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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# Binary Stochastic Neurons in Tensorflow\n", | |
"\n", | |
"###### A notebook and blog post by [R2RT](http://r2rt.com/)\n", | |
"\n", | |
"In this post, I introduce and discuss binary stochastic neurons, implement trainable binary stochastic neurons in Tensorflow, and conduct several simple experiments on the MNIST dataset to get a feel for their behavior. Binary stochastic neurons offer two advantages over real-valued neurons: they can act as a regularizer and they enable conditional computation by enabling a network to make yes/no decisions. Conditional computation opens the door to new and exciting neural network architectures, such as the choice of experts architecture and heirarchical multiscale neural networks, which I plan to discuss in future posts.\n", | |
"\n", | |
"### The binary stochastic neuron\n", | |
"\n", | |
"A binary stochastic neuron is a neuron with a noisy output: some proportion $p$ of the time it outputs 1, otherwise 0. An easy way to turn a real-valued input, $a$, into this proportion, $p$, is to set $p = \\text{sigm}(a)$, where $\\text{sigm}$ is the logistic sigmoid, $\\text{sigm}(x) = \\frac{1}{1 + \\exp(-x)}$. Thus, we define the binary stochastic neuron, $\\text{BSN}$, as:\n", | |
"\n", | |
"$$\\text{BSN}(a) = \\textbf{1}_{z\\ \\lt\\ \\text{sigm}(a)}$$\n", | |
"\n", | |
"where $\\textbf{1}_{x}$ is the [indicator function](https://en.wikipedia.org/wiki/Indicator_function) on the truth value of $x$ and $z \\sim U[0,1]$.\n", | |
"\n", | |
"\n", | |
"### Advantages of the binary stochastic neuron\n", | |
"\n", | |
"1. A binary stochastic neuron is a noisy modification of the logistic sigmoid: instead of outputting $p$, it outputs 1 with probability $p$ and 0 otherwise. Noise generally serves as a regularizer (see, e.g., [Srivastava et al. (2014)](http://www.jmlr.org/papers/v15/srivastava14a.html) and [Neelakantan et al. (2015)](https://arxiv.org/abs/1511.06807)), and so we might expect the same from binary stochastic neurons as compared to the logistic neurons. Indeed, this is the claimed \"unpublished result\" from the end of [Hinton et al.'s Coursera Lecture 9c](https://www.youtube.com/watch?v=LN0xtUuJsEI&list=PLoRl3Ht4JOcdU872GhiYWf6jwrk_SNhz9&index=41), which I test empirically in this post. Unfortunately, the results below show that binary stochastic neurons do not work so well as regularizers on the MNIST dataset, though they may serve as viable regularizers in other cases.\n", | |
"\n", | |
"2. More importantly, by enabling networks to make binary decisions, the binary stochastic neuron allows for conditional computation. This opens the door to some interesting new architectures. For example, instead of a mixture of experts architecture, which weights the outputs of several \"expert\" sub-networks and requires that all subnetworks be computed, we could use a *choice* of experts architecture, which conditionally uses expert sub-networks as needed. This architecture is implicitly proposed in [Bengio et al. (2013)](https://arxiv.org/abs/1308.3432), wherein the experiments use a choice of expert units architecture (i.e., a gated architecture where gates must be 1 or 0). Another example, proposed in [Bengio et al. (2013)](https://arxiv.org/abs/1308.3432) and implemented by [Chung et al. (2016)](https://arxiv.org/abs/1609.01704), is the Heirarchical Multiscale Recurrent Neural Network (HM-RNN) architecture, which achieves great results on language modelling tasks. Both of these architectures will be explored in future posts.\n", | |
"\n", | |
"### Training the binary stochastic neuron\n", | |
"\n", | |
"For any single trial, the binary stochastic neuron generally has a derivative of 0 and cannot be trained by simple backpropagation. To see this, consider that if $z \\neq \\text{sigm}(a)$ in the $\\text{BSN}$ function above, there exists a [neighborhood](https://en.wikipedia.org/wiki/Neighbourhood_(mathematics)) around $a$ such that the output of $\\text{BSN}(a)$ is unchanged (i.e., the derivative is 0). We get around this by *estimating* the derivative with respect to the *expected* loss, rather than calculating the derivative with respect to the outcome of a single trial. We can only estimate this derivative, because in any given trial, we only see the loss value with respect to the given noise -- we don't know what the loss would have been given another level of noise. We call a method that provides such an estimate an \"estimator\". An estimator is *unbiased* if the expectation of its estimate equals the expectation of the derivative it is estimating; otherwise, it is *biased*.\n", | |
"\n", | |
"In this post we implement the two estimators discussed in [Bengio et al. (2013)](https://arxiv.org/abs/1308.3432):\n", | |
"\n", | |
"1. The REINFORCE estimator, which is an unbiased estimator and a special case of the REINFORCE algorithm discussed in [Williams (1992)](http://link.springer.com/article/10.1007/BF00992696).\n", | |
"\n", | |
" The REINFORCE estimator estimates the expectation of $\\frac{\\partial L}{\\partial a}$ as $(\\text{BSN}(a) - \\text{sigm}(a))(L - c)$, where $c$ is a constant. [Bengio et al. (2013)](https://arxiv.org/abs/1308.3432) proves that:\n", | |
"\n", | |
" $$\\mathbb{E}[(\\text{BSN}(a) - \\text{sigm}(a))(L - c)] = \\mathbb{E}\\big[\\frac{\\partial L}{\\partial a}\\big].$$\n", | |
"\n", | |
" [Bengio et al. (2013)](https://arxiv.org/abs/1308.3432) further shows that to minimize the variance of the estimation, we choose:\n", | |
"\n", | |
" $$c = \\bar L = \\frac{\\mathbb{E}[\\text{BSN}(a) - \\text{sigm}(a))^2L]}{\\mathbb{E}[\\text{BSN}(a) - \\text{sigm}(a))^2]}$$\n", | |
"\n", | |
" which we can practically implement by keeping track of the numerator and denominator as a moving average. Interestingly, the REINFORCE estimator does not require any backpropagated loss gradient--it operates directly on the loss of the network.\n", | |
"\n", | |
"2. The straight through (ST) estimator, which is a biased estimator that was first proposed by [Hinton et al.'s Coursera Lecture 9c](https://www.youtube.com/watch?v=LN0xtUuJsEI&list=PLoRl3Ht4JOcdU872GhiYWf6jwrk_SNhz9&index=41).\n", | |
"\n", | |
" The ST estimator simply replaces the derivative factor used during backpropagation, $\\frac{d\\text{BSN}(a)}{da} = 0$, with $\\frac{d\\text{BSN}(a)}{da} = \\text{BSN}(a)$. A variant of the ST estimator replaces the derivative factor with $\\frac{d\\text{BSN}(a)}{da} = \\text{sigm}(a)$. Whereas [Bengio et al. (2013)](https://arxiv.org/abs/1308.3432) found that the former is more effective, the latter variant was successfully used in [Chung et al. (2016)](https://arxiv.org/abs/1609.01704) in combination with the *slope-annealing trick* and deterministic binary neurons (which we will see perform very similarly, if not better, than stochastic binary neurons when used with slope-annealing). The slope-anealing trick modifies $\\text{BSN}(a)$ by first multiplying the input $a$ by a slope $m$ as follows:\n", | |
"\n", | |
" $$\\text{BSN}_{\\text{SL}(m)}(a) = \\textbf{1}_{z \\gt \\text{sigm}(ma)}.$$\n", | |
"\n", | |
" Then, we increase the slope as training progresses and use $\\frac{d\\text{BSN}(a)}{da} = \\text{sigm}(ma)$ when computing the gradient. The idea behind this is that as the slope increases, the logistic sigmoid approaches a step function, so that it's derivative approaches the true derivative. All three variants are tested in this post.\n", | |
"\n", | |
"### Implementing the binary stochastic neuron in Tensorflow\n", | |
"\n", | |
"The tricky part of implementing a binary stochastic neuron in Tensorflow is not the forward computation, but the implementation of the REINFORCE and straight through estimators. Each requires replacing the gradient of one or more Tensorflow operations. The [official approach](https://www.tensorflow.org/versions/r0.10/how_tos/adding_an_op/index.html) to this is to write a new op in C++, which seems wholly unnecessary. There are, however, two workable unofficial approaches, one of which is [a trick credited to Sergey Ioffe](http://stackoverflow.com/questions/36456436/how-can-i-define-only-the-gradient-for-a-tensorflow-subgraph/36480182), and another that uses `gradient_override_map`, an experimental feature of Tensorflow that is documented [here](https://www.tensorflow.org/versions/r0.10/api_docs/python/framework.html). We will use `gradient_override_map`, which works well for our purposes.\n", | |
"\n", | |
"#### Imports and Utility Functions" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Extracting MNIST_data/train-images-idx3-ubyte.gz\n", | |
"Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", | |
"Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", | |
"Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" | |
] | |
} | |
], | |
"source": [ | |
"import numpy as np\n", | |
"import tensorflow as tf\n", | |
"from tensorflow.examples.tutorials.mnist import input_data\n", | |
"import matplotlib.pyplot as plt\n", | |
"%matplotlib inline\n", | |
"mnist = input_data.read_data_sets('MNIST_data', one_hot=True)\n", | |
"from tensorflow.python.framework import ops\n", | |
"from enum import Enum\n", | |
"import seaborn as sns\n", | |
"sns.set(color_codes=True)\n", | |
"\n", | |
"def reset_graph():\n", | |
" if 'sess' in globals() and sess:\n", | |
" sess.close()\n", | |
" tf.reset_default_graph()\n", | |
" \n", | |
"def layer_linear(inputs, shape, scope='linear_layer'):\n", | |
" with tf.variable_scope(scope):\n", | |
" w = tf.get_variable('w',shape)\n", | |
" b = tf.get_variable('b',shape[-1:])\n", | |
" return tf.matmul(inputs,w) + b\n", | |
"\n", | |
"def layer_softmax(inputs, shape, scope='softmax_layer'):\n", | |
" with tf.variable_scope(scope):\n", | |
" w = tf.get_variable('w',shape)\n", | |
" b = tf.get_variable('b',shape[-1:])\n", | |
" return tf.nn.softmax(tf.matmul(inputs,w) + b)\n", | |
"\n", | |
"def accuracy(y, pred):\n", | |
" correct = tf.equal(tf.argmax(y,1), tf.argmax(pred,1))\n", | |
" return tf.reduce_mean(tf.cast(correct, tf.float32))\n", | |
"\n", | |
"def plot_n(data_and_labels, lower_y = 0., title=\"Learning Curves\"):\n", | |
" fig, ax = plt.subplots()\n", | |
" for data, label in data_and_labels:\n", | |
" ax.plot(range(0,len(data)*100,100),data, label=label)\n", | |
" ax.set_xlabel('Training steps')\n", | |
" ax.set_ylabel('Accuracy')\n", | |
" ax.set_ylim([lower_y,1])\n", | |
" ax.set_title(title)\n", | |
" ax.legend(loc=4)\n", | |
" plt.show()\n", | |
" \n", | |
"class StochasticGradientEstimator(Enum):\n", | |
" ST = 0\n", | |
" REINFORCE = 1" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"#### Binary stochastic neuron with straight through estimator" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"def binaryRound(x):\n", | |
" \"\"\"\n", | |
" Rounds a tensor whose values are in [0,1] to a tensor with values in {0, 1}, \n", | |
" using the straight through estimator for the gradient.\n", | |
" \n", | |
" E.g.,:\n", | |
" If x is >= 0.5, binaryRound(x) will be 1 and the gradient will be pass-through,\n", | |
" otherwise, binaryRound(x) will be 0 and the gradient will be 0.\n", | |
" \"\"\"\n", | |
" g = tf.get_default_graph()\n", | |
" \n", | |
" with ops.name_scope(\"BinaryRound\") as name:\n", | |
" # override \"Floor\" because tf.round uses tf.floor\n", | |
" with g.gradient_override_map({\"Floor\": \"BinaryRound\"}):\n", | |
" return tf.round(x, name=name)\n", | |
" \n", | |
"@ops.RegisterGradient(\"BinaryRound\")\n", | |
"def _binaryRound(op, grad):\n", | |
" \"\"\"Straight through estimator for the binaryRound op (identity if 1, else 0).\"\"\"\n", | |
" x = op.outputs[0]\n", | |
" return x * grad" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"def bernoulliSample(x):\n", | |
" \"\"\"\n", | |
" Uses a tensor whose values are in [0,1] to sample a tensor with values in {0, 1},\n", | |
" using the straight through estimator for the gradient.\n", | |
" \n", | |
" E.g.,:\n", | |
" if x is 0.6, bernoulliSample(x) will be 1 with probability 0.6, and 0 otherwise,\n", | |
" and the gradient will be pass-through (1) wih probability 0.6, and 0 otherwise. \n", | |
" \"\"\"\n", | |
" g = tf.get_default_graph()\n", | |
" \n", | |
" with ops.name_scope(\"BernoulliSample\") as name:\n", | |
" with g.gradient_override_map({\"Ceil\": \"Identity\",\"Sub\": \"BernoulliSample_ST\"}):\n", | |
" return tf.ceil(x - tf.random_uniform(tf.shape(x)), name=name)\n", | |
" \n", | |
"@ops.RegisterGradient(\"BernoulliSample_ST\")\n", | |
"def bernoulliSample_ST(op, grad):\n", | |
" \"\"\"Straight through estimator for the bernoulliSample op (identity if 1, else 0).\"\"\"\n", | |
" sub = op.outputs[0] # x - tf.random_uniform... \n", | |
" res = sub.consumers()[0].outputs[0] # tf.ceil(sub)\n", | |
" return [res * grad, tf.zeros(tf.shape(op.inputs[1]))]" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 4, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"def passThroughSigmoid(x, slope=1):\n", | |
" \"\"\"Sigmoid that uses identity function as its gradient\"\"\"\n", | |
" g = tf.get_default_graph()\n", | |
" with ops.name_scope(\"PassThroughSigmoid\") as name:\n", | |
" with g.gradient_override_map({\"Sigmoid\": \"Identity\"}):\n", | |
" return tf.sigmoid(x, name=name)\n", | |
" \n", | |
"def binaryStochastic_ST(x, slope_tensor=None, pass_through=True, stochastic=True):\n", | |
" \"\"\"\n", | |
" Sigmoid followed by either a random sample from a bernoulli distribution according \n", | |
" to the result (binary stochastic neuron) (default), or a sigmoid followed by a binary\n", | |
" step function (if stochastic == False). Uses the straight through estimator. \n", | |
" See https://arxiv.org/abs/1308.3432.\n", | |
" \n", | |
" Arguments:\n", | |
" * x: the pre-activation / logit tensor\n", | |
" * slope_tensor: if passThrough==False, slope adjusts the slope of the sigmoid function \n", | |
" for purposes of the Slope Annealing Trick (see http://arxiv.org/abs/1609.01704)\n", | |
" * pass_through: if True (default), gradient of the entire function is 1 or 0; \n", | |
" if False, gradient of 1 is scaled by the gradient of the sigmoid (required if\n", | |
" Slope Annealing Trick is used)\n", | |
" * stochastic: binary stochastic neuron if True (default), or step function if False\n", | |
" \"\"\"\n", | |
" if slope_tensor is None:\n", | |
" slope_tensor = tf.constant(1.0)\n", | |
" \n", | |
" if pass_through:\n", | |
" p = passThroughSigmoid(x)\n", | |
" else:\n", | |
" p = tf.sigmoid(slope_tensor*x)\n", | |
" \n", | |
" if stochastic:\n", | |
" return bernoulliSample(p)\n", | |
" else:\n", | |
" return binaryRound(p)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"#### Binary stochastic neuron with REINFORCE estimator" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"def binaryStochastic_REINFORCE(x, stochastic = True, loss_op_name=\"loss_by_example\"):\n", | |
" \"\"\"\n", | |
" Sigmoid followed by a random sample from a bernoulli distribution according\n", | |
" to the result (binary stochastic neuron). Uses the REINFORCE estimator.\n", | |
" See https://arxiv.org/abs/1308.3432.\n", | |
" \n", | |
" NOTE: Requires a loss operation with name matching the argument for loss_op_name \n", | |
" in the graph. This loss operation should be broken out by example (i.e., not a\n", | |
" single number for the entire batch).\n", | |
" \"\"\"\n", | |
" g = tf.get_default_graph()\n", | |
"\n", | |
" with ops.name_scope(\"BinaryStochasticREINFORCE\"): \n", | |
" with g.gradient_override_map({\"Sigmoid\": \"BinaryStochastic_REINFORCE\", \n", | |
" \"Ceil\": \"Identity\"}):\n", | |
" p = tf.sigmoid(x)\n", | |
" \n", | |
" reinforce_collection = g.get_collection(\"REINFORCE\")\n", | |
" if not reinforce_collection:\n", | |
" g.add_to_collection(\"REINFORCE\", {})\n", | |
" reinforce_collection = g.get_collection(\"REINFORCE\")\n", | |
" reinforce_collection[0][p.op.name] = loss_op_name\n", | |
" \n", | |
" return tf.ceil(p - tf.random_uniform(tf.shape(x)))\n", | |
"\n", | |
" \n", | |
"@ops.RegisterGradient(\"BinaryStochastic_REINFORCE\")\n", | |
"def _binaryStochastic_REINFORCE(op, _):\n", | |
" \"\"\"Unbiased estimator for binary stochastic function based on REINFORCE.\"\"\"\n", | |
" loss_op_name = op.graph.get_collection(\"REINFORCE\")[0][op.name]\n", | |
" loss_tensor = op.graph.get_operation_by_name(loss_op_name).outputs[0]\n", | |
" \n", | |
" sub_tensor = op.outputs[0].consumers()[0].outputs[0] #subtraction tensor\n", | |
" ceil_tensor = sub_tensor.consumers()[0].outputs[0] #ceiling tensor\n", | |
" \n", | |
" outcome_diff = (ceil_tensor - op.outputs[0])\n", | |
" \n", | |
" # Provides an early out if we want to avoid variance adjustment for\n", | |
" # whatever reason (e.g., to show that variance adjustment helps)\n", | |
" if op.graph.get_collection(\"REINFORCE\")[0].get(\"no_variance_adj\"):\n", | |
" return outcome_diff * tf.expand_dims(loss_tensor, 1)\n", | |
" \n", | |
" outcome_diff_sq = tf.square(outcome_diff)\n", | |
" outcome_diff_sq_r = tf.reduce_mean(outcome_diff_sq, reduction_indices=0)\n", | |
" outcome_diff_sq_loss_r = tf.reduce_mean(outcome_diff_sq * tf.expand_dims(loss_tensor, 1),\n", | |
" reduction_indices=0)\n", | |
" \n", | |
" L_bar_num = tf.Variable(tf.zeros(outcome_diff_sq_r.get_shape()), trainable=False)\n", | |
" L_bar_den = tf.Variable(tf.ones(outcome_diff_sq_r.get_shape()), trainable=False)\n", | |
" \n", | |
" #Note: we already get a decent estimate of the average from the minibatch\n", | |
" decay = 0.95 \n", | |
" train_L_bar_num = tf.assign(L_bar_num, L_bar_num*decay +\\\n", | |
" outcome_diff_sq_loss_r*(1-decay))\n", | |
" train_L_bar_den = tf.assign(L_bar_den, L_bar_den*decay +\\\n", | |
" outcome_diff_sq_r*(1-decay))\n", | |
"\n", | |
"\n", | |
" with tf.control_dependencies([train_L_bar_num, train_L_bar_den]): \n", | |
" L_bar = train_L_bar_num/(train_L_bar_den+1e-4)\n", | |
" L = tf.tile(tf.expand_dims(loss_tensor,1),\n", | |
" tf.constant([1,L_bar.get_shape().as_list()[0]]))\n", | |
" return outcome_diff * (L - L_bar)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"#### Wrapper to create layer of binary stochastic neurons" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 6, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"def binary_wrapper(\\\n", | |
" pre_activations_tensor,\n", | |
" estimator=StochasticGradientEstimator.ST,\n", | |
" stochastic_tensor=tf.constant(True), \n", | |
" pass_through=True, \n", | |
" slope_tensor=tf.constant(1.0)):\n", | |
" \"\"\"\n", | |
" Turns a layer of pre-activations (logits) into a layer of binary stochastic neurons\n", | |
" \n", | |
" Keyword arguments:\n", | |
" *estimator: either ST or REINFORCE\n", | |
" *stochastic_tensor: a boolean tensor indicating whether to sample from a bernoulli \n", | |
" distribution (True, default) or use a step_function (e.g., for inference)\n", | |
" *pass_through: for ST only - boolean as to whether to substitute identity derivative on the \n", | |
" backprop (True, default), or whether to use the derivative of the sigmoid\n", | |
" *slope_tensor: for ST only - tensor specifying the slope for purposes of slope annealing\n", | |
" trick\n", | |
" \"\"\"\n", | |
" \n", | |
" if estimator == StochasticGradientEstimator.ST:\n", | |
" if pass_through:\n", | |
" return tf.cond(stochastic_tensor, \n", | |
" lambda: binaryStochastic_ST(pre_activations_tensor), \n", | |
" lambda: binaryStochastic_ST(pre_activations_tensor, stochastic=False)) \n", | |
" else:\n", | |
" return tf.cond(stochastic_tensor, \n", | |
" lambda: binaryStochastic_ST(pre_activations_tensor, slope_tensor = slope_tensor, \n", | |
" pass_through=False), \n", | |
" lambda: binaryStochastic_ST(pre_activations_tensor, slope_tensor = slope_tensor, \n", | |
" pass_through=False, stochastic=False))\n", | |
" elif estimator == StochasticGradientEstimator.REINFORCE:\n", | |
" # binaryStochastic_REINFORCE was designed to only be stochastic, so using the ST version\n", | |
" # for the step fn for purposes of using step fn at evaluation / not for training\n", | |
" return tf.cond(stochastic_tensor,\n", | |
" lambda: binaryStochastic_REINFORCE(pre_activations_tensor),\n", | |
" lambda: binaryStochastic_ST(pre_activations_tensor, stochastic=False))\n", | |
" \n", | |
" else:\n", | |
" raise ValueError(\"Unrecognized estimator.\")" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"#### Function to build graph for MNIST classifier" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 7, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"def build_classifier(hidden_dims=[100], \n", | |
" lr = 0.5, \n", | |
" pass_through = True, \n", | |
" non_binary = False, \n", | |
" estimator = StochasticGradientEstimator.ST,\n", | |
" no_var_adj=False):\n", | |
" reset_graph()\n", | |
" g = {}\n", | |
" \n", | |
" if no_var_adj:\n", | |
" tf.get_default_graph().add_to_collection(\"REINFORCE\", {\"no_variance_adj\": no_var_adj})\n", | |
"\n", | |
" g['x'] = tf.placeholder(tf.float32, [None, 784], name='x_placeholder')\n", | |
" g['y'] = tf.placeholder(tf.float32, [None, 10], name='y_placeholder')\n", | |
" g['stochastic'] = tf.constant(True)\n", | |
" g['slope'] = tf.constant(1.0)\n", | |
" \n", | |
" g['layers'] = {0: g['x']}\n", | |
" hidden_layers = len(hidden_dims)\n", | |
" dims = [784] + hidden_dims\n", | |
" \n", | |
" for i in range(1, hidden_layers+1):\n", | |
" with tf.variable_scope(\"layer_\" + str(i)):\n", | |
" pre_activations = layer_linear(g['layers'][i-1], dims[i-1:i+1], scope='layer_' + str(i))\n", | |
" if non_binary:\n", | |
" g['layers'][i] = tf.sigmoid(pre_activations)\n", | |
" else:\n", | |
" g['layers'][i] = binary_wrapper(pre_activations, \n", | |
" estimator = estimator,\n", | |
" pass_through = pass_through, \n", | |
" stochastic_tensor = g['stochastic'], \n", | |
" slope_tensor = g['slope'])\n", | |
" \n", | |
" g['pred'] = layer_softmax(g['layers'][hidden_layers], [dims[-1], 10])\n", | |
" \n", | |
" g['loss'] = -tf.reduce_mean(g['y'] * tf.log(g['pred']),reduction_indices=1)\n", | |
" \n", | |
" # named loss_by_example necessary for REINFORCE estimator\n", | |
" tf.identity(g['loss'], name=\"loss_by_example\") \n", | |
" \n", | |
" g['ts'] = tf.train.GradientDescentOptimizer(lr).minimize(g['loss'])\n", | |
"\n", | |
" g['accuracy'] = accuracy(g['y'], g['pred'])\n", | |
" return g" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"#### Function to train the classifier" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 8, | |
"metadata": { | |
"collapsed": false, | |
"scrolled": true | |
}, | |
"outputs": [], | |
"source": [ | |
"def train_classifier(\\\n", | |
" hidden_dims=[100,100],\n", | |
" estimator=StochasticGradientEstimator.ST,\n", | |
" stochastic_train=True, \n", | |
" stochastic_eval=True, \n", | |
" slope_annealing_rate=None, \n", | |
" epochs=10, \n", | |
" lr=0.5,\n", | |
" non_binary=False,\n", | |
" no_var_adj=False,\n", | |
" train_set = mnist.train,\n", | |
" val_set = mnist.validation,\n", | |
" verbose=False,\n", | |
" label=None):\n", | |
" if slope_annealing_rate is None:\n", | |
" g = build_classifier(hidden_dims=hidden_dims, lr=lr, pass_through=True, \n", | |
" non_binary=non_binary, estimator=estimator, no_var_adj=no_var_adj)\n", | |
" else:\n", | |
" g = build_classifier(hidden_dims=hidden_dims, lr=lr, pass_through=False, \n", | |
" non_binary=non_binary, estimator=estimator, no_var_adj=no_var_adj)\n", | |
" \n", | |
" with tf.Session() as sess:\n", | |
" sess.run(tf.initialize_all_variables())\n", | |
" slope = 1\n", | |
" res_tr, res_val = [], []\n", | |
" for epoch in range(epochs): \n", | |
" feed_dict={g['x']: val_set.images, \n", | |
" g['y']: val_set.labels, \n", | |
" g['stochastic']: stochastic_eval,\n", | |
" g['slope']: slope}\n", | |
" if verbose:\n", | |
" print(\"Epoch\", epoch, sess.run(g['accuracy'], feed_dict=feed_dict))\n", | |
"\n", | |
" accuracy = 0\n", | |
" for i in range(1001):\n", | |
" x, y = train_set.next_batch(50)\n", | |
" feed_dict={g['x']: x, g['y']: y, g['stochastic']: stochastic_train}\n", | |
" acc, _ = sess.run([g['accuracy'],g['ts']], feed_dict=feed_dict)\n", | |
" accuracy += acc\n", | |
" if i % 100 == 0 and i > 0:\n", | |
" res_tr.append(accuracy/100)\n", | |
" accuracy = 0\n", | |
" feed_dict={g['x']: val_set.images, \n", | |
" g['y']: val_set.labels, \n", | |
" g['stochastic']: stochastic_eval,\n", | |
" g['slope']: slope}\n", | |
" res_val.append(sess.run(g['accuracy'], feed_dict=feed_dict)) \n", | |
"\n", | |
" if slope_annealing_rate is not None:\n", | |
" slope = slope*slope_annealing_rate\n", | |
" if verbose:\n", | |
" print(\"Sigmoid slope:\", slope)\n", | |
"\n", | |
" feed_dict={g['x']: val_set.images, g['y']: val_set.labels, \n", | |
" g['stochastic']: stochastic_eval, g['slope']: slope}\n", | |
" print(\"Epoch\", epoch+1, sess.run(g['accuracy'], feed_dict=feed_dict))\n", | |
" if label is not None:\n", | |
" return (res_tr, label + \" - Training\"), (res_val, label + \" - Validation\")\n", | |
" else:\n", | |
" return [(res_tr, \"Training\"), (res_val, \"Validation\")]" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"### Experiments\n", | |
"\n", | |
"We've now set up a good foundation from which we can run a number of simple experiments. The experiments are as follows:\n", | |
"\n", | |
"- **Experiment 0**: A non-stochastic, non-binary baseline.\n", | |
"- **Experiment 1**: A comparison of variance-adjusted REINFORCE and non-variance adjusted REINFORCE, which shows that the variance adjustment allows for faster learning and higher learning rates.\n", | |
"- **Experiment 2**: A comparison of pass-through ST and sigmoid-adjusted ST, which shows that the pass-through ST estimator obtains better results, in accordance with the findings of [Bengio et al. (2013](https://arxiv.org/abs/1308.3432). \n", | |
"- **Experiment 3**: A comparison of pass-through ST and slope-annealed sigmoid-adjusted ST, which shows that a well-tuned slope-annealed ST performs slightly better than the pass-through ST.\n", | |
"- **Experiment 4**: A direct comparison of variance-adjusted REINFORCE and slope-annealed ST, which shows that ST performs significantly better than REINFORCE.\n", | |
"- **Experiment 5**: A look at the deterministic step function, during training and evaluation, which shows that in the absence of slope annealing, stochasticity is necessary during training, but that deterministic evaluation can provide a slight boost at inference, and that with slope annealing, deterministic training is just as effective, if not more effective than stochastic training.\n", | |
"- **Experiment 6**: A look at how network depth affects performance, which shows that deep stochastic networks are difficult to train. \n", | |
"- **Experiment 7**: A look at using binary stochastic neurons as a regularizer, which shows that they do function as a regularizer, but it is unclear whether it is worthwhile to use them as one. " | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"#### Experiment 0: A non-stochastic, non-binary baseline" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 11, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Epoch 20 0.9538\n" | |
] | |
}, | |
{ | |
"data": { | |
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Egm/I++tr+HBj7ZCP6wy7DNq/I4KgPWzJUCwb33YCwRA2R58IUMTBUIxb3WIjNy2OWUXp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\n54m0tAAAAABJRU5ErkJggg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7fa1e410cf28>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"res = train_classifier(hidden_dims=[100], epochs=20, lr=1.0, non_binary=True)\n", | |
"plot_n(res, lower_y=0.8, title=\"Logistic Sigmoid Baseline\")" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"#### Experiment 1: Variance-adjusted vs. not variance-adjusted REINFORCE\n", | |
"\n", | |
"Recall that the REINFORCE estimator estimates the expectation of $\\frac{\\partial L}{\\partial a}$ as $(\\text{BSN}(a) - \\text{sigm}(a))(L - c)$, where $c$ is a constant. The non-variance-adjusted form of REINFORCE uses $c = 0$, whereas the variance-adjusted form uses the variance minimizing result stated above. Naturally we should prefer the least variance, and the experimental results below agree. \n", | |
"\n", | |
"It seems that both forms of REINFORCE often break down for learning rates greater than or equal to 0.3 (compare to the learning rate of 1.0 that used in Experiment 0). After a few trials, variance-adjusted REINFORCE appears to be more resistant to such failures." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 13, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Variance-adjusted:\n", | |
"Epoch 0 0.0988\n", | |
"Epoch 1 0.0958\n", | |
"Epoch 2 0.0958\n", | |
"Epoch 3 0.0958\n", | |
"Not variance-adjusted:\n", | |
"Epoch 0 0.1006\n", | |
"Epoch 1 0.0966\n", | |
"Epoch 2 0.0958\n", | |
"Epoch 3 0.0958\n" | |
] | |
} | |
], | |
"source": [ | |
"print(\"Variance-adjusted:\")\n", | |
"res1 = train_classifier(hidden_dims=[100], estimator=StochasticGradientEstimator.REINFORCE, epochs=3, \n", | |
" lr=0.3, verbose=True)\n", | |
"print(\"Not variance-adjusted:\")\n", | |
"res2= train_classifier(hidden_dims=[100], estimator=StochasticGradientEstimator.REINFORCE, epochs=3, \n", | |
" lr=0.3, no_var_adj=True, verbose=True)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"In terms of performance at lower learning rates, a learning rate of about 0.05 provided the best results. The results show that the variance-adjusted REINFORCE learns faster, but that its non-variance adjusted eventually catches up. This result is consistent with the mathematical result that they are both unbiased estimators. Performance is predictably worse than it was for the plain logistic sigmoid in Experiment 0, although there is almost no generalization gap, consistent with the hypothesis that binary stochastic neurons can act as regularizers." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 15, | |
"metadata": { | |
"collapsed": false, | |
"scrolled": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Epoch 20 0.9274\n", | |
"Epoch 20 0.923\n" | |
] | |
}, | |
{ | |
"data": { | |
"image/png": 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LLp9AQ42Z2opOvEo3x/LbyNtXT0JSOPpgLdvrP6fGUo9P+DjQegRjbQYKfxCK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\n3k3hdyXe2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7tOlFZ/v/zyC48//jgTJ04kJydH6q8a6tKlC6NHjwbAarWi1Wqv6f/Ka6m7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\nQAghhBDA/wcgInqERE0apgAAAABJRU5ErkJggg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7fa2525597f0>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"res1 = train_classifier(hidden_dims=[100], estimator=StochasticGradientEstimator.REINFORCE, epochs=20, \n", | |
" lr=0.05, label = \"Variance-adjusted\")\n", | |
"res2= train_classifier(hidden_dims=[100], estimator=StochasticGradientEstimator.REINFORCE, epochs=20, \n", | |
" lr=0.05, no_var_adj=True, label = \"Not variance-adjusted\")\n", | |
"\n", | |
"plot_n(res1 + res2, lower_y=0.6, title=\"Experiment 1: REINFORCE variance adjustment\")" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"#### Experiment 2: Pass-through vs. sigmoid-adjusted ST estimation\n", | |
"\n", | |
"Recall that one variant of the straight-through estimator uses the identity function as the backpropagated gradient when the neuron emits a 1 (pass-through), and another variance multiplies that by the gradient of the logistic sigmoid that the neuron calculates (sigmoid-adjusted). In [Bengio et al. (2013)](https://arxiv.org/abs/1308.3432), it was remarked that, surprisingly, the former performs better. My results below agree; we see that the pass-through variant edges out the sigmoid-adjusted variant at higher learning rates, and interestingly, at a learning rate of 1, the sigmoid-adjusted variant performs very poorly and fails to converge." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 17, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Epoch 20 0.9448\n", | |
"Epoch 20 0.9434\n", | |
"Epoch 20 0.9506\n", | |
"Epoch 20 0.9226\n", | |
"Epoch 20 0.8894\n", | |
"Epoch 20 0.631\n" | |
] | |
}, | |
{ | |
"data": { | |
"image/png": 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KclS2ZnTWFrLbmjBLCVTVpOLMaSUYG/EGDE8ewpX5s6n4fBstGzZw3CqzrUcz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\njwCF71A+EyIROkZo+F0EUYOr+UFElZHREz8utm0V1p0m6tmOKOkwuwu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XUDHrmySCR4t1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Fkrwz9b0/G11+YwGJPxW1fsbpnT9Xqio+Fnqnw5lMtlpNPp+otJEhzHgRBouPPYY4/h8OHD2LNn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\nEELUIFL4hRBCiBpECr8QQghRgxi18CuKwpw5cxg6dCje3t5cunSp2OVmz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| |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7fa1f015acf8>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"res1 = train_classifier(hidden_dims=[100], estimator=StochasticGradientEstimator.ST, epochs=20, \n", | |
" lr=0.1, label = \"Pass-through - 0.1\")\n", | |
"res2 = train_classifier(hidden_dims=[100], estimator=StochasticGradientEstimator.ST, epochs=20, \n", | |
" lr=0.1, slope_annealing_rate = 1.0, label = \"Sigmoid-adjusted - 0.1\")\n", | |
"\n", | |
"res3 = train_classifier(hidden_dims=[100], estimator=StochasticGradientEstimator.ST, epochs=20, \n", | |
" lr=0.3, label = \"Pass-through - 0.3\")\n", | |
"res4 = train_classifier(hidden_dims=[100], estimator=StochasticGradientEstimator.ST, epochs=20, \n", | |
" lr=0.3, slope_annealing_rate = 1.0, label = \"Sigmoid-adjusted - 0.3\")\n", | |
"\n", | |
"res5 = train_classifier(hidden_dims=[100], estimator=StochasticGradientEstimator.ST, epochs=20, \n", | |
" lr=1.0, label = \"Pass-through - 1.0\")\n", | |
"res6 = train_classifier(hidden_dims=[100], estimator=StochasticGradientEstimator.ST, epochs=20, \n", | |
" lr=1.0, slope_annealing_rate = 1.0, label = \"Sigmoid-adjusted - 1.0\")\n", | |
"\n", | |
"plot_n(res1[1:] + res2[1:] + res3[1:] + res4[1:] + res5[1:] + res6[1:], \n", | |
" lower_y=0.4, title=\"Experiment 2: Pass-through vs sigmoid-adjusted ST\")" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"#### Experiment 3: Pass-through vs. slope-annealed ST estimation\n", | |
"\n", | |
"Recall that [Chung et al. (2016)](https://arxiv.org/abs/1609.01704) improves upon the sigmoid-adjusted variant of the ST estimator by using the *slope-annealing trick*, which slowly increases the slope of the logistic sigmoid as training progresses. Using the slope-annealing trick with an annealing rate of 1.1 times per epoch (so the slope at epoch 20 is $1.1^{19} \\approx 6.1$), we're able to match and even surpass the results of the pass-through ST estimator. The slope-annealed estimator is still more sensitive to the learning rate (performing similarly to the sigmoid-adjusted variant at a learning rate of 1.0), but it appears that it can be fine tuned to perform better than the pass-through variant. Note that the slope annealed neuron used here is not the same as the one used by [Chung et al. (2016)](https://arxiv.org/abs/1609.01704), who employ a deterministic step function and use a hard sigmoid in place of a sigmoid for the backpropagation. " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 21, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Epoch 20 0.9158\n", | |
"Epoch 20 0.9562\n", | |
"Epoch 20 0.9488\n", | |
"Epoch 20 0.9626\n", | |
"Epoch 20 0.953\n", | |
"Epoch 20 0.939\n" | |
] | |
}, | |
{ | |
"data": { | |
"image/png": 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vPObMmQPA+eefj9OZLLzCwkLuv//+rzzWf/pFfbzSb0hHb+uGJtYsrSE7P43C\nEg+uNAsWmynVBRsOxXnh8dWoqmDkCYWc9L0BNNR08d4rW7FYkwOtykckB1rtG7SmJFTMFgPtLUHe\nf21raoS52WJk3KRiAj0Rmup76OkKA2C1mxg0NIfG2m78XWEkWWL2ZWPw5rroaAtSuaKWISPy6D8o\ni3Wf1rFhZT0AE07pT1H/DN57ZQuRcIJMr4Oi0gy2bWhGUZID1vqVZnDGrAoSCZX5T67FZDZwyQ0T\nvnZvR0938tl5WvpXT88Kh+JEIwkysvZPW4tH2jFaMpDlb3bet6pqNNf3sHenD0mCk08vQ1N6MVmz\nvtHjfB3/br+/bZ072eXfw/dLZ2A2HHj+IkqU+kAjDYEm4lqcIlcBjcEWPqhbDIDNaOXX42/BarTy\n9Nb/RdEUfj76egxy8vrri4e4a80fkJCYM/D7VLZ9xi7/HjyWdG4YcQWFrnx6Yr08vOEJOqPduM1p\nGGQD3VE/bnMaVw27hAHp/QFoD3ewqG4p6ZY0hmQOpp+rALPBTDDex6qWSjZ1bKOlrxVFqMiSjNuc\nhj/Wg8Nk5/KhF1GROfj4Cwg++ugjlixZwgMPPMDmzZt56qmnePzxxwHw+/3Mnj2bt956C6fTyRVX\nXMEDDzxAVlYWF154IW+88cbXOta/00X9n+Tf7YZ0OJom6GwP0tLQi9liIC3dhjfXmeqa1DSNcF88\n9VISIPWWs30DmOY/uYZQMHbQYLDcQjczLx7J+k/r2bAqWQEbTTI/+vFE3vrHJvydYUxmA4m4isEg\nIUkSqqodtB/ZIDFj9jCi4QRL39+d6gI3GGWKyzIZMMRLyYAsDEYZTdPYs91HltdJZu7hWz57drRj\nMhkoGZis9AI9EVYvraG2qgMhwGwxcsqZg9i9rY2G6m4sViPxmIIQcNK0MkaOLzrsvo+FUPcWuuoX\nIBmsODNG4sqegNGc/o3sO9y7G02JYk0rJRHx4W/6ACXWjbf0QmzuQUe9H1UJIxts38ijg8P9/r6q\nJ0oTGhElisNk/zxNEULdm0hEfCjxHqyu/qTlTDnkPoQQbOrYRlyNU54xCLfFlVre17meQPsq0nJO\nxOUdf9jj70uf0BSUeA9KzE/cYOeez54iokQZmVXB1cN+BMCmjq2sa9/Ezq7dKEI9aF+ZVg9jc0ax\nqH4pA9NLUYVGTW8dABcNPp/JBRMBeKVqAZ80rWLOwO9zatFkhBB8WLeEd2sXIiFR5MonnIgQiHZz\nQd5wxpfKdlaNAAAgAElEQVTNRDY5WVz/Ce/ULkQIwZSCiQz2DOCFXa8RUSIHpMNtdtGXCKMKFYNk\noMCZR64jG1+4k5ZQG+WegfxwwNmkWdKQDeZjEhAc0xcTbdiwgSlTpgAwcuRItm3blvqusbGRIUOG\n4HIlMzV8+HA2bdpEYWEh4XCYq6++GlVVmTt3LiNHjjyWydTpjplEXGXzuka2rm8iGlEO+M5olBkw\nJBtnmoWdW1oJBeNMOm0AI8YV0t0R4oPXt1JQ7GHqjME01XXTF4gxdFQe40/uT1tT8sUpvb5KaqqD\nrFnmYteWNqw2IyPGFVK5oo53X9qCvzNM+YhcTpjSn8pPavB/3tKXDRIWiwmTOTkCXVU1Ro4voqjE\nDZKMJ8uBrzVIVo6TzGzHQa10WZYZPDz3KwO6L7+lLS3dxhmzKggFY9Tu6aS4LBPVaiC31MO6j6tp\nrPXjyXKQW5BGxZh/rjtdifcS9m8nGqxGCJWsktkYTF9989SUCP7mRUiSEUkyEOxYS1/nBtx5p+DK\nnogk/fODzKLBOjprXv7S0mRl2dOyBGvawKOq4GOhRnx7nsfiLMZbdhGS9PUH1wmhEeraRF/nBszM\nAAq/8J2gp2UxwY61WBxFWF1luLzjkQ37p1/G1DiPb36WhkAT/zX6Gqx91fR1rkdo8f3r9NUTVmJs\n18yEEmE0oTE0YzAFzjzm73qNLZ3bU+tmWj1kmKycbEyQRXIWiL/pQza1rGWjZgYkBqT3Z0bJaUiS\nxPrWz9hQ+w7f8xRgjrYByR4mDYkSSaHJ7GRz53b+suWvGKI+nFoEv6KQ48ihIrOcYlchZoOZhmAz\najzACTYbaqiOjIxcFvfU0alpjMyqYGd3Fe/XfsQJuWNo6WtjRfMavLZMpnweIMRCjZziKaTQcQkf\nN62mLVDHIJOBqRlujNFaOmpeInfQ1Zxecipl7hJe2v0ay5tXs7x5NR7ZyDV5A5Gs2WyLq0TCLRSr\nfiw2J7J7MEP7nYHdvP+aFUKgKWHadv+FmCWDnIGXfe3zfjSOaUDQ19eXqvABjEYjmqYhyzIlJSXs\n3buX7u5ubDYbq1evpn///thsNq6++mouuOAC6urquPbaa1m4cOFB03Z0uu9SJByntztCbqH7sOtU\n7/KxcvFeQn1xrDYT5SNyKSzxoKmCnu4we3f62LW1DUi+5MRqM36+foxdm9uIRhIEelopHWhj784m\nIDkdymY303+Ql2iwFh9byXRbWbbCjqoaGDupmOHjCtm8rolOXx9Gk8z4k/vjcFo48WQTQnNgcw86\nZEUiNIWWHY9gtGSSPeBHeHP3/3YjvXvwNy/EnTsVR8awf7n8HC4Lw8YUEFc1fr+5liyriR/PGHxQ\npahpCTQljKbGMFkykOQDb1lCaKjxXoyW5Dxw9fObpqaEU+u0732enAGXI4RCpGcXtvRyjOaDz1tP\n61I0JYw7bxpp2ScS8m+lp+Vjelo+JuBbg8VRgMmaDZKMJBmwuQdhth0Y8CSiXcQjbdjTh6byoqlR\nuurfAiTSciYTD7eAJJGedyrBjrWEurcQ9m/H6irB37wIocYx2/Mw2/MxOwowGPe1wsN01r6GEArR\nYDWBthWk5Z5M2L+NeKQNd+7JyIbDv/dACEE0sIeelo9JRJNT+HZvmU9xxdxUhR9oX0nQtxrJYCHW\nV0+sr554pJWskjlIkkRcTfDklr/R2lvHFKuJaM0LJBDIRifu3FOS15ZsoGX3syQ6VrOjL8LuRLJV\nvrjhk2RL2ihzaXomBkcha8JhWkPtDNV6yMJAdUJhq2phiilBMX5C8QQfhGLs9u8lpsYZ4ykh0fQ2\nM2wyRFvQ/HG01iiS2YnWX+L7TismVxkd/hrS1BZkiwSYmYQZmzsPk81GPLgNJdZNmhpBU8JEP38l\nQTFwtduOT3YxcsgcPmxYwYf1S3hu+3x6e/fyQ4eF7IwSlFAT/s51RHp2AuBGYo7JjuZOnidJMmKy\n5RIPN9Pb9gnOrDHY2z/iMrugwTaKjq27GT3SgxRthWgrJ0gyoIEBQIXgDjq370KSZCTZSFrOZFzZ\nE+msfR01EcAUzj7sOf5XHdOAwOl0EgrtfwHEvmAAIC0tjdtvv52f/vSnpKenU1FRgcfjobi4mH79\n+gFQUlJCeno6HR0d5OQc+X3Qx6L7RPft+DbO3ZrlNaxZVo0AHA4zMy8aTU5+Gqqq8drf1xMOJygb\n7GXoiLwjvtZUaILP1jaw+L2dRGIKM+eMYPSEfgR6Irz2/AbS3DbGTOzHrq1trF9Vh9EkM/m0gUw6\nteygkctCE9Ts6SDcF2dQRS6B3ghP/W0t75oVMtKMnDaxmM2V20h0z6c0X0FLjGfo8PxURVPdvAkA\nqyXKwLIG9taUMXX6YBwuCxOmlLL8oyomf28geUUe1Fg3DZteAqFhsXvJLZmKJ2c4BtP+5+w9vm2o\niSBqIki851MKB50NQKCrisa6VxGaQnfDW2R6s3GkF9PdsoHWmj7SMgZidxemggxNTdBevxybMwe3\ndyiaquBrWEGotwGzzYPRZCcSbCXS105X5mlEVWgKxWhHMNybBkA05KOtdgldrRtBJFuAJms6/cpn\nkpZVTqinjh7fdvztW0jEAuSVnkb+gDOo374QTQmTU3wKOSUn01a3DF/9CtqrnkKJhwBBpGcT5RN+\nhsGYrATj0R56fDvo69yA1ZFNacV0ZNkIOVNQysbSsncRPb6tRHqriPRWpcqrt3UpjvQSCgbOwOUp\nJR7tYef2v6PE+3AOlcgqnABA3bb3UBO9qTR+UcybybaV2wi2LyXQLkhEk9McI4H9xzHbMjC7+9HQ\nvpMMEWNNNM5wqxXaPiERriEcaAQgEaphwOgrMds8KPE+NE0BAUq8j2ikk86mSvr81YBEjTDjj/Ux\n1gpba+YzbeJNdDSuord1CTHJxHsxAzlppYxVuqBnJ6v2/C+7NZmG3haMUT/XetKwCJVeVSW75BSG\nDpqB/Pkze3+klzcjcc4yCmY67VgKT0LzDGJ73ad4AtVkSxoQQ4rUcceUXyIQbPv0DxisHhrtHqqa\nN2N3lnGGWWMoPkY6ctmypxVTzacomespMMr4ZRfyazsx+hKYBpei7qhG9pjQZhaSoBq3JBAdMUSv\nkaLTzqOztfKA82c0OTCZHRgsmdjJwxxyYezvprN7Pdm9DXRX/43vD57Jyta19PZUMdtpxSxJENyN\nL5h8X4EjvZi0jIEEuqpIxALYPBU43P3wFk5Akg3sWPUQgfaVhLo/Q02EAYkisZeCAg1JCeDtNwmz\nOY22HUuQNTOFFWfjyO9PZ9Magt3VCCGIRbroaVlMX/taFDWIWhuCdh/es4/NPfOYjiFYtGgRS5cu\n5YEHHmDTpk08/vjjPP300wCoqsoTTzzBTTfdRDwe5+qrr+aRRx7hgw8+oKqqit/+9re0t7dz5ZVX\n8u67735lD8F/wnPof0ffxhiCcF+M+U+uBZLvGg/0RMnMdjD78rFsXNPAuhV1qXUNBolZl445oHUs\nhCDQvhKzLZflS2LsavbTOzSduN1E7sYuZp01hBUf7cHfGWJfNzAkX+5y+nlDD3jP+j7xiA9ZNmO0\nJJ9PJyIdBLs28KZ/INVRjSKDketH5FK76UlMhiCaJiFJBnLLr8Biz0eJB2jZ/j+YrF40LYYSD4L9\nhxQPHggkB7A1VHfjLHTx2M5GxlrbGBlfgsXZn1hfPaARxUbAPoJRpSdjNNnorH2dcM92ZKMDTQmR\nnv89NDVG0LcGgcCdO4Xe1uXIBjOy0Y4S2/+SHoPRiafobKxppXTWvEw0WAOAyZqNpoRRlb5DnpuP\n1ZPYI4oBKLAozLGtR4n7UePJitFoycJszwUkwj3bk8GBZIDPnwXLBhtIBjSlD5d3IsGONZis2eSW\nX4siJKKKSqJ9MX2d6zDb8zEYnUQCVdg9I3DnTsbf9GEqrSCTPeASrK7+h0xrKNpLOOLHZQRVCRHq\n2kQ0WA3IeApPJ9S95fPWvwEJiZzBV9PXtZG+jkrM9nxyBl15yMcO3Y3v0de5AQB33qk4M0cTD7cQ\nC7cQ72smFm5CaMnZG02qzN5WA21WPxdkOjFI0CJMBDRBuUFBkk0gBEIoBx0HQHYUs8eQwWv1KxiT\nOYST1EYsCJo1iWIDRDTB/GCYAEYSWgKXJHFlmh2jBCtCMVwmM2MsBgyA6hnNQzXLKU4rZlT2MDZ3\nbCfc08Xwda2YYirek0aQnxNGUyMYLVkosU4ArM4yTBYvwa41ODJHI8tmgh1rySw+D0fGCOI+H6aM\nDJChfcNzxI2tqfSLqErfXon0sJfQunV8OsrBhqEO0gMKZ+4ykFPbieQ2YjRlYC0pJbh2NZLJhEgk\nkPOtYJLI+8GNOAYPp+O1V/B/uH/GAAYDrhNPwjwlh1DgMwA0YQQtgQSonwZxjhqDob8Li7MQR8ao\nwz7mUYIB/GvfJ5yxGwkJd9ZUYruaiTh2ItmNaNVRCs+6g8Any+h8/ZXkRpKEY+QosmbNwVKQnFkT\n62rBt/XvCHcCLZDA3lFO1jmzka3W429Q4RdnGQA88MADbN++nUgkwgUXXMCjjz7Kxx9/jMVi4aqr\nruL0008nkUgwb948WlpakGWZ2267jVGjRn3lsfSA4Pj0VQGBEu8l1L0ZZ+boo3oOfCiffrSHrRua\nU1PYlr6/i11b2hhUkcPenT5sDhPnXTKaxtpuli/cg8tt5YIrx6Za9LFwCzt2vUaTKGZTawmRbFvy\nVXeAq68P99pe7NYop0zZjMlRwe69ZVgsRiZMLcVkMiCEhhLrwmRNzr/XtATNWx9Cko3kll+HbLDS\ntutp2qMKr6pnAWCRBdfYP0GNttLQVEyX38no4TuQjTay+l9ANFhLoG05GUXnIJscdNa8jCSbkWUz\nBpMLT+EZWJz9eKfex2pfL1ZiXOlcTWH5NQQjPSxpqGNj0EoCI6WWMBcPKadrx0MYjE6ySn9A++5n\nU5WKJBnJ6j8Hm3sQfV2b6G54G5BxZo3Fmz8YX/N2Qt1b6NPM7JCG4dF8lKXZcJpMhP3bkCQDruwT\ncWaNRVP6UJUwJquXSLiN/96jYJFUvKY4NXE358pLKDKHMFm9uLwnYHMPBlWl+dH/wTqiDK04jBoP\nYHX1x5pWhtXZHyXup63qrwg1+fw5e+DlWJ3FvFTdys6eEDeUF5JtTmAwuRCaSvue52gP9WElhk2K\nYXEWY3OXY3MPxGTJIFJTTbjDx+6ERCi3gLjZSks4RkNfBFXAuKw0zizKwm40EO2rp7P21dQjCkfG\nKGzugXTWvpoKXLpUQbfnBL5XdlbqmtSERliJIITALoG/aSH2jGHY3YOT38di9C5fhn/Rhyg9ftqH\nONDKcyjZCrE9NcQy01h4ug1hkqlXNAySTIVJYrLdjlBlLI0BQmk2ot402qMBfEqMNkWjTU0OxnOZ\nnNwx4RakxF46di8AIBBQ6ayVyD33YgZlDMAf7aU11Ia1eSdmbf/4L0m2kFVyPjb3QJ5b9TSGdZsJ\n2WSiVgNTN/ThCO0PRgzZbmw/GIQiujAoaUTe3YvW3AcSWC7uh5RmBEnGYHaQP/Rn9Hy8mI6XX8Tg\ndGEdMIDQpo0Yh2diHz8MJWImuHo7htoWAEwFBSw9fzAum5txOaMoSetHrK6WRHcXzpGjkYxGepd/\ngu8fz2MdMBDX+An4/vdvmAuLyDjrbNqefhJTTg5pJ01GNpvpWbaURHsbksVKxiVnEqOZhNYOZhll\naS+iKYoWieAYNZrcK6/B4Dgw0I+3tdL94ftEa6qJt7aCEMhFdkRUQY5ZUUMhjDlubCeWE3xzFa4T\nJtK3YT2y3U7W+XPoXf4J0ZrqZGAwfARqOEyssQERj2E9qZSsU2djL6lIHe+4Cwi+TXpAcHz6ckDQ\nEopiNxpItyQr4866N5KVimwhPX8azqxxRz26Oq5qrGv2s25RFVkJiYuuG4/BIBOLKrz8bCWhYHIQ\n1FkXDKe4LDkHvXJ5LRtW1VNY4qFfaQaxhEKdy8emWEZqv+lGA5NYxla1lAZRgHd7B2cO3oLD6gMk\ncgdf+3mrdv9z31hfHVklc7B7hhLu2UVnbbJVkLCVETTmkR5YyTJ5OlWJTNwE6CWN2YYPKc4ooNU0\nld2BCAq9qKFaTpQ3YzLIIEkUDLsFWTbR3fjB561VUGJ+QGDJPJEnOoqJa8nyOi9XMK5wIE/ubKQp\nFMNlMuBQO2nTPGSaFM7U3qMgdwzp+dOIBmuJhZox2/Ow2PORjfsfLUSDdRjMaZgsGanz197bxnN7\nfAREcj0JyLdbKHOaGJXpINeZdtD52dMb4rmqFoZJuxkk1/GGegaZFgMD3U7sRgMZFhNeq5msjlYa\n77sbyWik5L4/YMo8+H0B0WAtHdUv4sgYSUa/s+mNJ/h/m+vQgFybmRuHFiFLEpu7gqxu76IprJAu\n9XFpdC/s6SXn4kuRLRY6Fy1kUX0bVeWjiVu/OG1RkGEWGGQzHVEFu1FmbJab/k6wqO3Ivk8wGe3k\nDfgRkmxMtfr3xhXeCUVJIHHL2B9T5Cpk/s5XWd++CUHy1lviHoPJPIqJ3iyG+ZoJrl1D36aNiFgU\nyWKhM00ms2P/iHRzQSHx5ibU0n74Lp7OqLxRxNU4L1e9ye7O3fxwoZ9sv4ImwSunewjmpDG1cBJe\nWyYb1r2PtaGd8cNPZ9DwyXgLs6jZ8g59lRuJLtkLQN4NN+Ialxzhr0Uj1P/uLhSpB8llBE0mY/I5\neCZPRw0EqPvDvWgdnfuLSZLI/P55OEeNIVC5hp6PFiJUBevwUqJbapCdTmylZSAEcVMXhhOTYx7k\nRifu/Cm0PvU4BocTZIl2iwOHxUT5dddjyf/8XRSaRuDTFQRWr8R70SVY+xUf+sf/BVoigWxK3k/a\n/vYsgU9XgCQhmc30+/VvseTn79/3qpV0vPwPtMjnfzDk8ZD34xuxlQ5E6e2h9S9PEdm1E3NBIYW3\n/AKjOzkWJbB2Ne3/+zdELIZksWAtLsE+ZhwL8gdh6u7ihBeewKAkKJh7G7YBA6n91S9Re5O9YHk/\nvgnX2HEIIQht2Uzna68Qb20BWcbk9eKZfgbuk6cifamXXA8IjkAPCI5PXwwIGvuiPLWrkUyLmZuH\n9QMtQfO2/0aSTQihItQYnsIZuLwnHHGfmhC8XdXKxkCIRHIBZ1kdTB5ZkFpne5WPdzY1Mszt4Jwz\nyvdvqwnefXkzzfU9xF0muio8KA4TbgKMlbfhUtwMLSvB3/g2QUMuL8ZOxkGc4fIOwoYs+mu76edy\nkj3gMhKRtmQwEOulEw+FThd5gy6js+5Nwv6tGG35/CM4km7ScUkR+oSNHKuRsbY23vNnMT3XzpSC\nPB7cXEefsn+61EnGHYxgM86sCewwjSfLaqY8fX9rJdbXQFf9W2yKZvGpNo5yqZpdoowSp5WRmS7e\nqu9gmMfJD0pziPi38W5tA1vFYEqlBi4bWpEKZr6oKRRlQZ2PUZkuTspJR/48KPN6Xexo6OLZ3c0E\nEgonZRpwWt3sCURSLWqTLHFRWS7l6U4a+iLs7Akx3uvmk1Y/lR29/MDTTH+nzDvBAezoOfhPZ4rj\nfUx64QnMiThpJ03Ge8VV1H70EcHduxk6+3wsBYUITSP42VpsZYMxeTL4qLmLpS3d2A0KYdXI6EwX\nvlCU5miy+9dp1AgqMqM+W8modctwjBiJ58yz+HDRMtZPmIZdUxnW2076ymUErQk+OgFUOY7VYGOK\n41w2JezEjZ+/llnxEU1sQtKaOTFvPP3TilnZ+hmBvjYispsTTYOo2/EhmZoJUZjHGlMLOfZsSkM2\nIt1uaotPgM/HXwzctYkJKxcSsUrsLXOyrdxJtyHGGQzipJ50HKNGYy3pT8sTjxLa+BnuU6aSdtGl\n9MQVcm1mmt98ifD7C7GUDSBWvRdDXi75v/glPRu30L5+PfadWw/7u+mePA1/ZxeD2urpf+8DyFYb\nbc/+hcDqlXjOPAtraRntzz2DFolgLiwCRSHe1kr69DOw5OcTb23FMWo09kGDU/uM1tfR+uRjJDo6\nsA4YSOY1N+DISgZ0Qgjadj5NIthO9O+1EBdIZjNF//UrfJnZPLmrGQMwMSedU/I8OE3/+pA3JRig\n7tfz0MIhcq+9nrQJJx60TqK7C9/85xHxBLlXX4sxff+000giwWvL12HdtZ3hHY3kTptG34b1hLdv\nQ7JYybnsClwnjEeSZTZ2Bni1NvnHTIMtErOkCGnDky8+6vlkGb7n/4Zz3Hjyb7jxgOMLTUPp8WNM\ncyMZD59nPSA4Aj0g+L8v3LMbSZKxuQemlnm9LtraetmyqYVFcpzezyu+SwbkUUIdXXVvkJBGU1Q+\nibadj2F2FFLbfDKd7X2YDe0YjSrCWExWtothY/Ixmgx89FkDS9UYckzF3hGhr8CB22LipxX9SGiC\n1e09rGrvQRECr9XETRX9MMlych60ohKMJlhZ08GmeAwBDJN2U9DaRkFOB1arCaM1g3ioibzyH7Ng\n1wY2KmWp/NilBHPkd/G48ogGa6kW/VgnTaBXNeKli/MHlCA3/B3ZaKXV+yPeqO/GQy99kpuESOa7\nyGHl95trGeS2MzLDxau17ZzgTWNidjp/2dWEDFyf38kurZQPmpOtjCHpDs7t5yXdYkJoGqqW4E/b\n6uhNSPw4t5UP+vpR3adglpPvIbi5LBuXSUYJ9uFreIFXmUwnHuYOK8Zrs9AdSxBKqBQ5rYQVlUe3\nN9ATT3YF93fZmFWSjUdN0NlQy196ZcIGE2fmujm5aP8I6Kiism75Kj5yZqNJMiUuGzXBZMvLJEvI\nSBhkmDeqFFkI2v72V3r6QqRfeS19muCDujU0hY0Yjfmkd3fQr62BDncmXbkFxD8fwHbmR68xatop\n9CxdQrR6L5aiIvLn3ckD22qJKHGCfS+TZ/8BIUPy/Q6le7ZS5tvOa8MjZFnmoBrtzProFVz1NSTM\nFl77/+yddZgc952n36pmnOZhnpE0I2nEZEkGkWVbMsUUsB1ONpx4N9m7XG692exusnsBJ3ubDdlJ\nHDNbtmQxWowDkoaZoZkL7o+WRp5IlmHNN+/z+PEz3aXqqurq+n2+fNdXSOo15BuPc3P5amp/9S+U\nt4VJlRcyWpnNQMdpZp+NIWs09BaW0zplJl0lUzMhJEVBQEUVL84TcIwOsXLzk1jDQTrmFmKbsZb9\nMZUxdzamaJgFB7ZRP2sxY95cNLFOZM1BDBo9CGDTWfnizHux6i+IPiWZpPNf/onTFhcnVqwnpsIc\no0D1b36KyWyi+P4fMfLs0wR37SButbFx3acIZ7ko8w8yP8vE2YRMs87M4v5Wqvvaic+az5/NOUiq\nyvIdLzDbYUaORomdbsBQUkrR338fQaslPTbG6PPPEjrwKqgqztXX4rnjrst67eRYjHhTI3XZxbzc\nM8qKfBcr8jKiQJGTyIkoQw89TLSulrwvfxXz7DnjXiy7Tksonbnv3AYdU7LMXFfoRSu+/R4MiY4O\n0iPD2OZf3rD4a9KKwh+b+mg/fw8nE0w9c5ypp4/j9Xlx3vs5JLcHh16LCjxQ38loMk2hxUhnJMH0\ncyL8/LMmWncK89QqRMPrV4ZcjklBcBkmBcEHm9DgfgJ9mY5gVs8CnPmrEUQtHo+VJ/54hANKiliu\nmQqhkxa1iAKzgVv1B0hFmti9bx5zls7Ga91KItrFtp2LkSQNK68+hE4rcfBIDWN+Bw63mcoqH5uC\nIeI+E9frTFR4bNSTZke/H5tOQyQtowJZOi0evUxrVGWRJcBir4kXRrLoiFwYK5ul07La0oonsh9J\nfxMmfSfpSCaz32AtJrvyXsL+Zl7tOInbPZ2gJpsdfWMUCANcKR5mN8vplZ1oBMg3yHQlNAiozBEa\nWO6z8MexSqKSzDemZmE1uRhJpCmwZhaun9V1EEpJeIx6+mNJ7qspwWXQsaNvjG29o8x226gfi2DQ\niHiNOjoiCQyiyFUjnWRv3UDbZ7/Oq1GZuW4bt5XlUD8W5tHWTInj1UPtlDz36Ph5ioUmutYtYJuy\nnJkxP1cVePnDSIK4qGW6UUNKb6A5FGNZtoOxZJrTgSgCUNLVTJ8vn6TRzKJ9rzAnEST/m99BYzaj\nqirDTzxKYNtWhnx57LjxbhIaLYUmHVPSMQ5IWmIIzHWYuK2ygKHHHyWwbQsAvns+Te/0HP7z1INY\ndVYqOytom3rh4W33j+CJhWjPK8Ua8nPTU79DK0sITgeqP8DZG9dzMLcGNV7HVTs2k5Vwcmr+cmYE\nB9E3HcYxmqB+XjaWMTu719yBU5vmpi0vUmd2cGzxShLJYyRTmaQyjaTy+f1g7Bke/3y/TcP2hTb0\naZW1yRJSIwlOFVQQdGQWOUFRMKRTWC1mhN4ukk4PLUWVaOUkeT3tDPkKSZgsCIpClZDm2iw95nQS\nbUkZf+7x0x6Oc09lLtMcr9/wqTuS4KXGTroVEa0s4TSbGE6mMUUjWKxm0jo9ZRY9xc8/xt6aKxh1\nenFqRfySMmE/AnBXeQ67+/30xZJoBQHSKW549iGc/mHMVdX47v0MLRoTeWbDeCgv2dNNqq8P64KF\nbxjCU1WVLb2j7O73Z+43Ab5WXUSO2TBhGyUeR2M2c3wkxNPtg8x0Wbm9NJtDQ0Eag1F6okkSssK1\nBW6uys2E8OKSjF4jonkH5ztcCllRebytnwZ/lOlOKwVmA3t7hoidSxJ1GXT4k2lUYIHXTqnNxJNt\ng8zz2Flf5OVPzRkhUWgx8smKXOx67fjxNwajWLQayuzmt3Qek4LgMkwKgv8e8WAT4eFDZOWuwGDJ\nv+y2UX89gd7tOAuuxezIuNulZABVldEZJ8Z3FTlFaHAfocF9aHR2RI2RdGIIrcGD2VlF84CLjUMi\nSZcRt+LnVt1mtilLaVcLWSfuxB4Ns3f/PMxWPTfeCuHBbdQ1VDJ/WRlJ/2YABI2V7qE1nDo6gqwX\n6F+Wi1uT4F5vDzqDE721hCd7VRqDUYptJmZm6SmJ7SYWbOZJ+ToimDGRIIaZEoser9lIll7HIo+Z\nsSr7HqoAACAASURBVDO/QNSYyJv+DVKxHgabHgLAVXQjVncm2VVVFQQho/r/3NxHYzCGiIqCQJXD\nwvWFHpw6OFT7F3ZJcwhjxaqkiIh6rsxxsrbwQutaKRBg4KHfc2jlTRw7N/22ymHh7spMnDMpK/x7\nbQexc56UuytymeawcGwkxMtdwyQVFW06haTTY9WIfLbIieHUMUyzZvMffVHMGoFV//VjRFHEVDkF\nUafDVDkF1WHiNwE9EbMNazhAyOHGHhgldG6Ry+/rZPWWp7AvWkL/FdewvaOfsSw3qCrrTAqlB3cR\nPnQQQ2ERtoWLSQ0NENq7B21uLsgywWic2NTpeGuPgiSRNBhprZxBaVcLkseIrW0AXXYO0tgoos3G\nH9e5CMgRvjf9i8T+5w+pqypm9zwHaw/14IspjN22gj59JU0hDXM7TmOxRthhhrIBCyPZRcQtNm5+\n5vc4RgbpztYxvH4ptVI3UiDAp18JoU1kxhw/c9tthN1TMYoSsgwpZGbYW8m3eHi+9WUqHeV8eea9\npHt7SXS0o6bSHC8ReK5rK+vLruXakhUApEeGUVIpdB4vkWNHGHz4T6ipFLrsHAru+y4nJA0vdg2h\nqGCRUpSM9rNyfg05ORNrygfjSX5V34XToOObM4rQ/lXcOCbJvNg5RO1YpmqjdKiHeVufw1NexqsG\nB2drFqPV6xAFgehrwkwLvHZuKvZx2h+hORRjWpYFq07LH5p6SckZkTDPY2dqloVHW/txJOPc6jVR\nXFnOM+2D1I5FcBq0fL26CONrmlSFUhIN/ghzPXYMl5gtAbCrb4wtvaO4DTqW5jh4sXOYQouRm0t8\nvNQ1TCQtc2uJj2KbibFkmt+c6SYhK3x7RvG4AIHM4vmzuk5SSua93liSx1sHMGpEZrisLPZlkW26\n2NoeTaQwaTWY38bAL1VVOROI8krPCCOJNGU2E5+ekodWFEkrCvVjEQ4NBRlOpMgxG4hKMkPx1Hit\n0bdnFuMx6kkrCs93DHFiNIxZK+I16hEFYTysBmDRalie4+TKXOebOrZJQXAZJgXB2ycebGa4/QlQ\nFQRRh6fktglu/dcSGjpAoHcrAKLWQl71V1HkBP1nfwuqTO60L6M1OJHSYQI9W4gHG1FVCY3eQXbF\n3Yg6K/6ezURHT9Km5LFNuQIFDSW6MMuVbQjpXPoTElvNy8gXBpg3NoCqm8vJQ92UVhioLt9KNJGN\n06UhFevH6plHZOQoeksBqbSFIxETx8QqlolHmSE2nztqAatvGTrXPNRwA6HB/ShSFIO1iBHzAh7p\n0wMqC8Va5mnbcRaswmgrZ7TzWZKRLuzZS3HkrSTR08NA828QzBp8efdiKry4PC2SlviPhm6SssL6\nYi9z3LZxC2q0awNjI/XsUq6gXc1Dn4zzNacGz/QLmcNDjz1CYPtWBtfezKbizOufnZpPhf3CuNt9\nA342do8w12VhzUgnxrJy9F4fXXv38cJojOHsAqafPMDsnmZE/xhqKoV5Rg2+r3+LeEM9g7/8Gc7V\n1+K98+MTjv1Q9xAvDGRm1y9U41xDjIMd/XSLepadOYo+HEIay0zuU4HdS6tpKNNzVV4F60pWM/bI\nI4T27RnfnzY7m0evthCVoty1O4ZpJIyc7SFcmUdSB4lICMfpbrKiMjGjwK5bprGwKY3nWAvbF9oo\nWn0jV8dy6f35/yF55XwOzLHTONZCQj7vxdGSZb0DhInZ3oIsU9FUy5Un9mJfv56f6w4RTGeeDx+r\nWMfCMRt9//EAGpsd7fe/w381HwfNjExL5/QpfrDgOqw6CwkpiV6jQ7xEE6eElMCoNV70+nmSfX2E\nDx3AsWLVeOJZIJkmIStkmy4/sfB8ZciafDdX511IZu2OJHistZ9ASqLAYuC6Qi/Z/V30/Nu/AqDz\neCm+/58QjUYUVeW0P8K+gQAeo45bSrMvaX32KjL/eawNh17L12cUYdRoeKV7hD0DGWveotUQleTx\n/9e4rNxZlpmH0RKK8UTrAFFJZpEvi5uKL26YM5JI8cv6Lkxaka9PL8Kq0/J4a/+4oBn/zoByu4m2\nUBwFWJ3v5prXnPt5znsPckx6huIptKKAThSJSjJGjchXqgvxGC90VTwyHOS5jiEgY8XPctu4Jtd5\nkdB6PV7uGubVwQAisMCbxdpCz+sKH8gkMj/dPki9P8Jsl407yi/k46iqyr7BAHv6/cSkjKcy12xg\nptNKKC1xajRMXFb4xvSJ3pPX0hWJIyJQYP0Qlh2+l0wKgrdHItzBcGvGfWzPXkpo8FVUVcbsnIHJ\nXo7JPgXx3IMv0L+T0MBeNForxqxKoqMnsHrmk44PkoxmmqMYrCV4y+5ksPlPpOMDKHov25RluC0u\nFud4yDUb8I9E2bTzDKeLrIiCygrNAUqFHgRJg7TBT6qgmO1zZ9NLDktdBlbl5/KXXx8klZRZtuQY\nWfZM4pkpayqe0tsZavkLyUgHqgqPyeuIYuG+Sj1mg5l0fIhA/w7kVPDCSctgcy7GUbYaQRBo8Ecw\nigK+ZD2B/h2oSjqT5KUqmBxVuItuAhm6fvSPpCODoBUw5VVR8K3vXPKaxpIpEATM+omNiJLRXgab\n/gB9es6cErDEwuRGghT9739E53Ihh8O0fe8+1FQKKb+QR9fdg8eo57OJIVLdnThWrUFrt5OORjh5\n8ChZm56HQACt203xD/6Rnl/8lERnB8U//ncCG14gtG8vWpcbQSOSHhmh9F//jbGNLxHcs5vC7/1P\nTJUT++dLisIfGnvxGvXcXOIbTxw8jyrLjO3ZSf+GZzibDYcWe7GbbAxEhsmz5PCVWZ/FNBhACgZQ\nE0lOOmI83rMJnahFTaWxxGWCtolJUnkmH2uVCtrFIDvjDZjjMp95cQzVYmLqv/6M4NYtjD7/LHlf\n/QbWOXORFZnRhJ/h+Ch1I6c5NNiLwXAlOiHEdUWVVDldKHt3QiKBY+VqNCYTp4YbeKrpBW6uuJ75\n2RmvTvjIYbROJ6aKSoZiI/zy1AvEFQfXF+axsujiRLP3krgk89O6DmKSQoHFQKXdQmckTkckjqrC\nijwX1+S5xr+f3gd+RrS+joK//R7mqdPeYO8T8XptnOwYxq7XYjuXtKeqKm3hOLv6x2gNxZnrsbG+\nyMeDjb10RxPM9diIpGWagzFEAczaTCjua9OLyDUbGIglScoKhVYjDzb20haO84nyHGa4MgtYOC3x\nq/oudKLAjcU+9BqRp9sH8CclfCY9V+c6qXHZLrr/zh/b78720BFJYNSIfHpKHvkWI0eGg7zYOYzX\nqOdvqgswajSc9kd4pKUfk1Yk32ykO5ogISvkmDL3d4HFeMnPOM9pf4S/tPTjNer5ZEUuPpP+dbf9\n62NsOxce0L+OeFBUFUlRJ7x/xh/h4ZZ+5nnsfKw0G1VVGUumcRoyHp/GQJSHW/ootBj5UlXhpCC4\nHJOC4K2jqir9Z/4TKeVHX3gXdXEnC21RIt3PIaczC6ioteAuWk8qPkCwfxeCxkHutHvQaK30n/0N\nUjJjMfpDeWg0CnbLAFq9Eynlx+qex3FxETvOxQ4BrNEkmr4EkQILsknL56YX4ek8wlj9BqTaIIwq\nqKkUqRkFbFz2cQKqnqtynXR2jtEtyxj1EmZNlCwhTJmnlBJ3PjkGgUi0j/0BHQdHEsxx27i97IIy\nV+Qkgb7tJMPdJPa3kz4xhKl0KgX3ffciS01KBRnrfJlEuA1H/ipsvkUIgsDgX/5McNcOsq5ZQaq/\nn/jZM+R/+2+xTJ8xfi1D+18lcvwosbNnATBXVWGpmYV98RWI+szDJNpeS++Pf46xuAzb4iUMP/oX\njKVl5H3jWwS2b2PspRfHG6nww5/gzMoi+P2/RYlEEI1GzNNnEK2rRU2lEI1GjKXlxM40YCgqJtnV\niWXWbPK//i1URSHR3oaxuCRTEvXQH3Bed0Om5Aoo++kvLipjeqN75exYM483PcdIfJRss48v19xL\neX4+vz3wGPv6DpFt9vGtuV/CrrchKzL/ePDfCaZC3L/4uzT5W+mO9OI2unAbnVj1Viw6M16Te9wC\n7wx1408Gydl2ktD2bTivvY7UQD/RUycp+z+/mJDtfZ5gMsTp0UZm+2ZiuozF/kaEUxFagx3UeKov\n6RF4r+mJJtjRO0ZjMMr5B3S+2cC1BR4qsswTtpXjcaSxsfFmNm+FN+oDEpPkcVf7WDLNrxq6SJ4L\nMfiMej5Wmk1clvljUx+lNhMlVhO7+sdQAbNWJCYpTHNYuLti4hjqlKygEYVxr0VKVhiIJ99wkYZM\nCGB77xjLchzkWS585+et+VyzAbtOQ2sojijA56YWUGg1kpQVNnYPc2Q4BIBeFCiyGlmV76bIOnEy\nZjCV5pf1XaQVla9UF76uxf5Ooqgqv6jvxJ9M83c1pRwcCrCr30+u2cB8j51XekZQ1YzHsMRmelcE\ngeb++++//x3f6/tALJZ6440mmUAi1Exk5AgWVw0HUxXsGwwwLBlYOnUlFmcVotZCItxOzF9HMtKB\nJFvYubsKjdZObqETSbGTCjcQixs4cKia/n47xUVDoEQx2sowFdzIE21DGDUiC5Iqw/44MbuBpMeI\nqhNZkefiuml5dP72UZIH2si54zPkfv5LaMwWknsOUdDWRFfNAppDcQIaAa2SRpBlIloro7hojcGx\nkRB7BkMc9kN3TMKq03BLiW9CiZIgajHaKwg9tofE8VY0Vjup3h70OTkYCiZO0xMEPf4HXyaxuRmN\nZMVSM5vg3t2MvfAc+vwC8r78VYzFJQT37CbZ1YVt8RIErZaRp59g5OknSQ8OoPN4EI0GEq0tRE+d\nJLh3N8gyqiwT3LKTVF8fvrs/jX3REtIjw8TqagkfPki8pRnBYMC5+lriTY3kVlZiSCcJ7tyBoaQU\nNZEk2dmBzunCdd0N5Hz+S2Qtv5JESwuJ1kx4xHvnXeizMy5dncuFIIros3Pw79xOtOkMJJIkaiow\n1tQgKRJpRUIrai7pwk7KKTpCXRzoP8rjjc+xs2cvCTnJNYXL+Mz0j5NlyCLLZqbUVEZSSVE3cpoz\no01McZbT5G9hf/8RluYtYkHOHApseVS7p1KaVUS2xYfT6MCqs0z4XIchixyLD3NlJeFjR4meOonk\nH0OblYV7/U2XvIeNWgOFtnx04n+vJM2g0ZNj8b0jEwTfCex6LbPcNuZ67JTbTawr8rIsx4nLePFI\nZlGnQ2u/uM/Dm8FiMVz22al7jWg0aTVUOSxMy7JwQ5GHK3NdZOm1uI16eqNJWkIxOiJxnAYt1U4L\nQ/E0OlHgnso8TH8Vv9eIwoSFXyMKZOl1b+r6m7Uaprus2PQTv/Nyu5m+aIKOSILRZBqDRuTjFbmU\nnQu3aUWBKoeVIqsRUYCUotIdTXJsJEQgJdETTXB4OMiefj87+vwklEzY73LJne8kgiCgFQVOB6K0\nhuPU+yOYtSL+pERTMAYqfKIyl8qsTIjMYnnnRcq7OstgkvcXRU6QjHQjaoxo9LaLxriGhjLtfG3e\nRTS1ZDqtNQVjbO0dY21hNnpTNmZHNaOdLyBLcbafmMlQqZPu0QDbD6WIRRUE4SYEgwGu0aENJDly\nPMrsOUkKSm9hc1+IpKJQMCbRfWyQPClOWeg40WIvthvWc2Wei2h7B7H6WkyVU7BfsRQA55prkeMx\n2PACH0+P0ZhbimPT8ziOvIoIaOd50az6OH5nOb3RJD3RBPF4gmkt9RTteJlURSXhq1dgmTUbUadD\nScQZef5ZonW1mKun4/vUvXT+w/cZfvJxTGUVaLKyxi14/9bNxJsaQRQJ7t6Fmk4TOngA0Wol76vf\nQNTrMRYVY1+2nNDePXT84H9gnlZF+OABdDk55H/9W+izM96J9PAwwb27CezczsizT49fd0NRMZaZ\nNQiCQM5nPo8hN4+R554BVcV90y2Yq6cztuEFYs1N43XInptvwVheSXp4CENB4QTrPueLX6Lrn+5H\n0OmwzKgBMhZ9c6CNuJTAnwwwWqxh5tmMrbnJ2kPnqz+acC+YtMaLLONYOj7ePEcnapnjncm1JSsp\ntE2cQigIAreU34CkSOzu2c+PDv0Uo9aAKIisKrrqrdyyAIhGE7lf+Bu6/vWfUFMpjKVlb3kfHxWc\nBh1Ow8Ui4P0i22S4ZOLe9UUeus8kKLGZuLXEh0mrQSpWkVX1sjH3dxKNkBEfoXQmn+B8ie1fU5ll\nGV9U28NxXugY4thIaPx9rSDgNOiY7rSw0Pv6w8veDWa7bWzpGaU/lmkc9qWqQhKSzJ4BP9VOK1Xv\nsjiZDBl8hBlqfZREqGX8b5tvCY68zAjR3d1dpIZ2U2NX0RTexc/qOim3mwimJEYSae6uyMUZlTFb\n9diyjLx8sI0DgoR6/setqqBmFgODVkQA4rJCbmsIbUeYK2+t5slwCI2k4tndi1sJUd3xCjnXLMe/\ndTOWmTXkfP5LBJ95jJE9+8j/5newzKwZP9ZkTzed9/8A6/wFZH/qXlq//XWMZeXYFixi+PFHADCW\nlaFzezJ1xcOZxCGtx4M0kumcJuj1mKZMJdnZiRwOoXW5Kfr+D9BmORh96UVGn392/PN02TlYZ88m\nsH0botlM/rfuo/eXP0cOBBC0Wgru++6EmLuSTuN/ZSNjL29AlSR0Xh8F3/0f6JwXZwjLsSiRE8dJ\nDw4iBfxkXb0SU9nERS7W1Ej05AncN96EoNXR8o2voPNloyYTyOEwZT//1Xi3tb8mkAyyt2UXVq2Z\na6auBuDF1lfY3LljfJucsMCdGwbBYKDlW7fQe27aXWaufZyYFEdRJ5akmbVmiu0FlNiLmO6eeskk\nute6nFVVpXakgVc6dtAV7mFhzlzurb7rksf8ZvBv3czwE4/h+8SncKxY9bb3M8nr807OElFU9Q3d\n/R9EJEWlJRRFL4q4DDrseu37eh7HhoPsGQhwV3kOuZcJVbwbIYNJD8GHBFWRMu02X2cme1pReKix\nlxmuTCe5RLidRKgFMWXCkj+HeKiR8NAB5HSYhHUmmwdAwwJmu4SMOwqY6bRRbDPyy/ouNjT2Y9vV\ni96goey6cvaLMoIKy7c+T35xJY3pAmrmFTB1ZsYa9ifT/Lyuk2CFHVd/hKe6h0nb9Vgb/WRZ9cw8\nuQFbeSme2+8k0dVJtK6W9u/dh5JKoS8oxDxj5oTz0ecXoPNlE62rJXz8KKgq1lmzca5ajTYri+De\n3cTOniHR1oZosWCZNRvn6msxTZ1GeqCf4N49ROtridXXIRgMuG+6BeeateNNQFxrM33lU329yOEw\n8dYW/JtfASD73s9iLCom/xvfZugvf8K55rqLEvBEnQ73+puwLVxE6MCrZF15zSXFAIDGbCFr6fLL\nfr/mKVMndHgzlpYRb8zkIljnL5ggBloDHbzSsR0Arajl9OhZpHPDfixZbvKtuWzt2oXL6OSagqUI\ngkiNpxrEvWiysphSefVlj+XtIggCs7wzqPFMpzfSj8/s/W/tz7FqDeZpVejz3npsfJL3ng+jGIBM\nKOG9Cgu8GeZ5s5j3HnsmzjPpIfgQkIz2MNT6KCZbOb32NQRT0kW1qucbz5g0It+bVcJo84O0RxUa\nOnNxu3wUVVWQG9iAHOtmvzyHWjWTjXxNrpPeWJKmYIx7PS6kQJKNwRB+i4bihgDSWJy+JdmgQs2x\n08zv2I2SiFP2k5+CIDDw0O+xzJyF46qr2dYzwo5+P7pkkrTBgKUviuuMnzXVMvKLf8F71ydxrlqN\nkkwS2LUD/5ZXkINBcr/0FWwLFl503uenkWnsduRQiOJ//OcJiVNyOIySSKD1eF5/6lgggKDXozGb\nL/n+eZREgsipkwiieMljea8Zef5Zxl56EWC8xaqqqmzv3sMLrZsmWPMeo4vlBUvY1L4NRVXwmNz0\nRQf4yqzPMt391rLO3yrvxbTKSd49Jr+/Dy+THoKPKOHhw0hJP1qDE4O1GL0pG8hk+D7T0klVYhs+\nNUHA38hzY7NIKSpVlgTaUC16SwFGWyn1/kxdb1xWONHbhC82yM7UDUQLrbQDR7v8LPZexxW2Fhr7\nvGilNIoqcnAoSFpRsSuw44lMn3ONywBzPDiW5BE504yqFXE0+rn2jhWotWaGn3gsM9Wro5342TNE\n62oxlpYyp6+XQzE9UVsWvmgC/dkAlcNHUDZmZpBb584DQDQYcF17HY4VK7Emw8SsFw+rAbDNm4//\nlY3IoRA6jxd93sTYtcZmQ2O7/I/iUpnpl0I0GrEvWvymtn23UFV1XNjoK861QxZFpCklnBquH3fF\n2/U2Pjv9ExTZC4lLcex6G6Ig4ja6+H39w/RFB5jnm/Wui4FJJpnko8WkIHiPSITbiYeaz8XwLyTZ\nhEeO4e955TVbCmRP+QwGSwEH+/s5E5YZZhZ3u13U+1OklIxD53jHMark4zB8CEnVckb5GBatjqgk\nc3goSCnlRLVW5pIg+/knOLjiRg4CEWc5STWCvTsjIEKlmexkoTeC2apn8VVl2J1GHhn1Ux8Io+bk\nYInHuG1hMY7sLJQrr2Zs40vjc8QNJaUkO9oZfOj3yNEo1+jNBO/5IivnlpOaEWb0v3aTDEoYyysy\nM85fg6jTY8krIfY6FoqhpBSty4U0NoZl9uvPHv8osKVzJ5s7dmDRmbHr7Qz6e7hHJ9Dr1fLAiZ8D\nICAwx1fDHVNuwq7PCCGD5kJt9BzfTNaVXsupkXpum3Lj+3Iek0wyyYeXybLD9wBVVRlpe5x4sAmD\nOW+8vW8i0sVo+zOIGiOesjsxWApIhJpJhNsx2it4rr2fBHpimCjPq+GAXySJDhUBWUkzy2nE4ppJ\nS0ykUS5gtnEYUY7Qp3oZlDL13Z+eMxX78ABZ+3bQWj2HgUQaVJXc5gC2hIw/xwyCgL01yNIaL1Xz\nCjHrFPz79tDlzkUVRW4uz6W6ONNeV9BqQVGInTmNoaSUwr/7e6RQkFh9HUo8Tv6yZcxafgUaUUBv\nNmKbtwA5HMa5ag0678Ux5cuVPQmCgBTwk2htwXPbnejcnktu92EjnIrw61MPcnTwJNlmL0cGTvBC\n6yYM2kx+w0BsCLfNh37eHNTZ1djMDsodJdxbfRdXFlyBQfP6iUaVzjKW5S++7DbvJG9UtjbJB5vJ\n7+/Dy2TZ4YeUdLyf9Lms7sjoSUxZU5DTUUbanwJUPKW3YbSVAuXI6Qihwb3UnX4Sv7qKXF2KgbSe\n5ztHiap2KoROAqqNPjUbU24NVpOD3nA3pBIUJY/ixEIPPlJaA8vcNgJ9IXJv/RjhI4dYcGgHBxav\nwjScoLDrBEaNzJDrShIuA554At0f/4WWR3WIRiMlyRRHS6fjtZqZ5ZvodneuWYvW4cRSMwvRYMB7\nx13EGhpQJQnX9esmbKuxWMj5zOfe9rVz33gL1tnzMFVeupXye01fZIDOUDdmnQmdqCMuxZEUmTm+\nGvTnJvCdGm4glAozP3sWJq2JhJRkMDaEz+whJaf55YnfMhDLVEWcGcuEUxyGLL4992/wmFzIiozm\nEhPzJplkkkneTSYFwXtAZPQUAIKoIx5sQk5HCQ7sRpGiOPJWnhMDGbJyryIRaedsKJNHsLqkmJOj\n4fHe39PFZnpVHyOKi9aYhmqDQmMohUOvocRRihrSsm8khGwy49/VxYtDMZatqqBg3Y1UPvYIfdYZ\nSGMy+YGz6JQk2d2zSbQLlI0dQGsyonW5SQ8PkX3VNXxzVhkm3cUlOIJWO94zADJZ9EX/6x9QFeUN\nk/feKqLB8IERAwkpyS9P/pZwKnLRe82BNj5VdTtdoR5+X/8wiqrwbMtL5Fty6Qr3IJ+rAtBr9KTk\nFNcULmOmu5oXWjcRTkf4+uzP4zFlQiqTYmCSSSZ5P5gUBO8yqiIR89cjai3YfVcQ6NtKoH8n0dET\naA1ubL6JiWyCIOIsvoPW+j6sGg2VWRZcBj11YxGyTXpKjA5M8TBHE1A3FqYlFCOpKCx0OXEXrmPo\nice47uAmepZ8gr6hOIIAr25v4eprq2koWosyKJMd6cS3ZD6Ro4eZPvIqidJZeFqacX3sDlzXXT8h\nue3Ncn6Ay0eZnd17CaciLMieS6Etj7SSxqQ1sb/vMAf6j1Dtnjqe6X9l/hXUjZymI9RFoS2fYnsh\nw7ERBmPDrCm6mrUlKxEEge+6vv62rvckk0wyyTvNpCB4B1BVBVmKotFmalnjwbME+nagM/kwmPNR\n5Dg272Is7lkE+ncQHc3MWs8kGGpesx+VvliSQ0NREorAcp8djSDgM+n5wrQC7DotTsOdeBUZR30P\npwOZIT+5ZgPLcjJu/WjdKaLaHPra4niyrSxbVcFLT9Sy85Vm0OfgiPUzQ9OJ75N/j9ZuR9n4Eoy0\noMnKwrFiJcBHYnHqCHXhM3kx6zI9yrd07qR2uIEv1tw7npAHkJbT/Lr2IbSilo9Vrif7dWrnw6kI\n27p2Y9VZuGvqzROa9ExxlvPjIw/wYP0jqKgszVvEnVNv5vYpN5JWpAmJf5fio3C9J5lkkg8/k4Lg\nHSA0uI9g/y40OjsavZ1UtAcAKTlKPHAGAIt7FhqtGXPWVGKB0xisxWisFROsww1dwxwcygwVsmo1\nLHpNc4oSmwlVlhl+7BGkYJBZ19/O7qEQC712bijyohNFUkNDBEbjnC1ejU6vYc3N1WQ5zay6sZr9\nO1qYMTef4pSIZdoqRJ0ex+o1+LdtQU2lcN2wfrxpz4edzlA3/370PyiyFfCdeV+hL9LPi62voKLy\nUMNjfH3258fb9G7s2EajP9PNsXGsmZmeaoKpMEk5yVxfDUtyFyII8FLbZhJyktvL1l7UsS/Xks1N\n5dfxTPMGnAYHt1TcAIAoiG8oBiaZZJJJPihMCoK3gKpIRMZOEhk+itk5naycTPe56EAtqKAqKVLR\nHgzWElyF12dG7w7sQqt3jvcWsPmW0BoTOCvPpelEG1flOllT4GEskebQUBC3Qcd1hR4qs8wTBouo\nkkT/b39N5PgxAKqSaeZ94ct4LBemdAVPnaI+5ypkNKy4bipZzkw8v3SKh9Ip5zP0Lwzz0drsGiFY\nSgAAIABJREFUeG69jdjpBrKWv/We8x9UtnTuBKAr3MNTTS/QGepGRaXIlk+Tv4WX27awvnwtnaFu\ntnbuwm10sr5sLc+3buTEcB2iICIKIhvaNrOhbfP4ft1GF8vyF13yM68uWIpO1FHhKP1vTd2bZJJJ\nJnm/mBQEbxI5HWGg6Q/IqYwFHxmJsy1eSXckwTIJPKNJXPm3YJxWjEZny0ybM3owO6vH96GqKttH\n9eyL1QASGgH2DPiZ7bZzYCiACqzMd1HtnNhGU1VV+n/zayInjmGaOg1BpyNWfwrNQ79lpLAINZlE\nX1TM4eNjRAzZTKtyUVHle1Pn5Vy1BueqNe/UZXrfGYgOcWq4gUJbPrIi82pfZoDTktwF3Fqxjh8f\neYBXOndwcriehJxEReWT025nqquC2d4ZBFMhnAYHKSXNkYHjnBiqw6Qz4TG5WJa3CO3rTNUTBZHl\n+e9vY6NJJpnkvUOSFQQBNG9hjPgHnUlB8CaJBc8ip4KYnTNRpBjhUCcnE2FkFZ5nFYuT+7mitwfr\nX/Xkbw/HCSTTTHNYODIcYt9gAK9Rx+2lOYTSEn9p6ee5jkF6o0mcei0zXRd33kv19hA5cQxjeQX5\n3/wOqCo9P/0J0ZMniJ48gQq0uOfT5ZyBTQmz/PrL983/KLOtazcqKmuLV5BrzeHfjvwSnajjloob\nMOtMfLnm0zzd/CKdoW4ScpIr85cw1VUBgE6jw2PK9IgwiRquLLiCKwuueD9PZ5JJJvkAICsKbX0h\nyvOyEEWBtKTwwz8dIRhJsXpBIRV5dvbW9tPcE+RLN02nIv/NJVkPjMX47YsNzChzceuVme6knQNh\nNh3qJBxLk5YV7l4zlUJfxkg82+lHFAWmFL65DqxvlUlB8CY5nxdgz15KMtrF2WAcWYVSXYz+tMj+\n3OU4O+pY+pp/IykqDzf3kZAVNIKArKpk6bR8Zko+DoMOVVWpsJtoCcUBWJ7rRHOJBLPIiXNJiCtW\n0d0dJhZJMeU73yXR3ISq0XLgVISuzhhWIc6qq3xodR/tsrWUnEYrai4a1zsUG+HwwHGyzV5qvNMR\nBZH/teg+REHEosuET/KsOXxjzhdRVAV/IoDT+O78sCaZ5INELCERS6TxOEyvu00oliKdVnBnvfch\nr9beIGc6/axeUIjhHXx+BSNJ/u9z9Vy/uJjZlW+vsZmiqvzh5TMcbBhk5dwCPrlmCluOdNE7HEUQ\n4Lk9bRO2/92GBu7/zEJMBi2qquIPJ+kYCGMxapladGEGTWtvkAeeriUST9MxEGZWuYc8j4VfPlOL\nP5wc3+7x7c383cfnMBpM8POnTlHks/L9e+a/vQvyBkwKgtdBToeR01H05sw0v2S0B0FjQGf0Iggi\n3eogAHOkMyzUDPBUai27faUskBX050YEt4djJGSFIouRlKIQk2Q+PTUPx7n55oIgcEORl1/Vd2HS\napjnsV90HKqqZvIGNBq6hBz2PpWZNxAOFTN3yXS2vXCG9s4Y3hwbN9xxBSbzRzuJLZgM8S+Hf850\n9zTuqb5z/PX6kTP8+fQTyKrMtcUrxsXC6y34oiDiNrku+d4kk3zU+P1Lp2noGOOHn1tItnNirxBV\nVdlfP8Cj25qQZZW/+/gcyvOzxhczp83wliph3koZbTSR5uldrew+2QdAY3eAb3xsJjrtBVHQN5JZ\neHPdljd9DOfZerSHlt4gj+9opqbcjShefFyDYzEe2drEzcvLKMub+AxWVZUnd7RwsCHzvN9+vId8\nr4WX9ndiNen4h08v4PCZQYYCcRZVZVPXPsqmg108saOZmWUent3TSv9obHx/q+YXcPvVFew83sOz\ne9qQZJVV8wvYdrSHh7c0Upxtwx9Osu6KEm5cWsIvn66lvn2MMx1j7K3rJy0pXD3n3Zv+Odm6+BKo\nisxA04OEhvZjdc9DVSWCfdsxWouxumchakxs7I+hIrBEPYApESZ5JklPQRmqolCRlblx9w4E6I0l\nub0sm7WFXpblOLHqJmowq05LkdXEAq99XCioqkp/d5CDu9rY9uIZemQnEV8lDW1JjCYdJouejuZR\nWk4PMdgXIq/Iwbo7ajCadBedywedt9o69cmm52kPddIXGWCurwar3sre3gP88fRjqKjcMeVmluQu\nmCzle4+YbH37wSeaSPPnVxqRZJXRYIJF1dnj7w0GEvzXc3VsOtSFRiMiSSrHGoco8ll5bHszD29p\nQlGhqviCZauqKhsPdrLxYCdzKr1oNRc8dYmUxI/+fIzNh7vQaUXyPVY0l1iEARRF5SePHOdUyyj5\nXgtFPisNHX66BiN4nSbC0TRP7Wzlz5sbebV+gMXV2ZiNE59xkqyAcOnS3WRK5ncbGkhLCtGERL7X\nSr5noqiIJyX+/fETtPWFGPLHWDozd/y9tCTzxM4Wth7tIddt5qu3zODwmSFONI8gKyp3raqkusRF\nZYGD2RUePA4TUwsdnGwZoa5tjCNnh4glJGZVeFgyI4dQNMWpllF2HOvhRMsIRr2Gv7l5BqvnFzIc\niFPfPkbXYIQCr5Uv3liNViOS4zaz51QfLT1BGrsDFGfb+OSaKQiCMNm6+L0iNHQAKTkCQCx4Bo3u\n3CAZSyZDfyyZJqRaKBW60Wgl5O4ky4QkbeEge4E5Xgceo44zgQhmrYZi2wU3nSrLxM6ewVhWjsaU\neb0iK6PYY5Ektcd6aTk9RDiYAMCiV4lrLfQpOsxWPevvmoVOp+H5R04QCiQoqXSz+qZqtNqPRphA\nUZWLQgHnaQ92cWjgGBatmagUY3PnTtaWrOSZ5g1YdRa+OvtzFNkK3uMjnuTDSCSeRqcRMejf/d/N\nmY4xEAR8DhMu+1uztt8uiqKOW8O1LaPIiopGFDjZMkJ92yh2i57n9rRxqnUUgGlFDj57fRVnuwI8\nuPEMP3sy011VqxF4aX8H5Xl2ZlV4UFWVJ3a0sOVINwBHG4cmLKKPb2+meyjTyfNPrzTyxI4Wsl1m\nfA4TPmfmv3lTfJiNWnae6KVjIMz8aT6+uL4aVYVfPVNLbesoteeOC8BtNzIaSvCnTWf5zp2ZIWeh\nWIoN+zrYdbKXG5eWsH7phW6v59lf3080IbG4OptDpwfZeKCT+VO9nO300z0cpTjbyubD3fSPxjDo\nNZztCtAxEKIkx07XYJjfbThN70iUbKeJ++6cjctu5K4VFTy8pYmibCtX1uRd9JlajcgX1lfz0ydO\nUpZr57ary8c9G6vmFfCbFxuobR1l2cxcbr+mHNs5j+7t11RwsnmERErmczdUjYus0lw7c6d4Od6U\naX1/54qKizrHvpNMCgIyjYXCQwfQGlzoTNmEBvYgakwocpyYv35cCOgtmcWmKZhxARUK/QBoUhZs\nJcUs3L+FndfeztPtA1xX6CWclpnnsU/IC/Bv3sTIs08jGIzYFy/BuWo1+tw85FSKDX/Yx1hcg06v\nYcr0bKpm5SI99mti7S1Y7/shzgIPFmtGFd5y91z6Ov1UVPsQPyJZrqFUmAeO/4Zsi4/Pz/jUBGGg\nqApPNb8AwBdm3sOTTc9zdPAkvZF+0orEPdV3TYqBSd4UQ/4Y//Sno5Tk2LjvrjkADAfi7DnVR2mu\nnapiJybDO/NobOgY46ePnxz/u6bczTc+VnNJ1/XbZeuRbrYd68ao16LXioyGEgQiKW5ZXsr6paXj\ni8nn1lXxuw2n+c2LDcQSEipQVeJi7cJCZpS6EASBZQ4T8aTErpO9rFtSQp7Hwj8/fIzfv3Sa6xYX\nc7bLT33bGD6HiaFAnAMNA+OC4FjjMHtO9VPks/K1W2ey7VgPdW2j9A5H6Ry4MNF048EuPnPdNJ7d\n04bJoOWTq6eML4Bfu3Ume2v7GQ0liCUkphY6WFSdzQNP11LXNspL+zuIxCX21vaRSGXagR85O3yR\nIFBUla1He9BqBO5cUYGkqBw9O8RPHjlOU09wwrbTS5ysXlDEL546xSuHurh+cTE/efQ48aTMNXPz\nuePqinHhePWcfLKsBsry7K/7HRZ4rfz8a8suet1k0PLN22oIx9LYLRNDu1kWPX//qbnEEhLFORMT\ny29ZXsqplhFmV3iY9hpPzbvBpCAAYv4GAn3bz/0lACqugnVERk+QjHQipzMCwGDJxG6agpkOgecF\ngd6ch76gkOKOvzAtMspZ3DzROgDAFNKkBgbQ5+SgShL+HdsQDAY0FjPB3TsJ7tmFde48+gZijJkW\n4o52c/1tV2CtKEcKh2hracJcXkHBtIlxI6vNwJQZOe/+xXmPkBSJ39c9zEBsiIHYEBvbt7GubA2y\nInN44DhbunYyFBthnm8Wlc4yri1ZwUMNj9Ib6WeOdyZzfTXv9ylMco60JPNq/QDTS1x4L5PE9n6Q\nSsv853P1RBMSDR1+RoMJ3FlGnt7VypGzmYFTWo3AV26eeVES2kv7O3jlUBcuu4E8j4XbrirH4zAh\nyQoPPF1L30iUkhwbsyo8LK/JRRAEthzOWNIr5xXQ2huktnWUlw90XNKiPY+iqBxvGuZUywjtA2GS\nKYlv3j6LAq+VVFrmhX3tFOfYWDDNx/GmYR7b3oxeJxKJp0mkZFw2IyaDlg37O5k7xUtd+yjZLjOL\nqrJp7gmy83gvBV4Lt19TwTULixkZmTibY/WCQlYvuNCv5FNrpvDHTWd5elcrACU5Nr51+yz+49k6\nznT4xxPg/rjpDDqtyBdvnI7HYeKulZXctbISRVUJhJMMB+Icaxpm29EefvxIJlH6k6unkPWaxVGv\n07By3sXC/t61U/nBHw7x3N52AOwWPbdeWcbRxmGaugNE4mmsJh1pSeFUywjHm4cZGIuxdEYOWVYD\n1y8u4ujZIZp6gpTn27l6dj69w1Ei8TR3rKjAYtRS4LVy9OwwZ7sCxJMyX1hXzZK/esYKgsDcKZfu\nZvpmEAThIjFwngKv9ZKv53ut/OuXFmN/D/LDJgUBEBnNNPuxuOcQ85/GYCnIlBcqKZKRTqTkCDqj\nD1FjJK0otIXjeI167EkJBRVTbhWGgswPaGntQfqvuolgWkIvChh+8wBd0QjF9/+IRFsrciCAY8Uq\nHLfeyZ6nD5HVcRyOHaUtN9M2uHSslvhJA9aKcsKHDoKqYpv77mSUvt90h/t48Ox2HBon/mSA1mAH\ns7wz6An38krHdlJyiuNDtfiTATSChityF3JLxfUAzPXVsKljO5FUhDun3vI+n8n/n7T3hxgKp/DZ\nLjyoOgZC/G7DafpHY+R7LfzDpxdMiDFfikF/jGhcwuc0YTFq31WX+qPbmugaioxbuIfPDrK8Jo8T\nzcNkO00srMpm48FOntjZMiEJrbZ1lGf3tGHUaxgOJugZjtI/GuP7d89j06EuGtrH0OtETjSPcKJ5\nBEGA8rws6tpGqSzI4pOrpxCJp/mHBw/zwr4OphU7qSzIJLxKssKh04NE42lSksKr9QMMjp0zQnQa\nkmmZB546xd9/ch5/3txIXVvGnX6wYZAznX4MOg3fv3seBT7reELfwdMD/PbF0/z8qVOk0gpzp3gQ\nBIGPr6xkSXXOuIX7Zq718ppcjOcs5JJcO94sI4IgsGRGDi29QQ40DHC6Y4xoQuJTa6aQ91dxelEQ\ncNmNuOxGphY5Kcmx8cdNZyn0WbnmTSbIuexGPn9DNfvq+llUnc3cKZnchVhSoqk7QGOXn3lTffxx\n01kONGSMsSyrnuuXFGeOO8fOPWunotOILJmRc0m3+9pFhfz+pTOEoik+vqryIjHwfuLJem+E9bsq\nCFRV5f7776exsRG9Xs8///M/U1h4QXk+//zzPPjgg9jtdm6++WZuu+22N/w37zTp+DDJSBdGWynu\novW4Cm8AMj8Us6Maf/cmQB0PFzQGoqQVlcL2RqRGP3IihPkrU9GYzWjdbtTONm4ry+bBxl6mGkQY\nGUYBBh76PWo6DWTKBxtO9dPcIyHqZzH3lmWM1oXIybPh6A4TOXYEz8duJ7h3D2g02JYsedfO/70g\nkAxi0pomtPGVFZmHzzxBb6R//LV8ay73Vt/FYHSInx77v2zv3oNO1HJ1wVJWFV01oWJAFETum/s3\nyKqCTX9pZT3JO8fhM4M8saOFlfMKuG5REXVto/zqmTpkRWXNgkLWLipi44FOdp7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KAAAg\nAElEQVQHTQtitKagKAohl4ve3R9gSEwi6/FvE2hro+Yfv0PnG3/CvmQZPf4grmCImAN7aH3hf2i+\n9Ysomsacq6JX/bfG5ADtzHSGqO0zUPLhaUo+PA3A8svyPvdgYHA4SVyMmcsWTrzO+daprQTUADfP\nuI4EczzekI9L0pdh0psoiMtjb/MBchzZZDvCRVl2Yww3zbiWa3Iu51TPaWYnzhzzcUOqOqlPhueT\nYEjlh78rw+ML8uzXL5v0/veJHu+Xb1XQ2OHmW/cuxmo20O8J8MrOKq5ZmjWqc9n2gw28s6+WPncA\nty+IXqewqDCZ0pPtvPHhaU41hbevrl02fgvnhQVJrF2aRWVd97i1AOtXZLOztAFNg9VSsS3EReOi\nCQgCA8OKjOZwpW7Prp1ofj/x16xF0esxpaXhWL6Cvn0f4Sov4yVDEg1uL188coSQTk9HWjbpNjNW\n28Qdo1RVpbamG5vdxDUPXELAH6LmRDsnK1rx+0IsXP7599t/90A9r+ysxmTQsWhmMvZhAYrHF+SX\nW46xblkWjgQ/e5r2k2pLYV3Oleh1I9fm9Dr9uIWBVoOFuUmjP1lqmsYv3qzg6Oku/r/7lp6365ed\nvV72VbRyrLaLmy/NoyAjjgPHWulxhSf7Ha7uHLX/W9U0unp9tHZ7cMZbxuwudqqpF4fVSEKseURD\nmjf21LDhykJe3naC3eXN1LX28/f3L0VRFDQtvKVuy95azCY9zjgLeTEObl6dx4yMOP7+v/fyXkk9\nqqaxsCAp6pjYjesmrstJSbBxw6pcWjrdkoYX4iJy0QQEQV+4gYjBkoQWDNI9MFMgbs3lkfsk3nAT\nffs+4vTOXdSuuh6AyqCO5JlzCAE59ujVxk11PXg9QeYuzkBRFExmA0Xz0z61uQNef5Cqhl5c3gAm\ng56FhUlntcf4aE0nv9t2EgXwB1XeL2vk+lW59Pn7w9sBOzM5cKyV2uY+8i85jqqp3FqwHr1Oj9sb\n5FB1O0a9nji7iRnp4w/4GNTvCfDarmrm5CawbHYKO0sb2X043Fr0528e5V8evXzM4zy+IP5AiLiB\nYU6qplFS2YbHF0QBFs1MHrWefDbaezz84s0Krl2Rw6LCkT3rPzjUxC/fqmBwLa25080/P7CCrSX1\nkft8fKItEhA0dbjYcbAxMl0NIDHWzL88csmILMippl6++z8HAHDYjPS5AxRlxdHR6+Xd/XU4463s\nLg//bKobe/n4eDuLi5L51UAzntQEK9/8wqJR8wHuuHwG//WnowCTatIzGXdeceHUuAghPh0XTUAQ\n8Ib7fxvNSfR9fIBgVyfxV69Fbxv6NGXOysZSUEi5MlSMdSq7AJ0+fGmYTEBw6ng4EzGjKDnKPc+e\n2xvku88fiPQ5B1ha5OSrN80dd4yrqmm8u7+OP++rJRhU8fpD6HTw13cU89wfy9n2cT3rlmfxiyMv\ncrzrJHGhbGAuHeZj9HVUMCMul4XJ83B7g3zvxY+pbR0ahDI/P5FHbp2HzWKkudMd7lw37GLV0ePl\n335XSlOHm+0HG7hkXioHKtuIsRjISXVQcbqLP+6sIt5m4MipTq5YlBH5dPsfrxyivs3Fv37tUowG\nHaUn2vnpH4eaVKXvs/HkV1ZEmuCcDV8gxI9fLae2pZ8+d4DigqRI0aPLG+DlbSewmPXcdWUhdS19\n7Cht5Kd/PEx1Yy/FBUnUt/VTdrKDYEjlcHUn//HqITQtPHBlxZwUOvt8nKzvobyqc8SAnA8HLvaF\nWXHUtfYzMyuOv9lQTHl1Bz97/QjP/7kSRYGHbp7L//+nCl7ZWUV5dTsflDeFB8rcXTzmgJMVc1PZ\nUdoIjJxbL4QQZ+OiCQiCA0sGBnMSfR+9DED8NWtH3c9evIgaYtFpGglqkOaMPAyEC+py7RMvF2ia\nxqkT7ZjMejJyPt2is3Db1gpaOt2smJNCUXY8+ytaKTneRuuvS9hwVQFzchM4VNXBa7tO0ef2Mycv\ngc5eH8frurGZDSTGmtHrdNxwSS5JqX4WzTez76CHPxzaxfGukxgUPT36OqyzveBoQwuYuSPvLoIh\nlR+/eoja1n5Wzk0lP81BeXUHh0918t3nS4izGTle34OiwDVLsli7LIvDpzp548Mauvv9XLEog6qG\nnkjjmEdumceMzDj+6ecf8cs3jkReY11rP//73sWcbu7jWG03ANWNPczKSeBoTTjDc/vlM2ho62df\nRStb9p7mlssm3oFyqqmXkso2TjX14g+GWDzTSU1zH7Ut/ZhNehraXRyr7Y5cSP+0uwaXN8iGKwu4\nanEmXn+Q8uqOyNz4tcuzKTvRztaSespOtvPb906g14Ub0SyblYJBr+N0cx9P/Wo/u8oaIwFBMKSy\n71gLDpuRb29cjKIoKIT36i+fncL2jxuorOtm3bJsVs1N43htNztKG2nudJOTaudv71k0Yg/9cDpF\niTymEEKcq4smIAh4O9AbHSg6I97qKgyJSZhSR6fxfXMX0NHmJ7ennaz+LnZnzqQOM7FGPfFRisga\nTnfT3+tj5rwU9FEmvE3kwLFWNu8+xX3XzopUs7+zv46S423Myo7noZvnotfpuLw4gxffPc6O0kb+\n7eUyTEYd/oCKooDDamTvwAV4SZGTL143K9JatD/g4h8/3ITf6MeYl8XujlZMRhNfLfoqPy55Hl1s\nG3oMuI4v4V/KK0ABf0BlaZGTh26ai06nsHZZNn/YWcWfP6qlpTM8U7y9x8vWkvpIal1R4O6rClm/\nMgdfIMTmD04RZzdHms48dNNcXtlVTWFmHKeb+6g43UV1Yy+7yhoiP4tjtd3MykmgsrYbk0HH9Stz\nCARVTtT38MaeGpbPSRl3zVzVNJ79fRl97nBAp1MUqhrChXcFmbHcdUUB//LiQbYeqGNObgKtXW7e\nK6knOc4SKcyzmAzcf90snv39IdKTbMzNTUCvKGwtqecXb1Xg8YW4aXUuq+YO/b+Um+YgN81BWVU7\nXX0+EhxmjtZ00ecOcM2SrFHFlIqi8OCNc/jwcDPXrQz3s7/lsnz2H2slwWHmb78wfjAw/DGEEOKT\nuCgCAlUNEAr0YrbnEWxvJ9TXh33Z8jHve8xkBzrJOVxCRlcbZIar5HPs1gn/6J6u6uCdPx5BUWBO\nlMr9ibR3e/jFWxV4/SGe/X0Z37x7EWVV7by55zRxMSYeuXVe5IJi0Ov44vrZrJ6fzkdHWyiraifL\naefOKwvIGJg25/WHKMiMHXHu79fvxR/yYzVY8aTUowKXJq+ju92M//gS8pbVcducK9njUiPNaXJT\nHdx/XVGkZkCnU7j7qkKWzHTiiDGSmmAjEAyx5aNaTjb0MD8/ieWzUyJ74c1G/agtbvNnJHHVyjza\n2sLBwPd/e5BXd1Vxsr6HBIeZ7j4fFae7uGpxJg3tLubmJWDQ6zDodfzFuiJ+/Go5Tz9fQozVQKLD\nwkM3zx3RVa6hzUWfO8CSIicP3DAbVYOPj7dxurmPWy7NIzbGRF6ag9KT7ZSeaOf1D04RUjXuurJg\nRHOThQXJ/OVt80lPDM+tmJkdF9nHnxRrGXOa3xXFGTz/diUflDdx8+o89h4NLxesmjd2K9vkeOuI\nbEe83cz/feQSLCb9BbcbQwhxYZq2AYGmhnB1lWNLmEdwsH7AkoznVBUAlvwZYx53uLMfvaqSdbwc\ns99HSn83rfb4CesHThxt4b0/VaDT67ju9nlknuM6rqpp/PzNcDBw6fw0PjzSzDO/LgHAGW/ha7cv\niBTZDVeYFUdhVhx/wcjq8cGRqMMFQgF21u/GarDw5CXf4vUju9h1tJqm7hR6zd1ofhtfmnUfOU4H\nC26Ofs7DR6kaDXpuuTR6E6mxzM6JJz89lqM1XQDcujQ8kKa6sYfDp8Lv36ycoZ/rkiIn16/MYe/R\nFgJBlcq6bv715VK+c9/SyK6JytrwYxUXJmEb+IR9eXEGFA897zVLs/j5mxX86JXwfIXV89NYPsb0\nuOHf0+t0LJ3lZGdpIxvXzRxzouDKuam8tO0EOw42kJpg5eDxdpzxlrOq2o+WFRBCiE/TtA0IXF2H\n6azdTNDXReBUO9jC9QPeU9VAeLbAmbp8AZo8fgoMGma/D4BFip/3FIWiuLHT0lXHWnnvTxUYTQZu\n2LCA9LOcNQ6w92gzx0530dbtpbKum8Uzk3ngxjnMzU/kF29WsHhmMl++Pjxe9WyE1BC9/j4SLEP1\nDPtaPqYv0M+6nCuxG2PYWLyeU4dKOHi8A5vZQIzFMGYgMdUUReGGVbn85LVyDHodly1Mp88d4HRL\nH2/trQXCQcNwG64qZMNVhWiaxsvbTvLO/jr+/fdlPH7vYsxGPZV14TqE4YHEmVbMSeWND2sIqRr3\nXTuLhQWTGyCz4cpCLluQTkHm2O/3YJ/8t/fV8bPXw3USK+dmS2pfCHHemrYBQcATXj/vbzuA/0gj\nhuUJGC1JdFe/Czod5pzRI2Ere8Lp8bkZThSTCc3vZ0V2Kpflz8AwRtq2urKNrZsrMBj13PSFhaSe\nw55tjy/Iz9+oIKSGdzKkJtr40vWzURSFS+alsaTIOamZ9gE1yA9LfkphQj63F9xIUA3yo9L/oqa3\njm8sfoSC+DxUTeW92vfRK3quzA53D1QUhZtX5/HvfziE2xdk8czkKRuVGs3iomSWzU4h2xmDw2Zi\ndm48f95XS2O7C5NRN+5Md0VRuPvqQnrdfvYeaeGd/XXcdEkux+u6SXCYccaNn90xGnR896sr0euU\ns7pY2yyGcYOBQRuuKmTprBQOV3fQ3OnmmiUTj6sVQojP0/QNCLxtAKiqB/388IVEbffgqz2NOTML\nnXl06r2yOxwQzE6MxbdiJe6Ko1hz80bNLVBVlX3v13BwT3hQ0Y13n1swAHC8rpuQqnHN0ixuvjQP\nu8U4Ym//ZIIBgKb+Zk731XG6rw5VU+n19VHdE+6O+Otjv+M7yx/jrVPv0uJuZVX6MuLNQxezhQVJ\n5KTYqW3tZ/YEn6anmk5R+Kvb5ke+npkVj05RUDWNmZlx4/bTHzz2/mtnUXaynW0l9RQXJNHnDrBq\nXmrUC/1Ej/tJ6BSFwsw4CqMEDkIIcT6YvgGBrx1FZ0RTAyhWPVpQpePlP6IFAlhmjK4fCKgq1X0e\nUqwmEsxGtC9+BTQNRT/ygqyqKm/+rpz6mi5i4y1ce9s8nGmT77bX0uWm4nQXl85Px2jQRdbMlxQ5\nx9xjPllNrnBGRK/o2V73AQAz4vLItKfzfsMefnTwPznVW0uKLZk7Cm8acayiKNy7diZ/2Fk1qvve\n58lqNpCX7qC6sXfCtP/w+69ZmME7++v49bvHAZh1EQ07EUKIT2JaBgRqyE/I34PZno+vpgaSNehX\n8VbVAGDJD9cPaJpGhy9AktlIda+HgKoxa6BWYLxphkdLm6iv6SI7P4F1t87DHGVd/zfvHKe6qYfc\ntFg8viD7KlrQNPB4g1y/KpeK052YDDoKMz9Zi9hmd7gF7pfmfoEtNe+hahoPL/giJr2Jio5KTvXW\nEmO08VcLHyTGaBt1/KycBP7+/mWf6BymQnFhMqcae1kwY3Jr+2uXZvHugTpO1vcAE9cPCCGEGDIt\nA4Lhcwv6Py7BeG0iBlMiPsLFaYMZgvebu/lzfTsrnHEMZpVnxY2+WA7yeQPsf/8URpOeq2+aEzUY\naOpw8d7H4T35p5rCE8WynHZau8Kd+1bNS6O+zcW8vIRPPMO7yRXe1jYrYSaLUxaiaVpk9sCX523k\n9aq3uKVgPU7b5C6s54vrV+awYk4KqQnjvy/DJcdbWVLkpKSyjTi7idSEiZtJCSGECJueAYEnXD+g\nC1oInezGPDeHhGXX4VLK0JnNmNLS8YZC7GwKd7/b19aDAlj0ugm7EZbsPo3XE2TVlTOwxURP7+8c\naCf70E1zSU+24Q+ozMyK43/+fIxdZU38fsdJAObmJX7CVwxNrlbsxhjspoHdEMOWzfPjcvjGkkc+\n8XN8Hgx63aSDgUHXLc+hpLKNubkJUtUvhBCTNC0DguBgQWGHDzSIMc/Flj4H571/gc5gRNHp2NPY\niSekcllqPDX9HupdPgpjbejHGdbT3emmvKQBR5yFBcuiV4sHgiF2lzcRazOyfE7KiMK1qxZnsaus\nKdJJ8JMGBP5QgA5PJ4Xx59YDYLopzIrjf9+7mMzkiaf+CSGEGDItA4KAN7xkEKwLF+wNbjFMuDo8\nu8AbDPFBcxdWvY6rM8MX452NXRQnjV8cuGdbFaqqcclVBRgmkd4/cKwNlzfIDatyR1Wx56Y5KMiM\npaqhlxiLgezUT7bvv8XdhoZGeszYXfAuRjLkRwghzs607Ika8LahM9jw14R74ptzckbcvre1B09I\nZU1aAha9Hotez3XZyaTZRm9FBKiv6aTmZAfp2XHMmDW5KYY7SsPPffmijDFvv3pxuFf+7NyET7zv\nf7B+IE0CAiGEEOdo2mUIVDVA0N+NOSabvtoDGJ3OESOOAUo7+zAoCqtSo+8PV1WV3e+F2x1fek3h\npNakD1V1cKK+h3n5iSPGAQ+3fE4KbT0elg4M+jlbJ7tPcarnNFdnr6HZFd5hkB5z/mwZFEIIcWGZ\ndgFBeG6Bhl5xoPb3Y5s1e8Ttnb4ArR4/s+JsWPTRU/9l++vpbHMxe2HapPoN9HsC/PKtCvQ6hQ1X\njm6PPMig151T3/9+v4vXqt5kb9MBAIw6I80DPQgkQyCEEOJcTbuAYLB+gP5wK+AzWxRHuhHGRy84\nqznRzt7t1dhiTKy8PPrFW9M0/ufPx+hx+dlwZQE5qZNvWDSWZlcr+5o/xmGyk2VP53DHMXY17MEf\n8pNpT6fD08mWmq0YdUZijDYcxs9+BoEQQojpYRoGBOEdBqGmfgAseSMv5McGAoJZ4wwrGtTW3Me7\nm49iMOi4/q752MaYMnim0pPtlFS2UZQVx3UrcqLefzzdvh7+cHwzpW2H0dBG3BZniuXWGdezJnMV\n75zewRun3gagIC5fttgJIYQ4Z9MuIAgFwh3qfKeaQVFGjDn2hcLtidOtJuLN44+WDYVU3vtTBcGA\nynW3zyNlnKE6Z3r7o3Djo/uvmzViHsFZnb8a4r/LX6Cmt5YcRxZrc64gpIWo72sk1eZkRfpSjLrw\n23Z1zhp2Nuymz98v9QNCCCE+kekXEAQ9APhOnMaUlo7eNtTUpqrXTUjTmB0/cWq9oqyJrg43cxel\nM2PW5Ir+app7OV7fw/z8RDKd5566f+PUO9T01rIsdRFfnntv5FP/irQlo+5r1pu4Kf9aflv5Krmx\n2ef8nEIIIcS0CwjUoBtQ0Po9WIpH9uY/Non6AZ83yP73azCa9CxfM/miv3f21wFw7fJzvzBXdp7k\n3dM7SLYkcs+sOya1BHBpxkqyHZlk2cfe3iiEEEJMxrTrQ6CGPChqOM4ZPtUwpGpUdLuIMejJjBm/\nHuDg3tN4PQEWr8qZVHtigK4+H/srWslIjmFe/rl1HfQGfbxQ8TsUReEr8zdiNVgmdZyiKOTGZkfm\nFgghhBDnYtplCEJBN/jChXjWGUPb/iq6XbiCIVanxo/bCCgUUikvaSDGYaZ4eVbU5/IFQuw50sx7\nB+oJqRrrlmWdc2Hfm6feocvXzfrcq8mLPfeCRCGEEOJcTKuAQNNUtJAXzaWimEyYMoZmDhxoDxcb\nLksev0CwvaWfYEBl1vwkDMbon7h/+sfDHKrqQKcoXDIvldXz087pvE/31rG97gNSrMmsz7vmnB5D\nCCGE+CSmVUCgDhQUqj0eLHn5KAONh7p9AU70uMmOsYzbnhigpaEXgLTM6LsK+tx+yqs6yEmx8zcb\niklwRN+WOJaQGuLFY6+goXHPrDsw6sff/SCEEEJMlWlVQ6CG3ABo3hCWYcsFB9p70YDlzokv9M0N\n4SxCamb0lsaHT3WGH3NOyjkHAwDb6z+gvr+RVWnLmJVYeM6PI4QQQnwS0yogGNxyyLCAQNU0Stp7\nMet0LEicuHNgS2MvFpuR2PjoBX3lVR0ALCyY3LCj4foDLjRNo93TyZvV72A3xnD7zBvP+nGEEEKI\nT8uULhlomsaTTz5JZWUlJpOJp59+muzsoW15mzdv5le/+hV6vZ477riDe++9F4A77rgDuz28lz8r\nK4tnnnlmUs8X3nIImkfFPFA/0OLx0+MPsiTJgVk/fvzT3+ulv9dHXmFS1MJAVdUor+4gwWEmyxm9\nBfJwpW2H+e/y50mxJmPQGfCrAe6dfSd249k9jhBCCPFpmtKAYOvWrfj9fl566SXKysrYtGkTzz33\nXOT2733ve2zZsgWLxcKNN97ITTfdhNkcTr8///zzZ/18aiicIdC8IfQx4Qtsk9sHQJZ94k/9LY0D\n9QNZ0ZcLqhp7cHmDLJudcta7Cg63VwDQ6e0iqIWYk1jE8tTFZ/UYQgghxKdtSgOCkpIS1qxZA0Bx\ncTGHDx8ecfvs2bPp6emJXFQVReHYsWO43W4efPBBQqEQjz32GMXFxZN6vsEMAd4QOmt47PBgQJAx\nQTEhQPNAQWFqRvSCwkOR5YKkSZ3XcKd6TmPWm3j60n+guqeGGXG5MoNACCHE525KA4L+/n4cjqF1\ne4PBgKqq6HTh1P3MmTO58847sdlsrFu3DrvdjsVi4cEHH2TDhg3U1NTw0EMP8fbbb0eOmUhoMCAI\n6VEM4ZfW5PahAKnWiQOCloZedDoFZ3r0CYWHqjow6BXm5CZEve9w7oCbZncrRQmFWA0W5iXNjn6Q\nEEII8RmY0oDAbrfjcrkiXw8PBiorK9mxYwfbtm3DZrPx+OOP8/bbb3PVVVeRmxseWZyXl0d8fDxt\nbW2kpqZO+FxOpwNXSxAAg86C0+lA0zSavX5SYsxkpY2/FBAMhGhv6SctM5aMjPgJn6ejx0Ndaz+L\ni5xkZ55dQFDaFB5+ND+9EKfzk41Gnk7kZ3Fhk/fvwibvnxg0pQHBkiVL2L59O+vXr6e0tJSioqLI\nbQ6HA6vVislkQlEUEhMT6e3t5ZVXXuH48eM88cQTtLS04HK5cDqjDxhqa+vD7QpvG0Qx0dbWR7cv\ngDsQYobdSltb37jHNjf0EAqpJDrtE94PYGdpAwCzs+Oj3vdMB2uPAZBqSD/rY6crp9MhP4sLmLx/\nFzZ5/y5cUxHITWlAsG7dOnbv3s0999wDwKZNm3jjjTfweDxs2LCBu+++m40bN2IymcjJyeH2229H\n0zS+853vsHHjRnQ6Hc8888yklgsgvGSgqRo6w9nVD7Q1hX8hUjImt1wAsLDw3OoHAPLjcs/6WCGE\nEGIqTWlAoCgKTz311Ijv5ecPTRC85557IsHCcD/4wQ/O6fnUgBu8Kjpr+MLe5AkHBOlRAoLW5oGA\nIEr9QCCocrSmi9QEK6kJtgnvO+rcNJWa3lpSbU5ijGd3rBBCCDHVplXr4lDQHd5yaAtfcAczBNEC\ngramPowmPfGJE1+oj9d34wuEzqoZUXn7UdwBD5n2dLwhH4skOyCEEOI8NG0CAk1T0VRfeMthJCDw\nYzPocUwwqMjvC9LV4SYjJz7q9r9DJ89uu2G7p5P/Ln+BkBaKNB6aESsBgRBCiPPPtGldrIa8gIbm\nVdHbbHiDITp9ATJsplEX+mOHmnjz94cIBsO7CwCcaZOoH6juwGzUU5Q98U6EQW9Uv0NIC5Efm0N/\nILzbQuoHhBBCnI+mTYZgcNKh5g2hS7TR5PEDo5cLNE3j4z219HR5OFnRhtcdAMavH2jr9vD+oUaa\nO9y0dLpZPDMZo2FkHKVpGp6gB9uw2oC6vkYOtBwky57BN5f+FSe6qun0dpFhP7cRyUIIIcRUmkYB\nwUBTIo+KzmaldaCg8MyGRF0dbnq6wsHDkY8biI0P70g4MyDQNI1dZY289N5JfIEQACajjjXFGaOe\ne3P1n9lau5P759zNirQlqJrK61VvoaFxa8H16BSdTDIUQghxXps2AUFocPSxL4TeaqPDG/7kn2wx\njrjfqePtAJjMBlqb+uhsd2G2GHDEjZx18PoHp9i8uwar2cBXrp/NvPxE4h1mdGcsP7gDHnbU70bV\nVJ4/+jLeoJfStsNUdp2kKKGQOYlFCCGEEOe76VNDMDj62BMuKuzwhQOCJLNpxP1qTrSjKHD5dTMB\nCAZUUtIdo+oMjtZ0oVMUvvvgCtYUZ5AYaxkVDAB82LQPf8jP8tTFmPUmXj7+Ryq7TjI/aQ4PzNso\ncwqEEEJcEKZNhiAy+tirhgMCdwCLXodt2Hq/q89Ha1MfmbnxFM5JYd+uU/R2e8csKPT4gtgsBhJj\nx5+SGFJD7KjbjUln5O6iW1mTeQmvnXyTSzKWsTp9hQQDQgghLhjTKEMwGBCEUKxWOr0BkszGERfl\nmpPh5YK8mckoisLC5VkAZI4xpMjtC2I1j79dEaC07TBdvm5WpS/DZrRREJ/H48u+xqUZKyUYEEII\ncUGZNhmCUGhgycCr4jKaCWouEs+sHzgR7iOQPzPcWGj+kkyy8xPHbEjk9gVJi9KNcEf9bgCuzL7s\nk56+EEII8bmafhkCT4guXTgQSDIPBQSaptHS0EN8ki1SQKgoypjBQEhV8flDE2YIWtxtVPfUMDth\nJqm26MOXhBBCiPPZNAoIPKCBouroCmkAJFmGCgq9ngB+X4iEKO2JATy+8DZD2xkZhuH2NZUAsCp9\n2Sc5bSGEEOK8EDUgaGtr+yzO4xMLhdxofg3dsC2HwzMEg70HYhPGLxIc5PYFAcbNEKiaykfNH2PR\nmyl2zvukpy6EEEJ87qIGBPfddx8PP/wwW7ZsIRAIfBbndE7UoDsyx6DDF+5SmGQZHRDEJVijPpbH\nGw4IbOaxMwTHu6ro8nWzJGUhJr1pzPsIIYQQF5KoAcHbb7/Nww8/zAcffMD69ev553/+Z8rLyz+L\nc5s0TVNRg240dzDSlMikU7Abhj7h955FQOAeyDDYLCNrLk/1nOZk9yneb9gDwEpZLhBCCDFNTGqX\nwbJly1iwYAFbtmzhhz/8Idu2bSMxMZF/+qd/YtGiRVN9jlEFAwMFhe4QykBTojvNq8sAACAASURB\nVGTLyKFGPd0DSwbxkwgIBmoIrOahH8/xrpP8+8H/inydbE2iIC7v0zh9IYQQ4nMXNSD48MMPef31\n1/nwww+54oor+OEPf8iSJUuorKzkoYceYteuXZ/FeU4o6A9PEtS8IbyOeAKqNqJ+AMJLBjqdgn2C\nRkOD3ANdDm3DAoLy9goAVqUtw6Q3sjhlgfQaEEIIMW1EDQh+8pOfcNddd/Hkk09itQ59up41axYP\nPPDAlJ7cZAX94RHGeEL0xScCjAoIers8OOIt6HTRL+KeMTIEFZ3HMeqM3DPrdoz68XcfCCGEEBei\nqDUE//mf/4nb7cZqtdLS0sK///u/4/GE0+9f/vKXp/r8JiWSIfCE6LXHAiMLCn3eAF5PcFL1AzC6\nhqDb10OTq4WZ8TMkGBBCCDEtRQ0IHn/8cVpbWwGIiYlBVVW+9a1vTfmJnY1gIJwh0DwheqwxACQO\nyxD0dnsBiJugfuB4XTeege2Gg9sOB5cMKjpPADAnSSYXCiGEmJ6iBgSNjY089thjANjtdh577DFq\na2un/MTOxmCGAI9Knyl80U8YswfB2AFBfVs///c3H7Plo9PhhxnsQzCQIajoqASQUcZCCCGmragB\ngaIoVFZWRr6uqqrCYDi/RiAEAkNFhS5DOBBwGIfOMVoPgoa28PEtneH7ub1DGQJVUznWdYJ4cxxp\ntpSpeQFCCCHE5yzqlf3b3/42DzzwAKmpqQB0dXXxve99b8pP7GwMryHo1xmI0esxDCsejBYQtHaF\nty129/uAYRkCs576vkZcATer0pfJrgIhhBDTVtSAYPXq1Wzfvp3jx49jMBiYMWMGJtP51Z1v+C6D\nfk1HgnFky+HeLg+KQmSo0ZlaB3oUDAYEbl8Qs0lPQPXzp+q3AVkuEEIIMb1FDQiqq6t58cUXcbvd\naJqGqqrU19fzm9/85rM4v0kJ+l0QUgjojPiAWNPIl9XT7cEea0GvH3uFpG0gg9DT70fTNNzeIBa7\nn38teY5GVzOzEgopds6f6pchhBBCfG6i1hA89thjxMbGUlFRwZw5c+jo6GDmzJmfxblNWjDggoAO\nj80OjKwfCPiDuPv9E245HMwQ+IMqHl8Qjy+Iln6YRlczV2RdyteKH8SoO7/qJoQQQohPU9SrnKqq\nfP3rXycYDDJ37lzuuece7rnnns/i3CYt6HeBX8Md4wAgdlhAcORgIwDOdMeYx/oDIbr7/ZGvu/p8\neHwhbOYe4kwO7i66dQrPXAghhDg/RM0QWK1W/H4/eXl5HDlyBJPJhM/n+yzObdI0LQReFfdAUyKH\nKVxD4O73cWD3aSxWA4tXZo95bNtAdmBQS5cHlRAhvRunLXlqT1wIIYQ4T0QNCG655RYeeeQRrrzy\nSn7961/z1a9+NbLj4HyiuUN44+KBoQzBRztPEfCHWL4mH7Nl7A6Dg8sFKQNLCo3tLhSzG5TwACMh\nhBDiYhB1yWDZsmXcdttt2O12XnjhBcrLy7n00ks/i3M7K5oniMceB4RrCNpb+jlW3kySM4a5izLG\nPW6woLAoK57WLg9NHQMBAeC0SoZACCHExWFSRYV2e7hYLy0tjXXr1mGz2Sb14Jqm8cQTT3DPPffw\nxS9+kbq6uhG3b968mTvuuIMNGzbw29/+dlLHjPtcrsBQDYFJT31NFwCLL8mZcKBRy0CGYGZ2OJho\n7HCjWAYDAskQCCGEuDhEzRAUFhby4x//mOLiYiyWoX38y5cvj/rgW7duxe/389JLL1FWVsamTZt4\n7rnnIrd/73vfY8uWLVgsFm688UZuuukm9u7dO+Ex41H7/bhz7CiA3WCgqyPcrCgpxT7hcZEMQXZ4\nuaGpw4UuYyAgsElAIIQQ4uIQNSDo7u7mo48+4qOPPop8T1EUnn/++agPXlJSwpo1awAoLi7m8OHD\nI26fPXs2PT09kQ6AiqJEPWY8Wr8Pt8VGjEGPXqfQ1e5Gp1OiTjhs7fZgtxpxxlvR6xT8ARWTWTIE\nQgghLi5RA4IXXnjhnB+8v78fh2Nou5/BYEBVVXS68ErFzJkzufPOO7HZbKxbtw673R71mPGoHhWX\n2UqKyYCmaXR1uIhLsI7bjAggpKp09HjJTXOgUxRiY0x09flQLG7MOitWw+TGJQshhBAXuqgBwf33\n3z9mD//JZAjsdjsulyvy9fALe2VlJTt27GDbtm3YbDYef/xx/vznP+NwOMY9ZiKBgJ6g3kBSjBmr\n2YTfF6JgVhxO59j9BwCaO1yEVI3s1FicTgfOBCtdfR4Uk4ckS+aEx4pPj/ycL2zy/l3Y5P0Tg6IG\nBI8++mjk38FgkPfee4/Y2NhJPfiSJUvYvn0769evp7S0lKKioXkADocDq9WKyWRCURQSExPp6+tj\nyZIlbNu2bcxjJuLShQsdzRqcPN4CgM1uoq2tb9xjjtV0AhBnM9DW1keM2YBi9qLoNOIM8RMeKz4d\nTqdDfs4XMHn/Lmzy/l24piKQixoQrFixYsTXq1evZsOGDfzN3/xN1Adft24du3fvjnQ23LRpE2+8\n8QYej4cNGzZw9913s3HjRkwmEzk5Odx+++3o9Xo++OCDEcdEoygGPOYYIDzHoLMpXAOQkDzxboia\npl4AnPHhpYF4h3nYlkOpHxBCCHHxiBoQNDY2Rv6taRonT56ku7t7Ug+uKApPPfXUiO/l5+dH/j1e\nG+Qzj4lGr7PgtoUbDzmMBrraw0sOCUkx4x7T4/Lz1t5arGYDC2aEL/7xdnNky2FqjPOszkEIIYS4\nkEUNCO67777IvwdT+//wD/8wpSd1tvSKCU9M+KXEmvQ0tbtRFIhPHL8o8HfbTuLxBfmLdUXExoTH\nOcfbTZEMQYZDAgIhhBAXj6gBwbZt2wgEAhiNRgKBAIFAYNKNiT4rOsy4beGLf6zRQFeHm9h4Kwaj\nfsz7V9Z2sedIM7mpDq5anBn5frzdjG4gQ5Bul4BACCHExSNq+f6WLVu44447AGhqauL6669n69at\nU35iZyNOVxTpUmgMqng9ARKSxg9a/vj+KQDuu65oRBfDePtADUHIgN00/nKDEEIIMd1EDQiee+45\nfvnLXwKQk5PDq6++yn/8x39M+YmdDb3fgttmR9E0/N3hSYwJyWNf0Js73VTWdTM7J56CjLgRtzli\n9CgWN7qAfcytlkIIIcR0FTUgCAQCJCcPDflJSkpC07QpPamzpfq8+CxWLIpGd8fEOwzeLwsXSV4+\nxsAjt9aDolOxqAlTd7JCCCHEeShqDcHSpUv55je/yc033wzAW2+9xaJFi6b8xM5GyOsjaLBhVBS6\nOwcCgjGWDIIhld3lTcRYDCwtGl0jUN8fDhYuK5w1tScshBBCnGeiBgRPPPEEL7zwAi+//DIGg4Hl\ny5dz7733fhbnNmmq10vIasCigM8TBMBqM426X9nJdnrdAdYuy8JoGF1wOBgQzEvLH3WbEEIIMZ1F\nDQgCgQAWi4Wf/exntLS08NJLLxEKhT6Lc5u0kM9H0GHEpNPh94drCEzm0Rf8XWVNAFxePHq5AKCh\nL3x7pj19is5UCCGEOD9FrSH427/9W1pbWwGIiYlBVVW+9a1vTfmJnY2Q10fIYMSg0+H3hYMVo2lk\nQKCqGsdqu8hyxpDlHD0SWdM06vobSLYmYTVYRt0uhBBCTGdRA4LGxkYee+wxIDys6LHHHqO2tnbK\nT+xsBHw+NJ0Ok15HwB/EYNCNGojU0uUmEFTJTR27/3O3rwdXwE2WfezsgRBCCDGdRQ0IFEWhsrIy\n8nVVVRUGQ9SVhs+ULxCuGzAa9Pj9IYxjLBfUt4XbGWeljM4OwFD9QLZDAgIhhBAXn6hX9m9/+9s8\n8MADpKamAtDV1cX3v//9KT+xs+HzBwAw6fX4fCFMptEvq661H5ggIOgLBwSSIRBCCHExipohWL16\nNdu3b+fJJ5/k6quvJiUlhYceeuizOLdJ8w8UOYYzBMFR9QMA9QMBQfYY9QMwlCHIkgyBEEKIi1DU\nDEFdXR0vv/wyr776Kr29vTzyyCP89Kc//SzObdL8gYGAQFEIBlRM5tEvq76tn9gYU2SQ0Znq+hqx\nG2OIM8VO6bkKIYQQ56NxMwTvvvsuDz74IBs2bKCnp4fvf//7pKSk8Nd//dckJiZ+lucYlV9VAdAN\ndFA0nZEhcHuDtPd4yXaO3c7YHfDQ4e0ky54hLYuFEEJclMbNEDz66KOsX7+el19+mdzcXIDz9mLp\nD4UDAd1AR+Uziwob2ieuH6jqCQ87yonNmqIzFEIIIc5v4wYEmzdv5rXXXmPjxo1kZmZy4403nncN\niQb5QwMZAnUwQzDyZQ3WD4zVfwCgtPUwAAuS507VKQohhBDntXGXDIqKivj2t7/Nrl27ePjhh9m3\nbx/t7e08/PDD7Ny587M8x6gCA/9VBgOCMzIEdQNbDrPHyBCE1BDl7UeJM8WSF5s9pecphBBCnK+i\n7jLQ6/WsXbuWn/zkJ+zatYtLLrmEf/3Xf/0szm3SgrpwRkAZWDowjpEh0CkK6UmjawhOdFfjCrpZ\nlDIfnRL1xyGEEEJMS2d1BUxMTOQrX/kKmzdvnqrzOSdBg3HgH+Glg+FFhaqmUd/WT3qSDaNh9Ms9\n2FYOwCLn/Kk/USGEEOI8NS0+EocGOycGBzIEw7YddvR48fpDZI6xw0DVVMraDmM3xlAQJxMOhRBC\nXLymRUAwUYagsT1cP5CZPDogqO45TZ+/n4XJ89DrRjczEkIIIS4W0yIgCA0EBOpAg6LhRYWDAUFG\n8uiCwqMd4RkNC52yu0AIIcTFbVoEBMGBJQMtEM4QDC8qHAoIbKOOO9ldjYJCYbwsFwghhLi4TZOA\nYCBD4BvIEAxfMuhwYdArpCRYRxzjDwU43VtHliMDq2HkbUIIIcTFZloEBINFhYNLBoNFhaqm0dju\nJi3Rhl438qWe7q0lqIUkOyCEEEIwTQKCSIbAOzJD0NnrxRcIkTFGQeHJ7nC74sL4GZ/RWQohhBDn\nr2kREIT04YAg6AsCRMYfN7a7AcgYoyFRJCCQ7YZCCCHE9AgIBosKQ94QRpM+MoRpqKBwZEAQUkNU\n99SQHpOK3TT2BEQhhBDiYjJNAgIjejQC/uCYPQjODAhq+xrwqwFZLhBCCCEGTJuAwKgo+P2hEV0K\nGztc6HWjdxic7K4GkIJCIYQQYsC4448/DZqm8eSTT1JZWYnJZOLpp58mOzs8UbC9vZ3HHnsMRVHQ\nNI1jx47x+OOP84UvfIE77rgDuz3cSCgrK4tnnnlmwucJGQwYFYWAP0RsnCXy3I3tLtISbRj0I+Oe\nmt46AAri8j7lVyyEEEJcmKY0INi6dSt+v5+XXnqJsrIyNm3axHPPPQdAcnIyL7zwAgClpaU8++yz\n3H333fj9fgCef/75ST9P0GDEqlMIBdVIQWFXnw+vP0T6GDsMWt1tmPUm4s1xn/QlCiGEENPClC4Z\nlJSUsGbNGgCKi4s5fPjwmPf77ne/y1NPPYWiKBw7dgy3282DDz7Il7/8ZcrKyqI+T9BgxDBQSGga\nWDKI1A8kjexQqGkabZ4OnNbkSPGhEEIIcbGb0gxBf38/Dodj6MkMBlRVRTesSdC2bdsoKioiNzcX\nAIvFwoMPPsiGDRuoqanhoYce4u233x5xzJlCBgOGgdsHiwp7XOFMQ2KsZcR9e/y9BNQATlvyp/Mi\nhRBCiGlgSgMCu92Oy+WKfH1mMACwefNmvvSlL0W+zsvLiwQHeXl5xMfH09bWRmpq6rjPo+oNWIwG\nAkBsnBWn04HZ2gFAUoINp3MoKGlrbQIgNyl9xPfF50fehwubvH8XNnn/xKApDQiWLFnC9u3bWb9+\nPaWlpRQVFY26z+HDh1m8eHHk61deeYXjx4/zxBNP0NLSgsvlwul0Rn2uwbbFIVWlra2Pru5wUyKP\n209bW1/kfsebagGwa7Ejvi8+H06nQ96HC5i8fxc2ef8uXFMRyE1pQLBu3Tp2797NPffcA8CmTZt4\n44038Hg8bNiwgc7OzhFLCgB33XUX3/nOd9i4cSM6nY5nnnlmwuWCQToNQgzVEASD4cmHBsPIY9vc\n4cyBLBkIIYQQQ6Y0IFAUhaeeemrE9/Lzh/b+JyYm8tprr4243Wg08oMf/OCsn0unauHjB2oIAqGB\nUchnBgSedgCcVgkIhBBCiEHTojERDAUEg0WFgYEMgVF/ZkDQgVlvItZk/2xPUAghhDiPTZuAgNBA\nhmBwyWCMDIGmabS522XLoRBCCHGGaRMQKAMBwJkZguFdCnv8vfjVAE5r0md/gkIIIcR5bPoEBMGB\nJYMJMgRt7oH6ASkoFEIIIUaYNgGBNlgzEMkQhAMEg35oaaDNM7DDQAoKhRBCiBGmT0AQOGPJIJIh\nGBqH3DqQIUiRDIEQQggxwrQLCIxn9CEwjpkhkBoCIYQQYrhpExCogRCKMtSIaKw+BG2edkx6E7Em\nadUphBBCDDdtAgLNH8Jo0ke2Ew5mCPQDuwxUTaXV3U6KbDkUQgghRpk+AUFQRT8sGxAIqRj0CrqB\ni3+Xt4eAGiDVFn0ughBCCHGxmTYBAQE1kg2AcIZgeA+CVncbAKkxKZ/5qQkhhBDnu+kTEARHBgSB\nkDqifqDZ3QogGQIhhBBiDNMmINACKrphOwoCZ2QIWgYzBDbJEAghhBBnmjYBgRqYOEMwFBBIDwIh\nhBDiTNMmINACoVE1BMMnHba4Wkkwx2PSmz6P0xNCCCHOa9MoIDhjySCkRnoSeINeevy9pElBoRBC\nCDGmaREQ6BRQNM7IEGiRDMHgckGKFBQKIYQQY5oWAYFJF34Z+oEMQUhVUTUtMthoMCBIk4BACCGE\nGNP0CAgGMgG6gf8GByYdDg42kh0GQgghxMSmRUBgGOhGOLhkMDjHIJIhcA30IIiRDIEQQggxlmkR\nEBh14Qv/YFFhIDhysFGLuw2z3kScKfbzOUEhhBDiPDetAoLBDEFwcNKhXhceauRpJ9WWIkONhBBC\niHFMi4BgaMlgdIagy9tDUA2SIg2JhBBCiHFNj4CAsTMEBr2OXn8vAHFmWS4QQnw6Dh4s4eabr+Xr\nX3+Er3/9ER555AFeeeXlT/15ent7effdPwPwzDNPsW/f3k/9Oc60YcMtBAKBsz7O5/PxD//wLb72\ntYf41v9r774DqizbB45/z2AdpoBaCu4diiOz3JVG5jYxTUVzouIWdwrGECUnpkiJipaL1629v3rT\nhiMHAi5wIiqKgohymIfz/P44cBI5KJaI0P35J3z2CJ7rue/7ua7pk0hNfVhomT17djJihBvu7sM4\nevQPADIzM5k1ayoeHqOYPHkcSUlJxdrfgQN78fGZX2Da5cuxjB07osh1xo8fTXz8DQ4e3MeRI78X\nmt+zp8sz9/nbb4dJTk7iwYNkliwJKNZxliXlIiBoam0B/PWVwZMtBI+y0wCwMrYsnYMTBKFcatGi\nJStWrGHFijWsXBnMli2bUavTXuo+rly5xB9//PZSt/l8f69rddeuHdSuXZdVq0JwcfmE9eu/KzD/\nwYNkwsO3smbNOr7+egXBwUFoNBr27NlJ/foNCQpay0cfdWHz5g3F2t8HH3QmIuIUWVmZ+mn79++h\nZ88+z123S5dutGnTzsCcZ5/79u0/oFarsbW1Y8qUGcU6zrJEWdoH8DLUNzfjT/7qMniyheBx9mMA\nLI0sSuvwBEEoQdt+ucLJmHsvdZstG1Si3wd1nrmMJEn6n9VqNQqFAoVCSWRkBKGhIUiSREZGOvPn\n+1KpUmXmzZuJWq0mMzOTUaPG0rJlK/z8vElIuE1WViaurgP46KMuBfYRFhbK1atX2Lt3FwC7doWz\nefMG1Go106bNxMamAtOnT8LGpgLvvtuGli3fYenSxSgUCoyNTZgxYw5arZb582cTHBwKwOjRX+Dt\n7Y+ZmSkzZ05Crc7A0bEaERGn2LJlJyARGOhPQsJtZDIZfn6BWFg8/+9ndHQkAwcOAeDdd1uzfv23\nBeZfuHCexo2bolQqUSotcHBw5MqVy/TrN0B/LRMT72JpWbyXN1NTU9q0ac/hw7/g4vIJOTk5HD9+\njLFjJ5KermbhQh/S0tJITr5P796u9Or1qX7ddevWYmdnT/fuvVi0yJe4uOtUqVJV3zJy7dpVgoKW\notVqSU19yNSps3j8OJXLly/h4zOfL79cgI/PfIKDQzl58jghIWswMTHB2tqaWbPmcelSLJs3b8DI\nyIiEhAQ+/LAzbm7DinVepalcBAS5eQGAoRaCx/ktBCaihUAQhJcnIuIUEya4I5PJUCqNmDx5Oqam\nply/fo15877Czs6esLBQDh36mbZtO5CamsrXX68kJeUBN2/Gk56eTnR0pP5BffLkn4X24eY2jN27\n/0P37r04ezaKBg0a4uY2jIMH93HgwF4+/9yNlJQUQkO/R6FQMGKEG7NmzaN27Tr88cevrFixBA+P\nSQUGVOf/vHHjOjp16kSnTt04efJPTp48oV+me/deODk1wc/Pm5Mnj/P++52eez3UarU+cFCpzFGr\n1QXmp6erCwQWZmYqfYuKTCZj4sQxXLt2laVLVxX3FtC9e09Wr16Ji8sn/P77r7Ru3QZjY2Pi4q7R\nqZML7dt3JCkpifHjRxUICPL99tshcnKyWbNmHYmJdzl8+BcArl+/hofHZGrVqs1PP/3IgQN7mD59\nDnXr1mP69DkYGRnpr+OiRf6sWfMddnb27NixhfXrv6N167YkJt5l48atZGVl0avXxyIgeFU0eQFA\n4TwEclLzWghEl4EglE/9Pqjz3Lf5ktCiRUu8vHwLTa9YsSJLly5GpVJx//49mjRpSs2atejRozde\nXrPRaHJxdf0MlUrF+PFTCAjwJT1djYvLJ9y+fYuFC79CJpPh4vIJVapULbDt+vUbAmBra0dmpq6p\n/M03q6BQ6JKwJScnUbu27lo4OzdnzRrdw/XJ1gytVvf3MS4ujs8//yxv2WYF9lOvXoNC+8k3ffpk\nMjMzqFWrDpMmTdNPNzc3Jz09HdA9/J9+0386SEhPT8fC4q9lli9fTXx8HJ6ek9i6dZd++uHD/yM8\nfBsymQwPj0n6Y8s/TrVaTVLSfQ4c2IOHx2T9cW/b9gO//voLKpU5Gk0uhty8GU/Dhm8BULnyG1Sq\nVBnQ3cP167/F1NQUtToNc/O/Apknr+XDhw8xNzfHzs5efx3Xrv2G1q3bUqtWHWQyGaamppiYmBrc\n/+umXAQE2tz8gKDwVwb5YwgsjUWXgSAIJS8gwJdt23ZjZmaGr68XkiRx7doV0tPTWbRoGcnJSYwZ\nM5z69RsSG3sRP7/FZGdn06dPV/bs+S8rVwbrtxUVdQZJ0ur/bejT6Sen2dvbc/XqFWrXrsOZM6dx\ndKyGsbExDx+mIEkSaWlp3LmTAEDt2rU5c+YMnTpV5dy56CK3+bRFi5YanN64sTPHjh2hQYNGHDt2\nhCZNCgYZjRq9RUjIanJycsjKyiI+Po5atWoTFraeSpUq4eLyCaamZvrgJl/Hjh/SseOHRR5P1649\n2LFD9yZeo0ZNAH74YRNOTk3o1etTIiJOcfz4EYPr1qxZi59++i99+/YnKek+SUm6rqdlywLx8vKh\nWrUafPddMImJdwGQy+UFAgIbGxvS09U8eJCMra0dZ85E4OhYzcCeJAPTXj/lIiDQtxAon8pDoNSN\nIZAhw8LIvNSOTxCEfw8Xl08YO3Y4ZmYqbG1tSUq6j6NjddatC+HQoZ+RJIkRI8Zga2vHgwfJjBkz\nDIVCyeefuyGXFxznXbWqA1evXmX79i1F7u/Jh/f06XNZunQRAAqFgpkzv8TW1o6WLVsxYoQbVapU\nxcHBEYCBA4cQELCAvXv3Y2dnj1KZ/yAu3L1QHL1798XHx4uxY0dgZGSMl5cPAFu3bsbBoRpt2rTD\n1fUzxo4djiTBqFHjMDIyolu3Hvj4eLFv324kSWL27PnP2EthnTu78Omn3Qu0VrRp045lyxbzv//9\nHxYWFigUSnJycvTnk//ftm07cOLEcUaP/oLKld/AxqYCAC4uXZg7dwZWVtZUrFhJ/8WEk1MTfHzm\n4ek5W7+v6dPnMHu2J3K5HEtLS+bM8eLq1StPXbuykQNHJj0Z7rxkkiTh5eVFbGwsxsbG+Pr64uio\n+58xKSmJyZMnI5PJkCSJmJgYpk2bRr9+/YpcpyiRJ26yZ2sk739SnwZN3uTXyNts+DGWkd0a8X+P\nN5KhyWRhu3kldZrCP1CxoiX37z8u7cMQ/iZx/8quY8eOUKuWA5UrV+fUqROEha1n+fJvSvuwhGKq\nWPHld4OXaAvBzz//THZ2Nlu2bCEqKgp/f3+++Ub3P5y9vT1hYWEAREZGsmzZMvr16/fMdYqSm6vr\nH/qrhUAX4yjzugxsTW1K6hQFQRDKpCpVquLj44NWqxtXMGmSZ2kfklDKSjQgOH36NO3a6b71dHZ2\n5ty5cwaX++qrr1iyZAkymazY6zwpN6+6YX5zW/4YAplcS2ZuphhQKAiC8JTq1WuwZcsW0cIj6JVo\nYqK0tLQCI02VSqV+hGu+X375hXr16lG9evVir/O0v8YQ5A0qzBtDkINuxKsYUCgIgiAIz1aiLQQW\nFhYFPjPRarWFBs3s2bOHIUOGvNA6T8vPQ2Bra07FipYYm+hOy8RC13JQ2dq2RPpbhJdD3JuyTdy/\nsk3cPyFfiQYEzZs359ChQ3z88cdERkZSr169QsucO3eOZs2avdA6T8vNayFIS8vi/v3HpD7SfTeb\n9PgBAMpcE9Es9poSg9LKNnH/yjZx/8quMjeosHPnzhw5coT+/fsD4O/vz759+8jIyMDV1ZUHDx4U\nSl5haJ3n+StTYV7q4rwAIVOr6zIQYwgEQRAE4dlKNCCQyWR4e3sXmFazZk39z7a2tuzcufO56zxP\nUZkKMyUxhkAQhJKxadN6Tp06gUajQaFQMHbsROrXb8D48aPx9JxNtWrVS/sQ/5H8fP/FKRa0a1c4\nKSkP+OKLkcXadmhoCEeP/oFSqWTChCn6bIH5zp8/x/LlgSiVSlq2bKXf8kdhFQAAIABJREFUbnDw\nKk6fPolcLmf06HE0a9biuftKSLjNxIlj2b59t36aRqNhwIA+bNjwAypV4Rw1+efeqJETR478xtCh\nBSsozp8/m969+9K0aXOD+7x27QqPH6fh7NwUL685zJ3rjVL5+qf9ef2PsBi0RbQQZOTqxiJYihYC\nQRBeori46xw58hurV68D4MqVy/j6zic09PtSPrLX36VLMURGniEkZAOJiXeZO3c6ISEbCywTGOiH\nn18gb75ZBU/PiVy+fAmQuHjxPGvXrufu3TvMnDmV9euff711yZgciIyM0D/Ajxz5jRYtWhoMBp5U\nt2496tZ9frf10w4f/gVbW7u8gKBweuvXVbkICIpqIUjPCwisRAuBIJRb/7myjzP3zr7UbTar1Jg+\ndboVOd/CwoLExET27dvNu++2pk6duoUeamlpaSxY8CXp6Wpyc3MZOXIMzZu/zaBB/XB2bsr169ew\ntrbGy8sXhUJJYKA/t27dzMtk6F7o7ff+/XsEBvqTk5NDcnISI0eOoW3bDgwZMoBmzZpz5cpl5HI5\nCxd+TWxsjMFqe/fuJbJokS/Z2dmYmJgQEOCPXK4iOHgVsbEXSU1NpU6dusyaVTCRW3DwKqKjI9Fq\nc/nss4F07PghUVGRrFjxNVZWVsjlCpycGhfr2kZHR/LOO60AXf2A3FxdRUFra12+mPR0NTk5Gt58\nswoA77zzHqdOnWDAgEEsWRIEwJ07CcWuigjQrVsvDh7cpw8I9u/fo3/rf9a5nzlzml27wvH29iM8\nfBv79+/Gzs6ehw9T9Mf6dFXFtm3bc/DgPoyMjKhfvwHz5s3i++/DSU5Owt9/Abm5uchkMiZN8qR2\n7Tr079+HJk2ciY+/ga2tHb6+i14oQ+TLVC4CgtzcpwKCvABBnaMWaYsFQXjp7O0rEhCwhB07thIa\nGoKZmRkjR46lQ4f39cts2PAd77zTSp8nf8yYEWzfvpusrExcXD6hSZOmrF69kl27wjExMcHGpgIz\nZ37Jo0epjBs3krCwbQX2eeNGHAMGDKZp0+acOxfNunVradu2A+npajp37sKkSZ4sWPAlx44dxdbW\n1mC1vVWrluHqOoBWrd7j9OmTLF68mPHjPbG0tGLJkiAkSWLw4H4kJSXp93v8+FESEm6zalUI2dnZ\njB49lLffbsWSJQvx8wukalUHAgMXFvvaqdVq/cMfQKVSkZaWpp+mVqsxNzcvMD+//oJcLmft2m8I\nD9/6QomUOnR4n5CQb8jOzubx40c8ePCARo2c8oowFX3uoOvGTkl5wI4dW/T3ZMQINwBu3bpZoKqi\nh4euqmKXLt2ws7PP6wrRPdyDgpbRr9/ntGnTjsuXL+Hvv4Bvv93InTu3CQoKxt6+ImPGDOfixfM0\nauRU7HN7mcpHQKB5qrhRXoCg1qgxN1KhkCuKXFcQhLKtT51uz3ybLwm3b99CpTLXv03GxFxk2rQJ\nNG/+tn6ZGzeu89FHXQBdAGFhYU5KygMUCiVNmjQFwMmpMcePH0UuVxAdfYYLF84hSRJarZbY2BiC\ngpbqKx82auTEhg3fsW+fri9co9Ho95XfrF2pUmWys7MADFbbu3r1KmFhoWzevAFJklCpTDE2NiYl\n5QHe3nMxNTUjIyOjwLavXbtCbGwMEya4I0kSubm53LmTQEpKClWrOgDQpIkzt2/fKnCNQkJWEx0d\niUwmY/ny1fq33ierIkLhyojm5oaqIv7Vyjtq1FgGD/6CUaOG4OzcTF8R8ulKkV279tCvo1Qqadeu\nI7/9doi7d+/o5xkbmzzz3J+837Vq1daPA2jYsBFQuKpiftbcp0mSxI0b1/VVJevWrcf9+4kAWFvb\nYG9f8Yn7l21wG69C+QgI9GMI8lIX5wUIj3PSsDGxKrXjEgShfLpy5TJ79uwkIGAJSqUSR0dHLC0t\n9a2UADVq1CQqKiLvj/89Hj9+jLW1Dbm5Gn1FwrNno6hVqzagexgMHjyUrKwswsJCqV+/QYHKh3Pm\neNKjRx9atXqPAwf2cvDgPv285zcxS3nHVIP+/Qfj5NSY+Pg4rl69yPHjR7l37y7e3v48fPiQ338/\nzJPV+apXr0mLFm/j6TkbSZLYsOE7qlZ1wN6+IvHxcVSrVoOLFy9gZVXwb+3IkWMMHknjxk1ZvXoF\nAwYMIjExEUmSsLKy1s9XqcwxNjYiIeE2b75ZhRMnjjFs2CgiIk5x+PD/mDJlBkZGRhgZGRXIUVO1\nqkOB6/W0bt16snr1Ch4+fMiSJSsBnnvu+RwcqnH9+jWys7NRKBRcuhSLi8snRVZV1FVFzE+oJyGT\nyahRoxaRkRG0bduey5djsbW1A6CUegcMKhcBgaExBAqFlgxNBo6WVZ+1qiAIwgvr0OF94uPjGDHC\nDZVKhSRpGTduEiqVuf7hPGjQF/j7L+Dw4V/Iyspixow5+gfY5s0buHv3Dm+88SajRo1DkiQCAnzw\n8BhFeno6ffr0LbTP99/vRFDQUsLCQqlYsRKPHqXmzTFcndBQtb2xYycSGLiQ7OwssrOz8fKah6mp\nNRs2fIeHxyhANwgvKem+fv02bdoREXGKceNGkpGRQfv2HVGpVHh6zuKrr+Zhbm6BSmVeKCAoSv36\nDXB2bsbo0V8gSRJTp84EICLiFNHRkQwdOoKpU2fh7T0XrVbLO++8S8OGb6HVavnll58ZM2Y4kiTR\np48rb7zxZrH2CbpUzRkZmdSsWUs/mLBRo7eeee75bGxsGDhwCO7uX2BjY4uZmZn+2jxdVVGj0VC/\nfgO++WYF1arV0F/7ceMmEhDgw5Ytm8jN1TwxVuHvVZcsCSVa7fBVCVtzjOuXkxjl2R6FQo536Enu\nPk5G7vQLb1duyhdvfV7ahygUQSRGKdvE/Xtxrq49+OGH/7wWn6GJ+1d2lURiohKtZfCq6LsM5H+N\nIVCa5gAiKZEgCK8bXcl3QXjdlH6I+hLkarQoFDJ9c4tGo0VumoUWkZRIEITXy5MJcgThdVI+Wgg0\nWhTKv04lJ1eLwljXQmBhJAICQRAEQXie8hEQ5BasiJij0SJT6j7/UClNS+uwBEEQBKHMKBcBgUaj\nRaH8a3SmJleLXKn7ltRMaVZahyUIgiAIZUa5CAgMthAodF0GZkaihUAQBEEQnqd8BAR5gwoBtJJE\nrlYCRV4LgUK0EAiC8HKdOXOa7t0/YsIEdyZMcMfdfRjh4Vtf+n4ePXrETz/9CICfnzcnThx/6ft4\nmqtrD3Jycl54vaysLObOnc64cSOZPn0SqakPDS6XkpLCgAF9ir2P9PR0evRwITMzs8D0YcMGFsqO\nmO/gwX0EB6/iwYNkliwJKDR/zZqgAomdnpaYeJcjR34HYOXKJdy7l1isYy3rykdAkCvpkxLlJylC\ntBAIglCCWrRoyYoVa1ixYg0rVwazZctm1Oq0l7qPK1cu8ccfv73UbT7f30uOs2vXDmrXrsuqVSG4\nuHzC+vXfFVrmxInjTJ3qQUrKg2JvV6VS0aZNew4d+lk/LTY2BktLa33q5KLY2toxZcqM4p9EnoiI\nU5w9GwXA+PFTqFSp8gtvoywqF58dajS5f6UtzstJIMnzAgKFCAgEoTy7v30Lj0+dfKnbtHy7JRVd\n+z9zmSdzCajVahQKBQqFksjICEJDQ5AkiYyMdObP96VSpcrMmzcTtVpNZmYmo0aNpWXLVvj5eZOQ\ncJusrExcXQfoax/kCwsL5erVK+zduwuAXbvC2bx5A2q1mmnTZmJjU4Hp0ydhY1OBd99tQ8uW77B0\n6WIUCgXGxibMmDEHrVbL/PmzCQ4OBWD06C/w9vbHzMyUmTMnoVZn4OhYjYiIU2zZshOQCAz0JyHh\nNjKZDD+/wAK1BIoSHR3JwIFDAHj33dasX/9toWXkcjnLlq1m+PDBz93ek7p370lw8Cq6dNHVrNi/\nfzc9evQGIDx8G7/9dojMzEysrW3w81usX+/u3Tv6cz98+H9s3LgOGxtbcnKyqV69BlqtlsWL/bh3\n7x7JyUm0bdueYcNGsWnTerKysnByasLWrZvx9JyNra2dweqVhqpNPq+s8uuqXAQEubmSflBhfqVD\nrTwHE4WxKGwkCEKJiIg4xYQJ7shkMpRKIyZPno6pqSnXr19j3ryvsLOzJywslEOHfqZt2w6kpqby\n9dcrSUl5wM2b8aSnpxMdHal/UJ88+Wehfbi5DWP37v/QvXsvzp6NokGDhri5DePgwX0cOLCXzz93\nIyUlhdDQ71EoFIwY4casWfOoXbsOf/zxKytWLMHDY5LBlMYbN66jU6dOdOrUjZMn/+TkyRP6Zbp3\n74WTUxP8/Lw5efI477/f6bnXQ61W6wMHlapggaJ8b7/9Tt5PL5aYqVEjJx4/fsT9+/ewsanA6dMn\nmTBhKpIk8fjxI5YvXw3AlCnjiYm5UGBdmUyGRqMhKGgZoaHfY2lpiafnRADu3UvkrbcaM2NGT7Kz\ns+nT5xNGjHBn0KChxMffoG3b9mzb9j1QdPVKQ9UmP/yw8wud3+uiXAQEklbSDyrMr3SolWWjEl8Y\nCEK5V9G1/3Pf5ktCixYt8fLyLXw8FSuydOliVCoV9+/fo0mTptSsWYsePXrj5TUbjSYXV9fPUKlU\njB8/hYAAX9LT1bi4fFKoYl9+Jb989es3BHRN4fl96m++WQWFQvfik5ycRO3adQBwdm7OmjWrgIKt\nGVqt7m9kXFwcn3/+Wd6yzQrsp169BoX2k2/69MlkZmZQq1YdJk2app/+ZBXDpysYFla4WyIjI4Pp\n03XBS8uWrRg8+IsC87t27cmPPx6gSpUqtGnTXp/6WaFQMn/+bMzMzEhKumewWuHDhw+xsrLSH5OT\nUxMArKysuHjxPGfOnMLMzNzguIb8a1dU9UowXG2yLCoXAQGgT0yU30KQK8vBTFk2m20EQSi7AgJ8\n2bZtN2ZmZvj6eiFJEteuXSE9PZ1Fi5aRnJzEmDHDqV+/IbGxF/HzW5z3dtqVPXv+W6BiX1TUmSeq\n5hkufvPkNHt7e30lxTNnTuPoWA1jY2MePkxBkiTS0tK4cycBgNq1a3PmzBk6darKuXPRRW7zaYsW\nLTU4vXFjZ44dO0KDBo04duwITZo0M7icTuEWAjMzs2dWK/zooy5MmeKBvb09Hh6TAbh69Qq//36Y\ntWvXk5WVyfDhgw2mha5QoQJpaWmkpj7E2tqGmJgLVKpUmQMH9mJpaYWn52xu3brJ3r079eefHzjl\nM1S9Mr9KY2kXJXpZyk9AkFfHQJMrARK5ZIscBIIgvHIuLp8wduxwzMxU2NrakpR0H0fH6qxbF8Kh\nQz8jSRIjRozB1taOBw+SGTNmGAqFks8/dyvw+TToSvpevXqV7du3FLm/Jx9G06fPZenSRQAoFApm\nzvwSW1s7WrZsxYgRblSpUhUHB0cABg4cQkDAAvbu3Y+dnT1KZX736t+rvte7d198fLwYO3YERkbG\neHn5ALB162YcHKrRpk27J4+62NvNZ2lpSfXqNXjw4IF+MKGDgwNmZirGjh2BJEnY2VUkKel+oXUV\nCgWTJ3syebIH1tbW+taFFi3ewdt7LufORWNkZISjY3WSknStLGFhodSr1+CZ1St1LTOvT7XCf6pc\nVDtcMHUvtRtU5KNeb3Et4RE+m45j9vbPvGXXgLHOw0r78IRnENXWyjZx/8quY8eOUKuWA5UrV+fU\nqROEha1n+fJvSvuwhGIqiWqH5aaFQK7IbyHQIlPmfWEg0hYLgiAYVKVKVXx8fNBqdeMKJk3yLO1D\nEkpZuQkI8vMQ5Gi0+qREYlChIAiCYdWr12DLli2ihUfQKxeJiQB9HoKcXC0yhahjIAiCIAgvotwE\nBPmpizUaLYguA0EQBEF4IeUoIDDUQiACAkEQBEEojnIXEGg02r/qGIguA0EQBEEolnITEOR/ZSBa\nCARBeBU2bVrPpElj8fAYxcSJY4iNjQFg/PjRxMffKOWj++fWrVvL7t3/Kdayu3aFExoaUuxth4aG\nMHLkEMaMGc7Fi+cNLpObm8vcuTNeqMKjh8coIiJOFZi2fPnX7Nu32+Dyd+/eYfRoXUZEL685hbIc\n/vnnMfz8vIvcX3Z2Nvv26epMHDy4T18hsawqNwFBgRYCpWghEASh5MTFXefIkd9YtuwbgoLWMn78\nFBYuXFDah1UmXLoUQ2TkGUJCNuDl5WuwPPHt27fw8BhFbOzFF9p2jx59+PHH/fp/azQajh79nc6d\nXYpcJz+ZkJeXrz5hUXElJyexd68u2OjSpdtTyZfKnnLz2aGhFgKVaCEQhHLv6C9XuRZz76Vus1aD\nSrT+oHaR8y0sLEhMTGTfvt28+25r6tSpS0jIxgLLpKWlGayON2hQP5ydm3L9+jWsra3x8vJFoVAS\nGOjPrVs38zIZutOsWYsC27t//x6Bgf7k5OSQnJzEyJFjaNu2g8Fqe7GxMWzevAEjIyMSEhL48MPO\nuLkN4969RBYt8iU7OxsTExMCAvyRy1UEB68iNvYiqamp1KlTl1mz5hXYd3DwKqKjI9Fqc/nss4F0\n7PghUVGRrFjxNVZWVsjlCpycGhfr2kZHR/LOO60AqFz5DXJztfqUwvkyMzOZNetLNm3aUKxt5uvY\n8QPWrl1FVlYWJiYm/P77YVq2fBcTE1ODVSifDABcXXvw/ffh+noSZmZmmJqaYmlpBRiuqrhxYyg3\nblxn/fpv0Wq12NnZ07NnH4KClhEdHYlMJqNzZxf69u2Pn583RkZG3LlzhwcPkpkzZz5169Z/ofMr\naSUaEEiShJeXF7GxsRgbG+Pr64ujo6N+fnR0NAEBuujQ3t6exYsXY2xsTJ8+ffRVsxwcHPDz83vu\nvgzlITAzEi0EgiC8fPb2FQkIWMKOHVsJDQ3BzMyMkSPH0qHD+/pliqqOl5WViYvLJzRp0pTVq1ey\na1c4JiYm2NhUYObML3n0KJVx40YSFratwD5v3IhjwIDBNG3anHPnolm3bi1t23YwWG3P1taWxMS7\nbNy4laysLHr1+hg3t2GsWrUMV9cBtGr1HqdPn2Tx4sWMH++JpaUVS5YEIUkSgwf3IykpSb/f48eP\nkpBwm1WrQsjOzmb06KG8/XYrlixZiJ9fIFWrOhAYuLDY106tVhd4+KtUKtLS0gpMyy/Q9KKMjY1p\n164jv/12iM6dP+bAgb2MGjUOgLi4wlUoO3f++Im1dS+V33yzgpEjx9CiRUs2b97AjRtxADx6lFqo\nquKQIcO4fv0qQ4eOYN26tQAcPfoHd+8msHbtejQaDePGjaR587cBeOONKnh6zmbv3l3s3r2TadNm\n/q3zLCklGhD8/PPPZGdns2XLFqKiovD39+ebb/5KjTlv3jxWrlyJo6MjO3bsICEhgSpVqgCwcePG\nojZrUIGvDPK7DBSihUAQyrvWH9R+5tt8Sbh9+xYqlbn+TTom5iLTpk3Q/+GHoqvjKRRKmjRpCoCT\nU2OOHz+KXK4gOvoMFy6cQ5IktFotsbExBAUt1Vc+bNTIiQ0bvtP3hz/Z322o2l6tWnWQyWSYmppi\nYqL7W3j16lXCwkLZvHkDkiShUplibGxMSsoDvL3nYmpqRkZGRoFtX7t2hdjYGCZMcEeSJHJzc7lz\nJ4GUlBR9TYEmTZy5fftWgWsUErJa/5a8fPlqfdP8k1URoTiVEQsLCPDh1q2bVKhgy4IF/gXmde/e\nk1WrVtCsWQvS0h7rr429feEqlE+TJImbN2/QsGEjQFewKT8gUCqNnltVEXTdSfmFnZRKJY0aOXH9\n+nUA6tXTtQhUqlSZs2ejXuicX4USDQhOnz5Nu3a6PhVnZ2fOnTunn3f9+nVsbGwIDQ3l8uXLdOzY\nkRo1ahAdHU16ejrDhw8nNzeXyZMn4+zs/Nx9/ZWHQEKm0KCQKTBSGJXMiQmC8K925cpl9uzZSUDA\nEpRKJY6OjlhaWupfTMBwdTxraxtyczX6ioRnz0ZRq5YumKlUqTKDBw8lKyuLsLBQ6tdvUKD635w5\nnvTo0YdWrd7jwIG9HDy4Tz/v+UV1pLxjqkH//oNxcmpMfHwcV69e5Pjxo9y7dxdvb38ePnzI778f\n5slqhNWr16RFi7fx9JyNJEls2PAdVas6YG9fkfj4OKpVq8HFixewsrIqsMeRI8cYPJLGjZuyevUK\nBgwYRGJiIpIk6asGFteMGXOLnFerVh3S09Vs376Frl176KcbqkL59DWSyWTUrFmbs2ejadXqPWJi\nLgBFV1U0VBWxZs2a7N+/h379BqDRaDh3LopPPunGn38efe2LH5VoQJCWllYg8lMqlWi1WuRyOSkp\nKURGRjJ//nwcHR0ZPXo0Tk5OVKhQgeHDh+Pq6kpcXBwjR47kv//9b6EqYE/Lz1SYka0BRQ6monVA\nEIQS0qHD+8THxzFihBsqlQpJ0jJu3CRUKvNnVsfL/zu2efMG7t69wxtvvMmoUeOQJImAAB88PEaR\nnp5Onz59C+3z/fc7ERS0lLCwUCpWrMSjR6l5cwxX2yv48NH9PHbsRAIDF5KdnUV2djZeXvMwNbVm\nw4bv8PAYBehqHCQl3dev36ZNOyIiTjFu3EgyMjJo374jKpUKT89ZfPXVPMzNLVCpzAsFBEWpX78B\nzs7NGD36CyRJYupUXbN5RMQpoqMjGTp0RLG28yxdu/Zg9eoVhIf/NcDQUBXKgnTnO27cRHx9vfjh\nhzBsbCpgbGyMg4OjwaqKb73VGI0mhzVrgjAxMQHgvffaEhFxGnf3YWg0Gj74oPNrN1agKCVa7XDh\nwoU0bdqUjz/W9dN07NiRw4cPA3Dt2jUmTZrEnj17AFi/fj25ubm4ubmh1Wr1F9fV1ZWgoCAqV65c\n5H4WTN3LZ1+0pL7TG0xf+TvXK+ygSgUblnct+nMRQRCE0vDBBx/w3//+FyMj0YIpvF5KtIWgefPm\nHDp0iI8//pjIyEjq1aunn+fo6Eh6ejo3b97E0dGR06dP07dvX3bs2MGlS5eYP38+iYmJqNVqKlas\n+Nx9PU7L5N69R8TffYTMXoOx3EQU7SgDRPncsk3cvxen1cL9+49fi4BA3L+yq8yVP+7cuTNHjhyh\nf//+APj7+7Nv3z4yMjJwdXXF19eXKVOmANCsWTM6dOhATk4Os2bN4vPPP0cul+Pn5/fc7gLQDSp8\nnJ6DOisLM7lWVDoUBOG1tH274SQ5glDaSjQgkMlkeHsXbLavWbOm/udWrVqxffv2AvONjIwIDAx8\n4X0pFDLuJKtBKbIUCoIgCMKLKj+ZCpVyEpLTkSlEpUNBEARBeFHlJiCQy+W6FgJ9HQPRZSAIgiAI\nxVVuAgKFUsad5PS/khKJgEAQBEEQiq38BAQKXQuBhbnuW1LRZSAIQkk5c+Y03bt/xIQJ7kyY4I67\n+zDCw7e+9P08evSIn376EQA/P+8Xqvz3d7m69iAnJ+eF18vKymLu3OmMGzeS6dMnkZr6sNAy4eHb\nGDnSjVGjhvLLLz8Xa7vp6en06OFCZmZmgenDhg0slB0x38GD+wgOXsWDB8kGiyetWRNUILHT0xIT\n7+orF65cuYR79xKLdaxlXbkJCHJytTx4lIWVlQgIBEEoeS1atGTFijWsWLGGlSuD2bJlM2p12kvd\nx5Url/jjj99e6jaf7+9l09u1awe1a9dl1aoQXFw+Yf367wrMT019yJ49/yE4eH1elcilxdquSqWi\nTZv2HDr0VwARGxuDpaW1PnVyUWxt7ZgyZcYLn0tExCl9auHx46dQqVLReXDKk3JT7TDpkS56tLCA\nZBCfHQrCv0TK7Z9If3jhpW5TZdOIClU7P3OZJ3O6qdVqFAoFCoXSYFW9SpUqM2/eTNRqNZmZmYwa\nNZaWLVvh5+dNQsJtsrIycXUdoK99kC8sLJSrV6+wd+8uAHbtCmfz5g2o1WqmTZuJjU0Fpk+fhI1N\nBd59tw0tW77D0qWLUSgUGBubMGPGHLRaLfPnzyY4OBSA0aO/wNvbHzMzU2bOnIRanYGjYzUiIk6x\nZctOQCIw0J+EhNvIZDL8/AL1xeaeJTo6koEDhwDw7rutWb/+2wLzra1tCA39HrlcTnJykj75XHF0\n796T4OBVdOnSDYD9+3fTo0dvwHAVwnx3797Rn/vhw/9j48Z12NjYkpOTTfXqNdBqtSxe7Me9e/dI\nTk6ibdv2DBs2ik2b1pOVlYWTUxO2bt2Mp+dsbG3tDFavNFRtUqUyL/a5vU7KTUBw72EGAKZmEmSJ\nMQSCIJSsiIhTTJjgjkwmQ6k0YvLk6ZiamnL9euGqem3bdiA1NZWvv15JSsoDbt6MJz09nejoSP2D\n+uTJPwvtw81tGLt3/4fu3Xtx9mwUDRo0xM1tGAcP7uPAgb18/rkbKSkphIZ+j0KhYMQIN2bNmkft\n2nX4449fWbFiCR4ekwymNN64cR2dOnWiU6dunDz5JydPntAv0717L5ycmuDn583Jk8d5//1Oz70e\narVaHzioVOao1epCy8jlcsLDtxEaupa+ffsX+1o3auTE48ePuH//HjY2FTh9+iQTJkxFkiQeP35U\nqArhk2QyGRqNhqCgZYSGfo+lpSWenhMBuHcvkbfeasyMGT3Jzs6mT59PGDHCnUGDhhIff4O2bduz\nbdv3QNHVKw1Vm/zww2cHk6+rchMQJD7UtRAYm2ghC0yVxY8+BUEouypU7fzct/mS0KJFS7y8fAtN\nr1ixcFW9mjVr0aNHb7y8ZqPR5OLq+hkqlYrx46cQEOBLeroaF5dPuH37FgsXfqWvcFilStUC265f\nvyGgawrP71N/880qKBQKAJKTk/Slg52dm7NmzSqgYGtGfjGeuLg4Pv/8s7xlmxXYT716DQrtJ9/0\n6ZPJzMygVq06TJo0TT/9ySqGz6pg+Omn/ejZsw9Tp07A2fk0zZq1ACAjI4Pp03XBS8uWrRg8+IsC\n63Xt2pMffzxAlSpVaNOmPUql7vGlUCifW4Xw4cOHWFlZ6Y/JyakJAFZWVly8eJ4zZ05hZmZucOxE\n/rUrqnolGK42WRaVm4DgTrIuGjU21v3bWG5cikcjCMK/laGqetf9YKD0AAAWXklEQVSuXSE9PZ1F\ni5aRnJzEmDHDqV+/IbGxF/HzW5z3dtqVPXv+W6DCYVTUGSTpr2p6hqrlPTnN3t5eX0nxzJnTODpW\nw9jYmIcPU5AkibS0NO7cSQCgdu3anDlzhk6dqnLuXHSR23zaokWG+/4bN3bm2LEjNGjQiGPHjuhL\nAOeLj79BcHAQvr75XRpGBbLQmpmZFTj3p330URemTPHA3t4eD4/JQNFVCJ9WoUIF0tLSSE19iLW1\nDTExF6hUqTIHDuzF0tIKT8/Z3Lp1k717d+rP/+kqhoaqV+ZXaXzdqxgWV7kICORyGfdTszA1VoBc\ndxONFOXi1ARBKGMMVdVzdKzOunUhHDr0M5IkMWLEGGxt7XjwIJkxY4ahUCj5/HO3Qmnaq1Z14OrV\nq2zfvqXI/T35MJo+fS5Lly4CQKFQMHPml9ja2tGyZStGjHCjSpWqODg4AjBw4BACAhawd+9+7Ozs\nUSoV+Vs0uO3n6d27Lz4+XowdOwIjI2O8vHwA2Lp1Mw4O1WjTph116tRj9OgvkMtltGrVulDLxLNY\nWlpSvXoNHjx4oB9M6ODgYLAK4dMUCgWTJ3syebIH1tbW+taFFi3ewdt7LufORWNkZISjY3WSknSt\nLGFhodSr1+CZ1St1LTN/73q9jkq02uGr4j/rAJfMFGgliXrtrhJ1/xwB7eZjYVQ2B3b8m4jiKmWb\nuH9l17FjR6hVy4HKlatz6tQJwsLWs3z5N6V9WEIxlbniRq+KQiEnKycXCzMjcrS6PiAjeelXEhME\nQXhdValSFR8fH7Ra3biCSZM8S/uQhFJWPgICpZysjBzsrEzR5OoGlChliuesJQiC8O9VvXoNtmzZ\nIlp4BL1ykZhIoZCTrdFiaqwgR6tBLpOjkIuAQBAEQRCKq1wEBHKFbiCHiZECjTYHI3m5aPgQBEEQ\nhFemfAQE8ryAIK+FQIwfEARBEIQXUy4CApn8rxaCHK0GpWghEARBEIQXUr4CAmPRZSAIwquxadN6\nJk0ai4fHKCZOHENsbAwA48ePJj7+Rikf3T+3bt1adu/+T7GW3bUrnNDQkGJvOzQ0hJEjhzBmzHAu\nXjxfaH5U1BlGjRqKu/sw1qwJKvZ2PTxGERFxqsC05cu/Zt++3QaXv3v3DqNH6zIiennNKZTl8M8/\nj+Hn513k/rKzs9m3T1dn4uDBffoKiWVVuQgIyEsGYSq6DARBeAXi4q5z5MhveVX71jJ+/BQWLlxQ\n2odVJly6FENk5BlCQjbg5eVrsDzxypVLWbBgIWvWrOPChXNcvnypWNvu0aMPP/64X/9vjUbD0aO/\n07mzS5Hr5CcT8vLy1ScsKq7k5CT27tUFG126dKNNm3YvtP7rpny8SuclhzIxUpCTJboMBOHf5ODN\n+5x98HLLDje2taCLY8Ui51tYWJCYmMi+fbt5993W1KlTl5CQjQWWSUtLM1gdb9Cgfjg7N+X69WtY\nW1vj5eWLQqEkMNCfW7du5mUydNfn+M93//49AgP9ycnJITk5iZEjx9C2bQeD1fZiY2PYvHkDRkZG\nJCQk8OGHnXFzG8a9e4ksWuRLdnY2JiYmBAT4I5erCA5eRWzsRVJTU6lTpy6zZs0rsO/g4FVER0ei\n1eby2WcD6djxQ6KiIlmx4musrKyQyxU4OTUu1rWNjo7knXdaAVC58hvk5mr1KYXzrV27HrlcTnp6\nOmlpaahUqmJtu2PHD1i7dhVZWVmYmJjw+++HadnyXUxMTA1WoXwyAHB17cH334fr60mYmZlhamqK\npaUVYLiq4saNody4cZ31679Fq9ViZ2dPz559CApaRnR0JDKZjM6dXejbtz9+ft4YGRlx584dHjxI\nZs6c+dStW79Y5/WqlIsWAplCdxrGSjkarUZ0GQiCUKLs7SsSELCEs2ejGD36CwYNci3UXJxfHS8o\naC1ffbUQf/+vAMjKysTF5RO++eZbqlWrwa5d4ezbtwsbmwoEBa3F3z/Q4FvzjRtxDBgwmCVLgvD0\nnM1//rMdQF9tLyhoLfb2FTl27CgAiYl38fMLJDg4lO+/1wUrq1Ytw9V1ACtWrKF//0EsXryY9PR0\nLC2tWLIkiG+/3cj582dJSkrS7/f48aMkJNxm1aoQli9fw4YN35GWlsaSJQtZsMCfpUtX8eabVYp9\n7dRqNebmf5VTVqlUpKUVDOjkcjnnz59jyJD+2NvbU7FipWJt29jYmHbtOvLbb4cAOHBgLz179gEg\nLk5XhXLFijW0b/8+hw79/NTaujfLb75ZwciRY1i6dJW+CBLAo0epLF++muDgUDQaDTExFxgyZBg1\natRi6NAR+uWOHv2Du3cTWLt2PatWhfDTT//l2rUrALzxRhWWLFnJp5/2Y/funcW7YK9QuXhyVm1U\nGeIfYGQsQ0ISXQaC8C/SxbHiM9/mS8Lt27dQqcz1b9IxMReZNm0CzZu/rV+mqOp4CoWSJk2aAuDk\n1Jjjx48ilyuIjj7DhQvnkCQJrVZLbGwMQUFL9ZUPGzVyYsOG7/T94U/2dxuqtlerVh1kMhmmpqaY\nmJgCcPXqVcLCQtm8eQOSJKFSmWJsbExKygO8vediampGRkZGgW1fu3aF2NgYJkxwR5IkcnNzuXMn\ngZSUFH1NgSZNnLl9+1aBaxQSslr/lrx8+Wp90/yTVRGh6MqIb73lxPbtewgJWc2mTesZNmyUfl5A\ngA+3bt2kQgVbFizwL7Be9+49WbVqBc2atSAt7bH+2tjbF65C+TRJkrh58wYNGzYCdAWbbtyIA0Cp\nNHpuVUXQdSflF3ZSKpU0auTE9evXAahXr77+Pp09G2Vw/dJULgICuZnuNIzy4gDRZSAIQkm6cuUy\ne/bsJCBgCUqlEkdHRywtLVEo/mp0NVQdz9rahtxcjb4i4dmzUdSqVRvQPSQGDx5KVlYWYWGh1K/f\noED1vzlzPOnRow+tWr3HgQN7OXhwn37e84vqSHnHVIP+/Qfj5NSY+Pg4rl69yPHjR7l37y7e3v48\nfPiQ338/rF8eoHr1mrRo8TaenrORJIkNG76jalUH7O0rEh8fR7VqNbh48QJWVlYF9jhy5BiDR9K4\ncVNWr17BgAGDSExMRJIkfdXAfOPGjWThwiVYWlqiUqkKlSWeMWNukWdaq1Yd0tPVbN++ha5de+in\nG6pC+fQ1kslk1KxZm7Nno2nV6j1iYi4ARVdVNFQVsWbNmuzfv4d+/Qag0Wg4dy6KTz7pxp9/Hn3t\nix+ViydnZnYuAEplXqVDERAIglCCOnR4n/j4OEaMcEOlUiFJWsaNm4RKZf7M6nj51Qw3b97A3bt3\neOONNxk1ahySJBEQ4IOHxyjS09Pp06dvoX2+/34ngoKWEhYWSsWKlXj0KDVvjuFqewUfPrqfx46d\nSGDgQrKzs8jOzsbLax6mptZs2PAdHh66N/AqVaqSlHRfv36bNu2IiDjFuHEjycjIoH37jqhUKjw9\nZ/HVV/MwN7dApTIvFBAUpX79Bjg7N2P06C+QJImpU2cCEBFxiujoSIYOHcGAAYOZNm0CxsbG2NnZ\nM3Pml8Xadr6uXXuwevUKwsP/GmBoqAplQbrzHTduIr6+XvzwQxg2NhUwNjbGwcHRYFXFt95qjEaT\nw5o1QZiYmADw3nttiYg4jbv7MDQaDR980Pm1GytQlHJR7XDTwYts/fkSY11rEXrjG1pWbs7Qt/qX\n9mEJxSCq5ZVt4v69OFfXHvzww39eeER7SRD3r+wqiWqH5WJQYUa2ri9Hkff7JVoIBEF4fckMNFcL\nQukrF0/OzCxdl4E8v8tAUS5OSxCEcmj7dsNJcgShtJWLFoLMvBYCuVwXdYtBhYIgCILwYspHQJDX\nQiCT5w8qFJ8dCoIgCMKLKB8BQV4LAQpdYCDGEAiCIAjCiykXAUFGlgYjpRytlPf5oQgIBEEQBOGF\nlOiTU5IkvLy8iI2NxdjYGF9fXxwdHfXzo6OjCQjQpei0t7dn8eLFGBkZPXMdQzKzc/Wlj0F0GQiC\nIAjCiyrRgODnn38mOzubLVu2EBUVhb+/P998841+/rx581i5ciWOjo7s2LGDhIQELl++/Mx1DMnM\n1mBipECjDwhEC4EgCIIgvIgS7TI4ffo07drpykE6Oztz7tw5/bzr169jY2NDaGgogwcPJjU1lRo1\najxznaJkZmnySh/r0luKLgNBEARBeDElGhCkpaUVKFqhVCr1eZ9TUlKIjIxk8ODBhIaGcvToUY4f\nP/7MdYqSkZWLsegyEARBEIS/rURfpS0sLFCr1fp/a7VafS5vGxsbqlWrRs2aNQFo164d586dw9LS\nssh1irJzUXf9z/2ad3mZpyC8AiWRglN4dcT9K9vE/RPylWgLQfPmzfn1118BiIyMpF69evp5jo6O\npKenc/PmTUDXvVC3bl2aNWtW5DqCIAiCIJSMEi1u9ORXBgD+/v6cP3+ejIwMXF1d+fPPPwkMDASg\nWbNmzJ492+A6+a0IgiAIgiCUjHJR7VAQBEEQhH+mXCQmEgRBEAThnxEBgSAIgiAIIiAQBEEQBKGE\nPzssSc9LiyyUvj59+mBhYQGAg4MD7u7uzJw5E7lcTt26dZk/fz4A27ZtY+vWrRgZGeHu7k7Hjh3J\nysrC09OT5ORkLCwsWLhwIRUqVCjN0/lXiIqKIjAwkLCwMOLj4//x/YqMjMTPzw+lUknr1q3x8PAo\n5TMs3568fxcvXmT06NHUqFEDgAEDBtClSxdx/14zGo2G2bNnc/v2bXJycnB3d6dOnTql87snlVH/\n93//J82cOVOSJEmKjIyUxowZU8pHJDwpKytL6t27d4Fp7u7u0smTJyVJkqR58+ZJP/30k3T//n2p\nW7duUk5OjvT48WOpW7duUnZ2thQaGiqtXLlSkiRJ2r9/v+Tj4/PKz+HfJiQkROrWrZv02WefSZL0\ncu5Xz549pZs3b0qSJEkjR46ULl68WApn9u/w9P3btm2bFBoaWmAZcf9eP+Hh4ZKfn58kSZKUmpoq\ndezYsdR+98psl8HfSXEsvDoxMTGkp6czfPhwhg4dSlRUFBcuXODtt98GoH379hw9epTo6GhatGiB\nUqnEwsKCGjVqEBMTw+nTp2nfvr1+2WPHjpXm6fwrVK9enVWrVun/ff78+b99v/Kzjubk5ODg4ABA\n27ZtOXr06Ks/sX8JQ/fv8OHDDBo0iLlz56JWq8X9ew116dKFiRMnApCbm4tCofhHfyv/yb0rswHB\n30lxLLw6pqamDB8+nO+++w4vLy+mTZuG9MQXrubm5qSlpaFWqwvcR5VKpZ+e392Qv6xQsjp37oxC\nodD/+5/cr8ePHxeY9uR0oWQ8ff+cnZ2ZPn06mzZtwtHRkaCgoEJ/N8X9K31mZmb6+zBx4kQmT55c\nar97ZTYgeFZaZKH01ahRgx49euh/trGxITk5WT9frVZjZWWFhYVFgYf9k9Pz7+/TvwjCq/Hk79Pf\nuV9PB3L5ywqvRqdOnWjUqJH+55iYGCwtLcX9ew3duXOHIUOG0Lt3b7p27Vpqv3tl9gn6rLTIQukL\nDw9n4cKFACQmJpKWlkabNm04ceIEAL/99hstWrSgcePGnD59muzsbB4/fsy1a9cKpbD+9ddf9c1n\nwqvTqFEjTp48Cfy9+2VhYYGxsTE3b95EkiT++OMPWrRoUZqn9K8yfPhwzp49C8CxY8d46623xP17\nDSUlJTF8+HA8PT3p3bs3AA0bNiyV370ym6lQEimOX2s5OTnMmjWLhIQE5HI5np6e2NjYMHfuXHJy\ncqhduzY+Pj7IZDK2b9/O1q1bkSSJMWPG0KlTJzIzM5kxYwb379/H2NiYr7/+Gjs7u9I+rXLv9u3b\nTJ06lS1bthAXF8eXX375j+5XdHQ0vr6+aLVa2rRpw6RJk0r7FMu1J+/fhQsX+OqrrzAyMqJixYos\nWLAAc3Nzcf9eM76+vhw8eJBatWohSRIymYw5c+bg4+Pzyn/3ymxAIAiCIAjCy1NmuwwEQRAEQXh5\nREAgCIIgCIIICARBEARBEAGBIAiCIAiIgEAQBEEQBERAIAiCIAgCIiAQhDJnwYIF9OrVi65du+Lk\n5ETv3r3p3bs3O3fuLPY2VqxYwaFDh565TH6SlJKwcuVKTp8+XWLbFwThxYk8BIJQRt2+fRs3Nzf+\n97//lfahvLDBgwczYcIEWrZsWdqHIghCHmVpH4AgCC9PUFAQkZGR3L17l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| |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7fa1e4089978>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"res1 = train_classifier(hidden_dims=[100], estimator=StochasticGradientEstimator.ST, epochs=20, \n", | |
" lr=0.03, label = \"Pass-through - 0.03\")\n", | |
"res2 = train_classifier(hidden_dims=[100], estimator=StochasticGradientEstimator.ST, epochs=20, \n", | |
" lr=0.03, slope_annealing_rate = 1.1, label = \"Slope-annealed - 0.03\")\n", | |
"\n", | |
"res3 = train_classifier(hidden_dims=[100], estimator=StochasticGradientEstimator.ST, epochs=20, \n", | |
" lr=0.1, label = \"Pass-through - 0.1\")\n", | |
"res4 = train_classifier(hidden_dims=[100], estimator=StochasticGradientEstimator.ST, epochs=20, \n", | |
" lr=0.1, slope_annealing_rate = 1.1, label = \"Slope-annealed - 0.1\")\n", | |
"\n", | |
"res5 = train_classifier(hidden_dims=[100], estimator=StochasticGradientEstimator.ST, epochs=20, \n", | |
" lr=0.3, label = \"Pass-through - 0.3\")\n", | |
"res6 = train_classifier(hidden_dims=[100], estimator=StochasticGradientEstimator.ST, epochs=20, \n", | |
" lr=0.3, slope_annealing_rate = 1.1, label = \"Slope-annealed - 0.3\")\n", | |
"\n", | |
"plot_n(res1[1:] + res2[1:] + res3[1:] + res4[1:] + res5[1:] + res6[1:],\n", | |
" lower_y=0.6, title=\"Experiment 3: Pass-through vs slope-annealed ST\")" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"#### Experiment 4: Variance-adjusted REINFORCE vs slope-annealed ST\n", | |
"\n", | |
"We now directly compare the variance-adjusted REINFORCE and slope-annealed ST, both at their best learning rates. In this setting, despite being a biased estimator, the straight-through estimator displays faster learning, less variance, and better overall results than the variance-adjusted REINFORCE estimator. " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 22, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Epoch 20 0.9302\n", | |
"Epoch 20 0.9632\n" | |
] | |
}, | |
{ | |
"data": { | |
"image/png": 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xW0HXbNWqVYSEhNgeWV1N0ADYCrrMzEwSExNZvnw5AI899hjTp08nISGBLVu28MEHH7To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\nCCGEEAJJCIQQQggB/D9aLZuaSVDjCwAAAABJRU5ErkJggg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7fa1e41448d0>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"res1 = train_classifier(hidden_dims=[100], estimator=StochasticGradientEstimator.REINFORCE, epochs=20, \n", | |
" lr=0.05, label = \"Variance-adjusted REINFORCE\")\n", | |
"\n", | |
"res2 = train_classifier(hidden_dims=[100], estimator=StochasticGradientEstimator.ST, epochs=20, \n", | |
" lr=0.1, slope_annealing_rate = 1.1, label = \"Slope-annealed ST\")\n", | |
"\n", | |
"plot_n(res1[1:] + res2[1:],\n", | |
" lower_y=0.6, title=\"Experiment 4: Variance-adjusted REINFORCE vs slope-annealed ST\")" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"#### Experiment 5: A look at the deterministic step function, during training and evaluation\n", | |
"\n", | |
"Similar to how dropout is not applied at inference when using dropout for training, it makes sense that we might replace the stochastic sigmoid with a deterministic step function at inference when using binary neurons. We might go even further than that, and use deterministic neurons during training, which is the approach taken by [Chung et al. (2016)](https://arxiv.org/abs/1609.01704). The following three combinations are compared below, using the pass-through straight through estimator, without slope annealing:\n", | |
"\n", | |
"- stochastic during training, stochastic during test\n", | |
"- stochastic during training, deterministic during test\n", | |
"- deterministic during training, deterministic during test\n", | |
"\n", | |
"The results show that stochastic inference and deterministic inference, when combined with stochastic training, are closely comparable (with the latter having a slight edge), but that deterministic training lags behind stochastic training. Similar results hold for the REINFORCE estimator." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 12, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Epoch 20 0.9554\n", | |
"Epoch 20 0.9482\n", | |
"Epoch 20 0.927\n" | |
] | |
}, | |
{ | |
"data": { | |
"image/png": 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YLNT+uMd+rY+PaxMen6Vl6b/P8PGWs9hsvy3/NSVVeJtbNxVGGEp+88ZCY4uF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\nt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Tm0vAPNzKFgMnl6LSVlfpSOfgCqywNENm0BFGoaz8Th8h/x9cSxU14u1+JkJtfv5CbX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\nSUVORK5CYII=\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7fbd0596d9e8>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"res1 = train_classifier(hidden_dims=[100], estimator=StochasticGradientEstimator.ST, epochs=20, \n", | |
" lr=0.3, label = \"Stochastic, Stochastic\")\n", | |
"res2 = train_classifier(hidden_dims=[100], estimator=StochasticGradientEstimator.ST, epochs=20, \n", | |
" lr=0.3, stochastic_eval=False, label = \"Stochastic, Deterministic\")\n", | |
"res3 = train_classifier(hidden_dims=[100], estimator=StochasticGradientEstimator.ST, epochs=20, \n", | |
" lr=0.3, stochastic_train=False, stochastic_eval=False, \n", | |
" label = \"Deterministic, Deterministic\")\n", | |
"\n", | |
"plot_n(res1 + res2 + res3,\n", | |
" lower_y=0.6, title=\"Experiment 5: Stochastic vs Deterministic (Straight-through, no slope annealing)\")" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"The introduction of slope annealing, however, as per [Chung et al. (2016)](https://arxiv.org/abs/1609.01704), closes the gap between stochastic and deterministic training. Although stochastic and deterministic training converge to a similar result, deterministic training results in faster training early on. Note that the slope annealed neuron used here is not exactly the same as the one used by [Chung et al. (2016)](https://arxiv.org/abs/1609.01704); this one uses a regular sigmoid, whereas Chung et al. use a hard sigmoid. " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 11, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Epoch 30 0.9668\n", | |
"Epoch 30 0.9676\n" | |
] | |
}, | |
{ | |
"data": { | |
"image/png": 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4mA8//JBRo0Yxb9488vPzmTNnDl999RVms/m8xwoL8z/vcuEh9dRyUlctI/XU\nMlJPLSd19fto1YBg4MCBbN26lWuuuYaEhAS6deumLXO5XBw7doxVq1Zht9u5/fbbeeihh0hMTNSG\nCfz9/XE6nbjd7gseq7DQ2mrf479FWJi/1FMLSV21jNRTy0g9tZzUVcu0RtDUqgHBuHHj2LlzJ7Nm\nzQLg5ZdfZuPGjdhsNmbMmAHAtGnT8PLy4k9/+hNBQUHceuutPP744/zxj3/E6XQyf/58vL29W7OY\nQgghxP88nVJK/d6F+DVIRHlhEnm3nNRVy0g9tYzUU8tJXbVMa2QI5MVEQgghhJCAQAghhBASEAgh\nhBACCQiEEEIIgQQEQgghhEACAiGEEEIgAYEQQgghkIBACCGEEEhAIIQQQggkIBBCCCEEEhAIIYQQ\nAgkIhBBCCIEEBEIIIYRAAgIhhBBCIAGBEEIIIZCAQAghhBBIQCCEEEIIJCAQQgghBBIQCCGEEAIJ\nCIQQQgiBBARCCCGEQAICIYQQQiABgRBCCCGQgEAIIcSvwO5ycKjwGC636/cuirhIEhAIIVpsW9Yu\nNqZ8/3sX41dX6ahCKfV7F+P/e9/uyWDT7rQmf3crNx8cW8V7Rz7iaPGJ37xcv4caZy21LnurH8fp\ncvPB1yc4dLqo1Y8lAYEQosU2Z2znm7QfKa+t+L2L8qvJtOaw4OeF7Mrd+3sXpdWk51nZczz/39qH\nWyk27Exl/Y5UHM7GWYBv0zZzpOg4APlVhefcR1ltOT9mbMPucvxbZfm9uZWbJQeWsyz+vVY/Vmpu\nBT8fzmXzwaxWP5YEBEKIFrParQD/Vb3AY8UncCs3J0tO/95FaTX/2nqa9748RlXNxTfEhWU2au0u\n3F4VPLd7CekVmQAopdicsQOT3ghAUU0JKeVpPB/3KoXVxY32sSVzB+tOb+KbtB8v/sv8CopsJSyM\ne5UTxacuavvU8gzyqvJJrcho8h1bKu5YHk+/v5esgsrzrpec7Qm+c4uqL+o4v4QEBEIIwNPrcSv3\nOZfXuuzY3Z4Gpb43+J9IKYXD7dQ+ny5LBSCrMuf3KlKrKyyzoYCCUttF7yMz39NwGQKLKHWU8F36\nVgAq7FZqXDV0DeoCQLGthISCo+RXF5BQeKTRPjIqPL3czRnbyasqaLTMrdysTdrIvqxDuN2tO3yz\nK2cvedUF7M0/2OzyC10L+/MTtP//+/YtrD35LcsTVvLB0VWU1JRe8PjllbV8/P1JsgoreXNNAhXV\ntdoypRSuhdilAAAgAElEQVRllbWUWmtxK0VyTjkAxRU11Nid59rlr0ICAiEEAKsSv2DhnlfPOZZu\ntZ/pySSWJGH/DcZPW8OnJ9fyzK5XsNorcbldJJenAVBQXfSbjAn/1txuRanV0+AUll18QJBR4MkO\n6bw8+zhSdJyy2nLyqz1DBFH+7bGY/MirLCKlJBuAtIqMM+VQbjKsWZj0JlzKxZcp3zba/8/Ze9ic\nuZ33967lnje2szsznoLqcw8/XEheVQFHi85kstzKzc6cPZTXVnCgrkFPK89odtt3D3/Ekv3Lmr0W\nXG4X8QWH8TF6A5DFYTZnb+FEySkOFBzi3cMfadfG8bQSknPKUUpx4GQhGfmeOvzX1tPYal3EtPOj\nMvp7ntz5Ej+k/4RSio2703lo+U7mr9jJ/317kpScM8NzeSWtmyWQgEAIAcCx4kQKqouosDefwqwP\nCPQ6PQ63k5OlrZ9iP1acyJvx72FzXlxDZnPW8Fb8e40ahlNlyZTbK1h/+muyKnO0m7dCkVOZ96uU\n+2KV1pRhc9Zc9Pa1LjsF1Y0nn5VV1uKq63G3NCCodFRRVlve6G9ahsDbsw+3crM7Z58WEIT7tiHU\nO4TS2nJSSz3ZltTydK1RrQ+4BoT3pZ1fJMeKE7UAzGqv1AKEEnsBdlMxnySt5tOT65otn91lp8h2\n/lT956c28LfD/6C0pgyAk6Wn+WfiGhbvX0ZRTYmnTLYiKh1VjbarcdZwvOQkGdbsZnv7p0qTsToq\naW/shrsyEJ25FpSOxwY/wIh2Q8iqzGHJ9lWUVNTw+meHeOnjAyxZHc+KdUd47bMEDp4qZPexfDpF\n+nPTpEj0XjW4DDbWJ39NcnkaccfyMBv1BPt7seNwDqXWWnQ6z7Fbe9hAAgIhBOW1Vq3BL667WZ6t\n0uFZ3j04FoC0ujHks+3Li2dn9p5fZdZ+XO5+TpWe5kRJ0kVtf6o0mZOlp9mV45kw6HK7KLJ5vl9c\n3n7tiYkewV2B33fYwO6y8+Le1/nkxL/OuY5Siu/TtjbqeTfc/o2Df+Olva83yuYUV5wJMFoSELiV\nm6UH32HJ/uWN0uYZBZUEWsx4WewolwGz3kxc7n6tFx/hG4aPzh+d3g1mz3HK7VZKaz0NcobVM1zQ\n0b8Dfdv0wul2kljiGcPfkrkDm9NGG59Q0IEp6iQAyWWpzWaiPju5noV7XmsStDRU/1sm1p072ZW5\nANo2Hf07AE2zBKnlGdr3rh9Oamhn3eTTzJOBUBHh+WNRNO0t7bjUcgXK7kV2bTKf/5SMWykMeh2J\nGWVYfExYqx2sWHcYY9tkRg43kF3lKaOxKhKAzYlHyC2upndMCDPGdqH+EuodEwJATnHj4OXXJgGB\nEP+fc7ldvLjndb5M/rbZ5eW1Vo4VJ6KUorzWyvHik7/4GA0bwmJb8wFBfSNTHxDkNuhNZ1pzyLR6\netufnPgX/zy5hn8c+ydO9y8b80wuS9MabICcKs/M+HOldhs6UXKKLGvjBr2+EcqwelLYRTUluJWb\nDpZ2GPVGjpd46mpMh+FA43rIrszlr9ufuaj6vBhZ1lxszhoOFybiOMcs/OzKXDakfMM3qZsb/d3m\nrOGTE5+TYc3G4XY2asiKys8EBOebQ5Bblc+p0mQOFhwmryqfstpyLdtgrbZTaq0lKtyC01CFqvHD\nVhRMUU0Jx+rqJ8I3DJ3D98wOlad5Sa377ep/i04BnoAA4HDdXJRjxYkY9UZu6DoJAEOAp2fuVK4m\njXKty87BgkM43U5OlSY3+10q7Fat559Y6gkI6rM/7S1tCTAFknHY0winnhVcnS5Lafb/6/d7qPAo\n4d7hlOX70st3IB1rh2NLiyWnsIodCXm4qwLQme3sOZWJt9nAwtsv49Zre/DK3GG0DfXF0O40pqgk\nthf/QLrVE1RP6n45AAnZSWC0E97JypAeEUQE+wAwql87z3coat2AwNiqexdC/NuKbMXkVOVR67Iz\nucs1TZZvSP6aPXkHuL3PbL5P20JmZQ4vjniCIK/AFh8ju0FDWnyOSVH1AUE7SyQWkx85VZ4bbLXD\nxpvx76DX6bm55x9wKhcmvYkDBYfoHdqDy9pe2qIyVDqqeCP+HTr5d+DhQfficDu13mdqRfp5ty2o\nLmR5wkoAugZ1Zk6vmYR4B2uNUGltGRV2q7a/geH9GBQxgF25ezHpTfQM7Y5epyfbmqvt80D+Iaqd\nNg4VHqVXaHcAvk3bgsNlZ1Izv8PZlFKsOf0VIV5BzAy7rtGyakc1Rr0Js8Gk/e1koadhcuMkpTyd\n7iGxTfZZ/33yqs9MyNuTe4DPTq2j1mUnyCuQstpyTpelMCC8LwDF5Q0zBI2HI2qcNbiVG1+TL/84\n9k+yK3PxMfpoy1PL09kVX8reRE9g1jbcQLLbSaA5iNrKYOzkk19dgMXkh6/Jl5pKLzB4tnWWhGMM\nzSOtIoNLI/qTUZGFDp0WjPmbLRwtOkF5bQXZlbn0CO5KlG80yq1Dp1folQm3zkFiSZJW/+CZu1A/\nufV0WSq6sg4cSMpBdYznsraXMjC8X6Ohn8SSJNzKTW5VHkadkXmX3M0rn8RjKynHJ8YTbCql2Lgr\njf0nC6loewSdlw6zwUTSWQFBXM5+3MpNjLkP6eiIbR+KxTuSk4cSOZhUyL7EAnyjg7FTiN7HyrDY\nGGpNJRzX/UC0fRzDRjn5JscTxBTZirE5bJj1JsbG9ufL9A0ovzLM0cfYXpHPsKpO3DahJ/sTCxjY\nrQ1+3kZyimXIQIjfREp5Onvzmp91/HuqH+8srimhou6xv4bq08cfHVtNZl0Pt9h24ZnODbUoQ1A3\nZOBvttDWL4IiWwk1zlq2Ze3C5qyhylGtZTEmdr4a8IzX13MrN8eKT/JN6uZGs/zrnSw5jVu5Sa3I\noLy2goLqQi11m2HNPm+2IbMuoAn2CiKpLIXF+5aRYc3SZrWDZ4Z7foP0dqhPMJM6j+ea6Csw6Y20\n9YsgrSKDvx3yjDvXp5rrG+Gy2nI2pX7Pd+lbqXJc+MacVJbC1syf+S59a6PhkxpnLQv3vMZHx1c3\nWj+1NFv7/xMlzT8Ol15XlmJbCXaXg9KaMj49tQ69Ts/EmPE8NvgBjHpjo151/ZCBxcdEibUGp8uN\nw+3ks5PreOznhbxQl3qvT6nbnDY6WDw90lOlqfxQ+jkV7TcT4G+gQwdPaz+kczTXD7xMO0a4b5in\njkoM2t9cRe1A6diWuYsntr5OSnk67SyRmA1m9Do9fUN7UumoYu3pjQB0D4mlstKNqg4AwKe8K0a9\nUevh16uf4W/QGThdlsrGXWkcKIznaPEJPjq2mtTydC1Y9TX6UOmoIqsyh+zKfOyVvsxfHkdmQTW4\nTLhtfiSXpfHV/mOs25FKbomVWlMRyhZAJ79oCm3FjYYlduftw6w3oSuLAqBLu0C6dQwCYMOOVBxO\nN73adgQgqI2dcYOj+CZ1M0eKTvDagRV8k/MlPkZvrogaBUCVs5oO/u0w6A3EBHRCZ67FEJxfV/fJ\ndIsKYtZVsWzP3kVIu2oKyyr5JnVLq811kYBAiDqfnlzLR8c/pby2aaP7e2qYQk89K3Vuc9aQX12I\nDh1OdeZlMecbW21OVmUuXgYzACU1pZTWlDW56dRnCPxNFtpZIlEokkvS2Zq5A6PO0xDkVOVh1BsZ\n1X4YPkYfrWFSSvFW/Hu8feh9NqZ+x8H8Q03KkNhgnsDR4hPa8c16E063k6TSFO3Z97Pl1jUAf+xx\nAzO6TsHqqOT9o6uodFRpPd4Ma5b20pz6BqyhG7tPp6N/B44Wn6hLv3sa3+zKPBxuJ7ty9uJWbhSq\nRRMqv03zpPUrHVUUVZewPGEln5z4nAMFCVTYrSSXpTVaP686D6VAuXUcK2o+IMis8AQNCkVBdSFr\nTm/E7rIzPXYS18Zcib/ZQkxAR7Irc6l2eIYH6jME3TsGoRS8ve4oz21Yw/bs3Sjlptxu5Zu6so6N\nGsn1sRO5u//tmPQmDuYfQu9Xgc7LxvQpZnz8PeP5IT7BDO/WBZ29boig1o/swkoKGrz76PIevbAn\n98NR40WZysPLEcLMbtPOLI8aiV6n1xr4HiFdKSy34SqNQLn1VOdFEBsYQ3ZlLhV2KzanjfWnv+Z4\n8Una+kYS5duJ/OoCMkuKMYZngtLhVG5e3/M+yaWeuh3ZfijgecOmSzlx2yy4lcLf18S00Z1x5nTB\noRx8W7AGPz/486y26PQKZ3kQOeleANqwRJGthILqInqGdCM9x4ZBryM60p/wIB/atfHDbDIwdmB7\nxvX2DIcM7O9NgL+O48WJBHkFYtQbCfdpw18vvZfxna5Ah2emYP1chr6Rnsc26/6sDVfsztnHF0lf\nUhS6FVPvnWxM/ZZDhcfOfeL9GyQgEP9R3MrNutObfvXn4Ksc1VoDlFzedCLR76nhbOq0igxqXXat\nx5lVNzY+qv0wBkcMYEjkQADKzwoIXG4XBwsO849j/2zSqHpmphcS5d8ef7OFopoS3j3yEYv2v0VB\ndRFbM3/m69QfqLR7xi8tZgvt/Dzjrx/Gf06Vs5qro68g1Nsz8Sk2MAYvg5kugdEU1fWwTpUmk1SW\nQqRvONB0bFYpRWJpkvZymyNFx7Ve3sCI/gC8ffgDFu9f1uwLhOp/u3aWSC6PGsElYX20ehsa6Rmy\nyLBmU2DzBE9hPqFN9hET2Im/DrqXjv7tSSxNQqEw6gy4lIssaw47c/ZqN/GTJUnUOGvZkb2blUc+\nJtuah8t9ZgJeSnk6J0tPo9d5brE70vdyouQUu3P3aVkUq6NSG+dWSlHmKkbV+OKuDCKnOqfJ0wIn\nMorJqDiTRUgoPEJ8wWFiAjoytMGwTGxQZxSKlLrHKYsravDzNhIVbvFsd7qQYuMp9OgZGjAeQJt0\n2b9Nb67oOJpAL386BXTAiSc1r0PH9+lbtSGXNt4h6HU6OvhEe+rjlIOnP9iL0+YNCnyMPtw4ug+X\nxwxmiG4mHQqmUho/mGPHzmRK2lvaanM3LCY/OljaUVRWgzM3htqEsVRXeBHtH63V59qkTfyQ8RPe\nBi9UbneSEj1BqKl9MnrfStxlETizY3Aba0goOoJBZ2Bs1EjMBs/kRwCLLoS37h/FK3OHMXZAeyht\njzOvEzqfKi4ZUcqJCs97E2L9u1GU6Rly25K5w3N+1mVtakqCyci3EhVuwWwyoNPpeOqWQbxx/0hu\nvro77QMi0ev05FblcajwKE7lYlT7Ybww4gmeuOwhIvzCsZj96BzYCTgTEMTUfTYbzAR5BXK6LA2r\nvZINyd/gZTDjZ/JF71MFJVEMDRvW5Pz9NUhAIP6j5Fbl82PGNr5Obf5NZ0W24kZpf6UUcbn7L5hC\nTylPQ+G5WdX3at3KzY7sOKpbkB5uTQ0zBAkFR3hi54t8kfQlcCaFHBsUza29b2R0e88NtuysVwtv\nSP6G949+wv78BH7Ojmu0LK08A4WivaUdbbxDKLaVkFmXol9x6H2+SPqSr1N/JK+6AB+jNya9kXYW\nT0CQXpaFt8GLsR1GcGldw90jpGtdmWIAT33uyPEc88Ye1+Nt8OZ0WSrVjmp25ezD4XJQaCuipKaU\n3qE9ifSLILHktDaRcES7IQDa8EF9z7uhnKo8/Ey+BJj9ARgffYW2rE+bngR5BZJRkUl+dSEh3sGY\nGozdN6TT6bgm+krtc/38h69SvvVkXUqiUE4jR4sSWbT/TT49uY74wiMs2v0eL639gS0Z27E5a7Qy\nju/kKcfXp7Zo+2z4mFteVQHxSYUcz87BpatF2fxxFXvS9a8dWNEo9b8p/ghK5ybA6ElR/5S1C4DR\nHYZrgUfDek8sTUIpRXF5DaEB3oQGmjGEp2MIz0TvZyXKuwsH9xpRTiNu5UaPnoTDTjbuSqOi2k47\nn/YAGB3+jO4wnNLaMn7K2glAqI8n+JveZwzeej/GdR+It9kISk87c2cuDe+HwaDnj+O6cdu1Pblv\nymAC/Mz8sC8Tp8tN/CnPM/nXxVxNsLENvQL6odfp6yZA6ogO8+w/SO85z9LKM0gsTcLP6MufOt9L\n6kkf3FbPOsYIz3niyIvCkReDcnmCynCfNgSY/RlVlyUAGNAhBrPJgI+XEYuPiTGXtKNt7aX4m/w5\nWnGQgwWHCPdpw41Dh0GNBa+qDmRaszlecpK9WZ5OyKF4HU6Xoku7M3N0vEwGvEx1AUpdJiCnMp+9\n+fEADIroj4/RG6P+zLS90e2HEewVpM0V6ejfnvaWtlwZNYoewV2xOW28c/hDqpzVTIy5mscve5Bh\nPlOwne7F9oR/7zXU5yKTCsV/lIZvlbO7HI0mZQF8cuJzkspS6GBpRztLJOnWTD4+8S96hnTj3kvu\nOOd+k5qZWXy06ASfnlxLsa2EqbETWuHbtEyRrRhvgzfB3oHk1s26r3+ZTv0YeUd/z5hmkJdn/PXs\nIYOjxYmYDWbsLjsldc9l19ucuR3wTLSrclRps669DV5aL1uhKKstJ9ynDQBt6zIE4GmQfE2+XNlx\nNDp0jGzvGVuODeoMwJ68AySWJNHOL5IugdF0CYrmWHEifz/6CadKT3OqNJlAL09D3iOkK+38Ivg6\n7UdOlSXjb7IQE9CJqV0m0M4SydbMnzlRcood2XFEB0QR5d++7pn0EmKDYtDpzqRh+4f1IbHkFB39\nOxAbFKOlpnuFnJmg1py+bXoR5d+eilorI9sNZWfOXk6WnsaoM1KZ2QlTVC1lxnyww/C2Q1BuA7vz\nd5MXspk1p2F//iHSrZl0CYzhiqiRfJP2IxW1nuGWwRED2Jcfz6CIS9ifn0ByUTafrbHTJqoC2oK7\n2h9XYRRdOweT7NzFW/HvMbP7VEa0u4xcWy74Q5CjMxW6g9icNnToGk24A+gSFIPF5MfOrP0c3RmO\n3ekmNNCbE86fMUefeR+Dt7UzxeV2TOVhGENz0dmC+OaIJwMRn1SIKcgL/KGP/6VcGz2ahIIjlNs9\ngWaIdzAA3UJjeO3yZwAY2a2Kn4/kct3g0fh6N25aLD4mLusZwQ/7M/lubwZrtqXQLSqIB27oR+7u\nQZSaDVzdoYqics8wR4+OwaTmWjHbQ9ChI77gMCU1pfRr05tvd3vKGKCLoDK7C2YfO6N6dOGHfd4M\n6xVJqbEP6SoBg8PTYPe1DOZH98/o9G4u79mjUblmX+2pu62ZTi3IHtH+MjqEWbi0exgHUjri3TeL\n9ae/JreiGOX0pntkexLTy+gZHXzOc6idJZK86gJOlZ6mS2C053HKswyKHMCgyAHaZ5PBxOND5gGw\nK2cfcXn7SavIoHNgNGM6jMCgN3DjZcMIUhn069J0f78GyRCI/yj1jXX9W8/quZWbvKoCrWGvT4sn\nlXo+nyg5RZGt+Jz/NOvpslT0Oj2dAqLIqcyj0l6lPap2dnq7OTuyd2tpV/C8YOazk+u1F7z869T6\nRs+Gt5RSiiJbMW18QugSGA140rcF1YUopci0ZuNj9KFNXY8twOyPDl2jDEH9kEDHujfJNXzZSoY1\ni2PFicQGxRAbFKPd6AHu6ncbvUK7a71c8AwXAPgYvWnjHYKXwaxNkLKY/Jjc5RptzL6jf3u8DV4c\nLz6JW7kZ0f4ydDqd1oM9VTcOvy//ID9mbCPQHMAlYX24OvoKhkYOAqCDfzt0Oh3jOl1O79AeWu/9\n05NrWbTvLXKr8smtykehtKxFvVt73cjTQ/+Kr8mHGd2m0LUuQIn0Cz9vnet1eh4YcCcLhjxIO79I\nbRhjUMhQVK0frnLPzbhXaHdu7DGdLgzFWdABV2k4HXw7ao+SXRt9Jb4mX60xCPdpw5xeM3lm6CNc\n3mEEAEdyPE9PlDk9wwMhZs/cBlNpDPddcgfeBi/+mbiGlNJMqvSeYLAkKwjqe8Fmz2/akElvZGjb\nQdhVDdl2Tx27gzOILzlAhE8E/UL64ixqy4mjnh6tu9RTHzUlwfTsFMzQXhGk5lo5dcJEu8LruHXI\nePzNFu7sNwejzkCQV6A236ShtqF+zLg8tkkwUO+yXp5n9tdu81xPReU2SipqUApstS6Wrz1CdmEV\n3mYD0W09gW251U2kb4Q2sfbUCT2Hk4uJ7RDIrdf0xJndlb7mK5jZZwIvzx3GbRN6cPOACbjLwyhN\nD8NW6+SDL1NxZHajs3cv2ge2abZsI9oNwd9kwagzaOfexOHRKFsAvlWdyanKQxkchJs68tdZA3jp\nzqEM6Nr8vgDa103K7OjfgT/1+eM51zuX+nM1yCuQP/e9GYPe81sZ9HomDo+mY4T/L95nS0iGQPzH\nUEo1SqGmVWQQGxSj/Qt89TOjwTNePIzBjdb/5MTnpFuziPBpw9ioUQyJHIhOp6PGWUOmNZtO/lH0\nCOlKekUmJ4tStJn36dYsal32Zm+CAHvzDvLpyXWYDWaGth2EDh2fnPicxNIkInzDsDltbKtL7/6h\n29Rf9J0r7JXY3Q7a+IQyIeZqugV34WDBYRIKj5JXXUCBrYjuwbFaz9igNxBgtjSaQ5BTmYdC0cHS\nDrvLTk5VvidFrNPzY/o2AK6NvgrwjA2D5zW0XYM70zW4M3aXnR8ztuFSLgLqAgKA2/vOxj/AC391\n5m8NGfQG7up3K2kVmXgZzAyvS/3XZw4AZnSdwpbM7QSYA7ij72z86/Y/u+cM+ob1oq1fRKN9xgbF\ncHuf2SSWnGJnzl7258XTxrdp1gLAbDBhNnh6iRaTH/dd8mcOFBzShjTOx8foQ0pOBYtX/0yX4Z1x\nGKx01A0AknAVdcDb14fbR09Fr9NzOqsCR1ofAAZ3jqbWtQ6d05v4ePjbsR34dbeAuZgeIV3R6/SE\n+7bB5vQ04lnWPKAtOh/PRNZuoVGU51SRX1pNt+C+/LHnDbx35P/YmhaHPqgAd603hbkmvIL90FvK\nMVgjKamoIaOgkktizzRQl4YO4seMbViicpkx/Eq+Kf8Qs9PMPZfcRqhPCPN+/plyh2eCoKskEvtp\nhassnIFXhjG6f1tKrbVYbQ7unzQAk9HTGEUHdOTBgXddsO7OJaatP2FB3tqjj6XWWgrrJjwGWczk\n1j1S1ynSnzaBntcCJyQVUuzrA574AGuRP8N6RzBtdGdCA7y5d3pfYuqCh7AgTyDaNiiISwwT2JOV\nz7I1h8kvqWb8kFHMHH7u391sMHPPJXdQ46zBYvb8Nh0j/Lkktg0JxxTmDnoMEalM7zsanU5HZIjv\nOfcFnuGAIK8ABob3b5LFbIkw31Du7HsL7S2R2jDYb0ECAvG7W3XiC1Iq0vE3+XFjj+uJaGYGOECh\nrYgKu5WYgI6kVmSQWu7pXSWWJGFz1pBUloLF5IfNWUOGNQu3cpNcnkqIdzC1zlqSylIw6U1kVeby\nfyc+41RpMsPbDeGrlG9xKzc9QmLpFtyZb9LgSN4J7TEst3KTWp7ebEOSX13IPxO/ANDS8RnWLO1R\nqezKHKrrXru7M2cv4ztdqaXHW6K4xpOyb+MTQqCXP5dGXKJNoKufKxEd0LHRNoFegZ4ejVLodDqy\n6wKb9pZ2lNWWk2HNxmqvwtfozZHiE4T7tNFeNtTevy0AA8P6afszG8x0CuhASnm6liEAT+8nrI0/\nhYXnfiqja3AXugZ3afS3jv7t8TP6EugVwOgOwxjVfih6nV4LasAzln9JWJ9m9zkwvB+9Q3uwLy+e\n/QWH6BnSre77RTa7fj2D3qBNumyOrdaJ3ekm0M8T+H2/LwO7w41f/mXcNaU363d4gkuLtxel6WFU\nVSm8A+FU5pkhmIzcWvITL6XW7iIdTwarJscPU7RnKCLueB7bE3K4fkwXLEYLFfpyenYKJtWvEuUy\n0CkkgoygXPJKqlFK0Su0Bz5Gb+JL96MzKszWaGrReV6Z61dBbqo/S3MOkV1YxYt/voy2oZ7GzFpm\nwlUeij2wEGdQKiUFpQyOGKiN/XcM9+dIZTE6IDTQh6ISTzDdOyYEk9HAIzcNQCnQ68/8JnBm4tvF\n0Ol0DO/Tlg0/pxJoMVNeadfe03/9mC6UVdaydlsKHSMDiAj2wWjwvOHP0MYPcwB4Gbx49c8T8DGf\nCcwHdmv+XnH5Je3YczyfxIwywoN8mD66c7PrNRTl367J364b3omE00XYs2K5re9E+kU2Xac5viYf\nhrYd1KJ1z6V/WO9/a/uLIQGB+F0V20rZlbsXo95IXlU+7x7+kOtixpFXVcC4TmMbRdf1vf3BkQMp\nqSnV3pNeUF2Ij9Gbtn4RDG07iB3ZcWRV5pBpzcbmrOGSsL509O/A/vwEZnWfhpfBzPtHVxGXt5+4\nPM/s4/5hfRjXaSwGnR5vgxe7Mw9SWlOujbvHFxwmsSSJLkHReBu8SCw9zdgOIzlRcgqH20m4bxsK\nqovIrcrjq+RvtcfwsipztMe/nG4n36Vv5oauk7WMwWWRA/E1+VJkKyEudx/jOo1tlImon1BYPyQA\nZx6Zqw8IYgIbBwRBXoFkWLOoclZjMfmRVRfYdPBvqz2eV1JTSpbTht1lp2+bXlpjHB3QkccGP0i7\nJj3zzqSUp+Nvaj4b8EsY9UYeGXw/XnXPo6O78DZn8zKY6RfWm/35CezI3k2g2V/LEDmcbr7dk86A\nbmF0CGu+vA6ni/U7UqmqcRAZ4sfIfm15+ZMDVNocLPnLcGrsLg6c9MyoT0wvA3Tk1fVgR/Vvyzdx\nGRxKLmJwj3Cyi6qI7RBIcnY5u4/m4XIrRvRpy9DekeQUVbF6s2LSpQNJPGZi4y7PxLRF/zyIubs3\neksRg/sEkVZQiasykPbdLIQH+5BRUMn7m07QtUMgfUJ6s6/ggOfYUYPYlFRGB9elhDsHs6eiimo8\nkxSPppZoAUFydjnO3BgMgcV8Xjc2Pqhu0idAxwgLR1KKaRfmR5d2gf+PvfuMr6u+Er3/26d3HR31\nLlmy3LsNxpgaCAZCccAJECAEUphcEwbCAzxJJpDMzcOd5DM35U5IyM0wGQgZmCR0QjM2OLgBtmVb\n7rIsq/d6etvPi61zJCHZkmzJ2Nb6vkE6desIa6+9/uu/Fht3NZLmMic74ymKgnISv5fRXHtBEQvL\n0uHGCoIAACAASURBVPnoQAtvbq2lql4LptJcFi6cl8PC6RmUFnkI+kL88KvLaO8JEjOW8HR1JaXu\n4iHBwImUF7jJSbPR1OHntivLk1mO8SrNTeGShbkEQlEunJ9zUq9xNpGAQBxXXI2zv/MQpSkl+CI+\nfrHzKW6beVPyimwiHOjStvKsLr2WjmAn6+v+ztN7/wRoV6ZXFl2afGxd/5p+SUohJSlFVLRV0hZo\npyPYxbSUYh5c8g+AVj9Q19fA3/ur6cvcJSzPWcrF+QNbdf5x8b1sbNiMN+wj05bO8pylyUrtWWkz\n2Nm6G4DzshaxqfEjPmzcBsC7g9oAmHWmZLOexRnzeevYeio7DtAaaGd++pxks5eYGqPMXUJnsJsP\n6jdzoPNwskHOa9Vv8Y+L7uWNo+9Q2XGAcCyCx5LKu7Xv89CS/0Fb/9azwUVJySYw/csCn84QJAoL\ne0K9WkDQ14hO0ZFjy0rWCHQGu5L1FvPSZw15/khXSvPT5/DusfcpdOYNuw+gpX8KW9YoqdSEwQHO\nyUoU5hkUPV+fdycmvQlVVXnu3YNs3NVEdWMv969ZMOJzN+1p5s1tA7/M1zfX4A9pjY92H+mgtTtA\nLK5iNRvwBiLUtvTR3OnHbNJzxZIC3t5Wx8ZdjdjM2p/QuSUe/MFosrXsqvMLyctwUJDl4IX1Vbyz\noRtfsJ3MVCvXLC/iv9dXoQYd4GjXGtG0qUxPy2dGoZtjLX18crCNzZXNbK5sxp5hhBKIB218fvlc\ncmwdlOS4aOsOsG3nbqxmPYFQjP01XVy5VCsurWrsId6bRoE9nzpfPXaDbUiGK7EFsTTXRWmui427\nGpld7BmSpZkMBr2OomwnVQ3a/7vVTf1Fii5tz39euh2nzUTQFyI/00F+pgNIR7XcSoErf8zvoygK\n37xuDo0dvlMuwPvqqpmjP+gcIQGBGFEgGuDpvX9iX8dBbph2NSlmF53BLna07J7YgKC/Gc1Mz3Qy\nrGnE1Digsq1pB+/VbuSS/BWYBjXMAUi3pFHsKqSirZKPmneiog5ZZih05rOJj9ja9AkKSrJAZzCT\n3sgVhZeMeEzz02cnA4LpqaXU9tVT19fIVcWX44/46Q33JdfwE8sB8zPm8Nax9cmr9jJ3Cc2+lkH9\n2wu4c9aX+e3uP9Doa2ZGahnlqaW8Vv02/175x2TR1Ib6D1FVFRWVfZ0HqfNqQVDuoPXxwT9rujUt\nue6ekNLfsrg71EOOPYsGXxPZtkyMeiMeizv5We5p34fNYGVaf7HiiZSkFPK/L/lnjLqR10N//udd\nKMAT35qc/dEjme2ZwfKcpcxNm5Xc071pTzMbd2kZkX3HughFYsntYAmqqvJ+RQM6ReHRryzm9S01\n7D7SQX6Gg/o2Lxt3NVLX5sVk0HHzJdN49p1DVFZ30tIVIDfNTqrTzMLp6ew41MZz7x5Cr1NYNjOT\npg4/je0+8jPs5PVnJlw2E7NLUqms7sRhNfLQlxeS7raybGYmezpd/Of+GtbVvg/AsmKtFuTzywpY\nUp5BMBJj274W3tiiYrQVYgll4bSZWDFXu1LNcFtZOS+HJTMyeH59FQdqu4hE43T0Bjna2EuG28oX\nSq/kN7v/g8VZC4ZseZs3LY0L5mTzuSUFpLnM7D/WxeeXFUzyb2xAmkurEQhHtK2kqU7LCR8/uBp/\nrIqynRRln77193OBBARimEgswv+p+H2yUr8l0EacRAvZ+hM9dVziapyDXVW4zSnaYBRF4UvlNwBg\n1Vt469h63qx5j2tKrsSoM9AR7MKit2AzWpNrmR81a6nULPuggKD/SkJ7vRuT66ZjNSdtJjpFlxyC\nc8/cO/BFfBS5tD+YsXiMf/zg+/3jXENY9GYKnHmY9abkONcyd4lWGdzfGj/fkUua1cNDS9dS1X2U\nmall6HV6mn2tfNy/V/mygpVsqPsw2fzmaE8tR3tq8VhSSem/6gdtfdJpdNAX8VLyqewADGQIdrft\nZX3t3wnHwsmq50SGoKKtku5QD8uyFiUrmEdjOk5RpS8YSQ7NCYSiWM0T/2dl675mDDodC6enY9Br\nmRy9Ts8ds7405HHbD2o9/s+blclH+1vZV9PJoulD15lrmvuobfGyaHo6ZfkpfOem+ew/1kVZfgo/\n/sPHVB7VgrMbVpawZEYmz75ziA93NxGJxslO0zIgly7KZcehNnzBKKvOLyQnzU5hloNt+1o4b9bQ\n5ZYrluTT1OHnnmtmkd5f+GY1G1iYOZv/OmRKBoO5Du1EryhK8nH5lzhQFHh9s8KMkqH/Hxv0Ou6+\nVsvu7KnuYP2OBr73u63JVsXzStOYmz6L+xd9i4JPZXasZgPfuG528vtvXn9616sTGQEAl92E0SAb\n3s4EEhCIIVRV5b8Ovsix3jqWZC5ge+suOoPdQ1rTjrT/P/Hcur4G8p25QxqlHE99XyO+iF+rzP9U\nqvKygov4oGEL7xzbwPaWCh5e+h06g13JbniFzjx0ii45iCexPx6gwJHHDaVXMy2lOLnFbTzsRhsL\ns2dzpKOWTFs6OkU3JMWt1+lJt3po9bcRiUfItGVoKXl7NjW9tZj1JvIduUP69SfWt816E3MG7Rtf\nXfYF9nUcJMuewU1l15Frz8FjcfO7Pf/JrrZK/NEAS1KHD7nJtGXQ1+MdscgrMdQoscxR5i7h2pIr\ngYGAIDH/YGnWwnF/Pp/W0DbQaKepw8/fdzdiMui59YrRq/lPpKXTj9NmpC8Q4XevamvvaS4z37ph\nLmV5Iw9u6uoLJdP6H+1vZVdV+5CAYPeRdl7cqC2VXLpIO0nqdEpyvOz5s7J4+cOjZLqtXLO8EKNB\nT3mBO1k4mKgun13sITfdTigc4/oLiwG4eEEu4Uiczy0ZmtqeX5rOH35YMqz40qQ3MT99drI/wvGK\nIm+8aBp2i5HyAvdxP6vZxR7W72igozfIvGlppLstXL5YO47yTxV1ngk8roGMQNqg4EB8tiQgEEPU\n9tWzrXk7Rc4C7pj1JQ53V9MZ7MLQfxUZV+M0eJsoSSnU+rqravIKc1dbJf+38lnunnMb01NLeaP6\nHa4uuYJYPM5bNeu4dtrnCccivHvsfW4ouzq5XDArdfiJw2Gy8+iy+3mp6nUq2irZ2baHUCycPKGZ\n9CbyHTnJXgGD0+iKovD5ostO6XP4xxVfp7m1+7iBTZYtgz1+rclLIljI7R+OMy2lGL1OT17/FZ9B\nZzjuzokUs5N/Wv4QRp1Rq8LOXQZAkaswuU9/8ElfVVVe3FiN2aqdHEpHSPcngiabwco9c28fsnZs\nM1ix6M0EYyE8ltRhTW1ORkPbQH+F6sYeNlY0YjDoWHNZafJqfiziqopOUVBVlb9tPcaLH1Qzd1oa\nC8q0NeBE4d5P/7SD61YUc8nCPFz2oVmLzr4QHqeZabkunDYjFVUdxOMqOp3C3qOd/OLP2lLQ4vKM\nZBAw2EULcjlU380NK0uShWhrvziPDyoa2FfTxbKZ2p59naLwvdu1HQsWk/Zn1G4xcsPK8QWgiToI\njyV1yJTBwXSKwlXnDc8EDTa3xMP5s7Moy0vh8sV5k14LcKrsFgMmo45wJI5nlOUCcfpIQHCO6Qn1\nUttXn5w3Pl6JrXYr85b3rzmnUt/XMGT98UjPUfZ2HODDxq1Y9Ra+u+R/4DDZkwNfGr3N9Ia9fNi4\njUg8SjQeZXvrLvoiXnwRP9U9x8i0pbO/f2vejOPsC0+3eliZt5yKtkoq27WT7+DGOcWuImr7Gvqv\n4Ce2c5fFYMZuPH6BnFbYlwgItPdOpHwT++ytBguzPOVYDJYTpuU/XQMAUDIoIBhcNNjc6eeNLcew\n2tO564t3kj9CAaC2h/lO8h15pFmHdlNTFAWPJZVGXzMX5p4/pkzOaOoHzWjfXNmMilbp39juG1MD\nlUg0xrNvH2Ln4Ta+f+dSKqs7+Gt/85rKox34Q1o//a9/YTatXX6eekWbTPfqphqWzszkuhXF5Kbb\nCUdieAMRCrMc6HQKi8sz+KCikR2H2lgyI4NXNmm7VB6+dREzi0buMpfqNPPQLUPXqx1WI9deUMy1\nFxQPud1mGf/+8k+b5Skn256V3PZ5skxGPd86zWn/U6EoCmkuC00d/iHZAvHZkoWbc8zr1W/z291/\nSA4hGSwcDfNvFb9nR3/B3EgGj4cF8FjcRNUYLb5WTP0FZa9Vv82bNesIRoO0Btr5973PEYvHkn0B\nOoLddPSvi37csjP5fnva91Pd/5gdrbuo7j5KviN3xBNiQq5dO8kmgo1EURwMbLdLt3rGvA4+UYYW\n9mlXmudlL+ay/JWszB0YC7t24df5+tzbx/36iZ/NoDMMqfqva9WuxgM+Pbt3Hv+f74KMucOCgYRc\nRzYmnZELcpYd9/mBUJReX/i497d2+ZMDlgYvGdQ0D6TFjzWPPjVSVVV+8efdfLinCV8wyrZ9LWzZ\n24Jep3DN8iJUFY409JLhtpDptjK3JI1/uXcFX7mynCyPjW37WvjnZz6h4nA7Xd4QoJ3UAa46rxBF\ngdc213Cgtpuq+h4WlKYdNxj4LBh0Bv7p/O8ma2emkkQgIEsGZw4JCM4xiT3nn56UBlDTXc/+zkP8\n6cBf6A2P/Md6eECg/fFUUSl1lyRH0Ra7CvlfK3/I/PQ5HOqq4r26jTQM2uPe2T9MKDEu9gslV6FT\ndJj0JrLtWdp8ezU2atc4l8mB3WgjEo8MOR4gWVmeY8sa8bmTKcs20P42kSGwG23cXH59stPZqSh2\nFaKgUOjMH5KdSQQEVrOBTZXNye9HUnm0gze3HiOuqkNuv2XGan5w/ndP2CDpN69U8th/fDRkgl/C\nJwdaefSprWzb34KqqjS0ecny2HBYh14x14whIGjq8GsFfXkp6HUKmyubONrUy/T8FK5cmp/cCz+n\neCC9b7MY+NySfP75nvP45vWzUeMqv35pDzVN2vslUtDZHhvnzcqirtXL/35BW6f/Qv96v/jsefoD\nN8kQnDkkIDiHqKpKs1+rsk5ULoPW0Mcb9iUn/gWiQV48/MaIr9Hqb8NmsCbT5YNPwGmWVGZ6ykk1\nu/nGvDuwGCzcNvMmDIqeN4+uS06j6wx20R7sxKQzkufIochZwFXFl3Hv/K/x7flf4/xB3eJGCwgU\nRRmy5W7w8aRb0/jGvDs/k8FDmbaBIsZ0y8QPGnGaHHxr/ldZXXI9lUc7klfjtS1aAHDL57QU8/sV\nDSM+/40tNfz8hV38+f0j/H1X45D7rAbrCXdeRKJxDhzrpscbTr7fYOs+0XafHDjWTWdvEF8wSn66\nndz+CvxUpxm9TqGmWdtjXlHVzn2/2Mie6o5hr7WvRvv/dOX8HGYWupNtbRdOzyDFYWZmofb7nl08\n/HgVRWH57GyuuaCIWFxlxyEtmE0ddMX5hRXFmAw60lIs3HX1zCET6sRna3q+O9mXQJwZpIbgHNIV\n6ibcv+2to7/DXbOvhZ/v+A0r85YzLUOrOjbqDHzcsgOzwcSXpt+QTLfH4jHaAh0UOfOTRUmDU/Sp\nllS+PGM1sXgsOT7WaXKwMHNeslJaQaEn3EsgGiDN6uHhpfcB2sCYRHW90+TglSNvYtAZKE0ZvQgr\n15GdbKIzOCAAjtveVvt54oQj8TFvg3tvez3NnX5uG0N1vNPowGqw9Bc6Hr/6+1TMS5/Nn9+v4s2t\nh7j7mlmsnJ9DXWsfHpeZFXOzefnvR9m6t5kvXVqG2TSwZLK/ppO/flBNqtOMPxTlL+8fYXF5Bk7b\nibu8RaJxorE4TR1+ojEtuDtY253sFQ9aAeGheq2pTE1zL8f6r8rzMuz0+owcqu9hen4KLV0B6lp9\nRGNx/rb1GL5glN++spfPLcmjoc3HV1fNxGU3sa9GC1LnFHsIR2Ls7f9+YX8h4RcvmcbfdzWdsLlM\nok6h8qgWcCSuPEFrdPPL71yE0ahDd4YX2k01F87LZvmcrHEVnorJJb+Jc0izrzX5daKJz6GuIwA0\neVvo8Gu3fXX2reQ5cviwYSuvH30n+ZyOYCdxNZ7shAdDT8Aeixudohs2S35l7sC88TJ3CXE1TjAW\nIs3iwaAzDEl5g5Zun58+hwtzzx/T4I/E0BqDzoBzHOn4v35QzUNPbiYQio762E8OtPLcu4d4b3s9\ngdDIExEBDtd3s6uqHUVROC97CUsyF05q/UJiHf7P71fR3Omn2xumIMOBXqfjovk5BEIxNldqe+Tf\n+aiWo029vLa5BtCq429cWYIvGOWtQV35jue3r1Tyvd9tTV61w0Cf/kAoyosbq3nm7YOAtge+oc3H\n9oPaBL7ibBe56drvpjQvheJsJ9FYnK17W6iq7yHVaSYQivL65mPsPNzO5spmorE4B2q7yEq1kpZi\nSQ7nyUmzkZmqZRtKc1O46+qZmIzH/4wL+7vuJX5vn25yYzbpJRg4AymKIsHAGUYyBOeQZl9L8utE\nhiDR/78j2ElHQDu5T0sp4sHF3+axLf+LTQ3buKb4Cpp8LcllhuMFBKnmkdOtZe4SCp15ROMxSlKK\nklfzx0tLK4rCt+Z/dcw/V2LJwGN2j6sqfk91B4FQlI6eYH8L1OFe/ns173xcRygyEAT0+ELHfc3/\nfOsgHT1Bnnzw4nEXgjV3+jEZdONaM00U7PX5I/yfv2rFmQX9V8QXL8jlb1tr+dO6w2zY2UB9mw+9\nTiEWV5lb4qEkx0V+hp2XNlaz+0gHay47fiV7tzdExeF2VODNbVrhp8Wk53B9N3FV5cUPqnlvh9aU\nKs1lYd40D+9XNPL21mMYDTpmFacSj7sJRWJcPD+Xiqp2Pqho5D/+pu3EuO2K6QTDMXyBCC+sr6Li\ncBvTcl0EwzEumKv9f5LutvL1L8wiK3Vs7Y8TUp1m7BYDvqAW+HmkSE2IkzKpAYGqqjz++OMcPHgQ\nk8nET37yEwoKBtpjvvzyyzz99NO4XC5uvPFGbr75ZgB+97vfsX79eiKRCLfddhs33XTTZB7mOSNR\nP2DQGWgPdvWPC9ZOzt2hHlr62tApOpwmBzpFxwU5y3i39n2e2vOf7O88lGxLO7iC3mqwYDVYCUQD\npFpGrs5WFIX7F90LqHzcv3QAWs3BRMh1ZGFQ9EO6EY4krqoo/ccTisSSfeV7/SNXy/uDEd76qBad\nojAtx4XJqGf/sS66+0YOCOJxldYuP9GYSq8/kpyKdzyqqvLWtlpMRj2LyzP40R8+xmY28D+/fv6Y\nljH6/GF6fGHmlHiIRGLJVH3iitjjsvDdLy/g1y9VUt/mY960NKobe/AFo3xhRTEARoOe6QVu9h7t\npKXLz5tba1k5L4ey/BSC4SiBUAyH1cDHB1pJlB5qtxmZX5rG5spm/r6rkfU768n22PjW9XNIS7FQ\ncbid9ysaCYVjLCxLT7YHTmzNO29WJq3dAV7aWI3bYWJB2UCHwU8OtnG4oYd3P9ZqEeYOqg9ItOUd\nD0VRKMxysv9YFyajLjlfQAgxPpP6L2fdunWEw2Gef/55du3axRNPPMGTTz4JQFdXF7/61a945ZVX\ncDgc3HXXXaxYsYL6+np27tzJ888/j9/v5+mnn57MQzynNPtaUVAoTSnmYFcVtX319AzaTVDTU4/b\nlJK8yr4w93zerX2f/Z3agKFEJf+nm+ikWVJp8AaTLXFHYjH0VwwPLkKcgAE2oBXB/ePie4e07/20\nQCjKo09t4ZKFeXzx4mnUtXhJFNf3+SMjPmfL3hbCkTg3XTKNay8o5v2KBi0g8I4cQHR7Q0Rj2ot2\n9ARHDQjWfVLPn9/XlmzW76gnFI4RCsd4ddNRvnz56HUKiexAUZaTG1aW8Md3DrLzcDvTB3Wsm1GY\nyuNfW8axlj4WlqXT1ReitSswpKvdnGIPe4928ttX9nKsuY+9Rzu57crpPPXqXsKROA6rEZvZgKLA\ntFwXRxp6Kc11MaPAzebKZv7zLW2Z4PbPlycLwIpzBgrBEo2DBlMUhetWFDO3xIPFpB+SGl5QlkZV\nQw/bD7WRn2Fn3ikOnwFtWM/+Y12kOi1nfFMeIc5UkxoQbN++nYsuugiABQsWUFlZmbyvrq6OWbNm\n4XRqf1jmzZtHRUUFBw4coLy8nG9/+9v4fD4efvjhyTzEs1Y0HqU71JPc8qaqKs2+VjKsaWTZMjnY\nVcW2/kE7qWY3XaFuVFUldVABXIYtjblpM9nfeZhvzf8qL1a9QXewh4xPNfm5sewauoI9w2oBRpI2\n6PUnKkMAo89hr23po88fYeveZlZfVMLR/gp3GDlDkBhwo9cprJyv7fN327Wgpts7coYg0a8foKM3\nyLTc4QFKPK7yzNsH2Hu0i86+IC67iWhUK9QryXHhDYR59+N6LpyXc9zRvAkN/RmOvAw7RoOOr10z\ni7tUddgJz+OyJJchBn+dMLtY+z0k6hE6eoP8n7/uwaBXWDozk4rDbXgDEWYXp3L1+UX86wsVzC72\nsHRmJkeb+wiEopRkO4dU+uek2ZKd5haUpXM8gwsSExZOz0g2Hrr98zMmZB25MKs/a+KU5QIhTtak\nBgRerzd5wgcwGAzE43F0Oh3FxcVUVVXR2dmJ1Wply5YtlJSU0NXVRWNjI0899RR1dXX8wz/8A2+9\n9dZkHuZZ6d1j7/O3mnX80/nfJdOWQYu/DV/UT6m7JNmQJjF57/ycJbxV8x4wvA7grjm34Y/4SbN6\nKE0pxhcJDCsaHM90w9Qh2xQnJkMwFonlgfaeIK3dgeSedBg5Q1Dd1EtDm4+lMzOTV/pup/bfrk8F\nBLuPtNPtDTP4NNzRE+Sj/S3sPNyO0aBj9UXTSHWa+csHR9i4qwmH1Uh+hoM7r5pBOBLjtc013HZl\nOR09QX75l9388Z1DPHLbohNezdb3twQeHDiczNVvfqYDl81Irz/CFUvz2XGojc7eEHd8fgYXLcil\nqr6Hv3xwhOtWFDOjMJX/75vLyXBb0Ot03HnVyK2N9Todn19WgKLX43aM7yScm2bjgjlZpKVYT9if\nfzyKsrXAI8Mte9qFOFmTGhA4HA58voEuZolgAMDlcvHoo49y33334Xa7mTNnDqmpqbjdbkpLSzEY\nDJSUlGA2m+ns7MTjOfHJJSNjau1lrdtXR1yN00UHczKm8efqlwC4onyF9oAqbYTxvKwZXDJ9WTIg\nyEnN+NRndbyvT57L7CAaj1GUmzn6gydIp2/gpF/b7h/STjeqgs5koLHdx7xS7Wr25U01AFxzYUny\n89CbtUAoEInz1Eu7OVzbzc++cxH//fttNHX4uHpQ61p/OMY766uS9QbFuSnMKvHw1rZa8jLs/Ov9\nl2Af1Kjn4mUDGY6t+1vZtreZV7YcY2aRhxXzctCPcJXc2h1Ep1OYNyMz2Vf/ZF2ypIC/VzRw13Vz\nWROIUNfSx/mJMboZTi5YNDCQZ6z/lr5108kPRvre3ctHf9A4ZGQ4+d5dy5hekJqcFHimmWp/o06F\nfFafjUkNCBYvXsyGDRtYtWoVFRUVlJcPXGnGYjH27t3Lc889Rzgc5p577uHBBx9Ep9Px7LPPctdd\nd9HS0kIwGCQ1dfTU86cniZ1rAtEgBkWfvHqv6dQqvg81HyNdyWTjsW1k2zIpMZdS79Ua0egVPauL\nr8MQHLhqMsetk/5ZXV9yNXHik/o+B2u7+KCika9dMxOjQc+Ruq7kfe9uO0Z9Sx956XYa2n20dvh4\n8s8VfLS/lX/66lKKspx8sKMeu8VAvmfg84jHVRQFWjt87KvuoKsvxI69TTS1+1CBD3YMjH7eXdVG\nd1+I0jxtzX334TZaO7Qg5NbPTcfvDeL3Bkc89psuLmHnoVZe3VjNq1TzuSX5fOXKoVmYWDxOTVMP\nWalWurv8p/x53biiiOuWFxIOhDEC07IcE/L7ychwnjH/9sqynaiR6BlzPIOdSZ/TmU4+q7GZjKBp\nUgOCK6+8kk2bNnHLLbcA8MQTT/D6668TCARYs2YNAKtXr8ZsNnP33Xfjdru59NJL+eSTT7j55ptR\nVZXHHntsyhcJxeIx/nnrz5jpKefO2V/GG/EliwWbfa180LCZuBrnquLLtTG8tiyKXAUszVxAll27\nSk/sFHAfZ+vgRLog9/g98k8krqps2NGAQa9w/uys5BS5kWzZ28zWfS1cuiiP8gI3je0+MtwWVBWq\n+qvxL12Ux5/WHaLPH07uUX99cw1XLC2gxxfmkoW5Q9avdTqFFLuJ+jZfsnfBh7ubktX3vmAUg15B\nr9clO/gtLEunxxumurGXHm8Yg15hev6JP+P0FCs/uHMpje0+XttUw3vb6/EFI6Q6zJw3K4uibCfv\nbW8gEIqxYs7ELLtoe76n9r8jIcSJTWpAoCgKP/rRj4bcVlIy0Jlu7dq1rF27dtjzHnroock8rLNO\nR7CLnnBfsqdAk3eg30Czr4WOQAcGnYFFGfMAMOqNyQ6BCRlWD7V9DaSaJ6er3qkKhqP87tV9VFRp\nMxhe2ljND+9adtw9+96AdsLu6gvR5w/T64+wIMdFfqaDt7bVctuV5Vy2KI/XNh2l1x9JFgruPNxO\nc6d2xX3erOEzENwO85Ae/Fv2Ng+5Pz3Fil6nJAv+irNd1Of52LavBW8gQnl+ypjS+/kZDvIzHBRl\nO/nJM9vZulf7nb65rZbSXBcN7T7sFgPXrywe9bWEEGIiyIbds0CiyVBnsItILEKTb+Ak1RpoR1VV\nSt3Fw4oBB8uxZ9PgbUpO5jvTvLTxKBVV7cwqSiUtxcKHu5vYebidyxfnEY3Fh51kvQGtZqCrL5Qs\nKMzNsLP6oml84YLiZCtfp81Ec6efWFzFZTfR6wvT1OHnvFmZzBihoE0rkBsICBLNbhKNbzLcVhRl\nYAdAUbaTxg4tIAAoLxxfwJWVauNf7r2Ajt4g7d1B3q9oYM+RDlTgzlUzRm03LIQQE0UCgrNAW0Dr\n0a6i0hboSE4VLHDkUtdfL1DmnnbC11hddi3Xzbkcp3rirW6fhR5fmA8qGkhzmXngSwvo6A3y4e6m\nZNvcF9Yf5iffWE7GoGIxX39A0NkXxGTU0v556XZ0OmVIX3+nzUhDu5b0v2BOFnNKPGSkWMnysLrg\nLwAAIABJREFUjNwNz+0YOAFbzQYCIW2ZYMXcHN79pC65LAGQnmLBYTVSljewRHAyVfNWsyGZMVg4\nPZ227gAN7T4WTMD+fCGEGCtpJH0WaA8OTIlr9bfR5G1GQWFB/xIBaO2DT8RpclCefuKg4bPyzse1\nhKNxrl5ehEGvI9NtJcVh4lBdN+t31BONqVQ39g55zkgZgrz04cHO4CvsTLeVuSVpxw0GgOQWOpvF\nkDwh56bZWVyu7U4oyXGRlqItYxT377EvyHRgNGjDcyZiml6G28rCsvQpXzsjhDi9JENwFmgPDAyb\nafG30ehrIdOWToFTa6ijU3SUuE7cuOdMs+6TOqxmA7OLPazf3kCK3cRF87VtcIqiMKPAzUf7W+nx\naU2FWgdV2quqii/YnyHoDeELRFCA7LThJ3qnbWAZJWMM29Hc/Y1tSnJTKM5xsXVfCwVZDmYUpvIv\n915AWoqFnf1jdqf1BwQGvY5V5xUSjY19sqIQQpxpRv3r1dbWRkbGiXvIi4kVioXZ13GQhRlzURSF\n9sBAhmB76y4C0QBz02aS3b+DoMCZl2wdfDZo7fLzp3WHAa0vfygS49Yrpg+pE0gEBAktg7oEhiKx\nZAvhrr4gsbhKutuS7Kc/2OAMQUbqGAKC/iWDkhwX86Z5+OsHOhb09y5IBBSLpmfwjetms6R84N/F\n6ovPzOyLEEKM1agBwe23305RURGrV6/miiuuwGgcfVytODVv16zn7WPruX/Rt5junkZHoJMsWyZt\ngXYavE0ALMqcT5rFw7UlVzItpfizPeBx+mBXY/Lr2lYvpXkuVs4fOtQmsRZvNumJROK0dgWIx1V6\n/WGisXjycYm5A8dL1bv6MwSKok3pG82solQ+tySf6y6ahhGVpx66dNhjdDqFC+Zkj/paQghxNhm1\nhuDtt9/mm9/8Jh9++CGrVq3ixz/+MXv27DkdxzZl7es4AEBboB1vxEcwFiLblpHcIWA1WJidNgNF\nUbim5EpmekYflHOmiMbifLi7CbvFwLeun0NJjpOvXjVz2Lz6nHQ7C8vSueb8QtLdFlq6/Lyx9Rj/\nz5Obh7QlTshNt4/4fokMgcdpGVPPfKNBz1euLCd3lDkDQghxrhnTgufSpUuZN28eb775Jj//+c9Z\nv349Ho+HH/7whyxcePLtS4WmwduEWW8i3ZpGX9ib3DnQHexJ1g+kWT3EidPqb2dBxlyMYxg0dKaI\nRONUVLUTDEXZebidPn+Ezy8r4PzZWZw/e3gvAACdovCdm+cDcLihh8rqTjbtbiIWV9lfq3UlVBSS\nFf95xw0ItAxB5hiWC4QQYiob9ayyefNmXnnlFTZv3swll1zCz3/+cxYvXszBgwf5xje+wcaNG0/H\ncZ6z4mqcX+54CoCHl32Hmt7a5H3doZ5k/UCGNQ2z3sSe9v2cl7X4MznWk7VlbzN/ePNA8vuiLCdX\nnVc45udnpdqopJPWbq2OIJEhyEq1JZsMHS9DkJZiQeH4AYMQQgjNqAHBr3/9a26++WYef/xxrNaB\nq6wZM2Zw9913T+rBTQU9oV58Ue2k9tTuP+AZNC2wa1BAkGb1cF72YmakTmd66pldwLavppN4XGXu\nNG3bXn2r1ub3xpUlzCnxMC3XNa4tdZ++uq/rf72CTAfNnf7j7jAArbPg9+5YQs5x7hdCCKEZdVH1\nqaeewu/3Y7VaaWlp4Ze//CWBgHaldtddd0328Z3zWvzaFja70Uajr5nKjv3YDTasBgtdoR6a/Vql\nfZYtE4vBcsYHA5FojF+/tIdf/mU3Tf3Dfpr7twxesbSA0ryUce+vz0odejJPFBUWZGrr/Blu64g7\nDBJK81KwWaQYVgghTmTUgOChhx6itVU7KdntduLxOA8//PCkH9hU0dofENxUdh1fn3sHc9Nmsark\nc6Sa3XQHe2jxtWLUGfFYJm4GwaG6bt7YUjPs9l1V7fzp3UMcbeoddt9Y7T7SQSAUIxZX+eM7h1BV\nleYOPy67CZvl5OoesjxahsBuMWA1D5z4i7OdKAoUZEkBoBBCnKpR/0I3Njby29/+FgCHw8EDDzzA\nDTfcMOkHNlUkMgTZ9kyKXAUsytS6D+7vPESjr5lGXws59ix0yqk1lezsDVLXEaAgzcrfth5j95EO\nVszNIbW/EY+qqvzXusO0dgdYt72eu66eycULcsf9Pome/nkZdvYf62LHoXY6eoJMP4mWvgnpKRZy\n0+3MKfZwsLaL2v4lg2yPjYe+vPCEnQeFEEKMzahnGUVROHjwYPL7I0eOYDCcPRXuZ7pWvzbdL9M2\ntPlTav+Y4pgaSzYgOhX/vaGKx3+/hW5viO4+bfJfjy+UvL+pw09rd4DSXBd6ncL67fXJ+17aWM2/\nv7HvuK+tqirPvXOIX/x5F7uOdJDtsXHnVTMArS2xinbyPll6nY7/+fXzufWK6cm2wQB2q5FZxZ7j\nTkQUQggxdqOe2R955BHuvvtusrK07WFdXV389Kc/nfQDmypa/G24TE6shqEnNbd5oNFOtm3krXnj\n0dDmQ1WhoydId3874N7+/wLJscOXLsrj4wOt7D7SQVOHjwO13by2uQaAG1aWkJ5iJRiO8retx/jc\n4nxSHGbe2HKM93YMBBDnz85iWq4Lq1nP4foe7WeYoKv4RLdAvU7BYhp9zLAQQoixGTUgWLFiBRs2\nbODQoUMYDAamTZuGySQjWU9VXZ/Wa6Az2EWpu3jY/anmgRT7qWYI4nE12fq3ozdIX38g0DM4IDjc\njqLA/NI0dDqF3Uc6+OM7h5ITB0GrPUhPsfJBRSOvbz5GNKqydGYmL22sxuMy840vzKa21cvF83PR\n63TMLExl52Et0JiogCCRIXBYjTL8RwghJtCoAUF1dTV/+tOf8Pv9qKpKPB6nvr6e55577nQc3zkp\nFo/xq51PEYqFUVHJsg2fFeG2DGQIck4xIGjvDSYr82tbvPT38klmCHp9YY409FCWn4LTZmLR9HRM\nBh37j3VhNeu5YeU0nn/vMIfqulkxNydZJ7CvppNoPI4K3HnVDGYUpjKjcGDb5Oxiz0BAMEHb/jJS\ntAyBwyq7BoQQYiKNWkPwwAMP4HK52L9/P7NmzaKjo4Pp08+eVrlnopreOvzRADE1BgyvH4CBGgKd\noiPDmn5K79fSOTApsKZ5YAdBr0+bGLh+Rz0qsHSmFnhYTAauXFbAtFwX379jKZ9bkofFpOdgXQ8t\nXX5qmrXGQLWtXj7a34rVrGd2sWfY+84p0W7T6xTSUyZmnT/drb2O/SR3LAghhBjZqH9V4/E43/nO\nd4hGo8yePZtbbrmFW2655XQc2znrQOchAEpchRztraXYNbxrX6KGIMOajl53amvlzR0DAcGx5oE5\nAL3+MP5glHWf1OOwGrl4/sCugpsuKR3yGmX5KVRWd/LOx3UA5GfYqW/z0esLs2xm5ohzArJSreRn\nOLCY9WOaIzAWGW4rTpuR/EzZaiiEEBNp1IDAarUSDocpLi5m7969LF26lFAoNNrTxAkc6DqMgsK3\nF9xDOB4eUkCYYDFYuLzgInLsp15Q2DwoQ+ALRpNf9/rCbNhZjz8U5aZLpmE+QZHejAI3ldWdbNjR\ngMmg49YryvnZf+0EYGHZyBkMRVH4f29fPGxw0akwG/U88c3lQ0YlCyGEOHWjXrZdf/313HvvvVx6\n6aX88Y9/5Otf/3pyx4EYKhaPsbXpE8KxyHEfE4gGqOmto9hVgM1oHTEYSLhp+nWsyD3vlI8rERB8\nujFQry/MrqoOdIrCZYvyT/gai8szsJkNzCpK5YEvLWBGoRuH1YhOUZhXmnbc51nNhhMGGifDZjFi\nNExMxkEIIYRm1AzB0qVLufHGG3E4HDz77LPs2bOHCy+88HQc21lnZ9sent3/34RjYS7OX5G8vaJ1\nD+/WfsAds9bQ4m8nrsZP68ji5k4/qU4zqS4L1Q3aNkCdotDjC9PZFyTLYx21i2BOmp1/e+DiIbd9\nddUMfMGoFPgJIcQ5YNSA4IEHHuDNN98EIDs7m+zs7Ek/qLNVi09r8dwaaE/etq72A16qegOAHa27\nCca05Zby1NLhLzAJguEoXX0hZhWl4rCbkgFBdpqNxnZt1sDckpNbj18y49QbJgkhhDgzjBoQlJWV\n8W//9m8sWLAAi2WgUnzZsmWTemBno/ZgJwAdgS4Aanvrebnqb6SYXPSEeznaW0soGkJBodBZcFqO\nae9R7VgKsxwoei11r9cp5A4KCPIyZDSwEEJMdaMGBN3d3Wzbto1t27Ylb1MUhWeeeWZSD+xslBhV\n3BHsJK7Gef7QS6iofHX2LfzpwF842lNLNB4h15GNxWAe8+uqqsoL66soynZywZzxZWjer2gAYOW8\nHA40aFsOXXYTKfaB98/PkIp9IYSY6kYNCJ599tnTcRznhPbAQIZgT/s+jvXWsTRrITM8ZZSkFPFx\ni1aVX5JSNK7X9QYivPNxHcVjCAiaO/3sP9bFpQtzaesJsvdoJ2X5KeRlOGjr0xoRuR0mXPaBdX/J\nEAghhBg1ILjjjjtGbBErGYKhQrEwvWFtj38wFmR3uzYMaGXucgCKUwoHAoIR+g6cSHtPEIC27sCo\nj31jcw2bKpspynKy87A2SfHShVp/gbT+Ln8pdjNOu9Z+2mTQJbv/CSGEmLpGDQjuu+++5NfRaJT3\n3nsPl8s1qQd1Nurozw4k7G7bi4JCgTMPgGmugazAeDMEHf0BgS8YxReM8Na2WkpyXCwuzyASjaPX\nKeh0WtDW0h80HGnsofJoJ3qdkiz+y0nXMgGZqVZSbFpAkJtuTz5XCCHE1DVqQHDeeUP3wa9YsYI1\na9Zw//33T9pBnS2O9dbhjfiZkzaDtv76AZfJSW+4D380QLYtM1krkOfIwagzYtIZyRxnK+JEhgBg\nf00Xb2w5RprLTHmBmx/8360sLs/gzlUzgYHgYe/RTmpb+ijNS8Fs1IoJc9LtPHLbIvIyHLT2DzuS\n5QIhhBAwhoCgsbEx+bWqqlRVVdHd3X2CZ0wdzx98kbq+Rh5Zdj8d/QHBjNSy5NJAoWug2Y9ep+f2\nWWsw6AzjntLX3jOwVLD9kLYM0NEb4pm3D9Lrj/DhniZuvHgaVpOB7j5tW+PuI/3HU+Ae8lqJ4UM2\ni4HVF09j6YzhcxSEEEJMPaMGBLfffnvya0VR8Hg8/OAHP5jUgzpbdId6UVF54eBLFDi1dfohAYFz\naPe/pVkLT+p9BmcIdlUN9Dj45IDW9yAaU9m0p4lF0zOSkwwTyj8VECToFIXrVhSf1PEIIYQ494wa\nEKxfv55IJILRaCQSiRCJRLDZJmaU7dksrsbxRrR9/Ed7j1Hv1bb3laeWJR/z6YDgZHX0DgQEwbA2\nIdFs0hMKx5hfmsb+Y118UNGY3D5oNesJhGIoCpTlHb81shBCCJEwakP4N998ky9+8YsANDU1cfXV\nV7Nu3bpJP7AznT8SIK7GmZZSRHlqGZF4FI8llTRrKlaDFQWFfGfu6C80ClVVae8JkuUZCMJcNiMX\nzcsB4NoLilg6I5PWrgAf7W8BtLkDAIVZTqxmGRMshBBidKOeLZ588kn+4z/+A4DCwkJefPFF7r77\nbq644opJP7gzWV/EC0CuPZtbZ95Ei78Ng6J9nBfkLCUUC2HWm075fXzBKKFwjNw0G6FwlG5vmIIs\nJzddWsqF83IoynbS4w2zZW8zH+/XlhDOn51FW3eQFXOlzbQQQoixGTUgiEQipKcPVMWnpaWhqp9e\nqZ56+sJaQOA0aWn6LNtAcd5N06+bsPdJFBSmuSx4AxEtIMh0YDbqKcp2AjCzKBUFCEfjAOSm2Xn0\nK4sn7BiEEEKc+0YNCJYsWcKDDz7IdddpJ7m//e1vLFx4csVx55JEQOAwTW7b3/ZurX4gPcVCIBTl\ncH0PhZlD39NhNVKc4+RoUx8GvYLbOfa2yEIIIQSMISB47LHHePbZZ3nhhRcwGAwsW7aMW2+99XQc\n2xktsWTgMjkn9X0SBYVpKVY8LguH63uYWZQ67HGziz0cbeojzWVBN85tjUIIIcSYlgwsFgu//e1v\naWlp4fnnnycWi52OYzujJZcMjBPb2Od3r+0l22Pj+gtLgIEth+kpFoqynSydOfLI4TnFHt7Ycoz0\nFMuI9wshhBAnMuoug+9+97u0tmrFana7nXg8zsMPPzzpB3am+3QNwUTo6guxdW8L7+9sSN6W6DyY\n7j7xib4sP4XzZ2dx0YJT39kghBBi6hk1IGhsbOSBBx4AwOFw8MADD1BbWzvpB3am805CDcGxZm04\nUrc3TK9fm0zY3hPAYtJjG2X7oEGv41vXz+G8WVkTdjxCCCGmjlEDAkVROHjwYPL7I0eOYDDI3va+\niBedosNmmLhJgTXNvcmv61q9yR4E6SmWcbc7FkIIIcZj1DP7I488wt13301Wlnbl2dXVxc9+9rNJ\nP7AzXV/Yi8NoR6eMGlONWU1/hgCgrsVLcbaTYDhGuownFkIIMclGPZutWLGCDRs28Pjjj3P55ZeT\nmZnJN77xjdNxbGe0vrB3QusHVFWlprkPk0H7ldS29iW3HKZJoaAQQohJNmqGoK6ujhdeeIEXX3yR\n3t5e7r33Xn7zm9+cjmM7Y4VjEYKxEE7jxBYU9vrCLJqezr5jXdS1eofsMBBCCCEm03EzBO+++y73\n3HMPa9asoaenh5/97GdkZmaydu1aPB7P6TzGM443MvE7DI42afUDJTkuCjIcNLX7ae7UhieluSQg\nEEIIMbmOmyG47777WLVqFS+88AJFRUUAU7qwrSvYzS92PoU/4sdi0E7QExUQHKzt4j/f0go3Zxam\n0tUXoqqhh52HtVHHo205FEIIIU7VcQOCV199lZdeeonbbruNvLw8rr322indkOhgVxXtgQ5SzW56\nQtrVvMcyvGPgeL1f0cBz7xwC4M5VMyjLT6EvEGbDzgaqG7X3kaJCIYQQk+24AUF5eTmPPPIIDz30\nEBs2bOCll16ivb2db37zm3zlK1/hkksuOZ3H+Zlr9mnNmb46+8tk27M43F3NnLSZp/Sa2w+28sxb\nB3FYjfyP1XOZUagFGAvK0snLsNPQ5sNs0mO3yDZPIYQQk2vUXQZ6vZ4rrriCX//612zcuJELLriA\nf/3Xfz0dx3ZGaA90EFfjNPu1gCDbnoXT5GBx5vxTHm+8q6oDgPvXzE8GAwA6ReELFxQDSA8CIYQQ\np8W4NtF7PB6+9rWv8eqrr07W8ZxRGr3NPL7lp6yv+zvNvhbsRtsp1w30eEP894Yq/MEIRxp7MJv0\nlGS7hj1u2cxMls7I4MK5Oaf0fkIIIcRYSC76BBq9TaiofNJSQXugk2kpxaf8mq9uqmHDzgZQoanD\nz6yiVHS64RkAnU7h26vnnfL7CSGEEGMhAcEJdIV6AKjr04YNZdtHnjQ4VsFwlC17mwFYt70OgGm5\nw7MDQgghxOk2cX13z0Hd/QFBQo791AYHbdvXQjAcQ69TiMZUAErzUk7pNYUQQoiJMKkBgaqqPPbY\nY9xyyy3ceeed1NXVDbn/5Zdf5vrrr+f222/nL3/5y5D7Ojo6uPTSSzl69OhkHuIJfTogyLadfIYg\nrqqs39GAosCaS0uTt0uGQAghxJlgUgOCdevWEQ6Hef755/nud7/LE088kbyvq6uLX/3qVzz33HM8\n++yzvPbaazQ2NgIQjUZ57LHHsFg+24Y8XaEe9Ioei94MnNqSwcZdjdS1ejl/VhaXLMrDataTk2bD\nZTu1nQpCCCHERJjUgGD79u1cdNFFACxYsIDKysrkfXV1dcyaNQun04miKMybN4+KigoA/uVf/oVb\nb72VzMxTW7M/Vd3BbtzmFJbnLKXEVYjbPLb0/tGmXqrqB7ILff4wf33/CBaTnjWXlWE26nnktsWs\n/aIUDQohhDgzTGpA4PV6cTqdye8NBgPxeByA4uJiqqqq6OzsJBAIsGXLFgKBAC+99BJpaWlceOGF\nqKo6mYd3QrF4jN6wF7c5hTXlN/DQ0rVj7gfw72/s51d/3Z08/g93N+ELRrn+whJSnVq2oTDLSU6a\nfdKOXwghhBiPSd1l4HA48Pl8ye/j8Tg6nRaDuFwuHn30Ue677z7cbjdz5swhNTWVp59+GkVR2LRp\nEwcOHOCRRx7hN7/5DWlpaSd8r4wM5wnvH692XycqKjkp6eN+7T5/BG8ggmowkOmxUVnThU6BGy6b\njsv+2S4RTPTndC6Tz2ps5HMaG/mcxk4+q8/GpAYEixcvZsOGDaxatYqKigrKy8uT98ViMfbu3ctz\nzz1HOBzmnnvu4cEHH+Tyyy9PPuaOO+7gxz/+8ajBAEBbW9+EHnt1j7bV0Ip9XK+tqiq+QASAXfub\nKc1P4UBNJ9PzUwj5Q7T5QxN6nOORkeGc8M/pXCWf1djI5zQ28jmNnXxWYzMZQdOkBgRXXnklmzZt\n4pZbbgHgiSee4PXXXycQCLBmzRoAVq9ejdls5u6778btdg95/mfZsrcrqNUAjLVuICEUiRHvXyqo\nbfXiD0VRgQXT0yf6EIUQQogJM6kBgaIo/OhHPxpyW0lJSfLrtWvXsnbt2uM+/5lnnpm0YxtNYsuh\n2zK+gMAfjCa/rm3po77VC8DCMgkIhBBCnLmkU+FxdIW6AUgdZ4bAHxoICA7X9xAMR8lJs5HtsU3o\n8QkhhBATSToVAtF4lGf3/zdV3QNNkAaWDNzHe9qIBmcIvIEI0ZjKVecVysRCIYQQZzQJCIDavnq2\nNn3CH/b+F8FoCH/Ez4HOQ7hMTpym8W0NTGQIDHrto/W4zKyYmz3hxyyEEEJMJFkyALxhbWtkV6ib\nvx19F7PBTDAW4uqSK9Ap44uZAv0ZgrklHiqq2rluRXEyOBBCCCHOVBIQAL6IP/n1e3UbUVCwG2ys\nzF0+7tdKZAiWz8nils+VkZkqtQNCCCHOfHLpCviiWkBwQ+nVLMqYh6IoXF1yBRaDedyvlQgI7Baj\nBANCCCHOGpIhYGDJoMxdwueLLiOuxse9VJCQWDKwmuWjFUIIcfaQDAEDSwZ2g3ZFf7LBAIA/pHUp\ntFkkIBBCCHH2kIAA8EW0DIF9nDsKRpLYdmiTDIEQQoiziAQEgDfiR0HBZrCe8mslaghkyUAIIcTZ\nRAICtKJCm8F6SksFCf5gFJNBh9EgH60QQoizh5y1AF/Yh900MTsC/KEoVqkfEEIIcZaZ8mcuVVXx\nRf2kW0cfsXwim/Y0EQzH8AejOG3GCTo6IYQQ4vSY8gFBMBYkrsZxnEKGwBeM8MzbB4nFtLHHWZ5T\nr0UQQgghTqcpHxB4w4kthye/w2BzZTORaDz5vc0sGQIhhBBnlylfQ+CL9m85NJ5chkBVVd7f2TDk\nNulBIIQQ4mwz5c9ciS6FDuPYMgQN7T7e2FJDZXUn37xuNhazgaYOP/OmpVFZ3YGK9CAQQghx9pny\nZ65kl8IxZgiefesAh+p7ANhxqC05r+DCedn0+ELUtnglQyCEEOKsM2WXDFRV5f26TdT01gFjDwi6\nvWEcViN6nUJtq5e61j4AirKclBe4AckQCCGEOPtM2TNXbV89fz78SvJ7+xiXDPoCEdJTLLgdZurb\nvATDMcxGPRmpVhaVpbPuk3py0k+9BbIQQghxOk3ZgKDR1zLk+9EyBKqqEourBEJRHFYjqU4tIGhs\n91Ga50KnKMwq9vCL76zEaZVdBkIIIc4uU3bJoLk/IFBQAHCaHMd97DNvHeAHv9+GN6BNMnTajBRk\nDjy+MNOZ/NplM6EoymQcshBCCDFppmyGoNnXCsDX595Oa6D9hAFBdVMvTR1+Wjq1AkSH1UjhoICg\nIOv4zxVCCCHOBlM4IGjBYbSzMHPeqI9NjDSua/UCWkBQkDWQFRicIRBCCCHORlNyySAci9AR7CLb\nnjmmxycCgvo2rWeB02ZK1hEoCuRlSBGhEEKIs9uUzBC0+ttQUcm2Z4362LiqFRIC1LcNZAgA1lxW\nSq83jNmon7yDFUIIIU6DKRkQJAoKs22jZwiCoShq/9fJgKB/muHy2dmTcnxCCCHE6TYllwya/VpB\n4ViWDBLLBQDhiDbASLYVCiGEONdMyYCgLdABQJYtY9TH+kPRYbc5JCAQQghxjpmSAUEwGgTAZrCO\n+lhfUAICIYQQ574pGRCEYmEATHrTqI/1fyogMBv1mKSIUAghxDlmygYERp0RnTL6j+8PRYZ8L9kB\nIYQQ56IpGxCYx5AdAAj0Zwj0Oq0dcWKHgRBCCHEumZIBQXgcAUGihiA7TRt+JDsMhBBCnIumZEAQ\nioUw681jemxil0FumtaNUDIEQgghzkVTMiAYT4YgUVSYm94fEEiGQAghxDloygUEsXiMqBob0w4D\nAH9QKyqclusCID1l9K2KQgghxNlmyrUuTmw5HM+SgaLAnBIPD9+6KBkYCCGEEOeSKRgQhAAw6ceW\n+veHotjMBnSKwsyi1Mk8NCGEEOIzM+WWDMLjzRAEo1jNUy5uEkIIMcVMuYBgYMlg7EWFdosUEgoh\nhDi3SUBwAtFYnFAkhs0iGQIhhBDntikYECRqCMYwx6C/B4FNlgyEEEKc46ZcQBCOa9sIx1JDkGhb\nbJUMgRBCiHPclDvThaKjZwjiqsqbW4+xbV8rAHYJCIQQQpzjptyZLhQ/cQ1BPK7ym1cq2X6wDUUB\ns0lPeYH7dB6iEEIIcdpNuYAgHD1xQHCgtovtB9soy0/hOzfNl1bFQgghpoQpV0MwkCEYuYagtsUL\nwOeXFkgwIIQQYsqYegHBKJ0Ka1v7ACjMcpy2YxJCCCE+a1MuIBitU2FdixezSU+6W4YYCSGEmDqm\nXEBwosZEkWiMpg4/BZkOdIpyug9NCCGE+MxIQDBIQ7uPuKpSmCnLBUIIIaaWKRsQmHTDA4JEQWGB\nBARCCCGmmEnddqiqKo8//jgHDx7EZDLxk5/8hIKCguT9L7/8Mk8//TQul4sbb7yRm2++mWg0yve+\n9z0aGhqIRCLce++9XH755RN2TOFYGIPOgF6nH3ZfXX9AUJjlnLD3E0IIIc4GkxoQrFszl2aSAAAg\nAElEQVS3jnA4zPPPP8+uXbt44oknePLJJwHo6uriV7/6Fa+88goOh4O77rqLFStWsHXrVlJTU/np\nT39KT08PN95444QGBKFYCPMI2QGApk4fALnp9gl7PyGEEOJsMKkBwfbt27nooosAWLBgAZWVlcn7\n6urqmDVrFk6ndjU+b948KioquPrqq1m1ahUA8Xgcg2FiDzEcCx+3bXGPL4zNbMBsHJ49EEIIIc5l\nk1pD4PV6kyd8AIPBQDweB6C4uJiqqio6OzsJBAJs2bKFQCCA1WrFZrPh9Xq5//77eeCBByb0mEKx\n8HG7FPZ4w6Q4Rp+CKIQQQpxrJjVD4HA48Pl8ye/j8Tg6nRaDuFwuHn30Ue677z7cbjdz5swhNTUV\ngKamJtauXcvtt9/ONddcM6b3ysgY27p/OB7Gbkkf9vhoLI43EKEkN2XMr3U2Opd/tokmn9XYyOc0\nNvI5jZ18Vp+NSQ0IFi9ezIYNG1i1ahUVFRWUl5cn74vFYuzdu5fnnnuOcDjMPffcw4MPPkh7ezv3\n3HMPP/zhD1m+fPmY36utrW/Ux8TVOOFYBF1cP+zxXX1aB0OrSTem1zobZWQ4z9mfbaLJZzU28jmN\njXxOYyef1dhMRtA0qQHBlVdeyaZNm7jlllsAeOKJJ3j99dcJBAKsWbMGgNWrV2M2m7nnnntwu938\n5Cc/obe3lyeffJJf//rXKIrC73//e0ymU0/ln6hLYY9PCwhS7CN3MBRCCCHOZZMaECiKwo9+9KMh\nt5WUlCS/Xrt2LWvXrh1y//e//32+//3vT8rxnKgpUa9Pu89ll4FGQgghpp4p1ZhoYLDR8ICgx6sF\nBJIhEEIIMRVNqYDAHw0AYDMOH1zU058hkF0GQgghpqIpFRD4IlpAYDfYht2XDAjsEhAIIYSYeqZU\nQOCP+AGwGSUgEEIIIQY75wOCaDzKv1X8nk9aKvBFtYDAPkJA0OsNoSjgtElAIIQQYuqZ1F0GZ4K2\nQAf7Ow9h0pvIc+QAw5cM4qpKjy+M02ZCp1M+i8MUQgghPlPnfEDg618m6Av34Y+kAEOXDF7ceIRN\ne5rxBSJkeYZnDoQQQoip4JwPCBJ1A71h70BR4aBdBodqu5NdCl1SPyCEEGKKOudrCBJbDfvCffii\n2lwF2//f3p0HxnS1Dxz/TmayTPad2hKJaBDRBvUWVYp6tVpbUa+1sSW2WBpLhMSW2JckSuz7D60X\nRfWttmhrqyK2iBYhIRESsq+Tub8/RkYmkoiiYZzPX8zdzn3mZuaZc889T7FbBqkP5x8AMaBQEARB\neH3pf0LwsIcgrzCf1Nw0FDK5dqZCSZJIzczDXGmIkcKAWlVEQQ1BEATh9aT3twyyHvYQANzNScbU\n0BSZTDNwMCevkHyVmnpONvh09sDIUO/zI0EQBEEold4nBEU9BKB5BLH4gMLUTM3YAWsLY4yN5P94\n2wRBEAThZaH3P4mzi/UQAJgpHg0o1CYE5qJ+gSAIgvB60/uEIKtYDwGAmaGZ9t+PEgIxmFAQBEF4\nvel9QpBdoNtDULywUdETBqKHQBAEQXjd6X1CUDRdcZHisxSmZohbBoIgCIIAr0FCkFOQo5MElDWo\nUBAEQRBeZ3qdEKglNdmqHBxN7ZGhedTQzFB3UiIDmQwLU8PKaqIgCIIgvBT0OiHIVeUiIWFuZK5N\nBMxK9BBYmRthIBMFjQRBEITXm14nBEWPHJopTLE00sxCaPrwsUPNLIX54gkDQRAEQUDPE4KiRw5N\nDZVYGJkDj3oIsnJVqArVYkChIAiCIKDnCUHRI4emClPslbYYyAywMrYEIOmBJlmwEQMKBUEQBEG/\npy4ueuTQzFBJy+rNeKdqY+2tgwvXUgBwr2VTae0TBEEQhJeFXicE2h4CQ1MsjMy1tw0Aoq4mo5DL\naFDbtrKaJwiCIAgvDf2+ZVDUQ1BsHgKA++m5xCVl4l7LBqWxXudEgiAIglAhep0QFB9UWNy5q8kA\nNKpj/4+3SRAEQRBeRnqdEBQ9dqhU6CYEF2PvA9Cojt0/3iZBeFqbN69nzJjhjBw5FD8/X65ciQHg\n+vWrnDt39qn2dfbsaYKCAp6pPb/8cpiUlGTu309hxowZT338Tz75kNGjfRg5cii+voP4+ecfy93m\n75xneTZvXk9MTHSZy8PDF3H3blKpy/Lz89m3bzcABw7s4+jRXyt0zJs3bxIZuQyA48eP4uc3HD8/\nX3x8vPnhh+8BWLt2JXv2/PdpTuWl1KPHpxQUFFRo3WHDvuDOnTtcv36VdetWVfgYs2cH8913e3Ve\n27FjK6tXryhzm86dOwAQFrbwsfc3Lu4Go0YNK/eYO3fuAODkyePs3bu7wm19leh1f3nOwzEExScj\nkiSJa7fTsLM0xt5KWdamgvBSuHEjlqNHf2H58rUAXL36F7NnB7Fu3VYOH/4ZW1s7GjV6+6n2KXvG\nibi+/vr/cHYOoFYtJ6ZNm8a9exlPtX3jxk0JDp4NQE5ODiNHDqVWLSfq1HErdf2/e55l6dt3YLnL\nR40aV+aylJRk9u7dQ6dOXejYsVOFjzl37lzGj58CwIIFoWzcuA0zM3NycnIYOLA377zzrwrv6+X3\n9NeXi0sdtm7dRELCbRwc3J+4/iefdGHVquV89NEn2tcOHNhHaOiiJ7Zr9OjxpS99wt/Fxo1r6N69\nJ82avfvE9r2q9DohKOohMJE/erTwXlou6dkFNHV3rKxmCa+oHT9f5VTM3ee6z6bujvT8oE6Zy83N\nzUlKSmLfvj3861/NqVPHjVWrNpKcfI8DB/ZhaGiIu3s9MjLSWbVqBcbGxlhZWTF58jTMzMxZvHge\n0dGXKCxU4e09DDMzM+Lj4/D39+PBgwc0b94Sb++hREWdYd26VUiSRE5ONkFBs3F0rMK0aZPIysoi\nNzeXoUOHo1IV8NdffzJrVhBTp85g5MgZRESs5ujRX1m/XvMLr25dd/z9K9YLoVQq6dy5G4cP/0Sd\nOm5ERi7j/Pko1OpCevXqg4eHp8555ubmsnLlV8jlcqpXr8GXX07m4MHv2b//WyRJwtt7KPPnh+Dh\n4cmtW/F4eTUhKyuT6OhLODk5Exg4nZCQ6bRr14GUlGSOHz9Kbm4uCQm36dOnPx07dmLUqGH4+weQ\nlpZKRMQSDA0NMTY2YdasuWzcuI6bN2NZv341arUaOzt7Onfu9licW7ZspT3HuLibSJKEpaXmkWcL\nC0u+/nob77//AbVru7BlyzcoFLofxRERSzh/PgqZTEb79h347LPPCQmZjiRJ3L2bRE5ODoGB06lV\ny4mdO7dz8OD/kMlktGv3Id2799LZ1+HDP/Hf/35NYWEhMpmMkJD5XLt2lS1bNmBoaEhCQgLt2n1I\nv35fEBIyHUNDQxITE7l/P4UpU4Jwc3uTn3/+kR07tiKXy/H0fIthw0Zw795dFiwIpaCggJSUZIYM\n8aVly/cBCYC7d5OYN282+fn5GBsbM2HCFBwcHImMXMapUydxcHAkLS1N2842bdqxc+cOGjWa9sTr\nxtPzLdLSUklKukOVKlWJiYnGzs6eqlWrcv36NSIiFqNWq0lLS2X8+Ml4eDTUblv0/pqZmTFjxlQA\nbGweDS4vLV67d+8kIyODRYvmUq9eA27evIGPz0j+7/828/PPP6BQKGjUyAsfn5GsXbuSxMQEHjy4\nT1LSHUaPHkfTpq9GwqfXCUGOKhcTuTFyA7n2teu3NRega3WrymqWIFSYvb0Dc+cu4ptvtrNu3SqU\nSiVDhvjy/vsf0LFjJ+zs7HF3r0+PHp1ZsWINdnb2fPPNNtavX0PDho1IS0tj1aoNZGZmsn37Fry8\nmlBQkE9o6EIKC1V0794Jb++hxMZeZ9q0mdjZ2bNp0zoOHfqRli3fJy0tjYULw3nw4D7x8XG8+25L\n6tZ9E3//AAwNDZHJZBQWFrJkyXxWr96IlZU1W7du4u7dJBwdq1ToHG1tbfnzzyucOHGMhITbLFu2\nivz8fIYNG0hExEqd8+zduxvLl6/F2tqa1atXcODAPhQKBRYWloSGLgAgMTGB8PBIbGxs+eijtqxe\nvYGxY53p2bMzWVmZOsfOyspi4cIwbt2KZ9KkcTq/+n/99TBt27anR4/e/PbbETIy0hkwwJvY2GsM\nHDiYtWtXAppbKCXjXDwhiIo6w5tvvqn9/+LFEWzbtoXg4Cmkpj6gS5fufPHFEO3yY8d+486dBFau\nXI9KpWLEiCF4eTUBoHr1GkyZEszx40f56qul+PiM4qefDrJ8+RokSWLs2BG888671KxZS7u/+Ph4\n5s9firGxMfPnh3Dy5HHs7R1ISrrDxo3bycvLo0uXf9Ov3xcAVK1aDX//APbu3c2ePbsYOnQ4a9eu\nZM2aTRgbGzNz5jT++ON3AHr37sdbb3lx8eJ51q5d+TAh0PzSXrZsCT169KZZs3c5ffoUy5eH07Pn\nf7hw4RyrV28kOzuL3r27advp6urGmjWRFbpmADp16swPPxygX78v2L9/L507a/YVG3udkSPH4uLi\nysGD3/Pdd9/qJARFNm5cS/v2HejUqQs//XSQPXt2PoxX3GPx6t/fm507dzBu3EQOHNiHTCbj+vWr\nHD78E5GR6zEwMCAwcALHjv0GgJGREQsWhHHq1Em2bdsiEoKXQbYq57HxA9dupwPgWs2yMpokvMJ6\nflCn3F/zL8Lt27cwNTVj8mTNr6aYmMt8+eVovLyaatdJTU3F3NwMOzvNINlGjd4mMnIZ1tbW2g9C\nc3NzBg0axtmzp6ld2xWFQoFCoUAu13wEODg4sHjxfExNTbl37y6enm9Ru7YLn37aleDgAFSqQnr0\n0PzylCQJSZK0x09LS8XCwgIrK2sA/vOffk91jnfuJOLo6Mj161e5ciWG0aN9kCSJwsJCEhMTtes9\nePCAlJQUpk2bhCRJ5Ofn07RpM6pXr0GtWk7a9aytrXFw0PQAKpVKatVy1sYgPz9f59hubnUBcHSs\nQl6e7rJ+/bzZuHEtfn6+ODg40qBBw8e2B83955JxLi4tLRVHR817k5GRQWJiAr6+o/D1HUVycjJT\npvjz5pv1tOvfuBGLp6fm9ohCoaB+fQ9iY2MBze0WgIYNGxEevojY2GvcuZOIn58vkiSRmZnBrVtx\nOgmBjY01s2cHY2JiQnz8TTw8PAFNN71MJsPExARjYxPt+nXrvqmNyYUL57h9O57U1Af4+/s97EHK\n4fbtW3h6vsWGDWvYt28PACqVSue8r127xqZN69iyZQOSJKFQKLh1K057rqamZtSu7apd397enoyM\ndJ19rFq1XNtTsnTpcp1u/Q4dPmLMmOH06tWHs2dPM3asP6C5ltevX42JiQlZWZmYmZlTmvj4OD79\nVJNEeHo20iYENjY22njFxT2KV0k3b96gQQMPDAwMHu7jLWJjrwHg5qaJYZUqVSgoePyaeVnp9aDC\nHFXOY08YXEtIQyGXUauKRSW1ShAq7urVv1i0aJ72w7ZmzZpYWFgglxtgYGCAJKmxtrYmKyuL+/c1\nk22dPXuGWrWccHKqzeXLlwDIzMxk3LhRZR5n7tzZTJkSTEBAEPb2DkiSxPXrV8nOzmbevCVMmRLE\n4sXzAR4e91FCYGNjS2ZmJhkZmrEES5YsKHfQXvFts7Iy2bt3D23atKNWLWcaN25CWNgKwsJW8MEH\n7alevYbOeTo6VmHOnIWEh0fSr98X2l/ORR/KpRyt1OMWKe++8Q8/fMdHH31CWNgKnJ1d+PbbXdoe\nkeKcnV3KjbONjQ3p6ZovuoKCfIKCAnjwQDOw2dbWFjs7ewwNH1VcrV27NufPawZRqlQqLl48R61a\nmi/4K1cuA3D+fBQuLq7UquWMi4srYWErCA+PpGPHTri6PhqLkZWVyZo1K5k+PYRJk6ZiZFTWzKyP\nYlMyJm+8UZ0qVaqyePEywsMj6d69Jw0aNGT16uV07NiJwMDpeHk1KRZf6WFcnPHxGUVY2Ar8/SfT\npk07nWsyJyeHGzditcfJyEjH2lp3orghQ3wJD48kLGzFY+2ysrLGyak269ev5v3322ivgSVLFjB4\n8DACAoJwcSmewOu+/7Vru3DhwjkAoqMvlRovY2PjMrd3cnImOvoSarUaSZKIijqrTUyfdZxOZdHb\nHgK1pCZXlYdS8SjzzS8oJP5uJs5VLTBU6HUuJOiJ999vQ1zcDQYP7o+pqSmSpGbEiDGYmprx5pvu\nfPVVGE5OtZk4MZCAAH8MDAywsLBgypRgLC2t+OOPkwwfPhi1Wq3tli7tw6pDh48YPnwQSqUptra2\nJCffo2ZNJ9auXcWhQz8iSRKDB/sC4OHhyaxZ07TjBGQyGePGTcTf3w+5XE7duu64u9dn8+b11K3r\n/tiAubNnTzN6tA8ymQFqdSGDBw+jZs1a1KxZi7NnTzNixBBycnJo1ao1SqVS5zz9/Mbz5Zd+SJIa\nMzNzAgNnkJR0p8TZyEr9d0U/pIvWq1evAXPmzMTERIlcbsCECVOwsbGlsFDFihUR2i+Lli1b6cTZ\n23uozv7efrsJy5cvoUePftja2jFmjD8TJoxBoVBQWKimRYv3aNq0mfbL6d13W3LmzGl8fLxRqVR8\n8EF77S/OEyeO8euvR1Cr1UyZEkzVqm/g5dUUX99BFBQUUL9+A23vCICZmTmeno0YOnQgCoUcCwsr\nkpPvUbXqGyXiUXZsrK2t6dXrP4wcOYTCQjVvvFGNDz5oT5s27YiIWMymTetwcHAkPT1NZ1/Dh/ux\nYMEc8vPzyM/Px8/vS9zc6tKs2bsMHtwfOzs7bG0f3bu/dOkiTZq8U6H3qMgnn3TB338MW7fu1L72\n739/RGDgRCwtrR6OU0jVaVfReffv78306VP5+eeDvPFGtXLjBZrEb+bMado2urjUoU2btvj4eCNJ\nEo0avc1777Xmr7/+fKpzeJnIpNLS5ldQyZHO2QXZ+P8ajKd9A4Z5DgDgekI6szb+QbvGNfhP+7qV\n0cxK5eBg8dQjwl9XIlYVU16cjh79FaVSqf0V/zqbNm0CY8dOxsbm70+VXjQYUr+eSHhkxoypDB06\nnIYN64q/vQpwcHj+vdx6+zM5W5ULoNNDcOueZkBRDcfS7ykJgvD8uLnVFcnAQ/7+/mzfvqWym/HS\nunbtKtWr16Bq1TcquymvNb29ZZCjKqp0+GgMwe17WQBUdzCrlDYJwuukok8ZvA5cXFzw8Rn5TPsI\nCAh6Tq15+bi61sHV9Z8dsCs8Tn97CB5OSqQsNqiwqIegmp1ICARBEAShOL1NCErtIUjOwt7KRBQ0\nEgRBEIQS9DYhKDmGID07n/SsfKrbi94BQRAEQShJjxOCh5UOH/YQPBo/IAYUCoIgCEJJepsQ5Gh7\nCIoSgodPGIgBhcIr5HWtDlgkJGQ6Awb0ZvRoH4YPH0xAgD+JiQnlbvPtt7semzzoWQQGTihz2f37\nKSxaNLfM5Zr3IgqA4OApj83mV5ZNm9Zz5UoMkiQRHr6YceNGMnLkUPz9/bTn/zRVBV9WT1N9s3hF\nwjVrInUmNSpPdnY2n37agdzcXJ3Xvb37cPv2rVK3OXBgH5GRy8p8f1esiODAgX1lHjMp6Y72Wi/v\n7+Nlo8cJwcMxBA8HFSYka3oIqolbBsIrpnHjpoSFrSAiYiWLFkWwZcsGrl79q8z1Dx/+mdjY68/t\n+H37DsTdvX6Zy0eNGlfmEwVF1QEBOnbsRIsW7z318UeM8CMsbAVffbWaXr36MG3apHLX37RpHWq1\n+qmPU5ZZs+aVuczW1o5x4yaWuVzzXmimsw0Onv1YEaPS3L2bxPXrV3nzTXdOnDhGSkoyixZFEBGx\nks6duxEWVlTR79WcDa+kp5nVr2jdXr36sGzZkgptY2pqSosWrTh06FEifeVKDBYWVlSvXqPcbZ/0\n/pblzJk/tBNNlff38bLRu9F1KrWKzIIs7VMGRbcMMrI1mbSNRVlTdwpC+f57dR9n7154rvt827Eh\n3epUvIzuy1YdsH//3s+9OmB5GjV6C4XCkNu3b2FoaKhTTc/fP4Dffz9BSkoKQUEBhITMfyw+rVu3\nZdSoYdjY2JKRkU7bth9y4sRR8vLySElJoUePz/n11yPExl5jxIgxtGzZis6dO7Bnz/8YNWoYbm51\nuX79GtnZ2cycOQdJkggKCiAych2RkcuIijpNYaGa1q0/4MMPO2rfi2bNvBg92o+tW3eSlHSHOXNm\nUliowtjYhOnTQ7R1IAB27fqG1q3bApppj2NiLvPTTwdp0qQpLVu+z7/+1UInJnfuJBIaOkNbnW/M\nGH9cXevQo0dnPDwacutWPK6udZg0aSpZWZmEhs7U1gzw8xuvM72vWq1m/vwQ7t69S0pKMi1btmLw\nYJ8yqyB+/nk3PD0bERd3ExsbW0JC5lNYWMiCBaHcuhWPJEkMGeLLW295lVpFsLiiioomJkbUq9eQ\nYcNGkJKSXGpFQnNzc4yNTbh+/WqJ6YlL98knnYmMXKYtXrV//x4+/bQrADt37uCXXw6Rm5uLlZW1\nTrvu3EnUvr+HD//Exo1rsba2paAgHycn51Lj5e09lM2b15OXl4eHhyfbt2/B3z8AW1s7ZsyYSnZ2\nFoWFhQwZ4ouXVxMGDOjN2297cfXqXxgYGDBnzkJMTSvnh6ve9RD8cPMQQcfmEJ9xG3g0qDAnX9NV\nZ2KkdzmQ8JqxtbUlNTVVpzrg0qUr2LBhDUqlko4dO9GrVx/c3eszd+4sQkIWEB4eib29g7ab08LC\nkmXLVtG4cVMSExMYNmwEEREr+eab7XTv3pNVqzZw/nxUqdUB581bzJw5C9myZYPOsqLqgOHhkXTp\n0k1bHdDZ2YWBAwdr1yteHTAsLFI7P39F2djYkpaWqq2mFxa2gs8/78uKFRF06tQZOzt7ZswI5cSJ\nYyQmJujEJzNTcz7t23dg8eJlyOVysrNzmD9/KX369Gf37p2EhMzH3z+A777b+/CIj37B1q/vwZIl\nX9GkyTv8+OP/NEsf/mr98ccfCAoKYdmyVZibW2Bv76B9Lzw9PSleBXDAAG+WL19Ljx69+fPPKzrn\nFxV1mjp1NPUI3N3rM3HiFH799TD9+vVi8OD+XLqkm5RGRCyhZ8//EBGxktGjxxMaOgOA5OS7DBni\ny6pVG8jJyebIkUNs3LiOJk3eYenS5fj7B7BgwRydfd29m0SDBg1ZuDCMlSvXs3v3N9plVatWY9Gi\ncLp378mePbsASEy8zdChw1mxYi1paalcvnyJfft2Y21tQ0TESkJDF7BwoeYYRVUEly1bhZOTMydP\nHtfuOz09nbVrV7J06XK2bNnC3btJnDp1UluRcOnS5bz3Xmudtrq61uHs2dNPvmAevm8ZGencu3eX\ngoICTp8+xfvvt0GSJDIy0lm6dDmRketQqVSP3R6TyWSoVCoiIpawdOkKFi0Kx8TEpMx4GRgY0Lfv\nQNq3/zctW7bSXh8bNqzhnXeaERGxkpkz5xAaOhOA7Ows2rfvSETESuztHTh+/FiFzulF0Ltvx6Ts\ne6ikQu5k38VAZoCxXNMjkJNXiEIuEzUMhL+tW51OT/Vr/kXR9+qAFTv/KqVW09OQtMWZYmIul4iP\n5v570TnCo+p+5uYWODlpXrewsCQ/P++xYxevBFhUoKjItGkzWL48jAcP7vOvfzUvte2SJBEXd5MG\nDTTnX9otlNTUVO2v4WvXrlKzphPBwbMBOHXqBFOnTuLbb/+n3d/Nm7E0aqSpjujmVpd79zT3q6tU\nqUq1atUBaNDAk7i4m1y/fpUzZ/7g558Par8Mi7O0tOTy5UucPfsHSqWZzhiFklUQQXPt2Ns7aF/P\nz8/n2rVrnD8fRXT0RSRJQq1Wk56eplNFsHjVRUCnoqJCYUB6eiYJCbeJj48vtSIhgJ2dvbbOAGiK\nJU2YMAaZTEbTps205ZyLfPxxZ77//juqVatGixattNeLXK4gKCgApVJJcvLdUsd5pKamYmlpiYWF\nZrrgoraXF68iRdUBbt6M5cMPOwKasubm5mbaa6j431Vp190/Re8SglzVo4EjSoWJNjvLzVeJ3gHh\nlVRadcBZs+Zy8+YNGjdugr9/AJIksWHDmjKrA5qamvHbb79gampKUtKdF1odcMQIPzZtWs+33+6i\nY8dOpVYHPHToIKCpDjht2mQWLQqv0PmfOnUCpVKJvb0Dzs7OfP55Pzw8GhIXd4OoKM1ASgMDTdGk\nouqJJeNT8jyefA+7eBxKX1elUnHo0I9Mnx4CQN++PWjb9kPte1G0H5lMhrNzbaKjL9GkyTv88MP3\nZGSk0717T+2+bG3tyMzMwNTUlD/+OMmNG7FMmDDl4bYuKJXKEvtzISrqDC1btuKvv65ga2sHwL17\nd3nw4D42NrZcuHCOf//7Y9LSUunQoR7t2nXgwYMH2tLFRb77bi8WFpb4+wdw61Y8e/fuqnCcit4n\nZ2dnHB2r0K/fQPLy8ti0aR1yuZw1a1by3//uR5Ikxo4dobNt8YqKVatas3Hj/+Hm9iZxcTe4cOEc\nrq51tBUJi2RkpOvcRlAqlYSHR5bZvg8/7Mi4cSOxt7dn5MixgCbh+vXXw6xcuZ68vFwGDepX6nVv\nY2NDZmYmaWmpWFlZExMTjaNjlTLjJZPJHhvH4uxcm3PnzjxM2u6SkZGBpaVVhWL7T9G7b8gcnYTg\n0aREufmFKI3lldEkQXgmr0t1wFGjhpX6gb58eThbtmxAJjPAzMyM6dNDgdKr6YGmLr2//xjCwlY8\nFh9TU9O/8eGrWyWvNAqFAktLK4YOHYiJiQnNmr1LlSpVte+Fp2d9ilcBnDcvhI0b12JiYsLUqTN1\n9vX2242Jjr6Io2MVPvvsc5YtW8LAgf/B3NwcmUxWbH3N/kaM8GPu3Fls27aZwkIVkydPA8DQ0IhF\ni+aRlHQHD4+GtGjxHg0behIaOpM9e/5Ldnb2Y5UZGzd+h+nTA7l48TyGhobUrOlEcnLyE2NTPD6f\nftqNuXNnMXLkULKzs+nW7bNyqy6CbkVFAwMZ9vZV+OCD9qVWJCwSHX2RYcMqPlZaKBkAAA2PSURB\nVB20hYWmB+j+/fvaxLBGjRoolaYMHz4YSZKws3PQ6XUoIpfLGTvWn7FjR2JlZaXtXSgrXq6uddi0\naR1167pr49K37xeEhs7g8OGfycvLY+LEKcjl8lJjWFn0rtrh7JOLSMjSfODVsqjOxKZ+AAxfdARH\nayXB3k9XXlOfiAp+FSdiVTHPM07h4YsYNWrcc9nXy+Zp4nTnzh2WLVvCzJlznrxyOYoGQ75qKhKr\n9PR0QkKCmTNnUbnr6TNR7bACivcQmCpMAVBLErn5hZgYiR4CQXhZff5538puwkuhatWq1KnjxpUr\nMc+4p5ejG/pF2LFjK0OHjnjyisJT0btbBrmFuSgMFKjUKm1ho7x8zT1ME1HDQBBeWkUDGwUYMGDQ\nM+9jz57vn0NLXk6DB/tUdhP0kl59Q6olNbmqPFysnGloX4+6Nq6AZvwAIIoaCYIgCEIZ9OobMq8w\nDwkJpcKE9k6tta/n5GkeI1GKWwaCIAiCUCq9GkOQU6LCofb1okmJRA+BIAiCIJTqtUgIcvMejiEQ\nPQSCIAiCUKoX+pNZkiSCg4O5cuUKRkZGzJ49m5o1a2qX7969m7Vr12JpaUmXLl347LPPnrhNeYoS\nApOSCUF+0S0D0UMgCIIgCKV5oT0EP/74I/n5+Wzbto3x48cTGhqqXfbgwQPCwsLYsmULmzZtYu/e\nvSQkJJS7zZPklnXLoKiHQExMJAiCIAileqE/mU+fPs1772nm6m7UqBEXL17ULouPj6devXrauaEb\nNmxIVFQU58+fL3ObJ3nSGALRQyAIgiAIpXuhPQSZmZnaL3zQTO9ZNL+zs7MzV69e5f79++Tk5HD8\n+HFycnLK3eZJtAmBvOQYgqJBhaKHQBAEQRBK80J/Mpubm5OVlaX9v1qt1hZVsbS0ZNKkSYwaNQpr\na2saNGiAjY0NFhYWZW5THgcHC7o7fEj3tz98bJl3F0+8u3iWstXr50VMd6mvRKwqRsSpYkScKk7E\nqnK80B4CLy8vjhw5AkBUVBR169bVLissLOTSpUts2bKFxYsXExsbi5eXF2+//XaZ2wiCIAiC8GK8\n0B6C9u3bc/ToUT7//HMAQkND2bdvHzk5OfTo0QOArl27YmxsjLe3N9bW1qVuIwiCIAjCi6U31Q4F\nQRAEQfj79GpiIkEQBEEQ/h6REAiCIAiCIBICQRAEQRBe4WqHzzLFsb7p1q0b5ubmANSoUQMfHx8m\nTZqEgYEBbm5uBAUFAbBjxw62b9+OoaEhPj4+tG7dmry8PPz9/UlJScHc3Jw5c+ZgY2NTmafzQpw7\nd44FCxawadMm4uLinjk+UVFRhISEoFAoaN68OSNHjqzkM3w+isfp8uXLDBs2DGdnZwB69+5Nx44d\nX/s4qVQqAgICuH37NgUFBfj4+FCnTh1xTZVQWpzeeOMNcU2VQq1WExgYSGxsLAYGBkyfPh0jI6N/\n/pqSXlE//PCDNGnSJEmSJCkqKkry9fWt5BZVjry8PKlr1646r/n4+EinTp2SJEmSpk2bJh08eFC6\nd++e1KlTJ6mgoEDKyMiQOnXqJOXn50vr1q2TwsPDJUmSpP3790uzZs36x8/hRVu1apXUqVMnqVev\nXpIkPZ/4dO7cWYqPj5ckSZKGDBkiXb58uRLO7PkqGacdO3ZI69at01lHxEmSdu7cKYWEhEiSJElp\naWlS69atxTVViuJxSk1NlVq3bi19/fXX4poqxcGDB6WAgABJkiTp5MmTkq+vb6VcU6/sLYPypkV+\nncTExJCdnc2gQYMYOHAg586dIzo6miZNmgDQqlUrjh07xvnz52ncuDEKhQJzc3OcnZ2JiYnh9OnT\ntGrVSrvu8ePHK/N0XggnJyeWLVum/f+lS5f+dnxOnDhBZmYmBQUF1KhRA4CWLVty7Nixf/7EnrPS\n4nT48GH69u1LYGAgWVlZIk5Ax44d8fPzAzTzqcjl8mf6m9PXWBWPk1qtRqFQcOnSJQ4dOiSuqRLa\ntWvHzJkzAUhISMDKyqpSrqlXNiF4limO9YmJiQmDBg1izZo1BAcH8+WXXyIVe5LUzMyMzMxMsrKy\ndOJlamqqfb3odkPRuvqmffv2yOWPpq1+lvhkZGTovFb89VddyTg1atSICRMmsHnzZmrWrElERMRj\nf3evY5yUSqX2vP38/Bg7dqy4pkpRMk5jxozB09OTiRMnimuqFAYGBkyaNIlZs2bRqVOnSrmmXtmE\noLxpkV8nzs7OfPrpp9p/W1tbk5KSol2elZWFpaUl5ubmOl/2xV8vimPJi01fFb9O/k58SiZORevq\nm3bt2lG/fn3tv2NiYrCwsBBxAhITExkwYABdu3bl448/FtdUGUrGSVxT5ZszZw7/+9//CAwMJC8v\nT/v6P3VNvbLfoOVNi/w62blzJ3PmzAEgKSmJzMxMWrRowe+//w7AL7/8QuPGjWnYsCGnT58mPz+f\njIwMrl+/jpubm85U0UeOHNF2Uemz+vXrc+rUKeDvxcfc3BwjIyPi4+ORJInffvuNxo0bV+YpvRCD\nBg3iwoULABw/fpwGDRqIOAHJyckMGjQIf39/unbtCkC9evXENVVCaXES11Tp9uzZw8qVKwEwNjbG\nwMAADw+PZ/oc/zuxemVnKpSKPWUAmimOa9euXcmt+ucVFBQwefJkEhISMDAwwN/fH2trawIDAyko\nKMDV1ZVZs2Yhk8n4+uuv2b59O5Ik4evrS7t27cjNzWXixIncu3cPIyMjFi5ciJ2dXWWf1nN3+/Zt\nxo8fz7Zt27hx4wZTp059pvicP3+e2bNno1aradGiBWPGjKnsU3wuiscpOjqamTNnYmhoiIODAzNm\nzMDMzOy1j9Ps2bM5cOAALi4uSJKETCZjypQpzJo1S1xTxZQWp7FjxzJv3jxxTZWQk5PD5MmTSU5O\nRqVSMWzYMFxcXJ75c/xpY/XKJgSCIAiCIDw/r+wtA0EQBEEQnh+REAiCIAiCIBICQRAEQRBEQiAI\ngiAIAiIhEARBEAQBkRAIgiAIgoBICAThlTNjxgy6dOnCxx9/jIeHB127dqVr167s2rWrwvsICwvj\n0KFD5a5TNJnMixAeHs7p06df2P4FQXh6Yh4CQXhF3b59m/79+/PTTz9VdlOeWr9+/Rg9ejRNmzat\n7KYIgvCQorIbIAjC8xMREUFUVBR37tyhT58+1KlTh8WLF5Obm0t6ejr+/v506NCByZMn06xZM5o2\nbcrIkSNxc3Pj8uXL2Nvbs3TpUiwtLXF3dycmJoaIiAiSkpK4ceMGiYmJfPbZZ/j4+KBSqQgKCuLM\nmTM4Ojoik8kYMWKEzpd8UlISX375JTk5ORgYGDBlyhRiY2O5ePEigYGBREREYGxsTHBwMKmpqSiV\nSqZOnYq7uzuTJ09GJpPx559/kpmZia+vL507d+b48ePMnz8fAwMDrKysWLhwIdbW1pUYdUHQDyIh\nEAQ9k5+fz759+wDw8/Nj9uzZ1K5dmxMnThASEkKHDh101o+JiSE0NBR3d3dGjx7N3r176dOnDzKZ\nTLvOn3/+ydatW0lLS6Ndu3b07duXXbt2kZuby4EDB0hISNAW2Sru66+/pk2bNnh7e/P7779z5swZ\nvvjiC3bu3Imfnx9ubm707t2boKAg3N3duXbtGiNGjOD7778HNAnFjh07uHfvHt27d6dFixYsX76c\nGTNm4OHhwebNm4mOj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"<matplotlib.figure.Figure at 0x7fc00e4e1a90>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"res1 = train_classifier(hidden_dims=[100], estimator=StochasticGradientEstimator.ST, epochs=30, \n", | |
" lr=0.1, slope_annealing_rate=1.1, stochastic_eval=False, \n", | |
" label = \"Stochastic, Deterministic (Slope annealed)\")\n", | |
"res2 = train_classifier(hidden_dims=[100], estimator=StochasticGradientEstimator.ST, epochs=30, \n", | |
" lr=0.1, slope_annealing_rate=1.1, stochastic_train=False, stochastic_eval=False, \n", | |
" label = \"Deterministic, Deterministic (Slope annealed)\")\n", | |
"\n", | |
"plot_n(res1[1:] + res2[1:],\n", | |
" lower_y=0.90, title=\"Experiment 5: Stochastic vs Deterministic (Straight-through, with slope annealing)\")" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"#### Experiment 6: The effect of depth on REINFORCE and ST estimators\n", | |
"\n", | |
"Next, I look at how each estimator interacts with depth. From a theoretical perpective, there is reason to think the straight-through estimator will suffer from depth; as noted by [Bengio et al. (2013)](https://arxiv.org/abs/1308.3432), it is not even guaranteed to have the same sign as the expected gradient during backpropagation. It turns out that if we keep the learning rate constant, both estimators start to fail as we increase depth. However, if we lower the learning rate dramatically (300x for the ST estimator and 25x for the REINFORCE estimator), we can start to get the deeper networks to train. In constrast with the results of earlier experiments, the bias of the straight through estimator starts to show and the REINFORCE estimator is the clear winner at higher depths. " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 24, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Epoch 20 0.95\n", | |
"Epoch 20 0.2428\n", | |
"Epoch 20 0.098\n" | |
] | |
}, | |
{ | |
"data": { | |
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p5qxeMQzuE9dp5/yPFhwHShuw2l0MTI9t8X7yeH1U1jmIMoectnEJ4kcS/MFN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\n9++lqKiQ+fM/5O233+eTTxZTX18PwJgxY3nzzfmXZUUICws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/vR83AdPY8ZgmTMQwZCiq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\nERERD6LGLyIi4kH+D/xvJ+vIeYXmAAAAAElFTkSuQmCC\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7fa1f01037b8>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"res1 = train_classifier(hidden_dims=[100], estimator=StochasticGradientEstimator.ST, epochs=20, \n", | |
" lr=0.3, label = \"1 hidden layer\")\n", | |
"res2 = train_classifier(hidden_dims=[100, 100], estimator=StochasticGradientEstimator.ST, epochs=20, \n", | |
" lr=0.3, label = \"2 hidden layers\")\n", | |
"res3 = train_classifier(hidden_dims=[100, 100, 100], estimator=StochasticGradientEstimator.ST, epochs=20, \n", | |
" lr=0.3, label = \"3 hidden layers\")\n", | |
"\n", | |
"plot_n(res1[1:] + res2[1:] + res3[1:], title=\"Experiment 6: The effect of depth (straight-through)\")" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 25, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Epoch 20 0.9302\n", | |
"Epoch 20 0.8788\n", | |
"Epoch 20 0.2904\n" | |
] | |
}, | |
{ | |
"data": { | |
"image/png": 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mapr0qJRyVE4KtHozcpkMmxD0i/NzRCC7ubugbdVzMK+Ot9dmEODtwhWDI9h8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