assignment-3-answers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment 3\n",
    "In this assignment you will be working with an accelerometer dataset recoreded when participants in a study where executing some task, for example, jogging. Our task is to predict the activity of a participant given the accelerometer data. This is known as the activity recognition task.\n",
    "\n",
    "The data is gathered from a laboratory experiment which is described in more detail [here](http://www.cis.fordham.edu/wisdm/dataset.php) and published in the paper \"Activity Recognition using Cell Phone Accelerometers\" (Kwapisz et al. 2010).\n",
    "\n",
    "Your task in this assignment is to train a neural network predicts the activity being performed. You can use what ever network architecture you want to. In order to pass this assignment you will need to achieve accuracy higher than 80%, for a higher score you will need to achieve accuracy higher than 90%.\n",
    "\n",
    "In order to make the assignment easier we provide you with the data preprocessing code so you only need to implement the model. As a starting point, try these layers when testing architectures.\n",
    "- `layer_dense`\n",
    "- `layer_conv_1d`\n",
    "- `layer_gru`\n",
    "\n",
    "In order to achieve accuracy higher than 90% you might need to preprocess the data differently. We leave that up to you. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we start by loading the required libraries. In the `assignment-3-helpers.R` you will find the function `create_sequences_x_y <- function(data, sequence_length, target_shift, step_shift)` which we used to generate the sequences. You will also find the function `load_activity_dataset <- function()` to load the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(keras)\n",
    "library(ggplot2)\n",
    "source(\"assignment-3-helpers.R\")\n",
    "\n",
    "set_thread_count(2L)\n",
    "# This specifies the number of threads TensorFlow will use. You can change this number, \n",
    "# but it depends on the model, what the optimal number of threads might be.\n",
    "# In my experiments 2 threads performed quite well for simpleRNNs.\n",
    "# Using more than 2 threads increased communication overhead between CPUs and decreased the training speed.\n",
    "# To change this value simply set the value which you want and then -\n",
    "# click \"Kernel\" -> \"Shutdown\" and then \"Kernel\" -> \"Restart\".\n",
    "# If you do not shutdown the kernel, the change will not take effect."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Prepare the data\n",
    "In this assignment we will do the usual preprocessing steps, read the data, split and scale the data. You will not have to do much in this part but we recommend reading it through as you might want to adjust it later.\n",
    "\n",
    "## 1.1 Read data\n",
    "In the cell below we load the dataset by calling the function `load_activity_dataset()`. This will download the data into the folder `data/WISDM_ar_v1.1` and return the contents of the file `data/WISDM_ar_v1.1/WISDM_ar_v1.1_raw_cleaned.txt`. We also reccomend reading the `data/WISDM_ar_v1.1/readme.txt` file supplied by the researchers with the dataset.\n",
    "\n",
    "We save the data as `data`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1098203</li>\n",
       "\t<li>6</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1098203\n",
       "\\item 6\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1098203\n",
       "2. 6\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1098203       6"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data <- load_activity_dataset()\n",
    "dim(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>UserId</th><th scope=col>Activity</th><th scope=col>Timestamp</th><th scope=col>x-acceleration</th><th scope=col>y-acceleration</th><th scope=col>z-acceleration</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>33          </td><td>Jogging     </td><td>4.910596e+13</td><td>-0.6946377  </td><td>12.680544   </td><td> 0.50395286 </td></tr>\n",
       "\t<tr><td>33          </td><td>Jogging     </td><td>4.910606e+13</td><td> 5.0122880  </td><td>11.264028   </td><td> 0.95342433 </td></tr>\n",
       "\t<tr><td>33          </td><td>Jogging     </td><td>4.910611e+13</td><td> 4.9033250  </td><td>10.882658   </td><td>-0.08172209 </td></tr>\n",
       "\t<tr><td>33          </td><td>Jogging     </td><td>4.910622e+13</td><td>-0.6129156  </td><td>18.496431   </td><td> 3.02371720 </td></tr>\n",
       "\t<tr><td>33          </td><td>Jogging     </td><td>4.910633e+13</td><td>-1.1849703  </td><td>12.108489   </td><td> 7.20516400 </td></tr>\n",
       "\t<tr><td>33          </td><td>Jogging     </td><td>4.910644e+13</td><td> 1.3756552  </td><td>-2.492524   </td><td>-6.51052600 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllll}\n",
       " UserId & Activity & Timestamp & x-acceleration & y-acceleration & z-acceleration\\\\\n",
       "\\hline\n",
       "\t 33           & Jogging      & 4.910596e+13 & -0.6946377   & 12.680544    &  0.50395286 \\\\\n",
       "\t 33           & Jogging      & 4.910606e+13 &  5.0122880   & 11.264028    &  0.95342433 \\\\\n",
       "\t 33           & Jogging      & 4.910611e+13 &  4.9033250   & 10.882658    & -0.08172209 \\\\\n",
       "\t 33           & Jogging      & 4.910622e+13 & -0.6129156   & 18.496431    &  3.02371720 \\\\\n",
       "\t 33           & Jogging      & 4.910633e+13 & -1.1849703   & 12.108489    &  7.20516400 \\\\\n",
       "\t 33           & Jogging      & 4.910644e+13 &  1.3756552   & -2.492524    & -6.51052600 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| UserId | Activity | Timestamp | x-acceleration | y-acceleration | z-acceleration |\n",
       "|---|---|---|---|---|---|\n",
       "| 33           | Jogging      | 4.910596e+13 | -0.6946377   | 12.680544    |  0.50395286  |\n",
       "| 33           | Jogging      | 4.910606e+13 |  5.0122880   | 11.264028    |  0.95342433  |\n",
       "| 33           | Jogging      | 4.910611e+13 |  4.9033250   | 10.882658    | -0.08172209  |\n",
       "| 33           | Jogging      | 4.910622e+13 | -0.6129156   | 18.496431    |  3.02371720  |\n",
       "| 33           | Jogging      | 4.910633e+13 | -1.1849703   | 12.108489    |  7.20516400  |\n",
       "| 33           | Jogging      | 4.910644e+13 |  1.3756552   | -2.492524    | -6.51052600  |\n",
       "\n"
      ],
      "text/plain": [
       "  UserId Activity Timestamp    x-acceleration y-acceleration z-acceleration\n",
       "1 33     Jogging  4.910596e+13 -0.6946377     12.680544       0.50395286   \n",
       "2 33     Jogging  4.910606e+13  5.0122880     11.264028       0.95342433   \n",
       "3 33     Jogging  4.910611e+13  4.9033250     10.882658      -0.08172209   \n",
       "4 33     Jogging  4.910622e+13 -0.6129156     18.496431       3.02371720   \n",
       "5 33     Jogging  4.910633e+13 -1.1849703     12.108489       7.20516400   \n",
       "6 33     Jogging  4.910644e+13  1.3756552     -2.492524      -6.51052600   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset contains 6 columns. The following description is adjust from the file `data/WISDM_ar_v1.1/WISDM_ar_v1.1_raw_about.txt`.\n",
    "- UserId: nominal, 1..36. A unique identifier per study participant.\n",
    "- Activity: nominal, {Walking, Jogging, Sitting, Standing, Upstairs, Downstairs }. The task which the participant is performing during the measurement. We want to predict this value.\n",
    "- Timestamp: numeric, generally the phone's uptime in nanoseconds.\n",
    "- x-acceleration: numeric, floating-point values between -20 .. 20. The acceleration in the x direction as measured by the android phone's accelerometer. A value of 10 = 1g = 9.81 m/s^2, and 0 = no acceleration. The acceleration recorded includes gravitational acceleration toward the center of the Earth, so that when the phone is at rest on a flat surface the vertical axis will register +-10.\n",
    "- y-accel: numeric, see x-acceleration\n",
    "- z-accel: numeric, see x-acceleration\n",
    "\n",
    "A datapoint is collected every 50 ms, or at 20Hz. We want to create sequences which last roughly 5 seconds, which means that we will need to have a sequence length of 100 using this sample rate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will not use the \"Timestamp\" column, so let us drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data <- data[, -(3)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Some data exploration\n",
    "The histogran plot below shows that the activity distribution is not balanced. Walking and jogging are by far the most common activities. We will need to take this into account when splitting the dataset into train/val/test as we want each dataset to have the same distribution of activities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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5H0Gw9fpO4pN+/cfcf43WH+nD19D07Y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\nAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "ggplot(data, aes(x=Activity)) +\n",
    "  geom_bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us also plot the number of examples we have for each user."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uOUQCqTBBKQgJRx9DJJIAEJSBlHL5ME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\nYSIMkBgmwgCJYSIMkBgmwgCJYSLM/weGbTO4i9+KkQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "ggplot(data, aes(x=UserId)) +\n",
    "  geom_bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 Dataset splitting\n",
    "We want to create a neural network model which can correctly recognise the activity of a new user. We will therefore evaluate the model on users we do not train on. We will therefore need to sample users to use for our training, validation and test sets.\n",
    "\n",
    "We will do this manually and check the class distribution in of each dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.174537858665474"
      ],
      "text/latex": [
       "0.174537858665474"
      ],
      "text/markdown": [
       "0.174537858665474"
      ],
      "text/plain": [
       "[1] 0.1745379"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CkgSbt2G1wGJMvBUkCSdu02uAxIloOlgCTt2m1wGZAsB0sBSdq12+AyIFkOlgKS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\n3bDguPmfeSHcPHtB+OLJc6/as+SkZ8YffeKXqrRuwZw/e+rg/B8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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We select users from 24 up to and including 30 to be in our validation data.\n",
    "val_idx <- data['UserId'] == '24' |\n",
    "             data['UserId'] == '25' |\n",
    "             data['UserId'] == '26' |\n",
    "             data['UserId'] == '27' |\n",
    "             data['UserId'] == '28' |\n",
    "             data['UserId'] == '29' |\n",
    "             data['UserId'] == '30'\n",
    "val_data <- data[val_idx, ]\n",
    "# The fraction of the total data\n",
    "nrow(val_data)/nrow(data)\n",
    "ggplot(val_data, aes(x=Activity)) +\n",
    "  geom_bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution is not exactly the same as we saw over the whole data. We seem to be lacking some \"Sitting\" examples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now for the test dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.181972731817342"
      ],
      "text/latex": [
       "0.181972731817342"
      ],
      "text/markdown": [
       "0.181972731817342"
      ],
      "text/plain": [
       "[1] 0.1819727"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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a98Cbds0HXyZ1d/1duD75HqI8f3SGFdhwWXGER6+NgfdC+bZq5KaeushwZdEaki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\ngf8PinHHo4YxJP0AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_idx <- data['UserId'] == '31' |\n",
    "              data['UserId'] == '32' |\n",
    "              data['UserId'] == '33' |\n",
    "              data['UserId'] == '34' |\n",
    "              data['UserId'] == '35' |\n",
    "              data['UserId'] == '36'\n",
    "test_data <- data[test_idx, ]\n",
    "nrow(test_data)/nrow(data)\n",
    "ggplot(test_data, aes(x=Activity)) +\n",
    "  geom_bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our test dataset seems to have a similar distribution as our whole dataset, and in the cell below we will see that our training set is very similar to our test set. We thus expect our model to generalise well from the training data to the test data and we expect to perform better on the test set rather than the validation set. This means that our validation dataset will be harder than the test dataset. Keep this in mind when training and evaluating your model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our training dataset will be the remaining rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.643489409517184"
      ],
      "text/latex": [
       "0.643489409517184"
      ],
      "text/markdown": [
       "0.643489409517184"
      ],
      "text/plain": [
       "[1] 0.6434894"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cmgZCGIpMF5Y0QyKFkIImlw3hiRDEoW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\nIgEkAJEAEvD/AXRf7aDeNTfyAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we select all entities in the validation set and the test set, and take the compliment.\n",
    "train_idx <- !(val_idx | test_idx)\n",
    "train_data <- data[train_idx, ]\n",
    "nrow(train_data)/nrow(data)\n",
    "ggplot(train_data, aes(x=Activity)) +\n",
    "  geom_bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we drop the `UserId` column as we will not use it to make predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "\\begin{enumerate*}\n",
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   "source": [
    "train_data <- train_data[, -(1)]\n",
    "val_data <- val_data[, -(1)]\n",
    "test_data <- test_data[, -(1)]\n",
    "dim(train_data)\n",
    "dim(val_data)\n",
    "dim(test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4 Creating sequences\n",
    "Now we want to create the sequences which we will use to train and evaluate our model. Our sequences will be 5 seconds long, that is, a sequence length of 100. We use the label of the last element in the sequence as a target, `target_shift = -1`. Since our dataset is quite large we get plenty of examples, so we shift 50 steps for each sequence to keep the number of sequences in an acceptable range (this sequence length and shift was set after some experimentation). Feel free to adjust these values if you feel like."
   ]
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   "cell_type": "code",
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       "1. 3995\n",
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     "metadata": {},
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   ],
   "source": [
    "train_seq <- create_sequences_x_y(data = train_data, sequence_length = 100, target_shift = -1, step_shift = 50)\n",
    "dim(train_seq$x)\n",
    "dim(train_seq$y)\n",
    "val_seq <- create_sequences_x_y(data = val_data, sequence_length = 100, target_shift = -1, step_shift = 50)\n",
    "dim(val_seq$x)\n",
    "dim(val_seq$y)\n",
    "test_seq <- create_sequences_x_y(data = test_data, sequence_length = 100, target_shift = -1, step_shift = 50)\n",
    "dim(test_seq$x)\n",
    "dim(test_seq$y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A minor note: When creating the sequences we created some sequences which contain data from two different UserIds, which will never happen in real-life. We do not care that much about this defect in our data processing, since the number of sequences which are from two different UserIds are **very** few (at most 36*2 out of 14132+3832+3995)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we drop last few columns which we do not need. We drop the label from the input sequences and drop the accelormeter data from the targets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
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       "<ol class=list-inline>\n",
       "\t<li>'Walking'</li>\n",
       "\t<li>'Walking'</li>\n",
       "\t<li>'Walking'</li>\n",
       "\t<li>'Walking'</li>\n",
       "\t<li>'Walking'</li>\n",
       "\t<li>'Walking'</li>\n",
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       "\\begin{enumerate*}\n",
       "\\item 'Walking'\n",
       "\\item 'Walking'\n",
       "\\item 'Walking'\n",
       "\\item 'Walking'\n",
       "\\item 'Walking'\n",
       "\\item 'Walking'\n",
       "\\end{enumerate*}\n"
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       "1. 'Walking'\n",
       "2. 'Walking'\n",
       "3. 'Walking'\n",
       "4. 'Walking'\n",
       "5. 'Walking'\n",
       "6. 'Walking'\n",
       "\n",
       "\n"
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       "[1] \"Walking\" \"Walking\" \"Walking\" \"Walking\" \"Walking\" \"Walking\""
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   "source": [
    "# Keep drop labels for the input features, x\n",
    "train_seq$x <- train_seq$x[ , , -1]\n",
    "# Keep the labels for the y data.\n",
    "train_seq$y <- train_seq$y[ , 1]\n",
    "head(train_seq$x)\n",
    "head(train_seq$y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We do the same for the validation and test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_seq$x <- val_seq$x[ , , -1]\n",
    "val_seq$y <- val_seq$y[ , 1]\n",
    "test_seq$x <- test_seq$x[ , , -1]\n",
    "test_seq$y <- test_seq$y[ , 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.5 Scaling\n",
    "To help our network train faster, we need to **scale** our data. In this assignment we will scale our data using the Min/Max approach, the same approach as we did for for the last assignment.\n",
    "\n",
    "We will scale the data so that the largest value in our training data will have value `1` and the smallest value will have the value `0`. To achieve this we do:\n",
    "\n",
    "$$\n",
    "x' = \\frac{x - \\min(\\boldsymbol{x})}{\\max(\\boldsymbol{x}) - \\min(\\boldsymbol{x})}\n",
    "$$\n",
    "\n",
    "Where $x$ is a single example and $x'$ is our new scaled value. $\\min(\\boldsymbol{x})$ is the smallest value in the training set and $\\max(\\boldsymbol{x})$ is the largest value.\n",
    "For the interested we recommend the [wikipedia article](https://en.wikipedia.org/wiki/Feature_scaling)."
   ]
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   "cell_type": "code",
   "execution_count": 14,
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    "# We initialise our arrays in the shape we want them to be.\n",
    "x_train_scaled <- array(0, dim = dim(train_seq$x))\n",
    "x_val_scaled <- array(0, dim = dim(val_seq$x))\n",
    "x_test_scaled <- array(0, dim = dim(test_seq$x))\n",
    "dim(x_train_scaled)\n",
    "dim(x_val_scaled)\n",
    "dim(x_test_scaled)\n",
    "\n",
    "\n",
    "for (j in 1:dim(train_seq$x)[3]) {\n",
    "    # For each feature we compute the max and scale\n",
    "    min_train <- min(as.numeric(train_seq$x[,,j]))\n",
    "    max_train <- max(as.numeric(train_seq$x[,,j]))\n",
    "    \n",
    "    # For each dataset and feature we scale the values according to the max/min\n",
    "    x_train_scaled[,,j] <- (as.numeric(train_seq$x[,,j]) - min_train) / (max_train - min_train)\n",
    "    x_val_scaled[,,j] <- (as.numeric(val_seq$x[,,j]) - min_train) / (max_train - min_train)\n",
    "    x_test_scaled[,,j] <- (as.numeric(test_seq$x[,,j]) - min_train) / (max_train - min_train)\n",
    "}\n",
    "min(x_train_scaled)\n",
    "max(x_train_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also need to map our labels to the one-hot encoding representation, as a first step we need to map the text to a numerical value and then we use the `to_categorical` function from Keras. The `category_to_label` function below maps the text to a numerical value which the `to_categorical` function can translate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "category_to_label <- function(category) {\n",
    "    if (category == \"Downstairs\")\n",
    "        0\n",
    "    else if (category == \"Jogging\")\n",
    "        1\n",
    "    else if (category == \"Sitting\")\n",
    "        2\n",
    "    else if (category == \"Standing\")\n",
    "        3\n",
    "    else if (category == \"Upstairs\")\n",
    "        4\n",
    "    else if (category == \"Walking\")\n",
    "        5\n",
    "    else\n",
    "        -1\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train <- to_categorical(lapply(train_seq$y, FUN = category_to_label))\n",
    "y_val <- to_categorical(lapply(val_seq$y, FUN = category_to_label))\n",
    "y_test <- to_categorical(lapply(test_seq$y, FUN = category_to_label))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. The model\n",
    "In this section you will implement the model. You need to construct a model which assigns one of 6 classes to each sequence.\n",
    "\n",
    "To achieve a score of `1` for the assignment you only need to implement the baseline model and achieve accuracy higher than 80% on the **test set**. To achieve a score of `2` for the assignment you will need to achieve accuracy higher than 90% on the **test set**. Keep in mind that the test set is easier than the validation set.\n",
    "\n",
    "We leave the model definition completely up to you but suggest that you start by trying some of the layers below.\n",
    "- `layer_dense`\n",
    "- `layer_conv_1d`\n",
    "- `layer_gru`\n",
    "\n",
    "**Answer**: this is just an example model. You can try different models. We have found stacked GRUs to work quite well. In our experiments we found convolutional or dense models without any recurrent components quite tricky to converge reliably. Regularisation may help a bit there, but be aware that you will need spatial dropout after a convolutional layer rather than ordinary dropout."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "________________________________________________________________________________\n",
      "Layer (type)                        Output Shape                    Param #     \n",
      "================================================================================\n",
      "gru (GRU)                           (None, 100, 16)                 960         \n",
      "________________________________________________________________________________\n",
      "gru_1 (GRU)                         (None, 16)                      1584        \n",
      "________________________________________________________________________________\n",
      "dense (Dense)                       (None, 6)                       102         \n",
      "================================================================================\n",
      "Total params: 2,646\n",
      "Trainable params: 2,646\n",
      "Non-trainable params: 0\n",
      "________________________________________________________________________________\n",
      "Epoch 50 - loss: 0.3332282, acc.: 0.8826069, val loss: 0.78216493079956, val acc.: 0.734864294528961   \r"
     ]
    },
    {
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eOxLNvwq+Xl5YGFJZiaVRcJSLC3V89KAYDdu3cfungRftsFX/4XFi+FXXvPPUrY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\nqVQhISEkSYaEhOzdu1ehUCgUil9//VUqleI6dkj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cz8wesHVLmzZt3nrrLW3dz4vgE6sl2LAcx704E73U/PoAACAASURBVGpimn9gxzA1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Rs3yi5IXiVqT2DlFF1MU0oaFVEUiWgpSSGE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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model <- keras_model_sequential() %>%\n",
    "    layer_gru(units = 16, input_shape = c(100, 3), return_sequences = TRUE, recurrent_dropout = 0.2) %>%\n",
    "    layer_gru(units = 16, input_shape = c(100, 3), recurrent_dropout = 0.2, dropout = 0.2) %>%\n",
    "    layer_dense(units = 6, activation = 'softmax')\n",
    "\n",
    "cat(summary(model))\n",
    "\n",
    "model %>% compile(\n",
    "    optimizer = optimizer_adam(lr = 0.001),\n",
    "    loss = \"categorical_crossentropy\",\n",
    "    metrics = c('acc')\n",
    ")\n",
    "\n",
    "history <- model %>% fit(\n",
    "    x = x_train_scaled,\n",
    "    y = y_train,\n",
    "    validation_data = list(x_val_scaled, y_val),\n",
    "    epochs = 50,\n",
    "    batch_size = 64,\n",
    "    callbacks=list(Progress$new())\n",
    ")\n",
    "\n",
    "plot(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you finished tuning your model evaluate your model and report the loss over the test set. Methodologically, you should only evaluate your once (or not very often). Try to keep to that convention."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2 Evaluate the model\n",
    "Evaluate your model and report the loss over the test dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$loss</dt>\n",
       "\t\t<dd>0.412324329925344</dd>\n",
       "\t<dt>$acc</dt>\n",
       "\t\t<dd>0.872340440750122</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$loss] 0.412324329925344\n",
       "\\item[\\$acc] 0.872340440750122\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$loss\n",
       ":   0.412324329925344\n",
       "$acc\n",
       ":   0.872340440750122\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$loss\n",
       "[1] 0.4123243\n",
       "\n",
       "$acc\n",
       "[1] 0.8723404\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model %>% evaluate(x_test_scaled, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Improving the model\n",
    "There are various ways of improving the model: increasing the model complexity, changing the model architecture, and preprocessing the data differently.\n",
    "\n",
    "Increasing the size of the data set is one of the most effective ways of improving the performance of a deep learning model. The cell below increases the data size five-fold (using the `step_shift` parameter) while reducing the sequence length, and evaluates the performance of a stacked GRU that has been regularised quite heavily.\n",
    "\n",
    "An even more effective, and also cleaner way of preprocessing the data would be to sample sequences per individual. In that way we prevent sequences from having data for two individuals caused by sampling across individual's data boundaries.\n",
    "\n",
    "We have found that it is quite tricky to train a model without recurrent components to have a performance higher than 0.90. It is certainly possible, but stability may be an issue. It also appears that preprocessing the data with convolutional layers before sending it to a GRU or LSTM has generally very little effect: a stacked GRU is usually just as effective."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>70658</li>\n",
       "\t<li>50</li>\n",
       "\t<li>3</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 70658\n",
       "\\item 50\n",
       "\\item 3\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 70658\n",
       "2. 50\n",
       "3. 3\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 70658    50     3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data <- load_activity_dataset()\n",
    "data <- data[, -(3)]\n",
    "data <- data[seq(1, nrow(data), 2), ]\n",
    "val_idx <- data['UserId'] == '24' |\n",
    "             data['UserId'] == '25' |\n",
    "             data['UserId'] == '26' |\n",
    "             data['UserId'] == '27' |\n",
    "             data['UserId'] == '28' |\n",
    "             data['UserId'] == '29' |\n",
    "             data['UserId'] == '30'\n",
    "val_data <- data[val_idx, ]\n",
    "test_idx <- data['UserId'] == '31' |\n",
    "              data['UserId'] == '32' |\n",
    "              data['UserId'] == '33' |\n",
    "              data['UserId'] == '34' |\n",
    "              data['UserId'] == '35' |\n",
    "              data['UserId'] == '36'\n",
    "test_data <- data[test_idx, ]\n",
    "# we select all entities in the validation set and the test set, and take the compliment.\n",
    "train_idx <- !(val_idx | test_idx)\n",
    "train_data <- data[train_idx, ]\n",
    "train_data <- train_data[, -(1)]\n",
    "val_data <- val_data[, -(1)]\n",
    "test_data <- test_data[, -(1)]\n",
    "train_seq <- create_sequences_x_y(data = train_data, sequence_length = 50, target_shift = -1, step_shift = 5)\n",
    "\n",
    "val_seq <- create_sequences_x_y(data = val_data, sequence_length = 50, target_shift = -1, step_shift = 5)\n",
    "\n",
    "test_seq <- create_sequences_x_y(data = test_data, sequence_length = 50, target_shift = -1, step_shift = 5)\n",
    "\n",
    "# Keep everything but the labels\n",
    "train_seq$x <- train_seq$x[ , , -1]\n",
    "# Keep the labels for the y data.\n",
    "train_seq$y <- train_seq$y[ , 1]\n",
    "\n",
    "val_seq$x <- val_seq$x[ , , -1]\n",
    "val_seq$y <- val_seq$y[ , 1]\n",
    "test_seq$x <- test_seq$x[ , , -1]\n",
    "test_seq$y <- test_seq$y[ , 1]\n",
    "x_train_scaled <- array(0, dim = dim(train_seq$x))\n",
    "x_val_scaled <- array(0, dim = dim(val_seq$x))\n",
    "x_test_scaled <- array(0, dim = dim(test_seq$x))\n",
    "\n",
    "dim(x_train_scaled)\n",
    "\n",
    "# For each feature we compute the max and scale\n",
    "for (j in 1:dim(train_seq$x)[3]) {\n",
    "    min_train <- min(as.numeric(train_seq$x[,,j]))\n",
    "    max_train <- max(as.numeric(train_seq$x[,,j]))\n",
    "    x_train_scaled[,,j] <- (as.numeric(train_seq$x[,,j]) - min_train) / (max_train - min_train)\n",
    "    x_val_scaled[,,j] <- (as.numeric(val_seq$x[,,j]) - min_train) / (max_train - min_train)\n",
    "    x_test_scaled[,,j] <- (as.numeric(test_seq$x[,,j]) - min_train) / (max_train - min_train)\n",
    "}\n",
    "\n",
    "y_train <- to_categorical(lapply(train_seq$y, FUN = category_to_label))\n",
    "y_val <- to_categorical(lapply(val_seq$y, FUN = category_to_label))\n",
    "y_test <- to_categorical(lapply(test_seq$y, FUN = category_to_label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "________________________________________________________________________________\n",
      "Layer (type)                        Output Shape                    Param #     \n",
      "================================================================================\n",
      "gru_2 (GRU)                         (None, 50, 32)                  3456        \n",
      "________________________________________________________________________________\n",
      "gru_3 (GRU)                         (None, 50, 32)                  6240        \n",
      "________________________________________________________________________________\n",
      "gru_4 (GRU)                         (None, 32)                      6240        \n",
      "________________________________________________________________________________\n",
      "dense_1 (Dense)                     (None, 6)                       198         \n",
      "================================================================================\n",
      "Total params: 16,134\n",
      "Trainable params: 16,134\n",
      "Non-trainable params: 0\n",
      "________________________________________________________________________________\n",
      "Epoch 30 - loss: 0.2444631, acc.: 0.9158623, val loss: 1.52518201224011, val acc.: 0.732136309146881  \r"
     ]
    },
    {
     "data": {
      "image/png": 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0880bp16wi/hCTZhFALLBEEuyTme7xr\nftt6+ESOl+Xi69RJadHCw6vsouAQRI8o2UTRK4luMcbfFy5g4TiOIRPL8gzDuN0Z//xD9VNC\nVyJKRHt31/J5b+/bl4hMLMMSGVhWr1ZrWJYXAgwxZpahM+emg6OPRGTkWO7MrTCV8CVoNBqT\nyRSNYpeYmBjZA0K0odih2JUNxS4cKHblslqtPM9XZLI0hyge9vgOer3/eH0HPN7DXu9xf+BK\nf21pGaahRt1ArWqoVre0WpIEoQ7P1VerNAxDRIWFhd26dSu8404afgcRkc9Hb0zu7vMuXbr0\nqmOXS5KkESNGbMg8QrffQXFxtGO7euWKJUuWKOGyd6PRWOB2O30+uyASUfGZocTgBxKR7cw4\nnCiR7cxnfUQuQSAityT5JCIipyj6JSkgSQ5BJCKbKEpELlH0SZJHkjyi6BElryS5RVH2kcVo\nC9Y+liETyxKRjmWD//aCE9xoGObMtYwswzBqIv2Zc9YsSWqW1Z9zpWNwY5YYM8fSmQsczVpN\nHYtV8rhR7ACnYgFAQUpr3AGXe1dJyXGOz/Ff4VVxkkjEkESUcUj104p3/vvfXs2aJqvO/q6z\nWq02m+3c52GsXbu20GQOtToiUqvp0cd+GzIgOzu7Xr16EfiqyvLjjz9u2L2HPp9HFgsRUffr\nfBbrk08+uWnTpii94hUxsqyG4+I5johSSFUJrxisek5RDEhUIggCkU0QAkROURR5lcjzeU6n\nNxCwiZJPFF2S5BJFnyiViKJPEl2iFOyLJYLokySX8h524jwTqZCidZvO+Nq1ptWpFaWDQxWC\nYgcAl+Pz+ZYuXZqRkZGUlNSnT5/k5OQIHtwtSoe93gMe72Gf74DHe8hz0WicWE6ri+e5Jmp1\nU426sUb97Ttv/6XV07DbiYgYoqbN/Ut++PV/M+6cMePyBykqKqKEhPNWWeOI54uKiqJX7Hbs\n2EFdu4VaXVDv2/7+8nOHw2E0Gi+9X1UlimJ2djbLspf6lmoZRstxZ6ZoPlslt2zZ8u233546\ndapBgwbjxo1r3LhxOC8X7HnZRcUHMzIYvT65QYMAy9oFUSAqEQS/JDlF0SuRRwo1QpsgiFJo\nTDE4MBn8/xJBJCK7KAqKH1PUhDGRIVQHKHYAcEnZ2dlDhw496vZQ8xZ0KvfVV1/96KOPwpkM\nrExeSTrs9R32eA96fQe93oMeb5bPf0VDK7VVfFONpplG3USjbqJRN9NqEs6ZrH/2mtU04T/n\n7dC8xdEt5Q+ANWnShDIyyOk4O4P/X/tUDNOgQYMrSXdleJ4nn++8VX4/wzBh3gtZtaxYseK5\n5547ceIEEaWkpLz99tth3iUwc+bMl15/nfr0o+at6O/98264YUF4U8rpWVZPZE1MaJOYUO7G\nYQpWQK8keUSJ12jsguD1em2CKFLoFubSE9MuSfKJokiMXRRLd5QkKVgTvZLkkSQisgmCSBQc\ngCSi4NNKgqXzKuJpGExxAkQodgBwGQ8//PDRBo3oueeJVxGRc+WKRx99tEOHDuGM2/kl6bDX\nl5WXf9Dr3V1UcsDjyfIHrmbYw+2i/82Y8cTjtzVvHryo6FKSkpKyT5w4b1XO8XAeltWzZ89u\nrVpufeE5eugRql2H/tpH0995+OGHozrxXq9evWZ+8QXlHA/NCENE3y7s3r27VquN3ovK4vff\nfx8/frz3wYfplt4kif8u+3HMmDGrV69u0aLF5Xc8duzYlClT6K13qW274BrftwsffvjhXbt2\nyVJ/g2UxSKfVEJE7moNkwQeZCESOc0YNS080l4giETlFyS+KPiIfw/RMiOJsLFCFoNgBQNlO\nnjz522+/0XdLgq2OiKhPP/sP369Zs2bMmIvmuSXKDwh/eTz7Pd6/Pb79Hs9hr++K7lStpeKb\nazTa3JOrvvmaxo0/+wmdnvbs1mT8Y27V8vJHGDNmzM5Jz1PbdtSkKRHRrj9o6ZIx8+eV+9Ic\nx82ZM2fSpElLJ4wTBMFgMDz00EOPP/54+OGvQs+ePUcPG/blg/dT33SyxtGO7dajme+uXh3V\nF5XF9OnTvQMG0cDBoeU77nRn/fv+++9//PHHl9/x119/9aU0KG11RESDhuTO+t/Bgwcjfvuw\nAhnP3E6bEMYThEvvio1yKKgCUOwAoGwlJSXEMGSNO29tfELpXcAFgrDL5dntDv3viub+TeC4\nljpNM42muUbdTKtprlEHL676/fTJVYu/ozFjSXWmTQYCVFiQkFD+CbU77rjj4MGDMx4aTykp\nFAhock9OmjSpZ8+e4eRJTEz85JNPPvjgg7y8vNq1a3NhvJVW3LRp03r16rV69erif4+06Nnj\ngQXzY/IOxCNHjtDdPc5b1brNkZ/Lr7CBQIAuGJnjOGKYQORmmQaIPSh2AFC2lJQUrUbj+XMn\ndeoSWuVwEMv+07HzvVk5ezzebF+4k83Gc1wLraapRl36//GXaE5paWnN6tc/NOMDeuS/xHEk\nCPS/GalJSZ07dw7nhV5++eV77rln586dPM937ty5du3aYSYM0mg00btbokzp6ekjR44Mf4Li\nqighISEz79R5q07lhlNhO3fuTM88S0cyqdGZR9qv/9mi15d7DhegOkOxA4Cy6XS6iRMnTv5i\nDhnN1KgRSRLp9fT61IVEZHNcZkcNwzTXalppNa20ms6JCW3NJuaC58ZeGs/zn3766V133ZV9\n5+3UsBEdO1pXxc+eN0+tVod5hAYNGkT1pge4UqNGjdrxwgvUqQs1aEhEdOggfbforpkzy92x\nRYsWE8bd9/GTj9Edd1LduvTXPlr83dT338dTswAuA8UOAM7jl6Q9bs/vLs82l3vHrX3pxt7l\n7mLluDS99hqttpVW01KjTtWoSx/TZLWYeZ6/oumJW7Ro8dtvv61du/bYsWMpKSm33HKLLvgM\nAKiaRo4cuW/fvk8fGEtNm5Egqo9kPPzww/369Qtn31deeaVly5aLFi3KWbWicePGD3799fXX\nXx/twABVGoodAJBNEH93u7c7Xdtdnj9dbk95Nz0YWfYanbadTpum07bTaRuoIzyBrVar7d+/\nf2SPCTJ6/fXXR48evX37dpZlu3fvnpqaWv4+RETEsuzIkSPvu+8+vV5vs9l8F0wQAwAXQbED\niH1Op3P69OkrV64sKSlp06bNU089lZaWdioQ2O5yb3W4trrcBzzey0+cxRA11Wo66LSd9LoO\nOm0zDSZDhSvTvHnz5s2by50CIPah2AHEOEEQ7rjjjm0FhTRiJNWseSovb8OGX5I47Ynynk1u\nZNmOel0nvbajXtdBp7VUyo2ioGSSJO3bty8rK6tOnTpt27ZlK+Xx9gBwRVDsAK6G1+v94osv\n9u7dq1aru3btOnz4cGW+yQmS9MGPy7Y1TKXJb5bOHCEQnbjE9jV5vp1O20Wv7WLQp2m1aozL\nwRm5ubn333//tj/+oFrJlJeX1rLFrFmzcJ8KgNKg2AFcMYfD0adPn4MuN11/A/n981966Ycf\nfliwYIFCup1blP50u7e73Duc7h1ut71JC2pyuekhmmjUXfS6rnpdN6O+vqoyHvcOVY4kSQ8+\n+OA2iaHvl5LJTC7XrmlvjRs3btWqVTH5DDSAqgs/kABX7PXXXz/IcvTZF6FHMtw5at39Y+fO\nnXvvvffKFSnXH9jp9mxzunZl5eyyO8t95APzz+FxXTp3N+i7GvSJPM6xQjkOHDiwZdt2+n4J\nmcxERHo9PfXMnqGDtm/ffu2118qdDgDOQrEDuGI///wzjb737IO2zBbql7527drKLHYBSdrv\n8W53ufe4vXs8nkMebzk7SBKFpiCR6J2pN3k9rw/GbacQrpMnT5LVSmbL2VUaLSUlnThxqbP6\nACAPFDuo7oqKinJycurWrWu1WsPcxefzkfr8KVK1umhPxCARZXp9u9yeXW73n27vPo/HJ5Yz\nLBfPc510ui563R9ffL7i44/pppvIYqHt22oW5L+zZk1U00KMqVu3LhUVUlEhxZ150rzLRbkn\n69evL2suALgQih3ECEEQRFFUXcklYkVFRU8//fSSJUsklmVEcfjw4a+//rrFYil3x/bt2+es\n/5m6nzkDJQi0YX2HvrddXfLLyPH7d7k8uz3eXW73brfHJlx+ThIioiY6XQetuote10mnbarV\nhO59mPjE6mtar1y5sigvt+2ggffdd184XyZAqWbNmt3Us+e6ya/QpBcpIYFsJTT1jU6tWnXs\n2FHuaMqSlZU1c+bMgwcPJiYmDho0KD09Xe5EUO0wUnnX4lR1+fllTHqvVqvNZrPL5XK5XJUf\nqUwqlUqr1drtdrmDhHAcFxcX5/V6lROJZVmz2Vz6BPpSBw8efPHFF3/99VdJktLS0l5++eVO\nnTqVezRJkkaNGrXmxEl68mlKaUDHjtLbb6Y3avj555+Xu29WVlavXr1sXbrRTbeQz0dLFzco\nyN+wYYPRaLzKr+2M/ICwy+3ZfeZ/eWE87FzHMm212g56XSe9trNe36xmjcLCwgrGiCCr1crz\nfJk/hnKxWq02m00Uy2/JlcNkMintWbFGo9Hr9fr95z0LOD8//5FHHvl53XqKj6eiwuu6d//o\no4/q1KlTCXn0er3SJigOPg3F7Xafu3L37t39+/f3dOxEaR3o9GlavnTCqFGTJ0+uhDwajcZk\nMjmdzgsiVVw4T/UFRUGxQ7ErW1Updrm5uT179izo3JUGDSGep5/X6JYsXrNmTblToe7bt+/G\n3r1p4fdnTy3ln6Y7hv2yYUM4jxjPzMx84403/vzzT57nb7jhhqeeeqpGjRpX8UUVC0Kww+3x\neHe7Pcd9/vL3IWqkVnfU69rrNB31ulZaTenzu4goPj4exe7yUOzKVWaxCzp27FhmZmb9+vWb\nNGlSaXmqSrHr0aPHgfYdadz40PKxozRh3MolSyphXBPFDkrhVCxUbe+//35BamN66tnQ8gMP\nuu32KVOmzJs37/I7Hjt2jGoln211RJRYg2omHT16NJxil5qaOnv27ISEBEEQLh5EvAy7IO71\nnB2TOxZek6ul4tN02jSdrp1Wk6bXWjFXMMikQYMGmLuuTHl5eQcOHKDJb55d1aAhpXXYtGkT\nTlhDZUKxg6pt//791LX7eau6dd//8Yxyd0xMTKSCAvL5SK0OrfJ6qKiwZs2akU3okaR9bm+w\nxu1yuzO9vnBGiuJ5rq1W0y70PFZdsgo/qgCKFhpwveCPLo5TztgwVBN4t4CqzWQyka3kvFUl\nJeFc69axY8cWKfUPfDCd/u9xUqnI56P33m3duHFaWloFIwUk6aDX96fLvcvt2eX2HPL6AmFc\n8GDi2Gu0wRqnbafTpqgxUTBAVVKrVq2UlJR/V62gO+4KrTqVS7v+6PrEY7LmgmoHxQ6qtgED\nBqx5/nnq3YdqJRMR2W20YP6gu0eVu6NKpZo1a9Y999yTOWIo1a1L2dlNa9b4dO5c7qrOch7x\n+f50eYJzkexzez1hNDk9y7bRatrqtMFhuVQ1Ht8FUIUxDDN9+vQhI0ZQbi61a0/5p+nbb25P\nT7/++uvljgbVS+zfPOF0Oi9eyXGcVqv1+XxlXh0sC47jeJ73esubZrayMAyj1+sDgYCiImm1\n2osvDR4/fvxXixdT1+6k4mn7tps6dvzuu+/CnPfE5/Nt3Ljx2LFjDRs27NmzZ/izpRQLwu8O\n5x5fYLvNtt1mLwyUf9m7hmXb6LVpen17g76DQd9Mq+GYyFc5vV6vnFuCiEin07EsW+aPoVx0\nOp3H41HOrz6NRsPzvNvtVs45O41GEwgElHMzh0qlUqvVHo9HUZGI6OJ3kF27dk2bNm3//v01\na9YcNmzY2LFjr+5vxSvF87xGo4n4m5ooiiaTKYIHhEqAYodiV7YqVOyIaNOmTRs2bAgEAl27\ndu3Xrx8ThcIkSNIBt2ebw7nD6frd4czweMv9yeEZprlO28GgT9PrOhj0rfU6VRSCXQDFrlwo\nduVCsSvXpYqdXFDsoFTsn4otsweo1WqtVhsIBCJ+Z/hVU6lUDMMoJw/HcXq9XhAE5URiWVat\nVpeZp3Pnzp07dw5+7PF4IvWKJYKw0+3Z6XLvdHn+cLvtYcwP3FCtStPp0vTaNJ22jVajZ9nS\nTwU8nsCZhNu2bcvNzW3WrFnFL+m7gE6nU85/MiLSaDQsyyotksfjUU6L4nme56qmsm4AACAA\nSURBVHlFtRaO4y413YksGIZRq9U+n085050EKecftkaj0Wg0fr8/4pFQ7Kqc2C92AFfkhD+w\nzeXa4fL85nAeCuMO1gQVn6bVdNDp2uu1aVptHF/OaZfff/99/Pjx2TYbJdag48d7dOn82Wef\nhf80MwAAgMtAsYMyFBUVffbZZ4cPH7Zarbfccsstt9wid6Lo+sfr+83p2u5yb3O5s8ubWE7F\nMG10mvZabQe97sbatRqp1eHPY1dSUnLfffed7Hkj3fcAcRwVF//y8vNPPPHEZ599VuEvAgAA\nAMUOLpKVlXXrrbcW1EqmTp2pqPjzceMeGDVqypQpcueKsH99/m1O1w63Z73DWe7zHswcm6bT\nddZru+h1nfV63Zn7VxN0uis6fbZq1aqTvIrGjafgKVqrlZ58Ztnokfn5+ZjeHQAAKg7FDi70\nxBNPFHToSM88T8Er/QcPnTVhXJ8+fa677jq5o1VUts//i9P1i8P1q9N16rKPYWWImmo1XfW6\nznptZ72+QYRmlcvLy6PatemcC++odm2JmLy8PBQ7AACoOBQ7OI/P5/vll1/o08+p9P7N+inU\n/dqff/65ihY7pyjudLl/cbo3OZx73Je7r4JnmFZaTWe9tqtBf51BHx+FSQrq169PR4+Q30+l\n86pk/MNzbOU8SR0AAGIeih2cJxAIiKJIGs15azVa5Ux6Eg5Bkna5PesczvUO5x63V7j0xBZq\nlknTaq816K816Dqdc441Snr37t38nXcOTp1Cjz5GZgsdyaQ3XrvnnnssFktUXxcAAKoJFDs4\nj16vb968+cF1P9Poe0Kr7HbavrVjVbjGrkAQ1tudP9udG53Oy8wYrGKY9jrttQZ9d8N5F8xV\nAq1WO3fu3EceeWTHoHTS6ViPZ9SoUS+//HKlBQAAgNiGYgcXevvtt/sPGUIOO3XuQsXF9PVX\n17dsOXjw4DB337hx4+rVqx0OxzXXXDNq1CidThfVtBLRPrdnjd25xu7Y4/ZcZnaSFlpND4P+\nBqOhu0FnOPcqt8rVqFGj5cuXZ2Vl5ebmNmnSJD4+Pvx9T58+PXPmzL/++stsNvfp02fo0KHR\nmIoZAACqLhQ7uFDXrl1X/vjjtGnT9r/7ttVq7TNo4P/93/+x4TWhSZMmzfryS+p1E5lM38z6\n9JNPPlm1alU0bgtwi9IvTucau3Ot3XHSf8nbIGry/I0mQw+DvodRn8Qr5V87wzApKSkpKSlX\ntNexY8duvfXWooaNqGs3KrH9+OyzGzZsmDFjRpRCAgBAVaSUtzpQlI4dOy5atCguLs7r9drt\n9jD32rBhw6x58+jj2ZTSgIjogQf/ff7ZZ555Zvbs2ZEKdsLr+7aweLXdsdnh8lziyjmOYTrq\ntDebjDca9W102pgZ0Zo4cWJRl2709HOh5QEDF427Z8Dq1b1795Y1FwAAKAiKHUTMmjVrqOeN\noVZHRBxHo+5e9cR/JUmq4BnDvz3eVXbHKptjt9tzqfsgavDcTUbDzSbjDUa9tVKeul2ZAoHA\n5s2b6eNzKnLNJLqux8aNG1HsAACgFIodRIzL5SKd/rxVeoPf7xcEgb/y06ABSfrN6Qr2uexL\nn2xto9XcYjL2Nhnb6bSVeBdEZZMkSRRFuqCw8nzgsrPxAQBAdYNiBxHTtm3bBR98SA+MJ402\ntGrjhtatW19Rq3OJ4nqHa6XNscbuKL7EQx20DHO9UX+ryXiryVhbVS3+DatUqvbt2/+xeiVN\n+E9old1Gv23pWhXuVgYAgEpTLd4UoXKMGjXqq6++2jvxcRo5ioxG2vorff/tGz/8EM6+BYKw\nyuZYaXNsdDi9l7h4LpHn0hPie2lUvYzGypyjRCHeeuutvn37em026tqNSkrou0U927QJ/25l\nAACoDlDsIGLUavW33347derU1TPeLykpadu27bOLF3fu3Pkyu+T4/evsztV25waH03+JPpei\nVt1qMvQ2Ga83GeMtluLi4ujEV7prrrlm3bp106ZN2/vl5xaLpe/ouydMmBDm3coAAFBNoNhB\nJMXHx0+dOnXq1KmX3+yIz7e8xL7C7tjlKvtmCJaog17bx2TqZzE2UqtDK6v9nG3NmjWbNWuW\n3CkAAEC5UOyg8hzweJfZ7MttjgOesh9QpmaY6w36vmbjbWZjTcVMOwcAAFBV4L0Tou4vj/fH\nEtsymyPD6ytzAyPL3mwypJtNN5kMRpxbBAAAuFoodhAte92epTbHjyW2Yz5/mRvE89ytRkO6\nxdTTaNBU+9OsAAAAFYdiBxG23+NdUmJbWmI/eok+V5Pn+5mN6WZjd4OeR58DAACIHBQ7iIwD\nHu/SEvtSm/1S51uTVXw/k3GAxdxFXw3nKgEAAKgMKHZQIUd9/h9KbD+U2A9e4n6IempVsM91\niOknQwAAACgBih1cjRP+wNIS+w8ltl1uT5kb1FGpBliMA82m9nod6hwAAEDlQLGDK1AUEJba\n7ItLbNudbrGsDZJVfH+zaZDF3FGvRZ8DAACoZCh2UD6PJK20Ob4vtm1wOn1iGTMKJ/LcAIt5\nsMXUWYfr5wAAAGSDYgeXJEjSmuKS+SfzfrLZHWIZI3Rmju1rMg6xWq436HB/KwAAgOxQ7KAM\n+zzeb0vsPxzIyPWVcYurlmFuMxsHW0w3m4xq9DkAAADFQLGLWU6n89133128eHFeXl7Tpk0f\ne+yxAQMGXH6XE/7Ad8Ul317iFleOYXoYdEMt5n4WE54PAQAAoEAodrFJkqT7779/beYReuAh\nqlnzr7177nvk0Y/c7hEjRly8sUsUl5XYF5XYtjhcZd4S0UGvG2oxD7KYavBctJMDAADAVUOx\ni03r169fu20bfbmArHFERM2aU1zcCy+8MGTIEJVKFdxGItrmdH9dVLzM5ijzErpUnXZkYvwA\nnbahWlWZ4QEAAODqoNjFpn379lHrNqFWF3T9DUVTXs3KykpNTc3y+xcW2RYWl/xb1lO/rBw3\nyGIaEW/tXbeOz+u12+2VlxsAAAAqAMUuNul0OnI6z1vldJJGu47jnzia/ZvTdfGcJWqWudlo\nHG4x3Wo2qhmG4zjcFgEAAFC14BL42HTzzTdrDh2knTtCy5JEOce5JcsmOTy/XtTq2uq0b9RO\n2tc0dW792ukWE250BQAAqKIwYhebUlNTX3nllWemvkn33U+du1B8PLW5Rjh/m5o8P8xqvsNq\nbqHVyJMSAAAAIgrFLgaJEm1yun695Ta+y7UBunD4Tc0ytxoNI+MsNxoNmFUYAAAglqDYxZST\n/sCC4pKvCouz/QEiovNbXQut5s44y3CrOYHDrCUAAAAxCMUuFgiStNbunF9U8rPDKUgX3hdh\n5tjBFvOdVkt7vVaWeAAAAFA5UOyqtpP+wFdFJfOKik+EhujO01WvGxVnGWg1a3HKFQAAoBpA\nsauSRIk2Op1zC0vW2B2Bi4boEjhuRJxlVJyliUYtSzwAAACQBYpdFZMfEOYXFc8rLMnyXzi3\nMEt0vdFwd5ylj8moZjFEBwAAUO2g2FUZO1zuOYXFy2x2n3jhEF0iz90ZZx0dZ0nBs78AAACq\nMRQ7pXOK4nfFtjmFxX97vBd8iiG61qAfE2/tiyE6AAAAQLFTsn+8vjmFxYuKS2yCeMGn4nhu\nhMU8Jt7aOIauohNFcdOmTRkZGcnJyT179jQajXInAgAAqGJQ7BRHlGiz0zmroHit3XHxE13b\n6rSj4yzDrRZdbA3RnTp16q677tpz5Cg1akS5uUkkffrpp926dZM7FwAAQFWCYndlBEHIysoS\nRTElJYXnI/zdK/QHPskvnFNQlH3R3CVahhlsMY1NiGuni8256B555JE9egMt/J50OhLFU/Pn\njhs37tdff7VarXJHAwAAqDKUWOxycnKmT5+ekZGxZMmSMjcoLCycM2fOnj17fD5fo0aN7r33\n3qZNm1ZCsLVr1z799NPZx48TMUk1a0yZMmXgwIEROfJfHu+ck3nfFZW4xQvPujZUq+6Jt460\nWuL4mH1cxIkTJzZs2EDf/kA6HRERy9Ld9+StXbNmzZrbb79d7nQAAABVhuKK3ebNm2fPnp2W\nlpaRkXGpbV577TW1Wv3KK6/odLoFCxa8+uqrs2fP1mqjO5S1f//+sWPHesaOo/6DiGFOrV09\n7qGHatWq1aVLl6s+pijRWofj04LiXxzOC866BucuGR1v6WcycrE+vXBBQQFxHCXWOLuKYahW\nrfz8fPlCAQAAVD2KK3Z+v/+dd97JzMzcuHFjmRvY7fYaNWqMGjWqXr16RDR69OhNmzZlZ2c3\nadIkqsE++ugjz4030/A7QsvpAyjn+Lvvvrtw4cKrOFqJIHxVVDK7rLOuFo67M858b3xcwzNz\nl2zatOnPP/9Uq9W9evVq2bJlBb4IhapXrx7PMIGDf1PzM1+dx0MZGampqbLmAgAAqGIUV+xu\nvPFGIsrMzLzUBiaT6dlnny1dLCgoYFk2MTEx2sGOHj1KPW86b1XLVke3b73S42R6fbMKixcW\nlTgvOuvaSq+712oebjXrWTa4xu/333vvvas3b6F27cjne3nKlMcefvi555672i9CoaxW6/33\n3/+/KZPp6eeoZSvKPUkfvt8htVHwHwMAAACESXHF7orY7fYPP/xw0KBBcXFxpSvffffdXbt2\nBT+uWbPm1KlTL96RYRgi0mq1anW404UkJydTbu55q3JPJicnh391/682+0cnTv1QUCic/xAw\nlqiX1fJInVr9ExNEQTj3U6+99trq/ftp/tcUF09E9M/h6f/3n549e/bt2zfMF60gtVpdObcv\nvP322xqNZsbjj/oDAklienr6hx9+WKNGjQs24zhOUbdTMAyjtEgsyyoqD8dxRKS0SGazWe4U\nZwW/RWazWbro8YByYVlWpVIpKg8RGQwGvV4vd5aQYCSNRiN3kJDgm5pOp4tspECgjKeQg8JV\n4WJ3/PjxyZMnt2vXbsyYMeeuz87OPnDgQPBju91+mXtXWZZlz4yNlev+++9ffscd1K07tWpN\nRHQkk+bPe+CD98u9N9Yrit/k5U8/fmKPw3nBp8w8N7ZW0sN1klPP3OvKnn+0r7/+mu65L9Tq\niKhJU0of8M033wwYMCDM2BXEMEzEb/4tE8/z06dPf/311zMyMurUqRMfH3+ZLSshT/gq7VsU\nPqXlIeVFUloeOlPv4DIU+C0K/x2kclzRm1o4lFPuIXyK++0Wpj179rz11lsjR45MT0+/4FPT\np08/d7HMC/DVarXZbHa5XC6XK8xX7N69+9OPPjr98Ud9KQ2IZSkz88H7x/Xr1+8yF/gXCsIX\nhcWfFRTnXfRHTyO1+v7EuDusZiPLktOR73SoVCqtVmu328/drKCggM4ZjCQiik/IPXygEu4q\n4DguLi7O6/VeECnakpOTRVEs8wtkWdZsNhcXF1dmnstLSEgQBEFRkeLj4wsLC+VOcZbVauV5\nXlH3wVitVpvNJl50LYRcTCaTRqMpKioSzh+wl5HRaPR6vf6LHkgtF71er9frbTabz+eTO0uI\nTqcjIrfbLXeQEI1GYzKZnE5nxCNVwpVOEFlVstj9/fffU6dOfeKJJzp06FCZr/vkk08OHz58\n69atgiB06dKlcePGl9rymM8/q6BwflGJ+6LnunbW68YnxoV5r2vTpk23/vknte94dtUfO5t3\nqtSvGgAAAKoKxRW74J+twVGi4F/5RqNRq9WuXbvW4/H079/f5/O99957AwYMSElJKR0GCG5T\nCfFSUlJSUlIus8E2l3tmfuFqm+OC0QA1ywyxmCckxLXSXsEFEM8991z/oUPJYqGevcjno28X\nJhzNfOjzz64qOwAAAMQ4xRW7iRMn5uXlBT8eO3YsEY0bN27AgAG7d++22Wz9+/c/cOBAbm7u\nggULFixYULrX+PHj+/XrJ09iIjozI937pwt+d3ku+JSZY0dYzQ8nJtRWXfF3u2vXrvPnzHnp\npZcyZ37IMEznzp3f/O67WrVqRSg1AAAAxBTFFbvZs2eXuX7ixInBD9q2bfvjjz9WYqJyuEVp\nQVHx/wqK/vVdeD1Kqkb9YELciDiLtgIzDPfu3bt3796FhYUajcZgMFQsLAAAAMQyxRW7KqRQ\nED4rKJ5dUFR40SXPXfW6hxLje5uMbISeGXGZu0QBAAAAglDsrka2P/C//IKvimyu82+sY4lu\nNhn/WyOhk74yLvgDAAAAOBeK3RX7pKDo5dzTgfNn99Gz7F1x5gmJ8fVVKrmCAQAAQDWHYnfF\nOut157a6BI4bm2AdFx8Xzytu8kwAAACoVlDsrliaTtvdoP/N6aqvUo1PjLs7zqqL1JV0AAAA\nABWAYnc1nktKPBUIhDnJMAAAAEDlQLG7Gl30OrkjAAAAAFxIWQ8wBgAAAICrhmIHAAAAECNQ\n7AAAAABiBIodAAAAQIzAzROVxO12r1u3Licnp379+jfddJNarZY7EQAAAMQaFLvKsG/fvjFj\nxmR7vFSvHv17LNVqnT9/fuPGjeXOBQAAADEFp2Kjzuv1jhs3LrtTV/r6W5r2Pn3zXWbT5vff\nf78gCHJHAwAAgJiCYhd1W7duPZJfQA89TBxHRMSr6P8e++uff/bu3St3NAAAAIgpOBV7BQRB\nWLRo0ZYtWwRB6Nq161133aVSqcrdq7CwkBISQq0uSKMliyU/Pz+KWQEAAKD6wYhduAKBwLBh\nw9587rk6xzIaHj82c8prffv29Xq95e7YqFEjyjlORYVnV508SafzcY0dAAAARBaKXbhmzZp1\n4q99u+67642e1752Q/c/x94pnjj+zjvvlLtj27Ztb+3Zk16YREcySRDo0EF6adLwYUMbNmxY\nCbEBAACg+kCxC9fatWsfSGtj0YSmKdHx/MMd2q1Zs6bcHRmG+eijj25v1YIZdw/dfAP3n/Fj\nund7++23o5wXAAAAqh1cYxcur9er1xnOXaNX8T6fL5x94+LiZsyY8dZbb2VnZ6ekpOh0uuhk\nBAAAgGoNI3bhat++/XeH/hElqXTNogOHO3ToEP4RDAZD8+bN0eoAAAAgSjBiF67HH3+817Jl\nwxYvH9euDccw8/86sKWgeOOkSXLnAgAAAAjBiF244uPjV65cae7U9f927H7wt51CyzZr1qxJ\nTk6WOxcAAABACEbsrkDt2rU//PBDuVMAAAAAlA0jdgAAAAAxAsUOAAAAIEag2AEAAADECBQ7\nAAAAgBiBYgcAAAAQI1DsAAAAAGIEih0AAABAjECxAwAAAIgRKHYAAAAAMQLFDgAAACBGoNgB\nAAAAxAgUOwAAAIAYgWIHAAAAECNQ7AAAAABiBIodAAAAQIxAsQMAAACIESh2AAAAADECxQ4A\nAAAgRqDYAQAAAMQIFDsAAACAGIFiBwAAABAjeLkDRB3Pl/E1chxHRCzLlvlZWXAcxzCMcvKw\nLEtESoukqDxBCoykqDwMw5DCIhERz/OiKMqdIiT4LQr+BpA7SwjLshzHSZIkd5CQ4K8jjuOU\n8w8pGElpeSL+pqacfwMQPqX8o4wenU538crSn8kyPysLlmVZllVOntL3Y0VFUtS3iJQaSVF5\ngj9rSouk1WqV844V/DtTaZE0Go1KpZI7SEjwW6RWq5VTpEpHB+QOEhJMolKpIhtJEIQIHg0q\nh1J+SKLHbrdfvFKtVqtUKp/P53K5Kj9SmVQqlVarLTOtLDiOU6vVfr9fOZFYljWbzcrJQ0Rq\ntVoQBEVFio+PV1Qeq9XK87zSIjkcDuWM2JlMJo7jnE6nct5EjUaj1+v1+/1yBwnR6/U8z7vd\nbp/PJ3eWkODfKm63W+4gIcEi7vV6Ix5Jr9dH9oAQbUr5awMAAAAAKgjFDgAAACBGoNgBAAAA\nxAgUOwAAAIAYgWIHAAAAECMic1esy+UqKSlJTk4mIrfbvXDhwoKCgsGDBzdq1CgixwcAAACA\nckVgxO7gwYMNGzacO3cuEQUCgR49etx7771PPvlk+/btd+3aVfHjAwAAAEA4IlDsJk2alJSU\nNHz4cCL65ptvdu7cOXPmzIyMjFatWr3++usVPz4AAAAAhCMCxW7Lli3PPPNMamoqES1evLh1\n69YPPvhgamrqf/7zn+3bt1f8+AAAAAAQjggUu+Li4uDVdYIgbNy4sW/fvsH1NWrUOHXqVMWP\nDwAAAADhiECxS0pKOnLkCBGtX7++qKjotttuC67Pzs5OSEio+PEBAAAAIBwRuCv21ltvff75\n5zMyMr7++uvU1NQePXoQUV5e3vvvv3/ttddW/PgAAAAAEI4IFLvJkyfv37//zTffTExMXLZs\nGcdxRPToo4/++++/8+bNq/jxAQAAACAcESh2ycnJW7dutdlsOp1OpVIFVz755JPvv/9+UlJS\nxY8PAAAAAOGIzJMnXC6X0+kMtjq32/3FF19s2rTJ6XRG5OAAAAAAEA5MUAwAAAAQIzBBMQAA\nAECMwATFAAAAADECExQDAAAAxAhMUAwAAAAQIzBBMQAAAECMwATFAAAAADECExQDAAAAxIgI\nFLsgv9+/efPmEydOsCxbt27d7t27m0ymSB0cAAAAAMoVgWIniuJTTz31wQcf+P3+0pUGg+Gl\nl16aOHFixY8PAAAAAOGIQLGbNm3atGnTBg8enJ6enpycLIpiTk7O4sWLn3rqqaSkpNGjR1f8\nJQAAAACgXBEodp9//vnjjz8+bdq0c1c+8MAD48ePf//991HsAAAAACpHBOaxO3LkSL9+/S5e\nP3DgwAMHDlT8+ArEeL3833vlTgEAAABwngiM2PE873K5Ll7v9/uDU5/EGO7USe2P37MlRW6V\nOtCkudxxAAAAAEIiMGKXlpb27rvv+ny+c1d6PJ6ZM2d27Nix4sdXFDb/tP6rOWxxIUmSbvVy\nxlYidyIAAACAkAiM2D377LPp6elNmjTp27dvnTp1JEnKzs5esWJFbm7u6tWrK358RRETawSa\ntQqdh3W7dCt+cI0YTWwE+jEAAABABUWg2PXt23fx4sXPPvvsxx9/XLqyTZs2n3766c0331zx\n4yuN59a++twctrCAiLjjWerffvFd11PuUAAAAAARmqB40KBBgwYNOnHiRE5ODsMw9erVi+Fn\nTkgqtWfAcN282YwQICLNts1C3fpCg0Zy5wIAAIDqLpLnEGvXrt2pU6eOHTvGcKsLEmrU9N1w\nU2hBknQrfmCcDlkTAQAAAFRgxK5587BuCD148OBVv4SS+dp35rKO8RmHiIhxObUrl7qH3kkM\nI3cuAAAAqL6uvtglJiZGMEfVwzCePgMMX37KlBQTEX80U/37Vl/n7nLHAgAAgOrr6ovdli1b\nIpijKpK0OnffQfqFX5IoEpFm8/pAnXpinXpy5wIAAIBqCvN0VIhQt76v+w2hBVHULV/MeNyy\nJgIAAIDqC8WuorxdrxPqNwx+zNpKtD8tJUmSNxIAAABUTyh2FcYw7vTBksEYXOIzD6t//03e\nRAAAAFA9odhFgGQwuvsPKX3+hGbzBu54lryRAAAAoBpCsYsMoV6D8y62+/E7zGwHAAAAlQzF\nLmK8Xa8LNEwNfsw4Hbrli4N3ywIAAABUDhS7yGEYT78hotkSXOKyjqm3bpY3EQAAAFQrKHaR\nJOl07v7DiOOCi5qtv/DHMuWNBAAAANXH1U9QHCUOh2PWrFl79+71+/3NmjWbMGFCzZo1L9jm\n+PHjn3/++aFDhwKBQMOGDe++++6WLVvKkvZiYu063h43aTasISKSJO3yH5xjxksmk9y5AAAA\nIPYpbsTuvffey8vLe+mll95++229Xv/qq6+K51+pJknSq6++GhcXN2vWrLlz57Zu3frll1+2\n2+1yBb6Yr0OXQNPQg3QZt0u37DsSBHkjAQAAQHWgrGKXn5//+++/P/DAAw0bNqxdu/aECRNy\ncnL27dt37jY2my03N/fmm2/W6/UajaZv374ej+fkyZNyZS4Dw3huGyBa44JLXE62ZuMaeRMB\nAABAdaCsYvfPP/+oVKqGDUMPcjAajXXr1j106NC521gslubNm69atcput3s8nlWrViUlJTVo\n0ECGuJcmabSeAcMkLnSmW/3n7/zfe+WNBAAAADFPWdfY2Ww2k8nEMEzpGovFUlJScsFmzzzz\nzIsvvnjXXXcRUVxc3IsvvqhWq0s/u2zZsmPHjpXuPnz48ItfiOM4IlKpVAaDIdJfxBmNGksD\nhzKLFwaXdKtXSHXrS8l1LrU5y7I8z0cxzxUK/ldQWiSWZZWTh5QaSVF5WJYlIqVF0uv1kmIe\n/cfzPBHpdDpFRWIY5tzfq/IKfou0Wq1KpZI7S0gwEssqZXAk+KamVqsjG0nAdURVkLKKHZ3p\nE5cRCAReffXV5s2bT5kyRaVS/fTTTy+99NKHH34YFxc69bl+/frNm0PzjNStW3f06NGXOpRK\npYrur4ku1wZO5AjbthARBfzsovmqRyYyl32H0+l0Ucxz5TiOU1okpeVhWVZpkZSWh5QXSavV\nyh3hQkqLFCwuiqKcollKOUUzKOJvan6/P4JHg8qhrB9dq9Vqs9kkSSqtdyUlJaWNLWjfvn1H\njx598803g78Hhw0btnLlyi1btvTv3z+4wYMPPjhy5MjgxxqN5uIBPzozEOXxeLxebxS/HiK6\n4Wb18Sz2eBYRSUWFnnmf+UbcTWX9RcXzvFqtdrlc0c0TNpZlTSaT3+9XVCS9Xu9wKOiRHmaz\nWRRFpUWy2WxypzjLaDRyHFfmj6FcjEajy+USFTN/uF6vV6lUdrtdOZF0Op3f7w8EAnIHCdFo\nNFqt1ul0KioSEUX9HSRsKpVKr9dH/E1NkiSr1RrBA0IlUFaxa9Kkid/vz8zMbNy4MRHZbLbs\n7OwWLVqcu40kSZIknfsb8IIf9aZNm567mJ+ff/ELBYujKIqV8OdIYMAww9xZwSeMsUczmF/W\n+a7tWeaWPM8r58+j4MB+5XyLwsSyrCRJyskTpLRICsxDCvu7P/gtUk6LCiYJBALKOe2l0WgC\ngYBy/qsFR6EEQVBOpOCIpnLyBM/AKupbBHJRyvUBQfHx8d26dZsxY8bR+/da+gAAIABJREFU\no0dzcnKmT5+empoanKNu7dq1y5YtI6LmzZvHxcXNmTPH4XD4fL7Fixc7nc6OHTvKnf2SJIPR\nPej2c2Yt3swfPiBvJAAAAIhJyip2RPToo4+mpKS8/PLLTz/9tFqtfv7554Oja7t3796xYwcR\nBSe3KywsnDBhwpgxY7Zv3/7SSy8lJyfLHfxyhNp1vT1uCi1IknblUrawjHFEAAAAgIpQ1qlY\nItLr9f/9738vXj9x4sTSj1NSUl588cVKDBUBvo5d2ZM5qoP7iYjx+bRLv3OPGiupFHctMAAA\nAFRdihuxi2He2waINUKPR+Py87Q/LSHFzG4AAAAAMQDFrvJIKpV74O3SmUkN+MMH1b/9Im8k\nAAAAiCUodpVKjIt3pw8tne5Es/UX1aG/5Y0EAAAAMQPFrrIJDVO9198YWpAkzU9LuFNKetAt\nAAAAVFkodjLwde7ub9Mu+DETCOiWfsu4lTIJMAAAAFRdKHby8N7SVzjz3FimpFj3w0JSzNyk\nAAAAUEWh2MlD4nj34BGSyRRc5HKyVT+vlDcSAAAAVHUodrKRDEbXgOHSmSdScH9sp53b5I0E\nAAAAVRqKnZzE2nW9t6afXV6xhMs+JlsaAAAAqOJQ7GTmb93W16FLaEEQdEu/ZYsKZE0EAAAA\nVRWKnfy8PW8RGqYGP2bcbt33XzNut7yRAAAAoCpCsVMAlnX3HybWSAotFRXqlixkcJMsAAAA\nXCEUO0WQNBr/7aPIYAwucsezNGuWyxsJAAAAqhwUO6WQLFYaNVbiVcFF1V971Nu3yBsJAAAA\nqhYUOyWpU8/bdyAxTHBJs3mD6uB+eRMBAABAFYJipyz+Zi193XuEFiRJs3Ipe+K4rIkAAACg\nykCxUxxvtx7+lm2CHzOBgH7JIqakWN5IAAAAUCWg2CkPw3hv6y/UqRdacjr0i79mPJgABQAA\nAMqBYqdEwSfJihZrcJHNP637YSEjBORNBQAAAAqHYld5RFHMy8uTJCmcjSWd3n373ZLeEFzk\njmdpf/yeRDGaAQEAAKBqQ7GrDE6nc9KkSSkpKa1atWrYsOGUKVO8Xm+5e4nWOPeQOyRVaAIU\nPuOQdt2qKCcFAACAKoyXO0C18Pjjj/+z+Zelg/q0SIjfk5f/xJdzS0pK3nrrrXJ3FJLrePoP\n1S1ZFByrU+3eKcbF+zp2jX5kAAAAqHowYhd1+/fvX7506eKh6TfUr1vToL+lYf2Fg/vO/eKL\n7OzscHYPpDb13NyndFGzcS3/996ohQUAAIAqDMUu6g4dOtQ4zlLPbCpd0yIhPtloOHToUJhH\n8Lft4Ot8bWhBknSrlnH/Ho14TgAAAKjqUOyizmq1nna5xXPumfAKQrHHa7FYwj+It8eN/hah\nye1IEHQ/fsfm50U2JwAAAFR1KHZR161bN3Vc/JRfdwSbnShJz2/6LTklJS0t7QqOwjCePgOE\n+g1CSx63/ruvWFtJxNMCAABA1YWbJ6JOp9N98skno0ePXnI4s2Vi/J68fJtas2DBAp6/wm8+\nx7kH3a5b8AWXn0dEjN2uWzTPdee9pVOiAAAAQDWHYlcZunTpsn379uXLl2dnZ1/foMGAAQMM\nhqtpY5JG6x5+l37B52xJMRGxRYW6hfPcI8dIWl2kIwMAAEDVg2JXSaxW66hRoyp+HMloct9+\nt37B54zTQURcfp7++69dt99dOt0dAAAAVFu4xq7qEa1x7ttHSbrQKB174rhuySISBHlTAQAA\ngOxQ7KokIbGma8jI0lE67limduWPFN7DygAAACBWodhVVWLtuu5BIySOCy6qDuzT/rxS3kgA\nAAAgLxS7Kkxo0MjbZyAxTHBRtXunevN6eSMBAACAjFDsqjZ/i9aem24rXdRs26LZ+ouMeQAA\nAEBGKHZVnj+tk++6XqWL6i0b1Tt+lTEPAAAAyAXFLhZ4u13v7Xpd6aJm0zr1jt9kzAMAAACy\nQLGLEb7rb/R1ubZ0UfPLOtWu32XMAwAAAJUPxS52eHvc5OvULbQgSdp1q1R7/pA1EQAAAFQq\nFLuY4r3hZn+7jqEFSdKu/Yn/e6+siQAAAKDyoNjFFobx3NzH37ptaFGSdCt/VB3cL2smAAAA\nqCQodjGHYTy9+/tbtAktiqJ2xQ/8fozbAQAAxD4Uu1jEsp6+A/3NWoYWRVG3cqlq3y5ZMwEA\nAEDUodjFKJb1pA/xN20RWpQk7erlqt07Zc0EAAAA0YVip3SnT5+eNGlS7969Bw0a9N5773k8\nnnD3ZFlP/6GBVmevt9P+vFL9x/Yo5QT4f/buPC6K8n8A+PPM7O7szXIfIuKBFyoqKt4nmmne\nWp55ZEmWRx5pZeVRv8wOtTzxyKNMS1PLNC+8UUO/4n0riiggLLD37O7M8/tjCFFXQF3YET/v\nP3yxs88885kR2A/PCQAAwOMgsRO1jIyM1q1bX9+1Y6S/ro+C3rx0cc+ePR0OR0nPpyhr526O\nOvXzXxLCJOxkjh4qpWgBAAAA4FmQ2Ina559/3sxb+/frPUdERY5uGHVoyOuGm9eXL1/+FFVQ\nlK1zN0eDxgUHZIf3MQf3uj9WAAAAAHgaJoR4OobS9aQbxFh09/54SKGhoYuaR3euEl5w5MvE\nf5O9A7Zu3fp0VRPC/bmJK9RWR7fvRHfqWmw86MkP0CPE9r8Gj6hY8IiKBY+oJEQYktiUxiNy\nOBwymcy9dYLSJvF0AKUuOzv78YMymUyr1VqtVovFUvYhuSSVSuVyudFoLHyQ5/nHf04dDofL\nmypGi7aMwyE7cUx4xSXssun1tthXEcYui9M07e3tzbLsIyF5EEVRWq02NzfX04E84Ovry3Gc\nqELy8fHR6/WejuIBnU4nkUie5Tu21Oh0OoPBwPO8pwPJp9FoGIbJzc3lOM7TseRTq9Usyz7F\nqI9SplQqlUql0Wi02+2ejiWfQqFACFmtVk8Hko9hGI1GY7FY3B6Sn5+feysEpQ26YkWtZcuW\ny0+fK0jtTHbHr+cvt2zZ8lnqwpht14lt1rrggDT5hGLr75hzuiFQAAAAAIgAJHaiNmPGjCSD\n+dUNm5cnn/vxRHKLtRu8q0WMGDHimSu0t2xrb9W+oJVOcvWS4o8N2CGWP4IBAAAA8DwgsRO1\ngICAgwcP1u7afXWO6S8nfuO9MZs3b5ZKpc9TJ9u0pbVzd0Tl/9fTKdcV69dgq1i6pAEAAADw\nzMr/GLsXna+v74wZM9xbp7NOlFUul/+5SeiHpdPvKtetsvQbRLRe7r0QAAAAAMoStNi9pJzV\nalj7DCD/TXei9FmqX1dR+izPRgUAAACA5wGJ3cuLq1TZ2n8oUSiFl9iQp1z3E516y7NRAQAA\nAOCZQWL3UuMCgy0DhxX0wGKrVfH7L9JL5z0bFQAAAACeDSR2Lzvex888YBjv5y+8xJxTvu0P\n2fHDno0KAAAAAM8AEjuAiNbLMugtZ+Wq/70mzMEE2fYtSDTLpQIAAACgJCCxAwghRGQya+8B\njqiGBUckZ045flqKbGJZVx0AAAAAxYLErtwymUzTp0+vW7ducHBw69atN2/eXMwJFGXr2JVt\n3qZg+WL+6iW0YjE25JV6rAAAAABwB1jHrnwihIwcOTLr7JkFLWNCNeojd+5Oev99m802YMCA\nok7D2N6iDe+lU+zalt8Pm3FPtW6ltecbXFBI2UQOAAAAgGcGiV35tHfv3lNHjpwZOdhPqUAI\n1Q/091Mqxn32Wd++fYvduMJZJ8qi0Sr+/B3bbAghbDQqfl3Fdu7mqFW3LEIHAAAAwLOCrtjy\n6dy5c00rBAlZnaB7RJXc3Nzbt2+X5HSuUmV28FvY20d4iZ1O+bbNzMG9iJBSCRcAAAAA7gCJ\nXfmkVCpzWXvhI3k2FiGkUqlKWAPvFyAdMxkVTJVFSHb8iGLTOqEZDwAAAAAiBIld+RQbG/u/\nLP3OG/nbSBCEZhw+3qhRo6CgoJJXglUqNPQde8PGBUckN68rf14OO48BAAAA4gRj7MqnKlWq\nzJw5s9+0aZ0rhYZpNYfv3E3j0V9//fXUFVEU2+FV3sdPnrAT8TxCiMrRK3/5yfpab65QYx4A\nAAAAxABa7Mqt4cOH79mzp+prPXJq1unz/tjjx49XrfqMqZijQWPLG28SZX43LrZZlZvWwZA7\nAAAAQGygxa48q1WrVq1atdxSFRcaZhn8lmLzeup+JkIIESI7foS6n2nr0pMoFMWdDQAAAICy\nAC12oKR4L51l4AhnRM2CI5IbV5Vrl9EZ9zwYFQAAAAAKQGIHngKRyaw9+rHtX0E0LRyh8nIV\nv6yUnTzu2cAAAAAAgCCxA08NY3t0jOX1wUSlzj/AcUzCTsXfW7DD4dnQAAAAgJccJHbAhbS0\ntClTprRu3fr1119fv349eWySBBdayfzm21yFigVHJBfOKH5ZASuhAAAAAB4EiR141OXLl1u2\nbJmWsLs3Q9XLTp81dcr777//eDGi1ljeeNPeqCnCWDhC389UrlkmPZtctvECAAAAIB/MigWP\nmjBhwuDqVebFthFeDq1bu/FPv+7p1Ss2NvbRojTNtuvEVawk37E1f2NZh0P+z5+SG1dtr7xG\n5DBbFgAAAChT0GIHHsKybFJSUlyDugVHKmo1r1YLP3DgwJNOcVarYRn8FhcYXHBEcuWicnU8\nlZZaurECAAAA4GGQ2IGH8DxPCKH+610VSDDF83xRZ3n7WgaNYJu3KeiWpQx5qvWrZUcOoCJP\nBAAAAIAbQWIHHqJQKOrXr7/67IWCI5lmy/brKc2bNy/mTJq2t2hj7TOgYIMKxPNM4gHl779g\nQ16pxQsAAACAB2CMHXjUd99916VLl3smS6fKlbKs1gUnkpu0bdulS5eSnOusXM08PE6+favk\n5jXhCH37pmrVUrZNrCOqYWlGDQAAAABosQOPqVev3r59+5yR9b5NSdtqJ29/OGXFihX44c7Z\nIhClytpnQOFFjDFrk+/apti0DptNpRY1AAAAAKDFDrgSERGxZMkSb29vlmWNRuNTn4+xPTqG\nCwmV/72FyskWjkluXFOtWmrr1LXwpmQAAAAAcCNosQPup9frb968aQ8IsgwbZY9pUTCjAlvM\nii2/yf/ciK1Wz0YIAAAAlEuQ2AF3unDhwquvvlqjRo0mTZrUrFlz5dq1bOsOlr6DiNaroIz0\n8gXVmnj65nUPxgkAAACUS9AVC9xGr9f379//1QCfDe8O95bLt1+/+d6n0zQaTd++fc1DR8kT\ndkrOnxZKYkOecuMvzjpRtradiALWMQYAAADcA1rsgNusWbMmFJEFndpV0KiVUknfmhH/17bF\n7NmzEUJELrd26WHtM5Co1AXlJedOq1Yukpw77bmQAQAAgHIFEjvgNtevX29WIajw4sYtQkNu\n3brlcDiEl84q1czDRjlq1C4ogC1mxY6tis0bsOnpp2gAAAAA4GGQ2AG38fX1vWN8aEGTVIPJ\ny8tLKpUWHCFKla17X2ufAYVH3UmuXVatXCw7eRwRUnbhAgAAAOUOJHbAbfr16/d3SupfV28I\nLzPNlo/3HxkwYMDjJZ1VIszDRtkbNnkwYZa1MQk7lb+uou9nll3EAAAAQPkCkyeA20RGRs6e\nPXvIRx/VTPzXWy4/cS+jSatW06ZNc1mYMHK2Q2dn9Zryf7ZRuXrhIJ2WqlwTb2/YxN6iLZHJ\nyjB2AAAAoDyAxA640+DBg9u3b3/gwAGj0TgxKiomJqbo8lzFcMvwOOnxI8zxw4jjEEKI52Un\njkkvnrO17uCsE1UWQQMAAADlBSR2wM1CQkJcdr8+CZFI7C3aWCpV1q9eXlWW/w2JzSbFjq3c\n+dO22C68r1/pRAoAAACUNzDGDojCxz8s6P/L74/MvaBvpyhXL5UdSsAOu6cCAwAAAF4gkNgB\nz9Pr9WvWrFnRtWOoRv3IW5jjmGOHVcsXSs6dhjmzAAAAQNEgsQOed+fOHUxIvYCHulyv6HMK\nvsYmo2LHVvnqeJJyo8yjAwAAAF4YkNgBzwsICHDy/G3DgzWKeUL6/PH3v37BRP5gwzE64x6/\nfKF82x/YkOeJMAEAAACxg8QOeF5QUFDnzp1H7dirt9kQQk6e//RgokWhDO3b3zTyPUeDxoj6\n7xuVEOnFc6oVi2RH9mM7DLwDAAAAHiK6WbEmkyk+Pv7MmTMOh6NGjRpxcXEBAQFPKrx37975\n8+d//PHHTZs2LcsggdvNmzdv2LBh1ZesquHjfdtglPv6rVixQqPRIIRssa/ao6Ll+3fR//XD\nYqeDSTwoSz7JNm/tqNcQ0fTJkycPHz7McVyjRo1at27t0VsBAAAAPEZ0id28efNMJtPnn3/O\nMMy6detmzpz5ww8/UJSLlsXc3NzVq1fLYBnbcsHX1/fPP/88ceLEtWvXgoKCmjVrJpfLC97l\n/QMs/QZLb6fI9+1EmRnCQWwxy/fsYJKOrk3LHBe/vE1YRYamF3z3XZtOnZYvX07TtIduBQAA\nAPAYcXXFZmVlJSUlvfPOO5UrVw4JCYmLi0tLSzt79qzLwkuWLGnbtq1SqSzjIEEpwRg3btx4\nwIAB7dq1K5zVFeDCq9DvT2LbdiSKBwPvcF7um2rZ3bHvbOnbbUOvLmdHDj536OCiRYvKMHAA\nAABALMTVYnf16lWpVFq5cmXhpVqtDg0NvXz5clTUozsQHD169Pr16+PHj9+/f/8jbyUnJ2dl\nZQlfK5XKunXrPn4hiUSCEKJpmmEY997CM6NpmqIo8cQjtJKK6hFhjLFEglu2dTaKoY4coJOO\nIadDeEshyf9ODlKrpjRrtGjz5kmTJpVNSKL6X0MIYYzFFg9CSGwhyWQyIprVc4SfNZlMxvO8\np2PJR9O0VCp12VXiEUIDvFQqxf/tLu1xwoeIeL6xhXgkEol7QxLP9yQoOXEldgaDQaPRFP7R\n9fLyyst7dAqkyWRasmTJBx984LJdZ/Xq1YcOHRK+Dg0N3bJly5MuxzCMeH4sBVKp1NMhPEQi\nkQgD3cRDo9EgjQb17EfaxnK7tzuSjlEP/67vEVH1UI6hzMKmKEqMj0hkxBaSWv3oiokep1Kp\nPB3CQ8T2uwghpCjUVC8SYvsEcfuHmsPhcGNtoGyIK7FD//19X7QVK1Y0bNiwfv36Lt997bXX\nCt7SaDRms/nxMjRNy+Vyu90unu9amqYlEgnLsp4OJB/GWKlUOp1OUYUkl8utVmv+a6kMdek5\ne+/BDiZ9swrBBcV8FPKVbZrZ5n7Fx7Qk9Rqg0vwTX6VS8Tz/ICQRUCqVFovF01E8oFAoKIpy\n+WPoKQqFwmaziafFjmEYiURitVrF0zrCMIzT6eSE7ZtFQCqVymQym80mqpCQmPIeoa3O7R9q\nPM+LMMUHRRNXYqfT6QwGAyGkIL3Ly8vz9vYuXCY5Ofl///vfggULnlRJhw4dCr8s6JYtTCaT\nyeVyp9Mpno9koZdBPPHQNK1UKjmOE09IFEXJZLJH4ukT9267du0+bVRvRN1IhfTB9zNOv0dv\n/Z07csAe05yrUZvQrr/V//zzz59++unWrVvh4eFvvfVW165dnyokpVIptsROoVCIKh6GYSiK\nEltINptNPFmURCKRSCSiylpommZZVjxZi9B7brfb7SJb5Eg839hCW53D4XB7SGJrbgfFEssQ\nCkFERITD4bh+/brw0mAwpKam1qpVq3CZ3bt3m83muLi4QYMGDRo0KC8vb+7cuV999ZUn4gWe\nFxoaunHjxg0Gm+/8pW1/2XgoM7twEx2dlan4e4tq4ffyXdvojHuPnDt//vyJo0d3pLi5jaPa\nI8e4Ue8sXry4bMMHAAAA3AmLpz9CMHv27IyMjLFjx8pksuXLlxsMhm+//RZjvHv3bpvN1q1b\nN6PRWLhz8IMPPnjzzTdjYmK0Wq3LCp/UYqfVai0Wi3g6raRSqVwuNxqNxRctEzRNe3t7sywr\nnpAoitJqtbm5uS7ftVqtTqdTo9HQGfdkxw5Lrl56fG9ZLijEUbe+s1Ydwsjv3bsXHR29o2+3\nlhUrCO/uu5XaY/P25ORkf3//Eobk6+vLcdyTQvIIHx8fvV7v6Sge0Ol0EonE5Y+hpwg9A+Jp\nsdNoNAzD5OTkiKfFTq1Wi6rFTqlUKpVKg8EgnhY7YcCfqFrshKFHbg/Jz8+v+EJATMTVFYsQ\nGjt2bHx8/PTp0zmOi4yMnDZtmtAtm5ycbDAYunXrptFoCrcMY4w1Gs2Tsjrw8igYWM0FBlt7\n9KOy7jPHj0gun0eFPizp9Lt0+l2yb7ezes0soyXCS1OQ1SGE2lWq6CeTJScnd+zYsayjBwAA\nANxBdImdUqkcP37848cnT57ssvyaNWtKOSLwQuL9/K1de+K2sdJL5yVnTtFZmQVvYadDeuFs\nc4SOvtm/8CkEIQfPSSSi+6EAAAAASgg+w0B5RlRqe3SMvWETOi1VduYUffkCdj7oXWIkD+1O\ncTYzq15ohUYNGpSk5szMzLlz5546dUoqlbZo0WLMmDFiW64CAADASwgSO/ASwJgLDbOGhuEO\nr0gunpNdOEvdvfP4CLx6AX5/93yVrFrMV6jIhYY5K1bigyu4nE6bmZnZtm3bKJXinVoRDo5f\n8fOaf/75Z+fOnWJb1AoAAMDLBhI78BIhjNxRv5GjfiNsNkmuXZFcu0Sl3KT4h0asY4eDTrlB\np9yQIURoCR8UzAWFcAFBJCiY8/FDFIUQmjFjRmOtalOfbsL828F1arVYs2Hx4sUuRxEAAAAA\nZQYSO/AyIiq1I6qhI6ohdtglN69Lrl6mb17Fj80mw5yTTkul01Lzz6IlfEAgHxBU6f69zjHR\nBauqyCX0gMgaexMTIbEDAADgWZDYgZcakcoc1Ws5qtdChND3M+nUFPrOLTr11uNJHhLyvHtp\n9L20WY0f3fWkcXCg1UGorEyi8yaSohZqt9vtixcv3rhxY2ZmZrVq1caNG9epUyd33hIAAICX\nGCR2ACCEEMKYCwjkAgJRdAwihMq+T6feou/cpu+lUXnFL1PXJiy0DULopyUIIaJSE50356Xj\nvbyJTsdrdcRLx6s1iKYRQmPHjk1O2DO1WeNK9Wsnpt19Z9iwb3/4oW/fvqV9fwAAAF4GkNgB\n8BiMeb8A3i/A0aAxQgjbbFRmOp2ZTmekU5n3KH02KnJtW2w2YbOJ+q8Dt6BOotbkYaqnw7x4\naH+tTIYQah1WoYqX19iPPurevbtMJivNWwIAAPBSgMQOgGIQuZwLC+fCwoWXmHNSWfdtd24n\n794lzcutqJBXVMrpYndwIQQbDTqE+tWMKHz49drVW4VVYNYsY4JCeJ2O1/nw3j7Ex5colI/X\n8e+//86ePfvcuXNarbZLly4TJ0708vJyz00CAAAoFyCxA+DpEFrCBQZLA4MbR8fkbymm11OG\nPCpHj3P1VF4ulZeLc/VUXh5mbSWpMFitQjnZKCf7oaswct7bh9d5E19/3s+f8w88eulyn169\n3m8U9VnndllW2/db/hhw4sSff/4JKyoDAAAoAB8JADw3iuJ13rzOG6GqDx23Wui8XMqQhw15\nOC+Xyst1ZGWymRm6Eix3h1mbsAFawZGWHHdp1NBgdf4yyJ0qhzVY8cuGDRsGDRrk1psBAADw\nAoPEDoBSo1ByCiUXFFL42PKlS3/8v/8bUT+ytp9PttVGSaR9O7T3IjxlKKaFT07TBVkdQkgl\nlZ56a1BayhX53n84vwDOz5/4BZAnp4xpaWn79u0zGAx169Zt1arV898cAAAAEYLEDoAyNWrU\nqEaNGm3cuHFDenqNenVGjBghDQiwIIQQwlYL1mdTOXoqV0/l6KkcPZWbU0S2p5JKqyOE/vdv\nwfIqRKXmAgLlai3vnT9Wj/f2IbRk3bp1U6ZMqa3T+sjlc+9l1G3S5Oeff1YqXQzjc4ll2Rs3\nbgQFBXl7ez/XzQMAAChlkNgBUNaio6Ojo6MfP04USlJByVeoWPggNhrorPvU/YyjWzZXwqSy\nTltEzdhs4m+aHllGz8HIo2/dPj9iQAW1GiFktNvf/Wfv1zNmzPj662JDdTgcX3311dKlS+12\nO0IoNjb2m2++CQ0NLf4mAQAAeAIkdgCIGtFonRotqly1QliVDq+8EsQ7+9eqoZJK9KytU8OG\ndf39qLycx/e9LUzK2hoFBRS81MhkP3d/FSFE5n1FtF5ErSFqLafRELWGaLS8UkU0WqJUCavu\nff3119vWrtne57WmFYJTDcbJCYeGDRu2fft2WJwFAADECRI7AF4MQUFBR44cWb169bEzZ7y8\nvLp06VKlbVszQtjpoLLuU1n3qewsKjdbmpfHZ2Vijiu2Quxw4OwslJ2FXP0iIEqVUy7vlH57\n0uB+/koFQqiyzuvnHp1f+23rvh07XunRoyQx6/X6f//91+FwVK1aNSgo6GlvGQAAwNOCxA6A\nF4ZarX7vvfceOUgkUi4opGCKhsLHR5+dTRkNWJ8ljNJLST7FZ6TX9H264XHYYpZazB0qPdTr\nKqclewb0QVdOk7nniVpDVGperSEqNVGreZWGqNREoyVaLyKTIYTWrVs3bdo0NeEZmr5rtY0Z\nM2bq1KklD8Bisdy5cyc0NLTkYwEBAABAYgdAuYMxr/VCWi8uvCpCSNkwpl27dq21qumtmvoq\n5Kcy7m+7duP17t3rVqyITQZsMmJDHnY4nu4KTifOzUG5ObSrdwnDWCTSaucvnhjYp5KXBiF0\nOVs/6td1f4RX6t1/QLGVG43Gzz77bN26dTzPUxT1xhtvzJo1C5ZiBqAc69+//7Zt20wmk6cD\nKQ8gsQOgnPPy8lq/fv2HH34YsWQVQsjHx2fKlCnVRoywFiqDWRYbDdhiwiYjZTZjkxGbzZQx\n7961az4SSvmUayBjllWxbIfwB7NAavj67B/cF6VeJT/OIRotr9ESpZrXaIlKRTRaXqkmGk3B\nwL4JEybcPpZ4eHC/ugF+F7Ky39+5b/z48T/99FNJLn337t05c+b8+++/Uqm0VatWEydOhJm8\nAJQnycnJDRo0IMVu9vMSg8QOgPKvevXqW7ZsycvLy8vLCw0NpShNCDSVAAAgAElEQVTqkQKE\nYQjjj5D/I8cdWVkdBg7MuHq1VViInSNqtXr8W8OrBQdTJgM2m7HJSJlN2GIuevZGYdhmwzYb\ndT/T5btEqbLRktEKKnpAH5VUghCKCvD/s1+31zdvv378WLV6UUShKKLyjIyM9u3bN/XWfl67\nhoPnV2z/q/Pu3QkJCSqVqoizAAAvkEOHDnk6BLGDxA6Al4WXl9fTdmj6+fn9888/hw4dunz5\nclBQUNu2bbVarf2RQjyPLWbKZBR6dSmTiTIZrv/vpM7pCFQ93fA4bDErEGpdsULhg95y+e4B\nvdHBXejgLkRRRK4gShWRy4lCSeQKolAQuYIolEQu37hsWf/w0O86tBZO7Fczou0vG+fNm/fJ\nJ5+U5OqJiYm//fZbenp6RETEO++8U7FixeLPAQA8PULIrFmzli1bdv/+/YiIiM8///yRAuvX\nr//+++/PnTsnkUiqV68+adKk/v37I4Q6d+68c+dOhBDGODo6+sSJE0UUfmlBYgcAKApFUW3a\ntGnTpk0RJYhaw6k1hY/pw6rFvPrqd22aDa9Xh8LoVEbm+gtX3uzbp0ZQIM7LxSYTNhmw0/nU\n0fA8tpixxezyzakBOvRfVocQklDUttd7pLMG5e+/8AyD5HLCKAjDEB8fCU94huFlDGLkRC5H\ncvmCpfGzv5g1KLJWO532+N5dzVet2rhxY0xMTAnjSktLO3/+vFarjYqKUhTZrAgA+Oabbz7/\n/PNBgwYNGzZMr9fPmDHDUWiY74YNGwYMGNCrVy8h4VuwYMGAAQM0Gk3Xrl1//PHHyZMnb926\nNSkpSWiJL6Kwp+7O4yCxAwC4X506dRYvXvzhhx9+mHBYRlN2WjJ16tSwEaMeHthnw0Yjtpiw\n0UiZTdhkxFYLNhlTzp8LlMk0jBuWytMxjI5hUMr1wpM8eIQe33ltIs9PHBcnpfM7qS9l55z5\nKV5mNWC5oiAp5BkGyRVCIkjo/F+ePM9/8sknq1auDFarjKxd7u09b968Dh06lCQ8QsjWrVt3\n7dqVl5dXo0aNuLi4gICA4k8D4EVGCJk/f36dOnV+/vln4UirVq0qVapUsDrmjRs32rdvv379\neuFIq1atfH19f/31165du0ZERPj5+SGEGjVqVGxhD9ybOOByPwIxKyvr8YMymUyr1VosFovF\nUvYhuSSVSuVyudFo9HQg+Wia9vb2ZllWPCFRFKXVanNzcz0dyAO+vr4cx4kqJB8fH71e7+ko\n8tlsttTUVLvdXrFiRa22qD0zCrt06dKQIUO47OyYCoF6G+vn7//ZhAmhPt7YaqVsVmQxUxYL\nslkomw1ZLU87n9ddiESKFHKeUaRlZ1++cbNRcKC3nCGE7E9NW3fp2idffhlQKZzIFUQuJ4z8\nSZWMHz9+xx+bRtSL9Fcqdt28dSLHsHPnzqpVq5YkAIfDsWPHjosXLwYGBr7yyivBwcHuuzmk\nVqtZlnV46Nk+TqlUKpVKg8Eg7IAiBkK7rNVqLbZk2WAYRqPRmM1mt4ckJFJudPv27UqVKo0d\nO3b+/PkFB5s3b37mzJknzYqtWLFi5cqVDx48iBAaOXLkihUrikhdChd+OUGLHQCgtMjl8saN\nG0skEpd/Xz1JzZo1jxw5smfPnlu3boWFhcXGxjIM86QUA3McslmxxYJZK5ub++2sWX68MyYk\nWEZT98xmfx+fRpGRlMOObTZks2Gn2zIV7HQgo4M2GsMQCqscln8Q43Zhoe3CQtGB3YWKYiG9\nI3IFkj9o9ruedtf78vnTIwb5q5QIoXGNG3y8/8jUiRN/37Kl2Kunp6f36dPHfO9udFDAfpN5\n+vTpCxYseO2110oYfG5u7qlTp1iWjYqKcm9GCECx0tPTEUL+/g9N1QoJCTlz5ozwtcFg+Pbb\nbzdv3nz79m2z2YwQ4jiuUqVKLmt7qsIvCUjsAACiI5PJunTpUpKShKaRSk1UaoSQJLTSuFVr\n4+Pjvzp+XCaTtWzZssXQoVZpob1zOQ6zNi9GZs7O5i1mimWRzYZZa8bt27u2bH69Zg0tk1/Y\n4nAShISZuc+LEGy1YqsVoRyEUEGncC2Evm3fqnDB/2vbAiFEvvsCMQxh5ISRI7mcMAySMbxM\nRmQMYhjCKBDDrPnuu+7ems9eGyqTUAihn89dHDNmTIMGDSpUqICK89tvv3388cdyp0Mhkdy1\n2kaPHl3CySUCo9F4586dkJAQWFkQPBuXjW1coc1yunXrduTIkSlTpnTu3Fmn02GMX3nllSfV\n9lSFXxKQ2AEAyg+FQjFu3Lgnvk3TRKlCOh0nZXieL/gk8W7S4t+jJ75a+fOkmOjKOu2xtPT5\nSafmL1rUq0cPzLKYtWKbDdms+YmgzYpZG7Zakc1K2WyXkk9V1qiVbkkBEUIIYZ5H+YngE82q\nHo6qhxe8HFynVp+aEWT9KrmXFxFmhMhkSMYQmYwwDJExSMYQmRTJmGt30n75evZPsa26VK2M\nEEq6l9Fz6dJKlSoNHjy42MBMJtMnn3yyfv164ZO5V69eX331lY+Pz3PfMXi5CG11QrtdgZSU\nFOGLa9euHTx48O233/7yyy+FI06nU6/XV65c+fGqnqrwywMSOwAAQHPmzFkdGbl2w4Z756/U\nqFFj7fr1wkRgolAULJ7ncv/dbdnGpd98s7F314ZBARwha85e/P16ypK53/ur1RRrQ1Yrtlkp\n1vZQRsjanmVGcJEUEgniOZRTzPDKOggl9O9Z8LJxcGDKeyOMKZdUy34kjDw/F5RKsVotkUgp\nWkJkMiKTIUZOZEz8/PnOUydODh9Qy9fnUrb+vZ37Ro8e/euvv2KMiw0vJydn3rx5x44do2m6\nRYsWY8eO1Wg0xZ4lMBqNO3bsyMrKCg0NbdWqleRplst2OBw3b95UqVQlacsEZSM8PFxYR0nY\nWgYhdOXKldOnTwubBwojO0NDH2xmuHjxYpvNVtCkJ3y/OZ1OiURSbOGXE0yegMkTrsHkiZKA\nyRPF0ul0TzvGrrTpdDqDwcDzvFtq43l++vTpy5Yt85MzJrvdyz9g3rx5bdu2LfoszHHEZp30\n3nvGa1ffrFvTT6G4os+9kps3fOCAEB8fxNowy2LWhliWsrOIZbFNLIP0CxCEcq02hY+PVKNB\nUhkp6CyWyohUimQMYRgilSGp1Gh3vDt+vA/n7FQ5zObkfjpz/r5SvWfPnpLsAnzs2LG33npL\nydoq67zO3c/yqxS+bt26wh/kRVi+fPlXX31lMhp5QiIjI+fOndugQYPnvu98MHnieXz22Wez\nZs3q3bv3oEGDMjMzZ8+e7efnd+nSJZPJ5HA4qlatyvP8okWLfH19N2/efPLkSYTQyZMnt27d\n2qRJkzlz5sycOXPGjBmRkZHdu3cvuvDLuTg5JHaQ2LkGiV1JQGJXrHKf2AkyMjKEdezq1Kkj\nlz9xGuwj7Hb7kiVL/vnnn5ycnMjIyAkTJtSuXftJhTHLIjsr/Ltt48YDW7eMrF+nho+30WFP\nvHNPotF0btuWctix3Y7sdmxnMctihx2JsunCyfN2TCl0OiKREIkUKRSElhCpND8vlEiEtkOr\nwzlt5syWfj5vRFanELY5uQ8TDqVovNb9vpHQLrcpfmDTpk1Txo1d8mps16rhJrtj9tGkX26m\nHjhwIDAwsNjwrFbrDz/8sG3bNr1eHxkZOWnSpCZNmjxSpojEzmQy3bhxw9fXtyybCV+sxI7j\nuGnTpq1atUqv19eoUWPmzJl79+6Nj49nWRYhdOLEibFjx54+fVqj0fTs2XPOnDkHDx4cPnw4\nx3HHjh1TKpXdu3c/d+5clSpVLl26VHTh6tWruz148YPEDhI71yCxKwlI7Ir1kiR2z0Oj0TAM\nk5OTU/L+I57nlyxZMm/evJycHKlU2q1bt1mzZrlcAw9zTmR3YDuLbVbksN+5fuOrGdOHR9Zs\nVTEEY3zbYDxwO7VlTNOKgQHYbkcOB7ZZsd2OnQ4kmrVOnoTIFYimiURCGDmWSIhMRqQyQlHC\nKoNr16+vp1Y2DgkqKP/xgcNVWrYdOHw4oWmhiRFhTOSPribN83z//v0zzp6eFBMdoFImpKQu\nPHn6199/b9XqoZkuLhM7p9P55ZdfxsfHY45jOa5Jkybz58+vVq1aqT2DB16sxA6UKkjsILFz\nDRK7koDErliQ2BXrGRK7Aunp6T4+PgUru5bE9u3bP/zwQ5Nez9C0FePJkyePGTPmkTJqtZq1\nWp1mE7bbsZ0lLItY9stPp0VQZEhkLQlNcYT8e/deNoc6tW1DO52ItWE7i+125HRgln3au/Aw\nmiZSGZJICE0TGZNnNF64di0mOKhgteqT6Zmn8oxvjnybYEwYBlEUYuQyuZzIGNbpQDIGUTSR\nyQhFzV+0aONPPy3q3D4mJCjbapuy7/BxmyMhIaEknc52u3358uX79u2z2WwNGjQYO3ZsyTOq\nmzdvrlmz5vbt28HBwf37969Tp86zP43HQGL3woHEDhI71yCxKwlI7IoFiV2xniexezY2m+3C\nhQsWi6Vu3bouVy1xuUDxjRs3hgwZknMntaavz7WcXJmv3+rVqyMjIx8/HbMscjqEpBCx7NqV\nKzKS/zeteQyFEULISfg/r9ysUr16ZI3qlJ1F/6WD2OFADgd2iGUJYjcg6I7RqPbx1fj6Ypom\nMhnBmMgYhDFiGCQ0OiKEZAyPUPyqVTl378WEBEpp6mha+nmj+ZtvvvEJCUEYE6kU0RKCMZLJ\nEEJExiCKKrjIvn37hgwZ8kpYhfqBAVf0OZsvX/t23jw3bpYKid0LBxI7SOxcg8SuJCCxKxYk\ndsUq+8SuWE/aecLhcBw4cODmzZsVK1Zs164dwzy+N5sLBoOhQ4cO/qx1QGQNjidrzl7k/AN2\n7tz5pMGImGUR58QOB28xfzR5sjYn+73oekFqVUquccuVazWj6nds0xrb7YhzIpbFTifmnMhm\nQ04nFoYY8jxmbc/7CEQvv0OZkLvZ2VqpRPvf/8XWq9eH79yflJTkru3pILF74cByJwAAAEpE\nKpXGxsY+7VlarTYhIWHhwoV/HD9OUVS3t9+Ji4srYooJYRiEGIIQ8tJ98P28qVOnVl28Srj6\n22+/PWzKJ2wJup6dNuvEsWNPHjhQL9CPEKR3cqPffrtDyxaY5xHLYo5DDjty2DHPY5sN8Ryy\n24Uc0azX37h6tbafr4ymir2KB2GOQ5wVIxSqfmjiZ4+IqiH7ExMTE3v27Pmkc0H5BokdAACA\n0qXRaKZOnfoMJwYEBKxcuZLjuOzsbH9//5KsmSeQyBXz45clJyefOnVKo9G0bNkyKCioJIsH\nYoTWTp+++sf4gZE1A1XKPTdvX7ayu7b/HV6hAnI4Mc9hO4t4nkEE8cRhNCBCkM2GCI9Z9s+t\nWx330npVr6qQSBBCV/S5Vw3G9s2byaRSZLMhhIRla7CdRQQJ9TzDMymWUiphX7iRjsB9ILED\nAAAgahqNJjAw0GAw2O1PNwKvfv369evXf9rLTZ8+vXXr1tu2bTuZnd1yYOtVI0f6+PgQhJAc\nFQxdEpatdjw8BbV5rbrDhw8fu2B5bT/feyazjZEvXLjQ2a5dEQkldjgQ51y3du3GJYs393lN\n9d8OeLMTk+iKlca8N5o4ndjpFBJHhJDQyog4ThiM6DCbd+7Y0bxCUOB/8zPO3s+6mKWPjo5+\n2rsG5QYkdgAAAMBD2rdv3759+6c9S6vVbtq0KSkp6dKlS35+fi1btix2gw0ilSKptN/wEWs3\nb2mx5rd3GtRRSaWbL1//N9eQsGCJowQr4d3MMrw7Y8akptHRQQGXsnO+Pnbi7bi4slljBYgT\nJHYAAACA2zRu3Lhx48ZPdYpEItm8efPixYs37ttnNuc1bh/77fjxJVlLGSH09ttvBwYGxsfH\n/7AvMTQ0dPKMmSXZ+ReUY5DYAQAAAB7GMMz48ePHjx//DOd27969X79+pbRAMXjhiHrWDwAA\nAAAAKDlI7AAAAAAAygnoigUAAABA2XH7zgglXwfnZQCJHQAAAADKjslkcm+FarUacrsC0BUL\nAAAAAFBOQGIHAAAAAFBOYLd3dYvN4/tYI4QwxhKJhOM48WwEjjGmKEo8u4ALj4jnefGEhBCS\nSCROZ0m2BSojUqmUECKqkMT2iCQSCcbY5Y+hpwg/++L51UfTNEVRTqdTVCHxPC+eeCiKomla\nVI+IoiiEkHg+QYRH5PYPNafTqVAo3FihwGg0urdC6IotrPyPsXPZly+VStVqtd1ut9lsZR+S\nSxKJhGEYs9ns6UDyURTl5eXldDpFFZJKpXL74IznodPpeJ4XVUheXl6iiker1dI0LaqQhOW+\nxPORrFKpZDKZ2EKy2+3iScflcrlCobDZbKIKiRAini1ZZTKZ8L/m3g81QkhpJHagVJX/xM5l\ngxNN0wghQoh4mqMoihJVPAJRhST8sS6eeASiekQCUcUjzv81UbXWC49IVK3jQjDiiUecjwiJ\n6RtbiEdUjwh4CoyxAwAAAAAoJyCxAwAAAAB4wOl0Yoz37NnzzAU8CBI7AAAAAJRnCQkJJ06c\nKHl5mqb37dsXHR39zAU8iJ4+fbqnYyhdFovlSW85HA7xjLPBGItttJYwmVFUISGERDXlE2Ps\ndDpFFRIS5SMSz5h3gdgeEc/zdrvd04E8gDEW1cRhhBAhxOFwiCck4X9NbJ8gpfGhplQq3Vsh\nQqjY73ae548fP3748OG8vLyQkBBhDnIRZDJZ0bNix4wZo1arGzVqVMIIMcbh4eFFTBwptoAn\nEQAAAACAsmIo0oULFxo2bKiRyer6+3kxsjp16pw8ebLoU4TVeZ6kXbt2GGOGYRo2bCj8Ubds\n2bLw8PBhw4YRQs6ePduxY0dvb28vL69OnTpdvXqVECL8Lbp7926haWPdunWdOnWqVatWWFjY\nqlWrSlKAEJKcnFyvXj25XN6wYcOEhASE0OnTp0v/6ZLyv44dAAAAAMRDWMeOungOucpAvvji\nC53JMKphPYVEYndyK8+cv4Xp6dOnC8tZPIKvURvRdLHr2IWHh0+dOjUuLg4hJJFIGjRosHTp\n0oiICI1GU7NmzZiYmEWLFnEcN2LEiHv37h05csTpdEql0t27d8fGxkokkvr162/fvj0gIGDF\nihVjx47NzMxkGKboAgqFIjw8vHXr1osWLUpJSRkxYsTJkyfPnj1bp04dNz3FJyr/y50AAAAA\nQGwkmzcgVx3H02tXK/haJqHjGtZDCKGtv7usxDH5M+Iq4Staz549GzZsKHx99OhRhmGEHueB\nAwf279//8QavIUOGBAQEIIQ6dOhgsVhSUlJq1KhRdIG8vLzU1NRZs2Zptdp69eqNHj36rbfe\neto4nw0kdgAAAAB4iVSr9iB3PHXq1BdffHHhwgWEEMuyLkeWh4WFCV/I5XKEkNVqLbbA7du3\naZoODw8XjpflNAuYFQsAAACAlwjDMMIX165d69KlS8eOHVNSUtLT01etWuWyfLH7lT1egBAi\nbKgovHTZj1xKoMUOAAAAAGXNOeJdl8c/+ugj49Urn7eMCVarsqzWL4/8aw8Mnjt3rsu5sUQq\nfZ4YTpw44XQ6J02aJJVKEULHjh17ntoKCw4OZln27t27ISEhCKGTJ0+6q+ZiQWIHAAAAgLLG\nB4W4PD7xu7njxo2rvGilt5zJsbGdOnVaMP9HFBT0POu4KJXKa9eu5ebm6nS6wsfDw8M5jjt2\n7FiTJk3++OOPxMREhFBBNvY8mjdv7ufn9+WXX3777bc3b95cunTpc1ZYctAVCwAAAACx8PHx\nWbt27cWLF9dt3nL+/PmNGzcGBQU9Z52jRo1atGhR3bp1HznetGnTyZMn9+jRIyQkZO/evVu2\nbImOjo6KikpJSXnOK8pkso0bNx48eNDf33/UqFGzZs1CCBW7IJ9bwHInAAAAACg7wnInblTs\ncice4XQ6eZ6XyWQIoaNHjzZv3jwvL0+r1Zb2dct/YpeVlfX4QZlMptVqLRZLEftSlDGpVCqX\ny93+7f7MaJr29vZmWVY8IVEUpdVqc3NzPR3IA76+vhzHiSokHx8fvV7v6Sge0Ol0EonE5Y+h\np+h0OmFFU08Hkk+j0TAMk5OTI55dXtRqtTA90NOB5FMqlUql0mAwiGd/DmHLgcdnR3oKwzAa\njcZsNrs9JD8/P/dWiF6OxI4QUr169ZYtW86dO9dqtQ4fPtxqtR44cKAMLg1dsQAAAAAA7oQx\n3rRp0+3btytWrFivXj2VSvXzzz+XzaVh8gQAAAAAgJvVq1dv7969ZX9daLEDAAAAACgnILED\nAAAAACgnILEDAAAAACgnILEDAAAAACgnYPIEAAAAAMpO2azT+9KCxA4AAAAAZUelUnk6hPIM\nEjsAAAAAlB23r8VN07R7K3yhQWIHAAAAgLLj9j2fRLjzhAdBPzcAAAAAQDkBiR0AAAAAQDkB\nXbEAAADAC+z69eurV69OTU0NCgrq379/VFSUpyMCngQtdgAAAIDn5eTkJCQk/PXXX6mpqSU/\nKyEhoXXr1ul7dzU2ZFuPHu76yiu//vpr6QUJxA9a7AAAAAAP+/333z/66CMVxymlkttmy4gR\nI2bOnFnshAC73f7+++/Pbt10dMP8Vrqe1au+OWVK+/btAwMDSz9qIEaQ2AEAAACedObMmQ8+\n+GBpx7b9a1dHCJ3OvN9j7ZoaYRWHvP46ttuRncUOO8WyyG7HdhY7HMjOYpsVORxsZuYvHVq2\nqlihoKpuEVUq7D+SmJjYq1cvz90Q8CRI7AAAAIAHcnNzv/7667/++isnJycyMnLy5MkdO3Ys\n4bnp6enLli27cOFCQEBAz54927VrhxDKT8VYG2ZZxLIUa0PC13YWsyy2s4oTJ44O6lvbz0eo\nJCrAP+W9t1BuOor/oejL+SHUOiz0kYMKicRutz/lTYN8TqdTKpXu2LEjNjZWKpXu3r07Nja2\ncIGUlJTKlSufPXu2Tp06Jazt8UpKFSR2AAAAQD6n0zlo0CB05/aSlk0CVMqElNSRQ99cuvKn\nzp0755fgecyy2GZBNhudm0vZWcTasM2GWDbn3t2T/+zopVG/Xy2Uwti592866ZCSphDPF33R\nhowEMT5uif905v1L2fro6Gi31PYyo2l63759zzYTJSEhQavVNmrU6HkqeWaQ2AEAACiH9Hr9\nggULkpOTtVptbGzswIEDXW9RSgi22bDQuWmzndi/rwVxTh/Qm6FphFDDoIDXa1XP3veP8u5N\nym5DNht2OApOVT5cUxBC/WtGPFp/cVndM6JpIpMRGZNtMt+8k+qvULIctyfl9jfHTsS99161\natVK5aJlRa/Xb9iw4ebNm2FhYf369fPIeEGMcdu2bZ/t3O+///61115r1KjR81TyzCCxAwAA\nIHZOp/MpSvN8ekrK8P5vNNRp3q8UxiPyvw2/bDuV1Pe1rhTLYtYm5HDYZsV2FttshU9th1C7\ndi0KXmKEKnlpKiGEsu+76VYeRRjGSUuu30nTMdJAlRIjfNdk2nbtZqM27SIbNCAyBsnlRMYQ\nmQxJZYRh8r/+bxMtOULXt22bumzZjRs3QkNDP/ry/wYMGFBKoZaNpKSkvn375vgHoKrV0KHD\nX3311S+//PI86VHTpk2jo6MXLlwovNy/f3+HDh1u376dk5MzYcKEEydO8DwfExOzcOHCwglx\n4V7U5OTkUaNGnTt3rmrVqh999FFBmXPnzj1eQ/v27ffv379nz55ly5YdP368oJKMjIzx48cf\nOHAgNze3QYMGc+bMadGiBc/zNE2vW7du1apVqampZrN55syZQ4cOfeabRZDYAQAAEC2r1fr9\n99+vX78+MzOzZtWq4955u2+XLpi1YqsVszZstWKbDdssyGqlWBuyWrHNilkWszYNQof7dSuo\np1f1qgghdOJYqUbLE4IQpgrNZD10O01aMaxBs2aIYQijQAxDGDlhGCST8VIZYhgiVwglL+7Z\nM3HixOyMDIVEwlLUpEmTIt4ZXcKBcq+99lqfPn00Go3ZbLZare6/sdIxIy398cZMjuNW7dhl\nmP4FqpfffWm8dHHAvoPDKlWRyWSPV/JxcADjsiG2kIEDB86ePfvHH38Ummx/++23du3aVahQ\noUOHDjExMampqRzHjRgxYujQoUeOHHn8dJ7ne/Xq1bp164SEhOzs7MJZV9++fR+vISEhITw8\nfOrUqXFxcYX/IOnRo4dOp0tOTlar1Z9++mmXLl2uX7/u5+dH0/R33323ffv2gICAFStWjB49\num/fviqVqiTP0CVI7AAAAJQIIeTkyZMpKSlhYWHR0dEl33k9LS1t9uzZx44do2m6VatWH374\nob+/f/57PI9tVmyxYJsV26xYSM6sFmy1YJv1zunTb1mt0wf1klE0hTG6n4pWLy2t23uCdLPZ\ngKiqkZE8I0eMnMjliJFLNBrCyO0URRg5kTGEYQgjb9C06fgaVd6Pzs9I7ppM/Tb/Hb92rb1V\nu2KvEhsbm5SUdOnSJavVWqtWLa1WW8q35XnzM+47CXHxRtduD72sWctcs9ZCfa7LSiYFBTDF\nXeiNN96YMGHCkSNHWrVqxXHcpk2bvv76a4TQ0aNHGYZRKpUIoYEDB/bv35+4iufYsWMpKSl7\n9+5VqVQqlWrcuHH79+8X3nJZg8tFak6dOnX8+HFhVg1C6Isvvli6dOmOHTuGDBmCEBoyZIhw\nvEOHDhaLJSUlJTIysrjbeiJI7AAAABQvLS1t5MiRF0+fDvfSphqMYdWrL1++vGrVqk88geOE\njC0v/e73kye38POe0r6FlKZSDbmp/ze9QlQ9qcMuNLwVcdFaCgYpiv3gfgpEJkNyBc8wSK4g\ncgUR2swYOZEreDkjfLH2t9/nLVrUokKwTs7su3Unl5Fv377dEhJSuB5KoUAIOR9uHvv222+H\nDhqUZjS1Catw12j+7t+TrTp1EibGloRMJqtXr5677hQUCAwMbN++/caNG1u1arV//36j0din\nTx+E0KlTp7744osLFy4ghFiWdTgcHMc9fnpqairGuFKlSlg6yzYAACAASURBVMLLiIgHwyhd\n1iCRuMisrl+/TlFUzZo1hZcKhaJSpUopKSnCy7CwMOELuVyOEHrOZldI7AAA4KVjt9s5jlMo\nFCUsTwiJi4sLztP/PXqEFyOzOB3TDx6fO3HCgjmzpXY7tlqw2YStFmy1UjYrspixxVKQsakQ\niu/QqqCqat46hBC6n+GueyESCVIIWZqcCOmaXP7r5i1yk/GNWvmfwSaHI3bdpmFTPho0ZEix\nFQ78YEKN1m22b9+elZU1qM8bgwYNEppkitWhQ4dNW7d+//3364+cCAgIGDx2/DvvvPNc9wbc\nZODAgZ9++um8efM2bNjQo0cPjUZz7dq1Ll26fP7559u3b5fL5Vu3bu3Zs6fLc1mWRQgVtMMV\n9K6WvAaXeJ4vWJWm2JWonwokdgAA8BK5fPnyJ598kpiY6HQ6a9euPX369EeGpWOrFdksVF6e\nxGjAVgtlNiOL2XDv3rSwwLYtG0kojBBSSqRz2rdECKGtv5dSnEQitSCUlpFZ3de74KCd48ft\n3j/ps8+Cq1YljILIFUihIK4aSOqGVu7YsePf1250i6iSZ2MX/e+0tlr1/gMHlvDq0dHRz7Zi\nSExMzIYNG57hxJeQvmFdl8c/+eSTH3/fiOJGo4jq6OYNtHTxm+3aLliw4Hmu1bt373fffffo\n0aN//PHHmjVrEEInTpxwOp2TJk2SSqUIoWPHnjj+MjQ0lBBy69atypUrI4QuXrwoHC95DQih\niIgInucvXLgg9LGazeZbt24VbvxzI0jsAADgxcOy7ObNm69everv79+1a9eKFSsWewp2OHLS\n7sx4Z2TXIL/lIwcrJZLL+pzLK5faUq74MLKCgW7C8hyPDFP3Qyg2PMxdwefa2LsOZ/X6DZBC\nwcsVSKHkFf/9K1fy/6VrmZmZzWNifmzTfEBkDYQQQWjagSOJrNO/dTuuuBaOiIiI/fv3f/fd\nd18mJ6tUqp7vxL377rslHxQIPGj69Ok+Pj5LFi/IyMjw8/MbOXLkhAkTnrNOrVbbtWvXzz77\njKKoTp06IYTCw8M5jjt27FiTJk3++OOPxMREhNDdu3dDHu5zRwg1a9bM19d3xowZc+fOvX//\nfsHs2ifVEBYWplQqr127lpubq1arhcJRUVHNmzefPHny2rVrGYaZMmWKRqN5qha+koPEDgAA\nXjB37tzp1asXzs2JCQn6n9E456uvFnwzp1v7dshioSxmZLVQVgsSOkatFmQ2UzYrslgw51Qj\n9NdrDzZRaF4huHmFYHQ/3S1REamMKJVEqSIKBVKo8nM1leq3v7Yl7tnzwyvtZBSFEDLZHa1/\n/v2N994f27t/0RUGBATMnTs37v331124VM1b9+/d9Jt25x9//FHCfqvw8PAff/zRDTcGypZU\nKp04ceLEiROtVmvJRwsUa9CgQb179x4zZowwBq5p06aTJ0/u0aMHxrhXr15btmzp2LFjVFRU\nUlLSIycqFIq///579OjRISEhERERc+bMefXVV3mef1INp06dGjVq1EcffSQsxVdQz6+//jp2\n7NjatWvzPN+kSZNDhw6V0hQZ7HIOSHmSlZX1+EGZTKbVai0Wi8ViKfuQXJJKpXK53Gg0ejqQ\nfDRNe3t7sywrnpAoitJqtbm5rudGeYSvry/HcaIKycfHR6/XezqKB3Q6nUQicflj6Ck6nc5g\nMPCltGzs07Db7fHx8f/8809eXl7t2rXHjx9fq1atRwtxHBYSNZMp/wuLed+ff/pzjobBge4c\nmFMkolDczc3LNRqr6LwUEonZ4Vh/4Uq6XDH+40+wWsMrlEipJLTrlgKr1frqq6867t19vVYE\nx5NfL1wOqFFzy5YtQgdWsVJSUv7666/09PTw8PB+/frpdDq33tkzEhIO8awtwjBMKS134ufn\n594KEUJu/1hRq9XuHab2QhNdi53JZIqPjz9z5ozD4ahRo0ZcXJwwB7iw9PT0n3766cKFCyzL\nRkdHx8XFeXl5eSRaAAB4NoSQYcOG3Tl1clKT6JDKIZeysn7+YMyEt0eG6Lyw2UwJcxEsZuzq\nc7pzgHu2n0IIcYRk2Wx+YeFIqeTzG9uUUp23U8Y4GIbIlUSpJAoloiinXv/pBx9sX7nOT6nI\ntljbtmv3w7ez+aCgYi+hUCh27dq1atWqo0ePUhT13rTXBw8e7HLmoEvh4eFTpkxRKpUGgwG2\nQAWgWKJrsfviiy9MJtOoUaMYhlm3bl1KSsoPP/xQeB8Yh8MxZsyY0NDQ4cOHO53O5cuXcxz3\nf//3f0+qEFrsng202JUEtNgV6yVpsbt48eLZs2c1Gk3Tpk29vb0Lv4XtdmwyYLMJG42UxYyN\nBmw2UyaDKSOdGI06xsWaq+5BUUSuIEpl/r8KJa9QXrp1O37V6inNGoVpNQihHNbW/udNnd8c\n+vHHHxc+Va1WC2s3PF7rnTt3bt26FRoaWrD6QxlQKpViS+ygxe55QItdqRJXi11WVlZSUtLc\nuXOFuSdxcXFDhgw5e/Zs4Q10b968effu3S+//NLX1xchNG7cuBEjRty6dassf8sAAMqfpKSk\n3377LT09vXr16iNGjKhQoUJJznI4HOPGjj22e1fLCiEKCZ2sUQ/s9lrN4CBsNGCTCRvzsKv0\nCCHkhRB61qzO6nQihBWS/KkALMfPT/pf29596sY0JQolUSiIQkVcDU6q0grJrtyotyw+NjxM\nIZHsvZVat0nMpEmTSn7p0NDQ0NDQZwsbAFAGxJXYXb16VSqVClkdQkitVoeGhl6+fLlwYif8\nEVmwtYi3tzdN09euXYPEDgDwzOLj42d+9tnrtao30Xkd37a1+fLlmzZtatSoUUEBbDFjs5ky\nG5HJRJmM2GykjEbKbDLdvbMqPJAeVWhvR30m0mc+eygURRRKXqkkKg1SqXiFkiiVRKUWmtyQ\nSk2UqvhVq76bNevb9q1ahVVINRg/PZjorBD2Tv/BzhLM+pw5c2bPnj0PHDhgsVgWNGkSGxsL\nTR0AlCfiSuwMBoNGoyn8W8bLyysvL69wmSpVqmi12nXr1r311lsIod9++w093K778ccfFywn\nExISsnbt2iddTqFQuHHGzfPDGLvcC8+DGIYRVUgYY6GlViQwxhKJRGwhiS0ehJDYQnqkt/Ta\ntWvfz569a0DvmJD8EWMHU9MyVy7V3ruFLSaSl0dMRuRqSXqEkBYhVNxWlY8GoFIjjSabdexN\nTOxUpZIXk7+zwv8l/ntKpty6ew8qLtMS1kqYNnt22t+7lEpl//79Z8+eXfIus44dO3bs2LHo\nMgzjzv0e3EKj0Xg6hEeVcOHiMiP0WbuxQpfd8UDkxJXYoRKsv6xQKKZOnfrjjz/+888/DMN0\n7949ICCg8OpESqWy4OdfpVK5HEmDMcYYE0LEM8RQuHExzNQrQNM0IUQ8IcEjKgmapkUVD0VR\nGOOyDMloNJ4/f14ikURGRj74y83hIEYDMhmRyYiMBmIyIZORGPKQ2YRMxpCcnLR3hxWupHXF\nCgghcun8s/2CsBGiCApGGi+s1SKdN1KpsVaHNBqk9cIaLaJphFAAQntHjhwfv3ZkVKS/Srnz\nRsrx7NzExESeEFSC30vvvvvuu+++m5ubq9VqhVHIbnzIFEWJ7dej2H5ju/2ZP6dSekTieeCg\n5MSV2AmDmgvvoZuXl/fI39YIoTp16ixdutRsNgt/U27cuPHBftIITZs2rXDhIiZP2Gw2mDzx\nJMLkCbvdLp6QYPJESfj4+OTk5Hg6igeEyRNlEBJmbdhk2rnp991/bAqUyQJVyhSdV4s6kb5y\nOTYbMcsWce6z/B6kKKJU8Rrt/hMnQ+VMjUK7I/TY+FdU39cnv+lqLymCkMFQ8GrOnDnNmjXb\ntWtXUk5OrS7d5r77bmBg4NM+q0f6NNyiiMkTHiE0RJlMJpg88STC5Amr1er2kETYdguKJq7E\nLiIiwuFwXL9+vVq1agghg8GQmpr6yMJOHMclJibWqVNHSPiSkpIIIbVr1/ZMxACAUnDixImV\nK1feunUrLCxsxIgRjRs3Fo5j1kYZDNiQiw152GSkjUZsMmCTERvy5yj0Rqh3h9YPKrKYkMX0\nbDHYnJzMxwdptLxCSTRaolTxag1RqYhaw6vURKkSemAvcpI3P/10TbdX2lUKtTics48mJeUa\nvi3BhqQIIYxx7969hw4dyjBMTk6Oyw3IASh/JBKJe9sCYZxoYeJK7Hx8fJo1a7Zw4cKxY8fK\nZLLly5dXrVpVSNp2795ts9m6detG0/SmTZsOHz789ttvZ2RkLFy4sFOnTqW0fDMAoOxt2LDh\ns0mTxjSqPyQs2Oq0XJ33dZWYphW1aspowDabu65CaBoJuZpSRZTKfxKPnj5zpkVoiJec+fde\n+uwjJz6dM+f1118vtp6hQ4fq9fq+c+cih4PluIjq1X/++eegEqzuBsBLS1Sj28sf0a1jZ7FY\n4uPjT506xXFcZGRkXFyc0DL3zTffGAyGWbNmIYTu3r27cOHCK1euyOXyNm3aDBs2rIi1LmEd\nu2cD69iVhDi7Yl+sdeyw00npsyl9FqXPpnJz+Oz7uTeuByjd83tfb7VdtdqiW7fhVWqiUhOl\nkldrNUHBRkI42UMdTA6HY+XKlRs2bLh3715ERMTYsWNjY2NLfiGz2Xz58mUvL6/w8PCn3ZBU\no9GIrcVOnF2xsI5dEV6sdexAqRJdYud2kNg9G0jsSgISuyKwLLt06dLExESn0xkVFfX+++97\ne3tjlqWyMqms+5Q+i8q+T+mzKUNeSeYKuESkUqL1ImoN0Wg5jXbRmrVVEd89okpBgUl7D6aG\nhi9fvrzwWeLZUkwAiV2xILH7f/buOz6qYu0D+HPatmTTGykEBAKhV+kghC5EAaVcQa8KAr4K\nNhAbCF5FUa8gYOGKqAgoSJMivYMCgnQIHVJIb5tsO+39Y0MIIZBANtlN+H3/4DMzOWfOkw2b\nfTLnzEypkNhBIfe6FQsA1YPdbn/ssccsVy9PaNMizM/n+sUzcVPf6tawAW/KLf3k2yTnm/nA\nIO9atRUvb9XLW/XyUYxG1eilanVFD/NLzRo9/uWgJ2LbhdUgog0Xr3x37OSvU6Y751sCAKgK\nkNgBQCmOHTt27ty54ODgtm3b3n2KHGMxs6kpXHrq5d075zer37B7h1ueaS4tq1N5QfTy2nTo\n7yb+frV8CjaA3nk1PnbVhiNHjmiDg+9++qBBgy5cuNBr9uxwg15SlAxZ+WDGxx06dCjL9wgA\nUD0gsQOAO8rJyRk9evSfe3bX9fVJMuUbg4O//fbbVq1aOb7KWCxsRhqbkcamp3GZ6WxqCmPO\nd3ypKREFlLIoserlrfgFyH5+in+g4uev+AWonkYiuqB+9+8pU15q1axJUMCptIy5h4+99957\nwaVldQ6TJk0aOXLkkSNHBEFo1aqVWy2MDABQCZDYAcAdTZw40XY+7tyYfwd5GERF+enk6W0f\n/6f1v5/R55nY9NTCNK4s7LL8d1pG60cHKEHBSkCQ4h+gCiVvajJq1KjatWt/9913q85eioiI\n+Gbhwl69epX9QjVq1Hj00UfLfjwAQHWCxA4ASsDkZJuvXqmddO2bwf0de14JLPt808ZERKeP\nl3q6THQxI6uOrw/HMkSkEg1bvcHYum2T7r3LcvWYmJiYmJhyfQMAAA8kJHYAD4QzZ85s3rw5\nJyenSZMmAwYMKLZCEGMycempbHqq474qm5HG2O2eRB91LdsDagyjeHkrAUFKQJASFCz7B9qM\nXk/168ekXB/VrLGG45afPXfCbNs5dWqFfG8AAHADEjuA6m/evHkfffRR17AQf71+2g8Lv5k7\nd/X8b7wsZjYthUtN4dJSyHIP6/6oDKN6+ygBQYp/oOwfoAYEKn4BqiAUPUYgWrdu3bx5837d\nt89my2vZq+/cV1/FugkAABUNiR1ANffPP/98/tGHu4YPahEcRESKqqqqyq1cWsbTRVmWVdLx\nnEWSfjx+eltO3lfLftN5epZ6osFgmDhx4ocffnj3BYoBAMCJkNgBVE9Mbg6XcI2/nljr74NJ\nL78gcKyjnWUYusu+ihwn+/kr/oFqQJDsH2Dx8p78yac/L17srdXk2OwtW7WaM2duWbI6AABw\nCSR2ANUHm5GuXIzTx53hEq4xOQX7YdRkiYi90ymKl7cSFCIHBqtBwXJAkOLj69jb3kEg+vyL\nLya//fb58+dr1KhRq1YtbLYNAODOkNgBVCWKoiQkJHh7e3t7F6zfy+SZ+MsXucsX+PirjDlf\nLsO7+kx65m/xSa/P/EwJDCq2eUOJAgMDAwMDyx07AABUOCR2AFXGggULPvnkk6ysLA3HjerV\nY9LAxwJzMri01LudwzCyt+/Gk6dSMjI7hNUwaPgNF668s2v/V999J4fXrKzAAQCgkiCxA6ga\nfvnll8Wff7Z+QK+mQYEsQyzD0IUzJR/KcUpwDSksQo6IlEMjVL2+TmLij1OmvLr4N5vNVqdO\nndlff92vX7/KDR8AACoDEjsAt8bIEnvtKn/lYveTh0c/O/yOx3GcHBohRUR6NGyc7elVbPGR\nsLCwBQsWyLJstVo9PDwqPGgAAHARJHYAboeRZTY5iUu4ysVf4+KvMpJIRHW8jbcfmSErXi3b\nSLXryJEPqRoNEXn6+amZmSV2y3EcsjoAgOoNiR2AW2BEkU1N5hKu8dcuswnXGEkq9ZS3du5T\n23d+pxf2RQUAgAJI7AAqlSRJP/zww7JlyzLS0ro3bTzusdhobyOXcp1LTiJZvsuJ6bKy79Ll\nmMgIT42GiH4/f+mrY6e2fDarsgIHAIAqAIkdQOVhTKbF/5muv3ZlRbsWQR56nmXp4h0mQBAR\nkarVyeE1pdp15Np1Fa1u7lNPjdnwQ5vQ4AyL9UyOaebMmdHR0ZUWPAAAuD8kdgAViLHb2bQU\nLjmJS7nOJlxjc7JfCjBSQJO7nKIaPOQaYXJYhBxeUw4JJY5ztOuJVq5cuWfPnuPHj3t7e3fv\n3j00NLRSvgkAAKgykNgBOJWqspkZXFICl3iNS0pgMzNIVe9+hqyqNm8foVYdOSxcDqup+Prd\n5eDOnTt37tzZqREDAED1gcQOoNxkmbuewMdfY5PiuaRExmop+6n5ohQ577vte/fVrl274gIE\nAIAHBBI7gPuiqlxqsnzmhHL1sue5M4zNVsrhGo0SGJyh1b837+uxLZq0CAlytH9x8HBQeESt\nWrUqPGAAAHgAILEDuAdMfh5/IY6/clG9dFGQRMcsVqbEQ1lWDghSwiKkGmFKjTDF148YRk8U\nHp/c/ZNPRjaJjvTy2p+YtC3h+qpVqxim5D4AAADuCRI7gNKxOdn8hTg+7jSXlHCXZ+ZUjUYJ\nDZdCI5SQGnJYTVWnu/2YV155pXXr1suXL9+ekhLVs8/+0aMjIiIqMnYAAHiAILEDuCMuNZmL\nOy1cOMemp979yK8OHzM3bDrmjTepDGNvnTp16tSpk5NiBAAAuAmJHUBxjNXCnz4hnPiHS00p\n8QCbrOTZbf56fWGLpKqbjx0fgzuqAADgUkjsAG5QVS7+qnDiHz7uDCOXsKOXqtPLdeqJdet3\neX7063VrDWsYVfilbKvVwzugEmMFAAAoARI7AGIsFuHYYeHEP2x21u1fVY1eUt36Yt36ckSk\nY7ngmD59P1u86NG6tYwaDRElmPL+d/Tk1JmfVnbcAAAAt0JiBw80xpyvOfSXcPQQY7cX+5Iq\naKQGDcWmLeUaYcWenHvjjTf27dvX7LvF/erWsknymnMXe8XGDh06tBIDBwAAKAESO3hAMbk5\nmkN/CsePMFLxu65KaLi9SQupQSNVoynxXK1Wu27dulWrVh0/ftxDEOa/2z4mJqbiQwYAACgF\nEjt44LDZWZoD+4RTx0iWb/mC3mBv1FRs0lwJCCq9E5YdPHjwCy+8IMtydnZ2RcUKAABwL5DY\nwQOEsVq0e3cKxw6TohRtT87L//ZUHN+u09iOj2juMEoHAADg/pDYwYNBVYUTR7W7tzEWc9Hm\npLz8TIvFS6tp6eczef431zMyZsyY4aoYAQAAygmJHVR/XMp13baNbGJ80Ua7h+e7a/94oXmT\nxoEBRFTTy6uun0+rBQvGjh0bGRnpokgBAADKhXV1AAAViLFYdJvWGRZ9VzSrUz08LX0fWxlR\nb8PFK3X9fArbo/39IryMp0+fdkWkAAAAToARO6i2hJPHtDs3MxbLzSaWtbd82N6hq6rVeqRn\nZVqtiqqyN5YykRQl22r19vZ2TbgAAADlhsQOqiHGZtNuWS+cOVm0UY6oZe3Rp3DGa9u2bXW+\nfh//+ffbHdo4Wj7af9ArKLh169aVHS4AAICTILGD6oZLTtKtXclmZxa2qJ5Ga+fuUqOmRdcZ\n1uv133zzzdNPP73+wqWGAf4n09KvivLPP/+MWbEAAFB1IbGDakRVNYf2a/fuvLlAHcPYW7Sx\nd+muCiWka+3btz9w4MCKFSuuXr06pFatwYMH+/r6VmrAAAAAToXEDqoDVVWTzp8LO7BXm5x4\ns1Gvt/Z9TKoTdZcT/fz8Ro8eXfEBAgAAVAYkdlDl7dy5c9Wnn3zycHOjQV/YKNesbXn0cdXT\n6MLAAAAAKhkSO6ja4uLi9n/+yYJH2hc+PScpypXIusFP/otYrOYDAAAPFnzyQRXGSJL4y08z\nutzM6oho4bFTLy5fhawOAAAeQPjwg6qKMZn0S39ob9AWaw/zMl66dMklIQEAALgWbsVClcQm\nJRhWL2Py827/0tWc3MDAwMoPCQAAwOUwYgdVj3DmhOHXnwqzunxRPJeR5Sifzcj8aP/BESNG\nuC46AAAAl6n+I3aCINzeyHEcEbEsW+JXXYLneYZh3CcelmXJzV4ihmEYVdXv2sof3F/YqPr4\nLsmX3vhqYUMfL55lj6emP/3cc8899xzDMHfpyrlRuc9L5OBW8Th+EO4WkiAIiqK4OpACjvca\nz/Os2zwYyrIsz7vRp4PjleE4zn3+I3Ecp6qqW8VDFfASqarqxN6gcjDV/sdms9lub3TkK5Ik\nyYUr2boay7Isy0qS5OpACjAMo9FoFEURRdHVsRRgRJFZsVQ5feJmU81aNPwZ8vC8fPny3r17\nRVFs165dw4YNKy0krVarqqrdbq+0K5ZKo9G4WzwMw5T4NnQVx3vffX718TzPcZzdbnerkBRF\ncZ/cl+M4nudFUXSrkIjIrT5BKuJDTZZlg8HgxA6hElT/xC49Pf32Ro1G4+XlZTabzWZz5YdU\nIkEQdDqdyWRydSAFOI7z9fW12WxuEhKTn6dfsZRLuV7YIrZoY+3WizjOhVH5+/vLspydne3C\nGIrx8/PLzMws/bjK4uPjw/N8iW9DV/Hx8cnNzXWfFMFoNGq12qysLPfJEjw9PW02m/v8UWcw\nGAwGQ25urvv80aLX64nIYrG4OpACWq3WaDTm5+c7PaSAgADndggVzY0G2wHuhE1LNaxcyuTm\n3Kiz1pg+YvPWLg0KAADA7SCxA3eRkZHx008/XbhwISQk5IknnoiOjna0c1cu6X//jbFZHVVV\n0FgHDLr7RmEAAAAPJnd5VhcecCdOnGjXrt2OHxeGXj4fv3F9jx49Fi9eTETCyaOGlUsLszrG\ny9sy/BlkdQAAACXCiB24nqqq48aN+3e92h936+RoWX/h8si33nrCx8N4+njhYUpwCP/MGLm6\nPxUKAABw3zBiB6536dKlc3Fxb3d8uLDl0bq1f+zTPaBIVifXrmMd/izj7e2KAAEAAKoGjNiB\n61mtVpZhPG5dfmlAvYcKy2Lz1taYPqw7LawFAADghjBiB65Xp04dncGw9nxJG7wyjK1rD2vP\nfuQ2a7cCAAC4LXxYguvpdLpp06aN/WPbgmMnzVKRpbNY1tq7v/3hDq4LDQAAoCrBvS1wC888\n80wNnfbhM8cMfMENWZXjrP0HSVHRrg0MAACgCkFiB26BTUsdlHmd8dA7qqpOZxk0XA6LcG1U\nAAAAVQsSO3A9LuGqfuUvzI3tRFVPo/mJp5TAINdGBQAAUOUgsQMX465c0q/6lbnxaJ3i628Z\nMkLxwrImAAAA9wyJHbgSf+mCbvUyRpYcVTm4huWJf6kGD9dGBQAAUEUhsQMnkyTpypUrOTk5\nUVFRRqPxLkfyF8/p1vx2M6sLi7AM/peq1VZKmAAAANUQljsBZzp06FCXLl06dugwsH//hg0b\n/ve//73TkULcaf2a5TezuvCalieeQlYHAABQHhixA6dJTk4eMWLEc/Vqv/XKGIMg7Lwa//Ts\nWb6+vs8++2yxI4UzJ3UbVpOiOKpSrTrWgUNVbCwBAABQPhixA6f5+eefG3no/9O1g4cgMETd\nIiM+6dZp9uzZxQ4Tjh/RrV91M6t7qK51ELI6AAAAJ0BiB05z9erVliG3rFHSMiQoMTHRbrcX\ntghH/9ZtXk+q6qiKUdGWx4eqHLI6AAAAJ0BiB04THBx8MSu7aMvFrBw/Pz+NRuOoCif+0W39\n42ZWF93EOmAwcVxlBwoAAFBNIbEDpxk6dOi2xOQfT5x2VC9n57y5Y2/hA3b8yWO6TetuZnWN\nm1v7PUYs/gcCAAA4DW6BgdPUq1dv7ty5r7/++n/2HfTT6c5kZD42aNAbb7xBRMK5M7pNawuz\nOqlxM2ufAcQwLo0XAACgukFiB84UGxvbuXPnAwcO5ObmNm3atEGDBkTEnzurW7uicLaEGBVt\n7Y2sDgAAwPmQ2IGT+fr69unTp7DKX76oW3czq5Oioq0DBuMOLAAAQEXA5ytUIO7yRd2qXxlZ\ndlSl2nWt/QchqwMAAKggGLGDisJdvaxf9Wvh3hLSQ3Utjw/FHFgAAICKg7ETqBBcclLRrE6u\nVcf6+BBkdQAAABUKiR04H5uVqV+xlBEL1iWWwyIsjw/BKsQAAAAVDYkdOBmTm2NYtogx5zuq\nSmiY5cmnVEFwbVQAAAAPAiR24EyMxWL4bTGTm+OoKr7+5oHDVEHj2qgAAAAeELg7BiUQRXHl\nypUXLlzw8vJ65JFHHMvRlYqRRP3KpWxGuqOqGr3MyL2vPAAAIABJREFUQ0aoBo+KjBQAAABu\nwogdFJeZmRkTE/PBG68nbPh99/f/i4mJ+fbbb0s/TZZ1q5ZxSQmOmqrXm4eMUL28KzZWAAAA\nKAIjdlDc5MmTQy15v40eqed5Itp1LSH2gw/at2/ftGnTO56jqrr1q/grFwtqGo3liacUv4DK\nCRgAAAAcMGIHtxBFcf369dM6t3dkdUTUtWZ4n5rha9euvctZuq1/CHGnHWWV4yyPD5VDQis8\nVgAAALgVEju4hdVqtdvtgQZ90cYAg95kMt3pFM3BfcLRvwsqLGvrP0iOrF2hQQIAAECJkNjB\nLYxGY3h4+ObL1wpbbLK861pCdHR0iccLcae1u7cXVq09+4lRJR8JAAAAFQ3P2EFx06ZNmzBu\nrKqqvR6KTDNbPtx3UAipMWzYsNuP5K4najesIVV1VG0duohNW1ZusAAAAHATEjsoLjY2VhTF\nmTNnjt+yU6fT9e3b99dp07RabbHD2Jxs/cpfGEl0VMUGjewdulZ6sAAAAHATEjsoweDBg4cM\nGSIIAsuyZrO5hCMsZv3yxYXbS8jhNW39HieGqdQoAQAA4FZ4xg7uyNPTk+O429sZWTKs+pXN\nynBUFb8Ay8ChaklHAgAAQGVCYgf3SFW1G9dyifEFNb3eMniYqtPf/SQAAACoBEjs4N5o92wX\nTp9wlFVesAz+l+Lj59qQAAAAwAGJHdwD/tRxzYF9BRWGsfYfKNcIc2lEAAAAcBMSOygr7nqi\nbtO6wqqtWy+pXgMXxgMAAADFILGDMmHy8/RrljOy5KiKTZrbW7V1bUgAAABQDBI7KB0jS4bV\nvzKmXEdVDouw9nzUtSEBAADA7ZDYQem0WzawSYmOsurtYxk4lLC4CQAAgPtxuwWK8/Ly5s+f\nf/z4cVEU69evP3bs2KCgoGLHJCQkLFy4MC4uTpKk2rVrjxw5smHDhi6J9kGgObhfOHHUUVYF\njWXgMFVvcG1IAAAAUCK3G7GbNWtWamrq1KlTP/30U4PBMH36dEVRih6gqur06dN9fX3nz5//\n448/Nm7c+P333zeZTK4K2J2lp6cvWbJk9uzZf/zxhyzL99PFhXPaPdsLygxj6xsrBxbPswEA\nAMBNuFdil56efujQoRdeeKF27dqhoaFjx45NTEw8ceJE0WNyc3OTk5N79OhhMBi0Wm2/fv2s\nVuv169ddFbPb+uOPP9q2bfvdfz44svTnN8aNjYmJSU1Nvace1LRUWv4z3Uis7R26iPUxMgoA\nAOC+3CuxO3/+vCAItWvXdlQ9PT3Dw8Pj4uKKHuPt7d2gQYONGzeaTCar1bpx48bg4OBatWq5\nIFw3lpyc/NJLL/2nbctDzw5fOXjA2ReeibTmv/LKK2XvgbHZxB/nk8XiqEpR0bb2XSomWAAA\nAHAO93rGLjc312g0MkX2kvf29s7JySl22OTJk6dMmfLUU08Rka+v75QpUzQaTeFXt23blpCQ\n4CgbjcbevXvffiHHFqg8z+v17rIXFsdxHMc5K55t27bVM+heaNHEUTUI/OcxXRr+b1F+fn5A\nQEDp56sq99sSNa1ghE8NDmEGD9MLmrufVNEYhmFZ1n1+ZOSuIblVPCzLEpG7haTT6VRVdXUg\nBRy/jnQ6XbHHTlyI5/nCf92BIxKNRlPi7tUuIQiCq0O4heMlcnpU7vN/EsrOXd63hYpmdSWS\nJGn69OkNGjT48MMPBUHYsGHD1KlT58yZ4+vr6zhg3bp1e/bscZTDw8MHDRp0p640Gk3RjNAd\nOOs3qclkquHpUbQlzOipqqrVavXw8LjTWYXk3dulMycdZcbDQ/PsWMbH1ymBlV9Z4q9MLMu6\nW0juFg+5X0gGg9tNAHKr3JfcKasrpNPpXB1Cce72CeL0DzVRFJ3YG1QO93rr+vj45Obmqqpa\nmN7l5OQUZmwOJ06cuHz58scff+x4kz/xxBN//PHH3r17BwwY4Dhg3Lhxw4cPd5S1Wu3tA35E\nxPO8h4eH1Wq12WwV+P3cC57nNRqN2Wx2Sm8RERFfJafmi6LHjT/gdscn6vV6Hx+fEl+QopiE\na9qNa29UGFv/wRaWo9LOqgQsyxoMhry8PFcHcpOXl5eiKO4WUm5urqujuMnT05PjuFL/11Um\nT09Ps9nsPkMRBoNBEASTyeQ+Ien1elEUJUlydSAFtFqtTqfLz893q5CIyH0+QQRBMBgMTv9Q\nU1XVx8fHiR1CJXCvxK5evXqiKF68eLFu3bpElJubGx8fHx0dXfQYVVVVVS36G7DYWz0qKqpo\nNT09/fYLORJHRVHc6s8RnuedFU+vXr1m14savvqPj7t1ivDy3BOf9NKmHa+99hrHcXe/BGPO\n91j5CxVOoe3W01azFrnHq8SyrKqqbvUjIyJ3C8kN4yE3+7vf8RK5TxbliESSpPucul4BtFqt\nJEnu81Nz3GGUZdl9QnKMaLpPPI5nHtzqJQJXca/JE35+fu3bt583b97ly5cTExO/+OKLOnXq\nONao27Jly9q1a4moQYMGvr6+33//fV5ent1uX7lyZX5+fuvWrV0du3sRBOHHH3/UNGrS+oel\ngbO+fXrTjmcnTBg/fnwppymK/vffmLyCtWPYeg2oS0yFxwoAAABO4l4jdkQ0fvz4+fPnv//+\n+7IsN2rU6N1333WMrh09ejQ3N3fAgAGOxe1+/PHHsWPHyrJcs2bNqVOn1qhRw9WBu50aNWos\nXLjQYrGkp6eHhoaW5aFj7e5tXPxVR1n18uaHP2Nn3Sv1BwAAgLtwu8TOYDCUuCrHxIkTC8uR\nkZFTpkypxKCqML1eHxERUZYj+Qtxmr//cpRVjrMNHKrz8CC3eYIEAAAASoXxGCAiYrMzdRvW\n0I0FIGwxfZUaYa4NCQAAAO4VEjsgRhJ1q5cxNqujKjVqJjZr6dqQAAAA4D4gsQPSbtvE3ViL\nWA4Msvbq59p4AAAA4P4gsXvQCXGnheNHHGVVq7M+NkTl3WtFdQAAACgjJHYPNCY3R7tlfWHV\n1rOv4uvnwngAAACgPJDYPcAURb92BWOxOGpi0xZidBPXRgQAAADlgcSuarDb7U5fKF+zbyeX\nlOAoK75+tm69nds/AAAAVDIkdu5u//79vXv3joyMjIyMfOqppy5duuSUbrn4q9qD+x1lleOs\nsU+qbrabNQAAANwrJHZu7dixY0OGDOmj4/4aOWTrk7F+CVcff/zxzMzMcnbLWMz6dSvpxhCg\nrVsvOSi43MECAACAiyGxc2vTp08f0yR6Sqd2jQP9W9cIXti/Vx2emTt3brk6VVXdxrWFG8JK\nD9UTm2OnXQAAgOoAiZ1bO336dJ86tQqrDFGfh2qdOnWqPH1qDh/gL8Q5yqrRy9LvMWKY8nQI\nAAAAbgKJnVvz9PTMtFiLtmRarEaj8b475FKTNbu3FVRY1tJ/IOkN5YkQAAAA3AcSO7cWGxv7\nyZ9/59rtjuqFrOyFx0899thj99cbI0u69asZWXZUbe06y+GRzgkUAAAA3ADv6gDgbiZNmnTw\n4MHG/1vUu3akRZI2XLw8dMTIAQMG3F9vmt3b2fQbW4eF17S37+y8SAEAAMD1kNi5Na1Wu2bN\nmnXr1h0+fNhfo/mle/f27dvfX1dcYrzmyEFHWRUEa99YYjFeCwAAUK0gsXN3LMvGxsbGxsaW\npxPGZtOvX3VzfZOYvooPtg4DAACobjBm80DQbt/I5GQ7ylKdKLFJc9fGAwAAABUBiV31x1+I\nE04ec5RVvd7au79r4wEAAIAKgsSummMsZt2mdYVVW6/+qoenC+MBAACAioPErprTbl7PmPMd\nZalxMzEq2rXxAAAAQMVBYledCSePCufOOMqq0cvarZdr4wEAAIAKhcSu2mJysrXbN92oMJZ+\nj6k6vUsjAgAAgIqFxK6aUlX95nWMzeao2Vs+LNes7dqIAAAAoKIhsauehJNHuSuXHGXFP8De\nJca18QAAAEAlQGJXDTF5Ju3OrTcqjLVPrMpjJWoAAIDqD4ldNaTb+gdjtTjK9tbt5NBw18YD\nAAAAlQOJXXUjnDnBnz/rKCvePvaOj7g0HAAAAKg8SOyqFcZi0W7ffKPCWHsPUAXBpREBAABA\n5UFiV61ot9xcjlhs3lqOxExYAACABwgSu+qDv3hOiDvtKKtGo61zN9fGAwAAAJUMkyUriSzL\nhw4dSkhIiIyMbN26NcMwzu2fsVl1W9YXVq29Y1WtzrmXAAAAADfnnBG75OTkOXPmFFbT0tKm\nT5+emprqlM6rgYsXL8bExDw1ePC8Ke8Oefyxvn37JiYmOvcS2u2bGJPJURYbN5Nq13Fu/wAA\nAOD+nJDYxcXFtWjR4o033ihsMZvNU6dObdas2aVLl8rff1UniuLzzz/flKTL//fc38/+6+K4\n58Jzs8eMGaOqqrMuwV2+KJw67iirHp62R7AnLAAAwIPICYnd5MmTPT099+7dW9gSGRl5+vRp\nT0/PiRMnlr//qu7gwYMJF85/1bu7hyAQkbdW822/mH/+/vvkyZNO6Z+RRN2W9XQjTbT26Kvq\nsScsAADAg8gJid2+ffvefvvtNm3aFG2Mjo6eOHHili1byt9/VZecnBxuNOqL7P3go9UGGfTJ\nyclO6V+zfw+bk+0oi/UbSlHRTukWAAAAqhwnJHZ5eXkajeb2dk9PT1mWy99/VVezZs0rObk5\nNnthS0q+OTnfXLNmzfJ3zqalav7+01FWtVpb997l7xMAAACqKCckdi1atFi0aFGxHM5kMs2a\nNatFixbl77+qa9WqVdPWrZ9euzEl30xE8bmmEb9v7N6jR/369cvbtarqtqynG6+8rUuM6mks\nb58AAABQZTlhuZMpU6b07ds3Kiqqb9++gYGBiqLEx8evW7cuIyNjw4YN5e+/qmNZdv78+ePH\nj681b4G/QZ9utvTr1++LL74of8/C8SNcYryjLIeEis1alb9PAAAAqLqckNj17t1706ZNb731\n1rx58wobmzZt+sMPP/TujTuDREQhISHLli27du1afHx8ZGRkeHh4+ftkzPna3dsLKixr692f\nnL02HgAAAFQtzlmguGfPnj179szIyEhKSuI4LiIiwmjEPcHiatas6ZTn6hy02zczVoujbG/d\nTg4KcVbPAAAAUEU5c4Fif3//Jk2aNGzY0Gq1YoHiCsXFXxHOFqyWonp52zt0cW08AAAA4A6w\nQHHVw8iybvOtC9cJJcxKBgAAgAcNFiiueoQ/d7OZGY6yGBUt1YlybTwAAADgJrBAcRXDZmVo\nDu53lFWNxta9j2vjAQAAAPeBBYqrFFXVbVrH3HhV7Z1jVExSAQAAgBuwQHFVIpw9ycVfdZTl\nkFB7cyxcBwAAADdhgeIqg7HbtTu33qgwtl6PEuucSc0AAABQPTDqjcmV5bFly5a33nrr8OHD\nhS1NmzadMWNGv379yt95OZV4O5hhGJZlFUVxyrfvFAzDMAyjKMqdDlD++F3Zs8NRZtt2ZB97\noqJD4jhOVdW7hFT5HD81V0dxkxu+RBzHudUjECzLMgzjbiG51Y/MPV8iVVXd6tejG/7GJiK3\niqciXiJJkrRarRM7hErgnMTOodgCxXl5edevX69Xr56z+r/vqG5vFATBy8vLbDZbLJbKD6lE\ngiBotdq8vLwSv8pmpBt++MaxLayq1+c//xIZDBUaD8uyvr6+NpvtTiFVPpZljUZjTk6OqwO5\nyc/PT5ZltwrJ19c3KyvL1VHc5O3tzfN8iW9DV/H29jaZTO6T23l6emq12uzsbPfJ7Tw9PW02\nmyiKrg6kgF6vNxgMJpPJbre7OpYCer1eVVWr1erqQApoNBqj0VgRH2r+/v7O7RAqmnN2nnDw\n9/cv+j/gwIEDQ4YMcfkv9Ltnru7z95YjkjvFo9m2kW780rd1jlH1eqqsyKvKS+RC7haSu8VD\n7heSWw1HObhVSOoNrg7kFm4Vknv+OnKrlwhcxTmJ3fr165cuXXrt2rXCP4JlWT516hSGcJ1C\niDvNX7noKMvBNcQmzV0bDwAAALgnJyR2v/zyy/Dhw3meDwkJSUhICA0NzczMtFqt3bp1K7od\nBdwfRhK1u27OmbD26Is5EwAAAFAiJ6QIn332WZ8+fTIzM+Pj4zmO27Rpk8lk+vLLL1VV7dy5\nc/n7f8Bp/tzD5GQ7ymLj5kpouGvjAQCAO0lLS5sxY8a///3vN954o+iGTACVxgmJ3blz5156\n6SXjjZVyVVXlef7ll19u3rz5W2+9Vf7+H2RsdqZw6E9HWdXpbV26uzYeAAC4k7Nnz7Zv3/6/\n27avDwr5MSNr4LBhn332mauDggeOExI7URQ5jnOUPTw8srMLhpcGDx68atWq8vf/INNu21i4\nz4St0yOqwcO18QAAwJ28/PLLOd170Ky59PSz9PokmjX3ky++OH78uKvjggeLExK76OjoBQsW\nOGahR0REbNq0ydGemZnpVstAVDn8+bP8pQuOshIYJDbDPhMAAG4qPT396NGjNPypm00NoqlZ\ni+3bt7suKHgQOWHyxGuvvTZy5MisrKytW7cOGjToo48+Sk1NDQ8Pnz9/frNmzcrf/4OJkSXt\njs03Koy1J/aZAABwXwVr7BVbC0Kns9lsLokHHlhOSOxGjBjB8/yVK1eIaPLkyX/99df//vc/\nIoqIiJg9e3b5+38waQ79xd6YMyE1bCKHRbg2HgAAuIuQkJDQ0NCkbVtp4OCCpsxMOnqk9f+N\nc2lc8MBxzjp2w4YNcxQMBsPmzZsvXLggimLdunUFQXBK/w8aJj9P+KtgOpWq0Vi79nBtPABQ\nQZKSkv7888+8vLw6dep07NjRsVEVVEUsy86cOXPEc89RRga1aEnpabR4Ub/OnWNiYsreiaqq\nkiThoxPKw5k7TxSqW7duRXT74NDu3saIBTvn2Nt1Uj08XRsPAFSEX3/9deLEiZbwCPLxpbOn\n2zduvGTJEk9PvN9dTFGUpUuX/v777zk5OdHR0RMmTKhVq1ZZTuzdu/fKX36ZNWvWmc1/BAQE\nDBw54sUXXyzjRa9evTp16tSdO3fa7famTZu+9957HTt2vP/vAR5gFZLYQXlwqcnCqYJZVIq3\nj9i6nWvjAYCKcO7cuTfeeMP65jv0SDciIpPpz7cmvvvuu7NmzXJ1aG7HYrGkpKR4e3tXzuVe\neuml5du30+ODyM//8N+HfuvcecOGDU2aNCnLuZ07d76PBVxzcnIGDhwYX7sOffI56XSHd+8a\n+vTTi5cvb9asmVlRRVUlIrOi2FWViKyqalUUIrKrZFEVIpJVsjB57VSmEYcRX0Bi53602zYW\n7gNrf6SnyuFnBFANrV692tq8ZUFWR0RGI437v99ef+Wzzz7jeTd912/dunX+/PnXrl0LDw9/\n5plnBgwYUNFXzMjIeOedd1atWqUoil6vHzNmzMSJEzUaTcVdcceOHcs3/EELfqDgECKifv2t\n33378ttvr1q1OldRiChfUURVZWUlX1FsNptJURVSrYpiVVQiypEVlVS7ShZVJaJcWVaIJFXN\nkxUisqqqTVWJyKTIskoqUY4sE1FmTq7pmwVkMBQEUaeu7dnnnyCiMxfKHvkbKjUKCXTmawFV\nk5v++nhgCWdPcQnXHGU5LEKs18C18QBURZIksSzLuvdE8szMTAoIuKUpMNhms+Xl5fn4+Lgo\nqLtZuHDhpKlT6Ykh1K3HxStXdk2Y8M7Fi6+88krFXVFRlFGjRu015dH87yks3HL65Kz/fma3\n26dNm1bsSJOsyKSaFFVR1RxZVomyZTkzM/OX9euvXLnC+/hGR0c3ad+BYRmToipEJlmWHWNg\nimpTVauqiqqaryiKql7z8Kafl5KP783eR405RRR19h5yrHum15e/j+tpaVlaXqfTlb8rqNKQ\n2LkTSdQU2RbW1r034UlqgHtx5MiRqVOnHjlyhOO4jh07Tps2LSoqytVBlaxu3bq0fQfJMt1Y\n4J2O/RMUFFQJNxz379+/Y8cOu93esmXLAQMGlCUDzs7OnjJlCk2ZTu07EBF17kotWs58bfzg\nwYMjIso6Zz8/P59lWY1OZ1IUm6paFdUx+pUtyzJRnqI4xr3MqmpXFJOiXLkWv7dTV+regxyT\nCVq2pv99/3VCwsazF2SWzVcUSaU8RZFu3OIoQedu1JmI6BzRmrSMMkVp8KCquRT84mXLFn/x\nWf/+/T/99NOAYn8zwIMEiZ072buLzS1Y0lls3FwOCXVtOOCGVFVNTEz08vLy8vJydSxuJy4u\nbuDAgeb+sfTcCyTLW9esOvrYYzt37gwODnZ1aCUYOnTot99+e2XGf+j50eTrRwf+pLlfTn5/\nakVPjJ00adLCJUupfQfS6eiXX9r+738rVqzQFlt97TbHjh2z+vsXZHUOjZuIfft/G3euvqeX\nTVHMipKvqCKpObJiVxSzI2m7Uc2yWtNNeRLDkIfHPSzJqfeg3n1vbTGo9aIuSTKRfE/ftQv5\ncBwR8Qx5sCwRGVhWwzBEZGRZjmEYIi+WTU5OPnjoILVuQ46pcpJM27a0Cq3Rr0cMERk4znHv\nWccyOpYlIi3D6BiGiASGmffZZ5vPnKWnn6UBj1HL1uv++2numDHLly938xFrqDiMepe/daqF\n9PT02xs1Go2Xl5fZbDabzZUfUokEu0337WyyWolI1WjyR73k2smwHMf5+vrabDaTyeTCMIpi\nWdbLy6twzzp34O/vL8typYW0cOHCjz/+ODMzk4g6deo0c+bMevXqFTvGz8/PcYCb8PHx4Xm+\nxLeh0z3zzDMbiKWJk282vfn6qKZNZsyYUSyk3NxcRVGKnZ6fn7979+6UlJS6detWzsojFy5c\nmDRp0p49exxRTZo0afTo0RV0LYuimlVlzeYtb37+Ob0zhQKDiIjsdlr+a4dmTR/u1t2sKFZV\nzZZkq6qKLJMlSmZZtiiKSVEtimKrdp8UjnzLyLEskYFhNCyrZRg9y/JEcUePJqSlUmAw6bQU\nH88nxP9r8ODIGiEcwxhZloiMHKfXarQMw4piYVd6ltUyDMOQF8sWVssez1tvvfXdokXUoRMZ\nDPTXn61qRa5atUpf2i3a+Pj4lq1a06IlFBZe0GQy0dBBa5cta9fOORPvMPhX5WDEzl0I2zc5\nsjoisrfrjCVOoJhly5ZNmjadXnuD2rajXNPeHxYMHTp0x44dlTZV0P2dOnWKRo29pal9xxN/\n7SvLuQcOHBg9evR1SabgYLp8uVWD+osWLQoMrNhH0evWrbty5UpZlvPz8729vUv9M9umqvmK\nYpKVXEXJVxSzouQpSo6s5BdU1WxZNt9adoycOR47IyKKqE2z5t7sUaOhp0buJ9pfxtuUlU+S\nSFHIMVtCVSk52ctibtG0qZFlWGK8OZZlGG+OY4mMLONIvDiGMXLsuBdesI98hiJr3+zqpx8G\nyvbPZn7qxZUylKXUDF28ePHaJT9mZGQ0btx4woQJDz30ULFjHCmXxWJx1jc6Y8aM2NjY7du3\nWyyWVjM+io2NLdyE/S6uXLlC3l43szoiMhqpZuSlS5ecldhBlYPEzi1wKde5k8ccZcXbR2zd\n1rXxgBuaMWMGvfgSde9BROThSW++HT921OLFi8u+UFaVk5GR4efnV/aRMw8PDzLl3tKUm2s0\nGks9MScnZ9SoUck9e9O/nyeWpfy8wx+8P378+KVLl5bx0seOHTt+/LiXl1eHDh3ung5Kqpqn\nKCZFzZflfEXJUxRRo7UbPFMzs3MlyaSoebKcryj5iprjSN1kOV9RcxWllIfJ3IaGZQwM68Gy\nAkPeHKdhmFOHD5sDg26ZKXI+Luxc3PhRo3iG8eE5nsiDZR1DXB4sKzCMF8tyDOPFsXv37h01\nalSGhyeFhdG5c/X8fH/99deIiPA7X7/AElK3LV1Ck98pqJtyae2a7lOnlJrVERHLsiNHjhw5\ncuR9vgT3q3379u3bt7+nUwICAigvj/LyqHD5Q1mm1NSgoCDnxwdVBBI7N6CqWOIE7s5msyUk\nJFDT5jebWJaaND1//nwZe5Ak6fz587m5ufXr13fPSZeFbDbbZ5999t133+Xl5Xl6ej777LMT\nJ04s9Z4UEcXGxp5evJg6dSFHMpeaSqtXxk57v9QTt2zZkszz9OyogulKHp702qStQwclJSWF\nht7yqKsjuzIrql1Vc2TZrlKu3fb5118fOnmKIiOJGM3+A4/06xdWr55FVc2yklPw8JmSryi5\nipIvFyxF5p6MHKtnWD3L+AqCjmG0RN4sq2NZHct4sayGYUypKUf3789MSQnxMvbs0qVxVJQj\ndfNgWQPLahjyLmmQqc+LLxzu1JUeH3Sz6cBfUZcuPPfmxFJD6tSp019//bVr167U1NSaNWt2\n7969jLsyfPLJJ927d899503q2Jny82j1qkcaNxoyZEiZX4yqoUGDBm1atDj0+Uya/DZpdSRJ\n9M282j7enTp1cnVo4DJIIFxPOH+WS4x3lOWISDEq2rXxgBvSaDQeHh756WkUXmSsIi3Vv2WL\nspx+6NCh8ePHX7h8mbQ6jSS++OKLb7/9ttvuXvX222//tGMnvTeN6tTJu3x5ztzZ6enpX375\nZeEBjrUt7KpqKVg5TFaJbKraYtTopimpx+d8QY0ak6zQ5Uutxk+w9+rzXWa2WZaJSCEyKao2\n22S327MlyZFh5cryhYAQemPyLZPQg4Jo4aI+mbmaPGuuoqhqwYJkJRswkAYMdBTtRJuJKNM1\nT4JqGMaDY71Y1oNlPVjGg+W8HAWOM7CsN8uQ1frlzJk5AQH0cFviePr7kGblb0sWLGjdpLFH\nkWftPT09bTabKIrFLxAcQE0a3WtUgwYNOjxrNnXsVPBgX1IirVg+6IMPyni6j4/P8OHDDQZD\nbm6u3W4v41mRkZF79+6dPXv2ka2bjEZjrxfHPffcc9VvPgHDMN98882IESPODB1MkbUoMbGm\nh+G7hQux6MmDDJMnXD15QpY9Fn7NZmUSETGM+enRclCIi0MiIkyeKJvKnDwxadKkhfv/pE//\nS0YvIqL9e7XTp27evLlhw4ZFD7t98kRqamohkx0pAAAgAElEQVSXLl0yevWhZ54jrZZOnqD3\n35s+Yfy4cZWxN/ntkydsqpotyyZZMSlKriznKIqj7HgyLCEra+W69dSl683FWiWR0tJ8Q0NV\nlqsqtyPLyYfjPFjWwDIeLOvNcZ43EjUjxxlZxpPjHFVvljWwrCfHebCMF8t6sixfhmT90qVL\nU6dO3bVrlyRJzZo1mzJlyu23/+6Y2N0XRVHGjBmzetMmat2GZJkO//300KGff/552XswGAz3\nmthVNKc/Y1cekiQdOHAgPj4+ODi4Q4cOpc5xvieYPFHlILFzcWKnOXJQu21jQaV5K1PPR10a\nzk1I7Mri/hK7bdu2LVu2LDU1NSoqauzYsbVr1y79HCKz2fzUU0/tPXaMGjainBzdlcszZswY\nMWJEscNuT+xmzZr14Zrfac7XN5t27giYN/vMmTP3FHapJFXNlOUMWUkTxTRJzpSVdEnK47gs\nWUm2mLMkOUtWsmTZfNt01OqHUVVvnjdyrJ5hDCzrfSNRM7CsD8cZCp5CY7w5zsCygZ4evjod\nk5+vI9WDZT0qZVRJURRJku60hYNzEzuHffv2/fXXX471Bdu0aXNP5yKxK5VWqzUajfn5+U4P\nCYldlYNbsa7E2O2aP/cUVHiBYvq4NByoDDNnzvx07lx6dAC1aLX3+PHFnTuvWLGibdvSp8sY\nDIaVK1fu3r37+PHjPj4+3bp1Cw8v/RFyIoqPj6d6ty7SGxWVnp5uNpsNhaNiZaASpUtyuiwn\niWKaKKVJcqokpctyiiSlSXKaKGXIVWZpMQcvjmWJISIjx6qynJGRYUlLIyJGkUO9fR566CE9\nx+kYhmPIyHEMFTxA5s0yROTDcRqWNbDM5AkT0jp3pYdvzEBUVXp9wpu9e73++utlDMNoNGq1\n2ixJlCvxBWRZtkI35rpdx44dsas9QCVAYudKmr/2MuZ8R1l6uD3v7UNuMzwGFeHMmTOfzppF\nX35FDaKJiAY9aVv80//93/8dPHiwLE//MAzTtWvXrl273tNFg4OD6fSuW5ri4729vUucjpAh\nyymilCRKKVLBv8milCpJyZKULslixQ/w8wyj5OUpqnpzll9Skj4xPqZbd1+BZxjGp0h2pWc5\nLUMMw3hzt6zaqmPZggJTsKBr4fKwRFTL37/EdeyI6qWlpV2/fv2hhx7y9CzrekN5PXtMePc9\neutdergtmc3008LAhPinn366nK8DAMD9QWLnMozJJBw+4Cirer3cvjN+GNXe3r17KbphQVbn\nMPCJqwv+d/ny5Tp16lTQRYcOHfr111/nrVpBjw8ihqGkRFq6uM/rE9eZ8q+LYqIoXRfFJFFK\nEsUUSXb6nE0ty/oLvDfD+HCsH8f7cKwfz/mwrBfHGR3/cqyRZY03HvkXGObIkSNPPTUi3dNI\ndevRpYv+OdmLFi1qU6tMw5PlFBgYeK9r1/3rX//Kysr69MNp+TY7SWKjRo1mLVlS0QvgEdGZ\nM2fmzp177ty5wMDAwYMHDxo0yG1nwwBAZUIu4TK6PdsZqeARFnuHR1QtJjFVf5IkUbG1bHie\niCriHlyqJCWKUqJdTPL06rJ02bbjx23nzlJQMAWH0Kw5vxL9ei2x/Ffx57hAgQ/kuWCe9+c4\nf54P4jlHwZ9ng3g+3M/vXneeaNmy5YEDB9asWXP16tWafXvHxsa6+fos//d//zd69OgLFy4Y\njcayb5xaHvv373/yySft3XtQr76UlrrlzTf//vvvYhtsAMCDCYmda3BpKfzp446y4u1jb9ay\nTEszQRX38MMP04cfUvw1iqhZ0LTpj8CAgNvXtS8jm6rG28UEUUwUpQRRTBCllITrV83WBFG8\nZezN04s63M+6VhqGCRb4EJ4P5PkaAh/IczV4PpDng3kuiOf9eU6omFEiLy+vyl8etjw0Gk2x\n6ckVR1XV8ePH2595jv51Y+pM127fjXl+8ODBrVu3rpwYAMBtIbFzDe3OrUVXJKYybB0D1UCr\nVq2eGTbsx1dfpqHDKSSUThyj1Sv/+/33PH+3d6JKlCpJSY7hN0mKt4uJopgoSfF2MU1ywlCf\nH8eFCHyYwIfwfA1BqCHwwTwfJvBBPB/A3///TKvV+vXXX+/fv99utzdv3nzChAl+fn7ljxYS\nEhKuXr1KAx672RRZi5o237dvHxI7AEBi5wL8lYvclYuOshIaJtZr4Np4oDLNnDmzWbNmy5cv\nT0nZEBUVNX7NGsfSD3ZVTZHk66J4XZSSJSlJlFIlKcEuXpek66JU/kffAnguVBBCBT6c52sI\nfA1BCBP4EIEPE4R72qq8jOx2+4ABA47m5NKjA0ij2b9j28qVK3fs2IGlEwAAKhQSu0qnqppd\n2wpr1kd6ER55fjBYVTVZlFIkyeexgQP6DUiRpGRJ+lSSki9cSRGlTGc8Zmdg2Vp6XTBD4YIQ\nphHCeT5MI4QJQqjA33f2JknSli1bLly4EBQU1LNnzzKOun377bdHM7No/gJyPDzaPzb5rUnT\npk2bM2fO/YUBhcLDwyMjI6+uXXPzVuy1q3T8aKcPprk0LgBwC0jsKptw6hiXmuwoS1HRclhl\nPGoNlcOiqNclMVWSk0QxTZIdo26Fy4Xk3nlLqnsVxPPhGj6M58M1QoSgCRf4cI0QzvO+PHf7\nAsXlkZycPGTIkDMpKVSvPqUk+7733tdffx0TE1Pqifv27aNevalwShDD0IDYvd/Mc1ZgDzKG\nYWbPnj1kyBB7/DVq3oJSUmjl8lEjR7Zq1crVoQGA6yGxq1SMJGn37iyocJytS3dXRgP3JUuS\nkyQpURSvi1JunvmK2RJvsTiWCzE5L3UjIj+eczzr5hhyc6RxoYIQJvCayhrlHT9+/Blff/pi\njiNFy1qxfNy4cfv27auE5TzgLjp27Lh169a5c+fG/bEuMDDwiU8+GTRokKuDAgC3gMSuUglH\nDjCmXEdZbNpS8fV3bTxwFxmyfNlmvyaKCXYxQZQSRDFelOLtYr7zdsTyZNkaAh/I86ECH8Bx\noYIQLHChghDC8zXKcfPUWVJTU3fs2EG/rrw58Db4yawN6zZu3FjqlNWOHTtu+/EnemJIwbmq\nSmt/79TpfmbmQomio6PnzcMIKAAUh8Su8jBWq+bgfkdZ1WhsHbq4Nh4olCpJ52z2y3bxil28\nbLNdsYtXnDT85sdzQTwfwvNBPB8scI5CiMAH8VwNXtCzbv14ZWZmJjEMFZvuEBhUllu9Y8aM\nWbNmzbFxL1D/WNJoaPvWkOtJU3/4vqJiBQAAIkJiV5k0h/YzN7ZnFtu0Vw0ero3nwaSodE0U\n42y2c1b7Bbv9rMV61mwxl2PbdT3Lhgp8DZ4PFYQQgQ/h+RCBD+a5EIEP5l0/6lYeERERGkGw\nHz9GzVsUNFksdC6u3otjSz1Xo9GsXbv2m2++2bdvn81ma9m+HZY7AQCoBEjsKgmTn1d0AzF7\nq3Z3Px6cxawop622U1bbSavtlNV62movfi+1DFmdwDAhPBeuEWpqNOE8H64RQng+TODDdboA\nrcZms1VU9C7l4eExYcKET2f8h16fRM2aUXIyzZvTus5DPXv2LMvper3+1VdfnTZt2r3uPAEA\nAPcNiV0l0e7fzYg3NhBr30XVal0bTzVmV9STVtsRi+WIxfqPxXrJZr+nW6p+HFdbq6mlEWoJ\nQi2NUFurCRf4EJ7nbh1727FjxwtTppw9e5Zl2TZt2nz88ceNGzd27jfiDl577TUimjftPbPZ\nzLJsv379ZsyYIQjYJwUAwE0hsasMbE62cOIfR1n19hGbY3V4J4sXpYP55sMWyz8W23GLtazL\n+Yoi8fzNdQTtNho+9Oeff2oT3ebu5x05cmTkyJG2p5+lqR8oknRgxfLBgwfv2LEjNDS0fN+H\n2+F5ftKkSa+99lpiYmJgYKDBYHB1RAAAcDdI7CqDZs8OurH8rK1DVxUbiJWbrKpnbfY/880H\nzZYDZkuSKJV6CscwtTVCI522sU4brdMm7Ns3+d136Yefbx6h0ZJWk5aWVmpXM2bMsMUOvLk8\n7ITXMq9fnzNnTnXdhZ3n+cjISFdHAQAApUNiV+G49FTh7ElHWfHzFxs2cW08bi4jI8PHx4cr\nKfe1K+r+XNPmtMy/8s2HLJZS1/vVMExTva6pXtdIq2mk0zbU6YrOQj0RFkpJiZSVSb43nuhP\nT6O01IceeqjUIOPi4qhP/1uaWrc5e+xIqScCAABUKCR2FU6zayvduDNo6xJTlkf1H0CyLH/1\n1Vdz5szJysrSarVPPvnklClTfH19JVU9ZbXtyss/YLb+aTaXugRJMM+3Nejbeuib6XXN9bq7\nTEpt3Lhxr27dNk95h954kyJr0eVL9Nkn/fv0adCg9K17vb29r2fduuRHVqaPj0/ZvlcAAICK\ngsSuYnGJ8fylC46yXCNMqlvftfG4rVmzZn383QJ65XVq1MSWcv3nTRv3/rw0tP+Aw2aL7a4P\nzGkZpoVe187D8LBB31qv8+XLepubYZi5c+dOnjx51bMjVY5jZPnJJ5/86KOPynLu4MGDP1z4\nA7VtVzDad/UKrVk9eC52QQUAABdDYlextHu2F5ZtXbpTJa5qJsvyTz/9tGLFivT09KioqPHj\nx7du7aaTNvLy8v47axbNmkv1GxAR+ftTw8ZXiK7km0s83ofjHjbo2xp07TwMzfU6DcPk5uYe\nO3rkmCw3bdq07Iul+fr6fvvtt5988kl8fHzNmjW9vb3LeOJLL710+PDhjSOGU6vWZLfTkb/H\nPv98//79Sz8TAACgIiGxq0D8xXNc/FVHWapVR65ZuzKv/uqrry7dspWGDqfg4IvHjv4RG/vL\nokVl2b69nCwWy4kTJ3Jzcxs1alSjRo27HxwvSjvz8tckJtmXrSSj112O9OO4Dp6GGH//FixF\na7VFt2z45Zdf3n333RyViON0FvObb7750ksvlT1gHx+fe72LyvP8okWLdu3adfLkSUEQHv7P\n9ObNm99TDwAAABUBiV2FUVXN3h0FZYaxd+lemRc/cODA0lWraMGPFBpGRNS5K4WGvfrqq0eP\nHmUr8iG/HTt2TJgw4XpOLnl48FmZzz333AcffFDsirmysjc/f1e+Zacp/5LdTkREbIlZnack\ndffz7eCh7+hhqK/Tcizr5eWVnZ1d9JhDhw69/Prr9Obb1L0HEVmP/D3tvbcjIyMHDBhQcd+m\nQ9euXQcNGiTLcrGQAAAAXAWJXUXhz5zkUlMcZTEqWg4uZezKuf766y9q2qwgq3Po0+/6nFnX\nrl2rVatWBV302rVrzz//vOmpp+nJocSy0oXz8995MyQk5OWXXyaiq3Zxkylvsyn/T7PZrpS2\nztyZU8YF//vzp5+CgwLvfuB3331HfR91ZHVERC1b04hnvvnmm0pI7AAAANyN2yV2eXl58+fP\nP378uCiK9evXHzt2bFBQUNEDTpw48c477xQ7a8yYMY8++mglhlkaRdHu31VQZll7526VfH2e\n50m6dWk3WSKiEpcRcZbFixebGjSkocML6nXr0fhXZx86eD7h+vY8c4p0x6XmNAzTjGPO/7I0\n+/gx0mgpMdEz4dpXX39dalZHRElJSdSu4y1NtWsnrv+9XN8JAABA1eR2id2sWbPy8vKmTp2q\n1WqXLFkyffr0L7/8sui9vAYNGnz//feF1dTU1Pfff79p06auCPaOhFPH2RvLYYiNmim+/vfd\nlcVi+f777w8fPqzT6R555JEnnniiLPdSu3TpQh99ROfPUb2ogqYVv9WtWzc8PPy+IylVUlIS\n1br1OcKOnXI6dl6anVvi8ZEaoaunR1dPQzcPDyPH2t56c82aNefPn6/Rq8ejjz4aHBxclouG\nhYXR5Uu3NF26VKHfJgAAgNtyr8QuPT390KFDX3zxRe3atYlo7NixI0eOPHHiRLNmzQqPEQQh\nICCgsDp79uyBAwdGRES4INw7kWXNn7sLyhxnb9/5vnvKycnp3bv3RUWlTp1JFJe/997vv//+\n008/lZrbNWnS5JUXX5z1ykv0aCyFBNPRf3SHDs5dvZqpsGm5oqpKTZqRVGydueKX8+W5RzwM\n3T09uhk9gvlb/vtptdohQ4bc63Wff/75FY8/Tk2aUUwPIqLDf9Pin8bOm3ev/QAAAFQD7pXY\nnT9/XhAER1ZHRJ6enuHh4XFxcUUTu6L27Nlz/fr1qVOnFm00m83Sjbt+LMvePZWpiERHOHmU\nzSl4ml5s0kL18S3LNRyRFIvngw8+uOhhpP/OJsct1GFPbRr17yVLlowcObLUDt999922bdsu\nX7489Z/DDRo0GDfri/vbFeruL1G+omw15W3Izdtqys/pUHIKyxI11et6GD1jjB4t9Trufl/z\nEl+ihx9+eN4XX7zzzjvZs/9LHKe3WSdPnlzJD9hVXLp8f9wtHnK/kBiGQUh3wdzg6kBu4VYh\nlfjryOXc6iUCV3GvxC43N9doNBb9f+nt7Z2Tk1PiwYqiLFmyZNiwYfytAz/vvPPOnj17HOXw\n8PDVq1ff6XIGg8H5m5pLov3AvoKpAbzg0S/W0/seltLQarVFqzt27KDR46jwwThvb+rbb/fu\n3a+88kpZehs2bNiwYcPKfvUS4ykWkkOqXfw9I3N1esa2rByrcofdIPLyonKy3onp1sfPL0gj\nlCeMovz9i9/XfvHFF0eMGHH48GFJklq2bHn7ARWK5/lKvmKp3C0ecr+QfH19XR1Cce62c0mJ\nb3zXMhqNrg6hOOd/gpSP0z/URFF0Ym9QOdwrsaN7+QNo3759Vqu1W7fi8xKioqJsNpujHBgY\nWOL/S4ZheJ6XZVm5U1Jyv9T9u9Ubw3XMw+0lgweV7Y3BMAzLsrIsF2202+2k1dxynFZrTbdW\nwpvN8RIpilI0pAS7fWV65uqMrD9NefIdNoSoo9W2slub5Jn+1bRRxI117JwVMM/zUkmTMPR6\nfadOnZx7rbIQBEFV1RJDcpU7vUSuwvM8wzBu9fHgbi8Rx3Esy0qSpN51k5XKxHGcoijuEw/L\nshzHudVL5HgexumfIPfN8RI5/UNNkiRBcNqf5VA53Cux8/Hxyc3NVVW1ML3Lycm509/WO3bs\n6NChw+3TPMeNG1e0mp6efvu5Go3Gy8vLZrOZzSXvbXB/GEny2L3dEboqCHkt2qh3GG68nSAI\nOp3OZDIVbWzRosUf27ZSm7YFdUminTuaP/nEnUYxnYjjOF9fX1EUTSZTvF1cl5u3Ntf0t9lS\n4q9VlqiNQd/Xy7O30bNukUzUuXGyLOvl5VUJ33vZ+fv7y7LsViH5+fm5VTw+Pj48z7tbSCaT\nyX0+ko1Go1arNZlMxf6ucyFPT0+bzeY+6bhjIMpsNtsLVr50Pb1eT0QWi8XVgRT4//buPT6K\n+tD7+G/2MnvJbjZZQkAIYCAYEZSrkSgvVC5HLnKzB1SUUFBCrKeBRytWxdbbg0pBQcSHRg60\nllLUHkVBRaJWwFcRAQHp0VhExYAgJCTZzWUv2Z3nj4nrmgRIIGSGyef91+7MZPPd4Zfky2/n\nYrPZ3G53IBBo8UjqO8UFRF/FrmfPnuFw+ODBgxkZGUIIn89XXFzcq1evhltWVVXt2bNnwoQJ\nrZ7xdKyf7pAq65pZuH+W4kw4xxd89NFHtw0bVvnkE+L6YSIYFK//T09J5OXlnXPSJjkUCP7t\n6LFXT5TsqQ402udkSRrqco5JdI92u1KafJNWAABwnuir2Hm93uzs7OXLl+fn58uyvHLlyh49\nelx22WVCiMLCwkAgEDso/quvvopEIme8Y1VrksIheefH6mPFKoeuHHzur5menl5YWPjUU0/t\nXv6cLMvDhg277777zvdRHSdqI29U+F73+XdWNT4/5zabRrpcYxJdw90JrvN5HwsAANAs+ip2\nQoj8/PyCgoJHHnkkEon07t17/vz56seye/fu9fl8sWJXVlYmSVLTb/feCuRdO6TqKvVxaNDg\nc5+uU2VkZKxcubJFXur0KiKRt/1Vr5X7tlVVN3r8XKLZNMrtHpfout6dYOPEKwAA9Ed3xc7p\ndDZ6yud9990X//S666677rrrWilTE0jBgHX3j9N1Nnt40FWn314/QopS6K98pdz/nr8y1Fif\nk4PB0R73lA7tr3MlyPQ5AAB0THfF7gJl3bld+vGQ1fCV2Yr9AjjadGd1zavlvvU+f1ltY4ds\n19aKYFCcOB5a8cLX1ZXXbdpEqwMAQOc4QKol1FTLn35S99jhDA3U9XTdd+HwouOlWf/+eszX\n360+WV6v1cmS5Ni9S+z8RFgsIiFBXJwuHn1if+nJdevWaRUYAAA0ETN2LcC2c7v045XzglnZ\niiyffntNBBRlQ4X/b+UVH1VWN/zA1STEEJfzP5M815pE3+vmirWv/rTOZhMDBn7++eetGBYA\nAJwNit25kmpqrJ/uVB8rCa7wgCxt8zS0pyawtqzitQqfL9LIhbsy7bbJHvfkJE8nq0UIEQ6H\nrVZr2O8T8WccV1S4M7q3WmAAAHB2KHbnSt65XQrXXTMzlHW1YtHLRbrLaiOvVPj+WlbxRSDY\ncG2KxXyTJ3FKUmJfhz1+udVqHT169Jv/XSCeeEqoFxzf/5nYsX3s7+a3TmwAAHDWKHbnpqba\nuqfu6DrF4Qj3HaBtHNWO6po/nyx/s8IfbHCWq1WS/sPtujU5cbgrwXKKkyEWLlz4xbhxB6ZP\nFf0GiIpy8cknv3/owX79+p3/4AAA4JxQ7M6JbdfH0o+3uAldNUSxanl0XXkk8kq576Wyii8b\nm6LLtNumJiVOSfKc8RYR7dq1+/DDD19//fUDBw4kJiYOX/B/e/fufX4iAwCAlkSxO3tSTY31\n07jpun4DtUqyu7pmdVnFG+W+QIMpOrfZNMmTODUpcaCzGVdgkWV56tSpycnJwWCw3u1rAQCA\nblHszp511/afpuuyrmn96bqQomzyV64oObmzOtBwbV+HPSfZ84ukxATu+gUAQNtAsTtLUiAg\n792lPlYcjnC/Qa353Q+FwqtPlq8tr2h4bWGXyfSfSYk53qTL7bbWjAQAADRHsTtL1l0fS4G6\nebJQ1tVnvHZdZWXlpk2biouL09PTR40aZbfbT799oxQhPvRXFZws+8Bf1fDKJX0d9unepJs8\nbqboAABomyh2Z0MKBuQ9P91qItzvytNv/+mnn06fPv2Y2SK6dhWrVnd57LG//vWvvXr1avp3\nDCnK6+W+5aVlDa9dIpukUS5XjtdzrSuhOW8CAAAYDcXubMRP1wWvHHz66bpAIDBr1qxj1w0X\ns2YLk0lEIsXPPZubm/vhhx+azWc4QVUIcSQcXnWy4i9l9e/9JYRIk63Tkz23Jyed8URXAADQ\nFlDsmk0KBuQfbzUhHM5w/zPcamL79u3fVVTUtTohhNksfvXroglj9u7dO3Dg6U6k3VVds6K0\n7C1fZe3Pz3WVhBjqSpjp9dzgdplPcS06AADQBlHsmk3evUMK1KiPQ4POMF0nhCgrKxPJXhF/\n3JvNJhI9ZWVljW4fVURhZeWLpeVbKqvqf2tJmuhJ/K/23l42Pd6OFgAAaIti1zxSMGDd/dO1\n60JNuDNsRkaGOHJYlJaIdil1iw4fFiUlPXv2rLdldSS6+mT5ipKyr3+8ikpMJ6tlpjdpWnKS\nl09dAQDAKVDsmsdy6BspWHd0XXjgmafrhBBXXHHFjTfcsPGh34o594j07uLfX4oli2+bemu3\nbt1i25TURlaXlP13ycnS2tp6Xz7Q6ZjdLvnGRJeVT10BAMBpUeyaJ3xJr8iMu2w7PrJ8/VWo\n/xlOho1ZunSp99FH/zbn7nA4LMvyjBkzHnroIXVVcbj2/5WUrimrqIn+7EA6kxAj3K7cdkmc\n6woAAJqIYtds0XYpNWMmSsGAYmvqtegSExMXL1785JNPHj16tFOnTlarVQjxv4HgspKTb1T4\n650b4TBJtyR58lKSuzdhOhAAACCGYneWmt7qYmRZVj9+3V5Vs/RE6QeVVfVu7Jpqtcz0Js3w\nJnmbcBkUAACAeih2rUcR4j1/1bMnSndW19RbdbFsndOh/YxOHcNV9c+EBQAAaCKKXWuIKuJt\nf+Uzx0v2N7hvxOV2W377duMSXXZZtptMYU3yAQAAQ6DYnV9RRWz0+xceL/2yQaXLcjry23v/\nw+3iZFcAANAiKHbnS1hRXi73LT1R+m3oZ9NwkhCjEl1zU9oNcDb7KD0AAIDToNi1vLCivFbu\nW3yi9JufVzr1CibzUtv1dVDpAABAy6PYtaRQVFlXXrHkRGlx+GfXGbZK0i887rnt2/XgVmAA\nAOC8odi1jFBU+Vt5xdKSk8U/n6WTJem2ZM+v27frYmVXAwCA84u2ca7CirKu3PfMidLDP690\nNkma5k36dYq3E5UOAAC0CjrH2Ysoyt/LfYsanB5hk6RpyZ789u0uotIBAIBWRPM4G4oQm/2V\nC34o+fznFzGRTdItSZ7fUOkAAIAW6B/N9kFl1SNHj38RDMUvlCUpx5s0J8XbkUoHAAA0Qgtp\ntq9D4fhWZ5WkSZ7EeantuslWDVMBAACYtA5w4clJ8qinuJqEGJ/o/qjnxcvTOtLqAACA5pix\nazbZJN2TmrK1suq+1JSeXJcOAADoBsXubNye7Lk92aN1CgAAgJ/ho1gAAACDoNgBAAAYBMUO\nAADAICh2AAAABkGxAwAAMAiKHQAAgEFQ7AAAAAyCYgcAAGAQFDsAAACDoNgBAAAYBMUOAADA\nIIx/r1i73d5wodlsFkJYLJZG12rCbDabzWb95DGZTEIIXUWSJMlkMuknj9BrJF3lUQeSriJJ\nkmSz2RRF0TpIHfXXkc1mi0ajWmepYzabZVlWg+mBxWIRQsiyrA4nPbBarYqi6Gdgq7uoxf+o\n6WdMoumMX+wkSTrNwkbXakJNop88MfqJxC5qIr3lETqLJEmSrvKodJVK+pHWQerTVSRd7aLY\nr8eWjaSfN4imM36xq6mpabhQlmW73V5bW9voWk1YrVZJkvSTx2w2O53OSCSin0gmk0mWZf3k\nEUI4nc5oNKqrSA6HQ1d5bDabyWTSW6+6IWQAABKNSURBVKRAIKCfqQiLxWKxWAKBQCQS0TpL\nHbPZHAwGw+Gw1kHqSJIky3IoFAqFQlpn+Rn9DGybzWaz2cLhcItHcrvdLfuCON/0Mq0NAACA\nc0SxAwAAMAiKHQAAgEFQ7AAAAAyCYgcAAGAQFDsAAACDMP7lTlpQIBAoKCj46KOPotHo4MGD\n77rrroSEBK1DAQAA1KHYNVUwGBwzZsx+n1+MGi1M5i2vr//73/9eWFjINX4AAIBO8FFsUy1b\ntmx/VbUoWCVuuU1MuUW8UHDQ4Xzqqae0zgUAAFCHYtdUW7duFWNuFLJc99xsFuMmbt26VdNQ\nAAAAP6HYNVU0GhX17j9tNunnrkQAAAAUu6bKzs4WmzeJ2tq654oi3nkrOztb01AAAAA/4eSJ\nppo7d+6GDRsOzv0vMeZGYTaLzZs6HTv64Nq/ap0LAACgDjN2TZWQkPDuu+/+etj1/Qo3XfH2\nhtxBAz/44AOv16t1LgAAgDrM2DWDx+P53e9+p3UKAACAxjFjBwAAYBAUOwAAAIOg2AEAABgE\nxQ4AAMAgKHYAAAAGQbEDAAAwCIodAACAQVDsAAAADIJiBwAAYBAUOwAAAIOg2AEAABgExQ4A\nAMAgKHYAAAAGQbEDAAAwCIodAACAQVDsAAAADIJiBwAAYBAUOwAAAIOg2AEAABgExQ4AAMAg\nKHYAAAAGQbEDAAAwCIodAACAQVDsAAAADIJiBwAAYBAUOwAAAIOg2AEAABgExQ4AAMAgKHYA\nAAAGQbEDAAAwCIodAACAQVi0DlBfZWVlQUHBZ599Fg6HMzMz8/LyUlNTG2729ttvv/7666Wl\npZ07d87JybnyyitbPyoAAICu6G7GbsmSJcePH//973//hz/8wel0PvbYY9FotN4277///ssv\nvzx79uwVK1aMGDHixRdfrK6u1iQtAACAfuir2JWUlOzcuTM3Nzc9Pb1Tp055eXlHjhzZv39/\nvc1efvnl6dOnDxo0KDU1dcKECQUFBU6nU5PAAAAA+qGvYnfgwAGr1Zqenq4+dblcaWlpX375\nZfw2paWlx44dE0Lk5+dPnjz5N7/5TVFRkQZZAQAAdEZfx9j5fD632y1JUmyJx+OpqKiI36a0\ntFQI8d57782bN8/j8axbt+7RRx9dsWKFx+NRN/jTn/4Uq3per3fOnDkNv5HJZBJCyLJsNpvP\n03tpLpPJZDab3W631kHqqP8KVqtVV5F0tYuEXiPpKo/6I6a3SC6XS1EUrYPUsVgsQoiEhARd\nRTKbzQ0Pg9GKOoocDofNZtM6Sx01kvpvpwfqHzWbzdaykSKRSAu+GlqHXgZlTHyrO42bb745\nLS1NCDFz5sx//OMfu3btGj58uLpq375927ZtUx+npaXNmzfvVC9isVj082Op0s+vLZXJZNJb\nJL3lkSRJb5H0lkfoL5Isy1pHqE9vkfTzn94Yq9WqdYT69PYXpMX/qIXD4RZ8NbQOfQ3KpKQk\nn8+nKEqs3lVUVCQnJ8dv4/V6hRAJCQnqU7PZ7PV6y8rKYhvMnz8/EAiojy0WS/yqGKvV6nK5\nampqYltqzmKx2Gy2qqoqrYPUMZlMHo8nFArpJ5I6F+Xz+bQO8pOkpKRoNKqrSA0nubWVmJho\nNpsb/THUSmJiot/v18/0WEJCgizLFRUV+pkhczqdoVCotrZW6yB17Ha7w+GorKzUT89Q/68S\nDAa1DlJHluWEhIQW/6OmKIr6NxcXEH0Vu549e4bD4YMHD2ZkZAghfD5fcXFxr1694rfxer3J\nyclFRUXqNqFQ6MSJEx06dIht0K5du/jtS0pKGn4j9T+jiqLoZ57ZZDLpKo9KV5HYRU2kqzxq\nf9JbpGg0qp8Wpe6iaDSqn72k7iJd5RH620VCTwNbHc+62kXQir5OnvB6vdnZ2cuXL//mm2+O\nHDny7LPP9ujR47LLLhNCFBYWbtiwQQhhMpnGjRu3bt26vXv3lpSU/PGPf7Tb7VzHDgAAQF8z\ndkKI/Pz8goKCRx55JBKJ9O7de/78+erHsnv37vX5fOPGjRNC3HTTTdXV1c8880xlZWVmZuYT\nTzxht9u1Dg4AAKAx3RU7p9M5d+7chsvvu+++2GOTyZSTk5OTk9OKuQAAAPROXx/FAgAA4KxR\n7AAAAAyCYtdsR48eLS4u1joFAABAfRS7ZtiyZctVV111xRVXDBgwYMCAAZs2bdI6EQAAwE8o\ndk1VVFQ0bdq0nM6p3/961rE5uf8nM/3OO+/cvXu31rkAAADqUOyaatmyZTd17/bb7Cu9DnuS\nzfarAX1zL++1aNEirXMBAADUodg11cGDB69J6xS/5Jq0TgcPHtQqDwAAQD0Uu6byer1H/JXx\nSw77K1NSUrTKAwAAUA/FrqmmTp26bNfevT+cUJ8eOFn+9Padt956q7apAAAAYnR35wnduvHG\nG/feddfQ558f2DHVYjJ98v2xW6dNu/3227XOBQAAUIdi1wzz58+fPHny9u3bI5HIY1dd1adP\nH60TAQAA/IRi1zyZmZmZmZlapwAAAGgEx9gBAAAYBMUOAADAICh2AAAABkGxAwAAMAiKHQAA\ngEFQ7AAAAAyCYgcAAGAQFDsAAACDoNgBAAAYBMUOAADAICh2AAAABkGxAwAAMAhJURStM2jg\nk08++e1vf/vLX/4yJydH6yw6dezYsalTp15//fUPP/yw1ln0a9SoUV26dHnxxRe1DqJfubm5\n33777ebNm7UOol8LFix477331qxZ06lTJ62z6NRf/vKX1atXL1iwYPDgwVpn0amtW7c+8sgj\neXl5U6ZM0ToLNNZGZ+xqa2t9Pl8gENA6iH5Fo1Gfz1dTU6N1EF3z+/1VVVVap9C16upqv9+v\ndQpdq6mp8fl80WhU6yD6FQwGfT5fbW2t1kH0KxwO+3y+YDCodRBor40WOwAAAOOh2AEAABiE\nResA2khJSRkxYkSPHj20DqJfDodjxIgRffr00TqIrg0bNiwlJUXrFLqWlZXVrVs3rVPoWu/e\nvcPhsNPp1DqIfnXv3n3EiBH8rJ1GamrqiBEj+FmDaLMnTwAAABgPH8UCAAAYBMUOAADAINri\nMXaVlZUFBQWfffZZOBzOzMzMy8tLTU3VOpS+5Ofnf/vtt7Gndrv9lVde0S6Ojhw5cuTZZ5/9\n6quv1q9fH1vIiIrX6C5iRMWcPHly1apV+/btC4VC3bt3nzFjxiWXXCIYRXFOtYsYRfGKi4v/\n/Oc/f/HFF4qipKenT5s27dJLLxUMJLTNY+yeeOKJysrK2bNn22y2tWvXfvvtt88995zJxOTl\nT2bOnHnTTTfFrgVqMpm8Xq+2kfRg27ZtK1eu7N+//4cffhjfWhhRMafaRYyomHvuuUeW5dzc\nXIfDsXbt2j179qxcudJutzOKYk61ixhFMbW1tXfeeWffvn2nTJliMplefvnlHTt2rFq1yuFw\nMJDQ5v6xS0pKdu7cmZubm56e3qlTp7y8vCNHjuzfv1/rXPri9/s7duyY8qM2+9uznnA4vGjR\nonrXvmdExWt0FwlG1I/8fn/79u3vvvvu7t27X3TRRTk5OT6fr7i4mFEUc6pdJBhFcaqqqiZM\nmJCXl9e5c+eLLrpo8uTJVVVVR48eZSBBtMGPYg8cOGC1WtPT09WnLpcrLS3tyy+/7Nu3r7bB\n9CMcDgeDwe3bt69Zs8bv92dkZOTk5HTu3FnrXNobNmyYEOLgwYPxCxlR8RrdRYyoGLfb/cAD\nD8SelpaWmkymlJSUoqIiRpHqVLuIURTP4/FMmjRJfez3+9988820tLQuXbrs2rWLgYQ2N2Pn\n8/ncbrckSbElHo+noqJCw0h6U11dnZSUVFtb+6tf/er+++8PhUIPPPAAN846FUbUGTGiGuX3\n+5ctWzZx4sTk5GRGUaPidxGjqKFoNPqLX/zitttuKy4ufvzxx61WKwMJog3O2Akh4gc9GvJ4\nPC+99FLs6bx586ZPn/7Pf/5z5MiRGqbSM0bU6TGiGjp8+PDjjz/er1+/6dOnq0sYRfXU20WM\nooZMJtPSpUvLysreeuutBx98cPHixYKBhDZY7JKSknw+n6IosdFfUVGRnJysbSo9czgc7du3\nLykp0TqITjGimosRtW/fvoULF95666033nijuoRRVE/DXVQPo0iVlpaWlpbWu3fvqVOnbtmy\nJSUlhYGENvdRbM+ePcPhcOwYIPWw3F69emmbSlcOHTr0/PPP19bWqk8DgcCJEyc6duyobSrd\nYkSdESMq3ueff/7000/fc8898ZWFURSv0V3EKIq3Z8+e3NzcYDCoPpUkyWKxCAYShBBtcMbO\n6/VmZ2cvX748Pz9fluWVK1f26NHjsssu0zqXjni93u3bt9fW1t5yyy2RSOSll15yuVxXX321\n1rm0V1ZWFolE/H6/EEKdKnC5XIyoeKfaRYwoVSgUWrJkyfjx47t16xabbWIUxTvNLmIUxfTs\n2TMQCCxZsmTq1KlWq3XDhg2BQGDgwIEMJIi2eR276urqgoKCPXv2RCKR3r175+XlMVNdz9df\nf7169Wr1fM/MzMxZs2Z16NBB61Dau/POO48fP15vyfjx4xlRMafaRYwo1b59+x5++OF6C2fP\nnj127FhGkeo0u4hRFO/QoUOrV6/+/PPPJUnq2rXr7bffrp76ykBCWyx2AAAAhtTmjrEDAAAw\nKoodAACAQVDsAAAADIJiBwAAYBAUOwAAAIOg2AEAABgExQ4AAMAgKHYAAAAGQbEDAAAwCIod\ngAvGiBEjLr74Yq1TAIB+UewAAAAMgmIHAABgEBQ7AGdpy5YtI0eOTExMdDqdAwYMWLVqVWzV\nwIEDs7OzP/jgg6ysLKfT6fV6Z86cWVFREdvgnXfeGTp0qNvtdjgcffr0eeaZZxRFia0tLCy8\n9tpr3W53x44dp0yZ8tVXX8VWWSyWb775ZvTo0W632+1233zzzSdPnmyd9wsA+kexA3A23n//\n/eHDh4dCobVr177xxhtXXXXVHXfcsXjxYnWtzWY7ePDg/fffv2TJku++++65555bs2bNjBkz\n1LXr168fO3ZsQkLCmjVrNm7ceMMNN9x7773333+/urawsPCGG26w2+0rVqxYsGDB7t27hw4d\neuzYMXVtJBKZNGnS0KFD16xZk5eX9+qrr957772t//YBQKcUAGi+/v37Z2RkVFVVxZaMHz/e\n7XbX1NQoinLNNdcIIbZu3Rpbe8cddwghvvvuO0VRLr300q5duwaDwdjaiRMnWq3WkpISRVEG\nDRqUnp4eDofVVTt27JBleenSpYqiDB8+XAjx2muvxb7w6quvTk1NPb9vFQAuHMzYAWi248eP\n79mzZ+zYsSaTKfCjMWPG+P3+/fv3q9skJCQMGTIk9iVDhw4VQvzrX//6/vvvi4qKxowZI8ty\nbO24cePC4fDHH39cWlq6a9eu0aNHWywWdVVWVlYwGMzPz1ef2u32iRMnxr4wIyOjpKTkfL9f\nALhQUOwANNv3338vhFi6dKkjTl5enhDi8OHD6jYdOnSQJCn2Je3atRNC/PDDD0eOHBFCdO7c\nOf4FL7roIvVljx49KoRITU091beu97JWqzUajbbcOwOAC5tF6wAALlQzZ86cNWtWvYUZGRmN\nblxbWyuEMJlMai2r18YURVHXmkymhmsBAE1EsQPQbF27dhVCRCKRwYMHn2qbo0ePRiIRs9ms\nPv3hhx+EEB06dEhLSxNCqPN2MerTtLS0Ll26CCGKi4vj1x46dMjpdLZv376F3wYAGA4fxQJo\nNq/Xm5WVtX79+vLy8tjCl156af78+erMnBCipqZm8+bNsbXvvPOOzWbLysrq2LFjnz59Nm7c\nGAgEYmtfe+01p9OZnZ3tdrsvv/zyjRs3+v1+dVVRUdHFF1/8wgsvtMo7A4ALGzN2AM7GwoUL\nR44cee211957770dO3bctm3b008/fdttt8VOeujSpcvcuXMPHTqUkZHx7rvvrl+/PicnJzk5\nWQjx9NNPjxs3bsKECXfffbcsy2+++eamTZuefPLJxMREIcSTTz45fvz4kSNHzpkzp7KyctGi\nRampqbNnz9by3QLAhULr03IBXKi2bds2cuRIt9tttVovueSShQsXxq5Rcs0111x66aW7du0a\nOnSo0+lMTk6eNWuW3++Pfe3mzZuHDBmSkJBgs9n69++/atWq+Fd+6623Bg8e7HQ6U1NTJ02a\n9O9//1tdPnz48G7dusVvqV5F5fy+TwC4cEhK3NXeAaBFDBkypKSkpKioSOsgANC2cIwdAACA\nQVDsAAAADIJiBwAAYBAcYwcAAGAQzNgBAAAYBMUOAADAICh2AAAABkGxAwAAMAiKHQAAgEFQ\n7AAAAAyCYgcAAGAQFDsAAACDoNgBAAAYxP8HfXaGK6I17twAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model <- keras_model_sequential() %>%\n",
    "    layer_gru(units = 32, input_shape = c(50, 3), return_sequences = TRUE, recurrent_dropout = 0.5) %>%\n",
    "    layer_gru(units = 32, input_shape = c(50, 3), return_sequences = TRUE, recurrent_dropout = 0.5, dropout = 0.5) %>%\n",
    "    layer_gru(units = 32, input_shape = c(50, 3), recurrent_dropout = 0.75, dropout = 0.5) %>%\n",
    "    layer_dense(units = 6, activation = 'softmax')\n",
    "\n",
    "cat(summary(model))\n",
    "\n",
    "model %>% compile(\n",
    "    optimizer = optimizer_adam(lr = 0.001),\n",
    "    loss = \"categorical_crossentropy\",\n",
    "    metrics = c('acc')\n",
    ")\n",
    "\n",
    "history <- model %>% fit(\n",
    "    x = x_train_scaled,\n",
    "    y = y_train,\n",
    "    validation_data = list(x_val_scaled, y_val),\n",
    "    epochs = 30,\n",
    "    batch_size = 32,\n",
    "    callbacks=list(Progress$new())\n",
    ")\n",
    "\n",
    "plot(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$loss</dt>\n",
       "\t\t<dd>0.259405222264554</dd>\n",
       "\t<dt>$acc</dt>\n",
       "\t\t<dd>0.933066308498383</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$loss] 0.259405222264554\n",
       "\\item[\\$acc] 0.933066308498383\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$loss\n",
       ":   0.259405222264554\n",
       "$acc\n",
       ":   0.933066308498383\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$loss\n",
       "[1] 0.2594052\n",
       "\n",
       "$acc\n",
       "[1] 0.9330663\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model %>% evaluate(x_test_scaled, y_test)"
   ]
  }
 ],
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