MLP_CNN_mnist_cifar10_practice.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## MLP_01\n",
    "---\n",
    "\n",
    "In this notebook, we train an MLP to classify images from the MNIST database.\n",
    "\n",
    "### 1. Load MNIST Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The MNIST database has a training set of 60000 examples.\n",
      "The MNIST database has a test set of 10000 examples.\n"
     ]
    }
   ],
   "source": [
    "from keras.datasets import mnist\n",
    "\n",
    "# use Keras to import pre-shuffled MNIST database\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
    "\n",
    "print(\"The MNIST database has a training set of %d examples.\" % len(X_train))\n",
    "print(\"The MNIST database has a test set of %d examples.\" % len(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 2. Visualize the First Six Training Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import matplotlib.cm as cm\n",
    "import numpy as np\n",
    "\n",
    "# plot first six training images\n",
    "fig = plt.figure(figsize=(20,20))\n",
    "for i in range(6):\n",
    "    ax = fig.add_subplot(1, 6, i+1, xticks=[], yticks=[])\n",
    "    ax.imshow(X_train[i], cmap='gray')\n",
    "    ax.set_title(str(y_train[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 3. View an Image in More Detail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArEAAAKvCAYAAAB9BpfGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzs3X1czff/x/HnOZ1TKCFzkTRlrkpRocYwG9Zsrodpsws0c70voo0N5Wozi1zNbC42Nk25/snYGEaEkauUToVOIsxIMcn790dfn2+N6qg+Z+dtz/vt1u27Tuc8ent/Uy+f8zmfNEIIEBERERHJRPtPL4CIiIiI6HFxiCUiIiIi6XCIJSIiIiLpcIglIiIiIulwiCUiIiIi6XCIJSIiIiLpcIglIiIiIulwiCUiIiIi6XCIJSIiIiLp6Mz5yTQaDX89GBEREREVSwihKek+PBJLRERERNLhEEtERERE0uEQS0RERETS4RBLRERERNLhEEtERERE0uEQS0RERETSscgh1t/fHwkJCUhKSkJwcLA0bbX7srbV7rNt/r6sbbX7srbV7rNt/r6sbbX7srbV7svahhCi1G8AXgaQCMAA4EMT7i9KetNqtcJgMAhXV1eh1+tFXFyccHNzK/Fx/3Rb5rVzX56stsxr575wX/4NbZnXzn3hvpirbcocWuojsRqNxgrAIgBdALgDCNBoNO6l7T3g6+sLg8GA1NRU5ObmIiIiAj169ChrVvW22n1Z22r32TZ/X9a22n1Z22r32TZ/X9a22n1Z22r3ZW0DZTudwBeAQQiRIoS4CyACQJlX5uTkhLS0NOV9o9EIJyensmZVb6vdl7Wtdp9t8/dlbavdl7Wtdp9t8/dlbavdl7Wtdl/WNlC2IdYJQFqB943/vY2IiIiISFU6tT+BRqMZAmCIqfdPT0+Hs7Oz8n7dunWRnp5eLmtRs612X9a22n22zd+Xta12X9a22n22zd+Xta12X9a22n1Z2wBQlhd1tQawvcD7HwH4qKwv7LKyshLJycnCxcVFOQnY3d29XE4wVrMt89q5L09WW+a1c1+4L/+Gtsxr575wX8zVNmkWLcMQqwOQAsAVgDWA4wCalnWIBSC6dOkiEhMThcFgEBMnTiy3LwK12zKvnfvyZLVlXjv3hfvyb2jLvHbuC/fFHG1TZlHNf4fLUtFoNK8AmAfACsByIcSMEu5f+k9GRERERP8KQghNSfcp0xD7uDjEEhEREVFJTBliLfI3dhERERERFYdDLBERERFJh0MsEREREUmHQywRERERSYdDLBERERFJh0MsEREREUmHQywRERERSYdDLBERERFJh0MsEREREUmHQywRERERSYdDLBERERFJh0MsEREREUmHQywRERERSYdDLBERERFJxyKHWH9/fyQkJCApKQnBwcHStNXuy9pWu8+2+fuyttXuy9pWu8+2+fuyttXuy9pWuy9rG0IIs70BECW9abVaYTAYhKurq9Dr9SIuLk64ubmV+Lh/ui3z2rkvT1Zb5rVzX7gv/4a2zGvnvnBfzNU2Za60uCOxvr6+MBgMSE1NRW5uLiIiItCjRw+Lb6vdl7Wtdp9t8/dlbavdl7Wtdp9t8/dlbavdl7Wtdl/WNmCBpxM4OTkhLS1Ned9oNMLJycni22r3ZW2r3Wfb/H1Z22r3ZW2r3Wfb/H1Z22r3ZW2r3Ze1DVjgEEtEREREVBKLG2LT09Ph7OysvF+3bl2kp6dbfFvtvqxttftsm78va1vtvqxttftsm78va1vtvqxttfuytgHA4l7YZWVlJZKTk4WLi4tyErC7u3u5nGCsZlvmtXNfnqy2zGvnvnBf/g1tmdfOfeG+mKtt0lxpaUMsANGlSxeRmJgoDAaDmDhxYrl9Eajdlnnt3Jcnqy3z2rkv3Jd/Q1vmtXNfuC/maJsyV2r+O1yahUajMd8nIyIiIiIpCSE0Jd3H4s6JJSIiIiIqCYdYIiIiIpIOh1giIiIikg6HWCIiIiKSDodYIiIiIpIOh1giIiIikg6HWCIiIiKSDodYIiIiIpIOh1giIiIikg6HWCIiIiKSDodYIiIiIpIOh1giIiIikg6HWCIiIiKSDodYIiIiIpKORQ6x/v7+SEhIQFJSEoKDg6Vpq92Xta12n23z92Vtq92Xta12n23z92Vtq92Xta12X9Y2hBBmewMgSnrTarXCYDAIV1dXodfrRVxcnHBzcyvxcf90W+a1c1+erLbMa+e+cF/+DW2Z18594b6Yq23KXGlxR2J9fX1hMBiQmpqK3NxcREREoEePHhbfVrsva1vtPtvm78vaVrsva1vtPtvm78vaVrsva1vtvqxtwAJPJ3ByckJaWpryvtFohJOTk8W31e7L2la7z7b5+7K21e7L2la7z7b5+7K21e7L2la7L2sbsMAhloiIiIioJBY3xKanp8PZ2Vl5v27dukhPT7f4ttp9Wdtq99k2f1/Wttp9Wdtq99k2f1/Wttp9Wdtq92VtA4DFvbDLyspKJCcnCxcXF+UkYHd393I5wVjNtsxr5748WW2Z18594b78G9oyr537wn0xV9ukudLShlgAokuXLiIxMVEYDAYxceLEcvsiULst89q5L09WW+a1c1+4L/+Gtsxr575wX8zRNmWu1Px3uDQLjUZjvk9GRERERFISQmhKuo/FnRNLRERERFQSDrFEREREJB0OsUREREQkHQ6xRERERCQdDrFEREREJB0OsUREREQkHQ6xRERERCQdDrFEREREJB0OsUREREQkHQ6xRERERCQdDrFEREREJB0OsUREREQkHQ6xRERERCQdDrFEREREJB2LHGL9/f2RkJCApKQkBAcHS9NWuy9rW+0+2+bvy9pWuy9rW+0+2+bvy9pWuy9rW+2+rG0IIcz2BkCU9KbVaoXBYBCurq5Cr9eLuLg44ebmVuLj/um2zGvnvjxZbZnXzn3hvvwb2jKvnfvCfTFX25S50uKOxPr6+sJgMCA1NRW5ubmIiIhAjx49LL6tdl/Wttp9ts3fl7Wtdl/Wttp9ts3fl7Wtdl/Wttp9WduABZ5O4OTkhLS0NOV9o9EIJycni2+r3Ze1rXafbfP3ZW2r3Ze1rXafbfP3ZW2r3Ze1rXZf1jZggUMsEREREVFJLG6ITU9Ph7Ozs/J+3bp1kZ6ebvFttfuyttXus23+vqxttfuyttXus23+vqxttfuyttXuy9oGAIt7YZeVlZVITk4WLi4uyknA7u7u5XKCsZptmdfOfXmy2jKvnfvCffk3tGVeO/eF+2KutklzpaUNsQBEly5dRGJiojAYDGLixInl9kWgdlvmtXNfnqy2zGvnvnBf/g1tmdfOfeG+mKNtylyp+e9waRYajcZ8n4yIiIiIpCSE0JR0H4s7J5aIiIiIqCQcYomIiIhIOhxiiYiIiEg6HGKJiIiISDocYomIiIhIOhxiiYiIiEg6HGKJiIiISDocYomIiIhIOhxiiYiIiEg6HGKJiIiISDocYomIiIhIOhxiiYiIiEg6HGKJiIiISDocYomIiIhIOhxiiYiIiEg6FjnE+vv7IyEhAUlJSQgODpamrXZf1rbafbbN35e1rXZf1rbafbbN35e1rXZf1rbafVnbEEKY7Q2AKOlNq9UKg8EgXF1dhV6vF3FxccLNza3Ex/3TbZnXzn15stoyr537wn35N7RlXjv3hftirrYpc6XFHYn19fWFwWBAamoqcnNzERERgR49elh8W+2+rG21+2ybvy9rW+2+rG21+2ybvy9rW+2+rG21+7K2AQs8ncDJyQlpaWnK+0ajEU5OThbfVrsva1vtPtvm78vaVrsva1vtPtvm78vaVrsva1vtvqxtwAKHWCIiIiKikljcEJueng5nZ2fl/bp16yI9Pd3i22r3ZW2r3Wfb/H1Z22r3ZW2r3Wfb/H1Z22r3ZW2r3Ze1DQAW98IuKysrkZycLFxcXJSTgN3d3cvlBGM12zKvnfvyZLVlXjv3hfvyb2jLvHbuC/fFXG2T5kpLG2IBiC5duojExERhMBjExIkTy+2LQO22zGvnvjxZbZnXzn3hvvwb2jKvnfvCfTFH25S5UvPf4dIsNBqN+T4ZEREREUlJCKEp6T4Wd04sEREREVFJOMQSERERkXQ4xBIRERGRdDjEEhEREZF0OMQSERERkXQ4xBIRERGRdDjEEhEREZF0dP/0AoiIyoOVlZVq7SpVqqjWltnIkSNVa1eqVEm1NgA0btxYtfaIESNUa8+ZM0e1NgAEBASo1r5z545q7U8//VS1NgCEhISo2qfS4ZFYIiIiIpIOh1giIiIikg6HWCIiIiKSDodYIiIiIpIOh1giIiIikg6HWCIiIiKSjkUOsf7+/khISEBSUhKCg4Olaavdl7Wtdp9t8/dlbdvY2ODAgQP4/fffcfz4cUyZMqVMvfnz5yMhIQH79u1TbvPw8MD27duxe/du7Ny5Ez4+PhbZL+/25s2bMWfOHHz55ZfKbT///DMWLVqEJUuW4Mcffyx0iaXLly9j2bJl+PLLL7FkyRLcu3evyHZkZCSmTZuGuXPnKrft2LED8+bNQ3h4OJYtW4abN28qH0tOTkZ4eDjCwsLw1Vdflbj2BQsW4J133sHo0aOV2+bMmYMxY8ZgzJgxGDJkCMaMGQMAyMzMxOuvv658rOCftyR16tRBZGQkfv31V+zatQuDBw9WPjZw4EDs2bMHu3btwqRJk0zqffXVVxg6dCgmTJhQ6Pbt27dj3LhxGD9+PH744Qfl9gsXLmDy5MkYP348goODcffu3SLbaWlpeOGFF+Du7o6mTZsiPDwcADB16lQ4OTnBy8sLXl5eiI6OBgCcO3cOFStWVG4fOnRosW1/f394e3vDx8cHCxcuLPTxefPmoWLFirh69SoAICwsDH5+fvDz80OLFi1ga2uLP/74o8j+pk2b8Pnnn2Px4sXKbTt27MDChQvx5ZdfFvpaPHHiBJYsWaK8hYSE4NKlS0W2i8Pv6eZva4QQpX+wRnMOQBaAPAD3hBAtS7h/iZ9Mq9Xi7Nmz6Ny5M4xGIw4fPoyAgACcOXOm1Os0R1vtvqxttftsm79vqW1TrxNra2uL7Oxs6HQ67N27F2PGjEFsbGyxjynqOrGtW7dGdnY2Fi9ejLZt2wIAoqKi8OWXX2Lnzp3o1KkTRo0ahR49epi0NnP2y6Nd8Dqx58+fh7W1NTZu3Ihhw4YByB8mXV1dodVq8csvvwAAOnXqhPv372Pp0qXo2bMnateujZycHFSoUAFa7f+OqxS8TmxKSgpsbGywdu1aZZi8c+cOKlSoAADYv38/MjMz0atXL9y+fRtffvklBg0ahKpVq+LWrVuws7N7aO0FrxN7+vRpVKhQAeHh4Zg/f/5D912xYgUqVaqE119/HZmZmZg+ffoj7/dAUdeJrVmzJmrWrIlTp07B1tYWP/30EwYNGoQaNWpg9OjRePvtt3H37l1Ur14d165de2Sj4HViz5w5gwoVKuDLL7/E7NmzlT/Lxo0bMWHCBOj1ety4cQNVqlRBXl4eJk6ciOHDh6NevXrIysqCra1toT0H/ned2IyMDGRkZMDHxwdZWVlo0aIFNm7ciLVr18LOzg5BQUGFHnfu3Dl07doVp06dKnJfHgyOGRkZuHTpEry9vZGVlYU2bdpg7dq1cHNzQ1paGoYPH47ExETExMTgqaeeKtTYunUrFixYgJ9++qnQ7QWvE/vga3HDhg0YPnw4gMJfiz///DMAoHPnzoUaly9fxo8//ljoHzMPlHSdWH5PL/+2EEJTYr/MKwReEEJ4lTTAmsrX1xcGgwGpqanIzc1FREREqX8AmLOtdl/Wttp9ts3fl7X9QHZ2NgBAr9dDp9OhLP+QP3DgAK5fv17oNiEEKleuDACwt7cv9VEdtfvl3a5Xrx4qVqxY6LZnnnlGGZLq1q2rHC1NTk5GrVq1ULt2bQD5A+vfh6mC6tev/1D7wQALoNARxbi4ODRt2hRVq1YFgEcOsH/XtGlT5c/9d0II7N+/H+3atSuxU5LMzExlyMvOzkZSUhJq166Nt99+G4sWLVL+HEUNsH/n5ub20J/vl19+Qffu3aHX6wH87x9gJ06cwNNPP4169eoBACpXrlzsnjs6OipH4itXrgw3Nzekp6c/xp+2aI6OjvD29lbaTZo0wcWLFwEAEyZMwIwZM6DRPHp+Wbt2Lfr161ds35SvxaysrIced+rUKTRt2vSx/zwAv6f/E23AAk8ncHJyQlpamvK+0WiEk5OTxbfV7svaVrvPtvn7srYf0Gq1OHLkCDIyMrBz504cOnSoXPuTJk1CSEgITpw4gdDQUEybNk2avprtY8eOoUGDBgD+N6StXr0aS5cuxf79+0vV3L59O2bNmoW4uDjlqNrVq1dx+/ZtfPXVV1iwYAF+//33Mq07Pj4eVatWRZ06dZTbMjMzMXbsWEyaNAnx8fGl6tatWxceHh44duwY6tevD19fX2zZsgVRUVFo3rx5qdd76dIlJCYm4pNPPkFoaCiSk5OV2zUaDWbNmoWJEydiy5YtJjfPnTuHY8eOwc/PDwCwcOFCNGvWDIMGDSr0D6HU1FR4e3vj+eefx2+//WZS+/z584iLi0OrVq2wZcsW1KlTB82aNXvkfXNycvDzzz+jZ8+eJq/9UeLi4pSvxYJOnz4NT0/PUjX5Pd38baDsQ6wAsEOj0fyu0WiGlMeCiIjUdP/+fbRs2RL16tVDq1atSn3kpSgDBw7Exx9/jGbNmmHSpEnFPu1saX212r/99hu0Wq0yINy/fx9paWno3bs3Bg4ciISEBKSkpDx219/fHx999BG8vLxw4MABpZ2eno6BAwdi0KBB2LVrF65cuVKmtRc8ClutWjUsXboUYWFhGDRoEMLCwpCTk/NYzUqVKuHrr7/GlClTcOvWLVhZWaFq1aro1q0bpk+fjiVLlpR6vXl5ebh16xZCQ0PxxhtvYP78+RBCIC8vD4mJiRgxYgSmTJmCw4cPF/vU/wO3bt3Ca6+9hnnz5sHe3h7Dhg1DcnIy4uLi4OjoiHHjxgHIP7p64cIFHDt2DGFhYXjjjTcKnadcVDsgIACff/45dDodZs+ejcmTJxd5/61bt6J169ZwcHB4vE0pYO/evYW+Fh8wGo3Q6/WoWbNmqdtkfmUdYtsKIXwAdAEwQqPRtP/7HTQazRCNRnNEo9EcMSWYnp4OZ2dn5f26deuW21MYarbV7svaVrvPtvn7srb/7saNG9i9ezf8/f3Ltdu/f3/lKNemTZvK9MIuc/fVaMfFxeHs2bPo3bu38hSxvb09nn76aVSqVAl6vR4NGzYs02kX3t7eykBWpUoVNGrUCNbW1rC1tYWrqysyMjJK1c3Ly8PBgwfx3HPPKbfp9XrY29sDyH+Kunbt2spT4abQ6XT4+uuvsWHDBmzbtg1A/jmiD/47Li4O9+/fL/Wg5uDggFatWkGj0aBBgwbQaDTIysqCg4MDmjRpAnt7e9jY2MDLywupqanFtnJzc/Haa6/hzTffRO/evQEAtWrVgpWVFbRaLd577z3lmQwbGxtUr14dANCiRQs888wzOHv2bLHtgIAAvP766+jZsydSUlJw/vx5+Pr6onHjxkhPT0fr1q0LfV1ERkaib9++pdoXIH9vk5KSCn0tPnDq1Cl4eHiUus3v6eZvA2UcYoUQ6f/930wAGwD4PuI+S4UQLU09Z/bw4cNo2LAhXFxcoNfr0b9/f2zevLksyzRLW+2+rG21+2ybvy9rGwCeeuop5RzBChUqoFOnTkhMTCy3PpD/tO2Doad9+/bK07ky9Mu7bTAYEBMTg/79+yvnaAL5w19mZiZyc3Nx//59nD9//qEX8JTkwSvXgfyngWvUqAEAcHd3x7lz55CXl4e7d+8iLS2t1EfXjh8/Dicnp0Jru3HjBvLy8gDk71dGRgZq1aplcvOLL76AwWDA0qVLldu2b9+ONm3aAMg//9fa2rrYV98Xp2XLlsopDhkZGbh37x4qV66MZs2aIS0tDX/99Rfy8vJw5swZ1K1bt8iOEAKDBw+Gm5sbxo4dq9xe8B8EGzZsUAa/K1euKPuSkpKCpKQk1K9fv8j20KFD0bhxY3zwwQcA8q+MceHCBSQmJiIxMRFOTk44cOCAct70jRs3sG/fPnTr1q1U+2IwGLB///6HvhYfrCc+Pr5MQyy/p5u/DQC60j5Qo9HYAtAKIbL++98vAQgt64Ly8vIwcuRIbN++HVZWVli+fHmpzzkyZ1vtvqxttftsm78vaxvIf8pz+fLlypGkqKgobN26tdS9pUuX4rnnnkP16tVx8uRJfPrpp/jPf/6DmTNnQqfT4a+//io0AFhSv7zb69atw/nz55GTk4O5c+eiQ4cO2LdvH/Ly8rB69WoA+UdhXn31VVSsWBHPPvssvvnmGwBAgwYN0KhRoyLba9asQUpKCrKzszFz5kx07twZCQkJuHr1KjQaDapWrYpevXoByL8CQKNGjRAeHg6NRoNWrVopg1BRvvjiC5w+fRo3b95EYGAg+vfvj06dOmHfvn0PvaArPj4ea9asUb6Ghg4dWuSLwv6uVatW6NOnD+Lj47Fjxw4A+a+qj4iIwBdffIGdO3ciNzcX//nPf0zqLViwAGfOnEFWVhZGjhyJ1157DR06dMBXX32FCRMmQKfTYdiwYdBoNLCzs8Mrr7yCjz/+GBqNBl5eXsqLqx5l//79WLVqFTw9PeHl5QUAmDlzJtasWYO4uDhoNBq4uLgolzDbu3cvJk+eDL1eD61WiyVLlhR5NDkmJgY//PADPDw8lPNsQ0JC8PLLLxe5ns2bN6Njx46wtbUtcV/WrVuHc+fOIScnB2FhYYW+FletWgUg/2uxa9euAPLPy7W3t0e1atVKbBeF39PN3wbKcIktjUZTH/lHX4H8YfgHIcSMEh5T+pcBExEVw9RLbJVGUZfY+rcreImt8lbwEltqKHiJrfJW1CW2ykPBS2yp4cElttRQ8DrB5a3gJbbUUNIltqj8mXKJrVIfiRVCpAAo/UsoiYiIiIhKyeIusUVEREREVBIOsUREREQkHQ6xRERERCQdDrFEREREJB0OsUREREQkHQ6xRERERCSdUl9ii4iK9vTTT6vWtra2Vq394LcGqaFt27aqtQGgatWqqrVfe+011dr0zzAajaq158+fr1r7wS91UEtWVpZq7ePHj6vW3rNnj2ptslw8EktERERE0uEQS0RERETS4RBLRERERNLhEEtERERE0uEQS0RERETS4RBLRERERNKxyCHW398fCQkJSEpKQnBwsDRttfuyttXuy9K2sbHBpk2bsG3bNvz8888YM2YMAMDZ2RkbN27Enj17sHDhQuj1+lL1ra2tERUVhc2bNyM6OhqjR49WPjZmzBjs2LEDP/30E95+++1S9Tt37owZM2Zg5syZeOmllwp97OWXX8a3334LOzs7k1orV67EuHHjMHXqVOW2zZs3Y8KECQgNDUVoaChOnjxZ6DHXrl3DqFGjsGPHjhL7ixcvRmBgIMaNG6fcNnfuXIwfPx7jx4/HiBEjMH78eOVjGzZswKhRo/DBBx8gLi6u2HZaWho6duwIDw8PeHp6KpdTCgkJgbOzM3x8fODj44Po6GgAwKFDh5TbvL29sWHDhieuLfPaL168iNdffx0dO3ZEp06dsHz5cgDAjBkz8OKLL8Lf3x9DhgzBjRs3lLU0atQIXbp0QZcuXTBx4sRi92XRokUYNGiQ8vcdAMLCwhAUFISgoCAMGzYMQUFBAPIvQTVhwgSMHTsWEyZMeOjvgDn3xWg04tVXX0WrVq3g6+uLxYsXF/r4ggULYG9vj2vXrgEAfvzxR7Ru3RrPPvssOnXqVOLaZ82ahW7duhX6fmQwGDB06FC88847CA4ORnZ2NgAgIyMDHTt2xMCBAzFw4EDMmTOn2Pbf2draYurUqfj222+xcuVKuLu7Kx/r27cvfv31V9jb2z9W81H4c9T8bY0QolyDxX4yjabET6bVanH27Fl07twZRqMRhw8fRkBAAM6cOVPmz69mW+2+rG21+5baLuo6sZUqVUJOTg50Oh2ioqIQEhKCwMBA/PTTT9iyZQtmzJiBM2fOYPXq1UW2i7tObMF+REQEpk+fjmeeeQZ+fn4IDg6GEAIODg74448/Hvn4oq4T6+TkhOHDhyMkJAT37t1DUFAQVq5ciczMTDg4OGDQoEFwdHTElClTcOvWrUc2Cl4n9uzZs7CxscGKFSuUQXbz5s2oUKHCQwPyA0uWLAEA1K9f/5H3KXid2Pj4eFSoUAGLFi3CF1988dB9v/vuO1SqVAl9+vSB0WhEeHg4Zs6cievXr2PatGkIDw+HVvu/f+MXvE5sRkYGMjIy4OPjg6ysLLRq1Qrr169HZGQk7OzsCg3OAJCTkwNra2vodDpkZGTA29sbRqMROt3Dl+mWtS3j2h9cJ/by5cvIzMyEp6cnbt26ha5du2Lp0qW4dOkS2rRpA51Oh1mzZgEAPvroI6SlpWHQoEH4+eefH7kPAHD48GHlvx98LS5YsABz58596L7ffvstKlWqhL59+yIlJQVVq1aFg4MDLly4gOnTp2Pp0qWF7l/wOrFq7MuDwfHSpUu4dOkSvLy8kJWVhfbt22PNmjVo0qQJjEYjRo4ciaSkJOzduxfVq1dHbGwsGjVqhGrVqmHHjh2YNWsWfv3110Kfv+B1YuPi4lCxYkXMmDED3333HQDgvffew/Dhw+Ht7Y2tW7ciIyMDgYGByMjIQHBwsHK/R/nkk0+K/NiHH36IEydOIDo6GjqdDjY2NsjOzkaNGjUwfvx4ODs74/3338fNmzeLbOzevbvIjwH8OapGWwihKbFf5hWWM19fXxgMBqSmpiI3NxcRERHo0aOHxbfV7svaVrsvWzsnJwcAoNPpoNfrIYRAmzZtlCMl69atK3KIe9y+TqeDEAIBAQFYuHAhHvyDtagBtjh16tRBcnIy7t69i/v37yMhIQEtW7YEALzxxhv48ccf8Tj/IG7UqBFsbW1Nvv+xY8fw1FNPoU6dOibd393dvcijwkIIHDhwAM899xyA/IGjTZs20Ov1qFmzJmrXrg2DwVBk29HRET4+PgCAypUro0mTJkhPTy/y/pUqVVKGhDt37kBalLMOAAAgAElEQVSjKfr7sqxtmddeq1YteHp6AgDs7OzQoEEDXL58Ge3bt1ca3t7eyMjIKLJRnJK+FmNiYpR/4NWvXx8ODg4A8p+huXv3LnJzc4tsq7kvtWvXhpeXl9Ju3LgxLl68CCB/mJ82bVqhx/v5+aFatWoAgFatWin3LYqXl9dDRz/T0tKUz9myZcsSB0dT2NraolmzZsr32Hv37imD+ogRI/DVV1+V+XMA/Dn6T7QBCxxinZyckJaWprxvNBrh5ORk8W21+7K21e7L1tZqtYiOjsbRo0fx22+/4fz587h58yby8vIA5B9ZqV27dpn6mzdvxsGDB7F//34cP34cTz/9NF599VWsX78e33zzDerVq/fYXaPRiMaNG8PW1hbW1tZo3rw5HBwc4O3tjevXrxfap7L49ddfERISgpUrVyo/aO7cuYPt27eja9eu5fI5zpw5gypVqsDR0RFA/lBfvXp15ePFHan+u3PnziEuLg5+fn4A8p869vLywuDBg3H9+nXlfrGxsfD09ETz5s2xePHiIo9mPgltmdeelpaG06dPK4PUA2vXrkWHDh0K3a9Lly7o168fDh06VGK3KH//Wizo4MGDcHV1Nfn0IjX35fz58zhx4gRatmyJrVu3wtHRURn8H2XVqlXo3LmzSesuyNXVFb/99huA/O8FmZmZyscyMjIwaNAgjBw58rF+81ft2rXx559/Ijg4GEuXLkVQUBAqVKiA5557DlevXkVycvJjr/NR+HPU/G3AAodYoifZ/fv38corr+DZZ5+Fl5cXnnnmmXLvd+/eHe3atUOzZs3QsGFDWFtb46+//kLv3r2xdu1a5anRx5GRkYGtW7diwoQJCAoKwoULF6DX69GtWzesX7++XNbeoUMHzJgxA5988gmqVKmCyMhIAMCWLVvQqVMnVKhQoVw+z/79+5WjsGVx69Yt9O3bF2FhYbC3t8fQoUORlJSEo0ePwtHRUTnPEcg/SnXy5EnExsbis88+w507d57Itsxrz87OxtChQzF58mRUrlxZuX3BggXQ6XTK0/g1a9bEgQMHsG3bNnzyyScYPXp0qX9V6759+x7565jT0tKwevVqvP/++yZ11N7zt956C59++il0Oh3mzJmDSZMmFXn/vXv34rvvvkNISIhJay/oww8/xMaNGzF48GDcvn1bGeCrV6+OqKgoLF++HKNGjUJoaKjyj9ySWFlZoVGjRti8eTOGDBmCO3fu4J133sGbb76JFStWPPYaybJY3BCbnp4OZ2dn5f26desW+/SIpbTV7svaVrsva/vmzZuIiYlBixYtYG9vDysrKwD5Tw9eunSpzP2srCzExsaiffv2uHTpkvJiqB07dqBJkyalau7duxdTpkzBzJkzkZ2dDaPRiBo1amDatGmYM2cOHBwcEBoaiipVqpSqb29vD61WC61Wi3bt2uHcuXMAgNTUVKxbtw4fffQRdu7ciejoaOzatatUnyMvLw+HDh0qdO6vg4OD8uIUIP/I7IOndIuSm5uLPn364I033kDv3r0B5D8tbWVlBa1Wi8DAwELnRT7g5uYGOzs7nDp16olry7z23NxcDB06FD179kSXLl2U2yMjI7Fz506Eh4crT53b2NgoT5t7enqiXr16SE1NLXZfHiUvLw+xsbEP/YPq2rVrmD17NkaNGmXSszJq78uAAQPQr18/dO/eHampqTh//jyee+45eHh4ID09He3atcPly5cBAKdOncLIkSOxZs2aQs9umKpevXoICwvDsmXL0LFjR+WInbW1tfJ9pXHjxqhTp47Jz/5cuXIFV65cUc7B3LNnDxo1aoTatWvjm2++wZo1a1CjRg0sXbpU+f+1NPhz1PxtwAKH2MOHD6Nhw4ZwcXGBXq9H//79sXnzZotvq92Xta12X6a2g4ODcg6YjY0N2rVrh6SkJBw4cACvvPIKgPwXEBX3gpGS+g+OINnY2KBNmzZISUnBL7/8gmeffRZA/vlJpfmBC0BpOzg4oEWLFti/fz9GjRqlvMr6jz/+wOTJk5VXcT+uP//8U/nvY8eOKee/TpgwAbNmzcKsWbPQsWNHvPLKK3jxxRdL9TlOnjyJOnXqFPoB27JlS8TExCA3NxeZmZnIyMhAgwYNimwIIRAYGAg3N7dCrzgveM7kxo0b0bRpUwD5Q/i9e/cA5D8tm5CQABcXlyeqLfPahRCYMGECGjRogPfee0+5fffu3ViyZAmWLVuGihUrKrdfu3ZNOf3nwoULSE1NLfKFnMU5ceIEnJycCn0tZmdnY+bMmXjzzTdN+sem2vsyYsQING7cGCNHjgQANG3aFCkpKTh16hROnToFJycn/Pbbb6hVqxbS0tLw5ptv4uuvv0bDhg0fez8AKKc93L9/H999951y7uT169eVPb948SKMRqPJ58dfv34dmZmZyiDl4+ODs2fPonfv3ggICEBAQACuXLmCIUOGFDrt4nHx56j52wBg2glOZpSXl4eRI0di+/btsLKywvLlyxEfH2/xbbX7srbV7svUrlmzJsLCwpSjjf/3f/+HXbt2ISkpCQsXLkRQUBBOnz6NH3/8sVT9GjVqYPbs2Up/27Zt+PXXX3HkyBGEhYXh3XffRU5OTrFPBRZn1KhRsLOzQ15eHlatWqW8iKw0vv76ayQmJuLWrVuYMGECunfvjsTERKSlpUGj0aB69eoYMGBAqfvz5s1DfHw8srKyMHToUPTr1w8vvvjiI08lcHZ2RuvWrTF27FhotVoMHjy40JUJ/m7//v1YvXo1PD09lRfVTJ8+HRERETh+/Dg0Gg3q1aunXE1h3759mD17NvR6PbRaLRYuXIinnnrqiWrLvPYjR45g/fr1aNKkiXIUdvz48Zg6dSru3r2rfB16e3tj5syZiI2NRVhYGPR6PTQaDWbOnFnoyhh/N3fuXJw+fRpZWVkYMmSIcjmvR30tbtu2DZcuXUJUVBSioqIAQDm9xtz7cvDgQURERKBp06bKOidPngx/f/9H3v+zzz7D9evXMXbsWAD5Ly7ds2dPkfsydepUHDt2DDdu3EDv3r0xaNAg3L59Wzk96fnnn1f+cX/8+HEsW7YMOp0OGo0GQUFBj3VJrPnz52PSpEnKVRk+++wzkx9rKv4cNX8bsMBLbBE9CUpzZMZUxV1iq6yKusRWeXjUuX/lqbhBoqwKXmKLngwPLrGlhkc9fV9eCl5iSw2mnmtaGo/zgqzHVdwltspDeVwpgR6PlJfYIiIiIiIqCYdYIiIiIpIOh1giIiIikg6HWCIiIiKSDodYIiIiIpIOh1giIiIikg6HWCIiIiKSDq8TS/9KXl5eqvZL+2tRTVHaX+tKJJP79++r2h80aJBq7Vu3bqnWVlvB3/ZV3sryG7FKkpiYqFqb/hm8TiwRERERPZE4xBIRERGRdDjEEhEREZF0OMQSERERkXQ4xBIRERGRdDjEEhEREZF0LHKI9ff3R0JCApKSkhAcHCxNW+2+rG21+2q0tVot1qxZg/DwcABAq1at8MMPPyAyMhKhoaGwsrIyqWM0GtG9e3c8++yzaN26NZYsWQIAmDFjBtq2bYv27dujd+/eymVtbt68iYCAALRr1w6tW7fG999/X2w/LS0NHTt2hIeHBzw9PTF//nwAQEhICJydneHj4wMfHx9ER0cDAA4dOqTc5u3tjQ0bNrD9GG2Z1y77vnTq1AnNmjVD8+bNlXZoaCjq1auHFi1aoEWLFti2bRsA4Nq1a+jUqROqVq2K0aNHF7vuv3vppZcwc+ZMzJw5E/7+/gCA1157DdOnT8e0adMwfvx4VK1a9bGaBXXt2hXz5s3DvHnzMGbMGOj1evznP//BggULMG/ePIwYMcLk7y/mbK9fvx6rV6/Gt99+i+XLlyu39+nTBxEREfj+++8xYsSIUrWtra2xdu1abNy4EVu2bMGoUaMAAH5+fli3bh02b96MTz/9tNRrL0i2n0Xm6svatrjrxGq1Wpw9exadO3eG0WjE4cOHERAQgDNnzpT586vZVrsva1vtfmnbJV0ndsCAAXB3d4etrS3+85//IDo6Gu+//z4uXLiAYcOGISMjAxs3bizy8Q+uE3vp0iVcvnwZzZs3R1ZWFl588UWsWrUKderUgb29PQDgq6++QmJiIsLCwhAWFoabN29i6tSpuHr1Knx9fZGQkABra2ulXfA6sRkZGcjIyICPjw+ysrLQqlUrrF+/HpGRkbCzs8O4ceMKrSsnJwfW1tbQ6XTIyMiAt7c3jEYjdDrdQ38Gth9uy7x22fal4HVi/9728/NDVFQUoqKiYGdnh7FjxxZqZ2dn49ixYzh9+jROnz6tDL0FPeo6sU5OThgxYgSmTp2Ke/fuYfz48VixYgVu3ryJO3fuAAA6d+4MJycnrFy58pH7ABR9nVgHBwfMmDEDH3zwAe7evYtx48bh6NGjuHHjBo4ePQoAGDNmDOLj47F9+/Yi+2q2i7pO7Pr16zFw4EDcuHFDuc3Hxwfvvvsuxo0bh9zcXFSrVq3Ya8EW97FKlSohJycHOp0O33//PT799FOEhYVh4MCBOHfuHEaNGoWLFy9i3bp1j3y8KdeJtcSfRZbQt9S2lNeJ9fX1hcFgQGpqKnJzcxEREYEePXpYfFvtvqxttftqtGvWrIm2bdsqR4aqVq2K3NxcXLhwAQBw8OBBdOzY0aRW7dq10bx5cwBA5cqV0ahRI2RkZCgDLJD/w1yjyf+7qtFocOvWLQghkJ2djWrVqhU5NACAo6MjfHx8lH6TJk2Qnp5e5P0rVaqk9O7cuaN8XrZNa8u89idtXy5evFjk/W1tbdG2bVtUqFCh2DX/XZ06dZCcnIy7d+/i/v37SEhIQMuWLZUBFgBsbGxQloM/VlZWsLa2hlarhY2NDf744w9lyASApKQkVK9e3eLaj9K7d2+sWrUKubm5AMr2ywxycnIAADqdDjqdDnl5ecjNzcW5c+cAADExMXjppZfKtF7ZfhaZqy9rG7DAIdbJyQlpaWnK+0ajEU5OThbfVrsva1vtvhrt8ePHIzw8XDkSdP36deh0Ori7uwMAOnXqhFq1aj1298KFCzhx4gRatGgBAJg+fTo8PDwQGRmJjz76CAAQGBiIs2fPwt3dHW3btsWsWbOg1Zr21/TcuXOIi4uDn58fAGDRokXw8vLC4MGDC/1wiY2NhaenJ5o3b47FixcXOySz/WSu/UnYF19fXwDA4sWL4e3tjcDAwDL/Rqj09HQ0btwYdnZ2sLa2RvPmzZWhr0+fPpg7dy7atGmD9evXl6r/xx9/YNOmTfjqq6+wbNky5OTk4Pjx48rHrays0KFDBxw7dsyi2gAghEB4eDhWrFihDCHOzs5o3rw5vvnmGyxevBhubm6lagP5R+w2bNiA/fv3IyYmBidOnICVlRU8PDwA5D8l7ejoWOo+IN/PInP1ZW0DFjjEEv2T2rVrhz/++OOhpzo+/PBDjBs3DqtWrUJ2dvZj/0rMW7du4Z133sHMmTOVo7Aff/wxTp06hb59++Lrr78GkH8agoeHB+Lj47Fnzx5MmDABN2/eNKnft29fhIWFwd7eHkOHDkVSUhKOHj0KR0dHBAUFKff18/PDyZMnERsbi88++6zQUSa2TWvLvHbZ96Vfv3744osvYG9vj/fffx+JiYn4/fff4ejoiPHjx5e4vuJcvHgR//d//4fx48cjKCgI58+fV/6uR0VFYcyYMYiJiUGnTp1K1be1tYWvry+GDRuGwMBA2NjYoH379srHhwwZgvj4+FI9jatmGwCGDh2Kd999F2PHjsVrr70GLy8vWFlZwd7eHoGBgVi4cCGmT59eqjaQf/pIr1690KFDBzRr1gwNGzbEuHHj8OGHH2Lt2rXIzs5GXl5eqfv0ZLK4ITY9PR3Ozs7K+3Xr1i32KSlLaavdl7Wtdr+8215eXnj++eexdetWfPrpp2jVqhWmT5+OEydOYPDgwXjrrbdw9OhRnD9/3uRmbm4u3nnnHfTp0wfdunV76ON9+/bFli1bAAA//PADunXrBo1Gg/r166NevXpISkoqsd+nTx+88cYb6N27NwCgVq1asLKyglarRWBgIA4fPvzQ49zc3GBnZ4dTp06x/Rhtmdcu+77069cPAQEB6NWr10PtwYMH48iRI8WuzxR79+7FlClTMHPmTGRnZ+PSpUuFPn7gwAG0atWqVO1mzZrh8uXLuHnzJvLy8hAbG4smTZoAAPr16wd7e3usWLHC4toAcOXKFQD5z0zt2bMH7u7uuHLlCnbv3g0AiI+Px/3798v0ojcAyMrKQmxsLNq1a4e4uDgMGDAA/fr1w5EjR5RTC0pLpp9F5uzL2gYscIg9fPgwGjZsCBcXF+j1evTv3x+bN2+2+LbafVnbavfLu71gwQK8/PLLePXVV/Hhhx/i8OHD+Pjjj1GtWjUAgF6vx7vvvouoqCiTekIIjB49Go0aNSr0yt3k5GTlv6Ojo9GwYUMA+X/B9+zZAwDIzMyEwWCAi4tLsf3AwEC4ublhzJgxyu0FX5yxceNGNG3aFACQmpqKe/fuAQDOnz+PhISEIvtsP5qsa5d9X9577z00adLEpHZZVK5cGQBQvXp1tGzZEgcOHCh0+pCPj0+x5+MW5+rVq2jUqJHyQk1PT08YjUZ06tQJXl5emDt3bqnPt1WzXaFCBVSqVEn5bz8/P6SkpGDv3r3K6VHOzs7Q6/X4888/H7tfrVo1Zd9tbGzQpk0bpKSkwMHBAUD+993AwEBERESUav0PyPSzyJx9WdsAYNoJTmaUl5eHkSNHYvv27bCyssLy5csRHx9v8W21+7K21e6rvfYH3nnnHbRr1w5arRaRkZGPPJr0KLGxsfjxxx/h7u6uPLX3ySefYNWqVTAYDNBqtXB2dsYXX3wBAAgKCsKIESPw3HPPQQiBKVOmFPtCjP3792P16tXw9PRUXvgyffp0RERE4Pjx49BoNKhXr55yaa99+/Zh9uzZ0Ov10Gq1WLhwIZ566im2TWzLvHbZ9+X777+Hh4dHoXPKC7ZdXFywePFi5TENGjTAzZs3cffuXWzevBnR0dHKee3FGT16NOzs7JCXl4fvvvsOOTk5GDx4MBwdHXH//n1cu3at2CsTFCcpKQkHDhzAnDlzcP/+faSkpGDHjh1Ys2YNrly5glmzZgHIf/FoZGSkxbQdHBzw6aefAsg/t3bHjh04ePAgdDodJk2ahNWrV+PevXuYNm3aY3UfqFGjhnIJLY1Gg59++gm7d+/G+PHj0aFDB+WSh7GxsaXqPyDzzyJZ1672vljcJbaIzKGkS2yV1YNLbKmh4CW2iJ5Uj3ve+eN61CW2yktRl9iSQVGX2CoPZX3hXXFMucQWyUXKS2wREREREZWEQywRERERSYdDLBERERFJh0MsEREREUmHQywRERERSYdDLBERERFJh0MsEREREUmHQywRERERSYe/7ID+lR78OkO1lPU3yxSnfv36qrXJ/NT8WgFQql8DaqoXXnhBtfbdu3dVawP8pSFElo6/7ICIiIiInkgcYomIiIhIOhxiiYiIiEg6HGKJiIiISDocYomIiIhIOhxiiYiIiEg6FjnE+vv7IyEhAUlJSQgODpamrXZf1rba/fJsz58/HwkJCdi3b59ym4eHB7Zv347du3dj586d8PHxMbmXkZGBAQMG4OWXX0aXLl2wcuVKAMC2bdvQpUsXNGrUCCdPnlTuf/fuXQQHB+PVV19Ft27dSrz8UlpaGjp27AgPDw94enpi/vz5AICQkBA4OzvDx8cHPj4+iI6OBgAcOnRIuc3b2xsbNmxg+zHaavdnzJiBV155BW+++aZyW1JSEt577z0MGDAA48ePR3Z2NgDg3r17mDZtGgYMGICAgAB89913xa4bAObOnYuAgAAMGzZMuS05ORljxozByJEjMXr0aCQmJgIAfv31VwwfPhzDhg3DuHHjkJKSUuK++Pv7w9vbGz4+Pli4cGGhj8+bNw8VK1bE1atXAQBhYWHw8/ODn58fWrRoAVtbW/zxxx+PbBuNRnTt2hW+vr7w8/PDl19+WejjCxYsQJUqVXDt2jUAwNmzZ9GpUyfUqFFD+f+nNPh90fxttfuyttXuy9qGEMJsbwBESW9arVYYDAbh6uoq9Hq9iIuLE25ubiU+7p9uy7z2f+O+ODg4PPLt1VdfFR06dBDx8fHKbbt27RJ9+/YVDg4Ool+/fuK3334r8vEP3pKSkkRSUpLYv3+/2Lhxo0hKShLHjh0TLi4uIjo6Wmzbtk1s375d+Pr6ivXr1yv3nzJliujdu7dISkoSBw8eFE2bNhWJiYnKx5OSkkReXp7yZjQaxeHDh0VeXp74888/RcOGDcXJkyfF5MmTxezZswvdNy8vT2RlZYm//vpLeWyNGjWU9//+xvbDbTX6MTExytuiRYvEihUrhKurq3JbkyZNxKJFi0RMTIyYOHGiePfdd0VMTIyYOnWq6Nixo4iJiRG7du0StWvXFuvWrSvUi4mJEdHR0crbZ599JubPny/q1aun3Obt7S1CQkJEdHS0CAkJEZ6eniI6OlrMmTNH/Pjjj8rtjRo1KtSKjo4Wt2/fVt5SUlJETEyMuH37tsjMzBQNGjQQR48eFbdv3xZnz54VnTp1Es7OziItLa3Q427fvi2ioqLE888/X+i2GzduKG+JiYliz5494saNG8JoNIpnnnlGxMbGihs3bojTp0+LF198UTg7O4uUlBRx48YNYTAYxK5du8S4cePEtGnTCrUevPH7ouW1ZV4796X826bMlRZ3JNbX1xcGgwGpqanIzc1FREQEevToYfFttfuyttXul3f7wIEDuH79eqHbhBCoXLkyAMDe3h6XLl0yuVezZk00bdoUAGBnZ4dnnnkGly9fRoMGDR75SwsMBgNat24NAKhevTrs7e0LHan9O0dHR+XIcOXKldGkSROkp6cXef9KlSpBp9MBAO7cuQONpuhrSbNt/r63tzfs7e0L3ZaWlgYvLy8AQKtWrbB7927lY3fu3MG9e/fw119/Qa/Xw9bWtti1e3p6Kl/LD2g0GuTk5AAAsrOzlV8E4u7urty3SZMmylHOojg6OsLb2xvA//bl4sWLAIAJEyZgxowZRf7Z165di379+hXZrl27trIHlStXRuPGjZX2Rx99hNDQ0ELtGjVqoEWLFtDr9cWuuTj8vmj+ttp9Wdtq92VtAxZ4OoGTkxPS0tKU941GI5ycnCy+rXZf1rbafbXXDgCTJk1CSEgITpw4gdDQUEybNq1UHaPRiPj4eDRv3rzI+zRp0gQ7d+7EvXv3kJaWhlOnTiEjI8Ok/rlz5xAXFwc/Pz8AwKJFi+Dl5YXBgwcXGsxjY2Ph6emJ5s2bY/HixcqAxfbjtc3RBwBXV1fs3bsXALBr1y5kZmYCAF588UVUqFAB3bt3R69evRAQEPDQAGyKIUOGYPny5Xj77bexbNkyvPvuuw/dZ8eOHWjRooXJzfPnzyMuLg6tWrXCli1bUKdOHTRr1uyR983JycHPP/+Mnj17mtw+ceIEWrZsia1bt6JOnTrw9PQ0eW2m4vdF87fV7svaVrsvaxuwwCGWyNIMHDgQH3/8MZo1a4ZJkyaV6hy77OxsjBw5EpMmTXroSFhBffr0Qe3atdGrVy/MmDEDPj4+sLKyKrF/69Yt9O3bF2FhYbC3t8fQoUORlJSEo0ePwtHREUFBQcp9/fz8cPLkScTGxuKzzz7DnTt32H7Mtjn6D0ycOBHr16/HwIEDkZOTowzA8fHxsLKywubNmxEVFYWIiIhijwgXJTo6Gu+99x6+++47vPfeewgPDy/08ePHj2PHjh0YNGiQSb1bt24hICAAn3/+OXQ6HWbPno3JkycXef+tW7eidevWJv0q6Fu3buGtt97CrFmzoNPp8MUXX2DixIkmrYuInjwWN8Smp6fD2dlZeb9u3bql+sZs7rbafVnbavfVXjsA9O/fH1u2bAEAbNq06bFe2AUAubm5GDlyJLp37w5/f/9i76vT6TBp0iRs2bIFS5Yswc2bN+Hi4lJiv0+fPnjjjTfQu3dvAECtWrVgZWUFrVaLwMBAHD58+KHHubm5wc7ODqdOnWL7Mdrm6Bfk4uKC8PBwrFixAp07d1aOYuzYsQN+fn7Q6XRwcHCAp6cnEhISTO4+8Msvv+C5554DALRr1055YRcApKamIjw8HJ988olJR3lzc3MREBCA119/HT179kRKSgrOnz8PX19fNG7cGOnp6WjdunWhU3IiIyPRt29fk9pvvfUW+vXrh+7duyM1NRXnz59H27Zt4enpifT0dLRv3x6XL19+7D14FH5fNH9b7b6sbbX7srYBCxxiDx8+jIYNG8LFxQV6vR79+/fH5s2bLb6tdl/Wttp9tdcOAJcuXVJ+yLdv3x7JyckmP1YIgYkTJ+KZZ54x6UjW7du3lfMT9+3bBysrKzRs2LDYfmBgINzc3DBmzBjl9oKnIGzcuFE5Lzc1NRX37t0DkP+0bEJCQpFDMtuPpnb/7x68Yv/+/ftYuXIlevXqBSB/aP79998B5H/dnD59GvXq1TO5+0D16tWV866PHz+uDMmZmZmYPn06goKCULdu3RI7QggMHToUjRs3xgcffAAg/8oeFy5cQGJiIhITE+Hk5IQDBw6gdu3aAIAbN25g37596NatW4ntkSNHonHjxhg5ciQAoGnTpkhOTsbJkydx8uRJODk5Ye/evahVq9Zj78Gj8Pui+dtq92Vtq92XtQ0AFnd1AgCiS5cuIjExURgMBjFx4sRye3Wf2m2Z1/5v25eirioQFRUlMjIyxN27d0V6eroYNWqU6NKlizh27Jg4efKkOHLkiHjhhRdMvjrBmjVrBADRuHFj0aRJE9GkSRPx9ddfi0WLFolatWoJvV4vqlevLtq2bSuSkpLEr7/+KlxdXUX9+vVFmzZtxO7duwtdmeDvVyfYs2ePACA8PT1F8+bNRfPmzcWWLVvEm2++KVHle4EAACAASURBVDw8PISnp6fo2rWrMBqNIi8vT6xcuVK4u7uL5s2bC29vb7Fu3boiX4XPtnn6Ba8k0KlTJ1G9enVhZWUlatSoIT766CPxwQcfCGdnZ+Hs7CwGDBgg9u/fL2JiYsQvv/wiXnjhBeHq6ipcXFzEiBEjHroywd+vTvD888+LatWqCSsrK1G9enXxwQcfiM8//1w0aNBAuLq6ikaNGonw8HARHR0tXnrpJWFnZyfq168v6tevLxo0aFDs1Ql++eUXAUB4eHiIZs2aiWbNmokNGzYUus/TTz9d6OoES5cuFX369HnoagV/vzrBTz/9JACIpk2bCk9PT+Hp6SkiIyML3efpp59Wrk5w9uxZUadOHVG5cmVRpUoVUadOHZGWlvZYVyf4N35ftIS2zGvnvpRv25S5UvPf4dIsNBqN+T4ZUTFMOf+uLEq6vmtZPOqqBiQvNb9WAODPP/9Urf3CCy+o1r57965qbQCoUqWKqn0iKhshRPGXioEFnk5ARERERFQSDrFEREREJB0OsUREREQkHQ6xRERERCQdDrFEREREJB0OsUREREQkHQ6xRERERCQd3T+9AKJ/woPfgqSW8ePHq9bu2rWrau1jx46p1p4/f75qbbXFxcWp1u7cubNqbQDIzs5Wrf3gt5Kp4cFv/SIiKgqPxBIRERGRdDjEEhEREZF0OMQSERERkXQ4xBIRERGRdDjEEhEREZF0OMQSERERkXQscoj19/dHQkICkpKSEBwcLE1b7b6sbbX7MrW7deuG8PBwhIeHY+zYsdDr9ahZsyY+++wzLF68GOPGjYNOZ/qV71asWIExY8Zg8uTJym2bNm1CUFAQQkJCEBISghMnTigfS0tLw8yZMzF58mRMmTIFubm5RbZ37tyJ5cuXY82aNcptd+7cwaZNm7B69Wps2rQJd+7cAQAcPXoUERERiIiIwJo1a7B48WLlY4+SlpaGjh07wsPDA56ensrlt0JCQuDs7AwfHx/4+PggOjoaAHDo0CHlNm9vb2zYsOEfaQPApUuXMGTIEPTp0wd9+/bFDz/8AAC4ceMGhg8fjp49e2L48OG4efMmAODIkSNo3749AgICEBAQgKVLlxbbL2jx4sVITU3FoUOHlNt69eqFw4cP4+bNm/D29ja5VRI1/h5ptVpERkZi0aJFAIDQ0FCsW7cO69evR1hYGCpWrGhSZ+XKlRg3bhymTp2q3LZ582ZMmDABoaGhCA0NxcmTJws95tq1axg1ahR27NhR6vXz+6L522r3ZW2r3Ze1rRFClGuw2E+m0ZT4ybRaLc6ePYvOnTvDaDTi8OHDCAgIwJkzZ8r8+dVsq92Xta1231LbPXv2fOg2BwcHzJw5E6NHj8bdu3cRFBSE33//HS1atMDBgwexb98+DB06FKmpqdi+fXuR7YLXiT179ixsbGywbNkyhIaGAsgfYitUqAB/f/9Cj8vLy0NoaCgCAwPh7OyMW7duoVKlStBq//dv2YLXib148SL0ej1++eUXBAQEAABiYmJgY2ODFi1a4Pfff8dff/2FNm3aFPo8qampOH78+EN7UPA6sRkZGcjIyICPjw+ysrLQqlUrrF+/HpGRkbCzs8O4ceMKPTYnJwfW1tbQ6XTIyMiAt7c3jEbjIwd+NdoFrxN75coVXL16FW5ubsjOzsaAAQPwxf+zd+9xUdX548dfMzBqongBNEQSUhRWR2E0yFIyNS9tq+lqmb9qt8CyjdosL2mreUFd8VamZJbobmZoWYqmKXkrNZEyFDUMDBHwrmCigTh8fn+Y5yslMCgH57jv5+NxHjJnznnNcR5n8OOZM2dmzmT16tW4u7vzzDPPsGjRIs6fP8/LL7/Md999x4cffsjbb7/9h20FCA8Pv+58gPvvv5+CggLef/99QkNDAWjVqhUlJSXMmTOHMWPGVHhtX0euE3uj+3pF14l9+umnad26NXXq1OHFF1/Ezc1N254RI0Zw9uxZFi5ceN11r71O7NX9fNGiRdpANiEhgVq1atGjR4/rrj9//nwA7r777usu89xzz5W77fJ7sfrbeveN2ta776xtpZSpwv5Nb2EVCw0NJSMjg8zMTIqLi4mPj6dv375O39a7b9S23n2jtV1cXKhRowZms5maNWuSl5eH1Wplx44dAGzevJmwsDCHey1btsTNzc2hZffv30/Tpk3x9fUFoE6dOqUGsL/XpEkTatasWWpeZmYmgYGBAAQGBpKZmfmH9dLT0wkICCh3W7y9vbHZbADUrVuXwMBAcnNzy1y+du3a2qCysLAQk6ns3216tgG8vLwICgoCwM3NDX9/f06ePMnWrVu1/2A88sgjbNmypdyOI7Zv305eXl6peQcPHiQ9Pf2m29fSY19v3Lgx4eHhrFixQpt37YC6Vq1aOHoQpTL7OVz5z5inpydNmjRxfIN/R34vVn9b775R23r3jdoGJxzE+vj4kJ2drd3OycnBx8fH6dt6943a1rtvpPbZs2dZtWoVCxYsIC4ujgsXLnDo0CEuXLhASUkJAKdPn8bDw+Omt33Tpk28+eabLFq0SBs4nDhxApPJxOzZs5k4cSLr1q2rdPfixYvaYKJ27dpcvHix1P3FxcUcOXKE5s2bO9w8fPgwKSkp2uB93rx5BAcHExERUWoAl5SUhNVqpV27dsTGxjp02oWebbhytDotLY02bdpw5swZvLy8APD09OTMmTPacqmpqQwaNIiXXnqJQ4cOOdSuTnq8jkaNGsWsWbP+MFCdNGkSW7duxd/fXzsV40Zt3ryZCRMmsHjxYm0/LywsZP369Tf9zXbye7H623r3jdrWu2/UNjjhIFaI25WbmxuhoaEMHTqUiIgIatWqpR0xrEpdunRh6tSpvPnmm9SrV4/ly5cDUFJSQkZGBpGRkYwaNYoffvjhpt4uMplMfzhqefjwYby9valVq5ZDjYKCAgYOHMisWbNwd3dn6NChpKens3v3bry9vRk+fLi2bFhYGKmpqSQlJTFt2rRyz7nVuw1XBvQjRoxg+PDh1KlTp9R91z43gYGBrFmzhvj4eB5//PE/nM5wO3rggQc4e/YsBw4c+MN9Y8eO5cEHH+Tnn3+mV69eN/wYXbp0YfLkyYwdO5Z69erxySefALB69Wq6d+/u8D4ohDAupxvE5ubmam93AjRt2rTctwKdpa1336htvftGardr144TJ07wyy+/YLfb2blzJ4GBgbi5uWlv6//+CN6NqFevHmazGbPZTHh4uPaWf4MGDQgICKBu3brUrFkTq9VKVlZWpdq1a9fWjnhduHDhDx/MceRUgquKi4sZMGAAgwcPpn///sCVt6BdXFwwm81ERkaSnJz8h/WCgoKoU6cO+/btuyXtq/0RI0bQu3dvunbtCoCHhwenTp0Crpw327BhQ+DKaRu1a9cGoFOnTly+fPkPpwjcalW9r4eEhNClSxfWr1/P9OnTCQ0N5d///rd2f0lJCevWreOhhx664cdwd3fX9vPOnTtz+PBh4MopLytWrGD06NFs3LiRtWvXsmnTpkr35fdi9bf17hu1rXffqG1wwkFscnIyAQEB+Pn5YbFYGDRoEAkJCU7f1rtv1LbefSO1T506RcuWLalRowYAbdu2JTs7m3379mkfjnrwwQdLfRL9RuTn52s/7969W3vrpnXr1uTm5lJUVITdbuenn36q9DmDfn5+pKWlAZCWloa/v792X1FREUePHi01ryxKKSIjIwkKCmLYsGHa/GPHjmk/r1y5UvvgUGZmJpcvXwYgKyuLtLQ0/Pz8qr19tT9p0iT8/f158skntfnh4eGsWbMGgDVr1vDAAw8AV04RufqW+r59+ygpKaF+/frlP0HVrKr39bfeeovu3bvTs2dPRowYwa5du3j99ddL/WP24IMPXvecakddu5//8MMP2r48cuRIpk6dytSpU+nWrRsPP/yw9h+NypDfi9Xf1rtv1LbefaO2ARy/lk81sdvtREVFsX79elxcXIiLi7vuW1LO1ta7b9S23n0jtdPT0/n222+ZOXMmJSUl/Pzzz2zYsIHvv/+e1157jcGDB5OZmclXX33lcHPBggUcPHiQgoICRowYQZ8+fTh48KB2DpKnpydPPfUUcOV0hoceeojJkycDYLVaadu2bZntDRs2kJubS2FhIYsXLyY0NJT27dvz5Zdf8uOPP1K3bt1SV0D4+eef8fX1xWKxVLjd27dvZ8mSJVitVu2UiujoaOLj49mzZw8mk4lmzZppnzDftm0bMTExWCwWzGYzc+fOxdPTs9rbcOVKBV988QUtWrTQrtrw4osv8ve//53XX3+dVatW4e3trR153LhxI59++ikuLi7UrFmTqVOnVvjhsasWLVpE586d8fDw4ODBg0yePJm8vDxmzJiBp6cnK1asYO/evde9GkZl6P07AK6cYjFlyhTc3NwwmUwcPHiQSZMmObTu+++/r+3nI0eOLLWfm0wmPDw8Sv2HoirI78Xqb+vdN2pb775R2+CEl9gS4nZws4OK8tzsB1bKU9Hlmm7GtZfYMpprL7FV1cq7xFZVcOQSWzeqokts3YxrL7Glh4ousSWEuLUMeYktIYQQQgghKiKDWCGEEEIIYTgyiBVCCCGEEIYjg1ghhBBCCGE4MogVQgghhBCGI4NYIYQQQghhODKIFUIIIYQQhiPXiRXCYNzd3XVrnz9/Xrf2e++9p1sbICIiQrd2VV9I/1off/yxbm0hhDAquU6sEEIIIYS4LckgVgghhBBCGI4MYoUQQgghhOHIIFYIIYQQQhiODGKFEEIIIYThyCBWCCGEEEIYjlMOYnv27ElaWhrp6emMGjXKMG29+0Zt692X9hVz584lIyODb7/99g/3RUVFce7cORo2bHjTjwOwcOFCjh8/zt69e29o/cWLF/Paa68xfvx4bV5CQgIjR45k4sSJTJw4kdTU1FLrnDlzhpdeeokNGzaU287OzqZbt260adMGq9XKnDlzAJgwYQK+vr7YbDZsNhtr164FYNeuXdq8kJAQPv/880r9XXr16sW0adOIiYmhV69eALi5uTF69GhmzZrF6NGjcXNzq1TzeuQ1enu19e4bta1336htvftGbaOUKncC4oCTwL5r5jUEEoH03/5sUFHnt/VURZPZbFYZGRnK399fWSwWlZKSooKCgipc71a3jbzt8rwYq+3u7n7dqVevXqpz585q//79peYHBQWpr776SmVlZSk/P78y13d3d1cmk8mhKTw8XNlsNpWamurwOgsWLNCm4cOHqzfeeEM1adJEm/fII4+oAQMGlFru2slmsymbzVbmMna7XdntdpWTk6OSk5OV3W5X+fn5KiAgQKWmpqpx48apmJgYbbmr0/nz51VRUZG2rpeXl3b76vTEE09cdxoxYoQ6cuSI+tvf/qb+3//7fyo1NVW98sorKiEhQS1dulQ98cQTaunSpWrVqlVlNm71vujM+/rt2jbytsvzIs9LdbUdGVc6ciR2MdDrd/NeBzYqpQKAjb/drhKhoaFkZGSQmZlJcXEx8fHx9O3b1+nbeveN2ta7L+3/s2PHDvLy8v4wf+rUqYwbN46q/GKTb775hrNnz97w+i1btqzU0ckffvgBT09PmjRpUuGy3t7e2Gw2AOrWrUtgYCC5ubllLl+7dm1cXV0BKCwsxGSq8PraGh8fHzIyMrh06RIlJSX8+OOP3HPPPbRv355vvvkGuPJcdejQweHm9chr9PZq6903alvvvlHbeveN2gYHTidQSn0N/P5fq77Af377+T/Ao1W1QT4+PmRnZ2u3c3Jy8PHxcfq23n2jtvXuS7t8Dz/8MEePHmXfvn1V3tbD5s2bmTBhAosXL+bChQvAlYHl+vXreeSRRyrdO3z4MCkpKYSFhQEwb948goODiYiIKDXgT0pKwmq10q5dO2JjY7VBbUWys7MJDAykTp061KhRg+DgYDw8PKhXrx75+fkA5OfnU69evUpv+7XkNXp7tfXuG7Wtd9+obb37Rm3DjZ8T21gpdey3n48Djatoe4QQVeSOO+7gtddeY8qUKbd6UxzSpUsXJk+ezNixY6lXrx6ffPIJAKtXr6Z79+7UqlWrUr2CggIGDhzIrFmzcHd3Z+jQoaSnp7N79268vb0ZPny4tmxYWBipqakkJSUxbdo0CgsLHXqMo0ePsnr1akaPHs2oUaPIysqipKSkUtsphBDixjh2uKEcSillMpnKfJ/SZDI9BzznaC83NxdfX1/tdtOmTct9K7Ay9Gzr3TdqW+++tMvm7+9Ps2bN2LZtG3Dlf8Rff/01Xbt25eTJk1X6WFXB3d1d+7lz587MnTsXgMzMTHbv3s2KFSu4ePEiJpMJV1dXunbtWmaruLiYAQMGMHjwYPr37w9A48b/93/tyMhI+vTp84f1goKCqFOnDvv27XP4FIAtW7awZcsWAB5//HHOnDnDuXPnqF+/Pvn5+dSvX59z58451CqLvEZvr7befaO29e4bta1336htuPEjsSdMJpM3wG9/lvkvolJqgVKqg1LKoX8RkpOTCQgIwM/PD4vFwqBBg0hISLjBzay+tt59o7b17ku7bAcOHKBFixa0bduWtm3bkpubS3h4uFMOYAHt7Xe4cg7s1fNfR44cydSpU5k6dSrdunXj4YcfLncAq5QiMjKSoKAghg0bps0/duyY9vPKlStp3bo1cGWQfPnyZQCysrJIS0vDz8/P4e2+Ovj28PDgnnvuYceOHezevZvOnTsDVwbk33//vcO965HX6O3V1rtv1LbefaO29e4btQ03fiQ2Afgb8O/f/lxVVRtkt9uJiopi/fr1uLi4EBcXx4EDB5y+rXffqG29+9L+PwsXLqRTp054eHhw4MABpk6dyocfflgl2/t7H330EV26dMHT05MjR44wfvx44uLiHF7//fff5+DBgxQUFDBy5Ej69OnDwYMHyc7OxmQy4eHhwZNPPnlD27Z9+3aWLFmC1WrVPuAVHR1NfHw8e/bswWQy0axZM+bPnw/Atm3biImJwWKxYDabmTt3Lp6eng4/3iuvvEKdOnWw2+0sWrSIixcvkpCQwMsvv8yDDz7I6dOnefvtt2/o73KVvEZvr7befaO29e4bta1336htAFNFn1g2mUwfA10AT+AE8CawElgO3AVkAY8ppSr8qHJ5px0IIRxz7dvuVe38+fO6td977z3d2gARERG6tW90QO2Ijz/+WLe2EEIYlVKqwkvFVHgkVin1RBl3dav0FgkhhBBCCFEFnPIbu4QQQgghhCiPDGKFEEIIIYThyCBWCCGEEEIYjgxihRBCCCGE4cggVgghhBBCGI4MYoUQQgghhOHc9NfOCiGq1y+//HKrN+GG3OxXr95KQ4YM0a29bNky3doAJSUluvaFEOJWkSOxQgghhBDCcGQQK4QQQgghDEcGsUIIIYQQwnBkECuEEEIIIQxHBrFCCCGEEMJwZBArhBBCCCEMRwaxQgghhBDCcJxyENuzZ0/S0tJIT09n1KhRhmnr3TdqW+++tKu/f7Pt5cuXM378eGbMmKHN+/LLL5k5cyazZs1iwYIF2nVlL168yOLFi5k5cyZz5szh+PHj5bazs7Pp1q0bbdq0wWq1MmfOHAAmTJiAr68vNpsNm83G2rVrAdi1a5c2LyQkhM8//7zcfkxMDP379+fZZ5/V5h06dIioqCgiIiIYM2YMFy5c0O5bunQpTz75JE8//TTJycmVe6J+07RpU7766itSU1PZu3cvL7300g11yuPM+8vt2Na7b9S23n2jtvXuG7WNUqraJkBVNJnNZpWRkaH8/f2VxWJRKSkpKigoqML1bnXbyNsuz8vt1XbWbZ8+fbo2vfDCC+qf//ynaty4sTZv0qRJ2s99+/ZV9957r5o+fbp64IEH1EMPPaSmT5+uRowYoVq0aFGqdXWy2+3KbrernJwclZycrOx2u8rPz1cBAQEqNTVVjRs3TsXExGjLXZ3Onz+vioqKtHW9vLy021enTZs2adPs2bPV/PnzlZ+fnzavVatWavbs2WrTpk1qxIgR6sknn1SbNm1ScXFx6u6771Zffvml+uijj5S3t7dKTEws1TObzRVOTZo0Ue3bt1dms1m5u7urgwcPqtatWzu0rlH3l9u5beRtl+dFnpfqajsyrnS6I7GhoaFkZGSQmZlJcXEx8fHx9O3b1+nbeveN2ta7L+3q71dF++6776Z27dql5tWqVUv7+dKlS9rPJ06coEWLFgA0atSIs2fPcv78+TLb3t7e2Gw2AOrWrUtgYCC5ubllLl+7dm1cXa98eWFhYSEmk6ncbW/Xrh3u7u6l5uXk5NC2bVsA2rdvzzfffAPAjh076Nq1KzVq1MDb2xsfHx/S0tLK7V/P8ePH+eGHHwAoKCggLS0NHx+fSnfK4uz7y+3W1rtv1LbefaO29e4btQ1OeDqBj48P2dnZ2u2cnJwq+2WtZ1vvvlHbevelXf19Pdvr1q0jOjqa3bt307NnTwCaNGnCvn37ADhy5Aj5+fkOf4Xt4cOHSUlJISwsDIB58+YRHBxMREQEeXl52nJJSUlYrVbatWtHbGysNqh1VLNmzdi+fTsAW7du5eTJkwCcOnUKLy8vbTkvLy9Onz5dqfb1His4OJikpKSb6lzLqPuLUdt6943a1rtv1LbefaO2wQkHsUKI/129e/fmX//6FzabTRsUPvjgg/z666/MmjWL7du306RJkwqPlsKVI5YDBw5k1qxZuLu7M3ToUNLT09m9ezfe3t4MHz5cWzYsLIzU1FSSkpKYNm0ahYWFldrukSNHsmrVKp5//nkuXryIxWKp3F/cQW5ubnzyySe8+uqr5R6NFkKI/wWVO9xQDXJzc/H19dVuN23atNy3Ap2lrXffqG29+9Ku/r7e2w4QEhLCwoUL6dmzJ7Vq1eLxxx8HQCnF1KlT8fDwKHf94uJiBgwYwODBg+nfvz8AjRs31u6PjIykT58+f1gvKCiIOnXqsG/fPjp06ODw9t51111Mnz4duPLBsp07dwJXjryeOnVKW+7UqVN4eno63L2Wq6srn376KUuXLq3ww2eVZdT9xahtvftGbevdN2pb775R2+CER2KTk5MJCAjAz88Pi8XCoEGDSEhIcPq23n2jtvXuS7v6+3q1rx3s7d+/n0aNGgHw66+/cvnyZeDKlQT8/f1LnT/7e0opIiMjCQoKYtiwYdr8Y8eOaT+vXLmS1q1bA5CZman1s7KySEtLw8/Pr1LbfvXUhJKSEpYsWaINkDt27MimTZu4dOkSx44dIzc3l8DAwEq1r/rggw/48ccfeeutt25o/fIYcX8xclvvvlHbeveN2ta7b9Q2OOGRWLvdTlRUFOvXr8fFxYW4uDgOHDjg9G29+0Zt692XdvX3q6L90UcfcejQIS5cuEB0dDQ9evTgxx9/5NSpU5hMJho0aMBf//pX4MoHu5YtW4bJZKJx48YMHDiw3Pb27dtZsmQJVqtV+4BXdHQ08fHx7NmzB5PJRLNmzZg/fz4A27ZtIyYmBovFgtlsZu7cueUeLZ00aRJ79uzh3LlzPPbYY/z973/n119/ZdWqVQB06tSJXr16AeDv70+XLl145plncHFx4eWXX8bFxaVSzxXA/fffz1NPPcXevXv5/vvvAfjXv/7FunXrKt26HmffX263tt59o7b17hu1rXffqG0A02+XvqoWJpOp+h5MCOFUrr7drpdXX31Vt/bWrVt1a3fv3l23Nlw5OiyEEEajlKrwww9OdzqBEEIIIYQQFZFBrBBCCCGEMBwZxAohhBBCCMORQawQQgghhDAcGcQKIYQQQgjDkUGsEEIIIYQwHBnECiGEEEIIw5HrxAohqoWbm5uu/dWrV+vWfuCBB3Rr9+7dW7c2wIYNG3TtCyGEHuQ6sUIIIYQQ4rYkg1ghhBBCCGE4MogVQgghhBCGI4NYIYQQQghhODKIFUIIIYQQhiODWCGEEEIIYThOOYjt2bMnaWlppKenM2rUKMO09e4bta13X9rV36/qdmxsLJmZmezatUub169fP5KTk/nll18ICQmpVC8mJob+/fvz7LPPavMOHTpEVFQUERERjBkzhgsXLmj3LV26lCeffJKnn36a5OTkctvZ2dl069aNNm3aYLVamTNnDgATJkzA19cXm82GzWZj7dq1AOzatUubFxISwueff+7Q36Fp06bExsZq02effUa/fv2oW7cuU6dOJS4ujqlTp1KnTp1KPTdlMdL+cju09e4bta1336htvftGbaOUqrYJUBVNZrNZZWRkKH9/f2WxWFRKSooKCgqqcL1b3Tbytsvzcnu1nXXb3dzcypx69Oih7rvvPrV//35tns1mU8HBwerrr79WnTp1Knd9Nzc3tWnTJm2aPXu2mj9/vvLz89PmtWrVSs2ePVtt2rRJjRgxQj355JNq06ZNKi4uTt19993qyy+/VB999JHy9vZWiYmJpXp2u12bcnJyVHJysrLb7So/P18FBASo1NRUNW7cOBUTE1NqWbvdrs6fP6+Kioq0db28vLTbdrtd9ejRo8KpV69e6syZM+rJJ59Uy5YtUx988IHq0aOH+uCDD9SyZcvKXdeo+8vt3DbytsvzIs9LdbUdGVc63ZHY0NBQMjIyyMzMpLi4mPj4ePr27ev0bb37Rm3r3Zd29ff1aG/fvp28vLxS8w4ePEh6evoN9dq1a4e7u3upeTk5ObRt2xaA9u3b88033wCwY8cOunbtSo0aNfD29sbHx4e0tLQy297e3thsNgDq1q1LYGAgubm5ZS5fu3ZtXF1dASgsLMRkqvD63X8QHBzMsWPHOHnyJB07duSrr74C4KuvvqJjx46V7v2e0fYXo7f17hu1rXffqG29+0ZtgxOeTuDj40N2drZ2OycnBx8fH6dv6903alvvvrSrv6/3tuulWbNmbN++HYCtW7dy8uRJAE6dOoWXl5e2nJeXF6dPn3aoefjwYVJSUggLCwNg3rx5BAcH+XIwqAAAIABJREFUExERUWpQnpSUhNVqpV27dsTGxmqDWkd16dKFLVu2ANCgQQPOnj0LwNmzZ2nQoEGlWtdj1P3FqG29+0Zt6903alvvvlHb4ISDWCGE0MPIkSNZtWoVzz//PBcvXsRisdxUr6CggIEDBzJr1izc3d0ZOnQo6enp7N69G29vb4YPH64tGxYWRmpqKklJSUybNo3CwkKHH8fV1ZV7772Xr7/++rr3V+dXhwshhDOp3OGAapCbm4uvr692u2nTpuW+Vecsbb37Rm3r3Zd29ff13na93HXXXUyfPh248uGsnTt3AleOvJ46dUpb7tSpU3h6epbbKi4uZsCAAQwePJj+/fsD0LhxY+3+yMhI+vTp84f1goKCqFOnDvv27aNDhw4Obfc999xDRkYG+fn5AOTl5dGwYUPOnj1Lw4YNtfk3w6j7i1HbeveN2ta7b9S23n2jtsEJj8QmJycTEBCAn58fFouFQYMGkZCQ4PRtvftGbevdl3b19/Xedr1cfXu/pKSEJUuWaIPMjh07smnTJi5dusSxY8fIzc0lMDCwzI5SisjISIKCghg2bJg2/9ixY9rPK1eupHXr1gBkZmZy+fJlALKyskhLS8PPz8/h7b72VAKAnTt30r17dwC6d+/Ot99+63CrLEbdX4za1rtv1LbefaO29e4btQ1OeCTWbrcTFRXF+vXrcXFxIS4ujgMHDjh9W+++Udt696Vd/X092osWLaJz5854eHhw8OBBJk+eTF5eHjNmzMDT05MVK1awd+9eHn30UYd6kyZNYs+ePZw7d47HHnuMv//97/z666+sWrUKgE6dOtGrVy8A/P396dKlC8888wwuLi68/PLLuLi4lNnevn07S5YswWq1ah/wio6OJj4+nj179mAymWjWrBnz588HYNu2bcTExGCxWDCbzcydO7fCI71X1axZE5vNxttvv63NW7ZsGW+88Qa9evXi5MmTTJ482aFWeYy2vxi9rXffqG29+0Zt6903ahvAVJ3nU5lMJjl5S4j/UW5ubrr2V69erVv7gQce0K3du3dv3doAGzZs0LUvhBB6UEpVeCkXpzudQAghhBBCiIrIIFYIIYQQQhiODGKFEEIIIYThyCBWCCGEEEIYjgxihRBCCCGE4cggVgghhBBCGI4MYoUQQgghhOHIdWKFELeF5s2b69bevXu3bu2q+NrY8mzevFm39nfffadbe968ebq14co3rwkhnJdcJ1YIIYQQQtyWZBArhBBCCCEMRwaxQgghhBDCcGQQK4QQQgghDEcGsUIIIYQQwnBkECuEEEIIIQzHKQexPXv2JC0tjfT0dEaNGmWYtt59o7b17ku7+vtGateoUYMVK1awevVq1q1bxz//+U8Apk2bxubNm0lISCAhIYGgoCCHejk5OTzyyCOEhoYSFhbGu+++W+r+d955h3r16nHmzBkAfvrpJ7p3746Xlxdz5swpt3306FEef/xxunXrRvfu3YmLiwNg8uTJdO3alZ49e/Lcc89x7tw5ALKzs2nZsiW9e/emd+/ejBkzptz+woULeemll3jjjTdKzU9MTOT1119nzJgxLFu2TJu/Zs0aRo4cyeuvv05qamq57Y0bN7Jw4UKWLl2qzSssLGTVqlV8+OGHrFq1isLCQgCKiopYs2YNH3/8MUuXLuXAgQPltiv6Ox0/fpy9e/fecKMs8hqt/rbefaO29e4btY1SqtomQFU0mc1mlZGRofz9/ZXFYlEpKSkqKCiowvVuddvI2y7Py+3VNvK230y7efPmZU5Wq1U1b95ctWrVSv3www/qr3/9q/r000/Viy++WO56V6dz585p08GDB9XWrVvVuXPnVE5OjmrevLlKSkpS586dU/v371ddu3ZVvr6+6ueff1bnzp1TGRkZatOmTeq1115TkyZNKtU6d+6cysrK0qZdu3apNWvWqKysLLV//37l7++vEhMT1YcffqgOHTqksrKy1NChQ9XQoUNVVlaW2rZtm2rZsmWpxu+nxYsXa9Po0aPV+PHjlY+PjzZv1KhR6k9/+pN6//331eLFi9WcOXPU4sWL1eTJk5Wvr696//331fTp05WXl5eKi4sr1YuKitKmfv36qccee0w1bNhQmxcSEqI6duyooqKiVMeOHZXNZlNRUVHq3nvv1X6OiIhQNWvWVC+88EKpnslkcmgKDw9XNptNpaamOrzOb9csl9eok7WNvO3yvFR925FxpdMdiQ0NDSUjI4PMzEyKi4uJj4+nb9++Tt/Wu2/Utt59aVd/34jtixcvAuDq6orFYrmpC93feeedBAcHA1C3bl1atWrF0aNHARg9ejQTJ07EZPq/a3R7eXnRvn17LBZLhe3GjRtjtVoBqFOnDi1atODEiROEh4fj6uoKQEhICMeOHbuhbW/VqhVubm6l5m3atIk///nP2va5u7sD8MMPPxAWFobFYsHLy4vGjRvz888/l9n28fGhVq1apeZlZmYSGBgIQGBgYKn1L126hFKK4uJiatWqhdl8Y/8cffPNN5w9e/aG1i2PvEarv61336htvftGbYMTnk7g4+NDdna2djsnJwcfHx+nb+vdN2pb7760q79vxLbZbCYhIYGkpCS2bdvGnj17AHj11VdZs2YNb7zxBjVq1Kh0Nysri71799KhQwe++OILmjRpog1Cb1Z2djb79+/XBsxXLV++nC5dupRarnfv3jz22GPs2rWr0o9z/PhxfvrpJyZOnMjUqVO1gWZeXh4NGzbUlmvQoAF5eXmVal+8eFEbNNeuXVv7z0Tbtm3Jy8tj0aJFfPzxx3Tu3LnUwN8ZyGu0+tt6943a1rtv1DY44SBWCCGqWklJCX369KFTp060a9eOgIAAZsyYQY8ePejfvz/16tXjueeeq1SzoKCAp556iqlTp+Lq6srMmTMrPCfVURcuXGDo0KGMGzeOunXravPfeecdXF1d6devHwCNGjXi22+/Zd26dYwdO5aXX36Z8+fPV+qxSkpKKCgoYOzYsTz++OPExsbq8pWsJpNJG6geOXIET09PnnnmGR5//HG2bt3KpUuXqvwxhRC3N6cbxObm5uLr66vdbtq0Kbm5uU7f1rtv1LbefWlXf9+obYDz58+zc+dOwsPDOXXqFHDlbe0VK1bQtm1bhzvFxcU89dRTPPbYY/Tp04fMzEyysrLo1KkTVquV3NxcwsPDOXHiRKW3sbi4mKFDh/Loo4/Su3dvbf4nn3zCxo0befvtt7XBYM2aNWnQoAEAVquVZs2akZmZWanHa9CgAR06dMBkMnH33XdjMpk4f/48DRo0KPU2fV5envZYjqpduzYXLlwArgzM77jjDgB+/PFHmjdvjslkon79+ri7u1f6KK/e5DVa/W29+0Zt6903ahuccBCbnJxMQEAAfn5+WCwWBg0aREJCgtO39e4bta13X9rV3zdau2HDhtrRzJo1a3L//ffz888/4+XlpS3TvXt30tPTHeoppYiKiqJVq1ZERUUB0Lp1aw4dOkRqaiqpqan4+Pjw9ddf07hx40ptq1KKkSNH0qJFC4YMGaLN37JlC/Pnz2fhwoXaQBDgzJkz2O124MrRzczMTO66665KPabNZuPHH38ErpxaYLfbqVu3LiEhISQlJVFcXMypU6c4ceIEd999d6Xa/v7+pKWlAZCWloa/vz9w5Vziq28xXrx4kfz8fO1cXGchr9Hqb+vdN2pb775R2wCuVVaqIna7naioKNavX4+LiwtxcXE3dfmV6mrr3TdqW+++tKu/b7S2l5cX06dPx2w2YzabWbt2LZs3b+bDDz+kYcOGmEwmfvzxR8aOHetQb+fOncTHx9O6dWs6deoEwLhx4+jRo8d1lz9x4gRdunTh/PnzmM1m3n33XZKSkq47aPvuu+/47LPPCAwM1I7CjhgxgvHjx3Pp0iWefPJJ4MqHu6ZMmUJSUhKzZs3CYrFgMpmYMmUK9evXL3Pb3333XdLS0igoKGDYsGE8+uijhIeHs3DhQt544w1cXV2JjIzEZDLh4+PDPffcw5gxY3BxceGpp54q98NX69evJzc3l8LCQhYtWkRYWBg2m43169dz4MAB6tatS69evQDo0KEDGzdu1C7Hdd9995UanFfGRx99RJcuXfD09OTIkSOMHz9euzTZzZDXaPW39e4bta1336htAJMe5z6V+WBXLmsihBBVrnnz5rq1d+/erVs7Pz9ftzbA5s2bdWt/9913urXnzZunWxvQ5bxfIUTVUUpV+GlPpzudQAghhBBCiIrIIFYIIYQQQhiODGKFEEIIIYThyCBWCCGEEEIYjgxihRBCCCGE4cggVgghhBBCGI4MYoUQQgghhOHIdWKFEKIC/fr10629aNEi3dqA9m1lRjNmzBhd+//97391ax87dky3thD/K+Q6sUIIIYQQ4rYkg1ghhBBCCGE4MogVQgghhBCGI4NYIYQQQghhODKIFUIIIYQQhiODWCGEEEIIYTgyiBVCCCGEEIbjlIPYnj17kpaWRnp6OqNGjTJMW+++Udt696Vd/X2jtvXoP/LII7z11lu89dZbDBs2DIvFQu/evZk3bx6fffZZpa7TmpOTwyOPPEJYWBj33nsv7777bqn733nnHerXr8+ZM2cAUEoxcuRIQkJCuO+++0hJSSmznZ2dTbdu3WjTpg1Wq5U5c+YAMGHCBHx9fbHZbNhsNtauXQvArl27tHkhISF8/vnn5W67nv21a9fyzjvvsHDhQm1eWloaH3zwAdOmTSt1XVa73c4XX3zBwoULiYuL48iRI+Vu97WaNGnCJ598wpYtW9i8eTMREREAvPbaa3z//fckJiaSmJhI165dHW6Wx6ivI6O9Rm+Htt59o7ZRSlXbBKiKJrPZrDIyMpS/v7+yWCwqJSVFBQUFVbjerW4bedvlebm92kbedmd9Xvr163fdKSIiQh0/flw9/vjjql+/fmrbtm1qzpw56tVXX1XPPfecOnHihHr66afLXL9fv34qPz9fm9LS0tSWLVtUfn6+ys7OVs2bN1c7d+5U+fn5at++fapr166qadOm6tChQyo/P18tX75cde/eXeXl5anExETVvn37Ur38/Hxlt9uV3W5XOTk5Kjk5WdntdpWfn68CAgJUamqqGjdunIqJidGWuzqdP39eFRUVaet6eXlpt683VXV/1KhR2jR48GD1t7/9TXl6emrzIiIiVGRkpPL19VVPP/20Nv+hhx5Sbdq0UaNGjVJRUVGqcePGauTIkaV6o0aNUt7e3n+Y2rVrp3r06KG8vb1VixYtVEZGhgoPD1czZsxQEyZMuO4615tu9b5u1LaRt12el6pvOzKudLojsaGhoWRkZJCZmUlxcTHx8fH07dvX6dt6943a1rsv7ervG7WtV9/FxYUaNWpgNpupWbMmZ8+eJTMzk1OnTlW6deeddxIcHAxc+aatli1bakcZx4wZw4QJEzCZ/u9LbNauXcugQYMwmUzcc889nDt3juPHj1+37e3tjc1m09qBgYHk5uaWuS21a9fG1dUVgMLCwlKPW919X19f7rjjjlLzPD098fDw+MOyp0+fplmzZgC4ublRq1Yth79B6+TJk6SmpgJw4cIFMjIy8Pb2dmjdyjLq68iIr1Gjt/XuG7UNTng6gY+PD9nZ2drtnJwcfHx8nL6td9+obb370q7+vlHbevTPnj3LqlWreO+991i4cCEXL15kz549VbGpZGVlkZqaSvv27fniiy/w9vbGarWWWubYsWOltr9JkyYODdgOHz5MSkoKYWFhAMybN4/g4GAiIiLIy8vTlktKSsJqtdKuXTtiY2O1Qeet7penUaNGZGRkUFJSQn5+PsePH+f8+fOV7jRt2pQ2bdqwe/duAJ555hm++uorZs2aRb169W56O436OjLaa/R2aOvdN2obnHAQK4QQRuHm5kZoaCgvvPACkZGR1KxZk/Dw8JvuFhQU8PTTTzNlyhRcXV2ZNWsWY8aMqYItvtIeOHAgs2bNwt3dnaFDh5Kens7u3bvx9vZm+PDh2rJhYWGkpqaSlJTEtGnTKCwsvOX9irRt25a6devyn//8h40bN+Lj41PhUeTfq127Nh988AHjxo2joKCA//znP3Ts2JGHHnqIEydO8Oabb970dgohbp7TDWJzc3Px9fXVbjdt2rTct6Scpa1336htvfvSrv6+Udt69Nu2bcuJEyf45ZdfsNvtJCUlERgYeFPbWFxczNNPP83AgQPp06cPmZmZZGVl0alTJ6xWK0ePHuWBBx7gxIkTeHt7l9r+o0ePlvv2d3FxMQMGDGDw4MH0798fgMaNG+Pi4oLZbCYyMpLk5OQ/rBcUFESdOnXYt29fhduuZ98RZrOZbt268cwzz/DXv/6VwsJCGjZs6PD6rq6ufPDBB3z22WesW7cOuHKKQklJCUopPvroI+2Uj5th1NeR0V6jt0Nb775R2+CEg9jk5GQCAgLw8/PDYrEwaNAgEhISnL6td9+obb370q7+vlHbevRPnz5Ny5YtqVGjBgBWq5WcnJwb7imliIqKomXLlkRFRQHQunVrMjIySE1NJTU1lSZNmrB161YaN25M7969iY+PRylFcnIy7u7u3HnnnWW2IyMjCQoKYtiwYdr8a08/WLlyJa1btwYgMzOTy5cvA1dObUhLS8PPz6/cbdez76ji4mIuXbqkPYbZbMbT09Ph9WfOnEl6ejoLFizQ5jVq1Ej7uXfv3hw8ePCmt9OoryOjvUZvh7befaO2AW7+BKQqZrfbiYqKYv369bi4uBAXF8eBAwecvq1336htvfvSrv6+Udt69NPT0/n222+ZMWMGJSUl/Pzzz2zYsIGHH36Yfv36Ub9+fWbPns3u3buJjY2tsLdz506WLVvGn/70Jzp16gTAuHHj6NGjx3WX79GjB4mJiYSEhFC7dm3mzZtXZnv79u0sWbIEq9WqfQArOjqa+Ph49uzZg8lkolmzZsyfPx+Abdu2ERMTg8ViwWw2M3fu3HIHg3r2ExISOHLkCL/++ivz5s2jU6dO3HHHHSQmJvLrr7/y6aef0qhRIx5//HEuXrzI8uXLgSsfMHvkkUfKe8pLCQ0NZeDAgRw4cIDExEQApk6dyqOPPkrr1q1RSpGTk8PIkSMdbpbFqK8jo71Gb4e23n2jtgFMv136qlqYTKbqezAhhKgi/fr10629aNEi3dpApa5T60yq6hzgsvz3v//Vre3o1RCEEGVTSlV4MrvTnU4ghBBCCCFERWQQK4QQQgghDEcGsUIIIYQQwnBkECuEEEIIIQxHBrFCCCGEEMJwZBArhBBCCCEMRwaxQgghhBDCcOQ6sUIIcQu1adNG1/6sWbN0a3fr1k23tt7ee+893dqTJ0/WrV2VX9kphDOT68QKIYQQQojbkgxihRBCCCGE4cggVgghhBBCGI4MYoUQQgghhOHIIFYIIYQQQhiODGKFEEIIIYThOOUgtmfPnqSlpZGens6oUaMM09a7b9S23n1pV3/fqG29+3q0zWYzy5cvZ+7cuQBER0ezbt06PvnkEz755BNatWrlcGvmzJkMHDiQIUOGaPMOHTrEyy+/zNChQ3nxxRdJS0vT7tuzZw9Dhw5lyJAhvPbaa+W2s7Oz6datG23atMFqtTJnzhwAJkyYgK+vLzabDZvNxtq1awHYtWuXNi8kJITPP//8lrQBPvzwQ0aOHMmkSZO0eWvWrGH06NFMmTKFKVOmsG/fPu2+L7/8kjfffJPx48dz4MCBctvX8vb2Zvny5WzatImNGzcSEREBQGxsLOvXr2f9+vV8++23rF+/3uFmWYy2n1dX36htvftGbaOUKncC4oCTwL5r5o0HcoGU36aHK+r8tp6qaDKbzSojI0P5+/sri8WiUlJSVFBQUIXr3eq2kbddnpfbq23kbf9ffF7atGlT7hQTE6O++OILtWXLFtWmTRu1cuVKNWzYsArXuzpt2LBBm2bMmKHmzZunmjVrps2z2WwqOjpabdiwQUVHR6u2bduqDRs2qM8++0zdddddasmSJWrDhg1q2bJlpVobNmxQdrtdm3JyclRycrKy2+0qPz9fBQQEqNTUVDVu3DgVExNTalm73a7Onz+vioqKtHW9vLy027+f9GjHxsZq07Bhw9Trr7+uvL29tXkPP/yw6tevX6nlYmNj1dixY5WPj496++231cSJE5Wnp6eaO3duqWV8fHyuO4WEhKiePXsqHx8f1bJlS3Xo0CHVpUuXUsvMnz9fTZ8+vcyGUfdzZ+gbtW3kbb+ZtiPjSkeOxC4Gel1n/mylVPBv01oHOg4JDQ0lIyODzMxMiouLiY+Pp2/fvk7f1rtv1LbefWlXf9+obb37erQbN25M586dWbFiRZVsY9u2balbt26peSaTiYsXLwJw4cIFPDw8ANi0aRP3338/jRo1AqBBgwbltr29vbHZbADUrVuXwMDAci/MX7t2bVxdXQEoLCzEZCr7uuZ6tgECAgJwc3Mrd5mr9uzZQ/v27bFYLHh6euLl5cXhw4cdWvfkyZPaEd0LFy6Qnp7OnXfeWWqZv/zlL6xatcqhXlmMtp9XV9+obb37Rm2DA6cTKKW+Bs5W2SNWwMfHh+zsbO12Tk4OPj4+Tt/Wu2/Utt59aVd/36htvft6tEeOHMns2bMpKSkpNf+ll15ixYoVjBw5EovFclOP8cILL/D+++8zePBgFixYwLPPPgtc+WaogoIChg8fzj/+8Q8SExMdbh4+fJiUlBTCwsIAmDdvHsHBwURERJCXl6ctl5SUhNVqpV27dsTGxmoDz1vV/r2tW7cSHR3Nhx9+qA30z507V2pAX79+ffLz8yvdbtq0KW3atOGHH37Q5oWFhXHq1CkyMzMr3buW0fbz6uobta1336htuLlzYqNMJtNek8kUZzKZyv8vuhBCiEoJDw/n7Nmzfzjn8q233qJPnz4MGjQId3d37bzKG7V69WqGDh3K0qVLGTp0qPY1tXa7nfT0dCZNmsTUqVP56KOPyMnJqbBXUFDAwIEDmTVrFu7u7gwdOpT09HR2796Nt7c3w4cP15YNCwsjNTWVpKQkpk2bRmFh4S1r/154eDgTJ05kzJgxuLu7V9nRcLhypHjBggWMHz+egoICbX7fvn1v+iisEP9LbnQQ+y7QHAgGjgEzy1rQZDI9ZzKZvjOZTN85Es7NzcXX11e73bRp0yr7rmg923r3jdrWuy/t6u8bta13v6rbISEhPPjgg3z55ZdMnz6d0NBQpk6dyunTpwEoLi5m5cqVtGnT5qa2OzExkU6dOgFXBm4HDx4EwNPTkw4dOnDHHXdQr149rFYrP//8c7mt4uJiBgwYwODBg+nfvz9w5ZQIFxcXzGYzkZGRJCcn/2G9oKAg6tSpU+rDU9XZvh53d3fMZjNms5lOnTpppwzUq1ev1BHf/Px86tev73DX1dWVBQsW8Pnnn7Nu3TptvouLC71792b16tWV2s7rMdJ+Xp19o7b17hu1DTc4iFVKnVBK2ZVSJcD7QGg5yy5QSnVQSnVwpJ2cnExAQAB+fn5YLBYGDRpEQkLCjWxmtbb17hu1rXdf2tXfN2pb735Vt99++226d+9Or169GDFiBLt27WL06NF4enpqy3Tt2pWMjIyb2m4PDw/27t0LQEpKCk2aNAHgvvvuY9++fdjtdgoLC0lLSyv1j9HvKaWIjIwkKCiIYcOGafOPHTum/bxy5Upat24NQGZmJpcvXwYgKyuLtLQ0/Pz8qr1dlnPnzmk/X/u8tG3blu+//57i4mJOnz7NyZMnK9WeMWMGGRkZvP/++6Xmd+7cmUOHDpX6O90oI+3n1dk3alvvvlHbAJU/SQgwmUzeSqmrr7R+QOX+i1sOu91OVFQU69evx8XFhbi4uEpdwuRWtfXuG7Wtd1/a1d83alvvvt7bftW///1vGjZsCMDBgweZOHGiw+tOmTKFvXv3cu7cOQYPHsxTTz3FsGHDiI2NpaSkBIvFwiuvvALAXXfdRYcOHXj++ecxmUz07t0bf3//Mtvbt29nyZIlWK1W7UNY0dHRxMfHs2fPHkwmE82aNWP+/PkAbNu2jZiYGCwWC2azmblz55YaoFdXGyAuLo6ffvqJgoICxowZw5///GfS09O10yc8PDwYPHgwAE2aNMFmszFp0iTMZjODBg3CbHbseNA999zDgAED+PHHH7XLaE2bNo1NmzbRp08fVq5c6VCnIkbez4267fK8VH8bwPTbpa/KXsBk+hjoAngCJ4A3f7sdzJXLIBwGnr9mUFteq/wHE0KI/zE3ezpARa6e46qHbt266dbW23vvvadbe/Lkybq1q/KtWCGcmVKq/MuK4MCRWKXUE9eZvfCGtkgIIYQQQogq4JTf2CWEEEIIIUR5ZBArhBBCCCEMRwaxQgghhBDCcGQQK4QQQgghDEcGsUIIIYQQwnBkECuEEEIIIQynwuvEVumDyXVihRCiWlXmK1Er6y9/+Ytu7UWLFunWBjCZKrwE5Q3btGmTbu2HHnpIt7YQzsSR68TKkVghhBBCCGE4MogVQgghhBCGI4NYIYQQQghhODKIFUIIIYQQhiODWCGEEEIIYTgyiBVCCCGEEIbjlIPYnj17kpaWRnp6OqNGjTJMW+++Udt696Vd/X2jtvXuG6n9zjvv8NNPP7Fjx45S84cMGUJSUhI7duxgwoQJN9zv0aMHU6ZMYcqUKfTs2ROAv/71r0RHRzNp0iRGjBjh8OW/srOz6datG1arlbZt2zJnzhwAJkyYwF133UX79u1p3749a9euBSAxMZHQ0FCCg4MJDQ0t95JXV9tt2rTBarWWavv6+mKz2bDZbFp7165d2ryQkBA+//zzcrd9xowZDBw4kCFDhmjzMjIyeOmll3j++ef5xz/+QVpaGgDLly/n+eef5/nnn2fIkCH07NmTX375xaHn6PeMtC9WZ9+obb37Rm2jlKq2CVAVTWazWWVkZCh/f39lsVhUSkqKCgoKqnC9W9028rZP0/8WAAAgAElEQVTL83J7tY287fK8VH27fv36150efvhhFR4erg4cOKDNe+SRR9TmzZtVo0aNVP369VWLFi3KXL9+/frqqaeeuu70+uuvq+zsbBUREaH+9re/qX379qnXXntNDRkyRFvmv//9r9q4cWOZjcuXL2tTdna22rVrl7p8+bLKy8tTAQEBau/evWrs2LFq2rRppZa9fPmySk5OVkeOHFGXL19WKSkpqkmTJn9Yxm63K7vdrnJyclRycrKy2+0qPz9fBQQEqNTUVDVu3DgVExOjLXd1On/+vCoqKtLW9fLy0m5fnRITE7Vp5syZKjY2Vvn5+WnzbDabmjx5skpMTFTR0dGqbdu2pdZJTExUEydOVMHBwX+Yb+R98Vb3jdo28rbfTNuRcaXTHYkNDQ0lIyODzMxMiouLiY+Pp2/fvk7f1rtv1LbefWlXf9+obb37Rmvv2LGDvLy8UvOeffZZ3nrrLS5dugTA6dOnb6jdpEkTDh06xKVLlygpKSEtLY0OHTpQWFioLVOzZs2rBzcq5O3tjc1mA6Bu3boEBgaSm5tb5vIhISE0adIEgNatW/Prr79SVFRUJe3atWvj6uoKQGFhYYVfmtC2bVvq1q1bap7JZOLixYsAXLhwAQ8Pjz+st3nzZh588MFy22Ux2r5YXX2jtvXuG7UNTng6gY+PD9nZ2drtnJwcfHx8nL6td9+obb370q7+vlHbeveN2r5WixYt6NixI4mJiaxZs4aQkJAb6uTm5tKqVSvq1KlDjRo1aNeunTZQGzBgALNnz+a+++7js88+q3T78OHDpKSkEBYWBkBsbCwhISFERkb+YVAO8NlnnxESEkLNmjUr3Z43bx7BwcFERESUaiclJWG1WmnXrh2xsbHaoNZRL7zwAgsWLGDw4MEsWLCAiIiIUvcXFhby3Xff0alTp0p1rzLyvmjUbZfnpfrb4ISDWCGEELeGq6srDRo04KGHHmLcuHE3/NWvR48eZc2aNYwYMYLhw4eTlZVFSUkJAJ9++inDhg1jx44ddO/evVLdgoICHnvsMWbNmoW7uztDhw7lp59+4vvvv+fOO+9kxIgRpZbfv38/o0eP5t1333WoPXDgwFLt9PR0du/ejbe3N8OHD9eWDQsLIzU1laSkJKZNm1bqCLMj1qxZwwsvvMDSpUt54YUXmDlzZqn7d+7cSevWrXF3d69UV4j/NU43iM3NzcXX11e73bRp03Lf2nGWtt59o7b17ku7+vtGbevdN2r794+zevVqAHbv3k1JScl13+p2xNdff82bb77JlClTuHDhAsePHy91/7fffss999zjcK+4uJiBAwfyxBNP0K9fPwAaN26Mi4sLZrOZyMhIkpOTteVzcnIYMGAAixYtonnz5hW2BwwYwODBg+nfv3+F7auCgoKoU6cO+/btc/jvAbBhwwbtKGt4eDgHDx4sdf+WLVtu+FQCMPa+aNRtl+el+tvghIPY5ORkAgIC8PPzw2KxMGjQIBISEpy+rXffqG29+9Ku/r5R23r3jdq+1tq1a+ncuTMAzZs3p0aNGpw5c+aGWlfPA/Xw8KBDhw58++23NG7cWLvfZrNx9OhRh1pKKYYMGUJQUBDDhg3T5h87dkz7eeXKlbRu3RqA/Px8+vTpw5QpU7j//vsrbEdGRjrczszM5PLlywBkZWWRlpaGn5+fQ3+Pqzw8PNi7dy8AP/zwQ6m3Vy9cuMDevXvp2LFjpZrXMvK+aNRtl+el+tsAlTuRpxrY7XaioqJYv349Li4uxMXFceDAAadv6903alvvvrSrv2/Utt59o7U/+OAD7r//fjw8PNi3bx///ve/WbJkCXPnzmXHjh1cunSJF1544Yb7L7/8MnXq1MFut/Pf//6XixcvEhERgbe3NyUlJZw5c4bFixc71Nq+fTtLlizBarXSvn17ACZNmsSyZcvYs2cPJpOJZs2aaacNzJs3j4yMDKKjo4mOjgZg3bp1NGrUqNz21Q94RUdHEx8fX6o9f/58ALZt20ZMTAwWiwWz2czcuXPx9PQsc9snT57M3r17OXfuHE888QRPP/00r776KrGxsdjtdmrUqMErr7yiLb9t2zbat2/PHXfc4dBzcz1G2xerq2/Utt59o7YBTI5+OrRKHsxkqr4HE0II4fC1WG/EX/7yF93aN3o+rqMquqrAzSjvurQ366GHHtKtLYQzUUpV+CJ1utMJhBBCCCGEqIgMYoUQQgghhOHIIFYIIYQQQhiODGKFEEIIIYThyCBWCCGEEEIYjgxihRBCCCGE4cggVgghhBBCGI5cJ1YIIYTTKSoq0rXv6qrfd/1c/UYvPfTs2VO3Nlz5ylshnIFcJ1YIIYQQQtyWZBArhBBCCCEMRwaxQgghhBDCcGQQK4QQQgghDEcGsUIIIYQQwnBkECuEEEIIIQzHKQexPXv2JC0tjfT0dEaNGmWYtt59o7b17ku7+vtGbevdN2pb7/7NtrOzs+nRowft2rUjODiYd955p9T9s2fPpmbNmpw+fRqAvLw8Bg4cSPv27bn//vvZv39/ue1u3brRpk0brFYrc+bMAWDChAn4+vpis9mw2WysXbsWgF27dmnzQkJC+Pzzz2/Jdl+Pm5sb48eP5z//+Q+LFy/mT3/6k3bfwIED2bx5M+7u7pVqXs//8r54q9p6943aRilVbROgKprMZrPKyMhQ/v7+ymKxqJSUFBUUFFThere6beRtl+fl9mobedvleZHn5epUVFSkTYcPH1Y7d+5URUVF6vTp06pFixYqJSVFFRUVqYyMDNW9e3d11113qdzcXFVUVKSGDRumxo4dq4qKitSePXtUly5dSvWKioqU3W5Xdrtd5eTkqOTkZGW321V+fr4KCAhQqan/n707j4uq3h8//pphIEXEQk0RuWKJgjJsKd7yanrJLE1J09yvJbhkaJm4/Kz0Um7wNc0F0tzSa4VXRcVvlqaZpiWSyuKCQqIBgnvKIrJ4fn94PV9IWRQOcrzv5+PBIzlz5jXHkTl9OHPOZxKUadOmKaGhoep6d76ysrLU+6elpSkNGzYs0SsqKtJ0uzt37lzq13fffaeEhoYqnTt3Vl544QWlR48eSufOnZV+/fopBw8eVDIyMpRevXqV2ZCfxZrX1vO2V6ZdkXFljTsS6+PjQ3JyMikpKRQUFBAREYGfn1+Nb2vd12tb6760q7+v17bWfb22te5XRdve3h4vLy8A6tati4uLC+np6QBMnDiR2bNnYzD837zoJ06coHPnzgC4uLhw9uxZzp8/X2rb29v7nu17sba2Vj8oIS8vr8TjVud2/1mdOnVwd3dXjxgXFhaSk5MDwNtvv83SpUsr1CnPf/vP4sNoa93Xaxtq4OkEDg4OpKamqt+npaXh4OBQ49ta9/Xa1rov7erv67WtdV+vba37Vd0+c+YMcXFx+Pj4EBUVRZMmTXB3dy+xjru7O5s3bwYgJiaG33//vcyBafF2bGws7du3ByAsLAxPT0/8/f25evWqul50dDRmsxkPDw/Cw8Mr9OlfWm43QOPGjfnjjz+YPHkyn3/+OUFBQdSqVYsOHTpw6dIlfvvttwp1yiM/i9Xf1rqv1zbUwEGsEEIIcS/Z2dkMGDCAuXPnYjKZCA0NZfr06XetN3HiRK5du0a7du0IDw/H09MTCwuLctv9+vVj3rx52NraMnr0aJKSkjh8+DD29vYEBQWp67Zv356EhASio6MJCQkhLy/voW33HRYWFrRs2ZKoqChGjhxJXl4ew4YNY/DgwaxatapCDSH0RrsPj35A6enpODo6qt83bdq0wr+JPsy21n29trXuS7v6+3pta93Xa1vrflW1CwoK6N+/PwMGDODVV1/l6NGjnDlzhnbt2gG3j/D89a9/Zd++fTRu3Jhly5YBoCgKrVq1onnz5mW2+/bty6BBg+jTpw8AjRo1Um8PCAigV69ed93P1dUVGxsbjh49Stu2bat9u4u7ePEiFy9e5MSJEwDs2bOHN954g8aNG7N8+XIAGjZsyOeff85bb71V4sjy/ZCfxepva93Xaxugxl3YZWFhofz222+Kk5OTehJw69atq+QEYy3bet52eV4erbaet12eF3le7nwVv5gpLy9PGTx4sBIYGHjXhU53vpo1a6ZeIHX+/Hn1Aqzw8HBl8ODBpV7YVVhYqAwZMkQZN25ciYuz0tLS1D9/8sknyuuvv64UFRUpycnJ6v1Pnz6t2NvbK+fPn7/nhV1abHdZF2XFxcUpQ4cOVTp37qysWrVK+frrr0vcXhUXdv03/iw+7Laet70y7QqNK2vaIBZQXn75ZeXkyZNKcnKyMnXq1Cr7IdC6redtl+fl0WrredvleZHnBUoOYn/44QcFUNzc3BR3d3fF3d1d2bx5c6mDwT179igtWrRQnJ2dFT8/PyUzM7PUQeyePXsUQDGbzYqHh4fi4eGhbN26VRk8eLDi5uammM1m5ZVXXlEHtV988YXSunVrxcPDQ/Hy8lI2btx41wwGWm53WQNQf39/JTExUUlOTlZ++ukn5ZVXXqnyQex/489iTWjredsftF2RcaXhP4PLamEwGKrvwYQQQujWzZs3Ne1X5GKsB1VYWKhZu1u3bpq1AX788UdN+0JUlKIopU/98R9yYZcQQgghhNAdGcQKIYQQQgjdkUGsEEIIIYTQHRnECiGEEEII3ZFBrBBCCCGE0B0ZxAohhBBCCN2RQawQQgghhNAdGcQKIYQQQgjd0W62ZyGEEOVyd3fXtN+3b1/N2u3atdOsreWHEWjt+PHjmrX37t2rWVsIvZEjsUIIIYQQQndkECuEEEIIIXRHBrFCCCGEEEJ3ZBArhBBCCCF0RwaxQgghhBBCd2QQK4QQQgghdKdGDmK7detGYmIiSUlJTJ48WTdtrft6bWvdl3b19/Xa1rqvRdtoNLJu3ToWLVqkLgsMDCQqKopNmzYxaNCgCrc2bdpESEgIixcvVpft2rWLsLAwwsPDWb16NdevXwdAURS++eYbPv30U8LCwjh37lyZ7Xnz5jFgwABGjx6tLvvtt9949913efvttxk3bhwnT54E4JdffuGtt95Slx89erTMdmpqKr6+vri5uWE2m1m4cCEAwcHBODo64u3tjbe3N9u2bQPg4MGD6jIvLy82bdr0UNqZmZmMGDGCPn368Nprr/HVV18BcO3aNUaPHk2vXr0YPXq0+pyvXr2a/v37079/f/r27cszzzzDtWvXynxu7qVp06bs3LmThIQE4uPjGTt27H03yiKv0epva93Xa9ugKEqVBst8MIOh3AczGo2cOnWKrl27kpaWRkxMDAMHDuTEiROVfnwt21r39drWui/t6u/rta11/0Hb5c0TO3ToUFq3bo2NjQ1jx47Fz8+Pdu3a8eGHH6IoCnZ2dly5cqXU+xefJ/bMmTNYWVkRGRlJYGAgAHl5edSqVQuAAwcOcOHCBXr16sWpU6c4cOAAQ4cOJS0tjW3btjFq1KgS7eLzxCYkJFC7dm3mzp3LkiVLAJg6dSq9e/emXbt2HDx4kA0bNhAaGsqNGzeoVasWBoOBlJQUZs2axbJly0q0X3zxRfXPGRkZZGRk4O3tTVZWFu3atSMyMpL169djY2PDhAkTStw3NzcXKysrTCYTGRkZeHl5kZaWds+5Z7Vox8fHA3Dx4kUuXbqEq6srOTk5DBo0iHnz5rF161ZsbW0ZPnw4K1euJCsri3feeafE4+zZs4cvv/ySzz//vMTyZ5555q6/w581btwYe3t7jhw5go2NDTExMfTp06dCP+e3bt0q8/b/xtfow25r3a+pbUVRDOX2K72FVczHx4fk5GRSUlIoKCggIiICPz+/Gt/Wuq/XttZ9aVd/X69trftatJ988kk6duxY4mjf66+/ztKlS7lzAKKsAeyfOTk5Ubt27RLL7gxgAfLz8zEYbv9/IzExEU9PTwwGA46OjuTl5ZGVlVVq22w2U7du3RLLDAYDubm5wO3BX/369QGoXbu2+jh5eXnqn0tjb2+Pt7c3AHXr1sXFxYX09PRS17e2tlYHleX1tWw3bNgQV1dXAOrUqUPz5s25ePEiP/74Iz179gSgZ8+e7N69+677fvfdd7z00kultsuSmZnJkSNHAMjOziYxMREHB4cHav2ZvEarv611X69tqIGDWAcHB1JTU9Xv09LSquzFp2Vb675e21r3pV39fb22te5r0Z40aRLz588vcXSsadOmdOvWja+++oqwsDD+8pe/VOoxAHbu3MncuXOJj4/n73//OwDXr1+nXr166jq2trbq294VNWrUKFasWMHQoUNZvnw5b7zxhnrb/v37GTFiBNOmTWP8+PEVbp45c4bY2Fjat28PQFhYGJ6envj7+3P16lV1vejoaMxmMx4eHoSHh1foE8C0bJ87d46TJ0/i5ubG5cuXadiwIQANGjTg8uXLJda9ceMGP//8M76+vuU/IeVo1qwZnp6eREdHV7oF8hp9GG2t+3ptQw0cxAohhIBOnTpx5cqVu952s7KyIj8/n0GDBhEZGUlwcHClH+uFF14gKCgId3f3KhvsAHzzzTeMHDmSf/3rX4wcOZJPP/1Uva1Dhw4sW7aMadOmsWbNmgr1srOz6devH/PmzcPW1pbRo0eTlJTE4cOHsbe3JygoSF23ffv2JCQkEB0dTUhICHl5eQ+tnZubS1BQEEFBQdjY2JS4zWAw3HU0d+/evXh6epb4JeJB1KlTh/Xr1/Pee++VeRRdCL2qcYPY9PR0HB0d1e+bNm1a5ls7NaWtdV+vba370q7+vl7bWveruu3p6Unnzp3Ztm0bISEhtGvXjlmzZnH+/Hl27doF3L4oy9nZudLbfoe7uzvHjx8Hbh95LX5R0fXr17G1tb2v3s6dO+nQoQMAHTt2VC/sKs5sNpOZmVnuBUwFBQX07duXQYMG0adPHwAaNWqEhYUFRqORgIAAYmJi7rqfq6srNjY2ZV48pnU7KCiIl19+WT2yWr9+fS5evAjcPm/Wzs6uxH22b9/+wKcS3GEymdiwYQNfffVVmRef3S95jVZ/W+u+XttQAwexMTExODs74+TkhKWlJQMGDCAqKqrGt7Xu67WtdV/a1d/Xa1vrflW3Fy5cyIsvvkj37t2ZPHkyMTExTJ06ld27d6sXVLVt25azZ89WaruLv5WdmJhIgwYNAGjVqhWxsbEoikJqaiq1atW665zX8tSvX5+EhAQAYmNj1bcRz507p57Tm5ycTEFBQZkDZEVRCAgIwNXVtcSpBxkZGeqfN2/eTJs2bQBISUmhsLAQgLNnz5KYmIiTk9NDaQcHB9O8eXOGDh2qLn/++efZunUrAFu3bqVz587qbVlZWRw6dKjEsgexfPlyTpw4UeLod1WQ12j1t7Xu67UNUP6JPNWsqKiIwMBAtm/fjoWFBStXrlSPDNTkttZ9vba17ku7+vt6bWvd13rb71i5ciWzZs1iyJAh5Obm3tfpBOvXryclJYXc3Fzmzp1Lly5dSEpK4tKlSxgMBurVq0evXr0AaNmyJUlJSXz66adYWlrSu3fvMttz5swhPj6e69evM2TIEIYOHcq4ceNYunQpRUVFWFlZMW7cOAD27dvHrl27MJlMWFlZMWXKlDIvkNq/fz9r167FbDarF2HNmDGDiIgI4uLiMBgMNGvWTJ0VYd++fYSGhmJpaYnRaGTx4sXq4Lw627GxsXzzzTc4OzvTv39/4Pb0aG+++SaTJ09m8+bN2NvbExoaqt5n9+7d/PWvf73rArz70aFDB4YOHUp8fDyHDh0C4IMPPuDbb7994OYd8hqt/rbWfb22oQZOsSWEEP9Nyptiq7KKT7FV1YpPsVXVik+xpTd3ptjSQkWm2KqM8qbYEqK66HKKLSGEEEIIIcojg1ghhBBCCKE7MogVQgghhBC6I4NYIYQQQgihOzKIFUIIIYQQuiODWCGEEEIIoTsyiBVCCCGEELpT4z7sQAghHkSrVq00awcGBmrWvvMxp1pp3Lixpn29Kioq0qxd/NO+qprM4yrE/5EjsUIIIYQQQndkECuEEEIIIXRHBrFCCCGEEEJ3ZBArhBBCCCF0RwaxQgghhBBCd2QQK4QQQgghdKdGDmK7detGYmIiSUlJTJ48WTdtrft6bWvdl3b19/XUtrKy4t///jebN29m69atjB07FoD27duzceNGoqKimDNnDhYWFhXqffnll/y///f/mDVr1l237dq1i7Fjx5KdnQ3AjRs3WLp0KbNnz2bmzJkcOHCgzHZ6ejp9+/alc+fOdOnSheXLlwPw8ccf06lTJ1544QX8/f25du0aAPn5+YwfPx5fX19eeOEFfv7551Lbqamp+Pr64ubmhtlsZuHChQAEBwfj6OiIt7c33t7ebNu2DYCDBw+qy7y8vNi0aVOZ265lX+t2165dcXd3x8PDg0WLFgHw0Ucf4eTkRNu2bWnbti3ffvutep+QkBBcXV1p06YNO3bsKPN5mT9/PgMHDuStt95Sl/3222+MHz+ewMBAxo0bx8mTJwHYvXs3Y8aM4a233mLChAmcPn26zHZZ9PQarc6+Xtta9/XaRlGUavsClPK+jEajkpycrDRv3lyxtLRUYmNjFVdX13Lv97Dbet52eV4erbaet70y7VatWpX65eXlpbRq1Upp06aNEhsbqwwYMEA5d+6c0q1bN6VVq1bK4sWLlalTp5Z6/0WLFqlf77zzjjJp0iTF3t6+xPKPPvpIcXFxUZ544gll9uzZyqJFi5RXXnlFeeGFF5RFixYps2bNUqytrZX58+eXuF96err6dfjwYeW7775T0tPTlZMnTyrNmzdXdu/erXz11VfK2bNnlfT0dGXMmDHKmDFjlPT0dGXmzJnK66+/rqSnpytxcXGK2WxWUlNTSzSLioqUoqIiJS0tTYmJiVGKioqUP/74Q3F2dlYSEhKUadOmKaGhoep6d76ysrKUmzdvqvdt2LCh+v29vrTsa9HOz89X8vPzlbNnzyrR0dFKfn6+cvnyZaVFixZKbGys8sEHHyhz5sxR17vzFRsbq5jNZiUrK0s5efKk8tRTTyk3btwosc62bdvUr5CQEGXhwoVKs2bN1GVeXl5KcHCwsm3bNiU4OFgxm83Ktm3blLlz5yrr1q1Tl7ds2bJEa9u2bbp+jT7svl7bet72yrQrMq6scUdifXx8SE5OJiUlhYKCAiIiIvDz86vxba37em1r3Zd29ff12M7NzQXAZDJhMpkoKiqioKCAM2fOAPDzzz/z4osvVqjVokULrK2t71oeGRmJn58fBoNBXWYwGMjLy0NRFG7evIm1tTVGY+m73UaNGmE2mwGwsbHB2dmZzMxMnn/+eUym259N4+3trU6mf+rUKTp06ABAgwYNsLW1JS4u7p5te3t7vL29Aahbty4uLi6kp6eXui3W1tbqY+bl5ZX4e1V3X+u2l5dXifa5c+dKXX/r1q28/vrrPPbYYzRv3pynn36amJiYUtc3m83UrVu3xDKDwaD+TObk5GBnZwdA69at1XVdXFy4fPlyqd2y6PE1Wh19vba17uu1DTXwdAIHBwdSU1PV79PS0nBwcKjxba37em1r3Zd29ff12DYajWzatIn9+/fz888/Ex8fj4WFBW5ubsDtt7vs7e0fuB8fH0+9evVo2rRpieWdOnXi/PnzfPDBB8yePZvXXnutzEFscampqRw9elQdYN0RERFBly5dgNuDnh07dlBYWMjvv/9OQkJCmQOwO86cOUNsbCzt27cHICwsDE9PT/z9/bl69aq6XnR0NGazGQ8PD8LDw9WB4cPsa92Oi4vDx8cHgM8++wxvb29GjBihts+dO1fi39nBwaHMAfW9jBw5kpUrV/KPf/yDFStW8MYbb9y1zo4dO3jmmWfuq1t8m/T2Gq2Ovl7bWvf12oYaOIgVQoiqduvWLXr37k3nzp1xd3fH2dmZCRMmMGXKFP7973+Tk5PzwB9Dmp+fz44dO+jRo8ddt504cQIHBwdmzJjBlClTWL9+PTdu3Ci3mZOTw4gRIwgODi5xFG/BggWYTCb1o2oHDBiAvb09L7/8MtOnT6dt27blntubnZ1Nv379mDdvHra2towePZqkpCQOHz6Mvb09QUFB6rrt27cnISGB6OhoQkJCyMvLK3fbtexr3e7fvz9z587F1taWUaNGkZiYyK+//krjxo2ZNGlSuX/3itq2bRsjRoxgzZo1jBgxggULFpS4PS4ujh07djB8+PAqe0whHkU1bhCbnp6Oo6Oj+n3Tpk3v+7fch9HWuq/XttZ9aVd/X69tgKysLKKjo+nYsSOxsbEMGTKE119/nV9//VU9teB+Xbp0icuXLzNnzhymT5/OH3/8QWhoKNevX+fAgQN4eHhgMBho2LAh9evX5/z582X2CgoKGDFiBL1796Z79+7q8nXr1rFz504WL16svj1uMpkIDg7m+++/Z9WqVVy7do2nnnqqzHbfvn0ZNGiQOhBu1KgRFhYWGI1GAgIC7vnWuKurKzY2Nhw9erTcbdeqr3W7f//+DBw4kN69e9/V9vf3V9tNmjQhLS1NvW96evp9H1nauXOnehpIx44d1Qu7AFJSUliwYAEffvghtra299Utvk16fY3qddvlean+NtTAQWxMTAzOzs44OTlhaWnJgAEDiIqKqvFtrft6bWvdl3b19/XWfuKJJ9SjmY899hjPPfccp0+fVs9DtLS0JCAggIiIiAfqN2nShNmzZxMcHExwcDCPP/44kyZNwtbWFjs7O06dOgXA9evXuXDhAg0aNCi1pSgKEyZMoEWLFowaNUpdvnv3bj777DO++OILateurS6/ceOGem7l3r17MZlMtGzZstR2QEAArq6ujB8/Xl1+5/xagM2bN9OmTRvg9mCqsLAQgLNnz5KYmIiTk1OZ265VX+v2yJEjcXFx4d13371ne8uWLWr7lVde4d///jc3b94kJSWF5ORk2rVrV+rzci/169cnISEBuH3U9c4g+HSWJn4AACAASURBVMKFC8yYMYOgoKC7Tk25H3p7jVZXX69trft6bQNU7ASnalRUVERgYCDbt2/HwsKClStXcvz48Rrf1rqv17bWfWlXf19v7YYNG6pTaBkMBr777jt+/PFHJk6cSOfOnTEajXz99ddER0dXqLdq1SqSk5PJzs7mww8/pHv37jz77LP3XPell15i7dq16nRcfn5+2NjYlNqOiYlh48aNuLq60rVrVwCmTJnCtGnTuHnzJgMGDABuX9wVEhLCpUuXGDRoEEajkcaNG6tTT93L/v37Wbt2LWazWb1IasaMGURERBAXF4fBYKBZs2YsWbIEgH379hEaGoqlpSVGo5HFixeXOQDXsq9l++eff+bLL7/Ezc2Ntm3bArenNFu3bl2Jdnh4OABt2rShb9++eHh4YGFhwYIFC8o8hSMkJIT4+HiuX7/O0KFDGTJkCOPGjWPp0qUUFRVhaWmpTvv21VdfkZWVpT6W0Wgs89+0NHp7jVZXX69trft6bQMY/jP1VbUwGAzV92BCiP8qrVq10qwdGBioWfvOW+Naady4saZ9vXrQc6ArYufOnZq1i59iIsSjTFGUsqdEoQaeTiCEEEIIIUR5ZBArhBBCCCF0RwaxQgghhBBCd2QQK4QQQgghdEcGsUIIIYQQQndkECuEEEIIIXRHBrFCCCGEEEJ3atyHHQghHh4t5xQdOHCgZm3Qdi7Xsj6pSmjj119/1bQ/c+ZMzdpV+YlEQojSyZFYIYQQQgihOzKIFUIIIYQQuiODWCGEEEIIoTsyiBVCCCGEELojg1ghhBBCCKE7MogVQgghhBC6UyMHsd26dSMxMZGkpCQmT56sm7bWfb22te5LW/t+kyZN2LBhA3v27OHHH38kICCgxO2jRo0iIyMDOzu7CvV27NjBkiVLWLNmjbrs1KlTrF69mvnz55OZmXnXfa5fv87ixYvLnXrp3LlzDBo0iG7duvHSSy+xatUqAGbPnk3Xrl3p3r07o0eP5vr16wBcvXqVQYMGYTab+ec//1nutqempuLr64ubmxtms5mFCxcCEBwcjKOjI97e3nh7e7Nt2zYADh48qC7z8vJi06ZNj1xb6/758+d566236N+/PwMGDCAiIgKAa9euMXbsWF577TXGjh2r/pueOXMGf39//va3v7F27doyt7s4BwcH5s+fr359/fXX9OzZk+bNmxMaGsr8+fP55JNPcHZ2rnCzLHrdv+hp3/WotLXu67VtUBSl7BUMBkdgDdAIUIDPFUVZYDAY7IB1gBNwBnhdUZSr5bTKfjDAaDRy6tQpunbtSlpaGjExMQwcOJATJ05U6C/0sNpa9/Xa1rov7artlzZP7JNPPkmjRo1ISEigTp06bN++neHDh3Pq1CmaNGnCJ598QosWLejWrRtXrly5Z6P4PLFpaWlYWlqyfft2/vGPfwBw+fJlDAYDu3btomPHjndty9atWzEYDDRu3Ji2bdve1b8zT+yFCxe4cOECbm5uZGdn4+fnx5IlS8jMzOTZZ5/FZDIREhICwOTJk8nNzeX48eOcOnWKU6dO3XMgW3ye2IyMDDIyMvD29iYrK4t27doRGRnJ+vXrsbGxYcKECSXum5ubi5WVFSaTiYyMDLy8vEhLS8Nkunuabr22tegX/2Xl0qVLXLp0CRcXF3Jychg2bBihoaF888032NraMmzYMFavXk1WVhaBgYFcuXKFzMxM9uzZQ926dRkyZMhd21vePLFGo5GVK1cyceJE3n77baKiojh8+DDPPPMMvXv35oMPPij1vhWZJ1av+5eauu96lNta92tqW1EUQ7n9CmxDITBBUZTWwF+Btw0GQ2tgCrBLURRnYNd/vq80Hx8fkpOTSUlJoaCggIiICPz8/KoirWlb675e21r3pV09/QsXLpCQkABATk4OSUlJ6iAzODiYjz/+mPJ+IS6uadOm1KpVq8Sy+vXrl3okNzk5mXr16lG/fv1y208++SRubm4A2NjY0KJFC86fP0/Hjh3VAZKnp6d6tNfa2pq2bdtiZWVVoW23t7fH29sbgLp16+Li4kJ6enqp61tbW6uPm5eXh8FQ+n5Zr22t+w0aNMDFxQWAOnXq4OTkxMWLF9m7dy89evQAoEePHuzZswcAOzs7WrduXeqAuyLc3d3JzMzk4sWL6vbe+W9pv6jdD73uX/S273oU2lr39dqGCgxiFUXJUBTl8H/+nAWcABwAP2D1f1ZbDbxaFRvk4OBAamqq+n1aWhoODg5Vkda0rXVfr22t+9Ku/n7Tpk0xm80cPnyYbt26kZmZyfHjx6ukfS/5+fn8+uuv/PWvf73v+6alpXHs2DE8PDxKLN+wYQPPP/98pbftzJkzxMbG0r59ewDCwsLw9PTE39+fq1f/742p6OhozGYzHh4ehIeHV2hwpde21v1z585x6tQp2rRpw5UrV2jQoAFw+5egqhhc3tGxY0f27t0LwPLly3njjTdYsWIFb775Jv/6178q3dfr/kXP+y69trXu67UN93lOrMFgcAK8gGigkaIoGf+5KZPbpxsIIR5h1tbWrFixgmnTplFUVMS4ceMIDQ3V9DEPHDiAl5dXhY+U3pGTk8OYMWP48MMPqVu3rro8LCwMCwuLSh8NyM7Opl+/fsybNw9bW1tGjx5NUlIShw8fxt7enqCgIHXd9u3bk5CQQHR0NCEhIeTl5T2Sba37ubm5TJkyhfHjx2NjY1PiNoPBUO7R4ooymUz4+Piwf/9+AF5++WVWrFiBv78/K1asYOzYsVXyOEKIyqnwINZgMNgAG4F3FUW5Xvw25fb7iPd8L9FgMIw0GAy/GgyGCn0Qdnp6Oo6Ojur3TZs2LfMtqfuhZVvrvl7bWvelXX19k8nEihUriIyMZNu2bTRr1oy//OUv7Nq1i4MHD2Jvb8+OHTto2LBhZTe/hIyMDPbt28eKFSs4cuQIBw8eJDY2tsz7FBQU8Pbbb+Pn50e3bt3U5Rs2bGD37t3Mnz+/UgOegoIC+vbty6BBg+jTpw8AjRo1wsLCAqPRSEBAADExMXfdz9XVFRsbG44ePfrItbXuFxYWMmXKFF566SW6dOkC3D5t4NKlS8Dt82afeOKJMrevory9vfntt9+4du0aAF26dOGXX34BYP/+/VVyYZde9y963Hfpva11X69tqOAg1mAwWHJ7APuloiiR/1l83mAw2P/ndnvgwr3uqyjK54qitFUU5e4rMe4hJiYGZ2dnnJycsLS0ZMCAARU6Sf5ht7Xu67WtdV/a1defN28eSUlJLF26FIDExETMZjM+Pj74+PiQkZHBiy++qJ5DWFX69++Pv78//v7+eHl54ePjg6enZ6nrK4rClClTePrpp/H391eX79mzh2XLlrF06VJq1679wNujKAoBAQG4uroyfvx4dXlGRob6582bN9OmTRsAUlJSKCwsBODs2bMkJiaWuFDsUWhXx7bPmDEDJycnBg0apC7v2LEj33zzDQDffPMNnTp1KnX77kenTp346aef1O+vXLminmft7u7OuXPnKv0Yet2/6HHfpfe21n29tgHKPQHJcPtwxQrghKIo84rdFAUMA+b8579bqmKDioqKCAwMZPv27VhYWLBy5coqO99Oy7bWfb22te5Lu3r6Pj4+9OvXj+PHj/P9998Dt6es+uGHHx6ot23bNlJTU8nLy2PZsmU8++yz1KpVi927d3Pjxg22bNlCw4YN1aN59+PQoUNs3ryZVq1a8corrwAwYcIEPvroI/Lz8xk2bBhw++KuGTNmALcHLdnZ2RQUFPD999/zxRdflHq0bf/+/axduxaz2axeyDRjxgwiIiKIi4vDYDDQrFkzlixZAsC+ffsIDQ3F0tISo9HI4sWL1fM4H5W21v24uDi+/fZbWrRooc408NZbbzFs2DCmTp1KVFQU9vb26owDly9fZtiwYeTk5GA0GomIiCAiIuKuUxDu5bHHHlPP0b0jLCyMgIAALCwsKCgoKHHbg9Lr/kVv+65Hoa11X69tqNgUW38DfgISgFv/WTyV2+fF/hv4C3CW21NslXlWfUWm2BJCPDylTbFVFYpPsaWFO1NsaaGsI5BCG+XNB1xZ5U2xVRlVeaRJiP9WFZliq9wjsYqi7ANKC/ne70YJIYQQQghRWTXyE7uEEEIIIYQoiwxihRBCCCGE7sggVgghhBBC6I4MYoUQQgghhO7IIFYIIYQQQuiODGKFEEIIIYTuyCBWCCGEEELoTrnzxAoh7l+jRo00a7du3Vqz9uLFizVru7i4aNYWpYuOjtas/T//8z+atbdsqZIPgSzVrVu3yl9JCFGjyZFYIYQQQgihOzKIFUIIIYQQuiODWCGEEEIIoTsyiBVCCCGEELojg1ghhBBCCKE7MogVQgghhBC6UyMHsd26dSMxMZGkpCQmT56sm7bWfb22te7rpd2kSRM2btzI3r172bNnDwEBAQBMmjSJH374gZ07dxIREVGp6bnq1KnD9OnT+eKLL1i1apU6HVfv3r354osvWLlyJSNHjqxQKyMjgzfeeIOePXvSq1cv/vWvfwGwfft2evXqhZubG0ePHlXXj4+Pp0+fPvTp04fevXuzc+fOUtupqan4+vri5uaG2Wxm4cKFAAQHB+Po6Ii3tzfe3t5s27YNgIMHD6rLvLy82LRp00Np633bZ86cSffu3Rk8eLC6LCkpiREjRjBkyBAmTpxITk4OAIWFhXz88ccMGTKEgQMHsmbNmjLbf/bKK6/w6aef8umnnzJ+/HgsLS15+eWXCQsLIzIykrp1695X716aNm3Kzp07SUhIID4+nrFjx1a6WZzsF6u/rXVfr22t+3ptGxRFqdJgmQ9mMJT7YEajkVOnTtG1a1fS0tKIiYlh4MCBnDhxotKPr2Vb675e21r3a2r7XgPRJ598kkaNGpGQkECdOnXYsWMHb775JufOnSM7OxsAf39/WrZsWeYLvax5YidPnkxCQgLbtm3DZDLx2GOP4ezszODBg5k6dSoFBQU8/vjj/PHHH/e8f/F5Yi9evMjFixdp3bo1OTk59OvXj4ULF2IwGDAajQQHBxMUFISbmxsAN27cwNLSEpPJxMWLF+nTpw+7d+/GZLo9HXXxeWIzMjLIyMjA29ubrKws2rVrR2RkJOvXr8fGxoYJEyaU2K7c3FysrKwwmUxkZGTg5eVFWlqa2i5Oy7Yet734PLFHjhzB2tqajz76iC+//BKA4cOHM3bsWLy8vPjf//1fzp07x8iRI9mxYwc//fQTH3/8MXl5eQwaNIiwsDDs7e3VXmnzxNrZ2TFz5kzeeecd8vPzmTBhAocPH+bMmTNkZ2fz8ccfM3HiRLKysu55f6jYPLGNGzfG3t6eI0eOYGNjQ0xMDH369KnQa7S8eWJlv1j9ba37em1r3a+pbUVRDOX2K72FVczHx4fk5GRSUlIoKCggIiICPz+/Gt/Wuq/XttZ9PbUvXLhAQkICADk5OSQlJdG4cWN1AAtgbW39wP06derg7u6uHqUrLCwkJyeHXr168fXXX1NQUABQ6gD2zxo2bKgOmOvUqcNTTz3FhQsXePrpp2nevPld69euXVsdPN28eRODofT9j729Pd7e3gDUrVsXFxcX0tPTS13f2tpabefl5T20tt633cvLC1tb2xLLUlNT8fT0BKBdu3b8+OOP6m15eXkUFhZy8+ZNLC0tqVOnTpn94iwsLLCyssJoNPLYY49x5coVUlJSuHjxYoUb5cnMzOTIkSMAZGdnk5iYiIODQ5W0Zb9Y/W2t+3pta93Xaxtq4CDWwcGB1NRU9fu0tLQq2ylp2da6r9e21n29th0dHXFzc+Pw4cMATJkyhUOHDvHaa68RGhr6QM3GjRtz7do1Jk2axNKlS5kwYQK1atWiadOmmM1mwsLCmD9/Pq1atbrvdnp6OidOnMDd3b3M9eLj4+nVqxevvvoq06ZNK/VoZnFnzpwhNjaW9u3bAxAWFoanpyf+/v5cvXpVXS86Ohqz2YyHhwfh4eEPva33bb+jefPm7N27F4AffviBCxcuAPD3v/+dWrVq0atXL3r37s3AgQPvGgCX5sqVK2zZsoWlS5eyYsUKcnNziYuLu6/tul/NmjXD09Ozyj6hTPaL1d/Wuq/XttZ9vbahBg5ihXjUWVtbs3z5cqZNm6YehZ0zZw7PPPMMGzduZPjw4Q/UtbCwwNnZmaioKEaNGkVeXh4DBw7EwsICW1tb3n77bZYuXcq0adPuq5uTk8O7777LlClTsLGxKXNdd3d3oqKiWLduHcuWLePmzZtlrp+dnU2/fv2YN28etra2jB49mqSkJA4fPoy9vT1BQUHquu3btychIYHo6GhCQkLIy8t7aG29b3txU6dOJTIykjfffJPc3Fx1EHz8+HEsLCyIiopiw4YNRERElHlUuLg6derg4+PDW2+9RUBAAI899hidOnW6r+26H3Xq1GH9+vW89957ZZ6iIIR4tNS4QWx6ejqOjo7q902bNq3wjvNhtrXu67WtdV9vbZPJxIoVK4iMjFTf9i8uMjKSHj16PFD7zjmsiYmJAOzduxdnZ2cuXrzITz/9BEBiYiKKolCvXr0KNQsKCnj33Xfp0aMHXbt2rfC2PP3001hbW5OUlFRmu2/fvgwaNIg+ffoAt88ltrCwwGg0EhAQQExMzF33c3V1xcbGpsRFZdXZ1vu2/5mTkxMLFixg1apVdO3aVT1KsmPHDtq3b4/JZMLOzg6z2az+bJXH3d2d8+fPc/36dYqKioiOji5xTnRVMplMbNiwga+++qrcC9vuh+wXq7+tdV+vba37em1DDRzExsTE4OzsjJOTE5aWlgwYMICoqKga39a6r9e21n29tefPn09SUhJLly5VlxU/v/Sll14iOTn5gdpXr17lwoUL6g7D29ubs2fPsn//fvWcx6ZNm2Iymbh27Vq5PUVRmDZtGk899RRvvPFGueunpaVRWFgIwLlz50hJSSn1bSNFUQgICMDV1ZXx48eryzMyMtQ/b968mTZt2gCQkpKits+ePUtiYiJOTk7V3tb7tt/LlStXgNsXOn3xxRf07t0buD1wPnToEHD7or1jx47RrFmzCjUvXbpEy5YtsbKyAsBsNpOWlnZf21VRy5cv58SJE3z66adV2pX9YvW3te7rta11X69tgPs7eaoaFBUVERgYyPbt27GwsGDlypUcP368xre17uu1rXVfT20fHx/69evH8ePH1emnZs+ezcCBA2nRogW3bt0iLS2NSZMmPfBjLFq0iKlTp6pXq4eGhpKXl8fEiRNZsWIFhYWFhISEVKh1+PBhoqKiaNmypXpE8N133yU/P59Zs2Zx5coVxowZQ6tWrVi2bBmHDx9m+fLlmEwmjEYjH374IU888cQ92/v372ft2rWYzWb1QqYZM2YQERFBXFwcBoOBZs2asWTJEgD27dtHaGgolpaWGI1GFi9eTIMGDaq9rfdtnzZtGkeOHOGPP/7Az8+PgIAAcnNziYyMBOD5559X3wl47bXXmDlzJoMHD0ZRFHr06EGLFi1KbReXlJTEL7/8wty5c7l16xanT59mx44ddO/end69e/P4448zf/58Dh8+THh4eIWa99KhQweGDh1KfHy8OuD+4IMP+Pbbbx+4eYfsF6u/rXVfr22t+3ptQw2cYkuIR0Fl5notT1lTbFVW8Sm2qppWbyeLslXVhU73UtoUW1WhIlNsVUZ5U2wJIR4uXU6xJYQQQgghRHlkECuEEEIIIXRHBrFCCCGEEEJ3ZBArhBBCCCF0RwaxQgghhBBCd2QQK4QQQgghdEcGsUIIIYQQQndq3IcdCHGHnZ2dZu3in5ilhTufkKWFp556SrO2uLeff/5Zs/Ynn3yiWRtg+/btmrVv3LihWVsIIcojR2KFEEIIIYTuyCBWCCGEEELojgxihRBCCCGE7sggVgghhBBC6I4MYoUQQgghhO7IIFYIIYQQQuhOjRzEduvWjcTERJKSkpg8ebJu2lr39dqu6v7ChQtJTExk37596jI3Nze2b9/Ojz/+yK5du/D29q5wLzw8HH9/f9577z112bx58wgKCiIoKIgxY8YQFBQEQFZWFv/85z8ZMmQIy5cvL7edkZHBkCFDeOmll3j55Zf54osvAPj22295+eWXadmyJQkJCer6+fn5TJ48mR49etCzZ0+io6PL7KempuLr64ubmxtms5mFCxcCEBwcjKOjI97e3nh7e7Nt2zYADh48qC7z8vJi06ZN0r6PNsDs2bPp2bMn//jHP9RlycnJjB49mmHDhjF58mRycnKA2//+vr6+vPnmm7z55pvMnTu3zPaf9ezZkwULFrBgwQLee+89LC0tefLJJwkJCSE8PJwJEyZgMlV+psQxY8YQExPDr7/+yttvv13p3p/pdd+lp/3io9LWuq/XttZ9vbYNiqJUabDMBzMYyn0wo9HIqVOn6Nq1K2lpacTExDBw4EBOnDhR6cfXsq11X6/tyvRLmyf22WefJScnh/DwcP72t78BsGHDBj777DN27drFCy+8wNixY/Hz8yu1XXye2OPHj1OrVi0WL17MvHnz7lp39erVWFtb069fP/Ly8khJSSE1NZXff/+dgICAe/bvzBN74cIFLl68SJs2bcjOzqZ3796Eh4djMBgwGo18+OGHTJkyBbPZDMDatWtJSEggJCSEy5cv4+/vT2RkJEbj//2+WXye2IyMDDIyMvD29iYrK4t27doRGRnJ+vXrsbGxYcKECSW2Kzc3FysrK0wmExkZGXh5eZGWlnbPgZC0/69dfJ7Y2NhYateuzcyZM1mzZg0AI0aMYMyYMXh5efHNN9+QkZFBQEAAGRkZTJ48WV3vXkqbJ9bOzo5Zs2Yxbtw48vPzCQoK4tChQzzzzDMcOHCAffv2MXr0aFJSUsqcC7a8eWJbt27N6tWr6dSpE/n5+WzZsoVx48Zx+vTpMu8HFZsnVq/7rpq6X3yU21r39drWul9T24qiGMrtV3oLq5iPjw/JycmkpKRQUFBAREREmYORmtLWuq/Xthb9X375hatXr5ZYpigKdevWBcDW1pbMzMwK91q3bo2Njc09b1MUhV9++UUdLNeqVQtXV1csLS0r1H7yySdp06YNADY2Njz99NOcP3+eFi1a3PNDC5KTk3n22WcBqF+/Pra2tiWO1P6Zvb29etS5bt26uLi4kJ6eXur61tbW6uAsLy8Pg6H0fYS0783T0xNbW9sSy1JTU9VfXNq2bcuPP/5YZqOiLCwssLKywmg08thjj3H16lXMZrM6qN69ezft27ev1GO0atWKX3/9lRs3blBUVMS+fftq9Ov/UWhr3ddrW+u+Xtta9/Xahho4iHVwcCA1NVX9Pi0tDQcHhxrf1rqv13Z19AHef/99goODiY+P56OPPuLjjz+uku6JEyeoV68e9vb2lW6lpaVx/PhxPDw8Sl3HxcWFXbt2UVhYSGpqKkePHiUjI6NC/TNnzhAbG6sOasLCwvD09MTf37/EoD86Ohqz2YyHhwfh4eEVejta2mVr3rw5P/30E3B7YHnhwgX1toyMDIYPH05gYCBxcXEVbl65coUtW7bw+eefs3LlSnJycvjtt9/Iycnh1q1bAFy6dIn69evf17b+2fHjx3nuueews7Ojdu3adOvWjaZNm1aqWZxe91163i/qta11X69trft6bUMNHMQK8SDefPNNPvjgA9zd3Xn//ffV8yAra9++fepR2MrIyckhMDCQ999/Xz1ifC99+/alcePG9O7dm5kzZ+Lt7Y2FhUW5/ezsbPr168e8efOwtbVl9OjRJCUlcfjwYezt7dVzegHat29PQkIC0dHRhISEkJeXJ+37bP/ZlClT2Lx5M/7+/ty4cUM9Ul+/fn02bNjAypUrGTt2LB999JF6vmx56tSpg4+PD6NHj8bf359atWrd17neFXXy5EnmzZvH1q1b2bJlC/Hx8RQVFVX54wghRFWrcYPY9PR0HB0d1e+bNm1a5luBNaWtdV+v7eroAwwYMICtW7cCsGXLlir5n31RUREHDx7kueeeq1SnoKCAwMBAevXqRbdu3cpc12Qy8f7777N161aWLFnC9evXcXJyKrfft29fBg0aRJ8+fQBo1KgRFhYWGI1GAgICiImJuet+rq6u2NjYcPToUWnfR/temjVrxrx581ixYgW+vr7qkQYrKyvq1asH3H7bvkmTJiWOSpTFw8OD8+fPc/36dYqKijhw4AAuLi7UqVNHPUe6QYMGXL58+b629V5Wr15Nhw4dePHFF/njjz9ITk6udPMOve679Lxf1Gtb675e21r39dqGGjiIjYmJwdnZGScnJywtLRkwYABRUVE1vq11X6/t6ugDZGZm0qFDBwA6derEb7/9VulmfHw8TZo0qdTbtYqiMHXqVJ5++mmGDx9e7vo3btwgNzcXuH0U2MLCAmdn5zL7AQEBuLq6Mn78eHV58VMQNm/erJ6Xm5KSQmFhIQBnz54lMTGx1EGytCvuzqkJt27dYs2aNeo5X1evXlWPap47d460tDSaNGlSoebFixdp2bIlVlZWALi7u6unmNz5xapLly4cPHjwvrb1Xho2bAjc/h9Mr169WLduXaWbd+h136Xn/aJe21r39drWuq/XNkDl52apYkVFRQQGBrJ9+3YsLCxYuXIlx48fr/Ftrft6bWvR//zzz+nQoQP169cnISGBOXPm8O677zJr1ixMJhM3b94sMV1WeT799FOOHTtGVlYWo0aN4vXXX8fX15f9+/ff81SCMWPGkJubS2FhITExMXzwwQclftMs7tChQ2zevJlWrVrRs2dPACZMmEB+fj4fffQRV65cYcSIEbi6urJq1SouX77M8OHDMRgMNG7cuNwpmfbv38/atWsxm83q0ecZM2YQERFBXFwcBoOBZs2asWTJEuD2wDg0NBRLS0uMRiOLFy+mQYMG0q5gG+Cf//wnR44c4dq1a/Tp04fhw4dz48YNIiMjAXj++efpT/IBOAAAIABJREFU3r07AHFxcaxYsQKTyYTBYCAoKOiui8JKk5SUxC+//MInn3zCrVu3OH36NDt27ODQoUNMmDCBQYMGkZKSws6dOyvUK8tXX32FnZ0dBQUFjB8/nmvXrlW6eYde91162y8+Cm2t+3pta93Xaxtq4BRbQtxR2hRbVaH4FFtauHOluhbuNauB0FbxKbaqWmlTbFWV8qbYqoyKTLElhBAPQpdTbAkhhBBCCFEeGcQKIYQQQgjdkUGsEEIIIYTQHRnECiGEEEII3ZFBrBBCCCGE0B0ZxAohhBBCCN2RQawQQgghhNCdGvdhB6JqtW/fXtP+xIkTNWv7+Pho1r7zsaCi+tz5JDKtLFy4ULP2rFmzNGvn5ORo1hZCiEeZHIkVQgghhBC6I4NYIYQQQgihOzKIFUIIIYQQuiODWCGEEEIIoTsyiBVCCCGEELojg1ghhBBCCKE7NXIQ261bNxITE0lKSmLy5Mm6aWvdr+r2pk2bWLt2LWvWrGHVqlUABAQEEBUVxZo1a1izZg3PPvtshXthYWEMHz6c8ePHq8vmzZtHUFAQQUFBvPXWWwQFBQEQFxfHpEmTeO+995g0aRIJCQllts+dO0f//v35+9//jq+vLytWrABg5syZdOnShRdffJERI0Zw7do19T4nTpzg1VdfxdfXl65du5KXl3fPdmpqKr6+vri5uWE2m9WpmoKDg3F0dMTb2xtvb2+2bdsGwMGDB9VlXl5ebNq0qdTt1rKt521PS0uje/futG3blnbt2hEeHl7i9oULF1K3bl0uXbpUYvmhQ4d4/PHH2bx5c5nPy8aNG5k1axYLFixQl33//fcsXLiQRYsWsWrVKq5fvw7A8ePH1eVhYWGcOXOmzHZx4eHhpKSkcPDgQXVZ7969iYmJ4fr163h5eVW4VR7Zdz1aba37em1r3ddrW+u+XtsGRVGqNFjmgxkM5T6Y0Wjk1KlTdO3albS0NGJiYhg4cCAnTpyo9ONr2da6/6DtsuaJ3bRpE2+88UaJgV9AQAC5ubl89dVXFdqu4vPEHj9+nFq1arFo0SLmz59/17qrV6/G2tqafv36cfr0aR5//HHs7Oz4/fffmTFjBp9//nmJ9YvPE3v+/HkuXLiA2WwmOzubHj16sGzZMjIzM3nuuecwmUzqXJ5Tp06lsLCQ7t278+mnn9K6dWuuXr2Kra0tFhYWQMl5YjMyMsjIyMDb25usrCzatWtHZGQk69evx8bGhgkTJpTYrtzcXKysrDCZTGRkZODl5UVaWhom093TLmvZ1tu2F58nNjMzk8zMTDw9PcnKyqJjx45ERETg4uJCWloagYGBnDp1ir1799KgQQMAioqK6NWrF7Vq1WLo0KG8+uqrJR6/+DyxKSkpWFlZsWHDBt555x0A8vLyqFWrFgA///wzFy5c4NVXX+XmzZtYWVlhMBjIzMzk66+/LvGLGJQ+T2yHDh3Izs5m2bJl6s9rq1atuHXrFgsXLmTq1KkcOXLknve9oyLzxP437rse5bbWfb22te7rta11v6a2FUUxlNuv9BZWMR8fH5KTk0lJSaGgoICIiAj8/PxqfFvrvtbbXhVat26NjY3NPW9TFIWff/6Zv/3tbwA89dRT2NnZAeDo6Eh+fj4FBQWlths1aoTZbAbAxsaGFi1akJmZSadOndRBkre3N5mZmQDs3bsXV1dXWrduDcATTzyhDmD/zN7eHm9vbwDq1q2Li4sL6enppW6LtbW1+ph5eXkYDKW/zrRs63nbGzdujKenp9pu1aoV586dA2DKlCl8/PHHd91/yZIl+Pn5qYPasjRv3hxra+sSy+4MYAEKCgrU/mOPPab+OT8/v9znvLj9+/dz9erVEstOnjxJUlJShRsVIfuuR6utdV+vba37em1r3ddrG2rgINbBwYHU1FT1+7S0tCr7dCUt21r3tWgrisLChQv54osvSvxQ9evXj7Vr1/L+++9Tt27dSj3GHSdOnKBevXrY29vfdduBAwdo3rw5lpaWFWqlpqZy7Nixu96qXbduHZ07dwbg9OnTAAwZMoTu3bvz2WefVah95swZYmNj1SPYYWFheHp64u/vX2KwEh0djdlsxsPDg/Dw8FKPlFZXW8/bfvbsWeLj42nbti3/+7//S5MmTdRfWO44d+4cW7duJSAgoELPRWl27NhBaGgosbGxvPDCC+ryY8eOMX/+fNasWUOfPn0q9RhakH3Xo9XWuq/XttZ9vba17uu1DTVwECuqz6hRoxg2bBjjx4+nb9++eHp6EhkZyWuvvcbQoUO5fPky48aNq5LH2rdvn3oUtrjU1FTWrl3LqFGjKtTJyclh1KhRTJ8+vcQAe9GiRZhMJnr37g3cfuv5119/ZeHChWzcuJHt27ezb9++MtvZ2dn069ePefPmYWtry+jRo0lKSuLw4cPY29ur5/PC7dM0EhISiI6OJiQkpNTzbaujredtz87OZsiQIcyZMweTycQnn3zC+++/f9d6kydP5qOPPsJorNwu68UXX2TSpEl4enryyy+/qMvbtGnD+PHjGTx4MDt37qzUYwghhKgeNW4Qm56ejqOjo/p906ZNy3wLs6a0te5r0b548SIAV69eZc+ePbRu3ZorV65w69YtFEVhy5Yt6tvxlVFUVER0dDQdOnQosfzy5cuEhoYyduxYGjduXG6noKCAUaNG0bt3b15++WV1+fr169m1axcLFy5U3wq2t7fHx8cHOzs7ateuTZcuXTh69GiZ7b59+zJo0CD1SFyjRo2wsLDAaDQSEBBATEzMXfdzdXXFxsbmobX1vO0FBQUMGTKE119/HT8/P1JSUjhz5gzPPfccbdq0IT09nY4dO3L+/HmOHDnCm2++SZs2bdiyZQvjx49n69atZT4vZfHw8ODYsWN3LW/evDlXrlyp0Hmq1Un2XY9WW+u+Xtta9/Xa1rqv1zbUwEFsTEwMzs7OODk5YWlpyYABA4iKiqrxba37Vd2uVauWes5grVq18PHx4fTp09SvX19d5/nnn1fflq+M+Ph4HBwcSrRzcnKYNWsWgwcPxsXFpdyGoihMnDiRFi1aMGLECHX5jz/+yGeffcaKFSuoXbu2urxTp06cPHmSGzduUFhYyIEDB3B2di61HRAQgKura4kLejIyMtQ/b968mTZt2gC3LxoqLCwEbr8VnpiYiJOTU7W39bztiqLw9ttv06pVK8aOHQvcPhqakpLCsWPHOHbsGA4ODvz00080atSIo0ePqsv9/PyYP38+PXv2LPV5uZfiMx2cOHGChg0bArd/mbpzgWt6ejqFhYV3nU/7sMm+69Fqa93Xa1vrvl7bWvf12gao2Ml21aioqIjAwEC2b9+OhYUFK1eu5Pjx4zW+rXW/qtt2dnaEhIQAYGFhwY4dOzhw4ADTp09XB3sZGRnMmTOnws358+dz7NgxsrKyGDlyJP3798fX15f9+/ffdRT222+/JTMzkw0bNrBhwwYAPvzwQ+rVq3fPdkxMDJGRkbi4uPDSSy8BMGnSJKZPn05+fj6DBw8GwMvLi9mzZ/P4448TEBDAK6+8gsFgoEuXLvj6+t6zvX//ftauXYvZbFYvZJoxYwYRERHExcVhMBho1qwZS5YsAW6fGhEaGoqlpSVGo5HFixeXerGRlm09b/svv/zC119/TZs2bXjuuecAmD59Ot26dSv173o/1q1bx+nTp8nNzSUkJARfX19OnTrFxYsXMRgMPP744+p54MeOHePIkSMYjUZ1J1vRi7tWrVpFx44dqV+/PidPnmTmzJlcvXqVuXPn0qBBAzZu3Eh8fPxdMyncL9l3PVptrft6bWvd12tb675e21ADp9gSVausKbaqQvEptqpa8Sm2qlpVnlguKqb4FFtaKD7FVlUrbYqtqlDTTl0QQoiaQJdTbAkhhBBCCFEeGcQKIYQQQgjdkUGsEOL/t3f/wVHXdx7HX5+ExF/Y4vHD0hBNUBgSBQlT8bT25qYac7QzBjoUpf1DS2211VY4dGL5o2jrIdUTCGAFr2p/cEcGqnDqMAO0tWNrbIrQqCCBLAma7ASE1pZfMuTgc3+w7CRAkgX2s/t9L8/HTMZks3nux8+Xr/N2+e4GAABzGGIBAABgDkMsAAAAzGGIBQAAgDkMsQAAADCHIRYAAADmRO43diG9Jk2aZLpvVTp/I8nJXnvttWDtE782NoSnn346WFuS/v73vwftAwCihWdiAQAAYA5DLAAAAMxhiAUAAIA5DLEAAAAwhyEWAAAA5kRyiK2qqlJTU5Oam5tVU1Njph26f67tlStX6sc//rHmz5+fvG3dunVasGCBamtr9fzzz2vfvn3J7+3YsUO1tbWaN2+eli5d2mu7ra1Nt9xyi6699lqNHj1aCxculCQ99thjKi4u1rhx4zRu3DitWbNGkvTnP/85eVtFRYVWrVqVtX7I9q5duzRt2jRVV1dr4sSJWrZsmSRp7dq1mjhxosaMGaMtW7Yk719fX68pU6Zo0qRJmjJlihoaGnrdl1//+td6/PHHtWDBguRt69atU21trRYuXNjtmLa0tOjRRx/VwoULtXDhQv32t7/ttb1q1SrNnTtXixYtSt72m9/8RosXL9Yzzzyjn//858n2O++8o8WLF2vRokV67rnn1NHR0Wv7ZIsWLdL27dtVX1/f7fZvfetbamhoUH19vR577LEzavYkyudottqh+7Qz37faDt232g7dt9p23vu0Bnt9MOf6fLC8vDxt375dlZWVam9v14YNGzR16lRt3br1nB8/ZDt0/2zbc+fOTX7e0tKiCy64QCtWrNCMGTMkSYcPH9aFF14oSXrzzTf10UcfadKkSfrkk0/07LPPatq0aRowYIAOHDig/v37n9J/+OGHJUkdHR3q6OjQuHHjtH//fl1//fV6+eWXtXLlSvXv318zZ87s9nOHDh1SYWGh+vXrp46ODlVUVKi9vV39+p3+Xd9C9kO0T7zF1p49e7Rnzx6Vl5fr4MGDuuOOO1RbWyvnnJxz+tGPfqSHHnpI11xzjSRp69atGjhwoIYMGaLm5mbdd999pwybXd9iq7W1VYWFhVq5cqWmT5/e6zFtaWnRG2+8obvvvvu0eyx1f4utnTt3qrCwUC+99JK+973vndJ+6623tGfPHt1+++368MMPNXjwYF100UXavn27Xn/9dd17773d2r29xdZNN92kAwcOaMmSJbrpppskSTfffLNmzpypO+64Q0eOHNGgQYO0d+/eHhupvMVWFM/RbLdD92lnvm+1HbpvtR26H9W299712T/nFabZ+PHjFYvF1Nraqs7OTtXV1am6ujry7dD9dLSHDx+uiy66qNttJwYSSTpy5Ejy88bGRl1zzTUaMGCAJJ12gO1q6NChGjdunCTp0ksv1ahRoxSPx3u8/8UXX5wc+g4fPiznev+zGrIfsj148GCVl5dLki655BKVlpZq9+7dGj58uEpLS0+5f1lZmYYMGSJJuvrqq3X48OFux+VkpaWluvjii7vd1vWYdnZ29rm3PSkpKUn5z8sVV1yRvG9xcbH+8Y9/nNFj1dfX6+OPP+5227Rp07RgwYLk4/Q2wKYq6udoNtqh+7Qz37faDt232g7dt9qWIjjEFhUVqa2tLfl1e3u7ioqKIt8O3Q/ZXrt2rZ544gk1NjaqsrJS0vGB4ZNPPtHSpUu1aNEibdy4MeXezp071djYqBtuuEGS9Mwzz2js2LH65je/2W1QaWho0OjRo3Xdddfppz/9aY/PwmayH7Idj8fV1NSkMWPGpPTvuX79epWVlamwsDCl+3e1du1azZ07V42Njbr11luTt3/44Yeqra3Viy++qN27d59x98S6nnrqKb377ru65ZZbTvn+xo0bNXLkyLNqd3X11Vfrxhtv1Pr16/Xaa6+poqLinJtWz1H+25Vb7dB9q+3Qfavt0H2rbSmCQywyr6qqSj/4wQ80duxYvfXWW5KkY8eOKR6P6xvf+IamTZum3/3ud9qzZ0+frQMHDuirX/2q5s2bp0996lO677771NzcrE2bNmno0KF66KGHkve94YYb9N5776mhoUE/+clPdPjw4az2Q7YPHTqkGTNmqKamps9ntSUpFotp/vz5mj17dp/3PZ2qqio98sgj3Y7pZz/7WdXU1OjBBx/UjTfeqF/96ldn1a6srNTDDz+sMWPG6E9/+lO377W0tGjjxo267bbbzqrdVb9+/XTZZZepsrJSP/zhD/Xiiy+ecxMAkDsiN8TG43EVFxcnvx42bFivf7UblXbofui1S1JFRYU2b94sSfr0pz+tkSNHqrCwMPnX4H29WKezs1OTJ0/W1772NX3lK1+RJF1++eXKz89XXl6e7rnnHm3YsOGUnysrK1P//v2Tj52Nfuj2jBkz9OUvf7nbs6I92bVrl6ZPn645c+Z0O+ZnY+zYsckXjl144YW64IILJEmjRo3S0aNHdfDgwbNuX3fddd1+ve6uXbu0evVqff3rXz/lEoezEY/H9eqrr0qSNm3apGPHjmngwIHn3LR4jvLfrtxqh+5bbYfuW22H7lttSxEcYjds2KARI0aopKREBQUFuvPOO/XKK69Evh26H6rd9TrDLVu2aPDgwZKk8vJy7dy5U0ePHtWRI0fU1taWvFbzdLz3uueee1RWVpZ80ZikboPv6tWrky9gam1tTb6I6IMPPlBTU5NKSkqy0g/dnj17toYPH6677rqrx3+/E/bt26f7779f06dPP+u/Pu96TN9///3kMd2/f79OvJCzra1N3vszHjb/+te/Jj9vamrSoEGDJB1/UdXy5cs1efLk5G3nas2aNfrCF74gSbrqqqtUWFjY7fHPhsVzNHQ7dJ925vtW26H7Vtuh+1bbkpTaRYgZdPToUT3wwANau3at8vPz9cILL3R7tieq7dD9dLSXL1+ulpYWHTx4UHPmzFFlZaWampq0d+9eOec0YMAATZo0SZI0ZMgQjRw5MvlK+uuvv16f+cxnemy/+eabWrZsmUaPHp18kdTjjz+uuro6vfPOO3LO6corr9SSJUskSX/84x/15JNPqqCgQHl5eVq8eHGvw0/Ifsj2X/7yF7366qsaMWKEJk+eLEn6/ve/r87OTs2ZM0cff/yxvvvd72rUqFFaunSpli9frra2Ni1ZsiT5eEuXLu3xGcjly5ertbVVBw8e1BNPPKFbb71V27Zt63ZMJ06cKEnJyx/y8vJUUFCgqVOn9vqirxUrVqi1tVWHDh3SU089pS9+8Yvavn17t/btt98uSfr973+vQ4cOJZ85zcvL03e+850e2yf72c9+ps9//vMaOHCgNm/erLlz52rZsmVavHix6uvrdeTIkTPq9STq52g22qH7tDPft9oO3bfaDt232pYi+BZbSK+ub7EVwom32EJ36TxJT9b1LbbSretbbKVbb2+xlQ6pvMUWAMAGk2+xBQAAAPSFIRYAAADmMMQCAADAHIZYAAAAmMMQCwAAAHMYYgEAAGAOQywAAADMYYgFAACAOfyyAwAAAEQKv+wAAAAAOYkhFgAAAOYwxAIAAMAchlgAAACYwxALAAAAcxhiAQAAYE4kh9iqqio1NTWpublZNTU1Ztqh+1bbofu0M9+32g7dt9oO3aed+b7Vdui+1XbovtW2vPe9fkgqlvS6pPclbZH0YOL2RyXFJTUmPr6UQsv39ZGXl+djsZgvLS31BQUFvrGx0ZeVlfX5c9luW147+5JbbctrZ1/Yl/OhbXnt7Av7kql2XzOl9z6lZ2L/T9JM7325pH+WdL9zrjzxvfne+7GJjzUptPo0fvx4xWIxtba2qrOzU3V1daqurk5HOmg7dN9qO3Sfdub7Vtuh+1bbofu0M9+32g7dt9oO3bfallK4nMB73+G935T4fL+krZKK0raCkxQVFamtrS35dXt7u4qK0vNwIduh+1bbofu0M9+32g7dt9oO3aed+b7Vdui+1XbovtW2dIbXxDrnSiRVSGpI3PSAc+5d59wLzrnL0rYqAAAAoBcpD7HOuf6SXpI03Xu/T9Kzkq6SNFZSh6Sne/i5bzvn3nbOvZ3K48TjcRUXFye/HjZsmOLxeKrLzFo7dN9qO3Sfdub7Vtuh+1bbofu0M9+32g7dt9oO3bfalqQ+L5pNvCCrQNJaSf/ew/dLJG1Oxwu78vPz/Y4dO3xJSUnyIuDy8vK0XGAcsm157exLbrUtr519YV/Oh7bltbMv7Eum2inNpykMnk7SLyUtOOn2oV0+nyGpLh1DrCQ/YcIEv23bNh+LxfysWbPS9ocgdNvy2tmX3GpbXjv7wr6cD23La2df2JdMtFMZYl1iuOyRc+5mSX+Q9J6kY4mbZ0maquOXEnhJOyXd673v6KPV+4MBAADgvOe9d33dp88hNp0YYgEAANCXVIbYSP7GLgAAAKA3DLEAAAAwhyEWAAAA5jDEAgAAwByGWAAAAJjDEAsAAABzGGIBAABgDkMsAAAAzGGIBQAAgDkMsQAAADCHIRYAAADmMMQCAADAHIZYAAAAmBPJIbaqqkpNTU1qbm5WTU2NmXbovtV26D7tzPettkP3rbZD92lnvm+1HbpvtR26b7Ut733GPiT5vj7y8vJ8LBbzpaWlvqCgwDc2NvqysrI+fy7bbctrZ19yq2157ewL+3I+tC2vnX1hXzLVTmWujNwzsePHj1csFlNra6s6OztVV1en6urqyLdD9622Q/dpZ75vtR26b7Uduk87832r7dB9q+3QfattKYKXExQVFamtrS35dXt7u4qKiiLfDt232g7dp535vtV26L7Vdug+7cz3rbZD9622Q/ettqUIDrEAAABAXyI3xMbjcRUXFye/HjZsmOLxeOTboftW26H7tDPft9oO3bfaDt2nnfm+1XbovtV26L7VtiRF7oVd+fn5fseOHb6kpCR5EXB5eXlaLjAO2ba8dvYlt9qW186+sC/nQ9vy2tkX9iVT7ZTmyqgNsZL8hAkT/LZt23wsFvOzZs1K2x+C0G3La2dfcqttee3sC/tyPrQtr519YV8y0U5lrnSJ4TIjnHOZezAAAACY5L13fd0nctfEAgAAAH1hiAUAAIA5DLEAAAAwhyEWAAAA5jDEAgAAwByGWAAAAJjDEAsAAABzGGIBAABgDkMsAAAAzGGIBQAAgDkMsQAAADCHIRYAAADmMMQCAADAHIZYAAAAmBPJIbaqqkpNTU1qbm5WTU2NmXbovtV26D7tzPettkP3rbZD92lnvm+1HbpvtR26b7Ut733GPiT5vj7y8vJ8LBbzpaWlvqCgwDc2NvqysrI+fy7bbctrZ19yq2157ewL+3I+tC2vnX1hXzLVTmWujNwzsePHj1csFlNra6s6OztVV1en6urqyLdD9622Q/dpZ75vtR26b7Uduk87832r7dB9q+3QfattKYKXExQVFamtrS35dXt7u4qKiiLfDt232g7dp535vtV26L7Vdug+7cz3rbZD9622Q/ettqUIDrEAAABAXyI3xMbjcRUXFye/HjZsmOLxeOTboftW26H7tDPft9oO3bfaDt2nnfm+1XbovtV26L7VtiRF7oVd+fn5fseOHb6kpCR5EXB5eXlaLjAO2ba8dvYlt9qW186+sC/nQ9vy2tkX9iVT7ZTmyqgNsZL8hAkT/LZt23wsFvOzZs1K2x+C0G3La2dfcqttee3sC/tyPrQtr519YV8y0U5lrnSJ4TIjnHOZezAAAACY5L13fd0nctfEAgAAAH1hiAUAAIA5DLEAAAAwhyEWAAAA5jDEAgAAwByGWAAAAJjDEAsAAABzGGIBAABgTr8MP95eSR+cwf0HJX4GuYHjmXs4prmF45l7OKa55Xw5nlemcqeM/sauM+Wce9t7/7lsrwPpwfHMPRzT3MLxzD0c09zC8eyOywkAAABgDkMsAAAAzIn6EPtctheAtOJ45h6OaW7heOYejmlu4Xh2EelrYgEAAIDTifozsQAAAMApIjnEOuf+zTm3zTkXc849ku314Nw553Y6595zzjU6597O9npwZpxzLzjnPnLObe5y2z8559Y755oT/7wsm2vEmenhmD7qnIsnztNG59yXsrlGpM45V+yce905975zbotz7sHE7ZynBvVyPDlHu4jc5QTOuXxJ2yVVSmqXtEHSVO/9+1ldGM6Jc26npM9578+H97fLOc65f5F0QNIvvffXJm57UtLfvPdzE/+zeZn3viab60Tqejimj0o64L3/z2yuDWfOOTdU0lDv/Sbn3KWSNkqaKOlucZ6a08vxnCLO0aQoPhM7XlLMe9/ivT8iqU5SdZbXBJzXvPdvSPrbSTdXS/pF4vNf6Ph/YGFED8cURnnvO7z3mxKf75e0VVKROE9N6uV4oosoDrFFktq6fN0uDlwu8JLWOec2Oue+ne3FIC0u9953JD7fJenybC4GafOAc+7dxOUG/NWzQc65EkkVkhrEeWreScdT4hxNiuIQi9x0s/d+nKQJku5P/FUmcoQ/fl1StK5Nwtl4VtJVksZK6pD0dHaXgzPlnOsv6SVJ0733+7p+j/PUntMcT87RLqI4xMYlFXf5eljiNhjmvY8n/vmRpFU6ftkIbNuduG7rxPVbH2V5PThH3vvd3vuj3vtjkv5LnKemOOcKdHzg+W/v/cuJmzlPjTrd8eQc7S6KQ+wGSSOcc6XOuUJJd0p6Jctrwjlwzl2SuDBdzrlLJN0maXPvPwUDXpF0V+LzuyT9bxbXgjQ4MewkTBLnqRnOOSfpeUlbvffzunyL89Sgno4n52h3kXt3AklKvGXEAkn5kl7w3v9HlpeEc+CcG67jz75KUj9J/8MxtcU5t1zSv0oaJGm3pNmSVktaIekKSR9ImuK954VCRvRwTP9Vx/+a0kvaKeneLtdTIsKcczdL+oOk9yQdS9w8S8evo+Q8NaaX4zlVnKNJkRxiAQAAgN5E8XICAAAAoFcMsQAAADCHIRYAAADmMMQCAADAHIZYAAAAmMMQCwAAAHMYTcAJAAAAE0lEQVQYYgEAAGAOQywAAADM+X/gOTI21RArLQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def visualize_input(img, ax):\n",
    "    ax.imshow(img, cmap='gray')\n",
    "    width, height = img.shape\n",
    "    thresh = img.max()/2.5\n",
    "    for x in range(width):\n",
    "        for y in range(height):\n",
    "            ax.annotate(str(round(img[x][y],2)), xy=(y,x),\n",
    "                        horizontalalignment='center',\n",
    "                        verticalalignment='center',\n",
    "                        color='white' if img[x][y]<thresh else 'black')\n",
    "\n",
    "fig = plt.figure(figsize = (12,12)) \n",
    "ax = fig.add_subplot(111)\n",
    "visualize_input(X_train[0], ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 4. Rescale the Images by Dividing Every Pixel in Every Image by 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# rescale [0,255] --> [0,1]\n",
    "X_train = X_train.astype('float32')/255\n",
    "X_test = X_test.astype('float32')/255 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 5. Encode Categorical Integer Labels Using a One-Hot Scheme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integer-valued labels:\n",
      "[5 0 4 1 9 2 1 3 1 4]\n",
      "One-hot labels:\n",
      "[[0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
      " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
      " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "from keras.utils import np_utils\n",
    "\n",
    "# print first ten (integer-valued) training labels\n",
    "print('Integer-valued labels:')\n",
    "print(y_train[:10])\n",
    "\n",
    "# one-hot encode the labels\n",
    "y_train = np_utils.to_categorical(y_train, 10)\n",
    "y_test = np_utils.to_categorical(y_test, 10)\n",
    "\n",
    "# print first ten (one-hot) training labels\n",
    "print('One-hot labels:')\n",
    "print(y_train[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 6. Define the Model Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten_1 (Flatten)          (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 512)               401920    \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 512)               262656    \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 10)                5130      \n",
      "=================================================================\n",
      "Total params: 669,706\n",
      "Trainable params: 669,706\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "\n",
    "# define the model\n",
    "model = Sequential()\n",
    "model.add(Flatten(input_shape=X_train.shape[1:]))\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "\n",
    "# summarize the model\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 7. Compile the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# compile the model\n",
    "model.compile(loss='categorical_crossentropy', optimizer='rmsprop', \n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 8. Calculate the Classification Accuracy on the Test Set (Before Training)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 8.5900%\n"
     ]
    }
   ],
   "source": [
    "# evaluate test accuracy\n",
    "score = model.evaluate(X_test, y_test, verbose=0)\n",
    "accuracy = 100*score[1]\n",
    "\n",
    "# print test accuracy\n",
    "print('Test accuracy: %.4f%%' % accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 9. Train the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 48000 samples, validate on 12000 samples\n",
      "Epoch 1/10\n",
      "47616/48000 [============================>.] - ETA: 0s - loss: 0.2765 - acc: 0.9145\n",
      "Epoch 00001: val_loss improved from inf to 0.12577, saving model to mnist.model.best.hdf5\n",
      "48000/48000 [==============================] - 7s 137us/step - loss: 0.2752 - acc: 0.9149 - val_loss: 0.1258 - val_acc: 0.9612\n",
      "Epoch 2/10\n",
      "47488/48000 [============================>.] - ETA: 0s - loss: 0.1150 - acc: 0.9646\n",
      "Epoch 00002: val_loss improved from 0.12577 to 0.09561, saving model to mnist.model.best.hdf5\n",
      "48000/48000 [==============================] - 6s 132us/step - loss: 0.1151 - acc: 0.9646 - val_loss: 0.0956 - val_acc: 0.9712\n",
      "Epoch 3/10\n",
      "47744/48000 [============================>.] - ETA: 0s - loss: 0.0797 - acc: 0.9760\n",
      "Epoch 00003: val_loss improved from 0.09561 to 0.08974, saving model to mnist.model.best.hdf5\n",
      "48000/48000 [==============================] - 6s 135us/step - loss: 0.0797 - acc: 0.9760 - val_loss: 0.0897 - val_acc: 0.9741\n",
      "Epoch 4/10\n",
      "47488/48000 [============================>.] - ETA: 0s - loss: 0.0627 - acc: 0.9817\n",
      "Epoch 00004: val_loss did not improve\n",
      "48000/48000 [==============================] - 6s 133us/step - loss: 0.0626 - acc: 0.9817 - val_loss: 0.0970 - val_acc: 0.9747\n",
      "Epoch 5/10\n",
      "47616/48000 [============================>.] - ETA: 0s - loss: 0.0516 - acc: 0.9844\n",
      "Epoch 00005: val_loss did not improve\n",
      "48000/48000 [==============================] - 6s 131us/step - loss: 0.0520 - acc: 0.9843 - val_loss: 0.0958 - val_acc: 0.9763\n",
      "Epoch 6/10\n",
      "47616/48000 [============================>.] - ETA: 0s - loss: 0.0432 - acc: 0.9863\n",
      "Epoch 00006: val_loss did not improve\n",
      "48000/48000 [==============================] - 6s 133us/step - loss: 0.0433 - acc: 0.9862 - val_loss: 0.0939 - val_acc: 0.9795\n",
      "Epoch 7/10\n",
      "47744/48000 [============================>.] - ETA: 0s - loss: 0.0389 - acc: 0.9880\n",
      "Epoch 00007: val_loss improved from 0.08974 to 0.08933, saving model to mnist.model.best.hdf5\n",
      "48000/48000 [==============================] - 6s 133us/step - loss: 0.0389 - acc: 0.9881 - val_loss: 0.0893 - val_acc: 0.9793\n",
      "Epoch 8/10\n",
      "47616/48000 [============================>.] - ETA: 0s - loss: 0.0339 - acc: 0.9902\n",
      "Epoch 00008: val_loss did not improve\n",
      "48000/48000 [==============================] - 6s 133us/step - loss: 0.0338 - acc: 0.9902 - val_loss: 0.0959 - val_acc: 0.9804\n",
      "Epoch 9/10\n",
      "47872/48000 [============================>.] - ETA: 0s - loss: 0.0323 - acc: 0.9904\n",
      "Epoch 00009: val_loss did not improve\n",
      "48000/48000 [==============================] - 6s 134us/step - loss: 0.0324 - acc: 0.9904 - val_loss: 0.1130 - val_acc: 0.9771\n",
      "Epoch 10/10\n",
      "47744/48000 [============================>.] - ETA: 0s - loss: 0.0272 - acc: 0.9918\n",
      "Epoch 00010: val_loss did not improve\n",
      "48000/48000 [==============================] - 6s 131us/step - loss: 0.0271 - acc: 0.9918 - val_loss: 0.1190 - val_acc: 0.9772\n"
     ]
    }
   ],
   "source": [
    "from keras.callbacks import ModelCheckpoint   \n",
    "\n",
    "# train the model\n",
    "checkpointer = ModelCheckpoint(filepath='mnist.model.best.hdf5', \n",
    "                               verbose=1, save_best_only=True)\n",
    "hist = model.fit(X_train, y_train, batch_size=128, epochs=10,\n",
    "          validation_split=0.2, callbacks=[checkpointer],\n",
    "          verbose=1, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 10. Load the Model with the Best Classification Accuracy on the Validation Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# load the weights that yielded the best validation accuracy\n",
    "model.load_weights('mnist.model.best.hdf5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 11. Calculate the Classification Accuracy on the Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 98.0100%\n"
     ]
    }
   ],
   "source": [
    "# evaluate test accuracy\n",
    "score = model.evaluate(X_test, y_test, verbose=0)\n",
    "accuracy = 100*score[1]\n",
    "\n",
    "# print test accuracy\n",
    "print('Test accuracy: %.4f%%' % accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## CNN_01  \n",
    "---\n",
    "From your random image...visualize four **activation maps** in a CNN layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-16-81f811d421cb>, line 8)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-16-81f811d421cb>\"\u001b[0;36m, line \u001b[0;32m8\u001b[0m\n\u001b[0;31m    conda install -c menpo opencv #????\u001b[0m\n\u001b[0m                ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "# for canopy ?\n",
    "(base) C:\\Users\\Minkun> C:\\Users\\Minkun\\AppData\\Local\\Enthought\\Canopy\\User\\python.exe -m pip install --upgrade pip\n",
    "        \n",
    "(base) C:\\Users\\Minkun> C:\\Users\\Minkun\\AppData\\Local\\Enthought\\Canopy\\User\\python.exe -m pip install opencv-python\n",
    "'''        \n",
    "# for jupyter ?\n",
    "conda install -c menpo opencv #????\n",
    "\n",
    "# for AWS linux ?\n",
    "conda install opencv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'cv2' has no attribute '__path__'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-d81c40ca15de>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__path__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: module 'cv2' has no attribute '__path__'"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "cv2.__path__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:15: DeprecationWarning: `imresize` is deprecated!\n",
      "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``skimage.transform.resize`` instead.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "import scipy.misc \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# TODO: Feel free to try out your own images here by changing img_path\n",
    "# to a file path to another image on your computer!\n",
    "img_path = '5WD.big intersections 0.jpg'\n",
    "\n",
    "# load color image \n",
    "bgr_img = cv2.imread(img_path)\n",
    "# convert to grayscale\n",
    "gray_img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2GRAY)\n",
    "# resize to smaller\n",
    "small_img = scipy.misc.imresize(gray_img, 0.3)\n",
    "\n",
    "# rescale entries to lie in [0,1]\n",
    "small_img = small_img.astype(\"float32\")/255\n",
    "\n",
    "# plot image\n",
    "plt.imshow(small_img, cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "###2. Specify the Filters\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "# TODO: Feel free to modify the numbers here, to try out another filter!# TODO: \n",
    "# Please don't change the size of the array ~ :D\n",
    "filter_vals = np.array([[-1, -1, 1, 1], [-1, -1, 1, 1], [-1, -1, 1, 1], [-1, -1, 1, 1]])\n",
    "\n",
    "### do not modify the code below this line ###\n",
    "\n",
    "# define four filters\n",
    "filter_1 = filter_vals\n",
    "filter_2 = -filter_1\n",
    "filter_3 = filter_1.T\n",
    "filter_4 = -filter_3\n",
    "filters = [filter_1, filter_2, filter_3, filter_4]\n",
    "\n",
    "# visualize all filters\n",
    "fig = plt.figure(figsize=(10, 5))\n",
    "for i in range(4):\n",
    "    ax = fig.add_subplot(1, 4, i+1, xticks=[], yticks=[])\n",
    "    ax.imshow(filters[i], cmap='gray')\n",
    "    ax.set_title('Filter %s' % str(i+1))\n",
    "    width, height = filters[i].shape\n",
    "    for x in range(width):\n",
    "        for y in range(height):\n",
    "            ax.annotate(str(filters[i][x][y]), xy=(y,x),\n",
    "                        horizontalalignment='center',\n",
    "                        verticalalignment='center',\n",
    "                        color='white' if filters[i][x][y]<0 else 'black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzsvXmMZdlZJ/i7b1/jvdgj18qt0lXlcpVdeMdgDxjcjIbBLSGEgXY3toxbGmQhIU0DA0OrkRAzbGppJDy2aHVD4fa0xHgaEJoG24AZFVTZuOyscqVdlVmZlZmRERkZy3vx9vXMHxG/E7/7xX1ZWblUPYL3SaF47767nHvO+bbf953vBM45TGhCE5oQKfZGN2BCE5rQeNFEKExoQhMK0UQoTGhCEwrRRChMaEITCtFEKExoQhMK0UQoTGhCEwrRfRMKQRD8syAIvhMEwYUgCH7hfj1nQhOa0L2l4H7kKQRBEAfwIoAfAHANwFcBfMQ598I9f9iEJjShe0r3y1J4J4ALzrmXnXNdAJ8H8CP36VkTmtCE7iEl7tN9jwC4Kt+vAXjXqJPn5ubc8ePH71NTJjShCQHAs88+u+6cm3+18+6XUHhVCoLgZwD8DAAcO3YMX/nKV96opkxoQv8kqFgsvnI7590v92EZwDH5fnT3mCfn3Gecc293zr19bm7uPjVjQhOa0Gul+yUUvgrgwSAITgZBkALw4wD+5D49a0ITmtA9pPviPjjn+kEQ/CyA/wYgDuA/OOe+dT+eNaEJTeje0n3DFJxzfw7gz+/X/Sc0oQndH5pkNE5oQhMK0UQoTGhCEwrRRChMaEITCtFEKExoQhMK0UQoTGhCEwrRRChMaEITCtFEKExoQhMK0UQoTGhCEwrRRChMaEITCtFEKExoQhMK0UQoTGhCEwrRRChMaEITCtFEKExoQhMK0UQoTGhCEwrRRChMaEITCtEbVqPxftOFCxfw4osvYjgcot/v7/udpe2DIEAsFsNwOEQsFpaReiwWiyEWiyGXy+Hhhx/G/Pz8vvMtvdrvw+EQjUYD169fx9LSEorFom/LqOuHwyFqtRoajQYWFhb8OZ1OBxsbG5iamkKhULjlc7e3t/Hcc88hn89jcXER8/PzSCQSGAwGuHLlCs6fP49WqxXqp6j+6Pf7GAwGSKVSAIDBYIDhcIhEIoF+v49UKuX73zmHZDKJTqeDdDoNAGi320ilUhgMBgCAeDyO4XCIIAiQSCTQ6/UAAKlUyt8jkUjAOYcgCHzb2CYe4/9Op4PhcIhkMolisYi3v/3tKBQKCIIgsl+cc+j1eojFYojH4yP7zzmH17o1QtT52Wz2ls95o+iOhUIQBMcA/AGARQAOwGecc/8+CIJ/C+ATAG7unvpLuwVXXlf6+te/jieffNJ/52QZDAb+LxaLIZlMhibJcDj0TAnsCYN4PI54PI5jx45hamoK5XIZsVgMQRCE/khBEGA4HPpB5/N7vR6uXbuGc+fOodvt4syZMzhz5gxyuVyI4dhW+/3q1atYWVnB448/HhIa6XQahUIB3/zmN3Ho0CEcP34ciUTCn8N3cs7h2rVr+NznPodHH30Ujz/+OLLZbOh9/vqv/xrPPvss+v2+Z3AVCOyjfr+Pfr+PqakpDIdDtFottFotFAoFNJtN5HI5DIdDtNttZDIZZDIZVCoVAPBto0DpdruIxWJIJBJIp9P+3ROJRKj9FPDD4RDOOQwGA/8/avwA4MEHH8QnP/lJOOfQaDRCgoN/FCCZTCY0jlYAWObW77zuVtfo58XFxYMlFAD0Afy8c+7rQRAUAfxDEAR/ufvb7zrnfuvum3fnxIkRj8cxGAz2MWw8Ht83iPyu2lqv5TmxWCxy4HmeCghO1sFggK2tLXz729/GtWvXsLS0hLe+9a0ol8v72hxFzjnU63VcvXoVZ86c8cyklMvlkMlk8PTTTyOTyWBxcXHfORRWfC8KKra/VCrh0KFDOH/+POr1OpxznhHZL2RICoVut+vvTSEai8XQ6XS81nfOeaajgCHDx2IxFAoFL3Scc/5zv99Hr9dDr9fzzyGphcf+TiaTiMfjSKVSyGQyKJVK+MQnPoFHHnkkJEQ5ds45bxXlcrmQoNBzdXztd3ue/WznAts+rnTHQsE5twJgZfdzLQiC89jZ72EsiIyvg6NuhP6uGp2kWpGfqcV4HRCeXFYY8LpqtYqLFy/i5ZdfRqlUwvvf/34cOnTott+FTHz58mUUCgXMzs5GCjAAmJ2dBQB8+ctfxhNPPIGTJ08inU6HJjQ1P039Tqfj3yOdTuPEiRMoFArY3t5Gv9/370HhYSd0q9XyzM/fMpkM+v2+72e2kcLMWgDdbjdkBajbR8FAQZZKpZDNZpFOp5HP51EoFDA9PY2ZmRnMz89jdnYWc3NzmJmZQalUwtLSUoiRowQCLYQooQAg9JtS1Dzg+XwX21+cc/djd7Z7QfcEUwiC4ASAtwF4GsB3A/jZIAg+CuBr2LEmtiKuCe37cD+IfigHgRPQ/o/FYhgMBiHrgZTJZAAgdB+6H7vv4f/sIDebTVy9ehUvvPAC+v0+HnvsMZw9e/aWWsJqID53fX0d29vbeOyxx0ICTAVCPB7H8ePHUSgU8Kd/+qd48skn8b73vQ/veMc7UCqVvDDr9XoYDofodruIx+Po9Xqh9h8/fhzlchk3b97EYDDwzKnvH4/H/Xskk0nP5CoYKACIBSijd7tdPyZRJj9dtiAIUCgUkMvl0G63UalUcObMGbz73e/GsWPHsLCwgJmZGZTLZWSzWf98i4Vof/J/p9PxYxxl/dlxibqH9ssoinr+ONNdC4UgCAoA/hjAzznntoMg+D0Av4YdnOHXAPw2gI/Z65xznwHwGQB44okn7nlPcaCSyeQ+85ekQoGTXM8hAxB3IAPp79ScNGGHwyF6vR7W1ta8CX769Gk8/PDDyOVyvm1RjK9aRn9vt9u4cuUKjh49GgIR9T5sy3A4RKFQwLve9S5cv34dTz31FFZWVvC2t70NJ06cQDqdRqvVQrfbRbPZRBAEqNfraLfbXgAsLCzgyJEjuHz5Mnq9XsjCUnCPOEGUkCMQSQ2vjG8ZIx6Pe9eH+MbMzIwHQQ8dOoTZ2VmsrKzg6aefxgc+8AG8+93vDlkbar0RrIwCk9lPxBCy2ey+No3CFPhZrUCdR69GVpiMK92VUAiCIIkdgfBHzrn/GwCcczfk988C+LO7auGdty303xK1kPql1MDEIag91CVQq0BNck647e1tnD9/HpcvX8bhw4fxgQ98wIN4nJCKS+j1Ue13zuHy5cvodruYm5vzVopOVt5XJ+3CwgK++7u/G5VKBclkEs888wyuXLmCxx57LCQAUqmUR/md24kQzM7O4m1vexsuXbqEa9euhZhZ+5PvRGwhyhpjnxFDSCQSyGQyKBaLKJVKmJ6extLSEg4fPoyjR496sz+XyyGRSPgx6fV6OHToEB5//HFMT0+HrBCeY10qden0swUVrUCwboZ1K6gAXgtz38r1GDe6m+hDAOD3AZx3zv2OHD+0izcAwD8H8PzdNfHOiIzW7XYjgUISJ9Ao6U/XgtZAIpHwkQAd1Hq9josXL+LSpUtIpVJ49NFHcfjwYXS7XayuriIejyOdTqPX6yGRSCCVSoUEk/VFOdErlQo2Njbw4IMPesBO3Qe+Z9QkffTRR3Hu3DnMz8/jwQcfxLlz5/DFL34RN27cQKVS8SHBbreLTqeDfr/v3Yx3vOMd2NjY8Oc3Gg1vMQwGA7Tb7ZDpT6GaTCaRSqWQy+WQy+VQLBZRLpexsLCAubk5HDlyBEtLS5ibm8PU1JQ3+dmvVivT0kgkEpibm9snTKMAv1F4y3A49PhJNpsNnRelxUdhDK/VMohSTOMqEIC7sxS+G8C/APBcEATf2D32SwA+EgTBW7HjPlwG8Mm7auEdkmpNq9GjUGiNsVP7RU0WCgdgb5JdvXoVzz//POLxOB599FGcPHnST3Jey+vUH1drJmoCdbtdLC8vY2ZmBrOzs/ssFztRKRz4e7FYxAMPPIALFy7g0UcfxQc/+EF861vfwoULF1Cv1z1AyLBhMplEr9dDo9HAYDDAm970JnznO9/BzZs3kUwmUavV0Gq1fD8lk0lkMhkvBJj38Mgjj+D06dNYWlrCwsICSqWSB2jtO7PNfB+2RzEPDRNGMWmUYCBp33a7XTjnPE4UdY21FOy4cCxH4QR2fKKuV2tjHOluog//H4CotxqLDWAGg4EHktQMBfYQ4SghQXBS8QVOgEQigWaz6ZlodXUV586dQ6vVwtmzZ3H27Fnk83l/rfVpdTIoM1gflv+vX7+ORqOB06dP+3ZbLcnjmtOgk3p2dhY3b97EK6+8gkceeQSPPfYYLl68iKeffhpXrlzBP/zDP6BareL8+fNIJBJotVoeN4nH4zh16hTe9KY3odFo4KmnnkK1WkUikcDZs2dx9OhRHD16FAsLCygWi8jn80ilUpiZmfH+vuZpWJ/fWm3sf1pT1OZR76zjaPNKotyddrsNAB5D0Hto31sNrq5I1DX6nKjr9b56zoGPPowjaYeT0YFwqIgaT4/rZ40w6L1u3LiB559/HisrKzh16hTe8573YGZmJiRoSFboRJn5PF9/Y+Ti+PHjPrav50cBYFHCJZPJ4PTp0/jmN7+JixcvYnt7G0899RQajQbW1tbwm7/5m5ifn8cP/MAPYH5+HkEQIJPJIB6PI5/Pe/O/XC7jh37oh5BMJhGLxTA1NeXNfn03y5D2XQnYahtp0fX7fe9mjYoG3Epja1/quXQZGJq9HRdhlJaPEgj2/Kjr9P+404EVClGgk/6mqbGvdg/eBwDW1tbwxS9+EQ899BC+7/u+D0eOHEEqlQpNLgtEqlUAYF8402oR5iRks1nMzc3dUgPZtkZN+Fwuh+PHj+PLX/4ynn/+eTSbTaTTaSQSCZw4cQI/+IM/iEcffRTlchm5XA7pdBrpdNqHGjVz0z57lC9u+8D2K88nSJlMJvcx7a2uVYB4lI+vbdNcjah78r8VKrbvNbcl6ln2uaPGbVxdB+AACwUAIWxAs+X4m5rywP6IhZ1wQRAgl8vhe77ne/DEE094YUCcgKTCgX+azabhM+tjO+ewubmJzc1NPPTQQx6HYDvteyjWoc/WewPAwsICzpw5gytXrmAwGCCXy+F7v/d7cezYMbzzne/EiRMnQsCnvot+vh3QLIpB9HqNWNBNsO9irZ8o5ho1TkpMsR5lTY0CKW0/jhJUt4pCjBIKACbuwxtJmnarVgMn5ihNSLJavlQq4ciRI6EYORfR8N7Ma+D9NWQW9Qx1OzqdDi5cuICZmRkUCoV9roVNlVVgLgoc4+dEIoFHHnkEL730El544QW0220cOXIEp0+fxrVr17CwsOBzIKxAG2XC62drJZEsYzN0qW6CnvdqjBhlHUVZMRyXqD6Pek4UvqM0CmuI6oOo86OSs8aVDqxQoMbTBU06ERhtsIKBxImlg8d0XSb/xGIx1Ot1bGxsoN1u48KFC2i1Wjhz5oyPpTPuz9WAynCqLfic5eVln2eg50eh1dZ8jtKY2v5cLoczZ87g2Wefxfr6OqrVKubn53Hx4kVcuHABjzzyCFKpVEij2z5l34zSkFHaV4WBhh+jSAXkKE0exYSWKKiZz2DdtFGuSRSz6+coi8TOGxUM1robZ7eBdGCFgroJqvUsgKXYgtUYKkR4ba1Ww1/91V9hcXERuVwOtVrNJ8O89NJL6Ha7uHz5Mh588EEcO3YMnU4Hly5dQhAEWFlZwfr6esjHrdfrXmilUim0Wi2kUilcvnwZyWTSt4G+PgWacw7FYtGHBNk+tUyIG1CgEHBjEtSFCxdw7do15PN5vPTSS5idncXs7Cyccz4UaPuH/vwoc14tBmprhhaZmxE1Tvrdul+3a/artRUEQQjrUeZUgWetmqhnRT2Hc8dacqOszVEWxDjSgRUK1Eiat6/WA7CnGZLJJIC9dGcmKwVBgHa7HZrk9Xodzz33HF566SW/5l81UCKRQKPRwHPPPYdvfOMbXiixXsHKygqy2SwKhQIGg4E3cRmK4xp7/lmXhKFWYiVBEPgEIL434/F8LpOlut0uWq0Wtra2kEgk8Pzzz2NjY8NbJS+88IKvj3Dq1Cn88A//MMrlciTjvpoG58rG5eVlPPvss5iZmcETTzyBcrl8Wya63su6cLciJqtRINj76ncLMI7CY+z7Rrkfo0DPUSHS202AeiPowAoFZYgoQcCJFo/HPUPR1KzX6z45B9gbTF3tB8AXGaFPz6IizI9g1t9gMEA6nUaj0UCr1fKMq5owlUohlUr5JB8yPNcetFot/w4M32kqMZcMAzvhTKYyA/AChkILAKampjAYDNBoNLC6uopkMom1tTVsbm76EKimP2u/ap8o8Rq6CfF4HPV6Hc8//zxOnjyJxx57LHT+rbSwnmMFjrVI+JkuHV0gbdetcIMooDnqne1n1f5qPenzNCVahcMEU3gDyOYg2EFRzUBcQc08rcBj8wz0HnqMAkDP6Xa7qNVqAKK1Bp/PyUzGprAh6boLPjMWi6Hb7fr3ZBSEi8BUKCiuQqGlgoT9NT09jU6n44WKRj74DtqnfBcKELW8AHjhqmnMt8IjbgVq6vPscb4TqzppG9VijLrva/H5R7VdhRNJwV+Cq/w8sRTeAKLk1nULNL+padXEZlotmZOMaiv9aGKNMiknFjW4ag1qaq59SKVSnpHpGqgwcM75FGS2hdaAujh8LgFTBTBTqZRvE7U2mYfH+Z/ZfrQQ2CdsH8kyNQUa7xWl/Sik9H5RTBWVwKXPj8J7+KcYgr2GnxXL0AiO9uGtgNRR1oNtH8O6FkPg+brsflzpwAoFNd2A/aiwnXgUGDTr1X2gJQHA++f2Wv1OK0PNfQC+GhCZW8/RKkQE+rSiEZk8KufeWhEUhJpqrBaB1lWgdWGFFE1wTQnXTEQtoKImcpSmHxVtsEz2ausJovqafaTWiT3Xtol9roLAmv+cP2pV8t56TyuoRgGgwF6RH+JDt4uTvN50YIWCNdfUMlCrAdgDGGn+0+QcDnfqMeTzee/fK2MQKCRTqbVAM3o4HCKdTvsJr8ytZqS1ZNjudrvtsRGCmr1ez5vLADzT8Tx1JxRM5TksocZMQtXwtCp4vjI672cxDPXvLQinEZHXwgijhASfZS27KBqFT3B+jLrmdlwW/f1W2Ii6mdaFGlc6sEKBg6VLnKnZiIwrMMbJrv6lIsrUiGo2s3qQHWBrlQRBECoQq0JB/XYyM5cy09RX4UZBxGco7qGaTa9Rbaaf9X353wo9fRdaB1aQjLIS+Jt1K6wbMkqz38qVoEWnwsuew3voc+04RR23GJRaDVFtsW1Vt0pdCiDsJo2rYDiwQoGT3mpj1iQksEazU8+hhrda3C7k4WRRc1SzGxk50BAnz6P1QqFEiyIej/vjCkrRqiFmYRlcsxrpFlD4cekxrQTmDuj7qXDhvSgYVBjYugeWLNBGt8MyzajrR93Tgrd0qUb5/lFttBr6dnADdSmsAoiyXlqtFprNJmKxmF8xq++rYzaudGCFArDfBOXA2iQfCgYKESLpwF7cW8E9Cg19hjKvFiMB9qo8JZNJn5BkBZZaDdZioeBQv5m/AQjVWAyCwDMxgBBWoNWX+QwVBCTtB8UcFJC7VdIXn8fz1H27HdM5SoDwGN81CgjV8y1pJS37LNuf9tnW8rPPUqtKXbMgCLzraZOlRiWAjQPdixqNlwHUAAwA9J1zbw+CYAbA/wXgBHYKrfyYiyjeej9JtRxJNSNzCsiMqhlJWh6emAPxAsUjOMAKvqlJzUlhtY71mZmUZP1RCi87kRRY5Pn8TnBRrRgylYY7lakpkNg2Ygt6nrY3ShjYtinT2He+lXCIcik0TKyMqtqYQl/7g+fovUeBtvb99BorQNinfCbPYdp7LBbzyWhqJTJaMq5C4V5lUPx3zrm3Oufevvv9FwB8yTn3IIAv7X5/XUkRf5vARI0J7K2kVGYEwj42GV73cNDkIWD/BFRNqUJD/XROIt6v3W6HSpkrss92aFTB5g6opcG26sS1bVa3gWAdn8/fyMyKzVhByveP+j4YDNBqtdBut33+A4WfCopRDMI+YpRBd4iyzB+FS1gfXv+injPqHP08GAxQq9VQqVQ84KtKoNPpeGuBVt5gMEC9Xsfm5iYajcY/SUzhRwB8YPfzfwLw1wD+zX16ViTR3FYEnsyvexlYiW9JfVi9t9V+ZCCrzZneTPcBwD5NzXtq1EI3ULFYBAVMVMiM91TLhe4Q26CuEH1+W95en6k4g1pBqm1VE7NNnU4H1WoVlUoFqVQK9XodxWIRQHgPDg3PRuU70ELQdHTbzlFMZi0Oti/KMtD/VriQ+F6sncn6kfytXq/jypUr/t2np6e9pcdoz+LiYghjGje6F0LBAfiLIAgcgP/T7ZRuX3R7xVtXsbO13OtKQbBXtZcTTSc2mUZJcwWAcGk2MpOiyjqRdEWjCgG6Duq/E/DjAiUtMhKLxXwIkxOOn3mc5cqs0FBmoelPt0cZzrpUai0wV4MWA8+xCHwU85Ax+U6NRsNnczabzZBwUhOe5+tz1DWiu2fxBX1elNU2CmyMEv6j3CP+pgKZJem134fDvTqXrVYLGxsbiMfjfm9P1nRIJBLI5XIjw6jjQPdCKLzPObccBMECgL8MguDb+qNzzu0KjBAF93kzmCAIfKae5hDoJCJzEHTU7DySamQtPWaZQu9NZiQD2ggFBQMLpZIhVeurNUHTmQVHNRoB7AGlyih0OdQ14LVRTKHvSEGoDGQxEn1nZVIFTNvtNkqlEsrlsjedy+WyX1LONjKiwX7j+6tpzkVe2l5NmorSuiow1Z2wOEsUsKn31MgD2zkzM7PP4uj1eqjVan4D4HK5jOnpaUxNTe1bcXqg3Qfn3PLu/7UgCL4A4J0AbgS7pd6DIDgEYC3iuvu6GYwFk6xPzf9kdN3kRY+r4FAtxgFmNEHTlPlZ1xhwYhM5Z9ozIxnU0nwevzNRaLfP/LvZtGidYJp8pIxh92ZQcIzvROEUZYnYNpDBVCCw/a1WC9ls1gNtiUQC5XIZpVLJu1oM2WoiFl0nXcehzM9naH6HxWj4bqSozxQIo1wJBS61r27lenBbwXw+j2w2i36/7zfaJY2rIFC6281g8gBibmcvyTyAHwTw7wD8CYB/CeA3dv//17tt6GslZvYR8FEEX/9r8RWa0YpBMAOR16tmI4MzTKbLqDXbEdhhyK2tLaTTab/fI+9FK4XaX7WbDQHatnJtBEknKBmVx4kxaB9Yq8GGcDWFWq+z4J4ySqvVCkUubNvJ+FbY0UJoNpv+PC2Co8+IqsugjKyWlAo+i73YfouKDFmhFGWZaL+zKna73fYWRdQzx1VA3K2lsAjgC7svlwDwOefc/xsEwVcB/JcgCD4O4BUAP3aXz7kj0nAeGTgKoVa3gKY9mUDDeuqnc9J1u10veKrVKkqlkp8YuVzOJxBRcGisn21hlqUWMVU/m5NSNaoNOWpuAr8De5YLAVNqXuIVFIJkAr6X5gFYIcX+ZB+zPcPhEM1m09ddVC2rQKbeRy0PClOWildfnoyu2ZzaF4qZcJyj2q5RE1UI2m9UIryPjUzZOaZjxOdzA1zudKVkcZlxo7sSCs65lwE8HnF8A8D3382975YUECSpb0+tZLUlB1cXE+mAq7bUrEBukkLmJkCoGMLU1BSAPUa1jKXuiU58Bd7UkuG9FAvRJcwWTFWUXHELNcVtFEG1q2UwYg9cvMWqUYqPRIUbNbxJ0uiLCik+l8JdhRXfXUFK3p/voTU12C8qDNSyswCxti0qt4PvoeOjY869NtUVU3ziQAqFcSZlKCC6OIhqah10vZ4ThoKCkzcIdjIHy+VyaBKwBJpODk4qThBrmWiOgroL2i7VrDoptaiJBdY4YW2snp95DrPw1EXSPrOWlfZtLBbz6zTS6bQHNZUBGK/XlG/rz6trw3fTPrKWBdupIK1ewz53zvliN2Re9qkKCgVntX/0PdUtsQJU+1xX13LxGi0cnhfluo0LHVihAIQH02plu1DKxsl1VZ8FJzmxtrZ2kjTn5+cxNTWFarUaAvjIwBbJVyQ7ak2DrpLU5ytgCIRTidVaUCtE3z8qB4BkQUVbXIbnKKMMhzu5/o1GA1NTU97tsQyvfa9ALJ/JhV8adrTPVCa1ApKfSWRuXS5u26Vugc0Z4L0sbmExH31e1LMZytYt80bhC+NEB1Yo0P/XQdBcfjWBLSnTOre39oCDzHu1222srKz452levoYiFeTUCaMWADMvaYFYoIzP1+sskwB7IJ4y4ihBQFyBGINqP5sirUKGQqher6Pb7aJUKvkoSpQvzz9r9TDDkVEYns+2WNIxsVEHFSZWsFhzXfNF1F1RoNL+2X7T89k2EjEkvmOn09mH0ViBM050YIUCsKfROdGiJD2ZV0N8UWadrq4kqDYcDtFoNPzv3G5N76PMaZdA24mvUQ0KL2VY3idq8uqCJWB/CE39bb1Wlx6rW2KZWifycDjE1tYW4vG4z9jTd1ULi8/SNRfU0pVKZZ/LoddruFOFi1pYbKO1JKIwC8vE6saowNHj9npgD1RVXEH7Vce/3W77kKS6OLzHONKBFQr0Za2vCYRTbDnZo/xHfteVjZoeTJQ5KkMwCILQZiTqk9oJS9OZ7eGkVEaKmkCWWcmI9O/JPOpaWI1pgUvbh7y/CpVGo4F8Pu+Tqfhsy7yquZvNpgdjiYUUi8WQNWfBPxU0+rsFItmuKOtEhUgU0KzjZS0O7QeLZXCMyNzMreC6Bx7T9mv/j6vrABxgocBBVKYA9pvTuviI//V3HtcaiMDOxCwWi35isUybanuNrSvjqTayxPM0/4HX6ERjDoRGRkjWfdB7q1akFUJSd4H3UeuAGXvpdHqfQGAbo8z4IAhCodt2u+0rV/Pe1i9XTMYy6ygLwmIRvLcyYRTyrwrAHovqu1GuCnGmS5cu4aWXXsLRo0f3WW7WohpHOrBCgaaxEiU7sLfIhufa8xR80glEjcNai0SVFdji5FEtyOuUycj8ihGoCU+XQ5OAeA3vSYuFlaDJRFyJCOwtAWcbKPwUT2BpMxXdsFfdAAAgAElEQVSMnMS9Xg+NRsNnJVqsRvtwlIBIp9NwzmF9fd0LFSvweP2t7mXvqwyufaf31D6346yMGYW/2GdZIcPjXAV59epVPPPMM3jllVd8ngqtVp47yjIbFzqwQoGAH7MF1Y/XSafAmfrawB7Cri4DgTw1IW3iEAUCGSrKtyfDWySev2kuA88F9rIggb01D7YkmbadE53PttEJEp/P63q9HqrVKprNJpLJJAqFwr7isVGWjgVp6SpUq1W89NJLWFpaQrlc9oLK5kXYforSyFFCWt+h3+/7athE/pWx7X31uL5blADStlrSkGe32/WLwLSNGomKusc40IEVCs45n27LQaAWpHanma2pyABCzMl7WeTbMqEWANHVidoeZRg9rv4w3R0LeGqxE7UyaNXY+ymop++uIBoFIs17ZQLG9un3W6DTvkOU6a7mfjKZxMLCAqanpyPLvfOzvrOCtkpRMX4F+Jzb26GKFqPem+9tnx/1Trc6R5/LsVeXi+6SjRSpRTiOdGCFAhDWxmqyUXKrBaBWg8b91apQX119fFojZHirGbQ9usBJ10rwngqOKhNbYcIJqBNONab+afhL26UAm1aLTiQSyOfzIYEQZWqrkIsCBdV850Ih9rtl7Cgg1Y6Pnqvam4uqhsO9/S7o8vA87rCVzWaRz+f9faOeF+XrqyVlrQ1+p6Am3qKVtCy2M654AvBPQCjooib1bzko1LaaY0ABQS2jzKypwjwf2Ntyji4GGZyWiuIO3LWa1yjT6v4SmkdAUvNTIxnKpJquy5BrlEuiAkzTiHO5HIrFou8bMksUIMh3tv+73S7W1tbw3HPPYXV11ReLZS0Iko3GqPtgtal+VmHdbrf9np+0cFqtFra3t+Gcw+Liok8xj7qX9q11H9hGbS+fq0KVc+XQoUN4//vfjzNnzqDZbHoBZle1av+NGx1YoaBgGplR89uBPckOhJcgk5HJ9NQQ6p+y+Kn1z61FQv+c5jrboOAgAC+Y9H4W8OM9o9B5kk44zRK0C6h4r8FgEAIpgXBMn/1h8y7s8/Q7MQTnHJaWlrC8vIxGo+Gfrcx+K9Pc4gtsm8b6g2CnOCq1P/ub/1UBWFLXS/tN2xTl+6sgVqFNIDabzSKZTOLll1/ed60mso0rHVihAOxtADsYDPzkIENzD0YOrA6eNauVSSwyriAgSc9V316jB6ppLKlgiXJD2DaLqiueQEGgVoGtJM3344pEanJaOLQkeG/9bz+r0KrVaojFYpiensZgMPA1FXieDdNZXEHvz3baMLHeS/uRNSCZL0BLb5Qwi3qvqPdUF0qtTrZR5wizXbXmphVuUfkq40IHVig0m01cv37d+8i0GIiGc0DI9PzM87hMeWlpyS8D1jCnrvNX60JBLNaIpJXBsJxupsL/vJ8Km1HhMdXqasIr8yhYqviHXVfB/uB7a3+pW6VkBRQFBxObAHhBo1aZlmTT97HAmzKavpuGXK1Aigq1WhfLAsWjwD5rbel3XmNdC0040301+J2YE5XIxFJ4AyiVSmF6etrHwzV7zW6owuNcxcgdl7e2tlAoFDxzAzsTg0lKRJdtfJ9YBhmNQkDXNPAYz6UGj8qdoPbvdDqIx+MoFAqRYCjdE5ahU2bRyaz4g93DAthhiu3tbSwvL/v+ITPPzMz4mhHKtKzJmEwmfZ+rUBgOd1LCdc8JZa6o949ykfSzRpbIbBQIsVjMl7FTa862SdugzxiFn9jvKhy4KjIIAuRyOV9zk/3M8dYxGUe6Y6EQBMGbsLO3A+kUgP8VQBnAJwDc3D3+S865P7/jFt4hOeeQzWa9WawVihQsJMPlcjlMT08jlUqh0Wggl8v5AVS/s9lseubU6AEnF4ULJ7mCTAxV0tqIx+OoVqu4fPlyaGKra0GG04IrdqmwWglBEIQ0kyY+WbdDtTi/U+hdvXoV+XzeM16v1/PtZxSBRIHAakoqEHTiR20Tr8xlBQLbphaCjoW2P8qa0fwTXmPniLW49LjFFKxVpudrgZxEIoFCoYBsNhvCH0ijFnyNC92xUHDOfQfAWwEgCII4gGUAXwDw0wB+1zn3W/ekhXdI1Ehra2t+kLa3t0NCYDjcWyeg/radPGRGNQc5OTUfgZOZG4HwOO+vrgIn3ebmJl555RWv1UhRpqq2y2oqyzSavajXaKSBv/MYXZvjx4+jWCyi1WqFXCotSEvGYNqy1puMAgf5fM3UJFlXIApA1e8831obeh6fYRPXRpEVENYdsxaFMjsFD90/DSfrXh/2PgcdU/h+ABedc6+MiwTkYOZyOT9xOTEYvuJETyaT3uRWy4DFR9RMVgai5aELYDgR1XIguKjFWaixGV8nqY+uFDUhrU/Ma8kY6vKokOFxjYCwLwqFAmZmZlCpVFCv1zE3N4disegtCPssClVdKRk16Xu9ng/R6RjZc6N8bauhrSUVJRg0iShKmI56hv3OZ1mMhtYT22RT6hWotW7euNO9Ego/DuA/y/efDYLgowC+BuDn3eu8ZRypUql4JqX5y8ScZrOJYrHoK+4CwIkTJ/yEYlhLrQcyskX96ctTK9E9UXNY/ViSakW7UtOeO8q/tZ+jmISk91fmY9XlVCrlE2/onxNfUDeIk73b7frS5TZlGQjvWEUmslqT/9Vc177g80a5JFa763G7OM0yvoKcUf1JYWBdAA0za9hY+53KgwpD33FcFOcouhd7SaYA/I8AfnH30O8B+DUAbvf/bwP4WMR193Xfh3h8ZyMOhqdYQTkWi6FcLqPf7yOTyaDVavlYvfUjNUKg5iAnGrW/xSis/6/hUDsp1ARW8NOa0Ldjcupv1pqw/rs+I5/P46GHHsJgMMCVK1eQTqdx7NgxDyZ2u12/cY1aIdls1t9Pw5/aF7yGUQ5tk/Ytj6trwt/1HXRMLEBqXRZd+arvboWR7S89j8/hu1Hr2/UmdlzUwlBgN6qt40b3wlL4IQBfd87dAAD+B4AgCD4L4M+iLnL3ed8HrZXIYqJaf5+Rg0Kh4BH7drsd8rc1qsAVkWp60y3QiaGoPDMp+Tya9lpV2lb65X04IaO0vgW5+H+UxlNm5v0oCGdnZ/Gud70LZ8+exYsvvohLly4hCAJks1kPmLLt3BWp3+8jm816C0ExF9XMTPllRIdlz4lB2JWS1jXi/yhBaN0QvYbJYXZpubW8oqwLFVbahwoa2/wKe1/OHQ2LUkhpiPggYwofgbgOwe4mMLtf/zmA5+/BM14z1Wo1bG5u+vxzRgWAHQa9ceOG9wMJ8rVaLZw6dSpkGRBoU9CJms8CWDZFmkQtw4QpTYm2kzsKqFOKsh54XD/fjh9NgXD8+HF0u12fImzBPu7QDexte08hqQJBtT6FDwVqEOxERSh0FhYWQkuwralv2x7FvFHuBAWzc873M8ff9qFlatuHih9w3PQaCle9NwUj3UwFGtkH3Ify0KFDfm/NcaJ7sRnMDwD4pBz+34MgeCt23IfL5rfXjQiC9Xo9pFIptNtt7wNzz79yuYzV1VU/KVgjQSsraz4AsL9EOLCX7krtqTsxaXKLJvTYpcO0DKIYOgoBtxpVKYpRtP3Ajvk7NzeH4XCIarWKeDweym9gGFfXbvC6ZDLpfWUF2Gxykbax1Wrh5ZdfxnA4RCaTQb/f9yHPYrHod47Sd7KuVNT7jvqultbGxgby+bzP79C+UOCV80DdP77vKEGr2p/fNY1d789nKB4xjnS3+z40AMyaY//irlp0j4ixdG6/pvUVc7kc6vU6KpWKBx05AXVhFAdWXQliEGoN8FwKEgUgGYqk1cG2WDBSGUI/Kx5wKwGg50UJEXvPVCqF69evYzgcYmZmBvl8PrSBLVOVGWvXQqQUrmSWqDUg+nxGf/r9PnK5HPL5PHK5HMrlsr9P1PX2He0xSyow6f+3Wi2sra3hgQce8K6MuhlkWvX71ULkeVYojHLpdKw0uUrbFo/HMTMzM7abzB7ojMb5+XkA8BqQkz6fz2N+fh6dTgfT09N+j0cbwwf2tIYCaQpAAeHyZ5okxGOccMQhyCjOOTQajVDI83b8TKv5bYKNBUrZBjXVdS8G68IQFGQeB7EUCgBlkqjlxlEJP8DeAq9arYbBYKdSEbfRY0UnkrUQLDhqgVSeowlplsltVIfnc2zVPdDzLKnQ13PsuNBqoJXq3E6ND62hOY50YIUCE4g6nQ4KhYKPGjDVln5xs9n0YBqwgy8QpFSG4YDzPgzfcQEOrQouV6b21Gs5CaOEgIYtSVZA8HxlFptspfe2+Q5WcBFn4boMMi3rH/B9VeNT0+m9VTBFgZpsE1OgWVthdnbW1zewRWksA1vm53/rJtly/MlkEkeOHEGpVNqXyanhzyhg89WYX/veCmBGvRqNhi9YS+BV9xgdRzqwQiGZTGJtbQ31eh0zMzPodrs4evQoAGBrawvJZBK5XA7VatVfMxwOUS6XvVZRoaD1Bsg8NKnj8b3t3judjhcIFlPQfSes9oryWW8FOloGscd5P518qmkJ8uVyOTzwwAM+LHzx4kVMT0/j8ccfR6lU8vn7fA/WWdB3J3OoJaRam0yayWRC27OXSiVfFt8y3q0SfZQBrQvAftVxIdajfaT9N6ov7TOiIiGqLPinqyPVBaWwYPTlIEcfxpIYaej3+9jc3PQbfubzebTbbe9frq6uwjkXsiYUBNTJTWyAABzDnZoMpJ81StFut71A0f0e7Z6XdoKOAh9HAY0Wf7C4go2oLC4u4uGHH8bMzIxfAJXNZrG4uIiZmZnQM8io2hd0AwB4awMIV8HmH8PAN2/eRK1WQ6PRwNzcnK//qO+t72rBQQUEW62WzxXR5+v7NhoNb90R7LVkrYNbCQ0eo5uiBXKJWbGWA4AQsMgtAcZ5peSBFQrVahWJxM6mrjR7o8JbuVwO2WzWDyJj6nZhjyYp6TJY9bF17b6GNYnSsw3UIATY2Car5a1JO8rFsKE0YP/k5oTVycjvNvwGIGTF8L76nfkGzWYT1WrVRxWKxSIKhYIPCxLw63Q6aDabWFtbQ6/Xw+bmJorFIt7znvfg5MmTIVzGkrUgqHEbjYZ33bi8XdO8+f5/8Rd/gSAI8OY3vxknT57E1NRUqE7krUgFMMdai7FqhqsKCAoBKg1miEZhMONGB1YoMHNRE44YOcjn8z7J6NChQ57p6ffRF7UMxfNo/tFH1FoKugpSIxI23BaLxXxSEClKS1pNr7iC3i/qmL0P38+G/JiyzLbq/W41eWl1UBgyVRrYM7l1mTrN+F6vh+3tbZ8pqaBgVLtJjIxozQINJ6q7xWcPBgN861vfQqvVwurqKt7ylrfgkUcewfz8vA/D1mo1by2yIIwVaGoNcJwZzl1bW/NuKH/LZDKoVqvY3NxEqVQKpXtTeE3chzeAmJ9Ahq5Wq35nIzIEIxLOuZClwKgAASuNS8diMeRyuVB2mw2b0UKwtRI0g5FgJd0VG/O2fqyazlHIPCnK3aApbYUKGYyWjloSKmTItNo2WgtayUoBSjKxgmxkmNnZ2dBCKq48ZVtVIGodCm2/gpi6cpV9zEVYzKS8fv26dxsvX76Mra0trK2tYXt7GzMzMzhz5owPFXY6HWxtbWF7e9u/u7aNgrDZbIbOoXWQSCRQqVSwurrqAVZardvb20in0/tWxo4LHVihwE7nGod8Ph/amCOfz4fCVVFINCewIuNR5qSNsWt6KxCu38DJw88WU7CLrfSzRi6siazHrDAhA6n7wN+JkShISM0d5UvzOclkEsViEblczr9voVBAsVj0qz81byOXy+HNb34z3v72t3sLgaFhmv5alcn2Db+r8GBIlRYZBR/7iDkKjEQNBgOsrKxgc3PTRyoo4Ov1Oi5fvoy1tTUvpLWcH90G7UP2lUZkbD8PBgNcvHgRrVbLr7lpNptIpVJ43/ved4ez+/7SgRUK1DCcMNwcRDUoB42CgUxizVgKDL1OP9v1EvY8muj8ncJHy8JZba/XWkanMLOfOSH5PL0vtZj+WdfD3teG+9geUrPZxPLyMq5cueIzBmdnZzE9PY0gCHyiE4Xw/Pw8SqUS6vU6ksmkT5pybid+f+7cOTzzzDNoNpt+7Obm5jAYDFCtVv07nT59Gk888YRPIw6CILSJK7ATBVpZWcGFCxdC40dXgG5UKpXywoyp21xmb1OUOTf4mxa/4bOZrMb+1XHt9Xo++qACftzowAoFDjYnJbAHBMViMVSrVY/+a8iIkYJut4vt7W2USiUfmaC5yvOVqVQ48HgUwDkKQxj1WQWOgph6rjKqTTWmC6RRBy2coma6IvC0FDRWz+ep67G+vo7z589jenoaCwsLvl8YZanVamg2mz4vgRq+1Wohk8mE9mlYW1vDuXPnUKvVPCOfOnXKr1UhDYdDPPDAA8jlcj4HIJ/Po1aroVarYXV1Faurq6hWqx4EHQz2SvSzIAywVw2K485zyfCW7Hhy7GlRElOyQl2xFxUa40gHVijU63WsrKx4TULG0ElLRmu1WvuQ/+Fw6Gs8KgA3HA59lWBb39ACeGp98B5WQ/N/FA5gySYNWcvCYgzWSrB+MSeqmtwUlL1eD/V6PbTLEpOceG8yGEFTmylIk1uLl+rGLbbdvFZDflF4Cldc0q3ju9VqNVy/fh2XL19Go9EIAXtkRuYtaC4DAA9I03WilaDpzuxPHtN3YLq2Rp3UzUgmkz4cakv5jxsdWKGQz+extLQEIFxwA9ivlS0D8biajcCee6AbuVDj0pS1RO2uzEwfl+YkyQJ8nNDK2HpPChw9174Hf1d0X5F1tQb4mfsgAvDvyr0MtO8ymQwOHz6M7/qu78JwOMTCwgLy+bxH5DudDlZXV7G1tYXhcIhvf/vbWF5exubmJoIgwMzMDAqFAoIgQL1ex7e//W3fHwokKnLPcwkaMuafTqe9v06tTTeAYUoKEALKnBscO1UgxFkIhqqroGAxgWj2sbpmarEpWYxm3OjACgW7oMUmjKhW1xi9WhJkZn5XBiIpOq5+vV1uzXOBsNbTHPhXsxQ0O9K+g73Gmq82UqLtoiZVYJRWhIYWCcwRsGN/lEolL3ir1SpWVlZw5coVbG1tYWNjA9VqFf1+H+fOnUM2m/XuWTKZDG0ld+3atVBaNdtm+7tSqeD8+fP7CsEmk0mfJcmxpxWoYU++n3POp6ZTo6sQ5ViyfSQKCFpLHBsqCItN6YIrtjUKuxoXOrBCQf13mo70K3WAlVnIABq+s2nCPK7JTNYXt5aB9c019KYMwAnFe/GZ+meTkiwwqBiBdRls3ygwqgJJNbPNAwCARqOBz3/+8+j1eigUCjh58iQOHz6MTqeDzc1NbG5uhkKsdC06nY63tBid4HvrIiw7NuraUcBzkZuey/dlmJn4kbY9k8mEKkax2IviQczbYB9ELRzTsaJwsFgThbHeSxXQuNKBFgo6iBomVFdCtQcnpzIsB1+ZkPF8/ulvnBRqrquloUxv3Qc7WaIsCMsk1HjW1SGpz22jI/oMvTcFBnEEYP8WdizPRsZzzmFrawvXrl3D2tqaTwUvFouoVCp+Ixi6TcxrYARGF5FRMB8+fBjlchlra2shQaXvTIGv33UsbJTG1jmwlpQKVO0jtRS1/xU/onWlY6b9q5aD3n/c6EALBUp9os0cDEXdlRHUb1fmUdcjnU77dQya1qq5CFETgvfUSauaRtNlLfCoboP69fa/XqMxez7LCkCbWqzWEsG8er3uy68Vi0UvJI4fP+7vT7yh2Wzixo0bWFlZQRDsVFciE+pekqO0JCtv0zQHEMpdIGPbXA5aAnQDKJCoAEg2R4OCiNdohEczWGn+k2zlZlUSFg+yoUdVRuNqLdyWUAiC4D8A+B8ArDnnHt09NoOdzWBOYKfC0o8557aCnbf+9wD+ewBNAP/KOff1e9/0V20zgiAI+YwWSNQBslYF/1O78DxqKkXwo+7N823VJqLPtFr0OpsgZV0ZamhtG7C3QIlkhZIygyVGUCqVCtbX19HtdtFoNHD16lVsbGygVqshHo/7DET2BYE9fuZ9Go0G2u026vU6Op0Ostks6vV6CMBT/53jk8lk/CpKZcJ4PO436bFamUJMGZthTgKKmi2qAlGtHNsvUf1EYcTrGbXiHGMxGsVmdDwY3dBw77jS7VoK/xHA/wHgD+TYLwD4knPuN4Ig+IXd7/8GO4VcH9z9exd2qju/6141+HZJ/XRqAAoCNeVHmY/WRFQNTO2nZqwyLq+zSU+qRRTA47kaaeA9tI1qZej7RFkMCjRqXyiwCuyEbq9cuYJWq4VLly6h2+2i1WphfX0d5XIZ3W7X52rQ2mq3254hVXMz3Mjnrq+ve6FJ03pqagpBsFPXIp/P+7Ri267BYOAFTCqVwuHDh32NC62ozZwHWgVsAy0aCgLtd4ZImXpNnIMan0KFbg4BSd6TCUitVsvXz6A1yb6ncNBMTFo9B0IoOOe+EgTBCXP4RwB8YPfzfwLw19gRCj8C4A/czlv/fRAE5SBczPV1IdWyCrZZjcpJo+i0Dd8BYf+R5jKZQcNelnmtr8tJohODIKhGR/TZFkewlgTPUWFgrQW9ThmlWq3ixo0b6Pf7fr2Hhit1sxheWygU8Ja3vAXD4dD/3mw2PWNmMpkQmMiY/o0bN3Dz5s5ugkwa4xJ3C9DyfUqlEnK5HAaDAcrlMvL5vBdGw+HQb+PH/iROozkd6pbpCkcKdv7Ge3A8FAylQGHqe6FQCFkfKpDYfpJ1P+z4jhvdDaawKIy+CmBx9/MRAFflvGu7x0JCIbjP+z6oIFDmUX/SMjGwF6mwFgavVTDSane1DhT15mfd9wDYwwp4Dw19qebUZyjzK4jG+/E7hZb62rxvNptFuVz2C5q2trZC78O2Eo9RN4iaf3p62n+u1+tIJBJYXFzEm970Jly/ft1XtNL3VeFIi0Pbz3N0vDqdjq8mrbgL26v1HzOZzD4trMysEQ7WVtAl4jZak0gkkMvlQu/B61utFrrdLmKxGEqlUmjRHM+1SohtV5dzHOmeAI3OORcEwWsSe+4+7/sAwANFFsSzeIKCkoPBILRfg04qThS1Kqj1W62WByLT6XQkw9MPtpYIhYzNdbCuhAoCvbdqOetKEJzkcvHt7W30+33U63W/Gc7y8jJSqZTf1q3f76NarXqXgcfIBAQi+WxGFbLZrNegUSneaqFo1MICdypIFTPgu2jBWK6C1YpGvEYtMvazWiLpdNpbfUyB7nQ6qFarqNVqfp0EwUZaP0yKGg6H3v1JpVK+TD6VirXcFFA+qBmNN+gWBEFwCMDa7vFlAKr6j+4ee11JGQfY0yz8TLKMZ0NRqp0VtCKesLW15RF6ACgUClhcXPQCQE12ThCux9Djqt1UQGgY0LZL2w6EY/WqpRYXF/H444+j3W7j7/7u77CxsYEg2InnHzlyxC+dVgG3urqKWq3ml5ovLy8jn8/7mgjr6+uhvh4Oh75CdqVS8dowFot5xlKBQGalQNCIA8eNFh3BXfr1XBLdbDbRaDRCYC7HhfUheK1aQizFxxRtYM/cb7VauHnzJq5fvx5Kx2abFdQNgsC7GPV6HY1Gw+duaCTCrkexgnvc6G6Ewp8A+JcAfmP3/3+V4z8bBMHnsQMwVl9vPIFEpuJEAfaEhOIB1EzAnv+uiS+8F01Y1WCVSgW1Ws0LjeFwp86jBRjVGqC20r0V1V3QCcU2q2UDIPQ8q5Gsmd1qtfDNb34T29vb2Nzc9AxK7W+vHwwGqNVqflEYLQHmLTDXQH1zJumkUilfun1ubg6dTgcbGxu+ZBv72PYL22ojQezrer3uIyObm5t+bcPU1JQHABUkjnK39By6AlzGTMuBlo+6UnxH9pUdC84nXfvA9yERbKTwVbdi3Oh2Q5L/GTug4lwQBNcA/Cp2hMF/CYLg4wBeAfBju6f/OXbCkRewE5L86Xvc5tsi9f05GPRNdVDJoNQ2aqIrUGXdDWCvQInek5PCJqioGctztFSX3sMuelKLwWoXa56rRgJ2fPLLly/7rD32A7VnrVbD+vq6X2VIrU4/ncg8P2t4lsT8AhbHPXHihE/f3tjYwLlz5zwQqe6O7XcVCPxPjV+v133thlwu5904HmN7gXCKeyqV8u+zvb2NZDKJhYUFlEolOOe8m5DL5ZBKpby1Q9eBVgUVBcu/afEcCmIFQS2ewrkWlf4+bnS70YePjPjp+yPOdQD+p7tp1L0gajYOmAI7HDiauBQaFAg8V5OWVFjwj1l6APwE4iTWDWtJan3oakELZlpBoD6qnWx8V9t21ZIKrCaTyVB5scFgZ1NZDbFSM05NTQGA19LMBCTC3u/3sb6+HmL4Wq2Gubk5zM7OhnABRfnVxydzqAWl2lzNbfr+ml3IcaKbMDU1hYWFBWSzWQRBgGKxiAsXLvi06Gq1ihdffBHXrl1DEAT7ytCxP7UiloZZ1d1huxW/UZzHzkeLJxxETGGsiZOEk1wZRN0FxRHIPExQ4XWaQccwWL1ex9bWli+pxQnERBY1P8kYFD4WYee9o9rHyTVqrYIyEH9jGJDvx4SeVqvl/WwKzVgs5usXMJxI/1n9fApYMi5LsHFzEz5rY2MDly5d8gVWNjc3PeaiYULe11oKHCcrIDQLU6tmqctFbZ3P5/0q1nw+j2KxiHq9DuccpqamMD09jVwuh0ajgWq1GlpNyRL0BGWZhKWFd6lQFITkuhodU507/D6ugkDpQAsFMiG/q3bixNNJCewthdZjwJ5Jv7W1ha2tLZ/7H4vtbDijwoVaWRFnHgfCGYb6LIYktSAHyYYgbdTBJj51u13vlpCJFOBTbEWZXRNumN5ME5ztdc6hXq/72oQK5nW7Xbz44otYX1/3uIRNNQ6CwEcqWEm7Wq36hUfsK8VvKOi4cQzfVdPNueBKmY++PqMLpVLJL4gC4K07JikxwkB3iO3QecW+ohCNyq5kX/E3a+GpBTRudGCFAgBvxtNFAMIAoyL8JPUT1eRlPCaqPRYAACAASURBVH51dRXAHhrNLD+6BjQ3ab4qKKkhOk2UUcAtCjvgdwXl2C4VEIpF0Oqh5meJM96PfaCrDbVtdIfoMmjuBYlWFa0Fakb6/4VCwddS0IpPAFAqlXD06FHkcjkvZNVaUvCV70WXZjgceuZlcV7u/xCF7tNNVCZlIdVisYjr16/7EGupVMLS0pIXnBT4AHydCRVyankpQK1gMd9Bhe04WwwHVigwAUdNfzItAUJqEWA/uAeEzb/BYIDl5WU0m02vwUiMmzM1dn19HbOzs/48zQZkW4A9EI3P1KxInm/j2app1NzWYxptoRa0GXUW8FNiHsNwOPSuBzMFOcGJ/utWedqGTqeD48ePo1gs4saNG55ZyTQEDxnpYCk0fW/7vvzf7/dRq9XQ6/V8mrJiKZYoDJLJpE/cKhaLPlzKMeFcoDLJ5XKhdqnronkrbJdGslQ4cS7SemMUY2IpvM6kzMBJQVNThQI1e9SCGbUatre3/QShlaDAkQ5wu93GxsaGz1dQ5lOE3Sa5qOYHwoxr8xVUYESBlRo+5T2U6fh8vUYZt9VqhQqabm5u+r0aFGwMgr36BbrghyXS5+bm4Jzzy5+Z8ry9vY1qtRpiRCuknHMhsJh9W6/XQ7t8MYfBWmMkZWAy5fb2dijjky4SXSJaehyjcrmMxcVFZLNZ/xwClBRMtHT4u4aztXgwV9mOq7VwYIUCzTROBsUQODh2Amp4iffgZNfUXYJLFDLK0LwHJ+3MzExI+yvYxvvHYjF84xvfQDqdxk/91E/h2Wef9cyj4Ja1CNSv1nNVq6o2tPchqQ8cj8exsLCAD33oQ5iamvL3YubjhQsXsLy87KMR+Xzely5/5ZVX/DNbrRauXr2Kmzdv+g1WFDxk3D6KQTKZDE6fPo0HHngAm5ubiMfjWFpa8qseZ2ZmQlYVLYZKpYKXXnoJN2/e9Mxfq9Xw4osvotFoIJHY2dODQKIKMI5HtVr1IUriHnNzc5iamvIhbQAhfGMwGPhEquFwGFqdSSXS7XZRLBYxNzfn60tOtqJ/nYlLcRkvVz+QnzlwimID+90HxsA5idR3tSagahD6vnp/dSHUkpmenvZpt2o625i2hr/0mIYsKTB4b2uqRrWZ5/O3XC7nt1fj38mTJ3Hy5Ek89dRTeOWVVzA3N4dMJuOjMXQjyPRqpTDpicJacwqsQDhx4gROnTrli7AofsD2qYXFcvEEP+nWEMBkP1MpMBlrOBxia2sLN2/e9PkHjFwMhzu7PzUaDWxsbHigkpZRLpfzFgItJ+ecBzE5vo1Gw/dPv9/H1NQUut0uZmZmQgV2xokOrFCwzEQm1SQXJhhxEuvKQL2Oew4wN96Gy2ySk7UECMhx8nOyM2ynTBEVxybTUrtp4REVOOrDWqFlBYZer4IjCALk83mcOHECJ06cCPWFAqhnz571bkClUsHU1BQajYYP0Sq20ev1UCwWPbORYbrdLtLptN9Uhkupc7mcr9a0vr6OYrHoMQxdEh2Px30VZW6Ge+3aNZ8CTauNrgFrQNTrdbRaLbRaLWxubnrLkQKjWq16i4dZjgRti8UipqenAcAv6FL3jIlS3Dlqc3MTlUoFvV4PlUrFA9/VatUXxx03OtBCgROUE55CQtNM6Vdq/X+bFEN0XRNs1JpQ1JyuBVF/LhEmyMnn8x7WlKfJqXgA287fFHtQjEHBLu0HxTzU79Y/xTxqtRrOnz+PWq0GAN5tYuLQ9PQ0SqUSKpWKn/jUqhYQpB9PTa/JQGxPu91GsVjE7OwsMpmML+xKYJN7RtCSUgGn4d3FxUUUCgUvFJiKPT09jfX1dTjnvPVDa4KYgOIkFGYU5IzQcG+K5eVlD1xSUPf7fZRKJczNzXlBSmCzVqv5/SmGw53dshRbGjc6sELBmvb03XVhDVN4OXHpTijzUvLrvTRsqKY5Bzoej+Po0aPe9LUJLRpDV7CPpJEIfleLJCqJSa9Vi0UFFn+PehYBN+ccGo0GvvWtb2FlZcVrdbpcCwsLOHz4sC+W8sorr+DFF1/0DEgLjVuuc+L3+31MT0/jgQce8LgOhXMul/NI/6VLl7C1teWzKhVIXVpawkMPPYREIoFnnnkGly5dCgl7XvPQQw/h9OnTGAwG+Nu//VsA8FqZ1Z5pNSYSCR/9oLDQ0CkAP2cY/qxUKgDgw64UKNPT0yiXy6HCsMlk0lsmLBJLvELzH8aJDqxQ0AQTG1okI2r4SKMUQNj0Vw1sNbXiC5zAxWLR30cZ2Pr26qeS1LfXqAR/A8J1CVTLaxv4XRF8krUu9H34eXZ2FvPz86FwHxlYN+Vlv/F5APYJV8VGHnroIW8xaBi42+3i0qVLftUjFyDF43EP8n7kIx/BAw88gPX1dXQ6HXz1q18NFZeNxWJYXFzEj/7oj+LYsWO4ePEims2mz4OIx+PY3t72y8dpsTHLUd1IJi3lcjmUSiU/TrQE+Z42FV3nELELjkG5XMbs7KwPd6rgGScaz1bdA1JBwMlL/7bdbvu6gOrzAwhJb/rFTBHmb7oIysak+VyNfuj9OEESiZ2NRrR2A8m6FWoVWGHBz1FuBfGBUqnkTVgFO20Ug/dIpVI4evSo30xHr1GglIJTl4IzByEIAg8ManuY16AbqPA6ll/XqEwQ7Kzy3NjYwK//+q8jFothbW3NbzaTy+V82nK328ULL7yAX/3VX8XU1BSy2SxyuRzm5uaQz+dDKckAvMXIHaPULWG7tXiKjVjZ8dIIEzNdCXZrVSuGxSfuw+tM1DaaE6Cfo/x5IFwEldfQnOTEtmsleC4AFItFPwF5PSe4Wg1AdAKRZT57XK+zWt66G/H4XvFSe19rtaibwTAdEXnViHymalTFYfhcDUECOxYbcxY0ysF+ZC5AuVzG1taWT2uOx+PIZrPodDp4+umnQ3uDOudCu21Vq1VsbW1heXkZhw4dwjve8Q7Mz897F8muu9DENh5nhMK6hFGRIBUaDH8Sx2KkRZPAGCnhZry2uMy40IEVCgokqnbTElxqvrFij7oIRPqphVh2TCcHmYLRAfVHKQy0TbrCTyccSZlcj9n30nfSc6zAaDabaLfbXnvbe1sXgvdkdMC6AJoIZMuK8T+voUDVtQlMfrJYCMeKx48fP45yuYxWq+UjCdSuZF7iH3QzKKyCYCddWWstqCCyAkn/67hoZIHn6HcKI1qg7CNmqhIonZ+f921mToMqlHGjAy0UOGkBhAZSNTiP26jCYDDw4cJkMolisQgAHkmnRaEpzOqyqPbn86JARTKZJTVHFf9QBlQGt2FItksLg2impsUVlGgpaMhWIx98T1ZbVsBVhRxRfN5/c3PTryVQRqMrwugFq0erxcJScTqmjIRMT0+jWCwiCAKsrq5ie3sb5XLZr0uJspJ0jKyrZvtK22lJcRu6S3RTNMzN+1Aw6HuMG72qUAii93z4TQA/DKAL4CKAn3bOVYKdis/nAXxn9/K/d8796/vQ7tsigkV2kuqKQB1smrxqFnOSMBynCLsuJ+a5DCXqgNOVsNiFze/nZwou1f6KK1CQ2Amv9+H96TezXUTadaKqcKTWu3z5MiqVip/sah3QT+ayY7V4uJ5ETXz+MRFoamoqJAw5BplMBqdOnfL9T0uFpeHVrOc7E2hk27jmhKFBWnAq/HQO8P31ntrvKkAsPqSk80FBbQviElNgv44j3Y6l8B+xf8+HvwTwi865fhAE/xuAX8ROeXcAuOice+s9beUdkC6LBsK7L2vo0GbZ0SckcfB7vZ5Hxjn5aZrSalAkWjEMYI/xKSBuFVLkf+sa2ESlqAlMTc7f2U4WTOF7KAZgGY1to1ZjnJ7PZzUiLndWQaURG96L/Vqr1VCtVv37a0VlAros+c7nE8uZm5sLRWqUwS2ewghAFEBLUivGWlnWoohy6RRg1nG2/R91H1oW/2gtBRex54Nz7i/k698D+NF726y7JztY6ovyu0YaNMtRfWdOcjJ+LpfzGXCaNqv7QGhEQxmZNRLIAOq7kqL8fX5WkFPPjRI+GiKjNnXOhZguilQgKEBqtSYLlWqNCHVz8vl8yJ9vNps+gsNwnLpqVjiy/Xy+roa0TKikwkL3bIgClUe9u8Vn+E6jogXWguCzdD6MGuNxpHuBKXwMO9vHkU4GQfAsgG0Av+yc+9t78IzXTBbgU/DQ4gg62FGIMO/FgqzqdtBvpwVBZteJpa6CugbWElBSDa7IP++pGIVqZTsBLe7BcKCtvaDPVM2m4TntC7XErAYm0Dc3N+erUK2srPjy6FyhqH/K8Np+Clz7Thw3C5TyT7W/fVerwe1nOw7WpbPWlZIKD+s6aPvG1UoA7lIoBEHwvwDoA/ij3UMrAI475zaCIPguAP9PEARvds5tR1x7XzeD4aBRY6tA0OgAyQ6SjQpoDvxu+0OhSWYvqsAJgr3KvWpKK7bAe+lzlRkt4MXzowDCUUQ3gBWFVPPbCcz7c9GPZRZiNLoWgO/Ovkmn0ygUCj701u12sbS05LMKOQ5aSZq+v/r/7G/ndlLNaXmp9aJjraFDkgoMK3CUbH8qI1tLw34f5VrwXMUjrJAdR7pjoRAEwb/CDgD5/W63B5xzHQCd3c//EATBRQBnAXzNXu9eh81gNGxoj5Go9dW8t66GjUxQ0/IcnXQ6Iag1qFUVy1Aa5X/yv9WEnGgUTMrc6s/yuLo69OO5qo8hQz5bkX2buGP/F4tFnDx5EkeOHMGNGzdw5coVv+6D797tdv3GsQD8mhB1HxS3Yf+wXcxE5K7VGlVSF8O6N9p3luFHuQHa76PGJ+p41G/WUmUbCPhGJa2NC92RUAiC4J8B+J8BvN8515Tj8wA2nXODIAhOYWeT2ZfvSUvvghTciwIAORF09aFlSobngL29H8nwBJfIhLocm/fQNmjUIJVK4YMf/CCmpqYQi8Xwcz/3c/ijP/ojfOlLX0Kr1fJt1L8onzcKcFMBoxulaq6DrpC0wkeZzU5gMiNTlnO5HHq9Hra2tvzyYy1VxlRhMrfuzMR+0/7UEChzFNR10far5aBCQsliTDq2Ot72d/7XvrfHR1kOdpx0TOzCtXGi2wlJRu358IsA0gD+cvclGXr8XgD/LgiCHoAhgH/tnNu8T22/JampD+zfbl4BMiXLVFpPj/fgYp4oE55ajv8paDSpiRM0m83iJ3/yJ/Hxj3/ch6c+9KEP4b3vfS8++9nP4nd+53dCNRTtxNI4e5Q5zc/6XE1JBvYqHun5VmiqQOVz1bVh1uH8/LwHXNUHbzabqFQqfkt5tkMxHYbp6EaolcOxVCGoboY13a2pb8PEOm6vxpjK2K92jp5rBYj9fitX5o2m24k+RO358Psjzv1jAH98t426F0SGJjNGZY9p6EzzGexgBbsAXSwWCxVcsYkuPFcFQFSUgRPk/e9/Pz75yU/6xChSsVjEz/zMz+Dq1at48sknAexNXhUEt7IMrKaiOc6NUXmenchWk0XhDrxOmZa1DjKZTGjLdZr7TEBi3QNiK0yAYiUn5kAoXmMtFbuYSnMedEzYBh1ny9wWxNXxt5bTKIrCMCxZUHRcsxmBA5zRCOxpbSC8rNmeQ42m6xX0ejIAY/Ss5sTinzTN0+k05ufn99VP4IRgzJ2JLh/+8If3CQRSoVDAT/zET+Cpp55CtVr1E0/bFwWYajuDIPDgJ4FBa3lECUC1PMjwUS6MBdMsAwLwYCSrEM3MzPgx0EiN3gOAB0V1A1ktQENif2jY0AoIvhfHmO+kGIUKHrVAtE1W6Fqyz7J9a/vsH62l8I+VaJKqyanRBx3oqIHXz+qzU4i8733vwy//8i/vEzIKzkX5mnqfI0eO3PIdHn/8cTz55JMhrENplGWjffDpT38af/M3fxNa6DVK89nj1rLRZ9rrWWadgor/2b/ZbNZHcIiVUIgMh3vl2hih4T14PYUZmZdJT6Pe2+I9NtqjayVINrKh/ao5DDqn9Pco3GIUjatAAA64ULDJKOoTR5nE/K84gBUW1LyFQgFnz571WMAo7XErejX0OZVK4dSpU6/pnkrOOSwtLeHIkSN+q3luP3/z5s194KINVTJNXI8TcLWuRr/f98VFms2mL91GDIOuQTy+V/PSrj7VPtRoSjKZRKFQ8OtRuDEs26mFUWjdqWWl2YsK8rKPNEKjv1tBeCuGV3fm1caEFuu4CoYDKxQ4wagJoiwBYG8nJYJRCoBRO1kffTgc4qtf/So+9alPhcKYXA6rPi6fywQc/mUyGXz0ox/F8ePHR77DysoKfv/3f9/v2KyT1WILagnxHOcczp8/75cls16gLiGOAr/0/io0bN/qNczS5BZs3JKdtQqSyaRnZrUi7JiQQUe5X8BeCT1dlAbsYQ1Ry9/tePC99DkAQusRlIGjhIX2mwp4O04WD6FwmgiF15ko+RUQ1AHRxUh2YZQi60r8Ho/H8fLLL+PixYu+oMpwOMTU1JSvTKTmqgXL6MIsLi7iYx/72D4XhOd85StfwRe+8IXQHgu8n80mJHCneRdqOtN0p+lMRnW7kQDVXtpejSLw3a17RFxFw675fN6Ds8xN0HsoQ/E9VPBaLa3Xx2Kx0CIoYieMCg2HQx9VUcVgMQYFUUm6I5RamtpmCh4VjqOiP2rx6HP13caNDqxQIONQKFAYAHt5BhxUTeBRKa5FOZUUYwD2BpqWheIWZAydHLRIPve5z+Hhhx/Ge9/73pA2cc7hhRdewBe+8AVfqWiUeWo1IIlttj5zNpv1xU+5DoJb4eVyOf8+NoRrQ30K8AVB4CtZ6fZrpVLJV8LWIqc2Rm8Zywpo/a9CjYlYbAOrNnGzHy7E0kxTTTnXZ1O4KRitFqJaHupysO81a1XHUd0ufc9xTVwCDrBQYDEL1XLKmBptAPbMSa0apJvIqtDQfQ85SVXTaTGQURQEAb7zne/gV37lV/CpT30KH/7whwEAlUoFX/va1/CHf/iHWF5eDq1bAPZW9/EeKhSUca0QsW4BzeRYLIZyuewtG2pK3XszSqNp6K/T6SCdTvsKzHSPMpmMXzil6xcoKHUZOt/HhhqLxaIXJHoO3RS2g7gDS9xpclm73Uan0/GCguPH+/F99L3sWPFPgUh+V6vQunBRwKUNUY8bHVihkEgkfCntRqPhLQJFuC24RmGg5q4thqFaiJqFTMuqxJosBIR3clZEfDAYYHV1FZ/+9KfxwQ9+EIlEAp/5zGfw9NNPo91ue82nk4eTS7UQsLeQS5k+Kr17MNgpbc4iJsPhTpXkeDyOWq2GtbU1xONxDwryGhuZsH56v9/H1taW/05sJZ/P+35jjUh1rwhSamYl39MKpChfnMKdi62Y45BKpTz2wJ2uWJ+TFaAoHOx6ECt44/G4x5q0TZoKrq6WWhpqVahSmuQpvEFE5qY5r4PNYiEUBMDOICvDc2IlEgmfk7C5uenrAlIgMKSWzWa9SW5TcgF4RiFGQERey6PfvHnT318FkvVD1VIh6QS0GkxdGCX2DQC/MYu6AgqWqSBQE7zT6fg9IljSnhaHujGtVssXzWVkQisRZbPZfZEivheZk1YASVetsr8tvqErMdnnHDu2idfwj8/VPud76HcdI0ZKdBm99hWtHAt+jxsdaKGg5bCA6FRnNSNtsVVeT43NXX80b4Dn9no91Go1v+kJfWjnnEfgFZBjaC7KqrCMoVrMahz66dalUORc383G521ClWpsJhBpG/R81dw09avVqn9f55z3+RmNCIIAjUYDwJ4bQbBQ1ztYAFixgKhkI8UaNIoCALVazfcbLUGuvaBwqtfrqNfr+6IRHJtRERsdJ86pbrfrx1/dvyhQchzpwAoFFvNQ85CDN8p044Qi01FocKCDIAjlJUSBjcCeBUDkXZmbsXwAfoMQuxpRY+6agEUiA4wKg6m/q++mqb/KUGpd2P6w/jNNXwq7qakpNJtN73o45/yCKPYFGYNpzhSKtKqU0VTDUmgo2EihSitPhaRdks5ryejdbtdv6VYqlbz1F4vtFM9JJpNot9u+MpWmU6ug4T11/Pk8AKEFXJrMRcHGaNC4go0HVihkMhnMzs4C2JP0ZC7VulaK83xFwW1Ys9VqoVKpwDnnKwbrAFPjasktFSK8L0E01hLk5GQdAxs10Tbze5SFoO+g76UYgYJd9lxO+ihBwfvU63UfjiXOwqgDN4shM1IAcD/F4XDoN2jVCIZqUhVgKtDIvGyfhh8toKzHuGgrk8mgVqvhxo0bfgfqTCbjhRjv32g0/FZvNmTNfuYxGyHRseI1GurU3JZxpAMrFFSTUjPp0mc72VXKK0OoUODv3GDEXquaS011jQjwfmyPJvPovZRRVJvzHGsZ2MrVGlkBwpiEJlJpmFbDdjrxydjWLeFektTeFAYE/Fhw9dChQ4jFYtje3sbVq1e9NuY5VpixjxSzYRuZ3hwlyJR5bdKQErMjt7a2fGiTQlyzWPk+BCgpJLRPFePRflaLR10ztntcrQTgAAsFG2Gw5jawH0gapWXVolCz2/qV6otyYkYxFACvZbvdLnK53D5tQwGjroeGuMjENkymQoL3i3Ip+FmtEPXnNcqhAoLPyeVyfucsLnrie6tVdf36db+YqdlsYnNz0wsEviejBHxPmwdCN47fmQ9CxlO8hKClFTJqNQTBzs5ZdHtKpZJ/R44VK0+xH9vtti9US3xJ3xPYw4OIeViwkREiuqHjKhgOrFDodruoVqs+c0+Zi8xAIFHX/1ML2opECv4RuCPgSN8xqpa/CgEF5RhxSCaToe3os9msjwDcCoxSRFsFhR6zFgYQjobwfN2khUzVbrd99IGLmYDwykxuPFupVPwWbXZ3bt6LApL7NjLCEWW1jWqnAndqfqvAVUtPLSs7HqlUCvl8Hpubm0in05idnfW+Pl0bzhHek+Bkv9/3DG4tUv5GIdBqtVCv171y4DwZ192hgDvf9+HfAvgEgJu7p/2Sc+7Pd3/7RQAfBzAA8Cnn3H+7D+2+LeIE1k1dAIQ0AiU6/1MbcuAsKKloOScpj0VtU9fr9bwmVNOUYcxCoRBiZgoFuxCJ7Y5qs5quCoBZS4f30NAlsLc7FoCQsGJ8nns2Kiiq/vzU1BS2trbQ7Xb9LsyaRajvTSEQFbJjWyl0STTj2W+kUZaefrc4jrpJ7HcCkIlEAq1Wyy/15nb2fAf2Ly0RCtBMJhOyGHSuMNKjuSqKkYwj3em+DwDwu86539IDQRA8AuDHAbwZwGEAXwyC4Kxz7vaWkN1DSiaTKJVKoUnEgWC8GwhXYFILgpOTZh41E60Bph7zN7Uy1CRWTchJQYYslUrI5/M+L8Htxs3JhAo0qovASWUtCTWRNeHIJiEBYY2vrgcFD8+n4LI1Fe1GLVzwxL4tFAoefNR+ts9UUvdGhY8KNQX21FqwglHHjMe1WhMjBNzerdFo+BJy6XTaM7KGSXX9A8+l0GC0gaCzFtlhfUoVSLeyAt9ouqN9H25BPwLg826ngOulIAguAHgngL+74xbeIdF8UxeA/1WbUuorkykiDoQrNFmT1IbRdPIplqEMR0HDRBdGH4Ig8Ih8u932qLjVKmyPdSGA8H4NqiFVYLDdtj+cc946isfjWFxc9BqfYCItiHg87hdZAfBVnXlPFT6aDMQ22X0y2Haep0CtjoXtC7Y7KiGIY0HhpG5Go9FAtVr1ayNYkZop25rUxvszfV3zX6hAmLnJqlasQpXJZEKupQLRo+pBvNF0N6362SAIPoqdSs0/75zbAnAEO5vDkK7tHnvdyfpt1NxR8eYoBiLz2pi+jfVbhJ/lxGhx0HcG9ucQkNl4PwoTVnbSWDcjBpz86rcrpsD7ahKUvpNzLpR8pes2tO9UY9Pk1TUHurO2goNaP4HP48Ilvj99a5IVWOzL4XDod75WYFWFN7MROYbaT2rV0bxn9uW1a9fQbDYxNTXlLSEy+8bGRqh6lq0yzf53znkMYnFxEaVSySsiHU89xmstEDlOdKdC4fcA/BoAt/v/t7GzKcxtU3Cf932gya2CgdKdmWwaxtptUwj114mmk8GCYBZMZPKLxsvJXMxLYKxaAT0rlGjF8BzNsyfZEJx1Bdhmi1HYGDkFCd9D+4BE3IQCj8xCrIFMqOY5S7zbNQI2ykLSdQNkLO0bTVnWMdb35vU8xpAiBUUymcTMzAwKhQJqtRo2Njb8cQqP4XDohTnbR0ERj8d9xIJRJCZH8R0o8Gnt8HfFbEatfH2j6Y6EgnPuBj8HQfBZAH+2+3UZgHL40d1jUfe4r/s+MNd+FIgVtfuxIsTUNmpdUAtFmeO8jybS0MSn4OFmKN1u11sDViipmWmZn5aDlkWPQtat5ld03Fo2ViBaEJC4ii1A69xOGjRXQTJCEYvFvIWkACMZBwiv9NQirdTW6qKR+a1AYNt0YZeOI/un3W6jXq+j//+3d22xcR7X+ZslreVluVqtSMmCrKslW3EjR5GdNIljo2mctgkCOOlDmz64cRHADZAACdA+uE0fggIB2gLJQ4AiQIsEsIs0FyCubSB10thIYCOJfJdlS/RFsqw7JUrUktwlKe0upw+73+z3H/6rK+VdEfMBBHf//S8z/8w5c853zszUasjlcujr60Mul8Pw8DBqtRqmpqZQqVRQKpUAtJQSI0B2AFBLL5PJoFKpYHx8PJDE3vswBV3fvfYNACEy0Y240n0f1njvTzS/fh7A683PTwD4b+fcd9AgGrcCeP6qS3kF0MYDFi6prWFAJQupFJSQ0musz6qhSloRNmKg6yxQKem8C/WZh4aGMDQ0tGDeBstUq9VQLpdDPJw+K++TRrylvRtaIjZ/Q0lJfTf8zOur1Sp6e3tRKBQCe18ul8MkNCoZXU9CZxnyu5KoVBb0wVUJqctgrSq+Z56jsyGr1WpicVy+dyp9Km6d2s19MDVbk6O+Zp+yznQ/AKBSqYRQrfIhGi61fbHbcKX7PvyRc24HGu7DuwD+FgC893udcz8FsA+N7eS+0onIA2Ej3gwupgAAIABJREFUAs0yJpbcUoZbR152MhUYJZ6Uydfr5+dbKylrGIwrETnnEgt/MBmGHZ17J6qbokk6QGuHKkYAmJpLUo1l0jqqK6CknVWcfG86QrPO/M3ej7tQ12q1ICje+0S+hSoh+3xaTHSjqBQ0AUvPYx30vbG8MzMzYQXogYGBEIGiUFLImaE4OTkZlDYtonq9jtOnTyesm56exoSvmZmZsNYl21hXwaKrZBWZ9se0yEs3YVH3fWie/y0A37qaQi0GdITjdzVblexhp6UgUpgJ7ZD6Wf1Ym+sAJEcvjp46NTeTaU2YevbZZzE/39iIVcurI6UKiVo1VDI0X/m7Bcuugqkd1FoJPKYcheYepJ1DQWQSk4bt1I0hNEmLytsKm75LbSsNjfL8TCYTlBHQsp743rnPBN1LTvlmCJhtRCVCQWd6NaMMfAfONWZ9MmdBrUy1dPj+1QXpVsXQnTGRRYBlqnkMSC6oaTu7JeP0HHbYC02tpYmsnYosup7PjkNW/pFHHglE1YYNGzA4OJgov2X5qRCoPNREp2mvoVENx1nrSd8JP1shtO5Yu/RoDVfSzZiamkKtVkOpVMKpU6dQLpdRKBRCyC6fzyfyIFgGy3+QpOOkNBKR2Ww2hABVybGtqtUqyuVyUAaMVqj/r8qSO2Vb18pmavb09ISVpthGjMDYslu3TgePbsOSVQoUGrL8SjICC6fl8pjyAvyd/9VM1NFKO6HmOKj5rUKlJCXJR6JWq+GNN95AT09jBeZCoRBGU80R0DJZ/oB+r+YXUBgKhQIGBwdD0g5HQB2lbdmZlKTWF8vPkKESeyQn2QbFYhHz8/MoFovYsGFDmC15/PhxzM3NoVQqYXBwMPj+JHVZH/2sOR7aZnQ9MplMyDdg2bmgq0YIaHnpSk18b6q0+Z7OnTuXCPvy3ff19SWsB7ahtgffHTcS6unpSaSOdxuWrFJQwaOpqwKpAq1xecbjKfRqwmvID0iSlUDS/Ob9eB8NkbF81r9UayCTyYQZlIVCIUF80hohbDhOFRAXiGF9eU/yKuQ29B1p+NJGWCzXoinIVCxqObEuGkHg3IKRkZEwuYjlKJfLCQuJZB/LxT+bIemcQ7lcDkpZBZmRBSo/hoR1OX6NGGn+CMOvjKoQzrkQim0XWlTlwuevXLkyrDcRlcJ7DApXWoKNMuuEstocIer1+oLZbDZEZUkjCr/63ux0aiLzOo6ouiQ62XBVWHYClgqOKho1u3keuZS+vr5QfxJylUolTFJiuUmW8r2oUtS6p/nE1g3hPXguw3KqQDjRCGiM/KVSKazXUK1Wg0+v91FTXVONmVw1NTUVrJahoaFwD20z9f3VglClqu4Sz+Uak7So+E5ZH3UnaLmw/WZmZnDDDTdgeHi47c5fncaSVQo2bm1Neh296INrhILmOhudHRlI7imh51MJ0U3h/5tuugnbtm1DNpvFyZMng6JSBWNnRVIYeU8tN81cwvqv9rOGCHmc/nQ+nw/n6L3V2lGiUS0Ha/nwXE1PTgsf6vwPHuM1g4ODwb0pl8sYHx8P4UAy+2wvKgXmSnDxVpYtn8+jr68vrH1Bk1/blJaAhip5rq5oDbRWwKaVofXWd6Wf2Y90QGJ0g4qo27BklYL64BpxABDCTsyJ996Hbc40dm4nEqkbQFIPaLkIHA0oKL29jQ1fHnzwQWzfvh1jY2P4yU9+gkqlEjqdjqYKNd9V4JQEVCHT64BkSJUukI6OapEoQakhVR1VbZSCgsR7MknLCruWS5UEn0WooAENt2RwcBArV67E9PQ0jh07huPHj4doAUdc3odtxIlwTFTKZDKBDNRkNP7Nzs4uSPvWfAqd6sy+pPXVAcHyUNoO1qrSQaXbsKSVgjX/NBlIE1iUiVd2mw3JzkUOgZ1AE2/Y6Rl5AIAtW7bg3nvvxfDwcNhwRUdczjDcsWMHnnvuOezYsQO7d+/GyMgINm3ahN27d2PFihXYvHkzJiYmkMvlcO7cubDS8ujoKG6//XbUajW89NJLiXCncg42AcgeZ4cmCaZuFwWAnIPmBvAevK/mcaiQEBoBsVECdXl4L5J8zA1gJIMjLrev12nd/M9ysD3pIvDP5iCom0jXoL+/f0FIlrChayWZ+bu6ke3cvW7EklUKJK20cUioqZWgMXuacxp+VFNQGXibEMXncOSqVqsoFovYuXMndu3ahW3btoVJRIODg6Ec3nvccsstOHv2LD7xiU+gUqlg69atWLlyJcbGxvDZz34Ws7OzmJqawsaNG3Hw4EHMzc1hw4YNqNfruPvuu/H8888vcGfSyC8bBrPmO98bf+O9aMrPz88n1jMEkovO8Hz1y3lMlYNuBmNH1dnZ2bAwCZd6Y8YkR2sSpVwgl+FPhVoFzE3Q/5lMJhCeJB5ZD0um8t1aC03dDZY/7f3xPelxzaDsNixZpcBQF5BcSFV3RgJaRKDG74GFC3boyGDDguwcTIelCX7rrbdixYoVuPPOO1EoFNDT04P3v//9KBaLuP322+G9x1NPPYXR0VHccccdKJfL4dxdu3aFTvv4449jbGwMDzzwAM6fP4+XX34ZW7ZswV133YV6vY7XXnst4Qpp57Xmq/rMlmgldISjRTQ3NxeIMiYHccS3VolaU2k+troSQGtZdCYTkR9gvJ9hTTL27Val0nak1aTRi1wuFxbG5bwDWhJp5LNaS9YlsqO+vjN+13raUGc3Y8kqBd0FSQWdJBUbTf1tIDkqAMlGp8nPqIGdq69rD3jvUSqVMDo6CudcyI/v7e3F8ePHQ4c9ffo0jh07ho997GN45plncM8996BarWJ0dBSrVq0CANx8883I5/MhHn706FEcOXIE27ZtwyuvvILx8fFQ7zQewpqu6kqoecz6W3+3p6cn7NkAIIQ2qRCq1Wp4H/pM5Rc0W9Q5F9K7NYTI51J5s92si6bKjmVWl4KuIt09hv54PJfLJcK87CNqNSl/wL7D/0o0k8fge1Glwnvx2TZK1K1YskoBSDLxJBc1skCfld/VDSBUQagfrBulDA4OYsWKFfDe4/Tp05icnMT8/DyefPJJ/PKXv8TAwADuv/9+5HI5/OIXv8CqVatw6NChRFTh8OHDGB0dxfr161EqlXD27FnMzMzglVdewZYtW5DP5zExMRFy9cfGxrBx40bs2bMnkX7dLlSodVGyzKYSqzLgOSTV6HZxKjfXTVi2bBmGhoYCMaejJu/Ja7gRjHILnLfBP41Y8Fzrz/OPW78peawTnebn5zE5ORkiK8wgtWFdC32+ci/6O6EJZGqZqSXFQYXnX6idOo0lrRSA5MzEtNRSZcNteCxN89tRlDzB6dOnAbRWCibjzXs9+uijqFQqOHr0aCJESd/6iSeewMzMDB577LFEmOznP/85stlsGI3n5+eRy+WwdetWHD9+HAcOHAjZe1YgObra6AVHZ5rM7NQUKE5h9t6HEds5F1KFOWmLC5wsW7YM5XIZ+Xw+wTeQ0NOwaibTWrKM77YdC28JTb5T5TBYb9aJC7JSMGu1WiBQyUewLJrBqQqC74a8j3JJtv9YwbZuRBo0Wa4bsaSVAoWSWlnNWQo8yUFCc/qBZIKSCquuRMROQKKSC6potOLs2bPBT37rrbdQrVaxadMm5PN5ZDKN7EXOMmTZGfKjqc7ftm/fjoGBAbzwwgth7QIKhfq/ynWo2U6LidOL7ToLrH9vb2+YfqwkLNDKDmQ2oq5JMDc3h1wuh1wuF1h9XcVKlUGaKa38jUZUVPBp5TBVmyBxqJOadHMdCy2PksjWbbDX04qidanhSFsPWjC0irRPdSOWrFJQbkAJQdX+QHKpMhsi03i2nqsjnyoF8gmEchXsHBTSffv24ciRIxgZGUGhUEB/f39YwJVmLjsPeQx21qNHj+Lhhx8O4TnORJycnIT3fsG0Xn0uy85RnmVnnoDG+9XF4v6INMkZKjxz5kwQ2r6+vjBfo1AoBGG1jL01wzU3hG1BZc7VjyjgGgZmGcnnkKzUBCZOo7ZKPs1qpCJPSyNPsxzVbWNdNArDPz7f5nV0q7WwZJUCSUEr2Gw0G0LjaEmiix3Q5tjrn819UL+TDa5ZkRx9c7lc6CDcZESfoYJCxh9AYkYg/enZ2dmQ6svpwCQFSQIqAap1ZicmZ8AyU2moyc6RDkCCXOUEK7L6jBDQMqOA6Xvjc9UCUMIOaBGgFF4VZs6X0O88h66U7izNVGltJ1VSSnjqe2c7Mo+BZj/bE0imvdv+p0pBSW32m4u5GZ3CklYKVrDbscnAwr3/gGSDa8cBkn4wLRA2PNBaOFb/KNBUVjyP12tiDZ/JRCoSavV6HcuXLw9l185Vr9fD0mPsiDZFOq3sej2fy7wEWiysH2P6TCHmRq98h3pfSwbymJrinGbNa2wUQdtIlQgjPHwuLT2SlnTx6Eow18GO9jZpiZwH3wfLQKWYxhnw2WpNKKFrozDd6jYQV7oZzE8A3No8pQCg5L3f4RpLwY8CeLP52y7v/ZcXu9CXAu8bayJqY+ioDySXHNPwFmEZagqFmq4ksHSfAhUSmzKsfjLNeXYmjvgUQo5YSoJyqzOm8Wp6MkdQACEbD2gJm+4CRcuC5dSRsLe3F/l8Hj09Pcjn84l78Xc9V7kWloMjLb8rK8+EI7WeaDWRxOTvMzMzIaeAo66O1JzWzWvsqk28jpajLq+mbUTBpUBrW9KSpLLV/sA6qrKwLoUqAXVLuhVXtBmM9/4v+dk5920Ak3L+Ae/9jsUq4JVCRxOrmdWX1NFP1xXgiMGOQiFQsxZIxvXZSVSYOc8faC0Uwgk+fD5BoeAzeV8KFU3j8+fPo1QqJSY0keybnp7G1NRUWG5cLQ8V6P7+/oRvTtNflYP6vmrZAAijP98n68vyk1ikIOk8BfX71VRXl4LKV98fOQpaHGqZsRwaRWFbqXuiCoQWmioDuiEEBxXtF+oiqttp21PvyzKo1WA5jW7BVW0G4xrS9hcA/nhxi3X16O3txdDQELz3C9YGVMaZcwk4dVlHIWWX2fHL5XK4P9ByK9Qs1FFfE13UgiCDTrOafr/1benXU5mwLr29vYm1ApkFqH42y0mB5whNwVeBsMSXHe3Y8ZlkpAQrwXJxQVma7QASCo6LoKgg6YhM0tRGK1Qw+Uc3jQqJxzRzVdte72MHDL53VTpsP3UF1F3Q/sZ3xe/ad3h/vtulHH24G8BJ7/3bcmyTc+4VAFMA/sl7/2zahe4a7/swNDSEtWvXJsJCQDLcxWOlUink1nOk0QZU353Xa7wfaDW4dg5l0vWzKgOWrVQqhRFpfr6RMWijGuqGcCox1wwgCUmh4Mo+/f39QRnoysrW31U3y0ZZ1HIBWsKvoUbWmyP/1NRUWLOBx6n4nGtMNhocHAx7Z3LSWb3e2CJ+amoqkKi8v+6/QWFV64euheZbUKGwfiqIOoLbUduuQanX6Pl2GTnrNlh3oZstBOJqlcJfAfiRfD8BYL33/oxz7g4Ajznn/sB7P2Uv9Nd43weONurP8rOOxhyBOQqrElHCz5JxQLKj0SzViVSc6w802HCa8iTCeG+OnrQmSD7ys3MurCVoU7SBlpByrgSVgCbnNN95gkwklJegKa/LtPHeGsajIqWFREHku6R1A7Qss/7+/vBdJ0VNTU2hVCoFZcRVk5g1yXpTsfJaWi004ZXQZZ2Zx6GCaC0cjRSwXdm26lboIKAjvUadNKytFicVmIapl1xI0jnXC+DPAdzBY76xh+S55ueXnHMHANyCxtZy7ynK5TImJiYWhAnVnNf1C3W0Uc2v/jgFlZ1GIw08l37//HwjtXdiYgKZTCYItQqjKhDdVo3mtya60MLQUVpdAV6nI5xaRRRANWfPnTsX7kfCjiM2R3JaE7Qa1JXR++mzVHipKIGW8FjXiJYY/xOq+PQ3KgFtGz6fCoAKTE13a67baIwKuipTvj8ti5LL7a7V7/rfTs7rNlyNpXAvgDe890d5wDk3AmDCe193zm1GYzOYd66yjFcEJt1QSwPJnZbn5uZCFiHNU26swg7H7D81uYm0DkZiky6A9z4R8qIA8nz1d6mQdB4AhUrDfkDLclGOgtYFVzlWF4BhPyoZloXrN9KyYMem1cJcAA3p8dm6irGOpKwbR3YqYQopFZsFz9eyU2FqXfQ5utiqvkPeXzkTfefKU+jv9lzel5/5m0Yn2kHfje0z5LlUgXcTrmgzGO/999HYcv5H5vR7APyzc64KYB7Al733E4tb5EuHhsn4nRbC7OwsZmZmEnFzTo4idKTRDmEZahVO7aCZTCYQgRrBUKGn0qDga2dRQot/NJc1XVlXBtIysYzkFnp6ekKGH015NYn1ORoGpJLQejN6ofXWd2XNa55nozcqzO3ePc9jvdRi4rXqVtiRWtuqnUDb513sPLVALPmoVom+A3tdt+JKN4OB9/6BlGM/A/Czqy/W1eP8+fMYGxsLHVmFmv6vChVHegCJ0UbDTWres2Nbd8L6+ST9crkc+vv7w+9MkKHZTHNaQ5DKeVAolQDkngkkFW0iEe/Bsmaz2ZD4xPLaab+0ClauXLmAKFPB1TklvJ4Wl/2NsMes6W0FSC2PtIQhfSd6P72H/bP3UQG2g4AqOx35tT/w3LTvWj5VMmlWRDehO+2XRYDGvG3SEhtVRy1rLqpSoIBTwdBs1aW/lGykMmAyTX9/f1gkhFOIVTFVq1VUKpUw+jOcqaM4/VcKL12LzZs348Ybb1zQeVkmHbHsCJ4WrrOWA6/l+XpffSZdC74fKlpL0KUJAt+duhv8DCSXedP2TRM269IQ1sJTd0Tfj9bXKh3tOzwvTdHoM3R+hFp73YwlqxS0U6mlYH1UCpjGwCn07Nyaqmx9fz7LjjA8XqvVMD09HabwslPU642pudxiTZOJOBV7eno6sSuSEnckBCmI1p2x03MvNCql+fFAMvnGTjfWenvvw8QlfZ5dwrzdiM5nAEnLiWVr50rwsxV+Xpd2nuWG1Oy3rpq1DthftAyqECxxqeeppWLv321YskqB5rKST7r5B4CQ5KJKge4EmX1lzoFkhhsbloKrLol2Mo5++XwexWIxTCmmG6ARAQBhfsPBgwcD72EVGRN1+LuSauzomuegCkIFxIbS9Jw0IdHUcP1ulTChSlKvVcuB74BQoWrne19M4K3A2fawbcrv+jzLG9j72aiFKjfN/0hLYEq7Z7dgySqFYrGI973vfQvIKIINSeFWl8J7H8hI1fAUOJ1RmKbtbcdkctLMzAwGBgawatWqkHikmY80w6lkmPhDroCuhjX7tXyWn2CdWGee345pTxMUDf3ZOmqYN00BpL0TVZw8j0rMsvxWKVuoctCojz7XWj/a3qqQVGHYcluLhX2EERpuUGsVOPM29P3xfcbNYN5jDAwMYGRkJDHKsQOomafmOUdczf1nI6sfrUk8NH95jubnE4zN05XIZrOoVCohuUq3L9ekHQ1het9aBUn3IGC9OBvQMubtyD39rJmKdhRtZ5orwaqJXZqtmXa9Kgv1862C0rTxtNE9zcS3/y05yzKqhaYKQcud9lnfN4+TBE5zHbTttQwkZONmMO8xvPeoVCqpPh9Nft0BiPkAupsPY/8MVTITURN2rOk/Ozu7QLgseWkFm0pFJ0RpYoyW1/rjnEVIjoFWEbAwVMhjer1aSsor2KiDmsp6PZAUBJ2qzeeljdy8Tkd262+rVcFzKWR6vpZbv6ctMGPLq9dqudrNlrXcAAcDq6z0/eg927mM3YQlqxQAhFi8Jiaxg3Mhkbm5uTAhih1ec/dpkrOT8TMz+9RE17x8rp2gocq+vj4MDQ2FXP+tW7cCaHSYU6dOYWJiIlgJTL7SZ3BEpVVhuQOgtSGrFTStB9EuBKj+sRUACpAu8WbPsaOvKgl1qawroWUi7O+sk9aN78ZyBkRaOXRk15Ge16uCUivRWixqsdn6a3nVKqHSVwXbTViySkGXHNc0YCXDGFlQE1AbkD4+R3W9p4YUGbFgeJIdlJaG5R/ID/D6Wq0W1nDk71RgnPCkLgrnN/C+4+PjYUKVFfC5ubnAjVgznYKvhBhNY1ozfK5O5AJarhffo5YxTYhtVCaNZFMlY4+lkaM2H4DnW/7A8gpWEdCK00Q28gP8o/VklaCW2ZKlPEcJ1jQyttuwZJUCG5Bxf6b/qkBqZ1HTnMLHUUD3IaApyok2/f39iY7IEVQTo5hC7L3H+Ph4WPm5Wq1idnZ2QcSClgz/uK8ieYNarRYEHUiSZ6wzj+uEIc0h4DMUdKFsh/XehzUgLFnL+9iZhO2Enu837TflbvQ8tYpYBo76tJz0HtZa4GfLt2gb87NaEywPc1R0xqhaBPZ9aRYtlYN126L70AGQsNNZkKrV2dE0MqEJTzqa6EKg/KM7kM1mE7MsmanIe/A6zhwEELIPuX4DhVbLZ7MrOVopxwEgwWxzdGeny2azGBgYSHAkmlKcNjITlhOxrD2tA/XvlYBkmawfr9aKHuO1Cg3xqaAr56GfbfvymB2hKeT8Xc/nb/Z3KgMbPtV6WC5F3TBNYrLKqduwZJVCJpNJpBLrsutAMnxFFlk7g44GOoLryETQBalUKonRVDuAmtl0NRjtALAglZn3ZVnr9ToqlUqIjGgWJglTWjFMbmKeBoC2QqcmP4AFI5hVGkqsUQnRWtBRWhUCn6tCQ1Bg1EpIcx9UadrIkI7Etuy0iiwsh2KVXhpRyvvpPWg96DvVa+y71Xkb3aoYlqxSUOiuQUBywo76qGwo9avpO+s8B21k3e8QaJmO6sPSpeD9KUQULO3oluEGkokw7aZ36/qIugaBjqisE+/fLt02jUwjqBjttVZgLJOvyWHWxLdKwJbBCrAKml5H/18VpuUb+D/NTdEBwPI4SiKqdaGuA9+TVULko4CW0tDZs92GJasU2Gg33HADCoVCCOlZM46jrk5gqtcbKyiT+eeIyCXb7NZpeh9VLFQYzI4keK66KO0mHmnnV56E1o1df0BTobXjpykTfVd2RLSgtWJHYvvZXpsmPPaZ+tkKsXVFdAYpv/MeyntQ6NRFSbNKtIxq2SipqmXReqiitkpG3RrtY8pTcY2NbsOSVQof+tCHsH79+tAhObrZUQJYuECGjuRAq0PoxCi1CKww8b+y1tbK4HnaIW2Hs9COq24G65BWFwvlEi4FllO4HHKsHV+R9rt+TnMf7HX2czsrIK0cl6vY7Pe0EV7PudD99f2PjIwsuE83YMkqhWKxiGKx2OliRERcd7ioU+OcW+ec+7Vzbp9zbq9z7mvN40Xn3K+cc283/69oHnfOue865/Y75/Y453Ze60pEREQsHi6F6agB+Dvv/W0APgLgK8652wA8BOBp7/1WAE83vwPAp9FYhm0rGqs1f2/RSx0REXHNcFGl4L0/4b1/ufl5Go0doNYCuA/Aw83THgbwuebn+wA84hvYBaDgnFuz6CWPiIi4JrismIhzbiOADwJ4DsBq7/2J5k9jAFY3P68FcEQuO9o8FhERcR3gkpWCcy6HxvqLX/dmHwffoFsvK2fTOfegc+5F59yLTPu9WnCxUeLw4cM4cOBAavLKhcD8A37eu3fvopTvQjhz5sw1f0ZExKXgkqIPzrkb0FAIP/TeP9o8fNI5t8Z7f6LpHpxqHj8GQLd8uql5LAG/yJvBeO9x8uTJMBdh//79yGazOHz4MIaGhrBq1SoAjclDPT2NLdqz2Wwi9TibzWJycjKkLBN79+7F8PAw9uzZg7vvvhv1eh3j4+PI5/OYm5tDPp/H5OQkli9fjnK5jL6+vsTy8sysPH/+fJi/UK1WEwvGHjp0CPl8Hs45nDp1CrlcLmyt9uqrr+Lee+/t2mSXiKWFS1ni3QH4PoBR7/135KcnAHwRwL80/z8ux7/qnPsxgD8EMCluxjXD/Pw89u3bh3K5DO89Jicn8alPfQqnT59GoVAAAExMTOD3v/89CoVCEP5SqYTVq1eHCUbT09O46aabwrZtlUoFs7Oz+N3vfodjx47hox/9KKampvCb3/wGhUIBy5YtQ6VSCZbFO++8g3Xr1mFoaAgnTpzA3NwcNmzYgHPnzuHQoUNYuXIlRkZGwloPy5cvR6FQwKFDh1AsFpHP5/H0008jm83i4MGDYT/MT37yk9f6FUZEALg0S+EuAPcDeM05t7t57B/RUAY/dc59CcAhNDaaBYD/BfAZAPsBzAD4m0UtcRs451AsFnHjjTfC+8a+BS+++CJyuVxwH5hZyISifD6PsbExOOewZcsW7N+/Hzt27EChUMD+/ftx+PBhZDIZbN++Hf39/cjn88hmszhy5EhYdHV6ehpr165FPp/HxMQEenp6sG7dOhQKhWA5FAoFnDlzBlu2bMGqVavC7kpr1qzB8PAw3n33XezYsQP1emMLte3bt4d5FCtWrFiQ8RgRcS3hLidD7Vph586d/plnnrnq+9h8ejshB0jmpZMv2L59Ow4fPoyBgQGsXt3gS8+cOYPf/va3WL9+PT7wgQ8kMtF0RqCdaGQn9tgy2Sw+pi3rEm82XVaPRURcKYaGhl7y3t95sfOWlFKIiIhoj0tVCpG5ioiISCAqhYiIiASiUoiIiEggKoWIiIgEolKIiIhIICqFiIiIBKJSiIiISCAqhYiIiASiUoiIiEggKoWIiIgEolKIiIhIICqFiIiIBKJSiIiISCAqhYiIiASiUoiIiEggKoWIiIgEumKRFefcOIAKgMVZ1rlzGMb1XYfrvfxArMOFsMF7f9ENLLtCKQCAc+7FS1kVpptxvdfhei8/EOuwGIjuQ0RERAJRKURERCTQTUrhPzpdgEXA9V6H6738QKzDVaNrOIWIiIjuQDdZChEREV2AjisF59yfOefedM7td8491OnyXCqcc+86515zzu12zr3YPFZ0zv3KOfd28/+KTpdT4Zz7gXPulHPudTmWWmbXwHeb7bLHObezcyVvoU0dvumcO9Zsi93Ouc/Ib//QrMObzrkJEfeeAAACwUlEQVQ/7UypW3DOrXPO/do5t885t9c597Xm8e5pB+5i1Ik/AD0ADgDYDGAZgFcB3NbJMl1G2d8FMGyO/RuAh5qfHwLwr50upynfPQB2Anj9YmVGY+u/JwE4AB8B8Fyny3+BOnwTwN+nnHtbs09lAWxq9rWeDpd/DYCdzc9DAN5qlrNr2qHTlsKHAez33r/jvT8P4McA7utwma4G9wF4uPn5YQCf62BZFsB7/wyACXO4XZnvA/CIb2AXgEJzd/GOok0d2uE+AD/23p/z3h9EY3/TD1+zwl0CvPcnvPcvNz9PAxgFsBZd1A6dVgprARyR70ebx64HeAD/55x7yTn3YPPYat/aYXsMwOrOFO2y0K7M11vbfLVpXv9A3LauroNzbiOADwJ4Dl3UDp1WCtczPu693wng0wC+4py7R3/0DdvvugrtXI9lbuJ7AG4GsAPACQDf7mxxLg7nXA7AzwB83Xs/pb91uh06rRSOAVgn329qHut6eO+PNf+fAvA/aJilJ2naNf+f6lwJLxntynzdtI33/qT3vu69nwfwn2i5CF1ZB+fcDWgohB967x9tHu6adui0UngBwFbn3Cbn3DIAXwDwRIfLdFE45wadc0P8DOBPALyORtm/2DztiwAe70wJLwvtyvwEgL9ust8fATAp5m1XwfjYn0ejLYBGHb7gnMs65zYB2Arg+fe6fArnnAPwfQCj3vvvyE/d0w6dZGKFXX0LDWb4G50uzyWWeTMarParAPay3ABWAngawNsAngJQ7HRZTbl/hIZ5XUXDN/1SuzKjwXb/e7NdXgNwZ6fLf4E6/FezjHvQEKI1cv43mnV4E8Cnu6D8H0fDNdgDYHfz7zPd1A4xozEiIiKBTrsPERERXYaoFCIiIhKISiEiIiKBqBQiIiISiEohIiIigagUIiIiEohKISIiIoGoFCIiIhL4f8GqEVYSzvQVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### 3. Visualize the Activation Maps for Each Filter\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers.convolutional import Convolution2D\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "# plot image\n",
    "plt.imshow(small_img, cmap='gray')\n",
    "\n",
    "# define a neural network with a single convolutional layer with one filter\n",
    "model = Sequential()\n",
    "model.add(Convolution2D(1, (4, 4), activation='relu', input_shape=(small_img.shape[0], small_img.shape[1], 1)))\n",
    "\n",
    "# apply convolutional filter and return output\n",
    "def apply_filter(img, index, filter_list, ax):\n",
    "    # set the weights of the filter in the convolutional layer to filter_list[i]\n",
    "    model.layers[0].set_weights([np.reshape(filter_list[i], (4,4,1,1)), np.array([0])])\n",
    "    # plot the corresponding activation map\n",
    "    ax.imshow(np.squeeze(model.predict(np.reshape(img, (1, img.shape[0], img.shape[1], 1)))), cmap='gray')\n",
    "\n",
    "# visualize all filters\n",
    "fig = plt.figure(figsize=(12, 6))\n",
    "fig.subplots_adjust(left=0, right=1.5, bottom=0.8, top=1, hspace=0.05, wspace=0.05)\n",
    "for i in range(4):\n",
    "    ax = fig.add_subplot(1, 4, i+1, xticks=[], yticks=[])\n",
    "    ax.imshow(filters[i], cmap='gray')\n",
    "    ax.set_title('Filter %s' % str(i+1))\n",
    "\n",
    "# visualize all activation maps\n",
    "fig = plt.figure(figsize=(20, 20))\n",
    "for i in range(4):\n",
    "    ax = fig.add_subplot(1, 4, i+1, xticks=[], yticks=[])\n",
    "    apply_filter(small_img, i, filters, ax)\n",
    "    ax.set_title('Activation Map for Filter %s' % str(i+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "## MLP_02\n",
    "---\n",
    "train an MLP to classify images from the CIFAR-10 database. \n",
    "\n",
    "It has 10 classes.\n",
    "- airplane\n",
    "- automobile\n",
    "- bird\n",
    "- cat\n",
    "- deer\n",
    "- dog\n",
    "- frog\n",
    "- horse\n",
    "- ship\n",
    "- truck"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.datasets import cifar10\n",
    "\n",
    "# load the pre-shuffled train and test data\n",
    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 36 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the First 24 Training Images\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "fig = plt.figure(figsize=(20,5))\n",
    "for i in range(36):\n",
    "    ax = fig.add_subplot(3, 12, i + 1, xticks=[], yticks=[])\n",
    "    ax.imshow(np.squeeze(x_train[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Rescale the Images by Dividing Every Pixel in Every Image by 255\n",
    "\n",
    "# rescale [0,255] --> [0,1]\n",
    "x_train = x_train.astype('float32')/255\n",
    "x_test = x_test.astype('float32')/255 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (45000, 32, 32, 3)\n",
      "45000 train samples\n",
      "10000 test samples\n",
      "5000 validation samples\n"
     ]
    }
   ],
   "source": [
    "# Break Dataset into Training, Testing, and Validation Sets\n",
    "\n",
    "from keras.utils import np_utils\n",
    "\n",
    "# one-hot encode the labels\n",
    "num_classes = len(np.unique(y_train))\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
    "\n",
    "# break training set into training and validation sets\n",
    "(x_train, x_valid) = x_train[5000:], x_train[:5000]\n",
    "(y_train, y_valid) = y_train[5000:], y_train[:5000]\n",
    "\n",
    "# print shape of training set\n",
    "print('x_train shape:', x_train.shape)\n",
    "\n",
    "# print number of training, validation, and test images\n",
    "print(x_train.shape[0], 'train samples')\n",
    "print(x_test.shape[0], 'test samples')\n",
    "print(x_valid.shape[0], 'validation samples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#################################################################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#################################################################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten_1 (Flatten)          (None, 3072)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1000)              3073000   \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 1000)              0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 512)               512512    \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 10)                5130      \n",
      "=================================================================\n",
      "Total params: 3,590,642\n",
      "Trainable params: 3,590,642\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Define the Model Architecture\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "\n",
    "# define the model\n",
    "model = Sequential()\n",
    "model.add(Flatten(input_shape = x_train.shape[1:]))\n",
    "model.add(Dense(1000, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Compile the Model\n",
    "\n",
    "model.compile(loss='categorical_crossentropy', optimizer='rmsprop', \n",
    "                  metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 45000 samples, validate on 5000 samples\n",
      "Epoch 1/20\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 1.83431, saving model to MLP.weights.best.hdf5\n",
      " - 66s - loss: 2.4322 - acc: 0.2648 - val_loss: 1.8343 - val_acc: 0.3294\n",
      "Epoch 2/20\n",
      "\n",
      "Epoch 00002: val_loss improved from 1.83431 to 1.76676, saving model to MLP.weights.best.hdf5\n",
      " - 66s - loss: 1.8401 - acc: 0.3388 - val_loss: 1.7668 - val_acc: 0.3514\n",
      "Epoch 3/20\n",
      "\n",
      "Epoch 00003: val_loss did not improve\n",
      " - 66s - loss: 1.7999 - acc: 0.3507 - val_loss: 1.8933 - val_acc: 0.3374\n",
      "Epoch 4/20\n",
      "\n",
      "Epoch 00004: val_loss improved from 1.76676 to 1.70775, saving model to MLP.weights.best.hdf5\n",
      " - 66s - loss: 1.7775 - acc: 0.3617 - val_loss: 1.7078 - val_acc: 0.3898\n",
      "Epoch 5/20\n",
      "\n",
      "Epoch 00005: val_loss improved from 1.70775 to 1.70772, saving model to MLP.weights.best.hdf5\n",
      " - 66s - loss: 1.7670 - acc: 0.3698 - val_loss: 1.7077 - val_acc: 0.3912\n",
      "Epoch 6/20\n",
      "\n",
      "Epoch 00006: val_loss improved from 1.70772 to 1.69477, saving model to MLP.weights.best.hdf5\n",
      " - 65s - loss: 1.7653 - acc: 0.3684 - val_loss: 1.6948 - val_acc: 0.4022\n",
      "Epoch 7/20\n",
      "\n",
      "Epoch 00007: val_loss did not improve\n",
      " - 66s - loss: 1.7661 - acc: 0.3721 - val_loss: 1.7117 - val_acc: 0.3646\n",
      "Epoch 8/20\n",
      "\n",
      "Epoch 00008: val_loss did not improve\n",
      " - 66s - loss: 1.7610 - acc: 0.3741 - val_loss: 1.7361 - val_acc: 0.3948\n",
      "Epoch 9/20\n",
      "\n",
      "Epoch 00009: val_loss did not improve\n",
      " - 66s - loss: 1.7538 - acc: 0.3765 - val_loss: 1.7034 - val_acc: 0.3980\n",
      "Epoch 10/20\n",
      "\n",
      "Epoch 00010: val_loss did not improve\n",
      " - 66s - loss: 1.7632 - acc: 0.3755 - val_loss: 1.7481 - val_acc: 0.3590\n",
      "Epoch 11/20\n",
      "\n",
      "Epoch 00011: val_loss did not improve\n",
      " - 66s - loss: 1.7622 - acc: 0.3772 - val_loss: 1.7417 - val_acc: 0.3866\n",
      "Epoch 12/20\n",
      "\n",
      "Epoch 00012: val_loss did not improve\n",
      " - 66s - loss: 1.7691 - acc: 0.3782 - val_loss: 1.7052 - val_acc: 0.4090\n",
      "Epoch 13/20\n",
      "\n",
      "Epoch 00013: val_loss did not improve\n",
      " - 66s - loss: 1.7627 - acc: 0.3779 - val_loss: 1.7804 - val_acc: 0.3922\n",
      "Epoch 14/20\n",
      "\n",
      "Epoch 00014: val_loss did not improve\n",
      " - 66s - loss: 1.7682 - acc: 0.3758 - val_loss: 1.8257 - val_acc: 0.3526\n",
      "Epoch 15/20\n",
      "\n",
      "Epoch 00015: val_loss did not improve\n",
      " - 66s - loss: 1.7776 - acc: 0.3777 - val_loss: 1.8694 - val_acc: 0.3396\n",
      "Epoch 16/20\n",
      "\n",
      "Epoch 00016: val_loss improved from 1.69477 to 1.68285, saving model to MLP.weights.best.hdf5\n",
      " - 66s - loss: 1.7716 - acc: 0.3802 - val_loss: 1.6829 - val_acc: 0.3850\n",
      "Epoch 17/20\n",
      "\n",
      "Epoch 00017: val_loss did not improve\n",
      " - 66s - loss: 1.7713 - acc: 0.3784 - val_loss: 1.9004 - val_acc: 0.3434\n",
      "Epoch 18/20\n",
      "\n",
      "Epoch 00018: val_loss did not improve\n",
      " - 66s - loss: 1.7742 - acc: 0.3778 - val_loss: 1.7927 - val_acc: 0.3496\n",
      "Epoch 19/20\n",
      "\n",
      "Epoch 00019: val_loss did not improve\n",
      " - 66s - loss: 1.7771 - acc: 0.3755 - val_loss: 1.8010 - val_acc: 0.3546\n",
      "Epoch 20/20\n",
      "\n",
      "Epoch 00020: val_loss did not improve\n",
      " - 66s - loss: 1.7750 - acc: 0.3784 - val_loss: 1.7382 - val_acc: 0.3706\n"
     ]
    }
   ],
   "source": [
    "# Train the Model\n",
    "\n",
    "from keras.callbacks import ModelCheckpoint   \n",
    "\n",
    "# train the model\n",
    "checkpointer = ModelCheckpoint(filepath='MLP.weights.best.hdf5', verbose=1, \n",
    "                               save_best_only=True)\n",
    "hist = model.fit(x_train, y_train, batch_size=32, epochs=20,\n",
    "          validation_data=(x_valid, y_valid), callbacks=[checkpointer], \n",
    "          verbose=2, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Test accuracy: 0.3993\n"
     ]
    }
   ],
   "source": [
    "# Load the Model with the Best Classification Accuracy on the Validation Set\n",
    "\n",
    "model.load_weights('MLP.weights.best.hdf5')\n",
    "\n",
    "# evaluate and print test accuracy\n",
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "print('\\n', 'Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## HAHA IT'S ONLY 40% ACCURATE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#################################################################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#################################################################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "## CNN_02\n",
    "---\n",
    "train a CNN to classify images from the CIFAR-10 database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_4 (Conv2D)            (None, 32, 32, 16)        208       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 16, 16, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 16, 16, 32)        2080      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 8, 8, 32)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 8, 8, 64)          8256      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 4, 4, 64)          0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 4, 4, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 500)               512500    \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 500)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 10)                5010      \n",
      "=================================================================\n",
      "Total params: 528,054\n",
      "Trainable params: 528,054\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Define the Model Architecture\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(filters=16, kernel_size=2, padding='same', activation='relu', \n",
    "                        input_shape=(32, 32, 3)))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Conv2D(filters=32, kernel_size=2, padding='same', activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Conv2D(filters=64, kernel_size=2, padding='same', activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(500, activation='relu'))\n",
    "model.add(Dropout(0.4))\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# compile the model\n",
    "model.compile(loss='categorical_crossentropy', optimizer='rmsprop', \n",
    "                  metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 45000 samples, validate on 5000 samples\n",
      "Epoch 1/100\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 1.48946, saving model to model.weights.best.hdf5\n",
      " - 39s - loss: 1.6011 - acc: 0.4207 - val_loss: 1.4895 - val_acc: 0.4810\n",
      "Epoch 2/100\n",
      "\n",
      "Epoch 00002: val_loss improved from 1.48946 to 1.19265, saving model to model.weights.best.hdf5\n",
      " - 39s - loss: 1.2948 - acc: 0.5346 - val_loss: 1.1927 - val_acc: 0.5738\n",
      "Epoch 3/100\n",
      "\n",
      "Epoch 00003: val_loss improved from 1.19265 to 1.08361, saving model to model.weights.best.hdf5\n",
      " - 39s - loss: 1.1759 - acc: 0.5833 - val_loss: 1.0836 - val_acc: 0.6152\n",
      "Epoch 4/100\n",
      "\n",
      "Epoch 00004: val_loss improved from 1.08361 to 1.05841, saving model to model.weights.best.hdf5\n",
      " - 39s - loss: 1.1041 - acc: 0.6141 - val_loss: 1.0584 - val_acc: 0.6406\n",
      "Epoch 5/100\n",
      "\n",
      "Epoch 00005: val_loss did not improve\n",
      " - 39s - loss: 1.0692 - acc: 0.6285 - val_loss: 1.1687 - val_acc: 0.6106\n",
      "Epoch 6/100\n",
      "\n",
      "Epoch 00006: val_loss improved from 1.05841 to 1.05527, saving model to model.weights.best.hdf5\n",
      " - 39s - loss: 1.0439 - acc: 0.6405 - val_loss: 1.0553 - val_acc: 0.6338\n",
      "Epoch 7/100\n",
      "\n",
      "Epoch 00007: val_loss improved from 1.05527 to 0.97342, saving model to model.weights.best.hdf5\n",
      " - 39s - loss: 1.0321 - acc: 0.6498 - val_loss: 0.9734 - val_acc: 0.6706\n",
      "Epoch 8/100\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.97342 to 0.96238, saving model to model.weights.best.hdf5\n",
      " - 39s - loss: 1.0229 - acc: 0.6494 - val_loss: 0.9624 - val_acc: 0.6798\n",
      "Epoch 9/100\n",
      "\n",
      "Epoch 00009: val_loss did not improve\n",
      " - 39s - loss: 1.0299 - acc: 0.6532 - val_loss: 1.4200 - val_acc: 0.5932\n",
      "Epoch 10/100\n",
      "\n",
      "Epoch 00010: val_loss did not improve\n",
      " - 39s - loss: 1.0292 - acc: 0.6554 - val_loss: 0.9794 - val_acc: 0.6876\n",
      "Epoch 11/100\n",
      "\n",
      "Epoch 00011: val_loss did not improve\n",
      " - 39s - loss: 1.0380 - acc: 0.6539 - val_loss: 1.0049 - val_acc: 0.6582\n",
      "Epoch 12/100\n",
      "\n",
      "Epoch 00012: val_loss did not improve\n",
      " - 39s - loss: 1.0376 - acc: 0.6520 - val_loss: 1.0682 - val_acc: 0.6622\n",
      "Epoch 13/100\n",
      "\n",
      "Epoch 00013: val_loss did not improve\n",
      " - 39s - loss: 1.0615 - acc: 0.6499 - val_loss: 0.9819 - val_acc: 0.6852\n",
      "Epoch 14/100\n",
      "\n",
      "Epoch 00014: val_loss did not improve\n",
      " - 39s - loss: 1.0689 - acc: 0.6464 - val_loss: 1.1328 - val_acc: 0.6232\n",
      "Epoch 15/100\n",
      "\n",
      "Epoch 00015: val_loss did not improve\n",
      " - 39s - loss: 1.0794 - acc: 0.6420 - val_loss: 1.0942 - val_acc: 0.6316\n",
      "Epoch 16/100\n",
      "\n",
      "Epoch 00016: val_loss did not improve\n",
      " - 39s - loss: 1.1010 - acc: 0.6389 - val_loss: 1.1690 - val_acc: 0.6394\n",
      "Epoch 17/100\n",
      "\n",
      "Epoch 00017: val_loss did not improve\n",
      " - 39s - loss: 1.1164 - acc: 0.6340 - val_loss: 0.9826 - val_acc: 0.6680\n",
      "Epoch 18/100\n",
      "\n",
      "Epoch 00018: val_loss did not improve\n",
      " - 39s - loss: 1.1379 - acc: 0.6272 - val_loss: 1.2999 - val_acc: 0.6268\n",
      "Epoch 19/100\n",
      "\n",
      "Epoch 00019: val_loss did not improve\n",
      " - 39s - loss: 1.1615 - acc: 0.6211 - val_loss: 1.2822 - val_acc: 0.5668\n",
      "Epoch 20/100\n",
      "\n",
      "Epoch 00020: val_loss did not improve\n",
      " - 39s - loss: 1.1870 - acc: 0.6140 - val_loss: 0.9771 - val_acc: 0.6852\n",
      "Epoch 21/100\n",
      "\n",
      "Epoch 00021: val_loss did not improve\n",
      " - 39s - loss: 1.2050 - acc: 0.6061 - val_loss: 1.4482 - val_acc: 0.5902\n",
      "Epoch 22/100\n",
      "\n",
      "Epoch 00022: val_loss did not improve\n",
      " - 39s - loss: 1.2257 - acc: 0.5991 - val_loss: 1.1364 - val_acc: 0.6062\n",
      "Epoch 23/100\n",
      "\n",
      "Epoch 00023: val_loss did not improve\n",
      " - 39s - loss: 1.2496 - acc: 0.5891 - val_loss: 1.4621 - val_acc: 0.5540\n",
      "Epoch 24/100\n",
      "\n",
      "Epoch 00024: val_loss did not improve\n",
      " - 39s - loss: 1.2775 - acc: 0.5829 - val_loss: 1.0779 - val_acc: 0.6396\n",
      "Epoch 25/100\n",
      "\n",
      "Epoch 00025: val_loss did not improve\n",
      " - 39s - loss: 1.2906 - acc: 0.5784 - val_loss: 1.1378 - val_acc: 0.6194\n",
      "Epoch 26/100\n",
      "\n",
      "Epoch 00026: val_loss did not improve\n",
      " - 39s - loss: 1.3068 - acc: 0.5717 - val_loss: 1.3889 - val_acc: 0.5926\n",
      "Epoch 27/100\n",
      "\n",
      "Epoch 00027: val_loss did not improve\n",
      " - 39s - loss: 1.3134 - acc: 0.5684 - val_loss: 1.2523 - val_acc: 0.5754\n",
      "Epoch 28/100\n",
      "\n",
      "Epoch 00028: val_loss did not improve\n",
      " - 40s - loss: 1.3440 - acc: 0.5609 - val_loss: 1.2279 - val_acc: 0.5544\n",
      "Epoch 29/100\n",
      "\n",
      "Epoch 00029: val_loss did not improve\n",
      " - 39s - loss: 1.3509 - acc: 0.5599 - val_loss: 3.2866 - val_acc: 0.5230\n",
      "Epoch 30/100\n",
      "\n",
      "Epoch 00030: val_loss did not improve\n",
      " - 39s - loss: 1.3790 - acc: 0.5470 - val_loss: 1.3194 - val_acc: 0.5800\n",
      "Epoch 31/100\n",
      "\n",
      "Epoch 00031: val_loss did not improve\n",
      " - 39s - loss: 1.3852 - acc: 0.5427 - val_loss: 1.2625 - val_acc: 0.5604\n",
      "Epoch 32/100\n",
      "\n",
      "Epoch 00032: val_loss did not improve\n",
      " - 39s - loss: 1.3933 - acc: 0.5407 - val_loss: 2.1160 - val_acc: 0.4844\n",
      "Epoch 33/100\n",
      "\n",
      "Epoch 00033: val_loss did not improve\n",
      " - 39s - loss: 1.4323 - acc: 0.5317 - val_loss: 1.1874 - val_acc: 0.6062\n",
      "Epoch 34/100\n",
      "\n",
      "Epoch 00034: val_loss did not improve\n",
      " - 39s - loss: 1.4476 - acc: 0.5242 - val_loss: 1.5032 - val_acc: 0.5864\n",
      "Epoch 35/100\n",
      "\n",
      "Epoch 00035: val_loss did not improve\n",
      " - 40s - loss: 1.4614 - acc: 0.5133 - val_loss: 1.5007 - val_acc: 0.5466\n",
      "Epoch 36/100\n",
      "\n",
      "Epoch 00036: val_loss did not improve\n",
      " - 39s - loss: 1.4958 - acc: 0.5058 - val_loss: 2.2443 - val_acc: 0.4402\n",
      "Epoch 37/100\n",
      "\n",
      "Epoch 00037: val_loss did not improve\n",
      " - 39s - loss: 1.5122 - acc: 0.4986 - val_loss: 1.1612 - val_acc: 0.6046\n",
      "Epoch 38/100\n",
      "\n",
      "Epoch 00038: val_loss did not improve\n",
      " - 39s - loss: 1.4963 - acc: 0.5012 - val_loss: 1.2818 - val_acc: 0.5660\n",
      "Epoch 39/100\n",
      "\n",
      "Epoch 00039: val_loss did not improve\n",
      " - 39s - loss: 1.5321 - acc: 0.4947 - val_loss: 1.3027 - val_acc: 0.5922\n",
      "Epoch 40/100\n",
      "\n",
      "Epoch 00040: val_loss did not improve\n",
      " - 39s - loss: 1.5460 - acc: 0.4867 - val_loss: 1.9664 - val_acc: 0.3844\n",
      "Epoch 41/100\n",
      "\n",
      "Epoch 00041: val_loss did not improve\n",
      " - 39s - loss: 1.5604 - acc: 0.4824 - val_loss: 1.2313 - val_acc: 0.5862\n",
      "Epoch 42/100\n",
      "\n",
      "Epoch 00042: val_loss did not improve\n",
      " - 40s - loss: 1.5621 - acc: 0.4752 - val_loss: 2.0235 - val_acc: 0.4340\n",
      "Epoch 43/100\n",
      "\n",
      "Epoch 00043: val_loss did not improve\n",
      " - 40s - loss: 1.5658 - acc: 0.4767 - val_loss: 1.3849 - val_acc: 0.5356\n",
      "Epoch 44/100\n",
      "\n",
      "Epoch 00044: val_loss did not improve\n",
      " - 40s - loss: 1.5967 - acc: 0.4669 - val_loss: 1.3367 - val_acc: 0.5524\n",
      "Epoch 45/100\n",
      "\n",
      "Epoch 00045: val_loss did not improve\n",
      " - 40s - loss: 1.5845 - acc: 0.4703 - val_loss: 2.1942 - val_acc: 0.4166\n",
      "Epoch 46/100\n",
      "\n",
      "Epoch 00046: val_loss did not improve\n",
      " - 40s - loss: 1.6002 - acc: 0.4646 - val_loss: 1.4180 - val_acc: 0.5140\n",
      "Epoch 47/100\n",
      "\n",
      "Epoch 00047: val_loss did not improve\n",
      " - 40s - loss: 1.6087 - acc: 0.4594 - val_loss: 1.5365 - val_acc: 0.4912\n",
      "Epoch 48/100\n",
      "\n",
      "Epoch 00048: val_loss did not improve\n",
      " - 40s - loss: 1.6191 - acc: 0.4538 - val_loss: 1.4533 - val_acc: 0.4770\n",
      "Epoch 49/100\n",
      "\n",
      "Epoch 00049: val_loss did not improve\n",
      " - 40s - loss: 1.6333 - acc: 0.4501 - val_loss: 1.5451 - val_acc: 0.4680\n",
      "Epoch 50/100\n",
      "\n",
      "Epoch 00050: val_loss did not improve\n",
      " - 39s - loss: 1.6280 - acc: 0.4466 - val_loss: 1.5158 - val_acc: 0.4476\n",
      "Epoch 51/100\n",
      "\n",
      "Epoch 00051: val_loss did not improve\n",
      " - 39s - loss: 1.6476 - acc: 0.4422 - val_loss: 1.5911 - val_acc: 0.4438\n",
      "Epoch 52/100\n",
      "\n",
      "Epoch 00052: val_loss did not improve\n",
      " - 39s - loss: 1.6543 - acc: 0.4423 - val_loss: 1.9135 - val_acc: 0.4380\n",
      "Epoch 53/100\n",
      "\n",
      "Epoch 00053: val_loss did not improve\n",
      " - 39s - loss: 1.6615 - acc: 0.4385 - val_loss: 1.4856 - val_acc: 0.4768\n",
      "Epoch 54/100\n",
      "\n",
      "Epoch 00054: val_loss did not improve\n",
      " - 39s - loss: 1.6470 - acc: 0.4434 - val_loss: 3.4793 - val_acc: 0.2082\n",
      "Epoch 55/100\n",
      "\n",
      "Epoch 00055: val_loss did not improve\n",
      " - 39s - loss: 1.6547 - acc: 0.4404 - val_loss: 1.4363 - val_acc: 0.4878\n",
      "Epoch 56/100\n",
      "\n",
      "Epoch 00056: val_loss did not improve\n",
      " - 39s - loss: 1.6726 - acc: 0.4324 - val_loss: 2.0621 - val_acc: 0.3942\n",
      "Epoch 57/100\n",
      "\n",
      "Epoch 00057: val_loss did not improve\n",
      " - 39s - loss: 1.6900 - acc: 0.4283 - val_loss: 1.5148 - val_acc: 0.4700\n",
      "Epoch 58/100\n",
      "\n",
      "Epoch 00058: val_loss did not improve\n",
      " - 39s - loss: 1.6821 - acc: 0.4266 - val_loss: 1.6951 - val_acc: 0.3676\n",
      "Epoch 59/100\n",
      "\n",
      "Epoch 00059: val_loss did not improve\n",
      " - 39s - loss: 1.6904 - acc: 0.4258 - val_loss: 1.4426 - val_acc: 0.5130\n",
      "Epoch 60/100\n",
      "\n",
      "Epoch 00060: val_loss did not improve\n",
      " - 39s - loss: 1.6986 - acc: 0.4186 - val_loss: 2.2813 - val_acc: 0.4494\n",
      "Epoch 61/100\n",
      "\n",
      "Epoch 00061: val_loss did not improve\n",
      " - 40s - loss: 1.7183 - acc: 0.4138 - val_loss: 1.6254 - val_acc: 0.4378\n",
      "Epoch 62/100\n",
      "\n",
      "Epoch 00062: val_loss did not improve\n",
      " - 39s - loss: 1.7207 - acc: 0.4142 - val_loss: 1.6555 - val_acc: 0.4404\n",
      "Epoch 63/100\n",
      "\n",
      "Epoch 00063: val_loss did not improve\n",
      " - 39s - loss: 1.7174 - acc: 0.4144 - val_loss: 1.4151 - val_acc: 0.5316\n",
      "Epoch 64/100\n",
      "\n",
      "Epoch 00064: val_loss did not improve\n",
      " - 39s - loss: 1.7349 - acc: 0.4092 - val_loss: 1.7997 - val_acc: 0.4244\n",
      "Epoch 65/100\n",
      "\n",
      "Epoch 00065: val_loss did not improve\n",
      " - 39s - loss: 1.7322 - acc: 0.4058 - val_loss: 1.9543 - val_acc: 0.4054\n",
      "Epoch 66/100\n",
      "\n",
      "Epoch 00066: val_loss did not improve\n",
      " - 39s - loss: 1.7435 - acc: 0.4024 - val_loss: 1.7596 - val_acc: 0.4096\n",
      "Epoch 67/100\n",
      "\n",
      "Epoch 00067: val_loss did not improve\n",
      " - 39s - loss: 1.7494 - acc: 0.4025 - val_loss: 1.4058 - val_acc: 0.4946\n",
      "Epoch 68/100\n",
      "\n",
      "Epoch 00068: val_loss did not improve\n",
      " - 39s - loss: 1.7877 - acc: 0.3886 - val_loss: 1.6594 - val_acc: 0.3984\n",
      "Epoch 69/100\n",
      "\n",
      "Epoch 00069: val_loss did not improve\n",
      " - 39s - loss: 1.7842 - acc: 0.3934 - val_loss: 1.5021 - val_acc: 0.4812\n",
      "Epoch 70/100\n",
      "\n",
      "Epoch 00070: val_loss did not improve\n",
      " - 39s - loss: 1.7818 - acc: 0.3928 - val_loss: 1.6924 - val_acc: 0.3736\n",
      "Epoch 71/100\n",
      "\n",
      "Epoch 00071: val_loss did not improve\n",
      " - 39s - loss: 1.7822 - acc: 0.3902 - val_loss: 1.6851 - val_acc: 0.5090\n",
      "Epoch 72/100\n",
      "\n",
      "Epoch 00072: val_loss did not improve\n",
      " - 39s - loss: 1.8017 - acc: 0.3853 - val_loss: 1.5865 - val_acc: 0.4330\n",
      "Epoch 73/100\n",
      "\n",
      "Epoch 00073: val_loss did not improve\n",
      " - 39s - loss: 1.7660 - acc: 0.3937 - val_loss: 1.5371 - val_acc: 0.4688\n",
      "Epoch 74/100\n",
      "\n",
      "Epoch 00074: val_loss did not improve\n",
      " - 39s - loss: 1.7846 - acc: 0.3851 - val_loss: 1.4659 - val_acc: 0.4964\n",
      "Epoch 75/100\n",
      "\n",
      "Epoch 00075: val_loss did not improve\n",
      " - 39s - loss: 1.7887 - acc: 0.3827 - val_loss: 1.4703 - val_acc: 0.4856\n",
      "Epoch 76/100\n",
      "\n",
      "Epoch 00076: val_loss did not improve\n",
      " - 39s - loss: 1.7813 - acc: 0.3844 - val_loss: 1.5454 - val_acc: 0.4616\n",
      "Epoch 77/100\n",
      "\n",
      "Epoch 00077: val_loss did not improve\n",
      " - 39s - loss: 1.7802 - acc: 0.3876 - val_loss: 1.5218 - val_acc: 0.4642\n",
      "Epoch 78/100\n",
      "\n",
      "Epoch 00078: val_loss did not improve\n",
      " - 39s - loss: 1.7663 - acc: 0.3892 - val_loss: 1.8756 - val_acc: 0.4144\n",
      "Epoch 79/100\n",
      "\n",
      "Epoch 00079: val_loss did not improve\n",
      " - 39s - loss: 1.7922 - acc: 0.3854 - val_loss: 2.1090 - val_acc: 0.3432\n",
      "Epoch 80/100\n",
      "\n",
      "Epoch 00080: val_loss did not improve\n",
      " - 39s - loss: 1.7995 - acc: 0.3819 - val_loss: 2.7735 - val_acc: 0.3404\n",
      "Epoch 81/100\n",
      "\n",
      "Epoch 00081: val_loss did not improve\n",
      " - 39s - loss: 1.7723 - acc: 0.3854 - val_loss: 1.4647 - val_acc: 0.4720\n",
      "Epoch 82/100\n",
      "\n",
      "Epoch 00082: val_loss did not improve\n",
      " - 39s - loss: 1.7674 - acc: 0.3886 - val_loss: 1.8146 - val_acc: 0.4028\n",
      "Epoch 83/100\n",
      "\n",
      "Epoch 00083: val_loss did not improve\n",
      " - 39s - loss: 1.7688 - acc: 0.3869 - val_loss: 1.6815 - val_acc: 0.3954\n",
      "Epoch 84/100\n",
      "\n",
      "Epoch 00084: val_loss did not improve\n",
      " - 39s - loss: 1.7658 - acc: 0.3893 - val_loss: 1.8253 - val_acc: 0.3132\n",
      "Epoch 85/100\n",
      "\n",
      "Epoch 00085: val_loss did not improve\n",
      " - 39s - loss: 1.7814 - acc: 0.3853 - val_loss: 1.5000 - val_acc: 0.4830\n",
      "Epoch 86/100\n",
      "\n",
      "Epoch 00086: val_loss did not improve\n",
      " - 40s - loss: 1.7706 - acc: 0.3845 - val_loss: 1.7314 - val_acc: 0.3712\n",
      "Epoch 87/100\n",
      "\n",
      "Epoch 00087: val_loss did not improve\n",
      " - 40s - loss: 1.7650 - acc: 0.3848 - val_loss: 1.4873 - val_acc: 0.4448\n",
      "Epoch 88/100\n",
      "\n",
      "Epoch 00088: val_loss did not improve\n",
      " - 40s - loss: 1.7881 - acc: 0.3839 - val_loss: 1.5330 - val_acc: 0.4330\n",
      "Epoch 89/100\n",
      "\n",
      "Epoch 00089: val_loss did not improve\n",
      " - 39s - loss: 1.8096 - acc: 0.3764 - val_loss: 1.9514 - val_acc: 0.3246\n",
      "Epoch 90/100\n",
      "\n",
      "Epoch 00090: val_loss did not improve\n",
      " - 39s - loss: 1.8041 - acc: 0.3716 - val_loss: 2.2819 - val_acc: 0.4162\n",
      "Epoch 91/100\n",
      "\n",
      "Epoch 00091: val_loss did not improve\n",
      " - 39s - loss: 1.8045 - acc: 0.3723 - val_loss: 2.2530 - val_acc: 0.4240\n",
      "Epoch 92/100\n",
      "\n",
      "Epoch 00092: val_loss did not improve\n",
      " - 39s - loss: 1.7967 - acc: 0.3747 - val_loss: 1.9414 - val_acc: 0.3392\n",
      "Epoch 93/100\n",
      "\n",
      "Epoch 00093: val_loss did not improve\n",
      " - 39s - loss: 1.8138 - acc: 0.3672 - val_loss: 1.7395 - val_acc: 0.4168\n",
      "Epoch 94/100\n",
      "\n",
      "Epoch 00094: val_loss did not improve\n",
      " - 39s - loss: 1.8259 - acc: 0.3669 - val_loss: 1.8528 - val_acc: 0.3532\n",
      "Epoch 95/100\n",
      "\n",
      "Epoch 00095: val_loss did not improve\n",
      " - 39s - loss: 1.8097 - acc: 0.3688 - val_loss: 2.3644 - val_acc: 0.2644\n",
      "Epoch 96/100\n",
      "\n",
      "Epoch 00096: val_loss did not improve\n",
      " - 39s - loss: 1.8203 - acc: 0.3642 - val_loss: 2.5597 - val_acc: 0.3460\n",
      "Epoch 97/100\n",
      "\n",
      "Epoch 00097: val_loss did not improve\n",
      " - 39s - loss: 1.8017 - acc: 0.3653 - val_loss: 1.5717 - val_acc: 0.4348\n",
      "Epoch 98/100\n",
      "\n",
      "Epoch 00098: val_loss did not improve\n",
      " - 39s - loss: 1.8310 - acc: 0.3635 - val_loss: 1.4476 - val_acc: 0.4802\n",
      "Epoch 99/100\n",
      "\n",
      "Epoch 00099: val_loss did not improve\n",
      " - 40s - loss: 1.8167 - acc: 0.3632 - val_loss: 1.7195 - val_acc: 0.3718\n",
      "Epoch 100/100\n",
      "\n",
      "Epoch 00100: val_loss did not improve\n",
      " - 39s - loss: 1.8344 - acc: 0.3599 - val_loss: 1.9443 - val_acc: 0.3570\n"
     ]
    }
   ],
   "source": [
    "from keras.callbacks import ModelCheckpoint   \n",
    "\n",
    "# train the model\n",
    "checkpointer = ModelCheckpoint(filepath='model.weights.best.hdf5', verbose=1, \n",
    "                               save_best_only=True)\n",
    "hist = model.fit(x_train, y_train, batch_size=32, epochs=100,\n",
    "          validation_data=(x_valid, y_valid), callbacks=[checkpointer], \n",
    "          verbose=2, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Test accuracy: 0.6735\n"
     ]
    }
   ],
   "source": [
    "# load the weights that yielded the best validation accuracy\n",
    "model.load_weights('model.weights.best.hdf5')\n",
    "# evaluate and print test accuracy\n",
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "print('\\n', 'Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Visualize Some Predictions\n",
    "\n",
    "This may give you some insight into why the network is misclassifying certain objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# get predictions on the test set\n",
    "y_hat = model.predict(x_test)\n",
    "\n",
    "# define text labels (source: https://www.cs.toronto.edu/~kriz/cifar.html)\n",
    "cifar10_labels = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 32 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a random sample of test images, their predicted labels, and ground truth\n",
    "fig = plt.figure(figsize=(20, 8))\n",
    "for i, idx in enumerate(np.random.choice(x_test.shape[0], size=32, replace=False)):\n",
    "    ax = fig.add_subplot(4, 8, i + 1, xticks=[], yticks=[])\n",
    "    ax.imshow(np.squeeze(x_test[idx]))\n",
    "    pred_idx = np.argmax(y_hat[idx])\n",
    "    true_idx = np.argmax(y_test[idx])\n",
    "    ax.set_title(\"{} ({})\".format(cifar10_labels[pred_idx], cifar10_labels[true_idx]),\n",
    "                 color=(\"green\" if pred_idx == true_idx else \"red\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "## CNN_image_augmentation\n",
    "\n",
    "### Create and Configure Augmented Image Generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "# create and configure augmented image generator\n",
    "datagen_train = ImageDataGenerator(\n",
    "    width_shift_range=0.1,  # randomly shift images horizontally (10% of total width)\n",
    "    height_shift_range=0.1,  # randomly shift images vertically (10% of total height)\n",
    "    horizontal_flip=True) # randomly flip images horizontally\n",
    "\n",
    "# fit augmented image generator on data\n",
    "datagen_train.fit(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABIEAAACLCAYAAAD24om0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzsvXecZdlVHvqtm++t3NU592TNjCZLo1GW0IBAiSBjeEYWItkGbJn4DAYh8HvYz4YHmGRjG2QwMkECSSgjoZEGpdHMSJrpyT0dpnNXd+W6+d7tP9baZ61761RXV5ipQb2+369/dXuftM8Oa++z9re+TSEEOBwOh8PhcDgcDofD4XA4vrGR2egMOBwOh8PhcDgcDofD4XA4nn24E8jhcDgcDofD4XA4HA6H4zKAO4EcDofD4XA4HA6Hw+FwOC4DuBPI4XA4HA6Hw+FwOBwOh+MygDuBHA6Hw+FwOBwOh8PhcDguA7gTyOFwOBwOh8PhcDgcDofjMoA7gRwOh8PxvAARvYeIAhHt3+i8PNcgoquJ6K+J6IyUwfTzIE+BiO5Zh/vcQ0RhHbK00uful3d4z3P97NWCiG6UPP/OOtzrPBEdXI98ORwOh8Ph+MaBO4EcDofDsSyIKEtEP0xEnyWiSSJqEdE5InqIiP47Eb15o/O4nnguHQhElAXwAQDfBuDDAH4ZwH9YwfV3ENEfEdFhIqoR0SwRPUxE/4mIdj1L2f6Gganrlfx79Ubn+x8iiOjH18vJ5XA4HA6HY3XIbXQGHA6Hw/H8hjgpPgzg9QCmAXwEwAkABQA3APi/AFwH4EMblcd/4DgA4HoA/y2E8COXehEREdhZ9LMA2gD+FsBfguvlpQB+GsCPEtHbQwjvW0W+XgCguorr+vFPAVTW4T7PFqbBjrd+/JL8TTt29FnKy5Pgcp9ah3u9BEBnHe7jcDgcDofjGwjuBHI4HA7HcvhesAPo6wBeFUKYsQeJqALgzo3I2DcIdsrfUyu87hfBDqCjAN4YQnjEHiSi7wLwvwD8GRHdHUL4zEpuHkJ4fIX5Weo+z6zHfZ4thBCmAby7P52IfkmOLzr2LOalCWC9yv3QetzH4XA4HA7HNxY8HMzhcDgcy+Gl8vc9/Q4gAAghVPsdDET07qXCZi4h1CpDRD9JRI8TUZ2IThDRbxDRcMq9biKi/01ER4moQUQTRPQgEf0mEeX7zs0R0Y8S0ZckZKpKRF+VEJWMOe/dAI7If9/eFwb0/UsXU8+zbiei90vIXIOIjhHR7xHRjr7zAoDPyn9/yTzn3cvcfz/YCdQC8OZ+BxAAhBDeD+AnAGQB/H7fO35/fB8ier3o9sxY7Z6lNIGIaIeEn52T8LOvEdHbiejVaXlP0wSy5xLRLUT0ESKaljr5LBG9FH0gop1E9C4i+jyxdlKTiE4R0XuJ6PqLldezBRLdHSLaRES/TUTPEFGbiH5aju8lol+RNndW8nyCiP6YiK5KuV+qJhARvU/StxDRO4noUekbp4nod4hoYKm89aXFcKy3Sr3/PRHNS91/IC1Pct0NRPQhqaN5IrqXiF5n77fGcvw1uc8d0ia/Jm3rBBH9KhHl5LxvNXm+QER/SEQjKff7Zjn2OBHNSbt6iIh+jvrsgrlmLxH9Lym3KhE9QET/mIjeKHn76ZRrtkjen5D6mCKiT1C63SsT0U/Lu00T0QIRHSGivyKiV66l/BwOh8PhWAmcCeRwOByO5XBB/l7zHD3vNwC8EsBfAPgggG8B8K8BvIKIXh5CqAPsAALwZQABHIp2BMAwgKsA/CiAXwA7SSAffn8j93oCwHsB1AG8BsBvg5lMb5Pn3wNgFMA7weynD5i8fW25zBPRGwG8HwABeB+AYwBuB/AvALxF3iE6mX4ZwH4Abwc7g+4xebgY3gEew/8ihPDwRc777wDeBeBaAK8C0M8GeiuY5fUxAP8FwL5l3m0rgC/KeZ8D8AUA2wH8HoBPLpPnNNwBZjN9UfK6F8B3Afg0Ed0SQnjCnPtKAP9G3uH9AOYBXC3v8GYielkI4euryMNaUQGXRQEcKlkDcFyOfTOAnwTwdwDuB4fXXQsOoXwzEd3Z947L4XcBfJM85+MA7gbwY+D6eNMK7vOPAXw7OMzz9wHcDOAtAO4goutDCLPxRCK6GcC9AAbB/fExsC34GICPruCZl4KfA5fZB8Fl9q2SNkxEXwK3kb8B8CVwe34HgCEA/6jvPu8Ct8svy70GAbwCwK8CeDkRvTGEYB2eu8FtcCeATwP4CoBdAP6nvOciENE1ksdd4Db5EbD9eTO4/b4thPBec8mfg+voqwDeA6Ah174SwGvBbcjhcDgcjmcfIQT/5//8n//zf/5vyX8AbgXQBNAF8CcAvhPAvmWueTfYOfPqlGP75dh7+tLfI+nn7f3BrNX3y7FfNOm/LmlvSXnGGIBMSn5+G0DWpGcB/I/++yyVx0soq0Gw06wD4BV9x/5vuecn+9JfLenvXsFzPi3X/PAlnPuncu4vmLTvl7QugNcvcV0AcE9fWiyr/68v/WbwR+2i9wA7tMIS7xwAfH/fsX8m6b/Xl74VwFBKPm8GO4Q+dintbAVlHPrznXLOeagTspRyfDuASkr6nWAn5F/2pd8o9/udvvT3SfqTAHaY9ALYuRQAXJ+St4N9aT8u5zYAvLTv2G/LsR/tS4+O1rf1pb/V1OFbL7FM4/P73+/XJH0CwJUmvQJ27rbkfV5sjmUBfF7a8NV997tiief/hjznDX3pf44++yLpLwHrbQUAP9137H459ua+9HFwSN8sgFFJ2yH3+CwA6jufAIyvpo36P//n//yf//N/q/nn4WAOh8PhuChCCF8F8H0Azsrf9wM4KuEYf01EK2EgXAp+K4RwzDy/C+BnwB97P5Byfi0lz1NyHYjDoP4lgDMAfiKE0DHndQD8FPgD7Z+sQ97fAmATgD8PIdzbd+zXwfo9dxPR3jU+J4aVHb/oWb3n7Ew59sEQwscv5YFEVADrQ80A+H/sscAMnD++lPv04fMhhPf0pf0h+OP6xX3POBdCmOu/gTz77wC8ZqlQn+cAPxGEoWYRQjgTQlgkrh1C+DKYRfXNK3zOu0IIp819mmC2CtBXXsvgj0IIX+hL+4P++xDRdfL/h0IIf2JPDiw2/uUVPPNS8GshhKfNM6pgsfPIervPHOuAGX0E4Ka+vB1e4v6/IX+/JSYQ0RDYsX0OwH/qu8+X5Pk9IKKXgdl9fxJC+FDfNRcA/DswQ6l/18RGCCH0nR/kGofD4XA4nhN4OJjD4XA4lkUI4S+I6K/B4VMvB7ODXg4OKfl2IvpjMKMjXOQ2l4rP9ieEEA4T0XEA+4loNLCY75+DQ7Y+QETvA/ApsFPh6b7LrwE7Zp4C8AtElPbMGnhXprXiNvn7dynv0Caiz4EZKrcCeD4IJt+3/CkJrgVQBnB/mjMGwN8D+KEVPv/+/oQQQouIzoLZXD0gojcA+OfgMLLNWDyP2QzgdP91zzImU9pcAiL6TgA/DK7zcfTlmYiGlijPNCwqL6iTb1F5rcN9bpW/n1/iPvdifUXh0/IVBdMfSDl2Uv7utonE+mE/AXbKXgVm6NmOv8v8vhFcJw+kOfLA7fp7+tLukr9bKF2/K97/BQAQQjhNRJ8BO4AfAPBX4LK7b4lnOhwOh8PxrMGdQA6Hw+G4JIQQWmDdl08Cydbx3wVmbvxTAH+NXv2c1eLsEulnwNonIwCmQwj3EdErAPxbcGjK2yRfTwD45RDC/5brxuXv1dBtv9MwuNaMS96ApR0RMX10jc85A/7A3HMJ58Zz0nYfO7OCZ8Z3W6p+lkq/GKaXSG+Dw30SENE7AfwmePv0vwU70apgFte3g8PCiqvIw1qxZBkS0b8Fs6bOg52Ux8EOxwDgu8F1WARwqU6gtPJqy99syrG13ufZqPOLYZHwPDRfFzuWMMCIqAR23LwQrOn1XnCIZgscPvdz6G0nq3nHaFPeIP+WgrUpbwbw82A9psikqxLRnwH4mRDC5EXu43A4HA7HusGdQA6Hw+FYFSQc4y+I6IVgEebXQp1AXfmbNs4s5wDZBhZv7sd2+Zt8DIYQvgjgjURUBIdnvB4c+vVeIpoIIXzKnP/XIYTvXObZa0V81vYlju/oO2+1+HswK+t1AP7bUieJo+7V8t80NsdKmFtRLHjbEseXSl8zZHeod4MdLrfZkCg5flfadc8RUsuQiMpgB+UxAHeEEM73Hb/7OcjbWrFhdb4GfA/YAfS7IYQftweI6GqwE8hiNe8Y++8PhhD+8FIyFUKYBzuBfp6I9oGFrX8QHOK6EyyC7XA4HA7Hsw7XBHI4HA7HWhFZDDbcYkr+pjFV7ljmfq/qTyCiK+ReRyUUrAchhEYI4QshhHcB+FeS/Bb5+ziY+fCSFWjGRN2glbArAN75B1DHSwJxZLxC/vvgCu/bj/eA8/gdRHTDRc6LH5hPICXMboV4HMxiuUl0VPrx8jXe/2LYDHYefiHFATQIDcN7PmEXOHzusykOoDGwo+L5jtieX7bE8WezzleLuM39+1OOLbItAB4GM4puFxZRP9Le8Uvy9xUpx5ZFCOFYCOGPwTu9nQTwzeI0dDgcDofjWYc7gRwOh8NxURDR9xLR3SKw3H9sO1jvBOjd4jhqzbxDnB/x/D3g7ZsvhnfKSnm8JgMWbM0A+COT/tIlPpziyn0VYC0e8M5HOwD857RriGgHEV1vkqbADI+VCjh/AMAkgO8lopf0HfvXAA4A+FQIYU16QCJ8+6vgMJgP9eUdAEBE3w7gt8DOon8RhbLX8MwmWIdpBMz8ss+6GRwS+GzhHLg+bxenT3xuHvyOm5/FZ68Wx8Flf6d1Lghr7ffAwsHPa4QQHgPr9NxERG+zx4joreDds55vOCp/X20TiehasGBzD0SP6QPg3ed+pu+aO7F4+3mAHaoPAvg+IvretEwQ0W3i7Iv2JU1zbAjAAHj3xU7KcYfD4XA41h0eDuZwOByO5XAnWID5DBH9PXjLZoAdGm8Asx0+CN7GGgDvfiQiyK8EcB8R/R3YOfMmAJ/AxbVsPg/ga0T05+Cwi28B6708AOA/mvN+FsBrieheydM8gBvAYRVT0N2OAP74uxksKvwmyc9J8Iff1WCmw78F8Kjkf56IvgzgFUT0p+CtuTsAPhRCeGipjMt1PwDeUeizRPSXYO2a28E7QZ0Bb4G+Hng3+APyJwF8nYg+AeARsGPopeB6qwH43hDCZ9bpmf8GHPb3s/KB/AWwc+27AXwUrM2zJmdTGkIIXSL6z/L8h4nog2B9l9eARb8/I7+fNwghNIjo98Hboj9MRB8G95Vvkr+fx9IMm+cT/hnY6fE/ReT6UbDY+lsA/A24T697na8B7wM7mn+RiO4AcBAsxv4mAB8Ca/L046fAjJ9fIaJXAvgKWGz6u8Hv2NOuQwiBiP4RgE+DQ09/Sq6Zk+tuBXAdmO01BeBKAPcS0VclPyfBzLY3yd9fFSerw+FwOBzPOtwJ5HA4HI7l8OvgnbVeB96K+VsAlMBiq/eAhVffm7Iz2FvADJ63gHV6ngI7bj4J/rhaCj8B4DvADKP98pzfAm+PbXfS+T3wB9ad4A+4HIATkv7rfdvMt4QZ830Avh/AG8GirRNgB9IvAvjTvny8Dbyl9OvBW6OT3H9JJ5A864OyhfTPg8tqBOz8+S8A/l0IIU2gecUQZs9PibPsx8AOt28CO6uOguvtN0MIJ9bjefLMs0T0UjAL6dvAZf8EgB8FsAD+WJ5d+g5rwi+C6+uHwI6JGbBA9C8A+OVn6ZlrxU+BBbnfDuBfgFliHwfn+T9vYL4uGSGEB02dvxbA3eAwsW8D97s34dmr8xUjhDBNRK8G8B/A4VqvBXAIrAX0R0hxAoUQnhHm3r8H99mXg51dbwc77Ba1a9mx8Fawg/w7wEw4Aou/PwK2fYfk9McB/AqYnfQ6sLD0BQCPAfjXIYT3weFwOByO5wi0Prv5OhwOh8PhuJxBRP8v2PH1+hDCJzY6P45nH8LIejOAPevpbHw+gYh+C6wz9vIQQpq4usPhcDgc/6DgTiCHw+FwOByXDCLa2c9mkh3ivgDWNtnVx9hy/AOG6C6NhhAm+tLfBA4DvT+E8OINydw6Yol2/SKw1tkkgH2iL+ZwOBwOxz9oeDiYw+FwOByOleB+IjoE1jZZAGsqvQEs3P3P3AH0DYcRACeJ6NPg0L8ADgt9LVis+19uYN7WE48R0YPgUK46gGuh27b/mDuAHA6Hw/GNAmcCORwOh8PhuGQQ0S+BNVL2g3c3mgZvmf1rIYR7Ni5njmcDsrPZ/w92+uwEa+ScA+uB/WoI4ZGNy936gYj+PVjnaC9YL2wKzG77jyGEL2xk3hwOh8PhWE+4E8jhcDgcDofD4XA4HA6H4zJAZqMz4HA4HA6Hw+FwOBwOh8PhePbhTiCHw+FwOBwOh8PhcDgcjssA7gRyOBwOh8PhcDgcDofD4bgM4E4gh8PhcDgcDofD4XA4HI7LAO4EcjgcDofD4XA4HA6Hw+G4DOBOIIfD4XA4HA6Hw+FwOByOywDuBHI4HA6Hw+FwOBwOh8PhuAywJicQEb2eiJ4gokNE9G/WK1OO5eFlvzHwct8YeLlvDLzcNw5e9hsDL/eNgZf7xsHLfmPg5b4x8HLfOHjZP79AIYTVXUiUBfAkgLsBnADwFQDfG0J4dP2y50iDl/3GwMt9Y+DlvjHwct84eNlvDLzcNwZe7hsHL/uNgZf7xsDLfePgZf/8Q24N174YwKEQwmEAIKI/A/AWAEtWZr6YD6VKAe1OJ0nLZLMAAAIlad2uHCdNi8e7nXaSFuQ+2awSmjIZ/h3M/eLz2m19LonvK0NybbDPD/K3q89CkCzpeWlInGopvjV7bSbD753NaN6zMc28DxGhVq+j2WraB6+o7DeNj4fde/aiUa8naa1mEwAwMDCgz8/nL/JiSx9aEsn76sUXL72LHQ0pvy4RaRdQ6s9Fl3z9qw+eDyFskf+uqNw3b94c9u/fv3xWLvWFJKPWcUtJmj1t8Rt145OlnTcbTXOM+xTlikna6bPcVtod0xbB/SGX1Yflc3y/oUG9tlLmdpzL2P4bet4B0D6aVv4PPPDAqssdAMoD5TAyNoRux/ZhyYK1KxSzZdOo9wLzn+X6f1p99Dvau8G25aXtRa/9W5zPtIKLt+l5ZvLiiy8NfQ+em55HrVqPh1dc7pVSLowMFFLLKS3vaW11ub5pLr4kXOJpF8VyXTT1PZa5oh/Hz82sus1ns5mQy2WWLeO09habChlDVCwWAADlSklvJ2NVMj4DCODfobu4TcehrdtdXHo97SMsXZ8hJZ/ttvbpVpN/234eb5TJXLxOYh+p1xtrsjVDA+UwPjaSTqtO7Qd2PKQlz+vLLACg02lpUrTHxm5TX98Iqe3y0p5lz0obfVNH5FRzFuxt5XfAhelZzC3UVm1rKJMJlO0r9eT5y4yRqeW9eL5yMSRzRwDZHI95+RxPqXMyr+XnpswnYxmbeyR1Zs+TvtZqar135PiAPgKVXFq/lnubfk0Z/n22ilW3+crAQBgZG+2ZO5aKJXkfLdeFWhUAcOrEiSStWW8AADI5nWsWinyfblffMc6Bx7eM6zMK3M7npqeStLrMaedm5wBo2QBam7FuAKBY4nwODg4mabFe6jWdHydt1qREO9Yx3xHx2srIqJ5I/LyMKfdWnctifnZ2TbZmZGQkbNu+vS9nMo+ixRbINvOY1xC0jHLSXnumKN3F7x4Ls2Nsfzd+V8m3RL3RSI5l5MFDw8NJWqXC9ZzN6WdnMq+51O+qtGO9Jy7KZ7W6gHNnz2FmZnb1tqY4GFAZX+rw8wiX+DFx0dMu9YMkZY6ZhtmTq27zm8Y3hz179vakxUq04/vFxvr+OS7Q195T0uI42mlpm65PXeBnNTmNUr7DbC66sZ+Z+8Z+Ydt7/DbL9MyhGB07vsk1WfOUTDK2ap+O3xePTvTMJ5fEWpxAuwAcN/8/AeDO/pOI6EcA/AgAFMsF3PLaG3F+djI5PjjExjOf0azML7BBz5qBIi8Grjo1naQ152cAAGPDOgkqiaFpdvXa83N8v8kJHTwy8g08WJCBrKvPX5jnSl4Qww1oweasAZNK6RinVrvNH9R2UkwyPcxnNU9DZR6EhitDSdqIDExDgzq4FotFfP7BL6MPy5a9Lfddu/fgw3/7WTz12CPJ8dOneGC+8069bNOWbfw+wQ4m0uDs+yw3Ye07LzoPgN6Bkc+xDryl72s/nGN5X7KpSunx2R5HQPxo0LzEZ4wPl46Zy1ZU7nv37sVX7r9fHTDoKcYEuY6U0+JxvceAhUwc4NSBE8ssdI2zRn6T+SxphOgI5fZ74ohOzKo4AwAojV+TpP27X+O2MrGg7TOPBQDA+KA6Yndt4Tb96pcdSNJuvnEMALBlMGuulTybptUm/tDMpTk7iFZU7nJNUvZDo4N424/9Y1QXtA/HiUEur304l5VJu2mHRbE7lNLmbP9PHM7mvFxu8YdyUyZJsZ1VWzrZbcSPV+sYirbGfEjE/EVHMd8Qi9CRROtoj3m3Hy0xn622/agMeN//+JC93YrLfXggjx/4tmuRN3mPzoMM2bKjRXmK3T/T5wS3f3vTenKx6LzeI5cw0Vx0hTmW4hC05+m9M4tOS3uutTXRJrzzNz+yaluTzWawa8cwslkt42xcYDFlHCcLdkGk0+YMZHP6kldetRsAcOPNL0jS8iXur7XabJLWDvMAgEaPU5nHz2IpI8d0IpU4aEjHbIS85NecJbauUTd9sM33uzCxkKSdOsHPX5jT51PgG5XL+oxuiDZL6yLag0cPPrkmWzM+OoR3/fj3oWiqPrbrkNExPx7OQ+0nxZcm8/IpyEkDnJ5Uu90OXK6bt+zXZ2QK8lz+fxeF5Fi4SB+xIGmQlLII1jVjeUfaUicYp2CKwyOWs50Ttdtt/PLvvtc+dsXljgwhPzrUs2gW23XPQklc50tzApmyyMoctNvjSIjOlcXzn1JJHaSjozyP3bl1KwBgfGQkOdYUB87CwnySFp+Ry2v9ZPLcBhoy/wWAplxz6vipJG1hvgYAePGolvvNm+VjwDhIu23OX8hqeytWOC//8YGwalszPDqCH/jxf47bX/Si5Ph1114HACiYhcT7v/o1AMC7flYjPo4+/gQAYHjL7iRtzzW38XvNnU/Shke57/7TH/7+JO2a/fsAAPd86K+StENPPAYA+NQn7wEAzFdrybHYB0dGdQ5zzfXXAwBe8rKXJml1KeMnHns8SUscHObjaqHKZTczrfVYXeDnvehb3qLXltnxUSG1Saef/DoA4N5PfHRNtmbrtq343f/6ez0ffRB7l8uVk6Q4DlBGz6vVOd/tptrjTePs2OgZ3+Y53y1jz7pij+fq+u4L8i127vhJAMCTR59OjpWlHXzTN92dpN18B7eXkU3qTClQnBssHkttH47zfzsfVqfW4rS5OR2jHnzwPrzzX/2kecFV2JryJtBrfz5xvAOX7sBPX75eep6SfpPFd0ldyEvaxeJFkZ4ZTnLYzjvjL9u25HfPh0vo+wtAyp3sY2OdffLnVm1rdu3eg4/97ed63j8nTt3Bso6ZpWIuXrsol52g9i+OWV2zuB2n4HaxKh/YLk+dOpKkPfW+PwYADBzndp7v6n3z0j/IjIVNmed3zVy8lOP7WjtZE2dEaUHvF4egyRGzwFPk36NmHl3qRn+D2r34fXHj7/yNLfclsRYn0CUhhPAHAP4AALZt3x7u2P9y3PfIF8wZXECUNx8DJTZm9mOg1eAXa5gBuioFUA46kBakRrNmxawol5TNBLlLUjGyMlFv6PnVmjyrZVhHUsnWGZFF/OAzE56USQikYdgBugaZqBbUcJM0bjL5TF/JWx623G+65bYQAqFjVkvjymlY3I/RSenwdBFPPLDUx1pqvgDYj6A0I7T4/G7KgJCGno8reaFuyupQx14UV+VSnEArhS33O+64I1AAMoZlFsfk3qJJS5TyzFijJh9QZmKvq112Isx/Z+a0TT98aAIA8KEPPwAAqE+rI/Y138ofelsKaphmG2xwdBoDZDI8mZqc0X6xMM3PffrQySTthlv5We94mzqVdgzxh5vlmmWjo/YSnYrLwZb99l1bQ7fb6bEhylAwzjH5QG2Z+3Qy/L+cOS/WkV2JLxbNh2y8thPPu/iHUUR0llh7EZ1GbdMPi+XyovOirbF9rhWv6WEdLl4hjHfJm8Eok8mkriYuB1vuO8YroRt65wxiOns+IClZpTb3SRyfqQ9ZlPc0Bh1dhFa3GidQnOilO4HsveP5iz8Wez8+sfh+Ye02vlDIBWbIGLZrXDnqcarFSbexpxlhGxjn5PHjpwEAA8O6Yr73iu18bc4weTvisCzYZ8SP49g+rfOR25u1Yd1udEjqRCYrDMNe9gSfNzau/S4yas+d0Un/7BSv6NcbaqeMKxDrAVv2V+zZHkr5LPLm1pmUD4RMdMSbD6mc9L9C0cwDZLHIkpuowWVDTXUQRF920cy8o3O1E//C2r8UJ1DCPtH2kJF+munpm5mev4D28bZxeHfk2jhfAoBswiwwaZlOql1aDrbcM/lcQAa9jhxxpFj7m/6cxR9GXSnHrinPorxvIWfmmDI/GxlU58KA1B/JfHK+rQ6NONeKjAlO47Y5b9Jy4lQi84Ef29HwiDIq4seFHUwpFx13+t5ZcULYxWR9i5XBlvuevXvCpqFBZI0zs9niPmfL/awsNM6e17IYKFcAADu2b0vScmInMsZ23XrH7QCA/Vfs1zzIX1sW0d7v3MW26fBh/e7JSj9qGMfQ04+zo6dZ1T7YFifd/Jz2rcgoyphFl9je82YRaXSIS3Tugn7XxvxNT6jD9uSTD2G1sGV/3QuuC6VSuXeROVlstSOYLDxl7JgrznnScq4Ik6pp2s3x40cBAK2Svufu/VcCAMpZHQ/y4nCOzicyi9fPPMXOvo98/KNJ2vQcl/ldd708Sduxnesta8o0nfUTLnJMEcviyLGjSdpctZo691oOttxpbF8IFPrIL5e6DJ1690tIMaCeB/edn+agWe5Zi53bqecnx1Oesdy1qywfW+4333JbIFDPsJ02r9IlFjt3i9+O9t5xRWDxmNCmxX0k8A0+AAAgAElEQVRqsFJJ0irShOJaWcF8L7WFMNJqKpswLjZbxmTsji2zMNYW21ktaB8ojnA/22yuncnz+FCbVzvVnWMiTKutz10Y1AWIS8FahKFPAthj/r9b0hzPPrzsNwZe7hsDL/eNgZf7xsHLfmPg5b4x8HLfOHjZbwy83DcGXu4bBy/75xnW4gT6CoCriegAERUAfA+ADy1zjWN94GW/MfBy3xh4uW8MvNw3Dl72GwMv942Bl/vGwct+Y+DlvjHwct84eNk/z7DqcLAQQpuIfhzAJwBkAfxhCOGRi10zWB7CS29+NYqGWvvEcaZJTnWUnhlZVlZLpRMkRMvQTWPM6ryhsUPuk28b/piwVq0OTIwrjHosWRu+0RaKbk9IhyTZcMk0WlyU+OiJjYw0Y02KlF5LpY4i2T0CzUTop7CvtOyJCIV8oYcC1+1GSp3Vi1hMqUuN/7wIeuNz492sJlA8T2JILb08JcY3OZaStrxIbwyHWKy30HPlohC19Dysps0DfWErkZsYTJvNM6XPRoV0hLjdhYYJZERDp2PyuSAswIOPnk7SDh/jcKyvfvWJJO2JYxz+NT/PbWursgzx5FGmF37uEaUsn5ri8K1OTmmL48Os9ZPPa54g4SCNlpbxFw7y/SoffTJJe/t3MJ14vGDiaIV2T8v4oldT7pQhlEqlhBIOADURqrzUDRGtrg/RYjpxs8U3apuw0diebduM7S+hypvnK7Xcir2Jro/R64laY5ZuH/uL7UPNqP+T0ubttcUil0uvyDj1/H+17R0wonhAYvjyJuQ3TeQ8MRO9nTNmLkmJ+gG9NN8YtmqvTdMO6scy4UFph1OoxEgNh10cgpSmP9ZNCQdbcdkHoNshhKy1v7F9mJCAFAXDGFJh21F1gcfAY0d1kW5ghO3P5i3GeCBqVdl35LYXKcp5M97H0LR6XcNgshJWUDAhZY2EVr1YA6tY1GnLlu0cGlauKAX6zEl+n7OnZ5K0VjPex9bd+tj4gAw6mXKfsKT8DUbXK3BYSt6ESnUbbGfbRhSXckz7bpoJQ0to31UjXlsYYDvc6okLktAM6UxZ0w8XK68BQSY0dnxtS3Bs1xZVyoYbXfnd7VGf5fqwTTokoRwmtC/T6c3HGmxNb5enxWmJCTF5j/OPnq7MaYW81tn4ELergYLq/0QBz4rR84lhY9tECLfd0fYdxYtLRa2nYolDyapms46O9JuiOS9GeduwhGj3Gk0Ns2rK84tW/0f6fd4OBfkYSqnPXWnZF4oF7Nu7EzMXziVps1tYC6lttK0efvhhAMC80WcZkPJsm1CyM0c5RGtsm2qYXpjkaz7ywU8maVcf4NCh7dt3JGk7drK20JmzPL85cUrnQSOjrD0TRaMBYHR8MwAgV9T6RNTtMjYp6gR1TD3G+YDV5Yth+I2Dqtu5Yzu/x5nT58x5Wn8Rq2vzBATq1dLMSgi6CXHLtDjfRaOLlq2xrWkuaHhcMuc3Ye6DDW4bI2Obk7Sy2ONsz3DN/xmQtpkXPSwAKMuYcuSYhufde++9/MNE6b7um1kzaHTMCGsneVssAdGjkRnTrO2SMMuJC9o3Dh890qNZt6pyJ7ANDGlhZct9h8SzeuzdovOWCXQzP/vO7Bl3Li3cOdDieZUetOJ2MS1FNyRNxHSpjCW3WcVcnnrnJsncyW7mFKe2PSFdcr6xScmmEsHWxWIdgjj0BrMRVfzqiWNgtWb0x+Q8qxMXpTxaJuS3LbIzBVNeoyWeT9U26byqKnY6o4/A+RmeA5Saak+3yFyivHl7kpbd2iukvRzWpAkUQvgogI8ue6Jj3eFlvzHwct8YeLlvDLzcNw5e9hsDL/eNgZf7xsHLfmPg5b4x8HLfOHjZP7/wrAtDW2QyGVTKg3jVi16bpG0d2wQA+OLjKhZ9bIa9+dYh2RbvYwd2NYlPqNmV+MBet4px2LY7suuDZaRE4UTx4JHZcSnbEfaD8VhHJ2HHrOIlzsRURWP9GZ2TaSr2VrUvV+CVJSuS1m611yQ/BrDoY6PRRssIU0fvZ6tlt9sVAWuz0qECokY4MrOY7ZC+6i4Mm57dnzgtCuhms9Zzu1jUMflt75Em9JiI4+Hi5yWMgXRZtfVEQEC720Bomx17IhPA1HtXVsLbduca8OrKVFXL4qFHWJX+qRO6wv30Mf79tYcOJ2lxcXFqRnfGOnuOd8bbI4KMoxW975mzvPXhlx4/mqQtNNmz3IW6ogvSFrpGwGzHZu6/3Zr2n0KeGUP3fn4iSdsn573lteqxpg6vTOWyi1kEa0UIcacYrd0o5DxvdmmJO4hYtf5ciG3JbKWaX7wTWGybUcSW7ycsCGOTIhMo9puCeceWiDs3zUqV7jqo94hbBPcKn8dd8vT5TbmfZckMDsYtePV94o41lm2UzWV7dx1ZLQJQKqiA5NAAr/6WzWp63DGoanZgjIyVZlvZZ7rqbURIpdyLxk6WE1VeI1DcZ5PSGUEpLJnUd1p8rIf1kyo8nMYOirbT2th1ECtOVstSxpgUsWh7XlrOYzOfmlRbc/rkWQDA2Kb9SVq7FcUX9X5NWTVuST1WzC6Bcfhsmo0YYpMeGdXV8mbcwcaKLcct6s1uH3GL+pExZScWCswAsBvpnXpGVsEbdmBeh7YOoNkJOD7VQo/2aLLliD5vMMNtffegplWE9txsmN09FjivxcGxJC1X5LKhkooRN7M8bpyYNCKTMpbEgrZCwalrtcnGC5r5tpRvx7KIYmFa+0xxRxYzhxFB2p6dPlOYOEQZdFYpiG5v2wmhZ3U+dSU8NrAekW7Jr5kiRIaZZd2MVdiOlQybrV3j8h4x520VhsnIMNvaebPD16CwMSwbIbblYkHrc3KO20dtXsenUiErf/X5m2VHp9Y53SWvISvHIwNmp9w2796UMzaukbB47XboK0O73cKFyQlMnleWYJDx89y8tsUH738QAJA1rJvioLRjM47d/koWCt68S5lAjz7ELOILJw0TMcMbWAzuUybQ8Ai/z9gojzGbN+vOU4ODzC7ZYhgqw7KLW7mi9qIltmbBMmlkWCqUtDyjiHvP5jDSpsbHdLw7fYJ3Wy0bUdeQWa9PLUKG8j15SMbNqjIEpo6xKHWzpW1uapLbQ80IZR+M81ETTbGty22uMqdjM8b53mQ2ChgU+xO3rC4VtY2Wt+8EABRNH8lluSyv2HdFklZJ2fAijeGjXyEXn63H3V63jCuLqZDLrcPeI8RhDGks4OVwqR8YF9kdeVFegPTvzvgdlMKSvtRn9Q6LKczKZHeLtNsahk3atatACKGPsS6799pdTyXPdtyZnua5y6mzurPi3v0H+FrLaI3XpjCg2ma3r1aT+00Qe5EzFMv4OdduG9aPzMVzZqOnhDVnmPpV2U21Oau2czrwNecy2qc2Fbj/7B7WeUEcHxYG1O41ezbEWB5r0QRyOBwOh8PhcDgcDofD4XD8A4E7gRwOh8PhcDgcDofD4XA4LgM8p+Fg1eo8HvjqF3HNgauTtP07+Xejq1So5sOfBwCcndVwkoZQtTImpCBSjwuGbpUT+lbXUG+zovJUMSKxMfxi365dAIArdumudQWhNU5MXkjSnnz6KADglITVAMBsjSmUHSNeacj46EcwPLtAnE8blhQpapa9xyEnayPUhRDQbbd7bpzNcZnZ8JIYqhW6hk5GMaTLhIPFkCorNRlDxHpEUOMxK2omQqJChzt29Ehy6JnjxwEAd9x2W5IWhba6hpaXsLvNO6bonaYiRFHZnrR4j8V5XyvaBJChDcZnnTqj1N3pBX632Zq2z6MnmML4+GEVuPvyA08BAM7WNPfzba6LUmGTPlSohE0jwlyRcCxkmUK+sKCihTOTfL+FWaWID4wybbKcUep1Rvpot65U99oc/251lDochTVDGE7SPv1JpkjvGVF69R038ftmM7Y21scvHUJAq9XqCW/qdhcLgMffGUstlfady9q8xNAvK8gobaljwyz5rxUjj+0qitFbsdsYltVta13lpG/a/pqEwpiYk5qIxNq0jFDWOyYMpS7nWSp1FPLtEbDOZLB8D7o4CvkS9u28BqWc0u3PTzAN/enHn0nSSkID37NbqfqFsohVm1CKKCrcqWnapIiGHluYTNJ2bGWa7ZYRQ8FHFH+VOk6xo/b9Y3GnhUX1XrM47eLHbGK0k5kljq8OBEIul+l5fpBQoG6PPU8Jm01ErRePBR0TLlyXkM/YZgFgfoH7fY9guYQTDQ1zHdtQy9iOKxUdsxv1OBZq6EC5zM+fntY6howBoW3DhSV0ydjYsggm79qrYqMz02y7amdtSA7WBfVGC48dPoOOEXIuSehpxYRIDBHTycfLmlYpSb5N2FZZQsQGB7U8stKfJhd0/nF2ksNX5pr6IlNVLq+O2J9WT8hVpOfbWAF5vLEhJbFTedMe6hK+Z4oeJOEdts3lcnye1aWPfajf7lbrZnOEVYJYNVT/H0NuTQZimH2no+9dEjteNPOaQamr8REVGY9CnjljCEbH+Pi2zRq+NFCW8O1prh8VNgcyEkpHJk8NCb+xdrohYQatuobrUEc2CzGCz+Uct4/Czn1J2rYreZzeAh3Xa+e+zs83fXi+uvYybzYbeOaZp9Ba0DnM+Vm2A4fPavhoU9rUtXfofK4rauPX3nBDkrZ9J5fj1+9/IElrTPF8gUzI77nTnHbFHp2T1Ga4vZ89HcPGtC3MS1hl3oytW7dxOPzM1LQ+SwSTg7FhO4z4tN6P+1vWfG9sGuNwvlMnNeQkL4LhVSM0XSit36dWCNQzz26LqPP999+XpB19mMW2F+a1Lc3PS0is6cRb9rGwdtHY9FmZj54/ouN1R8Icx69U0dlK3HhFyrcoZQsAo/KNt2Vcw1S27OBnXXeFfv/F7xA7fsTxKBib1EkZnFPDt+W0gaLazgN79qJYKC4+d1VYTgw5DSnnLTtPuNhz+y+zg360MSnl2bM1QBRITgm5s/Y0bppjBbmjHbeaA/F4z74L6zDABp6323l8S2xnpmsEnyXL+YLONZ45ziGRH/nYR5K0H/yhHwIAlMsahtuRb6icHRZlDGybNhjlZOLmFi1jV2Loe8F8M1RkTMiZcS8Kl7drZtMX2Yhh2oi4T0vY9/CgymfsJbZ15Zb6RS6IH+N4TedJg3Nqgy8FzgRyOBwOh8PhcDgcDofD4bgM8JwygWbnZvDpv/sITl97U5J2yy0vBgBcse3aJG1Atsd+4LEvJmlPXGCWSN161GVrx0LOsIPiak9DPaF33XwrAGD/zgNJ2pOPHgIAvOxOFqW7aq96uHMZvrbR0NXnrz14PwDg4BNPJWlfOshCvYcNqwPCNurZIjXZlc5472Xlr91SBlRciQiWWdDtpC9HrwCZDFAqULIVMKDCxCXjfSyXohfVsJNk9ZVsWup2yFiUFsU9H3zwYJK2Yzczr666klcFnj70dHLsv/3BfwUA/OzP/FSSdtdL7gQAtM1KgYpepnjlzWpssvWf3ZpxsX5s4tC2K8OXrNF2ETSabRw+MY1z59XrOznDZfLlrz6epD36GHtxqw1dvZgUzcfZunq2qcCrU7Wc3i9X5kxXjXBuXsqgZLZcLuV5JYdkS+JKXstu6qQIsdeVRdEWkUGyW6xXeZWzWtXVxtaIsHmy2o4LBV5BGhrUPnV2ltv0pz6j7Lo9228BAOzeYbacXyeZbiIWc26bFa5sFLYum2VqWZksdOwSk4jQZ4x4rSwTdDNma1lZQShk7BIC369tVvZDELaPiKo2zZaRUVzObiGbCCOaRdu4YlY3WwqXpe/2MJFE/L1m2kOrE4Ub1XbGRf6eVZ9u6Onnq0HoAq0FwvETh5K0xw4z2++UESrvChvqFa0bk7QD21nM0YpFF2VFqzqtDIhh6aiTZlX78DN8vHCFvuNQRbarbsetfQ3TJUUgORPtROrCnV0xk/oxRkKZWovZW3Yf6shUsEyxYGkTqwXJ84yNj7aul6kmtpMW571nZTVhDGlaQcRnO+Z+8XlWHH2gxP15YID7it2GO0j5FExbbMrKXr1hxlFqS97ManAcH+32rnK4VjMippKnYknLdesOXllbmNd2VFtYmYDiUshQBsViGVOzmoehoVgGatvKsSAKhiUoxTA4pPYiMq2mJ5UFunfflQCATlPt7MwUv0t5k7LpSl2eOzVjezXCsEgZo5O6NOL8WbFnpYrausjq6Gki8jqtlua9Lv2621rcX6pVLZ9MJoNm2zJAVw4iQjaf69myO77b8JCu9EZ2ZNUI4g4KO2jYMMgq8m4Hturq69QMt8lofwEVZh4eVqbr7Cyfd2GWGSZNszFAXp7fNivHNdle2N63Lv2gaNimBWHCdE1ZVYQdfcuLXpyk7dvMeXnmK+9P0oo5vsZOmScvrAMTqNHAM0cOY3bW7F88wrY75HQOceBaFnIOlqkvoq5t01/vv+dTAIDZCWXnRFZW3Yg1XzjD3wBf+4q+w6GneB51ZkIYcIbtFRmJmzarSPDUND9jelJXzrvSR8YNsysy0Ken9LzhYW5To6PKMDxzhtkGVoV+37XXAADKI+a8E7p1/drRvwEAv2fNCOZvvpLFlytVIxp/iudtHbNFfH6IRWaHDFuzKRuFPGMYBVXp/0MVFaXNtnkucvSphznhlLKhbh+UPnKlRlgMDLGodMawuyID0TI94ps1Gpr3vPTXnqiCNHaQTOxLOf2uOfLUk2jUVy+E3ou0ycHFPxbiHCusSQjfzsviFu1LM6GsgH9Ssub5GUntme0l35mGFR/vk/KpZb8n4wYNdj65HjP5AKAbMj3F3koYzobtJO0ia9pRcYDb2+iIRkm0xS+QKVo7wWktO43vSARKWx8cx7R2lftZKBnWT4Xt3oCZ7HQX2E41O9qPZuW7q5hVW9MQO1EbUYZwUepnR0OF8Qti444G7b/n5fsvazbwGSuvrOSdCeRwOBwOh8PhcDgcDofDcRnAnUAOh8PhcDgcDofD4XA4HJcBntNwsGaziWMnjsJoLSXCji+8/uYkbd8uFg6zLPkLX2CqYaOp9NBsFEE1lNooWnztziuStNe8kMPBiJTyWLmB6V45EZo+GWmdAApCj9+1fXeS9sIbb+cfJkYjn5Mwg68dTtKOn+dwm5ql4EcKY9B8BglDqbWUprhQ43ezAoKddhPdNYaDATHUSelzeaEcFwwdOj7WPi4rdMAeb2GKuGjMow2pikK4jz6i4WAf/sjfAADe8Y63y7M0T4eefAIA8MmPfzxJu+nG6wEAQ8NK724J/bVthLZjFEaPFlmaqGvydxlq5jooQ5+bmMbv/8EH8NghzcipScm7CZ/ISnjA5u0qSJkfYHrhEA0maZUxpqnXakZEvSrtra5hVvk2t9GyDXnrihB4kBAZI0zWEmHWgayW8UKdxcdOTd2bpE2dYzoxGaHZ7g4OUZszAq6hzmEKxRENcdp7xX4AwANfU5ru1Xv5+Hd/50uStGx2fcLBEAI6nQYIhpYvIQ/5koY5hBaX/ZARMp6rMWXcCkUO5rke6k1DZY7lmzeizpBw0K4Rppe6ju3Vtvms5Ilsx4lt2ciXt4SybgX6ciKC12ioDWkSn5cv6ztGSqulUpelDDptGyrUxVpFihuNJg4dOw4YYf6rJJRltynPk2e4LT35jNLka0K37TQ1LCibZYrs9JyG8dSlkx/YreKdpWIMaTI0aAmDy0t4b71hQuSkDRcMNT0fxfINpTf5RYtp6GRCDzSm1JzWSaG6Ryqzoe53m2sLi7EIVugxpKWFRWkRmZRwORs6VJCxsljUvlKMdGQbThSLVDY/6BGNXqxHibKEZ2ayRsB2XgSnuyZ8ReqlY5K6CcXdCOw2JNQmq/ncJra129J8Hj60PiEamWwWg0ODPUKwUdzdtrl8hfNzTs03Cnl+5wNjGjpy7LCEmZ/TfrBrH4ey796mNPIQQ2sKamfzFf7dScLB9B4ZqUsrDBuzl8urXZ6aEfq6Kb/yCI8NRRNSEKNqu22tkI7kpWHsCmW4LE6cOqvXDgwjk1vb9JOIkC8WktB2AAjSJ61AaFZCmsmEAJTEFo4PaUjXJhGr37lFBW5LRW7zVRNWMxDHj47W97yEgc1I+JgNx42hnzHsEVC7M2A2OonR+EXzPk0JiWmbdnTHbXcBAF750tckafd95TMAgAef1lDtm7fzvcslDRWYbqx9fK3XG3j80SOYXdA63rY7I89aSNIW5llOoWNCrfPSBquzas+jAHA+p7a7LsLaW7dqe98mc41zEzrXacq9y2WeL1VrJtRJQoLKA9o/op3eNKZhTXUR4u6ase/8JOevZQS+5+a5XzSb+oxzZ3nes3WbhmQuSNhheVTDUOZn9btl7QiwA03s10PjGvYGElF+E/q14yoOz3visSeStCEJm8yYMWqKuA1dcYuGahe3cGh/dou+Z67Jdb39Stmgwtxj8iSHsWzeoWXQERHvxoDOaTPSN2yYewzpWjDfRmObOLwsn9WQma7YuG5KSHe5rHXerDV6RKZXD+rbUUH+LPutsD4CycnPi4SVkYy5WfOtN1bkd8/ByFI02Ta0bLGk3Ve+WYOZV5HMbW2Yd5wM9246sA6aGhSATKc3lC1+x5twtEQNxJy3Wez43a+7O0krJuOC2UxFwp+7Nrsx0tAkZuTehbjRQF4LoCE2oVvTNhvDlDMlbe/jEi47Mahp0+ID6U5p/ZCExtfbajc6Mh7TiI5P22UI3RZ0fCrnV1buzgRyOBwOh8PhcDgcDofD4bgM8JwygdrdDqZqM6gfVVHc8xO87eOxZzTtRbexGPAVe1XI+cAYCwrXZnU1Kcjq44Lx8g7KtqXbjNDqo3/PW85b4aftV7BX/OwpZgDNz55Jjm0eZu91wbAwtm7ltB3G458LsmWkecfjU+yBffK4rsgcO8usCitklRNXY7OuKyfT4ilvm1W0bLZ3+9bVgABkQicReQaASETJ2M3SxSPZ40eUMg4pXtI0R6/Ne3SQX3vtVUnan/7pnwAAHn+U2UH796pwXE224Py42dLvmqv2AwC+7Q1vSNIGZPXCrgBEH2q7ZxWfM5BLSet5HVmWtuW81jIHeDvd8zPAxLyuSjaCCCca4brrb+O2de0LVRz94CFuP89MqCf4+NwxzltdV2ibF3jFfNC07bhWkjFL5t1ka2p+86JZdd+8hdv59BnjTS6yt78TdNtWCtyOS0Xd9rM6z/kbqOj7ZLrsxW42lV0X2pzPyUld+bz3c/z7jXcrC3BoxIpErwHETTdjRClbdX6/fcO6OnXLHcxCKgzpSvzkBS776dMqdNgRBlfHsKAmWtxfzwcVfsuKWFzBsI2yov6aawoLpEcgXurIdKYoLmyFz+Nv226jHnXLrMRXhZ1oxUXzIqCfN0wgEjHXktk6tRnaaybANdptHD53Dts2aXmOi5DqGIz4pAj0PWTEQEfLcVVS7VRBxK+3mu1nZye5He7cpiufY8PchhtVXYlpd7i+hQiEIbMgfPjMMcmH9oOybOudtVup1vl4y6zEB8l7WFAmQDR7HSuMKIwLapp6jLcxTFHLzFgTQv9K3GIRaD20eJtwmxavseygorAiioa90KG4JfhikcjkZXsEJBnZnvYZt8HWtE6H7WS9rjYpso06ZhyN9VcxIsZRqDhu/crX8nnj27QNttrcRg8f0rF6NWh1gTO1HBp5bfPtprBPTNlfkGyfM6vzDfldNgLO5SJfs++ACuvHBcl8TvO/VdhDU3Vtm+emuG+0ZVV+MKsCvAkjxbD0IlGSyHSOBtv0uqHVNnPcv2pmW96OZVgkidJfzPiZF9bLoGFG5IsDydbpqwVlMihWyj0MhI6wbbrGTsfOWTKr2UVpiRXDRhoeZPtjWVFlETnPkhHtFGbRwoIyUkhEn+dneD43Na/zugEpp8FBFU0ekpXgoYqmlSQvA2V9VkuEwMtmy+uXyJiVMzbp4OMszntYCTbYKaLkA0W1iTOm6leLVrODs6emUSzpWD1x7Cj/sFs5y7hkmZVRJDtjRfVljO4Yux+39N5qRLqjmLNlrw8K46M9LKvpxoaNCxOgXFTbMD0VGT5aEJPC+rFi/aUSP//CeRVnHxCW3dEjOq/J5hZv9tCW3089qoybltnsYG0IzLA05rspY9TZszoP2buPGVRZ0nffv3sfAGBhSs97wQ3XAQBmzus4/LUjzETcvUvn5tnxnQCAWRMJEZkOefnmyg1puz0v7549oWVQFDbhwhXXJ2m5Ue4Hs7O6CU9BGA/1IbV1VwibuGBYRFTI9fwFgHyJ+9OxY8eStLnZGjo9m8isAoQUyoR8G9kU6j2Wdv7Sz0hj4iwWv07miils3iB1sqOsff4VO7ntNRa03j82wW2h1bSq2nE7eJMmrJ+uab8U2dNmbhCjXYKdsy5+mxWj22mjOj+BUkGjJLLitrBMpDh3CD0bc8gmAcPaZnLJvGPxeSY4IxGYz/SwzEQYuiXl2TTf0zI3yZmNliDs9G5b836swedNtHTOMSaMt0JRx4KcsFIrwzqnqMkEdiConRoSRiXNa/+d7KxM/N+ZQA6Hw+FwOBwOh8PhcDgclwHcCeRwOBwOh8PhcDgcDofDcRngOQ0HyxWyGN29CWcOa5jFzCxT1KaqGuZVbzOVdmHutiRtbIDpjXtHlSI9f4YFmUNWaVn7JURgsxGqPPHYkwCAkc0aypUVyjMJtWrmtFI8GxNMqc60lXaVy7JY9fi4ikUXhEvd6ioNsjzMVKwtO/YnadueYZG0U0cOJWl7d/D7WFHrpySErGPEqPL5TE8Y2WqRzRBCx1CkhbabMxTEbCaNwMfn9RxJcR1G0VlL0YvhBTnDs4u0ufvv+zIA4MGvfDk51owCfS2ly/+PP/ivAICv339/knbni5gOfeerXpmkDe/YuiifkW6eMZFdFIVRzXnx93rQFy2y+TwGt+5A/ZgRupXqDiZMKZtnmvyJU0rhPHqcy+DQCaVwdoKI+Zp6youobKWobTBSQuHvt2sAACAASURBVJuGzh+DKgod7luFrN63nOf8lYpKPRwaZBr2/ryG3FTaM5J3FZCudkSAsKIlumsb0x9f86pv1fuNMCVz717tP/d+lkWnP/OJjyVpr7r7dVgPEIBsBijllA6dEfG6nQP6Ti++/g4AQBgzAoYSmjFhRIuPPCUhSKbxn3jmKQDAwbOPJWnnG2zHglYHuh0Rz5XQFSvQnBNquw27CULLzRb0JrMt03fjeRJGVMrreZGqW2kplXo8iKirEQWtZrmtLZARdc2mMoxXhFyOsGVLDgMVve9U9TjnM6e03F27ub9eMKYt32Sa7fAWDQFoF7h9zRq6fVfq9ORppeqfPRVDBbRfzVXZxneqfO3tt6nQ5bYhtr/HDqlNjtT/tgllmW3HMECTTykjay+zYrPbVnBQbOuAaTNlCQ20Fv1MWFy3K0dAB51UscTlRPC7kueMsRdRzDqXzS8634ZSRFF7G35DMQ+RXm41tZNHmAKN4pMm1GhwYChmJEmbFWHWjBnbY9F2ey0/59OGg8kYVCjpO+7aM471QDtkca49jJC11HEJp7OhyBIeM2jCXkpdbqNjLQ0t2i3s8b271B5H0VwbqlQUynrHiPI3Z9lOEWIooNL4Y9hU3tRzDFVtVTXvY1IPHROiNl3ncI16VynrFxpSzlkNVcpRDCk3faMW24YJGa520V5jiAYRIZ8roG3mNVFbOGvHfAkRGxvVvA+LqGfWCPgXZKLQaWqZdUQYeGhAhYSLJba3M/Na7nM1vk9Z2m1pUAWnKxIikDe2aUg2fqiUtExiaNrmca33OD3buWNXkhakrz309a8laU89zXZs1oQGnrjA7z08pP2q2l37LCeELmq1as8GB7G6rQh8SAlHjULEzZoJn4rhzKY53HTTTQCA02d0DG6IePCuXbohwIKEcHXkWbuNvEBGwqDPnzebZkgDaTQ0zLRa5fouFrUu5kQcvWHCUeNvKxUwJNe021ruUa5g6y61L48efBjrhUC9kUMxnJhMmEhXbENPeE4M2TFjRCvO7zPaNmPfPXroSJLWOcvhJoO7dO5UlEFxQuqo0NT+dU422jlr3ntEwi1Pbn5EnyX9MGPmryUJG581Qsanh7gssyParwYkzKc0pPOKvBy/YITZX/zCG/EXZqOMVSOT6Ql3TEOIFdMT2kV9f9cJ8Rl2yJc+ed2IfkNdneHv5MNGNqDdEXtiNktKxJXNK+4d5pt/8+t07vTx+3k+d2JSn5G8mh3s1xqCB2BmZgof+fBf4or9NyRpt9/2skXPakmIfrmsIao5+Q6qtdXWVMoijWBEneP4QWaukU3EojWt2WA7kZHwrWET3puV77mmCcWqzvL5E+Zj4Lio/+83RbN3UGyIsfsLWW6vdRv2KffrTKo9a1f5dxsm7JtMSNolwJlADofD4XA4HA6Hw+FwOByXAZ5TJlCxUsJVt1yDglnNOvoIe5unZpUFcfAJZu5MnFV20LX7mUGwZ4d612/ax+ycp4+qx3qPiLftNN7hkRtYcDdntrbdNChbOefYS1esK0soevUbCxNJ2tOH2SVnxaBItm+bnlOP6MGDLIS20DXbY+9h9tKrblAWxJYxWc2e1JWGxw6xh3WuqyJpxWIenfXY3jCEHi92ZORYMc6YZtkAcVW83bEiWOxhtdtaJ9sgm2ufllX2//me9yRpR4+wV7osq2mNmvVgivfVrOydPsWssS+ZMj7zNJfTY48qA+O1b3kjAGDbvn1J2pCsysUVIQDIS957F8RkJdyuhqyL0554C8yebZY5L13D3njwPl41MU0LdVkdH8gp6yYvonitoO1j5y5Oa07oqmQUFrZb58YV6ShSa1fxolAokQqYzchKWLumgmNlyXN5yKyo5th7ffzMM0nakGxj/EPv+OEkbWCA2/uFC8remDrLv7/+0INJ2h0v1+3i14aA0G0ja8RAKwXO99lTurp48vBRAMDeW3SFKysr1rmKeuaLu9l27BlXxtCIeOvLDa3fB8SOnWxqfTREoLgofSl07JbG3B5yhhEYxSbJdPtCFK00DLK2rHDnjMj4kPj1x+tqfw502GaW25rPoyXO39NNrd96ptMjrLka5HKETaOlxEYA2vy7XWXEXRBB/NmqtuXzp5kxObZ7f5JW2sKrvk889miStmOc67Fd1LI48jS3P8vOGRAm1b7N3Ic6ZnVwsMJtvbBb6/jwNNuaC2YsiqXRsWLRYusqhlFRkFWnnCm/QbEnNXNtV1aY5o0Y9OwW6eNTOt6tFCFEpodho8Rt3s15cVXeskuTbePNtVEYEcYWx5VkK/6fL0ZxTDuV4N/dZAXObnQgttZoAke2kR3mSBr/wKC24yg0a7TMkS/weVFo3N68R3C7EQWsDUNlhVupLgkiUL6QsG8AIMg723Gmm2F7MdfVsqqJeOTgdp2vjA3ymFgwDNo4J8mal88IS6Uxrza1IIyivLSzfEfbck7Gg4Jpt60oOG+YhkXZ5j2X1VXNobwIJFsb0uU8z0FX57tBxNWNncqITcpY1nHoJpsVrBYEIN/NotuybB6u+4phsOXjuN7QucbQIPf/TWZDg4owyrNtPS8nbG8rGlwVO3L6grJpZ6pcVpvGeR65aZOWSVvEnetVFYvesomfPzas5+WkzbTMFuRT0yz8GUx/PSfj5pNPHNXzROx30DCQZpqcdmJOy2KmtT4i9Jks9QjJx/ZpGTHxsGXDVRekPZoV9nqd669ihMYrssp+yDA1S7LKbwkGFyajErYIs+b1XSdkK/mi3fxAGEDz87rhRmQYWTZuTdJaTe0D8XjePKMpxycnVZE7e5znc5Uhwyhrr8McHgBz/LI9hIs4D7AbP1QlwqJhWJsLdZ6XzZv29ehTzGYO86bepCynzDsVxJCNbda5X7LxgDDTspYNJey3tmF3FquyMUZDbVImw3XamDaba4jgeaarZT9zitv8nNWSl9dtmedGge6qsZ2ve+e/7LHNq0aGLJU1HRcRhrYk3ZDKCrpYHi37Vebycdw2/Wswz2Vx4zY9f5Ocf7Ch37jJ1vA9r7NYcPrul/AGSj/yHXclaZ87+GdL5y+NALUGzM5M4xMf+zA2jX0lSetIpMjAgH4bnTjBfe6mm3WTmZow/IaG1Ca2Wjzfs4yhTZt4Tt8wGybErlRv6fd5R36HNj+/taB9Wpo2GqYumkW28XMjai+ulALf11abRG2eA8+ab6Pz0m/IzGHGJX+DWbMNfZH7yEBB+2UxtzLWmzOBHA6Hw+FwOBwOh8PhcDguA7gTyOFwOBwOh8PhcDgcDofjMsBzGg5GmQyKA2Xsu+7KJG1hjilbZ5/WEI2ZKU6Lgm0A0GoyZSpLVyVpd76YhYEHshrKUZtlSn2joly0vVexqNT8nFIOTx/jELLmAlN0R8dVbG5kx06+lxHsjMTEibNKkTx58hjft6k033NCET4zqe8zLsJPY1dep/erMqVry7CGI1wlIW8HDx9P0gr5MaydVxfQ6XZ6BE9jiJkN/4jiepYO2BTe4Myshm2MDksohRHXCjHUxXB1jzzF5RNDNfjWIsgY6eoZPT8KSFtKb8xf0wj5NWc5rORTH/pQkva5z94DANhztbaPA1dyuOD+ffuTtN17WDhw/1XaBseEDthD2F0HMW4iQjab7aEPd+pRxE/fOy9hCdu2q/hkFCRrGkrsjt0SUjWptMHxUW5bz5zXtp3JMK06ZEzoQKQpSj11bKiEhCSEoO1sZp77XrOq1MOyvEfoaHvfvotDHU+fMUJrIk5ZMmGfOQnJyxiTMzLM5X7k5N8laa2uPm8tIAD5bEZDAQBERvaZOe3DX37wSwCAHfuuSdLKo9wnCxWlp5fFFC0MKh959Or9AID9k1qWNQkvrU49kaRNZoTST1xuJUNVrooYuu3hHQnN6JLeN9LSrShlXkIL66ZvFDMconH1sIZFbjvNZVA2On6TNCfPUBvXzvWGE60GcwstfOaBU8ibd9y/g+3Fnq0a8lKLYUYFfZ9TVc7gkyb0a+dZFsu/Yo8KoxYH+X3OnlQbWxGh1ZsPXJGkbR0V4UgRf63PqQ1rSHjrsAmfGN/Nzz/8uG4SUJcwI8sCj2EQAyamqSL1UjFpTXlG1YjPloSafftrXpOkfc9bvxMA8Jl/8k+wFoRAvbG8kmcrzKpirfaFxCYYm5cXevFV11ybpO3cyX1kYd5Q9wvc9vJFrccgoQNdCXUka+NjyE3OhFlI2FFo2xA1ruOcoTaPjfE4nzd2JWZ5elrrdvIChzg2G/qMGPbUyWh7z+fXIUQAHCaXKxIrtiaJ8YcJEcuwbWh1NP+NAtfDkAlLHCyyvaguaMhKvc7v0rJ2W+qwXNQ2l+9W5e/i8bgLPq9thDLj4bwNWRaTWTZ1VO5yPWdyWn5RCzxv2tx8nkNOWhkNd+7KOBRMiFanG3rCGFYDCkCmE9AxY1RHynvTTi3PPds5DGJ2UsP7Nw9yOMDWYbXxJI2JzBhUlDCwKCgMAMdOsN05e0FDaYdEuLZS4X4zZMaOOQlXKxd0LNq1lctp+1adu549zeGox2UjEUDHhzPnziVptRrnc3ZB67FY4n5SGVAbmxGh4COTZ5K0ycbaYzS63YB6rYWuCcvMJvICJgxYDFDbtNk4fg0bgV8Sm7lli4arnBQZgAETInbVdRyacszIP7RlXKQct61z57SOcxK+vrCg85WKhIHYsLXNW7YsOi/ZVMTY8ziPs/a0LmLRZSM8fP4M19WQaZdzc3rvNSMQMmZMaUrofrauzzs/eRQAMDGlIrItEXefm9L547kz3DaKHSNaLn0oa8Isswt8bXFK504Rg9L/s3P6vXZgO39PzW/XMbp6nq/NblXZga4IeXdPahudle+F+qiWaV42iJg34W05sR/dltZRNMHNBc3LU0ee7hECXx0IQEY3PTDoiaBP2WUmE6UXei6N5W2+oeIONlboOWlri8drku/TYCYnuwfZXuwaUtu9czP3m0xD7W8Q8eCeuYE8PtfzKG63f/iBe5K0MyJhYsXEkzIwhRFSymqlaLc7mLxwAZMmVOqvPvCnAIBSUW3DgnyvTE6q7Tw3wde88lV3J2lHxHZUTV+/8aYXAujd8OIF194KAKjN67xiWr67yvP8/dk0r5crieTAuNqwE2Vu5+M1nS9tnuf+uGCuLUS5BCPrkJHviNEB7QOb8xIOn1d72hb7UzDhnCiurNydCeRwOBwOh8PhcDgcDofDcRngOWUCASxilauod2vvNeIJNgJMF06xx7jV0tXFs+fZc/dA+2m9WYE9+Hfd+uIkaeo4e8tGzZbPRfHgHzfiY198kLcpbNbYe379deoFvEZWhzdv2Z+kzYoXfaSseVoQgdzdY9uStG1b+ffJM+qBv/lGXk2tGIG6RiKkpx7bvTv5fQ4eOpqkdddBXSvIv1bLbBEozw8pK/82pSNLhVG8DwDqsvrbs32wuOAvmO04Hz3IK/oL8+p1LcYtUdtc7mWzNWRGtoa0q1k1EYmzeW+IsJwVlT51gVeAzpzXFbNHDh7kZ5TNFrYiDn7tC1+YpL3hjW8CANzxojs0L5m1+0c7nQ5mZuZ77tVBXAnXtNHN3Ka2bNU+UBLdskHDPNm6lcvg/Dktn/o8l/uEWWVstbhMO2ZrwijcHcVa2xktz3yey8du2VuVvtdtGsHiKEQXtB8dO/o438Os2JdlNbRg6jZfjvdZzFQ4PaF11qX1E1DMURYdI4TZbXNZXlhQ7/49X+Jt6nduVybQK1//bQCArHGu52a5Dc+Z+siNsff/nBHK7raZgbNtWG1CY45XueozzDDZpgthWJjjd1+o6ap/S1b5guWmiRmwW97v3iTsmI7aiPFhXom4tqyMmIUTbDMzZsvsqFjbNqsfnRB6xD5Xg2qjjQcPTSQsBQB4+iS/24EdKuSXkZW9iUm1DS0RvssYUdkoNjlktm3unuc63VvWfrBnn6zEG7HAlghy1+TaktkCOCOrxCNmm88Dkudj51TU+9FTvNrfNmUXWXxtM3rGxbiaYWq1pdKs/nBRGAIvfNUrk7SXvEJZQWsD9axKRhZI1/Q5SlG878pWv5m8tu3veuv3AABe87o3JWn7DzCz8n+/971J2tPH7gcAbN+p980I2yYyO+220XH7YStMb49HREYBQfMUBYabNcMikjobHTZtS57Rs/rejWLRulpux961gCggX+z0MIEigyD07EDA5VzM2Odym++YzQuoyHOXULfqr2K3OzpGBCmbgQGzbXKR7UMUa63W9Pyc1H3I2IbLbcMK0xeELVEx87SOrGZ26sp+2dYR1hHUnl4gEUEu6YrofIYNXsOI9CNfWPPuC6HbRXthASVzn4owUsaN4PO2UX7+qBGSH5bDm0d0btCRrX8tIySK7ZbNVu6xXQ0ZUecd29jeBxGNztvylLF+06YtSdrWMb52xKz0Hq3xfadmdHxtyZbDFwzTbXaO89kxbKt8hdtPzYhPU5HTZpr6jBEZfE5NPIW1oNvt9rDM4tzBCs5Htnne2POO2POmYeK04tzfbFwyJ2zvG27SedqIMHRbTW3To8L2nJjgdrlQ0/49tpnHhJERtQ3nz5yVR5nVdMlLwTC2Wznub1nDpo5zF7uVfLz33JyO35tGuW5nprWv2KiGNSEAmW4XVNfnnX+KN9Jpm410QhRIPqXMiNNVvmZkTCcgJbFZefOtVRLBefu9UhJWUPaMztVawvQgmRt1DRNp6wuYaT90xYEk7SlhNgbDCK7uYjuRmVaa8oKo7B4d1joqlGSTBfOdWJK+nssaxnmW+0Qzp8K/Dz75JKr1tTKBACwhL51mxnrIQVFlOOXinqQ4ZzL3i+M5mblgnGsEGdPKHW1b+4m/g+bOqQ05TvsBALPaZBAijcUwtuNmHsOGWXr2qfv4/JYdrxdvLx+3pl8fbq2CiFAolFCtqf175hlm81iG4egI24bDTysDvybsr5OT2i+a59muXDilzLP7v84b02wa102n8jluo9XHHtFrzwlrTuxzN2ibnRPTfiTo5k+zYot2mOeHDF+zYMxKIc/PpZGdSVpH7P68YePGzS8KhtE60uX7TS+YeswpC/ZS4Ewgh8PhcDgcDofD4XA4HI7LAO4EcjgcDofD4XA4HA6Hw+G4DPCch4N1AyFvaOfZEtOYxvaaGAnJ1fQJpbQt1CQs6YJSsEjCfTYZcc/BFlMwH/vqsSRtQChv3Yqe99g5vs/MgggrjSpla5NQy0JT81lvM+Vx1gg+b9/MHLBNmzTvuzfxM67commVCt9nMKdkuQURyHz4sYeStHNC4ctmlNrV6bbXHKIRYcOSrFhxP6z4bKSl2dCUeoPLzlKkc0L5ffTRg0naZz7Dgr9TRpwuZqErwrD2XceE0p8zNNSuCF0ODSq9szzI9OYwaUTIJMzKil7WhP5am1ce5OwC5/2hx59M0h565DEAwA/94A8maXfddRfWim63i+pCFVUjyJsVCqcNgbjqaqYQ3nqripM/+STnb/8+FY7MEN9o6rRSqYfLfLzbVupxaHNZUTDdW8oxREFeU58tyUvH8FBDjmnyU3Ut96aIrw0NKDWzJuKU5aEXJGmTUsYf+dRnNZ8iwFkZ0PtVo15eXkNyMibcaa0IbaDR1LKqFJjiSUYEsSKhXAsdLY+Txyfkr1Kf21W+z+yMvvv9s/yetaB5Lu3k+lioWxFUDu8YzLJNeMVdSpHOB7YrZ84rbfu82JjTF04laRMi5himNZ+3X/USAMD2IQ09Kw9xH2oeVsrovFCou0a0c1YoxDVDJS5UBlPDhVaCLAHDhSwGTAjA+SrbkK88pWFWw9LXW6a/BqGcF7JKgZ2XMNTDRvCzUoyiedpuHjjBZdXuWNslNkFCY246oFTdF1/PIbqlEWOnJQxvx2YNHzgxzfUzWdU8BSmjljHLk/Ksgim+ghi7ohlmZ4Sm/9DjX0/SRu5TQfi1oNsNsFzyRKvRUsnlb8bU8+AQt9+rX6B9+JZbXwQAOPaMjouzVc776GalTYcjnPd2Xd9xcFCEpsE2KQS1v51OFLXU5xO4PvMmpDXS+m1IeLcTJO9G4FJCSKJwOgCUJfS0VNSQ8HhNraHGeHpGx6W1oJht4+rBiR57G4WtCWaTAwlBabWNkLvUSN6KEYvt7Za0HRK4HcbxGADqUpnFsrbhcpnrI9Ph+7Xbi+u+aDcqkHZbMqG7wxJ2M7pJy68xz32tfk7bQ0k2xBjr6vsUAv9eyGs+o9mZMcKX7Vxpzbam025j9sIENo1peMk1+9m2bh/TspuWEHEb8jYkcgE7d6jtpA6378lptVMTc5w2Z0K05kUslAomzFLCDDeN8n2HBjXMbN8ODnnZvllp+vW5acmbsdMSNt81fePCNIcvzJtwfEj9zSxoWy5JP8h2NO2cbDCQLWndXncFh3I8dnD14WBEQC6X7ZmX2jljRDaGRJq0uInBoEkbFPFnW2ZbSjyPnp9RIeIo3Hr1lbqpx+GnDwHQcCwrG1CWkN9ZU3cTE1zeZSM4HfPebBgBfwn5aprQs9h+Npl+EcNWazUt98nJSblfw5y3TuvtoQtqNXDukIa9zB7iuizOzCZpA7JpxNhWDUGM4uF25h9DgKNtBYBM4DIsmzF8eJjnbVlTRk35rqrGMdzMjeZEyLeeMfZHwrfaNtxSQlyyTT1vWtr6/2HvTYMsS67zsJN3efet9V7tS+/LTPf0bJgFg2UogARIgotM0Ka4RIgUJTFE25At2aEIWWE5TAWDPxThsIKKsCmbtmyDIVEUKdIEKZAEaCwEIBGzYfaZ3ru6q7v2qrdvd0v/OCfvOa/rcaa7qqfBiM4TMehCvvfuzZvLybx5vu87zZDHbbJLVLKOGPM0D0w/AwC4RDkd+lz3Vr0OUSK4/fsxRf/p28pG/+CPJKVL0mDN5zTH1YiQsikbc/uU66+oXxTxwoIG++RoiEmFXrjBdKfT53Cs/+izn8zKPvQEzq+JqnivojVg5fKbWVn3Brb7Dc1jPjT+SQr7Z6LWko9+cCkTx3GhWCxDf8D71L4RY88XxPewLjeu83t/jsSVByuc6CO3hmu+vrqclb1BcymY5rly6TWUuTjU5rXgIyQ0nQuxTVoBP9/VIT7/jS6v408QrXJWjPcu0cw2Im67Fp0LlIfcZ+UQfVYgBkMvj+8WQcj1hAH6R08myxD07DsxiwSyZs2aNWvWrFmzZs2aNWvWrFl7AOy+IoEwtWQfnByf3IJLgnI+nwRXZjGKo0Xqv901EmMSYo4mctOq8wna8aMYbdnaYcTO1RWMEnfzjATaoDTWLRJufPsWn9ZVK5ii8JTDJ+vDLp5Yr69zVFNt4MngZXHKHFOkUZ7cGmFAKQY8pFPS7RafHG418TfJiLheDAeW29KYGjgUIrAmCicF60yZRAkZ8czBgNvHCCiWhXBkSEJjV64wwmZzC/slJ5BfqWkXOuHM5wQqzDSPaE9Nkc/ZWY7inzpxHAAAVnf4BFz1zOknt92QhBZzLo+3wDMCofy9i+8ieul//xf/S1b27tuM0NqvOY4DxWIRPE9ETyjKIbImw/I1M2Z4vF1fxu/dXBaRV0r9t3ydI9g9EjmOI5HOnAQjHRFx9kisdUhzZhCJk3UKrsiUj7U5FGwPfb5GuIaCbGHCJ82aIi5OjoVAX34bxWJ//r/8R1lZkMcxdegwo50CEq6cmGBkQb54d6Jm72XKAZiZ5Ujv048/j3UViLiFRYyMzk6zKNu1qzhuz1+6mpXllGlf7rgGQUF6Iu309lvYXzd3uH27IZ7WP3oc59fR438l+6xEgocnYxamHoYGpcjoro0djGavXWHE0ASl1J0/wpFRneKzbZ5nnxgQGiB12ceGFKmPRIr4RPczZMV+reA48ESxCE8WeSxtEAJgK8djNCbxWUeIi96s4/O2BHohIF/ou1J0l64rIuHGn4RCwDCiCG8uj0gJV6CzUhII1GVGUQQBoh0mChxVKdH6tNrnOWzAi+LukCMUS1GgHUJqS4kOKpIw9OQco3/ce7AKa0AEwYgMMUXqtEQHpdjvU1McRfv0j6L4qgKee//6N/8VAADs7vL4OH7yKAAA5HLs93WC99jdkffA9TvIk29XUlQSfYcWgVmD0ink+f4RiXkPxZplotU5j8dRqYj3SoEv2KUUrq5IXGAEpqcmud2rVYNHeBEOYkU3hg9VtyFNBUIpRR/sKV57Itpr9ERKXyNqOpXn9nNpDXPEXDSpiV2BZqFAJ+TEvPIISWkSAPgixXVi0CIy7TWl1q7WeB5M1LCNcgI57dA9ygnXvRehL0pFgoZSnqL4KSNcIkJUJz6Pm65bHJtu+W5MpymE/R7MnDiUlT12FsXL+zt8/5BQnJWq7HtK3yvEQIu0F5GItOYQ/W7c5n3n4cUFAABo9xil0mvgmnzu1GkAADhJ/wIATJBI9c7ajaxs/SZGrJXoizohKdoCOlwi8Wm3KJASLWxvTwgUezQYoiGvzSGh6MplIfB9QP8OgMH+OB6PUJd7x2w/meOyCqHbYpF+3KBC0xGxZvztkaMLWdkWJXY5fJjX6oskityhfeyCQL5srOE7wKZAW5Uo6UggUAQGndiTCUxovMu9gtk/r6/z2jpL6eWlWLQRFpfoqDyhcEAgBfZjOk0h7LbhnRf+PCtL1/E5q9Lf0T7bE/XPkf9UYo9cIGR24LMPyVdxHVQCHZgGeB25R6zO4HzapXnsCdRml/beQ3kvTSidq9x+BRKQHw5479vN4VybF31JoDZoyzalNg/k+ySNyVQsMIcmJyF3LxZZdRsUyAjtj3xlDPpF7cVamLTxjvh1SnV3BDvCoTXDEft1RWNI0cY9V2f0S28G225hmt+XPvGxjwMAwA9+/0e5AjS+t1vsL96+iAlENuuMMrvcxPu/OGAkUORS8gH5nmaqLJ91DDrwrk0BKOWBL5AuIe2Po0jcn27liLXVob2gprTwAAAuIYBKa4zyL91CpNDWGvuJF9/GpEYfXuDnfkbhMUbOAAAAIABJREFU/A+HhI5ymaVxkdgXPZHgpdXDeywLv7bq4ZhuuOx/aoDvWNMDwUZq4ZzWnkgCRElzHLHzDHtYZ+XxHHAifm+4E7NIIGvWrFmzZs2aNWvWrFmzZs2atQfA7CGQNWvWrFmzZs2aNWvWrFmzZs3aA2D3lQ6m0wSGnTb4AUMPDXzO1QyLjQhCWJxnOHKORMimBbzxE08+AQAAUxMCMtVFqGGnz9DOW02EW11bFSKXMULvYrr/1SbDNL0r+L04ZQhenmgWDfG9QYxwNEecpbkEgfMchoAZ7k/kMqRtrYffW20xzLhJlKpEQOpSSA5KBoMwCmFlZQVaLYZcGqjqjRsMUb5yBeGAUrT46HGkADRbDDEzQnuPnXs0KxsQhPmb3/pGVtbt4v18IdxmaB1+EeFwBV+IYBP1LE0lBQDbuNtj2OLaJkLlQkF30gSVi6UAHD1GLETvzHjLqb3CyGvXL2dlX/rDbTioRVEMG5ubkKY8jhWNX0eIxTUIcvnGW/yMhSLCZDd3uS3KJWxHXeD5s76FY7UsIOwFgi3mNNPGFD1vn8ZslPB1BwOsSyz6PXEQ8lmcPZeVHaogDLu1zUKLm22cZ37xSFY2e6xIz8p90SCxzU7C0PRGG/vv8PHjXE+HYZIHMa01DJMhnHyIYfnnHkN/EQTsLxyCN0vo9qFjCEGvCgFID3DuKkFH3djCOfEnX305K+vdwnHjCSHPQNN8Of40AAAUCzwfQhKyVAmPh7yHMOtCjekLc9PHAADgzLEns7KEBJ+1gG37RFuT4uoejflQc92B5mFRCK7vRD1I4WAQXp0LIDp8CvpV7ucPV7EuE2UWa90gKt2WEO1cIn87J9aHI1V8NleIDOYIft4R1K82/f27Kwzp3aJ5f+rYUwAAsHCUIdKVCbxXTrSTT5TFx57+SFY2dQLH/9UNruf2Dv69KQRk2yQaGPbZJxlYt4y0VKrYBpUKj8HeQBLLDmhi7TAa2RKibghjiea2y5exnTbWeH18/Q2kyC4t8vwvl5BC0xN0lRxRfKamF0QZtulggH0xMcE0r24P1+cE2L+6uZiuy1DqiIR2Rxc//F4KvLYDUX0lTdzMadfhvu0TZSkWwsrFoqARHMAc0JBXMey2eb8wWUP/nfe47fuUhKImKK8zM/g9uYdJEvye8riPPKIF+ImknOG/sRBfBZrjMbWfFEM2FOR8ialfZeqbSpXrlCsiHUQLAW6P6DNVZk9BNMR71KVAMNEh80Pea0yTcLQrqEJpUAHngL7G9VyYrFXgyXM8Rk3uDR3z2rM0j7D9paMsyF+mvk99QQuiBhoIWgvQOnhkkR98bgavLfRGibYPEBkh0R77gfkl9Ds7l17iOtXQ398Q63udaIxtQdEvaKxfJBIc6CH28WSBhfGNWOyu2PeGNI7SkPes3cbdiYaOM601xHECnsdrlqFNSWFm43eUEEXOEx3WE/OiQMkZQNBgdhroC2YG/Nx9+vjWGje8T/PfPH8o/MAWiRNPT3HftSlJyOIkj/edLdwnGeFkAKS7AbDwMwDTweReYXV1deT58blpjyccf79/MBqYsVQD9CMNrTb3o1vHcaMEZVpR3ziCitIiCQRJ+a6Qr5yStMgj6JN0gefGgCh9xUn2HaVDSLV3Sri/Dab4Gds5EsFPuT/ql7DfHCGB0SfK3m7Mz9Mrk4C0WA+S1Dwbz5eInifUPF8MvdwV82VhcRH8A4rQAygcn1LseMw1x7+r0TwQbiUgGqgj1mGT3EaJqxi6biUv6G159DHHTtFewn8o+2yS2u6Tzz2TlT32GNK9v/wiS1x8++1lAAA4v8J7mOVtbMdOxM/VS44DAMCO4rnhmHdbqQE9TgT6wG2O+xWlHIiFTzb7mpH3OqJ7p1LyhNo9bPB70FSIe/ZCyOtTnt4Z8+I9tUiUyLpiP74+pOQ69O62JujrmzmR2IrsLZIXWBbyAh3ab1YGXKfTu+hDph1+x94lyttmjt8dgybSrycG/D0vNPIe3D5xIBbpOzCLBLJmzZo1a9asWbNmzZo1a9asWXsA7P6miNcaRX+FGKhKqQqxN/o9APBLfDJXJXHPQ0J4c30DI5dvv84nnOYkNBIpfRsxntxJQcY4E6ukE2vgE/9ru1iWDvkkerGM9StITWuKsimB6jAnln0RRaPs9rDbERGENp4+dlLx3HQZV5zoY3rfg2GB6vVd+He/91tQEgiSLqXUXF/lqMqrr2P09/wFRsTMzCIaQma4NGndTxw/lpWZVIcX33knK/Oo3p4QhytStCWlCEEYyZNbSvcbSyFROu0X6TbXbuHJ6VBGnShS0RPf8x0sc8VZp0OnwznxPJru4QsxuWHr7sS1xlmaJtDtdiFNOMpboOhKKFJjF0v4txJq0ZGiiK6I2vRNtE0Is8YBXk8K8KUK+zbnsPiZT2nYQ0JgVYFPmEEZQXK+f0KCpqGIALh5nHu+OAFPOvhb7fLpc2XmON1TNHKAEYeCmECeh89WqfIcuBdacgAASZpCu9uHl199JStrN7CNHjr5WFa2sICiojkhLmiif9UaR8ICKot6HMXSNK5+5qefz8p6JArZG/D3TJrmk4fxXm4oB59J98lFKX0/Fo2Rkr/SYoyQW4PVjZWsbKJAaSQXuI92Kb1rJPxPPsbxkOsLMfByYURIcj+Wag29aAj/cZvH98ttbNt8gctMG+82GGGT1rHNPnyUEVh1jeNvdYORI0Y0eFGk+a3No+D4h54/k5UtzCM65dEl9FNxIFAULt7r3KOMrJqoomhfcUIg90gsNh0KxCZFk7c2V7Oyd15GNNg73/hWVrZzFVGWtwTSZ4ZETR2RwnZZpDXdrylQ4LrubamacaxIAVeTGr7X4ef5D19Hf18qcLvnaJ2V12vRc09McNTr7FlEYVQFas2l6HKriT5hY43byaRtLeVFytkYEUODIQvtDqi9dSoQA4Te8IQAaq9LY0o8Y0J7CVe0cY4QH6lAw8m0zgcy7QCkJZiocBsUAhxDnkBmFSi6WK3w3DQCxb5AHKSpu7eM5mUiFuKUfIEU1DVoAIO6yQm0hk/rRnmSfXWhTPUUa4pD6IpkZNtByEGBPpmeRl+jBfq2F9LeqcN7pwlCpg4HPIc9bxeUqPd+rBAE8MjpkxAIn3XpPIqanljiBAQVQjspgYoKabO12RQoAhJf3a5zlFjlMNouhbPLOZN4gcdPs49IhgLdolbkNX+LEOhzFfZXwQyOx+u7jMQ2c60oRIvzefyNTvlelSL2Racn0Lz0uURbxSTC3qhz1LlUlsnZD2J6BP1ibKSMFrVE7HV2t3FPcvpRRm8ZpKCv+XtXr+K+dHaB14fvvIFIquInP56VNUjYdo5EhBOBBJpfwLI05oFcId/uC9TPgBBtWoxH4ydlkhaD8FECsZQQYigO+b7pmHHtCAHwg1iUaFhrRZCbZP/Zu4BjyC0L0XjaZ6YN9qkxoQYSkSglJXRiEDASZ+kICgjHoo2A0nKL1yRo7OAeORfjmIuFeK9JBpAMeX4NHbxGKDY7XUI8hKLNcoSgyAlQv0trd03zHIoI1dkW4ytHfqoi/Bk0WgDJwXwNmoLxuAmJeNEj/+CnhAwTe3nTU54cSzS+UllX01YCOTdBaOKlIs6N4ZD3mjfr2D5//E1OdvDHb+H6+o0LjPRdp/U/FgLFiYvzsONINLFPz8D+J3s0OabNPuGgtJU9psBzcyMi0OY1xZdrliIk4JDrmVK773ZE4gJKNR8JBNaAKh2I95WPErq0dWs5K+sk2M59Qv1cEd20S+Pc7ct3OJwPuxHPyyCmJDF9vu5CF/slzHN7tilhyZbLz5gHmmcCKW/06EOBqh1mn3MSmfcyiwSyZs2aNWvWrFmzZs2aNWvWrFl7AOz+IoEAJQuUONX0gCIdsiqEgpBpgTVxfi9t88n2zlU84Twi0r72SF9lY5u50YpOCaUOCtDJWZZUV5xgDiiquCzSgraGeBI5I07rypQ20RFptA0QZTvm73XohDcSUdWEonxSm8ZE/pWIHrrKhYMyK/v9Hrz+xitw7mFOQ91uYLTr2xc5fd7qBrZtChxNun4NowzNBkfxChQhWLnGv82bKJvg4ufpRNkX0UuTXn5IDeWIp/OoTSSfv0xRsZI49Q2He/nVRjkpkml1TXt6Ik2mZ9BBIupjvjcSRD945EBrDeFwAH7AdfcpfTsMOcJ1412MlEeiAopS/WqpAUHjNxbpLocxXq8sUqG7Jp2wiK6kdAJuInUDJfjepNmgHb5XjlJjpyJqMxhinZqh6AuKNA8Vz61SgvXL+xwNni1QWkuRktz0gRtxf/pK6NYcwLQGiGIFt1b5NLzfIW2wZUZezM6iltGxo8ezssVF1HCYEjoZLmlXea6ISCT4fNMLQmtmEU/wA6l5QHxp1afx3ZVjD9stkig0ioj2BuzDBoQs6gw4qtHs4Rx+9bXXsjIV4r0+/dz3ZmXTH0UtpO2XGeH3xDRGyhs73PbXGldvD//ftSVhCM1b1yEUad6NPx3I9MQ0Dw3nHYD94+plRjbNzyGa55FHnsjKzh3HPju0xDpUx44h2mdGoLdypJVi0vcKICiUKxhJrc1wemmH6pIK3Rije6JFkHOKUCxTVdY4OnQU6/LEubNZ2ctf+SYAAHzrrfNcJ0pLvrXFKL18cHBtGqXUiH4FACMLJJrHpIsXAXO4eh7HURAMxY/xWnNz7FdOP4woK0/0WalCbSA08Da2UPdn7RbOvUhokph0vjtCC0WT3pbrMgIrJDSo44oxk9L3FH8visyayV8LSB8tL7S/YoriDYY8p4YCNXoQU8qFvFcFJSLsAfnvktDVKAbmOQVymJBOQgYPFO2FtOZ2dh2T8pn9xID8u+xfk9a9RPpbI0ggWodyeaElQ32ZiHigQfU6olETmhMyA7DRE5IaFv02rmuNiNE0fdI7a4o1N0h6oA6Yrtz3XFicrsHqTfYXIaG72kKTLW/aeCD0oKivXIHYHvbx8/LkXFYWUWC5IaLJfpXaVOwrTMpzRchcLSaY0dDavLHGdcoTIlnMpSppMXVE2uZNQrsPIok00XRdoZ1I63pQ5r41+ylP9GN0TxAR+jbEIZvUBMoROluiYAzKdeXaclb2zMcRSTs3wSnBX375dQAA2FgT6cQJnTg7yd9bK2CblqgvJKK2SSiYdZEi/rFnUSvlnTcZsR6QTke/x77BoCddgR4zCPWyQHn1Sb8pHApEOzW3bKN75Wv6gwG8/s4lOD3NbdCge1ePsa9OaK5HW7zPdIfYD2pS6Iws4rO0Bap148oyXkMgQvwWXSfi5xjQM0W039MiTXXXaAEJbTDdQTT40BFoHlpffeFYNKE7cymvG9N0bTfiDq4PSMuzzu+ERVpLjx9hjTpIhplPO5ipkYVm/HsZpY0XH5o1V4pEheTwE+ETzTuhFlcmYCWEAgH1ah0/r3mkW6X4s29v4Jgr5XjsLV/CvcYgxynNHYfaVqD/gXTNIJVovpTqJPEi5u8RuNOeonsFC0pTDUHA6+iQ9hOxQPiVaE4WxXu3Il22KOT2bDUIsTPkddSst67QHxu8fRUAACYEOrFHZwYr9H68JjT7YkKCVnK8J4yMTpzQO86TLnA55TJNrIu+J5DYMc7fpYbQ8c2GkXj/ozOSyOO28EJeq+7ELBLImjVr1qxZs2bNmjVr1qxZs2btATB7CGTNmjVr1qxZs2bNmjVr1qxZs/YA2P2lgykAcFSWvhAAIPAQyuaLVL0mPayEGRsxNimUW5upUhnDvd65jvBRJQTyTiwhdHJ7yLBQMEJuBt4ncKQGdjUQaLatkASfBV2iYmgvgrZm6FD+JEP0UoIwppEUcKTrCNqEGgMwPKBOKwAAJHECrUYzg64CALSbDfqXoZSKKCmGPgEAEOQQhqcSfh6XoJV50T8ewZ8TIfRsxE9lOvSBSeGX3EbHAxZwXphlKGdA/ZiKfm/TNUKRDtCIO+cEVcrRRtxP0CGo7oGAKOeIaiZFAKXo9H5NKQU5z4fmUKRj9rEuEyLN8QTRGeOAO7tvmmkgKB5EmzKitgAATeqzkkiTaWhgqRDYzvl4nT5RjN7eYpE4I8hdnDqalbVXrgAAwOT8SXF7nG8pcP+YDLupaPe4g3Ow13kzK0sHWOYLKHBrSKKOHkM9vfTn4N6YgiR1IYoZvtzu45i/eo3F0K9c+gMAACjkGcb52KOYUvx7nmPBZyNC/PSzj2dlNaINdIc8D156DWHVCzNMmXviOAqxRgN83uZFhrj3dhFm/dbNd7OylQ7SG5qCMthrUfpOkZ67S+nIl68yLbO7i8/48qvfyco+98N/CwAAlk6dzspOUprotMbzb/virWzO7NeUQvqBJ0V5yU8UxBwe0HztD6X6I47/YUukAK8idP3TH/9EVvTUUyjm7ArKnWNEchOeG0bo3CtgXxQLDOOvkAi0K/xARLD6KOQxYwTpYyE+aYSpZZrjgMQsp09yGz9MXJLcLN9XEVVp7hAL1/p5wTXbr6nRdOBoRhhalJj+FT7ZIUrMCMWD6J3lCtfts5/9MfxIwJEna9NUxm1xfXkZAABeexXFststTkMbU+rezU1ed3K0xmxvsA8zItTT84LKS/SScMj17LT7I58BAMzOzO55nj451LwQVYQDjnVjSilwHBcGfa5/nuoT5LleIY3HvhCP7Hew/sNQ7GvKOF6rYk/kU3/EgmYU0fon9z8J7aeMMGySE8KfRLnoJIKmQhB4SaeboT4vSHYhiT+PiFDT2u2LeTUkGlZQZCptFBNkvS2oSGGfc9zv03SaQhz2YSCE16dn0Sf3ErknRH8/vcRlk8QNTcT8z+ew7QpCwLkR4Tjc2eXx6pGA6OSMoFeQL2hs4ljvCmrRUx9+DgAAzue5ni9+G6miIls2zNO43djkfWq1gm2bExT57V28h/R/AaU3lmLitQlc0+oNTnIRievs3xS4rjcyv8zfvth3u7QniMUe1yUfL/fC3S62ce0E7z+mp3FPtHqL1+pnnsF1WQtKUJnob11aF1duMK2pQXSw2VmmTrnkfw8fFjRgmhfNBvspM7dkP87QdbpC9Dyi/a7cp7skXC/3p4ommD4gLWk4DGF5eQVma4K6ZvyKoEB2m9gekzFXzKSRLs6xMPzkQ0hl31q+lJWtvYTJNHI5HktFkgzwhe8wFKFeEcfZhS63y3YXfeGZCvuGBRp6iUjznlK/SSpZQr46FVSyXAHbdGKCr9ejPca1q1eyslkaDx8VIr9DnYy8S+7bbkt5rscXY9nI94iOKsoiolzFko1lXsedvReMNLfPQGMbXEixH6UYfC9AEfi8x3sYt4D9I/T7ITGUY8mfNH/uZYONH7fywce8R2djfu8v79iSNIF2pzmynywU0TdUhTi6Ed/Oiz3QgHz3UKRPT2m9c0NJi3WonlxTTXR9p8T02pUa7kU3aJ1IxJg1M0XunzVR8rTLa3aHqGlXczwHJ0hUWgl679AkEBKJo1KgZD2Sdkn7C3dS7DF32I/diVkkkDVr1qxZs2bNmjVr1qxZs2bN2gNg9xUJpJTCaKU4QcxTlLYgBBRbbTzJSsTRpWvSMyohxkRihXUhuhZMY4S70+Wo3M1tPB0dJnujINkBpzx8NVrRoiyk/9MQx7mdPl6vLM7SpikF9uQ0Iwu2NzGiPyJaTG2QilNfc6IvowqOf1vd9mG+78Pi/CI44jR3bgZPIpcWl7IycxLd6fOJpEkHL/Swob2FItGbN1l0Nx4YsS4+9RwQCiMSUXTPnIpTf0rhzDlKOSsPp+s72HeJPEcnVEtBpLzvk3BdTjSeomi3RAKZSJg3kjoXT1M9JUQA70Gucp0CpBFAyeeT2+Y2okC2tlksFihVulfj7+Ur2BYOcGrafh9FEF3FUSAPcP6EbY5U7pJwmufJdIlGpBE/S8XpeEin4vmaSG/YRdSKIyJhPgmVHpplFEMpweiYViyO3A8xletWnZ+x7GBUZ6bKUTlP4T1ch8fgaLxk/6Z1CkkSwmAE1YF9vrLCIufbm/h3r8OpeltbGEFIOuxr1pYvAgDAf/ULv5CVfeanfxxrLMZ3kdA50bYQ5T6OyCmnQKiSXW7T5DLeq3GJRUPfqr8AAACdMn8vR9GfwGV0gCKR6nyV+61B8MWvfetPs7LNC4gU+qGHP5KVnZw9jnXjgD1MlufAE+Lg+zJNPlVGhOjPQPhJI1ibFz7J9FRbiEROUArtJYGcyRdx3oeREMAzAoZSuZZ8jUEYypSmAxKQXVtjkfBVEjJ2hZC9EdidmuaovxHGT4RoYEQhPS1E9ZeOIorOEaLJHYo2T9ZYnNPLC3TKPk2BAsdxRlM0G0FCqa2s9wo9ZkuQlugg9IXLVy5mZbubiFA7ffaxrKxD4uRKIIumpjBCd/I4RvZXOTgPZULUnTnLiKkpQtTV64x8e+U7/xEAANa3L2RlXRJfdAXyVhHaoCnQY406CqDXRHTMoKS6XYFUdQ+4sGaVAPACFyoCdZPLU8pakYxip43zda3FY75NkUElci9PdtBHn5jh+pdIUL8rkIADQjMPE+7LTUr9S0FQUAJdaqaaRIP0KfWzDOqeWiK0xLQQGTbrZSqEYem3idgoKYrS+kokQyCUUyRQx1G3NZKSe1+mFKTKHUkB7JSw7SpiTgVVHI9Omedc36QP7vHzTNEmZ5jy86yu43r97gUehx968lEAAHjo1LGsrEQCr/VdnA+NFqNvbpFwdXGKEbQnH38WAADWbrLfr05TiuYeC0PfWCUErXjsiPZY/R7DiIz4skQ8GDRaXiC77wnqEBAZMC5FvLSQxqcnUHoB7feUiOxfehv3CTNTvBj52Xjbu3eTa7pBAFwjMeOW8AMf/TgisKS49NuvoOC08esAAAG1iRLP0zH7HlHWamO/hANud03+NBaoa+NrlBQRzv4+YOKFNIV2uwfvrvLeqkb3azWFCHOP9rlibnZJaHlmiVEIipgNvvCLZbNcCphKTCK4iUDYpeTvrlKyiq+ts0B7lxAXSYf7am4C56RE4WtCFnXFWjpPKK2ThznxQ9kn5LBIntK4hf2aLvGecpeQoY1V7vPU8SA9KAKOWCwjsFrTFOOgQCO/NYgY0XbZPkugT7I1YAS6Sz+QgtT49xvr6ORTkcTGzaGP2xmwD0l8LNMy0ZIQ3eYLa/qH2zhDWI9hU9z2Y/zfMe/RB7E0SaDdboKf43ejSWJOPHqWk3B45Gs2L3Lyk3VKUOEWhPg+CXIHmt9TY6CxKsZ2mdDOMqnQFvmxusIx7coEGSTEHgvfYA4QPE++0OPfrRnez75GqC2ZCCahfW/f5z2FpvppMX9ierfMF3htG87enY+3SCBr1qxZs2bNmjVr1qxZs2bNmrUHwOwhkDVr1qxZs2bNmjVr1qxZs2bN2gNg70sHU0odAYDfAIB5QJzar2ut/7lSagoA/i0AHAeAZQD4Ka11/S+6Dl0NlOuMQMsMjNURgnbmcykGbCBoypHCZAiD04Las7CIMKu2ECm7eRlhinEs8Gnm+IuQbSOCqOa+EvWWQf8EvDo14sYMx6uUENLaFTDjIQmOjuggjhH/MnqbucCDNEmh1wph2I8hSVJQSv39/bb7zPQ0/M2/8fPw2ssvZGXNXaTBhBHD14Iiws89IbznOQZDzvC5Xh0pDRWB/Vs4htB/N+AhtdNGSPRuk/siJJHAlKhv5SrD2HZJyO+W4A+4BBtemJ/PympVhPKWQv6toXw1WgyD9AkGnQhqiqEEaiFwlyeopUfUgjCO4Va9CTGK1z6637ZXOgUnGkLO4Tp160jNaay+w1/UCOONbnJfzBw5g/WN+Rm7JLhcdJg2EZLAoldmSGyfYMHbHkNgNUFGvTzCC50Bj9nJGsJvF+YYrg4e3msiz4LFuovXu3jhtaxs9sTH6EN+xjDF8TEImXaVI+pXHIqxRbBKx8H7drtd+LEf+yxsbW0BHKDd8Xk1hMMBDAVVNBpiP29ts3CagW6Xy0wfcGji5wKGoK7XUazz937nd7KyJ595AgAApk+wyOSZU9gPPSEKuUl01MMLSCmaPc0CmLub2A/PnmCKTa+E378AV/mBXKxL4DHUs93Gfp5bYoG88jRCvSuTgvK2hpSD3/zGb2dlM0TbmD+2CJ1BH7751pswTCNodXoH8jUakH2kR8r2cm7NlMwLaGtA36tWmQL5qecR0i/0+SAcYhWM8DOAFKEXlFuC3qYEszaCnQAATRJL/eK//4Os7Mv/35cBAGB2mtvz+Y+jOPgTT34oKyuTcGx/wDQUz0M4cE7AlhU9ZOowNebKFRTgvHrtMjRbHfjdL34DohhM08zh7/axtioA13VHYdsZvVhAzs06OoaqoMTaZtblen0nK/t//u9/AQAAf+2nfzYrqxDVpiPEUoFEL3u0Bscxt9P6JlLulMPzcnPH7AH4/qcexuseOsIUxgatO80Gj+2QeE+uJ0T9YyMIyc8YJzhvUvL7nXYPvv7VV6CHlJoD+RpQCrTrwkSJKSYezdNmm5/z5gaOuQ2BGO/F+OxaiOcaMeIasE9XxNHoCfHXPvn+W9u8HixvoZ/1SJjZkzQIbf7heyWpodlzW11bx8cdCGj74jTuDUpCcDU2STJEUoBilagmnqQs47XPBFNQb7bhN//gS7Db7EC73T6QrwnjBFYbLdAFdg6a+qAwM5eVBUQDi8UeMxwSlU7Q69yEoPUCvj/o43NcvsI0gzAhGpzwNTHt8QZ9arM1psY0uniNw2cf4srTPjHUvNcZksjwFIkiAwBskgh0LHzdLNEtNX8to14NxPioE03OcV1IkhS6nT70+hmFf9++RjkKvMADR4iwatofynFkKF3SJw2JQlit8FxJqO7rghpnkpM4Yn1YXcXPT59hKqlP1NzhAK9x6iSvxQHJBrzzltjDGMoLMF3oCaL3HT7G67LZN+xKkVV6DHeEBmfmD5cklPTA+FqtNSS83zyYr9Ea0jiGHSH23SVqc7fPP53uxgN3AAAgAElEQVSgOef5vF/QPklarHFSkIGRtBDC9Ir2jVq8a6XaUJp4feuTWLFJMrIl/K2mNe9am8fjoxO4rk/IJECUICfyuZ8bQ3yenW0eD5oo1S1Bu9vawectTPB+YdgztMwutOMYvrRdhz4ANMPoQL4GAPbSmwzN6/3oYGN+rgV9ms209146mBLvMC59Lw2IWimurIHmoeh3837qSgrUGIqaue1ogiIzX/Z8bawppUD3dkG/8H8B9Fumjfbta9I0hcFgMEJpTcjXODJJQg/XzFad9wYRJebopYJCrc3ZgngeoiV7knJnzhRENxWKWIc8rSO5kqCDZecI4p3Lx3UpEkLOIbmOWyJhzQ0X1wKd53c9X+P8ESxJ6CkSURfnCA5RY7V47x3C3SU1uhMkUAwA/0BrfQ4APgoAf1cpdQ4A/hEAfEVr/RAAfIX+v7V7Zgry5RzMLJVNFgjb7vfJFAAsTtbgYdQheRds298XU8qBX/mVX4EXXngBwLb7fTNHOfDhh8/Cz/z4p6FWLQPYdr8v5jgO/ND3PQf/0z/9x/DL/+M/AACYs+1+f0w5Cp7/xBPw1//GZwCsr7lv5joOfPb7PwE/+9M/CZN42Gvb/T6YUgDFUh6Onz0NRx86AWB9zX01x1FGa876mvtkDij4xGQV/vPTJ2AK9Vxsu98PUw6oJ38SnM/8Eqjv+4cA1tf8pbb3RQJprdcAYI3+biul3gWAQwDwWQD4Xvra5wHg6wDw373nxRRFJUbE3iJzn6zMUQaZIYQqXRLPFVEaHeBJXNTn0zcjFjq/wKfDDh2drVwVp4QCIQAA4IkoaCVP6TbFmWeWNl5EWsqUQnGpyuJNxQC/uLrNJ3MmLe5IJJyiCZ6IfrokTBiI3Kyui4KfCST7bvcwimDt1upI+tDqBJ46johv0wm9FFw2QoO76xw5WbmBIrpqwG0Ykxjx5Ay3e57S1y3OsxBdl9LgmfP8tW1Oh7pKEQWnyCesJx/G6Nnpk6eysjJFf0Jx/ykSur50idNFblNaVxlhNiMvEmJqQGMwO69VKKpIYzIFXLjvuu1LpQA+9uET8I0XWVwVIqxfrczR24QihSIbIDiUHrxW5NPhqI31DBscITGolX6XRQHNtB7IZ3Sxrfp9bIGC5n5yazgufNFOjsb+fuY5jrIGhF5b+z0WJZybxX7Z3uV2d6BPdeI5MF0lMW+RzjIhkTYzFqsTE/Dcc8+Zj/fd7gAYQej3hzAYcjQ7pQPyfJEbOqRTc5keUlHq3UKJ50GhjJHwl17n1Ot/9iUUX/7Jv8lp7eeP4jhc3WEERaOO7TDVwzYvnWSR4Tylyp69wNGKx9NHAABgbU1cw8P+HQLPV79A4nXCrxTyJLheYaHu7iJGjntbPEaKhIrxZwKoQgBVAIhyqUn1ve921wAQaQ0CfDISHebv4RcS0e4x+aKcmAgqpZTiq8tZWadN0X4hhlepYDQliYW4nkEb1hAdqoCjSYUCfnZWiAtuEkql1+Qo6/oNRGNN1ni+zC1i266tcxTfCOLPzIjUn2VcF2TK+VYb67dd5zl8fSWbO33YZ7srUOC6bhaFBgBICakwHvXDZUbg1RkR1Td/cf+89NJ/AACAphD4ff57PgEAo0i6hBC3PUrd7bo8Znfr2J7KY+SQS2v6qBa/WR+FwK9P80fMS58+Hw75uctTeCFH3LfdpXWGInW5vIKpyayvDuRrHMeFoFCFYkmI+A+wDjsNRkENCPWTLwj/Q4KsScLzv5rHNi+J1LJ9EnodCLHW1Q30D5evcxrtZoxjvEI+zhd9mtAYTUQEdUhon0SINLdps9Nqs79ot7DPHz3BIv7KoLiFQKYRqzbjHABggtC+h2drALM4T29tNSCPqOF9t7tyPfCq0zA5yYLCxQreKxLrjElrH4kkBw75hoJoH4OoaInkIxWK/h5aZGTROxdRJLolUtNX6b41gixOH2M04ZB82KsXOA23ua0S87VC27PDR1kQt0NIpc0t3rtWPfR/S0cY9WJ8XSRQ3G+dx31Hb4XHR54Tcezb17iOA5VKGfJF3hsY35GK5+l2cN0bCLRVTAjZjWWu0xyJ/kt0S5FEWOeXWED11KnjeH+Biuh0WnR/2gcJBP7rr6II9LnH2cfXKKlFtcJ79rNncQ+TIduA94mvv8Ko524Hx4+UM5doH1G6pwz96cH2k9mlkxiqs7zOpKQfP4i4/Xo01lsN3oMFhKLJvc7IqEO0lz4ViDTj1Jc5ieoiVyrRdHVCX7XJ358+dY7rRG25c4nRzGuEfnBSnnP9gdmU8RxuNrHOl2/wnvK0mdcuo2qNgLEjEEsBCem2YhfABcgDQM8tmKQQ+293UPTf3Qt7m+E6Di8kkTiK87GLz2ksib4wf5tEPpKJYl5UU4E00hl0RHxPjXv1N+N2zEdyb2DGvITTmIdMAVS+hv2ZpqD8AugD+BqtNcTDEPIC2eRQm924tpyVeYQO6vZFshB6X1GJ3GN6VHex/yHBZa/A94gJCdQVezfHMHrMow55f2Feo31xrLC8iSjCbVcgRqlbdMR9UUvxzXNCJGUpGDaCgP10KYFQM+UziA3ySR3BPMgL9NCd2F1pAimljgPAUwDwAgDM0wERAMA6IF3M2gdgtJjYdv/uWA5s2383zLb7d8EItm7b/T4bZcYqgm3374ZZX/NdMMqoZNv9PhsFz6yv+e6Y9TXfBSPggW33+2y6uw1gfc1farvjFPFKqTIA/C4A/Dda65aMJGqttZKkxdHf/SIA/CIAQFC6N+kpHyRLUw0xpvPed7tPTU2O+4q19zFaOE4BwM/dadvLdq9Up2//2NodGOmL3FW7A4y2faGUH/cVa+9hURSbFLv79jXuHfLjrbENhyH8q9/8IgDAyn7b3ffveCm3Joy0VA7ka+Zm7Pp6tzYMQ9jBg8/97yfvUbrzB8mSJIHriJbet69x3buKHVsjo2DugXxNkC+N+4q197AwSaCJSIl9+xooz4z7irX3MB0PQP/5rwMcwNdINpC1D8buaOeolPIBD4D+tdb696h4Qym1qLVeU0otAsDmuN9qrX8dAH4dAKA6W9VBLg9Sq3BIImAgqCsGHu54kg6G//qBWIBI6MwdcJmhCMUJvwTOLOHLeK/PWK2NFaKJEBTMEULNx2hTd2KSYeceCZNtbLCoWkBAqvIEQ0ubBLWOG/yQLDrN0C6zjuZy3AX5MsLBCmUShdIa1q41wfUciMNk3+0+Pz+nz5+/BFMCApvP4T3kK5uBdUcCPtdoIWy/LYSE6/T5UEDQQoKLD4UYsUdUm6GA+XcIrmygxLUphoHmCOZfnWQo9aEjCI2uioOsIn2vJODIC0ePAQDAUUEb+9Y3vwUAALduZmKIkCMRy1RAGQ00UKIbU61hZWsHAGD3bsa8bPcnPvS0/m//6/8Mcv/Hb2afvz2BNKvld1hospfgYJg/ysKELRq/iQDrVQmyqyNxwEE4xJ6YUyn1qqME9cohKKPCLxaFbw37CCNutZh+lKcD21QJyp+L1/jED34sK2vWCV65yU3hkQBdJShnZSUSzO21eBwlBE9tNpl28JWvfAV+6Zd+CeAu2x1gtO0nZ6qagaNULxI1nZ6VYtv476Aj4JQBtb2gSCiasH0hBvonX/4SAAA899yHs7LjH3kcAABmF4RYM0GZhz38rS4wHaNyDkWxG6ssQHlMnQAAgFMdpmC+NURo+0AxvU0Z+KiAtuaJDgaeGN8BURQW+VDS0AiHRONMkxS+8vuvQL6Qg067v+92z7kuIZy5Tun439AfghpMRf0+P+Ob76CAuuvz85SKOP9rE9yPUyQE6woayNwhnE8B0XuTmClIwx6294mj7Fem/uoP4P27TBtZW8M+6PUYar+9gW381S/9SVZ24xbSG6pC6P57Pobj4sknH8/Knv/UDwMAUw3jOIZf/pX/GX78x38Mfu3X/k9zk7tu90Ihr5VShs4HAKNipVy2lw42riyDhEtBSoKEX7rAdIJ1eu6ZGR7vM3MY4KtNIW1F0rK8HK73+RzPLUPzElr9kGr8nucxlaQ3wP7bqXNfGPbJRIXH9uQkrgW+gJD7NC860scVCvCHX/gWwAF9zaMPndAzlQoMhb/YbGNdtwRkXBVJGDXgdp7O4VivTLNPny6TcxaC2j0Sbt3qsj9eb6DPml48npXN5HFcGWFLX4yHNMX2iCIhXklJFhLBAYgV0eEF7DyncE44cjY7RhiV9zBbTazfjU3uo5Mk1jzh4fXiJIF/8/tfhEoxDzvN9r7bvVyt6sQPwC3wOpMYippM4EEDKxEbT011jwUHMSEH1NhmP9Ftop84efJYVtaM8BlvCoHddhv7MaJ9ytIM993CPO5xNlfZn3dp4ZkX1OR8D31Xryf6nf4uMY0rS0hSE+LKi+TrQAuxUpoTA/1tAMA9zbWr12Hx8CG4euHSvn1NLshpz3WhmOcxWyVKnpQXqNOa2ZYiuDTMuoIitoOJIMAXQtOHzjwMAADnlhgsMEmyC90WU+mH1D4mwcPNm1z9Rx7BveBzz2cUc/AL6KeWr/P+6+VX3tpTp3YD7xHJMUNzZER8f4zQvjMm6UuaauNTD+RrapNzulopwWyFaZmRprWkz/uoLonoh2Weh0Z8fiCos0XyMR2xIcxTkgPo8TpsRIUHHj/7GtEngzk8IKkKemI/JLHqNt+/38c2LYr3Op/2KalIzGPmri825Ir2OLEr1i3aU3oFFt4tTWD/FiiRRpKm8MdvvAqFIIBWf//7GjV7SgOokcRA2R7nfRhi+rZ//6JSrccdru69uOkBlYmcC7kLyi6U6r2v9qkec2guuV80T2XiAPOnlEswbaDk9xLTL4YqFuMB0LGPADRu7NvXVMolfWTpEBw7whTZCu371lfZ/y7N4j56/Rb72Bb5c1f4lR3aCzuK9+BNeq9JxRw2MgW9kL+ntpryEUeSgMSUCEIrLmuTP2mkvGZ36bxDC4HzkKQmumLvCiRXkxfNXqB11gvY79KxBGgxLttmjo4q3vyF9r5H+gq9278EgHe11v9MfPQHAPDz9PfPA8AX7uyW1u7EtNawudKCIO+D54+chtp2/4BNaw1rO3XI4aTcEB/Ztv8ATWsNv/qrvwpH0OHbdr9PprWGP/vyd6A2NXE7gsq2+wdoWmv41X/+v8Gxo0fgp3/qP5Uf2Xb/gE1rDX/0h9+C6ZkagPU198201vDr//aLcGhuGmqVEVSDbfcP0LTWcH35BhRLJThy/Kj8yLb7B2xaa/mubX3NfTKtNXztnTdhulyGUmD3NffLtNagX/o8wMQiqId/QH5k2/0vod0JEuh5APg5AHhTKWVU0v57APinAPDbSqlfAIDrAPBT73slpcB1/SwVJgBAlKXZ5ZMxz6T7dccggQRyBgI8pS9EAvFA3nYoxA8LlOq5MsWOoNPCk7seIXZiceLfoRPuQUmmL8ZjtYVJjr4oOrltCzGqPKF4vByfovuUBlGmuwQSMwvEPaoUOS0Ui9Bp9qFdH0C+mINwEAO1/b7aPZ8vwsOPfgjWby5nZZoiNrNCyHRxCQVPV9f5gPb8ZfxNp8tRkqCM9dxpcETGiM4NxOm9ORPtCLHCLomzlmsY4ZlZEgK2hAAYikjllVuIMMpvcxs//iim9PzUpz6dlU0TesikagUAqFK05Dc+//msLKQIheMKRAr1fUj/DoYhNHs9k+L03H7b3vcUzE0G8Lm//dms7Ld+AyPR6S6LK1/ZxYPyyZpIm9qhtI7iFDvuYlleCGGas+uNLY64mHSxSkTgDPLJCKwXi9zGJsNmt8NR8jTA6NI3vsEn666LbZfzeB4lEc6BzVs8ZnIxjouyOLwckgCqEUcFAEgcrOcLL74EAAD9fh9u3rwJU5gCd9/tDoBClcVCCYoFUdeEkHZFjogUKfXy1jqL3ZqI2eaWiPSSyKUSaLqLqyiQ/qU/+mJW9nOncTwHYl4VKVJV72C7bK3yvQ4TgqVwjiMd/TcwvfC5eRZa3FpGKvV6wv0xpDCAL1KuGmRhJCIdCfnbWCAVDPqt2RnA1vouXH73BkxOT0Cr2TmQrwEgRIezFwkko0mmLBXwj5Q+HwjfvbaB4+rwNkeEV9rLAABQLnLfHj6Cc/3E2Sezsmpthp6fBEo7LK7aauBYH/TZryUk1ucGHM0p1HCuDers6ypVfHl9+tmns7IijYuCqNNZ8lOnzjzK18vSWSfwyiuvwle++k04ffok/J1f/HsAOOZ/BPbZ7gCjYo2M5tmL+hmBCWVIIHEdEiDXAqmlMrFm9kkmJfY1IdJo1ooTLo7pyWmByCE0XjTkm5UoOifR4X1KptCsMypDk6BokBOp2MnHRBF/b2P7bfyeL/0k1nmSxsTKjXV4680rMIdJCw7ka9JUQ7+jYUegHN+9hOjTQKCJS4Tw8UTUe6pEopAiwp7G2KZdIR65QwKQm3Uh7l5DH+NLJAwhFesNQgIIhLXxE5GIQprob6XKotYdQi+lfSG2jVkDISd8TbdHiQrEvG6H+HdTiFOubOPa9PDhObh47SZ86+W34OjiDKxvNw7ma5QC8DxQcp9Ieyw5D1JqA0lVNVsxnQg/SWNuIJCpbRKJnzzM+5S5eRQX3q7znsSgbnd30U9cWWH0cWUSx9wxIS69vYX1mxVI6C6lOVYO+x9TUU/GAWlO7orkAydOoPhxbZLRGC6J7UYKYHVtDV75zuswkSTw6rdfAjior9Eauh2ec2li9hwClU+iquWSPOzDz7VYH4xwuOyz2Rlss/kF9vse5UveuMmi0r029kGEkgkwUeG5sLKCwv3X/83/m5VNzWEfDIXfDynqLvE7JnmNFqif8YjJMUiNdFSQX6KFtNYH8jWuq2CimoOpeR6PjTY+y6TPda2YpDkpI13NvnAgED5T9G4SCtFyFWKbSiROTN+LRcaHXULxeHMT9Ny8pzRJHibEfmkQ4jrszkrUJo7RcJLrGRCK0hHjxuyR18WY607jb2fmmKrlESLDK9Xg1sY6XFhfhdnJSah32gfe14Dj3IacuaNfjUcKjf3tGGSRQfvc4W0Zj3O7KPnoL7NxO7Lo70W1ZZ+PeYZRUWv6raMAti6Dvv7nANVDoP/0lwEO4GvKpRJ89MPPwdoaJ9K4RGNV5GiCiQkcA3VJiSjhuNwWwuG3GrT3Fu+Y8USJvs77igHtmSORiMHsmTX54h2RXMohBpKfcJskhJwWCShghhz5hmCntEkIvStotgExdbbFuURKSPaKYh83NYXPPSnQuGsN3DOHXSGS/R52J9nBvgV/8bj79F9Qbu2AVq4W4JlPPgxJouDCa8vQaw8+JD627f4BWj7IwQnasF1b23xHa/2s+Ni2/QdkhUIBPve5zwEAwK/92q/Zdr9PNrswBT/1iz8ChXwA//63vgbbG3Xra+6DPfPMU/DuO69Ct4cvjs8++73vaK3/iD627f4B2pGjC/BPfuXvAgDAP/kf/lfra+6TPXzyCHz+n/1jKKch/MNf/Q24vLJufc19sKXFRfh7n/sv4PoWvkx94Xe+YH3NfTLHcaBI9Pped2h9zX2yQ/ML8Pf/1t+GwHHg81/4fVjf2rK+5j6Ymn0I3J/5l9kBavrbf8f6mr/EZhXerFmzZs2aNWvWrFmzZs2aNWvWHgC7rylFFCjIeT50hOBhOCBhUgE7N+Kensc0r4DKUkEVUHmCSgl4dZ9gXnHCWLFUIyQxJwTEqjMEqdJYl36dIZI3tjEyu9lkCkCVYOyTBYZDB0Qb0EI8aomgX57D4qIOQb39HJ+5GUR9SYhKBxl1RYqkKVB3jDscb47rQbE6DYPr17OylKDPjywyDeWhc08AAEBz8GpWVqwgTHx6ZpYvSJWXtK2UBGZ32gyzPTSHFK3TJ1nEThH0f3IaYWyB4OoaOthug+HYjSH2QSgENnfqCHd79fVXsrInH8e6F4RYYYvEXGMhzBXSuFAC3xgTjHUEQp6Ow3DepWkNKokgL8SEozaOi4kcC8i6OXzGjd0bWVmzhxD20ydYcDVHkL/VW2JcTuB4O3KYIYe3VjDaJ1H/AQnGxYkRzuTx6XoIR2wNmKY0u/BxAADoRyy03WoTPSrkvgiJJhX2BTWQ9N4Cl+d5TGKEKfB4D4kCevrU8aysIsQuD2KO40CxWAbX5baPY5x/3Z6AeBJp0Q/YFUZEw9jZZrj9kKCfusQ+qUkQ0D/9s69lZU8+i2LAH/vhH8zKEqIeVmpIDXEFFbFH4url4zy/ctvYN7MDbuczE2cAAGBbUADiMvkFcZRvxNpTQR8ISCQxDaUIKtEmIvaJOueN+NL9WAoahmkM7hiRQz3yt95TFhFdTdIYjxhh+GmeB12C8quAqTZlosYUijy+2m0cz702Xrezy5TFBsGCI0G52W4Q5WaHx3KbqE2Lh9lPHj2BoqVPPcsB3TRB3xVH7P+M0rECXlvaLZwvkpLT7TPE/SCGaO4xUG5JXzCUhpHf7e0LNinrPeZ7dL1imaH7PgkmGkH+Ynkh+yyMcK1MBC10ooL3cATVQJMQ6cYGUz/M2C6VGA5dq+G4WN9YzcraDZyXruI6TU/h/FpYEHXx5bPt37rdIbz44hWoNwWdLULf98gCr0e+au6pV6VoRF15DHRIUHJDCGBvEPXaE9mBCiUc61LcWNMcMv+GQx57hron8j6AQ+LyQcD7mph8Xdjh+6cEt9cuf2+9getBJ+K1pN43gst8j80dvM6UoOoEtRLoA8Yg0ccXbxPlNaKlbDr7jMs8Q13pcrv7ORyTSlDzciQqPxSwfJcocbNLQpyXko6kRIHabvEeZn0HaaiPnj2Tle1uIq13IDgNpRLutXp9bk8j9KocSe9w99zjtbdRrN3zmWYQUyu4gsJXKLDP3K8lcQKNemtkHg5pbI2jR/WFQqlJ5rG0sJiV5SjLm/zlOrWPn+f1tlNHAentLab1GrqwoQROzvI6Ud9FSqSOuD/bhg6i5V5PbJTIjHi6lInImDF6r08cSTQCRv6Byw4t4fpx5dK1Pfe6G1NKg5sbQnvI145o/uU9ITZLftsTAsE1oru4QtC2RnPdv8J+tjykPWqev5cnmrkSNN6UaDaKdAQPLfJ8aBN99cbmVlaWzKIPnvvk92Rl/SLWqSHlOFZxDF/d4r3OhU3s86bwZ4sTOP6eEu8mjTaur7tDnkP5IBgRvt+XKcABsI/tkXoPvefx6bHGfG9c2XteZMwFx9X9fdpFjckuwVQyUWaoj7C37CA2DEO4fuPayJxrEJW/Ns2UyDpRqrboXwCARaKSDlMeM0XyNWnCc+UIJRWanmW67mtvYCKWzavLWdm5M0i5TejBz19hcfkOvQe5orOPLeFeY2qGKb8+nWNUBL/38jK+94lXFZgk8WtVYP+3SWLvPeFrvA6uAYGQmvHEGcmdmEUCWbNmzZo1a9asWbNmzZo1a9asPQB2n5FAAEorSEUUNDGnVgItkaOolOsKJBCdnKW+EPKj9LGOOAF36ZROpneOSRhQ+Xwy6Rfx/Ksyi9G0cMjXHZC41CDeizDa6vKJdSHAU/TDExydS0gYyhVihdqcmIv7exR18sSJoKl7XqS2VdoF54AnqmmaQqfbHRGmNqe0Uni4RNFcR4TMkkxQmL/32BNPAQDA5DSL9rWaGGHxgdtngYTb5sRJaLuFUcHBAE9OC0WR0nkJo41hlyOQTRL87Aw4wv72ebzXjVssZtdoYeRossYCc29dQERTJ2SES0yRf5VwW6gxR/Vezt9TdremwAEPAvBTjqicPY4isa98/TtZWaIwGtjqM1IhJBGyI0c4cnf2JKZm324IodkiIiA6dY6a/P6/+10AADh67ERWRnqVsL6B7VTIM0on1fih1iywl5CAdzsSUURKJ5wXzeVOYdTm+oXXsrJygFGYapG/uEWRbhPZBAAAisRvbLIAc63GY+UgppQCx/MhEWmOI0KBdUUKcoMEkUiGRgfbvtEWUeIC+aeAx02/j2P96jqL1n39jzBt+KOPsUDxxCGMeqoBzqWJVERQye90Bnyv4iJGhHWDoxqnotMAAHB+l8f8Sh/RD4nwiTFdzxXjV9F8joUIahhimfQtURTeFuW8e9NawzBNwBX+3DWxBuHGTGROAoZC8jV5kSrz1Gkcw0si/ezsPEZsZqeF4B6lKJeRqAFp7fQozfOF77zI9SSUTm2JkW4XrmA/vvjyS+IqOD7m5q9wyRD75eRRRpWAhw8yL5AmeYo6JUIwM08pvLVon3GR6Hth46J4xkYidqYvUvl9ivyOyqUCwKgAqEMpe2dnuH96PUpvSmmtd3bYn88vFugz7vgo3aF7idTFhHI784i4rkGAKR4fZrguLrLfn6N060kkUsMSGmPlOs+zgs/z8CA2HEZwdfkW5AQKdW4O/6747Gs8is4XBJoHKIrf7fFcX93C8VrvMYIiTyKXQVEgOah/HdGbAa3n83MYHVci4hnTvkumk83WdbE3qJisXRV+nlIZ7xuKFLixi2WDkH1snr43X2GhaSMa3Beime1YpgLZnymlwPf9kXFuRHlH91/mX55zPvlJJ+Q2dmkvNtDcZ0MaYLHYs84S4qE8z8iHBqG2+m2MzOpI+GTyv02R+j0hn3j+IkeTq7T2dXo8Rhu0XxpGYs0i1ESrxWMmehPXBePX8XnxeeYPMVJi/ggjcPZrWmuIohS6Qng0ICFiuZcyqdJ94c8japdWm1FMaXOvCPP6KvrilWuMnHFo/tSFcLcRO6+QuPS0EB02KIeW+L7Zb+fFXDX16wjRYUUMhVhE1ROaSyNi+dnYEyh/EngtlhmVOreE7X5QJNAw7MG15dehU2V/l6M2b7UZwWrQmNPTvKeLaQzJdyOfsoLUTh7LyoILF+lmPDciQnInQ4E4KKLvqNKee0KgsNbpfSAViRLWCC1xVcyNyxcQwXbh+nJWtrON6KHeLvdbj/b/UcJrzySt4a5ADm/QuJEIl1qtBtFdoiPGmgN3gLoZY+PQNOpOUTx77T1/ksS/a/8AACAASURBVOk9j0kGIb82ThiaP7yzisif0lxPkwMirm4zRwEEOQcc0ceGOdBs8fi8cvkdAADQwP08Re+fG+uMjoxI8Fm+9xqR+PUNRq1tEgpNCtgfpftGIc6FW7f4un1CZZbFnH/uuaezZzB2k34zM8dz5co6vpMlAhXaG6Iv8gSK0zfvTqJ/jLuNxHO7Bj3J7uA9zSKBrFmzZs2aNWvWrFmzZs2aNWvWHgCzh0DWrFmzZs2aNWvWrFmzZs2aNWsPgN1XOpjWGuIogUTAASMSFA6E4HKO6FASiu6SKK7jMhwZCB6VpEIsmqC3CUi4eUy/FTAquoxPeKrKoqCD3ELYokAFQwIIRwsFiDnnYT1LAvo8QVDqioD+troIFYuFCCmh6CEnKG+lHELJAo8hlDrUBxaG1mkCUb8L8ZDr5BBE2xPCn0OCpHcFrLROMPpNQdl5/EmEuVVqDGmbmkHY6dnTJ7OylWsIUb65xTSn115Hwa0mCSR//w/8QPbZmaeQ5vTudf5+N8H7uwWG2f3Qj/wQAAA8+8wzWZkRHEyEWPWXv/RNAACIYx5bUUICjmIMOgTfHaHBHVRIzphSUJtm6O5nfvyvAgBAscbC0K+8gQLX33nj5azsjTffpgpznylSh1ucmhVlOC4rk3yPv/bZHwMAgElRdvHSCgAAtI4jXHprl+kt202agwnDofMkEt0RkMI6ie1VckKQt4D1GwpB3OIUiT8eZqG1W+tIF4tj/l6/j7/NiaY+fvw43AtLUw39QQieoGD2Q6KPCh/iEAXGEyKxQR79SrnAItUeCSgPukJknMZI6PI9vvbnOOae+hKPzeef+RQAAKy/heKLkRB89ogeFAkx0qmTCGOdEO0XkXD9o4cey8q2biJtpFvgNs2T7zCQVQCmizg5rmdIbV+ZYN81HA73JXwoTQNAnGqQzKKEqE9SPDObX4J9FhO8+6HTLPh35CjSgWam2NfkA/QFSvy4QSL+UiQXNAlDEzT94vWV7KOUKCyHUkGbo3aKBeS8Xkf/0xN0nYkS+gnffSQrM6KCrS5/z9CQJeUuTnEOSdHE5B7RwdI0HSuSm0rh4DF+jcukuCm2QZLIuuG1Dx9mGvChIyT2ucC+pkti2gm1Y6kiaNABjmNfTPokxfVRaZ6XHlGNUtEXroN+fG1tNyu7eWOdnlWMbVq3wyH7c0Xrd+Bz2cLswYUrAQBcV0Gl6sPSCV63p2p4v5zP9Z8qYxsZKiIAQIOEHdfF+rrbxLlZmuR2Doygr+J2S6nfBFI+63+H9kaOgL1nrGRJfybKkBYT31AVHI/3RIbG2xWsCpf8Y1H0ZUrXlqLP2f5F0Ajr/Qjie5F8AUbHdEZtlMK/5nlE23n0+US+IH6L43arxeMrdZAyWBEi9A757JyYazMk0p0M0E8pQR8rULKD9pDh/mY/E4s9xyVK3NERe8cuiUT3hCCuR1SfkqAwFyp4vaKg64W03+yKPVFHUA4OYmmaQJpy3UPypyN7KPIdOSFEbKwtBLldGoNKzGEjCdHb5bYwNAhfjOmJKq5fhr7f2GXqfxTufVZDE/TENYpEsZQUsRYJDMtxNCSKmCfWDCMcLemCOaJwTM4wNU0mdDiIJUkE7fYtiPpijA5ofRXTqUxjTgq+G+q7pP1GRIGbT3kPsUDjNC+kHULaI/e1kEkwovIe9m91kqn8Jx9CmnWzyf1xmRIF/MYX/igrq9O62hmIJCLUpiJ/Bihl6ONc911a899+652srE00Q0NPBAAYBMEI1XB/psYIQ9/Z+qHHErj2UuTlL8b9md01Y5JlSuWiRiSMv5/3lyyRxN6qjKWUjSSh0CN1kxc6iJfP5Xw4emgOQpF8yczJdouplZvr6DurVX6vUpT0qSVeYhqUYEEmotraJaF5IVWxS35kUkgOKB/HuRMbqij7F7PXMRIAAAAunWOUS7x2qDVc57d3eP4WfLyvbM9E4/Ui8b4RUWIXLfZkleIElXH7pHc51i0SyJo1a9asWbNmzZo1a9asWbNm7QGw+4sEgr2pt00EQSKBTERAyQMtbVLbcpUdEohMBWTHCNPlRLSfhUmFoBsdFBuRy2CCT46LXYwIDDb4dNycrslUfQmVSeG7EqV2mxMCWUYQTeZNzdFJdeDwcwOd9A16nN4wHiaQJgc7xU5TDcNuHwoBR446bTw5vHKV08abdPAtcXrfH5q0vIyKMCexUvx1m9A+uzOMLFD0jI0et6NJIV8oY9TAK/NJ681tPMVv9jkCoR3sx2KR7z8/hyJ284ss6uqSwGVHiPmmCvvR9TjaZ46loxEBMxLuHhGYvAdRSgUADkdsAQCKVeyDH/hPPpOV/ZVP/RUAAHj7rbezstdfR+TMoUMsjBpSX4SiPRMqGwoBsWoFx6BMb/rIGewLrfAU+Rt/ziJoq+t4Ol4I+DS5lsMT6KVJHjPNBv427rJgX6ffoEfleblNQrCrt9azslYL+6VW4/HxmU9jG/z1n/2ZrOzDH+a02wc2pUZOxfMUFcsJ4fU+zTXP4VP92nFsv4kyoxtWN3fpkjy+koii3iI6vtbEZ//CH34hKyus4ReWBhhVLg7YN0Uutm9c4nB+i1Lalo4yEiiXw7F84giLfb+2jag6I5gLAKAKeB1XIB40RfFk5CIoYH95AX9vGDlwYCgQIMpHRvmz4T8mwpSI/qlSCtvv++RHs7LFBUSauJ5IZW/S8o5MUXyO3R1OH2zEdn0f2+T4o49zHckXJ2KOlB3sp6eeOpuV9Sj6OytEL0+cwHS/p09zXxQpXfKIsPbtETvgFOkjadvvTaZyABiNxI8rMz5OzguH1sxUDOQioZ1qk/zcLiHejh5nn12uUGS9wNG2shl7KY0xMRZ9SjU8EKjUkHxxLBALASHz+gOup0fLrEH1AABUq1NUN75Hs45ztFXndbTdMmhgbvdmk+93EPN8BQvzOXADvl+zj3NNpxyZnJnAMZwK1GSDkGb1BiMFSpSOvSjSeTuOGf8cBUxJDFJGNY1IqpMhcoQAuXn0ESSQidY64ns0cB0hAk0bn+5QhOcJCe2J3LZGBF2un0bUU4k9WZweHJGiFI7n9xQ+BR7zvhCLdj098hkAQEjRVH+CEQ1ujv7OMUrEtMVoO+I/PiF8ChKxTl8fijolhFypzDKqN6T6OQPez+ZpTsyJPWaekCulCgube/S51vyMBp0ifaxE2xzEtNYjvi6K9gpyJ7Rv1cKv+BT19j2BpqF2T0Uqd03jJ5Fjm1DzYcjztknrrXl+mfo5Jl/rifXe+MKhFD2m70mf2CU0lkwQYp4tFL81v/Fze31cTwh33yu5XNcBqJQ8iIcidb0ZNwLhMxhgHTc2GVVfJTHpWoX3NT16lk2B0ivSehkLsfgmjbVtdnHQormzRP44FvuQiFLEe8JXd3vYztt93j8mivyymIcBGEcv5osRdRf+26DjtsUzzhIjQa45njPeR9yVKUCkyJj1XXau2lvEhe9bBXNtibDZi/bJ3kHHiDsz6nIk9cPtl3jPysjEASaBhWw/41tHUD+p8ad7G+MgY3/Y78Hl82+AJxITGbaPk/JgPPcwvifJJBurN1HgvNXgvYnZ90nfNeiavQGPVbNGpwmvU+fOIfK7Sai+r3yF/YDxscUiv2suLmGdYuGT+uS7dur8jl0zLJcit/EO+bUk5tYz/jwWCEeDgM+L8R7fZYtbJJA1a9asWbNmzZo1a9asWbNmzdoDYPYQyJo1a9asWbNmzZo1a9asWbNm7QGw+0oHc5QDuSAAT4gha4JnKcGzYujZXnio0vLcyqPvC6gYnWsFAo7rGdqYEIOLCyS8FCFkMxZCwbkywU5bXBb3DFySoVYVgopOlBmWCwWED5+uLmZFuySU2ewxBKyQx/oJ3UvokKJlW8BIu70BUwj2aVqnMBwOYVKIqz7yKELbXI/bs9FCmKYUNzXwtscfO5eVnTqFNIiXX34xK9vdRurPKy/zM05NIezUExDOMw8/BAAAcwsIlUuEoN758xewLGSYXZ4Qel7M9IHG6mUAALj2tlDEJOiqpHfMlbFswmN4dUTQ5L6gSIQEuRsFSN4rAO8oHcNQtCRc3Yj4feRjTIN56umnsG4C+jcgmPhQ0MEiooMNBITc/D0QYpKtBkInry+jmNoI/J+gkQr4Xr32KgAAlPIzWVmYIgxSuwyNz/lE5WgJKDWNHzlTP/48PttP/MRPZGU/+iM/AgAAc/N8j1Eh2v2bUgo81x2hSvgkUBkOue3N+DdiwwAARks61jyWi2V85n6P29QP0J9EWtAXCRb+nYtvcl12cTw/M/8wAAAcri5lnxWKBM1WTP3or+F9Xc3UM0Ol8Kvc9sU8+h0/Yl/XG+D4lgKKHsFcBRs1E8vvizGilQcHpYMppSDvS7l5gNhQr0aQ1NgHeSEa+vSTSNc6d4ZpnjHVs9XkMW+oBJKGu7a+BgAA2zsMDT9zFn1NqYSUOy3WDkX+YoQWZWjIYr4amoHn89phhE4l9YHns4BSG9raOGFmKVx7YNFKAFAAnueO+BXjCpWciXovYN38JBHjuEzUvHJVCAuT0PXVZRaVbzXRr5QqPH/m51HQuEqirYUCt1OZxrnv8lo0JMh1schjIckGK9czJLpIucIUqwmqp6SczM3jPRYW+Xm2N3Gc13fEWsDLzIFMQQoKOrC1xdd2aYx0XYas+xpFId2hoEcTfTEv6PBG3FILOgbve6R4N1G5xFx3wdDm0VJHQvvN+Pb2lKVi3sdUNkILyJgKXOa6RvBdULuMqOsYAVUphq6UO0Jj2J8pcNzRMW/WD7mOGGkARyYLoXZpxuK5Ab8XVBeyMoeSICSpoApQW4yITxMNwcgRSEi+0U8Wt4KE7q8qTLeukajohNSFpXvJPbMySuBi+2OmtaRUAlFonBFmyL3jnrqComF82CgdlcabeG4jyCx955D2MFJcORzifJb0NZ/2Gq4nfku+w+wj5f2NjELq8HU1ic9LwfkeJbyIxD57nBqAEXQfJ5YvhYd7XdyrRkII3BNr1cFMgUodcMSrWzqGb5QkRjyX/c/1a7inKwtphyef/hAAAEws8p5kdQWlArpNpm1daOCeZEdQ8SrTOE/O5HEMB5L2TPcNxCBNiKLmie/laAzlRL+ZZD1yfffKODfcgJ+7THu2apHfv4o0hyQdPpfzD04HM9oOtxcBvJ+O8/uY/MVYMhl9JOo/hgbGV9tLcx1LxzftPW7vIa/rGFrxfuxeUE81uDqEQMx5Q4memeS98OI8jmlJB2vS+D1yiCm3Zg8o6aBmT2/mLQBAlfYskfAdy1fOYxlRMYdi72xsGHLZzBzKOUxN8/2vUXKSd989n5UdPYrvP80+339I6+igL84gqEy62KJJMBDK95K78zUWCWTNmjVr1qxZs2bNmjVr1qxZs/YA2H1FAimlIJ8LRo4VUzqxdkTUy8nSlu41eUJv0sA7HkfRfIpyFUSadWVE6LgoE6HrO3giGIccdTen7Hkh8hRTCkMpeNijk+0b6yxGmp/D0/FHjp3Jyp6k08k3L77G13PopM/lk74+pY7ebnFdojAeTXu8D0vTFPqDNkxNcwR1dh5PJyMRDq2TWFVXIE2KlDZzcoJ/a4SJQyEEd4iivwuLnNbWpM1b7tzMyqYpvbkRupXRtICQVbkcD8suCZMFvogokLDc7hqLWocUOQ4CjiY/dBTRWG+IKPUmKdsZdBgAQKRGo6cA41Mi7tfkM44Tbo3HoF9MdDcocN1LhBiSaCdTT5mG0kTF5H37bYxKzdL4fOm1F7LPLly6CgAAZx85mZUtHUbx7c029/FyD8dlJ+QT68UpFImt5bjfF+fwOlLk+ZOf/CQAAJw7x4gygzKTEbh72e7KcSDnSIFOiha63H4lSu3rye9Rf0Qi6uXlcPwVy9wfAxI6TATExgjPNhJuo6+uoIDzC6soVDclUvuWfLzehM/RuSqlqM+L9K5OGZETs6cZNbWjUKw69WSUyIiw8nzpUVrgdk+mYTWRa5E22fUPHJ33XQeWasURJFCPIif1HkdGjXD03AyLVM5MYhv0BBLRoXmvRTriwQDH5DqhfwAAVldvAADA4WMc0TTCqXlK6ewJcWnfpPuUYrEmOi+jaGOeMUsSMCI+S9+XuogmbaqIZpt08CMogv2kc73NHOVALpcbiyqKxfg0KAIpeAgKx/HsFPv4SZrXaSoj8fjvydPcZyaVvElfCgAwDAn52sGUwI2WeP4VHNtBnpFvboCf54W4ap5Sd8tUzgaJJJEAnjKRPfmMe31shcaC54h9Aa3zb7655+t3ZRo0aAghTUTSCkojHcW8vm5uYBRQCSSQIoRHbZLntU8i/6PanpnKuDCK0koUtfnXaBePi+qK8WhSv0uUXpIFmh3xU+znUCayMGmbBfgkJtiJL4Rms88EEk9DDg4aKdZa41oo5zD9HYk10g0V3V+g5FKDKmGfoACfTQkYk0EMOo6IrhJyOI153TJ+n9d34UOoDyRi1CCFYjGWFY11KfBrHOkIAl6b6LxY81MjbjxGmFVsL+5JwosxZsZZkuxFVkrRdhM9ly7P/D0WESnfFehzlQjkm+nvMengWQSfPzMoz0QggTIWgLiXQY2NojhH/5Wfy+8Zvy+RTVG0dz7sy7QCiAOARKBQ0733MHuqQZ/9j0EFbe+wUO7mJqJ+SmLFTmhfL+d1h9AXffGe5lI7bG/h+08uz3NkaQnX4ctXGDXqKCOiLVC1Ae2rBOqnTO8GObH39cu5Pb/N0xoeiN/67l7kruu6I8LT99zc97n2e67vB6jXuGfKxJglYnTc1/aiM8dChm4XoRb2vjrT96DJPc+FmcnKCCrMoPvDhMe2cjLYc2bBDK75SuwdC7SvkGhvsxfTEc/XDiFwdns8Cd76zrcBAGBrF/enroBYlks4FjsiHf23X8B3rOeeY4bH+jqigQ3SEQDAId/RaTPybhju9Ulmf+oJ9o7xralY08e9Y76XWSSQNWvWrFmzZs2aNWvWrFmzZs3aA2D2EMiaNWvWrFmzZs2aNWvWrFmzZu0BsPtLBwMFLjiZyDMAQBAYOoaE75Hgs7cXghtGLIBk0JZ+TlBmAoSZG9gXAEBKl449hnt5BFf2PfyekwqxXaKBKCF85xL0MBUUkU6I11urM32rVkfI5ZKg0Zw6jEKnfSH8dH4VRZBDn+uUlvFMruxUszIdJ7B9dQcOYlprGCZJRp8DANDU3lrQQRyPIPoOt90uCQovX2dK18mTKAytQQpM4r/TMyyqaCCpW9tM71g8hDQjQ1FZ22IqnREXzQlhK59oOPkJbhO/hveYOX5WfA8hpLGAaOerKMz10MVrWdna178KAACJEAbzxtAH7pX9/+2dS48kx3WFT2RWZT36/Zqed5Mcj4AhTcAa0PLGtraGNvbSO/0Ee0lAv0Ba6AcY8EILA95IgLSVBC8ELQwbsiyAIsR5gBQ9j2YP59ndVV1VmaFF3Mi4VZXsbna3Kyanzwc0piYqqzLiZmRkVOY9J+xXpILqNP2kkUxt6yUk49IUkZfoFHIzvZ1pTJ/WaepS3W/ecjLFt9/5s/K9X/36VwAApT7C5U13LC5thr7w5287ucjey3B8lpacEfrS6vtl2dbbLhX41q1bZdn6upM76LTOqthow8jTYJIEraxVylUA4EBSxnv9cL7Od50Eqd0K5oIDMYrT3SJtybm5GLYbDkTKasK5no/cPloqbdkbyb2UdNOnvSBjyp/LOKSMoROpc6pyQVdXnNzuShrOr+W3XN0bXdUfRJLS64cUeJt6GUjYzssMjZLBmbRx6hTeNHGmeioTvhx1ejrdVgystRzLp+jeuXO3LFsQ49RmO0jochls7t67U5ZdvOz61/qFYMI3kJTaTkfSaJuhrf4ak1SkBRcV8s2x86t8Od1/x7Kr/Yti+rOJGnfRPP34Y4yZkoP5PaTqmpWIWezScpBFrV9wfWFtPUgSOx0Xq7m5IP3a3XepzoPR87JsedV9T6r6USHp12lDJDdKSuPfg5JkWpEJjAbaaFfOKSXXyST9OmuFeHYars6tfhineiLdbqq4DnrufDBJGPcLO23seBKsLXAw7ANGL3ghcstR2MfByI07RulzOouXpK6h/km50IVO6Z9Ody+Nd6H7VzK2mTZD9p2vUOfcUPrmF89DKnrvwNVvQclW50W2/rgXjltLJBfPnj8Nnx26OF9eVMet6frIzosw7mZZC6P8MJnE8cjzYvw09NIiJUvycpm8Yn/aEDv146PeTJqbpqHQSzq1zNPLrPz5nSsDZm9MnVo9OIhs14Rj4ccdXXfIPqyqeyFyZatk5LaYlpT7scs21Hh2BlN+a62T4h2xiIOXhmmz1lK+PiablfGiwmhfj89ZOUGZltKWkvrKOUUo87KxXMerQgXjjaMLHfcK6ZevZ5UR78WLYXEY37Y7d8I160TYBHbYwlBdS/dF8tXr6cVB3Ph5oGQnucjoCtUP93dfyXuhna/8+Kp+k7VyL0EMn/V1eP7CXRc2tkJ7X4nk9dEXj8uyrOPGxwU1h2rPuWPqJWAAsCCvm13l3yHXLa1874hVRaYWl/BziExZhCRpiiQ9ozl+hQTrSAuDw95X/fXYqvDDvm9SDwyMLbpU4seYyu+qcLqu2k5X2I+FWtF5BhK8xBi02h2kqR5rZfxV57U/BloG7H9H6iOfylxDf19Dxv1GO/Sj5SU3r1hR58+KLBi1t++sGfr90N+9aXNf/a786H9+DQD47W+C9cbjh27hkqH63k8fuN/WPSXn9HP/RN2z6IpdSktJHROxneg01XmJr/cbiplAhBBCCCGEEEIIIeeA2S4Rnxi0s3Zp3gUA3a6Ydqoy/+QwURkPuRhZ9tUdNCt3trt6icB5l62Qqae+eeLNOMOdcmNd0xvG3U1uNcN3jA7cEyurvGLbS+4/w6fhCXsuVdkbhtufr/bdncCd7ZDhcvO6y365/f5flmXPZVlKbZrclX1058P3FaMhnv3xdPfq0kYDK8sXMDcfnupmYkTbVLfWE+PK3tp6ryxrZS4DZ/NCMK68fsMtvbywFu6E7uxsS1lY+tffCP5gJWy3JMv7+iyvzx4+KN+7d98Z5/qnGEB4ArqrTKg/+dTFbHcUuu/mZbfkfEMvESrH5+Z7f1GWfSpL9N37JCzRl/ol0lXqRzAkDUZfJ+WoJwVVGTGHfUY/gbRVqRsVn03lTvGcZL5d33qrfK/bcU98F+bU8uPS91tZOAe7C+5cXZrfKsvWN9yx3bgcnmQvr7p+Nj8fMgv8k7Cqdp1V9s8kJrHoq6djmdxJX1oK9fJP3fv9cBe+J6ZwTWVG7o3Jkzz0kfUNZ57bbqnlyyVwQ5WJ05Mn4IVkv2TzypxWMlLyNDy56lm//xCrC7LcZGc9PJ23kp00UE81u033PfopeyJPDrRZtJHlkEeD8NlRnp90LdCwLxhkiRnr0/6pS6qX+5Xz6+rVMDZsbLo2Pvzsfln20e+ca+9QZdPsS4bPteshK+r69dsAgJbKCvVGmf4pp37q7s8RO2bgOt2eqtVVv8IFuuLD3mFXG+y6fxsqy/WsLsKThoCJ36+q2sZFN55fuaayfubc+LffC2Pdk2fu+vX5w2Dsud932SL6yX7DZ40W4frpQ9ESg3OVUIu5Bdc/uypTxJt9NlT2XLfrtsu6oawh8wGrjGF3992T7EKdl7lk2uTKnHgwcNuZNJyrMGezXLa1FvlgiNxMZyikypR2JFkyOtO11XJjgTYthzyZ1BlDoasdPhcIhtCyaIZe5t0bSadhXztiFvv0WYhLZ9GNa4+ehMzlzTn32e1noWx90Y1FiQ3HyNdZG4X7xSce7zwry1aXl8czXk6KBXQHL42Px8x7xfhz7HT1psHFVJmeBxSlqboyPG5MX6/8g2Vv6q3Ny33263i2iDcP1n3U9Q+bTmefjEzoC00/nuuMFHmtn4T7b2mqcUHPHU6KMQZJkkxkR05nJJdP7M30E3u9EEx1Ns10JpBvkTZ1TSY+O27W74+xMgQfTZvLlwb+av8jf+6pjC5fF91nfDaUXtzDH+/V1TAX9tkqp80EshYocoOBMnw+8Au1DEKZ71eFGgN9bMaSYiQjbaQVAf580nNp/7aKR08WCNl54a4Vrc9Cxn1PFrcZHYTx4uKGi8fCYrj2NCWbp62yG1qy31TNv3y39dk/gPqNp7q0z/BNjRqTjBnLljwxxkxc5qf73KEfP7LwmHU8ZH9Vcxj/taZi/BvfTtozZtB+SNsq93X4fOrrYmFgkY7tq9lyxzYz7antU2UQ739XqKEG3stZ/05sygY6s9HKj/uV+TAvX5JMNr9Yhj4M/vdiT2UCXXri5lP3/xiy4dY6TiWxr89fWd69Y8O8YL3prq2NZjh/u9LfMzV3zKWeI3UtfdV3dQm/rA+HmUCEEEIIIYQQQggh5wDeBCKEEEIIIYQQQgg5B8zcGDpLM2RZSLEaSvpUrpxEvfGtTpkfeuM9dd+qm7mUKS0BMJJWPaxIEdaGaD7zK4Xbx9xcMB4uJPdwlIeUrY7INbpKMvPsoZgpqvT0p09dyvO2Slt88NClg21ubpZlH7z3LQBA/+PQ7i8PnMFiqtptW+lY+utJ6M4t4PZffRtNlZdfiBwk1waK8v7Nd79Zlr1/+wMAQLsVPptJ2v5lJSn6Ytu18f79kO7alrT3VSURS72Zpezrna1roaKFi/eX25+rMvfPi2fBHHtx3knP7PpaWbYtEpI1kc0AwIqkq3/jRqjn41vfcP9+/mlZNhIDa5163KgwVz4JxpjKNGfN102b1Jsfu2fIhj4F/2++/bflW94EcXk+SDq2rjrJ1/KKko0sufOy1QoxzsSIvdHW5o+Szqtla2UacVVjz/5etDFAo2F0RnOZgp6p1GMjJmq7yjy3IQaU2pfeiCyxp4zpGzImLK8qeZmY0RYqtXQw58ankZjTdrtBjtEW2UuuDQJ9+q6q+5KYKWat8NmRHDcUaEqVswAAByxJREFUakORDcwrU8VCZFDWKlmATwNXcpXOXOfUcgELi6KwUBn4Zdu04fLGmpMM3ngnSAvXZJwoVIy96eTjR9tlWVfkdFevBSlZt+vO9VTJihMxK96XdPnOnEpDl1RhLRUI6i0ttxxvg9vO+h2ohk9LCioVYoU32AyMjjBYPQ5eoqFTmv1wP78Q+syNm+4alNtg7vxEjPsHw5C6v7e3J9+rx0QX23wUynZ33Zi991LLIaRPS//U/Xj9gjvGzRdKLiRyvbaSfq1fcMFbXQtl83OuHWNqhkRkD1YZoMq52szClkuL7hzcPQiLFOTDMzKGLgr09veAduhfhchJjDrS/pqStYMJfCqx0nHOS0lTVSr+UdID3+mKqa/w44o+N/f3XayGw5Azv9x15+aDnWAWvbbg2lYoTUG77ep+dTPIzLefun6VDEKcveK0sNoE2ZxWeQqIQbHG938t2SklVWrRiNJUW1+bZbtGU6fbixlwhVn8wkI43t7WwI8dVW3r90N/8/WsMlfWC6eMlFmox8trtbzVj096bu3lNINhaLeeU58Uay0Gg0HlXEbPocIxmB47dX+vknJVScS8XG7sWHjJl8wv7Ji876vnFbrmQUKp+4KdaMO03FbXU1/vc4n3nbtB1twfDnEWGONkLtoMOdRPXXvK17rfelsM1b/M9Dw3Ky+EocwbqCdKlt0TS4snXzo52O7LIPfMZLP1lcWybGHBzWFaatGepJTBhjh7hwptRdCRBYTanfBZL3nT7WlK/7ZqUj0ajo4t2TqMBOOSKl/no0StZU2qqmCqXk4Xjp0bx9vbsSqltw4e0BV2E2M7PZ4crTyvj1ejSprNJi5uXIA1an5esZBOQyY7ei61u+skic9Vv2yKFjJR46SVcUL/FvZjjY6Fl3mG/SorFRkbWuq8vHHFzbW2LobfSz35DXCg7CL6ch7tq7IhRFaNvbLM+LljPj0mjkah7q19eR2Gn0NhJhAhhBBCCCGEEELIOcCcxR3SY+/MmB0AewCeHLXta846ZtuGLWvtxtGbVcO4n4oTx17i/hni1PusYZ+PA+MeB4418WCfjwPjHgeONfFgn48D4x6HOo41b0Lcgde0z8/0JhAAGGP+21r7wUx3esbUsQ11rPMkdW1DXeutqWMb6ljnSerYhjrWeZK6tqGu9dbUsQ11rPMkdWxDHes8SV3bUNd6a+rYhjrWeZI6tqGOdZ6kjm2oY52reF3bQTkYIYQQQgghhBBCyDmAN4EIIYQQQgghhBBCzgExbgL9S4R9njV1bEMd6zxJXdtQ13pr6tiGOtZ5kjq2oY51nqSubahrvTV1bEMd6zxJHdtQxzpPUtc21LXemjq2oY51nqSObahjnSepYxvqWOcqXst2zNwTiBBCCCGEEEIIIYTMHsrBCCGEEEIIIYQQQs4BM70JZIz5O2PMH4wxd40xH85y3yfFGHPNGPMfxpjfG2M+Msb8k5SvGmN+boy5I/+uxK7rV8G4x4Fxj0Md4w4w9rFg3OPAuMfhTYg7wNjHgnGPA+MeD8Y+Doz7DLDWzuQPQArgHoB3AGQA/hfAu7Pa/ynqfQnAbXm9AOATAO8C+AGAD6X8QwDfj11Xxv31+WPcGXfGvh6xZ9wZd8a9PnFn7Bl3xp1xZ+zf7Ngz7rP5m2Um0LcA3LXW3rfWDgD8O4C/n+H+T4S19pG19jfy+hWAjwFcgav7j2SzHwH4hzg1PBLGPQ6MexxqGXeAsY8F4x4Hxj0Ob0DcAcY+Fox7HBj3eDD2cWDcZ8AsbwJdAfC5+v//SVltMMa8BeCbAP4TwKa19pG89RjAZqRqHQXjHgfGPQ61jzvA2MeCcY8D4x6HmsYdYOxjwbjHgXGPB2MfB8Z9BtAY+pgYY+YB/BjAP1trX+r3rMvv4jJr/w8w7nFg3OPB2MeBcY8D4x4Hxj0ejH0cGPc4MO7xYOzjUJe4z/Im0AMA19T/r0rZa48xpgl3MP/NWvsTKd42xlyS9y8B+CJW/Y6AcY8D4x6H2sYdYOxjwbjHgXGPQ83jDjD2sWDc48C4x4OxjwPjPgNmeRPovwDcNMa8bYzJAPwjgJ/NcP8nwhhjAPwrgI+ttT9Ub/0MwHfl9XcB/HTWdTsmjHscGPc41DLuAGMfC8Y9Dox7HN6AuAOMfSwY9zgw7vFg7OPAuM8CO1vX7O/AOWXfA/C9We77FHX+a7i0rd8B+K38fQfAGoBfArgD4BcAVmPXlXF/vf4Yd8adsY9fJ8b99fxj3Bl3xr5+sWfcGffzFHfGnnF/k+NupNKEEEIIIYQQQggh5A2GxtCEEEIIIYQQQggh5wDeBCKEEEIIIYQQQgg5B/AmECGEEEIIIYQQQsg5gDeBCCGEEEIIIYQQQs4BvAlECCGEEEIIIYQQcg7gTSBCCCGEEEIIIYSQcwBvAhFCCCGEEEIIIYScA3gTiBBCCCGEEEIIIeQc8CcnXhY8o29H6QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x144 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x144 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize Original and Augmented Images\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# take subset of training data\n",
    "x_train_subset = x_train[:12]\n",
    "\n",
    "# visualize subset of training data\n",
    "fig = plt.figure(figsize=(20,2))\n",
    "for i in range(0, len(x_train_subset)):\n",
    "    ax = fig.add_subplot(1, 12, i+1)\n",
    "    ax.imshow(x_train_subset[i])\n",
    "fig.suptitle('Subset of Original Training Images', fontsize=20)\n",
    "plt.show()\n",
    "\n",
    "# visualize augmented images\n",
    "fig = plt.figure(figsize=(20,2))\n",
    "for x_batch in datagen_train.flow(x_train_subset, batch_size=12):\n",
    "    for i in range(0, 12):\n",
    "        ax = fig.add_subplot(1, 12, i+1)\n",
    "        ax.imshow(x_batch[i])\n",
    "    fig.suptitle('Augmented Images', fontsize=20)\n",
    "    plt.show()\n",
    "    break;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_7 (Conv2D)            (None, 32, 32, 16)        208       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 16, 16, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 16, 16, 32)        2080      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2 (None, 8, 8, 32)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 8, 8, 64)          8256      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_9 (MaxPooling2 (None, 4, 4, 64)          0         \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 4, 4, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 500)               512500    \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 500)               0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 10)                5010      \n",
      "=================================================================\n",
      "Total params: 528,054\n",
      "Trainable params: 528,054\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Define the Model Architecture\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(filters=16, kernel_size=2, padding='same', activation='relu', \n",
    "                        input_shape=(32, 32, 3)))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Conv2D(filters=32, kernel_size=2, padding='same', activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Conv2D(filters=64, kernel_size=2, padding='same', activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(500, activation='relu'))\n",
    "model.add(Dropout(0.4))\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# compile the model\n",
    "model.compile(loss='categorical_crossentropy', optimizer='rmsprop', \n",
    "                  metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 1.32809, saving model to aug_model.weights.best.hdf5\n",
      " - 42s - loss: 1.6497 - acc: 0.4023 - val_loss: 1.3281 - val_acc: 0.5436\n",
      "Epoch 2/100\n",
      "\n",
      "Epoch 00002: val_loss improved from 1.32809 to 1.11556, saving model to aug_model.weights.best.hdf5\n",
      " - 41s - loss: 1.3454 - acc: 0.5189 - val_loss: 1.1156 - val_acc: 0.6116\n",
      "Epoch 3/100\n",
      "\n",
      "Epoch 00003: val_loss improved from 1.11556 to 1.02076, saving model to aug_model.weights.best.hdf5\n",
      " - 41s - loss: 1.2434 - acc: 0.5556 - val_loss: 1.0208 - val_acc: 0.6362\n",
      "Epoch 4/100\n",
      "\n",
      "Epoch 00004: val_loss improved from 1.02076 to 0.96348, saving model to aug_model.weights.best.hdf5\n",
      " - 41s - loss: 1.1854 - acc: 0.5817 - val_loss: 0.9635 - val_acc: 0.6684\n",
      "Epoch 5/100\n",
      "\n",
      "Epoch 00005: val_loss did not improve\n",
      " - 41s - loss: 1.1518 - acc: 0.5980 - val_loss: 0.9681 - val_acc: 0.6708\n",
      "Epoch 6/100\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.96348 to 0.95772, saving model to aug_model.weights.best.hdf5\n",
      " - 41s - loss: 1.1380 - acc: 0.6020 - val_loss: 0.9577 - val_acc: 0.6706\n",
      "Epoch 7/100\n",
      "\n",
      "Epoch 00007: val_loss did not improve\n",
      " - 41s - loss: 1.1299 - acc: 0.6079 - val_loss: 1.0566 - val_acc: 0.6532\n",
      "Epoch 8/100\n",
      "\n",
      "Epoch 00008: val_loss did not improve\n",
      " - 42s - loss: 1.1340 - acc: 0.6114 - val_loss: 1.0242 - val_acc: 0.6252\n",
      "Epoch 9/100\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.95772 to 0.92255, saving model to aug_model.weights.best.hdf5\n",
      " - 42s - loss: 1.1345 - acc: 0.6123 - val_loss: 0.9226 - val_acc: 0.6888\n",
      "Epoch 10/100\n",
      "\n",
      "Epoch 00010: val_loss did not improve\n",
      " - 41s - loss: 1.1512 - acc: 0.6070 - val_loss: 1.0575 - val_acc: 0.6360\n",
      "Epoch 11/100\n",
      "\n",
      "Epoch 00011: val_loss did not improve\n",
      " - 42s - loss: 1.1634 - acc: 0.6031 - val_loss: 0.9305 - val_acc: 0.6812\n",
      "Epoch 12/100\n",
      "\n",
      "Epoch 00012: val_loss did not improve\n",
      " - 41s - loss: 1.1850 - acc: 0.5989 - val_loss: 0.9623 - val_acc: 0.6702\n",
      "Epoch 13/100\n",
      "\n",
      "Epoch 00013: val_loss did not improve\n",
      " - 41s - loss: 1.1978 - acc: 0.5947 - val_loss: 1.0371 - val_acc: 0.6498\n",
      "Epoch 14/100\n",
      "\n",
      "Epoch 00014: val_loss did not improve\n",
      " - 41s - loss: 1.2073 - acc: 0.5928 - val_loss: 0.9577 - val_acc: 0.6850\n",
      "Epoch 15/100\n",
      "\n",
      "Epoch 00015: val_loss did not improve\n",
      " - 40s - loss: 1.2373 - acc: 0.5857 - val_loss: 1.0576 - val_acc: 0.6378\n",
      "Epoch 16/100\n",
      "\n",
      "Epoch 00016: val_loss did not improve\n",
      " - 40s - loss: 1.2551 - acc: 0.5735 - val_loss: 0.9797 - val_acc: 0.6674\n",
      "Epoch 17/100\n",
      "\n",
      "Epoch 00017: val_loss did not improve\n",
      " - 39s - loss: 1.2645 - acc: 0.5780 - val_loss: 1.0252 - val_acc: 0.6628\n",
      "Epoch 18/100\n",
      "\n",
      "Epoch 00018: val_loss did not improve\n",
      " - 40s - loss: 1.2921 - acc: 0.5659 - val_loss: 0.9885 - val_acc: 0.6722\n",
      "Epoch 19/100\n",
      "\n",
      "Epoch 00019: val_loss did not improve\n",
      " - 41s - loss: 1.3119 - acc: 0.5614 - val_loss: 1.0873 - val_acc: 0.6242\n",
      "Epoch 20/100\n",
      "\n",
      "Epoch 00020: val_loss did not improve\n",
      " - 41s - loss: 1.3319 - acc: 0.5549 - val_loss: 1.0854 - val_acc: 0.6368\n",
      "Epoch 21/100\n",
      "\n",
      "Epoch 00021: val_loss did not improve\n",
      " - 41s - loss: 1.3516 - acc: 0.5463 - val_loss: 1.0217 - val_acc: 0.6474\n",
      "Epoch 22/100\n",
      "\n",
      "Epoch 00022: val_loss did not improve\n",
      " - 41s - loss: 1.3804 - acc: 0.5373 - val_loss: 1.0295 - val_acc: 0.6498\n",
      "Epoch 23/100\n",
      "\n",
      "Epoch 00023: val_loss did not improve\n",
      " - 41s - loss: 1.3933 - acc: 0.5316 - val_loss: 1.1026 - val_acc: 0.6272\n",
      "Epoch 24/100\n",
      "\n",
      "Epoch 00024: val_loss did not improve\n",
      " - 42s - loss: 1.4139 - acc: 0.5254 - val_loss: 1.1751 - val_acc: 0.5906\n",
      "Epoch 25/100\n",
      "\n",
      "Epoch 00025: val_loss did not improve\n",
      " - 41s - loss: 1.4382 - acc: 0.5188 - val_loss: 1.1351 - val_acc: 0.6030\n",
      "Epoch 26/100\n",
      "\n",
      "Epoch 00026: val_loss did not improve\n",
      " - 41s - loss: 1.4539 - acc: 0.5103 - val_loss: 1.1579 - val_acc: 0.5984\n",
      "Epoch 27/100\n",
      "\n",
      "Epoch 00027: val_loss did not improve\n",
      " - 41s - loss: 1.4582 - acc: 0.5083 - val_loss: 1.0773 - val_acc: 0.6324\n",
      "Epoch 28/100\n",
      "\n",
      "Epoch 00028: val_loss did not improve\n",
      " - 40s - loss: 1.4765 - acc: 0.5034 - val_loss: 1.1596 - val_acc: 0.6074\n",
      "Epoch 29/100\n",
      "\n",
      "Epoch 00029: val_loss did not improve\n",
      " - 41s - loss: 1.4936 - acc: 0.4968 - val_loss: 1.3006 - val_acc: 0.5790\n",
      "Epoch 30/100\n",
      "\n",
      "Epoch 00030: val_loss did not improve\n",
      " - 41s - loss: 1.5209 - acc: 0.4841 - val_loss: 1.1936 - val_acc: 0.5826\n",
      "Epoch 31/100\n",
      "\n",
      "Epoch 00031: val_loss did not improve\n",
      " - 40s - loss: 1.5338 - acc: 0.4797 - val_loss: 1.2344 - val_acc: 0.5726\n",
      "Epoch 32/100\n",
      "\n",
      "Epoch 00032: val_loss did not improve\n",
      " - 41s - loss: 1.5523 - acc: 0.4768 - val_loss: 1.3335 - val_acc: 0.5688\n",
      "Epoch 33/100\n",
      "\n",
      "Epoch 00033: val_loss did not improve\n",
      " - 42s - loss: 1.5325 - acc: 0.4803 - val_loss: 1.2764 - val_acc: 0.5526\n",
      "Epoch 34/100\n",
      "\n",
      "Epoch 00034: val_loss did not improve\n",
      " - 42s - loss: 1.5679 - acc: 0.4661 - val_loss: 1.3783 - val_acc: 0.5116\n",
      "Epoch 35/100\n",
      "\n",
      "Epoch 00035: val_loss did not improve\n",
      " - 41s - loss: 1.5745 - acc: 0.4641 - val_loss: 1.3881 - val_acc: 0.5256\n",
      "Epoch 36/100\n",
      "\n",
      "Epoch 00036: val_loss did not improve\n",
      " - 41s - loss: 1.5962 - acc: 0.4566 - val_loss: 1.5142 - val_acc: 0.4608\n",
      "Epoch 37/100\n",
      "\n",
      "Epoch 00037: val_loss did not improve\n",
      " - 41s - loss: 1.6120 - acc: 0.4467 - val_loss: 1.4584 - val_acc: 0.5054\n",
      "Epoch 38/100\n",
      "\n",
      "Epoch 00038: val_loss did not improve\n",
      " - 42s - loss: 1.6070 - acc: 0.4490 - val_loss: 1.4627 - val_acc: 0.5020\n",
      "Epoch 39/100\n",
      "\n",
      "Epoch 00039: val_loss did not improve\n",
      " - 43s - loss: 1.6149 - acc: 0.4505 - val_loss: 1.5264 - val_acc: 0.4964\n",
      "Epoch 40/100\n",
      "\n",
      "Epoch 00040: val_loss did not improve\n",
      " - 42s - loss: 1.6213 - acc: 0.4431 - val_loss: 1.2373 - val_acc: 0.5732\n",
      "Epoch 41/100\n",
      "\n",
      "Epoch 00041: val_loss did not improve\n",
      " - 41s - loss: 1.6330 - acc: 0.4448 - val_loss: 1.4221 - val_acc: 0.5296\n",
      "Epoch 42/100\n",
      "\n",
      "Epoch 00042: val_loss did not improve\n",
      " - 41s - loss: 1.6523 - acc: 0.4344 - val_loss: 1.5305 - val_acc: 0.4816\n",
      "Epoch 43/100\n",
      "\n",
      "Epoch 00043: val_loss did not improve\n",
      " - 42s - loss: 1.6644 - acc: 0.4251 - val_loss: 1.4329 - val_acc: 0.4856\n",
      "Epoch 44/100\n",
      "\n",
      "Epoch 00044: val_loss did not improve\n",
      " - 42s - loss: 1.6761 - acc: 0.4176 - val_loss: 1.4359 - val_acc: 0.4924\n",
      "Epoch 45/100\n",
      "\n",
      "Epoch 00045: val_loss did not improve\n",
      " - 41s - loss: 1.6981 - acc: 0.4178 - val_loss: 1.5534 - val_acc: 0.4464\n",
      "Epoch 46/100\n",
      "\n",
      "Epoch 00046: val_loss did not improve\n",
      " - 40s - loss: 1.6959 - acc: 0.4121 - val_loss: 1.4287 - val_acc: 0.4748\n",
      "Epoch 47/100\n",
      "\n",
      "Epoch 00047: val_loss did not improve\n",
      " - 42s - loss: 1.7033 - acc: 0.4107 - val_loss: 1.3281 - val_acc: 0.5256\n",
      "Epoch 48/100\n",
      "\n",
      "Epoch 00048: val_loss did not improve\n",
      " - 41s - loss: 1.6970 - acc: 0.4135 - val_loss: 2.1347 - val_acc: 0.3532\n",
      "Epoch 49/100\n",
      "\n",
      "Epoch 00049: val_loss did not improve\n",
      " - 41s - loss: 1.7190 - acc: 0.4019 - val_loss: 1.3692 - val_acc: 0.5194\n",
      "Epoch 50/100\n",
      "\n",
      "Epoch 00050: val_loss did not improve\n",
      " - 41s - loss: 1.7296 - acc: 0.4007 - val_loss: 1.5468 - val_acc: 0.4282\n",
      "Epoch 51/100\n",
      "\n",
      "Epoch 00051: val_loss did not improve\n",
      " - 41s - loss: 1.7370 - acc: 0.3950 - val_loss: 1.5567 - val_acc: 0.4422\n",
      "Epoch 52/100\n",
      "\n",
      "Epoch 00052: val_loss did not improve\n",
      " - 42s - loss: 1.7658 - acc: 0.3791 - val_loss: 1.5268 - val_acc: 0.4604\n",
      "Epoch 53/100\n",
      "\n",
      "Epoch 00053: val_loss did not improve\n",
      " - 41s - loss: 1.7795 - acc: 0.3750 - val_loss: 1.6509 - val_acc: 0.4034\n",
      "Epoch 54/100\n",
      "\n",
      "Epoch 00054: val_loss did not improve\n",
      " - 40s - loss: 1.7839 - acc: 0.3694 - val_loss: 1.4826 - val_acc: 0.4674\n",
      "Epoch 55/100\n",
      "\n",
      "Epoch 00055: val_loss did not improve\n",
      " - 41s - loss: 1.8141 - acc: 0.3640 - val_loss: 1.4849 - val_acc: 0.4704\n",
      "Epoch 56/100\n",
      "\n",
      "Epoch 00056: val_loss did not improve\n",
      " - 40s - loss: 1.8299 - acc: 0.3545 - val_loss: 1.5365 - val_acc: 0.4634\n",
      "Epoch 57/100\n",
      "\n",
      "Epoch 00057: val_loss did not improve\n",
      " - 40s - loss: 1.8688 - acc: 0.3410 - val_loss: 1.5733 - val_acc: 0.4560\n",
      "Epoch 58/100\n",
      "\n",
      "Epoch 00058: val_loss did not improve\n",
      " - 41s - loss: 1.8573 - acc: 0.3357 - val_loss: 1.5639 - val_acc: 0.4348\n",
      "Epoch 59/100\n",
      "\n",
      "Epoch 00059: val_loss did not improve\n",
      " - 42s - loss: 1.8547 - acc: 0.3361 - val_loss: 1.6497 - val_acc: 0.3920\n",
      "Epoch 60/100\n",
      "\n",
      "Epoch 00060: val_loss did not improve\n",
      " - 42s - loss: 1.8561 - acc: 0.3369 - val_loss: 1.5513 - val_acc: 0.4412\n",
      "Epoch 61/100\n",
      "\n",
      "Epoch 00061: val_loss did not improve\n",
      " - 42s - loss: 1.8426 - acc: 0.3380 - val_loss: 1.5813 - val_acc: 0.4322\n",
      "Epoch 62/100\n",
      "\n",
      "Epoch 00062: val_loss did not improve\n",
      " - 42s - loss: 1.8279 - acc: 0.3387 - val_loss: 1.5463 - val_acc: 0.4354\n",
      "Epoch 63/100\n",
      "\n",
      "Epoch 00063: val_loss did not improve\n",
      " - 42s - loss: 1.8458 - acc: 0.3329 - val_loss: 1.5979 - val_acc: 0.4008\n",
      "Epoch 64/100\n",
      "\n",
      "Epoch 00064: val_loss did not improve\n",
      " - 42s - loss: 1.8442 - acc: 0.3359 - val_loss: 1.5378 - val_acc: 0.4512\n",
      "Epoch 65/100\n",
      "\n",
      "Epoch 00065: val_loss did not improve\n",
      " - 41s - loss: 1.8415 - acc: 0.3406 - val_loss: 1.7233 - val_acc: 0.4010\n",
      "Epoch 66/100\n",
      "\n",
      "Epoch 00066: val_loss did not improve\n",
      " - 41s - loss: 1.8569 - acc: 0.3341 - val_loss: 1.6623 - val_acc: 0.3706\n",
      "Epoch 67/100\n",
      "\n",
      "Epoch 00067: val_loss did not improve\n",
      " - 41s - loss: 1.8498 - acc: 0.3369 - val_loss: 1.6522 - val_acc: 0.4066\n",
      "Epoch 68/100\n",
      "\n",
      "Epoch 00068: val_loss did not improve\n",
      " - 41s - loss: 1.8493 - acc: 0.3338 - val_loss: 1.7817 - val_acc: 0.3654\n",
      "Epoch 69/100\n",
      "\n",
      "Epoch 00069: val_loss did not improve\n",
      " - 41s - loss: 1.8334 - acc: 0.3384 - val_loss: 1.6757 - val_acc: 0.4020\n",
      "Epoch 70/100\n",
      "\n",
      "Epoch 00070: val_loss did not improve\n",
      " - 41s - loss: 1.8403 - acc: 0.3368 - val_loss: 1.5439 - val_acc: 0.4320\n",
      "Epoch 71/100\n",
      "\n",
      "Epoch 00071: val_loss did not improve\n",
      " - 42s - loss: 1.8543 - acc: 0.3355 - val_loss: 1.5424 - val_acc: 0.4238\n",
      "Epoch 72/100\n",
      "\n",
      "Epoch 00072: val_loss did not improve\n",
      " - 42s - loss: 1.8476 - acc: 0.3349 - val_loss: 1.6032 - val_acc: 0.4146\n",
      "Epoch 73/100\n",
      "\n",
      "Epoch 00073: val_loss did not improve\n",
      " - 41s - loss: 1.8299 - acc: 0.3391 - val_loss: 1.6016 - val_acc: 0.4134\n",
      "Epoch 74/100\n",
      "\n",
      "Epoch 00074: val_loss did not improve\n",
      " - 41s - loss: 1.8384 - acc: 0.3315 - val_loss: 1.6180 - val_acc: 0.4132\n",
      "Epoch 75/100\n",
      "\n",
      "Epoch 00075: val_loss did not improve\n",
      " - 41s - loss: 1.8554 - acc: 0.3292 - val_loss: 1.6050 - val_acc: 0.4036\n",
      "Epoch 76/100\n",
      "\n",
      "Epoch 00076: val_loss did not improve\n",
      " - 41s - loss: 1.8632 - acc: 0.3276 - val_loss: 1.6563 - val_acc: 0.3748\n",
      "Epoch 77/100\n",
      "\n",
      "Epoch 00077: val_loss did not improve\n",
      " - 41s - loss: 1.8404 - acc: 0.3339 - val_loss: 1.6101 - val_acc: 0.3906\n",
      "Epoch 78/100\n",
      "\n",
      "Epoch 00078: val_loss did not improve\n",
      " - 41s - loss: 1.8585 - acc: 0.3299 - val_loss: 1.6744 - val_acc: 0.3638\n",
      "Epoch 79/100\n",
      "\n",
      "Epoch 00079: val_loss did not improve\n",
      " - 41s - loss: 1.8708 - acc: 0.3259 - val_loss: 1.6025 - val_acc: 0.4272\n",
      "Epoch 80/100\n",
      "\n",
      "Epoch 00080: val_loss did not improve\n",
      " - 41s - loss: 1.8550 - acc: 0.3299 - val_loss: 1.7239 - val_acc: 0.3994\n",
      "Epoch 81/100\n",
      "\n",
      "Epoch 00081: val_loss did not improve\n",
      " - 40s - loss: 1.8585 - acc: 0.3289 - val_loss: 2.0295 - val_acc: 0.3876\n",
      "Epoch 82/100\n",
      "\n",
      "Epoch 00082: val_loss did not improve\n",
      " - 40s - loss: 1.8749 - acc: 0.3235 - val_loss: 1.6415 - val_acc: 0.4010\n",
      "Epoch 83/100\n",
      "\n",
      "Epoch 00083: val_loss did not improve\n",
      " - 41s - loss: 1.8671 - acc: 0.3291 - val_loss: 1.5935 - val_acc: 0.4124\n",
      "Epoch 84/100\n",
      "\n",
      "Epoch 00084: val_loss did not improve\n",
      " - 41s - loss: 1.8581 - acc: 0.3254 - val_loss: 1.6417 - val_acc: 0.4260\n",
      "Epoch 85/100\n",
      "\n",
      "Epoch 00085: val_loss did not improve\n",
      " - 41s - loss: 1.8677 - acc: 0.3222 - val_loss: 1.6345 - val_acc: 0.3974\n",
      "Epoch 86/100\n",
      "\n",
      "Epoch 00086: val_loss did not improve\n",
      " - 41s - loss: 1.8706 - acc: 0.3253 - val_loss: 1.7197 - val_acc: 0.3546\n",
      "Epoch 87/100\n",
      "\n",
      "Epoch 00087: val_loss did not improve\n",
      " - 41s - loss: 1.8691 - acc: 0.3197 - val_loss: 1.6584 - val_acc: 0.3892\n",
      "Epoch 88/100\n",
      "\n",
      "Epoch 00088: val_loss did not improve\n",
      " - 41s - loss: 1.8723 - acc: 0.3179 - val_loss: 1.7077 - val_acc: 0.3638\n",
      "Epoch 89/100\n",
      "\n",
      "Epoch 00089: val_loss did not improve\n",
      " - 41s - loss: 1.8667 - acc: 0.3211 - val_loss: 1.5768 - val_acc: 0.4104\n",
      "Epoch 90/100\n",
      "\n",
      "Epoch 00090: val_loss did not improve\n",
      " - 40s - loss: 1.8728 - acc: 0.3203 - val_loss: 1.8180 - val_acc: 0.2986\n",
      "Epoch 91/100\n",
      "\n",
      "Epoch 00091: val_loss did not improve\n",
      " - 40s - loss: 1.8939 - acc: 0.3189 - val_loss: 1.6246 - val_acc: 0.4102\n",
      "Epoch 92/100\n",
      "\n",
      "Epoch 00092: val_loss did not improve\n",
      " - 40s - loss: 1.8662 - acc: 0.3176 - val_loss: 1.6390 - val_acc: 0.4086\n",
      "Epoch 93/100\n",
      "\n",
      "Epoch 00093: val_loss did not improve\n",
      " - 40s - loss: 1.8890 - acc: 0.3163 - val_loss: 1.7946 - val_acc: 0.3274\n",
      "Epoch 94/100\n",
      "\n",
      "Epoch 00094: val_loss did not improve\n",
      " - 40s - loss: 1.8933 - acc: 0.3121 - val_loss: 1.6817 - val_acc: 0.3894\n",
      "Epoch 95/100\n",
      "\n",
      "Epoch 00095: val_loss did not improve\n",
      " - 41s - loss: 1.8978 - acc: 0.3118 - val_loss: 1.6703 - val_acc: 0.3794\n",
      "Epoch 96/100\n",
      "\n",
      "Epoch 00096: val_loss did not improve\n",
      " - 40s - loss: 1.8804 - acc: 0.3119 - val_loss: 1.8480 - val_acc: 0.3424\n",
      "Epoch 97/100\n",
      "\n",
      "Epoch 00097: val_loss did not improve\n",
      " - 39s - loss: 1.9021 - acc: 0.3113 - val_loss: 1.7205 - val_acc: 0.3814\n",
      "Epoch 98/100\n",
      "\n",
      "Epoch 00098: val_loss did not improve\n",
      " - 40s - loss: 1.9112 - acc: 0.3092 - val_loss: 1.6089 - val_acc: 0.3956\n",
      "Epoch 99/100\n",
      "\n",
      "Epoch 00099: val_loss did not improve\n",
      " - 40s - loss: 1.9048 - acc: 0.3060 - val_loss: 1.8215 - val_acc: 0.3298\n",
      "Epoch 100/100\n",
      "\n",
      "Epoch 00100: val_loss did not improve\n",
      " - 39s - loss: 1.9076 - acc: 0.3093 - val_loss: 1.7290 - val_acc: 0.3582\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f955410f2e8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.callbacks import ModelCheckpoint   \n",
    "\n",
    "batch_size = 32\n",
    "epochs = 100\n",
    "\n",
    "# train the model\n",
    "checkpointer = ModelCheckpoint(filepath='aug_model.weights.best.hdf5', verbose=1, \n",
    "                               save_best_only=True)\n",
    "\n",
    "# Note!!\n",
    "# fit_generator(data.flow(), steps_per_epoch) ..... instead of fit()\n",
    "model.fit_generator(datagen_train.flow(x_train, y_train, batch_size=batch_size),\n",
    "                    steps_per_epoch=x_train.shape[0] // batch_size,\n",
    "                    epochs=epochs, verbose=2, callbacks=[checkpointer],\n",
    "                    validation_data=(x_valid, y_valid),\n",
    "                    validation_steps=x_valid.shape[0] // batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Test accuracy: 0.678\n"
     ]
    }
   ],
   "source": [
    "# load the weights that yielded the best validation accuracy\n",
    "model.load_weights('aug_model.weights.best.hdf5')\n",
    "\n",
    "# evaluate and print test accuracy\n",
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "print('\\n', 'Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}