diff --git a/Books/ESLII_print4.pdf b/Books/ESLII_print4.pdf new file mode 100644 index 0000000..3f7ff5f Binary files /dev/null and b/Books/ESLII_print4.pdf differ diff --git a/Notebooks/Caffee/00-classification.ipynb b/Notebooks/Caffee/00-classification.ipynb deleted file mode 100644 index 1950f08..0000000 --- a/Notebooks/Caffee/00-classification.ipynb +++ /dev/null @@ -1,780 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Classification: Instant Recognition with Caffe\n", - "\n", - "In this example we'll classify an image with the bundled CaffeNet model (which is based on the network architecture of Krizhevsky et al. for ImageNet).\n", - "\n", - "We'll compare CPU and GPU modes and then dig into the model to inspect features and the output." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1. Setup\n", - "\n", - "* First, set up Python, `numpy`, and `matplotlib`." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# set up Python environment: numpy for numerical routines, and matplotlib for plotting\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "# display plots in this notebook\n", - "%matplotlib inline\n", - "\n", - "# set display defaults\n", - "plt.rcParams['figure.figsize'] = (10, 10) # large images\n", - "plt.rcParams['image.interpolation'] = 'nearest' # don't interpolate: show square pixels\n", - "plt.rcParams['image.cmap'] = 'gray' # use grayscale output rather than a (potentially misleading) color heatmap" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* Load `caffe`." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# The caffe module needs to be on the Python path;\n", - "# we'll add it here explicitly.\n", - "import sys\n", - "caffe_root = '../' # this file should be run from {caffe_root}/examples (otherwise change this line)\n", - "sys.path.insert(0, caffe_root + 'python')\n", - "\n", - "import caffe\n", - "# If you get \"No module named _caffe\", either you have not built pycaffe or you have the wrong path." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* If needed, download the reference model (\"CaffeNet\", a variant of AlexNet)." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CaffeNet found.\n" - ] - } - ], - "source": [ - "import os\n", - "if os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n", - " print 'CaffeNet found.'\n", - "else:\n", - " print 'Downloading pre-trained CaffeNet model...'\n", - " !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2. Load net and set up input preprocessing\n", - "\n", - "* Set Caffe to CPU mode and load the net from disk." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "caffe.set_mode_cpu()\n", - "\n", - "model_def = caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt'\n", - "model_weights = caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'\n", - "\n", - "net = caffe.Net(model_def, # defines the structure of the model\n", - " model_weights, # contains the trained weights\n", - " caffe.TEST) # use test mode (e.g., don't perform dropout)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* Set up input preprocessing. (We'll use Caffe's `caffe.io.Transformer` to do this, but this step is independent of other parts of Caffe, so any custom preprocessing code may be used).\n", - "\n", - " Our default CaffeNet is configured to take images in BGR format. Values are expected to start in the range [0, 255] and then have the mean ImageNet pixel value subtracted from them. In addition, the channel dimension is expected as the first (_outermost_) dimension.\n", - " \n", - " As matplotlib will load images with values in the range [0, 1] in RGB format with the channel as the _innermost_ dimension, we are arranging for the needed transformations here." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "mean-subtracted values: [('B', 104.0069879317889), ('G', 116.66876761696767), ('R', 122.6789143406786)]\n" - ] - } - ], - "source": [ - "# load the mean ImageNet image (as distributed with Caffe) for subtraction\n", - "mu = np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy')\n", - "mu = mu.mean(1).mean(1) # average over pixels to obtain the mean (BGR) pixel values\n", - "print 'mean-subtracted values:', zip('BGR', mu)\n", - "\n", - "# create transformer for the input called 'data'\n", - "transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})\n", - "\n", - "transformer.set_transpose('data', (2,0,1)) # move image channels to outermost dimension\n", - "transformer.set_mean('data', mu) # subtract the dataset-mean value in each channel\n", - "transformer.set_raw_scale('data', 255) # rescale from [0, 1] to [0, 255]\n", - "transformer.set_channel_swap('data', (2,1,0)) # swap channels from RGB to BGR" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3. CPU classification\n", - "\n", - "* Now we're ready to perform classification. Even though we'll only classify one image, we'll set a batch size of 50 to demonstrate batching." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# set the size of the input (we can skip this if we're happy\n", - "# with the default; we can also change it later, e.g., for different batch sizes)\n", - "net.blobs['data'].reshape(50, # batch size\n", - " 3, # 3-channel (BGR) images\n", - " 227, 227) # image size is 227x227" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* Load an image (that comes with Caffe) and perform the preprocessing we've set up." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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vAqVQbxvPBEJwHEd8Z1W22xNju7F/2Pn4crC/TAKKBefM4juKyeIIujtFkh+lMaFlclzN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w6H4LmXKSzzup33Bz5+8wlhecbNmlhypNnYsffcQww19/4aGX0xC9tmSOwIiMA6PXHcbHOgTy5Z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tOZ2GOkEfd/eMIXJ5/VRdu52rHkQSIIUR61t9XmNahA8ixFnJxmQfsrLSb3DM2bPJk4068z08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oxBRRmbs00dNMLX3vo6437HNKpj8/n9Gp9DwfQbQjhi24YwjEt763B9y7t/8SOeP7vGyj0C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h6kLU2FmzlEsB8fNYTiQhRjENYmNFVkwA1AxYSvG6UBvZmxdENBNq6mK5MI8hjHGUMmj3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\nmqAYfsg3sKhv50hcYl4QBQuCKPZGII9KmRWZQdvzQqRSHcb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P2RhGixNUQEtBnCfPplcM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6nVoYasNuXKPTAT83XmLl2g4sxfaMNRiD45o7Fyjrz942Zr4G1iVhJSqMmfOe6xnm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\nuVwu3OdlH5m9PP/E8/MLzjn+7d/+jZTrTjAOIfDy8sLnz59JqfD6+mdaH4e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caPfQS7zDQ01LG6qKyqDo+sa6G0xJdvfsKLu0wqAqIFQfAJ2qxso9ZoJj53D5hBHE4malsx\nNMT0mx/ljDTUlr0uK0gfbUkjJcjZdOSE3Tev1iwNg/PCHC/MH96yzn/Ub2BLuJ+wEsA5ahG2TE8h\n0Kzh/pMfIINlzSquBJgOB9zWRWSAavZ2e8lAGyktscaKd+CnCVO37CyDs5ZmLcZYBn+C3mlaecJW\n27OoGrhGBwqTpGFsYBxucUwEmRj76Ve85yrvyfm9MqmqIeUufubYN/wVE10Ht268L0MqmVQSNRfG\nw0HzHVGxtfeeeVlwzvHZZ5/xxRd6Fvrm9Wt++6/8Ds4bfv/3f4/peODjk3Kk7m5PPDy+4/WbL4nx\nGWYJMIaBlCN3dy949+4dxjhOXcA+rwspV6bJs8ZM8HbvYh5OJ5rAuiaMd8SSSf37nU43rEuh1srt\n7YC1wrIusBOOdaMMIWiA7/XKlmowHQ4IuuZ4ryPxrevnfSDGxGE6Ms8LLjwvkbIJt43FT0Jd192I\ncD0/8vR05nBQ6n9rYPw2vlvVUVwz0i5MYeDSwaHGTMTzSggWZzze2F22MKPjMNMst7f35NT2CY0b\nJ809a7qIt9b2zlJKKnSvOsOmpbYXkTU38pgYxlGfrrJ53XRUX6ThnVFnV2vP47mq48wN2GqM2uBN\nN8UEGm2NiIzYMFJtoMlzygBNsOEG96JQH9+RHtUC728+xroJWibLI46JvDnM5Ah2pdQH6nJB5rZn\nCC+jbn7Hm3vu5yferu/w73tRV87abcHoWuYbtVen1QqlWmoLiBs4SNhdi9ZpCHyVyuAt1sqeFxmM\nqDQkCiUaTcnoRd0wOHJZuFwfkLLisrrzQE0BRQqpNlIqpFaQvl5KS4jLlNrA6//nnbqcR3siGL1P\nS1WuX+lcrlSidniifucxZy2WNt9L0mOh90rKt7Y9m5doeKN5cq1jbrZpgLXama1tJeWVlAy5P08J\nFXjHVZMXUkq7NEVENHvQC4gChbff2cTgfOgHzEgl705mZxrRas5mxdJMo9bujjPqKk8Iqa00hKkz\ncVItxLhgzUBtTXEjW2ZeKSDSUwYa1+uZFvp36CAXdTLXorTy+O3C1TuKKXifCX7l89868flvnXBO\nzTH/03/zh/yy68/qSP014J8Gfl9E/k7/b38d+KdE5B9Bd9v/G/gX9KZpfyAi/xnwB/21/Yttzyz5\nxSvOV1YD46AdlFS0Mi9ZKNlQbHuueBXD0cc7D8QS2XqVS7xCW7GmgzXNoK4K6BoWAWpfABpLUs1K\nLQvG6iy10qjGsWyLqRmoRmflYjpfaXsbpkKrut8Fz2gOrD3mZokXfB3ABFwYMKaSsv6Zcw4xgZiu\nmKq6i9pPia45hjBinGPum8Fh1HFIXt9xzWdd1DrfZmtFixov9KRr4bq85+VNx/1bhQoaOzGNwv39\nS56eXjMv+v6NUWVVrongLDU31u0mrjMmzthgGdwA/aSy/ZwNWdv3rWhMSS8WRIRUVuZ5IWZ1PbbO\nShpCxRnBYahNaNg9YBmBIXh8CLTSdMS6PRhVsQwvDjfUWnj4sPD4pKdLbME0USsutdPSN+t46mO1\nhpgDLRXmVbUXX7/5I0qdeXH7GUde4nFs5L2SMyUvlLRyOBwILWDb9phYKo5WDYOdcHaksbGgREOj\ni1Bq4zFfEDGMN1scgoX+s+t6pbn4TLK3jVoiDnX2lNYoW7vdGfx4IISJUB1OKnbrnNobbFlJ9Ynq\nZkSuGKMFf8oJ36wumGIQ3J6QvqZF09ZzxvuB0+m0c2/WZdk7R69evuTNmzf8+Md/DMBf/sv/MN/7\n3vf4W//z/8C7d2/4q5//ozu36fHxAz/9kz8ml5WXL19wc3PDsZPIt1FaSonHx0du7m73kdAyR6ZJ\nw3UFhZlKX6CDMyzrgg0eEcE6j/TNaxwmxYI0xYOkeFV787Z5iz7zKaueUkSeN0xrieuK8wNhGMil\n7CfoJhDGAbEWEzxirWpjQMetzlKbYIKB3JjGjdIsvH/7jiE0Dqcb3r57vSMHwnigFL1HJSViZf+8\nY4wcjiPLcsXbikORFgCx6EneiuCsZwwHUtkwLJVmwVjHuq5Y8/z+aq3kGJXSXfUB27pQ1/TEu3fv\nORwHjsOJRt07UsYYpDkiBSsaEr4dWmLKIJlBJoI4xAd9XrsmrQSPGFEWExsU+bn7jwgFg7W3yJRI\nH5ScU0SY7r4HZsUw02rG9/GWwcLtLaVFljeF+d1XO8Q25QNzSjzGM8Mw8PLmBZ8eP9o/0ygV7zRk\n2tB2GYUNgWoCNE1BCIz7IUI3TAFR7lEYnP779tkQiCJUYxEC0gv+cbKISby6v9LWJ9L6uMd/LXHR\njoaprKmS10ZJnSQ+BjILuSSNkfFu5y9N7oQ3Th2A6P64ucpLqlBhiYmUK7kpCmeTprSSECkMXrDe\na/fJPK+1PlgMra/hVTs10FEAWpy1Koh4zLcKFBEtlIw1DMbv+7MxGR90/+2AqufUhnVldJZxGFjX\nrKDMzpQQYPSGULSAqs3sujNKpaRIqkIpBlOeuwcimhzRStMAaRFcf++pRFqpVFNVe2gVmQPQEpjW\nECtEGktNlC3CKziaN2AT1mecd8qeBJqUb01d/vTrVxZSrbW/xZ/u7Pubv+Jn/gbwN371X6uF0eX6\n9K1sOI80i5gAYrVqr53uDPow5TOyCOtSaX1MkduKQRjMkeAPWHvYM4By69lUFZqZMc48Z1XVQq1P\niECwB0zzLMuW4+TAG2onediaMK3DBfsXWp2HKhwGh9zp73y4vCfOj2TvmJxhDG6Hekm1NJMordJw\nqCauxwH4oy7YbaAWyxozpW/e1R+wUsFlvYkpOzxSmkcqJCK4yuXywPVGO1nj7T2VSi1XxEaO04Hb\n+xvmt72QcrpGGpzGGrTc41Ig18QQLAFPmhsEYVNGWzdhWqHGBesHHGEXwObSul3Wcl2ekGZ2G79p\nFgkK8GhNMMYhssVLaBHmnKGIPlRb7Ix1gcM4cRiPSCuMtuCdMngeHt9QC9q+r8pe3rRsUlVfVUrr\ni6hnAAAgAElEQVTDeKd8KtGidp7PvP7wBSklXt0kjmNEyqZN6JsZWmB7PzIddbO8u7nX0UdMGLci\nMlHiU7+hlfFkKqRamVMBOe8dwmkael5aoUbd4PLWXsgjYjK4Rm2ZlNO+0TbjKXWhWfDBa9bXrpFb\noC5YKnTWzEZpbqZRSyHGSLCOYP0edbKuq7KKqm6+pQlvO9vlcDhQqxZYr795y4eHd3uG22/8xg/4\nO7/3v/H111/yV3/nt7HW8tOf/hSAkhKWxsv7lxwmjSbZRmJLXBhk0Lys8YBgeXzUMYxzjmk6kFJE\njDxr5UCp2s5DbdgOEd1+Z6XrPIyh1KTdp8E+j1Rq1dNqKczzrDqnba2pgncDTFYPWM7uo49UGj6M\nmOAxRTDW4vv9vcQZ7x3zdQFjuL2/Y91yylwAI3zz5i2/cTxw9+IF797oZ9qkcRgnatFuwNPjB6Yw\n7K9znjvRvEQuy7yf9IfxQC6R4Dw1a86D66LXnKsS4WtVllSte0cq90OpftezxtD0lqs3nsE6Ht++\n52zfM00TY38tGENpmg23tArjSOn2d+89rjZEHMs8k2PhcHuL9AKt+oCxjoojAZIjTraum6ca7ZKD\no/pbhr5m5utMfHiDP40Y57WrUjZmj+oTw4tXVIR3Dx/I/SA4HAzFQCLzxeuv+fLD1zxlXfeNd4x9\nHOasRYQ9CqQ1PdR4G3DuqH++bYOi+4q1FrENMX43hNALThGnrC3j9s94Wa8YKdgGkzsSYE+CSClS\n00yOGk0irWwSIaSJcvWMFqmIkOnFUomKQ5KRWi0pVh37AmlJlFRZY2RJWQ/lDbaturVCbZUqAdMy\n4swOpTTWYp2FrCkaxhg2vN5m9IpJu0pC3knyRgRnqo47jQXxzB3VkGtnnHm3SzK2fNJWCutVzRxS\nLXGWHTEiovFjVbTT3FpjHLYkj0YtQm2WnDK2WsyG/hCdNhgRfDNQyq4PM7kqviElKgWxlrV/pqsx\niFO4ZkyFZj0u9DrCFrzThI8hCHYA+lSk1Yrd4mJ+yfWrueffXd9d313fXd9d313fXd9d312/9Prz\ny9rDUWvh6fweAG9HvIxUIrgBsYc9sVtPCkoWHpvCzXLp3YX1CW8GPrr5jOohtaqzdUDSlSIJoWK2\n6nJDHKDhjjSw1cLq6YUrBcgZxmkkBI+UvM+njT1As9QMVgZaXTgOOsIIXY9TW2K+PmJq4jCokNH6\ngLGeJRYaBusnUtXTVWyJYI54c8Q0R44L526dFu9peCQXxGvlu31puVR1dCCUshKb5f2jtsxPxzuM\nCZQaaZJAqoYKd6dYzgtDuFUxoRjENEzZTsmRlDOTHTUexlli2wTASV9PHXAtMNgDtmcGpqqwzZTR\n8UApuF6re+PxbiCmC7UWRAzWdWF4U0ecmIQzKHG7fxnH442OOYuO7j56MTB1XUpcLjw+PiJh0FGR\nZ6dCN+OgBXJdGcR2sWAPvZWEaXC5vIWciacro1PNjuVEKwnvhFzAuoEXdxrzc3+8I66NOliuh0bM\nllq6SFmshpO2RqKSjfB0WXFeu2fDKOAyAYt3QiptP3i3Vik204LoKbs6PbmiGrGUz8zpzGADdNQF\ngKlRc8yKZmeJs9g9gNZCcpSk+pBaM3ndQJ4GSRUjGnfx86++3Lsue1ZVnlnmiDWeX/u1Xwfg3bs3\n/OxnP+Evfv5DRBp/7+8+awZujkfECyUlzOhouT5ry+5OXK/XfTzw+PjI3Z1+3qfTjdrsje3arbR3\nznwYKakg1jOOAyIG10dJKSVKity/uMNKwfrNcdY7cjwLzsU0So57N8uaHoUyzztQdbdWO4sbp665\nKFgb2EZUgwukfpoOzuOd53HV7/fmcGRdE+/eveP+/hYbLDc3er+dz2ecFS6XCzfjif+HvTftsSNL\n0vQeO5v7XSJIJjOreqoxarT+/x+SBI1quqdnqjKTScZyr7ufzfTBjvvlCK0WMI1B6UM6QBBgMCL8\n+nKO2Wvv0ptjHeG718sJbSY+8d6RpuuRidhq5XS6UkqjIoYuDOTM7lNny5uhSmE6+GNtoFNaG7Us\ntLocooj72oghcD0/GTd0LSw3QxbmaCHGGiNumljLigx35zabern41VDP6CilEfckkFZp3uNjQqeT\nqT8HVaCXO9q9JT+kMxI97mTr4hwCrDfKfUXmj4Sk7B63qsUW43Bm/vATn35859e/GF9v217okigZ\n3mvhr99+4dttBG10cDJTfSaF6b/b5HJZDC4JRh+wUfA8ngGLiPEpmgKcdgg7PIHeC107c0yIK0dg\nc+8F1UDsndoquWSWgVS2VojOs+pGKQtKPnh3rYmJS7wSQx9Uk52zk4FqzvH9arYS4/7mXrnnO60p\nLRe6dJwL7Fiu987QJe1ENwQ/Iz4meGeorQt4cYYAtzGe7QXxnTbQzhQC/nBNcGTdSC4iLiDJo4NG\n8L7e2NSUi94ZArkr7FxXenbc10rXZqPNfaozwqxPF8H7RN70WPe8t2g4C1ZuiJOdh08fe7jWRhSx\nqKd9RGdpryau0IqxToaa0XnytpLbSvCJ0+V0II7edbwrXOZEOnnE98MWIzSh/HtGe/8zD0ellfqI\nQygZiYEuDu0NCeWYz87ziSATvQXeXt8R/5D6ShdUHB8//wkRuK3vLGX3nxq5X/0FZaXRj5BC78RG\nKhrYbg3aejh7e3Hmm5I8k4/4eH7I0YvxB2IIpmoQT2fPjIvE9MQ9v1gxUjaYH/wDL4FzTGyloiy4\nIZHV4lH1Js10jhgn3tYRI/C2mEdH8ATpNApuL0B6Bwkkd0ZbpsvK2/0/A/DbS+DD059ozUZ3XVYq\nhbC7tneHdwUXVmoVaonHwhC40reN996ZpoivcsCqrXlkOLhbAeo5GNgSmeZEKI3oOr3WIz5HvDPi\nZrMNIPiKj7vsuOFjtv8jDV/LMbpNk+CD0p3JWpXGacRL/PDDGe0rrXVqL+QtGeyMSdU7HRS6CtGl\nI9cxxjMMtczb8sKSM5fz2EzCidQmnFqq+jR7Pgw/pDk90+ud09Q5ZfPZ2RPQA2lECXlT6fRGjG4U\nhfDt6908wWIjJI9LjV2elbdKkUIY/LAglj0Iw1NKV0r5K5tvSHiCfcysjS62kBpvzhPHQ5zEUyRD\n8iATpW6m7gPoyrq9m/VAbrTS+XAeXjL3N+bThV4zouZBtY/L/vznf+Lj00dCiPzTP/0ToMS4PxeN\nUjMxeprrvC83nq5DDaViI8aU+PbthcvlcvB5tm1F1UZ8MQbWdeU0vIJETN15Pk2oeO45G3cRuC93\nTtNMiAntjTgiSfZ1oTVT1e7WBarCPMjvDeMbih9qx/7gF+2EasSbe7dz5D2uJ1hkVJonnHO832+H\nHUHvyloLS6nct8wsifmyk+1nylrIW+XuF67XC68v1kBeOOGnmdJtnDGlAPPDZV0EhGZ/1B0cMFV7\nVqI2K4ROcuTidTVV4Nv7G65DLUKcRsyRFN7fX2la+OHjD6RUKHmsl63S8kLWCpjoYF8vXXXUZuP7\nKA3nK7XdCHU/H4e2Ri8RzwWdLtRdXbq9Esud3hpabsh5ou0u+zHg4rONYvqdki3E2M4HQOntFVc7\nT+cTX2e7v3/5urHVlbU2RBpznLh6K85fl9/Y3CtMEcoKQShj1NZ7JzhHqcMlXy1I2Z43oTZBMrht\nZOnN4z10xWJKWkfrwul0Opzy6YLQ8Hi8AC2geef6qDlpB+WEZ1V/EKodQpChjO4NaKD34/52HX58\nZUWbqYQBnE4Imd7vpiBvUHs9osPoihNYy8qUZrTLsc+aI7gFLffeKaXRhpt4VuNA0YRSjcObdlf/\nCs5dLbalK17c4b0210roQOt0tZisnZpQu60NOeeRQODJQynXmiBeSNOJHsy7q38XPqxUanHk1jDS\nwhCfOcEpiKt4EaagyO6/5WTUFZlNbIy4x/wULWwlE0IkXoLFoIW90W9MKRCcJ6g338W94NNGXv99\nETH/044YBK+WzQNGoNv6go+CBCOmerfPfDtK4f32hTQ6Uv8dcda6M0XwTOFEGhX/WoW6vdKWQkpK\n8BxeHL05q867N7Sr3YjJuq80gndbqZQ14mfP4OPR2p2mjRgDPnq0BqjDGqFmnO+c0xVxFe1GrgNI\nXiwR3XtSiHZD93YOofWF2gw5cL4N6auR6Z0rhC4mS01yeG202nDeJLk4kKS0MYD/dv8V5yNOTrTq\nKGVBRBn1CTFafpW6gKp1C7qTqrujtZ102dHNjcBji87Q7gkjiX3JmTTtKrKEqvE2zBSNx/BYO9qq\nKSWbsXxUdrJiAVdoutri5PT4xnvJxPAZ76706vBBaYPcf73OhPQT2k3+/fLN/HPsPim4RpVC6Akh\nHKTSiCN6h2hhaRvvt2/ch4x/mk98Tj8gJQOODz/+hweHBFvgQkjUZj9bzmPTK9mUdyLEZtL9NCeC\n33lnL7y8WtBz0gnf63Ft1BnZX9SBGmF+V6HM08zsT9SWKe2dzsJuXFa2Dr3ik8MFIfbpyD5z/gU/\n7j806OHBAyqbCREQ1u3O6RSPEOFWKj4k1uVOLsrf//AjP/9sbif3+50//PgDX79+pZS9mB8oiDOf\nm+v1AyEE3pc7l7HwvY1YGFXlfD5zuVx4f7frnVLi48cfmKZIKcUI+vNO0LeGKcTJzGO6HERdbZDS\nzFaaRaf4SPd6FFLGFwqklNi2jf5dlmTZNmpXy3JTRapx4ex3jnQx53DB4308SLy78af3dq45P3KV\nXt5uhGSo2X3ZLGB5nKt3kaymECylEJ4/HIrG9+XOp/kTXYXSChE9PIhyLrRmOZvGhQpHtuN+TiEk\nal15eXkhfacunaaJLznThyXITii/zCeC+8yy3Pj68o3nj1euH63g7bkSxJnZp3OEEA/fJuccDAI/\n4qm1UjQjbohJrqdBVm7UtxekF+KHUdgloW/dCggBqXqgMoqnK4SYKDWjZd0pRITSDWHMnXpb+Xp7\nO+K/pukTL7ff+O3bF+7lKyFVTiMiR+UD35avlFwQhJ47feA1peuQx0+sNdt7sDeJ6EDzuvk7qed0\nGjYss2XD9u6IwXyi9nvocLhu/kkJKO0VN67LrnurvZNCoPbNuH9gnn4dRAO0huZGG956BDcUzzOK\nWWrsnKwdZQ3eszVrRMV76uCBpSnSVC0TL262p+6GrNGa0a6dkvOwQWF8fmjacEMpSy/IeVzTFhEC\naYjAnMqxF8vpSq4b2jgUdDtXtXfj8/UOtahx+8oDOe1NRjB7wOkjFm5bjTy/LgtaHLl2/PCV9D7j\neiF5z5wcQUamKIZiNqdIdWxZKbmNQgxKE1oX0uxxUZG4HdzfKUai93hpJtQYmbCA8RPrv82R+psV\nUpf5xPpdsrr3luFWWiWOBXFfpN9vr+aNIp5ttU1jGoGJ920xYp7MOCfU3o88JqcdHz2pTHjvMIhv\n5ByJ4gJ4dSMo8pE87ZxDxKTtLcp4APauxZPzQm03I7/GD/ihtNiKor2hXY/wz7aNytxlQpqNxCcV\n7fkgyM3pyrZkttYQLWz5/VDXreWGUJjEU9WGOs7vBeZwZQ6TZWllxc12Xdat8M3/yhyv1DyR6zu1\nrQeUWXrB48xmwMnwHBk3xxm0rVKovSGtH+cq3cJ/a+nMp+GtMhapiOCcedZ4L4jztHGugnXT3o8x\nbSkU3aWuBUIh90R0At5CnwHaXUkSebqcYUDRuyFpCJ1LSKR4RupHUrjx5asRfHNfjbzZK0sWgquc\nT3afoo9m8uYD6TxBg1+/GULw9v6VeC7EEIjhgvBQoOggROKEbb1R28J8HUqpqrApmpUgncvkrFMe\nrsExdFwI+HgixmAb50ACFCHg7TOrw/l4dF+1KKf5yvl8Zd1eWev9IBXnVdEameqF+ZzIVhrZ+ehC\ncDPRO2pztmAPt2Fxar5GtVJrJqC8vRsC+nR5Yl1uvLx8449/9x8HsmEu5E9PF95uN7ZlIcaIc3Js\nJq2ZgsjHQGud8/V6qN1ev31jns2v6Pn5A6UU3sfv++mnn4zIHBIvLy+2sYU9YaAzn8+omD1YDOlB\nos4WbNc7dFHznxF/mHt+r9LDO1v0x9iE0bVGH/AiVPdwLxeRocSQIxOw752+eBQr0krtZpA61q9l\nWUxFKELZMsuyUMaI7vPnT8QY2cqGaue+Lse5qXRK7YRpRnLntmxcxnOa5gntOgjSewrAjizYBmUB\nroHeN758MUuB9PbG8/Mznz9/4ssvf2XZliMzDk58+PDENEVy3cAH3LBoiUHsmozPrarHeiEobjw7\nvVe6eMIoxgGkBVowtaTrhf7tN1obxqIfr7TLjHtv1LpCz0wD6WCeLfetVqKABkcfo128QGn0+533\nl2/89vKNMoxj0+nK9WOjvfwX3t6+UXRhN8lrNPCOvm1sraDtYUfQWiP3ClgDHnwyOReG4HRtlFpp\nNSNdeV92FNsDhuKYRYAehcQcIud44iSOKQZaX1A/Cv7eAFOMiyjROcoYX/VWKUUQBOcmWstI347z\ndDIRZX/mHWHsM94XS98UB10Jzp5L/X94X/fezVstyPEVxXyW6CZGQcV8z4C8bXSsUWxZKK3ih3lo\nchM5V5IkpsnbtHsfoztPUWco73hXd2W54W46PCHbsErYvzKKcx3fKO04z1obJatZeXR7T3fvC6eG\n9k8pELypWnUUil0rdatUCZTmWHI73Pe7CiFFJCgSOt1Vgn+M+9GV1hytVfp3xWDLjVy+M9z6V46/\nWSElPuGCQhi8DWeVeW9Q+kbUcKQ9563z+nLjw/Un40YsGyoGVWtfWPM3Ynyma6TkhTq8i/LgNcUZ\nQgwghm6BdR/SwTVFfSfN/vAEKptDY0S0sZRvqHuG3XG2VpzzqGR6y6gsyG666DylRLRX8lKIXo5R\nWh+8BQnG3+i60Iq9iEEyKrBsFcGKnNKtY1/WN5xUpA+n3R5Qv3OLxt+y4bxnWzshjrDIUMjljrYO\n2DitM9QhmJpCnSlG/FC97S6vNHO87WKGjt7pgQ7WgfxtqyliYozoKLJa1SF9FbSaIR67+lA9pTka\nAfWJJsq6WvFSayYRmFok7mPCMTIo2TyaSmkEJrx6zgPeTz7gJDCnM2E64+L1MND79u0bt3zndDmz\n3TL3+zuX69igThe82iueJNDPkfVmN//l/Te+tt/44cMPnJKn9sYyYN04GYS+1YX3+xcchWmMy5oI\n6sCfzWcnt4wUd7jFBx85nZ+IpwvOCdIifVe4rBt042CQBHeZjqLW+YmuiRAmruK437+Sh5JKHah3\nrFs21c75sRCtUkkOzpqI3aHN48a92J2YSyk4sTGfl4dz75cvv/J8fSaFyF9/+YXTdTeB9NzeXm3c\ndrYA0H2DL6UxpRNOAvf1zvPz6XA9z7Uw6TQCis10ci+I0jyhwFIqa21mtLcrFlWt7VEsysQ/VLM4\noeRGvEToQ5Wlj6LXxcRt8FTO5zM+xD0sABcSoVjR053QuxwjQysizOjSeUFcOPhMu9rUeyHnaojU\neGdyXrkvNy6XM/PJEPXffrOifp5tM1SFeT6bm/hYFy6n0/AEymNvErZRDPoYbdSidhF674eaNcQJ\nr8q6VuN1hXBw0n775WeWtzN/+vt/4PT3/8hf//pfWRZ710q903Ti8vTMp+CpvRxFpBsO7977UThA\nGdEb4hwqFqtCV3NWDwnZ43waaM50VYJEpGwsfxleUfUj6cNnuD4ji0eXN+r7sIWJT4RwwsgszvbT\nUYSIN9+birDUlWW98+XN1J7xaaZKRaXS1ZFLIw9/osLKVu60vo3zdYfqSnCmaENwYlEu2nfvvQrO\nmW0Odajf7Jmptdq1ieBDR3onVzsXWkDahvMz2oJRMfZmF0ej2oRiIHqad5jesa4LqtF+b4j0UdR5\nJ4iAiyDB06t7WDiQmMOJHpQQ7Xyr6Ch1B2JFN/uNzmEDYK9NAFGjXHyHGoHtbVUDvQdamam1cN/f\nmcmezS5WQwVxB7IWpONdoPQ+UG99WPTgiNEKUO/tndrHft4betV7hybU2g9LGGvQI9oStTk0cOwz\nwXtSdMRUmSUPhO0xum3O1pxWPL578uDbqjS8rwYiB6OM7NeltXfwkZyVLqYW3JMZWpPjOfh/O/52\nHKl4JhFZd5OxkonTieSS5fmU7XCwjj4Q3IAWp8jr28IvX/9qP0dX7uXGpjdO/jqiQgZxNLnhNWMv\naIzh8TD2jtZmC6YON+LxfL/evxGLEfjacuN0ylyvJgF33QiBIcyIDkLtgFSdzPRqVgdufKbmdgv6\njThD34wEKFQz6sOq7/N8Qlw1aXFrB0dGgvG1Sqk4h5m37WNGtaiDjFnYq/PHLDcGh4ojt7vJqcXI\nuGWMUkMItFrxiCWDN44HRxw4n2h4c+2lj8R2rDtwNpKpXdHa0PGg+qzEOI3uqo1onoHmSKc16HRU\nBOfjMZ5dbyslb9QCITS8iwdpesmNr2+/4fjCZTrz4fyMipF4ny9ni9gIJ+bpidROpNPgVvnEX778\nFfGOUzzTtpVt2FvUUydOs3m0dMG7yHmMk7bVU5tQWqNJZ8kbt7uhg7OeyC2zrK/c1i94XwxBA6Yp\n2bPlFO9h6o6ehTZWouAnzpdnfLxQ84qTR9GY2WjZEeJEyQ0/C/Mg8Hud0BLJm3COV65zPArw93Uh\nq9Brp9cNEPooMn0S8BX6wkk9s5vZZ3tePIFA7UJT4y7JKPheXl6OjfXXLz9zuVyYR2TJcruR88r5\nfDVEise4AYTn54+UYp5RT09PfPn16/GsXZ+feP32iveRZVl4Gs7mTgJ4x7ZWeyb8gwfUtJFrG2NI\nx9byYawowZNrIZVkgKp4YkxGSsU4S62ruZr7AIcRhxUMaZ7GqKZbIRJ3c0GHjIKidxsR755XOy9J\nRKi1DtRgPN/Nuv8P1wspJW63N26rFQuvt3c+ffpshqJj9FxG0bNfx/tt5XKKuOCpZZfAN0SNj7Ib\njO5E/JP4gdILtTZSnA7riy0vfPnllSCBH//wJ3788Ue2PBxe1cYsuWVCmDmdHny1TjAE2vtjpDeN\nBkqCx6cJphNxPhNiwvlk4hvAxRntlbJlSt0ITkg7SvD6RsPjLh9J04w6OdZh1TaQrxNNJ1tzx3vR\n7u9E54mnM0/Xj3y53bl9Mx/or6//mRCVWt85zTPNF7a7Ef9Lfce5leaMW7TlQPAPDzEd6I53CZF4\nNIl4ExGhHiECgTKoGeqU1oYHpzp88KTBD5x9xONp4qkiBB/2ZFT0QDY6wZl/+87jrDVSy92MOV1g\ndtP4veA0DGTaGVrjjJwN2DMxuGspzthJ23oFEFLAeTG0xpvBtexunRhiVKqVXU7cDiwh2MiWajSP\nVpU6njdP5DTZO9O0Haas9n0RH5WSK7U9/KzGyVoTQcd5c3k/+M2OYcJtBra1tEdcTwdo5ALavXFk\n3T6JMFuKEC0CruaNOgqp0uz+lpapXc0cd6B8KTpCqqRoPLwYJzhWBTMvxju0G+K434uuQv23KVK/\n2x/8fvx+/H78fvx+/H78fvx+/I8efztEKkw0hWm4H0u8E4NniidEPG3tBxn58vTE8/MPhnJUNTL5\nGFP89vWFTRdc8SwuEzQdBFAVR/IzrTt6XslN8dMOcQpEkK6UniELVYdkVVfytpH88xgv3JFgCNjs\nZ7RWYncEmWi1U4ZyC604iegYG9gHG0S3CqXVAaUbp6MPjlAuK608IR7UZWoph2usjw4lIGLuvLn2\n7wKfB0xdg41GgzBoN8Tgkbnh3UTrAR/AS2BOezitOSLXzXgWKT6uqVZnLsXqzECyPRRmMUZT0fVO\nrZlOII4xhROlt2LVvBgisKe8B5cIGFyNOKpW/OBQPKUA6liXQlbl/PSJGIfsOm7k9RvvtzeW+6t1\nTH4ffTjm8wemy5UpOSZ35lSmcWU8W1v45esX0nxCposRb9k/foJm44OOEgYiFU9nfLF/yy3T2mbq\nS6BLpna4L+9U7cxzwg1+mItK8IJqJ3jB49FJ6MOQc0pXzqerqYTUIVTKLhF2Jkn2XgxGzp14sW7X\nlTjGHt64NO7MdQTe9nYj375R8mYRKasiA5UIqpYnmbpZgvRM3JMC3Aho7iOjK+dDFFGrAZ6lFD59\n/sECgxcbYZRiGXpzmqi5cbmceH8fasf5ZOq8bePzTz+iqry8mpP8H//4d/zyyy94sfFN6/3I2mvo\n4c7cml3D/dmvpQ1T16EcQ74TaJixaB/8i+48aT4fHKmmnTRfCckMR52EY8zeWqMwHPSrDShE9hG0\nIQZNTExhXfIYQ8bZQrqXZbjCR4ssAtZ15Xy6cDpdWLeNdSuH8/WyZj5idgH0TM35IP9eLo00nVnu\nN7ZcjdP2XdRLcA43XMtF5CCNL8tykPHv9wW6Htf0dn+h3hvrduPt9S+EdCLFYf3h4nAs3yh9mM/u\n76+PTO78MD3tnekykNEQkMFli3FmnmdCMt6iPYuB7gyxUQy1P3hnvVNfX0lN4XpBLrON7YDWG67d\nUaeImxC5HDFPyEZbV1wXzqePPF/eCcmED+Xtzvv9nbJlpuApzJzHmqHnhZAzy6rce8fHdPBrDFWM\nY0QbCfLgFomzddnO25CkXY6/5UZMRlto3q7Ng+cWEBfpTSmtH+pOgEqniZrFrwFTONmfww1HpNaC\nW02Xtocf92A/s7qOpVrod+OrbOIf6ZhHeUCdHGTs+STH9Z+mgHMcjvBBPKUXnAuWrYgQ9jFuLPhu\n98TIcf14vv3YSy2/0iLc4qA1aLAUEONccWRcwi76sBxC50yVu7uXG2/M/lajbR2eua0a37g1M5tN\n+nAv9wGaFnp3rE4o8CCpN+W+de61smHB1GmYfM7niA8d7y3CK7iHurCJiZ/yls01QE6P51ATTv+d\nocX/s46inYqHkQLuXR9Ktcp5PrEWy18D+PzxB7yLtLzRxfxl3MGjEHrDVEfBIT4ctqpODZJGImin\ntZW2DQ5FsGyrvsOOEfzwUfKhsLWNll9wEpCuEAaJd17wEtEy0aQYWW/IUmvdULWkdSd+cMq0aI8A\nACAASURBVBp2crvgljxiAJTa2wGNbnklx0aanLkK58J9QP+iHe88vVXqKKKOmXYzp2GbeTsjng7+\n1LZWnEuEk7cirNvcOYxr6pIwp8jiVvLWLBtuVzXdZWQfgaint3K4sKuzsRzSSCPAdt4l5mUl58yy\nFXot1OpofcC43hNjGFELRhy87EVdF5JMbICTCefO6MijSqkTThf6+jO3+6+88UZKu9LimVOC50vA\npYnJO06DONtyYf34AzlvrFtlmmbSZXCkQiRGj4rQarewyp04ev1A2+646AiTQ0InF+Or9dIQf0KI\nPF3+AK2bFxUQmrOFRSpeza8F15GRt3aanpmnhFZP9IJoYll2ObNnSmfEdeYY2XLn/s0KlPMcwQvS\nO2EOFNGD+Bj0I9c50Ntv3NaFCX+EUqtYSVVHFpXxQoZidbg0tzZS5HM+xhveO+bziefLM61nvr2+\nH9l31+sVEeG+rvz4+e9oNR9E7BAnfvv6wvXpzPl85i9/+cvhUl6rkcv/9Hd/4n5f+fz5J1sNgftS\nuFwTuq3mi1P1GOkXtSJeuy140T9koN4Fgu+ENOEFWnPU4coMME2zZYOVTowBHZRXGBzFUUT7mPAu\nHrmerltep9IRL9jkcZdIO2qz6JU9y2vnzzW1YjLXxuv7zRzLs33+T+lEaUZOd11YlpXzZbdlMJ6I\nF2HbVojpGLXkXJHJuFD2LqSjkMo5c7/fCSGYDceymiAD+MMf/sDLl1+G8lhxoR9kXHFWegaXrEkT\njnEhXjidHrEoxmkZKrnTiel0wfto60y05m1Pg9C2MT2fiC5B64dnGUDwiqsrNXd8EbycH41mXqDe\nIW64+QnUk8azwfWJngtSNlq3wjdNIwLo1QQOuRZCCMyaaGKF5HQJvDgrwvHZ+IDLo1gKweoE5xvB\nTcd6CjPeKbl2SlF6s00XMJ6TgB8cHysMRnEmbjhtB0rf0M6xJ3SpoN3UoGLP8W7erw3jvCJQHW0z\nv0F7EBM0Rxdrahv9aLy7bkC1+0u1ZmNkooJ9rhgjopiH4vBasudNCH5GQqS1ZsXb3kRMDu8rW8ls\na7ZGY3g23W+NyWXSdEKxIu17QrkTCw63c3yM9uz/OWI0Ba33kyUDYAW3FUsmqnHf/cxWBe1u+I8F\nnPpD+NDdhoZKFVuTay07VZF1g9f3QimOpiBR8NO490GJsyPGRoxKqwthbwSc5U7W2tHu8e4x0m+9\nEf4/SFJ/s0KqdYxcN8iT0q3A8GN+nKZHvIrDHoTzaTajsaqs48XI92YbRBq6BcmEPkjcTHg3oQit\nv5HzAuPFCOpo0ui92kKdG133FyMSp04vSq3LUXABVNog0ZpxoTM97+NztW4/t4F2OXyUejfZ6J75\n58JD4o42NsmEKEzJUcqGDgWhmtxqPJCWLXQkd4pDnLesPCodx7btMREb6iwPzzx2EgjH5ubE4WPk\n+YNjed/Ia0UH236KzyATwRU0RLQn2igmtl5QMqezdaUpzWODg+5NgVFKoVaI3j+uqSoNxQMuRILI\nEZ9S147Lged0JcUnSvFHkjm+EeNMT51ye8f1lbLuMozIeleWu3L9OA1kzL52mmaer0/cljtf2ldU\nYBr2Ft7Z9fRToLRq5PjxAnsBmTw+elyw+6RuGLq1Si+Bro7r9CPuwqEuXNZXU+mdI146iqK9EkZx\n6vwFlY4PSpBgi/EoiOZ0sYVQOmsTYlSWwctqpTPNz0zd0UomzZHaRqGRK86bXUATodQ7ZSCA0jyh\nBSSbN8rk0mFwK3BsyCJCTOa3BnA+ncF5Xt9faX1j2VY+DkPS/Tm+Xp5s0WnKdXhFvb3dmOeJHz79\nyLdvL7y8vBwd+7IsfPjwwew/RHh6eiIPa4A0n1i2ypo3Gkrtj27WjEHrcZ7NO7TuRNVAjKasE1VS\ncKMg2J+38S62ZiHRUzxCsu3fhorUjWiLY1MwlMcFuC0ZvlP/tZbJeaWUzQQFeWWPiwzRAlSX9Q7i\nyNs2lEpGqH8f9ho1N4vmGBtUzpnTZBusG7YCj1gSKxJUPCLOUImDqOsHof03TqezZScOBeHzhzNO\nPtM3y0zzCG6sezEZ/0SrIcYiD86K0rkvb7QaeL5eB79nbEK9I62auMApW7kTej+yDyWFYSSqzNNk\nQbXsyrUVrw7ViisFLRVxI8y9N9r2im93enk3XtD50zifGbmc0W1heXthqytT3HPxArk2iip1q4gG\n5vCIEGnTZ0M3eqBrPe6Fl8Gb9R0k0+SBAom4QcDuaLxC32gjS9NjfC4fvKHOg5S/34vgLPartE4p\n0HZRD8VMKjGUyDl3FEROwrBiMPSrN4fu6GCP5rOk3bJjm7JPBboWeisEncAFcs02kRj1S4zePPO6\nAn5YWYTjdxIjTqKJiLwn7WadMbEuCy4IHTFj4pHR9/pWOIVMihbK7X1kT0l2aoTy1Bu5FLt+u+jD\nWSybd2FY0Dj8dX68321BABHbq/ZM196VViu1Gb9Ux5Rj/EL6KC5z6fTiWca+tyzCbfO07lDXOE2J\nNHy9nTNsJqSAaDn8ysBU5OZH6MwKqOvx7PemNP5tktTfrJBKzg9fo508Og3Y1Vlp5adj9LHkF378\n4dkKK6lI6LTRCd5uG843ppG9JT7hd4TEedQHPB2pjtLc4WEx9UIKGY9n2TZqzY8KW+zhDikSQjTL\ngvGQBn9CnWNjtU649KPT7c4TUjSYsxvJbvd8UnWUulme3VAY7Z2fDMnxtjaWtCJhFEyYrD4EU8gg\nxdRL47Z5iVA9q2v4aLJflWELkRfy243bkgk+83SZeDqdkeFG21ujeYXgOF2uXOYLXa3QcOFKiM9M\n3kZQua7cViNyvi2/0UrmfAqcz8kCpnfrk0GKpTccHR/C4cJdW0dqx0u0rD6XkOGxZKZvgsMTw4yL\nATeeWxGHTKA/FO71Z2oEP1LApXZO0xlxnlhmiJ3GyNvy5jOUUmKKjq3dyWOh/Tg94Yp14+EiNNdw\ng8gZQxwbjAzp+4KqvfhTuJC3TlkyUjeiXInBFv2Vhe1+h+64Xk5E6WZ2OeD4Jbzg9QIu0oCpQxze\nTR+u1qW37vG1s5R6IHnrklnrGydfcSkS24nJjxHGQYKc+Hg6ofJ8FB49Z0MTfKe7Ar4fkLorgRaK\njaKLIP0xMlrvb7RmxfAxBtllzuLoeLbmOIcrp4vwf/35f7Nr+vEjP/30R75+eWfLb4NAPKwfphkX\nEu/ryuVyQZ0Q93sYvPmL2eyb0zwzDx+l++hcT6cT2gq59sPvappMVdtrRVQJKeHcxP1u97+1yjRN\neJ8Qb/d6l3mreEOGnZFKbbIxCgaB4IVS+phz6ggcNoRo2coQpphqaXcVEBHW5YYITDHxmt/48GwC\nFWmNt5dfuZwnclkOpSNA35QSC62V0US2o/ny0VGXQpkcl3Ni2e6HDYuSSVHQUqm68vH5wrevQ5l3\nS6Tpgp8f9zWMws2CrAXiIzRcdhFC79ZUlsytZObzyRB+oCyG8K9+M78dB5oc6dnMarUllvxCz3di\nK/jTE262IltrALcQSqfmgltf2cO+ZTpBnND8Cr/9C7l75GzWGOH5CecCm4d7y7y8ZNZvhtS3+2/k\nt9/YekTdCe2OOAjlThLT3Cla6MsXzrFR2AnOJ2oD7dXG6XHFYUaerYK4xuSHEi10BuCGb6bVaM3E\nGU6m4dFmeaBFoYq5u5dSDkqH9kz3ninOo4nupN1NvzW6i6hYg6HN08vwQROP9GjpG/KOcxzmvw2z\nYDGXzDsurgQ/MQ0rkjBVOgWHx9GMRrLbVJCIEpimZPuvYiawQIuYtcy9MjloQfHzoGassDVHbcLk\nIkuWo5C8xIBqoStU56DXI9swhQnnohHSMZWvfNd41xJofYNaKKWhbr9ujVqqFbZSUCfoQM5UbQS7\nNsia2Qosg7Vxfyu0HnDB4edAOJmKEiDOSohlqCoZvnTjPaxCJ9G6x8kEjEkUDL+v3d3sXz/+dohU\ns/DaEPduwPgz2vNxsWUsYLt1gKqyrneaPCIWRI3FH0O0wqm77zhEUNsdEYMFkw/UQb/f1g7RHxJM\nLw9Fn6PT+zD6C0OeOYrhbVsIoSEu0Jt1kbtJXBBTH8TgENeMHzSUFK2aMkWjObpr2Q4FTy8NnKCu\n4FslRk8f8KhxlUwl1HuzDnzA9KZwEqI4WvUIjQ9Xc4VO8acDxXJeucQZ190xFrtcT6gora5M3nGe\nnrhcfrKvPf/E+fTBAi3Lxtv7+xE98+u3yPvLOzqCYrVV9mjs6ptV83TbkNDjXLUCXWm9UHPFTe6A\nlH2MRPG0UlBZOM+fKAPJadrprTDFxA8fP/Mtu0NW7/yFFC9M4UrrHtF0ICt5W1HMB6W3bWzq+0Yq\nuMnThnJTRDhfbNHPOVPLHedH118fHKk0zyPQ1NG24XGyv1/NFsKyOt5L5zpPpBApO3pYMzUKrUda\nt5fUja7Vp8AcL6CRUCttXWjjM25ZeX9/Z3OOy9VMVJu8j+s207UhXqi9EZwwDR+x3p2NLSVAmOl4\nhrmzcTTUeFkNU8rsViOqyraZ35GqmUP2MaZoxXzFnq8fSJPw5z//pwMh+fz5M798+cLby4uhPyVz\nPg8EMMyj47PCbJ5n3m+j0++V09NEmidyLcTphI6uxTrpvSkSutZjDCVi17+0xhQjYQQaHxqc3pkk\n4Henc5VjhJPSRAiDs6TOVFXjPXXD8fn7P3t33Vqjd+PXGerajzHk1jdaU2L0dr+2hTgapW3bWJYF\nr1YkXS7X42eKKMvtna7VeF3xYeGQs43KS16tOA+B2/uIerm9cJomnq9P/PrrF54/Xnn6cD3Oc1d1\n7cq+/Wfun8c5xzTb59hNPlFbT3pr3JeVXCrzk60nLp2oGG+FGHFuJs0foO3rVGI+PbG2yrIWLhPH\nOEniydAtzRb7sr6CGzFe/kxMnyCcgYn2yxe+/ctf7Fx//pnz/Al6Zy3Kfb3z9d34et/eF+7rStGO\nxkDeGk+z/cwQAhWHD4mQPB5Fw3jenMe3NEKE6xhbt/3FwEkyxZxkK1ZGo5+LjagVQzhtT9kVZmYk\naS7ww/xUdsQ3GKoSoNeOkaSGKlUMZZIQzXePeHB9egdxlT7UeKKw9/kqle4aIRkSG3siTekI4FXv\niV7R1vH+jIoeESoiFZFESoEQEtKUMMxoVRU00oqZeQbXDiRzmgOtbKzr3byrvkPkajfOsYggWmjd\n0KX92qQUcBLpRRDCMRINXnHS2HIhF3sud35kK7Z/PjAjPZqCrtaYd7Xw4Vrl4OK21gatQTmliSk6\nwuDUzj4QnUOk4HobTus7+g0dpfVG083ibmSvTfT/x4VU3xDiQfJUbbaYjwVcnDsIiVUar8sbran5\nRA2pMViExXQKnC8zjETu7+WVORtUrpinkx/+RNu24eioDqloePAk0Dacai16Q3hcyK5QqhVQHk8I\n8TD1ci4gHXvZfLe57/gMvYnNgreBvPgzee90uznqdu30PipzeSx8LfTByYi0qrQ9VSkFYpzQbI7Q\nz9cP/MMf/lcA/viH/8jz8wezKdANLZ332+1wBZ8ulqs1RWHL77S68fRkI5zPn37g6foDzntu68aU\nTseo8b68cpM762bkQqd6dB/ee5PvY2iEtE7Y7bQl2P0jo+O6unkUoAZ8A5lcXonpcpi2tZLZ2kpp\nd2ouXON8jH+RQExnfDgNyfJD4t+bIK3iesY1ZWuPtiXHiJ9mUCvgg4uG+AExQKsTSENcQ7UcHCnL\neYuWKh/j0cnbMxMIciIS8MWxZejJ4YYdg8RsERokYKar4ken5N0o0JxnijAzUcYYKqWZ69lzv79z\ne99Mrj+c5B3WyQYJBsdXpcsuEfYjpd7TqgOXDuNYh6DG8hxjCXdIq1uzYr134+TYYjMEE7mRThMh\nwn/5l//E2/tX/vEf/xGAn3/+efgYOcqycT5fjYwMvL292Qg4TngfyVs9CjC0oc1sJnLOtFbYtmVc\nVLMgcR62pVBbOzg7uRa2dUXkBARYK+dzMm4JGJKEgFoxkXM+DPacj+S8GR/LC9u2HVEyTsIoLMvx\nuR+FVDeLBRFut/sYHx5nCmKf7Zcvv6KtH2alb++/UWtmXTvX84VpmliWgZyhlLzitFuklcrxDN9v\nixU7eeG3r56Pn35gHd+3LTfquvB8+cgcI+9vL0ch5QcXcS+avpej24jE/s5bOcYu+/sbnf/O884f\n17N1W4t8DGYGOV9BIm2Q+8M04aczsxNeX35mWd85uR11DLQ04Z0jNHPj3+02tHU0KuqecJePRAJp\n5AL++c//Oy/v/wniRJbG2/2Fr3fjJK55Ge7qoC6RKyzb2KBbYOvZpOwECBW/W7S4htTTwf1xbj7W\naEMpgKqIh+QiTXcJfKLWQoqzcWjioyCgW9FSuzmG05UuO6fUJhOuVGPpyQPJcQ7E69G0eTcddBYh\n0HWjUUzGz3f2B04J3jG7wbNycVhWjLFnmhBvXlLqJjqNPu6T8544TzQ1PmmaT6QRH3SeL/QG385f\n+PLbX/jtt1/pdUTW1IUezDfQeL7+2C9zzrhoTbSIh16peR9TeGQynqF4G53uvKTgjdBe6ma2Cv1R\nSJWmePGIRkTiQHF3vnG097sWWhVK7cfYXoLgUKbZEXxlCnCKo4ikMgWPVthqBRzSdxBE2Kr5JXas\nUN6pN14gzg+Ry792/G5/8Pvx+/H78fvx+/H78fvx+/E/ePzNECnG2GeHAA3WqxZB0DpUfcCcGk0d\nNMjVRm5jfO3MPCeCD/gUDzUSQHSRFCdaXxFXELejCgbT1nanq0PUk8zVDwDnOs7CwUf3Zhwn2Em6\nDSUSYnykaQMycrJMOims2+0Y3+2S0EJAYx+u6vYZpNqIoTNTeqLJdqA8YK7RztlcvvdGG1+73zIx\nQpo7Hy8X/uOP/8CffjCE4D/88Pf88e9+5NPnjwC832/c7q9s1dCVdbvRS7UMqfOZrb6b4gbw2pG2\ngUsWe9f7QQKkC7UWtnyjSCf09IBHZaWtBVcd5xiYfTJnYQbMr8paNpo4gni2YTxIF2gJ7cpWF9S9\nHPEia76z6orzjaoLbc18/PjDuE+Da1CrwbuD9wBDJamOczwzpztvt3dat+7q5k0d6tUxxzNhfh40\neBuJoSdqu5H7mxEgxZCspXwj+AsuOYpWcJ2YdmM8j2szc5jQZvytulTq6CKrNurccCJ48ST1xJ3r\n1gNaG2EOJB+IavYaAGdmYpiM4Ht7J2+VnY3sKSZDd57eBefbGB/Y+DJMyWIkmpqSit3+QAnOoRrA\nGcT9iB7p9vMQts3y4XahhQ5LhX/+l39iLRv/4ac/HlEvpXZutzdSSnz48IlpPlPqg+fXpDKni9mN\nlAfac7qcyTmz3u4PN+0d5QhmIrusNywp/nHPb7fFMr+8pdEvax6oST2+t7ZmyKFzbLmSjqy9TCmV\nENIQGvAwAW22Dm3bdkS+HLykobDLOQ/0rB2jRlXY1sJyf2dZNj59+Higbvf7Qh/xIjEmnIQDUUcb\nXjuuN273V4LzXC82onq/vSHuwpwCv/zy3zidpkMQ0mohpYmymZxct3bItSUYtGLKMo7PYe+FRYDs\npqL75wJoI/8xTpFpjoTo4BD8WGxWjAn1gaVm7rk8Rsn5lRQ+4c4nZv8RWRcYzvLdB1tH54BrV4ub\n2hHJdsfpmSYzBU88/8DzH+2aXr/9lf/jn/9P/vnXX1B3J06VezERxi2/knWlqKNuFob73h4mpzgh\nr5txaCj4OJ6pKJazKYlWxUweB4/ROYvIsegh20tSeBCjQ8iomtABdcdzaujfmJ7UQtd6IMoqgguR\njsMP7u0hQkgDApM+xA9ypBh1xeJquiFS30cnTUFsFFhMDHM6TcaDGki9S/FAvpoqpWRSfEw4DMmf\nSfOZj0+feL7+EYB5emZbCtF/4jx/4nr+mf/63/6LfY7+za6BT/iQcF4OJLO2Qi+Z3ouhX72PRA3I\ndWPzK+F6xjlPqQ8jT/oYYeLta8UdvD1Vyws0xfq+Lw8UUxsQaNVI5rn0g5og3jPFYKKe0G0v3xW5\nATPqpNOwRINdkRxwbNLMyqJPqDzij3yoRPdAdf+1429WSFmWU//vFqmuFWmd1gQnjT7Ud7TA6fRE\nCI68WfCnDp6ISjhyoVpVxMl3garm9eG8wfm1V5zuyjRP696yedo23MaHCmE8tG53b1Ubu4GpPpxz\nSK84qSh5+OOYumCOZ1NHiQWe3u92nltebNxyjfRsvhdlcBNSrcbV0IBrgpKO8OFc1iMTzYk3Fc53\ni75qRoH4ceLDh09cJ4P3T+HMHCJziCb1pltg7ljAHI238sL7+wIe8+III8OtNPy60nNjy8p9zdxv\ntritS6XWOmI+Gq65YzPpWvC1M6upU7wTK4oxz5Si3fL1QkDVHSG6tXSkdIN6Oyz3rwekXtpG1YZo\nI0yO29c7eR35blNmud+JTNyb4EI+iJMOCDJzmT9xmjfCcqeO4OX71unSmUM0kuZ0QkdESmiCSkSa\nktc31JeDJ2CO0ApE8B1lQ2WM4OZI7Ebk1LKHik6sDHLsiHFxHrxWXBAYC2PojdS7KdLEMyVBxhhu\nDY3FVcSdmIMjbwu6cwVcRbqM35UOcjiAqHH7Gmrn5IaJzfgcHo9IMB5GX5DDCyzQ6fQ6bDxiQPbv\na4XX327c15VPnz6NMeDgci0ry7Lx4cMHSqskVcpY3Jz4g1eUcwDXKSOSfZsK9EbVztPpid7B7bw6\nNd6FjeAiOS8PRV/pzPOJ4KKNeMouw9+5TsMnKCTbs/sjtqK1RkrzsA6wgmQv7Pavt7bzo3iMITGu\n4/222niycTRf27ZyX14p2aJETqeJr4P8vW0baRJKKWzLSgqRZV8X3huXeWKeomX0lY3LKM7bdmeh\ncP78mbLcefv6jdPZnou3tzFyDZ0pTXQ5sw7LAXumR1hzB5FHIRVjPOJOdguH/egYsd57E/1od+jg\nCE0xcTpdcGkidyXnu41B1zFuapUujnn+gXi6EsSz20FL36DbfVAfEB/QnbBXCpIXZIpYKPKEDG/B\nP/7pH/hfbp2/vH/jn/7ln2nhnXQe1IzyztIWttZYFiwpYHiMbS3gxLOVjfftjTCLOfvbY4BIMV8s\nl9BajyIo+DO42QQyLgOPcGVfI949LCuUdrilmyK50Zpa0HQtR3g6IRJFaM4KG3XDawoTb5gAxDp2\nI4UPP7feyb2Sa6PVbvST4RMlzuHU2c+eIn6K+DAf9AQbJw6hQctIj0e0UAzJlKY+ENOFp+e/Y5qM\nbF/WTm+e4E9M8wc+f4TXr5azeZsWclusIXZGGd73rz2CxoWx9uluZQStdrPp8DPOT7QOdbcgcg5o\n1D3DtPehTrTDRY8XP/5fP0h3rRloUUqD5tDS0TGqFRHEQxjFphcdjYX9W2k2zsY5S3jwo1FQmNVT\nu8UGSXXH7xMxDuq/dfztfKRKoXc9jMsMTQiAFURdMsGPjC+foPZj/iyiEAYXpHOYfdkPemT51Fpp\n2kjRZrvnmWNByVmopQ25Y6H2fHSlqoFaG14c6nd13VigteNF8MHhyaZeGWjGnD6SwpkYTlZIiDsQ\nty3fya2RvOBjQMtsdw/Am4WD/N/svUmT5EiSpfmxbABUzbeIjJzsrCoamvv8/3/Sh5qm7umirqrM\njAxfzExVAcjCMgcWwDybKnuI+hKXwMmJzG1RVUCEhfm976FIs0iYcLwG6Tin5LripeNYTnR9V6ut\n963z5duN5+evfByC8dqyIf6LUrvFWexlPwu0rSpNHKUJ+7aZvmUU3fu7wnV5oqnw2JRvz6/8/Itp\nE3758oXtsaE1Wo5Uq6cIMvSJ4CAOvMCWM9vQu6y1UoNQvCJ0fPRoHY6ggSAIJCZxo9s1TtAhQCtm\nq+3CNS5sz/dxX9zZ0o3JWfAwPtNHWnlwnjhdcXiW5cbyeOYxIjtk3CPahb10UhLS0MiEGNHu0brT\ndwNsHleXTi0DOqqKknHDeRjniCuGQ4jR8AYSOVEceA9ecN44YA8669i8rxGamBFicn4A5IZwNAly\nMUF+jorzlTrcO8ErpbzSNJLmq8Fnj4gJF6BlWqvUGChdmUahjIv0HpBiYZ+Wen9YfRmiWTe4L556\nhCvXRsuVyXkmHxC1KB2AL1++8fRuseK+deoCfiyKbsRi3G6mlcqPO3kcoO55Y54SU5rp4s7PCMyt\nh/OkeWF73On6xmzrvXO5XHDOsb6+2nMd4lks9QaoRWPUUgf/aLx858+ol/+5mBCxTty6rqYfC+Fc\nT0IIbHUjl42Y/OCkvWkH93xH9fi7ha8vIyJn7AP7upKnme3haLu9p+LURLUSkS5stzt52MMdhbxl\ntL7HI9yeX/hhsud7Xp6oebdsuxB4ShPr6ADd73dETBtVq464prfNqxTFuco89F6nZseb9sW7hBOH\nE49MhwOnk/NGkIkpzUQX7QA1MBYuBMp2J67mlGzV44f4mQ5dO64J6g+x9nx8ySKm+t00qXhkMoH7\n9d3v+d0P3/jpd5/4y7f3/HL/xuP2+fz8t5bZajUBvvOEZM9wrYrrji1nSn3w0//xO67zEM2HhwnJ\nJVALlJpPhINznt5mlID4PnShb599CNZ9t4gwf977Fhpd2HcD29bWKGPjdcXRWqaVSolmejoc2TF6\nJglE73A+nQfk40GsrbMXpddi+Iijk9MHcmGakZToIdHdROtHMrNl1rkOwRnPbh1ojJQmYvTUmsm5\nsm8NOeJVNpuEqCitZTPZDM3lPBnvTvXQ8LkT4tudEpJHurfumRhiBiDMNsG4P56NQdUFHeBnM4pZ\nnqnSR1fuQGbYIaY7tSgYsWh3e2+UvWbytlOLWr7h4UpNDnoxc1mYSDHRh6a4NuusBhRxZgpr43Nq\nrbPIQveOrINhN7Rce3UntPbvXb9aIbVudzxxCM6ALsQBxWpFwQem8CYAfTx2Wxwsh9EqciBOdvpw\nzhgs+74a9RUsL00rdIOuhWh8IYDghE0auiluBGYeXAzkYFcJTq09ftzDMSVrY/aKfByXWwAAIABJ\nREFU93aiOE7QIQrLPBHkgniHc/EMTla+2AkxVjzB3C6H60OGk8Z7kp/JbaOO73POIc2yn9ooGo/c\nJHHmCtTueH5+5d/+/BfeXcyOHONED51Vd3x0PPYHz8/fzoc4t2rjzarc7g++fvl3I7wDn5eFFBd6\nFV7vma/fXnh+NfzBMX5I82RhrDpmn4BUg1FGJ+M0r+xjE1o105rQOqjajX4EnnoNlJbJqlxcZwnn\nj6SJ0HpHujOxb5/JwwLfHjv75cFNAluEi3864YnXGM25Arx7+sC7+wvbY1iutdNbo4qa62VKp9hY\nuqP3aeAHZDi1jizFySB3neHaqvgjlLp2KmVkvTXw5kyVcDgsPaRI9w6pNr5uY7QyFSAIEqz971s7\n7co+OnwXvDqIgrsmQh1fM/ISrSulHbyIwx7v6aoYwMJGcvUQoh9WcFF6r2Z00INR00xs2wtahf3x\neOPseGNpBedAjfB9IAoucyL5wLruXJaBvBiL1NPlifv9Pt5L5eX2yjbEqPM8s8zmLFu3BzQ9O5zT\nNFnAbzW3nqqyjWLBGGaJ2+12hiiXUoinA8kyLFV1HNosUw/GAas5A046x7quJysKONEPh9vtKDRa\na9zv9/P/NS1nUHDVYiHSreHDzO12O8f6Tx+eoL0VZjnnc0QXFxvX51pAunHtxkl/iokt7+RtJ41w\n8CO/7+n6kVVeUFX2fWVaPpwuo6qNl9dnLsv1ZNf5cWAt23q+pnIYew5RbQhMlyvBR1LyzDHBfFj1\nAWek766NeZmNe3WOYSPaKtuXz1yuH+yQOB2FgaeWiuYHDgs6Pp59ghUIvSkuZDoROcwN28rr1xdq\nbixPV+IWuI3sykpjz7AVpRbjZB3Fy7ZZx7CUyqdPP/I+/pGJ8XzrK25+4EMj940W1+9MIx6RhPQn\nhIiT4ym0LmmMCefMFVa0nRMMG/NWO5D3yl7LeYCuoxNaveCzN4q4HAakMVZNE3RB+1sodWsN3Rtt\nr7RWaWroCgCXHC0442aJJVLUqufvtO5NxfU+5Cs7XYakI99oJKTNaN0Rdn734z/Zveg9Zd+pXSFk\nSr0hw2S0LDOTj+xZyAVoyqhbkRCJHspeSZMMoNUbqqAdUwuJiPhzf5ZuTQsVy8MNLtBOt6Mf+IbD\nGQn1gJw6aLVRaqcW63odKCHVSpwOCoCtj90fY7+BMglKTMkYV6dLsNK0mHC/GF6hHt2x3uG8E/7j\n69dz7dXNHAAcTho/dDTNghSz0MNY3L0p7G2PHDPPeuhyrIJWNRbUuq9nsVC146tHW2eaTQ/xBlGL\nXBdLuG59wZdyPoiNRvIBH+xjlO5Op0HPlnDdSqf2Qs0P4mxv+BbvfPrwB3z3qDqcK+cCHaKnrg96\nDQgQKefpubaOdgujjS6O0eLxRpk9P4bLGKVEwtAteOfpBJoqrWb+8vNnpP43AP79T3/l3fuFOEdj\n9jg3Tqb2O6cp4oNjz5l1/8q3l5+pw6ER7zO9CfnReNwr93WjjqpeeyXO75EODs80L2/U3PpAjiiD\nbg+UO9xQTlE60Qe6K8YDOk6COUK2+XufApcpnvP+Ip05JDZVVhSVwhSPxb3y+vyFlgtxTvQgLOPp\nvrpImALJw7IvvL9+Oje9R35Fc6a0SPNteAb7+JlvQLljtHMUdbVVA5t2j8OhValjQ1SaQRN7QcUc\nLS7wBpGj2zizM7Q69WDDUkqnSwXvaFks3ucIbm0jJsV3iNBcQ8br12Yog9BMk5C1npE1ogaxXdIC\n0qh9Zzs0eV1wvZMI1rrpejrFXO+0Zg5DFzy5VGTcM0V3WlOuH95TtfHt5ZllWM6nKVFr5fX1lXf/\n5we713jTjTwed67zwuvzV758fT5Dqa/LTBJPqZmy7RwhtmCL8LquBCf0/6mIuV6vbNtG752np6ex\nkQh6njDNkXUURYcGE6yzeBQP27aS8346fY9i74B2xhi/o4nv5Lwj4ti2jcfjcY4o24hRCiFQa+Vx\nf3CZB9cnBNbcWS4XXAhI76c+KWcIKXFJkRSF+6s7O7w+JkKDPRfj06G00w01M8cZoXC/33l9fpwa\nmuAV36FuGyA01dPRJQdWRd2IoPHnHlFLxq029g/uikuBkwmD4P047Wug5Eqj0w+95l65hoTUQr1/\nw71baDKd3xtDoHZBWkddwc/DnTbN9t6VhneO7jo6nIlffv4zX375K8+3G+tWkCrsY5S4N6UW2LPa\nOh8crR7BtY31sfPh6Uf++Lv/i2v4hBvdk3BVk2ToHVjB3c8C2/FAizONo0xDQzcK0FIQ78+DmvZ2\nMgmP+0vVNJFVy1mc9e4J4ul0cm705r/De1S6RkKPyAjBPriKtVb2hxVo3TWKKmWMbnXuuGBuzt4m\n2yelvR3MVW3CI5XaHnTZ0H4U/JV9j0zzE7XtuJeNI3T+aXkHPVK0kbc7r/fPZyzNcYl0YopjBGfv\nTUxifK3YUW/rmTs0WUXODn3O2fASY23v2im9msavd8Md9cPR5w1V4T29gTqhHn+KFGqrlDyecy8n\nONVG+CYl8MHhfEXisZbuuGiHCO/DOCAeAOuKup3WoKnjtm7UUWPM04X/dUDMr5m15xZar8RBXpyT\nvWGOTm32cB+CTI+QklKL2uy5F/bVRIe1bPiYaN2hfWVb7+jIxVGUJpEyTtGuB2QeM32FVuAyLXQK\ntTkoR5aRUrtDayGFjo8JHTN2tNFrQ2untMqelXmMb3q78e7ywsenGfC0avZ5+4U7hB2Vjej7oCOP\nRQ3BaRqz9jHUGZus84leZXRxjA57tBm9E2JIqDhazWyPlX/59/8ODJ6G64gXi8O5CL979zueLtbi\nXp6uLJeI1kbTne4n+hjhrPuD6CditJOGWdDHQuQTwXW626mt4IqNCOxDjMZHKma9LeOkYZ9hoLY3\nbZBqMQs+ZuetubBHpSyJ3IUwWrxzDOAXSstEUdoyU1e7L2KvlP2VTTrqPzCVTl0HmyvYphGisCwL\nH+aPbBdj0OT9Qa47iGVWrdvzqU3woVkxxIPWG6rT2ZGi7UCk4RA1XcOZw4cVQHG5mg5EFVqmlkNj\nElmmiAqodpa0MGRZbNgJq9dC7Uouj7FxQuuWvdYB6YXQ25tdPUQbt6nSvdBqpRwj6BiIQxRs3SZD\na4B1D+bgQY2ybc4Kex1Zs3VB4xvMsQ5qXa+mLRLtbK8rTmEZG+LnL58R8VwuTzaOVj01JH/+y88s\nMdFb569fvrJuGx+OrL3SuD9WEyA74eX5FTc2/Q97HvEvcF9X1nXn40f7vm3b8N6zLAvrurLvxVAf\no0BJKRGj6SndOEQcr8c0F0IplXXdTgH5cRmzTc8N8vhaG0iDdd3Ytm1oQw4dWKD1SFWl7tb1OfAH\n3iVinGwTipFe3g5RPhrHKDiFpkxzPAX1U1wQ9ZZwXzPzfMEPTcdeNov/6CZgfzy+cVBR6NZpu1wN\nAqnoaewIg/uUyYAyzxfSAKDKuJ87BSfNaPuMYlCijZi8o2tlW4dBYRwwffA0sSJO+h3y26bofEJR\nwpRgCma8ODRtSek+kciwrgiO+mLP6Xq/89oaj9aoe+P2yOyjcH3kRi1CzULryuYKBzW5VpiXd/z0\n0088zVdm706hcuRK08LWX1EpJCcnBNIMUJXW7kNy4vHjIDzpNHIdu5kvFBiFlPdCVYv9KnVFa/nu\nXrOOC9V0Nlvu+JEJ6LoirbF3Z5IH8fSxXvQ27P1lJ7eKOqWnsV7GwLQXShdIFSQhrpOOBUUaWV5R\nKgSlaz5lJEbHb+jjG146+/bt1PFe5p+4zk+E4OjNIT4yj3vY5cbLmg154xT8W+arjZET1W9INoyB\n5PE6UPQ4lKrgopzZmSLN9JdNiTjEReTQB4p1vX23RobA+WyrKrXUsS4Jqu5EuywXT0qVmCzqLQZP\nHZBq8ZZJ6pMVVa0V9EhOUMuVLcV0wU39Gy7GRfT/p1T6DX/w2/Xb9dv12/Xb9dv12/Xb9b95/Wod\nqatPiGukg3IaIuL1pA3jTQgIVp2Xx4boCM+c3qH5aLcrJY9TpyrOJfJ+tHgzrRdCi3hxLJLQM5+z\njxZ+RQfR/4gzUVXKrsRoeXpb2043lMNTskeL0BgicTmcK43XlztzvFkitxZaO8KHd8v3caZdQco5\nD7egJIORNd2s83Q4AbunC+wogoncj9GOeG9IiN5Z5nd4N72Nr16f2df1pBi3z8r6lPmHf/iH8b0Q\n3MQcLtayd/AYo9SiO80FXBQkZWJV/Gjxe/W42JhcJIyIjXWcLlOPJuKdZ1rPdKe4UatHHK4VE7RL\npLnpbcwqEXnyeK8U14juLbg0xJmq4EVI4smqEA/DQKdLZ9cN0YlSH2wjePpRIte+kHqkebhcYLlb\nN8OHL2iu+N4o+52X2194N9rb13QhxEatjdCmIbC2j8kduWu1o7WScISL4SUUR4oLaTYyt9J4PB7E\naGOK2YuN9LoHhzmXxntTc6aJokUpJVuI9hAjH5BZEJrY94WjNd47LTjLYivFfu75tcZeKwFPQZDa\nzkBjdUoXMeoEhdbr6Xpx3aEt47STWxkuuqF3aDZCKNuN21p4//4jz99MUL2tD/7whz+y58p+e/D0\n04Vv42u1VpZ54uvtmb3s414fLfVaeH15phRDOdxvr1zfmWtre9zxYt2k2+1hDr52OGkU7z33+51S\nyhBW57Njc70+UauNW0upQyT8ttaUUnh9fSWEYGObEw8gQ3dk4751Xf8GE6BqY7lt2/5GqH3Mf2ve\naNqIIXG9jtO8QG8b4hIpBF5uL4RpdJ2mCa2N3mDyiSktp8uoA3FKQ/Q8TDYjp63kxhQipXa8OC6X\nK69D3F7KjtadUhfSZHmYh0GlNIXeKNsO3gwrAeuQHHT35Bd6rujUSOPZFudRr6i3+J21KCmEE1nh\ntKOYVq97h68NN8ZJ4iwJFAduNidlGetJL3q60VDh8fUbP/9sKQr/7y9/5sv+jcZKCo45LHzNA1Oy\nN1x3iHamNFm02DBMVBf58ccf+en9D8xBiP5N59ZptCrmHqvgorw5CN2GExsjqXpD0ozRpXOeeZ5N\nC1kzrb/dT60UailIZYwF36jnvdr9L+Lp3dFKRbFnOwZPmYXeVkIopGQYFHvTjOSd90Z3gnghHbu1\nZkpb8TQg0vuOp7OOb60USr1ZBJbzBKeW2TreAaGiTVn3HSfKhK2LbvK0KnjfmeeEJ7K60XFvd4QN\nH8z85L1nmscfJIWmK75ViJ4Q0+nI3rZXez5ljKxbRfwbSsd5P3ReDaSfmigRgWgOeXWC0ijjOWza\n0A7dOYIEYoLRwGeKynIJTLMgwTqxR7ZhiKbRrVt7Syw45DW1U9WT94Iwm5tP30alLnwHX/4Prl+t\nkLpEZ0RZZzexF/BhMoQ9pqLXo6WuO9RAilfeX3/genniaVg2v3z7ypeXf+OxPlv30oVzBivqqXWn\n7TYzzsVxGdwTi3EJlDrcFkU4khJyNbpzoVFrZ07htJy3UkADvRoJtTU96b7uWrm9vDKnCynO1LZ9\nxxoJeHEIivegUt5QKmoclFosIsC7Gd+Hxb9YKzTIEOVJ47DXhTAhOKSaXTum6dSehJhYH3deXl7Y\n1g3nPF+/vPL0ZKLxH3+84rThteFDZMsYARubEbea6S4SU+ApRNqgBvdiwuSgjtlN9OhPPlGHEyOh\nMuOCMKQ+5sYQc7wYp6WfpGkTvyredYJYbJCM14EIPSpJvBWf+j0nbKY7IUsnt5UtRx7FNtJUAu/q\nlXdMTDh6SlwmWzCe5iuP7Sv7munLA5crcx7FWe8k6RYrI43olTAMClEug3C/Q1h4CjMMQ0SMEzEt\nqIuoVta80haFEaYZqPSqiDrDLTjH6yiWWqn0Vii5sW8WSaPHiM45Qoi4kJiWRAoeObQZYkgpFUtK\n17adGg7nrCXuJZCcseOPEdW9PnBzBPFotdDuU5ijNt7xVYfDCPqIX/Bi2rDt8UB7Y1vvbGP8/uOP\nP6C1cL+98uOPn/j29TPrw+799+/f03rl+fFy3PCnMFZERrHSho6pnSHY2xH0W4207b3hCsBGd1+f\nXwjBsSzLGH92ljGmOoqh78d68/wWhXG/298+i1Bbwx9YEBG2bTsLqZNePX5mzuboa0O7+LfxMVbw\nOIQUPPNY3df7A6ESwzzGhpVlFFmHV8MheGfZnqfINRimwzuHH1KHMDZ9wUN0hCBmzoE3Dlct9P6g\n7JXedvK+EQcPKY1cT6HStZM35XUcWKM3d3P1wdavqrjxfnufSEsiTBdyLnSx9/cxPuNlttiR6KM9\ns13emFeu45LpZHTP+KcrcTaatjaHlIrmHZeVfd34/HzEwNz4+eef+eXLX4dzNDCP/L68vbKXTEqB\neZ6HQ9ret8vTlacP7614koaERh332/1x556fqfJAA0yihCNFom2IF0KaaLmiPeAOI40311jVTgjG\n3DqQONI7HmGvalgRvgt77jb+7M0aAlL8qTl04tBssSRuYAX8KKJz3uhtjM+8Q5ISxgjSBaXLRhXT\ns7VW8S5zgAl7V3AZ7xJeCt7Fk8CfxFvsmG+EbnmBB1vRIl6EVoTmhWl5y1rMecMHE7HHyQ9H73iG\nfUYwXEnLfmjpLuczsz0eZrDoDPH6KE59MJSDBLTbCDoM0bhDqMGb5VX6KUs4nt/eO9F7XPTMC0xx\nrLNuvGfO3OA61jGAWgK5VZw6ZOxBbZDNTaM8Cl6MIxnGmDE6dxrK/t71KwI5LU2uHxlXHtPyYCK0\n1pwVUNgidZlmni7vmdOM9EQc89kff5goWnj90yu1bThXztmtF8/kAlvLrLWzrUodhdQ0TYgPtGGt\nN6jkm0Mg183yhERQzczpyKOKaMuWjq2WHZSHijf6nS3s3O93cjiCG4+ibiLJAm5nSp3WO9vQ+mjH\nRLJiRZMXTkfIIejrvaGt0LCQWYDuE94H+uDEfO8wulwuTCkyTRMv3154ub0irrNupi17PG6kfqGJ\nJwbBSX8rFvdMoxHnGfGB6Ou52LirQ3LHK/Sy09TT/FEtOcAh1Y9kczgCWl2yzoqTgZUQOTtLpY2s\njWYPmxKpwyJbaoekNj8Xjw+NMbpGjYZhzrswgZMzKmCtG/e8EejELvgOl3GC+rAsvLwEvu03pGVC\nX8jDCZeiI+iEc51lmQagcmR4uU/knIlqAslAwk/LeT/FZO9nyStTDfjXyuPIhFRnzhLpSN9t0ekj\nYFlMdGkb020AKw+7uhUNPe4Ef2EJCxyareBYm1KbsG6FUgt+nNoQ5RI/ME0TySeCi+jJaDGhsRMl\nqjkiD1G8eQSSdVN7tUPJIX72thnueQcnFLczT0N/oJ2//PJn/uEf/4lSCl8/f+bTpx/H91kMi+gQ\n5OaGe/9WoDw/P1Ob8unjx9EhOoqsfDKPrNvwJkT/+vyNWiu///3vqbXy8nLjw4d3Z4TMy+g2mbN1\ncHrG79v2nT3nE0p55Hja3+rZ991y/IZQ/VhPDgfguhrP6nq9spf1fD4PB1xDWa6X8/WZaN20do/b\nCz4IS7J7at93upj5Za8N8W8aKeuCN7wT8r7RSuXy7mAXFfasFr0zRySHs3OY0kyTjg6ES3AdGady\ny7t1pGlhLQ9oje3gfc0LczTej/iJ2vqpf4zLO3y4oAR8dCwu8Ci3k9tVteCbMLlklvPeOYIoXfD0\nacLFiSrQENwhuA4BaqU/Vh6vK1veuT5ZR/Ly9T2SE69f7jzf/oqb9RSjpHnCxWFSMlYzH97b4frd\n+/fDSm8bcik7z9k+p+fXL6z7Z/y0458E590Aj9pt3ilovyMevMznBIMR8GuG1TZirkbhMswexkLq\nODXAo32IgiK0aqYW9XI0/nDdQJLRRYIzHMcJgRRHE6U7swmbKes4JDW6r/iwgvN0GrXmM1LNh44P\njegq0U2mXRwByzUXKwLVEZzgZTpwdqRoe6a2QC2Cd0oazst3HxcIF9btFR8789JAjtiZNt5n2xPJ\n1dyJwPsPV1IQ1odQsplzjj0qeLP5iEAQg4eer7/bZ1pdRamo5jPKx4nYCSRgWijf8eMzDHEyxlVV\nA/IGQdtbbik4VDxOldI6Xcf65RLBR1rreAc+ybkmen3r5v+969dz7eWGC3KePqW/wfRsgVOOfDsv\n8LRc+PDunbVbG2cliXamZKG1pcI8vXWkokCiD+im3bT7fgjKnZHVXaeVTin9fOPitOAH2VZCwPdO\nHo4J5yyryjnwwQqFg7x6uH0ejxvzbHgEN0JNpXjjErmKDurwOb5ygneefTVbb9eM6DHaGifsbgyd\nmttpj46+4JI7Nw/p3W4yexPxKZFS4np9Yv72jXV9pg1L9uvXb1yjJ+eAC5ElJnRkLrU2sot6pKP4\n4ClDNN+0cglXKN0ejAZ6jhoHsFQEp53aK84fHUAZDriO8w0v/nR19a70ZoBPFbgVJXxHdtfHho8z\nToyHpAehuu3maIzOMBqi7NWK00ub0PqgOqF3R8lvp1LFMS8Ll7BT2Y0vMoqlNNkC6pNjjrN1HMfC\n533gkgTn3tHajnRHXEbhOk/GoCJQfUWyp7aJ/PpmuRexrpvQ6do5InY1NOOZSEXFnD9yulkFuoFk\ndS+4WXlarJJsAq511q5svVL3fJLNBU9D8CkSJDHFRBzC0V4E1wI0R9VBWz/zxgIuLahLaFvplLMA\nUSra7DQYxBO9nMG8X7/8woenjyzTzF8//2Lk4+9Ce19evxHEkdeVy+VyLqalFD5//kJMid/9+OM4\nEDC+VrndbszzROvKnCa2Yf8vrXK9Xokx8jo4UtO0nBiHl5cXpmnicrl859AbHcDBVToKpZMyjgEr\nt23jyJsDzsKm97exXghhMKfeuFfOORQ5he7H31JKoeSd5IGQef/+I/N8JCwY0d6HSN43nJ+IY4bz\ntFxo1fIHy2bFnegQf8eAc8PsEC2xYR7Pr3PQnKe5OhxckNJxPxmWJE0zMblx+BrbQHfszZyB6aJc\n5isyIJdhnuku4FJickaHTymRvwMVCtHMXM5b9mM5AHMRCYb+iGmixXSOkptEfDBx9p/+8q/85fkV\nRvcsq5HrXfCoa+zbK0ejmuhJQ3qhXbkuH86swWmKdsLC7u/Hduf1ZmDJl9szVQtJHVGETWf8Qc72\nnVpXYgTvjDN1dHjpQqfbiLN8lxXJ0Y3seBFqU6TqeQyW4FEvuOCgGe5Oxs+MMSLROoHW/eRvCvqQ\njMHkguCCHPGAZDXOYM5t8K38SOg4ujnO/j+2DnjnB5YFpjmhJVGKhSG3Wr8zRGUuT56udmDqbOQj\nfUIK8+KI05XWN0r7diZFpJSIwSDUqkrVcqJPWlPU70yLvZ68y8l8cs5CsAUG1V3P9zuJZ9NmHUU6\nOMPJgI2jxStOxr3rnYWkAt07Ss0EgRgmmhSOaWktnRCNLZ9VcBLeBPMIItbhtfJBz4lRTNMJ7vx7\n169WSO2PZqGuZ2CmM+YDGK3cRebBhCl1HSfoQG/QRGEo8TvGZanVgJ4xTEyjxE5J6RXEz2wlW6vv\nAID2bg6zHkj+SvDtuy6A4/oUR3jrAEgOHlDOGZrSSsWnhpBxYyFqZafphvZIx5xcehQ9MRL9xL6t\nPEq2Ec0RMhmDjWO12Uy9N2TYOYP30BUvBvis341F9n0f7WsZnJzwN1qPEEzD5EPnD7//B0p5z+Nm\nOorHPXO/bcyfnujVdDfvLrYQx/CO5/uNrRVz/qkjDxqt4KnRYiZoDnHTudE4DDgaUsLHTJDpb4pM\n+wFK7Q3n2uB3QfeK9GZB8s5a3QdwVUShedRbx67rW0BlE1MZebURiNKHsw5Wd+MRPL51a207h4wW\ntqZAfHriY1e2+kBCPKMurFt2I8YF6Z6ezVVoL7DhAgRfkebpWnHjIOCjEea1Gyflfn/l67fP5DK6\nTq3iFJoDjQEJij+szm2naUFiZbpEc8WMK0VHDI7oGlNQnC/IcBJpKUM/03B9w2mhtUHqlQn1ApfO\n5BJRFtKwo4fJoSXQm1DFXDX+CO/sDumNsmc8mVLz2daOwVFRnA9EZ06hMoqQy2yaoHV7WDHt/dk1\n/vzlC2XfcSmRdyukjo3m27dvlFL46aef3nhGo8jato2Xl2fgPe/eXU/m0/GMLvPFumxd+N2PPxFD\n4mU4vg7d0+FIO0Zxx73YWmPbNqZp+hsy+tGhOp6hxxhLgIE2j7VmWWbu99vpPjv+fx+6qe/Dntft\nTvBKbpngphEGPT7fZBE/pVVz9DU9bdcinmm2Yir5wKtzMIj/yS+Di1PIe0V6ZR6okSm+o5Zgp/Ju\n68VxiHA+GcunVlyww+Xx+foYSHHBx3CG057w3y62ocyJUt44X4fNX3ulajP0CxYC686RkXH6JCR6\nMOexOxxmBHAJYuLb/uA//9f/zssoeKs0vt7/TA8rPipaPdIPncw+OGG2kT+9u5yjW+dsM+3AfVt5\neXlmu1kn3qkQ3JWeO61XWp9YhwgyTQ1GceYGifzo/jfN0MOptTMtHePeUkOZiMOFhA4AMtjhFrE1\nTlWJLhAHoiVO6Yzx8b6f+AIwCYliHeLeZfzbfqEPHicRLUJljFHhjEByzTRHXYQqDYl6RsSklJBp\nhubZ68N0f+OkeLs/E6K357MLIpFjJliqdYSc97TSyeV+hrnnHEix493CXobQ45w0KVEbjYbzAS+B\no2nuxKHtGMnaqG46KmW1e8rGeG1IYg6Xt2lmQ3ADjp2MfYUd9Kfk0d4sBLkbXPP4vjY+E/GOGD3p\nmKbosT44ajOJwaHvtXSFt4P9f3T9eoVUqRYFM6ps7z0uWhcjV6XhyKMIyftOLb9wvXzkevmdteIP\n8KDulG3FSzKhWXLEMTKaJ0cP0+BBHSC3YcsUj8eyyFI0Uu1xI4uHMAU7OUeLtjhYG1qLWSTXO3m/\nDyGavYbWM1lfuLhApeC6QLeHRgEfBd/GSbX3s3ugmmnNodXRq6c0hhYKeq8mUhyQRttkvt8QLNJB\nW2UrmXToeWJEtdni3LGWq8z8+MMf7HvbbhqJIgiZkC6nffhp+Qhuonz9xVDJb5pAAAAgAElEQVQP\nPVE2KwhKabh3jku64udkp4pDbC8d5zecvyGhM8UL7kBDFECE5jz38uCRb2f7O7hEnDzeKZWN2BV/\nbrUOF2cr1lxnnhxhGyLtYp3Lpjvd+QHttM/p28MejFIt/5AQ0dGn93PkaX4PzrPkiaLt5J40McKy\nk0xvr2aCHmMYHVBUMRwguVYYsRHr/kC7pyrc7hvfvj1zvz3QdWw6AhVrQbdJ8aExjfyvPgjLPlRm\nN41R1qCXO0gxEiUwTZY6P2pTumuIFuP+RCVEh25DPKmwy86+r+h0GVqpYedNAt6jFVxbDPh6RKvY\n2cwKcyZCiicLrTfwviFiXamq9eyehBgodTUel3SW6YKOk6e2QgyObd+Zrk+IjycV+fb8yqdPn/Au\ncnt98PT+Sh2L95Y3bo87KSU+fvz4NzEuMcZxb3c+ffpE7/3M/Tu+fkRHmT6jn9DVA/IJVmAZyHfA\nWoehIOdsYMeczw162zZeX19xTs5C7OTgddM6pkFC792yB8E6RNflwu2lML1/wrvI/WFf82Jj1r3u\nPD09Mc8zt7HpP/aNeU60Vokh8umHH9gGR6m5sYnEiJMyOoDHwu9Nk5n6iB0xgbTdT94kArWRD7bS\nWL8ulyeuT+8JcWZ5upKmCUmjyxUXgvfsq+nHKA0f3DnC1GajUIfFDyGBHs+Z8DDUYM9PyfSBcBED\nDxFS4tPv/4j+8//gv/zLfwZg73dy/ozQ8GFoN/WQOij3baf3zo8/fmJZ4tt4ulmH7ratvL6+8ni5\no/uQX8SEOE8txnQq/S37rvdCUKVHQcJGipHG4/yZzg0yt9oI68x1HCJ7csGJs+gnOeDOna4GV+ke\nXOCMnGptJ8bZxu+BNzArWIepZaR1Wm+46M+8t+hA1OQONStFlRQ8/hhT+Y54RbwjxAkvZk6wK+Od\nI/qZyzzhggnM7e+Bx/qVEAvzfDXe4dD5eWcj8dp2gvNcpo92KgRutxvP5Rn6jRQmA2mOZz+II4ZA\n6R1RoUd3rl9IoIijIkTvucZ4MgnztnH1Zq7ZG2SxuCswUZA4hwTH5CfDIPQ3FEXpagJzbz25E90T\nwkhZsMbNFP1JUkeUVjv7XqnFpl7H4bI1ztfz967f8Ae/Xb9dv12/Xb9dv12/Xb9d/5vXrxcRUyrB\nC3E4TlpxoCY4QwxKeGgatJkI909/CfzTf5qozfHYra3oOuzbimuOEBtROtNxMqOQkrkDW/Nk7ZRj\nnMQQ941MqquPxDEG8JMQ5khIi7XCL47S7GSy7wXvN9OSiMUilHKkjlsw4t6eCbKY0VQP6zSo3ymy\n46ZhhR/ld9WdWgu9BkoRcunoIapsm/0etdlwioF5OsY3gSATKXii99zXB4/V3pdJJ1KwCt97T1OD\n6cnQbC3TAr3Q1SCbeynI6TSBmcglzNzWSkaoZVTnteFkAX/Bhxn0fsaTlLqi+iDGjHRHKasJ7AHv\nZpSED54gnd5W8hhf9m6nrZAcdj7RE9oWQqBLRXQx4r3YKR5g18zeG60XNsmELrQjS7EHyEaxXorp\nLI6IFO15gEoj0/KO2Ee0AeAjtM3CLe3krqRhAe4jobxKwAWDeu7r+L400TB7/KPAo1oMSBogV6ee\nTkVCQGJA/Q0XRtyJW/BhIWyZx8ODc4TR5dPW0C6UMJF6txzA8fqnZSbsgu+ZHDou6ilkrY+d295Z\ncmDXwqR6fl93nhDA9z70ChPuECNHI+2H4PF+Bu/Qcmh9dhORus5WNhP6H0kfEbKa83Ce3pOmhX0b\nI9iDFu4c03KxMO+bPU+XdOH64QO//PKNaUlc3XvqeBFryVTtPNZtoAzeNCQhROvqqjJNE8/PzwbB\nHN2jx+NBVyV4z1qr0YzHCHrfNrZ95+PHjzjn/kYjxdBB5ZwHvuJN63SI0p1zPB729x+xLF4CrTcQ\nG7GXnOkjE/H90wWaEMOVKb1jK29jgrpnasuEYCT6719jaZlJEq131tszH96/Yxo5fPu+I3guy0LZ\nTd9z6DjMNbvguulrVJU2xmzee7oWG1eLp+Zi8gEgxZkpXbh++IHperXsuqHRTOlCr0IrhYhDe6dV\nPSndMVjMUt+adZ9T4sBB9+ARiTTvzbmsSs7DwZkV97Jzq4qPV/7hj//If/3X/wLAf/vX/8FWXqAr\nl4tlk/bRPTkimqb5YqkG6Kn/nKaJXAr39cH2ckOydb0BQ5aoULtnx9MemdvodM7XZNmMfQVWGyUd\nXaB0pWY9x6K99zNPzgWgOsRBHtb8dnxtdFjEC905qtTz/fY4JHacdGITtMMb/cAMMrQd52aCBNKY\npkjNOK/ktaI407j6ZAgUoO87frZUudozpcgxbCCXnWlS1FvqhuvLG6w0esQ1cs4IM9PkzrGfc8HG\n0A1Khd7EYMkAlyuPx8br65371y+klLiMxIPW7NmYXKLWRmsZN+7TUjKte1xa8E7omk+XpHeNS7Q1\nOovw6Mp67KWWloci1D5GdWcyhaJScUlB1fRbQx8YormYg6umO5VOGxTbUjt1M0hvzQaqdu1AHjWc\n/q9LpV9PbF5MzPWoR07GTmze3iBneV+uH9bbiPrG68tX/rX/C84F9vwYP0nNri0b9EBvgTA24UQA\nbeTu8S7g3Q56tPDNjROd4LuxMa7L0PoEQYKQohusJk8Ig0GkO2t1IAU/LfZ3nFEvO61VXO2UuhF9\nwI+bP5PZ2g2VhuuR5vypLRKZwVV6UMqWKdWf2iJiI6UJbR3vEv07EezlkohJ8T4RUkK849tgybw8\nfybFmWVZSFMYgsBw6qsUT+tKRHHBI+JZh1W/9MI0zVb8tNVyBcdYaF7SKW4PzlM0nCPB3hul7zxa\n5jpZYLML1vr3XfGaLVvYCe/myGMdoxh/pwUhhkg8wivHQhRdwEkxC3H31NrPOAAXd6Tccb0SekWb\npw2qfRPHoz9wdWZ1CxIS03DYxSAWpaIL4oTgH9aSx6YQ7snTsmkUHvfPvDwMGXGZr2bRdxYsXXKj\njBFcfmS6ClupeH+lt9nmuWMhChqZUsL5IXZsDT90fnGyuBgnJjhdV08v9r4Vxuy+QZHAqp0DP6YK\n4gLNCS525vSW/deDoTte607cN/xUqUOIP4vQ40yIHi+KNENp2GO4Iaq4lAALaD5GJiE6a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WLbkfnv6erUbMyx+5vW7kMlCo7Jd4zZUpZEaj3UxrLJPXQmPJEapFWiOnim35wB+kIogN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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "image = caffe.io.load_image(caffe_root + 'examples/images/cat.jpg')\n", - "transformed_image = transformer.preprocess('data', image)\n", - "plt.imshow(image)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* Adorable! Let's classify it!" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "predicted class is: 281\n" - ] - } - ], - "source": [ - "# copy the image data into the memory allocated for the net\n", - "net.blobs['data'].data[...] = transformed_image\n", - "\n", - "### perform classification\n", - "output = net.forward()\n", - "\n", - "output_prob = output['prob'][0] # the output probability vector for the first image in the batch\n", - "\n", - "print 'predicted class is:', output_prob.argmax()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* The net gives us a vector of probabilities; the most probable class was the 281st one. But is that correct? Let's check the ImageNet labels..." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "output label: n02123045 tabby, tabby cat\n" - ] - } - ], - "source": [ - "# load ImageNet labels\n", - "labels_file = caffe_root + 'data/ilsvrc12/synset_words.txt'\n", - "if not os.path.exists(labels_file):\n", - " !../data/ilsvrc12/get_ilsvrc_aux.sh\n", - " \n", - "labels = np.loadtxt(labels_file, str, delimiter='\\t')\n", - "\n", - "print 'output label:', labels[output_prob.argmax()]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* \"Tabby cat\" is correct! But let's also look at other top (but less confident predictions)." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "probabilities and labels:\n" - ] - }, - { - "data": { - "text/plain": [ - "[(0.31243637, 'n02123045 tabby, tabby cat'),\n", - " (0.2379719, 'n02123159 tiger cat'),\n", - " (0.12387239, 'n02124075 Egyptian cat'),\n", - " (0.10075711, 'n02119022 red fox, Vulpes vulpes'),\n", - " (0.070957087, 'n02127052 lynx, catamount')]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# sort top five predictions from softmax output\n", - "top_inds = output_prob.argsort()[::-1][:5] # reverse sort and take five largest items\n", - "\n", - "print 'probabilities and labels:'\n", - "zip(output_prob[top_inds], labels[top_inds])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* We see that less confident predictions are sensible." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4. Switching to GPU mode\n", - "\n", - "* Let's see how long classification took, and compare it to GPU mode." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 loop, best of 3: 1.42 s per loop\n" - ] - } - ], - "source": [ - "%timeit net.forward()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* That's a while, even for a batch of 50 images. Let's switch to GPU mode." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "10 loops, best of 3: 70.2 ms per loop\n" - ] - } - ], - "source": [ - "caffe.set_device(0) # if we have multiple GPUs, pick the first one\n", - "caffe.set_mode_gpu()\n", - "net.forward() # run once before timing to set up memory\n", - "%timeit net.forward()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* That should be much faster!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5. Examining intermediate output\n", - "\n", - "* A net is not just a black box; let's take a look at some of the parameters and intermediate activations.\n", - "\n", - "First we'll see how to read out the structure of the net in terms of activation and parameter shapes.\n", - "\n", - "* For each layer, let's look at the activation shapes, which typically have the form `(batch_size, channel_dim, height, width)`.\n", - "\n", - " The activations are exposed as an `OrderedDict`, `net.blobs`." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "data\t(50, 3, 227, 227)\n", - "conv1\t(50, 96, 55, 55)\n", - "pool1\t(50, 96, 27, 27)\n", - "norm1\t(50, 96, 27, 27)\n", - "conv2\t(50, 256, 27, 27)\n", - "pool2\t(50, 256, 13, 13)\n", - "norm2\t(50, 256, 13, 13)\n", - "conv3\t(50, 384, 13, 13)\n", - "conv4\t(50, 384, 13, 13)\n", - "conv5\t(50, 256, 13, 13)\n", - "pool5\t(50, 256, 6, 6)\n", - "fc6\t(50, 4096)\n", - "fc7\t(50, 4096)\n", - "fc8\t(50, 1000)\n", - "prob\t(50, 1000)\n" - ] - } - ], - "source": [ - "# for each layer, show the output shape\n", - "for layer_name, blob in net.blobs.iteritems():\n", - " print layer_name + '\\t' + str(blob.data.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* Now look at the parameter shapes. The parameters are exposed as another `OrderedDict`, `net.params`. We need to index the resulting values with either `[0]` for weights or `[1]` for biases.\n", - "\n", - " The param shapes typically have the form `(output_channels, input_channels, filter_height, filter_width)` (for the weights) and the 1-dimensional shape `(output_channels,)` (for the biases)." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "conv1\t(96, 3, 11, 11) (96,)\n", - "conv2\t(256, 48, 5, 5) (256,)\n", - "conv3\t(384, 256, 3, 3) (384,)\n", - "conv4\t(384, 192, 3, 3) (384,)\n", - "conv5\t(256, 192, 3, 3) (256,)\n", - "fc6\t(4096, 9216) (4096,)\n", - "fc7\t(4096, 4096) (4096,)\n", - "fc8\t(1000, 4096) (1000,)\n" - ] - } - ], - "source": [ - "for layer_name, param in net.params.iteritems():\n", - " print layer_name + '\\t' + str(param[0].data.shape), str(param[1].data.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* Since we're dealing with four-dimensional data here, we'll define a helper function for visualizing sets of rectangular heatmaps." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def vis_square(data):\n", - " \"\"\"Take an array of shape (n, height, width) or (n, height, width, 3)\n", - " and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\"\"\"\n", - " \n", - " # normalize data for display\n", - " data = (data - data.min()) / (data.max() - data.min())\n", - " \n", - " # force the number of filters to be square\n", - " n = int(np.ceil(np.sqrt(data.shape[0])))\n", - " padding = (((0, n ** 2 - data.shape[0]),\n", - " (0, 1), (0, 1)) # add some space between filters\n", - " + ((0, 0),) * (data.ndim - 3)) # don't pad the last dimension (if there is one)\n", - " data = np.pad(data, padding, mode='constant', constant_values=1) # pad with ones (white)\n", - " \n", - " # tile the filters into an image\n", - " data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n", - " data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n", - " \n", - " plt.imshow(data); plt.axis('off')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* First we'll look at the first layer filters, `conv1`" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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hhBAN0EuUEEIIIUQDjt6JokGafol49vvy4e971FhiHpOvYm1oSOehuxD6Pfy9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\nAau7yDDmZ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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# the parameters are a list of [weights, biases]\n", - "filters = net.params['conv1'][0].data\n", - "vis_square(filters.transpose(0, 2, 3, 1))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* The first layer output, `conv1` (rectified responses of the filters above, first 36 only)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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Z7nrH+wULyFbd0NdYl1dXV2PnrbVGW1lAGC4Zk0qlPEE6SUT1oNoLCAgICAgI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KfAolWVToe9N9VCm8XyZqv0slo0qQGED99oNGr0xfl8vjIBmJ2lTMH7slcUoExdwGg0arSrioNj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GFX\nsbm5GUxP77//frhmmV0TrvPPYHME70T8DiLGGCnVYa63373wbjyl5xXbiaSocL4HrmFzX4py5mMI\n/caOkRkzzrmHa2AqfPLkSTIoIbVjYr0XHh+qjVLjiIMSltmlt4Uqi3c23t3dDfIcrDuE32BSiplB\nS8a7Ggcxs1COGU4dyzmbt21zfl6JGVeVsdSUEGPCUmjrtqDm0RjbkZoHgFwSZOVoXVrG0vNT7h88\nf7KbA2eYMFuU9lDPZVcFnMdMfE66wGyR1eZ5LWeRKIXqw7ZK5KhHzkSdY204eAD/ppj6V155xcxO\nWW1oDGLuVbqDg8EglDkVNJFLZFzSFlyfWMAF9VNlpCoqKioqKioqVokLYaRGo9HcbHGlqVbWOX8n\ntaMqcV5fRpkXWFtbC9er7N9s9y/ZReb8k1Lg3WIs5Nn7L+X8CBg5nyfAs0BKKC62C/QhxuPxODwP\ndWEmL5erqcRHSvk0TadT6eANKPYhtZM/DyMVG4sp/yCFXBi8bz/uD95xpnak7NeTUqLO1S2F0nmA\n/SDaskVtUOK0HDsP4L4pcb5WPh458PkpZqu0fdU1yp8n5yRe8p6VhvGbNfN/cluBdWVRZPzGuTnx\nm8rIwGVOBdQw1Dyb8sdRzJ/PtuC/X5xlg5+rfKhK3gHlE8hI9W+v12vMi7PZLDnelL9WygLAz16G\nuc5ZYFLn53ykLty0B8c/mORiKKES/+/eC+cxeEDzgge/qYkMlC/uF1NWXcZx1oOj0FRaBnZuN8tP\n2uxoy05/JZEKZotmQjy3NDLP14kXuSriAmXa2NgIEXxqcsOzBoNBMOlxYmQVTQiohUVO9yulW5Zq\nx1Iz0zJmodRvakLm39A+0+lUpr8pGRvKRKXSS5ynHv44nq82Rfyu+DbPfRyA3IYmZ5os0eTi8zg4\npfR9bGsSzfWrcilImWeUGZSjimP6RwxVH1WnmElRffhKF6IerJEGxDaBqXqo53E5Vd3wm/qAqwUw\nJx7nBQv+LjURlpoUU2OSvyu5717JOI7Ns6Ubx5Kx3WY8qWPV2byioqKioqKi4jnhwhkpzwjEdkWp\nsE0c6/f7jd1n6Y4UzzFLU+esGZRjM8CAgFGJOZum5AX4fn7VnqPdcX9+Hqs/58xkapetGKlVSE6o\nMqWer2jjnHK8clCNOZ6bLVLrqTB5RoodKcWqzV/qHWAGJuds7E12MR02vjdQogXF9V2G2WV2yuul\n8XuTY6tLFLdzztdty1xqslNl4fG5jLlRHU8FhCinapWbszSwgJ+5ikwOXPYUE+JzZTJ4bCvJm9zz\nc33oy5S6NjYPKNNdLkjIz0XMKjFQZy/Jwc+IWYMANZ8oa0Bbh/vzsPdqHM/n84VxDnjmz7VVZaQq\nKioqKioqKlaJC2Gk+v3+3GwxT45yhsbfSjk65mgNvxmswJXzIB/P+SC0tSmjHqUsWE4+oO0O3Tso\n+t1Lzu8ntzsuES00a4p4qhx6KI+ZdtwvcbhmKOf1mE+O90HLKWADLEaZYqmUP0/pDt2Xtc21pard\npc/g9svJYSifhlI17hLkdvzz+bzBSOXELdvmmYupjitGzY/tWKh2qs39c/i8UnYsJjOifKQA1QYp\nRqrb7SYlFhQUQ8y+QyX+K+p4G4ZTPRcoze/6PN+BUhHUnJ9Y6h3OsZSl30AvpxLrN8/4dLvd8N7i\nt+l0Gp6bkj9g5BT9U5YYHh+q3Dkfqb768XkDHcLJBlMTJDckm3YAfpnxIVVOcoBS0mWVbTYVeuq6\n2+2GwYZrJ5NJo9OV06JZM4pOOVxz2ZXzN09ifqLIDTpF8/P1qYnESeWb2aI5lSPmfH/GBi9/ZPx5\nCsrkAKi6xxbKSKwJZ9PSCVRFleYmGH5uW0drtbFIXZs6v+QZpcdQJx4Pvj9yJgC+V8l5JRG9fnym\n5gG+Dzvzpj7guXKmlO35488bx5QzN9fHP1st/hRimxM1dpQJCFAfc/5/KpVMakGAunioRaw3M8cc\nwUuit9lko9qvdOGj3gGey9sudtkNIxWhZ1YW5BBbJPj78SYb8+PJyUn4jvGYVXOfJy3UHK3eqZOT\nkwZR4gkBX15+V1GWlP5bbMO/ig2eWTXtVVRUVFRUVFQsjQt1NgedZ3bGJvDKcVkFcYZa2f5fGRb+\njYUhL/M8s9P6pFiWnAnLn7dsPyka2JvdOh2d0ymlQ6TqppzmuU0Vw7Uspb65uWnPnj1bqBszjal2\ni5kZvSoxX8tsG3ZrzKgC3I5tnc1LzXg5el7dt8TUkWOGUs68bGbiceD78ryOxUqSA+Cdcso8x2hr\nzs/lwUzlbuTzcmO8JCBkmbZsqx0XMwsCan7hey2jeeXvl0uoft450kPNUynzcKpNlwm7Z6i6lZr2\nUmbrfr8f/i6V3WBLTSrIga/z1/LfKogI8ixPnz6V3wYFxaJ6lkohJ0OBMh0fH1dl84qKioqKioqK\n54UL8ZFicUisCOEk/uzZsyInWXb6ZZE7rESRsf7DDz8M1/AuT+2gUkwUX8tq2DimfJSwm0yJqins\n7OwEtiUVmsy7Yz7Gwp4enU4nMEbKFg+wvZyZKSV4ieNqd4027ff7jeOz2UzeL+UnlVIgVuySGkOD\nwSAcZ/Ystbvi83w9mPVkllWhhE2KOcinxk6KUcmFyat7KKT8a7g+3D6+L2O7y1JWAectwxqXBpik\nfHvatJHyGQMwJ0wmExlwoZT8lZP7eeoGcB1XIUOQYkJyz1Xn8xyn+n9ZPzz+LZURIednF5McMYtb\nI1KBLcziqP7k/2PsxKwAZqf+Tp49j31bU/OC+sbl/CtLfUIx9mM+vP5aFaiA8XR0dCQDELzVJcbE\necuKZ8EVLsS01+1252anhUZSYK8wyxgOhwvmGzOtXG22mNQWWDZBqIoCi8HL3ne73YUIrth5s9ks\nfHyxSLh//37j/jHnu9xA9QOPIyDVgpUXYSVmMkZOa8l/lJSyMIPPT5nTSnWxgLW1tfDioiy8ME/d\nW01KDBUI8HGiTaRUCWKJh4G2pg4VtbNMPRSU43Fp+Usd43NlTY07/ljy4kltJvy4O0/6jpxpN6es\nXrL4ZnNKapOQS27NSJlxUyjdOMRQqqhdAnZBUMmB22xo/EJAzS/r6+uNb5a6H89j/E3y/aAIi2VT\nmgH+PjmdyGWws7NjZmffi9Io+hioDatpr6KioqKioqJilbgQ0x7vEjwjocxVR0dHknr1O6l+v7/A\nRJnFdZr8TuXk5CQ8lyUW2OQYq4eiamMra2VCQxmYiXr11VfNzOydd94JbYD2SDnS5XZOMaoZuwSw\nY3t7e3bz5k0zM7tz546ZLebTwnOU+jezWQy/C3vy5EmjX5U+TL/fb7BFvENXTFSKDVL9pcwk3W63\n0V6sfcZImQZW7QybQsy5vjSPnP8tFq6e2rWnGKlVmI48UtR7zCxT4sha6qheCnY94DlJscopk6iq\nx6rGVttABlXG2DtsFpd7UaxYadv7cimV6tg9YgweIyanoOADVpgdYmY0pfWmJHli+mUoK+Yf9Z1i\nywqex3OYagN2v4HViJk1Ne5UP6hvVUlGhfF4HNpSzbeqP/Dt2tjYCEmqcTyW3UE53Ke+STFURqqi\noqKioqKiYklcqPxBLOM6VtlYFW9sbEi/IX++WXoHonwRSoFV6nw+b6xsz2szBlDfy5cv2wcffLBw\nbDgchuemnJL5PpPJpOEboXYn0+m0sTu4evWq3bt3b+G+pXkL+TzV5uzvdOnSJTOzsIMwSytoK5G5\nWN1juHTp0sLzcP9UP7LDvZeFUP2g/HVwLv+rpATU+TFn3VhAQewafz3KsCyUJAKQc3xmLMvaeSd8\nDtE2yys0p5S+S57ty8y/KX/IXKh87Pmqr8/ju+PvHStTLhNCSv4g9gw48+I3ZnG5/5SPqQrjz+XV\nPC86nU4joCb2XGZy8Hw/FnNMGDuQKwkarvuyY7XX60kZglKkxir72fogDQ7MUs9lRs/7Ravz+/1+\nuIbHKcYYvkNcTrTpdDpdYKxwP5Udg94NOdAvxLQHc5lK0ru1tRUispTJC87kDx8+DL+xSQGDEI3O\nUWzcQF7OXjnamZ01sDLZMLzaOSvaqkmGy+zp/g8++CA8F/dQSsn8MqI+BwcHyYmP24Dvl1ogXb16\n1czM7t27l4wcVOXitsffaPO1tbWwoOHJxk/oMS0whZJFwf7+fqMeSrWdF4RqUafKhJc6Bk+J59J8\npCbcmIM3T7Q45qMKVWQLY5lFVi6CB2UvdbAt+RjGjrEZPBVxpyI9Sz9OuTqpyDvfnsqVQS242CzI\n9Sktc6otl1lI+3Eec+pPZUdQQS7KXM9gs5fZ4nymFsWxhM0p+HZeX18Pc5Z6v7mOfvHH9VWpZJS5\nnL+LqXkvFxWJ5/EGA2UpHeODwSCUC32yu7sriY2S7BS5jTiPoZSbBi+uvKlwPp831haj0agxtniR\ny6mbUmmDYqimvYqKioqKioqKJXGhpj2zs5UlqLj9/X25yr5+/bqZmd29e9fMFndAKXNOTJ8jpf2B\nFa5iKdbX18O12C0oRW21g2Tw7sRTvzk6nZFLVqnyN/FOBdeqsiqlck815xTGVd/gWaPRqJGPUI3H\nNoxUya6ImSYVTMC6ZErfpiRxc8y0p1DCKuRMO6tqP7537H7quDKvL2N6Oo8JkBkagGVI/O9m52Ok\ncmib2UCZcZReTsw0mcKyZtyY+dgzPmon3+v1ku8h3CUmk4lMQK/Kohhd7+S+qv7j56acwxmsho1y\n+rHKEjSpMcfvLTs849ytra3AlDELzSxmrHyTyUSOT+VqAfAYwryJ5x4dHTX6WrGUw+FQftuWfffY\n7Mrm1JTSu2KalMZkxJG+yh9UVFRUVFRUVKwSF8pI8U5erSDh92N2pmSd2xHkRPfwLwtUmp2uZvHs\nElaDEdu1lQiYxcJaS8NyFeDgfXx83HD6Pjw8TIaO8k4wJf2QcgiP1df3sWLeuO6p+8XavCSgIMZS\nelE4Hp/sJ4Z2wTG1u+MxlmJRYj5SivEp9SNS411BOaCW7giXZTja1APjBf/y2FRjYz6fL4zz1PNS\nz82Vn58XQ6wt2dHVLC9rwFilUKR/XuyZOdaTj6UYM2aLvF+KmZ7/fV/yc3P5Ln1d2gjLlji0l7K8\n/Nwc66LqVJrfMPc+puZyQPW1D+Ywy1tM+Fuy7DyBMWKW9qUzO6s7+xV7Vu487wpbWCzCSF3IQurS\npUtzs9NO9Q6Z165dC9FiXDYVKeWdbnlwg8Lke+c++qmEnUDsxfDRQmoRFouyKOmD2EDw7XL79m17\n//33G/fmhSNeAvWR5jbwi5LSMphZQ7Geow5zSUiBZZR0c+YRM22KY6Vf1tRCkAHfF6q5jx49CteD\nEkd9eUI+TwQRX1tyn9gCExsG7vvnbSri8ZJTz/blVIEoufuoyMEclOZRSkuJj6cWnbEFiFJ1Ts0x\n/p64T+xa/o0X1LkAhRIoZ2meC5X5K/UOp5Kcc9AE1x9zEmv8pPqtTZ3wDP9cni9KTehtIzAvX74c\n5o6Yg7xyjE5FM3MAknfI7na7xe4jXG6zxfaFefbw8DCayYCv6fV6Ded1heFwKDcbANdbjW2gdO5V\n43R3d9fMTkkcKms17VVUVFRUVFRUrBIXatrb3t5uSB0wwGpMJpMGq6R2DhsbG2EFyhSmX5XG8qoB\nOcezEjAdXJrEM5WQVyGX943DdvHc3d3dBekILq/Z2Ypc5ati5/WUU6iiwlm7iXeinrVhZ/iUZpTa\nJfJuh8tVsiuK5UFDn7BDvdc1UTR/zLSXMqelzsupbOeQCsjgvvfKzPO5VnVW4zg1ttuyBW2oeGZH\nPCOVM1emGBBfHo9UyHmMRS2RDynVRsq1EctfpHb3pVCO78zk+Od2Ok2F9jb9mmJ11FyZGn+KITRr\nsotq3mO2jecQ/946E5CZLZrBeGyAycGzUpYRXIv/K4kAZttxPMfkcwYP3E8x9T4gKGf+V/2GunMA\nAp+37LjsdDrBbIm2VIFNZs0chSo4hSU72D2kOptXFJsGhgAAIABJREFUVFRUVFRUVDwnXAgjNRqN\n5mZpP5YSwDlPsVBglTqdTnhOyom8dMef21GlmBqzxd2a2WLeIhUyq3bCyibMjENK2fxTn/pUyN8H\ncDgu38/vpObzuWRP1C7c+1cpvyR+LjtDKgZJ7YI8y8JsUWrXtLu7G8qaYv9Go1EYMxhbzOhx32Bn\nw+KqJf46bRSrS5yD+X6pXGY5Z9kUg9RGUb1U1RkoZW8VlOQE79BTkicnJzo/pEe3m85Un3r/+R3g\ndsk5PwPLsuNtggiWaXNc5xmO6XRa5LPI40mdz7I0KX+YVMh7qR9gqWN5LhNCKvdm7P0pZW3ZCV6F\n8uM+KAMzPspfGNjc3AzXcg5czM2YZxWDtIwzN7+rql9hucAxDpTi/sz5VZY8X1kIlMixfZKczTuk\nI4VGUErkTNHBwRd4/Phxo+OGw2FjUWLW1KriYzyx+ReDJ02ljqxMMWpAnMfZ2JfH3xsTj08fg/P9\nwqdNaoBUmg0gNkF73a9Y+UuSQnO9UhNUzGxZep5fmKlUMmoSVB/I4+PjhpkshhIzWZtotxRWYaaL\noUQLJhcFdh6ohZSi9s2amyqVgkmBMxGo90JFGCrTuNes889VEVylC66SD8uqNo5sUvLzSc5xm8vp\nF1Cxhb5/R3kzVpIeKgY11wBqIcqpthioE9p2PB6H82IBSDi/dJ7wJqplkOt/mE6Pjo6i+oy+LCXt\ndl6UzBkqeIqvWcZdopr2KioqKioqKiqeEy4k1x4756ldk89NNBgMFkLNzbSWBe9gwIg8ePAg3E+x\nGGoXk1ML9ytb3hX5cO/YfbBzYSZMtUUqN1rO2TxGf6dCdGGSm81mckfoqeTt7e1AA/MuWpXLO/H1\ner3ARLEpMLXrUCwanvvw4cPGTkntgLlsqRx/CicnJ40d//HxsV27ds3MzD766KNwbgnzonZyKhii\nVP4g5xStkoKq4xzWXuoMnwrZV2XJsW3LyF8AOTOJl13JOZvz/VjjBkjJEKRM8RzQwsA7EjEvJFGi\n5J67V1u2MJccmvX61DV+ruH/87zCTJTZ6TuoLBgp82FKm0lBzY/MRjFL7ufwmPm8bS7AHCOtdJd4\n3Pnz+B1WLCq7PCDoi39T40mxVP58Pp6q23A4bOTI7XR0PtwUUi4I6n2cTqdLsWiVkaqoqKioqKio\nWBIX4iM1GAzmZqcr/ZLduFnakRWYzWZ25coVMztjn/herLILpoePe5+W0WjU2FGcnKRz6Cm03d1x\npmr1DJWjCvC+Xt5WHGM2SsKyvdinv5aZuhJ/BeWXwCG9amfNYpiqH/xzuR6vvvqqmZ2yRsrJ3Ksw\nK2dZZmh4d6p2aCU+DGo31uk0xWZLr2VflbasQxv/pdQ7oMKLl1FM99eW+Ov4XbZytOfjzI7HpDwY\nLBGREhfN+V+kfP0UVFh+m2ADj9wYa+v4zPdjZ2glAXP79m0zO/Of5LB7ZiG9n5gKWPHX4FkxIUyu\nU6mDdC7AgAEWBfdl8VLV3hyk4sdfrL6pQCT+neeuEv8lNSa2t7eTPl7oo8PDQ+mk7bGM4r+6Fn2+\nu7sb2g1zumrfGKObCw4DPpHO5r1eb252WrlUkkRgNBqFBub0HYpOB7jTVZJeABQxRyLwcdXQaoJn\nxVicn1Is5ug9H1WknOVSjrJcD08t+5duY2Mj1IWvZ3l9PE/1TWoS59Q0fhG0tbXVWLx0u80kpGbp\nj4wqE/d7asL7+te/bmZmP/rRjxo6NLwAyUX++QleLTp5EZGauNVvsRQMJR831aarclRPXZtzLF02\nVUTsuYA3FaqPSy4C0WwxgTYfW7YtY87Lfu6IRX/597nUcTemh6bKuYo+UfX1OnB8/OSkmTGBHbe5\nzKp8qTIrs1tqjOfSxpRCma2WScJbEuHIfw8GA5mNwwdNdLvdBTcZM51ah5FKwdPv90O5UurkvV6v\n0Q6z2Uz2oWovNrf5YynE3Gr8gntjYyO8X7mMC9XZvKKioqKioqLiOeHC5Q8USpMGp8xHWN2PRqOw\nAgaL0e12w4pbmXhwLZse25pYzMp2JTENldIdUo4Z4N2Lmc5/xWVQu7rLly+b2anjPrcN/uUQXvyr\nWKVUQuGcKRB9iOezKjqbFlEGLjPKgnKyRgrAzIAafypvFcBmF8g5PH36NGnay/VvaW66FEPD91I0\nvrqmZNwpx12z5jtSqtCdO8b9oRzQlVOtuicQM4l59omZQR5/KiikVK7C4/Lly/bgwYPG78syUjiX\nEbtuFfITXF9+R1COlGlfSZ+k5FA2NzfD75gvTk5O5HfCK36fR44mJqvjnxVjW/xzWQYD2NraCnVT\nDCarq/uMDh6pMmBe7Pf7YWxx3kKe19tgc3Mz9Hvba0vHNuYBXIN/UQ/l+K4sHqp91LepjY5UZaQq\nKioqKioqKpbEhTNSKR8gdtgDwC4cHx+HlTmkDtixlIUggZs3b5qZ2Z07dxrHYv466jzlm8W7DZRF\nOe56Bewc66agHOiYzWIHWe/EGbMZp4Qa0b4ffvihXb161czM7t27F85Tea/UeR5KUV3VczabJZ3I\nAWaGeIfxj//4j2Zm9t3vftfMzG7cuGEffvhh9NpcWdQx35/sr5OTDSj1UXleatfLsp+4j1neoZl3\nz/g7l1+shJn2ciRtRQr5ub5+yq9PibRynVL9qliq2P1K1f0VStlMhbbjietbcg2/89xXsBAwE4U2\nYHmDV155xczM/vjHP0bLPhwOwzyRyvtWqiofc8w/jzxHCV5++WV76623Gr+z9SElDpwK3GgDZveB\n3d1dMztj91UmAp6jWX4h9z6jbr6sMbbdS3qoa9sI0Kq+/kQ6myNqz8waDr5KK2J7ezu8GKWqtTzI\n/YTc7/fDcX6pUw7eypk75bgZ60yvitzpdBqLHFZZZqXskoWej/TwizkVCaKcm3kxBgfv+/fvhwUN\n2uPx48fyRfNtzu2r6Hv10cTLv76+3vigKSn/fr8vF0NeByXWfmpiLPmYxzSDPL3Mx0vfO5VQeJkI\nOPxdmig0tfhrA++0qiKglHOoGqfc5/w+cpTnKhdS6r1g3bSUqTVmtvSmdlbATznG+rKqe8eeq47n\nUpyUIhVBFpvHAKXqrcw3qc0WpwDKzY8l43g4HIaylOhxxYCN5OHhYagzuzsoM7haeKu65xavqQVe\nygy5trbWiPjlTTY21I8ePZIpxbz5M1Y2FfQFIgB9pL7zpdHMOfBizWtW8jzLm+Nq2quoqKioqKio\neE64cNPe88b6+npwIFO7orbImYAU1E4o5XitrmXWg++TY8pSO8YcA+MdxllJHav20WgUjiv2hsui\nwnvRDtiBxHSz8AxOjIpy830927WzsxNU8VOMjmJAFDU9n88XEt3G7hczM5Wa9lJ9w0yc2nV6hpPL\njDrGNGq8Ka5UNy1mKkwlLU4xOoPBIJlrDcfm8/mCg7/S7Mnp2pidtlVKdkPlTVS74xLWyF9bGlyz\nKgkJ3KvU1JGS7GD2TrEJvo953HG9fR7UUukRRlsWj8FK86lxws7raj578cUXzexsPr53754MlFFl\nV3MNA3Mlxmen00m2G9cdYxuYTCahTnh/2KLA3x0VqIL7KSmdZcyI3nVjNBotBC/hXyU9xNqHZovS\nSDwHegsGW5JSciQuOKAyUhUVFRUVFRUVq8SFMFLdbndudmpH5txk/3dM5kTyu43d3d3ANGA1O5/P\nw4q2xIlwBfUIz/CinzFHRr+LQdnNFjOao54pPwa1eo4pmyt2hG3BWJFzPjzfXi+99JK9/fbb0fIw\nPOPG/hIpx032l4Bv05MnTxZ8J8wWd0ApdVpmpBipcHXF8qlwa8Uk4DyWZ1BgO70Xt1OZ5dmXTjEX\nqszKj4h3iSkmRPlSpHxQYjIe6hl+XOX8ofgeaneP40dHR9IvLeUzwmXF38rHQzkoK+HWnB+OKotn\nu84zJ8f8sM4z96VY1JL5xUyryi/j+KxUzPHOMZOoBIYVSgMafB8pdkT53nLOOGbRMD+iHur5nolH\nH7JcgZoTztO+pfDfu16vJ9+bku+w8jH9uNclucCBT6SzOZv2WN/IbDGyjVNx+CSe/PFXExruwQ7I\nuUWV11/he7bt4JiOjHLM5mtSz0ilzOCPAP/mF5a8sCiNOkktVGILX1+XXq8XFkYPHz4M1yP6g38D\nmO7lRZUHL9oU1a0+VOygjrp51fThcNhom5wZR2kaqfO4TUsXOanxoZ6R0lwqHcelJqCY87IfO0oV\nmSPX2Lymyqo0y1I6Ur1eT2oJ+fsx9Z+b/DkCCfVI6RapKFuVPJoTKadMYoCKeuV+yCUCL40CTS3C\n1EKqdCwCObM6R3H769fW1sLYUgvanHNyLCOEKiOXfzQaJR2u8bwrV66EsY3zR6NR6GuVwgzXxtKl\npRavrLWVi97016tv73mgzPhcfoW28x476XNAjf/mdzqdxjqhTSRsdTavqKioqKioqHhOuFDT3tbW\nlmQYFEpCOjkUlp3NlKOo3xnGVqZ+l6BMcVw+7yDHYEd1vi/YFrBuaufFYdeluyiuJ5I5KyYshlIl\ncrAOPvw1BnYoToW7AltbW+He2G2x4zvvYhCiyzpi3kGV9YFUKKz6DYiphPv3qI2mUWn+NeVEHrtH\nDGz68mwmM47cpiUs1jK54HJQ7eLrzs9VbV76PGZFSiUMUoxZzLTP5TJbdIbnepckno6povv+jOXf\nW4XsAreFypjAjK9ZPKm2d17mDAzM8vh5u9/vF0nixN6pVIAB1yf1/WEnZ8zJHJCirvGahkpqY21t\nLdyP3Ud4nGJuw2/KapAbs6tCiQsAA/qDLBGRA9oA38Bnz57J+QvjHOfv7e1Jtkt9t3E/lsGojFRF\nRUVFRUVFxXNCP3/K6oHVXSkbpdgCXu3m8gP533n3qXZjbHP1uzHeYXDeuZjt38zs2rVrZmYLatrY\nUb3wwgv23nvvLTyD/Ui4rdiRGed5QTHelXOZ1E4lx/JxzizUU4lppu7NDIF3Rrx9+3ZSoZh3Nnie\nej7vMLxKtPJzYnFPDg7ALocd/f2OVfmCMLxTvKoPX+vyOJmZZsI4BFf5zbCYq+8PtSON+WsBOR+F\nFEuhdn4phivGLsZkJbhMzEIrxHbFvowqbFzdl5WZ1b35GowdFaqv+p+duUsYgxir5MH5Abm/VN+l\nZBtyDuuKqfVjUY2rfr8vGSGMSxxTzusbGxtJRgqs+traWmDv+B1NXcvfnBS7zPNFyp+UoYIrAOVP\nxN8ihhpbav709+v1euGZubL6+8UkgEqYX2ZHeR7245jPw7dkY2MjzO/q+8NzNe7DmTDQ5vwN8esK\nvp+vfwoX7mwOsJI2R2SYxScJ/5FTVLJyMmPgpdra2mpl9vLwzo39fj9Ql1gw5qJnlKMtOp3pT36h\ncA0PLKUjBZOXj5LE+X4cKKdFnvRT6QdiJkxApY9BmdfX1xuTw8bGRrieF3eqP7/whS+YmdkvfvGL\n8Js3USqF3G63G0yscMaPpbBJBQwAMdOeMn8A3EZqMZLS3FJ6XfzBL03B4ifrTqezYFpD2ZXZzX/4\nlIq5WVoLiPvKtxWPSWV2zplTuczLag6pj0gsHQwW5lxGPBebopgeknfIj6FUg0pBRR+WLpr9nMrv\nVOqbwhFwagOHNuN7cLqXErN6v99vRO1xn8HN4f79++E3jmBGWyqtPNYp82mhdnd3ZdCMfwabADEO\nut1uw4XCm/vwfuG3nZ2dMH7aJgfnZ3O0OMBBSm0jPXlM+o2vWpTm9PD4mP+t3++HuQALs7W1tdAn\nqUAqHjuq/xnVtFdRUVFRUVFR8Zxwobn2Op1Ow/Ewp6R7nh0YwCv93P04v5DZ4uqZnVyV4nIK2F2s\nr69LnSOFEmc+dsg1W0y8aRZXtC7JdcThuGzWjJlAzHTovQobVyGpYPT6/X7Y6aWcQ81OExKbnZlR\nmUFQUgtgRw4ODlpT0+z8XeJsrpgL5WSKcpst7qQVQ+uZGe5/ZkkVq1SqLZTKI5lyXubz+JhnpDY2\nNsJ57Kzr5wZuK2YFVa495VSfCwf3O3QFrlOqj9iJnNsAu34c8zkkgZTcB8OzDsyitg1AUPIROT0s\nzCVXrlyRDG1KeoTHnR/Hin3a2toKx9m9wjOEV65cWWCbfFlUjje2YKjvz87OjpnpuQPjWDHY3OfM\nPvJ8jOejfCjTdDpdkAjBnMdlBZgpS8l9cH19/s3cNxX1mEwmjTbKacEppCSIOEhM3YNZd4xFfC94\nHLLjeEluybW1tdAn/O5VRqqioqKioqKi4jnhQn2kBoNBY2XLu0pmi/wOSK12x+Nxg2Fip2lmcryE\nwGg0auRQ6/f7xQ7x5wHvQPB87/jODqPsx8K7BLOm3INnpMyau0R2xFMifmybV6KgyufB7+5556hE\n4dQ4/OpXv2pmZj/96U8bx3gHxCyL38mz425KLoBFX5k5Y2d0/KYyhisHT7+7Z9kFbh/FAqTahdkY\nJXwKpHaDMcFYgMeB70sVSs5skWINcb/xeBx29aqOiuXh+7366qtmZvb73/++cXxvby/cE887PDxs\n1DMmiVDqKAzk2Gx/nNWplZ8b79BTfjyA8vXK+SfGHJlxnr8mNk68I73yTSuVxIg9w9dtZ2cnvNe5\n8gF4Vx4+fChZFJS7tM9zgTdAyg+w1+s1/KvMrMEQzWaz8O5Np9NGTrxut5v0N4uVG+XDGPQ+yXxe\nzN+oNCuCr5t672ICpegbBGs9e/ZMBk2UIDZOSuUgcozUhS6kYtoeKfBkV0LFm2nFWG82iA1o/yFl\npBR3d3d3Q4QBf+TwN5tGfPnxEprpFzEFduZj0ymo6cePH8t2y0VaAX4RlnNGTH3YedLnly9l1uCB\n7xeRSm04VqZUJGdOXdcvFJQ5hRdXpeA2SH3Uc6lfPDY2NkJZMf54UQcMh8Pw8eLNhx8v3Faqr5TJ\nk/XXUuZv0PNPnz5t9MPly5fD3yg7K/XzApTbz0+WpR9udV5sEabGjP/QqgwNavHiyw+grzkqN/Uh\nSJnBuW6qrYDY9wFZAND2bIpVSI3TmHnbtwvPF9xXPGZwP/VhLinL+vr6grnN7PRdQF+irR48eCDd\nF9AurLbuF7YbGxuhrEoTEP12cnKyEKGNMmAjwgsyFX1WushaFfw7sLm52Uhoz+XJLbL92M6lkil1\nVcB4OTg4SKZY4wAoWixX015FRUVFRUVFxSpxIYyUmV3IQysqKioqKioqlkRlpCoqKioqKioqVokL\nUTZv6zvycaBNDrBPEnLOct6hVLU9q+ayT8ayeZlYEI9zRS2LbrcbfGzYOdTbt1955ZXgc4AcVnt7\new3xS84Yngr5ZWdO+EaYLYp4om64D4tg+mtPTk6KnVpVWdCm8Hd78OBBkQxIv98PTrecgxD+Tegb\npTSslNcHg4F9/vOfNzOzF1980czM/u3f/i1ZBvTL0dFR8COBtMivfvWrxvmj0ci+9rWvmdmZWv2b\nb77ZOG8wGIR7P3nyJOr0bHbmk8E+XrkQbHW81DdzlVDh5ezPoYIXuN9SecaUT1jK34TPYz+rVPg7\n+2v5HHrsK4N6bGxsBL8UvDP8DPgLPX36NPyNehweHoZgA0ie3L17tzFfxAJWgNu3b4fn+gwMV69e\nDU7fOO+jjz4KYwLtd/369TAXAWtrayGYAO177969hkwLB0p1u93wfrKvnxeCzuVp5LaP+YiZnbUv\nzxfLoMRvSY3tra0te+2118zs7Bvy61//Ojtf555ltpiHL+WDzDkUY3I74dzk0T8RrGJi48S4zzNS\nL5WWZRmULlBSHxiVkoTvzQ68ePn4fDjH48N8cnISoo3Os4DiNDPoW57AUX4sLO7fvy91eXw0DI8T\npa/FOizeuZF1mtAGsZfbJ7DOKUdz3fDhQ3LTd955J5QbE/hwOAyLiJgekdlpGiKcxxMjp2PwUNkA\nWG8Gx3/7299Gn8vgiFSUJbWhWltbC+MqpSB/fHxsn/rUp5LP9rpaMQdv5YSsJmVWfTaLR1niA/v+\n+++bWflmTTm5K+0rvldK/dtsMYoZZUl9lEqc8UvOR1nQ5/v7+wtK9Wanuk9vvfXWQj0uXbq0sGEw\n00lpeSGFaw8PD+2FF15YeAaPe36XfRtwQACculEOvpazMqBu6+vroQyYC1XwC+t1+SwEZmeBHuPx\nOLTfjRs35DuulLmxUcEY39vba0Tora+vh+McdYgFA9rtpZdesrfffrvx3FKo8a6yImDhi2/v06dP\n7YMPPjAzC5G6t2/fDuNEAW3BTu6p93d9fT30LdK0Mdp8o6tpr6KioqKioqJiSfxZMFJYdcZUu0vA\nu+znSd2DOv049KkYzC55poTbDDskzrsEdDqdsMMCW9DtdsPOLSfVADobz/jwww8Xdqq4n9/5xnbx\n2NVhpxYzm+F6dZzNCyn2DGWaTCZF2jhcvlxSbexe0Q97e3vheWA19vf3A8vHZkS0KcOr7H/pS1+y\n3/zmN43z0F8w+zHUbgx93ul0wi6VVflhinvnnXfMbJEFYLMq2iVFl/d6vdCvKbZtPB4HnZlSKHV1\nJcXR6/Ua80nOhA4MBoPARH372982M7P//M//LCqfekYuwTDrfoHN9JpVfE1M3T1VBtYb8mNZjRd+\nLsrH16Jtb9y40WAaHj9+HPr15ZdfNjOz3/3ud/LeYG1//vOfh9/AlP7TP/2TmZ0yDsgzyvntfPlZ\nPZ3/xZwFc9Mbb7zRuPaFF14IcyHKh1B7BmeDYCYR9+G6oR/w3WCwaZf/9d+tl156KczbeCf39/ft\nS1/6kpmZvfvuu2a2+C7jvMuXLyf1ssB+Xb16daF/UvBj5+DgILxzbAKGSdSPlxjQVs+ePWswgmru\nf/jwYRgLXkKDUcJMVUaqoqKioqKiomJJ/FkwUlipLuPEzs53fvX/PBip8+QIXAVyPhpYufMKHrsx\nFlUDVLbzfr/fYJWm06nMf+Vt8sfHx0X5CrvdrvTX4jKYnTp74hk4j5lLjBm12zJbdBTn82Ngp3Tv\nC8BsG7cvlwvg3HlmZp/97Gdl+/kxura21vC/yImmqvbDjg6O3rgPysk+ImanzNDf/u3fmplJnwrU\nYzQahWuVbxZweHhof/jDH8ws3jdmp6rHalxymZVjtGdPuSzMIHoBWoZSZFcsbykTpcQZGSqrgBIM\nVv5QnCcR56fUqZU/lPKbS5WPgZ3+tWvXAtOAsXv//n37whe+YGZmv/jFL8IxLw5rdjZPKH8p9Vyw\nVK+99lpgpMCOsbMxGMperyeFTPE8BFkwI4V73Lx5M4wDlJn9q4DpdLogIosyoY3QzoeHh6FcakwM\nBoOG6KZqg7fffrvB7j18+DD4nn3rW98yM7PvfOc7jbHA/omKucT8sLe3F+6d8mOKAfVjSwjGLPKm\nXr58OZtrFSi1+PhMCCn2O4U/mYVUiUf+Mo7NKslo6WInVyYVtVWivH1epKh6/pjjX7NFc5GHWlyx\nwy3MS3iZ9/f3F6JIzE7bAH+jDabTaUNZnhcGmCDZ7KJSazDQ1pxOAefiZRmNRjK9EMqAyWttbS28\nsPiYx54LapgXUgArGqMNuC1xb2Viw0T1yiuvNI51Op3GIoOj+9B+Dx48CJORSl2hVMmR/JkXUuzw\nj3pign769GkoCz5YDFx769Ytufj22Nvbk6ZitBGetbW11YiKYqg5gdv+6tWrZrboPMzpZbyjOv/N\nHzFvOuAxi/7Y3Nxs1J0XQ3gvVCJeVRcenz5tEf/GZeYIPb+AZrV7fj9KP14Ybz4ll9nZGMMC3ezM\nLHRwcBAi1Rj4qHLEHN4/vG+TySQ5nn75y1+amdk///M/23//93+b2VkbvfTSS2GsYnxubGyEsY36\n3rx5M4wxNX+jnm+88Yb9/d//vZmZffe73zUzsw8++CC8I/xe+Lbc2NgI4wTtcu/evdAP/B4CnCII\nY3YymchIafVOYowhGvby5cuh/1Vfo3yXL18O8wkv5PEOpaK9R6NRaF9etGAsokzr6+uNMjx48CBE\n+r700ktmdhrJh+chpdiPf/zjcA3m3m63KzeMqAfmgWVRTXsVFRUVFRUVFUviT46RAlal+cSrZn9P\nzuOlwHpC3rHY/212ulLnBMDPCylm7uTkpOH8zOHb2H1ybjTs/o+PjxuMy3w+DztCZr3Ujho7GqVb\ng/bY3NxsmN3m83myvVDWnZ2dhhwA7yBRJrW7Y+B47Dw8D7uYjY2NUCemwtG+YC76/f7C32anbYE2\nV+H+KWdJhdlsFhhCmBXu3r27wOCkgDIo51aU5fDwMDj4vv7662Z2ugP/zne+kyyX2elu25uNsJs2\nWzTjeIzHY/v0pz9tZmfhys+ePTtXeDbMpSztgTJwDjjF7uJ8xSBxHjyM5xhz4oNbjo6OQvtzklZv\nXt7f329ILPCuO2WeUzv0Z8+eLTitA6WSDT5YR7E3zJhhPtja2pJsgf+NWWO+DyewNjt93zAPgNX8\n3e9+F57ncxbysyaTSXjvwZ595StfCYzUD37wg4XnmJ2xt2+++aYMfODxDXjn5r29vcA64729d+9e\n8v1XSYQ3NzcbJtaTkxM5f+LeqFu32w3zO5hfHsc4n1lw1rECg4RrY64IkCtBHzFbxontP/vZz5rZ\nosQKnODx761bt0L5UZZXX301jAkfWIU2Qj188MU3v/nNMJ9AfoFz38ZQGamKioqKioqKiiXxJ8NI\nPa8M1uyI6oUbSxHbsWFnxjugZZ3NO51Ow/GVHeTbQPl9eHDoNzM+eLbabQDcHtjVDQaDsDv0wpeM\nyWTSUJ1mvx8wIWtra2GHjx0N7N0epQrtpedhd6dE3BieuZhOp6FOvDu+deuWmWnJCQAhyur+jOFw\nGHbA2PXeuXOn4SgcE4fEb9h1XrlyRcpLsLAfritRQe52u8EfBv22sbHR8IdQ5WNhVjASOckNBWZP\nuG/wvBxr7McJtwtnEMB9sJPncHWA2VZmkPAOoEzb29vBzyjn++L9ofgaL7JrtrhD94zg5uam9MNT\nYwdlVs7/LH8BsEikb5ft7e3Gc09OThr+S9fwElbeAAAgAElEQVSuXWu0QbfbbYyL73//+/bNb37T\nzM7kOd55552Gg79yMGd2XjHOYAXffPNN6aysHMBT3xj0AUtFxN5VH5gzGAwajOBwOAysmWIu4ZM1\nmUwa44OtFWBmNjY2AusENmg8Hof+UlYcDlRBG37uc58zs9MxgXYHGziZTAIThT5XrNydO3dCmf/3\nf//XzE6/DZ515Pqq4BV8z46OjkLgA9oxZZUC/mQWUs8LvCgB2IEOC4G2uk8cPcWTa9tFGpfJp3nJ\naR/FkHJGx4BWzobT6bTYWd7TwbH286YEXmiib7gseHF7vV6jLJcuXQoTGU88qo1UG6BvWIcHv/HE\njMUDypxb5LNDvR8TnOJELU7xEqci17g+4/G4sQBgUwf+5ehIBuqJsly/fl2aF7wi8GAwKDY/ejVm\nNiOi7Orjc3BwECZXKFcvs8Hi9BhqHOTSJPnxFPvg+Xd9a2srjG/uTx95pfSrnjx5IqNAU6luuA5e\nDX02mzXMSzw/oSzPnj1rmB5jEYz4nfvEl5k3O3juzs5OYwEyn88bpvXZbLaQDshsMRqP6wvAjPTO\nO++Ev2G6OTw8bCykjo+Pw9jGu/fDH/6wUV8Guzuo8Yh3itsR9+Z+w3yChcatW7fkBkoB44CV3lG3\nBw8ehDmN20a5caDNWZvRz7MnJyf2V3/1V2a2aO7z+nXsMM6bDtQP7/J4PLa//uu/NrOzhfZ//dd/\nheel5hV+F9HO+/v7jXf0hRdeyG58zU7bD/3t5/kUqmmvoqKioqKiomJJ/MUzUpznzJtiTk5OAoXJ\nKsFYrSvqH7sTzgXHuwD/WxtGyVPNpQrbjGW0tnwC4hIos4dHzCk9BSXTwOVL1Y8ZPezWmEZn9WU8\nA7scllDwasLsRJ5yij8+Pm4wITs7OzJ/F6B+U2C9GezWscvb3t5eSPxqlleBR/+9//77DTPJP/zD\nP4S/EZpeGvzBUgHY+T9+/DiMaW+C8PBjcFk5EZW9wLMnSsE7lgdPaTL5NmZnc5Zd8DkP+ZnMmPg2\n5v/nZGGUycb363w+XwgEAXwiXpaK8debLdYb4xK/xfLF+X7k8/BezmazBjug5EZibfHv//7vC/+/\ndeuW/frXvzazxfGAAIrvfe97ZhZ3GfBl7XQ6sn5gn1CPW7duBUdmZn587s5lrA38fQLjOJ/P5TwC\ntoitB15FXJlxJ5NJaDe+1pvGr1+/3kj2zGAGHmwR2LTzYDgcNuZhZtEgiaHQ7XYbciQlqIxURUVF\nRUVFRcWS+ItlpLyvCq/kYaft9XoNwbvRaBSuZZVdFbIP8C7fO10u43zOIaKrQK/XW9i9mJ3WF7uT\ntrv+mCggdvzYpXImeGBrayv4pbGysXJkhpM2yvf48WPpkO/DwIfD4YJIptlpv6KvOeTcK98eHBw0\ndordbrdIzoJD2H1b4D4xxBx8vbgqi6FCBuGNN96QavEIt1a7RrQFswvwGXjxxRftRz/60cLx0jHy\n6NGjsGNFG9y/fz8wApA32Nvbk/ITOA/vTYkjKMCMk3rvvFwFK5srqRD29Uq9x2CrBoNBaFcwBOvr\n66HfleM82jXmfK+YJg+l7s7q/izJ4eUeWNohx4D5ZzAQJKLG2t7eXtJ3FONub2+vaJxdunQpsB1c\nFrBFcK7m9w19NJlMWs+rYKxeeumlxnjkAAOwaV/84hcDIwWw9AD6wNfVC/YqdpSzGKBOX/nKV4Lo\nZsqHSyHG7rHTvdnpXIN+Qt0ePnwY5mhf3xhSvqDj8TjZ//iGbW9vN/J5zmazMLcAipnK+dbG8Be7\nkMKHh18a33AcyaOc0pmKx/2YpveT23Q6PdcCypdzGepXXTObzYI5iBeYKXOBovk5JYmP+BuPx2FQ\n48VlEwZHjuADyqY2fPRh6hiNRmFxxSYTX2aO4OA29xR8LDUArmW1Zr+4KjVr8bW4Zm9vL7QfJiJl\ndhuNRkmnR44+g/4KnvXuu++GfodJaXt7uxGlyuNYlQGqwt///vfDB4NN2SXgRKHc5j5y7datW3Ih\nheM5LTAFtJFKEWTWjLJUpj0+Dzg5OZGaTYAylwHKZMf6dUpRnRdPKcd3FXnFc5E3Jc5mM6lppeDr\nospiVuaou7+/39At29jYaJjV2fHZa9IxPve5z4WFFOYIXtDgvmw+xgf80qVLjQ8sO02zKRFtgwCY\n3d3dxrhS5Xvw4IG9+uqrZnamgTWZTEKdUF/fdn6jxf3KQH+y1pJPUMwZH3LwOlj9fr8xX96/fz+Y\n6lGPx48fh/kGSuRPnjwJ74pa7KbQ7/fDYtIv5MzO5vfHjx83TO0PHjwIaX2ggL63txciOP1zuFwl\nLjTVtFdRUVFRUVFRsST+YhkprDYVlecpb7OznUWMacK5ii1iJ+dlHMSfF7geflfA7A2H1rOau9mi\nuUo56bETtld/H41GwbTl1dHNNM2rQo05tNebPcfjscwVWIqUBgzut0wIPutxsbnFbHF3zwmj1e6e\nFZnNTnfPoNOVOQ/O3Hfv3g1h46hjv98Pu0+f29DszBz17NmzBQYRUHn8PCaTyYI53QM7TaWs3ul0\nGsmclwGPbW4b/16rY/53/J9N/2andVNyBR4nJyeN3fOjR48azBCzD0rqgHfPPqCA2WW+FtdwvynH\ncg/FPrHbAl+DOmGs7e7uNhTej46OGrn2uP9x/s7OTiMzgDIJYvxzGzDQV0qSZTAYBFMd2G/uN/5G\ngG1Bm/3xj3+0L37xiwvnKXz44YdBvoPhszGwLIGZng85m4TZomkPx37961/ba6+9ZmZnAR7z+Ty8\nS8y2ewkYNZf3+/0wj6CtOp1OeO/hUrC3txdYMZ5XlIkVbcg5XHEN+unZs2fBfPftb3/bzE7z6qFd\n2BUFfcf9D/YJv21tbTXkd1j/zZsHU6iMVEVFRUVFRUXFkviLZaT8DpKh2CQ+5neYvAtQuaw4R1aJ\nz8DHBeVroXZSzDS19cvikGh/7dHRUZGT9mAwCG2udpjs9Ot37Sq/GcsfKEdagBk4perdForhODw8\nDD4W2KFdunQp7O4Q0DAcDpOisKw+jLZK5dd6/PhxI+8eyzjEVOdxHp7Bu7USJ11uO35n8DeYCxXW\nriRKOp3OUuxUidBm6hjKzfUwW2wjf83h4WFjB6ycvmezmVQ79/51jFQwhPLNY2Csceh3Tk5B9bX/\njfuVRSmVA7+fhz/66KPA7sBR+vj4eMEp3OyMYWHwmFRyH16IlMFsKiuDA9wuf//3f29mZj//+c/D\n/XANGLX9/f1GGfb39xviu71erzFX+T5I+dVyEIsXVd3f3w8ipAjkMTsbv8gPePfu3dAnmHcODw8b\n36xOp9Nw3J7P56Hd8fxutxvaGP1148aNcD8cYxaVrTwoA9i79957L1wDFfObN28GYU+eu9APapyi\nnY+OjhptzPVog7/YhRSAhh4OhwvKwmaLZjx8eGez2YLcvdnpAPd0Ki+uMBnmHIYvCqWLI6Z+lcnL\nR8KZLTpr+w976XPVBDIcDhsOnfyRTplQ5/N5Izqp3++HyRz1iKl/nweeijdrKrhvbm6GNmTTngKP\nX9QDY0wtIlmJHAs3To+gHKO5PcxOzTNY1PFiSLWVdw5lJ2c2FbCyNP+L9jBbXCSwQ/gyC6mSsZc7\nhz9efrzxWGQtKDa3mZ3WUzmqK2dlv1kzs2ASQx9yBCHfV5kZfQRp7J1RC8aYmTKHvb29MLZ5Y6A+\nXn5scxRlSsuP1cDVQir2LpmdjjFOmYNrEXWId2o2m4VFHJshf/e735mZhTQjv/jFLxoBHPP5PNSd\n00L59p9MJgt9WOIWwt8n7jfvnM1uEGgjTtKNYA5emHFkMO7NmT9SGz0868MPP2yMt+l0GtoBbfrg\nwYMQcKH6CyZDdhnBHHL9+vVwHHXrdrthXvSJileBatqrqKioqKioqFgSf/GMFNDpdBYSyZqdrlyx\nuwN1rsx+rL/CFLoPQ07thP5U4OvETA7a6uDgIOxePNNgpnfA2FkfHx+H3Wlqh9uGKULfoSzMrDE4\niWoJmC3yIbM5qN0Qns+SDRgzbdgC0N+4lrWA0Aa3b9+W6r+4D3bZrH3Ez/TluXHjhtT6Umyi3+HG\ntGHYrInrUA92Ok/pb8VQmlmAMx/gX3+tkg3gcnPIvnK09+NNJTfG9WaLqtNgojiQQ7FZ/r3l8aLG\nFteRXRO43h7Kod1jMpk0WNbj42OpvA1mCczp48ePG1IhKn/au+++G+YTZmU595zZInPBwL3ZuRvO\n1ewUjbEIJob7jU1o/v3p9/uN70rMUoEy7+3ttbIc8L8M1H1nZ6chQ3Lp0qWGOv1gMAjvK9rg+Pg4\nuCPk3j0/FmLzNtqaNRpRrpRJk7UD2d0AYwz9sbW11XAeb5s/N4XKSFVUVFRUVFRULIk/K0YqJqCX\nAlasvHpXdnfelXn2YTAYNJw05/P5gn3WbLU22WURE85TSO0sOQxY7Rj8jnowGIQdEI5du3YthBBj\n5/j+++9L5fBS8Thf9vF4vJBPz+y0H1jOIlZHBvpyOByGemCHw34EuR2a8tnwZWCfOyUmyvBjam9v\nL+zg2YcDz4N/yrNnz8KODDvwfr8ffoNPyB/+8IfGM1Vo/7Vr16R6scrxB4ZGsS7cfoqV80wiB3+0\nQeodYN8cNRdwcAPK5RlpxdodHx8HMcCf/vSnjed5lfJcmU9OTpL+TTmHej/u+D1j30E1R6aUyNkh\nHHMl+n9/fz8cB+MQY4bYZxD1wW8Q6FX9w75+wOXLlxvO5bu7u/K5AMtCKJYHTut4Z7a2tsI8ptgO\nlmzwEgxbW1vhHWcfLfy9t7cnAzCWxaNHj8I8gecqVpDZYoiHvvzyywvlMjttK99fr7/+eqjzj3/8\n41CnFNBHHAijfD1ZUNu/F8+ePWuIG5dmQNjZ2Qn3xrepJP/fn9VCiqOJSsEvHDs1emBS6nQ6YQLi\nNBroOJ5g/GTzSVhIlZoylOZVr9drmBdUndhpkaMYvcnuzp07gSLOARMn+uvhw4cN50GzJlXOtHrb\naLvhcBjqi5drb28vOxmk7ud1mszO6HuUlZMb84JQAZM9+mNvb88+//nPm9lZG/CkjkmBE7FC9Zgd\nRlOK5Tl9L6DT6YRxAFPH0dFRI+2SmTUWouPxuJFA9enTp40F73Q6lZG3vhy+LiWbhNls1nA85s0a\n/mXTqVJDRh91Op2wgOLIMb8wOz4+bqTWUAsXjvhTpsVcfVUSZN/vx8fHDfPmYDCQJhqchzE+mUxC\nuTgtlB/LHDjCZUZkGKKy1tfXG9GMauOys7MT2g2biddee83+53/+Z+E87iPoLP3ud78Liyulh8Qf\nZLQVfnvllVfCRx/jRS1EJ5NJKBeu3draCosIrhPemzt37qw88AXvOlTHEdmXw1tvvRUWYWx299F4\nb731Vqgn+lzNHa+//rr97Gc/W/gtlmkC4LnDb9DN0tpPePf29vbCWMWCcDKZhL/xb0mAWDXtVVRU\nVFRUVFQsiT8rRqpEkygGpUEUU/XFcd4h4Nk+ISv/nVJ8zj33eSDlPMoJe4HxeNzIKcimLrA3T548\naTiUKs2O0vJduXIltDXnx8K9sdtRZTZLh3fH+hhlbrsLjDFHZou5uADW7uHnowzsCOqlBEajUbgf\nytnr9cIuF8eYafB5s8zOduN//OMfg6aMMk0gh99vf/vbwDD4vF6+Hqgb2uXJkycNZpDZTzaR453C\n+FJ9tb29vfA7M8e4xivHs+lU5YwEptNp2EEr8zyPE2+yVbndrl27Fu6ndtxcD8/UqrofHx8vSCug\nTL7NDw8PG/pLvu4AjyOzRbZNzXvA5uZmaA+VFYHHuGcvWd2fZQPAHHCYPID6qna8detWYKTAZCvW\nksf4pz71KTM7NWl/73vfW6hHr9dbYHC53IzLly+HpMx4/vr6engOO/z7d57HFZeL3UNW/V3wmTy+\n9a1vNVi7GCCPwCrgvnxPnjyROS0BjMkrV67Y//t//8/MzP7jP/5joWwlaPvd5zHjvwnMPrWxIFVG\nqqKioqKioqJiSfxZMVKliOWKwuqZBdaw2sWx8XgsV8s+rJh3i8zOeLVzBs6fTqfhealntVm1c7Z0\n7DbZLwV/w7asdpCKUTs+Pm5cyztW77xqduYvcXh4KFf9LBBndrr7UbtgZlzwDNWu/hmq/zm3UyqH\nHvvh8T3Ujt9jc3MzKHczlJ8HmD+c3+/37fr162Z2xkj1er2GL8DGxkY4zjsv+NywjwLGOc6bTqfB\nL+SXv/xloyzf+MY3zOyUkcJvuIdyCFW+a+PxuCF3wO3Iwrep7AN8X95Fphy2WagSfagcrXEt+/op\n8U0WV/Xjcz6fN9ii+/fv28svv2xmp/4jKAvuw/2hHNp9kAsrOCsFdJR9MBg0nOGV4zi/J+wH5tvy\n0qVLgZEA1tbWwviEEjk/T/mislgvwE7GXkRyMpkEnzUc47GEMcbvBBykP/vZzzb8yJhF/cxnPmNm\niz5QOJ+FYPHva6+91vAp4vbDWHvhhRca7O6rr74ahDuB2FyOd3M8HrdW3C79ToA9Y2mKtkx8t9sN\nvpm/+tWvzGzR5xLzD78/aKPf//73YQzgHbh8+XKor2f+VgmvgM5tpQRwY/iTWUh5PR1OF9EWKqJG\npXmIKRajYeFIx0kScS1PQBx9hAmFE8WiM5n6TUX6pRZXsfPY5KGUwFPgl0qZ7Lz+kopiYwVaTsuC\nDzeo9el0GiYZ3C/2UvskyDngY7O2ttZQrJ/P5+GFxVjY3t5eSLpsplNwjEaj4rKo6DtMHlioHh8f\nN57BzpyAmlj7/X5YLPFHBh8+Hido1x/96Edmpk2PZmcpJBjomxdffNHMdHQfA+1y+/bt8HFTgPny\n7t27Yezwx82bo+7fv59U2eZFMy82OKiCz+ffvLI06uHVtdl0yk7naH+M8fv374fIKKVmjkXE5z//\n+dAneAYv6ric/j5sivOuCvw3z0/KaZ7hFxGs5wTcv38/fEgZuB/G0MOHD8OHGwufZ8+ehX7nsaHU\nuPFc5bSM+rC2FD7kP/nJT+yb3/ymmZn98Ic/bFyLMcbvHco3m80aC8ejo6NGGe7duxdMiTDxHRwc\nNBbUL774YmMhxfMGFqR3794Nzz04OEhGSipwpokSzSRub8xJnIxYgccLnvG1r33NzM4i9czOvhfX\nr19fyKRgdrapYBwcHAQtMBVIwUC/43usNnVmZ2PQz/Nm6cVmiUm1mvYqKioqKioqKpbEnwwj5RN7\nxlaJJXQm78bUeRxS7E0AfA0rNPvcbb1er6EtdXJyEnaQTBt6J06+thSKweJdI+8cfLgoX4PdU7/f\nD7sSVvPFPVO7FE5CiXqyUzprOGF3wPQtmyEB7A7/5m/+xsxOdz9vvPGGmaX7fG1tLTyXzZZcJ5TZ\nm3b29/dDGbwejtlZO7JuTUrr5eDgQOprYeeF3WfMyV3pvABgMxRzZXa244Y2yhe/+MVgmsAOfjwe\nR53Gzc6Ss5ot5t0zs8YO22yRRYGzLkwoMWDXzrtvZsl83rKTkxPZVjwmMGaZMVXMoWKJUo7xLAvg\nGeGTk5MwjrHT5xB3jGe+PwIp9vf3G2Xh3Tgz3b58R0dHjdB6PofnCf++KEmOyWTSyA/J+fy4vkrK\nRJlH0AaszwNGClIHXH68Z5y4WznKc55ITnQLgOlR+Nd//VczM/v//n/2vqxJsqu6eueclVlZ89Dq\nbnWXBqRGtDWAZGMsIwYZjMGBI+zAfnA4wi+8+lf4zS+2wy84eLPDDvwAtokAYxNCHrAghJAEgtaI\n1OqWeqiuKatyqJy+h/zWrnX32fdmdknQ6PvOemkpK/Pec890z15777V/67f0M4zH+fPnE7pfIuP5\nbBNLLl++LL//+78vIkeM1Pb2dvCuunz5ckKjSiS5//E4YC2PRqNAzyhNAR9g9yyzomlgRhJ7Je+f\nrEVnCxQvLS3pPEGb6/V6ICFw48YNufPOO0UkOdYAz138ZlIwOe4LSYZms+n2C8IkwKJvbm6mVlW4\nWURGKiIiIiIiIiLimHjPMFLApBiUm61HZAUmRY6ssUKhoFYA/K79fj9h/YuMT9vW8ioUCpkp01bU\nk3GzwpH2HoDH2lUqFVel1zJlXuyTiASB6l6sWqVScauzT5um6gWoY3xeeuklEUmyM5zubS0MTk0H\nC8BsB1t8HqvA42nbx3EHHDOWhmazGVjyuVxO2wCLygtIT7MkbRweP7/H1OHvjUZD2SQwBK+++qpa\nrpA6eOutt9QyZ+kJy1J4lh3PGzz3pMBRjOv8/HwQlMoV6+0zpoHTsjkmyAuwZsbK/o0/s3FVhUIh\nEVeJ72FNsaAo4jR4jNHnGIednR2dq57kwCQFdDA5YJRbrdZEhXzcC8+E8WSWgpl1mwBQKBRcZXuA\nawKi/RxjZJWnmc3g4H/0kbfmMV71et19D6B9zM7Y/f/NN98MGEwPg8FAY3tYFsSyxsPhMGC1X3nl\nFQ2+ZpYcf+e+4DG2MVLTvicODg70nYX512w23b2S54zIeH8Ee8Z7oZW6YHkIj5nE9zc2NlwmynoN\n+L+zAuDPnTuniTFgftfX13XueIrmqOHYaDR0z8W9ms2miqB6wsFpeM8dpN4tZG3AHMnvBWxy9o9I\nMqMCg12pVFw3HjBtoVVu53GD6xmTAva8zB3OovMOSDaYdxJdypkZOLTCrbW9vR1M3NXVVf07b1Q2\niyktY88Gik4CF5lGH3j9xoWbs15QQFo5C7xU09TBRcZ9ytmkgH0p5XI5vQ5rNwHYUOfm5twN7/3v\nf7/eT8QvClur1dTAyMok8g733iHRQ7PZDIobsyvLuo4t2MXuZVnatnnlUURCpfx8Ph+sFXZle24Z\ndtPhIHDu3DkRGWdH4pCBsTk8PNTfei9Se8BEu9AfNouWf4M5wer5XsA9cHh4GNyXQxn4+nhOgF27\nrO5t+3RpaUnXNWul2eSUra2twCVWKpWCg9TS0pK7HhHA/8UvflFERL70pS8F86BSqehhA4cDz93d\n7/eDosXValWefPJJbZfIeJ56yT3e3PDWCx+ejltRQeTocHPy5EkRGbtV0desc4U5wwHe6GscOryE\nG5Gj+YvnXFpa0v0DfZj2DsM8QWmq3d1d7aOsg8yFCxcC44QLMntECdZKs9nUPsWcrFQqQXWHaRBd\nexERERERERERx8QtZ6SOo4n0bsBLL7cWweHhYWC1cUo0/lYoFFz2CffgIEgrCzCp0PK0/eIFyjKY\niuf6d2gz2u0F18PyOjw8dJWjsxSNcdIfjUZB/Tu2mDmQEpYALKC9vb2A1vVStcvlcqY6fJbCuUjY\nL9O6Inu9nlo+HtNoLWuRZEFUWNSTmLwsPS+Me61W0/tx/wGsoeM9n00I8OZTq9VSNfSsAPhJAMNW\nLpcDCp7nFf5llWisC7b4RcIxHgwGQa04z6XN+kuYO6VSKQjc53nFv/UKbXsaSvg93BFzc3P6TDyu\nGDvMiW63m2CEcF2PCbPB8Pl8XtvAc8ym5TPYkrfXY/kI7zfA8vKyuoO537JqlyHYeGtrS/uU9aG8\nwHeLdrudySbwPmX3iatXr+o92GUHeK5ZsC1cMw73WFtb03nAjBLuy+8Nuy/V6/VEX2WFDTA7b+cd\ns60Yj2KxqHMMLv56va4aUNgbWKcNzH6j0dCx5jkLthVSNjMzM/Lwww+LSHK+c1C4iO8WXFhY0Pvi\nXrlczp2rln28ePGism14NmYVvcQxvIuKxaIyazfDAEZGKiIiIiIiIiLimLjljJQNyPxFgxknyzSx\nNctxGjYWIJ/P62c4PReLxURwJn5rg4NZZfmdYFJAHLMPHntmLTNP/VvEt+rt94rFohtMb9miUqmk\n/eCd/rPiaTzrbFI9P7as7XOw2B/+ZQub46bwGx43W7dKRAL20UvjnwRPER5WIDOEHEzMbA3+RX/B\notvc3AyCg+v1ugZiIhj2xIkT8uabbwbtglXJaeFZDIcHsF+ecjkzHp4CNtcgY9i4Pm9O8Nz2xtBj\nWJmhscHIpVJJv5vFiLICuhUEZdTrdbX+IVfhiYPi+Ww/2H3Hq0FZLpcDqQlOHOHnsTFhafUswU6i\nLSwPgDm7tLSkQcGelIrHdIEhaLVaQeD7wsJCwGZeuXJF+83D1772NREZsxVWvPHSpUuyvr4uIkfr\njQGGw9ub9vb2Asaq3+8r2857HNclFRnvB/Z76+vrKqPggZ+dGWnIPGAt9/v9IC6t3+8H0jOLi4u6\n7sFceYKiBwcHGsuEtctintgvVldXlQnCM+3v7+t4ZnlbeEwx7yzjab+LNl25ckXbg/FaXl5OtB+w\nVTSazab2FYLOp4mxveUHqXfjEPFO4LkWvc2Qg1ex8bDrAYPMGULe4SZLg+oXAVZX94K0+TCRFZCb\nhbRDnQ26Z/VnoFgsBgdadrHwOGGRwEV0/fr1qeaTp2Jdr9e1LXxIw7gyBTytuxVuCl6I78SVzYd5\nwM6nbrcbuLcqlUrCPStytMmKHPUfU/a4rjc3V1dX3Q0eG+S0BynOfrRtrtVquvnipc5ji/lgXTi4\nDo+hPYCw+4NdRTa7zyvzwu323GpZRbC5fXiW3d3doBg1b/T832g/Z53ZteLNK2/dcqUBL8g+K1TA\ny3BdX1/Xly8Cmr0XkGeA8RyzWkkiSde0va/nJhwOh/oC5yw028/YPywwRpyMYY0iD9vb29p+Pih5\nRpP9rN/v68sc8/7g4CDQTWO02+0gy67VaqmBhP1nc3MzoZAv4q/r7e1tVxXcrufhcKhjjeet1WpB\n4snW1pauHxxK9vb23ELTWWAXZdb+icPr4uJiIhlBZLyHWMPt4OBA+5WLuWPusMtzEqJrLyIiIiIi\nIiLimLjljNQ0DMe7DS/VmdOG+XuWgudiquwKsG4Bpsn5XxtAO0kH591ClhYHt99jawDWOskKMOff\ncbC5peU9a5cDd+E+KhQKAVNSrVbVCocFNmkuMQtoCx6nBRZmuZxZZT+r/iE/pw1uZLcQwC4RtgZt\nwWvvt+VyWfsKFhpLInh9BKtydnZW75O0KAQAACAASURBVI0AUM/i7/V6bn0srJVpC5+iz7lwL8Br\nEX3AxXJZ74gpfxsY7en48BzzdJ94vKw1zskh3vPxnMB/e8Hm6KN+v68MCe8daW4MkWQtMW8NeXpo\n3ne8PvcSZDzXvb0v3wN9trOz47qFALjf2E3Gc9+yLbOzs0GfX716NWBZeF1wf9si2Gk12fA9DlDG\nXpQVQtHpdHTewb129epVfXZupxckb13cOzs7iXp5Nsmk2+3qfZgNslUb+L+5QoPtS89NOglYw9Vq\nNXAfDgYDbTP3C/qDPRPTuM8mMVJYl9vb27q+MMbValXbgnnFlUu8/T9tfrhtm/qbERERERERERER\nCdxyRurnBU+IC/BSq9mK8wJdPWFOZnHYj2vh/SYrfffnAS/uxwuCRvtYAZ2ZELZUAVhS3FewfGAp\nHSeZwFZ/Z3ipxAzPcuEkAg/enPHkHmDReHFbHPuQVY/Q60evLcyIsCo57msrmc/MzLjK0WgXsy54\nDp4HuAd+6/VtGnvHbRCZzEixfAizbPw3kSOWolqtupYrx9VYdoLZE49N4ABqq2zOf8cz8Xgw6+Wx\nWXZsWVDU6xtmtTEH8S8zCGzJW8vcYym95Ar+nScL4sWJegwrwPFEsOS5nh8zUxyXKJJkpJgRsTE3\nKysrGtvHgdl2LXEfMEuFWCvEvqTFpnqfewkP3u/uuOMOETlidFmigq9rFf5Z/Jevh98uLCy4+wlY\nMy+gHWAPDMZ1fn4+CJjf29tzr2PjnA4PDwPmitvq7b24/40bN3T/4jYhSJ/FZDk2TiTJGnvrh5Nx\n7N85tplZNMwF/G1nZ+dY9ff+nz1ITQo69g5G1o1XLBaDgxRn3rHmkp2orEvDG5V3kLIbmUe7vlO8\nGyrclUol0BliNwQ2mXeagZlVUHZa9fTjBHN7bjLv8GU3+EKhoJtNWtHoNHjj0e12dUPjwExspCj6\n+dprrwVtL5VKgbJ5u90Osk/L5bLeA3PD05vy5mGlUnFfpviNl4XHsK6/NH0tAAc+u7GKhEkRaIP3\nQvAOO2mGkf07v0Cty4mvyQcQW2h9UmYt7wO2/d688q7X6/UCN673PX65clJCloI/rynP9YjvYd7x\nGPKh0ipgMxDEvLq66mbGWffc/v5+sF9448dzbJLxat8Do9EoqFzhBVeLHBXl5iLe6A/8dnl5OThI\nraysuCV28BtPF+u2227T32CeeGPoGUMHBwfqFsRhbDgc6rzjLDarc4dkBwYXgs/CYDDQQxjvSVj3\nGF/WVwN6vV5QKNo+kwW/q3EQZD1DPBMfFu27BW7dLETXXkRERERERETEMfGeYKQmqX8fB54SsWWf\nisViZiFbz+Jj6twyEl7aMOsXsWXo6RK9E0wqWmq1kzzrotvtup97dLdlJTjdml0T1qXDgeVefcOs\n2m74Pa4tkl64mduF33nK9t717fe4RhXDc+1YLR7PFTMajQI2YzAYBOwY9zv3KfclnsOmb1cqlcBF\nORgM9JmzXLJp7kj8BpZfGqDT4+lTeTUfwcpxwXB8NhwOE6yTp0tmGaETJ06oK4rnh+fatf3W7/eD\nsfbkBXisuc4drF1mx7K0p2y/iBzNSy/om7/r6Vx512PpDKsCz//NBcuzXCvsOsHzehpgniwA+tYL\nLOZ5xfVQ7V7ujd8kpphZKE8OwhZQXllZcdc82BowUxcuXFB9Iy6+beEVKma2dWtrK5CGGI1GAXM5\niRVi9zCYKGZqWGUc97AscavVSsgZHBcY//n5+UDnrtPpBMHrItmeJo+F5vcoWECPkeQKAkiCgC7a\nNExbZKQiIiIiIiIiIo6J9wQjNRgMMoPHPbC4XlZquidNwCnMNvaJrQkb72Rh78sMjCcACjSbTRVT\nw4l/WpmINNFMtlytMBnXAPQCvNlitqwSB+nyc3ineCso2ev1gtgnT8i02+1O/fxWhsIb/263q6wI\nLByOWeB5YNuSpkTvzTHrs2fRQvR92tyBheTFpeC6zEiwMCMzm/jXsq21Wi0QROz1emple/EGXqwP\nxzvZtnixfrlcTgNLPUYK1ifLW3DAcJbaPp6f21oqlbQvYeFeuXIlsF45Vo3Xvx1/jkHJEuvkfYIT\nB2ycFgcZ835i7+vNE35unu+W8ej1egGDMBgMphIU5VgV3O/w8DCIkcrlcsq8oDbd6dOn9b85rgyW\nvqc+zqwXfoO5yIwUPpudnQ0CrHl9op38jB6DzvutXd885uiDer3uxnDhs8cff1xExowUM4giybmB\n/26323L69GkRORLL5eBqFhTle2EtYW5fv35dWVu0eWlpKZEMIJKcT8wq4R5o140bN4L+aLfbQRKG\nJ67M1+G9CJ+Bhdvf39cxAfvFSQlAsVjUNWcliESOxv0LX/iCfOUrXwnaYr/HYKYb83NaGReR98hB\nSuTdLSHD9C27EqyLrVQquWrH0yiW22vj/ydtiAAmN2/W0xwmJn2H3ZVZtOzCwoK2i7/nldaw9Lmn\nIzUcDnVhey9i7+DgIUtHZFrldREJKGyRI2qY9a6wsK3+k8jR2OTzeZfmt4c6vBhEkocXe8BkFws/\nD/4bv11dXdVgU7S50Wjo/dAmnu8InDw8PHSz+7ICoq1RwW3m58TzpM31rPI4mAcrKytBQeTZ2Vn3\ngOcljLBbDZs469fgmfEC9UpSeIG73W43oSwukjxseAHtXGzYGh28r/GYW60d3p/YHW4PYfxC48ML\n2ocX5WAwCDIgOdsW2NvbC9yp3AZgZmYmcJnwGvP0kNhlxwc8XN+u8eeee06zrJAZ6GWpcekXL3kh\nLZMbbbJrwHODpu1TL7zwgoiInD17Vj+zVQDm5+e1sC8bE7gvu5Ywht1uN2gXjyGyBUulkvYJvr+1\ntRUQEXwwQ9/3+31dB3xow3zysuLQH/V6Xf/OLjSruZbP53Xf5PmJPp6U6Yzr2DUtcnRAvnjxonzs\nYx8TEZHvfOc7wXW8wtOY2+vr6zpvca9ptB6jay8iIiIiIiIi4ph4zzBSgBc8mIU0V4DHonhuEqb5\n8VurxcISBlnuPmaksqh4vh9O2VwE2cM0Aasi41O9xzrAYkFK7Pb2dkJ9W8QP4pydndXPcV1W1/Yk\nETjQ2rbFCwT0CsvydbICyycB7oJCoRCoIefzeW0/f2ZdOqxi7aUpo32eS6FerysrcvHiRf0c3+W5\ngb6Cxeml5TYaDbUIYbW12221FjkVO6uemgcuFOzJUDDjloXLly+LiJ8qDnjrttfrueveK9gLeKzS\naDRSppSZKPQR+j6NGUT/gsFMY21smzy9qXK57Cat2Psyk4M5wS4Hzw2B9VsoFNQKZxePZWs4yYGZ\nKftMXpB7q9UKmJ5ms6nsCkIGuB9ffPFFERmn8YMVwbNtb28r84fnaDab6v4C4+S1ZX5+Xv/uzSOe\ns3hO9AGHkQD8XBjnNDYf4/aNb3xDREQ+/vGPyxNPPJH4zvXr1+V973ufiCQZKYwNuzxxn5mZGfed\ngrn6k5/8RESSelO4zt7enrpdMd93dnbcMA7sGZhPd911l4Yj8HWxB2G+7e/va7gE3iHD4VDXCsa1\n0+kE92i321PL1UwT+P3UU0/JF7/4RX12EZFnnnlG/462Ly8vJ6QQRMYsoHX7T9O2yEhFRERERERE\nRBwT7zlG6t2SQfDieTzVX0/Z2gso9Wrt2WukMSaeyKUNSp8UIzYtG1OpVNQSgIXBzwS2QOSIiWK1\naRvT5ClNTwrOQ3941sW04+sxVxxXwaxilkXhWZZsjdt+zeVybjyUJ0lghRvZJ89BmDYQlMX+WCXc\nxmmxOCyzXmgLGKu9vT39O8Zrfn4+YE/TFOJtnBj3KfctntcGsVtgjiFO5Pbbbw8Cz/f393Wu4bpp\ndcC8OCOeY5Z9LpVKATvIkgi4Bsfc8Rq1EguNRkNZG2acvPRty54cHh4GCRy8HgH+fzA1m5ub7jrw\n2DH7bIVCQde/x/J7UhGcbOCxI+gPDmwHA8p75WOPPSYiIk8++aSIjONSEOuHMe/1esqicSKIXcvn\nzp2TH//4x4nPeJ5MqpeGvuLf2L2W+4WFQD0gtggMyPLychBE3u12XXkT/DdiA3n+DYfDYM5y7UmA\nn4PrDGIM0ZadnR1tKzNTuAczttj/wZzz2GDdFgoFnW/eWvHqVzKLj73Ce6cCzBROeh9+6UtfEhGR\nj370oyKSjLnEM7bbbV2vHJeWNrZZeM8dpN5t2OBUET+4jF133gvHvlg8lwxro3h6OXyvd1LM2boj\ncW+0xRa/TWuHLVPBitYcmH2zKuyTCiNngQ8OXjA/kOWuyufzuijRlkajoc/JLyAuLovvY/F5gfcM\n/NaqIoscjdGNGzf0etiA+KCGDa3b7U7MWMP/20yZcrmsmwNvithYvPkCNBoN/Y2nz8JAW6cte4QX\nC5TaGazXlhaQ7cF+zpmDOFjyywcbqJfJJ5IdjI7nbTabQWkakfClxC/CrDIaHPSNZ2d3FMZtZmYm\noQGGz6wrztO+S+s/nm/oFz7c2LYyoEuEgHCRo5c0+nZ3d1cz+ezvRHxtKYbVp/O+f3BwILfddpuI\niKsWDtTr9WA/TtvfgUl6fNYwe+mll9StxsB84jVv5xXPF6880u7ubmCs8Xy3yRoiR270kydPqssO\nfSVy1F+e24+f7eTJk4m2sm4iniOtjzC3OFzCJqhwggwfNm/2vfif//mfIjI+QOLQDAOiVCppW9DX\n3uF0GkTXXkRERERERETEMfH/PSOF07NX2FFEglMx6xJ57Ii9Ll/Dq+uVy+UCBeSbcV966b2edgoz\naqyFIpIsrAkqvtvtBoVr2dpmq91aUF56fLlcDtKe7X/jt547zQbzT6vl5IFrSvEYeoHCHtvmsV0s\n8wB4rijWyREZW21oi2cFsvWEa6NNXgA/W1PMJNqaXayRlVUTkFOYuTisZxl6ystZwN9tejjua2Uh\nRI7mLJ7D1iyzGA6HavWjbyqVij4zrHZPj6jRaARjmMvlEkH8gHVjspq4l2SQtQZ4jXrsONa3Nw+Z\nufJ0rhg2QL1UKiWkP/AdG2bAlRcYNq2cZQjuvvtuERmPAVy7YEKYkeLrgjHh57Bj9PbbbwfJMJNY\nLaBSqQS6Y6yV5z3jJG0huLrOnz8vIiI//vGP3XECg8ShCPgeFz7G/F5eXnaZEvyeGTjLrJ44cUL3\nNuwnb731lj4nWKhSqRTIS/D7jlXvwU7BjXv16tWEzAe+bxl9kfB9yPMOYL0p3J+V8rPekffcc48y\nb2CUr1y5ErCxvO9lyS5Mg8hIRUREREREREQcE7eUkUpTHQemTel/t+5rq6p7AprsC/ZU0YFJ9QGz\nLM2bER/1TtK2nlfaNfm3sKg9ViZL2XwwGCQkGkTGloNls7z7c6wSCwt6FuGkeKSbxSQmA0BbEOfA\nfnU8W7Va1etNSs+Fnx6/ZSubxx/9y2yCrfvGfcpWI9qMGKTbbrstwU6JJIPSs2KavLiYNIE6WKc3\nG6xZrVaDeVwsFtWaxb+7u7uaYo01mjaOHPSNdnvxTmA79vf3g/XHFQbwG0/BmQUlOVbK7l8sdcJ7\nmg325jgXtGlmZkbvwWOCvvEkFLgenf3+4eFhwLbzdRGIvLOzo99DWjszSAzIWYCR4r0EbArvi2BC\nVldX9Te8Z9k+FQlrbV69elXZLsRepdW+tPAU2rkNLMhoWfw0sFguYPe+fD4f7LMPP/ywPP300yJy\ntI4ODg50frNcCQN7L7PBdv1duXJF5zEzTp43wAbnVyqVgNEVOZpTYOLz+Xywx3C/efGJzDjbZzs4\nONB5ydUvpvE6cHINyy/Y3xYKBTl37pyIjBXo8b0zZ86IiMgbb7wx8V76LFN/8+eASZ3ybh+gsu7L\nAZm8AdqDFB+umPK2dKEXvO6pQI9Go4DmfbfAm6qXDcFaUFyGQyS5CTLtbQ805XI5UcZAZLyQ8Rt2\nFVqKu1qt6maVFRw+Go3cA1QWBe/BBnWLJN1k1pW0vr6u/eIFrSLQc3d311XBtcUv8/m8/oZfxtbt\nJhLOfaa1efO3/cIvKlxjeXk5oMT39vZu+mXjZeAAvOHe7EGK3b5c+BYvc958sb5spiOAFwX/Bv2A\nwxD3JSuI47nYlYTf4IVRr9eDIN69vT33kOEFy6L9fA87f70i6F52Kbsj+WXDGlVAlkozJ2twAD2e\nAdfBs7GrmOFlQAE4KHGGJuYVZx+iv/k5+CCCgwO7EZH9iYMUlyvKAs8xXkf22UajUWDwpQEvYTzj\n0tJSMHZeMhGvRfQFv8gvXboUFAPn8Uf/8rV5XlkX9STVfqzHTqejawTP1mw29cCF/YyDtDFejUYj\nKEbM8Iw0DgXBbzm0wDP6rAF05coVnR9o0+Hhoc5j7DHb29s6T3Cg6nQ6OsceeOABERkr6k9CdO1F\nRERERERERBwT77lg85+Xu4+vaVNiGRz4zBa6R5PawE3vND3JvflugVWbYQ2jXWy9e64kDjJF/+PZ\nWP6AmTf8hl029r7HqZ/IsgXWOiyXy3oPrvFkGZq0wEI7Pqw07s07WHRpyutgKWCZM3PJyuVcDBbP\nYV07bGHD8veCmHu9npv4YFPY2bWDPkurFzgNY1qr1QJGdxI4HdkqTPN/o5+LxaIG5KfNnSzXRFYB\ncP4Ma2BhYUEtftaegVzDa6+9JiJjFgxsjefC8FgMXme233is8d8sdYB56ulTFQoFrfPGjIYNQGYG\nDv3DRasBZu84qcRzM6HvmTGFKxb1y7ifMYalUkldT2BWWJ0crNHm5qb25UMPPSQiIt/61rfceWvH\nmIP/GVydQMQvtC2S1FDKwiOPPCIiIl//+tdFZDw3rATE/v5+IM8AZXKRpLvam0/AiRMnAikJlmfB\n/ba3twOZhG63q+8E/Hvt2jW9N+QNGo2GKtBjP3zwwQeV4cKzLSwsKJuFedXtdrUvvZAH3vfAtoEJ\n5eoj6PvRaJS654kcMWYXL14MCkqz+5jZKjwT5uypU6d0n0BfYA5nITJSERERERERERHHxHuOkZqW\nibrZOmIMVkDOCkrn73uMFNeUw2e2/b8INkokGbcC64DTSmGxcNA3Wwcik/ty2u/BgkhLo/fAfcjX\n4L/djDgoxgbxHP1+X/soqzo8Y1K8hFfjzcZLzMzMqJVog1xFfNVpZrPsPB+NRmqJ4jdbW1sBK4d7\nixz1n8eOen3hWe21Wi1g1tISLmydPo5PA5gJAfr9vrJULGQIRs22B3+38X88Pz0hTWB/f1+/e889\n94jIWGCRmSiRJIvGNeNsvCT3hxcM780xXIMtccwJb64PBoPAGseziBz1Cz+v9+yc7GDbwNIZDMhY\ncM1FO/67u7uB0vfi4mIQfM1t4n2U9w4AjAlLwVjpB66lyeyy7cPTp09ru7x16DEiDLDQttYo/41j\nszj2EnMC9xc5YqJqtVrQ1v39fVdyAs+MPpidndX+YiYM8xZjWa/X9R6QnuB2g3V99tlngzXHc5v7\nftrkIKuevr29rb/l2pZgsbHX8B4BBvPs2bM61ngOHgdcjxPH0BcXL17UscFvbWyah/fcQeqdKGrf\nLDh7ygtA5w0Qk5EPYdZFyJsrJoKXJTct0uhqD9xmuwkOBoNUrRnG3NycbnDo+9FopC8jzz3DbgN7\nD6ZvuV9sH/GmyUHuXmYba2OJJLOn2HWCa3svnWnBKrzehuEFCGO8bJkeBpfl4Gezh0mRoxcOv0Ts\nC3lzc1M3B9y/Xq8H5Tu8w39aoWW79rgANWeNev3Ch3XAHuC8gxRfm7OncCAUCV1YfB1eK5iXeM5u\nt6vXwff7/b5u3C+99JLeF2PGmXz2ZbOwsBAE+ObzeTf71AtKt39jTR7uF0/XCPPDU8jGoZ3H2tPp\nwX29PSbt5WhLztRqtSAwfzAYBJlv165dC9xCrVZL3Ut4od19993qyoKrUORoD8UcPzg40HHjsQQ4\necbuhWtra7oncJ9Ou8/CVYR1ydl07J7zsk2xHjFeS0tLiWLPdj2wGj/QbDZ1TuCQduLECd2LvKQZ\ntLXb7bpF170sTZsRure3p/MCrjBOrmDNOm9dYx3iEJ7P57W/uFi7PfTPzs5qf2EeNJtNPfzANcpE\nAt/ftoUTTCaVpmJE115ERERERERExDHxnmOkpk2tZkvD032aFjgB43rFYjGg7Fn7iP9mdXo8V+E7\nYdVuJlibLUyvrewaEhmnzCOtGHj11VddBeeswsr4frlc1vt6tf6AarUauB49dW0PtVpN28BWsxfE\nC3CtKNsHpVJJLRpOB85qv6eDw+DUYL6uyJFWFTNSDFuMOJ/Pq7XI18lS/+X6iWgDp8vDIs2i5Eej\nUSLdHv+C7YCFmKap5a0/KxFSKpVcXTUuzow28/Xs/sAWp+fGw/cffPBBefbZZ0Uk6f6wc9oLsi4U\nCjoOzEzZAsqcCJDFAnE/sEvWWs+cwo57VSoVd4/MqrWHNnU6nSAh5PDw0GXKvIQCsCOY9ydPngzc\n1qVSye1DXI9rDELhG4wUjzMngvDeBqCvuIAykLVvVqtV9++T6seJiHzyk59U9ya+t729rW3g/Qfr\nm4Pxbd8zk+itR9a0Q993u129DvYE1pH61V/9VREZu2Et683SCdb9auGFzqCNuO/KyoquOXy2vb0d\nML/tdluZN8yXRqOh85JrluIeYJ96vZ6uVzBH7XZbxxuuz9XV1cALVCqV3CB+WzA+TTePERmpiIiI\niIiIiIhj4j3HSB0Hnppw1vc8i5mDRG08FCt+878cxIt/p63CPi1sm9PkFGz6axpgfd64cSNT/ZsV\nlfEbtphhheH50phEW1/Qs/imFeRMezbPovBqFHpts7EAaWymlcQQ8cfWKlEzYL15VdtFQmaNWQFu\nn9dXds7u7e1pH4AVbTQaas1CUNB7Ru85qtWqywx41/DaZ1XMR6NRQvDUAtew4pBg2by0aw46t0zw\ns88+G9Qe4+sAaerOmHscT4Lx5HVh5w+3j/9mpVhYRBbX498yi+ExzvZ6HDCO+9ZqtYAdq9Vqwbqa\nVLUBbAUHLHNAsze3WF5CZDwPXn755cR3WNqC9xPME2ZwwDqAVffYYQ+dTkf3IGbi0G9ZlQseeeQR\n+au/+isROZqHHLPEfe8lf2DdY122Wi2N+0oTZMXnLCyMfRuJNLu7u8rWPPPMMyJylCgh4otI43oc\nE8jg6h/4vq020Ov1glglZiMxp/v9fkJOJ+15vXi9XC6n6xXzgGPLEO9WKpUSsXsi4/7z9hYA88lL\nZrF4Tx+kptWU4k1XZLLLwStGzBlJXqaUPax5elPc5knZH9MC7cJizefzrgI1b4aYGKAue72ebkys\ncgw6mLVHvDIg1uVQKpX0+TjLBv2GvmL3J8MGPHuHK3Yp4jlLpZJuQlkBhcVi8aaLVE5yB3sFp9My\n3kT8wwE2O55j2PA4KJ5dnxhLfM8LJhWR4OXabrf1xYRrVKvVTPc3NiAO1scLgwOLvYBVYDAYuONq\nD7vdbjeg9g8PD4M1b1/y+H8eh6zSOqw9gz7k4tG2XWxI4SXNek3YY/L5fFDEm5MrOCzA9gdn93pG\nCq8Pq+GWz+fdl7Tdx7zMRe5LvPg4YBjXSztEYU9gd5UNeK9UKjpH4ZJptVq6HlDu5Wc/+5keSnEN\nfgmj71999dWEa1IkefjDv3x4yTKiNjc3g3mSy+WCDDcPOzs7ej/0QbPZ1EOBzTgTOVrXrBOWpbnE\n4EManu22227T/kWbe71eoKV2+vTpIITi7rvvVlce+h6aZHy9RqPhZnTbTMm9vT29H76/vLwcHP64\nQDH6vlwuB4epNCPbGpje93q9nn7OxvHGxoaI+IXTgWkSsaJrLyIiIiIiIiLimMj9onSMEjfN5X7x\nN70JsMvEWrNpmiuWkWLGBJZ8LpcLgu9yuVxAk5ZKpYAO5jp3HmOW9hnShGEtFAoFtXyygn7n5uYy\naU8P6KN6vR4wbl7dP77/O9H98twV/LdbMce9QtYifko6wCrGsJphtb/yyiv6GQJLr169qhYV2KW0\nMYM1jDFqNps6FznoHKxYluuxVColmCg8F6xYtkgtCoVCEAxrry0yZjfwPQTh7+/vJ9LBAV4jtphq\nuVzWPsH1WNUdWFtb0/ZjLs7NzSXYKeDUqVMiInL58mUREfnQhz4kL7zwQuLZGTzfbVA1uw6z3Mcc\nlO7V7sPzMFvAbIWn8WPBxWNZUZ2lJETG1j3GHWO8vr6u/cduLQvuZ+Azn/mMfOMb3xARkQ9/+MMi\nMmaksHdhjm9ubgas17Vr1xKVF0TGrAe7FUWSteA4qBv9z+w8F7LG81hXMctbIDC7Wq1qADeYKZ5n\nYPNbrVawT6UlmHB1CcsczszM6F4ANrhSqehnHJqBz7i49X333SciokkWXPuU2w2WEIxOWjFfq2nl\n4fTp0+quBPu1t7fn1rmE/AX2tnfLi3Mc0Jp0I88jIxURERERERERcUy8p2Okpg2gtmBhPPa7W6FA\nttDYh4vTOv/Wux7HLQEssIff2e/1+/3A+pwUqG6vy20W8Zk0WI6FQiHwM09io9BmjquxwmhpYIub\n46Vs+7OUnvlvXtyHx3DBopufn1cLjWOMbDxKt9vVayN+ZjAYqBXO8Rfoy0mBibDq0L5CoaBjw32O\n+3G8EeY7Bx7bIF37G5Ex64ExgSXv1VT0hPcqlYr2AadaM2MhkmTgPCYK44a4O36Ora2tIC6O155X\nH4xZVV5ztg5hu90OWMBCoaB9hOe4du1aEEPpsVHFYlEtbjARP/jBD/TvuBeLG3Jfs6yAyJilQH/x\n3Lbsc6fTccU37RppNpvKYoKlGAwGbqyVZafy+bw+O+ba/v6+zkWupefJtuAemGuzs7OBIKY3x5iZ\nRJvPnj0bsOkcl4m/cTwU2u4xwTxH0KaHH35Ynn76aRHx6xbiOvv7+4EiPLcF8+CZZ54JPATMEPLa\nt3t4q9UK9iwb1G/XdafT0XkCtqvdbus8BpN0+fJlbS+zYmCi+HqIGcRYF4vFoJ5fGjwmysYEXrp0\nSZ8Z8573aJ6fYBWnkR+41XjPK674jQAAIABJREFUHaS8wws6ml9iPFGt240PQ15wONOfWQcp1ofh\nAw/+tTR+LpfTxcSL1Qb48md8DwALZTgcalswAUulUqBfw+D2vxNVdZtlYWG1YliJnP+GBc595FG4\nVtWbD4Yc1I/28GaE3+IlnMvllPbGdbwisgyeG2grB5hbrTIPHFAK8ObIJUzgzuJipvg7U/ZchBjA\nIdHrRy9xgEt62Ofd2NjQEhysEI+5hT69ceOG++zWZbe3txckYbDaMbvDMT/5pYXfcBFmngvWZcKu\nbE/jzVOJ5wMNuwNxfRya4ZpoNBo6Dtznnl4SwIdOLwnGfjY7O+tmvtrD0NLSUiJrCt9B//Oaxz5g\n1eBFjuZJrVYLkhJGo1FgmN24cUNdNnihcsKA57ZEP//4xz/WzzC+r776qn7mKUtb9yqDs169agBA\nuVwOClBXKpUgm3FmZiYITOd5hvvxXPOAv83MzAQH4H6/HwTx28oA3l7rVZrAAQRr5MyZM3oYmpS9\nyPpc7xSVSiVIpOh2u9o+Tl6x3+NDpDdnvDJEXt+zvhr6nOcuFPU525fbym3KQnTtRUREREREREQc\nE7+UjBROkczycFo+/oYTN8sLACwBYJWoPVkDPrF6bJEHL32b9aYsI8SsF5+e8T1YsPl8PnBh8bWY\nisdvOJjQXo9xM7pVsEARBM31rfBZtVoNatl1u91A6+jw8NCtewVLJCvFtF6va7+xRWWtGE6tZ1eG\ndbuyrg6+Nz8/r/3mFQXm4NGs+nFZSCsyizYjpXtra8vVxvLU3dEfGKtut6v34efA35mRwPW8dnl1\nEzHmrPEES+7q1asuc2D1objmIp6bLX58L82yt+vRrjEweQjcZQ0gb+7js8XFxcBFxAHKaGu5XNY+\n5DR5y9DUarXMKgz8HPa3rN3D68fuWeVyOdA5a7VaibRytM+uLw5u9nS40P88/zDvvISQwWCgvwUj\n1ev1ErpgImN2BKwH+p7njce64u/sBuW9F8wq14wDwJJ54R83btwICtIWi8WAgVhYWHDnI4Khs3Bw\ncBC47FgPiZlzrqjAfwOy2C7vPYVxndY1Ny1YUd9Tjgd47Xn7I8amWCxqW9FXXCM1K4RlaWkpOBvs\n7+8HSTPs4kd4wc7Ojs6zLM3EaRAZqYiIiIiIiIiIY+KXhpHidH8vxseKpHl1n7zYh9FopKd1vq61\nZDkAfdJnHjzmyAZwl8tlPXFzvIE9ZTPDxvX6rKWZ9ltPOC8r7b5er+vfcW0OKIb1ceeddyoTgXE4\nODgIGJBmszkVQ+PFDLHgIcNaPJ7UwezsrPYvW7mT1NXTUCwWA0u+UCgkFHlxfcu88H+jfxYXFzWA\nGf3NgbuYE+VyWa127m9PFNBjCSzDVKlU1PrjmBJYvt5YYczffvttDTZG345GI2VAwVLt7e25zBbX\n9sM1bA1KZr081XaAGUKgXC4n2g+Wha1OjnURGc9Tm6q9t7eXqH8nkpwvrLxvGV9PLDNNJsWuV547\n6D/veoeHh8H3uL+hYs2JKjbmi+ElkywuLibUw0WSsVnoA09hWsSPucRaYeVt+zdmiDF+HCPDiTeW\nNRY5GiewkSx9gHlqn0tkPPYIrsZ1eS56MaYcm4X7/PZv/7aIjJkfK/3AcawMOyZeQkKaeC3ACt7T\nAnPozjvv1GdlpXnMI7TH2zN5bmfFXHGMsQeM/8zMTDBXWe4Da3l2djYhmYB/WdEc98UaRu1LPgfg\nudfX1/U9kaVYPw1+aQ5SXtYZwAcGprft5GKXmBcYyy8dr3gw7sEaNFjMWRkh7LLjbCybsdBut3XQ\nsUA4M4z7wAtex2/swZDvweDPeONB5hYXCMV3sXB2d3dv+uDhAcHIIkcLB8/k0aneIcpTIPael9vL\nyQG2ZAk/bxa8LEA+XGPTTGs3gA1+YWEhSCIYDAbqduDAcrzoQZ1fuXJl6iLV9kAzPz/vHnKwqWbR\n2oVCQV0YOIQ1Gg3d3PDC2NracgOA7UF0ZWXFDXwGME9nZ2eDNdXpdAIjgV0iIskDlEhyQ+Zr4wCF\nMdzf39frcJtZ28tiUsFxnjPcZpGk4WXnIv+/VzaGgb7EQaHf7wfuFk6QwDxtt9vy4IMPikhSR8i2\nndeU9xxAvV53s7bsGLPyPtq+sLCgYQMcuG3B+wCvBfQLf8Z6ZCLjsbKB+VtbW+pOR9A5B7l7riUU\n/f3qV7+qn3G5FcwXb26z9hbeMUDamLN2l93H+v1+kFE5CZizFy5c0Kw+1unyCmhPg0ajEQS+iyTV\n5tFm4Gc/+5mIjOeOPchwZiPrdWF+8Bq1LnnuS0+xnMcV/40Ddz6f13mEQPRpsgajay8iIiIiIiIi\n4pi45YwUToR82rap5B4lzpQhW644TbLlaqUOWKcF4FM0u12sOrlnhVYqFbXu8RzswmA2yAY+V6vV\ngO3igGZWfLWMRLFYDKxe/u+04rEcNP5uAtZRrVZT6xGWPBfT9drFOijTMi8A+qBYLOq1Ycltb29P\nVStpeXlZf4u2cwICW/zAtEq7H/jAB0RkTKHbgqhpEhRgE5jFy1Kiz0qK6Pf7Aet07tw5te6yaO21\ntTVtgyeX8NJLL+l/gw3yUtIBZpCy9H481W6uAoBrWHYE/ZlVo44DgPn7uBbLAXhMlO1r/i36YBJD\ncO7cOREZMwM2HMELIvcC0Eulko4dMziYO+wKRL+xmj2YKK/emMdIoF+8Prnrrrvk+eefz3xmAO0H\nk+B5IbzxZ2AdcRUFMGLsymYGDO3nOYG9nsfLehJ4z0dfMbi4spXL4bkC9rPVagVB/fyMrHrPa9P2\n07SFmNMwbRA6J3iJjPcB67JdXl7WPv/hD38oIsl9xQv2z5Lh4X2W5zb6/8KFC6nXm5ubSzBMIuN+\nxryF7lez2dT+x9rikBG4EadBZKQiIiIiIiIiIo6JW85IWcG0NNVrWwme61axKKFNheT/5pO1tSrZ\nssX1yuWyMhscbGqRJqpp47XYyuIK7bBAOCWf1dXRFhbixL2sIGO1WnVr6YFN4Odk1otju0T8uKRy\nuaxWB/ql1WppHAKsujRrHJagZxF6wagcX2XTbRuNRhBovb29rdZNVoxCr9cLgpq9OCHvGjMzMwGD\nMDs7GyilixwFP7Iat2VSvNgqjvVDX83NzalVx32UxUR5sWiIY3rggQfk29/+dupvgfX1dWVZeN6B\n1WQWA8+C5/XmUKfTCZIwRCSI9RgOhzrWHFxtxSO5cryIH79jBUCZoWHYAHRWzeYgWGtdMyPFdek8\nJhTBzWxRW/FN7iNc9+DgIJBx6Ha7QTAySzag31jqwJMIYAFLAGPIsWNZweY25sf2C7M8rJouMmZW\nsmoAeorufF3LWM3Pz2scKDNSmMd43nK5rMzgU089pd+za4r3M0+8FnUWRSRgTBksFcLinCJ+ZQKe\n76z+//MCMz/MqNm9ygtwv3HjRlAf9O23385ku7EXvfXWW5n7GDOD2BP4HgCusbe3F7B1pVJJ2+/F\nTfH8wzzB2pqm5uwtP0hZtxYfmmzBYPxdZDyonk6ThXcw48XnqZ1jMXMGlqdcbNvEbbbPJOK7I/kQ\nxs9pD1fsjvQKtvKGyoHWAA4Fi4uLCVpcZDwG3mTBwsfk7fV6uqlkLZByuaz3yFogy8vLeh30a7Va\n1RcaH9DwTGgnfzZtYCS/ALOyYfBSbzQaunHivrxwgU6n476MkAGDse71elO5A+v1ekL/RCQZBDkt\nvL5HAOXBwYH78rO4++67g1IO1WrVdStgzuK6aQcpO3eq1arOMcyvfD6v6wbjViwW3WfyXr5oQ6fT\nSSjQi/hrbjAYBDpYPF/s3sDgQxnGiMv44GV4eHio2ZhZRh3fjxX1vQxim5XW6/USCv4i40QFjDsO\n1ZwxywHlmO9sRHgB9zYby9s/OLyB+54Ltov4Sul4PhHf2GClfO8g6pVqsskzS0tLGmzOyNpPPBcV\n+rFUKgXFktOAeeKFOfC+zfPIzr0HH3xQ24P7djqdIAwlrUC1LYJcr9f1Nzxe0yTmiByt97Nnz4qI\nyB133OG64OyzLS0tBUbxYDAI2s1hBDeLtL0TbeDyYShjhPcQl7VKQ3TtRUREREREREQcE7eckQLY\nXWKL0LKEgVeslosIWzcep1Gyu8wyOaxBhb81m003ENIGQTLlzG4I1mTC/S1lz5pWzKh5Qe02gNHT\nm/IsV5EjVmlnZyczyA8sHNeDygoe9jApLRxglxO7TnA/Hmv0If6t1+tBSvek4Ekea8scLC4u6n/D\nck2rO2XZLK4j6Lkm2e2bJZOQlco8TcA8YINlGawqDcs1K2D98PBQqXxeC1kyFZ7VjjZ1u91g3KrV\napA63+/3g/lZKpUCliuXyyWsdqsz5GnB8W94HngSJ/ge+p9V2FkzyLqfOKCdXRNWh82zlDmwHBiN\nRtqHXNPOzhV29+Nfri1p5SFsv1iUy2WXbbLj7z2HxzLNzMzo/OD72T1tMBi4Ol0Ar0HrPtzb29O9\ngJk4uDcZXOcPsAwXeys4sNwijTUGO4Z6fvxMvEZtwW3+HrcBePbZZ+Xee+8VEUnIkfC+JJJMXvL0\nGjEnDw4OpmafsgAZl0cffVQ+85nPiMiR1MG1a9d0P8FnH/vYxxLvL5HxHoI5+8Ybb7zjNuXz+UTB\nZpFkMD/+bbVaymaDmQIjm3n9d9zCiIiIiIiIiIj/T5HLimH5ud00l9ObwrLguB5PndxazZ46ucfu\ncAwCp1FbC4hji5jpsQwSB4zzfe31OA6LA+Rt4KYnC+DFQImEgYzdbletHY6l4LgvxLKkCYnivrCk\nPSvxZsE1AFnpGf3AytHTxDeVy+WpWS6A41KsYnyv10vUdBJJr5WH34At6na7qZIFjNFoJA899JCI\njGMFRESeeOKJQCWaWVQbT2JhmVCOQWFZjTRpAL4G+/294FGwaevr69pHHFOFmDBmNsCUgCXx5g8z\nP4An7cCfecHGQKlUSgTmc00/wIrb9vv9IMiXg8NZwd2LSwQ4dshejwPVgZWVlUCVnNkWMANp4pZW\nNZuRJRUgcjQ2GGsOGEdgNsuioC38DB5jiv6ZnZ11Y+JsEpE3J7gtfF+vDYA3J5hxRj9kXePMmTP6\nLBi3fr8fMM48NxBTxWrg3j34GllzbRIQZ8kxmOjDkydPaswms7x2LnDMJdYAi9v+MgGs0ezsrPZr\nlqehXC67iV4eM4h9AGMzNzen4zBJSoLmrRvxf0sOUiJyS24aEREREREREXFMuAep6NqLiIiIiIiI\niDgmbkmweZaicVqRRwC03MrKipuKDrcR9EGg3mvvj2KGCKTsdDoubWvdfWm0IWDrV4kc1Vq7cuVK\nZiCwp7+D+1er1YTLQWRyEd6FhQWlrr0+92oOpbkV+b78W/4Na3FY6n2Sy2kSslwXXno20+k2uJnd\nRjawlO+Vz+f1N5gvCECcBE4s4H7ziht74wjXJOZaWuCudfewq/hm5RJEQvXkfD6vfcSp2jZYeWlp\nSduC+cuJDUy7Z9WMw/dKpZL2EbfJ1gLj4OrhcKjri2uU4Tqe64nvD7eCpymGumTtdnuq5IszZ864\nyQpeUVi7rofDofYHu67+5E/+RESO9qcnnnjCvbetUTgcDuWee+4RkaM+8LR07rvvPnn00UdFROTv\n/u7vRMR3ia2urupehYBhT5rCSxZpt9tBmAbvB3BBLi0t6TiwKxFjhO9dunTJTXz40Ic+JCIiP/jB\nD/SzLBcx/tbv97XdHKiO8YAOGNfk43eJ3d953WZpZYkcjRun5OO+7XY7scZFpqsBZ69t1yPjOGEd\nrAOJfvUkh96J54v30Wmuw6El+P5x9kLGpPtGRioiIiIiIiIi4pj4pZE/8GrreLWdJlW7xqmYGSFY\nTziVzszMKEvEAZaW4eLAWBvUK5KsjYWTOd/3/PnzIpKsXu+lHdvnZcuZA6RhqUxSWsV10gLMJ1kj\ngCcUyoyBSNLi8pSH+f/BHHl94OE41gyYKE/8NEt1mH9jVc9Fklakx45lMY0MG2w8MzOjcxr9MhqN\nNDD15MmTIjIOhrTjztY9B1yjDZj39957rzILLJDn9a9li4bDYWDxe2KHW1tbbtC6xWg00jHi/rVs\nUb/fD8RL0+auba/I0Vr32IpisahsA9Z/r9dTIcH7779fRJKMDwJez507F4hb8rOAKfn85z8vf/3X\nfx3c22NhrDgw2iiSrM/2H//xH4k2Lyws6P04td4mLVSrVZ1jWXvHcDh0g74t6vV6IFLIrDYnd4Dp\n8VhXGwDP/726uqptBra3t3UPASuXtt4wRvAybG5u6nziChGeeDGA8d3Y2JDf+q3fEhGRv/3bvw2+\nh71weXlZJRZ4P+VaoICXOIJ2YS2wcv20welcFYHnvpWISJPJ8ZI+rDA2s3Ye08NzJ2u9en3OQrQ2\n+apYLGpbMC+5Mgiel+ex977mZDJ7XxYg9ZLP0nBLDlLei4g/w2LxFjFcK5ztlPUiWF1d1QmKIoRc\n4gJYX1/Xz7xDgpctyNkC9kVQq9V0YLFh8AEC96jVajpwXjFh3GNtbU03GX5evCxZERb09ySVXe4r\nr5SD/d7s7GyiBIaIrwtkr402e4c1W5ZDxJ8fWVldnHFhDyCs8eO5x/i63j0APlxZmlzkyDXAL/9p\nDoLtdls3SZQ9KBQK6j7Cv7fffrsekLEGer2eu0YAzMlnnnlGP3v44YdFRORHP/qRW/B2Grert6kX\ni0Wd514mDG+unro7l0JCO3hjFPFdD1z6Ae0QSbpx7Wbf7/f1BYu27u7u6sv5d37nd0Rk3G/24PHi\niy/qcwKcVYoXeLFY1L5++umnE/fm+/LzMTA2WB+5XC5Yz+fOnZNPfvKTIiLy53/+58E1+KCCg1bW\nXNze3paf/vSniXZ6WF9fd3WugGndKKyEbbWCXnzxRfn0pz8tIkdrYHt7OxF2kdW+b33rWyIi8sd/\n/MciIvLNb35T5x33i90TSqWSu3d9/etfT70fxujtt98Ofnvy5Eldo7z/2+9tbGzoekG/sLbhJLD7\ni+c+PgP4et57DICxUyqVErpLIuO1inWGA/XCwoL2ZdYYsX5VlptxOBwG+0xa5radb6w7OclgyYKn\nDZj63amuGBEREREREREREeCWMFJ8IgR7AmthOBxmugiyaqSVSiW57777RETkueeeE5Gx1WFPoGtr\na2rlwMpnNihNbRjttS5Aj+m4/fbb1f3Iljnux8GmntUByxzBqc1m070PrA/0S6FQUCvBY6QmBs2R\nbpZlp9KUrbOsVwb6A8GZvV7P1csBg8jMhXXPeYWiGWzNgD1DsCarBPPcsPPEK/DMVDID9+A2MyUt\nkgwwR192u12dbzxeNjD1jTfe0N+DsWWFdrSPA1Qx71jDC+uiXq8HbutcLpdZcDgL/X4/qFF1/vx5\nZYHxvCdPntT1zesc45tVi9CzJO1aRV+jD7imHF/7hz/8of4dwN8xTz/96U/LV77ylcT1R6NREIy+\ntramzw5L+Cc/+Ylbf9FzrXkFWG3CALs68Nn29nbAjjGgX7a1tTVVDbirV6/quGdZ4XNzc67atLeP\n2bqed9xxhwZqo7/feOMNOX36tIgceQ1ERItqY82fOnUqwcZaYN6x6xlB85/73Ofke9/7nogcjQHv\np2Aod3Z2dAzBgPCz8hjAFYxwjqWlpaCo9urqqrbVC2lA8HqtVpsY8pClsO2tDeyL1WpV3wlo1/7+\nfkLlXCS5n3hrBuBxxhz35rqIryNm5wnv5awDibXJHoVpapVOYpzYxWfbMjMzk/DuTItbGiNVLBb1\nReEtDExKkaMJwC8jW0rmgQceSNDo9ntWhJGvy6JwnMmFCYjFyZudF0ewsbGhbcKGywcNuJwmFWDE\ngoXPPW1yINYLz9ZsNlWiPw1ZLieOybLf58/wzDwZOSvG3oPjzfCC5/FFVtFLL72UWcSV/997Dq6W\nDti5Va/X9VDlHczsc4sk50yWj90Djz8WMcfA4GXIWUqe+4zLj9h2eBsf5me5XA7cZN71+fnxktjb\n23PjEfHyh5jjzs6OZoLhcHz9+vXgQHhwcBBklRUKBW0rDtnD4TB4+ZfLZe0rzLnd3V3XpYxNeGlp\nSecb9w3WF8fu4L/xss5ymzKGw6Fm9eFlfvnyZX258Nz2gL6GW5DLlqAP1tbW5PHHHxeRozIlL774\novzFX/xFartwYPj4xz8eZPjV6/VE6Ro8h41lOn/+vK4fxIm9+eabQbkV76XEwJzlZ0N5k4sXL7ph\nDdZAKxQK2hYY4GykckiG3RsuXLiga88bV8zPRqMRhCCwSwn7yx/8wR/ousBBa2trSz7wgQ+IiMgL\nL7wgImPD5dSpU8H9sFdykW5c2zuULCws6LVvFp1Oxx2vaQ8MXua6hff+5vJMWQZ8sVgM3s3D4VDH\nid81GHcucmxFczmMxHMfshFt3Yw3a0Dqsx7rVxEREREREREREbeWkeKAUgasSZzQ+TSN0+na2lpg\nxXBGErsoYBXbsiX8GWfFMfsAupVpVViOnoUDi86zKvL5fEDfzs7O6v04Y8bq5Yj4Vhj672YKO3qF\nk205Di4hwKwSrHp2JbJbFrAWSK1W0+fEszErwi6KSWyJ/YytT1uYcjgcTmVllMtl13XqwbPgpmEv\neK7Dym21Wvo55uzdd9+tlirrgOG+WcHalUpF+88rWcGMFOY77l+pVLSv8O+pU6fUosYcazabysxi\nDS4uLiorg/W6v78fjGWarg4+Z/bLsgqHh4cBa1wqlRLjwQGx+C3uA9ZrOBzKr//6r4uIyH/9138l\n+gDPJzK5CDaQz+eVmcN9d3d3td2T3GrYK973vveJiF9I99q1azq3P/GJT2j7PC09APd95JFHgr70\nCkB7pZhmZ2d1XNHPno6Wl901KUgX1zt79qz2NRi9RqOhAfLYo1mXyyvpgWe8cuVKsJe/8sor8tGP\nflREJFGEG+8aDjFAP4NJfPnll4P5+frrr6uLkrXcoKvFsP1VKpX0vmANm82m3HnnnWEn/V+srKxk\nZlx6iRie1h8wqSQWXwu/nRTCYdmdNJbeJoT0er1grpRKpaDcW7fbDcadM/m4SLgteTY3N5fQlhMZ\n97nVQDw8PNTP7P6Y+ewTvxEREREREREREeHiljJSHsvACqmeRYPga2aj2BoHIwRra3Z2VlkWnOg5\nDgCWer/fd0+eYErYuocF4qkDZ2E4HOqzseYKTr5sbeMzWED1ej04jVcqlSAw3yuWOgnMSHEgtR0f\ntEnED65miQBrjaSlrt51110iIvLUU09ltjFLwgDMZbPZ1D7EuG1ubgbzyGO8CoVCEBiZZlF5z3Kz\nyr3MjmJc8W+lUlF2CnNtUlFNWJitVkt/g7lt44gA25etVkv7AJbc5cuXdS2BMWm1Wsr+oo9u3LgR\nBGEXi0W9ryeRgXFYWVnRWD/07auvvupazZ7quAc8W7PZVOsffcosFdgHXkdg1Cal2oPhqlQqOt/Q\nH81mU+MrcW2WWGFgjSM262Mf+5gGw6OPut2usjaIs5xUzBv7QLPZ1OfEWtjY2FDmC2PiXeupp57S\n6yAg2xuDwWCgc4ZVpb15h7aA0RmNRtp/CL5mVm7aoF9er94+YfUERY7mNHsKsNbALs3OzmqbwVz9\n+7//u34f8YLNZnOqvbfX6+k7iJXu8T6Bx+Ouu+6S73//+yIyZtS4soDFpLhSy+54exuz3vZzkdCT\nwX/jSg5Yb51OJ2CB+D2bFavb6/Uyq1lw0H8WY8QSOVl7KOZLpVLR/XNSQXvGL40gJzAcDoNAPJEj\nmtXb3BCsydQvNrlSqRQcNlgPCfCq0s/NzQXuqIWFhaAN5XJZX+Zo5+bmppthiHZh4c7OzgbUf6FQ\nCFyD3gJdWVkJaGMvo8zC00mxQmcevL+lBRnaeywuLmof8sS0QZAPPvigZsHwy9dSzhzcyoc5zJms\nDBdOLADSKsZnLWbenNLET/lfvkbW4u92u1O7GT3ANTUJXrKE95yYq9CjqlQq6oJB33e73eCZ+P+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PwW3wODycw/2KBOpxOMWaFQ0PXI8Bguj5nGfoE102g0tC+xvuv1uvab1y82TlXkSFqGa5D+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\nQeA3mUxG9ybGqlgsmv4mP/rRj0RE5A9/+IN+B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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "feat = net.blobs['conv1'].data[0, :36]\n", - "vis_square(feat)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* The fifth layer after pooling, `pool5`" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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d6fwfzz33XNa6H/3oR7UeN7r2jh49msQmJiaS2LVr12rNZXNzs3jbfjUZN+Hi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\nos+lqLE+V9S4/fTTT2dte+7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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "feat = net.blobs['pool5'].data[0]\n", - "vis_square(feat)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* The first fully connected layer, `fc6` (rectified)\n", - "\n", - " We show the output values and the histogram of the positive values" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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NZ03jXHv2o7Vr/aW1cqW/tCTplFOyLZ+nBaGp27mp6xWDY49Nd2e9j3Mmk5RW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\nFLIAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "feat = net.blobs['fc6'].data[0]\n", - "plt.subplot(2, 1, 1)\n", - "plt.plot(feat.flat)\n", - "plt.subplot(2, 1, 2)\n", - "_ = plt.hist(feat.flat[feat.flat > 0], bins=100)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* The final probability output, `prob`" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "feat = net.blobs['prob'].data[0]\n", - "plt.figure(figsize=(15, 3))\n", - "plt.plot(feat.flat)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note the cluster of strong predictions; the labels are sorted semantically. The top peaks correspond to the top predicted labels, as shown above." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6. Try your own image\n", - "\n", - "Now we'll grab an image from the web and classify it using the steps above.\n", - "\n", - "* Try setting `my_image_url` to any JPEG image URL." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# download an image\n", - "my_image_url = \"...\" # paste your URL here\n", - "# for example:\n", - "# my_image_url = \"https://upload.wikimedia.org/wikipedia/commons/b/be/Orang_Utan%2C_Semenggok_Forest_Reserve%2C_Sarawak%2C_Borneo%2C_Malaysia.JPG\"\n", - "!wget -O image.jpg $my_image_url\n", - "\n", - "# transform it and copy it into the net\n", - "image = caffe.io.load_image('image.jpg')\n", - "net.blobs['data'].data[...] = transformer.preprocess('data', image)\n", - "\n", - "# perform classification\n", - "net.forward()\n", - "\n", - "# obtain the output probabilities\n", - "output_prob = net.blobs['prob'].data[0]\n", - "\n", - "# sort top five predictions from softmax output\n", - "top_inds = output_prob.argsort()[::-1][:5]\n", - "\n", - "plt.imshow(image)\n", - "\n", - "print 'probabilities and labels:'\n", - "zip(output_prob[top_inds], labels[top_inds])" - ] - } - ], - "metadata": { - "description": "Instant recognition with a pre-trained model and a tour of the net interface for visualizing features and parameters layer-by-layer.", - "example_name": "Image Classification and Filter Visualization", - "include_in_docs": true, - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" - }, - "priority": 1 - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/Notebooks/Caffee/01-learning-lenet.ipynb b/Notebooks/Caffee/01-learning-lenet.ipynb deleted file mode 100644 index 1c32826..0000000 --- a/Notebooks/Caffee/01-learning-lenet.ipynb +++ /dev/null @@ -1,1288 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Solving in Python with LeNet\n", - "\n", - "In this example, we'll explore learning with Caffe in Python, using the fully-exposed `Solver` interface." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1. Setup" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* Set up the Python environment: we'll use the `pylab` import for numpy and plot inline." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from pylab import *\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* Import `caffe`, adding it to `sys.path` if needed. Make sure you've built pycaffe." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "caffe_root = '../' # this file should be run from {caffe_root}/examples (otherwise change this line)\n", - "\n", - "import sys\n", - "sys.path.insert(0, caffe_root + 'python')\n", - "import caffe" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* We'll be using the provided LeNet example data and networks (make sure you've downloaded the data and created the databases, as below)." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading...\n", - "Creating lmdb...\n", - "Done.\n" - ] - } - ], - "source": [ - "# run scripts from caffe root\n", - "import os\n", - "os.chdir(caffe_root)\n", - "# Download data\n", - "!data/mnist/get_mnist.sh\n", - "# Prepare data\n", - "!examples/mnist/create_mnist.sh\n", - "# back to examples\n", - "os.chdir('examples')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2. Creating the net \n", - "\n", - "Now let's make a variant of LeNet, the classic 1989 convnet architecture.\n", - "\n", - "We'll need two external files to help out:\n", - "* the net `prototxt`, defining the architecture and pointing to the train/test data\n", - "* the solver `prototxt`, defining the learning parameters\n", - "\n", - "We start by creating the net. We'll write the net in a succinct and natural way as Python code that serializes to Caffe's protobuf model format.\n", - "\n", - "This network expects to read from pregenerated LMDBs, but reading directly from `ndarray`s is also possible using `MemoryDataLayer`." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from caffe import layers as L, params as P\n", - "\n", - "def lenet(lmdb, batch_size):\n", - " # our version of LeNet: a series of linear and simple nonlinear transformations\n", - " n = caffe.NetSpec()\n", - " \n", - " n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb,\n", - " transform_param=dict(scale=1./255), ntop=2)\n", - " \n", - " n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, weight_filler=dict(type='xavier'))\n", - " n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n", - " n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50, weight_filler=dict(type='xavier'))\n", - " n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n", - " n.fc1 = L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))\n", - " n.relu1 = L.ReLU(n.fc1, in_place=True)\n", - " n.score = L.InnerProduct(n.relu1, num_output=10, weight_filler=dict(type='xavier'))\n", - " n.loss = L.SoftmaxWithLoss(n.score, n.label)\n", - " \n", - " return n.to_proto()\n", - " \n", - "with open('mnist/lenet_auto_train.prototxt', 'w') as f:\n", - " f.write(str(lenet('mnist/mnist_train_lmdb', 64)))\n", - " \n", - "with open('mnist/lenet_auto_test.prototxt', 'w') as f:\n", - " f.write(str(lenet('mnist/mnist_test_lmdb', 100)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The net has been written to disk in a more verbose but human-readable serialization format using Google's protobuf library. You can read, write, and modify this description directly. Let's take a look at the train net." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "layer {\r\n", - " name: \"data\"\r\n", - " type: \"Data\"\r\n", - " top: \"data\"\r\n", - " top: \"label\"\r\n", - " transform_param {\r\n", - " scale: 0.00392156862745\r\n", - " }\r\n", - " data_param {\r\n", - " source: \"mnist/mnist_train_lmdb\"\r\n", - " batch_size: 64\r\n", - " backend: LMDB\r\n", - " }\r\n", - "}\r\n", - "layer {\r\n", - " name: \"conv1\"\r\n", - " type: \"Convolution\"\r\n", - " bottom: \"data\"\r\n", - " top: \"conv1\"\r\n", - " convolution_param {\r\n", - " num_output: 20\r\n", - " kernel_size: 5\r\n", - " weight_filler {\r\n", - " type: \"xavier\"\r\n", - " }\r\n", - " }\r\n", - "}\r\n", - "layer {\r\n", - " name: \"pool1\"\r\n", - " type: \"Pooling\"\r\n", - " bottom: \"conv1\"\r\n", - " top: \"pool1\"\r\n", - " pooling_param {\r\n", - " pool: MAX\r\n", - " kernel_size: 2\r\n", - " stride: 2\r\n", - " }\r\n", - "}\r\n", - "layer {\r\n", - " name: \"conv2\"\r\n", - " type: \"Convolution\"\r\n", - " bottom: \"pool1\"\r\n", - " top: \"conv2\"\r\n", - " convolution_param {\r\n", - " num_output: 50\r\n", - " kernel_size: 5\r\n", - " weight_filler {\r\n", - " type: \"xavier\"\r\n", - " }\r\n", - " }\r\n", - "}\r\n", - "layer {\r\n", - " name: \"pool2\"\r\n", - " type: \"Pooling\"\r\n", - " bottom: \"conv2\"\r\n", - " top: \"pool2\"\r\n", - " pooling_param {\r\n", - " pool: MAX\r\n", - " kernel_size: 2\r\n", - " stride: 2\r\n", - " }\r\n", - "}\r\n", - "layer {\r\n", - " name: \"fc1\"\r\n", - " type: \"InnerProduct\"\r\n", - " bottom: \"pool2\"\r\n", - " top: \"fc1\"\r\n", - " inner_product_param {\r\n", - " num_output: 500\r\n", - " weight_filler {\r\n", - " type: \"xavier\"\r\n", - " }\r\n", - " }\r\n", - "}\r\n", - "layer {\r\n", - " name: \"relu1\"\r\n", - " type: \"ReLU\"\r\n", - " bottom: \"fc1\"\r\n", - " top: \"fc1\"\r\n", - "}\r\n", - "layer {\r\n", - " name: \"score\"\r\n", - " type: \"InnerProduct\"\r\n", - " bottom: \"fc1\"\r\n", - " top: \"score\"\r\n", - " inner_product_param {\r\n", - " num_output: 10\r\n", - " weight_filler {\r\n", - " type: \"xavier\"\r\n", - " }\r\n", - " }\r\n", - "}\r\n", - "layer {\r\n", - " name: \"loss\"\r\n", - " type: \"SoftmaxWithLoss\"\r\n", - " bottom: \"score\"\r\n", - " bottom: \"label\"\r\n", - " top: \"loss\"\r\n", - "}\r\n" - ] - } - ], - "source": [ - "!cat mnist/lenet_auto_train.prototxt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's see the learning parameters, which are also written as a `prototxt` file (already provided on disk). We're using SGD with momentum, weight decay, and a specific learning rate schedule." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "# The train/test net protocol buffer definition\r\n", - "train_net: \"mnist/lenet_auto_train.prototxt\"\r\n", - "test_net: \"mnist/lenet_auto_test.prototxt\"\r\n", - "# test_iter specifies how many forward passes the test should carry out.\r\n", - "# In the case of MNIST, we have test batch size 100 and 100 test iterations,\r\n", - "# covering the full 10,000 testing images.\r\n", - "test_iter: 100\r\n", - "# Carry out testing every 500 training iterations.\r\n", - "test_interval: 500\r\n", - "# The base learning rate, momentum and the weight decay of the network.\r\n", - "base_lr: 0.01\r\n", - "momentum: 0.9\r\n", - "weight_decay: 0.0005\r\n", - "# The learning rate policy\r\n", - "lr_policy: \"inv\"\r\n", - "gamma: 0.0001\r\n", - "power: 0.75\r\n", - "# Display every 100 iterations\r\n", - "display: 100\r\n", - "# The maximum number of iterations\r\n", - "max_iter: 10000\r\n", - "# snapshot intermediate results\r\n", - "snapshot: 5000\r\n", - "snapshot_prefix: \"mnist/lenet\"\r\n" - ] - } - ], - "source": [ - "!cat mnist/lenet_auto_solver.prototxt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3. Loading and checking the solver\n", - "\n", - "* Let's pick a device and load the solver. We'll use SGD (with momentum), but other methods (such as Adagrad and Nesterov's accelerated gradient) are also available." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "caffe.set_device(0)\n", - "caffe.set_mode_gpu()\n", - "\n", - "### load the solver and create train and test nets\n", - "solver = None # ignore this workaround for lmdb data (can't instantiate two solvers on the same data)\n", - "solver = caffe.SGDSolver('mnist/lenet_auto_solver.prototxt')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* To get an idea of the architecture of our net, we can check the dimensions of the intermediate features (blobs) and parameters (these will also be useful to refer to when manipulating data later)." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[('data', (64, 1, 28, 28)),\n", - " ('label', (64,)),\n", - " ('conv1', (64, 20, 24, 24)),\n", - " ('pool1', (64, 20, 12, 12)),\n", - " ('conv2', (64, 50, 8, 8)),\n", - " ('pool2', (64, 50, 4, 4)),\n", - " ('fc1', (64, 500)),\n", - " ('score', (64, 10)),\n", - " ('loss', ())]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# each output is (batch size, feature dim, spatial dim)\n", - "[(k, v.data.shape) for k, v in solver.net.blobs.items()]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[('conv1', (20, 1, 5, 5)),\n", - " ('conv2', (50, 20, 5, 5)),\n", - " ('fc1', (500, 800)),\n", - " ('score', (10, 500))]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# just print the weight sizes (we'll omit the biases)\n", - "[(k, v[0].data.shape) for k, v in solver.net.params.items()]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* Before taking off, let's check that everything is loaded as we expect. We'll run a forward pass on the train and test nets and check that they contain our data." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'loss': array(2.365971088409424, dtype=float32)}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "solver.net.forward() # train net\n", - "solver.test_nets[0].forward() # test net (there can be more than one)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "train labels: [ 5. 0. 4. 1. 9. 2. 1. 3.]\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# we use a little trick to tile the first eight images\n", - "imshow(solver.net.blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray'); axis('off')\n", - "print 'train labels:', solver.net.blobs['label'].data[:8]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "test labels: [ 7. 2. 1. 0. 4. 1. 4. 9.]\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "imshow(solver.test_nets[0].blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray'); axis('off')\n", - "print 'test labels:', solver.test_nets[0].blobs['label'].data[:8]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4. Stepping the solver\n", - "\n", - "Both train and test nets seem to be loading data, and to have correct labels.\n", - "\n", - "* Let's take one step of (minibatch) SGD and see what happens." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "solver.step(1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Do we have gradients propagating through our filters? Let's see the updates to the first layer, shown here as a $4 \\times 5$ grid of $5 \\times 5$ filters." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(-0.5, 24.5, 19.5, -0.5)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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jFTwwn93j7J28IyBzl56sTpaurot9FallO3DApo6nS85qMkS9O7qfyl2WnLeCHYvQYu8o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\nDppK7vaW09lj2ulwVUYVfUUegxuDGe4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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "imshow(solver.net.params['conv1'][0].diff[:, 0].reshape(4, 5, 5, 5)\n", - " .transpose(0, 2, 1, 3).reshape(4*5, 5*5), cmap='gray'); axis('off')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5. Writing a custom training loop\n", - "\n", - "Something is happening. Let's run the net for a while, keeping track of a few things as it goes.\n", - "Note that this process will be the same as if training through the `caffe` binary. In particular:\n", - "* logging will continue to happen as normal\n", - "* snapshots will be taken at the interval specified in the solver prototxt (here, every 5000 iterations)\n", - "* testing will happen at the interval specified (here, every 500 iterations)\n", - "\n", - "Since we have control of the loop in Python, we're free to compute additional things as we go, as we show below. We can do many other things as well, for example:\n", - "* write a custom stopping criterion\n", - "* change the solving process by updating the net in the loop" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration 0 testing...\n", - "Iteration 25 testing...\n", - "Iteration 50 testing...\n", - "Iteration 75 testing...\n", - "Iteration 100 testing...\n", - "Iteration 125 testing...\n", - "Iteration 150 testing...\n", - "Iteration 175 testing...\n", - "CPU times: user 12.6 s, sys: 2.4 s, total: 15 s\n", - "Wall time: 14.4 s\n" - ] - } - ], - "source": [ - "%%time\n", - "niter = 200\n", - "test_interval = 25\n", - "# losses will also be stored in the log\n", - "train_loss = zeros(niter)\n", - "test_acc = zeros(int(np.ceil(niter / test_interval)))\n", - "output = zeros((niter, 8, 10))\n", - "\n", - "# the main solver loop\n", - "for it in range(niter):\n", - " solver.step(1) # SGD by Caffe\n", - " \n", - " # store the train loss\n", - " train_loss[it] = solver.net.blobs['loss'].data\n", - " \n", - " # store the output on the first test batch\n", - " # (start the forward pass at conv1 to avoid loading new data)\n", - " solver.test_nets[0].forward(start='conv1')\n", - " output[it] = solver.test_nets[0].blobs['score'].data[:8]\n", - " \n", - " # run a full test every so often\n", - " # (Caffe can also do this for us and write to a log, but we show here\n", - " # how to do it directly in Python, where more complicated things are easier.)\n", - " if it % test_interval == 0:\n", - " print 'Iteration', it, 'testing...'\n", - " correct = 0\n", - " for test_it in range(100):\n", - " solver.test_nets[0].forward()\n", - " correct += sum(solver.test_nets[0].blobs['score'].data.argmax(1)\n", - " == solver.test_nets[0].blobs['label'].data)\n", - " test_acc[it // test_interval] = correct / 1e4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* Let's plot the train loss and test accuracy." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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tVTHRhi302f4ujBpV6KY4juMUE41VdXe4oKofBgVME5HEAnpNRIbWqmlFwmHv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\nS+3661Nh9gMGwD332HhWz56Zj9Wnj103LkD5oAj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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "_, ax1 = subplots()\n", - "ax2 = ax1.twinx()\n", - "ax1.plot(arange(niter), train_loss)\n", - "ax2.plot(test_interval * arange(len(test_acc)), test_acc, 'r')\n", - "ax1.set_xlabel('iteration')\n", - "ax1.set_ylabel('train loss')\n", - "ax2.set_ylabel('test accuracy')\n", - "ax2.set_title('Test Accuracy: {:.2f}'.format(test_acc[-1]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The loss seems to have dropped quickly and coverged (except for stochasticity), while the accuracy rose correspondingly. Hooray!\n", - "\n", - "* Since we saved the results on the first test batch, we can watch how our prediction scores evolved. We'll plot time on the $x$ axis and each possible label on the $y$, with lightness indicating confidence." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for i in range(8):\n", - " figure(figsize=(2, 2))\n", - " imshow(solver.test_nets[0].blobs['data'].data[i, 0], cmap='gray')\n", - " figure(figsize=(10, 2))\n", - " imshow(output[:50, i].T, interpolation='nearest', cmap='gray')\n", - " xlabel('iteration')\n", - " ylabel('label')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We started with little idea about any of these digits, and ended up with correct classifications for each. If you've been following along, you'll see the last digit is the most difficult, a slanted \"9\" that's (understandably) most confused with \"4\".\n", - "\n", - "* Note that these are the \"raw\" output scores rather than the softmax-computed probability vectors. The latter, shown below, make it easier to see the confidence of our net (but harder to see the scores for less likely digits)." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for i in range(8):\n", - " figure(figsize=(2, 2))\n", - " imshow(solver.test_nets[0].blobs['data'].data[i, 0], cmap='gray')\n", - " figure(figsize=(10, 2))\n", - " imshow(exp(output[:50, i].T) / exp(output[:50, i].T).sum(0), interpolation='nearest', cmap='gray')\n", - " xlabel('iteration')\n", - " ylabel('label')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 6. Experiment with architecture and optimization\n", - "\n", - "Now that we've defined, trained, and tested LeNet there are many possible next steps:\n", - "\n", - "- Define new architectures for comparison\n", - "- Tune optimization by setting `base_lr` and the like or simply training longer\n", - "- Switching the solver type from `SGD` to an adaptive method like `AdaDelta` or `Adam`\n", - "\n", - "Feel free to explore these directions by editing the all-in-one example that follows.\n", - "Look for \"`EDIT HERE`\" comments for suggested choice points.\n", - "\n", - "By default this defines a simple linear classifier as a baseline.\n", - "\n", - "In case your coffee hasn't kicked in and you'd like inspiration, try out\n", - "\n", - "1. Switch the nonlinearity from `ReLU` to `ELU` or a saturing nonlinearity like `Sigmoid`\n", - "2. Stack more fully connected and nonlinear layers\n", - "3. Search over learning rate 10x at a time (trying `0.1` and `0.001`)\n", - "4. Switch the solver type to `Adam` (this adaptive solver type should be less sensitive to hyperparameters, but no guarantees...)\n", - "5. Solve for longer by setting `niter` higher (to 500 or 1,000 for instance) to better show training differences" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration 0 testing...\n", - "Iteration 25 testing...\n", - "Iteration 50 testing...\n", - "Iteration 75 testing...\n", - "Iteration 100 testing...\n", - "Iteration 125 testing...\n", - "Iteration 150 testing...\n", - "Iteration 175 testing...\n", - "Iteration 200 testing...\n", - "Iteration 225 testing...\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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ObSIiqKpElpsBC4ETgFXAVOA8VZ0f2Se1Qs3fVXVgMdoXxwV3LzAXKzInWALC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\nrSpA4X5RUjuk0AIKO7N58+we3HOPCXu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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "train_net_path = 'mnist/custom_auto_train.prototxt'\n", - "test_net_path = 'mnist/custom_auto_test.prototxt'\n", - "solver_config_path = 'mnist/custom_auto_solver.prototxt'\n", - "\n", - "### define net\n", - "def custom_net(lmdb, batch_size):\n", - " # define your own net!\n", - " n = caffe.NetSpec()\n", - " \n", - " # keep this data layer for all networks\n", - " n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb,\n", - " transform_param=dict(scale=1./255), ntop=2)\n", - " \n", - " # EDIT HERE to try different networks\n", - " # this single layer defines a simple linear classifier\n", - " # (in particular this defines a multiway logistic regression)\n", - " n.score = L.InnerProduct(n.data, num_output=10, weight_filler=dict(type='xavier'))\n", - " \n", - " # EDIT HERE this is the LeNet variant we have already tried\n", - " # n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, weight_filler=dict(type='xavier'))\n", - " # n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n", - " # n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50, weight_filler=dict(type='xavier'))\n", - " # n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n", - " # n.fc1 = L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))\n", - " # EDIT HERE consider L.ELU or L.Sigmoid for the nonlinearity\n", - " # n.relu1 = L.ReLU(n.fc1, in_place=True)\n", - " # n.score = L.InnerProduct(n.fc1, num_output=10, weight_filler=dict(type='xavier'))\n", - " \n", - " # keep this loss layer for all networks\n", - " n.loss = L.SoftmaxWithLoss(n.score, n.label)\n", - " \n", - " return n.to_proto()\n", - "\n", - "with open(train_net_path, 'w') as f:\n", - " f.write(str(custom_net('mnist/mnist_train_lmdb', 64))) \n", - "with open(test_net_path, 'w') as f:\n", - " f.write(str(custom_net('mnist/mnist_test_lmdb', 100)))\n", - "\n", - "### define solver\n", - "from caffe.proto import caffe_pb2\n", - "s = caffe_pb2.SolverParameter()\n", - "\n", - "# Set a seed for reproducible experiments:\n", - "# this controls for randomization in training.\n", - "s.random_seed = 0xCAFFE\n", - "\n", - "# Specify locations of the train and (maybe) test networks.\n", - "s.train_net = train_net_path\n", - "s.test_net.append(test_net_path)\n", - "s.test_interval = 500 # Test after every 500 training iterations.\n", - "s.test_iter.append(100) # Test on 100 batches each time we test.\n", - "\n", - "s.max_iter = 10000 # no. of times to update the net (training iterations)\n", - " \n", - "# EDIT HERE to try different solvers\n", - "# solver types include \"SGD\", \"Adam\", and \"Nesterov\" among others.\n", - "s.type = \"SGD\"\n", - "\n", - "# Set the initial learning rate for SGD.\n", - "s.base_lr = 0.01 # EDIT HERE to try different learning rates\n", - "# Set momentum to accelerate learning by\n", - "# taking weighted average of current and previous updates.\n", - "s.momentum = 0.9\n", - "# Set weight decay to regularize and prevent overfitting\n", - "s.weight_decay = 5e-4\n", - "\n", - "# Set `lr_policy` to define how the learning rate changes during training.\n", - "# This is the same policy as our default LeNet.\n", - "s.lr_policy = 'inv'\n", - "s.gamma = 0.0001\n", - "s.power = 0.75\n", - "# EDIT HERE to try the fixed rate (and compare with adaptive solvers)\n", - "# `fixed` is the simplest policy that keeps the learning rate constant.\n", - "# s.lr_policy = 'fixed'\n", - "\n", - "# Display the current training loss and accuracy every 1000 iterations.\n", - "s.display = 1000\n", - "\n", - "# Snapshots are files used to store networks we've trained.\n", - "# We'll snapshot every 5K iterations -- twice during training.\n", - "s.snapshot = 5000\n", - "s.snapshot_prefix = 'mnist/custom_net'\n", - "\n", - "# Train on the GPU\n", - "s.solver_mode = caffe_pb2.SolverParameter.GPU\n", - "\n", - "# Write the solver to a temporary file and return its filename.\n", - "with open(solver_config_path, 'w') as f:\n", - " f.write(str(s))\n", - "\n", - "### load the solver and create train and test nets\n", - "solver = None # ignore this workaround for lmdb data (can't instantiate two solvers on the same data)\n", - "solver = caffe.get_solver(solver_config_path)\n", - "\n", - "### solve\n", - "niter = 250 # EDIT HERE increase to train for longer\n", - "test_interval = niter / 10\n", - "# losses will also be stored in the log\n", - "train_loss = zeros(niter)\n", - "test_acc = zeros(int(np.ceil(niter / test_interval)))\n", - "\n", - "# the main solver loop\n", - "for it in range(niter):\n", - " solver.step(1) # SGD by Caffe\n", - " \n", - " # store the train loss\n", - " train_loss[it] = solver.net.blobs['loss'].data\n", - " \n", - " # run a full test every so often\n", - " # (Caffe can also do this for us and write to a log, but we show here\n", - " # how to do it directly in Python, where more complicated things are easier.)\n", - " if it % test_interval == 0:\n", - " print 'Iteration', it, 'testing...'\n", - " correct = 0\n", - " for test_it in range(100):\n", - " solver.test_nets[0].forward()\n", - " correct += sum(solver.test_nets[0].blobs['score'].data.argmax(1)\n", - " == solver.test_nets[0].blobs['label'].data)\n", - " test_acc[it // test_interval] = correct / 1e4\n", - "\n", - "_, ax1 = subplots()\n", - "ax2 = ax1.twinx()\n", - "ax1.plot(arange(niter), train_loss)\n", - "ax2.plot(test_interval * arange(len(test_acc)), test_acc, 'r')\n", - "ax1.set_xlabel('iteration')\n", - "ax1.set_ylabel('train loss')\n", - "ax2.set_ylabel('test accuracy')\n", - "ax2.set_title('Custom Test Accuracy: {:.2f}'.format(test_acc[-1]))" - ] - } - ], - "metadata": { - "description": "Define, train, and test the classic LeNet with the Python interface.", - "example_name": "Learning LeNet", - "include_in_docs": true, - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" - }, - "priority": 2 - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/Notebooks/Caffee/02-fine-tuning.ipynb b/Notebooks/Caffee/02-fine-tuning.ipynb deleted file mode 100644 index 07ca8df..0000000 --- a/Notebooks/Caffee/02-fine-tuning.ipynb +++ /dev/null @@ -1,1175 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Fine-tuning a Pretrained Network for Style Recognition\n", - "\n", - "In this example, we'll explore a common approach that is particularly useful in real-world applications: take a pre-trained Caffe network and fine-tune the parameters on your custom data.\n", - "\n", - "The advantage of this approach is that, since pre-trained networks are learned on a large set of images, the intermediate layers capture the \"semantics\" of the general visual appearance. Think of it as a very powerful generic visual feature that you can treat as a black box. On top of that, only a relatively small amount of data is needed for good performance on the target task." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, we will need to prepare the data. This involves the following parts:\n", - "(1) Get the ImageNet ilsvrc pretrained model with the provided shell scripts.\n", - "(2) Download a subset of the overall Flickr style dataset for this demo.\n", - "(3) Compile the downloaded Flickr dataset into a database that Caffe can then consume." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "caffe_root = '../' # this file should be run from {caffe_root}/examples (otherwise change this line)\n", - "\n", - "import sys\n", - "sys.path.insert(0, caffe_root + 'python')\n", - "import caffe\n", - "\n", - "caffe.set_device(0)\n", - "caffe.set_mode_gpu()\n", - "\n", - "import numpy as np\n", - "from pylab import *\n", - "%matplotlib inline\n", - "import tempfile\n", - "\n", - "# Helper function for deprocessing preprocessed images, e.g., for display.\n", - "def deprocess_net_image(image):\n", - " image = image.copy() # don't modify destructively\n", - " image = image[::-1] # BGR -> RGB\n", - " image = image.transpose(1, 2, 0) # CHW -> HWC\n", - " image += [123, 117, 104] # (approximately) undo mean subtraction\n", - "\n", - " # clamp values in [0, 255]\n", - " image[image < 0], image[image > 255] = 0, 255\n", - "\n", - " # round and cast from float32 to uint8\n", - " image = np.round(image)\n", - " image = np.require(image, dtype=np.uint8)\n", - "\n", - " return image" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1. Setup and dataset download\n", - "\n", - "Download data required for this exercise.\n", - "\n", - "- `get_ilsvrc_aux.sh` to download the ImageNet data mean, labels, etc.\n", - "- `download_model_binary.py` to download the pretrained reference model\n", - "- `finetune_flickr_style/assemble_data.py` downloadsd the style training and testing data\n", - "\n", - "We'll download just a small subset of the full dataset for this exercise: just 2000 of the 80K images, from 5 of the 20 style categories. (To download the full dataset, set `full_dataset = True` in the cell below.)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading...\n", - "--2016-02-24 00:28:36-- http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz\n", - "Resolving dl.caffe.berkeleyvision.org (dl.caffe.berkeleyvision.org)... 169.229.222.251\n", - "Connecting to dl.caffe.berkeleyvision.org (dl.caffe.berkeleyvision.org)|169.229.222.251|:80... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 17858008 (17M) [application/octet-stream]\n", - "Saving to: ‘caffe_ilsvrc12.tar.gz’\n", - "\n", - "100%[======================================>] 17,858,008 112MB/s in 0.2s \n", - "\n", - "2016-02-24 00:28:36 (112 MB/s) - ‘caffe_ilsvrc12.tar.gz’ saved [17858008/17858008]\n", - "\n", - "Unzipping...\n", - "Done.\n", - "Model already exists.\n", - "Downloading 2000 images with 7 workers...\n", - "Writing train/val for 1996 successfully downloaded images.\n" - ] - } - ], - "source": [ - "# Download just a small subset of the data for this exercise.\n", - "# (2000 of 80K images, 5 of 20 labels.)\n", - "# To download the entire dataset, set `full_dataset = True`.\n", - "full_dataset = False\n", - "if full_dataset:\n", - " NUM_STYLE_IMAGES = NUM_STYLE_LABELS = -1\n", - "else:\n", - " NUM_STYLE_IMAGES = 2000\n", - " NUM_STYLE_LABELS = 5\n", - "\n", - "# This downloads the ilsvrc auxiliary data (mean file, etc),\n", - "# and a subset of 2000 images for the style recognition task.\n", - "import os\n", - "os.chdir(caffe_root) # run scripts from caffe root\n", - "!data/ilsvrc12/get_ilsvrc_aux.sh\n", - "!scripts/download_model_binary.py models/bvlc_reference_caffenet\n", - "!python examples/finetune_flickr_style/assemble_data.py \\\n", - " --workers=-1 --seed=1701 \\\n", - " --images=$NUM_STYLE_IMAGES --label=$NUM_STYLE_LABELS\n", - "# back to examples\n", - "os.chdir('examples')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define `weights`, the path to the ImageNet pretrained weights we just downloaded, and make sure it exists." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import os\n", - "weights = caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'\n", - "assert os.path.exists(weights)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load the 1000 ImageNet labels from `ilsvrc12/synset_words.txt`, and the 5 style labels from `finetune_flickr_style/style_names.txt`." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loaded ImageNet labels:\n", - "n01440764 tench, Tinca tinca\n", - "n01443537 goldfish, Carassius auratus\n", - "n01484850 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias\n", - "n01491361 tiger shark, Galeocerdo cuvieri\n", - "n01494475 hammerhead, hammerhead shark\n", - "n01496331 electric ray, crampfish, numbfish, torpedo\n", - "n01498041 stingray\n", - "n01514668 cock\n", - "n01514859 hen\n", - "n01518878 ostrich, Struthio camelus\n", - "...\n", - "\n", - "Loaded style labels:\n", - "Detailed, Pastel, Melancholy, Noir, HDR\n" - ] - } - ], - "source": [ - "# Load ImageNet labels to imagenet_labels\n", - "imagenet_label_file = caffe_root + 'data/ilsvrc12/synset_words.txt'\n", - "imagenet_labels = list(np.loadtxt(imagenet_label_file, str, delimiter='\\t'))\n", - "assert len(imagenet_labels) == 1000\n", - "print 'Loaded ImageNet labels:\\n', '\\n'.join(imagenet_labels[:10] + ['...'])\n", - "\n", - "# Load style labels to style_labels\n", - "style_label_file = caffe_root + 'examples/finetune_flickr_style/style_names.txt'\n", - "style_labels = list(np.loadtxt(style_label_file, str, delimiter='\\n'))\n", - "if NUM_STYLE_LABELS > 0:\n", - " style_labels = style_labels[:NUM_STYLE_LABELS]\n", - "print '\\nLoaded style labels:\\n', ', '.join(style_labels)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2. Defining and running the nets\n", - "\n", - "We'll start by defining `caffenet`, a function which initializes the *CaffeNet* architecture (a minor variant on *AlexNet*), taking arguments specifying the data and number of output classes." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [], - "source": [ - "from caffe import layers as L\n", - "from caffe import params as P\n", - "\n", - "weight_param = dict(lr_mult=1, decay_mult=1)\n", - "bias_param = dict(lr_mult=2, decay_mult=0)\n", - "learned_param = [weight_param, bias_param]\n", - "\n", - "frozen_param = [dict(lr_mult=0)] * 2\n", - "\n", - "def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1,\n", - " param=learned_param,\n", - " weight_filler=dict(type='gaussian', std=0.01),\n", - " bias_filler=dict(type='constant', value=0.1)):\n", - " conv = L.Convolution(bottom, kernel_size=ks, stride=stride,\n", - " num_output=nout, pad=pad, group=group,\n", - " param=param, weight_filler=weight_filler,\n", - " bias_filler=bias_filler)\n", - " return conv, L.ReLU(conv, in_place=True)\n", - "\n", - "def fc_relu(bottom, nout, param=learned_param,\n", - " weight_filler=dict(type='gaussian', std=0.005),\n", - " bias_filler=dict(type='constant', value=0.1)):\n", - " fc = L.InnerProduct(bottom, num_output=nout, param=param,\n", - " weight_filler=weight_filler,\n", - " bias_filler=bias_filler)\n", - " return fc, L.ReLU(fc, in_place=True)\n", - "\n", - "def max_pool(bottom, ks, stride=1):\n", - " return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)\n", - "\n", - "def caffenet(data, label=None, train=True, num_classes=1000,\n", - " classifier_name='fc8', learn_all=False):\n", - " \"\"\"Returns a NetSpec specifying CaffeNet, following the original proto text\n", - " specification (./models/bvlc_reference_caffenet/train_val.prototxt).\"\"\"\n", - " n = caffe.NetSpec()\n", - " n.data = data\n", - " param = learned_param if learn_all else frozen_param\n", - " n.conv1, n.relu1 = conv_relu(n.data, 11, 96, stride=4, param=param)\n", - " n.pool1 = max_pool(n.relu1, 3, stride=2)\n", - " n.norm1 = L.LRN(n.pool1, local_size=5, alpha=1e-4, beta=0.75)\n", - " n.conv2, n.relu2 = conv_relu(n.norm1, 5, 256, pad=2, group=2, param=param)\n", - " n.pool2 = max_pool(n.relu2, 3, stride=2)\n", - " n.norm2 = L.LRN(n.pool2, local_size=5, alpha=1e-4, beta=0.75)\n", - " n.conv3, n.relu3 = conv_relu(n.norm2, 3, 384, pad=1, param=param)\n", - " n.conv4, n.relu4 = conv_relu(n.relu3, 3, 384, pad=1, group=2, param=param)\n", - " n.conv5, n.relu5 = conv_relu(n.relu4, 3, 256, pad=1, group=2, param=param)\n", - " n.pool5 = max_pool(n.relu5, 3, stride=2)\n", - " n.fc6, n.relu6 = fc_relu(n.pool5, 4096, param=param)\n", - " if train:\n", - " n.drop6 = fc7input = L.Dropout(n.relu6, in_place=True)\n", - " else:\n", - " fc7input = n.relu6\n", - " n.fc7, n.relu7 = fc_relu(fc7input, 4096, param=param)\n", - " if train:\n", - " n.drop7 = fc8input = L.Dropout(n.relu7, in_place=True)\n", - " else:\n", - " fc8input = n.relu7\n", - " # always learn fc8 (param=learned_param)\n", - " fc8 = L.InnerProduct(fc8input, num_output=num_classes, param=learned_param)\n", - " # give fc8 the name specified by argument `classifier_name`\n", - " n.__setattr__(classifier_name, fc8)\n", - " if not train:\n", - " n.probs = L.Softmax(fc8)\n", - " if label is not None:\n", - " n.label = label\n", - " n.loss = L.SoftmaxWithLoss(fc8, n.label)\n", - " n.acc = L.Accuracy(fc8, n.label)\n", - " # write the net to a temporary file and return its filename\n", - " with tempfile.NamedTemporaryFile(delete=False) as f:\n", - " f.write(str(n.to_proto()))\n", - " return f.name" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let's create a *CaffeNet* that takes unlabeled \"dummy data\" as input, allowing us to set its input images externally and see what ImageNet classes it predicts." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "dummy_data = L.DummyData(shape=dict(dim=[1, 3, 227, 227]))\n", - "imagenet_net_filename = caffenet(data=dummy_data, train=False)\n", - "imagenet_net = caffe.Net(imagenet_net_filename, weights, caffe.TEST)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define a function `style_net` which calls `caffenet` on data from the Flickr style dataset.\n", - "\n", - "The new network will also have the *CaffeNet* architecture, with differences in the input and output:\n", - "\n", - "- the input is the Flickr style data we downloaded, provided by an `ImageData` layer\n", - "- the output is a distribution over 20 classes rather than the original 1000 ImageNet classes\n", - "- the classification layer is renamed from `fc8` to `fc8_flickr` to tell Caffe not to load the original classifier (`fc8`) weights from the ImageNet-pretrained model" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def style_net(train=True, learn_all=False, subset=None):\n", - " if subset is None:\n", - " subset = 'train' if train else 'test'\n", - " source = caffe_root + 'data/flickr_style/%s.txt' % subset\n", - " transform_param = dict(mirror=train, crop_size=227,\n", - " mean_file=caffe_root + 'data/ilsvrc12/imagenet_mean.binaryproto')\n", - " style_data, style_label = L.ImageData(\n", - " transform_param=transform_param, source=source,\n", - " batch_size=50, new_height=256, new_width=256, ntop=2)\n", - " return caffenet(data=style_data, label=style_label, train=train,\n", - " num_classes=NUM_STYLE_LABELS,\n", - " classifier_name='fc8_flickr',\n", - " learn_all=learn_all)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use the `style_net` function defined above to initialize `untrained_style_net`, a *CaffeNet* with input images from the style dataset and weights from the pretrained ImageNet model.\n", - "\n", - "\n", - "Call `forward` on `untrained_style_net` to get a batch of style training data." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "untrained_style_net = caffe.Net(style_net(train=False, subset='train'),\n", - " weights, caffe.TEST)\n", - "untrained_style_net.forward()\n", - "style_data_batch = untrained_style_net.blobs['data'].data.copy()\n", - "style_label_batch = np.array(untrained_style_net.blobs['label'].data, dtype=np.int32)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pick one of the style net training images from the batch of 50 (we'll arbitrarily choose #8 here). Display it, then run it through `imagenet_net`, the ImageNet-pretrained network to view its top 5 predicted classes from the 1000 ImageNet classes.\n", - "\n", - "Below we chose an image where the network's predictions happen to be reasonable, as the image is of a beach, and \"sandbar\" and \"seashore\" both happen to be ImageNet-1000 categories. For other images, the predictions won't be this good, sometimes due to the network actually failing to recognize the object(s) present in the image, but perhaps even more often due to the fact that not all images contain an object from the (somewhat arbitrarily chosen) 1000 ImageNet categories. Modify the `batch_index` variable by changing its default setting of 8 to another value from 0-49 (since the batch size is 50) to see predictions for other images in the batch. (To go beyond this batch of 50 images, first rerun the *above* cell to load a fresh batch of data into `style_net`.)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def disp_preds(net, image, labels, k=5, name='ImageNet'):\n", - " input_blob = net.blobs['data']\n", - " net.blobs['data'].data[0, ...] = image\n", - " probs = net.forward(start='conv1')['probs'][0]\n", - " top_k = (-probs).argsort()[:k]\n", - " print 'top %d predicted %s labels =' % (k, name)\n", - " print '\\n'.join('\\t(%d) %5.2f%% %s' % (i+1, 100*probs[p], labels[p])\n", - " for i, p in enumerate(top_k))\n", - "\n", - "def disp_imagenet_preds(net, image):\n", - " disp_preds(net, image, imagenet_labels, name='ImageNet')\n", - "\n", - "def disp_style_preds(net, image):\n", - " disp_preds(net, image, style_labels, name='style')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "actual label = Melancholy\n" - ] - }, - { - "data": { - "image/png": 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yvUJS3DfmLUsURECnW6oGgUxGBSVh7180Q52jz0UjoWnYDZxRmdmA1bqxNeau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6EmVpSTd7osozItUDOtN5ksBa2bdacbpJGyRKLhrAxEttNG0O8AWK1TShCtYaElalB19S3dVz6K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\ncDPDu2eav33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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "batch_index = 8\n", - "image = style_data_batch[batch_index]\n", - "plt.imshow(deprocess_net_image(image))\n", - "print 'actual label =', style_labels[style_label_batch[batch_index]]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "top 5 predicted ImageNet labels =\n", - "\t(1) 69.89% n09421951 sandbar, sand bar\n", - "\t(2) 21.76% n09428293 seashore, coast, seacoast, sea-coast\n", - "\t(3) 3.22% n02894605 breakwater, groin, groyne, mole, bulwark, seawall, jetty\n", - "\t(4) 1.89% n04592741 wing\n", - "\t(5) 1.23% n09332890 lakeside, lakeshore\n" - ] - } - ], - "source": [ - "disp_imagenet_preds(imagenet_net, image)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also look at `untrained_style_net`'s predictions, but we won't see anything interesting as its classifier hasn't been trained yet.\n", - "\n", - "In fact, since we zero-initialized the classifier (see `caffenet` definition -- no `weight_filler` is passed to the final `InnerProduct` layer), the softmax inputs should be all zero and we should therefore see a predicted probability of 1/N for each label (for N labels). Since we set N = 5, we get a predicted probability of 20% for each class." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "top 5 predicted style labels =\n", - "\t(1) 20.00% Detailed\n", - "\t(2) 20.00% Pastel\n", - "\t(3) 20.00% Melancholy\n", - "\t(4) 20.00% Noir\n", - "\t(5) 20.00% HDR\n" - ] - } - ], - "source": [ - "disp_style_preds(untrained_style_net, image)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also verify that the activations in layer `fc7` immediately before the classification layer are the same as (or very close to) those in the ImageNet-pretrained model, since both models are using the same pretrained weights in the `conv1` through `fc7` layers." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "diff = untrained_style_net.blobs['fc7'].data[0] - imagenet_net.blobs['fc7'].data[0]\n", - "error = (diff ** 2).sum()\n", - "assert error < 1e-8" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Delete `untrained_style_net` to save memory. (Hang on to `imagenet_net` as we'll use it again later.)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "del untrained_style_net" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3. Training the style classifier\n", - "\n", - "Now, we'll define a function `solver` to create our Caffe solvers, which are used to train the network (learn its weights). In this function we'll set values for various parameters used for learning, display, and \"snapshotting\" -- see the inline comments for explanations of what they mean. You may want to play with some of the learning parameters to see if you can improve on the results here!" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from caffe.proto import caffe_pb2\n", - "\n", - "def solver(train_net_path, test_net_path=None, base_lr=0.001):\n", - " s = caffe_pb2.SolverParameter()\n", - "\n", - " # Specify locations of the train and (maybe) test networks.\n", - " s.train_net = train_net_path\n", - " if test_net_path is not None:\n", - " s.test_net.append(test_net_path)\n", - " s.test_interval = 1000 # Test after every 1000 training iterations.\n", - " s.test_iter.append(100) # Test on 100 batches each time we test.\n", - "\n", - " # The number of iterations over which to average the gradient.\n", - " # Effectively boosts the training batch size by the given factor, without\n", - " # affecting memory utilization.\n", - " s.iter_size = 1\n", - " \n", - " s.max_iter = 100000 # # of times to update the net (training iterations)\n", - " \n", - " # Solve using the stochastic gradient descent (SGD) algorithm.\n", - " # Other choices include 'Adam' and 'RMSProp'.\n", - " s.type = 'SGD'\n", - "\n", - " # Set the initial learning rate for SGD.\n", - " s.base_lr = base_lr\n", - "\n", - " # Set `lr_policy` to define how the learning rate changes during training.\n", - " # Here, we 'step' the learning rate by multiplying it by a factor `gamma`\n", - " # every `stepsize` iterations.\n", - " s.lr_policy = 'step'\n", - " s.gamma = 0.1\n", - " s.stepsize = 20000\n", - "\n", - " # Set other SGD hyperparameters. Setting a non-zero `momentum` takes a\n", - " # weighted average of the current gradient and previous gradients to make\n", - " # learning more stable. L2 weight decay regularizes learning, to help prevent\n", - " # the model from overfitting.\n", - " s.momentum = 0.9\n", - " s.weight_decay = 5e-4\n", - "\n", - " # Display the current training loss and accuracy every 1000 iterations.\n", - " s.display = 1000\n", - "\n", - " # Snapshots are files used to store networks we've trained. Here, we'll\n", - " # snapshot every 10K iterations -- ten times during training.\n", - " s.snapshot = 10000\n", - " s.snapshot_prefix = caffe_root + 'models/finetune_flickr_style/finetune_flickr_style'\n", - " \n", - " # Train on the GPU. Using the CPU to train large networks is very slow.\n", - " s.solver_mode = caffe_pb2.SolverParameter.GPU\n", - " \n", - " # Write the solver to a temporary file and return its filename.\n", - " with tempfile.NamedTemporaryFile(delete=False) as f:\n", - " f.write(str(s))\n", - " return f.name" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we'll invoke the solver to train the style net's classification layer.\n", - "\n", - "For the record, if you want to train the network using only the command line tool, this is the command:\n", - "\n", - "\n", - "build/tools/caffe train \\\n", - " -solver models/finetune_flickr_style/solver.prototxt \\\n", - " -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \\\n", - " -gpu 0\n", - "\n", - "\n", - "However, we will train using Python in this example.\n", - "\n", - "We'll first define `run_solvers`, a function that takes a list of solvers and steps each one in a round robin manner, recording the accuracy and loss values each iteration. At the end, the learned weights are saved to a file." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def run_solvers(niter, solvers, disp_interval=10):\n", - " \"\"\"Run solvers for niter iterations,\n", - " returning the loss and accuracy recorded each iteration.\n", - " `solvers` is a list of (name, solver) tuples.\"\"\"\n", - " blobs = ('loss', 'acc')\n", - " loss, acc = ({name: np.zeros(niter) for name, _ in solvers}\n", - " for _ in blobs)\n", - " for it in range(niter):\n", - " for name, s in solvers:\n", - " s.step(1) # run a single SGD step in Caffe\n", - " loss[name][it], acc[name][it] = (s.net.blobs[b].data.copy()\n", - " for b in blobs)\n", - " if it % disp_interval == 0 or it + 1 == niter:\n", - " loss_disp = '; '.join('%s: loss=%.3f, acc=%2d%%' %\n", - " (n, loss[n][it], np.round(100*acc[n][it]))\n", - " for n, _ in solvers)\n", - " print '%3d) %s' % (it, loss_disp) \n", - " # Save the learned weights from both nets.\n", - " weight_dir = tempfile.mkdtemp()\n", - " weights = {}\n", - " for name, s in solvers:\n", - " filename = 'weights.%s.caffemodel' % name\n", - " weights[name] = os.path.join(weight_dir, filename)\n", - " s.net.save(weights[name])\n", - " return loss, acc, weights" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's create and run solvers to train nets for the style recognition task. We'll create two solvers -- one (`style_solver`) will have its train net initialized to the ImageNet-pretrained weights (this is done by the call to the `copy_from` method), and the other (`scratch_style_solver`) will start from a *randomly* initialized net.\n", - "\n", - "During training, we should see that the ImageNet pretrained net is learning faster and attaining better accuracies than the scratch net." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running solvers for 200 iterations...\n", - " 0) pretrained: loss=1.609, acc=28%; scratch: loss=1.609, acc=28%\n", - " 10) pretrained: loss=1.293, acc=52%; scratch: loss=1.626, acc=14%\n", - " 20) pretrained: loss=1.110, acc=56%; scratch: loss=1.646, acc=10%\n", - " 30) pretrained: loss=1.084, acc=60%; scratch: loss=1.616, acc=20%\n", - " 40) pretrained: loss=0.898, acc=64%; scratch: loss=1.588, acc=26%\n", - " 50) pretrained: loss=1.024, acc=54%; scratch: loss=1.607, acc=32%\n", - " 60) pretrained: loss=0.925, acc=66%; scratch: loss=1.616, acc=20%\n", - " 70) pretrained: loss=0.861, acc=74%; scratch: loss=1.598, acc=24%\n", - " 80) pretrained: loss=0.967, acc=60%; scratch: loss=1.588, acc=30%\n", - " 90) pretrained: loss=1.274, acc=52%; scratch: loss=1.608, acc=20%\n", - "100) pretrained: loss=1.113, acc=62%; scratch: loss=1.588, acc=30%\n", - "110) pretrained: loss=0.922, acc=62%; scratch: loss=1.578, acc=36%\n", - "120) pretrained: loss=0.918, acc=62%; scratch: loss=1.599, acc=20%\n", - "130) pretrained: loss=0.959, acc=58%; scratch: loss=1.594, acc=22%\n", - "140) pretrained: loss=1.228, acc=50%; scratch: loss=1.608, acc=14%\n", - "150) pretrained: loss=0.727, acc=76%; scratch: loss=1.623, acc=16%\n", - "160) pretrained: loss=1.074, acc=66%; scratch: loss=1.607, acc=20%\n", - "170) pretrained: loss=0.887, acc=60%; scratch: loss=1.614, acc=20%\n", - "180) pretrained: loss=0.961, acc=62%; scratch: loss=1.614, acc=18%\n", - "190) pretrained: loss=0.737, acc=76%; scratch: loss=1.613, acc=18%\n", - "199) pretrained: loss=0.836, acc=70%; scratch: loss=1.614, acc=16%\n", - "Done.\n" - ] - } - ], - "source": [ - "niter = 200 # number of iterations to train\n", - "\n", - "# Reset style_solver as before.\n", - "style_solver_filename = solver(style_net(train=True))\n", - "style_solver = caffe.get_solver(style_solver_filename)\n", - "style_solver.net.copy_from(weights)\n", - "\n", - "# For reference, we also create a solver that isn't initialized from\n", - "# the pretrained ImageNet weights.\n", - "scratch_style_solver_filename = solver(style_net(train=True))\n", - "scratch_style_solver = caffe.get_solver(scratch_style_solver_filename)\n", - "\n", - "print 'Running solvers for %d iterations...' % niter\n", - "solvers = [('pretrained', style_solver),\n", - " ('scratch', scratch_style_solver)]\n", - "loss, acc, weights = run_solvers(niter, solvers)\n", - "print 'Done.'\n", - "\n", - "train_loss, scratch_train_loss = loss['pretrained'], loss['scratch']\n", - "train_acc, scratch_train_acc = acc['pretrained'], acc['scratch']\n", - "style_weights, scratch_style_weights = weights['pretrained'], weights['scratch']\n", - "\n", - "# Delete solvers to save memory.\n", - "del style_solver, scratch_style_solver, solvers" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's look at the training loss and accuracy produced by the two training procedures. Notice how quickly the ImageNet pretrained model's loss value (blue) drops, and that the randomly initialized model's loss value (green) barely (if at all) improves from training only the classifier layer." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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ROQeFwjsdShwuuADYvx84etT1dX2eYOtWYP16632UlgI9e/LPRnFISFCzsnYE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\nvXQO+gS2sfonkpC9x6oq6xXnvDkHOcDswgs5dAa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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot(np.vstack([train_loss, scratch_train_loss]).T)\n", - "xlabel('Iteration #')\n", - "ylabel('Loss')" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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rQuOqcvDiVuL2qW1z41YqRg4clLQiB7U2kAoeIaZShjGxcyu5UQ1WKEdAmg1O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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot(np.vstack([train_acc, scratch_train_acc]).T)\n", - "xlabel('Iteration #')\n", - "ylabel('Accuracy')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's take a look at the testing accuracy after running 200 iterations of training. Note that we're classifying among 5 classes, giving chance accuracy of 20%. We expect both results to be better than chance accuracy (20%), and we further expect the result from training using the ImageNet pretraining initialization to be much better than the one from training from scratch. Let's see." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def eval_style_net(weights, test_iters=10):\n", - " test_net = caffe.Net(style_net(train=False), weights, caffe.TEST)\n", - " accuracy = 0\n", - " for it in xrange(test_iters):\n", - " accuracy += test_net.forward()['acc']\n", - " accuracy /= test_iters\n", - " return test_net, accuracy" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy, trained from ImageNet initialization: 50.0%\n", - "Accuracy, trained from random initialization: 23.6%\n" - ] - } - ], - "source": [ - "test_net, accuracy = eval_style_net(style_weights)\n", - "print 'Accuracy, trained from ImageNet initialization: %3.1f%%' % (100*accuracy, )\n", - "scratch_test_net, scratch_accuracy = eval_style_net(scratch_style_weights)\n", - "print 'Accuracy, trained from random initialization: %3.1f%%' % (100*scratch_accuracy, )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4. End-to-end finetuning for style\n", - "\n", - "Finally, we'll train both nets again, starting from the weights we just learned. The only difference this time is that we'll be learning the weights \"end-to-end\" by turning on learning in *all* layers of the network, starting from the RGB `conv1` filters directly applied to the input image. We pass the argument `learn_all=True` to the `style_net` function defined earlier in this notebook, which tells the function to apply a positive (non-zero) `lr_mult` value for all parameters. Under the default, `learn_all=False`, all parameters in the pretrained layers (`conv1` through `fc7`) are frozen (`lr_mult = 0`), and we learn only the classifier layer `fc8_flickr`.\n", - "\n", - "Note that both networks start at roughly the accuracy achieved at the end of the previous training session, and improve significantly with end-to-end training. To be more scientific, we'd also want to follow the same additional training procedure *without* the end-to-end training, to ensure that our results aren't better simply because we trained for twice as long. Feel free to try this yourself!" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running solvers for 200 iterations...\n", - " 0) pretrained, end-to-end: loss=0.781, acc=64%; scratch, end-to-end: loss=1.585, acc=28%\n", - " 10) pretrained, end-to-end: loss=1.178, acc=62%; scratch, end-to-end: loss=1.638, acc=14%\n", - " 20) pretrained, end-to-end: loss=1.084, acc=60%; scratch, end-to-end: loss=1.637, acc= 8%\n", - " 30) pretrained, end-to-end: loss=0.902, acc=76%; scratch, end-to-end: loss=1.600, acc=20%\n", - " 40) pretrained, end-to-end: loss=0.865, acc=64%; scratch, end-to-end: loss=1.574, acc=26%\n", - " 50) pretrained, end-to-end: loss=0.888, acc=60%; scratch, end-to-end: loss=1.604, acc=26%\n", - " 60) pretrained, end-to-end: loss=0.538, acc=78%; scratch, end-to-end: loss=1.555, acc=34%\n", - " 70) pretrained, end-to-end: loss=0.717, acc=72%; scratch, end-to-end: loss=1.563, acc=30%\n", - " 80) pretrained, end-to-end: loss=0.695, acc=74%; scratch, end-to-end: loss=1.502, acc=42%\n", - " 90) pretrained, end-to-end: loss=0.708, acc=68%; scratch, end-to-end: loss=1.523, acc=26%\n", - "100) pretrained, end-to-end: loss=0.432, acc=78%; scratch, end-to-end: loss=1.500, acc=38%\n", - "110) pretrained, end-to-end: loss=0.611, acc=78%; scratch, end-to-end: loss=1.618, acc=18%\n", - "120) pretrained, end-to-end: loss=0.610, acc=76%; scratch, end-to-end: loss=1.473, acc=30%\n", - "130) pretrained, end-to-end: loss=0.471, acc=78%; scratch, end-to-end: loss=1.488, acc=26%\n", - "140) pretrained, end-to-end: loss=0.500, acc=76%; scratch, end-to-end: loss=1.514, acc=38%\n", - "150) pretrained, end-to-end: loss=0.476, acc=80%; scratch, end-to-end: loss=1.452, acc=46%\n", - "160) pretrained, end-to-end: loss=0.368, acc=82%; scratch, end-to-end: loss=1.419, acc=34%\n", - "170) pretrained, end-to-end: loss=0.556, acc=76%; scratch, end-to-end: loss=1.583, acc=36%\n", - "180) pretrained, end-to-end: loss=0.574, acc=72%; scratch, end-to-end: loss=1.556, acc=22%\n", - "190) pretrained, end-to-end: loss=0.360, acc=88%; scratch, end-to-end: loss=1.429, acc=44%\n", - "199) pretrained, end-to-end: loss=0.458, acc=78%; scratch, end-to-end: loss=1.370, acc=44%\n", - "Done.\n" - ] - } - ], - "source": [ - "end_to_end_net = style_net(train=True, learn_all=True)\n", - "\n", - "# Set base_lr to 1e-3, the same as last time when learning only the classifier.\n", - "# You may want to play around with different values of this or other\n", - "# optimization parameters when fine-tuning. For example, if learning diverges\n", - "# (e.g., the loss gets very large or goes to infinity/NaN), you should try\n", - "# decreasing base_lr (e.g., to 1e-4, then 1e-5, etc., until you find a value\n", - "# for which learning does not diverge).\n", - "base_lr = 0.001\n", - "\n", - "style_solver_filename = solver(end_to_end_net, base_lr=base_lr)\n", - "style_solver = caffe.get_solver(style_solver_filename)\n", - "style_solver.net.copy_from(style_weights)\n", - "\n", - "scratch_style_solver_filename = solver(end_to_end_net, base_lr=base_lr)\n", - "scratch_style_solver = caffe.get_solver(scratch_style_solver_filename)\n", - "scratch_style_solver.net.copy_from(scratch_style_weights)\n", - "\n", - "print 'Running solvers for %d iterations...' % niter\n", - "solvers = [('pretrained, end-to-end', style_solver),\n", - " ('scratch, end-to-end', scratch_style_solver)]\n", - "_, _, finetuned_weights = run_solvers(niter, solvers)\n", - "print 'Done.'\n", - "\n", - "style_weights_ft = finetuned_weights['pretrained, end-to-end']\n", - "scratch_style_weights_ft = finetuned_weights['scratch, end-to-end']\n", - "\n", - "# Delete solvers to save memory.\n", - "del style_solver, scratch_style_solver, solvers" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's now test the end-to-end finetuned models. Since all layers have been optimized for the style recognition task at hand, we expect both nets to get better results than the ones above, which were achieved by nets with only their classifier layers trained for the style task (on top of either ImageNet pretrained or randomly initialized weights)." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy, finetuned from ImageNet initialization: 53.6%\n", - "Accuracy, finetuned from random initialization: 39.2%\n" - ] - } - ], - "source": [ - "test_net, accuracy = eval_style_net(style_weights_ft)\n", - "print 'Accuracy, finetuned from ImageNet initialization: %3.1f%%' % (100*accuracy, )\n", - "scratch_test_net, scratch_accuracy = eval_style_net(scratch_style_weights_ft)\n", - "print 'Accuracy, finetuned from random initialization: %3.1f%%' % (100*scratch_accuracy, )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll first look back at the image we started with and check our end-to-end trained model's predictions." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "top 5 predicted style labels =\n", - "\t(1) 55.67% Melancholy\n", - "\t(2) 27.21% HDR\n", - "\t(3) 16.46% Pastel\n", - "\t(4) 0.63% Detailed\n", - "\t(5) 0.03% Noir\n" - ] - }, - { - "data": { - "image/png": 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yvUJS3DfmLUsURECnW6oGgUxGBSVh7180Q52jz0UjoWnYDZxRmdmA1bqxNeau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6EmVpSTd7osozItUDOtN5ksBa2bdacbpJGyRKLhrAxEttNG0O8AWK1TShCtYaElalB19S3dVz6K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\ncDPDu2eav33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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.imshow(deprocess_net_image(image))\n", - "disp_style_preds(test_net, image)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Whew, that looks a lot better than before! But note that this image was from the training set, so the net got to see its label at training time.\n", - "\n", - "Finally, we'll pick an image from the test set (an image the model hasn't seen) and look at our end-to-end finetuned style model's predictions for it." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "actual label = Pastel\n" - ] - }, - { - "data": { - "image/png": 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WKs9hFZYitTN2cR87zMqTyhlYiOUvdRVeXdJREPOPBUQLuaLGrEgo3mWYO8JX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9p9Ybt4Of19W7OXfbsAgKNmoNTwI0vwBGcQKvGY5rN9HPF6L//O5aJjNCQtQWBMX/OJhfm4G1Ge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\nKJVy+/ZPkPUZu9dfpDdYJ8gc51qO7ryOOHqPk7d+SLY8oaEk6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EIa92mO1lg/t8jkyCSB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\nv+3j8qpvhwWF6TsmyabsE1+C94x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FzzNvLUUeNPY7UYZSIpOA1VfGkiSSHlslWq/o8cor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\n5hZiy0DMGbTzHHtQis736yIk4/HdR7ljZNC2bRM/4Rx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1GfjHzby8CfyVm27/NX3428DP\nA58HPkfYQPdveR/+AiE84bPA7zV/P3bb5uLgjnyAAxwAOHggHuAAB2jggAwOcIADAAdkcIADHKCB\nAzI4wAEOAByQwQEOcIAGDsjgAAc4AHBABgc4wAEaOCCDAxzgAAD8P7tWdgG4qV/gAAAAAElFTkSu\nQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "batch_index = 1\n", - "image = test_net.blobs['data'].data[batch_index]\n", - "plt.imshow(deprocess_net_image(image))\n", - "print 'actual label =', style_labels[int(test_net.blobs['label'].data[batch_index])]" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "top 5 predicted style labels =\n", - "\t(1) 99.76% Pastel\n", - "\t(2) 0.13% HDR\n", - "\t(3) 0.11% Detailed\n", - "\t(4) 0.00% Melancholy\n", - "\t(5) 0.00% Noir\n" - ] - } - ], - "source": [ - "disp_style_preds(test_net, image)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also look at the predictions of the network trained from scratch. We see that in this case, the scratch network also predicts the correct label for the image (*Pastel*), but is much less confident in its prediction than the pretrained net." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "top 5 predicted style labels =\n", - "\t(1) 49.81% Pastel\n", - "\t(2) 19.76% Detailed\n", - "\t(3) 17.06% Melancholy\n", - "\t(4) 11.66% HDR\n", - "\t(5) 1.72% Noir\n" - ] - } - ], - "source": [ - "disp_style_preds(scratch_test_net, image)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Of course, we can again look at the ImageNet model's predictions for the above image:" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "top 5 predicted ImageNet labels =\n", - "\t(1) 34.90% n07579787 plate\n", - "\t(2) 21.63% n04263257 soup bowl\n", - "\t(3) 17.75% n07875152 potpie\n", - "\t(4) 5.72% n07711569 mashed potato\n", - "\t(5) 5.27% n07584110 consomme\n" - ] - } - ], - "source": [ - "disp_imagenet_preds(imagenet_net, image)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So we did finetuning and it is awesome. Let's take a look at what kind of results we are able to get with a longer, more complete run of the style recognition dataset. Note: the below URL might be occassionally down because it is run on a research machine.\n", - "\n", - "http://demo.vislab.berkeleyvision.org/" - ] - } - ], - "metadata": { - "description": "Fine-tune the ImageNet-trained CaffeNet on new data.", - "example_name": "Fine-tuning for Style Recognition", - "include_in_docs": true, - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" - }, - "priority": 3 - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/Notebooks/Caffee/03-fine-tuning.ipynb b/Notebooks/Caffee/03-fine-tuning.ipynb deleted file mode 100644 index 05b9657..0000000 --- a/Notebooks/Caffee/03-fine-tuning.ipynb +++ /dev/null @@ -1,888 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Fine-tuning a Pretrained Network for Style Recognition\n", - "\n", - "In this example, we'll explore a common approach that is particularly useful in real-world applications: take a pre-trained Caffe network and fine-tune the parameters on your custom data.\n", - "\n", - "The advantage of this approach is that, since pre-trained networks are learned on a large set of images, the intermediate layers capture the \"semantics\" of the general visual appearance. Think of it as a very powerful generic visual feature that you can treat as a black box. On top of that, only a relatively small amount of data is needed for good performance on the target task." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, we will need to prepare the data. This involves the following parts:\n", - "(1) Get the ImageNet ilsvrc pretrained model with the provided shell scripts.\n", - "(2) Download a subset of the overall Flickr style dataset for this demo.\n", - "(3) Compile the downloaded Flickr dataset into a database that Caffe can then consume." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "caffe_root = '../' # this file should be run from {caffe_root}/examples (otherwise change this line)\n", - "\n", - "import sys\n", - "sys.path.insert(0, caffe_root + 'python')\n", - "import caffe\n", - "\n", - "caffe.set_mode_gpu()\n", - "caffe.set_device(0)\n", - "\n", - "import numpy as np\n", - "from pylab import *\n", - "%matplotlib inline\n", - "import tempfile\n", - "\n", - "# Helper function for deprocessing preprocessed images, e.g., for display.\n", - "def deprocess_net_image(image):\n", - " image = image.copy() # don't modify destructively\n", - " image = image[::-1] # BGR -> RGB\n", - " image = image.transpose(1, 2, 0) # CHW -> HWC\n", - " image += [123, 117, 104] # (approximately) undo mean subtraction\n", - "\n", - " # clamp values in [0, 255]\n", - " image[image < 0], image[image > 255] = 0, 255\n", - "\n", - " # round and cast from float32 to uint8\n", - " image = np.round(image)\n", - " image = np.require(image, dtype=np.uint8)\n", - "\n", - " return image" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1. Setup and dataset download\n", - "\n", - "Download data required for this exercise.\n", - "\n", - "- `get_ilsvrc_aux.sh` to download the ImageNet data mean, labels, etc.\n", - "- `download_model_binary.py` to download the pretrained reference model\n", - "- `finetune_flickr_style/assemble_data.py` downloadsd the style training and testing data\n", - "\n", - "We'll download just a small subset of the full dataset for this exercise: just 2000 of the 80K images, from 5 of the 20 style categories. (To download the full dataset, set `full_dataset = True` in the cell below.)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# NOTE: this data is bundled into the hands-on lab for you\n", - "# so we will skip some of these steps.\n", - "#\n", - "# Download just a small subset of the data for this exercise.\n", - "# (2000 of 80K images, 5 of 20 labels.)\n", - "# To download the entire dataset, set `full_dataset = True`.\n", - "full_dataset = False\n", - "if full_dataset:\n", - " NUM_STYLE_IMAGES = NUM_STYLE_LABELS = -1\n", - "else:\n", - " NUM_STYLE_IMAGES = 2000\n", - " NUM_STYLE_LABELS = 5\n", - "\n", - "# # This downloads the ilsvrc auxiliary data (mean file, etc),\n", - "# # and a subset of 2000 images for the style recognition task.\n", - "# import os\n", - "# os.chdir(caffe_root) # run scripts from caffe root\n", - "# !data/ilsvrc12/get_ilsvrc_aux.sh\n", - "# !scripts/download_model_binary.py models/bvlc_reference_caffenet\n", - "# !python examples/finetune_flickr_style/assemble_data.py \\\n", - "# --workers=-1 --seed=1701 \\\n", - "# --images=$NUM_STYLE_IMAGES --label=$NUM_STYLE_LABELS\n", - "# # back to examples\n", - "# os.chdir('examples')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define `weights`, the path to the ImageNet pretrained weights we just downloaded, and make sure it exists." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import os\n", - "weights = caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'\n", - "assert os.path.exists(weights)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load the 1000 ImageNet labels from `ilsvrc12/synset_words.txt`, and the 5 style labels from `finetune_flickr_style/style_names.txt`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Load ImageNet labels to imagenet_labels\n", - "imagenet_label_file = caffe_root + 'data/ilsvrc12/synset_words.txt'\n", - "imagenet_labels = list(np.loadtxt(imagenet_label_file, str, delimiter='\\t'))\n", - "assert len(imagenet_labels) == 1000\n", - "print 'Loaded ImageNet labels:\\n', '\\n'.join(imagenet_labels[:10] + ['...'])\n", - "\n", - "# Load style labels to style_labels\n", - "style_label_file = caffe_root + 'examples/finetune_flickr_style/style_names.txt'\n", - "style_labels = list(np.loadtxt(style_label_file, str, delimiter='\\n'))\n", - "if NUM_STYLE_LABELS > 0:\n", - " style_labels = style_labels[:NUM_STYLE_LABELS]\n", - "print '\\nLoaded style labels:\\n', ', '.join(style_labels)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2. Defining and running the nets\n", - "\n", - "We'll start by defining `caffenet`, a function which initializes the *CaffeNet* architecture (a minor variant on *AlexNet*), taking arguments specifying the data and number of output classes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [], - "source": [ - "from caffe import layers as L\n", - "from caffe import params as P\n", - "\n", - "weight_param = dict(lr_mult=1, decay_mult=1)\n", - "bias_param = dict(lr_mult=2, decay_mult=0)\n", - "learned_param = [weight_param, bias_param]\n", - "\n", - "frozen_param = [dict(lr_mult=0)] * 2\n", - "\n", - "def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1,\n", - " param=learned_param,\n", - " weight_filler=dict(type='gaussian', std=0.01),\n", - " bias_filler=dict(type='constant', value=0.1)):\n", - " conv = L.Convolution(bottom, kernel_size=ks, stride=stride,\n", - " num_output=nout, pad=pad, group=group,\n", - " param=param, weight_filler=weight_filler,\n", - " bias_filler=bias_filler)\n", - " return conv, L.ReLU(conv, in_place=True)\n", - "\n", - "def fc_relu(bottom, nout, param=learned_param,\n", - " weight_filler=dict(type='gaussian', std=0.005),\n", - " bias_filler=dict(type='constant', value=0.1)):\n", - " fc = L.InnerProduct(bottom, num_output=nout, param=param,\n", - " weight_filler=weight_filler,\n", - " bias_filler=bias_filler)\n", - " return fc, L.ReLU(fc, in_place=True)\n", - "\n", - "def max_pool(bottom, ks, stride=1):\n", - " return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)\n", - "\n", - "def caffenet(data, label=None, train=True, num_classes=1000,\n", - " classifier_name='fc8', learn_all=False):\n", - " \"\"\"Returns a NetSpec specifying CaffeNet, following the original proto text\n", - " specification (./models/bvlc_reference_caffenet/train_val.prototxt).\"\"\"\n", - " n = caffe.NetSpec()\n", - " n.data = data\n", - " param = learned_param if learn_all else frozen_param\n", - " n.conv1, n.relu1 = conv_relu(n.data, 11, 96, stride=4, param=param)\n", - " n.pool1 = max_pool(n.relu1, 3, stride=2)\n", - " n.norm1 = L.LRN(n.pool1, local_size=5, alpha=1e-4, beta=0.75)\n", - " n.conv2, n.relu2 = conv_relu(n.norm1, 5, 256, pad=2, group=2, param=param)\n", - " n.pool2 = max_pool(n.relu2, 3, stride=2)\n", - " n.norm2 = L.LRN(n.pool2, local_size=5, alpha=1e-4, beta=0.75)\n", - " n.conv3, n.relu3 = conv_relu(n.norm2, 3, 384, pad=1, param=param)\n", - " n.conv4, n.relu4 = conv_relu(n.relu3, 3, 384, pad=1, group=2, param=param)\n", - " n.conv5, n.relu5 = conv_relu(n.relu4, 3, 256, pad=1, group=2, param=param)\n", - " n.pool5 = max_pool(n.relu5, 3, stride=2)\n", - " n.fc6, n.relu6 = fc_relu(n.pool5, 4096, param=param)\n", - " if train:\n", - " n.drop6 = fc7input = L.Dropout(n.relu6, in_place=True)\n", - " else:\n", - " fc7input = n.relu6\n", - " n.fc7, n.relu7 = fc_relu(fc7input, 4096, param=param)\n", - " if train:\n", - " n.drop7 = fc8input = L.Dropout(n.relu7, in_place=True)\n", - " else:\n", - " fc8input = n.relu7\n", - " # always learn fc8 (param=learned_param)\n", - " fc8 = L.InnerProduct(fc8input, num_output=num_classes, param=learned_param)\n", - " # give fc8 the name specified by argument `classifier_name`\n", - " n.__setattr__(classifier_name, fc8)\n", - " if not train:\n", - " n.probs = L.Softmax(fc8)\n", - " if label is not None:\n", - " n.label = label\n", - " n.loss = L.SoftmaxWithLoss(fc8, n.label)\n", - " n.acc = L.Accuracy(fc8, n.label)\n", - " # write the net to a temporary file and return its filename\n", - " with tempfile.NamedTemporaryFile(delete=False) as f:\n", - " f.write(str(n.to_proto()))\n", - " return f.name" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let's create a *CaffeNet* that takes unlabeled \"dummy data\" as input, allowing us to set its input images externally and see what ImageNet classes it predicts." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "dummy_data = L.DummyData(shape=dict(dim=[1, 3, 227, 227]))\n", - "imagenet_net_filename = caffenet(data=dummy_data, train=False)\n", - "imagenet_net = caffe.Net(imagenet_net_filename, weights, caffe.TEST)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define a function `style_net` which calls `caffenet` on data from the Flickr style dataset.\n", - "\n", - "The new network will also have the *CaffeNet* architecture, with differences in the input and output:\n", - "\n", - "- the input is the Flickr style data we downloaded, provided by an `ImageData` layer\n", - "- the output is a distribution over 20 classes rather than the original 1000 ImageNet classes\n", - "- the classification layer is renamed from `fc8` to `fc8_flickr` to tell Caffe not to load the original classifier (`fc8`) weights from the ImageNet-pretrained model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def style_net(train=True, learn_all=False, subset=None):\n", - " if subset is None:\n", - " subset = 'train' if train else 'test'\n", - " source = caffe_root + 'data/flickr_style/%s.txt' % subset\n", - " transform_param = dict(mirror=train, crop_size=227,\n", - " mean_file=caffe_root + 'data/ilsvrc12/imagenet_mean.binaryproto')\n", - " style_data, style_label = L.ImageData(\n", - " transform_param=transform_param, source=source,\n", - " batch_size=50, new_height=256, new_width=256, ntop=2)\n", - " return caffenet(data=style_data, label=style_label, train=train,\n", - " num_classes=NUM_STYLE_LABELS,\n", - " classifier_name='fc8_flickr',\n", - " learn_all=learn_all)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use the `style_net` function defined above to initialize `untrained_style_net`, a *CaffeNet* with input images from the style dataset and weights from the pretrained ImageNet model.\n", - "\n", - "\n", - "Call `forward` on `untrained_style_net` to get a batch of style training data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "untrained_style_net = caffe.Net(style_net(train=False, subset='train'),\n", - " weights, caffe.TEST)\n", - "untrained_style_net.forward()\n", - "style_data_batch = untrained_style_net.blobs['data'].data.copy()\n", - "style_label_batch = np.array(untrained_style_net.blobs['label'].data, dtype=np.int32)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pick one of the style net training images from the batch of 50 (we'll arbitrarily choose #8 here). Display it, then run it through `imagenet_net`, the ImageNet-pretrained network to view its top 5 predicted classes from the 1000 ImageNet classes.\n", - "\n", - "Below we chose an image where the network's predictions happen to be reasonable, as the image is of a beach, and \"sandbar\" and \"seashore\" both happen to be ImageNet-1000 categories. For other images, the predictions won't be this good, sometimes due to the network actually failing to recognize the object(s) present in the image, but perhaps even more often due to the fact that not all images contain an object from the (somewhat arbitrarily chosen) 1000 ImageNet categories. Modify the `batch_index` variable by changing its default setting of 8 to another value from 0-49 (since the batch size is 50) to see predictions for other images in the batch. (To go beyond this batch of 50 images, first rerun the *above* cell to load a fresh batch of data into `style_net`.)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def disp_preds(net, image, labels, k=5, name='ImageNet'):\n", - " input_blob = net.blobs['data']\n", - " net.blobs['data'].data[0, ...] = image\n", - " probs = net.forward(start='conv1')['probs'][0]\n", - " top_k = (-probs).argsort()[:k]\n", - " print 'top %d predicted %s labels =' % (k, name)\n", - " print '\\n'.join('\\t(%d) %5.2f%% %s' % (i+1, 100*probs[p], labels[p])\n", - " for i, p in enumerate(top_k))\n", - "\n", - "def disp_imagenet_preds(net, image):\n", - " disp_preds(net, image, imagenet_labels, name='ImageNet')\n", - "\n", - "def disp_style_preds(net, image):\n", - " disp_preds(net, image, style_labels, name='style')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "batch_index = 8\n", - "image = style_data_batch[batch_index]\n", - "plt.imshow(deprocess_net_image(image))\n", - "print 'actual label =', style_labels[style_label_batch[batch_index]]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "disp_imagenet_preds(imagenet_net, image)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also look at `untrained_style_net`'s predictions, but we won't see anything interesting as its classifier hasn't been trained yet.\n", - "\n", - "In fact, since we zero-initialized the classifier (see `caffenet` definition -- no `weight_filler` is passed to the final `InnerProduct` layer), the softmax inputs should be all zero and we should therefore see a predicted probability of 1/N for each label (for N labels). Since we set N = 5, we get a predicted probability of 20% for each class." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "disp_style_preds(untrained_style_net, image)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also verify that the activations in layer `fc7` immediately before the classification layer are the same as (or very close to) those in the ImageNet-pretrained model, since both models are using the same pretrained weights in the `conv1` through `fc7` layers." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "diff = untrained_style_net.blobs['fc7'].data[0] - imagenet_net.blobs['fc7'].data[0]\n", - "error = (diff ** 2).sum()\n", - "assert error < 1e-8" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Delete `untrained_style_net` to save memory. (Hang on to `imagenet_net` as we'll use it again later.)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "del untrained_style_net" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3. Training the style classifier\n", - "\n", - "Now, we'll define a function `solver` to create our Caffe solvers, which are used to train the network (learn its weights). In this function we'll set values for various parameters used for learning, display, and \"snapshotting\" -- see the inline comments for explanations of what they mean. You may want to play with some of the learning parameters to see if you can improve on the results here!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from caffe.proto import caffe_pb2\n", - "\n", - "def solver(train_net_path, test_net_path=None, base_lr=0.001):\n", - " s = caffe_pb2.SolverParameter()\n", - "\n", - " # Specify locations of the train and (maybe) test networks.\n", - " s.train_net = train_net_path\n", - " if test_net_path is not None:\n", - " s.test_net.append(test_net_path)\n", - " s.test_interval = 1000 # Test after every 1000 training iterations.\n", - " s.test_iter.append(100) # Test on 100 batches each time we test.\n", - "\n", - " # The number of iterations over which to average the gradient.\n", - " # Effectively boosts the training batch size by the given factor, without\n", - " # affecting memory utilization.\n", - " s.iter_size = 1\n", - " \n", - " s.max_iter = 100000 # # of times to update the net (training iterations)\n", - " \n", - " # Solve using the stochastic gradient descent (SGD) algorithm.\n", - " # Other choices include 'Adam' and 'RMSProp'.\n", - " s.type = 'SGD'\n", - "\n", - " # Set the initial learning rate for SGD.\n", - " s.base_lr = base_lr\n", - "\n", - " # Set `lr_policy` to define how the learning rate changes during training.\n", - " # Here, we 'step' the learning rate by multiplying it by a factor `gamma`\n", - " # every `stepsize` iterations.\n", - " s.lr_policy = 'step'\n", - " s.gamma = 0.1\n", - " s.stepsize = 20000\n", - "\n", - " # Set other SGD hyperparameters. Setting a non-zero `momentum` takes a\n", - " # weighted average of the current gradient and previous gradients to make\n", - " # learning more stable. L2 weight decay regularizes learning, to help prevent\n", - " # the model from overfitting.\n", - " s.momentum = 0.9\n", - " s.weight_decay = 5e-4\n", - "\n", - " # Display the current training loss and accuracy every 1000 iterations.\n", - " s.display = 1000\n", - "\n", - " # Snapshots are files used to store networks we've trained. Here, we'll\n", - " # snapshot every 10K iterations -- ten times during training.\n", - " s.snapshot = 10000\n", - " s.snapshot_prefix = caffe_root + 'models/finetune_flickr_style/finetune_flickr_style'\n", - " \n", - " # Train on the GPU. Using the CPU to train large networks is very slow.\n", - " s.solver_mode = caffe_pb2.SolverParameter.GPU\n", - " \n", - " # Write the solver to a temporary file and return its filename.\n", - " with tempfile.NamedTemporaryFile(delete=False) as f:\n", - " f.write(str(s))\n", - " return f.name" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we'll invoke the solver to train the style net's classification layer.\n", - "\n", - "For the record, if you want to train the network using only the command line tool, this is the command:\n", - "\n", - "\n", - "build/tools/caffe train \\\n", - " -solver models/finetune_flickr_style/solver.prototxt \\\n", - " -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \\\n", - " -gpu 0\n", - "\n", - "\n", - "However, we will train using Python in this example.\n", - "\n", - "We'll first define `run_solvers`, a function that takes a list of solvers and steps each one in a round robin manner, recording the accuracy and loss values each iteration. At the end, the learned weights are saved to a file." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def run_solvers(niter, solvers, disp_interval=10):\n", - " \"\"\"Run solvers for niter iterations,\n", - " returning the loss and accuracy recorded each iteration.\n", - " `solvers` is a list of (name, solver) tuples.\"\"\"\n", - " blobs = ('loss', 'acc')\n", - " loss, acc = ({name: np.zeros(niter) for name, _ in solvers}\n", - " for _ in blobs)\n", - " for it in range(niter):\n", - " for name, s in solvers:\n", - " s.step(1) # run a single SGD step in Caffe\n", - " loss[name][it], acc[name][it] = (s.net.blobs[b].data.copy()\n", - " for b in blobs)\n", - " if it % disp_interval == 0 or it + 1 == niter:\n", - " loss_disp = '; '.join('%s: loss=%.3f, acc=%2d%%' %\n", - " (n, loss[n][it], np.round(100*acc[n][it]))\n", - " for n, _ in solvers)\n", - " print '%3d) %s' % (it, loss_disp) \n", - " # Save the learned weights from both nets.\n", - " weight_dir = tempfile.mkdtemp()\n", - " weights = {}\n", - " for name, s in solvers:\n", - " filename = 'weights.%s.caffemodel' % name\n", - " weights[name] = os.path.join(weight_dir, filename)\n", - " s.net.save(weights[name])\n", - " return loss, acc, weights" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's create and run solvers to train nets for the style recognition task. We'll create two solvers -- one (`style_solver`) will have its train net initialized to the ImageNet-pretrained weights (this is done by the call to the `copy_from` method), and the other (`scratch_style_solver`) will start from a *randomly* initialized net.\n", - "\n", - "During training, we should see that the ImageNet pretrained net is learning faster and attaining better accuracies than the scratch net." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "niter = 200 # number of iterations to train\n", - "\n", - "# Reset style_solver as before.\n", - "style_solver_filename = solver(style_net(train=True))\n", - "style_solver = caffe.get_solver(style_solver_filename)\n", - "style_solver.net.copy_from(weights)\n", - "\n", - "# For reference, we also create a solver that isn't initialized from\n", - "# the pretrained ImageNet weights.\n", - "scratch_style_solver_filename = solver(style_net(train=True))\n", - "scratch_style_solver = caffe.get_solver(scratch_style_solver_filename)\n", - "\n", - "print 'Running solvers for %d iterations...' % niter\n", - "solvers = [('pretrained', style_solver),\n", - " ('scratch', scratch_style_solver)]\n", - "loss, acc, weights = run_solvers(niter, solvers)\n", - "print 'Done.'\n", - "\n", - "train_loss, scratch_train_loss = loss['pretrained'], loss['scratch']\n", - "train_acc, scratch_train_acc = acc['pretrained'], acc['scratch']\n", - "style_weights, scratch_style_weights = weights['pretrained'], weights['scratch']\n", - "\n", - "# Delete solvers to save memory.\n", - "del style_solver, scratch_style_solver, solvers" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's look at the training loss and accuracy produced by the two training procedures. Notice how quickly the ImageNet pretrained model's loss value (blue) drops, and that the randomly initialized model's loss value (green) barely (if at all) improves from training only the classifier layer." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [], - "source": [ - "plot(np.vstack([train_loss, scratch_train_loss]).T)\n", - "xlabel('Iteration #')\n", - "ylabel('Loss')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "plot(np.vstack([train_acc, scratch_train_acc]).T)\n", - "xlabel('Iteration #')\n", - "ylabel('Accuracy')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's take a look at the testing accuracy after running 200 iterations of training. Note that we're classifying among 5 classes, giving chance accuracy of 20%. We expect both results to be better than chance accuracy (20%), and we further expect the result from training using the ImageNet pretraining initialization to be much better than the one from training from scratch. Let's see." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def eval_style_net(weights, test_iters=10):\n", - " test_net = caffe.Net(style_net(train=False), weights, caffe.TEST)\n", - " accuracy = 0\n", - " for it in xrange(test_iters):\n", - " accuracy += test_net.forward()['acc']\n", - " accuracy /= test_iters\n", - " return test_net, accuracy" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "test_net, accuracy = eval_style_net(style_weights)\n", - "print 'Accuracy, trained from ImageNet initialization: %3.1f%%' % (100*accuracy, )\n", - "scratch_test_net, scratch_accuracy = eval_style_net(scratch_style_weights)\n", - "print 'Accuracy, trained from random initialization: %3.1f%%' % (100*scratch_accuracy, )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4. End-to-end finetuning for style\n", - "\n", - "Finally, we'll train both nets again, starting from the weights we just learned. The only difference this time is that we'll be learning the weights \"end-to-end\" by turning on learning in *all* layers of the network, starting from the RGB `conv1` filters directly applied to the input image. We pass the argument `learn_all=True` to the `style_net` function defined earlier in this notebook, which tells the function to apply a positive (non-zero) `lr_mult` value for all parameters. Under the default, `learn_all=False`, all parameters in the pretrained layers (`conv1` through `fc7`) are frozen (`lr_mult = 0`), and we learn only the classifier layer `fc8_flickr`.\n", - "\n", - "Note that both networks start at roughly the accuracy achieved at the end of the previous training session, and improve significantly with end-to-end training. To be more scientific, we'd also want to follow the same additional training procedure *without* the end-to-end training, to ensure that our results aren't better simply because we trained for twice as long. Feel free to try this yourself!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "end_to_end_net = style_net(train=True, learn_all=True)\n", - "\n", - "# Set base_lr to 1e-3, the same as last time when learning only the classifier.\n", - "# You may want to play around with different values of this or other\n", - "# optimization parameters when fine-tuning. For example, if learning diverges\n", - "# (e.g., the loss gets very large or goes to infinity/NaN), you should try\n", - "# decreasing base_lr (e.g., to 1e-4, then 1e-5, etc., until you find a value\n", - "# for which learning does not diverge).\n", - "base_lr = 0.001\n", - "\n", - "style_solver_filename = solver(end_to_end_net, base_lr=base_lr)\n", - "style_solver = caffe.get_solver(style_solver_filename)\n", - "style_solver.net.copy_from(style_weights)\n", - "\n", - "scratch_style_solver_filename = solver(end_to_end_net, base_lr=base_lr)\n", - "scratch_style_solver = caffe.get_solver(scratch_style_solver_filename)\n", - "scratch_style_solver.net.copy_from(scratch_style_weights)\n", - "\n", - "print 'Running solvers for %d iterations...' % niter\n", - "solvers = [('pretrained, end-to-end', style_solver),\n", - " ('scratch, end-to-end', scratch_style_solver)]\n", - "_, _, finetuned_weights = run_solvers(niter, solvers)\n", - "print 'Done.'\n", - "\n", - "style_weights_ft = finetuned_weights['pretrained, end-to-end']\n", - "scratch_style_weights_ft = finetuned_weights['scratch, end-to-end']\n", - "\n", - "# Delete solvers to save memory.\n", - "del style_solver, scratch_style_solver, solvers" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's now test the end-to-end finetuned models. Since all layers have been optimized for the style recognition task at hand, we expect both nets to get better results than the ones above, which were achieved by nets with only their classifier layers trained for the style task (on top of either ImageNet pretrained or randomly initialized weights)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "test_net, accuracy = eval_style_net(style_weights_ft)\n", - "print 'Accuracy, finetuned from ImageNet initialization: %3.1f%%' % (100*accuracy, )\n", - "scratch_test_net, scratch_accuracy = eval_style_net(scratch_style_weights_ft)\n", - "print 'Accuracy, finetuned from random initialization: %3.1f%%' % (100*scratch_accuracy, )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll first look back at the image we started with and check our end-to-end trained model's predictions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "plt.imshow(deprocess_net_image(image))\n", - "disp_style_preds(test_net, image)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Whew, that looks a lot better than before! But note that this image was from the training set, so the net got to see its label at training time.\n", - "\n", - "Finally, we'll pick an image from the test set (an image the model hasn't seen) and look at our end-to-end finetuned style model's predictions for it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "batch_index = 1\n", - "image = test_net.blobs['data'].data[batch_index]\n", - "plt.imshow(deprocess_net_image(image))\n", - "print 'actual label =', style_labels[int(test_net.blobs['label'].data[batch_index])]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "disp_style_preds(test_net, image)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also look at the predictions of the network trained from scratch. We see that in this case, the scratch network also predicts the correct label for the image (*Pastel*), but is much less confident in its prediction than the pretrained net." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "disp_style_preds(scratch_test_net, image)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Of course, we can again look at the ImageNet model's predictions for the above image:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "disp_imagenet_preds(imagenet_net, image)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Huzzah! So we did finetuning and it is awesome. Let's take a look at what kind of results we are able to get with a longer, more complete run of the style recognition dataset. Note: the below URL might be occassionally down because it is run on a research machine.\n", - "\n", - "http://demo.vislab.berkeleyvision.org/" - ] - } - ], - "metadata": { - "description": "Fine-tune the ImageNet-trained CaffeNet on new data.", - "example_name": "Fine-tuning for Style Recognition", - "include_in_docs": true, - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" - }, - "priority": 4 - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/Notebooks/Caffee/DIY Deep Learning for Vision- a Hands-On Tutorial with Caffe.pdf b/Notebooks/Caffee/DIY Deep Learning for Vision- a Hands-On Tutorial with Caffe.pdf new file mode 100644 index 0000000..b22b9e9 Binary files /dev/null and b/Notebooks/Caffee/DIY Deep Learning for Vision- a Hands-On Tutorial with Caffe.pdf differ diff --git a/Notebooks/Caffee/brewing-logreg.ipynb b/Notebooks/Caffee/brewing-logreg.ipynb deleted file mode 100644 index c053b73..0000000 --- a/Notebooks/Caffee/brewing-logreg.ipynb +++ /dev/null @@ -1,1164 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Brewing Logistic Regression then Going Deeper\n", - "\n", - "While Caffe is made for deep networks it can likewise represent \"shallow\" models like logistic regression for classification. We'll do simple logistic regression on synthetic data that we'll generate and save to HDF5 to feed vectors to Caffe. Once that model is done, we'll add layers to improve accuracy. That's what Caffe is about: define a model, experiment, and then deploy." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "\n", - "import os\n", - "os.chdir('..')\n", - "\n", - "import sys\n", - "sys.path.insert(0, './python')\n", - "import caffe\n", - "\n", - "\n", - "import os\n", - "import h5py\n", - "import shutil\n", - "import tempfile\n", - "\n", - "import sklearn\n", - "import sklearn.datasets\n", - "import sklearn.linear_model\n", - "\n", - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Synthesize a dataset of 10,000 4-vectors for binary classification with 2 informative features and 2 noise features." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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ZsmQhkpQhEGjir//6Ty6pBHu1mI5x9/v9yLKMy+W67OvV7Xbz29/uJBBQOHLk\nGCbTbFatWkJl5WR0aXS0l0WLzJcUkuvr6+Oxx3YiywXodCZSqQDl5Rq+8pXPTU15ZrNZfvR3f4fg\n9/P68y9il+y0pWWicgWqoKeqrJqEVuQT921GlntYs2YBPl+Y8vJC1q5dcd6UrCRJDA4OkkgkKCkp\nwWw2c+TIcU6ebEejEVm9eiGrV6+asRL9Symw5iIjV4EPmy/yTt6qqvk4OCPXIq7iYtyZzAXtXaOj\nuKMO1q9fgyiKKIpKvnMBh1sHmVVWTJ7VyqnmZjTjY1RqU9SYVRJj3fiKi2lu3seZM01UV5cRCiVY\nvfo2GhqWTj1ty7JEU9N2SkpWo6oqo6Oj9Pa6SSbTOBxa2tsjiKLC8JhKUd3N5DkLKC2rRa8Xeeml\nfdTW1iLLMhaL5QNP2VwMg8FATU3NR+7/caOkpISvfvVLfPWrX3rPdcLhMBZrEYI9hFYUCceSSKpC\nQozSF0gSH8ly9GgHa9fOJ5lMk04XU1Y2OTWWzWZpaTnJN7/5D1SV52OITrC4opy6bIwTLz9K+fIt\nJDMKqVQREKCwcBZlZfMYHBzBZOph3rw5SJIFj8dzTT4ZXw4+n4+nnnqJkZEIIOJwaHjggTs/8nEm\nEgm2bXsWo3EuTmcWi8WH3T6HpqZOrFYreXlOioqqOXXq4HnOiKqqtLS0cPjwKSKRGO3tXcyevQWX\n6y2HoYahoTYOHjzCHXdsRpZlTpw4weFDhyjMZCgw6SESRsBKucWFhIBFryMlZiktLSMQCHDLLeun\nhBDfjVarpa6u7ry222+/ldtvv/UjnYerSc4ZuQrs2/f+EvDvxZYt8LOfwV/91ZW16eNOOp2mt7eX\neDxOUVERVVVVF9XPsNnt9MViZFtbWTJnDhqNhpGJCbpDCaprb5l6+hJFgcr62XQ3eejo68dlMhJ1\nuzEYjSxZtgijrBBNJnnu1z/AJtqYV1iKIRZn0BviyJGTKIrEvHmTURBZlrBarahqgo6OLtrbPVgs\nReh0+fT0tNLdfYpXX60im63FbLYzMOhmbNyLTleIu/8A3U37mFVRTuOZM6TCYfLz87npk5/ki1/5\nyjUpFnUtkMlkaDp+nLbjx5EVhXnLl7NqzRqMRiNer5dwOExeXt55XwLZbJa+vj56enoY6uoiHYuR\nX1TE6ltueU+JfpvNRklFAUdbJhiZiJDOykSyXRjUJEWClipRy3D7IK9FRcbHu7n11oeByemCw4eb\nCAZVYjHnFT5hAAAgAElEQVQTwkQfc8xmgsIYa5cuJho5TPOBHUzoyigsvAlIUFlZhCCIOJ2l9PYO\nMGfObFRVuiYUia8k6XSaRx/dgSxXUFU1GRWMRoNs27aTb37zwSmF3Ev1d7vdqKpKRUUFJpOJ7u5u\nEgkrRUUFhMMTgIxWq0OnczI0NExenhNZljAYzhcQ27HjWV5+uQmns4pYLMKBA4McP76L1asXM3v2\nLLRaLU5nGcePt7JmzUqeeuwxTrz0EtXRKG1uN8PxOBZFISxniWf8OG3lDIx5qFgyh2h0gpGRbjwe\nD/n5+VddvCydTk/9j6jA/BUrWLl69XtG2d6a4pIkibKyMkzvKG+/GDfWVXkNoqqTkZF//deP1v/W\nW+ELX4BkEt5nLHN8QLxeL9u2PU0kYgCMqGojc+YU8PnP3z8VvpQkiaeffoEzZ0ZICA283HWE3Sef\nZfnyBdQuWsTakmq83vPzJ+xOJ4OhGM8caMOeiqBXs9gqy5lfV4fNYsHf0UmlIrFo7mKKi0twDwxQ\nFPQwGkuyd6INNZti7sINjI52c9ddmzh1qoNDhzopL1+LLEuMjfUSjXYCTgTBTlFRBVqtgZGRCH19\nHRQXRLAEAqiCluf37acyI1Gj0aKbiHB04P/lbGMjP3n00WsiRH8tIcsyT23fTryzk3ydDlVV6dq9\nm/ZTpxAt+QwMhBFFK4oSZd68Uj7zmbsJhUJs2/Y0A/0BPKePUamHObUlFGi1vPLrX5P49KdZvnLl\nBfsymUxIUojO8SE0SQ06PBSSxIELPZAaclOdn4ffI+IZnyCTyWAyafF6vQQCEgUFVQSDpymxmCgu\nrOZ05wk6BnspMhopExOMBdqpX3cHDQ2b6Oz0AYVoNFokSSUQGMNuVykvL7/AruuZrq4uIhE91dVv\nH5fNlkc0WkJz8xnuvHPzJfs++eTLpNNGQECni3PvvbcRiyXQaCb/T+z2AqxWgUTCj1ZrIJFIATA6\n2s2mTW+LKe7e/TI//ekObLZFtLV1MDp6FkEoRFEqOXq0k4MHT1JVVY1GI5OfP8Kzv/sdo0eOkB0Y\nIBEIUC/LVGs0jMhgFJP04yaiL6C4ah5e7wDPPTfG7Nn17NhxnNdfP8rDD3/mqlUVSZLEjt/8hkxf\nH/WFhQiCQM8rr9DX3s7nv/rVCxyjkZERnt++HSESQSsIxESRW97niTznjEwz7e1gtcI78h8/FA7H\npObIwYOTUZIcl4eqqvzudztR1Rqqq9+ea+3sPM2hQ0em9BgaG49z6pSfkpIltLY2kRZKiAkWmvqD\nfOnPbj/3NPYaLtekzHc4HObYsVaKS8tZt+4ezjS+Sqj7BI7RcUw2G/50mu7ubgpNDiwWK8ODA2iT\nSZZXzuLA6BBkzYw2vcqor4vqaheiWMGiRbUcOtROZ8cOxga60clxjFqIC1bCjnFSqTSCYMDn85NK\nBUkFTzLfZsIdimOPpFhSVIogCkRUlQJDPh0nTrJt2zYKLBZSySR1CxawdNmy931iudHp6+tj6Phx\ntF4v0VQKURDIiCKdjSfQNmxmxYo7gMlrp739LHv27KW7ewhZriId7Gd5aS02kwXvqBu7YxydovCz\n73+fOx94gOXr1lFfX48gCPT29vK3f/t/eOWVY0jJECIyKhksmBFRQNSQSerwDrox1emw2cz09LTg\ncJTS0dGNIFhJJoMYjTIqKqcHOxkY6ufW6gIWlZdTptWCOoa//zhr7/gi0ehhRkebyWYNiKIPRTHx\n4IP3X9bU3bVIKBRGo7lQYdZksuPzBd+zXzgcZvv23eTlLaG4eDIpOJVKsGPHm9x99zpkeVKnUxAE\nVq26icOH9zI+Hsdur6C7+yBWa4pIxM7vf/8UAwMD7Nixj1DIgkaTJBrVkJ9/Fx7PbkTRRyIhUVTU\nQColYren8Xrj/PSf/pk5skJsYhSrIhMTRWRRRNEbWGGy4Y/7CGbaSA1PkEzKNDTMZv36WzGZLPh8\nw+zY8SJ/+qcPT8MZvZCenh7ifX2sfMe07aLqak4MDNDZ2cnChQun2lOpFM/8+tc06PW4zn3xpTIZ\nDjzzzCX3kXNGppnLyRd5iy1b4NVXc87IlWB0dJSJiSxVVecnpJaWNnDkyMkpZ+TIkdMUFs7m2LE3\niURsOByrycvTMDJyip/8ZBvf/e6fsHp1FcePH8NkKqG9vZNEYpiVK5dSWTmHrpZD5IlayrUGLKpK\nntNJm0ZDLBpGECATi+M0GkhlMpiMOoqrHVQXWDnq9tDQcBOHDk0wMdFNJOIhO3gKl2RBK7rQZTOk\n0nEmEmGyWT+h0DCSZAJ0aCQn7ZKKkhlgo8aAJMmYjAbUdBKTyYbiG2DPY4/x5U99CrtOR+dLL9Ha\n1MSDX/vaDa8fkk6nOXz4KMeOtZDNSixdOoeNG9fjcDjoam9nor2dpS4X5nOy68lUiqYzbVjL3laq\nFASB8vK5vP76qxgMeVRU5JMOT6CYrHiC48iKyM49e1lRWUpVJoPa2cnLbW0s2rKFJcuX853v/IC9\ne9tJx6spoIgkKURG0WNCRxGyopLJBonHMwjxCAaXjdbW/aRSJUSjKuGwl4oKA2VlLg6cOImYsSJF\nXbzUn2BCHaDKYWXD+pW8caqHgYE2Vq68hYGBNsbGzvCZz9zB5s2bb0jHs7i4CFluvaA9FvNTVVXz\nnv3a2jqQ5XzM5rerk4xGM1ptCcPDo2i1Qfbu/T11dUsoLKxk3ry51Nf3sWRJOY2N7XR2Btm5s5Vg\ncIJUyoAoFqHX5+P1pgmHB6ioqMVun43PdxiTaSmCkMDr7cPpzMc/kSLtD5MSVKyyQCFgU1WCksTZ\nrIQVC1m1EINaiyIUUlbWAIR5440XWbRoPfn5eXg8Q0xMTFyV6MhQXx+ui0RUC00m3H195zkjPT09\nmBMJXO+YHjPq9VTmpmlmlr174VOX+dbuLVvgkUeuiDkfeyRJQhAufDLUanWk028nqiaTGSTJTyik\nkp9fg6oqBINj+HwRjh5N8rOf/Zy/+Zs/Z8mSUTo6eggE4tTVraeqah6yLKHJJHEUVjA+PoQ9HMas\nKGQFAbvTQCDgJhKNEgjEiWczuNUY5pSTrvEM+Y4FVFbOIRqN0tc3QfPhw1gkGZtWi1YMEZJjJOUC\nbNZydDoBrdZFPB5FVUfRaWzEMhLJtJ1+wU2dzYqkyAhaHVlFIp5IsrKkZKr0uMBuZ29LC//PD36A\nWafDUVDAqo0bWbR48XX5/hlVVeno6OBMYyPpZJL6RYtYtnw5BoOB7dufoqcnQ0nJEjQaDU1NQ3R2\nPsEjj3yZiUAAslnM7yhb1ogiRkSi0cB5+9BqdWSzCjrdpEpon3eU4ZQRRTLjDfchJfyYtBZsRoVb\nCgqYZTJx5M036ekfoLV1nHSqAIs8jIUUIJHETpIoIgkU1YSs2AlH/UwEwG6ppqZmOdlsnGwWTp92\n43aP4/GEkbJ2wv5xnEIJouTghfZ2FlUH+MrKlaxcWIPP6MXvTzJ/fgmPPPK/b+hE5NraWsrKDjI8\n3EFpaT2CIOLzDWM0Blm69L3FneLxBFrthV+wkUiQ3/3uDHV1q9DrDRw69Ab5+Spf+MJ9rF9/Fz/7\n2eP09vpobGxFlm1kMilEUUNenp1oNIjLVY8sTxCPj2AwFGAwGJg1Kw9RjJOfbyeRiNLT1YQ5EyeE\njBWRJBpKFAktYEWkW5LRGGYhmEqIZFRisTCiaKG1tY9IpAWTyYDD4SObzV54YFeQTCbD6Ogo8WSS\nxEUS+VPZLPnvKjVPJpNcTADA8j7TwzlnZBp5S1/kRz+6vO2sXj0pC5+Thv9wqKqKx+PB4/FgMBio\nr6+npKQEnS5NKpXAaHw7GjA+PsjixW/rFyxcWMdLL50mnRaJx8P4fKN4PEHikTSkdOz4zRsc3nuA\nW9cuwe5wUF7ixB+SSKUSDPSeZmSgFcFkJ2E0gKIgjYxg1Wg56fFQnEwRD8mYTA4CWg0r5t6EgoXW\n/hZW35RPLBbjqadewusNo5UUarFjkx2IapaQLDFEnHTcQ3Rch6ToUFUrorgKFQ3JTBIVhS51lCXR\nCGbZhLmwnB6vG7eUYZ3JRJfbTU1JCf5IhKH2dvItFjZ84hNEEwn2P/EEkXCYmzZef++ufG33brr2\n7WNWXh4FOh1du3bRduIE6zZvpqdn8gWEb1FePpvBwRZOn26hqKiIgKIwPDaGThCwmM2IBgMhDdh0\n599WQyEfNpuO8fFeWlrOMjoSQI3ESCsQl/KxaxfTOBTBbpMZ/f1zbF27HKNWy8GDx/H5YuRlo1SI\nIhbFRkJNEwQ8pKnAjwE7KVllPO5FE5LBsRqDwYokaYhEWtHrHWSzVrJZG4piQNBZULQxNAYb2WgR\np1s7+I/+/w/ZaeMvfvpj7j33Vs5sNktXVxepVApRFDGbzRQWFl4xwa6ZRqvV8qUvfYbXX99Pc/Mh\nFEVh7txq7rzzc5d8dUN1dQWvv94BvF1xI0lZTpw4zKpVdzJr1lxmzYL16zfR33+SoiIXfr+fEyfa\naGrqRKtdh1ZrIJtNksnE8HhOYjbn4/EcRhAs+P3d2GxGZs2qpLS0jlBoDFkOcLyxBX1GSxFWYsSw\nI6NBoQuIA8WiliFZRk4rCEYdopghHg+Sl1eORuNCp9NjsRTh8ZzG5/NRWlo6Zb8sy/T39zMyMord\nbqWhoQGLxfKRzmtbayt7nnkGQyZDNJHg9OnT2HU6Ks7tL55KMaYobHmXlEBxcTHHVPUCPZ6xcPiS\n+8s5I9NIeztYLHC5r/LQaid1Sp55Br797Stj242OLMu8+OyzDDU3kyeKZFWVvQYDW7/4Re6551ae\nfPJNjMYKzGYbkYgPkynEpk2fn+q/ePE8/uM/HqWrS8JoDDMx4UNULdSXFqDTxsmmbaQGoniERjbe\nfx/tw8PsPXOcrpNmKhGYbbDS1t+FN2tC9DhwaTQYNQplZXMYj8UZFQSMogGnvZjBcT+ptIdI2oCi\nifPUU48zOJhAq9XiEi1YMYGaBlnBKBgpVQ10qglKjXb6giF0ujVI2QyqIICgQ1bLiQvdvCFHKIor\nKKk+xjJR5liM+Bsb8bW1sVsQELRa6vV6Cisq0Go05NlsrDAaOfbGG6xYteq6Cun7fD7aDx1i3axZ\naM5VOOXZbLQMDnJg/0E0GucFfez2Inp73VRVFTGWhj2+CewqoGaQrXpmLV5A2CLi949is+UxNjbC\niWPPs7bOgdTTzujxFgozhehEDVEpRkY1kJUyCKEJSjGTDMV5dqQfvctFprIOJemnXO9ASKdJk0ZG\nwIqBMFoGMWMSRUQD6PR2MpECAr4Es2YVk05HOXXKSzxuxWBwIEkZdDoztXXr8AzvwTvmwSSI2I0u\nQqqEM6njX//+ByxfvhyNRsNvfvMsPp9KW1s/4fA4lZUuZs+uZNOmFdx22y3XZRTsLbq6unjttUN4\nPBPk59u4++6NLF68+AIdDUVR6O3tpadnAJPJwPz5c6mtraWhwUFX10mKiuoQRZGOjqOYTHZqa89X\nGi4qquP48bPMn1+N2+1Ho6lCq3Uhy0kUJY0sZ1AUSKVkLJY8MpkBDIYQc+euorJyFmfOvEEqJTM2\npkdNCLiIMBstCibCxMgCGSAN9CpZMjow5NdgtdUQj7tJJMbwet3I8jDpdID8/E7Wr1/LgQNNLFq0\nCEEQSKVSPP74U/T1RdHp8pHlJAbDAR5++D4qP+RTrNfr5bXf/palhYVYz90HHILAb/ft45ZVq9Dq\ndMS0Wu743OcumCaqrKykeP58TrW2Ul9cjE6rZWh8nPj7VPLlnJFpZO9e2LTpymzrM5+Bf/7nnDNy\nKUKhEMcOHaL37Fm8ExOIY2P8wbp1Uwl7oViMndu38yff+Q6PPJJPY+MpAgEfK1dWsnz51qmnKFmW\neeGF11mz5n5k+SBDQ0EEwYFBFNCIaZLpQQpEA0X5DiLBTqLhMCtnz6alr4/R7jN4EwKtfh/eRClW\nTRXxjERCzKA3pCiNxkCykJAkwolCDPkLUDVmFFOU6MRejhxpwePRAAZQ2pmFTFIQMCigQ4eWLJBF\nxICMOBl+Q0WjETDozICMJOvQaRysWLOQU61dFDqMfKV8Pgm/HzUYhFAI0WzmaDCIoNMh2WzIsoxG\no0Gv02FSFPx+/7RIh2ezWYaHh1HVyaqOK6XoOjw8jBOmHJG3KM/L49iIG0V9W3tBVVUSiSih0Djz\n5xdz4MApXLM3YA6OYRa1CKKALzCKuaqKP/nTr9PYeAavd5CQr4utC4upLS0l0d9HUGcgJlmQxSwO\nfSmJbAi9EqEIM4VaEzqzBa3opz0UwhvrQZRCZKU0gupAxIJIBshgJ4MsGrBozYQyPrKSCY1YiWck\niHTsBbLZDH5/FFEsQ1WTmEx6FEVLNJpAVgyoSGTVcbLZJBadws3FVXSEJti+/XcIggVRrGd0tBud\nbh41NesIBM6iqsW8+morBQV5LF265IqMwdWmo6ODbdteIT9/LtXVi4nHwzz11FHS6SwbNqybWk+S\nJH73u2dpa5vAaCxCkjLs2XOS++/fyEMPfZrjx5tobGxFkhTWr6/A4XBcUP4siiKSJGMwGEgms8iy\niVjMg06XRzY7iixnEMX5aDRxZLkQVc0gCDE+97lNrFq1guee0/DDHz5BKlWBhiCFJLFiRESLDRNa\ntIwi0YtIBoWSWZsQdUb0eiPhsIwoGrFYYqhqgpKSCkKhCIcOtdHTkyCdlti6dRNDQ5MCiu+MAEYi\nfn772xf5i7/4+odKXD7T3EypTjfliAAsb2hA1WgoXr+e+fPnU1lZedHKPEEQuPezn+V4YyNnjh4l\nm0jQsGYNf3DTTTzyne+85z5zzsg0snfvR9cXeTebN0+W+Ho8cJkK59ctQ0NDNB08iH9sjJKqKlZt\n2DClPhiJRHjiv/+b/ESCxS4XPZ2dREdG+N1EmDWrllJTUoLTasUUCDAwMEBDQ8N7ftkODg4yNpbF\narWyZs1qDIbDnGruRIOZSCJLfWkFxlgGSUlj0wpkzs2leodHMSk2KuY20Nh4ltLCm4gGxzEIEoqx\niHRmAk/ET4kYpiibJYGe4EQnpRXLSUZjpNMFjI0lkaQ0kmRClgX8REgIZkRE7KQwq3rSBDCYZpHM\n6EBJIMs9aDT5KGoSUUij12rRaGQWVhRTlU2Q1etZWFZGp6LQOzYGySSGTIZMNovF6STU0cHRvDzW\n3Xzz5BOWokxLQmt3dzdPPrmbVMoACOj1KT796duvyLb1ej3SRdrT2SxOp5Pe1mb6+tyYTA4mJkZI\npWTi8VFEcR7JpIk1Gx9gcKCVsb4WFFnGvmQTliId8+bNY86cORw/fpwfv/ECnUYjPW43TllGI4JF\nryGUzoAgYhLBoqpkVJlAPIE2o6LVRtFozZjslag6hUx2BAMqKiZkkoiEySAjCn6S0iiV2jJCIig6\nO5psDLe7B1F0oKp6stkker0BURSQJC/hcJpU0oNR9lEsxrApkMjE2NfXilar49iRYyxcfAc2m45Q\nKE1+/mTpq8VSTX9/P8uXr+Xgwebr0hlRVZVXXjmIy7UAm21Sjt1stmOx1PDznz9JPB5n0aIFlJaW\n0tJyltbWIDU1q6aiQJlMFc8+u5fZs+vZsGE9GzZMvr8lmUzS2/vfpNNJDIa3v4jHxwe4/fa5pFIp\nFCVFJhNDFPUkkyNksxNAMYoSJpuNotdnKSurQZI07N7diM1mZf/+Luz2CqTEODBGmAwqWRQUFKxo\nERGRkbEjYSca9dMwbxFu935kOYBGk8LlWoNeP5eBgSEyGQ2K4mHBgltQ1Vq2bduFJMUoLz//7cx2\newGDg714PJ4PFR2JBAJYL+Jo2A0GCvLzmT179iX763Q61m/YwPoNGz7wPnPOyDRxufoi78ZggHvu\ngSeegL/8yyuzzeuJttZWXt2+nVkWCw1WKxOtrfzu1Cnu+9rXqK6upvn4cRyxGLMrK/EGAjT3+rEp\n5Qx2C4ylRygvGubu9UvRqCqSdLGvrbcZGRmhqakZnW4cVdWTTpsxWyTs2hKKHDryHA6CoQFEYZyi\nAhM2m41YMknfeJRyjHjOnkbKGtCZ9BhNNmKxIBpVJC2DVZGoMGnQiQKyDOFAN2OJOGkljSZtIZEd\nBIpQFdCIeUwoDThVAT02EoSwa8PIRjuzS6rxBLvQaoJojWFk2UAmq0OnAUHTy9wKFxPBIGaLBUVR\nGPSO4Rkfp9Bmw5fNUpCXR6UsozGbKREEetrbyZosZESB8lUryT9XVXKlCIVCbN++C4djMUVFkxGo\nZDLGE0+8ekW2X1tbyx6DgXA8juPcHHkqk+HpIycYl/MYG1Pw+Y4RiXixWudRUVHGli2fJRbzc+bM\nfurqNlBXv5S6+kmp43Q6STx+CkmSeOJXv+LNxx/H0N+PYDRyNpFAr6oUWAx4fGOgOhDEGLKaBkFA\nEE2EpBR52hglRiPjURFZTJORjQRELWUY0QoSqiwTx0QEEYccRSeaUWUZORsnqo6CXoC0DUkqRxDC\nqOoYgjAPScpQUmJkdLQZnWaEGiFOiaDHrSrYNbNJyzqG0gnkwQnKK+MY/3/23jvakqu+8/1UPjnc\nc27O3X07t7pbqRWtlkBIIIlgTDBGFphneCyHGfOMZ72ZZy8P4zULz4yXjbHxMMxgyUZgokCAQBLK\nodU5qm/O6dx7cq683x+naakVAIHaYOPvH2edqjpVu9bedWr/9i98vwEbSXqBB0JVdRzHJhiMUCxW\nX7E/fxGwtrbGsWOnyOfLbNjQx+7du87nP9i2zfp6hcHBJM1mk0xmjTNnRikULGTZ4Xvfm+Rb33qK\nPXsGWFsrEYlsZm1tnuXlBQB6ewfw/Thzc3MXyCcEg0He9rb9fPWrj6Pr3RhGmFptjZ4eiSuvvJw/\n//O/IR43qFbnAAnPk5DlAKChqg6a1o7rqhSLdSSpDgzxhS98h2YT6vUywoYQm6jR5BirXEoNnSYN\nVDIIXGIochu2XWRoKMzu3W/hxIlFyuVRGo1l8vkeXHcETQPXlVhdrbJ1aw3DGGBy8kH6+1/u/ZAk\nCd/3X1Pf92/cyOjoKJ0v0d0pui5XXKTV8EU1RiRJ+kvgMuDYSxV8pZaJehz4tBDi/1zM+/h54OxZ\niEZ/en6RV8KHPwx33gkf+9gvl3Ce53k89p3vsLu9ndi5l1EkGCRULPL4Aw9w10c/ytzYGP3JJL7v\n8/CRMdJte/CLNSIKxAM9FCp1njs7gZKKv+oKQQjBsaNH+ctPfoqFSR8jGKZmSShKCNtJs1R+jqA2\ngOP2smpNcknCZ2B4F9FolLu//xCzWYGjmGBbNO0gS41xAlqMqusQqJfx3Dy+lCPvBLF8CSFKRHAo\nNMcQSgRHZBBeCkXtR1JquG4Fj80UKGJgIROjLvvs7NlOpdIg7BbZoHvMm5P45FDQ8b06QqqhB/rQ\n+vtxCkUef+Ygg00H1bFZtuukFBklEGBzPE5Q1/nW6AyWEua5Z/N0Dfexp6vC4uLiy/rJ933m5uYo\nlUrEYjGGh4d/Ytfv2bNjuG4b4fALCYXBYARZfmVa69eKYDDI7b/xG3zn3nuJ5HIowJH5JfL043ld\nbNmykURikZmZGaJRi1isjVAoRGdnJ0eOHGRhYYyNG18gsFpfn+P667dx6uRJRh96iMtDISqpFPV6\nk05P42ipiK27uD6oWgBPNKl4WUAhShXTd3FwyDd91kSc5fI4IfKU0GmSRQdsEjhSP3WhUuIYQb8N\njU4cqvj2BK6nYbubgQay3AYEqFbHgAKRSJwrruigOrtGaGqFrKsSYATZk1BwUSUD2Wrn6ae+z/vv\n3I0QJkL45yTo19i4sYdCYZUtW17HF9TriNHRUe6990FUtZtgMMrY2DjPPHOc3/7tXyeRSKBpGqGQ\nxvz8LEePTjA/nyWTqSLLMXR9ikhExfNCnDhxEsOoUCweI5HoIxZrJfDNz58kEqni+y9Xr927dw9d\nXZ2cOHGGSqXOyMil7NixnW9845t85jNfxjS7sO04vn8SWXYJBpPYtokkbUBRBvF9C9MsEo/rPP/8\naWy7hKYlaFQVEsiAjIlChTSPUGcIGxMokkClkyIGwvI4dWqca6+9Ec8rsGXLVpaW8pTLMUyzTDgc\nIxbbQDw+wMTEWS677Fqi0SBra3P09b2QiN9s1jAM+zULhu685BKOPfMMk8vLDHZ04Pk+U5kMsY0b\nL1pl1kUzRiRJuhQICyF+RZKkz0iSdLkQ4siLfnIHsA78y1fIegW8HvwiL8VVV7UI1B56CG699fW9\n9i8yisUiolYj9pLJsSOZZHRhgWazSTgWo7m0hOt5VJsqG3qHONucolQsEnQcNCPMI6fH+ONP/r+v\nWkXw2A9+wIH77iNgBujEYWl2kpoaRgr3IUQMI6gzsjdJVxy6N1/O/MwsD5yZ4O8fe5rZTINYaBdF\n6shOEVkxqDtLVN12NDWALcq4zBIXYHoyAheBQRafmujAd5PYBFGQEH4ORQnjMAw0ELRjSyaCOLK3\nzPHZI3RIdS5LBxm1NPr9JEI1qMoyycAQkuYhhTx+86Mf5a5fez+9cgpLrxHAxDcbzHo2Vcvi1t5e\nZvJF9Mg2+vt3kt66lW07drK0NMOnP303n/jEx8/HhOv1Ol/7x3+ksbhIVJKoCYHW1cWv/eZv/kQU\n85VKDV1/eejHMF6/cNDGjRv58Mc/zuzsbKuC5MsP0F/uYH7eRZJkTNMiHt+I40wihMrKSoadO9vY\ntm0HS0vHCYXCGEaYanWd9naXa67Zx31f/CJKuUxHKkW5UmNyvobhx0hKMGF6GDSRnAyOD2HC+JKg\nLCqEhIxiaRRwWKaIgUyEXiK0USCMiU+TMogeFGYR7KGBS5gkqtyFI6XQpTFsmijKELIUBOI4bgdw\nglxumksu2UK+UmUoEmClqhMUKhYCkAjoGlu7h5jLHubAM/fR1bWZublRFEUiEqkTiezA8xa54Yb3\nvm79/3rBcRy+/vWHaW/fSzDYIjNLJjtZWZni8cef4e1vvw1Zltm3bwd/+qf3YpobaDRkQqF+XDdH\nta8YfDkAACAASURBVNrk6acX6O3dRTSaIp3WmJqaQ5IS9PR0IUkQDLaxsPC9V72H7u7uC6pUJicn\n+aM/+hTV6m6CwSFUVcKy5nHdMVKpIbLZs1hWFsfRaeVwLVGpuAiRwjBK5HIVVLtBDIcaZdpQCSFT\nIsACBg4uDbrxUZE0hZDeydLSCo8//nUuu6wbIWx0Pcnw8GZMcwpN8+nrG8IwIlQq01Qqea6//kpy\nuQoLC6eJRNoxzRqum+HXf/3m10wdHw6Hed9v/zbPPvkkzx0/jqqq7LrpJq669tqLRpp3MT0j+4Af\n+mB/AFwNvNgY+XXgn4B/uancPwKPP96qgHk9IUktjZpPfAJuuaW1/csAwzBwX6FUzHFdJEVB0zT2\nXHUV3//85xmMREBIaKrGQF8fU+EwUmcnUsBgZPhSrn6VGOaJEyf427/4O2TT4+xMFb8Woy3Wg2HV\nydslgrEudD1B3+Al/NEf/Rb33fcAZaebTPkgIalKSotjNvNURSex2OU4jeexXQuZEopkEg/UqLsl\ngiKGoIGMR4MaTXpwSWKjoRHFI4HrL+L7ZUBHkaIEVBNDjdFwwHIlQuS5IdUFdh3f1ekPpzA9H1lR\niUXj6LrD6vIs/+k/fQqzHqcW0uiMJPEkn2YsTjM7j9m0qDYaHM1U2TxyExVFQ9U0nn7oQYK+T7Y8\nyZ//8R/zgd/9XQYHB3n0wQdRl5e58kWlYdOrqzz07W/zrve//8eO4dBQH08+OQ0MXbC/Xl//aR6J\nV0UwGGT79u0IIfja1x5GVXUkqRWWC4VClMs1JEnD973z56TTQd73vndSr1uUSjU2bdrJrl07W9VE\nQoAk4QnBaqmJpqXwPAlD+KjRXvJ+GSs/T1uom6JVw3drDODThoctxQgLnY0ssUQAkz5AoGEjiKHS\npMHTKKQIsBeTCSTqyHIQWSRxXA+JBgouEhauB62aCx/fTwJhHKFhOS5dkQSqpRNUFGwhWPN9NF1n\nc28fqQ6bbVe0oapzlMtFurp62LUrxP79b3xFwTXbtpmcnCSXK9DenmJkZOSfVQclk8lgWTodHRey\nqraE6Z7l7W+/7dx2mlQqwPPPn0QIGceZw3VDQBxFGcS2VQoFk0plFtcNMjMzRr1eprMzRTgsGBzc\nzIMPPkYms8baWoHV1TyJRJTrrruM7du3X/Cu+dzn7qFcThCPb8I0m3heASFkXDfG4uI03d39lMsa\nrlvE95sIIROL3UC9foxLLtnGwWeeJolOlRJDBAkRxkMQokYJwTgqgk48JFRvEd9PY1kSrhsgmezk\nrW/dz3/9r/+A60ZIJGoEAh1EInHq9RyBgIZtL3Lzze8ikUhw+vQZpqeXSCbbuPTS/a8qqvfjEI/H\nuelNb2LD5s24rktfX99FlZK4mMZIApg5970M7PjhAUmS3gQ8DngX+R5+LvC8ljHyl3/5+l/7ve+F\nT34S7r+/lUPyy4BoNErftm1MT06y6UXuxrHlZbZfcw2qqjIyMsL6W97Ccw8+SMlcp5yJoUUTXHPj\n9aTTHWSzi+zdO4BpmmSzWQKBAB0dHQDMzs7y2c/eh+QMsqG7h1Nj36diGqhRn/ZkJ47TRAkIzLrN\n4ccf5BPVWZ47usjC7BKaVSFol3CcDlwM8GE1uwZSP4gsirJKW8AhHPCxm5ew4EwQRidAlCpNaoRx\nCKMDCgY+DXzCeKwisQ6iHUky8YgQCoaxqgeRfZvJWpE2VQYEIc1AVVwaeoB0uovJ1dPU/BTZbAir\nprJqyeT0Em/avYuB1FWMTR3nxPoEZm8velWiCHQODpKdnmY4kURVFFQpywZd51v/8A/85u/9HlMn\nTnDNS1y9G7q6eGpsjGq1et7b5HkeuVwOTdMuyDvZtGkTw8OHmZs7TUfHMJIksb4+R2+vcv68TCaD\nEILu7u6fafWVyWQ4fvw0xWKOUsnDtkGIFMlkO6urSzhOFlnupKMjxerqNO3tcPXVVzM2Nsbc3DF+\n8IMDjI1NceON17F1717GHnmEtWKRarWJpnUjB1SqTZ9yM0Oz7hMX3VTrSQQbUDhOBBUXH4FAJYiO\nQpgoDhEkwvg0MLDRMbCQ0KQYQpKRfAlV8nDcZZAcDNlGEusgrSNIokgSmlzE9UFRgoyOTiNLMnOe\nBG6Zphcn5ofI0+LeODt1hn27QrRFo9ilVS5tDxLoCFERAr9ZxjRN6vX6BTwUxWKRz3/+yxQKCqoa\nw3XHSKef5oMffDeJxMvLoy8GZFlGiJfnOPi+h6a9MF1IkkR39zDVaheVyjxTU5NIUh/gIssGjuOg\naVWKRZ94vIdIRBCNqjQa60QiERYXK9h2ie9+dwqwue66G5DlNPfc8whXXjlGZ2cnuq6zadNGDhw4\ngusKbHsZyyoCXUhSAlhHiCqWlUSWQwgRwXVtIECxOE806tDTM4BBCZkgPiZ5gtRoEMDCwCZEkAg2\nTeZB6kfXb0CIdTStjUajwhNPPMd//s//D29+8xm+/e1RYrFOCoUcJ0/OEIlUuOWWS7nrrjvOh2L2\n7buSffuufFn/vVZMT0/z3S9+kZBloQAPA1fccgvXXn/9z3ztV8LFNATKwA8DxHGg9KJjHwJ+k5Z3\n5FXxp3/6p+e/79+/n/2vd9zjIuHoUejshItQGYmiwF/9FXzwg60w0C+LCOstb30rX7/3Xg7OzxOW\nZaq+T3rzZva/8YVqjGuvv55L9uxhz6FD/J///WVqqwuMPXGahtOka6SXK698G5/85Gfx/RBCWAwM\nJHj3u+/g4Yefpq1tOyXjDKqi0RFLUG+UydUtdAVqVhHDnqMzGqaemeM7X6+yWo6iy31EJRNdXscX\nJRQXFCw0SSEoy1RFgLDchuXMo0lhqk6eJl0EiKJQw8E85xFJo1MEmgSJUSMH2AhsfJZo2sMgLyCb\ns3RRZqPWQRhB3q4gsMjaOYJyDFeSKTWy5Os2qBEKhQJFO0hU9FBqFvjusVHevAd8TeVtH/wAN99x\nO4GnDrC4aFAvuyR1HVVRsF0LVa6zqWcXoysrjI+PI/k+6ksMBEmSUOB8QvDo6CiPfPObSI0GnhC0\nDQ1x2zvfSTKZRFVV7rzzXTz33CEOH34e3xfs37+da665kt/93Q/xP/7HZ6lWWyvRSMTjPe+5jeHh\n4df8nJw8eYqvfOVRNK2bWGwHJ08+QqOhYNtNFMUgHM6hqg0SiUVM02Dr1j4uv/x6vv3tBzh0aIlm\nwyM3c5oT1QLf/Pt7eNeH3k/fNddw/FvfYrVWRJEDZJGYrTVRnQrd/gBlXKLnckFUyoRxAI2q8JBQ\n8YmiIOEio6ISIEQDHxkPmSSuqOOKs0hoWITQMJGpkqCB6Vcoq0tIcgPflRGehC9q2GY7lVKKSjlN\nRNIZjgXIl7IseyZtSpCU6pPwS/jZOIfLa3zgppvo6u6mWCxSHJ/ie489xw8eP0PvQB/XXruLm2++\nEUVRuP/+h6jX2xkcHDrfp6urMzzwwCO8733vfM3j8dOgu7ubZFKmXM4Rj7/AYbG6Osn+/S/Qjg8M\nDOD7WebnJ7CsGKbZhudV8P15PE8QDA4hSQrR6FYcZ5lYrJfNm3eTy2WZnDzFxo0JDCNFIpHEMAxO\nnTrFzTffTqnk8Fd/dR/XXvtGNE1hbu4fWV218X0Xxwng+0k8bwEhZKCCLAdpNFZoNs+g67sIBAaw\nbR/fX8DzTOYmjhMRddYoI9OHSgcNTBRW6cejHZ8F6tTpR1W3oevteF4WVRVEo3vI5+9nfHyctbUm\nQ0P9rK3l6OhQcF2ZK67Yzp/8yR++ovFerVZxHIdkMvmauWTq9Trf+cIX2BWLET/nWXFcl0MPPEBP\nX99P9d/8cbiYxsgB4CPAV4E3AH//omObgW8CvbRyWZ8SQky89AIvNkb+JeF734M3v/niXf8Nb4Db\nbmtxjtx998Vr5xcJ0WiUuz7yERYXF6lUKiSTSXp6el72J4tGo/T09LC7I0g6HcB3fBLJGDPZdT7/\nP/+Jm279CNo5Vs1MZo577/0GKyt5+vv3szK/wlJ2nZgeJ6larNdnyDamMYLtDMV6mV/+AYZQkOUA\nqmPS8BRsRQNS6H4ekzwmDr7I47gVVIq4dhPDdik3Qgh60elFJ4GPisIaEtPIbMJDR5PqNES95RGR\nVBDrGGoHshKgYZ2mC4cuwri2oOFAQArj+XXG/ClcX0N3e1ht+lSbYSJxGd9PEk724VWrBJRuzIbN\n06OjbNya5M4PfoC+vj42bNjA5z73JZ4+O0abb5CvNLGdVd54+TC6pqFLEpIkkeztJVMo0PUib0eh\nUiHQ1kYikWB5eZkHv/AFdqfTxM7Rzc+trvK1e+7ht37v91AUhUAgwP79v8L+/S9nd1XVzQwMtDL3\na7US99xzP//+39/1mlbjpmly332P0Nl5+Xl23XS6hwMHHsIw1ujsbGfr1ut5wxtuoKuri6NHj/PA\nA0/x9a8/xfHjZ0inImyNBNjTPYye7GQ9t8o3P/W3+H0biG+8nNVMgXzBQ5MTBNxVVKEikyGAi8Ya\nKg0MAqh4RNDQcClSBNooUEdCoOHi4uHi47CAhEWIKj5RbAbQRDsSIMk2hhwj5RdpupOY0lUI38AW\nU0AvshOlmF1HyBGaUgTFkBiIaqjlBVSRISVLXNq5DUfXyVSrVIslThw8TSZToVhsEIoEqK8X6bnq\n3Tz++AmCQYPLLtvL5OQK/f0Xrnw7O4c4e/Ypms3mPwsRnizLvPe9d3D33d9gYWEFWQ7ieSWGhyNc\nf/01538Xj8dJJkM0mzKmGScUGsSy6nheHM87QiSSplqVMc0GqrqIqtbIZqNkMjkcZ5GRkV3MzdWI\nRKI0GnkKhSpHjjzE2hrEYpcSDCZZXZ3mkUcOUy4ruG4Ty3oWaIOWxjIQQJI6MM0yUEOILK5bRwgT\nwxjEdWuYmWmajRpRBvEIE8Qijg4MUWAejRoRoEQVH+ucQF+FZHI7rlsmHk/z7LOHCQSGuPLKITzP\nxXUddD3AwsJhFhcXGRoaOifkOMoTDz7I6MmTqJJEX1cXoXSaN77tbWzatOkVevuVMT09Tcy2z1em\nAWiqykA4zKkjR/5lGSNCiOOSJJmSJD0JHBdCHJEk6a+FEL8vhNgLIEnSXYDySobIv2R8//vwZ392\ncdv47/8dLr0UvvIVePe7L25bvyiQJImBn6A86eBjj7Gnr4/0i9xGS4urqDUTy2qeN0a6uoaYnz+I\nqvo0GhWGRkb4yqHj2PkiZr1K06vSsHUi1MnVH2WbESBq9NEwA0SaFaZFnbLbS1oJYfsanVhILBBC\npYJOJxJhIpiYlLEJ4lChggJ4aHiE0RG4HMUnjilsJGwUJBTJRw/txnHXca1ZAjRpQ0dHxcDHE4KG\nkAkQxpU2YmsRPL9KSAkTikUJhVPU6ypdXX1UAnnKhUU0CcxQisTwEI899iQ33vgr9PX18dGP3kk0\n8gWe/MZ3Gerq4rLNW+lsa0MIQUkIenp6SKVS/OOnPkXP6ipDfX1Umk2WHIe3fvCDSJLE8YMHGQgE\nzlc7AQx1dpKbn2dmZubH8hL8kCsCIBJJUCqlOXPmea677ifnKVheXsZ1wxfQ/EciCa6//naazVP8\nh//wO+f3j4+P88UvPsrhQ1lKOY9GdRtLaweIGhU2Jzrwg0FOTo5SLS2hFhroO2N0dWxHzz2HUl+l\n5pWoIdFNEpUoHg2iyExjMYtDFwohHFxsVqlRpx2PBXRiBFCBDBEKdNMggo6OQZkFcqzg00FE6aSJ\nRRyJhFRgnWN4IoFKiAhRPCwc4aF6UUzZ58jqKEm5Qb8eBlSiMYlco8GO3btZOXqUZw+fZahrO5Y1\nSzo9iGlVmZ+dQAhBX99OnnrqKN3dnVQqNVz3wnCILLcqQDzvhTyb14JcLsdzzx1hfn6Vjo42rr76\nsh9LqNfb28vHPvZ/8dBDD3Ho0Ck0TWdwcATHcS5IqvY8nQ0btrG+7lMuF/F9j2RyAKhRqZzFtoPE\n4yPs3v3rmGadRmOOtjaXVKqb7dsvZ2HhEUZHH8N1gzSbPmtrpxDCRVV1stlnKZddms2OcwaODxSA\neaAdCKIovSiKjOcFgABC6EiSTTjcg+PMIUtVVqbPAjoBwqhA85wejYxMgzDzrBElTESxUZIKnrdO\nIBDEMGyi0RCRSALT9IhG26hWi9TrZYLByDkelBDlchnP8/j/Pv5xjn3zmwTLZYKKQrS7m/K2bWyN\nx/nuPffwnt/5HTo7O1lfX8dxHDo6Ol7GVPtDmKaJ9grelICuU67Xz4djVVUldW7x8bPiouZrvLSc\nVwjx+y/Zvuditv/zQD7fKuu97rqL204k0uIcectbYN++n51y/l8T8pkMm9vbqVarGIaBrus0GhZx\nI4hp1olEXlhty3KQvXt7OXBglFMnMgQ8FU+P4gcF7Uo/LlHKzQmGEwphKUWhWsF3JQIYpGmSwSXr\nrSJYYxEDmTQeJpuQ6SeBikKZBhpNMlRJEEJFxUelgE8DCZdFZAoodKNiEGQdzbdoNgW+GkOlgQVU\ncEgjE0XDRKAhaGDjIYjEdmEYEIkUEKJKtVrC91NYlkkkGiUYSlMtNWiW11k/Nsl3R1f4+hfu43c/\n/n+zZctm7FIRXTRZnHieeinLnp07qTgOvXv2UCqV+cY3fkBZGWBqZZbH545y82038d63vvU86Vxh\nfZ3eFxkinuexsLDA5PHjrLgub37b29h72WU/MeOqrocpFF4bB0Zr0nx5cZ4Q/svc2E8+eZjTp7N4\nVYO+ZJoFu4zqx8HxOXXiEGpbnGApx6ZIEikUxS1kWJsfozOQYKnUoIZHHI8FsjRwiCCzEYkuLHJo\nrGADDnUUihjodOKSI8w4CjYCkxAufWg4KIBEnCAGLgUcgk4dIepoVBBey1Sp4iDRRYUSCJ0AKkEU\nTF+mikHRV4hIXQQCDs2Yy1UD7Tj1Otl6g75oDJDOiURKmELgG53kcstEox0cfvYwgcoS2clpZs4W\n2XXFNedXvysrc6TTQSKRyEu79sdieXmZz33ua0hSN7HYIGNjZU6c+Bq/8Rtv+rHnHjp0hAMHFojH\n92IYQZ56KsOJE1/gwx9+H/F4HFmWWV9fY23NwzD6aW9PUCyu0WiUcd06vi/T1dVFT083oVAbkUg7\nuZwCTJBKtaMoKq5boFYLkUyO4LpTZLMGlUoDSZrB8xx830GStrWIjonSSnPspJV5EMP3LRSlm1a4\nxsIwQshyGFVdIxzuo5k/go+LQKNGa8IVCBq4+EjY+KSAHBKebuCYhzAMQSg0RKMxi++3iAG7urq4\n7yv3o9fLRCSJhhBo7f3EO1ueyT/7L/+FQ1/6Em+Ix7EMg6Ask1ldZdX3yQ0P0xsM8vjDD9OsVKit\nrKDJMrauc8Mdd7B7z56X9X1vb+8r6suslsuEBwf5u//235AaDVwhaBsY4LZf+7WfmZvoX13y6M8b\nDz0EN9zQIim72Lj88hYB2p13wmOPtfJJfplhmibT09NMzC0w98RhkpEEkuSxcWMvbW0xjuazbA+9\nwHPh+z6+X+a66+4gGj3NN//p74kpHVhuk4F0Nz3JnTRMi6PT8+TyJQip6Kqg0nRxPIGBh8Rp2sji\n0INGBw0U6mRRcaljESGIAdhIxAEXExcNFY0YFnVypOglgkWDOt04xAkiIcj5a0zYRSzS+KSp4jND\nmRQmYWK41Cjh4nt13EYGzTfIN6ZJd/QhUaRWPYrr9jAwMITnWhjNKkPdIa7auBVD1VkprvHXf/43\nXHf5Vna3tXHp7bczNzvL+OQkDx47xgc//nE2b9nC3/7tl0mn99DVFWHbthup1UrMr5y5IPGxe3CQ\n3MGDJCIRfN/n2KFDmJkMOA5bNI3nv/Mdps6e5T133fUTVWY0mzkGB694TePf19dHMGhTq5UuMDjX\n1qZ4y1suFPOan19hZSFPQu6iUM/h2Daup4JskC0XSEsWKUkiGolSlCTkZp1EtcKEYyH5JjvwqWKg\n0odOhDwwSx2DOkUEMu0oSpKGX0ARGhYT9FJkMxIughwBYlgohGhioeMRRkEH1qkghEGMCjEsbDTS\n+NjUEFg4BAjRhYyPSRmXLAE68PCpOSYJLUjdSnE2X8Irl8kImfL6Moqs4fgW9XqOJR+8YA9jY2NU\n84dI0mD/xo3sSKX4+pOnOPzotxnvTrE8eQrTKrL9ssv4O+Nu3vGOW88boD8Jvve9x9H1DaTTreTK\ncDhOo5HkW9969EeeV6lUePjhIwwMXI2qtp6XSCTB0tI4Bw4c5tZb34hlWZRKJYRQCAbjWJaF78dR\n1QaRSIhotIuNG6+jUDhBqXQISQpiWVn27esiHo/z1a9+jrNnFxBimEbjII7ToNkMIkQQ36/j+wJw\nECIA5IEarcyCyrlt+Rwzrg3ISFIJIdpR1RS2vYDVnKXHKdIEumhiUSFMG2vnjJAoHj55isSwpR34\nis727duYmlqgXq8yMLCDdLrFniucGdypgwx1bCOd7gQEo7OnyPtJvvmlJl/69KfZ1myy1GiQUFWS\nsRhdQjBRLDI9O8vuLVu4/0tf4lf37eOScyvXhmny+Fe+QiKZZPAlq9menh6GLruMI4cOMZxKtfRl\ncjnygQD5Y8e4rKvrfDh2YX2dr959Nx/6/d9/GY3+a8G/GSOvM+677/Uv6f1R+MM/bIWF/vqv4Q/+\n4J+v3V80TE9Pc++932Vqao0zZxyMYoPL+1MM9Q1ydnQBO9Qk3N1GvV4mEAhjWQ1WV8e46qpNpNNp\nduzYxp7BNqy6RFt4C/FQB9VqlWKxiun5NEWCSqOIobUhRA5V0iiLdTZRwSZMmF4UVKIIVpCBIHXK\nGFRwMFHwcXGpECCKgU4Nj3UC+ARQCAMGFXpox8HHQ8PGJkkHJgFcZCw8PFJYLJKmcO7KAwgaBJtn\nUJs2ilyhXp1HGB1IfglV1FhbKmBbBTalA+wZ3IyhtlyzPclODp04i70UIX1uFbxl2zaGN27kzMIC\ngUCAM2dGkeXO83wP0JoUisUkR48eJRAIkc0WCIcDLHsegWwWzfOorKwgFIW2vj429/cjSRJHZ2YY\nHx9n586dLxu/xcUxurpa6qlrazN0dAi2bt36mp4BTdN43/tu5x/+4X4KhQSKEsBxCoyMxLniisvP\n/87zPNbWlinkM3hytMVO6kuU7SgLTNKmOSimDbJgrVYg1LuJSiGD1xBUnTLbhcBAJUMPKRK0AS0h\n9zgreIBPlC5qnkqNXiTWiZNjBEECnyJtxFEJ4uHRoBOFKjWglY8hISiyxCaqrKEQBXqwmCeNQQyH\nOWxcZMJ4FHFZIcJeaizQoEDOcmlmE5xdz7Ip4tMX62CtUOPhwhmMcBJLSuOIIbCzTE3pVJaf473X\nDIAQdCSTvOuG3fyvr99H4WCG3QPb6Nx0JflykwNPnCWfr/Lv/t0HiEajVCqV8wR4r5TbY9s2c3MZ\n+vu3XLA/FIqSz//ohMrFxUUKBY9yeQJNU+np6SYWi5FO93HmzCi33vpGzp4dY/Pmq6lUDjI39yTl\ncgDHMYEMgYBLKrWBWKwd1x1h375NhEIG+fwiV1/dzpe//AMymSKWZeF5RRynAoQACVmO4Dg+rfqL\nTqDj3AhngNO02CiWz42XgRBFYBlNCwMSlcoChlEmqrQ4VTcA/cAKy5SxaSPBMi41ckSoUmUPrjaA\n45SZnVymv30r5foU/b1h9t/0Zh577AEWRr/P1akeFhdHWVh4nv7+LnZvSPPE1BhHZsaJOw5pVSXu\n+5SrVQxZJhoK4dbrTE5NkZ2aotpocFhVMXfuZNvQEKFAgMFQiOMHD77MGJEkibe87W2c3rCB04cO\n4VgWm2+5hdjKCmJi4oJw7EBHB9m5OWZmZti8eTM/Lf7NGHkd0WjAgw/CZz7zz9emLLcMkTe8AT70\nIfgRitn/atFsNrn33u8QDm+nUllh8+bbqNeyHJh6hBVpHiMSRo/E+OM/+RiHDp1kfPwJotEQb33r\n3vMlcOFwmO7BbhZPr+ILn9XcWQrrrcp0WXKoRbro0CzMYo5oKEamlsehTpeksiRCaGh4qC32SyKU\nKaBjo1IjhIqNYIU6ARZJoiPhoOJgE0BlnQYuKSwqgIeMj0MdCCEjkPCQUZGBKFWKODiEGUJBoLHK\nIHUC2Bi+w2lMio0QshKnadYI6E1810aWui4QkvM8F6deJrucYWpqmmQywdzkJMVMhlytxnSjwYZL\nrsAwkq/Q5y533/0N+vsvR9ejmOYKshwml4hy/OmnMS2LS3ftYs/WrefdvJ3hMPOTk69ojFx1VZoj\nR54DYN++7dxww7U/lYje8PAwH/vYbzE2Nk6tVqe//7KXMcVOTEwACZAzWKKLqN6LikNI+FT9MHV/\nGa+p0ysHcH2ZwNIa9doalusihENckqmIIAoxXCRUBGFgDZsyQWQiNIjiUEeiSBAFHROBxxo+UEZH\nIYdNGy4aBjFM6tRYRUFHIoRDiADtuMzgsEAAhw4celAw8aiiUSJMhBIBPPJ0Mk8HbWieh9coYCpV\nRoauY7h/KxMTz1NswKwcw7YNFGWZUKhJKhVhg9ZBIdtgdHqabZs2sV4s0icJIvE+to/sBaAtIjhT\nyLC+3sPx4ycolaocOjSOJIXx/TqXXrqRO+649YIcBEVRUFUZ13XQtBf2CyEQwnnVMXRdl+9//1FO\nnJggnY7j+w3GxpbYs2cTbW1RQqHWc9FoNIlEktx222/wla/cQ7k8gWGE0PUkoZCNbWeoVjOAiiQp\nuK5DJNLg1KkJ5ucNQqEOHGcBy6oihInjFDGMTbjuGJJUQ4gewIVz1W1QpTVlmuf2jwEKkESSYkhS\nO77vEwjkSCR0vHwOC4ihAUH6ESTJ0WQNF58SgmXa8JTtqFKIgOIhPJl0JEpQ6aK8NM+BZw+Qy6m4\nbpih/u0M9m+jVMoSDDbo7m6H44fpiETI6zrTtRqaEDi+T65SQZJl5k2T3aqKo+tcm0gwGApx6tgx\nouEwfe3tREMhlnI5stksE+PjeK7L8MaN9PX1oSgKe/bsYc+Lwjj/+NnP0v0iQ+SHCEoStVrtQROY\nDAAAIABJREFUJ/+jvgL+zRh5HfG978GVV8JLFJUvOnbuhJtvhr/5G/iP//Gft+1fBExPT2NZEZLJ\nII4jiEQCxBP9yFveTiTR4KqrrmBl5QipVIr3v/9dr3iNaDTKdW++lYcy9zB27An6/SBJT8bGIqL5\nrGKzHuukWMogOWuUZYHigSU8fBwcLGQ8fCQUNFZRgRrteDTxWcWjAwkXFwUXG4Mh1BY5FjI1JBoI\ngmjn+EaccxOYi4dBHIMcTSJoqKh0YtCkSQGIoSNRIEKTLAKX7ej0oalBQuF+XH+VqjtGZlXigcoB\nrh8ZJBpJsLK6zHrdo9AMcPr0OssLj7KzK8amri4cIC7LTB47gBe6hHS693xfCSE4deowu3btpb//\nh/RBA6yuzhBqU3jXRz7CwsMPs/0lq62mbdPxKnkHt912C7fddsvP/jCcG8sXe0JeijNnJggG0wxv\n3M3i5BEK9bMoko5wqshKDREfphFOsVZcpU1OY5oKubLHqrBwZZWSbyEh8PAxkRF4FBAU8RCMIAMe\ncSQ8QmSIUEMF4jg0kKgisAENmMI7R4JnUDznNRvGYJwqPjXSCJ4ihMNGVEJAOzICQQkHCYUKGhY6\nZ+khDugIDAy/wCZNIVPMMTKsMzKyjUxmiZnZcRwpyh13vI+Rkd3MTJ/m6LefQZFkHvvBElMLCwhJ\nAsshHH0hHCNJEklJomZZPPLI0zhOOwMD1yLLSissd+wMhvEot9/+AjW0oihceeUOnnlmnMHBF8Jk\na2tzbNjQ/qrjc+rUadbXVVKpOK5rEY93AUlOnBhny5Ywd97ZqvYZHh7g0UfHKBRsOjuvRte3k8/X\ngCIbNmzAthfx/THW1mZZX8+zY8cG3v72d/GBD/wBCwslarUA9fogLa9HCiGWMM1naBHzdaOqG3Dd\nCrBGi5liIy3GigawH5gCQJZLqGonjjOB72eRZYdiVsa2ZcIEkFAJoaIDEVQsGjSxKCLhE0OSSgR0\nlWgghus1cFwXaNDXlubkidN0De3FikapNmtEgxGSiQ7yhRnmFxZoui6pcBjP97F8wWlfIiFUHNvl\niXweO5Eg0t/PrpERlk+cwFBVBgMBxqen6WtvZ71UopZKce+nPkW7JKFIEicfeohNV1/Nrbff/rJq\nxZ6hIbLPPkvyJSzWVfiZc0Z+iRROLj6++lV41yvPdRcdf/iH8Hd/Bz9GA+5fJVqquRqaZmAYCrZd\nB1pue9+X8TwXTXNflQb+h7jtHe9gy037GUkr4K6Rs9eoej6qI9ArM8xmyuSlGyiKfTT8bdTpp0Ib\n7cg0KSHwEZg4uKi4RLGwUWmgEEZhHYlWASDY2CwDSSR0ylTwKBMFfEJYRPDoACrUafGPyASAJllc\nGlSQMVGJ4+OTJ0AAWwqzIMWJyn24eNTtALlSjXwpiuMolM0m5ZzN6OGHmTj8EKfnz7Lzqjeh9G6k\n0LRQ3BgrhTpzpRJWOMzlW7awo60N15lnYeEsltXENOuMjx9G05qMjFyY+NYqAZ1l85Yt5GWZumme\nP2baNhnXZccll/DzhqqqVCoFanWZZM+t6AEVYU0Q8vO0oxKs1Fgv+ORjVzLtS5QTYTJGGFkOEABm\nMVFp4rGGiUsJgwoy0IXAwyOERoMYLgZxmpSJ4bKMioJKGwptaPgEaBBkDZUSUVxU6nisYeEQYZ4Y\nxxCYpPFpR0dFZ50gEgphBFUazBFgmQgFPBp4WPjUkKiQFBHW1tao1QoYepDBgREG+zfS07OJgYHN\n1Gpl8qMHuaS9nxSwJRCg27I4OTZGQ5GIxi8s47UROE6D5eUCvb07kOWWt0mWZfr6tnPo0CjNZvOC\nc2666VcYGTGYn3+OhYXnmZ8/TCJR4ld/9bZXHZ+DB0+yuFijXteYnn6aw4cfYG7uBKXSJIODynmV\n4eHhYbZsSXLy5JOsrJxifX2M9fXnKRQmKBSWWF1dYWFhieHhLQQCQTo7U8RiMcbHp8jnG5hmL9CN\nJA3h+01a5qFOKzlVxXWrtKZIC0jT8oJYtIyVNmAEGML3+/H9CqoawzBiCNGN5wxgYOKTZA6HWZrM\nY1FDYKKxBvgI9GAPsZiJrsgkIhF0xaTaWCYUMOlIdGGbTSxrmUv33chUs0q+VsJ2HdaKBZ4YH6fa\nbHLk7FlSwQiWNkRd6WJGSnBGDtFUI+zp6sJrNCjX60Q7O1nM5zEUhWq1ylwmw5zjUJydpaPZpDgz\nQ35mhh5JYurpp5mamnrZ2Oy94grWFYWlbBYhBLbjcGZhgfiGDS8L9bxW/Jtn5HVCpdIK0Xz60z+f\n9vfsaYny3X8//Oqv/nzu4eeF3t5ehHgaEGzbtoOjR88Si22l0SjR1xdnaekkb3nLZa9axgYtVdmn\nnjrA6GSGuhbHNFxCag+hUArXtVnIjeI0VFS1gXBqaMJGsJcZTtNDjjDLlMlSQkWiyTAV4ki4KARw\nCOPTB5hILNN6nTnIrCII4OChIpNkmgopXGR86oBBGZ8yDnVUSshUiRNApYlLCJsmISyC8gCyZGN7\ndVyh4QGSMHBcFRUXSQoSD0govkLBW6Rh19mx7XqqlRxD+9/F6aOPUVhegqpFNJnkjVdcga5pdMTj\n7NuYJt3Xw7FjJ5BlmRtuGEDXG+cno5cilUpx83vew8Nf+xpRp+WOrygK+9/5zp+amvr1xPbtm/iL\nv7gH3/fRtAgJPUZc24hMCl3PYEgKpmWwnjcIaJuo5uboV8O4nkvEL+D6IaYxcciSw6VBJy46nPtU\nEOeopR3ARCZHGwplBCVU9HOekSweEjIr+PRQYxANH4l1HGqoWASpUsImgIJElE5McsgU0RB4ZFDJ\n0Y6HeZ5EDRQ5iCTFsHwf0xKsr2eIRVMUamX0VCfBWgnDCDI/dYIeTSc1tJ0Zr0EtJPBtm/ZUikh7\nO2FTUKnmCYXj5GtlFhpVtncF8LzIBWEXAEVREULDNM0LuEgCgQB33fVelpaWKBQKRKNRBgcHfyTD\n7tGjp1hfb6en53I6O3dTqaxQLC6wYUMfN910PUIIGo0GBw48x+zsEtnsJOvrHu3t27nkkp3YtsTs\n7CP4vsQ73vGOc2EGwZEjJzl9+q8QohMhFIQwgBCuWweStAyNTlrrfAkYp1XCa9OaKldoVWuZ57aD\n/NCr4rrrCNGO5xWRuJwgzxDBAVxCqISwkYEJbDLI54I9EvFEB8PD/ayvTIJYYKDTJhjI0ZHoZ25t\nHD2co6trM7t2XUexb4T5iaOcmjtLLjvP22+8num5OU4ePoxiR0gEU9TtAnWzylAwTk9bL3qzyuXx\nOKfHxhjZu5dYWxtHjh+nmk4jjYxwSTTKE5/5DFkhCAcC1C2LyUwGP5Xi+WPHXlaS39bWxrs//GGe\neOghnpiYQFIUdl5zDdffeONrJlZ7Kf7NGHmd8MUvtvI22l/d+3jR8ZGPwOc//8thjPi+z+zsLLOT\nk2iGwbZtaZ5//jDJ5Aa2bx/gxInHUVWXZHIPt9xyzY+kR65UKnz2s/fSaKRIJK/g2ewkwgvTZtgE\nfI9ms4arhPH8BN1BF8Jp8uUMvhfGZAvjxElSQOAQwKWDLGFUTBya/z977x0k2XVeef7us+lt+aqu\nqvYeaABNeIDgACIJUoRIkKJoRqQiKAwHK2mGoQ3tajY2JrgzmphQbEyMQitNjIbQcClSIClB5A4E\nwpuBa5h2ANpWd1V3+cwy6fNlPn/3j5doAoSTYCkGzz9Z3ZmReStvZt3zvu9852CxGw2JpIBKC4HT\nM3ufwGQOFx0TH40uMboMEOCRwsFEkmANQQmdVUJ89qKQoEsHnTZhT5mv0RUuSuCh49DGjqynhQPS\nROATyhopN48es9iV2kCrtUhltkQ1WOTOWYuNW3YxNHk1cX2NK/ZuI9U7UGqWxdjll/Phj3yEj33s\nRiBq08zPr1CtlikUflrKX12dY+fOSQzDYPeePWzavJnZ2VkgcstMvk6v+YPAwsICzcUT6JUabfcJ\nYp7ElwUMI0QIiZQORiBoBevYMkWBFqqn0Qna5KRNXBjY0uQ0DiYeCarYmFiEpBnFx8eigodHnBqj\n1PDR2YiBQoxST9ZcQAA+HRS2otEGLBIkSNCPywxtCkCONi1sIIfGAEl8AioEhPTh4JCgi0JAnBh5\nZBgdkeeVFewwwVx5DpHJsBz4jO7awc2XXMmZM4dYWTjDuGtT85rsvWgLl122DyEEG+bn8TdvZunE\nCV549ghTRxvYJBjbMsrOnZMsLVWwrOarEpht2yIe53Wrj0IINmzY8IZp2a/E+vo6UqpoWkRiVVUn\nn5/ANLOsrDzCuXPz3HHHD3nqqcOsrHhkMgU6nTSp1Aie10ZV24yPb6NS6UOIBHv27EZRBCAYHd3F\nnXf+f0xMXEyj8QLNZr3nogpRRQSECJCyiGEoxGIZms0zQAlIEnl15omISpWIlGSIyEuMIDiGoqRQ\nlDKqX8fApQ+LHAlCTAQhWQQruOg4dBimP+0xMGCzdct2lPoCt3zoWoYKBU7MznKmVuNr/+Z/45ln\nTnL27BEcq0Wn02Z5aYor8hlYXmZIVTmcSLDc8hh25hgwTMZ1g2IqxXRnnVwhzVKlwlg8zvT581yy\ndy8TN9zAF2+/nf7+fr733e/i1evUTJOTi4vEga6UtMplsldc8bp7NDQ0xG985St4noeqqr2R+neO\nX5KRdwl33AH/4T98sGv49Kfh934PqlV4h+27n2sEQcDdd91F6cUXGTBNvCCg4vvs27ubrtsgnY7x\n6U//Frt37yKfz7/ll+X554/QbudQ1RTPP/MAK5UGBUxKVg0z4xEI8DQNPVQZLBRpOj4JN0273cIA\nTIqkSKHSxmaGIhrbgAVUHEJUAuqE6Ki4PSO0ZQJapAAXnxQ6ghY2CuN06ZImIKCNyjp9+HjQm6oI\nMQhpEaIQo41gARWJRUYEBNLH4Rymso0gbBFgobBCDB9bVsk66wS+RA8DCF36EzlSqX7qMwusxgP2\njDkUeirocrVKRdP4xL59dLtdjhw+zJkXX8QwDHbu3MiBAydYWKhgmpleZLrNzTf/NAU2Ho+zc+fO\n9+pj8LZQLpf503/7b9nUaTHUN0izXaVULVPFIS7zKH4fZuhiB7M4cgSVButBHU0aKHho6CBVAkIm\nUAEVjyI2cZZZoc5pdEbJ0kGlTp4l+ghoAhJBGxUXDZ0akyg0CIjjAx5J4rRIoZHBpE4KA5MiSWx8\nVmmiESPLGm1ggX5WaQuJJRMIusyzQg4bjQwtFFpMMjCSwRpysYaSfORDl/KpT32UiYkJFhYW+MGd\nDq0jR7hyzy76+/svfE+awCdvvJH5rVuZbWb55PU7GBgYIhYzOXXqBENDKsvLL1Is7iSTKdJq1Vhf\nP8XnPnftOxrthMjCfGhoM1I2KJVeQNf7CUMP3y/j+xaPPDLF0aMVZmaS+H6GatUlCBqo6lFisU1U\nq48zPDyFEAoDA/3Mz8+TSCTo7+9H1018X0XXmxQKozhOCdtWEKIPKdeBFlJ6vYqfiu+nUNVBgqBK\nNA/jEBGPFFADqgiRQAgLqBKGKaBA4DtYFEjSZoIOChIXlS4CgUKCDvPo9PdfzK23/hbd7gJf/vK1\nmIbB8489xvTyMkNbt/Ivb7qJjRs3kojH+es//TP0eptYu0mmvkwuNcaGdJry8jJmt4siXYbFIKpQ\n0BGors94zKCbMhneu5eZqSlOrqyw95Zb+PyNN9Lfu2r2u13mLIvJep1LUynUXijko8vLzM3Pv+le\nvdvhib8kI+8Cjh6FtTV4RUzKB4JMJhKy/vjH0WTNLypOnjxJ+ehRLt+48UJpcNx1OXj6NF/9/d8n\nn3/t9MebYWpqltnZGoeevg9/pUaKLO3ARYYxzjXmsLR+wvhu3MYx1prD+BI6TqvnNDFESJI2FinW\nSVJllCQ2HgKBT9RpdntmXAYGoneVtIxgEYcueTwgxAamUDDwqZPHoojKBAo+IS2i4cIuUCNGiIYD\nxEizFsSQmoIIimRknIRxhooT4kufJA5xfFJCMKLk8JxZjHQcIx5yxm2QBVRh01w7TmtkjG8/+CCj\nGzawYccOPvtrv0Y8HufOO+5AKZUYLxbxOx3OPPwwOy66iA0bN7O2VmN09GJ27dr5ntqFB0HAzMwM\nCwvLZDIpdu7c8YZGXJ1Oh9nZWaSUjI+Pk06nCcOQP/6jPyaYr5NWBuk4cSy7RlJR6PiSjq8iRBct\ntLFkGykGCMIhfKmzQgtBnQJtcsACARuFiUJAS64TMMIAWSzmkMxRwGcTHgqSaRTqyF5wnkqXBntQ\nSCPwep+GGC4NQKGKTZcYIVkySEyKnEdjlhOs0iKOiiQlbEJVRTOS9Hc8cgi8HglZwaXNFmTYRlFc\nLt42yXDW5Ogj9zJz5Bkuufxy9l9/PV+//et898//nI7rEkqJ4zgcPHmSNdPk+NGjPPXcCXbuvJZU\n6qdOxuPje1hcfJpf//XrefbZl5iff5GhoSK/+Zs3snv37tfdi38MisUiQtjs3389lUqJ1dUyum4S\nBBNMT9fpdAKmp+v4fj+x2CS2vYjnmQgRI5EYIZEYJZ9XKZWOUC7HOXZsACFCNO0UIyMxOp0aS0tl\nVlYCwjAEziFlFuggxCCalkfTHLrdFcJwClV1CQKFaOy3DiwQVUYyQIBhLAORBX0YdglDE8igUSVB\nnCRdEnTpItCBdSQdIK7vIqsaHDt0CN2AH3z/x9x66ye45Lrr2Lx5M6qq8vyBA9x9550cfPJJrt6+\nnX3XXcMT999P0N+P0e0yPTtLu1plUNNQNRsbl5SSpuF5xH2frBlDSRps2bYNLZNh186dfPpnRI0D\nQ0OERHWhpuOgAE3fZ6xQwG9EYYrvZVLvK/FLMvIu4I47osP/58F07AtfgP/2336xycjpF15gPJd7\nVY8yZhjkw5DZ2Vlc1+X5p55i6fx5csUi+6+77g1zGWq1Gi++eJQDB9ZRay1MJU88FafdXqUrWxBk\nqPgJTPspUrSprbfw1RGk1FFFB0O+RBMFBUmKLgY6XSQuCjU0qqiksYkDDQJiRCOgAVnWcKmxA3pe\nFAIFjSQhDh7rbFQmcMMmZymRwqIBvSFBlX5cXHy6qKQIWcaj6ruoKKg4KHaMBDFU+rA5jUQwExiU\ngxr9+GyLJTGyWbZn+/G9Ek5rjvGUzyevuAJD0zjf6XDtRz/K6Ogozz/3HKJUYu8rBGqFdJpnT5zg\n2htu4KqrXr+c+27Ctm2+9727OHeujWEU8LxZ7r33aX7rtz79KuGc4zjceecP+c53foJtxxgZGWLr\n1iKf/exHyOWynH5xhuH0GDlVZ3WthnTj1P0UIQ5tqaArSVbdOg5pNJnClTaCUUIkgjwv8TxZYiSJ\nEZCkLj18mricw8dlHJcEISqCNRQsBB10fCRTSPLUyOIz33sMgItCdNxraMQJUakBTTQKKCjoGIok\nEebQ1H4ShommmMTjktA6Qh4Fg1wkvxQxEjLgNOcJyeNZ61w+dg0vHDnCPlWls7iIk0jwt0eOkNqy\ng4GxCeaWFzgzO8vS0hJ6EHDl1q20jx7lyOOH2HbJMHsuuujCd01RVBQlzsjICL/zO5e9xqHznSKT\nyXDVVTt58smXGBnZxeDgBPX6GtPTj7Np0y5OnjyF62bQ9SwQEoYemjZEEHRpt8vkcqOo6gCtVo3N\nmzeSSCQIApfDhx/jwQfPo2kC284AwxhGAts+CawBY4CC570cJVAFRnrieAdYJDqydSIXVguoIMIs\n6dxVVKtPAOMopDGoksTCQec0XbYBKSQqkTtJlT7G1Szx0KOoKCwvn+aZIwdZvu/HxLNZzq6vs1qv\nszUWY8fWrWwKAmaPHuWl06dJdbuM9PXRWFmhu7SEbpoMKQpdTTCRCbGFi9MU1N11AquLKnOcmZ9n\nzTD4woc//Jr3e9O2bSRTKTYXi9SbTYIwpD+ZJC4EWl8f3W73l2Tknwo6Hfj+9+HFFz/olUT4+Mej\nRN9m8xfXc+TN/gCurKzw+I9/zAZNY3cuR3NlhXv/8i+55tZbuexDr3b0nJmZ4a/+6h7OnOlgtSrE\nLImi+cR1FVWXGHaZJB36WGYUSZYBVCT1YI51kUIRBUy9xXbp0PQyLBAgaaP0klrBQMNiGpcUYJBg\nCUkdgy6CEIMCVXwa2IwTMg69Ir5Dg+PhKcaJnF6XEdhI9gA6KiM9HcoUPt1e8medgD6GWKeOYJYM\n0EGlQhKbUTSRwqLNmjRIxlPctO0ycskMUy89SdI0aKViFNJphgoF8s0m//Pee9n8e7/H+VOnGP6Z\neGhFUcgLwdLS0oXo8vcSBw48y7lzHtnsVoSATGYjltXg+9//e/7gD25HVVWCIODP//y/861v/T1B\nsA1dz/LSSw0WFhbx/UfZvXuE/MAmuqsvMZTKkM04rFaqaGKAmlijLAxW3XWSBJikcREIhoAcLiEh\nIZIsDiOkmSMnJRr53mTLAtsxaBISoGGg0KSLRoKNGHi4lOlSxyeNZITIQ6aKZBqd55DEsTHxWOlN\nYWXwsKgxSxc/TJE1Btk1Psnp1TXSMkbouUhHoNCmQxadJFLaqDiktIDQyJNLDvN3Dz3OTUMFBlIp\nOqbJs88dJpYZ4aEnf0g2UyCpehQ2FDGF4IvXX3+h1bJndI5zp48xPDpKX8+vIAh8oEu293l4N4kI\nRG2a6667imw2zRNPHGZ11WF0tJ+vfOWT3HffiwgRIIQgFkvQ6TSQEnTdRFV9gqBLPN6H45TYsuVi\nrrjiEp577mGOHTuB72/oCUyzKMoGwnAJ14Wo/aIBOlJG31nPOw9swPfD3n1JoubVKpGg9WUH1mFc\n36FWe5AwjKqe+V5wQ546W1EooXAc0FHw8VkAhsUoQ6bGuiKwvTbVc8+zPxnSWFxk7exZUkGALwSD\nus7y2hpNVWXvhg1Ynkc1DMlms3Q0jdPtNkXPo+j7+PE4xWIerdsl7wY4ZpoZIYh3uzyzuMjvf/Ob\nDAwMvOb93rZtGxv27WPpzBmKiQQh0FFVNu/dSymReMsJxHcTvyQj7xB33QVXXgn/AG3W+4JUCq65\nBh56CD77/iR+v+/YsW8fB06eZOAV1RHX86gC3tmzbI7FGOn98UzEYmSTSZ6+7z72XnzxhYka3/f5\nm7+5j2x2DwMD0GhAuXEA6bdx2rOkFIsNSpxS6DKCJIVOjDiSgCKCCk2qoUB4MRzRxMannxQuBm2a\npHBQiGGSYAEoEzBAhnKPUqh4FDB7QsQmAySps4ZLkYA10sTJkmKELuD3wuYDmigUCOn2JjZymD3d\nQZKscPBx2CQtBkkTss4yOgED1BgmkCNIbBQlzanVk+wtzbJit2ksnqWhhrS1flYrFYYKBYqZDCcX\nFuh0OpiJBI73WpMqT8q3ZUz2dnD//U9y+rRKENSQUhKPC/bv34NlqSwtLTE+Ps7MzAz33fcUrruB\nvr5dgEIqNUalMsNLL62QyYQMTezgXGmeUrVBYFkgJV3h0lJTbExMoHWmaYo4bZkkCH/qJhIgUaij\nk8Iki0OWVVq9molCokdCJGmSdLAI2UCS8wSoCLLkCQko4DEE9CMoI1lgiARFFhHM0kVQJ4OPTo4u\nISm6JPBZpEvM1xBhSLNjkZAeigAwGDBCyn4TIQwMYTCUH6AtfdpqPzvGN7A0+zTaYNS6bLQtqtWQ\nlFtBX10iHsaJ6xqNI1PkCianJyfZsylywr181wTnHjnF/Ow0fX19OE6X5eUTfOQjF5NIJN5oq94W\nlpaWuPvuh1laqgGSrVuH+epXb6VYLBKLxZBScvLkOc6cyaKqp4F+YjED2345h2adfH6A/v5h1tYW\nyOU0ZmfnCcM+hNiFlEVcdxpwUZQlwnCNqNoxQNSCKRGpss4TNUJHiGTALyfKvPy4nUQTNTPAKjHj\nKtzgaVTVJ+W3MUkRsMwoGlHjdQCNFHVCygR4VMhpK6yGUZjE8alH2CzbZF0NYVloUQgOaSlRwpCE\nomA6Dvb6OhuHh2noOiuLi5SlZHM8zkqtxiHPY9vgIPNS0m42SQpB1TT5yD/7Z9x4+eUcn5+nvLzM\n5OTka953RVH44m238dB3vkNe08glk+jxOGdqNa78lV95xxqgfwx+SUbeIe64A77xjbd+3PuJX/1V\nuOeeX1wysmvXLqb27uXg8eMMJhJ4QUDZdfnQzTfzzP33s+9nkn3jponh+6yvr1+4ii+VSnQ6GsVi\njtHRfhqNnbSWz5FqzWG6PmOxUVa9EjVaTJLCRCHExKJLmQaeTKMqQ7RDSVfa5FkijkYkJczRxKLJ\nGi5Z6kyQxCdkDYMxPHQUPGwEKlkCAhSaZDEoMYegTtiL1/JRERgIII6CisDCI0acGAKTAI8AlSaS\nGMgaGzBJoGMRw8YkQR8OPm1CBElgF51wkZ8cP0QxtNBVyKUL7MvlOPD005imyaaREaSqYhgGF+3f\nzz1HjzKQy1FttbBsGwE0dJ3Nmze/6r2ODNFe4oUDB+i0WmzavZvLr776H63jeSXq9TqHDp0gl/so\nmUxkO27bFgcOvMCOHbFe7x/On59ndbVFPL6Jly2UwjBEyDgnj52ikFgglh5jx/W3MHXwUWbKB7F1\nj3XPZyJ9MYHTICMV1sIYScOJQtD8EFvWEbiYrAASnxYJ4jTpI2QdQYsskCWFgeg5j4QkUbGRrBOw\nikUaBxCkEawTMkueOEN0UImiFzfiUCKgTZJ+dAJCkvjMYGBTCRd48lwdX5gsS7OXCtym7DoEqHiK\ngaEUOFlfYcnoR9PLKGwBJU613aaQTjO7UgVVo7I0j6EXySQnMVSdaqVNRmszdeYMu3tarC2jo1y9\nd41p/ywLCx6mqXDzzZdyzTVXve29fD3UajX+8i//DsPYzPj4Hubmpvjrv36c733vXq6//jJuvPFy\nrrzyCr70pVsJAo/p6VMsLh4iCAT5fEizWSUeH2fjxkspl9tUq3M0GnW2bPlVarUynpcE+hCigpQL\nhOFGYC+RGLUCzBM5qp4h0oJMAONEFRCDaLR3Q+/+gMhzBKCBH6yiaWOE3gkUynjEMLDGinESAAAg\nAElEQVSwelVLA4McGhlirCIISWDJWUayK2SLAYtrdTKhz1q1RdgjIjGiOR1HStQgoKiq1C2LUrlM\nF8jbNl3Po6vrbFNVxhWFmWqVaqNBfzxOODzMTVdfzXWXXIIQguFcjtmpKa68+urXff8v3rcPTdM4\n8PDDlNfXSWka1/3Gb7Dvkkve1X1+K7ynZEQI8Z+By4Ajr0zwFUL878DNRIPa/5eU8t73ch3vFc6c\ngamp6PD/ecInPwn/7t9BGEZ28b9o0DSNW7/wBWZmZjg3NYURi3Htrl2MjIxw5PHHsV2X+Cuu2KWU\nOGH4hlfxGzdOsri4Qt/Gq1k46xHzXqTpdWgqPt0wjyISGLJBiVIvaTVGgE4tXGZRMQjCEQxMHEoo\nlDGJkUOBntOqhU6LJIIVHEx8kuRIEiJx8LHpUKeFgqSPFfKoRN6vHVZIkiaBJEaDCmlCYsQIcXAQ\nVPExkTTx6cpt5FjGQOD2vCc8BDomKiGR0XwKpEvgqQyaSQbjBVxrnnBlhedbLcYyGX50773c8OEP\ns+umm9B1nU2bNrH9+uv5z//PHXQaCpIErtrlV371mtf4RTx0331MP/EEW/r6iMdiLB88yJ0vvcSX\nb7/9dfNL/iE4duwEg4MbaTarJBLRc8RiSZpNhXp94QLBTCRi6LpGEDR6rTxJbX0RaTWJKR2uGB2l\nUilx+NmzJArb8YqDxB2LXXED11XxhIHlOnTxietpFHeJroyUOjotUijI3lxMAYN1YihsQlLGw0JB\nw8VihIB1YAqfBAqbcPGALiFN4BzREdclTYiKRQyfJAEGkX5oCYOANBqCPpax6eDhM0KHNDEJtljB\nlOVeey5BjJBuWGM1rOOJNMNGko5tcfeTPyGT1ngqqKAJQdN18QMdGx01kSemR3qAVHqYWvsURis6\nFFUh8IOAWD7H//H1r9Pf349pmm/qD/J2ceTIi/h+kaGhIebnz3D48Alyuatptyt0uyPcffdRXNfj\nhhuu5/bbv8bll+/j+9+/h3K5w/z8EktLPpbVYGrqfxKLZRga2snc3NPMzc1g2wFh6CCEi6J0CQKT\niEx0icjIIBHZiIIYIlLysj7kZS8VB5gj0om4vVsAEz+okIyn6XptTCx8bAZ79c0kGh1WaWHgk0RS\nJIsJYYzyaouZhovvVMm4Ts93SMUjIEGkLRkLAkwhQFFwhcA2TdKAqygE1SodRcFKJhkQgqrj4Os6\n1WSSf/HFLzJUKFyoGndsm8RbtFt279nD7j178H3/fa2GvBLv2asKIS4FklLK64UQ/0UIsV9Keah3\n93+SUv6xECIJPAD8kyQj3/52lJj7Jl5aHwg2boz8Tg4ehDcYFf8nD1VV2bZt22uCmfZdey2nH3iA\nfZOTF76M58tl+jZtIpfLMTMzQ7VaJZlMEos5FxJer7/+Cs6enaJSOUjTLzCRyaI6LVrVJOthnRCH\nBC55BO0ekSigYuiSs04LSYoadSbxGCdOEoM1QlZp0UWlTAKHDCEpQiQWARAjwCAEHNbpQzJIFg0J\ndJkkxyIOLoI4g6xgU6XFKHEswMOhRYCCShMDlBSKSNINnJ7pVhTG1qGFQxKJhkJAIJeJ44ENcV/S\npyfZogcctm2cdhsvDDlnWdz2ivGwetNmcs8nicf7UBSFvr4+SqUpHnroMT71qZsBqFarnHj6aa6Z\nnLyQgbNlZAS5tMTzBw7w0U984m3tdbXaZOvWi5maOkmlchrTLBAEXRxnmssui8zsPM9D1zVE2MBx\nVHxfIESBwFpFBquM9tt8aMcOThw7ycmZMqKok86M0lk9Sdhao23PUGp1cUJBwAgGmygagjhLrLsN\nBA4F4dKnWpT8CgYZBBYWMAyYDHKOMoM0kMAyAhXYi8oAkjYBXejFxUv6gRINfIawMemiEOIQXZ8p\nhAgsAnwC6uQICTEYwCWOg0ST66ToMsgoCUXBEQHrYYu09BjWM3jxPkJ3goItaTgO5YTJXefnuGT7\nBs69eB7HTLE/Fwl/wzDANAV+rI9V3+dcqYSUkheXl9EzGR675x627N3Lpfv3v+vtGYBSaZ1ksoCU\nklOnjpNOb8cwkgjRZnW1QqFQ4Ec/epjLL49e/7LLLuPiiy+mVCrxF3/x//KjHxmY5ibS6SGCwKPT\nqZNI5CmVjhPlxtgEgUSIABhHiDJSWkROqw3oEcwIacAkIiA6Efmgd7udyPisS1S/aKGoBWz7PDLw\naBGyB4c4HgUixZjA5CwBAQGrVOnShxamGGKQZtvBl3XWUVEx6UPDwqGCT0BADbClpBwEOLrOzkyW\nkzMzbAoCNoYBg0FAw/c5bxjENY1sKsUx30fXtFe1rxc6HW7Z/8bxCK/EB0VE4C3IiBBiJ/BrRLnJ\nEFHGu6WUp/4Bz30F8GDv54eBq4BDAFLKl03LX56X+icH34fvfAceeeSDXsnr4+VWzS8qGXkjXHXN\nNVRXV3nqhRfIKgpdKYmPjvKxj3+cb33ruyws2AiRJgwthOjQbD5HrTZOo9Hmwfv+FsVPU8xfitVu\n4DgNRuQKhpJlKagwgkeNJD4q0aEhEY4HdHDQSNFmLJKA0SSkhUIOgzhVVMo9X9UYsBmfIgK9N1Gh\nodKkAOgYSBwC+mlhoeOwRhuTKg0CNGKs4uFh0EawAR2dOGvUaIRnkEiO02QIhXEy9GNxnHPYbAVK\nhEg0lkgiSAD4AZom6cZi+EGAD2QGB9kwMIDrupimiWVZHDs2y6ZN177Ks2V0dAeHDh3gYx+7EcMw\nKJfL5BXlVWF8AMOFAlNTU/A2ycj4+DDPP7/Mddd9jOXl86yvr/UOxW1cc82VOI7DD7/zHezZWT5z\n0Th/++hJak6Flu1hum0y8S5bY2lePHqUUsll+/A23NFR/I5Gvdal1Foh57S5Ss+z7rmUCVm3aoAg\npcUpGBod9wSTisOwkiTAIkGZPAs0iOGQpIVPnQ4KHm0EGgINDZ2AKiE6MjKUIjr2GkAWm7O08BgH\nBgioEbLYIyYaBmlsVkhRJodCiMsKBh1GUAmpELIRDSkDFKnQJyVC0XFCi4pVIaH3Y8YDFEPhM1/+\nHWy7hq4vMGYUWZxXmWuWGDLT6CIkU0ywFMT4nf/z36ApCs8fOEDeNLlocBDDcZh58EFOHT3Kl2+7\n7V0nJMPDfZw5s0QymaHb9SgUUniex/nzMzSbSdLpkGZziT/5k7/gd3/3a2QyGTRNY8OGDdTrbdrt\nBqmURbt9Dk0r0G7PsLbWRFEyGEYUrNdsnoladsLr+YpIfmpeNgm92lWUP1Mh0oqUeo9ZI2rbxIi0\nIy/7b1j4fgdNjaOjEcPpua5G9ZUQH40QE8k6kMWnSpuicEgmYhRsn6Zt0MRE71XNVOI0emsxCJkl\najWOdbu8cHaGYUXQlZHTSRwwfZ96ELBqmuQtC5JJTnY6JBsNNKChKFx1yy2vqxf5ecMbkpFeK+WL\nwA+A53r/vQH4vhDih1LK//gWz50jqkhC9N171RC6EOK/AJ8B/vnbWPcHjgcfhIkJ+DnzdLqAm2+O\n8mr+/b//oFfy/kFKyfFjxygvLFDvdrFSKS65+mr27dvHU089x/Ky/prArlyuyuRkjm//2Q+YzPej\nyRymbbNor9Jvq4Qa+CHY+jAydCFMEso2BsmekXsHnyRNzpIjqkis4LFAHJc0BiEuVs8VxMdlFYFJ\niIuLgsRCIY8giU6ITg6XRk+bouIxwDo+Oi0mSZLt6VICPEos4xCjjUaLIllimPhIHOp0WKGBg0kL\nvWdAPk+AJEaXNFXUwKQR1rFVG9ouxSDAVFXqzSYnT53i+WefZfn8eTzfp1JZZ8OGV09OqKpGEIDn\neRiGQSwWw+npN16JruOQfAeakV27dtLff5DV1TnGxrYwNraFcnmGvj6V7du3c/D55wlmZ7lschIm\nJ9m1ZTN/9aN7OXhukdEk7M8a9LWbPPOT+0mN7iYxPEG12oB2m1jOILHsMaRkKcYLKGqbQXOQY515\nLK+fPIK0ZlAKMjSCGRYCcCjSAZI0MGkSkqRBHy6SChUyNCgi0YloZiSBFGioBBcSZKJ8Z4cVQlIE\ntJE4wBIOEwhySOr0UaJAiMYgEpc8JdYokSUJOMxTwpQ6KTRUbDoySoT2lRihoUIQ4rg2lmUxOjrJ\nmTPH+fCVu7hv/WncRJ516ZM0DJqm5FOf/wo33ngjq6urvPDww1y1d+8FYplLpTg2N8cLR49y9TXX\nvO29fD1ceunFPP30SzSbGUxTxfO6nD07jRAhY2N7EEKiKEUsq4977nmIL30pEsM1m03Onp2jVpth\nfb1GGBqEYYUgECjKJpJJE9936HZNwEbKJELUCcMi9N6/qBLyMjFJwIUIQ4XoGvzlz7xBJG4NiZoo\nBTTtIlR1EZwSfXTIYpMAQgQJFNqAQxcLBXAR+Oi47NbSLHrreFLFJaAfhZAODmCh4JBgFcEkbXYC\naSFYl5LlwMMMoCjoBQ1Eq0FKAtfFMgyEovAbt91Gp9PB933Gxsbe0Ivn5w1vVhn5bWCX/JmsZyHE\nfwJOAm9FRhpEaiCI6mGvqoBIKf8XIcQfAg8RVVFeg29+85sXfr7hhhu44YYb3uIl3z/8zd/AF7/4\nQa/ijXHNNTA9DSsr8HMQB/K+4MBTT3H0nnvYPTTERVu28OCBA3z3kUc4tHcvh0/Nsm3/Z141Fjww\nEDlRDvW12NbXx0KQo7ZcoZBMUnJrDKEjEkWqzjLzXQ2fBEPY6EhCyjjorOHg0iFPFQVBA411Rsky\nQBsFH0GdPD5LbKfDEh6SLGHP0j2a1eniM0yDWTQCPOq4JCkyRAuQhAgytFgkSUCHkDQ2OwmYw2aV\nAjFGGUFHQdIWA3TlPBohTeVSUmERlwUkJVI4ZHqeoekQHEKkE2KEAbF0mqG+PoZzOZ45eZLj/+N/\nsHNyEtd1sU4f5AU/ySUf+mmybqOxztBQ9sKV8vj4OGE+T7laZahnAewHAdPVKh/55BsHo70VTNPk\na1/7Ao899iRHjhwA4PLLd3HDDddiGAanDh1i8ytyGDKmyf7+NFZFoHW7JDoevqKQDwJKs1OcLa1g\nG3O4zTYtuwqeYEVRSYdtNL9BsqOSQqGCRjUUxKSNGbRYIUvIZnTypFCpUKfFKllSCEwG0fHI4TNL\niyYxVM4RUCAetccQ+GhIVDQcTGCYBCYNuqywRgKHAME5DExSlNmIT4CKTQoFhwE0BAKLFiYew/gE\nCEI05nDpkxpFTKa9NVw3hiEUhnMxzhw6xLkZFb32Ah8uXsGndgzxzJFjrKgmm6+6kZtvvp4bbojS\ncJeWlsjBaypco/k8M8ePv+tkJJ/P87WvfZa7736YbNbh9OkH6HQM0unNvPTSSTqdc/T1KUxNrXL2\n7DmuuWY/ExMTPProk6ythWQye6hWk0hpEgQVohHcDratMzKylZWVKWzbBOpIGQMOE9UVbCJSso8o\nibdFRFDC3q0kIiUWkXVhkUhX0o8mCkjp4TgO/SyRxydFRCb6UFCRJBBU0Glh9yIDciSJ4XuSVtig\nEgg20iWGQR5BCkmHgLO0AZ9rAVMIuopCIgwREgSSEaFSlQHne79FG7DCkPOOQ9Jx+Ls77+Rf/ut/\n/a47pL7XeDMyEhBRw9mf+f+R3n1vhWeArwN/C9wIfPvlO4QQppTSIfo0vKHE8pVk5OcJrhsF0v3R\nH33QK3lj6HrkCHv//fDVr37Qq3nnkFIyNTXFC888Q6fVYnLnTvZfcQWZnpmK4zgcfPRRLh8fx9R1\nHjt4kEStxseHh6lUq+yKJ1k/8QyLiRQbxqNyVrPZZGmxjG/NYYqA6fNnURuSmj9Lo1Nlg56IFPtC\nRe2pRBZYJYeNjo+F30thFYSk8XA5h06CPmwSaMRo4qKRxUPiMc0mOkyzgsZAL1qtAqQI8SihIpnt\nmWaN0QUsBD5xQix8dBZZZQQVEw0bqBFEjpy0CEkCCppQ8WSBGDX0cBUpTJBLZDCIY2CyRsqIsy49\nKl6XSRk5u/brOiXHodvtsj2RoF/XKaTTCCH43Iev5r8/+Bi5vlGGhzfSaFRw3Xk+97lbLpA7TdO4\n9Td/kx9/73sszM1hiCileN9NN7Fr1653tP/pdJpbbvkEn/rUza/1thCiV3aP0Ol0aFkWRrvNaDpN\nyfdJBQFl22K6a9FJmghXUG2n6QY2MbYShAI9dJG0aLFIGhMFE4nJoj/NBiwqTNBFQ0Hi4+OQRpIl\nYIEcSSQZFNqEjONzjg4WXbSePZaPBUgSpNFpE1AmidvLOilQRKfEHBVyJOjv0ZIscTSgzjJ27zUd\n2rTx6UNhmKBnrOYzQZQZ7fgwqFVphnHqpBlMj1Gfm2fphWf4Xz//CZ555FHmz8wQFwG5MOTAA1Wy\nqsXRxx9l8+7d9I2M8Hrh347nEXsPNCMQhV3efvtX+fKXm9x114/55je/hZQKQeDg+4J6fRDDsFFV\nn//6X3/Abbf9OocPT5FMRjEOUrYBBSHSBIGLlGVisQmWl2fx/Ty6vgvPqxO1YLYSNTpWoOd6HGXO\n1IiqI3GiVN5a7/HF3s8pBHXiqGSlgR10qbOOT5x6r0GzQpMGHdIIIKSER4wUa7RpA2lqtEjTDmwU\numiAiUM/gjiCHBD0/EhswJbQDCQ1wERQQtKRkrGe79BxwBaCLarKiGHQ0TTmnn6avx8f59bPf/49\n2av3Cm9GRr4BPCyEmCbywIWoTbMV+N23emIp5VEhhC2EeAI4KqU8JIT4UynlvwL+RAixg0gp9H+/\ns1/h/ccjj0TtmbGxD3olb46bb4b77vvFICOPP/ooxx96iE35PIOGwfITT/DXR4/ypa9/nWw2S61W\nw/R9TF2naVmsLy2xN5mk1mqxXKuRGRqj6CosnD7E2IYdnDh2jPlTLxHnHNQFTx15kW6QxXE8RjwX\nIaHaXUHpqeLzSOpAnSIhKxQYoEiSLCUCDBbQ8NhCi2WqKL35FY82MVIkkRSok2ALFi3m6PTG/jwc\nurhopNAZp8QcGdpExd7IdjrGEgUckkQpow4dFgkYBsaIAwErVPHQ0YE0cRCgywo6NjW5ikqCgAHa\n+Lio6LTI6pAgJCZCxnSd5XqdgYkJQtfFWl3lqcceY25qilx/P1t27uSmS7fTSK3geTbbtw9y7bW/\n/hrDs8HBQW77xjdYWFjAcRyGhoYuGGS9G3g9k61d+/dz6ic/YV8viC8ei7HUaEAYsq+/nxBYbDbx\nu126dpfDto3fBTfwkEyikkUDWiyRpQ8bWOEsgjI6BkpvJxQSOOSw1X5kUMekjUlkFNVFYtIghgqY\nxJEMYHCaGN3eYLdLSBaX5d7clMYQBkM41KhxnkyvbtIBuowT0GaNKgUc0kAFGwOPfgSjKBjoLCF7\nkuiA7cBpFKQSkghaKPE1AlPSDstIu0nGbXHfPY/i1nz64/0I6dO1SyRnZyk/9BBf+e3fZnFqiudP\nnMALQ+rtNrlUCikljWaTk6USt3zmM+/aXv4sLMvi+PGTHD16mv7+IrFYnrU1lb6+EQzDZH39GLt3\n50ildnD//Y8TBBJdN/B9gaZlUJQYnicRQiUMY3heA8+LoapJpFxHCAUpt0LPuye6Do5M2qNqiAKc\nJiIk/URk5eUCfxYoo+OiMIRAwcAmhk2XjeTQKQCCUTrMIahxjhALA4lJiiY7aKAgWaRLkyRp0viE\nxHDp4gABZjSgTz+Rk0kRlQEERSSrQJOQ00IwIASdMKAM3KRpWFKCVKh3QortJN/+s2+TLfZz440f\nec/2693GG5IRKeX9QojtwOVEFRJJ1Cw79AoB6pvileO8vX//q97t7W97xT8HuOsu+NznPuhVvDVu\nvhn+4A8ise0HKJJ+x2g0Ghx97DGunphA640WZpJJphYXOfjMM9z08Y+TTCZxpCQIQzqOg2NZnFhc\nJB4E6EGAlkhQry+ylhhkevoUMy88y2C6wy3XfIjVhSWWjfPUGxXyYZdYoGBKGbmEyIBy70/GOiWg\nQJEcJgkCVlFIoJCnwDpdTDL0U+7pBWIoBHh4tFBp4hFjjmZvlmaaOApZTJJsQidNnDQdBBnO0aAD\nDKJhMYxDmiRQJUkKHY8yHrMo7ESngyAgZAU7muMJXRJKhZT0sOnSpYhkE6aSJwxdJGuUvUOMCY+C\nIrB1k5KAzdksumHQcRw6ts2wZTFmmvjNJkeffJLY5s3ccsvN7N279012K5p0ej8Fc5ft38/506c5\nOD1NfzxO07JYkhLVNJGAoSgonQ6649DCJww8pHTRCYEsDiYhDhKDDh0sEjgMMcogGXS61LERWNgI\n0YdupEm6bcxAp0GVDII+oiqVQGBTY5yAGAF9gE8fBio+UKaBwzB5bJKATQWI4VDAxSZAA7ZiYZOk\nwCo2AHEaqEAhsq0jhUoBWOplFg0QHacNQjYIA0MIwsBBemt07QyJWJFKdxat47OjkCNlxmj7DqEL\nA5qOu7rK4cOH2XfxxXiVCv727ZxYWCCcm2P69DSLlkd2YhvavY+TSCQY/xkvn3eKer3Ot771fer1\nOMePB9TrA1QqTxOGg8TjIfG4h6YtMTR0HbYtOXLkJL7vcPbsWWx7C4pikkymUNU4rdY6llWl1bKB\nQXz/PLo+hBAuUsaJWi4hEQkpAFuIVBgpovbMRqKRXwvYRGQVv45BHLDwOYaHi40NDPQiIOhlvCgI\nBrFpUyTEIoFGh0tQieHTBoZQmEayjE4bm1xvIk4Q0ui9apeoMrIdgQdUCVBRKKJiSYkUgrimkZSS\nlqahC5VOIke+f4zhwQkW13UeeOAo27dvZezn/aq5hzc9oqSUAVG75ZfoQUp44AH4wz/8oFfy1hgZ\niUS2zz4L1177Qa/m7WN5eZksXCAiL2O0WOT0iRPc9PGPk06n2XTJJZw6epQEcOT0aa4m6gL3b9zI\n5tFRuv4c9nACq3aQyzb7XLv3YoqZDLNT5+mkB2kul0hLE1u2yeCxQQacJ2CWOA45QrKkadKlQxsH\nE5UkCXQ0VFRavWvgNKuY6OgkySLwWKFLDYcGJgExBBNIXEJO08coWRwkFVxU8njEUSjTJUcCCxMN\naKLjEGLiolBA0iKg05u9UEj2pHIGNhVS4RoOIS4Ck1F8ErjSJqZI/NBiVOqM+m1ihomZTnG2WuWs\n51FotTjvumzIZinG49RrNUbHxmi7LscWF/laz50TIjv9px9+mJWFBf5/9t40RrLrPNN8zrlr3Ngj\nMnLPrMpK1s4q7otsipS1WpQs2ZIXtVsaW24IXjRtWN0YoH8MMOPGdKNhoNFAw2gYltFDy54RxrIl\n2RJNSaZESaSK+1ZksVhbVu5r7NuNu5x75kcES9RiayNZlM03kciMzIzMgzgZN77zfe9SGBvjtp/7\nOU68LMfktYLjOPzab/wGL774In/7N5/n4nafyvE72Dr1Fb68vs5iNst6q8WO1nhCMKZDYjQNQnw6\nGJTpo0jQdDEZOj7EtNmhhwW0ECPfmEDvIKM2adVB08JhDYsKARqBokoHk21CAiYRHKTHWTaQjKMw\n6ZCQRwMJDiYWBn0kIQ4ttrGYxiWLJkLjE5JnA02AZB/QwsAblbMuMSmGwtQ0Q43HLDCpIhIMAtWD\nwCfxt2kYNWqJS1abDGJFxoG+CpFxTFdp8qLHxunTdHd3mT9+nKjX48Mf/zh/+If/FfPo3dy17yjp\ndJ5Wq8o993ye3//9/+XH9o35fnjggYfodkvMzR2g03kW111kZmae1dUvoFSfXk+SywnOnt3FthUr\nK0+TzU5i29DvryPlDO32JqnUgHTap9ttMHz2S7QuEYYvyXgVw3N1PHrUphhyTEyGihmPITMhx7Aw\nyWHQRLMD9LEZYDNgHwGbaHpYlBEw6mg1UYSYJFiME3EOn4P4ZDFIYRIS4+FwHJM6PnUUY2iySDSC\nNBqfYVekgGCbhOFuatIYHDJclnMGhudhKoWMY9xUClvmEG6K3NxBmv0OXnmSdHqWM2fO/fMoRt7A\n9+LixeHHfyR37XWHl0Y1P83FiG3bfK8Z+XCGnRq15gHe9d738hc7O9zzZ3+GqTXLWnMwlyPudDi3\nvEzHcTh85BALCwsU9vYoj/gmlxsNNtspUvYEBSONJy0azRUUe2gqSGbRjGPh0eI5BGUkFm0G7NGg\nREgXQRfQ1HGJ6bCJGp2Z5oio0mEfAxawWUYxAbSu6Cte8iQYkJDgkydhF82FUWN+eEk0cYgAjxjN\nsHGcEBMxTIRNgCYNynSJyBOSp0OEjyI34hYYQuHIFqnEoSMkYymXQb/PQi7HqudRy2a5sVCgZJqc\n2dgg12jQ9jwaQlCZnyc9erzPnz/Pfffcw6F8nmNzc7R6Pb756U/T7/X+UafHVxOmabK9vUd3MMGt\nt7+DKAp43Ae9dYFOKma316PS6ZD1PNrNgG2ajDNDnzXC0ahLMIWmgcUSRUzKdJAE7I46Fg49UsYZ\nHCFIGYJy0qeke9Tx2SFNhEDh4TPNJXaYGyX8HmWPHk2WsHAQlCggcNhDYRMTk9BmA4GJwsXHRZAi\nS0iCxKRMkz0UPiaCJopk9GYwfMndYsj7uQbQwmBJgyZFBYdV5VPCpEmKy0jCRpWJfouqjrC1yRFb\ngJVwYHoaz3V5+rHHuP7IES5fXiabPcj8/Lc7Yfn8GJ1OldOnn+fOO1+Zi4rWmqeffpGpqTfTaDTw\nvDz9fpt+P0LrEkLMo1TC9vZZZmYMOp0NPG+MhYV3Y5rfwDC28P1LowJE02jskSRzOE6FKGqSJGmG\n3A+PoYtqkWEnJM+wSxKN3jdh5BIzfFT72Oxg0UPTwxj57MYoNtlCUMekR5/0yHl36CLk0yVFlwaQ\nJRoepEhGPS5BMhr6pYhxsPBxeBqfAkO2Sm3019MI6gydlwU2jjSJ1ADblwSzs6SmpnhnpcKlp5/B\n7GlKhQkSy2Ep6HHk5ncQxyFh+P2unK9PvFGM/Ih44AH4uZ+D1/jw92Pj7rvh3/5b+E//6Wqv5IdD\nkiSsrKzQbrcplUrMzs6yb98+4myWaqvF2Ih/oJKEi9UqP/uOd1y5r+M4ZDyPt6endnEAACAASURB\nVN1+O7tnz5I1DNZrNaIoot9s8raf/3l0qcShkyd58q//mlgpNqtVLtd8svYEm8pAOJJoMMCQHmsi\nhaMnGM6VDUJqKBYZ0GEKcHEIMOmwRJs0C5TYxcJhHg+FRAJF6iyTocc0AhMLF00aQYwmTY8WXSQ2\nPRxs0iTYDNgCQhKyNEc9kgSBZhuHmF2GF645bLaQLCPIigwd3cJggUk5Ri9JaI48T3q0SXSWWA9P\n/QkCX7vsdBMwYsYyGVKWxeTiIrbvM5lK0QkCKgcPsn/fPlKOQ310EvZ9n3/4/Oc5Uixe2Y9CJsMN\nts2j99/PDTfd9Jpl1ryEKIp46KFnmZ29DcMwMQyTE3f8Ii888RV21p4ikpJSpQJxTLmboh9t0KFL\nCoOQU2hmgBibHuNksbEZcJFrTPDiDgMhqcgsUdqgZAgGvR45FZEa9cVMMuwgCSgjUMTs4ylWuI6I\nZCTtLBGN2Pq7GMziIOiiGNAmRJDlRgLqWKPk3g4SQY2YLpqQHgazxHjEWKMXtXU0PeA0w9SUCoIN\nDRFlPPIk2GgMmtIhpfaoJmNckHkaKkUca6R6kXGjxfT4PMVslkEcs95uc0RrVlc3sKzvlYW6bpa9\nvcYrtndCCEzTIEkUcRyTyRSJ4ybVah3XzSJElyRJ0Ho/zz33AJVKjiRxqFYvks0eplzOkiRj7O5u\nc/HiswwGFkKMAwGZTI4wXGIwaL3sL3b4dkfEA8qYZgelqmidY0iNLGJxDmgg6GGzDw8Tn5ABafaY\nQ494QgEzpLDJktCkg6BGQkITgwnGaNKmR594xP8YoGihRt4kmjHydDAJiUbXhiFvJAZyCApImkTI\nRBEIRSmBXr3Or/7hHzI7M8PD3/oW9/3t15HFWVR5muMHb6BQGGd5+TGOHn3nK7ZPrzbeKEZ+RDzw\nALzs9e91j9tvh5UV2Nwcjm1e7/jzP/kTBhsbeELQ05r8gQN84Nd/nV/6yEf47Kc+xcpIpdHUmiN3\n3PE9/IX1pSVuPXKEL66uspDJcGRiAqU1m60WHd/n5htv5NDhw9yzs8MjX/saqcGA7maHmrmH8gqc\n7q5QDGIsLWng4BouKND4KNoIKmQRRIiRY+bQnyBLyB41IvJYRKQYOhMoEkIKNKjSxsAc6RRqDAmR\nFSQtdukwSUgOaGFTJc80vVH4/A6akG2mAJuIVQQ9NB6SdQR7aBoYZGSdIjlaKk+YRCgSOgwQbNFj\nEoFPlEBBGmRtjWkWCY2EOO7zYqvNLjCXJHztwjZy4JAtFFmoRszPKp7f3OTw4cP82Z99ikuXtjnz\n4P34c1OcPHmEcrkMgGvbWFFEs9lk4jXWk/u+j1ISy/q2HXKhUOFNb/t1zp0bI370Xtxmk716E2TI\nIVPQjWucI6HLBJoU0hgw5e0nFWmSaEAsbUyjylGt2LQMTGtARlosuBkebNeICchh0sGkRkybcWyK\ntOlhYdKlzTkaCDw0CpsGNnCADZaoE5LGwkEzoMs8BiVsIOAsHgVMTJq0MdjlKHkkFhdoMYaFRcw6\nMWkkB4F1EraAKTRdPCxcDGKaRGhmSJIUItkE1kHup0caLRpImWI5nXB8ZoYndnd5YWsLQ0o2Tp3i\nvJRsdNJMTu7/jtFbv19ndvYnU0d9N2699QQPPXSRSuUAMCBJJJXKJFLGLCzczuOPfw3HyVAo3MLs\nbInt7T5bW3sYxgWuuWYW37/EhQvPYpoHEKKNlBXiOEGp85RKNxFF30CpNIZxDKWGKb1Di/ctII1S\nBlr3gY0h4VXVKZKiyy4KFw9JiD/yFcqhKCLRRGyTo0tCzCZtxhiQwmadDBJjRHGNCelzAxqFQYOQ\ndRJSpNhBUKCLx4AMFgYJF9FcA+RIuIxmhhgHwUWZMG9ZLGSzXLZtPv+pT3HPZz/Lzbfcwuy+a3j8\n8TWy2VmUUiwvP871109x4GVj1dc73ihGfgRoPSxG/vN/vtor+eFhmsPi6Utfgt/6rau9mh8Mb3eX\nk/v2Xbn9wuXLPPAP/8Dd73sfv/3v/z3Ly8sMBgOmpqauxJq/HLlikSiOueH663n2qaeYNAwcw+DF\nZpPj+/Zx0y238MRjj3Ewm2Unk6Hf7zNuSSYch1ONTZR3C1tJBx236ag9esrBoYeDZpceFiHmKHJc\nIbHwEFiUqbFDgEmKPsOJs00yIqN5+FSokeAggBaXgCKaAQYpYmp0iAhJ02c/HiE2adKELBFSoUWe\nPm0kAxYIOIJNH5PaqMAxCcgACTY2khYREX185oixMVnHICChS5wotrWDjQVCUpOKHRTT+TxfP7tF\nIhbpuwa5/ATbHcEf/X9/z5H9ZR459RidyGbh+reQKy3Q6xt861vP8Ja33EIul0MlCaHWr4pl+A9C\nOp3G8yS+3yWV+vZpPgj6+N099k9NUd3cxPT7TGEQaIMuMSaCAm18KwSzgko0uVKGuN8mCIZdLMuy\n8HVCEreRwiYXDFgkYs9Nc2GQ0AB6VEgzTY8mMR4WFj4CmxIpPFr4rKLRDMjQZ54+GRQ1CqxhoFEE\nbGGhKWPgsU2XGMkWk2QoYRASIxmjjsGwaxayw4A5BswQ8jzwOJIMDuZI7ruNjYUDiYOPgYnCtGMS\nsU3Gc5mxFsmXIk7cfDNPnTnDm2dniYXg9qNHMS2LP/v8vTzz9Fc5ed1bEEKwu7tKLudz4sS1r+j+\n3XXXz7K8/FesrZ2hXNacP/8McZyQycxx5syz9HoR6fTQ5tx103iez/LyGkppZmcnuHTpPElSJpeb\noNvNoJSDYZRRStFun8YwSijVRKlngSMM+SJ7DP1Fhtd2Kbto7SBEHi26QImMHtCjjsXGaGg2jiCN\ni4vERDFGgMsYIEgT4NPBxGYPix4WRRzKbNPgWfQoW0pSImKNiADBJpqXSoYtoIvAQuCSMDvqfOUN\nSdEwmDBN4lyOqUqFzd1dlpeXWVhY4Bd/8T0cP36BZ589S5JorrvuLRw6dOg7HJNf73ijGPkR8OKL\n4LrD7JefJtx9N3zhCz8dxcji1NR33D40M8OpJ5/kHe9+N5ZlcfDgwX/y/jffeScPf+Yz3DQ/Tymf\nZ2ltjeWtLfa985389h/8Af1+n2/edx/O7i6ZIKRcKONZPTabA4raYMUXpKxZEA1c3WOg+uQwmBUJ\nXe0T0kSQGo1gbLTo4emYGMU+JNv0KeKQIKiiaWKTEFGiyC4RCX0M0rQIuYCJIjVqA/fI0MFAsUMD\njxI2JRygho9BiYAUDmsMgCcJCQk5iM04eihUTGI6WpIjwibgMgUUZcqsMI2FS4YBJj3WkbLIM0GL\nUpKQd9PMiwin75OdOsFE+QgXL1+mvrcHZhurWSe7GVIODXAsLjxyL87i9WyjqRhZli+vcuLkcV5c\nX2fhuuvI/oBQrlcDhmHwrnf9LH/1Vw8yPn6cTKZAt9tkc/NZMrrH++66iy8Cz3/rWzhBm3WlCYXg\nWivNZjRgOXqRSE7hJzEEKaJBFZl0qQnJdhyxz7LYn06zEcfsdttoNFPeBHPpNP9QW6MD9OgSIbEB\nQUxEig08FAEBRSSCEjYBZ9khYQNBmxweeWCAJgIUIQo9UmjksJBYxISAQ4YSLpqQkIgWEXlqrNMC\nKhgEZGgS0EEQj8oYRExfV/FpEds34Jr7CII+nXiPJdGmGPl849Sj2NGAJJtl7vDhK66dH3jrm/mH\npUtsbVkkieb48QXe9a5fe8ULzlQqxcc+9mEuXbrEqVOPsrv7KI89tkuvN8XYWJnJyQqdTou9vSfY\nv3+GTuciSWJgWQat1jatVg+lMnQ6fUqlCWq1vZHfSEwYNkiSXYb0XpuhMbjJcERzA65rIcQ2YWiS\nJA1k/DApDCJcxjDI0yFDwDppQqwRXXUNh22y1FBkCMgiRs88E4VFjzQJHbaIEDijqwak6OJjoShh\n0EARSIeNRNMgoUiWioiJBfSdGKE1BAFSSjpas5PNMlYscnJ6mt16/UpitZSSw4cPc/jw4Vd0X15L\nvFGM/Ah4iS/y04af/3n4gz+AKBqaob2e8d1KDNMwQKlRENoPXvwNN95Ip9Xi4QceGJo7Z7Ncf+ON\nvOO97+Xez3+eldOnefab30Sdu8BEpkzOydPvden3a6gghWF3SI2ViH2T/h7k2KaDT09myakedVbp\nU8QTJYSM0WqTIgFbaI6SsMs2ISYl0phoGigKtBGk6ZKjRp42Xbp0yDODxw6CmBiP3ChzJKBJj00S\nepSJR56vc8AL5GhiM+QHSGCNkB2GoeeOFryAT40NuhTReKSoM43CI4WBgSCLS5Z2UmNWupycO0yj\nucViRiByeU5tLdFtCOa9LK0kZrO1xmICXrtDNj9DNp0nb9o8uHyG/W/9Nc5deIqdy3u0ijnmjh/n\nXb/wC6/4/8QPixtvvAHLsrj//lOsrnYolbK8850nuPxQE89xuOXECXZfeAHbttnc2OKaxGAjESRy\ngayyaUUCU/eRQZtFS1Fwsjzc2mbWcVjIZnG0JhWGpOKYs1pT7/bwk5CUdJhK1hEM8PFoU6VPjGSe\nAXlsNB5VcnRGlGWX4/RpIGhgItBYhIRU0aSwiTAJkGxiExAyRp0eeQoIJMMwgAFtTBxyXAYOYZKX\nKdAldvWANC6XUexhgu4RsoMlS8zQIzW4SDOOaJtpBsTk9+9nUzqk+xf5hXe/m8rL3GzHCgUWFzQf\n/w+/j9b6VXX1NAwD0zQ5d67G4uKd7O09RxBIBoMuvV6VYtEhijKcP/8AnY5FNjtNFC0TRT4HDtzK\n6dOP02q5OE4Nx0mhtSaOh/kycVwABFLeSpIsodQw4tAw0ii1jGl2kKSxSePg4rHNNA326HOYgF1W\nMRhHYSHpkGePa4gZR+ETUB/5EBk4VNklpMscHkXqbI06IHWgTEiEQYA14nw5kCS4lsZLDDJWiURG\nxFGTqVIZM+WyXKvRBuanp/mZxUXK6TQv1Grk5+aYnJx81fbjtcYbxciPgAcegKt4rf2xMTEBi4tw\n6hTcddfVXs0/jZeMll7CbqNBaWbmhz6JCSG4661v5ebbbqNWq+F5HmNjY/z5Jz/J0le/SkYpBrtV\nLD9mENWJ7JCKlyey8/i9JnnbZd/+GaoXn2PMqJHRIVbSAZr8jG3wWOjQZGg5nU0MIgYsEZBGcIYu\nGRJsLrFNaiTVy1Bmkuoo0M7BwMTExKdDjzQdAvKUGSNBY2FjMU5AiMl54lGqr+QiY9TxMEhj4hMz\nS4wFXETQxGBsFLuX4JPFZ4CDiySNh4kkRmOIAQkaW/l4TpogbCJogpXBD3wGO9v0MgHj9gniJEYN\nOkzYNpYMiCMfKJB3UqS6TSpTB8gUKhw4kPCBD7yP0sgC/mpAa02z2WRhYT+f+MS3o9BrtRpnv/kN\nBr5Pt9Fgu9FgXkoOmJKWMnDMfaSFRz9SlLWB0h4dq0OWEEsKKgiElCx1Othag2UxNjvLZKvFmQ7k\nE4c5YhwcGjQI2KJEgx0mCQmwCBkadm8giYmp0cXnSSx8EqZYITfqpLXoY2BjoZFoypi0SUZJRSnq\n9Cig6RDTR6Op0BKKvO2S0kM/T1+lsJI8FTmgpppUpYNpdLHjAQfdWaw4QcbgCZe0qrJqDbjzro8y\nP3+Er/7V/4XxXfL57XqdmcVFzp8/z4UzZ7Adh6MnT75qPjJf//qj5PMHqdXOMzd3y7Aj0Nljc7OG\nYQj29iCfX8TzQkyziGkWGQzq5HISKWOGVljXYpqCIFjG87p0Oj1sexi+p/XGiKS6C6yDWsdWyyTR\nDCZpbBwcHAzGCKhylIAIg8MElLnEKap4CA6RwaFLAUluJAjfo04RgUk0svof4BLSZ6jRSaGJCQkx\nEGimKTLAwULSVjV2RUzRtgktk9V4j0DF3D45iZnLUY8iStksO0HAmXabMJ/nX330o1fUbf8c8EYx\n8kMiSeDrX4f/+l+v9kp+PLwk8X29FyPP1Wrs6/UoZDLUOh02lOKXfowQoHQ6feWJ+q2HHuK+e+7h\nsO/T6nTIVJssJQmq30PGCstJsWOYeCmb1d4l4mcusyjSxJTRepsiUDQMlG1zhC5PRGV0cZJ6bJMK\ndjlqZZH9LcaThJ6IMSSMqR67aM6i2aNCE4sskjYJXTpoQlJcxAQMZsgTMyAgGRUUKUx6GOzh4jKH\nQ4sBCZIZOhToEHCWHml8Ekx28NEkRICmQIoeWdaImUAP83mR9EG3cQ2fnojQsklhfIKcnOPFS5dw\nADv28bvrXF7z8caOEqKQRkA5k6EjEnp+B2naCMsmCHwMo84v/MK/uqqFyMWLF/na3/0dg3odBcwd\nPcq73vc+stks5XKZ9NQUn/3sZ1lwHOYyGc7t7GDFMU2RJpVYBMYw/0cmCZZTxHELBP4Wu40NsiIh\nbVgcdF3W2218z+NnT5zguW99C5006NCjiQAEWQQz9IgAmyYrPIcmj0lMiZgiimEic5FlYvK4ZJG4\n2Bh0mUPRoM8c7kiT4eAQ4bFNljw7pGiPylkHjwECw+0wNbWAO2jQbncxYxsdmWidxpaCijdOM9jA\nNk3qoYWTShGqHcYMkwk3S+JpCoUKF194hEa7xf/9mc9w0+HDXH/DDbQHAy5HEelqlW/+xV8wnc3S\nj2P+7uGHOfn2t/OWt73tFd/Lzc1dSqUFCoU8GxtVisVrSKcr1Ot92u1LJIlHkgQkyYAwbJHJzOH7\nfXZ2HiOTKdLrPQ+kEMKlUhHs7DRQqkQcTwIBUvYwTQs4iRN+mVlcUhgkhLS4QJU0FlkcbBI8cgzY\nRqKRmMAENWIsEnwEARGKPjYaF5eEAQbJyLU5h0cfyQCfoyMjxB1MKgyVZnsoXBi6riYWgehR728x\n6xhMl4tk982zoTW3f/CD/MbHPsaXvvAFls6d4/jkJG9597s5cuTIK/74X028UYz8kDhzBnI5eIWN\nB18z3H03/PZvw3/5L1d7Jf80fuX3fo8nTp3i8vY2EydP8qE3veknUmb4vs9XP/c5JqQkFYaUSiVC\nP8YiZnnQZTcO6SIpZMtkHBOr3sHsSLAN0AGG9uiKBC9pcjkMiaRkttyjbvYxUwGVsElReAipyeiI\nIrCsk9HJFTQhe+zSZ54WXTQtxKj/McsYDeoomkN3Tzw0NgEWfUx6WKN4tI3RC1OFMgUkki45HBbp\ncZkUEh/JOsvMETNGSGNEp2uywy57jOMNg+mFT94K6VoObsZjMp/jwbNnGfd9xgFDaBztszTYwo8N\njh5bpLa+QiqO2X9wga2dPc7ubqOm5hgb6/L+97//NVfOvBxbW1t88Z57OF4oUJqfJ0kSli5c4DOf\n+hS/+bu/i5SSdDZLnE5zanOTM80BO0GelLSQSlByJFkp0IlNbAoKxSy9aA1LtrDNEKklPYaBf3nb\nxgUeeO45lBAURERJD5jHwEEwIKbPUKfRJ2Eanwl8miRMAT1sUuQJSJhjQHcUvhaPvpMmoUqHNtDH\nGmUNCXwcxuiQIaSJBBzadNBoJp0ZqvUd9lVsylaFRqNPNxIkGPiGxhANXLdMRoxjDNLYOkNiubTk\nOrOFFIEHF05/k0qvw1v37ePAsQWeOnOGJ778Zd734Q9z/fw8F77yFW55mSpjVike/trXOH7y5HeM\ndF4JTE1VqFbrzMwscu7cBbrdHYRI02w2yedzQMzCwhFM0+T5559gff15kqRKGO5hWRLHWcQ0u2Sz\nA3Z26kSRjdaHYNQzTBKfMOxiscQ+AnKiB0aKMA6pEBLhYzCNQhCi6KDQIw3cCkPaK0QIIloMnUhK\nlJFIEjQJE2yzyTgdBsAukhRDGX6foS+QQpLHZJUAQQphDB1hXRQHUwmeSMBKc6hYZCUImJ2fp1Kp\n8JGfBtLfT4A3ipEfEj+tfJGXcOutQ3nv2hrMzV3t1fzjmJmZYeZXfuUV+31ra2tULIvLQcCsGPpo\n5jyHvg8FM00vVSApH4RikUL/HBN9jzE34ICbod1StLsBwsjSlgN6RsKJxUXGMhn+/sIGGdNlSvcx\noj62TEhLi140MqaybaJIMNAWIRUkBgkdBNMYNCmSxkFToIdLgywFIvps4qLZh0+DHLMkuCMxoIFg\njBbhyO2zABjElOmzTZYsmjEGtMhiM4PFEm3ejOICbeq0SUlImYJOvsgvnTjBkzs7PLW1hb+3x2Q+\nz04YkjJN4jDkoOfQzNrcde1RPrOxynq3y97ODrmJCRZuvZn/8Du/w7XXXvuau61+N5569FFmLYvS\nyMBOSsk109M8vrLC8vIy+/fvZ+3cOcbyec69uMJ04UYWCymWqytUeytsqy46M42VTSOcNIYlmLYy\nTAQGYStiO9KMlfN8q1rFiCK2+n1Uq8VNlQpV2SGlJAaKGYZFyCrDAQBoimQR9BgHJrFYwyAixCUg\ng0bTo4RFh5ABHj4+EYLeyEg+pkSTbUz2cYF1Jigxh80AgzoJpjtgpjjN+OR1nF97gMPlNCKWxLpJ\nR8LC+HUkCNb2mmRSFsWyh21niaIsW9UOzXiL8sxJ7PoOKRtOnjzIwsICJw4e5MzaGvsPHWL90iVm\nv8tp1TQMysDy8vJPXIw8+eRTbGzsUqkUufbaY7zlLbfxyU9+gYmJ67jjjrfwzDOP8txzX8UwOmht\noVSKzU0fpboEfpc46KCFTyp1LYXCtSgVEkXnabdPI8QUhlFEykmUMhim9kZAmxQvMmYZLBYKNBPF\nVr2FpR3KxNRpoIWJpQUXEHgINhnQJeYAXMmOMYHLSDZHPJA6OfoESAps08UiYhNNaXSfFlAmoU/M\nMHvKQGAQiBjsGNvymJqZQdo2fSGYue46rp+Y4ImXnDb/meONYuSHxNe/Dh/84NVexY8Pw4B3vnMo\n8f3Yx672al47SCkxTJN98/Ocf/pprrVtxvJZ1ttdLvd7ZNxD2KkUY4UQGSpKqRyptCTvuGSzHsZy\nSDeJiByX2w/u55b5ef726ac55BgMgCnHJei3aMQRNoIMUEfTAkzpcFBJOtSoUqfD1KhbUiZhA2hy\nkiwderToAS5pelzmIhnyFCmhCEby3Tox0+xSI0Mbkx26OPh4CAwWsOiRQlJDGjF1FZEixkxnmFcx\nOA69YMDhUpGbTpxgZmwM37J4vtFAmiY1rZlwXYq2TRDHnGs2Ob20RCqb5e133UUum+Xi5iaFY8f4\n3U98gmKxeDW39Qqqm5vMfh/1jscw00gIQaPVoreyQsaeZCw9A0ClUOCJrQJxSZJIiW1kmSzN0Ouc\nx00VOLNURSWKfYUSniU5UipxanWVbDIc5zy/s8M4MGFIVlWCy9DX02CYKjpNPCosCljUR2oYA5uY\nMRyaCDSaDAYwoE2GXYbprMaIwloTETU9TgkBLJAQ0qSPLVykSIGuUG10yGamiZJZelMOa/2A9NQN\nxHubBFFErbeF607iO5L9+2ZwY0VjZxfXlbREF91Z45pigZ/5mVu+gwxZ8jx21tYwTPOKYuPlSOAV\nkY1+7nPP4rpFguAcX/3qo/zWb/0yH/7wO7jvvm/SakUsLmY5ceJNPPjgBeAa0ukq1eoqYauN6nWw\n2AXSqMEWtVqM605g2ybttkCILKZpY9t5Op0mWqeBKpZlk7LLeFZMpAIKQhNbEX0FXRXSZxWpfXpk\nCRBMk2GXy5SAArB/tPY6Q8v2s/g0yJCmzyyShIAaGo3B+Mh5dYOh3D8BTGJWEAgEkWxgmAI5WeFn\n5+e5ZWQGdbHZZH7fPsI4/h4ezz9XvFGM/BBIEvjGN+CP//hqr+Qnw3vfC5/+9L+sYmRubo6B43Dy\n2DGam5uc832CIKBTzHLjscOs7lQ5fGCCA1MTfH33IuPTLuXytVxceYoSCYFrstWuMj2V5fb9+3ls\ndRVDKd581x18+f6vc9FvsRBHeGh2SSghaekENwwpGC5pu0QnEuR0xCV26ZIjwaGHjUuEwMLDJUVI\nlQZZskygscgTEWKM3DaLbDMgZgZNhpgMEk2HZfo0KAMZNA22cNhNCiQ0MHF4st8lMisU1CQ50ef5\n1h53uC79KCIwDOwkwQfsIGA8n8cwDFzDwDVNcqkUH37f+6iMTsa3Hz/Oo8vLNBqN100xMj43R+2p\np76D9AxQjyJWV1fZXF6m5fv02226kUUq7OJZaYI4ZqI4wfytt5Av15Ay4oUXdrnxxndh2yk2u19g\n7ZwkaA2Ya9SpDmoc0pprpEQIwZpSvACMm5I8Q4pqn2HIWQEoo2mzi4VNlYQWMRJBcWRz1kPRJEWG\nLhaC1uinO7hs4wEesZ5DEdJmlWFUXohijEg7xLqPFbawASlsTDuHHwTYRgt/5SvMWGmETmglHWLd\nZ9IskESCFzebVNLTVGbyfOjtv0q/3ebZS5cY+64OR8v3mZuYYHxykq+dPs1kqXSl+BiEIQ0hWFxc\n/In3b37+5Lf3rL7NZz/7JT7+8Y9y7NhROp0OjuPQ7Xa5777/lfHxSfL5fQStv8YwtumwjtIeUk4h\nhIM/2CGKWhgGxLGFZcUI0cYwBJ6XIwxD4jgml5W41ji1wTJWp8Gs7TBueTSMGDHwmRIeDeHQVbO4\nrONTZQJ1JV6vwdCjdQA0Saig0XRJk8fDQI7cRVboo0ZhA0vA4dH/RgSY0kAZUDUFh8r7kIUUmXKZ\n1mCACkPGpqeRUnJpe5tr77wTpRQXL15kaWmVbNbj2LGjV5Wn9WrgVS1GhBD/DbgJeOrlCb5CiP8D\neNfo5v+utf7aq7mOnxSnT8PY2E+Hg+k/hfe8B37nd6DdHvJf/iXAcRze/aEPce9f/iVji4vUl5dB\nKdLlMvPHj/ORt76VielplFKEuRypZpsnzm1TnDpGu1ejSUIv3UdOTfDpS5fYbvvcNDXP9t4e/qBL\nJwo4x7BF30ZwUYCvh1mfYZJgjjxAPOGQ0w3qVLGxR06dWzTpYZFcoZ6CSUAyokUqUpjEdDmMossu\neVzSmLgYSDR5DM7QYosIiSZHBVvXmcIgNqbwVYySNiuBom/MYSdpHlrdoVzO0DcMVKtFWymeCQLO\n9npkDIOMbbNjGNwyN0et3b5SjAghGLNtVi9fft04O9502218+oknyDYaneN8BgAAIABJREFUjBeL\nxErx9KVLPLe0RN6yKHkean2d51dWcJJtlL2HbzgUxw4yNjXPzt4Ga5tLyHaT/tYaDzzyRdqDASI7\nx/j0fnqrZ9mLYnKxoiw0hm1jAGNJwpTWvBjHaDw6o6jCPooe0AQOAYcI2QMeYZi4a6CoIhEYzBBS\nQ+NjsYVAonHsHMgpBkEWLQSmcLBUlg5NFIsoLCJcBA6RPosXNrh08RkaqV2mqhnGe1VOlCcIkpiG\n7VIY2DS31kkbAieIGA+6qDTcfnQfJw4cQMUxz507x1MXLnDriBBZbbWoGgZ3X3cd+Xyei7fdxiOP\nPUbFNIm1pqo1d/7iL76iQXkApdIkq6sXaTQalEol8qOoAa01x45dw9raBu22ifQbTHsOe8EEQbuC\nZU1BkiNWk2hxAdBkMhopPXy/Sxg+BYyhdRvDWAK/RcW1UHmPc36LgT+gr/u0ZY5Js4inNFqFNFlC\nsk6eYecrYcgZCRhG6aWACUIUJvmRF8w22xRRGCjGcBkwT4c2JXYI0CwBApMcLq6EnWyOQAgcy2Dx\nwAEefPRRMqbJzaUST6ys4M7NcdOtt/I//+f/w+OPr1GvJygVMjFxHx//+K/9wATtnya8asWIEOJG\nIK21vlMI8T+EEDdrrZ8YffvPtdZ/KITIA38HvK6LkZ92vshLyOfhzW+Ge++FH0Og8lOLgwcP8tF/\n9+849+KLPPn445x97jlEHOONj7N/cfHKCS8YDHj8c5/jPW9aYG23TtfPEOj93Pmr/xtBrLn//jMs\nhJLdR/+e5cef5SgG0jSRWrChE2qJ4pA0aWtBBo0vBvTV0PY7RmLj47IE5GnhjaytUtgoDAQCkyoB\nNQ6TZQJFnw6X8aiPFBoai5gsGsUAR0ik1uSEpqfbCA6R0GfWMECb9BITyypT9jSGaVOzp2l0HZ5s\nrXPbhEm62aSlBChBRRtkREKgNWtxTCafJ+U435OUHCqFm0q99pv4j2B8fJxf+jf/hq998YucW1tD\nC0E1jnn7sWMcmp/nxdVVgmqVGz2PTpzguibYHjuiwWrbpVOvMZd1mHZKPDVYRw0cckFCRu9Coqks\nzBHuKirNLlIp/DDC0oKE4UhmlRIm8+SwqdPHZx0HnyoaTUiZYYs+C0hsamgOjmzkfTQCn1UMMkgK\nRo6cK9hSyyhrnjgp4WWzDFrPIxOHAAtBGTDQBECWml5HDJ6iqGOauwUWLY1fXwVtsO53mQAOOQZG\nMaLd2cFSAzaaDW45/GYMKTFsm5uvv5510+Sh1VUE4JbL/NJHP3ql+/We97+f9ZtuYnlpCcuyePfh\nw1ciAF4LpFIpbrrpKJOTKZSSPDsokokU+SBHJ3CG4ueBj0pctExhGOso5WPbDbJZjyQxiaJlXHcD\nR9eZljZuCE5fUlUuLWlhqIis8nGUSYSmS4tjRPhoxhkm2USAP/p8iaF9WoKmTsQcEpeIPgEDXGwx\nQ6S79HFGe9YCNA55DBwUKbphl90utKTNe2+5mfItt/Cbd9+NaZpEQcDU7CyLi4s8/PCj/P3fn2Ew\nmMB1h3ty6dIW//E//jF//uf/7ao4Hr8aeDU7I7cBXxl9fj/wJuAJAK318ujrIcPj4OsaDzwA//pf\nX+1VvDL44Afhb/7mX1YxApDP50mlUoQbG7z7wAFKuRy1dpsvfPKTvPMjH+HY8ePccuutaK159P77\nMYpZsuMWb77jDo5eey3//b//vxw+/GaSRPHEVz/NjDTwEk1fQ0vFOEJwGMmGkKgkoacTbjYlfbNH\nXYdIrVlNFCWuoUMGC0WPhE22yAEu3ijjJEcegaSFRFHCo4LNDjEOklkEeQRdBIFWgMDRCTlAYQwz\nehUINCBRiabvD0ikgozk5E13kc+fJZ2P6CuH/mbAkcx+DL+Go8Ggx5vGy1yQkmfqdd72ssKj6/vU\npOS9R49epV38/pifn+c3f+/36PV6JEnCn/7RH3HNzAyb1SqfvfdeDvT75DMZLu3uktg1TEJ6zQ3S\nsynmKyex1s7z0NklUv4MXmLSU122/Sol1USYFrHrUUUxrgVd5JWc111sEsaoUCLBwKPEgDQRz5En\nzTYNHiceJS1beDhkCEZGZ9BDs4cY/TaJr3yCXkzaVvTj00hb0e/3iZMeHm1MIKSFSY4RFRsTg0OG\n4qiTYrnXRpoCO5NB+j4MumSEIJcqkEjNkUPzDHZ3kd0uZ5aWmB9xRLTn8ZHf/E3K5TJaa0ql0ncQ\nk4UQzM3NMfcqM9/r9W2mpnLfdwT4nve8lT/9079CiArjC0dZevSrCF1i/9QcShlcWF0lMbpIqXCc\nIun0LP3+CoaxwvXXH0HrFLWdacrxcUS7T70zoK42ucbw2Io6TOgsigSPJpcJOcDQSPBpIMNwNBOM\ndipgOKLpM+SNDMMhQopIuphskkZplxYhFh6aAX3StDFJiQyBFiTapCey9PE4du2bMMwJbr/zzu+r\nTPvylx+k3c4wNbX/ytfS6TxLS6ucOnWKt7/97a/0VlwVvJrFSIFhAQnDsvD49/mZ/xP4k1dxDT8x\nlIIHH4RPfvJqr+SVwfvfD5/4BPR68M/IL+cHQinFN++7j+smJsiOThITxSKOZfHNL32JI0ePIqXk\ntttv56abb6bb7eJ5HrZtc/r0abQuXEmDzVdm6W2vcNFvg9YYhoFONF0U22j6QlLUirptM2GaNAYD\ntqOEgAoGeQY42FSZI6FACoFPj5A6eQxmmCfGISIgxsYmwsWjj41JE0EBjcZEY7EBBJh0GeDRIDey\nUbNxUYBKmkjl0tQJnV5Mr7fLjTdOYw5qbEeKnHSZ8Fx82yQaNEmihEa/z0YcMz02xl/eey9zMzMc\nWlig57q860Mfet3wRb4b6XSaMAwRQJwkPPLkk5S1ZjaTIW2aCNNEFApkFhb4mZkZXkxconCMs089\nQhQWKCUupu0QhQGmnqKRrFPqh8wcuYHHd85jIyhjEiNoolghRZ5xxDDlB1eaw3EBGXrEZLHYI0Yw\nfPGysRkONhLWiWmP+mE2ES2mMZjBxqMxaBDpS0jtY4UdDuHjookx6LA3GvPM0uU8HppqFOBHMJV2\n0VJSazQ4XCyS831UPCyGnDCk0+2yvbtLLQw5++STFJUiOztLw3F4+pFH6DQazC4ucvPtt79mnY+V\nlZcIrG1ct8UHPvAr31ehNTExwS//8tt55pnTjI/Po9UBBqcbdLtdlM4hrDymziE5RxLvEQRTuO5t\nSLnBkSN3cPbsw8xPLTKOyelHn6LolNjtakJ8UjLDcwoEggwRNkOy6R4ODULmeCnTeTh+qwNVht2u\nYwwzgBtoAgy6aDSaXUIGLJBgMC0zbCU7uKRAplGYiP+fvfcOkus873SfE7tP5zDdk/MMMMiBIEEk\nBjFZFEVSDBJNK8uyZFq2ZO8trVy+tavau7Vbdde7sl1717K1srQSFUnRIiWKFDNBEETOYQYDTE49\n3dM5nXz/mBFIkJAs2SRBQXxQqJ7pme7+vv56zvmd733f3+v1krF1PMFl5PMG+/fP8rd/+w/81V/9\nuzcYmaXTGWS59Q3viSwHOHdujMtEi7ylYqTA4poChFlcx/MIgvABIOq67vd/2RN8+ctfPv/1dddd\nx3XXXfemD/Jf4vDhxVyRS2il8KYSj8OVVy5W1fw2Vwf9ppRKJZxymeDSiTSdz/PioSMMTcwxr9vI\n4SS33XYTTU1NyLJ8QTzcMAymxk+RmTyHxxfCwEXXK2zUAqiCTaZWZco2MIEGy0BTVaquQkZVSVkW\nZU1DFyziZoJzjoyIh0bmWYaKB9/Shr6FQYFpqjjImMhIiDhYlLBIAjIiKVSmqRFEwQJsNHQEIiSp\nMEMdD1lcOgUJwa0huHXKroPpbUUxyoyfe5L3/94djKfLLBR1BFGialbxKF4sT5i0DVHBpKchwu07\ndtDe0cFLJ0+irVjBh++9F+0dFKK5GKqq0r16NUf37UOq12mNxcim04iOg+r309nRwUg+Dx0drFje\nx8mTNeZqBoIToWhbOLaBJVjYtoWJh4VqESmdRot3cDJdIGAvih0dmTwhmvFi4yIiYLkOjgg4BjI6\nfYCDSAaJSbwUMQkjECCEhYFLnSgV5kji0oVfaMBxRWLEmMNDWT9GD14SGLhYlEgTJUaNUbLMEydF\nNyYBDAZrFusjQQRZZjSfx6/rVIEZ22bAtmk3TUZGRxGDwcW+ED4fh8fHqZfLrO3pwTs5SdLnI7V/\nPw8eOMB9n/3s2+Ifc9dd65mZmSeRaGX16pXne+K8ltnZWb73vcfIZm1ARFUN/vgLn2fnzlf42c+O\nMjVVwHGzSNYQXjGP6zp4dA91yjS3tCIIISyrjWx1jliwEUeAumWiuF7yroDggoBIEyLdCJi45FDI\n48WPQQSWmlAuhmaqLIbo9KX/IRa3+McQmQGyiNRoJ0AzNUYpOgI2Yc5iEhN1LNvCcPwUnOUELC+q\nGiUUijA7W+Ghh37Cxz9+3wXzHxjo4uTJcRKJVxuImmYNQcjT2vpGkfLbylspRl4BPgM8BNwAfOMX\nPxAEYS3wAPC+X/UErxUjl4rLJV/ktdxzz2Ko5ndJjHi9XixBwLJtRqaneeyJp3ALDnE1TFm3ePon\nrzAxkeZzn/swyWTy/ONyuRwvP/kknsmjJMJdWNk5zPHTWKLMkFEjKELdsnBZPEi1qCr9wSBnczmq\nts363l6C4TBPHjlOwdSXDNynaMTCgw8BGwkFBYkEZSaYo0A7USQEDCzK2FSoATHqeBE5RRAFDx4s\nXFTiRABlqe9MnozoZ9iZRsHEFSPoqkpQNUA/zQpfDenMGZZ5PBxLnyZVMxeTVG0Dv2gTxCErOIQb\nG+jo6MDn93PtunUcmphAVdVLs3i/IdffcgtfPXmSVKHAikCA/bpOwTRZ39lJ1TA4lU6ztbkZj+Qw\nfOCf0StzZKt5olaABknBsnUWbIuiPU/ALRGrD1JVROpqJ07dwREUYq5M3p0iTwUJHw4SritQcecR\nKeMDMtg4gIFCHD/TWOQwiFNGRSGEgQpUiOEniO66eHGwBQnNjWIikqCKi42AhMoCUCCKiYxCKxEi\nFBnwhcjaJsfSGbY1N5GTJF6pVmny+ZCBY5UKQ6USHlkm6PGgNjTwodtvJ+T384+PPMKqTZtoWuqA\nHfT5UFMpXnrmGe55G2LTV1yxkSuu+OU/r9frfOMbP0KSeunoSC7dV+GRR3bymc/cxY03XsP/+v++\nzlNzzxBVE/ikIOlKAL8nSNouEA63kc9lmRqfo5AfYUHMItk2ZbsAjoDhGNQlmUYcNCxqS2aFKjYL\nFOgFirDUFnOppHnptorIThQkXBwsFGSUJV/dGhMUKQEeMpSQWMAQNdJSF5LWhmmGkBUFxznHzIyI\nohRYseJ9nD17hnQ6fYF/y513vo+nnvoy8/NH0LRGbFvHsmbo729gw4a1XC68ZWLEdd3DgiDUBUHY\nCRx2XfeAIAh/57runwH/L5AEfi4IQsF13TvfqnH8W3n+efjUpy71KN5c7rwT/vIvQdfB47nUo3l7\n8Hq9LN+0icMvvcSpI0eI1CAe7yZfqbC+o52SXmF8NMczz7zI/fe/arq2+8UXaTJNlt98DXv3HsOo\nufQIAuOyRFqWKdSqFAWBLklinSRRVRRcrxfF66Wo64zrOpmzZ8nXKxSccWRcbEREFFi6OnZRAQsV\nEw9zzOJSxYeHMrBAEBsBkJAok0SjCYFWatQwmKQBFw2BHDYFXHplL343goSD6VGZlQUCfpOkXGZ1\nwM/siROEYjGi1TQZw0tWVVHsxS4qBSNLTzBAczxO1bLwAZrHg12vYxjGO35nBCASifBnX/oS/6VQ\nQK1WuXlggPlSiSPT05wZncBs6eXJn+ykySxyx5pl7K8VOZIfIS1UKLgJfJJIULAQJYO4pjFYLCB4\nFOpuAUXxgAkTrr3UeWYSF/+SSXsZDwV0oAmJ8FIb+CISU5h4UcjSgsUCSSxEROpAHYdG1MWTngt1\n10Zf7CCEjIlNAAkPXhxqODjUacNApopfVfGGEnRIMiOpCU46DlVV5ffb2ghLEs/NztIMFHI5CATo\nSiQwQyHms1lkWSbkuojuhWl7bYkELw4O4rruJTe1O3v2LJWKRmfnqxcIXq8fj6eNw4dPcMcdt3Ll\nql7W3HszLz53BEPQyJs2sqwTkGB+6hRmWqJazGMZVRasAj67gIRLCQMdA8lWcaggY+BHRsIkgEuZ\nxV2PEIu5InU4H3LTkanRu9R4wWWaGiJlkiTxYZLDi0wCv2wTliRcX5i8MAlSnVptGsMYRhAMTDNK\nrTaB67rs2xcjFBJIpVIXiJHu7m6+9KU/5MEHf0o+P46qKiSTEe666/p3d0Z+XV5bzrv0/Z8t3f7e\nW/m6bxamCS+/DN/61qUeyZtLUxOsWQNPP73oPfK7wg233MLfDw2RzWRQTC/pSpVANIbtlTk7OUVq\nPM/k9AiyLHPbbbfg8/k4c+wYVyeTqIrCzTfvYGpqisP6DBVJX4wZRyPsnZ2l33UpuC4dfj+Cx0N7\naytnUykmZ2ZoFgRcF0RBIOmWmcRmHoMwAgomDjZQogxICMjM4CyVBIssuj3WgGOEiRMnisMcZST8\nBGklz/jSgTJPhxIiqQaoOiDa0B1OINdnyNbyJEUTpVxGF0SmZ+Zortap2xUMNUA0GMcTThKpN7Gi\nWSYWDDI2PU1DOEy+XMYfj+P1ei/d4v2G+Hw+PvWFL/DY//k/yI5DdyTC0Og0gb6tdK/eRvn4S3RG\nOxkbmaC/s4VmCV4YnmCmlsVybJq9dSzTYtpQ6fM0kKmVSZg5VFHG8YdIV03SjkRFaMJVbApGCa9i\nUjdVolSJoiCjYlPGxiCAiI1DUIzgdTRs8jhk6UFiigVKVHHxYQoCNdemTAYJhTlkQsholGlEwGax\n1byDS0xSaO1aSaqwgF4okkVkygoTFV3OpNJEQgFCfj+dmsa8x0NVkli/ciW6ZXH83DnaGxupOA6B\n1+Uo6KaJ6vVeciECUC5XEIQ3fu40LUgutxj5lxWF3v5+PLLK0WNnSBk5CjUPgqHTKAfw1EWojOK1\np1km+tAQsalRpMwkMg1AGxYhBKax6QECLFbEpYEmlnY9WcwPOQmUSaISwAVsQCOIC+jMUUHDpIkg\nOn5JwdIUou2dSEacSqWI1+tBkuaBdYiih3q9Rj7v4fDhCaLRIt/9ro9PftJLX1/f+flee+0OVq9e\nyblzi2mYPT3dNCztZl0uvGt69is4cAC6uhY9Ri437rkHHn74d0uMeDwebrr1VmpDQ2TOZkgk+igb\nVfacnUOVl6PJMi0t3Zw4UaVS+TGf+MT9KIrCbCqF5Lr4/H66u7spZrMMFoso9TpV10VyXYYti55Q\niKCiMF2pcNa26dU0Vvb00NXQwO7nX8Jvw6SrkMBDBgMBacl7orrkTSEQQkNk8QBYQcYEIhiogkna\nDaIjISIgksfGRMODSYWkkCcU8iAZOhUzh+ANYEoWs4URwn6LoqsjlmukLYeKINKseHHkALJdIuzX\n6I34UFobcVCZmzlCNBZE13UyhQKnFxa45WMfe0ecnC6GbduMj49TKpWIxWK0tbUhCALd3d189POf\n59SJE5w8dgyh5yqu3fBehk/tISJ7UBUPshwjnx+luzHBdSKcnp+noaYj2i6vpC00xUfF0tEcmwZ/\nktNVnWo9giOGUKUyqBLBhqtJ5Q0UpURAnCeYOYbhmBjYpLGJ4SJi4RE8qLJN2TCIiQuschxkwWW1\nu8AejmHTRt6VsMhhU0ZCYJbF0tVOXEDCRKYL/2I3I8emXiyhCgF0nx/L6+H3P/lXjOx6FH8tiz8q\nUJ+dJdbSQkKW2TM4SKlSIeDzUanVGF1YoGntWjKVCh1LgsR1XQZnZlh3882XdlGXaGxM4jgH3nB/\noZBiy5bFknw1HOFvvvUIfkUjHo7SbZU4euowspigWi6QM3M0Oym8ro7HNrGExRwRyxVZjoPiCeHV\n67QKMn7BIuW4eHDIsHiCHFy6nWcxEXKxzD6KKMu4jkPUlSm5NhYaNtNIWIi0UNci1II2a9atxCWK\np7JAqTRCNLqMYtHAMGaAJkxTADJUqyI9PU0kEpv4wQ+e4Itf/CyKopyfczwef1tLqt9u3hUjv4Jn\nn+WyyVR+PffcA1/+8u9WqAags7MTT3MzsXwFwyhzdj6DV2mjaFhIIR99fV20tDRz9uxuBgcHmZqd\n5fSBAwyEw+hAoKmJxq4ugv391Gdn8WoafsdhNJVCsixGMzlmJJmaKOH1qajA3MwMogBRV2RSqCG6\nQcr04VLHYIoAAapYKFRYi0AFnRoyDhILQA4PYUXEMHREFEQUBExMKthk8ZOlJSBz1S03c+LgMfSi\nij/aiizLGPU8ucooJdNk3nFZHoihGTU8jk3etKiJKq5hEvEHmc+liLX0kFi3kjPpFAHbJujz8d7b\nb2f58uWXeOUuTj6f5+FvfQsrlUJj0Qk11t/PB+67D6/XSzQaZduOHXg0jYnZQRRFxeMPUbNNAFTV\niyiGyOplBFUlEY8TrtbYNTTDrOMj4PaRqRdosHMUfAEawmuYK5WwaUCUVEpM0NueJFedwrY1dLOO\niUYBmRwLrMDGi4QMDIgCs8IpDGrojskkILkuUwg0kyFLHrR2VKUFHIlCrYzHPkEbMhryeWs8Cx8C\nVUZcG7eQwXQE5rwe1tx4L21tvUw1NBKp+YlHbGxVxefzsZDP07p8OSOlErMTE9DXx7o77uDu/n5+\n/J3vkBofRxMEiq5Ly9q1bN2+/dIt6mvo6upi2bIYZ84cpampH1lWSKXGCYUqrF+/jscff4K//+qT\n5MvLsSo29dIUlXKKHk2nM6GTnRvHqlVxnMVEbwmRblfGoE4JhyAiJaNKQZCYdR0k16GCy2EWLwgS\nLPaVmWJxdyTDYn6BLjn0xWIUSmWKdQsEiUbJS5MUYNysYggVOttV7vjQBzl0aJBQqAPXncfv91Kt\nzmNZLqLYi2XJSJKLJMWQ5exiY0d/mIUFlampKbq7uy/dm/82864Y+RU88wx88YuXehRvDS0tsHbt\nYlXNHXdc6tG8fQQCAW64+26eePBBFk6dYXR2BkvwIIRCXLttHS0tzQCIop+f/OhHbIhGGRsYIDU3\nRxg4PjjICzMzrN20iWHXZd40ufvjH2f3zpd4cf9J8gGVcKIfKzOGJXo5fOIcEcFGMnQEPAgYFBDw\niiuoOwUsSngDYVyjRtKYQBFUfK5AlQCNRChTZBoHy1bwoFPFwcJCRKIBFYsUQXSkZAud3d38fN8w\n/c1NrFuzHgGBil7jx3sKbBzoJz94hgkHFNvCEGVG3BoBT4SqEuRMMYsjimhWmoamLnbcdAMf/OhH\nL7gyeyfy+COPEMnn6e58tdLgxNmzvPT889z03veev6+hYdGBE6CpuYfDp/YS16votRLLlnVQrpY5\neOoUjZEIu0emGXZ9mGIPkhsFVyFDBq8RIhnQCAQsXCkAikbZ7SczP4EquiwUT2OLEFQizFvzNNou\nAVFBR6LiLuaPLHfqpEWQXR9zrr3kmqvQ4/Fx3IWG+ADxjk7OTE3hzM0Qtb2I1HGR8CAhITJHFQUP\n86KfghDGkXJcc9O93HjThwEY2HQTh5//IZVcnraOFn528CBiqcTylhYc18UOBBhYvpxlS+Zln/zT\nP2V8fJxKpUJDQwPNzc1v7yL+CgRB4Pd//y52797Dnj1HMAyLDRuWce21NyMIAn/7tw8SDl9De3uC\nYrHA0T2jNKLQ4Cgk8nmKtk0KCT82OiYrl2ytHPx40QnjkHclDCFCmQVUJHQcFFyaWTxBVpbGEgCi\ngsCCoqDIefJmlbwLs5IIrkNFzVL2hljRsIJW18QTlphPzWEYBtnsIJ2dCRKJVk6cOIHrCkiSh2BQ\nRlESqKqA1+vHthd+MfOL9gS6nHlXjPwSKpXFMM0111zqkbx13HcffP/7v1tiBGDd+vW0tLZy+sQJ\njB8+RlXvYP36q87nRLiuS6WSxlPO0NLdjUdRyDc24tg2YydOEC4W2ZZIsDWR4LlDh3h43z7KrkTX\njR8i1jjA6Ngguq0QkDyUigUSfhmjVqMq18HWqLkutqtTJ01IUgjIDZjOKGFbRRIdRFNBEfzYUhDN\ncZhwBOpiDVWsYVjnsFwvIcIg1GkQ6rQrCoZtsmtoBG/HFeghmVPZFKIAZUFGa1pFZ6uXytQsCTXM\n0PwkXlGiTVEwIw2YokTOA/6uVjp3bOWq665j3fr154WI4zhYlvWOq6bJ5XJkRkbY9jozrmXNzezZ\nu5f33Hzz+SZjnZ2d9PVFOHv2OM3N/fRvfi8HXnwYv71ATIoid3fz7z79aRYWFnj2L/4LHu8yCnNn\nSbkLqGqSuhmmbgrkqkWaOluZmiuRqeoYYgBLytMaDeEXDUr5SYoo1KU6cWBBFDEEAdN2aBJdVMlL\nTdWIuX4ClskRq8KsJJATaxhyG7IjENc0Iok4DYSRiyLFwjBJ16aKjICLItgsKBF6269ix/qreXHf\nj2hOtp2ffyzWRNvqq1i5wktnWyspVUWemcFSFJKJBNd0LyZuP/3Tn3Lfxz6GJEnvGHv/i+HxeLj+\n+mu5/vprL7h/9+7dVKseksnFZE9Dz9DiFmkLd5HNH2XecAgJcUbdMvPotCwJERsXCRkBizKg4GCL\nrWSxUZ0Kc66LgEkclqwDF0WII0ko7e0079iBFgqx84UDGLqXgKnRohisab6C1ngjHlUlkztDRasy\nNf0ilmWzYcON9PWtpVyuMDs7TioVxLbraFqMej1HKBRGVS2CwRj1egVFqdHW1sbvEu+KkV/Crl2w\nYQNcpOz9suHuu+FLX/rdM0ADSCQSJK6/nu6+Pv7hH36Erpfxer2YpsHMzCD9/QmG9w7xreH9uG4Q\nw6xS1edotcu0hUJoHg+SJHHH9u0cHhvjYNZgzdp70PUqI6MjtK+4jcGD3yOm+JiXBeZljVmjSDTg\nIlbKmKQJeFup6gZ61UU3XXQsPJKLLflQtRAeRaOIztrGZczO5XGCmPoxAAAgAElEQVTkEtFyng7H\nIGPnqQkeyoJGOa5xxYb1RK/cSO2kgW1HKNhV2toaWdu7gice+x7RkMXWrVfxyv5TBKKNDOfn0SSF\njoYWYi1+bn//rdzzB39wgeCwLIuXd+7kyO7dWPU6Da2tXPN7v/eO2To2DANZEN6Qy6LIMo5lYdv2\neTEiCAL33383O3e+zCuv7MM0be75xJ2sXr2MWCxGS0sLkiRRq9VINH6DppZuqkYdoxLCFURUVyVn\nnULEJeDzkRWylEwL0Z1HC0BLg5/NK1cSN5rZe/osycRGRgcPU67mcU2wXIm07VI2dbyqjCHpWB4P\nii9BR/d6duzYzp49exBJ0LN+A97jRynpQbpDUQ6ZM5RdHbFuIeFlVlSpBNu4YdVGNK+GLxIlX5oi\nlRpHUTwUCjP4fAVsJ8jx0+M4+TzrBgao1uv4NA2PotCRSLBzeJhqtfpbYyeeSqWoVqskEgk8Hg/5\nfB7bNrBtE0lSMMszRGQNB5ui4zBv+bCQ0F0PFQKUBJcZ18DCRcDGQWEUERcBxRUxhSAVf4SaEEap\nzKFLRVo9IhGPiCZJpE2TfCjEn3zxi6xatQrHcTh37hxf+crfkz8yRFu8AcuqMV8ap6xITKShZeUq\nArU5JifP0tExQDgcZseOq5mfH6KhIYIgSGQyZbxeCU2TCQZVUqlD3HffjXg8HmZmZtiz5xCzsxna\n25NcffWmC6wHLifeFSO/hMs5X+QXJBKwefNir5oPfvBSj+bS0N7ezsc/fhtPPPEiExOnkGWB7dtX\n0dKS5JHvPc3KxBrS+RIT8wILOchWxnGXd2Ga5vkTXcLvJ6rbZDLTeDwaum5Snj+Aa+vM2TV0b5L4\nuvVUp45wY1cc+9wEQ9kKmM1kHB+qM4fk6mQQEAyDqmzS4FcQfS6NwTa0YDPnpiZxzAqapDBhFnGU\nDiJaK4gGkY5GTqdmaRqZYnKySjC4EVVt4tSpDOXyYbr6w5hKiTVty/CJKqdGZnDDEUgmSa7u4/6P\nfIiVK1e+oVX5k489xuz+/WxqbcWrqqTzeR793/+buz/72bfcGvzXoaGhAVfTKFWr5111AWYXFmju\n6XnDTo7H4+Gmm97DjTcuGgf9QsTUajUqlQrBYBBN09i8eQUPPXSApqatOI5CoZCnXlNoiOaJRIKY\ndp2tq3vZd/wAATFLXPLQGizwge3XUCuXOTo1xdnxU8wYJh5LJuBG8QBlTKYEGZ9VJW2V8doOXk1E\nsg0aGlpZvnwZmUwev1+jUqkyl7fJZE6wIqBQdwTGzDp5W8SMdLH9ipsJB4JMZuZo6Ejwl3/5RwwO\njlCt1vF46szMBMjlElQqRXbvOkzOe5jVnW3MCQLHPR6u3bqVd2Y68hspFov84AePMjaWRxS9zE6f\nwO+WaAoGkAtDnDtt0738NgRRxhPwMTZ+jAUnQlDsQLQkFu3fatRFC9suIgoyuGFEbGwcJonhOosO\nq5satiJIIvkFP2eLBxF0A/xhch6VcizGbZ/6FKtWLZqJi6JIf38///W//kf++j/9J0rnxulpbeTk\nrId0PkqstZv1668FHF555Z8ZHHychoYmgkEPn/vcnYyP14lEuhEEheHhY+j6OHfddQtbtlxJY2Mj\nZ86c4VvfegJVbSMY7ODw4QUOHPgun/703e+Iv783m3fFyC/hiSfgH//xUo/irecXoZrfVTEC0NfX\nx+c+10utVkNRFBRF4Zvf/AGrr7iJE3uPkElbxIIteD06enUKpDgHDhxl+/bNAFR1nTUb1vDzZ15k\nZqbO2JFn6EUm4bq4kgvVPKPDu2mK+xgvlUAR0bwulj6CIDrMCzJBQaMqiKTsDGFRJJ2fJhFdTkvj\nCvYfPYpHrhGPN1GuKxQzDg1yM7YmI3g0snWVVEnCjTrceuu9HDiwm0plDkFwOXv2EP/jf3yRYDDI\n848/zsu5DNOFeYIhP9duWcVH/vAPL5ojkM1mGT54kO2dnedbxyciEQzLYs8LL9D+kY+8rWt0MSRJ\n4j133MFT3/kOnZpGJBAgXSwy67rcc8stv/RxvxAhlUqFpx9/nJHjx5FcF38iwQ23386f/MmnePzx\nT1AsTgM+dL2EKM3S1pYgOz9MIOSlpaGDzf0O72lajiZJTFaraKqK7fGQtRVSngEWhDSGqKEJWRTB\nwbBUFFej6o6wTqjjmiYLog9vfpxj+56kf00fW7e28fjjP+HU2DBiZZ7WgEzehahXISQrpJ0gXWu2\nUxZFTs7PoATqfOELn2XFihWsWLGC2dlZ/uf/fIje3i2IosjMxGm6/WHiepUg0B2LMV8u87MXXmDz\nPfec3xV5J3iKXAzXdfn+93/M7KxGZ+dWpqfP4UzlUJ0aA1e3ErxqLU/tOc7ZU9/GH+1hqnyOolAn\n5l2JYEnoGJioyMTIOwUmhDxBt4SJQVGQkEWNsD2BhYimBFhYGCYYbydj1BCU5YwrVUoehUSDxvIN\nq/jQa5p6jY+P853vPMThw2cIBn30b1rPXD7PyQWblWuvom/5MhRl8RR7xRXvxbYHeeCBj+FdKpse\nGhpiz56jVCpVbrqpC8fpJJstMTh4Bq/Xy6OPPksstppAYNEN2u8Pk8v5efzx5/nsZz96SdbjreRd\nMXIRRkYgnV7cNbjc+cAH4AtfgGIRQqF/+fcvVwRBuGC7Op8v0dXVz/BwajGh1IJgyzIq7lk8/iCZ\nTIVSsYigKIzVauhn54jHVzF6bj+JuoCKQVMihGTJZDPTJCToD8RJxmKEW1sxhgsk3TYo1XFMAdGR\nKbsZfChMyCqaaFIzchw58zSmZXPDlbcQi8Q5OXqUQ6U4k7UcYb2FRLCFuYLJfD5IZt9eNC3GypVr\nCQYjuK5LLteDJEk0NTVxamQGa15ne9s6FFFi7Ok9/PW5Ef78P/8/b4hPLywsEBLF80LkFyTCYfaP\nj78ta/LrsHLVKoJ//Mcc2rOHsfl5mjZu5Pqrr77ANOpiOI7Dw9/+NsrsLNtbW5FEkYVikUf/6Z/4\n0AMP8Bd/8TH+5m+eplCoIMsZRDHCwkID1UqesD9JyCfSs3EDqbNnWR6LobAobh5/4WX8TetY1r6R\n6pGn0PVOysU8siDjUACy+IlgClkUbHx2Gr+kkFSy3HvvjYRCIc6cyRGNtpLd9Qir/B0YtsVsaYGN\n63v4wMqV7J4r0tLVTDLZzo03bmXNmtXn5zU1NQVEEUURx3FYGB9kVf8GJocOcm56DsnjwTQM8rUa\nG66+muHhYZ55ZjdTU/PEYkGuv34zGzasf8cIk1Qqxfh4gc7Oxd2IqaH9dIdiKILA8PA41123hWRj\ngof3HaJ5bZhCxyZe+fleRNdDsVTHkAW8bhCPq5BHIhnx4ubqGG6dXimIxy0RFGHOcahLQQJCltOZ\nFEgDtERF4ppGvK2NBTNP88Dq894e586d44EH/iO1Whex2JVMTxc4ffo473vfCrZct5z29gudUTXN\nz8zMhSGxgYEBBgYGOHLkKA899AK1moeJiXFmZ59A0wySyQauuWbTBc8TjTYyMTH0WxVe+3V5V4xc\nhJ/8ZNF/43XH4cuSSASuvRYefRTeARe77xiWLetg3745QKCrpw9BEHFdh2lpgAnZQCxmCY6PI8fj\nSIkW6tkoy5ev4cS+PSzr6iGAwnx2mIhdpi8RwTUN5MZGtl5/PYqqcmzs6xSLs0QIoFtgUkKT0kQ8\nQSqRTuKdrcRDDuXiHJrRSctSgmJf6zLSBZfxlIo/2YkajUK2ileTse0C2azM3r1HWLu2D6/XRyo1\nRqWyil27dnP24BA7utagSIt/9qFAhLGxEzz12GN88oEHLph/MBikepFs/kKlQuRfONG/3fxrOspO\nTExQmZxk82uqcOKhEO3VKgf37KGrq51wGKLRBubmFLzefnR9AUP1E/S3MTo3zcDmOLphsHd8nHy5\nTH16mgnJz8CK6zh+fBqv10s+n0aSu3GMaTRkFEFFcMFCpFNV0DUNOexn06oBNE3j3LlxQqEOgkEH\n3+w5ZFEE06CjuYeurijdra1oy5fzkc985qKCwTAM5udHEUWJaLQR13XweQM0da9ClhaQW5tpCYXY\n4Dhks1l++tP9xGIr6OxcTaVS4Ac/eJlqtcb27Vv/zevyZlCtVhHFV11/66Uc/kgScCkV60iSRG9v\nDzepCjd96lNMjI0xd2aGsRmRQkkm6Q0jIJCrFwhIVUTTZkqSWa8FUZxF8/aQ6sdfmueEk8Un2zSL\nXvrX9REOxDg1M0N81SpWtzRTqZ4+P46vf/071Ou9dHSsw3VdZNmPpsV54omfsXnzWizLRJZfrURb\nWJilv7/jDfPTdZ3HHnsBVW3l8OH9eDw9dHVtIpUa4eDBl4nH97FmzdXnf9+2LSQJZPnyO3VffjN6\nE3j00cXdgt8V7rsPvvvdd8XIa9my5UoOH/4O9foEo6NDGEYJUTTYuHEt27a9lzNnnqR180qGhmZ4\n+eeHMM12Dh8eZXp2gZBTJRlvIeI0EbZTtEWjjGcyeAMBTh89iuu6rOjv4czZs8jFNHa5QrMWwCfL\nLCh+RL/G1VtuwDSnUNUeDj47cX5cIX8EgQK2GyORbCSfL6NpYQxjHMtqpF6vI8uNPP3Yd7iyJYFg\nLfDCQ1VOp6s0aMHzQgRY7ECsxRk6ehRd1xkdHaVWq9HY2EhLSwux3l4Gx8ZY3tqKIAjUdJ0z2Sw3\n3377pViSN5VCoUDgIifzXzjP5soW1113K4cO7UYQHFS1SGtrKzMz8PyhIxjVIq8c2cna/h7i8QBr\nbrmFprZ2njz8CNVTs0xPT+K6Bo5TQJK6sB0Tecmj0ydW8Msa0WiUsmWgOxbpapW5uTlmZqaoVEza\n2/sZUb30BGOoskI6O0PF1Dk+McG2+++/qBA5uH8/Lz/2GM7QIRbGhhhXvJiKh1Qxg2yVuPrqjSST\nSfLlMvO2zf79p4jHVxIKLRpp+f1hOjo28swze7nyyivwvAMMiBKJBK5bWjoJy/ijjRSrJSTHoiER\nJpfLYdk2RdsmGo3i8Xjo7W0il5tHleqYehkZgYCQYWWjh4DsYTqVpaOxk9m5aTTJg+O6uIoXza2h\neV3Uss7xcyNEIlUGrljPmrVrMU2dbPbVq9MDBwZpbHwfhUKBqak5LEvAdR10HWIxifHxA0Sj3QQC\nEfL5NIYxxg03vDEWPjc3h2F4mZo6h6p24fVGmJ8fJJOZpFCo8OyzP6O1tYtYrAnXdTl9+gBdXTLj\n4+N0d3dfVqLk8pnJm0Q6DQcPXv7Jq6/l9tvhj/8YslmIxS71aN4ZRKNRNm1azs9/vovy7Bmikoqm\nqQzuew7HKXLDDes4fjxDW9vVwBip1OLByNaDDOspXH2cZEjGFUUmymVGqlWSU1NkRRndssk7Juu3\nb+fs4cNItQwzehVFTUA8xA23vB9BgN7eJFddtY4De/6OsUyaiFfDdh18oRhaaQYYoVzO4/Op9Pb2\nUiiUqVRmKKfm8TsOpj6PFGxg6FSKfUOnafVHWZNsQ3hN6qJh6biCj6985WuUSh7AA+xi3bp2brv7\nbp55/HFeOnkSjyhiqSrb7r6bgYGBS7Qqbx7hcJjy63qyAGRLJeT2doZPn2B8pEokHKSzM0g83k+h\nUGByfBLLcLGtAF6jjV3HBHpWBPBN5Ribgra25VQqPlav3sbJky/j8WQwzb04gk6VOiG3hCtaOIqC\nIIrUXJ2cAJnRNMVnxigWC+zc+SQdHWsIhps4lBpBrBTIzA7RlomhJJNEDh5k2fLlRJc6UMNiOOOl\nH/+Ybe3trAkE2LfvBGGjzrHMFEdlm609LTiyzPD0NCng5vvu4zvf+RkdHRc6eiqKB9v2ksvlaGpq\nequX4ZdSq9U4cfw406OjRII2p0+/SFfXFbQt28iJZ79Hwi4gGgoHZyYZK5WQenpIp9P09fVx8z13\n8vJL/xdeVcJnqVhOhTafSYfoZdbrI97VS1H24/g85CpFJGRM0ULxenFtlaxeo5QXqQkefFN5unoy\n1Gpptm1bDBWVSiXq9RpnzpymWHQIh1vw+RZtAYpFg6GhMeLxRl544QCmWWfr1rV85jMfpaWl5Q3z\nXEwat8lkMgSD3YyN7adUElHVARoamqjXR3j00X9iy5YbOXPmBI5Tw+vdwje/+RwNDQIf+9g9xC6T\ng/a7YuR1/PCHiyGayywc9ysJBODmm+GRR+AP//BSj+adgWEYvPjiIdoDPrZvuYpSrky5XKVuVShP\nHWNhoZdEYhWOs+hiK4pVgsEV5CkjeRWGS8PMVebobYwxNz+PX/VSLUBF8DKpl6gEQ+R27uI9mzai\nrVjB4ROnmLBVVm29CV3PIElVtmy5i/7+fj76yffyxBOHyBRMRElg2fouulY20tW1hV27DtLQsBZR\nFJGko7S2rCMzeJZ6XcLwttAUWoFX1ehv8XJs+AgnRs6wpmfRTbVaLzNbzxASWmgQes83I3Ndl0OH\nDtHRMcTd999PoVCgXq8Ti8Xe8SZovy4dHR3429sZmp6mr7n5fM7IvulpggsLNDkO2dkxdMlPei6D\n359k8PQggl0n5G9CEnK0NCxHEBTKxTkOHEixaVMbfX3N7NnzIrVaI11dfRQKJxHFKA0N7VRmx/DX\nDKq1EVJMUzcF8pqXWMsyrt/xCRRFZWTkGXy+fo4enaSxsZFaNU+sNsotG9awdv06ko2NTGYy/PN3\nv8snHnjg/A7J4KlTNEoSXlXF29jIzbdEWchkaJoN0HTNNbR3dTE7NkYymeTGDRuIx+P4fM9Qr1fx\nel892DmOjevq+C9hrX+hUOC7X/saWj5Pg99PS61GujpDOq2jqn5W37CJ48//HMlw0Xw+Nm7eTFdL\nC49/+9t85POfp29ggFves4ORI0doEUX83hBhRaFkWWiJBB+/9X08/a0fkuhZRXVmhKDokDWrxDw+\nTDtISzxKXVAp1irkciWeffbH3HnnFezYsYW5uTm+/vWHCIUaOHnyGKK4FtNMkUw2U6/Pomk6w8M6\nnZ3LueuuO9H1GtPTJzl5cuiiviEtLS1EoyKi6JDPj1Ms6gQCa6lUsrS0NCFJPpqaHGCUrq5u1qy5\n7vyaz89P8PDDj/NHf3R5bGm/K0Zex4MPwn/4D5d6FG8/990HX/3qb68YcV2XiYmJ8/1JLnYV8puQ\ny+WYm0nTKIgko3GS0VevII+P7eP0yVNs3b6VVCpFMNiDIMyTyx3FcQykqBdL9hGLd7L6+m1Io6Oc\n2HkIFR+OJBPvvhIxN423lkYzTa65/np2bNnCqXPn2Dl9hEIhikCI//bfHmTlylY+/OG72LRpPYOD\nZ1EUmRUrllEul/n+958gmZSYnNxDOOxhw4b1GIbEPAfxeGWa4otCBKCtoYMCDvvmTpFHRxOgKuls\ne99NLBSDRKOvehcIgkBjYz+7dx/h6quvIhwOEw6H/03v5zsNURS55yMf4enHH2fXUjWNGo3i1TSu\n6enB5/EQEGXOnJmhVzE5efJBMmmHiBbCdso0NnQT8i1+JvKZWfJ5i/37dxEItCOKSUqlCTStQF9f\nAsMQCQb9GHqE1OxhwsE6hhjC6mrhve+/nbrehd8fYnDwIPm8l56e6wiFpolGTeq5Cp0G3HDTjchL\nQrCrsZG94+NMTU2dz5Ux6nWU15Rmq6pKc0sLhiThC4XYum0bbNt2wXtw7bWb+MlPjtLZuQFJknFd\nl6mp06xf30MwGHx7FuIivPTcc8TKZTqbm0mlUgiVCuvjceb9Ap/7y89zYN8+OvUSfc3NKLKMtJTc\n11AscuLYMVxBIKFpdOzYwdjYGF6g5rqUBYE1mzfzmc9+hpUrV/D0j39MbjbEuZERMtM18pk6nmA7\nPjdOLKjSGJMoSnl6+qLcffet+P1+vv3thxGELm69dRPDw/+ZTGY/lUqMyclDNDbKdHevw7J8CMLi\nWng8Gp2d69m1azfbt1/9BpEniiL3338Ho6N/xwsvvEyt1okgZAiFVGTZIZEIsGJFD7t3/5Cbbrrz\ngvBcMtnBxMTLLCwsXBY9a95SMSIIwleAK4BDr+3gKwjCJ4H/G3jZdd13jKw7exZGR+Gmmy71SN5+\nbr11UYikUtDYeKlH85tRKpV4+MEHqU9NoQkCJdelcWCAOz74wX913FvTNMx6AY94ofeGZZsEPApu\n0EuhkEYURQRBoLPzShoaMqTTu9mxYwfh8O2o6gR/+qef4qtf/RqZUS+9rb2osoppm+RmBwmpcYql\nKrAoADRR4sSeE7R330BIFUhlFtj93EkeffTn/MEf3MWtt15/QaLmv//3nQwPD/PCC7uYmCggSRUM\nY454Usetxs8LEYCSbnDte+4gn+9h69Y+/H4/GzdupF6v881vPveG+SuKSrGo/6veu98W/H4/d37w\ng9Te/35M0ySdTvPsN76Bf8mJd9WqFXS0t7Jibo6WfJ69x8cJOiqFSpRocHEd3KV/lUoBVU3Q0XEF\nALHYajKZo1iWzo03/gGnjh6lI6KwLHo109OTLFQLXNXeRObkKeatDC0tfYyPj+PzLWdmZpSZmSnm\n5yFEms6Qh1w+f0GFkCaKVCqV89939/fz1M6ddL2uRDdVrXLDL+krtGXLZsrlKi+/vBvX9eE4Ndat\n6+S22y5dkzzXdRk6fJh1gQAvP/sscr2OVxSpOQ6nbJuz995LqVAg4PHgfZ2PjF9R2Pn00wjVKudO\nnGBlMEiD10vrwADhUIj5UokV110HwI5rrmHb9u1Uq1X+6Z++zU8efBGhHCQS6EVAoFAeRwhU6Vm5\nnkTCRNO0pfyQPKFQG3v3HiEY7KdeLxIICGiayX33/RHPPXcQRSnj979amihJMoKw+PiL7Tg1Nzfz\n13/9H/nv//0rPPTQCWKxRrxei8ZGkQ0b1pLNTqOqMrL8RgdkQZAxDOPNXYRLxFsmRgRB2Aj4Xde9\nRhCE/yUIwibXdX/RfvFR4EXgy2/V6/9r+OpX4cMfhssoJ+jXRtMWbeG/+1348z+/1KP5zfjZP/8z\nvlSKta+pjDg+OMjO5567oD/Jb0IoFGLdplUc/fHzJMNxBAQc1yWfnyXcFGfzbTeza9cZQqFlKIpJ\ntZojmx0kENAYGxvDsg7zmc/chqIodHZ2ootHEEQZUZTANrEdh5ptEXtNS+h9+w4jWyrdDUmmp2Zx\nqiJ9kV7GUmcYGbH52td+xAMP3Hc+lu/xeFi9ejWrV6+mWCxSKBQIh8M8/9RTfP3vvomnmMWraOSq\nFZREgkSiAUEIcOedd563vl+sVqhgmjqK8qpwS6cn2bSpj98FNE1D0zSy2SyvzyIJhkK0KQrFxkYa\nupaz86cnUKQihllBVfzkigvImkNIdQmFGqnXK3i9fhzHxrJUgkGNsZGz+A2B/q4BRoaHWRZMsqCa\nmKUit2zbxvee2MXouWMYRo2hoacplUwkyUNr62r0ksGZ0VfYXh44L0YcxyHvOBeIk56eHpJr13Lg\n6FE6o1EEQWAilyO+ciW9vb0Xnbcoitxyyw1s33412WyWYDBIJBJ5q97mXwtBEBAlieOHDxNxHKKv\nyYcYnpjglZ07uXLrVkZ27qTrdY89OTZG1XG4c/NmGlyXyaEhIrUa506epH31atzmZtZv2HD+90VR\nRFEU5uZKRJPL8MpFcqU8IV+MgK+V8cxeevwi7e1RotEoxWKRarXCkSMHkOUkXV1XUK+fwjA8SFId\nUZTR9VmSyQjR6KtXdI5j4zj1C3abTNPk2LHjHDx4EkEQueKKlXz+83+KaX4N02whHm8iEAhQr1cx\nzRm2bt1AJjNNIvFqqKdWK6Npzr9Yxv7bwltZvLoZeGrp62eALb/4geu6C4D9Fr72b0y5DN/8Jnzu\nc5d6JJeOT34Svv51uEhe3zuWQqHA7NAQva8z7lre0sKJvXux7X/9x+zTn/4E/p44J8YOkl4YI50Z\nQgxatF15BTfeeCOf+MStBAKztLUVmJx8mIWFUarVRtJpcJwQu3YdI5VKceWVG+lYnmSsWGAyu0C6\nVCEneXECAv39Pdi2zdTUFEdOnkZUQhQKRfL5GgF/BE3VkG1pqVSwnV279l10rKFQiPb2dkKhEO+/\n6y7u/fTvM+dkKHpV2jdsZMOVm5iZOcXWrWvOCxEAn8/He9+7hampA6TTU5TLeSYnT+PzLbB9+9UX\nfa3LldbWVkyfj3z5/2fvvKOrOO+8/5nbe1HvXYgqBKIIsEEU2xg3XDCucUm8sZMTbzbJ7mb33fPG\nOduy3k2yb4pTbMdxCTHGFVfAdEQRAgQICaHeu65u0e135v3jyjIyoltISPqcoyNp5s7cZ+5zZ+Y3\nz/P9fX+uIctrOzqYuWABz3znm6TMMCGp3LT07KOyZQd9wePkzTOxcOFcli1bgFzeS2/vGbzeJubM\nyWLmzBm0Nx3BoJbj83rxu1wE8BCt9xApkyEolczPSeZY0bs0NNTQ0eFFkrIQhBja22tR6Ey0ywTO\n1DcREkVcHg9H6+uZsmDBkKF5mUzG2nXrWPTgg9hjYuiLjmbB+vWsuu02iooO8MYbb7Nt2w56enq+\netjo9XqSk5NHPRD5gtRp06hrasJ6Vi0Om8eDJSaG7sbGcBZJYiIVTU14/X78gQCnm5qo6+vjhqlT\nkctk5E+bxtzFiyEpieZQCOu8eTz81FPneHP4/X5kMjV5i5egshjxi100d9fR1NuFoFYQHd3Pvfeu\nAcLnmN9vx+0OYDCYkctVWK1ReL119PRUUVW1jbVrp5GYaCUQCI8qhkJBmppOkZ+fNRiMhEIhNmx4\nh7ffPoLdHkNfXxSbNh3m/fc/5ckn78Vq7aW3t5ympiPYbEdYt66QBx+8h2CwnpaWKlyuPjo6Guno\nOMaddy4fNxk1I3kUFqB24G87MGME3+uqef31cFG8tLTRbsnoceONYTFmcfH1Y/jm9/tRDkyVnI1K\nqUQKBAgGg+fYnF8qZrOZn//u13yyeTMni4sxaDTMveEGlixbhlqtZsqUKUyZEtZv/Oxnv8PnS0Qm\nU2K1WrBarXR1NfP553t5+OH7eODBVXz2WQn9/RokCaYmzLPjJDQAACAASURBVKGn4TB/+XQ7/V29\neGVKmgUFViKprm4iFJSh1wuIkoggOdDrjVgs0dTXV1y03TKZjIcffpDk5GR27z5KMNhGd3cjS5fO\nYOXKZee8vqBgITEx0Rw6VIrd3sLs2SnMn38npgnmgqdUKrntwQfZ/NprWG02NHI5vX4/xsxMFhQU\noFareeH3v6C4uJjKyipkMigoKCAmJoZf/OJVoqOjWLEilmAwXBOnq6uJ7OzpqHxd2Jtqae8N4PTV\nE2dUU5AcS63bDYLAogX5bG1owapKpbvbBTgJBDR0dMjp7d3PLbfcQ73YwJ7WVvRGI3Pvuot5Cxac\n036FQsHs2bOZPXs2AF1dXfz+9xvweCwYDBFUVXWyd+/rPPnkWtLG8IVu/qJFfPDKK5zs6cEsl+MR\nRexKJTcuWkRlfz9yuZz1jz/Ogb17OXz4MJIkMb2ggOlKJaaBYEMQBFJjY0mNjcXa2MiMWbPQarXn\nvJder8diUSOTKVhx2+10dXXS1dVNIODFYDDx93//nSHbJSUlUF/fQFvbCVpanASDXkwmBTrdPEKh\nAHfeuYbm5lZee+19amu78Hpd5OVlMmvWjYP7qKqq4vTpPtLT5w8uM5ujOHXqEAsX+vi7v/sbWltb\nCQaDJCQkDE41f+97j3Lo0BHq61tISbFQUHDfuCqmN5LBiB344mpmBvq+sv6iz9/PPffc4N+FhYUU\nDsz3fd34/fD882Hx6kRGEL4cHblegpGIiAgkrRaXx4PhrItGe28vUSkpV+2VoNfrWffgg6w7ywb6\nqzgcDgTBQHb20Ln5qKhEKip2I4oiK1cWMnVqNqdPV+H1eikudhEVtZ6uzm6qvNUotXqsxmpEWyfI\nU3HYHOhNdlz+NqxRemJj03A4ekhKsp6nFWECgQCnT5+mvLwGjUbF+vWriYyMxGAwDHsx/oKMjIwx\nXbn1WpGens6TP/gBpysq6Hc6mZOSQkZGxmBAq1QqWbJkCUu+IgYtLMxj27YSYmJy0Gj0dHW1Ego1\nUli4HpNOgb2khEi9ngM7bUy1WvGHQniVSqLMZlq7u9GaY1mUvxq7fRf9/SpAQKk04ve7qa5u4bHH\nbuCppy7PAvyvf32PigoParWG6GgPiYmpeL0RvPPOFv7u7546x113rJCUlETBypUoOzoQg0GidTpS\n4+Lo6usjKTsbtVqNWq1m1erVrFq9enA7URRpKSkhKzFxcJnNZuNYTR3sPUxPj43c3JlDdBsymYw1\na5bx+utbMJuziYmJRquV09dXxUMP3X/OOZOZmUZ/fyxHjpRisQhERKRjMiXicFQSEZHKe+9tY8aM\nDMzmJJYuLcBsjqK/384rr3zI00/fR3JyMmfO1KPTnSvM02pjqa6uJzs7e1gTv4iICG69dfwKGkcy\nGDkAfBvYBKwEXvnK+ov6DZ8djIwkf/oT5OScIzafkDz2GMycCb/4xfVRsVgul7P8zjv5fMMG0vR6\nLAYD3XY7zaEQ9zz88DVpg1KpRBQD5ywPBv2o1YrBUZvExEQSExM5fvw4+/c3k5GRS3t7CQkpi9Hp\njHR26pBrK5HsrXilJjp664lLSWbuyocIhQI4HDXccMP5DccCgQBvvLGJM2ecGI0JhEJeDh7cxqpV\ns1i5snCkDn/cYTQamT/MyMOFWLmykMhIK/v2HcFmc5GdnUxh4Xri4uJYsmwZf62qwmezEZeRwaGT\nJ/EplSwsKKChs5M2ICtnCjIZ+P39aLXRaLUWQMDhaCQQ6EcQznXDvRCHD5ewceN2rNblqFQKWltb\nqalp4oYbFtDVFaCnp2fMag0EQWDNunW8/8orREsSZq2Wus5O7Fot6y+gASu44QY2lJUhNjeTEBlJ\nfWMjHx8oRZ+1hK4uCx99VMG+fUd56qkHh0xJTZ8+jaeeUrNr1yFaWqqIi4vi3ntvIyvrXM1UQcFc\nDh/eSCCgISsrF0mScDpbMBpF0tJmUFu7m+bmg2RmrkQ+YDD4Rer0tm37ePLJB9Hp1ASDX302h2DQ\nh1Y7+kZzo8WIBSOSJB0TBMErCMIe4JgkSSWCIPxKkqRnBUG4HfhHIFMQhE2SJK0bqXZcDLcb/v3f\n4Z13RqsFY4uEBCgsvL70MzNmzsTw7W9TUlTEmY4O4mfO5IElS4i9RmlB0dHRJCeb6OpqIjr6yyea\ntrYzLF8++5wppIaGVrTasHBVLg/bzANoNHEkZkZhtVjRn9iNKULAHJmGJPXidHaxfn0h6enp521H\nWdkpzpxxkZ7+ZT2LUCiRHTsOMHv2l3U1Jvn6EQSBOXPymDMn75x1ZrOZbzzzTNjEq7YW3fz5eN1u\nfH4/UZmZPLx4McePl/Hmm8UkJWXgdHpwOuvx++3odJ0sX343fX3eS26L1+tl8+ZdGI3xmEyRyGRy\ndDoTNls7tbV1GAyhK566vFakpqby6LPPcuLYMWydnWQkJ5M7e/YFU46tVisPP/MMJQcPcrqsjN21\nbWQue4y0tOkIgkBERBwtLdXs2lXE2rW3Ddn2UkcGk5OTefDBVZSU/Ce9vQEgRFSUiblzVyAIMjwe\nFxpN1GAg8mXbYqmrOw3AzJnT2LnzOH5/MipVWL/l93sJhTqZMWP8jnxcjBFVvpydzjvw/7MDvz8C\nPhrJ975UfvYzWLQILvNBaFzzox+Fs4qefvr6ySxKTU0l9axsmmvNunV38Oqrb9PQ0IEgaJEkB1On\nxrB06bnDbVarCb+/HoCUlHhaWirR6UyEQm5MphjiEzJRKHv50Y++iSiK+Hw+IiMjLypUO368Eotl\n6PBu+KIYQX19/WQwMorodDoWLFx43vnPG29czIkT5ZSXH8ZqnYrB4EajUbFs2XcJBv1ERjov+b1a\nWloAM2lpCpqbG7FYwgGs0RhBRcUJ7rkn57pw7YyIiKBw5crL2sZisbBq9WpyZsyg2W4gOXmoVDEu\nLo1jx/adE4xcDnPm5PE3f3M/JSXdJCZOGUzj7exsJDMzjq6uwDlVkD0eJ1araaANcaxdeyObN+9B\nFMMjNHJ5H/feWzhmR6uuBdfJrWZkqK6GF16A0tLRbsnYYvHi8AjJu+/C/eeWU5hkGCIjI3n22W9S\nX1+Py+UiKiqKxIGaLl8lJSWJhoaNVFRUYLVGEhMDLS0nCAZbCIXMdHaWcO+9Ky5bQKpUKhDF4bKH\nxHGjuB9puru7OXz4GE1NHcTHR7JgwdxrMsKmUql49tlv43S66OiQExc3n6ioBEKhIM3NFdx776Wn\nqMtk4dG26dPzsdu309NzEpnMhN/fh1rdzD33XGe5+1eAXC4fHHE8m1AoeNnngt/v5/jxE5w8WYVS\nqSA/fwarV6+ks3MT7e1V9PWZCQadWCwBHn30fj78cCt1dVUkJGQjCAKhUJD29gruv38woZT58/PJ\nycmmYaACdlpa2qgazY0FBGmM5nEKgiCNZNskKWz0tWIF/P3fj9jbXLd8/DH8wz/A8ePXbnREEATG\n6vfx66Knp4c//vFNWluhtrYLl8tHINDCtGkWVq9eRmpqCtnZWUPqjlwqFRUVvPbaTlJT5w+KE30+\nD52dh/nRj54csy6qY6Xfm5ubeemld4A4jMZIXC4bwWALTz551wWnx75ObDYbGzd+QHOzE0FQIZd7\nWL16EQUFl64oDwQC/Pd//wGNZhparYGurmYcjj5stiYefngZK1YsH8EjuHRGst9FUeSXv/wjopiO\n2fzliGBjYxnLliVz000rLmk/gUCAV1/dSE2Nl4iIZEKhIH199RQUpLJmzc3U1tbS2dlNRISF7AFx\nrcvl4u23P6KqqmOg4nA/hYVzWLFi2bAPJxOJgT4f9kOYsMHIn/4Ev/41HDoEqnON7SY8kgQrV4ZH\nRp5++tq851i5KY0kb7+9mVOnfMTHZyCKIna7HZ/PSyhUxT//83euKvtHFEU2b/6E4uI6FIrIgVGS\nbu67bwV5ebO/voP4mhkr/f67372K3R5FRMSXBeIcjh5ksnr+7u+eumY3EkmS6OzsxOfzERMTM8QX\n5uzXXKg99fX1vPrqBwQCZuRyLYFAD1OmWHnooXtRjZEL3kj3e0tLC3/+87u43QYUCh2BgI20NB2P\nPrrugpllZ1NaWsrGjYdJT587uCwUCtHUdIjvfvceEs/K3Pkq3d3d9Pf3ExUVNaq1fsYSk8HIV2ho\ngHnzYMcOmDVrRN5iXHDsGNx6a3h05FpoQcfKTWkk+clPfk5c3A3nCNyamkr41rdWX7XuJVxfpJkt\nWz7n5MlqFAoN2dmp3HTTkvM6cY42Y6Hf+/v7+c///CMpKUvPWdfYWMQPf/joFY1Wfd2Ul5fz+ef7\n6eiwERtrZeXKRcyYMbyFk8vlorLyDC5XP8nJiaSlpY2pdN5r0e9ut5szZ87Q1+cgMTF+SJr2pbBh\nw7s0NqqJiIjD6XRy+nQ1ra1d9Pe3c889OTz99LfGTHB3PXChYGTsfDOvEaIITzwRFmlOBiIXZs6c\ncL2aJ564vlxZxzIajYpA4NxaEpIUvGBF3FAohN1uv2gdCkEQqKtroKqqn7S0leTkrMHhiOGllz6k\nqqrqqts/XlEoFMhknKO5Cd8sr73mJhAIYLfbhzgIl5Ye57XXtuH3p5CauoJAII3XX9/O0aPHht2H\nwWAgP38uy5bdSEZGxpgKRK4VOp2OvLw8CguXkp2dfdlZRGq1kkDAj9vtZs+ew3R0SFgs2Wg0MRw7\n1sabb76H3+/HbrcTDAZH6CgmBhNO1fbzn4ddRn/0o9FuyfXBT34STvX98Y/hv/5rtFtz/bNo0Wy2\nbj1DWtqXKaC9ve1ERamI/4ql/RccPnyEbdsO4HaLKBQhliyZzfLlS4e9QXq9XnbsKCElZcFgrRmL\nJRpBENi6dR/Z2dkjc2DXOWq1mtzcTE6erCYx8Uvzuvb2OnJyEq6ZuDAUCrFr11727i0lGJSh08lY\nuXIB8+bls2XLPuLj89BqwwZARqMVhWI2W7YUMXt27phP170emTt3JiUlH9LZ6SAUMmCxRBIIeFAo\nHOTmrmbLls0cP16JTmdFrZZYuXIBBQULJ7w25EqYUMHIvn3hYKS4GCbP20tDqYTNm8NW8QoF/Nu/\nhZ1aJ7kyFi8uoKmpndOnDxI2KPZiMgV48MF7h72AlZYe55139pOQMJuoKD2BgJ8dO8oIBIKsWXNu\ndVWbzUYopB5S9A7CdtONjScJBAIXHIGZyNx660q6ut6ioeEwgmBAkvqJjZVx553XLqVs+/bd7NxZ\nTVJSOJj0et28914xPp8PlyuE1TrUiVCrNdDdLeFyucasQPl6Jj09nVWrcvmf/3mdQCCF3l4bMpmd\n/Px8mpqqqa0NkJQ0heTkbHw+Dx98cARBEC5LcDxJmAkTjHR1wYMPwiuvQErKaLfm+iIyEnbvhjvv\nDH+GL74IEzwL7YpRqVQ88sg6mpub6ezsRKfTkZmZOey8syRJbN9+gNjYGWg0YQGcUqkiJSWXgwf3\ns2zZknOEcXq9HknyIYrikGF5r7cfvV4zmeJ7AfR6Pd/+9mPU19djs9kwmUyXrTG4GjweD0VFJ0hJ\nWTTEvTM+fhZ795YikwUJBPwolV9+V4LBAHJ5aFiR6yRfDytXFtLQ0ERJSSdRUYlERSUglys5evQI\nJlMaBkM4BV+t1pKQMIsdOw4zf/68yZGqy2RCTCJ6PLB2LTz+eFiQOcnlEx0dFvyazTB3LpSUjHaL\nrl8EQSA5OZn8/HymTZt2XgFcMBjEZutHrx/6xBu+UWlxOBznbGMymcjNTaW5uWJQHBgKBWltLWfZ\nsvzJ4eOLIJPJyMjIID8//4o0BleD0+lEFFXniJu1WgMeT4B586bR0nJqUNciiiGam0+xcOGMq67B\nNMmFuemmQiwWBVFRCWg0enw+Ny6XB6NRNsRMUKPR4/GE8Hov3TF3kjDj/jFJFMP1VlJS4Kc/He3W\nXN9otfCHP8CmTWGPlh/+MKy9mXwAGBkUCgVWq57+fvuQgCQUCgKe85qi3XnnrYRCH3PqVBGCoEEQ\n3Cxfnjs5dDzGMRqNyGR+gsEACsWXU2kejwuTScstt6xEFLdx+HARgqAH3Myfn8WqVYWj1uaJQnJy\nMuvXL2fz5l10danw+91otXbmz585JGD1evvRauWTI1VXwLhO7Q2F4JvfhPp6+OwzmPx+fH00NMA3\nvhEO9l57Db4OT6ixkOJ5LWhoaODo0TJcLjc5OWnk5s4678WrtPQ4b765h4SE2Wg0Yc1Ic3MZN96Y\nOqxm5GxsNhsul4uIiIgx7XMwHvvd4XBQWnqCurpWoqMt5OfPviQn161bdwxoRmYOakZaW4+zbt1i\n8vPnDu7bbrdjMpmua53I9djvgUCAjo4OlEolp06dZuvWCpKSZqFSafD5PLS0HGft2nkUFCwkFApR\nUVHBiRNnkMkE8vKmMWXKlAmZ1fQFE9JnxOUKT8vY7fD++zCGr8XXLaEQ/PKX4Syb558Pf95XMwtw\nPV6cLpf9+w/w4YeH0emSUKm02O1tJCTAk08+gE6nG3abr2bT3HBDHoWFN44b/cd46/fu7m5efHEj\nbrcZozEKt9tOKNTOo4/eypQpUy647RfZNEVFx/H7hcFsmgUL5o+7Kbbrvd9FUWTv3iJ27z6G3x9+\n2P0im0YURd566z1OnOjGZEpCkkSczmbmz0/m7rtvH3d9ealMuGDk0KGwN8aiRfDb306OiIw0J07A\no49CRgb88Y9hfcmVcL1fnC6Gw+Hg+edfJj6+YIgIsaHhJLfcksXSpTecd9tQKITL5UKr1Y47k6Xx\n1u9//eu7VFVJxMWlDS7r77fj91fwox89fUk6lEAggNvtxmAwjFsh5Hjp9y/6Sq/XDz4gnDlzhlde\n2U5a2pdBpCRJ1Ncf5Omn7xjVop6jyaiZngmC8EtBEPYIgvC/X1meIAjCDkEQigRBuLyyjOdBFGHP\nnrB9+T33wL/8C7z88mQgci3IzQ2nS0+ZArNnw0svQSAw2q0aezQ1NQGWIYEIQFRUCqWllRfcVi6X\nYzabx10gMt4IhUKUl9cREzO0erJeb8blEujs7Lyk/SiVSsxm87gNRMYTX/TV2SOVFRXV6HRxQ0ZA\nBEFArY6hqqp2NJo55hmxYEQQhLmAXpKkpYBKEIR5Z63+MfB/gJuBf7mc/UoStLdDURG8+ir83/8L\n994LMTHw3e/CDTfA6dPw0ENf37FMcnHU6vB0zbvvwltvhQOT//iPsLZkkjDhi9W5VXVDoSAq1aT3\nx3hAEATkchmieG7FWEkKjZuptUkujEqlHBCaD0UUQyiVk9+B4RjJT2UhsHXg78+BRcAXCaEzJUk6\nACAIglMQBKMkSc7hdrJtW/inpgaqq8O/tVrIzISsrPDPfffBr34FF6hZNMk1oqAAtm4Nj5S88grk\n54PVGp4ymzIF0tLAYgn7lBiNYDCEl08EUlNTUam20N/vQK8PZ8JIkkR3dy0rVkxmuowHZDIZ8+dP\n58CBKlJSpg8u7+lpIz7eMCQNdJLxy8yZU9m79z1CoZTBVO1AwEco1MHUqZdWMXiiMZLBiAX4YjzK\nDpxdzenssUf7wGuHDUbs9vDNbP36cOCRmRn2uphkbLNgQfjnt7+F8nI4eBBqa+GTT8J96nCA0xme\nzjl1arRbe23QaDQ89NAa/vKXj+npMQNKJMlGfn4Ss2fnjnbzJvmaWL78RpqaNtHQcBiZzIwoujGZ\nfKxbd9+EFS5ONJKTk7n55jy2bTuAIEQhSSKC0Msddyy6pKyqichIBiN2wn7XAGag76x1Z49hmgDb\ncDuYPHEnBmd382SfT0wmQr//+MffHe0mjDkmQr+fzb//+2i3YOwyksHIAeDbwCZgJfDKWetOCIJQ\nAJwETJIkuYbbwXhQWo8Ffve7V7Hbo4iIiBtcZrd3o1Y38b3vfXPMXBDGi7p+kstjst+H5/DhEt57\n7/iQooqhUJDm5oN8//sPEX2laWtjhOu93+12O//936+QkFAwxKSuoaGMW27JvGB23ETlQveaEROw\nSpJ0DPAKgrAHCEqSVCIIwq8GVj8P/DuwbeD3JCOEy+WipcU2JBCBcOG0zs7+YS3FJ5lkktHn5Mkq\nrNakIcvC+gMrjY2No9OoSQZpbm5GkkxDAhGAqKjki2bHTXIuI5raK0nS9yVJWipJ0t8O/P/swO8W\n4FlAAv6vIAi/G8l2TGTCqYHiYD2LLwg/kYiTqYOTTDJGUatVBIPD5chPZuWMBc6XHRcMBlCrJ7Pj\nLpfR/EZXSpK0BEAQhD8JgjBnYDRlksvgC8vhimPhj25qXh7Tp08fDDK0Wi2zZqVTUVFHQkLW4Hbt\n7XXk5CRiMBiG3e8kk0xy7Whra6O0pIS+ri4S0tPJmzuXefNmUlb2OVZrDDJZ+Hz2eFwolQ4yMzNH\nucWTpKWlodFsGVI7SpIkenrquOmmgsHXud1ujpeW0lBZic5oZPb8+RPW9OxCjAkHVkEQ/gr8syRJ\ndWctu+raNOMdSZL44O23aTt6lJSBFKNmu53IWbO4e/36wYDE6XTy6qubaGsLIAgGRLGf2FiBxx+/\nf0zVtrje55AnuTImer+fPn2aLW+8QaJKhVGrpdvpxKbRsP6ppyguPkpRUSUyWQSSFESh6OOBB25h\n2rRpo93sq2Y89HttbS2vv74Zn8+ETKYayI5L46671iCXy3G5XGx48UVU3d3EWyy4vV4a3W4K7rqL\nhQUFF3+DccaYtYMXBOFOwpqREkmSnvjKugkXjHg8Hg7s20dZcTGiKDJt7lwWL12K0Wgc9vW1tbV8\n+uKLLExLG2I5XFxfz6onnhhSByMUClFbW4vNZsNisZCRkTHmhnrHw8VpkstnJPvd5XKxf88eyo8c\nQSaTMWP+fBbdcMN56wBda4LBIL9//nlm6nQYz2pTfXs7wpQp3PPAA3R0dNDY2IhSqSQzM/O814Pr\njfFyvvf391NTU4PP5yMhIYGYmBgOHThA6f79nCkvR+3xcOvixZgHCqT5AgEOtbfzNz/+8ZguYDkS\njNlgZLARYWHrh5IkbTtrmfSTn/xk8DWFhYUUFhaOQuuuDaFQiL+8/DJSYyNZcXHIBIG6jg6cFgvf\neOYZtFrtOdts37KF3gMHyEhIGFzm9fs5WF6OmJDAHffdR1ZW1pgLOs7HeLk4TXJ5jFS/+3w+Xvv9\n79F1d5MeF4ckSdS2txNMTOSRb30LpfLrndcPBoPU1NTQ3tqKyWwmZ+rUiwY9ra2tvP/CCyxMSRmy\nPCSK7Glp4Qc//em4rfI6Hs93SZJ4+y9/wVFeTnZcHAd37ULweLBpNNxSWIhWraalq4tDlZUsuPtu\nblm9ekIFJBcKRkbtLiUIgkqSJP/Avw7gnKIbzz333DVt02hSXV2Nu76e+Wlpg8tykpI40dDAqbIy\n5s2ff842CpWK4Fm20912O7uKigj29BDlcLDn9dfZn5DA+scfn1Bf+EkmASg/dQpZZydTz5qfn56S\nwpH6eqqrq7/WqQ63281br72Gr6kJi0JBXSjEXo2Ge594gsQLWEMrFIphJJDhhxO5QjFm0u4nuTSa\nm5vpKC+nIDU1XItGpSJKLge3m7KaGmw2G4HubkSXi4YdO3i5vJy1jz1GyleC0YnIaIbcqwVB2CUI\nwm4gCfh0FNsy6rQ1NxMxTBG0aIOB5trhCytNnT6djmAQXyCAJEnsLykhRRSJMRiYN2sW+ampqNvb\n2bNjx0g3f5KrIBSCn/8c5s2DtWvh+PHRbtH4oKW+nqhhRiYiNRpavuaiSUW7dyNvbmZeaipZiYnM\nSklhikrFRxs3Dlun5guio6MxxMfT2t09ZPmZtjZmLVw4GYxcZ3R2dmIWhMF+S0pPp8vlIkqrpbS8\nHEV3N9kGAzGRkSyZOZOpWi0fvfkmodBwIenEYtSCEUmSNkuSVChJ0jJJkh6XJOn8Z+wEwGg24w6e\nW1jJ5fViiogYdpvY2FgW33UXxW1tHKiooK2lBWcwSOqsWVisVgAy4+OpKCm54AVxktHl2WfDBQb/\n3/+DW26BVatg167RbtX1j9Fqpd/vP2d5v9+P8WsWbpcVF5MVHz9kWbTFQrC3l46OjvNuJwgCt61b\nR5NCwdGGBk43NlLc0IA8LY0ly5Z9rW2cZOTR6XR4z/o/OSUFQ1ISFZ2dtLS2Ig+FaPP7yV24ELlc\nTqTJhGC309raOmptHitcH2KCcYIoirS1tREIBIiLi0Oj0Qyuy5k6lSKVil6HgwhT2EXf5fHQHgqx\nMi/vvPvLys4m/tvfpry8nA6nk4UzZgxJ15XJZEiiOO7mZscLH30ULix45AiYTLBkCUydCvffD0eP\nQlLSxfcxyfDMzM2ldNcu4vv7MQ1MU9qcTmwKBdNmzLjI1mGHTVEUsVgsQ0YoJEmio6MDj8dDdHQ0\ner2eUCg0rLZDBhd9EIiJieFb3/8+1dXVuJxOomNiSE1NHbdakUtFkiRsNhtKpfK6EO329vYik8no\nUyho7+0lLiICuVxOzqxZtKnVpPT2kpGeTkJ8PKqzRsFlsuGrPE80JoORa0R7ezsfbNhAqLcXhSDg\nVihYevvtzM3PB8BgMLD28cf56M03ERobkQkCPrWa1Y88Mqzt85kzZ/jgg+04HEEkKcTUqQlEpqXx\n1bGVho4OMmfNmjQ3G4MEg/D978MLL4QDkS9Yvhy+9z146qlwYcHJkforIzIyklsffpitb7+NoqcH\nBIGgXs9djz+O6ewP/Ct0dXXx/vtbaGjoAQTi4gzcffctJCYmYrfb+eDNN7E3NqKRyXABcwoLycnL\no/74cbLO0oc4+vsJ6XSXVBhNpVIxffr0i75uolBdXc0HH3xOX18ASQqSk5PIXXetvmC/jRZ+v59P\n3n+f+hMnMAgCfqeTTxsamBIXh1qhwKtUcvuTT9LR2oqtuHhIIOLyePCp1SSclYQwURkT2TTDMZ5S\ne/1+Py/+8pekA7ED0ycen48jra2sffrpIQY4oVCI1tZWRFEkISFhWMV/S0sLL7ywicjIWRgMloER\nlxoUikaMfiexgoBJp6Pb5aLfaOSBp54i4jxTPWOJn4czWgAAIABJREFU8aiuvxBvvQW/+hXs23fu\nukAAZs4MT92sXn3t23YtGel+DwaDtLa2IggCCQkJFwzMPR4Pv/rVKwSDiURFJSIIAjZbB15vFX/7\nt4+xeeNGdB0dpMeFyysEQyFKGhrIXbOGskOH0NrtRBsMOD0e2kWRNY8+OiTFfpIvOV+/t7W18cIL\nb2GxzMBotCJJEu3ttURGunjmmcfG3IPVlo8/pnX/fmalpAwe04n6ejRTp7KksJC4uDjUajUOh4O/\nvvQS6t5eog0GXF4v7aEQNz/00LjwjbkUxmQ2zUSipqYGlcNB7FlBh1atJkWno7S4eEgwIpfLSU5O\nvuD+9u8vQaNJxWCwAOFhvsTEbBoaerlj3c3Yurvp6+5mamoqM3NzJzNpxii//CX84z8Ov06phJ/9\nDH7847COZHJ05MpRKBSXnK1QWVmJw6EhNfXL+TGrNZbmZhs7d+7C0djIjLPOV4VcTk50NGeOH+ex\n736XshMnaG1oICYigpV5edd9MbvR4NChoyiVSRiN4Qc3QRCIj8+koeEw9fX1Y8p91ufzUV5czOKk\npMGpPEEQmJGSwv7aWuIeeAC1Wg2AyWTiG888Q9nJk7TU1RFttbI8L4+YmJjRPIQxw2QwMoJIkkRZ\nWRkbNryL/VAJQq+drKz0wflPo05Hu802ZBufz4fD4UCv15/Xo6CtrQejMeOc5YKgRyaTsXzVqsFl\nbreb5uZm9Ho91oFRmUlGn8pKqK+H228//2vWroXnngtrSm655Vq1bGLT3W1DqTxXn6DVmmlsbEI/\njI7DoNXi7O5Gp9OxoKAACgrweDz09PRgs9mu6rzzeDy4XC5MJtPgTe1y8fv9HD5cQnHxKUKhEHPn\nTqWgYMGYMX77Km1tPRgMw6VD6666sKcoivT29qJQKLBYwg9zLS0t7N59iKamNqKjI1i6dD5ZWVkX\n2VMYr9eLPBRC8ZXRGoVcjlwU8Xq9Q/pNq9Uyf8ECsrKz6e/vvy60MNeKyWBkBNm5cw9bt55ELs/E\nraihuclPS8thli2bj9FopMNuJ3nuXCAcuOzZs49du44QDKoAHwUF07n55hV0dHRQXRmuApmVk0Ny\ncgwnT/ag0w39IkuSa/DCJ0kSe3bu5Nju3WhFEY8kkTRtGmvuvnvMXoQmEq+/Dg89BBfyoxME+MEP\n4Be/mAxGrhWxsVEEAjVDlvn9fmqqy8nKCNLa1sbU6Gi0Z4nP23t7SRm4eUmSxN5duzi6a9dVnXfB\nYJBt23Zy8OApRFGFXO5n6dI5FBbeeFnCVlEU2bDhHSor+4mJyUKplLFjRwPl5bU89dTDVxzgjCTJ\nyTEcOdIzOPL7Ja6rCuxqa2t5772t2GwBIERaWhT5+TN4553daLXpmM15dHb28dJLH7F+fSFz5gyf\nOHA2RqMRpcmE0+0e4qDb3ddHl8PBoaIiomJjmTZ9OlqtFrfbzcfvvktLRQUamQyvTMbcwkJuLCyc\n8Gnck5qREcLpdPJv//ZblMpkRFGks7kSTWczRkFGUqKKqKQ4ujUaHv3OdzCZTBw8eIj33z9CcnIe\nSqWaUChIU9NJ9NoujB4nsQPakY5AgPjcXE6casdonIrFEo3LZae0dBc6nYeHH76L2bNzqTpzhoOb\nNpGfmopSoUCSJCpbWlBNmcK6Rx4Z5U9neCaKZkSSICMjnM47Z86FX+vzQVoa7NwZzrIZj4xWv4dC\nIZqamggEAiQkJKDX6/H5fPz2t3/G6bQSG5uOy+Vi19YPUftruHvJTPaePEl7XQOLZ85i6pRMRIWC\nhkCA9c88Q3x8PEePHOHApk3kp6QMOe8UWVmsuPVWFArFJd1Qt2zZzq5dtaSk5CKXKwgE/DQ1Hef2\n23O54YbFl3yMNTU1vPzyVtLSFgxZ3tBQyr33zmXu3It8AUeQ8/V7V1cXv/nNBnS6bKzWWEKhIG1t\n1SQlhfjWtx65oiyjzs5OfvObDZjNMwd1KF1dTRw9+jHz5t1FVNRZLtbefhyOUn784+9cknt12cmT\nbN+wgRyrlQiTidPV1Xy4bx8ZWVksmDIFh9+P22jk/iefZOuHH9J/6hTTU1Lo6+ujqbmNKlsvq554\njDW33XbZx3W9MakZuUQkSUIUxa9FIPXRBx9w6OP3MPn8iJKEXaEkKjMPnULB6dp6vnX7am5etgyT\nyYQoiuzceZiEhFyUyvCTilyuQK9PYu+nm/mndSsxDNjBp4VCFJ84wZo77uDo0dOUlx/i+PHTREdP\nIStrEVu31rNr11E0wR7mxcWhHDiZBEEgJzGRotOn6e3tvS4EreOV48dBLofzZGwPQa2GRx6B116D\n//iPkW/bRKG1tZUP3ngDweFAATgFgYJbb2XR4sU8+eR6PvlkOxUVezlx9BgZhhB3LL6RM83t+PyR\neNVaPizvZndTL1NnZ/GDf/oH4gc8Rop37WJ6bOyQ886qVPLmq69SV1qKVqcjMi2NNffcc95z0Ov1\nsn//CZKTFyGXh/ejVKpITJzJrl0lLFq08JKvUQ0NzSiV576PwRBLVVXDqAYj5yM6OppvfesePvpo\nB42NlchkMGdONjffvPyK051LSkqRyeKH6FCs1nja2yEQ8A15rUajp6tLQW9v7yXpOWbOmoX6ySfZ\nu2ULb2/dSlN1NckaDbKeHqrq6rgxP5+O3l7+66c/pe34cfIMBjZu30lAZiI+aSpKv44//OIldHoj\nhYVLr+j4xgOTwQgQCAQo2rOH0v37Cfp8JGZmsuyWWy4p3UqSJGpra6ksKwNgyowZiKLIvk2bmOK2\nkx6Zhlwmw+ZzU1J1lLjCdSxYkc9ta9cOef/+fj+RkUOFpj1dXehlOvyBAAwEIwq5nFiVCo/TyXe/\n+wS/+c1LREbmEhv7pUCvu7uVo/u3sXztUEGCIAhoBQGXyzUZjIwin30Gt9566aLUxx4Lv/5f/zUc\nxExydfj9ft599VWy5HKiB4St/kCAwx9+SExsLJmZmTz00L10dnby5/9pZ0VmJj0OByWV3cRHzEKt\n6KKsthSjPpqq8iYOFBVx3/33A+C02QgZDJTX1RH0+1Hr9TRWVJAokzErJoZYq5Wm9nbeeuUVvvns\ns8Nmy/X39yOKShSKoevUai0+n4TX671kUbrBoCMU8p6z3Ofrx2Qau9eA5ORknnnmMdxuNwqFYkg6\n7JXQ0dGLXh8ORPx+P9WVlbTU1dHe3MmR4v0sWxE1qN8QRRFJ8qNWqwev7ZIkkT19OpmZmcMGRNnZ\n2dSeOcPCzEyyPB6mREYCUN3ZyeGyMswmE40HDpAdEUGEXI7dr8Ar89PvtBGfkElDr8C2bUeZOXM6\nUVFRV3Ws1ysT21VngM1vv03N9u3Mt1opTE5G39bGpj/+ka6urgtuJ0kSn330EZ++9BLeEyfwnTjB\nZy+/zMu//jUml4sUq5lAIHwhsKp1JCmUVBz5lIKCXERRJBQK0dLSwsGiIvp6m2lrG2r77g/4kMt8\n6L9SJE8gPMTscrlob3cNCUQAoqISCAg6mr7i/BgMhXALApEDJ8rl0tnZyb49e9j5+efU1dVNiCmV\nkeCzzy4vXXfmTIiJgUlX/6+H2tpa1C4X0ZYvNQkqpZI0o5HSQ4cGl6nValQqFYIg0NzVg0yIxObs\npqvuMBlikPkRceTqItnzl43sGugcbyjEzs8+w11Xh9jeTunu3Tiam/ErFJj1egRBICUmBllPDzU1\nX2pTbDYb+4uK2LF1K+3t7cjlQfz+oUGEx+PCYFAMWzTzfEydmoNC0Ud/vz3cPm8/VWeOUHNmF3q9\nmuAwrs9jCZ1Od9WBCEBKShwuVw+iKHG0uBh7TQ0ZRhNZMSp8nY0U79mDx+MBoK2tiunTkzi4bx8f\nv/gi7tJSfCdOsPVPf+LDd98d1qDM7/dTXlxMTmLiEO1HutlMS2MjpyoqmGI245HJ6Oq2oVLridKZ\n6O9ppdvZi0JnpKm+gw/ee4/29nZqamrYsW0bRfv20f2VUgHjldEslLcQ+AUgAoclSfrBaLSjra2N\n1rIyFg0UNgJIjIrC19ZGcVHRkBEMCA+h9vf3YzKZaG1tpXr/fgrOcktMEkUObNxIos9HdJSFhsYW\n3G4FKpUGwecgLimFloZ6ij79hDOVlQguF/lTppAZ6mf3Z3+ke+4aZs1eitfrJiR2EZtgQHnW47Ao\nirT7fMyfNm2gvRKSJJ3jEBmXkUO104larSbGYsHl8VDe3s7sVauuKNW3pLiYfZs3EyOToZDJOLV9\nO8n5+dx+991jLu9/LONwhN1WL7cA9UMPwaZNcNNNI9KsCYXX60U9zLCUXqOhxW4f/N9sNmNNTKSt\npwe5TABBor2tkngUqKxWFHIFdo8Tp7ud3/30p5Ts2UNjTQ1mlQqZUolFq0UF1Pf1kTNjBtqzxKJ6\nmWwwM+T06dN8tmED0YBaLqfS60UmyGhoKCE5eQ5+f4iWlkZ6e8/wxBM3X9ZUhclk4pFHbuPNNz+h\nttZNw/H9WAJO5s7I4vRnn1Fz6hTrH3ts3Iva8/Pz2L//JNXVZXi7uki1Wumyt5KXZcSs11J8upSS\nw0FSUmNISNCi1WrZunEjy3JyMGi1YcuFmBgOHTlCzezZZGdnD9m/3+9HCIXQ63SYoqOx2e1YDQbk\nMhkyUaTb4SA7KgqXw0FxbTWxgpYIawxuKURVfTmWiFisoQC9Bzt57uOPMZlMzM/IwB8Kcfizzyi8\n917yLiYwu84ZzWmaemC5JEl+QRDeEARhpiRJZde6ET09PZhksnOUzDEWC2fq6gb/DwaD7N6+nZP7\n96MQRUSlEpnBQIxKNeTiIJPJiI+I4NT+/ZitVkxIdPXb8fTL8FnNBMUgtmPHSNNq6bHZSFOrsVVV\nsXD5clIio3jn4BZOKRzExkbyyCPL6OuezqGDB4kfeBpq83jIWrKE1IHgKTs7gcbGBmJj0wbb0NnZ\nwLx501i2rIB927ZxqqEBvdnM/LvvJn+Y6r8Xw2azse/DD5kfF4dm4CklQ5I4fOQIldOnTzpHXgY7\ndsCiRXC58eDatWGr+N/9bnKq5mqJjY2lb6BEwtnnfX17O/boWF588Q0iIy0sXDiHm9euZdPLLyP3\n++hzNtDb20GcOYr4pESae9ppqzvB8pxU/HI5hq4uZE1NxM2eTYfLRUVvLz0aDYbYWCxfCSCckkRk\nZCRer5ctGzcyJzJyUBeWDhyrrUXIMXDwwLs0nK4lUiMwLS2G0u2fY9TrLus8zsrK4oc/fIp//M53\nyKaPaLMOf1srFoUcSRTZv2cPq8aJs57b7ebYsVIqKurQ67UsWDCbzMxMLBYLTz21jt/+9k+4nSfo\nlpuYmhJBwfS5aNVqspJaaFarWbg0j5Jt2zhRtB2pupo3SkpAoyEtLg5LVBRJiYlUlZefE4zo9Xp0\nERHYnE6mzZrFkaIiPL29SECP30+vTIa9t5fcyEhMU7I5VllHVVczHRodU2NSmReXhtPRQEpkJFJT\nEz0+HxF5eVgMBlJ9Pna+9x6ZWVnjOhV41IIRSZLOnkMIwDlO5tcEg8GAe5hhN3t/P9azjJJ2bttG\n/Z49FCQno1Qo8Pr9bN63D4fBMMQCWpIk+vr6kMxmqlwujH5A0NHg9dAi+tDKfcSZzdQ3NxOrVBJp\nNBLq66Oxro5pM2dyh1xG1JKFrLw5/AQkSRJ1s2ZxprwcgDXTp5Oenk5tbS2lhw7h7Gimpakdu70D\nozEGv9+OxeLn5pvvoquri/iUFKbm5TF9xowrNj+rra3FKoqDgQiE9ScpZjPlR49OBiOXweVO0XxB\nZmZ4qubQIVh86ckUkwxDfHw8qXPncqSkhKyYGNRKJZUNDXx6soqsOTmIQixtbQ6OHNnEgw/exBN/\n+7eUnTyJO2oPH7/9MYYIA263naraEhYnRBEXGUmtzYbVZCLbaKS7tZW7b70VuUyG0+3mvU8/pc/t\nRpIkQqJIVWsrfQoFH7z9NmdOnULe0cGUG28cDEYA0mNiONrcxMxoDY/krkSv0SAIAl6/n70ffEBq\nevplaQuOHDmC88wZbk5KQq1UIkoSHU1NiG43pw4fHhfBSH9/Py++uIHubhUWSzydnV5OnPiE1avz\nWLbsRuLi4njoobvZ6beTn5Y2xBtELpeTnZPDkR07mG2x0GC1UtrXx2KdjgafjxhJQmO3c7C9ndUL\nFpzz3oIgUHj77Xzy5z+TaTAw94YbOF1dzeG6OizTpuGsqaGpuZnMiAhmp6ai9Po53NRB0B/E6PXg\ndDSQl5dNZ3MTcUYjgtdLc2cnFoMBrVqNVRSpq6sjNzf3Wn6k15RRF7AKgpALREuSdPpavm8wGEQu\nl5OSkoIqLo669vZBi+d+r5c6p5M7lywBwsZDZQcOsDglZfALrFGpWDJtGm/u3MnM9HS6+voQBAG1\nUonb5WLdbbexYfteetp6kYWCKMxxyC3pJFhj2XqoDJWvF6GpCcliQWsy0dfTA4RHVpRK5eBoS1j1\nbWX+okVEREQgCAKHDh7k0AcfkGY0Mk2jwRCl4YzrNLm5yWRmzic2Npb3//IXFD09WNRqmv1+Dm7b\nxrpvfpO4gWO8HCRJ4nxay0ndyKUjSfDpp+EqvVfC2rXw/vuTwcjXwe13383RlBSOHziAz+2mRaYm\nZ959pKaGA2uTKQKPJ4o33tjMo4/eSXpGBosWL2ZWXi57/7KBZIsGuyeSzPh42mw2olNSiI+Lowzw\n9vfj8niwGAyolEpkERFUB4P8z6ZNKGQyQlot9qYmcnU6TJJEa3Mze202Zi9dSmZWFoJMhkwQaG9u\nZnFKypAgRaNSES0IVFVWXl4wUlREpEaDekAwKxME4q1WKjs6CF1C7ZyxRiAQ4MyZM3S1t2OJjCQn\nJ4dDhw7T3a0hJeXLhyOLJYZt2w6Sl5eL2WwmMzOTXVFRdNhsJA58ft19fVT29bHQbEbv92PS6wkE\nAqhEEZ1KRZIg0NLby+KsLMq6ulCe5TFzNlOmTEH97W9zcPduztTWUtHXR1tXF3pBQNvfT5zZzOdV\nVSgUCvweD2lJUTjbOvDZ60ibt5y0tFQ6m5uAsC5wyLVVkgb//+LeNd58SUY1GBEEIQL4NbBuuPXP\nPffc4N+FhYUUXu5E+zBUV1ezZcveAZc/LYWF87j74Yf55N132Vdfj0oQCKhULLv/ftLT04GwQ19T\nXR076usxGo1kp6Uh+nzUV1dT09LC7994gxkWC429vZxqa0MSBLxuNz29PqLMuZgUOiRBRpXTi1cP\nJ8tqWByvxBYIINrtlDU2okxMxGA20yCKZJhM9Pb2EgwG+fTdd7E3NyMIAprISJatWcOBTz9lwcAT\nzhfUNjZydN8uYmPu4eTRo1idTjIHbKt7HA7ONDXx6u9/zw//5V8uKXf+bNLT09kHBILBwZRFgKa+\nPhavWXPVfTJROH06HJBcaRmKO+4IZ9Y8//zX267rHZvNxoG9e6k+eRK1VsvsggLmLVhwwe+5XC5n\n/oIFzF+wAFEU+clPfkFi4lRCoSAejwtBUFB5qpy6imPEeDtQ6PXETJnCHffdhwBseecdytracPT1\nMXP6dAyxsXy6dy8dNhtlTU10BgLERkdz/NQpLHI5sRoNETod7R4Ptro6kCTE2Fhio6Lo1unob27h\nrY1vY03OICM1Ho9Sjs3no6yqCrVSSfxZonO5IAwRnvp8Pnp6etBqtcN6mLS0tFBRWkpXTw+RoRBp\nsbGD1w6b283UMWSvfik4HA42vvIKdHZiViqpDQQoMpmwBZRERg7VVYQzksw0NzdjNptRqVTc9/jj\nfPTWW9TW1dHQ2EhHWxvZ2dm8+fvf42pqIjh9Ok6Hg9j4eLptNtx+P/VOJyG5HK3VytZPP+WvL72E\nEAgwfd487n/00cF7RWpqKpa77uKnP/whvUVFzNVqCblcnOrrIzk9nYK4OKo7O5k/fTodLS3UygV8\n/U6KP/8cq9VKXHIytSUldMtkJCiVHDp5EpfbTZdMxjSvl9/85k+0t/diMulYtmw+CxbMu+ZBSSAQ\noKKigpryctRaLdNnzyYtLe2q9ztqpmeCICiAzcBPJEk6PMz6r930LGwA9CFW6zTM5ig8HhdtbeWs\nWjWVVauW09PTg8/nIzo6ejDlrquri7+88AK127czKzISTyjE0dZW7HY7gt9Pl8NBjMVCj9OJzucj\nW6mkeyD17pTLh9yYSnrSPLxBcKnVVLe2kqhq4ZsLsjjZ2srp6mrUHg9Gs5mA1UpbMMisOXNISEig\nvLqam2bMIHugjny33c6+5mZilEoWDxTfaurs5MCBA0SJIn2iSNbcuWwpLuabt96K2WBgd2k55Q0O\nZDIzjX1d3HjLfJ5++mEiIiJoamoiGAySmJh4UQHbvj17OPrZZ8SpVKgUCtr6+4maMYO7H3jgsoOb\n8zHeTc9++ctwQPKHP1zZ9qEQxMZCaSkkJV389dcLV9PvDoeD1194gSiPh+SYGHx+P1UdHcTMncva\ngXTbiyFJEv/6r/+Ly2mgrfIoqlCA1q4uQkE18dEaHrkplwiTifLGRtTTpuHo6SHU3ExfSwtdtbX4\nNBqcPh8Zfj92mw10Opp7eijzeFibk4PP4cAoimjNZiobG2lzu5lrMNChVJKp01Fud3DU5sMnRKG3\npGHzdGBU2nji9hV0VlYiNxpJnzaNuVOnIooiBxsbWfvMMyQlJXFw/34ObtuGVhTxiSIJU6ey5u67\nB6dky06e5PM336SvvBx/dzc1bW0Y5XJmZGXhFkXKAwH+7cUXB2+m15Ir7fcPNm3Ce+oUWWdZL7R0\nd7P51Bly8h7EbB46YtTYeJRvfGPZkIKFkiTxwbvvUrNzJ/MyMzl55AjO1lYO19SQnZSEEAyiDoVQ\nKpUcqa+nD5AQqLSJKBXRJFsjsep8ROv8BGKi+D//+7+Dxe5e+sMfKPnjH4n2eknW65EJAlU2G0d6\ne1mRlUVbVxexOh2N/f1U+f0oRRFZfz9anY4lq1dT3dmJy+/H7PcTJZPhE0X8Viu1XhULlz1KZGTC\nwL3rFDffPIMVK5ZdWQdcAX6/n02vv467poZ4oxF/MEiz203eLbew9BIGC8aq6dk6YB7w/EBk90+S\nJB0cyTfctq0Ii2Xq4JdVqzWQmjqXPXsOsGjRgmFTXvds24aupwedysD+smqiDDpa6mtQhSRUgpw8\njRrR7abF5WKGRkOG2YxOqaTV4SBJEjjtbOVkWxkR0dOYkZJKbWsVhkg1x+12Gt1u+mUykjIysPv9\nGICFGg2H9+yhVqMh6HTyTkMD999+OxkJCUSZzUQ1NdHa0UF/Sgrtvb3sOXiQfIMBtUyGUpKYnpTE\nyf37OV5R8f/Ze88gya7zTPO5Pr0t76u6u6od0OhueDRIEBCFJSFBIAlShDCkSE1IS2mMxmzM7MRE\n7Eq7ignFxIxiRVKz0gRWIkVJ9AJIkDCEITzaolHtu6vLZLmsSu+vv2d/VLMFECAJAg1H4vlVkZH3\nVN57Mu99z/m+7/3o7unl5ILLQHYXkiTRFgkcZ4AvfvFv6Ar56O02qiTRUhT23XYbV75KLPRH7Hvf\n+xibmODU8ePMnjtHSwik9XUe/8EPuPr66y/2eXiPn8xDD8HnPvf6j1cUuPlmePRR+MxnLtnHelfz\nwsGDpNptNl9oLmloGnvGx3l+epr8jTdeNCP7aUiSRF9vjB88eC9Xje5AU1TkxTw1c52CBM3OZqLh\nMFNDQ/ztgw+yZ3CQ3Zs3E0xMcK6ri/1PP83S8jLhaJSJ0VGiqkrccci3WpxfXKJlB2hSgLm8zEA0\niul5rNk2Z+p1cprGuaaDK2/B0NI0HJ0w/Wh08eQzB8n4Fs7sAodPnWHlhuvoGh5m0w03MDQ0xMmT\nJzn83e9y9fAwIV3fcHs9d477v/lNPvmZz+C6Lo/fdx97enuxolFefPppbtq+ndOFAicti8GREX79\nllveFiHyenFdl9ljx9g3+PLeNYNdXfRE5llZOUE8/r6LYe5Wq0YoZJJOp7n//geZnp7BMDSuvHIb\nc9PTXLdtG2dnZljN5RhKp5nIdPHkuVmuGBggn19GVxRMVWVPLMahdY8hkSUphWhZIVSjm3JnjYlW\niy/9j//Br915J4eeeopvfeUrdAUBS40Gc9UqUV1nIBIhrKo8Wy7TabU4Vq+D5zGsaUiqihmNQijE\nkVyOu//dv+PBv/s7YqurWJ7HyMQEtaZNy4F6rUg2O0A4HGN4eDdPPnmA6667+ucq934jHJuexjx/\nnj0v+c4M+j7PP/IIOy+//A35V72dCaxfBb76Fv4/lpbWGR3d8bLXFUVFiDCVSuUVCZ6+7/P844+j\nLdaJxTfBaB+Hpg9hN3zCqoatRag3bYTXYjDwKNsbTn66qhKoKprjEA7ahK1lvJbE4nqHRLjADZdd\nyUwuR6fdZmcsRtjzqDYahDUNVZbpqtdpttuEAKVS4YknnsC+/no0TcNxHI7mlplfsZDkGLWFAuFE\nnUwyxJZrrkHVNCbHxzk8O8ty3ScTn0SSJJqdNnoiQV/fCPd/67t8et84Wy+EcSzH4dl776Wnr++n\ndjcdGhri3OnT+KurXN7VRUhVWd2/n7+fnubu3//99wTJT6HTgeeeg298442N88EPwiOPvCdGfsTi\nzAyDPxaakCSJJLC2tvYTxUg+n2d5eRnDMNi0aRN+o8bekQzN+jK+r1GrLyFcE9OLct8zOcLGDB/Y\nPUF+YQEpm6VSqZBOp9m6YwfFfJ5SqcTE1BSburo4evgwlXIZu+NwptMmLsEWI0rClhCyxZrjEPU8\nQpJEyPWQ/Ch9vo4wFLRIHKVawLEcVtptskkZLAep3eH+pw5y/YdjfPaWW5AkiUNPPMHW7u6LieWS\nJLF1aIhnZ2YoFArYto1m20RDIaKhEDuvv55zx4/Tn0rxgmnym5/4BO+/5ZY3e4ouKcGFKij5VUIT\nfT09pKb6OXv2eSANOBhGmzvv/BW+9KVv0Wql6em5Es9z+e53p2nMHKPV08/+I7NInQiPn1shpgnC\n8RFy6gAzfo3BmMQIgobt4Mn9RBUJ1XWJBgFsqMUAAAAgAElEQVQV30dXFAp+ifKTT1Kbnd3IOcnn\nWbdtmpLEFbqOAI6VyyDLBIkEDV3nGkUh6jgMGQZ+EPBss0mmp4fJwUEe/s53CHI5tvX3E9J1aqUS\nszMLDG69kfziWSY27QI2XHmFCFGtVt8yMXLu2DGGf0xwqIpCGsjlcu9OMfJWs5EIGqPdbhCNJi6+\nLoRACItYLPaKY4rFIgf3H2HS06gb64STvSihOCktjRBN+uJdeO0ish9GFQ5tz8MXgpLjEFEUwpqG\nG4uxeaAf1+lwcvkwgSzznQcfZFc6jVevk3NdhmWZZr1OtqeH5XodPwjQLmTO14Vg2Pf5+29+kxEj\nzGKpzIzoJ9M7ykhXCl9EKHcMOprLBy6sDjdv386Ty8uUi2W6Exa1jkVLlhkcHeOpxx+nsVbCbHVf\nLG0M6TrDkQjHDh/+qWKkVqtx7Mknue4lmeiTQ0PMrKxw8Lnn+NX38kd+Ik88AXv2QDL5xsb54Afh\nP/9nCAJ4nc7Yv1DEUilalQqpH/v92pL0qqHHIAh44LvfZf7QIVJslPA9rmmsFwp87OYbqVQqFAsF\nFuYk+tJTqIFDMjqEomj8xbcfgc4K66EQlZkZjEyGPddcQ6q7G1cIooZBo9HAbTTwfImikEkKi7Ss\nsWQ1CaPhmR2MIGDO9+lRFFwhSCATlWVMy2Z9NUe/LKEjaCIT9n02J7s51a4zvukKrDM5vvftb/Ob\nn/40tXKZmBDsP3qUpXwe1/cZGRxES6VoNptEIhH8l4RBent76enpodFqEWq3+eCHPvQmz86lxzAM\nhicnWcrlGHlJ4m2pXifc3c2nPnUXa2tr5PN5DMNgYmKCI0deoF6PMjKyUY6raQaTk9fwN498jWYj\nS1dyB6X6ClGtl45fx/bXuHbqfdSaBp4yT0hqUa61aHZsDAvCikJI03AkibgRolyts94K2JNO49dq\nqKbJsO/TAc46DklFIQLMyjKXJZM0LYtqq4XpOHQsi1XfR1EUcktLeJEIc60WtwwMkLjw/e1Jp+lS\nFllbnUPeeuXFcw6CgCB49WfXm4Wiqni+/4rXA3jDflO/VLezm266mvX1U7iuA2xM5tLSaSYnewn9\nWIZ0vV7nb7/wBeLexkUa1EI4q7NIdoty4OGgEdUiYMTpIFFFogmcrlYxg4CsqrIkSQhJwq3XMest\nCqU6fqnKaLtNtNlk1LbxWy2KnseAqrJUKBD2PCRV5YpYjN26jtluc2ZhgXSlQdwzcPRuxjO7EU0X\n0j3ENm+je3ycTHaEaqUCwFqzySc/9zmuu+1mWhGF7LbtRFMpCqdP0V5eIqgucu6ZZzi4f//FmG00\nFKL9M9pz5/N5EvCKdtkD2SzzF0qP3+PVeb0lvT/O2BikUnD8+Bsf6xeBK665hoVWC8txLr62Xq3i\nxGJMTEy84v0nTpxgcf9+rh0eZvvoKJePjnJ5MslqLsd6pUI2m0XVNAaGtlG3XVabTeqmzfnFFay6\nzo6p7Xiaxmg6jSiVODU9Tba/n0o0SqPToVwokIzHOdJq0yfrTEoymzWVTSKgHLSoex67ZJkpVWVr\nJMIaoGFhCw9VSOhCouEJLKeDGrToM8K4gaABhGWFuBThmQcfZGZmBisIeOrxx2mdPUvP2hoDhQKz\nhw/z4v79HD10iN7eXozubtYu3BcA6rUa33vyWY7P5vnCF/4/pqePvevytD7woQ+xqqqcWlpirVLh\nzPIyZ9ptbv3oR5Ekif7+fvbs2cOOHTsIh8OcOHH+ohX8S3GVbqptGVnTqLRNJFnFJoKnZujYHURY\np1kuUy6V6LEtwrRwBLT9gKbjogBtv047sJA8D7tYpJDL0SfLTMoyU0AcCPkBM76Kq6fQLBvqdcKA\n7LrMmSYpz+NyXWdU19msKCQsixXHoeO6Fz/r2HA/82tzZAY38l6CIGBl5TS7do2TSCRecW5vFjv2\n7mWxXn/Zd6ZjWdQV5Q2H+35pdkYA9u7dQ6vV5oc/3I8QYTqdKnZzkaNLHU48+QhTu3dz25130tfX\nx6MPP0xu/wsYFpxs1TidXyKiqaybDiUtzFAApusSVhOsGxbztAkrEqeBoN3mNDAxOUlSUZhbqaKp\nXQSSQ7dQ0bw21UqVrnAII53mSL1B0nFwfJ9pySUjZM74Nl6goMgqrWqNcDiJn+4jYgRYPhiKz9lj\nR/jNT9/NzAuP0pqbofrDVULZLsavvoqPfOhD7LMs/sr7KsvLRZqrS0idJtW1Y3SpbXosheNPPEGp\nWCRmGORqNbZ/7GPYtv0T24obhoHzKjcu07YJv4U/iHcjDz30xkM0P+J974Onn4Zduy7NeO9mJiYm\nuO6OO3jugQeIBQFuECBlMnzs7rtfte/L8QMH2JTNvsyoMBmNsmV4mANzc7zfMLAsB8t2WTJr1IMw\nh6ZP0DZNdm8ZYXw4wsriLCcPvkBCSKydmWHyg7fwr//0T7nvnnvIz8xQaTYpeQ4DKLQlnZKr4MmC\nXgnmhceKFGBKEnFZJgmocoeGWMYJ+lGESkMKcKRVRr0Gp2qCNV/gSgpHjh1EFTahtThf/m//jcWV\nFXo8D7nRYCydxgsCRL1OJR4nf+wYq/v2cftdd/GtL32JfC6H22zyxJFTyL3buG7vJ3Acm3/4h6eo\n1eq8//03voWz9sbo7u7m0//yX3Li+HHWl5boTiTo1XQOHz5GLrfEZZftIJVKkc/nefz73+foY4+w\nXvTJDE2RyPSi6yEisQxt04ewRmGlTMlxEW6drswgrtfh8eP7KZXOk6yXmJNtNFkhFph06GFZxLFc\nD6NZJZlqMzA4gFso4DSbqEFAUpLQFAUzCDCBAIOYEsELTXBkxcToWPT2xil4HqF2m6Qss9Bo0IlE\nOLuyymAsSiyZ5Hi7TVYIdEli1feJT41hOwscPTqPpkns23cFt9/+1vrDbN++nfmrruL5w4fpUhQ8\nIShLEr/yiU+8YUO2t62a5mfxZlTT/AjTNCkUCvzZn/wXzDM5umN9SEDdKhLbOsof/p//B//+9/8d\nifUW5YVTVBot8D3CwqGNxGK4ByMSZyAaR/geprAZito4QLanh4bjkOp0GO3qYmZhCWGnsD04WjjD\nVfEBVLcOdp3BmEal1WZBVllyN+yEB1BA6iKhJfBVmVrYp93J8/5rPkxP3xiPvvgU7VKbrBqh6Jr0\n7dlFod6iNP8iQ9ku5HCUaF83//sf/W9cffVVVKtVPv/f/5yzzx7GLxe4YbSXpXKZVqlEq1olpCgM\nbt1KJ5ViYssW4lNTfPKzn33VLTff9/mff/ZnjAYBvRfi9H4QcGhhgRt/67fesCHPL2o1zews7NsH\nq6uvvTneT+PLX4YHHoCvf/2Nj/VO4FLMu2marK6uous6g4ODP9Ey/W/+4i8Yse1XhHWOLy6S2ruX\n9fl5Th45wjNPTaNFJqhUJAyh0O5U0UWR9+9N0x0bQ2gRWlabmXqF8Suv4g//8LeQZZnf+ehHaZyZ\nIeEFDKLQwEMnhAvEZIeiojCpqfiSoOF71H2fRgCDikJDinDOE0iBTVKTqLsBcSNJ3EjiN/P0Sy66\nBqvhEH2bNqEIwaZ0GrVYRPU8FFUl09vLOVnmij17GPvVX+XG978f27aZnZ3la1+7F9PsZfPmKy6W\ng7quQ6FwgP/4H3/vLbeEvxTzXq1Wueeer1Gvh4lEMlhWE0Up8dGP3swP772XcVXFkCTu/ceHqJQr\ndCJxJke2cjQ3z7lqkyuv/l2i0S7W1haYmclhGCrV6vOEHItsZ50hOaAjPPJBh7gkMIOAtpYhMBKE\n3DJXbhlAjcV4/vhxrpJlFMsi5rqoksTpIMBHoQeNBaAQGUAXfWj+OXZ16XTHYqyurBAxTeqSTDYz\nRDgaJ1dfR/R28a//+WdZKZWwLIt8u40+MEDINNEcB0dV6du6lTvuuut1m1m+XoQQLC8vk1tYQNd1\nJqemXnO+4Du1muZtIxwOc/78eQon57lqfC+ytHHjSvk9nDkxzT985SssLxWIzZ8kC0S9JqOygaJG\nKPgd4nHBiuSjpgZJxQPSahO5WSVIJolLEoOaxtFymfOHD2OaPiotam6ATQTTcvGFwHV9OnWHsPBp\nagoJI4ZhW0h+QEfWKfs2JhKe42MqEWRNY6m0woBwMWWTZrtDJhrHzp3Dy+f4X67Yx+iFmOh8aYX/\n579+kb/6my+STqe55tor0efO0RWX6QQBzU6HlXabfKuFEYmQTSb5tZtvJhoKcXh+ntnZ2ZeVwf0I\nRVH4yKc+xb1f+QpLi4voQE0ILrvpJi677LK3cAbfXTz8MNx666URIgA33gj/6T9teJb8gvke/USE\nEMzOznLixDkkCXbunGJiYuLiQzUcDrPpJX4ZjUaDSqVCPB5/WZXc5OWXM/PQQy8TI57vUwPu+MAH\nSN5xB3/zV3/F9PHznJlbYzw2SkwPM2sugbfKwUMLXHNtLxOpPqKhCPVEhp6endx//2P0ZMOkLQvP\nCzDxkXBJAlVa+MiIICAUSqAlsviNElHbZglwkFjSo+R9n15NokuJ4AmNbiWM6VWpNHNcK+mE9Qht\n2eXm3l7m6nVWfZ9+WWb76CjpWAxJkrA9D9U0EZJ0seTeMAy2b99OEDzwMiECG0mQQRCmVCr91Hyx\ndyqPPPIknU6WkZGXzn2Zv/yLv+aGnjgDfX1UKhWymkciJDPbrtKq5+nzm0SGeiiXX6BcTiJJGrK8\nxtLSGRJqjRG6CCshDE3Qth2SSHgajBgRlh2TuNsiIxSqC4usS4IQcM406fU8JCFYEoI0oKEg8Mmg\nYlst1qQaE5E+RNzlaK1G07YZRUJS44wOb0ZXNUxV5flmi8emp5kaG8NUFNR4nD7HYdfmzRfP88Ts\nLP/zC1/g8t27yfb2snXr1lekG7wZSJLE8PAwwxdyFC8Vv5RiBODQgSNkjORFIQKgKhpRLcVj3/8+\ncVdgywpOELAlHEPzPdqyTzSZ5Io9O3HTacz+fqKqSm5uDl+FPt9nRyaDLEmUKhUO53K4rocd+Dhy\niKScYcZrkwnAFN1U/IAKJmVPYkzIKNjUgaIfYBKj5cu4bhhVtbjnuacYDsMWz0KzTUKuh+9orNY8\ntqR6yWS6L57HaKafhaWTnDp1Csdx+NY/fIMXHn2cPkUhpavsiscJC8FYKIScSNDudIhcCM1kdZ3l\nhYVXFSMAfX19/O6//bcsLi5iWRb9/f2varT0Hv/Egw/C3XdfuvHGxzdEyNzchk38LzpCCO677/sc\nOLBANDoICPbvf5Drrpvg13/9Qy97uPq+z4MPPsL+/aeRpBhCdNi+fYiPfvQ2QqEQu/fu5fTRoxzL\n5RhIpbAch8VWiz233koqleLEiRN87RuPsFIIEREW+fZ5pGaRlN+kK7BABEwffpTTqS7UnhGMvh0U\nDhxibe0kVm2B7rVVdgI1VFwcugAbKBDQRKLftWg3y0TDcXzfA99lRUTx7H40JYotPOasFbo0l3Sk\nH9n1aLdbLAufFDpJI0ZXOIzrOBQtizXXpd9xSG1cKM7XakR7ezm+tsZ2wyAIgou7RMlkFNNsEYm8\nfDs9COy3rBrjUlAsFrEsi0wmw1NPHaTZNPjhD793oYFpmq1bL2N1YQWjZysAM7OzLLQ8TClNyW9R\nb9XoicdJqAr5VhFfKLRaJpVKAUURJOQEulAoyLDiJDFEjHpQo2TZFPwQtUCmX5UIxxPYTpFBJcA0\nTWRJIq+qnHRdMkACiOABMlEgE9hU5DqFQGXYgp2axmlZRng+Ud9lbmWOeKqbZijCxOAEXt8mrr7r\nE6TTab72l3/J9pc8/FutFsWzZzlRKNDdbJIDnk+l+MTv/M7r7sr+dvNLKUYKhQKtjkXLe+UWYdu2\n8ITLeCJDqd3NSnmVkO8SkmRqrs1IJM3i3ByJLVuQh0Z48VyRej3E/LFl3j8Qp1tVqdTrHDl9ml5f\nourHcUUUJfBZI0+NOKtyFlW2UKUwnr8ZxRes+Dmy1KgQp0YajzQ+48hyN67bpuqfptmeIUaNXkIY\n0V4sXaNdX2W5UmSw0yIeSyGEoGG1qFSr/O2Xv0z+hROMJ0a5enQX0yeeQ3IdZsNVLEUmJUmonQ5u\np8N6tUpfJoPpeUR/RuxPVdVXTQ58j1di2/Dkk/ClL126MSVpY3fk6ad/OcTI/Pw8Bw8uMDZ2zcUH\naxAM8/zz+9m1a5HRCyXqAE8//SzPPrvI6OgNyLKCEIJTp06h6z/gzjtvJxKJcPfv/i7Hp6c5f/Ik\nuaUlCo0GC9/8JoePHOHh+59EdPrwOnUyUgJJsVCcFfZqKiE1Sd1s0PJslptVch2VjDuFpKuY5hTt\n2hpdlkQRBY8ABZl5YBFBAYVhPOZcFd+NItoBARJtWUOTJrADHTfQMFGwkCmwSoYQZsdGDnRspZ92\nANVmkeZaiYIPdQ1i24ZYjUY5v7BA27ZZaPuIdY0tl1/Pvfce5sCBY3zqU3eSSCR43/uu5N57jzA2\ntgdZ3gjDrq6eZ9OmLN3d3a926d9R1Ot1vvGN75LLVfE8OHnyMC++OEurpWPbNrKsEg5bzM83iYXX\nWBnP0tfTwxMnl7CdYXriXSxV8xSqYc40ZnH8EoQmmdw6jOdVSSaH6HRm8bwTeGqSuqmT0rtoOHk6\ndOMTYs0tE0fGlDxy9Rp4HXrkgEFJ0JQkNqsqR1yXMqAALio6BilCyHicDOo0Oy7raxJRCXo9jzYS\ny75NuZQn6cDwwCbCkRSxWJLt27djmib4/svcr09NT5PyfQaTSfqzWVKxGAvr6zzyve/xyd/+7bdt\njt4I72oxYpomrusSj8dfkyVurVbjG9+4n1yuyvq6xQtLi0TQmBwZR5Ikap0my16b3ZdtI9W0yK8v\ns61nmFatSMpzSXsmqu8TURTue+owlaeL7Lj8NzBNl0J1lSeq60yfPsNoREaYFhU/i08ciRQyGhHW\nWUXGD7oJy2lkZFQZvEDCo0KDNG0UTDRk+pHpRvgCgYwIxggos05Av5HmjFVDop+OkmXBaWCdneFX\nIzGWqxVm1posN6OUHpgjIytszoaZnNzLmYWzJBtlKj5M9nWzXqsxEIlQqlQ4eOwYK7kcq7bNP9u2\njd1796K/pDHee7w+nnkGduyAS71Y+ZEY+WXwGzl9eoZwuP8V3bENo4+zZ2cvihHf93nmmaMMDl55\n8WErSRJDQ1t58cXnuPXWJvF4nHA4zJ4rr+TBB37Ac/c+hNpwQRh8v/Q12sTZsVkinFBolW10N8+w\nL+EhkHQJTVUYiSdpIVEJulDaHZbWO2yZnKSz7rNIliYJdCRsGkg0UJDxyTCPRgKbftJoKKzSphiU\nmEQnKoWoIeOIAJ1h7KBOvjKLQjcVfJK+RlG4rIkunIKOp/QQGAZpa4Ade8ehf5gXnn4BRx+jOztJ\no6mwPbmFYrHE/ff/gLvvvpOdO7dz6NBhnn/mS0Ti/SSTcTZtyvLxj9/xtszrz4MQgr/7u29TLicY\nGdnO9PRz1OvDVKtLCDGKYWxGCBPfL9FoFInFhji0mEfSdRRjGEW2OLW4zGpHJhXvxnXLtJw2YWOM\n2dkcluUQiWSIxUaoFo6heGtIYoKWaVFno5Ori4nMIB4tal6BAJ1tUgpfKOT9GprUJh6OkDY9cvgI\nYsioOARYtDmPjEmaQMRYtwVlSvRSYlhS2KxFORU4eI7LWnEJN5birl0bu9OhUIhkXx+lep2uZBLb\nsmgWi/THYghFIX4h12e0p4enZ2bodDpvSv5PEAScO3eO0y++CMDWXbuYnJx8wyW9P+JdKUaazSYP\nPPAoJ07MI4RCT0+U22//lZ/qjy+E4O///h8pl5N0dY3Rqp9HT8/wwNmznG+WyaTTWIrPHZ+9C6eU\nZ3sohCsHvHDoBTrCpmY16TYMKh2Llbag6fahyyMcnz5COqySimex6y6S0mS2sQi+ioKBTJYADRUJ\nnTgyYOEgBxK2JIhIKhISATJtJDqEAZ0AiYA6Ej4brgM6EKGMxlm3himGkB0VSxJYRNGqIR5+8SC2\nE6PSiaBl+ymvrdOSY1TMF/nIVXuIRLJEZBXTqqOl02wbHqZcLLJ/bg4tt0S/EaIrFue+/+v/5tCT\nT/Knn//8T6yseY/XxqUq6f1xbrwR/vzPL/2470RkWSZ4lc7aQgTI8j8tQjzPw7J8dP3lcXNZVpAk\nDdM0L2b8P/PMszz/wBN0+wmi3cMslmfpB3AcpMV5RhMhjikOmt3GEz6uFNCwWhixED3pGMfXy/iy\nT6Ndw2l7rMx0aHQC4ozhYaIh8BikTQaHZcKkMOmiSg1BA40wPjoyMRpI6GjoSoSOVyMseSAMbGrU\ncCjShUcNKwhhouP5Q6hKlr7eCXK5JjMzzxIELUAnHI7QqpZpVeDBymPc8ZEP8/zzD2FoAYeffprR\ncJhfGUqQb+TRYhKf/OQ/f1e0pV9eXmZ11WJ0dBe+75HLLeL7Bqo6jGWF8DwbkHFdn3A4TSymEaQy\nfO/IMYr5OJWqRaXhEtUnKFZatFyVQFMAj3J5CSGiBEEI37dwgjAl0cAINpaGFh1cokSZwsfDwUXn\ncgSncMQiVQE+EULC40y9Qx0N0DmPoAefECqLCPL0oJLBAQQKHikWkFBFkaZTZx0Z5CYhVyLIHyWV\n+gNgQ1B/4Lbb+M499zDuOER1nXKnw3yjQf+WLSysrTHc3X1x5+TVfitvFCEE37v3XpYOHWI4kUAC\nnpie5uzu3dx+550/MWH85+FdJ0aCIOArX/kWa2shBgf3IcsK9XqJv/7r+/gX/+Iuenp6yOVynD8/\nj65rbN06SU9PD0tLS6yu2mQyWQ488UPivs++TbuZDUVYa57jun03ctdv3cnU1BRHjxzhvnvuwZND\njOy6ivOLZ1mcPU9RN0CN0w5iBJIg4s0Tb7XodkJ0VIWFwKIiDKYMg1O2TTcGCjoyAgcPkwAPBQkJ\nlQ6qiGAJgUOJAAedISTigIlCkShNDMACOsh4WMi45AOZFh08F2QphSd5+G6bUqGCEckwtHk3rZaL\nUFQ0OUqlHvDo8eMMaGHWnBaabjA+NETYMDixXqTlCe4Y20k6nkSIANNscO7Jp3j44Ye5/fbb3+YZ\nf3fz0ENwzz2XftydO6FUgvX1jX41v8js2DHF009/B98fQVE2blm+7+G662zbtu/i+3Rdp6cnQaNR\nJpH4p60o2zbRdf9lGf8PP/wEXqPJWlvgN+rY7SLbJJVlbJxWm15ZYVgymVHDVLw63bqEpqWQFZmq\n4xDEQsT0GOt1lRBJ8CAIRmlhEsbGJsDFwEKlSoIQGiFsdFQcqnShoqNhIihgoYoUwndxUaiIGioN\niqgojOFhsMT8hU8eA+IYkqDValKrdggrIeKhJpbTwa61kGM1Nvf2srowz5f/3z8hK5VxjzxGxjBY\nTCa54dprGUineeHsWf76L/+S3/nc597xgqTVaiHLG3ktvu8RBBtiVIgwQdDC8zr4vgW0iMf7aDVr\nlOZbvG/bJr63toywJISexY/00GqtYwkPYeexnWlAA2o0Gg2EUDCMOKF4CqsxhxckcbCQ6UKWNYRw\nkYWOi4GKg0ubBCFaBKwDHTxsIshIxAnI47BGCIchYIiAAB+oI5NBxWOQdWwaNGjgs0UOyIZ8+ge7\nOfTYY+zbtw9VVRkfH+fjf/AHHHjqKY7PzDDd6TAsScQLBc6trzMdibBt61Z6xsbeFBO0hYUFFg8f\n5trx8YtRiP5slgNHjzK/d+/LksdfL2+bGJEkqR/4PrANiAohXpOcW1hYYGXFYnT0n8pIk8kuOp1B\nDhw4gut6HDmyhK53EwQeDz98mNtvv55EIo4sh5g5c4aUEGRTG0mX20a3M2IFhJwWlUqVw4ePsLKS\nJ+/3UW26rOZOotccJsJJMANMP0TJCfCtKhlhoAgZ2THJygYudVbcDrLrYuFTpE0WjwCfBg5rZAAJ\nnTxtZAxa6Pj4rOARQpW3IQIPicdIECXCCApRQnTQWKNKgzYxNr7y4ygM4gvQdIVYtIzl1AgZGUzT\nIR4fpxOXcBpNVDmO6wraSgdX1pCyaY61WnTKZQ6U62wNpVGFRzk/h4RAyCoRYfPce2LkDbG8DPk8\nXHnlz37vz4ssw/XXb4SBPvaxSz/+O4mRkRE+8IEdPPHEARSlGxB4XolbbrmcwZf0KJEkiZtvvpbP\nf/7rRKPjDA1tQgiHUukMH/nItRfDjr7vM3PiGEp1nSE1S8drUTMrnPcCYr6HJMm0TYWwJIjrYcqh\nBO10FLecR7cDTrQc7EyKhr2G8JOE1BC+00KRJXw5SjnwCGhjI2GTQaNNhigyOj6QQMGmTJ0uHCQU\nVrBREKILFxA4CKZQMHGo4qMDo0AE6AC9eF6DUqlESIvgBTKarBKSPRzhYDoqrXaLZmmZpL9KtCuG\n75iku2XSaZl//M53mEqliEgSh86dw282ue3uu5mamnrL5/a10t3dTRBsmG3peohMJkGj0aTTOUYQ\nuAjRgyz3ADqVygxRzeeK7Zsw81U6lQVUsRndd2m2KzhuCWgDOxEiDmjIskQQzANrxB0YUUA1fGyn\nSt43WWcZV4SJyTotHzxWGMIkQxRJEnQJmQQ2cwR0sJiiH6gT0MUAQ8zRxCOGShxoYyFYxcFDo0ka\niJDEQ/brhC2F1fl5lLk55ufn8TyPSqVGd3eW2z/+ce7/9rf5eKNB5fx5NMdhQNdZKBb5YRDwX/7V\nv3pTrv/s2bP0hUIvS4eQJIm+cJiZ06ff3WIEqAA3A/f+PAfV63U2VgcvJx7PcOTIEWw7xvj4NS+p\nox/j/vuf4zOf+TWCoE5xtcLkS1ZIjXaNiGRy7Jmj5Dv9aJrOgQPPcN11v87UVIZT3ho7dmznh48+\niBxY9BtZys0laq6FL0wCKQMiguuB7FmochnFSDFBh8O4FGkhMOgwjI+OwfKFtU2FNutk6eDhUyeD\nHZzFR2MIixFc8tSxkIgQkMJEJo3JFG3OARkCDMDDkARBkMV2bZT2Kh0nSrari0z3AAV3nk5zlY4V\nZyGoMzCU5sbNw1irqxiOQ8sKcJwOXgnnP8UAACAASURBVK1EJrJRHugFPsu1BrWf4cj6Hj+dhx7a\nsG+/RCHVV7Bv3y+HGAH44AdvZseOrczMzAIwOfmBV/SdyeVyPH7ffYypZWbOnubkIZOtV+zkd//X\nf/ay0vPz58+zOWawFImg2Q56ENCPyrLkI0kevYaKMEyKnTbZrizjmz5Mbv0oiS6JTrNFRIM7dmzh\n74+vYmp5DL1Oo15HCmzCwRS+FEYWPiZRoEEIgUcEmSYuRQx8QLBInV5aJJBZx6PFIj4D+OxEYBFQ\nw6cC+IDBxgr+gnu0rwN5LL9NSPVxOm2Gw0mK7jLldphcJ8Bz1hhWbTxbxZdVmq0AaW2NoFgk1d9P\nNhKhR5LYlUrx4Ne+xsh/+A/v2Kqarq4u9uyZ4PDho/T3b2Xbtst56qn/vtGnRt5NEGh4Xh5JaiPL\nUSrVc9TWY4x1bWeya4WZQgHfE9Q7pxB0I0lRhBgBWkiSjiR10LQsuDmGRJuoJ0hkN9Go1gkLj04w\ng0wcEegXsvryJHAQJKjjoNFBIkBFIosgTkCRGB79yITR6OBSRZBEI4lNHUEPgioBPURYRUFhxQtx\npWEgYjHOnzrFn/zxnzI0ei2yHCMIjpHNPo69NsctmzfjjoywsrxMu9Fg5+QkMXjT5k/VNLxXCf94\nQXDJcgvfzkZ5NmC/lsTTl5JMJtlQtS+n2azQajXp6tr+Y3X0BpChVquxd+9mDj79LcyQRiQUpdaq\nUG2cQng2RqyPkZFtNJtV4vEdnDy5hOvWSSPhuS7xZD/rq+exrRKq7yGLJhqDBMJH9uv4kkdKMVjz\nFc6bdWqoZGljskKDIaCOTp0oJhmSdEsKFSERx0YDNEwC1nBooiFIY6BhowIaMg2giE2YAoEcJhBt\nECBQ8R0LW5h4voZJEbww5XIKSeqQTDkks5sYGMjS0zPEH//xv+fzf/zHJHp6SMfjjLVcls6eZlO7\ng4aCLElIisw6gvGurldc5/d47Tz0ELyZG0v79sG/+Tdv3vjvNAYGBhh4Sdv4l2JZFt/5279lRzSK\ntGULw6kUruezblsX854syyKXy/HUY4+xY2QYrd7m1OGjCNMi8H1CgUtb1YkmMxiGwQnfozszSmFl\nmVDHYuvgJH4XVCvnyNVapFMJ4rEdjKdTlHM5iuuLrLZm8Ajj4BER50mwTjc6RVboIOi5sLep4BMh\noE2UBlFCxNEI8OnFQkKWokiShRQECFRAAAk0TGTOIsgicFHFCpoWJmUM0+q06YvGKHamcSSFkNwg\nJat4QYRio023EWF1qUAopOH5Pvlmk76BAUK6jl+pcOLECa666qq3bD5/Xn7jNz5MT88BnnnmBRYW\n5uju7sc0DSBBu93C93tQ1RVisV6E3WJ6waI76TDcP0yzukLWSFD2PBwxhO8XgSaSpKIoGYSQUaQ6\nBg79OBiujyifIwhkCBR6NJ8VdxlBlBg+AXkMLHzCSEIgYxElRJWAEC4FypTRCfDx8Uig4FPAQUHQ\njU8HmRoGRUbQiOGRQWZZwFHLYnMshu0GmCtNBq7biabpWFab8+eP0lo8zwc3bSIUCrHpJb4ja4uL\n+K/SN+ZSMLV9O9OPPcao66JfcDZ2PY91x2Hfjh0/4+jXxrsuZ2RsbIzBQYOVlRn6+ycu5oz4/grj\n4yO0Wq8UNz8y+fuN3/gQ+ZUcj3/7YcKaRrPTxmwLFmo+QbLMWKuOLCvIskCW41SrdZIIgsDHskxk\nzQABaqDSljQGhQ50iEgeiiRR8y2glzAuGRRWcNBxkVnExyGLSokYKhCWAmTRwiOMJffhixhtIZBx\nkGmzjkySFBFkZGQ8dDpY9MgeqA4tR0KideGG5OM4PopsE1N7UZIgxAqyHKFYWEcWgkZxgUqXw10f\n+23Ckkyp2mGp1EEEOq6bZtVtMmmuMWiEWREOmdEh+t7zD3nduC489hh88Ytv3v+46io4cwaaTXiH\nh/zfdGZnZ4lYFssdi6eml4AsSBLNdh77S1/h7s9+mq9+9UEcJ8Li/DyhpWPcfPUutk5t4v7v/YB6\n1cNyagS6itKVoiAEUS9Gp7pIYKTpjcVpreUptktMXXYZ/f3jrJ49ykxzHTGxCWutgCJFiOtNbGee\nsHDpR0bBoIlLlF4UNHwcVlnGRiJJjA4KDZKY6EADQRgZA0lYBMJFEAZWgUFULFTiCAx8FtCokGSV\nkNTFwMDV5JcWWGnNktJsrg6nceQ+/FaRlA0Fz+HFxfPEhEszGsE+dQ59sJctgyN8+aEDrNVszpr/\nyPLyOrfd9qvvyEo6VVW58cYbuPHGG/j61+8jmaxz7737SSRGWV8vADFaLRXTrG7c8xoGp5ZzXLdl\nB/m1VY7lFxHqAJIHmiajaT6eJyHLHr5fJ+Q7GLjoaGhyAgWNbs2koNp0rCYhPLov5IMEBLjIGHh4\nCHQkmkgsoyGRoY8MPi4VaqRpI4gQI0qHIi1WCVAxUBghQhQByFiEiOFxtGZx0LTZPpJkWI/QbteZ\nnz9LLrcCGCycLzCSPMBt1119MXG0WKsR6el50zqn9/f3c9WHP8yBhx7iR0vUkhDsvfXWl4VK3wjv\naDHyR3/0Rxf/vummm7jpppuQZZlPfepOHnjgUY4ffwbYqKb5+MfvoNFo8NWvPk8m03fxOM9zkaQK\nY2NjKIrC733u9+hKJfj6l76Jqk6AEuBFVYbHLufgwRe44YZrUNUmth0lGs2ylDNpLKzgtFaRLRdD\nTbKChSV0FqgwgEASEoosWEMjLEeIizaBkIkhs0QMiQIx6kj0k0KhgosfFHFwgC0kRAIZFSEreISw\ngjZ5JJIEuAgsHPIIArqoBhauE8HjJGHGMdCIyjbt4DzxoIZlKXRtuoVWq0ixUMQ263THE3RHksSs\nEGvrZc6as0jydlR5ENtzEXh0mOeYKLKutbl2bCcqLvlS6WKvGtu2WVlZQVEUhoaGLlk51y8qBw5s\nmJP19f3s975eDAN274b9+zfCQb/MOI6D2W7z/Lkq2cRONHXjYRoxMhw8eJZa5x8YHX0f0WiCVGqE\nF0sFjh6b41duvpI77/x1Dh3JsehDQ1bINysMxlKIVoFoT8C822C9UsDwAqZ608i+i6GHGO4fZqVU\noNZ4EV9ZYz04hxENkNw2k3h0ozKDgsc4CSJE8VklgksXAVUy9OKzhkDCI0RABZXOhTNqIJNEQ8Vm\nAVghYAyJOBIOITQU+oixRsxa4/zSg0RCcXylyoQWpeM4rJoetlDZLAn8QFAVcZZCglQ8SWzscsq1\nNfYfrzI+eDm1oMXWrbdw+PA88AM+8pFfe5tm8rURjYaIxz02b+7lzJkFHCeE43QuGLg5DA/vprRU\n4Nj8CQa6ssgj44yNb6UyfYxWK4+mDREETXw/huPkgCKqlCdNE6Fo6JKDGfh4gaDjudhoTIYMunyf\nhusyTxiLEINI2Ph0UChSp8lmAgaAGll8YkiUqNCNTBhloy0AGz1uQtQwCJDx8OmmjY0hRUiIFFVl\nivlSi7o8R+WH36ZcEfQNXkckksYchCdnZhHyYa7duoVqp0NRkvjoXXe9JouL18v1+/axZWqK+bk5\ngiDg1s2bL6k/zTtFjLzqFXypGHkp8Xic3/zNj3D77S/3GfE8j507z3LixEEikT5838Nx8tx66166\nLoQcNE1jz7XX8uyhAvH4OPPzp1iZPsf6+ll0XSGXm+Wqq67lBz/4Jo6zk1LTo2rlGehRWFxYY9Fq\nUvZtegFX6sEWMit4GL5BgI4brFBCRiZAxaIXAxUPj3UsGkTUGAQOlcDGJY1OgqYI8LAIiygBOnWS\ntNE4LasEQQcXhYjcQ1wIPCoowqWPAgFFHDRUyeJyuYNPjJO+w9raIoaRRhVrRKM62we3Icsa6/kV\n/MDHcTLoxjCBEKjKIIFvIiETCelIkkm94VMNZMrnXL7whb/m6qu2c/SJJwi7LgHgx2Lcfvfdl9wO\n+BeJN6uk98f5Ud7IL7sYGRgY4Hy5CmQvChGApmWjRbtZXfXZvn2jmWMikWV07y2ceva7yEdeZGJi\nlFW1hdazjWsmr2Zl6RxnzzxFYqCHrTu2M1E32X+sQcVqEA08lpbOcaZawIkmiWk6qUqOTfEomyeG\nqBZXOImgB4UeIIeOh8IaAQ4yHdIYxFBwUdCQ6SOgjEwBjyYBLyITB7rQqKIrDpo/hMkcG8bxKioG\nOgmggCCEosh0peKEMpvozw6ycmIOW8rScRxUyeL5zgpCSP8/e28eZMlx33d+su53H/36vubEAJjB\nOeDgEAYEQYgERVoUbZ20LcmW7XVQEd5VSOHwWmuH5A2FLa8VCiu0a8mK0EqytCS9lEDxAgiBlEBg\ncMyAmBlgMEfPTN/X69fvrld3Ze4f/QiLIqmDJAaEuN+/qju6XlZn1qv6Zeb3QDhljh46ihv1kIbF\nRk9i+QZrhT7zR4+SzxfIZo/y5S+f4nu/172h0fR/U9x55zGef/4PefTRB5HyWc6cWUSpHJa1wvT0\nfg4evBfbvsDq6iKrdobbbjtJHLu0e2ssLY2RJCauu4IQKaYZkCY+k3qfo+Yo6yKiE/UoJiEDUhQ6\nFSS9IGXK2MsZyjCNoMgSXbqEJFSJyWMxiaBIE42QXUwkWTQcrtPDooKDwKaPR0iIYE+ZiYpR9HFE\nBaEpbOXQ7iW4wSIjgcd0fh+7V5+gXb2V2fkpjh49yfWrT9KfnGRqaorH7r6barX6pvf76Ojom2aQ\n91aqaQzgSeAO4PNCiH+tlDr9N/mMTCbzVYQdwzD40R/9u1y7do1Ll65j23mOHbvva16aruuSz49Q\nry/SbCZkMvOsr6/geRssLPS4/fabufeeaRynz+jNFncd/CAvnz7N8soybqpRZBSJRKoWA2wERTwC\nUpoUGcXDIcAjpcEhJJMoPCRX8fbY09KmRIUuPfpsY5PDxCEmRaITIPDQ0LRpkA2yosNArtNDYZMn\nr8GYzFPT8phCI5Q+QhkEOJiMYBpH8bwQP7yAaVXZ2lrBRhAGIZ2kgxAV4iTANCooFEoohFZkEPnE\nicUlu8/0wZMcOHAn9Tr85n/6df7xu+7n+sY2l1Z2GPgBl69c4xf+0y+/ba2H32w88QT86q+++e2c\nPAm/8itvfjvf6RgfH2fy5pv50ucWyWd8dF2j47qk+QKVrM1g8D/20l3XpVKd5faHfxjLWkUfK3GL\nEHRabdavPsFdD9zLT/3TX+TXfvF/53PPXkYzcuSsSSIrz8vbV5nM5rhl8gC9JOLCuWex99/G/OF7\n8DyX9trjTKWSNjqzCAZo7FAixCYaElEDUmxidBJiTGzKBGwAFjYjJNRJcUnQCFIHnTp5BAkRGayh\nl+cmNpKILL6KGLPzTFR1zl2/yk5XYsgGFS1LzRxlJfTYlRqjWo7W+hrduM/V8XF6dhHbLnBofpSF\ns0/x4pO/i+aUKU/kWFxc/JaDL79VxHHMxYsXee21q2QyNnfffeyNmPqZmRm+//vv4zOfeYF77plj\nY+M1Op0NDh++l0ymwksv/ja9notpZbh69QoTExazs7MIMcatt97DhQtL5HL7UKpHtWri7X4RG4Fl\n6uQSE+HtzZBrCKZxSPA5TUw9SXEpEWHgoTGghs8IUEHwIibrCBr4lOmSwSJlFJdRAjJIJskTo+ES\ncAnoEFJVMQa7SDJ0VERHr+AmMUI0cKIWG9sN9MkC406Fla1THH70f6FSGWF0fIb73/Uuoiii2+2y\nvLzMwsICa2tN4lhx5Mg8Dz103xvFQxzHvHDqFOdPnSIMQw4ePcqDjzzyHeO++12Z2ru6usp/+A+/\nzdJSSqVyB0tLL9JoJPh+hiReY74WcGA8ZkRzuXrxEkLosLvLRlwgZIYaOgMkCV08NhghYBMNnVmq\nTJKi0SQiIibHEgfx0FDsorONjsJkHEUJhUaRCI0dNGJG6WGjWKFMH9CpkJJlDA2LgAF1DHpoTGoR\nU8LBSm0GrJNi4uOwzigyfysYWbz+GUqqwfHCrSgUbhizlrbZkgaGdYw0cZAqj5QKaKLU6xSLBykW\nDQxD8s533k4+U2P9tT/m0HSVVq9IpbCnYLi8foXb33mAX/zFf/VVUe1SStI0/brx7X8V/rak9tbr\ncOQINBrwTXTD3widDszOQqv15rf1ZuHbNe5LS0v80i/938igTJqkjE5PM79vH5cufZE0Tbnppndy\n9uwFWi0PIXQ87zoPnCiyTxPcOj2NY1l0XJfXdnZQ4zP81n/5PDVtklqhSm+wy+L6KQ4Jn4nZee66\n83t48dXniVeuUaiNkK9NEbgd+o1NaG6wpBlkkpizjAIPkFIkpYlNhKKHYA3BNDpzeGwiAImOQQbJ\nANghQ4BGFp0ONhox00hCbMDGpI9Bnw0y+BQrOnMTk6ys6thMYEvYjToIvUUqbcy0x035KcbLNTxS\nusYuvVKFYn6csrtN2Y+pZPN0wgGX/S3e9/e+j//pX/7LN/VF9ZeN+9raGr/yK7/BykrE7OxNVCpl\nomiLu+6a5v77TzAxMYFpmnS7XVZXVzlz5mU+85lzbK6usnbtVcJokkLmZiJMZg5O4AcXEaJOv1/C\n9w/R7QYUi6OYpoVSDdLupzlsWcRpj2jQYzYJqAEeii32CMcdFE1AUqbPPmL24xIAk1hsMctl8pgI\nxvDosEFCQgGTZW6iwTgGghJqaHtXJ6VNQoGIKWx8YuqiQKDfjpbWmVMRJbOLQYKRszDGppmaPcjo\n/e8nlyty5dVPcOe+Sbrb21xaWCASBn4yhpGd4dDtd1EqZ4ANPvKRD1Or1fjEH/wBvYsXOTI5iWWa\nrO3ssKFp/MOf/ukbli/2/6f2/gXMzs6SySS4rsI0d+j3I/L5g+j6LkZcY6aq09i6RqPxOtlen0tJ\nQkSOiCoawVDnEpFjQJYMGRExjUFXtVlBIMhQRFHBIsWkR4IA9mNgo2GQUEURDK1zUookpKzSxCJg\nFhtFHp1dJAYDdnHQMTEYQzDAYFu20dBwMUkpIKnSZ7BnveN2MTUHhxRNuPSSDlWripA+IokxtC6w\nQZKWMYwymuYRxysIMUMc17HtO5mcnGV7e5vRiiSMQrabBnPj+9/ow4nyLOvrMVeuXOHYsWPEccyX\nvnSK558/TxDEzM+P89hj73xbJoF+q3jqKXj3u29McVAuw4EDcPYsnDjx5rf3nYx9+/bxgQ+c4KWX\nVrGsGlImrK+f4/77b0Ipye/8zsewrJsoFMYZDLYoFDK8+OxFHvj+B9A1DaUU5XyesXab3/nUKfLl\nm9na2WG9fZVsJkM/hg3VpXPtFda2rhCEPlWpSFfqdFcWiDWTduLRVdCTDhFFIvYh2USnTg0bRYTO\nOiOENFiijYdGG5MykhwFIixcBClyaHqYEuOh49JGYxxBOnR47TCPICFHs9dh0W2TSWexzIRspsCY\nElwJQgzR5hbNIok8BlFAdXwKJ1B0u4uIuE0pzjFVHiFNE7KE3DdWJN3Z4fSpU7z/B268Vfz586/y\nn//z77KwALXaMa5d62FZTYTQ+NKXHuell5aoVCw+9KFHOXr0Vqanp9nc3ESpPyHjbeJYoxSc2wm9\nFFuTXL94hVC2ieIVRqtH6fV30bQa3e4FdD1Cyh62YRDEHUrFHEvuLmViFCbbWJSBKSTZYQrzIik9\ntkmpAGVgjTHWKOMAGtClSIqOzzp9ptlFAQ0SDFok6AxQlBHkMQlJWSPGxCCnYtz0GnNYFPWQimFh\nGBVSLUZPfbw4wPN6XD7/BA9M5bi9UuHMuXM8Oj7Ob56+TmqNYrLO+kad9/zAD5DNzvDMMy9w//3H\nqV+6xH3z82/wSvZNTBCtr/PK6dO8+73vveHj/BfxXVmMCCF43/seYXPzedbWLuH7KePjKRk7R9hy\nWV/fIGqHpF6GKNVwKQEFdCYIiLnOKlV65EmpkDKpxBs3mE8TjztIMHHZpUjKLcCrQB+Fjc4oERlS\nBkgkASYpgogCkhIaITkkigwpc5gUEfj4tEgQmMzhkEXQIEUwjaRAiMYIBQIWqbJBTjokCFIxoBNe\noB3mQQhqeUFWRiwG59FUiTR5HYRJsTiHpqXousn+/dOMjc3RbscMggZh4pPLfPUMyZOSydGDLC6u\ncezYMR5//DOcPdtkevoeTNOm1arzW7/1R3zkIz/yNX4Qf9txo/giX8GDD+7l1Hy3FyNCCB599GGW\nln6XZ575AmCSz8cUjVGCgUu3cR5vcAZTSqYO3sqBmx7m+cVL/B8f+wyzo1NkbJ2j+0fJ2hYLV3dI\n+y5lI4/ApNt4jblkwEHb5oBj0gwHXA4CrqcJUxjYGOgyZVqZ5DEok2GFGEUJ0JjkEgUsUgbYZDFx\nqOEyYIVD6CgkG3QwgTk0MggiYhZp0KFCeZhr02MHH7AZkCOmiINODi/dQU8zjBmCKB7gJwGxkcMx\nKiSyS94YIdVjlBGSph0sM+Xm2RnGHRt/aYPewMUyNY7MlSnk81z1PNavXbvhY+j7Po8//gWCoMT4\n+DSZTIk0zfHKK88xOjpLsXgbmcwkhcIMH/3oU5w8uc5/+2+fZmXFpbnTJ9xp0vQylO2YbKaIHzcI\nwwaBrKFURBLrJMl5krSEaR4lCPYC9my7xrXBi9xt+Uxo0EtBUkQSM4eOR0qIQ0TIGDoturg0kITo\n9Mmzt2W/5wnjY5BQI49HwjyCTRSjQA1FnwQFdIA8CU0Ek2iYpFgMsFSHCBstLWIkORIVU63V6Hhr\nXNy4xJGZEaqWx4lb72FtZYWCpnFhe5vdZsyoqFMrFtn1ff74936Pv/MP/j4LC5scPDhLcfgd+fMY\nK5dZv379ho/z18PbrhhRSrG1tYXv+4yNjX3TNsZ33nk7Bw++ytzcDM899zpxnLC7vUXSX2c6n0NK\ngUDDVyNo1Gjh4RAiKOAwgUGdPCk1FAmgI8lgMQssEZJhlJQMATuUgBFglZSbiWgQ4LAXLd4gQhJy\nCJhAR5JyiZABFrdjksFAAAUkNXTOEpPFYZoYiU6LLCkGo0gGRIygGGUeG0WfmIpRpGX02WdajBYy\nXPFderLKhFMiTcfJ2iHewGWAR7W6nzDsEIZdPK9Hr9dg8kjMWOkQ29cbFHNVpJTs9Hpkx8fJZi2K\nxRyNRoPz51fZt+973rjZq9UJ4jji2Wdf4od/+Ds/iOvbhTTdWxn59//+xrX54IPw3/87/OzP3rg2\nv1PxiU98mna7zHvf+4959fwLbL/yBa68fom4u05lo46jCSadLPGru3z+7Ck2wwnK+Wk2Gi6apvGn\n53aQ6TLdVo792ZtJgxgvdRlJAipKI0gDBAYiSdBR5IAmkhCfg0qjSwZFFQtFlYgWa8AMFXIUiUiZ\nJiTAoUsFnS4FPAJq5JiijcKgRIGQATYpBnAYjSwCDZs6Pm1sBsRMM4lOjIXHGII6McVsnsGgj58m\nJFKASFHE4BiMViYQms/BgzOEYR13PMOIZZGRkrlKBW343d1xXZRlUXoL+GAbGxskSR7HSfD9BIB+\nv42mVfC8lHxekiQxtp0lTSv88i//V3T9CDMzD2JwFaFPsXX1GRIkUkU0exuQHtqzIxM9vIEDqYUu\nfUg3kdKnVLoNkOTF8wSBhhuX2CAmi0WZgG0gxSLCISahhEeFPFVGaQ7doHy22YdLAYVAYwB0iNCA\nhL0SxWIv2qMB7AcKwCowj04ZyTopOoIq+t45ukYvDggl9NoprhYwfc/3Mj19guXnP8mXnnmJ0Voe\nKSUXlpeZ0HLkEER+RE4aSNfnjz/+UX7qp99LPp8n+Dr97fo+xW+TNPdbxduqGOl0Onz0o59kfd1F\n0xyU6vPOd97Bo4++628saarVanzwgw/yb//tr3H58jVI9yNkgu438ZTED9eoqAjFKB4JJXRCeoCG\nTg4XsEmwSUnRCVHIoV5cMCAiRKOHTZFN6kigTIoCfKCIoDoktrYQrCKYw2GAYB9TLLKLwEESE2Dy\nFfW4hsLBp0BMEYsWfRwEOoKIOnnyCAxMEiwEMSY5ZdNBMHA9LvsZ9s08QC/aZLO+Rr8TorCwhcCP\nm/TkZbYyEl2PmJ72+Tf/5ucA+Nf/6j9yrdcm4+SZPnqM8ckxOp1Xue22x2g0Gmha8WvGoFweZXn5\ntW9t0N9meOUVGBuDG7k7dfIk/It/seen8yYq+77jUa/XuXp1l8nJe3jh2S9x7YVPsj9IaHRbBOEm\nN2kSgc2OH6KSmCqjdC0I4ipKFQj81xm1LNa7PlkVEScuQuWQaUQuVQh8FDELgxA/itgnJWswjL+U\nbAF5ShjYpLSpElGkToc8PQY4w2+pzi4OAX1SYir0gSIBRVJ6aCzhEaIjKFPFJ4ciQsNHYeAxTQ+J\nCdTp4FAhQaIhadAKJsiZOZABiYqIjQ5COGxHG+QHEMcer53b4MDxwzz02GOsLiywfOkS9fV1Ctks\n5WqV1SjCOnCA4w8++Jf295uBvWeIYm5uH5ubr5HNjhDHEUKYuO4Og8FlGo0svv8JNC2h0+lz330f\nRNdN8uUxuu06JSNPc3CR2B9FpkVi9jhxhrBwaKNLGx2PDD4t4RBFCWmyQCXxSLUyeSQFUjzGacHQ\nyF9HADnUHocDG5MBBj4BCXkiSmjopBgoypg08MmR4CI5CpSG/2MRaA6Pm8AoKQkaDAUMCRkUA3rp\nDgE6BaWBC+nIGNMTh5ifv5Wtyy8zCCJodnEHA5wwxBAJzahMza6iSBjJZtloL7C1tcL+/ft5ulJh\nY3eX6aGy1A9Dll2XH7jvvhs6xt8Ib5tiRCnFxz72SRqNAvPze9bOaZrw9NMvU6tVueuuO7+pzy0W\nJjh+UNJ3u8RpluVNn91gkxE5IKub9NImZWYoIEgI0WmxQ4IkokdABguFRoygT0yARUrAGG1y6PjY\nBGh0kTjACpIJoIjCxUaiUyOhRcw2ARpj5AFBSkyOFn3U8IbX0EhJGSEmDxjEhHj00TGo4lNhG48p\nXCpkAI1ASgap4mrQJNWyZJ0RwrRBvHuFmTgmQ5mEhFW1jef3mHYEXm+NTkchhOBjH/sMt912mP/5\nZ36Cz3/+RcLQQYg+g8EOH/7wyU7g/gAAIABJREFUexkZGcH3faT0vqZvB4Mu4+PfXcZpTzwB73vf\njW1zZgZyObhyBW6++ca2/Z2EXq+HpuW4cukSg/Vlct6Agp4hFQZSaRhphCNhRwly1BBKJxP02ZJd\nIk1REGPEchtTd5jTdTr+ZfTMDJqICFSHMSJsJ89uv8mUUsNYeROPPBlCyiS0aVIgpUKMIMsIkgwN\nunTJIBmljU7MJiaN4WRDkmJiEA23Yk0mKJPBZ4MMI0RAjEsMHMYhJsRHUcZmlS4uIT4SjZRL0Ws4\njBKj4RGQyYxzoHInmmqyK7qIqEN5tMxGmqA5OTZ3d2kCWhSx0euxtb5O8cgR/tef+ikOHz58w8dw\ndnYWxwnIZCocOFBjefksYWiwu3sOwxhQKtWIooM4zihbW1/G9yNWVq5z8OAtBEHKUnNAEpaw1UU8\nuUrAHDoBGc0gVZIqNq4Fg6iPpklymk6iBpCuMy4lUxEIFFlghy7rFGnSZwaBhiQlYIDERGfPVXsM\nhx2ajNAloEKPGh4agjIShkEA1nCqKofviiwpy0AeQQ4NHUkCSEAHmuToEHIzCl3GRELjxP5jhEuv\nU5/Yx9zR+9h85U8ptQfUxsbovPIK++KUvr7ObuhjmAUMtU2tGJAkOVqtFj/4Ez/Bpz/+cVZXVzGE\nIDJN3vnDP8z8/PwNH+evh7dNMbK1tcXamvtGIQKg6wZjY0d49tmXv2ExkiQJp0+f4bnnzjIYBNx6\n634eeeRBRkdHeeWVS8Sewe37b8cyTFy/R95J2NidpNdbxk8TsrSACIlCAg4h42zt6c7JsEyEiSRA\nEWPSxMKjQg9FSsSABjEWXSBkDIs2k8TsksceljgJITYD1vCYBvqk9IBtukyjoWHQISTDnjxRsheX\n1SWiS0zKPgzyaPgEuDTZJk9ChEacSpZQhNpxUllGRTHXN5aYUTZFMmRETETEvIrZMBXj1jircYNs\n9gRKjbGzU+Kll1o4zlX+2T/7EVzXBfakdV+x2Z6enmZursDm5jUmJw8ihCAIPLrda/zgD77/zbol\nviPx5JPw7/7djW/35Mk9v5Hv5mKkUqmQJF22l9tkNIOWlJiWTqKSPTK3puGmCtDRsRFAojTCUJIx\nFMqxidK91c7tKGYmtSFpkcnmGWgF+v42WVEEJUAIWsogS40+GnVARyMgIksLgzxbKHJYTNEHEs4S\n4yPoU0NjigIlBClNNmkT4uHgkmGKIiERPhqSiIQ8/tAA3iDLgAExCR4RBTw2EFg4QJEs1aHsVBBj\nYPUUy+EipZzivrvvYTTfppimvN4M+MNPnKOz3ObQ5AHuPnmSOAwpZjKsJQmzbxHx3LIsfvRHv4/f\n+q3/FyEM5ubKuO4W29s75HJ34fs22ew8vj+gWp1na2uV7e1FNjcv4/saStVIjCxRLHAYwRQJphhF\n4mKIXVAm3XgbiY/UZknSAZGXkKWDhiCWHjoZspiUGbBByDZV+jSx6ZIj5SYgosMqWVIiUqYwsdEJ\n6dBiwBKz9BgAPWAOSRMxZAplSBH4JDQIOILBFinTQ3ZRDx1JiR1ixsgOowU6WDJi+fwLSEvjwuY1\nPvgP/zeM+76PV1/8KO04ppfL4Xd73GNJenoLp6LTQVCYOUapNM5gMGD//v38o5/+aer1OnEcMz4+\njmVZKKVYWlri6qVL6LrOkaNH3xIPqbdNMeL7PprmfM3vM5k8zab7Dc/75Cc/y8sv15mcPEqh4HDl\nyjoLCx/lIx/5+wxXBEEIhBAUsiXuOHQHazufoS8Tso5FNY6IktfxsdHQcelzFy5tBKOYbCEJSBlD\n0WFAkxIJ49Rp49CggqRLmSZT5ICUPj46BUbQ8PCIh7TVHCGCbTyyRFhkEAja9Mmh6ALXgX3sJfMs\nAC0kBiNoRHg0yAABkpQSkg0cLLZJ8fV9lLP34voNklQiVJ/WMCenqPZ2ox19hF3hIg1BlDiMjx8n\nTSWuG3HkyC1sbAhOn36FD3zgMaIo+iq7aCEEH/7wh3j88c+xsHAKIUxsO+GHfughDv257IS/7Wi1\n4MKFPQ7HjcZXSKz/5J/c+La/E9Dv97nw6qu0ti6ycHWdQ8VZsAs0YxdFj4Iu6aRyz3hKWCgGxEqj\nj7PHyJAKP+ii00THpJvuEUirYYqXtOkJl3qxxlYUkgjopFCgRAdFnxKCeVr4BHQYsEobF0WB3DDY\nLouJjQfkyDGPTZ6IkAgPkyLX2CZmBgtoopNHICiyQ4sqGhBjkBDj4aGIEZQIKaChYWNhorDwMOlR\nRDBGlRSD6xTDEgPlsbn1IvOzcyRyhHKxRBBrjBVnuHDpZV5/+XmKpqBULDIyMcHFixffEuJ5kiS8\n9tpl0lQjCHx8v8uxY1NUq+9jedljYSHC9xuUy0XK5RkaDWg2d5GyiqYZJMl5TFmnYs0i5TioLlJb\nQZMFIpVQlz2KhiKr52jJa4SJRqC6FOngk6GFRQHQ0BBYSFx0IvLESDwOoOEhmcRliQ4B48AILg3G\nSTAp4jNNRMiAGIAmigoGHtnhC1eniyRgmjouBVLO4zFCRB1FG4VGmQo+RRQaEkdJqnqJSA4Y1Ff5\nwid+jcLUfnZ3Vgg6be4ZH+dCqohSKAvBxd0t1NwR3v3IjxFFK2+YfgohmPhzttBSSj77yU+yeuYM\nE5kMqZQ8/swz3P7ud/Pwo4/e0LF/2xQjY2NjKNUnTRN0/X9cdrO5xeHDX7+Kr9frvPLKMvv3fw/9\nfpvFxddIkhhN03jhhTMcP36UP/vTSzS7PaaqIygUC4uXCL0E26iRxSagwbzuM6IN6MUDBuwVBQKF\nTUTMnjlOAjikWGwhUURUMJkgQOJjYlFGZ4seJXbpkCPFJ0ZSIcGmi4eigk9Ekw2OMoaDQ8w2KXVs\ndLKkVNnbc9xFUCE7vMGzuCQMhku+XTQGCCQJKSNYTJEmHXKmopP0SNGGUuAN8iTowiJRCikTemEd\nu3SYTKZIp1Mnn99jq9Rq0zz99BMsLKzQankUizYPP3yCEyfu2SvkCgV+/Md/hE6nQxAEjIyMfFNe\nI29nPP00PPQQOF9bM7/peOgh+KVf+u7kjfR6Pf7gN3+TYr/P3z16E+2FK6wsfQmhNOpWxEjFpNUQ\nXGOPRDiuYnaI2UQRaNMouYUrB9hym5xtIeQ8OcOgIzssCrBNg1TYPHjvu9havYzePM/abodamjJg\nDItxBkhCchiM0yRgigYaAp0KPoJt+pgEgEGeGJ82Ol9RVwh8Cgh0FAk9Egpo2OSpo+PjUQC2SIjQ\nSamSZ0COzFBbs6fms9HYoEeMg8MODhUUNjkG5JTD6uISxa5PKTvDej7PROEA64svMuYOsOKUe2ar\nbIchFxYWMD71Ke69994b7sL6zDPPcfr0NocOPQIILl8+x6c//UWazXVqtTkqlTIHDhxBCI2rV5+l\nXD6Obbu023WSxEDTSki5TNU8ThR2ULaNr/r0k12StEuU7lJFYKQGFVGko3JETBDyZSQGHlX2prZ7\n4uoQG4M2RVI8LK4TUQLaMGT8GUA6zFiWlJFILDYxqGAREZBDZ4BJljzu0NCyDmTYT4MlMkhqJFhI\nWuTIMUaMTgufPH0msEi1mDAN2Yg8ktRm48JLaAtneMfMBIdzOZw0JbZN1o0csjBKycowe//78bw6\nDz982zcUely/fp3VM2e4d9++Nzh/82nKS1/8IkeOHr2hBenbphgpFAq885138PTTLzM2dmS4IrJF\nmq7y8MM/8nXPaTQaCFHg/MtPcfb5L5AEDik5Qs1nefkMv//7/yePfu9RPvGxP6W50iD2XV67/gpT\nI8e46eABvnzpVeptBwcJ6Z7Zb0CECxg4hOQpIAhwuQgUGCNLZhgR3iFBEFEiZXvozCcokGWLEMmA\nAikWgt6elgUde88RFYsBG+hksYgoYxITkWDTwqSAR4QiIsCjRYKBhomDIEAh8DCokGg2jjlHEPrE\nyZ75kSSHRw+TXaSAQFPYacyO2iVQAaXxKWb2HyEMfaDPzMxeIuPi4utcvFhn3753Mz9fwvddHn/8\nNFEUcfLk97zR529WUNPbATda0vvncfPNe0XI5ctwyy1vzTW8VTjzwgsUez1uHi4t/6MPvp/Pf+EL\nrF25womDBwl1nRdWsojtXa65DldVAUtM4SsbKa8BKYKYskhJUoUhE8iViSOLJHFA5lEs8uyzTzBZ\nc6glkqI5xdXUxSKPhiIhg45GQojDCDvscIAOPj4esA5UMEiJ0AgxMRgHUjQ8FFkCSjRoEuNQxWMM\nG0FKhh086mhoOPiUKKFj0mYTHZc8o8MZ9YBxTPLAHDoxTZYQDEgwySQ90iSk3m3Q1HNMTt1KY3uR\nrOeRkzoYBoamIZSi2etx/cwZfuHnf565yUlMTePQbbdx4oEHhqnpbw7SNOXUqfNMT99DFEV88alP\nsXDpGhlznmSg6GAQJDvAC2hahYWF1ykUbkPTYkqlMlLOYdsj7Ozs0KJPpNqEgYnUJkGPEXKH4nCs\nHcaoKYcsLhLwGWObNgZdYgzSIVMvpUwRNbSc3KMNl9gjnhZx8WgBWSxSxrBQpCTADAYxMS1SHGJW\nsLGICUnpUwKKxHSGE9iQEj59FBEplaEix6fKJq09zomS7MQBG1JQDmtEaUArMrmw02d6JIs0TcIk\nIRu2yOdt2m6HNFnhQx/6MO94x/Fv2OdXXnuN6Vzuq8QHhq4zoutcv3btu6cYEUL8KnAceEUp9VeG\noT/66Luo1ao8++zLNJsuhw/P8fDDP/JVy05/Htlslu3Nq6ye/TIFbR/l2l78eMftsL64zRNPfIGf\n/Mkf4x3vuJ1PfepzPPcnT7FvbpaDU7OsLb5Eqdd9I43xdTQqWLhEZCiQ4wAmJqCoI0kZYJElh0GM\nTg8XxSIainFSBEVidLr4aGRYQVDCwyKLokqKwqFPFh0LgxEsegTEQBYDHfARFJjmFZZIycBQ0Gvi\nksNBYQIBAV2Ucyta2iGKru9ZzMd3DylULjFjdNnAVttIVcbVoKsSzHyF/cdP4Lpt+v1LnDz5ILlc\njjRNOHv2OY4ffy+53N7DKJPJMzNzJ0899SJjYzWy2SxTU1PftQF6Su0VIz//829N+0LsFUJPPvnd\nV4xcu3CBm2u1N34eq1T4O489xh9mMpxPEgqZDCcee4zC6iqbT2+TBkdxdJ0wvoZSxwAf05Ts0kbS\nQSiDvBIILU9WS5FyF2GZFEoPkkQdlj2FH6wiUMRUsZkcFg4Rii1SJBGC6yiqhOTQmCWlQ0INxTYt\nRqkRoDEgJcYnM2SRtRnHJYNLGx2JYJdRLPJkcSlQx2QbgyIDRpGUcfCIcHEwyCOxhg4lNglTmLhk\nuRmpDfAZMFHSiQyLWrHI9vVFJss1mptr1DIxX97dpT0YcFcuR9Ju03rySZKDB3n05EkaL7zA/3Ph\nAv/gn//zb9pO4a9CFEVEkSRNFU8/+SRXz79MxrqZcqFMxkjIWSFbkc7ly18gm53GtqFUMhGixvr6\nGqYZ4LpLgKQVLaPELJo2yszsETqdy/Q6ZSQ+GTJoqkeHNhYlMvgEzLOFh8HMcA06Q4JEZwFFiGSU\nyaGt5AIhNw25I2dZpI7Y85kZqmEMWm8UFaCwgDIuu5TQuQWHHB4mfbrErNGjzQCQCEr0celgUwFK\nNClh0ERTijBJyYs5UAYJKQX24fu7fGH9GjdlLO7IZunFMQfnZ3mt0+HY0QPce+87/tI+/0YuuG9m\n4N43wluZTXM3kFNKPSSE+L+EEPcopV7+K87hrrvu/GspZ5RSKKVYufw8zUbI/NieZl7KFIOIuZFp\nTp06z9Gjh/n0p5/B90sYWoFosMH28lkmIkFPq+AKgzElWGdAAXBhaFHjkKIRI4nJYVMipI5JdviQ\ncocxSCktJDoFSkzikNJnB53NoQ+Jjk2Chc8IgpgUGw0bmwkkl/BpkkMiGRBS0ix6skKNSQJSJuhT\noMkme/SXSQSjCFajJqldIoxbQA3J6tB2RyBYJBW3sCVK5PMOtdodzFiKD3zgfuL4Gj/0Q49w6tR5\nms1r9PvrSNlkYqLM4cNfzY7c2trhuefO43mCTCZDuQwf/vAHmZqa+mZuibc1Xn11T9Fy8OBbdw2P\nPQa/8RvwMz/z1l3DWwE7kyH0PPLDnCrX9zl9aZHlpmT/HXdzxx2Hec97HuLXf/23ObA0zeZml157\nhVQVMHAw8NBlH1MZ+LKFQ4By92LkHWqAhQq3kL0+AzWOTDM4RGQp4tNgL5tVJ48a0ld7GJSYo4IA\ndGIqtMjSxwDm2aKNiyCHT0iOiAxlQozhs2Ufih3gVWbJkCMH1MhjUyRiEZOAeSR1JD4hPgkV+nhE\nWMAA/w1XCYcOAaYcYNpTFCoCLQxY3XidBIU0QsoVxcH5g1xYW+Ph2VnWBwN6nsc7Dh8m0nWur67y\n4F13cXF1lbNf/jIPPfzwmzKOjuNQKBh89rOfo764hZIWMi2wVd8ll+9z9+GDeOvr2Ifu5qGH7mdt\nbQnPG8dxRllb3STsvk6a7Hk+RWocTdfR9E2SJIPvB1R0k/3SokQBhUlCnWgok44JgCwJkJADQjR8\noEwHjxIpBg4mITaKDLCNYIQ+fRYIKdBGo4RiBJ8x9mTAPiY1IiqkPE8A7L1/JC4+OjFjRPTZj84O\nEFIiJsVnG8kaBSJiFA4ZcoyhqyJd4eEzh6VZmDJLT2RQvotvGHhKseW63HfiBLvb2+zs7DA2NvYN\n+/ymY8f4/OnTzEiJpmkAJGlKI0l45Aarqd7KlZF7gaeGx08D9wN/aTHy14WUkk9+8rOcObOCrpWI\nwnXWN5YoFPLkcxazs6P4mmAw8Pn93/8so6PHaTSWaA5MOu0GZijJ5MaJQg+dAQEuY3sBz3joGDhI\nMhjEWOhDYZaFQCdFsMsuBhXKjGAj8YiAHVIsiuToYBGRJ4eH5AqSGjFZAiChQZkiPUDHwKBImyki\nLEJ8zsltbKoUyBDSYwyNPCYGFikeOQp4+NTlDp0gYi8LQaIIMfQupl4hSWdJ0gTDqKFUmTBscttt\nx9i//wCbmz5KKUZGSiwvX0TKiOPHjxJFLi+9dBrXDSmVCoyMlDh37hrZbI39++/Fshw6nQa/8zt/\nxM/+7D99Q2nz3YK3covmK3j3u+HHfxw8D7LZt/ZabiTuvP9+Xvj4x6nk86RS8sfPnWWnU6IweoKj\nR99Dvb7JRz/6Wa5fv06nk2Nu7j7WREK37aCSFVKqCFEjVbsUqVKlTo48UsIa20CWMQFG2GY7Xkdq\ngiwCkwIjeGxxHcEIEnBpokgYYxSbLCEhkhDQKLK3XeMgMWkzi4uBThsLnwod3KF35zIQ4WDTw8Ig\nIY8iJiaDSQ6fPlPUSelzHZuQHfzhk6iLgyImj4GNICKlRaSK2GmZ82sX+LG79rFohZTzVbKpT7UH\naBrjto2XJPSlJGuaVKpVpKbxzMVLqH5Iz/dZihPue+CBryKwf7sghKBUcuh2NzF1DWVaJNIjli5h\np8XVBYOVRhM9l+A4Ze6//3s5depp6vUt9LRLrHrk9JiCWWUgSiTmKAmbSNkim61SdZexhA5qj2Uj\nKJBnQERKgg8cBuaAbaAPjKMRIHFYAlr4VPCHPqsaNyMJEFTwWMRDASV02ggm2dvKibGRxOhozCFJ\nqKOw6aNYR0dis4uFS4Ye0yhqOCR4hEgiPLbZxULDJkuwxwyU4xj6HJFqIJBkdY2CnWELyE5Pc+I9\n72F0dBR/bY2dnR0uX17g3LkraJrGiRPHuOuuO9/g8x06dIgr997LSy+9xLhtI5WiniTc9Z73fMMd\nhzcLb2UxUgYWh8dd4Oi364OvXLnC6dOr7N9/H143Im5/DlOMoJKQAwdnkKSstHaYN7OY5gwXLpzm\n9ZfPQOgRqRSZ9vC7bQrSRCiFFAkdpbEJSFIaDDDJkEcMX/MJCT3ytAjYIaHACCM4QweQDGN0SfHp\nYGKgMMlyjJQGWfpodOixRgEHRQELAYQoasR0cdExGMemSsx5fCx2yOOQkNBEwwD0PWdIsqTsmcyj\nYgQFFDXAIU27CNkF4aDpLXS9jGkGnDx5Bw8++C6klPi+yx/90VOMjBznjjs+hFKSCxde5LnnzhDH\n0zjOIQwjots9i2HEzM6WOX36PNVqifn5Obpdh+vXr3Prrbd+u4bzbYEnn4Sf+7m39hqKRTh+HP7s\nz+D7vu+tvZYbidvvuION1VWeP32a3s4OlzcTSqOj3H3f/RiGwfj4HAsLO3hejK776LqBbWcwjS5R\nOgvo6JoiKy0s5nFJKKPQgRl0VnGZkzYFaZLDoyNDPCK6DNhPiVli6mwjEaQ4CIqkKCRtqvjD77MY\n5vQamOTxcblOhEM69CEasENhmGI1B1QweR0NQYMtFC1sphEoNFIUET4B00PL+RwRBjGjQxXeMikJ\nLUYoI0SONmVSdHYCyZnlVQrHjlGd3I+SFud2nydqrqF3O9xaq1GZnSUvJZZp8vrVRXb6ETOVDOvt\nHpv9VX73dz/OT/7kj74pBPVWK+Cxx97Hn3zm9yBq0h7skmUKRyuSKAMzC7lChbNnl3jkkRrvetcH\n+OwffxyT62gyS8W6CYFOHLRQqU2+Nk6reQWlbFQSkqg9awSDGAjwCIZr3tNABbDZezVV0fkzCoyQ\n4SYECR59VqlTZpkRFK8DDopDwzNfRKDQSUjpYhKh4ZCgUFRI39hcj4ZKHUGWhDYNymRwyLCPCIcO\nA2I0bGYJsND0LmZaJtUShDxAUbMIRUSkFG7SZdIM2E0Utxw9yrvf/37K5TJKKbpxzBNP/CmdToFa\n7QBxnPL44+dYWFjmwx/+e2iahhCC93/wg6zedRfXLl9G03UevOUWpr9NrqxxHCOl/GtNTt/KYqTL\nnjAE9jhBnb/4B7/wC7/wxvHDDz/Mw3/N5cFz5y6RJBbnzj1Pv++ilRw6javEgcOZs5fRZIhdMpHe\nzTSbdV5/9tNMRGBGA3Sl0UoCpsX/R96bx8p1nmeev+/sp/b17hsXcRNJSdRCUfISS/JuOZmOGu7E\nTmzHnU4aDQSDHqAHg5luoOe/AN3TE8CNtJG4jU564ni3IUuWJVsyba2WSErcl0vyrnW32pdTZ//m\nj6pIliXZkkVZivMABMl76xQO6pyqer/3e97fA6YeE8cGMlDQUAkIhh8sK6ziIxhDwUTnKrN0SZMk\nIiCHQ48FDAqESBTAoECdOg18FGYJidEp4jANNGhykRwWOUZQUQjwcPHoIWAYUT3IQdDxaeNRQqCy\nhsAeelZMVBQiNomHu9M9DMZQyeAREDNNLK+AXEDVFQwjx9hYgCIkjz30AIHTpd45ya7r72T37snh\nvqHC0tIajcY0qRR0OhcIQ41a7QyqajM1dR+Ok6Zeb3P16tNs357BcV4JQPtNVqsFx47BW9S9fkP6\n+Mfhm9/8p1WMKIrCR3/7t9k8coRvfet+tps++/YdQtNe+niLY4Nkcozrr7c4c+YJpIxx/WVUeRBd\nC5Cxhxr7DGLpJD06ZIbuiwiVRakyi4skoolNQBEfn1N4pMiioNHCwx9azLfoMUWPJBYR0ZCdOuih\npumzk8QwuzdmhTQdtgE6YrgIgS4+MRERERYNKqTxkaRw0FDYIk2XHAqrSPZhs84q/nCxY+PgksZQ\nbkAooEifWDg4scFl1eaW8vXs23cvqqoxO3eE5565n/ryTxm9fh93HDjAc0ePsrK5yXPLa3QSeX5y\n7FEEMcXtB/nul7/Djh2T3HXXXdf8WlqWgWWVuf7g7Vz8yTfJKh1W2wu0RYK2NcpNt95BtxsRRRqL\ni8uMjKQ5f/40fd/BNqcwEja2aqEndK40NnA2G9iGS9dZphk3SZEgIhpeiYAmOhEpBnD2KgYLDIJH\n0+ioWIwQ46PSwKJDiMMkCmPI4bwiXAXKQBqNEEkfSYuYzBCT1mfwZdelh0tEjE1rOIo9eI7t9FlC\nRcWnNdwGCohp4RJhxC1GkxN4Xp96fJFeXEJHQ7BKuehx042Habgut/zWb5FIJDh16hRPnzzJpmGg\npkPuePfHSSaTAKRSOc6e/SkLCwts374dGHSkZmdnryn8rNfr8djDD3PpxAlkHDO2fTvv+yU0yDdV\njAghPiul/NKvePhTwJ8AXwPuBl7xPD9bjPwiOY7D8vLyiy/qqVOnee65GpnMdajqKCQPoESnCBdO\nM5fKcNONB7n55hu4sLjIl37wd2R7LmkpuOJpxOwiEC5rsUPek2gq1PBZxycDjAI24FBlnRYb2EwT\nkyXCoEo4nGux6RMN1zkeEQEMp2QS6BhYNIdMP580eRpMUWGTgCVSgEtAnTImE6SIabKJSxlJbzgt\ncwELC0mKeRpk6aFj0CKiSZGIJipZTNEf0CcxCKgRE5IUPlG0jShqs77u8Hj9GLlEj4PbM+wdz3Ll\n4vNcLc+wfeeN+L7L4uIamjbL2FiBYnEUz+tx/LhPq1UjkchgmgksK0mnozM/f5zR0X9C34TAI4/A\nnXcOPCNvt+67Dw4dgr/8y19PavCvW77v02g0sG2bTCbzst+NjIxw+PDNLC0df1khAhDHLomEzsGD\nH6RQOM7y0lWajR4ibJG0snhOi0D4CNlgikEKt0FABh0HjSo52nRp4xEzToxApY5A0sABAgIidBQ0\nfDzSrFIDfFRCuni0UCkQkUTSQ9ImiTZMuupyCckcEtCooNFERaXPAlMEJFGwCFhmHY8UxjAir4eL\nwCKNSYzAo4YJjAPzdHGEjipsAsWgWL4BVVewiwbbt9+Gqmpsba1w4anvMgVk1RRLly9zYXGR8WKR\n7x/9Md1+SKrRYFSzSOSnGZUCVbf50l98nsuXV9naajI2VuK9772NHdfAMHXkyI381RfuR11b4X3X\n305l4RLJaI2O5jF5+Dbec88n2NhY4sknH+PUqeP4voLjWZhM0HFrdPomxYSBptmEUkcVq8yNlbi0\nfJGaK9BxyZNGxaGPQoUdhERYPM84giQhEVs0CGniE1OlSIM8KjFdAnrDrTSDBCFZIqr4VBBIIqxh\nyOEmEVkENuGLOIhJfBaijUDoAAAgAElEQVRZYAuHwTp8MNigYhOi0eM8U4yiYhAPmayCdTIGpOwG\nrpcmjYFUmviyScLYZHLbu1gKA/bt3cu3nnqK1cVF0pbFPXfeyUQ/4txiyI8eeojRqSmIY0pjYyAy\nLC4uv1iMXGtFUcTX/vZv0VdXuWNiAlVRqKyv8/W//utfeNyb7Yz837xKEfF6JKU8IYRwhRA/Bk68\nmnn1gQe+z4kTF9A0hdtvP8iRI4df0e45fvwE3/nOUaIoBUjCsMaVKxUMYweZzMBImUyWuVhb4ODk\nBP/yU7/7ohv80L59fOd7D9PvNdnQR9CYRkEllDpdSrQVnURCpePFGP4qeZxh3yJNDkESFw0HkwQS\nQRYbicYWfUwMOvjYGENk+zIuNiZtTExGAYsEHj4NPAI8Yq5jCQ+VLaIhEdDGw8Qjg4rLPDFpErSY\nQeKygUmAjoVFihoaOZIEtGkRozKKLxNo+IT0hoVIQMoURGrA3L5306pVmMr2Gc1vp945yVY1ptGE\nS9/9ez5wr8rMzG46nQ6GITEME1U1UJQY295Gu32eSuUUExPXI4TA89YwTYfR0dFf5Zb4R6sHHnjn\ndCJmZmDnzsFWzfvf/3afzbXVM8/8lO9//2mCQENKn/37Z/j4xz9E4mcMMrt37yaTeYJqdZVSadBq\nbjQ2yeV8um2Xow98g9FkkVmrwOXUKJuNCkHQwVKgFwckZGOYs6vho9EDiiTxkLTJ4lLGZhIDlz5l\nBB45ikQ06BMCPUL6xKzRQqWFQOBRJGaUDBa9oesghYsG9LEBEw+Xi4CKjkSjgIpDGRUTixiXPkkK\nlOkiaWHi0CVLTJuILj49HGYRFJEIBDYBi9Eqm6RI5m5A1Vvk82mE6LCxcolLJx5l5cppbh2bZbQw\nTl06vPvdBzg5P8/RCxfYO72XpY0uOa+NJlXcbptKZZ1SKUVjucrp02127bqFzc06f/VX9/PJT97D\ngQP739Q1vvXWm/nbv/wCVtgiCgTpfJau2+X26+/mfLdJr9cmkymQzdpsbW2Ry23HD+bpRUVUKQlk\ni5XuYBGo6QpTI5OUsjP0+3NcrvyITWFRi/ooSFy2E9NCUGMKkyxlwGeAcO8T0yCNyTg5xBAnOY7N\nKgE1wEDgIigi2EAyi8oY0CMigUGMwkUU+hiEGCzSxQeSbKNND4VJBKVh7zwC5hG0McjSp0tMjSml\nR1tRsIM2uuijpWzSqQR5c5p1WYbSJNnaJT60cyf+1BRPP/oonqpSyOWIFQfX61FfqGB2u4yNjrK6\nvk5dtvngB986wu7CwgLu8jIHfqbTMlkq0V1Z+YXH/dJiRAjxi5LOXtum+zr0y8Z5n3mmzujoLURR\nyMMPz3P16gqf/vS/eNH1u7a2xte/fpTx8VswzYGT/uLFc1QqF9i2TadavYphZInjmMBpUxhPvWws\nTdM09m6f5ZTTod52yeoKoQ5dNU0utsgbJXTFxWMTBYUkBhJ7ePMwZDcGtHEoYjHImDBI4LBChyom\nJiF9mqTwiOkzgkmXNVyKGFjoxDjDeRuFUSQKksxwXj2ij0/IFho9LAx67MVgFQWXPB6ThHiI4Uos\nxsWmQ4CgjUIdmKZPjKSCRYAiN1ECF6EomGaOhNlHU302aldYWaywq1hC6XXpu3meeeZ5NM3AslQ8\nb4l0eoDiFwJarVNomkK3e5WTJ0+Tz2c5fPhdpNPWP6nx3jge5NH8+3//dp/JS7rvPvja136zipEz\nZ87wzW8+zfT0zRiGRRzHnD17Ed+/nz/8w5c4Q7Zt89nP/nO+9a2HWFq6CghGRhLcccchnvrmAoXx\nFtV2hWq1TV408ewuaqpAoyFx/SYl6vhoNFDxyeGRRgyZp5LpF/0gGgYSiwgLl7UhciyFzxgqxwkx\n6FFiBoMYjxZtBB2SQHI4VxMNUYkhYKBTAHzWh8kmZVRaJBmE8EWMopBFYJCnwSY+GlUCVDwC1thg\nGoXkkBA66MZGJHGRURa916ftbmFZBtPZJMr8C4xbCdxem97iebaiCE0PSKVS5A0DtdUml93Jc1df\nICMKWHqGMOhSq7Vpt1eY3n0I206i6waFwhiWleTBB4+yb9/eN/X+V1WVbTMTHNq/h3a7jabNcuFC\nkna7iyWh2dzi6aeP4roqmcxtNJtdfL9MLBKgTKAQEQdnBujH2CKhJWj1wDBzqEqBIA4JlAmiWAKb\ngE6CSZK0GIRswD+EbWTwSNNEYiJQCVHw6ZPBoIJJjEsKjyYDlHcZSY1B3kwdyWUStJnFZBLQ2KCG\n4DIKdUIK6GTRuEySKjY9wKPDZWKSpERESsREsUovjLms+kxmTGZGZ0mbaSpui3L5FipXj3HPVB7b\nNKlvbTFimqRTKc6eP8/OnTtZXP4J16X3oMUx6UQSXVNorp2k33ttavmbVa1WI/0qo8Gln+tk/rxe\nT2dkBPgQA+jcz+vJ13Nyv6qmpnYBoOsmc3M3cOnSMy/b63rhhTMYxviLhQiAbaeBIhMTOa67rsz6\n+haappJJHMByLnD58hXOnr2E02pSKmUhl8MeHyenBlhS0PZ8AplhWSRwogZ6u0FLBiQxyKPC0O0h\nsBHDkbABKU8jRmKgkyKJh0OCVQQKNnlcSgjajOGj0GWLGm1sAsDGBXbj4iMwiTBQ2UbMGhYhFkkk\nW0T0UTlNgioGBhERHaCIO3TsCzrDqR+DSSRNAo4iKZEiJEGXDJJcZNDwVrly6VkSVpqkUiVav8p+\na4QdmQJRIsextWXaW4s880yV97xnlvn5Jq3W87iuxurKCTqdJrnsPnbuvAfT1Gk256lUrvLpT9/9\nT6oYOX4c8nl4izqev5Luuw9uvRU+/3l4C4Ye3hYdPfos5fIeDGOAt1UUhcnJ3Vy48CRbW1uUy+UX\nHzsyMsKf/Mkf0mw2ieOYfD7P/d/4BrtLJe7as4eVjQ1+8OjTHL7zPTx75jlOtJfRoi6GKOCyjb5k\nOFqro6pp+pFDG4mHTUyfiMH9HSKQ2EMGahuN5HBjVpBiJyYOnSF/WVLgypAjtA0fQR+BIGQCl01m\nGMUiRDCKS5VlHibAYJCINYYgiyAANGIUFGIMfLpDA+U6An/4x0USWgmSGCiBSjr2SBgO28pzdPDZ\nVbQxui61rosX+liKwsr8Me7+6D0YhoHT7WLpOkvNFpn8XtzGCskoQpUqMtaBHmEiSSZTfPE1TyTS\n1Gox7XabfP5XD8cUQjA6NUXQbjMzzMcplUqcOXOeZ4+d5OT3v0SvV+SWW+7k8uUllpaWkXIGaBBL\nD4Zb15BAyg5Xl1bQlBaKZRLEPmFcYmBW3WRgZZ1A0h4uMBWghUADSnisE+MQsjksRNyhgdgiJEWX\nFB2abBCRIuDcYGyAJLBORJMsJrsxsZCAQYoIlZgLQJqYy8xQI42GwBhu73nYqJhSJxJjuIpEigZl\nI4fob7JWOc4FK0Np90dIJCfpVB5namI7hmFgWhYBoAjB2fl5NtfXmYrWubq2jhXuwqzH6GqHf/au\n61mdn/+Vr9EvUzabpRfHr/h5s9f7hce9nmLkASAlpTzx878QQhx9vSd4LaSqeSqVtReLkW7XwTDs\nlz2mXC6hqjHdbpsdOw4wNjZGHMecipY4f6rNwnceJx0bGJrgwsJFotEks7fcwnM/epyO28XIFhCd\nOoq6k7WogicLJNUsUXSWGmuksVAxkEAHSY08VbKEQ9Jikh4+FgUmqLNGBR/JBAaQYpMCCgliZgno\nDIPuLmJjDt8SLlU8RlDx0ZHD/UOXmCwudWaIKJJmlIj0kBdYHb79JghoMErMDHUsAgrDRvFJyuik\n8NCYIMSlpBdota6y2UugdTfYIWwsXUFTVbq+x+GDN3EldulbgmJxhPX1Gt3uGv16C9mvsXt8B1JE\nnD35OGNTO7AsnV6vyuHDr037+03UAw/AR99hWYBzcwPw2YMPwu/8ztt9NtdGW1sNRkZeTnMTQqCq\nSTqdzsuKkX/Qz9KAQ9/HVFWEEJhCUEiWSNkpxkbGSHTOIa1ZanGalnRIxavkZBaNKjJq0REatpwg\nok2AhqCFTwaNMgHrSEwUZvBYQKFBjMGAzzn46ukhSBKRxWSSOj08VCQOOZqskac4ZAulEOiYQxPk\nwMAekiUEPMSQFdrGIYeKT4EqDQpojA0hAjYCC5V1t4elK6Q0nTlpMp5VaGs1Vrckj56skU9q9NrQ\nbUZI0SaVkmRzg5VrByhNTrB0pspU6UY2ZA/fdxCRSiadoOY28dMFisWX6JxxHCFE+IZH+p977hiV\nyhblcp79+/eRTqe54557+O4Xv4iiKBQzGcI4puI65GZ2o7pJRkf3cv58jaWlVXzfJQxd4jiJogw+\nlVVVEEdNEiSxUdD9Llv+KiGjCDE2hHzlGBQfmzjk6bJKnjKgo1Anoo5DGp/OED5nIlFpE1PHw6VP\nmzweJmmyhLjUucIBurQQ9DBQySExiAGfCBUFiyJdBBGrZDEpMsgGG3ReXAoINvBIUMKPDVapMxKq\njMQmydI+4rhNTk2zWb1Kr98gXxDsvX6waB8plzlvGDx27hzlIODmfJ5Ks0mcilhLd9i/XeG2vbfT\n7fdZewsNZdu3b+dHpRKLm5vMlMsIIWh0OlTC8Bcep/yyJ5ZS/pGU8iev8bvf+xXP91dSHLukUi+5\nBK+7bpZud+Nlj7Esi+uuyyHEBouLp1lZucDS0tNMTZnk5t7Fpp5nw1BZVjWMmUP0XZXq889z5Mb9\nWOlNOvoauhngxs8Thw6WKKFKaBFTGVJBNnBZos15FNpk0MnTJ8c6B7nALJvkuELMVTIMgqS3cFmh\nRMQaznBTRWISEiKIh6sckxCbEJOQCA9JSDSMxuoOQTsJVPq4LNBjARedkCqDOCdBkgwFTFTygEGI\nyggaGbLDTSZJHVXpoUaLmMESpuzT7Tu4fhXDUql5LunRMYqlMtWVCp0OjIzcwZEj/wK/ucL1hQ77\nZgrcdfNBPnDLDdw8lyFlNXjXu/Zz6NCtxK9SEf8m653kF/lZfeYz8D/+x9t9FtdO09NjNJtbL/tZ\nHMdEUZtCofBLj9+5fz8rzcHAXrXTYb5ymZ+ee4anL7+A03bQ/QgrqkIk6cgkawhWiOkoLpoaoyh9\ndObJUB0G3g1oqwZXh2b1JpAc+j00PCQ+kzTQ0YlRgCQxE5jsIYeGTZsyggxJDFw82lRosU6LLgbq\ncIpGY5kmDgs4VFlkmTYeHpIIgxiNNAYT6AhUfMRwqSSJg4CW0CnqKQpWilSzR+g0aPdMms4+hDXD\n3gNHiCfnqMkUR595jmOLi+RuuIG5m28mnTXp92skkzu4oqQ4p/g443n0PXspTr588mJ19QI33rjz\nZf6d16Nvf/sUJ0+6PPjgBf7iL75EpVJh586dfOSzn2XJMDi6tMSxZpMVNc3hd/0+IyMTRFFApyMp\nFg+QTicIw8tI2UGIDqoSoylFdKoorCHoEyltVCKS+EgZYbFEglMIOsAaUKWCwxZX6FOnRZsVdCz2\n0KXMOmUccvRJsIzOMnnW2UaDMTzKRMOZSpMyHRTqQ4pJQIxHjzbzRJxBcB6HKjYhOfrY9DCRGITI\nIY+mi00dlYsoXBZFUAukZI7Q69LYWkfxHbLSIaw+z5EjGf7s//jfuFSt4gUBmq4ztns3K+025UKB\ndhSxEUVMzM5yZGaGTqOBoWlcrVY5ePjwG7pOb0S6rvPPP/MZ+uPjPL68zFPLy8wD937mM7/wuHd0\nNo3r9rCsQfHRbtew7Q67du168fd79+5lZuYEi4unKJVmkTJma+sq7373Lu699wNcvnwFz/OZm3sP\n99//Q0zDYM+2W8ilBpmYl64+R7kfcrBgMzU6yu8cOcIXvv51SlGbZrvHla0YRV4hKVWmUPDIs0LM\nBbIoWJgUUemRpkNIhz5JYJzm0FKmAAKLPDaCrWGdHLFBxDKDtY4gHg4JLhOhopJGskrIGCE2AR46\naSQNEjhDAoFGn4AeA4iSisTGo4FFhAa4wADBFhHiYxHSxECgYJEw+mSTNj1pkE6qdEONXMomXU6R\nzY4RBiGXLp1nq9fk3js+h2FYLC6ex2lHnKu6xEYP26qwbXya2dExLtZrJJMJgsB/w9kV8c+Q//6x\nqVKBS5fenpTeX6b77oN/+29hawtepWnwj053330HX/jCt9A0nWy2hOf1WV09yx137H5deUh79+7l\nzJ49/P3DD+OtrpJ16pytNtihqJxzffKKpKhZrEV9ivoEfjhCPwqZVerodswp5yrTuCi0qWDjkxqM\nXZIjRkPFJ0QhwsQnCdSH70Abn4iANjuJsUkQD6HjVaq4ZGlTZRzJGBoK0AdO0ySgiMRig5gaCQZr\nx+0oTBHioFCniYNLSAadEgkW6A4NrlAmZCLSsWyDVr+PFwkMPcALBcXMDFJGbNRPs23yBuoTHls5\nl/s++lF27NhBp9OhF8EjD53H0MrsO3APB2+8kTBsMznpkUwmOH36CRQlTRx32bNnjA9/+I2nvM7M\nHHjx3/X6Ot/85kP8m3/zWXbt2sXOnTs5f/48Tz11jPbZGv1+j7m5HVy69BOkHMW2c9RqSdLpNp3O\ncaScIYosFFEnTxpLqTFtW4SxQtTziahRYIscaQQGDudZxSSgSMwcFaoonCfCJ2YHSRKE5GiwnS06\nxGwQsgtYQyegiIaNwEMZGlot+kgKmOwkwXE2sOkyQwaDDH0c2lwlpktMGWgDq8OeCdiodJD0GKHH\nBMgCRryIpuhoQYRu+piqj4lD2YzQpceHP/YxHk+n+elPfoIaRWwFAXfcdRd37tlDHMfsvf12zj73\nHFq3y9VajacWFpg+dIiDN9zwhq/VG1GhUOCTn/sczWaTMAwpFAq/9HP+HV2MNJsnCAIbKSMyGcmn\nP/2/vKzyNgyDT3/6Ezz77DGOHz+Pqgo+/vED3HLLIXRdf9mKybJMBBDFMaqi0u13iDtbjFhJhJBo\nmkZ9a4s9hoGaTLI3myVsnccOPFQp8KSKis00PvNECBKotCgS4lGlT5uIERJDTqJLiErICCUgRJLE\np0eGFFk6mKj0iDiHHDZ8+2S4wAIWAn24xklh0EejA2wyiktumFJjEJNGQ+JTYYCpr+FhUsdEwx9S\nChxcJD16Q+BOmwZrngnYCF3HFAEpXaVS20Dz+ixdPE4ymWax0yBRnsK2k6yszHPixDlCuZ2ibWGm\nupy6epYwitkxMU0Y+ayuvsDHPnYz1uuMrD116jQ//OFTVKstRkZy3HPPHdfsvvl16atfHWyDvBN9\nGZkM3HsvfPnL8Gd/9nafzZvX7Owsn/vcvTz00I9ZWjqNZWl86EM3vSyk8Rfp6tWrbK2vc+7cObYJ\ngTk2wlQcU45V6o0F1sI6aX2UYgT9qIFCSNrssWu8yPjcNPOPP8EuVBxiIppow3DKRTw8UUaXKjGL\nBKQYWBgvo7CMSZ6AGmm2yBAwCIT3UFGZJuQFAlR6CMxhalWEQGeEBB269DEAB4OABDkGqVcXCJkm\n5DoUYs5xnhAHH58yxjAMThIC1aDKaqDRq0KsmDiGgp1IstnpYCgKPQ/KusZdd32UhYUneOZHP+K5\nBx8EYLRU4pN/dA/Ly10UJYPjXGF6OsEnPjGYSNza2qLZbJLJZK7JBF2hMMbS0jyNRoNWq8XnP/9F\nzpypkctNsbQErdZT7No1zZ49szz22NOEYYlW6wK2nWPHjg9Sqy3Q711BFzGG7LPNyJHTc9S7ywhi\nkjQoUERgAh2ymIQ4tDmNiUaAJMAmiY2vp2gFl1HwsKkQ0MchiU0HQRKJR2JYIFr0CfDo4RMzSh8N\nmw4jtEjRG9JGIEGXEg0qSOo0yVCmSoMcKhoGDj2qNHBQiEkiSOKLMn1ZR409jKDOdrNExwsIgpDn\nv/cwRz9+lAMHDpBIp4njGMuy+NJ//s/MVyqMj4wwMTZG4f3v54ULF9hVLvM7n/scU1NTr8iekVKy\nvr5OFEWMjY29YjT+V9UbCU4VrxaS806QEEK6rsva2hqqqr7pELbTp0/zhS88yMZ8m225PB2nQf3i\nE4yoPcKkIDk1xebSErYfcLrt0uhHGNUFDgoDNR6MadWIWIj7rKEwY+6kH8S04wo5Ohik6aPRwKZN\nAZcQjRzTWLj4xPQxqDNCmyJ1dGKuoLFCmiRpFDpk6SERLBPhk0cjg02XUQwEgjIb5PCxULEAC8Ei\nEQtIkgyC9GzSSBK0EISkqCEwlSS6oeC65wmYwWAKzS6gqSFJbYM99jqFnIYE/CCgretcv2MXWphn\nWdNx9AKqupfNygJyc5EDe2Zwwy7nlp+lkEkxuns7/+pP/5DbbrvldQUsPfvsMb7+9ScYHb2eVCpH\nu12nWj3Ln//5//qqoU3vVN1+O/zH/wgf/ODbfSavrh/8AP7dvxuYbN/Jeq2wrteS7/tomvaaK60w\nDOn1eiQSCXRd5+LFizz4pS+RCkM2zpxhezrNseVlQseh53o8v9Sm0tfwRQkpBabiMmb0GbE9bhgr\nIIpZHn7mGNkgIoOkhaCJJCLFEkkke1BED1UOMkXybFAmpEsWjyQhKjY10nSZG1pSLwEtFFQk25Ck\nhu6uPgYZcoSEnKdLjSlS1NiGh0kZSOAiWcamxTQKPXTOU2CTgwgGGeMKbQIksGpnsLQ5EnYJaaUR\n5RlWV09hajlscwypLfH7n/okqqrz5GN/xR//1s1MDkMHK7UaV8OQez/1KVzXJZ1OMzk5ec1C1IQQ\nfOELLyc6LC09zt1338Df//0jnDvXY2TkFly3zerqBUZGtqMo6xw5cis/fOT7XDn/JKpZRrdvIJkc\nod2ukM9bbFSeRmxcYI+9A5B0fZ9q2MeigkkRhsuyPgKLDjYaJjkEClt02aSLmtpG2kpypXYBi70Y\nZHDk1nBIYUDXTTCKiYGPR5M+CXqMk0FD0CHA4wJ78dCB9NB1IoAFoApsMM7A7uqjEQI+ATqSEIMs\nQmSRukLsLzNGj722xLLz1FWTyfIcjV6LjZLOvXe/ixFd53Klwvzly0ykUiitFsK2KW/bxnVzc1xx\nHO770z9lamrqFddhbW2N737lK3QqFTzXRc/n+djv/R579ux5xWOvxTWXUr7qDfSO7oyYpsnc3Nw1\nea7rr7+eD3xgmW+0f8Txy+dQgg6N9iJVJcBoSrbX6/jVKhU/JizMMF7ehtWtY4Qemuaj2gnMOKbh\nSGxTI6muQOSwU8Qo2DSjHCPCIC9dLtPCZxLw6RFQQBIS4KByfuiLzyDoksIkj8cIEQm2WAcqWLSI\nkfQp4TFNRBWNy5TwkECdEAOFFBqgkWXgC9+DQX24OWMSskadLAkyWRvUBh1jBz1nB7qwscw0buDS\ndzs0DJO5lM3h2VlMXed0o8Hc7p1cOrVEUPdZDR127ryFXGmS1U6Fmu9jawkyuRIf/md38kf/+l9j\n2y8ZieM4Znl5GcdxGBkZoVh8yXUfhiGPPPIUk5M3vrgFl8kU0LS3tm14rXXlyuDPWwChvGZ63/sG\n2zQnT8LBg2/32Vw7vVYuipSSJ598mkcffRbPA9OE9773EPMnn2dvoYDjulQAU9dJRjE/vrqEq02B\n3MakEhMbFltBC1fUmTWTuFqWuqmyVKsRIUmhcYUEYhg4ucUWMZuM0COQJl0UtgF5Eug0KeOwSEiF\nJDY2DvAEHQwGUDKdmDEGAPISETkUloio0cIgGiZTVdlNSAmTPgzh4oJRunSJgRwqY5iEBLRIoNBH\noqsFIjPAkVlSdp7MzDhbjoLaF0zmdtDsXMBSDAzV5aeP/4jCqODOHaNMlkpcXF7h2QvLNLs+buBQ\nnNvGJ//gU2/5da3X1ymVbI4efZ4gyKCqKarVBpqmkc/PDUM7XX747c8zqUp0xUHX4YX1h9hghnRm\nB92ug6KrRCMW56oL5IVKFNk0FEEhTmASIoQKcjAVNI5JG0GMii4ylKSgQZtOaFFMz1CWLRrtTfxg\nA4U0EQV8XHTqSM4g0AiJSZAlT4kYFUlEEp0OaUw8Br0BiQvDecxBCZJjjZASdQqU0MiTponAwSfJ\nKj5pUtokTqSzFlUQUYtZmSCDTWOrSl/VaKw2GFFUZstlTp84wd2FAtUwZOrWW1m5coXTp0/jjY3x\nyT/+41ctRPr9Pl/74hfpXL5Ma3OTBLARBPy/Z8/yf/2X//Kqx7xVekcXI9dKjuMghODeez/Mbbcd\n4uzZs2xubvLtv+3ROXmSu0slkobBfKMNsUdWqKw21ygnyxhxQBh12H/TAZLJJFcefZTrtm1jLpvl\ne8ePUwojGlGMSoiJSlFobEqXDjVMmhQYI0nMJgF9iuRIoVGkT5sMCjBKlyI61pDqGFPARUOhS4MG\nApeIKWwauBSQTKLhAwEKPcQwolqiIDBR0YfDwnn6rODiBRq+n6HlG6CYyMjH6rcxkQTo1NGIFYWN\n9U3y2TSWlLRcl7q3SXtxgXac4WRNML59B/f9/n3EcYzj9Mh2VT7zJ3/yskKkXq/zP//nN9jYCFEU\nCylbHD68m4985AOoqkqn06Hfl5RKL8eVJhJvTSz5W6WvfGXgy3gnU05VFf7gD+Bv/gb+0396u8/m\nrdeTTz7N/fefYGpqwCLxfZfvfvcE/bWfcsO730WQTPKMYVCpVok6IX0thU8ZFAm6gh/4xOSI0Hm8\nf4UUSUjmcXUDYpgXRSy5HV2oRNIlN+SM7KBHjMcGfTwSKEP3VxeF7hA+NoFBjZBZFEaI6QATwByw\nyAC1paJTGhIvemh4RIwBoxiYgIeDiUGMIETDpkVAjEoKgU0PhwgwRBpTt7DzZURHx56e4siHPsgD\n9z9EdekChmqimXV2TCjMjpVxojW27z3I9iDgzMIijx5fJ5/eyXghSaW6xne+/VP27d//utLS36gW\nF1/AsvJ4XhvLanHnnUf4xjeeZX7+ImtrKRKJBFL2iWOHQkGnkG6xW01zzx1H+N73HuOFhS5pdRpV\nK1AojZFKZdjYaJLIqcQli42NBu22glCmiL1HSVHEVJMY4QDKEGMhyGESEQgHofWxlDJ1NLZaAaO5\nEfLhPOttE12qeGpdLmAAACAASURBVITo5IlZZS8qeXyeRJBjDJMULjExLgY+EWkuUWM3kgSD/JrO\n8O8J4AmgiTmcjlSH0Ag5/C7QB7QbpUChmMerbZHWk6QMmy2niuL7tMOQpm7wdz98io/efoCClOSS\nSTr1Oplcjrs+8hH21ev0xsdfE/V+8eJFKufOkajXuTWfR1UUojjmmeVl/r///t/53//Df7jm1/y1\n9BtdjGxtbfHd7/6Ay5fXAcmuXVN89KN38773vY+1tTUu/OQnVNbXWe90iFotXN2gVCyx0GywFUky\nepZm4JAxFBr9PpeqVTaA6zSN6sYGGWDSMtF6Dg4OKdUiUkISfocCTRQ8fHq0SBGxHYOAcRQ8knRI\nsU6DHFkkHv6wg6KTRiHLHnTWqKMREuEwSoYWME+HAhExkhqSHaTp0Adieiho2OjYWEhaGHSxiPwy\n3XAdKQ104TKSGUPTVFynjQkE/Sat/hSLtQ7NusuFoE3e87i1UGD2gM17Rif46SUXRfExTRPbtuh2\nV7n77ttezDyAwcr0y1/+Np1OmdnZaWDQJXniiWOMjh7ntttuxbZtFCUiikJU9aXbLwi8X+Od8eb1\n5S8POB7vdH3qUwP42Z//+aA4+U1VGIY89tizTE4eepFFYhgWMzM38dgLj9Bot8lnMtx522187Vvf\nQQQRnoR13yMIIIw0YpHATOSw09PkC7sIN07jXlyiUJ6hY1q0nRSSiEjGRPioCCzKdIgoEDBKSIUW\nm2i0sbEwSGPRpsUWHbYNbYtjCCpIthhYUgvAEhoGBn0ittAJmCXGJ6ZCC58CARbgvugvUYnxyFIi\nwKRPhEAjQGWmUCKfTHK6uUooLG45chunTs0T+TpjiSTtfpUiLreMZrjrfbdzYXmZeGKC2rlznDi/\nSim7D1MfvIaxYjAzt5+HH36CgwcPXHOG0O/+7k2srm5QLk+yf/8+2u02q6tfB4pE0TyNRpUo8tE0\nm36/R3Ksyt333kW326Xd8djoeqSMfThOh+XlDRKJTTwvIoqKTEyMEMcRfXcRMOnFsyyGfUrhBhkk\nXaHTkBKFPoHw8OM+kTqOq5bpuxLHPY9fXyVPTHZIto5IIxFkqNFFsIxAxULio6Kgo6CSQBKTIaCP\n5HkG0YdVBm6iHQicYVdsDR8dlQBAERhxQIQcbEFqgkIuYqt9CaFsUBcmtrPJTKyCZmHEfXTFprnR\n4ZHn5rnJHnyGKgyQ7EIINFX9hROOzUaD6soK9wyx7QCqorC3WOSZEydwXfd1+wDfrH5ji5Fer8df\n//VXiaJJpqffDcDi4iL/9b/+DXv3zvHkk8d54ejTxLUm+/NToEKtt0JWU5CaiZkbp6kZ7LB30dg8\nw4mri4RxiKEonD11ilQyiapprPo+NhJb6VCL+tixSkJXGAs8dCICOixiUMSlBej8Q2rjIF7aAMwh\nmSCFRoTEISamwziDTJoIgU8aC5MsPQpDHmSbmDYtVtBRMMiTQyMmNVxBNYiBBP2gSCwFQvRQ5Do9\nV2d8dAdS8ag1LnM4o2JnSvi6xfn6BpuxSm5jk7plsfumm5iemSGTm+eHx07z/AnJ9MwIhw/v4kMf\nerl7fm1tjUrFYXb2pS0XRVEYHd3N448PihHLsjh8eB+PP36GmZn9KIpKHEesrJz5Nd0Zb15nzkC9\n/s6covl57dsHIyNw9Og7e0vpzcpxnOHWzEtdujAMWLxyknq1yn/76le5ee9ebj5wgJv3H+RrR59B\n6hp5GTCSmORqo01PzaPp0yhqjd7GRSadDpae4OraOmt9UEnTRyfAwxp2InVCejiU8RlF4BEjMJhF\n0iAmQR4DSZZNVCQqgy+GMoNJuDqDLdaQNC55asT4zKEzSsgabZbRCRhDUAIaRKzisoWBSUiWFQTQ\nIWAThyIhC72Q+UCnRkyhmODRR+9HylmcbozldkiZfWazu3juuSvMzk7R0XXuvOkmHl5cZL3eZ9eU\nQSxj6p0OoZ1gbttONjaexXGclxGsr4UOHbqJQ4de+n8ymaTd3sDzsoShxPctFGWKTqeCql4lt6eE\nnUjw2KNPIWWWXncVjyqRpiFlRL2+STKZJY67VKs1HEfD8wIUxSCObbraDH2/PkhlVnv0qFGSLiUr\nQ62vUlEEm65HHFexccjjMwKk6dEmZp0GZTJkgTYRdQpIygg2iTFJksBA0qaDR5VRBtsy8XBzT0dS\nR+KgoqGTxqE9zMkhtkCoxLJGXm7hSwur7XFADekkBItxgIhj6qFAEGEKhUzYwHM1ongn5xqX2VMu\n4/CScXSp0eDWe157yimby+GHIcbPFZn9ICBfLtPv93/zixEhxIeB/weoSinffa2f//TpM/R6SWZm\npl/8WS43zre+9Sirqx7j4zew1nsKK9RpdQJmxyeQocblpbP4+REmdr4Hw8pz8ux3UIkQrQ6782k+\nPD7OheVlgm6XVSG4EoaMAHMK9GWfdQmmYeFFkkwsyCG5OjSYdgEPiImJEAjauLgEqJhECAIMQjQ6\nFEmioJIf1uMKDQSCIjpJVCBik4hlNNaZQxARE5HGpEsXhwYuJhpFTC1FV2rY5gKG5xP656l1VzFV\nl905n9HJbVztO4xNXkfq+iP4q1fobpzlpve+98WJpNv37aKUSRBu385v33cfqVTqFa+553koyiuh\nR5aVYHPzpSTf97//fXje9zl27AkUJUkc97jzzr2vOO6dqq98BT7xCfjHMpH8yU/C3/3db3Yxkkgk\nME3wvP6LBcmp5x5BrFzijtEx9u6a5oUXXuBvFhawR0cxNMHhsSkqjQ6doEtGl0TBGn2/Qd9vMKXV\nKdg5amGTZcdCFbN4skmCDGBTQ2IQYNHDpEsKC4lKnZgpYAYTj5AuDgIdG406LjEggBKDdv08kESh\nRUwP2CKPSQGNEJUUPjHbUNkAzhEQkKKHTZIiWYq4bDBJjTIRHSXCVwWFnAnZEu/ddiurdoJK2+XE\niVNomo6i1zAUnc1eGyXWeeDo47z3D36fffv2US6Xeeb5/5OL9RqKolIcH+eW6/cjhERV41/Ll5IQ\ngv37r+PcuSdIpW4miiSeV6NQyJBK3Ul2wuHHx49z5coGuj5NxirQDWM8BLqewjQN+v0mnreEYeRx\nXRW4DinHABfTrBGLBJaw0UWCltKlH7Wp+222hEqLcbw4BibJscwUMaN0h5wSyRgKK7jUEbQpkSZJ\nl/EhHXuVNpIEEZIGc7jDxaZOE0GPQQYzgIpGC4kgoE2b/5+99w6y7DzvM5+Tz7k59e2cpyf1DGaA\nSQgDcJBIMIOEQIJUpiibWluyWbRL3l17xZLtqq0SVdLW2pIs2aZMizRFIZAESQggiDwAJmNy6u7p\nnG6OJ5+zf3RjKJAiBZIARgD3qerqvrdu+Pp8957zft/7vr+fyjYiUgSJFmEosiI22K/DUBQEI4bi\n6fRkMkxfnkRFIqNEcQOLnJGm4FgslRaQO+J8Z3qa7WNjVE2T84UCyU2b2LZ9+4883uPj46jd3Uys\nrDCUyyGKIqV6nYYskx8cJBaLsbKygud5dHZ2vmFdNn8f13Jn5CVgB/C9N+PFFxcLGMZrZYnn5xfw\nvAyKkmB29hKR5GYCocbp6jms0jIKIfOigqeKDGs2krzCyNbdLJ57llxrhc4wxHVdeuNxJM9jvt7A\nkTW0wGM5CFkCOhFI2jZtQaAuKchBAGGTJdo4RCgBEQQ8QmK0KDOJRTcKPiI+MM8QFgEhdXRcJCSi\n1KmSQkZCwETiCj4NQiqkyZJAQcRe98lpE0GhQQcBBWL4bgNBCknEtiEGj5MUYFN/J4P5EZbmp5By\nvRzYfy/5fD++7/HKK89yfuEiFxZX2ShJ5Na1Qxq+z94bbvh7AxFYk+GGJq7roCjfLzIsFObZsmX4\n6m1FUfjIRz7AnXfWqdfrJJPJN3zF9WYRhmspmq985VqP5PXzwANrBaz/6T/BW7TIecuRZZnbb9+z\nXjOyg1arTnv+Mr2Cy/ato4xt3MCGsTHOTk1RSKfJ2S7LZy6xI5lh0SxgmlVkVUXOpGlZUbpjGmLN\nYs42iQTddGudXDTPUKRMSIIAlYAi/azSQ0gNhyoiATJr1ngCBiIeDh4CC/hXjbxmgUXWTr5N4PK6\nQJZEDIUMbUxkIvi0kQlwsRGAAilEehCQya5rQGtEWGKB3TQoEGKGMqFpocQVPEkk17cRr1gkpkwj\n2WVMq8UFL4YgugSCS1qw+T/uugtJkujq6uLXP/0AzzwzRX//dlRVw/c9ZmdPceedO1DeogKpnTs3\n8/TT8+RyA3iei65vQJZVTHOOeMrh7OQ5lnyPqFdFVmTq4SqysQvTtNC0KkEwj+M0CYIxZLkbx5km\nCDQMo4Ourh5UtcrS5CE0b5UeNaQ/HmWqUqLpK9jCPNCPKBooQQ0DH5MQAROQ6ENiAZcGMUQSSOue\nYyEbqFFHZhWROt2E9CEwS0gTlxIiifU0XRsoIjFDAos4Gn0IkouotpCDNnGjAxMJxVimY7CbRCLO\ngmVhWhaCCGnJRwxMFEVHlXXagoPrtunespdP/MoD2K0WjmXxri1bGBsb+7EBhKIo/Oa//tf89R/+\nIY1ymYiiEMvlkFMptt14I1/6sz/DXFlZW/5GItz5kY+wZcubs3C8ZsFIGIZV4A1rE/tBurpyHD9+\nEfh+NXChUAEcLl8+RaHQpNnKg5TFi2xiISnR099DIl9kbGyInTtvQdMiPP34N7CLMwxKAn2yzGq9\nzmKzScPzMH0fQ1FpiyIdhMRDuC4MCEOBMqCEIpfDEAmHNvP45Kmh0yREogqkSJMiwKHJEtCilxZR\noIXLDB4iOgYZVnCp0sBCoAZ4dNHCRidCBwJrkjkGDlGWcLAo0MLGoIiMTCho1GorpOM9OOYKVr3G\nRLNIW4OuXC+5XC+u6/Dyy99jaqqAH9nG9060OHLhJAeu70NRZMTu7h/b7hWNRrn77j185ztHSac3\nYBgxKpW1DqEDB35YrDeRSPyQDfw/do4fXwtIdu++1iN5/fT1wY4da/LwH/3otR7Nm8fNN99IGIY8\n/fRR5uYWUe1lrtuzk3x3Jy+dPMni0hIBUC0U2DzQx9zlS0zVFsjFInxs1wgHryywJIuEkgSGjNNa\nIAg9YqKG5/tEyaNgElAixCbKLCJwEQGXEGe9AqxBi9y6tJWCj4RJNz5p1hYiLiFzwOV1fYkBIoio\nrFJGQkdBosgc4lolAUskcbCIkiCBQR1nvSvDR2bNWjMpwkwYMixA6NhcbpTJqjq9sRSTz32TZOU8\nTS9BRttMLJGh6FRw9C6EsMmTTz5/9Xt9110HcF2Xw4cPIYoGYWiyf/9WDhx4wzevfyQ33bSPv/zL\nx3HdJooSw3HaOM4q27cPEYsto27ZhxTdxMnj52iJG+hNDNNuL2NZx9F1HcfxEIQOfD9LEEiE4Sqi\n6OO6FtBHJhNndfkMmaaB7kisOiZRUWOIkCuBQEvciBxeJIJJ13o9xyohVTzqeDiAgbuu5yTiMIdD\nhhgRbGSgTAc2K0AL0JDYjYiHSgGXZaBOlCgD1LHQSCMKEiJNtEQaSdVR2iZ6RCGXzbBiWaTyPZSn\nZ7loWogIdEYiJKMpCoFHKGukDIHf+Myn2LNnz098vPft20f8936PF598ksrqKlo2y0233srhp55i\nIAjoXvcIapomj3/5y6T+2T+ju7v7H3jVn5x3bM3Itm1beeqpw5RKS2Sz3YRhiOfVKRZPMTR0J729\nClNT08Ri4xR9mQ3jg9xyy02cPPldRLFFo7HmC1hfPU9SDAlliflKhdB1UXyfqr+20km7DjIipwnI\nI7AKZAUJTVEwXQ8NEQWJEWRmKNEAdHrQ6GMRD4mQCFEidGFxnovruWiLGAIxEsSI4KJgIOFj4yMw\njEYch0vEsAjR10WOFGQCFGxsXAaIs0wFWQBXUtEljYYTpS54dEcFNg70EE0kmKjNMTt7jnK5wORk\nge7uQfbu3cHK0iKzE5f52pGz/PPf+WVuv/vuH9lW+Sq33bafXC7DwYPHqVZn2LGjn/37f5HcunbB\n252vfnVtp+FNiqHfND75Sfjyl9/ZwYggCOzffzP79u3h7NmzPPU/bCzX5n899BCDqsp4ZyeFep2V\nQoE/uTxLVuhBtNNMNdo8PXeO0aTEoCYiakVGBzaR2XgDE098j3ZrmTBQEdcVJnrRaLJA77rRpUYc\nmTYOCQTSmMxwmjLV9VJTFZeQkGVUNEEiDH1AJoZKgI+OhkLIMBY+RZq0cNalwX06aFJnEyoTOLRQ\nsHDREfGQ1nv0fGqBQESWiItQCQMsQSI/ej0vv3wU1VKJxZNUyg6G7+M4ZdIy1ASHoXwPLx48zj/9\npy6KoqAoCh/60Pu4/fZbr+5a/qid0DeLvr4+7r33Vk6erCOKBrqeJJ/fQrF4ngMH9vDEE4fYu/cu\nVDXC88+/gGWBqgYMDg5g2wKFwiyath3f93CcC6hqDt/3cd0ay8szlMsNdNeiw8jgOw7R6EaajcvE\npAYpv06DFVLUEDGo0KaORzdrRagl1gKMEHO9eylKnCw+FipVVFbJ4rBAhEU8BHz6UREJ6UamjgDE\nCFHwkNDRceUQRU1TDxuk81ki0Rjm4gxXfJdkq4Vji/REkth+DEtJURR0Cq6DX1ogLovkDZXJhky9\nXv+pFa23jo+zdXwc3/eRJIlz586h1ut0/50unJhh0KeqvHLkCN0f+tAbNNvf500PRgRB6AS++gN3\nL78eX5vPf/7zV/8+cOAABw4ceN3vG4/H+Y3fuJ+vf/1xZmcngZDR0YDz56PE471IkkwiMUu5fAZV\n1VlaWmJq6ggjI1GSWoyLF55jaUlkMC+SNK7jpZdeYsDz6JckLgUBedaqlkNRQg1hGBHCtYzwDCEq\nAg0lSsVxWMGjSp000I1CkVUKVBggQZQAhwoFHEyi+AjY9JJkGAWJCk0KTCEj0E+U8/joZIggE6CR\noEWTMjpRZARs2sACEjJFIU5ccnFo4KMDJhIz/P6n7mfXpk3I60VLL5w/T2zEZ3V1iT17djI2thlV\nVUmnU2zeupWZmSFGNm58TefMj2Pr1q1s3br1dc/V24UgWAtGHnvsWo/kJ+e+++Bzn4NmE97ia8tb\nzqsX1WNnz6KurJA1TWRVZb7dJkynUWptOpUMUmIIt2lTKhbR/G6m2yts6OuhX49x6tI0d9y0k/fe\nfQdPPHGQpiVj+Ckqgc8yRfKsIgItEgTkKGOyiEGJMqCiENKPTwIfEDCQcUWNnJZANE08YJYWPgoi\nHj41NARsbFSSRJFp0oWERJsyq7iotGmjoJCigICKgySUSYYOy5JEjyoix+MM5vNMlBqcPn0cwxhB\nUxZRIhkijWUM1ScMfeK6gSu5DHfnKZuzP3QM4/H4NU2dPvDAvYjiN5maKiGKBrXaAnfeuYObb76J\nSqXOSy9NsHfvHQwMjHL48LOUSnV0PQREdH2YxUWfdvssmrYJUexBVetY7Tkss4rrxonqBq7tIQoC\nVvMSGc9HEUQs2tQ4RAKdBHnOMcX4+q5Wc/0nC1zBo8ACNn0k0NDRaFBEoMkMaXxUdCwSNEkTUMal\nik8CCREJDWjRwiVCIMwRixh0d/SghDZuo0B/3KJTiDB58hSBluTs8ip1IUum+05ka55Io0RO17H8\nFRqCS9Lz+OK//bccvesuHvj0p39kK+8/xKvdUo16HePvWXHFDYNKqfRTzuqP500PRsIwXAFu/2me\n+3eDkZ+Grq4uPvOZX6VWqyEIAo1Gg8VFi+XleTxPJpnswjAWSSZlHGeOvq4OgqUlutNp+gZ6mFxd\n5bjvcNPNN1O4fBm7VOIS666WskxGFJnzfbQwoFeUuOgH9AsiqqJRERVko5+WUEG0m3SgUcLAQ0Wm\nTZwWOlmixDDw0JlnEp0GOnGStFlkTe8xSUAHm6UJfCDwO4EUVVxsckg4dLNCCR8bGYUmEKKxB0lI\nEMo2hryIQIG+zm6SyX52jY2xvLREq9EglkgwksthaQq7dl1Hq9XzQ7sfgvDmpdPeTrz4IqRSsG3b\ntR7JT04ms6YY+9hjcP/913o0by6WZfHkQw/xwIEDfOnhh8m6LqYgUKpU6Ovvx2ovM5rsQOjvod4K\niCoKtutzpSbS3X0L8UgK6Ofw3Aofu/8uyOWYOHqMCydPkhHB8ltE1The6BM6Ph5NVgnx0WiSRWKR\nJAbDqLg0mMMmC/ihvdaRJwm0fI8IOgXaBMgYdODgI64rtK4JZOkoJLGZJE7IMAYlTIq0kQgpY0Eo\nUlSidBkxMnmVLWNjWKLI1myLC+Vp0ukeKmaZ8XSCTnuteJXAI5nSSCbzrNZW2PPuLW9ZPcg/xNLS\nEoVCgWg0yq/92gOUSiVarRa5XO5qcHT33bfTaHxr3Rsnxo4do/T2RqjVmijKOM1mhW9/+3EuX76C\n667iuiZRySEbKSOqgzTaEUK1TkgN0VGQvRqKIBEECp6s0OeHeOEiOhF0fCTgGDpV4gQY6NhYlGkg\nIePQoIiCRR6HZWJ4JEhRYs0BzUBDJk6TOj5tFCRsGuiokkqXItHbmadoTtBqWkiNKlHFZUtMZ3Nv\nL/MeFMM1w0QSGwiDDIoaY6ZxHFMIqDg+N2cjjGSzZLJZ5s+d48/+4A/49Gc/y+jo6E89D/nOTo4F\nAZVKhWKhgCSKdHR2Umw06P8pUkGvh2vZTbML+L+BbYIgPAF8MAzDN0Vs4lXzNl3X6e1NsnnzDizL\nQRRFksnbKZcX0fVewsI8N42MXO23zqfTXJqa4vDZs2waHMTTdUTf57GFBTKqhgZI7TaarBAEPkVg\nwYiRF2Wanstyu8RSmKYDn5KQxwiH8QgQKRPQYvWq/VUVGQmdNjl0EjRQUbDVKle8Nk6QZDbMkJdM\nDN/GpUVAEoFhpoQ2Q2GFDhqECCwiUaMPkQhOAJlQoxlkUaMucucAWc3kuSefZHllleWmjxcERBMy\nB37919m1ayePPPIKicT3PX0sq4Uk1X/qSPudxKspmrcr990HDz30zg9GZmdniTgOPV1d7N62Da1Q\nIK1pjAoCc/baKcZBIKnoBEaIpygU2xKGkkSRVQRBoCPThecneOq5Y5RKNi19I3bOJmwVSEkaVywX\nwbNBCij6AS6DSAwQoiOul53PsIQh+HRLMo1AIBF4NOwyrmhQElXEUCYMMxSx6UUDdJp4FKlTYYSQ\nDC4RImjri4wEGVSyNAnFCBdEjVkhRlmu0BFX6BkaogxYhsENGzeSdDV6enaxOBrBnjzOYNhgaukS\njp+k7QjEfI94UuDXfv0Xr+V0AeC6Lg899E1On15EEBKEoUkuJ/Irv3Ifw8PDr3mspml84hP3XfXG\nSSaTpNNp/uN//EOee+5R6vUy9XoFQUij6ypO6zyjuV5ynb2UGhk8r0AyMsicfZqcWyItRCg7izSV\nCIY+hhZYLHomEj4xFwqBhsUoBgkCZEJ8KqRxMYiwEREbhRqrTCICfdTpwyWKTBGXRaCfCDFMLuEQ\nCi4NESTBxQ9kanaJjWmXVnmJnbkkF02fvKIwOz1Nw/WIJXoItRiF5gqJoetYmiiSUPKkkjIJr0Fa\nhkRHB4eWqsy0NKIrMf73f/MFtm3tZc/uHWzYvJnR0dGfKH0zMDDAXLPJuWefZWMigSgIvPzyyyjj\n43zw7/Ziv4FcywLWY8Ddb+V7qqrKe9+7n4cfPkgyOYphJFhdncHz5ujujFO7WKKRSpFMJq/uBBy4\n4QYeO3+eWcch9Dy8MMTTdKpeSFaSERSNWDTFYmDSpyoIapwVNUrVh7ofxS86+GIMQejF8UXWNhLX\nFBVr2JjUiaADMfJMrUslZQipIzshXYJFSU4TejF8sZs2K0AJkRQiCZrhJs7wChEkZETipBlBx2Se\nFTTm3By6HGeoU2Vo+/WULjzDyckq7aCLmJpBDENemb2C+9RBPvbLv8yFC1OcP38IXc9TqRRYXjzF\n9ePdHD96lJ0/ppPmnY7nwd/8DRw8eK1H8tNz773wu78LlvXO7aqBNfG9V0+7Y6OjHFteplvT8H2f\naCSCEJFZtGwGYhlk2eKK59GyHeJJiabVoN6usVppMVFZpdG0ueGGe4nFZIJgjImJ57GSBrnUOCsX\nH8JoLJBAIY2BTZkGGgEeCklKzHCzENKhaBQdn3l8WsjUAp0udYBZr4Ec5pmlQZ0WFhY2nZhEUdmE\njIFPHYhRp4VHA40mCAaOoBKEKq4fUA0STAdZnqsK3Lqjj5vHRrnsurx/9z6efXaaLdtuZD6ZZvbc\nIQLnKJLUZGBDD/vvuJmPfeKB9S64a8vBgy9x/HiJdHoToiiRTCYpleb52tce5TOf+VWCIGBiYoLT\np8+wslKjoyPDrl3b2bBhAwBf/vKDLC9LVCoBtVoCVd1CKjWL58UwQhU9aVFqtJgv+viBSaUBnjzM\njCJQcJaJyCJpJY5gzeOJ4KJyzPNIBDI+SdLEAQUJmRYiFgOEVJBQCdGoYyOTZoACOlFUAiL4jKJx\nFptFVFQkCoJIPQyJCimQfBKqiy7LOJaFHARM1esUm00s0ySlabi2xfLqDLlNnQTFBolEmnp+gKZV\npmrXyMkeY5u3caLYpNjK05XtwxYtmpOzzC7NE5mdZLq7m1fGx/nIxz/+ultzp6amyGsa/bt2MT09\nje/7dG7YgGsYuK77pnwG3rEFrD+K4eFBtmw8xcFnHsETFPbetIdWS+GFg5cILi1SmquTz0fZdcN1\n+EFA6Pts2raNIysrqI5DUK6gCApnQwfJc0hFEpwWQnzF4IPvfw+ri0u8ePwUbVHD9XxERaHpeER9\nEAgIUfBRcXAQMPCoEpCnxTIxQMIkwCZEQCYgF3o0vGligkXLB1EaQlMlVMWl3ZrH912QN6GEFqOk\nINDxwwhJWSUatpkUHBDbjIyN8MEP3sgjpbOcmS/Rradp2gErdg05muGllyb58z//73ziE/dz001V\nvvvdpyhPvMw9I910RwyufPe7nD50iE/85m/+RE6M7xSefhoGB2H93Pe2pLMTdu6EJ56AN6H+7Jri\nOA7tdptY2LXCigAAIABJREFULMbAwAANWca0bQY7O6ls28ax8+exqlXiIyMM3Hkb80cucn72HDE9\nST2oU7YukI+lmT/xOPVmm1VBZr6tIIsjnDp0lGgqRVdfN6LYj22XqBWniLSLbAKm1zQv8bFRsKgh\nUsMhSkgs8AksGy0MiACxdeOGquNg4WHh44sixTCCFyZZ02Nt468Lg4OEjY+NikgLBIOokUQUNJqO\nTS7SS48u0BTjZFKbOHj+En4mxS/+1m/R39+P4zzB4cMvI8kRBrdt5IOfvJt7730fmvbDekDXkkce\neYKJCYMwXEszRyICe/bsYGFhjm9961t88YuPcObMFYIgSkfHEMPDvTzxxGH6OwS6uvIcObXCrt0f\nZnp6kXpdA1QEIYoorqJEDS7MriBgEoQ+o703AxJzlXmSmT784iqDchLHbKDG84SyDH4OQ7mFpZW/\nRQkb+AjEsHFxKCJi049ADV8MCQOTkAQiF0nQIIkNmECIh0iWgEC28AkY0VXKYTfbM9uYbzaZ9306\n4r1MLLQQPYuUKNAtywSui+m6a0XKIUzNnKcVTTA7+wzd3QNkEiO0lk/Sb/SgGAYLtQaCEAFFZGHq\nJLdsHCefy9Euz/KufQMcP32aM+Pj7Nz5+iT9z588yUgqRV9HB3vHx4G1VP252VkmLl9+jd/YG8XP\nVTAyMzPDV//kT6henMSo27Rcl6+ePMHorvcxvu3dPD85Sc6ROHVqhonzZ+nJpjlSKJDK5/n0u9/N\nzOQkZ44eR5HiTIsyHdtuxG01aBYX6FJ1Dh0/Sb20SmdHihs7OvifR07htpPYCISU0UkTEmIh0Aai\nNBFwaFGhSAsDbT0EKSEhoQImLZJIDIYRFqjTlnrJJg0UKYkYgGu2UGWNTCiRVFPU2xU0OYGhiqS0\nNHW7QLJD5cBtO9i6dRNfFzW2btqNiM58YQbHjxMzBqnYZb797Ummp/+Ez372U7iFZe7fuxtj/aSV\nSya5vLDAi889x/veaVey18GrQmdvd15N1bxTptD3fZ596ilOHjyIEgSEmsa+u+7iwIc/zLMPPUSX\nLJNLp2lv2UI7keDej3+cDRs2sLy8zLce/TbTU/NsftctPPbfJ1ArDUxHJm1oCO0aZcciHRlDN03E\nMOB8YQE12oGuN6A5Q1SQEBHR8PDXBQclPDygvC5o1UTEFEQaoURIgEkVEwMXkzo2Dhph2Ltu3iYA\nU0CNgOz670Vk4hRp00NISghw3BIrchQ9uoVsLKAj0UUiFiezZRtxt4+dt40xPDyM53ls2TJGJpNA\nURTGxsbIZDL4vs+JE69w5MhpXNdjx46N7Np1w2s8pt5KlpaWOHbsIj0996Kqa2MwzSYvvXSceLzM\nM88cxPdHkeU+VDVCvT7F2VeOszHuYBgesaEOWhdLvNQKCcOAsbFRBEHBNCOUiw5Tl6cxTZN0TCQW\nibJUOkLU6Ke7J0smW2H//fdRuXCBs4cuoSf6uVCaJ9n7AVZW6mTyN2OvfhstlNcbrUMiqNiUkBDw\nBBUHC58QnQYJFNZM8SKAhY6Dg0cylUEWBIqWDXIHK5ZFLp0mKsuEqRStYpqBeBeaXcNqOxx0PNRA\nQgoN0t3dlGWZ8b23cv31PYyNbUIUYfHKAC985zu8MDNDsS2jqTAzNU205bK6XEFXNdb0X2Ewk+HC\niROvOxjxPQ95PTvwd+sFRSDw/Tdq6l/Dz00wEoYhjz34IM3zk+hhlnxPFt/3WT18iBMvPkexWKfo\ndzB15SyRWhFdrkMsQm93N3q5zMLsLNfv3o2k6kxO1skJEpGtNzE0vG1Nn+OlR7l46G+4b3ycfDTK\nVw4fZpPfxldCzrsRChQRiBFg4BMFXHwCPJKY6ETopUaJHlR8JBSWiCHSwKCHTsAiRcCcM4PEZkQ8\nvNBE1Hqwg1WQFJpOk0S0A4QmggCSKON5i3Qovfztoy8yM2cxsbhMo2CjykkK1VW6MntYKbeomyHV\nqsGRIy0+97n/i/0DWYwf6CUfzOc5dPLkz10w4nnwjW/Av/t313okPzsf/Sh8/vPgOPAPdGm/LXjq\niSeYfu45buzrQ1UU2pbF4Uce4ZaPfYwHfvu3OXf6NM1ajRsHBti+fftVFVHDMLjnve8hk8nwuX/+\nO2Q9A0WScXBwbQHfDukWbXzJRhFiKJJM3BNYqsxjG4uklW4UqYHnlejAY4pFQnoBFZkQjSq6oDMn\nBqQCDYhiYFJDYJ4csBGPFUKShGEIoYAmRrCDLNBCQgRERGKkCBBwKdHAJiDiKxQDC0UpkjaGEGNx\nspkMg4MDrKy4HD78Cs1mi8unjpMHdEGgAcyNj/OhX/gFvvnNxzh6dJFsdgRBEHn00XO88soFfvM3\nf+ma7JicOHGazs5BLKtyNRgxjBhLS0tMTx+lo+M2Gg0BXY+jKFEcp4ZcPkZKHyGVzSD5HmKlwezK\nYUrRHkzzFQYHxykVFlldXCAd78J1SyTVTYSejqCbxHImd717D2E4x//5+7/LM888w6X2X1FtxvAs\nAd8XSCQ02uVVjFDEpIFCChUJnRY6V5BIE/gVFFw8ZogRsIRLJx0YqHg4FKlxmTabQpHhiM50INJG\npRHLEuIh+T6hopDLd1AtXERtOyTCDhzBZp4EuqJQFzJkh7dw662/wMLCy9x6676rUgkf/tjH+MaD\nD/LsF/4HUTmGksiQjYJh5JicnGbHjszVWpEf51Hzg2zcvp3nT56kO5u9Gox4vk8pCLjzZyiM/XH8\n3AQj1WqVpYkJ8HSSmbWJ9P2ApJ5hojTH/HydzZvv4UKrgyBaZrF5kRtynfQlI2iCwMqVK4xt2sTw\n0ABTU4eRggiubQLgOBaq5rA1lyOp63zn9GkqxSLZMGRQddGFIiecLA0maKMjEEGmC4FttDhNjAIZ\nerFJsiC01wpYQ4sm2npFvQzrYkkeJaaKa+1ugS8icIlIUsTIjKNWlmk1isQiCTKJkKnSLLqRYand\nSf1SiXOTT9Jq1SBo0J26DtNKc2G2SChI5DoT9PdvQpZlFhYe43TzErds3PiaY+j6/j+oM/JO5Nln\nYWhoLU3zdqe3FzZuXEs7vec913o0PxvtdpszL77IzQMDV9vUI7rOtq4uXv7e9/jMv/pXlHt7+dvT\nUxw6Mcvjj7/I3r3jVFeX15xKRZErxSIvfvcF3pXbALpD0GrTdhxCW8UNBQreDLIcxTcFfLdFu30S\nXRvAtpM05Tgxt0U6cBmkQZFLFNFwEUhKIWOyQTEQMYM1KXELWCCkzsj6vqdPgovIgosbBphBBpEe\n1hK6ZQIMNGqAj6pYjMSvR5XKKGYFz66x7DfoGNhANBLBjEaZn7/IoUOHuO666zl59GWU1iI3jefY\ntWNtm/2V06d5VJY5/soKg4P7mLlymsXLxwlsk4vH23R2Jrn//l94w+fJsizOnTvP/PwKHR1ptm3b\n+pq24XK5zpYt13Pq1GlqNQ9dz+C6LRqNC8TjBvV6gXq9TRhmSSRGEAKLuB/geR5B4LO4WEQQIK8n\nEBI5KpLFhQtHaNZW0JQUlrMW5JhSnVQkhZzsI5evEoul6OqSkSSJXbt2cf2u03R37+OZZ77D0pJA\nsShRqywyTIIo0GCKEJcMFmlclnBRxDphUEOnShSVWRLUaZOkRYhPC40GnZxprjJVWSYiGDjCBSpB\nmiDdTSzmsvfmG3n0a/8vu7uyHLsiI6AghiI5eRDJSKCoBno8hywrCEKKpaWlq8FIT08P4ztv4Na7\naywstJHlLipTcyStKkHQJJFYEyybrVTYe/frL9HcvHkz57Zt4+iZM3THYvhBwEK7zfjtt9PV1fWG\nfj5e5ecmGJEkiZZpooffv5gqioIkg+UERMUIYRgSuJA08iSiAtVGle3DGebm5ohK0lpRUTrN7t1b\n+OozL5J1yszOHkXXbT75yffyB//ycR6emUdpmvQGCh2BhW/btIBeMYURxjkdKrSIIyAhUEOhziht\nHC6uS0yrmFKCitdmEJ2YYGOGc9iYVAmw2UgmmUdsXkSTJRS3gtZyWBEGqfk6Eb+MZ03haDoVNUNn\ndjeTlSrZrndRLBZQVRNRXKYtTOFJMVwvjqa2GRnZgq5Hsaw6PT1DrBZPcmV+nuG+7yvYXl5e5rp7\n7rkGs3dtefBB+IU3/hx9zXg1VfN2D0YajQY6XA1EXiUeiWDOznLmzBm+/OXvkc9vZ2AghW2b/Nmf\nPMSIXuSB2/cjCAKF2VlkX2S1UWMglqHZahPV1gwsHUkkq1Upto6BJKNIJgMZCzUaY3G1jqdl8bUq\nlt1EDTwCbMBHVbuQwgY2Jj1KF/NehVVUJDpo0wQ8Qs7STZFuupBQkbCoscISPjY1XBoEgo6mQsL3\n6TQyxCIdSJKCGkkTbyxT90yWKleIiENs3jzE008/QTTaR6HQprRcY2vPGCcn5xntKdCfz7Opu5tv\nPvU0Wm4/kxeP0LxwhPFkB1o0xWplhSe+9GW2b9/2Y1WWf1Kq1Sr/7b99lUpFQ9NSOM4qTz55iE99\n6r6rjxkZ6ePChYscOHA3U1MXKBanSCRiiGKUK1dWaDZ9BKGDcnkW2y6jyRpe4BGL6bhuGYjQ2Rnn\n9OVzFAOTXPc4y8uHkUQRWayhyEkE4rRMH0NdQfKSeJ5FozHBJz/5EWDNWG7v3o28+OIp8vlujh17\nBlUdwREUbAISWMSQSay7L58jRBJWGNQ1BjSFY1Wfc6GKwFZMDFqYiLQJKRHFJu+YbAwVMqpKTXQ5\n1XiaOXsrSjPGxsIxtgxoZK0ogz2DVJsrBM0anudRtdtElAwpX8A0TcLQwbIsHnv0Ua6cO0ckHqcV\nCAwMbGFoSOLChdPUsxGWGysMdKSpmG0OX7lCduvWH+tR84NIksRHH3iAS5cucfnsWXRF4QPXXcfQ\n0NAb9tn4QX5ugpFEIkHv5s1cnnqRXHYt/SAKAkpCpi0odMoqvu/i49E0V9m1sQfbq9HX0cF0Os3l\nmRm2BAGVRoNV1+GDn/pF9txyC5IkkUql+OsvfYkLcyVGLBFVyODis+DXiYdV2qLMXNBEI4GERRKP\nAAFoIlImgUhaMlAMmVXXwfRUylIHS4FEOhRxKOMSsrpelKW3JxhWEihyDCXRTb1+Gs1cZdJTEeUE\nqtRBo1qhI97L5dIEqjhMu3gZ3woJtSix2GaGhtr4fouJCTCMPMlkliDwaTan2LVrE63uCBftCrWZ\nGXRBoBoE5DZvZu+NN17biXyL8X145BF44YVrPZI3jvvug3374E//FN5gR/i3lEQigSUIeL7/moCk\n3mphJJM8++wRstktxGJrBddBAKIVoeKEmLZNRNcRgoDhTILpqk0sYoEs4tkWS4JNUwy5LylQVmtk\nMhmmbIHs1l2UGxn8wCUQkrRtmRlTx2eOKG1iNBADG01NMG2Z9HtLJMOQFh4r654mEt66VJoCgoSy\n3qWRpEWFOQaI0RJCSmIaLT1KUHgO/AS12iqdnXG0WB+rQYgnWgjpkJY/z5NPHqbV6se1RFZmFjCr\nNVqrZeIJiVNT8/Tn86iKQhj4OE6TlYlX2JnpQhLXjpsuK2xJpnnxySff0GDk8cefptnMMDDw/a39\nSmWVBx/8vnLgjh3bOXjwBPV6ka1bd627eF+gUDjN1q3v5sqVNrWaQCKxkVptAriMLLnokSZDA528\ncPA8opTHjPQQS28kDKsMDm5n/sosqtSNLPZjaApBc46l0klyssm+DRv59Kc/xPz8PF/4wn9hcbHI\n2FgfW7eO8PDDT9PToyOKS1QqLkurTSQ0IkSp0+YMNRQkhjQZAai54ItxPL8PlRAdFYEoNgFNXDJc\npi90iAkigVsnEnoMEVL1TiHKnayuJNm3dRP2/DzWaoNcYgxLWGCh1iYU80Q9Ga+wzJPfeJDOYXjx\nb1fo8jyuy+Uw220OX7zIuZJIvmMUsTrDaFynFR9iFZt9t9zEu+66i9HR0auCZq8XSZLYsmXLm+ZF\n84P83AQjAL/0G7/BZw8d5/jMOfLxNE2gnk4zNu6Ty0Xw/SXGNqVwSjZ+YJNLKER1nXxvL8nrr2dJ\n15EkiZ0f+Qg7r78eRVEIw5D/+Rd/wdSh4wx1jtJZahJaDmXbwwkTtAQRCQkfmRKgYpATbaJBC5hf\nF3GXqBOQk/OkpAC3VaTpC9SkTpa9ZZI4ZEjTi0dVWMZwWyixNIYcx/NsQl+iQ47g6ClSiS2ksgkO\nTx6l3pwioSbpTWaQBYmV5golp0qk+zoURebGG2+mWv0WxeIS1WoCQWgwNjZAPt9PrVbit//l/8bc\n3BytZpPOri4GBgZ+7sTPDh6Erq63dxfNDzI8vOZX8/zz8BOIGv+jwzAMrrvlFk4+8wzb+vrQFIWW\nZXFmZYX9H/sYDz70PQYHd1x9vOM4aJKEQJR6u00IpLJZhnIGyy2TRTmOb4CtSJTCOOmEzmkBdN+n\nHIb0btnCvXfcwcPPncBQHRpehooZIjLLGAoddKIITaL4nLLKdIkinpGkaVu4YRJfjCGFQ3jOJSSK\nKEIeXa6ghhqBJyBjYdBAF0QIBQpCDVVr00j2ErSrJP0GXtPHDANMOUlv737uee89yLLAF7/4x7Sb\nIumURHeuhxU3JPAbBO0iFyYrvP/GG5gvFNh1880cOTWD7NhXA5E12fQK2zZfx6nl5auS4D8rruty\n5swUvb2v9bVJp/PMzU1cvR2NRvkn/+STPPPMQU6ceAlFkdi8OQ3so7d3J7J8kitXlqlW16TgslmF\nX/ml38Kcn+f8yXMUzBAvLhId2k+ucxvl8hUmJ7+OpPWiS3naZgvblRCEOEGo8Z57RvnCF36fb33r\nW/zRH32DaHQ7sdgYR44scPToU4yMpLjrrvvQNIOvf/1LPPXUYS7bGjpFQlKAyQAtpFiamh3Sr0Zo\nug5RP42NSQUNmTVPGhmRKC3SeCQFBUMUEXwJLQyYEj1ykgKFNi9UzvMv3n83Be8EpZrBWO9eGueO\nYXoLKGKTfCpLIlrDLtTRUhIbNm1a+w5oGnfs3MlTf/5f0Ram2dK/GVEQWS0vUtEt7njPe34m8bO3\nkp+rYKSjo4P/57/+KX/5l1/mzKkJjGiM/Vs3sG/fdr797RcJw05isTSXL59l7srLdA508OiFCwyN\nj/O+e+5hcHDwhy7Gy8vL1GdmcByRbKaPlFpBD0Wc2QUKdoAkRKlSx/WzxHBpe8s4oUkckx4CQlSW\nBJFUKBC6LkEoYqlxoqpMXqlRKrtsCvtoCh4zfoBHHy2/zVS9SV9GIWxWkUIPMZQRJYFcLo2PTRBU\nEF2ddgAzwQrZSAxdlolbFaKGRBi2Sac7ufHG61lYeIWengi9vTuAkOXlE9x//20kEgnG19u6fl55\np6VoXuW++9b+t7dzMAJw4K67kBWFw88/j+T7CLrOzffdx/U33MAzzx6l2axe3RmJRKI4goDXqvDM\nKzbleoDru1yaX0X1HHCitBwHXzR54Fffz+/9+3/Po48+yvN/8zfcNj7OQGcnkijy0duuZ2Lx60yc\nPkvoieRZREZnngRB2IUUFNGCImlDZu9tu1lYqDI93QTboRIskkhthraLgk5HMo5XKa/tUIUGi4GK\nISs4gktf2kKJNVC7N7G0NE9T3UbFbbFl0wBqRWJkZIB4PM7c3CXS6U0szh5HTvUiiCLxVJriqkXN\nLKC5EscuXcJKpfjkBz7Aputm+Q+f/TcUSyGCIAIm27cPoxgGUUV5QwKRn5RkMsmHP/w+Pvzh9wFw\n6dIlJiaeRdM0brllL9u317FtGwiIRhf57X/xaWZnZ/n85/+ITCpFoQix5DCe5wI6zWaBSGQzLSFK\ntT2H5/koikoi3Y9hRGm1Wvz5n3+dzs7biUbXRB6j0RQrKwaXL79If/8KqVQH9XqLWCxH1Vul6fci\niiDLBupwQBi4dPlJSmJIoLWor/qonkyUKjItQESigIdHBpADHz8IkQWRUBARwhCFFqOpPJe8Ns9P\nTbF/5xjnZpd54dRhdLnG/l0b2DHWT1cmQ1cmw4OPPIJlmq85dtVmk+GowZ7xQVrtOmEQctPuEeSI\nzomXX/7/g5F/rGSzWT73ud/BNE18378q4jUyMsKxYydZWFhlx46d9PXdw8MPP4HT1JiZi/AXf/Et\ntmzp5OMfv/c10smtVgtDFInoCslUhuV6gREtihGJogQ+juZhdFyPtiyguAYpzjEUBCTVPI5fQQpW\n6ZI6WPYdCp5DLt2F62tE5AhBMEVO8TDUDFfsJnFlE45rYoUCsh+naLl0RirEFJG6BF09m5Blicml\nKWSpn7hqokU0qo5JoRHSFQ8Y6siyvHqEnr5RCoUT3HHHBm666QGOHn2FqalFcrkkN930QUZGRq7V\nFP2jIQjg4YfhySev9UjeeD7+cdi/H/74j+F16iD9o0SSJN51xx3cfOutmKZJNBq9ejG9444b+cpX\nnkVVb0BVdSRJQEuKXLwyR0S/ha5MD4vFIiuOQ6bDZWC4nz4jRqZ3A6LaoN1u89GPfpTa0hJhpYK4\nvhDxgwBfCUjoeaz2moJyhX5SQgqEAFuI4QsxGuIqN910A+VyhW8//izTMz7Z2AjZnhEWZpcxPYuW\nLRCGEglkCqKFJkWJ6RpLboRMPM/1++9idPM+JifPMjFxhWZTRpIadHcPsmvX2q6PIAhoWpyYodJo\nnUeQhhFEATlWIqMHKIk06d27ues97yGdTpPL5fjFz/w6E9/7Hhs6O+nM5xFlmRNzc9z4BkbeiqKw\nbdsIFy5M09392jRNLvfj24gHBgZQlBaW1ULXo1fdvaenT/Gud62lDbLZLD09A/T17WdxcZHJyTlM\n0yYa9Umn80iSi2XV0I1OBEFHFFts3jxMoeDwV3/115imQmdn5jXvm8n0srSkU69foFYrsrRURFHi\nZDI70XWZZDKO51l40hE6t6coz0js6h3F9x2+9tQjWPUkQSgQEX1coYkQlnEDkWUEBoAAhUbocR4P\nTdIYzHUiCBDR4ux5/wcQXIeNvQWiI33kGg12/UDKLGoYtNrt19zXsiwMUWRoeOg1hqSmbXNmaekn\nnbZrxtv4NPSz8YM99dlslne/+w5grQ34P//nLyKKI4yM9F6979y5k7zwwkvcfvttV5+Xy+VohCHb\nR7pYLC2THdzJ6SuvUPr/2Dvv8KjuK+9/7vTeVEZlRgVJCASidwzIFHfcsB3XOE5sJ9kUO5v33fI+\nu1lvdt+0zSbZbHY3zbG9fh0n6xobG7BN7wgJECBUUe+j6b3d94+RZQQYYwcYCfR5nnnQXO6dOXd+\nM/ee3++c8z0hF/3RGApDLqX26xjwngSfnIRUR1RwoNEaUaJlwO1CL4pI5AayjSWU2Ivo8HrpiAUZ\n7PWQHY/QEeslIStFr8lAEY8S9QdxSiLYtAYybIV4BnpxBkTyBAn9njZ84QgWvQVJXM60ghwcPgcD\nvmGUBhmWbJFFCyp47IkvYLVaMZvNAKxff+lbQk90Dh4EoxEuYQh93FBaCgUFqaqaT5FkP275sDne\nmcyaVUkoFOK99w4Si0mBKHPmmpErbiEeUNLscjHgDZJXtBCtNkF+eT4lJakkv87OepqamlmyZDH3\nPvooW956i92NjUiBiFyON6QiJzMflzOKz9WDVbQSIYZcjKGUQFRmwiuJoNFqycrKwptM0r+tFyGj\niHy7jaysubSdPI3PN4AgJIknw4hIyJcJnE7GCQu5dA8PsUCViVKpprJyMRqNiqKiBLm5VmpqvKNl\nyhkZuUiltRjNNoqNAkpllKSYJMuk5qaFK4lkZHD3vfeOOmk9PT0MuiMcc8fZ33iI4vxM8ktLWHLL\nLcybP/+SjsuNN15PV9fLdHYGUKnMRCJeFAo3Dz+8gaef/vjjVCoV99xzA3/4w3sIQjZyuYpQaIji\nYg0LFy4AUuGd0tIcurq6sdkKsI0k22/c+BYzZ84jGEzS2hpBrc5FoVASjwvE4/3MmnUXTU3bgSiJ\nRAyp9KPvTSIRR6OR8+Uv38dLL72C398LzMZs1hKLeXA6XcRiYZRKCYWFRajVMpq7PLgH+jBJwiSN\nEZz+IAqlFJs6jgY9p71ymuN++sQYcjGBZ6RVXq7BgqhQEorHUWXpmTt3DoWFhQwPD1NXV0fNn/6E\nKIpjVuOlWVmEYUyeVCgSwa9SYbGMdayGvV6yz5LSH8+kszfNk8BjI09/Loriy+my5WwGBgbo7w9S\nUJByRGKxOC0trTQ19bF//xaGhpysWbOCjIwMTCYT05cu5fSuXcwqUrL1wBH8vhgRjYaoMEzUE6H1\nyA7CkQRJUYNOp8MVUZIpiZGIJUCpZUAI45PloBBktAeDqMxmQt1DZNiXEnAMIgsNkkgkCYaGkQgC\nSq2cqvW3kJGpJzvbgyIeoHX3ftzudjQKCMc0RCVqFBo1mTlWysvL8QW9BMLHmTarhM9/61tYrdYx\n5yyKIpFIJFVhNJGzGi8hV2uI5kMefBB+//urwxk5H4IgsGTJYubNm4vH40Gj0bB7936CQQ9mcy7H\namro7RsiFBzGOxRGLh2muHgGEokEiUQ2KnttNBq575FH8Pv9xGIxqqtr2b7bgSc6gKg34nCrCBFH\nhZQEEbQyGBRk5GVaCcdiqJNJBt1epEYj8xfPRKGQoddX0lR/imBchiBoMGhVqEUvnoQav5BHWBJG\nq4xxcMtL1NeVMHv+bGw2BQ8/fD+iKHLy5H8zNNRNZmY+SqWa/PwM/P4u/KIeSUJALgtgzwCvXM7N\nd945+pt2OBz85jevolSWsGrd1wkEvHR2nkRn07N8xYpLnhNmMpn4+tcfO6O0N/ec0t6PY8aMCp56\nKpsTJ+rx+YKUlq6krKxsjKT5bbet49ln/4eODjcqlZFIxIsodrJo0e34fG46O99AodAhCBCNdmG3\nz0MmU2E0WigocDA4eAKrdQ6CICCKIr29tdxxxxzKysp44IEN7N9/iqamOIFAP5CBVGohHnchCGHq\n6urJzzXjGuwhEJRAIkGZNI5QqKAyL5cSs5napn6csQCIGoR4EK3EgC4cZ1j0445GCPvdyDwD3Hv3\n9Vh54nTDAAAgAElEQVStVl7/4x/pOn4cNdDQ1kZfezvXL1qEXC6n3eGgYNEicu129u/dix6IiiJC\nRgZL77iD+u5upuXnI5NKcXq9tAWDbFix4uM+3nGHIIpiet5YEApFUewQBEEGHBBFccFZ/y+my7au\nri5+/et3sdsXIIoiO3bsoaWxG2JRIvFTLLnuegqL9HzjG49iMBhIJpMcPnSIza+9xrFdu9BqNPT7\nksQGHDjcTmKRKHqZlqRShVumIEIW6pgPpRAlGu0l02QigIGMjCLyc+w0dtejsuRSPn0FdXUdDLXt\nJOjxosSK0qjHmJ/Fo196lO7uYzzyyAoKCgrY9Oab7Hv/A9rbumkeTLB4+eeYOrWUhuPHCbtceHx9\nlJZL+cZffZupZ+mHNDY2smnTbhwOHwqFhOuum8PKlcsvuo/BpeLDC8J4QBRTiZ5vvw2foiJuQtHb\nCzNmpP5Nk/gmcGXH/ciRo7z22hGGekMkBgcQgJNtPhAi6LVB5qy6ieIpU+jsPMBf/MVdo7PtM3nr\nrU289adTDDU00dLWQrfTQSyaiVKMoRDC6DItzFy6kLwcP1PzTSSiUSIyGbu27GOKKR+v20PTgBd5\nViVdHdXEvKcREiqQq4gLMjItuUjCTczPUJMMBhkIhclcMJ9/+dWvRu0ZHBxk06ZtNDf3Iggwa1YJ\n8+dXcuLEKU7U1SEX40ydNo0Fy5aRn58/avs772yhutpNXt7YjOyOjoM8+eStV7Qh5qUa91AoRH39\nKQYGHGRnZzA87GTXrl7s9go2b36VYNDMwIAbv99JaWkZ8biHkpIQ3/rW4/zDP/yUnp44EomBaHSQ\nTEuMlQumYTKbmTZ3Lm++tZNNm2pxOm3I5RakUgkkuzCJpygyC8jw0edPoLVUkAi5KBUgEHQjWCRM\nyc7k+MlWuoN6kll5JGMhEt4+opEw/uAwZjlUTCnCNmMai267Fa1ez/Dhw8wcKRSIxeNsPnyYmF6P\nwWyh3x1DrTGh1SqZP7+cwkI7Go0Gm81GLBZj23vv0VhTg5BIoMvMZNWtt1JWVnYJRurSMTLm5/V4\n09kor2PkzwQfataOE7Kzs5HJQoTDQYaHXdTX1JGjMiCKESxGPbKuJmoHVHwwfTt33rkeiUTCoiVL\ncA4NkZVIcKjeiT4URqKKka9LYFLFcYpBDBmZ9Plj9EijWDJn4I/0My3LRnigjWF5gDVLsvBEvHil\nBpasvB+lUovD4cVkuof+/qP09fVhsBpYtnI+3d3HmDHDQnl5Sqjsvkce4fZ77yUej7N9+2727m1D\nJhOZt3gBXV3NlCp1/OVfPnHOUl5raysvvLAZi6WCggIL0WiY99+vx+8PcPvtt6RpBNJPTU1KoXTm\nzHRbcvnIy4N58+Ddd1MJrdcC06dPQy7fRmdrC3NspYiiiErZhcfXzbSC2dQfqwbBwdKlxWNu4mcy\ndWoxRlMTx4Ng1BajU1vodTkJxg1Y8kq46eblqNU+vvCFhykrKyMYDPKbH/+Y2yoK6e3y4nUFmK4x\n09y/H53ZgCzrTtzuBJFIAI3gIRZuZIE8zkyZDENWFr5QiJa2Nv7jRz/i+z//OZC6Rj366P2Ew2Ek\nEsmoGGFRURFLlixEKpWet39IR0c/BsO5DhbocDqdE7I7t1qtZv78jzrJBoNBGht/T1fXCUpKStm4\ncQvhsJHS0mmoVFoikSHASHd3Lz/84d/S2tpKd3cPDTWHmWM2U2C1EolGadyyhcJcCxaLSCjkGll5\nGcIQPMLSYjtqlQpP73FydHa640Hs5cvobqtFrzIS8TnRLChAIVOQ6JFQWHgLOl0mfr+DU0deoaqi\ngoVl2axYsQRRFDlYV8chj4fPzZs3ujoll8m4acEC3jh+AkfAgL1gFjpdSi9nx47jrFkjY926VGqB\nVCrllttvZ82NNxKNRtHpdBOu8nE85Ix8BXgz3UaciVKp5Pbbq3jllZ2cONaLOhYBZQCZdICybBuu\n/iEGBup58b96GWhtYt1dd1FaWopULmfviSYcwxn43H0ofUPMU6mRSBXIZUpMJugf7kenUpOdn0mV\n1Uq2wYhGM53qzk6SdhtVixczfdBDW1uEjAwrVVWL6ezsxmpdSF5eLStXTmfKlCwqK6dSXl4+JqSi\nUqlIJBKUl5fg87lpba0jmdSwenUZy5bdg9FoPOdct27dh9E4FYMh5aQoFCoKC2dz6NBeVq1aft5j\nrgU+DNFMsN/zp+bDUE26nRGPx0N7ezuQuqFeru+dSqViw4YbOF17kEGXDxBYWqHAnjWDfpefsMvJ\n5z//GNOnT//Yi3lZWRlW6xYEpYjaUkLY58KslWHTJjEYtITDjXzrW19HKpWyeeNGTp08Sai9nVVz\n5qBWNNDX14XRaKBIUNIuz6WkYi0+n5P6k7vJ1WYz2NtJkU6CZWS5Si6RMDMzk301NbS1tVF8Rh6A\n6oz2y21tbWx+7TUSHg9JUcSQl8ct99wzpitvTk4GJ058VGH0EcHRJNHxhNvtxuPxYDQaL9igMx6P\nc/r0afx+P5mZmTz++IPU1Z2gurqO4mI5Ol0WoujGZBIoKlpGe2s7//b9n3Pr8llE5XLkZjOVJhNl\nIytPSrmcecXF7O/s5NFH72Tz5gZCoSgB5wCzi8ooyi2mvb2eUCRBfoYJX9BNNB4jr2gup1pqcIWT\nzLXZ+Ml3vsOOHbv5zW/+hMtlJpkMUmKKUZpppnJmqjxXEARsRiM19fXIFi4cc15ymYzm1j4WVN0y\nOmZKpZrCwnns3r2P5cuXoNFoRvdXKpXjrgnixXLZnRFBEKzAH87a3CeK4oOCICwGbgLuPN+xzzzz\nzOjfVVVVVF3BOsS5c+dgsZj522/9DUFlN8VZRdgsJfSebkedTJKvkGOz6JkqlbLxhRe476tfpaWh\ngfbmNiQJPyFfNx5/P71yDRqNgazMDPIsRnqdQ0yxZrOyaiH27GxEUeRQQwtN/VFiViX+XQ2YTBLc\nbj86nQmt1sDUqaUYDG0sXLiUr33tsdFeA2fj8Xh44YVXGBhIIAhaRFGL1apl1arlY76wZ9LdPUBe\n3tgMTYlEikSix+VyXZPOiCimFEr/+Md0W3L5uftu+Pa3YXgYLkMjzovmxz9+jmQydbEVhO2sX7+c\nxYsXfsJRn43S0lLmzqugUq9HKZePNoPsdzopscz9xHJ2qVTKmjXX0djoJhpNIAhGCgtnk5dXQjgc\nRCZrwzE4yO7XXydfqUQyNETn0aM8d/gkGdkFhEIicnmAZFxErpTj8/Uz3HmATLGTmC9GKBQkIaQu\nzUlRJJhMYjObUQ0NMTg4OMYZ+ZDh4WHeev55ZhgMmO12AHodDl557jm+9NRTo07L4sVzqa19hUDA\njFab6oszMNBOdrb0sqprflpisRhvvbWJ2tpWJBIdohhg7twp3H77zeckKg8PD/P886/gdEoANeCl\ntNTMgw9uwGbLY3Awjt3+0Xep5sBBkoPDZCk1LLTZiMRi/Oatt1g7e/aY1xUEAaMgUFBeQm+vH71+\nOs3HdiLraaXx6E4kCT/xiI++4S7kKh0uzyAxVzfZkTAl2VmYXC7+9NJLPPDEE6xYsYzduw9y6lQj\nYnOU1UsWjlZyAqiUSlAo8AWD6M+4Vg97vYQSUiyWsRLsUqkMUI/mQl0NXHZnRBTFAeD6s7cLgpAP\n/Bi4/eOSQ850RtJBYWEht6+/gcPBNzFrTMQiMYRoFIVKSSjpY3pBHiadjjyfj7fffJNgWxtqFfja\njlEk09AvSElGQ6CU4w17GA5pyJ4yhZ5gkKwRL7+hs4s9dQ60pjlUVKxCJpPR29uC2RwmGq3H6RRJ\nJuOUleVw1133fKwjAvDmm5txuYwUFn5Ultvd3cCWLdu4667bzntMdrYFv989ujICqWTWRMJ/UUlm\nVyPHjqWa482dm25LLj9mM9x2G7z4IhesbrjcWK0LUShSN8xYLMJbb+2lsNB+WfpgqFQqFq9dy+G3\n3mJaVhZymYxBl4vWQIC7Hnjgol5DoVDgc7QijcmRKdVEQ1kAuFz9zJ+fza6332ZhXh4qhYJYPM4m\nX5wpskwkgpHcXA1DQ1763EOEtFORtG4jNxalrHQqCCLb+lup7Y+QJZEQFwQseXkERBGZwTCmdPNM\njtXWYgXMZ/xm8zIzGezooKmpiVmzZgGQn5/PI4/czJtvbsXpTCKKCUpKsrjrrnvHVeL6++9vp6bG\nQUHBdUgkEpLJJDU1x1Grt3PLLTeM2ffVVzcSDudQWGgf3dbScpwdO/awcuUyJJIwsVgUuVyB3+/H\n3d9HplIgw2BAEARUCgXFWVk0NzQw/awci7Aokpuby2OPFfHaa1voc50mdmI35Qo5Rr0ai8nA6f5W\nOrSZSEIhyhRq1CYT06dlsXj6dE739bF761buuPdeCgsL8fl8/PZHP0J2Vo+vLpeLdffey7ETJ5hq\nMmExGHB6vTS6XMyYM5Nw2I9G89HYJpMJRDE8xqGZ6KQzTPP3QDbw+shy6M2iKIbTaM95WbBiBW1H\njpDsdTA87CISceBIxMkttlI5osVh1GjYc+gQse5uLLEIsyx6YjE5apmE1rCHvHgYWUygYto0ZGo1\nCVGkoa+PLK2W96rrCQjFzFuwcDRhNC+vlI6OAb70pbuRyWQoFIpPXKHwer00N/dht183ZntubilH\nj+7j1luj521yV1W1iBdf3IZSOQ+lUj0ixXyKmTPt5405Xwu88grce+/VH6L5kMcfh699DZ56Kn3n\n/KEjAiCXK5HJcjhx4tRla8q1ZNky9EYj1Tt24B4cJK+4mA3XX4/dbv/EY4eHh9n2+utUquOEInHU\ngoaeul3s6ahnxtwS8vJKcVQnUI383hyeAFJjCcNBN6HBPkrKptLnH2YgoCUxeIxcuY6iwmIyMzPw\neh0sXzSLmlPHaFMomJ6bixfoCAQoXbbsY3M6XIODGM4zQ9bJZHhcrjHbysvL+V//qxSn04lcLr9g\n+CMdRCIRDh48ic22dHTyJZFIsNkqOHhwP2vWrBoNRTgcDrq6PBQUjE3uysubyoEDB7nhhtWsWbOA\nd96pISurnFAoSjTkJoyHxdMrRvefNW0ar23ZQjgaHR23AZeLmF7PlClTkMvlfOtbT9BUs5MhvQql\nQkGGRoMvEiGpkeCTBLFpLVizddjtmcyfl3L+Cq1WdtXVId5zD4IgoNfrWbF+PXvffJMchQKVXM5A\nIIC2tJS777mHzkWLOLB9e+r+kJfH+nvuweVy8+qrB7DZ5qBQqEgk4nR1nWThwtKrasKYzgTWr6Tr\nvT8NpaWlXP/AA+x7910s2k5aokMUFZWwasmS0Tpvh89HOJFAHwohV6koNZlwev2og0mighLdtDIi\nWg2GefNYV1VFYWEh9SdP0t3Whniii0UzricjY2yprUSiIhKJkJt7cfofqTJEyTlxbolESiIBiUQC\nSK16dHZ2MjAwgFarpaysjA0bQmzZsp9oVIooRpk3r5RbbrlK6z0/AVFMOSMvvZRuS64cq1ZBLAb7\n98OyZem2JoVMpiAUily21xcEgZkzZzLzM2QoH9q3jxxRZFnVStrbOmg93UmuKoo02cNddz2JXC7H\nFwzS0d6OIJEw5PZRbK/E43fT6epGIpNRcP3t5CTA2fke+aE4CmkIl6uVnBwT69bdhuFAJqe8Xhqk\nUjQGA+UVFdz/pS99bIVbTmEhpxsasI7oBn2IJxZj9lll/JAKNWVlZX3qc78ShMNhkkkpMtnYcIxM\nJieRkBIOh0edkVgshiCc+5lIpXKi0TjJZJIVK5ZjNhvZufMww8P9aAyD3LlkDjlnJPMr5HLKV66k\nemgIbTJJPJlEsFjY8NBDo2Ehj8eDp6uLu+bPp8PppNntRqbRMHPGjFTovqSEm2bMQKZQjK4yJRIJ\npGeN2YJFi8iz2aivqyMUCDDHbkcikVBXV4fdbueRJ58cs39KdiHK1q2HiMXkCEKEJUvKuemmtX/+\nhz2OGA8JrOOeJUuXMmv2bLq7u3n71VfJDoUwarUkk0m6h4YYVigoKiwk0NGBj9SFzmo2kmlMknRK\nKJ8/j1hBAQ8/8QQDAwNsfO01TtfX0+9w4O1u5EjvELqMfAoqFmOzl5NIxIHAxy7Jng+z2YzJpMDj\nGSYWixCNhtDrLcRiEez2DFQqFS6Xi3ffeAN3SwtGQSAMbNdq2fDYY/zN33wVt9uNWq2+qpb+Pi11\ndakb84IFn7zv1YIgpFZHfvvb9DkjZ4s7BYMDlJevuaTvEQ6nFl7PTPo8H/39/QwPD6PX67Hb7ec4\n+D2trZSZzUgkEoqKCwkKIk0tLfgG+tn05pvk2e0cqKkhIpejUShwuD0MCTFUBhuL166kuCS1otre\nfoiZ69ZgGhwkS69HJpOhVquJJxJ4gJKiImSiiNpiYfVtt41JRD2bWbNnc3T3btr7+ynIziYpirT0\n9SHLz6d0gjVW0ul06HRSgkHfmNBEMOhDr5eOuT5lZWWhVicJhfyo1ant0WgUh6OH6dOLRp2CyspK\nKkdq9Ddv3Ejn3r2YdTrUSiUOj4dmr5eHn3gCq9VKX18fCoWC/Pz8MWFxn8836gRV5ORQMbJqF08m\nOdHVhTori7d27kQai4FEgr2wEI1Ox4xVqxCEVNfdzs5ORFGkoKCAtTfdxJEjR3j2X/8VX1cXEkCT\nnc2ae+/lrnvvHf3eCYLA8uVLWbhwPh6PB61We9XkiZxJ2nRGPol06oxciEAgwM6tW2k4fJhkMknB\n1KlU3XQTB3fvpv7112lrakJ0OilWKklKpQS0WuQzZ3L7V79KTm4uv/7BD8gDApEIjtZWFJEI/cEk\nFus0ehMxsmatRCqLsXp1OVVVK+ju7iaZTGKz2T42S9rpdBIIBGhvb+cn//QT1GEJRqWaoXAAVa6J\nL3/jixw71szBg3V42lpZPbuERRVlKOVyBl0uOmQynvzWty6Yj3IlGA86I3/3dxCJwL/8S1rNuOIM\nDkJ5ObS1wZVetRcEgb/+619isaRCEC5XJxUVJh58cMMlyWNwOp28++5WGhq6EEUoL7dx661rzglD\nxmIx3nrtNXqOH8cgCIREEWVeHhsefng0TBoOh3n+l7/ENDREWWEhB44fZ6i5mRKDgQGfD1l+Poca\nGlizbBlNp05hTiSIRyK82TpA/sybWXvjnQgCnD59lIyMEDffvJqdr79OviiikctRqFRsra0l7PVy\n3223oVIocPv9nBga4ubHHjtHI+hMHA4HO7ZsoePUKQSJhPJ581i1du24nVxc6Pd+7FgdL7+8nYyM\naej1Fnw+J8PDDdx/fxVz5oxNND15sp7f//494nEL7e0D9PZ2I5UO8MADN3L//XefE+JOJBLs27OH\n2t27SYTDmHJyWHnTTRd02jweD52dnfz2Rz9CNzhIhcmEWi4nEo9zwuGgXasl22hE3tWFTa1GBhwZ\nGiKYn8/3f/5z/D4f7736KvpYDEQRn0zGgrVr+dX3v095PE6JxYJEEOj0eDgeifDtn/yE2Wcl1F4N\nXEhnZNIZ+YwkEgmSyeToEl53dzev/ud/UqRUUnfqFN3d3UgFAZ9Wy1/8/d+Tb7Pxg//zfzB1d5Oh\nUnGwtZXri4qwWq3UdXejzMxl2OOnX6vh6b/7K8xmE5v++EeU4TACEJDJWLdhAzPOWFYOBoO8/vo7\nNDT0AkpO1XzAbKOcfEsmXm8Ak0lPb8hPS1xLxcxbqd1/mByZFF+gH3t2iNuWpqSfD3Z2cvtXvnJe\ngacrSbqdkdSNKhWiWXh5CjnGNQ89lNId+fa3r+z7CoLAkSNHqK09hSiKzJ9fwcyZMy+J6F4oFOIX\nv3ieUCib7OyUmNTQUCdyeT/f+MYXxswwt3/wAW3btjHrjIaYp/v6iBUU8OBjj3Hw4CE2bdrL0JCf\n/iO7mJWbgdvjYL7ZTCAcJqRWYzCb6W1pQT1lCosqK+kcGCAcjdLvdqMom4HPH6eztQEjYSoK7AQT\nCY6ePk3M6UQVjeKNxwnHYvzlAw9gPKPU1uHx0K/T8ehXv/qJ5xyLxZBIJOMqIfV8fNLvvaGhgW3b\nDtDX5yA3N5PVq5cw7WN6M5w6dYp/+qefEwhoKSwsobCwDL/fidHo4Wtf+8I5FTgAyWSSeDx+3ly6\nD4lGo7z99maOHDmNIGhoOLYDS3gYk0qJJJEgnEjQPjyMRK2mNJHAqNUSUyhwxmIQChFJJLBUVOBw\nOrn/uuswarVASsL997t2EWpvZ/1IB94Pqe7pwXT99fz1d75zMR/jhGJcip5NdKRS6Zgfu81mY+0D\nD7DtzTfJKi3FWFyMxGxmw8MPY7FY+M2//itqj4eZOTmE43FyVSqcPT143W68bjdaUcSek4M6K4sp\nU4r5f//+71QajRhH4rqBcJj3//AHsr75zdHl2tdff4empih2+3J8Pid6UUfEHcU8xcCcOaklSc++\nQ3j7XBiXZxIKBvElk0gFDY0dQyye7ibLZEImCMTjn6w7FwqFqKmp5dixJuRyGYsXz2LmzJnj/qJ3\nsVRXp5rjXUshmjN5+ulU4u5TT1355nlz5sxhzpw5l/x16+tP4XYrKSwsGt2WnV1IZ6eXEydOsmhR\nyutMJpMc27ePhXl5Y8IyxTk57G1tpbq6mjfeOIDNtoi8PBUmYyFbNv4WZXc9UbMRiVpNxcKFRKNR\nsrVaTg8NoVGpmDaScNrY3U3hsgX4vV6kHceZmp1LptFIZ1sb+UNDyPPzUevNNHcP4Wk+RXVtLWvP\nkDLIMBg43tV1Ued8vhvvRGTatGkf63ycjcPhpKRkOXb79NFter2Zjo5ampubqaioOOeYM8XiPo7N\nmz+gtnYYu305EomErKxp7Hr/eVTaMFPsNuoaGjAYjbj6+kiKIgG/n7ZgEJvJxJLycnqcTojFCPX3\n09zRwYIRO9RKJUqvlyGXi6HBQdQaDTqtFgSBTKWSgZEGdynp/5McPFhHMBhm5swSFi6cP25Xu/4c\nrhlnJBaL4fP50Gq1l00UZmZlJeXTptHf349MJiMnJwdBEDh58iSaYJBMs5lgIIBGLicC+L1elIEA\neoOB4owMIuEwpxsbOXjgAOZ4fNSLBtCqVOTKZJw4dozV69YxPDzM4cNNBINWTp7cBQTRhCIYrHk0\nNbdTWJS6CAb9QaQSOe1tbTiHhoh4vZiVSgbCwxytq2PZwoUEZbJPTJQNh8P87ncv09srISOjgGAw\nzssv72fRok7uuuu2Caf2dz6eew6+8IVrp4rmbBYuBJsN3nzz6unJ098/hEplPme7Wm2mv98x+jyZ\nTJKIRlGedSMXBAG5ILB7dzVmc+lo1Y9ObyHXbCXq7GFueTkms5nBwUGGBQFHXz/tgoL/9/5+ymwZ\nzCi0MRyPkxOJ8Ny//AsL5XIcg4M0h8MMezzMKSzkD9XHyZlyPTr1DMIkePdgJ9m2VmaVprrduv1+\nTBfIGbnW6eoaQKtNjXMymSQUCiOXy5DJDAwMDHG2L+L1etm/v5rjx1tQq5UsWTKLOXNmj5lYhUIh\nqqsbsdmWjYawVSotVTd+ie7unax+aD0nvvtdpkSjBIxGTIEAaqmUEy4XMlEkKYrEAJUgUGo00nr6\nNHOnTUMqkeB0OvEMDDDkduPv7cUFyPV6CouKGPD5KPkwv2XzB+zc2YTZPAWFQsnWrZ0cOdLAk08+\ndNU5JOlNErgCiKLIwQMH+K8f/IDf//Sn/Of3vsfWLVsuaiXgsyCXy7Hb7eTm5o7eoCORCAqgvKyM\n9mAQqUSCVqulZ2QZT1CrUSoU9ESjzC0tpa66GvV5pqZqhQK/xwNAS0sLNTWn6e8XkcvzicetNPQ4\ncLo9BALh0eXPmCAiqPS0HDvGnKIiBL2eiCCglEZob2piW2Mj199xxyc6aHV1x+npgcLCSnQ6E0Zj\nJsXFCzh8uJ3e3t5L+yGmgXAY/ud/4NFH021Jenn6afjZz9JtxaUjK8tCJOI5Z3s47CEr66NqCplM\nRu6UKfQ7nWP2C4TDxJVKYjERtfqjZMqulqNMN2WhMGUxGAohEQRyzGb6O7qocUQISksJhm3sPOrn\n3zduJ3P6dI5s306mVEoyEqGvp4fwwACO9nYO1jehEAwYtRmYdEZycwpIJrPZdayDaCxGMBymfnCQ\nJatXX74PaoKTm5tBMOimt7eXne+9x8EP3mfnu+9Sf7warXZs4yW/38+vf/0Se/b0o1BMJxy28cor\nh/jTnzaN2S8YDALyEYGxj1AoVMjlGhQKBYH+fqZbLJTl5NCVSBCMx7HI5YR8PvqdTuRmM4XFxURE\nEeJxYvFUhU9ddTVmgwF9bi498TgKmYyA00l1UxNDJhPr77wTh8PBnj0nKSpaiNmcjVZrpKCgAqdT\nw+HDtZf7I73iXPXOSO3hw1S/+SbzTCaW2u0stVo5vWMHW7dsuWI25Obm4gYKsrMpmz2bWp+PiFRK\nu0TCEaWSgNlMXShE6ezZzC0rQxBFhqPRc15nKBCgoCQ1U6qtrUcmU6DTGZHJ5Oj1GZiLVrKn6SQo\npATCYVr7+sCWgyhPII+F0Ks1TC8rJaoTUJohs6SE4rlzmX0Ry+OnTrVhNI5dPREEAYnEQldX9yX5\nnNLJG2/A/PlwETITVzV33gnd3XDoULotuTTMmFGBWu3D4fjIYR4e7kOpdDNz5tjp8sobbqAlFKK9\nv59AOEzf8DBH+vpYeeutlJYW4HYPju4b9AyhU2uYkptLKCuLGpeLQ729HHf7Kau6m0Vr70TMtGKe\nMovM0hUkBAlmUcQfizHY30+hSsUUvZ4ilYqO7gECyFArUyuh1sxMFHk2uoMC7zc2ctTnY/k993ym\nMuRrhTlzZuH1tlC78wPyZDKmmMxkyBLIvC001tWN2bem5ghutw67PdWrRq83U1Q0f2Ry1z+6n9Fo\nRKUSiURCY44PBDyYTCr0Iwq+8XicDI2G6cXFdEuldMbjdMfjyHJymLd4MdnZ2UQNBpyxGKFIhM7e\nXtr6+zEXFvLF++4jXFBAHdAgl3NCLufbP/wheXl5I5M8ExLJ2DC4xZLHyZOtl+ujTBtXdZhGFB/0\nkiYAACAASURBVEUObt/OzJycUclnuUzGrIIC9h04wHVVVWjPCIVcLnJzcymaP5/Dhw5RkpVF3qpV\n7Dp+nAKplC/dcQeiRIJOrUYmlXK6r4+K2bPxOJ3UnT7NFKsViSDQPjiIaLUyvaKCRCJBd7eD2bPn\n0NBwAr2+FIVCi1ZvZTjDiqqynMZEAtvcuXxt2TI2b9rEOy+8Rr/LiUCcZTMNrJh1N06fj+RFyr1r\ntSpisXN1H0Qxilp94VLJicDPfw7/+3+n24r0I5OlElj/7/+FP/0p3db8+Wi1Wh5//D7eeGMznZ2t\ngEBenpG77rr3HMEom83GA1/7GtV799LQ3o7JZmP98uVMmTKFvPx8jh9/maEhGRkZecg1Rro66pgz\nPZ9582Yz7PXS0nqahpCa6TOXYTRmUjCSL+J2D9HWdoKMcBi1RIJXLmc4EsGsUCDT6Rh0DUBMRVIU\ncfl9OKJRqm66Gb+/kXsevYGpU6de8Q7aEw2z2UyZXUeo9RRuvweRJPmZKu6tWkl9Wxv9/f2jAnoN\nDe2YzWMnVqkwjJG+vr7R/WQyGevWLeH11w+QlTUdvd6Mx+PA6WzgoYfWYrFYyC0vp/30aXLUarK1\nWlR2O2GJBL9USkF5ORKpNLVCUlzMTRs20Dk4iEMQ0JWWsmbRIhRyOV+4+24cHg/haJSOZHLU6VQo\nFAjCuSv40WgYi+XqK+29qr/h0WiUsNeLvqBgzHaZVIoKRnNIrgS33HEHdUVF1B08SCQcZvXDD9Ny\n6hT9TielublIJRIcHg89iQQPLF+OyWTi4P79nKyuJhGPYywtJddioebwYcqnTUOjUWK1VqDRaGlq\nOoXLFcJiyWDp0ll85emnxpQtXr96NY6TJ5mRlYVKLkczorPQ09ND1Uhs8pOYP7+Smpq3icdzRsWI\nAgEvCoV3wukYnM3+/anS1jvuSLcl44MnnoAf/ABqa1PVNRMdq9XKV77yKG63G+CCiqNWq5Xb7r77\nnO3Z2dl8+cv38f77u2lq2oElV0ZMNFAwJeVwGLVaAvEYsswcjMax+kCRSJDCQhutgx1kq9XYpk7l\neHc3NU4noViM0jkzqHdATzSGISuL+aWlxOMBCgqMF2zYN8lZxGI8csNyYvE4kpEJHoDW6cTj8Yw6\nGXq9huHhIHr92blEsXM0aBYtWohKpWLbtoN0dh4hNzeT9etvHE2sXbdhA9WvvUYwEMATDKKwWJhq\ntzN97Vr6enrYdfIkSo2G5TfcQFVVFQqFgkgkwi9/+ENiiQQKuRypRILVbKaxu5uKxYtH37u4uBi1\n+n28Xudou45EIo7b3cYdd1xaDZ7xQNpKewVB+DzwJUAJ/FoUxd+d9f9/dmmvKIr86ic/YapEMiYZ\nNJ5IsL+/ny//zd+gVqsv8AqXl0AgwNZNm2g9dgxBFDHk5LBm/foxks+xWIzXX34ZZ0MDmQoF0WSS\nIVHENKWUxqYYRUVzRkvk+vvbsNujfPGLD53zXlveeYfmPXuw63RIBIEen4+MGTO4+4EHLroaZteu\nPbz3XjVgBuIoFAEefPDWS+aMpKu09447YO1a+MY3rvhbj1v+/d/hgw+uzOpIuku6Py0fCrQ1Nzez\nfeNGQsPDJCUSCioqOFbfg8k0Z7TDaiwWoafnMF/+8h001Nfzwj//MyUyGT0DA+jicRRqNXGzmSaJ\ngtlLb0MiMSEIETIyBD7/+Q1XdUuGSz3ur7z4IsquLvLPEIsURZF9HR187qmnsI4o0ba0tPDss5uw\n2xeOTqy83mGi0Sa+/e0nPjZ/7mxhPkhJPOzdtYuju3dDLIZErWbJ2rXk2Wy89txz6EMh9AoFrkgE\nMSuLz33xixgMBk4cP84Hf/gDeQoFWqWSIb+fiNnMA088MaZ7cmdnJy+++CdCIRUgRxTdrFpVybp1\nqyekkzoudUYEQZCJohgXBEECHBJFccFZ/39JdEaOHT3KzpdfZnZeHjq1mnA0yonubsrWrGH1uvEh\neR4Oh4nFYuh0unO+YNWHDnH0jTeYd0anzkA4zOGhIazTKqmv70MiMZBMhrDZ1Dz00IbztgIXRZHW\n1lZOHTtGPBajfNYsysvLP3VZrsfjoaurC5lMRlFR0SeqWX4a0nFT2rsXHnwQGhvhEp7KhCcchpKS\nVGXN5dZcmWjOyJmIoojf70ehUKBUKmlvb+ell94mFFICUiQSH7feuozFixcB8Mtf/IJ3/uM/WGYw\nkJ2ZiUKtptfnI15QwNIHHiArKwuNRkNRUdFVUzL/cVzqce/s7OT1X/6SGRYLFoOBWDxOQ08PuooK\n7nlo7ARt9+69vPfeIUTRCMTQ6WI8/PCdn1lrKRaLEQqF0Gq1SCQSfveLX5ATCIyRnG/u6UE7axbr\nN2wAoK+vj+NHjuBzubCXljKzsvK8yqrRaJS2tjai0Sj5+flYznjNica4dEZGDRAENbBZFMVVZ22/\nZKJnR2pr2ff++8T9fgSFgjkrVrB8xYoJ8WN/4T//k/xQaEw3ToCjHR0se/hhMjMzcTgc6HQ6bDbb\nBb1lt9uNKIqYTKZx6VVf6ZtSMgnXXQdPPpkq6Z1kLM8+m3rs3Xt5y50nsjNyPmKxGJ2dncTjcWw2\n25hQcG1tLX/68Y/R+nyQSCBTqymdORNBqaRfp+ORJ5+8pA7+eOZyjHtzczM73nmHgMOBKJVSsWgR\nVWvXnne1w+fz0dvbi1wup6Cg4JLl5TgcDn7/s5+x/Kz0gHgiwd6+Pr75ne8QCoWIRqOYR9oKXCuM\nW9EzQRC+AzwB/N3lfJ+58+Yxe84cgsEgKpUq7clggUCAvTt3cvLwYQAq5s9n+apV560bTyST53Uc\nPvwhZ2VlfWLDq4GBATa/8Qau7m4EwJiXx4133XXRTfiuVn7+c5BK4ZFH0m3J+OSxx+CXv0wp0j78\ncLqtmTjI5XJKRqrezkYqlVJcXEyFzUY8FkOuUHC6pYXj+/bRr1QSGB6mctkyqtau/VSTpYaGBg5u\n385Qby+ZubksWb36ogXDribKysoofeopAoEACoXigqJmer2e8rPUTz8rAwMD7N22jbZTp4gDru5u\nkjbbGEdDEATC4TCvvvQSfc3NyAUBqcHA6ttvv2R2TGQuu0smCIJVEITtZz1eBhBF8btACfC4IAiX\nVcFFIpGg0+nS7ohEo1H++NxzDO7fz+KMDJZkZOA4cIA//O53RCLnVqtMnzePdodjzLZwNIpXIvnY\nduJnEggEePV3v8PidLKioIDrCgrI8np55dln8fl8l+y8Jhp798L3vpcSOpsAC2RpQSJJ5Y789V/D\n8HC6rbk6KCwsxC2RkEgmUapUtLe10X3iBFJB4PrZs1litdK6Ywc7Pvjgol+z7tgxtrzwAjl+P1U2\nG7mBAFuef55jR49exjMZvwiCgE6n+0R11UuFw+HgD7/8JbS0sCIvj8UWCwNdXezZt2/Mfh39/fQ7\nHAhtbVxns7HUbmeqVMqm//5venp6roit45nL7oyIojggiuL1Zz0eEAThw29KDEgC50z/n3nmmdHH\njh07LrepV4TGxkYSvb1Mt9tRyuUo5HKm2e0wMEBjY+M5+8+bPx9FcTE17e10Dw3R0tNDdV8fK++4\n46IqgU7V16MNBMg7I6krx2LBHIlw4vjxS3puE4XqatiwAf77v2GCFwJddpYsgc99Dr785VTvnkn+\nPEwmE0tuuYXqnh6ae3qoqa3FmUigysuj1GZLSQ/Y7Rzft2+0y/CFSCaT7Nm8mdlWK5lGI4IgkGk0\nMic3lz1btpBIJK7AWV3bHNq3j1yg0GpFKpGg12jYsHYt1e3t1DQ20uNwcLyzk+ZIhAKTidK8vNEV\nE5NOR6FKxeGzHJdrkXQuE/ytIAhVpKpp/iCK4jnT9GeeeeZK23TZ6e3oIPM8FTyZajW97e3MmjVr\nzHalUsn9X/gCDQ0NdDQ3k6XTsbKy8qJDLM6hIQzniZcaVCqcAwOf7SQmKPE4/Nd/wXe/m8qFuOmm\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luroFURRRlDwTE+1cdlkFb7xxknRKxGi0YTIbaGx0cPfdX/pY1lH5pP9RUhSFn/zk/5BO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BaDQabrr9dqp7euizWvEFArT399PgdGLKyOCDrVs5sH8/DodjzHaSJNHW1sbJkyfp7e4e9yJQ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S5dN9iyZT8PP/wEx47d8IYo2UgJ/+2/we///pXek++HEIk6cvw4vOMdV3pvXjtc0UrZK43z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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "X, y = sklearn.datasets.make_classification(\n", - " n_samples=10000, n_features=4, n_redundant=0, n_informative=2, \n", - " n_clusters_per_class=2, hypercube=False, random_state=0\n", - ")\n", - "\n", - "# Split into train and test\n", - "X, Xt, y, yt = sklearn.cross_validation.train_test_split(X, y)\n", - "\n", - "# Visualize sample of the data\n", - "ind = np.random.permutation(X.shape[0])[:1000]\n", - "df = pd.DataFrame(X[ind])\n", - "_ = pd.scatter_matrix(df, figsize=(9, 9), diagonal='kde', marker='o', s=40, alpha=.4, c=y[ind])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Learn and evaluate scikit-learn's logistic regression with stochastic gradient descent (SGD) training. Time and check the classifier's accuracy." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy: 0.781\n", - "Accuracy: 0.781\n", - "Accuracy: 0.781\n", - "Accuracy: 0.781\n", - "1 loop, best of 3: 372 ms per loop\n" - ] - } - ], - "source": [ - "%%timeit\n", - "# Train and test the scikit-learn SGD logistic regression.\n", - "clf = sklearn.linear_model.SGDClassifier(\n", - " loss='log', n_iter=1000, penalty='l2', alpha=5e-4, class_weight='auto')\n", - "\n", - "clf.fit(X, y)\n", - "yt_pred = clf.predict(Xt)\n", - "print('Accuracy: {:.3f}'.format(sklearn.metrics.accuracy_score(yt, yt_pred)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Save the dataset to HDF5 for loading in Caffe." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Write out the data to HDF5 files in a temp directory.\n", - "# This file is assumed to be caffe_root/examples/hdf5_classification.ipynb\n", - "dirname = os.path.abspath('./examples/hdf5_classification/data')\n", - "if not os.path.exists(dirname):\n", - " os.makedirs(dirname)\n", - "\n", - "train_filename = os.path.join(dirname, 'train.h5')\n", - "test_filename = os.path.join(dirname, 'test.h5')\n", - "\n", - "# HDF5DataLayer source should be a file containing a list of HDF5 filenames.\n", - "# To show this off, we'll list the same data file twice.\n", - "with h5py.File(train_filename, 'w') as f:\n", - " f['data'] = X\n", - " f['label'] = y.astype(np.float32)\n", - "with open(os.path.join(dirname, 'train.txt'), 'w') as f:\n", - " f.write(train_filename + '\\n')\n", - " f.write(train_filename + '\\n')\n", - " \n", - "# HDF5 is pretty efficient, but can be further compressed.\n", - "comp_kwargs = {'compression': 'gzip', 'compression_opts': 1}\n", - "with h5py.File(test_filename, 'w') as f:\n", - " f.create_dataset('data', data=Xt, **comp_kwargs)\n", - " f.create_dataset('label', data=yt.astype(np.float32), **comp_kwargs)\n", - "with open(os.path.join(dirname, 'test.txt'), 'w') as f:\n", - " f.write(test_filename + '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's define logistic regression in Caffe through Python net specification. This is a quick and natural way to define nets that sidesteps manually editing the protobuf model." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from caffe import layers as L\n", - "from caffe import params as P\n", - "\n", - "def logreg(hdf5, batch_size):\n", - " # logistic regression: data, matrix multiplication, and 2-class softmax loss\n", - " n = caffe.NetSpec()\n", - " n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)\n", - " n.ip1 = L.InnerProduct(n.data, num_output=2, weight_filler=dict(type='xavier'))\n", - " n.accuracy = L.Accuracy(n.ip1, n.label)\n", - " n.loss = L.SoftmaxWithLoss(n.ip1, n.label)\n", - " return n.to_proto()\n", - "\n", - "train_net_path = 'examples/hdf5_classification/logreg_auto_train.prototxt'\n", - "with open(train_net_path, 'w') as f:\n", - " f.write(str(logreg('examples/hdf5_classification/data/train.txt', 10)))\n", - "\n", - "test_net_path = 'examples/hdf5_classification/logreg_auto_test.prototxt'\n", - "with open(test_net_path, 'w') as f:\n", - " f.write(str(logreg('examples/hdf5_classification/data/test.txt', 10)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we'll define our \"solver\" which trains the network by specifying the locations of the train and test nets we defined above, as well as setting values for various parameters used for learning, display, and \"snapshotting\"." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from caffe.proto import caffe_pb2\n", - "\n", - "def solver(train_net_path, test_net_path):\n", - " s = caffe_pb2.SolverParameter()\n", - "\n", - " # Specify locations of the train and test networks.\n", - " s.train_net = train_net_path\n", - " s.test_net.append(test_net_path)\n", - "\n", - " s.test_interval = 1000 # Test after every 1000 training iterations.\n", - " s.test_iter.append(250) # Test 250 \"batches\" each time we test.\n", - "\n", - " s.max_iter = 10000 # # of times to update the net (training iterations)\n", - "\n", - " # Set the initial learning rate for stochastic gradient descent (SGD).\n", - " s.base_lr = 0.01 \n", - "\n", - " # Set `lr_policy` to define how the learning rate changes during training.\n", - " # Here, we 'step' the learning rate by multiplying it by a factor `gamma`\n", - " # every `stepsize` iterations.\n", - " s.lr_policy = 'step'\n", - " s.gamma = 0.1\n", - " s.stepsize = 5000\n", - "\n", - " # Set other optimization parameters. Setting a non-zero `momentum` takes a\n", - " # weighted average of the current gradient and previous gradients to make\n", - " # learning more stable. L2 weight decay regularizes learning, to help prevent\n", - " # the model from overfitting.\n", - " s.momentum = 0.9\n", - " s.weight_decay = 5e-4\n", - "\n", - " # Display the current training loss and accuracy every 1000 iterations.\n", - " s.display = 1000\n", - "\n", - " # Snapshots are files used to store networks we've trained. Here, we'll\n", - " # snapshot every 10K iterations -- just once at the end of training.\n", - " # For larger networks that take longer to train, you may want to set\n", - " # snapshot < max_iter to save the network and training state to disk during\n", - " # optimization, preventing disaster in case of machine crashes, etc.\n", - " s.snapshot = 10000\n", - " s.snapshot_prefix = 'examples/hdf5_classification/data/train'\n", - "\n", - " # We'll train on the CPU for fair benchmarking against scikit-learn.\n", - " # Changing to GPU should result in much faster training!\n", - " s.solver_mode = caffe_pb2.SolverParameter.CPU\n", - " \n", - " return s\n", - "\n", - "solver_path = 'examples/hdf5_classification/logreg_solver.prototxt'\n", - "with open(solver_path, 'w') as f:\n", - " f.write(str(solver(train_net_path, test_net_path)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Time to learn and evaluate our Caffeinated logistic regression in Python." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy: 0.770\n", - "Accuracy: 0.770\n", - "Accuracy: 0.770\n", - "Accuracy: 0.770\n", - "1 loop, best of 3: 195 ms per loop\n" - ] - } - ], - "source": [ - "%%timeit\n", - "caffe.set_mode_cpu()\n", - "solver = caffe.get_solver(solver_path)\n", - "solver.solve()\n", - "\n", - "accuracy = 0\n", - "batch_size = solver.test_nets[0].blobs['data'].num\n", - "test_iters = int(len(Xt) / batch_size)\n", - "for i in range(test_iters):\n", - " solver.test_nets[0].forward()\n", - " accuracy += solver.test_nets[0].blobs['accuracy'].data\n", - "accuracy /= test_iters\n", - "\n", - "print(\"Accuracy: {:.3f}\".format(accuracy))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Do the same through the command line interface for detailed output on the model and solving." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "I0224 00:32:03.232779 655 caffe.cpp:178] Use CPU.\n", - "I0224 00:32:03.391911 655 solver.cpp:48] Initializing solver from parameters: \n", - "train_net: \"examples/hdf5_classification/logreg_auto_train.prototxt\"\n", - "test_net: \"examples/hdf5_classification/logreg_auto_test.prototxt\"\n", - "test_iter: 250\n", - "test_interval: 1000\n", - "base_lr: 0.01\n", - "display: 1000\n", - "max_iter: 10000\n", - "lr_policy: \"step\"\n", - "gamma: 0.1\n", - "momentum: 0.9\n", - "weight_decay: 0.0005\n", - "stepsize: 5000\n", - "snapshot: 10000\n", - "snapshot_prefix: \"examples/hdf5_classification/data/train\"\n", - "solver_mode: CPU\n", - "I0224 00:32:03.392065 655 solver.cpp:81] Creating training net from train_net file: examples/hdf5_classification/logreg_auto_train.prototxt\n", - "I0224 00:32:03.392215 655 net.cpp:49] Initializing net from parameters: \n", - "state {\n", - " phase: TRAIN\n", - "}\n", - "layer {\n", - " name: \"data\"\n", - " type: \"HDF5Data\"\n", - " top: \"data\"\n", - " top: \"label\"\n", - " hdf5_data_param {\n", - " source: \"examples/hdf5_classification/data/train.txt\"\n", - " batch_size: 10\n", - " }\n", - "}\n", - "layer {\n", - " name: \"ip1\"\n", - " type: \"InnerProduct\"\n", - " bottom: \"data\"\n", - " top: \"ip1\"\n", - " inner_product_param {\n", - " num_output: 2\n", - " weight_filler {\n", - " type: \"xavier\"\n", - " }\n", - " }\n", - "}\n", - "layer {\n", - " name: \"accuracy\"\n", - " type: \"Accuracy\"\n", - " bottom: \"ip1\"\n", - " bottom: \"label\"\n", - " top: \"accuracy\"\n", - "}\n", - "layer {\n", - " name: \"loss\"\n", - " type: \"SoftmaxWithLoss\"\n", - " bottom: \"ip1\"\n", - " bottom: \"label\"\n", - " top: \"loss\"\n", - "}\n", - "I0224 00:32:03.392365 655 layer_factory.hpp:77] Creating layer data\n", - "I0224 00:32:03.392382 655 net.cpp:106] Creating Layer data\n", - "I0224 00:32:03.392395 655 net.cpp:411] data -> data\n", - "I0224 00:32:03.392423 655 net.cpp:411] data -> label\n", - "I0224 00:32:03.392442 655 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/hdf5_classification/data/train.txt\n", - "I0224 00:32:03.392473 655 hdf5_data_layer.cpp:93] Number of HDF5 files: 2\n", - "I0224 00:32:03.393473 655 hdf5.cpp:32] Datatype class: H5T_FLOAT\n", - "I0224 00:32:03.393862 655 net.cpp:150] Setting up data\n", - "I0224 00:32:03.393884 655 net.cpp:157] Top shape: 10 4 (40)\n", - "I0224 00:32:03.393894 655 net.cpp:157] Top shape: 10 (10)\n", - "I0224 00:32:03.393901 655 net.cpp:165] Memory required for data: 200\n", - "I0224 00:32:03.393911 655 layer_factory.hpp:77] Creating layer label_data_1_split\n", - "I0224 00:32:03.393924 655 net.cpp:106] Creating Layer label_data_1_split\n", - "I0224 00:32:03.393934 655 net.cpp:454] label_data_1_split <- label\n", - "I0224 00:32:03.393945 655 net.cpp:411] label_data_1_split -> label_data_1_split_0\n", - "I0224 00:32:03.393956 655 net.cpp:411] label_data_1_split -> label_data_1_split_1\n", - "I0224 00:32:03.393970 655 net.cpp:150] Setting up label_data_1_split\n", - "I0224 00:32:03.393978 655 net.cpp:157] Top shape: 10 (10)\n", - "I0224 00:32:03.393986 655 net.cpp:157] Top shape: 10 (10)\n", - "I0224 00:32:03.393995 655 net.cpp:165] Memory required for data: 280\n", - "I0224 00:32:03.394001 655 layer_factory.hpp:77] Creating layer ip1\n", - "I0224 00:32:03.394012 655 net.cpp:106] Creating Layer ip1\n", - "I0224 00:32:03.394021 655 net.cpp:454] ip1 <- data\n", - "I0224 00:32:03.394029 655 net.cpp:411] ip1 -> ip1\n", - "I0224 00:32:03.394311 655 net.cpp:150] Setting up ip1\n", - "I0224 00:32:03.394323 655 net.cpp:157] Top shape: 10 2 (20)\n", - "I0224 00:32:03.394331 655 net.cpp:165] Memory required for data: 360\n", - "I0224 00:32:03.394348 655 layer_factory.hpp:77] Creating layer ip1_ip1_0_split\n", - "I0224 00:32:03.394358 655 net.cpp:106] Creating Layer ip1_ip1_0_split\n", - "I0224 00:32:03.394366 655 net.cpp:454] ip1_ip1_0_split <- ip1\n", - "I0224 00:32:03.394374 655 net.cpp:411] ip1_ip1_0_split -> ip1_ip1_0_split_0\n", - "I0224 00:32:03.394386 655 net.cpp:411] ip1_ip1_0_split -> ip1_ip1_0_split_1\n", - "I0224 00:32:03.394395 655 net.cpp:150] Setting up ip1_ip1_0_split\n", - "I0224 00:32:03.394404 655 net.cpp:157] Top shape: 10 2 (20)\n", - "I0224 00:32:03.394424 655 net.cpp:157] Top shape: 10 2 (20)\n", - "I0224 00:32:03.394443 655 net.cpp:165] Memory required for data: 520\n", - "I0224 00:32:03.394450 655 layer_factory.hpp:77] Creating layer accuracy\n", - "I0224 00:32:03.394462 655 net.cpp:106] Creating Layer accuracy\n", - "I0224 00:32:03.394479 655 net.cpp:454] accuracy <- ip1_ip1_0_split_0\n", - "I0224 00:32:03.394489 655 net.cpp:454] accuracy <- label_data_1_split_0\n", - "I0224 00:32:03.394497 655 net.cpp:411] accuracy -> accuracy\n", - "I0224 00:32:03.394510 655 net.cpp:150] Setting up accuracy\n", - "I0224 00:32:03.394536 655 net.cpp:157] Top shape: (1)\n", - "I0224 00:32:03.394543 655 net.cpp:165] Memory required for data: 524\n", - "I0224 00:32:03.394551 655 layer_factory.hpp:77] Creating layer loss\n", - "I0224 00:32:03.394562 655 net.cpp:106] Creating Layer loss\n", - "I0224 00:32:03.394569 655 net.cpp:454] loss <- ip1_ip1_0_split_1\n", - "I0224 00:32:03.394577 655 net.cpp:454] loss <- label_data_1_split_1\n", - "I0224 00:32:03.394587 655 net.cpp:411] loss -> loss\n", - "I0224 00:32:03.394603 655 layer_factory.hpp:77] Creating layer loss\n", - "I0224 00:32:03.394624 655 net.cpp:150] Setting up loss\n", - "I0224 00:32:03.394634 655 net.cpp:157] Top shape: (1)\n", - "I0224 00:32:03.394641 655 net.cpp:160] with loss weight 1\n", - "I0224 00:32:03.394659 655 net.cpp:165] Memory required for data: 528\n", - "I0224 00:32:03.394665 655 net.cpp:226] loss needs backward computation.\n", - "I0224 00:32:03.394673 655 net.cpp:228] accuracy does not need backward computation.\n", - "I0224 00:32:03.394682 655 net.cpp:226] ip1_ip1_0_split needs backward computation.\n", - "I0224 00:32:03.394690 655 net.cpp:226] ip1 needs backward computation.\n", - "I0224 00:32:03.394697 655 net.cpp:228] label_data_1_split does not need backward computation.\n", - "I0224 00:32:03.394706 655 net.cpp:228] data does not need backward computation.\n", - "I0224 00:32:03.394712 655 net.cpp:270] This network produces output accuracy\n", - "I0224 00:32:03.394721 655 net.cpp:270] This network produces output loss\n", - "I0224 00:32:03.394731 655 net.cpp:283] Network initialization done.\n", - "I0224 00:32:03.394804 655 solver.cpp:181] Creating test net (#0) specified by test_net file: examples/hdf5_classification/logreg_auto_test.prototxt\n", - "I0224 00:32:03.394836 655 net.cpp:49] Initializing net from parameters: \n", - "state {\n", - " phase: TEST\n", - "}\n", - "layer {\n", - " name: \"data\"\n", - " type: \"HDF5Data\"\n", - " top: \"data\"\n", - " top: \"label\"\n", - " hdf5_data_param {\n", - " source: \"examples/hdf5_classification/data/test.txt\"\n", - " batch_size: 10\n", - " }\n", - "}\n", - "layer {\n", - " name: \"ip1\"\n", - " type: \"InnerProduct\"\n", - " bottom: \"data\"\n", - " top: \"ip1\"\n", - " inner_product_param {\n", - " num_output: 2\n", - " weight_filler {\n", - " type: \"xavier\"\n", - " }\n", - " }\n", - "}\n", - "layer {\n", - " name: \"accuracy\"\n", - " type: \"Accuracy\"\n", - " bottom: \"ip1\"\n", - " bottom: \"label\"\n", - " top: \"accuracy\"\n", - "}\n", - "layer {\n", - " name: \"loss\"\n", - " type: \"SoftmaxWithLoss\"\n", - " bottom: \"ip1\"\n", - " bottom: \"label\"\n", - " top: \"loss\"\n", - "}\n", - "I0224 00:32:03.394953 655 layer_factory.hpp:77] Creating layer data\n", - "I0224 00:32:03.394964 655 net.cpp:106] Creating Layer data\n", - "I0224 00:32:03.394973 655 net.cpp:411] data -> data\n", - "I0224 00:32:03.394984 655 net.cpp:411] data -> label\n", - "I0224 00:32:03.394994 655 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/hdf5_classification/data/test.txt\n", - "I0224 00:32:03.395009 655 hdf5_data_layer.cpp:93] Number of HDF5 files: 1\n", - "I0224 00:32:03.395937 655 net.cpp:150] Setting up data\n", - "I0224 00:32:03.395953 655 net.cpp:157] Top shape: 10 4 (40)\n", - "I0224 00:32:03.395963 655 net.cpp:157] Top shape: 10 (10)\n", - "I0224 00:32:03.395970 655 net.cpp:165] Memory required for data: 200\n", - "I0224 00:32:03.395978 655 layer_factory.hpp:77] Creating layer label_data_1_split\n", - "I0224 00:32:03.395989 655 net.cpp:106] Creating Layer label_data_1_split\n", - "I0224 00:32:03.395997 655 net.cpp:454] label_data_1_split <- label\n", - "I0224 00:32:03.396005 655 net.cpp:411] label_data_1_split -> label_data_1_split_0\n", - "I0224 00:32:03.396016 655 net.cpp:411] label_data_1_split -> label_data_1_split_1\n", - "I0224 00:32:03.396028 655 net.cpp:150] Setting up label_data_1_split\n", - "I0224 00:32:03.396036 655 net.cpp:157] Top shape: 10 (10)\n", - "I0224 00:32:03.396044 655 net.cpp:157] Top shape: 10 (10)\n", - "I0224 00:32:03.396051 655 net.cpp:165] Memory required for data: 280\n", - "I0224 00:32:03.396059 655 layer_factory.hpp:77] Creating layer ip1\n", - "I0224 00:32:03.396069 655 net.cpp:106] Creating Layer ip1\n", - "I0224 00:32:03.396075 655 net.cpp:454] ip1 <- data\n", - "I0224 00:32:03.396085 655 net.cpp:411] ip1 -> ip1\n", - "I0224 00:32:03.396100 655 net.cpp:150] Setting up ip1\n", - "I0224 00:32:03.396109 655 net.cpp:157] Top shape: 10 2 (20)\n", - "I0224 00:32:03.396116 655 net.cpp:165] Memory required for data: 360\n", - "I0224 00:32:03.396138 655 layer_factory.hpp:77] Creating layer ip1_ip1_0_split\n", - "I0224 00:32:03.396148 655 net.cpp:106] Creating Layer ip1_ip1_0_split\n", - "I0224 00:32:03.396157 655 net.cpp:454] ip1_ip1_0_split <- ip1\n", - "I0224 00:32:03.396164 655 net.cpp:411] ip1_ip1_0_split -> ip1_ip1_0_split_0\n", - "I0224 00:32:03.396174 655 net.cpp:411] ip1_ip1_0_split -> ip1_ip1_0_split_1\n", - "I0224 00:32:03.396185 655 net.cpp:150] Setting up ip1_ip1_0_split\n", - "I0224 00:32:03.396194 655 net.cpp:157] Top shape: 10 2 (20)\n", - "I0224 00:32:03.396203 655 net.cpp:157] Top shape: 10 2 (20)\n", - "I0224 00:32:03.396209 655 net.cpp:165] Memory required for data: 520\n", - "I0224 00:32:03.396216 655 layer_factory.hpp:77] Creating layer accuracy\n", - "I0224 00:32:03.396225 655 net.cpp:106] Creating Layer accuracy\n", - "I0224 00:32:03.396234 655 net.cpp:454] accuracy <- ip1_ip1_0_split_0\n", - "I0224 00:32:03.396241 655 net.cpp:454] accuracy <- label_data_1_split_0\n", - "I0224 00:32:03.396250 655 net.cpp:411] accuracy -> accuracy\n", - "I0224 00:32:03.396260 655 net.cpp:150] Setting up accuracy\n", - "I0224 00:32:03.396270 655 net.cpp:157] Top shape: (1)\n", - "I0224 00:32:03.396276 655 net.cpp:165] Memory required for data: 524\n", - "I0224 00:32:03.396283 655 layer_factory.hpp:77] Creating layer loss\n", - "I0224 00:32:03.396291 655 net.cpp:106] Creating Layer loss\n", - "I0224 00:32:03.396299 655 net.cpp:454] loss <- ip1_ip1_0_split_1\n", - "I0224 00:32:03.396307 655 net.cpp:454] loss <- label_data_1_split_1\n", - "I0224 00:32:03.396317 655 net.cpp:411] loss -> loss\n", - "I0224 00:32:03.396327 655 layer_factory.hpp:77] Creating layer loss\n", - "I0224 00:32:03.396339 655 net.cpp:150] Setting up loss\n", - "I0224 00:32:03.396349 655 net.cpp:157] Top shape: (1)\n", - "I0224 00:32:03.396356 655 net.cpp:160] with loss weight 1\n", - "I0224 00:32:03.396365 655 net.cpp:165] Memory required for data: 528\n", - "I0224 00:32:03.396373 655 net.cpp:226] loss needs backward computation.\n", - "I0224 00:32:03.396381 655 net.cpp:228] accuracy does not need backward computation.\n", - "I0224 00:32:03.396389 655 net.cpp:226] ip1_ip1_0_split needs backward computation.\n", - "I0224 00:32:03.396396 655 net.cpp:226] ip1 needs backward computation.\n", - "I0224 00:32:03.396404 655 net.cpp:228] label_data_1_split does not need backward computation.\n", - "I0224 00:32:03.396412 655 net.cpp:228] data does not need backward computation.\n", - "I0224 00:32:03.396420 655 net.cpp:270] This network produces output accuracy\n", - "I0224 00:32:03.396427 655 net.cpp:270] This network produces output loss\n", - "I0224 00:32:03.396437 655 net.cpp:283] Network initialization done.\n", - "I0224 00:32:03.396455 655 solver.cpp:60] Solver scaffolding done.\n", - "I0224 00:32:03.396473 655 caffe.cpp:219] Starting Optimization\n", - "I0224 00:32:03.396482 655 solver.cpp:280] Solving \n", - "I0224 00:32:03.396489 655 solver.cpp:281] Learning Rate Policy: step\n", - "I0224 00:32:03.396499 655 solver.cpp:338] Iteration 0, Testing net (#0)\n", - "I0224 00:32:03.932615 655 solver.cpp:406] Test net output #0: accuracy = 0.4268\n", - "I0224 00:32:03.932656 655 solver.cpp:406] Test net output #1: loss = 1.33093 (* 1 = 1.33093 loss)\n", - "I0224 00:32:03.932723 655 solver.cpp:229] Iteration 0, loss = 1.06081\n", - "I0224 00:32:03.932737 655 solver.cpp:245] Train net output #0: accuracy = 0.4\n", - "I0224 00:32:03.932749 655 solver.cpp:245] Train net output #1: loss = 1.06081 (* 1 = 1.06081 loss)\n", - "I0224 00:32:03.932765 655 sgd_solver.cpp:106] Iteration 0, lr = 0.01\n", - "I0224 00:32:03.945551 655 solver.cpp:338] Iteration 1000, Testing net (#0)\n", - "I0224 00:32:03.948048 655 solver.cpp:406] Test net output #0: accuracy = 0.694\n", - "I0224 00:32:03.948065 655 solver.cpp:406] Test net output #1: loss = 0.60406 (* 1 = 0.60406 loss)\n", - "I0224 00:32:03.948091 655 solver.cpp:229] Iteration 1000, loss = 0.505853\n", - "I0224 00:32:03.948102 655 solver.cpp:245] Train net output #0: accuracy = 0.7\n", - "I0224 00:32:03.948113 655 solver.cpp:245] Train net output #1: loss = 0.505853 (* 1 = 0.505853 loss)\n", - "I0224 00:32:03.948122 655 sgd_solver.cpp:106] Iteration 1000, lr = 0.01\n", - "I0224 00:32:03.960741 655 solver.cpp:338] Iteration 2000, Testing net (#0)\n", - "I0224 00:32:03.963214 655 solver.cpp:406] Test net output #0: accuracy = 0.7372\n", - "I0224 00:32:03.963249 655 solver.cpp:406] Test net output #1: loss = 0.595267 (* 1 = 0.595267 loss)\n", - "I0224 00:32:03.963276 655 solver.cpp:229] Iteration 2000, loss = 0.549211\n", - "I0224 00:32:03.963289 655 solver.cpp:245] Train net output #0: accuracy = 0.7\n", - "I0224 00:32:03.963299 655 solver.cpp:245] Train net output #1: loss = 0.549211 (* 1 = 0.549211 loss)\n", - "I0224 00:32:03.963309 655 sgd_solver.cpp:106] Iteration 2000, lr = 0.01\n", - "I0224 00:32:03.975945 655 solver.cpp:338] Iteration 3000, Testing net (#0)\n", - "I0224 00:32:03.978435 655 solver.cpp:406] Test net output #0: accuracy = 0.7732\n", - "I0224 00:32:03.978451 655 solver.cpp:406] Test net output #1: loss = 0.594998 (* 1 = 0.594998 loss)\n", - "I0224 00:32:03.978884 655 solver.cpp:229] Iteration 3000, loss = 0.66133\n", - "I0224 00:32:03.978911 655 solver.cpp:245] Train net output #0: accuracy = 0.8\n", - "I0224 00:32:03.978932 655 solver.cpp:245] Train net output #1: loss = 0.66133 (* 1 = 0.66133 loss)\n", - "I0224 00:32:03.978950 655 sgd_solver.cpp:106] Iteration 3000, lr = 0.01\n", - "I0224 00:32:03.992017 655 solver.cpp:338] Iteration 4000, Testing net (#0)\n", - "I0224 00:32:03.994509 655 solver.cpp:406] Test net output #0: accuracy = 0.694\n", - "I0224 00:32:03.994525 655 solver.cpp:406] Test net output #1: loss = 0.60406 (* 1 = 0.60406 loss)\n", - "I0224 00:32:03.994551 655 solver.cpp:229] Iteration 4000, loss = 0.505853\n", - "I0224 00:32:03.994562 655 solver.cpp:245] Train net output #0: accuracy = 0.7\n", - "I0224 00:32:03.994573 655 solver.cpp:245] Train net output #1: loss = 0.505853 (* 1 = 0.505853 loss)\n", - "I0224 00:32:03.994583 655 sgd_solver.cpp:106] Iteration 4000, lr = 0.01\n", - "I0224 00:32:04.007200 655 solver.cpp:338] Iteration 5000, Testing net (#0)\n", - "I0224 00:32:04.009686 655 solver.cpp:406] Test net output #0: accuracy = 0.7372\n", - "I0224 00:32:04.009702 655 solver.cpp:406] Test net output #1: loss = 0.595267 (* 1 = 0.595267 loss)\n", - "I0224 00:32:04.009727 655 solver.cpp:229] Iteration 5000, loss = 0.549211\n", - "I0224 00:32:04.009738 655 solver.cpp:245] Train net output #0: accuracy = 0.7\n", - "I0224 00:32:04.009749 655 solver.cpp:245] Train net output #1: loss = 0.549211 (* 1 = 0.549211 loss)\n", - "I0224 00:32:04.009758 655 sgd_solver.cpp:106] Iteration 5000, lr = 0.001\n", - "I0224 00:32:04.022734 655 solver.cpp:338] Iteration 6000, Testing net (#0)\n", - "I0224 00:32:04.025177 655 solver.cpp:406] Test net output #0: accuracy = 0.7824\n", - "I0224 00:32:04.025193 655 solver.cpp:406] Test net output #1: loss = 0.593367 (* 1 = 0.593367 loss)\n", - "I0224 00:32:04.025545 655 solver.cpp:229] Iteration 6000, loss = 0.654873\n", - "I0224 00:32:04.025562 655 solver.cpp:245] Train net output #0: accuracy = 0.7\n", - "I0224 00:32:04.025573 655 solver.cpp:245] Train net output #1: loss = 0.654873 (* 1 = 0.654873 loss)\n", - "I0224 00:32:04.025583 655 sgd_solver.cpp:106] Iteration 6000, lr = 0.001\n", - "I0224 00:32:04.038586 655 solver.cpp:338] Iteration 7000, Testing net (#0)\n", - "I0224 00:32:04.041016 655 solver.cpp:406] Test net output #0: accuracy = 0.7704\n", - "I0224 00:32:04.041033 655 solver.cpp:406] Test net output #1: loss = 0.593842 (* 1 = 0.593842 loss)\n", - "I0224 00:32:04.041059 655 solver.cpp:229] Iteration 7000, loss = 0.46611\n", - "I0224 00:32:04.041071 655 solver.cpp:245] Train net output #0: accuracy = 0.6\n", - "I0224 00:32:04.041082 655 solver.cpp:245] Train net output #1: loss = 0.46611 (* 1 = 0.46611 loss)\n", - "I0224 00:32:04.041091 655 sgd_solver.cpp:106] Iteration 7000, lr = 0.001\n", - "I0224 00:32:04.053722 655 solver.cpp:338] Iteration 8000, Testing net (#0)\n", - "I0224 00:32:04.056171 655 solver.cpp:406] Test net output #0: accuracy = 0.7788\n", - "I0224 00:32:04.056187 655 solver.cpp:406] Test net output #1: loss = 0.592847 (* 1 = 0.592847 loss)\n", - "I0224 00:32:04.056213 655 solver.cpp:229] Iteration 8000, loss = 0.615126\n", - "I0224 00:32:04.056224 655 solver.cpp:245] Train net output #0: accuracy = 0.8\n", - "I0224 00:32:04.056236 655 solver.cpp:245] Train net output #1: loss = 0.615126 (* 1 = 0.615126 loss)\n", - "I0224 00:32:04.056244 655 sgd_solver.cpp:106] Iteration 8000, lr = 0.001\n", - "I0224 00:32:04.068853 655 solver.cpp:338] Iteration 9000, Testing net (#0)\n", - "I0224 00:32:04.071291 655 solver.cpp:406] Test net output #0: accuracy = 0.7808\n", - "I0224 00:32:04.071307 655 solver.cpp:406] Test net output #1: loss = 0.593293 (* 1 = 0.593293 loss)\n", - "I0224 00:32:04.071650 655 solver.cpp:229] Iteration 9000, loss = 0.654997\n", - "I0224 00:32:04.071666 655 solver.cpp:245] Train net output #0: accuracy = 0.7\n", - "I0224 00:32:04.071677 655 solver.cpp:245] Train net output #1: loss = 0.654998 (* 1 = 0.654998 loss)\n", - "I0224 00:32:04.071687 655 sgd_solver.cpp:106] Iteration 9000, lr = 0.001\n", - "I0224 00:32:04.084717 655 solver.cpp:456] Snapshotting to binary proto file examples/hdf5_classification/data/train_iter_10000.caffemodel\n", - "I0224 00:32:04.084885 655 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/hdf5_classification/data/train_iter_10000.solverstate\n", - "I0224 00:32:04.084960 655 solver.cpp:318] Iteration 10000, loss = 0.466505\n", - "I0224 00:32:04.084977 655 solver.cpp:338] Iteration 10000, Testing net (#0)\n", - "I0224 00:32:04.087514 655 solver.cpp:406] Test net output #0: accuracy = 0.77\n", - "I0224 00:32:04.087532 655 solver.cpp:406] Test net output #1: loss = 0.593815 (* 1 = 0.593815 loss)\n", - "I0224 00:32:04.087541 655 solver.cpp:323] Optimization Done.\n", - "I0224 00:32:04.087548 655 caffe.cpp:222] Optimization Done.\n" - ] - } - ], - "source": [ - "!./build/tools/caffe train -solver examples/hdf5_classification/logreg_solver.prototxt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you look at output or the `logreg_auto_train.prototxt`, you'll see that the model is simple logistic regression.\n", - "We can make it a little more advanced by introducing a non-linearity between weights that take the input and weights that give the output -- now we have a two-layer network.\n", - "That network is given in `nonlinear_auto_train.prototxt`, and that's the only change made in `nonlinear_logreg_solver.prototxt` which we will now use.\n", - "\n", - "The final accuracy of the new network should be higher than logistic regression!" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "from caffe import layers as L\n", - "from caffe import params as P\n", - "\n", - "def nonlinear_net(hdf5, batch_size):\n", - " # one small nonlinearity, one leap for model kind\n", - " n = caffe.NetSpec()\n", - " n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)\n", - " # define a hidden layer of dimension 40\n", - " n.ip1 = L.InnerProduct(n.data, num_output=40, weight_filler=dict(type='xavier'))\n", - " # transform the output through the ReLU (rectified linear) non-linearity\n", - " n.relu1 = L.ReLU(n.ip1, in_place=True)\n", - " # score the (now non-linear) features\n", - " n.ip2 = L.InnerProduct(n.ip1, num_output=2, weight_filler=dict(type='xavier'))\n", - " # same accuracy and loss as before\n", - " n.accuracy = L.Accuracy(n.ip2, n.label)\n", - " n.loss = L.SoftmaxWithLoss(n.ip2, n.label)\n", - " return n.to_proto()\n", - "\n", - "train_net_path = 'examples/hdf5_classification/nonlinear_auto_train.prototxt'\n", - "with open(train_net_path, 'w') as f:\n", - " f.write(str(nonlinear_net('examples/hdf5_classification/data/train.txt', 10)))\n", - "\n", - "test_net_path = 'examples/hdf5_classification/nonlinear_auto_test.prototxt'\n", - "with open(test_net_path, 'w') as f:\n", - " f.write(str(nonlinear_net('examples/hdf5_classification/data/test.txt', 10)))\n", - "\n", - "solver_path = 'examples/hdf5_classification/nonlinear_logreg_solver.prototxt'\n", - "with open(solver_path, 'w') as f:\n", - " f.write(str(solver(train_net_path, test_net_path)))" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy: 0.838\n", - "Accuracy: 0.837\n", - "Accuracy: 0.838\n", - "Accuracy: 0.834\n", - "1 loop, best of 3: 277 ms per loop\n" - ] - } - ], - "source": [ - "%%timeit\n", - "caffe.set_mode_cpu()\n", - "solver = caffe.get_solver(solver_path)\n", - "solver.solve()\n", - "\n", - "accuracy = 0\n", - "batch_size = solver.test_nets[0].blobs['data'].num\n", - "test_iters = int(len(Xt) / batch_size)\n", - "for i in range(test_iters):\n", - " solver.test_nets[0].forward()\n", - " accuracy += solver.test_nets[0].blobs['accuracy'].data\n", - "accuracy /= test_iters\n", - "\n", - "print(\"Accuracy: {:.3f}\".format(accuracy))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Do the same through the command line interface for detailed output on the model and solving." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "I0224 00:32:05.654265 658 caffe.cpp:178] Use CPU.\n", - "I0224 00:32:05.810444 658 solver.cpp:48] Initializing solver from parameters: \n", - "train_net: \"examples/hdf5_classification/nonlinear_auto_train.prototxt\"\n", - "test_net: \"examples/hdf5_classification/nonlinear_auto_test.prototxt\"\n", - "test_iter: 250\n", - "test_interval: 1000\n", - "base_lr: 0.01\n", - "display: 1000\n", - "max_iter: 10000\n", - "lr_policy: \"step\"\n", - "gamma: 0.1\n", - "momentum: 0.9\n", - "weight_decay: 0.0005\n", - "stepsize: 5000\n", - "snapshot: 10000\n", - "snapshot_prefix: \"examples/hdf5_classification/data/train\"\n", - "solver_mode: CPU\n", - "I0224 00:32:05.810634 658 solver.cpp:81] Creating training net from train_net file: examples/hdf5_classification/nonlinear_auto_train.prototxt\n", - "I0224 00:32:05.810835 658 net.cpp:49] Initializing net from parameters: \n", - "state {\n", - " phase: TRAIN\n", - "}\n", - "layer {\n", - " name: \"data\"\n", - " type: \"HDF5Data\"\n", - " top: \"data\"\n", - " top: \"label\"\n", - " hdf5_data_param {\n", - " source: \"examples/hdf5_classification/data/train.txt\"\n", - " batch_size: 10\n", - " }\n", - "}\n", - "layer {\n", - " name: \"ip1\"\n", - " type: \"InnerProduct\"\n", - " bottom: \"data\"\n", - " top: \"ip1\"\n", - " inner_product_param {\n", - " num_output: 40\n", - " weight_filler {\n", - " type: \"xavier\"\n", - " }\n", - " }\n", - "}\n", - "layer {\n", - " name: \"relu1\"\n", - " type: \"ReLU\"\n", - " bottom: \"ip1\"\n", - " top: \"ip1\"\n", - "}\n", - "layer {\n", - " name: \"ip2\"\n", - " type: \"InnerProduct\"\n", - " bottom: \"ip1\"\n", - " top: \"ip2\"\n", - " inner_product_param {\n", - " num_output: 2\n", - " weight_filler {\n", - " type: \"xavier\"\n", - " }\n", - " }\n", - "}\n", - "layer {\n", - " name: \"accuracy\"\n", - " type: \"Accuracy\"\n", - " bottom: \"ip2\"\n", - " bottom: \"label\"\n", - " top: \"accuracy\"\n", - "}\n", - "layer {\n", - " name: \"loss\"\n", - " type: \"SoftmaxWithLoss\"\n", - " bottom: \"ip2\"\n", - " bottom: \"label\"\n", - " top: \"loss\"\n", - "}\n", - "I0224 00:32:05.811061 658 layer_factory.hpp:77] Creating layer data\n", - "I0224 00:32:05.811079 658 net.cpp:106] Creating Layer data\n", - "I0224 00:32:05.811092 658 net.cpp:411] data -> data\n", - "I0224 00:32:05.811121 658 net.cpp:411] data -> label\n", - "I0224 00:32:05.811143 658 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/hdf5_classification/data/train.txt\n", - "I0224 00:32:05.811189 658 hdf5_data_layer.cpp:93] Number of HDF5 files: 2\n", - "I0224 00:32:05.812254 658 hdf5.cpp:32] Datatype class: H5T_FLOAT\n", - "I0224 00:32:05.812677 658 net.cpp:150] Setting up data\n", - "I0224 00:32:05.812705 658 net.cpp:157] Top shape: 10 4 (40)\n", - "I0224 00:32:05.812721 658 net.cpp:157] Top shape: 10 (10)\n", - "I0224 00:32:05.812729 658 net.cpp:165] Memory required for data: 200\n", - "I0224 00:32:05.812739 658 layer_factory.hpp:77] Creating layer label_data_1_split\n", - "I0224 00:32:05.812752 658 net.cpp:106] Creating Layer label_data_1_split\n", - "I0224 00:32:05.812762 658 net.cpp:454] label_data_1_split <- label\n", - "I0224 00:32:05.812774 658 net.cpp:411] label_data_1_split -> label_data_1_split_0\n", - "I0224 00:32:05.812785 658 net.cpp:411] label_data_1_split -> label_data_1_split_1\n", - "I0224 00:32:05.812798 658 net.cpp:150] Setting up label_data_1_split\n", - "I0224 00:32:05.812808 658 net.cpp:157] Top shape: 10 (10)\n", - "I0224 00:32:05.812816 658 net.cpp:157] Top shape: 10 (10)\n", - "I0224 00:32:05.812824 658 net.cpp:165] Memory required for data: 280\n", - "I0224 00:32:05.812831 658 layer_factory.hpp:77] Creating layer ip1\n", - "I0224 00:32:05.812841 658 net.cpp:106] Creating Layer ip1\n", - "I0224 00:32:05.812849 658 net.cpp:454] ip1 <- data\n", - "I0224 00:32:05.812860 658 net.cpp:411] ip1 -> ip1\n", - "I0224 00:32:05.813179 658 net.cpp:150] Setting up ip1\n", - "I0224 00:32:05.813196 658 net.cpp:157] Top shape: 10 40 (400)\n", - "I0224 00:32:05.813210 658 net.cpp:165] Memory required for data: 1880\n", - "I0224 00:32:05.813230 658 layer_factory.hpp:77] Creating layer relu1\n", - "I0224 00:32:05.813241 658 net.cpp:106] Creating Layer relu1\n", - "I0224 00:32:05.813251 658 net.cpp:454] relu1 <- ip1\n", - "I0224 00:32:05.813258 658 net.cpp:397] relu1 -> ip1 (in-place)\n", - "I0224 00:32:05.813271 658 net.cpp:150] Setting up relu1\n", - "I0224 00:32:05.813279 658 net.cpp:157] Top shape: 10 40 (400)\n", - "I0224 00:32:05.813287 658 net.cpp:165] Memory required for data: 3480\n", - "I0224 00:32:05.813294 658 layer_factory.hpp:77] Creating layer ip2\n", - "I0224 00:32:05.813304 658 net.cpp:106] Creating Layer ip2\n", - "I0224 00:32:05.813313 658 net.cpp:454] ip2 <- ip1\n", - "I0224 00:32:05.813321 658 net.cpp:411] ip2 -> ip2\n", - "I0224 00:32:05.813336 658 net.cpp:150] Setting up ip2\n", - "I0224 00:32:05.813345 658 net.cpp:157] Top shape: 10 2 (20)\n", - "I0224 00:32:05.813379 658 net.cpp:165] Memory required for data: 3560\n", - "I0224 00:32:05.813401 658 layer_factory.hpp:77] Creating layer ip2_ip2_0_split\n", - "I0224 00:32:05.813417 658 net.cpp:106] Creating Layer ip2_ip2_0_split\n", - "I0224 00:32:05.813426 658 net.cpp:454] ip2_ip2_0_split <- ip2\n", - "I0224 00:32:05.813434 658 net.cpp:411] ip2_ip2_0_split -> ip2_ip2_0_split_0\n", - "I0224 00:32:05.813446 658 net.cpp:411] ip2_ip2_0_split -> ip2_ip2_0_split_1\n", - "I0224 00:32:05.813457 658 net.cpp:150] Setting up ip2_ip2_0_split\n", - "I0224 00:32:05.813465 658 net.cpp:157] Top shape: 10 2 (20)\n", - "I0224 00:32:05.813473 658 net.cpp:157] Top shape: 10 2 (20)\n", - "I0224 00:32:05.813480 658 net.cpp:165] Memory required for data: 3720\n", - "I0224 00:32:05.813488 658 layer_factory.hpp:77] Creating layer accuracy\n", - "I0224 00:32:05.813499 658 net.cpp:106] Creating Layer accuracy\n", - "I0224 00:32:05.813508 658 net.cpp:454] accuracy <- ip2_ip2_0_split_0\n", - "I0224 00:32:05.813515 658 net.cpp:454] accuracy <- label_data_1_split_0\n", - "I0224 00:32:05.813524 658 net.cpp:411] accuracy -> accuracy\n", - "I0224 00:32:05.813539 658 net.cpp:150] Setting up accuracy\n", - "I0224 00:32:05.813547 658 net.cpp:157] Top shape: (1)\n", - "I0224 00:32:05.813555 658 net.cpp:165] Memory required for data: 3724\n", - "I0224 00:32:05.813565 658 layer_factory.hpp:77] Creating layer loss\n", - "I0224 00:32:05.813585 658 net.cpp:106] Creating Layer loss\n", - "I0224 00:32:05.813599 658 net.cpp:454] loss <- ip2_ip2_0_split_1\n", - "I0224 00:32:05.813616 658 net.cpp:454] loss <- label_data_1_split_1\n", - "I0224 00:32:05.813627 658 net.cpp:411] loss -> loss\n", - "I0224 00:32:05.813642 658 layer_factory.hpp:77] Creating layer loss\n", - "I0224 00:32:05.813663 658 net.cpp:150] Setting up loss\n", - "I0224 00:32:05.813671 658 net.cpp:157] Top shape: (1)\n", - "I0224 00:32:05.813679 658 net.cpp:160] with loss weight 1\n", - "I0224 00:32:05.813695 658 net.cpp:165] Memory required for data: 3728\n", - "I0224 00:32:05.813704 658 net.cpp:226] loss needs backward computation.\n", - "I0224 00:32:05.813712 658 net.cpp:228] accuracy does not need backward computation.\n", - "I0224 00:32:05.813720 658 net.cpp:226] ip2_ip2_0_split needs backward computation.\n", - "I0224 00:32:05.813729 658 net.cpp:226] ip2 needs backward computation.\n", - "I0224 00:32:05.813735 658 net.cpp:226] relu1 needs backward computation.\n", - "I0224 00:32:05.813743 658 net.cpp:226] ip1 needs backward computation.\n", - "I0224 00:32:05.813751 658 net.cpp:228] label_data_1_split does not need backward computation.\n", - "I0224 00:32:05.813760 658 net.cpp:228] data does not need backward computation.\n", - "I0224 00:32:05.813772 658 net.cpp:270] This network produces output accuracy\n", - "I0224 00:32:05.813787 658 net.cpp:270] This network produces output loss\n", - "I0224 00:32:05.813809 658 net.cpp:283] Network initialization done.\n", - "I0224 00:32:05.813905 658 solver.cpp:181] Creating test net (#0) specified by test_net file: examples/hdf5_classification/nonlinear_auto_test.prototxt\n", - "I0224 00:32:05.813944 658 net.cpp:49] Initializing net from parameters: \n", - "state {\n", - " phase: TEST\n", - "}\n", - "layer {\n", - " name: \"data\"\n", - " type: \"HDF5Data\"\n", - " top: \"data\"\n", - " top: \"label\"\n", - " hdf5_data_param {\n", - " source: \"examples/hdf5_classification/data/test.txt\"\n", - " batch_size: 10\n", - " }\n", - "}\n", - "layer {\n", - " name: \"ip1\"\n", - " type: \"InnerProduct\"\n", - " bottom: \"data\"\n", - " top: \"ip1\"\n", - " inner_product_param {\n", - " num_output: 40\n", - " weight_filler {\n", - " type: \"xavier\"\n", - " }\n", - " }\n", - "}\n", - "layer {\n", - " name: \"relu1\"\n", - " type: \"ReLU\"\n", - " bottom: \"ip1\"\n", - " top: \"ip1\"\n", - "}\n", - "layer {\n", - " name: \"ip2\"\n", - " type: \"InnerProduct\"\n", - " bottom: \"ip1\"\n", - " top: \"ip2\"\n", - " inner_product_param {\n", - " num_output: 2\n", - " weight_filler {\n", - " type: \"xavier\"\n", - " }\n", - " }\n", - "}\n", - "layer {\n", - " name: \"accuracy\"\n", - " type: \"Accuracy\"\n", - " bottom: \"ip2\"\n", - " bottom: \"label\"\n", - " top: \"accuracy\"\n", - "}\n", - "layer {\n", - " name: \"loss\"\n", - " type: \"SoftmaxWithLoss\"\n", - " bottom: \"ip2\"\n", - " bottom: \"label\"\n", - " top: \"loss\"\n", - "}\n", - "I0224 00:32:05.814131 658 layer_factory.hpp:77] Creating layer data\n", - "I0224 00:32:05.814142 658 net.cpp:106] Creating Layer data\n", - "I0224 00:32:05.814152 658 net.cpp:411] data -> data\n", - "I0224 00:32:05.814162 658 net.cpp:411] data -> label\n", - "I0224 00:32:05.814180 658 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/hdf5_classification/data/test.txt\n", - "I0224 00:32:05.814220 658 hdf5_data_layer.cpp:93] Number of HDF5 files: 1\n", - "I0224 00:32:05.815207 658 net.cpp:150] Setting up data\n", - "I0224 00:32:05.815227 658 net.cpp:157] Top shape: 10 4 (40)\n", - "I0224 00:32:05.815243 658 net.cpp:157] Top shape: 10 (10)\n", - "I0224 00:32:05.815253 658 net.cpp:165] Memory required for data: 200\n", - "I0224 00:32:05.815260 658 layer_factory.hpp:77] Creating layer label_data_1_split\n", - "I0224 00:32:05.815270 658 net.cpp:106] Creating Layer label_data_1_split\n", - "I0224 00:32:05.815279 658 net.cpp:454] label_data_1_split <- label\n", - "I0224 00:32:05.815287 658 net.cpp:411] label_data_1_split -> label_data_1_split_0\n", - "I0224 00:32:05.815299 658 net.cpp:411] label_data_1_split -> label_data_1_split_1\n", - "I0224 00:32:05.815310 658 net.cpp:150] Setting up label_data_1_split\n", - "I0224 00:32:05.815318 658 net.cpp:157] Top shape: 10 (10)\n", - "I0224 00:32:05.815326 658 net.cpp:157] Top shape: 10 (10)\n", - "I0224 00:32:05.815335 658 net.cpp:165] Memory required for data: 280\n", - "I0224 00:32:05.815341 658 layer_factory.hpp:77] Creating layer ip1\n", - "I0224 00:32:05.815351 658 net.cpp:106] Creating Layer ip1\n", - "I0224 00:32:05.815358 658 net.cpp:454] ip1 <- data\n", - "I0224 00:32:05.815367 658 net.cpp:411] ip1 -> ip1\n", - "I0224 00:32:05.815383 658 net.cpp:150] Setting up ip1\n", - "I0224 00:32:05.815398 658 net.cpp:157] Top shape: 10 40 (400)\n", - "I0224 00:32:05.815413 658 net.cpp:165] Memory required for data: 1880\n", - "I0224 00:32:05.815435 658 layer_factory.hpp:77] Creating layer relu1\n", - "I0224 00:32:05.815450 658 net.cpp:106] Creating Layer relu1\n", - "I0224 00:32:05.815459 658 net.cpp:454] relu1 <- ip1\n", - "I0224 00:32:05.815469 658 net.cpp:397] relu1 -> ip1 (in-place)\n", - "I0224 00:32:05.815479 658 net.cpp:150] Setting up relu1\n", - "I0224 00:32:05.815486 658 net.cpp:157] Top shape: 10 40 (400)\n", - "I0224 00:32:05.815495 658 net.cpp:165] Memory required for data: 3480\n", - "I0224 00:32:05.815501 658 layer_factory.hpp:77] Creating layer ip2\n", - "I0224 00:32:05.815510 658 net.cpp:106] Creating Layer ip2\n", - "I0224 00:32:05.815518 658 net.cpp:454] ip2 <- ip1\n", - "I0224 00:32:05.815527 658 net.cpp:411] ip2 -> ip2\n", - "I0224 00:32:05.815542 658 net.cpp:150] Setting up ip2\n", - "I0224 00:32:05.815551 658 net.cpp:157] Top shape: 10 2 (20)\n", - "I0224 00:32:05.815559 658 net.cpp:165] Memory required for data: 3560\n", - "I0224 00:32:05.815570 658 layer_factory.hpp:77] Creating layer ip2_ip2_0_split\n", - "I0224 00:32:05.815579 658 net.cpp:106] Creating Layer ip2_ip2_0_split\n", - "I0224 00:32:05.815587 658 net.cpp:454] ip2_ip2_0_split <- ip2\n", - "I0224 00:32:05.815600 658 net.cpp:411] ip2_ip2_0_split -> ip2_ip2_0_split_0\n", - "I0224 00:32:05.815619 658 net.cpp:411] ip2_ip2_0_split -> ip2_ip2_0_split_1\n", - "I0224 00:32:05.815640 658 net.cpp:150] Setting up ip2_ip2_0_split\n", - "I0224 00:32:05.815654 658 net.cpp:157] Top shape: 10 2 (20)\n", - "I0224 00:32:05.815662 658 net.cpp:157] Top shape: 10 2 (20)\n", - "I0224 00:32:05.815670 658 net.cpp:165] Memory required for data: 3720\n", - "I0224 00:32:05.815677 658 layer_factory.hpp:77] Creating layer accuracy\n", - "I0224 00:32:05.815685 658 net.cpp:106] Creating Layer accuracy\n", - "I0224 00:32:05.815693 658 net.cpp:454] accuracy <- ip2_ip2_0_split_0\n", - "I0224 00:32:05.815702 658 net.cpp:454] accuracy <- label_data_1_split_0\n", - "I0224 00:32:05.815711 658 net.cpp:411] accuracy -> accuracy\n", - "I0224 00:32:05.815722 658 net.cpp:150] Setting up accuracy\n", - "I0224 00:32:05.815732 658 net.cpp:157] Top shape: (1)\n", - "I0224 00:32:05.815738 658 net.cpp:165] Memory required for data: 3724\n", - "I0224 00:32:05.815747 658 layer_factory.hpp:77] Creating layer loss\n", - "I0224 00:32:05.815754 658 net.cpp:106] Creating Layer loss\n", - "I0224 00:32:05.815762 658 net.cpp:454] loss <- ip2_ip2_0_split_1\n", - "I0224 00:32:05.815770 658 net.cpp:454] loss <- label_data_1_split_1\n", - "I0224 00:32:05.815779 658 net.cpp:411] loss -> loss\n", - "I0224 00:32:05.815790 658 layer_factory.hpp:77] Creating layer loss\n", - "I0224 00:32:05.815811 658 net.cpp:150] Setting up loss\n", - "I0224 00:32:05.815829 658 net.cpp:157] Top shape: (1)\n", - "I0224 00:32:05.815843 658 net.cpp:160] with loss weight 1\n", - "I0224 00:32:05.815867 658 net.cpp:165] Memory required for data: 3728\n", - "I0224 00:32:05.815876 658 net.cpp:226] loss needs backward computation.\n", - "I0224 00:32:05.815884 658 net.cpp:228] accuracy does not need backward computation.\n", - "I0224 00:32:05.815892 658 net.cpp:226] ip2_ip2_0_split needs backward computation.\n", - "I0224 00:32:05.815901 658 net.cpp:226] ip2 needs backward computation.\n", - "I0224 00:32:05.815908 658 net.cpp:226] relu1 needs backward computation.\n", - "I0224 00:32:05.815915 658 net.cpp:226] ip1 needs backward computation.\n", - "I0224 00:32:05.815923 658 net.cpp:228] label_data_1_split does not need backward computation.\n", - "I0224 00:32:05.815932 658 net.cpp:228] data does not need backward computation.\n", - "I0224 00:32:05.815938 658 net.cpp:270] This network produces output accuracy\n", - "I0224 00:32:05.815946 658 net.cpp:270] This network produces output loss\n", - "I0224 00:32:05.815958 658 net.cpp:283] Network initialization done.\n", - "I0224 00:32:05.815978 658 solver.cpp:60] Solver scaffolding done.\n", - "I0224 00:32:05.816000 658 caffe.cpp:219] Starting Optimization\n", - "I0224 00:32:05.816016 658 solver.cpp:280] Solving \n", - "I0224 00:32:05.816030 658 solver.cpp:281] Learning Rate Policy: step\n", - "I0224 00:32:05.816048 658 solver.cpp:338] Iteration 0, Testing net (#0)\n", - "I0224 00:32:05.831967 658 solver.cpp:406] Test net output #0: accuracy = 0.4464\n", - "I0224 00:32:05.832033 658 solver.cpp:406] Test net output #1: loss = 0.909841 (* 1 = 0.909841 loss)\n", - "I0224 00:32:05.832186 658 solver.cpp:229] Iteration 0, loss = 0.798509\n", - "I0224 00:32:05.832218 658 solver.cpp:245] Train net output #0: accuracy = 0.6\n", - "I0224 00:32:05.832247 658 solver.cpp:245] Train net output #1: loss = 0.798509 (* 1 = 0.798509 loss)\n", - "I0224 00:32:05.832281 658 sgd_solver.cpp:106] Iteration 0, lr = 0.01\n", - "I0224 00:32:05.859506 658 solver.cpp:338] Iteration 1000, Testing net (#0)\n", - "I0224 00:32:05.862799 658 solver.cpp:406] Test net output #0: accuracy = 0.8156\n", - "I0224 00:32:05.862818 658 solver.cpp:406] Test net output #1: loss = 0.44259 (* 1 = 0.44259 loss)\n", - "I0224 00:32:05.862853 658 solver.cpp:229] Iteration 1000, loss = 0.537015\n", - "I0224 00:32:05.862864 658 solver.cpp:245] Train net output #0: accuracy = 0.7\n", - "I0224 00:32:05.862875 658 solver.cpp:245] Train net output #1: loss = 0.537015 (* 1 = 0.537015 loss)\n", - "I0224 00:32:05.862885 658 sgd_solver.cpp:106] Iteration 1000, lr = 0.01\n", - "I0224 00:32:05.883155 658 solver.cpp:338] Iteration 2000, Testing net (#0)\n", - "I0224 00:32:05.886435 658 solver.cpp:406] Test net output #0: accuracy = 0.8116\n", - "I0224 00:32:05.886451 658 solver.cpp:406] Test net output #1: loss = 0.434079 (* 1 = 0.434079 loss)\n", - "I0224 00:32:05.886484 658 solver.cpp:229] Iteration 2000, loss = 0.43109\n", - "I0224 00:32:05.886497 658 solver.cpp:245] Train net output #0: accuracy = 0.9\n", - "I0224 00:32:05.886508 658 solver.cpp:245] Train net output #1: loss = 0.43109 (* 1 = 0.43109 loss)\n", - "I0224 00:32:05.886518 658 sgd_solver.cpp:106] Iteration 2000, lr = 0.01\n", - "I0224 00:32:05.907243 658 solver.cpp:338] Iteration 3000, Testing net (#0)\n", - "I0224 00:32:05.910521 658 solver.cpp:406] Test net output #0: accuracy = 0.8168\n", - "I0224 00:32:05.910537 658 solver.cpp:406] Test net output #1: loss = 0.425661 (* 1 = 0.425661 loss)\n", - "I0224 00:32:05.910905 658 solver.cpp:229] Iteration 3000, loss = 0.430245\n", - "I0224 00:32:05.910922 658 solver.cpp:245] Train net output #0: accuracy = 0.7\n", - "I0224 00:32:05.910933 658 solver.cpp:245] Train net output #1: loss = 0.430245 (* 1 = 0.430245 loss)\n", - "I0224 00:32:05.910943 658 sgd_solver.cpp:106] Iteration 3000, lr = 0.01\n", - "I0224 00:32:05.931205 658 solver.cpp:338] Iteration 4000, Testing net (#0)\n", - "I0224 00:32:05.934479 658 solver.cpp:406] Test net output #0: accuracy = 0.8324\n", - "I0224 00:32:05.934496 658 solver.cpp:406] Test net output #1: loss = 0.404891 (* 1 = 0.404891 loss)\n", - "I0224 00:32:05.934530 658 solver.cpp:229] Iteration 4000, loss = 0.628955\n", - "I0224 00:32:05.934542 658 solver.cpp:245] Train net output #0: accuracy = 0.7\n", - "I0224 00:32:05.934553 658 solver.cpp:245] Train net output #1: loss = 0.628955 (* 1 = 0.628955 loss)\n", - "I0224 00:32:05.934583 658 sgd_solver.cpp:106] Iteration 4000, lr = 0.01\n", - "I0224 00:32:05.955108 658 solver.cpp:338] Iteration 5000, Testing net (#0)\n", - "I0224 00:32:05.958377 658 solver.cpp:406] Test net output #0: accuracy = 0.8364\n", - "I0224 00:32:05.958395 658 solver.cpp:406] Test net output #1: loss = 0.404235 (* 1 = 0.404235 loss)\n", - "I0224 00:32:05.958432 658 solver.cpp:229] Iteration 5000, loss = 0.394939\n", - "I0224 00:32:05.958444 658 solver.cpp:245] Train net output #0: accuracy = 0.9\n", - "I0224 00:32:05.958456 658 solver.cpp:245] Train net output #1: loss = 0.39494 (* 1 = 0.39494 loss)\n", - "I0224 00:32:05.958466 658 sgd_solver.cpp:106] Iteration 5000, lr = 0.001\n", - "I0224 00:32:05.978703 658 solver.cpp:338] Iteration 6000, Testing net (#0)\n", - "I0224 00:32:05.981973 658 solver.cpp:406] Test net output #0: accuracy = 0.838\n", - "I0224 00:32:05.981991 658 solver.cpp:406] Test net output #1: loss = 0.385743 (* 1 = 0.385743 loss)\n", - "I0224 00:32:05.982347 658 solver.cpp:229] Iteration 6000, loss = 0.411537\n", - "I0224 00:32:05.982362 658 solver.cpp:245] Train net output #0: accuracy = 0.8\n", - "I0224 00:32:05.982373 658 solver.cpp:245] Train net output #1: loss = 0.411537 (* 1 = 0.411537 loss)\n", - "I0224 00:32:05.982383 658 sgd_solver.cpp:106] Iteration 6000, lr = 0.001\n", - "I0224 00:32:06.003015 658 solver.cpp:338] Iteration 7000, Testing net (#0)\n", - "I0224 00:32:06.006283 658 solver.cpp:406] Test net output #0: accuracy = 0.8388\n", - "I0224 00:32:06.006301 658 solver.cpp:406] Test net output #1: loss = 0.384648 (* 1 = 0.384648 loss)\n", - "I0224 00:32:06.006335 658 solver.cpp:229] Iteration 7000, loss = 0.521072\n", - "I0224 00:32:06.006347 658 solver.cpp:245] Train net output #0: accuracy = 0.7\n", - "I0224 00:32:06.006358 658 solver.cpp:245] Train net output #1: loss = 0.521073 (* 1 = 0.521073 loss)\n", - "I0224 00:32:06.006368 658 sgd_solver.cpp:106] Iteration 7000, lr = 0.001\n", - "I0224 00:32:06.026715 658 solver.cpp:338] Iteration 8000, Testing net (#0)\n", - "I0224 00:32:06.029965 658 solver.cpp:406] Test net output #0: accuracy = 0.8404\n", - "I0224 00:32:06.029983 658 solver.cpp:406] Test net output #1: loss = 0.380889 (* 1 = 0.380889 loss)\n", - "I0224 00:32:06.030015 658 solver.cpp:229] Iteration 8000, loss = 0.329477\n", - "I0224 00:32:06.030028 658 solver.cpp:245] Train net output #0: accuracy = 0.9\n", - "I0224 00:32:06.030040 658 solver.cpp:245] Train net output #1: loss = 0.329477 (* 1 = 0.329477 loss)\n", - "I0224 00:32:06.030048 658 sgd_solver.cpp:106] Iteration 8000, lr = 0.001\n", - "I0224 00:32:06.050626 658 solver.cpp:338] Iteration 9000, Testing net (#0)\n", - "I0224 00:32:06.053889 658 solver.cpp:406] Test net output #0: accuracy = 0.8376\n", - "I0224 00:32:06.053906 658 solver.cpp:406] Test net output #1: loss = 0.382756 (* 1 = 0.382756 loss)\n", - "I0224 00:32:06.054271 658 solver.cpp:229] Iteration 9000, loss = 0.412227\n", - "I0224 00:32:06.054291 658 solver.cpp:245] Train net output #0: accuracy = 0.8\n", - "I0224 00:32:06.054314 658 solver.cpp:245] Train net output #1: loss = 0.412228 (* 1 = 0.412228 loss)\n", - "I0224 00:32:06.054337 658 sgd_solver.cpp:106] Iteration 9000, lr = 0.001\n", - "I0224 00:32:06.074646 658 solver.cpp:456] Snapshotting to binary proto file examples/hdf5_classification/data/train_iter_10000.caffemodel\n", - "I0224 00:32:06.074808 658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/hdf5_classification/data/train_iter_10000.solverstate\n", - "I0224 00:32:06.074889 658 solver.cpp:318] Iteration 10000, loss = 0.532798\n", - "I0224 00:32:06.074906 658 solver.cpp:338] Iteration 10000, Testing net (#0)\n", - "I0224 00:32:06.078208 658 solver.cpp:406] Test net output #0: accuracy = 0.8388\n", - "I0224 00:32:06.078225 658 solver.cpp:406] Test net output #1: loss = 0.382042 (* 1 = 0.382042 loss)\n", - "I0224 00:32:06.078234 658 solver.cpp:323] Optimization Done.\n", - "I0224 00:32:06.078241 658 caffe.cpp:222] Optimization Done.\n" - ] - } - ], - "source": [ - "!./build/tools/caffe train -solver examples/hdf5_classification/nonlinear_logreg_solver.prototxt" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Clean up (comment this out if you want to examine the hdf5_classification/data directory).\n", - "shutil.rmtree(dirname)" - ] - } - ], - "metadata": { - "description": "Use Caffe as a generic SGD optimizer to train logistic regression on non-image HDF5 data.", - "example_name": "Off-the-shelf SGD for classification", - "include_in_docs": true, - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" - }, - "priority": 4 - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/Notebooks/Caffee/net_surgery.ipynb b/Notebooks/Caffee/net_surgery.ipynb deleted file mode 100644 index d50d503..0000000 --- a/Notebooks/Caffee/net_surgery.ipynb +++ /dev/null @@ -1,6901 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Net Surgery\n", - "\n", - "Caffe networks can be transformed to your particular needs by editing the model parameters. The data, diffs, and parameters of a net are all exposed in pycaffe.\n", - "\n", - "Roll up your sleeves for net surgery with pycaffe!" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "\n", - "# Make sure that caffe is on the python path:\n", - "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n", - "import sys\n", - "sys.path.insert(0, caffe_root + 'python')\n", - "\n", - "import caffe\n", - "\n", - "# configure plotting\n", - "plt.rcParams['figure.figsize'] = (10, 10)\n", - "plt.rcParams['image.interpolation'] = 'nearest'\n", - "plt.rcParams['image.cmap'] = 'gray'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Designer Filters\n", - "\n", - "To show how to load, manipulate, and save parameters we'll design our own filters into a simple network that's only a single convolution layer. This net has two blobs, `data` for the input and `conv` for the convolution output and one parameter `conv` for the convolution filter weights and biases." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "blobs ['data', 'conv']\n", - "params ['conv']\n" - ] - }, - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAlIAAAHNCAYAAADVB5V4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvWuMZdl13/c/tx733np393T3PPkYDUccPsQZiaRkCYpE\n", - "CYklOwYhfwjCIAEiJDLswAkQf3AQIEoC64OcIEDiIHESBAiCCAkkJ4GtJHCM+KHQjmGZtmxKJBVC\n", - "wxkOZyac4Uz3dHe97q1bt+7Jh+r/rt/517499ER008xZQKGq7j1nn73XXns9/mvtfZq2bdVTTz31\n", - "1FNPPfXU0z86DR52B3rqqaeeeuqpp57+SaXekeqpp5566qmnnnp6j9Q7Uj311FNPPfXUU0/vkXpH\n", - "qqeeeuqpp5566uk9Uu9I9dRTTz311FNPPb1H6h2pnnrqqaeeeuqpp/dIvSPVU089/b5T0zT/RdM0\n", - "/87v97Xv0s4HmqZZNE1T1WtN03y5aZp/6v/rc3rqqaeeSE1/jlRPPfX0vUBN03xA0suSVtu2XTzc\n", - 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"net.blobs['data'].data[...] = im_input" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The convolution weights are initialized from Gaussian noise while the biases are initialized to zero. These random filters give output somewhat like edge detections." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAicAAACbCAYAAAC5xzv6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvVuMbVl2pvWvfb/FjkueW568VN5dXSUbl4sHbBBYbYRK\n", - "jRqEJW7qfkD90MItN4gGgQC3QHYJiwdejJFfcNvgRtBuaBAPyA9gt5FBcrnc1bbLVemqPFmZlZdz\n", - "TuaJc+KybxH7sniI8839rxFrx4lMU7mjKveQQhGx97rMNeeYY/zjH2POleV5ro1sZCMb2chGNrKR\n", - "qyKVdTdgIxvZyEY2spGNbMRlA042spGNbGQjG9nIlZINONnIRjaykY1sZCNXSjbgZCMb2chGNrKR\n", - "jVwp2YCTjWxkIxvZyEY2cqVkA042spGNbGQjG9nIlZJPDTjJsuyHsiz7x1mWHWVZ9jezLPuVLMt+\n", - "7vF3P5ll2TvrbuNGNvJxZKPbG/lBlY1uf3rlUwNOJP2Hkv6vPM/7eZ7/13me/0ye518uOzDLsrey\n", - "LPuL36uGZFn2lSzLXsmy7KUsy/4wfLeXZdn/mmXZ4HE7/s3vURv+8yzLfuOqXm8jH0m+X3T7Z7Ms\n", - "+2qWZZMsy37te9iGjW7/4MiV1+0syxpZlv3q4/sfZVn2tSzLvvQ9asOnRrc/TeDkM5K+ccljc0nZ\n", - "x7lJ9lgu+L4u6fk8z9+Q9EVJfxgO+W8kTSTdkPRXJP1KlmWf+zht2cinRr5fdPs9Sb8g6e98nPtv\n", - 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], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# helper show filter outputs\n", - "def show_filters(net):\n", - " net.forward()\n", - " plt.figure()\n", - " filt_min, filt_max = net.blobs['conv'].data.min(), net.blobs['conv'].data.max()\n", - " for i in range(3):\n", - " plt.subplot(1,4,i+2)\n", - " plt.title(\"filter #{} output\".format(i))\n", - " plt.imshow(net.blobs['conv'].data[0, i], vmin=filt_min, vmax=filt_max)\n", - " plt.tight_layout()\n", - " plt.axis('off')\n", - "\n", - "# filter the image with initial \n", - "show_filters(net)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Raising the bias of a filter will correspondingly raise its output:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pre-surgery output mean -0.02\n", - "post-surgery output mean 0.98\n" - ] - } - ], - "source": [ - "# pick first filter output\n", - "conv0 = net.blobs['conv'].data[0, 0]\n", - "print(\"pre-surgery output mean {:.2f}\".format(conv0.mean()))\n", - "# set first filter bias to 1\n", - "net.params['conv'][1].data[0] = 1.\n", - "net.forward()\n", - "print(\"post-surgery output mean {:.2f}\".format(conv0.mean()))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Altering the filter weights is more exciting since we can assign any kernel like Gaussian blur, the Sobel operator for edges, and so on. The following surgery turns the 0th filter into a Gaussian blur and the 1st and 2nd filters into the horizontal and vertical gradient parts of the Sobel operator.\n", - "\n", - "See how the 0th output is blurred, the 1st picks up horizontal edges, and the 2nd picks up vertical edges." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAicAAACbCAYAAAC5xzv6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvWuMbNl13/c/9eh6V7/uvT1zHzNDzgw5HNIWNInpMCEi\n", - "2wkCwYElBFASBTLg2DCM2LATSAkSJ5GlWDJi5EMAA0ngL/EjkQPFcuIQgREEcCIbAkJD9JhDgdJ4\n", - "yOFjHnfuq2/fflV1VXc9Tj7U/e3+1+pTfe+MqOkmWQtodHfVOfvsvfbaa/3XY++T5XmuJS1pSUta\n", - "0pKWtKTLQqWL7sCSlrSkJS1pSUtaktMSnCxpSUta0pKWtKRLRUtwsqQlLWlJS1rSki4VLcHJkpa0\n", - "pCUtaUlLulS0BCdLWtKSlrSkJS3pUtESnCxpSUta0pKWtKRLRT804CTLsk9nWfa1LMsOsiz7C1mW\n", - "/fUsy37+8Xd/KMuy9y+6j0ta0kehpWwv6QeVlrL9w0s/NOBE0n8q6f/N87yb5/l/l+f5n83z/K8U\n", - 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"net.params['conv'][0].data[1:] = 0.\n", - "sobel = np.array((-1, -2, -1, 0, 0, 0, 1, 2, 1), dtype=np.float32).reshape((3,3))\n", - "net.params['conv'][0].data[1, 0, 1:-1, 1:-1] = sobel # horizontal\n", - "net.params['conv'][0].data[2, 0, 1:-1, 1:-1] = sobel.T # vertical\n", - "show_filters(net)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With net surgery, parameters can be transplanted across nets, regularized by custom per-parameter operations, and transformed according to your schemes." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Casting a Classifier into a Fully Convolutional Network\n", - "\n", - "Let's take the standard Caffe Reference ImageNet model \"CaffeNet\" and transform it into a fully convolutional net for efficient, dense inference on large inputs. This model generates a classification map that covers a given input size instead of a single classification. In particular a 8 $\\times$ 8 classification map on a 451 $\\times$ 451 input gives 64x the output in only 3x the time. The computation exploits a natural efficiency of convolutional network (convnet) structure by amortizing the computation of overlapping receptive fields.\n", - "\n", - "To do so we translate the `InnerProduct` matrix multiplication layers of CaffeNet into `Convolutional` layers. This is the only change: the other layer types are agnostic to spatial size. Convolution is translation-invariant, activations are elementwise operations, and so on. The `fc6` inner product when carried out as convolution by `fc6-conv` turns into a 6 \\times 6 filter with stride 1 on `pool5`. Back in image space this gives a classification for each 227 $\\times$ 227 box with stride 32 in pixels. Remember the equation for output map / receptive field size, output = (input - kernel_size) / stride + 1, and work out the indexing details for a clear understanding." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1,2c1\r\n", - "< # Fully convolutional network version of CaffeNet.\r\n", - "< name: \"CaffeNetConv\"\r\n", - "---\r\n", - "> name: \"CaffeNet\"\r\n", - "7,11c6\r\n", - "< input_param {\r\n", - "< # initial shape for a fully convolutional network:\r\n", - "< # the shape can be set for each input by reshape.\r\n", - "< shape: { dim: 1 dim: 3 dim: 451 dim: 451 }\r\n", - "< }\r\n", - "---\r\n", - "> input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }\r\n", - "157,158c152,153\r\n", - "< name: \"fc6-conv\"\r\n", - "< type: \"Convolution\"\r\n", - "---\r\n", - "> name: \"fc6\"\r\n", - "> type: \"InnerProduct\"\r\n", - "160,161c155,156\r\n", - "< top: \"fc6-conv\"\r\n", - "< convolution_param {\r\n", - "---\r\n", - "> top: \"fc6\"\r\n", - "> inner_product_param {\r\n", - "163d157\r\n", - "< kernel_size: 6\r\n", - "169,170c163,164\r\n", - "< bottom: \"fc6-conv\"\r\n", - "< top: \"fc6-conv\"\r\n", - "---\r\n", - "> bottom: \"fc6\"\r\n", - "> top: \"fc6\"\r\n", - "175,176c169,170\r\n", - "< bottom: \"fc6-conv\"\r\n", - "< top: \"fc6-conv\"\r\n", - "---\r\n", - "> bottom: \"fc6\"\r\n", - "> top: \"fc6\"\r\n", - "182,186c176,180\r\n", - "< name: \"fc7-conv\"\r\n", - "< type: \"Convolution\"\r\n", - "< bottom: \"fc6-conv\"\r\n", - "< top: \"fc7-conv\"\r\n", - "< convolution_param {\r\n", - "---\r\n", - "> name: \"fc7\"\r\n", - "> type: \"InnerProduct\"\r\n", - "> bottom: \"fc6\"\r\n", - "> top: \"fc7\"\r\n", - "> inner_product_param {\r\n", - "188d181\r\n", - "< kernel_size: 1\r\n", - "194,195c187,188\r\n", - "< bottom: \"fc7-conv\"\r\n", - "< top: \"fc7-conv\"\r\n", - "---\r\n", - "> bottom: \"fc7\"\r\n", - "> top: \"fc7\"\r\n", - "200,201c193,194\r\n", - "< bottom: \"fc7-conv\"\r\n", - "< top: \"fc7-conv\"\r\n", - "---\r\n", - "> bottom: \"fc7\"\r\n", - "> top: \"fc7\"\r\n", - "207,211c200,204\r\n", - "< name: \"fc8-conv\"\r\n", - "< type: \"Convolution\"\r\n", - "< bottom: \"fc7-conv\"\r\n", - "< top: \"fc8-conv\"\r\n", - "< convolution_param {\r\n", - "---\r\n", - "> name: \"fc8\"\r\n", - "> type: \"InnerProduct\"\r\n", - "> bottom: \"fc7\"\r\n", - "> top: \"fc8\"\r\n", - "> inner_product_param {\r\n", - "213d205\r\n", - "< kernel_size: 1\r\n", - "219c211\r\n", - "< bottom: \"fc8-conv\"\r\n", - "---\r\n", - "> bottom: \"fc8\"\r\n" - ] - } - ], - "source": [ - "!diff net_surgery/bvlc_caffenet_full_conv.prototxt ../models/bvlc_reference_caffenet/deploy.prototxt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The only differences needed in the architecture are to change the fully connected classifier inner product layers into convolutional layers with the right filter size -- 6 x 6, since the reference model classifiers take the 36 elements of `pool5` as input -- and stride 1 for dense classification. Note that the layers are renamed so that Caffe does not try to blindly load the old parameters when it maps layer names to the pretrained model." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fc6 weights are (4096, 9216) dimensional and biases are (4096,) dimensional\n", - "fc7 weights are (4096, 4096) dimensional and biases are (4096,) dimensional\n", - "fc8 weights are (1000, 4096) dimensional and biases are (1000,) dimensional\n" - ] - } - ], - "source": [ - "# Load the original network and extract the fully connected layers' parameters.\n", - "net = caffe.Net('../models/bvlc_reference_caffenet/deploy.prototxt', \n", - " '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel', \n", - " caffe.TEST)\n", - "params = ['fc6', 'fc7', 'fc8']\n", - "# fc_params = {name: (weights, biases)}\n", - "fc_params = {pr: (net.params[pr][0].data, net.params[pr][1].data) for pr in params}\n", - "\n", - "for fc in params:\n", - " print '{} weights are {} dimensional and biases are {} dimensional'.format(fc, fc_params[fc][0].shape, fc_params[fc][1].shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Consider the shapes of the inner product parameters. The weight dimensions are the output and input sizes while the bias dimension is the output size." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fc6-conv weights are (4096, 256, 6, 6) dimensional and biases are (4096,) dimensional\n", - "fc7-conv weights are (4096, 4096, 1, 1) dimensional and biases are (4096,) dimensional\n", - "fc8-conv weights are (1000, 4096, 1, 1) dimensional and biases are (1000,) dimensional\n" - ] - } - ], - "source": [ - "# Load the fully convolutional network to transplant the parameters.\n", - "net_full_conv = caffe.Net('net_surgery/bvlc_caffenet_full_conv.prototxt', \n", - " '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',\n", - " caffe.TEST)\n", - "params_full_conv = ['fc6-conv', 'fc7-conv', 'fc8-conv']\n", - "# conv_params = {name: (weights, biases)}\n", - "conv_params = {pr: (net_full_conv.params[pr][0].data, net_full_conv.params[pr][1].data) for pr in params_full_conv}\n", - "\n", - "for conv in params_full_conv:\n", - " print '{} weights are {} dimensional and biases are {} dimensional'.format(conv, conv_params[conv][0].shape, conv_params[conv][1].shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The convolution weights are arranged in output $\\times$ input $\\times$ height $\\times$ width dimensions. To map the inner product weights to convolution filters, we could roll the flat inner product vectors into channel $\\times$ height $\\times$ width filter matrices, but actually these are identical in memory (as row major arrays) so we can assign them directly.\n", - "\n", - "The biases are identical to those of the inner product.\n", - "\n", - "Let's transplant!" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "for pr, pr_conv in zip(params, params_full_conv):\n", - " conv_params[pr_conv][0].flat = fc_params[pr][0].flat # flat unrolls the arrays\n", - " conv_params[pr_conv][1][...] = fc_params[pr][1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, save the new model weights." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "net_full_conv.save('net_surgery/bvlc_caffenet_full_conv.caffemodel')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To conclude, let's make a classification map from the example cat image and visualize the confidence of \"tiger cat\" as a probability heatmap. This gives an 8-by-8 prediction on overlapping regions of the 451 $\\times$ 451 input." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[282 282 281 281 281 281 277 282]\n", - " [281 283 283 281 281 281 281 282]\n", - " [283 283 283 283 283 283 287 282]\n", - " [283 283 283 281 283 283 283 259]\n", - " [283 283 283 283 283 283 283 259]\n", - " [283 283 283 283 283 283 259 259]\n", - " [283 283 283 283 259 259 259 277]\n", - " [335 335 283 259 263 263 263 277]]\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAXMAAAC5CAYAAADavt/0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvWnMbWl23/Vbz7T3PtM73aHGrqrurrbdbbttuulYVhqT\n", - "gcgycQwIGVlYFiCQhSCRAUu2IyHExxAhWSIKcsRoRSJBfLAi5AgMIU4CiWxIYqc73XZPRVXXcOu+\n", - 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"output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "\n", - "# load input and configure preprocessing\n", - "im = caffe.io.load_image('images/cat.jpg')\n", - "transformer = caffe.io.Transformer({'data': net_full_conv.blobs['data'].data.shape})\n", - "transformer.set_mean('data', np.load('../python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1))\n", - "transformer.set_transpose('data', (2,0,1))\n", - "transformer.set_channel_swap('data', (2,1,0))\n", - "transformer.set_raw_scale('data', 255.0)\n", - "# make classification map by forward and print prediction indices at each location\n", - "out = net_full_conv.forward_all(data=np.asarray([transformer.preprocess('data', im)]))\n", - "print out['prob'][0].argmax(axis=0)\n", - "# show net input and confidence map (probability of the top prediction at each location)\n", - "plt.subplot(1, 2, 1)\n", - "plt.imshow(transformer.deprocess('data', net_full_conv.blobs['data'].data[0]))\n", - "plt.subplot(1, 2, 2)\n", - "plt.imshow(out['prob'][0,281])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The classifications include various cats -- 282 = tiger cat, 281 = tabby, 283 = persian -- and foxes and other mammals.\n", - "\n", - "In this way the fully connected layers can be extracted as dense features across an image (see `net_full_conv.blobs['fc6'].data` for instance), which is perhaps more useful than the classification map itself.\n", - "\n", - "Note that this model isn't totally appropriate for sliding-window detection since it was trained for whole-image classification. Nevertheless it can work just fine. Sliding-window training and finetuning can be done by defining a sliding-window ground truth and loss such that a loss map is made for every location and solving as usual. (This is an exercise for the reader.)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*A thank you to Rowland Depp for first suggesting this trick.*" - ] - } - ], - "metadata": { - "description": "How to do net surgery and manually change model parameters for custom use.", - "example_name": "Editing model parameters", - "include_in_docs": true, - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.9" - }, - "priority": 5 - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/Talks/.DS_Store b/Talks/.DS_Store new file mode 100644 index 0000000..5008ddf Binary files /dev/null and b/Talks/.DS_Store differ diff --git a/Talks/AlphaGo_IJCAI.pdf b/Talks/AlphaGo_IJCAI.pdf new file mode 100644 index 0000000..7533889 Binary files /dev/null and b/Talks/AlphaGo_IJCAI.pdf differ