demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Copula-based conformal bounding box prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lab/bcyusamu/miniconda3/envs/TBD/lib/python3.8/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": [
    "from src.reporting import UQEvalResults, SummaryReporter, GifReporter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Uncertainty quantification results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "folds, version = 3, \"_release\", #10\n",
    "approaches = ['Bonferroni',\n",
    "              'Max additive',\n",
    "              \"Independent DE-CCP\",\n",
    "              \"Gumbel DE-CCP\",\n",
    "              'Empirical DE-CCP']\n",
    "epsilons = [0.01, 0.05, 0.1, 0.2]\n",
    "detectors = ['Correlated D.', 'Uncorrelated D.', 'YOLOv8 D.']\n",
    "filename = f\"results/UQ_experiments{version}.pkl\"\n",
    "res = UQEvalResults()\n",
    "res.load(filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Validity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> Reporting validity ...\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ps6yzs7PoeYUKFUTHDwoK0tomu+zrg4KCROtbtWolWm9qappnnUxNTYWQkBDRPmbOnCkq06pVq1zb7MKFC29to+yve9CgQVr7qV+/fp77uHfvXr7bYPPmzfn+/ZmamgonT54Ube/v75/n7ym/Zd5mxYoV+apjt27dRNv9/vvvb91GIpGInvv7+4v2MWjQoLf+TnISGhqa6zEPHTqU4zYqlUro0qVLgd5XWeu7cuXKfG/n5uYmqFSqAvwWiIiIqCjhCDciIqISqEOHDrh8+TKaNm2ar/JWVlaQyWSan9esWZPrXFjNmjXDzJkzdVbX/BozZgwaNWqU4zoTExOsX79edEleQXl7e2PkyJG5rh8/frzW5bo5mTJlyjvXIbuBAwdi7dq1MDc3z7OcjY0N9uzZgw8//FBnxy6Ir7/+Gj/88MNb65mdj48Pevbsmev6mTNnonz58u9bvRxVq1Ytx7+n0qVLo2PHjjluY2Jigl27dqF///75Ooa5ubnWnYPzw9raGlu3bs3XfHRERERUNDHFiYiISqjq1avj4sWLOHXqFMaOHQsvLy+UKlUKZmZmkMvlqFixInr06IF169bh6dOnosn2+/Tpg6CgIHTo0AE2NjaQy+WoUaMG5s2bh6CgIMjl8kJ/PTY2Njh79iwWLFiAWrVqQS6Xw87ODl26dMG5c+cwZMiQ9z7G6tWrsWHDBjRo0AAymQw2NjZo1qwZtm/fjmXLluVrH59++in279+P1q1bw87O7r3u2glkXMZ47949TJkyBQ0aNICdnR1MTU3h4OAAb29vzJ49G/fv30f37t3f6zjva+zYsXjw4AGmT5+OZs2awdHRUVPP2rVrY8SIEZgwYYLWdjt37sSCBQtQrVo1mJubw97eHj4+PggKCsKsWbP0Wufsl+QCQL9+/WBqaprrNlZWVvj1119x6dIlfPnll6hZsyZsbGwglUpha2uL2rVrY8CAAdi0aRMiIiLQpUsXzbYDBw7Eb7/9hokTJ+KDDz5ApUqVoFAoNO3UpEkTTJs2DXfu3EGrVq308pqJiIiocEgEQY/3syciIiIiIiIiIjIyHOFGRERERERERESkQ+xwIyIiIiIiIiIi0iF2uBEREREREREREekQO9yIiIiIiIiIiIh0iB1uREREREREREREOsQONyIiIiIiIiIiIh1ihxsREREREREREZEOscONiIiIiIiIiIhIh9jhRkREREREREREpEPscCMiIiIiIiIiItIhdrgRERERERERERHpEDvciIiIiIiIiIiIdIgdbkRERERERERERDrEDjciIiIiIiIiIiIdYocbERERERERERGRDrHDjYiIiIiIiIiISIfY4UZERERERERERKRD7HAjIiIiIiIiIiLSIXa4ERERERERERER6RA73IiIiIiIiIiIiHSIHW5EREREREREREQ6xA43IiIiIiIiIiIiHWKHGxERERERERERkQ6xw42IiIiIiIiIiEiH2OFGRERERERERESkQ+xwIyIiIiIiIiIi0iGDdridOXMGvr6+KFOmDCQSCQICAt66zenTp9GgQQPIZDJUrFgRP/30k/4rSkRExRJzhoiI9I1ZQ0REOTFoh1tiYiLq1q2LVatW5av8o0eP4OPjgw8++ADXrl3D//73P4wdOxZ79+7Vc02JiKg4Ys4QEZG+MWuIiCgnEkEQBENXAgAkEgn279+P7t2751pmypQpOHDgAEJDQzXLhg8fjuvXr+PixYuFUEsiIiqumDNERKRvzBoiIspkaugKFMTFixfRoUMH0bKOHTti48aNUCqVMDMz09omNTUVqampmudqtRrR0dFwdHSERCLRe52JiEo6QRAQHx+PMmXKwMSkeE8N+i45AzBriIj0zdizhjlDRKRf+siZYtXhFhkZCWdnZ9EyZ2dnpKenIyoqCq6urlrbLFy4ELNnzy6sKhIRGa0nT56gXLlyhq7Ge3mXnAGYNUREhcVYs4Y5Q0RUOHSZM8Wqww2A1hmczCticzuzM3XqVPj5+Wmex8bGonz58vjnn3/g4OCgv4oWI0qlEkFBQWjTpk2uozeMCdtDG9tEzBjbIzE2FQeWX0RqqhkSTV+h1Z8bYJEah6lD7OF9ry8WrhsJa2trQ1dTJwqaMwCz5m2M8T2TF7aHNraJmDG2R1JiGnYsOgk7dQrsHH9E+d8yPnvHfiWFq9IKLW/3wcSNY402a5gzeTPG98zbsE3E2B7ajLFNbhzdiIp/f49+Lq5Yul4NICNnlGYS1ItRYtPUWzrNmWLV4ebi4oLIyEjRshcvXsDU1BSOjo45bmNhYQELCwut5Q4ODrluY2yUSiUsLS3h6OhoNG+0vLA9tLFNxIyxPRwfb8UAxWzsEOZCll4B92uNRc3Qzfjk0UAkqy0B5N0hVVy8S84AzJq3Mcb3TF7YHtrYJmLG2B4Wt7bBVWKCOFTEw/hpqCxdAVlqDOQmtmjyeCxgIgNgvFnDnMmbMb5n3oZtIsb20GZ0bRK8Fd53VuB3m9JIlatgm64CAHyREoOVNna4m5Zx8kKXOVOsJkDw9vbG8ePHRcuOHTuGhg0bGscfCBGRIQRvBQ6MwfP9Zmhy9kfIkl8iRV4KV70mQkgvDVnyK0PXUGeYM0REBhC8FYo/RuEjm+kwNf0X1mmlEFxvHGJtPPBp6GjYpjohwTTa0LXUGWYNEVEhC96K1IApOBQ9HRFPv4V9op1mVeP4VDgk2KLjnc91fliDdrglJCQgJCQEISEhADJukR0SEoLw8HAAGUOnBw4cqCk/fPhwPH78GH5+fggNDcUvv/yCjRs3YuLEiYaoPhFRyadSAr/7Aci41OV0VDhiLn4nKlLl3h4DVCx/mDNEREVclpx5vt8cZU8uwfE/f0KSzDHLiZ2XaP3nz4auaa6YNURERdh/OaMUZIiNd4A61QGmGxzxr1QBABB+q4SBf42BIl33l+cbtMPtypUrqF+/PurXrw8A8PPzQ/369TFjxgwAQEREhCaoAMDDwwOBgYE4deoU6tWrh7lz5+LHH3/Exx9/bJD6ExGVeFIzoMsyABJENnqCSf9GYPL9a3gYeUtT5IJXb8PV7y2YM0RERVyWnHmdno4xD27jt5A9CLyyWVOkZuhmWKTGGa6Ob8GsISIqwv7LGYU0GvWvLceuoPk4cusABv8bjdfW7giuNw4p8lJ6uWrHoHO4tW7dWjNBaE42bdqktaxVq1YIDg7WY60yJi1NT0+HSqXS63GKCqVSCVNTU6SkpBjNa86LsbeHVCqFqalpiZgjhXTEawBuPXiKnou/RbpKQF335qhm7YTawd/jauOBUKQV3cmai2rOAIBKpYJSqdT7cYoCY/9czc7Y24M5Q1q8BiApORUTNwxHmFIFW4UjWtTw1awOajoYxyqsAoroALCimjXG9J3G2D9Xc2LsbcKsIRGvAQCAfSeG4cLNVJhITNC50VBcq9AUABBnHoVjlf2BX3V72GJ104TCkJaWhoiICCQlJRm6KoVGEAS4uLjgyZMn/EAC2wMALC0t4erqCnNzc0NXhYqAp0+fouO41YhJUaOiSy183PlLNDz/I2SpMdhdfRXa3P7C0FUsdhISEvD06dM8v6CVJPxcFWN7MGdITKVSof/3v+PiMxXkFpYY7bMELiYCav53YscmrTRaPfwcp3DW0FUtNoztOw0/V7WxTZg1JLbuchLmn08FANTqVRO1HJpq1p2sshUV01/ilI6PyQ63LNRqNR49egSpVIoyZcrA3NzcKD6c1Go1EhISoFAoYGJSrO6joRfG3B6CICAtLQ0vX77Eo0ePULlyZaNrAxKLjY2Fj48Pnj1/hrKl3DHh45moUGYmBvqZICNCYnGtUtGdV6coUqlUePr0KSwtLVGqVCnmjBEy5vZgzlB2giBg7NixCAgIgJnUHF91nA+FsxW8Tq/QnNjp+M9YWCcX3dHURY0xfqcx5s/V3Bhzm+SUNVSypKarcOL2C6TlMXrTXCpFuxqlYWEqxYEDBzBy5EgAwDeDe8LDsheUWTbtcO8ztGrzDL9ggk7ryQ63LNLS0qBWq+Hm5gZLS0tDV6fQqNVqpKWlQSaTGd2HcU6MvT3kcjnMzMzw+PFjTTuQcUpNTUWPHj1w8+ZNuLi44GTQcZR1LgfFo2S0ODQWl2UWkAuAZ+NROIALhq5usaFUKiEIAkqVKgW5XG7o6hQKY/9czc7Y24M5Q1ktWrQIa9asgUQiwZg+c1DPvTEcvE9iYIMEZJ7Y+bOuPzreGWPoqhYbxvidxtg/V3Ni7G2SPWukUqmhq0Q6FPw4BqO2v/2y/B3DmkLy8h769OkDtVqNgZ8NRvUyXyD+VSoU0kg4OK9F5IuRQKozrl+003k92eGWA2P8QCLKiu8BAjLmvqhcuTIuX76Mw4cPo2o1z4wV9gNgC6Dd735Al2V4VcEHwAhDVrVYKumjDYjywpyhTO7u7jAzM8P333+Pr4aNhDIlHQr7Zmhx2QWXT8+GvNGXaPLB//A87AVGfff2/dEbfJ+RseN7oORq5G4PN3s5nr5OhgAAJkkwtXoAQW0BVWIlSCRSuNlbopG7PR6k28PZ2RlVKldDG7chiH+VChsnGbp3AKxP30F8bwEBx2R48SJB5/VkhxsRkbFTKTPu3pONqakpfvrpJ0yePBmVKlUSr/QaANTtk7HdK93f0YeIiEqYXLKmb9++aNy4sSZnLOQZX09sGw1DO6/Bmm2sbC0KrapERFS0mUpNML59Ffjtvg5T65uQl9umWadOs0NS+BcY374tTKUmqFq1Ki5evAgzqQxBv9yH1CQN3f28YO3QDGj+CaylZuheKwU7Fp/WfT11vkciIiqyss93UOHxPtS9MQfX68zA4wo9AQC3L5/HuIHdYSWzgEQi0e5sy5TDFyciIiItwVuB/0ZFw2sAbt68CQcHB5QtWxYAmDNERFRgH9Utg2UnbiDZdgd+XZgOABgwQYpU8xhYl9oP+zgPAOUAAC4uLgAA37H1/htJ/d90Fv/ljLWDDJ2+qg0s0W0dOcaSCiQpKQkff/wxbGxsIJFIEBMTY+gqaQkLC4NEIkFISIihq0JU5GTOdzB+13Vc+L8f4HF9Jk7KTJF4Zz7O/d9yfDl7Nb79qi/ad+6KtLQ0Q1eXjBBzhqiECd4KHBgDqNKAA2MQdmgZOnToAG9vb/zzzz+Grh0ZKWYNUfFnKjXBhw1eQGLy5u4HqVEfQkgX8GjDSbRv1xY7d+4UbWMhN33T2ZaNPkZSs8OthBg8eDAkEonm4ejoiE6dOuHGjRs6Pc7mzZtx9uxZXLhwAREREbC1tdXp/nXBzc0NERERqFWrlqGrQlTkZM530Ft6Cu1st6KlezlMcC6Fka6lcUb5f3h96DsAAupWrwwzM44soDeYM28wZ4jylpquwu83IhAc8COEA6MhZMywg6gkFToNnoTIyEhIZQrYOjgauKZU1DBr3mDWEOVNnZSE0rJXsFC+WWYa64bIX54h4WYCpGZSeHh4GK6C4CWlOlXQW9PqWqdOneDv7w8AiIyMxLfffouuXbsiPDxcZ8d48OABqlev/l4f/CqVChKJRGsSy7S0NJibm79vFSGVSjVDRolIzFRqgu8r3kaFG/vQw84Ru/8bft2rrxLBq59CrVKjVaM6WLVyJSf1L4KYM/nDnCEyrODHMTi1axkWm64HAEgkQLJSQLedybj7Sg03Gwk6dmqJh7GAs5OBK0sihs4ZgFmTiVlDlLe7Xg3QFkDbLMu8NyxGyKsYmACYvno6mjRpYqDaZeAINx3KeqlWbo9R24MR/DhGL8e3sLCAi4sLXFxcUK9ePUyZMgVPnjzBy5cvAQA3b97Ehx9+CLlcDkdHR3z55ZdISHhzJ44hQ4age/fu+P777+Hq6gpHR0eMGjUKSmVGl3Hr1q2xdOlSnDlzBhKJBK1btwaQESqTJ09G2bJlYWVlhSZNmuDUqVOa/W7atAl2dnY4dOgQatSoAQsLCzx+/Bju7u6YN28eBg8eDFtbWwwbNgwAsHfvXtSsWRMWFhZwd3fH0qVLRa/T3d0dCxYswOeffw5ra2uUL18e69ev16zn8Gui3KVe+hVPzkgR8HoezJX2AIB/lUpErAiDOkmN8q5VMKTB50i/vMPANaWcMGeYM0TFQeOY37HEbD0kyOhsU6kF9N+XjAtPVLCTAYH9LLHWYQcax/xu6KpSNobOGeD9s2bw4MHMGiIjtDsmBmv/u5nbTGcXdPLpZOAascNNpzIv1cptTIhEApR3yLg1rb4lJCRg27Zt8PT0hKOjI5KSktCpUyfY29vj8uXL2LNnD06cOIHRo0eLtgsKCsKDBw8QFBSEzZs3Y9OmTdi0aRMAYN++fRg2bBi8vb0RERGBffv2Acj4AnX+/Hns3LkTN27cwCeffIJOnTrh3r17mv0mJSVh4cKF2LBhA27duoXSpUsDAL777jvUqlULV69exfTp03H16lX07t0bffr0wc2bNzFr1ixMnz5dU4dMS5cuRcOGDXHt2jWMHDkSI0aMwJ07d/TXoEQlQfBWKH+fiWSVLVTpzuh8dyxemiow/NlTvI5RwcmuDEZ2WASltDSUv8/MmHeHihTmDHOGqMgL3grpwbFIVVsiUe0IQRAw4mgq9t9Jh5kU2NfHEu5OpZGqtoT04FhmTRFTlHIGyH/WjBkzRrQds4ao6MqcdmD/tae5Pn6/EYHU9NxH2gJA1eCruLfMB1+MleJUQgLm/BsJAHDtWgoH5jrCzdqtMF5OnnhJqQ5lvTVtTgQBGN++Mkyl+unnPHToEBQKBQAgMTERrq6uOHToEExMTLBt2zYkJydjy5YtsLKyAgCsWrUKvr6+WLhwIeRyOQDA3t4eq1atglQqRbVq1dClSxecPHkSw4YNg4ODAywtLWFubq4Z3vzgwQPs2LEDT58+RZkyZQAAEydOxJEjR+Dv748FCxYAAJRKJdasWYO6deuK6vzhhx9i4sSJmuf9+/dH27ZtMX36dABAlSpVcPv2bXz33XcYPHiwppyPjw9GjhwJAJgyZQqWL1+OU6dOoVq1arpuVqKSQaUEfveDQpqGWudXIrjeOAClcKT8RwgPvQYbmS1Gd14CJyENZcvMhSItKuOOcnX78C5xRQhzhjlDVKT9lzWpajkOvZ6BZLUt5DazERQSBgkAt6HlsKJuOXS/NQbWJrHwtZ8DC2ZNkWLonAHePWumTZsGGxsbAMwaoqIscyTt2+wY1hTelXKf69Pkzl60vfwLFrqWwZnEBKgBODa3g8PHpVE/JRWl7xwFvAbosOYFxxFuOvZR3TI5nhXKPBvkW6eM3o7dpk0bhISEICQkBH/++Sc6dOiAzp074/HjxwgNDUXdunU1wQQAzZs3h1qtxt27dzXLatasCan0zXwMrq6uePHiRa7HDA4OhiAIqFKlChQKheZx+vRpPHjwQFPO3NwcderU0dq+YcOGouehoaFo3ry5aFnz5s1x7949qLLMJZF1XxKJBC4uLnnWk8joSc2ALssASCBLjYFXyArIkl+ibPnmGNN9FUb4LEI5M1N4haxAKUkUAElGeX4BKnKYM8wZoiLrv6xRCpZIVtsiTuWC+4lzsLFyHSwrUwbl6lRAk7vjkaRyQbLaFkrBkllTBBkyZ4B3z5qsI9GYNURFl05G0gZvRWrAFAjp9pjz8hWml3bGAhdXVOhfBmXTVZgQCaQGTDH4KGqOcNOx3M4KFcbZICsrK3h6emqeN2jQALa2tvj5558hCEKuE6BnXZ79roQSiQRqtTrXY6rVakilUly9elUUagA0Z6YAQC6X53j8rGEJIMd6CoKgtV1B60lE0JzhqZo+Ftslr3HA5ld0eDAe5UtVAQDsrboca1vEIzAyHfhopcHPCFHOmDNvMGeIiiCvAVAA+GDLNByRLIB1mjPuNZiIpqGbYR46EEK6E+LMo9DFfiYU3WYxa4ogQ+YMwKxh1lBJ994jaf/rbNsRMQESU2f0dJoBdf9INJLJ0DIuCjUTrHAweh7+NomFb8AUWAAGyxqOcNOD7GeFCutsUHaZd81JTk5GjRo1EBISgsTERM368+fPw8TEBFWqVHnnY9SvXx8qlQovXryAp6en6PEud9WpUaMGzp07J1p24cIFVKlSRSv8iOgdeA3A6rSuGLfsJcpeFt+1p0XYADROtkTpLj/wC1ARx5xhzhAVZSEmtVFnZTju75sKWfJLpMhL4arXRAjppSFLfolOp5fjVbshzJoirKjkTMax85c1WTvpCopZQ1S43nkk7X9TF7xONcec39Zg2ZGfsPvFDJgoHdAuKRl1/utsi9OMpJZlTJOjUur9NeWEHW56kNljm3kOo7DOBqWmpiIyMhKRkZEIDQ3FmDFjkJCQAF9fX/Tv3x8ymQyDBg3C33//jaCgIIwZMwYDBgyAs7PzOx+zSpUq6N+/PwYOHIh9+/bh0aNHuHz5MhYvXozAwMAC72/ChAk4efIk5s6di3/++QebN2/GqlWrRHMiENG727t3L77+fhteJanxPPwxZMkv0SD4e8SZR8E21QmNwmcj3v0TQ1eT3oI5w5whKqoeP34MHx8fxKvVOBvzElVv+4vW1wzdDFlqDGIqNMllD1QUGCpngHfLms8++0xzA4N3wawhKlzZP2MyvfWzRmqG9I7fYej+Z3j08iEeRl7HkzhTBETPQ0RaVQT819lmI41Ed4dvoZBGG3TqAna46Ulmjy1QeGeDjhw5AldXV7i6uqJJkyaaO/e0bt0alpaWOHr0KKKjo9GoUSP06tULbdu2xapVq977uP7+/hg4cCAmTJiAqlWr4qOPPsKff/4JN7eC3xXEy8sLu3fvxs6dO1GrVi3MmDEDc+bMEU0uSkTv5ty5c+jfvz8EQUCLGr7o2aIXOrtPQ0KvYDR1ngUb63QkJVggYFkwEl6nGLq69BbMGeYMUVETHR2NTp06ISIiArVq1MDH26bjnPfnojJBTQdj+BhH2FnYGaaSlG+GyBng3bJm5cqV731cZg1R4XqXkbSCIGD4+nMIvJcOuSkQ0McalRxMEKdywb7oRaLONmtptMGnyZEIOV1MXoLFxcXB1tYWUVFRcHQU3/EiJSUFjx49goeHB2Qy2Xsfa1/wU/jtvo7ln9ZFj/rl3nt/+qJWqxEXFwcbGxuYmLAPlu2h/V5QKpUIDAyEj4+P1lwTxqg4tkfm5L2vX79Gw+otMbr7PPSc2AjWYXsyhll3WYZ4908QsCwYcmtz+I6tBwt5/qb5fPXqFZycnBAbG6u5O5ixK6ysYc4UT2wP5szbFMf2SElJQfv27XHu3DmUK1cOxwODcOrXMKhiTSBLfomaoZtxtflXQLI1Yi2i0G9yM7iXzf/nFrNGjDkjxs9VbWwT8XtBKpUWu89VfSsKWZP5GZPpbZ81s2fPxqxZs2BiYoL934/FR7H+iEirgn3RizRlejp8A1fzfwrc2aaPnOFNE/SoR/2yqFRKgTrlbA1dFSIyYs+fP0enTp3w+vVrNG3aFIGHDsLMxBwKexngMACo2weQmsEaQI8JXjCTmea7s40MizlDREWBSqXCZ599hnPnzsHW1hZ7dwbg8q4XUMWaIN4iCs0uroAsNQa7Pb9Dx3/GwjbVCWfXhsNpglNGFlGRxZwhIn36qG4ZLD1xDf8qb6GUlQ0612qfa9kNGzZg1qxZAIA1a9bgo6++QvyZRjixS1zuROw4dP8UsPbqp8ea549xdnUXEolEgrpudrneSYeIqDDMnDkT4eHhqFy5Mg4ePAh7RxvxF5wscxoo7GXsbCtGmDNEVBQcPHgQe/fuhbm5OQICAlDXqy7k1uawcZKh9lA7DPRLQO+ppohWxOLPRrth6WAKubU5zGTMm6KOOUNE+vTHkxOIc/4f5OW2IcF+Lbr95ovHcY+1yr169Qp+fn4AgGnTpuGrr75CfHQKAo65ay4j7enwDWykkYhTuSDgmDviow0/RQ5TjoiohPvhhx8gCAL+97//wcnJydDVISKiEqZ79+5YtmwZypQpg9atWwMAfMfWgzIlHQp7GVpUPYfLkZchN5WjiWsTpMSmczQ1EZGRS1Qm4puz34iWPU98jvmX5mN9h/Wi5Y6Ojjh27Bh27dqFuXPnIuF1CgKWBSMuKgU2TjJ07wBYn36E7p2AgGMyxEVlrO8xwcugI6mZckRExURqugonbr9AmkqVaxlzqRTtapSGudREczba0tISGzZsKKxqEhGRkRAEQZM148ePF62zkL/pULO1sEW7Cu006xT2/ApCRGTsToafhFKtBAD86vMrLj6/iNUhq3Ex4iJeJL1AacvSopxp2rQpmjZtCgAwk2WMlAaA7n5esHZoBjT/BNZSM3SvlaKZl9rQI6mZdkRExUTw4xiM2h781nI7hjXFwY1LYWpqitmzZ/MyECIi0rk//vgDS5Yswc6dO2FnZ2fo6hARUTETFhsGizQBW5eqgIV90PSoP1b/t+5J/BMgAfj444/xww8/wMvLS7SthdxUNJIagGaaHGsHWZGZl5pzuBERFRON3O1Ft84GAFOka37OvJX2lcM7sXDhQsydOxfnzp0r/IoSEVGJduPGDfTo0QNHjx7FokWL3r4BERFRNrGpsaLnCnNrzc8RryPQtWtXnDt3DoMHD4Zardba3kJumuvlokVlXmp2uBERFROmUhOMb18Fwn/Pu5kdw0qHkWhtvQeACoIAeEvv4+uxYwAAc+fOxQcffGCw+hIRUQmgUoqehoeHo3PnzoiLi0PLli01d4wjIiLKL3VSEuwFS1hkiZiEuChYpAkwT1Zj8ZjFuHz5MhwcHLBnzx6YmBTPrivDd/kREVG+fVS3DJYf/wfuan/8USYEf8ABwFWUUt5CynVf/PjDQgiCgC+//BLTpk0zdHWJiKgYyT5XaIXH+1D3xhxcrzMDjyv0REJcDKZ93hPPnz9HzZo1ERAQAJnMcJNRExFR8XTXqwHaAmibZZlFt+HYIgiY9W8k9sTehkwmw6FDh1C1alVDVfO9Fc9uQipR3N3dsWLFijzLSCQSBAQEAADCwsIgkUgQEhKS5zatW7fGuHHjdFJHoqLCVGqChVWCccv1mmh57MtY/LNpHlJSUuDr64vVq1dz7jai/zBniPInc67Q8buu48L//YB616bDRKVEvWvTcWbnUgz7rA+ePLyHUs6uOHz4MOzt7Q1dZaIigTlDpBtrX73CnthYmJiYYMeOHfD29jZ0ld4LO9z0LdswfH0ZPHgwJBIJhg8frrVu5MiRkEgkGDx4cKHURd/c3NwQERGBWrVqAQBOnToFiUSCmJgYUbl9+/Zh7ty5Bqghkf6kXvoV/97dBBMlsHthOnYvTMdXT6MRtiwMaQlK1K/hjp07d8LUlAOYjQZzRueYM2SsMucK7S09hcWm6yFBxvygEgCxx1Yj9ektSC2scPTIYbi5uRm4tlRomDM6x5whY1c1+CqqBl/Fs53zNMt6tEzEqldRAIBVq1ahe/fuBqqd7rDDTZ+CtwILymT8Wwjc3Nywc+dOJCcna5alpKRgx44dKF++fKHUoTBIpVK4uLi8tUPBwcEB1tbWeZYhKk5SL/2Kg9viEfHsW1il2WmWt1Clwa2rO0o7lMOQZiMhvbHPcJWkwsWc0QvmDBkrU6kJVlT9G4uydLbhv3/9mpqjkr0Eq6cNQf16dQ1aTypEzBm9YM6QsTOxtISJpSU+rNJZs2zlqLXo1KkTpk6dihEjRhiwdrrDDjd9Cd4KHBgDqNIy/i2EkPLy8kL58uWxb9+bL9v79u2Dm5sb6tevLyp75MgRtGjRAnZ2dihVqhQ+/fRTPHjwQLN+y5YtUCgUuHfvnmbZmDFjUKVKFSQmJuZ4/AcPHqBbt25wdnaGQqFAo0aNcOLECVGZFy9ewNfXF3K5HB4eHti2bZvWfu7du4eWLVtCJpOhRo0aOH78uGh91iHYYWFhaNOmDQDA3t5edOYr6xDsqVOnomnTplrHqlOnDmbOnKl57u/vj5o1a8LFxQU1atTAmjVrcnytRIUueCuUv89EssoWEmVpdL47FikWdgAAabIDvrCeh2k9N8LCohyUv88stP8YkwExZ5gzRLoWvBVeITMyRrVJgFgTExy3lOO8XIZqzlLcHqXAl6qtzBhjwZwptjlTvXp1WFpaonHjxli7dm2Or5WMT2q6Cr/fiMD+a0+x/9pTHLrxHABw6MZzzbLfb0QgNV1lkPq1TYzEwYMHMX/+fIMcXx/Y4aYPmeGkuZegUGghNWTIEPj7+2ue//LLL/j888+1yiUmJsLPzw+XL1/G8ePHYWJigo8//lhzu92BAwfCx8cH/fv3R3p6Oo4cOYJ169Zh27ZtsLKyyvHYCQkJ8PHxwYkTJ3Dt2jV07NgRvr6+CA8P15QZPHgwwsLC8Mcff+D//u//sGbNGrx48UKzXq1Wo2fPnpBKpbh06RJ++uknTJkyJdfX6+bmhr179wIA7t69i4iICPzwww9a5fr3748///xTFMK3bt3CzZs30b9/fwDAzz//jGnTpmHu3Ln4888/MW/ePEyfPh2bN2/O9fhEhUKlBH73g0IahVrnV0KW/BKKVEfMNnHDQ3kZ/PV8DmzSnGCV9hq1nOdAIY0CfvcrtEtAyACYM8wZIl3773MlTS1HosoRx83k+GWdJZ6vMsfXDqXQpVwZPJQ6I00tL7TPGzIg5kyxzpn58+fj1q1bmD59OmbMmMGcIQDieTrH77qOb/bdBAB8s++mZtmo7cEIfhxTaHUKO7EWhyuEodqnz2BybAJMb+woUfNQc5IfXdMKp0z/hRQAeA3Q2+EHDBiAqVOnas6anD9/Hjt37sSpU6dE5T7++GPNz2q1GitXrkTlypVx+/ZtzVwC69atQ506dTB27Fjs27cPM2fORKNGjXI9dt26dVG37ptLDObNm4f9+/fjwIEDGD16NP755x8cPnwYly5dQpMmTQAAGzduRPXq1TXbnDhxAqGhoQgLC0O5cuUAAAsWLEDnzp2RE6lUCgcHBwBA6dKlYWdnl2O5WrVqoU6dOti+fTumT58OANi2bRsaNWqEKlWqAADmzp2LpUuXomfPnoiLi0Pt2rVx584drFu3DoMGDcr1dRPpndQM6LIMODAGstQYeIWswLeCCwJvBuCCbVn8r6EtrJNfwitkBcp6/gtAklFeambompM+MGc0z5kzRDry34mdVLUcB1/PQJLaFiukC3Dz+S1IJBJUeuwB0/JlEBg9GuWFaPjaz4HF735A3T7MmpKIOaN5XpxzRq1Ww9HREWFhYcwZAvBmns6nr5O13t1AxshmN3tLNHIvnBvivDy5Gp2GTMa9aDWUKgFTWlgUymdMYeIIN13KNZwy6f/MkJOTE7p06YLNmzfD398fXbp0gZOTk1a5Bw8eoF+/fqhYsSLs7OxQr149ABCdvbG3t8fGjRuxdu1aVKpUCd98802ex05MTMTkyZNRo0YN2NnZQaFQ4M6dO5p9hoaGwtTUFA0bNtRsU61aNVGohIaGonz58ppwAqCzO5P0799fM+RbEATs2LFDczbo5cuXePLkCYYOHQobGxuUK1cONjY2mDdvnugsEpHBeA0APlqJqr0icarsdQTeDAAAtK/XB2ZSc+yt9yvWf/ESpVVq4KOVJSakKBvmDHOGSB/+O7GjFCyRrLLFnRdJuL3uIdIBtLFSYLyZAt3+HgOLtFJIUNtCKVjyxE5JxZwpETmjUCg0WTN//nzmDAHImKdzfPsqub+7BWB8+8owleq/myjp/Ab4Dvoa96LVqGArwcC6mXlSeKNpCwNHuOnKW8Mpk/7PDH3++ecYPXo0AGD16tU5lvH19YWbmxt+/vlnuLi4IC4uDs2aNUNaWpqo3JkzZyCVSvH8+XMkJibCxsYm1+NOmjQJR48exffffw9PT0/I5XL06tVLs09ByGibvIaIZpbJSldDSvv164dvvvkGwcHBSE5OxpMnT9CnTx8A0Aw9//nnn9GoUSMkJCRAoVDAxMQEUqlUJ8cnem9eA3Dw1FVMPJJxmUGXhoPQrJoPAKB12Gf4yH4GO9tKMuYMc4ZIn7wGQAHAqf/3+Cn8MVLTklG5dDX8z6087j/8HDZyJ8iSX6KK51wousxi1pREzJkSkzNNmjSBWq3WZI2ZGTvHKcNHdctg+fF/8PR1smh55ug23zpl9F6H9L82ofeQ0fjzmQoOcgmOfGYJV+s3nXwJKgeYBUyBBVDss4Yj3HThv2H4bw+nTIJe51fq1KkT0tLSkJaWho4dO2qtf/XqFUJDQ/Htt9+ibdu2qF69utYtqAHgwoULWLJkCQ4ePAgbGxuMGTMmz+OePXsWgwcPRo8ePVC7dm24uLggLCxMs7569epIT0/HlStXNMvu3r0rOnaNGjUQHh6O58+fa5ZdvHgxz+Oam5sDAFSqvCd3LFeuHFq2bIlt27Zh27ZtaNeuHZydnQEAzs7OKFu2LB4+fAhPT09UrFgRnp6e8PT0hIeHR577JSosFy9eRJ9p66AWgGbVuqB79U5oEPw9zKX/wiq1FC4nrkS8+yeGribpA3MGAHOGSN9iKvpi7MNbeJ0UjTK25TDMZxH+bjgFKfJSkP03dUFyi0+K/RcgygFzBkDJypmsWcOcoUy5jXIrrNFtwtUtGDFiFH6/lwozqRl29nZBNac3Jx7jVY7YHz0PB19PR2rAlGI/0o0j3HQhy/xK+Qsp/c6vJJVKERoaqvk5O3t7ezg6OmL9+vVwdXVFWFgYvv32W1GZ+Ph4DBgwAGPGjEHnzp1Rvnx5NGzYEF27dsUnn+T8hd7T0xP79u2Dr68vJBIJpk+frjnTAgBVq1ZFp06dMGzYMKxfvx6mpqYYN24c5HK5pky7du1QtWpVDBw4EEuXLkVcXBymTZuW5+utUKECJBIJDh06BB8fH8jlcigUihzL9u/fH7NmzUJaWhqWL18uWjdr1iyMHTsW1tbWaNGiBczMzBAcHIzXr1/Dz88vzzoQ6dvdu3fRtasvUlJSULN8EwzzGYWPLUfDumIUaqruIiBlLeLiTRGwLBg9JnhBYS8zdJVJl5gzAJgzRPqUkpKC7t27435aGmwdLFHjq6qwjLbWrN9b71esbRGPdTW6GbCWpDfMGQAlJ2dsbGzQsWNHvHr1Cnfu3EFsbCxzhjQyR7m9jEsCkDG6rbxDIYxuUymxYMoIbAhOggQSDG77LSKtPBGv+hbW0leIVzkiIHoe4lQuAAClICv284VyhJuu/De/EvC24cKSQrnky8bGJtfh0iYmJti5cyeuXr2KWrVqYcKECZgzZ46ozNdffw0rKyssWLAAAFCzZk0sXrwYw4cPx7Nnz3Lc7/Lly2Fvb49mzZrB19cXHTt2hJeXl6iMv78/3Nzc0KpVK/Ts2RNffvklSpcuLarb/v37kZqaisaNG+OLL754622By5Yti9mzZ+Obb76Bs7OzZvh5Tj755BO8evUKSUlJ6N69u2jdF198gQ0bNmDz5s1o3rw52rRpg02bNvGMEBUJFhYWcHJyQqVyNTDu0/n4eFobWPeYBUjNYd1jFrpPbQkbJxnk1uYwk/FcSonEnGHOEOmRRCJBmTJlYGNjg+lrFuLDhC9F61uEDYCpYA83azcD1ZD0jjlTYnJm06ZNqFu3Lrp27YotW7YwZ0gk+yi3Qpu7TWoGZ+8+kEqAJR0d8IGnJ+JULgiInoeItKqazjYbaSS6O3wLhTS62M8XKhFyusi8BIuLi4OtrS2ioqLg6OgoWpeSkoJHjx7Bw8MDMtk7jg7Jc+6DwgmnglKr1YiLi4ONjQ1MTNgHy/bQfi8olUoEBgbCx8eHc0AABmuPV69eITkxDXbW9m9GsKmUmhBKeJ0CM5kpLOSF2+H26tUrODk5ITY2Ns95UYyJXrOGOVPssT2YM29jqPZQq9UIuXILfx+IQ3xUKmTJL1EzdDOCmg6GTZoTUq3iMWxaR1g7FP4oamaNGHNGjJ+r2tgm4veCVCplzmSRrlKj47IgjK2SgJX3rHFkfOtCuVkCANzZuxjVbi5EvMpBNKINgKazzVoaXeifNfrIGeN85+lTrmeGimY4EVHRpVQqce7cOc1zR0dHlCvvKr5cNMsZH4W9rNA728gAmDNEpEOnT5/WXDKXFJuGWwfiER+VCjM7AT83X4Vho57gt1orkSh/DYtEawQsC0bC6xQD15r0ijlDVOKZSk0wqo0nAGBUm0p67Wy7efMmXr58qXle7eMpwEcrYS2NRjvbFaKy7WxXGKSzTV/Y4aYPWiHFcCKighEEAV9++SVatWqFn3/+2dDVoaKGOUNEOrBr1y60bt0affr0QXp6OsxkppBbm8PGSYa+k5vj6KBDWN56OZZ2WYQvp3Xk1AXGhDlDVOK1rGYFAHB0eox0dbpejvHw4UO0a9cOzZs3x+PHj9+s8BqA+A/X4ETsOFH5E7HjEP/hmhLzWcMON33JDCmpOcOJiApsxowZ2LRpk2ZOHSItzBkieg+nTp3CwIEDAQAuLi6QSqWwkJvCd2w99JjgBWsHGWwtbNGuQjs0L9scdk4K9JjgBd+x9Tia2lgwZ4hKrGNhx9BpbycAgN8pP3TZ1wWP4x6/ZauCiYqKQqdOnfDixQtYWlrC3t5esy4+OgUBx9w1c7b1dPgGNtLIjDndjrkjPrpkjKRmh5s+eQ0A/vec4UREBbJu3TrMmzdP83OXLl0MXCMqspgzRPQO/v77b3Tv3h1paWno2bMnli9fDokkYySThdw01ztdc+oCI8ScISpxEpWJ+ObsN6JlzxOfY/6lvG/uURBJSUnw9fXFvXv3UL58eQQGBmrmRUt4nYKAZcGIi0qBjZMM3T8FXOWP0P1TwMZJhriolBIzfQE73PStGN9Rg4gK34EDBzBy5EgAwMyZMzF06FAD14iKPOYMERXA06dP0alTJ8TGxqJFixb49ddfIZVKDV0tKsqYM0Qlysnwk1CqlZrnw+oMAwBcjLiIF0kv3nv/6enp6Nu3Ly5dugR7e3scOXJEdMVO1ukLuvt5wbplP+B/z2Hdsh+6+3mVqOkLiv8rICIqIS5duoQ+ffpArVZj6NChmDlzpqGrREREJUhMTAw6d+6MZ8+eoXr16vjtt98gl8sNXS0iIipEYbFhAABzqTkAoJFzI826J/FPUNqy9DvvWxAEjBkzBgcOHICFhQUOHDiA6tWri8pkTl+gTEl/M6L6v459awcZekzwgpnMtESMqC7+r4CIqIQ4dOgQkpOT4ePjg59++klzeQ8REZEuXLt2Dffu3YOrqysOHz4MBwcHQ1eJiIgKWWxqLADA0tQSAKAwV2jWxaTGvNe+X79+jRMnTkAikWD79u1o0aJFjuUs5Ll3qOU2rUFxxA43IqIiYu7cufD09ESvXr1gasqPZyIi0q02bdrgxIkTUCgUqFChgqGrQ0REBmBrYQuLNAErlkXhwRwgIS5Ks87Owu699u3g4IALFy4gKCgIPXv2fM+aFn/8RkdEZECJiYkwMzODubk5JBIJBg8ebOgqERFRCRMTEwM7OzsAyHW0ARERGYcKNuITLlf/Ddb87Gbt9k77zJozpUqVQu/evd+5fiUJb5pAIhKJBAEBAYauRr7MmjUL9erVM3Q1iN6ZUqnEJ598gq5duyI+Pt7Q1SEqFMwZosK1YsUK1KxZEzdu3DB0VYgKBXOGKHfqpCR8WKo5FKo3N0PZHuIPizQBLRwavdP8bcHBwfDw8MAvv/yiy6qWCOxwKyEGDx6M7t27G7oaxd6mTZvyNZ/Jpk2bIJFIIJFIIJVKYW9vjyZNmmDOnDmIjY0VlR08eLCmbNZHp06d3nqcoKAg+Pj4wNHREZaWlqhRowYmTJiAZ8+eacoIgoD169ejSZMmUCgUsLOzQ8OGDbFixQokJSUByAjzrPV1c3PDF198gZcvXxawhUhXBEHAV199hcOHD+P8+fO4d++eoatElCfmjG4wZ6gw7dmzB35+fnj+/DmOHz9u6OoQ5Yk5oxubNm3SjDR6WznmjPG569UAT5t8gLUrUjTLVv6kwtalKoyddLHA+3v06BF8fHwQExOD7du3Q61W67K6xR473PQoNjUWxx8fx/ln55GuTjd0dUjHbGxsEBERgadPn+LChQv48ssvsWXLFtSrVw/Pnz8Xle3UqRMiIiJEjx07duS5/3Xr1qFdu3ZwcXHB3r17cfv2bfz000+IjY3F0qVLNeUGDBiAcePGoVu3bggKCkJISAimT5+O3377DceOHdOUq1mzJiIiIhAeHo61a9fi4MGDGDhwoG4bhfJt1qxZ8Pf3h4mJCXbt2gUvLy9DV4mKIeZMycacofdx5swZfPbZZxAEAaNGjYKfn5+hq0TFEHOmZGPO0Pt49eoVOnfujH///Rd16tTB3r17YWLCLiYRwcjExsYKAISoqCitdcnJycLt27eF5OTk9z7O0UdHhVqbamkeHfZ0EMJiw957v7kZNGiQ0K1bN83zVq1aCWPGjBEmTZok2NvbC87OzsLMmTNF2/zzzz/CBx98IFhYWAhVq1YVjhw5IgAQ9u/frynz9OlToXfv3oKdnZ3g4OAgfPTRR8KjR4+0jjtr1iyhVKlSgrW1tfDll18KqampmjJqtVpYvHix4OHhIchkMqFOnTrCnj17NOuDgoIEAMKJEyeEBg0aCHK5XPD29hbu3Lkjqu/ChQuF0qVLCwqFQvj888+FKVOmCHXr1hWV+eWXX4Rq1appXtPq1as16x49eiQAEPbu3Su0bt1akMvlQp06dYQLFy6I6pH1kb3NMvn7+wu2trZay//991/ByclJ6N+/f66/m/x48uSJYG5uLowbNy7H9a9fvxYEQRB27dolABACAgK0yqjVaiEmJkYQBEGYOXOmVlvNmzdPMDExEZKSkrS2zf5eSEtLEwICAoS0tLQCvY6S6n3bY926dZq/sfXr1+u4doUvKipKACDExsYauipFRmFkDXOGOZPb7yY/mDNF2/u2x99//y3Y2dkJAIQePXoI6enpOq5h4WPWiDFnMjBn3p4zM2bMyLG9S3rOCIL4vcCcyaBKTBRUiYmCMipKuFmnrhAQECAkRUZqludXUlKS4O3tLQAQ3NzchGfPnumx1oVDHznD7kc9SFQm4puz34iWPU98jvmX5hdqPTZv3gwrKyv8+eefWLJkCebMmaO5nECtVqNnz56QSqW4cOECli5diqlTp4q2T0pKQps2baBQKHDmzBmcO3cOCoUCnTp1QlpamqbcyZMnERoaiqCgIOzYsQP79+/H7NmzNeu//fZb+Pv7Y+3atbh16xbGjx+Pzz77DKdPnxYdb9q0aVi6dCmuXLkCU1NTfP7555p1u3fvxsyZMzF//nxcuXIFrq6uWLNmjWj7n3/+GdOmTcP8+fMRGhqKBQsWYPr06di8ebPWcSZOnIiQkBBUqVIFffv2RXp6Opo1a4YVK1bAxsYGd+7cwbNnzzBx4sQCtXnp0qXRv39/HDhwACqVqkDbZrVnzx6kpaVh8uTJOa7PHCa+bds2VK1aFd26ddMqI5FIYGtrm+sx5HI51Go10tN5trIwHTp0CCNGjAAATJ8+HcOGDTNwjag4Ys4wZ5gzlJtnz56hc+fOiImJQfPmzbFt2zZIpVJDV4uKGeZM8c+ZZ8+e4c6dO5gwYUKB2pw5U7KZWFpmPOTyN8vkcs3y/FCpVOjXrx8uXrwIOzs7HDlyBGXKlNFXlYs3nXXdFROFcTbot/u/ac4EhbwIEdaGrNU8/zfx3/fad25yOiPUokULUZlGjRoJU6ZMEQRBEI4ePSpIpVLhyZMngkqlEl6/fi38/vvvojNCGzduFKpWrSqo1WrNPlJTUwW5XC4cPXpUc1wHBwchMUtv+Nq1awWFQiGoVCohISFBkMlkmrMumYYOHSr07dtXEATxGaFMmXXJ/F14e3sLw4cPF+2jSZMmorMcbm5uwvbt20Vl5s6dK3h7ewuC8OaM0IYNGzTrb926JQAQQkNDBUF4c6bn9evXgkqlyqmpReVysnbtWgGA8O+//2raSCqVClZWVqLHnDlzct3/iBEjBBsbm1zXZ6pevbrw0UcfvbVc9jNCoaGhgqenp9C4ceMcy3PkgViKMl04dP25sC/4ScbjSpgQEBAg7LsSpll26PpzIUWZ9wiCpKQkwcXFRQAgDBkyRPTeKs446kCbvrOGOcOcYc6UbO/THv379xcACFWrVs3xM6i4YtaIMWeYM5lyy5nMNskta0p6zggCR7jlRZWYqBnhllLAz9Xt27cLAARzc3Ph9OnTeqph4dNHzpgWUr+eUQmLDQMAWEgtUMepDgRBwOqQ1QCAJ/FP3unOH++iTp06oueurq548eIFACA0NBTly5dHuXLlNBMbent7i8pfvXoV9+/fh7W1tWh5SkoKHjx4oHlet25dWGbpDff29kZCQgKePHmCFy9eICUlBe3btxftIy0tDfXr18+1vq6urgCAFy9eoHz58ggNDcXw4cNF5b29vREUFAQAePnyJZ48eYKhQ4eKRgylp6drnRXJ7TjVqlWDLgiCACDjjEymNm3aYO3ataJymZNmDx8+HL/++qtmeUJCAgRBEG2f17HyUw4Abt68CYVCAZVKhdTUVLRu3Rrr16/P17bGLvhxDEZtf3O7bAupgCWNgW/23USq6k377xjWFN6VHHPdj1wux+HDh/HDDz9g3bp1+f7dEWXHnGHOAMwZytmaNWsgCALmz58PR8fcM4koL8wZ5gzAnCnJTCwtUfXKZTwIDBSNdsuPPn364N69e6hevTpatmyppxqWDOxw04PY1Iy7uliaWkIikcDa/M0HfExqTKHVw8zMTPRcIpFowijzQzT7+qzUajUaNGiAbdu2aZUtVarUW4+f9Xi///47ypYtK1pvYWGRa30z65Lfu5xklvv555/RpEkT0brsl1G8z3HyIzQ0FDY2NqL/5FpZWcHT0zPH8nPmzNG6pKhKlSqIjY1FRESEJkRzUqVKFYSGhuarXlWrVsWBAwcglUpRpkwZrfan3DVyt4ebvRxPXydD+50DSCSAm70lGrnbv3Vf9erVg7+/v+4rSUaFOaN9POYMc4Yy2NjY5PieIioI5oz28ZgzzBnKIJFIMGPGDENXo1jgHG56YGuRcQYiKT0JgiAgPi1es87Ows5AtRKrUaMGwsPDRXefuXhRfBtgLy8v3Lt3D6VLl4anp6fokfUsy/Xr15GcnKx5funSJSgUCpQrVw41atSAhYUFwsPDtfbh5uaW7/pWr14dly5dEi3L+tzZ2Rlly5bFw4cPtY7j4eGR7+OYm5u/11wFL168wPbt29G9e/d836Ele/sCQK9evWBubo4lS5bkuE1MTAwAoF+/fvjnn3/w22+/aZURBEF0S29zc3NNezCcCsZUaoLx7avk2NkGAIIAjG9fGaZS7d95YmIiOnfujPPnz+u3kmRUmDPMGeYMZfXNN9/gu+++y7EDguhdMGeYM8wZyur48ePo2bMnEhMTDV2VYoUj3PSggk0FAECqKhU3om7gUsSbD1I36/x/KOtTu3btULVqVQwcOBDfffcdIiIitHqp+/fvj++++w7dunXDnDlzUK5cOYSHh2Pfvn2YNGkSypUrByBjOPXQoUPx7bff4vHjx5g5cyZGjx4NExMTWFtbY+LEiRg/fjzUajVatGiBuLg4XLhwAQqFAoMGDcpXfb/++msMGjQIDRs2RIsWLbBt2zbcunULFStW1JSZNWsWxo4dCxsbG3Tu3Bmpqam4cuUKXr9+DT8/v3wdx93dHQkJCTh9+jS8vb2hUChEw8uzEgQBkZGREAQBMTExuHjxIhYsWABbW1ssWrRIVDY1NRWRkZGiZaampnBycspx325ubli+fDlGjx6NuLg4DBw4EO7u7nj69Cm2bNkChUKBpUuXonfv3ti/fz/69u2L6dOno3379ihVqhRu3ryJ5cuXY8yYMejevXu+Xjvl7aO6ZbD8+D94+jpZtDxzdJtvHe2JQtPT09G7d28cOXIEf//9N+7fv8//HJBOMGeYM8wZyvTjjz9i8eLFAIBWrVqhcePGBq4RlQTMmeKfMydPnkTFihVhamoKhUKRY1nmDOXHtWvX0LNnTyQkJOC7777DrFmzDF2lYoMdbnrQrkI7zL44G0q1Ep8FfqZZ3tS1aaHNd/A2JiYm2L9/P4YOHYqmTZuifPny+PHHH+Hj46MpY2lpiTNnzmDKlCno2bMn4uPjUbZsWbRt2xY2Njaacm3btkXlypXRsmVLpKamok+fPqI34dy5c1G6dGksXLgQDx8+hJ2dHby8vPC///0v3/X99NNP8eDBA0yZMgUpKSn4+OOPMWLECBw9elRT5osvvoClpSW+++47TJ48GVZWVqhduzbGjRuX7+M0a9YMX331FT7//HNER0dj5syZuX6gxMXFwdXVFRKJBDY2NqhatSoGDRqEr7/+WtQ+AHDkyBGtodRVq1bFnTt3cq3LyJEjUaVKFXz//ffo0aMHkpOT4e7ujq5du2oCVyKRYPv27Vi/fj1++eUXzJs3D6ampqhcuTIGDhyIjh075vu1U94yR7n57b4uWp7b6DZBEDB8+HAEBgZCLpdjz5497GwjnWHOMGeYMwQAe/fu1fz9LViwgJ1tpDPMmeKdM8OHD0ffvn3x6tUrzJgxQ3TH1ayYM/Q2YWFh8PHxQUJCAtq0aaN1J2DKm0QwsrHncXFxsLW1RVRUlNZEsikpKXj06BE8PDwgk8ne6zjHwo5hwuk3t2AuY1UG6zus15wtKkrUajXi4uJgY2OT72HDmQYPHoyYmBgEBATop3IG8D7tUVJkfy8olUoEBgbCx8dHay4NY5KuUqPN96fwMi4JixurMOWyFKVtrPDHhFZaHW6zZ8/GrFmzYGJigoCAAPj6+hqo1vr36tUrODk5ITY2Vus/Z8aqMLKGOVN8MWeYM2+Tn/Y4e/Ys2rdvj9TUVIwYMQKrV68u0TfjYdaIMWfEmDPamDXi94JUKmXOZPO2rImOjkbz5s1x584d1K5dG2fPntW6gUdJoo+c4Qg3Peng3gHnXM/hcuRlyE3laOLaBKYmbG6i4ixzlNvU/RcBmMNEfh9j2/XQ6mzbsGGD5qzomjVrSnRnGxkOc4bIeN2+fRvdunVDamoqunfvjpUrV5bozjYyDOYMkfFKTk7GRx99hDt37qBcuXIIDAws0Z1t+sJPTD2ytbBFuwrtDF0NItIhud0tWHsuBjAdluV+xfqHgWjk+bPmbO+ZM2c0t3z/9ttv8dVXXxmwtlTSMWeISobUdBVO3H6BtMyJztUqmAA4dOM5YJJxd0JzqRTtapRGWnISOnfujNevX8Pb2xvbt2/XuoMhka4wZ4iM01dffYXz58/Dzs4OR44c0cx3SAXDDjd6L5s2bTJ0FYgKTaIyEdPOT4VJlhs8RyRGYP6l+VjfYT0AoHHjxujRowcUCgXmzJljqKoSlRjMGTIGwY9jMGp7sOa5hVTAksbAN/tuIlX1ZuTajmFN4V3JEVOmTMHq1atx8OBByOVyQ1SZqMRgzhBpGzt2LE6fPo0tW7agZs2ahq5OsWWcF3MTEb2Dk+EnoVQrNc+H1RkGALgYcREvkl4AAGQyGXbu3In169fz8h4iIsqXRu72cLOXI7fUkEiA8g6WaORuDyBjIvLg4GCtubuIiIh0oWHDhvjnn3/QqlUrQ1elWGOHGxFRPoXFhsEiTcCmZekAgEZRUQCA9Lh0zFs4D2q1GgAglUo5GSsREeVb5hyhud3JTK1Wo/yTo4iLjdEs452viYhIl3bv3o2//vpL85w58/7Y4UZElE+xqbGi51YX10KdqsbjFY+xct5KfPPNNwaqGRERFXcf1S2jGeXWw+Qc8N+/Egmg/ms7tq1ajNatW0OpVOa9IyIiogI6ceIEPvvsM7Rp0wZ///23oatTYrDDjYgoH9RJSbCPDINFlu85sekSPFsVjuSHybB1sMXQoUMNV0EiIirWMke59ZKewmxTfwDAbFN/VAxehqendgIAJkyYwBHURESkUyEhIejZsyeUSiV8fX1Ro0YNQ1epxDB4h9uaNWvg4eEBmUyGBg0a4OzZs3mW37ZtG+rWrQtLS0u4urpiyJAhePXqVSHVloiM1V2vBmg79QI2/JhxBzlBELBkdRJibybAQiLBtsVfoGrVqgauJeWGWUNExUE34Q8sMVuvmcvtt9spCDr2BwBg3rx5GDRokOEqR3lizhBRsXN9J168eIFuPh0QHx+P1q1bY/PmzTAxMXg3UYlh0JbctWsXxo0bh2nTpuHatWv44IMP0LlzZ4SHh+dY/ty5cxg4cCCGDh2KW7duYc+ePbh8+TK++OKLQq45ERmV4K1Il8qQYmGnWbR7927sjY6GCYCF5SujXfh2IHirwapIuWPWEFGxELwV0oNjAQDxUhOEhoZiwL54CACGNzDH/zq5GbZ+lCvmDBEVO8FbEf1/EzBnzhxERMWgVqVy2L9/P+dt0zGDdrgtW7YMQ4cOxRdffIHq1atjxYoVcHNzw9q1a3Msf+nSJbi7u2Ps2LHw8PBAixYt8NVXX+HKlSuFXHPKSevWrTFu3Lj32semTZtgZ2enk/oQ6UTwVqQGTMHdNl/i7+ZjENbbBPujX2HHjh0AgLq93GHXZSYOvp6O1IAp7HQrgpg1JQdzhkqs4K3AgTEABBy3lKOV1BHz589HWrqAUnUVmPSRFSQHxzJjiijmTMnBnCGjELwVKftGo9fOGDx9+hRlbUwQ6BsLu4cHDV2zEsfUUAdOS0vD1atXtSYZ79ChAy5cuJDjNs2aNcO0adMQGBiIzp0748WLF/i///s/dOnSJdfjpKamIjU1VfM8Li4OAKBUKrUmnVUqlRAEAWq1WnO3weIkMjISixYtQmBgIJ4+fQpbW1tUrlwZ/fr1w8CBA2FpaZnjdoIgaP5939f9vvvI3DavfUilUs3PlpaWKFOmDJo1a4bRo0ejQYMGmnWnTp1C27Ztc9zHs2fP4OLikuM6QRDw77//4ttvv0VgYCCePXuG0qVLo27duvj6669F+7x27RoWLlyIs2fPIjY2FuXLl0fLli0xceJEVKlSBWFhYahUqZKmvJ2dHWrXro3Zs2cX6Vssq9VqCIIApVIJqVSqea8Y3UTNKiVw+H9Iltgi2cQW8XAGXk+DxHIhTE0j8UmzUujsOh/xameYSoBkiS1MDv8PqPExIDWeOXaK8t8Fs0a3mDPMGV1hzmRxfScQOAkwsUCSRIIZpcvCLFINuVwOOAPlRlbG9+pU/PAiCjg0CVADqNvH0LUudEX1b4M5o1vMGd3kDABERERg9uzZOHHihFHmDCDOmszfR1H9LCk0/2VOKixgK1fC0tIM+wbYwMVeDaURZwygn78Ng3W4RUVFQaVSwdnZWbTc2dkZkZGROW7TrFkzbNu2DZ9++ilSUlKQnp6Ojz76CCtXrsz1OAsXLsTs2bO1lgcFBWl9YJuamsLFxQUJCQlIS0t7h1dlOGFhYejUqRNsbW0xbdo01KhRA+np6Xjw4AF+/fVX2NnZwcfHJ899xMfHv1cd0tPTkZaWpvkPwLtISUmBIAhv3cfq1avRtm1bpKam4v79+9i8eTO8vb2xatUq9OmT8QGRlJQEALh8+TKsra1F28tkslyPER4ermnLmTNnombNmlAqlfjjjz8watQoza2Sjxw5gkGDBuHDDz/EunXr4OHhgZcvX+K3337D//73P/zyyy9ISEgAAAQEBKBatWp4+fIl5s6di65du+LChQuoUKHCO7eVPqWlpSE5ORlnzpxBenq6Zvnx48cNWCsDqbUKAGADwAYJAKxQu908LH38GOXLl4dEItEsP41FGdscNa52ynyvFUXMGt1hzjBndIk5k5UNUHed5tlUALAHohdHw9TUFDY2NgCAQNf/CjwD8CywsCtpcEU1a5gzusOcYc7oWk5ZY5w5k9WbzPncS4VOz58jws0NEZmrjTRjAD3ljGAgz549EwAIFy5cEC2fN2+eULVq1Ry3uXXrluDq6iosWbJEuH79unDkyBGhdu3awueff57rcVJSUoTY2FjN48mTJwIAISIiQkhLSxM94uLihFu3bgmJiYmCSqV6r4cyPl64XbWacLtqNUEZH//e+3vbo0OHDkK5cuWEuLi4HNenp6cLKpVKePDggQBAuHr1qmZdVFSUAEA4ceKEoFKphJMnTwoAhMDAQKFevXqCTCYT2rRpI0RERAiHDh0SqlWrJlhbWwuffvqpEJ/ltbVq1UoYOXKkMHLkSMHW1lZwcHAQ/ve//2mOrVKphOTkZGHixIlCmTJlBEtLS6Fx48bCyZMnNes3btwo2Nra5vlaAQh79+7VWj5gwADB2tpaiIqKEr2OV69eFagtO3XqJJQpU0aIjY3VWpe5r/j4eMHJyUno1q1bjvvILJdTe4eHhwsAhDVr1uj97+JdH4mJicKtW7eEuLg4IS0tTUhMTBQCAgKExMRErfeNUTwubxHSZjsL0TOqCVvGbBPWjD0uBAQECGvGHhe2jNkmRM+oJqTNds4oZ+i6GuAREREhABBiY2N1mhO6UJKzhjnDnGHOlJDHfxmTNru08OMPlQXvn72E640aCwEBAcLl+Z5Cg00NhAabGgh/LXJj1hTBrGHOMGeKas6ULVtWePr0qajuxpQz2bPGqHMmW9ZkPhLnls9ok7nlRcuNNWv0kTMGG+Hm5OQEqVSqdebnxYsXWmeIMi1cuBDNmzfHpEmTAAB16tSBlZUVPvjgA8ybNw+urq5a21hYWOQ48Z+ZmZnWbdVVKhUkEglMTEze/84cWbbXyf7y8OrVKxw/fhwLFizQOvOhXS0TrTpJJBLNv1mXz5kzB6tWrYKlpSV69+6NPn36wMLCAtu3b0dCQgJ69OiB1atXY8qUKZr9b9myBUOHDsWff/6JK1eu4Msvv4S7uzuGDRsGABg6dCjCwsKwc+dOlClTBvv374ePjw9u3ryJypUri+r3tteRvYyfnx+2bt2KkydPonfv3jm+1reJjo7G0aNH8e2330KhUGht5+DgACDjzEhUVBSmTJmS474zy+VUB4VCASDj762o3gHGxMQEEolE632S0/vGKDQcAJgA9gfGoJ1iBfbHLQQACCoJ2tmsgL0kAui6EvAaYOCKGkZR/pso0VnDnGHOgDlTIvyXMep9YxGfngZJWipMUjPuiG2dkgykZXy2xEmUMOv6HbOmiGHO6AZzJoMuc2bevHmwsrLStEkmY8kZQJw1mZfxGmXOqJTAYT9AnfOIVzN1CszUKeKFh/2A+n2MapocffxdGOzdYW5ujgYNGmgN6Tx+/DiaNWuW4zZJSUlab+jMN47w33X7hqZOSsp4JCe/WZacrFmuD/fv34cgCKhatapouZOTExQKBRQKhShE8mvevHlo3rw56tevj6FDh+L06dNYu3Yt6tevjw8++AC9evVCUFCQaBs3NzcsX74cVatWRf/+/TFmzBgsX74cAPDgwQPs2LEDe/bswQcffIBKlSph4sSJaNGiBfz9/d+9Af5TrVo1ABnD0bMqV66cph0UCoVWO2WV2ZZVqlTJ81j37t0THTO/EhMTMXXqVEil0iI/5wFl4zUA8R+uwYnYcaLFJ2LHIf7DNUb7BaioK4lZw5xhzuSFOVNMeQ3A3f9zQY/1cmz4UaVZrPzNCVuXqrB1qQp23mOYNUUQc0Y3mDMZdJkzeZUBmDNGRWoGdFkGQJLPDSQZ5Y2os01fDDbCDcjowR8wYAAaNmwIb29vrF+/HuHh4Rg+fDgAYOrUqXj27Bm2bNkCAPD19cWwYcOwdu1adOzYERERERg3bhwaN26MMmXKGPKlaNz1aqC17F7zFpqfq98J1duxM8/sZPrrr7+gVqvRv39/0SSr+VWnTh3Nz87OzrC0tETFihVFyzKv/8/UtGlTUT28vb2xdOlSqFQqBAcH5/glIzU1FY6OjgWuX3aZ/0HJ3g5nz54VnSkzNTXVLO/cubNm+bp16+Dp6ZnjPnI7Vn41a9YMJiYmSEpKgqurKzZt2oTatWsXaB9kWPHRKQg45o44VQpszCMBWMHa5F/Epbkg4JgM3WulwNpBZuhqUg5KWtYwZ5gzOWHOlHxuDb4wdBUoF8wZ3WHOMGdIT/47YZMaMAVKQQaF9JVWkQSVI8wkKbDovpgneHTEoB1un376KV69eoU5c+YgIiICtWrVQmBgoGbixYiICISHh2vKDx48GPHx8Vi1ahUmTJgAOzs7fPjhh1i8eLGhXkKR4OnpCYlEgjt37oiWZ4aJXC7XLMs8m5b1Aza3u3FkHVKZORQ3K4lEUqA7+KjVakilUly9elV0Zx7gzbDk9xEamhH+Hh4eouUeHh453pq7YcOGCAkJ0Tx3dnaGUqmERCLB3bt38zxWZsjeuXMH3t7eb63brl27UKNGDdjZ2ekkjKlwJbxOQcCyYMRFpcDGSYau7YEzMUDXj4FDx2WIi8pY32OCFxT27HQrapg17485k4E5Q/pUNfgqEpVJ8N3eFqt+ylg2ZrgU8aZSNHJpjLWWpQ1bQcoVc+b9MWcy6DJn7ty5gw8//DDXYzFnjE9q9b44GFAKydHR6O7wLWQmiZp18SpHBETPg9zRAb7V20H7AnZ6FwbtcAOAkSNHYuTIkTmu27Rpk9ayMWPGYMyYMXqu1burGnwVQMaw68wzQZXPn4NJlpDQNUdHR7Rv3x6rVq3CmDFjYGVllWvZUqVKAcgI/vr16wOA6EP6fV26dEnreeXKlSGVSlG/fn2oVCq8ePECH3zwgc6OmWnFihWwsbFBu3bt8lVeLpdrzgBl1aFDB2zcuBGTJk3SmkMiJiYGdnZ26NChA5ycnLBkyRLs379fax+Z5TK5ubmJbqdNxYuZzBRya3MAQHc/L8isGwGBgVA0+wTda6sQsCwYcmtzmMkM/pFKuShJWcOcYc5kLZeJOVP8mVhawhqWmNZmHvDTdABAqhngaFcW37SaaeDa0dswZ94PcyaDrnKmY8eOWLNmDQYNGqS503Em5ozxUqakI1ltgziVOQKi56Gr01wAQILKAYeipyNO5QKoZVCmpMNCzu81ulB0ZzgspkwsLTMeWc/CyOWa5fqyZs0apKeno2HDhti1axdCQ0Nx9+5d/Prrr7hz547mDIxcLkfTpk2xaNEi3L59G2fOnMGMGTN0Vo8nT57Az88Pd+/exY4dO7By5Up8/fXXADLOovTv3x8DBw7Evn378OjRI1y+fBmLFy9GYGDBbj0cExODyMhIPH78GMePH0evXr2wfft2rF27Vuvsz4sXLxAZGSl65HYWDMi4RbdKpULTpk2xd+9e3Lt3D6Ghofjxxx81Z3+srKywYcMG/P777/joo49w4sQJhIWF4cqVK5g8ebLmEgIqGSzkpvAdWw89JnhpXTZq7SBDjwle8B1bj8FEhYI5w5xhzpRsbcu31fy8+IMl+L3n76hgU8GANSJjw5wp/jmzZs0aqFQqtGvXjjlDGgp7Gbr7ecHGSYY4lQsOvc44uXPodUZnm41TxnpesaM7/HZYQlSqVAnXrl3DggULMHXqVDx9+hQWFhaoUaMGJk6cKDrj9ssvv+Dzzz9Hw4YNUbVqVSxatAidOnXSST0GDhyI5ORkNG7cGFKpFGPGjMGXX36pWe/v74958+ZhwoQJePbsGRwdHeHt7Q0fH58CHWfIkCEAAJlMhrJly6JFixb466+/4OXlpVU2pwlDL168iKZNm+a4bw8PD5w6dQorV67EhAkTEBERgVKlSqFBgwZYu3atply3bt1w4cIFLFy4EP369UNcXBzc3Nzw4YcfYt68eQV6PVT0WchNc+1QYyiRMWDOMGeocJhYWqLqlct4EBiIphVbwtSE/10n48Cc0W3OXLlyBbNmzcKkSZOYM6Rh7ZDRqRawLBjxMc6wQQLi1c6azjbOSa1bEqEo3AqnEMXFxcHW1hZRUVFa156npKTg0aNH8PDwgExmPH9oarUacXFxsLGxKdK3dS4sbA/t94JSqURgYCB8fHyM7zbaOWB7iL169QpOTk6IjY3VumzBWDFrxPi5Ksb2YM68DdtDG7NGjDkjxs9VbWwT8XtBKpXyczWLiAex2L/8Csq2T8Cz4wr0GN8QrpVsDV0tg9JHzhjnO4+IiIiIiIiIyMjER6fghP8t0bIT/rcQH51ioBqVXOxwIyIiIiIiIiIq4eKjUxCwLBhxUSmwdsi4F6m1gwXiojKWs9NNt9jhRkRERERERERUgiW8ftPZZuMkQ9fR9QAAXUfXy7iRwn+dbgmv2emmK+xwIyIiIiIiIiIqwcxkppBbm2e5G2nGCDeFvYXm7qVya3OYyXizHl1hSxIRERERERERlWAWclP4jq0HZUo6FPYZNyzKZO0gQ48JXjCTmcJCzm4iXWFLEhERERERERGVcBby3DvUFPbGcVfjwsRLSomIiIiIiIiIiHSIHW5EREREREREREQ6xA43IiIiIiIiIiIiHWKHG723wYMHo3v37m8tJ5FIEBAQoLPjuru7Y8WKFTrbHxERFU3MGSIi0ifmDBHpAzvcSojBgwdDIpFoPTp16qT3Y//www/YtGnTW8tFRESgc+fOeq9PplmzZmnawdTUFE5OTmjZsiVWrFiB1NRUUdnWrVtrykqlUtjb20MqlWL48OFvPc7evXvRunVr2NraQqFQoE6dOpgzZw6io6M1ZdLS0rBkyRLUrVsXlpaWcHJyQvPmzeHv76+5O0zW36GZmRkqVqyIiRMnIjExUbcNQ0T0Dpgz2pgzRES6w5zR9q45kzVrRowY8dbjMGeI9IN3KdWx1OR0zW12s0t4naLX2+x26tQJ/v7+omUWFhZ6OVZWtra2ea5PS0uDubk5XFxc9F6X7GrWrIkTJ05ArVbj1atXOHXqFObNm4etW7fi1KlTsLa21pQdNmwY5syZA7Vajfj4eFhbW0OhUOS5/2nTpmHx4sUYP348FixYgDJlyuDevXv46aefsHXrVnz99ddIS0tDx44dcf36dcydOxfNmzeHjY0NLl26hO+//x7169dHvXr1ALz5HSqVSpw9exZffPEFEhMTsXbtWn02ExEVI8wZbcwZ5gwR6Q5zRltxyxkAmqxxdnbOc//MGSL94Qg3HUpNTsfBH0Owf2kw4qNTROvio1Owf2kwDv4YgtTkdL0c38LCAi4uLqKHvb29Zr1EIsG6devQtWtXWFpaonr16rh48SLu37+Prl27wtraGt7e3njw4IFmm1mzZqFevXpYt24d3NzcYGlpiU8++QQxMTGaMtmHYLdu3RqjR4+Gn58fnJyc0L59e83xsw7Bfvr0Kfr06QMHBwdYWVmhYcOG+PPPPwEADx48QLdu3eDs7AyFQoFGjRrhxIkTBW4TU1NTuLi4oEyZMqhduzbGjBmD06dP4++//8bixYtFZS0tLTXt5uzsDBcXF9jY2OS677/++gsLFizA0qVL8d1336FZs2Zwd3dH+/btsXfvXgwaNAgAsGLFCpw5cwYnT57EqFGjUK9ePVSsWBH9+vXDn3/+icqVK2v2mfk7dHNzQ79+/dC/f3+dDlsnouKNOZOBOcOcISL9YM5kKAk5k5k1zBkiw2GHmw4pU9KRHJ+GuKgUBCx7E1Lx0RnP46JSkByfBmWKfgIqP+bOnYuBAwciJCQE1apVQ79+/TBixAiMHz8ef/31FwBg9OjRom3u37+P3bt34+DBgzhy5AhCQkIwatSoPI+zefNmmJqa4vz581i3bp3W+oSEBLRq1QrPnz/HgQMHcP36dUyePBlqtVqz3sfHBydOnMC1a9fQsWNH+Pr6Ijw8/L3boFq1aujcuTP27dv3XvvZtm0bFAoFRo4cmeN6Ozs7Tbl27dqhfv36WmXMzMxgZWWV6zHkcrlmiDYREXPmDeYMc4aIdI858wZzhjlD9L7Y4aZDCnsZuvt5wcZJpgmpiAexmnCyccpYn9PwbF04dOgQFAqF6DF37lxRmSFDhqB3796oUqUKpkyZgrCwMPTt2xdt27ZF9erV8fXXX+PUqVOibVJSUrB582bUq1cPLVu2xMqVK7Fz505ERkbmWhdPT08sWbIEVatWRbVq1bTWb9++HS9fvkRAQABatGgBT09P9O7dG97e3gCAunXr4quvvkLt2rVRuXJlzJs3DxUrVsSBAwfev6GQEVJhYWGiZWvWrIFCoYCNjQ3KlSsHGxsbbN68Odd93Lt3DxUrVoSZmVmex7p3716ObfA2f/31F7Zv3462bdsWeFsiKpmYM28wZ8TlmDNEpAvMmTeKc85kzRrmDJHhcA43HbN2yAihzFDa991VANCEk7WDfsIJANq0aaN1bbyDg4PoeZ06dTQ/Z17PX7t2bdGylJQUxMXFaYYfly9fHuXKldOU8fb2hlqtxt27d3Odx6Bhw4Z51jUkJAT169fXql+mxMREzJ49G4cOHcLz58+Rnp6O5ORknZwRAgBBECCRSETL+vfvj2nTpkGtViMhIQEKhULz+jp37oyzZ88CACpUqIBbt27luI/8His3mf/JSE9Ph1KpRLdu3bBy5coCvjoiKsmYMxmYM3kfKzfMGSJ6G+ZMhuKcMwA0WVOxYkUAzBkiQ2CHmx5YO8jQbkhNTTgBQLshNfUaTgBgZWUFT0/PPMtkPXuR+aGZ07LModA5ySyT14duXsOKgYyhxXmZNGkSjh49iu+//x6enp6Qy+Xo1asX0tLS8twuv0JDQ+Hh4SFaZmtrC09PT6jVak1Am5hkDALdsGEDkpOTAbxprypVquDcuXNQKpV5nhWqUqUKQkND81WvzP9kmJmZoUyZMm8920RExok5w5zJijlDRLrGnCneOQNAlDUAc4bIEHhJqR7ER6fghP8t0bIT/re0Jh4tLsLDw/H8+XPN84sXL8LExARVqlR5533WqVMHISEholtNZ3X27FkMHjwYPXr0QO3ateHi4qI1ZPpd3blzB0eOHMHHH3+c723Kli0LT09PeHp6okKFCgCAfv36ISEhAWvWrMlxm8yJWPv166eZuyG79PR00W2yM/+TUaFCBYYTEeWKOfN2zJkMzBkiehfMmbdjzmRgzhDljh1uOpZ1QlEbJxl6TmogmgNBnyGVmpqKyMhI0SMqKuq99yuTyTBo0CBcv34dZ8+exdixY9G7d+/3ui1237594eLigu7du+P8+fN4+PAh9u7di4sXLwLImDNh3759CAkJwfXr19GvX788z1LlJj09HZGRkXj+/Dlu3ryJlStXolWrVqhXrx4mTZokKpuUlKRpt3///ReRkZF4/fp1rvtu0qQJJk+ejAkTJmDy5Mm4ePEiHj9+jJMnT+KTTz7RzJcwbtw4NG/eHG3btsXq1atx/fp1PHz4ELt370aTJk1w7969Ar8uIjJezJn8Yc4wZ4jo3TBn8qeo50xm1jBniAyHHW46lPA6RWtCUddKtloTjya81k9IHTlyBK6urqJHixYt3nu/np6e6NmzJ3x8fNChQwfUqlUr17Mg+WVubo5jx46hdOnS8PHxQe3atbFo0SJIpVIAwPLly2Fvb49mzZrB19cXHTt2hJeXV4GPc+vWLbi6uqJ8+fJo3bo1du/ejalTp+Ls2bNQKBSisj///DNcXV1RtmxZVKtWDWXLlkXfvn3z3P/ixYuxfft2/Pnnn+jYsSNq1qwJPz8/1KlTR3MbbQsLCxw/fhyTJ0/GunXr0LRpUzRq1Ag//vgjxo4di1q1ahX4dRGRcWLO5B9zhjlDRAXHnMm/opwzWbOmX79+ee6fOUOkPxJBEARDV6IwxcXFwdbWFlFRUXB0dBStS0lJwaNHj+Dh4QGZrODzE6Qmp+PgjyFIjk/TmlA080yR3NocvmPrwUJedKbPy2kumUyzZs1CQEAAQkJCDFM5A8irPYxF9veCUqlEYGAgfHx8ODQcYHtk8+rVKzg5OSE2NlYzT4ix01fWMGdKBuYMc+Zt2B7amDVizBkx5ow2Zo34vSCVSvm5mg2zRkwfOVN0PiVLAAu5KXzH1oMyJV3rVtnWDjL0mOAFM5lpkQonIiIqPpgzRESkT8wZIiLd4SeljlnIcw+g7KFFRERUUMwZIiLSJ+YMEZFuGOfYUsq3WbNmGd3wayIiKjzMGSIi0ifmDBEZCjvciIiIiIiIiIiIdIgdbkRERERERERERDrEDjciIiIiIiIiIiIdYocbERERERERERGRDrHDjYiIiIiIiIiISIfY4UZERERERERERKRD7HAjkU2bNsHOzk7zfNasWahXr57m+eDBg9G9e/dCrZO7uztWrFih12Nkf92LFi2Cl5eXXo9JRGSMmDMZmDNERPrBnMkwe/ZsfPDBB3o9JhHljR1uJUhkZCTGjBmDihUrwsLCAm5ubvD19cXJkyffeZ8TJ058r+0LIntIZLp8+TK+/PLLQqlDptGjR+P48eOFekwioqKOOaM7zBkiIm3MGd2ZMGECfvvtt0I9JhGJmRq6AqQbYWFhaN68Oezs7LBkyRLUqVMHSqUSR48exahRo3Dnzp132q9CoYBCoXivuqWlpcHc3Pydty9VqtR7Hf9dKBQK2NjYFPpxiYiKKuaMbjFniIjEmDO6pVAooFarC/24RPQGR7iVECNHjoREIsFff/2FXr16oUqVKqhZsyb8/Pxw6dIlTblly5ahdu3asLKygpubG0aOHImEhIRc95t9CHam2bNno3Tp0rCxscFXX32FtLQ0zbrWrVtj9OjR8PPzg5OTE9q3b//WY586dQpDhgxBbGwsJBIJJBIJZs2aBUB7CHZ4eDi6deum+bLSu3dv/Pvvv1p13rp1K9zd3WFra4s+ffogPj4+3+2Z/VKfzKHn33//PVxdXeHo6IhRo0ZBqVRqyqSlpWHy5MkoW7YsrKys0KRJE5w6dSrfxyQiKsqYM8wZIiJ9Ys7oNmeyX1LKnCEqfOxwy6fExMRcHykpKfkum5ycnK+yBREdHY0jR45g1KhRsLKy0lqfdViziYkJfvzxR/z999/YvHkz/vjjD0yZMqVAxzt58iRCQ0MRFBSEHTt2YP/+/Zg9e7aozObNm2Fqaorz589j3bp1uR578uTJAIBmzZphxYoVsLGxQUREBCIiIjBx4kStYwuCgO7duyM6OhqnT5/G8ePH8eDBA3z66aeicg8ePEBAQAAOHTqEQ4cO4fTp01i0aFGBXmd2QUFBePDgAYKCgrB582Zs2rQJmzZt0qwfMmQIzp8/j507d+LGjRv45JNP0KlTJ9y7d++9jktExoE58wZzhjlDRLrHnHmDOcOcISoUgpGJjY0VAAhRUVFa65KTk4Xbt28LycnJWusA5Prw8fERlbW0tMy1bKtWrURlnZyccixXEH/++acAQNi3b1+BthMEQdi9e7fg6OgovH79WlCpVIK/v79ga2urWT9z5kyhbt26mueDBg0SHBwchMTERM2ytWvXCgqFQlCpVIIgCEKrVq2EevXq5fvYmbIfO1OFChWE5cuXC4IgCMeOHROkUqkQHh6uWX/r1i0BgPDXX39p6mxpaSnExcVpykyaNElo0qRJrnXJemyVSiVMmTJF63VXqFBBSE9P1yz75JNPhE8//VQQBEG4f/++IJFIhGfPnon227ZtW2Hq1Kl5N0QRlP29kJaWJgQEBAhpaWkGrlnRwPYQi4qKEgAIsbGxhq5KkfEuWcOcycCcYc4Q2yMnzBox5swbzJmcjz1jxgyhVq1amtdkbDkjCOL3Aj9XtbFNxPSRM5zDrQQQBAEAIJFI3lo2KCgICxYswO3btxEXF4f09HSkpKQgMTEx33PJ1K1bF5aWlprn3t7eSEhIwJMnT1ChQgUAQMOGDQt07JzOZOUkNDQUbm5ucHNz0yyrUaMG7OzsEBoaikaNGgHIGLZtbW2tKePq6ooXL17k6xi5qVmzJqRSqWifN2/eBAAEBwdDEARUqVJFtE1qaiocHR3f67hERIbGnGHOEBHpE3OGOUNUErHDLZ/ymhcg64cWgDw/CE1MxFfxhoWFvVe9AKBy5cqQSCQIDQ3N8xbXjx8/ho+PD4YPH465c+fCwcEB586dw9ChQ5Genv7e9cgakNkDJ69jZ5034G0EQcgxiLMvNzMz06rb+04amtc+1Wo1pFIprl69qvX38L6TtBKRcWDOvB1zhjlTkqUmp0OZkg6FvUxrXcLrFJjJTGEh53/d6d0xZ96OOcOcIdIlpnY+5feMhT7L5sbBwQEdO3bE6tWrMXbsWK19xsTEwM7ODleuXEF6ejqWLl2qCcrdu3cX+HjXr19HcnIy5HI5AODSpUtQKBQoV65crtvk59jm5uZQqVR5HrtGjRoIDw/HkydPNGeFbt++jdjYWFSvXr3Ar0VX6tevD5VKhRcvXogmJyUiyi/mzBvMGW3MmZItNTkdB38MQXJ8Grr7eUFm/ebLbnx0CgKWBUNubQ7fsfXY6UbvjDnzBnNGG3OGSPfe66YJ2SfXJMNZs2YNVCoVGjdujL179+LevXsIDQ3Fjz/+CG9vbwBApUqVkJ6ejpUrV+Lhw4fYunUrfvrppwIfKy0tDUOHDsXt27dx+PBhzJw5E6NHj9Y625VVfo7t7u6OhIQEnDx5ElFRUUhKStLaT7t27VCnTh30798fwcHB+OuvvzBw4EC0atUqx2HfhaVKlSro378/Bg4ciH379uHRo0e4fPkyFi9ejMDAQIPVi6i4Y84UHcwZ5gzpjzIlHcnxaYiLyuhcS3idCgBIeJ2KgGXBiItKQXJ8GpQp7z+Ch7Qxa4oG5gxzhqikKXCHm1qtxty5c1G2bFkoFAo8fPgQADB9+nRs3LhR5xWk/PHw8EBwcDDatGmDCRMmoFatWmjfvj1OnjyJtWvXAgDq1auHZcuWYfHixahVqxa2bduGhQsXFvhYbdu2ReXKldGyZUv07t0bvr6+mlte5yY/x27WrBmGDx+OTz/9FKVKlcKSJUu09iORSBAQEAB7e3u0bNkS7dq1Q8WKFbFr164Cvw5d8/f3x8CBAzFhwgRUrVoVH330Ef7880/R/AxE9HbMmaKJOcOcIf1R2MvQ3c8LNk4yxEWl4NCqEADAoVUhiItKgY1TxvqcLjeld8OsKXqYM8wZopJGImTOUJlPc+bMwebNmzFnzhwMGzYMf//9NypWrIjdu3dj+fLluHjxor7qqhNxcXGwtbVFVFSU1uSPKSkpePToETw8PCCTGc9/aNRqNeLi4mBjY5PnWR1jwfbQfi8olUoEBgbCx8dHa+4HY8T2EHv16hWcnJwQGxub78mK81LccwZg1mTHz1UxtgdzJjeZl4/GxySjbPsEPDuugLWdHN39vGDtYByfF7lh1ogxZ8T4uaqNbSJ+L0ilUuZMNsxeMV3nDPAOI9y2bNmC9evXo3///qLJFOvUqYM7d+7opFJERGS8mDNEZKysHWRoN6SmaFm7ITWNvrNNH5g1RESkbwXucHv27Bk8PT21lqvV6gLdnYWIiCgnzBkiMlbx0Sk44X9LtOyE/y3ER3OOMV1j1hARkb4VuMOtZs2aOHv2rNbyPXv2oH79+jqpFBERGS/mDBEZo8zLSeOiUmDtYAEAsHaw0NxIgZ1uusWsISIifSvwfcVnzpyJAQMG4NmzZ1Cr1di3bx/u3r2LLVu24NChQ/qoIxERGRHmDBEZm4TXbzrbbJxk6Dq6Ds5cPImuo+vh0I83NJ1uPSbwxgm6wqwhIiJ9K/AIN19fX+zatQuBgYGQSCSYMWMGQkNDcfDgQbRv314fdSQiIiPCnCEiY2MmM4Xc2jzL3UgzRrgp7C00dy+VW5vDTFbgc+WUC2YNERHp2zuldseOHdGxY0dd16XIKOCNW4lKHL4HyNBKes4AfJ+RcePfv5iF3BS+Y+tBmZIOhb1MNIeYtYMMPSZ4wUxmCgs5O9x0qaRnDd9nZOz4HiBDM877A+ci81a4SUlJBq4JkWFlvgd4e2gi3cu8G15aWpqBa0JkOMwZbRZy01wvF1XYy9jZRvnG7zREGZg1ZGgFTm57e3tIJBKt5RKJBDKZDJ6enhg8eDCGDBmikwoWJqlUCjs7O7x48QIAYGlpmeNrLWnUajXS0tKQkpICExP2wRpzewiCgKSkJLx48QJ2dnaajgGiwlSScwYATE1NYWlpiZcvX8LMzMwoPmeM+XM1J8bcHswZKipKctYY43caY/5czY0xt0lOWaNWqw1dLTJCBe5wmzFjBubPn4/OnTujcePGEAQBly9fxpEjRzBq1Cg8evQII0aMQHp6OoYNG6aPOuuVi4sLAGgCyhgIgoDk5GTI5fISH8b5wfYA7OzsNO8FosJW0nNGIpHA1dUVjx49wuPHjw1dnULBz1UxtgdzhgyvpGeNsX2n4eeqNrYJs4YMr8AdbufOncO8efMwfPhw0fJ169bh2LFj2Lt3L+rUqYMff/yxWIZT5heh0qVLi+bPKMmUSiXOnDmDli1bcrgt2B5mZmYccUAGVdJzBgDMzc1RuXJlo7ms1Ng/V7Mz9vZgzlBRUNKzxti+0xj752pOjL1NmDVUFBS4w+3o0aNYvHix1vK2bdtiwoQJAAAfHx9888037187A5JKpUbzBpVKpUhPT4dMJjPKD+Ps2B5EhmUsOWNiYgKZLOf5mkoafq6KsT2IDM9YssZYvtPwc1Ub24TI8Ap8MbeDgwMOHjyotfzgwYNwcHAAACQmJsLa2vr9a0dEREaHOUNERPrGrCEiIn0r8Ai36dOnY8SIEQgKCkLjxo0hkUjw119/ITAwED/99BMA4Pjx42jVqpXOK0tERCUfc4aIiPSNWUNERPpW4A63YcOGoUaNGli1ahX27dsHQRBQrVo1nD59Gs2aNQMAzTBsIiKigmLOEBGRvjFriIhI3wrc4QYAzZs3R/PmzXVdFyIiIgDMGSIi0j9mDRER6dM7dbhlSk5O1rrrjY2NzXtViIiIKBNzhoiI9I1ZQ0RE+lDgmyYkJSVh9OjRKF26NBQKBezt7UUPIiKi98GcISIifWPWEBGRvhW4w23SpEn4448/sGbNGlhYWGDDhg2YPXs2ypQpgy1btuijjkREZESYM0REpG/MGiIi0rcCX1J68OBBbNmyBa1bt8bnn3+ODz74AJ6enqhQoQK2bduG/v3766OeRERkJJgzRESkb8waIiLStwKPcIuOjoaHhweAjLkNoqOjAQAtWrTAmTNndFs7IiIyOswZIiLSN2YNERHpW4E73CpWrIiwsDAAQI0aNbB7924AGWeJ7OzsdFk3IiIyQswZIiLSN2YNERHpW4E73IYMGYLr168DAKZOnaqZ92D8+PGYNGlSgSuwZs0aeHh4QCaToUGDBjh79mye5VNTUzFt2jRUqFABFhYWqFSpEn755ZcCH5eIiIomXecMwKwhIiIxfqchIiJ9K/AcbuPHj9f83KZNG9y5cwdXrlxBpUqVULdu3QLta9euXRg3bhzWrFmD5s2bY926dejcuTNu376N8uXL57hN79698e+//2Ljxo3w9PTEixcvkJ6eXtCXQURERZQucwZg1hARkTZ+pyEiIn0rUIebUqlEhw4dsG7dOlSpUgUAUL58+VyD5G2WLVuGoUOH4osvvgAArFixAkePHsXatWuxcOFCrfJHjhzB6dOn8fDhQzg4OAAA3N3d3+nYRERU9Og6ZwBmDRERifE7DRERFYYCdbiZmZnh77//hkQiee8Dp6Wl4erVq/jmm29Eyzt06IALFy7kuM2BAwfQsGFDLFmyBFu3boWVlRU++ugjzJ07F3K5PMdtUlNTkZqaqnkeFxcHICNolUrle7+OkiCzHdgeGdge2tgmYmwPMV22gy5zBmDWFBV8z4ixPbSxTcTYHtqKatYwZ4oGvme0sU3E2B7a2CZi+miHAl9SOnDgQGzcuBGLFi16rwNHRUVBpVLB2dlZtNzZ2RmRkZE5bvPw4UOcO3cOMpkM+/fvR1RUFEaOHIno6Ohc5zxYuHAhZs+erbU8KCgIlpaW7/UaSprjx48bugpFCttDG9tEjO2RISkpSaf701XOAMyaoobvGTG2hza2iRjb442imjXMmaKF7xltbBMxtoc2tkkGXecM8A4dbmlpadiwYQOOHz+Ohg0bwsrKSrR+2bJlBdpf9jNLgiDkerZJrVZDIpFg27ZtsLW11RyvV69eWL16dY5nhKZOnQo/Pz/N87i4OLi5uaFNmzZwdHQsUF1LKqVSiePHj6N9+/YwMzMzdHUMju2hjW0ixvYQe/XqlU73p+ucAZg1hsb3jBjbQxvbRIztoa2oZw1zxrD4ntHGNhFje2hjm4jpOmeAd+hw+/vvv+Hl5QUA+Oeff0TrCjIs28nJCVKpVOvMz4sXL7TOEGVydXVF2bJlNcEEANWrV4cgCHj69CkqV66stY2FhQUsLCy0lpuZmfGPKhu2iRjbQxvbRIztkUHXbaCrnAGYNUUN20OM7aGNbSLG9nijqGYNc6ZoYXtoY5uIsT20sU0y6KMNCtzhFhQUpJMDm5ubo0GDBjh+/Dh69OihWX78+HF069Ytx22aN2+OPXv2ICEhAQqFAkBGQJqYmKBcuXI6qRcRERmWrnIGYNYQEVHO+J2GiIj0zeRdN7x//z6OHj2K5ORkABnDpgvKz88PGzZswC+//ILQ0FCMHz8e4eHhGD58OICModMDBw7UlO/Xrx8cHR0xZMgQ3L59G2fOnMGkSZPw+eef5zrBKBERFU+6yBmAWUNERLnjdxoiItKXAo9we/XqFXr37o2goCBIJBLcu3cPFStWxBdffAE7OzssXbo03/v69NNP8erVK8yZMwcRERGoVasWAgMDUaFCBQBAREQEwsPDNeUVCgWOHz+OMWPGoGHDhnB0dETv3r0xb968gr4MIiIqonSZMwCzhoiItPE7DRER6VuBO9zGjx8PMzMzhIeHo3r16prln376KcaPH1/gL0IjR47EyJEjc1y3adMmrWXVqlXjXTSIiEowXecMwKwhIiIxfqchIiJ9K3CH27Fjx3D06FGt+QUqV66Mx48f66xiRERknJgzRESkb8waIiLStwLP4ZaYmAhLS0ut5VFRUTneOYeIiKggmDNERKRvzBoiItK3Ane4tWzZElu2bNE8l0gkUKvV+O6779CmTRudVo6IiIwPc4aIiPSNWUNERPpW4EtKv/vuO7Ru3RpXrlxBWloaJk+ejFu3biE6Ohrnz5/XRx2JiMiIMGeIiEjfmDVERKRvBR7hVqNGDdy4cQONGzdG+/btkZiYiJ49e+LatWuoVKmSPupIRERGhDlDRET6xqwhIiJ9K/AINwBwcXHB7NmzdV0XIiIiAMwZIiLSP2YNERHpU4FHuHl4eGD69Om4e/euPupDRERGjjlDRET6xqwhIiJ9K3CH25gxY3DkyBFUr14dDRo0wIoVKxAREaGPuhERkRFizhARkb4xa4iISN8K3OHm5+eHy5cv486dO+jatSvWrl2L8uXLo0OHDqI7/RAREb0L5gwREekbs4aIiPStwB1umapUqYLZs2fj7t27OHv2LF6+fIkhQ4bosm5ERGTEmDNERKRvzBoiItKXd7ppQqa//voL27dvx65duxAbG4tevXrpql5ERETMGSIi0jtmDRER6UOBO9z++ecfbNu2Ddu3b0dYWBjatGmDRYsWoWfPnrC2ttZHHYmIyIgwZ4iISN+YNUREpG8F7nCrVq0aGjZsiFGjRqFPnz5wcXHRR72IiMhIMWeIiEjfmDVERKRvBe5wu3PnDqpUqaKPuhARETFniIhI75g1RESkbwXucMsMpqtXryI0NBQSiQTVq1eHl5eXzitHRETGhzlDRET6xqz5//buPciqwr4D+G9hlwVxwIhKfCBCfEFQo6AC1jqgYNXqOLYN1ij4IM1GrQ+SWIhpBWPimFZCiIJvnKQ+MKKpbYm6TX2gZGxBiCI2WkRXDYisFVATWOD0D4Y1h7OYvXDOvcvu5zPDDPfsuXd/+5u99zv73bv3AlC0kgu3VatWxbnnnhtPP/107LHHHpEkSaxZsyZGjBgRDz74YOy9995FzAlAByFnACiarAGgaJ1KvcLf/u3fxtq1a+OVV16JDz74IP7v//4vlixZEmvXro0rrriiiBkB6EDkDABFkzUAFK3kZ7g9/vjj8R//8R8xYMCA5mMDBw6MW2+9NUaPHp3rcAB0PHIGgKLJGgCKVvIz3DZv3hw1NTWZ4zU1NbF58+ZchgKg45IzABRN1gBQtJILt5EjR8aVV14Zv/3tb5uPvfvuu3H11VfHySefnOtwAHQ8cgaAoskaAIpWcuF2yy23xLp16+Kggw6KL3zhC3HwwQdHv379Yt26dfHjH/+4iBkB6EDkDABFkzUAFK3k13Dr06dPvPjii1FfXx//8z//E0mSxMCBA+OUU04pYj4AOhg5A0DRZA0ARSu5cNtq1KhRMWrUqDxnAYBmcgaAoskaAIpS8p+UXnHFFTF9+vTM8VtuuSWuuuqqPGYCoAOTMwAUTdYAULSSC7c5c+bECSeckDk+fPjwePjhh3MZCoCOS84AUDRZA0DRSi7cGhsbo2fPnpnjPXr0iNWrV+cyFAAdl5wBoGiyBoCilVy4HXzwwfH4449njv/iF7+I/v375zIUAB2XnAGgaLIGgKKV/KYJEyZMiMsvvzzef//9GDlyZERE/PKXv4ybb745pk2blvd8AHQwcgaAoskaAIpWcuF28cUXx/r16+N73/tefPe7342IiIMOOihmzpwZY8eOzX1AADoWOQNA0WQNAEUruXCLiPj6178eX//61+P999+Pbt26xe677573XAB0YHIGgKLJGgCKtEOF21Z77713XnMAQIacAaBosgaAIpT8pgkAAAAAwPYp3AAAAAAgRwo3AAAAAMiRwg0AAAAActSqN02YPn16q2/wiiuu2OFhAOiY5AwARZM1AJRTqwq3H/7wh626saqqKuEEQMnkDABFkzUAlFOrCrfly5cXPQcAHZicAaBosgaAcvIabgAAAACQo1Y9w21b77zzTjz22GPR0NAQGzZsSH1s6tSpuQwGQMclZwAomqwBoEglF26//OUv46yzzop+/frFb37zmxg0aFC8+eabkSRJHHPMMUXMCEAHImcAKJqsAaBoJf9J6aRJk+Ib3/hGLFmyJLp27Rpz5syJt99+O0466aT4q7/6qyJmBKADkTMAFE3WAFC0kgu3V199NcaNGxcREdXV1fG73/0udt9997j++uvjpptuyn1AADoWOQNA0WQNAEUruXDr3r17rF+/PiIi9ttvv1i2bFnzx1avXp3fZAB0SHIGgKLJGgCKVvJruA0dOjSef/75GDhwYJxxxhnxjW98I15++eV45JFHYujQoUXMCEAHImcAKJqsAaBoJRduU6dOjY8++igiIiZPnhwfffRRzJ49Ow4++OD44Q9/mPuAAHQscgaAoskaAIpWcuHWv3//5v/vtttuMWPGjFwHAqBjkzMAFE3WAFC0kl/D7b//+7/jhRdeyBx/4YUXYsGCBbkMBUDHJWcAKJqsAaBoJRdul112Wbz99tuZ4++++25cdtlluQwFQMclZwAomqwBoGglF25Lly6NY445JnP86KOPjqVLl+YyFAAdl5wBoGiyBoCilVy41dbWxnvvvZc5vmLFiqiuLvkl4QAgRc4AUDRZA0DRSi7cRo0aFZMmTYo1a9Y0H/vwww/j29/+dowaNSrX4QDoeOQMAEWTNQAUreRf39x8883xp3/6p9G3b984+uijIyJi8eLF0bt37/jpT3+a+4AAdCxyBoCiyRoAilZy4bb//vvHSy+9FPfdd1/8+te/jm7dusVFF10Uf/3Xfx01NTVFzAhAByJnACiarAGgaDv0AgXdu3ePv/mbv8l7FgCICDkDQPFkDQBFalXh9thjj8Vpp50WNTU18dhjj33muWeddVYugwHQccgZAIomawAop1YVbmeffXasXLky9tlnnzj77LO3e15VVVVs2rQpr9kA6CDkDABFkzUAlFOrCrfNmze3+H8AyIOcAaBosgaAcupUyslNTU0xYsSIeO2114qaB4AOTM4AUDRZA0A5lFS41dTUxJIlS6KqqqqoeQDowOQMAEWTNQCUQ0mFW0TE2LFj4+677y5iFgCQMwAUTtYAULRWvYbbH9qwYUPcddddUV9fH0OGDInu3bunPj516tTchgOg45EzABRN1gBQtJILtyVLlsQxxxwTEZF53QNPywZgZ8kZAIomawAoWsmF21NPPVXEHAAQEXIGgOLJGgCKVvJruAEAAAAA29eqZ7idc845ce+990aPHj3inHPO+cxzH3nkkVwGA6DjkDMAFE3WAFBOrXqGW8+ePZtfy6Bnz56f+a9UM2bMiH79+kXXrl1j8ODBMW/evFZd7/nnn4/q6ur40pe+VPLnBKBtKTJnImQNAH6mAaC8WvUMt1mzZrX4/501e/bsuOqqq2LGjBlxwgknxO233x6nnXZaLF26NA488MDtXm/NmjUxduzYOPnkk+O9997LbR4AKqOonImQNQBs4WcaAMpph1/DbdWqVTFv3rx47rnnYtWqVTt0G1OnTo1LLrkkxo8fHwMGDIhp06ZFnz59YubMmZ95va997Wtx3nnnxbBhw3bo8wLQ9uWRMxGyBoDt8zMNAEUp+V1K165dG5dddlk8+OCDsWnTpoiI6Ny5c4wZMyZuvfXWVj8Fe8OGDbFw4cKYOHFi6vjo0aNj/vz5273erFmzYtmyZfHP//zPccMNN/zRz7N+/fpYv359av6IiKampmhqamrVrO3d1j3Yxxb2kWUnafaRlvce8sqZCFnTVrjPpNlHlp2k2UdWW80aOdM2uM9k2UmafWTZSVoReyi5cBs/fnwsXrw4/u3f/i2GDRsWVVVVMX/+/Ljyyivjq1/9ajz00EOtup3Vq1fHpk2bonfv3qnjvXv3jpUrV7Z4nddffz0mTpwY8+bNi+rq1o1+4403xpQpUzLHn3rqqdhtt91adRsdRX19faVHaFPsI8tO0uxji08++STX28srZyJkTVvjPpNmH1l2kmYfn2qrWSNn2hb3mSw7SbOPLDvZIu+cidiBwu3f//3f44knnog/+ZM/aT526qmnxp133hl/9md/VvIAW1+4dKskSTLHIiI2bdoU5513XkyZMiUOPfTQVt/+pEmTYsKECc2X165dG3369IkRI0ZEr169Sp63PWpqaor6+voYNWpU1NTUVHqcirOPLDtJs4+0xsbGXG8v75yJkDWV5j6TZh9ZdpJmH1ltPWvkTGW5z2TZSZp9ZNlJWt45E7EDhVuvXr1afIp1z54943Of+1yrb2evvfaKzp07Z37zs2rVqsxviCIi1q1bFwsWLIhFixbF5ZdfHhERmzdvjiRJorq6Op588skYOXJk5nq1tbVRW1ubOV5TU+Obaht2kmYfWXaSZh9b5L2DvHImQta0NfaRZh9ZdpJmH59qq1kjZ9oW+8iykzT7yLKTLYrYQclvmvCd73wnJkyYECtWrGg+tnLlyvjWt74Vf//3f9/q2+nSpUsMHjw48/TF+vr6GD58eOb8Hj16xMsvvxyLFy9u/ldXVxeHHXZYLF68OI4//vhSvxQA2qC8ciZC1gDQMj/TAFC0kp/hNnPmzPjf//3f6Nu3b/PbXDc0NERtbW28//77cfvttzef++KLL37mbU2YMCEuuOCCGDJkSAwbNizuuOOOaGhoiLq6uojY8tTpd999N37yk59Ep06dYtCgQanr77PPPtG1a9fMcQB2XXnmTISsASDLzzQAFK3kwu3ss8/O7ZOPGTMmGhsb4/rrr48VK1bEoEGDYu7cudG3b9+IiFixYkU0NDTk9vkAaPvyzJkIWQNAlp9pAChayYXbddddl+sAl156aVx66aUtfuzee+/9zOtOnjw5Jk+enOs8AFRW3jkTIWsASPMzDQBFK7lw+0MfffRRbN68OXWsR48eOzUQAGwlZwAomqwBoAglv2nC8uXL44wzzoju3bs3v4vP5z73udhjjz1Kfvc4ANiWnAGgaLIGgKKV/Ay3r3zlKxERcc8990Tv3r2jqqoq96EA6LjkDABFkzUAFK3kwu2ll16KhQsXxmGHHVbEPAB0cHIGgKLJGgCKVvKflB577LHx9ttvFzELAMgZAAonawAoWsnPcLvrrruirq4u3n333Rg0aFDU1NSkPn7kkUfmNhwAHY+cAaBosgaAopVcuL3//vuxbNmyuOiii5qPVVVVRZIkUVVVFZs2bcp1QAA6FjkDQNFkDQBFK7lwu/jii+Poo4+OBx54wAuMApA7OQNA0WQNAEUruXB766234rHHHouDDz64iHkA6ODkDABFkzUAFK3kN00YOXJk/PrXvy5iFgCQMwAUTtYAULSSn+F25plnxtVXXx0vv/xyHHHEEZkXGD3rrLNyGw6AjkfOAFA0WQNA0Uou3Orq6iIi4vrrr898zAuMArCz5AwARZM1ABSt5MJt8+bNRcwBABEhZwAonqwBoGglv4YbAAAAALB9rS7cTj/99FizZk3z5e9973vx4YcfNl9ubGyMgQMH5jocAB2HnAGgaLIGgHJpdeH2xBNPxPr165sv33TTTfHBBx80X964cWP85je/yXc6ADoMOQNA0WQNAOXS6sItSZLPvAwAO0POAFA0WQNAuXgNNwAAAADIUasLt6qqqqiqqsocA4A8yBkAiiZrACiX6taemCRJXHjhhVFbWxsREb///e+jrq4uunfvHhGRei0EACiVnAGgaLIGgHJpdeE2bty41OXzzz8/c87YsWN3fiIAOiQ5A0DRZA0A5dLqwm3WrFlFzgFABydnACiarAGgXLxpAgAAAADkSOEGAAAAADlSuAEAAABAjhRuAAAAAJAjhRsAAAAA5EjhBgAAAAA5UrgBAAAAQI4UbgAAAACQI4UbAAAAAORI4QYAAAAAOVK4AQAAAECOFG4AAAAAkCOFGwAAAADkSOEGAAAAADlSuAEAAABAjhRuAAAAAJAjhRsAAAAA5EjhBgAAAAA5UrgBAAAAQI4UbgAAAACQI4UbAAAAAORI4QYAAAAAOVK4AQAAAECOFG4AAAAAkCOFGwAAAADkSOEGAAAAADlSuAEAAABAjhRuAAAAAJAjhRsAAAAA5EjhBgAAAAA5UrgBAAAAQI4UbgAAAACQI4UbAAAAAORI4QYAAAAAOVK4AQAAAECOFG4AAAAAkCOFGwAAAADkSOEGAAAAADlSuAEAAABAjhRuAAAAAJAjhRsAAAAA5EjhBgAAAAA5UrgBAAAAQI4UbgAAAACQo4oXbjNmzIh+/fpF165dY/DgwTFv3rztnvvII4/EqFGjYu+9944ePXrEsGHD4oknnijjtADsimQNAEWSMwBsq6KF2+zZs+Oqq66Ka6+9NhYtWhQnnnhinHbaadHQ0NDi+c8++2yMGjUq5s6dGwsXLowRI0bEmWeeGYsWLSrz5ADsKmQNAEWSMwC0pKKF29SpU+OSSy6J8ePHx4ABA2LatGnRp0+fmDlzZovnT5s2La655po49thj45BDDonvf//7ccghh8S//uu/lnlyAHYVsgaAIskZAFpSXalPvGHDhli4cGFMnDgxdXz06NExf/78Vt3G5s2bY926dbHnnntu95z169fH+vXrmy+vXbs2IiKampqiqalpByZvf7buwT62sI8sO0mzj7S2vAdZ0za4z6TZR5adpNlHVlvdhZxpG9xnsuwkzT6y7CStiD1UrHBbvXp1bNq0KXr37p063rt371i5cmWrbuPmm2+Ojz/+OL785S9v95wbb7wxpkyZkjn+1FNPxW677Vba0O1cfX19pUdoU+wjy07S7GOLTz75pNIjbJesaVvcZ9LsI8tO0uzjU201a+RM2+I+k2UnafaRZSdbFJEzFSvctqqqqkpdTpIkc6wlDzzwQEyePDn+5V/+JfbZZ5/tnjdp0qSYMGFC8+W1a9dGnz59YsSIEdGrV68dH7wdaWpqivr6+hg1alTU1NRUepyKs48sO0mzj7TGxsZKj/BHyZrKcp9Js48sO0mzj6y2njVyprLcZ7LsJM0+suwkrYicqVjhttdee0Xnzp0zv/lZtWpV5jdE25o9e3Zccskl8bOf/SxOOeWUzzy3trY2amtrM8dramp8U23DTtLsI8tO0uxji7a8A1nTtthHmn1k2UmafXyqre5BzrQt9pFlJ2n2kWUnWxSxg4q9aUKXLl1i8ODBmacv1tfXx/Dhw7d7vQceeCAuvPDCuP/+++OMM84oekwAdmGyBoAiyRkAtqeif1I6YcKEuOCCC2LIkCExbNiwuOOOO6KhoSHq6uoiYstTp9999934yU9+EhFbgmns2LHxox/9KIYOHdr8m6Ru3bpFz549K/Z1ANB2yRoAiiRnAGhJRQu3MWPGRGNjY1x//fWxYsWKGDRoUMydOzf69u0bERErVqyIhoaG5vNvv/322LhxY1x22WVx2WWXNR8fN25c3HvvveUeH4BdgKwBoEhyBoCWVPxNEy699NK49NJLW/zYtoHz9NNPFz8QAO2OrAGgSHIGgG1V7DXcAAAAAKA9UrgBAAAAQI4UbgAAAACQI4UbAAAAAORI4QYAAAAAOVK4AQAAAECOFG4AAAAAkCOFGwAAAADkSOEGAAAAADlSuAEAAABAjhRuAAAAAJAjhRsAAAAA5EjhBgAAAAA5UrgBAAAAQI4UbgAAAACQI4UbAAAAAORI4QYAAAAAOVK4AQAAAECOFG4AAAAAkCOFGwAAAADkSOEGAAAAADlSuAEAAABAjhRuAAAAAJAjhRsAAAAA5EjhBgAAAAA5UrgBAAAAQI4UbgAAAACQI4UbAAAAAORI4QYAAAAAOVK4AQAAAECOFG4AAAAAkCOFGwAAAADkSOEGAAAAADlSuAEAAABAjhRuAAAAAJAjhRsAAAAA5EjhBgAAAAA5UrgBAAAAQI4UbgAAAACQI4UbAAAAAORI4QYAAAAAOVK4AQAAAECOFG4AAAAAkCOFGwAAAADkSOEGAAAAADlSuAEAAABAjhRuAAAAAJAjhRsAAAAA5EjhBgAAAAA5UrgBAAAAQI4UbgAAAACQI4UbAAAAAORI4QYAAAAAOVK4AQAAAECOFG4AAAAAkCOFGwAAAADkSOEGAAAAADlSuAEAAABAjhRuAAAAAJAjhRsAAAAA5EjhBgAAAAA5UrgBAAAAQI4UbgAAAACQI4UbAAAAAORI4QYAAAAAOVK4AQAAAECOKl64zZgxI/r16xddu3aNwYMHx7x58z7z/GeeeSYGDx4cXbt2jf79+8dtt91WpkkB2FXJGgCKJGcA2FZFC7fZs2fHVVddFddee20sWrQoTjzxxDjttNOioaGhxfOXL18ep59+epx44omxaNGi+Pa3vx1XXHFFzJkzp8yTA7CrkDUAFEnOANCSihZuU6dOjUsuuSTGjx8fAwYMiGnTpkWfPn1i5syZLZ5/2223xYEHHhjTpk2LAQMGxPjx4+Piiy+Of/qnfyrz5ADsKmQNAEWSMwC0pLpSn3jDhg2xcOHCmDhxYur46NGjY/78+S1e51e/+lWMHj06dezUU0+Nu+++O5qamqKmpiZznfXr18f69eubL69ZsyYiIj744IOd/RLajaampvjkk0+isbGxxR12NPaRZSdp9pG29fE0SZIKT5Ila9oG95k0+8iykzT7yGqrWSNn2gb3mSw7SbOPLDtJKyJnKla4rV69OjZt2hS9e/dOHe/du3esXLmyxeusXLmyxfM3btwYq1evjn333TdznRtvvDGmTJmSOX7ooYfuxPQAbKuxsTF69uxZ6TFSZA1A+9LWskbOALQveeZMxQq3raqqqlKXkyTJHPtj57d0fKtJkybFhAkTmi9/+OGH0bdv32hoaGhTYV1Ja9eujT59+sTbb78dPXr0qPQ4FWcfWXaSZh9pa9asiQMPPDD23HPPSo+yXbKmstxn0uwjy07S7COrrWeNnKks95ksO0mzjyw7SSsiZypWuO21117RuXPnzG9+Vq1alfmNz1af//znWzy/uro6evXq1eJ1amtro7a2NnO8Z8+evqm20aNHDzv5A/aRZSdp9pHWqVPF3/g6Q9a0Le4zafaRZSdp9pHV1rJGzrQt7jNZdpJmH1l2kpZnzlQssbp06RKDBw+O+vr61PH6+voYPnx4i9cZNmxY5vwnn3wyhgwZ4m+OAciQNQAUSc4AsD0V/RXRhAkT4q677op77rknXn311bj66qujoaEh6urqImLLU6fHjh3bfH5dXV289dZbMWHChHj11Vfjnnvuibvvvju++c1vVupLAKCNkzUAFEnOANCSir6G25gxY6KxsTGuv/76WLFiRQwaNCjmzp0bffv2jYiIFStWRENDQ/P5/fr1i7lz58bVV18dt956a+y3334xffr0+Iu/+ItWf87a2tq47rrrWnxKdkdlJ2n2kWUnafaR1tb3IWsqzz7S7CPLTtLsI6st70TOVJ59ZNlJmn1k2UlaEfuoStrae2sDAAAAwC6sbb3qKAAAAADs4hRuAAAAAJAjhRsAAAAA5EjhBgAAAAA5apeF24wZM6Jfv37RtWvXGDx4cMybN+8zz3/mmWdi8ODB0bVr1+jfv3/cdtttZZq0fErZySOPPBKjRo2KvffeO3r06BHDhg2LJ554oozTFq/U75Gtnn/++aiuro4vfelLxQ5YZqXuY/369XHttddG3759o7a2Nr7whS/EPffcU6Zpy6PUndx3331x1FFHxW677Rb77rtvXHTRRdHY2FimaYv17LPPxplnnhn77bdfVFVVxc9//vM/eh2Pq1ntfSdyJk3OZMmaNDnzKTnTMjmTJmeyZE2anEmTM5+qWM4k7cyDDz6Y1NTUJHfeeWeydOnS5Morr0y6d++evPXWWy2e/8YbbyS77bZbcuWVVyZLly5N7rzzzqSmpiZ5+OGHyzx5cUrdyZVXXpncdNNNyX/9138lr732WjJp0qSkpqYmefHFF8s8eTFK3cdWH374YdK/f/9k9OjRyVFHHVWeYctgR/Zx1llnJccff3xSX1+fLF++PHnhhReS559/voxTF6vUncybNy/p1KlT8qMf/Sh54403knnz5iVf/OIXk7PPPrvMkxdj7ty5ybXXXpvMmTMniYjk0Ucf/czzPa5mtfedyJk0OZMla9LkTJqcyZIzaXImS9akyZk0OZNWqZxpd4Xbcccdl9TV1aWOHX744cnEiRNbPP+aa65JDj/88NSxr33ta8nQoUMLm7HcSt1JSwYOHJhMmTIl79EqYkf3MWbMmOQ73/lOct1117WrcCp1H7/4xS+Snj17Jo2NjeUYryJK3ck//uM/Jv37908dmz59enLAAQcUNmOltCagPK5mtfedyJk0OZMla9LkzPbJmS3kTJqcyZI1aXImTc5sXzlzpl39SemGDRti4cKFMXr06NTx0aNHx/z581u8zq9+9avM+aeeemosWLAgmpqaCpu1XHZkJ9vavHlzrFu3Lvbcc88iRiyrHd3HrFmzYtmyZXHdddcVPWJZ7cg+HnvssRgyZEj84Ac/iP333z8OPfTQ+OY3vxm/+93vyjFy4XZkJ8OHD4933nkn5s6dG0mSxHvvvRcPP/xwnHHGGeUYuc3xuJrVnnciZ9LkTJasSZMzO689P6ZGyJltyZksWZMmZ9LkzM7L6zG1Ou/BKmn16tWxadOm6N27d+p47969Y+XKlS1eZ+XKlS2ev3Hjxli9enXsu+++hc1bDjuyk23dfPPN8fHHH8eXv/zlIkYsqx3Zx+uvvx4TJ06MefPmRXV1u7rL7NA+3njjjXjuueeia9eu8eijj8bq1avj0ksvjQ8++KBdvObBjuxk+PDhcd9998WYMWPi97//fWzcuDHOOuus+PGPf1yOkdscj6tZ7XknciZNzmTJmjQ5s/Pa82NqhJzZlpzJkjVpciZNzuy8vB5T29Uz3LaqqqpKXU6SJHPsj53f0vFdWak72eqBBx6IyZMnx+zZs2OfffYparyya+0+Nm3aFOedd15MmTIlDj300HKNV3alfH9s3rw5qqqq4r777ovjjjsuTj/99Jg6dWrce++97eI3QluVspOlS5fGFVdcEf/wD/8QCxcujMcffzyWL18edXV15Ri1TfK42rrzWzq+q5IzaXImS9akyZmd094fUyPkzLbkTJasSZMzaXJm5+TxmNququ299torOnfunGltV61alWknt/r85z/f4vnV1dXRq1evwmYtlx3ZyVazZ8+OSy65JH72s5/FKaecUuSYZVPqPtatWxcLFiyIRYsWxeWXXx4RWx6ckySJ6urqePLJJ2PkyJFlmb0IO/L9se+++8b+++8fPXv2bD42YMCASJIk3nnnnTjkkEMKnbloO7KTG2+8MU444YT41re+FRERRx55ZHTv3j1OPPHEuOGGG3bp3yrvCI+rWe15J3ImTc5kyZo0ObPz2vNjaoSc2ZacyZI1aXImTc7svLweU9vVM9y6dOkSgwcPjvr6+tTx+vr6GD58eIvXGTZsWOb8J598MoYMGRI1NTWFzVouO7KTiC2/Cbrwwgvj/vvvb1d/t13qPnr06BEvv/xyLF68uPlfXV1dHHbYYbF48eI4/vjjyzV6IXbk++OEE06I3/72t/HRRx81H3vttdeiU6dOccABBxQ6bznsyE4++eST6NQp/XDauXPniPj0NyEdicfVrPa8EzmTJmeyZE2anNl57fkxNULObEvOZMmaNDmTJmd2Xm6PqSW9xcIuYOvb3959993J0qVLk6uuuirp3r178uabbyZJkiQTJ05MLrjggubzt77d69VXX50sXbo0ufvuu9vVW2gnSek7uf/++5Pq6urk1ltvTVasWNH878MPP6zUl5CrUvexrfb2jj6l7mPdunXJAQcckPzlX/5l8sorryTPPPNMcsghhyTjx4+v1JeQu1J3MmvWrKS6ujqZMWNGsmzZsuS5555LhgwZkhx33HGV+hJytW7dumTRokXJokWLkohIpk6dmixatKj5bcU9rsoaOZMmZ7JkTZqcSZMzWXImTc5kyZo0OZMmZ9IqlTPtrnBLkiS59dZbk759+yZdunRJjjnmmOSZZ55p/ti4ceOSk046KXX+008/nRx99NFJly5dkoMOOiiZOXNmmScuXik7Oemkk5KIyPwbN25c+QcvSKnfI3+ovYVTkpS+j1dffTU55ZRTkm7duiUHHHBAMmHChOSTTz4p89TFKnUn06dPTwYOHJh069Yt2XfffZOvfOUryTvvvFPmqYvx1FNPfeZjgsdVWZMkcmZbciZL1qTJmU/JmZbJmTQ5kyVr0uRMmpz5VKVypipJOuDzAwEAAACgIO3qNdwAAAAAoNIUbgAAAACQI4UbAAAAAORI4QYAAAAAOVK4AQAAAECOFG4AAAAAkCOFGwAAAADkSOEGAAAAADlSuAEAAABAjhRu0E699NJLMWzYsDjyyCPj+9//fvz5n/95pUcCoJ2RNQAUSc6wK1O4Qc6effbZOPPMM2O//faLqqqq+PnPf172GZqammLcuHExa9aseOmll+K5556LI444ouxzAFAMWQNAkeQM7DyFG+Ts448/jqOOOipuueWWis3w8MMPxwknnBCHH354REQMGDAgjjzyyIrNA0C+ZA0ARZIzsPMUbpCz0047LW644YY455xzCv08t912WxxxxBHRrVu36NmzZ4wcObL5Y0uWLEmF0SuvvCKcANoRWQNAkeQM7LzqSg8AlG7OnDkxceLEuOOOO2Lo0KGxbt26ePPNN5s/3qtXr3j99dcjIuI///M/49lnn43DDjusQtMCsCuSNQAUSc7Q3incYBf02muvxYEHHhijR4+OPfbYIyIivvjFLzZ//Pzzz4/TTz89hg4dGsOHD4/jjz8+qqvd3QFoPVkDQJHkDO2dPymFCpo8eXJUVVV95r8FCxZkrvfVr341OnfuHHvuuWfsvvvusWzZstTHu3fvHgsWLIj58+dHp06d4vzzzy/XlwRAGyNrACiSnIGWqYehgi6//PI499xzP/Ocgw46KHW5qakpzj333Dj22GPjzjvvjD322CP69++fOucHP/hBzJkzJ6qrq+OMM86Iiy++OO/RAdhFyBoAiiRnoGVVSZIklR4C2quqqqp49NFH4+yzz87tNh966KG45pprUq9vAEDHJWsAKJKcgR3jT0ohZx999FEsXrw4Fi9eHBERy5cvj8WLF0dDQ0Mut79hw4ZYsWJF/PSnP40333wzlixZErfffns0NTXlcvsAtH2yBoAiyRnYeZ7hBjl7+umnY8SIEZnj48aNi3vvvXenb3/jxo3xd3/3d/HQQw/Fe++9F3vuuWecfPLJcd999+30bQOwa5A1ABRJzsDOU7gBAAAAQI78SSkAAAAA5EjhBgAAAAA5UrgBAAAAQI4UbgAAAACQI4UbAAAAAORI4QYAAAAAOVK4AQAAAECOFG4AAAAAkCOFGwAAAADkSOEGAAAAADlSuAEAAABAjhRuAAAAAJCj/wfeilBu5FOFuAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "validity_tbls = SummaryReporter(epsilons, approaches, detectors, k_folds=folds).report(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>$1 - \\epsilon^g$</th>\n",
       "      <th>Bonferroni</th>\n",
       "      <th>Max additive</th>\n",
       "      <th>Independent DE-CCP</th>\n",
       "      <th>Gumbel DE-CCP</th>\n",
       "      <th>Empirical DE-CCP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.99</td>\n",
       "      <td>0.992±0.002</td>\n",
       "      <td>0.989±0.003</td>\n",
       "      <td>0.990±0.001</td>\n",
       "      <td>0.990±0.002</td>\n",
       "      <td>0.989±0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.95</td>\n",
       "      <td>0.955±0.004</td>\n",
       "      <td>0.952±0.004</td>\n",
       "      <td>0.952±0.004</td>\n",
       "      <td>0.949±0.004</td>\n",
       "      <td>0.947±0.006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.90</td>\n",
       "      <td>0.913±0.008</td>\n",
       "      <td>0.902±0.002</td>\n",
       "      <td>0.906±0.007</td>\n",
       "      <td>0.901±0.006</td>\n",
       "      <td>0.897±0.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.832±0.006</td>\n",
       "      <td>0.802±0.002</td>\n",
       "      <td>0.815±0.008</td>\n",
       "      <td>0.806±0.009</td>\n",
       "      <td>0.795±0.010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   $1 - \\epsilon^g$   Bonferroni Max additive Independent DE-CCP  \\\n",
       "0              0.99  0.992±0.002  0.989±0.003        0.990±0.001   \n",
       "1              0.95  0.955±0.004  0.952±0.004        0.952±0.004   \n",
       "2              0.90  0.913±0.008  0.902±0.002        0.906±0.007   \n",
       "3              0.80  0.832±0.006  0.802±0.002        0.815±0.008   \n",
       "\n",
       "  Gumbel DE-CCP Empirical DE-CCP  \n",
       "0   0.990±0.002      0.989±0.002  \n",
       "1   0.949±0.004      0.947±0.006  \n",
       "2   0.901±0.006      0.897±0.011  \n",
       "3   0.806±0.009      0.795±0.010  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validity_tbls[0] # Experiment N°1: correlated dissimilarities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",