb0xHGmZWbcf9PDWEqF96wKJF7UgKPFKWb0FIiKyQgyAyKxWHb2Kzk7a8JdDaHdZvbbIz32iXTACKrrhUvxN/L739srESnk/YMQywCsYuBGhDYelxJV284mIStXIkSMxaNCgXPf98ccfaraTrI+Vd3koKQ0j63LJzChje/fuVY+VdDV6e8AAiMw6/LXSKP+nqMNfeu4uTnimW6ihF+hWhlEvkKhYFXhqOVCxGhB3Gvh5kJYbRERkJX744Qe18roUAZZFRfMji4suXbo0130//vgjatSoYaJWWicGQGQ2p64mIT7uCho7RGp3yDT2YhraOghVK7kjJikNv+w6f+cB3iFaT1A5P+DqUW2KfFpSKbSeiKhsffzxx3juuefU6geFrWYgq6zPmTPHcPvmzZvqOXJ/Xtu3b1crwXt4eKhFv59//nm1WoLezz//jFatWqmgStbQfOyxxxATE3NHr5MsMSXHeXp6okOHDjh9WlvGSBw+fBjdunVT55BaPLJE1b59+2BpGACR2aw6egUdHY/C0UGnJS1XKH61bjdnJzzfo47an7XlLFLTM+88yLeOFgS5ewEX9wILhmvVo4nIfkjN3/QU028lrDX86quv4t1338Xff/+Nhx9+uNBjZZHwbdu2qdUTxJ9//omQkBDcd999uY47e/Ys+vTpg8GDB+PIkSNYtGiRCoieffZZwzEZGRnqdSWI+euvvxAZGamG5fKSBchlSE4CG2dnZ4wePdrwmPRYVa9eXQ3D7d+/H6+99hpcXFxgacy6FAbZ9/CXTH9/Oif/B5L/U0KDW1bHN5vPqhlh83acx8SuWnJ0LgGNgCeXAPMGApHbgEVPAMN+0+oHEZHtkyKpH1Q1/ev+5zLgWq5YT1m1ahWWLVumelm6d7/770Z/f3/07dsXc+fOVctASW+QcUCiN336dBWcTJo0Sd2uU6cOvvrqK3Tp0kUNsUmekfHzatWqpR5v3bo1kpOTUb58ecNjsgSVPE9IgNO/f3+VhyTnkEDslVdeQf369Q2vY4nYA0RmERaTjLOxyejkeLRE+T/GXJwc8UJOL9B3W88i6VZG/gdKfaDHFwPOHkD4em0B1ax8eoyIiMyoadOmqgdn2rRpKvAQjRo1UgGIbBLs5CWBiwRA586dw86dO1Wgk5f06sgx+vOUL18evXv3RnZ2tlpaQkiPjayxKflDMoSlD3L0vUvGbdSrUqWK+r9+qEwWJR87dix69uyJDz/8UPU8WSL2AJFZrDhyBQ0couDvEK+t33WPa3cNalENMzeHq+rQc7ZH4oWeBfzFEdweGP4b8NtQ4OT/gL8mAg/PAhyd7un1icjCye8Z6Y0xx+sWU7Vq1dTML8mjkSEr6RFauXKlGp4Skr+TlwRF48ePx5gxY1QAU7ly5TuOkWBqwoQJKu8nLwl4JBdIAiLZfv31V7XguAQ+cjs9PT3X8cZDWvqZZhJIibfeekvlDq1YsUK1XQI5yUm621CeqTEAIrOQ2V89HA9rN0I63fNQlJOjAyb1rIvnFxzED9vPYWSHELVuWL4k2frR+dow2NHfARcPQFaV53RRItsl/76LORRlTsHBwdiyZYshCFq9erXqkSmI5OGMGDFCJU5L0JEfyQk6ceIEQkO12bN5HT16FNeuXVO9NpIgLUqavFy3bl21vfjiixg+fDh++ukniwuAOARGJhcWnaSGwLoY8n9KPvxl7MEmVVAvoIJaKX72tnOFH1yvL/DI94CDI3BgHrDmPyVOViQiKgsShMisKxlakl4YWem8MJK8HBsbq44tKLF6x44dKun50KFDCAsLU7lG+iRo6QVydXXF119/rYbSli9frs5ZHDIDTc4n7T5//jz++ecflQzdoEEDWBoGQGRyK49ehSduobXjGe2O0J6lcl5HRwe8+EBdtf/TPxG4npK7y/YOjQcDA/6r7e/6Btj0fqm0g4iotMhsKgkm4uLi7hoESfDi6+tbYPFDyduRXqUzZ86oqfAtWrRQSdNVq2rJ4TLkJTlCixcvRsOGDVVP0Kefflqs9jo5OaleJOmNkh6gRx99VA3Pvf3227A0DjqZjkO5yA9YpUqVkJCQoGoYUOnq88VWVI3Zgjmun2qVml84XGrDT/Lj/NB/t+PYpURM6FwLU/sV4a+OPbOBlS9r+z2mAZ0ml0pbiMg8ZDaSJPXWrFlTzUoi+/l8E4vx/c0eIDIpmfklBRC7Gg9/lWLujfzlMzmnF2jezkjEJOUuD5+vNuOAnjl/nWx4G9g1q9TaQ0RElokBEJnUyiPaoqS93Iq3+ntxdKvnj+ZBXriVkY1vNhVx+mXHSUCXV7X91a8CB+aXeruIiMhyMAAik1p57CqqO8QgMPMS4OgM1Oxc6q8hvUAv9dJ6gX7bHYUrCUWs+tx1KtA+pyLq8ueBo3+UetuIiMgyMAAik4mIS8HJK4m3h7+qtwHcyybHqmOoL9rU9EF6Vjb+uzG8aE+Sobhe7wGtpBKqDlgyHji1okzaR0RE5sUAiExa+0cMqnDqnpe/KFIvUE4u0O/7LuDC9dSiPhHo9xnQdBigywIWj9SqRhOR1eEcH9ukK6W5WwyAyKQBkDMy0SzjcJnl/xhrW6uy6gnKyNLh641hRX+ioyMwcCbQcCCQlQ4sfAKI/Kcsm0pEpUhfpTg1tYh/+JBV0X+u97rAKitBk0mcv5aC45cT0c4pHC6ZKYBnZaBK8zJ/3cm96mJ7eBz+PHAJE7uGoqZvESvBOjkDj/wAZNwCwtYAvz0KjFgOVG9Z1k0monsktWi8vLwMa1N5enoWWBuHrKvnR4If+Vzl85XP+V4wACKTFT8Uw3zOAEk5y1FIT0sZu6+GN7rV88Om07H4cv0ZfDGsRdGf7OwKPDpPC34itgK/PAyMXAEENinLJhNRKQgMDFT/1wdBZDu8vLwMn++9YABEJs3/6eRwxCTDX8YmP1BPBUDLDl/GM91CUSeg4PV07iDrhA1bAPzyCHBhNzB/EDBqFeCn5RcRkWWSHh9Zpdzf39+wiChZPxcXl3vu+dFjAERlThKQj15KgK9DInwST2p3Sg+QiTSpXgm9GwVgzfFofLE+DDMfv694J3ArDzz2OzB/AHDlsPZ/CYJ8apZVk4molMiXZWl9YZJtYRI0maz356mACDjI9PKAJkCFAJNeeVkjTFIAVhy9ghOXC19QMF8eXsATSwG/BkDSFS0ISrhYFk0lIiITYABEJguA+nmeKPPp7wWpH1gR/ZtUUfufr89ZhLW4ylUGRvwF+NQC4qOA+QOBZOYXEBFZIwZAVKYu3kjF4YsJcHTIRkj8LpPn/xib1LMuHB2AdSeicfhCfMlOUiFQmw1WKQi4Fq7lBKVeL+2mEhFRGWMARGVqVc7sr39VS4BTaizg4gnUaGeWqx7qXx6DmldT+zPWlbAXSHgFASOWAeUDgZjjWoL0rYTSaygREZU5BkBUplYe04a/hnrnBBwhnQBnN7Nd9Rd61oGTowO2nInF/vP30HNTubYWBHn4AJcPAr8NBdJTSrOpRERUhhgAUZm5HH8TB6PiVfJxk1v7tDtDzTP8pRdcuRyGtKyu9j9bew+9QMK/vpYT5FYJiNoJLHxMK5xIREQWjwEQlXnyc8cgD7he2qPdGdrT7Ff82e6hcHFywI6z17Dz7LV7O1mVZsATfwAu5YBzm7W1w7JYc4SIyNIxAKIys+qYlv8zouoFIDsD8ArWZlCZWXVvTwxrXUPtz1h3+t4X1gtqAzy2CHB2B86s0laRz84qncYSEVGZYABEZeJKwk3sP39D7XfAodvDXxayHo9UhHZ1dsTeyBvYGhZ37yes2QkY+gvg6AIcXwIsfw7Izi6NphIRURlgAERlYnVO70+rYG+Ui9pi1unv+Qms5I4n2wWr/RlrS6EXSNR5APjXHMDBCTj0K7Bqiqzed+/nJSIi2wyAZs6ciZCQELi7u6Nt27bYsycnXyQfXbt2VWu85N369+9vOGbkyJF3PN6nTx8TvRsyzv8ZEpoNXD8LODoDNTtb1MWZ2LU2PFycVJ2iDSdLqaBhwwHAoG9lJSJg72xg/TQGQUREFsjsAdCiRYswefJkTJs2DQcOHECzZs3Qu3fvAlfwXbJkCa5cuWLYjh07ptZ5GTJkSK7jJOAxPm7BggUmekcUnXgL+3KGv3q7HdMuSPU2gHtFi7o4vuXd8FSHEENdoOzsUuqtaTYUePBzbf+fL4Gtn5TOeYmIyHYCoBkzZmDcuHEYNWoUGjZsiFmzZsHT0xNz5szJ93gfHx8EBgYatnXr1qnj8wZAbm5uuY7z9vY20TsiGf6SkZ8WNbzgdWmr2Za/KIoJnWuhvJszTlxJxJrj2rBdqWg1Cug9Xdvf9D6w47+ld24iIrLuACg9PR379+9Hz563p0Y7Ojqq2zt37izSOX788UcMGzYM5cqVy3X/5s2b4e/vj3r16mHixIm4dq3g6c5paWlITEzMtVHJyYKj4sFGvkDEVovL/zHmXc4Vo++/3QuUVVq9QKL900C3/9P2174O7P2x9M5NRETWGwDFxcUhKysLAQG5VwaX21ev3v2vcckVkiGwsWPH3jH8NX/+fGzYsAEfffQRtmzZgr59+6rXys/06dNRqVIlwxYUFHSP78x+xSTdwt5IrcLygMqXgPQkwLMyUKU5LNWYTrVQ0d0ZYTHJ+PvI5dI9eeeXgY4vavsrJgOHOBRLRGQJzD4Edi+k96dJkyZo06ZNrvulR2jAgAHqsUGDBuHvv//G3r17Va9QfqZOnYqEhATDduHCBRO9A9uzJmf4q1mQF/yit2t31u4uXXuwVJU8XDC+s1af6Iv1YcjMKsXp6zLtv8c0oM0E7fayp4Hjf5Xe+YmIqETM+q3k6+urEpijo6Nz3S+3JW+nMCkpKVi4cCHGjBlz19epVauWeq3w8PB8H5d8oYoVK+ba6N6Gv/o3CQTCN1j08JexkffXhLenCyLiUrD04KXSPbkEQX0+BFo8AeiygT/HAGfWlO5rEBGR9QRArq6uaNmypRqq0svOzla327dvX+hzFy9erHJ3nnjiibu+zsWLF1UOUJUqVUql3ZS/2KQ07InQhr/613IBrhy+3QNk4SQR+t9daqv9rzaGIaM0e4GE9IA99BXQeDCQnQksehI4l1MfiYiITM7s4xIyBX727NmYN28eTp48qRKWpXdHZoWJESNGqCGq/Ia/ZHircuXKue5PTk7GK6+8gl27diEyMlIFUwMHDkRoaKiaXk9lR2ZRSQ5x0+qVUO36bgA6IKAJUCF3jpelGtE+RE2Nv3D9Jhbvu1j6L+DoBDz8HVCvP5CVBiwYDkTJdSIiIrsLgIYOHYpPP/0Ub775Jpo3b45Dhw5h9erVhsToqKgoVcfH2OnTp7F9+/Z8h79kSO3IkSMqB6hu3brqGOll2rZtmxrqorKz6pj2OfVrUgU4u8Gip7/nx8PVCU931XqBvt4YhlsZZbCel5MLMOQnrVcsIwX49V/A5ZylQoiIyGQcdKWyBoBtkWnwMhtMEqKZD1Q015LT0Pr99aoHaOvLXVDjpxZASgwwYjlQqwushQQ9XT/ZjKuJt/DWQw1VblCZSE8FfhkMRO0APHyAkSuAgIZl81pERHYisRjf32bvASLbsOZ4tAp+GleriBoZ57Tgx8UTqNEO1sTdxQnPdg9V+zM3n8XN9DJa1d3VU1tBvlpL4OZ14OdBwLWzZfNaRER0BwZAVKrDX30bGw1/hXQCnK1v2PHRVkGo7u2hkrp/2XW+7F5IlgZ5/A8goDGQHA3MGwDER5Xd6xERkQEDILpn11PSseOsVmm7v+T/6Ke/h1r+9Pf8uDo74vnuddT+t1vOIiUts+xezNMHePIvoHIdIPGiFgQl5s55IyKi0scAiO7ZuhNX1RISDatUREgFHRC1S3sg9PYSJ9bmkfuqIaSypwru5u6ILNsXK+8HPLUc8AoGbkRow2EpcWX7mkREdo4BEN2zFUe1ZUv6SfHDyO1Adob2Ze6jVVe2Rs5Ojnihp9YL9P3Wc0i8lVG2L1ixqhYEVagKxJ4Cfn4YuBlftq9JRGTHGADRPYlPTceO8Lh8pr/30CogW7EBzaoh1L88Em5m4MdtEWX/gt4hWhBUzg+4ekSbIp+WXPavS0RkhxgA0T1ZeyIamdk61A+sgFp+5a1q+Yu7cXJ0wIs966r9OdsjVLBX5nzraDlB7l7Axb3AgmFAxs2yf10iIjvDAIjuycqctb9U78+NSOD6WcDRGajZ2SaubN/GgSq4S0rLVENhJhHYGHhyCeBaAYjcpi2bkZlmmtcmIrITDICoxBJSM/CP8fCXvvenehttircNcHR0wOQHtF4gSYaOSzZRICL1gR7/HXD2AMLXaQuoZpXhbDQiIjvDAIhKbN3JaGRk6VA3oLzKlbk9/d16lr8oigcaBqj1zVLTszBrswmLFQZ3AIb/Bji5Aif/Byx7WlYLNt3rExHZMAZAVDrDX1kZQMRWm8n/MebgcLsX6Odd5xGdeMt0Ly5rhg2Zpw0rHlkErHgR4Oo1RET3jAEQlYhMC98WFnu7+OGFPUB6EuBZGajS3Oauape6fmgZ7I20zGx8synctC9evx/wyPcSigH75wJr/sMgiIjoHjEAohJZf0Ib/pKhrzoBFW5Pf5ceC0fb+7GSXqCXcnqBFuy5gEvxJp6Z1XgwMPC/2v6ub4BN75v29YmIbIztfVORSaw0FD+sot1hQ9PfC9Ih1BftavkgPSsb/91o4l4g0eIJoN+n2v7WT4BtM0zfBiIiG8EAiIot6VYGtuYMf6nqz7Jsw5XDt3uAbNhLveqp/y/edwFR11JN34A244Ceb2v7G94Gdn9n+jYQEdkABkBUbBtOxiA9Mxu1/Mqhnhr+2gRABwQ0ASoE2PQVbR3ig051fFXxxy83hJmnER0nAZ2naPurpgAHfjZPO4iIrBgDICrx7C9JfpbcmNvLX9h270/eXqClBy/ibKyZlqro9h+g/bPa/vLngKN/mKcdRERWigEQFUtyWiY2n9GGv/o2rqLVpbGD/B9jzYO80LOBP7J1wJfrzdQLJIFnr/eAlqO03rcl44FTK8zTFiIiK8QAiIpl4ylt+Kumbzk0qFIBiD4GpMQALp5AjXZ2czVfzJkR9r8jl3H6apL5gqD+M4CmwwBdFrB45O1glIiICsUAiIpl5ZErhjWycg1/hXQCnN3s5mo2qlpJXQOpSfjF+jPma4iUHBg4E2gwAMhKBxY+DkT+Y772EBFZCQZAVGQpaZnYdDom/+nvofYx/JW3F0hiwFXHruLYpQTzNcTJGRj8I1CnF5B5E/jtUeDifvO1h4jICjAAoiKT4EcqIQdX9kSjqhWBtGQgapf2YGhPu7uSdQMqYECzqmr/83Vm7AUSzq7Ao/O1nrj0ZOCXh4GrR83bJiIiC8YAiIo9+0uSn9XwV+R2IDsD8AoGfGrZ5ZV8oUcdODoAG07F4GDUDfM2xsUDGL4QqN4GuJUAzB8ExJo5MCMislAMgKhIUtMzsemU0dpfwjD9vYeWkGuHavmVxyP3VVf7M8zdCyTcygOPLwaqNANS44D5A4DrEeZuFRGRxWEAREWy+XQsbmZkobq3BxpXq6jdaWfT3wvrBXJ2dMC2sDjsjbxu7uYAHl7AE0sBv/pA0hUtCEq4ZO5WERFZFAZAVCQr8hY/vBEJXD8LODoDNTvb9VUM8vHEkFZBav/TNaehk6lh5lauMjBimTY0GR+lBUHJWgI7ERExAKIiuJmehU2nCpj9Jfkm7jk9Qnbsue6hcHVyxO6I69hx9hosQoVAYMRyoFIQcC1cywlKtYAeKiIiC8AeILqrLWdikJqehWpeHmhavVKe6e/2sfzF3VT18sBjbWuo/c/WWkgvkPAK0nqCygcAMceBXwYDtxLN3SoiIrNjAER3tfLoVcPK72r4KysDiNiqPWjn+T/Gnu5aG27OjjgQFW9YLsQiVK6tBUEePsDlA1qdoPQUc7eKiMisGABRoW5lZGHDyWi131c//HVhD5CeBHhWBqo05xXM4V/RHSPaBxvqAllML5DwbwA8uRRwqwRE7dQqRmfcMneriIjMhgEQFWrLmVikpGehaiV3tAjyyj39vXZ3bSkGMvh3l9rwdHXCkYsJWHdCCxwtRtXmwBN/AC7lgHObtLXDpDePiMgO8duLCrVKX/xQP/tLcPp7gSqXd8PIDiGGukDZsmS8JQlqAzy2EHB2B86s0laRz84yd6uIiEyOARAVOvy1/qR+9legdmdKHHDl8O0eILrD+M61UMHNGaeuJmHlMS2AtChStuDRnwFHF+D4EmD580B2trlbRURkUgyAqEDbw+KQnJaJwIoy/OWt3Xl2EwAdENAEqBDAq5cPL09XjOlUU+1/sT4MWZbWCyTq9gL+9SPg4Agc+gVY/SrU0vZERHaCARDdfe2vJoFwlAWvci1/wd6fwozuWBOVPFwQHpOM5YcttApzw4HAoFkAHIA93wPr32IQRER2wyICoJkzZyIkJATu7u5o27Yt9uzZU+CxXbt2Vbkoebf+/fsbjpHZN2+++SaqVKkCDw8P9OzZE2FhYSZ6N7YhLTPLkMRrKH4owyTM/ymSiu4uaihMfLk+DJlZFjrE1Gwo8OAMbf+fL4Ctn5i7RURE9hEALVq0CJMnT8a0adNw4MABNGvWDL1790ZMTP5l+5csWYIrV64YtmPHjsHJyQlDhgwxHPPxxx/jq6++wqxZs7B7926UK1dOnfPWLU77Lap/wuOQlJYJ/wpuaFkjZ/gr+hiQEgO4eAI12t37h2/jJBm6cjlXRF5LxZIDFtoLJFqNBnp/oO1veh/Y8V9zt4iIyPYDoBkzZmDcuHEYNWoUGjZsqIIWT09PzJkzJ9/jfXx8EBgYaNjWrVunjtcHQNL788UXX+D//u//MHDgQDRt2hTz58/H5cuX8ddff5n43VmvFUe04od9G+cz/BXSCXB2M2PrrEM5N2c1LV58uSEM6ZkW2gsk2j8DdHtd21/7OrAv/39/RES2wqwBUHp6Ovbv36+GqAwNcnRUt3fu3Fmkc/z4448YNmyY6uURERERuHr1aq5zVqpUSQ2tFXTOtLQ0JCYm5trsmXxRrztxNffwV67lL1j9uaieaBesetEuxd/Eon0XYNE6vwLcP0nb/3sycHihuVtERGSbAVBcXByysrIQEJB7NpHcliDmbiRXSIbAxo4da7hP/7zinHP69OkqSNJvQUHayt726p+zcUi8lQm/Cm5oFeKj3ZmWDETt0vZDbweXVDgPVyc80y1U7c/cGK5KC1gsqfPU8y2gzXhtpt9fE4ETy8zdKiIi2xwCuxfS+9OkSRO0adPmns4zdepUJCQkGLYLFyz8L/UytvKINvurT6NAOOmHvyK3A9kZgFcw4KMl91LRDGsTpCppX028hd92R1n2ZZMgqM9HQPMnAF028McY4Mxac7eKiMi2AiBfX1+VwBwdnXvJALkt+T2FSUlJwcKFCzFmzJhc9+ufV5xzurm5oWLFirk2e5WRlY21eWd/5Zr+3kP7kqQic3N2wrPd66j9bzafxc10C+4FErK8yYCvgEaPaEHvoieAc1vM3SoiItsJgFxdXdGyZUts2JDz5apmWmer2+3bty/0uYsXL1a5O0888USu+2vWrKkCHeNzSk6PzAa72zkJ2HH2GhJuZsC3vCva1MwZ/hKc/n5PhrSqjiAfD8Qlp2H+zkjL/1FzdAIe+R6o1w/ISgMWDAeidpu7VUREtjMEJlPgZ8+ejXnz5uHkyZOYOHGi6t2RWWFixIgRaogqv+GvQYMGoXLlyrnul5pAkyZNwnvvvYfly5fj6NGj6hxVq1ZVx1PR1v7qbTz8dT0CuH4WcHTWllGgYnNxcsQLPeqq/VlbzqoK2xbPyQX4109ArW5ARgrw67+Ay4fM3SoiolLhDDMbOnQoYmNjVeFCSVJu3rw5Vq9ebUhijoqKUjPDjJ0+fRrbt2/H2rX55yZMmTJFBVHjx49HfHw8OnbsqM4phRap8OGvNce1RPH++Q1/VW8DuNvv8OC9GtS8Kr7ZFI5zcSn4aXsEnuuhDYtZNBd3YNhvwC+DgagdwM8PA6NWAv4NzN0yIqJ74qCTwjmUiwyZyWwwSYi2p3ygbWGxePLHPfAp54o9/+kBZ6ecwHPBY8DpFUD3/9OmSlOJLTt0CS8sPISK7s7Y9mp3tVyGVbiVCMwfCFw+AJQPAEatAiprNY6IiKzx+9vsQ2BkOVYevWoY/jIEP1kZQMRWbb826//cq4eaVkXdgPKqzMCP287BakjP3xN/Av6NgORoYN4AIN7CZ7QRERWCARApmUbDX/2aGM2Wu7AHSE8CPCsDVZrzat0jqar9Yk8tF2jOP5G4kZJuPdfU0wcY8RdQuQ6QeFHrEUq6e70uIiJLxACIlN0R13E9JR3eni5oX6vynfk/tbtr06PpnkkPW8MqFVUi9HdbragXSJT3B0Ys0+pBXT+nBUEpceZuFRFRsfEbjZSVRrO/DMNfgtPfy6QX6KVeWi/QvB2RiE1Ks66fwkrVgKeWAxWqArGntMTom/HmbhURUbEwACJkZesMw199jWd/yV/2Vw7f7gGiUtO9vj+aB3nhZkYWvt181vqurHeI1hPk6QtcPQL8OkRbLoWIyEowACLsibiOuOR0NSOpQ23j4a9N2ppQAU2ACrnXVqN7I/WqJj+g9QL9svs8ribcsr5L6ldXC4LcvYCLe4AFw4CMm+ZuFRFRkTAAIsPwV6+GAapg353LX7D3pyx0quOL1iHeSM/MxsxN4db5kxjYGHhiCeBaAYjcBvw+Asi0osRuIrJbDIDsnAx/rTqWM/urqdHwV3Y2839M0gtUT+0v3BuFizdSYZWqtwQe/x1w9gDC1gJ/jgGyrKDSNRHZNQZAdm5fpAx/panCfPfX9r39QPQxICUGcPEEarQzZxNtWvvalXF/aGVkZOnw9QYr7QUSwR2AYb8CTq7AyeXAsqe1IJqIyEIxALJz+uGvBxoGwtU5n+GvkE6As5uZWmcf9L1Afxy4iMi4FFit0B7AkLmAgxNwZBGwYjLAQvNEZKEYANmxbKPhr/5NjYofGk9/ly81KlMtg73RtZ6fGo78akOYdV/t+v21VeThAOz/CVjzOoMgIrJIDIDs2P6oG4hJSkMFGf4KNRr+kunMUbu0/dCeZmufPdHPCPvr0CWEx1j5dPIm/wIGfK3t75oJbPrA3C0iIroDAyA7tuJIzvBXgwC4OTvdfiByO5CdoVX79allvgbakabVvfBAwwBk64Av1p+B1bvvSaDvJ9r+1o+B7Z+bu0VERLkwALLj4a/V+tlfxsUPc01/7yFTlczQOvvuBfr7yBWcvJIIq9d2PNDzLW1//VvAbhkaIyKyDAyA7NTBCzdwNfEWyrs5o2Mdo+EvweUvzKJBlYron1OK4PN1NtALJDq+CHR+Rdtf9Qpw4Gdzt4iISGEAZKdWHtV6f3o28Ie7i9Hw1/UI4PpZwNEZqNnZfA20Uy/2rANHB2DtiWgcvZgAm9DtdaDdM9r+8ueAo3+Yu0VERAyA7Hb2V8709wKHv6q3AdwrmqF19i3UvwIGNq+m9mesOw2bIMOovd8HWo7UllZZOgE4tdLcrSIiO8ceIDt06GI8LifcQjlXJ3Su65f7wfCN2v+5/IXZvNCjDpwcHbDpdCz2n78BmwmC+n8ONB0KZGcCi5+6PdRKRGQGDIDskL73p0eDgNzDX1kZQMRWbb826/+YS4hvOQy+r5pt5QIJR0dg4DdAg4eArHRg4ePA+R3mbhUR2SkGQHZGp9MZ8n/6NclT/PDCHiA9CfCsDFRpbp4GkvJc9zpwcXLA9vA47Dp3zXauipMzMHgOEPoAkHkT+PVR4OJ+c7eKiOwQAyA7c+RiAi7F34SnqxO61vPPP/+ndnftr3UymyAfTwxtHaT2Z6w9owJXm+HsCgz9WVtmRQLuXx4Brh4zd6uIyM7wW85O1/7qXj/P7C/B6e8W5dluddT6bHsir6ueIJvi4gEMXwBUbw3cigd+HgTE2tBwHxFZPAZAdkR6EVYUNPsrJQ64cvh2DxCZXWAldzzetoba/8zWeoGEWwXg8T+AwKZASiwwfyBwI9LcrSIiO8EAyI4cu5SIizduwsPFCd3uGP7apE1RDmgCVAgwVxMpj4lda8PdxRGHLsRj0+kY27s+Hl7Ak38BfvWBpMvAvAFAwiVzt4qI7AADIDui7/3pVt8PHq5OBSx/wd4fS+JfwR1PtQ+x3V4gUa4yMGIZ4F0TiD8PzHsQiAszd6uIyMYxALIT8sW56lgBw1/Z2cz/sWATutRWNZuOX07EmuPaDD6bUyEQeGo5UKkGcP0cMLsHELbO3K0iIhvGAMhOyJfn+WupajjljuGv6GNASgzg4gnUaGeuJlIBfMq5YnTHmmr/83VhqpK3TfKqAYzbAAS1A9ISgN8eBf75SqJ3c7eMiGwQAyA7m/3Vta4/yrk55z/8JdOSnd3M0Dq6m7Eda6GCuzNORyfh75zP0iaV99d6glo8CeiygXVvaEtnZNwyd8uIyMYwALKb4oc5w185q43nO/09lNWfLVUlTxeM61RL7X+x/gwys7JhsyQIH/A10PdjwMEJOLIImNsPSLThwI+IrCcA2rZtG5544gm0b98ely5pszZ+/vlnbN++vTTbR6Xg5JUkRF5LVTVlpP5PLmnJQNQubT+0J6+3BRt1fwi8PF1wLjYFyw5dhk2TtcPaTgCeXAJ4eAOX9gPfdwUu7jN3y4jIngOgP//8E71794aHhwcOHjyItLQ0dX9CQgI++OCD0m4j3SN98nPXun4on3f4K3I7kJ0BeAUDPloPA1mmCu4umNC5ttr/ckMYMmy5F0ivVldg3EbArwGQfBX4qR9waIG5W0VE9hoAvffee5g1axZmz54NFxcXw/33338/Dhw4UJrto1Isftg/v+Evw/T3Htpf3WTRnuoQDN/yroi6noo/9l+EXZDAfOw6oF4/ICsN+OvfwJrXgewsc7eMiOwtADp9+jQ6d+58x/2VKlVCfHx8abSLSokkzcqQSb7DX4LLX1gVT1dnTOwaqva/3hCGtEw7CQKkavTQX4HOr2i3d/5XmyV2k79viMiEAVBgYCDCw8PvuF/yf2rV4jCKJdGv/N65jp8aQsnlegRw/Szg6AzUvDOgJcsky2MEVHTD5YRbWLT3AuyGLNDb/f+Af80BnD2A8PXADz1YNJGITBcAjRs3Di+88AJ2794NBwcHXL58Gb/++itefvllTJw4sWQtoTJhmP3VJLDg4a/qbQD3ivwErIQsYvtsN60X6L8bw3Erw056gfQaDwZGrwYqVgeuhQOzu7NoIhGZJgB67bXX8Nhjj6FHjx5ITk5Ww2Fjx47FhAkT8Nxzz5XklFQGwqKTEB6TDFcnR/RsmM/6XuEbtf9z+Qur82jrIFTz8kBMUhp+2XUedqdqc2D8ppyiiYnAr0OAf75k0UQiKtsASHp9Xn/9dVy/fh3Hjh3Drl27EBsbi3fffbfY55o5cyZCQkLg7u6Otm3bYs+ePYUeLzlGzzzzDKpUqQI3NzfUrVsXK1euNDz+1ltvqfYZb/Xr14c90ic/d6rji4p5h7+yMoCIrdp+bdb/sTZuzk54vofWC/Tt5rNIScuE3TEumigL+a57M6do4k1zt4yIbDUAGj16NJKSkuDq6oqGDRuiTZs2KF++PFJSUtRjRbVo0SJMnjwZ06ZNU7PHmjVrpqbXx8Tkv+p1eno6HnjgAURGRuKPP/5QydgyE61atWq5jmvUqBGuXLli2Oy1NpF++Ktv3rW/xIU9QHoS4FkZqNLc9I2je/bIfdURXNkT11LSMW9npH1e0fyKJspU+UQbr5NEROYJgObNm4ebN+/8K0vumz9/fpHPM2PGDJVPNGrUKBVIydR6T09PzJkzJ9/j5X7pdfrrr7/UlHvpOerSpYsKnIw5OzurRG395uvrC3sTHpOEM9HJcHFywAP5DX/p839qd9eSS8nquDg54oUeddT+91vPIelWBuxS3qKJlw8A33dj0UQiKlSxvvkSExNVsUOpLSM9QHJbv924cUMNRfn75zPVuoDenP3796Nnz9vVhx0dHdXtnTt35vuc5cuXq8rTMgQWEBCAxo0bq8KLWVm5k0DDwsJQtWpVNSPt8ccfR1RUVKFtkUKOxu9FNluZ/dUx1BeVPPIMfwlOf7cJA5tXQ22/cohPzcCc7XbaC6THoolEVFYBkJeXF3x8fFRejeTeeHt7GzbpZZHhLwlOiiIuLk4FLhLIGJPbV69qX955nTt3Tg19yfMk2HrjjTfw2WefqcKMepJHNHfuXKxevRrffvstIiIi0KlTJxWwFWT69OmqhpF+CwoKgk0Pf6XEAVcO3+4BIqvl5OiAST3rqv0ftp9DQqqd9gLdrWhilh3mSBFRofKsi1C4TZs2qd6f7t27q+UwJBjSk3yg4OBg1fNSVrKzs1UP0/fffw8nJye0bNlSrUP2ySefqDwi0bdvX8PxTZs2VQGRtOv333/HmDFj8j3v1KlTVS6SnvQAWXMQdC42GaeuJsHZ0QG98h3+2qQljQY0ASrk8zhZlf5NqmDmpnD1mc/edg4v964Hu6Yvmrj5A2DrJ1rRxJiTwL9+1IbIiIiKGwBJvo2QXpUaNWqonqC8ZLhJHrsb6TGSICY6OjrX/XJb8nbyIzO/ZOkNeZ5egwYNVI+RDKlJEJZfr5X0VuVXuFFPZpPJZiv0vT8dQn3h5elayPIX7P2xBY6ODnjxgbqY8PN+zPknQi2aWrm87fw831PRRP+GwF9Paz/zs3sAwxcCflqPGRHZtxJlv0pujUx7z+vatWuoWbNmkc4hwYr04GzYsCFXD4/cljyf/EjiswQycpzemTNnVGCUX/AjpE7R2bNn1TH2Qp//0z+/4ody7Zj/Y3Okp69JtUpITc/Cd1vPmbs5lqPxI8CYNVrRRKl6LpWjz6w1d6uIyFoDIBkGKyjYkHo+RSXDTjKNXWaVnTx5UlWRlqn0MitMjBgxQg1P6cnjMgtMqlBL4LNixQqVBG2cdyTVqLds2aKmyu/YsQMPP/yw6jEaPnw47EFkXApOXElUuSG9GuYTAEUfA1JiABdPoEY7czSRyoD0xk5+QOvZmL8zEjFJt3id9ao0y100UdYQ2/4FiyYS2bliDYHp82Tkl+2bb76ppqzrSWKyLI3RvHnRa8oMHTpU9STJuWQYS54rycv6xGgZTpOZYXqSl7NmzRq8+OKLKr9H6v9IMPTqq68ajrl48aIKdqQ3ys/PDx07dlSFGmXfHqw8ljP8VbsyvMsVMvwV0kmroUI2o2s9P7So4YWDUfH4ZtNZvDWgkbmbZGFFE/8HrHwJODAfWD8NiD4ODPgKcPEwd+uIyAwcdAV15+SjW7du6v/SwyLDVMbDTrIvdXmkB6ZOHa02ibWSJGiZDSZT/itWtK41sh78ehuOXUrE9EeaYHibfHKx5j4IRG7TCsdJ7RSyKdvD4vDEj7vV8idbpnRFlUr8cs9Fft3tmQ2sfg3QZQFVWwDDfgMqlt3kDSKyzO/vYs8CEzJE9eWXX1pdcGDroq6lquBHG/7KZ3ZXWjIQtUvbD71df4lsx/2hldG2pg92R1xXC6W+/3ATczfJAosmjgf86gGLnwIuHwS+76rNGgtqbe7WEZGl5wD99NNPKviRhGQZktJXhS5GZxKV4fBXu1o++c8CitwOZGcAXsFavRSyOTI8/VIvbRr8or0XcOF6qrmbZJlqdQHGbQL8GgDJ0cDcfsCh38zdKiKy9ABIEpFlJXiZXt6vXz+13paQOjsvvfRSabeRijn9vV9+xQ9zTX/vof0lTDapTU0ftQBuZrYOX20IM3dzLJdPzZyiif2BrHTgr4ksmkhkR0oUAE2aNEnV45EkZeNEaElqliRmMj35S//IxQQ4OgC9G+VfR4nT3+2HfkbYkoOXEBGXYu7mWHjRxF+Azq9ot6VooswSu3nD3C0jIksMgNauXYuPPvoI1atXz3W/JD+fP3++tNpGxbAqZ/irbc3K8M1v+Ot6hFYHxdEZqNmZ19bGtajhje71/ZGVrcOX68+YuznWUTRxyFzA2eN20cRYXjciW1aiAEhq9Rj3/BgPjdlSRWVrsiKn+GG//IofGg9/VW8DuDN53Z56gZYdvoyw6ILXwqMcjR5m0UQiO1KiAEgWF50/f36uxEupzvzxxx8bpsqT6Vy8kYrDF+JVWk/vxgUNf23U/s/lL+xG42qV0KdRoJr5/Tl7gYpRNHEzUKM9iyYS2bhiTYPXk0BHkqD37dun1uCaMmUKjh8/rnqA/vnnn9JvJRVq9TGt96dNiA/8K+RTiTsrA4jYqu3X7sGraUdkjbA1J66q5VGOX05Ao6qVzN0ky1feDxixHFj5MnBgHosmEtmoEvUANW7cWC1FIVWWBw4cqIbEHnnkERw8eBC1a9cu/VbSvc3+urAHSE8CPCsDVYpeqZusX73ACniwqVbk7/N1nBFWZM6uwENfAv0+BRycgKO/Az/1BRIvl92HRUSW3wMkpNLi66+/XrqtoWK7HH8TB6K04a8+je+S/1O7u5bwSXZlUs86WHHkMtafjFZDpc2CvMzdJOsg/6jajAN867JoIpENKlEAtHVrznBKATp35iwjU1mVM/zVKtgbARULWIiWq7/btdp+5TGoRTUsOXAJM9adwbzRbczdJOssmrjwMSDmhFY0UXqHmj9m7pYRkakDoK5du95xnyRCGy+MSqax6m7DXylxwJXDt3uAyC690KMOlh+6jC1nYrEv8jpahfiYu0nWVzRxzFpg6b+BU39rRRNlMdWebwNOJe5IJyIzKtF4yI0bN3JtMTExqgBi69atVY0gMo2rCbew77xWsK1v44KqP8v6bTogoAlQIZ/1wcguBFcuhyGttLpdn61lfZsSF0189Geg8xSjoolDWDSRyJ4CIMn/Md58fX3xwAMPqOKIMiOMTGN1TvHDlsHeCKzkfpflL9j7Y++e7V5HrRK/89w17DgbZ+7mWHHRxNe1ookunsDZjTlFE0+bu2VEVEylmhEbEBCA06f5i8BUZGpzocNf2dnM/yGDal4eGNYmSO3PWHuGixffa9HE0WuASkFahfUfegJn2PtNZE1KNHh95MiRXLdlFXhZEPXDDz9E8+acZm0KMYm3sPf8dbXft6DZX9HHgJQY7S/VGu1M0i6ybM90C1WrxMvQ6dawOHSp62fuJlmvKk215OjfRwBRO7Q1xHpOA+6fxMWGiWw1AJIgR5KeJfAx1q5dO8yZM6e02kaFWH38qqrw26KGF6p6eRQ+/BXSCXDmEiUENVPwiXbB+HF7BGasPY3OdXxzTWCgkhRNXAasegXYPxdY/5aWHD3ga8ClgH+XRGS9AVBERESu246OjvDz84O7ewF5KFTqVhzR8n/6FzT8ZTz9PZTVn+m2iV1r47fdUTh8MQHrT8bggYZMjr/nookPfgEENAZWvQocXQxcCweG/gpUqsYfPSJbygEKDg7OtQUFBTH4MaHYpDTsidSGvwosfpiWDETt0vZDe5qwdWTpfMu7YeT9IWpf6gJlZ+fuyaV7KJo44i/Awwe4fBCY3Q24sJeXk8jWkqC3bNmChx56CKGhoWobMGAAtm3bVrqto0KHv6Sib3Vvz/wPitwOZGcAXsGATy1eScplfKdaKO/mjJNXEtXPE5WSmp2BcRsB/4ZAcrRWNPHgr7y8RLYSAP3yyy/o2bMnPD098fzzz6vNw8NDLZD622+/lX4rKZeVhuGvAnp/ck1/78GETLqDdzlXjO5YU+1/vu4MstgLVPpFE+s/CGSlA8ueBlb/B8jK5E8ikbUHQO+//75aEX7RokWGAEj2ZRbYu+++W/qtJIO45DTsjrhWePFDweUv6C7GdKyJiu7OCItJxt9HuMhnmRZN3DUT+PVfLJpIZO0B0Llz59TwV14yDJY3QZpK15rjVyF/rDetXglBPgUMf12P0GqTODprXfJE+ajk4YIJXWqr/S/WhyEzK5vXqSyLJp7bBMzuzqKJRNYcAEnS84YNOUMsRtavX68eo7KzKqf4YaG9P/rhr+ptAPeK/DioQCM7hMCnnCsi4lKw5OAlXqkyL5p4TqscfWYNrzWRNU6Df+mll9Sw16FDh9ChQwd13z///IO5c+fiyy+/LO02Uo7rKelqGYO7T3/fqP2fy1/QXZRzc8a/u9TCBytP4asNYRjUvBpcnUu1QDzlWzRxKIsmEplZiX7TTZw4EQsXLsTRo0cxadIktR07dkzlAU2YMKH0W0mG4S9JVm1crSJqVC5g+CsrA4jYqu3XZv0fursn24XAr4IbLt64icX7L/CSlXXRxJYjtQWKpWjiknFAxk1ecyIzKPGfeg8//DC2b9+Oa9euqU32Bw4cWLqto1xWHr1y9+GvC3uA9CTAszJQhcuS0N15uDrh6a5aLtB/N4bjVkYWL1tZF03s9yng4KQVTZzTB0jg8CORqd1TX3d6ejouXryIqKioXBuVvhsp6dhx9lrhi58a5//U7q4lYRIVwfA2NVClkjuuJNzCwj38N2zSoolXDrFoIpEZlOgbMiwsDJ06dVK1f6QSdM2aNdUWEhKi/k+lb92JaDX81aBKRdT0LVfwgZz+TiXg7uKEZ7uHqv2Zm8/iZjp7gcqczNAcv4lFE4msKQl65MiRcHZ2xt9//40qVapwMUUTWHG0CMUPU+KAK4dv9wARFcOQlkH4dvNZlQv0865IjO+sDYtRGfIO0YomLv03cOpvrWiiLKb6wDuAU4l+PRNREZXoX5jM/tq/fz/q169fkqdTMcWnpuOf8LgiDH9t0pIrA5oAFbjAJRWPzP56vkcdTPnjCGZtOYfH2gar5TLIREUTt3wIbPlIK5oYcwIY8hPg4c3LT2RJQ2ANGzZEXJz2hUymGf7KzNahfmAF1PIrX/CB4eu1/3P6O5XQIy2qqSFWKbkwb0ckr6OpSL5et/8AQ+axaCKRpQVAiYmJhu2jjz7ClClTsHnzZjUDzPgx2ahsZn8V2vuTnQ2czan/w+nvVELOTo54oUcdtf/91nNIvJXBa2lKjQaxaCKRpQVAXl5e8Pb2VtsDDzyAXbt2qcVP/f39Dffrj6HSk3AzA9sNw1+F5P9EHwNSYrS/Hmu040dAJfZQs6qo419e/ez9uI1L25itaGKNDlpJCymauP1zQKczfVuIbFiRB/g3bZL8EjK19SeikZGlQ92A8gj1r3D36e8hnQBnN5O1j2yPk6MDXnygLp7+9QB+3B6hlsuQ1ePJDEUTV00B9v+kFU2U5OgBXwMuHvwoiEwZAHXp0qU0Xo/KYvjLePp7KKs/073r0yhQlVw4eSUR3287h1f7cMKDWYomPvQFENgYWPWqVjQxLgwY9htQqZrp20Nkr0NgR44cKfJWHDNnzlT1g9zd3dG2bVvs2bOn0OPj4+PxzDPPqOn3bm5uqFu3LlauXHlP57RUkn+xLawIs7/SkoGoXdp+aE8TtY5smaOjAyY/UFftz/0nEnHJaeZukv1qPRZ40qho4vddtYrvRGSaHqDmzZurej+6u4xDyzFZWUUroiZrh02ePBmzZs1SgcoXX3yB3r174/Tp0yq3KL/K05J/JI/98ccfqFatGs6fP69yj0p6Tku24WQ00rOyEepfHnUDChn+itwOZGcAXsGATy1TNpFsWM8G/mhWvRIOX0zArM1n8X8PNjR3k+xXzU5a0cQFjwExx4G5/YEHPwdaPGHulhFZLQfd3SKaHBJoFJVUhy4KCVBat26N//73v+p2dnY2goKC8Nxzz+G1116743gJaj755BOcOnUKLi4upXLO/MhMtkqVKiEhIQEVK1aEuYybv09NgX++eygm96pX8IErXwH2fA+0Gq39UiQqJZtPx2DkT3vh5uyIrVO6IaCiO6+tOUlv79IJWtFE0e5p4IF3WTSRqATf347FCWqKuhWF9OZIMcWePW8P2Tg6OqrbO3fuzPc5y5cvR/v27dUQWEBAABo3bowPPvjA0ONUknOKtLQ0i5vKn3QrA1vOxKr9fk2LmP/D6e9UyrrU9UOrYG+kZWbjm03hvL7m5lZeK5rYJeePuV3fAL/+C0i9bu6WEdnuEJgEH3379lU9L7JfmAEDBtz1fFJIUQIXCWSMyW3p4cnPuXPnsHHjRjz++OMq7yc8PBxPP/00MjIyMG3atBKdU0yfPh1vv/02LMnGUzFIz8xGLb9yqFfY8Nf1COD6WcDRWVtbiKgUyZD25F518djs3Viw5wLGd6mNal6chWT+oolTgYCG2hIa5zYBP/QAhi8E/ArpKSaikgVAgwYNwtWrV1UejeyXRg5Qcclwlrz+999/DycnJ7Rs2RKXLl1Sw2ISAJXU1KlTVd6QnvQAybCZRcz+anyXtdb009+rtwHczTdcR7arQ21ftK9VGTvPXcN/N4Zh+iNNzd0kEg0Hajl/khd0/Rwwuwcw+AegXh9eH6IicCxu8KHfL2gravDj6+urgpjo6Ohc98vtwMD8C/7JzC+Z9SXP02vQoIEKzGT4qyTnFDKbTMYKjTdzSknLxObTsUWc/p5T/ZnLX1AZeqmXNiNs8b6LiLqWymttKQKbaMnRwfdrRRMXDAO2zWDRRKLSXgtM8mhkBXhj8+fPR82aNVVwNH78eJVPUxSurq6qB2fDhpwejJzASm5Lnk9+7r//fjXsJcfpnTlzRgVGcr6SnNMSyfCX5FzImkwNqhQy/JWVAURs1faZ/0NlqFWIDzrX9VNr0n25IYzX2pKU89WmycskCFkMecPbwJ9jgXQGqkSlFgC98847OH78uOH20aNHMWbMGJVkLDOs/ve//6l8mqKSYafZs2dj3rx5OHnyJCZOnIiUlBSMGjVKPT5ixAg1PKUnj1+/fh0vvPCCCnxWrFihkqAlKbqo57QG+uGvvo0DCx/+klog8lefZ2WgSnPTNZDs0ks5dYGWHryIs7HJ5m4O5S2aKDNA+3+m5QMe+wP4qS+QcInXiehec4DEoUOH8O677xpuL1y4UE07l4BDSN6M5OK89dZbRTrf0KFDERsbizfffFMNY0mtodWrVxuSmKOiotQsLj05/5o1a/Diiy+iadOmqg6QBEOvvvpqkc9p6VLTM7HpdEzRhr/0+T+1u2uJkURlqFmQF3o2CMD6k9H4cn0YvhregtfbEosm+tUHFj15u2ji0F+AGm3N3TIi660DJKSyclhYmCFBuGPHjmpm2Ouvv65uR0ZGokmTJkhKSoI1M2cdoBVHruCZ3w6gho8ntrzStfAeoO+6aL/kBs0Cmg83ZTPJTp24nIh+X22D/FiufqEz6gUWMkRL5nMj8nbRRCdXoP8M4L4n+YmQzUssizpAQnpRIiK01aEl6fjAgQNo1+72yuMS+BRUoJCKv/ZXocFPShxw5fDtHiAiE2hYtSL6NQlUC5N/vu4Mr7ml8g4BxqwFGjwEZKUDy58FVr0GZGWau2VEFqNYAVC/fv1Urs+2bdtUbo6npyc6depkeFzWAatdu3ZZtNMu3EzPUgnQov9dh782aQmPAU2ACtYxvEe2YVLPuloP0PGrOHYpwdzNocKKJg6ZD3TNyaPc/S3w62AWTSQqSQAk+T/Ozs5qZXjJ+5FNZl7pzZkzB7169SrOKSnPsgM3M7JQ3dsDjavdZegtfL32f05/JxOTdekGNKuq9tkLZOEkN7Dra8Cj8wEXT+DcZmB2dyCm4MKwRPaiWEnQUmdn69atamytfPnyuerxiMWLF6v7qWRW5Ax/9b/b8JeUATibU/+H09/JDF7oUQd/H7mCDadicDDqBlrU8ObnYC1FE29EAD/0BAbPBur1NXfLiMymRFOHJMEob/AjfHx8cvUIUdHdyrg9/NX3bsNf0ceAlBjtL7oat3OwiEylll95PNKimtqfwVwgKyua2DGnaOJwFk0ku8a50xZCKj+npmepdZaaVa9UtOnvIZ0AZzeTtI8or+d71IGzowO2hcVhTwQX47SaookjWDSRSDAAsrjZX3cpfmi8+ntoDxO0jCh/QT6eeLS1VhLjs7WnUYyKGmROTi4smkjEAMhyhr82nIwu2vBXWjIQtUvbD+1pgtYRFezZbqFwdXLE7ojr2HH2Gi+VtRVNHLEM8PC5XTQxare5W0VkMuwBsgBbz8QiJT0LVSu5o0WQV+EHR24HsjMAr2AtqZHIjKp6eeCxtjXUPnuBrFBIRy0vyL+Rllc470HgwM/mbhWRSTAAsgCrjl1V/+/T+C6zv4zzf2T4627HEpnA091qw93FEQei4rH5TCyvubVh0USyUwyAzCwtMwvrT2jDX/2bBt79Cfr8H05/JwvhX8EdI9qHqP0Za88wF8gasWgi2SEGQGa27UwcktIyEVhRhr/uUkvlegRw/ay22nPNzqZqItFdTehcC56uTjh6KQFrcwJ6sjIsmkh2hgGQma08ps3+6tM4EI6ORRz+qt4GcDftIq1Ehalc3g2j7g8xVIfOzuaMMKsumijriFWqcbto4ulV5m4VUaljAGTm4a91huGvu8z+EuE51Z+5/AVZoHGdaqGCmzNOXU0yBPZkS0UTP4NaBZfIRjAAMqMd4deQdCsT/hXc0PJuSwlkZQARW7V95v+QBfLydMXYTrUMvUBZ7AWykaKJY7SFlze8A/w5BkhPNXfLiEoFAyALWPurb1GGvy7s0f4S86wMVGlumgYSFdPojiHw8nTB2dgULDt0idfPJoomzgD6z9ByD4/9CfzUB0i4aO6WEd0zBkBmkp6ZjbXHtenv/e5W/NA4/6d2dy1ZkcgCVXB3wfjOWi/QlxvCkJGVbe4mUWloPUYrmih/gF05DHzfjUUTyerxm9RMdpyNQ+KtTPiWd0OrEJ+7P4HT38lKPNU+BJXLueL8tVQsOcCeApsqmjhuExDQWCuaOLc/iyaSVWMAZOa1v2T4y+luw18pcdpfXfoeICILVs7NGRO71lb7X20IV72dZCO8g4HRa4AGA7SK9MufBVa9CmRlmrtlRMXGAMgMZFhAXyulb5MiFD88u0lLQgxoAlQIKPsGEt2jJ9oFq+T+S/E3sWjfBV5PmyuaOA/oOlW7vXsW8OtgIPW6uVtGVCwMgMxg59lriE/NgG95V7StWfnuTwhfr/2f09/JSri7OOHZ7qFq/78bw9SCv2SLRRN/BlzKAec2A7O7AzEnzd0yoiJjAGTG4a/ejYow/JWdDZzNqf/D6e9kRYa2DlIL/EYnpuHX3VHmbg6VhYYDtKKJXiyaSNaHAZCJZWZlY01xZn9FH9MSDl08gRrtyr6BRKXEzdkJz/Woo/a/3RyO1HTmidikwMbAuM05RROTtaKJWz9l0USyeAyATGzXueu4kZoBn3Iy/OVT9OnvIZ0AZ7cybx9RafpXy+qo4eOJuOR0zN95nhfXVpWrrBVNbD1Wy1fc+C7wx2gWTSSLxgDIxPRLBMjwl7OTY9Gnv4f2KOOWEZU+FydHPJ/TC/TdlrNITmMvkE0XTez/GfDg51rRxONLWDSRLBoDIFMPfx3TD38VYfZXWjIQtUvbD+1Zxq0jKhuDmldFLb9yqufzp+0RvMy2rtVoYMRyo6KJXW//HiOyIAyATGhPxHVcS0mHt6cL2tUqwuyvyO1arQ2vYMBHq65LZG2kp3NSz7pq//tt55CQmmHuJlFZC7nfqGhiLDD3QeDAfF53sigMgMww/NWrYaAaGihy/o8MfzncZbYYkQV7sEkV1AuooBb//WH7OXM3h0xVNFFmiBmKJj4HrJzCoolkMRgAmdDkB+rhw0ea4LG2NYr2BC5/QTZCFvt98QEtF2jO9ghcT0k3d5PIFFzL5RRN/I92e893wC+PsGgiWQQGQCYkM7+GtamBZkFedz/4egRw/ayWTFizsymaR1SmJPG/UdWKSEnPwndbz/Jq21XRxFeBob9oRRMjtrBoIlkEBkCWSj/8Vb0N4F7R3K0humcODg54qZeWCzR/x3nEJqXxqtqTBg8BY9exaCJZDAZAlio8p/ozl78gG9Ktnj+aB3nhZkYWvt3MXiC7E9BIK5oodc1YNJHMjAGQJcrKACK2avtc/oJstBfol93ncSXhprmbROYomvjkUhZNJLNjAGSJLuwB0pO0OhpVmpu7NUSlqmOoL9qE+CA9MxszN4Xz6tp10cQvWDSRzIYBkCXn/9TuriUQEtlYL9DknF6gRXsv4OKNVHM3icyl1SgWTSSzsYhv15kzZyIkJATu7u5o27Yt9uzZU+Cxc+fOVb9AjTd5nrGRI0fecUyfPn1gNTj9nWycFAK9P7QyMrJ0+HoDe4Fg70UTx28GApqwaCLZVwC0aNEiTJ48GdOmTcOBAwfQrFkz9O7dGzExMQU+p2LFirhy5YphO3/+zkUWJeAxPmbBggWwCilxWvl4fQ8QkQ3XxRJ/HLiIyLgUczeHzMmrBjBmDdBwIIsmkv0EQDNmzMC4ceMwatQoNGzYELNmzYKnpyfmzJlT4HOkRycwMNCwBQQE3HGMm5tbrmO8vb1hFc5u0lZTlr+GKtz5vohsRctgb3Sr54esbB2+2hBm7uaQpRRN7Pa6dptFE8mWA6D09HTs378fPXveXujT0dFR3d65c2eBz0tOTkZwcDCCgoIwcOBAHD9+/I5jNm/eDH9/f9SrVw8TJ07EtWvXYBXC12v/5/R3sqNeoKWHLiE8JsnczSFzkyV/ukzJUzSxGxBz0twtIxtk1gAoLi4OWVlZd/TgyO2rV7VV0/OSgEZ6h5YtW4ZffvkF2dnZ6NChAy5evJhr+Gv+/PnYsGEDPvroI2zZsgV9+/ZVr5WftLQ0JCYm5trMIjsbOJtT/4fT38kONKleCb0aBkCnAz5fz14gyq9oYiTwQ0/g1EpeHrKtIbDiat++PUaMGIHmzZujS5cuWLJkCfz8/PDdd98Zjhk2bBgGDBiAJk2aYNCgQfj777+xd+9e1SuUn+nTp6NSpUqGTXqWzCL6GJASA7h4AjXamacNRCb24gPajLAVR67g5BUz/fFBll80ceFjwNZPoKJlImsPgHx9feHk5ITo6Ohc98ttydspChcXF7Ro0QLh4QXPJKlVq5Z6rYKOmTp1KhISEgzbhQsXYNbp7/IP3tnNPG0gMrEGVSriwaZV1P7n687w+lMhRRPfA/4YDaSzdAJZeQDk6uqKli1bqqEqPRnSktvS01MUMqx19OhRVKmi/QLNjwyPSQ5QQcdIwrTMLDPezDr9PbSHeV6fyEwm9awLRwdg7YloHL2YwM+BCi+aOKc3EG+mP1TJZph9CEymwM+ePRvz5s3DyZMnVcJySkqKmhUmZLhLemj03nnnHaxduxbnzp1T0+afeOIJNQ1+7NixhgTpV155Bbt27UJkZKQKpiRROjQ0VE2vt1hpyUDULm0/9HZSOJE9CPUvj0HNq6n9//vrKNadiEZKWqa5m0WWWjTx6hEtOfp8wZNliO7GGWY2dOhQxMbG4s0331SJz5Lbs3r1akNidFRUlJoZpnfjxg01bV6Olant0oO0Y8cONYVeyJDakSNHVEAVHx+PqlWrolevXnj33XdVT4/Fityu1b/wCgZ8apm7NUQm90LPOvj7yBUcvpiAcfP3wcXJAa2CfdClnh+61PVD/cAKqgQG2TF90cQFjwHRR4F5D2m9Qy2fMnfLyAo56HTMKMtLZoFJMrTkA5lsOGzlK8Ce74FWo4EHPzfNaxJZGBn++n3fBWw+E4ML13MvlBpQ0Q2d6/ipgEjWE/PydDVbO8nM0lOAvyYCJ5Zpt9tMAHq/rw2XkV1LLMb3NwOge7yApearFsD1c8DQX4EGD5rmNYkslPxdFnktFVtOx2DLmVjsPHcNtzKyDY9LvlDzIC90qeuvAqIm1SrBSe4k+yF/u8ussE3va7drdtYKKXr6mLtlZEYMgEx4AUvF9Qjgq+Zagt+UCMDdTEnYRBbqVkYW9kXewJYzWkB0Jjo51+Peni7oJL1Ddf3Qqa4v/CvkXh+QbNjJ/wFLJgAZKYB3CDBsARCgpUSQ/UlkD5DpLmCp2PsDsOIloEYHYPSqsn89Iit3Of4mtp6JVcHQ9rA4JOVJmG5UtaIKhmS7L9gbLk5mn+9BZSn6OLBgOBB/HnAtDzzyPVC/P6+5HUpkAGS6C1gqJKHv9Aqg+/8BnV8p+9cjsiEZWdk4dCEeW05rAdHRS7mn0Zd3c1Yrz8twWee6vqju7Wm2tlIZSrkGLH4KiNym3Zbfp51e1pbXILuRyADIdBfwnmVlAB/VBNKTgHGbgGr3le3rEdm4uOQ0bAuLVQHR1rA4XE9Jv2PKvfQMda7rh7Y1feDu4mS2tlIZ/D5dPRXYO1u73ehhYOBMbaFVsguJDIBMdwHvWeQ/wNx+Wm2Ll8NlNdiyfT0iO5KdrcOxywmG3qEDUTeQbbSSgpuzI9rVkt4hbXZZLd9ynGpvC/b9BKx8GcjOBAKbaHlBXmZa4ohMigGQCS/gPdvwDrDtM6DJEGDwD2X7WkR2LiE1A/+cjTMERFcTb+V6vLq3hyF3qEOorxo+Iyt1fgew6EkgNQ7w9NVWmA8u2goDZL0YAJnwAt6z77oAVw4Bg2YBzYeX7WsRUa6p9jKbTD+zbG/EDaRn3Z5qL4UYWwZ7a1Pt6/qhQRUWYrQ68VHaIqpXjwKOOUtqsGiiTUvkEJjpLuA9SY4FPg3V9l86A1TQql8Tkemlpmdi17lrht4hqUNkzL+Cm8obUlPt67AQo3UVTXwaOPGXdptFE21aIgMg013Ae3Lkd2DJOCCgCTBxe9m9DhEVW2RcCrbmJFPvOHsNNzOyDI9JzcVmqhCjFhA1re7FQowWXzTxU2DTe9ptFk20WQyATHgB74kU7zqyELj/BeCBd8rudYjonqRl6gsxagHR6eikXI97GRVi7FzHF/4VWYjRIp38G1g6AUhPZtFEG8UAyIQXsMSys4HP6gEpMdoKx7W6lM3rEFGpu5JwuxDjNinEeCt3IcaGVSoaFnG9r4Y3XJ05u9NiRJ8AFgxj0UQbxQDIhBewxK4cAb7rBLh4Aq9GAs4WvFI9ERUoU1+IMScgOnIxdyHGcq5OakaZfrgsyIeFGM0u9Trw+wgWTbRBDIBMeAFLbPvnwPq3gDq9gcd/L5vXICKzFGKU5TkkGJJeomt5CjHW8itnCIakBhELMZqxaOKa/wB7vtdus2iiTWAAZMILWGJzH9T++uj7MdB2Qtm8BhGZvRDj8cuJhqn2B6LikWVUiVEKMbbVF2Ks64fafizEaHL75wIrpGhiBosm2gAGQCa8gCWSlgx8FKL9g3vuAFC5dum/BhFZnISbGdgphRjPxGLz6VhcSchdiLGal4chd6hD7cqo4O5itrbalfM7gUVPsGiiDWAAZMILWCKnVwMLhgJewcALh7lYH5GdFmIMj0k25A7tPnc9VyFGZ8ecQow5AZEkVjtwYc+yE38BWDjcqGjip0DLkWX4glQWGACZ8AKWyMpXtHHnVqOBBz8v/fMTkVUWYpQgSB8QRcSl5HrcTwoxylT7en7oFOoL73KuZmur/RRNHA/0/gBwYk+ctWAAZMILWCJftQCunwOG/go0eLD0z09EVu/8tRTDVHspxJiafrsQo3QENaueU4ixnp/ad5LqjFQ6RRO3fQpszCmaGNIJeHQ+4OnDq2sFGACZ8AIW2/UI4KvmgKMzMCUCcC/jtcaIyCYKMe7XF2I8E4tTV3MXYqzk4YKOdW5PtQ9gIcZ7d2oFsGS8VjRR0hWGLwQCGpbCiaksMQAy4QUstr0/ACteAmp0AEavKt1zE5FduJpwy6gQYywS8xRirB9YwZA71CrYh4UY76VoouQF3YgEXMsDj3wP1O9fCp8glRUGQCa8gMW24DHg9Aqg+/8BnV8p3XMTkV0WYjx8Md6wiOuRSwlqFMe4EGP72r4qIOrKQowlK5q4+CkgYqt2u5v87n6Zk1csFAMgE17AYhfe+qgmkJ4EjNsEVLuv9M5NRCSj7CnpqldIX4gxLjlPIUbfctqq9vX80K5mZXi4OvG6FeV395rXgT3fabcbDgIGfQO4luO1szAMgEx4AYsl8h9gbj/AszLwcjjgyPWBiKhsCzGeuCKFGLWAaP/5G7kKMcoaZW1r+qihsq71pBBjeU61L8z+eVoKg6Fo4m+AVw3+CFsQBkAmvIDFsuEdYNtnQJMhwOAfSu+8RERFkHgrAzvCr+Wsah+Dy/kUYlS9Q3X9cH8oCzEWrWjiz0BwB/78WQgGQCa8gMVyaT9wYhkQfD9Qt3fpnZeIqASFGM/GJquK1KoQY8R1pGfmLsR4nxRirHu7EKMjp9prWDTRYjEAMuEFJCKyBTfTs7Ar4ppKppbcoXN5CjH6lpdCjFoydac6fvCx90KMeYsmth4H9JnOoolmxgDIhBeQiMgWRV1LxRZJpj4thRjj7ijE2LRapVyFGJ2d7DCnMb+iiUPmAeUqm7tldiuxGN/fDjrpB6USX0AiIlsnQ2P7zucs03H6zkKMFd2dVa+QBESSQxRYyR32XTRxARDQyNytskuJDIBMdwGJiOxNdKJxIcY4tcr9HYUYc3KHWoZ4w83Zyb6KJrqU04omcqkjk2MAZMILSERkz2RavXEhRtk3HlfwdHVCh9qVcwIif9So7An7KZr4ulbwVsYMySQYAJnwAhIRUe5CjNvD4wwBUVxyWq7LU9O3nKF3qG0tH3i6Ott40cSBwKBvWTTRRBgAmfACEhFRwYUYT17NKcR4WivEmFlAIUbZQv1tqBCjcdHEgCbAcBZNNAUGQCa8gEREVDRJUojxrL4QYywuxd/M9XjVSu6GQowdQn3VKvdWXzTx9yeBlFgWTTQRBkAmvIBERFTSQowphmU6dp27lqsQo5MUYqzhZcgdalTVSgsxqqKJjwFXjwCOLkD/T4GWI83dKpvFAMiEF5CIiEqnEONuKcSYExCdi81diLFyOVdD71CnOr6oXN7Nei57eiqw7Gng+FLtNosmWsT3t0VUrpo5cyZCQkLg7u6Otm3bYs+ePQUeO3fuXDVGbLzJ8/L+ZfHmm2+iSpUq8PDwQM+ePREWFmaCd0JERCUhq9J3reePaQ81wsaXumLblG54b1BjPNAwAOVcnXAtJR1LD17CpEWH0Or99Rjw3+34bO1p7Iu8jsys2z1HFsnVE/jXT0D3N7Tbe2cDPz8MpFwzd8vsmtkLIS5atAgjRozArFmzVPDzxRdfYPHixTh9+jT8/f3zDYBeeOEF9bieBEEBAQGG2x999BGmT5+OefPmoWbNmnjjjTdw9OhRnDhx4o5gKT/sASIishwyNCYJ1NIzJPWHZIV7YxVUIUZfQyHGKpU8YLFOrQSWjGPRxDJiVUNgEvS0bt0a//3vf9Xt7OxsBAUF4bnnnsNrr72WbwA0adIkxMfH53s+eTtVq1bFSy+9hJdfflndJxdCAiR57rBhw+7aJgZARESWK0YKMYbF5RRijEV8au5CjPUCKqglOiQgamWJhRhjTgILhrFoYhkozve3WQswpKenY//+/Zg6darhPkdHRzVktXPnzgKfl5ycjODgYBUs3Xffffjggw/QqJFWdjwiIgJXr15V59CTiyGBlpwzvwAoLS1NbcYXkIiILJN/RXf8q2V1tUkhxiNSiDEnd+jQhXicjk5S2/dbz8HDJacQY05AFFy5nLmbD/g3AMZtul00cdHjLJpoBmYNgOLi4pCVlZVr+ErI7VOnTuX7nHr16mHOnDlo2rSpivA+/fRTdOjQAcePH0f16tVV8KM/R95z6h/LS4bL3n777VJ7X0REZBoyW6xFDW+1TepZFzf0hRhzAqLYpDRsOBWjNhFS2dMwVNa+dmXzFWL09AGeWAqsfR3YPQvY9D4QfYxFE03I6kpwtm/fXm16Evw0aNAA3333Hd59990SnVN6oCZPnpyrB0iG4YiIyLp4l3PFQ82qqk1SIk5eScoJhmKwL/IGIq+lInLneczbeR6uTo5oXdPbMNW+boCJCzE6OQN9P9IWTv17MnBiGXDtHIsm2kMA5OvrCycnJ0RHR+e6X24HBgYW6RwuLi5o0aIFwsPD1W398+QcMgvM+JzNmzfP9xxubm5qIyIi2yHBTMOqFdU2sWttJKdlYodR79DFGzfxT/g1tX2w8hQCK7prwVA9P9xvykKM940AfOsCi54Aoo8C33cDhv4MBHcwzevbKbNOg3d1dUXLli2xYcMGw32S1yO3jXt5CiNDaDLDSx/syKwvCYKMzyk9Ort37y7yOYmIyPaUd3NGr0aBeP/hJmqa/YaXuuDNBxuqoMfN2RFXE29h0b4LePrXA7jv3XX417c78PWGMJVjJMt6lKka7bS8oMCmQGocMO8hYN9PZfuads4ipsE/9dRTagirTZs2ahr877//rnKAJG9HpshXq1ZN5emId955B+3atUNoaKiaCfbJJ5/gr7/+UsnUDRs2NEyD//DDD3NNgz9y5AinwRMRUb5uZUghxus5i7jGqCrVxnykEKNMta8nhRj94FtWhRhV0cRngONLtNutxwJ9PgScrHxZEBOxmllgYujQoYiNjVWFCyVJWYapVq9ebUhijoqKUjPD9G7cuIFx48apY729vVUP0o4dOwzBj5gyZQpSUlIwfvx4FSR17NhRnbMoNYCIiMj+uLs4GRZlBRri4o1UbD0jw2UxaohMVrn/69BltYkm1SoZhstaBHnB2cmxFIsmztHygja+B+z9AYg9DQyZB5SrXDqvQZbRA2SJWAeIiIj0MrKycSCnEKNsxy/fWYixY+jtQoxVvUqpEOPpVcCfY3OKJtYAhi/UAiOyjUKIlogBEBERFSQm6Ra2nbldiPFGnkKMMptMHwy1DvFRvUv3VjRxOHAjAnApBzzyHdDgIX44BWAAdI8YABERUVFIIcajlxIMuUNSiNE4X9rdxRHta1XOGS7zV3WIij3VPvU6sHgkELFFu931P0DnV6RyMD+kPBgA3SMGQEREVBLxqTmFGFVAFIuYpNurDIgaPlohxi45hRjLuRUxFTcr83bRRNFgAPDwLMDVAipbWxAGQCa8gERERPmRDJNTV3MKMZ6Oxb7z15GRdbt7yMXJQQ2R6ZOpZQ2zu/YOHfgZ+PtFIDsDCGjCool5MAC6RwyAiIiotEkhxp1nr6mhMgmKLly/mevxgIpuhqrUklRdybOAqe9Ru7SiiSmxgGdl4NGfgZD7+YGBAdA9YwBERERl3Tsky3JsOa0FQzvPXcOtjGzD444OUOub6YfLZNq9o9ypl3ARWPgYcOUw4OgM9PsEaDXa7j+0RM4CuzcMgIiIyNSFGPdG6gsxxiIsJvmOQoyd6viicx1tdplfBTetaOLyZ4Fjf2oHtRqjrS1mx0UTExkAme4CEhERlbZL8TexNSd36J/wOCSlZeZ6vFHVilrvUB1ftLo4F06b3pN+JSCkk10XTUxkAGS6C0hERFTWhRgPRsUbcoeOXcpTiNHNGf+uchrjYz+ES1aKVjRx2AIgsLHdfTCJDIBMdwGJiIhMKTYpTRVglGBIeon0hRhDHS7iB5fPEOIYjXRHD4Td/ylqdx5+b4UYrQwDIBNeQCIiInMWYjwmhRhzluk4F3UBXzt/iY5Ox9XjX2UPwcGQsehcL0ANmdX0LVf8QoxWhAGQCS8gERGRpUhIzcA/Z66i4rZp6HhNS45emdUGL2X8GzfhjiAfD8NU+/a1K6N8UQsxWgkGQCa8gERERJZId2A+8PdkOGRn4LxLLYxInYTzWb65CjG2CvZRRRglKKofWIRCjBaOAZAJLyAREZHFMiqaqPOojAPtvsKy+BBsPh2LqOupuQ71r6AVYpRp9jLl3svTFdaGAZAJLyAREZFFK6BoYmRciiF3aMfZuDsKMTYL8jIUYmxa3QtOxoUYLRQDIBNeQCIiIot3l6KJtzKysC/yhmGq/Zno3IUYvTxd0KmOFgx1rusL/wrusEQMgEx4AYmIiKyCTgdsnwFsePeuRRMv6wsxnonF9rA7CzE2rFLRkDvUMtgbLk6OsAQMgEx4AYmIiKzK6VXAn+OA9KQiFU2UQoyHLsQbluk4eikh1+Myk6xD7coqIJKlOoJ8PGEuDIBMeAGJiIisTswpYMEw4EYE4FIOeHgW0HBAkZ4al5xTiPF0LLaGxeF6Snqux2v7lVPT7CUgalvTx6SFGBkAmfACEhERWaXU68Afo4Bzm7XbXacCnacAjkUfzsqWQoyXEwzDZQei4lVxRj03Z0e0q1VZS6au54daZVyIkQGQCS8gERGR1crKBNb+H7D7W+12gwHAoG8Bt/IlOl3CzQzsCI9TwZBMtb+aeCvX49W99YUY/dAh1LfUCzEyADLhBSQiIrJ6B34G/n4RyM4AAhoDw34DvIPv6ZQ6nQ5hMcmG3KE9EdeRnnV7qv3DLarh86HNUZoYAJnwAhIREdmEqN05RRNjAM/KwKPzgZCOpXb61PRM7Dp3zRAQTepZF4NaVENpYgBkwgtIRERkW0UTHweuHNKKJvb9GGg9pkxeSvKHHEu5uGJxvr8tY+I+ERERmV+l6sCoVUDjwUB2JrBislpPDFkZpf5SpR38FPv1zfrqREREZFlcPYHBPwI9pgFwAPb9CMwfBKTEwZYwACIiIqLcZKp6p8nA8IWAawXg/Hbg+27A1WOwFQyAiIiIKH/1+gBj1wM+tYCEKODHXsCJ5bAFDICIiIioYP71gbEbgFpdgYwU4PcngU3TJYsZ1owBEBERERXO0wd4/E+g3dPa7S0fAotHAGm5V423JgyAiIiI6O6cnIE+04GBMwEnV+Dk/4A5vYEb52GNGAARERFR0bV4Ahi5AijnD0QfA2Z3AyK3w9owACIiIqLiCWoDjN8MVGkOpF4D5g8E9v4Aa8IAiIiIiIqvUjVg9Gqg8b9yiia+pK0nlpkOa8AAiIiIiErGxQMY/APQ862coolzgJ+to2giAyAiIiK6t6KJHV80Kpr4j1UUTbSIAGjmzJkICQmBu7s72rZtiz179hTpeQsXLoSDgwMGDRqU6/6RI0eq+423Pn36lFHriYiICFI0cdwGqymaaPYAaNGiRZg8eTKmTZuGAwcOoFmzZujduzdiYmIKfV5kZCRefvlldOrUKd/HJeC5cuWKYVuwYEEZvQMiIiJS/OoB4zYCtbpZfNFEswdAM2bMwLhx4zBq1Cg0bNgQs2bNgqenJ+bMmVPgc7KysvD444/j7bffRq1atfI9xs3NDYGBgYbN29u7DN8FERERKR7ewON/AO2eseiiiWYNgNLT07F//3707NnzdoMcHdXtnTt3Fvi8d955B/7+/hgzZkyBx2zevFkdU69ePUycOBHXrl0r8Ni0tDQkJibm2oiIiOheiiZ+AAz8xmKLJpo1AIqLi1O9OQEBAbnul9tXr17N9znbt2/Hjz/+iNmzZxd4Xhn+mj9/PjZs2ICPPvoIW7ZsQd++fdVr5Wf69OmoVKmSYQsKCrrHd0ZERERo8bhWNLF8gMUVTTT7EFhxJCUl4cknn1TBj6+vb4HHDRs2DAMGDECTJk1UgvTff/+NvXv3ql6h/EydOhUJCQmG7cKFC2X4LoiIiOysaOK4TRZXNNHZnC8uQYyTkxOio6Nz3S+3JW8nr7Nnz6rk54ceeshwX3ZOYpWzszNOnz6N2rVr3/E8yROS1woPD0ePHj3yzReSjYiIiMqwaOLy54Cji7WiiXHhQN8PYZc9QK6urmjZsqUaqjIOaOR2+/bt7zi+fv36OHr0KA4dOmTYpKenW7duar+goauLFy+qHKAqVaqU6fshIiKiQoomPjL7dtHEqi1gtz1AQqbAP/XUU2jVqhXatGmDL774AikpKWpWmBgxYgSqVaum8nSkTlDjxo1zPd/Ly0v9X39/cnKymh02ePBg1YskvUZTpkxBaGioml5PREREZi6aWK8/4FcXdh0ADR06FLGxsXjzzTdV4nPz5s2xevVqQ2J0VFSUmhlWVDKkduTIEcybNw/x8fGoWrUqevXqhXfffZfDXERERJbAz7zBj3DQ6XQ6czfC0sg0eJkNJgnRFStWNHdziIiIqJS/v61qFhgRERFRaWAARERERHaHARARERHZHQZAREREZHcYABEREZHdYQBEREREdocBEBEREdkdBkBERERkdxgAERERkd1hAERERER2hwEQERER2R0GQERERGR3zL4avCXSrw8ri6oRERGRddB/bxdlnXcGQPlISkpS/w8KCirtz4aIiIhM8D0uq8IXxkFXlDDJzmRnZ+Py5cuoUKECHBwczB7NSiB24cIFVKxYEfbEXt+7vb5vwfduf587P3N+5qVJQhoJfqpWrQpHx8KzfNgDlA+5aNWrV4clkV+I9vRL0Zi9vnd7fd+C793+Pnd+5vzMS8vden70mARNREREdocBEBEREdkdBkAWzs3NDdOmTVP/tzf2+t7t9X0Lvnf7+9z5mfMzNxcmQRMREZHdYQ8QERER2R0GQERERGR3GAARERGR3WEARERERHaHAZAZTZ8+Ha1bt1YVp/39/TFo0CCcPn260OfMnTtXVac23tzd3WFt3nrrrTveR/369Qt9zuLFi9Ux8n6bNGmClStXwhqFhITc8d5le+aZZ2zqM9+6dSseeughVZFV2vzXX3/dUbH1zTffRJUqVeDh4YGePXsiLCzsruedOXOmuoZyDdq2bYs9e/bAmt57RkYGXn31VfUzXK5cOXXMiBEjVPX50v43Y2mf+ciRI+94D3369LH5z1zk929etk8++cSqP/PpRfgeu3Xrlvr9VrlyZZQvXx6DBw9GdHR0oect6e+H4mAAZEZbtmxRPxS7du3CunXr1C/GXr16ISUl5a4VU69cuWLYzp8/D2vUqFGjXO9j+/btBR67Y8cODB8+HGPGjMHBgwfVPzLZjh07Bmuzd+/eXO9bPnsxZMgQm/rM5ee4WbNm6ssrPx9//DG++uorzJo1C7t371bBQO/evdUvy4IsWrQIkydPVmUCDhw4oM4vz4mJiYG1vPfU1FTV9jfeeEP9f8mSJeoLY8CAAaX6b8YSP3MhAY/xe1iwYEGh57SFz1wYv2fZ5syZowIaCQas+TPfUoTvsRdffBH/+9//1B+xcrwE+4888kih5y3J74dik7XAyDLExMTIumy6LVu2FHjMTz/9pKtUqZLO2k2bNk3XrFmzIh//6KOP6vr375/rvrZt2+omTJigs3YvvPCCrnbt2rrs7Gyb/czl53rp0qWG2/JeAwMDdZ988onhvvj4eJ2bm5tuwYIFBZ6nTZs2umeeecZwOysrS1e1alXd9OnTddby3vOzZ88eddz58+dL7d+MJb7vp556Sjdw4MBincdWP3O5Dt27dy/0GGv7zPP7HpN/1y4uLrrFixfr9E6ePKmO2blzpy4/Jf39UFzsAbIgCQkJ6v8+Pj6FHpecnIzg4GC1YObAgQNx/PhxWCPpzpTu4lq1auHxxx9HVFRUgcfu3LlTdYEak78G5H5rlp6ejl9++QWjR48udOFdW/nM9SIiInD16tVcn6ms3yPDGwV9pnKt9u/fn+s5sm6f3Lb2nwP5ty+fv5eXV6n9m7FUmzdvVkMl9erVw8SJE3Ht2rUCj7XVz1yGf1asWKF6tO/G2j7zhDzfY/L5Sa+Q8Wcow3g1atQo8DMsye+HkmAAZEEr0E+aNAn3338/GjduXOBx8ktDuk6XLVumvjjleR06dMDFixdhTeQHWXJbVq9ejW+//Vb9wHfq1Emt4psf+ccQEBCQ6z65LfdbM8kTiI+PV7kRtv6ZG9N/bsX5TOPi4pCVlWVzPwfSpS85QTLEW9jit8X9N2OJZPhr/vz52LBhAz766CM1HNK3b1/1udrTZz5v3jyVM3O3YSBr+8yz8/kek8/J1dX1juC+sM+wJL8fSoKrwVsIGUOVfJa7je+2b99ebXryRdigQQN89913ePfdd2Et5JeeXtOmTdU/dOnh+P3334v0V5Gt+PHHH9W1kL/wbP0zpzvJX8aPPvqoSviULzhb/zczbNgww74kgcv7qF27tuoV6tGjB+yF/EEjvTl3m8xgbZ/5M0X8HrMU7AGyAM8++yz+/vtvbNq0CdWrVy/Wc11cXNCiRQuEh4fDmslfB3Xr1i3wfQQGBt4xa0Buy/3WShKZ169fj7Fjx9rdZ67/3Irzmfr6+sLJyclmfg70wY/8HEjyaGG9PyX5N2MNZFhHPteC3oOtfeZi27ZtKum9uP/uLf0zf7aA7zH5nGQoU3q6i/oZluT3Q0kwADIj+atPfmiWLl2KjRs3ombNmsU+h3QPHz16VE0VtGaS43L27NkC34f0gEi3uTH50jDuGbE2P/30k8qF6N+/v9195vKzLr/IjD/TxMRENdujoM9UutFbtmyZ6znS5S63re3nQB/8SH6HBMEyPbi0/81YAxnGlRyggt6DLX3mxr2+8p5kxpgtfOa6u3yPyXuVP9qMP0MJACWXqaDPsCS/H0raeDKTiRMnqtk9mzdv1l25csWwpaamGo558sknda+99prh9ttvv61bs2aN7uzZs7r9+/frhg0bpnN3d9cdP35cZ01eeukl9b4jIiJ0//zzj65nz546X19fNYMgv/ctxzg7O+s+/fRTNYNAZkfIzIKjR4/qrJHMZKlRo4bu1VdfveMxW/nMk5KSdAcPHlSb/KqZMWOG2tfPdPrwww91Xl5eumXLlumOHDmiZsXUrFlTd/PmTcM5ZJbM119/bbi9cOFCNRNk7ty5uhMnTujGjx+vznH16lWdtbz39PR03YABA3TVq1fXHTp0KNe//bS0tALf+93+zVj6+5bHXn75ZTXzR97D+vXrdffdd5+uTp06ulu3btn0Z66XkJCg8/T01H377bf5nsMaP/OJRfge+/e//61+323cuFG3b98+Xfv27dVmrF69erolS5YYbhfl98O9YgBkRvKPJL9Npj3rdenSRU0d1Zs0aZL6QXJ1ddUFBATo+vXrpztw4IDO2gwdOlRXpUoV9T6qVaumboeHhxf4vsXvv/+uq1u3rnpOo0aNdCtWrNBZKwlo5LM+ffr0HY/Zyme+adOmfH++9e9Nprq+8cYb6j3JF1yPHj3uuB7BwcEq2DUmXxD66yFTpHft2qWzpvcuX2YF/duX5xX03u/2b8bS37d8Ifbq1Uvn5+en/niR9zdu3Lg7Ahlb/Mz1vvvuO52Hh4ea0p0fa/zMUYTvMQlann76aZ23t7cKAB9++GEVJOU9j/FzivL74V455LwwERERkd1gDhARERHZHQZAREREZHcYABEREZHdYQBEREREdocBEBEREdkdBkBERERkdxgAERERkd1hAEREJhUZGQkHBwccOnTIYq78qVOn0K5dO7U4ZfPmzW3iPRFR4RgAEdmZkSNHqi/rDz/8MNf9f/31l7rfHk2bNg3lypVTaxTlXXPOHObOnasWviSissMAiMgOSU/HRx99hBs3bsBWyIrTJSULTHbs2BHBwcElWpjUUsnCubJ4KBHdiQEQkR3q2bOnWm15+vTpBR7z1ltv3TEc9MUXXyAkJCRXb9KgQYPwwQcfICAgQPVavPPOO8jMzMQrr7wCHx8fVK9eXa18n9+wU4cOHVQw1rhxY2zZsiXX48eOHUPfvn1Rvnx5de4nn3wScXFxhse7du2qVqGeNGkSfH190bt373zfhwQA0iZph5ubm3pPq1evNjwuvV779+9Xx8i+vO+CzvPxxx8jNDRUnadGjRp4//33i9yDk7eH7fDhw+jWrRsqVKiAihUrqlWz9+3bh82bN2PUqFFISEhQxxu3KS0tDS+//DKqVaumeqzatm2rjs/7usuXL0fDhg1VO2XVbTmmTZs26jny+P3334/z58/n23Yie8EAiMgOOTk5qaDl66+/xsWLF+/pXBs3bsTly5exdetWzJgxQw0nPfjgg/D29sbu3bvx73//GxMmTLjjdSRAeumll3Dw4EG0b98eDz30EK5du6Yei4+PR/fu3dGiRQsVFEjAEh0djUcffTTXOebNmwdXV1f8888/mDVrVr7t+/LLL/HZZ5/h008/xZEjR1SgNGDAAISFhanHr1y5gkaNGqm2yL4EGPmZOnWqGjZ84403cOLECfz2228qMCupxx9/XAVle/fuVQHYa6+9BhcXFxUUSqApQZG0x7hNEvDt3LkTCxcuVO9lyJAh6NOnj+G9iNTUVNW798MPP+D48eMqCJUgtUuXLuo58vzx48fb7XAnkUGpLq1KRBZPVqceOHCg2m/Xrp1u9OjRan/p0qVqRWY9WZW6WbNmuZ77+eefqxWrjc8lt7Oysgz31atXT9epUyfD7czMTF25cuV0CxYsULf1q6F/+OGHhmMyMjJ01atX13300Ufq9rvvvqtWDjd24cIF9Tz9itBdunTRtWjR4q7vt2rVqrr3338/132tW7dWq1PryfvMuwK5scTERLUi9ezZs/N9XP+eDh48qG7LqtaVKlXKdUze61uhQgXd3Llz8z1ffs8/f/68zsnJSXfp0qVc98sq2VOnTjU8T17j0KFDhsevXbum7tu8eXOB74/IHrEHiMiOSU+B9KKcPHmyxOeQ3hNHx9u/SqRXpEmTJrl6mySvJiYmJtfzpNdHz9nZGa1atTK0Q4aHNm3apIa/9Fv9+vUN+Tp6MmxUmMTERNU7JUM+xuR2cd6zHCvDTz169EBpmTx5MsaOHauGI6Vnyfh95efo0aMqp6du3bq5rosMHRo/V3rEmjZtargtPUAyVCk9X9LLJj1i0qtEZO8YABHZsc6dO6svRhneyUuCGp1OOg9uy8jIuOM4GbYxJkMr+d1XnGTc5ORk9WUt08qNNxnqkTbrSU6LKXh4eBTr+KJcO8nrkSGq/v37q2FEydlZunRpoddEgkkZLjO+JhKcSVBj3Na8w1uSgyVDXzK8tmjRIhVE7dq1q1jvicjWMAAisnPS+/C///1PfUEa8/Pzw9WrV3N9kZdmnRvjL2BJmpYv9gYNGqjb9913nwoOJOFako6Nt+IEPZJHU7VqVZUjZExuS8BRVHXq1FGBRVGnyMu1S0pKQkpKSqHXTgKRF198EWvXrsUjjzxiSBaXXhzp7TEm+VByn/Sk5b0mktB+N/J8CXR37Nihks4lh4nInjEAIrJzMlwlCblfffVVrvtlllVsbKya+SRDLDNnzsSqVatK7XXlfNLjIbPBnnnmGTUlf/To0eoxuX39+nUMHz5cJQnL669Zs0bNjsobGNyNJFvLUJ/0fEidH0k2lmDkhRdeKPI5ZKbaq6++iilTpmD+/PmqPRLA/fjjj/keL7OzPD098Z///EcdK8GGzNDSu3nzpkpoltlZMhtLAjJ5n/oAUAI/6fGRgEtmvkliswRL8jmNGDECS5YsQUREBPbs2aNm8q1YsaLAtstxEvhIgCuvJcGW9KTpX4vIXjEAIiI1BTzvEJV8QX7zzTcqUGnWrJn6si1ohlRJe55kk3Nv375dTd2W6exC32sjwU6vXr1UkCbT3WUKt3G+UVE8//zzKt9GZnnJeWRGmbyW9OoUh8z+knO8+eab6toMHTr0jrwm47ybX375BStXrlSvuWDBglzT62UoS2a8STAjgY3MbpMp/2+//bZ6XIaqZPacvIb0JkkQKqSHSJ4j7ahXr56a3SWBk0zJL4gEYhJkDh48WL2WzACTAFNm5hHZMwfJhDZ3I4iIiIhMiT1AREREZHcYABEREZHdYQBEREREdocBEBEREdkdBkBERERkdxgAERERkd1hAERERER2hwEQERER2R0GQERERGR3GAARERGR3WEARERERHaHARARERHZnf8HJFb17HdbahkAAAAASUVORK5CYII=", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For the K-means\n", "The best number of clusters for shiluette is 5\n", "With a silhouette of 0.8066308498382568\n", "For the K-medoid\n", "The best number of clusters for shiluette is 5\n", "With a silhouette of 0.8066308498382568\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For the K-means\n", "The best combination of number of clusters and number of samples for the mean distribution similarity is 5 clusters and 90 samples\n", "With a mean similarity of 0.0052489564\n" ] }, { "data": { "image/png": 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3DaN3FbaVRdGXzV6zou1nUpbtFO5z5D18BZt+TA69ClA1j/Rpce2T9rraococ9G6u+o6jrBtFHbq7GutmsBFGG65xx44NwMaNG1V7sG7x4sWFujE7M2/9+vVTGZm33nrLajqOmrHBdjawwGvZppCxwcJG0LaLsKshk4N28vfff99uM4HefKIfERqP4DHPODp3BwR1+H6ht4dxnaH5DFkMrCN3Qd2HEeonkI0s73XpCKxH2ywMeqDgO1gW+P0U9dtBNgC1F8hSIPuArIrxYMJZWLebNm2SDRs2WKbhe4peKajvsa3VKC3UkSEzjHWH+V+1apXqtYZAQr+46r1wEIj3w8EdaodsnT17ttD33rjNQI2LbW1OWbZTeC62m/icjfNgrDXyJcyomAiKqxBto0saCsoQpOAHgOgaRxc4yiiuXR5NP+j3j8cjI4DulOgypxe9IWjAkQlS4IjwsXHDEVZp28Fx1IrXxlEr5hfdJLFRMXahxhEsAhgc1WGnjJQ8xoMxFrc6O29ov8aRFrJFqIdp37692jki1Y+Uuu1rlwTBFj4nFAPjtbBjw5EQupLaG/fElXBkix0ICk2RHUGdETZu+B5gOgofUZiL8SDQ1INlRzdeZAkQ3CCgQnDoDmgew8YWTRgoGta7J6M5w9Xnt3EGdl7o7oudGr6j6Jqsdz13N3R1RoYUnxHmEzt7fNf0bvKlhWVFV1p0i8dvEN8LYw0Kmn/QLR/fMTRplgW6y2L8Fax7FIriM8aO++DBg/LNN9+4tEkJmSBsA/G7GDhwoApWjAWmroIgBZ8ffo/IpKIpDu+NIQzQlI731wMOdEnGdhbbPnRRx0Eivvfo9o/fpSu2U+gOr5+y4IknnrB0T8a23dF6K2/CQMVEUGQH2CHhx48jH+z8EQgUV0gL6K+PHwPGBUAhI1Lv6BGCga/0wjGMf4ENCkZ6xI4RARGOyEsbqKCQEz8a/HCxs0fzBYIj41gQOFLBzh5NUfhxYqeLjArano2cmTdsCBG44fNCHQ4ehyM57DhtX9cRmF8092AjgpoUtFdjY49lefTRR6U8YVkwVgqOspChQo0M5gfNYthA6UW1KKLDzhYDZ2H8B7SjY96wMcXG0h1wRIsjXgxWh3WBdYjvHHaEZS0CLQvsPPH9wPpEFgUbd+zAUVTrbhgfBFkVHBmjyQc7QAQqJTXrlgSfP5pTURSO3yLWgzFQ0cfPwbhAZe05gmJ91LtgTBLsoLEcyNCgeRI7cFdDPRbWJWrIEBzhgMyRsYCchTFbkEXFWEjYluC7g6JjvK+xNySCB9T84LeI7RUCDmyDEIDoAxWWdTuFzA2CStQ0zZgxQwWy2C5i/ow9CX2FH/oou3smiIiofHXs2FEdACErQeRJWKNCROTl0PyF8T+MI8wSeQpmVIiIvBSKm3H+LjS/okkYYwMVV+tGZEbMqBAReSnUNaG+Aj1ZUADLIIU8ETMqREREZFrMqBAREZFpMVAhIiIi0/LocVQw3gXOdosxRlw9PDwRERGVD4yMgjF/MDZMSYMEenSggiDF9qyYRERE5BlwOhWMDO61gYo+WisWFEMgExERkfklJSWpRENJo657fKCiN/cgSGGgQkRE5FkcKdtgMS0RERGZFgMVIiIiMi0GKkRERGRaDFSIiIjItBioEBERkWkxUCEiIiLTYqBCREREpsVAhYiIiEyLgQoRERGZFgMVIiIiMi0GKkRERGRaDFSIiIjItBioEBFRuUjOyJaUzBx+ulQmHn32ZCIiMo+LaVmy6WCCbDyAy3nZfSpJNE2kWniQ1IsKl3rVwqVutTCpq26HqWl1IsMkNCjA3bNOJsZAhYiISiUxLVt+O3i+UGBi60JatlxIS5QdxxLtvk5M5ZCCQCZM6lYLl3pRYSqowbTYqqESGMDkvy9joEJERC4LTBpHR8gVjaqrS9dGURIWFCDHLqTL0YQ0OXohXY5dSJOjCfp1mqRm5cqZ5Ex12XL4QqH3DPD3k1pVQq2CF2Rl9AwNghx/fz+uQS/GQIWIiIoMTDYdyg9KcNl1suTAJKZyaKHXaRkbJC1jqxSarmmaXEzLlqMFwUv+dVp+YHMh/zorJ0+OX0xXl42SUOg1ggP8pY7KxFwKXi7dDpOoiGDx82Mg48kYqBARkUsDE0chgKgWEawu7epGFro/L0+TsymZlixMflamICNzMU1OXMyQrNw8OXguVV3sCQ8OsApebK+rhAZx7ZscAxUiIh/lSGDSyBCYXNEwSmKqlD4wcRaadGpWCVWXTnGF78/JzZOTiRmW7MuxguYlPStzOjlD0rJyZc/pZHWxp2oYCn3DpG5kQW1MQVYGt+tEhktYMAt93Y2BChGRj0hMz5bNqlfOedl48Lz8dcJcgYmzUGSrAouocLv3Z+bkynHVjHQpeFFBTUFAk5CapT6TxOPZsvN4kt3XqFEpxFIfY2xewrTYqmESHMhC3/LGQIWIyEt5W2DirJDAAGkUXUld7EnNzDEU+hqKfAuyM8mZOXIuJVNdth25WOj5qOFFoS+6W6sgxqbYF/ehGJjKxk9DNZOHSkpKkqpVq0piYqJUqVK4UIuIyJf4emDiStg14vO0DWQszUwX0iQjO6/Y1wj095PakWFWTUt692tcR1fy3R5LSU7sv5lRIY/YYKCdOSkjW5LSc9Rol/ptXCdn5EhSuvW0pIwc1X59ZePq0rdtrHSoG+mzGwTyXtiR/m6pMUmQv04kSp5tYFIjQrqqwCRKBSeo9yDHCn0jw4PVpU2dqna3Syj01bMwevCi/41eStm5mhxJSFMXkfOFXgPNRmoAvGqFx5DBdPZYyseMCpU7VO4jhYpgQgUVKqDIDyaSjcGF8X6roCRHcm23vk7CoFE3tqklfdvESqe4akzHkkfC78KSMWFgYmrYZp1OyrBkZC51uc4PZk4mphcKKu31WKpb0KR0qbfSpdsoBPaFjAoDFSpRdm6eJWthHWgYsxmGa5ugA+f6cEUDI9KoVcKCpEpoYMF1kFTG7dAgqRIWeOnvgvvSs3Nlxa7Tsmr3aTWolC66cojc2LqW9G1bSy5vEMVRL8m0GJh493b1FHosGYIY4+3TSZklvkbl0MBC48bkZ2Xyp0WEmLfRhIEKFQuBw7YjF1Q60rbZRM9gGAMRNLu4Qkigv1WgUTnUTtBhFYjoQUj+/aFB/qUauCkjO1d+2XtOlv55UlbsPq2WSVc9Ilh6I2hpU0u6Na4uQRyqm9wIv7n8ppz8rMnO44WbchrWiLA043RtWF1qVWVTjjfCduvERTQjpRcq9D1+IU3OpWSV+BpoOtKDl7p6fYz+dzX3nmOJgQpZOZOUIZsPXZDNhxLk98MJsutEUokpR3siggOKDCouZTaKvh8V+O6GUS7X788PWpbvOq1GxdQhjdq7VU3p1zZWrmxS3RTzS96NgQmVVlpWTkHXa+MpCfIHwsM16pdKgtMPWA2AZ8jGoAi4PA/cGKj4MBR47T+bcikwOZSfObGFL2KLWpWlalhwiZkMNKtUCgn0uiYSpF5x1Lp05yn5aecpOZ966QgFn0mvljVVpuWaZtE8uys5VZuA8TnQpfVscsEFXVwLro3TjIGyjhkTclUQfMzQSym/WenSbWNzuD3Grtc9mkXLiOuauHTFMFDxIcgQ/Hk8UaWLEZxsOZygzlRqhNaSlrWqSJcG1aRzgyjp3KCaGqiIrHcuOD390p0nZdnOU+oEacZM0vUFQcu1zaMlPNi87b5UfgcAaBo9m5IhZ5OzrAOO5PxxNvTg43xKplMZSwYmVNE0wzmWjN2vjYW/mTmXul4P6lhHZt7ZwaXzwEDFy6PkrYcvqEwJhr7efvSi1RdKrwXpUC9SLm+IoCRKOtaP5PksnOyltPXIBVny5ykVuGCIbh3qZK5rHqO6PF/fIkZlmsiz0+eFgg1j5iMlKz8TkpypzinjKBwcoP4Jo5qieDtav64ccmla5RCVekf3VyIz0Qq6XuuBCzIr6OIuvh6ozJgxQ8aPHy9PPPGEzJo1y6Hn+MKAb+jChkyJnjH5285p1auFB6mARM+YtKldlcM6uzBo2X7sosqyLNl5UrX9GsdAuKZptPRrW0t6tqzp0V0FvS3LaAw6jNkO22klpb9toVm0hk3goQci+nQEHyhi9LamUiKfHvBt8+bN8u6770q7du3E13eKe8+kFNSW5AcmGDTIVlz1cOkcdykwwdlMeRrz8oFB4jrWr6Yu4/q2UCN9LvnzpKprwdlaV+4+rS5BAX7SvUkNlWm5oWVNdTZYcn3dhzHgsMqA6HUgRdR9FAdZMpwBuEalYEPgEVqQ/Qi2yoS4s5cEka9ye6CSkpIid999t7z//vvy0ksvia91P0N9iV70iuAEXYNtC5pa166q6kq6oL4krhqHvHYTBIMYoRKXp/s0V2djVc1Df55UAWb8nrPqgnN7qBFx28RK79Y11Q6OHEs1HzybqgJAXA6cS1WpZwQhCanO1X1gzB2rphabDIix+QU1SAz0iczL7U0/w4YNk6ioKHn99dfl2muvlQ4dOnht08/FtCzZchi9cfKDkh3HEgu1e4cFBchlcZEFGZMo6VA/knUQHmDv6WSVZcFl98kkq0ATY11gcDkMMufr51VBjdUhPRAxBCW4YHyf0tZ92Da/oBmOp0wgMi+Pafr54osvZOvWrarpxxGZmZnqYlxQs0L8h6NBjFuiByb/nE4p9DhsdPUmHFy3jK3CQcc8UNOaldVlVM+maqeLItylf55SGbMNB86ry6Tv/1IZMWRaMJw/xinwRpk5uXLkfJrKiKggpCAgwd9omikKgjoMRIVeMLjg5HkY0yGmIPhg3QeRb3JbRuXo0aPSuXNnWbFihaU2paSMygsvvCCTJ08uNN0MGRW0oe85layacfSmnFNJl3qL6LDx7RKX30UYGRPUmzDt7L1QMa8X4tqeJh49s1CIi8AFO2RPq6c6kZh+qZnGkB3BWA3FNdMgOMeJ8vBbsA1KOMgekW9I8oReP4sWLZJbb71VAgIuFafl5uaqnba/v7/KnBjvKyqjUq9ePbcEKulZuao3iF70ii7DOPGebTs56hn0jAlOhsd6Bd+F4bARtOCy+XCCVe+tNnWqqIAFo+Jix20G2DSggFXPhhizI4fOpxbqFm+Ebtt6EKIHIrhuUCOCXeWJSDwiUElOTpbDhw9bTbv//vulRYsW8swzz0ibNm1MVaOCDTaCkt9VjUmCOgcHTuFtu3G+LK6adInLD0xwxBwWzF4CZP+0Bj/9dUoV4/528LxVBgIjBiNgwQBzaE6qiLFELLUiZ/ODEhWYnE0pVNxthJ5OcdULAhFDUNIwOkI11TBTSEQeHajYY5ZiWnwkGHb+0vglCbL/bGqhx9WsgvqS/KJXNOW0qFVF9fggcgbqNnCWZ3R7/nX/edWMqGsSU0n6tcGZnmNVAFPanT9OF4BmKGOPGj07Yq+J0qhOZJhVdgSBSOMalaR2ZCjHCiEi7y6mNasP1x+SFxfvKjS9aUwlS9ErghOcL4dHjVRWaA4ccnl9dbmQmqXO8Iwuz+v2nZN9Z1Jk9s/71AVBArIsaCJCU5Htdw8BNk4Nf+BcSqEeNQi8jQGQLRSqWjXVFAQkDapHcOwQInIrU2VUnFVeGRVkUYa8v1Ha1Y3ML3qNy68v4SBeVJFw9tOf/0am5ZSs+eesGnFVhyAZQQsGIDNmR9Kzix5pFV3fGxTUi1g11dSI4DDuRFShPLbpxyyBSk5unuTkaTySJNPAGCM//31Glu08qa4zsu0XsqLpsX5UuN3sSM3KoRxbhIhMgU0/ZYRzdASyBpZMBIXaA9rXVhcUv67Zc1YFLEGB/lbZEXTxDeI5ZojIi7BGhcjDhAcHquJaXIiIvB1P70lERESmxUCFiIiITIuBChEREZkWAxUiIiIyLQYqREREZFoMVIiIiMi0GKgQERGRaTFQISIiItNioEJERESmxUCFiIiITIuBChEREZkWAxUiIiIyLQYqREREZFoMVIiIiMi0GKgQERGRaTFQISIiItNioEJERESmxUCFiIiITIuBChEREXlPoDJs2DBZu3Zt+cwNERERUVkClcTEROnVq5c0bdpUpk2bJsePH3f2JYiIiIjKJ1BZtGiRCk4effRR+fLLL6VBgwbSt29fWbBggWRnZzv7ckRERESurVGJjo6WsWPHyvbt2+W3336TJk2ayNChQ6V27doyZswY2bt3b2leloiIiMh1xbQnT56UFStWqEtAQID069dP/vzzT2nVqpW8/vrrZXlpIiIiIucDFTTvfPPNN9K/f3+Ji4uTr7/+WkaPHi0nTpyQjz76SFauXClfffWVTJkyhR8vERERlUmgs0+IjY2VvLw8GTJkiGzatEk6dOhQ6DHXXXedREZGlm3OiIiIyOc5HaigSWfw4MESGhpa5GMQpBw8eNDnP1wiIiKq4Kaf+Ph4u717UlNTZfjw4WWcHSIiIqIyBCqoQ0lPTy80HdPmz5/v7MsRERERlb3pJykpSTRNU5fk5GSrpp/c3FxZsmSJxMTEOPpyRERERK4LVFB34ufnpy7NmjUrdD+mT5482dGXIyIiInJdoILaFGRTrr/+etU9OSoqynJfcHCw6qqMAd+IiIiIKjxQ6dGjh7pGb5769eurDAoRERGR2wOVHTt2SJs2bcTf31+dlBCjzxalXbt2rpw/IiIi8mEOBSoY1O3UqVOqWBa3kU1BM5AtTEdhLREREVGFBSpo7sGJCPXbRERERKYJVFAoCxjoDT17JkyYIA0bNizveSMiIiIf59SAb0FBQarHDxEREZEpR6YdOHCgLFq0qHzmhoiIiKgsJyVs2rSpTJkyRdavXy+dOnWSiIgIq/tHjRrl7EsSERER2eWn2eu+U4zialPQ6+fAgQNSUTCsf9WqVVWX6SpVqlTY+xIREVHF7L+dzqiw1w8RERGZtkaFiIiIqKI4nVGBY8eOyffffy9HjhyRrKwsq/tmzpzpqnkjIiIiH+d0oLJq1SoZMGCANGrUSP7++281tP6hQ4fUSLWXXXZZ+cwlERER+SSnm37Gjx8vTz31lDrfT2hoqBpX5ejRo+qkhYMHDy6fuSQiIiKf5HSgsnv3brn33nvV7cDAQElPT5dKlSqpLssvv/xyecwjERER+SinAxWMm6LXpcTGxsr+/fst9507d861c0dEREQ+zekalSuuuELWrVsnLVu2lH79+smTTz6pmoG+/fZbdR8RERGR2wIV9OpJSUlRt3GCQtz+8ssv1Yi17PFDREREbh2Z1kw4Mi0REZF377854BsRERF5dtNPtWrV1Hl8HJGQkFDWeSIiIiJyPFCZNWuWIw8jIiIiqvhAZdiwYfzYiYiIyJyBCope9GIX3C5OSUUxRERERC6vUTl58qTExMRIZGSk3XoVdB7C9NzcXIffnIiIiKjMgcrPP/8sUVFR6nZ8fLwjTyEiIiIqM46jQkRERKYdR8XpkWkhIyNDduzYIWfOnJG8vDyr+wYMGFCalyQiIiIqe6CybNkydfZkeycgZI0KERERuZLTI9M+/vjjMnjwYFVci2yK8cJCWiIiInJroHL69GkZO3as1KxZ06UzQkRERFTmQOX222+X1atXO/s0IiIiovLv9ZOWlqaafqKjo6Vt27YSFBRkdf+oUaOkovDsyURERJ6nXHv9fP7557J8+XIJDQ1VmRXj4G+47Uyg8s4776jLoUOH1N+tW7eWiRMnSt++fZ2dLSIiIvJCTmdUatWqpYKRcePGib+/0y1HVn744QcJCAiQpk2bqpFtP/roI/nPf/4j27ZtU0FLSZhRISIi8jzO7L+dDlQwQu3mzZulcePGZZ3PIl8fwcoDDzxQ4mMZqBAREXkeZ/bfTqdEcCblL7/8UlwNXZu/+OILSU1NlW7durn89YmIiMjzBJYmoHjllVfkp59+knbt2hUqpp05c6ZTr/fnn3+qwASj3VaqVEkWLlworVq1svvYzMxMddGVdCZnIiIi8rFABYFFx44d1e2dO3da3WfvrMolad68ufzxxx8q/bNgwQKVsVmzZo3dYGX69OkyefJkp9+DiIiIPJPpTkrYq1cvVf/y7rvvOpRRqVevnkNtXEREROQjJyUsTxiK3xiMGIWEhKgLERER+QaHApVBgwbJvHnzVNSD28X59ttvHX7z8ePHqzFT6tevL8nJyfLZZ5+psVlQ/0JERETkUKCC9Ixef4LbrnLmzBl1Jmac4BCvi+JcBCk33HAD1wwRERGZr0bFGRxHhYiIyPOU6zgq6enp6nw/usOHD8usWbPUsPpEREREruR0oHLLLbfI/Pnz1e2LFy/K5ZdfLq+99pqajvP2EBEREbktUNm6datcffXV6jbGPcG5f5BVQfAye/Zsl80YERERkdOBCpp9KleurG6juQe9gHBywiuuuEIFLERERERuC1SaNGkiixYtkqNHj6oeOr1797b04OGga0REROTWQGXixIny1FNPSYMGDaRr166WEwgiu6IPrU9ERETktu7Jp06dUmOftG/fXjX7wKZNm1RGpUWLFlJR2D2ZiIjI85T7EPoooMXFCL1/iIiIiNza9ENERERUURioEBERkWkxUCEiIiLTYqBCREREplWqYtoTJ07IunXr1NgpeXl5VveNGjXKVfNGREREPs7pQGXevHnyf//3fxIcHCzVq1cXPz8/y324zUCFiIiI3DaOSr169eSRRx6R8ePHW8ZQcReOo0JEROR5nNl/l+pcP3fddZfbgxQiIiLyfk5HGw888IB8/fXX5TM3RERERGVp+snNzZX+/ftLenq6tG3bVoKCgqzunzlzplQUNv0QEXkP7F+ys7PdPRvkAogNAgIC3DOE/vTp09VZk5s3b67+ti2mJSIicgaOl3EOuYsXL/KD8yKRkZHqdDtljQ2cDlRee+01+fDDD+W+++4r0xsTERGBHqTExMRIeHg4D3q9IPBMS0tTQ5hAbGxsxQYqISEhctVVV5XpTYmIiPTmHj1IwZAX5B3CwsLUNYIVrNvimoFcXkz7xBNPyJtvvlnqNyQiItLpNSnIpJB3CS9Yp2WtO3I6o7Jp0yb5+eefZfHixdK6detCxbTffvttmWaIiIh8D2scvY+fi+pWA0tTHDNo0CCXvDkRERGRywKVnJwcue6666R3796qkpeIiIhcm4VYuHChDBw4kB9raWpUAgMD1fD5mZmZzjyNiIjI62C4ji5dukjlypVVwSiCiz179ohZrF69WgU+nt7t2+li2ssvv1y2bdtWPnNDRETkIdasWSMjRoyQjRs3yooVK1TRKFocUlNTxdu6G+fk5HhOoPLYY4/Jk08+KW+99ZZs2LBBduzYYXUhIiLyBcuWLVNjiqFjSfv27WXevHly5MgR2bJlS7HPw1hkeA6G+8AYIyNHjnQ4I/LHH3+oaYcOHVJ/Hz58WG6++WapVq2aREREqNddsmSJuh+lGoD78Bx9/LO8vDyVDWrYsKHqRox5X7BgQaH3Xbp0qXTq1EnN57p168RdnC6mxQkJYdSoUZZpWCBEXLhGn3giIqLSwv4kPbvi9yVhQQFl6qmC4eAhKiqqyMe88847MnbsWJkxY4b07dtXPWf9+vWlfs8RI0ZIVlaWrF27VgUqu3btkkqVKkm9evXkm2++kdtuu001R2GYen1sEwQpn3zyicyZM0eaNm2qnnvPPfdIdHS09OjRw/La48aNk1dffVUaNWqkgh2PCVQOHjxYPnNCREQkooKUVhN/qvDPYteUPhIe7PRu0ZKlGD16tBoQtU2bNkU+7qWXXlKtEhiTTIc6l9I6cuSICkZw7j1AUKHTAybUz6DHLqDGdNq0abJy5Urp1q2b5TnImLz77rtWgcqUKVPkhhtuEHdzeo3ExcWVz5wQERF5KGQ2du7cWWwTCUZpPXHihPTs2dNl7ztq1Ch59NFHZfny5dKrVy8VtLRr167Ix+/bt08Nb28bgCAr07FjR6tpnTt3FjMoVei4f/9+mTVrluzevVv93apVKxUdNm7c2NXzR0REPgZNMMhuuON9SwM1JhgEFU0odevWLfr1C5peHOXvn19GiqYwne0orw8++KD06dNHfvzxRxWsoFkH5+R7/PHH7b5mSkqKusbj69SpY3UfalGM0JTkkYEKzpw8YMAA6dChg+WcP2hfQwHPDz/8YIo0EREReS7UiZS2CaYiIYBAQIBxT1CAiuLU4qAbc4MGDWTVqlWWQtfioGYETp48aakRQTGtLdSjYOgQXMaPHy/vv/++mq/g4GB1v7F2FIkFBCRoMjI285iZ098EFNeMGTNGFQLZTn/mmWcYqBARkc8093z22Wfy3XffqSAEZ4GGqlWrFpk9eeGFF1RAgboRFNMmJyerg317GZAmTZqoIATPmTp1qvzzzz8qW2KEuhi8TrNmzeTChQsSHx8vLVu2tJRqIOhDtqdfv35qnjCfTz31lNqPo66me/fuloJeFNwOGzZMTEdzUkhIiPbPP/8Umr5nzx51X0VKTExEPkxdExGR50lPT9d27dqlrj0N9j/2LnPnzi32eXPmzNGaN2+uBQUFabGxsdrjjz9u9ZoLFy60/L1u3Tqtbdu2WmhoqHb11VdrX3/9tXrMwYMH1f0jR47UGjdurPa/0dHR2tChQ7Vz585Znj9lyhStVq1amp+fnzZs2DA1LS8vT5s1a5ZlHvC8Pn36aGvWrFH3x8fHq/e4cOFCua1bZ/bffgUfjMMQ3c2cOVMGDx5sNf2rr75SURrSSRUlKSlJRa6IBhEJEhGRZ8nIyFC9SdFsEhoa6u7ZoQpat87sv51u+nnooYfk4YcflgMHDsiVV16ppiFl9PLLL6u+4URERESu4nSgMmHCBNXGhXYyFO1A7dq1VRuacRA4IiIiogoPVFCYgyIcXFAEBAhciIiIiFytTP2/GKAQERGRqU5KePr0aRk6dKhq7gkMDJSAgACrCxEREZHbMio4+yJ69qBWBWd9LMsJnIiIiIhcGqjgPAa//PKLGpmWiIiIyFRNPxhHxcmhV4iIiIgqJlDByQgxXP6hQ4dK945ERERE5dX0c+edd6pTRONMyeHh4RIUFGR1f0JCgrMvSURERAVDgOAkhwMHDuTnUdpABRkVIiIiX4eBTidPnmw1rXnz5vL333+LGaxevVqdpRknK4yMjBSfCVRMeWZFIiIiN2jdurWsXLnS8jeG7fA2mqZJbm6u25bN6RoVIiIiyoedd61atSyXGjVqlPjRfPjhhyrACQkJUcN8jBw5ssiMiJ+fn1y8eNEy7Y8//lDT9DrRw4cPy8033yzVqlWTiIgI9bpLlixR9yObArgPz8HwIpCXlyfTp09XJwsMCwuT9u3by4IFCwq979KlS6VTp05qPtHj1128L/QjIiLPhp6l2WkV/75B4SgSceope/fuVQOg4uzA3bp1UwFA/fr1i3z8O++8o07gO2PGDOnbt686ezBO7FtaI0aMkKysLFm7dq0KVHbt2iWVKlVSPXS/+eYbue2222TPnj3qDMUISgDz+Mknn8icOXOkadOm6rn33HOPREdHS48ePSyvjY4zr776qjRq1EgFO+7CQIWIiMwFQcq02hX/vs+eEAmOcPjhXbt2lXnz5qm6lJMnT6p6lauvvlp27txZ5ClmXnrpJXnyySfliSeesEzr0qVLqWf5yJEjKhhp27at+htBhS4qKkpdx8TEWGpUMjMzZdq0aaq5CoGV/hxkTN59912rQGXKlClyww03iLsxUCEiIioFZER07dq1U4FLXFycfPXVV/LAAw8UevyZM2fkxIkT0rNnT5d93qNGjZJHH31Uli9fLr169VJBC+alKPv27VM9d20DEGRlOnbsaDWtc+fOYgYMVIiIyFzQBIPshjvetwyQtWjWrJkKBuzRm14c5e+fX0ZqHGQ1Ozvb6jEPPvig9OnTR3788UcVrKBZ57XXXpPHH3/c7mumpKSoazy+Tp06VvehFsUITUkeGaikpqaqtrVVq1ap6BBFOUYHDhxw5fwREZGvQZ2IE00wZoEgYP/+/erEvfagOahBgwZq/6kXuhYHNSOAZiW9RgTFtLZQj/LII4+oy/jx4+X9999XgUpwcLC6Hz12dK1atVIBCZqMjM08ZuZ0oILobc2aNWpF8KSERETkq5566inV4wbNPWjSmTRpkgQEBMiQIUOKHXsFAQXqRtB0lJycrIpp7WVAmjRpooIQPGfq1Knyzz//qGyJ0ejRo9XrIJOD8VLi4+OlZcuW6j7MF3rvLF68WPr166cyOgiWMN9jxoxRiYbu3btbCnpRcGvKIUg0J1WtWlVbt26dZgaJiYnIh6lrIiLyPOnp6dquXbvUtae58847tdjYWC04OFirU6eO+nvfvn0lPm/OnDla8+bNtaCgIPX8xx9/3HIf9mkLFy60/L1u3Tqtbdu2WmhoqHb11VdrX3/9tXrMwYMH1f0jR47UGjdurIWEhGjR0dHa0KFDtXPnzlmeP2XKFK1WrVqan5+fNmzYMDUtLy9PmzVrlmUe8Lw+ffpoa9asUffHx8er97hw4UK5rVtn9t9+BR+Mw9DvGn209YjNnZKSkqRq1aoqGkQkSEREniUjI0MOHjyo9i3o4ku+sW6TnNh/Oz3g24svvigTJ05UVcNEREREpqpRQfsYioVq1qypioJsT0q4detWV84fERER+TCnAxWe0ZGIiIhMG6igqpmIiIioIvCkhEREROQ9GRUMHPP666+rIYIxYAyG3TVKSEhw5fwRERGRD3M6o4KTLs2cOVPuvPNO1a0IZ4EcNGiQGuoXg9IQERERuS1Q+fTTT9XwvDj7Y2BgoBqB74MPPlBdljdu3OiyGSMiIiJyOlA5deqU5XTSlSpVUlkV6N+/vzrJEREREZHbApW6deuqEyRB48aN1dkaYfPmzYXOvEhERESuc+jQIXX+HnsnJ/RWTgcqt956qzrzI+AkShMmTJCmTZvKvffeK8OHDy+PeSQiIjKdtWvXqpMS1q5dWwUPixYtKvQYnKUGpRE4iS9OCtirVy/Zu3evmMW8efMkMjJSvCpQmTFjhjz77LPqNgpqsaIeffRRWbBggbrPGdOnT5cuXbqoszniTJIYTG7Pnj3OzhIREVGFS01Nlfbt28vbb79d5GNeeeUVmT17tsyZM0d+++03iYiIkD59+qjz4HiT3NxcdTZmU46j0q1bN9XzB1Gls9asWSMjRoxQRbgrVqyQ7Oxs6d27t1r5REREZta3b1956aWXVEuDPcimzJo1S55//nm55ZZbpF27djJ//nw5ceKE3eyLDjt8BDhNmjRRJRX169eXqVOnOpwRwWsjw6Pbvn27XHfddSopgBMAdurUSX7//XdZvXq13H///arWFI/HRe+9m5mZKU899ZTUqVNHBVddu3ZVj7d93++//15atWql5hNDlphiHBX4+OOPVXSIsyJu2LBB4uLi1MrAGRKxMhy1bNkyq7+x4MisbNmyRa655prSzBoREXk47ODTc9Ir/H3DAsOsdvBlhX0kOqCguUeHMwZjp49951133WX3eePHj1e9azFmWffu3VVd6N9//13q+bj77rulY8eO8s4770hAQICqb8F5+q688kq170bTlN6agU4yMHLkSNm1a5d88cUXqmlr4cKFcuONN8qff/6pyj0AJyd++eWXVc/f6tWrq/23KQIVLCgWavTo0SrCQ7oHEFlhgZ0JVGzpPYiioqLs3o8IDxfjaaKJiMi7IEjp+lnXCn/f3/71m4QHhbvs9RCkAE7ia4S/9ftsJScnyxtvvCFvvfWWDBs2zNJxBQFLaSHT8fTTT0uLFi3U33qgoQdOCM5q1apl9fi5c+eqawQpgOwKkguYPm3aNDUNrSD//e9/VfNXeXK66efNN99Ukd5zzz2nIjNd586dVaRVWkh1Ifi56qqrpE2bNkXWtOBD1S/16tUr9fsRERGZze7du9UBec+ePV32mmPHjpUHH3xQZXZQS7p///5iH499OZIQzZo1UxkW/YJyDeNzg4ODVXNWeQssTSoLKSRbaJ8qS20JalV27twp69atK/IxSIfhAzdmVBisEBF5FzTBILvhjvd1JT1Lcfr0adXrR4e/O3ToYH8ewpybB39/f9VUZoRMhxHqTv71r3+psc6WLl2qTi6MJp2iamtSUlJUIgJlGMaEhLFpSJ9XVzaVuSxQQR0K2rdQl2KElFDLli1LNRNoC1u8eLHqQYRxWoqCYIhjtRAReTfs/FzZBOMu2F8iWMGQHnpgggNs9P5Bb1l70CyDAADPQRakJNHR0aq5CIkCFL2CvTFWkB3BZcyYMWpEeTThIFBBVkQv4dAhGYFpZ86ckauvvlrczelABRkNZD/QtQpR3KZNm+Tzzz9XzTIoqHEGno+xWFCkg2pirFQiIiJPgMzDvn37rFocECSgzhI9dRBwoaQBPYMQgGAfh7HHUPeB4TjsCQ0NlWeeeUb+/e9/qyDiqquukrNnz8pff/0lDzzwQKHHozA3PDxcDRsyatQoFQShY4ouPT1d1afcfvvt6v2PHTumBmi97bbb1P0NGjRQy4HACLUmeC0ENCjAxfhor732mgpcMA94DJp6brrpJqlQWil88sknWpMmTTQ/Pz91qVOnjvbBBx84/TqPPvqoVrVqVW316tXayZMnLZe0tDSHnp+YmIh8l7omIiLPk56eru3atUtde5r4+Hi1D7K9DBs2zPKYvLw8bcKECVrNmjW1kJAQrWfPntqePXuKfd3c3FztpZde0uLi4rSgoCCtfv362rRp09R9Bw8eVO+xbds2y+MXLlyo9slhYWFa//79tffee089BjIzM7W77rpLq1evnhYcHKzVrl1bGzlypNXn/cgjj2jVq1dXz5k0aZKalpWVpU2cOFFr0KCBmofY2Fjt1ltv1Xbs2KHunzt3rtp/l3bdOrP/9sN/pQ1y0DUJkVhpuyQV1baFlNR9991X4vORQkNRLXoLoW84ERF5FmTnkYnA0T6yCeQb6zbJif13qcZR0SFFhEtplSFGIiIiIh/gdKBy/vx5NY5KfHy8KrSxHTI3ISHBlfNHREREPszpQGXo0KGqeAhFPRi0piK6JhEREZFvcjpQ+eWXX9RYJ+U9Eh0RERGR0yPTYghedHciIiIiMl2ggnH9MXw+htJFvQoqd40XIiIiIrc1/eDkgwhIrr/++kI9eFCvYjvCHREREVGFBSoYrQ6nh/7ss89YTEtERETmClRw4sBt27ZJ8+bNy2eOiIiIiEpbo9K5c2c5evSos08jIiKiMlq9erUqs7h48aLPfJZOByo4ieATTzyhTnqEU0Dv2LHD6kJEROQLcDLeLl26SOXKldWpZHCiwT179hQaRh4n8q1evbpUqlRJnQzw9OnTYhYvvPCC5czOXtP0c+edd6rr4cOHW6YhumMxLRER+RL0fkUQgmAlJydHncG4d+/esmvXLomIiFCPGTNmjPz444/y9ddfq3PbjBw5UgYNGiTr168Xb5KVlaXO9myKjApOMGR7OXDggOWaiIioLHDgm5eWVuEXZ88/t2zZMnUC3datW6tBUNHScOTIEdXaADjh3v/+9z+ZOXOm6inbqVMnddLdX3/9VTZu3Fjk62ZmZsozzzwj9erVk5CQEGnSpIl6HUczIrNmzZIGDRpYNRddfvnlKnhCz92rrrpKDh8+rOZ38uTJsn37dpVwwAXTAE1LDz74oERHR6uTBmL+8Tjb9/3ggw/K/YSSTmdU4uLiymdOiIiIEKikp8ueyzpV+GfRfOsW8SvDiXYRmEBUVJS6RsCSnZ0tvXr1sho0tX79+rJhwwa54oor7L7Ovffeq+6fPXu2CoCQCDh37lyp5gmZHjRJPfTQQ/L555+rzMemTZtUUIIWEnSQQcC1cuVK9XhkfWDw4MESFhYmS5cuVdPeffdd6dmzp/zzzz+W5cPpdL755hv59ttvJSAgQMpLmc6eTERERKJO0Dt69GiVrWjTpo36SE6dOqWaQ5DFMMJ58nCfPQgEvvrqK1mxYoUlwGnUqFGpP2KMe4YAqn///tK4cWM1rWXLlpb7UTcTGBgotWrVskzDaXIQzODEw8jowKuvviqLFi2SBQsWyMMPP6ymIeiZP3++yrqUJwYqRERkKn5hYSq74Y73LS3UqiA7gZ18Wfzxxx8qO9GjRw9xhaioKNU81adPH7nhhhtU8HPHHXdIbGxskc9BE09KSooqADbC6XP2799v1cJS3kEKMFAhIiJTUfUSZWiCqWgokF28eLGsXbtW6tata5mOLAWyDqj3MGZV0OvHmMEwQnOLM/z9/QvV1qC5yQh1MaNGjVJNPF9++aU8//zzKmNTVNMTghQEMqhtsWVcDr1guLw5XUxLRERE+UW/CFIWLlwoP//8syoqNULxLEZyX7VqlWUaui+j4LZbt252P8K2bduqZiT0KHJEdHS0akYyBivIytjq2LGjjB8/XhXyomkKo8sDmqZsT31z2WWXqddEkxAKeY2XGjVqVPiqdzpQwWBvx44ds/yNdiy0y7333nuunjciIiLTQnPPJ598onb6GEsFO3dc0EQCKEJ94IEHZOzYsRIfH6+Ka++//34VpBSVzUBvnWHDhqkhQFATcvDgQZXZQN2KPddee62cPXtWXnnlFdUs8/bbb6sCWB2ejwAFxbno6bN8+XLZu3evpU4F74fHILhBwS56HKF5CPOIIlw8/tChQyrAwQmJf//9d6lwmpO6d++uzZ8/X90+efKkVqVKFa1bt25ajRo1tMmTJ2sVKTExESGkuiYiIs+Tnp6u7dq1S117Gux/7F3mzp1reQyW67HHHtOqVaumhYeHa7feeqvadxYHzxkzZowWGxurBQcHa02aNNE+/PBDdV98fLx6jwsXLlge/84772j16tXTIiIitHvvvVebOnWqFhcXp+47deqUNnDgQMtrYfrEiRO13NxcdX9GRoZ22223aZGRkVbznpSUpD3++ONa7dq1taCgIPX6d999t3bkyBF1/6RJk7T27duXet06s//2K/iwHVatWjXV/xvn+kHXKbR3YeAaRF2PPPJIhY6lgmpmRKyoaEY/byIi8iwYuRVH9OU9FgeZa906s/92uukHRTp6dyX0ux4wYIClb/jJkyedfTkiIiIi1wUqGIFvzpw58ssvv6iq4RtvvFFNP3HiRKGuTEREREQVGqi8/PLLaoQ6FPAMGTJEjZoH33//vRqil4iIiMht46ggQEFlMNqXUK+iw0h14R7U752IiIjMr1QDvmHUPGOQAsYTIBERETnD2RMCku+sU6ebfjCi3tChQ6V27dpqMBgELcYLERGRozAgGqSlpfFD8zJpBetUX8cVllHBOQMwqt6ECRPUELsY6piIiKg0cICLYdlxAjxACQH3K56fSUlLS1PrFOu2rEkMpwMVnHAJPX46dOhQpjcmIiIC/bw3erBC3iEyMrLIcxqVa6BSr149tiUSEZHLIIOCDH1MTEyhE+qRZ0Jzj6vKQZwOVGbNmiXjxo1TXZRZQEtERK7CWkdySaBy5513qranxo0bq7ZE2yKZhIQEZ1+SiIiIyHUZFSIiIiJTBio4/TQRERGRaQd8y83NlUWLFsnu3bst5//ByQk5jgoRERG5NVDZt2+f9OvXT44fPy7NmzdX06ZPn656A/3444+qdoWIiIjILSPTjho1SgUjR48ela1bt6oLBoBr2LChuo+IiIjIbRmVNWvWyMaNGyUqKsoyrXr16jJjxgy56qqrXDZjRERERE5nVEJCQiQ5ObnQ9JSUFAkODuYnSkRERO4LVPr37y8PP/yw/Pbbb2qEWlyQYXnkkUdUQS0RERGR2wKV2bNnqxqVbt26SWhoqLqgyadJkybyxhtvuGzGiIiIiAJLc5Kh7777Tvbu3St///23mtayZUsVqBARERG5fRwVaNq0qboQERERuTVQGTt2rLz44osSERGhbhdn5syZrpo3IiIi8nEOBSrbtm2znHobt4mIiIgqgp+GbjseKikpSapWrSqJiYlSpUoVd88OERERuXj/7XSvn+HDh9sdRyU1NVXdR0REROQqTgcqH330kaSnpxeajmnz58931XwREREROd7rB2kafYA3ZFQwforxbMpLliyRmJgYfqRERERU8YEKxk/x8/NTl2bNmhW6H9MnT57sujkjIiIin+dwoBIfH6+yKddff7188803ViclxDl+4uLipHbt2j7/gRIREZEbApUePXqo64MHD0r9+vVVBoWIiIjIVCPTHj58WF2Kcs0115R1noiIiIhKF6hce+21haYZsysorCUiIiJyS/fkCxcuWF3OnDkjy5Ytky5dusjy5ctdMlNEREREpcqoYCQ5WzfccIMqqMV5gLZs2cJPloiIiNyTUSlKzZo1Zc+ePa56OSIiIiLnMyo7duyw+htdlk+ePCkzZsyQDh068CMlIiIi9wUqCEZQPGt7LsMrrrhCPvzwQ9fNGREREfk8pwMVjKNi5O/vL9HR0VZD6hMRERG5JVDBCLREREREpi2mXbVqlfTv318aN26sLri9cuVK188dERER+TSnA5X//ve/cuONN0rlypXliSeeUJcqVapIv3795O233y6fuSQiIiKf5KfZVsWWoG7dujJu3DgZOXKk1XQEKdOmTZPjx49LRUlKSlLjuiQmJqpgiYiIiMzPmf230xmVixcvqoyKrd69e6s3JCIiInIVpwOVAQMGyMKFCwtN/+6771StChEREVGF9vqZPXu25XarVq1k6tSpsnr1aunWrZuatnHjRlm/fr08+eSTLpsxIiIiIodqVBo2bOjQJ4WB4A4cOFBhnyprVIiIiDyPM/vvwNIM8kZERETkUSclLI21a9fKzTffLLVr11bZmEWLFrlzdoiIiMhkHMqojB07Vl588UWJiIhQt4szc+ZMh988NTVV2rdvL8OHD5dBgwY5/DwiIiLyDQ4FKtu2bZPs7Gx1e+vWrSr7YU9R04vSt29fdSEiIiIqdaASHx9vuY3ePu6SmZmpLsZiHCIiIvJeTtWoIKsSGBgoO3fuFHeYPn26qhLWL/Xq1XPLfBAREZEJA5WgoCCpX7++5ObmijuMHz9edWXSL0ePHnXLfBAREZFJe/0899xz8uyzz0pCQoJUtJCQENXf2nghIiIiH69RMXrrrbdk3759qktxXFyc6glkhGJbIiIiIrcEKrfccovTvXuKkpKSooIe48Byf/zxh0RFRakmJiIiIvJtDg2hX17Qg+i6664rNH3YsGEyb968Ep/PIfSJiIg8jzP7b6drVBo1aiTnz58vNP3ixYvqPmdce+21gjjJ9uJIkEJERETez+lA5dChQ3Z7/WB8k2PHjrlqvoiIiIgcr1H5/vvvLbd/+uknlbLRIXBZtWqVw2dZJiIiInJpoDJw4EB1jUJa1JDYjq/SoEEDee211xx9OSIiIiLXBSp5eXnqGlmTzZs3S40aNRx9KhEREVHFdE9GF2J7hbSRkZGlmwMiIiIiVxXTvvzyy/Lll19a/h48eLAa96ROnTqyfft2Z1+OiIiIyHWBypw5cywnA1yxYoWsXLlSli1bJn379pWnn37a2ZcjIiIicl3Tz6lTpyyByuLFi+WOO+6Q3r17q2Larl27OvtyRERERK7LqFSrVs1y1mJkUnr16qVuY6A2d51VmYiIiLyT0xmVQYMGyb/+9S9p2rSpGqEWTT6wbds2adKkSXnMIxEREfkopwOV119/XTXzIKvyyiuvSKVKldT0kydPymOPPVYe80hEREQ+yq0nJSwrnpSQiIjIu/ffgY4On48mHoxAaxxK354BAwY4N7dEREREZcmo+Pv7q94+MTEx6naRL+bnV6EFtcyoEBEReR6XZ1T04fNtbxMREZHn0vLyJC8tXbT0NMlLS5O89PT86zRcp4qWni6BsbEScfnlnlNMS0RU3pDo1bKyRMvO8foP2y8oUPwCA8UvIMDds0ImpmVlXQoi1PWlQOJSYKHfZzPd8rw00Wweq2VklPjeVW6+2XMCFWRT5s2bJ99++60cOnRINfXgJIW33367DB06VP1NRN53xKVlZqpLHq4zMiQvA39n5E8ruJ1/jcdkiFbctMys/NewOy3/9fEcn+Pvnx+wBAWpa8F1wW171xKk/23zuILpEujE/bgdbOc99OcGFTUPBbeLKQnwuQAb32NLkFAQGFgFDQXTiwowrAKLS9eSnV2+M+/nJ/5hYeIXES7+YeHiH47rMHUd0rSpuFOgMysAhbJLliyR9u3bS9u2bdW03bt3y3333aeCl0WLFpXv3BIV8d2UnBzRcMnOLrjOEcn18qNxbBSzs62DhywEDiUFCnh88cGDui6YhiM5qgAICJFF8sTPOyCg6GDK37szRVpubn6mIrUgoCjvjrRBQVZBhLouIsDwD8+/9lOPCRd/9RjDtPCI/Mfg+aGhpk02OByoIJOydu1aWbVqlVx33XVW9/38888ycOBAmT9/vtx7773lMZ9Unjv53Nz8nbu+o0fkbm/Hn5N/36XH6vfbPB7T1d+G19KnFTwet8XyfJvHW55vvD/bzuNzLK9PFSQwUPxDQvI3aiHB4h8Sqm4XnhaSfx0SIv6hIeJX3DQ8L7hgmv5a+ushs2DSjafLgs2cXNGyswp+J/rvreA6K9vmd3Tp93jpdlbB78nwXKv7Da9RaLqj9xe8vr59sIVtCC7u+AxNKj84CLsUGBQEDbbBhJpunGYVTFwKNiwBSXCw+BqHx1HB+Xyuv/56GTdunN37p02bJmvWrJGffvpJPL3XT9KyZXJi3HjxegUBSrkfAbjrCA9t/t68k1P1DUFlDxRUYBBqmZZ/bbhtDB4QOJBPszq4MQRUVgcyhmtkiryaf4B1MFGQrWBzWAX3+oEdO3aokWiLgnFWZs+eLd5AHRk4UGDktfz8CrdBW6V0Ay+1Xdvcp1K9he4ztInr7eF6u7feFq/fZ2gLtzze8Fir+4xt+Ib5UPexzZyonDYPfgW/zUCR0FB+ylTuHA5UEhISpGbNmkXej/suXLgg3qDydddJ45Urxdv5+fvZFNUVXLP3AREReVqggoHcAotJ+wYEBEiOl9QKIHUXHB7u7tkgIiLyeU71+kHvnpCQELv3Z/pid0IiIiIyR6AybNiwEh/DHj9ERETklkBl7ty5Ln1jIiIiopJwOEEiIiIyLQYqREREZFoMVIiIiMi0GKgQERGRaTFQISIiItNioEJERESmxUCFiIiITIuBChEREZkWAxUiIiIyLQYqREREZFoMVIiIiMi0GKgQERGRaTFQsSMxM1EW7VskR5OOiqZpFb9WiIiIyLmzJ/uS30/9LhPWT1C3o8OipWNMR7ms5mXSqWYnaRrZVAL8A9w9i0RERD6BgYodQQFB0iG6g+w8v1POpp+V5YeXqwtUCqok7WPaS6eYTip4aVOjjYQEhFT0eiMiIvIJfpoHt20kJSVJ1apVJTExUapUqeLy18/IyZA/z/0p285sk62nt8ofZ/+Q1OxUq8cE+QepYOWymMtU4NIhpoNUCXb9vBAREXkLZ/bfDFSckJOXI3sv7JWtZ7bKltNbVPByPuO89QcqftK0WlNL4ILrmhE1S7cmiYiIvBADlQqCZNTR5KP5QcuZrSrzcjjpcKHH1alUR9W36LUuDas0FD8/v4qaTSIiIlNhoOJG59LPqUwLAhdc77mwR/K0PKvHVAupZlWg2zyquWpCIiIi8gVJbPoxj5SsFNl+drvKuiDjgpqXzNxMq8eEBYZJu+h2lgLdtjXaSnhQuNvm2ZNl52XLmbQzcir1lFzIuKAKnfFZhgeGW13jM/f3Y+98IiJ3YKBiYlm5WbLr/C5LxgXXyVnJVo8J9AuUltVbWupckH2pFlpNfF1uXq6qCTqZelIFIvrldNppy21ktDRxrD4cwQoutkGMMZjB7bAgm8cU8Xhco8cYEREVj4GKB0Gz0P6L+1XQsuVMfoEudry2GlVtZCnOxXXtiNpeVeeCep8LmResApBTaQWBSGp+IIJMSY6WU+JrBfsHqwLm6qHVJSsvS9Ky0yQtJ03Ss9MlNSe1UFOcKwX6BxYZ1BQKeGyvjQGS4b7QgFCvWtdEREls+vHsHTYyBnqBLgKXA4kHCj2uZnhNq8ClSWQTUzdlIGtkG4BYgpCCv22bxOwJ8AuQ6PBoqRVeS2pF2LmE15Ko0Kgid+z4fI3Bi73r9Jz0wtMNtxHw2D4Hr1le0JPMNqhR2aCgMFXbhM8E616/6H87PN2/8PTSviZeC/Or/vYv3etg3el/60EbeZbs3GyV3cQ4VAjecdAQFRbFWjyyYKDiZVBroY/lguBl9/ndhTILlYMr5xfoxuQX6Laq3kqCA4IrZP4w3oxtAKL/jUAEgZft+DNFwQZNDzpiI2LVNbIjemBSI6yG2vCZsTbGGODYC2bsXdsNigz3kajADMEp1j1GisZ1THhM/t/h0ZZpGL+ImafyH6IhISNBzqadVRlOBCJW12ln1W08xp7IkEj1G8f6QuCCa/3v6mEF16HVVVO3GX/n5DoMVLwcdmQoytUDFxTr2u7UUESqD0SHwKV9dHupFFypTMWp9pplcLmYedGh18KOxBiAqCAkvKbV7YoKrjwBmqgQBFoFPoZABsEfdhx4XK6Wq671i/436nry8E+/bXN/sc8tbnpenlOv68j7Wb1WwWs7A995PZgxBjbGYAa3sbM0c/bRHfBZ44DIGHCcSS8IPAy3USPm6HpBrV2N8BpqnSJwwXp2FLJyCFYQvCBwUUFMqHUwo+4Lq656UfK0Jp6HgYqPQTCxJ2GPpWcRAhjUexhhw9y8WnOr5iI0kSA9W1QA4kxxKlL0xgBEz4CobEjB30zhkzPQTIcAHDtP/Ugd13qTgnG6bUF6cfSmCEtWBkFMeP61cRp+H56+A8RnmJSVVCj40LMg+rRzaeccqv/StyUIGlQAGB4tMWEx+dfhMZbP0DYgRHCDAxqsu/Pp59U1ghf9NgIg/T5su5wJUvEeCFZsszJ6IGP8m0HqJQgg0dyuZ29xUIRr29u41K9SX7rX6S6uxEDFx2HjdDDpoGw7vc0yiu7xlON2j3gc2TihDsI2ALHNiDDtTu6EDSt2dHoQgx2xum0IbPSdozM7QAQrhbIyBYGNvoPGDrCix0HCbzwlO8Vu8KEvux6cOFo/pWcxbAMO420sc3kHcNiBIlhB0KICmYxzVgGNMdBBFsjRXn6A2ifMv6XpySZLYwx23L1Nw+eQkWsdMBiDCP0agUaRQUZuusrGGl9Hv9+RmkBdv4b95OVrXnbp8jFQoUJQK4Jsi16ki1MB4AdeZHGqISOCHzZT5eQtRZ7Y2emZGH2HbsnSFAQ2zjRxgDqiLwhe9CYm26YnXDtyAlM07dnWfBRqjkk/61QNEzIJxuyHbfDhroCrrND0iWDFNiuj/52QnmC57WgTtTHzpgc19upo9IxN5aDKliZaPSCwzUjYCyLsPcYSUGSnl2uBflFDNaCHoeV24KXbyMDf3fJucSUGKlTylyQrSW0QzVqcSuROel2FHswYm54s0wqusbN0FIreVdBSELgggMAO1BiEIFPizOup4MiQ4bHNiOA3zjO85wepWKd6hkZd7AQ3uHamKbEihBUEDFYBRVDhwELvEWgMMoy37U3Dd8MdB6IMVKh4OGH2md0iWSki0c1FQqvyEzO73ByR83tFTv0pcmqHyMkdImd2ieCoP6RywaVq/nVoFcO0gttYx7bTcI3HcpC6UkPWJTEz0SorU1Rg40yqHTsQNKtaZT9ssiAITvA4Kp+BOVVQYyeIMTY94RpF7SUFA5aAIihMwgLs3F9wG4GG7fRQLx1HyZlAhYfSviLphMiB1SL74/OvU89cuq9qfZGarURiWonUbJ1/qd6EOzB3yUoVOf3XpYAEwQmCkpwM+49Psz6Dt9MCQw3Bix7oGIIZq+DHeJ/NdA8vPC0NVcgZWk1dmkvzYmtKkrOTVdGqnjnBTg7ZFNtmGQQjEUERFbocZA29D/Vm8JJg3XpjIGEmfho+ZR+IyHxOZorIoXX5QcmBeJGzf1vfjyOxsEiR5JP2n49uwjWaGwKYNvm3K8eK8EfpOiln8gMSBCN6UHJ+HzZ/hR+L7uVYD7XaisS2y78dFCaSmSySkSSSiUvypesM49/6bcN0B8e2cRh2roWCGkPmplCWx05AhGX0Z9dhIm+XxIyKjzYNnNiWH5Qga3Jsk4hV27mfSO2OIo2uFWl8nUi9riKBISJpCfnNQDiCP/OXyOld+UfvaBY6/Wf+xSg08lLQogcwMS1FQpwfo8Wn5OWJXDhonSXBJeWU/cdXqnUpIMF1rXYi1Rq6dieO7wza4q2CGkNgU2hawbVVAJR8KdODwAeXopbJIX7WAU14lEhk/YJL3KXbVeqIBDAhTOQLmFHxVEiEJRy4FJgc/EUkM9H6MdiwIyhpdJ1Iw2vyN/qO7lQTj+QHLcYABjUSRfWEwHvZBjBRjXxzZ5KdIXJ2t3WW5PTO/OCvEL/8ZjbboKRSjHiMnKzCwUyJWR5cEq2zPHnZjr+nX4BI1TqG4MUQxFSLy8/8+WBTFJGnYDGtt0L2Q2/K2b86P5gwQsEkAhIEJghQECi4egd8bk9BALMzP/OC20UdQaMrZkwLkZjW1gEMdsLe0nyEdYLPwhiU4DOy1xMEtSD4DIwBCeqBglmPoALvnMzCmZvUsyIXj4hcPFxwXXDJLaHrJnqyVa1rHchUMwQzyFixiYnIbRioeAtsuI9svJQ1ObndunYBYx6gCUdvzkHTjjuOIlPPG5qN/irIwuwWyU6z//jw6tZ1L9hZR7cUCQ4394408ah1QIKLbbCoC6uWH4iooKQgMKne1DczTK6GjF/KaUPgcujS7QuHRRKPlZydQQ1W1XrWWRhjUONNwbSrfwcZifkF3OkX8gP19IT8vy23E6zvx9D5aGbGgYu6DjZch4oEBtvcZ5xmuM/yGqWYxnVpOgxUPHkjgJ28Hpgc/lXEdlAn7ND15py4K81bG4KdCXYgp20CGDRX2W0+8hOJapgftOgZGAQy1RpUfPCVmy1y7h9DQFJwnVHEgFHYsRkDElxXqc2No7vk5Yokn7LOwiCA0f9GIFPSeWews0QgY8zCqECm4O+IGp6/flGjhGCiyEAD17b3Xyj5szMjHNQ5HAAVBEuWgMreNMN9QaEiOMO3uoTlZ0hxjeJyHHzheZ7+XSkHDFQ8rduw6jKsdxs+a31/pZr5GRMEJriuEiseLTs9vweSVQCzy7q7tG3vJDQf2QYw2FG4ApoZMC8qKCm4IBtkr2kBzQkIFC1NN23z5wW9p8izdtDJJ2yCGD07c1gk6XjRtVg67JQsAYxtjUyD/IxaRe6c8LtyKNDQ708oXNPmDOyEUfOG5USGVN2Oyr/G3+p2tfxr/G7we0LRNbLE6rbhGtNtpxX5eEzLEsGYNFavgduZl+4rqWmwQvkZghg9oAk3BDT63+HWAU5xwU9QwW39+R7YjMlAxcywYzy0/lLWBPUMRvjSxV11KWuCHjW+EI2nnL0UtOgFvGf+LpxR0kXEXApaVDNSK5HoFvk/3KKyVWgusAQkBZkSZHjsCa5cuMAVg+PhKIq8GzJqCFZsAxj9Ng4uSjq/DLpZ2w1iCm6j95y93zW+p9hGlBho2NxfVDOrIzAvxQUatoEIrpFFMDN8jkUGNsZphvtsgx2HH5+Z//lnpeVf67edGOCvzAJDyxD8GJ9XcK0eq9+OKJesNgMVT+w2rAcm9S7nztCYwk84aAhgCgp4Mc3ejgLDQEc1vhTAoAsrmnD0oMQ2W6WrXNs6S4KgBDsXDzxKoQqAnRXqlewFMQhuHOmeja7X+I4hQ4oB/oxNK870frLtCWUVaJSU8YjKD1JYN1V+2y8VuKTnr2NcqyBGv11wbQxw7AU82baPNbxWRWk9SGTwXJe+JAMVM3Qb3v9zflOOvW7DSA3rTTnOdBumfPihI9tiG8CUNEIrApkazawDEly7qhmJSO8dhzoY2yJf/XZRzZy2TZ5WwUURgYbxfgQ/vpB9pUt1gMj2FBngOBjwGIMo28fqTaDth4jcOkdciQO+mbLbcI9LWRMUjVLpIRVZt1P+xapp54x1t2kc9dZoWhCUtM9vRjNzzyLyDmgWqdEk/2IPdgR6RgYjQ6OZyLZphd9TKom/f/73pLy+K/qQAQhe3BwAm6Kv5Ntvvy3/+c9/5NSpU9K+fXt588035fLLLxdTHzEd3XjpvDlFdRtujCLY60Vqd+DgU+UNP6TKNfMvTXqW+9sRlRp2LKh3woXIzNvUoFBT1CO5PVD58ssvZezYsTJnzhzp2rWrzJo1S/r06SN79uyRmJgY86TY0Myg9845vKFwkScKOvWB1up3M2+3YSIiIg/i9iH0EZx06dJF3nrrLfV3Xl6e1KtXTx5//HEZN26c+05KWGK34VqXBlrDdeWSz7JJRERE4jk1KllZWbJlyxYZP368ZZq/v7/06tVLNmzYUOjxmZmZ6mJc0HKxcY7Ismesp6ELV4Pul4pgfaXbMBERkRu5NVA5d+6c5ObmSs2aNa2m4++///670OOnT58ukydPLv8ZQ3dh9BBRZxsuaM6pi27DweX/3kRERGSeGhVnIPOCehZjRgXNRC5Xp5PIvw/kj0FAREREvhmo1KhRQwICAuT06dNW0/F3rVqFaz5CQkLUpdxhACQGKURERG7n1qE3g4ODpVOnTrJq1SrLNBTT4u9u3bq5c9aIiIjIBNze9IOmnGHDhknnzp3V2Cnonpyamir333+/u2eNiIiIfD1QufPOO+Xs2bMyceJENeBbhw4dZNmyZYUKbImIiMj3uH0clbIo13FUiIiIyO37b54eloiIiEyLgQoRERGZFgMVIiIiMi0GKkRERGRaDFSIiIjItBioEBERkWkxUCEiIiLTYqBCREREpsVAhYiIiEzL7UPol4U+qC5GuCMiIiLPoO+3HRkc36MDleTkZHVdr149d88KERERlWI/jqH0vfZcP3l5eXLixAmpXLmy+Pn5uTzaQwB09OhRrzyPEJfP83Edej6uQ8/m7euvPJcRoQeClNq1a4u/v7/3ZlSwcHXr1i3X98CK8dYvIHD5PB/XoefjOvRs3r7+ymsZS8qk6FhMS0RERKbFQIWIiIhMi4FKEUJCQmTSpEnq2htx+Twf16Hn4zr0bN6+/syyjB5dTEtERETejRkVIiIiMi0GKkRERGRaDFSIiIjItBioEBERkWn5dKAyffp06dKlixrZNiYmRgYOHCh79uyxekxGRoaMGDFCqlevLpUqVZLbbrtNTp8+LZ7gnXfekXbt2lkG6unWrZssXbrUK5bNnhkzZqgRikePHu01y/jCCy+oZTJeWrRo4TXLB8ePH5d77rlHLUNYWJi0bdtWfv/9d8v9qPefOHGixMbGqvt79eole/fuFU/RoEGDQusQF6w3b1iHubm5MmHCBGnYsKFaP40bN5YXX3zR6hwunr4OMYIqtitxcXFq/q+88krZvHmzxy7f2rVr5eabb1ajwuK7uGjRIqv7HVmehIQEufvuu9W+JTIyUh544AFJSUkpnxnWfFifPn20uXPnajt37tT++OMPrV+/flr9+vW1lJQUy2MeeeQRrV69etqqVau033//Xbviiiu0K6+8UvME33//vfbjjz9q//zzj7Znzx7t2Wef1YKCgtTyevqy2dq0aZPWoEEDrV27dtoTTzxhme7pyzhp0iStdevW2smTJy2Xs2fPes3yJSQkaHFxcdp9992n/fbbb9qBAwe0n376Sdu3b5/lMTNmzNCqVq2qLVq0SNu+fbs2YMAArWHDhlp6errmCc6cOWO1/lasWIE9uBYfH+8V63Dq1Kla9erVtcWLF2sHDx7Uvv76a61SpUraG2+84TXr8I477tBatWqlrVmzRtu7d6/6XVapUkU7duyYRy7fkiVLtOeee0779ttv1Xdx4cKFVvc7sjw33nij1r59e23jxo3aL7/8ojVp0kQbMmRIucyvTwcq9jYoWGn4MsLFixfVjh0/PN3u3bvVYzZs2KB5omrVqmkffPCBVy1bcnKy1rRpU7UD6NGjhyVQ8YZlxAYRGwN7vGH5nnnmGa179+5F3p+Xl6fVqlVL+89//mO13CEhIdrnn3+ueSJ8Pxs3bqyWzRvW4U033aQNHz7catqgQYO0u+++2yvWYVpamhYQEKACMaPLLrtM7ew9ffnEJlBxZHl27dqlnrd582bLY5YuXar5+flpx48fd/k8+nTTj63ExER1HRUVpa63bNki2dnZKu2lQ9q9fv36smHDBvEkSM9+8cUXkpqaqpqAvGnZkDa/6aabrJYFvGUZkXJFirZRo0Yq1XrkyBGvWb7vv/9eOnfuLIMHD1bNrx07dpT333/fcv/Bgwfl1KlTVsuI84N07drVY5bRKCsrSz755BMZPny4Srl7wzpEM8iqVavkn3/+UX9v375d1q1bJ3379vWKdZiTk6O2n6GhoVbT0SSC5fT05bPlyPLgGs09+O3q8Hicf++3334TV/PokxK6+kzMaIO86qqrpE2bNmoaVlZwcLBaIUY1a9ZU93mCP//8UwUmaAdH+/fChQulVatW8scff3j8sgGCr61bt1q1F+u8Yf1h4zBv3jxp3ry5nDx5UiZPnixXX3217Ny50yuW78CBA6qWauzYsfLss8+q9Thq1Ci1XMOGDbMsB5bJU5fRCLUAFy9elPvuu0/97Q3rcNy4ceoMuwiwAgIC1E596tSpKqgGT1+HqGHENhR1Ny1btlTz/fnnn6uddZMmTTx++Ww5sjy4xoGFUWBgoDrIL49lZqBiOCrHxh8RsjfBDg5BCbJFCxYsUBv/NWvWiDfAacefeOIJWbFiRaGjHW+hH5UCCqMRuKCg76uvvlJHdN5wgICjsmnTpqm/kVHB73DOnDnqu+pt/ve//6l1igyZt8B38dNPP5XPPvtMWrdurbY3OOjDMnrLOvz4449VFqxOnToqGLvssstkyJAhKiNG5Y9NPyIycuRIWbx4scTHx0vdunUtH06tWrVUqhZHQEaoyMd9ngBHa4j6O3XqpHo5tW/fXt544w2vWDZsJM6cOaM2GojmcUEQNnv2bHUbRwCevoy2cOTdrFkz2bdvn1esQ/QqQIbPCEetevOWvhy2vWA8aRl1hw8flpUrV8qDDz5omeYN6/Dpp59WWZW77rpL9dgaOnSojBkzRm1vvGUdoicTti3o1YIDpE2bNqkmOzTHesPyGTmyPLjGtte2iQw9gcpjmX06UEEdEYIUNIf8/PPPqnudEXbuQUFBqv1Vh+7L2IgiFeipR7CZmZlesWw9e/ZUTVs4gtMvODpHylm/7enLaAsbyv3796sdvDesQzS12g4JgFoHZI0Av0ls+IzLiGYGtIN7yjLq5s6dq9LlqKfSecM6TEtLU7UJRsg6YFvjbeswIiJC/fYuXLggP/30k9xyyy1etXzgyPLgGsG1MaOEfSjWObK+Lqf5sEcffVR1wVq9erVV90FUeevQdRBdln/++WfVdbBbt27q4gnGjRunejChy+COHTvU36jKXr58uccvW1GMvX68YRmffPJJ9f3EOly/fr3Wq1cvrUaNGqqHmjcsH7qVBwYGqi6u6Pb56aefauHh4donn3xi1VUyMjJS++6779T3+JZbbjF11097cnNz1XpCLydbnr4Ohw0bptWpU8fSPRldXvEd/fe//+0163DZsmWqVwu6z2P7iZ54Xbt21bKysjxy+ZKTk7Vt27apC8KAmTNnqtuHDx92eHnQPbljx45qWIF169apnpfsnlwOsILsXTC2ig4r5rHHHlPderEBvfXWW1Uw4wnQZRBjVAQHB2vR0dFaz549LUGKpy+bo4GKpy/jnXfeqcXGxqp1iJ0B/jaOMeLpywc//PCD1qZNG9X9sUWLFtp7771ndT+6S06YMEGrWbOmegy+xxgXyJNgbBhsW+zNt6evw6SkJPWbQ7AVGhqqNWrUSHXbzczM9Jp1+OWXX6rlwu8QXXdHjBihuux66vLFx8fb3fch6HR0ec6fP68CE4yZgzFl7r//fhUAlQc//Of6PA0RERFR2fl0jQoRERGZGwMVIiIiMi0GKkRERGRaDFSIiIjItBioEBERkWkxUCEiIiLTYqBCREREpsVAhYisHDp0SPz8/NRpCMzi77//liuuuEKdfLJDhw7iSXCm5IEDB7p7Nog8FgMVIhPu2BAozJgxw2r6okWL1HRfNGnSJHWeFZwHx3gOEiLyfgxUiEwImYOXX35ZnfzMW+AswaWFEzF2795dnaywevXqLp0vIjI3BipEJtSrVy91BtPp06cX+ZgXXnihUDPIrFmzpEGDBoWaHaZNmyY1a9aUyMhImTJlijol+9NPPy1RUVFSt25ddWZfe80tV155pQqa2rRpo05zb7Rz507p27evVKpUSb320KFD5dy5c5b7r732WnV28tGjR0uNGjWkT58+dpcDZ1zFPGE+QkJC1DItW7bMcj+ySDhLKx6D21huexYsWCBt27aVsLAwFczgM0xNTVX3bd68WW644QY1H1WrVpUePXrI1q1brZ6P13733Xelf//+Eh4eLi1btpQNGzbIvn371LIgo4PPA0GT7TrA8+rVq6eed8cdd0hiYmKR6w3Li/WKs9RiXtu3b6/mXYfgFGcAj46OVvc3bdrU7voh8hUMVIhMKCAgQAUXb775phw7dqxMr4XTr584cULWrl0rM2fOVM0o2BlXq1ZNnbr9kUcekf/7v/8r9D4IZJ588knZtm2bOq37zTffLOfPn1f34RTv119/vXTs2FF+//13FVicPn1a7aSNPvroIwkODpb169fLnDlz7M7fG2+8Ia+99pq8+uqrsmPHDhXQDBgwQPbu3avuP3nypLRu3VrNC24/9dRThV4D04cMGSLDhw+X3bt3y+rVq2XQoEE4O7y6Pzk5WYYNGybr1q2TjRs3qp1/v3791HSjF198Ue69915Vn9OiRQv517/+pT6b8ePHq+XE6yH4MkIg89VXX8kPP/ygPgd8Xo899liR6wNByvz589Xn8ddff8mYMWPknnvusQSCEyZMkF27dsnSpUvVsrzzzjsqwCLyWeVyqkMiKjWcwRSnVYcrrrhCnQUbFi5cqM5wqps0aZI63bzR66+/rs6YbXwt/J2bm2uZ1rx5c+3qq6+2/J2Tk6NFRERon3/+ufr74MGD6n1wqndddna2VrduXe3ll19Wf7/44ota7969rd776NGjVmcIxpmscRr4ktSuXVubOnWq1bQuXbqoMwrrsJxY3qJs2bJFvfehQ4c0R+DzqFy5sjpzsw7Pf/755y1/b9iwQU373//+Z5mGzwhnCNZhngICArRjx45Zpi1dulTz9/e3nAHZuD4zMjLUGZJ//fVXq/l54IEH1Jlo4eabb1ZnoiWifMyoEJkY6lSQlcCRdWkhG+Hvf+mnjmYaNJEYszdoKjlz5ozV85BF0QUGBkrnzp0t87F9+3aJj49XzT76BRkIMDaNdOrUqdh5S0pKUtmeq666ymo6/nZmmdF80rNnT7VcgwcPlvfff9+qvgfZnoceekhlUtD0U6VKFUlJSZEjR45YvU67du2sPicwflaYlpGRoeZbV79+falTp47V54bmHRT+2kL2JS0tTTVDGT87ZFj0z+3RRx+VL774QjUp/fvf/5Zff/3V4c+ByBsFunsGiKho11xzjWoKQdMD6k2MEHzoTRu67OzsQq8RFBRUqBbD3jTsXB2FnTyaghBI2YqNjbXcRl1HRUCwtWLFCrVTX758uWoye+6551TTFmpB0OyDZis0M6EgF7UwCChsC3yNn4vew8reNGc+K9vPDX788Uer4AYwT4C6n8OHD8uSJUvUMiEAGzFihGoaI/JFzKgQmRy6KaP+AYWdRii2PHXqlFWw4sqxT1DLoUPxLQpaUWAKl112maqvQOFukyZNrC7OBCfIbNSuXVvVsBjh71atWjk1vwgikImZPHmyqhNBbczChQstrzdq1ChVl4IME4ICY+FvWSArg6yQ8XNDENm8efNCj8Uy4b3xHNvPDcW4xnWL4OqTTz5RBdLvvfeeS+aVyBMxo0Jkcmh6QC+Q2bNnW01HT5SzZ8/KK6+8Irfffrsq5EQBJnb+rvD222+rphIEJ6+//rpqSkGxKuAIH80rKGBF8wR6D6FZA00WH3zwgcpwOApFuyjwbdy4sWruQA8XBFyffvqpw6+BzAnGV+ndu7fExMSov/HZ6IEVluPjjz9WzVdotsF7okeNK6BXFIIKZDzw2giIUFSMXlu2KleurIqBUUCLrAy6XKOHEAIprDe8zsSJE1WTGQKqzMxMWbx4sWU5iHwRMypEHgBdc22bG7Dz+u9//6sCCtRobNq0yW6PmLJkcnDBa6O3zPfff2/pfaJnQXJzc1VwgGAK3ZDR/dlYD+MI7NjHjh2revXgdRBw4b0QXDgKO3n0akLGpFmzZvL888+rnkRoRoH//e9/KtBCJgjdqPGeCGhcAdkQ9DDCe+OzQJ0L1ktR0LMIPXvQ+wfr8MYbb1RNQWiiAmSC0NSH10HTH4I+BIBEvsoPFbXungkiIk+EcVQwYrCZTjdA5G2YUSEiIiLTYqBCREREpsWmHyIiIjItZlSIiIjItBioEBERkWkxUCEiIiLTYqBCREREpsVAhYiIiEyLgQoRERGZFgMVIiIiMi0GKkRERGRaDFSIiIhIzOr/ASi4spuBIK2XAAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For the K-medoid\n", "The best combination of number of clusters and number of samples for the mean distribution similarity is 5 clusters and 20 samples\n", "With a mean similarity of 0.0038000795\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For the K-means\n", "The best combination of number of clusters and number of samples for the std distribution similarity is 5 clusters and 100 samples\n", "With a std similarity of 0.09857538\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "For the K-medoid\n", "The best combination of number of clusters and number of samples for the std distribution similarity is 5 clusters and 100 samples\n", "With a std similarity of 0.055438265\n" ] } ], "source": [ "# plot the resuls hyperparameter tuning\n", "\n", "# MSE\n", "# plot the mse in function of the number of clusters (this does not depend on the number of samples since it is a clustering metric)\n", "# so, we fix a number of samples to 20 and pick the mse for the runs with that number of samples but various number of clusters\n", "mse = {\n", " \"medoid\": np.array([v[\"medoid\"][\"evaluation\"][\"mse\"] for k,v in hp_results.items() if k[1]==20]),\n", " \"means\": np.array([v[\"means\"][\"evaluation\"][\"mse\"] for k,v in hp_results.items() if k[1]==20])\n", "}\n", "\n", "# plot the mse\n", "plt.plot(n_clusters_values, mse[\"medoid\"], label=\"K-Medoids\")\n", "plt.plot(n_clusters_values, mse[\"means\"], label=\"K-Means\")\n", "\n", "plt.xlabel(\"Number of clusters\")\n", "plt.ylabel(\"Mean Squared Error\")\n", "plt.title(\"Mean Squared Error\")\n", "plt.legend()\n", "\n", "plt.show()\n", "\n", "# print best number of clusters according to the mse (the one with the lowest mse)\n", "for method in [\"means\",\"medoid\"]:\n", " print(\"For the K-\"+method)\n", " best_n_clusters_mse = n_clusters_values[np.argmin(mse[method])]\n", " print(\"The best number of clusters for mse is \"+str(best_n_clusters_mse))\n", " print(\"With a mse of \"+str(np.min(mse[method])))\n", "\n", "\n", "# Silhouette\n", "# plot the silhouette in function of the number of clusters (this does not depend on the number of samples since it is a clustering metric)\n", "silhouette = {\n", " \"medoid\": np.array([v[\"medoid\"][\"evaluation\"][\"silhouette\"] for k,v in hp_results.items() if k[1]==20]),\n", " \"means\": np.array([v[\"means\"][\"evaluation\"][\"silhouette\"] for k,v in hp_results.items() if k[1]==20])\n", "}\n", "\n", "# plot the silhouette\n", "plt.plot(n_clusters_values, silhouette[\"medoid\"], label=\"K-Medoids\")\n", "plt.plot(n_clusters_values, silhouette[\"means\"], label=\"K-Means\")\n", "\n", "plt.xlabel(\"Number of clusters\")\n", "plt.ylabel(\"Silhouette\")\n", "plt.title(\"Silhouette scores\")\n", "plt.legend()\n", "\n", "plt.show()\n", "\n", "# print best number of clusters according to the silhouette (the one with the highest silhouette)\n", "for method in [\"means\",\"medoid\"]:\n", " print(\"For the K-\"+method)\n", " best_n_clusters_shiluette = n_clusters_values[np.argmax(silhouette[method])]\n", " print(\"The best number of clusters for shiluette is \"+str(best_n_clusters_shiluette))\n", " print(\"With a silhouette of \"+str(np.max(silhouette[method])))\n", "\n", "# plot the distributions mean norm similarity\n", "for method in [\"means\",\"medoid\"]:\n", " for c in n_clusters_values:\n", " distributions_mean_norm_sim = [v[method][\"evaluation\"][\"distributions_mean_norm_sim\"] for k,v in hp_results.items() if k[0]==c]\n", " plt.plot(n_samples_values, distributions_mean_norm_sim,label=str(c)+\" cluster\")\n", " \n", " plt.xlabel(\"Number of samples\")\n", " plt.ylabel(\"Distributions mean norm similarity\")\n", " plt.title(\"Distributions mean norm similarity for K-\"+method)\n", " plt.legend()\n", " \n", " plt.show()\n", "\n", " mean_sims = [v[method][\"evaluation\"][\"distributions_mean_norm_sim\"] for k,v in hp_results.items()]\n", " best_n_clusters_mean_norm = n_clusters_values[np.argmin(mean_sims)//(len(n_samples_values))]\n", " best_n_samples_mean_norm = n_samples_values[np.argmin(mean_sims)%(len(n_samples_values))]\n", " print(\"For the K-\"+method)\n", " print(\"The best combination of number of clusters and number of samples for the mean distribution similarity is \"+str(best_n_clusters_mean_norm)+\" clusters and \"+str(best_n_samples_mean_norm)+\" samples\")\n", " print(\"With a mean similarity of \"+str(np.min(mean_sims)))\n", "\n", "# plot the distributions std norm similarity\n", "for method in [\"means\",\"medoid\"]:\n", " for c in n_clusters_values:\n", " distributions_std_norm_sim = [v[method][\"evaluation\"][\"distributions_std_norm_sim\"] for k,v in hp_results.items() if k[0]==c]\n", " plt.plot(n_samples_values, distributions_std_norm_sim,label=str(c)+\" cluster\")\n", " \n", " plt.xlabel(\"Number of samples\")\n", " plt.ylabel(\"Distributions std norm similarity\")\n", " plt.title(\"Distributions std norm similarity for K-\"+method)\n", " plt.legend()\n", " \n", " plt.show()\n", "\n", " std_sims = [v[method][\"evaluation\"][\"distributions_std_norm_sim\"] for k,v in hp_results.items()]\n", " best_n_clusters_std_norm = n_clusters_values[np.argmin(std_sims)//(len(n_samples_values))]\n", " best_n_samples_std_norm = n_samples_values[np.argmin(std_sims)%(len(n_samples_values))]\n", " print(\"For the K-\"+method)\n", " print(\"The best combination of number of clusters and number of samples for the std distribution similarity is \"+str(best_n_clusters_std_norm)+\" clusters and \"+str(best_n_samples_std_norm)+\" samples\")\n", " print(\"With a std similarity of \"+str(np.min(std_sims)))" ] }, { "cell_type": "markdown", "id": "fae2e1f0", "metadata": {}, "source": [ "The hyperparameter tuning results have been showed in terms of:\n", "- MSE\n", "- Silhouette\n", "- Distributions mean norm similarity\n", "- Distributions std norm similarity\n", "\n", "From these results, we can see how the most appropriate number of clusters for nlbse24 is 5 (this is highly higlithed by the silhouette score).

\n", "The 5 clusters showed also the best results for the mean norm similarity and the std norm similarity between the sample and the original dataset, for both K-means and K-medoids.

\n", "About the MSE, the best number of cluster found has been 20. This may be due to the fact that having more clusters, means leads to a situation in which samples are closer to the center of the cluster they belong to,

\n", "\n", "Despite this, almost all clustering techniques were able to pass the Kolmogorov-Smirnov Test (only those with high number of samples and number of clusters equals to 2 had failed the test).

\n", "\n", "About the best number of samples to pick, from the distribution metrics we can see how a number of samples of 20, 90 and 100 are the most appropriate (20 and 90 samples minimized the distribution mean similarity while the 100 samples minimized the distribution std similarity).

\n", "\n", "Said this, about the distribution std similarity, the 20 samples had been the second best. Also, the 20 samples showd a lower distribution std similarity than the 90 samples. So, we can pick 20 samples as the best value for the number of samples to pick.

\n", "\n", "Between K-meand and K-medoid, the second one was able to reach better results according to the used metrics in the most of the scenarios

\n", "\n", "Resuming, the best results were obtained with the following parameters:\n", "- K-medoid\n", "- number of clusters: 5\n", "- number of samples: 20" ] }, { "cell_type": "markdown", "id": "308db2b5", "metadata": {}, "source": [ "Here we run again the K-medoid sampling with 5 clusters and 20 samples to save the results of the evaluation and of the sampling" ] }, { "cell_type": "code", "execution_count": 15, "id": "785652cc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Initialized MLflow to track repo \"se4ai2526-uniba/Capibara\"\n",
       "
\n" ], "text/plain": [ "Initialized MLflow to track repo \u001b[32m\"se4ai2526-uniba/Capibara\"\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Repository se4ai2526-uniba/Capibara initialized!\n",
       "
\n" ], "text/plain": [ "Repository se4ai2526-uniba/Capibara initialized!\n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "๐Ÿƒ View run Kmedoid_5_clusters_20_samples_nlbse24_train at: https://dagshub.com/se4ai2526-uniba/Capibara.mlflow/#/experiments/4/runs/fe29875b44df49ba9f4c321240c34a24\n", "๐Ÿงช View experiment at: https://dagshub.com/se4ai2526-uniba/Capibara.mlflow/#/experiments/4\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "save_figures_folder = FIGURES_DIR / f\"clustering_sampling/{dataset_folder}\"\n", "save_samples_folder = SAMPLES_DIR / f\"issue-report-classification/{dataset_folder}\"\n", "\n", "best_sampling_model = ClusteringSampling(n_clusters=5, n_samples=20, method=\"medoid\")\n", "best_sample, best_evaluation = best_sampling_model.train_and_eval(embeddings, dataset_name=dataset_name, save=True, save_figures_folder=save_figures_folder, save_samples_folder=save_samples_folder)\n", "\n", "\n", "plt.figure(figsize=(5,5))\n", "plt.scatter(embeddings[:,0], embeddings[:,1], c=best_sample[\"labels\"], cmap=\"tab20\")\n", "plt.scatter(best_sample[\"sample\"][:,0], best_sample[\"sample\"][:,1], color=\"red\")\n", "plt.scatter(best_sample[\"centers\"][:,0], best_sample[\"centers\"][:,1], color=\"yellow\", marker=\"x\")\n", "plt.title(\"K-medoid 5 clusters 20 samples \"+dataset_name)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 13, "id": "d9aca7bf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'distributions_mean_differece': array([0.00294518, 0.00240135], dtype=float32), 'distributions_std_differece': array([0.03862047, 0.04436064], dtype=float32), 'distributions_mean_norm_sim': 0.0038000795, 'distributions_std_norm_sim': 0.058816724, 'kst': False, 'silhouette': 0.8066308498382568, 'mse': 0.9374418131510417}\n" ] } ], "source": [ "print(best_evaluation)" ] }, { "cell_type": "markdown", "id": "a502c625", "metadata": {}, "source": [] } ], "metadata": { "kernelspec": { "display_name": "syntetic-issue-report-data-generation", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.19" } }, "nbformat": 4, "nbformat_minor": 5 }