diff --git a/README.md b/README.md
index 2c86052..750ff67 100644
--- a/README.md
+++ b/README.md
@@ -93,6 +93,11 @@ IPython Notebook(s) demonstrating deep learning functionality.
 | [tsf-convolutions](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-exercises/4_convolutions.ipynb) |  Create convolutional neural networks in TensorFlow. |
 | [tsf-word2vec](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-exercises/5_word2vec.ipynb) |  Train a skip-gram model over Text8 data in TensorFlow. |
 | [tsf-lstm](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-exercises/6_lstm.ipynb) |  Train a LSTM character model over Text8 data in TensorFlow. |
+| [theano-intro](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/theano-tutorial/intro_theano/intro_theano.ipynb) |  Intro to Theano, which allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation. |
+| [theano-scan](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/theano-tutorial/scan_tutorial/scan_tutorial.ipynb) |  Learn scans, a mechanism to perform loops in a Theano graph. |
+| [theano-logistic](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/theano-tutorial/intro_theano/logistic_regression.ipynb) |  Implement logistic regression in Theano. |
+| [theano-rnn](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/theano-tutorial/rnn_tutorial/simple_rnn.ipynb) |  Implement recurrent neural networks in Theano. |
+| [theano-mlp](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/theano-tutorial/theano_mlp/theano_mlp.ipynb) |  Implement multilayer perceptrons in Theano. |
 | [deep-dream](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/deep-dream/dream.ipynb) |  Caffe-based computer vision program which uses a convolutional neural network to find and enhance patterns in images. |
 
 <br/>
diff --git a/deep-learning/theano-tutorial/intro_theano/Makefile b/deep-learning/theano-tutorial/intro_theano/Makefile
new file mode 100644
index 0000000..dfe1f9d
--- /dev/null
+++ b/deep-learning/theano-tutorial/intro_theano/Makefile
@@ -0,0 +1,3 @@
+intro_theano.pdf: slides_source/intro_theano.tex
+	cd slides_source; pdflatex --shell-escape intro_theano.tex
+	mv slides_source/intro_theano.pdf .
diff --git a/deep-learning/theano-tutorial/intro_theano/intro_theano.ipynb b/deep-learning/theano-tutorial/intro_theano/intro_theano.ipynb
new file mode 100644
index 0000000..12203b3
--- /dev/null
+++ b/deep-learning/theano-tutorial/intro_theano/intro_theano.ipynb
@@ -0,0 +1,882 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction to Theano\n",
+    "\n",
+    "Credits: Forked from [summerschool2015](https://github.com/mila-udem/summerschool2015) by mila-udem\n",
+    "\n",
+    "# Overview\n",
+    "\n",
+    "## Basic usage\n",
+    "\n",
+    "### Defining an expression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import theano\n",
+    "from theano import tensor as T\n",
+    "x = T.vector('x')\n",
+    "W = T.matrix('W')\n",
+    "b = T.vector('b')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "dot = T.dot(x, W)\n",
+    "out = T.nnet.sigmoid(dot + b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Graph visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dot [@A] ''   \n",
+      " |x [@B]\n",
+      " |W [@C]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from theano.printing import debugprint\n",
+    "debugprint(dot)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sigmoid [@A] ''   \n",
+      " |Elemwise{add,no_inplace} [@B] ''   \n",
+      "   |dot [@C] ''   \n",
+      "   | |x [@D]\n",
+      "   | |W [@E]\n",
+      "   |b [@F]\n"
+     ]
+    }
+   ],
+   "source": [
+    "debugprint(out)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compiling a Theano function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "f = theano.function(inputs=[x, W], outputs=dot)\n",
+    "g = theano.function([x, W, b], out)\n",
+    "h = theano.function([x, W, b], [dot, out])\n",
+    "i = theano.function([x, W, b], [dot + b, out])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Graph visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CGemv{inplace} [@A] ''   3\n",
+      " |AllocEmpty{dtype='float64'} [@B] ''   2\n",
+      " | |Shape_i{1} [@C] ''   1\n",
+      " |   |W [@D]\n",
+      " |TensorConstant{1.0} [@E]\n",
+      " |InplaceDimShuffle{1,0} [@F] 'W.T'   0\n",
+      " | |W [@D]\n",
+      " |x [@G]\n",
+      " |TensorConstant{0.0} [@H]\n"
+     ]
+    }
+   ],
+   "source": [
+    "debugprint(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Elemwise{ScalarSigmoid}[(0, 0)] [@A] ''   2\n",
+      " |CGemv{no_inplace} [@B] ''   1\n",
+      "   |b [@C]\n",
+      "   |TensorConstant{1.0} [@D]\n",
+      "   |InplaceDimShuffle{1,0} [@E] 'W.T'   0\n",
+      "   | |W [@F]\n",
+      "   |x [@G]\n",
+      "   |TensorConstant{1.0} [@D]\n"
+     ]
+    }
+   ],
+   "source": [
+    "debugprint(g)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The output file is available at pydotprint_f.png\n"
+     ]
+    }
+   ],
+   "source": [
+    "from theano.printing import pydotprint\n",
+    "pydotprint(f, outfile='pydotprint_f.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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MmVKXQ0RERERERE0IgzCiluzXXzF3LoqLsWtXS24Eq2DlypVGo/Hxxx8XBGHGjBlSl0NE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\nQkNDw8LCguwCAwODgoI68t1/Z86cKSsrKysrO3XqlPGiqKiou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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {
+      "image/png": {
+       "width": 1000
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image\n",
+    "Image('pydotprint_f.png', width=1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The output file is available at pydotprint_g.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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cPT/wwAMtWrTYs2dPz549y2SPO+64o4oiS0pKkpKSjOHl8jWXmXQUbPD29s7P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T+Iu04cDkclU8y7Z2KdZ3c8648uke3ZfO76b0LvAXRDamJvWnYR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\nCedktmgAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {
+      "image/png": {
+       "width": 1000
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pydotprint(g, outfile='pydotprint_g.png')\n",
+    "Image('pydotprint_g.png', width=1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The output file is available at pydotprint_h.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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MmVKXQ0RERERERE0IgzCiluzXXzF3LoqLsWtXS24Eq2DlypVGo/Hxxx8XBGHGjBlSl0NE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\na//+/c8991ylFkzSVVdddeeddzpvz5w5Mykp6cUXX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3Ho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\nLfN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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {
+      "image/png": {
+       "width": 1000
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pydotprint(h, outfile='pydotprint_h.png')\n",
+    "Image('pydotprint_h.png', width=1000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Executing a Theano function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.79048354,  0.03158954, -0.26423186])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "np.random.seed(42)\n",
+    "W_val = np.random.randn(4, 3)\n",
+    "x_val = np.random.rand(4)\n",
+    "b_val = np.ones(3)\n",
+    "\n",
+    "f(x_val, W_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.9421594 ,  0.73722395,  0.67606977])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g(x_val, W_val, b_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[array([ 1.79048354,  0.03158954, -0.26423186]),\n",
+       " array([ 0.9421594 ,  0.73722395,  0.67606977])]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "h(x_val, W_val, b_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[array([ 2.79048354,  1.03158954,  0.73576814]),\n",
+       " array([ 0.9421594 ,  0.73722395,  0.67606977])]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "i(x_val, W_val, b_val)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Graph definition and Syntax\n",
+    "## Graph structure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The output file is available at pydotprint_f_notcompact.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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njEajo8shIiIiIqIGi6EDNXaxsejcGT//jJUrsXUrGsmDHdq3b79jx46///576tSpZrPZ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gv9uqmJkU11U1tLYztLY2sL2FgIFqaCXXUjc1OJpbmRlYXE3FRiW92ompmRhanEtrqRuanEykJi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\n06ZNa9euXbt27c6dO0tLS9u1a9ezZ8/o6Ojo6OioqKjraezG9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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {
+      "image/png": {
+       "width": 1000
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pydotprint(f, compact=False, outfile='pydotprint_f_notcompact.png')\n",
+    "Image('pydotprint_f_notcompact.png', width=1000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Strong typing\n",
+    "### Broadcasting tensors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(True, False)\n"
+     ]
+    }
+   ],
+   "source": [
+    "r = T.row('r')\n",
+    "print(r.broadcastable)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(False, True)\n"
+     ]
+    }
+   ],
+   "source": [
+    "c = T.col('c')\n",
+    "print(c.broadcastable)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 1.1  2.1  3.1]\n",
+      " [ 1.2  2.2  3.2]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "f = theano.function([r, c], r + c)\n",
+    "print(f([[1, 2, 3]], [[.1], [.2]]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Graph Transformations\n",
+    "## Substitution and Cloning\n",
+    "### The `givens` keyword"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.90651511,  0.60431744, -0.64253361])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_ = T.vector('x_')\n",
+    "x_n = (x_ - x_.mean()) / x_.std()\n",
+    "f_n = theano.function([x_, W], dot, givens={x: x_n})\n",
+    "f_n(x_val, W_val)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Cloning with replacement"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.90651511,  0.60431744, -0.64253361])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dot_n, out_n = theano.clone([dot, out], replace={x: (x - x.mean()) / x.std()})                        \n",
+    "f_n = theano.function([x, W], dot_n)                                                                  \n",
+    "f_n(x_val, W_val)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Gradient\n",
+    "### Using `theano.grad`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y = T.vector('y')\n",
+    "C = ((out - y) ** 2).sum()\n",
+    "dC_dW = theano.grad(C, W)\n",
+    "dC_db = theano.grad(C, b)\n",
+    "# dC_dW, dC_db = theano.grad(C, [W, b])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using the gradients"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[array(0.6137821438190066), array([[ 0.01095277,  0.07045955,  0.051161  ],\n",
+      "       [ 0.01889131,  0.12152849,  0.0882424 ],\n",
+      "       [ 0.01555008,  0.10003427,  0.07263534],\n",
+      "       [ 0.01048429,  0.06744584,  0.04897273]]), array([ 0.03600015,  0.23159028,  0.16815877])]\n"
+     ]
+    }
+   ],
+   "source": [
+    "cost_and_grads = theano.function([x, W, b, y], [C, dC_dW, dC_db])\n",
+    "y_val = np.random.uniform(size=3)\n",
+    "print(cost_and_grads(x_val, W_val, b_val, y_val))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[array(0.6137821438190066), array([[ 0.49561888, -0.14531026,  0.64257244],\n",
+      "       [ 1.52114073, -0.24630622, -0.2429612 ],\n",
+      "       [ 1.57765781,  0.7574313 , -0.47673792],\n",
+      "       [ 0.54151161, -0.47016228, -0.47062703]]), array([ 0.99639999,  0.97684097,  0.98318412])]\n"
+     ]
+    }
+   ],
+   "source": [
+    "upd_W = W - 0.1 * dC_dW\n",
+    "upd_b = b - 0.1 * dC_db\n",
+    "cost_and_upd = theano.function([x, W, b, y], [C, upd_W, upd_b])\n",
+    "print(cost_and_upd(x_val, W_val, b_val, y_val))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The output file is available at pydotprint_cost_and_upd.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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kAQAAUG/xzzUAAAAAAICm6+jRozt27DDavtjY2JKSkoED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U6dOuXu7t6II9Ay5k8sFgcGBvr5+RUWFi5cuPAjOz/KgQMH+vTp06dPH0tLy6SkJHxXoJiqqqpi\nZMhWfZTu3btra2s3dwyJRLJp0yYmk7lmzRoWC9esA0BTioqKGj58uIODw+XLl1vRZT1NBWP+ABQE\nPj8purKyslOnTh05ciQhIcHS0nLTpk1TpkzR19enOxcAAICc4nK51DdfHh4e1BaxWJyVlSVbB4aH\nh6enp0skEhaLZWRkVK8ONDMzo/cl0Cs3N5cQInuOTa0AlJeXR1smgLZCU1Pz559/Hj58uIeHx5gx\nYz7mIY8ePSopKdmwYUNzZ2tFamtrN2/evHXr1mHDhgUGBn58c0YvqvPbtm1bfn7+okWLli5d2sDQ\nz3+LjIwUi8V9+vRpvoQgn+pVfcnJyWKxWF9fn/qo4+rq6ujoKL1Yp2UEBwfv3bv37t272traysrK\nq1atYjAYLRkAANq23r17379/f+jQoe7u7tevX28t/9EDAHwSjPlTXDExMUeOHDl79mxtba2Hh8cP\nP/zQr18/ukMBAAC0EWVlZSkpKbJ1YGJiYkVFBSFEW1ubx+PJ1oGWlpbq6up0R24hjo6OsbGxYrFY\nOruOSCRis9k9evSIjY2lNxtA2zBt2rSbN2/Gx8d/8Mt6iUQyfPhwZ2dnNH9Sr1+/njJlysOHD/ft\n2zdr1qzP6RtabMyfbOfn5eW1fPlyQ0PDTzpCcXHxypUrjx07Rk36gjF/bVt1dXV0dHS9qq9Tp05O\nTk7SgX2dO3emMWFVVVVpaemlS5dWrlxZVVW1f//+hQsX0pgHANqkjIyMIUOGsFiskJAQIyMjuuO0\nHIz5A1AQaP4UDp/PP3r06IkTJxITE+3t7efOnTtp0iQFHNsOAADQ8nJzc6kJQqV1YEZGRl1dHSFE\nX19fOiiQagRNTEza5LTbo0ePvnr1amlpqXRZoJKSkvbt248cOTI4OJjebABtQ2lpaffu3YcMGRIY\nGNjwnocPH05PT9++fTvG01BCQ0OnT5+urq5+5syZ3r17f+bRWqD5q62tDQgI8Pf3z83NnT179rJl\nyxr33eX48ePnzJkjnfrlq6++ysjIePnypbKyMo/Ha9LIQIOamprHjx/Xq/r09PR69eolJ1Xf+5w+\nfXrq1Km9e/d+9OgR3VkAoA3Kz8//6quvCgoKQkNDbW1t6Y7TQtD8ASgIzPapQDIyMn788ceAgACB\nQPD111/v379/8ODBOMkHAABoMZ07d673zVpNTU1KSopsHXju3Lny8nJCCIfD4fF4snWgvb19G7hY\nx8XF5erVq5mZmXZ2dtSWrKwsQoirqyutuQDaDm1t7cOHD3/zzTfjx48fNmzY+3a7du1aSUkJaj9K\nXV3dxo0bt2zZMnny5MOHD8v/D9va2tqgoCB/f/9Xr17NmDFj7dq1nzNe4dq1axcuXKi30crKisfj\npaSkfF5SoEG9qu/Vq1cikahjx469e/f28PCQ56qvntGjRxNCpJMEAAA0rU6dOt29e/frr7/+4osv\ngoODXVxc6E4EANBk0PwphHv37h04cODatWtGRkarV6+ePHlyly5d6A4FAAAAhMPh2NjY2NjYyG7k\n8/nUiECqDgwODk5KSqqtrSWEcLlc2SUDra2tLS0tW9c3YpMmTVq1alV4eLi0+QsPD1dWVp48eTK9\nwQDakq+//trDw2P27NlxcXHvLLFCQkKysrLWrl0r3fLo0SOFXeMtPz9/woQJ0dHRJ0+enDJlCt1x\nPoDq/LZv356cnDxjxow///zT2Nj4M49ZXV0t+1vM9tnqCIXCqKioelVfhw4d+vbt27qqvnqKiooI\nIePGjaM7CAC0Wdra2jdu3BgzZsxXX30VEhKChZAAoM3AbJ9t2du3bwMCAg4dOpSenv6f//xn4cKF\nuJoeAACgNRKJRNnZ2bJ1YFxcXH5+PiGEzWYbGBjI1oG2trZ6enp0R27I2rVrr169Gh0draKiUl1d\n7ejoOGHCBCwzBtC0ioqKbGxsxo4d+9NPP9W76+bNm35+fmPHjqV+K5FIsrKyVFRUNm/e3OIx6RcR\nETF+/HgOh3Pp0iUHB4emOmxNTY2Kioq5uXlSUlJTHbOuru7XX3/dsWNHUlLSjBkzVq9ebWJi0lQH\nl4XmT/7V1tYmJiZSPV94ePiLFy+EQmH79u2dnZ3lfALPhvn5+bVr127WrFkqKipCoXDy5MlMJjMo\nKEhZWZnuaADQltXW1k6bNu3y5ct//PGHm5sb3XGaF2b7BFAQaP7apsLCwkOHDh05ckQgEHz77bcL\nFiywt7enOxQAAAA0JT6fL7tkYFpaWkJCQlVVFSGEy+VK1wuk6kAbGxsVFRW6I/9XXV3d/v37o6Ki\nLCwsEhIS+vXrt2jRIsw3CNDkgoKCpkyZ8tdff3355ZfSjREREV9++SX1s0JWamqqmZlZywak3/bt\n29etW/fll18GBQXp6Og01WEfPnx47ty5/fv3czicH3/8sW/fvvXGdn8qiURy8eJFX1/fly9fzpw5\nc9WqVaampk2V9t/Q/Mmhurq6ly9fSkf1PXv27O3btzo6Ov369WvVVV89q1evPnz4sJaW1jfffKOi\nojJw4MDhw4fjEwIAtIDa2tqpU6deuXLl+vXrAwcOpDtOM0LzB6Ag0Py1NSkpKbt37z558mS7du0W\nLFjwww8/dOjQge5QAAAA0BLEYnFWVla9OjA9PV0ikbBYLCMjo3p1oAJ+yw+gaMaMGfPs2bPnz59r\naGjQnUW+VFRUeHl5nTt3zs/Pb+XKlXJbLVCd3+bNmxMSEiZPnrx69WorKyu6Q0FLqFf1PX/+XCAQ\ncLlcFxeXtlT1AQDID6FQOHbs2IiIiPDwcEtLS7rjNBc0fwAKAs1f2xEcHLx9+/bw8PDu3buvWLFi\n0qRJmBADAAAASktLU1NTZevAxMTEiooKQoi2tjaPx5OtA62srNTU1OiODABNJi8vz8bG5vvvv9+9\nezfdWeRIbm7u6NGjk5KSAgMDpbOeyhuq89uyZUt8fPzkyZNXrVplbW1NdyhoRu+s+tTV1R0cHKie\nz9XVFZfsAAA0q+rq6q+++io1NTUsLMzIyIjuOM0CzR+AgkDz1xbcuHHD39//77//7tu374oVK0aP\nHs1kMukOBQAAAPIrNzdXumQgVQdmZGTU1dURQvT19akWUFoHmpiY4KMFQOsVEBDg5eV1//59rPlN\niY2NpSYSvHr16mdOwtl8goODt27dGhUVNW7cOB8fH7nNCZ9DIpEkJCRIq74XL16Ul5erqan16NFD\nOqrP0tJSSUmJ7qQAAAqksrJy6NChubm5YWFh+vr6dMdpemj+ABQEmr9WTCKRXL16dcuWLbGxscOH\nD1+1ahVO5gEAAKBxampqUlJSZOtAarQBIYTD4fB4POkcodbW1vb29u3ataM7MgB8rGHDhmVkZDx9\n+lR+1vuky6lTp7y8vIYMGRIUFCSfP8eCg4P9/PwePXo0bty49evX29ra0p0Imky9qi8uLq6srExV\nVbVnz56o+gAA5MebN2/69++vrq5+7949+fy08DnQ/AEoCDR/rZJIJPrll1/8/f2zs7MnTZqEiV8A\nAACgOfD5fOl6gVQdmJSUVFtbSwjhcrmySwZaW1vjy0oAuZWZmWlra7tgwYKtW7fSnYU2dXV1S5Ys\nOXDggLe399atW+Xw51VwcLC/v39kZOS4cePWrVtnZ2dHdyJoAvHx8Q1XfRYWFiwWi+6YAADwPzIy\nMvr27durV68rV67I4WeGz4HmD0BBoPlrZWpra8+cOePn55ecnOzh4bF27VpcBAoAAAAtRiQSZWdn\ny9aBcXFx+fn5hBA2m21gYCBbB9ra2urp6dEdGQAIIeTHH39cvHhxZGSkk5MT3VloUF5ePnny5Nu3\nbwcEBEyePJnuOPXdvn3bx8cnPDycOsWzt7enOxE0nmzVFx8fX1paqqKi4igDVR8AQKvw5MmT/v37\nT5w48fjx43RnaUpo/gAUBJq/VoNa4H3Dhg2vXr2aPHnymjVrLC0t6Q4FAAAAQPh8vnS9QOnowOrq\nakIIl8uVXTLQzMzMxsYG8w0CtLy6ujo3N7fS0tLo6GhlZWW647So/Pz80aNHv3r16sKFC4MGDaI7\nzv+4c+fOhg0bwsPDR44cuXbt2r59+9KdCD5ZXl5edHR0TExMeHh4TEwMn8/ncDhOTk7Sqs/c3FzR\n/tEBALQN586dmzx58o8//jhnzhy6szQZNH8ACgIXmrUCVOfn4+Pz6tWrGTNmhIaGGhkZ0R0KAAAA\n4L+4XC715aZ0i1gszsrKkraACQkJ4eHh6enpEolEWVnZ0NCwXh1oZmZGY34ARcBkMo8fP25vb+/v\n779+/Xq647ScxMTE4cOHs1isqKgoHo9Hd5z/d/fu3fXr11OdX0REhLOzM92J4GPl5+c/fvxYOrAv\nLy9PWvVNmTIFVR8AQJsxceLExMTEhQsX2tjYDBgwgO44AACfAGP+5N2FCxc2b96ckJAwc+bM1atX\nm5iY0J0IAAAAoDFKS0tTU1Nl68DExMSKigpCiLa2No/Hk60Drays1NTU6I4M0Nbs2rVr7dq1MTEx\n3bt3pztLSwgJCfHw8HBycrp8+bK2tjbdcf7r/v37a9euDQ8Pd3d337Rpk4uLC92J4AMKCgqioqJk\nqz42m92rVy+M6gMAaPMkEsn48ePv3LkTHR1tampKd5wmgDF/AAoCzZ/8io+PX758eUhIyNdff+3r\n6+vg4EB3IgAAAIAmlpubKztHaEJCQkZGRl1dHSFEX1+fagGldaCpqSmDwaA7MkArVldXN2DAAJFI\nFBERoaSkRHec5nXs2LG5c+dOmDAhMDCQzWbTHYcQQqKiotauXXvr1q1BgwZt2rTJ1dWV7kTwboWF\nhY8ePZKt+pSUlCwsLKiez9XV1dbWVk7+UgEAQHMTCAR9+/Zls9kRERGqqqp0x/lcaP4AFARm+5RH\nhYWFPj4+x48fd3JywsQvAAAA0IZ17ty5c+fOsltqampSUlJk68CzZ88KBAJCCIfD4fF4snWgubl5\nu3btaMoO0PpQc3726NFjz549K1asoDtOM9q4caOvr++GDRt8fHzk4YqBx48fr1mz5tatWwMHDvz7\n77/R+cmboqKiyMhI2aqPyWRaWlo6Ojp6e3s7Ojo6ODhoaGjQHRMAAGjQrl27M2fO9OvXz9vb+8CB\nA3THAQD4KBjzJ18EAsHWrVsPHjxoamq6b9++L7/8ku5EAAAAAPTj8/nUiEBpHZiUlFRbW0sI4XK5\n0kGB1A1jY+M2P5gJ4HNs2bJly5YtT548sbKyojtL0xOJRLNnzz59+vSRI0dmzpxJdxwSHR29evXq\nW7du9enTZ8uWLTjFkxPFxcURERHvrPoo9vb2uLIEAACkfv/997Fjx54/f97Dw4PuLJ8FY/4AFASa\nPzny22+/eXt78/n8NWvWLFq0SEVFhe5EAAAAAHJKKBTm5OTI1oEvXrwoKCgghLDZbAMDA2kdaGZm\nZmtrq6enR3dkAHkhFoupSavCwsKYTCbdcZpSTU3Nt99+e/369ZMnT44fP57eMLGxsT4+PtevX3dy\ncvLz80PnR6+SkpLw8HDZqo/BYFhZWaHqAwCAjzRr1qwrV648f/5cX1+f7iyNh+YPQEGg+ZML8fHx\nCxYsuH//vqen5+bNmzt27Eh3IgAAAIDWh8/nS9cLlI4OrK6uJoRwuVzZJQPNzMxsbGxwoRUorGfP\nnvXq1Wv37t0LFiygO0uTEQgEY8aMiYqKunLlyqBBg2hM8uTJkw0bNly/ft3R0XHbtm3o/GjB5/PD\nwsIaqPrs7Ow0NTXpjgkAAK2GQCDo2bOnhYVFcHCwPMwl3jho/gAUBJo/mr1582bZsmVBQUHu7u4H\nDhywtLSkOxEAAABA2yEWi7OysurVgWlpaYQQZWVlQ0PDenWgmZkZ3ZEBWsi6dev27t37/PlzHo9H\nd5Ym8Pr166FDh5aWloaGhtrY2NAV4+nTp+vXr79+/XrPnj39/f3R+bWkioqKJ0+eUD1feHh4Wlpa\nvarP1tZWS0uL7pgAANCKPXz40NXV9ejRozNmQXq3jAAAIABJREFUzKA7SyOh+QNQEGj+6HTmzJnl\ny5eLxWI/P78ZM2a0sZl2AAAAAORTaWlpamqqdFBgQkJCYmJiRUUF+WdooGwdaGVlpaamRndkgKZX\nU1Pj6Oiop6d369at1nvdOuXVq1dDhw5lsVh//fWXiYkJLRkSExM3bNhw6dIlBweHjRs3jhw5srW/\nq/KvsrIyNjZWOqqPWv/V2tpaWvV1795dW1ub7pgAANCmeHt7HzlyJC4uztDQkO4sjYHmD0BBoPmj\nR35+/rx5865evTp79uwtW7ZwuVy6EwEAAAAotNzcXNk5QhMSEjIyMurq6ggh+vr6VAsorQNNTU3x\nnT60AY8ePXJxcTly5IinpyfdWRrv6dOnX331VadOnUJCQmhZ0TMpKWn9+vWXLl2ys7Pz9fVF59d8\nqqqqYmSg6gMAgJZXU1PTo0cPExOTGzdu0J2lMdD8ASgINH8tra6ubufOnb6+vhYWFidOnLCzs6M7\nEQAAAAC8Q01NTUpKimwd+Pz5c4FAQAjhcDg8Hk+2DrSwsNDQ0KA7MsAnW758+bFjx1rvdetRUVEj\nRozg8XjXr19v3759Cz97cnLyunXrLl26ZG5u7uvrO3bsWMzj0rTqVX3JyclisVhfX19a9Tk5Oenr\n69MdEwAAFMvt27cHDx7822+/eXh40J3lk6H5A1AQaP5aVGJioqen5+PHj319fZcuXaqsrEx3IgAA\nAAD4BHw+X3bJwPj4eGrQCSGEy+VKBwVSN4yNjZWUlOiODNCQqqoqe3v7bt26Xb9+XbrxxYsXSUlJ\n48aNozHYx/j7779HjBjRu3fvK1eutHD1/urVq7Vr1166dKlbt26bN29G59dUqquro6Oj61V9enp6\nvXr1krZ9nTt3pjsmAAAoujlz5ly5ciUpKUlTU5PuLJ8GzR+AgkDz13IOHz68cuVKAwODX375pW/f\nvnTHAQAAAIAmIBQKc3JyZOvAFy9eFBQUEELYbLaBgYFsHWhra6ulpUV3ZID/ERER0b9//5MnT373\n3XcikWjbtm2bN28eMGDA7du36Y72X0+ePOnYsWOXLl1kN165cmXixIkjR448c+YMm81usTApKSm+\nvr7nzp0zNTXdsmULOr/PVFNT8/jxY2nV9+rVK5FI1LFjx969e6PqAwAAuVVSUmJhYTFjxozt27fT\nneXToPkDUBBo/lpCSUmJp6fnlStXFi9e7Ofnp6KiQnciAAAAAGhGfD5ful6gdHRgdXU1IYTL5cou\nGWhmZmZjY4PPh0Cv+fPnnzt37ty5cwsWLEhOTq6rq1NRUSkvL5eTSUrc3d1TU1P//vtv6ZSkFy9e\n/PbbbydOnBgYGNhiI2tTU1M3bdp07tw5ExOTrVu3ovNrHKFQGBUVVa/q09XV7dOnD6o+AABoRfbv\n379q1aq4uDgej0d3lk+A5g9AQaD5a3YhISHTpk1TV1cPCgpydnamOw4AAAAA0EAsFmdlZdWrA9PS\n0gghysrKhoaG9epAMzMzuiODAsnNzXVwcCgqKmIwGHV1ddTGsLAwFxcXeoMRQmJiYpycnJhMpr6+\nfnh4uLGx8cmTJ2fOnDl9+vSff/65ZWq/nJyczZs3//LLL0ZGRhs2bJg0aZKcdKKtwjurvg4dOvTt\n2xdVHwAAtF5isdjOzs7a2vrixYt0Z/kEaP4AFASav2ZUW1u7detWX1/fYcOGBQYGduzYke5EAAAA\nACBHSktLU1NTZevAxMTEiooK8s/QQNk60MrKSk1Nje7I0AbduXNn+vTpubm51IqVFDab7ePjs2bN\nGhqDUb755puQkBCRSKSsrKylpfXDDz9s3bp14cKFe/fuZTAYzf3sr1+/9vX1PXHihIGBgY+PDzq/\nj1FbW5uYmCit+p4+fVpRUdG+fXtnZ2dUfQAA0JZcu3Zt1KhRkZGRrWhdJzR/AAoCzV9zKS4unjBh\nQnh4+M6dO+fNm9cCJ6UAAAAA0Abk5uZSLaC0DszIyKCGYenr60uXDKRumJqa4nMmNFpVVdW6deuo\nCk061I/CYDAGDRp069YturJR4uPjbW1tpSetLBaLzWaPGjUqKCjo8//mP3/+3M7O7n335ubmbtq0\n6cSJEx07dly/fv306dNbcjXB1qWuru7ly5fSqu/Zs2dv377V0dHp168fqj4AAGjbXFxc1NTUbt68\nSXeQj4XmD0BBoPlrFnFxcaNGjRKJRJcuXerVqxfdcQAAAACgFaupqUlJSZFdMvD58+cCgYAQwuFw\neDyebB1oYWGhoaFBd2RoHWbPnn306NH33auqqlpeXs5isVoyUj3Tpk07e/asSCSSbmGxWO3atbt/\n/76tre3nHHnz5s379u3LyMho165dvbvy8vI2btx48uTJDh06bNiwAZ3fv9Wr+qifSFwu18XFBVUf\nAAAolFu3bg0ePPjBgwf9+/enO8tHQfMHoCDQ/DW9U6dOzZ49e8CAAWfPntXR0aE7DgAAAAC0QXw+\nX3bJwPj4+KSkJGq2Ri6XKx0USN0wMTFhMpl0Rwa5I5FIDhw4sGzZMkKI7FSfUhERETQuVZ6Zmcnj\n8f4djMViaWho3Lt3z97evnFH3r179/Lly5lM5ubNm2VnNM3Pz9+2bdvx48e1tbV9fHzQ+UlJJJKE\nhIR6VZ+6urqDg4O06rO0tGyZZRcBAADkyoABA9TU1EJCQugO8lHQ/AEoCDR/TUkikfj6+m7atGne\nvHl79uzBChAAAAAA0GKEQmFOTo5sHfjixYuCggJCCJvNNjAwkK0DbW1ttbS06I4McuH+/ftjx44t\nLy+XHVpHCGGz2Rs3bly9ejVdwZYvX37gwIF6qShMJlNTU7Nx5d+OHTu8vb2p2+rq6tnZ2Vwul+r8\nAgICNDU1N27cOG3aNA6H87kvoDWrV/W9ePGivLxcTU2tR48eqPoAAABkhYaGDhs2LCYmpmfPnnRn\n+TA0fwAKAs1fk6mtrfXy8jp16tSRI0dmzpxJdxwAAAAAAMLn82WXDKRuV1dXE0K4XK7skoHW1tYW\nFhb0Tu0IdHn9+rWHh0dUVJTsADt6l/orKSkxMDCoqqr6911MJlMikRgbG+/bt2/UqFGfdNjDhw/P\nmzdPduHAxYsXC4XCgICAdu3arVq1ytPTU11dvQleQGtTr+qLi4srKytTVVXt2bOntOrDjwgAAIB3\nov6XPHPmDN1BPgzNH4CCQPPXNAQCwZgxYx4/fnzlyhU3Nze64wAAAAAAvJtIJMrOzq5XB6alpRFC\nlJWVDQ0NZetACt2RoSWIxeJ169bt2LGDwWDU1dVRG1VVVcvKymiZy8THx8fPz08sFstuVFJSqq2t\ndXBw8PX1HTlyJIPB+KRjHj9+3MvLq94pMJvN5nA4ixcvXrp0qba2dhNEbz3i4+OlVV98fHxpaamK\nioqjDFR9AAAAH+P06dMzZ85MSUkxMjKiO8sHoPkDUBBo/ppAUVHRiBEjcnJyQkJCPnOdeQAAAACA\nlldaWpqamipbB758+bKyspL8MzRQtg60srJSU1OjOzI0i2vXrn333XfV1dXSOTYfPnzYp0+fFo4h\nEAgMDAzKy8ulW6jOz9HR0d/f/8svv2zEMc+ePfvdd99JS00pFou1aNGiXbt2fVbiViItLS0sLAxV\nHwAAQNMSCoWGhoazZs3asmUL3Vk+AM0fgIJA8/e5ioqKBg8ezOfzQ0JCLC0t6Y4DAAAAANA0cnNz\nqRZQWgdmZGRQxYm+vr50jlDqhqmp6acOwAL5lJKSMnr06KSkJLFYzGazfX19pavitZg9e/asXLmS\nmnqUxWKJxeL+/fv7+fm5uro27oDXrl37z3/+IzuXqSxVVdXMzExdXd3GJ5ZXeXl50dHR0oF9eXl5\nHA7HyclJWvWZm5tjfXoAAIDPt2bNmsDAwOzsbDn/jxXNH4CCQPP3WUpLS93d3UtKSh48eGBoaEh3\nHAAAAACAZlRTU5OSkiJbByYnJwsEAkIIh8Ph8XiydaCFhYWGhgbdkaEx3r59O2vWrN9++00ikQwd\nOjQkJKQln10kEhkZGeXn5ysrK4tEol69em3cuHH48OGNPuCNGzdGjRpVW1v7vpNfFou1cuXKrVu3\nNvop5EdBQUFUVJRs1cdms3v16oWqDwAAoFmlp6d37dr1t99+GzduHN1ZGoLmD0BBoPlrvJKSkkGD\nBpWXlz948MDAwIDuOAAAAAAANODz+bJLBsbHxyclJVGDq7hcrnRQIHXDxMSEyWTSHRkIIeTt27fl\nMvh8PiGkqqqqurpaIpGUlpZGRkYGBwcrKSktWrSImnjz7du30llAKUKhsKKi4oPPpa6uzmazZbew\n2Wx1dXVCiJaWFpPJ5HA41BSy2traDx8+PHHiBCHEwsLi+++/d3Nz0/xHI7rkyMhId3f3mpqaf8/z\nWS9hVlaWjo7Opx7/c0gkkoCAAAcHBycnp0YfpLCw8NGjR7JVn5KSkoODg4uLC6o+AACAluTu7q6m\nphYcHEx3kIag+QNQEGj+GkkgEAwZMiQrK+vBgwc8Ho/uOAAAAAAA8kIoFObk5MjWgS9evCgoKCCE\nsNlsAwMD2TrQzs5OU1OT7shthEAgKC4ufvPmTbGMoqKi4uJigUAgLfnKyspKS0vfeSaooqKiqqpK\nCOFyudSWnJycrl27du7cmTRY4DWsgcqwrKysrq6uurq6qqqKEMLn88vLyyUSyTuLOiaTqaWlpaWl\npSmj/f/q0KGDrq5u+/btNTQ0YmNjBwwYUFlZKX2xDAaDw+HU1dUJhUJqi46OjpmZmbm5+fz5852d\nnT/4WppKTEyMl5dXbGzsnj17lixZ8vEPfPPmzcOHD2WrPiaTaWlpKR3V16NHj4/5QwEAAICmFRQU\nNGPGjPz8fOnnKDmE5g9AQaD5awyJROLh4XHv3r27d+/a2trSHQcAAAAAQN7x+XzZJQOp29XV1YQQ\nLpcru2SgtbW1hYUFi8WiO7Lcefv2bU5OTmFhYU5OTkFBwevXr6lfqW6vuLi4pqZGurOysrJsHyZb\nlWlra2v+Ly0tLW1t7Xeu1Mjn89PT03v27NkyrzEjIyM7O7t///6EkLq6urKysrKysvL/xefzZYtM\nabtZXFwsFoulh2Kz2WKxWNogMplMbW1tAwMDHo9nbm7u4OBgbW1tZmbW8nPSFhcXe3t7BwYGUm/4\nxIkTg4KCGti/qKgoMjKygarPwcEBM+sCAADQrrKysmPHjvv27fP09KQ7y3uh+QNQEGj+GsPX13fz\n5s0hISHu7u50ZwEAAAAAaJVEIlF2dna9OjAtLY0QoqysbGhoKFsH2tjY6OvrN0eMuLg4MzMzaqpJ\neVBdXZ2ZmZmRkSH9NTs7m2r4pPNqKikp6enp6f9DT0+v3rg3XV1dLS0tel8ILcrKyqghj3l5eUeO\nHKmrq+NwOBKJpLq6ms/n5+XlFRYWUlPREkLU1dW7dOmip6dnaGhoYmJibGxM/WpsbKyiotIc8YRC\noZ+fn7+/f21trbSkNDExSU9Pl92tuLg4IiKigarP3t6+Xbt2zZEQAAAAPoeHh0dZWdlff/1Fd5D3\nQvMHoCDQ/H2yc+fOTZ48OSAg4Pvvv6c7CwAAAABAm0INDZStA1++fFlZWUn+GRooWwdaWVl9fmM3\nceLE27dv+/r6enp6tvByaHl5eYmJicnJyenp6dKeLy8vj7pXQ0PD2NjY1NTU0NCwU6dOXbp06dSp\nU+fOnTt16qSnp4flEhuntra2sLAwLy8vNzc3Pz+f+jUrKysjIyMjI0Parerr60u7QBMTEwsLC0tL\ny06dOn3OU4eEhMyZMyc7O1taPVIYDEZqampcXJxs1cdgMKysrKRVHybFBQAAaBXOnTs3derUgoIC\nuZ3wE80fgIJA8/dp4uLi+vXr5+HhERAQQHcWAAAAAACFkJubS7WA0jowPT2dOpHR19eXzhFK3TA1\nNX3nrJXvY2Njk5CQwGAwDAwMtm3bNmnSpOYo1WpqapKTk5OSkpKTkxMTE6nCr6ysjBCirq5uZmZm\n8g/pyLMOHTo0eQxoWFFRkexoS6oOTEtLoxpBLS0tCwsLqgWkbnTr1o3D4XzwsBkZGXPmzAkJCWEy\nme9cv1BTU7O8vFxPT8/JycnR0ZH6lVpbEQAAAFqR0tJSXV3dM2fOeHh40J3l3dD8ASgINH+foKam\nxsnJSVNT886dOx9zggcAAAAAAM2hpqYmJSVFtg5MTk4WCASEEA6Hw+PxZOtACwuL962CVldXp6qq\nKhQKCSFUX2hqaurv7z9u3LhPqg//rbCw8MmTJ0+fPqV+ffXqVV1dHYPBMDQ0lG2PzM3NjYyMPueJ\noLlJJJLs7GyqtX358iV1IysrixCipKTUrVs3BwcHBweHHj16ODg4dOzYUfax1dXV/v7+27Ztq6ur\nk12DUBabzfb09Fy9erWBgUFLvB4AAABoTgMGDOjatWtgYCDdQd4NzR+AgkDz9wlWrFgREBAQFxeH\nqy8BAAAAAOQNn8+XDgqkbmRmZlIzK3K5XOmgQOqGiYkJk8nMysoyNjaWPQg1KsvKymrTpk2fdLF2\ndnb2o0ePqJ7v6dOnubm5hBADAwM7Ozs7Ozt7e3uq8JOfBQXhc1RUVFDDN58/f/78+fNnz569fv2a\nENK5c2eqAnRwcBCLxevWrUtPT3/nOD8pJSWl8ePHnzlzpqWyAwAAQDPaunXr4cOHs7OzP/MysmaC\n5g9AQaD5+1hhYWFffPHFsWPHZsyYQXcWAAAAAAD4sNLSUqqeocZpUaqrqwkhXC7XwsJCU1Pzr7/+\n+vcDWSyWWCzu06fPjh07BgwY8M6Di0Si2NjYyMjIiIiIyMjInJwcDofTvXt3e3t7aduno6PTvK8Q\n5EZJScnTp0+pIvDJkyfPnz+nCj8Gg0H9dWrg1NvExCQ9Pb0FwwIAAEBzefToUd++fePj462trenO\n8g5o/gAUBJq/j1JTU+Po6Kivr//XX3/J5/UaAAAAAADwQXV1dZmZmdSSe4mJiXfu3ElNTX3fNIxU\nYTNo0KA9e/bY29sTQiorKx88eHDnzp3IyMjo6Ojq6uouXbo4Ozv369fP2dm5Z8+ebDa7ZV8QyCmh\nUBgaGhoaGvro0aOkpCSBQMBkMhkMBjUIVVlZWVlZWSgUUn/3GAxGaWmppqYm3akBAADgc4nFYm1t\n7f3798+cOZPuLO+A5g9AQaD5+yj79u1bt25dfHx8vbmAAAAAAACg9Vq0aNGRI0eodf7eh8lkEkKc\nnJzYbPbjx4+FQqG9vf2AAQOowg+r9MHHyMrKCg8Pj4yMvHv3bnx8PIvFMjQ01NbWrq6uLigoKC4u\nvnPnjpubG90xAQAAoAkMHDjQ3Nz86NGjdAd5BzR/AAqCRXeAVqC8vHzLli1Lly5F7QcAAAAA0Ja8\nfPmygdpPSUmprq6OmrMxJibGyMho586dHh4enTp1asGM0BYYGRkZGRlNmjSJEJKfnx8SEhIaGnrz\n5s3i4mJdXV0PD4+MjIyKigp1dXW6kwIAAMDn6t279zvnkwcAaDFMugO0Anv27JFIJMuWLaM7CAAA\nAAAANKX4+HjpbSUlJQ0NDSUlJUIIk8lkMpkSiaRr166enp53794VCoVpaWkLFixA7QefqVOnTtOn\nTz979mxBQcHDhw/nz59fUFAwa9YsPT29SZMmXb16taamhu6MAAAA0Hi9evWKi4t7+/Yt3UEAQHFh\nts8PKCkp4fF4y5YtW7duHd1ZAAAAAACgyVRXV6urq1ND+rhcrrGxsVgsTktLq66udnV1nThx4rhx\n43R1demOCQqhsLDw4sWL586dCw8Pb9eu3ZgxY6ZMmTJo0CC6cwEAAMAnS0lJ6dat26NHj3r37k13\nlvow2yeAgsCYvw/45ZdfJBLJ4sWL6Q4CAAAAAABNqby8fNOmTZcvX96xY0eXLl2ePn3K4XC2bNmS\nlZV1//79OXPmoPaDFtOxY8e5c+c+ePAgMzNzw4YNz549c3d3t7OzO378eFVVFd3pAAAA4BOYmZmp\nqKgkJibSHQQAFBeavw8IDAycPHmyhoYG3UEAAAAAAKAp1dTUVFZWenp6rl+/3sHB4eHDh9HR0UuW\nLOnSpQvd0UBxGRgYLF26NDY2NiIiwtbWdt68eUZGRmvXrs3JyaE7GgAAAHwUJpNpZmb26tUruoMA\ngOJC89eQx48fJyQkTJs2je4gAAAAAADQZHJycubMmdO1a9fTp08vWbIkKyvr9OnTffr0oTsXwP9z\ndnYOCgrKyspatGjRyZMneTzevHnzXr9+TXcuAAAA+DALC4ukpCS6UwCA4kLz15CgoKBu3brJ4YzM\nAAAAAADQCEKhcMeOHdbW1sHBwXv37k1NTV27dm3Hjh3pzgVNoLi4+PLly35+fnQHaUp6enrr1q1L\nS0vbs2fPlStXrK2td+/eLRKJ6M4FAAAADeHxeGlpaXSnAADFxaI7gFwLDg7+7rvvGAwG3UEAAAAA\nAOBzxcfHT5w4MS0tzdvbe8WKFaqqqrTEqKysPHLkyG+//SYSidq3b19XV2dhYdG1a9e8vLydO3dK\ndysrKztw4MDly5eZTKaOjg6DwbCxsTE2Nr5w4UJYWFiLpU1PT587d65IJPLz82vWayJtbGxcXV1/\n/vnnxj08MTExICBg165dFhYWa9asadpsUVFRq1evVlZW/vnnn42NjZv24B+DzWbPmzdvxowZ/v7+\n69evP3Xq1Llz56ysrFo+CQAAAHyMLl265Obm0p0CABQXxvy91+vXr9PS0gYOHEh3EAAAAAAA+FxH\njx7t1auXtrZ2fHz8hg0b6Kr9MjIyevbs+fvvv588eTI2NvbmzZs3b94cPHjwtm3bSkpKpLvFxcXZ\n2dk9ePDg/Pnz0dHRf/31159//jlw4EB/f/+ioqKWDLx8+fKQkJCffvqpuadC0dPT09HRafTDLS0t\n/f39mzCPrN69e//000+hoaErV65spqf4GKqqqps2bYqPj9fQ0HBycgoMDKQxDAAAADRAT0/vzZs3\ndXV1dAcBAAWF5u+9Hj58yGKxMNUnAAAAAEBr5+vr+8MPPyxfvvzevXsmJiZ0xaipqRk2bJhEIgkN\nDbW0tKQ2MpnMMWPGXL16tbKyktpSVlY2YsSIDh06XL9+vWvXrtLdvvnmm1u3bnE4nJbMnJiYSAjh\n8XjN/UR37tzZtm3b5xxBSUmpqcL8G/UHER8f33xP8ZFMTU3v37+/aNEiT0/PNja1KQAAQJuhp6cn\nFouLi4vpDgIACgqzfb5XZGSkra2turo63UEAAAAAAKDxDh8+vHHjxsOHD8+ePZveJCdPnkxKSjpx\n4sS/zzL69etXWFhI3T548GBWVtaBAwfYbHa93WxsbDZv3twSWf9RW1tLmrlUaxWod0AsFtMdhBBC\nWCyWn5+foaHhvHnzdHV1Z82aRXciAAAA+B/UMtKFhYW6urp0ZwEARYQxf+/1/PnzHj160J0CAAAA\nAAAa78mTJwsXLty8eTPttR8h5Pr164QQd3f3d947evRo6sbvv//OYrEGDx78zt2++eYb6oZAIPD1\n9fX09HR1dXV1dY2OjiaEVFRUnD9/fvr06S4uLmfOnNHR0TE3N3/8+HFYWJiLi4uKikr37t2fPXtG\nHSEkJERXV5fBYEjbxICAAGVl5ZMnT77vJUgkkj/++GP+/PmGhoZZWVnDhg3jcDh2dnaxsbHUDoWF\nhQsWLFiyZMnKlStdXV3nzJlTUFDQ8NtSW1t7/vz5adOmDRgwoIHjSySSqKioNWvW8Hi8xMTEAQMG\nUC/nzz//fOdh4+Pjv/nmm3Xr1s2YMaN3796RkZHU9oqKCl9f3+nTpy9durRPnz6+vr7UTFzvfD/l\n2Zw5c3x8fObNm/f8+XO6swAAAMD/0NTUJIQIBAK6gwCAgmJIJBK6M8gpCwuLyZMn+/j40B0EAAAA\nAAAa6Ysvvqirq3vw4AGDwaA7C3FwcHj27JlQKFRWVm5gNw0NDR0dnaysLNmN0dHRYWFh1JgzNTW1\nKVOmfPvtt0eOHOncuTMhZPz48bdu3UpPT2/Xrl1+fn6XLl20tbV///13CwsLY2NjfX39JUuWzJkz\nJysry8bGxsXF5d69e9RhAwICPD09b9y48dVXXxFCsrKy1q9fL9v8WVhYJCcnS08bJRJJUVGRhYUF\nn8/fsmXLjBkz4uPjhwwZ0rNnz+jo6Ddv3vTu3dvLy2v16tWEkLKyMmdnZ4FA8Pjx406dOjXwkgUC\ngaampoWFxcuXL993/EePHt2+fXvcuHECgWDp0qXffvttZmbmjBkzBAJBVFRUz549CSEMBsPCwoKa\nodTY2JjNZr969UoikXTu3FlDQ+PVq1eVlZVffPGFvb39sWPHGAzGsWPHvLy8zp8/P3bs2NGjR//7\n/dTS0pKGZDAY5ubmSUlJH/vn3fwkEglV6N65c4fuLAAAAPD/CgsL9fT07t69O3DgQLqz/I/z589P\nmDABjQBAm4fZPt+rsLCw4bNTAAAAAACQZ8nJyQ8ePAgNDZWH2o8QwmKxCCEVFRXa2toN7CYWi/8d\n2MnJSU1NzcbGRktLKysrKzIyMjg4ODg4WHafO3fujBkzRl9fnxCip6fn5uZGCDE0NExPT1+yZAkh\nxNzc3MjI6PHjx9KHTJ061dfX98cff6Sav6NHjy5evFh6r0QiKS0tlT0tYjAYurq6urq6fD5/7dq1\nhBB9fX1jY+MnT54QQvz9/TMyMry8vKidtbS0fHx8Jk6cuHXr1oMHDzbwkjU0ND54fCUlpSFDhujr\n6wsEgm3btrHZ7J49e+bn58+dO/fAgQMnTpyod8yFCxdSayJKJBI1NbXU1FRCyJ49e6Kjo8+fP0+9\nw1OnThWLxW5ubrdu3Xrf+yn9bceOHcvKyiQSiZz8dSKEMBiM9evXjxgxIiUlRbokJAAAANCO+hBS\nXV1NdxAAUFCY7fO9Kisr1dTU6E4BAAAAAACN9PjxYzab/b7ZNVtet27dCCHJyckN72ZkZJSXl/fv\nr4osLS0JIXp6epqampGRkXZ2dpL/RdWScxYOAAAgAElEQVRU9XqpeosFKisrV1ZWyv524cKFN27c\nSElJEQqFSUlJ0iUPampqdu/ezeVyjx07Vi9JvafgcDjUhJn3798nhLRr1056F3Wde3h4eMMvud4B\n33d86V3SF/X1118TQp4+ffrvYy5btuy7777bt2/foUOHampqqGvbb9y4QQgxMDCQHnnOnDkdOnRo\n4P2UOn78uI6Ozp49e2pqahp+OS1p8ODBLBZLts0FAAAA2qmoqBBC5OozAwAoFDR/7yUUCqmrMwAA\nAAAAoDXi8/laWlpKSkp0B/mvkSNHEkKuXbvW8G4jRowQiUR//fVXve1MJpP8U30JhcKUlJR67WBt\nbW0jUnl6eqqrqx86dOjy5cseHh7S7WKxmBqe+PEXRFLZMjMzpVt0dHQIIc13SSU1HpH6cq2eO3fu\nmJubOzg4LFy4UDqmkGo9qfF/sj7m/VRXV1dXV6+srKTmXJUTLBZLS0urpKSE7iAAAADw/6irlIRC\nId1BAEBBofkDAAAAAIC2ycDAoKioqKysjO4g/zVu3DhLS8tDhw6lp6fXu6u2tvbMmTPU7RUrVrRv\n337t2rWyg/PqsbGxqaysPHTokHTL69evZX/78bS0tDw9PX/55Zfz58/LjnJTV1dfv359amrq1KlT\nP/JQ1PDKkJAQ6ZacnBzyT+XZHPh8PiFkyJAh/75r+vTp6urq1KBD6WI2vXr1IoT4+flJtxQVFV28\nePFj3s8pU6ZkZmauW7dOXV29WV5Mo/D5/OLiYiMjI7qDAAAAwP+jLofCcnoAQBc0fwAAAAAA0DYN\nHDhQRUXl119/pTvIf3E4nKtXr3K53IEDB964cYMaUiaRSCIiIiZOnGhsbEzt1rlz59DQUD6f7+7u\nLjuPZVhYGCFES0uLEDJq1CgjI6OVK1cuXrz4ypUr+/btmzp16vTp08k/I9Wk3zRR82RKh6nVu5ey\ncOHCt2/f9ujRQ1lZWXY7k8nU0dF5/fp1vRdS7yAikYh6opUrV3br1m3Xrl1UIUcIOXLkiJOT08KF\nCxt+Z6h47wspPX69AISQ27dv83g8ahVD6uHSu96+fZubm/v06dOgoCBqSNzLly+nTp2qpaV1+vTp\nESNGBAQE7Nmz57vvvhs2bFgD76dUbm4ul8uVn0X+KKdPn1ZVVf3iiy/oDgIAAAAAAPICzR8AAAAA\nALRN2tra33///caNG4uKiujO8l/m5ubPnz/38vJau3atoaGhnZ0d1QIePnzYxcVFupujo+PLly9H\njRo1e/ZsBwcHNze3wYMH7927NyAg4M6dO4QQdXX1mzdvDh48+Oeff54+fXpsbOyZM2e0tLTevHmz\nfft2Qsjr16///vvv+/fvZ2dnE0K2bt1aUlISGBhITcV5+PBh2ffE1NR0+vTps2fP/nfgfxddp0+f\npg5y8ODB8vLyX375JSMjgxDi5+enqqoaGRn5zTffjBw50tvbe8mSJUwm8+7duw3P9llRUbF3715C\nSGZm5okTJ3766af3Hb+qqop6yE8//VReXp6Xl5eSkhIeHs7lcjMzM7du3UodJDAwkM/n79q1S01N\nbfz48bq6ukuWLGGz2bNnzzY3Nw8PDx85cuTff/+9aNGiqKioEydOaGhovO/9/OC7Qa/CwkJfX9+Z\nM2dqamrSnQUAAAAAAOQFA4OO34fBYPz222/jx4+nOwgAAAAAADRSaWmpvb09j8cLCQmhFlyBT2Jp\naZmUlCQ/p4005mEwGBYWFomJiS3/1O9UU1MzZMiQrKysp0+f/rukBAAAAHrJ53fL58+fnzBhgvx8\ntAOAZoIxfwAAAAAA0GZpa2v/8ccfMTExI0eOLC8vpztO66OkpERkptBsNMb7yU+X1gDqHWAy5eUM\nuqysbOTIkc+ePfvjjz9Q+wEAAAAAgCx5OW8BAAAAAABoDra2trdv337x4kX//v2TkpLojtPKWFhY\nEEKo6Tc/h+T9LC0tP/441Jp/0hUBW0x6ejohpFu3bi38vO/08uVLV1fXhISEu3fv2tjY0B0HAAAA\nAADkC5o/AAAAAABo45ycnB4+fKiqquro6Lhz506qPYKPsX379n79+nl6ej579ozeJBUVFVu2bElL\nSyOEeHt7x8TEtNhTP3v2zMvLy8XFZceOHS32pO8kEom2b9/u5OSkqan58OHDHj160JsHAAAAAADk\nENb5ey/5nIsZAAAAAAAaRywWb9++fevWrTweb//+/YMGDaI7UashFouFQqGamhrdQehRWVnJZrNZ\nLBa9MW7durVo0aKMjIz169cvX76c9jwAAADQAPn8bhnr/AEoCIz5AwAAAAAAhcBisdauXRsXF9et\nWzd3d3c3N7f79+/THap1YLFYClv7EULU1NTordnu3bs3cODAIUOGWFlZxcfHr1q1CrUfAAAAAAC8\nD5o/AAAAAABQIGZmZr///ntERAQhZODAgQ4ODseOHausrKQ7F0B9FRUVR48etbe3d3NzU1JSioiI\nuHjxoomJCd25AAAAAABArqH5AwAAAAAAhePs7Hz37t3Y2FgHB4cFCxYYGBgsX748NTWV7lwAhBDy\n6tWrpUuXGhgYLFy40NHR8cmTJ7dv3+7bty/duQAAAAAAoBVA8wcAAAAAAAqqR48eJ06cyMzMXLRo\nUVBQULdu3fr06bNr167MzEy6o4EiysjI2LlzZ+/evc3Nzc+dO7d06dKsrKzAwEAHBwe6owEAAAAA\nQKuB5g8AAAAAABSanp6ej49PZmbmH3/8YW1t7efnZ2pqSlWAGRkZdKeDtk9a+Jmamvr7+3fv3v3G\njRuZmZnr16/v2LEj3ekAAAAAAKCVwargAAAAAAAAhM1mDx8+fPjw4UKh8NatWxcuXPDz81uxYoWt\nre3QoUOHDh3av39/DodDd0xoI6qrq8PCwkJCQkJDQ+Pi4nR0dEaNGrVp06Yvv/xSWVmZ7nQAAAAA\nANCKofkDAAAAAAD4f7IV4I0bN0JDQy9fvrxr1y51dfWBAwcOGzZs6NCh3bp1ozsmtErJycmhoaEh\nISH37t2rrKzk8XhDhgzZsmXL8OHDUfgBAAAAAECTQPMHAAAAAADwDmw2e/To0aNHjyaEpKam3rx5\n8+bNm+vXr1+wYIG+vr6zs3O/fv2cnZ0dHR0xFhDep7q6OiYm5uHDh+Hh4ZGRkfn5+Vwud9CgQXv2\n7Bk8eLCZmRndAQEAAAAAoK1B8wcAAAAAAPABPB6Px+P98MMPtbW1T548iYiIiIyMPHDgwPLlyzkc\nTs+ePZ2dnZ2dnXv27GlqaspgMOjOC7SRSCTp6emxsbGRkZGRkZExMTFCodDIyMjFxWXNmjX9+vVz\ncHBQUlKiOyYAAAAAALRZaP4AAAAAAAA+lpKSkpOTk5OT08KFCwkhr1+/plrAiIiIQ4cOCYVCTU1N\nW1tbOzs7e3t7Ozs7W1tbDQ0NulNDMxIIBC9evHjx4sXTp0+fP3/+4sULgUDA4XB69Ojh7Oy8dOnS\nfv36de7cme6YAAAAAACgKND8AQAA/B97dx4f09X/AfwzM5lkksky2SSyJyKhlKjaHrRVpWqnv6Co\nqq2UKtqiShVt9UHx2EptVUuJtWqnPJZQUmoLsSYhIvs+WWdyf3/cJ9ORZEaSRiaJz/uVV15zz5x7\n7/fcG+09851zDhERUQW5u7sHBwcHBwcDKCgoCA8Pv1xky5Yt6enpUqnU19e3adOmgYGBDRo0CAwM\nDAwMVKlUpg6cKig1NfX27dsRERG3bt2KiIi4evXq/fv3BUFQqVRNmzZt0aLFqFGjgoKCXnjhBa7b\nR0REREREJsHMHxERERERUSWQy+VBQUFBQUHipjjr419//XX58uXr16/v3r373r17BQUFAFxcXMQs\nYEBAQMOGDf38/Hx8fBQKhUnDp+Jyc3OjoqLu3bsn5vlu37598+bNhIQEAObm5vXq1WvQoMHAgQPF\nm84V+4iIiIiIqJpg5o+IiIiIiKjySSQSPz8/Pz+/t99+WyzRaDSRkZERERERERG3b9++cePGrl27\nkpKSxHddXV19fHy8vb31f/v6+lpaWpquEc+F7OzsqKioqKio6Oho/d9xcXFiBWdnZzFT2717d3Hs\npq+vr5kZe9NERERERFQdsa9CRERERERUFczMzOrXr1+/fv0ePXroClNSUiIjI3XZpsjIyIMHD0ZF\nRWVkZIgVnJycXF1d3d3dXV1d3dzcxN91izAvWBY5OTmxsbGP9cTGxsbFxYm/dclXOzs7MefaqlWr\n/v37i699fX3t7e1NGz8REREREVHZMfNHRERERERkMg4ODg4ODs2bNy9WnpqaKuYCHz58GB8f/+jR\no/j4+EuXLiUkJMTHx+uqqVQqFxcXxyc5Ozs7OTnpl9TWAWoajSY5OTkpKSm5SGJiovhCVxgfH5+e\nnq7bxcXFpU6dOh4eHm5ubi1atHBxcfHy8vL29vb29maGj4iIiIiIaoHa2f0jIiIiIiKq0ezt7e3t\n7Zs1a1byrYKCAl0u8NGjR7oUV1xcXHh4uJj6yszM1N/FysrKtohKpbJ9kp2dnUqlkkgkCoVCHEQo\n5sAsLS0VCoVUKrWzswOgVCrNzc0rpXX5+flqtRpAenp6YWFhbm5uTk4OgNTUVAA5OTm5ubmFhYXp\n6enp6ekZT0pNTc3MzBRfZ2dn6x/WxsbGycnJ2dlZzHfWq1fP0dHRycnJ3d3dxcVF/C2XyyulCURE\nRERERNUTM39EREREREQ1iVwu9/Dw8PDwMFInPz9fN/QtJSVFP3OWnp6elpYWHx9/584dXWFaWpog\nCOK+DYCXgC1liMTc3FypVD61WlZWVkFBQZnapkcikZRMUvr5+dnZ2emX6A9trKzEJBERERERUc3F\nzB8REREREVFtY25uLi4EWK69cnJy8lJSbN54o8DKas7Wrdl5eXl5eeLYOwBqtTo/P1+/vm7onnEl\nBwvqUoZ2dnZSqdTCwsLKygpAsaGHREREREREVF7M/BEREREREREAWFpaWk6ciIwM2X//6+fiYupw\niIiIiIiIqNyY+SMiIiIiIiIAwH/+g927cewYmPYjIiIiIiKqmaSmDoCIiIiIiIiqgdBQfPYZZs/G\nq6+aOhQiIiIiIiKqIGb+iIiIiIiInnuJiRgwAN26YepUU4dCREREREREFcfMHxERERER0fNNq8U7\n78DcHOvWQSIxdTRERERERERUcVznj4iIiIiI6Pk2Zw7OnsW5c7C3N3UoRERERERE9I8w80dERERE\nRPQcO3QIc+Zg2TI0bWrqUIiIiIiIiOif4myfREREREREz6uoKAwahMGDMWaMqUMhIiIiIiKiSsDM\nHxERERER0XMpPx8DBqBuXaxYYepQiIiIiIiIqHJwtk8iIiIiIqLn0uTJuHkTYWFQKk0dChERERER\nEVUOZv6IiIiIiIieP5s34z//wdatCAgwdShERERERERUaTjbJxERERER0XPm5k2MHo2xY9G/v6lD\nISIiIiIiosrEzB8REREREdHzJDsb/fqhSRMsWmTqUIiIiIiIiKiScbZPIiIiIiKi58nIkYiLw4ED\nkMtNHQoRERERERFVMmb+iIiIiIiInhurV2PrVvz2Gzw9TR0KERERERERVT7O9klERERERPR8uHgR\nH32Ezz9H166mDoWIiIiIiIieCWb+iIiIiIiIngPJyXj7bbzyCmbPNnUoRERERERE9Kww80dERERE\nRFTbCQKGDoVGg02bIGU3kIiIiIiIqNbiOn9ERERERES13fz5OHwYJ0+iTh1Th0JERERERETPEDN/\nREREREREtdqxY5g2DXPnok0bU4dCREREREREzxaneSEiIiIiIqq9YmMxeDB69cKnn5o6FCIiIiIi\nInrmmPkjIiIiIiKqpTQaDBwIa2usXQuJxNTREBERERER0TPH2T6JiIiIiIhqqS+/xIUL+OMPqFSm\nDoWIiIiIiIiqAjN/REREREREtdHu3fjuO6xahSZNTB0KERERERERVRHO9klERERERFTr3L+PYcPw\n3nsYOdLUoRAREREREVHVYeaPiIiIiIiodsnJQd++8PXFDz+YOhQiIiIiIiKqUpztk4iIiIiIqHaZ\nNAlRUQgLg0Jh6lCIiIiIiIioSjHzR0REREREVIts2ICVKxESgvr1TR1KTZKcnHzq1KmbN29Omzat\n0g9+586dXbt2yWSy3r17+/v7V/rxiYiIiIiIdDjbJxERERERUW0RHo6xY/HxxwgOBnDixAmJRKJS\nqV566aVWrVpJJBKFQtGqVaugoCClUimRSB4/flz1MZowqvDw8EWLFomvBUGYN2/e559/3r59ezMz\ns/fee69v374///xz5Z4xMzNz5MiRvXv3bt++/aeffloy7bd06VKJRFK5J32mNBrNjBkzYmJiTB0I\nERERVZAgCEuWLAkODp45c+aAAQNWrVolCIKpgyKiysQxf0RERERERLVCRgb69EFQEObPFwuys7M7\nd+68d+9eCwsLABKJxMfH5/z58wDS0tLatm2bk5NT9WGaKqrDhw9v2bJl3bp14ubChQsXLFgQFxeX\nkZExaNCgyZMn79+//x+eIioqysfHR7eZkpLSsWNHjUZz5swZe3v7kvXDwsKmTJnyD09aYcWiLSMz\nM7OpU6cOGzZs7ty5fn5+zyAuIiIierbmzJmzadOmy5cvW1lZZWdnBwUFJSYmTp8+3dRxEVGl4Zg/\nIiIiIiKiWmHkSKSlYetWyOViQU5Ozqeffiom2IpRqVSjR482SebPJFFdvXp17NixS5culclkYskP\nP/zg4OAglUpVKtX+/ftfeeWVf3iKhw8fDhkyRLcpCMK777577dq1rVu3lpr2S01N/fXXXz09Pf/h\neSumWLTlolQqv/nmm549e6anp1duVERERFQF5syZM3bsWCsrKwBWVlZjxoyZPXt2ZGSkqeMiokrD\nzB8REREREVHNt3Qpdu7EL7/Aw0NX1rVr1w4dOhjaY+TIkfVNsRZg1Uel1WqHDBny/vvv29ra6gqj\noqIq8RQJCQndunVLSEjQlRw5cuTAgQN9+vRp1KhRyfqCIMyZM+ezzz4zyVSfJaMtL39//wYNGnz6\n6aeVGBURERFVDY1G0759e91mu3btCgoKNm/ebMKQiKhyMfNHRERERERUw4WG4pNPMH06OnbUL7ay\nsjIzM7jEg0KhMDc3z8zMnD179ogRI9q1a9euXbs///xTEIR9+/aNGzfO09PzwYMHXbp0sbCwaNKk\nyaVLl8Qdr1y50qFDh1mzZk2bNk0mk2VmZgJISEj46KOPJk6cOHny5Hbt2o0ZMyY+Pl6r1Z4+fXry\n5Ml+fn6RkZHNmzd3dnbOyMgwHtWOHTvEBf8WLVqk0WgAhISEWFlZbdq06cKFC9OmTatXr15ERMQr\nr7yiUCgaN2588OBBcd+SbRHLd+/efeXKlR49eoib+/btGz16tFarjYuLGz169OjRo7OysoqFUWpz\nxLfCw8N79uw5ffr0YcOGtWzZ8ty5cwB++OGHa9euiQcUq4nTijo7OwcFBZmbmzdt2nTfvn264y9d\nurR///52dnbGbuuTDh065OzsLJFI5syZI5asXbtWLpdv2LDBSNvVavXs2bOHDh06adKkVq1azZ49\nu7CwsGS0Zb99cXFx4i7du3dfu3bt7du3y94EIiIiqiZ8fX2LvT579qzpwiGiyiaQAQC2bdtm6iiI\niIiIiIiMSkwUPD2FLl0ErdZ4RQCBgYH6JVqttkePHo8ePRI3g4OD7e3tU1NTExISxAkqv/7669jY\n2KNHj0okkubNm4vV/Pz8PDw8xNcjR46Mj49PSEjw8fH59ttvxcK0tLSGDRt6eHhER0eHhYXZ2NgA\nWLhw4YkTJwYMGJCSkmI8KkEQxNXvbt68KW7ev3+/d+/eGo3m8OHD4tEmTZp08eLFXbt2qVQqmUx2\n8eLFUtuSlpYmCELfvn1lMllBQYHx8+pKDDXn8ePHgiB4eXn5+/sLglBYWOjq6iq+LnlAd3d3AOvW\nrcvMzLx8+bKvr69UKj179qwgCGfPnv3+++/FaoGBgWXvmK9ZswbAgQMHxM3o6OghQ4YIBu5jWlqa\nWq1++eWXhw8fXlhYKAjCjz/+CCAkJKRYtBW7fVeuXAEwc+bMMgZPRET0XKmeny1v27ZNTAroPxfl\n5eUBCAoKMmFgRFS5mPkzqHr+15mIiIiIiOhvGo3QsaPg4yMkJz+1bslc1+HDh0t+PXTXrl2CIAQE\nBOhnpHx8fKRSqfhapVIBWLZsmVarvXHjRnp6+qRJkwAkJSXp6m/duhXAuHHjdIfKysoqY1SCIMTF\nxSkUiuHDh4ubs2fP/u2338TX4tHy8vLEzRUrVgB47733jLTF3d3dzc3tqefVlRhvzoIFC5YuXSoI\nglar9fPzk0gkpR5QJpPp8qOCIISEhAAYOHBgUlLSsGHDtEVp2nJl/vLz8728vLp16yZufvHFF5cu\nXRIM30dxdOD9+/fF+rm5uStWrEhMTCwWbcVuX3JyMoDOnTuXMXgiIqLnSvX8bFmX+dNoNLrC/Px8\nAM2aNTNhYERUuTjbJxERERERUY31zTc4fRpbt8LBoQJ7nzt3rkmTJsV6iX369AFQbP05CwuLwsJC\n8fXixYtlMtm4ceNatmyZmppqa2t78uRJAOLgMNFrr70GIDQ0VHcopVJZ9sBcXFxGjBjx888/i+PY\nTpw40aVLF/Et8Wjm5ubipjiH5+XLl420JS4uzsrKquxnN96cTz75ZPDgwYsXL162bJmYgCz1IOJk\nqsWOcP369TFjxgwePPj27dsRERERERHit+wjIiLu3bv31MDkcvn48eMPHDhw9+7d/Pz8W7duNWvW\nDIbv44EDBwB4FC39aGFhMWbMGCcnp3K119DtE+vHxsY+NWwiIiKqbvSnOhdnbhenKyCi2oGZPyIi\nIiIioprp8GHMmoV//xutWlXsAPn5+Xfv3s3NzdUv1Gq1xvd67733wsLCOnbsePHixXbt2i1ZskRM\nDkVHR+vqODg4AChXvq2Yzz77TBCERYsWhYWFtW7d2tDSgK6urgAUCoWRtojD8sp+auPNOX78eEBA\nQFBQ0Pjx462trQ0dpGHDhrrRdQDE2VMVCsXevXtff/31hkWioqLEym+++WZZYhsxYoRSqVy2bNnu\n3buDg4PFQkNtz87OBvDUnOKzuH1ERERUnen/f//BgwcA2rVrZ7pwiKiSMfNHRERERERUA8XE4N13\n8fbbmDChLNVLTX01atQoOzt72bJlupJHjx7pb5bqu+++a9as2bFjx3bu3Alg+vTpHTt2BHDo0CG9\n6GIAdO/evQJRiby8vAYPHrxq1aply5YNGzbMULXU1FQAnTt3NtIWd3f3jIwM45HoM96coUOHKpVK\ncVRcsfh1wyIB9OrVKzMzMyIiQtxMSkoC0LZt29zcXP2RebrZPu/evVuW2Ozs7EaMGLF+/fqQkBBx\nRCMM38cWLVoAEBfw04WxY8eOYtFW7Pap1WpwfAAREVENJJVKxZH9otDQULlcPnDgQBOGRESVi5k/\nIiIiIiKimiY/H8HBcHLCunVl3EMcECbOLanTq1cvLy+vyZMnT5gwYc+ePYsXLx4yZMjQoUNRNFpO\nlzEqKChAUa5o4cKFKSkpAPr27evm5ubv7z958uT69esvWLBAzMMBWLly5csvvzx+/HjdXhqNpoxR\n6cycOTMvL+/Bgwf+/v7F3tINTPz999/r1as3ceJEI21p27ZtYmKiOACu6Prl48nRjWJ4Yonx5mRl\nZcXGxl6+fHnz5s3idbh58+bjx4+dnJzi4+MfPXok7jJu3DhPT88FCxaIm3v37nV0dBRX1DNi8uTJ\n3t7e69evN1Jn/PjxWVlZzZo1k8vlYomhtk+ZMsXOzm7jxo3dunVbu3btwoULBw8eLM6bqh9txW6f\nuG/r1q2Nt4iIiIiqm6lTpy5fvlx8DBOXAZ4+fbqnp6ep4yKiSsPMHxERERERUU0zdSquXkVICAzP\nNqnv2LFjEyZMABAVFfXll1/+8ccfYrlSqTx69GinTp1WrVo1dOjQS5cubdmyRcwViXNALV26NCMj\nY/369eKklN9++21OTk5iYmKbNm3mzp372WefNWnSZMeOHQ4ODufOnevZs2f37t2nTJkyceJEqVR6\n4sQJQRAWLlwYGRkJ4Msvv7x+/XpZotLx8fHp1q3b8OHDS7ZoxYoVGRkZjx8/vnv3bmhoqL29vaG2\nAHjvvfcAXLp0Sdw3IiJizpw5ACIjI1euXBkREREdHf3NN98AiI6OXrdunUQiKbU54uyXCxYssLKy\n6tevn7Oz88SJE83NzT/44AOpVPr1118LgjB//nzxLCqV6tSpU2lpaYMGDZoyZcrvv/9+5swZ3ZJ7\nhsTGxj548GCC0XGcvr6+Q4cO/eCDD3Qlhtru7+8fGhravXv306dPf/zxxxcuXPjpp5/EGUr1o63Y\n7bt06ZJEInnnnXeMt4iIiIiqmzlz5gwfPvz999//6quvhgwZMnLkyBkzZpg6KCKqTOVb7eC5IpFI\ntm3b1q9fP1MHQkREREREpGfHDvTrh02bUNsnZdJqtW3atPnvf/+rv+BcgwYNbt26Va6erCAInTt3\nbtas2bx5855BmJUsJiamW7duV65cMXUgT9G3b19bW9uffvrJ1IEQERFVR9Xzs+WQkJD+/fszI0BU\n63HMHxERERERUc0REYH338fw4bU+7QdgzZo1r776qn7ar2IkEsn69esPHDggTs5ZneXk5Hz++eer\nV682dSBPcfXq1fDw8EWLFpk6ECIiIiIiKs7M1AEQERERERFR2WRno18/BARg6VJTh/IMHT58eOLE\niRqNJiUl5ebNm8XeFVcc1Gg0Zmbl6M96eHhs3LhxwoQJa9asMTc3r8xwK9Xt27e//fbbar7QTlJS\n0hdffHHw4EF7e3tTx0JERERERMVxzB8REREREVENMWoUHj5ESAgUClOH8gy5ubmlpaXl5eXt3LnT\n2dlZV65Wq7/++uv79+8DmDJlyq4gEg0AACAASURBVMWLF8t12GbNms2YMWPJkiWVHG6latq0aTVP\n+xUUFKxZs2bjxo1+fn6mjoWIiIiIiErBdf4Mqp5zMRMRERER0XNqzRqMGoVff0WPHqYOhYiIiIiM\nqZ6fLXOdP6LnBMf8ERERERERVXuXLuGjj/DJJ0z7ERERERERkRHM/BEREREREVVvaWno1w8tWmDu\nXFOHQkRERERERNUaM39ERERERETVmCBgyBBkZyMkBGZmpo6GiIiIiIiIqjX2G4mIiIiIiKqxBQtw\n4ACOHoWrq6lDISIiIiIiouqOY/6IiIiIiIiqq1OnMG0avvoKHTqYOhQiIiIiIiKqAZj5IyIiIiIi\nqpYeP0a/fujWDV98YepQiIiIiIiIqGZg5o+IiIiIiKj60WoxcCCsrLB+PSQSU0dDRERERERENQPX\n+SMiIiIiIqp+vvwS587h9GnY25s6FCIiIiIiIqoxmPkjIiIiIiKqZvbswdy5WLIELVqYOhQiIiIi\nIiKqSTjbJxERERERUXUSGYlhwzB4MMaNM3UoREREREREVMMw80dERERERFRt5OWhf3/UrYsVK0wd\nChEREREREdU8nO2TiIiIiIio2pg0CRERCAuDtbWpQyEiIiIiIqKah5k/IiIiIiKi6uHnn7FiBX75\nBYGBpg6FiIiIiIiIaiTO9klERERERFTl8vOLl4SH48MPMXo0BgwwRUBERERERERUGzDzR0RERERE\nVOW6dsXKlX9vZmSgb180bIjFi00XExEREREREdV4zPwRERERERFVrdhYHD+OMWMwYADUagAYNQrJ\nydi5ExYWpg6OiIiIiIiIajCu80dERERERFS1QkJgZoaCAuzahfPn8c47CAnBb7/By8vUkRERERER\nEVHNxjF/REREREREVWvrVmg0AFBQgJgYzJuHzp3RrZupwyIiIiIiIqIaj5k/IiIiIiKiKhQbiwsX\nIAj/29RooNXiyBEMHozsbJNGRkRERERERDUeM39ERERERERVaNcuyGTFCwUB27YhKAg3bpgiJiIi\nIiIiIqolmPkjIiIiIiKqQtu3o7CwlHKNBnfu4NVX8fhxlcdEREREREREtQQzf0RERERERFUlMRGh\noaVn/szMUL8+9u5F3bpVHhYRERERERHVEsz8ERERERERVZWdO0splMshl+Prr3HjBtq0qfKYiIiI\niIiIqPYwM3UAREREREREz41t2yAIT5RIpWjSBBs2oFEjE8VEREREREREtQfH/BEREREREVWJhASc\nOvX3VJ9yORQK/PADLlxg2o+IiIiIiIgqBcf8ERERERERVYlff4VE8r/XEgnatMH69fDzM2lMRERE\nREREVKtwzB8REREREVGV2LkThYWQy2FujnnzcPw4035ERERERERUuTjmj4iIiIiIagO1Wp2fny++\nTk9PLywsBFBYWJieni4WFhQUZGVlia9zcnJyc3MNHSozM1Oj0VQgBjMzMxsbm1LfkqvVvY4dkwhC\nWkDAjc8+kwYGyi9fFt9SqVQSiQSAVCq1s7MTC83NzZVKZQViICIiIiIioucZM39ERERERFTVxIRc\nRkaGWq1Wq9Xp6elisi0tLU2r1aanp2s0mszMzLy8vOzs7Ozs7Ly8vMyMDE2BJi0tVaxQUFCQlaXO\nzcvNMZzAqzAbK6WZTFaBHTVabWa2utS33gU6AVOAFeHhwtCh5TqspUKhsFBYWyvlcrmdnZ1MJlOp\n7M3kZja2thYWFlZWVlZWVhYWFjY2NmZmZiqVSiaT2dnZiWlIlUqlVCqVSqWNjY2dnZ1UynlfiIiI\niIiIajNm/kypX79+27dvN3UURLXftm3b+vXrZ+ooiIiIaqecnJzU1NS0IuLrrKystLQ0MauXmZmZ\nnpamzspSq9UZGRkZGZnqbLWhdJ2d0lomk6qUNmYymY3C0kIutzK3sJSbK+TyugpLuUxh5+Evk0pV\nSmszqczGUqygEPdVKhTmZv/r4NhZKaUSKQCJBCora7FQbmZmrbAUXyvkcktzi2d7aYo59Gvui83m\nqBzmAACycnMKioYVpmVnCQIAaAu1GTnZYmG+RqMuukrZ+bl5BQWZOTmaQm2aOktbWJierS7I12TF\nxKcXFMQV5KvzcvM1moycbG1hYWpWZmFhYbo6q9QoLBUKpZXS1tbG1tZWqVQqra3tVCobGxsxO6hS\nqaytre3t7VVFxNeWlpbP9uJQeUh0q0USkekEBweHhISYOgoiIiKiUjDzZ2qtgUmmjoGodmPKj4iI\nqJwKCwuTnpSmJzUlJS01NS0tLTU1LS09La9ogk2RmUymsraxtVLaWSmVFgqlhYWtwtLTUqlUuSpd\nFXZWShuFpVKhUFooVEpra4WltcKy6LVCLqvV3ZMuvRSAomjLXmn9rE+Yr9Go83JTszLVebnqvNys\n3Jw0dZY6L1edm5uZm5OerVbn5qrzcjNi4h/lRqnz8rJyczJysjOy1WlZmRqtVv9QFubmumSgvYOD\nqig1KOYFnfQ4OjpyWGFVmAi0MXUMRM+zhaYOgIiIiMiwWt21rhE8gWBTx0BEREREz438/Pz4+PjE\nxMSEhISkpKTk5OSkpKSEhITExMSkhMSkpMSkpOSklGRBHIMGALBTWqusrVVKa3sra5WV0tVK2dDZ\nU+XTUKVUqqys7a1tVFZKldLaXmktJvNM2DrSZ25mZm5mXbEUY1ZuTqo6K02dlabO+t+L7Kw0tTpV\nnZmmzkq79yA2+2ZadlaaOis1KzND/fcEpxKJxMnB0cnJ0cnJ2amOc506dZydnXV5wTp16jg5Obm4\nuJibm1deQ59LrdmRJDIpzt9ERERE1Rgzf0REREREtUpeXl58fHxMTMwTv+PiHsXExMXFJyYn6WrK\nzcyc7FRONnZONrZ1bOya2Dg4ufmKmy4qeycbOycbOydb21o+FI9KIw7H9HR0LkvlfI0mKTM9OTMj\nMSM9IT01KTMjKTM9KSM9MS7l1p3I0KyMpIz0pPQ0/XGEdZycXVzqeHh6uri6enh4uLi4uLu7u7q6\nir+ZFyQiIiIiIqow9uGJiIiIiGqegoKCmJiY6Ojo6OjoqKioqKiouEexMTEx8QnxicnJumpOdipX\nlb27vZOrnf1LAU1cWzq4Ozi5quydbO3q2KpUz37CSXoemJuZudk7utk7Gq+Wqs5KzEhLykh/nJYS\nm5Is/o796/qfJ04+Tk1JzkjX1XR2dHR1cXV3d3d1d/Px8fHx8fH29vb29vbw8JDL5c+4NURERERE\nRDUbM39ERERERNVXXl7egwcPxPTe//J89+9HRUXFxsVptVoACnNz7zquXo7ObirHlxo0dW39v9ye\nu4OTq8rBgmkSqjbsldb2SuuAuh6lvptXUPA4LSU2JUnMCMalpTxKSY7569q5Y8ejE+Jy8/MByGQy\n97p1vb29ffz8dOlAb29vLy8vCwuLqm0NERERERFRNcXMHxERERFRtZCfn3///v3bt2/fEUXcunPn\nTszjWHHJPRd7By8nF08Hx+ZOLm93aurlVMfT0dnLqU4dO5WpAyeqBBZyuY+zi4+zS6nvxqenPkxK\nfJCU8CApITop/sHtyIN/hD1MSohPTQEgkUg86rrVr1+/foPA+vXrBwQEBAQE+Pr6ctZQIiIiIiJ6\nDjHzR0RERERU1QRBiIyMFBN8t27duhMRcefO3eiYh1qt1lwu93WpG+DqHuTqHtytqb+rm5dTHS+n\nOgo5cxj0/HKxs3exs3+5XkCx8tyC/OjE+IfJiXfjYu88fnT7zyv/3X/wflxsgUZjJjPz9vSsX9+/\nfoMGAQEBYkbQx8dHIpGYpAlERERERERVg5k/IiIiIqJnLjk5+ZrOlavhN8Izs7JkUpm3i2v9uu71\n69Tt9nq3+nXd69d193ZyMZPJTB0vUc2gkJsHunkGunm+8eJLukKNVhudFH/n8aPbj2Nux8bcOvPH\nvh07H8THawu1NtbWjRs1frFpkxdffLFx48ZNmjRxcHAwYfxERERERESVjpk/IiIiIqJKptVqr127\ndvXq1WvXrl29fPn69euxcXEAXOwdmnj5tveuN7ZVhyZevg3cvczN+EBOVMnMZLJ6Lm71XNy6BLXQ\nFeYVFETEPrwaff9q9P0r5y/tCdmekJYKwL1u3caNGzcJCmrcuHHTpk0bN24sY+qdiIiIiIhqMn7Q\nQERERERUCZKTk8+dO/fHH3+cDQ0NCwvLUqvlZmYNPX2aePpMeKNHkE+9Jt5+Lnb2pg6T6DllIZc3\n9fZr6u2nK4lLS7kaHXkl+t7VB5GHd+xevGhRgUZjY23dokWLf7Vt27p16zZt2nBEIBERERER1TjM\n/BERERERVURhYeH169fPnTt37uzZP0LP3rp3F0CAu2ereoF9+7/fqn6Dpt71OKSPqNpyVTm4qhw6\nN20ubuZrNJej7p6/E3HhbsS2dT99/fXXEokk0N+/9b/+1eZf/2rTpk2jRo2kUqlpYyYiIiIiInoq\nfhJBRERERFQODx8+PHLkyNEjR44dPZacmmJvY9vKv8GAl9q06vd+q/oNHaxtTB0gEVWEuZlZS/8G\nLf0biJvJmRnn70acv3Pz/JUbU3buSsvKdHJwfKPTG53ffLNz587u7u6mjZaIiIiIiMgQZv6IiIiI\niJ5CrVafPHnyyJEjRw4eunn7llJh+doLTb7sPaBTk+YN3DwlEompAySiSuZoY9u1WcuuzVoCEATh\n5qMHR69eOnL14ke/jlXn5rwQ2KDzW106d+786quvWllZmTpYIiIiIiKivzHzR0RERERUuqysrF9/\n/XXL5s3Hjv2u0Wqa+dXv1bjZ8ndGtA1szGk8iZ4fEonkBQ/vFzy8P+7aJ6+gIPRW+JErfx7dd2DJ\nkiVyM7NOnTq9M3Bgr169lEqlqSMlIiIiIiJi5o+IiIiI6EmCIBw/fnzN6tV79+4tKCjo1KT5mg8m\ndglq4WxrZ+rQiMjELOTy1xsHvd446DsgIT3t0OWwredOvjdkiIWFRa9evUaOGvXqq69yHDARERER\nEZkQM39E1UYycAq4CUx7Bge/A+wCZEBvwP8ZHJ+IiKhWSEtL+/HHH9f8+OPd+/dff7HZ94NH/V/r\n9k42NTvhl5yZcermtZuPHkzr846pY6mOnp/rk56ttrPioLTKVMdONeTVTkNe7ZSYkb793MkdF868\n/vrr9evVGzV69MiRI21tbU0dYDk90/4IVQ12KomIiIiImb+nEgRh6dKlp0+ffuGFF27dutWhQ4dR\no0bxK5zlcAJ4HbAD/AA5cAGwAJoCecAdIBuIBeo+T1GFA0eAiQAAAZgPpAJngHNAF2A/EFjZnbRM\nYBJwFlgN/Ku0CkuB8YBQqSd9pjTALOADwMPUkRARUS0SFxe3cOHCVStXSgWM6th15Mdf+ru6mTqo\np1t6cM/iA7vuxz+WSaVvvPiSmUwmCEKBVns37lFkQlz0is3ZeXlrjx9c8Nv2QDdPk2S2Gk0a0a5B\n41WjJlRs98iEuA/XLCnQar59Z1hL/wZioSAIq38/sOTgHjOpNDM35378YwC/fzn/9cZB5T1+xKOH\nFb4+Tw3jH7a9Ako9Y25B/tKDe/ZfOh96K7zgl0Ni4YW7EZ9vWSuXma0aNcHb2UW/vqRfJzOZbHLP\nfjaWVn1btQuo+79HrvCHUUeuXpzY7W1BEJYe2nP65vUXPLxuxcZ0aNR01BvdytJFMrSjRqudtWPj\nB29083B0Fmvefhyz6/yZpMz0hft2CoIghBythKvzLDnb2n34Zs8P3+x5+3HMj8f2z545c87s2WM+\n/HDixIl16tQxdXRlEwGsBRaUsz8iAcyAyYAN0BcIKFHhEXAYOAQ8BM4ZOMhtYBeQBCwEhNI6JuxU\nFlNrOpViQ4JMcSVLdirZzSQiIqJahJm/p5gzZ86mTZsuX75sZWWVnZ0dFBSUmJg4ffp0U8dVc2QD\nnYG9gAUAQAL4AOcBAGlAWyDneYrqMLAFWFe0uRBYAMQBGcAgYDKw/x+fIgrw0dtMAToCGuAMYF9a\n/TBgyj8+aYVFPRltGZkBU4FhwFzAr7JDIiKi509+fv7y5cu/mjnT0kw+vdeADzp1t7W0MnVQZfXR\nW73ffeUN+/f71HNxO/TFXF25IAh9F8wq0GoauHt+N2jEgt+2mypCFzt7B2ubCu/+6cZVhy6H3frP\nel3+CcDyw3s/Wrds5ycz+7ZqB+DQ5bABi795lJJUgeP/k+vz1DD+YdsroNQzKuTmE7r1/f63HRqt\nVlfY0r/BihHjG0wYNnnT6m0Ti/dufOu4fvPOMP2Sw1f+3HLm+LoxnwKYs3PzptPHLs9bZWVhkZ2X\nFzT5g8SM9OlvD3pqeIZ2NJPJpvYeMGzFgrkDh/u51AUQUNdjau8BAPZcOHsvPrZCF8M0Aup6LHj3\ngxlvD/7hyG+LVq5a+cMPs2bPHjNmjFwuN3VoT9MA+A5YUP4dfYFvDL/rDgQDw4FAw3UCgKkAgD3A\nvdIqsFOpr9Z0KnUNOWSKK1myU8luJhEREdUiUlMHUK0lJibOmTNn7NixVlZWAKysrMaMGTN79uzI\nyEhTh1Zz5ACfFj3BF6MCRpuok2aSqK4CY4GlgKyo5AfAAZACKmA/8Mo/PsVDYIjepgC8C1wDthro\noaUCvwKe//i8FVMs2nJRAt8APYH0yoyIiIieQ3fu3GnVouW0qZ+PfaP7ncXrP+vZrwal/UQqpTWA\nYiOuJBLJlN79rRWWAGRSUz7zH585f+7A4RXePeLRQwD1XJ4Yf7nh5BEAnZq8JG52CWqx/sPPYpIT\nK3aKCl+fp4bxD9teAYbOKJeZiX8n+vxd3QGEx0SXrC+VPHFNrkbfH7tm6dJh42RSaXRi/Jydm8a+\n2cvKwgKAlYXFmM49Zu/YFJkQZzw24zsqLRTfvDOs57wv07PV+nuZyWSlH656s7NSTu094M5/fvrg\ntS5TPpvcqkXLe/dKzWhVMxW72E/9B1T29LehbyazU6lTazqV+g0x1f0t2alkN5OIiIhqC2b+jDlz\n5oxGo2nfvr2upF27dgUFBZs3bzZhVDVMV6CD4XdHAvWrLpa/VX1UWmAI8D6gv9hHVKWeIgHoBiTo\nlRwBDgB9gEal1ReAOcBngEkmry0ZbXn5Aw2ATystIiIieg6dOXPm5ebNzbJzw79f/e07w2xqWs7P\niNuPY5p4+bnYlfoxbU2iLSxEieScuZkcwJydmwXhf1PL9Wrxr4YeXlUcWzUJo8LEq6o/ELBU2sLC\nIcv+/X6HN8Wk+OYzxzVabfuGjXUV2jVoXKDVbD79u/HjPHVHf1e3Bm6en25cVbHmVEO2llbfDRpx\nbcGPkqzs5i+9FBoaauqIaix2KkW1plNZrCEmvL8lO5XsZhIREVGtwMyfMREREQB8fX11JeLrs2fP\nVsXpBWAfMA7wBB4AXQALoAlwqahCONATmA4MA1oWrZqgBkKAoUBbYAvgAAQAYcAZoC2gABoDV/TO\nkgnMBkYA7YB2wJ8AgGQgwsCP+J3gLYASkACLAA0AIASwAkqmRK2MzimrAMxLi+Gpbb8CdABmAdMA\nGZAJAEgAPgImApOBdsAYIB7QAqeByYAfEAk0B5yBjKdFtcNAAzcBF4BpQD0gAnil6JIeNHo9AewG\nrgA9ijb3AaMBLRAHjAZGA1klwii1OaJSb/0PwLWiA4rEGWCcgSDAHGgK7NM7/lKgP2Bn+DqUdAhw\nBiTAnKKStYAc2GC07WpgNjAUmAS0AmYDhaVFW/bbp/tCeXdgLXC7PE0gIiIqEh4e3rN7j44vND0z\na5E4wWDtIAhCSlbm5E2rM3LUpVbIzMmevWPTiJUL282Y0G7GhD/v3QagzssNOXdy6PL5bWdM2HLm\nuMP7fQI+Hhp279aZiOttZ0xQDOra+JORV6Lvi0c4dfOaYlBX1dDeobfC07PVo1YtkvTr9OY3U2/E\nRAO4HHXPY/Q7a48f1BYWhpw7+d7yea/MnCTueCX6fodZn87avnHaL+tk/Ttn5mQbise4j7v2ATB/\nb8jb3896kJQAQCqR9G7RVnxXnZc7e8emocvnT9qwstW0j2bv2FQoCADCH0b1/PeM6VvXD/thQcvP\nx527faPUg5espi0sPH3z2uRNq/3GvRuZENd8yofOw/8vLi3FSBgl214oCHN3/zJk2b8/WrdM+W53\nSb9O4k+5rjyAhPS0j9Ytm7jhh8mbVrebMWHM6v/Ep6eWesas3JxPN64asXLhZxt//Hj9iqzcCo6a\n2X3hzJXo+z2atxY3z0RcB+Bb5+9/Mr51XAGcNXA9dcqyY/fmrdceP3T7cUzFQq2e/F3dQmcvei3w\nxW5du167dq2Kzmr8ub3U3kQxT+0SViV2KkW1o1NZsiHG768cOF/i4n8PWBSlGzOBVYC5XvbR0AUs\nVclOJbuZREREVPMx82dMamoqABubv2cnsbW1BfD48eMqiqAVsAWIATYC64H9wHVgVNG7XYGbwNfA\nWr0ZOSyBdsAG4AZQF7gORAJvA2HA78BV4BbwcdERCoFBwAhgDXAGcAM6A+nAeqChgR9x/Y6BwEcA\ngLeKntFbAG8WvVsuhmIw3va+wF1gJvAtMBzIARKBVoAbsAiYB+wHTgIvA48AS2AlEAnsAb4H3jAw\nkYg+Qw18B0gDlgH3gdXAYuAX4BHQA7hkuC0AfgFkwAtFx+8OrAQAuAIrgZVAsemXDDVHTHqVeutn\n6h1QFFoU+RkgDMgEehX16M4BGqBVOW4UAHQBvgMAvFxU0gkYCLxnuO3ZwGvAA2A9sBAYAcwEdpaI\ntmK37yVAALaUsxVEREQAgDGjRzdwdd/68RcW1X/9rTK4FftQzCRJ+3d2HNb317DSv6lWKAiDlswd\n0fGtNaMnnZmz2M3BsfPXU9Kz1ZbmFu0aNN5w8siNmOi69g7XF66JTIh7e8GssHu3fv9y3tUFP96K\nffjx+uXiQV5p+OLw19/KKyho7OljZ6VcOmyci529u4PTCx7eAF708m3o7jWsQxeZVPpWUIufTx5N\nSE8Td+y74Ku7cbEzg9/99p1hw19/Kyc/31A8uoB1w+n09Wvz6qaPpqqU1rsvhAZ+/P6MbT/lFuSL\nb2Xn5b321ScPkhLWf/jpwvdGj+j41syQDTv/OA2g69wvbj568PWA99eO/uRhcuKQZf8u9RKVrKYt\nLLQ0t1h5dF9kQtyesNDvh3zwRpOXLOTmRsIo2fZ5v277MmTDqlETlg4bt+DdDwAMfa2zEHK0XFc+\nMSO91bRxbvaOi94bM2/wyP2ff3PyxtWXp46NS0spdsZ8jeatb6epc3PXjJ40/91R47v2jktLKbW9\npV5hfb+EnpBJpeLNBRCbkgTARmGpq2BrqQTwODXZ+HHKsuNLvv6CIGw5c9z4oWochdx824QvGri6\nfzRuXBWd0shzOwz0Jop5apewWmGnsgZ1Kks2xDhtaRd/GOBdVMEG+EBvYUIjF7BUJTuV7GYSERFR\nzcfMnzFSqRRPLpoivi62jMqzIgGcAWcAwBdAXeANwBv4q6jC+KIcngBYFa2FLgXE79G6AB0AN8AT\neAhMBBRAAOAFhBUd4RjwG+AOSAAJsB1IBY4DnwKCgZ8zRfuKB9StAL8JqNgiJqXGcOJpbU8BYoDl\nQGFRJN8BUXq9ODtgJhADzAdeLromo4DXgF8MrE9QTKkNlAGdi442F3gJ6AN8C2iBJYavJ4DzgIvR\nbzIWY6g53wAwcOtLigM8gPcBa6Ap8G+gEFgGJANrgAllDkbfEMALWF60+WPRcQy1fSHwJ/BF0Rcw\nhwArSpvLpWK3zwOAgS8pExERGXXt2rXTZ878e+Bwc7Oy/++5Wgt08xRCjgohRwu3HUlcu+O1Rk1L\nrXbs6qXfLv7h/sEAMU24/dypVHXW8euXpRJJXZUDABc7+w6NgtzsHT0dnR8mJ07s9rZCbh5Q18PL\nqU7YvVu644x9s2duQb44T6OFXN7SP3Db2f9m5GQD2H/p/P+1bi8+MFvrpXkApGRlxiQnLj+8t1AQ\nJnZ/W2Fubigesb4gCGnZWa4qh5INGdS+472lP0/p1V+A8PXOze1mTEjKTAewcN+OP+/d/qLvQDGA\nIa90WjFifIfGTQGMf6vPx137QnyAsrC4F1/6l/lKVjM3M3u5XoB4fUa90e21Rk1/+XiavdLaSBgl\n2374yp8A5DIzAG+3ag/gr8i7AMp15b/bszUqMX7UG93ETTsr5czgd2OSE7/ZtaXYGdceP3gm4vr4\nrn3EzXoubqUObK1jp0rPVhtP/p2/E+FiZ69bb08mlaF4FwkoQxepLDt6ODoDMDQcs0azkMu/e2f4\nyVOnwsPDq+iUhp7bUbbeRFm6hNUHO5WlqradynI1xNzAxS/2aZZu08gFLFXJTiW7mURERFTzMfNn\njKOjI4CsrL+nzMjMzATg7u5edUEU60FbAIVFrz8BBgOLgWVAHiAY2MX8yU05kF30+hzQpERHrk/Z\nAnMBRgA/A48AATgBdClrm55gJAYjbV8MyIBxQEsgFbAFTgJ4cvX41wAUfUVRPJSyPIEZaaB4NN2F\nFWcpuWy0LXFAuVYOMt4cQ7e+GMWTd188wnVgDDAYuF00XU8eACDCcGdPnxwYDxwA7gL5wC2gGQDD\nbT8AoKjvBMACGAM4lbO9hm6fWD+2DGETERE9KTw8XCaVtgko44iDmkQikTjZ2E3o2ldMMhVz7vaN\nJt5+Yo5Q99OnZVuUyNyIi9jpyGVm2Xl5us0XPLw7NAr68dh+QRAiE+K0hYUFGu0vZ44D2Hjq2OBX\n3tAFo3+QxUPHyKTScWuXtvx8bGpWpq2llZF48goKvt+3w15ps/qDiaW21MHa5rtBIy7PW9XQ3evi\n/Ttj1ywFcOCvCwA8HP/3tGEhl4/p3MPJxg7AJz3+b3D7jov371p2aE9eQYGhdJehamJblBaKsoRR\nsu1tAxtptFox/ycuXtjxxZdKrWnkyp+8cQWA/oKUYoo39FZ4sePsOn8GgL+rm65EKiml67dm9CcO\n1jYL9+3MKygo9WoAiEtLGChqMgAAIABJREFUsbL4e3CTp5MzAP25QzNzcgC4O5R8wntCWXa0sbQE\nEJvylOGDNVTbBo1kUmnVTfhp6LkdZe5N1CDsVJaq2nYqy9sQlOfil/dTjpKdSnYziYiIqOZj5s+Y\nwMBAANHRf69j8ODBAwDt2rUzWUz6jgMBQBAwvsS0HmWUD9wFcp8s1JZ5UYfPAAFYBIQBrcvz3cOy\nxGDce0AY0BG4CLQDlhT1BPTDE78dXt4ehb4yNtAVAKAw2hZJOXvUxptTxlvfEEjUO699UZx7gdf1\npuuJKqr8ZtliGwEogWXAbiC4qNBQ28U081O7f8/i9hERERlWt25dbWFhTEqSqQN5Vnq1+JejjW1m\nTraYZNLJ1xTcjXukm5RSVKxOGY3r0utK9P2we7fm/bpt3uCRfVu1W/37gfCHUd7OdUqmx0Tvvdo5\nbO7yji82u3j/TrsvJy45uNtIPJpCrTo3V6VUWj15tJM3ruqve9fA3fPojH+bm5nt/fMcgOy8XAD3\n4koZz3f8+uWAj4cG+dQb/1afYgPyKlDNeBglfRU8ZE7/oUOXz//il3UTN/zwVfCQ7waVe8YMMbcX\nnahbpwsO1jYArMyLTzsozu2ZlVvs4aw4pYVCqVBk5+dqCg0+f0skEv0kadvARsViENc4bNegsfFz\nVXjHWuNhUqK2sLBKv0Va6nM7ytabqFbr/D0VO5WlqradyvI2pFwq9sdAREREVLsw82dM27ZtpVJp\naGioriQ0NFQulw8cONCEUf1tKKAs+s5dxZ6bGwHZwDK9kkfAsjIv6uAFDAZWAcuAYWU4XalBGorB\nuO+AZsAxYCcAYDrQEQBwSK9ODACge4WiEpWxgakAgM5G2+IOZDwtEn3GmzPU8K3X/+CuF5AJRBRt\nip9ttgVyn/z+Y2DRce6WLTY7YASwHgjR++6koba3AAB8qxdnErCjRLQVu33iGkBV+OEJERHVGi1b\ntnR3c1vw23ZTB/IMCYIwfOX3xcaTNfL0yc7LW3boV13Jo5Qk/c2y6/lyGw9H56+2b1Tn5Tby9Bnd\nqfvF+3fGrl36Yeeehnb5bs/WZr7+x2bM2/nJTADTt/5kJB6lhWLG/w2+F/e42IJ8NpaW49ct089W\nujs4OdrYOtvaAWjhHwjg291bdGP1kjLTd/xxCsDQ5fOUFgpxkJyR+S3LWM14GCUVCkJadtaFucu+\neWfY1glfzAx+t9RBmcZ1bNwMwKHLurn7EZOcBKB789bFavrWcQVwWK9mqd5d+l10Yvz0voMMJWsB\nuDs4ZeT8vfLiO207SCUScZShKPRWuFxmNrDd68bPVZYd1bm5KMPwwRpq/m/bPT08WrRoUXWnLPW5\nHWXrSD7rdf6EolFiFdixJHYqS1VtO5VGGlLeTzY0T74Qyv/HULJTyW4mERER1XzM/Bnj6Og4derU\n5cuX5+bmAsjNzV2xYsX06dM9PT2rLgjxu2m6x19xGh7xQTwLiAUuA5uBFADATeBxiV3EyroHYv13\newFewGRgArAHWAwMAYaWZ1GHmUAe8ADwL0NbxK/dFevgGYrBeNsXFjW5L+AG+AOTgfrAgqIuE4CV\nwMvA+NIuwlOjKksDdV8b/B2oB0w02pa2QKLePKsA8p88iC48scR4cwzdeicgHnhUtMs4wFNvVYm9\ngCMwyUBLdSYD3sB6o3XGA1lAM0A3GZWhtk8B7ICNQDdgLbAQGFw0xY1+tBW7feK+xT/pIiIiejpL\nS8vvFy784chv604cenrt6i0nPw+A9skxWwVazfSt6wFIJRKNVqur0KvFv7yc6kzetHrCTyv2hIUu\n3r9ryLJ/D32tM4pG2ulyXYVCIQBxX93u+pkwM5nsgze6HbocNrlXfwCvvtAk0M3TxtJKfz05cXfd\nQRbu25GSlQmgb6t2bvaO/q5uRuIRg3ewtnn05NBMf1f3Uzevvb9ivm7eyAN/XXicmjK19wAAU3oN\nsLNSbjx1rNt309ceP7hw347BS77rEtQCQFZuTmxq8uWoe5tP/y6GcfPRg8epKfrXx0i1YhfEeBgl\n2z57x8b9l86fvnnt0OWws7fCb8RE6ybYLPuVn9yrf/267gt+256q/t96BCuP7nu5XsD4t/oUO+Ok\n7v8nlUgmblgZeiu8UBAuRd4RRwEmpKfpX8zY1GR7pY3xJfraBjZKzEjXzTjq4eg8tfeA5Yf3iiM1\ncwvyVxzeO/3tQZ6OzgAmb1rt/eGg9ScOlzyO8R1F4r1uHdDQSDw11JrfD/54bP/CRYsUCoNJ1mei\n5HM7DPcm9PsjlbjOn/i3Uyyj8zmg0ssnlR07lbWgU1myITqGrmTJi18PALAEiAZWFbXxAtDd8AUs\nNaqSnUp2M4mIiKjmk3311VemjqGamjVrVnBw8Lhx4/Lz81esWHH9+vUff/yxd+/ekydPfury9WW0\nffv2G7jxxLwrxWwEfgYKAQegIbAF+BkQADnQAnADTgAHgGDAGzgD/AW8DqwDjgO5QHsgClgBaAAZ\n0AT4BdgMFAJ1AF9ABXQDbgG7gH2ALbAKcCxPG1TAJWAQ0PRpNY8BC4GLQBpQCFgWrf1mbiAG423/\nAtgDZAF7ASnwE+AGDAQeA98Dt4EDgBxYCwBYBmwHCgEN4ALUKUNUxhsormfuBDQEUoDfgeWAk+G2\nALABNgFvAV4AgAhgGXAKSAfqANaAGlgK/BfIBNyBhsDw0pojLmzgXNqt7w+4AUeAnKLUmgLoA+wF\n9gIXgL+AjYBfiVsjNueros0NwBngBPC54btpDzwApuottGCo7Q5AD+ABcAo4CCiBlUWTzNjpRWtZ\nodt3GNgDrCxt4UB9sxAcHNyoUSOjlYiI6LnTuHFjQRA+WzivQKt5peGLUmmN/Ercuds3vtn1y1+R\nd1OyMo9cufhr2NlfQk/8+PuByZtWH7ly8eOufZxs7JYe3PPfG1cyc3LcHZ0C6nq83br9rdiHu86f\n2XfxD1srq1WjJjja2CZmpC85uOf49b9yC/LbN3wxKjF+xeG9mkKtTCpt4u33S+iJzaePFwqFdexU\nvi6uuuk3A909UzIzR3bsiqKJKHs0b63L/Knzcpce3HP06qXM3Gwvpzr1XOrO2PbTnguhWbk5e/88\nJ5VKf/rwMxc7+24vtSoZj66Byw/vTc7M+Cp4iK7EQi5fdXT/2VvhKw7vPXnj6trjB/df/GP+u6NG\ndHwLgIO1TY/mrR8kJ566cfXgX2FKheXKUR87WNsCcLZTnQi/cuCv88GtX/V2djkTce2vqHut6zdY\nd+Kw7vr41HH1cqpTrNqZiPCEjLSDf10oFARNodZFZV/HTmU8jJJt1xYW/nLmxJYzxzef/n3diUMr\nDu9dcnB3XXtHdwensl95Rxvbge1ef5yW8v2+Hbcfxxz467xcZrZ2zCdKhaLYGTu+2Kx9wxcv3b8z\n79dtSw/usTCT52kK3mrW0tlW5eVUR1rUnZm1faOTrd24Lr30/6hmbd/oZPN3oY3CatPpY281a+Hl\n9L/nsA6Ng/I1BSsO/3b9YeSPx/b3btF2cq9+4h/AhpNHz0RcPxF++fM+75T8czWyo+jwlT/3hJ1d\nOfJjcWlGAMsO/VrsD6DGKdBqZmz7aeqWtbNnzx49enSlHHPWrFkIBsryeFvyuR0GehOtgXV6/REf\nwOBkt/qhAE7AOMMV/gAWA+eBLKAuYFH0PH8WuAyMKuoaiIp1TEpip7J2dCqLNcT4lVQbuPitgIvA\nBuAYMBq4DHQCnIEXgJ4GLmCpUZXsVJaxm7kdjdAoONjIBzpE9LwTP1uubp9HhYeH79ixgxkBolpP\nYmQGm+ecRCLZtm1bv379nt0p+vXrtx3bEfLszvDsaYE2wH9r73pspTawAXCrnPOQCEBnoBkwr3Lj\nezZigG7AFVOH8VR9AVvgp6dVk+BZ/1smIqKaa+XKlZ9MmtTEy3fZ++Oa+9U3dThUXIMJw27FPhRC\njpo6kH9EEIRlh34tFISPu/YRN7Pz8w5f/nPoivkZGyoy1WplkfTrFOjmGbF4nZFCQRA6fz21ma//\nvMEjy3LMmOTEbt9NvzJ/VQXi6bvgK1tL5U9jP9OV1PQ/gD/v3R67fll4TPSixYtHjizTBSwLiUSC\nbUB1eLyVAIEVGrpXqgr0s2oEdiqLMWFDSkZVslNZxm5mPwQjOCSkRn+gQ0TPVhV8tlwBISEh/fv3\nZ0aAqNarkV9tpmpkDfBq7U37ofIaKAHWAweK5lGpznKAz4HVpg7jqa4C4cAiU4dBREQ13OjRo8P+\n/FPqYNdy2rihK+bfi481dUT0BJlUiqL5MGuuL0M2jF+/XBwOCEAikSgtFK3qN/BxdjFhVOJVlZY2\nnYn+5LESiWT9h58e+OuCOPepcTn5/8/efcc1dbb/A78DCSsbCCNsyg4iw4GAWhU3dFh3W9tqtbaP\n1WodrbbO1m+rVq2jzmqdrbZqXS0K1jpwo6JsVIbslUASCJCQ3x/nMb88LBdwGJ/3H7xObk5OrgMn\n61znvq7qLw/+vOOj2S8Qz72sR4mPs9a9/7H+IFX7tCNKz899d9P3vRbOMBKZ34qLa8G0X7ujefoq\nz6qj/refBl8q66FrRxpG1fBLJb5mAgAAQKfw3F3lAQgh5AwhswlRE1JGSDLdwbSG5neQag6hfs4n\nkD0h+wj5jJCdhBi1TJitIo2QlYS0YS/LF1FCyCJC/iZESHckAADQbqjV6qKiIpVKVVVVJZVKGy40\nOqhbqKur2/Pv2X0XYsaE9Jsz8q1ebl507xAQQoin2D4pJyuruFC/fWCH829iPCFk/emj818fyzJk\narXae9kZ/3fs132ffkFjVBlFBYQQd1u7hr96WJg/b992Cy5vVO8wD1t7ewvRvhkLPvvlp53TPzdi\nNvcJOC0/d+XEKfqt+55Ribx80W+7/164UsjmEELS8nOOXr8sr6qkguxYrqYlrT195Oj1y6+4uO7b\nt2/ixIkt1S2inXpIyDxCLAgZRYjHC20hjZCjhMgJyWjh0GiGL5XNfKmkZUfqRdXwSyW+ZgIAAEBn\ngcwfvBAxITJCWIQcIeS5v9d3BE3toJKQdYQ8IoQQsoCQiYQEPc9mAwj5mpANhMxtyWBb2FNbNtKu\nlpCdhOwjREB3JAAA0NKqq6srKyulUmllZWVlZWVFRYVcLq+qqlIoFOXl5dSgTCbT/21lZaVSqZTJ\nZFVVVVVVVY1uls/nm5mZmZmZCQQCakEoFDo6OpqZmbHZbN1veTxeSUnJnt2/9F74qcTR5f3+g9/p\nO8hGYN7oNqFtfP/21MJy2Ydb1657/+PuTg1bS3UMv85auPyP/dtjTq8+cfgVa7GduUU/H78dH83m\nmtJWOiM+69HsX7aEekoa1vBstLRmgIvb12+9s+HvY3Mjm2tq9WL/o1qNeue5v/fNWCBgc6gRD1v7\nL94YTwj5dsLkF9ggLfKlZfsuRv9yMTr5cVbvnr0OHTo0atSoDtpA9Dm0SKkwD0KoJPi3LbG19gNf\nKpvX9juiH1XDL5X4mgkAAACdCPr8NQl9/gA6CfT5AwBoW1SiTi6Xy2QyxRMymUwul1PLFRUV5eXl\nCoWiqqqKWqisrKRye3WNFXVkMplcLpfH4+mSdqampmZmZnw+n8PhmJmZcTgcPp9PDQqFQl0aT/fb\n592FO3fubN++/eCBA0qlcpBf4ISQAa/16GPO4bbEnwdehFqjqVGrzYyN6Q6k86isrjZiMpmGhnQH\n0uGVyitO3Lp68Mr58/fvcDict995Z9q0ad27t+7FdO2ozx9Al4U+fwDwNOjzBwA0wpw/AAAAAKhP\nq9XWy9VVVFRQy09N6TWavRMKhZwn+Hw+l8s1NzenJuGx2WwzMzMqt2dqaspms3Uz8/h8PpvNNjJq\n64pmAQEBW7ZsWbt27YkTJ349ePCjHT9O2fpDgKv7ML+gYf49g929kS9pY0xDQ/zNWxbSqC9DrdFc\nTUuKunsz6l7cnUfpRizW8OHDf120ICIiwtTUlO7oAAAAAACgq0PmDwAAAKBzqq6uLi0trdforqm+\nd/Va3zXcmrGxsbm5OTXfzsTERCgUCoVCc3NzsVgsfEL/V7rljnse3NTUdNy4cePGjZNKpVFRUWfP\nnt195sy3Rw8KONxwv0AqC2hnbkl3mADQRh6XFp+5eyvq3q2Y+7fLFQo7sXjI0KHzvlk2bNgwgQD1\nAQEAAAAAoL1A5g8AAACgA1CpVNTEu/LycmqaXYUeqVRab0Qmk1VUVGg0moabEgqFbDabmn4nEAi4\nXK6rqyuHw+HxeFT9TAo1S49ak1qNyeyiHx2FQuGECRMmTJhACElISDh79uzZM2dm7tlSuXWtr/Mr\nIW5evd29ert5eds7GTAYdAcLAC1GU1eXnJt9PT35WnrKlQfJSVkZZqam/fv3X7pixeDBgyUSCd0B\nAgAAAAAANKKLnr4BAAAAoEttba0ugVdRUaGfsZNKpfVGZDJZeXm5XC6vqamptx0TExMej8flcgUC\nAZ/P5/F4FhYWLi4uXC6Xz+cLBAKq0V3DlB4te91p+Pr6+vr6zpkzR6VSXb58+cKFC1evXv113za5\nQsFjc3q6ewa7evZ29+7l5mnNF9IdLAA8twJZ2Y0HqdfTk689SruZniKvVPK43F69e7816Z0N/fuH\nhYUZo1AqAAAAAAC0b8j8AQAAALwIjUZTWFhYr5BmwxKaTy2kyWQyRSJRvVKZzs7O9UbqVdTsuCU0\nOw0TE5Pw8PDw8HBCiEajSUxMvHLlytWrV3+/cuXbowcJIc7Wtj1c3Ls5uvg6OPs5ubpa22JGIEB7\nU6fVPirMj896mJCdmfA48+ajtKyiAkKIp7t7cEjI2I+nhoSE+Pj4GKLNJAAAAAAAdBzI/AEAAAD8\nV1VVFTUV76momXkKhaLeFgwMDKj5dlwul8fj8Xg8BwcHiUQiFAp1IxRqTh4FabyOztDQ0M/Pz8/P\nb/r06YSQkpKSa9euXbt2LT4+fte181mHswkhbFNTHwdnP3tnXwfnbo4u3RxdrPhoDAbQ1orKZfey\nH93Pzkh4nHkvJzMpO7NSpSKEODs5+Xbr9m74h7179+7Tp4+FhQXdkQIAAAAAALwgZP4AAACg01Kp\nVM+SxtOprq7WvzuDwRAIBEKhUPCEnZ2dRCKhSmvqEnh8Pl+X1UMtTSCEWFpaRkREREREUDfLy8sT\nEhLuU+7dO/LnQVl5OSHEWmjuY+/kaWPnIbb3Ejt4iO2dRTaGBga0xg7Qeag1msziwrT8nJTc7LT8\n3LSC3MTHmUUyKSFEKBB069atV/jAD/38unXr5uvry+Px6I4XAAAAAACgZSDzBwAAAB1DbW1tcXGx\ntDFNldzUvzuDwbCxsalXQtPT01PYBMzDg5bC5/NDQ0NDQ0N1I48fP6ZygWlpaUnp6cf+PlpYVEQI\nMWKx3OwcPG3tPaxsqXSgp9jBgouEBMDTlcjLU/NyUvMep+XlpBXlp+Q9fpiXU1NbSwixsbb28PR0\n7xU47J3xfn5+vr6+9vb2dMcLAAAAAADQWpD5AwAAANqo1WqpVFpWViaVSnUT78rLy/VvPtecPE9P\nT2oSnqABPp/P5/Pp2lMAfQ4ODg4ODsOHD9eNVFRUpD+Rlpb2b2rqjotny6RSQoiQy3MUWTtaiJws\nRI6WVo6WVg4WImcrGxuBORoHQldTp9UWyMoyiwqyS4oelxZnlxRllRbffJBaJq+oUdcSQvg8noe7\nu5ePz8QRg92f4HK5dAcOAAAAAADQdpD5AwAAgBZWUVFBJfOa/ymVSisqKvTv2Gh1TR8fn4ZpPGTy\noJPh8XhBQUFBQUH6g6Wlpenp6Q8fPszKysrKykrPzIq+cSErK1tVrSKEGLFYdpZWVnw+kzD6enVz\ntbYVCy3szC1tBEJrvpCBpCB0WHVabVG5tEAmzS0ryZOWZpcUZRUXZZUVPy4tzikurFWrCSEmxibO\nzk5OTs5OAb69xaLHjx8XFRXl5+eXV1Tcvnu3TCaj3mKUSmVtbS11UQjduwUAAAAAANBGkPkDAACA\np1OpVLp0XaM5PP2bGo1G/758Pl8oFJqbm1M/3d3dqXKauhHqJpXMo2sHAdohCwsLCwuL4ODgeuO3\nb98+fvz4xYsX7927l5GSy2Aw8qsrCwsLlZWV1ApMQ0NrobmduaUNX2gntLARmNuZUz8tbQRCK74Q\nkwWBXlRuL19alictffKzNE9Wli+T5klLCqVl6ifvIxw228nR0dnFxdc3JMLZ2ekJGxubRrecl5eX\nlJT06NGjxMTEO3fu7N+/Pz8/nxAiEAheeeUVHx8fiUTi6urq4+Pj5eVlaGjYdvsMAAAAAADQVpD5\nAwAA6Iq0Wm1+fn6jPfMa7aKnf18mkykSifRb4umSeeiWB9AatFrt7du3L1++HBsbe/HixcLCQh6P\n169fv/nz54eHh3fv3p3JZBJC5HJ5Tk5OYWGh/s+E7MfnHiXn5OZWVlVRW6PygpY8vhVXIOLyLLl8\nSx7Pksu34gssuXxLLs+Sx7fk8g0NDGjdaeioNHV1JfLykoryEnlFcYWsqFxWIq+gRooVFcXy8pKK\ncv3cHtvMzN7Oztraxs7NKcymt52dnY2Nje7n8xbqFIvFYrFYd1Or1WZnZ6empqakpCQnJ6empkZH\nRxcUFBBCBAKBt7c3lQL09vb29vZ2dnY2wGEPAAAAAAAdHzJ/AAAAnUp1dXVJSUlpaWlpaWmjc/J0\nIzKZTP+OBgYG+pPwHB0d/f39G87MoxaQzwNoA8XFxTExMf/888+///774MEDY2PjXr16TZs2rV+/\nfiEhIWZmZvXW53K5VAKj0a3J5fLc3NyCgoLc3NzCwsLi4uKioqKSkpKskqKSh4lFRUXS/31NsOQL\nLHW5QDZHYMYRsDlCNkfA1lsw4wjYbFMj49b6E0C7UVVTLVMqZZUKqUIuq1TKlAqpUiFTKqiFEqW8\nRFFRKq8oLpeWlpfr39FcKBSJRJaWIkuRpZOXa08rK0tLS2tra3t7e+onh8NpvbAZDAY1R3DIkCG6\nQZlMRiUCU1JSUlJSLl68mJGRoVarzczMqFygRCKRSCQ+Pj7IBQIAAAAAQEeEzB8AAEDHUFVVRaX0\nSkpKdLk96qb+oEKh0L8Xl8vVz945OzsHBgbqcnj6WT1U2gRoD2pqaq5cuXL27NmzZ8/euXOHyWSG\nhoa+++67/fv37927t4mJyQtvmcvlenl5eXl5NbWCWq0ueaKoqKi4uFh3M6uk5G7uQ5lMJpOVyyrK\n1Wq1/h1NjIwFnCfpQDO2wJQtYHN4pmZ8MzbbxIRtbMIzZfPMzNjGJmxjE74Zm2tqxjY2MTNGvpAe\nldXVymqVvKqyvFKprFYpq1UVlZUVVUpltUqhUlVUKiuqKmVKhaxKKVUqZZUKmVIhlcura2v0N8Jk\nMgU8vkDAFwqFAqHQ0tUh0PK/RCKRSCR6csuSmpDarggEguDgYP06ujU1Nenp6YmJiQkJCYmJibt2\n7Xr48KFGo2Gz2Q1zgWiiCQAAAAAA7Vy7+xrW5fxOCL45AgB0bUqlkkrdFRcXl+rRnXOnblY+6eBF\nCGEwGJaWlhZPiMXibt26UWdaLfSYm5u3w1OuAFBPXV3dnTt3YmJiTp48ef36dY1GExgYGB4evn79\n+uDg4DZ7FjOZTBsbm6bap+mTy+UymUwmk1Gzh/UXZDKZtEyaJ5UqinNkMplSqVRWVsr/94oECoPB\nEHC4HBNTjqkp29hEYMbmGJuwDA35ZmxDA0OBGZtpaMg1NTNmssyMjU2NjE1YRhwTExaTyTdjGxoY\nCMw4+iu0wt+jfamsrq5W18qrKtUajaxSoamrK69U1qrVCpVKVVtTVVOtrFbVqNUVlUpNXZ1UqajT\n1pVXKmvUamVNtVSpUFarlCqVQlUlU8i1Wm3D7XM5HLaZGZvNFggEHA5XaGluLXTyFAgEAgHVh7Xe\nQqtO1Gt7RkZGVG5v7Nix1Iharc7Ozk5MTExKSkpMTDx8+HBiYqJKpWKxWA4ODlQukPrp6+trTMsR\nOI6QcTQ8LAD8f2PoDgAAAACgCTgbSKfZs2ePGYOPigBPp1arT548mZGRkZGRUVRUpNVqzczMnJyc\nXFxcXFxcnJ2d7e3tDQ0Nm7q7/jXdAG2mpqampKQkPz8/Ly+vXts8/UH9FnoMBsPGxka/SZ6Pj49Y\nLLa1tUXzPIDOR6FQREVFnTlz5uzZs9nZ2Ww2e8CAAevXrx86dKibmxvd0TWHy+VyuVwHB4dnv0t5\neblSqVQqlRUVFRUVFdRyeXm5/rJcLler1VlSqaZWVZ6dq1ar5XJ5dXV1ZWVVZVVldU3NUx+FbWJq\nxGJRy3w224BhQAhhMIjA7L9pKhaTyTH+77xJExbLlGXU5D6amDGb/mjRDLVGI1f990KNOq1WXlXJ\nN2PrfltVW6OqraWWFdWq2iezJ2WVCiofp6nTVDy5zqOmtlapqnrqIxobGZmZmpmZmRobG3O5XCaT\nKRAIDZmGfHtrNotlzeEIBAI2m81ms7lcLp/Pp5Z5PB6Px6OWMee7ISaT6erq6urqGhkZSY0oFIrk\n5OT79+8nJSXdv39///79ubm5hBAOh+Pt7e3r6+vj4+Pr6+vn56ffaLCVHD58uLUfAhrSarV79+6N\niopatWrVc70AQmdlb29PdwgAAAAAjUPmj059+vShOwSADmPChAnUQm1tbVpaWtwTv/zyi1KpNDQ0\ndHJy8vHxCQoKCgoK6t27t5WVFb0BQydWXV2dm5vbfD6vXkrPwMDA2tpal7eztbUNCgpqmM8zNzd/\nmVJ+ANAhFBcXnzhx4s8//4yJiVGpVH5+fuPGjRs6dGhYWBg9M4faBJ/Pf/n0EpUalMlkGo2mvLy8\ntrZWoVCoVKqqqv+mx5RKZc2TBGF5eXldXR0hpK6urvxJ5znqLtRyVVWV/gt1PXkVFeraJn/bDKYR\nk2tpTS0nJSRkZGQMGzaM9SQfKTQx0V26weFwdOMCgYCqIWlgYKD7QxkZGbHZ/80ampqampiYUHfh\n8/mGhoYCgYDJZHK1F9eDAAAgAElEQVS53BcIEl4Ah8Pp2bNnz549dSMymYyqDpqQkJCUlHTq1Kni\n4mJCiEgk8vf39/f37969e/fu3b28vFp85i6uH217tbW177333j///HPy5Mnhw4fTHQ4AAAAAQHOQ\n+QOADobFYlHlmCZNmkQI0Wg0WVlZiYmJVCJwy5YtRUVFhBAqs0IVYgoKCvLx8UFTFnhGVGur4uLi\noqKiwsLC4uLi4uLigoIC/VKc+j2ujI2NqdKalpaWYrHYz89PV2yTqr1JNTri8Xg07hQA0C4pKenw\n4cO///57UlISm80eMWLEtm3bRowYYWlpSXdoHQaV5RIKhXQH8qzKysq8vLxsbGy2bNlCdyzQ8gQC\nQVhYWFhYmG6ksLAwPj7+7t27d+/ePX369Lp169RqtbGxsa+vry4R2L17d0yy7HCqqqrGjh174cKF\nqKiofv360R0OAAAAAMBTMBpt8wCEEAaDcejQIV2nBwDoKPLy8uLi4qimLHFxccnJyVqtls/n+/r6\nBj3h5eXVTHVQ6MQUCgWVw9PP5xUXFxcWFhYVFVEJP/2snqWlpUgkEolEVlZW1tbW9VJ6IpHIwsKi\nk/U6AoAWpNVqY2Njf//991OnTj169Mja2vr111+PiIgIDw9Hzd4u4ueff542bdqVK1d69+5NdyzQ\n1nTNAqkL1G7dulVQUEAIoap5Ux9KJRJJt27djIyaLDwLtJPL5ZGRkQkJCVFRUT169KA7HAAA6DDa\n57nlw4cPjxs3DhkBgE4Pmb8mtc9XZwB4XuXl5ffv39dVB01NTdVoNEZGRm5ubrpEYGBgoJmZGd2R\nwkupq6srKCioV3JTt0wt6Fd1YzKZIpFI10JPv5eebhnn5QHghZWUlPz222979+69efOmra1tZGTk\n66+/PnDgQFT07Wq0Wm1YWFh1dfX169dx1VEXp9VqMzIy7t69S80LjI+Pz8rKIoTw+fzu3bv7+/v7\n+fkFBgb6+vrqysAC7UpLS4cNG5abmxsdHS2RSOgOBwAAOpL2eW4ZmT+ALgLVPgGgk+Pz+fqFmGpq\natLT03WJwCNHjlRWVjKZTA8PD11p0ODgYJFIRG/YUI9MJsvNzW0msSeVSnUr65rqUTk8V1dX/Xwe\ntYCsHgC0BpVKdfLkyX379kVFRXG53PHjx//444+9e/c2MDCgOzSgB4PB2Lp1a2Bg4Pbt2z/++GO6\nwwE6MRgMV1dXV1fXUaNGUSNSqTT+iUuXLm3durWmpsbExMTPz69Hjx49evSgSta3eJtAeEZU2u/x\n48dI+wEAAABAx4I5f01qn9dlAEDLUqvVqamputKg169fLy4uJk/aBOqqMLm6utIdaWdWU1OTk5PT\n1EQ9alB/up5AIBCLxU1N1BMKhRYWFsbGxjTuEQB0NWq1+u+//963b9/JkycNDQ1HjRo1adKkgQMH\nIuEHlFmzZu3bty8lJcXKyoruWKBdy8/Pv3XrFnWB2o0bN4qKiqgL1HSfS3v06IGpw23j8ePHQ4cO\nVSgUMTExHh4edIcDAAAdT/s8t4w5fwBdBDJ/TWqfr84A0NqoNoG6ToFJSUmEEIFAIJFIdOdcvL29\ncTL3uRQXFxcVFRUVFeXl5VHd9QoKCnQ3S0pKampqdCsLBAJra2uqu56NjY1IJLK0tLSysrKxsdF1\n3WMwGDTuDgCATmpq6v79+/fv35+ZmRkYGDhp0qSJEydi4jjUU1FR4e3tPWTIkN27d9MdC3Qkus+l\ncXFxV69eLS0trZcI7NmzJ652ag137twZMWKEra3tX3/9ZWNjQ3c4AADQIbXPc8vI/AF0ESgbAgDw\nP8RisVgsjoyMpG7KZLKEhATqhEtMTMymTZvq6uo4HI6npydVGpTSxUtHlpWVFRYWUpk8/Qxffn4+\nNV5bW0utSXXXs7KyEovFVlZW3bp10+XzbG1tqQUjIyN6dwcA4Klqa2v//PPPrVu3nj9/3s7Obvz4\n8e+9956vry/dcUE7xePxVq1a9e67777//vv9+/enOxzoMPQ/l9bW1iYkJNy6devmzZs3b9787bff\namtreTxeYGAgVRc0ODjY2dmZ7pA7g+jo6FGjRvXp0+fo0aMcDofucAAAAAAAnhvm/DWpfV6XAQD0\nUigUqampVGnQuLi427dvV1VV6S6+pjoFhoSEWFhY0B1pS6qsrMzJySksLNRl8qgMHzVSXFxcXV1N\nrWlgYGBlZSUSiajEni7DZ21tbWtrS+X8MF0PADq03NzczZs37969u7i4OCIi4j//+U94eDhe2eBZ\nDBo0qKio6Pbt2ywWi+5YoMNTqVR37ty5desWlQtMTU2tq6uzsbEJDg4OCQkJDg7u0aNHF7807cUc\nPnx40qRJr7/++t69ezGfEgAAXkb7PLeMOX8AXQQyf01qn6/OANCuUG0CdaVBqSpM5H/bBPbo0cPW\n1pbuSJ+iqqoqNzeX6quXn5+fm5tbUFCg+1lRUUGtxmAwqOydLpNXL8MnEokMDQ3p3RcAgFaSnJy8\nZs2a/fv3czicqVOnTp8+HdNr4LkkJSX5+/uvWrXqs88+ozsW6GwqKiquX79+9erVa9euXbt2TSqV\nslgsf3//4ODg4ODgvn37Ojg40B1jB7B169YZM2ZMnTp106ZN+EwLAAAvqX2eW0bmD6CLQLVPAIAX\nx2QyJRKJRCLRjei3Y9m+fXt+fj4hRCgU6pcGpaVNYFVVFZXYo3J7+j9zc3PLy8up1QwMDKytrW1s\nbOzs7Nzc3Pr162draysWi6mfVlZWTCbeOACgy7l69er3339/8uRJV1fXtWvXvv/++2w2m+6goOPx\n8fGZPXv24sWLx4wZY2dnR3c40KnweLzBgwcPHjyYEKLValNSUq5du3b16tXz589v3ry5rq7Oycmp\nX79+ffv27du3r5eXF93xtkdffPHF999//9133y1YsIDuWAAAAAAAXgrm/DWpfV6XAQAdi1Qq1ZUG\njYuLS0lJqaur43K5fn5+VGlQalKgiYnJyz9WbW1tYWFhdnY2lcyrl+eTSqXUagwGQ5fb0/9J5fas\nra2R2wMA0Lly5cqyZcvOnj3bp0+fefPmvf76621/6QZ0JhUVFd7e3uHh4Xv27KE7FugqysvLL1++\nfOnSpUuXLt26daumpsbKyiosLKxfv36vvfaai4sL3QHST6vVfv755+vXr//hhx9mz55NdzgAANBJ\ntM9zy5jzB9BFIPPXpPb56gwAHZpcLo+Pj6dKg1K5QJVKxWKx3N3ddTMC/f39ORxOU1uQyWS5ubk5\nOTn5+fmPHz/Oz8/PycmhMnwFBQW6l3QrKysbGxt7e3tra2t7e3tdhs/e3t7KygrthQAAnurq1atL\nliyJjo4ODQ1dvHjxkCFD6I4IOolDhw5NmDAhJiZm4MCBdMcCXU5VVdX169cvXrx46dKlq1evKpVK\nFxeXAQMGDBgwYODAgWKxmO4AaaBSqd57773jx4/v2rVr4sSJdIcDAACdR/s8t4zMH0AXgYkdAABt\nh8vlhoWFhYWFUTdra2vT0tKoFGBSUtLy5cvLysoIIZaWli4uLtbW1lwu19DQUCaT5efnP3r0SH/e\nno2Nja4IZ2hoqH5NTqFQSNseAgB0fBkZGV988cXvv//+6quvRkdHh4eH0x0RdCrjxo07fPjw5MmT\nExISmrnWB6A1mJqavvrqq6+++iohpLa29vr16+fOnYuJidm3b19tba2np+eAAQOGDBkyfPjwFqlI\n0f4VFhZGRkZmZWVduHChd+/edIcDAAAAANAykPkDAKCBSqV6/PhxTk7O48ePs7Oza2pqTE1NnZ2d\nmUxmcXFxSUlJSUmJbmVjY2NLS0sPDw9fX98ePXqEhoaKxWILCwsa4wcA6JTKy8u/+eabjRs3enh4\nREVFYZ4ftJLNmzf7+Ph8/fXX69atozsW6LpYLBZ1RdqSJUsUCsXFixdjYmLOnTu3bds2KkE4YsSI\n4cOHu7q60h1pa0lOTh45cqSRkdHVq1c78W4CAAAAQBeEap9Nap8zsgGgA6mpqcnLy8vJycnKysrJ\nycnNzaUWcnJyioqKqHWMjIzs7e3t7OwcHBxsbW2pZbFYbG9vb2trW1JSol8aNDk5WavV8ni8bt26\nBQUFUZ0Ce/bsaWxsTO+eAgB0AseOHZsxY0Ztbe3KlSs/+OADQ0NDuiOCzmzHjh3Tp0+/dOlSSEgI\n3bEA/I/8/PzTp0+fPn06OjpaqVR6enoOHz58xIgRAwYM6EzdoP/9999Ro0Z169bt2LFj5ubmdIcD\nAACdUPs8t4xqnwBdBDJ/TWqfr84A0N7I5fLs7GyqGuejR4/y8vKo5by8PJVKRa3D4/Hc3d2pUpyu\nrq76C6amps/+WBUVFffu3aNKgyYmJt66dau6urpem8CAgAA2m906+woA0Dnl5OR8+umnx48ff/fd\nd3/44QdLS0u6I4LOT6vVDhkypLCw8NatW0ZGRnSHA9AIlUp14cKFkydPnj59OjMzUyQSjR49ety4\ncX379jUwMKA7upeyf//+KVOmvPXWW7t378YldAAA0Era57llZP4Aughk/prUPl+dAYAuSqUyMzMz\nIyMjKysrMzNTV6gzPz9frVaTJ733HBwc7O3tHRwcHB0d7e3t7e3tnZycbGxsWmPuiH6bwLi4uLt3\n7yqVSkNDQycnJx8fHyoR2Lt3bysrqxZ/aACATuPPP/+cMmWKhYXFTz/9hJZ+0JbS09O7d+++cOHC\nr776iu5YAJ7i4cOHhw4dOnTo0L179+zs7MaMGTN+/PgO2hhv6dKly5cvX7x48ZIlSxgMBt3hAABA\np9U+zy0j8wfQRSDz16T2+eoMAK1NLpdnZWVlZGRkZmZSST7qp67xnrm5ueMTVG7P0dHRwcFBLBbT\ne82+RqPJysrSlQa9ceMGVVPU1tZWVxo0KCjIx8cH5zgAAAghKpVq3rx5mzdv/uijj9auXftck7AB\nWsSKFSv+7//+Lz4+3t3dne5YAJ5JUlLSoUOHfvvtt7S0ND8/v2nTpr399tsCgYDuuJ5JdXX1rFmz\nduzYsXr16jlz5tAdDgAAdHLt89wyMn8AXQQyf01qn6/OANAi6urqMjMz69XnfPToUW5ubnV1NSHE\n1NTUxcWFqslJoUp02tvbd6CSXHl5ebrSoLo2gXw+39fXV1cd1MvLC72sAKALys/Pj4yMfPjw4Y4d\nO0aPHk13ONBF1dbW9u7dm81mX7hwoaOXT4QuRavVXrp0afv27UeOHDEwMBg7duwnn3zSs2dPuuNq\nTmFh4ahRo+Lj4/fu3Ttq1Ci6wwEAgM6vfZ5bRuYPoIvoPA26AQAapVAoMjIyHj16lPkENYdPKpUS\nQphMpp2dnaOjo7Ozc58+fRwdHZ2cnJycnBwdHU1MTOiO/WWJxWKxWBwZGUndLC8vv3//PjUjMCYm\nZvPmzRqNxsjIyM3NTZcIDAwMNDMzozdsAIDWFhcXFxkZaWNjk5iYKBaL6Q4Hui4Wi3Xw4MGAgIA1\na9bMnz+f7nAAnhWDwejXr1+/fv02bNiwd+/enTt39urVKyIiYvny5QEBAXRH14jr16+/+eabfD7/\nzp07mGILAAAAAJ0eMn8A0EloNJrHjx9TST79n1TFS2NjY6cnAgMDnZ2dqWU7Ozsms6u8EvL5/LCw\nsLCwMOpmTU1Nenq6rk3gkSNHKisrmUymh4eHrjRocHCwSCSiN2wAgJZ1+fLlkSNHBgYGHj16VCgU\n0h0OdHVeXl5LlixZvHjxyJEjJRIJ3eEAPB9zc/PPPvvss88+O3369JIlS4KCgt58880VK1b4+PjQ\nHdr/98svv0yfPr1v376HDh0yNzenOxwAAAAAgFaHap9Nap8zsgFAv1CnPt0cPkdHR9cGcG73qdRq\ndWpqqq406PXr14uLi8mTNoEUiUTi6upKd6QAAC8uLi5u0KBBgwYN+vXXXztQ9Wbo3DQaTUhICIPB\niI2NRQlu6Li0Wu3x48eXLFmSkpKydOnSL774gvbe0hqNZs6cORs2bFiwYMG3336L5xcAALSl9nlu\nGdU+AboIZP6a1D5fnQG6lJqaGirJV28mn0wmI4Sw2WxXV1cXFxcqt0ctuLi4mJqa0h14J0G1CdR1\nCkxKSiKECAQCiUSiywV6e3ujLxEAdBSJiYmvvvpqnz59/vjjD6T9oF1JSUkJCAhYtmwZan5CR6fR\naDZs2LBw4cIhQ4YcPHiQzWbTFYlMJpswYcI///yzadOmqVOn0hUGAAB0We3z3DIyfwBdBDJ/TWqf\nr84AnVVNTU1GRkZ6enp6evqDJ7KysjQajaGhob29fcMkn5WVFd1Rdy0ymSwhIUFXHTQlJaWuro7D\n4Xh6elKlQSnIvAJA+1RaWtqrVy+xWBwTE2NsbEx3OAD1rVix4rvvvrtz546HhwfdsQC8rJs3bw4f\nPtzb2/uvv/7icrltH0BKSsrrr79eXl5+5MiR0NDQtg8AAACgfZ5bRuYPoItA5q9J7fPVGaATaD7J\n5+Tk5O7u7u7u7uHh4e7u7ubm5uTkxGKx6I4a6lMoFKmpqVRp0Li4uNu3b1dVVVFtAqm6oD4+PiEh\nIRYWFnRHCgBANBrNkCFD0tLSbt68aWNjQ3c4AI2ora3t06ePkZHRxYsXu04TYujEUlNTw8PDRSLR\n2bNnLS0t2/KhL168OGbMGHNz8xMnTri7u7flQwMAAOi0z3PLyPwBdBH4SgkAraiioiI9PZ0qFFmv\nIZ+pqamPj4+rq2tAQMBbb73l4+MjkUjQja8D4XA41CS/SZMmkSdtAnWlQVevXl1aWkr+t01gjx49\nbG1t6Q4cALqib775JjY29vLly0j7QbvFYrEOHz7s7++/ePHilStX0h0OwMvy9PQ8d+5ceHj4sGHD\noqOj2+ZzvlarXb169aJFi0aMGLF3714+n98GDwoAAAAA0N4g8wcALSY3NzclJSU1NTU5OTktLS09\nPT07O1uj0RBCrK2tPT09/f39R48e7f4EykJ2JkwmUyKRSCQS3YiuTWBcXNz27dvz8/MJIUKhUL80\nKNoEAkAbuHHjxjfffLNq1aoePXrQHQtAc1xdXX/44Yfp06cPHjx4wIABdIcD8LI8PDwuXrzYv3//\nN95448yZMyYmJq36cIWFhRMmTIiNjd28efO0adNa9bEAAAAAANozVPtsUvuckQ3QTlRXV6enp6ek\npKSlpSUnJ1MJP7lcTgjh8XieT+iSfLS094B2RSqV6kqD6toEcrlcPz8/qjQoNSmwtU8JAUBXU1dX\n16dPHzabfe7cOQaDQXc4nVNpaenFixeTk5MXLlzY4htPT08/evSooaHhG2+84ebm1uLbb4fGjBlz\n7dq1e/fuoRYC7XBst4iUlJTQ0NCQkJBjx461XiXb2NjY8ePHs1is33//PSgoqJUeBQAA4Nm1z3PL\nqPYJ0EUg89ek9vnqDND26urqMjMzdRU7qQWqYqeJiYlEInF1daVqdbq6ur7yyisCgYDukKEDkMvl\n8fHxVGlQKheoUqlYLJa7u7tuRqC/vz+Hw6E70nYtNzf3zJkzUVFRjx8/vnr1Kt3hALRHW7du/eyz\nz+7fv69r9aTVajdu3Hjp0iUfH5/U1NQBAwZMmzatYVLw/PnzAwcO5PP5rq6uLBbrxo0bxsbG3bt3\np659qayszMvLa/sKxjRGlZiYePbs2dmzZ5Mn9fSkUunly5evXr06bNiw06dPe3p6pqSktOAjyuXy\nOXPmXLlyZceOHSEhIQ1X2Lhx48yZMzvQ1xm1Wr1s2bKPPvrI3t6+mdVKSkq6des2cODAAwcOPO9D\nPMv7Ao7tenBsv7ynHtvR0dEjR46cM2fOd9991+KPrtVqV61a9dVXXw0ePHjfvn36Taaf8QUfAACg\nNVDnlkeNGrVy5cqdO3cWFBR4enrOmTPn/fffp/HNCJk/gK5CC00ghBw6dIjuKADamlqtTk9PP3Hi\nxKpVq6ZMmRIaGqr78mxiYtK9e/exY8cuXrz44MGDcXFxCoWC7nihk6ipqUlISNizZ8/MmTPDw8PN\nzc2po87W1jYiImLJkiUnTpzIz8+nO8z2qKKighDi6elJdyAA7VFxcbG5ufncuXP1B5ctW+bu7q5U\nKrVarVKpdHd3X7FiRcP7njp1asiQISqVirqp/0STSqU+Pj4PHz5s5fAbQVdUUVFRkyZNUqvV1M01\na9aIRCKNRiOVSkeMGHHhwoWXfyHKyMjQv1laWurv7+/r61tWVtbo+jdu3KDKhr/Mg76wetE+O4VC\nMXbs2Kf+m6KiohgMxoEDB17gIZ76voBjWx+O7Xpa79jevn07g8H466+/XjCyJkil0tdff93Q0PC7\n776rq6ur99tnfMEHAABoDdS55WnTpr3//vvbtm2bO3cum80mhKxfv57GqA4dOoSMAEBXgD5/AF1d\nVlZWUlLS/fv3ExMTqfl8VVVVhBBra2tvb29fX9+33nrL29vb09PTyckJLdmglbBYLKpN4KRJk6gR\n/TaB27ZtKygoIITY2trqSoMGBQX5+Pjgqm2U0gVoxooVK5hM5qJFi3QjWVlZK1asWLNmjZmZGSHE\nzMzs448/XrBgwdtvv+3i4qJ/36qqqrlz5xobGzfcrEAgmD59OvV22cZoierevXv/+c9/bt++bWho\nSI1s2bLF3NzcwMBAIBCcPn365R/i8ePHkyZNunjxInVTq9W+++679+/fj4+Pb7TipVQqPX78uIOD\nQ1pa2ss/+vOqF+1zYbPZ33777WuvvRYbG8vn85tabejQoR988MGnn37av39/Ozu753qIp74v4NjW\nwbFdT6se21OnTr127drbb799+/ZtZ2fnl42VEEJIfHz8mDFjFArFuXPn+vfvX++3z/6CDwAA0Ery\n8/P5fP6qVauomyNHjhwwYMDq1atnzZpFb2AA0Okh8wfQtRQWFiYkJCQkJCQmJlI/qQvDraysunXr\nFhoaOm3aNF9fX29vb7SWAXqJxWKxWBwZGUndzMvL05UGjYmJ2bhxo1ar5fF43bp1CwoKotKBPXv2\nbPSMIQB0TdnZ2Vu2bNm4caN+GeoDBw6o1eq+ffvqRsLCwmpraw8cOPDVV1/p333EiBFGRkZNbXzq\n1Km0XA3T9lFpNJpJkyZ98MEHPB5PN5iZmdmCbcmKiopGjhxZU1OjGzl79uxff/01evRoiUTScH2t\nVrtixYolS5b88ccfLRXDs2sY7fNyc3Pz8vKaO3fujh07mlntxx9/jI2Nffvtt8+dO6fLS7UIHNsU\nHNv1tMGxvX79+suXL0+ePLlFGq/u3bv3P//5j4+PT3R0tJOTU8MVnv0FHwAAoJXIZDL9N51XX33V\nzs6upKSExpAAoIvA9B2ATkur1T569OjkyZPff//9pEmTJBKJiYmJjY1NeHj4hg0b8vPz+/Xrt3Hj\nxlu3blVUVBQWFsbExPz444/Tpk0LCQlB2g/aG7FYHB4ePmvWrL179yYmJspkskuXLi1fvlwikcTF\nxc2cObNv375cLpeaNfjjjz9evnxZqVTSHTUA0Gn16tW2traTJ0/WH7x8+TIhRH+2B7V85cqVenc3\nMzNjMpu8SM7ExMTIyEguly9fvvzDDz8MCwsLCwu7deuWVqs9derUjBkzHBwcsrOzhw0bZmxs7Ofn\nd/v2beqO8fHxAwYMWLZs2cKFCw0NDeVyOSGkqKjo008/nT179vz588PCwj7++OPCwkKNRnPp0qX5\n8+e7urpmZGQEBQWJRKKKiormo/rjjz/YbDaDwVi3bp1arSaEHD582MzMbP/+/Tdu3Fi4cOErr7yS\nkpLSr18/ExMTX1/fv//+m7pvw32hxo8dOxYfH6+7DuPUqVPTp0/XaDQFBQXTp0+fPn26QqGoF0aj\nu0P9KjEx8bXXXvvqq68mT57cq1cvqhHdli1b7t+/T22QWm3Xrl2EEJFI5O/vb2Rk1L1791OnTum2\nv3HjxnHjxjUzYa6hqKgokUjEYDBWrFhBjfz8888sFmvPnj3N7LtSqVy+fPn7778/Z86c3r17L1++\nvK6urmG0z/7vo+avE0IiIiJ+/vnn5id1cTicI0eO3Lx58+uvv372PX0WOLapcRzbbX9sc7ncX3/9\n9dKlS9Tf4YXJZLLx48d/8MEHn3zyyaVLlxpN+5HnecEHAABoJd7e3vrXGGm12qqqqtDQUBpDAoCu\ngrY6o+0eQZ8/6Ghqamru3r37yy+/fP7550OGDNHVhmKz2T169Pjggw9Wr14dFRX1+PFjuiMFaGH6\nbQJDQ0Op0vmGhoaurq66NoGFhYV0h9laCPr8ATRQVFRkZma2Zs2aeuPdu3cnhNTW1upGqqurCSH+\n/v7Nb7DhE02j0URGRubm5lI3x4wZIxQKpVJpUVERdQHNN998k5eXFx0dzWAwgoKCqNVcXV3t7e2p\n5alTpxYWFhYVFTk7O69cuZIalMlk3t7e9vb2WVlZN2/epCo3rl279vz58+PHj6/XGKzRp/+CBQsI\nIcnJydTNR48evfHGG2q1+syZM9TW5syZExcXd/ToUYFAYGhoGBcX1+i+yGQyrVY7atQoQ0ND/b9Y\no4+rG2lqd6herY6Ojm5ublqttq6uzsbGhlpuuEHqM8yuXbvkcvndu3ddXFwMDAyuXLmi1WqvXLny\nww8/UKt5eno++9eZnTt3EkJ0PcaysrImTZqkbeL/KJPJlEpljx49pkyZQnUO2759OyHk8OHD9aJ9\nsX9ffHw8IWTJkiVPDXv79u0GBgZnz559xt2kPNf7Ao7t5h8Xx3a9/X35Y/uLL77g8Xi6wJ7XqVOn\nrKys3Nzcrl+/3vyaL/yCDwAA0CIanlumrg36999/6QpJiz5/AF0GnudNQuYP2r/y8vJLly5t3Lhx\nypQpgYGBVIkkFovl5+c3YcKElStXHj9+/OHDhxqNhu5IAdqUWq1++PDhiRMnlixZEhERYWVlRWXB\nbW1tIyIiFixYsGfPnoSEBOqEVyeAzB9AQ0uWLBEKhRUVFfXGAwMDCSFqtVo3QpW2CwgIaH6DDZ9o\nZ86caXhR3UEDc9wAACAASURBVNGjR7VarYeHh/7XaWdnZwMDA2qZKj26adMmjUaTlJRUXl4+Z84c\nQkhJSYlu/d9++40QMmPGDN2mFArFM0al1WoLCgpMTEymTJlC3Vy+fPnJkyepZWpr1dXV1M2ffvqJ\nEPLee+81sy92dnZisfipj6sbaX531qxZQ5Vr1mg0rq6uDAaj0Q0aGhrqckharfbw4cOEkIkTJ5aU\nlEyePFn3wea5siM1NTWOjo4jR46kbi5atOj27dvapv+P1AyqR48eUeurVKqffvqpuLi4XrQv9u8r\nLS0lhAwZMuRZIp84caKVlVVeXt4z7qn2pTN/OLYbHcGx3VLHdkVFhb29PZWefC5KpXLatGkMBmPa\ntGkNX94beuEXfAAAgBZB/vfccl1d3bBhw5YtW0ZjSFpk/gC6DPT5A+gw6urqkpOT4+Li4uLiqIZn\n+fn5hBBra+uePXtGRkYuXbpUIpE4OTm1bDMYgA6Hmu3n6uqq3yZQ98Q5efLkqlWrtFotn8/39fUN\nesLLywvPHYDOQa1Wb926derUqdSsFH0ODg63b99WKBS6enpUTULdRPlnd/XqVT8/P2qCSz31+lcZ\nGxvX1dVRy+vXr58yZcqMGTN27969YcMGb2/vCxcuEEL0Q3311VcJIbGxsbpNUVOZn5G1tfWHH364\nbdu2ZcuWicXi8+fPf/nll/qB6bqpRUZGfvLJJ9TEo6b2paCgQL9W3lM1vzuff/65TCZbv369gYEB\nlaRpdCNUwcl6W0hISPj4448//vhjXSFBavpOSkoKi8V65ZVXmg+MxWLNnDlz3rx5Dx48cHR0TE1N\nDQgIIE3/H1evXk0Isbe3p24aGxt//PHHz7u/Tf37qPXz8vKaj5mybdu2oKCgt99+Ozo6um3ep3Bs\nNwrHdksd21wud82aNRMnTpw3b56vr2/ze6cTHx8/ceLEgoKCw4cPjx49+lnu0oIv+AAAAC9v69at\n3bp1a/FC7gAAjUKfP4D2q6qq6vLlyz/++ONHH30UFhYmEAh8fX0/+OCDmJgYW1vbBQsWREdH5+bm\nFhQUnDx5cunSpZGRka6urkhdADQkFosjIyMXLFhAtQmUSqWXLl1atmyZq6trTEzM5MmTfX19zczM\n9NsEVlZW0h01ALygM2fOFBUVffTRRw1/RTXVyMrK0o1kZ2cTQsLCwp73UWpqah48eKBSqfQHNRpN\n8/d67733bt68OWjQoLi4uLCwsA0bNlAn0PVDMjc3J4SYmZk9b0g68+bN02q169atu3nzZnBwcFPt\n02xsbAghJiYmzewLNXXp2R+6+d35559/PDw8/P39Z86cyeFwmtqIt7e3bgYSIYSqMGliYnLixImB\nAwd6P5GZmUmtPHTo0GeJ7cMPP2Sz2Zs2bTp27NiYMWOowab2nXoXePjw4cvsb4vgcDi7d+++dOnS\n+vXrW2qbzcOx3Sgc2y14bI8bNy4oKGjx4sXPsnJdXd33338fHBwsEonu3r37jGk/0qIv+AAAAC/p\nxIkTZWVl33//fb3rqAAAWgkyfwDtiFarTUtL279//6efftqnTx+RSNS3b985c+ZcuHDB3t5+0aJF\nUVFReXl5iYmJe/funTVrVnh4uFgspjtqgI6Hz+eHhYXNmjWLSgRWVlYmJCTs2LEjPDz80aNHCxcu\n7Nu3L5/Pl0gkY8eOXbp06cmTJ4uLi+mOGgCe1a+//hocHOzq6trwVxMmTDAwMKDmrFBiY2NZLNbE\niROb2WCj6QGJRFJZWblp0ybdSG5urv7NRn333XcBAQExMTFHjhwhhHz11VeDBg0ihERFRenWycnJ\nIYREREQ0v6lmkhaOjo7vvPPOtm3bNm3aNHny5KZWk0qlhJAhQ4Y0sy92dnYVFRXNR6Kv+d15//33\n2Ww2NXOoXvy6qWOEkNdff10ul6ekpFA3S0pKCCGhoaEqlUq/eomuIuKDBw+eJTY+n//hhx/u3r37\n8OHDb775JjXY1L737NmTEEI1OdOF8ccff9SL9sX+fUqlkjzPxKOQkJAVK1YsWLDg3Llzz3iXZ4Rj\nu/lI9OHYbtlj+8svvzx+/Pj9+/ebX62kpGT06NGLFi2aN29eTEyMg4PDU7es82Iv+AAAAC0uKioq\nOzt70aJFurTf9evX6Q0JADq/1isk2tER9PmDNlFcXHz69OnFixcPGzaMupCWyWR279596tSpP/30\n05UrV5pqfwIAraS2tjYhIeHw4cNUm0CRSES9Y1JtApcsWXLixImHDx/SHeZ/Udfye3h40B0IQHsh\nl8vZbPaGDRuaWmHhwoUSiaSqqkqr1VZVVfn4+Dy12QY1RcbZ2Vl/UKFQODo6MhiMWbNmHTt2bN26\ndQMHDpTJZFqt1s3NjRCiayZK5SCpDl4ikai0tJQat7OzCwgIKC0tdXd3d3R0LCsro8bnz5/fo0cP\npVKp1Wrd3d0JIbW1tc8YlU5GRgaLxerfv7/+IJVO0HW9+vXXX1955ZWysrJm9oU6RU4FQ6HqELq5\nuelGamtrdSPN745QKDQyMrpz587+/fstLS0JIUlJSXl5eZaWljweLycnh7qLVCp1cHCYPHkydXPb\ntm0WFhaPHz+ut4/1eqHNmzfP0dFx165djf5BKI8ePTIwMFixYoVupKl9T09PpyoEDh8+fOfOnT/8\n8MPQoUPlcrlWq9WP9sX+fQkJCYSQJUuWNBNqQ+PHjxcKhQ8ePGh+ted6X8CxjWObrmO7rq7Oz8/v\nnXfeaWadAwcOiEQie3v7S5cuPXWDjXqBF3wAAICWQp1bPnv27KuvvrrxiQ0bNsydO/err76iKyr0\n+QPoIvA8bxIyf9BKysrKTpw4QWUUbG1tCSEGBgZBQUEzZ87cs2dPQkJCo2dAAIBGubm51NN2zJgx\nPj4+VCJQIBCEhobqnrnUec82dvXq1VmzZhFCjI2Nd+7cmZCQ0PYxALQ3Bw8eZDKZRUVFTa2g0WjW\nrl07fvx46km9bt06XRqjUdHR0dOmTaOe+F9//fXVq1d1v0pNTR0yZIiJiQmfz3/33XcLCgq0Wu3e\nvXtZLBYh5McffywvL9+1a5eBgQEhZMWKFVQ+w8PDY+XKlXPnzh0+fDh1GUFJScmMGTNCQkLmz5//\n2WefffHFF3K5XKFQ/PDDD1Qxwy+//PL+/fvPGJXOG2+8sXfvXv0RKp2wYcOG8vLyvLy8FStWUDE3\ntS9arfbMmTOEEN1p9+Tk5K+++ooQYmhouGXLluTk5MzMzKVLlxJCWCzWzz//XFZW1ujuUHf/+eef\nBQKBu7v7mTNnvv32WyMjo759+xYUFGzdupXL5c6aNUsXakZGxqhRoyZOnDh//vyxY8cmJyc33MF6\n2ZG3336bEMLj8Zr5b2q12smTJ9c7PJra94SEhIiICA6Hw2azx40bl5+fT43Xi/YF/n179+5lMBgp\nKSnNh1pPZWVlYGCgn5+ffrKqnud6X8CxjWOb3mN7586dRkZGjb5cp6Sk9O3b19DQcMGCBc0c8E/1\nvC/4AAAALYj6mGRqatpwKg6NFxMj8wfQRTxfb4MuhcFgHDp0aOzYsXQHAh1eTU3NjRs34p5ITU3V\naDQ+Pj5BT/j7+zfTDgQA2huZTJaQkKB7UqekpNTV1XE4HE9PT/2ndqOf7wGgtb399tsFBQUtXhSx\nw9FoNH369Pn333/1m3J5eXmlpqY+1+d/rVY7ZMiQgICAVatWtUKYLSwnJ2fkyJHx8fF0B/IUo0aN\n4vF4v/zyy/PeMTMzs0ePHsOGDdu/f38rxNUx4Nhuz57r2K6qqrKzs1u0aNHnn3+uG1Sr1T/88APV\njHnHjh19+vRprVgBAABaWfs8t3z48OFx48YhIwDQ6TXeER0AXlJVVdWNGzcuXrx45cqVGzdulJWV\nsdnsgICAYcOGLV68uFevXi4uLnTHCAAvSCAQhIWFhYWFUTcVCkVqampiYiKVCPzjjz+qqqqYTKaH\nh0dQUJBEIvHx8QkJCbGwsKA3bICuoK6u7uzZs/Pnz6c7EPrt3Lmzf//++qmRF8NgMHbv3j1s2LAv\nvviCKkveblVVVX355Zc7duygO5CnuHfvXmJi4rVr117gvs7Ozr/++uvw4cNDQkI++eSTFo+tQ8Cx\n3W4977Ftamo6ZsyYgwcP6jJ/t27d+vDDD1NSUpYtWzZnzhxqjikAAAAAADwvZP4AWoxUKr38xK1b\nt2pqajw8PEJDQ7///vuePXtKJBKqKg4AdDIcDoea5Ddp0iRCiFqtTk1NjYuLS0pKSkxMXL16dWlp\nKSHE1tZWNx2wR48eVLFfAGhZ9+7dKykpCQ8PpzsQ2pw5c2b27NlqtbqsrCw5Obneb6mmZWq1+rk+\nk9jb2+/bt++zzz6jSvO1ZLgtKi0tbeXKlQ4ODnQH0pySkpJFixb9/fffQqHwxbYwePDgpUuXfvbZ\nZ35+frprULoCHNud8th+6623tm/fnpGRYWVltXDhwp9++qlHjx5xcXESiaT1QgUAAAAA6PSQhwB4\ncVqtNikpKTY2NiYm5vLly/n5+SYmJmFhYYMHD162bFnv3r25XC7dMQJAW2MymRKJRP+MVV5enq40\n6Pbt2/Pz8wkhQqFQvzSot7c31S0JAF5GTEyMSCTy9/enOxDaiMVimUzGYrGOHDkiEol040qlct26\ndY8ePSKELFiwYOLEiUFBQc++2YCAgK+//nrDhg1z585t+aBbSPfu3ekO4Slqa2t37ty5b98+gUDw\nMttZuHDhlStX3nnnnRs3blhZWbVUeO0cju327IWP7QEDBgiFwg0bNpw7d+7BgwfffPPN559/jssl\nAQAAAABeEvr8Nal91mIG2tXW1l6/fj02Nvby5cs3b94sLCwUCoWhoaFhYWHh4eF+fn4oSgMAzZNK\npbrSoLo2gVwu18/PjyoNSk0KNDExoTtSgI4nIiLC1NT0999/pzsQgNZVWlrau3dvKyurf/75B+8X\n0HEVFxcHBwdnZGQMGDBg69at7u7udEcEAADQYtrnuWX0+QPoInAxHcDTabXaxMTEc+fOnT9/PjY2\ntqSkhMr2zZ49OywsrGfPnu25OhAAtDdCoVC/TaBcLo+Pj6dKg8bFxe3du1elUrFYLHd3d92MQH9/\nfw6HQ2/YAB3CtWvXvv76a7qjAGh1FhYW0dHRwcHBo0ePPn78uKGhId0RATyf6urq//u//1uzZg2X\nyzUyMvr777/xlQoAAAAAoKUg8wfQpIcPH/7zzz///PPP+fPnCwsLLS0tBw4cuHTp0n79+kkkEtTl\nA4AWweVy9ROBtbW1aWlp1HTApKSk5cuXl5WVkf9tE9izZ08bGxtaowZoj7KyskpLSwMDA+kOBKAt\nuLi4HDlyJDw8fOHChd9//z3d4QA8h4sXL86cOTMlJeXzzz8fM2ZMQEDA3bt3e/XqRXdcAAAAAACd\nBDJ/AP9DpVJdvHjx6NGjZ86cyczMpPr2zZ49e/Dgwf7+/sj2AUBrY7FYVJvASZMmUSP6bQK3bdtW\nUFBACLG1tdWVBg0KCvLx8WEwGLQGDkC/O3fuGBgYtP+GWAAtJSwsbM+ePRMmTHBxcZk+fTrd4QA8\n3YMHDxYsWHD06NFBgwbdv3/f3d1dq9VaWlpeu3YNmT8AAAAAgJaCzB8AIYQ8ePAgKirq77///vff\nf6uqqrp37z527Njw8PCwsDBTU1O6owOALk0sFovF4sjISOpmXl6erjRoTEzMxo0btVotj8fr1q1b\nUFAQlQ7s2bOnsbExvWEDtL07d+64urryeDy6AwFoO+PGjUtMTJw5c6abm1t4eDjd4QA0qbCwcN68\neQcOHOjevXt0dLTucGUwGP7+/nfv3qU3PAAAAACAzgSZP+i6VCrV+fPn//7776ioqPT0dKFQOHjw\n4E2bNg0bNszW1pbu6AAAGkclAnXnyyoqKu7du0eVBqUmBVZXV9drExgQEMBms+kNG6AN3Lt3DxP+\noAtaunRpQkLChAkTYmNjPTw86A4HoD6lUrl58+ZVq1axWKyffvppypQpTOb/nIjw8fG5ceMGXeEB\nAAAAAHQ+yPxBl1NYWHj69OlTp06dPXu2srIyMDBw7Nixw4cPDw4ONjQ0pDs6AIDnw+PxmmoTGBcX\nd/ToUaVSaWho6OTkpCsN2rt3bysrK3rDBmgN6enpb775Jt1RALQ1AwODAwcODB06dOjQobGxsWKx\nmO6IAP6rsrJyy5Ytq1atUqvVc+fO/fTTTzkcTsPVvLy89u3b1/bhAQAAAAB0Vsj8QVeRkJBw4sSJ\nEydO3Lx509jYODw8fO3atRERETg5AgCdSb02gRqNJisriyoNGhcXt2XLlqKiIkKIra2trjQo2gRC\n56DVajMyMpydnekOBIAGpqamp0+fHjBgQP/+/WNjY3F5B9CuvLx83bp1GzduZLFYCxcu/PDDD5sp\nP+Dm5iaVSktLSy0sLNoySAAAAACAzgqZP+jkUlNTDx06dOjQoaSkJFtb24iIiEWLFg0aNMjMzIzu\n0AAAWp2hoaGrq6urq6t+m0CqNGhiYuLJkydXrVql1Wr5fL6vr6+uOqiXlxfmQEOHU1xcXFlZicwf\ndFlcLjcqKiosLCwiIuKff/5pdGYVQBuoqKhYu3btpk2bNBrNF1988Z///OepR6OdnR0hJDc3F5k/\nAAAAAIAWgcwfdE6ZmZlUwu/OnTs2NjajR4/etm1bSEiIgYEB3aEBANCJahOoSwSWl5ffv3+fmhEY\nExOzefNmjUZjZGTk5uamSwQGBgbiaglo/zIzMwkhyPxBV2ZpaXnixIm+ffuOGTPm2LFjJiYmdEcE\nXUt1dfWuXbtWrlxZUlIyffr0L7/88hmnn1KZv7y8PD8/v1aOEQAAAACgS0DmDzqVoqKiw4cPHzhw\n4Nq1axYWFqNGjVqzZk3//v0xeQUAoFF8Pl+/TWBNTU16erquTeCRI0cqKyuZTKaHh4euNGhwcLBI\nJKI3bICGsrOzDQwMHBwc6A4EgE4eHh5RUVGDBg0aNWrUsWPHjI2N6Y4IugS5XL59+/a1a9eWlpZO\nmTJl4cKFVDLvGfH5fCMjo5KSktaLEAAAAACgS0HmDzoDpVL5559/Hjx48OzZs1wud9y4cd9++23f\nvn1ZLBbdoQEAdCRGRkb6bQLVanVqaipVGjQuLu6nn34qLi4mT9oE6joFSiQSugMHIIWFhZaWlkZG\nRnQHAkCzgICAf//9d+DAgZGRkSdOnMDMP2hVOTk5q1ev3rVrl7Gx8YwZM2bMmGFpafkC2zE1Na2s\nrGzx8AAAAAAAuiZk/qBju3r16tatW48cOVJTUzN8+PDffvstIiICVzcDALQIJpNJJQLHjBlDjVBt\nAqlOgb///vuyZcsIIQKBQCKR6KqDent7o7QytL2ysjJzc3O6owBoF/z8/GJiYgYNGvTmm2/++eef\n+GwMrSElJWXlypWHDh2ytrb+5ptvpkyZ8jLdJU1MTFQqVQuGBwAAAADQlSHzBx2SQqE4cODA1q1b\n79696+vru3LlyokTJ77Y5aUAAPDs6rUJlMlkCQkJujaBmzZtqqur43A4np6eVGlQiqmpKb1hQ1cg\nlUqR+QPQ8ff3P3bs2IgRIz744IN9+/ah9D20oOvXr3/zzTd//fWXp6fnjh07xo8f//LzrU1NTZH5\nAwAAAABoKcj8QQeTnJy8adOm/fv3q9XqMWPGbN68OSQkhO6gAAC6KIFAoN8mUKFQpKamUqVB4+Li\n/vjjj6qqKqpNoK40aEhIiIWFBb1hQ6dUVlYmFArpjgKgHenXr9+xY8dee+21cePGHTx4ELVw4SVp\ntdqYmJgNGzacPn1aIpHs2bNn/PjxTGbLnFLAnD8AAID/x959h0VxfX0A/y7L0mHpvYkVOyqiEY1K\nsWswwV6wJLHFaKLYX6NGxW4EFVuIYolgC2Iv2LEErCjYQZDeO2x5/5iwvxXYFRCYRc/n8fHZnZ1y\n7szOsGfu3HsJIaQWUc0faTAuX768YcOGM2fOtGzZcuXKlWPGjNHV1WU7KEIIIf+jpaXFNPKTHiaQ\n6Ro0Kipq3bp16enpkBomsGPHjp06dTIzM2M7cNIgXb9+/dmzZ5K3r1+/FovFa9asYd4WFxf37Nmz\nR48eLEVHiEJwc3O7cOHCwIED3d3dQ0JCdHR02I6INEj5+fmBgYG+vr7Pnj1zc3MLDQ3t168fh8Op\nxU2oqakVFxfX4goJIYQQQgj5klHNH1F0JSUlQUFBGzduvH//vqur6+nTp/v06VO7eSYhhJC6IBkm\nUDJFMkxgRETEzp07ExMTAejp6Ul3DUrDBJIqSkxM/PHHH7lcruQLo6SkdO/ePQBCoVAgENy9e5fV\nAAlRCM7OzpcvX+7bt6+Li8vZs2ep1TWplqioqC1btjBtRr///vvQ0NBGjRrVxYbU1NQKCwvrYs2E\nEEIIIYR8gajmjyiu4uLiP//808fHJykpacSIEQEBAe3atWM7KEIIITVXbpjAzMxMSdegkmECtbW1\n27Zty3QNyjQKVFNTYzdsopi++eYbHR2dnJwcoVBY8VNra2tHR8f6j4oQBdShQ4dr1665urr26NHj\nwoUL5ubm0p/m5+fn5eWZmJiwFR5RQEKh8NixYzt37rx06VKrVq02bNgwatQoLS2tutuiSCSi0SgJ\nIYQQQgipLVTzRxRRcXHx7t2716xZk5KSMnHixPnz51tbW7MdFCGEkFqmp6cnPUxgbm7uw4cPma5B\nIyIi9u3bV1RUxOPxmjZtyjQHbNWqVYcOHfT19dkNmygIFRWVUaNG7dmzp7S0tNxHPB5v5MiRrERF\niGJq0aLFjRs3XF1de/fufeHCBSsrK2a6SCQaMWKErq5uYGAguxESBZGYmBgQEBAYGBgdHd27d+9j\nx44NHjy4HtriFxYW0oM+hBBCCCGE1Baq+SOKRSwWHzlyZM6cOcnJyZMmTZo/f77kxgQhhJDPm7a2\ntnRFYGlp6fPnz5kWgU+fPl2+fHlGRgY+HCbQ0dHR1NSU1agJm8aNG+fv719xemlpqaenZ/3HQ4gi\ns7W1vX79uru7u7Oz88WLF5s2bQrg559/PnXqFICffvqpc+fObMdIWCMSic6dO7dz587Q0FBNTc1R\no0YFBQW1adOm3gIoLCxUV1evt80RQgghhBDyeaOaP6JAbt++/euvv96+fdvLy2vZsmWWlpZsR0QI\nIYQ1PB6PGSZw3LhxzBTpYQJ37NiRlJQEwMzMTNI1aMeOHVu2bEljwX45unbtamdn9/r163LTrays\nOnbsyEpIhCgyMzOzS5cu9enTp3v37ufPnz937tzWrVvFYrGysvLkyZMfPHhAw6x+gV69erVr1669\ne/cmJye7uLgcPHhwyJAhKioq9RwGtfkjhBBCCCGkFlHNH1EIycnJP//8c1BQULdu3e7evUt36wgh\nhFRUbpjA9+/fS7oGvXjxoq+vr1gs1tHRadOmDdM1aMuWLR0dHVVVVdkNm9SpCRMmLFu2TCAQSKZQ\nV5+EyGFsbHzhwoW+ffv279///fv3YrEYgEAgePLkyf79+yVPWpDPXmFhYXBwcGBg4KVLl4yMjCZM\nmDB58uQmTZqwGA+1+SOEEEIIIaS2UM0fYd+BAwd+/vlnHR2dv//+29PTk9pqEEIIqQqmItDV1ZV5\nm5OT8+jRI6ZrUKZRYHFxsfQwgR07dnRwcNDU1GQ3bFK7xo4d+3//93/SU6irT0LkMzQ0XLVq1YAB\nA5hqP4m5c+d+++23dJH87EVHR+/duzcwMDAhIcHZ2fmvv/7y9PRkvdatqKiI9RgIIYQQQgj5bFDN\nH2HTixcvJk+eHB4evmLFil9++YXH47EdESGEkIZKR0dH1jCBERERx44dy8/P53K5NjY2kq5BnZyc\njI2N2Q2bfCIbG5uvvvoqPDxcJBIxU6ysrDp16sRuVIQoshcvXnh6ekpOGYZYLM7IyFizZs3y5cvZ\nCozUqczMzL///nvfvn23b9+2tLQcM2bM+PHj7e3t2Y4LAMRiMdX8EUIIIYQQUouo5o+w5vLly8OG\nDePz+RcvXuzRowfb4RBCCPmslBsmUCgUxsbGMl2DRkREbN++PSUlBYCZmZmka1AaJrCBmjRpUnh4\nOPOax+ONGDGC3XgIUWSpqamurq4FBQXlav4ACASCNWvWTJo0ycbGhpXYSF2Q9OoZFhamp6c3atQo\nPz8/RRtbITc3VyQS6enpsR0IIYQQQgghnwmq+SPs2LBhw/z58wcPHvzXX39pa2uzHQ4hhJDPHJfL\ntbOzs7Ozkx4mkOkaNCoq6uTJk2vXrhWLxXw+v3Xr1pLeQVu0aMHlctmNnHyUp6fntGnTioqKQF19\nEiJXcXHx8OHD4+LiZM0gFosXLVq0f//++oyK1JFHjx4FBgYeOHAgJSWld+/eAQEBQ4cOVczeXDMz\nMwFQzR8hhBBCCCG1hWr+SH0rLi728vIKDg5evnz5ggULqGkFIYQQVjDDBEoqArOzsx8/fsy0CLx5\n86a/v39JSYmKikqTJk0kFYEdOnTQ0NBgN2xSkZaWloeHR3BwsEAgsLS0dHR0ZDsiQhQUh8MZN25c\nXl7evXv31NTUmPpyaaWlpQcPHpw5c2bnzp1ZiZB8uqdPnwYFBQUFBT179sze3n7WrFmjR4+2sLBg\nOy55mJo/XV1dtgMhhBBCCCHkM0E1f6ReFRYWfvvtt+Hh4aGhoX379mU7HEIIIeQ/fD5fepjAkpKS\nFy9eSIYJPHr0aEFBgbKycrNmzSRdg3bp0sXIyOjTN/3s2bPGjRurqKh8+qq+WGPGjDl06JCysjI1\n+CNEDhUVFS8vLy8vrxcvXuzZs2f37t0ZGRnKysqlpaWSebhc7uTJkx88eKCkpMRiqKS6bty4ERwc\n/M8//8TGxjZt2nTUqFGenp6tWrViO64qycrKArX5I4QQQgghpPZQzR+pPzk5Of3794+Jibly5Uq7\ndu3YDocQQgiRSUVFRXqYQIFAEBMTw3QNGhERsW3bttTUVJQNEygZKbBm91iXLVt2584dPz+/AQMG\n1HIxAXCKfgAAIABJREFUvgD5+fklJSVOTk5GRkapqakDBgxg2o7IoqWlxePx6i08QhRT06ZNfXx8\nli9ffu7cub179x4/fpzL5TL1fwKBICoqav/+/czVrxbJOTc5HA6196qZiIiIffv2nTx58s2bN40b\nN/by8mpAFX4SmZmZ9B0ghBBCCCGkFnHEYjHbMSgoDodz+PDhYcOGsR3IZ0IoFA4YMODu3bvnz5/v\n1KkT2+EQQgghn4QZJlAyUuDTp08B6OrqtmrVStI7qL29fVUazTRq1Ojt27cAevfuvXXr1hYtWtR1\n8HWqsLCwsLAwKyuroKCgqKio3IvCwsLs7GzJC2aRzPQM5kV2dpZIJAKQk5MjFAoB5OXlM7UR+QUF\nJaUldRe2lqYmT5kHQFNTg2l/qa6urqamBkBNTU1dXQOAiqqKppYWAGVlZW1tbW1tbTU1NeaFurq6\nlpZWxRc6Ojo0VCRRfM+ePdu1a1dAQEB2djaPxyspKTE1NX39+rW6ujozQ0ZGRkZGRnp6esaH0tPT\nS0pK8nJzS0tKc3NzBAIBc/JmZ+eIRKKsnOyaJZscDkdXh6+kpMTn63C5XB0dHR6Pp6WlzZyDqqqq\n+lIMDAyk39bqjlFcT58+PX78+LFjxyIjIw0NDYcOHTps2LCePXs20AvOn3/+OWvWrJycHLYDIYQQ\nQmqTYt5bDgoKGj58ONUIEPLZozZ/pJ7MmTPn6tWrYWFhVO1HGpYVK1Zcu3aN7SgIITJduHCBle2W\nGyYwKyvryZMnTF3gxYsX/fz8RCKRlpZW8+bNma5BGZLb6BI5OTmxsbHM62vXrrVs2XL06NEbNmww\nNjau1/LIIBAI0tLSsstklZG8zc7Kys7675PsnJysssq8cjgcjq6WtrqKqrqqiq6GlrqKijpPVbds\n0EQ7TW3mBd+isRKHA0BbXUOZywWgpabO43IBaKiqqSpX0lZPU01NRVkZQGxq8tkH//7o9pF2k3lF\nhaUCYcXpuUUFAqEQQH5xUYlAAKCguKi4tBRAUWlJYUkJgJLC0vysZACFQmFqcVFOYUFRaWleUWFO\nQX5hcXF+UWGlW+Qp8/g6Orq6fD6fr6enz9fT5fP5usx7qRfMa319fWr1QuqfnZ3d9OnTBw8efOLE\niVOnTr18+TIpKaldu3ZqKiopKakpaanS94aUuVwDHb6+to6+pra+ppYaj2eiqqairKZlosfjcpmT\nV0ddg6ukxNfQVOL87+kHXU1NWSN8i8XirPx8yVuRWJRdkC8UiXIKCwRCYW5hQalQmFdUWFxQWpCV\nnF1a+ib/YUZ+bnpOTkZuNnPmMjgcjomRkbGxsYWlpYmpqYWFhYmJibm5uampqampqaWlpaqqah3s\nv5rLy8tbtmzZmjVrqvKYiFgsjoiIOHbs2LFjx2JiYkxNTYcMGbJ69erevXsrKzfsvD4rK4sufeSz\nR3klIQqCrfyREELqWcPOEEhDsW3btj/++OPIkSNdunRhOxZCqufx48cXoy+iK9txEEIqegfcZjuG\nMrq6utLDBObl5cXExDBdg0ZERBw5cqSwsJAZJlDSNehXX31lYGDw6NEjyS11gUAA4PDhw8ePH1+y\nZMmsWbPq9CZ1Wlpaenq65P+0tLTU1NS0tLT0tLS01NT09PSU1NSKNXl8TS2+ppauphZfQ4OvrqGr\nrmmtqcc3suRraOpqaulpaqupqKirqOhpaqurqKjxVPS0/ntRdwWRcG/XycqgFkZerLGcwoLCkuL8\noqL/XhQXZRfkF5WUZBXkZxfkZxfkZ+XnZb1Pi38VG1VYwLzNzs8rLC6WXomysrKhvr6BgYGhoZGh\nsZGxsbGBgYGhoSHzv6GhoZGRkaGhoaamJlvFJA1XcXFxXFzcWymxb968efMmMTlZciEy1tVvZmFd\nWlqaFJ8w/mu3pp2/NtbRNdDWMdDW0dfS0dfS1lHXYLcU5eQUFmTk5abn5qTn5mTk5SRnZyVlZbzP\nTE98EPXvlWspWVkpWf+1KuZwOGYmJo0a2dnaNbKVYmVlxUqN4K1bt0aMGPHu3bvhw4fLeTiyuLj4\n/PnzwcHBp0+fTk9P79ix44gRIxpil55yZGZm0iB/5LNHeSUh7FOk/JEQQuoa1fyROnfr1q3Zs2d7\ne3sPHTqU7VgIqZGuQBDbMRBCKgoChrMdgwxaWlpMIz9moKyCgoJHjx49ePDg/v379+/fDw4OLioq\nUlZWtre3V1NT4/F4TIeWjNLS0tLS0sWLF+/YscPX17fGg/8JhcKUlJTExMT3798nJSUx/yfExycn\nJcXHx6ekppUK/rdRTTV1Qz7fSEfXUEvHUEvbzsDc0NbeQFvHmK9rqM1navX4Gpp6mlqfuGfqFLvV\nfgB01DV01DXAr95SJQLBf7WABflpudnpuTlpuTnpuTlpudmpCanPYl6l5eWk5+akZWeVCgSSpTQ1\nNCwtLExMTC2sLE1MTCwsLExNTZm2Tebm5tR6hgDIzMyMjo5++vRpTEzMs6dPnz19+jYuTigUKikp\nmRkY2hqZNjI07mVuN6FdV1sjUz0tLWMdXRNdPR73vwxRIBRmFeQZalfzC13vmPPO1shE1gwlAkFK\ndmZKTlZGXu7blOS3qUmx75KvPHj8NiXpfVqqWCzmcrmNbGzsW7ZsYW/fokULe3v7Fi1a1GlFVElJ\nyW+//ebj48PhcHg83rlz5yrW/OXn558/fz40NPTkyZOpqamtW7eeNm2ah4eHg4ND3QXGlqysLKr5\nI18EyisJYZcC54+EEFLrqOaP1K28vLyxY8e6urquWrWK7VgIIYQQdmhoaHTp0kXS8F0gEERHRzO1\ngH///XelQywIBIK4uLiBAwf26tXL19dXTtuO1NTUWClv37yJf/cuMTExJS1NWNYJnqaauqWhkQlf\nz0JXv6uxlUULB1NdfRO+rjFfz0BL21CHXz9t8kilVJSVjXT4Rjofr1/JKSxIzspMz8tJy8lOysp8\nn5melJWR8DI2PPJhQkZacmaGpGpQXU3NzNTUzMzMplEjGym2trYVu5wlnwehUBgTExMZGRkZGfng\n/v2oJ1EpaakAtDU07a1sWltYf+3s1trKtrGJuY2RiUoVOodU5nIVv9qvKlSUlS0NjCwrezKguLQ0\nLi3lZVJCVHzs0/i4qyGn/Lduyy3IB2BiZNyyVUuHDv9p1qxZbQ2hFxkZOXz48Ldv34rLhISELFq0\niPk0Pj4+NDQ0JCQkLCystLS0a9euc+bM8fDwaNq0aa1sXTFlZmbS8wqEEEIIIYTUIqr5I3Vr5syZ\nBQUF+/btq8rYFYQQQsiXQFlZuXXr1q1btx47duy5c+cSExMrnY2pt7t582a7du2mT58+Z86cpKSk\nly9f/lfF9/pNbGzs27jYgsL/hpcz0zewMTKx1jfqZdXEsl1XE109C31DE76upYGRpqpa/RWP1Bmm\nbVNTWMiaITk7MyU7Kz49NTk7KyEjLTEzPTbm9ckb4bFpybkFBcw8xoaGNlbWNnaNbGxtmbrAZs2a\n2dnZ8XiVDKNIFJlYLH727NmdO3ciIyMj7917+PhxfkGBMpfbwtK6g22T/v08WlvZ2ltY28huDEdU\nebymZhZNzSz6OXSWTIxNTX6WEPfk3dtHsa/PHTn+x+bNQpFIU0Ojfdu2HRwdHRwcunTpYm9vX4PN\nCYXCFStWrFixQklJSVBWT8+M3nfixImwsLDQ0NDXr1+bmpoOHjw4KCjIxcVFQ0Ox+latI5mZmQoy\nui0hhBBCCCGfB6r5I3Xo6NGjAQEBJ0+eNDAwYDsWQgghROGUlJS8ePHio/MA2LJly5YtWwBwlZTM\nDYxsjIwbGZp0bN3Rumc/G0MTGyNjGyMTardHTPh6Jny9NtaNKn6UkZcbm5ocm5Ycm5ryJiUxLj45\n7MHj2NTk9JxsAMrKyrbW1s1btGjeokWzZs2aNWvWvHlzc3Pzei8B+QihUPjw4cPr169fvXLlxvUb\nqelpPGXl1jaNOtg0GT1yUge7pu1s7NRVWBiy7nNiY2RiY2TSt70j87aguPhh7KvINy8jX7+4fuqs\n//btpQKBsaGRc3fnHl9/3aNHj7Zt21alOeCLFy9GjRr14MEDkUgkEomkPxIKhR4eHjY2NoMGDRo0\naFDPnj1VVL6s63lWVlbz5s3ZjoIQQgghhJDPB9X8kbqSn58/a9assWPHDhw4kO1YCCGEEEX09OlT\n6RH+AHA4HACS/j+VOBwtdQ0zXT07E/P2to2/tm/bo2Ubuq1PakBfS1tfS9uhUZNy03MKC14kJrxM\nSniRmPAiKeHWqbP79vyZlp0FQFtLq1mTpk2bN2vTtm2bNm3atGlja2vLQugEePbs2ZkzZy5duHjj\n5o2c3FwzA8PuzVv/35ARX7ds28rKVonDYTvAz5mGqmrXZi27NmvJvBWJxU/i3lx9+uh69BOf5Stm\nZaTztXW6devm4ubav3//Fi1aVLqSP/74Y968eSKRSCA1WqcEj8cbPHjwkSNH6rAYii0rK4vP/xy6\nliWEEEIIIURBUM0fqSurV68uKipiGigQQgghRCI+Pv7x48ePHj06cfy49HR1FVVTXf1GxqatrW27\nNLH/ulU7cz19toIkXwgddY2Odk072n0whFhmft6LxPiXSe+fJ8bHxMcfur37t4R3pQIBX1unTetW\nbR0c2rZt27Zt29atW2tra7MV+WcvPz//8uXLZ86cORN66u27OCO+bp+2HTeO/r67fZtmZpZsR/fl\nUuJw2trYtbWx+6nfNwCeJ8Zff/Y4LOqhz/IVv/76ayNrm34DB/Tv379Xr15MR52JiYnjx4+/dOlS\nuXZ+0kpLS2/dulV/ZVA8BQUFNAQpIYQQQgghtYhq/kidiI2N3bBhg4+PDw3VTgghhCQlJd1h3L59\n//79zKwsZS63ibmlibbu4E5dOzVu7tLaoWszew413CGKQU9Tq3OTFp2b/K/1UolA8DQ+9nHcm8dx\nbx7eunf8cFBiehqHw7G1tnZ0cnJycurcuXPHjh3p3v2ny8nJOXHixKGDB69cuVJSWurYtIVXl579\npjp2atyc2vYpoGZmls3MLCf17icSi/99FXP6/t0zF8P8/f1VeLzevXu3btPmzz//TE9Pl7TkliUx\nMfHFixdNmzaVP9vnqqioSE2NxqMlhBBCCCGk1lDNH6kTS5cuNTU1nTJlCtuBEEIIISwoLCyMjIxk\nqvpuh4fHxcdzlZRa29h91dR+zMjJbW3sWlnZ0LB8pAFRUVZub9u4vW1jyZTUnOxHsa8fvH11+8Wz\njZd8EtJTlZWV27Zq3aXbV507d3ZycmrevDlVZlddYWFhaGjo34cOnT59WiQSubfruPP7Wf0cHA21\nqQvEhkGJw2Hqy3/zHJeak30q8vbakKDTZ85UnJPH4ykpKQEQCoXSnX+eP3/+i635Ky4uVlWljqwJ\nIYQQQgipNVTzR2rf27dv9+/f/9dff1H+Rggh5MshEAjCw8MvXLhw/uy5yPv3SwWlBjr8Lk3tv+/m\n+lXzVo6Nm2mra7AdIyG1xkiH79LGwaWNA/P2XXrqrZio2y+ehV+6snvXrpLSUl0+v2fPnu59+ri5\nuTVpUn58QSIRGxu7bdu2nTt25Obmfd26na/X9KFOzvpa1I1qA2akw/fq2cerZ5/03Jzg8KsHbobd\nionS1NTs37+/s7Mzl8vNzMzMzMzMysrKyMhITU1NT0/Pysq6ffv29OnT2Y6dHVTzRwghhBBCSO2i\nmj9S+7Zs2WJlZTVy5Ei2AyGEEELq3IsXLy5cuHD+7NmwsCs5eblm+gYurdpP+WFW12b2zcwsqc0T\n+UJYGRgN/6rn8K96AigqLYl4/eJG9JNLj+//Ont2YXGxnY2te98+bu7uvXv3pq7gGWKx+PLly35b\ntpw8dcpMz2D+QM9xPdzMaGjPz4uBts4U90FT3Ae9z0zfe+X81vMnjxw5MmTQoBkzZ/bq1Yvt6BQI\n9fZJCCGEEEJI7aKaP1LLcnJydu/evWjRIi6Xy3YshBBCSJ0QiUQ3b94MDg4++c8/b+PidDQ0v27Z\ndoXnWJfWDq2sbNmOjhCWqfFUujVv1a15q3lDhheVltyKeXrxceSly9d27drF4Sg5duo49LvvPD09\nbWxs2I6UNefOnZs3d+7Dx4+dmtnvnzHv2y7deVzKyz5n5noGCzxGzhnsGRx+bfOZ471793Zo127N\nunVubm5sh8a+0tJSkUhEbf4IIYQQQgipRZRhklp2+PDh0tLSiRMnsh0IIYQQUstEIlF4eHhQUNDR\n4OCExMSWVrbjnHr0+b5T5yYtlOl5F0Iqo8ZT6d26fe/W7QFk5uddiXp4MiJ81bLl3t7enTt2GjZy\nxHfffWdtbc12mPXn3r178729w65e/dapu//KLV2a2rMdEak/PK7yKOfeo5x734yJ2nDyiLu7u5uL\ny+o1azp27Mh2aGxiBjtUVqZbE4QQQgghhNQa+nlNatnff//9zTffGBkZsR0IIYQQUmvevXu3ffv2\nwL1749+/b2FpPfErl2Fffd2amvcRUh16mloenbt5dO7m/73g4uPIoFtXVyz9bc6cOU6Ojj9OnTpi\nxIjPu7u/1NTUmT/9dDgoqEsz+5srNndt1pLtiAhrmEaxN6KfzNm/09HRccTw4Vt8fQ0NDdmOix08\nHg9l9X+EEEIIIYSQWkE1f6Q2JSUlXbly5ciRI2wHQgghhNQCsVh87ty5bVu3nj5zxlzf0KuHq2eX\nHm1t7NiOi5CGTUVZub9D5/4OnUsEgvMP/z0cfnXqj1N+nf3L+AleM2bMsLP7DE+x8+fPe40br8ZR\nOjxr8XddutMIoASAc4vW4Sv+CAq/Ou/gnnZt2u4N3Ofq6sp2UCxQVlbmcDilpaVsB0IIIYQQQsjn\nQ4ntAMhnJSQkRF1dvW/fvmwH8mVJT08/fvz4qlWr6mLlL168WLNmzfr161++fFkX6/9ypQPHgTo5\naEQB1OnxfQGsAdYDdFLWJbFYfPz4cceOnfr371+akHJ8zm9vfPetGO6l+NV+6bk5x+/eXHX8ENuB\nsCm7IJ/tEGruizqCKsrKAzt2CZwxL8H/0KLBw0L+DmrerLnXeK/o6Gi2Q6s1hYWFv8z+pW/fvq7N\n2zxYs92zaw92q/2+qC+YLIpzieBwOMO/6vlwrX/PJvZ9+vTx9vYuKSlhOygW8Hi8L7PgiobyygaJ\n8srPACWPhBBC6gDV/JHadOrUKTc3N3V1dfmz3bx509nZWVVV1cDAYOzYsSkpKRXnCQsL43A4urq6\nHTp0cHJy4nA4ampqTk5O7du319TU5HA4iYmJdVMIeViMKioqatOmTcxrsVi8du3aBQsWdO/eXVlZ\nefz48UOHDt23b1/tbjE3N/f777//5ptvunfvPmfOnCZNmpSbwdfXt2E9sS4QCJYsWRIfH892IEA0\n4AMMBap10DgAD1gE+ADPK3wqBrYAnsBSYASwAxDLWI8AWA5YAypAGyCgbM7ngA8wB1ACKj2wYQAH\n0AU6AE4AB1ADnID2gCbAAVg4KVmNKgrYVPZaDKwFFgDdAWVgfPWPb1XkAt8D3wDdgTlA+ZMS8JVx\n7ORjCsLKnhQAS4B4uVPYEB4e3sXJ6dtvv7VW0Yjw2XZmwcpBHbtwldj/1eR75kTjn8Zxhrkpj+jT\nd+WCgT6LB6xe5P77fLsZYznD3OLSUqIT3vmc+Hvo+t/2Xb3ASoStfpn8487NNV78TUpSv1ULXVd4\n331Zk4ofgVC46dTR3svmGk76tsYxfKJP3AN1egTvvox2WT6378oFsanJtb7yT6Svpf3LwO+iN+75\nc+qvdy5fadOmzbRp09LS0tiO61NlZ2e7ubr+uWvXgZkL9s3w1lHXqNPN0SVCvqpcIlg5Tfgamgdm\nLtg73XvHtm2DBw3Kz1eUisl6w+fzc3JyqruUQCBYvny5tbW1iopKmzZtAgICxOJKfv5SXlkO5ZWf\n7jPPK2Vli+VQ8lhdlDx+OkVNHgkhRDFRb5+k1ojF4hs3bvzf//2f/NkiIiI2btzo4+Ojqam5YcOG\n/fv3JyQkXL58udxsBQUF7u7uISEhqqqqADgcjq2t7Z07dwBkZWV169atsLCwjgoiB1tRnTt37uDB\ng3/++SfzduPGjevXr09KSsrJyRk9erS3t/epU6c+cRNv3761tbWVvM3IyHBxcREIBDdu3NDT06s4\n/7179+bNm/eJG62xctFWkbKy8vz58ydOnLh69WqWexJrAfgA66u/YCNgpYyPVgD7gQeABlAAtAdS\ngcWVzTkdKAEWAy+A7cBEIAf4GWgGzAcAnABeVbZgAeAOhACqAAAOYAvcAQBkAd0AFk5K9qI6BxwE\n/ix7uxFYDyQBOcBowBv41JMSeAvYSr3NAFwAAXADqOSkBO4BNTgpJQU5y8aeVAbmAxOB1YCdjCn1\nq6ioaNGiRZs2berctMWt3//o0tSehSBk+6nfN2N7uOpN8GhsYn520WrJdLFYPHT9slKhoIWFlc/o\nyetPBrMVoQlfT19Lu8aLzwnccfbBvZg/ApqZWdZgcWUu96e+36wLCRIIhTWO4RN94h6o0yPYuUmL\nbZNntpg10Xv/rsOzK/0LwTJlLndsD9eR3XrtuHhq6YF9R4OP7Ny9a8iQIWzHVUN5eXmuLi7JcfHh\nv/9hb2FdD1ukS4R8VblEsHiajOnu0t62cd/VC117u1wKu6yhUbf1xApFT08vMzOzuktNnz69pKRk\n8eLFL1682L59+8SJE3Nycn7++edys1FeKY3yynIor6yErGyxHEoeq4WSx1qheMkjIYQoMqr5I7Xm\n1atXGRkZTk5O8me7c+dOUFAQl8sFEBAQEBoaevPmzYqzFRYWzpkzh0mEytHV1Z0yZQorGRorUT16\n9Gj69OmRkZHMTgOwfft2fX19JSUlXV3dT8/NALx7927cuHHXrl1j3orF4rFjxz5+/Pjhw4eVpmeZ\nmZn//POPlZXV8+cVHxGsc+WirRZNTc2VK1cOHjz45s2bfD6/1mOrBm6NlpLV4igWWAGsB5h7RBrA\nVGAeMBpo9OGczwE+sLbs7QCgF7Duw1xO1l+GQmBO2S/7cnSBKSwlb6xE9QiYDkRKHcftgD6gBOjW\nRtoG4B0wDpB8zcXAWOAx8FBG5pYJ/ANYVfbcrhzSBWHr+GoCK4HBwE2AL2NKfUlISOjft9/rV6/2\nTPnVq6e7Yj5+rqupBaBcbBwOZ943w7XU1AGw2zbx8tJ1n7J4dMI7AI1NzGu8BmUuV0ddMzEz41PC\n+BSfuAdQx0ewiakFgKj42LrbxKdT5nKn9xk8yrn37L3bv/nmm5kzZ27atElJARrdVpfX+PFxr17f\n/n1LI2PTetsoXSLkq8olgsXTpLWV7fmFq7sv/WWCl9fhoKD6D4Aturq6WVlZ1Vrk+fPnfD5/7dr/\nftQOGDCgV69e69atq1jzR3mlBOWV5VBeWYmqZIvlUPL4UZQ81iJFSh4JIUTBNbwUmiisO3fuqKqq\nOjg4yJ9t2rRpkkyDw+FwOJyRI0dWnK1///69evWStZLvv/++adOmnxJtzdR/VEKhcNy4cRMmTNDR\n0ZFMfPv2bS1uIiUlZcCAAdJ9rp4/f/706dMeHh6tWrWqOL9YLF6xYsXcuXNZuSdeMdrqatKkSYsW\nLebMmVOLUbHvACAAuktNcQZKgQMV5kz6sCFgT8ACqGJvav0BmV9/4HuAhZOSjaiEwDhgAqAjNfFt\nrW4iBRgASH/NzwOnAQ+gkpMSEAMrgLnV7K2lXEFYPL5NgBbAHLlT6t6rV6+6de1akpUd6bNtQq8+\nilntJ8vzxPi21nYm/Eoz+4ZEKBKB7ZqJzxuzb1lsE1l1eppaf02bu2/GPP/t28eMHi1sCDFL27dv\n3/ETJ4JnL6nPaj9Z6BJRLeyeJi0tbY78suTI0aMHDx5kJQBWmJubv3//vlqLJCUlLV78vx+1PXv2\ntLCwqLSLYMorGZRXlkN5ZeU+JVssh5JHBiWPtU4xkkdCCFF8dGOF1JrHjx/b29tX+txipcRi8e+/\n/z579uzdu3dX/FRDQ0NZWWabVDU1NRUVldzc3OXLl0+ePNnZ2dnZ2fnff/8Vi8WhoaEzZsywsrKK\ni4vr27evqqpq27ZtIyMjmQUfPnzYq1evZcuWLVy4kMvl5ubmAkhJSfnpp59mz57t7e3t7Ow8derU\n5ORkoVB4/fp1b29vOzu7N2/edOzY0cjIKCcnR35UR44cYQZm2LRpk0AgABAUFKShobF///67d+8u\nXLiwcePG0dHRPXr0UFNTa9269ZkzZ5hlK5aFmX78+PGHDx8OGjSIeRsaGjplyhShUJiUlDRlypQp\nU6bk5eWVC6PS4jAfRUVFDR48ePHixRMnTuzcuXN4eDiA7du3P378mFkhMxvT/YuRkVH79u1VVFTa\ntWsXGhoqWb+vr+/w4cOr9WDj2bNnjYyMOBzOihUrmCl79uzh8Xh79+6VU/b8/Pzly5d7eXn98ssv\nTk5Oy5cvF4lEFaOt+uFLSkpiFhk4cOCePXtq7cHSs4ARwAFWlE3ZA/CAvQCAKGAwsBiYCHQGwitb\nQzoQLeNfFR83vwHgw+Z9zOtbFebs8WHKIQYKgW5V24qG3IbiaoAKkAssByYDzoAz8C8gBkKBGYAV\nEAf0BVSBtkBk2YIPgV7AMmAhwAVyAQApwE/AbMAbcAamAsmAELgOeAN2wBugI2AE5HwsqiNlIw1s\nAgQAgCBAA9gP3AUWAo2BaKAHoAa0Bs6ULVuxLIzjwENgUNnbUGAKIASSgCnAFKD8SSmjOIxKvyHb\ngcdlK2QwPcMYAe0BFaAdECq1fl9gePWfcCxXEPnHlwfcqbDzNwCqZRljLrADUJFKIGXtwEoNBPZ8\n+MxpxSl1KT8//5vBQwxU1K8v29jUzKKetlobxGJxRl6u9/5dOYWVjwuVW1iw/Mj+yf4bnZfMcl4y\n699XzwHkFxcFhV/12rqu25JZB29c1p/g0exnr3uvYm5EP+m2ZJba6P6tf/3+YexrZg3Xnj1WG93V\ne5hqAAAgAElEQVRf1+ubmzFR2QX5P+zYxBnm1mfl/KfxsQAevH1lOWXknstnhCJRUPjV8VvX9lj6\nC7Pgw9jXvZbNWRYcuPDQn9zh7rmFBbLika/ieg5cv6Q6qh9nmBuzwh0XQlVG/vdW4u7LaMcF09VG\n9+84b1pY1IOK+y004vaMPb5WU0fFpaX0XblAdVS/tnN+iHzzgpkhJTvrpz/9Zu/d7r1/l/OSWVN3\n/ZGc/ZGe6KT3gJz1i8Xiuy+jFx76s/FP46IT3vVY+guzw8/cv1vpaqPevR28ZsnivwMmbl/fecGM\n8OdPmen5xUXLj+z32rrul73+Tgt/Wn5kv0gsrtkeVmRje7iGzltx/Njxj/Ynr1CKi4sXzl/wo+uA\nHvZt2I2ELhE1u0Swq1er9pN791swb15JSQnbsdQTa2vruLi4ai3So0cP6RossVhcWFjYrVslP2op\nr2SmU15JeWWVfEq2WA4ljwxKHj/H5JEQQhoEqvkjtSY2NrZRo0Yfnw8AcPLkSRcXl2XLlm3atGnd\nunWVDsYun0gkGj169OTJk3fv3n3jxg1zc3N3d/fs7GwnJ6eDBw/Gx8cHBgYGBAScOnXqyZMnP/zw\nA7PU0KFDX758uXTp0lWrVk2aNKmwsDA1NdXJycnc3HzTpk1r1649derU1atXO3XqlJCQoK6u7u/v\n/+bNmxMnTmzYsMHV1fWj9ZqjRo366aefAPTr14/J5RwdHfv06TNy5MisrCw/P7/Xr1/v2rVr8+bN\nhw4dSkhIGDRoUGRkpKyyADh06BCXy23ZsiWz/oEDB/r7+wMwNTX19/f39/fX0tKSDkBWcZjkpH//\n/s+ePfv999/37NnD9G0CYOnSpZIVMith+l91dHS8cePGvXv3cnNzhwwZwqRz4eHhAoHgo326ltO3\nb18fHx8AnTp1Yqa4ubmNGjVq/PjxsspeUFDQs2fPuLi4gICAjRs3Tp48eenSpUePHi0Xbc0OX4cO\nHcRica09Sd0X8AEAdCqb4gaMAsYDAPoDz4DfgT1lXXBUFADYy/g3umoxMA9JS4+bwyRsHx1V+w6Q\nAdTW3VQRMBqYDOwGbgDmgDuQDTgBB4F4IBAIAE4BT4AfypYaCrwElgKrgElAIZAKOAHmwCZgLXAK\nuAp0AhIAdcAfeAOcADYArjI6GJE2CvgJANCvLDlxBPoAI4EswA94DewCNgOHgARgEBApuywADgFc\noGXZ+gcCzKljCvgD/oDWhwHIKg5zx6DSb8hSqRUybpZFfgO4B+QCQ8oyvXBAAFTvpKysIPIJK9v5\nEwGbshm0gR+lxpaQswMr1QEQAwflTqlLGzZsiI+L+2fOb4baDaOPmJj37zjD3DjD3JSGuxtMHPrP\nvYr1/AAgEotHb1k92aXf7im/3Fix2VzfwP33edkF+eoqqs4tWu+9ev5pfKyZnv6TjbvfpCR9u37Z\nvVcxl/5v7aP1O2Pev/s5YCuzkh72bSb17ldcWtraypavoek7cYYJX89C37ClpQ2ANtaN7C2sJ/bq\ny1VS6tfecd/VCynZ//XYNnT9by+T3i/1HLtq5MRJvfsVlpTIikcScKW/ByquZ3R3FxsjE+ZTbXWN\nH90G2hqblFvq8K2rWyZM3/nD7JdJCX1+XyCppZBwamp/8Mbl+PTUwGsXA6bNPbVg5ZN3b3/YsQlA\nak6208IZ5noGm8ZPXTvm+1MLVl59+qjT/OlJWfJ6CCy3B2StXyQWZ+Xn+53953Vy4q5Lpzd7TT30\n86KEjLRBa5ZI6h2l9V+96FlC3O8jJuyZ8uu79NRxfmsAFBQX9/zt17i0lIBpczaOnzLZpd/SoL1H\nb1//6B6Ws58VllvbjuvH/uDj4xMTE8N2LFUVEhKSnJK80KOSbi3qB10iPv0Swe5psmjoqIT376Xr\nSD5vlpaW7969+5Q13LlzJyMjo2aPCFBeSXmlnPJ+cXllObWbLZZDySMlj7Zlbxta8kgIIQ0C1fyR\nWhMbG2ttbV3FmV1dXQ8cOODr61tcXLxw4UJfX9/qbu7ixYsnT560sLBgugwNDg7OzMwMCwszMjIy\nMjICsGjRIjMzM1dXVxsbm/v37zNLZWRkxMfHb926VSQSzZ49W01NzcfH5+3bt5IUjs/nL126ND4+\nft26dZ06dTIzMwPwww8/9OzZ89ChQ5UOTlAOs9r16/8bZXv//v2TJk3icrnu7u7M2lavXt2hQwcP\nD49Vq1YJhcItW7ZUWpbLly8DuHPnjomJiZwHQsuRVZyVK1cCmDlzJjP0hVgs1tDQePWq0pG4kZSU\nZGlpOWHCBC0trXbt2q1Zs0YkEvn5+aWnp+/evXvWrFlVDEbauHHjrK2tt2797ybRzp07mfXIKvvG\njRv//fffRYsWMX2/jBs3btu2bRW7xKnZ4bO0tATA5Jy1YxxgDWwte7sTkOykmWWDIogBDRmDn88B\nxDL+3ahaAEwHutKddXAqTKlIDCwDlgFfV20rH3UROAlYAByAAwQDmUAYYAQYAQAWAWaAK2AD3C9b\nKgOIB7YCImA2oAb4AG+lsjs+sBSIB9YBnQAzAMAPQE/gkIxxC8phVru+7O1+YBLABdzL1rYa6AB4\nAKsAIbBFRlkuAwDuACbVGSdXVnFWAqjaNwRAEmAJTAC0gHbAGkAE+AHpwG6p71u1VKsgKjJ2frnf\nEZK3cnZgpSwBfPjwcsUpdUYsFm/z2zrdfZClgVF9bK82NDe3EgddEAddEB0+n7rnSM9W7Sqd7eKj\nyJMRty1+HMHUAQSHX8vMz7v85IESh2Omqw/AhK/Xq1V7cz0DKwOjd+mpswd8q8ZTaWZmaW1ofO/V\n/2pZpvcZXFRacuD6JQCqPF7nJs0P37qSU1gA4FTkne+6dGeu1cwoYhIZebnx6albz4WIxOLZA79V\nU1GRFQ8zv1gszirIM9XVL1eKiusBoMT54MtX7i2AVSMndm3WctzXbmvGfF8qFKwL+WDELA6HY6TD\nN9LRBbBo6CgzPX3XNh1sDI3vv3kJwOfE329Tk39wHcDMzNfQXOo5Nj49deWxj9xOkOwBOevnKim5\nt+vI7P/VoyZ1aNTUo3O3VSMnCkWiLadPVFznzH4eP/cfCuYioar6KjkRwMbQI/++er5o6Kj//kr2\ncNs2eWav1u3k72GGMV83uyC/YVX+TXEb2NjUYtu2bWwHUlXnz5/v0rwli5cUukR84iWC9dPE2tC4\nczP7c+fOsRVAPWvSpMm7d+8qtjmrIrFYvGzZsmXLln39dU1+1FJeWSnKK7/QvFJarWeL5VDyWClK\nHhU+eSSEkIaCav5IrXn37h3zw7cq1NXVzczMZsyYsWPHDgAHDlQcjuwjwsPD27ZtK/6Qh4cHgHLj\nBKiqqopEIub15s2buVzujBkzOnfunJmZqaOjc/XqVQDa2v9rLdWzZ0+UPZ/IrEpTU7PqgZmYmEye\nPHnfvn0JCQlisTgsLKxv377MR8zaVFRUmLdMXysPHjyQU5akpCQNDY2qb11+cX799dcxY8Zs3rzZ\nz8+vuLhY1u0MptObcmt48uTJ1KlTx4wZ8/z58+jo6Ojo6OLiYgDR0dGyMj1pPB5v5syZp0+ffvny\nZUlJSUxMDDMkpKyynz59GmWpFABVVdWpU6caGhpWq7yyDh8zf3VHE5FbPGAmcBp4CZQAMYBkvMtf\ngTHAZsAPKAbq6A6SFYAP+wlhuj2R32ehP9AGWFJ7YYQDbSskmR4AKtRBqgKistebAS4wA+gMZAI6\nwFUAHzZh7Amg7NFFZlXVOCkBE2AysA9IAMRAGNC37CNmbZLvO9N7yQO5ZUkCqnFSfqw4VfyGqEkF\nKVnDE2AqMAZ4XtaNTzEAIFp2EiitugVBdXa+nB1YKWb/vJc7pc4kJCQkp6b0adfp47MqHg6HY6jN\nn9V/KI9bSSIe/vxpWxs7pgJA8s+jczdU+FuposyTfsvjKhcUF0vetrS06dWq/c6Lp8Ri8ZuUJKFI\nVCoQHrpxGUDgtYtjerhKgpFeyWavqVwlpRl7fDsvmJ6Zl6ujriEnnuLS0g2hR/Q0tXf9OLtcKSqu\npyp7RpX3X4mGdPoKwOO4N5XuvQ8XUWE6zLz69CEAbakNMRUnN2Oi5G+03AplrV/ykUrZHdhBHbsA\nePD2ZcV1/jrouzHdXTafOuZ39kRxaSnzt/v0/bsALA0MJYWd6j7IUJsvZw9L7J7yq76W9sbQo8Wl\npfKLozi4SkpubRwi7snv+EmBvH3zxt7Miu0oALpEyCb/EqEIp0lLc+u3byq5cH2W2rdvLxKJHj58\nWLPF/f3927Rps2RJDX/UUl5ZKcorv9C8UlqtZ4vlUPJYKUoeFT55JISQhoJq/mTicrlClsZ1b6DS\n09OZhyKr5ZtvvgHA5XKru2BJScnLly+LioqkJ370kI0fP/7evXsuLi4RERHOzs5btmxhfsTHxv6v\n23t9fX0A1cqLypk7d65YLN60adO9e/e6dOki68lKU1NTAGpqanLKwuFwqvW4sfziXL58uVmzZu3b\nt585c2a57lyk2dvbp6amSrbLPNKopqYWEhLSu3dv+zLMgPD29vZ9+vSpSmyTJ0/W1NT08/M7fvy4\np6cnM1FW2QsKCgB8NPeri8NXQ5MBTcAPOA54Sk2/DDQD2gMzK/TjIfHp4zEwN3WlZ2aGSnGWvUgI\nkAGsqeaw3vKVAC+Bog8nfvQ6Oh64B7gAEYAzsKUsJOniMM/3f8pRnQuIgU3APaCL7GcVTQEAanLL\nwqlmpi2/OFX5hgCwB1KltqtXFmcI0FuqG5+3ZTNX5aSsbkGqpWZfBlJTQxy/MtDWyS0sEIpE0tNL\nBKUvkxKKSj8YKarcPFU0o++Qh7Gv772KWfvP4bVjvh/q5Lzr0umod29tjIw1VdUqXWT81+73Vm91\naeMQ8fqF8//N3nLmuJx4BCJhflGRrqamRoW1VVxPtSI31NEBoKcp5wQr778/LqmSMVWgr6UNQEOl\nqoMZVxfTjElNRaXiR5efPGj2s1d728Yz+3lImkwVFBcBeJVUvk/nqhxxTVU1TTW1gpIigaghnZMN\nq5FifdyQrQ66RMhR6SWigZ4mDZetrS2fz69ZzV9ISEhGRsaaNWvK1dJVHeWVlaK88gvNKyXqIlss\nh5LHSlHyKEF/gQkh5NNQzZ9MKioqxVKPspKPKi4uVqnsjpV8aWlpAL777js581SaorRq1aqgoMDP\nz08yJSEhQfptpXx8fBwcHC5evHj06FEAixcvdnFxAXD27FnJPPHx8QAGDhwof1VyEidra+sxY8bs\n2LHDz89v4sSJsmbLzMwE4O7uLqcsFhYWOTk58iORJr84Xl5empqazNOL5eIXSd3lGTJkSG5ubnR0\nNPOWOUbdunUrKiqSfoKyefPmzHpevqykjUJFfD5/8uTJAQEBQUFBzJOnkH0cHR0dAaxatUoSZ1pa\n2pEjR8pFW7PDl5+fD8DCQn6DuGriA5OBACDow2fTvADNsofsZH1lPn08hpGAUtljgIybAA8YVbbd\nchezs0AcsEgqkbtTtQ1JVFqWVkABIH0WJnz4tlI+gANwETgKAFgMuJQFKREPAPjISSk3FbEGxgA7\nAD9A5kkJZAIA3OWWxQKoxkn5seJ4yf6GSN96HQLkAtFlb9MAAN2Aog+fi2xetp6qnJRyClLdpE7w\n4Qtx9b8M+WUhyZlSZywsLEyMjM89bDDNiSolFosn+W8od9+zlZVtQXGx39l/JFMSMtKk31bd4E5d\nLQ2MfgsOzC8uamVlO8VtYMTrF9P3+E5zHyxrEZ8Tfzs0anJxydqjvy4FsPjvv+TEo6mqtuS7Ma+S\nEplx7OSvR/KRoOzOLPOi0j/N7zPSAXh0lvMoRHkurR0AnH1wTzIlPj0NwMCOXaq+kmrJzM8D4N62\nkoanXlvXaqqqMY0OJQV0bNIcwKrjB//3VzI3+8jta1U54mN9fWJTkxcPHS2rPkYBCUWii08edHRs\nMA1zbe0aPUv8pEHLah1dIlCdS4QinCZP38fZVnkM9YaOw+E4ODjcu3fv47N+6OzZs3FxcZKOHAHc\nuSPvRy3llfIjkUZ55ReaVzJkZYsV88oqouSx6ih5ZChw8kgIIQ0F1fzJpKqqWlJS8vH5CABAIBCI\nRKKq1PytWrXK19eXeRavpKRk7ty5np6ezOjlsjAzl6uIHTJkiLW1tbe396xZs06cOLF58+Zx48Z5\neXmh7KlGyS/70tJSlP2m37hxY0ZGBoChQ4eam5s3adLE29u7adOm69evZ/IlAP7+/p06dZo5c6Zk\nKYFAgAoqjUpi6dKlxcXFcXFxTZo0KfeR5AHSS5cuNW7cePbs2XLK0q1bt9TUVOZBRQbztZR+CpUJ\nj5kivzh5eXnv379/8ODBgQMHmP3w7NmzxMREQ0PD5OTkhIQEZpEZM2ZYWVlJhpQICQkxMDD45Zdf\nKi2phLe3t42NTUBAgJx5Zs6cmZeX5+DgwJP0sCSj7PPmzePz+YGBgQMGDNizZ8/GjRvHjBnD9G8j\nHW3NDh+zbJcutX0DdyaQBzgA0h1i5QHvgQfAASADAPAMSCz7jcscxk8fj8ESmA9sLXtKrgjYBiwu\n6wV0AaAr9bv/AsDcNPMD/ABfYC4QWs3CMhsq9/UfAlgD3sAs4ASwGRgHeEmVVJISMF1nMcnJxrI9\nMxQwB5oA3kBTYH1ZKgXAH+gEzJRaqpKTUkZUEkuBYiAOKH9SSj1OeAloDMyWW5ZuQCpQILV4yYcr\nwYfHV35xZH1DDIFkIKFskRmAldRoEyGAAfCRkxLwBmwAWSdlxYJIyNqTFXd+YwDAFiAW2FFWxrvA\nQNk7sNKomJJ2kTulznA4nGkzpvue++dlUgPoIKawpBiA8MNmKKVCweK/AwAocTjMDW5mhiGOX1kb\nGnvv3zXrr20n7t3cfOrYOL81Xj3dUdaMRvK3UiQWQeouObO49L08ZS73R9cBZx/c8x4yHMDXLds2\nN7fSVtewMzGTzMMsLlnJxtAjGXm5AIY6OZvrGTQxNZcTDxO8vpZ2QkZauSJXXA+AxiZmALacOR6b\nmrzjQmhmfi6Auy9jJM2VJF0R+p494dyi9RS3gQC89++ymTY6IOxcpcUsFQoAiMRi7yHDm5pZrD8Z\nzFTIAfC/ENqpcbOZ/eT0OlTJHpC1fsn8kmgvPY5sbGI+e+C3ksUlhzivqPB9ZvqDt68OXL/E7Idn\nCXHjerjxNTQDr10c4LN4z+UzG0OPjNni07e9o/w9zHifma6nqV3jxjGs8Dv7z6ukhGnTprEdSFW5\nu7vfjnkan55a/5umS8SnXCIkWD9N4tJS7j6PrmL7p89Dr169Ll26VK1FLly4sGbNGgB+fn5+fn6+\nvr5z584NDZX3o5bySsorKa/8ODnZYrm8suooeaTkkfFZJI+EENJQcH/77Te2Y1BQmzdv/uqrr5yc\nnNgOpGEoLi5euXLlmDFjmOf15Dh9+vTatWt37dr1+vXra9eueXh4LFy4UE5vnxcvXty4cWNERERW\nVpZIJFJXV2f66FdRURkwYEBMTMyxY8dCQ0N1dHR27NhhYGAQGBi4b98+kUikr69vb29/8ODBffv2\nicViHo/n6Oi4aNGiEydO5OXlhYSEKCkp/fXXX+bm5qNGjUpMTNywYcPz589Pnz7N4/H27NkDwM/P\nLzg4WCQSCQQCExMTY2Pjj0YloaurGxkZOXr06Hbt2kkmMoOZGxoa2tvbZ2RkXLp0aevWrYaGhrLK\nAkBbW3v//v39+vWztrYGEB0d7efnd+3atezsbGNjYy0trfz8fF9f3ytXruTm5lpYWNjb20+aNKli\ncZgxCYyMjMLCwk6fPu3p6WljY3Pjxo379+8PHz7c3Nz8/PnzhYWFTAqkpqbm4eEREhISEhJy9+7d\n+/fvBwYG2tnZlTs0THEk15C9e/feuHEjLCxswYIFso6mnp5eXFzc/PnzJWMkyCq7vr7+oEGD4uLi\nrl27dubMGU1NTX9/f6bHFT6fL4lWXV29Bofv3LlzJ06c8Pf3rzjAQ0XBwcFP8fSDjlZkFg+IA+Z/\n2JG9ERAGnAY8ARvgBnAf6AL8CVwBcgELwBZQr8L6lwGGwAwZn/YCSoBtwBNgJ/AN4F32kOYt4AHw\nA6AP3AL6Ay+BM1L/bgEBUqOdM0N//yY7kovARiACyAJEgHrZeNoqwAAgBjgGhAI6wA7AAAgE9gEi\nQB+wBw4C+wAxwAMcgUXACSAPCAGUgL8Ac2AUkAhsAJ4DpwEesKcstmBABAgAE8C4ClFJ6AKRwGig\nndREprCGgD2QAVwCtgKGsssCQBvYD/QDrAEA0YAfcA3IBowBLSAf8JU6vvbApMqKw3xPKv2GDAfM\ngfNAYdmoEmqABxAChAB3gftAIFD+pKxw7PYCN4AwoNKTslxB5O/JfBk73wmIAPYCF4EpwAPADTAC\nWgKDZezASqM6B5wA/AFD2VMqigKOoFZ+yXTq1Ck4+EjQ1YueXXtU7EpOcYQ/f7ry2KH7b15m5OWe\nfxjxz71bh26G7bx02nv/rvMPI37u72Gozfc9c+LK04e5hYUWBobNzCy/7dI95v27Y3duhEbc1tHQ\n2PHDLANtndSc7C1nTlx+cr+otKS7fZu3qcnbzoUIREKuklJbG7tDN8MOXL8sEouM+bqNTEwlO6S5\nhVVGbu73Lv1R1inWoI5dJLf184uLfM+cuPAoMreowNrQuLGJ2ZLDf524ezOvqDDk33AlJaW/ps01\n4esN6OBUMR5JAbeeC0nPzfnNc5x0qb337yq3Hj0tbaem9hGvX+y9ev7i4/tT3AY9ePvSrV1HIx1+\nU1OLZuaWKdlZ/hdO3nsV88+9W0Y6/J0/zlbjqQDYe/XCjegnYVEPFniMDLx2cd/ViyKxSF9b297S\n5uCNy/uuXhADPC63R8s24792T8zK2BB65Hli/On7d3hc5T1Tf9VUk/fdKLcHbsVEHb51tdL1OzZp\nvuPCqfTcHENtvr2ldUZe7qXH97dO/slQmx+bmix9BG2NTa0NjcOiHp6+f8ezy9c2RiY3oh/ff/tq\nqvugkd16xaWnXnv66Mz9e5pq6v4//KyvpaOirCx/DwNYFhxoqMOf0XdILXwj68X5hxETtq+bN2/e\nsGHD2I6lqpo0abJn956UjPQBHeo1laBLxCdeIiRYP0289+9KKS7w2+pXgzERGigOh/PHH3+MGzeO\n6RDyo27dutW/f/+XL1+ekXLr1q2AgABZa6C8kvJKyiv/IyevlJ8tSueVEpQ8UvLYUJJH1Gb+WEXL\nli3z9PRs1apVvW2xKqKioo4cOUI1AoR89qrX1fsXxdbWdvr06XPnzmU7kIZBJBLxeLy///5b0s/+\nF04oFHbt2vXKlSvSAwO0aNEiJiamWiedWCx2d3d3cHBYu3ZtHYRZy+Lj4wcMGFCzITrq09ChQ3V0\ndP7666+qzDxs2LBgBCOojmOqCg7QvEaPWFZXCyBG4cYoqgVCoCtw5cMhH2pQWDHgDjgADeCkBOKB\nAUClJyWLBakY1VBAB/hL7pSKgoDhtTb616tXr1x691YXc0LmLGtqRj3FsKPFrIkx79+Jgy7U3Sbi\n01MH+Cx+uG5H3W2iKuqhpLJwhrk1N7eK3vxn/W+6BvZfvzTJf8O3330XGBjYsKpA9u3bN2HChLCl\n63vYt2E7ls9HvZ047J4mYVEPXFfMCwwMHDVqFCsBsKKoqMjIyGjdunVTpkxhOxbFQnmlIqO8EqDk\n8WMoefx0tZU8opbzx6rgcDiHDx9WtMfXgoKChg8fTjUChHz2qLdPmQwMDJhu6ElVKCkpaWtrZ2Vl\nsR2Ioti9e/fXX3/96eOBczicgICA06dPM52oKLLCwsIFCxbs2rWL7UA+4tGjR1FRUZs2bWI7kBqp\nnzGuRR+fpUHaDXz9aSO9MzhAAHC6rH8VRVYILABknZRsFaRiVI+AKGCT3Cl1r3Hjxjdv3VLR1ekw\nf1pA2DlKhFjBVVKCVB+Yta6wpHjBwT27fpxd4zVwhrnJ+hedoFjjulWK2bdKDaGrz8z8vAnb14/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FJ0dPTDhw+joqJIJJKGhoanp6eXl9fkyZMFoZ/3Z5qamiZNmsRmsxMTE4lEItZxAOg9OK4E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\nfu3atcvExKTTlTscObe/e+XKFTwejxAaP358c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IBQKCS6HACIZLVai4uLCwsL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8jWuvfPat4yg7isivKP/wry0rn36Rw2QC4DCZL42b+OaWjdOHj+7kH9iE4N2U5cBncxqy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\n06dOG03G2LDIMT16j+nZ794evYUcrqc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VWwW0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\nu+uuu5KTk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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {
+      "image/png": {
+       "width": 1000
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pydotprint(cost_and_upd, outfile='pydotprint_cost_and_upd.png')\n",
+    "Image('pydotprint_cost_and_upd.png', width=1000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Shared variables\n",
+    "### Update values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "C_val, dC_dW_val, dC_db_val = cost_and_grads(x_val, W_val, b_val, y_val)\n",
+    "W_val -= 0.1 * dC_dW_val\n",
+    "b_val -= 0.1 * dC_db_val\n",
+    "\n",
+    "C_val, W_val, b_val = cost_and_upd(x_val, W_val, b_val, y_val)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using shared variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 1.78587062  0.00189954 -0.28566499]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = T.vector('x')\n",
+    "y = T.vector('y')\n",
+    "W = theano.shared(W_val)\n",
+    "b = theano.shared(b_val)\n",
+    "dot = T.dot(x, W)\n",
+    "out = T.nnet.sigmoid(dot + b)\n",
+    "f = theano.function([x], dot)  # W is an implicit input\n",
+    "g = theano.function([x], out)  # W and b are implicit inputs\n",
+    "print(f(x_val))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0.94151144  0.72221187  0.66391952]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(g(x_val))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Updating shared variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "C = ((out - y) ** 2).sum()\n",
+    "dC_dW, dC_db = theano.grad(C, [W, b])\n",
+    "upd_W = W - 0.1 * dC_dW\n",
+    "upd_b = b - 0.1 * dC_db\n",
+    "\n",
+    "cost_and_perform_updates = theano.function(\n",
+    "    inputs=[x, y],\n",
+    "    outputs=C,\n",
+    "    updates=[(W, upd_W),\n",
+    "             (b, upd_b)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The output file is available at pydotprint_cost_and_perform_updates.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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4UJmZmW+//TYAhULx2Wef3bx587PPPrOzs/Pz8zt06NCaNWssLCxGjBhRUFBw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\nkSNdXFxcXFz0u9j2mHTAEiLSJTOzq236+hampqYhISFXrlx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GhDB0pERERERESGxPHy9CyEhoYOGjQoOjp68+bN77zzjqHDISIievrKGke/ZQuyszmO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\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6nd6ccZvVo0ePNm/e7OHhQXaQ+ojF4nXr1p05c8bW1pbsLKj9oFKp27dv9/PzW7FiRUlJ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\nD2WogUqO3Ut/AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {
+      "image/png": {
+       "width": 1000
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pydotprint(cost_and_perform_updates, outfile='pydotprint_cost_and_perform_updates.png')\n",
+    "Image('pydotprint_cost_and_perform_updates.png', width=1000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Advanced Topics\n",
+    "## Extending Theano\n",
+    "### The easy way: Python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import theano\n",
+    "import numpy\n",
+    "from theano.compile.ops import as_op\n",
+    "\n",
+    "def infer_shape_numpy_dot(node, input_shapes):\n",
+    "    ashp, bshp = input_shapes\n",
+    "    return [ashp[:-1] + bshp[-1:]]\n",
+    "\n",
+    "@as_op(itypes=[theano.tensor.fmatrix, theano.tensor.fmatrix],\n",
+    "       otypes=[theano.tensor.fmatrix], infer_shape=infer_shape_numpy_dot)\n",
+    "def numpy_dot(a, b):\n",
+    "   return numpy.dot(a, b)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.4.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/deep-learning/theano-tutorial/intro_theano/intro_theano.pdf b/deep-learning/theano-tutorial/intro_theano/intro_theano.pdf
new file mode 100644
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diff --git a/deep-learning/theano-tutorial/intro_theano/logistic_regression.ipynb b/deep-learning/theano-tutorial/intro_theano/logistic_regression.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Logistic Regression in Theano\n",
+    "\n",
+    "Credits: Forked from [summerschool2015](https://github.com/mila-udem/summerschool2015) by mila-udem\n",
+    "\n",
+    "This notebook is inspired from [the tutorial on logistic regression](http://deeplearning.net/tutorial/logreg.html) on [deeplearning.net](http://deeplearning.net).\n",
+    "\n",
+    "In this notebook, we show how Theano can be used to implement the most basic classifier: the **logistic regression**. We start off with a quick primer of the model, which serves both as a refresher but also to anchor the notation and show how mathematical expressions are mapped onto Theano graphs.\n",
+    "\n",
+    "In the deepest of machine learning traditions, this tutorial will tackle the exciting problem of MNIST digit classification."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Get the data\n",
+    "\n",
+    "In the mean time, let's just download a pre-packaged version of MNIST, and load each split of the dataset as NumPy ndarrays."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import requests\n",
+    "import gzip\n",
+    "import six\n",
+    "from six.moves import cPickle\n",
+    "\n",
+    "if not os.path.exists('mnist.pkl.gz'):\n",
+    "    r = requests.get('http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz')\n",
+    "    with open('mnist.pkl.gz', 'wb') as data_file:\n",
+    "        data_file.write(r.content)\n",
+    "\n",
+    "with gzip.open('mnist.pkl.gz', 'rb') as data_file:\n",
+    "    if six.PY3:\n",
+    "        train_set, valid_set, test_set = cPickle.load(data_file, encoding='latin1')\n",
+    "    else:\n",
+    "        train_set, valid_set, test_set = cPickle.load(data_file)\n",
+    "\n",
+    "train_set_x, train_set_y = train_set\n",
+    "valid_set_x, valid_set_y = valid_set\n",
+    "test_set_x, test_set_y = test_set"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The model\n",
+    "Logistic regression is a probabilistic, linear classifier. It is parametrized\n",
+    "by a weight matrix $W$ and a bias vector $b$. Classification is\n",
+    "done by projecting an input vector onto a set of hyperplanes, each of which\n",
+    "corresponds to a class. The distance from the input to a hyperplane reflects\n",
+    "the probability that the input is a member of the corresponding class.\n",
+    "\n",
+    "Mathematically, the probability that an input vector $x$ is a member of a\n",
+    "class $i$, a value of a stochastic variable $Y$, can be written as:\n",
+    "\n",
+    "$$P(Y=i|x, W,b) = softmax_i(W x + b) = \\frac {e^{W_i x + b_i}} {\\sum_j e^{W_j x + b_j}}$$\n",
+    "\n",
+    "The model's prediction $y_{pred}$ is the class whose probability is maximal, specifically:\n",
+    "\n",
+    "$$  y_{pred} = {\\rm argmax}_i P(Y=i|x,W,b)$$\n",
+    "\n",
+    "Now, let us define our input variables. First, we need to define the dimension of our tensors:\n",
+    "- `n_in` is the length of each training vector,\n",
+    "- `n_out` is the number of classes.\n",
+    "\n",
+    "Our variables will be:\n",
+    "- `x` is a matrix, where each row contains a different example of the dataset. Its shape is `(batch_size, n_in)`, but `batch_size` does not have to be specified in advance, and can change during training.\n",
+    "- `W` is a shared matrix, of shape `(n_in, n_out)`, initialized with zeros. Column `k` of `W` represents the separation hyperplane for class `k`.\n",
+    "- `b` is a shared vector, of length `n_out`, initialized with zeros. Element `k` of `b` represents the free parameter of hyperplane `k`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy\n",
+    "import theano\n",
+    "from theano import tensor\n",
+    "\n",
+    "# Size of the data\n",
+    "n_in = 28 * 28\n",
+    "# Number of classes\n",
+    "n_out = 10\n",
+    "\n",
+    "x = tensor.matrix('x')\n",
+    "W = theano.shared(value=numpy.zeros((n_in, n_out), dtype=theano.config.floatX),\n",
+    "                  name='W',\n",
+    "                  borrow=True)\n",
+    "b = theano.shared(value=numpy.zeros((n_out,), dtype=theano.config.floatX),\n",
+    "                  name='b',\n",
+    "                  borrow=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we can build a symbolic expression for the matrix of class-membership probability (`p_y_given_x`), and for the class whose probability is maximal (`y_pred`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "p_y_given_x = tensor.nnet.softmax(tensor.dot(x, W) + b)\n",
+    "y_pred = tensor.argmax(p_y_given_x, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Defining a loss function\n",
+    "Learning optimal model parameters involves minimizing a loss function. In the\n",
+    "case of multi-class logistic regression, it is very common to use the negative\n",
+    "log-likelihood as the loss. This is equivalent to maximizing the likelihood of the\n",
+    "data set $\\cal{D}$ under the model parameterized by $\\theta$. Let\n",
+    "us first start by defining the likelihood $\\cal{L}$ and loss\n",
+    "$\\ell$:\n",
+    "\n",
+    "$$\\mathcal{L} (\\theta=\\{W,b\\}, \\mathcal{D}) =\n",
+    "     \\sum_{i=0}^{|\\mathcal{D}|} \\log(P(Y=y^{(i)}|x^{(i)}, W,b)) \\\\\n",
+    "   \\ell (\\theta=\\{W,b\\}, \\mathcal{D}) = - \\mathcal{L} (\\theta=\\{W,b\\}, \\mathcal{D})\n",
+    "$$\n",
+    "\n",
+    "Again, we will express those expressions using Theano. We have one additional input, the actual target class `y`:\n",
+    "- `y` is an input vector of integers, of length `batch_size` (which will have to match the length of `x` at runtime). The length of `y` can be symbolically expressed by `y.shape[0]`.\n",
+    "- `log_prob` is a `(batch_size, n_out)` matrix containing the log probabilities of class membership for each example.\n",
+    "- `arange(y.shape[0])` is a symbolic vector which will contain `[0,1,2,... batch_size-1]`\n",
+    "- `log_likelihood` is a vector containing the log probability of the target, for each example.\n",
+    "- `loss` is the mean of the negative `log_likelihood` over the examples in the minibatch."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y = tensor.lvector('y')\n",
+    "log_prob = tensor.log(p_y_given_x)\n",
+    "log_likelihood = log_prob[tensor.arange(y.shape[0]), y]\n",
+    "loss = - log_likelihood.mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training procedure\n",
+    "This notebook will use the method of stochastic gradient descent with mini-batches (MSGD) to find values of `W` and `b` that minimize the loss.\n",
+    "\n",
+    "We can let Theano compute symbolic expressions for the gradient of the loss wrt `W` and `b`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "g_W, g_b = theano.grad(cost=loss, wrt=[W, b])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`g_W` and `g_b` are symbolic variables, which can be used as part of a computation graph. In particular, let us define the expressions for one step of gradient descent for `W` and `b`, for a fixed learning rate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "learning_rate = numpy.float32(0.13)\n",
+    "new_W = W - learning_rate * g_W\n",
+    "new_b = b - learning_rate * g_b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then define **update expressions**, or pairs of (shared variable, expression for its update), that we will use when compiling the Theano function. The updates will be performed each time the function gets called.\n",
+    "\n",
+    "The following function, `train_model`, returns the loss on the current minibatch, then changes the values of the shared variables according to the update rules. It needs to be passed `x` and `y` as inputs, but not the shared variables, which are implicit inputs.\n",
+    "\n",
+    "The entire learning algorithm thus consists in looping over all examples in the dataset, considering all the examples in one minibatch at a time, and repeatedly calling the `train_model` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "train_model = theano.function(inputs=[x, y],\n",
+    "                              outputs=loss,\n",
+    "                              updates=[(W, new_W),\n",
+    "                                       (b, new_b)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Testing the model\n",
+    "When testing the model, we are interested in the number of misclassified examples (and not only in the likelihood). Here, we build a symbolic expression for retrieving the number of misclassified examples in a minibatch.\n",
+    "\n",
+    "This will also be useful to apply on the validation and testing sets, in order to monitor the progress of the model during training, and to do early stopping."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "misclass_nb = tensor.neq(y_pred, y)\n",
+    "misclass_rate = misclass_nb.mean()\n",
+    "\n",
+    "test_model = theano.function(inputs=[x, y],\n",
+    "                             outputs=misclass_rate)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training the model\n",
+    "Here is the main training loop of the algorithm:\n",
+    "- For each *epoch*, or pass through the training set\n",
+    "  - split the training set in minibatches, and call `train_model` on each minibatch\n",
+    "  - split the validation set in minibatches, and call `test_model` on each minibatch to measure the misclassification rate\n",
+    "  - if the misclassification rate has not improved in a while, stop training\n",
+    "- Measure performance on the test set\n",
+    "\n",
+    "The **early stopping procedure** is what decide whether the performance has improved enough. There are many variants, and we will not go into the details of this one here.\n",
+    "\n",
+    "We first need to define a few parameters for the training loop and the early stopping procedure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Define a couple of helper variables and functions for the optimization\n",
+    "batch_size = 500\n",
+    "# compute number of minibatches for training, validation and testing\n",
+    "n_train_batches = train_set_x.shape[0] // batch_size\n",
+    "n_valid_batches = valid_set_x.shape[0] // batch_size\n",
+    "n_test_batches = test_set_x.shape[0] // batch_size\n",
+    "\n",
+    "def get_minibatch(i, dataset_x, dataset_y):\n",
+    "    start_idx = i * batch_size\n",
+    "    end_idx = (i + 1) * batch_size\n",
+    "    batch_x = dataset_x[start_idx:end_idx]\n",
+    "    batch_y = dataset_y[start_idx:end_idx]\n",
+    "    return (batch_x, batch_y)\n",
+    "\n",
+    "## early-stopping parameters\n",
+    "# maximum number of epochs\n",
+    "n_epochs = 1000\n",
+    "# look as this many examples regardless\n",
+    "patience = 5000\n",
+    "# wait this much longer when a new best is found\n",
+    "patience_increase = 2\n",
+    "# a relative improvement of this much is considered significant\n",
+    "improvement_threshold = 0.995\n",
+    "\n",
+    "# go through this many minibatches before checking the network on the validation set;\n",
+    "# in this case we check every epoch\n",
+    "validation_frequency = min(n_train_batches, patience / 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import timeit\n",
+    "from six.moves import xrange\n",
+    "\n",
+    "best_validation_loss = numpy.inf\n",
+    "test_score = 0.\n",
+    "start_time = timeit.default_timer()\n",
+    "\n",
+    "done_looping = False\n",
+    "epoch = 0\n",
+    "while (epoch < n_epochs) and (not done_looping):\n",
+    "    epoch = epoch + 1\n",
+    "    for minibatch_index in xrange(n_train_batches):\n",
+    "        minibatch_x, minibatch_y = get_minibatch(minibatch_index, train_set_x, train_set_y)\n",
+    "        minibatch_avg_cost = train_model(minibatch_x, minibatch_y)\n",
+    "\n",
+    "        # iteration number\n",
+    "        iter = (epoch - 1) * n_train_batches + minibatch_index\n",
+    "        if (iter + 1) % validation_frequency == 0:\n",
+    "            # compute zero-one loss on validation set\n",
+    "            validation_losses = []\n",
+    "            for i in xrange(n_valid_batches):\n",
+    "                valid_xi, valid_yi = get_minibatch(i, valid_set_x, valid_set_y)\n",
+    "                validation_losses.append(test_model(valid_xi, valid_yi))\n",
+    "            this_validation_loss = numpy.mean(validation_losses)\n",
+    "            print('epoch %i, minibatch %i/%i, validation error %f %%' %\n",
+    "                  (epoch,\n",
+    "                   minibatch_index + 1,\n",
+    "                   n_train_batches,\n",
+    "                   this_validation_loss * 100.))\n",
+    "\n",
+    "            # if we got the best validation score until now\n",
+    "            if this_validation_loss < best_validation_loss:\n",
+    "                # improve patience if loss improvement is good enough\n",
+    "                if this_validation_loss < best_validation_loss * improvement_threshold:\n",
+    "                    patience = max(patience, iter * patience_increase)\n",
+    "\n",
+    "                best_validation_loss = this_validation_loss\n",
+    "\n",
+    "                # test it on the test set\n",
+    "                test_losses = []\n",
+    "                for i in xrange(n_test_batches):\n",
+    "                    test_xi, test_yi = get_minibatch(i, test_set_x, test_set_y)\n",
+    "                    test_losses.append(test_model(test_xi, test_yi))\n",
+    "\n",
+    "                test_score = numpy.mean(test_losses)\n",
+    "                print('     epoch %i, minibatch %i/%i, test error of  best model %f %%' %\n",
+    "                      (epoch,\n",
+    "                       minibatch_index + 1,\n",
+    "                       n_train_batches,\n",
+    "                       test_score * 100.))\n",
+    "\n",
+    "                # save the best parameters\n",
+    "                numpy.savez('best_model.npz', W=W.get_value(), b=b.get_value())\n",
+    "\n",
+    "        if patience <= iter:\n",
+    "            done_looping = True\n",
+    "            break\n",
+    "\n",
+    "end_time = timeit.default_timer()\n",
+    "print('Optimization complete with best validation score of %f %%, '\n",
+    "      'with test performance %f %%' %\n",
+    "      (best_validation_loss * 100., test_score * 100.))\n",
+    "\n",
+    "print('The code ran for %d epochs, with %f epochs/sec' %\n",
+    "      (epoch, 1. * epoch / (end_time - start_time)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualization\n",
+    "You can visualize the columns of `W`, which correspond to the separation hyperplanes for each class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "from utils import tile_raster_images\n",
+    "\n",
+    "plt.clf()\n",
+    "\n",
+    "# Increase the size of the figure\n",
+    "plt.gcf().set_size_inches(15, 10)\n",
+    "\n",
+    "plot_data = tile_raster_images(W.get_value(borrow=True).T,\n",
+    "                               img_shape=(28, 28), tile_shape=(2, 5), tile_spacing=(1, 1))\n",
+    "plt.imshow(plot_data, cmap='Greys', interpolation='none')\n",
+    "plt.axis('off')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.4.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/deep-learning/theano-tutorial/intro_theano/utils.py b/deep-learning/theano-tutorial/intro_theano/utils.py
new file mode 100644
index 0000000..d1cc2ce
--- /dev/null
+++ b/deep-learning/theano-tutorial/intro_theano/utils.py
@@ -0,0 +1,140 @@
+""" This file contains different utility functions that are not connected
+in anyway to the networks presented in the tutorials, but rather help in
+processing the outputs into a more understandable way.
+
+For example ``tile_raster_images`` helps in generating a easy to grasp
+image from a set of samples or weights.
+"""
+
+
+import numpy
+from six.moves import xrange
+
+
+def scale_to_unit_interval(ndar, eps=1e-8):
+    """ Scales all values in the ndarray ndar to be between 0 and 1 """
+    ndar = ndar.copy()
+    ndar -= ndar.min()
+    ndar *= 1.0 / (ndar.max() + eps)
+    return ndar
+
+
+def tile_raster_images(X, img_shape, tile_shape, tile_spacing=(0, 0),
+                       scale_rows_to_unit_interval=True,
+                       output_pixel_vals=True):
+    """
+    Transform an array with one flattened image per row, into an array in
+    which images are reshaped and layed out like tiles on a floor.
+
+    This function is useful for visualizing datasets whose rows are images,
+    and also columns of matrices for transforming those rows
+    (such as the first layer of a neural net).
+
+    :type X: a 2-D ndarray or a tuple of 4 channels, elements of which can
+    be 2-D ndarrays or None;
+    :param X: a 2-D array in which every row is a flattened image.
+
+    :type img_shape: tuple; (height, width)
+    :param img_shape: the original shape of each image
+
+    :type tile_shape: tuple; (rows, cols)
+    :param tile_shape: the number of images to tile (rows, cols)
+
+    :param output_pixel_vals: if output should be pixel values (i.e. int8
+    values) or floats
+
+    :param scale_rows_to_unit_interval: if the values need to be scaled before
+    being plotted to [0,1] or not
+
+
+    :returns: array suitable for viewing as an image.
+    (See:`Image.fromarray`.)
+    :rtype: a 2-d array with same dtype as X.
+
+    """
+
+    assert len(img_shape) == 2
+    assert len(tile_shape) == 2
+    assert len(tile_spacing) == 2
+
+    # The expression below can be re-written in a more C style as
+    # follows :
+    #
+    # out_shape    = [0,0]
+    # out_shape[0] = (img_shape[0]+tile_spacing[0])*tile_shape[0] -
+    #                tile_spacing[0]
+    # out_shape[1] = (img_shape[1]+tile_spacing[1])*tile_shape[1] -
+    #                tile_spacing[1]
+    out_shape = [
+        (ishp + tsp) * tshp - tsp
+        for ishp, tshp, tsp in zip(img_shape, tile_shape, tile_spacing)
+    ]
+
+    if isinstance(X, tuple):
+        assert len(X) == 4
+        # Create an output numpy ndarray to store the image
+        if output_pixel_vals:
+            out_array = numpy.zeros((out_shape[0], out_shape[1], 4),
+                                    dtype='uint8')
+        else:
+            out_array = numpy.zeros((out_shape[0], out_shape[1], 4),
+                                    dtype=X.dtype)
+
+        #colors default to 0, alpha defaults to 1 (opaque)
+        if output_pixel_vals:
+            channel_defaults = [0, 0, 0, 255]
+        else:
+            channel_defaults = [0., 0., 0., 1.]
+
+        for i in xrange(4):
+            if X[i] is None:
+                # if channel is None, fill it with zeros of the correct
+                # dtype
+                dt = out_array.dtype
+                if output_pixel_vals:
+                    dt = 'uint8'
+                out_array[:, :, i] = numpy.zeros(
+                    out_shape,
+                    dtype=dt
+                ) + channel_defaults[i]
+            else:
+                # use a recurrent call to compute the channel and store it
+                # in the output
+                out_array[:, :, i] = tile_raster_images(
+                    X[i], img_shape, tile_shape, tile_spacing,
+                    scale_rows_to_unit_interval, output_pixel_vals)
+        return out_array
+
+    else:
+        # if we are dealing with only one channel
+        H, W = img_shape
+        Hs, Ws = tile_spacing
+
+        # generate a matrix to store the output
+        dt = X.dtype
+        if output_pixel_vals:
+            dt = 'uint8'
+        out_array = numpy.zeros(out_shape, dtype=dt)
+
+        for tile_row in xrange(tile_shape[0]):
+            for tile_col in xrange(tile_shape[1]):
+                if tile_row * tile_shape[1] + tile_col < X.shape[0]:
+                    this_x = X[tile_row * tile_shape[1] + tile_col]
+                    if scale_rows_to_unit_interval:
+                        # if we should scale values to be between 0 and 1
+                        # do this by calling the `scale_to_unit_interval`
+                        # function
+                        this_img = scale_to_unit_interval(
+                            this_x.reshape(img_shape))
+                    else:
+                        this_img = this_x.reshape(img_shape)
+                    # add the slice to the corresponding position in the
+                    # output array
+                    c = 1
+                    if output_pixel_vals:
+                        c = 255
+                    out_array[
+                        tile_row * (H + Hs): tile_row * (H + Hs) + H,
+                        tile_col * (W + Ws): tile_col * (W + Ws) + W
+                    ] = this_img * c
+        return out_array
diff --git a/deep-learning/theano-tutorial/rnn_tutorial/Makefile b/deep-learning/theano-tutorial/rnn_tutorial/Makefile
new file mode 100644
index 0000000..70828a1
--- /dev/null
+++ b/deep-learning/theano-tutorial/rnn_tutorial/Makefile
@@ -0,0 +1,13 @@
+all: instruction.pdf rnn_lstm.pdf
+
+instruction.pdf: slides_source/instruction.tex
+	cd slides_source; pdflatex --shell-escape instruction.tex
+	cd slides_source; pdflatex --shell-escape instruction.tex
+	cd slides_source; pdflatex --shell-escape instruction.tex
+	mv slides_source/instruction.pdf .
+
+rnn_lstm.pdf: slides_source/rnn_lstm.tex
+	cd slides_source; pdflatex --shell-escape rnn_lstm.tex
+	cd slides_source; pdflatex --shell-escape rnn_lstm.tex
+	cd slides_source; pdflatex --shell-escape rnn_lstm.tex
+	mv slides_source/rnn_lstm.pdf .
diff --git a/deep-learning/theano-tutorial/rnn_tutorial/instruction.pdf b/deep-learning/theano-tutorial/rnn_tutorial/instruction.pdf
new file mode 100644
index 0000000..cbd9bd5
Binary files /dev/null and b/deep-learning/theano-tutorial/rnn_tutorial/instruction.pdf differ
diff --git a/deep-learning/theano-tutorial/rnn_tutorial/lstm_text.ipynb b/deep-learning/theano-tutorial/rnn_tutorial/lstm_text.ipynb
new file mode 100644
index 0000000..9436c66
--- /dev/null
+++ b/deep-learning/theano-tutorial/rnn_tutorial/lstm_text.ipynb
@@ -0,0 +1,508 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction\n",
+    "In this demo, you'll see a more practical application of RNNs/LSTMs as character-level language models. The emphasis will be more on parallelization and using RNNs with data from Fuel.\n",
+    "\n",
+    "To get started, we first need to download the training text, validation text and a file that contains a dictionary for mapping characters to integers. We also need to import quite a list of modules."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import requests\n",
+    "import gzip\n",
+    "\n",
+    "from six.moves import cPickle as pkl\n",
+    "import time\n",
+    "\n",
+    "import numpy\n",
+    "import theano\n",
+    "import theano.tensor as T\n",
+    "\n",
+    "from theano.tensor.nnet import categorical_crossentropy\n",
+    "from theano import config\n",
+    "from fuel.datasets import TextFile\n",
+    "from fuel.streams import DataStream\n",
+    "from fuel.schemes import ConstantScheme\n",
+    "from fuel.transformers import Batch, Padding\n",
+    "\n",
+    "if not os.path.exists('traindata.txt'):\n",
+    "    r = requests.get('http://www-etud.iro.umontreal.ca/~brakelp/traindata.txt.gz')\n",
+    "    with open('traindata.txt.gz', 'wb') as data_file:\n",
+    "        data_file.write(r.content)\n",
+    "    with gzip.open('traindata.txt.gz', 'rb') as data_file:\n",
+    "        with open('traindata.txt', 'w') as out_file:\n",
+    "            out_file.write(data_file.read())\n",
+    "        \n",
+    "if not os.path.exists('valdata.txt'):\n",
+    "    r = requests.get('http://www-etud.iro.umontreal.ca/~brakelp/valdata.txt.gz')\n",
+    "    with open('valdata.txt.gz', 'wb') as data_file:\n",
+    "        data_file.write(r.content)\n",
+    "    with gzip.open('valdata.txt.gz', 'rb') as data_file:\n",
+    "        with open('valdata.txt', 'w') as out_file:\n",
+    "            out_file.write(data_file.read())\n",
+    "\n",
+    "if not os.path.exists('dictionary.pkl'):\n",
+    "    r = requests.get('http://www-etud.iro.umontreal.ca/~brakelp/dictionary.pkl')\n",
+    "    with open('dictionary.pkl', 'wb') as data_file:\n",
+    "        data_file.write(r.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##The Model\n",
+    "The code below shows an implementation of an LSTM network. Note that there are various different variations of the LSTM in use and this one doesn't include the so-called 'peephole connections'. We used a separate method for the dynamic update to make it easier to generate from the network later. The `index_dot` function doesn't safe much verbosity, but it clarifies that certain dot products have been replaced with indexing operations because this network will be applied to discrete data. Last but not least, note the addition of the `mask` argument which is used to ignore certain parts of the input sequence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def gauss_weight(rng, ndim_in, ndim_out=None, sd=.005):\n",
+    "    if ndim_out is None:\n",
+    "        ndim_out = ndim_in\n",
+    "    W = rng.randn(ndim_in, ndim_out) * sd\n",
+    "    return numpy.asarray(W, dtype=config.floatX)\n",
+    "\n",
+    "\n",
+    "def index_dot(indices, w):\n",
+    "    return w[indices.flatten()]\n",
+    "\n",
+    "\n",
+    "class LstmLayer:\n",
+    "\n",
+    "    def __init__(self, rng, input, mask, n_in, n_h):\n",
+    "\n",
+    "        # Init params\n",
+    "        self.W_i = theano.shared(gauss_weight(rng, n_in, n_h), 'W_i', borrow=True)\n",
+    "        self.W_f = theano.shared(gauss_weight(rng, n_in, n_h), 'W_f', borrow=True)\n",
+    "        self.W_c = theano.shared(gauss_weight(rng, n_in, n_h), 'W_c', borrow=True)\n",
+    "        self.W_o = theano.shared(gauss_weight(rng, n_in, n_h), 'W_o', borrow=True)\n",
+    "\n",
+    "        self.U_i = theano.shared(gauss_weight(rng, n_h), 'U_i', borrow=True)\n",
+    "        self.U_f = theano.shared(gauss_weight(rng, n_h), 'U_f', borrow=True)\n",
+    "        self.U_c = theano.shared(gauss_weight(rng, n_h), 'U_c', borrow=True)\n",
+    "        self.U_o = theano.shared(gauss_weight(rng, n_h), 'U_o', borrow=True)\n",
+    "\n",
+    "        self.b_i = theano.shared(numpy.zeros((n_h,), dtype=config.floatX),\n",
+    "                                 'b_i', borrow=True)\n",
+    "        self.b_f = theano.shared(numpy.zeros((n_h,), dtype=config.floatX),\n",
+    "                                 'b_f', borrow=True)\n",
+    "        self.b_c = theano.shared(numpy.zeros((n_h,), dtype=config.floatX),\n",
+    "                                 'b_c', borrow=True)\n",
+    "        self.b_o = theano.shared(numpy.zeros((n_h,), dtype=config.floatX),\n",
+    "                                 'b_o', borrow=True)\n",
+    "\n",
+    "        self.params = [self.W_i, self.W_f, self.W_c, self.W_o,\n",
+    "                       self.U_i, self.U_f, self.U_c, self.U_o,\n",
+    "                       self.b_i, self.b_f, self.b_c, self.b_o]\n",
+    "\n",
+    "        outputs_info = [T.zeros((input.shape[1], n_h)),\n",
+    "                        T.zeros((input.shape[1], n_h))]\n",
+    "\n",
+    "        rval, updates = theano.scan(self._step,\n",
+    "                                    sequences=[mask, input],\n",
+    "                                    outputs_info=outputs_info)\n",
+    "\n",
+    "        # self.output is in the format (length, batchsize, n_h)\n",
+    "        self.output = rval[0]\n",
+    "\n",
+    "    def _step(self, m_, x_, h_, c_):\n",
+    "\n",
+    "        i_preact = (index_dot(x_, self.W_i) +\n",
+    "                    T.dot(h_, self.U_i) + self.b_i)\n",
+    "        i = T.nnet.sigmoid(i_preact)\n",
+    "\n",
+    "        f_preact = (index_dot(x_, self.W_f) +\n",
+    "                    T.dot(h_, self.U_f) + self.b_f)\n",
+    "        f = T.nnet.sigmoid(f_preact)\n",
+    "\n",
+    "        o_preact = (index_dot(x_, self.W_o) +\n",
+    "                    T.dot(h_, self.U_o) + self.b_o)\n",
+    "        o = T.nnet.sigmoid(o_preact)\n",
+    "\n",
+    "        c_preact = (index_dot(x_, self.W_c) +\n",
+    "                    T.dot(h_, self.U_c) + self.b_c)\n",
+    "        c = T.tanh(c_preact)\n",
+    "\n",
+    "        c = f * c_ + i * c\n",
+    "        c = m_[:, None] * c + (1. - m_)[:, None] * c_\n",
+    "\n",
+    "        h = o * T.tanh(c)\n",
+    "        h = m_[:, None] * h + (1. - m_)[:, None] * h_\n",
+    "\n",
+    "        return h, c"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The next block contains some code that computes cross-entropy for masked sequences and a stripped down version of the logistic regression class from the deep learning tutorials which we will need later."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def sequence_categorical_crossentropy(prediction, targets, mask):\n",
+    "    prediction_flat = prediction.reshape(((prediction.shape[0] *\n",
+    "                                           prediction.shape[1]),\n",
+    "                                          prediction.shape[2]), ndim=2)\n",
+    "    targets_flat = targets.flatten()\n",
+    "    mask_flat = mask.flatten()\n",
+    "    ce = categorical_crossentropy(prediction_flat, targets_flat)\n",
+    "    return T.sum(ce * mask_flat)\n",
+    "\n",
+    "\n",
+    "class LogisticRegression(object):\n",
+    "   \n",
+    "    def __init__(self, rng, input, n_in, n_out):\n",
+    "        \n",
+    "        W = gauss_weight(rng, n_in, n_out)\n",
+    "        self.W = theano.shared(value=numpy.asarray(W, dtype=theano.config.floatX),\n",
+    "                               name='W', borrow=True)\n",
+    "        # initialize the biases b as a vector of n_out 0s\n",
+    "        self.b = theano.shared(value=numpy.zeros((n_out,),\n",
+    "                                                 dtype=theano.config.floatX),\n",
+    "                               name='b', borrow=True)\n",
+    "\n",
+    "        # compute vector of class-membership probabilities in symbolic form\n",
+    "        energy = T.dot(input, self.W) + self.b\n",
+    "        energy_exp = T.exp(energy - T.max(energy, axis=2, keepdims=True))\n",
+    "        pmf = energy_exp / energy_exp.sum(axis=2, keepdims=True)\n",
+    "        self.p_y_given_x = pmf\n",
+    "        self.params = [self.W, self.b]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#Processing the Data\n",
+    "The data in `traindata.txt` and `valdata.txt` is simply English text but formatted in such a way that every sentence is conveniently separated by the newline symbol. We'll use some of the functionality of fuel to perform the following preprocessing steps:\n",
+    "* Convert everything to lowercase\n",
+    "* Map characters to indices\n",
+    "* Group the sentences into batches\n",
+    "* Convert each batch in a matrix/tensor as long as the longest sequence with zeros padded to all the shorter sequences\n",
+    "* Add a mask matrix that encodes the length of each sequence (a timestep at which the mask is 0 indicates that there is no data available)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "batch_size = 100\n",
+    "n_epochs = 40\n",
+    "n_h = 50\n",
+    "DICT_FILE = 'dictionary.pkl'\n",
+    "TRAIN_FILE = 'traindata.txt'\n",
+    "VAL_FILE = 'valdata.txt'\n",
+    "\n",
+    "# Load the datasets with Fuel\n",
+    "dictionary = pkl.load(open(DICT_FILE, 'r'))\n",
+    "# add a symbol for unknown characters\n",
+    "dictionary['~'] = len(dictionary)\n",
+    "reverse_mapping = dict((j, i) for i, j in dictionary.items())\n",
+    "\n",
+    "train = TextFile(files=[TRAIN_FILE],\n",
+    "                 dictionary=dictionary,\n",
+    "                 unk_token='~',\n",
+    "                 level='character',\n",
+    "                 preprocess=str.lower,\n",
+    "                 bos_token=None,\n",
+    "                 eos_token=None)\n",
+    "\n",
+    "train_stream = DataStream.default_stream(train)\n",
+    "\n",
+    "# organize data in batches and pad shorter sequences with zeros\n",
+    "train_stream = Batch(train_stream,\n",
+    "                     iteration_scheme=ConstantScheme(batch_size))\n",
+    "train_stream = Padding(train_stream)\n",
+    "\n",
+    "# idem dito for the validation text\n",
+    "val = TextFile(files=[VAL_FILE],\n",
+    "                 dictionary=dictionary,\n",
+    "                 unk_token='~',\n",
+    "                 level='character',\n",
+    "                 preprocess=str.lower,\n",
+    "                 bos_token=None,\n",
+    "                 eos_token=None)\n",
+    "\n",
+    "val_stream = DataStream.default_stream(val)\n",
+    "\n",
+    "# organize data in batches and pad shorter sequences with zeros\n",
+    "val_stream = Batch(val_stream,\n",
+    "                     iteration_scheme=ConstantScheme(batch_size))\n",
+    "val_stream = Padding(val_stream)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##The Theano Graph\n",
+    "We'll now define the complete Theano graph for computing costs and gradients among other things. The cost will be the cross-entropy of the next character in the sequence and the network will try to predict it based on the previous characters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Set the random number generator' seeds for consistency\n",
+    "rng = numpy.random.RandomState(12345)\n",
+    "\n",
+    "x = T.lmatrix('x')\n",
+    "mask = T.matrix('mask')\n",
+    "\n",
+    "# Construct an LSTM layer and a logistic regression layer\n",
+    "recurrent_layer = LstmLayer(rng=rng, input=x, mask=mask, n_in=111, n_h=n_h)\n",
+    "logreg_layer = LogisticRegression(rng=rng, input=recurrent_layer.output[:-1],\n",
+    "                                  n_in=n_h, n_out=111)\n",
+    "\n",
+    "# define a cost variable to optimize\n",
+    "cost = sequence_categorical_crossentropy(logreg_layer.p_y_given_x,\n",
+    "                                         x[1:],\n",
+    "                                         mask[1:]) / batch_size\n",
+    "\n",
+    "# create a list of all model parameters to be fit by gradient descent\n",
+    "params = logreg_layer.params + recurrent_layer.params\n",
+    "\n",
+    "# create a list of gradients for all model parameters\n",
+    "grads = T.grad(cost, params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now compile the function that updates the gradients. We also added a function that computes the cost without updating for monitoring purposes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/pbrakel/Repositories/Theano/theano/scan_module/scan_perform_ext.py:117: RuntimeWarning: numpy.ndarray size changed, may indicate binary incompatibility\n",
+      "  from scan_perform.scan_perform import *\n"
+     ]
+    }
+   ],
+   "source": [
+    "learning_rate = 0.1\n",
+    "updates = [\n",
+    "    (param_i, param_i - learning_rate * grad_i)\n",
+    "    for param_i, grad_i in zip(params, grads)\n",
+    "]\n",
+    "\n",
+    "update_model = theano.function([x, mask], cost, updates=updates)\n",
+    "\n",
+    "evaluate_model = theano.function([x, mask], cost)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##Generating Sequences\n",
+    "To see if the networks learn something useful (and to make results monitoring more entertaining), we'll also write some code to generate sequences. For this, we'll first compile a function that computes a single state update for the network to have more control over the values of each variable at each time step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "x_t = T.iscalar()\n",
+    "h_p = T.vector()\n",
+    "c_p = T.vector()\n",
+    "h_t, c_t = recurrent_layer._step(T.ones(1), x_t, h_p, c_p)\n",
+    "energy = T.dot(h_t, logreg_layer.W) + logreg_layer.b\n",
+    "\n",
+    "energy_exp = T.exp(energy - T.max(energy, axis=1, keepdims=True))\n",
+    "\n",
+    "output = energy_exp / energy_exp.sum(axis=1, keepdims=True)\n",
+    "single_step = theano.function([x_t, h_p, c_p], [output, h_t, c_t])\n",
+    "\n",
+    "def speak(single_step, prefix='the meaning of life is ', n_steps=450):\n",
+    "    try:\n",
+    "        h_p = numpy.zeros((n_h,), dtype=config.floatX)\n",
+    "        c_p = numpy.zeros((n_h,), dtype=config.floatX)\n",
+    "        sentence = prefix\n",
+    "        for char in prefix:\n",
+    "            x_t = dictionary[char]\n",
+    "            prediction, h_p, c_p = single_step(x_t, h_p.flatten(),\n",
+    "                                               c_p.flatten())\n",
+    "        # Renormalize probability in float64\n",
+    "        flat_prediction = prediction.flatten()\n",
+    "        flat_pred_sum = flat_prediction.sum(dtype='float64')\n",
+    "        if flat_pred_sum > 1:\n",
+    "            flat_prediction = flat_prediction.astype('float64') / flat_pred_sum\n",
+    "        sample = numpy.random.multinomial(1, flat_prediction)\n",
+    "\n",
+    "        for i in range(n_steps):\n",
+    "            x_t = numpy.argmax(sample)\n",
+    "            prediction, h_p, c_p = single_step(x_t, h_p.flatten(),\n",
+    "                                               c_p.flatten())\n",
+    "            # Renormalize probability in float64\n",
+    "            flat_prediction = prediction.flatten()\n",
+    "            flat_pred_sum = flat_prediction.sum(dtype='float64')\n",
+    "            if flat_pred_sum > 1:\n",
+    "                flat_prediction = flat_prediction.astype('float64') / flat_pred_sum\n",
+    "            sample = numpy.random.multinomial(1, flat_prediction)\n",
+    "\n",
+    "            sentence += reverse_mapping[x_t]\n",
+    "\n",
+    "        return sentence\n",
+    "    except ValueError as e:\n",
+    "        print 'Something went wrong during sentence generation: {}'.format(e)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch: 0\n",
+      "\n",
+      "LSTM: \"the meaning of life is i�ateisn ^ltbagss7tuodkca r9 msd,forreypoctlluoiasrn?at�netteofkotenni�cf/vattosnlrxisiovu�al.hahau�ootwo tuost! ]cw� eweunhufaaecihtdtk tticiss cvt2f etoct bllstsluohh-,retti?eusrv eikly an�ade'i stiel�doelnamtuartoci�ht.�woi 2kfs$an tpeo�miiadain9.e eegtamiaesboeinne�unlocityqe dansapeaeiyo�ihaewmtrt�'aa svteatae ,otrr.gsac.-perioswetgoc�io froaoeismhsgtulherbttrh fl�i el  nnltnta�sat yhomsnttwlnwnenaee.mhits r�us-thist sn man4lamhpac.osdopl g�\"\n",
+      "\n",
+      "epoch: 0   minibatch: 40\n",
+      "Average validation CE per sentence: 251.167072292\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-13-7c09df6ae427>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      9\u001b[0m         \u001b[0miteration\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m         \u001b[0mcross_entropy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mupdate_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmask_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m/home/pbrakel/Repositories/Theano/theano/compile/function_module.pyc\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    577\u001b[0m         \u001b[0mt0_fn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    578\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 579\u001b[1;33m             \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    580\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    581\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'position_of_error'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m/home/pbrakel/Repositories/Theano/theano/scan_module/scan_op.pyc\u001b[0m in \u001b[0;36mrval\u001b[1;34m(p, i, o, n)\u001b[0m\n\u001b[0;32m    649\u001b[0m         \u001b[1;31m# default arguments are stored in the closure of `rval`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    650\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 651\u001b[1;33m         \u001b[1;32mdef\u001b[0m \u001b[0mrval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnode_input_storage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mo\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnode_output_storage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    652\u001b[0m             \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mo\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    653\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mo\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "start_time = time.clock()\n",
+    "\n",
+    "iteration = 0\n",
+    "\n",
+    "for epoch in range(n_epochs):\n",
+    "    print 'epoch:', epoch\n",
+    "\n",
+    "    for x_, mask_ in train_stream.get_epoch_iterator():\n",
+    "        iteration += 1\n",
+    "\n",
+    "        cross_entropy = update_model(x_.T, mask_.T)\n",
+    "\n",
+    "\n",
+    "        # Generate some text after each 20 minibatches\n",
+    "        if iteration % 40 == 0:\n",
+    "            sentence = speak(single_step, prefix='the meaning of life is ', n_steps=450)\n",
+    "            print\n",
+    "            print 'LSTM: \"' + sentence + '\"'\n",
+    "            print\n",
+    "            print 'epoch:', epoch, '  minibatch:', iteration\n",
+    "            val_scores = []\n",
+    "            for x_val, mask_val in val_stream.get_epoch_iterator():\n",
+    "                val_scores.append(evaluate_model(x_val.T, mask_val.T))\n",
+    "            print 'Average validation CE per sentence:', numpy.mean(val_scores)\n",
+    "\n",
+    "end_time = time.clock()\n",
+    "print('Optimization complete.')\n",
+    "print('The code ran for %.2fm' % ((end_time - start_time) / 60.))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "It can take a while before the text starts to look more reasonable but here are some things to experiment with:\n",
+    "* Smarter optimization algorithms (or at least momentum)\n",
+    "* Initializing the recurrent weights orthogonally\n",
+    "* The sizes of the initial weights and biases (think about what the gates do)\n",
+    "* Different sentence prefixes\n",
+    "* Changing the temperature of the character distribution during generation. What happens when you generate deterministically?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "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": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/deep-learning/theano-tutorial/rnn_tutorial/lstm_text.py b/deep-learning/theano-tutorial/rnn_tutorial/lstm_text.py
new file mode 100644
index 0000000..d2b734b
--- /dev/null
+++ b/deep-learning/theano-tutorial/rnn_tutorial/lstm_text.py
@@ -0,0 +1,299 @@
+import cPickle as pkl
+import time
+
+import numpy
+import theano
+from theano import config
+import theano.tensor as T
+from theano.tensor.nnet import categorical_crossentropy
+
+from fuel.datasets import TextFile
+from fuel.streams import DataStream
+from fuel.schemes import ConstantScheme
+from fuel.transformers import Batch, Padding
+
+
+# These files can be downloaded from
+# http://www-etud.iro.umontreal.ca/~brakelp/train.txt.gz
+# http://www-etud.iro.umontreal.ca/~brakelp/dictionary.pkl
+# don't forget to change the paths and gunzip train.txt.gz
+TRAIN_FILE = '/u/brakelp/temp/traindata.txt'
+VAL_FILE = '/u/brakelp/temp/valdata.txt'
+DICT_FILE = '/u/brakelp/temp/dictionary.pkl'
+
+
+def sequence_categorical_crossentropy(prediction, targets, mask):
+    prediction_flat = prediction.reshape(((prediction.shape[0] *
+                                           prediction.shape[1]),
+                                          prediction.shape[2]), ndim=2)
+    targets_flat = targets.flatten()
+    mask_flat = mask.flatten()
+    ce = categorical_crossentropy(prediction_flat, targets_flat)
+    return T.sum(ce * mask_flat)
+
+
+def gauss_weight(ndim_in, ndim_out=None, sd=.005):
+    if ndim_out is None:
+        ndim_out = ndim_in
+    W = numpy.random.randn(ndim_in, ndim_out) * sd
+    return numpy.asarray(W, dtype=config.floatX)
+
+
+class LogisticRegression(object):
+    """Multi-class Logistic Regression Class
+
+    The logistic regression is fully described by a weight matrix :math:`W`
+    and bias vector :math:`b`. Classification is done by projecting data
+    points onto a set of hyperplanes, the distance to which is used to
+    determine a class membership probability.
+    """
+
+    def __init__(self, input, n_in, n_out):
+        """ Initialize the parameters of the logistic regression
+
+        :type input: theano.tensor.TensorType
+        :param input: symbolic variable that describes the input of the
+                      architecture (one minibatch)
+
+        :type n_in: int
+        :param n_in: number of input units, the dimension of the space in
+                     which the datapoints lie
+
+        :type n_out: int
+        :param n_out: number of output units, the dimension of the space in
+                      which the labels lie
+
+        """
+
+        # initialize with 0 the weights W as a matrix of shape (n_in, n_out)
+        self.W = theano.shared(value=numpy.zeros((n_in, n_out),
+                                                 dtype=theano.config.floatX),
+                               name='W', borrow=True)
+        # initialize the baises b as a vector of n_out 0s
+        self.b = theano.shared(value=numpy.zeros((n_out,),
+                                                 dtype=theano.config.floatX),
+                               name='b', borrow=True)
+
+        # compute vector of class-membership probabilities in symbolic form
+        energy = T.dot(input, self.W) + self.b
+        energy_exp = T.exp(energy - T.max(energy, 2)[:, :, None])
+        pmf = energy_exp / energy_exp.sum(2)[:, :, None]
+        self.p_y_given_x = pmf
+
+        # compute prediction as class whose probability is maximal in
+        # symbolic form
+        self.y_pred = T.argmax(self.p_y_given_x, axis=1)
+
+        # parameters of the model
+        self.params = [self.W, self.b]
+
+
+def index_dot(indices, w):
+    return w[indices.flatten()]
+
+
+class LstmLayer:
+
+    def __init__(self, rng, input, mask, n_in, n_h):
+
+        # Init params
+        self.W_i = theano.shared(gauss_weight(n_in, n_h), 'W_i', borrow=True)
+        self.W_f = theano.shared(gauss_weight(n_in, n_h), 'W_f', borrow=True)
+        self.W_c = theano.shared(gauss_weight(n_in, n_h), 'W_c', borrow=True)
+        self.W_o = theano.shared(gauss_weight(n_in, n_h), 'W_o', borrow=True)
+
+        self.U_i = theano.shared(gauss_weight(n_h), 'U_i', borrow=True)
+        self.U_f = theano.shared(gauss_weight(n_h), 'U_f', borrow=True)
+        self.U_c = theano.shared(gauss_weight(n_h), 'U_c', borrow=True)
+        self.U_o = theano.shared(gauss_weight(n_h), 'U_o', borrow=True)
+
+        self.b_i = theano.shared(numpy.zeros((n_h,), dtype=config.floatX),
+                                 'b_i', borrow=True)
+        self.b_f = theano.shared(numpy.zeros((n_h,), dtype=config.floatX),
+                                 'b_f', borrow=True)
+        self.b_c = theano.shared(numpy.zeros((n_h,), dtype=config.floatX),
+                                 'b_c', borrow=True)
+        self.b_o = theano.shared(numpy.zeros((n_h,), dtype=config.floatX),
+                                 'b_o', borrow=True)
+
+        self.params = [self.W_i, self.W_f, self.W_c, self.W_o,
+                       self.U_i, self.U_f, self.U_c, self.U_o,
+                       self.b_i, self.b_f, self.b_c, self.b_o]
+
+        outputs_info = [T.zeros((input.shape[1], n_h)),
+                        T.zeros((input.shape[1], n_h))]
+
+        rval, updates = theano.scan(self._step,
+                                    sequences=[mask, input],
+                                    outputs_info=outputs_info)
+
+        # self.output is in the format (batchsize, n_h)
+        self.output = rval[0]
+
+    def _step(self, m_, x_, h_, c_):
+
+        i_preact = (index_dot(x_, self.W_i) +
+                    T.dot(h_, self.U_i) + self.b_i)
+        i = T.nnet.sigmoid(i_preact)
+
+        f_preact = (index_dot(x_, self.W_f) +
+                    T.dot(h_, self.U_f) + self.b_f)
+        f = T.nnet.sigmoid(f_preact)
+
+        o_preact = (index_dot(x_, self.W_o) +
+                    T.dot(h_, self.U_o) + self.b_o)
+        o = T.nnet.sigmoid(o_preact)
+
+        c_preact = (index_dot(x_, self.W_c) +
+                    T.dot(h_, self.U_c) + self.b_c)
+        c = T.tanh(c_preact)
+
+        c = f * c_ + i * c
+        c = m_[:, None] * c + (1. - m_)[:, None] * c_
+
+        h = o * T.tanh(c)
+        h = m_[:, None] * h + (1. - m_)[:, None] * h_
+
+        return h, c
+
+
+def train_model(batch_size=100, n_h=50, n_epochs=40):
+
+    # Load the datasets with Fuel
+    dictionary = pkl.load(open(DICT_FILE, 'r'))
+    dictionary['~'] = len(dictionary)
+    reverse_mapping = dict((j, i) for i, j in dictionary.items())
+
+    print("Loading the data")
+    train = TextFile(files=[TRAIN_FILE],
+                     dictionary=dictionary,
+                     unk_token='~',
+                     level='character',
+                     preprocess=str.lower,
+                     bos_token=None,
+                     eos_token=None)
+
+    train_stream = DataStream.default_stream(train)
+
+    # organize data in batches and pad shorter sequences with zeros
+    train_stream = Batch(train_stream,
+                         iteration_scheme=ConstantScheme(batch_size))
+    train_stream = Padding(train_stream)
+
+    # idem dito for the validation text
+    val = TextFile(files=[VAL_FILE],
+                     dictionary=dictionary,
+                     unk_token='~',
+                     level='character',
+                     preprocess=str.lower,
+                     bos_token=None,
+                     eos_token=None)
+
+    val_stream = DataStream.default_stream(val)
+
+    # organize data in batches and pad shorter sequences with zeros
+    val_stream = Batch(val_stream,
+                         iteration_scheme=ConstantScheme(batch_size))
+    val_stream = Padding(val_stream)
+
+    print('Building model')
+
+    # Set the random number generator' seeds for consistency
+    rng = numpy.random.RandomState(12345)
+
+    x = T.lmatrix('x')
+    mask = T.matrix('mask')
+
+    # Construct the LSTM layer
+    recurrent_layer = LstmLayer(rng=rng, input=x, mask=mask, n_in=111, n_h=n_h)
+
+    logreg_layer = LogisticRegression(input=recurrent_layer.output[:-1],
+                                      n_in=n_h, n_out=111)
+
+    cost = sequence_categorical_crossentropy(logreg_layer.p_y_given_x,
+                                             x[1:],
+                                             mask[1:]) / batch_size
+
+    # create a list of all model parameters to be fit by gradient descent
+    params = logreg_layer.params + recurrent_layer.params
+
+    # create a list of gradients for all model parameters
+    grads = T.grad(cost, params)
+
+    # update_model is a function that updates the model parameters by
+    # SGD Since this model has many parameters, it would be tedious to
+    # manually create an update rule for each model parameter. We thus
+    # create the updates list by automatically looping over all
+    # (params[i], grads[i]) pairs.
+    learning_rate = 0.1
+    updates = [
+        (param_i, param_i - learning_rate * grad_i)
+        for param_i, grad_i in zip(params, grads)
+    ]
+
+    update_model = theano.function([x, mask], cost, updates=updates)
+
+    evaluate_model = theano.function([x, mask], cost)
+
+    # Define and compile a function for generating a sequence step by step.
+    x_t = T.iscalar()
+    h_p = T.vector()
+    c_p = T.vector()
+    h_t, c_t = recurrent_layer._step(T.ones(1), x_t, h_p, c_p)
+    energy = T.dot(h_t, logreg_layer.W) + logreg_layer.b
+
+    energy_exp = T.exp(energy - T.max(energy, 1)[:, None])
+
+    output = energy_exp / energy_exp.sum(1)[:, None]
+    single_step = theano.function([x_t, h_p, c_p], [output, h_t, c_t])
+
+    start_time = time.clock()
+
+    iteration = 0
+
+    for epoch in range(n_epochs):
+        print 'epoch:', epoch
+
+        for x_, mask_ in train_stream.get_epoch_iterator():
+            iteration += 1
+
+            cross_entropy = update_model(x_.T, mask_.T)
+
+
+            # Generate some text after each 20 minibatches
+            if iteration % 40 == 0:
+                try:
+                    prediction = numpy.ones(111, dtype=config.floatX) / 111.0
+                    h_p = numpy.zeros((n_h,), dtype=config.floatX)
+                    c_p = numpy.zeros((n_h,), dtype=config.floatX)
+                    initial = 'the meaning of life is '
+                    sentence = initial
+                    for char in initial:
+                        x_t = dictionary[char]
+                        prediction, h_p, c_p = single_step(x_t, h_p.flatten(),
+                                                           c_p.flatten())
+                    sample = numpy.random.multinomial(1, prediction.flatten())
+                    for i in range(450):
+                        x_t = numpy.argmax(sample)
+                        prediction, h_p, c_p = single_step(x_t, h_p.flatten(),
+                                                           c_p.flatten())
+                        sentence += reverse_mapping[x_t]
+                        sample = numpy.random.multinomial(1, prediction.flatten())
+                    print 'LSTM: "' + sentence + '"'
+                except ValueError:
+                    print 'Something went wrong during sentence generation.'
+
+            if iteration % 40 == 0:
+                print 'epoch:', epoch, '  minibatch:', iteration
+                val_scores = []
+                for x_val, mask_val in val_stream.get_epoch_iterator():
+                    val_scores.append(evaluate_model(x_val.T, mask_val.T))
+                print 'Average validation CE per sentence:', numpy.mean(val_scores)
+
+    end_time = time.clock()
+    print('Optimization complete.')
+    print('The code ran for %.2fm' % ((end_time - start_time) / 60.))
+
+
+if __name__ == '__main__':
+    train_model()
diff --git a/deep-learning/theano-tutorial/rnn_tutorial/rnn_lstm.pdf b/deep-learning/theano-tutorial/rnn_tutorial/rnn_lstm.pdf
new file mode 100644
index 0000000..225c255
Binary files /dev/null and b/deep-learning/theano-tutorial/rnn_tutorial/rnn_lstm.pdf differ
diff --git a/deep-learning/theano-tutorial/rnn_tutorial/rnn_precompile.py b/deep-learning/theano-tutorial/rnn_tutorial/rnn_precompile.py
new file mode 100644
index 0000000..cd10723
--- /dev/null
+++ b/deep-learning/theano-tutorial/rnn_tutorial/rnn_precompile.py
@@ -0,0 +1,234 @@
+"""This file is only here to speed up the execution of notebooks.
+
+It contains a subset of the code defined in simple_rnn.ipynb and
+lstm_text.ipynb, in particular the code compiling Theano function.
+Executing this script first will populate the cache of compiled C code,
+which will make subsequent compilations faster.
+
+The use case is to run this script in the background when a demo VM
+such as the one for NVIDIA's qwikLABS, so that the compilation phase
+started from the notebooks is faster.
+
+"""
+import numpy
+
+import theano
+import theano.tensor as T
+
+from theano import config
+from theano.tensor.nnet import categorical_crossentropy
+
+
+floatX = theano.config.floatX
+
+
+# simple_rnn.ipynb
+
+class SimpleRNN(object):
+    def __init__(self, input_dim, recurrent_dim):
+        w_xh = numpy.random.normal(0, .01, (input_dim, recurrent_dim))
+        w_hh = numpy.random.normal(0, .02, (recurrent_dim, recurrent_dim))
+        self.w_xh = theano.shared(numpy.asarray(w_xh, dtype=floatX), name='w_xh')
+        self.w_hh = theano.shared(numpy.asarray(w_hh, dtype=floatX), name='w_hh')
+        self.b_h = theano.shared(numpy.zeros((recurrent_dim,), dtype=floatX), name='b_h')
+        self.parameters = [self.w_xh, self.w_hh, self.b_h]
+
+    def _step(self, input_t, previous):
+        return T.tanh(T.dot(previous, self.w_hh) + input_t)
+
+    def __call__(self, x):
+        x_w_xh = T.dot(x, self.w_xh) + self.b_h
+        result, updates = theano.scan(self._step,
+                                      sequences=[x_w_xh],
+                                      outputs_info=[T.zeros_like(self.b_h)])
+        return result
+
+
+w_ho_np = numpy.random.normal(0, .01, (15, 1))
+w_ho = theano.shared(numpy.asarray(w_ho_np, dtype=floatX), name='w_ho')
+b_o = theano.shared(numpy.zeros((1,), dtype=floatX), name='b_o')
+
+x = T.matrix('x')
+my_rnn = SimpleRNN(1, 15)
+hidden = my_rnn(x)
+prediction = T.dot(hidden, w_ho) + b_o
+parameters = my_rnn.parameters + [w_ho, b_o]
+l2 = sum((p**2).sum() for p in parameters)
+mse = T.mean((prediction[:-1] - x[1:])**2)
+cost = mse + .0001 * l2
+gradient = T.grad(cost, wrt=parameters)
+
+lr = .3
+updates = [(par, par - lr * gra) for par, gra in zip(parameters, gradient)]
+update_model = theano.function([x], cost, updates=updates)
+get_cost = theano.function([x], mse)
+predict = theano.function([x], prediction)
+get_hidden = theano.function([x], hidden)
+get_gradient = theano.function([x], gradient)
+
+predict = theano.function([x], prediction)
+
+# Generating sequences
+
+x_t = T.vector()
+h_p = T.vector()
+preactivation = T.dot(x_t, my_rnn.w_xh) + my_rnn.b_h
+h_t = my_rnn._step(preactivation, h_p)
+o_t = T.dot(h_t, w_ho) + b_o
+
+single_step = theano.function([x_t, h_p], [o_t, h_t])
+
+# lstm_text.ipynb
+
+def gauss_weight(rng, ndim_in, ndim_out=None, sd=.005):
+    if ndim_out is None:
+        ndim_out = ndim_in
+    W = rng.randn(ndim_in, ndim_out) * sd
+    return numpy.asarray(W, dtype=config.floatX)
+
+
+def index_dot(indices, w):
+    return w[indices.flatten()]
+
+
+class LstmLayer:
+
+    def __init__(self, rng, input, mask, n_in, n_h):
+
+        # Init params
+        self.W_i = theano.shared(gauss_weight(rng, n_in, n_h), 'W_i', borrow=True)
+        self.W_f = theano.shared(gauss_weight(rng, n_in, n_h), 'W_f', borrow=True)
+        self.W_c = theano.shared(gauss_weight(rng, n_in, n_h), 'W_c', borrow=True)
+        self.W_o = theano.shared(gauss_weight(rng, n_in, n_h), 'W_o', borrow=True)
+
+        self.U_i = theano.shared(gauss_weight(rng, n_h), 'U_i', borrow=True)
+        self.U_f = theano.shared(gauss_weight(rng, n_h), 'U_f', borrow=True)
+        self.U_c = theano.shared(gauss_weight(rng, n_h), 'U_c', borrow=True)
+        self.U_o = theano.shared(gauss_weight(rng, n_h), 'U_o', borrow=True)
+
+        self.b_i = theano.shared(numpy.zeros((n_h,), dtype=config.floatX),
+                                 'b_i', borrow=True)
+        self.b_f = theano.shared(numpy.zeros((n_h,), dtype=config.floatX),
+                                 'b_f', borrow=True)
+        self.b_c = theano.shared(numpy.zeros((n_h,), dtype=config.floatX),
+                                 'b_c', borrow=True)
+        self.b_o = theano.shared(numpy.zeros((n_h,), dtype=config.floatX),
+                                 'b_o', borrow=True)
+
+        self.params = [self.W_i, self.W_f, self.W_c, self.W_o,
+                       self.U_i, self.U_f, self.U_c, self.U_o,
+                       self.b_i, self.b_f, self.b_c, self.b_o]
+
+        outputs_info = [T.zeros((input.shape[1], n_h)),
+                        T.zeros((input.shape[1], n_h))]
+
+        rval, updates = theano.scan(self._step,
+                                    sequences=[mask, input],
+                                    outputs_info=outputs_info)
+
+        # self.output is in the format (length, batchsize, n_h)
+        self.output = rval[0]
+
+    def _step(self, m_, x_, h_, c_):
+
+        i_preact = (index_dot(x_, self.W_i) +
+                    T.dot(h_, self.U_i) + self.b_i)
+        i = T.nnet.sigmoid(i_preact)
+
+        f_preact = (index_dot(x_, self.W_f) +
+                    T.dot(h_, self.U_f) + self.b_f)
+        f = T.nnet.sigmoid(f_preact)
+
+        o_preact = (index_dot(x_, self.W_o) +
+                    T.dot(h_, self.U_o) + self.b_o)
+        o = T.nnet.sigmoid(o_preact)
+
+        c_preact = (index_dot(x_, self.W_c) +
+                    T.dot(h_, self.U_c) + self.b_c)
+        c = T.tanh(c_preact)
+
+        c = f * c_ + i * c
+        c = m_[:, None] * c + (1. - m_)[:, None] * c_
+
+        h = o * T.tanh(c)
+        h = m_[:, None] * h + (1. - m_)[:, None] * h_
+
+        return h, c
+
+
+def sequence_categorical_crossentropy(prediction, targets, mask):
+    prediction_flat = prediction.reshape(((prediction.shape[0] *
+                                           prediction.shape[1]),
+                                          prediction.shape[2]), ndim=2)
+    targets_flat = targets.flatten()
+    mask_flat = mask.flatten()
+    ce = categorical_crossentropy(prediction_flat, targets_flat)
+    return T.sum(ce * mask_flat)
+
+
+class LogisticRegression(object):
+
+    def __init__(self, rng, input, n_in, n_out):
+
+        W = gauss_weight(rng, n_in, n_out)
+        self.W = theano.shared(value=numpy.asarray(W, dtype=theano.config.floatX),
+                               name='W', borrow=True)
+        # initialize the biases b as a vector of n_out 0s
+        self.b = theano.shared(value=numpy.zeros((n_out,),
+                                                 dtype=theano.config.floatX),
+                               name='b', borrow=True)
+
+        # compute vector of class-membership probabilities in symbolic form
+        energy = T.dot(input, self.W) + self.b
+        energy_exp = T.exp(energy - T.max(energy, axis=2, keepdims=True))
+        pmf = energy_exp / energy_exp.sum(axis=2, keepdims=True)
+        self.p_y_given_x = pmf
+        self.params = [self.W, self.b]
+
+batch_size = 100
+n_h = 50
+
+# The Theano graph
+# Set the random number generator' seeds for consistency
+rng = numpy.random.RandomState(12345)
+
+x = T.lmatrix('x')
+mask = T.matrix('mask')
+
+# Construct an LSTM layer and a logistic regression layer
+recurrent_layer = LstmLayer(rng=rng, input=x, mask=mask, n_in=111, n_h=n_h)
+logreg_layer = LogisticRegression(rng=rng, input=recurrent_layer.output[:-1],
+                                  n_in=n_h, n_out=111)
+
+# define a cost variable to optimize
+cost = sequence_categorical_crossentropy(logreg_layer.p_y_given_x,
+                                         x[1:],
+                                         mask[1:]) / batch_size
+
+# create a list of all model parameters to be fit by gradient descent
+params = logreg_layer.params + recurrent_layer.params
+
+# create a list of gradients for all model parameters
+grads = T.grad(cost, params)
+
+learning_rate = 0.1
+updates = [
+    (param_i, param_i - learning_rate * grad_i)
+    for param_i, grad_i in zip(params, grads)
+]
+
+update_model = theano.function([x, mask], cost, updates=updates)
+
+evaluate_model = theano.function([x, mask], cost)
+
+# Generating Sequences
+x_t = T.iscalar()
+h_p = T.vector()
+c_p = T.vector()
+h_t, c_t = recurrent_layer._step(T.ones(1), x_t, h_p, c_p)
+energy = T.dot(h_t, logreg_layer.W) + logreg_layer.b
+
+energy_exp = T.exp(energy - T.max(energy, axis=1, keepdims=True))
+
+output = energy_exp / energy_exp.sum(axis=1, keepdims=True)
+single_step = theano.function([x_t, h_p, c_p], [output, h_t, c_t])
diff --git a/deep-learning/theano-tutorial/rnn_tutorial/simple_rnn.ipynb b/deep-learning/theano-tutorial/rnn_tutorial/simple_rnn.ipynb
new file mode 100644
index 0000000..1d1252f
--- /dev/null
+++ b/deep-learning/theano-tutorial/rnn_tutorial/simple_rnn.ipynb
@@ -0,0 +1,393 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Recurrent Neural Networks in Theano\n",
+    "\n",
+    "Credits: Forked from [summerschool2015](https://github.com/mila-udem/summerschool2015) by mila-udem\n",
+    "\n",
+    "First, we import some dependencies:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "from synthetic import mackey_glass\n",
+    "import matplotlib.pyplot as plt\n",
+    "import theano\n",
+    "import theano.tensor as T\n",
+    "import numpy\n",
+    "\n",
+    "floatX = theano.config.floatX"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now define a class that uses `scan` to initialize an RNN and apply it to a sequence of data vectors. The constructor initializes the shared variables after which the instance can be called on a symbolic variable to construct an RNN graph. Note that this class only handles the computation of the hidden layer activations. We'll define a set of output weights later."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class SimpleRNN(object):\n",
+    "    def __init__(self, input_dim, recurrent_dim):\n",
+    "        w_xh = numpy.random.normal(0, .01, (input_dim, recurrent_dim))\n",
+    "        w_hh = numpy.random.normal(0, .02, (recurrent_dim, recurrent_dim))\n",
+    "        self.w_xh = theano.shared(numpy.asarray(w_xh, dtype=floatX), name='w_xh')\n",
+    "        self.w_hh = theano.shared(numpy.asarray(w_hh, dtype=floatX), name='w_hh')\n",
+    "        self.b_h = theano.shared(numpy.zeros((recurrent_dim,), dtype=floatX), name='b_h')\n",
+    "        self.parameters = [self.w_xh, self.w_hh, self.b_h]\n",
+    "        \n",
+    "    def _step(self, input_t, previous):\n",
+    "            return T.tanh(T.dot(previous, self.w_hh) + input_t)\n",
+    "        \n",
+    "    def __call__(self, x):\n",
+    "        x_w_xh = T.dot(x, self.w_xh) + self.b_h      \n",
+    "        result, updates = theano.scan(self._step,\n",
+    "                                      sequences=[x_w_xh],\n",
+    "                                      outputs_info=[T.zeros_like(self.b_h)])\n",
+    "        return result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For visualization purposes and to keep the optimization time managable, we will train the RNN on a short synthetic chaotic time series. Let's first have a look at the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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yn7WBheYMYiMWHw/b1mpEoZ8ZWN9jze2Lgbvn3EQJy6x1BjUscVQTFcCSiUpz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\ngYGBgQGGMxgYGBgYAP5/+4PE7UVXfAIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f0f3cac8750>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = numpy.asarray(mackey_glass(2000)[0], dtype=floatX)\n",
+    "plt.plot(data)\n",
+    "plt.show()\n",
+    "data_train = data[:1500]\n",
+    "data_val = data[1500:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To train an RNN model on this sequences, we need to generate a theano graph that computes the cost and its gradient. In this case, the task will be to predict the next time step and the error objective will be the mean squared error (MSE).  We also need to define shared variables for the output weights. Finally, we also add a regularization term to the cost."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w_ho_np = numpy.random.normal(0, .01, (15, 1))\n",
+    "w_ho = theano.shared(numpy.asarray(w_ho_np, dtype=floatX), name='w_ho')\n",
+    "b_o = theano.shared(numpy.zeros((1,), dtype=floatX), name='b_o')\n",
+    "\n",
+    "x = T.matrix('x')\n",
+    "my_rnn = SimpleRNN(1, 15)\n",
+    "hidden = my_rnn(x)\n",
+    "prediction = T.dot(hidden, w_ho) + b_o\n",
+    "parameters = my_rnn.parameters + [w_ho, b_o]\n",
+    "l2 = sum((p**2).sum() for p in parameters)\n",
+    "mse = T.mean((prediction[:-1] - x[1:])**2)\n",
+    "cost = mse + .0001 * l2\n",
+    "gradient = T.grad(cost, wrt=parameters)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now compile the function that will update the parameters of the model using gradient descent. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "lr = .3\n",
+    "updates = [(par, par - lr * gra) for par, gra in zip(parameters, gradient)] \n",
+    "update_model = theano.function([x], cost, updates=updates)\n",
+    "get_cost = theano.function([x], mse)\n",
+    "predict = theano.function([x], prediction)\n",
+    "get_hidden = theano.function([x], hidden)\n",
+    "get_gradient = theano.function([x], gradient)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now train the network by supplying this function with our data and calling it repeatedly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0: train mse: 0.0515556260943    validation mse: 0.0469637289643\n",
+      "Epoch 100: train mse: 0.0407442860305    validation mse: 0.0401079840958\n",
+      "Epoch 200: train mse: 0.00225670542568    validation mse: 0.00203324528411\n",
+      "Epoch 300: train mse: 0.00185390282422    validation mse: 0.00163305236492\n",
+      "Epoch 400: train mse: 0.00161687470973    validation mse: 0.00139373319689\n",
+      "Epoch 500: train mse: 0.00145859015174    validation mse: 0.00123134546448\n",
+      "Epoch 600: train mse: 0.00134439510293    validation mse: 0.00111229927279\n",
+      "Epoch 700: train mse: 0.00125755299814    validation mse: 0.00102029775735\n",
+      "Epoch 800: train mse: 0.0011889107991    validation mse: 0.000946390733588\n",
+      "Epoch 900: train mse: 0.00113300536759    validation mse: 0.000885214598384\n",
+      "Epoch 1000: train mse: 0.00108635553624    validation mse: 0.000833337020595\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(1001):\n",
+    "    mse_train = update_model(data_train)\n",
+    "    \n",
+    "    if i % 100 == 0:\n",
+    "        mse_val = get_cost(data_val)\n",
+    "        print 'Epoch {}: train mse: {}    validation mse: {}'.format(i, mse_train, mse_val)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since we're only looking at a very small toy problem here, the model probably already memorized the train data quite well. Let's find out by plotting the predictions of the network:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AO1uZMTBfhzXStMUmRRO7CLppI0SRGQNpqFAWoy6ETwg7cWP1a3NksILugxLu\n1jHtDTZRfB1qai+gvHlllHdiwribqilelhCvCtfQgGS169TjNdVfYrt+nzonVhG2ZWO9hK0rjM0t\nolXEHHZ9sCmRg1pJ/RlYw6uRWFuQPGlsmnjlB78DR8QN5nMHHiRXHvWO93Djcr3eywxHu7AHgL8U\no6kdRZ53qYzXBKF92kPr9B1IBRB2jI4JEZ5PhGeZryOyQBiYiudq4p05rL9T8/yJwCF8QuQCsXuJ\nGwNTc8vlL+5D4qTyiEyRMolUnHNkoJLQYwudE6dUyfgMxoCFLnhIAPyzUqIsVvNPTZ6ICDqIvEGj\nEp14e4YXzDtzFaLWaUgeNoWkRHBxzfvvo0f9cb03u7fWBksAIELKkmn0VCJwtNayvxqMwfYSwlZz\nf59JQVNTBJR5pQ2AN59MEKu/4Ctf3IvYSRE7aaM32Cz70T4J2IPjSAWQWObf7zxwNy7/13cajymY\naKAjzenvl9NXTfGaoBgeCAQdCWlsi25BaGMwXq0bk9b6CsJOAh6b95qlK4wzg1MV4XuIWuNGxyJL\n4pvov+1T1yLyjul7YD7//s86AEC3GSrdeehB0lB1fm2upQAAXPZxZVDMeReFHgRLUaNjwIOm6GoJ\nvfsJS/ffDcklgq5Zvzz2LX+Gb/yhP284twVKB5C0c/Gcu6WNmTHH4sIaSQTdZObOymchF7QxmLQ4\nNlWydh/Yi8hTRGrJ5YxMIBfOtoQz2Jzm1ROhS4QfNp7BO9MGCPKIjLVBmR4ZONvZOg0vgd9G7PYb\nvSF3U3tzBvjEW78MsXtS45NNG28/Xv5M4PG/8eLake4DPcROKo9IeRY1F/vQPpmBrfW12INlBL2k\n0RjwsIPInZYzcBC7UaNxzhKrpiZqnRN7dNfQBJSaFYAuxqLbKvmd7vHLYY0I9nADqQCCnlkBvPgF\nV+CVT3pOw9oEKB0gFTsrsgkFuMF7FgFH2G26Dg/WWCJsAeNVk3e+jNiJGznyPJxukMXYRdQaNBrk\njCRgfAYnDyG1PqecioZn6E66qdZhER46kGygHJsdIoPOg9er9SR1Y9A6tQyWEMJ2c71DE3+fBwfR\nOw4Ax6cSVJ74RuBJb2yI7CIOSgYaJtrBMdhSTR3JSA5pQYwlwm6KWRrunaVc0MYAWZhuKrv3zuxF\n7GpjwNIZYSI1vBzo65DWfA5r8Gwcpf9B3//I+vH2qQ4SO9Xr2Dlh5q1nD7ueCGLxClJ+ujm072sG\niKEGwOldIIOvAAAgAElEQVT3kNinkDZHBuCB2vBRu/4CttaXkDiaq047saIuQ/dBYLgPCA1UNzFe\nRtgNGq9DBG2E3aTRGIjA3cErzV7c+m+726tInEj1V2poqSEmDU/Lxw988noES76KNrlsNAZeYxE9\nwEPtDZ5FwZTd34vhvsg0EY0IBB4wRK2ood7ChRhLBD2JwEChtYdLSJxQG4O6sRF+F0FnCpTn24id\n7cZnwDQ/lwwtQ9qnlsDi/5zqVFhDpdTGK/X3QBXb9ZHY0Y4G1dtQWLsI6uvo3X+g4Bg07IWGyODA\nJ69DylN5RA6RcomkoQp8WjkODwV41Nd5vOnX4WzvgQINTL/jwR4CYTcGzkPPsAa50I1B5gHWb6Q9\nXEXiBAAyxkRT75FriPANDed34fQBlYSO0VRxev3fqpml/vL1tWPemS4SPS9X8ql5ByIItE9nv+FW\njnFY4/2w/M80KlFr1GwMrGEboLWp3tTSfRkNsM5esftdJJYyBimLMc2orX7uGkBKjFalkeom/CVE\nreZmbzzoIGxHU7xSB1Fr2AhVsciC35NgBraRNboMiR2oZH6Dx82D7E/lF6tz4hoE3SEAtaeilvnF\ni6e8jyzkoKSPlO9Mi/TWlzHct92wTgf2kBB2Apj3tk6U91IjU0b4qkmamvVRv48i6CDsJI3MNTGy\nELsbUwyyNgZpdR8TvHUP7dN3qt5CDZFyprz7B5fr5w5tgPo677KDEt08rNdRfyidU49H5I2QiqSx\nkjqDiaotP1qnr0HUVhtFComkCXKcsjweMAh/+6xak7TWlzE8MGqADD3YfYK/HEPOUNF9lnKhGwP1\ngEyRgTVcQayNgWRp44Sx6979S/ixfe9tOL9bYCQ1Y9TulvKkg14da7eHHaRaiapWtU1QkwXgt9A7\nlsEr1Yd+A/beGSFs3wHWoESFr5Q5MxgDe+QBUkUGrCEy2PM5Rb8zsVfsYQ+Jra+DJyCzR0cEgj36\nEQS9EVJbwl+qe3bWuIfIHYFScy9+FrUQtcJGRWSNLUTe1hRFJBB2zN6es3kYkbetorQGL6opMmit\n70fU3gagFEDsmL8/rU5BhBw86CO1o2mKjAhPR3vNg79yumHPdOFspQh6oVGRsVAZA38pgTQUIQq/\nB8l9zZGvKxju91TUUXcc9DMWSJz1xmeUJfEpqSqnJXSPA90TX4SckntiOroLevW9KEILlG4jdqdC\nbUR4OljylUgEjE7Yvs88DePVz+kIpQmKU9c/3Feul2itHUDkqaRMyiVkQ4fdafaehxzO9rbO4027\njmejc8LB4MAZo2PQO9YDpYoUcclHBqa27ZbfQipCAFlDNPPD2v+pFbTXQLcZcgrtk23theuK0QYv\nxh4qJRq79VkM9rCTe9R8Gkx0OV6z+t1YuZdh60rAXyo/1N59N+Oyj1u4/Ydvb1QiYjzFGAwc8PCk\nxqqbcg5Z87H6C2iNukitDHabFuFchWd/35NhjY8hsVNEXr3oiIddSNHc04WHLQUDNUQw1shC4m42\nso14KFS+yPDiWOObINl9SIUxmU8ENjEGVW9Q+B0ktjqY8ma68jRjwEMOa9xHyqbnDB79R2/A1f8I\nbB1+sKH1Rxd2H/B7gVlBHF+B5BKJmyLlBqgu6CAVzU3WeNhG1A4botA9cDclotaZxr3EYgeJMNF/\ne+icAoBTOjpqyDno78VePUrlgQUWb+nRoc338DH/6534utfvweAy31jAuHrXIyHZP+v3sin/pIzd\neE85wvU29iDWxkDyFEnDsKeGdvNEIIiA0Dq9rXoTTYkMbnzbO3DlvwLbV2wYcward+9D1Ir1dVyy\nxkB7H4aXihJbsRWAqd1CW+vqIdz9rPo83d6xHhJbJ01Z3PiWi3E26OOQ4VgHadYGmDUnkMVo7wRr\nDnoSo73lzXfde2/C4OA6Pn/LJgCzR83DNiSZKZn2wIK79YAyBg1KlutCIRabcNq24nVDRwYN96J3\n7DAOfwhw+t+GxEqR2gaYKNANukQDRBG6iL2RqQ5BVXQPCJG30cwmCgViL6zmeIjg4NC/fQW6J/5C\nGzSTg2Dr4rp64lU1FssUgETSwFCbagwCBndzC4kzvejs+r+5DP3L/hkbV5825qpY2IU9YgiWfJDB\n0eke34PYTXRTwbqi4kFXcfWnGOTYDYw5A4qfht4xiY2HnWiE8nhkI+ykBmZbG9aIARmlsikBrSOD\nRBgiA1+ARVvKWE0zBm/VinzlwWoEQgSGfXcsoXvivdrRq91DpbB9QmLVW34420uIvAwyTBs7Guui\nPsP76kCMJCxfFyBOcQwe9wcMkfcZbF+xaXQMvDOXI/YiHeFcssZARwYG48siG5LlCeQmy5/xpD95\n62+SNSozQNytAt4/JTLgmjnBkgOGYy2FSQLTGEk4+PGs1e8rkFgSQbcc2ntrVyDsnAGQMUDq5+FR\nS49kLB1TYf2Ao3Pi9NTIIOOGs9igwMMOkiwymMKKWvn8fvi9SB6Rn0BqJUiFITLw20hZf4oichE7\nTRTaHpzNFMHSdnNOwRc6wVx+MVh4Ax72PsDd+jP9TEwvsAvLB/we4K+U1y6CNlJehAbOyRioqCMg\ntNb6OpHfrACscQubV/0NUis0Kszle/Yh5SkSxxwBeRuriJ1YRUAG6IBFKwC2dMGU4RlEbUSueW7D\nY9/6KsTeKWwf2mxMgrPIQdROah55+8GejuTDqe/UJLpldcdEBBz2YBNhJ5h6D4NuirD9adz3lM8a\nDOoSevdLuFtf0PMETPpBOQZBN621/FAJeGUMJG+e78GjTDlVoTq1z4DxjmQCkjbOXPez6t01Rbuj\nQ4gd/9KODFg4ufBa2wOWODrpCz2msaFASaXo8fyXPwmv/urfLx2zh10NJ2BqlSDTGUMW12f6Cr81\n8ahT3rz5l+69GtuH+vKI/H2kol4d624fQOyuAYg17GX25sJODBZVf0NA+IAzGCK1gkZvKqsa5VEd\nY1aRgYbdpiSQW2t7EWtKb2IlxupYFq+AJWuNEAULHSR23+yVJl+H1hlguHcTrP48VLHViCP2BrUX\nfO+dh2CNSR6R9zV5g1CV1io6i1rVyMCD5JkCaPYGddLQ6A1aIwke+0it6d6gt+HBHt4DyQIj7Nc7\nfgBRK9bXYWKPrSjWFE+MSUV7uB+SP4hUAKO9dToiDz0krtkgX/7Rx2L78nchtfzmKDOyNNOpvLal\nYyuInSSPthveh6wNhjQZA5/DW99A1J4OE7nbHtZu+CWMV7cN93Af2qcA4PSU4je9F7pprZqdxV7B\nMWhu3MjDzBhUnSJX14H4SK0QMO8FXfVuoXf/vdqBMBgt+hqkYkvPd3jIWlhf2Magc6qlQjgPqNII\nKbEmxkD10tnBAwGweneZucCDdp78ZVFjG4fMoPCwTslkcQ6vgJLG7myttYOI2iMA0MagvPmcrT1I\nrJMAoubIIGwjakXgNXjF1dBHoHHWphdYG4PQoByiNlKhE/JTioWc/h7ErjIaqZVWaY9EuArW+CrY\ng8/oeQWm63CR2FtGo/XYP/wFCF9ieNlmQ2Tgwh5IhK1R7cVZuv8yRK0sj9SEE7vgARB2UkRu+T7w\nwIVk+hlNMQY5G6nuDQrtDUZeI8RBBIK7YWHp2BeRcvOcX5LfiNgdNSoAa7ishq3XIwMS/jNx6GMP\nQ/vUJ5AKiaBjet4eEntoLEpbureHxPk4UhE0RwaxhcgLa7BG+/QKYjd7p6YUDk4qm0vGgAgC1gjw\nNvoY7Ql0IaRZ3E0b7VP3IhVBDTrtHTuoK+o3IRtzFx6sMRB2klofK4pbAKm9oGDopgS0unebV1V7\nI2V7wVeV1I11Bl20T0t4G6cgRc0YEKGD697zTLTW33Npw0StU10klkTsSFSNAYttpBlMNC0yiGz0\nVUNA+Et+6ZhKsuUQT3NIayOxAB6aaHCeSpRBRwYNRkkEnhrYAaVEU6u8+exBC6mlYCL1/phC+xai\nVmBQ9s5k4yV2c2jN4swYGDDm0EPKM3ZW80tsD1YRO1mStT4r+fAHvxOPewth6b6/bIwMROAh5SeN\nXumj/vgKDPf9HFLhNzBZOrD7EmFvVIMovPX9iDx9jxtzBqoHU9ius3B46EGygb4HsnEOtLaZiLwq\n3OYWnsM0mMiCPQTcrS2TIiMCw41vfxmE/2E9+tEUJa4isczQwY1vfw4OfwhY+eLblOPhmthILiQb\nKCZOZc9aQws8OIXEMe01/fuRpXIOlbXZ/WUFbQEaK29qOaL/Pa3eQwfCV9FV2I1B0tgMUUeIhM6D\n65A8qP3O/k9dhaAX6gilKTLwIMZA2E1qFcosdiFJRwZTWrqziJAyoH/Z3soRVxuK8Q6V1B24mwCw\nCckCw7M+iIe/i+Bu/TRSEc88uOos5MI2Bk6/hdRKEHuEmjFIbYBlsMb0yEB53EDQLY9+4kEbicgj\ng2aYyMZoTwzh15NdLPIm8IqKDJoSZu4k4a2UaMUrjWxIGgCIGjtmsshD7Jp6F+VKKHamwESxh7Al\n8wre4u+HLUiR1W00G0Y1/Wk8+VzVCF/1j1+J04+4E790+j+QWqh2kyXCoxU8Z32hilcTgdBat0Dy\nfZqNY9qfHcWv7w5rEIWzvQ+xmyWAzfCKt9YBpUDUSpBW5vOqJnPb+tqM3iAR2CQy2LjG4A1qaEA1\nHGw2BgpeCCF5YDB6e3Dte1J4mz+sek3VEuUr8Fe+F7G7ofHwSr3EyStx8sY7FFzGJWJDwz6VP9oy\nzm2wRwLt06cQO+PGvcR9G7E3qHnk9mAZsZPl8sLGFtksEog8gGqN7iw9oS0CEGtjZTqH+hyPQ+04\nlNfh9HtIHI0cNMBVLGzpPlYR6s6mCyCHDJthaIbxqkTcKkPInRMdAKozQTq1ktoGDwEgQCrCGtzV\nvf9yWEMCcFJH7JcoTGQP2khFitgFYtsAE1HOfmlOIAtsX/F3+M/n/E5twygFmJ2j2RhQYsFfGUOM\n64lXHrmQPPNGo8bNT6mji7mA1EogK3RAHloABgBizQ03wSseYndoMAaO9lZ9xbJp2HiUuAiWEgi/\nvqEosSGZ1nLUHBlYo6WJMTCFrU5/FVFrXUpIJBYwXi0roq/67T/CdX8HhN07DMlLB/Y20Dm5rvFq\nQ41C2IE9JAS9YW2N9nAPYjcL7WNj0nDp2AoSJ0VqpbU+L6pCW0cGjTCRAzGWiFxguH9v5fuZN+jr\nOgrD19VKtQKIdGRQvk4WXob2GgG4V0EcFW9w/6eejRfcCvSO32Xk0Dtb+xC7ZwCoAUNpmUJLhAPg\n8V5APlhN8is2V5/QOr2GxA6n9JcSunVK+RmIcU9h5NjBwUqyWpHqOyXAJ8Yg0citaS9aEIFEfg8r\n6wh6SETmfJlbm7RPdyEZEHsxYHDOAL3PmVG/KMcgBIJeVGs93X2gN+lMMD1/ZOn3QF9HZU8f+OR1\nCJYC3YOtKQ92XuTCNgbWqI1UJIhdie0ry145i62cTURpYysJii0kzjY++4KP1KiMLG4rTBY7bVwb\nQa8Pe2hKlhbgFZ40e0Kxg2worSkyYJEAySGmwUQsdhA7A/CokkyfNMsLELX8RiXEEg9BN4DwDV5S\nnBvXlDW381aRUAYT1aEYa7CM1FpXx4VE0Ctf58PfdQCRexL/cPTTYEkVr+7C3ZIAtpFYNbxaJdu2\n3wLJJGLXr704CkfX3lxD4tU7s4TETpRBZlUFIADKueVmaEAZXn85ReyWc0jt012kXMojMkHYnRYZ\nZMYgRGqNawr10L9fj8RK5BGpe+FXruPyDz8WsR2jtf5qDXNWDfIeJNYpAAriqFJPr/n79+Jxb+nA\nGn0BCmkrnr8DZ1uCx33F2GpsKSKQ2Fu1ZyD8LhI7y9tMi7aFbkZYfafKkYGibprOUbiHvO448KBT\nMEpm56b7wLLaCzxGNTLgoYDkvr6OpsjAhgiUMZC8vBfczR5i3d4ldpqNAQvtifFLuSH3cfxqBL0s\nQrmEjYHw20isBLGTYrxSMQaJBUnFZGFTokoA8BG7w7oSjT3leUF79U2RQWwh7GzAHpjgFRcyg6to\nGiPJznMLIkF184lAgJJtKZEgtYCwZaZkpgYWTu94RyuhFEGvuScQix1ErTGskSFhWUzIT4lwWGoD\nWUQm6qNC7WEPKVvT1ylrvYsoaePBm27D4JCvIYDidXbhbBOAbSS2iclyJV6z7ybwiCB5PaTmgTuB\n7NKGJLiztawpmUnNG6SUATLLmzRRU5UxCJZipKKsADonurr9A+Avh2CpuWNorvBCJKKOyy9/8XIE\nvQyyq+Pu3pn9WH/4ffKIPG1UEHa/i1RoYyBSpLxsLJ7wq4oibY1uN+R1OpMWLVGrGXIUY47YOVNT\nXiwuRMAsmjIPgSNqhYYmfNZZRgb25B6mVh0mUvnAzPmKjPCtt6H6can3sQoZigKbqAl5cMADibAb\nQM15LxzZ7k3IKanVXC/RWveQCCjPXwS167AHS4jdzPmKZmyzf1ZyYRsDVQQU60rXsjGgRABUSBY2\nwkQckCNErQFYVI0MWhMFDWruOc4SC1HrNJxtUXu5WezkHsQURpKCYTJjENe6KLJQgEcDfRzwlw1J\n3shBYm/W2lW0T+Uh6Xg1aPRIKXERtfqwRqx+HSmf3E/JmwvwSol7Q5LWHrYBOqnPU6/itXwXvfuP\nwZQbcTaXNANkiMSu50aW7lFNySLvT5GyekjNY6tEjzUl2+xRD4kVG71BFnNQqpVw46AiGzwA/KUQ\n1Y6bYuxM5j4nTgyV8jDtqQJMZPk1xo417BW86zrd0PJ7SOyM9VTHkUVoQ/K+/n49MkgcgROP+zW8\n+faP16Z4sTAbGjXAeGVsciyIYMEeEWJvo6a8WOLkxaBiilMRc0SeD0rqxqAUGUzJGeRQW51swML2\nBL5tyh/ZAx0liriWd+Ehh+QZTNS0FxwNE40BWSaXiHGej1RGr8EYrLV05AENE5X3ggg6SKwsQjGT\nCc6TXOjGoKWNQYLULvORFUykMW6WNpe9xwIkfQRdA7wSe7lXz8OpIa2kNaScUPUgWGxDUu5NTosM\nUMpxVMNS1dMGUB51ZOgGykMbKd/U8Ep+Lc523jk1bGceaf3ZssSF5APNfi1vfpWD2TkyoMQqwF11\nr9Qa2iCZwUQpkpw1pYrj+gztU6ehXuJyPcXeOy9D7CXyiExVorxi1Fbv3o/xSih/dvRCpFadkslC\nu6AAzE3SxKiH1Iq011Z5lkkhMmjkpjvgIcFf9kFp2RhYY0/XvABAMmV0ZoYTh4r9VYEvhZ8bA5VI\nr0IxHaSimBspr5OFFiAL1bNUfUYuYu9zACLETnm+rzVyIHVL9uGBpgTyAVhDibCzbYBe8whzagfd\nkCN2R2C13kbZvYkBJHqvmiODDCZK7HprEx53cqo0M7OarGEPiRVpY1ExBlEeGTS3Z3HAI0LYGYKS\npcr33YmSj51mB80eeJOWFpLXmzcqoxbkxy9ZYxC0IXmkcb2yMVDhZ5FG2GQMOCDHGO0daFy9cCxy\nzwomYrGANdqAvwJ89vlXlY8lIs9dTMNIE3tieFIRo6qMRcBgD7fUcS4Ru3VjwEILJLdqOK896E6a\nzCVuMwODEgeQY4QdYO2G8uZlhUhragFeIiqJ+0of+FCAksyopZVB5DacLQkRbAKIaxXKrTPdHGf1\n6pGBt7FnwlRRDA1TZJDnPUwvjggUpm2KDChhYGkRJjIXzIkQ8JfGYHEZGlCRQW4MJDPj3dbQgXKe\nYyR2AFbxGoXfzfFu03X6HaRikF9nxRjwSICkToSLtBaF2n0bYvQAVGQCJKLwDNZcXfQIhIp9Vyyu\nIwLHk375GJbvI/hL9WaCLC60iZnyTvGIIfb6YHHZIFtDGyxV92Z6ZGBPEq+JHdSMEg9bSFlRiRqM\nwbiH1Io1/FJxzgIOyYpECQNsG6jIIGwPwJKqs2pP9kLQayYT2IPWxIFIjJFBG4nIYaJZBnCdpVzg\nxiDykIgIiRVDsnKFH48FQFlkkDTy+1VL4TG2ruqDR1SCR5SnXVTkTTCRACUj8DBB1Pq90jFKRcHj\nn1K4lli5MeClMn7Vuz5kaJ3WtEaRInarDJBvBtDTDbzKdEAxbmtcE8hfIFOS2AEoRNSWWH94ubUG\npTzPwbC4OTKIc9aRCYrhIQePtDHg1ZkCHtxtANhGXlyXf1959jrC6fo1iMLur+QzLAyUTBZahWR+\nrdCICNdicOAZSEWoDHK1nUXMcphImL3B3vEOEiERdUZgVW8wzL1BVUkOmJ5D+5SHREh5REoV4VS9\n2ij3alUX2ioe3ipUSpuMAQelOjIod28lAsEZWOg+cHzC+AoLRWn2IIe6TM9IjPbjG39U/Xm4f6Me\nGRTh0GnRdsQQu5tgUVkJexuuxtAlVHRlZtZZQzs3qFbdcWCRN4FvU16juFL71Hdi/6d/CqkI9Kzl\nqlPDJsagaUhP63QbkkFB0DVj4GiHQhnVpsjAGnlIJy3w6518edjK4apLOTJgcQuSR0jtGNVhMJQI\nSOS8+KbIgMcMLB0i7NYTljyy1eAJZIm6JgWoktD3PvV3cNU/PdqwjozVtIMSpUJysxS6C1hjwNvM\ne6FUO1He+uy/xqP+zII1vF9HBrki46FTUUISRmOg6aORm8DvlXnRyvs8mwinkKsxsIl4yCHGmVGT\nFbzag62Tk8qjK0MUPHKyxl/a864Yg+EKEls9c5Vgrrw4kZXDRKw+zP3G//U2vOjFN0FypQCq0ABL\nCCzO8j9mnLi11kFiS0TeCCwqGwMROAaYqH4fne3WJNEc2/UkrWr0p9ch6gpV+C2kPKuHqCcVWSjA\nYp0zENXrcOFsAZ3TGsqzyowva5Q/A+VYlPfSvs/mc4uH+zdrSrhERJjCr+chQ+ycAY/Kz8DdKBvU\npnvYOt1CYimDGrv1QksV9ee5vOpeeOxb/yue8GuARKSKuWQVNmUgmWH1ZsegtdZGYkkkTh8sqrQ2\nieyJMfCXg0YygWqBonMGhvvFI2/SPNFEJjiPcmEbAx55kDzUDdTKN1spgrzYq7nSkYNFY6iqUImi\nEmWRNakzAJuycWMOwhiffvH7IfzKi5fwSWSQ8ri5fD+1CjBMtc971k5CP3SRFicrEcHC9e9Wf+k8\neLc2BoWkX1KEJ6ZEBonKb8ReDCnKHHkW5wnk5oIvbfyoYEArRpiHDNZQzwSoRQY5Dz+DKEqRQWRr\nJQyFyVeV5Hhp0mJa8jqDhMUiN+6ivrZ9n0305yIdOdQVAIuLSUcTG6mD1EoRu4Pa8HIWFY1yM97t\nbHmT52WqKFd9ojIFUKcb8tAFaEuvs+4tipDl+SdWpZ6KScEboPNTrdxgC9+ZwER5kj8///K91+rk\n+eMRO6O6Rx7bOUXZEPVgEgkTEnt90r00E7vvFSITxSYyzaJ2t1qTz6UGsoEIHKRMRwaGRHbnQVUX\n0D3R1RFMxTFIGSALvbpMe2Fb7wVnu9bvSxFGNPzjJJpMUDdqInAnXRBU94DKXgi9QosUc1PD8yQX\ntjFgsYuUh6poozJrlcccoALfvQkmihhEMAIQQLVQL3jUkYU0g25YZByio9YhAOljcPAk7GH5xSvC\nK6Dm6VYqCa03l4hR9dYsXbmqrkdWcN42smiah3ciccoDS4peyDRjQIkNUKAbzZWLZEow0Q788FJu\nQZbhLhEQ2msZTFRuouaeaeuOlomUSJBY5bkOPHJLxqCaCBd+D4m6UUZKJosLMJEh8epsK6ixfapv\njgxiAg+mJw2z+RWxs1FLGrLENsB19ftoD3NFpirKy/uOh60CrbGeKBeBA0lZFFk3BtlMBXU8gUQ5\nCmUaXgHqfbIs3y1EN/W8jj3oIej58oj8mLEaniV5dNY8aEnx86P2+mQU6+T3R252b6SERCqAyECz\ndvqtiUENDRRYHlqTinrJ6nkXMW7DXzoFZ/A8U08gsJgVokQzTGQPs6LYLbDIQCw5i72gdJyGiQy9\noFjoQLLsWV/KxiByIXmARIRAWrW8Ig/jpvTSYTFBjAcAQsQuoWoMZEZFFGGN1ZGfg4PSMU496qSm\nZeb3rZgzMHkgk8/F1iTHUcfa8wZngKlJWgexKwE8Hr9+x3EkNhAWRjKq2Q75C2zqNwNktRmBmhAn\n91SOidwToinGILUAFBvaFa/DUt1T+1lDvqTUi7+13kFiS40HK690vJK/ROoFyrwpA17td5Fa+txW\nnU7II5EzLwzJS2ewjPVr/w9a69+sQ+46TMRDbZAbEshirAohpThVGxLEItcQGZjmUngTbzBYqrOm\nit6gUhDlcygjWKhDkFVjwOCd0dFZrS9P2RgkVoq0MLilmLcxNU1UBll9N2rXI4NiZ4Dmduqan99e\nhwirTKmiMQJSZp5FbQ28Sfv5uFWvreGBhcTK8j8GqC3w8OBjPyCPyH/WHnfZKFFCIFnY56a94Lcm\nxkAEFTJCWnQMMjKBoatA4E6MRmzoBSUKxsBEmjiPcqEbAweSBarneyUyKNEAWQIz40C9GO7mCAqj\nBkrwSmQVcgbBlMiAgVIf471jPf+28PLEvKxEm2CiREBOIplq2Olqfn1e8VjG2tuwhwTgkzrpJzHe\nk9+PYrJKeXPmpJtqG+yrBmTpSuUYA6GQfG2CiWI+ydXUKXsZ3FW4joLx9c70CnCWMgaxV7yXVuEF\nqlNPWexOkteJY0oaijzxajDMdr+N7Stul0fkmrF3PIsZnO0cJzZ5gyJUEI9kD4CHVVJDOUKTDclP\nUaCg+kt1RcZia2LUFExkMAayGMUV20lwCB/wzmQ1K1WjVokMeJnxVczbKCivnNcRhcpef2lYq3kp\nsuuaBy1lhXunwavGIPBKe0QKIHbruRsR5Fi7vzSuQ4phvhdUlFhNzLqQLMtt1Y0Bi9nEMWhqYinG\nqtFl1NoAD6pRpg1oqnZOJjBFBs6kC0JiaD+vlH+2Jy/NyIAu/+gPQfhfDUmB5pRX2zfwnSIDInDw\nEHC3RsiGxhQbp7FY5CGtCKcM8mBgiYaaKnkH5flkFbnTCtdE7mlUvLXWqTYggWLoXuSG9+5fAqSU\nR49Y5+MAACAASURBVGTW9VTCX2oXzm2dFUzEEgsgH7EzqrXjZgWYKJ3ScpcVWFwq8WaKcIrwXX6v\nrFG3bAwsibjQRrqIs+ZDfgrPKxWT5GRiGbjlkcgTryZoILBA2JxcY0EBEIHAIsDp53vKhBPzQNW+\nbF9xJ9qnK4WQsVPyBpuggcygAMBo37jWloNSAYk8CVuDDlIGQpH4UFyn6p3Eo0yBVNlGWe+frDAs\nLbXlKOZtTM9ANXdUvz3cPzbkDKxJpJxYTe+UAx4Ag/2nIPyqki7mXQDJJCJD11UR5DCRv2yODCZt\nUwwRigjyvItpjgBLCDwoFpMa9kLUQsoTRO0zEBW4qwwZNu8F1QVBRwZuvepeOQKZcTW3Oz9PcsEa\nAzzt596AJ/x6C5JGmn9bLxCipIjvNlQpBhI8ChX+aKEMSyRikjNQjIMmRZ4locNadEFJMfE65Ryx\nyIvTKrTH3vF8/KZaS7lH/fIX9iLycqJyYqWIvTwyoFJkkCIV5ulWee7jNLz1q0vHKOGFsLg5gawg\niyb+tlMyBmoofX6vrGF7Mi8a0PUUhQaErGQM6glmVXWulFhsigxinnvUBmig5FHXvEEOFgPWuJgP\nMTULVMbg7md9AO6mQ69dzZPIvIQTK5jIlPxkkTtRFIkT1+AkpQC00RP+1OuoJxWzpoU5WaHM+LJq\nMFGxVTcPnewZGFuj8DCPDEb7RnoeQgE2TUROoTYnkCdr3HjYg3VjEFVgIi6RWoa9HOaG15Rf4qFA\nauUsnCrUJnwbElkSPqy1Q2cxQQTTW7or2DKG31urEUsosQqRQTOZQEViek+79chAJbLzLrDmtu7n\nRS5cY+Ct6z8w3cEyrbQOiDhYUogMjNRSG0K3hwXUxgqW8vNQwkGTlyJshokKSWiVdygyeXJWk6p6\nbWLhqOI3oF4QZY3zQh8gC+2LWPte1VkxO26Vk34stTTGnSfdwk7dm+KRBZIjjFb/Dd7m9eVrjAsK\nZsocZZYUI7Jqz5eMLZRz9YteqeWXIYBUlOc6sFIivJ4zUEnuAl5dfXFiXqiBMBsDSguwYFKGT0oe\ncwOdUHmDsdw6fAKnHhXj5I3PKpx/sv48+emZzuGinlwsRkAFhpph2piiPRYr54vPQBTaOWhIsgIT\nKUglnwVSZHwVi6XU92Wp8ywP2pMkfeKE9eitwDaLPXNvIxaqKu77n3galIJuK8wvZ6V7o2jWiWXY\ny2HuUSdOPU/G4kKkK/x6ZODbYKmOEkVQq+WgGHC2dX6qQb8Iv4PECjE8sAYxru41O3snMS2BTEnu\nyJlyH0pHFffsJWgMVMIFsIbbSMUYLKkm+4qRQVPFrK17z5tpdEq5FGlwTcaAwIMRssiguDmLCkaV\nkzdHBjlvuUx7FOMiTpvlQPLfcLZWJwND1HWkSEQxZ2ChWOIouUTQNbxAgQ1Kt7Fx3T+gd6xSZ5AW\neNU0pQAv5oDMKyKLL5E1cPQyMoVa7vlijdxJ867sOoqtvFlqlY1BpQ5B0VrVucO2DxbVlWRmDEzQ\nAEsLxqAWGVhgkWqJDDQ7GCzyJnTk/qF1BL0n5McSa+LlqXOYk58sdAufy3Ij+W9RwRgkht5Fpeuo\nJRXLnr96N4q9h4Qu1tJN1Hi5LUc5OlNQXvGd4aEHKbIhUZEugCw8owKMGNs1ejBd/tHvw5Pe8O9I\nORAsDxF5svT7qvanbIwkNxnUPAlvvIcJL7eaqZINfAss0m2+uV+b2MZjgruRRwZGmEhHSWeuux/C\nZ6UxqArSzN7Z5shA0ZHV50y5D6VPLnGYqKUjg/2feZ8uMKo2FaNCgqep2CvvXwLoyl6ngFGnbHIs\nsZtzBjxmsAdjRYd0gPFqAaIpMTumRBdxwaPm5bCTRVYpMlCJ1yJ1tLjxgUSkkLywhtQuKaGUS8Se\nyZsSoHQbdz/rE/DOWHQbFT0plSQHprFAdKK5ACcVXxJn2y5Uj2aKpnAdQRFTV9eZsmKkZhW8wqg2\nHKfoMQeGojRKGWQBS6+zcAiUZC94lb9fjgxSZm7Wx2Jv0qgvWNoCDw8Vzu9AUvH6zMlPljiq4yvU\n71UjgyJObGrYV3ZADJFBDOSefzmvY41spEx1yQSgjDNVmGmsDOWVjEHUQsozY1CHTVVkoI2BV3ew\nrv7HF+EbXpvlBYYIO4TILeS/YqccGTCJVBjuYZU0UYuuCkrUkHfhIYPwsyR7qekhERhYDLTOFGpO\nDHuBh6pSfHjgXvQPSdz71K+YHKPELjhoGanDkDNIc5jIlPsgWYjYp4y0PQ9yQRoDIjiwRoTEul7+\njP83KjKIK5hexGCNCzmDpsigZAwkErscGeQFY8bIYMLOaJ9WvO3EkvCXC5s3LeK30yIDBkozmCgs\nKRrVFqPoDZUZIKz6gopyL35FWy2/wEltoIkHHgrwYAvbh0/DX5Yo1hqwmIGSotfcBBMVjEaFTeRu\nORWjVk5eCt8tGbWUp5C8mIy3M2Uqpc59RF4xP5PnDAYHh3UWTsLy/I0BGmAxA5tcY3Vko1WCV8DM\nje5YnPezitx+qT8RS/K+POr6JBLHgHdH7iQRnidpC16t5JOq9sStF9+xglGre4tVtlC5QtnbKD+j\ntFKHwKNKZCAkksKkNMXCGeu/mYxBnkNTLbDLz8DdUL8tAiYlRgg7EqcefVl+7SUlrx0jMt/D3AHK\n6iGKkUHR0TPUpKQMLNb3sNYC2yob1DpNmW746+NYvvf5SHkgJVJsXj3EePXp+fkL80GmteJmUV6k\nl1cq52tlpcigaRTseZEL0hgAWII1lOCR4tcm1kj11SkIiwnWSHOxG7tsqq6CecKzTKNTD1jd6Nht\n6jnultgZiZUi6OaUQor5BL9Np8w6ZTHPi1gqnoZK2pVhIipWSsdWJXQvz2yl1CooF0CKEs5K7dM3\n4WXPGsHdFBDjLQBDBD1C2O4VzsEmOZhp/deV0cg6ZpYTWtawDHellToEHjplY1BplUAlVpRSRP5K\n8TpzWG+8Guj5uIXEa1qOWmodIBPKFYCoQgNljzqtJw3p4e/6e7ROf/WEjhy3+uCFlhQUWxVFVjPK\nah0FOqGplTclbOKkRIbCLkoKRq3Wz0boZ1eEiQoGe9MtQ5I8AaEYndklz1w1Gywq+7zhojIGlRxa\nAasPugEK2xIA4K2XK9+DXojtKx7WcG9UZCC5IcqN3YLhrVdKqyR7rkTreReAB8W9ULyHmWNQuIcV\nx+DbnnsIX/W77UmUNNy/DRbleTgqMN8yNlFiGXIGae5AJE5UazJZzA8ZBj6dT5n7xER0CxHdSUR3\nEdFrGj7zq/r4J4joprM4bRdqYJUutrBG9cggJribGVvADBO5Gy4gIY/IDB8t8/dZksMKccsvOtfF\ns5QLwqwUiVWkdRIoyTH0Ro86KuQ4eFjaXDwuFvroBLIsbuyKt8bLg1lYxRikXCIV+Qt0zftvxrXv\nAw5+nMPd2pISKaJ2ivXr8mZ1SslnBm9KArlg1FIRllhc1rhq1MoQBQ/LkYHkSanSWnlTRThMlpqo\nFSM5Y4K54A2aoAFKCDzMFUDVGywlkA0Oxq3P+To88deum9CRY2dcarSmKL6F3A6XSIXBq01znDiH\nEMqJ8swoRe06HMYSAou00auMzWSh0OiEnu1dKUqzxuXIIKlUw5fzNhlMU2DyydzzN9XuUIFtNris\nXlDn9Mt03LAzRuJcXbi2KkyVAlT3qFXidVp0VYgMnHpnWJYwWH4hMoinQW3NRZhZE7mwuwUeXl5Y\n34Riq8gEHMY8XrF9Rz7ZrbKns/yQceDTeZO5TkxEHMCbANwC4JEAXkpEj6h85tkArpNSXg/gVQB+\nc+dVhS7EmAAoDzSxhypJmp1T1w9knHA0tMptn2xPGoIBOmHJzDBR0Gma6lThzleYPJQysDQLR6eM\nt0sYeJixE8rwCqskzapKlFU9ZqvcfpniPLEK6NCa5y+ou5kni7sPKAZF2E4Qu0WYiCD8IrW0KTIo\n5mrKXqkYV3MfMZAWIYiy15dWitIotVB0JVNRrUPIlaTKKVSK0gr5G1Ofl+La63NzxaQlcr72Io6c\nX2feitwvJ2eTSoTGJBJhUADRdEXGCkbN1LCPEgKPzDBRa81FylGgKZeTn8UBPABU7kJOi87KNOdC\nEl/l0GzA7xWj2LxNTNTJWmAXYI9KL6KoPQDklYVrt2v3sEh9zX/HLkdXFYPKkrwWwzTLmWLAGuq9\nYo0r0ZVVyR9FRRiaCIRAgwOZMYi8IVicIwblBHKWezHRvfMZHCa4iyU06aQb23UywXmUeU98M4C7\npZT3SCkjAO8A8LzKZ54L4C0AIKX8MIBlIjqA/5+8d4vVbcnOwr6qmpf/tta+nXP6uC/QBkPicHEQ\nIIgRkblFkEgkUSJIIhQSKVKc2MQi4iHiIac7EUkQCoK8OZEfLPECMgJZIsRGQBQwAXML4hpjsN12\nX073OWefvdf6L/NSVXkYNWdVjRr1r2bvjrTVnlJLffb/r7XmpWaNMb7vG9+4djz+mQO88f49v2jK\n71nk7oLZViKfEzLyzcexMQUg/X66SaaZ5PlpMbNWPf3JX4Vv+z9+V9YdbBsLr1PCKyElTR1eMZNG\newqVAdPA66kFVF66l/BJWjmwYCBVBklp3d3H0tzMscJxXX4dzSX4/nSlC2T8XhLU2IZqZs595Fmp\nmlsGQeSqKe2adWQiQA1RNrVKSGAiYJE1pvdJAYsFdTtAFb45Cu1p2QA4/spVOLwyiHDQag/QXKBT\nfbrLKwOCOIQNILF5XiCEcSevy9PbQmUwqwziSK9j84JDdbl3kRn4M5ozWw4yM2QwkUmfUeyBoEv2\nOL2VriODxRlAaoA0c4P3f8UP4fTsPwAATNsXUI5xBiZPbGrzwJFk1NRQyqC2AK9M21Klo61ClwaD\nmUFtjDPIpac9FpPSxTbEdmdomwpLWpagAdO+DGoqsbYX4a402RR6a76Bx+v+4k8B+Nnkv38u/NtD\n3/n01d/66Au3mDdxwdr+noaVr0c6JxVAxTp6+/xmnQAGBOI1w0fjgrk8GZCsQQDAv/qDfwq/53f8\n0bxXobHwqazTRiJq7q9n1N39Mqw9V7pQt2LqxzIDLi0VebY2Q/lUDtgmLwbgVd7B3J5SH6Kw4bcW\ntsmDQXu+rNdRVVZNCu1JVh0V3AdrSisgCJMP+aEpajlnkEpPy8oAyLPB6Cdju0sBDSir0N8tweKc\nwSub5x2ACCmWpGF85t6EzlXGO+iE4KbveXhdbmTJCNS1GXJ4lN4HAxXw7vOTsqEqC2rMsK89ssxf\n5c+APyMaBsQSj0zNk/chpHwGQIKK8ZC+D1F6TEPeWfU2Nji+9eP+D3/wJwEA8/Y5zPBO8vM5VOgq\nBLKyPcIskkRskFaZag1K1O+w3qNVLdSeF6z+nPmSNWcGGRbw721IRrHacVBHfPpOpnBaqBI7SVnW\nZUFNrAxCsklB7Y0NBv7hrwAA+AZ5/ef2X73FvIkLYt7cQ/NgkKqEKh2z3fEm63j1Ji9507b+y5MJ\nXuUv3fYjmms6p+ZqrYVXaQag0IS29XkjYu3B6kAFj6RlE02Jz5wT8GaGyrKDhmVruX4/J6vCdeo0\nGKQ+RMsUMgvoXTy/WWH7UbgOQRK43rNZYfdhJJBzvLpjShXmqOk4Hpwb2dF1pjCRQzrXgWC9CA2U\nfQgxuDNoYN0AmstCxuXZ4ObjTb6JFjjxBksRZC4LTHRK4cuSM9DccDB8b+4KCGHcpxxQch0biRtJ\ng1peGXBOgKrQJBhMOT/FFV88o6XKOllridJp+XzOpKGxX0WsDKYG2t2v/z3unqM9x/nBhRhCO0DV\n7mHcbElskPOBC/E63HBFlsn6Ybh8t7vP5bc8MTDDLbo7+n3bj/4a/f32iHSEZ7GWjYczUjCIUlxZ\nZhxt1ad95nCrPq+U+rzKnRle43hd06MvAvhM8t+fAWX+177z6fBvxaGU+hwA4ObbvwP/3McFPW8+\nQjPUZYDOzGJnnrkcYLv0gVgYy8pxn+B1DUIFQv+24MtNMi7TtTYnDZOS3fYjVHzPkqOBGYF22URY\nd6zmKhTWocwzaoJf0oXfJPDJIsdLJZ2P8PEvvMfjnzn493wIBm1aXWiYKVoxjAcR7lIKBt+b+PfY\nZgiwAR28YYjwanYdWeY8Z3g1Ecg5p5BeZzbDQjIeTPDVEhoguwkzLRtAjhNzlU0hm32+W4uW7fNl\ngPk5e06qeA4eUhcz38i88dlku1RBshDlZmoBDEpB43vSoNZdMjisuQhQXcZPlYqvlPdQxVqzMCqv\nWlAEi5TXYcGAZbpmNFBJMJgOH6A9R56x5AwcREmmTWGigMlv07WiVsXVcMttwpvQYLjYQJxgknt4\n+MqWOQLk7+v2o0fhPd/grZ9YIJxjpnhUidU7EJRlQjBQGYE8wTKTSeK5hnAdZ+hZKaW+C8B3wbzz\nGL/hq7+v+J2veLxuZfC3APwSpdRnlVIdgN8N4IfZd34YwH8MAEqpXw/gY+/9+9Iv895/znv/OfzL\n3/Oj+PQhPuiXn34f7Snd7Jf+gQUmKpQf6l/509+Hmy/9ylK9kmLU2UtXZjHdfZkdc/O1FF6ZhHby\neL7pQBGWUXNOgLmacpjIN/lgFtLfpy9GLtnUc48Xv+DHAPzS+J3GAqsTbK6rnrelJHD5XnrfufcM\nbXJp1plnpco1OQzEhs7T5/lGk5KHqeYawjwEIlZjdZNDXQ1MsgFYNpilu99erQwefeEWc+/w0S/+\ns3j80384XP8547J4ZeB1XqHF62xZVuuKYLAoSEr3Vh7UclLRDAyqU/lGZkbeR5D3IegC6+b8lcnX\nWptbiphJJzCRUBmMBmaIwWA4fBXtMZFq83vI1nL+veQetswB18a+mfOzIXgoLe9m3osx7k/ZOu7u\n+qzfkCMP7bGDa7x/zw8rYmDb+zwYpHNOQMFAbJ6zDaCTykCAiZZgcHrrAj0r7/3/6b3/HN760R/C\nbyxHpb/q8VqVgfd+Vkp9L4AfAWljf8B7/4+VUv95+Pz7vff/u1Lq31RK/SRIKvqfPviLadxl3DS+\n+su+CjNo9XllAqbbhk03hYnyTfh3//t/DADw4bd9sP4b74hVNuKK0sJthpLw8WxkpbYK/cuQje5q\naqK8+c2bXOnCpaO8a7TM1qY8m+MZqXbFJuvV7N/z/3T9N9vOaNYSs8kI+fOTGoHcoxk8Ysd1vuEW\nRDezqyggCFYZENw1rP/ttc3x6tgx7j28+h4+DyFR2Qy3HOrKN4B5mweDZkinxZV8yPYjMhP84z/5\n767/Rv0v6fXl2aAzHnwaG7BUQHlWi6zSixVOCYflQW3e5PLNfFKZIFaYuUx5KqsbDjkih1YX/T4Q\nKgOfVgZxdKgcDDSay8v1v4dH76O7TzmHtljLIoFsO3h1l5ynh1UpTKOgA9Y+3oxwOiW/87Uw3B5D\nPxId7fl6YtDf5XAoANjuHnpKE5eIMACLRFa6jgbL+4TFZHKX71FmDFB2NOTz73mH9tQX5/Eax2uT\nEd77P++9/5e899/mvf8fw799v/f++5PvfG/4/Du893/n4bOyuR59vH2JeesBUAZhhpxA9k1dB+ya\nNMvgwUBD21TjC2QLdygjeeIbRDj0BPR3y8OqqYk6mCFRP7W5JJNLR2meAN9kUs5gZKU9I6tYNqWc\nzjZhIMADa3mfN9mcnwzgtsrxOoBaZyeV+CkEUVYGnEAuKgOGR+cEcwrrLeRlOhwHq+3wcMNVOCwb\n3J0yb6PmvGE9Evma2nx8C9vnjSgkeU6fYy4nRGWOMj3vGPRc4+GbPOjptDLIOIP8OqZdjneTfDeF\n6hhMlFkrL+9Onnhk1RkfYOTyysA2rOfFxjkApPLh8xA0+pdxEz+9/SV091xEkAYD2TCQrLLzzTbN\nvNUcGwzLjDu/h6e3T9lchu7YM6iNVQbMSwxAGH3JO7GZskyXyWXiZkyzShqfiQm0VejuFxg654/a\nc1ecx2scb2YHMo2CSzevOwy3CgDZBfcvt/Aq8VdpMrZfqYSwzoKBzgdfp0y95CzIbWkBDm3kErTL\n4/omSrzDgrVfWGWQZ9Rln0GerfHJTNpG/3uAiEswAjpdmADg2inJRnOYyG5KW2U6+lDhRPmmzmAi\npkQpgsEDlYE1LGPm0tM8GLjGw/Y5mb/IBUVoIAl4hL+yTbRhG0Cq0jnlYgQAmDfHTNiQq52uZIPJ\n1DuAgndKlGurGEwE1ILBmJOKwdsnvY68D0HbnEC2TS7/1UysUFQGGVRHCVbWDW9jvwqrDNbZxzdf\njpXBh9/2ZfQvOUz19d1Dz6ow3+T3cIFXSlWTCfeQrvP+3RPMrNa1osfcRdiztdCw5koAsJu7ci34\nHCaSlWVpZUDo8HCzBVZhB9Y1zSFDcvn9hgWD1yWQ//859LzNMkjgguEWcPopgJ/D9sNt3kxmuIpn\nA2cIno4NHWFz8XnGvJBMcQBFChM1eP+Xfx82L/7u+m856clxaBvKUY2w0NQv+1N/AL/uS09yn3/G\nGSgvwUAso0aazY1Qbp99joyQtJkcjxwc88qAZiosL0+TS3XDS0wlbPpzHcwY4bm5zzsiCwiiGdNm\nnRCUUghiKoJe9nnDTdRSjof6EFzWwQzc/uzSETqyZ5FvoudnOTRAWHsK1eWVQXu6Cc1+8Zg3x4x4\nVIl9MxBkmVJlMHXw6rz+tzOOwX56gTi8h1Xf1wLnJzwBCUGNNaVp1utBTrrJM5g7Vp2xgF3wT3mH\ncmruCJCgwoz5Jryo6xDmIUT9P827WHgdAHjx2Ts0A0+M8sog6dZXCnv86//9f4fHYwdnTvF7jQNc\nnuhlQelKZbBYcRNJP9KQ+oxkz2HdZmgLeGbanjL5O1UGKeTpAF+uBTM1QLoWGoe5X65jkcDmXfft\nmd4ZLhN+zePNrAyUyyoD7+Ex7izuPk165P7lhjWT8cHbB8SJhHVYIm3rX/C6y21S0p4NlP/7/o9+\n4a+s/5Znszm8Io23+03v/RH8jt//BzOff2p4SiZbzQJ8wjJqXKsM5lRls0BZzO3T5xuZbUbEGRH8\nOgrLXaWgYYY+9FwsfvXsOjj3wZp1CgksUxsRt8Eqg+x55cEgmd+7Ske3z7kgQM6oj++cYKaYDZJV\nRh7I0jVlhj1sm1dX0/4eekqz7hKuUxLePXX5BsBmXutZwUx5BXR6Gvmd7DpYU1phQW1yxReXMTs2\nDF4xqIsPgyeoLiX55yxgk03MBUCc6XB+vKyzHs0lCikA4PTsGKppE87fMM4gh4l+8Y/8R/jN/+1/\njUdfuF27f5d7mDvgKnR3V6qrRFpKDYweS9DTrEr0jD9SLOACoc8gTQxc7IZfzi+pDJTCVn3m//61\n1HDqj8n3PNzqZ5U/aw4Zcrfj1zzezGCgWRciAEy7GdOOOpf7u10euRtOIB/QhHXS3cWXjDaiXK8d\nSknv4eFN3vzTXDT2X/04Ow+v0x4A/rBsOK+4AS4iJ9v6Fdbi3j/a5a6jXuXeRaRMSDmDgeG8OTzh\nDSOQGX4JBKzYJ4suGXhCQc0jDWr/9n/yHP/h7/zzwaI6DCLf5OopqnDSjSb3zeFwVVEB8Y2AwUTK\naiibQwhutfKmZ7F5EQNz7nGfQ3rjzSX0DYQXi/lDeZ1ng2RXnK/J4XDOqgvayPLKIA2ov+Cv/Xvq\n9/6WI5qxBXyS1RqHzKPJRrkyQEqZyI3ka+7ydAAQN1NVVGeseisaGPPKQBf6eE7yM6jOzEuFucIa\nS78Kfe4xrYOW2rDM4++3/RjmitPfIE8fzn/F83vyU9St/OgLj9buX/o7uXKLEoOUuwDStaASMz/u\nvmqmTREwM8WWkJHPm1yQoGaTEe30/fjO/oo/8b/hP/vOHw+28lFdRcN8kmedIA/cj4tLuV/zeDOD\ngZo3OREHYNqNsC3ZKjRsYpZt8w7BRz/zeNX793cRz3RmQtrZm5eStHCDz8qaae6/FrMPoISJ0g1G\nqgzm8I7PfXK+G0mFk76gOURRNpUNSMf00XeTF5RVBmTaxTiDZlw1/JGQTzdSur7l+PY/c4tv+9Ff\nmN334ebMYKK8wqEK5tp15OSlZuRkAVFYnahsEOb3RqiryKKKbDDdAC7ZPGvyx2cZc7aJbguobTzk\nkkSCWBg0kDyHb/nb/wZ+0V/a4eaLO0Cl2WDeF6L4umw85q0cDKQNooSBrkN5JUyU94KoK9UZCT3y\nZ2CmvNcidgbnWD0dQ56VF1BbXpm0R2qgPLzfI1ppI/TWJO+uBbr71PMnEtlmyAf8AGPYFpa1wHmV\nfC1wY0kAmHannD/LOrEDZJgEqyc/Fa7jyx20jYR67hIrPOtEWWam9psfJtK2DAbzdgAU2SqQ9Cu1\ndM5lhE/++dtrB3NaxhMxm7903THJYhqPaZ88CJbFAEvD15XKgG2ii/Q4tcWwbd4RWahsdJkx5xYA\np8wThzKSnDPIXU/zjJuuI8597V9sso7LqAJJOCW1/O14HZcn56wfoeyHGDLVVNmUxbyLWIXjjGVE\nuYJyeWUQFRr8WeQ4sRlyN0/ggnkLLBsZNWPxjDnZ6BmvAwCXpxyv14zUtEj7RXZfo2Tm5ss7KJdW\nBhwOiwoSOhefeDQZltXmpGLhcFs8A2b30EyZvxSH6oq15PhaWysDcH8nugf5uZfv1AAaYtiH82PN\nWjq/h0u38uYF8sogGxVLQacZeMCk39OeWrbeL9k4W+WYmqjNpdZ6bIuMfN6dGFzH+C3tsrV++Ard\nk9svdtBjrAyc8UmVeB0m4s/6NY83MxiQxzcLBpsLgKcAwoCNNHKzKWV6IoO6L/7an8J4+JPrvzs2\nblLPwObjVNUBuDbJYiaPPItZ8PzIGRQZNasMlstI4YRxzyoDx19Qpg3npTsLBsqZ1YobEHBeV8JE\nKVa8eZH72UiVwTLAzSXB4PRsyFUYroNXadDjMBHf7Jmscc5hJN6hTF2lKXxisXS/Hr7UkpITMaCl\nlUH/os/cPIELpq1af14XWHruYku8Tv7i3X3LOVMsKWdWbyS6Z3ll0AxE+h++YqBcUhk0DtBLF77z\n8QAAIABJREFUVssVJAEOC9BBmdUyHJl5CznN5L+uzSBYkgfXoTrqxE6raZ0H5AxubBmsETLdlme6\n6TuVQzQl8Zqv5eacGAaaY/K9VPUkJ2nL1LzNc67PHzFvgHEX1gIb8FPsL3OH1FgSAM5PGEzEq1jm\nsaRnWgv7DxTapO+CJLK163CwbRz4xOecvObxZgYD3q4PECanLJVWuhiSknMGzdDBG+//1x//Rf6P\n/mzSrq3XbkuBcFyymNqDCN/RU5Kp5MPFWWWgFBS6UAGmA7PnDZMD8tKcadx56W7bzNI7+MGkL9Cc\nZVOUpTDOIMnK+xfbIhgkk5mUQrtyH2lFNh3GYBYb8OrC5IxBFCyocSULl47m/AxdRyT8KfP2IYva\nf23DNvs8G+RunpQNYs1aNYNXOK/DqzeAdN8p0a6tBtLnYCzSgGom4jeaAdBzghNnsB5XkNA9Xwz7\n+NhKDhPx6sx2D0GSOcGsWa+Hb+bsGpRTUC59Z6ZcUCFUBnFzMwVnAAwhK08qgwzynCPxBqAZ4jwE\nPaWbqEVsBs3e3YLI7u/6VOTmPTzmjcf9u7fhHuSjN22fIw/cWBIATs9OjD/K30nH3Ff1HCXR3ct4\nHbmVjHQdHucgJuCQ32seb2YwoFmyHCY6QlvKCsy0zR4GGcQlMsFRvknkYVSDeIjkSReuBBORtXEN\ne1zG2y0rbYM+BAOTbP6Xx8w4y+XST+5RTwRySrodSYWQfP4QTFRwBklW3t/zTImUVXO3nMMOSyGS\ney/lUExhqMdM1HiFUjSlMTUOJy9z7XhOMG8/3GQWAhwn7vJrXDeAu0/ehnNjIys5NOBafvGINtoL\nCV3KCflwn/X3TSwYFDxUnl0v3Mjuaxv2rFhlwE0PmatpWYWytVY0S/EOZVad8cqAv1PGJ9X2wtuk\nQXXG3AOX2xCUZ4NMd8/WcnO5wflJGJ15+Zh9r16BuMZjeESbaHvMezEAYO4chkchGLBNdu5ZlejK\n/eXy+AyVuMvqWSed2KB7nlXrkcvcf5B2Ul+rcHIxgXJtcR2vcbyZwYAWNKsMugv0vGRxmyxyzxv2\nsKzcmXcd719KNP45h4mSTfRF/wDWfkB7Ks9jeMQqA/aCes204S7X+89dPt+BKgOWUWdqJJ05TdJ1\nxI3YDJs0uK6Z1LAOLVk6j4HumO64eTMPqYXS7JpvREIwsPl55kGNcQpWrQZtAOHEy3hQyvby6ib1\nuG9PZdeo7R3OTwmD1kx/b5mNNw/I6/WneP3MKgPNKoMkGLSnlDRM8W6uIKENYpFNdvf8OnJSkWf+\nc3dm70bu/0Qe+blyLYfymGmi1YDjAT1uXjlsmvM6ZjCMt1ksvD3Ozw7h9zNFlrKZ75iedrh/l4jj\n7UfPk3vE7qEQlBb/p2ZshbVgMW1vwj3K+aNpxyqDWagSNwvctSQGKg8GrHlO2y3u3qV3rr9LVVE2\nIZoNEz0EXnOzVAZtBsu+5vFmBgNlu2xBAIvWNyo/0oc17vOHVWXZM2JWeunSeatSSZtvopsXGwav\n8MqAMqVs1C7KwddcZcOzNc085OdNPt9BJUPqgaAmYqSfQn4/042YBtU79nnaFr/ow4H2zILBNYii\n5xAFq2C4asoyOV7hm4N1lgKd44zFsE9PXZH5EzSwDddY+rjYzq0bgLZdUGHRMTNogPyh+IvHs3Ih\nGLAZ0PefoOvv7/LKoOxqzyuDhVQs/WimEPSWTYgFgy23DGGjRdtcQqtng9xRNYVFKSCnPRC+GTKJ\ncg6bZioftKcucw5Yz6FzuDzah9/PobYc8lSuwfkxcQW3X/ww3iN2D/m76xq3zhMwF6GDuHOwXVwL\n6T0c2RRE3ssRvhX2gkUiyyoD7fJRt0O7BrWlGZWu1wGeVwapQs/B9rQWlP95UBkk80PXI220MvMm\nf1g37GFNNZhozIOBgG9Gl8wKZ5BsUF0BryxEVcopAF/61S9wevZR/NY6+DrAK4wTKIac+5zUm3Z3\nMGMeLFIcl+SAebBIsXh+L5qBQw8hA9kmlUFi4x2PXL/NcfW5yy0fiOhOs8oh5wxsXhmUWanC5gXb\nKMLL14w8MAdZY9C4N0OJ887dvG4AHCaatrzazKue8C0GE2lom24ANhcsTD1Oz6gi2H81qQwyKERa\nl7ETu7lklQENdmmB4SZsEKzKJK/+9DryeQFEINdJ/qJxMDEDBBZlXE39EiCugIF3d2VGDtCsaxum\n7unZQCHnv/K1bHB66ysAgO3zn4nfayLv0pw5fLvwgSExGIXEoJ3hTboW4s9eHrPEwJbS0kiEx8qg\nOTPTRaSJgcH9u8sayHmwpYJo79vAU6fCBg8XbDfUzwcCmcpCrn65rFJINfdZA9B4mBjBI0dMIjST\nYMDwS591glZgIh2HwHd3m/LFNMC0zT1QPvPXfwF2H34m+S15QxSHIFzLiFeG+Y8HHgwUlE001+wF\n4goQutaoWKJKK79fXseyur3fwMzAVMzRyLX8fF6BY+MzC/vjhhv2MQ27KQn/nFidseCrZixhoDSg\nNZdNkQ26zsLrwBnMeePfdOCVQc6H0DEyiEblRD6Tlpqxw/kZTUlrzx/m32OyyLxijVr+Zig3shQP\n50FtPOTyV16FTlsesHlA5moihWXYCrDwQss64YquoPIJwaB/uYEwkBCus7B9CAbWQFmmJkqrR2/Q\nHf8OvvCdv8W/5xPOQEd+qbvjJDsyM0A9lomB62Z4TVCVYqM/L49ynyuqrqRgEOWp1MPEEoMiGCzv\nbAp3Rc5g92Efmjx99rk32yvn8crHGxoM2AANIJdCmrnPFnSEXZaH1Yk3yZkxwR8lAjl1mTRhI+ME\ncgqvdCK8kvYq0O84+/eSjlMue1TMA8gZIVtLNtHT2y9gxrS0Tz2WSjURf8GBBYoKnMFYEmqp//rj\nnznAth4f/pIZ5yfPk2/NTMlirm40mjfHGU5e5i38OUwkZcxxfq+ehE3SeMybwDONZTZH2WDYANiE\nrcttWRnwprMFJpq7pDIYc9IwgwbGFpfHPxX+K1qr58aElcpgDXrlmrNNvE4+YauAOJg/lO3PLCAb\nFpBj8kOf5xmvbU5QIRhsXsqb1xIMumOFy2tiJ3k+HCesZbDq0c/+B37sL+W/I3FX3X1QJgapGaAR\n1optZ3gsUFUOtY23U1DNpbCulBgAazCYFTYv8mCQqaIuBsr9XwCwznpfv7fIoe+Eit3EmdTyebzy\n8YYGA7/4jseDpkoteFw+Dcn2ub6f3DMrlYFNKwOg4AweqAzIs2bZRMuH5Q0w7iNhxstVOqZMhaNd\nHvykjDqFiV5++mUWDHIP+bC5ZPCLyl5wIIeieMMVfR6DwebjPWzn0d1/C7bPv5VdRyTMaeNjWGsG\nUeSd0KVdRQ53cVjPFBxP3ETJ6ZZnzFGzbeZyI7L9BI+FuMyrGhokEr+rfPHikeVwC4wLRMOUNpTR\np4NlWpjLj+ALv+Gz/j2fw3oPwkR+sUoo8W6XBgPGCRzfYZWBzYMFSbZzfXwZkHOYKHb2Igz4WZoX\npYAczfrMIHN5qQ02DaVJMfS5UNaBNaQCCyQX+giECiT1f9JTmVG7dgKWYOBbFvjzvhseLMJVwHbA\n+UkfxtwC3X3OGeT8kcbm4x/z7/kcfs2e9aVcs8QfLTLcnw8wkeS/3wyrtp5seNPPWeevSPYxBc2F\nd6SCrJ9X2agJJS9XE0V4pRGIKGfi4OsaYVaYqHFpqeFdo/kmevzEEQDU51UIJrPO7AsKzoBtskAg\n/tzSH9AX98sn+vD+xR62df6P/7MP/Hv+RfKtxZUyVjhZdv2IQxR51ukLe2WdQwSGEf4FHh017qQG\nKjmDRZ/PrZ2Bxb55gScYvHI7AUhIfmsEzoA24tNblNWaWaE5p5UBMxy0Btqd/A/81Z/Jf0fWWMVt\nM5agEonyAtJrHGy3NM/laqHz0wtSW3XuDzXc5M1SHJL0TPGlbd4QZ7vj2gDZvygDMsEjgde5lENh\nlvNf+kU47+J4z40rrVWAUCWGe0jSUSEoBUWWdlKVOEKFyX+0llgwSDzHhGCw9gCcnu4g2nJoC5U5\nImu4JkULwveSueB8njWwcDAhGPhWgC5f+XhDg4EEE7Xn9cXSDCbiD6uGpaWb7PY570hd8LqY5fDP\nl9+xLE7ugU+/w8OFzbFGmPGGKM2IVa5k0YzUAy6Ye4/VSoF52XhdKkDSTXa9DrtcR06Y0edxGEd7\nOoRu3/xSl8z4suLVOQRxeptnpdz+mHd2ssyaq794MNBTUi2Wm71r3NpEKDXouHbEmg0yG4YCyvOS\ntDTwEqFzVWX2zWVloJPh5umRK3YqcFgW9MrrXElFnvlv51yswGCk4fZYPqPMe2gdKSvyNrY9rT0v\n7bnc7FMC2VT890lOuZCieZULRsLTmi2fQ1pdmbHMqH0T4SopcXBJMOCQITCHx7O8r1JlQMOWhkc7\n0HhYVsUyWw09a0Bdit+Rwl18njVdZxQT0MjMb/Zg4Nd5AOthm6iH5p7nrGO22qbtdfTXp2DAs5gY\nDDYfywuXoKYFjipfTJ90MZPVtnSFeUNUQax2Y9na7lkw2ABLMFBWoX+Z2Pnqucy4GYFMg2lSApkT\n5bGsbs872EYuR73xGB4t55FvmJcni6NmaMRhHcakRGHXadON6Lr6K+1DkLpCaTBKyKLmtrhG2yUb\ngOUbwMSgAUlNRJvd3C9ZrUqcMoXKwOcjI9fv6aiaau9bAHk1mTZUcRM1Oge/4uElt8EgSaZcOz/L\nB7tQFZkG5JTkL5+BTXpezFBKlFMM3AjELZ1/JMh5sxZ1/HOCW7qHMXEwEsmevNviWmhHwMW1kCej\nHCbK4dB4HQ7TNlYGuSqLBzVVWQuxShSvI+m6Vz8vCGSnM68dAMGPZ3kYXREMsofl2yLTBQiWWFrv\nu7vyRqf+87WRcnlGvSkjt/ZwJlQGx9okoolJMvNgMG3OOWfANtEYDLY0PWoC2mMtow5Y9pTfT9tc\n1uuQOirTyUzNZV8MdlnvR+PXYRzcbqIkyvk8grxDWXPuI3M9lVQ2kWBWToLsItkmacOpy3iBBnL4\npFB8XakMXLuN9s0fph77OWmoBO6GfkeE9XYfZlYJdJ06SmjFyiDJrPVDFQ6fGdEOWeVASUgqdUy9\ni5rCryvthm8Ea+e0u5q4PLkySInXTIVTzPbQhTvB+j0fz6OsDOwKr4hroRmgfKxwHQuoaTOpzBkA\nrvVw3R6lZ9lSueS2HqnoI34v3gvJKju9V8qXQ6te43hDg4EAE9n2vGYg9EKkC2LhDBZP90aGiZLW\nfKkj1es4Iaw9yvhm6uXCveOBAK80UfkhD58gu4fFcIpru+cNqwycZhvVgHmrgq8ObZLdiXfu5pts\nqg0HQmUwL63zHCNdsuplUe4yL6j8friVvITjz407auacAJGPORGe2k3kfQgSfDIiExXw6iaR6VU3\nAJe09j+UDUqVgXFBv07dt82YQgNc4qugZzmrXTYKWUESzRHl64h4uGKcQeldZNi7M2bPiM4xfQZT\nqNQByaJl3tzDLO+lkPkTBh5+dwUmoh6BhehXaI9MJs2rXCGjdgnUJinH0spAQg5c0i+hH6oMSjEB\n/Y4mrgWdTQ4slWXKKfDeHzrPeB1GcEdN71UtKL3i8YYGA1fCRK49R3iGzUkVKwMxcsdg0AhDrZ1x\naxbTXsrPgaXfITkPEV4J0tOzqJ5YyabLo2WjMtkM43nLOx6zysB7zJg3wOmtA+LCY5tQSrrNuTYc\nAOb+vOL12vbiRrpgvcpuxUoLWFRHyUak6huRZjr8QnpqFZp0qEuTQxSKEasEI9U3yVTWKL04tjtj\nmfbGR1YCHGuvZKSriVwFGsjIz3S2cfI7UpjoKMkiY9ar5i5sbPw6o9GbLwJyeh2cfxpYF3kuU84V\nX2VAThsgJclm2g2vK+oX18xYO8lnhc3zNBikwQhAFSaK95Ck52VlENdCKT237QV6WQuuhKF5YiAR\nt87Y0APQlmvBzFmVyKfZxe9F23YjJpvxXtUSlFc83uBgwI3qNqdgYrU8rPqLSwPkhYelI04udSGm\njSF6lGVwZG1M941kfkJloJeuVzmg0O8BxpvtcmLZ9V4enVnHoy7ghbl3OD17BCpJPfKFlyswtFNo\nz3llYJP5xSVenvMnlDXL2GRKXmrLoRRmV+FyTmDaH/NgMCm0pzQYXN+InLkkMFG5SaZDY6RgQTxU\nQn7zDSCFBlwFJ169d/jo0IUYzmEibaWsNsJhEgnrE2dQKQFxxsIv2WJhVz6zzZ5/zkY+MrGBy+wq\nymcwHu7WmSEih9ak8t8KgaxnKPRKwUBPwOaOVSbcmsVdv4ekuLoCVwlNqZTkpb0a6d+gSn7YL82V\nvPoK32odnF6CAVOECZUBV/gt17tWOLMEZUe1Ua1CecXjzQwGNLOXBYP+BLMuuhZgWZxjmB4kmKhN\nNnIpGCTOglKwAIIcct1EhcWvI9auxbb1cC4p8ciytcvjUp/PF47tLKbdDaTKwGk2YcwqtPd5MJj7\ny7oRK+bFQteRZNVOht2AgMs39Y2IVwapWuj89Aid2f4qdC9ZMFg2SaG71TWXVeOunNQ4d72kdu1l\nrQyoOruSDTot4rOusfDLBsCDsrIM7y7hOmDJapdqUiDC9ZQFvRImigoT5Zq0yvQ+6N+HQ9gIhcrg\nusGaRCDH+3h+EnteqAtcIJCTyoAHbPobS/fwgrUna13PyIYMCYki/Z14D8V7lJDwEnLg2vPaSc3s\nYVafq/EmuccSf2SoX8IMi2HflcrAKphLGQyQVDgyPxQ7rSlR+iYPBoSn5Td73p1ZJpveyMUg7gFM\nr72sC0vUayf+86qSxRDpuXQidqUKJ8lGzdjBK7ky8MavwYBLLodHl8zITgt9ArazsH0IBgU8sdpZ\nJMNSWGWxS2EimTNY+JOavcdyvXEj4hsqV+RoqASuOr29atxX2WKWFSZTuqTuVsJ5l01SeMFNCg1I\nMNEJi8UJyfRKHmruksZAUdLoQlbeBptyrnZi0tlJ2MiaOPOa1EJlZbBuEMJ1ZH0IMw/IJHk8vbV0\n1/KpdwNTtmmYMSX5U6O70rzx7lMxGIhKpzQYzC1QgYnUEgwGj7xLvUxsUrXTeg/SeygkYVlGLa2F\n9rx2UlNnL7dv8RgPEVKUlWUWwBabj7ehvyixuuGJgQW6U3kdzsxrP4KkekplxrTPfbMTyCJMFDXr\nes7wXfIEaoBpm7D9IqYXs2HJvoAw3kUGV5I3ABl/qXUTFV5M7QGVnEe1MohEM8dxbc/waluSTbaz\ncM0NZOVCWlrrQDDn93PcRwMz7lez3osFp3by4qfriA1DJQSR2ytrq9Am/RCnZ6fQEKUhN5XFjUjq\nbrXdaW1ElBVRkQPiTpT0+0/QU5rtredW2ngL0+Lon5esXMCJ2WAW5RTak8QZRMtyqcM4ldCKFY6Z\n13nW0kZme495G1RTxWyLEXM2aSzvWbFdTH6khrjTW/cgH/+mwtvEAUXKyZVBtBXp0Ax0TvlnqaWJ\nhvJCUE7uoUgQJ/p9LWD+rj2tVSapFrlRZvS54hLq9TvNDK96bD/kw6JCZYB8LTRnuedk7aeq3E8k\n76V0Hq94vFHBIGqdBcXA5TaSjZpNQwLoYS2EbM2zg3Dyxb+oL6AP2jwWEqpSGWyS85gFeCVpXDOT\nHFDofFPZIyfFclmjnsumMdsuxlpCZZANQZfdV9PuYPJyYsEgmcxE97N+HSvRXATxOe9QnhVMQhDb\nPvVzKS3FCSai6yh9/IPCzKbQQLlJ4sommnbPqrk0R/TGr8GAnlEFGkAPcxGCMifyXU6Qx98Rx7GK\nJmrJBiFWQM2MteekgIGoMpg3S0OVRjqABxhhewXbLFlz7qnjsuE4Qp9Bf4HtPYBevMcpfFOrDPxq\nSNjDjHRO8TM2i1qokuk84z1UQs9JyhnIlcExSSwa8HkqrvGw/dL5K2fkxDlusX2+g2s5BM1UUTOw\n+9p1yLAaXFfRhNwV/4rHGxUMkOKznDMYbuNYOXpYTCrZANM+eSGEiDn3bCMvFsycLChZ+WCby6r0\nkV5MrxN4RVA1rOdr4ijD0mI6dwNVVhfKA9fN8KDKgJNVeQu/gZ7zTRbIu4Npvi/bCHXMqq8Fg6wj\n0pkU3is6lLVTGQQBDAl5WQ5Gse1lFQ00Umt+d4qNiJVnsQy/oUllQjBYN4DcRA9A5npa6zPwoWGq\nO5ZOmYUscgba43XyU1KQEOyXwEQ8q23GRGEiOf562HYXP4/vlvdw5KnzdLdCiu0xBoN5EyXIwnAa\n0NhKANjIm5eOAbluEzMF6KMLHBLvmcnlxyJMlDaD2q7gDNMRqsq14J276Vooq6flHsZkU9pfSBW1\nQXvaChD0nHRyK2gL9HfXCWRpD8oGdPlv3soAMRPNB2QA6+alPq9U2ADyBZFOMqrpb9PxddKwkpRA\nrikfpn2yiQoqHJdUF9QNWtlEm9jhW1ZCfJyk0DTWToDawwyS4d54NZsD0u5gEzgBHgxi5yj3yM++\nl8kaNaDKDfXyJNo1dNkLwCqDIuuMhH8jmHbZB4JBXhk09N/pz/dHmCnZAPi5Gw+7Sa5N1LfPAHrs\n399WsP6EE7FAK0EDWVe71HUaO7GVYEHgmikS4a68Dts5eLU8A8HBtnW4PLkBoGGmHFKckwbIzcfL\nZL/0/GI3vGIDgpb7E7F8+X2g8+9ghg7NCORmhnkwIP8q4Tk00ZFYEnZka0FYz7ZnlQEfrpX0D9Vh\noglAj/a0g2v53jKDN1B2J6ExVnOYiCMPkUAu1XuvdbxpwWC5CQJM9OQCMvgzgSRjC1pHl03tjLh5\npd7uNNmqLCUX8kZPchYz3Oa/g2ebXjtApwHlGryyVDI822FzAqxCM/KmsQnwO+w+2BYeSqS/58GA\n34/YA6DnrtDQu2RkI2VCtetIOiJd3i8BALZ1GA+HNRtKTc5iMOghwV1zAuuRj39+DnN/XGWNYmWQ\nnVtZGczd3do9q21ZGaTWIsrLnAFVkx26Yzlcp2jYmgEzScKGWMnJFifRLE4OelNSGZRwlmsdXKgM\ntGBnbjuHebOHFJDTBki5EfOCaasAbAhzFzev+rkv569ci90HuyASSBVjDCayyJri1u8lAVX5kpsg\n/X7CGbDzmDfHdXogQYY8MXBwJlVkSTDRDOV7PkY2uQ9pzwwgW1rEKlEJ55nOl7jG5b3C8aYFg1gZ\nlNF/CjhcRw8TucmTN27FplEhVs5P06xeUhwkC7cyUu70Vvo7ymyT4JWIkX49WLsuGmnyCWJaspPo\nRii/D4PgpUwy34TKxRu7gykD4RthWhmYqrQ0U7JIWWfnMPcHRCI7PY8Rcw/Qhl0OU5+3kfA3Y1mp\nzZtj3ntSBOY5I8F5FTft7la5MmX++ZoiB9pU8VXhDHyH7m4raNvzbFAaowosuPjS1V4qSDyrDApH\n33ZcR09S8OFVpIXXybwAx6s3FziFtuBt0gSqfylZtIQGyKd7udpOIMuagaQ3I+Aa7L62E4wfZ3D/\nKrFXI2lQVFIzaGoGKMA80+YOZkztYYTKwKTSU+kcKCg3Q3kdJXQLyAHlIQI5qqZqMttXPN60YBBh\nEb6gs0x2aqCQ27+6xHK51hRy96nT+s81fDMlu6SFe//uGYslsJRtkj5/cbqsBwMOr+QvKDOym4Hu\nmG9UNniptCchGDSpG6gME6UNYdSXwWC3RFl1bYhGqt+GMGvZthau2UPeDCciL1sZJpp2cZi7EaTA\n0+4YN3MBB86Cu7CJUvfs8uLJBLI3NXI8+Rvo0QxyZYCkMjDM12f9XkKUi3YTSTCQHDOpckggjALv\ntvBm8WDKjeiA8IxMWhmkPS+RI+uOPdicAOKFeofzsxvqIyiSo2R0aYV7IoinxfajQ0G8pveG7g/n\nncLfyTLqCpG9QMDCep72SfOcUCU6nQglfKUbfakMprKKRZYYlPb58XdMV68jqwzYUKzXPN60YJB4\n2BRdhhFf1rOBcufsU9/4VZ1Ta8a4PJmAgJOLC0alckq5Mpi3UzASa8AHogAhU16tA64Fg6iB1wUp\nNhWVQf+Cw0QD4LYETxTdlA+riXKYqE1llXR+iT5cKlez72UD4TkebeGXjSbfDNeGqMvjxekxJ8KH\nm+tS4OHmHnpaej7KZ5EOSS9N9IDx9m79eT3nE7aAAA3olDOQsnrCu5vLRtgApiwoV6GBhOOpZoM2\ngQ4K364xas8FiNW1Fh5RTVQMOmotXEPPgJP4l8dRgkzzlyW5tcNwcytKuhPiVJRiAwvE06I7lhm1\nbUbkpo0KWmjcS1VPUsBMFVuSJPPy6C72Swj8UToSt0ogB68sPW4LL68cMmyqVWIGE4mBP1GW+XLu\ny2scb1owiJWBBBMtnZJ6NoBnlUGWkdeaMeIGKHcPx+hNEI+0ASa/Q2pOUREmulYZuKw7lvvcU8OT\nbdq1GYv3CZBNAFUGBeHYpvMQltGb9cqAXlIeDFx8edgEs/w65mwj4n/HthZO7wAsU984xOFxeSQT\n4edncSOSsPTL03uYNRgIvRKpMkOoFo/vvFh9dZQzRSDLNgABfgFAVgq+hTxpLa8M+PWt3zNjYn5Y\nrhnbXlYzOOk6yNBvSSxK7x5nZkDHSWLwrDJoLKBS6+UkGCRCAzPKrqO2s5g3N2J15syExcddVbg8\nb2h6YHPZwbK5GbZLAyrEBkr6OzEBUq70HkorFGmE6ent2DzHp73ROaY+VzWfKrINIRUhTzRzyFCe\ngMg2e+E8M5lx0dfzWscbGgxsaawGjGHz6mAmA+XyYEA2ENcrg3IDLEvJpQ6u+/Gk8Iq0ASX+Rldc\nBV2i42ecADU8tcDlcWqAxjbZhoy1tDBgJx8as+CTAoEcGsJIkcM5mBmpuVmVM2hmZBVdkXXOgKLK\nQMqGbOsw7fekVGFE+OlZhOS0YND28lN3MKNaIbuycY5tACyLevnJl2iGRaGmoRyrDLQDdLrJypUB\nfAczlJWBV5Og6pII5CSrlTTw/TkhkMvr8M0IvXRie+kZTACiootbJ7t2hq8Eg9SWQ7Kh920qAAAg\nAElEQVRUBkIDZHtTkfcmKh9X6UBuyZ22uWxLzqAZkfl0VZq1Ujt0qUM4D0plZv/iMy/RhLWkbZkY\nEMdXl++u1+o6mLmsDHwCGW4/bAFIExAR/LZCQirA0JmyzH0Tq4kWAlgLCxaYMPdh85o0zHTMPvXG\nwZuU8JQrg2VhyyXYnJeSD1UGhdVtTiDToqxsojqtDIQX1PjQN1F6yAMgK4Z5QyobzSWXsTLo7kpP\nn+I65gpn4OuZVHYdPtkwC3JyBm1EclByrcPc70Iw4H0EKSRXEv6XJ+fwfjXiC57OCZA+vzw9hfXQ\nkX7dl3LldQMQ4BX6DmG42pYQimtzNVENGqD+lzo0YNshqqasdJ1nrLYcEolvZngkM4Zn3qMzB+np\n4gzLLUVIaCCR+HR+Fs7ciAE37YanQFaDyRoYAV6Z4zzwtQ+iv7sOE0HwHvJmWmeXS66j0+EcOrF7\nuTJIvLpqaiLaqFuoYhIjcQFLsLr5Uo9kkmh2pFb94h7VDEliIDu4vuLxZgUDyoQXXJBfJGnvp20P\nM2mYkVUGWScs919ZjnwjFyuDK0z+8jtiQBGUHZkks14ZUEkZx1aasdyIxn2POrxCxlpmLKdLzUkw\n6AVPHwS8nu5nF0zaWGWgU2VVvdOR7AISFRiHINolGJQwEADYxsE1O3T3kjFg3IiUCNsNsF3ofpUy\nZp127koKszH8fAc96wKLTrvJq30GmpqAtOBg6zPOoFahLRr3Omcw92eYOd1QeTAYEtmkKvg2Ckpx\nKp7hwaCdoUBme5wzSJVtEokPEJQDtRerGqenvKoRpaWEk5tpW177JuUM6vLcaX8f76EAGbrEQZbW\nM18LSyf1BnwWNyCsBZFADpyB7UsiPakMuvuOE/HrkfXOiDBRXhlI3MUrHq8VDJRST5VSf0Ep9RNK\nqR9VSj0WvvMZpdRfVkr9Q6XUP1BK/VfVXzjcLnbCgMlhotDN6nF+uoWeNNrTffazKexCN6kOE02b\nVlZlqEh21QdHUFCa+zboldmiSGadXiNeXTMiLd25xbRrHGxH+nsZXqFgQAuPZVPJPATJ0yf+DY/z\nkx20baB4MEhGNtawXiAs8gwm4kqWBaKoVQakdOnuSsw9I7nF4LxIU3uRM3CrzQFQTjKjn4+iBA1l\nOUyU9inIU8ryyoBl9IzIl7kbYNzfJxLXct3N23NUulSCwQoTCXYNzkxI52Vr3rNiZsBvxZGbac8L\nybElW/cZXh1kqC4ZtFTTxXtzgbINmvOBAkty2G5EnO1R513G/X3kf8RE76HEYJ0eSOuYVQYubcKs\n8UeGnoPIGSSd1O1JnqIIALZPrfobeP6+pFbdAj/0GsfrVgb/DYC/4L3/pQD+YvhvfkwAfr/3/pcB\n+PUAvkcp9e3ibxt3ccCFZOjlGo9xv4MZFfoXJUwUI7eoA17tEY7vbCHr0tOFK26A6++4++QuqIm4\nBC3hDK7MAbDtiGifXFpMx6ExMmdA2WAjZqTTLgaD9tzWF17jMR62tFF6zhnYhEA2BTG4fi+13BW4\nHvKMCRCEsBm61sLpLZqhJMJzfkayAx9gk2DAf3e+AUjQ4bQGA2VL36B0TelKZUAa+TZM8Sq5G57V\nSpXB8OiYq6K47HF7zjaIYgpgBhOVsmxSqMQ5zfw6qYGxDyM3yz6ClWerNFG6liSVctWSErsyTGQ7\ncqdtLjc0izi99n3KGciQKQCcn91lYoICvk3macvE6xiCQSfKb/P9pbTYp78x0FqYNyEAJ/fBpMGg\nblNDzW8JJMgTnGZI/LhKGfFrHK8bDH4ngB8M//8HAfw7/Ave+6947/+f8P/vAfxjAJ8Uf5vtl8pA\noTkJJXnrMR52MJPC9iMWDFL/fa+rN2neeAy3B5Fkyj3RZbILoKB0ebwNWDtToGiXkNBXKgNSAyUW\n07wy8HBt2pnLFwXZN2tB0zzeRJioPV2pDFqPebMlqa7PpbouJdNtU3QWr99LpnBpq0o8uh0Bf6Uy\nCBp4M3QF3BUrg65Cxg+YNwprMChgIl7dlKKEWBkomKmUlmaVgWSQFmYNmGkTfIrSn48EshlMWE7l\neji+fQ8zxkbGojlun24QJQRKkGHMFlXBP8WmNEq0+FobofwW/cuyVyKVOeu5NtB+BtlRlEmYyzgD\nI8NEwarcjDckmU6O4Sad+leTSQP3n7iPaiCRyE6a38TEYAycZEfr2HLYNJVa12Ei7Vo0ww6245xD\nDAbS8J3lmHbHq1Wg16n0VEO9OX0Gn/Devx/+//sAPnHty0qpzwL4VQD+hvgFgkUg2BbQ4RqH8eYG\nZii9wCljWR5WHUubNx6uva2oTx4mu+g8PMb9JjwsLkdMNqArY+litiZLR0nWeK0yuEBbIlY5PHF+\nEl+g5nLFLK9xsN2Gsk7et5Ga9lVhtwATrderObwXrrNPpKNlMAC2onUzOWoCMRiUlcHcA7btAynI\nIarh4WwwbABmVuju2T1Iu7DF3hfAm0UJc4DtePd1rAza1ciufBZ3n7qHmZX6vNKigmTaRU6hHCBE\n4ztXXx2hW53uQxQr9C950JugXI/+pVQZTGsDJA2Vki2o4Tfy5mXyykA2eKNRsmY8wLX5uV2epDLp\nOkx0fnYPeLLSFuGVJrH0ENfCDNsRJ6mshp7KSjnrpxFhIoK7zHCAb5j0PemXaC612ejAeIjcBwRO\nIB3/q4S+ntc4HgwGgRP4+8L/fmf6Pe+9ByBfIP2eA4AfAvB9oUIoD9dsE119+cBt6zDub9CwARgA\n5wzqWNq8cXDmQL0IEkzkHs7qvfGwXYBXuApHJQTyFc6ApHBEEMsZs4NbgoGV4BV6gcjYTAoGJMk0\nV0dv0shKPWloLtXNPGV0PajpKasMOBFOG80m2QwFAzFsK+Tkkvl3UkMTOW72wPnpIcB7HBqIYzHL\nCV9AugHw2bvAovhKyHFxfjHBD2Y6wDZcvz+tG8D2I1mJA5CV9zJTQMpqz0/yyqGAidpLskEoaHae\nvhkB163vFjfLo+qtF51h02l1ysmW7KTaCtWZwNusks8K97SMYDXjHq7JN+FpN4VBTzTJsE7CD9FK\n28u9GDpL9LLPg5w7cGizQjNcrwzExEAPQRW1gzNSMEhFAsWPAwAuj9MKR2gw7BJOoVKhvOJR0TfF\nw3v/22qfKaXeV0q9673/ilLqWwB8tfK9FsCfBvAnvPd/tvrHfuQf/C6g+3b8jR3wIX4N3sM/zU/G\nOMwbqgxK6VcauevNGPPGobkcRDzOJyP29JWs3nYO3uzCi8mxxRRrr0syaeraY9ACLy2mXWOhzQY1\nHxOa39tA2ZKsspsZzlAnZTNcGbBjHJTZEPGm8o0QSZOM8qYuLc2Ms8ru0CUrrW2GriWIwQzSRhRG\nNt5sq8os2zqcHwdZo+YQxSXiq2U26D28+i9aj5efuqHZuy9LnHjtYJ6VrCYKDVOUDTLLkH6VRRJp\nWH3dBrJhGHsRJrr/lsgpaAEnpsEsC0xUzr5wzQDtDohqHKFpzfdoxoK38R5WfU8LTNsaib8oZXqy\nQi8qgzi6lN4pqUeAYC4z7eGafB3aPq7la/Jc4IK5B9rzJlSJ3BYl7dWQ+wRs6zHc7IMiqxQTqDTZ\nFDLyZb2ZaQtv3s8+82ZelWV6rr+T9+8eV+5DqrTmzQk/d+yUUp/DL//FO7x4+V3i73mF43Vhoh8G\n8HvD//+9AIqNXimlAPwAgH/kvf9jV3/bb/3VPwKM/wO+8xb4Pfix4nPbWNjuphiAASwET6q1rsFE\nDvCHqhRx1Xxd2cjnjQPcQZZkKsuw9hpMdIaar3ECNFu3u2uhfJlR0wQqU8HSY6OQnnq4SkbqG5qp\n0J4NlP0o//uZR8q164iku8h9tBQM2nMvboaL7a9AThJZ33kc3zpU1V22d5j2h4p1czoWUybbaCTk\nTTGzlu5BXhnwjBtYYJAGetrD8mDQRmlpd6zDdQvcRRVQWbGe3j4BWCCQMtGZtkm2KBHEa4dyOUAI\nWIjujqrIgrchWPT01r5qr+KCJYd2JWdg28RO3cucge0uQdpbZtSADZVtg2ucQZyr0EOSlqe8Sg05\ncK3DvN1BTwrdvQATrRWwDBP55kwS2XELZ3L0wzYxMbg25+T89AiaHGdEWG3eHvGtGt77z+E7vm3E\nd33HnxN/zyscrxsM/icAv00p9RMAfnP4byilPqmUWk7yNwD4PQB+k1Lq74b//Xbxt1EHsakqBnzj\n4NpDKJkFeGYp464SyDN8c1sNBmkWU+MM5t5Bu0PQA1/hDK6ocCgbkg3agIWU22L7odygYrsz1Gyg\nBM4AmNcX6Nr8Yhd4ifZkcPuzeVWXKat8PbhSaRynZPUvSrxa24601VJlYGYotw3kpLRRLJt9vTKw\n3V7eAJpzBlHUNoDx5gZ69MXnGU5sFcwgwUQD4d3TDt7kWe28HVaI3QyylQMduSqqJDeTfgrBXnne\nrLOkw3ny+3AJz6iRe1aaC5TtKRiITWUe424LabAO/fwYYCLpGaTwiGysNvc0WtOMW3h95J8izjc3\nATKV3qkloG5kxVWbKLIqnKJtHGxPMNH2oxIyjE2YNTEBDWMywwZe58HAN1FMQIZ+NcgwedaCWGBM\nCWahp+Q1jgdhomuH9/4jAL9V+PcvAfi3wv//q/h6gw51e0rTlOhwjYVXh7CgyxdXpWy/ELkBytac\nvg34JicMYxZzjUCeNxbNeQ9lTWFu5jNten0SkW1PQRsuw0C0MW6raqB5e6LKYO6EhZ1UBtemrTUW\n8+aA7g64/VJeGXgzA4kKpEbIU1AjD6XfJ+DRhGd31L0qatQn2khE6SjxGvN2L3a3AiRNVX4vZszU\npb00EcprYuGhpMogw4mdgpEM0gKBbIYdnHmZfTYe4nq6RuSTrJFUUbLjLhHp7aUj8pLPB9/FYKCc\nQsdlymtTWgNdNJUFqMt2MFPlGbQUkGu27ktlIBGzmWliZVoc2WRrmHEDbzifuKxlg2sGb8CAeasS\nMQHH2mNnr3YaXiKAW4dZ7YlXYdL2dC1oqwVlWqj2rYEZNwDytWATmbGqDM5ar6On+R3Smh5uj4mE\nVpY7v+LxZnUgk6XzNUe/Gcrewra+UGU4beFXl09TND8th+0HeHMrt86rKSGZjGiDDQBzb6HcnjIN\nVXbu5hhpjTM400ZeqwwCli7N/gViNkik3jWY6PpMhWn/CP2dB8AymaxZqO6OSOqGDlU8OmSlzbnG\nGQSYyJb9EgBt1q7ZQTJBAwg69HpHWRS3z25OiV9NxU/GONj+RnwGme3xDLSCQZo3QRY5b+AZxDFt\nx3UJmam9Hgx6wDbyRkaVAbBki/w6Lrf3MJOOMuUiGCxqo9KiGlj4q9BHIHUYNw623VUFEbThd3Kf\nQRuVbbqSkU97MiQ04wZe8WBg17Xc3TUVaxUSE8y9x/HtGzF5mbdJwKysBUqOKDFoRp5spiNxJe80\nap4jnq4DcMd+dxIMXH3oVTrwSVoL52f3SeDXRcf/axxvVjBYK4OKYsAZCzPdhrm57GcTgzhcGfrg\n2hFe3wR8s9TwhnFqIna7HLafoXAI7qlCMFh5BxkjBZDMXG3CafBNlDbJ9iRn1FNY3ETqCZVBG9QX\nlYybTs9i7h6hPSoUwSAlkK9IdWP7vCz7c80F2i0adQkmIulpraFpmYcgdRgDVBl4vQ2aay7tTJu1\nys+BYIdhDg9XBlZuhLTBV0iCOC5P4gag5yo0EOYQe5yf7ivyy7TTuoRAL09OgWCWlXiuOQfospJ4\nmEvonK30EbQWrtlKii66gGYEfCtCcQVnIKyj4fYUgkEPr+/Yp/PqT9W/KM0Ms/PsHM5Pb8SpdJQ8\npZyBlNk72PYQYGq+FqK6TtdgoobWAokFPsz/fh87qQnyfAgylKvA4zv3qzmjnhX0/M1aGeAhmGiG\nnm6K+aJAwPRcfFg1/a3tzlD+JmQxPPpPq0QPon9JOI92gFe31ODD9fkmXTR1mGjul020EXHQBUsn\nD/ly4Yw3J5iJKoPrMNGV0ZuNhXLP4Frv3/M8MCYEstfF+S2HbU8x6xTISVJxtFVV0xIMlDDvFVg2\n+111IyLocANpqMvcJc1Yc0UB0lq45kBqoQJ6nKF8c3WA+bg/woxNGDKUb2TTblxlkTWTt+Wwncfl\nyY2Iu6fNdZRd59ng3bccYSaFxz9dqzKTykCQKdvuHBsYxQ5jB6931AMhDqcZoGwrwkRkjZLOCSif\n4emtM8yk0AwtOLySruXuWMfa6TpIbSithWmX8iqlbQpdxwzX7gV/pqAmSiBDafQmvQsa3X0PM34x\n//k2dlJL9vnxSBophft5eXJBNGeULVJe8XgtzuAbfyiCG+rjAS3MtIeVggHXxQuDSACSZGLFmPkD\nH0NDCS1c2eyONjivb6AnDa8lT5+kQahCvNruCD0twaC83gVLb0Y5s788DjCRbalbODviC6RcI2Z7\nAC3Q/sW7GA/SvZ7YddRgt2MMauJGRMFCi01lCDYBXSDV5ATAq129MghEu7YGVteJVTKak7I5C6ha\nZbCsKRkCA4Dh5ojm0qC7O8A1X8o+s71dZZHXiHwgENn7emVge2DabkKWzTa6wwXOAJ/6G70sUzbn\nQN5eCRa2gXY1qM7C6OUZyEZz2pHhIb/Hcx+bxqSqBqDGMqeB5tTBdS/4X185g+7uGu9C56ncQaz6\nx0Ni+VGZA0ANkHsRSktNLAkmkppiifvoXhroKQ8G4z6FieSJb3RMmJdnLb53RDDrcw9tFTy+SWEi\nskKWM2WANkgz7gqbW4CpiSp2wwDguhOU24tMfcr4Xxs27doLoG6CNrqcA5B27lYrg+099Ew4KCA0\nYwW/GD3KlcH9J47QkyJFUq6v9x4OrgHGPWnDUfFHmjcXbD/6JC6PLsVnLm17t7paJc39KQwSlxuC\nbBc2mrmVlSor3lyZghVcT6UXHFiCwSYErvzFmLenpLVfXhNUHR3khrg1G6wnKKe37mAGg/7FDsr+\nNPs0Qhx6vAYNBG6k3Unr0nt4zJ3H5fGusrZHuNbj9ud2ck9KMD+rdYHb5gI115xhwz3WmyvV2QXK\nNZAsP+ZNHJtZFyKQO213bKFmHgxiYtMMD1UGFrY9iMTr8R2qnug8ZOTANTNVQHNZPaXvtRZ8rADA\nbqhTfPNS451/+HPsswQmstUqcZVTn54dxAonqqZCp7QwD/oVjzcsGCCtDKQMZA7BQKoMLBRSN7+a\nmugM5SjL4aWiV3EozLWN3LZHKHsDPSuomXXuqhlLe+E1Fc64J2OtrWgOFuATHxQeqvx8eEyL0Uzl\nuEeAkpjzk01Vnw+QNfLuw7cw3nCcNgRGF4mqmjfRtLsPWGylOS7AYXru6hCDa6sbjW1nQC2bZEVU\n4LdU+rNnPtzexw1A8Khffl5PN4InT5oN1uXOx0/coz0bbD/usH3+z/jZB77HPEAaLtzIQWw0AgDX\neVweHYJhXhkM5h7oX27lgNyeoKzB/qsbEXO33Wm1NpEVXRbArh6wGxpbKb130y6vDOQKkwwDu/sG\nzfgx+8zCNcR/NeceXnhO8TpIbahsqRY6vn2CmZZBSLXEYAbUPuRWvDKIYzP1LMuMCZbU2DwHmuF5\n9tnlUQITXa0MCO4abg8V24sxkSGj6Ph/jeMNCwa+u2ro5ZqZ/Ptb4bPUf9+qasS03T30vIOeTeEs\nOPeX5IHVIZ7h0cdoz0/ISvvCDN5MaoNdDwZTsNwlc7Dyc9dMpP2uwERLNticN0XnLZ2Hx3ggLJ5P\nCFsO2x9x+PIe446/gIxAvtJnMG/vQ/Ytw11zT9m5tjJcRcM6uhAs5AQAfnMdJvLLQJL8xTk/yfX3\nvDN3+f16PsjBYM0G5WsDgI9/4QuYwWD3NY13/x4PBrEyMPP1ysCFofQ1iwHbOMzbA6QhPIsCpX+5\nqzwDMrrrX0o24Yvs0pAyrVIZeGyqmL9rqfNWUumMNwOW0aWq8vPLFMP+pUF3/5x9FkbAtkYc5FSc\np9lBOVMYuE2HIfTrNJWAulSZO7EXw7P9pX8p8Ec3R7RHFZpiczHB6a1LXN7C8J3sPNrlWcsw0VIZ\naKtgirkvr3y8WcFAocPm47qhl9dTIFQljJsNWXFyxJx2L2CGfcBQmQSvG3L5WYUzOD/9CO3pEZpB\nw4xMhaOZjUPld5zeogHc3VHuI3DNEBp5algzbQDNpRODgTce86an66xWBkdsXirMu68JPz9mlUHt\nXlweLV78y5QsthFtjtCzCUR25TodzZcQOYMwslHy5KGfJ9WVZBFw/y5BaQBVBlIHMVUG+0plsMhr\n6wq3y9MPoR2w/wBoz9yOJeLdSjTaS6/TAmpbTUJcZ2HbfRAt8Osg6amet1dhou6+F6+TKgNTD8jN\nBIVNtSuf5MONqNg6vsNgIrFiJ2fU/qXC7qtZYrJCnvOmg55kqDFexxT5wGITpeQpTjKTEqgZyu5E\nyDC814mYoNxfjm8f0YzAeJiL/Wu8maA81OeVDgTydciQGiklBdy4+nVJDrSvcbxZwQC+RXdXv1Gu\nmYLjn8AZJB458gtDx3h4gWYImwsLBtP2EoNBRQYHAKenX0FzeoT2pHD7hVxClk40umaLcf/uHfRE\nygN5kwzEalUNFIOBuEkaD9s9ABNtCB6a+68IP58Eg4qXPwBcnt4FYy2ZnJy2xzCzWt4MbcgqIcyX\nAMJG5DYVlc3yeQ9pOtXl6QUAtfZL9tr083PoHpYUaks3eb0yAGjzutzMwkzbRNX1QDCwYQ5xLQlZ\nJbbi2qYNQs8bOSBvT2EgVK2B8biKEaqcga9LS+3qoFsG5Gk/AQibYBVupHncm5fA/gNeGQBeeww3\nfcXZNjnPJXGowCvz6oBbqRLbCXq+khgskGFFTPDxZ6kaGA/S3mNDZWKqRPz6zdbC6X0F7iZIbdps\nqMFQCEqveLxZwUC57qqhlzcTmksbyrn8cCa6Z5KNr7x5jTfP0Vw2YsPYtEuCwZWN/PT2P0F/9wjt\nEeiPZbNW7tJY+x0nQAHbj7YyZ9CMBJ/Ymvd5UhmI3ZAerumpa7RGIPchGGx+uvx5VhnUfNNffpIc\nNbu7RvRQGm+PST9EZQC4bYNsUeojoJGNNL9WgpGWDmYJPhlCTwoNr+HjHgHiRvRU4aGYmkiW19Lm\nNe+kl9KuVgoPVwbEjcjuqqnEVtrIaIPQdi+e5+WWGpVqMuU5eBspJ1uCLMo27WTDQvKAMlCifDdR\ntlUncw2YaZQJmoETyLSWbd/BjNcVWbaZoLCvOBDE2RU14tU1I/S8kyv1dryqyAKA47v0XKb9ufgs\ntdVQV4ZeAVQFkhFmcb+iX9c71CgpyZ1f8XjzgkHNwwagh9WemwpMlE4ykptCAODy6AM0l6DXZpvH\ntLtEwvEKxPOTv/1vQbkWh68CHBt0Jh1YXYeayGXRY/fhQc5KDXWFUuu6jLNeg4lcQ75D1yqD8eZ5\n+Ft/W/j7KZletwQfHp/hDPD2Pzos7hXZcQ6WvLpmcrbizRXi1IyA76r30od5CdJQF2CEW4bXCPba\n9PsnmHkjwg/Ud0IbQEVN5CNRKd2faKXwEGnozASPbUVBQtmi1zvoUta4bhC2OYgVDNkiqyr/NAYN\nvjRLAQhQnO/DvRA+D1YPsmIr+mQpL8KN3mPAuF/+s7S3J8izQ23sZjyPEcptKwE1GWRUk4aaCWba\nylDaOg/BiP00y98AAK9KdV5quEdWONeC2gyv9yL0CRDBfHlyA2XlCuUVjzerz0DbjpqTasHAjOju\nDV5+Sn6Q2VBwoQwEgNOzr6K5dFB2BoeJzk/OK9mlr3iFu/af4P3v0PjWvwzwxUsYeKJqqnqHkMti\n//GhYg42BIVGLaOkwSzN0GLupUzSw6sNNb5VgsGXfs0/AQDc/txfKX8+6zMoG7LiQYPEH33hIL6o\n9+/ek767AhORn8sCo0icAQ0Bqm2SVMEQfqoLnijNBivBoJnIPrtSGaycQdUgDfiLfwjw+GHhE7sS\nyNcbjRBGR27q83UDjKSthpe4j9YDeAQIMuW7T5KqqrnIBDKN3VRBWlqput0mWLJLm9M5BGMNr/k9\nXgLiojaSN6+ArApQG+AMYLs+WHpcC6hjgLMk76G0i1uqIimxMOMzuVIvKgNpLdD65I2odMTxoTVV\nVvxbRGTXVJGudRiaW2ye0+/9Bh1vVjBQtguzZOucQX+nwAdgAHlloIVpT8vx8WffR3tqgxwyf2jH\nTzACWb7R3uOofuNvA771L5dE9zLCL5zU1crA9kD/Uq4MVhuH6iZCg1nMpcW8kQ3Y9Ly5uvC++Ot+\nGH/o+K4fdwJn0KQdk/WXeFE3HN4/iC/R+S2S3jZDxW6iX5Qs5TwCOo8BysqmXXSdI+AIGuDTqTKc\neFaV6XlTZbAOEhvvawZpwF/5g78SwE8Kn0RbkAcrgzAruuqbY2YobINSTup+ddD2SeUZUNdqd7cV\nz+H01nGtDKoB2S/eQ9ximiTKejYhUJUQlgtjM2s2EA8dXjs4s/SiXCOQl8RBSuQm2F7BNl2AFCVo\ndSK1omjjPQbTwypM5D2s+jyA09vv88+QDwlqrsNEzUxiAqvl4Ns6eHNblTu/4vFmwUR67q6OhHNh\nkpQUDChDXCSd8thMAHjx2Q+gPNAeDTSbc3r3yTPMRPYBlKHVZVt/7Q98N/6Xn/gj4jlGq9p6J/RS\nGXT3+4qaKDQCyZUBQQOtpw5liTNoHRY730ow8B5f9uPu8+LZUUYeK4Ma7LZcx+4DuTKg7NyjO+7E\n81gGm1TVQqHfokas+uDVL8NAA2y/2Dgo9C+lYDDCDBVRQhiiHuXO4gvsPf6+95CyQVLJ2CZUeFc2\nANuMQUIreyitzXdWob1I5KeDcjfrOI78oIC9eSGvtfPTE8yMYGooPQO6xzWp9GqaKAaqWBmoq+9D\n/aB54F3VOyl+70LVVWnTEPyfgOHRLvQJSOjCWE1GbTuGJO+amAD4n3/2R/BnfvC7hU9srAyuzEqh\nvzUBfltFOGxnYTsZEnyN482qDPTcwdQNvdYoWQ7AWDLZWBm0p9pG/hLDjcf24wyZutEAACAASURB\nVAbHd/hkqhm2AczcXFXQAPC2/X7gl5Qf2DZWBtfH0g2YtgrteV+BT6gyqHn2AAQNtC9byPNYHUhG\nJzelPXSkbpMUFOpBzfYgOwbxudHnzakXO6EpqwyZs5I2uQu07arqLpri1UKJXaE0Ke30bAs9lbbE\nAG0A1NkqdWGTtHTzcQdn4D8/17NS4fAeXv2XLXB5vH24Mmgj9yFabTe0QagZaI/ludrGQtvb+jNo\nPfq7vbiZ2n7A3AHNZSPeYwqKN6jNMJ72oTKYvfAMpwDVtdVABwBt+UrHv689nCHLklo3PUBrVtkt\ntNNwFSO6y6Nd2B+kxGCgtSBUBrZb3mu5uXI51Zeflme15JXBA9exVokaWrwOC68PV+CqVzresMrA\ntrT5CR23AD1sAMWwaWCtDFYbXykLpOMlhltg89xAWf5SzYE8qmeiDx22i5XBlbJ4dao0460Mn3Tk\nxU9ZRM1byKG5lBO+6DNLmaavNfp8PdexuCyq6ku8GGt1x5tKZTBg7j3a81auDPpzsOKWsyWaENXV\n8dO1aQ3oX2TZ+Vo9vfj0TVUO6JsBzaWpSEupOto8vy5pvHa4xmO4CaMYr+HETQJxiGqixepbYfOx\nsJF1Fnq+rT4Du1FoT7srMuWlgVGSjoYu8Yo6bjicyBpl1lAQoLqEt6klWH/ze4C/+d1lvwtAMBEU\nzcG+VhnY5gxtqY9AgoFs5zBt99Q0JkgyXTOguciKJXof6v00Dx8WtlOIMNE1zmAC/OaqHNob2Xb9\nNY43rTJor8rH5p7IWq/LYECdrMT2X2fZ73B5rPHW/wvoif8ewuHbSwvlFV4F36RxlGkwuFIO9g7a\n3ogdwgt8oit+MQB1pTaXsl9i+azmif71HJn/uq1OjvMeVn33BtDjbWXDJKimOW9FmGTa3wfpqeS+\nijCgRrZHpvOk4TLaKvQvpMrC4/zkFvuvQf55M6I5G0y7Su+KNWhP1z1xrh2ucZh3W5I9X9sAzBDg\nsFp37AhgGyyqpQ5lCz0dKs8gKM9O+8q7RU1rzdDJiUWQOVPzYfmMLqHT24xeeKfGANUtPy+/Uz/+\nvf8aJCURQD0zTlMwuF4Z0MQ2bVXh7Eqf06CkzccQO4jdkhhoofLqh8AzXhu9ee1ICeTriYE3I8Fd\nVsEIzbOunaHs/hsNE71ZlYGybdj85BdvuP0AAMBHygEpPHON7Yf3mDEeHNozCpO5JRiQ/My8Er5p\n+6RX4YphHkCZip5vKpnIAp/UgwFVBhXPndZCrZOz/sUrgykx1iIMtl4lEXn5qJK1hclNl168jnG/\nDOmpNZVdAgxUU1aEPoUZ6IThM7Z1GG9uYSpyQNcMaM9alCsva6o79RUs/uHDNR5zT53FD8JEtr9i\nqDcCkI3o6HMLbUWozntYzD1gpkNlLQ2JTLkSkB1ZVEvP6PTsBDMpmEmjf5m9m94HqG44hExXXkfe\n4697j38gfUbvh7qmrKPDLpCiKDMOgbmnTVRKFl0zwFy0PH61pWDQ3bXX+KPaQUhAC0ybjixOriQG\ntl0aKWUPJNvOAPahQvlmhYnmoDevBIPLE2r3LwdgLHDBgunVdMB0hK5INBdO+qXR+xo0Uj/mzSUn\nkK/8DttZmEGGidJJZrWF4xqL7qgqTWczIoH8Lx7UcoOxehMfsFQ4j+oS2I1CM8gQxHB7ghl1IJAl\ntRBJT2sqG9ssU7qAZpBecIe5MskMIOixPSkZHulG6LmBGWqNfw8ftk36Pa5VBs0lkLT1ykD5TVXj\nvnTPVuddt54SjxpM1CtSfNV4GdsG/b4AEz0e4BXQnIGbL/MZxvS3L08OlBxVhQj1wxtHMysqliXL\nQXbqXVVN6BoLuzlcCagXtGddVb0pF/mj2oCda4drPMabDa4NvaLvDQEylCeqkQWL7EP1GsebFwz0\n3FcX9N0nSbLFZ80CgWh0X18ZN+3oBm9elMO30y7Fa2qi6u9OLS0eGFg99zOaQa4M5k1U2dQ2c9dY\nmAnwpnwBvQnB4BVhovGQjiu8piaihig9iZxB5EamjRjUTm9drwyoQmorDo7Lc2/F2b4A3SPXXtsA\nBmi7BE/+tymwkyz2FWEi4+GaRaN/PRhoe8UqoRkAvxVN1ACCDvS0qw9abx3MeLhqbWIuXcXnnwJy\nfYLgCNt6dPdAM9bmTO8efB9qhzMWUP2DxKvt76GnHmqW51Xb1sK2N/WA2gzo71VQbuXHkpy1x1dP\nDFzrYTvyC7vOGVyoMqh4D63Ksm8sZ/AGBgPXVAnkD34p6eFtV+p47SrpvGYdQMcUrAO2H/LKwAbG\nv7vqb3TtoOHeqQqn/jvmfkJzuREhisWLv6b9BoBlroOyUqU0U9eor7uvXjsuT5LK4IoLLEAVjp5v\nquSe7Tz0KEtLT89OUA5hJoK8EVGHcgU+CZPUpIEk9LdprGU1GJggV26lkZYEDeipvdr5eu3wjYM3\nS1C+ZltMs6LrhnpkXFi/jhlm2l4RXzhyZy0z61V22YyymojgF6oM5K784Jp6R/9f+tvzZgftFPAK\n7xS5FF+VSQMAps0dzNTBzArKCeqwhpxh61Ui7QdWCAbTjoQO/Z3s7/R1XUfjMW23ZHFyJUGjapgk\nsjLRTZ3Wkv3LaxxvWDCYrrs7Xp5+BAAYbv9e8RkRtw/rgAGynQCAroi6S2VAMNEVaWn1OL91SrqY\nH8io+xFm2IUsPj9WLP2KCmWx8tZzJRgsBPIrwESXxwPMHHounIKpNPHR37KUdVaDgaOB8ZLVQUdZ\nZXNpZZhoaUqrwCeL0V2dE7DU2l9RgCw9K0tQSI95Q8HAVBrmvp6D/n6YWnUNJ+6P0FNfVZDYdgB8\nX6+A2gl62tSfQeugJzkgAyQ4MGMvVwbtJUJ14iZGMBM5wsuVge2oMri2jmqHMxZe9Vf3BgCYdi9h\nxhZ6UujvBLiqCZYelWBg27AWGmFWwfb8+pVB42H7zYNqInKZ3VRVkbYZYaZdML77hh1vlprIzIt6\npvbAfxaf838CwF8tPiEpZt09Mz3G/ZI1cCJ6jsoH+2qcwf07Z5g5DNH4Tg1VUU8AFAyayw7OlOZc\nl6dkIaBcA1+pcpaKwoxCMFgM3K4M2Ll2DI8meEWzDJQDtEDIrdfRzeiO+yqe61pLGnZdwnur0uXc\nitwHWWA3gbSTrRCUNVc2+xle1SsDG7JB15RNY/PmsprsvfIGYBy87h/kbmx7hJnbKwqSCB3I3MkE\nM17hDBpL7qy1xKJzMEOH4UbazANk6Wq9N4vtByAPELJwZotmfrWZva6xUNg8CK9M+5fQUwMzKaHq\np7WgZ9nMD6A9hL5XnuPllmaON5fXqAyMg22DTfm1faE7Qdv+ihx6CoH91c6jcrxhlcFsyGVTDgbe\n4/9r78yDLLvKw/777vZev57pWZgwo81IYCAkKWSgjFPYVETFcdhKxlUpTDlxKKCclO0EUqlKAMdV\niHIW29kQ2JgYQwKhjMHYcQg2MQIDJpSDAkggFiHJQbGEpNEyW3e/5W4nf5xzX7emz7m3+9wevdej\n86uamu5337193nn3nu98+6ZS/LSylcGtBk1IZ7MwuG+a2VpzQ1siH1LIRwOTIONh38wKU48mNpm7\nbeaVGenm0LpIbM5t6Ym1mids2bkH522VHpviYu6Ce+1s1eLXGZvua9TNQuTadaY1UTHA/p2YpLRp\ngm2xLoy5zKUZFKbiptt8UiHqkDMCpFkAVLxz8Wiq2OqaOL4LQGWcn+0RJOVwY+78tBfU0z4FW69m\naOrq2HsYQ+PXsed66OO1jrG3hlWaQnQOJ/5WYhnYc0FqVLSKq0NYF7q/eWbWBvccTo+c0609C1g5\nZzETNTV/XBuDbGL+3s75nzxFa/xdDXbaqJPa+I/aHchVtoFUQ/c4k5mzoF4PlkwYFN3VHV3oXqtG\nM+gwE201gLfdMLVOWXeo67sYifmS0tYaSQBlNiUbp1Yz0WxtYmzp9ggP2BIGq4/sFAYqLhCVOUsi\n7+5zgK4pI62lcqusIM5XrCUdQD+E2glr+xy6nEU6Ti1FzozjroqIHLvKcqh3bLFjt1cnOia7SuwR\nIHUjDCyLYDGaGVOdv5lIJRUw6FwAyuE6UZHognu2CJJsqh3pjnu7SnJdldSxUKmkIs7tEV2gFypt\nqrN8B8ZUh7N6bb4t9Nb2HVQQrWhzo8XB3MXjTZ7uOdw8eY5kFptOY/beF7qBkF2LLAdGS7QJg2MT\nokqcZcB39TliIwzcdc/MOHRUVJy7Isdy4tnw8hYGcdHYyPf+4OldnOg6MhW0aQbrV20AjsUhVvPI\nBx/7ZlOPBlKkFKszcP63sgnZemKPr5/b0tvKUejXR4/tzLuo543m/TKQH9+YBbIWYVAnJfF06DQT\nVWlFPHOVxZhqYbCZWIVBvrapHellBBbzSTHSoam27lQAKimJS3tNHoAy00lStSWRMT+kBU1b7ksX\ndWIiYXZh4ojzlCgXS8izNtXEeeYWakmuEzadO/+SJB84x1AnFel4Z1l3aGzYsanka+9UZrCOTTfu\n0XkG2cbeN1i6lWwTTeSew3NPO6e7D84U9hIt7VE4xar2MyhbaRLzPA7P2xsh7QbV+I86ahOVw3Xi\nIjVCzWZ2m5LM7AX1erBcwiAqTLellqgLF9Oj2qwyPDtwts1suOOnPsa/Pv8X1mNN5IPU0mond1PM\ncxWi2l4Qq6HKJgzWI90dbQe5MZ/YO5kBJs0fkpm9cB+qKSHgqRnEsHEy1YX/nLWedDy+Vp/dGkwy\nc5lJdI2mbJxi09Qmx7TvRNvuLQv24bHOE3AVN4xLosJVNwlmR7QfwxaeOz061lFMtasPdTd1XG1F\ndbX4bmaH14nzmLgUVs7s/JxVOiaZZs7dYJ022bMOn0FcEs+yFpNjRTqOrBpSvmqqmpaxrYqmUh0J\nWCoxLT0re2JgF9rkmZnEvRbtarSpo5o27NUDdF/xFafJsBgZzcAyBzp8VveZ9tcMKhNZ1l6ZoFhZ\nJypS01vFFp01M8UVL2fNoBRdlsBDM5gc05rBYL0zDFAp/oOarT3NerBKKup0RWsZFtttN8U8PFXX\n0G8zE40RhbV0cxO7HU/dZqL8cPNg2MLo8q1Kkz3MRJsnmnDGtlIKubE3t+xKp1ZhMM9DSDcTRO3c\nEY9P6JaNUSmWjHHd9D4uoMxcyVYFru5VABunHtQDkZ3CYHJ80wgivw0KNLvRbs1gcuy8FgY5rJy1\n2+2TaeIWBnGHMEhLrTm0JTBuCLZ7qVg130EVWTWHLuqkREV6R+4uIOlG9zfPOqNwYEIxwulXaTqZ\nubSr6VGjGViCCfRzrUg37IUld0Md1yi6w73zQ+vEeWI0A3sEXTJtr+DqwXIJgzJVpGO3XbONyfEJ\nUQUrZ4e42mbuBpVoj79OBfdZRLc0A6lobVhdDczN5xIGGaTTzLmz3+q12iIMWruttaGFQb6WdZrd\n6mxGOrGXPwa9IKfjiNqqAekM5mwzBoswmDyl6YcgJJOdxzdPbpoxuBbJgqjF2Xb26Q8AIGrnbnx2\nRPcB0BEknqaBuNI1h5S9rk/D+pVbwsC6AAzGpOO4JcN4RrYZWZPn9DgK0nHi0EJNpE2N9TuYrTVh\nzhFiMdUB2xzI9msrWXGWgehCV/HMOp3wMKFYaX62awZRseLcGJx5hum/rOyaQZ1hCi565pzEJcjA\nZCC3aIlHzL3gFGpTvTFI960UBSybMKgGkE4GXruwOtPlp1dPr/YKudKxyNrJ5OqJ0M6Wz0CXym0L\nLTWF96x2WBO7PbMXcAOIyhpAvc0S7qeb/eiywX2iicpBhpTtTTTqRBd7c5uJCtJNlzlM7+oH64LU\ntjrGudEcIrJNS8TPqqlk69oRJ6atpWMRzQ8/BkAybcms3bQX2dsNWjPQta7aMsHHJ9aRuinlvHMs\nZTYhHUdUqStaaEbaIgzqpDAtY13CwCQwWvoybJza1L6TKrImc+m/b31ZXzs25RM6NEzn+VFjJurK\nmZlQDQBQb1M2R3au810c60M50ibDOLeNUVdfTccr/iZD4z+KOhzI4xMXyDYiqtTlH5qQTuLLWzOo\nBhjNwOdDFtSJYvSovcH8bqkTbdeLSsg2PB3ImVClOit2eL7FTDRsqrDaFyJd4M0tDO565Vke/qv2\na+syC4l+gD2S56DWmsHqsDM6q0qnZJtRizDIiUtXb1iosyaT2qaez6gGutSBLXZ8q++sI4om1pEX\n7g3COT7/i3Dnq2xtK5tqn7735FZYJMreP7ihzqaUK+4s3nK0STLDuatVyZS4pGWxL4xPwH4/6no3\ngKVl4+ywiWzL7Q5mwGjDduZRPB2birbzpU6NQG3XDFqFUpIT5W3RQDpfJ57ZhYHenNm7xe0GHW03\ngA4/3vqV5/V36dBmq2zaqiV6slxJZ+UAXerYI2O2Mc8MLrgjR3ZDHVfU8QipIJn4O5CnR7vV4nJo\nksUs4XxNm75kljLL7Nd48AXv4d3fUPy65Zgu4JY4Sz93oBRK3hRDMVrp9hkkU71QtWgGYA/fBB1t\nBBCVLs1AGJyHxOrDab4jd9/suHCaeZSiFPmlDwFfthzWTWHSqf8CoCNhdPnmrl1tsQLZJtiEQW4i\nXVwRJPPkudix2Mc56aY4hUGjtUUWYVBneh6SSUQ5dAj0lqWkTgsUQ6fZowut5R4GZS/K2LxNUcjP\nt40jmZl7wbU+nOWTN0OVfMxyTGv8SQ9hMNcSO0rbz44Y/5UjCKbKxqQTWov2ebBcmkE5FFPq2E8z\nqFLINvxteqAXLhVrlTbJ9yyUTEMVGJ/oVouLkRYGlht8XvpXh5a6GuT8jlK8xP450hyp+piJdJht\nsbrS6TOYJ25ZNZxtGZ1WoadrGwEkuU0YmBLYjtjx+cLpqsljQi5btEWTyNgS0TXpoxkUOqqrwzQA\nE8q5vXvnWJoFwh2+axKmEsd3EOekU6zhu7CVsxKVdu1La+0RUWE7juk1badOCp1B7Kgf1UWjGbha\noz7+vS3H4llbRJZSTPnSGz/Cl3/us5bDenMW5+4Q6i6UCSbAUf11C/08Nf1ELqYy4dA+vtUWlksY\nVANIZqu9NIN0POplS1NxRR1174bbqBPF9MiqszpiQ2FslK4HtEprE0LmE8FhNIOOMtqt14gVVbZC\nWzN4gHLQ3JyOhajpUCf2haRKTUnx8c6InqYEtsYtDJwF2uJZj9pCWitJpkNnQl0XeiFr2na2LYTT\nZiGz5kuMn2JMik4HciOQXTv/Rmi6hQVAZBXI2neSTtwRdh/7CPzat+52XLsAhqaUxt6fbd3n2pTQ\n7hIGrWYi3daycsfnK8VrlGJHEud8c6ZLevTQEuuBafLTdi/o58S11pfDJlN6X4XBspmJatOtyU8Y\nVGk/bz8YVS5a6eyJ0HqNRFGsdncimq2dA+xJLoBpXpNiSzzpHsPUlBDwFwYqUTrMtqO8x1youRaa\nxIzf6TPQc7SzpDg0jVc0dkc7uB8c3bwmmQukvbFNGHjuwuqkNG07u5L/pq272o2TjX/JVQywEciu\nnX8jkO3Hm0q+ycwukKsBpJtCnNu/w41Tz2Pj1KOOv52DWvXXDGKtGVCXneXY65YmRHU6JZmm1A4n\nfOc4kpqoXG0to92GirSWGDlrPDW0awblsF1L9GS5NINypSKdjPyFQQbJdB/MRFG/WuF1qnutdmkX\nG0/VwqBYsRVw05pBOklQltDRLnQ1T3dNn91Qx4oqGZk1rCXZpxm/QzOoGmFgCd/Ux/V5o0dtx4sm\nQgT7QqJ3SfHMvgqoZEYySebRMntD31Nx7q7p04WKCx0SqVrtxEqZJD8XdZbP32mj2S3aSilsf93l\ntylW9dwP1u1monIgpGOxRnTpUd2uFPfb/3aSI/XA+Aw8QktT3dI22kU2fdvXVKczknHSQ8tTSDWi\n9t4YFEDW6UBu7mmHsks5bAS/3/rkwFsYiMhxEblFRO4SkU+JyNGW98YicpuI/I/Wi5bDimQy8qqy\n2UTxxFN3Gd/doB6Xsu77pdfU6ajTvPLYs/VOyr4b09dJJjE4dnttVNkUqU2lSa+kM1CRQsXdPoNi\nZDQcl2Yw35U7zEQDfe3BzlIFSqG22aN3XF81FV2Tmd0+UMdTE4a35+/StCpURKVvUIPZ1arUaAbt\n30N7a009N65NaTVo1wyUKdXt8tvMDunvZnjunOVok/MCw7P277CNOp4RaYluDfnsPj/X7Vvr9vBc\naA9xrRJzL3htDDA9IVa8kmKhMRNpB7J0+AzqlqU5P9TkJy2NZvAW4Bal1LOAz5jfXbwJ+BauiI+G\nYliSTuxVPLvRPoM+Nj1obKfd4ZTt16h1Ew1HWeX5+9LHeOyZcOYZO/sz6OO6raU9CaadeR+Aur2n\nQhsqVroxtwJaSg5smbtcYYdm/JaEJoBy0NzU9nFumYnci2lc2O/lOpnpKBpLMcDdUKeqtfRzF41m\nIB0RJGCUByd6bqPK/gwVK80C4TDFzb8bhzA4ooVJtm4XBo3fxlZfv4s6nWnHq6dVuk6m2zSDdoF6\n3w/Dxkm3XyXbFG9hUCUVUelvMtSN7tOuDZpSqNaNwfRIU0NpOTQD4EbgA+bnDwCvsr1JRK4GXg78\nFmC3gTWUw4J0PPBp0ziP4omqfsJAh9gNejWbrpN6HpHUfo3TvOsu+J83f8F6tEoqI5T2LgzKwQQx\nxcV8asiDNhNJtdrZ83X8lDPmb9o1nCbSJbImlUE1bObIsavVD4ZzDOPjsH6F/dp1PCGdNmF9e6fZ\nDfprBrn2aqruqK72xbI9hHYejujyy5gS3a5cj/EJfd1svKM3xtx5CpDkPtFAM6KiT1OYXGu5HYl7\nAJ/5VznvvPt267EqmyCq370Qt5QB72IrsqzbdNum4Ww+tfEf7asw6ONAPqmUatpPngZOOt73H4F/\nBqx1XrEaFKSbh/xDIZNm8eqhGZg+AP01g04zkVJcEGFFuXwC86xQX82gipAKa0/d3aDiGqm7m2is\nX3nGvH9nkx1g3k7QnmEMZdY8XPbz23fM8O5vQDy7hX9n+9tJ06/A77usklp3EBPbjrmbOs6RunHk\n93l4Gwew/d4+84zGVGe/V1Tjt3Hca9Oj7ZrDbrQzF3UyI87dRfa6aLrZieo2eVaD51IN3Mla4B+F\nUycVUTFwznHn+XGB1Id6a4nTo8Yk6GVOd9IqDETkFuCU5dC/2P6LUkqJ7PR2iMgrgYeVUreJyA2d\no/n6N4c89GjCI3KdiNyglPpc5znbqZMaqdx9YHd1jTgH1no5kHUNe3d1xO1vbXMOz0sLOMwrbRSj\nsan0CbW3ZqCFQZtjE2DjCr0QxTNX6KjRDKxJZTTx9c5+rqpdoWTjin8IfN56bKutpecGI63INgbO\nMg/d5+fbzETt38NuzEQuTXPzpK6r49IC68TMveNeuvsVd/NvzqOma46FtNWJ306dTklm/n2ktQPZ\naAbtu2Gl+I7zYNO8xlsziGvTM8KuAXehosZMJJ2aQTGCwc7K9Hocf/e5fBZ47P89VURu8hqLhVZh\noJT6W65jInJaRE4ppR4SkSuAhy1vexFwo4i8HBgCayLyQaXU37de9NkveoTnf3ON7z7nW+o7939u\n15+iQdt3V3vF36o4J6oGvZpN13GNKG1eeXvZQzCZxtxiyQrtIj+se7ZKqYgKTwdyrO3lXTu6OtXR\nROXgEevx5iGMZ/a7+8wz2x+uDq1cKX6zZWxNTLavtlkTFwMqRxZ45/nxjKhOdpF0Bg9dD2v3u+65\nZvFwTYb+DiKHGacxE9lqD2luZrZmK8lh/moPzaBqCqt5auxVOp2biXz6kjds5cP4hoyXxHlGsep7\nfj7P/ekqEfP+L8Lxu7/C22wHf/eLvETgvmvvU3c8cJOIWN+1V/r4DD4OvNb8/FrgDy5+g1LqF5RS\n1yilrgNeA/yJUxAAlIOp3k17momqrCIqVt0xWbugjnPifEiV9qtvFJWrvWok6eu0ZYW2MzH9HXQf\n5b2fD01Tmt2U9zjNO74LH/vIjnsA2HoI69QuLL756q/z9pbny6s3j6Ey3atcCXFd1EnVWiywC5Xk\nupm8o7vWdj7xn+DXvv2Y9TLzVq/OcZznw/8dvvSm37UebTQDZWnio6//mFLWkhyaLWHgExo6JZnG\nPTSDqWmJ2175tYsmfNZ7Y5BWJLPMW0ucBxMoe5+C7Zz5/j/mnpd91HHURJZ59Vtx0sdn8MvAR0Xk\nDcC9wKsBRORK4L1KqVdYzmm/GcqBsem1NItuo8r04tUnGaNOZsSznsIgrUjGqx2hgrsZixEG1gJu\n7UyPjYkLIc7FWU+miyqrSWaHdtFR6Tznrv0VwJ6Bmq/pnf/wzIOO838RFb/DefU+frJ8pLWRupcw\n8Apq0OfHjfOz20xUJzcyPWY3pTU02do7eZTv3Ahwp/XobE37Y5ow4L2inwd7v4POcwcTkmm8rd3s\n3mha2mq/i79mMP5Leg78zUSl6dvhaXaNTCZ1FRFVrZ9DKV7acrgJM/bzBTrwFgZKqTPAj1pefwDY\nIQiUUp/HZddtaBJnvDWDtCK7MPIOHQOTmDIb9OovWqUFg5buWrtlnhU6aV8gbMyOTFECyVTA0rhl\nNzTCtcOBrBSKttDiu15xgQ99Ev7ey1y73geAB5zneyZ8ArBxSpcWUI4Cbl2ouCTJV3toFjOd/Ke6\nShCgFO15OL95K0j1Ed5pPbdZrO2cvU7ntEhlM+d2Y5RtL9NplU5Ix8JszdNMNJiatqe+jZo0834F\nvoI9KUkmibcwUE1UVC1Q+3+Oraz7PtfYwXKVo5gnznh+4brPq38YIOiQzHTsH/kA6O5ahbu71m5p\nKlUm070LA513oUjHwuCCpzBIKwbro969VlX8be55aXt4aus4WoqgdXH++4ww8HyAq7TUvYF9I9zS\nGVLrHs5V1k+tf+AHS3ROz96570UP89XXw9mnf93r/GLodRqgn6mowvs+KlZ0+9Gos6ZPO/lhfS94\n7gtQSaFLR3veS2U2ISpTpBKiXrv6MZ94NxQjW3VVb5ZLGBQrZtHzffCyWPSO1AAADcJJREFUgng2\npE7tIYq7oVwZk25mvRbAKs2Jy+6QzO6x6M9hr9nThe6HMLgA6dQzFC4rifN+/SEApfjfdOWYtPH7\nH4JDD16wO9M6OHednkOpPUNL06YPgN89WaxMdI2oMqLKeqn1StESfN7B7OgmH38fsLMI264oV9o1\njza2/Da+wmA677TWx4EM2mQYz/x8pVVWMthwlwHvohxqYRBVgvjv6pUiF/lZgNt8r2FjuYRBNWwW\nPc9dXFKSTEfMBme9x1CsTEjHMcXI/6bTDav776jzQ/rBXX3Yal7pYEa5AsML+mcfqrQySTb72kRj\nz1y4+tNcuPour3ObUhjxzHchK8g2xTv3pRhtEhUJUdlai/8J4P8ATzPmpL2jI9taelu2UKz0Ewaz\nI5MtYdBDM9jKY/EzI899mr5a5mBq7oUIqfw2aFs8RSnO9LzG41guYVDMm714OmiynGR6lOmxHhEH\nozGD9Zj8sP8urkpyktmx3sJgtqY1pXRiL2TXzpR8tVkAfR3ype4Str/VEfeKUjhDnHdB8z36Rqg1\n53kKg9UN4iLWpgHP5L99wAiBv/C+gJ4HP2EwPWqc+J6hpdMjEx0MUUbzBdkPPQ5RnlrivG+H3/dY\nrIyJzcZAPBzx29hvQQDLJgzKlSbSwddnULQ2Zt8N+eqmsW/2cEJnM+LpsJffAeDs0xtNyefG2d4s\nxVO4piVx3q8/xOIxORArfuaRatBU+/Sbw9naBnEeE1V+UWHLwmf+5Tpf+Qerfqa6a5u+HX7P1PjE\nGKmFOI8pVvoI1Al/+OuAsodAdzFvIOQo9tdFsarrhUWFkEz6agb7znIJg+kRHYfune6dzEg3Y+9s\nU4D8cFMrvI/PYEo6HfSuN37vS87y9gpVe/kephQtnbN2Q5XOSGbHF24m6sc5fuNrUEcf8jq7zPpp\nFpNj60RFTFTWROXSLQC7Jl/7Ag//tau9zp2cMMmGngEEdVZQZYp0M2a25i1QlUKJ/Bzoopl7Zx7g\n4rk+5YfH5l4QBheWbmOwXMLg9PW6HnqV+Ua/5L2cfQDTo00scp9chSnJNKMc9DULfMQ39V0pKnmD\n1uq9ygZDk3ORUaeLtHX3Zczp5wI85HV2NegnDDafuk6cR0Slu1zHweAn8Q8C0MLAv+LylHKoGKzH\nbJ7sK1BvAG71OrMRBr4+uOnaJnEeExcwPOcTIXhJWS5hsH6Vqe8/9a9hEtXuuvq7YXyiiUv3FwZV\nNiUdJxSjXl+4UnwF+Ir/BXonvek2gbPBgV3E9G6QK9DFFPdOZWzUdeI3B9NjY1QE6US8r7EEGJ+D\nr9lTb2j8hcGEYgTZeow46lvtEqU6cp3aKLMm2tFPIE2PbhIXMXEOg/WF+Y9cLFens8a+m0z9ooGq\npolKD82gKbrWp3FElemIpD5+h/2gq8Bc5/nzmjKL/Rw9UYqHvKNoqsQ4P1PfhXxKldVkG0tpGniC\nMMLA25WnTZ6D9YjYozTLflHNK8f5CYPxCaMlFtAnee4SsVyaAZzl3z4E+eof8l88zp4XJeuhGTQ1\nXPo0EaoGE9KJ7Hcnoj3j20xk6/wJySRe+OdYJHXWlLPwM13CjHII2TpExcH1GfRDz504GvN0M6FY\nFbJNiL0SMPeHYqUJTfUTSLOjG9QxpGMIwqCT+9g8CY2GsFcazcA38kNjyi33+K6q1BRm299ORHum\n9s9R0ucnU+ODefIKg3xey8dRT7iTKeUQRmcgGy+daeAJwnxu77oiE4qR9lekCxQGY9OzvE5874UJ\n1UDp7oVBGLSiM+s4pJRvklTWJLf0EQaNZuCfMdt0/FpwfH5nU5guqnSsfTBPYmEwOd7Ec/uFpsKU\ncqW5l5ZuAXgiUAolL3kbRMUXPc+v5XVDnQE9emRxwuD8NTr5c3bkUc8rTCiHWsPxDfe+hCyVMABQ\nCl91fEsY+Catacw1PHsZgC5pAf4NVfaLrqYwXczNbk9iYbB+pS7sNjvqqrjaxYziyS0MAPjcTQB+\ndZEAimEFJKwuUBic+f4HzP/3e15hsq0U+NI9U0snDHqRmzK9/dL+9c0mnjHRsE0z2N8epXvmE++B\n227FK1EIoE4vSXu9A8X3XvgAVQrf+8Hvel5Bm4k0T15hAH+Cb5E9gGqon8dktrgddTU4y00K4Bue\nV5g0QR3eRRsvIZeXMNgwrf+ark5+6HPnkUkeFIdMw+oFawbrV8GdP+F/fuMwW7S5a5GoZJNfygHu\n8zpdUcrr58LgSStUleJv9rpAsdJo6ot8pv7c/O9rMpz0Dve+hFxewuDCVVqlL0feVUuVIpdTt4OS\nD9vqxu+Ks9caobRgzQB+Chh5nz09ZsoILFioLZZ7AJRvWXUgaAb7QDlsNiQL0wyU4rQIq0q19zVv\nYdbbj3cJubyEwdmna7tuY6bx5fT10JiLfGhq6Ef1ogu8fbjXBRpNq4+WdMBRiu/Sp/w2sMx24gND\nOVwGzQCl/NcFpVDyj3r0hbjEXF7CoBzpyI9q4JdtusXPAn/sfbZKtGYSz5ZXJ9wN56/RoXQ96/A/\n6THNeZbRTnxgqNOlEAa9yVcXPQInl5cwgHO86ztw7ml/2uciSvGenuPQmoFUB3snuHFKm4mkV0OR\nQNmjU1vAoAXpgReofRNBLyHLOzI/zvPYs8A3aW0/xwG65+lBRiU6YzbOF+37ONj0adsZ0BSjg72x\naggO5CeMB4C3Ar4x4fvFeT54C0yP/vaCx9EXLVSjMgiDPkhVs3x1wA4WX/mZC5y+/rh3mPSyMD6x\n6BE4EbUkWpeIKKX6ZkktByIIUAN/Ryl+b9Hj8UWEk9xw00Ocv+afqK++4eZFj+egItf82SZP+9OR\n+l9vvizu70Ugwp3As5Xq6cxfMHLkPsXKmQfVQ9dfuW/X3Ke1MwiDS4QIF4C/odT+Nq1+IhEhRsfG\nv1wpPrno8RxURDgDHDvoC9kiEeFdwAuU4kWLHksfRFDAaaU4tX/X3J+183IzEy0NSrG26DH0RSkq\n0bfYvvdbfZJxF7C89oGDwRvpG+K7HPwq4Fei/xITNINAKyI8A/i/3v0AAoiwBtRKeVc+DQScBDNR\nIBAIBPZt7QwRDoFAIBAIwiAQCAQCQRgEAoFAgCAMAoFAIEAQBoFAIBAgCINAIBAIEIRBIBAIBAjC\nIBAIBAIEYRAIBAIBeggDETkuIreIyF0i8ikROep431ER+ZiIfFtEviUif91/uIFAIBC4FPTRDN4C\n3KKUehbwGfO7jZuBP1JKPQd4LvDtHn8zsEtE5IZFj+FyIczl/hLmcznpIwxuBD5gfv4A8KqL3yAi\nR4AXK6XeD6CUKpVS53v8zcDuuWHRA7iMuGHRA7jMuGHRAwjspI8wOKmUahrPnwZOWt5zHfCIiPxn\nEfmqiLxXREY9/mYgEAgELgGtwsD4BO6w/Ltx+/uULn1qK3+aAM8H3q2Uej6widucFAgEAoEF4V3C\nWkTuBG5QSj0kIlcAn1VK/eWL3nMK+DOl1HXm9x8B3qKUeqXlestRSzsQCAQOGIvudPZx4LXAr5j/\n/+DiNxhBcZ+IPEspdRfwo8A3bRcLvQwCgUBgcfTRDI4DHwW+D7gXeLVS6pyIXAm8Vyn1CvO+64Hf\nAjLgz4HXBSdyIBAILBdL0+ksEAgEAotj4RnIIvJSEblTRO4WkTcvejwHBRG5V0S+LiK3icit5jVn\nIqCIvNXM8Z0i8mOLG/niEZH3i8hpEblj22t7njsReYEJqLhbRG5+oj/HsuCYz5tE5H5zf94mIi/b\ndizMZwsico2IfFZEviki3xCRN5rXL+09qpRa2D8gBu4BrgVS4HbgOYsc00H5B3wXOH7Ra78K/HPz\n85uBXzY//xUzt6mZ63uAaNGfYYFz92LgecAdnnPXaNS3Ai80P/8R8NJFf7Ylms+3Af/U8t4wn93z\neQr4AfPzIeA7wHMu9T26aM3ghcA9Sql7lVIF8DvAjy94TAeJi53urkTAHwc+rJQqlFL3om+WFz4h\nI1xClFJfAM5e9PJe5u6HTATdYaXUreZ9H8SSePlkwDGfsPP+hDCfnSilHlJK3W5+3kBXbbiKS3yP\nLloYXAXct+33+81rgW4U8GkR+bKI/Ix5zZUIeCV6bhvCPO9kr3N38evfI8zpxfxjEfmaiLxvm0kj\nzOceEJFr0VrXl7jE9+iihUHwXvvzw0qp5wEvA35eRF68/aDSemHb/Ia5d7CLuQt08xvoCgQ/ADwI\n/PvFDufgISKHgN8D3qSUWt9+7FLco4sWBt8Drtn2+zU8XpIFHCilHjT/PwL8N7TZ57RJ9MOoiA+b\nt188z1eb1wJb7GXu7jevX33R62FODUqph5UBHVremCXDfO4CEUnRguC/KqWaHK5Leo8uWhh8GXim\niFwrIhnwk+hktkALIjISkcPm51Xgx4A72EoEhMcnAn4ceI2IZCJyHfBMtGMpsMWe5k4p9RBwQUR+\nSEQE+GksiZdPVsxi1fAT6PsTwnx2Yj7/+4BvKaXese3Qpb1Hl8Bz/jK0t/we4K2LHs9B+IdWv283\n/77RzBtwHPg0cBfwKeDotnN+wczxncDfXvRnWPD8fRh4AMjRPqvX+cwd8AL0IncP8M5Ff64lms/X\no52VXwe+Zhagk2E+dz2fPwLU5vm+zfx76aW+R0PSWSAQCAQWbiYKBAKBwBIQhEEgEAgEgjAIBAKB\nQBAGgUAgECAIg0AgEAgQhEEgEAgECMIgEAgEAgRhEAgEAgHg/wOaqv+HR+GwRwAAAABJRU5ErkJg\ngg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f0f35c39350>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "predict = theano.function([x], prediction)\n",
+    "prediction_np = predict(data)\n",
+    "plt.plot(data[1:], label='data')\n",
+    "plt.plot(prediction_np, label='prediction')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Small scale optimizations of this type often benefit from more advanced second order methods. The following block defines some functions that allow you to experiment with off-the-shelf optimization routines. In this case we used BFGS."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warning: Desired error not necessarily achieved due to precision loss.\n",
+      "         Current function value: 0.000218\n",
+      "         Iterations: 5\n",
+      "         Function evaluations: 31\n",
+      "         Gradient evaluations: 19\n",
+      "train mse: 0.000217512235395   validation mse: 0.00018158860621\n"
+     ]
+    }
+   ],
+   "source": [
+    "def vector_to_params(v):\n",
+    "    return_list = []\n",
+    "    offset = 0\n",
+    "    # note the global variable here\n",
+    "    for par in parameters:\n",
+    "        par_size = numpy.product(par.get_value().shape)\n",
+    "        return_list.append(v[offset:offset+par_size].reshape(par.get_value().shape))\n",
+    "        offset += par_size\n",
+    "    return return_list\n",
+    "       \n",
+    "    \n",
+    "def set_params(values):\n",
+    "    for parameter, value in zip(parameters, values):\n",
+    "        parameter.set_value(numpy.asarray(value, dtype=floatX))\n",
+    "        \n",
+    "        \n",
+    "def f_obj(x):\n",
+    "    values = vector_to_params(x)\n",
+    "    set_params(values)\n",
+    "    return get_cost(data_train)\n",
+    "        \n",
+    "    \n",
+    "def f_prime(x):\n",
+    "    values = vector_to_params(x)\n",
+    "    set_params(values)\n",
+    "    grad = get_gradient(data_train)\n",
+    "    return numpy.asarray(numpy.concatenate([var.flatten() for var in grad]), dtype='float64')\n",
+    "    \n",
+    "    \n",
+    "from scipy.optimize import fmin_bfgs\n",
+    "x0 = numpy.asarray(numpy.concatenate([p.get_value().flatten() for p in parameters]), dtype='float64')\n",
+    "result = fmin_bfgs(f_obj, x0, f_prime)\n",
+    "\n",
+    "print 'train mse: {}   validation mse: {}'.format(get_cost(data_train), get_cost(data_val))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Generating sequences\n",
+    "Predicting a single step ahead is a relatively easy task. It would be more intresting to see if the network actually learned how to generate multiple time steps such that it can continue the sequence.\n",
+    "Write code that generates the next 1000 examples after processing the train sequence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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pc75Js9QpjiWWNXE3Z7/L8bCzga3aZV2FlgoAPk/61vSsA2cKria0Y1PH/Phif5wIPxbh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\ng7jM5lHgSCAfnId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P11tQwzsc119gjOmGBQBTGn7h8UXAJ2q975Nr7YBLI22M6YYFAFM2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\ngg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f0f3ca042d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_t = T.vector()\n",
+    "h_p = T.vector()\n",
+    "preactivation = T.dot(x_t, my_rnn.w_xh) + my_rnn.b_h\n",
+    "h_t = my_rnn._step(preactivation, h_p)\n",
+    "o_t = T.dot(h_t, w_ho) + b_o\n",
+    "\n",
+    "single_step = theano.function([x_t, h_p], [o_t, h_t])\n",
+    "\n",
+    "def generate(single_step, x_t, h_p, n_steps):\n",
+    "    output = numpy.zeros((n_steps, 1))\n",
+    "    for output_t in output:\n",
+    "        x_t, h_p = single_step(x_t, h_p)\n",
+    "        output_t[:] = x_t\n",
+    "    return output\n",
+    "\n",
+    "\n",
+    "output = predict(data_train)\n",
+    "hidden = get_hidden(data_train)\n",
+    "\n",
+    "output = generate(single_step, output[-1], hidden[-1], n_steps=200)\n",
+    "plt.plot(output)\n",
+    "plt.plot(data_val[:200])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "#Things to Try\n",
+    "The quality of the generated sequence is probably not very good. Let's try to improve on it. Things to consider are:\n",
+    "* The initial weight values\n",
+    "* Using L2/L1 regularization\n",
+    "* Using weight noise\n",
+    "* The number of hidden units\n",
+    "* The non-linearity\n",
+    "* Adding direct connections between the input and the output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.4.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/deep-learning/theano-tutorial/rnn_tutorial/synthetic.py b/deep-learning/theano-tutorial/rnn_tutorial/synthetic.py
new file mode 100644
index 0000000..ad8ff23
--- /dev/null
+++ b/deep-learning/theano-tutorial/rnn_tutorial/synthetic.py
@@ -0,0 +1,85 @@
+import collections
+import numpy as np
+
+
+def mackey_glass(sample_len=1000, tau=17, seed=None, n_samples = 1):
+    '''
+    mackey_glass(sample_len=1000, tau=17, seed = None, n_samples = 1) -> input
+    Generate the Mackey Glass time-series. Parameters are:
+        - sample_len: length of the time-series in timesteps. Default is 1000.
+        - tau: delay of the MG - system. Commonly used values are tau=17 (mild 
+          chaos) and tau=30 (moderate chaos). Default is 17.
+        - seed: to seed the random generator, can be used to generate the same
+          timeseries at each invocation.
+        - n_samples : number of samples to generate
+    '''
+    delta_t = 10
+    history_len = tau * delta_t 
+    # Initial conditions for the history of the system
+    timeseries = 1.2
+    
+    if seed is not None:
+        np.random.seed(seed)
+
+    samples = []
+
+    for _ in range(n_samples):
+        history = collections.deque(1.2 * np.ones(history_len) + 0.2 * \
+                                    (np.random.rand(history_len) - 0.5))
+        # Preallocate the array for the time-series
+        inp = np.zeros((sample_len,1))
+        
+        for timestep in range(sample_len):
+            for _ in range(delta_t):
+                xtau = history.popleft()
+                history.append(timeseries)
+                timeseries = history[-1] + (0.2 * xtau / (1.0 + xtau ** 10) - \
+                             0.1 * history[-1]) / delta_t
+            inp[timestep] = timeseries
+        
+        # Squash timeseries through tanh
+        inp = np.tanh(inp - 1)
+        samples.append(inp)
+    return samples
+
+
+def mso(sample_len=1000, n_samples = 1):
+    '''
+    mso(sample_len=1000, n_samples = 1) -> input
+    Generate the Multiple Sinewave Oscillator time-series, a sum of two sines
+    with incommensurable periods. Parameters are:
+        - sample_len: length of the time-series in timesteps
+        - n_samples: number of samples to generate
+    '''
+    signals = []
+    for _ in range(n_samples):
+        phase = np.random.rand()
+        x = np.atleast_2d(np.arange(sample_len)).T
+        signals.append(np.sin(0.2 * x + phase) + np.sin(0.311 * x + phase))
+    return signals
+
+
+def lorentz(sample_len=1000, sigma=10, rho=28, beta=8 / 3, step=0.01):
+    """This function generates a Lorentz time series of length sample_len,
+    with standard parameters sigma, rho and beta. 
+    """
+
+    x = np.zeros([sample_len])
+    y = np.zeros([sample_len])
+    z = np.zeros([sample_len])
+
+    # Initial conditions taken from 'Chaos and Time Series Analysis', J. Sprott
+    x[0] = 0;
+    y[0] = -0.01;
+    z[0] = 9;
+
+    for t in range(sample_len - 1):
+        x[t + 1] = x[t] + sigma * (y[t] - x[t]) * step
+        y[t + 1] = y[t] + (x[t] * (rho - z[t]) - y[t]) * step
+        z[t + 1] = z[t] + (x[t] * y[t] - beta * z[t]) * step
+
+    x.shape += (1,)
+    y.shape += (1,)
+    z.shape += (1,)
+
+    return np.concatenate((x, y, z), axis=1)
diff --git a/deep-learning/theano-tutorial/scan_tutorial/scan_ex1_solution.py b/deep-learning/theano-tutorial/scan_tutorial/scan_ex1_solution.py
new file mode 100644
index 0000000..04a8f95
--- /dev/null
+++ b/deep-learning/theano-tutorial/scan_tutorial/scan_ex1_solution.py
@@ -0,0 +1,28 @@
+import theano
+import theano.tensor as T
+import numpy as np
+
+coefficients = T.vector("coefficients")
+x = T.scalar("x")
+max_coefficients_supported = 10000
+
+
+def step(coeff, power, prior_value, free_var):
+    return prior_value + (coeff * (free_var ** power))
+
+# Generate the components of the polynomial
+full_range = T.arange(max_coefficients_supported)
+outputs_info = np.zeros((), dtype=theano.config.floatX)
+
+components, updates = theano.scan(fn=step,
+                                  sequences=[coefficients, full_range],
+                                  outputs_info=outputs_info,
+                                  non_sequences=x)
+
+polynomial = components[-1]
+calculate_polynomial = theano.function(inputs=[coefficients, x],
+                                       outputs=polynomial,
+                                       updates=updates)
+
+test_coeff = np.asarray([1, 0, 2], dtype=theano.config.floatX)
+print(calculate_polynomial(test_coeff, 3))
diff --git a/deep-learning/theano-tutorial/scan_tutorial/scan_ex2_solution.py b/deep-learning/theano-tutorial/scan_tutorial/scan_ex2_solution.py
new file mode 100644
index 0000000..c4a76dd
--- /dev/null
+++ b/deep-learning/theano-tutorial/scan_tutorial/scan_ex2_solution.py
@@ -0,0 +1,50 @@
+import theano
+import theano.tensor as T
+import numpy as np
+
+probabilities = T.vector()
+nb_samples = T.iscalar()
+
+rng = T.shared_randomstreams.RandomStreams(1234)
+
+
+def sample_from_pvect(pvect):
+    """ Provided utility function: given a symbolic vector of
+    probabilities (which MUST sum to 1), sample one element
+    and return its index.
+    """
+    onehot_sample = rng.multinomial(n=1, pvals=pvect)
+    sample = onehot_sample.argmax()
+    return sample
+
+
+def set_p_to_zero(pvect, i):
+    """ Provided utility function: given a symbolic vector of
+    probabilities and an index 'i', set the probability of the
+    i-th element to 0 and renormalize the probabilities so they
+    sum to 1.
+    """
+    new_pvect = T.set_subtensor(pvect[i], 0.)
+    new_pvect = new_pvect / new_pvect.sum()
+    return new_pvect
+
+
+def step(p):
+    sample = sample_from_pvect(p)
+    new_p = set_p_to_zero(p, sample)
+    return new_p, sample
+
+output, updates = theano.scan(fn=step,
+                              outputs_info=[probabilities, None],
+                              n_steps=nb_samples)
+
+modified_probabilities, samples = output
+
+f = theano.function(inputs=[probabilities, nb_samples],
+                    outputs=[samples],
+                    updates=updates)
+
+# Testing the function
+test_probs = np.asarray([0.6, 0.3, 0.1], dtype=theano.config.floatX)
+for i in range(10):
+    print(f(test_probs, 2))
diff --git a/deep-learning/theano-tutorial/scan_tutorial/scan_tutorial.ipynb b/deep-learning/theano-tutorial/scan_tutorial/scan_tutorial.ipynb
new file mode 100644
index 0000000..5827dad
--- /dev/null
+++ b/deep-learning/theano-tutorial/scan_tutorial/scan_tutorial.ipynb
@@ -0,0 +1,644 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction to Scan Theano\n",
+    "\n",
+    "Credits: Forked from [summerschool2015](https://github.com/mila-udem/summerschool2015) by mila-udem\n",
+    "\n",
+    "## In short\n",
+    "\n",
+    "* Mechanism to perform loops in a Theano graph\n",
+    "* Supports nested loops and reusing results from previous iterations \n",
+    "* Highly generic\n",
+    "\n",
+    "## Implementation\n",
+    "\n",
+    "A Theano function graph is composed of two types of nodes; Variable nodes which represent data and Apply node which apply Ops (which represent some computation) to Variables to produce new Variables.\n",
+    "\n",
+    "From this point of view, a node that applies a Scan op is just like any other. Internally, however, it is very different from most Ops.\n",
+    "\n",
+    "Inside a Scan op is yet another Theano graph which represents the computation to be performed at every iteration of the loop. During compilation, that graph is compiled into a function and, during execution, the Scan op will call that function repeatedly on its inputs to produce its outputs.\n",
+    "\n",
+    "## Example 1 : As simple as it gets\n",
+    "\n",
+    "Scan's interface is complex and, thus, best introduced by examples. So, let's dive right in and start with a simple example; perform an element-wise multiplication between two vectors. \n",
+    "\n",
+    "This particular example is simple enough that Scan is not the best way to do things but we'll gradually work our way to more complex examples where Scan gets more interesting.\n",
+    "\n",
+    "Let's first setup our use case by defining Theano variables for the inputs :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import theano\n",
+    "import theano.tensor as T\n",
+    "import numpy as np\n",
+    "\n",
+    "vector1 = T.vector('vector1')\n",
+    "vector2 = T.vector('vector2')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we call the `scan()` function. It has many parameters but, because our use case is simple, we only need two of them. We'll introduce other parameters in the next examples.\n",
+    "\n",
+    "The parameter `sequences` allows us to specify variables that Scan should iterate over as it loops. The first iteration will take as input the first element of every sequence, the second iteration will take as input the second element of every sequence, etc. These individual element have will have one less dimension than the original sequences. For example, for a matrix sequence, the individual elements will be vectors.\n",
+    "\n",
+    "The parameter `fn` receives a function or lambda expression that expresses the computation to do at every iteration. It operates on the symbolic inputs to produce symbolic outputs. It will **only ever be called once**, to assemble the Theano graph used by Scan at every the iterations.\n",
+    "\n",
+    "Since we wish to iterate over both `vector1` and `vector2` simultaneously, we provide them as sequences. This means that every iteration will operate on two inputs: an element from `vector1` and the corresponding element from `vector2`. \n",
+    "\n",
+    "Because what we want is the elementwise product between the vectors, we provide a lambda expression that, given an element `a` from `vector1` and an element `b` from `vector2` computes and return the product."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "output, updates = theano.scan(fn=lambda a, b : a * b,\n",
+    "                              sequences=[vector1, vector2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calling `scan()`, we see that it returns two outputs.\n",
+    "\n",
+    "The first output contains the outputs of `fn` from every timestep concatenated into a tensor. In our case, the output of a single timestep is a scalar so output is a vector where `output[i]` is the output of the i-th iteration.\n",
+    "\n",
+    "The second output details if and how the execution of the Scan updates any shared variable in the graph. It should be provided as an argument when compiling the Theano function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "f = theano.function(inputs=[vector1, vector2],\n",
+    "                    outputs=output,\n",
+    "                    updates=updates)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If `updates` is omitted, the state of any shared variables modified by Scan will not be updated properly. Random number sampling, for instance, relies on shared variables. If `updates` is not provided, the state of the random number generator won't be updated properly and the same numbers might be sampled repeatedly. **Always** provide `updates` when compiling your Theano function.\n",
+    "\n",
+    "Now that we've defined how to do elementwise multiplication with Scan, we can see that the result is as expected :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "vector1_value = np.arange(0, 5).astype(theano.config.floatX) # [0,1,2,3,4]\n",
+    "vector2_value = np.arange(1, 6).astype(theano.config.floatX) # [1,2,3,4,5]\n",
+    "print(f(vector1_value, vector2_value))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An interesting thing is that we never explicitly told Scan how many iteration it needed to run. It was automatically inferred; when given sequences, Scan will run as many iterations as the length of the shortest sequence : "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print(f(vector1_value, vector2_value[:4]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 2 : Non-sequences\n",
+    "\n",
+    "In this example, we introduce another of Scan's features; non-sequences. To demonstrate how to use them, we use Scan to compute the activations of  a linear MLP layer over a minibatch.\n",
+    "\n",
+    "It is not yet a use case where Scan is truly useful but it introduces a requirement that sequences cannot fulfill; if we want to use Scan to iterate over the minibatch elements and compute the activations for each of them, then we need some variables (the parameters of the layer), to be available 'as is' at every iteration of the loop. We do *not* want Scan to iterate over them and give only part of them at every iteration.\n",
+    "\n",
+    "Once again, we begin by setting up our Theano variables :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "X = T.matrix('X') # Minibatch of data\n",
+    "W = T.matrix('W') # Weights of the layer\n",
+    "b = T.vector('b') # Biases of the layer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the sake of variety, in this example we define the computation to be done at every iteration of the loop using a Python function, `step()`, instead of a lambda expression.\n",
+    "\n",
+    "To have the full weight matrix W and the full bias vector b available at every iteration, we use the argument non_sequences. Contrary to sequences, non-sequences are not iterated upon by Scan. Every non-sequence is passed as input to every iteration.\n",
+    "\n",
+    "This means that our `step()` function will need to operate on three symbolic inputs; one for our sequence X and one for each of our non-sequences W and b. \n",
+    "\n",
+    "The inputs that correspond to the non-sequences are **always** last and in the same order at the non-sequences are provided to Scan. This means that the correspondence between the inputs of the `step()` function and the arguments to `scan()` is the following : \n",
+    "\n",
+    "* `v` : individual element of the sequence `X` \n",
+    "* `W` and `b` : non-sequences `W` and `b`, respectively"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def step(v, W, b):\n",
+    "    return T.dot(v, W) + b\n",
+    "\n",
+    "output, updates = theano.scan(fn=step,\n",
+    "                              sequences=[X],\n",
+    "                              non_sequences=[W, b])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now compile our Theano function and see that it gives the expected results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "f = theano.function(inputs=[X, W, b],\n",
+    "                    outputs=output,\n",
+    "                    updates=updates)\n",
+    "\n",
+    "X_value = np.arange(-3, 3).reshape(3, 2).astype(theano.config.floatX)\n",
+    "W_value = np.eye(2).astype(theano.config.floatX)\n",
+    "b_value = np.arange(2).astype(theano.config.floatX)\n",
+    "print(f(X_value, W_value, b_value))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 3 : Reusing outputs from the previous iterations\n",
+    "\n",
+    "In this example, we will use Scan to compute a cumulative sum over the first dimension of a matrix $M$. This means that the output will be a matrix $S$ in which the first row will be equal to the first row of $M$, the second row will be equal to the sum of the two first rows of $M$, and so on.\n",
+    "\n",
+    "Another way to express this, which is the way we will implement here, is that $S[t] = S[t-1] + M[t]$. Implementing this with Scan would involve iterating over the rows of the matrix $M$ and, at every iteration, reuse the cumulative row that was output at the previous iteration and return the sum of it and the current row of $M$.\n",
+    "\n",
+    "If we assume for a moment that we can get Scan to provide the output value from the previous iteration as an input for every iteration, implementing a step function is simple :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def step(m_row, cumulative_sum):\n",
+    "    return m_row + cumulative_sum"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The trick part is informing Scan that our step function expects as input the output of a previous iteration. To achieve this, we need to use a new parameter of the `scan()` function: `outputs_info`. This parameter is used to tell Scan how we intend to use each of the outputs that are computed at each iteration.\n",
+    "\n",
+    "This parameter can be omitted (like we did so far) when the step function doesn't depend on any output of a previous iteration. However, now that we wish to have recurrent outputs, we need to start using it.\n",
+    "\n",
+    "`outputs_info` takes a sequence with one element for every output of the `step()` function :\n",
+    "* For a **non-recurrent output** (like in every example before this one), the element should be `None`.\n",
+    "* For a **simple recurrent output** (iteration $t$ depends on the value at iteration $t-1$), the element must be a tensor. Scan will interpret it as being an initial state for a recurrent output and give it as input to the first iteration, pretending it is the output value from a previous iteration. For subsequent iterations, Scan will automatically handle giving the previous output value as an input.\n",
+    "\n",
+    "The `step()` function needs to expect one additional input for each simple recurrent output. These inputs correspond to outputs from previous iteration and are **always** after the inputs that correspond to sequences but before those that correspond to non-sequences. The are received by the `step()` function in the order in which the recurrent outputs are declared in the outputs_info sequence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "M = T.matrix('X')\n",
+    "s = T.vector('s') # Initial value for the cumulative sum\n",
+    "\n",
+    "output, updates = theano.scan(fn=step,\n",
+    "                              sequences=[M],\n",
+    "                              outputs_info=[s])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now compile and test the Theano function :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "f = theano.function(inputs=[M, s],\n",
+    "                    outputs=output,\n",
+    "                    updates=updates)\n",
+    "\n",
+    "M_value = np.arange(9).reshape(3, 3).astype(theano.config.floatX)\n",
+    "s_value = np.zeros((3, ), dtype=theano.config.floatX)\n",
+    "print(f(M_value, s_value))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An important thing to notice here, is that the output computed by the Scan does **not** include the initial state that we provided. It only outputs the states that it has computed itself.\n",
+    "\n",
+    "If we want to have both the initial state and the computed states in the same Theano variable, we have to join them ourselves."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 4 : Reusing outputs from multiple past iterations\n",
+    "\n",
+    "The Fibonacci sequence is a sequence of numbers F where the two first numbers both 1 and every subsequence number is defined as such : $F_n = F_{n-1} + F_{n-2}$. Thus, the Fibonacci sequence goes : 1, 1, 2, 3, 5, 8, 13, ...\n",
+    "\n",
+    "In this example, we will cover how to compute part of the Fibonacci sequence using Scan. Most of the tools required to achieve this have been introduced in the previous examples. The only one missing is the ability to use, at iteration $i$, outputs from iterations older than $i-1$.\n",
+    "\n",
+    "Also, since every example so far had only one output at every iteration of the loop, we will also compute, at each timestep, the ratio between the new term of the Fibonacci sequence and the previous term.\n",
+    "\n",
+    "Writing an appropriate step function given two inputs, representing the two previous terms of the Fibonacci sequence, is easy:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def step(f_minus2, f_minus1):\n",
+    "    new_f = f_minus2 + f_minus1\n",
+    "    ratio = new_f / f_minus1\n",
+    "    return new_f, ratio"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The next step is defining the value of `outputs_info`.\n",
+    "\n",
+    "Recall that, for **non-recurrent outputs**, the value is `None` and, for **simple recurrent outputs**, the value is a single initial state. For **general recurrent outputs**, where iteration $t$ may depend on multiple past values, the value is a dictionary. That dictionary has two values:\n",
+    "* taps : list declaring which previous values of that output every iteration will need. `[-3, -2, -1]` would mean every iteration should take as input the last 3 values of that output. `[-2]` would mean every iteration should take as input the value of that output from two iterations ago.\n",
+    "* initial : tensor of initial values. If every initial value has $n$ dimensions, `initial` will be a single tensor of $n+1$ dimensions with as many initial values as the oldest requested tap. In the case of the Fibonacci sequence, the individual initial values are scalars so the `initial` will be a vector. \n",
+    "\n",
+    "In our example, we have two outputs. The first output is the next computed term of the Fibonacci sequence so every iteration should take as input the two last values of that output. The second output is the ratio between successive terms and we don't reuse its value so this output is non-recurrent. We define the value of `outputs_info` as such :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "f_init = T.fvector()\n",
+    "outputs_info = [dict(initial=f_init, taps=[-2, -1]),\n",
+    "                None]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we've defined the step function and the properties of our outputs, we can call the `scan()` function. Because the `step()` function has multiple outputs, the first output of `scan()` function will be a list of tensors: the first tensor containing all the states of the first output and the second tensor containing all the states of the second input.\n",
+    "\n",
+    "In every previous example, we used sequences and Scan automatically inferred the number of iterations it needed to run from the length of these\n",
+    "sequences. Now that we have no sequence, we need to explicitly tell Scan how many iterations to run using the `n_step` parameter. The value can be real or symbolic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "output, updates = theano.scan(fn=step,\n",
+    "                              outputs_info=outputs_info,\n",
+    "                              n_steps=10)\n",
+    "\n",
+    "next_fibonacci_terms = output[0]\n",
+    "ratios_between_terms = output[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's compile our Theano function which will take a vector of consecutive values from the Fibonacci sequence and compute the next 10 values :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "f = theano.function(inputs=[f_init],\n",
+    "                    outputs=[next_fibonacci_terms, ratios_between_terms],\n",
+    "                    updates=updates)\n",
+    "\n",
+    "out = f([1, 1])\n",
+    "print(out[0])\n",
+    "print(out[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Precisions about the order of the arguments to the step function\n",
+    "\n",
+    "When we start using many sequences, recurrent outputs and non-sequences, it's easy to get confused regarding the order in which the step function receives the corresponding inputs. Below is the full order:\n",
+    "\n",
+    "* Element from the first sequence\n",
+    "* ...\n",
+    "* Element from the last sequence\n",
+    "* First requested tap from first recurrent output\n",
+    "* ...\n",
+    "* Last requested tap from first recurrent output\n",
+    "* ...\n",
+    "* First requested tap from last recurrent output\n",
+    "* ...\n",
+    "* Last requested tap from last recurrent output\n",
+    "* First non-sequence\n",
+    "* ...\n",
+    "* Last non-sequence"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## When to use Scan and when not to\n",
+    "\n",
+    "Scan is not appropriate for every problem. Here's some information to help you figure out if Scan is the best solution for a given use case.\n",
+    "\n",
+    "### Execution speed\n",
+    "\n",
+    "Using Scan in a Theano function typically makes it slighly slower compared to the equivalent Theano graph in which the loop is unrolled. Both of these approaches tend to be much slower than a vectorized implementation in which large chunks of the computation can be done in parallel.\n",
+    "\n",
+    "### Compilation speed\n",
+    "\n",
+    "Scan also adds an overhead to the compilation, potentially making it slower, but using it can also dramatically reduce the size of your graph, making compilation much faster. In the end, the effect of Scan on compilation speed will heavily depend on the size of the graph with and without Scan.\n",
+    "\n",
+    "The compilation speed of a Theano function using Scan will usually be comparable to one in which the loop is unrolled if the number of iterations is small. It the number of iterations is large, however, the compilation will usually be much faster with Scan.\n",
+    "\n",
+    "### In summary\n",
+    "\n",
+    "If you have one of the following cases, Scan can help :\n",
+    "* A vectorized implementation is not possible (due to the nature of the computation and/or memory usage)\n",
+    "* You want to do a large or variable number of iterations\n",
+    "\n",
+    "If you have one of the following cases, you should consider other options :\n",
+    "* A vectorized implementation could perform the same computation => Use the vectorized approach. It will often be faster during both compilation and execution.\n",
+    "* You want to do a small, fixed, number of iterations (ex: 2 or 3) => It's probably better to simply unroll the computation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "### Exercise 1 - Computing a polynomial\n",
+    "\n",
+    "In this exercise, the initial version already works. It computes the value of a polynomial ($n_0 + n_1 x + n_2 x^2 + ... $) of at most 10000 degrees given the coefficients of the various terms and the value of x.\n",
+    "\n",
+    "You must modify it such that the reduction (the sum() call) is done by Scan."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "coefficients = theano.tensor.vector(\"coefficients\")\n",
+    "x = T.scalar(\"x\")\n",
+    "max_coefficients_supported = 10000\n",
+    "\n",
+    "def step(coeff, power, free_var):\n",
+    "    return coeff * free_var ** power\n",
+    "\n",
+    "# Generate the components of the polynomial\n",
+    "full_range=theano.tensor.arange(max_coefficients_supported)\n",
+    "components, updates = theano.scan(fn=step,\n",
+    "                                  outputs_info=None,\n",
+    "                                  sequences=[coefficients, full_range],\n",
+    "                                  non_sequences=x)\n",
+    "\n",
+    "polynomial = components.sum()\n",
+    "calculate_polynomial = theano.function(inputs=[coefficients, x],\n",
+    "                                       outputs=polynomial,\n",
+    "                                       updates=updates)\n",
+    "\n",
+    "test_coeff = np.asarray([1, 0, 2], dtype=theano.config.floatX)\n",
+    "print(calculate_polynomial(test_coeff, 3))\n",
+    "# 19.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Solution** : run the cell below to display the solution to this exercise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%load scan_ex1_solution.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercise 2 - Sampling without replacement\n",
+    "\n",
+    "In this exercise, the goal is to implement a Theano function that :\n",
+    "* takes as input a vector of probabilities and a scalar\n",
+    "* performs sampling without replacements from those probabilities as many times as the value of the scalar\n",
+    "* returns a vector containing the indices of the sampled elements.\n",
+    "\n",
+    "Partial code is provided to help with the sampling of random numbers since this is not something that was covered in this tutorial."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "probabilities = T.vector()\n",
+    "nb_samples = T.iscalar()\n",
+    "\n",
+    "rng = T.shared_randomstreams.RandomStreams(1234)\n",
+    "\n",
+    "def sample_from_pvect(pvect):\n",
+    "    \"\"\" Provided utility function: given a symbolic vector of\n",
+    "    probabilities (which MUST sum to 1), sample one element\n",
+    "    and return its index.\n",
+    "    \"\"\"\n",
+    "    onehot_sample = rng.multinomial(n=1, pvals=pvect)\n",
+    "    sample = onehot_sample.argmax()\n",
+    "    return sample\n",
+    "\n",
+    "def set_p_to_zero(pvect, i):\n",
+    "    \"\"\" Provided utility function: given a symbolic vector of\n",
+    "    probabilities and an index 'i', set the probability of the\n",
+    "    i-th element to 0 and renormalize the probabilities so they\n",
+    "    sum to 1.\n",
+    "    \"\"\"\n",
+    "    new_pvect = T.set_subtensor(pvect[i], 0.)\n",
+    "    new_pvect = new_pvect / new_pvect.sum()\n",
+    "    return new_pvect\n",
+    "    \n",
+    "\n",
+    "# TODO use Scan to sample from the vector of probabilities and\n",
+    "# symbolically obtain 'samples' the vector of sampled indices.\n",
+    "samples = None\n",
+    "\n",
+    "# Compiling the function\n",
+    "f = theano.function(inputs=[probabilities, nb_samples],\n",
+    "                    outputs=[samples])\n",
+    "\n",
+    "# Testing the function\n",
+    "test_probs = np.asarray([0.6, 0.3, 0.1], dtype=theano.config.floatX)\n",
+    "for i in range(10):\n",
+    "    print(f(test_probs, 2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Solution** : run the cell below to display the solution to this exercise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%load scan_ex2_solution.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.4.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/deep-learning/theano-tutorial/theano_mlp/theano_mlp.ipynb b/deep-learning/theano-tutorial/theano_mlp/theano_mlp.ipynb
new file mode 100644
index 0000000..e5349fa
--- /dev/null
+++ b/deep-learning/theano-tutorial/theano_mlp/theano_mlp.ipynb
@@ -0,0 +1,787 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multilayer Perceptron in Theano\n",
+    "\n",
+    "Credits: Forked from [summerschool2015](https://github.com/mila-udem/summerschool2015) by mila-udem\n",
+    "\n",
+    "This notebook describes how to implement the building blocks for a multilayer perceptron in Theano, in particular how to define and combine layers.\n",
+    "\n",
+    "We will continue using the MNIST digits classification dataset, still using Fuel.\n",
+    "\n",
+    "## The Model\n",
+    "We will focus on fully-connected layers, with an elementwise non-linearity on each hidden layer, and a softmax layer (similar to the logistic regression model) for classification on the top layer.\n",
+    "\n",
+    "### A class for hidden layers\n",
+    "This class does all its work in its constructor:\n",
+    "- Create and initialize shared variables for its parameters (`W` and `b`), unless there are explicitly provided. Note that the initialization scheme for `W` is the one described in [Glorot & Bengio (2010)](http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf).\n",
+    "- Build the Theano expression for the value of the output units, given a variable for the input.\n",
+    "- Store the input, output, and shared parameters as members."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy\n",
+    "import theano\n",
+    "from theano import tensor\n",
+    "\n",
+    "# Set lower precision float, otherwise the notebook will take too long to run\n",
+    "theano.config.floatX = 'float32'\n",
+    "\n",
+    "\n",
+    "class HiddenLayer(object):\n",
+    "    def __init__(self, rng, input, n_in, n_out, W=None, b=None,\n",
+    "                 activation=tensor.tanh):\n",
+    "        \"\"\"\n",
+    "        Typical hidden layer of a MLP: units are fully-connected and have\n",
+    "        sigmoidal activation function. Weight matrix W is of shape (n_in,n_out)\n",
+    "        and the bias vector b is of shape (n_out,).\n",
+    "\n",
+    "        NOTE : The nonlinearity used here is tanh\n",
+    "\n",
+    "        Hidden unit activation is given by: tanh(dot(input,W) + b)\n",
+    "\n",
+    "        :type rng: numpy.random.RandomState\n",
+    "        :param rng: a random number generator used to initialize weights\n",
+    "\n",
+    "        :type input: theano.tensor.dmatrix\n",
+    "        :param input: a symbolic tensor of shape (n_examples, n_in)\n",
+    "\n",
+    "        :type n_in: int\n",
+    "        :param n_in: dimensionality of input\n",
+    "\n",
+    "        :type n_out: int\n",
+    "        :param n_out: number of hidden units\n",
+    "\n",
+    "        :type activation: theano.Op or function\n",
+    "        :param activation: Non linearity to be applied in the hidden layer\n",
+    "        \"\"\"\n",
+    "        self.input = input\n",
+    "\n",
+    "        # `W` is initialized with `W_values` which is uniformely sampled\n",
+    "        # from sqrt(-6./(n_in+n_hidden)) and sqrt(6./(n_in+n_hidden))\n",
+    "        # for tanh activation function\n",
+    "        # the output of uniform if converted using asarray to dtype\n",
+    "        # theano.config.floatX so that the code is runable on GPU\n",
+    "        # Note : optimal initialization of weights is dependent on the\n",
+    "        #        activation function used (among other things).\n",
+    "        #        For example, results presented in Glorot & Bengio (2010)\n",
+    "        #        suggest that you should use 4 times larger initial weights\n",
+    "        #        for sigmoid compared to tanh\n",
+    "        if W is None:\n",
+    "            W_values = numpy.asarray(\n",
+    "                rng.uniform(\n",
+    "                    low=-numpy.sqrt(6. / (n_in + n_out)),\n",
+    "                    high=numpy.sqrt(6. / (n_in + n_out)),\n",
+    "                    size=(n_in, n_out)\n",
+    "                ),\n",
+    "                dtype=theano.config.floatX\n",
+    "            )\n",
+    "            if activation == tensor.nnet.sigmoid:\n",
+    "                W_values *= 4\n",
+    "\n",
+    "            W = theano.shared(value=W_values, name='W', borrow=True)\n",
+    "\n",
+    "        if b is None:\n",
+    "            b_values = numpy.zeros((n_out,), dtype=theano.config.floatX)\n",
+    "            b = theano.shared(value=b_values, name='b', borrow=True)\n",
+    "\n",
+    "        self.W = W\n",
+    "        self.b = b\n",
+    "\n",
+    "        lin_output = tensor.dot(input, self.W) + self.b\n",
+    "        self.output = (\n",
+    "            lin_output if activation is None\n",
+    "            else activation(lin_output)\n",
+    "        )\n",
+    "        # parameters of the model\n",
+    "        self.params = [self.W, self.b]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### A softmax class for the output\n",
+    "This class performs computations similar to what was performed in the [logistic regression tutorial](../intro_theano/logistic_regression.ipynb).\n",
+    "\n",
+    "Here as well, the expression for the output is built in the class constructor, which takes the input as argument. We also add the target, `y`, and store it as an argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class LogisticRegression(object):\n",
+    "    \"\"\"Multi-class Logistic Regression Class\n",
+    "\n",
+    "    The logistic regression is fully described by a weight matrix :math:`W`\n",
+    "    and bias vector :math:`b`. Classification is done by projecting data\n",
+    "    points onto a set of hyperplanes, the distance to which is used to\n",
+    "    determine a class membership probability.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, input, target, n_in, n_out):\n",
+    "        \"\"\" Initialize the parameters of the logistic regression\n",
+    "\n",
+    "        :type input: theano.tensor.TensorType\n",
+    "        :param input: symbolic variable that describes the input of the\n",
+    "                      architecture (one minibatch)\n",
+    "        \n",
+    "        :type target: theano.tensor.TensorType\n",
+    "        :type target: column tensor that describes the target for training\n",
+    "\n",
+    "        :type n_in: int\n",
+    "        :param n_in: number of input units, the dimension of the space in\n",
+    "                     which the datapoints lie\n",
+    "\n",
+    "        :type n_out: int\n",
+    "        :param n_out: number of output units, the dimension of the space in\n",
+    "                      which the labels lie\n",
+    "\n",
+    "        \"\"\"\n",
+    "        # keep track of model input and target.\n",
+    "        # We store a flattened (vector) version of target as y, which is easier to handle\n",
+    "        self.input = input\n",
+    "        self.target = target\n",
+    "        self.y = target.flatten()\n",
+    "\n",
+    "        self.W = theano.shared(value=numpy.zeros((n_in, n_out), dtype=theano.config.floatX),\n",
+    "                               name='W',\n",
+    "                               borrow=True)\n",
+    "        self.b = theano.shared(value=numpy.zeros((n_out,), dtype=theano.config.floatX),\n",
+    "                               name='b',\n",
+    "                               borrow=True)\n",
+    "    \n",
+    "        # class-membership probabilities\n",
+    "        self.p_y_given_x = tensor.nnet.softmax(tensor.dot(input, self.W) + self.b)\n",
+    "\n",
+    "        # class whose probability is maximal\n",
+    "        self.y_pred = tensor.argmax(self.p_y_given_x, axis=1)\n",
+    "\n",
+    "        # parameters of the model\n",
+    "        self.params = [self.W, self.b]\n",
+    "        \n",
+    "\n",
+    "    def negative_log_likelihood(self):\n",
+    "        \"\"\"Return the mean of the negative log-likelihood of the prediction\n",
+    "        of this model under a given target distribution.\n",
+    "\n",
+    "        Note: we use the mean instead of the sum so that\n",
+    "              the learning rate is less dependent on the batch size\n",
+    "        \"\"\"\n",
+    "        log_prob = tensor.log(self.p_y_given_x)\n",
+    "        log_likelihood = log_prob[tensor.arange(self.y.shape[0]), self.y]\n",
+    "        loss = - log_likelihood.mean()\n",
+    "        return loss\n",
+    "\n",
+    "    def errors(self):\n",
+    "        \"\"\"Return a float representing the number of errors in the minibatch\n",
+    "        over the total number of examples of the minibatch\n",
+    "        \"\"\"\n",
+    "        misclass_nb = tensor.neq(self.y_pred, self.y)\n",
+    "        misclass_rate = misclass_nb.mean()\n",
+    "        return misclass_rate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The MLP class\n",
+    "That class brings together the different parts of the model.\n",
+    "\n",
+    "It also adds additional controls on the training of the full network, for instance an expression for L1 or L2 regularization (weight decay).\n",
+    "\n",
+    "We can specify an arbitrary number of hidden layers, providing an empty one will reproduce the logistic regression model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "class MLP(object):\n",
+    "    \"\"\"Multi-Layer Perceptron Class\n",
+    "\n",
+    "    A multilayer perceptron is a feedforward artificial neural network model\n",
+    "    that has one layer or more of hidden units and nonlinear activations.\n",
+    "    Intermediate layers usually have as activation function tanh or the\n",
+    "    sigmoid function (defined here by a ``HiddenLayer`` class)  while the\n",
+    "    top layer is a softmax layer (defined here by a ``LogisticRegression``\n",
+    "    class).\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, rng, input, target, n_in, n_hidden, n_out, activation=tensor.tanh):\n",
+    "        \"\"\"Initialize the parameters for the multilayer perceptron\n",
+    "\n",
+    "        :type rng: numpy.random.RandomState\n",
+    "        :param rng: a random number generator used to initialize weights\n",
+    "\n",
+    "        :type input: theano.tensor.TensorType\n",
+    "        :param input: symbolic variable that describes the input of the\n",
+    "        architecture (one minibatch)\n",
+    "        \n",
+    "        :type target: theano.tensor.TensorType\n",
+    "        :type target: column tensor that describes the target for training\n",
+    "\n",
+    "        :type n_in: int\n",
+    "        :param n_in: number of input units, the dimension of the space in\n",
+    "        which the datapoints lie\n",
+    "\n",
+    "        :type n_hidden: list of int\n",
+    "        :param n_hidden: number of hidden units in each hidden layer\n",
+    "\n",
+    "        :type n_out: int\n",
+    "        :param n_out: number of output units, the dimension of the space in\n",
+    "        which the labels lie\n",
+    "        \n",
+    "        :type activation: theano.Op or function\n",
+    "        :param activation: Non linearity to be applied in all hidden layers\n",
+    "        \"\"\"\n",
+    "        # keep track of model input and target.\n",
+    "        # We store a flattened (vector) version of target as y, which is easier to handle\n",
+    "        self.input = input\n",
+    "        self.target = target\n",
+    "        self.y = target.flatten()\n",
+    "\n",
+    "        # Build all necessary hidden layers and chain them\n",
+    "        self.hidden_layers = []\n",
+    "        layer_input = input\n",
+    "        layer_n_in = n_in\n",
+    "\n",
+    "        for nh in n_hidden:\n",
+    "            hidden_layer = HiddenLayer(\n",
+    "                rng=rng,\n",
+    "                input=layer_input,\n",
+    "                n_in=layer_n_in,\n",
+    "                n_out=nh,\n",
+    "                activation=activation)\n",
+    "            self.hidden_layers.append(hidden_layer)\n",
+    "\n",
+    "            # prepare variables for next layer\n",
+    "            layer_input = hidden_layer.output\n",
+    "            layer_n_in = nh\n",
+    "\n",
+    "        # The logistic regression layer gets as input the hidden units of the hidden layer,\n",
+    "        # and the target\n",
+    "        self.log_reg_layer = LogisticRegression(\n",
+    "            input=layer_input,\n",
+    "            target=target,\n",
+    "            n_in=layer_n_in,\n",
+    "            n_out=n_out)\n",
+    "        \n",
+    "        # self.params has all the parameters of the model,\n",
+    "        # self.weights contains only the `W` variables.\n",
+    "        # We also give unique name to the parameters, this will be useful to save them.\n",
+    "        self.params = []\n",
+    "        self.weights = []\n",
+    "        layer_idx = 0\n",
+    "        for hl in self.hidden_layers:\n",
+    "            self.params.extend(hl.params)\n",
+    "            self.weights.append(hl.W)\n",
+    "            for hlp in hl.params:\n",
+    "                prev_name = hlp.name\n",
+    "                hlp.name = 'layer' + str(layer_idx) + '.' + prev_name\n",
+    "            layer_idx += 1\n",
+    "        self.params.extend(self.log_reg_layer.params)\n",
+    "        self.weights.append(self.log_reg_layer.W)\n",
+    "        for lrp in self.log_reg_layer.params:\n",
+    "            prev_name = lrp.name\n",
+    "            lrp.name = 'layer' + str(layer_idx) + '.' + prev_name\n",
+    "\n",
+    "        # L1 norm ; one regularization option is to enforce L1 norm to be small\n",
+    "        self.L1 = sum(abs(W).sum() for W in self.weights)\n",
+    "\n",
+    "        # square of L2 norm ; one regularization option is to enforce square of L2 norm to be small\n",
+    "        self.L2_sqr = sum((W ** 2).sum() for W in self.weights)\n",
+    "    \n",
+    "    def negative_log_likelihood(self):\n",
+    "        # negative log likelihood of the MLP is given by the negative\n",
+    "        # log likelihood of the output of the model, computed in the\n",
+    "        # logistic regression layer\n",
+    "        return self.log_reg_layer.negative_log_likelihood()\n",
+    "\n",
+    "    def errors(self):\n",
+    "        # same holds for the function computing the number of errors\n",
+    "        return self.log_reg_layer.errors()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training Procedure\n",
+    "We will re-use the same training algorithm: stochastic gradient descent with mini-batches, and the same early-stopping criterion. Here, the number of parameters to train is variable, and we have to wait until the MLP model is actually instantiated to have an expression for the cost and the updates.\n",
+    "\n",
+    "### Gradient and Updates\n",
+    "Let us define helper functions for getting expressions for the gradient of the cost wrt the parameters, and the parameter updates. The following ones are simple, but many variations can exist, for instance:\n",
+    "- regularized costs, including L1 or L2 regularization\n",
+    "- more complex learning rules, such as momentum, RMSProp, ADAM, ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def nll_grad(mlp_model):\n",
+    "    loss = mlp_model.negative_log_likelihood()\n",
+    "    params = mlp_model.params\n",
+    "    grads = theano.grad(loss, wrt=params)\n",
+    "    # Return (param, grad) pairs\n",
+    "    return zip(params, grads)\n",
+    "\n",
+    "def sgd_updates(params_and_grads, learning_rate):\n",
+    "    return [(param, param - learning_rate * grad)\n",
+    "            for param, grad in params_and_grads]\n",
+    "\n",
+    "def get_simple_training_fn(mlp_model, learning_rate):\n",
+    "    inputs = [mlp_model.input, mlp_model.target]\n",
+    "    params_and_grads = nll_grad(mlp_model)\n",
+    "    updates = sgd_updates(params_and_grads, learning_rate=lr)\n",
+    "    \n",
+    "    return theano.function(inputs=inputs, outputs=[], updates=updates)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def regularized_cost_grad(mlp_model, L1_reg, L2_reg):\n",
+    "    loss = (mlp_model.negative_log_likelihood() +\n",
+    "            L1_reg * mlp_model.L1 + \n",
+    "            L2_reg * mlp_model.L2_sqr)\n",
+    "    params = mlp_model.params\n",
+    "    grads = theano.grad(loss, wrt=params)\n",
+    "    # Return (param, grad) pairs\n",
+    "    return zip(params, grads)\n",
+    "\n",
+    "def get_regularized_training_fn(mlp_model, L1_reg, L2_reg, learning_rate):\n",
+    "    inputs = [mlp_model.input, mlp_model.target]\n",
+    "    params_and_grads = regularized_cost_grad(mlp_model, L1_reg, L2_reg)\n",
+    "    updates = sgd_updates(params_and_grads, learning_rate=lr)\n",
+    "    return theano.function(inputs, updates=updates)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Testing function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def get_test_fn(mlp_model):\n",
+    "    return theano.function([mlp_model.input, mlp_model.target], mlp_model.errors())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training the Model\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training procedure\n",
+    "We first need to define a few parameters for the training loop and the early stopping procedure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import timeit\n",
+    "from fuel.streams import DataStream\n",
+    "from fuel.schemes import SequentialScheme\n",
+    "from fuel.transformers import Flatten\n",
+    "\n",
+    "## early-stopping parameters tuned for 1-2 min runtime\n",
+    "def sgd_training(train_model, test_model, train_set, valid_set, test_set, model_name='mlp_model',\n",
+    "                 # maximum number of epochs\n",
+    "                 n_epochs=20,\n",
+    "                 # look at this many examples regardless\n",
+    "                 patience=5000,\n",
+    "                 # wait this much longer when a new best is found\n",
+    "                 patience_increase=2,\n",
+    "                 # a relative improvement of this much is considered significant\n",
+    "                 improvement_threshold=0.995,\n",
+    "                 batch_size=20):\n",
+    "\n",
+    "    n_train_batches = train_set.num_examples // batch_size\n",
+    "\n",
+    "    # Create data streams to iterate through the data.\n",
+    "    train_stream = Flatten(DataStream.default_stream(\n",
+    "        train_set, iteration_scheme=SequentialScheme(train_set.num_examples, batch_size)))\n",
+    "    valid_stream = Flatten(DataStream.default_stream(\n",
+    "        valid_set, iteration_scheme=SequentialScheme(valid_set.num_examples, batch_size)))\n",
+    "    test_stream = Flatten(DataStream.default_stream(\n",
+    "        test_set, iteration_scheme=SequentialScheme(test_set.num_examples, batch_size)))\n",
+    "\n",
+    "    # go through this many minibatches before checking the network on the validation set;\n",
+    "    # in this case we check every epoch\n",
+    "    validation_frequency = min(n_train_batches, patience / 2)\n",
+    "\n",
+    "    best_validation_loss = numpy.inf\n",
+    "    test_score = 0.\n",
+    "    start_time = timeit.default_timer()\n",
+    "\n",
+    "    done_looping = False\n",
+    "    epoch = 0\n",
+    "    while (epoch < n_epochs) and (not done_looping):\n",
+    "        epoch = epoch + 1\n",
+    "        minibatch_index = 0\n",
+    "        for minibatch_x, minibatch_y in train_stream.get_epoch_iterator():\n",
+    "            train_model(minibatch_x, minibatch_y)\n",
+    "\n",
+    "            # iteration number\n",
+    "            iter = (epoch - 1) * n_train_batches + minibatch_index\n",
+    "            if (iter + 1) % validation_frequency == 0:\n",
+    "                # compute zero-one loss on validation set\n",
+    "                validation_losses = []\n",
+    "                for valid_xi, valid_yi in valid_stream.get_epoch_iterator():\n",
+    "                    validation_losses.append(test_model(valid_xi, valid_yi))\n",
+    "                this_validation_loss = numpy.mean(validation_losses)\n",
+    "                print('epoch %i, minibatch %i/%i, validation error %f %%' %\n",
+    "                      (epoch,\n",
+    "                       minibatch_index + 1,\n",
+    "                       n_train_batches,\n",
+    "                       this_validation_loss * 100.))\n",
+    "\n",
+    "                # if we got the best validation score until now\n",
+    "                if this_validation_loss < best_validation_loss:\n",
+    "                    # improve patience if loss improvement is good enough\n",
+    "                    if this_validation_loss < best_validation_loss * improvement_threshold:\n",
+    "                        patience = max(patience, iter * patience_increase)\n",
+    "\n",
+    "                    best_validation_loss = this_validation_loss\n",
+    "\n",
+    "                    # test it on the test set\n",
+    "                    test_losses = []\n",
+    "                    for test_xi, test_yi in test_stream.get_epoch_iterator():\n",
+    "                        test_losses.append(test_model(test_xi, test_yi))\n",
+    "\n",
+    "                    test_score = numpy.mean(test_losses)\n",
+    "                    print('     epoch %i, minibatch %i/%i, test error of best model %f %%' %\n",
+    "                          (epoch,\n",
+    "                           minibatch_index + 1,\n",
+    "                           n_train_batches,\n",
+    "                           test_score * 100.))\n",
+    "\n",
+    "                    # save the best parameters\n",
+    "                    # build a name -> value dictionary\n",
+    "                    best = {param.name: param.get_value() for param in mlp_model.params}\n",
+    "                    numpy.savez('best_{}.npz'.format(model_name), **best)\n",
+    "\n",
+    "            minibatch_index += 1\n",
+    "            if patience <= iter:\n",
+    "                done_looping = True\n",
+    "                break\n",
+    "\n",
+    "    end_time = timeit.default_timer()\n",
+    "    print('Optimization complete with best validation score of %f %%, '\n",
+    "          'with test performance %f %%' %\n",
+    "          (best_validation_loss * 100., test_score * 100.))\n",
+    "\n",
+    "    print('The code ran for %d epochs, with %f epochs/sec (%.2fm total time)' %\n",
+    "          (epoch, 1. * epoch / (end_time - start_time), (end_time - start_time) / 60.))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We then load our data set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from fuel.datasets import MNIST\n",
+    "\n",
+    "# the full set is usually (0, 50000) for train, (50000, 60000) for valid and no slice for test.\n",
+    "# We only selected a subset to go faster.\n",
+    "train_set = MNIST(which_sets=('train',), sources=('features', 'targets'), subset=slice(0, 20000))\n",
+    "valid_set = MNIST(which_sets=('train',), sources=('features', 'targets'), subset=slice(20000, 24000))\n",
+    "test_set = MNIST(which_sets=('test',), sources=('features', 'targets'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Build the Model\n",
+    "Now is the time to specify and build a particular instance of the MLP. Let's start with one with a single hidden layer of 500 hidden units, and a tanh non-linearity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "rng = numpy.random.RandomState(1234)\n",
+    "x = tensor.matrix('x')\n",
+    "# The labels coming from Fuel are in a \"column\" format\n",
+    "y = tensor.icol('y')\n",
+    "\n",
+    "n_in = 28 * 28\n",
+    "n_out = 10\n",
+    "\n",
+    "mlp_model = MLP(\n",
+    "    rng=rng,\n",
+    "    input=x,\n",
+    "    target=y,\n",
+    "    n_in=n_in,\n",
+    "    n_hidden=[500],\n",
+    "    n_out=n_out,\n",
+    "    activation=tensor.tanh)\n",
+    "\n",
+    "lr = numpy.float32(0.1)\n",
+    "L1_reg = numpy.float32(0)\n",
+    "L2_reg = numpy.float32(0.0001)\n",
+    "\n",
+    "train_model = get_regularized_training_fn(mlp_model, L1_reg, L2_reg, lr)\n",
+    "test_model = get_test_fn(mlp_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Launch the training phase"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "sgd_training(train_model, test_model, train_set, valid_set, test_set)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## How can we make it better?\n",
+    "\n",
+    "- Max-column normalization\n",
+    "- Dropout\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ReLU activation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def relu(x):\n",
+    "    return x * (x > 0)\n",
+    "\n",
+    "rng = numpy.random.RandomState(1234)\n",
+    "\n",
+    "mlp_relu = MLP(\n",
+    "    rng=rng,\n",
+    "    input=x,\n",
+    "    target=y,\n",
+    "    n_in=n_in,\n",
+    "    n_hidden=[500],\n",
+    "    n_out=n_out,\n",
+    "    activation=relu)\n",
+    "\n",
+    "lr = numpy.float32(0.1)\n",
+    "L1_reg = numpy.float32(0)\n",
+    "L2_reg = numpy.float32(0.0001)\n",
+    "\n",
+    "train_relu = get_regularized_training_fn(mlp_relu, L1_reg, L2_reg, lr)\n",
+    "test_relu = get_test_fn(mlp_relu)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "sgd_training(train_relu, test_relu, train_set, valid_set, test_set, model_name='mlp_relu')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Momentum training (Adadelta, RMSProp, ...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# This implements simple momentum\n",
+    "def get_momentum_updates(params_and_grads, lr, rho):\n",
+    "    res = []\n",
+    "\n",
+    "    # numpy will promote (1 - rho) to float64 otherwise\n",
+    "    one = numpy.float32(1.)\n",
+    "    \n",
+    "    for p, g in params_and_grads:\n",
+    "        up = theano.shared(p.get_value() * 0)\n",
+    "        res.append((p, p - lr * up))\n",
+    "        res.append((up, rho * up + (one - rho) * g))\n",
+    "\n",
+    "    return res\n",
+    "\n",
+    "\n",
+    "# This implements the parameter updates for Adadelta\n",
+    "def get_adadelta_updates(params_and_grads, rho):\n",
+    "    up2 = [theano.shared(p.get_value() * 0, name=\"up2 for \" + p.name) for p, g in params_and_grads]\n",
+    "    grads2 = [theano.shared(p.get_value() * 0, name=\"grads2 for \" + p.name) for p, g in params_and_grads]\n",
+    "\n",
+    "    # This is dumb but numpy will promote (1 - rho) to float64 otherwise\n",
+    "    one = numpy.float32(1.)\n",
+    "    \n",
+    "    rg2up = [(rg2, rho * rg2 + (one - rho) * (g ** 2))\n",
+    "             for rg2, (p, g) in zip(grads2, params_and_grads)]\n",
+    "\n",
+    "    updir = [-(tensor.sqrt(ru2 + 1e-6) / tensor.sqrt(rg2 + 1e-6)) * g\n",
+    "             for (p, g), ru2, rg2 in zip(params_and_grads, up2, grads2)]\n",
+    "\n",
+    "    ru2up = [(ru2, rho * ru2 + (one - rho) * (ud ** 2))\n",
+    "             for ru2, ud in zip(up2, updir)]\n",
+    "\n",
+    "    param_up = [(p, p + ud) for (p, g), ud in zip(params_and_grads, updir)]\n",
+    "    \n",
+    "    return rg2up + ru2up + param_up\n",
+    "\n",
+    "# You can try to write an RMSProp function and train the model with it.\n",
+    "\n",
+    "def get_momentum_training_fn(mlp_model, L1_reg, L2_reg, lr, rho):\n",
+    "    inputs = [mlp_model.input, mlp_model.target]\n",
+    "    params_and_grads = regularized_cost_grad(mlp_model, L1_reg, L2_reg)\n",
+    "    updates = get_momentum_updates(params_and_grads, lr=lr, rho=rho)\n",
+    "    return theano.function(inputs, updates=updates)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "rng = numpy.random.RandomState(1234)\n",
+    "x = tensor.matrix('x')\n",
+    "# The labels coming from Fuel are in a \"column\" format\n",
+    "y = tensor.icol('y')\n",
+    "\n",
+    "n_in = 28 * 28\n",
+    "n_out = 10\n",
+    "\n",
+    "mlp_model = MLP(\n",
+    "    rng=rng,\n",
+    "    input=x,\n",
+    "    target=y,\n",
+    "    n_in=n_in,\n",
+    "    n_hidden=[500],\n",
+    "    n_out=n_out,\n",
+    "    activation=tensor.tanh)\n",
+    "\n",
+    "lr = numpy.float32(0.1)\n",
+    "L1_reg = numpy.float32(0)\n",
+    "L2_reg = numpy.float32(0.0001)\n",
+    "rho = numpy.float32(0.95)\n",
+    "\n",
+    "momentum_train = get_momentum_training_fn(mlp_model, L1_reg, L2_reg, lr=lr, rho=rho)\n",
+    "test_fn = get_test_fn(mlp_model)\n",
+    "\n",
+    "sgd_training(momentum_train, test_fn, train_set, valid_set, test_set, n_epochs=20, model_name='mlp_momentum')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.4.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}