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Miki Tebeka committed de4fd43

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Files changed (13)

+{
+ "metadata": {
+  "name": "01-Numpy"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### --pylab creates some shortcuts for us"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "np"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 1,
+       "text": [
+        "<module 'numpy' from '/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/__init__.pyc'>"
+       ]
+      }
+     ],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 2,
+       "text": [
+        "<module 'matplotlib.pyplot' from '/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/pyplot.pyc'>"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### I feel the need ... the need for speed"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "x, y = np.random.random(1000000), np.random.random(1000000)\n",
+      "%timeit np.dot(x, y)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "1000 loops, best of 3: 1.55 ms per loop\n"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Array operations"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "a = np.array([[0, 1, 2]] * 4)\n",
+      "a"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 4,
+       "text": [
+        "array([[0, 1, 2],\n",
+        "       [0, 1, 2],\n",
+        "       [0, 1, 2],\n",
+        "       [0, 1, 2]])"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "a.shape"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 5,
+       "text": [
+        "(4, 3)"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "a.reshape(3, 4)  # Will create a new \"copy\""
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 6,
+       "text": [
+        "array([[0, 1, 2, 0],\n",
+        "       [1, 2, 0, 1],\n",
+        "       [2, 0, 1, 2]])"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "a"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 7,
+       "text": [
+        "array([[0, 1, 2],\n",
+        "       [0, 1, 2],\n",
+        "       [0, 1, 2],\n",
+        "       [0, 1, 2]])"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "a.ravel()  # Flatten"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 8,
+       "text": [
+        "array([0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2])"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "a[:,1]  # Select 1st column"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 9,
+       "text": [
+        "array([1, 1, 1, 1])"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "a[:,0:2]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 10,
+       "text": [
+        "array([[0, 1],\n",
+        "       [0, 1],\n",
+        "       [0, 1],\n",
+        "       [0, 1]])"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Plotting"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "xs = np.linspace(-10, 10)\n",
+      "plt.plot(xs, np.sin(xs))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 11,
+       "text": [
+        "[<matplotlib.lines.Line2D at 0x106f7a750>]"
+       ]
+      },
+      {
+       "output_type": "display_data",
+       "png": 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rRBZ9m3BbuaaOSvZOaam7Knd04uPV6ZOkL3B0egfUnng85vn6you+mcuTrcSt\nmX5sLPDFF8D586IjGRg3+vkAVVhFRZnbydEqKiqos6ZbFjh2xSxfX2nRT06mNritraIjGRi3iv6g\nQVRppUK271bRB9SxeNxo7ehwpg+q4Jk4UY0KHreKPqDOZC6LvugoBsbNos+Z/hVUqOA5fx44c4Zu\nUG5EFV+fRV90FAPjZtHnTP8KKkzm6q16Byn/1w4MFTL9s2eBc+doDsKNmN3J0SrcLPoxMUBTE82R\nGUF5GVJF9N1q7QBqlG3qnTXdVrmjk54OHDggd58kTXNvFRxA16YZN2dHiL7sFTxu9vMBesqprJS7\nJLCkxH2LsroydixV8VRXi47EOydPAkFBFKtbYdEH3fWrquSu4HFzdgLQhHt0tNwlgW7283XS04H9\n+0VH4R3d2nHr0xhgjq+vvOiHhdHmzjJvqOL2TB+Q3+Jh0QeuvVZu0T940D2b23iDM/0ryOzrt7fT\nghK37LnqDdknc1n0O319Wdm/H8jIEB2FWDjTv4LMZZtHj9IuTEOHio5ELDKXbZ47B5w+TU+MbkZ2\ne2ffPmDaNNFRiGXyZKChAWhuDnwMR4i+zJk+WzuEzJm+fo7cWlKrk5ZGNqmME+6aRjek9HTRkYgl\nKMj4CndHXOYyV/Cw6BO6py9jSeDBg2ztAEB4OK1TkHF+7NgxYPhw4JprREciHqO+viNEPymJbJRL\nl0RH0hsWfWLsWGqS1dAgOpLesJ/fiawWz759nOXrGPX1HSH6YWHUeU/GDMXtC7O6IqvFw6Lficyi\n73Y/X4cz/SvI6uu7vUa/K7KWbbLodyJr2SaLfiec6V9Bxgqe06dp0we37cTkDRkreJqbyXJy26Yc\n3pC1bJNFv5PERJrjCNTOdozoy5jp79tHmZObVxB2RUZ759AhmhMKChIdiRwkJAD19XJtenPhAokc\nPzEToaFkZwfaUt5Roi9bBc+ePcD06aKjkAcZ7R22droTFERPZDIlUCUldGMODRUdiTwY8fUdI/pJ\nScYeeayguJhFvyuTJtHm401NoiPphMs1eyPbZO7+/Wzt9MSIrx+w6J85cwbZ2dlISkrCzTffjLNn\nz/b5uri4OEybNg3Tp0/HnDlzAj3cgISG0mo1mTaCYNHvTlAQ+ZEyWTyc6fdGNtFnP783QjL9VatW\nITs7G2VlZVi0aBFWrVrV5+s8Hg8KCgpQXFyMoqKiQA/nEzL5+hcvUs8dN7fr7YvMTLK9ZIFFvzcs\n+vIjJNMjPf0cAAAQG0lEQVTftGkTcnNzAQC5ubnYuHGj19dqNi3DlEn0Dx6krHbwYNGRyMXMmcCu\nXaKjIC5eBGprgfh40ZHIhV62KcPqaU0D9u5l0e9JcnLgE7nBgR60oaEBkZGRAIDIyEg0eFlq6fF4\nsHjxYgQFBWHFihX43ve+1+frnn766av/zsrKQlZWlt8xpaUBa9b4/TZLYGunb2bMAP7rv0RHQRw+\nTIIfEiI6ErmIiiKxra8XX25cX0/Vb+PHi41DFgoKClBQUACAEsqWFv/H6Ff0s7OzUV9f3+vnzz77\nbLfvPR4PPF7qErdv346oqCg0NjYiOzsbKSkpmD9/fq/XdRX9QJGpgodFv28yMqgOvK1NvNiytdM3\nHk9nvb5o0dfbL3DZM9E1IS4qArZsecbvMfoV/W3btnn9XWRkJOrr6zF+/HjU1dVh3Lhxfb4u6spV\nExERgbvuugtFRUV9ir4ZJCYCx4/T3U+0rVJcDNx7r9gYZGT4cGrqdeiQ+F4qbt8isT90Xz87W2wc\n7Od7Jy0N2LLF//cF7Onn5ORg9erVAIDVq1dj6dKlvV5z4cIFnL+yyqO5uRlbt25FuoWf9NBQWlkp\nuoKnvZ0+MJmZYuOQFVl8fS7X9I4sk7ks+t4JdBexgEX/8ccfx7Zt25CUlIQPP/wQjz/+OADgxIkT\nuP322wEA9fX1mD9/PjIzMzF37lzccccduPnmmwM9pE/IMJlbXk4bp4wcKTYOWZkxA9i9W3QUbO/0\nB4u+/AR67Xo0u0pr+gvC4zGtwueZZ2iT9B7TDrayZg3w9tvAX/4iLgaZ+egj4Cc/AT79VFwMly7R\nTfncOV7p2RfnzpGff+6cuBYVbW3AiBHUwyo8XEwMMnP2LDB6tP/a6ZgVuToyNF7jSdz+mT6dyvDa\n28XFUFZGi/lY8PtmxAggIgI4ckRcDIcP0ypuFvy+GTUqsPc5TvRlqOBh0e+fUaOAyEix+x+wtTMw\noi0etnaswXGin5gIVFcHVr9qBprGjdZ8QbSvz6I/MKLbLLPoW4PjRD8khBbciOrvUltLNcWi65tl\nR7To791LK08Z73Cm70wcJ/qA2Aoe3drhxST9I1L0NQ0oLATmzhVzfFVg0XcmLPomw36+b+ii39Fh\n/7GPHaOb8qRJ9h9bJZKTabHjxYv2H/v0adrIhc+R+ThW9EV5kezn+8bYsTShW1lp/7F37ACuu46f\nxgYiJITmyEQURuzfz7vOWYUjRX/OHHp8F7ECgTN93xFl8RQWAtdfb/9xVUSUxcPWjnU4UvRjY6n3\nTkWFvcf94gvaGSohwd7jqsrMmWJEX8/0mYHR2yzbDYu+dThS9AFg3jxg+3Z7j7lnD12ogxz7VzWX\nGTPs78Fz8SJZf7Nm2XtcVRGV6fMWidbhWHn6ylfsF322dvxDt3fstOF276ZGVbzK0zdE1Oq3t1Mh\nBpfUWoNjRX/ePOCzz+w9Jk/i+sf48UBYGFWI2AX7+f4xcSLQ3EzVNHZRWUktILhhoTU4VvSnTaOV\nuWfO2HdMzvT9x25fn/18//B47Pf12c+3FseKfnAwVfHs2GHP8Xgj9MCwu4KHM33/sdvXZ9G3FseK\nPmDvZO6BA0BSEtkVjO/YOZlbU0MtladMsed4TmHmTGDnTvuOx6JvLSz6JsF+fmDoom/HZO6OHZTl\n84If/1i4kPZAsGvCnUXfWhwt+nPnkqC0tVl/LPbzAyMmhsSkrs76YxUWsp8fCPHxtJGKHa2wz58H\n6ut5rYuVOFr0R46kC7a42PpjsegHhsdjn6+vZ/qMf3g8ndm+1ezcCWRkiNutyw04WvQBeywefSP0\njAxrj+NU7PD1L12idsq8KCswbroJ+PBD64+zbRuQnW39cdwMi74JlJXRTlBcVxwYdmT6e/aQZTB8\nuLXHcSoLFwIFBdZ3RWXRtx7XiL6Vk1A8iWsMO2r1uVTTGBMn0r65VrYsb2ykPXl5nwNrcbzoT5pE\nnmRVlXXHYD/fGHFxQFMTcPKkdcfgRVnGsdrief99ICuLWjoz1uF40fd4rG/JwKJvDDsmcznTN47V\nk7ls7diD40UfsNbX7+gg0c/MtGZ8t2Cl6NfVAefO0YYgTOAsXAj87/9S4YLZaBqLvl2w6Bvk//4P\nGDcOmDDBmvHdgpW+vl6fzy2vjTF+PBAVRXNYZnP4MD3xJSWZPzbTHVd8DDIzydM/e9b8sTduBJYu\nNX9ct2Flps9+vnlY5evrWT6vlrYeV4h+SAhlkoWF5o+dl8eibwYJCXRTrqkxf2z2883jppus8fXZ\n2rEPV4g+YM1kblkZCRUv+DHOoEHAnXcCf/mLueO2tdETxJw55o7rVm68Efj0U3Nbm7S10VzBokXm\njcl4x1Wib7avn5dHQsVesTnccw+wfr25Y+7bRyWhvHDOHK65hlqbfP65eWPu3EljRkSYNybjHdfI\n1fXX06Tr5cvmjcl+vrksXgyUlgK1teaNyX6++SxcaK6vz9aOvbhG9EePplWFe/eaM15DA61OzMoy\nZzwGCA0FcnKAt982b0z2883H7MlcFn17cY3oA+Zulv7OO8Att/CmKWZjtsXDmb75LFgAFBUBLS3G\nxzp7lpoV3nCD8bEY33CV6Jvp67O1Yw1mWjwnT9KG3qmpxsdiOhkxAkhLM6ca7qOPKBkbPNj4WIxv\nuE70zajgaWoCPv4YuPVW42Mx3THT4tm5k6p2eKLdfMwq3WRrx35c9XGIjwdaW4Hjx42N89575BNz\nRYg1mGXxvPMOz7lYhVmTuSz69uMq0debrxm1eDZupFJNxhoWLwZKSowt1GpspBvH8uXmxcV0Mm8e\n9Zxqbg58jKNHqSdSerppYTE+4CrRByhD2bQp8Pe3tQGbN5MFwVhDaKjxhVqvvAJ8/eu0uQ1jPkOH\nUusMIwnUtm10g2f7zV5c9+f+7neBDz6gBk+B8MknZBPFxJgaFtMDIxZPSwvwu98BP/yhuTEx3THa\napmtHTG4TvSHDwf+/u+Bn/40sPeztWMPehVPIBbPmjXUZG/qVPPjYjoxUq/f3k7JF4u+/bhO9AHg\n7/4OyM+n3jn+oGncYM0u9Coefy0eTQNefBF49FFr4mI6ue46mnv58kv/31tcTNZbdLT5cTH940rR\nHzECePhh/7P9PXtIjNLSrImL6c499wDr1vn3nvffJ+HnDNJ6wsJoP9v33vP/vVu38jkShStFHyDR\n37IFKC/3/T16gzXu+W0PixcDhw75Z/H88peU5fM5sod//EfgH/7B/70q2M8Xh2tFf+RIYOVK4Nln\nfX8Pr8K1F38tnpISaqP8zW9aGxfTyc03A3fcQfNkvlJURN1Pb7zRurgY77hW9AG6UN99FzhyZODX\nVlUBJ05w8y678cfi+dWvgIce4iX9dvOzn1Hp5saNA7/2xAnga18D/vhHKqpg7MejaZomPAiPB6LC\neOopoLoaeOON/l/3619TdvL66/bExRCtrbQv6969/ZfJNjbS/qplZdyXXQTbtwN3303nady4vl/T\n0kLZ/Z13Ak88YW98TiUQ7XR1pg8AjzxCXn1lpffXVFYCr77K1o4IfLV4Xn6ZngpY8MUwbx6Qmwus\nWEET6T3RNOBv/xaYPBn4p3+yPz6mE9eL/ujRZAk891zv37W3k2UwZw5w//3A7bfbH5+/FBQUiA7B\ndO65B3jtNe+7NbW0AL//Pd3AzcaJf0+reOYZskr//Ofev/vFL4ADB4DvfreAJ9kFE7Dor1+/HlOn\nTkVQUBB2797t9XX5+flISUlBYmIiXnjhhUAPZyk//CGwYQP1AtEpLQXmz6ef79hBFQoqLBd3okgt\nWUJZ5N13A7NnkxV34ULn7//7v6klgBWltE78e1pFWBjw5pv0Wamu7vz5li20diIvDygsLBAWH0ME\nLGPp6enYsGEDFixY4PU17e3tWLlyJfLz81FSUoI1a9agtLQ00ENaxpgxwPe/T9l+Wxv9d8EC4Fvf\nomXmiYmiI3Q3QUFUGnjkCGWTGzYAsbE0EV9ayouxZCIzk87L/fcDHR1UcpubSy01YmNFR8cABkQ/\nJSUFSUlJ/b6mqKgICQkJiIuLQ0hICJYtW4a8vLxAD2kpjz5KvvHs2dQr//PPyfZRIbt3C0FBwG23\nUcvk3bup+mPhQvr5okWio2N0fvxj4Px54PnnadL2+efJ82ckQTNIVlaWtmvXrj5/t379eu2BBx64\n+v2f//xnbeXKlb1eB4C/+Iu/+Iu/Avjyl2D0Q3Z2Nurr63v9/LnnnsNXv/rV/t4KgMqJfEETXzXK\nMAzjCvoV/W3bthkaPDo6GtVdZnSqq6sRwz2JGYZhhGGKY+0tU581axbKy8tx9OhRtLa2Yu3atcjh\n3UcYhmGEEbDob9iwAbGxsSgsLMTtt9+OW6/sEn7ixAncfqWgPTg4GC+99BKWLFmCtLQ03HvvvUhN\nTTUncoZhGMZ//J4FMJF169ZpaWlp2qBBg3pNBj/33HNaQkKClpycrL333nuCIlSXp556SouOjtYy\nMzO1zMxMbcuWLaJDUo4tW7ZoycnJWkJCgrZq1SrR4SjPpEmTtPT0dC0zM1ObPXu26HCU47777tPG\njRunXXvttVd/dvr0aW3x4sVaYmKilp2drX3xxRcDjiO0INFbrX9JSQnWrl2LkpIS5Ofn46GHHkJH\nR4egKNXE4/Hg0UcfRXFxMYqLi3HLLbeIDkkpVFljohIejwcFBQUoLi5GUVGR6HCU47777kN+fn63\nn61atQrZ2dkoKyvDokWLsGrVqgHHESr63mr98/Ly8I1vfAMhISGIi4tDQkICXyQBoHFVVMCotMZE\nJfiaDJz58+dj9OjR3X62adMm5ObmAgByc3Ox0YdWp1IuPTpx4kS3Kp+YmBjU1tYKjEhNfvvb3yIj\nIwPLly/HWX93uXA5tbW1iO2yhJSvQeN4PB4sXrwYs2bNwh/+8AfR4TiChoYGREZGAgAiIyPR0NAw\n4Hv6Ldk0A6O1/jq+1vy7CW9/22effRYPPvggnnzySQDAT37yE/zoRz/C69wX2mf4ejOf7du3Iyoq\nCo2NjcjOzkZKSgrmz58vOizH4PF4fLpuLRf9QGr9e9b319TUIJp3UO6Fr3/bBx54wK8bLMNrTKwg\nKioKABAREYG77roLRUVFLPoGiYyMRH19PcaPH4+6ujqM87aZQReksXe6en05OTl466230Nraiqqq\nKpSXl2POnDkCo1OPurq6q//esGED0tPTBUajHrzGxFwuXLiA8+fPAwCam5uxdetWviZNICcnB6tX\nrwYArF69Gkt92fTDsvoiH/jrX/+qxcTEaIMHD9YiIyO1W2655ervnn32WS0+Pl5LTk7W8vPzBUap\nJt/+9re19PR0bdq0adqdd96p1dfXiw5JOTZv3qwlJSVp8fHx2nPPPSc6HKWprKzUMjIytIyMDG3q\n1Kn89wyAZcuWaVFRUVpISIgWExOjvfHGG9rp06e1RYsW+VWyKcV2iQzDMIw9SGPvMAzDMNbDos8w\nDOMiWPQZhmFcBIs+wzCMi2DRZxiGcREs+gzDMC7i/wFa4fKS4KyA7QAAAABJRU5ErkJggg==\n"
+      }
+     ],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.hist(np.random.randn(1000), bins=20)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 12,
+       "text": [
+        "(array([  5,   4,   4,  14,  40,  48,  81,  96, 107, 130, 121, 108,  90,\n",
+        "        68,  35,  25,  13,   7,   3,   1]),\n",
+        " array([-3.15881716, -2.84097859, -2.52314001, -2.20530144, -1.88746287,\n",
+        "       -1.56962429, -1.25178572, -0.93394714, -0.61610857, -0.29826999,\n",
+        "        0.01956858,  0.33740716,  0.65524573,  0.97308431,  1.29092288,\n",
+        "        1.60876146,  1.92660003,  2.24443861,  2.56227718,  2.88011576,\n",
+        "        3.19795433]),\n",
+        " <a list of 20 Patch objects>)"
+       ]
+      },
+      {
+       "output_type": "display_data",
+       "png": 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Pz1dFRYUGBgbiGhYAEJ3LsqzYfhsiqa+vT319fSopKdG1a9e0aNEiHTlyRAcOHNCDDz6o\n7373u3rhhRfU39+vpqamu5O5XLIxHWLgcrkU6y+4pGSNSeZc5HNiDK/vxIilO22dqWdlZamkpESS\nNGvWLBUWFqqnp0ctLS2qq6uTJNXV1enIkSN2vjwAwKZJv6Wxs7NT586dU1lZmUKhkNxutyTJ7XYr\nFAqNen59fX3ksdfrldfrnWwEADBKIBBQIBCwNdbW8suHrl27pq9+9avasWOHqqurNWfOHPX390c+\nn5GRoXA4fHcyll8SjuWXyYxJ5lwm5uP1nSgJX36RpJs3b2r16tVat26dqqurJd05O+/r65Mk9fb2\nKjMz0+6XBwDYYKvULcvSxo0b5fF4tGXLlsjxqqoq+f1+SZLf74+UPQAgOWwtv/zxj3/UV77yFS1c\nuPB//9yXGhsbtXjxYtXU1OjSpUvKy8vT4cOHNXv27LuTsfyScCy/TGZMMucyMR+v70SJpTsntaYe\nK0o98Sj1yYxJ5lwm5uP1nShJWVMHAKQeSh0ADEKpA4BBKPUUlp6eEfONCgBMb9wkI4UNDvbL3i+4\nAExXnKkDiJPYb4HHbfDijzN1AHES+y3wJG6DF2+cqQOAQSh1ADAIpQ4ABqHUAcAglDoAGIRSBwCD\nUOoAYBBKHQAMQqkDcFjsV6JyFeq9cUUpAIfFfiUqV6HeG2fqAGCQaX2mvn17g9566+2Yxrhc0o4d\nW/TlL385QakAwL5pfY/Shx76gnp7N0vKjmHUOkk3YpxppqSbMY75UOrej9K8e2wmcy4T8yX3e0ql\nLkm0WLpzWp+p3/F/kr4Qw/OfUnJ/2AFg4lhTBzAF8Y6Ze6HUxxRwOsAEBZwOMEEBpwNMUMDpABMU\ncDrABAUS+LU/fMfMxD/u3EnsYwkDiczojLiXeltbmwoKCjRv3jy98MIL8f7ySRJwOsAEBZwOMEEB\npwNMUMDpABMUcDrABAWcDjAuSn0cw8PD+ta3vqW2tja1t7frl7/8pS5cuBDPKQDAptFLNg0NDcYt\n28S11E+fPq25c+cqLy9PM2fO1De+8Q01NzfHcwoAsGmsJZvvj3Fs/GWbVBbXd7/09PQoNzc38uec\nnBydOnVqxHNcrlR7R0fBPY43RBlj53uw+32PN26snMnKF8uYj+ZMxXxOzGU3X4Oi/3zGa654jJlo\nTif/zsfPmHq9dW9xLfXxvvHp9L5SAHBCXJdfsrOz1dXVFflzV1eXcnJy4jkFACCKuJb6l770JV28\neFGdnZ0aGhrSoUOHVFVVFc8pAABRxHX5JS0tTS+99JJWrFih4eFhbdy4UYWFhfGcAgAQRdzfp75y\n5Uq98847+uc//6nt27ff83l79+7VjBkzFA6H4x0hLnbs2KHi4mKVlJSovLx8xLJSKnnuuedUWFio\n4uJirVq1SleuXHE60ph+9atfaf78+frEJz6hs2fPOh1nlKlwfcWGDRvkdrtVVFTkdJSourq6tHz5\ncs2fP18LFizQvn37nI40phs3bqisrEwlJSXyeDxR+8ppw8PDKi0tVWVl5fhPthxw6dIla8WKFVZe\nXp7173//24kI47p69Wrk8b59+6yNGzc6mObejh49ag0PD1uWZVnPP/+89fzzzzucaGwXLlyw3nnn\nHcvr9VpvvfWW03FGuHXrlvXII49YwWDQGhoasoqLi6329nanY43yhz/8wTp79qy1YMECp6NE1dvb\na507d86yLMsaHBy08vPzU/Lv07Is6z//+Y9lWZZ18+ZNq6yszHrjjTccTjS2vXv3WmvXrrUqKyvH\nfa4j2wQ8++yz2r17txNTT9j9998feXzt2jU9+OCDDqa5N5/Ppxkz7vxnLCsrU3d3t8OJxlZQUKD8\n/HynY4xpqlxfsWzZMs2ZM8fpGOPKyspSSUmJJGnWrFkqLCzU5cuXHU41tk9/+tOSpKGhIQ0PDysj\nI/UuNOru7lZra6s2bdo0oXcQJr3Um5ublZOTo4ULFyZ76ph973vf08MPPyy/369t27Y5HWdcL7/8\nsp544gmnY0w5Y11f0dPT42Aic3R2durcuXMqKytzOsqYbt++rZKSErndbi1fvlwej8fpSKNs3bpV\ne/bsiZy8jSchW+/6fD719fWNOr5z5041Njbq6NGjkWMT+T9Potwr565du1RZWamdO3dq586dampq\n0tatW3XgwAEHUo6fU7rzd3vfffdp7dq1yY4XMZGcqWgqXVgylVy7dk1r1qzRiy++qFmzZjkdZ0wz\nZszQX/7yF125ckUrVqxQIBCQ1+t1OlbEq6++qszMTJWWlk54n5qElPqxY8fGPP63v/1NwWBQxcXF\nku78s2LRokU6ffq0MjMzExElqnvl/Li1a9c6egY8Xs5XXnlFra2tev3115OUaGwT/ftMNVxfEX83\nb97U6tWr9fTTT6u6utrpOON64IEH9OSTT+rMmTMpVeonT55US0uLWltbdePGDV29elXf/OY39bOf\n/ezegxK+wh9FKv+itKOjI/J437591tNPP+1gmnt77bXXLI/HY7333ntOR5kQr9drnTlzxukYI9y8\nedP6/Oc/bwWDQeuDDz5I2V+UWpZlBYPBlP9F6e3bt61169ZZW7ZscTpKVO+9957V399vWZZlXb9+\n3Vq2bJl1/Phxh1PdWyAQsL72ta+N+zxH91NP5X/2bt++XUVFRSopKVEgENDevXudjjSmZ555Rteu\nXZPP51Npaak2b97sdKQx/eY3v1Fubq7efPNNPfnkk1q5cqXTkSI+en2Fx+PR17/+9ZS8vqK2tlZL\nly5VR0eHcnNzHVsOHM+f/vQn/eIXv9Dvf/97lZaWqrS0VG1tbU7HGqW3t1ePP/64SkpKVFZWpsrK\nSpWXlzsdK6qJdGZS71EKAEgs7nwEAAah1AHAIJQ6ABiEUgcAg1DqAGAQSh0ADPL/xLgWV8dKTsgA\nAAAASUVORK5CYII=\n"
+      }
+     ],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Two Plots or More"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "fig = plt.figure()\n",
+      "ax = fig.add_subplot(111)\n",
+      "xs = np.linspace(-np.pi, np.pi)\n",
+      "ax.plot(xs, np.sin(xs))\n",
+      "ax.plot(xs, np.sort(xs))\n",
+      "ax.set_ylabel('$sin(x)_{-\\pi<x<\\pi}$')  # I can haz LaTex"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 13,
+       "text": [
+        "<matplotlib.text.Text at 0x106fde2d0>"
+       ]
+      },
+      {
+       "output_type": "display_data",
+       "png": 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jR9G3b18AQFpaGrRarcKJrMPrDojIXahqKMyePRuHDx9GmzZtEBQUhGXLlikd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\n"
+      }
+     ],
+     "prompt_number": 13
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}

02-Regression.ipynb

+{
+ "metadata": {
+  "name": "02-Regression"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Load data"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from sklearn.datasets import load_boston\n",
+      "boston = load_boston()\n",
+      "print(boston.DESCR)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Boston House Prices dataset\n",
+        "\n",
+        "Notes\n",
+        "------\n",
+        "Data Set Characteristics:  \n",
+        "\n",
+        "    :Number of Instances: 506 \n",
+        "\n",
+        "    :Number of Attributes: 13 numeric/categorical predictive\n",
+        "    \n",
+        "    :Median Value (attribute 14) is usually the target\n",
+        "\n",
+        "    :Attribute Information (in order):\n",
+        "        - CRIM     per capita crime rate by town\n",
+        "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
+        "        - INDUS    proportion of non-retail business acres per town\n",
+        "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
+        "        - NOX      nitric oxides concentration (parts per 10 million)\n",
+        "        - RM       average number of rooms per dwelling\n",
+        "        - AGE      proportion of owner-occupied units built prior to 1940\n",
+        "        - DIS      weighted distances to five Boston employment centres\n",
+        "        - RAD      index of accessibility to radial highways\n",
+        "        - TAX      full-value property-tax rate per $10,000\n",
+        "        - PTRATIO  pupil-teacher ratio by town\n",
+        "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
+        "        - LSTAT    % lower status of the population\n",
+        "        - MEDV     Median value of owner-occupied homes in $1000's\n",
+        "\n",
+        "    :Missing Attribute Values: None\n",
+        "\n",
+        "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
+        "\n",
+        "This is a copy of UCI ML housing dataset.\n",
+        "http://archive.ics.uci.edu/ml/datasets/Housing\n",
+        "\n",
+        "\n",
+        "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
+        "\n",
+        "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
+        "prices and the demand for clean air', J. Environ. Economics & Management,\n",
+        "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
+        "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
+        "pages 244-261 of the latter.\n",
+        "\n",
+        "The Boston house-price data has been used in many machine learning papers that address regression\n",
+        "problems.   \n",
+        "     \n",
+        "**References**\n",
+        "\n",
+        "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
+        "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
+        "   - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)\n",
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "boston.data.shape"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 2,
+       "text": [
+        "(506, 13)"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Some shortcuts"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "indexof = {name: index for (index, name) in enumerate(boston.feature_names)}.get\n",
+      "data = boston.data\n",
+      "prices = boston.target * 1000"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Have a look at the data"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.scatter(data[:,indexof('RM')], prices)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 4,
+       "text": [
+        "<matplotlib.collections.PathCollection at 0x106eafed0>"
+       ]
+      },
+      {
+       "output_type": "display_data",
+       "png": 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Azp19WLJEe7po3bp1tG/fB5vtXcAdbaD5ISAHSMLHZxdDh95PdnYugwb1pnXr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\n"
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Learn (rooms only)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from sklearn.linear_model import LinearRegression\n",
+      "clf = LinearRegression()\n",
+      "X = data[:,indexof('RM')].reshape(data.shape[0], 1) # Make it a list fo 1-length vectors\n",
+      "clf.fit(X, prices)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 5,
+       "text": [
+        "LinearRegression(copy_X=True, fit_intercept=True, normalize=False)"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### What have we learned?"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "clf.coef_"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 6,
+       "text": [
+        "array([ 9102.10898118])"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "clf.score(X, prices)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 7,
+       "text": [
+        "0.4835254559913339"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Plot the results"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "xs = X.ravel()\n",
+      "fig = plt.figure()\n",
+      "ax = fig.add_subplot(111)\n",
+      "ax.scatter(xs, prices)\n",
+      "ax.plot(xs, clf.predict(X), color='red')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 8,
+       "text": [
+        "[<matplotlib.lines.Line2D at 0x107f2cfd0>]"
+       ]
+      },
+      {
+       "output_type": "display_data",
+       "png": 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5eXmSlZUlUVGtxWy+TsBDwChgERgg8IxzqqqRuLu3Fdgg8H9iNvvL+vXrL8rO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\n"
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Now learn on all the features"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "clf = LinearRegression()\n",
+      "clf.fit(data, prices)\n",
+      "clf.coef_"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 9,
+       "text": [
+        "array([ -1.07170557e+02,   4.63952195e+01,   2.08602395e+01,\n",
+        "         2.68856140e+03,  -1.77957587e+04,   3.80475246e+03,\n",
+        "         7.51061703e-01,  -1.47575880e+03,   3.05655038e+02,\n",
+        "        -1.23293463e+01,  -9.53463555e+02,   9.39251272e+00,\n",
+        "        -5.25466633e+02])"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "clf.score(data, prices)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 11,
+       "text": [
+        "0.74060774286494269"
+       ]
+      }
+     ],
+     "prompt_number": 11
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}

03-Classification.ipynb

+{
+ "metadata": {
+  "name": "03-Classification"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Load and View Data"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from sklearn.datasets import load_iris\n",
+      "iris = load_iris()\n",
+      "print(iris['DESCR'])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Iris Plants Database\n",
+        "\n",
+        "Notes\n",
+        "-----\n",
+        "Data Set Characteristics:\n",
+        "    :Number of Instances: 150 (50 in each of three classes)\n",
+        "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
+        "    :Attribute Information:\n",
+        "        - sepal length in cm\n",
+        "        - sepal width in cm\n",
+        "        - petal length in cm\n",
+        "        - petal width in cm\n",
+        "        - class:\n",
+        "                - Iris-Setosa\n",
+        "                - Iris-Versicolour\n",
+        "                - Iris-Virginica\n",
+        "    :Summary Statistics:\n",
+        "    ============== ==== ==== ======= ===== ====================\n",
+        "                    Min  Max   Mean    SD   Class Correlation\n",
+        "    ============== ==== ==== ======= ===== ====================\n",
+        "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
+        "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
+        "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
+        "    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)\n",
+        "    ============== ==== ==== ======= ===== ====================\n",
+        "    :Missing Attribute Values: None\n",
+        "    :Class Distribution: 33.3% for each of 3 classes.\n",
+        "    :Creator: R.A. Fisher\n",
+        "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
+        "    :Date: July, 1988\n",
+        "\n",
+        "This is a copy of UCI ML iris datasets.\n",
+        "http://archive.ics.uci.edu/ml/datasets/Iris\n",
+        "\n",
+        "The famous Iris database, first used by Sir R.A Fisher\n",
+        "\n",
+        "This is perhaps the best known database to be found in the\n",
+        "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
+        "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
+        "data set contains 3 classes of 50 instances each, where each class refers to a\n",
+        "type of iris plant.  One class is linearly separable from the other 2; the\n",
+        "latter are NOT linearly separable from each other.\n",
+        "\n",
+        "References\n",
+        "----------\n",
+        "   - Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\n",
+        "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
+        "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
+        "   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n",
+        "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
+        "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
+        "     Structure and Classification Rule for Recognition in Partially Exposed\n",
+        "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
+        "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
+        "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
+        "     on Information Theory, May 1972, 431-433.\n",
+        "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
+        "     conceptual clustering system finds 3 classes in the data.\n",
+        "   - Many, many more ...\n",
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 20
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import pylab as pl\n",
+      "from mpl_toolkits.mplot3d import Axes3D\n",
+      "from sklearn.decomposition import PCA"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 21
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "X = iris.data\n",
+      "y = iris.target"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 22
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
+      "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
+      "\n",
+      "pl.figure(2, figsize=(8, 6))\n",
+      "pl.clf()\n",
+      "\n",
+      "# Plot the training points\n",
+      "pl.scatter(X[:, 0], X[:, 1], c=y, cmap=pl.cm.Paired)\n",
+      "pl.xlabel('Sepal length')\n",
+      "pl.ylabel('Sepal width')\n",
+      "\n",
+      "pl.xlim(x_min, x_max)\n",
+      "pl.ylim(y_min, y_max)\n",
+      "pl.xticks(())\n",
+      "pl.yticks(())\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 23,
+       "text": [
+        "([], <a list of 0 Text yticklabel objects>)"
+       ]
+      },
+      {
+       "output_type": "display_data",
+       "png": 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C12bM4Cz/CxcuYPTYcZjw1jJY2Tlg24r3oNYswvJlyzibkxBC/otWI5MaZWVlwc7OHhvP\nJsKYX/G57OPpo9EuKABffPFFZdzMmTNx5NxlvLnmBwCARq3GuAhPpKWmwsHBoTJuxKjRkDYJQIf+\nwwEA18+dxImt3+B09AnOjmHhokW4lVWCQVNmAwDSk+9g5ewJSE2mbj2EkLpFq5GJXiQSCRgYivJz\nAVQ8D5yXnQm5XF4lTiaToSAnq/JNVlSQB8agE2cqkSA/O7Py67zsTEgkYm6PQSxGgc6cEk7nJISQ\n/3rimW1paSl++eUXpKSkQK1WV3wTj4e333675kHpzLbBaN+hI27cSUKXwWNx8/J5JF2/hNTku1UK\naVFREZxd3dDEvxV8Wofh8I7N8HR3RfTJqmesN2/eRJu2bdGm52CIxBIc+Wkjftv5K6KiojjLPyMj\nA62Dg+Ef0QmWdg44/ON3WLXycwwdOpSzOQkhjVOtzmz79u2L3bt3QyAQQCqVQiqVwtTUtM6TJPXT\n0SOHMWLQACSc2AeFKR+Jt27qnLFKpVIk3roJO6kACSf2YUj/Pjhx/JjOWN7e3jh/9iz87M3hKjXC\n4UN/cFpoAcDOzg4xFy4g1NsVdsal2LF9GxVaQshz98QzW39/f8TFxT3boHRmSwghpJGp1ZltREQE\nYmNj6zwpQgghpLGo8cy2efPmAACNRoPExES4u7tDJBJVfBOP99gCTGe2hBBCGhu9NiJISUmp8Zt5\nPB5cXV31mpDUTKPRwMjIqE52/9BqtVCr1RAKhXWQGVBWVvZUY2k0GhgbGz82hjEGxhiMjOpmMfzT\n5vaie5rX9mk97c+gLt+ThDR0el1GdnNzg5ubG956663K//3vfyN1p7CwEP0GDIRYIoGZuQU+/9cz\nrPqYNGkSTCQSmJiIYefgiFu3buk91unTp2FpbQMTExOYSEyxZMmSauMOHDgAe0dHiEQiBIeGVX5Y\n+zfGGBYuWgSpVAaxRIIJE19GWVmZ3rlt2LABpjI5RCYmkJmZ4+eff9Z7rPosPT0d7SPCIBIKYWdl\nWavjZIzh/ffehcxUArGJCCOHDUFpaalOXH5+Pnr36AaxiQjmchm+WrO6NodACGFP0LJlyypfl5eX\nMx8fn8d+z1MMS/5l9NhxLKrXQPbd6dtsxa5oZu/syvbt26fXWOvWrWMSmZwt3X6IbTp3h3UeNJo5\nODnrnZvc3IINnjqXbT5/l73z7S9MJJaww4cPV4lJSkpiFpZW7O31P7PvLySz4TMWMP+AFjpjrV27\nljX1bc5WH4xh60/Es1Zt2rOFixbpldfdu3eZyETM3li+ln1/IZlNefczZiKWsOzsbL3Gq8/ahgSz\n2WFNWdaMLuzYsDBmYyZj169f12usH374gbnZmrH1fTzYj4M8WYS7NXtt+lSduKED+7MuzWzYjiHN\n2Jqe7szOQsYOHTpU20MhpEF7XO2r8cz2ww8/hEwmw/Xr1yGTySr/s7W1RZ8+fZ7fp4FG4NixY+j3\n8usQicWwc3ZDVN9hOHbsuF5j/frrr4jqNRgunt4QCEUYMn0eHj3M0Gus9PR0FBcVot/EGeALBPAK\nDIF/SBv88ssvVeLOnz8Pv+AIeLcKhTGfj17jpiLpzh3k5+dXiTt89Bg6Dx0PCxsFJDI5eo6bhsNH\njuqV24EDB2Dr5IKQjj1gzOcjqvcgmMrNceIEd92oDEGtVuNMzCUsDHGHwNgIrezM0L2JLc6cOaPX\neEf++B3dXExgYyqAWGCMgZ6mOHroD5244ydOYKi3HEJjIzjKRWjvZIITx4/X8mgIabxqLLYLFy5E\nYWEh5syZg8LCwsr/cnJysHTp0ueZY4Nnq1Ag5VY8gIrLfH8mJkChsNVrLAcHB9xNuAatVgsASL2V\nAKHIRK+xrK2tAfDwICUJAKAuL8O9O7fg4uJSNX9bW/x5NxHlZSoAQEZaMng8HqRSaZU4hcIW924n\nVH6deiseCoVCr9w8PDyQnZGO4sKKgp6fnYnCvBx4enrqNV59ZWxsDHOZFPFZRQAAtVaLhOxi2Nrq\n9/6wd3BCaqG28uu7eSrYVvMzsLG2QnJuxeVlxhjSilm1cYSQp1PjAqnLly8DqPhFq25xRKtWrWoe\nlBZIPZPTp0+jd9++CIzsjNzMDGhLCnAq+qROsXoaRUVFcGviATMbOzi4N8XFo7/jzfnz8M477+iV\n28SJE/HjTz8jqH1X3Im/Cj60SLp9C3z+P3tYMMYwZNhwXI1LgLtPAK5EH8HHH32ACRMmVBnr4cOH\nCA0Lh10TL4jEYtyIOYMTx47Bx8dHr9xCQsOQlJoG/9BIXDt9HEGBLXG4mrO0F9327dvx2uRX8JKH\nLeKyimHn7Y/f9v+u12KpnJwchAW3hhWKYSowwrWHpTh07DhatmxZJe7EiRPo36cXgh1MkanUwMjc\nHifPnKNWl4Q8hl6rkdu3bw8ejwelUolLly5V7vISGxuLoKAgnD17Vq8JSfWSkpJw5MgRSKVS9OvX\nr1Z/1IqKirB48WI8fPgQI0aMQK9evWqV25YtW7Bv3z54eHhgyZIlVQrt37RaLfbu3Yv79+8jJCQE\nrVu3rnasvLw87Nq1C2q1Gj169KiyUYE+li1bhsuXL6NNmzaYweHuQYZ27do1nDlzBgqFAn379q3V\nquSCggLs2rULKpUK3bp1g7Ozc7VxiYmJOHr0KORyOfr37w8TE/2ukBDSWOhVbP82YMAAvPvuu5XP\n3cbFxeGdd97RuW/3tBMSQgghDVGtOkjdvHmzstACFe0bb9y4UXfZEUIIIQ3cEzePDwgIwMsvv4xR\no0aBMYatW7eiRYsWzyM3QgghpEF44mVkpVKJr776CtHR0QCAqKgovPrqq4+9f0OXkRuW0tJSpKam\nwtbWFhYWFjXG5ebm4uHDh3Bzc6P7ew1AaWkpoqOjYW9vD39/f0OnU0VeXh7OnDkDPz+/x3azI+R5\nqtU927qekLxYLl26hF59+sBYIEJ+TjY+/PADzJg+XSdu9Zo1WLDgTZhZWkFdVoq9u3cjKCjIABmT\nunD+/Hl0aR8FI6aBUq1FgL8/zl++WmctNmtjw4YNmDr5FUgERihSaTBk6FBs2brN0GkRol+xHTx4\nMHbs2AF/f3+dR39oI4LGgTEGVzd39J86H2FdeyMz/R7enzAAhw4eqPKoSGxsLDp27orFG36FraML\nLhzZjx1f/A/30lKpp+4Lyt7aAu3tjTHM3xrF5VrM+yMVQ16ehhUrVhg0L7VaDZlYhFnh9gh1kuFB\nYRlmHUzBj7/8ht69exs0N0IeV/tqvGf7xV/9effu3ctNVqTeKywsRGZmJsK6VvwRs3Fwhm9QGOLi\n4qoU27i4OPi0DoWtY0Wzi5BOL2HtktnIz8+Hubm5QXIntZOXX4DObdwrmpMIjdHWVYYL588bOi0k\nJiaCMYZQJxkAwF4mhJe1GMeOHaNiS+q1Gq8J/f384+HDh1FeXq6zGQFp+GQyGaRSKRJiKp6pLszL\nxe1rl9C0adMqcR4eHki8fhmFebkAgBuXz0MsNoFcLn/uOZO6IZWIEZNe0bWqXKNFTHoxvPVsPlKX\n3N3dwQDcyCwBAOSXqnEnp7TG57oJqS+euBo5LS0NkydPRnJyMoKCghAVFYW2bdvqdJwhDQ+Px8O2\nrT9gyLBhcHL3xP3Uu5g86RWEhYVViQsNDcXL48fhzaGd4eTmgXt3b2P7tm314v4e0c+GLdswZGB/\nHEzKR55SDTMrG6xZs8bQacHExASLl7yHd5a8DSczEdILyxAWHoGRI0caOjVCHuupF0gplUqsXbsW\ny5cvR3p6OjQaTc2D0j3bBuXhw4eIj4+Hg4MDvL29a4y7efMm0tPT4efnp3fPY1J/pKWl4ddff4WN\njQ2GDx9erz48xcbG4tChQ/Dz80P37t0NnQ4hAGq5Gvn999/HmTNnUFRUhJYtW6Jt27aIjIx8bJs9\nKraEEEIam1oV28DAQAgEAvTs2RNRUVGIiIiASCTSe0JCCCGkIar1c7YFBQU4ffo0oqOjsWPHDigU\nCpw6dUqvCQkhhJCGSK9Hf/52/fp1REdH4+TJk4iJiYGTkxOioqLqPMkXUWxsLJKTk+Hn56ezQvdZ\nZWdn49y5c5BKpYiMjKxxV5eDBw/ixIkTCA4ORv/+/Ws159NKS0vD1atX4eDgQI0q6lhxcTFOnToF\nHo+HyMjIF3ILu/379+PUqVMIDw+v8fEbrVaL06dPIy8vD6GhoXrvx/ssGGOIiYnBgwcPEBgYWOPu\nRn93ytJoNIiMjKxxa8sHDx4gJiYGVlZWCA8Pr9Uz5Gq1GtHR0SgpKUF4eDgsLS31Hou8INgT9OzZ\nky1dupSdPn2alZWVPSmc/XWm/FRxL7J3lrzLbBR2LKRdZ2ZhZc02b96s91jXr19ntnZ2rFWbdqyJ\nly/r2LkLU6lUOnFjx45lJhJT5hsUziRSOevZq3dtDuGp7Nmzh1lYWrHgqE7MztGZTZ/xGudzNhYP\nHz5kPh5NWLi7Awtzd2D+zZqyzMxMQ6f1TIYOGcTEAiMWoDBlEoER699H9z2pVqtZ7x7dmJutOQv1\nUDAbCzN28eJFTvPSarVs8isTmYOlnIV52DELuZTt379fJy4nJ4f5ezdjvk7WLMDFhnm4urD09HSd\nuOjoaGZjbsa6eruypgorNnzQQKbRaPTKrbS0lLVrE86a2lmwIHcFs7OxYgkJCXqNReqXx9U+ateo\nh4SEBES174j/bfsdZpbWuJ+ciHfH9UPGg3SYmpo+83iRUe3gE/USOg4cCa1GgxUzx2PCsIGYNm1a\nZUxSUhJ8fP3w8Y7DsHN2Q25mBmb3b4+DB/ZzdqVBq9XCytoGMz/7Dp4BrVBSVIh3RvfEj1s2IzIy\nkpM5G5PJEydAEHsSH0Y2BWMM80/dAT+oM1Z9/Y2hU3sqcXFxaN0yAKteagKFVIDM4nJM23cX0WfP\nIzg4uDJu8+bNWL5oFpa0sQbfiIfo1AL8kSfHtXjudg87duwYxg3tj2XtFBALjHAjswSfxOQjMye3\nyhnpzNdn4NYf2zG5pSV4PB62xOVCHNARm7ZsrTKel7sr3guwQo8mtlCptei2KxaLPluNgQMHPnNu\nn332Gbav/ADzQ61hbMTD/sQ83DJxx7HoM7U+bmJYtdpij+hKS0uDq6cXzCytAQCO7p4wlcnw6NEj\nvcZLSUmBb0gbAICRsTE8W4YgOSW1SkxcXBxk5pawc3YDAFjY2MHW0QXXrl3T/0CeoKioCKWlpfAM\naAUAkEhlaOIbgNTU1Cd8J3kaKUmJiHKoaPzB4/HQ1l6O1LtJBs7q6V27dg2WYgEUUgEAwMZUABtT\noc57MiUlBV5mPPCNKopcc4UE9/78k9PcUlJS4GkphlhQ8SfO21qMgqIiKJXKKnHJdxLhZymoLMB+\nVkKkJN3RGS/1/gNEOVdc6hXxjRCqkOr9e5ByNwk+5kYw/vv1sBXT71QjQMVWD35+frh7Iw7JN64D\nAC6d+ANMq4Gjo6Ne4wUHB+PIjs3QarUozMvFxcN7EBxUtSNOeHg4igpyce3MCQBAYuwlZNxLRocO\nHWp3MI8hk8ng4OCAE7t/AgCkpyQhIeYsbbFYR0LC22Dz7Syo1FqUqjX4/nYmWoeFGzqtp9a2bVvk\nKtWIfVgMAIh/VILM4nKd92RwcDDOPyxHrlINxhgOJBUgkOOmOIGBgbiWUYQHhWUAgEN38+Hu4qxz\nTzwkIhLH76ugUmtRrmE4ck+J4LAInfGCWjTHutg/wRjD/cJS7E/J1rtrVUhYOE5nqFFUpoGWMRxM\nKUJQUPCTv5G82J73deuG4pdffmFyM3NmaWPLFPb27OzZs3qP9fDhQxYcGsbMLCyZWGLK5s6bx7Ra\nrU7cl19+yYQmJkwilTGBUMTeeeedWhzB07l+/TpzdXNnltY2zFQqYxs2bOB8zsZCqVSygb17MbnE\nhMnEJmxwv77V3quvz5YvX86ExkZMIjBiQmMe+/DDD6uNW/L2YiYWCZmFVMICfL3Zn3/+yXlua7/5\nmpmKRcxSZsrcnB1ZfHy8TkxZWRkbNnggk5gImUxiwnp268JKSkp04pKTk5mfpwezkUuZqYmIffLx\nx3rnpdVq2czXZjCxSMjMTMUsPLgVy8rK0ns8Un88rvbVeM/2cU29eTwedu/e/dj/v4ZhGxSVSoXM\nzEzY2dne+1c4AAAgAElEQVSBz3/iwu7HYozh4cOHkEgkj+0pXFJSgvj4ePj4+NS4arKuaTQaZGRk\nwNLSEmKx+LnM2ZhkZWWBx+PBysrK0Kno5Wnfk0VFRSgsLIRCoXhu3aiUSiVycnJgZ2dX4wp/AMjJ\nyYFGo4G1tXWNq4y1Wi0yMjJgZmam19qM/8rPz4dSqYRCoaDdsRoIvZ6zPX78+GMHbd++vV4TEkII\nIQ0RbR5PCCGEcKxWTS1u376NhQsXIj4+HqWlpZUD3r17t26zJIQQQhqoJ944GT9+PKZMmQKBQIDj\nx49j7NixtJ0VB+Lj47FixQqsXbsWBQUFtRpLqVRiw4YNWL58OS5fvlxj3I4dOxAQEIDWrVvj5MmT\ntZqTNE6MMezduxfLli3Drl27ntsVrenTp8Pb2xvdunXTeZznWR09ehSBgYEICAjAzp076yhDQv7j\nSaurAgMDGWOM+fv76/ybPiuyiK4jR44wC0sr1n3YeBbWqQfz8vZheXl5eo1VUlLCWgeHsNaRHdhL\nIycyS2sb9vPPP+vELV++nAlFJiyqzxAW1rU3E5qI2a5du2p7KKSReeO16czd1pz187NlHgoLNnXy\nJM7nbOHvx8xExqy3lwVzNxcxc4mJ3qu4f/rpJyY05rG2LjLW0V3OhMY8tmrVqjrOmDQWj6t9T7xn\nGxERgejoaAwaNAidOnWCg4MD3nzzTdy6davG76F7ts8msHUQOo2eitbtugIAvn77DbwUGYz58+c/\n81jffvstvtr4A2Z9sQk8Hg+3rl7EhiUzkZaaUiXO3Moa/V95A12HjgMAbFv5Ec4f3ImH6em1PRzS\nSKSlpaGFnw9Wd3OEVGiMknINpv+RjnOXrta6V3hNMjMzYa+wxTe9PWBjKoBayzB1710MGPOyXpvb\n21qao709H6Na2AAAdt/Mxq+3C5FTVLuzZdI41aqD1Oeff46SkhKsXLkSMTEx2LJlCzZt2lTnSTZm\n2dnZcHD754+TnWsTZGfn6DVWTk4O7Fw9Kh8lcHT3RG5urk6cVquFg/s/czo18UR5uUavOUnjlJOT\nA0upCaTCikdqJAJjWMvEyMnR7737NJKTk2FsxIO1pGK5Cd+IB3uZEPfv39drPE25Cs5mwsqvneQi\naDX0e0Dq3hOLbUhICGQyGczMzLBy5Ur8+uuvCAsLex65NRrdu3XDjtUfozA3Bym34nF851Z07dpF\nr7E6dOiAswd3ITH2EooK8vDjFx+gcxfdsewUtvhx5VLkPMpAxr0U/LL2c/j7eNX2UEgj4uXlhTKe\nAL/fyUdxmQaH7+ajoBzw9fXlbM7WrVuDb8TDtutZKC7T4OL9ItzILMErr7yi13hefi3wY1w2HhSW\nIbO4HN/HZsLexbWOsyYET765euHCBebv789cXFyYi4sLCwgIeOKOHU8xLPmX4uJiNnL0GCaVyZmd\nvQNbt25drcbbsWMHc3J2YaZSGevbfwDLzc3ViSkpKWEOjk5MIBQygciENfPyYmq1ulbzksYnISGB\nBTb3Y2KRkLXw82GxsbGcz7lnzx4mE/GZMQ9MzDdir7/+ut5jqdVq5uHmyoTGPCYw4jEnO9tqO0gR\n8jQeV/ueeM+2efPmWLNmDdq2bQsAOHXqFKZOnYrY2Ngav4fu2RJCCGlsanXPls/nVxZaAIiMjKx1\na0JCCCGkMXnime0bb7wBpVKJ4cOHAwC2b98OExMTjB49GgDQqlUr3UHpzJYQQkgjU6t2je3bt39s\nk+xjx44904SEEEJIQ0S9kZ8BYwxbt27FoSNHobC1xdw5s2Ftba0TV1JSgk9XrMCdpLsIahWIqVOn\nPnZXkecpNjYWr0yajILCQvTo3g0rPv202rgzZ85g0+bvwefzMWXyJDRv3lwnhjGGDRs2IPr0GTg7\nOmLOnNkwMzPj+hCeikqlwmcrVuB2Qhz8W7bCa6+/XqtbHMuWLcOWDeshNDHBR598ii7VrOLWaDT4\n6qs1uHz+HNw9m2H2nLk6e6Q+i02bNuHTj5eCxwPmLXyr2u5sjDF8//33OHboD9g7OmLOvPmwtLTU\niXv06BGGDRmMB3+moUVQCLZs+aHe3PLZv38/XpkwDuWqUrRp36nGTk0nT57E1s2bIBQKMWX6jGpX\nNqvVaowbOxaXzp+BnaMTfti2HQ4ODjpxeXl5WL5sGe7fS0NUx04YN24c57vrKJVKrPh0ORJv3kBg\ncAimT59R7d+Fu3fv4ovPVqC4qBCDh41At27dOM3rWZw7dw4bv10HY2M+Jr06tdr9qxlj2LhxI04e\nPQJHZxfMmTcP5ubmBsi2fnls7XvS6qoHDx6wCRMmsG7dujHGGIuPj2fr16/Xe0VWffe/Dz5grk29\n2ISFH7JuQ8Yy9yYeOqt5y8vLWZu2USyiSy82cdFS5h8UxkaPGWuYhP/j5s2bzERiyroOHcfGv/kB\ns1TYs779+unEHT58mFla27CRMxezIdPmMgsrK3b16lWduFlz5jBPvxZs4qKPWIe+Q5h/QIt6sVpT\no9Gwbh3bs5e8nNjnnXxZx6YObHC/vtXuA/w0Zs+axcxFAvZxO282N6QJE/ON2eHDh3XiJowZxSLc\n7djnnXxZf19n1jY0mJWVlek155o1a5gJ34iNbWHDxrSwZiK+Efv222914t5+axHzUJizV4MVrLuX\nDfN0d2MFBQVVYgoLC5mFTMIinGVsarAd87AwYX5ennrlVddOnDjBRMY81tvLgk0JUjALE2PWulUr\nnbj9+/czhbmMfRjlxRaGezJrMzmLi4vTiQv092Xu5iI2NdiOtXWRMzNTE5afn18lpqioiPl4erCu\nzWzYq8EK5mlnwebPncPZMTJWsbK5XZtw1qaJNZsabMdaOlux4UMG6cQlJyczG0tzNtjfhk1qrWAK\ncxnbunUrp7k9rWPHjjFLuSkb19KGjWphwyzkUnbp0iWduPlz5zBPOwv2arCCdW1mw3ybNWVFRUUG\nyLh+eVzte+KZbffu3TF+/Hh88MEHiI2NRXl5OQIDAxEXF6dfda/HGGOQm5njg22/w8bBGQCwcu4r\neGXEYEyYMKEy7tSpUxgzcRLe3/o7jIyMUKoswWs9gpGUmAhbW1tDpQ8AGDp0KFJzivH6sq8BAPeT\nE/HWqJ4oLSmpEte5a3d4t++FNj36AQD2bvoawoIH+G7Dt5UxZWVlkMnl+PLARcjMLcAYw9IpQ/He\nwnno16/f8zuoaly5cgWDunfGxWGtwTcyQqlag+bfn8O5K7Fwd3d/5vEUZlJ81dEbnd0qrmK8E30b\nZ3mWOHcxpjImKysLHq7OuDGuDaRCPrSMoe3PV7F66w5ERUU985xuDgr0cjJC16YVZwT7E3NxKMMI\nSff+6eKl1WphKhHjq+7OsJQIAAAfnMvGa+9/VuUs+JNPPsGX/1uML19yA4/Hg7Jcg1G/3sGdu8lw\ndTXsc6P+/v6wLrqHWREVZ593c0vx5uFUKMu1VeI6tgnHeAsl+nraAQA+uXAXOX7tsXrt2sqYR48e\nwcFOgc0DPCEVGoMxhjd+T8WEWQuxePHiyrgdO3Zg6dxpeDvcEjweD/mlary8NwXFJUrOzvbPnTuH\n4X17YEUHBYyNeFCptXhl/z3E30qEo6NjZdxbixYh/re1GN+i4r0W+7AY2++LcP1mIid5PYuXunSC\nV9ENdHCvuHq161YOypq1x/fbtlfGqNVqmErEWN/LDWYmfDDG8N7ZHCz4ZDUGDx5sqNTrhVqtRs7K\nysLQoUMrL4UIBIJ6c2mKC2p1OSSyfzZvl0jlKCsrqxKjUqkgkUorN8AWikwgFIp04gyhtLQUEtk/\nl3klUjm0Gq1OXFmZCqb/Pk6ZHKr/5K9WqwEA4r82yubxeJDIdF8PQ1CpVDAVCsD/62cgMjaCWCCA\nSqXSazytRgO56J/3tbkJH+py3Z+70NgYYn7F74IRjwe5SKD366FRq2Eq/OdX0FRgBM1fr/nfGGPQ\naLQQC4yrxP13zpKSEogFRpWXSYXGRjDmVWzYbmhlZWWQif6dvzGq+3ukUpVCLhJUfm0m5ENVVlol\nRqlUgsfjwYRf8brxeDxIhUYo+c+HyYr3xz+vh1hg9NdryV13KJVKBYmQD2OjijkFxjwI+ca6fz9K\nlRD/68qyqcAYZWXlnOX1LFSlSkgEVd+TKlXVn4FGowFjDGLBPz8DU6Hue5JU9cRiK5VKkZ2dXfn1\nuXPn6s09u7rG4/EwbNhwfL34dSTGXsbRX7fi6umj6NGjR5W40NBQFOdmY+e6L3An7io2LV0Ib2/v\nKp9eDeW1117D6QM7cWL3T0iMvYSVC6bCx9dHJ27s6FHY9vn7iLtwCleij2DX+s8xeuSIKjESiQTd\nuvfAN+/MxJ3rV/D71m+RkhCLDh06PK/DqVHLli2hMTHFe+fu4lJGPt48nQRbRyd4enrqNV5Yuw6Y\n9kccTv+Zg12JGfj0YjJeeXValRgHBwf4NW+OmSdv41JGPpZfTEFGecX7QR8Dho3EukuPcOVBMS6n\nF2HDlUwMGjG6SoyxsTEGDeiHlZdycCtLiQN38hCXqULXrl2rxI0fPx73CsqwIz4Lt7KU+PL8A5jJ\n5fDx0f3ZP2+zZ8/GoaQ8HEvOx43MEnx65j6srKx04kaOfxkLziTj5L1s7E96hE+v3cfw0WOrxLi6\nusLG0gKfn32AW1lK7LyRgzs5Krz88stV4rp06YJb2WXYl5iHW1lKrLyUg94v9YBIJOLsOIODg6Ey\nFuPHhFzczlZi/bVcNGnqqXNlYciw4TiYqsTptALEPyrBN7H5GDVuPGd5PYsxL0/G5oRCXM0oRkx6\nEX66XYzR46u+tiKRCL1f6lH5ntyXmIdb2WXo3LmzgbJ+QTzpGnRMTAwLDw9ncrmchYeHs6ZNm1Z7\nb+9pr1vXd6WlpWz2nLmsRWAr1qVbN3blypVq41JTU1m/AQNZ8xaBbNz4CSwnJ+c5Z1qzjRs3MoW9\nIzO3smYRkZGsuLhYJ0ar1bKvv/6aBYWEsrCISLZjx45qxyoqKmJTp01nAS0DWY+evdjNmze5Tv+p\n3b9/nw0d0I8F+nqz0cOGsMzMTL3H0mg0rE+vnsxWbsrsLeTs/fffrzYuNzeXTRw7mgX6erOBvXuy\nlJQUvedkjLGJEycya5mEWcsl7NUpU6qNUSqV7PUZ01gLXy/WtWO7Grs0nThxgrnY2TILUxPm16wp\nu3fvXq1yq0vz5s1jchMBkwmNWRMXZ1ZYWKgTo9Vq2aovv2ShLZuztiGt2W+//VbtWPfv32fNvZsx\nC1MT5qSwrvbeOmMV60u6d+7AWvh6semvTqn296CupaWlsf69e7IAn2ZszMjhLDs7u9q4Q4cOsbZh\nwayVvw/76MMPmEaj4Ty3p7Vu3VoW3MKfhbZqybZt21ZtTHFxMZv+6hTWwteLde/cgcXHxz/nLOun\nx9W+p1qNXF5eXrnLj5eXFwQCwWPjX9R7toQQQoi+9Lpne+HCBTx48ABAxX3aS5cuYeHChZg9ezan\nu3oQQgghDU2NxXby5MmV9zdOnjyJBQsWYOzYsZDL5Zg0adJzS5AQQgh50dW4rFir1VY+OL99+3ZM\nnjwZAwcOxMCBA6t9yJkQQggh1avxzFaj0aC8vGI5+uHDh6usQFX/5/GExig9PR0jRo1GaHgEpk6b\njsLCQkOnRGqBMYY1q1ejXVgwundsV20bUgAoLCzEjFenoE1QIEYNHaL3puXPoqysDG8tmI/IoFYY\n1LtX5fqJ/7p37x5GDB6INkGBeH3a1Bof+zl8+DC6d2iH9mEh+Obrr6u9x6TVarHs46WICG6FHp07\n4sKFC9WOlZOTg1fGj0WboEBMGDMKWVlZ+h8ogA0bvkWH8FB0bdcWBw8erNVYLzrGGFZ+8RkiQ1qj\na4d2iI6ONnRKpBZqLLbDhw9Hu3bt0KdPH0gkksqdfxITExt9W66SkhK0a98BpSYW6DZxFm7ce4g+\n/frTorAX2MovPsfqD5bgDXuGQaJcDO3fV6fAMMYwsE8vZJ3+HYvcBbBPu4qOkW1QXFzMaW5TXp6I\nC7/+gDfd+WhdmIQOkW2QkZFRJaawsBAdIiPgkh6HRe4CPDixD0P699V5T549exYjBw3AEHEeXrPX\n4vN338JXa9bozLnk7cX47otl6CHLQtPCG+jepRNu3LhRJUatVuOlzh3BrkVjkbsAooRz6NahXeWH\n9Ge1fv06fPTmPExTqDHCtABjhw3ByZMn9RqrIVj28VJ8+dF76CrNhF9pIvr26oErV64YOi2ip8eu\nRj579iwyMjLQtWtXmP7V2OD27dsoKiqqdrefykEb+Grko0ePYvrs+Vi8oaK/q1ajwYzuQbh+7Wq9\neNaWPLtAX2983NwMYQ4WAIAVF+8iv1U3fLbyy8qY9PR0BHg3w+3xEZXNNLrtuo7/rduMTp06cZKX\nRqOBRGyCpJfbVTbdmHD4FnrNfgfjxo2rjDtw4AA+em0S9vbyAwCUa7RouuEUbienwsbGpjJu+uTJ\nUCQcx+tBFV22Tv2ZgyW3S3DhWtWOcM72CixoZQpns4p1GxuvZaHl0Bl4++23K2Pi4uLQt1M7XB4e\nVPk7H/bTFfyw9/fH/n2oSWRwK8xxNkJH14rOSl9fSUWiewjWfbfpmcdqCLyauGGyJ9DUSgwA2BaX\nBZdu4/DxJ58YODNSk8fVvse2ggoPD9f5t2bNmtVNVi8wgUCAMpUSjDHweDyoy8uhVpc36M5aDZ1A\nIIBS/U+nrRK1VufnKRAIoNZoUa5l4BtVnOkq1RpOf+48Hg/Gf7Wj/LvYKmvITVmuqXxPlmm1UGu0\nOk3w+UI+SjX//DGoKX8+nw/Vv+LKtKh2zjKNFmotg8CYBw1jKFWr9X49BAIBStT/dCFSarTgP+Ex\nw4ZMIOBDpfnnKkGZBo369XjR0a4/eigvL0ebtlEQ2zjCL7Qtzh7YiWbO9tj+4zZDp0b0tHXrVsyf\nMRVzAx2RXarGV/EPEX3uPLy8vKrEjRo2BOkxpzGsqRWOpRcgmW+Ok+cuQCgUcpbbwnlz8fu2zZjs\na4trOUocyixDzLXrVTq5qVQqtAluDW9eMdraSbH1Tjbcw9tj45atVcZKSEhAu4hwTG9uB3MRH8su\n38dnX6/FkCFDqsStWb0aH76zEAM8JMhUanH0fjlirl6Ds7NzZQxjDL27dQHu3UYfV3PsS8tDia0b\nDh49XtnK9Fns2rULU8aNwfxWTigs12BlbDoOn4hutAsyN27ciIWzXsMAT1PklWpxME2FczGX4OHh\nYejUSA1oiz0OFBUV4cOPPsKdpLto3SoQs2fNojPbF9zevXvx05bvITY1xeuz59S4vdtnKz7F5fPn\n0MSzGRYsegsymYzTvBhjWLv2G5w49AfsHByx4K3F1W54kZ+fj48//AApSXfQOiwCb8ycWe32btev\nX8eXn62ASqnEsDFjddqR/m379u347eefYG5hgbkLFqJJkyY6MSqVCp98/DHir12Bt38A5i1YALFY\nrPexHjp0CFu++xZCkQjTXp+Jli1b6j1WQ7Br1y5s/+F7SOVyzJ47X+fDH6lfqNgSQgghHKvVrj+E\nEEIIqR0qtoQQQgjHqNgSQgghHKNiSwgqVpjPmPYqbK0s4Opoj/Xr11Ubd/78eThZW0IsMIaNzBTb\ntlW/Av3QoUPw8nCHlbkcg/r1QV5eHpfpAwA2b94Ma5kEYoExnGysEBMTU23cN19/BRcHOyisLTHz\ntRnVdoRTKpWYMHY0bCzM0cTFCdu3b692rEOHDsHaTAYR3whWcin2799fbdzOnTvh5eYKhaUFxo0c\nwXkjEKCip3sL72awsTDDgJ4v1bq7VX2VkpKC9pERsDSTIahlAGJjYzmfMysrC31f6g4rczn8vDwb\ndfORp0ULpAgBMH/ObBzesQlTWpijUKXGJxdzsOGH7VVW6paVlUFhYYbJzR0wMcAFR1OzMOvYDVyN\nv1Fl0/qbN28iIjQYrwWaw81chB9vFsDYrQX2/X6Is/zj4+MREtgCKzv5IcrZEt9cTcV3CRl4mFdQ\nZZX8nj17MGXcKMwJtoRUaIQ1V/PRc9Qr+N+HH1UZ7+XxY3HjxD5MDDDHo+JyfHoxB7v2H0RERERl\nTF5eHhwUNhjhZ4m2rnKcuVeA72OzkfJnepXV0jExMejZuSO+6+KNJuYSLDpzF7KWkdj4Q9XHkupS\nSkoKglsG4MuopmhlZ4YVl9OQKLHHkZOnOJvTENRqNfy8PBEqV6KTmwyXHhRjx90y3Lh9h9NOf+0j\nIyDPvYMBzcxwJ1uJNdfycfnadbi5uXE254uAFkgR8gR7du3ESB8ZFFIBmlqJ8ZKbGHt3/VYl5tKl\nS2AaNd4MawqFqQjDfR3hayXFTz/9VCXu6NGjCHU0RSsHKSwlAkwMsMChI8eg1WrBlR9//BEtbOUY\n7G0PhakIiyM8UVZWhri4qp2hdu/8Bb3cxfCwNIFCKsQIbyn2/Parznj79u7FOH8zWEsE8LWRoKOz\nCQ7+/nuVmIMHD0Iq4KGPtyUsxHz0bGYJCxNj7Nu3TyduRDNbRDpZwkFqgg8jmtR4BlxXTpw4gQ6u\n1njJwxZ2piJ81KYpTp09j9LSUk7nfd6Sk5NRmJeDQT4WsBDz0bmJGWwlxpy2dVQqlThz/gLGN7eE\npZiPECcZWtpL6ez2CajYEgLA3NwcD4v+6V70UMlg/teuV39zcHCAUq1FtrKiq0+ZRov0IhUUCoXu\nWMWayk+4D4vLYSoR69Xo4WkpFAr8WViKck1FQc8sKYNKo9V5HtfCyhoPS/4p+hlFZTAz0z0DMjMz\nQ0bRP92LHpUCZv85U3J0dESRSgNluQYAUKrWIr9UDQcHhypx5ubmSC7+57VNzlfCTM7ts8nm5uZI\nLVBC+9fP4F6hEny+MafNRwzBzMwMhcoyFKoqfgZlGi2yCkurNDypa0KhEHxjY2SVVLw/tIzhYVE5\np3M2CIwDHA1LCGeOHz/OLORS1t/XhnXytGHODnYsIyNDJ65dm3DmLDNhs4PdWQtbOWviaM80Gk2V\nGKVSyYIDW7BQN2s2yM+G2ZpL2fr16zjNv7y8nLna2bJWCjM2K8idOcpMWOd2UTpx6enpzEFhwzo3\ns2b9fG2YhVzKTp06pRO3e/duZimXsoF+1qxdU2vm6e7G8vLydOKa+3ozR5mQDfK1ZM5mQubj6aET\nU1BQwPybebK+vs7sjWAPpjCXsR07dtTNgdegrKyMtW8Tzjp5OrLZIR7Mxdqcrfzic07nNJQ5M99g\n7rbmbIi/NfN1tGRDBw5gWq2W0zm/+HwFs7eUs8H+NizI1ZpFRYSxsrIyTud8ETyu9tE9W0L+EhcX\nh927d0MsFmPUqFFVGvj/2+LFixEdHY1mzZph1apV1Z4tKZVKbNq0CY8ePUL79u0RFRXFdfooLS3F\njBkzkHTnDqLatcOSJUuqjXv06BG2bNkClUqFvn37VtspC6i413rgwAGYmZlh7Nix1Z65aLVazJ07\nF5cuXUKLFi3w2WefVXsGX1hYiE2bNiE3NxfdunVDSEhIrY71aZSVlWHTpk1IT09HZGQkZ5tFGBpj\nDLt27cLVq1fh6emJ4cOHc3oV5W9HjhzBqVOn4ODggLFjxza4qwb6oA5ShBBCCMdogRQhhBBiQFRs\nCSGEEI5RsSXPXUlJCQoLCw2dRrUKCgqgVCrrZKysrCwcPXq02qYRz4oxhpycHJSXlz85uA7l5eVB\npVI9Nkar1SI7O5vTR5sIedFRsSXPjUajwauTXoaVhTkU1tYY2LtXnRW22iooKED3Th1gb2sDS3Mz\nzJ89q1brDjzc3WBna4PuXTpDJhZi9erVeo919+5dtPDxgpuTIyzM5Ph2/Xq9x3pamZmZaBMaBEd7\nBczkMnzwv/erjTt16hQcFDZwd3GCwtoKx48f5zw3Ql5EtECKPDcrv/gC2z5fih09/CAyNsIrR27C\no0tffPrFl4ZODRPHjobq6imsbO+JApUa/fbGYdaHyzFmzJhnHmvChAn4acsmfNrNDXZSAX5OyMav\nN3JQXKbRK7egFs3Rz1yNGYEuSMorQc9dsdh35BhatWql13hPo+9L3cFLvYJxARbIK9Xg7VOZWPPd\nFvTq1asyprCwEE1cnTE1QIbWDlJczSjGysv5SExOgYWFBWe5EVJf0QIpUi+cPXkc45pZQy7iQ8Q3\nwiQ/O5w7XT/a5507fRqvNrcH38gIlmIhRjS1wrlT0XqN9ccffyDMWQZ7mRA8Hg/9vK2gLNfq1b1I\nrVbjSlw8prV0AY/HQ1MLU3Rxt66x73FdOXfhAno3lcGIx4OlmI829gKcP3euSkxiYiLMTfho7SAF\nALS0M4WtTIRbt25xmhshLyIqtuS5cXFvgnOPiis/+Z3PKICTi6uBs6rg7OKCc+n5ACruj57PLIaT\nnn1eXVxccDNTWdnN6WZWCYTGPJiYmDzzWHw+HwpLS1x4ULGRQalagyuPCuHk5KRXbk/LydEBN7Iq\nLvFrtAx3ChicnJ2rxNjb2+NRQUllJ6EcpRoP8op1OkgRQugyMnmOcnNz0S48DPLyIkgEfNwqUOHE\nmXP1onl5QkICOreLQksbKbKVZeBZ2OJI9GmYmpo+81ilpaWwMZNBIgCc5CLEPypBu05dcPDgQb1y\nO3DgAMYMH4o2zta4mVWIoKgO+P7H7eDxeHqN9zQuXryIl7p2hpe1BFklZXDw8MHvh4/qNC5Y8ely\nLP3fe/CxNcXNR8WYOXc+FixcxFlehNRn1NSC1BslJSU4cuQI1Go12rdvX6/u7WVmZuLkyZMQi8Xo\n1KkTRCKR3mOp1WoMHDgQ9+7dw8SJEzFt2rRa5ZacnIwLFy5AoVCgXbt2nBbavz148ACnTp2CXC5H\np06dquwe9G/Xrl3DjRs34OXlhcDAQM7zIqS+omJLCCGEcIwWSBFCCCEGRMWWEEII4RgVW9LgMcaQ\nkJCA8+fPo6SkpNbjZWVl4cyZM/jzzz8fG3fnzh2cPXsW+fn5tZ7zad27dw9nzpxBVlbWc5uTNE55\necWG+rkAABN4SURBVHk4e/YskpOTDZ3KC4GKLWnQNBoNRg4ZjC6REZg0uC+aezdDUlKS3uPt378f\n3h5N8PrIwWjh640vV35ebdy8WTMR0ToQ04cPhE/TJpw/FwtUrAxu7uuNl4f2QzMPd71XPxPyJKdP\nn4anuxsmDumL1i388fZbtAL9SWiBFGnQNmzYgHXvL8Jvvfwh5htj5eVUnIA1Dp149oYVpaWlcFTY\nYnsPX4TYmyOtQIlOv1xB9IUYNGvWrDLu0KFDmDZ6OA4PaAFzkQC/3nqAj28V4EYSd2cACQkJiAoP\nxbIOClhLBEjILMGyC7nIyMyifUZJnWKMwclOgYneIgQ5SpFfqsb8E4/w854DiIiIMHR6BkULpEij\ndetGAro6yCDmGwMAenvY6N3hKCMjA2K+EULszQEALnIxmttZ4M6dO1XnvHULUY7mMBcJKuZsqsDt\nlFROG/UnJiaima0U1pKKOX1tJDCCFo8ePeJsTtI4KZVKZObkoLVDxTPoZiZ8+NpIqHPYE1CxJQ2a\nX/MA7P+zAEVlFTvv/HL7EXx9ffUay97eHiotEP1nDgDgbl4JYjNy4eXlVXVOPz8cvZeDbGUZAODn\n2xnw8Wjy//buPK6qcl8D+LNhI4NMMiqiAgaooMDG4eAQKBRamgWODR610rTjPZ6b5ofMocHmbmVH\nj3WPqVfPPVqpJ/3kLc0pB5wQJzAFERUQAUX2BmQDm9/9gxNlDonyskCe738b3/2uZ++Nn4e117vW\ngpWVuv9uwcHBOFVgwqXS2m0ezS8DrPTw9vZWtk1qmezt7dHWyxP7ckoB1F45LK2gDCEhIRona+JE\nAUXTEtWbxWKRieOeEU9nRwlq6yHB/n6SnZ191/Nt2bJFPFycJaS9l7g6Osjnny256bi5ryRJG0cH\n6dbeS3y9PeXIkSN3vc07tXjRp+Lc2l46t3UTd1dn2bZtm/JtUsu0f/9+8XJvIwFt3cTZwV7eevMN\nrSM1CbfrPh6zpRbh7NmzKC0tRVBQ0D1dGQoASkpKkJWVBV9fX3h6et5yXG5uLoqKihAYGAgHB4d7\n2uadKigoQF5eHgICAuDs7Nwo26SWqbS0FJmZmfD29ka7du20jtMk8ApSREREinGBFBERkYZYtkRE\nRIrd/DYe1CIUFhZiy5Yt0Ov1GDJkCJycnO56LhHBDz/8gJycHPTq1QuhoaENmPTemM1mbNq0CSaT\nCTExMejYseNNx+Xl5WHr1q2wt7fHo48+Cnt7+5uOS01NxZEjR+Dv799od+DRwp49e7BixQq4u7tj\n9uzZcHR01DpSnWPHjiElJQUdOnRAbGzsffsZ0H2ksVdkUdOQmZkpbT3dpd8D3tLb30s6+3WUgoKC\nu5qrpqZG/vjUWOnSzkPGhAWIl4uTrFy5soET353y8nKJioyQvv4+MqK7v3i6usi+fftuGHf06FHx\ndmsjT4T6yYMPtJeIkK5SUlJyw7jFiz4VTxdHievSVjp4uMifprzQGC+j0S1ZskRsra0kytdJAtrY\nibuzoxQXF2sdS0REli37Qtydaz8DPy9XmTDuaampqdE6FhFXI9ONRjz+GBzPH0BCl9r7yf796GUE\nxI7Gxws/rfdcO3bswAtjR2LniHDY661x8nIpHlp7GMVGE6ytrRs6er188skn2LLkA/wjvht0Oh3W\nnrqIJReB/alHrxv3UPQADGtVjPGhvhARTNr6E0JHPos5c+fWjSktLUVbL098FOcDb8dWKK+y4C9b\n8/F/235EeHh4Y780pdo42mNSuBv6dXRGjQjm78hBl+ihWL16taa5Kisr4ebqgvcGtYOvsy3M1TV4\nafsl/PNf36Jfv36aZiPiAim6QW7OBQS2+eUyfg+46JF74dxdzXXx4kV08/zlKk1d3FqjpqYGJpOp\nQbLei4u5OTC42dV9zWjwdkF+fv6N4/LyYPCuPVVGp9Mh0t0BF3MuXDfm8uXLaG1rA2/H2vfNwcYa\nHdo44OLFi4pfReOrMFciyL32a3QrnQ5dPeyQ+zs3XmgMRqMRVjrA17n29C1bvRU6tbFHXl6exsmI\nbo9l20LFDIrDxqxyVFTXwGS24PtzZkQPeuiu5urZsyd2nSvE4fwSiAiWHL0A/44d4OLi0sCp66//\ng9H438wryDFdQ5WlBh8duYB+/fvfOC46BguP5MJcXYNLZWasOF2EAQMHXTemffv2sHVojR+yal/n\niYJyZBaVISwsrLFeTqNp6+2FL9OKYKkRFJRV4fszJYgfPFjrWHB3d4e3lxc2ZVyFiOBU0TWkXTKh\nZ8+eWkcjur3G/t6amoaKigp5cvRIsdFbSysbvfz5Ty+KxWK56/nWrVsnbs5O0kqvl7CuwZKRkdGA\nae/Nu2+/Lfa2raSVXi+DYwfe9NijyWSSJx4dIq30erFrZSPzXp190+OAx48fl0D/TmKjtxZPN1fZ\nvHlzY7yERpeZmSlt3V3FSgex1kGGPzZM60h1Tp06Jd2CHhAbvbW4uTjLhg0btI5EJCI8Zku3UVlZ\nCSsrK+j1974wXURQUVFxy1W8WrJYLKiqqoKdnd1tx5nNZuj1+t891lxeXg57e/v7fhXs1atX4ejo\n2CC/Hw3t2rVrsLOzu+8/A2o+eAUpIiIixbhAioiISEMsWyIiIsVYti1YWloa5s2di9dffx3Z2dla\nx1Hm5MmTGBwfj/59++Lzzz/XOg4RtUAs2xZq//79iOkXBeN3/0D+huX4Q6QBGRkZWsdqcCdPnkTv\n8DC4XTiB/jUFmDHtRSQlJWkdi4haGC6QaqGGxcchviYf40J9AQDv7s9CUdf++GzpFxona1jxDz8M\n95w0LInvDgDYfv4yJnx3AlfKrmmcjIjuN1wgRTcwlRjh6/TLaTC+TrYoNZZomEiNUpMRHZ1/ORXJ\n19EOFotFw0RE1BKxbFuo4aNG47WDF3Ci0ISU/BJ8kJqL4SNHax2rwY0bPwF/Sz2HHecvI7O4DH/e\nlobgLl20jkVELUzTO1OdGsWfp/8FZaWl+OPf/xt6vR4zX1uAUaNGaR2rwU2ePBlZWVkY/9eFsFgs\nCO7SBdt279U6FhG1MDxmS0RE1AB4zJaIiEhDLFsiIiLFWLZERESKsWwVO3LkCJ56ZhweTxyBL7/8\nUus49WaxWPD+e+/iiUcG48XJk1BQUKB1pHpLT0/H+KeeROKwR7By5Uqt4zQLIoLly5chcegjmPD0\nUzh16pTWkYiaNZatQunp6RgUFwcrb3+0j3gQ02e8jC++aF4XjZjy/HPY+NkneFyXD6vUbejfpxdM\nJpPWse7YmTNnENOvLwLOH8ajlly8MXM6Pl24UOtYTd7HH/0X3k2aiaE1ufA7dwgPRv0BZ8+e1ToW\nUbPF1cgKvTRjBrJNFox44SUAQPqhZPzrr2/h+NFUjZPdGbPZDBcnJ5x5/kE4tao9S+yJTWmY+tbH\nSExM1DjdnZk/bx4uf7sKCwYEAgAO55dgcnIuTmWf1zhZ09a5Q3v8T3QndPd0BgC8/ONp+D4xEa++\n+qrGyYiaLq5G1oiIwMrql5uQW+v1zeqPkJ+zWv/q5tx6nVWzew36X/2W6635h+CdEJHrP3crvm9E\n94IXtVBo3DPPYFBsHFzcPeHi5oGvFr2DWf85XetYd8zOzg4jExMwbvNuTA7xxoFLJpwurUJcXJzW\n0e7Y2CefxICFn8DX0Ra+TnZ449AFPPcfL2kdq8l77oUpmLTkU8yO9MUFUwXWZBRh75gxWsciarb4\nNbJiycnJeOe991FeXo6xo0dhwoQJ0P1qj6Gpq6ysxFtvvI49O7bDp0NHvPnue+jQoYPWserl8OHD\neGv+XJSajBg+cgxemDKlWX0GWhARLF60CBu+XgNnF1fMfu0NhIeHax2LqEm7XfexbImIiBoAj9kS\nERFpiGVLRESkGMuWiIhIMZYtURNXUFCAHl2D4WLfCt5tnLFq1ap7mm/t2rXoHdYdPYID8faCN1FT\nU9NASYnoVrhAiqiJ8/f1QVudCWO7e+DMFTM+O3QJ23ftRlRUVL3n2rp1K54ekYDFMYFwtbPBS7uz\nMHrqdMxKSlKQnKhl4WpkomaqsrIS9na2WD0iCLb/vjrHe7vzEBibgGXLltV7vinPP4tOGXswNcIP\nALAvrxivpJtw6Hh6Q8YmapG4GpmomdLr9dDpAKPZAqD2/NerFdVwdHS8q/kcWjui8Fp13ePC8krY\n2zs0SFYiujXu2RI1cYMffgiH9+7EY8FtkHG5AqkFFcjMvgAvL696z5WVlYW+vXpiTOc2aNPKGotP\n5GP5P9dgyJAhCpITtSzcsyVqxr7bvAXPTJ6GQ2YP2HSOxImfMu6qaAEgICAAew8egv3ARJQYBmP9\npu9YtESNgHu2REREDYB7tkRERBpi2RIRESnGsiUiIlKMZUtERKQYy5aIiEgxli0REZFiLFsiIiLF\nWLZERESKsWyJiIgUY9kSEREpxrIlIiJSjGVLRESkGMuWfpfZbEZmZiZKSkq0jkJE1CyxbOm2UlNT\nEejXEXF9e6ODTzv8bfEirSMRETU7vMUe3ZKIoHNHX8zp7oHE4HbILilH/Pqj+H7nLvTo0UPreERE\nTQpvsUd3xWQy4VJhERKD2wEA/Fwc0L+jB44dO6ZxMiKi5oVlS7fk5OSE1g4O2JNzBQBQXFGFg3nF\n6Ny5s8bJiIiaF73WAajp0ul0WLV6DZ4aNRJdvZyRUWjEhOefR1RUlNbRiIiaFR6zpd+Vn5+P48eP\nw8fHByEhIVrHISJqkm7XfSxbIiKiBsAFUkRERBpi2RIRESnGsiUiIlKMZUtERKQYy5aIiEgxli0R\nEZFiLFsiIiLFWLZERESKsWyJiIgUY9kSEREpxrIlIiJSjGVLRESkGMuWiIhIMZYtERGRYixbIiIi\nxVi2REREirFsiYiIFGPZEhERKcayJSIiUoxlS0REpBjLloiISDGWLRERkWIsWyIiIsVYtkRERIqx\nbImIiBRj2RIRESnGsiUiIlKMZUtERKQYy5aIiEgxli0REZFiLFsiIiLFWLZERESKsWyJiIgUY9kS\nEREpxrIlIiJSjGVLRESkGMuWiIhIMZYtERGRYixbIiIixVi2REREirFsiYiIFGPZEhERKcayJSIi\nUoxlS0REpJhe1cQ6nU7V1ERERM2KkrIVERXTEhERNUv8GpmIiEgxli0REZFiLFsiIiLFWLZEiixY\nsAChoaEICwtDREQEDhw40KDz79ixA8OGDbvjn9+rb775BidPnqx7HBMTg5SUlAbfDtH9SNlqZKKW\nLDk5Gd9++y1SU1NhY2ODK1euwGw2ax3rnqxfvx7Dhg1D165dAfCMA6L64J4tkQL5+fnw8PCAjY0N\nAMDNzQ3t2rUDAKSkpCAmJgY9e/bE4MGDkZ+fD6B2T3H69OmIiIhA9+7dcfDgQQDAgQMH0LdvXxgM\nBvTr1w+nT5++4xxlZWWYOHEi+vTpA4PBgA0bNgAAli9fjoSEBAwZMgRBQUGYNWtW3XOWLl2K4OBg\n9OnTB5MmTcK0adOQnJyMjRs3YubMmTAYDMjKygIAfPXVV+jTpw+Cg4Oxe/fue3/jiO5XQkQNrrS0\nVMLDwyUoKEimTp0qO3fuFBGRyspKiYqKkqKiIhERWb16tUycOFFERGJiYmTSpEkiIvLjjz9KaGio\niIgYjUaprq4WEZEtW7ZIYmKiiIhs375dhg4desO2f/3zpKQkWbVqlYiIFBcXS1BQkJSVlcmyZcsk\nICBAjEajVFRUSKdOnSQnJ0dyc3PFz89PiouLpaqqSgYMGCDTpk0TEZHx48fL2rVr67YTExMjM2bM\nEBGRTZs2SVxcXAO+g0T3F36NTKRA69atkZKSgl27dmH79u0YPXo03nnnHURGRiItLQ1xcXEAAIvF\nAh8fn7rnjR07FgAwYMAAGI1GGI1GlJSUYNy4ccjMzIROp0NVVdUd59i8eTM2btyIDz74AABgNptx\n/vx56HQ6xMbGwsnJCQDQrVs3ZGdno7CwENHR0XB1dQUAjBw58ro9afnNOfQJCQkAAIPBgOzs7Hq+\nS0QtB8uWSBErKytER0cjOjoa3bt3x4oVKxAZGYmQkBDs3bv3jueZM2cOYmNjsX79epw7dw4xMTH1\nyrFu3ToEBgZe97P9+/fD1ta27rG1tTWqq6tvOA7723L97b//PMfPzyeim+MxWyIFTp8+jYyMjLrH\nqamp8PPzQ3BwMAoLC7Fv3z4AQFVVFdLT0+vGrVmzBgCwe/duuLq6wtnZGUajsW7vd9myZfXKER8f\nj4ULF16XA7j5Vd50Oh169eqFnTt34urVq6iursbatWvrCtbJyQlGo7Fe2yeiWixbIgVKS0sxfvx4\nhISEICwsDD/99BPmz58PGxsbfP3115g1axbCw8MRERGB5OTkuufZ2dnBYDBg6tSpWLp0KQDg5Zdf\nRlJSEgwGAywWy3V7lzdbEazT6ep+PmfOHFRVVaFHjx4IDQ3FvHnzbhjzaz4+PnjllVfQu3dv9O/f\nH/7+/nBxcQEAjBkzBu+//z4iIyPrFkj9drtEdHM6udmfuETU6AYOHIgPP/wQBoNB0xxlZWVo3bo1\nqqurkZCQgGeffRbDhw/XNBNRc8c9WyK6zvz58+tOPwoICGDREjUA7tkSEREpxj1bIiIixVi2RERE\nirFsiYiIFGPZEhERKcayJSIiUoxlS0REpBjLloiISLH/B1h3Zba+d8riAAAAAElFTkSuQmCC\n"
+      }
+     ],
+     "prompt_number": 23
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Plot first three PCA dimensions\n",
+      "fig = pl.figure(1, figsize=(8, 6))\n",
+      "ax = Axes3D(fig, elev=-150, azim=110)\n",
+      "X_reduced = PCA(n_components=3).fit_transform(iris.data)\n",
+      "ax.scatter(X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2], c=y,\n",
+      "           cmap=pl.cm.Paired)\n",
+      "ax.set_title(\"First three PCA directions\")\n",
+      "ax.set_xlabel(\"1st eigenvector\")\n",
+      "ax.set_xticks(())\n",
+      "ax.set_ylabel(\"2nd eigenvector\")\n",
+      "ax.set_yticks(())\n",
+      "ax.set_zlabel(\"3rd eigenvector\")\n",
+      "ax.set_zticks(())\n",
+      "\n",
+      "pl.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "display_data",
+       "png": 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vv53Q0FDNnV2npVpF7TKZTJhMJqDyZIfx//0/Nq5ejtFk4ro7RjN69Oh6q+OZ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\nPpDDAAAAAElFTkSuQmCC\n"
+      }
+     ],
+     "prompt_number": 24
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Bayes Classifier"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from sklearn.naive_bayes import GaussianNB\n",
+      "gnb = GaussianNB()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 25
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Learn a naive bayes model\n",
+      "gnb_model = gnb.fit(X, y)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 26
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Prediction\n",
+      "pred_y = gnb_model.predict(iris.data)\n",
+      "num_errors = (iris.target != pred_y).sum()\n",
+      "print(\"Number of mislabeled points : {}\".format(num_errors))\n",
+      "print(gnb_model.score(X,y))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Number of mislabeled points : 6\n",
+        "0.96\n"
+       ]
+      }
+     ],
+     "prompt_number": 27
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Cross Validation"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from sklearn.cross_validation import train_test_split\n",
+      "train_data, test_data, train_target, test_target = train_test_split(X, y, test_size=0.15)\n",
+      "\n",
+      "print(\"Original data size\",X.shape)\n",
+      "print(\"Original num target labels\",y.size)\n",
+      "\n",
+      "print(\"Train data:\", train_data.shape)\n",
+      "print(\"Train target:\", train_target.size)\n",
+      "print(\"Test data:\", test_data.shape)\n",
+      "print(\"Test target:\", test_target.size)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "('Original data size', (150, 4))\n",
+        "('Original num target labels', 150)\n",
+        "('Train data:', (127, 4))\n",
+        "('Train target:', 127)\n",
+        "('Test data:', (23, 4))\n",
+        "('Test target:', 23)\n"
+       ]
+      }
+     ],
+     "prompt_number": 28
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Learn a naive bayes model\n",
+      "gnb_model = gnb.fit(train_data, train_target)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 29
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "test_pred_y = gnb_model.predict(test_data)\n",
+      "num_errors = (test_target != test_pred_y).sum()\n",
+      "print(\"Number of mislabeled points : {}\".format(num_errors))\n",
+      "print(gnb_model.score(X,y))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Number of mislabeled points : 1\n",
+        "0.96\n"
+       ]
+      }
+     ],
+     "prompt_number": 30
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### SVM"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from sklearn import svm\n",
+      "clf = svm.LinearSVC()\n",
+      "clf.fit(train_data, train_target)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 31,
+       "text": [
+        "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
+        "     intercept_scaling=1, loss='l2', multi_class='ovr', penalty='l2',\n",
+        "     tol=0.0001, verbose=0)"
+       ]
+      }
+     ],
+     "prompt_number": 31
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "svm_test_pred_y = clf.predict(test_data)\n",
+      "num_errors = (test_target != svm_test_pred_y).sum()\n",
+      "print(\"Number of mislabeled points : {}\".format(num_errors))\n",
+      "print(clf.score(X,y))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Number of mislabeled points : 1\n",
+        "0.966666666667\n"
+       ]
+      }
+     ],
+     "prompt_number": 32
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Decision Trees"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from sklearn import tree\n",
+      "clf = tree.DecisionTreeClassifier()\n",
+      "clf.fit(train_data, train_target)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 33,
+       "text": [
+        "DecisionTreeClassifier(compute_importances=False, criterion='gini',\n",
+        "            max_depth=None, max_features=None, min_density=0.1,\n",
+        "            min_samples_leaf=1, min_samples_split=2,\n",
+        "            random_state=<mtrand.RandomState object at 0x1002ab2e8>)"
+       ]
+      }
+     ],
+     "prompt_number": 33
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "dt_test_pred_y = clf.predict(test_data)\n",
+      "num_errors = (test_target != dt_test_pred_y).sum()\n",
+      "print(\"Number of mislabeled points : {}\".format(num_errors))\n",
+      "print(clf.score(X,y))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Number of mislabeled points : 1\n",
+        "0.993333333333\n"
+       ]
+      }
+     ],
+     "prompt_number": 34
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}

04-RandomForest.ipynb

+{
+ "metadata": {
+  "name": "04-RandomForest"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from sklearn.datasets import load_digits"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "digits = load_digits()\n",
+      "print(digits['DESCR'])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        " Optical Recognition of Handwritten Digits Data Set\n",
+        "\n",
+        "Notes\n",
+        "-----\n",
+        "Data Set Characteristics:\n",
+        "    :Number of Instances: 5620\n",
+        "    :Number of Attributes: 64\n",
+        "    :Attribute Information: 8x8 image of integer pixels in the range 0..16.\n",
+        "    :Missing Attribute Values: None\n",
+        "    :Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)\n",
+        "    :Date: July; 1998\n",
+        "\n",
+        "This is a copy of the test set of the UCI ML hand-written digits datasets\n",
+        "http://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits\n",
+        "\n",
+        "The data set contains images of hand-written digits: 10 classes where\n",
+        "each class refers to a digit.\n",
+        "\n",
+        "Preprocessing programs made available by NIST were used to extract\n",
+        "normalized bitmaps of handwritten digits from a preprinted form. From a\n",
+        "total of 43 people, 30 contributed to the training set and different 13\n",
+        "to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of\n",
+        "4x4 and the number of on pixels are counted in each block. This generates\n",
+        "an input matrix of 8x8 where each element is an integer in the range\n",
+        "0..16. This reduces dimensionality and gives invariance to small\n",
+        "distortions.\n",
+        "\n",
+        "For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.\n",
+        "T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.\n",
+        "L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,\n",
+        "1994.\n",
+        "\n",
+        "References\n",
+        "----------\n",
+        "  - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their\n",
+        "    Applications to Handwritten Digit Recognition, MSc Thesis, Institute of\n",
+        "    Graduate Studies in Science and Engineering, Bogazici University.\n",
+        "  - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.\n",
+        "  - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.\n",
+        "    Linear dimensionalityreduction using relevance weighted LDA. School of\n",
+        "    Electrical and Electronic Engineering Nanyang Technological University.\n",
+        "    2005.\n",
+        "  - Claudio Gentile. A New Approximate Maximal Margin Classification\n",
+        "    Algorithm. NIPS. 2000.\n",
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Show the first image\n",
+      "X = digits.data\n",
+      "im = X[0].reshape(8, 8)\n",
+      "plt.imshow(im, cmap=plt.cm.gray)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 4,
+       "text": [
+        "<matplotlib.image.AxesImage at 0x10705ded0>"
+       ]
+      },
+      {
+       "output_type": "display_data",
+       "png": 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WFs+oaW64BHGno/83lZQn122PHo8smuE1eveY3ydtjQOh5nPslBVxqT4WkBq4GuTepl17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\nEFoDdmONDaH9P8aBWGceKCHzAAAAAElFTkSuQmCC\n"
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Target of first sample\n",
+      "y = digits.target\n",
+      "print(y[0])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "0\n"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Train a classifier\n",
+      "from sklearn.ensemble import RandomForestClassifier\n",
+      "clf = RandomForestClassifier()\n",
+      "X, y = digits.data, digits.target\n",
+      "clf.fit(X, y)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 6,
+       "text": [
+        "RandomForestClassifier(bootstrap=True, compute_importances=False,\n",
+        "            criterion='gini', max_depth=None, max_features='auto',\n",
+        "            min_density=0.1, min_samples_leaf=1, min_samples_split=1,\n",
+        "            n_estimators=10, n_jobs=1, oob_score=False,\n",
+        "            random_state=<mtrand.RandomState object at 0x1002ab2e8>,\n",
+        "            verbose=0)"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Check accuarcy\n",
+      "print(clf.score(X, y))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "0.999443516973\n"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Use Cross Validation"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from sklearn.cross_validation import cross_val_score\n",
+      "values = cross_val_score(clf, X, y)\n",
+      "print(values)\n",
+      "print(values.mean())"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[ 0.93155259  0.94991653  0.93656093]\n",
+        "0.939343350028\n"
+       ]
+      }
+     ],
+     "prompt_number": 11
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}

05-Clustering.ipynb

+{
+ "metadata": {
+  "name": "05-Clustering"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Load and Show the Image"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from scipy.io import loadmat"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "img = loadmat('bird_small.mat')['A']\n",
+      "img.shape"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 7,
+       "text": [
+        "(128, 128, 3)"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "imshow(img)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 8,
+       "text": [
+        "<matplotlib.image.AxesImage at 0x108b0cd10>"
+       ]
+      },
+      {
+       "output_type": "display_data",
+       "png": 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7wSUr9ObmlHJmFUFiglqhTHg0lu6pBlxXwleWc2AmrAZ9XdBYuZuMuWTjjV4C\n01xgm8fW0QDDfE3NPjEafASNVGdX2fiVyIIgH5CzSMb12deFMME8sMgMQ+aus1NT1SG3cRmViKCu\n2KhfEPnwpPSwUSOgFK3QYMXoKyy9QFGkKrQJs8qrD15xfHnP+XGhrwutVg67A7vdjlYn5nlmfziw\n399wOBwoNQ1BqzOtNaZWstOR6iiuCvpyYjmfOJ5PnNYz3h2/bVgt9LKnyESRhsk01KBJ+nVX1AO8\nZwkIbaSnne5QzC7kn3sZzH46krCsI0licWI377i7OfDO8xMvX3WO7y0sBuZZPo2OAiv8AuNJHJuI\nAHliBJ7G+fn30wXsA9YHW7XoCAfHnJdLWp3LGS+pwo1IexpGfI/x8SMCXy9WL292bO0FEorCJT7K\nvwuhgnjkBN6yAVozxy16CZG2yrirl78AsyTYdOgYy5YHv0o5UxmrKEljuo/+CAimjWg3oAviPYtv\nSpJRW1OIlUw/2lDSpUotVW+nNfPd62qUcJqkuMnc6NWJmsao4KPllSVdZ3bNGQeIBeqjmEez2CcJ\nvqz0M09cFZ6vdRsZgGEE+kVvD7XkfdLE+BBZlCSRKUvD8zlcUrkjVelpXEIalEI3Zy1w9onCRIlK\ndOG0Gu+//8D59UO2RBNlmmdub++43d9RSqXNM/v9nsPNDTc3N6gWSm3UtktDUAulFmpNDQMCtp45\nPd6jL9/HTw+cljOxKNEKrjMmRregGvRUXpALKuN3DaNEBlEJgPLnxbY2Y0OPNozvhho2jYeoMLWJ\nu9sbXjx74Pld5dv3K7UKWktWXNqG6q6xvw4//oTSu8zwq4e/5hEuP4utuGg74ilFeP3RJixSuZ5v\n+wg/BFf4IxAUJQhFR/2+xiBvZKRuxgS+gKqR6vOBszSUKFw8uAxy60KwaNbGVxXc2kjZjQcjaSxS\nBZidb8Q3K1rG8UOiig1BCqn0a6kdkMjctMgglTD2sVLMWOyUYhXIFmDFaSV7FawI51VGVx04ded4\n7tiUFWMTQQnD/ZyGYMBKiTSb1rMQabWOlsjuQFXoHlgfBU9m9FVI7cBGGMZFTbgpKwHw7CuoKiND\nkvfRbUmWPqCM6etBCpIjayHCdPAEgpfAihL7A1F3iE4cHxdevTry6oOX+Hnhdt5R5+xPuD/csd/f\n0urEbrdnf3vD7rBnt9+NatSS/QxbY26pvyitUEryJOGdaZ5prcL7wun8AKuhJ4dDVnOej49gp2wJ\nxiglVxnctP8lAAAgAElEQVQQ+6pQFSmo1qwz8CxNNstQMoWWim9txUbvMe9GrYWbuxsO+8rNHj7x\nvHI8V+5Pgjwo5+PggKRQZdO0jI4MT2THA7w/WbAbv7Ahheuqycm7yYiepAovnANsxXZsywcGWTiy\n7t9nfOyGQCUhpwJIeuit8k8iYe5WupXpmy0pBjHgcY6c/HIRDG2xWZ5Hi+IiKRSJjVTMc1w7HMEK\nQKSkdWQUU+uexqNoZhVUCngBT3Lz7Fvzziw4Ujean3GpI9YelluEJs6uQq8QqpSqucAlm6aUItlt\nKHqSqBH0LdUQI1kaazYMdbuoM0M0oe4Q8qQ8eHixkZ4dAeT4emjrt+DVY5B+2TLMPROHWdWog9DK\nmozUHtQk1TwNo5aKaqVQUNkTrpzPwcPLB44fvCaWlaaV3e6G3f6GaX/gsD8w7/bsph27/Q27mzvm\n3cw0t4S+IhdD0KaJNqUxqDU7VyHGbrdjN++yytBXTn1h6QvEjCGsSwqKak0D0jSSFNlk1UhqSbQh\nWmEIqMwM6yu1Jbnqg3SGEeN71iOoKvPuwM3tzItnjdPJOT6rnPrEtzC+fXSS2chS+1HRBb5lx+Qi\nALq66+H4ZMMJ18V+RREjhNj4sCe+fhMzX0XNcRUZyZODvsf4+HUEmuWybDoC2YiNLUglDcQI/CUC\ndSNGUj+kk4q5kjUKPghE2ToYZeUbkg8hVD9kPU03cmu0SvME4mGWPQO3kl2JzLsXzdZlmgq/MOHc\nK2cLTiuIGxNQWWgYEgXzSnih90LXbFF2KEHswFHmXQV39gH7WdhNAj1g7dnCy7OfwNaIpdIRX1mW\nBfMY11WGdDY7LNdBAHZNGbVH1ltoyLWXnjpENi6RbK2c91621mH5MXoIRMFsS7MtGIVuFfOCx8Rc\ndtRppnDIAjJrHB8XTq+OPHz7Faf718yq3OxvuLt9wf7mjt3+hlqFNjXm/Z75cMN8uKW1Rqkl0R/Q\nSqPVidImapuY5sbUZmptqKaR89s7ylzRufDuu3/L8vo9iAkT4WSJgCafmIunbPCCCjdjOBSmUkfP\niSGispU26lzC00iw8Tcj7JOyQ+vM3d2Bt5/vOL42lrtKnfewLLx8d8mMkIxqVnGeTMKrzHgjFeMa\nEqegaFviT4L7Jwv5w3iAD8uTt1Bg+34LFL6zduE71+UPt3w/upHNGzZGlMF4pAXY2jfJ6D6UUMjT\nc5btWnxcWlzUV1sFY97HQogi0hCpo5WXpDBpVIElAhjs8OiMa2ZEN/qA0ym/DVrxQdSNJiUlsBC6\nkzE6haBdJNKFgFio5tmuPCrUijRlIkOPqkpIdsrdOrC5lDxaKkhHI73/umZJtJIZlvBUVXYz1tAs\nD7YhThpipBQhecqNRdPrRyRZ5YxQgexpkrcieQSXUTCTKOvscel1mB5yMORasC6cunE6HjmfOsdz\nsJ4W1tOCrUfmuXJ3eMb+cMdulyhg3u9prTJNjWk+UNqUCCy2yZxdprtLEp01IbZoo0d66XneUafs\nOXCzb/jtHrk9sH/9HieMx9MDfT0jZ1jtEaMQ9DH3wAentKWpQbIIYsixMtM00CGyKbQvvFUWWzWk\nzNze3PLi+R3vf+uRF1158dYt9y9f8+/aPbBe+mTEZSFmaf2mbBloP0PjnO7fpQR+mijcFIVbhjI1\nAxc+8LISLktrMyQ/BEnwIyhDLvkgtkl1MQTJhmbQPspjJciM/qgmjMhq3giIzAxk2e8mfs143kuh\nMCOjOtFCswItdIQgY2HEmsbAAMvccfdIya+NcKTBtMF3jUy7hQxjkMU1MgxHPgijRqfGkdw2oEHs\nEGaqBkWgSKZCVTMMMhlNNUKAgkp2EgjvdOsUCcCHKi5hZu/GaSUn7diDIUYTku7OapZ18iWh6SZs\nSWkzo9FLGlMXYR0h1NaEJRAWD9aAHSWfjW1cjGam4Lzw/nuvuX/1wOnxRFhHyZ5++9s7nr/1Nrvd\nLVIqbZccwW6emaaZUmdEKx6jdZhmdychU3m9Q+1CscJMxXuim6ozZZ6RqdJuDhyeP6e9eM7Nwwd8\n++W38JffpsdC3Bv9PpGMXxqHyqgAHeK0gY4ybCCL24ihBRjOyflQCMXo7KTSuDnc0p/dcth9E6Hw\n7Pkt3/gPJ+q0gI4U+RACxUj/qYzqV2REjwPpbqTgFudvnj02Duvi/saz3n7GBbGkVPn6s215Jdf0\n/dfljyA0UAJPLmAggg0h5AWOyTnIk7Sc9cIppFhz4we2gH4U1aBkbcEOZwIKEZ3w5eoRRzupyCqc\nrGeiUMSZ1GkaRA2W0WhDyPo8M2ihl5Zi+yLsGsOY1bGwlBoGtl3jip1WFlMWF9AUHM1TJyI4n09g\nDfWJx/uOnTu7psxaKBgqktkJzRRUGgzBu2TL8T5SXi6MFAEu/dJnwT09oZac7BolWWVJGbKN0utt\no5kYJGl4DNYhqxqXEIoIpQpBZenC8XzmdFp4XBeWfmI5vqbVkpmB/R3Pbt/mcHjOvLuh1EJpO2rd\nIToDLYlWyYlrHvhqhEmmIMsBZIesM+f7xv25Mu8m2n7i+LBD10ZMQ2AlU4Yzk6A3xiEMYuHkZ5bl\nnlUKa6/ZC6eA65ZfT+OLy2gOk9Gli7B6ksGFRGDuluIpSCMZgdsKCLVN3D27YTdV7vY73r6beOft\niZevT2QbvRxbXp/tPseVJEizIBdPH9ufp3H95Xcb/8Qlk7GBhi3DIxffKk/ChH9koYFGJy4cwfA+\nIza6hgsgQz2fY6QJGaRN9EQGPm5w2LC6sN0ZH+Zw4wOw9WJZr+UmafUFo4gnBNe8YapZ4rtBV4Mh\nWk5DUIqMXH5+ri1+1jDCMgAxhG4nbO2sXZIAxPE1oaj6Ge/BKZTHYxqCqoVpFPhkKz29quBK7ncQ\nll7ffE0Cz7KXQ4SDpnhbRo48UVMdxFVBhobCgdW2ieTDe6Q56O70IHWKUjJUkMwwWCjLmhmPx9NC\nX3KjlIhOrY39fs/N7XNub99it7tlmneUmiXSohUGQiup/EHqjqBmefYKfa1om9CyQ2RP08rSlSUa\n1Sr26HjtRKtEK0ireNsjxdi1M7v9mVgfsNMrzrViVuheqXIt+000kKlC0avIZ6tNyd8nYto2Vskw\nK9GQeyfWAAwtlbu7PdYK+zLz9t2ez/zYnne/eY+KpzRcPjy3MyV8lTjr9VfABVx+17iA4e/84eAD\nkgLJ3z5tZ57L6x8ZWWj95YhnyYlRapL4bIGOjP9HP7tgQPqUeZqAyrQ9rryhYhArEmeQLA0Ksklm\ndiNqQFbqqXe8JMEoWlPnPfrwh8ZVV0B6QbjyNEUjPXTR4UW2Lj5DnBMCXnFtiMx4NLqBSyfO57zu\nriynzlyVF1Nw9oWjGR4nRDolGOXLOfmq1IT14ZTm9FjHpjDJpSiOyejRG4UWgmpHNNWYEpnuy5xr\nNjZJpXWnj3bvEaPL0ijOMo9sZiIN14lsf54S39WCHifCFXVFzlC8sD8cuL17zrNnb3P7/G32N8+o\ntY7qxpYcg/XkUkog7YY631Kmt1l9R++N+4cTj6ckgqcJbm8L027HPO04PZx5+d6Roxxzv4R6R+xm\nfDdRZmPawTu7mVn31DIn2VizddlqMrbK2XL62QNC1ZHouUXcCHlqyd2zAGJsMWfm2T1JBBHD+kLv\nK1pTyXm4mbHa0D7zzvNb/tPP3PK1/+cDvlZk9F3cPPiWkfqOvMBYyJrx22Wxbwjkoq8Zf8llrXBB\nBNuORhuPcOUI48JvfL/x8RuCKJfPybYY2WJ9u1QWbR9bNjMIgI8WUyN+04EoIuP/vPDR8ts7EWeE\nduEUGDUHFrDKBus35KCXWGpr1JE/uVY9bo9OLt9LqhMHY+8uI19bqFJoszP1hexYtCLmqVjs2TBj\nqkoJyxoFNaSmoSk60qSiXHd7GrltyUKS9HCaBU0Bq8ggSuUaG0ZWSWaPvs3jZ+ZA0dHRV56Qo1tv\nxxEikO3So85EaeMO7JDIrtK2dOgrUynU/XPu7t7m7tk77OZbap3R0gitrGMHKtFKnQ6U+QYpNyx2\n4PFxz9IrZ1PcZ0ptqYyMwnI2WO9Z7l+znBfOy8JjHDEp1LYS8wGfZ+YXGf70wyhesgO23NLPz9Bj\nIZaCy4prYKWhsaLaKZLoyTwVllpLzoWxU5OFUAYiiCFRzwItw/qRLYE/1YYZmFWe3+z5yR9/wec/\n9QF/9+4D3/j2wv3JLrF/ZgxizOaNyXvC6Mm2tuVJouBJVu0p+XdBz9dQgCe/HlDgh+ILP35DUO4G\nfPHc0Ugr+BnxE2JrxrkbTBvtrnIhn1HJ2vigDmVYqg6dlCOntLYgdoYwRAykw4iX3RWzmpV7HqMl\ntIK2QdBtXnZrNCpjsxUdDT/9kvnM5aKUkkUrl0yI5y+lVFor7KYJtSUbYyxZB29ueE/du6qyF6XV\ngKK05pSSW6alxn2H2zkLjAw0sk4hGfZIJYUGrspKRpwxJjGRegUUIkZpcSgRlRrCTiorQyy0SYpV\nkVjJTktrViG2idL2hBZq7JE+Y+trltNC4cQ07bm9fYe7u3e4vX2LIhXxAnXCpGYrsToztQO7208x\nz+9wWmZePQbvvTpx6ifMVz759oG372ZUGuviPDweef36PU6vv0XbVUotLD1S9t1O0PZ4PTDvbtC3\nZ6zVNAz1DuWM0umvd/hDA3+NiyH1gPkJjSMu2Rlq9Uw1z1KAwPuZrkJEoeYKx62jmiKqLMbqWdka\nWTaNQCe428/s61v8zE98wHsvH3h1fJWGYKx3LZm5kRjb1D3hBjJ9mlWmsTmeEfLKFhLIyDpcFIeA\npygsQUFaixgHyOA0ftD42A0BTJmoGaTayJ8g5MQOyTLZZAx9kDSSMmMy/y0jng/VAeclc76ihBnd\nRspRUk4MevGG2e0XmhRWqVlEM7T2W22eieLbXgtyhcU9htZgXam6Eq3kHRykofnGCI/+/6UgteC1\nsdRGGfv/bW3BTkteh5SBcbZrjGzLtZwXTscTWjOTUkYrtR4jwhwNOFUyG9Ejsx1oIoc+qutsHZue\nMOJ+Yhi2sTvQhj4kqx4TQDRCZ6TdUucXSD2ACetjZ3l4RE5nZoHd4Y7D/pbbw3MO+2dMuztKmRGd\n6THTafRoiOwI3XNa73hcKi/vHzgtHY9gEkHKjB1XHmyhtpLdgsbuS7nrtSPaKSH0c/Dw+jXTjXL3\n4o5yFrhX5OaGOk/spwnfCbSVpZ5ZNS6afCWoRZmkUcOGN9UBrSP5l8hNdKXUS5ZBSGGVOPR1pZ8W\noKR6sFXQIHSlTo1puuOffuaTvL5f+PffPPP6cWE5S/aefFIc9KEvZPtrK5of3w94Hxu5GNcFfmUJ\nn5zsSbRxeacfAhJ8/DqCQcjYRoj4NgkbqoXk6EE8tyFXTdQQOj7q0HFnbYATmu3DQiXLZCH3IhiT\nW0XzeIGQlWChSBbrhLQUyHSQof13lFVGN2Mt1LGDciCsLqx9RfpKxZBJkamkiIhk4VXkkiIsJfXn\na61onbK5plj2RHTjtHZUfWzjlbswxdCCqijL+cjLl6/ZHSrTXFArl3ZbxmgdvrV4HzJrG2hFgtGY\nxLNfYWzdhvVSbBMh2IA3IpWyEYOjGUzUPdLu0PkFsMPXYHl8n9N7r9Ho7Jtyc3PHYRiBeb6jzc/Q\ndsBkx3KqLDYTekBjpsXE68eJ43HlWy/fB1Ze3O2Y64HGzPn4ipevH6jzkOUOhWed9kg5gqzZ8syC\n+/szd3pgeqHw6PQPOn4zwzRRd5XQM7N+kOhBsq+AZlKCSQs7nRL5eBLPec/GTkghSUSW3Nsy99fI\n1DAe9GVlPfZ0QLVSp5qtzXRFtTKVmc/9+Dus54X/+999i3fff+CDNUZ79rjUp1wqA7dgU0ZMN+KD\niKuw7soPPA0dtuKmeHIWLhwE4+x58u+/Lv+jDMHXvvY1fuM3foN3330XEeG3f/u3+Z3f+R3ee+89\nfu3Xfo2/+Zu/4fOf/zx/9Ed/xIsXLz50rNjxskAZyj/RoYwKQ3xFTAlbSPlwbhkeZUfu8Z1MowyV\nF5HhxFaYLMWQxkiPOcvqQ5osEJUIuex4MxFUhf1cRh6/ZXGSkeFHZNfhGO3Abe1YP6K2ZD1CJIue\n24cL67DapaQmvkggTWnW2DsssrJKx7qxjlSZS6eHgRdWU7pFknjinE+d83mlzlBjFECNwiTz3Bk5\nG6nkrsgypMu5YUmmwjzjCcSDMBt7Ohipz7j2HojeQbIuo3t68YhGREW7sZ4fOX9wZHl4RY9Hmk5J\nys131N0dMR+I6ZNE+TSvHgoPp+DheEaqcrib6I+Vh9fB4+v3OJ+OhJ6pE5zPgZhBObMuZ86nM6eX\n9xhrxv3nlfXxzLTL3Ypv7t5h/+zAj9/d0FfjP/zdX0Pt6M759vszz95qfOIOZl6j67dZT/eEOzfz\nLft5Yr/bM9kj9XTClkxbClCL57bwpVDKjtpmWtuho4GpaMnNXtY150cptP2BaXegTdkkJdaVdTFe\nP+Z8e/5sxz/93HO+9frMq9cP2JroS0cd4lZ/AMPRb/wUXLQxXO3ChbW+bG6ykQSxcQ5b/PF07Q84\n8A+BCFpr/N7v/R4///M/z/39Pb/wC7/AF7/4Rf7gD/6AL37xi/zu7/4uX/nKV/jyl7/Ml7/85Q8d\nuymtLnlOiXFrdGxWqqNrZrYd80gREjKPffOGwbA+NAfOZRsySTkvbWyCFaMVePhonV5SACQLYLSR\nDopSWNEMGrrDGnhks9F8+I4tY+cfy1bl0EfDTqGiF2VejCYXlYoUyXbntTBXLqSk+5qlqnrdoWl0\nSSPKeMDunBZj7R33OmogfNQiJBDY+g66S27qmQxnEqK+OZcCYoMADXDLCsltc9Btm2iyySceLFIz\nYxAtMw6PZ86PK8eXr3FbEA1KbbTphjq9wOtzTnGgn/ccfcf9Q/B4dJZeKA0ohvfOelo5PrzC1hO3\nd1ku7GtwWk6c/YHT45nH44mH4yvWOKMtiGXFjgvzvGN3OGDxyO42qFVZjo988N4rTE5QV6Zj4dWr\nyvl549m8si+PlNJp04HpsGe3m9lPlbo69Easa/I8Ak09jUFtlDZR2kyp0yVeB0lNwZq9K+r+wHRz\ny7TbZ09KPNOr3TmdOoedcHe34/OffsY3vv3Iv/2bI8czhI3sQ0iGuMPdbbolxpK++vMr/N+MwYhi\nnq6qa1bhKSyAv+e1f//4jzIEn/rUp/jUpz4FwO3tLV/4whf4+te/zp/8yZ/w53/+5wD85m/+Jr/y\nK7/yXYbgf/o3/+f4sM4v//zn+Bf/1T8ZxT4Kbc69A3XGa8PFwMZWVJY7GcXYqxDJ3LhQs5sMPoQz\nSbCopHy3klV7qS5sBFN28wlh0gZSOYlm/bpOtGJMzXlcC30JfDmznjqnUx+psDkVf+TGI1LgBqVt\nEun/l7k32bElS+s9f6u1Znfufs6JJjPhZlFSDRggMWLCrOYIKSVmSCUxrAkzeAPGPACDlHgMpsyu\nhKi64kKJLpPMiDitd7sxs9V8NfiW7XOSC8nVRQS5Fa6IOO4e4W7bbK31/VsxUK32GmJAKro2GWr1\n5NJ+RhrgVFpYZkvRsU57AU/nhUvKDUbxUD2lZAUNRZONjOQGjmrteG1UajWFbAtWPK56rKg4qDac\nwxT1V+RmATdGQdYshaVksrcU31MJ5KmSpgfKZUEuZ0LXE4c9XdgS+1sIn3HJO47Pnst8Zl7+gdAH\nur6j77dQhIc378jlSJUjLgTivqffjEQ3Yorn+eEDj+9fc7w8clnOWmFGpsrEpovshw21Bo5Pwv39\nj0FmQksZrsYiUTB9JV0qdIY+em13coXD3Q27mwObYOltJcisuI8f8J3BO0+wBe8E6w0mWEwI2OD1\nxFa1J5FWaweFMAx08Qbvo1bqSWqxcKrtKLJgQmToRr73as8vv3zmxfYDZXJMk6o1C/VnQTzhytjo\nqKdjgpFPgkmuqwFcjwrrH8vH04M0/CclLen5n3n9uzGCf/zHf+Qv/uIv+I3f+A1ev37N559/DsDn\nn3/O69ev/4ev/7//r/+TVdZhW1S5QRF+KUKRj7194uLH31lqo3VWLXxzd7WOAGOqqhGN9u8ZK025\npruhFcGKJUvbw8XgrNfTRlXBTLUO14CYWA1iK1Obs+fc5MGtS9G1AlFYNQya8FOLVcNRbQNpWKXT\nDb0VaYk/gnMWI5ZSDDmX5mYESmWeqqY5O32Ic9Hi0VJFXYc0oUobE6qsBapFNe51tR/rbqGSaFUG\nrnpTve2UwbHruFGEHBzFRlJqR/PThKSMrUIIkX6zJ4QXYG85niKXJFyWTCqqQbDeklMhmUXBt/lC\nrmcKE2IEYx3H0wnLTE2Zy/GR83JPsTN+qIQYcT4iRIZhYDvuWC4wTwvL+UzJZ6xA348Mmw22i5gx\nYA7Q7YSwSYS+0g+V8cUrdjd39CYT8gUzJQVknQqXnNOxwKqAA+cjPkas99oajaA0d1QnqlMsy1iP\nd1EtzkXvvZQLpWaVKhuHD5GXt3d87/OJ733+AVMNjw+Zp7lwyh8VjtdbHPnoLPzZT/DpkP9x+VjN\nRipU+6g20q+IwWOjno4Fw+U8/6vP8b9rITgej/zgBz/gj//4j9ntdj/zuX+tVKE6MLKmDQNUfJwx\nMpOWEzVZkAhxgDhggmr5JSdNiqmapa+qsE6LNNqioGMEZGNaRLcel40p2iQs2r3nrBaUIJ5cLDbr\nUlqg0UFCZzPWFRZbqUYotqUGF6H3nug7QrA4NwNLE+8DEhARRfaD13JTdHUvtZJL1opzhBgV/yi1\ncjlnpmlhmSZqNczF0jnHELSwJOVCKgoUmuatd9Yys5BqapRq22mKweTGdFAbJw6L6CIpzqmtyFTU\nrqA9iFrA6iloF8E0FZbTAvOkAGvo8MOWbneD899lnna8f/PEtEyYzjBubths9qQayAmeL2ccid6L\njiB1pEyG5bzwIX+tpqD8TAyVfmPZjxuG8cBmO2iKUQx6OjQdl+cHpuMHuskjtSfEyH57x83+FaHf\n48Ytdddj+oLz9xy2M7eHyn57YBy2hHzGXQRJYIw6OIN3BBPVauA0D9H5QAhBG6AbUIgN2LjB+6AS\n+flCmS8aY2ZdA50N86KFtcYKtTpgZH8T+fLLzK/8l7c4hNecSY/C6dScTA00/LjrcwX/uCJf6zO+\nehY+sTFjMKaBuyoK149PdAvtq37us/y/vBCklPjBD37A7/7u7/Lbv/3bgJ4CvvnmG7744gu+/vpr\nPvvss//h+6poxJhptAymqkcA3yzGWYU/aVIw0AddvZ3HmE7DRat2B+jl0kQBI6Z5zFrUOE2D0NiD\n2mTKWIuYwmp5tRai/0ilSa2UVHAsOApjVDMO1jLnQKoW5yaCnYgWHFnVg0UThUotFFH03ZhAThFj\nVvSmYI0WoBjAWa8+hmrwzuG8JS+qGcjN/1CqZU6VYjXItbVeKxviPMabtvtrzn7JTYYsNBZkbS7S\nG1asUzVnC1fxje3ItdWKY0kFlgbSyZxxJtL1I+Pmhti/QupLnj5YLqcLUjIhetywwXUd1Ql9Jxhj\nSTnirGcINDv3+r6ba/7gfD7hA/SDZ7MZGTcDfoi4zmGjaViK5fBZhyl3pGUiF120SoFTgXw5Uc8X\nfBrpdrAdz8gYMG6H9XucHxv+MmF8xIZAwBOBYC242NKpKs6pHFo9VgZjA85HnG9JR/mMlFlB6kYs\nItoqlSYFrWOMlDIzT5W+j+y2A7/03ZekSShn4XG58LSUds3bWHzdyeWK+uuJ79PUAaWYP54O5JO/\nfSQdPx0hVhn9fwhYKCL83u/9Hr/6q7/K7//+71///Ld+67f44Q9/yB/8wR/wwx/+8LpAfPqqEjDG\nKg3UTERVPEY6MBVjJ0w9IXmh5gnYYU2nuYCt2MPUBVNnzcOX5qVtAhq9CI619MygNFltLIWOEgtG\nklavGcF7CFW197lUcio4s+BtZYwacOqc8FR6aunxOLxAkIRtR/tapGkYMkVolWiFsjSdgF0pIn2g\naSCRcsQKKIbqmKZCzolqDMUokyClUG0hBlUxVpFGi6qPX8t0mjagtqIUEXJVRkKsVwDWOZULW69V\n7kaDW7QK3qqpCS1HSWmhTBMmQRi39MMLtrtXVPeCNB+4f/vM5TQzbh3jpifs7jRI0ReGEYboKfR4\nZ+m8wXcB10dcCFjvkWpIU+H0mDAOfDTstj3DGKjRUHyl+IJxGeszh2HDGHtqMcxL5TzNvL9/4PW7\ndzw+PnA+ntjkkYMEOleQeou1O5zdYm2vjIjtsV5/Bk8gWEO0jupHvUY1t8Qmd1VbRudxvlHbZSbP\nz5iSr2Aeir9SciXNmRAg+sA8X8jLhS7s2QyB7375gstT4vjuwvaU6KYFFmkymuZyXKXlrHiB+Ug1\nsp6yPxkj1hyDdQHRO6zRwe1ZXXGE/4gTwZ//+Z/zp3/6p/zar/0av/7rvw7AH/3RH/GHf/iH/M7v\n/A5/8id/cqUP//mrSivNBEXKq+CNUUOMCxjvMV3ASlPXNaMOeWmOxFUyGxR5FYclK3PQLlhFHWXG\nal7AanUu1TGXQCgZL4KPQfv5rOq8BwvnZMjZslRLqU1lWDLDslyFJZRELgoKVZPUJtw87LauK3rF\nFu0tcN7jvGfOVQNVxUOu5LpQ04StF4UaakW8hmGYkihFmIwheC0VsThcsw8753HOEJwjusjcnGfS\nMIgsheq8zrZ+ABuouJY5KJRiKUYXjVwNqUKpTbFYgLJgg8H3G3b77+HcC47PG47nM8+X9yw5YX1H\n2HyH7d0d27s9/dDTd3q0Dj5guzV/wBI6h+sdJlpsUGl0KYV5TjqbB7jdRLZDwDkFNmfRXIUsGZsy\nLie8SWArZTAcdntuvhj56deB9/eWvo8cDj03rzbcvbzh9sUd275n9B7jK8KZcnF46+icNjOpBUPN\nUKo81fvSujXKzWtfQl4oaSYtSRudQkScI9fM+XjP6fmBJSewEBLUNKGy+Y4YLC9vOr7aeBYrbA+e\nl3zQRY4AACAASURBVKbj9dvCvLRez0YdXtOM1l3crL5QWVnB6+FhffSvLsVPx4BPsEWVIvz8I8H/\n0kLwm7/5m9Ra/8XP/dmf/dnP/V6dZQxSTfN6G5xrYn1jrztWe+ShZCgZaXP1Ne5YjEpw2+VZQUFd\nKZUP17+0tdcCWTypBKSoTNkQMQ30c1Z7GXPUtuKaC7kaXPPydyZT6wWpmTlXqAlQc49uC7p6m5YG\npHkHDdlvZh5pWWi2SadrzlAWjEyYqpZYY9TQRNW3vxQDTlubbPUE02gn6/De4LOCWCLlWkNWatXO\nRByYgDERTGgUlY4wWT5qH7JYHVHEaouTGJxx0G9w4Q4TXpLyluMFzpfEvFwIm47Nfsf+5Wccbl+x\nvd2w3YxshgHrAi5Gwjjg+4CPltgbwgAmgvF6w9daSSW1k6Cwj4ZtsHROT3Zz1kKYXIX5+ETKE3Z+\nwNSEj5FtizO/1IE6bBm7jpv9hlef3XF32LPfbRmcR3NMMjV1LE6Tka04jHNtktR7SjcZPVkFt/pg\nFIxemZ0qFms84rTxuZbCdDkyTSdSSZhsWLJiWtZUalnwPnAYHOMQcJ3nxg2Ic9zfLzzX3BYBPm7a\nKwvASgR8oimAtgj87MiwfqP55Gs++eNfvDyCtX1Ie/cMLuhJAOu0bqoZPIxp2gDxzf3ZlIai7L2s\nXgOa8s8oVGZKxuQLtQbERazzuoLbpsQzgO8VAApOKcfWKeCcQA/BCpfZqd+fgrFake2fnwmXD1Tp\nWpqtSoldW9gMtkmIpS0g7eBWKnnJmFrw1hCjLl5ZmjtNwJSKK+BFCz3pDZIFyUJKjrkGxFnEwxgg\nREeIHpsdZo5IOVFyorSUpYyofLhUbJowRrULq7S4tPKTIlonV3xgKcI8Z1y/JXa3LO475HrHu0dP\nSmdmObJ/seWL2/+N7e5LttvP2Wxe4sJAdVYNQ94Tho4wRsLGYzuL8eC20G3bNRZhmTIlFcgfsZUP\naeKpJsZgCQaMVIK3DMEi85lluufD13/LdHxAfEfqt6R+j/WeF/uR29tbXtzs+Wy/Y9f1DCbiscoE\n4ZX/twGsJ1mjfRHNRCTNIyLWUavHWQfWqaS7tNp4E4iD1w1KDNRVbFaoNSFSyMUwL4IXlWzVpMpF\nL8KmC7y4PZCTEM3Mj8PxmrOxUoEK6qnnAdZUYnO1vn0aXrIyQius+BFA/DgwYAz2P0pH8O95rXXR\nRSrO6c6eV8Bd7CeiCrhmFuBw1WFqwtaiO4gK9lkvoB4SnCKnYluW3wwmKBBkLcGZJuxpZaEN6LGs\nzkW9UbtOq0pxlSoJWzQcxPsTvcuqhENXam1pMg28WjngdsAxSs/VXDUXXxqjqMmjqv5LiuQ7k/Cs\nun+1GUtjPqQxBkRDFz19H4heG3j4pGugFFpOgeLHtQoFtRtrP5e7Lp5qsVW6tthAlYFqA8VbsC+B\nl+Syo0rABcvQB8a45e7ujrsXrxj7l/TDLV2/xcRIDY7YG/xg6beefnS43mIDWFfBF1ItzNNMnibm\n00RJFaOxq4hU8uWIzCdO5YIvEz7PRAddtJxy5ThPHB/eMc9nsg8KgcbA7fYVm9s7NuPIGCN+yZQ8\ncbGZGDtqcGrEqAYxQelpq70GpZ3G9QSpo5GzXlO2r2qcZuG2puk3aqOBadfagQ24EDAUTeG2HuOg\nJEGqGrq2fceXL+84nzM5nRhixHt939rzzTXI/PocrCfbNbznY6zZutN/XADWUcCwRvh8zD/8+a9v\n33RUW1i4ZGqx5KpacNeOu8Y4THVKBVYVEGEsrjj1v2fBuqo3F4tKZ6tBCFQXMagk1NYLVRKmxkZX\n6lF6Y4V50bYgbQeyGulllIT0LmN9IYiKbapYJKvCMXRNnjo/k/OClEBuRZcpV5aU8E5ry2lGpbW/\noFaDC2pEitarD96WFrPtEZcQp7kItehsjGheYU0LuYANA9Z7tuOAEcuSDFImbYtSkxypavdesxwi\nkqnOUE1txpWguYhNmlzEU6SnskPcHhP2JHnFnA6k+YHgn7h9ecPh5gt2+/9CP+zougHEaWFMcIRd\nINz2uNHgB9hsK2NfwYG3QrCF03nh8XHi+P6ey/0j+fyMMRCHERc81luW6Zl0vqfef415fos7PeIk\n4ZxwYmR2A64fkL5jiYZ+jPSHnu9+8ZLPX35BmReW86RNyxisjwz7A8NmhJoxRfDi8bbDmkKpM1Ka\n96Q97NYEvA0fsyZaf4Z1Ua9nmduuoxL2XAUxPc6PxD4jOUHNCjB622TMggvCbuj5pc8H3rw/83Qs\nDF1HFwLTmgmBYjz6Pq3S48Z92XXaN5/QjeqZsfDPCISmEqXlbGL+TXXhf0LT0dLADxrAZyjOaPlI\nLdhWK7KmHOvqpwyAiG+mk6wYhWkAiwiwQC5rnIHKeMkYnjEkMCPYiLEBbyu4ZkFuF7pgKdUhokf6\nnMtHsNAUfKjYzmFqj1QVtKhbUiXQ1aynmWY8ss0UsiL6pTkNEUoyLHPhfNJ0H2M+jiZeMnntdlgB\npLrgESI9Tiw5WaQW5mlRujHPzW/QmpraNWtZpkhWGk5LGMGuvzuWyoj4FzB8F2NuMWXL4DqCc3Q3\ne8besdu/IA53uNARYo/vN/gQsX1ARo85OMKtwUddoJdlRpZM5wy1zkzTE8fnE89PJ44PmnBclgum\nJmYjmDIj5aLU4HImT8/IfIFlZi3Akb5gN56x2xGGAeeFTTdyGA6UOfPh3Xvy5UJZLuQy47qB0AWq\nrxTbMBVGXH2BuVjKpCdAWePcbNMLuNCo6vYg4qnGX23fUptHtQqn08L5dOF0fyZPk5qYyoyUhLUd\noGEsDg2z6bzjMAbmSdhvF3a7nvHRs5SFmuWqGlA5vOYsXrF++dlyFGCNQ/hkx/9kfGCVousJ+ZNw\ng3/x9Z+wEEwowNc07lJasqxgSm2Z/Zodb9zHxOOKa29G0JKIXJoZxNCCuJE6NUedbTmFGVMXLAtQ\nwQ+IR+d3L2RxV9vw9fuaISTn9UiXcTYTQ1VAr0Tq4jHFqHAEw9J2k9py7a4LAS30oyqSb4oqodJS\nmc6F4ynhTSZ6ufYROpsbSOpbmCkYSXiEIBVbaB2KiXk+agdiKTqCGN05dBFQtsWA/tw4sE3n7rSs\nBAmI3VHjK+z4fZzc4eeRTTezH2du9z2bccD7lxS2TNVgncP2Hd1+xG07lh7MtmJ3GUvG1cz8dGK+\nTIivMD1yuf+G09Mjp+ORy+XEPF90oU5nzPRMPX+gnO9VQi1CcZ5inGZOWIdYSx9GRlsxMRD6iDPC\nNvbs4pb5PPP8+ITME4ZE6AUbPHbjoDMojOSxftNcjUK+nLF1QapH0DQn6wLOhsY2tftOWrr02sdJ\nUL1GER6PMw/3E5f7M+QzXVignTKsa3qWnPAWfI14a9j2nmnsOWwXDvuOzRh4Ps9IkStUYK+jClyf\ndFlFQisK0Bgyo1iBYR1xGsfQBEpytfn/gi0Eki96YQkIDme9RopjVBctyrM7a3CieQLWKk1ojAZD\nCKp+001Z0XlbF2zRdCJN4WlRZtYgNWHK6TpQiYkIAa0IXzAkqnhEIkUyIolaFqoksix0rhKCwZis\nTrdocRKalLfJpI1RqslV1a23t8Q24Q4iTYIM02XhMmlCMVawFai+qcYK1hWck0bz6RtdJXE5P2Fq\nJpgNhkwps3YPFDVeeauGMGPRZl7TMhpaeaw4r3iGGKpsKeaADd/Fhy+QuqHvB272WzbdhjGqpPv5\n4hA3IL7DhB7bd5hDJLwyxF2GUsnpwvNPn7F5xpUZkybMdGQ5vyc9vWZ6+Irl/MhyPrKkCzlNiCxQ\n2ocsGBb63mO1Jx1CRFxHyY6cNFw0LBMcH1UiNfYs84nL02tC9NyMkfHVS4Z+pAsdEjtS6Ak+EJxG\nj5EStTjKUlkuM65q/Zm3AY/DFpVo5xWBE0fJVTcVFANKOZOzZymBD0fH8zkyTxBFsB5cY7RSrhij\nxjZjNdTGmELwhU0Pt5vAq13g9SZw/xi0Iq+0zg2zbgArHyhaeLmKi9a14SNMqMxCA6et2Havt19E\nj60/9/WtLwSOZtAQ5Ta1gNJqgnmtLQu+IE5BwmYx0osgTq3B0gI1ij6ItrZOwJqxMmNM1rAJa9vX\nV0xNmoQkDqSxDQIireFQMq6mRr+tJwyVA1sRqtPTizGqpZfqqItGzRgpWKuBF6HNxdboacBZWj6B\neh1qhWUpzElHD4xahK3o76sKQNW+m7oe8lSeXOuMt4aU9Y2vJZOzRn9jlKlwxmpqm2jaEFZ7Iltt\ni0phiyHLjmo+x4Xv4OMr8CN9N7DbD3TeEaxRiXFxFLfF+YEw9Nh9j791+APYPmNOM+XywPz2LXJ+\nwkxHbH1G5g+kh5+Snl+TT++g2bedKXiTsUazGIwDGwwuOvoxEocNfthj+x2221JzJM9eMZCqDMSa\n7WOlYNLMMHp2m57d3S2b7S2D35CwnEpp/Y66g+eSWeZKuiTS5YJFd2vxDUTN0vwW6D0iVj0gWWf/\nlDLTlFhKx5INzwucJkuaYQCGaDTpulryuhDEVSHYimxtoY+O7WA5jJ7d4Ok7pxmRtrHl14e2Hfob\nS0DDLq9gYdvlV8bgU2PSGlCmgrrrl/6rr299IRh6fVNTKmQplJqQrFFLtOQdPdYUpMx445WmabFQ\nyr8XcpopaUaSYgNWEtZMeJdxvuJ9BOMorU/PmKB5BTK3lbaSiK3/UPAkPNNV3FGN0w8xlFJYslar\nu9IWKOsQW3FSqVIIVlSA450K+OxHLYA3Wb/WKrug4SDatVhrIsmCb292IxcUT7BoRh6qAfDeY4LT\nPr6qFWRTKpxTaYgzupCJlmBItSo/NtruPNfKOXtOJeK7W0L/JbG7ox/2xDgQuoFgPHkxGr/uN4Rh\npBtH/NjjtoHxM8/mpcLV8zExPT6xfHiD3P+E9OFr0oevWY4/YTl/RVneYeRCMMI49Gy3A8OmZ9js\n6DpLjI4QLCEGfBfo+h1x2NGNO/rxlm7zAhd2WL/h+Xjm6fnE/cOR50tiro4x9hx2d2y3t4ybA8Fv\nMXQIGts+uPWaCEuuGr/+fCIfT9TLBWcL3mvWY0WYksp2VIkZAe3OzHlhuSTmS+FyVt9HLokshbQU\n5kWfs2mWpn71kA3WVEz0OEOTdIOxhuAqMegpM0ZDF2mjmuN8LkxT+ZhFshKEKzjIx7EAY5o+4KPO\nQNbTDJ8uGPKLtxAY63Drv7TAjypZFwNAAzNVNqyZ97kp4hSBNyVRliN5eVb7sVSscXhniL7HBHDB\nKODjnNp40RRfK5oQbESjzS2GKqEFjCg1VIsGnLrQohFEY8CFcpUqG+saPVQwUjE14xwq2fUad75y\nv4IoXtA0DHpyVKXjmjwrK8orrSRTDM6iBaNiri1PYuXa3FRK5TIl5tx4blQ3kKVSGjWqJaiFLIml\neqZqmelJ5obgX9ANL+j6LV3XE2PEOqeUYrUIEdfv8LstcRfxG48bPXEQnMlIqtTzkfzwluX9Tyhv\n/pZ8/xPy01fU+TUufyC4C11n2A49u23PYb9jbItB17nrQrB2G/o4EuJI10di74gjhM7iu8h+47nd\nj9zsd5wuC5dUwfY4X7BlJl0mqokUCdS+FbtYLYlNBZ5T5jgvnKcZWQquerz1Gksnnrl4dU+agSp7\nnKhPwjmoZmZePBdOnOtCLpZSnGIaqWghizHM2eCsxZkWa2bQrkgUmxGjY0atBqQQvWPsVE3ZRU/0\nntf1xDRpZ8LPzvUrJKgSo0/DSj9Wo8v183yitP3Z7/+XX99+eGlRPtZFh2T1zk+ptiM4GDKYpjYT\n7Zyv1arWIC2wXMjTI2m+R5zFxEgcd7hxQ+hHuhiIQamztfVnDdywpuJKbQ21ix6j0Xy+XD1p/X9I\nZgxCCAaib9JT23IEtN8Qq3mEawmpLgJWKULNHwcaLOpAqlEqez29WW1t0lFurS5TK7ZBtQbiVCrs\n3NrFUluSkmNaEo+nk9KboKlHVSvNkyIGzbEIc8pMNTCbEeIWN3yHYfyM/faGrh/wQR+cWoWcMiYE\nXBdxmxG/H4l3hjCCi8qi1FPB5hlzeqQ+fEN68w+cf/rfYf4aW94xdInh4Bi3t+x2PTf7DfvNhu12\nQ4yBEFXe65xRmq7N0dYmrDnh0ozhRC73yLSnhltifEG/ueHlzQuqMSxl4v7xxNt395yfjiwPj/jD\nl4Q9dGIIfcQHz1zhkivvl8TTvDClhK+O0e2owVGjYymVWhwXs6GYA2LviMbTG8emtziTMHVDzY+U\n+ZE1WKcsEymVZn23LFUzp70x1+yClA2+uVBrKUieSclTc6FzkW0/sB8nrLXstj2n88z7h4KmVpr2\nsH/c5lUt/4kNvdaflQ+3U7MxawLnerT4BQMLNc7c6vHJWbxxRBSdl0wjFoWcCkvKTFNlnqte8Jrx\nzBizYHzExQHfDbh+S+hGfNfjg0pIRZYWZeaaw6tiJeGkUKzehIaKMQln1bmnNmZF3EW0V862yNmW\nasCV7biqGrM2JcMVudWFuSXJYprJSpqef0VyV/lxBSVByNngjeCa+CnX1HoNdXHIGWYKF181zZe6\nxliQ2vE3tXDTLFrOUcWB6fFhA+Ez3PBduu13GPsD3gZMiyNLeHBKr0oYKXHE9h2lcyyN0nVOsGXG\nXk5MH77h8u7HnL/575THHzGYr+i3Z4bOshm2jEOgHwJD7xk7Rx+FyIQvM26x7fpAtRUtT7HNFOX0\nZJIdJjmMf6SEe0gPmHyL9BqkGqxjN4C56zldhOfLzMPzNzweT7B9STzcMN4cWCpMGU7ZsRDINiJ+\nx0RPCh4bPLlWlmqYckfKnrIkbJnwUtlvI0M0SArkNCqmE7SbweaEqws2LETTSnPLRCnN/inCkioh\nCN7rub1WjaorueKNZRM7bscOHz2H/Yafjo+EqG1WV+7fmHbPtWSjTxmF6+f1pYnaay6iZnvKJ1/6\nr72+9YUgi4dmbvHOEXxBo7QKSxHtqJfCMiXm88zxeeZ8WkgpY20hxkq/CXTjFtvtsP2W0A+ELuKj\n10oyi4qJRH/FKmrJNabgRCvTHY7gRSkvk1s8mldgDgUlac3IKuU0jVpU88+ap4jxWKvW6dUvsorR\nVgpKlYYqGa5F1YhIVTt1S6rJxZBKO1UY8BYVHeV6valKFmrxOKuFqKqulAaeCqkKKTXXYW03jzW4\n0GG7O0L/Xbrt9xh2XxLcgLWaS1iqxo5b32PtiISB2g24LlCDYZGKLYlYF2R6Qp7umX7yNxy/+mum\nd/+NYN6x3yUOh8Bh37MZN/Rdj3cebwXPgq0JmS6IkRafvs7NCmpa6/BeP4wPiDdNkRjA90h8T10O\nmPQK291i44ExDIx3PadLIjxPfPjwyIfne05doZs8B78ns6Y7ewqRanskqMxYfEBsYEJPpZcLLKeJ\n9PREnSdMWbjdj+w3PaNvRS2MmgHpBBsWvGSChw5DqBUzV3JeaJHRpKWSfaWGljZUtTClloo3hjEE\nbseO2EcOu5HdJtJ3lll04ZdPHnbTqO21yXGNNL9igWsGAXr61D9qqMK/oSj69ktQxSuij9PjvxNs\n1Sai82Xi8enC4/OZ58czx6czkmcchWFwjNtI2PS4zRbbb7FhwPtOb6B2zMTUjw+acJWD5qKLjQJ0\nlVLBSMA7g7GFYCs2FvXMF6MON/Qsv44WNB/EanHO7ZiOCdiWcaDlqQJVeecrcGM0085ZtJVBlJ3I\nJZFqImVLqS2v0TZASCxSrQaHVE0sEqlMktv851srkbTRoclkm45e/BbCDQzfw/TfwQ5fEvuX9P0e\nTACCOj5jVITedRQbsZsOu4mYwWBcojdn7NMHLl+/5vT+n6jvf0Q9fo1Nb7jbTex3A7e3B4aopS22\nCvU0sWTLLKJsS5Pmqk1M2mlJ3xtnwVlDiOrDiDHgo8H5igsOFy74uOC6GZczpAnpzhi/gTDgK4wu\nc9ic+HBy/NM3HSxbTt3nxN7hwxo5Fql1h5TCkgwpqct0OlYujzOnD/ek53vq5Z7OZoYIZrjB5lFZ\nBwy52CugbAEf9/Tjnt7f0skeOb+mWM0dkFJIRVOfSs56ovKu3aN68hu6yO2+xwVHHww3u56Xtxve\nvk+krBJ7hQPMVU68iuaun1sf+qYZuAKJjb4W2u34c17fvsR4jQc3LbE3w3QuPD/NvHnzxLv3T7y/\nP3I6npnOF8ZY2Y6G3W6kH3v6ba/jQBhwLmBtKyY1Or/XVlqJlNZq21yJTaqnlGptBRP6QLvmPgym\nEtZiFbvWgdG0/Hqcp5ZrhkAWdIRoacBW+KQVBxUJrepH4Ko6NKIJSiKUkpnTTMoq/VWVogJ9axPT\nqk6U5meY0qL/bdcqyIo08ZEqCK3xiO2gewX9F9jx+9j+S1y8JQSlAlVqHMAGxHUYPyChI1tHHANh\nZzEuQT4jl7fkDz9m+ebvSe//jvz4I8ZwZhwzL/aOm8PIYT9iMVAqKS2kqVBm1dGXq4lG8KJx76wa\nyFrb9RdiqMQoxK4SI3if8bHiO6EEwceETwU3zJj5jO12mLjFWEc0sIlnonGcHzqSXDB3le3BYbGU\nLJTFUZdImjLzpOPAknURuHx44PL2K5jv6cyZboCt9+xcZOP0PRMc2XbkLJRcqFIQ54n9LV13h2eD\nOI+xajfPl0QSSLVSSm65vA4hNTrR0gVlKKyzhOC42418dnfgeHpmmmdqO4sqRbiSyZ9M/EpUXNmC\n6wJhP/kCPlEo/iuvb19HEFoJiBhO58rxaeHtN/e8/uY9X715x9PzE/M8EX2h74XNruPl3cCrz7Zs\nD3tiv9FjHRoXbtEHL+cmF65FY80aaOKcLhLB1faQNrUdomIca6jiW7NRxhmHs1apH+o1dKJk1RZA\nq7tuqz1VmQUjLWSlVl002kwdXROJ4HXXFg1Ztd5gsqHOlWVZyLWCVdCqWFUFFqk6azTFtamVWhIp\nN9GJabSQWSlNi7OW6jaIv0GGX4bhl3D9l9hwA7ajSIRq8UFzCrI4PTKLJzvHMljiKHR9pl6OlMfX\nPL35G8rrv6a+/mu8vKcLz9xuR+5uR253I70PcPaKUaTKnD05OUrqqeKp1q7OcaIxLX+iMTimYiXj\nSibLzFIWuiQsTuPlYufphkiyC85fCFPFjzNumPD9GdefIWj/QDQLne0wOeOWQr9An6CLhstcyafM\n/Jw4H2eOzwuCRpSf371hfvgaOX3FzSh88WrHzSZyGCP7zcim7whBuykk9Ezzwvl84Zs377mc4XD3\nBSaMVBlxu56w2ZIy5DSRSeSqjI7D4ls+Rq36tjmn4SjeO2IIvNrvuSyW9/eF86UwZ026VsnzOiho\nRbygJ836CUOwbjm2dR58nCx+wUaD6VS5TAtPx5n7Dyfu3z/z8OE9T0+PnNIZ4zK7rWE3BvZbw93d\nhtvbLfubfavY7miPvwpS0EDTXFQ8RK1NkaezuHdoxPnKt1quFuFKS4hZn7UmynCsvQlCXopae0vG\nmKRipVrV6JN1QTCoqMk0T0Ettc1wGgWGQK1G048qyjVb5QQF20pN9HeRNgLY6xKuN03JKLWXhZQV\nqTauRZa1ABYxkWp6aniBxM+w/Xew3ReYcANupIg2EacsBKO/Z3GR6jw1GKQDNxqkPJPunyn3r8kf\nfkx68zeYx3/ET18zDJXN0LHb7NgOO6IbMBLJ2ZGqJYmhWE+NAVyPNVpVRqPzonVEa5vtQcctK6UB\npxcsM5BU1allaZgSsOWELVpTVmvB5TN1LvjLghk6aoi4pGaxaFQI5k9nsJGSHGkqLJfMck4s50S6\nLNRiqDkzP7yjHt8wyhN7Gzi4gUEEtywYu2Ckw5sB5zPGFMQkMhcGlzHi8FYTjJdZCGFHHyPdzYVg\nK3J6TZazXvtiSM21CK1otuiWHrxn23fcbByn2bHfnLh/mkll4tqOtN7D1yNB2+2vSUV8PCqYVZn4\nUVr0817f+kLw8C7z9v0zf//jt7x584EP7z5gzZHYZ24+H7k9bHmxDex62HaGcbelG7ZYP2JsBHEY\nC95dByNqLWRWhR4YcaScyEUbb51rKVpWcE7zBpytpAaimPpp+mHLlK9Qs1DmTCqJIlk9ECZjqqGW\nSk4ZKS1LsKkbZc0tbBFpocVSlwopqetR8wx9C0wJqoNolA+tg32tasNY5amTtIwWIeeK9bqzKhak\nEuJqembzAnFfYvrv0sXP8PGA2J6K17ajCtVUljxjrWB2HUQHvRBGiBuDvHnL6fXfkd7+LfXxH7CX\nf6LnyGZT2e8O7Hc37IYt0Y5IiSQ6Mh3V9dTQ4VzE2oAxDue8YjjR44Kjc55gbdNdoKuwqDpTDTsT\nLDOSZ0gLYiBbh5UjTo6wHKl1IpRMvZwpXLC7AcYeKYIvjt4XlrLA0xN5jmTvuCTHlFXARlaCNS+J\n5XyhnN/i5/fcjJk9gjs+Mz8vzHVm6R3L2LG53RPHHhuDqhOnws5Ftn1H9BeWZeHp/kTXb2H/kv0L\nQ7wZefzRRL3MCLoAp1qaY1GUFUuCDbBzjt3QMeeO3RDYjSNDd+J0nihVdO7k44amU2DbzFaTAvDp\n8/5xGfi3rcjf+kLwX//r33CeJz4cj0DhxRcd++3IzU3HzYuRTWfoZCaYQnSVYdgRNzto3gQRS8GR\nW5qMaf573xYHzSE0hGCxth2XamUuQrKC82BCA+1sBZN1Zq2QqrYgWZMpLX8wVVWQiXwsZYE2s5dK\nTolSFmUjGjC22kQr+vDXqlmIS9YCVLkakVBU3zvl060lrhJlVGDtTEAbnpVvd047FIxdBSeOSmCu\nI4s9kP0rbHiJj3fYuMP4gUokVy0QzaJJRKGLxHFDdzjgdyPVC3J5Znl4T/nm/6F+898w57f4/EDv\nM9thp6eA8ZbN5oZu2BKHkeCDBpK4CE5r7lcxl7Ua8BG8xXmHDw7v9MO6xoMD6y27BoTUvFBz0hGv\nSU1t1SzLmu4hP5LLMxQNCy1nIefMQiJfPHU6Uu0TdXrEmC3ejPRGufwknloX3JKQ8xP56R67U1ud\nxQAAIABJREFUfCDUB2TW0ppTMex2Hbu7jsN2ZLvd0e+2+H7A+kjOsCzCZSrMqZIvj1Tx9MGQlzPv\n3yXsq5H95vuMr57h2DEf30CZoYXEVrEaf54EFyzeOmKwRCtEA70PdMF/kl3YJEZmZQJWdRqfCIc+\nlSDJFWT8n3l96wvBX/6//x8uCm6Az77Y8+V3bvny1S0v7/ZsNz0mX/SolmdECqHbEPvt9XhdqqVI\nJNdILRmp6uATV9R77VrnoGutwNmwJEhZw7tMNYSWXOxNaYIWyNWSsiM4dT/qQlBIUtSrAKyloyva\nLbVQcmKZJyUT17HdKP9XKixVKDWzlEwqGgZiVgAQbVIOwWGt0prBCU7aqUAMVpxiEKiQxDlHxDWF\nIYg4MoGpbljsLfiXuPACH/ZYtwHbUSWQxbCUQq6WYgIujtj9nu5mTxg60nxifvzA/PVfUV7/Bbz/\nS3qX6XrHdtiz279kt/uCbX/D2B3oN1u6caDvAl3XuhnbGOZcS2V2Hmct3oLzWjvunFdnnmvRdIrk\noPJJp+nBpaijspSWs2C0CzNPLKf35NNbyvENpSxYA2k6sywnFjOzXAz58kz1z8jyhIuBjh7jFFSe\nqtcW6nlBjo+U5zfE/ICvT9R5Zi4JSuLu5StefvYFd7dbtrtbfL/D+QHrOh2vCjw9PvP0dOLpeAQ8\nm2HL/eOR+/uZ2P3vhOFzbl4l6AzPz0+UpEpYivZo5qR4kjcB76wyX6biaiV6S/S+JRQ1dewn4+Kq\nV9Hczn+JElgTvlSv8QvnPvzerwTGzcDN3Z4XL++4e3HLbuzpg0dSVkPIdMF6wfUBEyLWB8X3BWzV\nh9u0na0WhyHhRRMOxDiK9ThX8KYQr3FUlqWo0CY1zt1YLfYQUfmyRTA1KapblKNXyk+RZ1NTy09c\nkLJAzUgRcjJ4wzVhGNOq0g3XRGE90YlKkhvP4F1FAljr2/ep7n0R3eFSafVkAtRAEmlpTrYtijCJ\nYzKREg4QXhDiDc5vMKYjVUtdKjnNpNYmFYeebn9g++KOcb9HSiW9+Yrjm78lffhr6v1f0S1vGDrP\nbnvLZndg3B3YbG7ZjC/YDxt2w4Zx2zFuIkNviZ0lROX9VwDM2gaIWsU71DKu1nLrPCusLbUiktFa\nV02KqiYrFmAMEnXkUEvthrjdkS43LI87lucHzsdncvXk7JjLQp4U9PV+oesWjDmTi2+LqoeSydOJ\ny9Nb8uUel44EmwkuY/IjQ5f57JXns1vDi21kEzp6OmwJKh+2RX90bxle3bLdH+junzgdZ+Z5wpcL\nnTlz/+ZHzPMLxv/jJf1OqLt7Kj+B+R3HWS3oqSSit+w3ji5ATgtpyapDkKosgzW4Jk5bd3dp2hF9\nKROlHpWPVmZouRSNcfiFkxj/8q/s2G62vLh7yW5/y2Z7wFsDuXA+TiznibRMBN8Ruk6DO1vY5xo/\nHliLPIRUKlK0WCKJba7Ddipou5E1CpKRKpJVILTUii0KCtrmDluLVasUVT+i2nED2m+YKyUtUFNT\nLapmQSqs0fTrg2tbLbGyFJ+kyNRVAiI6mnj9+VJWX4EYS5HC3E4kOkpoL0EqhSSqVEzFsmQ4m57Z\nbTH2Bu/34EbEdBTx5KwVZyVrVFnoOvr9hu2LW/rNiLfC+cNrzq//nvmrv0Se/46QfsJ2cBy2txzu\nXrHZv6Df7BmGHdt+x37s2I+RzTYwbBx9r/y/D6bVp9VrToRxKqUGlDs3muFg7Jq+I0jJ7ZrAGkcv\nJiMtHQovSHTNSm0Rdvh+wFhHlo40e9JyIiWY5wckVzajw20dN3tHKomUTqrPkAg5UZYT8+kd5fwB\nszzgu5nOJaJ/ZttnXu5G9l0iSoZ5IZcZ4wzFzXhfdcQJns3mlrgdQCrOWB7zCc+CKycejzOXaeHV\nl5+x37xCtr8MdYb6xGmZeDhpld+mtxy2gSHaVoGXNMzEgvcW7yzJmibB0gP/Gjjzs2qCTwDDVbvy\nz5iEn/f61heC73//ewQXGOKAdY6UVCNf5onL8YF0eabkjGUDbkMpjmWuTTCkOu7BF3pfmMlMsnBJ\nF1LJ1OwoUlS8Y2xbSfVIOHZcker1QTKlUq2oUagtBOo/1zqyYCyO9WHP5HQhzWe8UaTWIGo/9mo1\ntlbIzcZckta/Vyqh7YqqAzBNZ6Df69piUjPkYnRRRBcmzcSzJCAhLFJJVX+PKVueL5463iLxFfg9\n1XRkDFIMZbZko6Cl9ZZu7Nm+fMmwvaPf3HB5fuDp4TVPP/1Llrd/jX/+Edswc7PdcHt7x83dHdvD\nLeN2R9f1bHo1x+wHy36Ebqx0A/jOYYPu/qsU1tj2z/ZjipTuS6oi1aZXg9EU1ev821YOjJW2aBTE\nJTAX1TwYrxkL3iP9nryFpQTmD2+Zjxfm2eFt4Htf3jDcveD25S1vXk+8e3ekmKitTiRKObNMH6iX\nt9j5Ae8SQ5zYdWdu+szWGOT4yOP0FmzC2Cd1sAKWQoyGvvNs775Ld3jFTb/DHbYIwun4RDldkAyz\nVH70dz/h5uWB29vvE8yM1A9M+cTzMbH1hmEM3B4GvIVlWpQJs0WzKaPSlnNayLk5SqH9/WeEAxpT\nL6u6fZ0b9Loqg/ULNhpstzvNBjSGKpm8TNRcKNOFPD1Ty4TxTscBE6lFyCSVJXuLWNNsvlpQE9A5\nelpEd4VSKUvLOrQOXEPXrcE76GOzmgpQHaUaTLUYo9iAegicBo00oYbUqmNAa1aWNqNZI3gjFKuA\nYGp+dvUTZBUj0STNzugCUXXuNUZwVWnOnCs56/GuM60bNytrkXNLq201XSKO8+I45Y6jGQnuFu9f\nYv0t1h/AjlQitTjECjZYNrsdm8Mt4+4WsFwe3vP0+u95fv23LO//Cn/5KTs3cbvd8OLlC25fvOLm\n9gWb3YZx7BmiZ+wtm94w9paxF0IvWhCkz6eWV68GmVXu9kmq5pqeo4YSBT9XXtSsmX3G6y0prgGJ\nKxeuY5aCEHo+k74nlkpfLfNlguMzpTpcsHz2csvm5cD24Dg9LXzgmXk2ZDa43uNNwsqFWjRZyFPo\n3cw+zgwmw+KYy0ziwmmpXJYH5nnBSqGzhcPgOWwiy+mR7fGOeLgj9LcctgceOg+ScbaSq+Hx4RuK\nzbjuBp8GpByY6jvA4p3QR8tmiHpizJUuFIbo2PQ0V6Jlni1S7PWSXaPMTb2SCD8bZKb3i1n//d9Y\nBOA/Q1koDlB7bKmFkmckFcp8QfIz1mb8oP30VlyLis6AZswb566hIzEauuDoOsfpkrk/ZqYCp6VS\nHbpNY6lOy7+Nq0RbGn9tyVlzEA3aaIvJmmuIwxjls1XRVxpNaDR6vTnLnFFbsDfCXCrzUlutubmG\nptSK5uaJ0abcWkhVMQ5trK3MqbJUzdj3zmCKQZKQF0gFags+6dz/z9yb7Fq6neWazyj/YparjNil\nTaFzTrqHRDdlOvRpQQtxDXQMEn3LCITEBdCwuAOD3KKBO9xANlJ5Mk+Csb33jmoVs/qLUWXjG3NG\nOLF98qQOxlNajYi9V+zYc81/jK943+f1TMGyP1mOumdw1yhzhTdXOHuHsWuKEfpSzjKA9K3j+u6O\n1fYOzYKnd694/eP/yv6L/43h7f9OzzuWbebu5obb2xdc3b1ks9myXq9YLiyL1rD00LXQtmC9iMLO\nA9nLFkWdP57nm6pWBWcPfamHQYn1MIiQAirOQpDKAYUD5QFfmQDnKByJd8NQjUky72iyRxXNdFpz\nOu3IBzlwr7cdi63FdRNte8S5Z/bHiZgHFv0tnSt4K+lRELE60unAQgdcikyHwOwSg4YvHna8fjjx\n8PiEyRPbNvHxpuej7YJh/6+sHyzL1ZrFy/+Fq6/9r7zrPcYVvFKkEhnm1zw/z6BFK5GmJSF3tM7T\n+kDjFI13Qq+yhd4XVm1hEyKrztI1mmk0EOVCSjlXS7O8B6WSr4VAeZ5WX97sesAKmPcXvX7pB8GP\nf3KS21BnvNE0VmMIWGbazlbt+4KsGk6z+LithUYbrJVfZ2RkrqjCFKtpO8NGafycGUJGFdkWkArR\nZLwVI4/VSmzIJuHqZkFXv7hCMhBKrsqtoiRtqSoCRapcDTNFtN2qHhKpkoetkdI+iQ8JqDd6rjBJ\nENxaEkFJjIEYAlFFlHakJIlI+ykxz2d0lacowxgVh2DZJUvQPdpt0G6Jcl2l7Cg43xwl0S0qczDP\nxN1r5iGxf/sTTm/+L9T4JSt34qrvuF333N694PbmjqurGzG/LFuWnaJvoXEZ3xSsF9aC1jKnkMGI\nEXBegvIheLZ8sF0p57pVmJQS9JIgJ8gBlQLkuX4cHei2Zl1YMKlSpKSSKFZyLVQJOAOqtTjv0cZJ\n9VFmbHnGscBqRdfOrJaJ02kgnArDwTANx9rWJYoRJqUxSVL1jKYYy2FOvB0P/Oj1ni/f7Hl+3kGa\naV3mNE3kMjNnmQcp9UB5igxO8fw6cDo8Y/s1i6bDZkuIE89vXjHNj0zjW9a5sN2u2C4im40XDQng\nrKL1hq5x9JNh4TvWzZLYaEwJlGmSdK2qJ76wCj8suy6TAfG6KFXqVuHf6SBIKfHbv/3bfPrpp/zd\n3/0dDw8P/MEf/AE//OEP+frXJe5su93+m+/75x8eZBpqC5vesu0NfTPT+0jfObRryGbJGBrGWdcU\nJLl9jFWXMijVKXguAgNxXrM24G3EDoU5K0JA2PVVvKLrDtaYhNPvPd+l1BILcUaKmEgOBHXex0os\n03vHV2UlUkRElLMAROXaUlLSZ+rf+fyDo4qJZHaQYpSDIM5kK7bnEBrGKXOYEzlkLIZsPal4jnNi\nNxkOpcHons6uMX4BviFbEUOYCtxTqtA0DYu+h3lg2L1m//DA4fEL5scfsdBPrBeZ++srbq9vuLm9\n42Z7zc16w2rhWPWGRVNom4x2InoRm4iqE/9Kxs11Slof/EJElSgD1RSkDcjVnZnlkJD3kos5jPNB\nUDRClpnAepTxlCxAUNII8QS2ByVbAIOvxrMacUeBPKLiIzq3aKXp2on1Cvb7wDAkjofCeDogLUyi\n6BkJpxVOprKGYhzHU+TV044v3jzxk9c7huNRMikRrmHTZKnSbKbpZtLznjg98Ph6xfF5ydYtWHQt\nrdXshsC7xyd2x0fG6R3dnaK7XrNdRda9wlQrujXgnaH1gj1b+My6WRE7jdUTiSy6liSD8jPkVNet\nQqmHg3Rn55Wi8ED/3SjGf/3Xf803vvEN9vs9AN/5znf43d/9Xb71rW/x53/+53znO9/hO9/5zr/5\nvv/7R88yRDKZVdOw6RrWy8zVRvHi1rFyDd46nDZkowhJM2SDmQ1JG1pfU2nPEUExo+bKDSyJkmda\nPWEwRAy5OFH2RRmcJTKoyiM4O+DI4n847+f1ec0nQyuK9PT6bPo4HyrU9ZiBBgQ4qpIIdxDclDUC\nYFWqgi3rhHyOhXnOhChYNGdalGmYJsM4JJhlD1SMqQdD4PkEQ3Ro09P4JX27RDctyRlCTqg84zH0\ny57F9orWN5RZcThNDPtnjrtXlOEdK31k01q2iw3Xq4brpeKqy9wsFffXnq5VdE3GWQGxKlu3MUVO\nVFG3qnoIjvLQlwnKBExS8ucAZb4cAiUlcorvKwHU+69aKVxQzvYoAiXbVqFScxkkvt/BWIpaUPQK\nE57wPGLzEZ0HbDlhygGdHYvWcX3dSpSbLoQ3crDkmDmdJqb9nitXmPxE9BEXq+x4mhimzBRmUooi\nQ8+aUDLHGR7HzDYqxvoxVGmC6ZF8isRDJHbXJL+GbkGMM/vHr8BZNjefc3Ub2W4nHO8oeSKm2iqm\nUisMg9Kaxnm2yxbnFIEO81iIz4HjcSam8yAWzlXAmVgsh2y5rBEvOvr/2QfBj3/8Y77//e/zZ3/2\nZ/zVX/0VAN/73vf4wQ9+AMAf/dEf8Tu/8zs/8yB4/XASZJOO7H1i38L1rMBYNltDX42q8gABcyZl\nVddlNZNOyf+0mIFE869KFDCmTvi667VIpJc8smcRkLxZqkg/pWqqkEJBsbW8ynXNLbX85cA4l7SA\npC1JqWydRpuCL9RDoP4r9XN9TlrTVMNVbW2mOcl/2Vi08pAt4yTBmLqmNxdlGObE8zGznxyz9ji/\npG1WdM2KYDoiQlfSStN4j1/0LFYbSgjEYeC42zPsHpmPb+nKnnWTuVn13Gx77m48N9ue7abjetuw\n3Xoal7EmVAJz4ZxLqYp+f8GcH+gUasjKIDd3kYNAbvIKh8lZsiiqWKfkdBklvPfQQ/UjQ2USKNOi\nbCsHgdb1UM6XjUpWHVkv0fOEK3tsEZy5URHNgCmazm9g3YHqKapwHJ44PMuNmbPMaET6LY7BbBB0\nXtKoXDAqcY7mrD9VMoqAIikllCyjxRQUT5RZEUdFHI7EfkQ3LTHOjMcnms0Ny/UNqxvNYjtidgdy\nHEklo2qitgxKNcZY2ga2y56+92QLYwnsw8g4RgqxzmHqX+v9AoHLBJEzEflCOfy5r/9fB8Ef//Ef\n8xd/8RfsdrvL77169YoXL14A8OLFC169evUzv/eHP/w/kFs1c3vzEv/R59LRKMFzmSJGH+cQCIlO\nlArqZPZMKUh6jsoSJRYzU4jELAO11mnwDb4eCLoIASdWFoDRhVIkm15XtVYuMxqPwUtbUAQ+qtAS\nH5SjDDVjgFjTiHRdMVqDxgs0NCVBkqlMowO5BpdArZyVQZlz7oDEmGknct80QwgTx3kgAG7ZkRLM\ns1QDT5NiVC3KL1h0Vyzaazq7JWVDnAwJg+tWrG4/wrueaSiCdBveMh5ek4fXtOGZTZO4Wy+4v7vi\nxd0VL+4WXF/1LFcdi9WSdtFjmGrQDOf59Pv2KVEfbOH3U2oLEOZ6MFBp1JVInaOsCHMN/6jS4RxF\nFUqZa4WlwXmUdWA1ynqsblCuQ9sOfCc4dWuF+zfsSPFByMaxwyWxdgvhSHIPTNrTmA5jNc71GGuY\nA8zTyO7pma5dofprrJ1BKZKyJBLoQOc0163iuNTMs+VtDKSSsMBqYdiuPasldH3BdwpjC2VWsjVK\nkZhGQjyhgiPGiVIyGk2jLX59j7sBNb6ixAMFoayWmKUdVQ5vG/rWk5aJQKZ4xdvjRPt0wuhRqq1S\n14Lq/60VkApgnhPTHOvP73/yQfD3f//33N/f81u/9Vv84z/+48/8d9QZzPkzXp98/pvVf55ZLRZs\nVobrjeZmY1g2mkYLLyBnTYjyF5R0PLldVJnFkGP+7QAkl0LKipgFzKFMxtVbXyt9UWnJ0C9XfmA9\nMUuNYUMm4BUKJJFlMZPnalZJso0uhcrZ02inyOnch8mAT59XjLLvES9RQPbBuRBCIFdkW86WVBKx\n1PmvkhVZTII9PwY4JktuV7h2g/IbsmoEXIrcov1iRb/c4H1DiZKBEE9vCcc3lNNXNOmJdZu427S8\nvF3x8u6Kl3dbrq871puOdtHim0YGcLlAKheoy3kjWJT8fkmS5JxzkAc8RsR3myhRHv6SQz0I6vdU\nb0VJ1HDXelicWy1dA25sApNQOlLUjDYT2p7AtSjbUJwkROb5RI5RaD/xSBkLxAHlvSgAS0LHA9qs\nsa5gGyjaMYcV42ng+WHHfOwIJ08qmSkaQjZknfAu0gVYN4m7NSilWXhHiBKNd3/rud8qrq4Uy03B\ndiL2OoTCnA3atGSlCSWi40yocequ7di+/JRmewNdBL+A4OWzfJYKKklhtrah8ZnYdqg8M5MwSnQm\nVkuATko//fn/Kf2gQujQvq0r2MzpePq5z/X/8EHwT//0T3zve9/j+9//PuM4stvt+MM//ENevHjB\nV199xcuXL/nyyy+5v7//md9vSsRohdeaq6Xlk7uGzz9WfHRr2Xgj+3YypwTHwdAahdcJy4xVM1Zl\nUI6sGorxoCwehUk1yKSIbThZQ9YajPgJKjhYICZybFwyAq3WlJyJcZTEYYMwB6K0JSUW8hQhBHSq\nhOWiKFZCPI2xsh5UkHIkJDmFVSnn0AFyVoQhMk+BEGemBNo4cjHMkwzesQarLGVWzCNM48xxnDnG\nhlEtaJprdHtL1GuOUXOa9xTb4XrP9faKfrGmhMR0eGR8fkM87MinB9r0mlU7cHe94uO7NZ/c3/Dy\nesX91ZJ+aWg7g/VOZMGp3vBnwH49yOQNzLU7EsOVmKkghwxTrodBIOdZDokk5ZV4K4QWXCrkpWTB\ntJ8l9KaASjIwJQUUkVwmNCcx3lQtSDGGYn11VAqCLgx75tNInve4biNUyTxDeEL7W7RNKD2zWHhe\nqgXTcMXz05794yvevRGM+WmGMRuWBnwbaVOkD4o77VgtLOmuBRzGeLousVxGbm4My41GucjpmHkz\nZE7JY90VmFaKpxQJ88Q0jvjlhvv/9A3azpB5BL9AxaZSqhPKKpQW/YxxDlsKNjoYJsZhIMcRjWxL\nnNGXDZasad/f+LJaPAuJykVs/Ite/8MHwbe//W2+/e1vA/CDH/yAv/zLv+Rv//Zv+da3vsV3v/td\n/uRP/oTvfve7/N7v/d7P/P55Glj0ns264+OXS379a7dcrxWLrvbxKmFUQjRyijnBYS6QMk5FOpNo\nfaLxcmMkHEFrQYZo5KErgrGOCZKq8t5zgjKIDLbuuEuRhzdn5FbPChWlFM4pEkOgxAlFrN+XxJ6c\nkHmCK2gn5YMqRViIWjQLUiILRSinOuCsN2DOkWGcUdpjbUs2Etw2h0QImZjgeYAvd4ahdBi7Qpme\nki0xJGAGFKvtLcvrO7pmhY6G0/7EvHskPn+Jnh5o0zO3i5n7q5aP75a8vF3w8qplu/YsFxbfOWzT\noHwjnIUwyjpP0EK1yqlzFqXk4T4/yMmIZDvUdWko5JApoZCjxLKVnOTPrQlW6uwJ0Uo2XEraMKoX\nQWsl6kMSpQgERqVYJd1BUPcKRB/iZKMyjuRphDyg6MQFGuRg935ElxHKDqcSfbvg5ho+/9oNz0/X\nPD1vKAPs55nX+6ZuECaMKVxvYJEki7LwniXZ+kTbJLpGqFeHU+ThGd4+OkJq6dYL/GpF8S0PD0/s\n9gPN6ha/uSP3G8Z0oMwz7ZkTaaLAMLRYlOcg1VYOUnmFmJhDpmtW3N8YivYUs+Ph6UAOlbx1Hhae\nW7n6u7kOw9W/N7Pw3AL86Z/+Kb//+7/P3/zN31zWhz/rFeMJ7zS3155PPl7z9c9vqzowk9OJXAaM\njrREWjLvgmGYkLkBiWIjLhecmlDWUrRD6wZVwaO5SJmezjRfJbJirSVmnJLF+FIEZFJykZYgFwGC\nnN+vLElCIQwQR2yVxyol4JFcwzCVquYheb+xStNosTOHmJiTqki2XIk8YiTJOTGejmjTQWPJtiEq\nzRxm5pCYo+ZxhC/2Ftf3uEbCO3JWlDiDdijr6FZrtjf32NQRjjPT7kB4fiDtvqRVb9n4Ax9vr/js\nfsEn90vurnpuNi3tyuF7g2qFWYhxEu0VztWAQDeQCE/OFN1qeaRO1eQgSBW6IQUBZVLkGVJI5BQk\njaqmLWgl7VRx+mKnNcpQtEVpqeJqaqQMxJgpZaoapCjbiTwKDNW15HZFmmdymGTeQEJnRZ4l0cj2\nIzqdKCVg9Iyygc2241O14c3bK16/veLpVeB4HGDfokrEq8h2nVkuM/35AFMGqzJeBxoDzkCwhSEU\nnnaZtw+eh8eGtuvZXi0xyyVj1Dy8e+IwFvrbz3CbOybTMI/PpGHgijMwdhQhmhb16TwHYlLkuRBD\nkBCVAH23oem3ZGOZS+H5ODDFKEPw+uADdXZQWYVnNde/x9bg/PrmN7/JN7/5TQCur6/5h3/4h//u\n93x2H/noo4b/8p9fcH+3QRsnf+ESyUpoNClnTJnRjGxbS2cNw6gJwTFHOAQZ1nUq43Sog8aMUZaQ\nJZRUlUJRkmkQsrACTF1/qwsPQGzBJZ0lwdUBlxXTFOShjBM2R0yCNBdJaMriD0gpQrBo5chRkYMM\nboyVOLZIfv9DKZk5BlKIlWJUaK1MP1IMaGdxToNxHKLix8+Rx0mjnEfZJUUv62AsoXSi729YbT7G\nuWvmk2Kcd8z7R8bdDzHjv9Krr/hopfh0u+Lz+46P7lrublpW245+3WO8Q1sjJek8UoSnjoqx3vof\nTqHfqwelb7Iyq6FWVkoArsVAdDK4TFqQazmJNFvliMoRXaJ8X5IKSkdF1CLXRgepurJsJFTd0pw3\nPgpLZgHFofJEGRNl3FOKqSBah7VblFqT54npucW6gHNPlKYFH1E243Vk3bXc3cLLj3vmk2E3G4xf\ncwyG//Y6s9yNrLrI1RLWnaJ1EkNXChyD2IiPz4r9pHl89hyGHq239OsXrO4/4dW7Ha9fP7B73KMX\nd2w+/s/o9pqHhycW+USbM6oGpaYiIb1FW7msECZDmjNpiuS5UAI472m9Z9Es6fwBZy1Gz+RUlddK\nfCwiP67itfoLpQ2/6PVLVxZ+7eOWu/uOu5slzjlO47kPjaLASwqVLbbMOBLOKhyKWVkmNEMyhBkC\niWIKC5NqIrJMuEWCoyuOTIZR59ubIlbjVLXYZ/txrnYupSK6CoJCnJnmiZQkVfkMDg2VIXfmwcWY\nmIoSLXhSNdG4yGFWswijku+JKTGHIIjrVF16FLKS0FOsYg6GU0y8OmSGZEU1qBsyDrLCGIPxHU2/\noV/eo2iZhpl0fCQdvkSffsSCV2z7A59dr/i1+55PXiy4u+1ZXS/wqx67aGRXXXtLlWTQV83/kATF\ndoanyhrqvK0yVU9QE5ydwihLpkHbhM6JHCPEmTJO5BiJWX5NnCSdOkfJASyS+ajyeexby/801aqk\n0o+rrVtrKEoDTg76OFDmgVzXjM41WLcisyBFxzxscIeC88+oxSTahiZJi9Ak7m8M+883nHaPpDBi\nMoTo5DM2nZjyQCozUwh0JgrpWslAcEzi+TjNnuNpQdEbfH+Nabck3fD0vOf16zcch0C/6XFXH5Ns\nz/55T9dFrDMo5aFY8hwr6t5c4Ls5JlLIxJCI9eIwytDYlsa2eOOx2lQxEe/xZVXc9WHLG6BiAAAg\nAElEQVRqslIX7eHPff3SD4Jf/9qnGLdk/xx5LgMpF3KYyDmK/1wJecficCrTWo3TikhmynBKlsYq\nolK4OQl7zxTQqaquM7ZYnBZeoC6CGa9ZJyioKkCx5oImq7NlWUAQJReyUWSFQEyr2SjlRCpRBldK\no7HkCOOYaqgnaGXQqnKDqlItpchEqdPpxOEYiTljqn3XeYVvJadx/5R52BWex0wyFu97YSWEGetb\nXLNmef0JvrmWQV06QZwwuy9pTj+hzV/wYjnyyU3H1194PnvhuL5fsrq5wq23mKYT3X6VUEsOQ67C\nn1S1FlLR5JQoRViIZ3t3qQcApkHrFmNkd6VjqZFyEHMWnsI4M40DcToS54E0D+gcMDli1YcmWZkN\nlTCQQyHlWbYKIaGSZCIYK6RjRfXoG13BsqLqhCIwFOsJpSGXlmhfMk3PqKcDNs64FAT84TLozP2t\nx7qXpClitOftF084v+Dl9WdoNZLTgbePX/HDd29J0wFdMl5rtDEo7TG6xdolTXuNW95gVzdMUfP4\nzz/m7Vdfctg/Ee2K0KzY4+hDphlOuFVLv1pjx56SHGk2pCEQxkiYRlIMpOyJRRGQwN9UZEiteV/2\nS0KUJn8A5L1oiKqQSNfD/lcu1+A0b0izI+4TqYzEnCghyOpPi+dcwB1glMHVAQ1auPJBKRZek7pC\nZwqtEUR2QUixIh7SAjYxIgcV/JVYjZMSHoG4AqtmHiMx7Fp6/oSwDa1G1mjVUZgvb6YIg0JChoAp\n1SCPGoEOl2gzYxUmXjQ5oKgZhZAxWCOJTSEbTjO82yV2x1Jj2jWpTn2VAut7mmZN4zYY00qq9LwX\nBPfwL6zzV7xY7PnsRvG1Fw0v7htu7xb0Nxua9QbdL8QuWJ+/UvmIshJEBlZRkaKRVicXmaOoqizM\nnGMX6oDPom3FkylXf24CTEkpM80zehphOhHDSAojKs6oGGqUvbQLlIgqgVw5hwVZAec4itYgRGw8\nR9Vl8YO4VgAtygugJoP3S1yzEhCJW+KvlxC/YE6vSMdAzke8ipg+YJrMot2i7YLPv35HzGIDnk6B\nOWrBvjcLbG9xZQl+D2mWrAvjJE/DdDi3wDZbsus4zYqn52fevXvg3cNbjmOgvfkEs9hI3kVJ9F6Y\nmWckfs6qzlcK0xyrijHKAYwSzUqO5BggCzujaRq6TvIq7TgSYu3/yzldi0tLUFFYv3oVwX/9cUNR\nEgiJnkGFilNSJF2AhE4gu/33QgjFWalXuFqCypqlg84VXI0Rm3Kp5qBMaXKdUIustSR5U7SKF+Kx\nttT8PQtG8OO5nry+zhNiXXelDBRJSCpoYoJxkhyFc22rSiHHGYAsU0HJRjLgjCIY8U5YU1VpyoNZ\noO2S3ZB5uwu8eY4choIzDQFFmEds61He0nQbWr/FFC8x6jqh5ifU8Ue08//JTfPIb962fP1Fw2cv\nYHnb0V1fYVZX6G4NuoE6IC0p1VtXLM/lg/YmBUtK5wi4s6+toFTClkqF1u817do20PQYXSusokm5\nYGLApIhNAnIpKZCmkTxNlDhT5gnmEeKMyjMYTzL+0grFrIgpyOAxytZFq4CxRio6ayl2WTF00LVb\nmuaKnDRusaa/f8G0a5ifIY6vifMzqCOeCWvANg192/PJpxuKht3zIz/657f85CevWK+vuLq55vp2\nzUefJBQnUhyZpgllZI2oaMjFM8WGw37k4c0jD4+veHh6xe5wIpue5eaKdrVBhUirM1frFq8iaTwS\np4kQJScjlsKcMimd/SxVwVoFW3meISWMMSyXSzbzhq5rOQ4npjlyDl0pfGhEkrZX6/Ovf/7rl34Q\nTPg6QKuljM4onNywtVe3CXI5twnUSTsYI0nECXHiCbBhonEBpUSQI+IdLaIXJcaelAWC2ZqMJxGT\nJRaHKXLLZ5dxRXrWFKsHJpaKORfYiQhozqYO+VDGMJNzQWl1ESKdrbhFaQlhLVBywpAlzGVWEu1W\nFKYoYiicTomvHk98+TSzGyFkuQ20sSjb4GyHdx3Od2jvwGYoR9Q04k//Qj/+M5/1e37tOvP1+8yL\nO8fqeoVfbzH9FmU7KJYSFTkKryGnUvNbXc191JcglZQlTRktK0OrxE9hbRFAjDWyafBO4sN8C74T\neKkyGGXENF4KvkhLlCuYNE1j/VBHURmGiTIdyfMR4kQJI3EamKcj87BnPD4xHJ/I+wPj8YiKR0yM\n+DiiGwONZa59n1us8d2CcdpR5iWol+hmjelfygo3FNg/UdSEtjuUatCNY9EsuL9p+Y3ffCEP5TQT\n48jbt+/wjdjcu9bi7AJllmQ0MWnG48Rw2nE4Bvb7A7v9I/vDM4fTEWt7utVLVsuXLJZXtAuDd6Cz\nonGO3rbo2VGyxOvl8v5nQJYcRW+KEJk9TK1Fe4uylt45lmGi6zvMzlSVVvW+XAwINW5PiwvxVw5n\nnl0tTVOmqHT5yxek16HIvKpc6EK1J7RgrcFVGsaULXM8EGNAu4zVgawzc4KCkd11TMxTJKZIUQVT\nEgWJsQ5Z4soyhWxSjSUvYpOfFCkINCRj5AdfTU6SniQ3ao4TKRVZe9WB2hkaV1SuJXKquLJMiIVx\nLkyp9nhZMQdJLvrq4cgXTxOJjowVSb+ymHoINK7DOo+2Shxz4YAa3tEN/8J1/CG/tjb85r3jsztY\n3XiaqzVmeYVut6BaSpLUoThnwhBJNdq74MjFiYG4TgTFeCXvu9NKHn6ncB5Je7YKmlaCRYwRSIET\nrBzWCmBEGxG9KM0ZMJJLJs1Vqp0L5CRVwrgnnp7r0DKQw0icB6bxxOHwjNo9cHr9ljG/Je/BzHsI\nE7oYMIWQI8o32H6N73qm4xvK0FPSjLYdZnEn254YiacjqAnX7DHaoY2m9ZqbTYf6jTsoieNhz1c/\n2fPu7Z5UFM56Npsty+WSvu9IWcr4p3cDTw+P7J6eOQ07xrAXuXuC29st1zefsl6/YLnc0KwMjVOQ\nhFDct4Wyt+RanRY0VOiNLuA0FAvRV7Bu8bjGobyjX7SscqDte6yzVGN8HQrqy+RF5im6bq1+xWYE\nugqFlDKElJlzkbKwyLWvlCDLlTEoJ8k9Tmuc1VgrwyBnNc6B0Vlgo3lGmUjjJXdvThGU3ERCONQo\nJf3XlCZiER8iICsXA8lASsIyHIuARDIKpSTYRCvRIRQKMUVyzjhvUTHJh6wI11ApfRErnZn9pc4k\nUhZMuqoUnhy1+B7UBBq0scxJRj6uhoJkhdCavAcSaXomHAba8Q3d+AUvumc+v3Z8elu43SraZYft\nl+h2i2pWYBfk7ImzYZwEcXbKnqgs2VpUFq5A0bX3L8JzMRq8LjQWYTk4jXHilVCm3jolSV9aZddk\nJ18miWc5mxrkIoIhowzatxTn6wq3lsH9grK55pwSfWb3dXNgGUdCGDi8e8v+1Zc8/+t/Y3r9I+b9\nV+gyoedMsQ7jFmhjJbrOZGCE+RFtZP6j/ZrsZ9nhnx4oj3uaHPDlgOkGlL1i1S357JMrtPpP9MtX\nZPOa3dNIDBBi4jicOM0nhuOR4+7A6TQxDiPTdCKmQMHSLhf4fslHn/0Gtx//Ov56QbPMeBtYeM2q\nk+ZyHgfCfoc9HCRQBzlwvfeUbNC6xReNTwtcI7Fvul9gG0+3WDLlxKpf0jUtWhvIlwaufq5FvFag\nYvV+8euXfhCEEFBFy3Q55RobJlmE1poL9UbV6axw8C3OaJkKO4e3hc5FnAqoPJHiRNZZGPpGpstk\nGcRkHEVLSjJ5ZoqZUul1qQgU0zkl2PM61Z9TkhurnKGR1ZJ8NiDkDBmMtoIvKFGMRBdJh/7Aq1CZ\nhFSlYcloDEVZFGJSiRLPAtqQZKmOMYYiIQzVRGNQaYBpjzo90MRXbNIrPrpWfHanuN/CeuXwix7T\nrtHNFuyapBbEZBiD4jhljtlwwpKNBW3loafUPXQWurIGr6E1BWcL1tV2wJjKI4Q6bZQVX314BTQi\nv3c5DIRxXglDEsuGMpchFgCqEVgLfDDVEhkzNZa+u3+kvXuN6bY8+wXDv2bS+CCux7bHNEtAoYoA\nZ5RO2HhEmR7rWvJiSyqKcdgxhYmyP5A4UDjiUsL2iaY13FwvafsVISuGEIEnHt8NDMPI8XQi5Ilh\nv2fY7wlJZkilJJSzeNvRb7csb265/exTbu7vKI3FNBFvC94YrAmQEzENTKcjaZzorJI1qgJjHb7x\naNNRsLiiKLaQbCa6FozBuYau7SV1uu2w1pJjlmf/jIsr53Uv1eH5K1YRPO6DzPVLEPm6lp3neR8q\nqcFZ1jxK+mRtpTIwViSonQls7ECnBkwemceZHBPOKxpvWDhLUI5JeXmYCjgHKgZSbrB5xuSZKRmS\nVjTaEGNhf4oEIrFkdJYpeQkyRMt1Ml1iwWQxFMWkIOlK7FHiZuRMCKrPRJE05VQHmdK3GbQ2GK/k\nUp3F6x6zzDfAkLXF6BZjlvjSYGOG+QEbdnTzG+79wMerwidXkRebQr9oMW2P8Ru036LsNbGsCLFn\nDIZjVDznzFg3L84Y+SLjC7giidBeZ7yR8CNjJaZd1QyCM0fwMoXOZ7Xm+dcyYClalIpKG9D18JDJ\nrBwMqjIjz1zD8+viKRAJslQTDrSiWzmc39C3Nyy6DV+cBqZ3Gqa36HaDXlyR4kyejjSuwTgvdmRv\n0d0Sv2xwXU8ugeEJhn0i80iOR9r4RHOWhzeazXrJ179+jbYiNjsdB9589Y7jYWAOs4ijEKJ01gbd\n9HTLJeurDevbOzZ3d2xWW3xbyGoStqVtKSTGaaJVCpsGgfRGRcZDlWQrJXAebVoKjlQMioxWiZzP\nJO2MKpqu7+gWHa5xhKrDoeoGpMV4367+4mPgP2JYOIyCG1cKazSm6Mutcc59zSpTSKgs5XUpSYZy\nOeOJdHpkaU40akKVc/ahQUdQpuCshJcVBbOWIYxBVmEpWWwJNTw1oLJFl0SsoIqiM+iCKeannYNK\nrLJaGXLJlyddU/BW4Y0WAnGp4Iwi+/mc6747yRZY6wJI2If1juGU2A2RYyjMSZFLwaDkoNANVvW4\nkvHxgA87lvmZjX7ivgu83ML1EladxTcd2m/A3RK4YY5rxtAyFsNpVpwinCqxSeuCN9BbaHSh0QVH\nxumaGKXlmVVG1YdYvd991v10JXXAT33EgvxzIxJo6kGAMZcUJIxUCeoyU7moYC5/lPgQPvi+4iSc\n1jWYzTXx5iO6609I454wHzD9Br/akONIHg1Nd4X1TWWdRpSe0NYBnrC5ocSJMI7McyaFQFaJoo54\n9YBTDudartYG9dkVw34kTjPTODNME2EAhcPoBuUdumkwix63WdBdrWhWPdYbUpmZ5wNKW5RpOad0\nkRVlCpRwlHlIKiSlSdEQoyXrlmJaiqlp1bgqmw6YiLy/lU7Udx1929E4L4zLlKoItFxmBarkGoj0\nK1YRlHASrJQVqaqA/eVDpqquPSnBgOmUIARSKUQ0vkiv1emRhR1x+bx69JdBCVlMQkaNeGY6FYk4\naRWqQabo80RcWHimFMaQ2J9GWe9ZUQNqpDIpWrYFxhosMIdAjoFcIsZmYScYhdOamBQhnWVetkqZ\nz9BJJ1UN0vY41zCmmTeHwG4sjFF+gE4VaXO0w+kWl0808cAyPbHVB27akftV5G6TWbYdznqMW6Hc\nDdm8JOQbhtOCQ3IcY2HIiTlJa+KV9P1LbVhZTWc1jRNZtOQSiB1Qnfe1VCAIQD3ggNoGRCk5q72Y\nKt5S1URUjAUj0es49543YCqYtArIpE1I7x2P+rImAtuAbS8Hj04J6xzdzT3z/jXz01fYxZpmu4Hx\nHWUquPULycSwilJO4hol0eiW9WpBibdMY2B4CgzHCfQga2z9fFEw9v6W9nqD+i8vaFrLlApJO2I5\nEoOiFItdLjHLHr1u8EuDWShymRn2j0RrGZ2nbTtanTDRoZzGaYOeBsrpAPMEKcu8JltCaimqA9Vi\nqk6jKA85omtYT8ZKzibQNy2LrqVznmyqoE58cwLeyWdW5q/gsLDERKrgCkwBDaZOlUOJFRCkyEoR\nK1LMW03bGBa2cNVFlj5KNJnRKDxOG6yR283pVMGiEcg0SHpRSIhpJUVSTmiT8aYAgeE4cDpFjscZ\n7zWNN7IT19XmWeRGLJVRpkxBG8GRq6pKFLWZFkUehaysVD7WMoaZcYygHN57clKEbNgd4emYeB4j\nUxTyguQxOGxpsEVh88gqPrI1Tyz1wNYHrvvMqoHGKKxtKHZFsPeE8oIwbhhGzykHQkkEZObgtES9\nLZxi6TWLttA14CuRWIBI9cPyXp7GuSQS6KhM+1XOECdpBer+WjLfq2bjzDWscwGUvRwEFYMMxlBq\nJXguNM5sR/KHh0OGICwIogyaXQks+45T2zGj2TQ9/XKDKyesliSl84FGFUOBwEeapqNf9szzFWna\nMZ72nE6ZVE4UPVzEU64ojFds+synL1tO37ij6XraZeB5D8dBU1oPrYVGkXVmTJFQdRZNzvgcmdPA\nFDPtFCitRzeeVg006YiOMxRJ3spJKFw5B0osaOeFn2g1MUtbmmsADtrgfcf29p6r/TPr9ReS2B0m\nzkTjrGRmlc/CzZ8Zi/b+9ctfHwZJAIoaiilSEAC5JLIRv7vCcM7IcDpjvaLpLOtGcd0leidgBum1\nHc4amXATq5ORalqJYj0umjlWolHKZCWnZ+s0uUR2xyOHY+Z0nCshRtNo6U1zjZoySos/QdWDwBYE\ncyRtjlYyds9IBZPPk3JryLkwTAF0j7MNETjNhcdj5vGY2E2JWJxEiGuN0RZDg80FXw6sywM36oGF\nh02j2HaavtFYhfjezYak7pnSLbvjglNSjHFAm4K1ir6xLIxl4S3LxrBsoakJRaoOZ+URzO9nTecd\nFHIIkDPq4kWIEKbKWqA6EjMXEQbng6C2fcqAtfWrAVtnN5evc2z6+QuqBFMGKKXANMphYCw2jiwa\ni3NWgtN9S9evMfMzVoESymqV2J6Hk0F+Tr6jX7TksmXYP6Oenxkn2UygJ5TKWJOkMiuK3ja8uLEY\ne0W7vML0ii/eKt48KmaTCTohudozUx4QO7CizYomJWyINONE2O1RzmAbzaqN4AI6zpQiAbkpF3Gp\n5lGWgWmJ8h7FexvIhT1gDL5ruGp6rvfPrDcbhuHEcJKtTy7iuBVWzv+XwLP/iK3BtH9fNlaLZ84J\nHY2Y2BRQAmiZWG/Xmru15eMrw80KFr7gjFhXMR5lLFkXiqqsulL971paBVslxU5Jf5zsWc2kLjvW\nMIzESTQGMWRmoOh6wyuFM2KuySgi6n1ZS66QU+nH8hnAkRJziCht8c7JbCIqMOIPH5LmaUz85OnE\nwzExRyM+CI2AQo0CFWnLzDZPLM2R1mRar+vDC0VZinIMec0YNgz7hpHIEN/inGHRapauYdm39AtL\n1zZ473BO45wQodU5Bho4rzlKrkKvGuV20alL7JI86ClK4EISjb9cV/Wf5cBPEXNL3WjrWiFoe6kI\nRHfg3h8El+QaMX69n0cUGE+UeaSQKGFGnY7YNNA6i4kzjCe862mMw+BQoUASnBc5Q+MotggG3WoW\nC8/mek2cbnl+FxhPkd1DJM2BEvYsNgq1Cmi/xNoF20XP1z5e4NoV1289Xzw4hgynlDlNI8N8ZI57\nIVKnQCgjihmdpb3sKbSqXD6PYS4wiaoQm4TuVOrMIMvWQ6VImScxHRWByGqnyURK8bRuwbJds1qs\nefLv5Oyj5h8iHZbRum7lfsVagxiO8mEoXiLItRhcdDSVLFy7aycHwbrx3C4N14vCwlfTUNESfYW4\nt1SSPX/WgVKiiIOU5pyf4Mg0EYqrCTC5ypgqaKOkWVoVxCxDSiRNFQpZsjOYIh9SwWoACrKo3eus\nS1OyqlJdiDFKCGjjQAmhSOt6EAR4GgNvDyOHWZOKAFu1gqy03Cp5xKuJFScaFbFGDEraKpIyBDxD\n6TiEnjS0jCMk5MO3tI7rpmOz9KxWhq53uLbGl9t6416ArNTBJ/X/47L3rH15LdUvB0Gqh0H9NZXb\nHlM1LoX6Z8KZYEw62wsVpWpFqlSxHgbnX38wIFRaBq91NlCmE8wDKU+kMJHnCTPvaaxChwFOO0zT\nYk1Tr9CIIghXMTnpvTWyMjUNTetYrpfEcMU0jYzjyOk4EKY9aTyR5ohKA36xwfaRvnWYradfbenX\nHYttw2427CbYjTP78chx2jFNJ+bpRAnPqJhwIdDmmY5MExMmJ3KEoEBHublVzGLDzwJNLdlC9Rek\nWCooKgulu+RLPiJZ2sjGeayx9RJVnMNNLts43gen/7zXL781SAMKh67715wTKkt/DNSSX+GMpmsM\nq16z7ER6eRwVoUgPaJ2vVlrQJZDUhNUz0Qif3lh9CTRxWshCNmZcyqRoydmQsngFGquYrWJMmhxh\nTqkKiRRFteRYYJZ9ujN1705NylH5Ymm+KCZTPdysxtiE1WJJ1qYQcuI4zuyGmWPIhGxR2stMpIjW\n35UZFSasjzRNRBlDNI6sISnDlD0x9Bz0kik50inR+SfWfcPd2nB37bi9b2mWPa5foF0NANG67urL\nxetfUqrGKirF4nwLw2WSX78ktqwSWMr5wa9fSazkwhJABlWp9vZB/l3hPdQKofa6GCf4MaPEn6+N\nVAm1tM85SVBqnChpIqWJEAbCcILTkVYH9PhEflawuQUUJc+1zUmouYDXqLSAtJZipTUY29AtPKX0\nzNOGECaG08DwPHJ43DENI3k6sLoZWRCxuqfpEq6TVqOxhlfHhnZsWGMZUuQUTwzjgWHYCzD2AMvn\nE8tppFfgY5asSwqhfoa0ks9QLsLOAI/RilISKY6EWKXHShFigjng8wKwxPzMcb9nniv8RWuRzJcz\n2KUgaUg/XaT9rNd/QORZ/aAkyDERSZiSAHN5oCyKnD0lacYx87TPHHSkoIlZYVzBuYLSEa0KpgRa\nEiddcFYGdNYWGlfoXcLrXOcQVfGnHQVTEdZgVJHVmSnkrCvoIgp2m8gcpQu0TuOdrApdxZ2d24dz\n+fyBQbGyCeoK7gOZbUyZmAohK8GtX05toGRsKvQq0hUxPxlJWCUqxSk7ytygaNE06KJoTWDdWF6s\nLS/vWrYVQKI60dLL9Fl/8Gl4L3RQpVwkqpxvlJ+qDH7qh8dFSHQ+CNL8ftp/riKK5kOGnuQf1Ic5\ny2ZB/nv1oa9bnHI2lWhzeS/ON6Vg02diGUlhIg0DxIQ1BceMjQNMJ2FLpBprpwrYIlS3OEhICnJY\nqV7hTKbrPZttSxgXDMct8zTy/HQglwNxHolxoJSJngZvOpzfsGo0eqUJRZO1pVE9DR1d6QnzgjAu\n2KmRaXpHVya6dKJRYhHRCakQVUGZWFFiNfymiOIWTJ32R0qsn1ulq1U/YoyjKE0IgXkcMKXgtMFZ\ne7HLvzd5l/e8gl/w+qUfBBLykaFMZCT/8BylnVIShVUxhFkxKMvrtxGhpsubIYTXc58rCDJdIo6C\n11WJ6BTeQusKK5/ofKKxGq8lxDIbT8GQonDhVMkYnWmczAZImhKkBAspMcaJIUxYa/HesfBe+nVt\nBYGmVd2mVcgJtcXRGqMdMMsDnyBVk5LSGoohcZ41GKEoEWlVYaMyvdZYZbBKJtETlpQ8Y2rRyeOy\n5b5J3HYzX7vrePnCc3W/wi2X6K4TyKfRH6j1Prjtz+U7VcBzkUVr+Qfp/UGg6m5aHk75xnMEGXGS\n/rYODOVQcfXGt/Ig1iqhFCAFchJRGcrIIVI5B+cJ5UUcWxK5REGM16yEWGZSCpQpChPCOLzVNAaY\nT6R5lKFm1X0UA9iCCgdU2EE6QhyBgmkavNesVwZiQ5ivGOeZt89HpqeB/dORFALkncxvjEXZNd6C\nbhXLKTCkRKGhqCWGFuNabNPRHr5iT8KlAR8OOKXQ2aCyqZHxSlSTpraxRaOSEp+GMpeqqp4QInmv\nPprsZnKBsWTyPNIZQ+scrffENBOInH8S5yLvv3cS/NIPAhlQnXfPUmYWm+VWpRYLGeYoH7aUNaeJ\ni+LM6Kp48xJnbgznSBQU+hLOaXWpUuREazONKTQm05ks+YpaQ1CoaFBZU4hkleuHi8stJyNCeVtD\nlIRjbxQu25pzaKFocszEkMVFpupGo2LPRDWoiRGGCCkZQCoakrRIuoApGkemM7Dxir5qcjCOiGfO\nnognK8u20dyvFJ/ceD6+W3B/v2J9s8GtNmI59gvQQnmm/j0uBwCKUjMW+IC2JDe55hLigrqIhsoZ\nyni2u15ah1wrg3L5s846AowH5cE3EDpUM1HmATUPEAVhRinSTtU/q1ycitJy5CJhHqWGpeYSiKkw\nBUVZ9HRX17TbFW7Rwun/Ye7NfSXLsvrfz1p7nyki7phjdVcPwAM9CemHgdrHwcHCQGBgICR8sOAv\nQDQeErio1T4WNg7CRELCQO893o8fQkBXV1VWZt4hhnP2sJ6x9ombTf9onhB0d0i3MpV1h7gRZ6+z\n1nd9hwO2HKAWFztJ5wettN+vOiuQkh072F6h0wV9F9heXfK8RC/8S+b+DRzv4d3dO1RnNH6OMCAy\nErYZHZRdNzEPyml+YDks3B0CenykO73j9Oafqe+/oJwSKVXfWlR1bUeMvs4TB71D7FzuLdLWhKWp\nXIXQ4JWAOzhmq6R5TykzWRSphT74qDIE5disJWW1l2uj2I+dDFmi+PXUwt6tCU/Wne+6kk7tgCwl\nOw9eIWgTkHSgtRB6FyJ1BITY2mxtGQagjTLbhUqnMAaYFGL0ghLp0NqhNdCF2XMTg6JiKMkvwNYb\ni0LJnj1Xyiru6LyC1+iOxzlj4kXN8YuAVQcKc1WOBQ5JyMW7AVFBWtqyiJux9FbYKFwOkamFjlbp\nKTJxrANVI31UrraBr90E9yN8fcH0/Iru6ho2V9BtsbjxOwstpHSd69fXWgNmK+d/rRDt9uHKVURr\nO/Tt69Y/10XDh2NCKeeDTKAVg8H5Ayow+npRjgeYDw7+pRlLjYtwJr4ULM/tw10kHAEAACAASURB\nVA++0TT5ZaFYdQlw7olXG7a3rxmut3RDIB8eqfMeqBjN/8/Dt8/PW8oRyhHSI9SXvi7cXLHZ7dqB\nFHLx7c/+ELjbG5Y/Z+ze07Vrp5dACFs2EVIfeXs05kfl7eeGvf+MeP+vyOET5PAFtiRqqtSyoNUI\npoh1qDmZTnG6uZPp/DyUUpyFaEIgEITmZejv0jFlco5NvFWJIfi4GqRtYQ2rH4yBNLLdD3j88EeD\n0PJygzeYtDAUtScZrM9I4tx8y9RgiAXM3J04FHf9sWZdFXTd47dUY13ncSFVpZiyKCxFOQVBmj4g\niqA2IXWgK5k+5+ajl9GkhBIIuvhFpREJM0pGQyWo0cXgfn1Nv58bKu7cgohoRyqBVJRUhFPrCNyP\nwCv+WlLMKlqMqwDXIozRffqSRZY6kOuAxMDVCK+vjK+/6PiJVztuXl4zPntG2N3AcImFjR9AYmv1\nPyAFtGvD1pXfqkqz2goSbUPwwdaAej7sUt1TgFqpuWA5U1KGJSFpaQYjCRUHAek8oYjYCkKMsLmA\ncYvkEywn5HTEZv9Y0X7LC5Znal6oDSDE3Kknl0SyjkUHQhgZ+iu6cYeOgsYeU/XNjYnLv6XZsRVb\nV07A3PCDztfEEgjTNeO44+ZZi5UnUqXj4TPj8QhfvPkc1SOh+y4b3TDoiHSJWAtdDmjqybkn54Wc\nFmJKdDm52WxxH0KaKexiUIsR2+snJXuylrg0PdjK5nS8yn0wipPcQqR6pSWt11vsKBpIOPZg4r+r\nmhHEmoanfN9Z/PDxw+8I2rzyBEav7WUDmdr+0Mx8NsQRUDU7JxOZrHO4UjCK+gti4glDa+u6TrMi\nQHWmYqorycWJO65w6PwNVaewakloCgTr6cLRW0OZUQlEOTlZqP0sXxS0QPAQVhIioYWvpOw236kY\nc4Y5ec5BWZNtbN3bF6IIV6pcRqWLkaI9i40s0mPasesqz7fwtVvlqy8nXr2+Yby9pbt+BsOVjwNx\n+mAceJoxn1aC7e/NJwHhLAn2AyNPnwtPRcDpaed50xBKVZbqmw6KEXJG8+zFVATCCemOSDchwwjD\nBP2ARN8UnKPP1VemrBwSCrUs7veQTpR08MYG93co2lP7AZ22DJtL4rhpic0DNXh3Vg1kNYpZ5+1s\nmBpI9W4nPPqYGAckDmg3sd3tCLEnW6TQUVLmbk7cPRyJ4ZFhfI/ETwndiEyKSiWmjrAM2Gmkzg9I\nPnpHkxKWFmpZqdj+kZInGgeFmrN7OMYWzEtw8hqrZsXj0NwApyVpq2diIkoJkdj3LrKKPRJOXvyk\nWefb/594kx+FH0Eb2dwVx9+UbOZwqmpTrEXKmn5hpdF4C6oFCaVVe3HTkBpJ6vv1arVVwEaqMDub\nbUpQitr5DizismMRJYbh7NKroa106gj5hOU9wokgM0E29OHIUBamUkgloxT3oF9ptK3l19hhVViW\nwikl5rQwp9CitVr3U9eiZY0LD1dd4KLvkW5DkolTnZAuMI3Gq23mK1eRrzybeH57xXTzEr18Dttb\njwsPQysC8lQE1rSixpNoSS7epreRBGjFGBd61dYFtKIs0NgpnktoxRWSpUROomSJoD1R28q2zISS\n/E6XjgS5d7yg65Fhg/UTdCNoRMIEY0DjQJ0fsSNYfsCW7KNAmsnLAmOkdpGiI/QXxMsb+psb+ptL\nQqdur95vYJioh7vmfNy3FVrjP5hh66hUQFJBTkcIb8/jUhivmS6veF4jqpE0L5RlYf/dR969N4Z+\nT+gf6MbvEC1gIRGXHd2pJ94renygz/fYfMDmmZyOKIkQ3HFYpLhjU3FRXa1OSptqcNMXrSChXf/u\nbYgELHbUJkLqtCOESBc655do5XK75WG74zjPzCQMp9oHc0zux45HkEsie3oYZi19SJUSlKDBZcdW\nWR2GzxutWqnqpAoEXz8WpWgkaIYQsc49BTsCa+qLURtK2/AFwY0cJFJaqEYfgl/EjbGoolD71osI\nYj1qiagbki106YjJzJIXOvG7pQZX9cna0jVXIy8ChSVXluyGp5nWoba7sxgMAXZB2I4dcRhZwoYc\nRkQ6thPcbI0vXSlfuhl5fnPF7vKGsL2G6Qr6Cx8HtHtCnL8nwbkBhStxqFTXC1hpo9n67qydQ/MV\naNoKkCeiDwFq12bZnqWPzDZQwtw2HIFe9nT5SKyJUBLUGTggRw83JY4e99UNSIyt0vhKUUKH9htq\nWiA5s87DlZQqgdrMTTY3N4w3t8TLG89EKDOyvUXmExz3WF2oJMAzLaju/mPqWylKcArzkpBwOK8t\nBSPEju2mQ57f8PhwZN4fON2953RIvHub6afEtHvPoAM2VDRfElKPnhQ5LYR0ZDmdSEe3UBdLLiLD\n6MwLk5nfoJIUjiVAddBag+tW3D3Lb4ZVXY5cpek92pZ10GbcE0ZuL50LcTieOBxOTqfHwLSxX3/M\nCsGcTq0QGKtvYVZ3JIpdR8SIVhxAkfb0KhR8CStVqeJGH2vYqtAjjZQSUGJtOL/UVmwEla7FfDd1\nnARq8Krfa0cfA32nxLglxAnWvVkYnABlxU1+qeT5xONyZKcPbOKBKcwMXWXoCp264KVUI+XMnBaW\nnEkFUvFgDGt75CZpQhC2qlz1gWnaIMOWg2ygC0yDcLspvL6AL98OvL654PLyln53A+MlxC0mo28I\nQvQ99OobUDOIJzQ14obfCbMXBxfmPLX7QGPyfTga0KTIinUNfDQ9KypzHpl1YS5HtJl6jqljDJEh\n7emy78NlWWBZms9rgDgiXY/2nYe4dN06NxKGS8yUkg1LhTLPLERyDQQT+n5gunnG5vY54eLGvywn\nJFd0XuD9WyzNVI6INBsw2s8lABGx4Ou6XGFegAfMZh9NVOiHW8LlBS9fXrMc9rz/4o675cT7uxPT\n9sjl1R7p3yIUNO0JeURSR02eUHQ6zMyPMxIaySoXeoUhGB1GFPfrNIETgVqEmoWui84SdDN+d7tW\no0bBpDimYQtRE50ZfeeyZeGWEJS37+94f3dHWhYfrFfuiPyYFYLj0RNsU2qHQHBFXxFKTZQQyEFR\n9UOqH1p/lVYhxW3J17WXMPv8FJrEl+ArKIcjcRg8sEZpPQlcHLFdtJl0xECIR4IOKB0qAQk+viBG\njM0lSSOLTsyiPOaBUWe2ObPNiSkkhpAIwRONQM5JzIVKqgULQsHJRZgRUC76wPU4EPst9BNBI0Mv\nbCd4uet4fRl4djmy3W3opgHtoxvbBXmiDJu1Xb34h62c/YCTmyNmkWrRVW7m1tkihahO91YF6QJ0\ncWVEYatYqBtd3yEdmqA7VcYpUY8H6qkj74VTMeZ54bAsbFJiUzMjnkZFsDMdWXJCFkFPoDEiscc0\nONajQimJGjpPQe43EINzIlC0Gxg2G7phbKGhgnUB2d0iy4Le31MI5OUeLJ1Zoqtll6/S3C6OGtqf\n4iKq5R4O3paHUbnYRZ69uODZywvy4YLHxy2nfeLxzUwcTvQiaD4RbUeUK5YizHPhlGaWsmBlZVsa\nC5WDGIMIvUAX3RlKqG4fr0KILnqKWpwHIe7unTJk8a5IYkesykChB6J6/sVui3srPjwwp0IuSxsK\nnoDif+/xny4E79+/57d+67f4u7/7O0SEb33rW/z0T/80v/Zrv8Y//dM/8fWve/7h9fX1vykEydNc\nSm6GNOIYoQqlZB8RgtOI/SN4aIhJMypxc00Rv7RXG2cRt/dCFCE4SmvVD7OsGwmX0FrnF7i2Qyrq\nJikxxmY+0hFtQ5QeUQeYqla6oXcv+RiZQ89ROyIjnSQu80LKJ3K4p8TFZdIIam7PFYK7Gi/FQAPZ\nngpBVLjoB66nkdhPSDfSBVcJ3k7Gy13Py4ue693EZjMRhg7pozuIqLS7esMAWoQbWRrtuaHo1UHN\nbK50LHWh5ISlRCAxxErXQx+lHfzmMHQuAv2TN0AYCVXosrHZLvA4Yg+BgwlzhuXkcmS35i4E9dZY\nQnNkoiA5I5ZRUvMA8M2MhY4aI1W9NbbYI8N0NkkRjYRxQ7/dEPvR7/Kq3j1uOjRDuHqkZCEvitgd\nasmdLnTdgNAYfBEjIuZcEGqBZY/pCdGAhJ7N1HPzbMPzV1uOdzsOn+1YTkf2b/dsLhZiXwgmRApR\nN2SDx2Uhldl9DGs5r12teqjrKIFBlbFAFwyl+tbJFImVEAqDZrrgN6qsxqywmJAJSDRiDSTzfNAx\ngGjH0E/sthdc7HbcP+xZlkxt24L6H1SC/3Qh+O3f/m1+6Zd+iT/7sz8j58x+v+f3f//3+cVf/EV+\n93d/lz/8wz/km9/8Jt/85je/5+ty9rnN5cHfC2ij1e20s/sFhFCJXcRVpRHVD7cNLhf2EdYFFrIa\naVjbIlTOrFrTtk1tXAW32Qqsaq1qRq4FVUMRqs3ujlsKFWe3xRSJMdKFkT529L3nFdYYuMs9swmn\nrnCBcMlMVxw1JldUlJQrx6VCTS6LRumCsYvKdtowbnZYJ0hX2PbK1RS52QSup8jl2DGMA3EckWlC\nhg10W3cPjo1CTAOGUqXORkqZlDK54rHtCLk6MSq3RJ1h6BiHkdB3xL5iA0h022xvxNoaxDwazUg+\nd2ogxEC/GdC+o7+8YPdi8XSjwwP54Q7ev0HuP2N5/IR4qoRycu/C3k1ZpfDBjr8Rimry3L/WMfma\nsmD4nbC/umF49oKwu0HG7RmEJAakFLRUws1LJFXyQamnQklHhr6imtEijichSPSxBHEQVUqzEy8G\ny51vocZnTH3k1fOJfHfF6e0JPWTyklhOJ/rTDAG09kT2HrhnhZxn5rzQt3VmWjygNUhhLpkMpGru\nEt1k7330rjVTKQaxGJ2Hq6N4wK7rElz3NVPJeeF0PFDLwpJOlIcv6LIDlPir2HwJ/htGg7u7O/7q\nr/6Kb3/72/5NYuTq6oo///M/5y//8i8B+I3f+A1+4Rd+4fsKgVOKfVIz5HxHR3zvL6tdeLWWouvV\nLAS/+4n6qTZcSy/qqxLgSfiDnfkItbVFsvIpztZY6x/SqnX10BH87lpozsqN3ZYtodkpv53O5DhQ\nxoEy9NShJ6kwl0hiJPtly1SNUDzwJAZPDpqXghVjyV58hihcDoHtNNFPG2qj329GuBgDl0PP5RDY\nTR3DNKCbDbK5hHEH/cYBQppSr1QnryyVMleWObGkzNKiswpCzpklzb6OEiHEjtIJJRpJPKMwasS9\nmFoiT+H8ekoYsTD5ui0MdNoRx45eRyo7ahXScsty2LO8v6a+3SHvBmS/I5w2kBZYZurxCPNMTerY\nhWW/cK20kcUFaSae7SAqaDfQP3tF/+IjdHOJxMHfQw2+MQn+fOPNc/RUsXtrAqU9Gk4ETSjJXbPL\nDPYkbvLTZZAbTj0/gC1ICPRhw82ucLrteP9sx8kuKA8H8pzIpxM2GMGOBB6gdJQFUp5JZSGqP8dc\nCgEfv6ys5jxCUaWXiHZGDO0SxdWGHoJDS+MUN/MVSIYzWWsizzOzHCjpRFoOyPxIX2YiHihr4kXA\nVszn33n8pwrBP/7jP/LixQt+8zd/k7/927/l53/+5/mjP/ojPv30U169egXAq1ev+PTTT7/va0+H\nU/tlhdhFQuzgDJrVxoESDyixSqGgpmjMED1UVD90wFGFD7IJV+wrVLfqLurW5kqTHYOvcfAx43uW\nK4bTQFWxFpdWKb67Nk/nrZJbQGXyjLrcU+tAjEqNq8np4McoVLZ9JqRKHwWhklNmWZRUIZXKMHU8\nu+gYtwMyTR693QlTHxijsgmRTR+Ypo5wMSGXF9jFM2S89pVhBUtGTdXprIeZnBZySRTLFDx1V6uR\n50Q5HiiHB8ZnzxmvnyFaMDuyzO/Iy1tkfss4dYybnhh9/0FuwZF0EC+QeAXdBdJtIYwQBkIYUB0w\n7YkxMFxcUjYb6ouXkH4SPb5Djl9g7z6lvvuM+vZzyt0d5fHREe4qYBmz7Hdo9YtXhh1h84JQj+jU\nM3zpJ+g/+il03HkRWI4OZGpw0HHYIJfP0LkQDwuHdGI5ZkL6HK0PyJiI+RGxEyIuWiP6dopSELe7\nbFunAwRBwkjPge24cHMjvD8OPB42lLKnLAdUXbIc7T156bh/VObTkZozSwjN0vbp+wZrm4zspi0W\nBNQTo0NxlKv6/tu1XI1nokERNUIjdVnxwikkLDuJqa+JgYVeM8fTiff3HidY/zu2Bjln/uZv/oY/\n+ZM/4Rvf+Aa/8zu/8313/hXR/7ePaTOtRNbvIzo4mCcreOy881ooudkxS2wAno8B1tR+Yr4br8VV\nV4pBATX9ADBs66P1mzdikmOV6zPCn5FVpDkVrUo5MTkr9XJNFCueU9Di1IehR8xVjSft2GfoNTPG\nIwQI6vIiq8aS/VnFGNhOAzcXG4a+R4PSiTBGZQodu65j1yvTNhIvJ/TqGtveUsMl+QD58JayuJlG\nXmZqOrnCTzKE6o1C0+CrBrqhQ4PR9SPDVujHxVdmYgQbYN5AOGL2SD68wzShtmApQbPLFh2RsEGH\n50h/C8Mt0l9Bv0PCBguDa/41EINicYCph80EqT3/69fU27eE+7eU92+pD19QH99i84OvDA0vDBod\nJ6gF3WzR21u6m5fEi2tco2quKkwr+NsCVsYN8eKS/vmR4+FIejhyOO0hHRFdwBIxzO4vIYb0Y+OA\nfLBBadsWk7cQ3eB2jMb1VlhGOIUeqgeYSpyRshBRSkocDq4BQIWlCemidNTq2Ra9OXGoFr8eg3he\nZCrFbfzbfryYkfLq0eEZn6E0Hs6ZiRv8Oq2rxyZ0ovSq3O4GYpxIzQHp4W7/X1sIPv74Yz7++GO+\n8Y1vAPArv/Ir/MEf/AGvX7/mu9/9Lq9fv+aTTz7h5cuX3/e1Z/6KrezA9ifOGlAagCjW2K6VUtY1\nmBuAxtU6LOfzKGCtEFT3byFYIRAaxVQa97rFqAWBYC1pCTq6ZmnlQJZZIZTJ8wdaYdAavZiYKybN\nMotlihWofshD8++3GlkKzDWwiDQxU/IWz4JHokdlmnoudhuuLy4ZY/SIsABT6NjqhotOuJ6M4bJH\nb3bI9Uvq+Jwlbzh8+o7Hf/5XltPMfDowp/dIODJtKv0k9GMkhA4JHaaKjhf0168J4y0aL6jLA1Ye\nCf1EmDbE6Rq1l7B8ifzm/ya/fUMpD5D3lMWDTJkPBLJfbP2XCNPH6MVPINuPsOkFhBmRkdVnbiWH\nnQ1M+xvsxXP0FWAJO7zH3n2H/J3/SfqX/4fyzigPC5aKFwDtXKr+8Ibu+f/B8NFXCReXfuevCmVB\nyowtbSQMPegIoSNst4x2w/5uj7275zBvKfMjwQ7OHu0LIvdoPUG9Btt5kstqlVYMUoV6785GUemD\ncjkKh65w0IiUSE2B2ngZAdcVzAdhHCKhCxyXghQl6kAx45Tn5uOoJCsEc8fspbosX2JHEKFKYamF\nU3JJcqDSLx6guupZigaQShAlqDlhloiGnj709ApRzF/L/47R4PXr13zlK1/h7//+7/mZn/kZ/uIv\n/oKf/dmf5Wd/9mf59re/ze/93u/x7W9/m1/+5V/+/kLQmHS14QIf9gwGLZTBnj4XXxVSBam56bnV\nU4dqpZ4tz5vgRazxBVrDb9KUcvUDflVtqHHDIASqhYY9+OdUWXDgsWAt89A5Odo0EF4gCplsQu4i\npe/RWqmmDvgAZ88+IpDb+GMMQXg29VxvR6bdSDBnlQ2xZ9t1XI0du40w7oywuaV0Lzk9jOT7E3l5\noMxH4mUhbI2hRmq4RXqlnwa6sScOPRL8wyQi/YawuUHj6PmR8+RMvjr7vFyd7BO2Vwhfp4xjk+we\nkDRj8wP1dEc93lGP96T5gJz+F2H/CXF8Tth+CR1eosMzLLY146p+lOA8h9jYhR5ThXQb7OZjYtwg\n1x9Rv/gXytvvkL/4FDnNFBnIx0fy/h3jIAxT8MTqxoegNM+IdEBKAq1gF8iwQUNHnHaMV9dsbw8c\nS6JYYp4f0fwA+ZGYgC4gZUbKEbEtWO+Eo5xBZggn6AoyTWgVYigMQ2UcC4hnSDqGUgnMaBFsCYSh\np9OBkx0dghClmJCTkJuLtTVW7SEXisGIkRcHEi0UimVOOVFzQGqkdkB82pa5YrGjytA86BVKoSNw\n1UXmrvA4F7LQgPT/4kIA8Md//Mf8+q//Osuy8FM/9VN861vfopTCr/7qr/Knf/qn5/Xhv33Ime5u\n58HpA/juA42MF4l6jnl2Fx0t2Y02rLg1WIXiUcVOhGPlpTTQcf1+jYRUWnqx5tJ2uG6f7oUksvIR\nRZ9GgjWoZBUlu1TXR5JqlVxdeZhzdisqE1ZraRqL0YiIVF+VCUyd8mzTcbkdGDYjljw4pY8dm77n\ncurZTELcAP0VS73h8Qsjnd5i+Q3dRc/46sKBvbglbHbouEOHS+f2xxEJg9+NpQcZEB3aHH6kpoly\n2pAePqXmR+riceM6XKDDVwnXH2HlhJXZyTnLPeX0lvL+E8rb75DefELdf0q4f0sXNwzTl4m7rxG3\nX8H6C6S7gLAF6aBq8ynskX7wTUc/Otg53hC2zwkf/Qz27l+on/0Ty/h/IXfvMOvg/jNKek8YlX5U\nglSHzHNtcuLkhcEBA1/Vaed31mHLdHlJfT6TFyPNM/PhMzRXND1AzNAZWk5IPSLcILZBbHAGHyfQ\nR+gLooaaoHqk64VpIyzz0gxxpXUEC1oVUkSbjZjaqeViuAlNzUJuiU8qToufc6ZWpzslCtEq9I6P\npZxIKbizMW6iUy37WFykXfcRGRQ6xaoSUS66wL4LxJUbUj+85f4XFoKf+7mf46//+q+/79//4i/+\n4gd+na1iiA+O/zmT4HvGhtYhrHqBRiQiuu981CZ8wZ5SivH7vhZrB/8D950GmBQzwhppJtp4Squ8\ntvoaTtTv22oNeQWxxk9rXcrK0TCDQiHXRKoz0ZzrH8W5CRp8lVksQNcTp8DYVaZtz7gdiTrCPBEM\nuhCYupGh94DRWQfuykD57oH6yf9LyW/QLjFebOgufpLhxU8Q+pHQDX6HjR0axyeGoUZEOj+MTbG2\nvrBCJMgG7Bl1Hqk2YxZ8TNYODdELmA7UsLjzcL9B4gadrpGL5+S7l+S7T8j7B053b+keH+m7/0no\ntp64tHmFjs/R/ha6nYOL+fiBbmFEhqkViYiUgmyu6F98jRom8ru32DjQvXhGfP6acP3aOQUUyI+Q\nFiS33EXBZX0asW4C2TkfY7djXBaGd/fUYYTdR6TjghwfqPmBPp0IdUFtj3JC6yXUC0w9Gs9k9tVi\nOIKC2kzfR8ZtpC5COolbZ1aPaDPcS7PmQl1mOhNH/2tBgtJtBnI1Sq5M6jkeS02NCaputy9Cr359\njhYJJqRaqFmYxcODjbpaOXgMvCW6qvTRqLoK6D1V2b0wf8yYhX7Yn2wSrJksnouA/+P572szfzbW\nMUBcp78Cf7XUpxnInjIKHRxcf2jjlLUiFOzpX/1guFPROduvfVTsHMsmrYmp4qQmTzUyHyetEGoi\n5kTOgZKFeREeg3JaIg/Wk3u/SU7VGKce7TdkGzicotuRSeBkHXsLhCIclkDcdzDfIfU9/fCGqQsw\nduimI+x2aL9ta7TshS06xdka6WoNw/Tf5+nvIs0PwS6o2pGXvXsUNB8F1ScPfa2ryajbkOs4IdMO\ntpeUcUd5813K8gl1eaDOb1ATQhgJm0+JmxeE6RXa3yD9FSodoj6y0A0wO7iIRC9mVMI4IeOGam+h\nU7rNRNht0WnruEMpYAnKEUmnNhKyklEaLdzBwzD09NcXjJcbyn5HkZfkeqAe3npIzXKgq0eiHQmU\ntsY0LAgWDGvmrBKOWDDUFro4MG5geV+dKr+4k1ItGaudF9BcMcmEtqnKJSMKwzRwOC7kVBjUC3Ou\nfmilCCrNUj57FsUgwfN/dPXncKo9zQ25VMjZJfAZBYmI+k1PpUXXZTuv2P+9xw8/4CSuMc04Et8O\nq9E47qz6AX/4tsC3BELLkQdMDZXo5B9aKq/Z2W1LKme3YdrXrgElUVroZwtBdQt+oYMzzVWCdyIO\nZTRWmylShSwONHplqq0YOFFnXjJiiTorj4+VT4OSas8pK/ve0fQNQhciB+uw1PFQKjEYQ6m8s8gu\nKdsTTI+ZsXvkcvPI9WVhfPlV4u0l7LbUvqPMn1Pmz9rBNjR0SLd1Sy1VVH1OF50QHZswCX8t8L29\ndB2qgdiNPrqsBaS9dtIM76R5J1i7m8t4TQwDOmwpu1vKzUvq3TvywzvKw+fY4R49/QPd+/9F3/WE\n/oYwvCBOHxM3HyGbK4TJD3PzGfSuJGNV4XRA85GoJ0I8tB39fZv7AqadXx3l5L6JtZ6zFsTwf8tH\ndHNB3O7YvrpC7MTjZ7AsL1gOJ8oBSlooFHqb6TisaxYfXTrnaFg1qpyooSCa3eOyN9DkwqY8U8qJ\nvGRq7Xxjg/laLDqWlZdKPwyMw8gpFWpq0bfm+JRmV0j67A9ZhajKGLWFtghzrSwtr2O9RRrZ2a/V\nXeNmeq/ZAkMUhh66LITlB5/LH40fwQcQ4dPdu40JK1rQdv3SzD89aLTt/G1NFfY7W5B4FhnJSjpa\nuQKhMRCdSYCpuKOwgNVV920tj7PZZ5vPX+bvUgMxpRE7vPSoPHUn1vAIdwXOZAKH7Gw/o6dopGil\nqCC9Es0r92wdhcBJ3Kumq8pxMe6tMGQ8kjwYVzlzhXDT9+ysZzwEdseFi/1bj1vrhX4a6MfQHMG1\njQT4toXZX9V2iNorT9udInReNGjuzR8sdr0FbdJly0ht+gRVtN8gMaL9hjBdkrfv4f4d1kdKhHJ6\nwJYT+XBEjwdC+IJ+uqM/vSGcbtHhEu12QIeYUsuM1YxpT57dWpzwiPQHbHmLnT6FPgOjP596cmXj\nsod0QrSD5dDATzcfkWEk9BuG62usJlI6kU5b5ocb8nwg1UxZImb3IBk4YbxH6wapkzNUQ7sqO0Fi\nIFglsrgLu0bI6mKj3EZUaes92lrT3FClM4g6EJkJNvs4gYewBCBobZjPeYZmpQAAIABJREFUqkIU\nYmyW5I2daKkB17V63B8FQnWLflPfYIiDiAHYqLGX4uPUD3j88AtBBb/rO/peVw92awODrK25E3s8\n86J1CLreiTOU2DY9ip7bodSszN3NyESwGFCEuM70IucdplrLJTQ3Fgk0hZdUSqMqywoxKKyxZ4K1\nRJk2gDV2o1JQy2CRhY5ikWK9/94YqGfYOc1BMDqKKrlzI9TSXJqPqXowRmpOSnOkvxe2n+/ZTZWL\nzY6bq8qzm8z1Tc/17cRl2CLTDTFeIN2G0I0gMyJu7Y1kTJze3VYz/j6sdXd1d1pb7DZqmRS3C7MF\nKwnLR6TsGwDpLkQSBrTbopsr4tUteVLytic/3pPuH5nv9tjpDtJ3mU5vGff/QDfe0I0vCNOXob9B\nuquzZ0BVyPOB4/49dI8McSYfPsP2wb0GdecBJnnG6ows93B88JEo9li+9/Z53CJpQXoj7m4Y1NjM\nn3PaR5Y3ExZeEqSnlJFaPwfegpww7lCbfbUY3TvBpHfiDx6hF8pCrxN9COR0gCVRa2mAMhQCpYW3\nGgulnDxFi4G+Bt92iaP/Udy+vw9KPw4Mw0AXA12EEGozyHWTXopRkms2FCdeEVxd21lTnlqg1o5Q\nAzuBe5mRHzeHonruOtcosdCcfz8YC87nq83m7Sscqaf5ZVasby9EbOBddSeXkot/vgpkP/y5+hfb\nmu9nTx2cYRSUGXW/f6rHsTUnorU2uWfSBwCCCqJGDEKngUAgV0d+syyYFkAJ629hOAGEFSXxpBtd\ngUsVsrhwkNaBRPOcxCUJczEe08K70wNv9if+9YvoiUZTz8Xle66uLrm+ueTq5oqr2ys2O2XcKCFG\nJATfhEhpP70BqbX5FdA3vUJj2bFal7ldnEePnZp0tsl5JfiKUA1UW7htROzo1gjTAMMI3Y5y2FJP\nl6S6YDUzHx4Jx4X48J7QXxH6K0y3WNhSukseDwuP+yP9mBg2YMuBOr9rGMMMJbqXwHyC+Qjz0Q1P\nQ4B0au8d3h3kE7K9IYSB8fbLbObA5qg8as++dJSTYCVADtT5HaPcE2lBLUPwgqdA7VHpCexdLNUO\naW0Cp5SUWkN7rf01L3V1F3JCGil7I7Z2ru0VHzWwi8Ju2LHb7Zi2G8app+9ddp8sEe8fCQ8P7PcP\nLMuh0d8Ny22lWt1XU9To+omCcqzuyvAfWBb+8AtBqT6Del5gYx+aEx603a3lfPhWP7+1FKz+c470\nVyoaypNTbcHnsdJciaq2dCH3OjR1x2SakWW0liEPOB0kEKygtroawfoKijx5z7MSlYAgQq9ClIBI\nRzKnDhddEJROHZ8ItAQb1juvgBWkrIYmDvCtdmcm4tmGACbkKiwF9iVhy4Le+3Yklkpvwmboubrc\n8PL5JR99+RkfffU5z17uuHm+Y5g29IM0B5zmMoy/vmYLNBssLLoHXqNeO5fKXx3IiKQ2WfS4Zfka\n3W3O91LFYofqS7RXGAzpe3RIpMcb0iFRDg/k4x22f4ss79DyCV030vUX2PCS2r9k6Y39PnM4zkio\n3n6XhM0PmEQIiztILQXmGZajuyPno79eyQNapRycTZiaffn2OcP1l9nUnt2cOCyR494DRu3UlJFL\nQTm09WEB7ZEw4lHkHYELihRU9n7taaFmIy/OEajVqe8eihvIlinma2Mxw3ICq0iQRgASRJ1JetF1\nXG0uuLq6ZXd7y3S5o98MVCukvNCN79D4BdZwsVzM49AXZ+BqLaQaGodrIgP7UliozhD/AY8fPlhY\nCqJuHBLUD1TU2MJN1rm0If4Ipa7+a+0+1fzdC0buzfMSad3DB6QiW8lBtbY7sAtSpDSWodFGgtZi\nCBCaK4zYBz7zrUMxFz+VBrUFc0VhNCXWQFB3pk14odEKotWLiQk1tPwCMVc9mqImZ63A0ma40NyZ\nausYoFCbBFfNDS5ryATzwIw1Rv5UZh4eCm+Xme/cP3D5L5/w7HbDi2c7Xr56xvOXN1w9u2Gz3dBP\n0/m1Z+WtS2r4hzsGmXnBUtFz9Lzplidtu68k/Xtkb8XXwtptUK7o6uwybHtwCfOolO015fiSMn7B\nfP+W+f4dcpqJp0dilzB5y8PyLyRGwhQYXj5n+5XXhH6hhoWaH5C0R+uELNpchh6Q5Q7KAa9eB+AA\n9QFq82akuovRxSu6GNleXTNOe4JkFgKl9tRj87JMAuZ3e+kSWhbEBgckmwDL5/CMaSKb33xK9o9a\njKBCjxu4aGOiripWkULE5dBd7BjixNV2x9XFBTc3z7i5veXy9jm762umywtMIZXE9OYN/e5TV01q\n5PHxHTnvycytW/Z1utRMOR441copQfIUvx/4+OGvD6lNJShNbdguNnFVtvHENnR14tNBb+t7avFR\nohSPJuf8Nf6GP5UTJ3oIbvMs7eDx1BScUX/FhSD+KW1VWIXV2ttrh7QBpllLtq5GxX3qbd1cnPUJ\nDrZVVlWjt9BU33rQiEpurOrAaadr27426BWPRhTPXxS8BaY5P7fnlK1iaeH94cib9/cMAa52A7dX\nG7700R2vv/SMVx89cPv8muvbK6bNln4YoCTEiuv5Na47W4B2sTshyg0/n9Scq0uy4asszLMm/X/1\nwIYwXftK1toYNyiyBGwoIEopyukIubyHZSHOj1h+4GF+IOye8+yjj9l89JLx9Zex/B5L76jLW5/7\nywFS9GKQHiDtoR79za0LYguUE7a6ObeiKqGj04HN7oLNbmLcRh6OgblGah5QRnomYlfo8gnNK2lp\ndW9ete0KzMDSBG9ORvRPs1ZE8ZuPuegNgUpBpbbOLNDHju04sd1dsbt9xuWzZ1w9e87V7XN2N7ds\nrq+RGKgU4nRJ6LeUUsi5kFJhXipFatuctYV7zZyWmaWa867q2tn9+48fwdagcfobUFe1BZQ86YSf\nQKv2OPsVtL2+UqnFqDlTwgeHkNWvwD74wrZ9sOAHsdZzPtwZlyD43bAUt4VSVy+uJqimrnsQCh04\nw6x5ITzFnkGxTK7Zu4zGNVCrSGlmqu2gFXVXZsWaKYdiyZDihUhXyEnx1VDrWBLORtPsv2tuF5e1\n18XRapxSWpV398bjIfPZuxNX//wFL24mvvLlZ3z96y95+fGXuX35jFj3nuMgEUqHSH563dteGula\nR1ba5oaGLaxotPmGxMpTV6fB7cynS1Qr3XGPhCMiCSuZOhlD6qhlw/Ghctgrjw9H8lzR8Zrdy4+5\n/en/wcWrG8LViB1Ozvw8FOp8QuqMJrAZJM9Q2wqxmc94tS0ge7Av/Hm2tVvYvmaannH5/JLT4zX7\nxxPHd5B1R9DnbDsofO4BPNmQ7NmCtTQLdzOsBKRUJLcE42Y1ViuYGFkrOVizhQz00rtXAeamOBIZ\nYmToBrp+pB8mhmHLOF0ybq8Ydpd02wvCuCUMLki71Z7Y9aTlkSUdOJ0Wjothi1HrjJWF0Hg0C4ls\nvkZ0UtEHB+p/8/gREYr84Tv6FQH80GlV1jN6Xid+6KtQzCtuLgUphaArEC6sJB+/f8vTt1tXfOdo\nrRXya7gEfgDt/LO8/aiN32C4K5jjeM0QRf2wJ2v+fdVNJWoDQv1381VjqwqgRrHQyFDOpygmaAln\nnMDXSeY+ocEvbN/tK0VCA/Vah6K0Tcb6mhq5DfgpF45z4uF44v5hz/t3kfv7PXfvHviJh8zXTgvX\nl7DZDn6HCvbBC/3B63X+zit3ws5F4HvvM9aQ8LZxaIGmQo/aiYBRyoKmmdCfCGOmT8KSOyxNsB2J\nu4Gr11/h2dd+isuv/jTjZY90GcuP2DFSa0etgTV7UUrCagLzu7Y0TYhDrR2iGdMTHB/O2yKpmRCU\nzShcv7jm3ed3HB4GcggswBKNHBasnvx2mvHMhdhCY80chM6+Lrbiq8OUwaqzSv3e7w5BnsqtzjYF\nT+KKkakb6IeJbtqw2W652O7YbHdM2y3DZks/Th9YlUds6+/CzcuP2B9PPO4zx1Q5LJnZjFSWJt83\nFhw2E5N2Pf7gY/nD7wjaAT+/KdZWWOdiwLkIeFe+FgLzdd7qMGSGrtbcjQ0ozcbEGiDZdLOrRwvr\nhSoizZ/gqRj4FmPtJNaOxCjVXZOD+aGr2sYUxVF+/A0oxcNNrX1TBYKu44D4nVYLphkLQzvMdnZR\nkkaFLioutdY1aK0SxdxqC185JfHPjlRqUExpm4mnzn41WBGfQ0gl8zgvvHs48a///JaHu4W8P/GT\nP/OMvn9O16/aTz4oKq62dH3Cgtl85llIG5VW6FtYC/n6UTlTMVV8B28gaUaXPRKPaDejQ0J6QfqB\naXfJ5uYVX/o//wc3H3+d/uo5qq0IhLdUIsVGJ9RIwqiEWsES2ILU4tiMGLZa3wQHMkkJjg9YbiIr\nCmP3jKvbG25fvGU+Hnl3X8lZWahk21PZY2V2CXbKSEwQXG1Kqm4Llw1yoeTMUnw0GoMitZKzh5Za\n8Ei7qDCI0KkSozKOA/20oZ+2XOx2XO+2XGw3TJsN4zjRDwMxxMahMUIf6dhy+fw1t3Pm4ZA4LZn5\neOKhzm2t6HZ0SSCZJzZleyrO/97jR4ARPF1mnP/WaD3r3Uis3dm/97GSiZ7+wb7nIFtTHq6MQIci\n6pMRSbsDrz9vfS5Nx0Uw8c0A7oZkZ2KSf+pKQfB23n+FUnxWK01R6Y5JPioAbkhZDZVyzlqgCibh\nzJC0RoEMeKVZW0sxcZyiua2YFFDfaBiOsaz3v6fm50M6aRNj2fq7GqUU9iXz9v17Pv104vmrC66f\nefZIoLb22YHNc5wZCY8UXvz7re4vgFfE9WdlTDLypCx72nObAoHQV2yYqengd1OUPkeGHNi8eM3l\nq69z8eo1w8UVEgOWjtTTI2n/3j0S0h6tM7KugZVW3DNqzeBEfJMvdnLmqBlYB7WD3AIlw0DYKWOn\n3FyN5JfXoDNyNELNSLnAyqMXf5azJbog5/V1KS46o91xS7tmg9QmOXfVoSp0AfqodBG204bN6N3A\nMO4YNldcbi+YNjuGfiRK50W5ghXDAu732N6S0A1MF5dcPXvG8bDnuH/klI/YcX92rqf69Zgavvkf\niA9/FKNBO5A8HcPzfxsgeC4VZyvm1hGwNgv2tF40J/f4t26FgOZl0GZ1a3j4E0Bo5zrk8VAOvPmH\nngG4leuwgmAinJmLzkx0a7A0p3ao/etVaUgGDdeohDbOaGNEuulFKzzt/wmK4sBcMQjmlu7SsCkL\nBXdbAkwpVdvzWruOdSSyp8PYHtrmRJVKlcLj4cDbt++5v184HozNrhC77BqDNRTDDfxovTFGfmox\nz2zK9i6a4cy8D5iJ6qPWahSqrRAweKSZWcXCQF96pjpx/fojrr/yNaab54RhdAxp3pMf37I8viXt\n3yL5RKjVXycEVB3P1HJ+PgJoKcAJZ4nOiHVQ+3OwCtoTwohqz9Wuw/IFZRGSFmKuSLrA0iNVvbBZ\n9e7DU6HMi0Brw1cm63rdqNTmSqRUIirmLNGodFHYbXdcXVy6QcywYZwuudhdMI5bujh6ZFw1Bya1\nOmUjNgv54lb/wzhxcXPN4fDA48MDd/t7uHvXgFt/72s1UnuLfuxGg1UEJCvQttqVA2fU83sox/L0\nv2RdKq54gFdpKw2wage95av697YPWubWseKQzbnj8M2EterppqJSnfhBozYjftBNfH2ZrFJyJmWX\nIVfWG2BAq4NBGFhro1Wb1bqsxB6BFlpJUDr0LFChAZAqHn8VcAS6nF8RJzrV5ovf9hytiK7vuJ65\nDoKeX++ggRgjoZ/IYeDd3Z7PPn1D11fginG7Y9Wue2EJIL1redCn94iEH/rS3qvqBwbDwxNKew8i\nUkfQ1EDHiIQeHbaEauQcGC9v6a9ecPn6y2yuX9INbkNW5j3z/aecPvsH6uGemiIiG6RXauzQkrAY\nsJBBk4uEFiEsvTs5l+RbERVMRkQ3SAhOyj89QngDtdDVicngQo7MmiAEgk2IXOImhtWl2+WEpDs3\nVC2lTT4CIVMVUunJNVDa9ieqY0GdGNugjF1gGCIXux1XlzcM/YZ+3DKOW8ZpS98MY6sVck7Y4oUs\nYvSqvi2r5pkFOdEFZTNtuL655t27DV3fkfPsBrzFGgX//O78wHP5IxgNgA8OuciH5eqDOdP+zQbA\nT1VrS9eTaa3y+REp39M1tHJxLgT1Kfi3/b9VToxYA1Z8SKjipCKMs0ZhLVbVjOy2wKRSyLUZUdIE\nT1ZbYOoHB7FtQdxv0YkmGgMa5WzAGsVJR7nNIGFNdW6Cn/VZ02jPtr5WrcrVc47hGgjbtiUrsNhk\n2IbnPiwFHk6Z7376jqjGNBZihH7s0RCfijMK0jf8ILZOo567GcRtydeOTc7wriDq2QEiHdSMcQR6\nKj0eMuoYw7B7Tn/5VbbPXvlIID153pNP70iHNyz7N87p0OkJmwktWi4UTBbQisTiEQUWoGY85tAR\nf0lwtjOTI5jboEMh6iWjdez0RIyVLEqUwQuHncB8TWg1U/Oeml1yTG2iISkUUar17qEgDvyKutlo\np8agwtR3TNPExfaCq4srhn5LP27ph4nYD4QY0RWRptnd1+KdUV1HQCHnRE5OBOu7nt3FJZvdjmEa\nsXSE1KYjWa9v/sOW4IffEbSE2nWuXcU+4PtXzNr8t/Y0rLd3zujeeahoF2MTbxR8faVr12FP8Jl3\nC9Z2vOBdgeMNAUf4S4Wq653MOQJBGhap7kJcSiFlo5bauo/Kqg3HrCW1qbO7YvQPFUeNW2hL0M7D\nWKK1jkj94H8Avq3FsoLvgM3AFCm4Ldu5DY1+x2tMSzHf0avEtpflyWHJfCyoIZNz5fHxxMObd5we\n79iMiXGM7K4u6GRAQ9dGhOaSLC15qm1ChIhKB3g6kNOQcX2HCR7bwrl/c+rhkZwn5tOG42OgLBFl\nYNh8me3zr9Bvb9DYUdNMWd6z7L9DKffoJhLYoDa0pOQTaTm6diMqOu4I3YCmBQkLRvF1rwXI4q7F\nixuynKneGCyNCqxCLB2b3mPL5yxoar+z9aiN+OlaKNWDa2rxZOhq/x9z7/IrS5Kcd/7M3eORr/O6\n76quIrub1LQ05BCQOFwIs9CG0FYQIHFH7fQPCGj9AVqQ2mkvEAQBLvRHcMMVN8LMStBKQHNGFLu6\n63HvPY/MiHB3m4WZR+at7q5FC2J1Fs69p87NkxkZEW5u9tn3fabMpRqjL/SkONDVwSzTo2VyUY1U\nNIYNN+OBq+2Bw27POOzohi2h31imIhC6ZBO/hh4ZBkgdqe8IKdg51YhqZZlnTscjtVT6YcPusGd/\nc0WeH6nT0e30Wtm4Lplf+PiWMoL2OB/dB6xCbRU6LW/3LtYZM7jEHKuXEtX1BHD+7M2zQDz9Nwac\nf68tu8DTp3NBUmn0Zu9WKGiu1JzJ2aYZa2wlTlgByZDsQvY+nTbGjhDFnYlsWItNHoJS23I+l0JV\n/DOIBZfgI7HtjPhOLW7ESjDzFJdmGzDYnJEqXQiMQ6CP0XzwqqK1oDWT88JxntAjjGnhq88HXry8\nIi9HYhKC1Sx+Ftvf7UK0s0Sr2ew5Kzu0XsATDnDIALIhlx2n48LDe0XYsLu6odu9ZDg8I/ajlRr5\ngbq8Bb0n9JUubQl1NI/AqVKqjwonUmUk9gbGhmWyQBBmRJzTMEf7qguyZAgn31AUQkQJSJyJ2tNL\nQmMyT4GYLbvSjsAIOlv3qU42bbks1jIuwumYWKaeICNdHC0zCGZFFutCR2EQ2HSJ/XbLdrNhM24Y\nhpHUD0hnOo8qEKNvHslclkjJNBzRGLgSRzb7HafjE0/3916mVMZxw/XNDY/3b9Gne3c4Nq6KfpB1\n//zHt9A+bIShlmq3P9ZnOBJr33uW5Ku6pUctOWi7vU1WNnGHBQ5dZXXiUKEBZavkWG2OghcoPiWs\nOmDXJDlqpAzXNtVczTW4YmktVvunYFOZYojE1JFSR596glNBLUswqytUyKVSqzng4C2eoH6MmJBJ\nYiVWiEVQWVwmP1rq6GImvKlYgxloVm8X2tCXQt8nrvaR2/2OTd+jFabTxNPTI4+PJ47HmWUqPD3A\n+3fveXq4Z1me6PpoBicINg0I1gWNZwRq7TnLqkyaba3GgvENyhrcLIZ3SNySyxXHU+Hh/RPdMHC1\nfUm3uyONO3u9cqJOX0L+ipgWYp+gu0ZPxb6KZWfU6DqHPZIKocvEbkDSQk0LhIKQQXpb2Mu9tQ2X\nxStL3wS0QDAbtygjUToiyYxto2LozQh1QuuClNnmR5TFxsLnxNNDYj5uSGnHkAYgsQhUzchyopPK\ndrNhN3Rst6O1Dcee1A3ErjeDWR+LF33MXwjRzV/PqtDU9/RpY2c9Z+6/+opSKvNxYuwG7m6f8fmX\nn8F9RJfcmk0uI/sVCwTQMrNLK3Fv6+k5G2jPVDyVb7u3soJlrZ1oUmL7G/yDiyJSTDOwlgJnDOGS\nOHNuw+GtNuxitzorRAhNax5oZ1jcJNWIQla7VrXyYa4TYTHXIHE7JEGcQGQHEV3j7OqFi8/lwKOD\nfOoONRIK4iPcrAAuIHG1oQpil7wJulql0Y+Rw2FkO4wIlVqumY4Tp9PEcprYbiI3+5Ghd+Ybl7dN\nK6VaZlBW/Ib1mS0km1JROTMM7SWq8/QVqUKKI7ubHcP+jv2LNwz7g33OFphjIPQ9XdqZyUzq0FTR\nrhJkRvQIOSKlI+c93Q7CtiLLA6SJ0IOGyTAJrW5WsrHZmTWjSwGOZwS5K0hnu3FSyJI9M8NGnzVd\nhamTQGegWCBYIqc5MS0jNfSoRqpnh1GFkcI2CruhZ7/dsTscGDY7Yjci0fGEYOCwfX9m3l7Oamwy\n/SCBYdywu77m5sULci5MxxOpS2x0ZLvbMGxHnub8genuzzbjP3x8C10DJxA1aEnOAaA5F/kPPQic\nAQ/xf7ObrtmEFfM1qGqptgYTHLlRaGzpajgDbCtzsLUr9UN2f2sBVFXrFEQ9n6rQxkw7DitmiIL3\n3VWNW2BTf89tR9tM7SdVrFToQ3A5avC03xa5ivEMVpu12PwT3TYtBLNjD45PED0WtfLDsA3UTVeD\n0G06bq537DaJsRe0LNQl8/R4IlC5u+7ZbQYzMPXxae18XC76dpbO/+YhQzGKMQtt1h8+Ks0dZlf3\noL4f6bbP2Ny+5PD8NWkQzDHJSE0hdUQZCVx5EO6gEwsEOiH1Ec2B+hRYThvG0BM2YlzabJRzQmeZ\n5VKRuUmtI5QjmmczLlF8wxBIHUEqMRRSqJQqFALNhdoyWQsENjfKPtKSE6elZ8kDmjqqeNtYTR0/\nBth1gd1mYLfbsdtfM2x25i0pacW9QghISHbtwbOrunKypIrfU5YZbA9X3Lx8yel04u2XX5K0o6dj\nu9uw3W14/zjBUi/Uo79ihKJzOo/Xui0bcJzAMQFg3bUb5mc8/La4nJDjQaBWXQE7tJqzDPhMAV1p\nwm02vYWS8+18bi3a/5fmHuPbahF8J8bHsRXjmFexnZpMIHopcm6LBjF/RUv9m3w5riIUfOGvNZCo\nkW989S1yzmJaGo7a5CKjJIcPMBPjCliZMc2Vr97N5OUdD+8Xvjwcub0aub0ZuD4MHHZbrm+v2Y49\nh/3I/tkN/XBL8FHw1lVxToCe2Zlri7A99BycGzojlzd0mSnzI+X4QIyJ8bCju/qI4XBH7AdrO6qa\nirHaWPIgG1R6b9/GNbjrJiDzBLEyT0eOn7+n2z1nuLomDi8IwwLLE2hvKT2PEB/R90/oUzSdULFr\nKLUieYbeOB1EK+VCtfrfXJIzVRbDXjCVJpKtnCzCXBOl21DriIbOduGazcWoZgYJjMPI5nDNZn/N\nZnNF6kaCzzYwggdrBqtqBqZ1iUhYCCnZOD6KjbOPAjHYedxs2V1dc3j+nMf3X3CaH9nttlxf7fny\n3QNxmmxepf4KBoL1Dm9pup6/Wo/6Z7IYDwItPjSMoQWP4tnEmRavqNTzBORgt2g9v+sHAadtWurY\ngcCaUlFbCeDdJlrwqWvLrPXxA9kjvI9o9x0/qinggvxs2t04jsYHLzQrllBNnlAaPqpmiVb9c4q0\nyu8CwGvAqFOoc1GWkslz5fiYeXw/83A/8ngcmecDIpHddsfh+orru2s2hytSf7jgdVi9b4vABr+s\nx73eXI105BNNW+BYz3NGy5GaJ+qSif01qX/BcPuSbrsnREAXKx3qyeYsoEjozAfA23A22NY4I2Hs\nCF2g1Jnp+MjxfkP/sGNze0UYBSQhzBjBaYQwojUZSy/7dOKqpigsvmuqBwI6m7y9nGwgKwVlpk2q\n0PUzC7kIS+mo3WiqxRhZstGcYyn0WhhTYjNu2R5u2OyuGIYtIfbn+j8EN8xtjFhvc5eMloSWQhXz\nTWzGutJFYxhKpB9GNoc9p9MDSGQzbtltd6SU3FfHjrf+qgWClumbWs0XgbISb871e9uvzxQZQdwP\n3nbbVgMBF+QZyzqcALaWFK7ZsTRMw7rYG8iozkL6OqxSVRGnCDdkvlQbIXUpyFk1EEAb3mC9++jo\nhHUELKM519lNkl2aiq/tqJzJU9TGGsSwAQpoRGo0cFTVd05Zy6loiKExLAXmUrh/nJimhbdvH/j8\ns3s+u97y+GtHtArbm1u2aSCEzi9SG1VczL5tBQHbYnem4broq6fbp4sLXS0VXx4sjw4but1r0u5j\nus3BzDt0Qau1b7UuUE+2iBGgMyAvDi7gWqDvqGNH3PTETYIRHu8fKH8TeZ56Undlo9hkIugTyojK\nFqaCzid0OQGdD8pxE9Aa/NrbwNUYOmQyMVMpE6ITJZxQXXzAjVmN5ZzIpSN0G8bUE1JFZuMxpDIx\naGEzdGz3B3bXzxk2B0JI3rKMZ0KdOZR4iWk6BSPKZUpe7F4KwTeCAjmw5Mzp6Yk8L0QJpNhZ6zLu\n6OLOuBsOkuvlDf0LHt9a+/C84Hyv1g+rUfunlmbbwgjBUqJ2Euu6s6/0ofV1VZoCwcBFuaA2o7oa\nR56JRurr6fxKFphswUuo550SWNMZByL9cPl5wIyumY7ipo32LHW73ZMkAAAgAElEQVQ0r1ZqqCsL\nzFRTddUIrFhDE2hhng6qwf0WsB7/+hl9xw5wHh9n6WVp2vlF3cjnhNbZsYd2fGdascmqzJLF8hbb\n/c3BZ1kzA/HWGnryC6CW5lcjvoS0R9I1afectLlyx54z/6JlECINwwGjC0dEej/PidAtpGGi7o/0\nV5nxOjMfI4/vFzZfvidGZbjaEEKAzgVKBXSzM1uzZTZwLntXIVSQDheW2PXwtF21OnGoILFlQ8lw\noAq5BpTEMO48i3vHEpQpRmIUhtib78HhGZvdDd2wM7JWND5JKw3tGrHK3q1KdGVjnkEr1XkGVlFH\nm6FQrUPUd4mh6xn6DaXu6LsdMfbGJbkAyb/p8S2Ahb6DKzTzBtvZ64oFtCz3rBK0rxgSKSaPjmAp\n6iXlGD+xwbE5MfaZ2KDJpjOotRonwHWJrRppmQoiFzwE3ESCdZdtr9VuWGkB64PcxUNS476vN3tt\nHx6VhDTJrmcwJp6yRdsGpXmxYLMYqqd7XjpYiXnmMVQPXsWE8AZ2BllJTpsxcLUN3G4Szw6R22th\nv4UuZURmRBNGD7YgcK4tW/3UlGzeIqwLJlRqGcHkwSSbI1ARAgOMr5DxE2K/tfq4TFZuSNsIzu67\na61YqlsPJiBCcHL4qKSqjKdAPQXe/qTw9K7w8NMvifWelK4J2wN0eyhPiFRks0WyjX0ndoRsOA8U\n97FsrVEHagkXJat4NWAdBFWc7Wfsyc1uT1cW8sMDnQpdHEl9ZIgj2+sXbK9fMI5XdP0G6TpisgEy\nqsV1AMEz17reS6oVrYv5VFAR70iUWoliHaYUIn2X2Awd0zgwjVtUC2O/J6XBgo1GmkX6Nz3+7gMB\nHvEuFvLaIuQSHjjXzqZktTYLwVyAitfoLaquCb2cf3auoZ1yq9WstIJAZ6Oh1jp/bV1eIOb+q4bS\nG6prgSmvmMP5SM/Hbm8f1n61ahOnuMYAKwnEJzL5Rk5Vcze0S28mo9FvyLVNic95BL9pnU8g7owo\n5oEYXQMfo21wQ4L9AHfXiZd3Ay+e7Xnx7MDL18+5e3nLsB2IERRzLFprfu+OrB0cD5l23jxz8Jah\nsIAsiE5e9wNygP4FMjwnDAfLLKrRdu32dG8BoM1IlKZpcK0Itaxn1oZ87kljYrjeQrnhON1zPL7n\n3f07cpnohrfsbpV+19teGG2CNl2AYfAy0QaX2lhxo0iT5zMmUTOlCnk2FmnqIHgkbkgB0pG6kfGw\noTIyx2fkp4njsSDSE7sd3eaaftjbDh07I5gFm6XByqmJjoH5BO5gXaK1rK24aY35bgoQusjYbQld\nIPbJytViY+X7fmDcjnRjBycDmuS8sH7u45cOBH/0R3/En//5nxNC4Ld/+7f50z/9Ux4fH/mDP/gD\n/vqv/5pf/3WbfXhzc/PB71VtlF086sFanOu5OaX+Y3BmlO9qhimYCuvrpc85cz+/zlnLYLhAcA2/\nSKMWNxqv3Ri1uR2tDD9/Xyd4WECa/HjP792CyVo+tD6+v07BptaKWSbYc2vLNgrF37eVM5liQcFb\nm2vq6HV7rZbPxJCAjkogaSVGu6hdFMZOGTthSMJ+CFzvEq+eD3znzZ5XHz/nxZvn7G6fMe4PxL53\nareNEGtcxVayCZU2Rh6wmn5VJ3p2IBYYRI+WkrOBsIf+tU06SgNS7qE+oe4SrCQPugLulwidZQhY\nYJbq5CQnhyEbYr9FDleEuDDe/4T0Ft6+OzE9ndgP7+lE6PuNtZPbwgsCXW9JGcVwqVqM2KEgebLd\nNwZrrxZhmqFkpQ8G2IdQfAiuIMECwX43ICmxdC+Z43vu6z0aN4S0Iw17YrclrPTydNEl8Jkboc3X\nwPUQLRBcZM/V8CpPGwkxkvqeNHSkTU8pmTxP5GliaIFg8K5Lxchs3/D4pQLBj370I/7jf/yP/Nf/\n+l8ZhoE/+IM/4D/9p//Ef/kv/4Xf//3f54c//CH//t//e/74j/+YP/7jP/7wl1sa3tKulpqvFGPO\nAIKj9bYr2wepDd1HzrWlFppVWau3jJdRHX7w4SLqvfhLzUIwLmGInpq5TbdFfSfBrJJb1583S6xW\nvohfWx8H3tyH8H8TMZCzpfjr32IcxuAf3MDr6nFC1/hoFYYYJ0CAFVjEsQIQFiRU+hQ57BJXV4nr\nbeJm23G9Gbg5bLi93XH7/Jq7Fzfsb2/ZXd+QxpHQJctWcM+E1a68XbRmge4kbD1fG0vZHRnRxauz\nHuKW0L2C+Brtnlk/vjyhOiG62G2/osPtrcRq9RA/DPLqGMUaeNtWCdLDcNWxvdvx/v6K033h85+8\nRfQtQ7gn9jskbuyaRYHeJzRHrF1ZFot9aoaDkiuSFeaFnAtzSZS6IWjyUig4cS0QukQUKI/v6Hcj\nVzcDebhlGQ6cjgGpA0WEXOvZkQqbxG1dAytjzyf6vPjNAau1F80iPfisxDLN1FKAgUqx76v5bqSu\no+tHQrSJ0xZQzkzeX/T4pQLB1dUVXdfx9PREjJGnpyc++ugj/uiP/oi//Mu/BOBf/at/xT/5J//k\nZwLB+pEvUxW/6VvQ0ovttkXM4Kl+VWv3XHIG1leWc/99JSg6Q6tWA//O/gyeKfg4tDZjwUQ8kSi+\nGItidaTVsTZn0dLm8wyGszW7+mQmDR4gHKNohiWiTpq5kAa3T9yIzYYr6IofiGc01WcXNjKTkVxa\nRM1EqXRBOGwDr54NvLrb8up2y+1hz+3Ngeu7A7ubazZX16TNnjhs3dq8eH3fWoCDLRbCRebjx2L9\n1JXc1QRJDbS1hbwFOSDdx5CeI3FnQGI9s/LOKIy/wdqN8JH1xsu2gOHgaWtNrnRmMRZiv+vY3u7o\nvzgxPZ746v6JLt5ztX3PuM90mwLamd16169ZnpQZzQE0m68kRnyysesGxlUSRZLPu/TaXQEiaeiJ\nEpkfHxBZ2F1t2e63XKWR9JAps5HFSnVcyjO7EGwsOim2ChnW121eG61McPAy+PlSpeYFLcHl1ZVS\nM6UUV6DaHIu+7+mSlSHq3YhvevxSgeDu7o5/82/+DZ9++imbzYZ/+k//Kb//+7/PZ599xqtXrwB4\n9eoVn3322c/87jRNazCIKZLS2XC9LQlpuw5Gm40eCExYVFclXWv5hbamDUmzrr4DKtAyCENPC+q0\nY2eNqXVZDYBMJLHR0ylYBJY+Yosjs0wnprysF1WcIRjlDGg2YI5gyHEXva0o9SIImDIvrs5Ddmyi\nauaTYvEnSGMwWvqcm/GIbxwhqBmdhEyqQqrQSeZqEN5cj3z30xd88p1X7G5u2B6u6Lcb0jAYJz/Y\nfET1YCqOB+ja2fCFTZNrtwyl/ddaUx5ImouuRAjPIL6A/pVJh7VgI8vd0x88aCfrdqgBjtZ1yGhN\niPRIGLDhIq5rkIq4LRk+u1EF0qZjc71nf/XA9H7kfrrm/Ry5fwhIF0lDQdRG0NHZtdEQkVmQUiH2\n4MpN1QplRsCuX5/sfKdHkzvXEyodEgfGYctx6vnpVw8cp0fS8EDef0S3f822K+hUSQvUWjhNM2lT\n6WMgpETsTFEIpmGpxa7liiE5zhXafex6GjP9NfpwmVmHn0zLiaflxCkb9nIYN5zeH/nif7yl5OqW\n7r/48UsFgv/23/4b/+E//Ad+9KMfcX19zb/4F/+CP//zP//gOZfCosvHZrPBa4E101vLgfa79gK0\nDgCIjz6vLq9sGUH7jfBhquG7+WpwdHEcLVEQ93RaAWEAaZTZlrpH9wswuayqUEollUxj+TUr9rbr\nNywjBJMWdxKooquaMbDSGc4bImui7+/sACntM7jWUKt3BVb4xBZWtc8YgTHB9WHgzcsb3rx5zquP\nX9IfbkibPcEDwOUuLCsoeHlALQ1oR2znZqUOa8vZWkbiWEEYQW4gvkbicxv9DKATaxtS8IzHy6j2\nftLo5tXajo34Ea130gxbDVwEHOQzL79It4NxB/0uIqdrJkl89RjoNjObMZuCT5QVhwhiJoLag2SQ\nsir2XPJgJqOd2HuFE+iJqosBml3POI7suh1vt0IpM9NJYdfRbTbEpGjK8O5oWEQIJkxLHbHriL3x\nNdQnea9+DiIOTLvYqHmPNect1IFM24xqMD+EZVmYp4npdKLmzGHc8L1PPmaaHjkdJ/KS+fyn739m\nPbbHLxUI/vN//s/843/8j3n27BkA//yf/3P+6q/+itevX/PjH/+Y169f87d/+7e8fPny5/x2W51e\nDjSQxH/WlqGxrkyEoeA68GIGoe1ZnpKvZGG/iIaqe5++0QHbEEMsrQ0uGKrB0HpD5QuoD/RQq8mR\nakhv2hKDMAbLKwTzL1jrL9cMNHvyIIEkkUSkUCmhOGux0sxOzsGvXWJbkIZrNYguNGzJrNFFqSGs\n7cZQ/XfFxEbbIXB3s+P1xy+4e3nH7u4a6XdIGn3n8/JpbfkZX8BWqMuNRdZalZaiOqFHVx/Dtnmd\nW4nEZ5A+gvQSCXuEE+gRM/Yo5xvbOyEf4Ayx9/NqO7LV4+pgz+AbQjzjSh6IYyjQCWEsdLvMcIDN\ncqBOA18eE7unL7navKcbqnkg1gC1R7AR74TeuQQF1ZMRHGNYF2PqrC2qPKF6RDGz3Bg7NmMP7Hl4\nfcvj48KcH0i6M5AuCYWFWZ/Qqm5ZPtL1vXkNdMnAveLj1D3uSgw2Mm29NRoxzf0vqFQtBg6W4tOp\nhTpnyjQzPT5R54WrYct+3LjBDf9rPAt/8IMf8O/+3b/jeDwyjiN/8Rd/we/93u+x2+34sz/7M/7t\nv/23/Nmf/Rn/7J/9s5/95VbTNiBMPwgF6+IWp1/S2H9FTQ3oDsM/82U59hnodwpuaGBEu/HwZbfy\nitdlacgslazFrcXO7ACRSpc6hB2iQgod8zSjqnavBisTCPGMC/jBtfTu7C2v56wHS76jKs1ArRUP\n1ikwarKgJIKfPiEF6BPc7TZcb3uGHva7jtvbLW/e3HL36hmbm1vCePCb/WLm1Vr3y7nOb8fzwR3T\nFpzhJPZ09ZrTxUVECDeQRoh3EG8RGVj9Hlzss2ICPppK1tworCWINI9DxTEAtbKhnmyhSkS1lRiL\nX0mhTA8s919Rnj5Dn95RnnrmeeSJDTengZvjFhkjfd8h2pkKMasRMapiKs5mza5orQYKamCZYcnG\ni7A5V0IYEmHoqRFEM6HrkL6ncKDr9/TD6MOfAgwjvfRe4ttGVVUJxQf9tPzPM79aFRs4065BXUnc\ntvurZS65MpdsGWIQci5UdQxNIdIxdiPX+y21Fpb8zaOOfqlA8Du/8zv84R/+Ib/7u79LCIF/+A//\nIf/6X/9r7u/v+Zf/8l/yJ3/yJ2v78OsPU3DBh2gh52xAxMd6W2pkdF5PnfSSRuy/05B5YM3pLqvY\ntYaQ9S0b0852f5vD2FIwkxpbZoAEorOcRAspRCPEhGi68fpELQtNhmyZgbHSxKViFTUfQ4JrDZzn\n1UoJ7PB6bD82XZuYJr7VxWKmrF1wD0OB7RDYj4Ffe3XNm2d7Nhths+vY7Ld89PELbl4+YzjcIMPe\nOwvnIGTZVPu24Sjt581nABqTw0wAGgYAEhoZR0E2EG+hew1hA9J7CeEDR/zT2Hi02CBgf1/BLZwx\ntpCeNwdtfluL0ZSDSbrRGV29Eo1dWU735PsfU59+Qj1+RXlcOOVnTPETHk4dj6cdPQagIRubUSAn\nWIq3JrNnSB67SqGUSCmB6WTThEQrKQmpS6S0IW33zBLQxS3q0wByIA57hnFLXiqlROK4ZcBs6WiD\nctxtWdfA2Lh/una5WqeqKpRqU72qny4VIzQt00JWy2qXpbhqtWW9iTFtuLnac5xmHh6nb1zTvzSP\n4Ic//CE//OEPP/jZ3d0df/EXf/HNv+gmJKuJafu5iLME7YPYDPjs4GBFLm5kVRfpNA7+quuXNast\nGi6m0LaTaxFYPB2xBUZTCK9VcUtQzLG2goMtOdkMQ+k6Os9slpMwTzOlKEUcNBPci94ujLiHXQzQ\nCXQxkIIRf4q2yfVOUw52BmJD5X0HTVHY95Fn+4HXdz0ffXzHR5++4PmbF9w8uyFGIaVA7CK7mxuG\n6xeE4UBhZ68tvquv4qDgASvQOiv2mc/nQddtyS3Q3LDUWqdbkBFNdxB2aOixTkebOrTQugAWMHpT\nE7bgRsMavCRaSUx4kGiTHMSPd2lHBeAuwlYuaimEONIfbtmcKtvTW/L7J+bj/+Cp3/L2tGE3FzZ5\ngWQUX8YdcESLc/m9LV29X7/kzNOT8u5t5ekpE1Nke9hz2F/T332X7cvf4JT3TPeJ/PaRKonh9pb9\n8xdc373keP/IlB/hsGOTevYvbunHrU2m7pJd16UFV8dLHCexc1M8M7Exa7lZ5qOmptZKmWcKSgli\n56BAR6RKD8DQbbnZvuBtPxPC0zcuy797ZuGaqZ8X/RoNpGEGDg46MGig3rmpuMqGsZPWqv+mUAz+\nRoo6kObf0wAje981CZb2fM4psxfh2mqykE0JGKEPkdAPJLeX1tNCroVFC000HpyqXAUkFIKYkaX5\n2EWbxehpfwPqBSEGH0VaTTgUU2Cz7TjsR17dHPjoxYFPP9rzyXdf853vf8z2+TPG62vW9B41kU7a\ngGxQhotPmtcv9RvO8wDEh5ied+h2YtpJaYs1gIwgOwgHSLcerBaoi4uIZscePNSvvITOKeae7Ep2\nTsaa/Fok8kBgWZy39dS5DGI4Qc2FmhdqMYFSHEeG6y3bMrFMM6UuLPMDqgPHkphzoVbLHs2soTMm\nYWAtTRS7zwrKtCw8HmfevoeHx8owDIRxx9V4RXf4DsPtR+RTj8yZpRxR6Rhvbtjd3LLdX1vcmipS\nA8MwMF7tCaGjFjnfqO0eDl4sXmSIhslWd0MqlFzIbpYrAQqFvMxkwSZoG2BFjB0pVUpR+m7L1f6O\nzfDVCpX9ose3YF6Kyy1bP/ws2AnRasmquOa/IShAcxG6PIEf1PDQMPd18pGDWeq7qydbJOcmeOSh\ntrRMFXU/wVrc9ksDJVQKs7EPi7nVJIlmRdZHpFOL7nlxNaT1d9s6MpGUpXa5CicfjDEkbxOLEquQ\n3IA+CnRS2XSB7SD8xq/f8f3vv+LXf/M7vPjoJfvbGzZXV2z2e8LQm7cdsNKjxdtyyLqAIFjby9N0\nXAdpPYrUTuAaqRW5oBr7Oa/Vdv94BcEzAalYa9B4AlpOnDkHburhWIddqPP7W4Bvr6/+rs2ivfVW\nHIhU95qM0Xqr08nKkyCEsUPGAe0CxI6YXpDGma47EcKGkDZUMceILokJrOpk4HAMaBL7UsxjgsIx\nL7w7Fb6YEo9T4qAH9tzQb14RuhuqZpZamEpmzgX6nu31HcNmb/LgboCNaQS61FFDsinep0yI1Qhm\njeDmg29rrc5fMB9CLT5KLxdyySxlNuv8xejES8nUENGusww5RULfIf4aXT9wdXXFuOkI4X8BRvA/\n9fCSoNWb68/aQtUzkKfaUGXP99vTG8jWUim5yCyk7XHtzxZMXNjRoohnAGeqhf2fSrCb1kbcOFTj\nr1msnamaje0VEjEOpG6waK2zv5KuoJeVGnUlO1RVFpRQhd7bcDHYlKVOAkMSDpuBZ4ctz55f8/zl\nLd/9/hs+/e5HvPr0NYe7O+Jma4s/NpCvHWKgjSvnwtzkvDAvALu2+65GD+GD59LOSfX0lQiyR8O1\nYQJhj5GOTlZf18VISfXki99JQZJQ6fz78MFLn/O3FsCghXeHAc9PbviPinEg0obAjIbFPq7YpKau\nBqgjZYmUU2GZZrS8ZZ57prknbnpCUJp6kmByavX2ptHXK9OsPE7CU00c48hm3MPmObL9iDDukL5j\nfnzguJxYtNKlxGa/oxsHD3rmOxj7QAyRuuhKUmr3f21qU79+JsX2NqZnxKVYh6CUhZxnCwZrdlCt\nzzIvlOZVgRBTZFkyKUV2Ycdu2LIZhp+7HNvj796PIPjOc9GfFk+jSzsRzb4b9VbbuhRXVKF1C9a0\nTmRVNqLFwMa19JBz0PBiotQKzRW4TQmSYjducDPRGoxS7FcrlkBHIdUTIXRINxJlw5CUHCuzzLR2\nzzlx8ZBUbCcicJ5upNBYjUGEXoSbXvj02Y4ffO8Tvvdb3+fXf/v77J6/Yry5I3Ybq28FVDImEGrK\nvbaokn0uohdNZX1O6wqsmZMKqHUTmuryXLs3UZEHAwaQa4g3kK4MYGNCdLZAoAWqafhDtFkGVRoh\nKNH8GC1g5DM41hiE4inzKvoy/0PDFMS7Hg7ExkTcbQg8onxFpVLq4iamShgCaYz0W6EuX5FPJ6bH\nLafhinF3h4rNPUBn0CPqk4TNGahSZpiXxCkLU9iQNwfC3TPCzUvYvIDtHtkOTF/+iMdpYqbSdZF+\n0xP7SAlK1kpRpY+JoEJ5mo2BemFPl9W4McHv91h8hLm63EuUoplSZ0qeyHlhyQtLziylshRYSmFZ\nsrXbYyR1kRgCEoqJzcLIftxzGK+/cV1+C2PRWUklHyj/cI+AswiBc8rQvvu6stqCymVmYIBhA18a\nEr7WCazLc6UuB8AdiCT6cTgesKK3AjVSJbNgwqSgau3A0tpp50yjNYUvx7urqONzxkQMTjDZ9ZHD\nEHh1veejZ9d88slrvvPJx7z5zic8++g1t69fErc74jDSQD9bhH5GRExYcxEk7TxZb/wM2LVFdnn+\nImfqbl2vA3VG6uLPCRCvQbaGCYTBX2/2heQy5Nq6QRa+rXvioFjb4VX9MyQ7txq8VizYoNViC7R6\nRtiyExHMFHUGTRA2SNyDFEQjWguik2cQGWWCmIlDIA4FzSc02PSg6X5Cx57UJbQe7UuPVD1RysJc\nZk5LZiodEwPaH+jGZ2yffUwYtrz/4ol5eU/31czbrx6YHisxjgzbLf24oRbl9PiI5EwXAzEIUs1h\nSL0tbmPV88qQzVi2QKmUMhtHoBZb8MtMnheWeWGpmVwzS87M2UqSeSnMS6bkTK2F2HXQRWoyM1aJ\nHZv9geuvif++/vjWMoIG8qnvjhYEzn74F/0Ef1ykUS2bXZF1x1maTyBCDWcgrGUNa/XgO2OKkSC2\n6wdv0ZWqVM2UAIRAErXdteLmHpmsYZ1hGKoi2oahQ4Mx1i9vY1Spzo0wX4M+Rcax53bX89Gh5+99\n9xV/7zc/5X/7P36LF7/2Xfq7jwjJ2nHr7qkNPdfzG/H1YIAHAb2o8ZWGSrPiMkYPllUj0CzIM1JP\naJkR6QwHSM9cPzD46zkmoAYKSgN1W9khEW1BoJUDa4C0QCBNV+BZiWp0KYFx/Vu7uJGOlJmqs+Ec\n2hGlQ4K78NSC6Lx+XtUTBCUMQhwUcoa4kPMTx/fv0LyFw5V5ItQjVScKE0UX5rIw5cKpDswyIuOB\n/vCM3bM3pKQ8vf+Mx/efofFz7pctU9nTdRvG7Z7Uj8ynmeP7Bzapo4vJsACx9m0jwpVqVnei5pRd\na+MGZEqeKHnhlBfmZWGeZ/Js05aNVFRY8sKcF6Z5YV48IDw9MZ9OSEqEcaC7OTBse7TvGPc7rm9+\nxTKCUi7aVMpqNdbS6LaDfri7nXfY1pZrQ0Px3VUa+OfAlMm6zxlCcN/4EFtP3TQMtDS5gZfB9AAm\nOZW13rPdtPqOZ/VbYLHuRjbRB/WsVBSfmSihLUL7LCJCF5VX1wN//7vP+Hu/+TG/+Rvf4eVHn/Ds\n1Udc3b0k7W8gbn2heifCtLMYpu2ip1aAtBLazrCfNWEdSCpNyARgi39lY65/VlTNtx+SKfbCte28\nYcDYgK2FOBvYppPJeFU9AzAKs8YNyAi+UO1z17UcWsVFK1VGEJzhp9h7lAy6mINSPpkWnwBpCzFR\nZELqEeVE1Zm6OilBilCSkFMgxMAilWl6Ik8LS4pkXdCgdo1VySKmEtTKrHCqkSX0aL+l6/ds9nu2\nuw3XN1s2n97y9rPE53/zyMOxMMvC9m7HfrsnBBteqktlKTMSC0PsSGKlWrOSs3JLvWFSafM7S81k\nrSzOHCzzQj5OzKeJeZpsc5Nq7om1YQcWJDRA6JO13OfK8lZJWVFJDKHjarv/xnX5dx4Iaj1Xzisw\n2NLoi+fJ+sf5B03BF4P3vz0LaFH3DEZ53e9obOBsJCoxnIGnNbXwRQ6IJlcGum2Wg3yhaRPWGQhn\ndDd7IFD1nbihhGfSnB0SMMTAi5sNv/HpM/7Rb/86P/jfv8v3f/A9ts8+pT+8AhlRki9k1/efrTD8\nOC8APV3/gLWub6fhIiNoWAXR8YKGyntp4NbZSjSOQNgh8RZk64vYwbW1JLjgCkiEYNiEZR+DsRlX\nKbO3SNcJzY1N2o47nK9dC7qqZh6aZ8rp3jIO6ZBxi5ARHpDwgMqJWhf73F4mhhiInRB7MVqxRKbT\nDPmRKdjsQK2FYRjous7OsgQyMGvgVCM1bojjgTDuGcbRMo6Q6PYD9Yue49LzeDqhEZ7tDmx3h/WG\njd6BqaVSA84OtTKoajEFa3XuQnUmo1YnurVJ3sYSLDmTl5llngxcDEoN7ujl3YHqfg3qpj1aMvV4\nYpZIl3qSBvab3S9Ykfb4FtqHF3yA1uu/BPbWmwTW3WoF9C5ou0GcZXV+jRScK+87pIplBW0/tzK4\nXmAIbvzRrMN8tkDwQR4a6kpIEm9fhXZ0apOT1acW4a2gdQ4iF204Gown3GwGfuf7b/i93/v7/J//\n1z/i9u6aYbMhxA2qHeKtPGnkn1V63DQOznpc36F1BC66Kusic4yjZe1NZtxahXZB7KtW29HjzhiC\nYUTpHWcoKBPqhiPScAG1notKQoPrGJA1yCjtfTwpvvje3t4DQEPNiaDGtDPL8IAWpcyZOj+iWrAw\nvdj7coRg5YlNsgsQOrQPhCLEDNKPaNgwL4H8aMrF6ckMTHZXB3b7PRCNTkzHpHDUDrorNukW7Q9E\nUd59/tcs72cefqLc30/MNTCVgHQ9w+0N/fWOpWQkCJvdBoq5YXXdYK3mCnkp5HnyqcaYjkZ8YQuo\nBkIwBWytSlh8tmMXCTW5hqYF7ZU7a3dBqeRSIEQ02bmoy2TODL8AACAASURBVEz+6kuGTcfYbb9x\nXX4LhKKmsrJF3PCC1jICfCHgG7astb/IhQYhWH+aYIuz6QKs9j3XlmeM0P+tLQrORQewimHOAJ+f\naPHXD+Lj1sUie61UH4ipxb3nog3CsM9n/Y5GfxaEm93Ar72547d++/v84B98n1dvXtGl6Gu5hQtP\n+JtTsM6ououwzkaiWRY7clEXzJw7L+2VkEbNap/lHGrtBPh5JSF0toOLOwq1dh8VAyZn0JOxBtd0\nvoF+5+M+BwEPQhfXXVaAh/XfWY/Wrw02++HcUoyENJKGK2oUVGdCBxIXL8FOVD154DOVopWPkdAJ\ncYS02RE2Gb2/Z9Yj5TQzHY9MD0eWeSYvi5uKDpTQUVNHTRuQPZEdabcnxEQ+PvBw/8TT23ecFjjN\nEelGhv0VabuBFKnVNAJdP5C8EyQafI5COV+JAE2kdi7XvFQqDqzmQEiR1Hd2prpkbMJSmLMdd6nZ\npiqJuFDJ7nsT3omVD8tMiDtSf6E1+TmPb6E0OO/ILV/2mAbAavFM4xC0JR5XIU9ofWpn58X1Rveq\ns93nF0Ggtu5C8Bu8pajB6nZpvt8BNFRfxE0AhYNxfpTZyB/LMttIdnWaQDLjzTYbIYhRiks1KvOb\nZzv+wW++5rd+9+/zyW/8GkGFOrUWoItsVlFWtYVfj5Yil5mcT+TjE/nxgSZtFc/Ira3o5CzaRmvn\nMrhFmomX7L8QEhJ7JB4gbpFwa4GAzj9l8/GfMOuxGdF5zbTEbVUtYImZebhvwIc6knOQaO9u4ddd\nkVsA1pZFYGWBCkhHHK5JmytM+dewAlMpVgc27Xc7rIyyeyWmQBoDaR/p5gQPJ8rjzJRP6PHIcbln\nOR2Zjkd2N88Y9rfEbgfDHun3MO+hDvTbHd24YQ7Caco8fvE5y5LJEtm8uOXw7DkhJbuvKQQVQuwZ\ndz1dipweZkpxHwYxB6HqmFHwm1/Bp3VlEKharLxJkW4YCH1PL+Z0lEumPj0w4TMrsGsfg0KCmjPi\nVvtFK7nMLHP/IT3k5zy+vfZh+/8Vav96n0DOf/kO37oA6uulZbm2wYnv3u1G9fS5/bt6puCLGlg1\n30Ewc8sQDRMAApEYok8FtvwhVhC1E4w60ltNW6DBvAuCv29QJbR2HIUQhJvnB55/dEs/9pweHnl4\n9xnj4cDu2R26LMjje0px6uj0xHR6z3y6Z55OzKcjp4dHnt7f8/j2nekoYrQdMtmYMBut3UBKY2rG\nGOliokuBrhNSinRdot/cMGxvGTYD3ZhJ3URMEENGQjXKsTQTEPMxNAMVS+fN6VfWmr8RvmQlYNlZ\n/Pq1t4uR/Mb0rGFtIVYvOawjIVKd9dw6D9YRUGZPkRfPIC5apJKQMBDp0JTou4lxFMbDLfNpIU9G\nyCnzzDQB708QHqkyMO73aBxhvKayZZkTp2lmzoXT45HTMXOcI0XF/Aiu77h58ZLD9a35bFRL2YNG\nUpcMk4ImYLC7N6b1/je3rZZdWq0/L77b52z3e0oO7uq6cFLX09fKICAxkxebrCS1UiQhpboxbqEG\nIZeZrBfY0c95fCuBAC6SxIZ4q66AoXjuro6yXxSYTkg01PWclbZSoC1ifwev/9dgIFZeS/ViogWC\nhlOE4NCapcwhdLZz4tgCitRiQ1P9AmqtvhHbAgkxGaDpaHAtlrrHKOxutmxvtszzwhc//pz7/+9v\nuP3kO8jNLTw8UuuJ6fTA6eme4/17Ht9/xcP7rzg9PvF0/8jDV/e8+/I9X335jlLVmI2dELtI6Drz\nqQth7drFLpK6jrHrGPvEOASGsWPcDOxvPmJ/o+yue7b7xLgtDENP33ekToidIiFjg2RN6be2BUkX\ngiTAg520xdx+Ku1qysXPfFG7xNy6Dg1zyNjUYbc084zQLrXxCFQfqTq13IFL4pTdXwlhhDCQ6Oi7\nQB2VcX/DPBWW45E6z5RpYikT9VghHSEdCSOUNMJ4oJaRZYnk45GaC9PTo2ELORC6jn7Ysr15xs2L\nV1xd39B3iTw9IRoIkqw7VdWDgGFJ7R5rZWmt1RmDJp6q2QxGlrysHgUaI6qmN2hoUex6eoESA4SZ\nyozWxTKBYAI3qRWIaAhkzeScv3FdfguBoFWpusKClwzAxguwzePc4mp6AWm7jysOG+ho9buj4eH8\nchc2RbaAzeqOIE3WcpGO1rImr1WEUiHk6s+vzGVhnmbmfCQvM6sPfWtPXhCFggcLpFhZk+BvfnzP\n//3//IjP/vsDmxRYjo88f3vixdsH5kfl+LTw+HTPw/tHHt8duX944P7pgXmpLHNhnjLTvDDlGcWB\nUM9aZB38kgzgDCDJEPQ+BPpoX10X6frEZvPEbvdT9vtrDocDV9dbDtcbrq5HtvuB7WFks+3YbHqG\nfiD1o5mb+Ilt8u61LbpiAI4RrIDkZauSi5Ag/jwwDCh6Z6J4QJiNPSkRpTMsxEuIsBKjvE7RAjWc\nuQfgnJSFkCppjGx2A/m0Yd4NaD6ACCUfyWXmFBKaB2ROVDoyIwuRuSwspxPLdKJMJ/LxgXl+Yr+/\n4+rFC25fveLm7o4uBWcmnghpIPYj+TiRHyfmxxM6W6u3aj1P19K6mpKUvJiwqBb7fslW/6uRkEz3\nsTZiKWKDYUPy+zMsqLrPQ6iEWNFo3bJUBkrNztL8xY9vLRC0OnK9MeTrT2xxwevL9mt+48GZz69N\nwWd5GFZ9NSDyDEZ9eBysNyrSMlUfSSZQ1YeFaHXsSin5xLycWJaJWgrRBSM4X2GdSIwFA4ISHOkP\nUvn88yfmp8zf/r9vGbpAiMrNT99z+zc/5Xg/cXw88XQ68vQw83SfeTxOPEwnkyproLbzkKARpxpe\nIjFbx0OTg6k0Ri5JhE7E/BhDQGJg6J4Yui/YbUZ2u5Grw4brm5Gb25HD9ZbDzZabmx03Nweur22A\n57BVYmfzD9Y0fwUk4evF3UVr4ms/9pO/dlc8ILSdXdu8hMVTQJ/tQBtR3iYu+a/XYN0GBJMvB5rs\nWiKkwei/437D5sn66aHfM89PLPORUiuzjpxyh4bIEiJLrcx55nh8ZHl6QpeJspwoNdNvBq6f33F1\nc8N2vwOXQxsPxcrMPC1MjyfqtNh9FZqY7RwISikmpV7ymRfgLEELBNUrCguUvuv5/Wsahpg6Upcp\nIpQC5FZGqGWFJJqs+Zse30LXQFbYCDjv/rB2E1qa3wKBjTc7P8nAFQuRFgwuxpGZawNVjDUYNJxL\nB9cUBARqoDSFYAhe5lrkjmK8RxUlB8skQhFyvuAM1GKuRAZAsMqXWsniGULsoo2uqguP95nTQ+bz\n+EiUaoNI/vsXhBjtNWk1YUCysBSYaySHShXXT6xJ04daiiYeavyHZugsYh+PoCZM8Swp55ljzTzk\nhfT0RPoi0XeBsRc228R+3/P8bsvLF1e8efOS5y9fcPfyBYfra7b7AzENFuxWJlO9yPHw89namOrt\nLmj7mj3Ld38tvmMtKM2QxNN+SYYpqLVmhS3W0Tja72gjUJm+oakbz6xEg5JTX+i3A9u7O8KwI50y\n3fTEMj+RnyYokaw9pcCkztibTuR5Ji+Zmk2mHdLIdn/F3fNbhrFfgd4QhNTvUAlGXJoLOVs5EKIR\nqkqpZm2uXDBpnVuQ88WXlQulYQtutGt4tn2uICZT77tEkA3zMjPPwjwvLHMmeAdDknevPpCX/+zj\nW8gIGrJ80f++uJ/tZ1/bY/Rc/wvSVv+HkoR2z9GcB5Sqti22DkRQuUgrzRLb9AjRcYDmIHNuIao0\nGrhSircL23AFwlpW2LDUtqOZiUaIcSUwqQbykplqoZINNwh6gSMpIQa6cUsXAx1CrsLscIg1NNQv\nqn3KFqwM1cAdkBtByE9JDXbztEDhqL7RcitLrbAEmmVbCkrXBcYh8OUXD/z0Jw989uNHXr58y8vX\nX/Dq9XNevH7G/uqacbe3Ed8h+pG1x3ox7PLoZXHQAoV3Dho2QLbSQDPIhdWrg3/NJNn7h6w8BnXG\nYWhlideFbedoXpIp0g0dw24PcUD6QlpGumXL3B1ZjpVcEtOpcKwn5mOlLvUs0sxKiJFhHBk2G/qu\nR2qhzDYUxd4rURYlTwtlLsa18s/ttqN2jzmJqFbLCM7fe4aQC2VZ1iE+IdqUr/A1vKy11WOIXvqC\nhMm4Mb4vSVTjM+ivWPswBiNGrB4Areb0/1qt3e7kVmGew4A0GOG8//gfUh0cpDoBt/rLG9AXNBA1\nUaWYNmCdPecbm9jvIeYJID5jsdZmgrF41sAatRqFvk1tCqpEtZpMpHM032+I7CCRAI2+HIzyLMG7\nANLZ8EqxNuhSvQQKQhWrxbW6NXs7XqAL0XUDxVD/4vVltf762s0IhncErztX6nIIbhAeyEWZj5XH\nx4mf/HTmR3/9jturH/P6+Ybvfu813/veG15/+obnr1+yu7pF+i1mUfZh2dfKhjVArBfLdlFtAGMr\nA5pFuR2paewlEeNoi0ft53bARm5S6noubdMQPJpjTsfBZ4QIsUukcaSGBDHT1Z6aN8Q0QpiYviwc\nHxcepiPzEpEiRCKViBYldont4UDfD2hR8ulI7hKhTxASWoX5VJgeZmQu3sY75z+rR2F1bMAXvvpM\ngtL4KSVT52XNOiUKKWHeCSLkqms7PGC0+YqQC6Q0keNMyZlmw5eiOEnuFz++JdGR2IJclXFc1Ae6\n/n8DAvEbd21Htd2unWAHjQqsJKO2wMG2U+N1eK0k2CJpph01rG1I2zV9kdezM0xZFkotVCl+GJYt\nNLekoPhCbdOVC5qhRIxeGoIRAotnHu4oYxfTBEMVyMWswIoqOS/mh9CCAV4qVVw27Gm38/XXD6GN\nWyhrXYqqazKcei32d/QWV3Hn5+psPuvlQ1a1dltdOE4zx1l5+/bIJ1/c89Enb3n98Ruunz1nd3VH\niD1nQ5RmR9YMSdsn4By4W+mwlhDtmhvQZ6VAb59DAtpcltUyGIhWArXysmVCaufMGM3q16qpP5vk\nuTp1dyYXKPQUCkuJTNNMqT0rHRtLw8dx5Prmlt1uT5ciUhbqfCJ2e2yBq+3m84KUTFBjgRojsKwk\nTnv4zdCuqWKGIrlQl4xmCxDZ7+eoZo1vEm/1DcKu70q/CkLXRXKfoNr9uszFWsjpVy4QqCvOrIit\nVc+LUHW9gVsGWaWVEeGcrvtu1gKBIbK2AJrnn4SGKuNSAln528GJSOoRZfU88SAQgMYeXBav13yW\ngQalUXzrerz2y170+H1dTcZfBI1+w7aEvtr8PGvwRBr9sLkD17K4KKVNHsbb5GZsYqVBO4fuJ6iN\nriteKJxLp1aPSg0rmOV8LDoPCJILhWBv4zql4A2rpRbeHzPvnhbeP8z85Cfv+eKLd3z15VtOTye+\nsxRiNzCMga7rz3mAWhem2ctD6za0k3YJGPoF8N9TXSyYhnKR/dn1lPZ73icVmnzag0ptX/5youuX\n+Og5VSXPE6enE1UHiiaqJkqpLPNiGYZvFqKFLkU2my3Xt3fsDjuGPiFaqHnGQMxknP9sYqHg0uzg\ngak48WzdpVo26wS5SKYUtUCQM2QvEQQkCbVYi7gPdg80/puRS5uMXkhdoOsjtUTKXFiWYjMXVxer\nn//4FrQGlaYitJvXd8+252nT0kPbWRQoqp7ihzXlt2fIGvVXVx6n7DYdgqKWPq5L1m9LN++MmIVm\nKbYCVECqUEphWSayp24tTWs2Z/Vieownab4QXerTymCwhV2sPZTL4uOpsHIk1JUTo47y2bBY99hb\nsyW5AFHPwayuhJxWZnm5Yr+EjTe3VDtoJKgNZI0x0veRGIQslbm0AKVYI88CXmkLicypKm+fKvV/\nwMMp8/79zFdfHfne08SrNx9x9+KVD/wM0FJ39WvZroU6LqTna9J+3KYsC/XiM4oLvE5IUFRsAMo5\nh/CFJdEwkyjn26EsxtirxTCQOKLDgOpALoklP7FMZn+meTYEf56pFAiZspiv4eaw5/rlHbcvn7Hb\nj8TOSoYiA5JGJCbqUtCSYZlogbjWNp2rSas9cXEmYQwwjpESbUhqznafVM9ERAM1wzIpUmeWZDrL\ngvgwXwPFo7rs3AZoWps1JvoUqSqc5uYv8fMff/cUY/Whn45sBw2OjvoNI21hc9HiY929VwdcPIXX\n863Q5LaK2OAPMVEH1fnXclFQaLA5hQjg5B/N60Km4gNVWpfAUnxRT69p+n4LKEZ/Duf45cdVqydw\n4TyqrRa7CQSowVJkVXWmpN3URSFGn5HXyFa+G66YEa1Egmaztc5xWI/D39970oCRjhQ3SLG5DDFA\nrJVQ2yANI/HiQRIsEC+lUrJZfD8+LRwfTpxOixNiKn0f2Oysjrb6v11AOZ+YRqNeo1/7Kufv8RmI\nLfFVA2HNvu5cCp03EGHlkKhnRE0RWrP34RMhjVYCaU+3BNKcbNxYyIRQCWJj4bUoNmA0k4aB7fWB\nw7NbDrfX9J0B0KqJSo+SsE6FQi0m5XYnKJvHUfzLVIfnmZlqU6uHQO0iMXYcH72UaCY9qAuvKnMt\nhGZrHg2/CqFtQZU2IUtayRkikoRlMjOTb3p8C+3DSsWYT20ykFYbHWH/frnIm97A617hXGSdN4Jz\nTX8hZDFGQKP4ng1PCJaVqGAX2hdQW0gq0Y7POwHn47lIY7112QKCgYseDDwC1IYfOJzXhp62cWjq\ntWPz/repTNYSb8FK1dycw2rmcv7o1hoz7KSu/oS6ZlxrB0ZWGgRNBVwBQkWzMi8YIal6AK6LTWYM\ngarSJoYb8u9TpoqCiHKaKp9/dUT1C5aTBYguLrz6zhtu7m5pI2JWpkGLsCteoDSLMxtkYjZmZxzH\ng0D14kCqk6bkXEpW94lc0yR7vtYZXZ5c7w+BDsKASufAYSD2Qjf+/8y9u69t23He+asxxpxzPfbj\nnHN576VMqsFAEiQ2BGe2EgECDCo0DAcC6MSQQoUGDBsKnZB/gTMHigw5kyOCcNBMhUajHYiBZKBl\n8yHSvLz3nL3XWnPO8aoOaoy51r6ijmE3WuQ63Nz7rsd8rVk1qr766qsAFGqJDCuMS2V3KSyzkbfE\nwzAF7l49cHx8YNxNODyaFWvOGm21drlRjKsdZ7tvqxrOlGreZPimaWAYPcF7pgHuJqFqYB0cz581\n0Lc3szXYW1xAnX3Xdm+19LhapchaDUz3MouQmrOXZnNafsEcQUN12DTcLUZvL9wY8m1tcLOAFgyK\n9s3Qcy3ZSipct8tmUqaD0O692leMDkC3XnCLOnoloukN3ABZfZ9b18ONI7J7U18YIDe4gIlW6jYS\nraqBcp1eDXb6hpS3FKCvCC0l2A5Yri7umo/3aokh5X4DDrmpsphDrAVKtmeSJsS3yUrVRPOcF5yH\n1AT0pA9crM0ZNyUn0cK8KG/fXtBc2e8d01QQyQSXOByOhDBZLt8f2/W8cQSNSWij0VasegB0xeUW\nTdj32k9owADJtF2Ja9WijVSrxfpK3ACyw+mOqiM+DEgI5LKS80xKzxRZTWp+sDKj6cwoMo0c7+95\nfPOG+4d7wjBAtrkK4gUqlBjxgQ3B98E1ZrFpYxYtW6s6aqQjHzzeWZt4LoWiNnyJDgg6h+Q224BM\nvenA1WCOuutdbqYlxoEJzjH4ADVbAxP84smZX40ftjAdg+i0rxb9vtNr79qWB7aadBcqVbXOvg7u\nuIY/9IvUxU+0NuNphucRtunJzWgtrLQvy+q7hT6g8kpY6uH2lV7cE4zuCOz7bNbnrsfQkXPxfttV\n22Tf8BZJu+5oes9/dVtDVC+xmXJhD5/t87Vl9qKea2v25g4b3wGSCrmIjcJq7MMW4NrN7B252nxE\nUVtla3Vb7i3aoikcy5qoRfne93+CMjMOyhQK/pc+xh/vETfStQs7Cm8XSq1yowl0RdUGjUIEBoQB\nVd/9fbt5jEYuMkIXnMWmI3epO62ttVtB3IDIHtUDqiNaPd4fGIcHil5I+R356YklLm2EmODHkaGC\nBMdwOPL4+jVvPnjD/f0Dgw/kaKmDYVUJTZbGhDFQhkAaPTUWSm5RZqO9Wjs7eO8R56koa6qsMZEr\npCokBR8GamiS5jWa+A11m9cpbsQEdJr4rqhVpdr96J1nHAQSxGw4hA2T/dsfPxc9Amhkvm01bKIe\nrQ6kcl3J+kO2VaTn5XK9N5pTkRfvbmG9Xp/ZuG0bNmCf60oxVWrTAekOR+maBHYzNq4DV85Dp392\nYtRG7xQxry2NA98alMxxmTMIzt2ASQaY9nBOWn5vvqNHBnZtqvYUxGIApBXWtlp6pdZrbV1dR82t\nO65oxVVj3JWW8vTZjB6hZAMIU1FyqYgaB8OOTbahMaiBVrF6NAtvn2fcjzKvHvccJ+tTGIbAuBva\nd7B9HTffyzWFosmK2wUI4HZGiCoLaEKkNKccsFFre0QOaD1DPWOEJAeyAy9IWDGtxXtEDyB7gj+Y\nDJs/oJfCuiSWy8x8OnN+yqxnJUdhPBx4PBzY37/i8fUHPBzvOU4T4zhSRcnBUiHViqdArqSU0VTw\nfiDlTFlSIw/1UqYRh3JMdv94S/3WJZKLGq29YFGHNias50o17pFlNiwrgwmjOpvSrU22rHYOAcYv\nYQAZr8D2z3q8N2D4gz/4Az7++GN+8zd/c3vu008/5Wtf+xq/9mu/xu/+7u/y9u3b7bVvfOMb/Oqv\n/iq//uu/zre//e2fuc0eIvefFwW4Kxx+DRteOIMWO3SDdF3D8HPpQMvttxusUYddz+Ub+i8tH6fV\nf3NMVi5M0UZpVUNtTfughWat9NiIm2yj033X8e/OwZpjXPMiG2FELX8M3jOOI+MwMARrWYWmZdAj\nXdgAxr7E2WRcI5BYvl8pFDYl5Rax1DaFKdVqXACtZAqJTNRM0rJJbtuQWYvSqULOsKYGDFbd2JRC\nxasSKlYjt3k7ZC3EqjxdIj/59MQPv/cpP/xvP+Hp07fE5WKlS7iG7tx+p1eEZ6PxiQc3gewMJ0mz\nNfW0KoSKA3dA3CP4L4A8gO6hdoWjnRl72CHhiIR7XLgnDK8ZDx8Rdh+AvyMluMwzy2VhOS9cnhfm\nSyTGzHQ48sHf+yW++KUv89EXf4mHw5H9MHHYTdzdHXl4uGc3DQwOgiikTDrNlGjluporZU3mLIoa\nwSur8VKWSFpWalZSVM7nxOW8sp4XtFRc8PhpJOwnwmHCTyNuCHafYSBoTYmSYutW7A6g8V2SCdg4\nrdaCPg6E6f1r/nsdwe///u/zrW9968Vz3/zmN/na177GX/zFX/CP/tE/4pvf/CYA3/3ud/mTP/kT\nvvvd7/Ktb32LP/zDP3xRXtu+eNfQ1D7J6NYvbPlue69Fj31cYvu8bm+ztk6/lQF7+tlXy2v1Qbp9\nIsLW+ZWaTnwuJh7R6Z4NirFt9mhj62KkkXIcvVR3K0QiOEQDaLCbuNQ2qKLaOffb39mcwjB6htF0\nA0yowr4Sbeh5Bx+tFMU2IUxrYySqtE5H2V6vjRKdtVC0l8+qzYpsxqINZ7YIx8LKgiNrIFeLCjQL\nUgRXBdTIRhYw1W62Te9Pya6QqCwFTpfCu3eRp7cX5vNi33N3+tsaYCUuY9u1PgG3Q+SAuHtEDlQG\nak3U/A6tF7RmVD0wIXId6QYjyAT+HpxJj6Eekb39uB0y7MnieHp+x19//6/4L3/+f/LD7/0Xnp7e\nQtixe/iQ3f0r9vf37B/vuHt1z+PrRx5ePXB3f8d02DPudwz7A8Nhz3jcMx0mhiFArOhqlGctlbxa\nZKB9LFv70ZypuZLWSJwX0rxQl4jEiCwrOi/UZUXXCGtCcsVLIIyT7Xe/J+z2+GlEgoHNNUfKOlPW\n1WYhxkhaV5bLmZgTbghGk0/vjwje6yZ++7d/m7/6q7968dx//I//ke985zsA/PN//s/5nd/5Hb75\nzW/yp3/6p3z9619nGAa+8pWv8Cu/8iv82Z/9Gb/1W7/10hG0ZdvKiNJxLnqQ+Dc46Vt+IHQgkJuf\na3YOG9jYcKMr3qhX4R9poXgP1RsJxW7Suu1gm55ED89fnAQbGrgddz8We+5aE+/lwmtdHMyZuNDQ\nehEjLW2dQnpNE1rZcIsQ7OJZNWDziRtCwSabvWEwlt6ANEGWa0lOOydD7Xi7nrBmNh2HfnYKW5oi\n27HIVSpLmjw9wpoq85yYLwtxiTcp2s2JbEfXvyRnzkCCofvsWkUxouWEYlUmkz5v2EHj1Nu1FpTR\nfqtaalQDJRZKnamixAjPT2fe/vQTPv3xj8itNXcYDozTHbt9wHvjYxyOBw6HA8fjgcNxTxhG/Djg\nguXl6mGYBjQPsGQqjuCFVDLa6MVOaEN7+v3XhUgsyvPOGtLqmqgxorluN61da4HQNC6GAcTuldIQ\nXy0ZzZaioAUpGUpCcyTHSPBiYjl61Tb82x7/0xjBj3/8Yz7++GMAPv74Y3784x8D8MMf/vCF0X/5\ny1/mBz/4wd/4/DpHOng1DEPLh2i5n7609BvhTWg3qdwYXfscrX7vuhAmN3arduFNr6BxtDuw1kKN\njsF1PkBPN6yxqRF/VLYbj3J1Ut340FaX72cnbF96TwsCrkUWt9GFw6vD4Y07L/0F+9XLYqI9m26A\nHxYphZ7itHZk51wrFyrbqGd3XflN99rIRQaiWnpBuz4q4Ks1NpUAxRnNuHdYqnjr7Ky1nf9VIi04\nYecdPijqCwVLPa6DbGjfmcUS5jq6KEmhT41W2ZmhlxUtF6hz4/IHRAdQR8lLk5JPDR9YrBKjDlTJ\nOZNS4vmzd5zeLixrZZkzy2XmcrlwmU/4YWSY9vg64GRiGkcGZ84s+IDUytBSOLwNvyk5bs45DCDH\nwXLxWChaWZ4u1LOF5Ew7NCdKrQZox4omtbRUHNVhpcW4UuYVjWUrMeIFgkWWvg3iEW8sVIdYd6Fz\n1LpSayJIxksikfCacKXw//y3/8r3/u//a9NAeN/j/xNYuKkKv+f1zz+Ox/2W+/Zl5nqIn7fi5gx6\nJnkTATQroSN5zY9u5b7aWWs9RWjAm9JBRW0FCsFaBnS9oAAAIABJREFUhu0119IM6fu/rbu0/ELF\nauzb0y3DQevmRLY+hJ4CtWrINSc2yrO7WdP7qffrYZfnGpGYXZuXl85jaOVIJLQUSV5cPjPWtr/b\ntEnsupQWqVhsYD1y0mIF1/6yzMBQ8qpGAtsCzWq8CA8cd543j4E3jwOPd4HJGeNtKxFqm43Q2o61\nj1nfHpayWC9GpObZ1Ioks5URq1CTqRjVmqyBJ5/RuqIyohoo1bHMM6fnZ9795C1PnzyxRCXGTIrR\nUsKSGariCKS82ORpNxAmU3ryzttgkQbCgaI5UXO9ziXrTW3e4XYOqY7xOEFV3OxgFvJS0dQigdZk\nRKXNJMiUNZLnSFkiGq3cp1qN4VUylEBVIbiAH0CGAQkeH6y0WgR8LewCjE4YXGR0gSDKb9z/Mv/7\nb3zVMIbR8+3v/B9/wx7743/aEXz88cf86Ec/4otf/CJ//dd/zUcffQTAl770Jb73ve9t7/v+97/P\nl770pb/xeekSVXo1lE7KsZtWt3JdX/W3m7r3tPeSnVxLhPS+bu0MPq5chJZ6WHnv6niuI9K85fdb\nQCFXJ+f81UjbfDl1jcPVVmZRaaBdxWO1XHpZpzm8jiNsQitYJlLb/bTNQ+xIv16Tndoc2uAEvOBC\nwHkDPb3rij3XtizBwvaGJrINctXmSLQ2/fwe4AtOBqtINN/ntTcpueYMrGbv1OGqUBuNV1TxqozO\n8Xg/8MUv7vilV3u++GbHffAMVW18WnXNyG2smdYLEA3klABuaPRqpdaFmiOaz6Az4mujiDs0F7Se\nqPGtlRtdMVAuVSQcKEzEPPHu7YlPf/wjnj55x+nt2foIVKjkFq5j4J1Wap3xvjId7xinHeNhj5NA\nXDLrsjJNCyE4dM3kU6Ss0XoMnODGgfB4xE0jOGF6ODDd3yE/fWtYVF4psbLGSFptfNk2i6BUNGXq\nHCmpYQnOvhvrzBZYHT4rWj11D0E8MgQYAy6MBO8YtHLcKw9TIe2V5QxPo+d5gdN5ZtqNHO7277Xr\n/2lH8I//8T/mj//4j/lX/+pf8cd//Mf8k3/yT7bn/9k/+2f8i3/xL/jBD37AX/7lX/IP/sE/+Buf\n3+abdpOT29faOmOWgPZMoa+aN2/WXoPe/jYUbasWbFnFDSotn9thX/23aL3zEz4Xo7xgCb14ATAK\nKRsf30pqBjnUpnJctpC4tp6AnvV0skntCjItcgAsDHR+UyEKPuC9KQwhnXDXsI3GrzBn2TbgFJXS\nopPtsrb39fGyfnNO0lKb3iBVxMJk1z5TxUBF3cbOK06tL0CrULKjpMDhMPDmwzsePnxkd3+wa3R7\nfp07QBvfJoIy0Mu2NSU0r2hJLX2agMEcZ35G8wLlLcoKUsgxkqJtNhVhmQuntwvnU2bNUF2gVGmq\nQJkUE3FNlGFlHFYGv2fYCWMQ9vuJ4/2drcDqSKfPeHv+jCE4vFioTirU1AaqRFMSdrsMIdj9ptan\nghNcEEQz+fTcHIhpWtTSoo2U0RSpjTxk/7QxXu1+cjlSUiTEPTntcWmP7CZkGgEYpolxJ4w7Naq4\ndzhmQij4CD7I7S39Mx/vdQRf//rX+c53vsMnn3zCL//yL/Nv/s2/4V//63/N7/3e7/Hv/t2/4ytf\n+Qr/4T/8BwC++tWv8nu/93t89atfJYTAv/23//Znpga1NQRd0af2nx2wo61tikUL/oaBiK2o22QY\ntdXS2Fct7JWb8FptdRNa1GBAAl1fz0Z3SzPiFpHcCH3Ytloy0Y1ls9N2AzeOQC9FGmZgx2595vlm\nda/NCdyUN7UYMr7x8ntXWsMPgjkC12SprI3bhMZLVXx3rL16oGGLPExmrWyGe/UxgqeBSL0UK10X\nH8Q1zcaGTYSmKqWtA0q7E8CUckSVWpS0CvFivIHHD8wR7O8Pbd8NiNXaVH2aIInDjKuliOYjMpqt\n7ckcdUA1QK2U+ITmZ1w9o7JSJZNzJCZIeUeMnvM7OD3NXC6FXAM6AKVaya1U0rIyn87UMcBuIewK\nXiaGAIfdwMPdkRAGnFaefvTXnJ/eEYKJxkzHR1x1FmlFm5MppSBrwu12dh6lUAqodzgvCBldztS1\nlaVTpSZtI88TWmJbIaEWa0EvFIoUI4ilhbLMlLTHpwMSF1w8tOG4I8Mw2hTk0QDoEDyDQggRH5U4\nOP4HRYP3O4J//+///c98/j/9p//0M5//oz/6I/7oj/7o/TsMQxNqTJsj6IYln3NbrUL3wrJL0bay\n6pYubEDURjKyDv+NcbrtpK2Y1xCgv2r/1ZfNduP2EPvKlreOM2lAmTSn0zkAitiN3FqYtdQGLLZG\nITWwsEcwli7XpmNnKbATu3k09N6F5hpqpTTR0o5z3FYLaOW/Sm59DxahdBWbzs/f+BTO5NpveZG0\nooL5FaWNebXGJDHn4THZK22ToyvmgF2186lFCRIYhx1+3G2zHqhtZqNm6w/Q1RxB9XTXa9fCnKgd\n94Bqosa1fa6CnkFmqkRyWUhlZo0D6zoxryb+en6emWfTGlC1FmGb0CRU9WgYGY7KOHnGacCTkXRC\nT+9gf8/wStmNI8MUKJd7JEfS+UJMZ2qsOD/g8Has4vAlEEpGKsRUWdfC7nBgCAPpAn4cefjSF3h+\n+8TTp2+pKVNrIsVIyX2atCV2VQ2nMUdfm4MsqOQW4VXjLUijo3Og+ANL9vjsOe7v8GOg5sQgcBwy\nYwikYXy/Xb731f8fHl4ciKHJfYnqYNgVEOT6+4oEtpy6h/+2uva3XBN5uWII24tmOrKZjxnGVmaT\nGwfUduf68z3XvzE/rc6wBmeVglvHUrvaTGmJf+3dgrrtF4xgRMaihnzlGNhqK1fAUvu+26y7dkYm\nXNranrdrpo1F2KsebruuPcVxmLPxzuF9QxX6dWxFhXYhLS662b7D47RPc7Kmru7MHcLgHbvJs98N\n7KYRP5gOA6U0fkZhm5uoVj0y2TFz0LX149usCAMxta3k2qoLqhdqvVBKJKXVJgKvI8vieX5eOZ0y\n8+mZFCulePAOcdZY1IjhiHOEMTDsBqb9yKTK5JXJFyZfGQfPtAsMux2H+yOaVsiVkjI1xzbMpqek\nVqHQVKlemOfM+ZJwU2C3m1AnuCkQjnec40z8xFLFWnPDDxKm12j8E22NZ73sqArOtVKuV3z/cb0t\n35S21mTVoN1hxxDATSO+rgQqLkAI7282+LlMOjK1YCthbcxC1RfTi7dVU9tS2aVokBe4AbClFNsS\nLRXa/CM204HrLIOrHoFtrI+j0g2/cG1kurbkYGt/vmXJNeWjjsxbi7Fu+V4/tx6OuxukHxVyLC1X\nLJtB1daCKhloIpVVG2lqU0x2TdLNbnRE8L3jrWnbuQ02/Fx6JtykNMXICE6s7CZdg5m2zdZq7QE8\nrlq4UFujhLQw34syeuHxYeKLH9/z+s3E/k5wLlNrtOafGpGyAosZtROQgU7kroiBiDlSmUEsNag1\nN5wlorpQyomcz6S4EKOQ1pF5GTjPjufThcvThXi6UEsAOeL2DoI0ynU3ngw1MQ4j++PEcRy53x+5\nv3/Dw+tHjo87/M4jQRmPe5A3jMdHyrJQL2f86PHjQFpW1jUzx8qcV+qSOM8L5/OCTA6VR3QQUE8p\nKylG1nnBV/sOrZhire603hgJxmJ1NlkTUPw4MB4m9g9HprsDYdzhxz0y7SjOE6kmtLqsHAcI+5Ya\nUklpRmrA8wsWEVjLbzPMF3H/TXng88BGjxpegHg9GtgQxReYxLU34ebdct3EtorSTbXdkp8DBKW9\nsbYypfRj7asu5oi0GmhY2rCKzj+wAKU5oJ6rt/eXRjSq3bnQcunqEKmbgTgndnOIGblijMomhnhj\n6w1jkE4Oaieq122LWB9BdRawSLX4QsUUjCAY5UjM+doQWPu7NrVnRfGNGShYPX03OV49THz00Z67\nx8AwKSLZOFo1o2WGcjEDlwp1RN2AaqsmVDGCTFlQvQCrHWcplFSoOlPrhZRO5gTWRIoTKe05n4XT\nOXJ+emY5z+TFSpISEhKB4klxIaeFkhecFIYRhtExTJ4wBWRyyJDAr/YjAzDgh4Fhp7igRBHiGtvF\nrK2ByJx7ToXLsvL8/Mzl6RlqZX4+WYoRHGHwjHuLMNJpJS1tgEm7o6pWqBEb0OLopUmcI0wj+8c7\njq+N9Sh+AD9Q/dQWjYKmTE6V8+mCU89uGJh2e6grqh6bC/G3P/7OHUG54c2bgXq6uOgWBGt3DHpV\nA9pKgm1DzTFYyCqbM4Bm78q2nxd8hxe8hbbe9xTFmYE7sXKgaktl+vYakLBVBzs2oA0Fbt691qY+\n20J876ybz7XPG8lIDUNonQOdU6lVce4aRagK1XkbmkLTZ2orv+n9mxpxpW78BdOwKqD+Kg3Wohlt\nHALv7bp59QjFbpQGRCptnqQbjMJcBfVKCRY9NHixBQqOYRT2B8/jq4kPP5w43HncoNe8vkSknNF6\naqT2piSkYsQhjB5uvIALlJMRZZQGqBVSvZDymbieiEtiXYRcAqXueX6eeX53Znl+Jq4rhRFCwclM\nnVdKVHI+U8qCSLLpwIcj487hByhSWfOCu0T84NkdHxA3EfwdzmW8d4b218qSwaVMWE0kFieEnYdS\nmeeFy7sTp59+xvnTd4zTxIcffcyrL7xm98EDd69fQYGf/NcfcfnsZPdHMKZnLYlcMpIt8hLBulTH\ngWE/cXw88vDmnv2rOwqOpJ5VnRGUVBCfKbnwfDpT68Du4zuOh8DdwRNXJcbPRYafe/x8pMpu0P1u\nrPV2tZdex3dbh19volTYpJ60domzv/mQjjngtn0prSNv4wtcS5SbenILHbohdiGULdAWGifASP29\nxbiU2pSMOupnkYDz3lhp3tuZaqXkutFON1JK35/Ii95zY7F14RCsxuz6Sv65a9cWki5aIv1iXa+c\nXYU2h888SoukFCNhiVCsj5nO97DPVDRXG5DiXA9a8T1lCUJ1WHNTsfzXt6qDhfURY44GVALaCEJa\nlKrWpVfTYqlBbWSbUkhpIaaZGC/EONukqeiIac+6Vtb5HfPzmeV0sUaeqjDYdlM1/ceaI3F+puTV\n5g/4IzUfoTE6a66kCoMXYkpczu/I0TOEjKZEiZG4RC5PM+fPzkDBB9OvzFVZ58zzOfJ0Xrk8ny1q\ncEqVzHk5o2/hdHrXrgXcf/SKw8ORmlfysrDOZ4vWPGiCHJX1NKPimO7vOD7esX88QvDEWEgKUYXs\nINcWoQ0BJ7CcztRz5HDyyF7YO4doQsov2oCT1ojkOoAmunXA9blw29Rj1yMGbwS/TsZp9OBamrJw\nKyVumEDH+JozaXtuUfK1HKn1ptXYNVCp9cu75hC2xiWlacvDFpq0FTarbk1Ftu8+RbmF8K1XfOsK\nzNlwEhHDK3pZQzBSjbSUoF8HbMEuWrdj7416SpOuunFg0lqOu2xZj3g2CEUVyXZdREqLbAxTMVFM\n3drRjNhpjoBaEe/x0qontNq6CEWUNcP5kjlfIsd5ZpJEcIo4G7ahbfaAtgEkpohkdfVSIjUvlGyU\n2ZKtE3RdTyzrW9Z5YZ0jKQox74h15HLKnN4+kZcLZbUhJeIGQnCUoqYNqTO1nEnrMyUlQhgpcaJE\nIJuTLdlW96KOmDPn0zuiFAa94LJSY2adrTvx/HamekUGS79SSsxvT1wukXO0/v+UKhIyFOWyzqwp\nks5ndvs9x4cHHt88cthPpDizns8szxPeCdM+kC7K+pT4TN9SFe5fP3B8fc/u/kAuymVOrLmQ8OgY\n2o2s+OAQH4hvE8u8Mu0cUgfCHsOsyvpeu/y7FyZpv29BQrpx0m5lbbr9LXfuIJ4Nl/QE8XgcuQzk\nkkjNIdgM+hYjSGf9X1mKhntd2XvSYmnt4X0rwNkzNzTjtrmee5sDqK09t+2vK9CIWEek67x/scih\nlmvDCWZo9eYaePc5p6W1ORW3CYpWLJoKxZb+Lm8mWElPbNI4SHd6PQqoLfVpqHlLRlyLJkTBNb1D\njxJqa0dypbH9WgSgGEGpWAWg4KmuosnDJfDffpip5cK7k/Llz2Y++mDHw/3Aft+IUNoAQc3UslLr\ngJZEKUYLzulCyjO1zJQ0k+PCGk8sceYyw7yOlDyxLoH5vNpQ0suZmhJUGHcH/HhA/UiJF3I8GzJf\nCiIHxr1j2E9MuwOD96YRECvTNBBGmyGZcqSUt/h8IeSRoCMkIT7PrOdInQtZCsVVYwCnisaMpIIk\nw1mcGylJWXNBy4mwG/FDQFMif/IJ609/yuiFMJoikgwDOCGXjNuPTNPI4zQi4jm+ucPvBjKOOSUu\nS+L5vJKKWlfibmDYDcxzJM4r82VGc+bTT54gHxjdHaECv2izD+FFOn+FCLtx3j5/k+cbt9+aN5xz\nBNe59Y0Ln5XyYr7bCxTtBtzr8MwtrtCMsxmc0xvcoEUa5ni1dX6ZYdfcIxGaw4FtAKm7UqB79NAr\nJi0SvwKQLVK5gqINlGz0yEY9oohV9mvtpTC26U0/Czxt6CZ9hJhWweYD0KoRzVFCU8G1wS5OLQJR\nKdgKfnOdauMOtGiuNsyBBX70iTLPmcuceHpaOP29HR9/tOPNmx27XWCamnJQVUpWSg7UnMnVU4oj\nphMpnyhlNUewLqxpYYmZ8xKY14Gcd6RZmJ9X6wWogJoCsJvukNFWzlyUtM6UmlCFcdwzTjuGw8Q4\nTYRgzW4lRobR7iXDbqw6IdHhoifUHbI60mcz61xICbJXsq/UCDU1NatcmtZY60dRRymVdclkNSzG\npYQsM6eUCKLcPR7Y3x8Y7r2lfTkzHAfCceS4O+D8yHQYybWypMicCpeYmeeFGDMhJIY4MJSB+XRh\nOc2QbLE414T3MA4jx6Ew/Q9s8u/cEVx7eLqhapts1BP5zuXrTS8d8rZQvuRsWnEInQ/r8LgaWmhe\nW3nwZpuf+7M7ASsLttDfkuR2CNLL94ZpSFeYaeXFVt9ti7a5lR4BNOGSdsjW8y9mTjioTUG0NqfS\nxU1Urts347ZopRbAWRRjwJ42PYAri3GLFhrnwWEsw2uM5TejleZMLWpqV9eUXOntz0VbRUGKAafe\nW3pAQVUoWW7SH0VzIpbIUw0sMXCaM//9k8R//1Hky78088v/244PvzDwwZsBLx5XHSlirde5kLKS\ncjZp8TKTayTn3OYPwhoD8xJYFk+MUBJUPEx3DP4Rl01Aht2RDMTzM+t8IsYTbvD4/ch02DPtDsbP\nHyeGcaRqZF4vVO8Ym7pw8EoIdv+oFNJaqM9KfndhvRTmLHAc4G6EYAuCzb7A2jWBKgkZHCFM+DBR\nYub8oxO1REQzTpyNn2dCSmB9XvGl4nNGdGDwE+G4g2ARyryunJaFWISsahoIRYnryjJfKJ9m0rxQ\n1sh+2jHsBgjCZZ35wV9HPny944PHX7DyYX/cDhhBena/oXo3xtxDUugSXIXG1++NLw1P2Epr3H62\nL7F93W7RgOqLyOQGYWBjK2IpQFHtCxmbwBI3gOSGBzg28lGTZ68VnNSNO24H1aKMjg10LEL6sV33\noTQj7QNbpKsuXEFS3YJ9wxy2rsyb1Vw3B9s+0Zzv1ty1vbfrLrceg6qoZNsupQmgOHCGJ3hxaDWV\npKyVlKvNDzxn5kvk+Rw4XVZOX57IceRuPzL6QFohRSHl1fLqEkl5JZeVVDMpV1KENVYDBRdljQaO\noQ4nAe8HnJ9QjI8RYzKS0ekdcTmTYmIcAkMYET8gEnBuQMQbhTo3YQ9mUiqE4NjtRoZxxLsBcYH4\nvJIuK+myEOdMLkIdBXTEhZbCXqDkauzJVsHBBSvzOTH03zlqNsGXGmy0XfET2Y3GGq1WnZG1Us4L\nXg37SXNkXjKXmNEwgh/wITDsjIWY10SMEc0Zp2plyt2E23vWeeHTT94yuNfcHR7fa49/92AhN6s1\n1gHnqS2csvd0RWLtOobQyEWu3ehtzgC9AqCbjkDtE0NbGtDFRtr8ouYYeqYsG7GpOxSLyAUbxNnn\n2debtmno7TrbCLFOvNlW9R4qGK220tMBC9ud9ATFUHU7RN1Wftk8EtjZVZO7aj0R6u1FtxGn2uc3\notTLFCtzTXFcc4S93dqc55VEbXtzVHHU6lrDkdX+VbQh34016f1GwaYWG/vWmntqyszR8XyJvH3r\nuJwSGvd89IXKw91AWrSt9kqskVRXUs7kXMi1khKkVVjWwrJmYnKkosjQBrMgoAHqQCqVNSfSciLO\nJ9bL86ZX4HVA2VGio2BNOdUpa8rkmKmxkNKFIUSmKRBCwIUjIRzxOpH0p+R1JsZMThFVRyqFnA3c\nExcoWDORloyqjcXTZYLijA04Bg5v7knnkfV5NsDVTxAmGCe75hVLZ3KCz07I0ztySqynlVyEogP+\n7shwdIS7HcPkcN5BgOyKCc5W2D/cMR0PyH7gckl88sln3O0CHzwc32uXPwfxUgBt+ed1RYWrQMg1\nJ3XXVbs7h2ZYBvA5C4EbmWebeHS70nceQMu57R19Wx1Qe5k/9JVetVukvNxsjwRal16tfat6PfrN\nb9Rt63b+vQeiH4N0U7oe5+YqtX3eTLVHFFv/zs3xbIasGzzYoqpbkpRcX2vOgFapsWsIqaW56lo/\nkCqa1WaHiBhg64KJgFSQPh8NmtMsLWURfKmk5Fhm5XKunJ6Ew04QVZbzyrpElhhJmshky+0r5Crk\n7MgxsF4q69zEPan4Auo9SCXVM2SImoklMZ/esZ5P5MtsTnHwhCETfEQm46uoLMQlU7JNIxIxsVAV\nI0mtceH56TOCXHBlIJ7OxLiYdqMKpfWMu6yWNlQl4iihqXBXa+bKtaI5InXP5EZ2u4A/3sFHBoLj\nHPvHO4ZxgFpZU2WJhbhkwz6WmbKspCWi4pEBQszolNDorErg2kDdUhEfcIM3EZmY8Ch1TUiq5Dmz\nnOLfYpH2+DmUD7WVppQuodWtr+P0Rdvd2EEwoeWvrR1DelhvYbAW3RSCX4KAN/tt9fQrltZFOK7/\nthRFaOU5RzcxIy11MpHbVt1ae3PIDWBIcyS08Jlm1N1IGwAhqkgTOun99t2LuF5u1NoneG26BL03\nwMaiYVtvYcQmmS6mUiQdMxDZIiy9vb7b7MBqeofqkF6V6m6sYqQiBxICQSbr7ddq1GGn4NtMvnor\nFCsEafSGLCyzcD6B1mJCofOZJV7IWkwZGE9R+8lFKBHipbKeIqZunAilUL237rw1kueF7IWoldO7\nJ+LzmTon3DAQ7vaEEAluQWRAvKdSKOuF9PzOBEJ3k6kyjZ4CrCmS362G/mcHp4rGVq4WR5eL96os\nayGWShahhsaQzIIrrgmlZJzC6EceDgcODzt2DyNrjORSCNNk338q1DlyzpVVYY6V9Bwp54WSsmke\n+AK1QM6kueCCtzJpTtSUceOEhoGYK7mujKWgsRDwaFTi5Rds5JnzV1rsNTxno/IaZHBrnrrZtvag\nvNFfQRuS3+cPQF8nNxR9cxjdQK6r43XVblKeKjeuoa+KRmXq1YErUYlG2bWhJXqLHF5/3RCh7Hg3\nHKAfw6Yq5JojanvXnkpYmdDUjGgGfy1/mjlr629q5cHmza7NSDTA0jCM3likqlCKrSpSwZuTENpI\nb9+dtV3n2iTa1CsiI955ghsRV7CJfIq4yiF47nbCq3vh1b3n9f3Iw53Dj4Xz5YnLJbGuKzElUoGM\ns1mRMlI1kKMjJ6WsJsQZczIJdVZqXuyalEjJlZKEWAtryqxPF6MXqxCC4nIiRsclOAvBB2/6AE3F\nWmuhxEwdQHUkhHvG3YT3YsKjS0b9SmW1zk91hHFHOB4Ij3cQEywRWQuxOrIO1ixUFBcGZPL4MRAO\nA8MHR4bDZApI087uJy+UWskuU84zy/M71vNMuizkuFKlopNDdgNuP+F3NqXp9OkTcVltEWjahWGX\n8FNGnLCbRnb3e3jYc/ngyPHhwP7w/rrBz2HAyTVQls0Z2PO2ZsuL5/sqLX3lQ+lyxEppeoStU6uF\nzLcxfA+r23/SFvbt0QN04wjZSqkqW9SxxSk3gcbVEVwJQdoEUbvRbLtrIYj92j7JtfNRDFC6dQJX\nV7QdG8iGIfRjcJsT1U3otM+EuDqL7VI0vFFa2VK2CAW0kYq4wScaT0EsqqDxH2otZBcJJpRo1Qzx\nFrW4ShDh9Z3jzb3jC28cjw+e+6PHuUqtC5d1oeRIKplcoaijmOuxcV7VkRYlr5m6ruS8kktCqlGp\nRaBqIZWFnIWcQ5MgS5Q52bS0MFj+kwrOZ8Rn/C7h88BQB2ORDkMDUJ1x8Qmmdhz2uMFRWVtjZmwV\nnoI4z3DYMxz2hP3EUCtFWjtwtZJlLS2CDAE/GX/ADR4mUxWSECydFRBn/RUlZnJOpPViEc7FIoGK\nQnDo4JDBg7fS8fJ85vzuCUpp/Q6CW1fcNBsp7nhEH49M+5GHDx+4Ox453O3ea5d/91UD7WHrtXxX\nb6oGVpa6tkz2TkG3leS6/kAzEP1c7n8rx3PTZdh5/rZy95v4miYoajVp6byFNn9AoHOSe47unIX0\nvk/vwRk7rpGatnHmekUhuu/pkU+31z4n4eYdN3GSZfRbd55YBOLFbU5gcxbVb02a5sA6NiHbNhQB\nbWPEsAhC+jWW1tpcGziJjV/vJENpDUd9qnTRaOQgAqMERj8xDbAf4YMPKx9+oLx5CEyDjVJblpW4\nps0pm+FbWtVpXClWUozEOVLiSk0rtRjzkDafWRFyVWJR1rWyLJWSKiWrCbgE6+tAhboYQ9R7NTXj\nVMh5JPgJ2TVHKh4ZdqgLJAVXCuqqRQS9pRwo4gi7HeHVI26/M+5KzWixoam1RmJccQSCHyycH0e8\nC9SsnJ9mE3m586wxUmpht9+TcuX5MhNTJDigFpvIXEwhytIyW2hSjGitxLSQ0kqJq1UKnFDPtZXC\nB/L9I7vDxMPrOz78pS/wuJ/Y737BxqIDzQJant96/q9mQKsaSP/fFjF0Z9GHk2qLBDrdtj9uV83+\n22KDzsqXlxF627KiG223v9arDPY3zSE0xF5vxPFkAAAgAElEQVQaaaiH+Grtrq4JVd7OTpSG2vdz\nUqThmFa224RJtzOGq9trzkQ7K7LRg7U/30P6K8zaL/M2UVev8QbIVk7s5y/at9n339IpqdvhdIdl\nWyyNnSitmmN6BPtRCGOBoBQcsSilIfQ5m+IQYpWaUk1oplSl1EKcE3Ep5HU1deIt5dNGdXCkpKz9\nZ4VlZlON8t61GoprIjAdiTcjSmsgDQEGj3ehfdeCxgRcAChpz+4w4ooi1UqMuRQKViIuwTVNwYTS\nxpBVkxKXUprnFEQHRCuaMzlGmB1rcAzecblcbDBJAxvX84oWYZoOxCETvfVaiIJzJqQqDuJ8Yb0s\nLKcTcZ6pJVs/R/CUNpsDNar4u59+ZsNbH1/jpwHG95v6z6V8iEWmbaWnebJ+i9pqti1t/SE0kM5D\n60jTrvW3LblXoksnBPWw3LB3GwrRkfXrpnsaYp9x25G0KKAbcwupcQYSikpTEcaUbMWEPamV4oSa\noRTdmqzo0YfIpi3Ahg3QRE9tdeyAomsCo2ZwPaLoeXvDDxooKGiLtEwzQBt2QDXU36qqHqe9FoFR\nk2vDQbqTpTdjFTrHwPCJHmEJ6joGYUNXKwXnhCEIKQvPs2FbAwXZtAeH7fzMwNQaflImpUw+R9Kc\nqCmiWm3ugwPxSpWBrJ5zysxztJLi6khrYBoKw9BA5t7r773l4AKxJMIi1gUaHOhg/R/V8nlkYXWw\nPD9zvL/D1ddMfsCro+RKTKl9JwWXI0hFncM7CMGTc8HnwiA2Qq7mFUmeGj0p25VlENbZbrDL08kq\nJsOCiCOXinOB/fGRNENaoMQzgjIOE2Ec8EE4fXbm6SefMp9mcky44PDDYF2itQCJSiLFM08/+SnT\nbuTxgwfiWBlvpwT9jMfPZRpyR943V94MZFsx5Ro+SzNu2QBCMUEQOi34mo9jm7muizeGt7U+vwgd\nbj7Hy787kvgzX2+7zXKdC+jxVh829REcsgl1d7GRvsPOpOz5ed/69ciuyUQf4uyksR9qD/Xt/695\n/jWl6eW72o+5KSVp27c2BqJ5ZNuWjTrv6EXbbrvcytaoiMMo2FLdFtGpFHAr+EqVgTUq5QQlJEZf\nCV5tte49C1UpWUhJSRHSmklxwRVMoHUc8cETRhv15UJgUc85gsYn4vzM2sJnaVyPXO1e0YZ9eGc4\nQBgcYXStU7CQUjRB2GlsYYQpDddUYC6kLJTiub+75zgdyOqpEtDBoyFQKSa0VGzitDiPnwZCVcac\niblQauOgNOMMbmTcB0QgXqItIs6agXoU59p0491xZ6SkasxK5x21FJY5sS4LMa22mHmH9wHnfCN3\ndVVqE069nJ+5nO5Y5oXd6BnDL9gQVMtHrWzVk9AO8G0rcwfPWvtxX4Ho5Jfaooi2Wnfb3hzAVqun\nrbbdxLYwof1Pt9UY7azDa+h+u1XZwpgrrbcLffQox0arBTO20I8Xm3Ovddv2z6po2KwEbhxVT39M\nhMjSi4addODuxqn1oL12YFFlO3Ju0obqWvQg8gIENLzCUgFRafp+bTvdYW4pmRBsSALqQSWjrlBE\nyUBZBUmQXWYalWkPQR3eeSxMao4gCnGBFCs5ZvbTjt1+z+64Z9rvGMcJN0y4YcdTEvI5w5MjkVjL\nBSmJUYo55cJGjqpVCQFw3oaYDB51K5najEvb/AKAakBdzuQ1s0aIEWBiCHeU6qkM1hgUvFG5S7sW\nbdye30+MTsirUFfIySozNkQmM0plmAKahbQk8BBGDxGkC/S2W2HcT0gQYlrgsoJzxJxY14V1tbIj\nTgje4/2AeNdwNm0IiinO5PnCfD6xXGbifiSOv2gYQVvZnfMtItC20LfcrusCNOmm/pHNgNtN7bCV\nTbELcc2key59VejdHnqNMAC7aB0T6L9bCH7d8fXzvRHo1vy6g9ACGUtVvO9qRA4vVl4rUjb15dtP\ntz9Q7Ivsx+D0Nr/f9r6F5pW2im+XRq+ZRj/XTa69FUUFEJND34DSatiGpz/Xrk8bk6X0Kk4DH1tf\nQlXTFVTB2nmjclElp9aq7IXslZQhZY/34N11xJylKMIweg6HiWn8gIdXr7h/fMXx/g4/jlSENRWW\ntRCeV+RyQsW6RFO1NMi7qxEppr6EKhoTWeFYlYOMTDsbVqvqKVXIWfGDlRGdH1sakShV0Jg5rpm4\nZlK06cLeGZU6RxOSsaYQR3CecAgwCsUL2Q2Ir1Yp8ELOlXlZePrsLbtpx7DfWbkbTxgGS6tESdFW\nfB+sc3V/f48fJ3Ku5FMmnSJkIYSdHXdfZDS3Po1iaUi1dC4ER86F57dnjocDh/ePNfh5RARu67Pv\n0l3X0N+Mp1fHe6pgN3kD3noUYFvjGt9fHcU1/bjhDXQeQQ8IbmP+F86gb7JHAFcH04Pwa1zRjqJ9\nvhYbJVKlDaIQh/Ntu55tiMk1GbhGH3rzT7BSmrS9XSMdXpRf7ZgbiNp6J7oOQne2rjmCa9glNhdh\ny3vMETh1XYIAsC5F6ZlUbwevuukltIHs1neQDQepObNGtWm+wRMdRO9YVxPbdM7IRt47pnFgP43c\nHQbu7w/cPxy4f/2a+8dX7I9HcI4lZp5PC/npjFwaaUsrRUsjndl5dS2GTrhSMOyhRCsiOQU3mFN0\n4Fwh+YRKT+V8w3ksjUvVBElzTGhpalO+CbJUsaiJYg7WCzIGG0eWvGkiqFiUVIpNKl4q8rYij49M\nr/eImiqR96N9LyVT10qMK74O+ODx48ggDl1WUKUu1mrt3dBEZ43FWbIdq7XEGwZmuJt1Py7nhbTm\njQPytz3+7lWMnWfj07dV27XyXv9C+6P/VaE5gLINMC19opGwrZi3C/im7rMZ7wuqUHuPbqu/II2m\n9/KCbe3IthHMBPrRvUTpBW2zBCHTRUSv2+/CKNuntyiktuiGph5ESxfMMbiNDNXC8V7G1H79LOd3\n3kJGm5EXrLYvgvQ2ZLoRc/WfPWpqTqqfX5c+sz6C2vxvMwKRrexZVcjF2J3mf2obr6h4EUZnCsFd\nPn0Mgf1+5P7+yOsvPPDxx6959eae+1d3+HHCt1mYKZWm+pTJmpvx9xKtOSbnwLX9aY/MWipTG7Ax\nL5GcEjkO5L05H0q1MmHdoTrh1eTdp8kb/VebilZa8V7wu4lw3CPDSBWHF4f3jmG0KCOWwiVlTnEl\naaD6gbImalwoRake5rwwjgP54YFhmAg+GLaRE3FeSfNCXhM5VnAWTRQ1DUwtBVeNq6C1Utp3VRst\nnGI9IYNTnAuGGWDNYVqaGE79BVMo6oNKN5rwi+rAbejdoK6bPLrWSs2lTRe+rqDXh96s9LoBYbaJ\na2h9fcjLwoT01ZktH6897RC6eteLjfQVvhvVlnPL1bipHeGv9G5+lI1TcFvG6NvS7Vpc8Y4rlmLH\nvg1sbV2P4rnpgmyr/nXTti3tTV/9SGS7Nv0AqljF3vAZ3RxH/2VOs4GP7RnLzdupVKNWF7Hrl6Uy\nOGEMjjEEpmnPw+Mr3nz4mg8+fsPdw5H9YbdpJZRiSVJpwq6qpf3UViq+OllxunEytJVitWMgraGr\nqBKXgqvN0TaOx24YCRoIQ7CKglZyLpSUoGZyWhl8QIYRN9ggVFdMUgxvbMiSC5c5cT5Hzs8rVQPq\nMqRoGgXFGsaKQlxmluWMOJtwXLWQU2RdZtNazE1arULxDZuplmoejjskLtTVAM+SayO0gXPB8B0U\nP9jQnphyq+J4grcBOe97/JyYhT0EZgvtajdc7QxBk/eSFu7bEEnT+6t9nLn0m+Bljt95+n0ffbUA\nk9TaGHnbeq0tZG4RgWLTbFo0UNtx0pxBjxs2A2ukf4vA23lU6NOTqrZZd+38tpkK2k2oGVsHI4Fe\nBdhQg+sp2GooAuItvfLSwt+2T1Wo5WZ2hB1tbalRpyohrWRJ00lsjnHrtG50ZG1pmiVsLWVzFVwX\nOLN2XNNBaMzM5jmKdhqQMlSYpoHHhyNf+Og1bz78gOPjPcMQbFiIlq0SUquSsTTglk8gTWewcy6M\nKNaulYSWumxtVZs2Q81K1IqwoFRUPEM1zYDdcccwOGJaietqo81LJsYFmR7w40R1wZxISaj3FAms\na2FdE+dnkzFbniM4wY3OhFLApkZbTE9aF07P79qKb2XwnCLzupBTtOhnyaRUSADeMY4jYTey33vc\ns1CeE8tq8xwEmyY+ToHuosMuoFQyGT8Ku/3AOI0mxPKex8+BUHTNkI3AU+j03m4CAthwTMd1KtDN\nIJC+6vaUoFnHtq7plTjT9/Wix+Dmt7S8ubc+SgOfnDiofYW+JgObQ6CnJJ87M70u8re04S2lvbkG\n3YHdrLPmh+R6Xq71MshWSWkrcJdGv6E4Uw0s65Iu1/3dRDptP70S63rjUdMTrN3gpZGmnDlladfU\nSqXGG7ielHVC9LIldGEUmrybyaczwLAPTHcT035qykDmtMuWfrQIbBN+6DSwikhfjaXdG0KpmNgJ\nmNMSuM60cAzOGp+ojbNSxcaNuURNyaYTOzFcQ2EogkweshJLgRgx9qThCT4EikJeE8vTYl2Ua6LE\nghfINbHOiWHYE/ywcUi0KOu8UvQdJcE6R0sPciGnTFwX4vnc6MaVLB43jk17YGS827FzlUwhl4IL\nEdxg+gNjMMWn0hS2vHD/6p7Xb17z+OFrdvsRre9vOnLve/EP/uAP+Pjjj/nN3/zN7bl/+S//Jb/x\nG7/B3//7f59/+k//Ke/evdte+8Y3vsGv/uqv8uu//ut8+9vf/lu22r9g1yplN0MsNoVjuwFUjNtu\nkUDZ5gDccHzazd6EvsVdtQLazSjb2nE1pI64Nnujz1brlQbnxaYNe4f4NlxEe+dhb8d125a7C3qR\npmiPHm6W8ttrsE1sorH+blyTXo/XSZ963LEAcwJVrBRoJUOLlmq2iKlLgHcs4VpGbNTqpqPQpx27\nPv7Me9SbLuTgbNy5ybH71izWtBilTzy6Opy+/S36aiXI7YtyigQI+8B0GAlTK31pA1n7d5xvHL5e\nIyJL+6p1RnpBnaNiFQCtDhvm2hzYJnjrCEEYB2EYxEA2dTaEJmVKisYhUEWbulQYhHEabAUvyhIj\ny7KwpkQGahhJCOuaOb+bOX16Zn6eKSk3EK+Q00LNaZtMVRFKqcTLyuWzJ55+8lM++/FPeH77xHxZ\nTI1pXZifn1jmJ2J8pqSZkpINvwkOf9wx3R043N1xuL9n/3DP7vHI7uHAeLcnHEZkDIbtOOH+9SOv\nPvyAhw/eMOwCNS3vM/X3O4Lf//3f51vf+taL5373d3+XP//zP+c//+f/zK/92q/xjW98A4Dvfve7\n/Mmf/Anf/e53+da3vsUf/uEftiacl49eEuszhPoNIyrYUFLLbxUxJLRUE3xo4qC3pKNbleJeJbi+\nZlLiYojS1oLQV1/EesuL3qggdwNsjEHxrgmRNt5Dczb96GtLS7bJSdwQnehpzpUP0P2ERRydNXhz\ns8s1VbiyAF7+A24o1k3OvZGautiIKTaxAXq9u/PK82/dig1sqsX6LOqGZWgDo0AtzaWooE29uDSG\npzUbDXgJeA1bhQEpKJmsRq6hNuBw8AyDxwVHUaPupmTlr1IKpWZyydaQlIsZbLUKShd+aUnx9Xsl\nUDAJL+PVWSOQOCE4i8BdALxSnVKdDXfNAmuO1gq9zOSU8M4zBFtl/TCYcIhA1UzKkTVnVrVGKevX\nMEe0rJl5Xokp4ofA3evXjIeDcQ+GwcDjlMiXSH5KLJ+eOf3kLW9/8lPeffYp6+VEzclA3sZToSlC\n5byyLivny0oqAmHC+4lBAq4xI2u2+8cNjnG/Z393z25/xA8jWeEyzzw9v/1fdwS//du/zevXr188\n97Wvfa0NHYF/+A//Id///vcB+NM//VO+/vWvMwwDX/nKV/iVX/kV/uzP/uxnOwK9DeNv0fwGOrWb\nVBtnv5YOtDVT3ioCbQ292vYWOm/v2UJoNh/QP6At7NyETtANA+iftcjAs40qa4X6za5vt9sPZjsW\n3TQR+3G7FlFcz/hmOzfbe2H8+rOiCm1dh9cQ2hwMV6n461st0qoYa+3/be/8Yiwrqv3/qaq99zmn\nuwf5o4AyQzIZmcEeFDD4903JSGKY8Q+EyBhIjPFBn/wTw7MPzECIifrAGxO4aoIvJhqDBJGIJqD5\nmcH8Esm9Euy5d2BgrjA0TPc5Z+9dVes+rKq9TzMM3ssduknuWUiwT3efU733rlXftdZ3fZfEbsiK\nbsDUYBNCmlOoCC0PaPU+avY7xfsh3Yceezls+qfPAeUkn8bIzhrK0lEUmmmPxG7T5zWE6PXf1Ggk\nQVKokUe+2VwqSGGAIWIJYmgFfO5ETZfKOTqHiBMd8GoUTWAtIUaaplHGXtMABue09GmcU8KQs6nR\nSvMdPjlFRMlKRTlAxKpEW9MSsRSDBWw1wJSlUrFFCG3Q5qgm0o5b6tMTxq+dTnMaJ4hEirLUxF9C\ntogQmoZ6MmF8ekzTBmIShrGZat8qA9EHPdCsKyjKAeVgiC1KgkTWx+u8curUG2/yZP+rHMGRI0e4\n9dZbAThx4gQf//jHu+9t376d559//ozfWV8bp71iNPYZDhRip+RgFrdQmmXoT1TSNrAJbnana58X\n6MgvuZyUgnVDUvfJp2NWO8oJRtEHV/U7tWGFqCdPnnSMc4kmnOJYkynL0MX4iTadT+IkAKTeussZ\nqABpTCPPIG1SQWv3M2XPnLzLW6tHCLki0DclZbcye/IrFZsZ6C59SG+yY9XNROydayQSEwlJjCXG\nQqsENhDTfbJpbaRZkjYxRnWHekwSkMHo5CZXQFloiGFS7ifEoC3GOApru3F4uddCYsRGQyEFzpS9\n3iAWn5ybM5qRJ2onIWTquiSfne6nka6N21lL6UoKq1ONfdvQNBOG5aKiQJTeFQnYsqIsh7hihDEV\n0VvtwxLHaGkbzg1oQ2R9fUzdtMg0YkqPKSst4zUa0kpM1bEqyboD+KDT4WOBK0pcWUF0EBoEpyRM\nvA59bWvK0aImLhMSbZsGL0IssnCvEEtDVUScLfj7sX/n78ePM85j2N7E3rIjuPPOO6mqioMHD571\nZzbMEUy2tG2pT3rNnqKQTqrYzw7sYGr+OboM3evjcWbeLn8xcxbPhA6zJ6ty1Xtlo5yEzHr/uhkU\nAbn0vV7vMC89/5mKJBQxZASh6sYpq2/6cIjcvShsWFM/3WgGLeQ4PIcvmT2XS3v5AqT3ymilEzmX\n/p1yJaSvJIAxWQ8y/bzpy4xi0gkrVoVYrVV4TYLrErHWptFwTp0ZOd+ijUjWWu32syVgCVHwPtD6\nFuf0HhhxMw1PuUaeJkJFk8q5utEFQxC9Rvm/qrWQNlvMSKnnWTijDtgER+FKyrJiOBgxGg1xziW/\n2ucaEk8UnwJBmyoxMcTkBB3FQHn+1WigjUnTSGgC07UpxZLDVi4FiTO9Gl3omm9LUnWylrIa4JsA\nTehmaUYJOrWphRCF0gfE++4a+VQdiqmfhGjwQw8Y9uzayYeWd/Hcs8d47tl/54n//69n7Mdsb8kR\n3H///Tz00EP89re/7V677LLLOH78ePf1c889x2WXXXbG78rMDs2pPJKHi7MQNT/iKV7NwqLZBcyA\nAr3Z0m2l9DCmkzLJVOto7IzcU9LMQNYly33+WR7MWJNkxZP0ltW6b8glLDFIZpfNhK8pA5BW5hIC\nyGyvJLUm+Xy3XYIrzMYYs4zGmdKEBM3kW6QvuZp8VXvPkUN1ZzaSmlREdfY9+2QlmYZs6Zyc/liP\nHCQlXxFDa/praKOhtBYxBZFCP9c67c5zBmcKrB0AJSFaWq/ioWVjKKxgJYJLyM8ASXAmBEkhgqon\np8aG9Ddaolh8EMocBiUkZ/DqPFLXpDUo4sBixFG4irIasrC0jW3bFok2dM1ikPJKpiFKYBpavGjS\n2IpX/cZyiK1UMhzRkl1Vl8QotHXNdLpOVQxUPdlaxIG3ggmaDDeORPNWcl1uFHNlgSmsgoJWpz1F\no4nfkMK5UHucTejUOEjNTcFrKEXQ6Us5514EIUwbpmvjs29o3oIjePjhh7nnnnt4/PHHGQ571ZMD\nBw5w8OBBvv3tb/P888/zzDPP8NGPfvSM3+82byKxBFJCLeUE5HXTgaGP+/X3c54gvZ/pHcBssJ6T\nkqTqdw4b0iO9IYY2Cer2LENFAySYam12WnoimZxUmzmzTbdOZtBJPlm1TBcSGshz73uiUC9Dwszr\nGzGOdBRl/Tu6mKFHP9k5piA5MxRCh6LSq4auvNndk7TBFAW8wQ3LnxOTYOZM7kNshiv5/jidrFxY\nMAW4khCMDioZTyiGBlt6jB1gTEkMBZXr5jdjSVWQVnSwbAyE2Gr+QFqieD1JRWgSitDZxdKHhkYR\nJtYpYcgVYBSNuMIgLoIDUxSURYktDCI2idPo3S6M00M2eDw1LhikCVjjkGqIGME6GFYFYahTt2J0\nujYf8E1L6Ry2HFAENLRKaMV4RQEihtCm4acUBInYosBTa2hsWuVXxECUguAdwVmwUJWOorJEq5vf\nt6nf1QWC1NQNxNoTphPKcGbiftbe1BHceuutPP7447z00kvs2LGD733vexw+fJimadi3bx8An/jE\nJ7j33ntZXl7mlltuYXl5maIouPfee98wNMhiFkB3+sYgaSioPlDdrL6NeL8vW8/AcWZq9d3Zl2P/\n7sTMGyNvgtS4lE7mDhnEvEPyk5/exSQGX4K91qgjiFh64ZK8fv1drT7k99E1GHJHX18ayzAx7+lO\nNr3bpAb3OnKRUq5zTqS7EF1ZVdeoV7kTcKF3mjYjsRmnkzkZ+RpoflJ3v8nfw5C7qXvSjsLzTgPJ\nBIyURBQdCE7Vf7wwqWuKMZjSYwuPdV5lvUMJrkjoyeBIQ2V9TI7A46VVJxB1YxhRqm2eOSFGKDLi\nsfq1OgIdKzYsh1jraKTVcMeqYKoYgytLnHM60TrGxLgyFMbRKodXh5N4QWqv48hz3spCNSgJTUHd\nWCRqf4eXSGhbqjKFHlHSIBSPaT0mCtYV4CE0rdIwjcGUJaZwCdUFxAQktqp8FVqwBd4JrnIsjkps\nWYAtaHxN42taHzGFEGiYNp7xdEw7HWu36JuYkW4CyNtvxhguvPTiLiQ4Q1gkQ3/TS4BvoBnN7Os+\nys8xV4Ja6VXp9Acy5LPpAU+qwBredo6FtEkNiVtkUKZhUVA4S2kVikYxnSPJjsyL0Gn9pwueS47G\nOHUWScIsig4o1IRcOlij6aB9tyGTg9PW2uwgNf+Q1yc5+QnqGJIDtBgNc9JF6mTQDYn/0DcddVWN\n5EA62bTcqGRSWZespJTnMujFs2I0I+rSXRXTVRC0VdbhioKislSVYTQqWVqsOP+iBS64YIELLxix\nUFUMXdkzPhJRqPGiJTvfsjauWT095sSJF/nHP1Y59cp6p/BjjSYNR85QpS7HQWFYKCzDUclwWDJa\nWKKsBgSTul4lsriwyNLCEsNhhbM6CRkTsQVIq4SjkNSsh66i9IKdtFAuwvA8qm0DVT9er2nWa+rJ\nVCdBx0AjBnEFo21LiA+MX17F11NiyI1MgisHWpGIkWphxPC8Jeom0NQtfjwmNI1qGsYWHxtMUWFs\nRRRDUZYsLi1iixLBUi4UuKGlntY457jg3RcRvWd99VVeO/kSp/+xyr/88f9xtu2+BZqFmQRjulCg\ntx5qz1ru7uvfo/cEfRGudw7dV6Z/N33nlETKtLqMEExmxEFOtmV5spzRV+iv/25IwolJk4I1Z5BP\nYAt6MolmoCHz5nWTdS0WwXRhgpE+2Mit1Ooc+r4AI3llM2BJ6G9wQjOSaJEdRjE5ztd3EtOjkv5K\nZoWjlF9J+EGTqL34uyKXjIJIMN0p5BVtWxJjkKA8DYtPGoUGCS0SPNZFChcZVoILAVfGjvYtUZOB\nbSC12DYE3yZ9wCwQmw4BMar2i9AkJmiJUCReRPA6jCSjg6JIY8miJ8ZI29Y4G4kWmqZGTEwqUyof\nroN0HCIuIYCgw1PGYIfnQTEgWLBVwdCMCKHFR50nELAUZZFQXkSbCJQ4l++TMQZTGFxpKUrHZDyl\nHo/BKwoQPxM6G1WKRhwxOB1Nl1BTOSoZVEPldhSOqqgYT1pOvzpmfdIwfac1HUn06fTShJ7tstwJ\nysHrtrZJ/8sZ5fT/pa88dMm6tJGMTfwA0zfYKL9LuvjPYhM9V7p8RZ4N0M9N6Kv9URRVuA6nkA5O\ngxObBnskAJ+zuASM8al2H1Jok5yRkGJEZjaPxtfpSuk/0XY6BS5vYukdWFoGs5crzuQM+tM9rVos\nIaUms6PUECNnKdIOk74ZTEtp2f9aTNTeBP1KqztxplRZKtRArBKCbGt0AGvULkUAWwjOCYMyYMMi\nxSh1U6IViSAG702afqS6f8E32ss/U6LNt1ckUuN06oCzuIhuRiOINQy8YRigNOoQgtE6fRsajK+x\nRsfV6xNTJN5FzL3jGqtjCEUgNmNCPaEeQ8s2fZ6qgrIaYKY1si7Y1qsDmHqdHu0SuymU3fNqRUfL\n5wa7KEJbT2nW1yjysxk1yUjI11dDH4xJ16hlPF4nmIhEw+LSiIXRAoPhAuPX1pm+eprp+oSmfYdN\nQ5aYk3SzSCCfTP3p1FUFZsIAFSPJKEC/aTa8S/fsvh4epO/ZRLixZI77TMpQT0PT9T2q4xD1xl0Z\nyWTXYrq/oT+Ze2JSejrJp7PGrmkzdGxKm2L9Hu30DlBSDC/9a13lhH6/kuL4mXJsDhE2XCV5/dVi\n5rVcNuz/9tnrqyIkdDkImeGhdTRggUyVlrTW2DnZ1IgkBknvxZrKiQ1Kh5OaAuUB2MTcDKKHZ9sq\nF79talXujV4PjO7vz0hJZdGJ0IbE27AWWgMmUo09BR47dFin90STkKpo5GxQ1GEsxAKLlkRt4icI\nSiyyFBhTg2+J43V8dNiFBYzT2oXFUtqC6HQIiovaK7K4uIR3Bd7VBO+1RG6ApHJFFPABFzxVbJW/\nIiQsaQg4rZwEy2Aw1OEo1uGbmno6ZmGUTTMAAAs4SURBVDQYYCJUlU57FlGewXR9khqp3mEDTtrG\nU1alTkWWHGe/7sFNNzkmiN0lA9Mm7nIGs+FCeil/T0gJr4QCJNeSrc0EYSb1lLIcgFEn4MTiTWpd\nzhswRmWSJaeuz3iCyzl7aSW1GkvnDEyG5oImGp2DqCPcOyIROrpLFWp0PHe3I7s24qASX536aKoa\nSBLrzMnGJOXW8fslpJ/N6Kvf8voa5I1ueyxDtIZmOmE0GG68vMlpzjpozCwCCoqzNBGh1z4lVAFc\n5/cVOcVosSZS2ogzrcqHFQXOuZQ/0nbkvx87yXsveTdNO8X7aRpzHvJBTSDixOrparTppg66JZ2x\niNfrbaRGgsVSURaCUKd+DUOswDmdEKRgqKCwlWoNmhJchZiKlRde4gOXXYgxnii6yaK3MBiqJmIT\nKKISp0yew2nBFKU6gumYZv00k6mSj9oQiWKw0SI+II1nIAFXKJLykuTOjKG1Gpo4LOVokX+sneZ9\nC4sQtbfBGRgNKoajAa4smLY1k8mEOlGfvbzDHIH3nrIslZI+s8FnM+5q2mjTlwrIAbq6gJkTTh/M\nzIxDPQm27wA0Skl1LklbCRCNTssdDDZsDmNTa2sHLfrseybjbDgVJSaon/5FNmw4Y9QRZACoicMs\nbhK707RpPEVZ0JGO8nugRB66foaMNPISE56Q2DVZ0YVAfQal4wKQna7d+J18/aPQTGoWBqPMOdLh\nskhHXdb8Q4L/6Vq5mcYpg2i3X/eXBMAS03BaE/ThbtqG9amhWo+UZclw4KlKpSsTDTEI//Ef/8l7\nLjyP1nuaEJRbELOD16GmSo1I1yWqY45RMzPGaHPWpG7To2RYHJYsDJU1aoAYGoJ4lE/isFLQlXmd\n+t8mRv7tuRfZ8e4hZWi1o9G3yGRC8+oabiQMB0NyubYcDbCFAx9Ux3I0JFZOhUTbEXXdMJ3UhGmL\naRpcDBTR44YD5R20nlbHQetBZQtMOcJVQ8rCcvzkC7x72wgrkfOXzmdx2zbKpRGtRKZra5w+vcrp\n1VVC2yJeUih3dtt8PYJ0M8gOID+6mS1HLvMpI02bjRJgnUW2+akjvV9Hz535HDIKAHGS2sCNJmDS\n77u0YVRERDev2wi6QfJaNTzQph8DRmWpTExJs4QCclXEGO1o60KR1Lqrz2si6iQEIKIQUOcVptSg\nOCwuNUYJqoeakYhm8GPerbFHB/ni9NGCbsdMHMpuIIGyPjSRlLzKLiTOXGqbKxvZYaTQZrZte8Yp\nGQxWkkx6QlmdepDROYvew6QWqklkUApEiwkutYBbJKSEo29pQlIIDpK0C1IJOLFBOyeb4mpBhVSt\nVWQ5bVva1MpOXGBYLuCsuqo2qg6icw5rKpypyCUXMZpvamKgDYG1ep1RgJExKjrip9TxNYoIg2Gl\n7dYWBgsVg+EAJo0+J8MKMyhhNFC146alem2NljV1FibxVRaGwABfT3DTiMiEIsKQAjsaYAdLBKuJ\nTD9dp3AV573rQhbO24YZVUybhvH6Gq+8/J+sra4SW6/FLN5hjkA1/TMczYA0PVwJ8+bKFSn5lzd5\nOmjS76USo8mnUi6cQR5SKtCR/FIzVxLrUEBsjNEZ9wk5WEpISkJaBtQOhOwWVHQjYpN6bRDNefQT\nkKw+9KIPlpGABENMrb4RUmiRFQPSKZ3Cf5t2rm6mjL0TPy4NGpn9+7Nt8InpumjoYnpnRjrNE/8g\nN1p1jMQcaqVGoSD9+5hMvc48WUBEaaw2XeuMlrqKCiA6FqRDaiIJ8qMbNETBt0LbCHUdsEZb0vMb\nBIE6NKy3U8ZtYNIKtVfardMOKr2+OaxBr1FInBARFTdxEikBojBuWmQ8oSWwuK1icbGirJYoy5Ky\nqrqJWr5VXQBjKowtUn+BQbzBh0gbBGegsAHaV2nGgdfKUmnL1QAfItQ1NkQKZygkYKoCKQrMa6qE\nPBwOGJYF9rwl7KDALgyopzVtXeMGi5QL72J4Xo2PUYfCUhCxqYGr4PwL3qUVgmFJMBNefbWmWZsy\nWVtj/fSrTKZTJlZog+iU6zexTecRzG1uc9s6e0fwCDbR58xtbnP7H9ibBw5zm9vc/k/Y3BHMbW5z\nmzuCuc1tbpvsCB5++GGuvPJKrrjiCu6+++7N/GiOHz/Opz71Kfbu3ctVV13Fj370IwBOnTrFvn37\n2L17N5/5zGdYXX1zbbdzaSEErr32Wvbv37+la1ldXeXmm2/mAx/4AMvLy/zpT3/asrUcPnyYvXv3\n8sEPfpCDBw9S1/WmreWNxHrf7LP/e2K952Yd/3vR4H9isknmvZddu3bJysqKNE0jV199tTz99NOb\n9fHywgsvyFNPPSUiIqdPn5bdu3fL008/Ld/97nfl7rvvFhGRu+66S+64445NW9P3v/99OXjwoOzf\nv19EZMvWcvvtt8t9990nIiJt28rq6uqWrGVlZUV27twp0+lURERuueUWuf/++zdtLb///e/l6NGj\nctVVV3Wvne2z//rXv8rVV18tTdPIysqK7Nq1S0IIb9s6Hnnkke7977jjjnO+jk1zBE888YTccMMN\n3deHDx+Ww4cPb9bHn2Gf+9zn5De/+Y3s2bNHXnzxRRFRZ7Fnz55N+fzjx4/L9ddfL4899pjceOON\nIiJbspbV1VXZuXPnGa9vxVpefvll2b17t5w6dUratpUbb7xRHnnkkU1dy8rKyoYNeLbPPnTokNx1\n113dz91www3y5JNPvm3rmLWf//zn8uUvf/mcrmPTQoPnn3+eHTt2dF+fTdx0M+zYsWM89dRTfOxj\nH+PkyZNccsklAFxyySWcPHlyU9bwrW99i3vuuadThAa2ZC0rKyu85z3v4Stf+Qof/vCH+drXvsb6\n+vqWrOXCCy/kO9/5Dpdffjnve9/7OP/889m3b9+W3SM4+z05ceIE27dv735uM5/nI0eO8NnPfvac\nrmPTHME7hUy0trbGTTfdxA9/+EO2bdu24Xsbpie/jfarX/2Kiy++mGuvvfas3IrNWov3nqNHj/KN\nb3yDo0ePsri4yF133bUla3n22Wf5wQ9+wLFjxzhx4gRra2v85Cc/2ZK1vJH9s8/ejHW9VdHgf2ab\n5gheL256/PjxDZ5sM6xtW2666SZuu+02Pv/5zwPq5V988UUAXnjhBS6++OK3fR1PPPEEv/zlL9m5\ncye33norjz32GLfddtuWrGX79u1s376dj3zkIwDcfPPNHD16lEsvvXTT1/LnP/+ZT37yk1x00UUU\nRcEXv/hFnnzyyS1ZS7az3ZP/rljvubQsGvzTn/60e+1crWPTHMF1113HM888w7Fjx2iahp/97Gcc\nOHBgsz4eEeGrX/0qy8vLfPOb3+xeP3DgAA888AAADzzwQOcg3k47dOgQx48fZ2VlhQcffJBPf/rT\n/PjHP96StVx66aXs2LGDv/3tbwA8+uij7N27l/3792/6Wq688kr++Mc/MplMEBEeffRRlpeXt2Qt\n2c52Tw4cOMCDDz5I0zSsrKycVaz3XFkWDf7FL35xhmjwOVnHW8hjvGV76KGHZPfu3bJr1y45dOjQ\nZn60/OEPfxBjjFx99dVyzTXXyDXXXCO//vWv5eWXX5brr79errjiCtm3b5+88sorm7qu3/3ud13V\nYKvW8pe//EWuu+46+dCHPiRf+MIXZHV1dcvWcvfdd8vy8rJcddVVcvvtt0vTNJu2li996Uvy3ve+\nV8qylO3bt8uRI0fe9LPvvPNO2bVrl+zZs0cefvjht20d9913n7z//e+Xyy+/vHt2v/71r5/TdWxq\n09Hc5ja3d6bNmYVzm9vc5o5gbnOb29wRzG1uc2PuCOY2t7kxdwRzm9vcmDuCuc1tbsB/ASGTtyfn\nkETiAAAAAElFTkSuQmCC\n"
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Cluster"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from sklearn.cluster import KMeans\n",
+      "vectors = img.reshape(128*128, 3)  # Flatten the image to vectors (data set dimension 16384 x 3)\n",
+      "k = 5\n",
+      "km = KMeans(n_clusters=k, n_jobs=-1)\n",
+      "km.fit(vectors)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 9,
+       "text": [
+        "KMeans(copy_x=True, init='k-means++', k=None, max_iter=300, n_clusters=5,\n",
+        "    n_init=10, n_jobs=-1, precompute_distances=True,\n",
+        "    random_state=<mtrand.RandomState object at 0x1002ab2e8>, tol=0.0001,\n",
+        "    verbose=0)"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Map Pixels to Cluster Centers"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "code_book_image = np.array([km.cluster_centers_[i] for i in km.predict(vectors)])\n",
+      "\n",
+      "nimg = code_book_image.reshape(128, 128, 3)  # Back to image\n",
+      "\n",
+      "subplot(121)\n",
+      "imshow(img)\n",
+      "title('Original image')\n",
+      "\n",
+      "subplot(122)\n",
+      "imshow(nimg)\n",
+      "seg_title = 'Segmented image with %d centers' % k\n",
+      "title(seg_title)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 10,
+       "text": [
+        "<matplotlib.text.Text at 0x108e40210>"
+       ]
+      },
+      {
+       "output_type": "display_data",
+       "png": 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