NN_regression_RTWu.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"VSoklTpd64He"},"source":["## Demonstration: Solving a regression problem using Neural Networks (NN)\n","\n","### - Tutorial : https://machinelearningmastery.com/pytorch-tutorial-develop-deep-learning-models/\n","\n","### - You can work directly on this script using Google Colab or jupyter notebook.\n","\n","### - Write write down your observation with markdown in this notebook briefly.\n","\n"]},{"cell_type":"markdown","metadata":{"id":"Qz9X8m2NJwdj"},"source":["#### You will train a regression model in this part. The data is **Concrete Compressive Strength Dataset**, you can find the details of each column at https://archive.ics.uci.edu/ml/datasets/concrete+compressive+strength. In this part, please predict the **strength of concrete**."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gFMwtIeAJwdj","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1648931677986,"user_tz":-480,"elapsed":20814,"user":{"displayName":"Rih-Teng Wu","userId":"04870307609359018979"}},"outputId":"5f2716d0-a332-4a62-9a36-ba4f7ae19217"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/gdrive\n"]}],"source":["# Import necessory modules\n","%matplotlib inline\n","import pandas as pd\n","import numpy as np\n","import random\n","import torch\n","import torch.nn as nn\n","import torch.optim as optim\n","from torch.utils.data import Dataset, DataLoader, random_split\n","import matplotlib.pyplot as plt\n","from tqdm.notebook import tqdm\n","from sklearn.preprocessing import MinMaxScaler\n","\n","# for data that saves in Google Drive\n","from google.colab import drive\n","drive.mount('/content/gdrive')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BpSVExq8Jwdl"},"outputs":[],"source":["# For reproduce the result\n","torch.manual_seed(0)\n","random.seed(0)\n","np.random.seed(0)"]},{"cell_type":"markdown","metadata":{"id":"fFN95MvCJwdl"},"source":["### 1. Define the model and dataset (10%)"]},{"cell_type":"markdown","metadata":{"id":"o1xJhvMbJwdm"},"source":["#### 1.1 Please follow the tutorial to create a class ***ConcreteDataset***, for loading the data you need and also do the ***Min-Max scaling*** to the ***feature and label***. (5%)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"f6qRprzVJwdm"},"outputs":[],"source":["class ConcreteDataset(Dataset):\n","    def __init__(self, csv_path):\n","        df = pd.read_csv(csv_path)\n","        self.X = df.iloc[:, :-1].values\n","        self.y = df.iloc[:, -1].values\n","        self.X_scaler = MinMaxScaler()\n","        self.y_scaler = MinMaxScaler()\n","        self.X = self.X_scaler.fit_transform(self.X)\n","        self.y = self.y.reshape(len(self.y), 1)\n","        self.y = self.y_scaler.fit_transform(self.y)\n","        \n","        \n","    def __len__(self):\n","        return len(self.X)\n","    \n","    def __getitem__(self, idx):\n","        feature = torch.tensor(self.X[idx], dtype=torch.float32)\n","        label = torch.tensor(self.y[idx], dtype=torch.float32)\n","        return feature, label\n","        "]},{"cell_type":"markdown","metadata":{"id":"VVC7FOQkJwdn"},"source":["#### 1.2 By following the tutorial, try to create a class ***MLP(Neural Network)*** with ***three hidden layers*** as your network architecture. Also, for the convenience of implementation, please set the numbers of hidden nodes and the activation functions as input variables to the foward functions.(5%) "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vWPTZrVdJwdo"},"outputs":[],"source":["class MLP(nn.Module):\n","    def __init__(self, input_ch=30, first_layers=256, second_layers=64, third_layers=8, activation=nn.Sigmoid(), output_act=nn.Tanh()):\n","        super(MLP, self).__init__()\n","        self.model = nn.Sequential( nn.Linear(input_ch, first_layers), \n","                                    activation, \n","                                    nn.Linear(first_layers, second_layers), \n","                                    activation, \n","                                    nn.Linear(second_layers, third_layers), \n","                                    activation, \n","                                    nn.Linear(third_layers, 1) )\n","                                    \n","        \n","        self.output_act = output_act\n","    \n","    def forward(self, X):\n","        X = self.model(X)\n","        X = self.output_act(X)\n","        return X        "]},{"cell_type":"markdown","metadata":{"id":"VPm02AdTJwdp"},"source":["### 2. Train the model (60%)"]},{"cell_type":"markdown","metadata":{"id":"LxwWJdZkJwdq"},"source":["#### 2.1 Please load the ***train.csv/ validation.csv*** in ./data, and turn them into dataloader with batch size 64 and determine whether shuffle or not. (5%)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lj7cuSDJJwdq"},"outputs":[],"source":["train_ds = ConcreteDataset(\"/content/gdrive/My Drive/Colab Notebooks/TKU_talk/data/train.csv\")\n","val_ds = ConcreteDataset(\"/content/gdrive/My Drive/Colab Notebooks/TKU_talk/data/validation.csv\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"chnUUwXfJwdr"},"outputs":[],"source":["train_dl = DataLoader(train_ds, batch_size=64, shuffle=True)\n","val_dl = DataLoader(val_ds, batch_size=64, shuffle=False)"]},{"cell_type":"markdown","metadata":{"id":"rK_23b6YJwdu","tags":[]},"source":["#### 2.2 Create two MLP model from the table below and ***print the model*** (10%):\n","| | Hidden Layer 1| Hidden Layer 2| Hidden Layer 3| Activation in each hidden nodes | Activation for output |\n","|:---:|:---:|:---:|:---:|:---:|:---:|\n","|***Model1***| 256 | 64 | 8 | Tanh | Tanh |\n","|***Model2***| 64 | 16 | 8 | Sigmoid | Identity |"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4yZXH5etJwdu"},"outputs":[],"source":["models = {\"Model1\" : MLP(input_ch=8, first_layers=256, second_layers=64, third_layers=8, activation=nn.Tanh(), output_act = nn.Tanh()), \n","            \"Model2\" : MLP(input_ch=8, first_layers=64, second_layers=16, third_layers=8, activation=nn.Tanh(), output_act = nn.Identity())}"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"eO9b6KstJwdu","outputId":"735cfacf-4c42-466e-a5de-d6cf4fa8d6bf","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1648931766998,"user_tz":-480,"elapsed":291,"user":{"displayName":"Rih-Teng Wu","userId":"04870307609359018979"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["MLP(\n","  (model): Sequential(\n","    (0): Linear(in_features=8, out_features=256, bias=True)\n","    (1): Tanh()\n","    (2): Linear(in_features=256, out_features=64, bias=True)\n","    (3): Tanh()\n","    (4): Linear(in_features=64, out_features=8, bias=True)\n","    (5): Tanh()\n","    (6): Linear(in_features=8, out_features=1, bias=True)\n","  )\n","  (output_act): Tanh()\n",")\n","MLP(\n","  (model): Sequential(\n","    (0): Linear(in_features=8, out_features=64, bias=True)\n","    (1): Tanh()\n","    (2): Linear(in_features=64, out_features=16, bias=True)\n","    (3): Tanh()\n","    (4): Linear(in_features=16, out_features=8, bias=True)\n","    (5): Tanh()\n","    (6): Linear(in_features=8, out_features=1, bias=True)\n","  )\n","  (output_act): Identity()\n",")\n"]}],"source":["for name, model in models.items():\n","    print(model)"]},{"cell_type":"markdown","metadata":{"id":"r_BjKmc1Jwdv"},"source":["#### 2.3 Train the above two models with the same hyperpameters below and do the validation in every epoch. Choose the appropriate type of loss function according to the task. (25%)\n","##### (Note. You should record the training/validation loss every epoch)"]},{"cell_type":"markdown","metadata":{"id":"uibgj0BQJwdv"},"source":["\n","| | Learning rate | epochs | optimizer | momentum |\n","|:---:|:---:|:---:|:---:|:---:|\n","|***Hyperparameter***| 0.01 | 300 | SGD | 0.9 |"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"referenced_widgets":["55b2addf32794e0281e8c41be0d287a6","a97a3eb9d7ef49e880b922f21d373ba9","fc4e4a02c10e4504a8dd93f56de187b7","5ebd03015ab14a71983db6b528ec357f","5095633f4ea2407b80187564547c452a","3130ecc31760438b826c108d0f3b0351","71ab9cb9025a4ac28ff2951a9432991e","6470f0b43041484489118c3265f9cae7","97377de524bf4ae6ae9d0bbebd4e5ece","b85d889d36e649c8bad2769e0b855f84","26096a9dd0044b358440a3a72bc58417","03e9e78dfb004bec9ee666fbb0093484","81cf6a7640074210ab681c9502c47263","e5b45879acfa4fbf97f109db0d66f5ca","4d0b0764430544a88282744bede4d4ba","c5d47ac6615c481fa137d93651c28ae9","de842f81a9604cb4ba584e8416113eb2","6e5c2d2754a24e0b976b4e8714e2709f","d9452f9391cb446d901e06f98c4c2835","98ff65eef67e437aab5610ba268949e0","224d2659b0f34e29ab5f1664e4878635","ead764c5c777433a835fbad2d32de9a0"],"base_uri":"https://localhost:8080/","height":81},"id":"nWN7hP8PJwdv","outputId":"a973faa0-13f7-46ad-af34-e0707b524805","executionInfo":{"status":"ok","timestamp":1648931825676,"user_tz":-480,"elapsed":20752,"user":{"displayName":"Rih-Teng Wu","userId":"04870307609359018979"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/300 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"55b2addf32794e0281e8c41be0d287a6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/300 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"03e9e78dfb004bec9ee666fbb0093484"}},"metadata":{}}],"source":["learning_rate = 0.01\n","criterion = nn.MSELoss()\n","max_epoch = 300\n","result = {}\n","\n","for name, model in models.items():\n","    optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9)\n","    pbar = tqdm(range(max_epoch))\n","    result[name] = {\"Training Loss\":[], \"Validation Loss\":[]}\n","    for i in pbar:\n","        training_loss = 0\n","        val_loss = 0\n","        for inputs, label in train_dl:\n","            optimizer.zero_grad()\n","            pred = model(inputs)\n","            loss = criterion(pred, label)\n","            loss.backward()\n","            optimizer.step()\n","            training_loss+=loss.detach().numpy()\n","\n","        for inputs, label in val_dl:\n","            with torch.no_grad():\n","                pred = model(inputs)\n","                loss = criterion(pred, label)\n","                val_loss+=loss.numpy()\n","        result[name][\"Training Loss\"].append(training_loss/len(train_dl))\n","        result[name][\"Validation Loss\"].append(val_loss/len(val_dl))    "]},{"cell_type":"markdown","metadata":{"id":"5C9_OteKJwdv"},"source":["#### 2.4 Please include the plot that shows how the training/validation loss vary with the training epoch. Show the plots using the above two models and write down the observation. (5%)\n","##### Here is the example figure\n","![train_ex.png](https://github.com/tingyan08/Deep-Learning-and-Computer-Vision/blob/main/HW2/img/HW2-2.png?raw=true)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ddGvQYu_Jwdw","outputId":"c1348f0d-cebe-4661-84ac-7a7729f9fc8d","colab":{"base_uri":"https://localhost:8080/","height":404},"executionInfo":{"status":"ok","timestamp":1648931845562,"user_tz":-480,"elapsed":1649,"user":{"displayName":"Rih-Teng Wu","userId":"04870307609359018979"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1152x432 with 2 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA7YAAAGDCAYAAAAf0oyvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeZycZZno/d9V1Ul3p5PuNEmHbEASWQNhDTDuIKMHhYFRQWFwBtSj4qvyijMjzLwziowcYY4jHs7ocfS4HXFAZI4cOESZEQVcZpSAIIQQDRBIwpaE7Etvdb9/PFVJp1K9pNPV1cvv+/n0p+p5nrvquaoTcnPVdS+RUkKSJEmSpNEqV+sAJEmSJEk6ECa2kiRJkqRRzcRWkiRJkjSqmdhKkiRJkkY1E1tJkiRJ0qhmYitJkiRJGtVMbKVRKCLmRUSKiLoBtL0sIn4+HHFJkjRe2TdLtWViK1VZRKyKiI6ImF52/jfFDnDeMMfzdxHxWER0RcQ1/bS9JiJuHqbQJEkaFiOpb46IGRFxS0Q8HxGbI+IXEXF6H+3tm6UKTGyl4fEMcHHpICIWAZNqFMtK4JPA3TW6vyRJI8FI6ZsnAw8CpwAHAd8G7o6IyTWIRRq1TGyl4fEd4M96HF8K/K+eDSKiJSL+V0Ssi4hnI+JvIiJXvJaPiM9HxPqIeBo4p8Jrvx4RL0TE2oj4bETkKwWSUvp2SumHwNYD+UARcV5ELIuITRFxX0Qc0+PaVcU4tkbEiog4q3j+tIhYGhFbIuKliPjCgcQgSdIBGBF9c0rp6ZTSF1JKL6SUulNKXwUmAkft7weyb9Z4ZmIrDY//AJoj4phip3YRUD6M6L8DLcAC4I1kne17i9c+AJwLnAQsBi4oe+23gC7g8GKbtwD/ecg/RVFEHAncAnwcaAOWAHdFxMSIOAr4KHBqSmkK8J+AVcWX/jfgv6WUmoFXAbdVK0ZJkvoxIvvmiDiRLLFduT8fxr5Z452JrTR8St8MvxlYDqwtXejRof5VSmlrSmkV8A/AnxabvAv4YkppdUrpFeBzPV57MPA24OMppe0ppZeBG4vvVy3vBu5OKf1bSqkT+DzQCLwG6AbqgYURMSGltCql9FTxdZ3A4RExPaW0LaX0H1WMUZKk/oyovjkimosxfSaltHk/P4t9s8Y1E1tp+HwH+BPgMsqGOgHTgQnAsz3OPQvMKT6fDawuu1ZyWPG1LxSHHm0C/gmYMWSR72t2zxhSSoVifHNSSivJvi2+Bng5Im6NiNnFpu8HjgSejIgHI+LcKsYoSVJ/RkzfHBGNwF3Af6SUPtdbuz7YN2tcM7GVhklK6VmyhSreBvzvssvryb4xPazHuUPZ883xC8AhZddKVgPtwPSU0tTiT3NK6dihjL/M8z1jjYgoxrcWIKX0zyml1xXbJOCG4vnfp5QuJuvYbwBuj4imKsYpSVKvRkrfHBH1wB3AGuBDg/w49s0a10xspeH1fuBNKaXtPU+mlLrJ5rRcFxFTIuIw4BPsmetzG3BFRMyNiFbg6h6vfQH4V+AfIqI5InIR8aqIeGOlACJiQkQ0kP33XxcRDb0tNFWUK7Yp/dQX4zknIs6KiAnAn5N14L+MiKMi4k3FdruAnUCheO/3RERb8VvkTcX3LwzwdydJUjXUtG8u9qO3k/WXlxb7yP7YN0tlTGylYZRSeiqltLSXyx8DtgNPAz8H/hn4RvHa14B7gEeBh9n3W+U/I1to4glgI1kHOauX+3yNrEO7GPj/is//tJe2FNvt7PHzVEppBfAeskU11gN/BPxRSqmDbA7P9cXzL5J9A/xXxfc6G1gWEdvIFqu4KKW0s497S5JUVSOgb34N2SJUbwE2RcS24s/r+wjbvlkqEymlWscgSZIkSdKgWbGVJEmSJI1qJraSJEmSpFHNxFaSJEmSNKqZ2EqSJEmSRjUTW0mSJEnSqFZX6wCGyvTp09O8efNqHYYkaYx46KGH1qeU2modx2hm3yxJGkp99c1jJrGdN28eS5f2tgWZJEn7JyKerXUMo519syRpKPXVNzsUWZIkSZI0qpnYSpIkSZJGNRNbSZIkSdKoNmbm2ErSSNDZ2cmaNWvYtWtXrUPRADU0NDB37lwmTJhQ61AkSVVg3zz6DKZvNrGVpCG0Zs0apkyZwrx584iIWoejfqSU2LBhA2vWrGH+/Pm1DkeSVAX2zaPLYPtmhyJL0hDatWsX06ZNs+McJSKCadOm+S2+JI1h9s2jy2D7ZhNbSRpidpyji39ekjT2+W/96DKYPy8TW0kaIzZs2MCJJ57IiSeeyMyZM5kzZ87u446Ojj5fu3TpUq644op+7/Ga17xmSGK97777OPfcc4fkvSRJGqnsm4ePc2wlaYyYNm0ajzzyCADXXHMNkydP5i/+4i92X+/q6qKurvI/+4sXL2bx4sX93uOXv/zl0AQrSdI4YN88fKpasY2IsyNiRUSsjIirK1yvj4jvFa//KiLm9bh2fET8e0Qsi4jHIqKhmrFK0lh02WWXcfnll3P66afzyU9+kl//+te8+tWv5qSTTuI1r3kNK1asAPb+lvaaa67hfe97H2eccQYLFizgpptu2v1+kydP3t3+jDPO4IILLuDoo4/mkksuIaUEwJIlSzj66KM55ZRTuOKKK/br299bbrmFRYsWcdxxx3HVVVcB0N3dzWWXXcZxxx3HokWLuPHGGwG46aabWLhwIccffzwXXXTRgf+yJEkaBvbN1VG1im1E5IEvAW8G1gAPRsSdKaUnejR7P7AxpXR4RFwE3AC8OyLqgJuBP00pPRoR04DOasUqSdXwmbuW8cTzW4b0PRfObubTf3Tsfr1mzZo1/PKXvySfz7NlyxZ+9rOfUVdXx49//GP++q//mn/5l3/Z5zVPPvkkP/3pT9m6dStHHXUUH/7wh/dZcv83v/kNy5YtY/bs2bz2ta/lF7/4BYsXL+ZDH/oQDzzwAPPnz+fiiy8ecJzPP/88V111FQ899BCtra285S1v4Y477uCQQw5h7dq1PP744wBs2rQJgOuvv55nnnmG+vr63eckSeqLffPY7ZurWbE9DViZUno6pdQB3AqcX9bmfODbxee3A2dFNlP4LcBvU0qPAqSUNqSUuqsY6273rXiZp9ZtG45bSdKwuPDCC8nn8wBs3ryZCy+8kOOOO44rr7ySZcuWVXzNOeecQ319PdOnT2fGjBm89NJL+7Q57bTTmDt3LrlcjhNPPJFVq1bx5JNPsmDBgt3L8+9P5/nggw9yxhln0NbWRl1dHZdccgkPPPAACxYs4Omnn+ZjH/sYP/rRj2hubgbg+OOP55JLLuHmm2/udRiXRr+UEj9d8TLPrN9e61AkacjYNw+9at5tDrC6x/Ea4PTe2qSUuiJiMzANOBJIEXEP0AbcmlL6+/IbRMQHgQ8CHHrooUMS9H/+9lI++IYFfPLso4fk/SSNX/v77W21NDU17X7+t3/7t5x55pn84Ac/YNWqVZxxxhkVX1NfX7/7eT6fp6ura1BthkJrayuPPvoo99xzD1/5yle47bbb+MY3vsHdd9/NAw88wF133cV1113HY489ZoI7Rr33mw/y/551BFe++chahyJplLNvHhojsW8eqasi1wGvAy4pPr49Is4qb5RS+mpKaXFKaXFbW9uQ3DifC7qLY9ElaazZvHkzc+bMAeBb3/rWkL//UUcdxdNPP82qVasA+N73vjfg15522mncf//9rF+/nu7ubm655Rbe+MY3sn79egqFAu985zv57Gc/y8MPP0yhUGD16tWceeaZ3HDDDWzevJlt2xxtMxZFBBFQsG+WNEbZNw+NaqbPa4FDehzPLZ6r1GZNcV5tC7CBrLr7QEppPUBELAFOBu6tYrwA1OWC7m47T0lj0yc/+UkuvfRSPvvZz3LOOecM+fs3Njby5S9/mbPPPpumpiZOPfXUXtvee++9zJ07d/fx97//fa6//nrOPPNMUkqcc845nH/++Tz66KO8973vpVAoAPC5z32O7u5u3vOe97B582ZSSlxxxRVMnTp1yD+PRoZ8hImtpDHLvnloRKpSR1FMVH8HnEWWwD4I/ElKaVmPNh8BFqWULi8uHvWOlNK7IqKVLIl9HdAB/Ai4MaV0d2/3W7x4cVq6dOkBx338NffwjpPncs15I2OYgqTRZfny5RxzzDG1DqOmtm3bxuTJk0kp8ZGPfIQjjjiCK6+8stZh9anSn1tEPJRS6n+fBfVqqPrmI/+/H/L+18/nKqcJSRoE++bx0TdXbShySqkL+ChwD7AcuC2ltCwiro2I84rNvg5Mi4iVwCeAq4uv3Qh8gSwZfgR4uK+kdijV5XN0F/xWWJIG62tf+xonnngixx57LJs3b+ZDH/pQrUPSKOdQZEk6MOOhb67qTN6U0hJgSdm5T/V4vgu4sJfX3ky25c+wykXQZWIrSYN25ZVXjvhvgTW65CIo2DdL0qCNh755pC4eVTN1OTtPSZJGklyAXbMkqS8mtmXyOSu2kiSNJLmci0dJkvpmYlumLh90F1f3kiRJtZeLwLxWktQXE9syVmwlSRpZci4eJUnqh4ltmXyEqyJLGrXOPPNM7rnnnr3OffGLX+TDH/5wr68544wzKG3J8ra3vY1Nmzbt0+aaa67h85//fJ/3vuOOO3jiiSd2H3/qU5/ixz/+8f6EX9F9993Hueeee8Dvo9ErZ98saRSzbx4eJrZl8jk7T0mj18UXX8ytt96617lbb72Viy++eECvX7JkyaA3Uy/vPK+99lr+8A//cFDvJfWUzbGtdRSSNDj2zcPDxLZMNsfW3lPS6HTBBRdw991309HRAcCqVat4/vnnef3rX8+HP/xhFi9ezLHHHsunP/3piq+fN28e69evB+C6667jyCOP5HWvex0rVqzY3eZrX/sap556KieccALvfOc72bFjB7/85S+58847+cu//EtOPPFEnnrqKS677DJuv/12AO69915OOukkFi1axPve9z7a29t33+/Tn/40J598MosWLeLJJ58c8Ge95ZZbWLRoEccddxxXXXUVAN3d3Vx22WUcd9xxLFq0iBtvvBGAm266iYULF3L88cdz0UUX7edvVbWWC0gORZY0Stk3D0/fXNV9bEejfC7nHFtJQ+OHV8OLjw3te85cBG+9vtfLBx10EKeddho//OEPOf/887n11lt517veRURw3XXXcdBBB9Hd3c1ZZ53Fb3/7W44//viK7/PQQw9x66238sgjj9DV1cXJJ5/MKaecAsA73vEOPvCBDwDwN3/zN3z961/nYx/7GOeddx7nnnsuF1xwwV7vtWvXLi677DLuvfdejjzySP7sz/6M//E//gcf//jHAZg+fToPP/wwX/7yl/n85z/P//yf/7PfX8Pzzz/PVVddxUMPPURraytvectbuOOOOzjkkENYu3Ytjz/+OMDuoVvXX389zzzzDPX19RWHc2lky4WrIksaIvbNwNjsm63YlqlzKLKkUa7nkKeeQ51uu+02Tj75ZE466SSWLVu219Ckcj/72c94+9vfzqRJk2hubua8887bfe3xxx/n9a9/PYsWLeK73/0uy5Yt6zOeFStWMH/+fI488kgALr30Uh544IHd19/xjncAcMopp7Bq1aoBfcYHH3yQM844g7a2Nurq6rjkkkt44IEHWLBgAU8//TQf+9jH+NGPfkRzczMAxx9/PJdccgk333wzdXV+pzvaZHNsax2FJA2efXP1+2Z79zL5CLrc7kfSUOjj29tqOv/887nyyit5+OGH2bFjB6eccgrPPPMMn//853nwwQdpbW3lsssuY9euXYN6/8suu4w77riDE044gW9961vcd999BxRvfX09APl8nq6urgN6r9bWVh599FHuuecevvKVr3DbbbfxjW98g7vvvpsHHniAu+66i+uuu47HHnvMBHcUyeUciixpiNg3D8ho7Jut2JbJ5wLzWkmj2eTJkznzzDN53/vet/sb4S1bttDU1ERLSwsvvfQSP/zhD/t8jze84Q3ccccd7Ny5k61bt3LXXXftvrZ161ZmzZpFZ2cn3/3ud3efnzJlClu3bt3nvY466ihWrVrFypUrAfjOd77DG9/4xgP6jKeddhr3338/69evp7u7m1tuuYU3vvGNrF+/nkKhwDvf+U4++9nP8vDDD1MoFFi9ejVnnnkmN9xwA5s3b2bbtm0HdP/RLCLOjogVEbEyIq6ucP0NEfFwRHRFxAVl1y6NiN8Xfy4drpgdiixptLNvrn7f7NfVZeryQXtXd63DkKQDcvHFF/P2t79997CnE044gZNOOomjjz6aQw45hNe+9rV9vv7kk0/m3e9+NyeccAIzZszg1FNP3X3t7/7u7zj99NNpa2vj9NNP391hXnTRRXzgAx/gpptu2r0wBUBDQwPf/OY3ufDCC+nq6uLUU0/l8ssv36/Pc++99zJ37tzdx9///ve5/vrrOfPMM0kpcc4553D++efz6KOP8t73vpdC8RvKz33uc3R3d/Oe97yHzZs3k1LiiiuuGPTqkqNdROSBLwFvBtYAD0bEnSmlnmPfngMuA/6i7LUHAZ8GFgMJeKj42o3VjjtLbKt9F0mqLvvm6vbNMVaG9ixevDiV9no6EJd989ds3N7B//no64YgKknjzfLlyznmmGNqHYb2U6U/t4h4KKW0uEYhVUVEvBq4JqX0n4rHfwWQUvpchbbfAv5vSun24vHFwBkppQ8Vj/8JuC+ldEtv9xuqvvlN/3AfC2c1849/cvIBv5ek8ce+eXTa377Zochl6nLhqsiSpLFqDrC6x/Ga4rkhe21EfDAilkbE0nXr1g060J4ciixJ6o+JbZls5UU7T0mSBiOl9NWU0uKU0uK2traheEP+6/a/4bRN9xz4e0mSxiwT2zJ1eSu2kqQxay1wSI/jucVz1X7t4EVwUvdvmdb5fNVvJUkavUxsy+RzOQomtpIOwFhZu2C8GGd/Xg8CR0TE/IiYCFwE3DnA194DvCUiWiOiFXhL8VzVdZMjklsWSBq8cfZv/ag3mD8vE9syzrGVdCAaGhrYsGGDHegokVJiw4YNNDQ01DqUYZFS6gI+SpaQLgduSykti4hrI+I8gIg4NSLWABcC/xQRy4qvfQX4O7Lk+EHg2uK5qiuQA0xsJQ2OffPoMti+2e1+yuRzzrGVNHhz585lzZo1DNWiOaq+hoaGvbYrGOtSSkuAJWXnPtXj+YNkw4wrvfYbwDeqGmCl+xJWbCUNmn3z6DOYvtnEtkw+gq6CnaekwZkwYQLz58+vdRjSmJLIgZUWSYNk3zw+OBS5TD5vxVaSpJGkEEGk7lqHIUkawUxsy9Q5FFmSpBGlQI5wjq0kqQ8mtmXyLh4lSdKIkg1FNrGVJPXOxLaMFVtJkkaWRDjHVpLUJxPbMjkrtpIkjSgpHIosSeqbiW0ZK7aSJI0sbvcjSeqPiW2ZfC5HdyG5gbMkSSNEgZyJrSSpTya2ZepywVV1t5D+9VP9N5YkSVWXwsRWktS3uloHMNLkc8Hrc4/BqtW1DkWSJFFcPApHUkmSemdiWyafC6bGNthlMVuSpJEgq9h21zoMSdIIZmJbpi4XHMRWaK91JJIkCZxjK0nqn4ltmYmpnUnRTmp3yJMkSSNBtt2P/bIkqXeOty3T1L0FgOjugM5dNY5GkiThdj+SpH6Y2JZp7Nq856B9S+0CkSRJQKlia2IrSeqdiW2ZSV0b9xy0b61dIJIkCXCOrSSpfya2ZRo6e1Rsd23uvaEkSRoezrGVJPXDxLZMY6dDkSVJGkkSDkWWJPXNxLZMQ+emPQe7TGwlSaq1FEEkK7aSpN6Z2Jap7+w5x9bEVpKkWkvkyNFd6zAkSSOYiW2Z+o7NbEyTswMrtpIk1VyKHFixlST1wcS2zMSOjaxObdmBFVtJkmrP7X4kSf0wsS0zsWMj69JUuusmWbGVJGkEyIYiW7GVJPXOxLbMhPZNbGQK3ROnWLGVJGkESBHk3MdWktQHE9syE9o3sjFNpmuCia0kSSNBirxDkSVJfTKx7anQzYZXvZ2lhSOzxNahyJIkjQBBOBRZktQHE9uecnlWv/a/cE/hNLrqJluxlSRpBEiRIxyKLEnqg4ltmbpcANA5YbIVW0mSRoLIkXMosiSpDya2ZfKlxNaKrSRJI4Pb/UiS+mFiW6YunyW2HXXFiq0bwkuSVFOFyJGzP5Yk9aGqiW1EnB0RKyJiZURcXeF6fUR8r3j9VxExr3h+XkTsjIhHij9fqWacPeUjS2y3NR0KXTthw8rhurUkSaokci4eJUnqU9US24jIA18C3gosBC6OiIVlzd4PbEwpHQ7cCNzQ49pTKaUTiz+XVyvOcqWhyC9Of2124vf/Nly3liRJFTkUWZLUt2pWbE8DVqaUnk4pdQC3AueXtTkf+Hbx+e3AWRHFkmmN1OWyX8nWxjkw7QhY+eNahiNJklw8SpLUj2omtnOA1T2O1xTPVWyTUuoCNgPTitfmR8RvIuL+iHh9FePcS744x7a7kOCIN8Oqn0PHjuG6vSRJKpMiR45EoeBwZElSZSN18agXgENTSicBnwD+OSKayxtFxAcjYmlELF23bt2Q3Li03U9XIcFRb4PudviPLw3Je0uSpEEoJbYuICVJ6kU1E9u1wCE9jucWz1VsExF1QAuwIaXUnlLaAJBSegh4Cjiy/AYppa+mlBanlBa3tbUNSdC5KFVsCzDvdXDsO+C+6+GpnwzJ+0uSpP1UHIpswVaS1JtqJrYPAkdExPyImAhcBNxZ1uZO4NLi8wuAn6SUUkS0FRefIiIWAEcAT1cx1t32qthGwLlfgNb58J23w38M2+LMkiSpKEUUE1szW0lSZVVLbItzZj8K3AMsB25LKS2LiGsj4rxis68D0yJiJdmQ49KWQG8AfhsRj5AtKnV5SumVasXa015zbAEaW+FD98Ohr4Z//5L72kqSNNwiT45kFyxJ6lVdNd88pbQEWFJ27lM9nu8CLqzwun8B/qWasfWmVLHt7jneaWITLLoQ7v4ErP89tO0zKlqSJFVLcY5tt5mtJKkXI3XxqJrJ9xyK3NPhf5g9rnRfW0mShlXkiHDxKElS70xsy+SjQsUWoPUwmH6k+9pKkjTcIkeeAsmtbCVJvTCxLdNrxRayqu2qX7ivrSRJw8nFoyRJ/TCxLRMR5HORbfdT7vA/zPa1XfXz4Q9MkqRxKhUXj3KOrSSpNya2FWSJbYULh70W6hodjixJ0nCKHIFzbCVJvTOxraCut4rthAaY/3oXkJIkaRhF5MhRcLsfSVKvTGwryEdUnmML2XDkV56Gl5YNb1CSJI1XxcWjrNhKknpjYltBPh/7ropcctwFMKEJfvaF4Q1KkqTxKpcvDkWudSCSpJHKxLaCulwfFdumaXDaf4bH/wXW/354A5MkaRxKkSNHomBmK0nqhYltBflc9N15/sFHgAQrlgxbTJIkjVdRSmwdiixJ6oWJbQV1uVzvFVuAKQfDlNnw0hPDF5QkSePV7jm2tQ5EkjRSmdhWkMvR+xzbkoMXwssuICVJUtXlcoSLR0mS+mBiW0G/FVuAGQth3Qro7hqeoCRJGq+cYytJ6oeJbQX53vax7engY6G7AzasHJ6gJEkapyLyxTm2tY5EkjRSmdhWUJfrY7ufkhkLs0eHI0uSVFURQS4Shf6+dJYkjVsmthXkB5LYth0FkXcBKUnSqBIRZ0fEiohYGRFXV7heHxHfK17/VUTMK56fEBHfjojHImJ5RPzVsAWdy/53pVDoHrZbSpJGFxPbCvJ97WNbUlcP04+EF387PEFJknSAIiIPfAl4K7AQuDgiFpY1ez+wMaV0OHAjcEPx/IVAfUppEXAK8KFS0lt1UQdASlZsJUmVmdhWMKCKLcDsk+D5R8BVGiVJo8NpwMqU0tMppQ7gVuD8sjbnA98uPr8dOCsiAkhAU0TUAY1AB7BlWKKOAKDQbcVWklSZiW0Fdbmgq3uAie32l2HrC9UPSpKkAzcHWN3jeE3xXMU2KaUuYDMwjSzJ3Q68ADwHfD6l9Eq1AwaI3UORrdhKkiozsa2gcWIdW9s7+284+8Ts8fnfVDcgSZJq7zSgG5gNzAf+PCIWlDeKiA9GxNKIWLpu3bqhuXPkAUjOsZUk9cLEtoLD2yaz8uVt/Q9HPvi4rLM1sZUkjQ5rgUN6HM8tnqvYpjjsuAXYAPwJ8KOUUmdK6WXgF8Di8huklL6aUlqcUlrc1tY2JEGXKrYmtpKk3pjYVnD0rCns6izw7IbtfTecOAlmHGNiK0kaLR4EjoiI+RExEbgIuLOszZ3ApcXnFwA/SSklsuHHbwKIiCbgD4AnhyXqYsXWociSpN6Y2FZwzMxmAJ58cWv/jWedAC+4MrIkaeQrzpn9KHAPsBy4LaW0LCKujYjzis2+DkyLiJXAJ4DSlkBfAiZHxDKyBPmbKaVh6QAjShVbE1tJUmV1tQ5gJDri4MnkAp58YQtvWzSr78YzjoFHvgs7XoFJBw1PgJIkDVJKaQmwpOzcp3o830W2tU/567ZVOj8c9gxF7qrF7SVJo4AV2woaJuSZP72J5QOp2LYdkz2+vLy6QUmSNF7lSotHub2eJKkyE9teHD2rmRUDSWxnHJ09rjOxlSSpGkpDkQvJxaMkSZWZ2PbimJlTeO6VHWxr72fYU/McqG+2YitJUrXs3sfWxFaSVJmJbS+OLi4g1W/VNgLajoKXh2dhSEmSxptcsWKLia0kqRcmtr04auYUAJ58cUv/jduOdiiyJElVErvn2LoqsiSpMhPbXsxtbWRyfR1PvjCQebbHwI4NsH199QOTJGm8cbsfSVI/TGx7EREcPXPKwCq2By3IHjeuqmpMkiSNR7srti4eJUnqhYltH46eNYUnX9hKSv1sL9A6L3s0sZUkacjt2cfWiq0kqTIT2z4cPbOZre1drNm4s++GUw/LHjc+U/2gJEkaZ2L3UGQrtpKkykxs+/AHCw4iF/DVB57uu+HESdA0AzY+OzyBSZI0nuweimzFVpJUmYltHw6fMYU/e/U8bv7Vs/z7Uxv6btw6z6HIkiRVQc6hyJKkfpjY9uPP33Ik86Y18d5v/Zr7Vrzce8PWeVZsJUmqgj3b/TgUWZJUmYltP6Y0TOD2y1/NodkMgIYAACAASURBVAdN4tN3LqNQ6GUhqdbDYMsa6O4c3gAlSRrjwu1+JEn9MLEdgGmT6/nImYfz7IYd/HxlL3vVts6DVIDNq4c1NkmSxjq3+5Ek9cfEdoDOPm4mBzVN5Ob/6GW48e6VkVcNW0ySJI0Hpe1+cPEoSVIvTGwHqL4uz8WnHcK/LX+Jx9du3rfB1EOzx81rhjcwSZLGuFLFttBtYitJqszEdj988A2vonXSRD5z1zJSKptrO2VW9rjlheEPTJKkMSy3u2LrUGRJUmUmtvuhpXECV775SB5ctZFfPfPK3hfrJkJTG2xZW5vgJEkaoxyKLEnqj4ntfrrg5LlMrq/j9ocqDDlung1bnh/+oCRJGsMiVwe4KrIkqXcmtvupcWKecxbNYsljL7C9vWvvi1Nmw1aHIkuSNJQiAoCCia0kqRcmtoPwzlPmsqOjm3994sW9LzTPdiiyJElDLJfPFo9yjq0kqTcmtoOw+LBW2qbU8+PlL+99oXk27NwInTtrE5gkSWNQaVVkrNhKknphYjsIuVxw5lFtPLBiHZ09tx5onp09Os9WkqQh46rIkqT+mNgO0puOPpit7V0sXbVxz0kTW0mShlxEcR9bV0WWJPXCxHaQXnfEdCbkg5+u6DEcuXlO9mhiK0nSkHG7H0lSf0xsB2lyfR1/sGAa9y5/ac/JKbOyx60mtpIkDZXc7jm2DkWWJFVW1cQ2Is6OiBURsTIirq5wvT4ivle8/quImFd2/dCI2BYRf1HNOAfrzKNm8NS67Ty3YUd2on4y1LdYsZUkaQjtSWxTbQORJI1YVUtsI5sQ8yXgrcBC4OKIWFjW7P3AxpTS4cCNwA1l178A/LBaMR6oNx09A4CfPNmjats828RWkqQhVBqKnByKLEnqRTUrtqcBK1NKT6eUOoBbgfPL2pwPfLv4/HbgrCjuwh4Rfww8AyyrYowHZN70Jha0NXHvkz3n2ZrYSpI0lHZv9+OqyJKkXlQzsZ0DrO5xvKZ4rmKblFIXsBmYFhGTgauAz1QxviHx2ldN5+FnN1IoDY9qnmViK0nSUAq3+5Ek9W2kLh51DXBjSmlbX40i4oMRsTQilq5bt254Iitz7Oxmtnd0s3pjcZ5t8xzY9hJ0d9YkHkmSxpwoDUV2jq0kqbJqJrZrgUN6HM8tnqvYJiLqgBZgA3A68PcRsQr4OPDXEfHR8huklL6aUlqcUlrc1tY29J9gAI6Z1QzAE89vyU40zwZSltxKkqQDZ8VWktSPaia2DwJHRMT8iJgIXATcWdbmTuDS4vMLgJ+kzOtTSvNSSvOALwL/JaX0j1WMddCOmjmFXMDyF4qJ7ZTZ2aPDkSVJGhqlxLbg4lGSpMrqqvXGKaWuYpX1HiAPfCOltCwirgWWppTuBL4OfCciVgKvkCW/o0rDhDwL2ibzxAs9K7bAlvLitCRJGhSHIkuS+lG1xBYgpbQEWFJ27lM9nu8CLuznPa6pSnBDaOGsZh56dmN2sDuxfaF2AUmSNJYUE9twKLIkqRcjdfGoUeWYWc2s3bSTTTs6oLEV6hqs2EqSNFR2b/fjUGRJUmUmtkNg4exsAanlL2yFCPeylSRpKO1ePMrEVpJUmYntEDhm1hSAHvNs55jYSpI0VEpzbF08SpLUCxPbITBjSgPTJ9fvWRl58sFu9yNJ0lDZPcfWxFaSVJmJ7RA5ZtaUPXvZTj4Ytr1c24AkSRorHIosSeqHie0QWTi7mZUvb6OjqwCT26BzO7Rvq3VYkiSNfrsTW1dFliRVZmI7RBbOaqaju8BT67ZlFVuA7VZtJUk6YO5jK0nqh4ntEDl6ZrYy8u9e2gpNM7KT29bVMCJJksaIYmKbs2IrSeqFie0QOWzaJCLgmfXbYXIpsXUBKUmSDphzbCVJ/TCxHSINE/LMbmnk2Q079iS2DkWWJOnA5fLZo0ORJUm9MLEdQodNm5RVbCdNB8KVkSVJGgpWbCVJ/TCxHULzpjfx7IbtkK+DSdMciixJ0lAwsZUk9cPEdgjNmzaJjTs62byjs7iXrYtHSZJ0wIqJbbh4lCSpFya2Q2jetCYAVm3Ynu1la8VWkqQDF0GBsGIrSeqVie0Qmje9Z2J7sItHSZI0RBLh4lGSpF6Z2A6hQw/KtvxZtb64MvK2l+2EJUkaAgVygBVbSVJlJrZDqGFCnlnNDVnFtmkGdO2C9q21DkuSpFEvEc6xlST1akCJbUQ0RWQrN0TEkRFxXkRMqG5oo9Nh05r2DEUGt/yRJFXFeOubC+QIR0FJknox0IrtA0BDRMwB/hX4U+Bb1QpqNJs3vYlV64uLR4HzbCVJ1TKu+uYUORePkiT1aqCJbaSUdgDvAL6cUroQOLZ6YY1epS1/ttZNy064MrIkqTrGVd/sqsiSpL4MOLGNiFcDlwB3F8/lqxPS6FZaGfm59uzRociSpCoZVN8cEWdHxIqIWBkRV1e4Xh8R3yte/1VEzOtx7fiI+PeIWBYRj0VEwxB9ln4lcuAcW0lSLwaa2H4c+CvgBymlZRGxAPhp9cIavUp72a7cPhEib2IrSaqW/e6bIyIPfAl4K7AQuDgiFpY1ez+wMaV0OHAjcEPxtXXAzcDlKaVjgTOAzqH7OP2IIBWs2EqSKqsbSKOU0v3A/QDFhSrWp5SuqGZgo9Vh0yYB8Owr7dA03aHIkqSqGGTffBqwMqX0dPF1twLnA0/0aHM+cE3x+e3AP0ZEAG8BfptSerR4/w1D9FEGpECe5FBkSVIvBroq8j9HRHNENAGPA09ExF9WN7TRqWFCnlktDcUFpGbA9nW1DkmSNAYNsm+eA6zucbymeK5im5RSF7AZmAYcCaSIuCciHo6IT/YS1wcjYmlELF23buj6wBQBVmwlSb0Y6FDkhSmlLcAfAz8E5pOtvqgK5k1r4pnSXrZWbCVJ1THcfXMd8DqyOb2vA94eEWeVN0opfTWltDiltLitrW3o7h45knNsJUm9GGhiO6G4N94fA3emlDoBN5PrxSEHNbJm485sL9ttVmwlSVUxmL55LXBIj+O5xXMV2xTn1bYAG8iquw+klNYXV2NeApx8wJ9igBI5K7aSpF4NNLH9J2AV0AQ8EBGHAVuqFdRoN7d1Euu2ttM1qTjH1g3lJUlDbzB984PAERExPyImAhcBd5a1uRO4tPj8AuAnKaUE3AMsiohJxYT3jew9N7eqUuScYytJ6tWAEtuU0k0ppTkppbelzLPAmVWObdSa29oIwKZcKxQ6YefGGkckSRprBtM3F+fMfpQsSV0O3FZcUfnaiDiv2OzrwLSIWAl8Ari6+NqNwBfIkuNHgIdTSneX36NqIuc+tpKkXg1oVeSIaAE+DbyheOp+4FqyBSVUZm5rtjLyutTCdMgWkJp0UE1jkiSNLYPtm1NKS8iGEfc896kez3cBF/by2pvJtvwZfuFQZElS7wY6FPkbwFbgXcWfLcA3qxXUaFeq2D7f1ZydcAEpSdLQG199cwS4eJQkqRcDqtgCr0opvbPH8Wci4pFqBDQWHNzcQF0uWLWrKTux7eXaBiRJGovGVd+cIk9QoFBI5HJR63AkSSPMQCu2OyPidaWDiHgtsLM6IY1++Vwwe2ojT23PKrdsX1/bgCRJY9H46psjR5DodDiyJKmCgVZsLwf+V3E+D8BG9qyYqAoOOaiRFVs6IfKw3YqtJGnIja++OXLkSHR0Faivy9c6GknSCDPQVZEfTSmdABwPHJ9SOgl4U1UjG+XmTp3E6k3t0DQ9WzxKkqQhNO765siRp0Bnt1voSZL2NdChyACklLaklEp75H2iCvGMGXNaG3l5azuFSdNhm4mtJKk6xkvfHMWKbWe3Q5ElSfvar8S2jCs39GFWSwMA7fXTHIosSRouY7dvzmWLR3V0mdhKkvZ1IImtY4H6MKslWzhq+4SDHIosSRouY7dvLs2xtWIrSaqgz8WjImIrlTvJABqrEtEYMbNYsd0ULUzftg5SyvbgkyTpAIzXvtmhyJKkvvSZ2KaUpgxXIGNNKbFdn1o4vGsndGyDen+dkqQDM2775lyOHN10do3dorQkafAOZCiy+jC5vo4pDXW80N2cnXA4siRJgxa5PHkKDkWWJFVkYltFs1oaWN0+KTtwZWRJkgYt5eupj04Xj5IkVWRiW0UzWxp5ZldTduDKyJIkDd7EJprY5RxbSVJFJrZVNKu5gd9tK67j4VBkSZIGLU1oYpKJrSSpFya2VTSzpYHfb88WkXIosiRJB2DiZCaFia0kqTIT2yqa1dJAR6qjUD/Viq0kSQdiYhOTaKfdObaSpApMbKuotOVPe8M059hKknQAcvWTaYp2Oru6ax2KJGkEMrGtolkt2fzaHRNaHYosSdIBiPrJAKSOHTWORJI0EpnYVlGpYrs51+pQZEmSDkCuPttlIHVsq3EkkqSRyMS2ipob6pg0Mc96WhyKLEnSAcg3ZBVb2k1sJUn7MrGtoohgZksDL3VNgV2boau91iFJkjQq5RumAJA6ttc4EknSSFTVxDYizo6IFRGxMiKurnC9PiK+V7z+q4iYVzx/WkQ8Uvx5NCLeXs04q2lWSwNrOovfMm9fX9tgJGmk6twFy/8vpFTrSDRClRLbnHNsJUkVVC2xjYg88CXgrcBC4OKIWFjW7P3AxpTS4cCNwA3F848Di1NKJwJnA/8UEXXVirWaZjY3smpnNi/I4ciS1Ivf3grfuwR+/bVaR6IRKl9cPCo6rdhKkvZVzYrtacDKlNLTKaUO4Fbg/LI25wPfLj6/HTgrIiKltCOl1FU83wCM2q/wZ7U08NTObHVkV0aWpF7kJ2aPv/zvtY1DI9fESQDkukxsJUn7qmZiOwdY3eN4TfFcxTbFRHYzMA0gIk6PiGXAY8DlPRLd3SLigxGxNCKWrls3MpPGmS0NvFRozg5cGVmSKkuF7HHzc7BuRW1j0cg0MRv9lLNiK0mqYMQuHpVS+lVK6VjgVOCvIqKhQpuvppQWp5QWt7W1DX+QAzCrpYH1qSU7cCiyJFXW3bnn+e//rXZxaOSamA1Fznc5x1aStK9qJrZrgUN6HM8tnqvYpjiHtgXY0LNBSmk5sA04rmqRVtHMlgZ20kBXvtHFoySpNz0T203PDe49OrbDjcfBQ9/uv61Gn2LFNu9QZElSBdVMbB8EjoiI+RExEbgIuLOszZ3ApcXnFwA/SSml4mvqACLiMOBoYFUVY62aWS3Z/NqdEw+CbVZsJamiQjGxbTl08Ilt507YvBq6dg1dXBo56hroJkedFVtJUgVVW2k4pdQVER8F7gHywDdSSssi4lpgaUrpTuDrwHciYiXwClnyC/A64OqI6AQKwP+TUhqV5c7WSROYWJdjS24qUxyKLEmVdXdkj9NeBZueHdx7dO7MHuv2mbmisSCCXTRQ172z1pFIkkagqm6hk1JaAiwpO/epHs93ARdWeN13gO9UM7bhEhHMamlgQ0xljkORJamy7uL6gNMOhzUPZvvZRuzfe5QqtRMahzY2jRg7o4EJ3VZsJUn7GrGLR40lM5sbeLl7ikORJak3pYrtQQugYxvseGX/38OK7ZjXnmtkohVbSVIFJrbDYFZLA2s7J8OO9VDornU4kjTyFDqzvWxbD8uOBzMc2YrtmNcejVZsJUkVmdgOg5ktjTzb3pTt07hzY63DkaSRp7sTchNg6qHZ8WAWkLJiO+a15xqpL1ixlSTty8R2GMxqaeCl7uJetg5HlqR9dXdCvmdia8VW+2rPNVKfTGwlSfsysR0GM1sa2EBzduDKyJK0r+6OLLFtaIGGqbBxEImtFdsxrzM/ifqC2zlJkvZlYjsMZrU0sC4VK7aujCxJ+yrNsQWYNA12bdr/97BiO+Z15hupTya2kqR9mdgOg5ktDaxPDkWWpF51d0KuuANdXT10te//e1ixHfM685NodCiyJKkCE9thML2pnh25JrojD9vX1TocSRp5untUbPMT9mz/sz+s2I557XVTaGIHdA3i74ckaUwzsR0GuVwwo3kSW/OtzrGVpEpKc2wB8vWDS2yt2I55WxpmkyPB5tW1DkWSNMKY2A6TWS0NbIwW2GbFVpL2Uejak9jW1Q+uIleq2JrYjllbG+ZkTzauqmkckqSRx8R2mMxsaWBdodmhyJJUSXdHto8tDH4ocufOrNqbs2sbq7ZNmps9Gcx2UJKkMc3ef5jMamlgbecUkkORJWlfe82xrYfuQSwe1bULJlitHctiykzaUx1dG56pdSiSpBHGxHaYzGxp5KVCczYUOaVahyNJI0t3J+SLqyLnJ2TH+6tzJ9S5cNRYNqOliTWpjY71JraSpL2Z2A6TWS0NvJgOIrrbYccrtQ5HkkaW7o49FdvBbvdjxXbMm9Fcz5rURnplVa1DkSSNMCa2w2RmSwMvpIOygy1raxuMJI00hZ5DkScOfo6tFdsx7eDmBp5LM5iw9blahyJJGmFMbIdJqWILwJbnaxuMJI003Z2QKw1FHmRia8V2zDu4uYHVqY2JHZth1+ZahyNJGkFMbIdJ2+R6XmJadrBlTW2DkaSRpufiUYPd7qdzF0yYNLRxaURpnTSB5+Pg7OAV59lKkvYwsR0mdfkcuckz6CZvxVaSynV37tnHdrDb/XTtdA/bMS4i2DDpVdnBy0/UNhhJ0ohiYjuMZkxtYmN+momtJJUr9ExsB7ndT+cumOAc27Gus2Ue7VEPLz5e61AkSSOIie0w2j3P1sWjJGlv3R2QKyW2EyEVoNC9f+9hxXZcaGtp4pk4FF56rNahSJJGEBPbYTSzpYHV3a0kK7aSxpvf3AzrVvR+vburxxzb4uP+bvnT6eJR48HBzQ0sKxwCLz7mvvCSpN1MbIfRnKmNrOluhc1r7YwljS93fRyWfrP3690dkC+tilxfPLefiW2X2/2MBzOa63mk81DYudGpPZKk3Uxsh9Hc1km8kKYRXTuzDlmSxoPuzmwObV//7u21j+2EPa/bH1Zsx4WDpzSwvHBodvCS82wlSRkT22E0t7WRF3bvZes8W0njROfO7LG3xDYlKHTtmWNbV6zY7s9Q5JSs2I4TM1saeDIVE9sXnWcrScqY2A6jua2NrE3Ts4NNz9U2GEkaLrsT21cqXy9VZvM9Fo+C/dvyp5QEW7HtV0ScHRErImJlRFxd4Xp9RHyveP1XETGv7PqhEbEtIv5iuGLuac7URrYxiW2T5lqxlSTtZmI7jFoaJ/DKxFnZgYmtpPGiq5+KbSmB7Sux3fICdGzv/x5WbPsUEXngS8BbgYXAxRGxsKzZ+4GNKaXDgRuBG8qufwH4YbVj7c2sqdmXFy82HO6WP5Kk3Uxsh1FEMHnqDHZFI2x8ttbhSNLw6G8ocqFUsZ2492PPochffzP87At93GNX9mjFtj+nAStTSk+nlDqAW4Hzy9qcD3y7+Px24KyICICI+GPgGWDZMMW7j/q6PDOm1PNMfh5sWNn3Fx6SpHHDxHaYzT1oEi/EDNhkYitpDLjzY/DzL/bdpmdiWyjse700FDlXXBW5NMe2dL6rHTavhu3rer+HFduBmgOs7nG8pniuYpuUUhewGZgWEZOBq4DPDEOcfZrT2sjjhUOBBC8vr3U4kqQRwMR2mM2Z2siqwnSHIksaG56+D1b9rO82pcQ2FaBj677Xu3up2Ja2+ykltH3NubViOxyuAW5MKW3rq1FEfDAilkbE0nXr+vgy4gDMmdrIr3fMzg5cQEqShIntsJvbOolVXdNJG1e5l62k0a9jO+zc1HebUjUVYEeFBaT6m2O77eW9j/u6hxXb/qwFDulxPLd4rmKbiKgDWoANwOnA30fEKuDjwF9HxEfLb5BS+mpKaXFKaXFbW9vQfwKyiu3DW5pJE6e4gJQkCTCxHXZzWxtZnWYQHdvcy1bS6Nexo/9/yzp7JLaV2ha6ssdSQltXmmNbltj2tf2PFduBehA4IiLmR8RE4CLgzrI2dwKXFp9fAPwkZV6fUpqXUpoHfBH4LymlfxyuwHuaO7WR9m7obFvoAlKSJMDEdtjNm97EmtKWPxtX1TQWSToghe6sUrqrn4ptKemEyoltqRJbmmNbXrHdbsV2qBTnzH4UuAdYDtyWUloWEddGxHnFZl8nm1O7EvgEsM+WQLU2pzX7c9485Uh4aZkjoCRJ1NU6gPFm3rQmVqcZ2cGm52DOybUNSCpZ81BWOTv09FpHouG0eQ3UT4GGlv1/beeO7HHnpiyxyBbO7b0d9JLYls+xLS0eZcW2GlJKS4AlZec+1eP5LuDCft7jmqoEN0Bzpk4C4PmGw2nr2JotyNg6r5YhSZJqzIrtMGucmKdzSnF608ZnahuM1NO9n4F/+9taR6EDkRKs+FFWSR2ob50LP/jw4O7XUUxYUze0V1gUqqSrv4ptKbEtzrGtK9vuZyCLR5XuYcV2XJhbrNiuSIdmJ16q2e5DkqQRwsS2Bg6e0cYrudZs/z1ppGjfuidR0ej0/G/glndnKxUPRPvW7Au2FUsGNzWis8f+oX0NR95rjm2FdoWyxHYwi0eVqsJWbMeFpvo65k9v4udbZgDhPFtJkoltLcyb1sTThZmkDU/VOpSxodAN9//X/ldmVd86d+y9eq1Gn9KKwzs2DKz97i/XEiz9xv7fr6NHYtvXAlKlxHZCE+zsY1XkXCmx7W0o8gC2+7FiO24cO7uZh1/sgIMWuDKyJMnEthbmT2/i910zSeut2A6Jl5fDTz8LK39c60hGt84dey/yo+r4xU1wx0eq896lPWJ3bR5Y+/W/zx6nHQ7L7hjE/XrOne3ji6WunVmyOmlaL0ORy1ZFLlVu91k8qo85tqUvZazYjhvHzWlhzcaddEw/xsRWkmRiWwsL2pp4Js0kt2OdVcahUKoadTqM9oB07Nh7LqSqY/Wv4KmfVOe9S/Nc27cMrP3630Pk4Ki3webVe+a6DtT+DEWe0AiNU/teFTlfXM+wrlixLc2x3Z/Fo6zYjhuL5mQLnr1YPz8bSt/X3w9J0phnYlsD86dP5pk0Kzt4xeHIB6z0P9dWGw9Mp4ntsOjuqN4e1u3bssddA01sfwdTD4O2oyAVshWS98f+DEWe0AiTDoLt6/e9XihbFTnXo2Lb1b4nae4r8e7amW0XlHex//Hi2NnNAPyue1b29/eVp2sckSSplkxsa+CQ1kZW5+ZkBw5HPnCl4ZDODx28QqE4FNnf4aAVCn3PAS3pas/+rpZ/EfP0ffD9yw5sP879rdhuWAnTj8ySW9j/BaQGOhS5lNg2z4Utz+97vZSwlhLaXC573t2xZ0Xk3IS+hyJ37rJaO85MnTSRua2NLN02LTux/ne1DUiSVFMmtjVQl88xcfoCCuRcGXkolIYgW7EdvFKlNnXvme+o/fPrr8I/ntJ/u1ISVz5096mfwrIf7F0F3V+759iWJbbdXfDja+B/f2jPMOhdm4uJ7RHQWkxsNz27f/cb6FDkrmLS2TIXtr6w7xcA5dv9QDYcuatHdXvKzL6/OOja6fzacei42S3cv6G4B3NpzrgkaVwysa2Rw2cdxHPMgoe+CU/8n1qHM7qVEgErtoPXc36yv8fB2fB72PRc/3vIlqqO5RXO0oJPHdsGdr/uTrj/7/fMP4UeFduyPWV/8UX4+Y3ZvzX3/z2s+gXcMD9LOA8+DprnZMN4N+5nYlv6b6+uoZ+hyDuyim3LXCDB1rKq7e45tj0S23yxYlv6LE3TrdhqH8fNaWb5hgKFKbNNbCVpnDOxrZEjZ07ho+0fprtxGtz18VqHM7pZsT1wPauE1f49/uqf4PH/Xd171EIpAeuv4lqqOpYngqXEtjwp7c3v7oGfXgc/+4ceMRST4p5DkV95Bu6/AY59O5z4J/DSE8V9bhP86Q/g+HdDLg8th+x/xbY0FLl5dj9DkXdlie3UQ7Lj8rm85XNsIVtFubt9z+9j0vQs0e1tqLYV23HpuOICUlsmz3cosiSNcya2NXLUzCk8nhaw9pA/yvZ1PJDhh+Odc2wP3F4V2yontr/+GvzmO9W9Ry2Uhv+Wfpe/+G/w2O37titVHcuH7paS0f7mxxYKWaK4rPjlwG++u+feHRUWj1r9qywhfOPVcPBCaN8MK5bA9KPgVW/K5rNCNhx5f+fYdm7PqrWTpvWzKnKpYttLYrt7jm2PhZ/yE7PzuxPbaXu33eceu7JYNK4cOztLbNfm52ZD6w9kjrokaVQzsa2Ro2dOAeC5rqnZiUoLqmhgar0qcqEA//q3o2dFzpU/3nfRsuFMbDt3wI4N1b1HLZQSsNLv8pf/CI/8877tSluSvPIMfOvcPcMnB1Kx7e6Cf34XfHERrPghzD01m1f76C17v7ZnclwaXtw6Lxt2DNmen7OO3/u9px7W+1DkQgG+9qZ997rt2A4TJkFj694V6Pate8/V7iomnc2zs+PNq8s+V4WKbd3E7HdV+ixN04ttexmO3FVcoErjStuUemY2N/Bk58zs78rWF2sdkiSpRkxsa2RmcwPNDXUs35FtV7Df22xoj1pXbLc+D7+8CZ68uzb3318/uDybb9lTz9Vth2Jl5L4W+enYBtuHIbG9/f3w8DBWhksJWMeO7Gf7y3tW9O2plMQ99RNY9TN45LvZ8UAS25/8Haz8t2zobucOOOtTMHkmvPjbvV/bs2K76VmYMisbpjvjmD3nZ52w93u3HgY71lfeKmj7y7D2oeynp44dMLEJGnrsT9uxAz43F5b8+Z52nTuyBHhCIzS1VajYVppjW7/3HNtSxba3v1tWbMet4+Y08/OtM7ODFx6pbTCSpJoxsa2RiGDR3Bb+fV3xf8Ss2A5erefYlhKBHa/U5v77I6UsAdn+8t7n96rY9qiI3Xst/PRz+3eP/5+98w6Pozrf9j1aSaveZcmqLrLc5G5ssMEG00w1vZcAvxRIAiSBhCQk+eg9QAKhlwCmF2OaIeCKjXvBvUi2ZUm2utX7zvfHO0czu1rJMlgu+DzX5Wvb7MyZM7Pyuc/zvu+pyIP7U2D39/4/bz4I0aozJgAAIABJREFUjm1bi4TqbpnVs8dxqskRirw3X577O0/lOCoY3fyFPLaDbRfFo75/FwadDb9aCNP+A5nHQ2iMfQ86HVuPR55X7hC3FiAk2g4HTvZxbDOO826PU9WF3ueo1FJngW20feyV/5XHjZ86tmu081+j02Cvj2PrsdzdgE6KRxkBcp6gHVutDspJjebLymRMwwUFyw91c7S0tLS0DpE02B5CjesTz8JSayCnBo5a+6+WQ+zYqsF+wxEAti31AhF1ZR3fV3L244LHYN6D+67061RFnjiKxes7ftbaLJ+1Nni7xAda1YVgemzA3Jdm3wtLnv9xx3QWj1K5qnWlHXP+lONYWyyPpZukz/bl2Ho8MiGROBCiU2HUlZIf645yuMUKik37eeVOe51agKSh8pg8zHv/6cfKdmv8hE9XKbD1aZsKRXZHymetzZJbDLKMkFJLg2wHArb+HFvDZef7grXcj1U8yh0pDq7a1p+0Y3vUKiclmnrTTUPsQCjUYKulpaV1tEqD7SHUuL5xNJnBNLvjNNj+GClAOhAhtN1RxXZvd1i5ZV0td3K4SLXVF2y9QpH9ON+FKzu+V1UoQKicwfZjWIBW6yfXzbnuaX1Zx88PlFSuqG8upz+ZJix7SQoq/VCZpnfxKFVd2BlKq+R0HIMj5HHDTDu3ubPiUQ0VMikRkeT9fkiUw7GtFfdU7ae1Wf62xDrAdvglMOpq2wFVCgiAEZdD3jwbZJXU3yffMGUViuyOlLaVbJB1asE7DLu1wYbO6Ay5Lk7gb2vxzq8F7+JR7igBXeg8FLm1UTu2R6lUZeRdYUPkb5Xv3yQtLS0traNCGmwPoUZlxBDkMih3JXYcSGp1Xz1dPGrjp3Z12+Z6+M9xsPQ5+/OmIygUWUGnL1Tuq3jU1i87vrfuA5j/CFRu9zmG1R81liNZXwG7lslzZ/XvfYUjf/s45C/pepvOpJzahkr/OaNO1ZZYlck7CQGuLhLn2lkMyVetTfaSNc313kWYnIBnmt6OY8ooCO8F+Yvt9zpzbFVRnIhe3u+7o+S6trUKQEalyvuN1RbYm3YoMkDOhTDtKf/HGHGpbP/9O97vK4e1g2Nba4Mt2AAc29c+77YWgV7l2EYkyv3mnIhqrLLBVckVbC33U205thb4dhaK3NKgHdujVElRbhIiglnj6S/3S/m2fX9JS0tLS+snpx4FW8MwphqGsdkwjG2GYdzh53O3YRjvWJ8vMQyjj/X+qYZhrDAMY631OKUn23moFBLkYkRaDPmtsdqx7a7aWmHGr70dxJ4uHrXoX7Dgn/K8Ik+O48wfbTqSHFsLbFvqvV1aJ3AqsHWGH2/2k6uqwm19AdXXsf3uafjv2QJ1zmN2BbZtrZLfu+TZzreBzicznOuxdubaejyw9X9QYoVMd7bk1rKXpC1r3+28HU7gczq24H2evmG00ekSmusM2+4MbFXockSy9/sh0XIPNlvfU2DbVG1fI2cocleK6ye5tmve9nZUO82xrbdDkcGeoIvPkvugtdkGWJVjG2I5xWp5II9HKnVnTvDetytYvq9CkbVjq9WJDMNgaEo039RY+eNFqw5tg7S0tLS0Dol6DGwNw3ABTwNnAEOAyw3DGOKz2Q1ApWmaWcDjwEPW+2XAOaZpDgOuBX6Ci16KJmQlsLkhEo92bLunql2w+g144SS7sm5PF4+qLbEdTrWkT/lW+/PDtXhUa1PH/E4nmDhdW6d7pp4rwIrtA8VrYddS730pePMNa27ycWxr9wh0NNf6hCJ30V+1xZIju3u1nEfplo7b7Foq1Xd9CxGBt2Pq73OAvNkw/SKY/6jV7k4c2x3fyuO8hzpfQ9XZry31ApQqZNjp2PqCbUyG5MtWOXKBO3OO28HWx7FVocjqeqkldRqr7WsU202wBRhxGZRthiLH5JEqbtdVKDJAteXsxveXx/pye6JEQacKgW6wwLZopYDz4HO99x0YbIdyuyPtisnasdXyo2Gp0cwvj8LEOHKWXtPS0tLSOqDqScd2HLDNNM080zSbgbeBaT7bTAOsEpq8D5xsGIZhmuYq0zRVmeD1QKhhGD5xaj8NnT40id2eeAKaqrquhvpTVWO15PR1V06AmGfNgyinracc27pSGaCbJlTkyntl22xo7K5ja5qwc9HByf9qrIJHB0hlYN/3lZzA5QROVRVZndexN8k6paookFJ3HVsFsI3V3q6oLxA7pfI0K/Lgy7/Cc5O8qzWDVBX2tNjtWPYivHutPN+bDwkD7ef+VGGFUO9cKI/+gLK5Tpa46T1SjrNxpv99Oe/L5joB69Qx8tp5nr5uY0w6RKV5v9ewF1442buqMDjA1ifH1h0lsKeug9Ox3bNWHNXI3v7b7U9Dz5dCTWs/sN/rqniUE2xVyHJcP3msK7UnngItsFWOrfq9bPgYAgJh4FTvfbvcVihyN4pHtbWA2aYd26NYOalRNHgCaQnvbf9N0NLS0tI6qtSTYJsKOK2SAus9v9uYptkKVAHxPttcCKw0TbPDNL1hGL8wDGO5YRjLS0v9rBd5BGhI7yhqwq3wqeJ1MOMmWP3WoW3UwdSKV+G1ad13O33X5wT/jm15LtzXG4o3/Lj2NdcL8HhaJXSy3ALblrqOLlZrQ9cFrPIXwytnyPqlP6Y9r5wJq97oervdawQw96z1fl+Ff4L3WrLN9bbbpSYI1HlFJMExP4dNn9qA42lzLGnjA6j+cmzBCpftZiiyMzR/xSvSJt/tVf+3A9JM2DBDQHKvBZaBId5hwU75Vub1B7b5iwWep9wJoXESuuyr8lyodfz9qdktYcEpo+W1l2Nr/RlTa7Iqx1YpNFbc0sLlsH2+93FqS6TYlDvC+31VLEr1h3JsGyqlTwacBgGuju3uTCHRkD4OdlpOtafNmmgwvJcRAvkdOEORVRvilGNbZoO9r2Or7sXcOZA5Uc7dqdAYuXcaq/cdiqx+d9qxPWo1LE3uq4rglI55/1paWlpaR4UO6+JRhmEMRcKTf+nvc9M0nzdNc6xpmmMTExMPbuMOkAzDIHrIaTSbLlrnPQKrp8O69w91sw6eKrcDZkfI6EzKMQqJsYHJmWOrXNTdqwV493zfcR/7I+d6r3Xl4iCqtTZVOLJXeK8PoL9/PayaLs8LrFDeijzY9FlHR647mnOfOIxb/BRzUvte94GALUD1bu/PGzsLRa4XcAN7gqC9r6Mg7Rh5rpzUmt22c+brvLbn8dbJPhSQNlZ7w2OXYOtY11mtcdoBbK22KEAq3SSPeXOlfbF9BBx9c2znPSw5s1W7ZH1UAAw5H19o2vGtuIkZx0H/KZIL6gS71mZ49gS5LkoK+GPSBUSd7Vaus3JdVY6tUnSa7Tb5Os01ezq6tSCOLXQE282fyzXOuaDjd/alzAkyKdJYLcc12yCuL2Da7n5rs1yb4HC7DVWFsmxPTIa8XvoCvHiyPA+z7q8QRyiyxyOFfpJyOrYhJkN+W3Ulsv+uikf5hjtrHXVKiQ4hISKYnWYv7dhqaWlpHaXqSbAtBNIdr9Os9/xuYxhGIBANlFuv04CPgGtM08ztwXYecp04MovFniEE5n4tb5RsOrQN+rHKnQ2z/ty9bVX+Y83urrdTUhAZ20cq2ZqmQJkRIDmZKgdSQYETkH6InE5cfZmAY5/j5XWZAltHeKYzHNk0YeMnkDdHXquCJlX5MOd+mPtgx+PlL5ZiS/5UVQCL/yPPyzv5SSx5XmBagW+Nz/l7hSL7gK07UqDdd9kZd7TtyKlzdeaw+sK8E/Rriu31fZuqbXc9KGzfYOtyC/i1H8dn+xqHY1tfYYfqqj6K6ydw5AuIW7+SqIiqAgHW4ZdJ+C10dG1LN0lIszsCsk4R99U5WVJdKKCnJhLAnqQJjRNn1l+O7bCL4aS/yn3sDEV2nq8vkNeW+AfbEAWV1nEjkyEoXH6HwRHi2O6vMifI72nXEvj+bXmvl1UioX3JKOu83FH2skU1RdKeCGuicfMX0gfXfgp9J8t7Tse2do9MSMX17dgGVfDK9HhXRfYNSQft2GphGAbDUqPZ0BAnfwt6cp1sLS0tLa3DUj0JtsuAAYZh9DUMIxi4DPBNUJuJFIcCuAiYbZqmaRhGDPAZcIdpmgt7sI2HhUZnxLIw6FjrlSEFWHYslOq/XS0xcrhqw8ew+BnvqrqdSQ3euwugjQ6wra+wBrSm7TaqMNoDBbZOx3ZvvgB45kQZyCuwbay2nb8GB+Q11wrIKIBUYLt3lwBy5faOxZ1WvQ5f3Sm5i8113p+X58ogP2WU5Pr6y9VVcLdjgTz6OrZN1RCWINDoBK7meggOEzBQYKv62h1ph7+2g+0OeQyJ9hOKXAXBFgjXFNmw7wxFjsnYN9hGpYjbmDZO3us0FHkvlG6W5y635MSG94JBZ0luqVomp70PaqRdJRvlPrrgOXFjoSPYVhfaocJZlvO4zRGO3A7N1nVyR9uTNWHxEJ7oPYGgwDY+Cyb/EQzD3n9AoHdhKN+iV7V7ILIrx9aaN3RHwnWfC7BP/uMPczHTjpH2zLxZ3O2BZ9rFndTERd5cecw41p748LRKe0Ji5PuYMnnQ9wQ5V5A+wpDrpiZoVLEpp5wFr9yRUkwK/Bfw0o6tFjA8LYbVtdbESWcpCFpaWlpaP1n1GNhaObO/Ab4ENgLvmqa53jCMuw3DUOUvXwLiDcPYBvweUEsC/QbIAv5uGMZq659PKdCfjlwBBp6BZ1NmRtM6+jp587M/SPXf4nXyevkrsOS5zndyOKmuDDC93cGqQgEOp0zT4dj6wEdnarL2GZspjo8qRqRyFlUYrXIUnU5wXTnM+kvXebC+qnWAbYG1Fmt8f3EDVSGppio7nNTpXqrn9WXyXMFgwXJruZ3ajmG89ZUCr1u/gkeyvIs/qT7KnCgD+eoCKWL1UF8bsp3tNQI6OuGNVQKj4QneoNhSLy5fUIijKrIFMCFRNrgo8KvcIfvvPcJ/KHJitjwv3SznA96hyL5gu/xl2Pmd/bq6SIognXo3XDbduz/bt7HOraHSDkNWYbfH/05APSJJ+sQ5CaDgvKnavm7B4db5+Sz5owAbBDp7DZECYEq+rmpkkr3sTlhcR7BVoc7ONVsjkgQCQxzOOMh91eDIie7UsbVybFX+c3AEpIwUYJ94S8ftu6PgcJlAqSmCyXfApdPt35jqv61fysRB8jABSsPK4w2JEogNt1zb9PHe+w4IsCo577V/Q3F+wDbGB2zbi0dpx1bLv4anRbPTYw0VdDiylpaW1lGnHs2xNU3zc9M0s03T7G+a5n3We383TXOm9bzRNM2LTdPMMk1znGmaedb795qmGW6a5kjHv5KujnWka+LIIYxteoa58ZfKG6Ub5VHB4LePw8J/HZrG7a8UsDjBZc598MZF3g5kQ6Wdr+cbMtuZGqtl8BqRJMCkKu+GJ8hjB8fWEf2+5k1Y/LS44d2V09VU30sYIMdXwNJYbQ/CnaHI6vzrym23NibTXhIFOhY5UY7v/McENtfPsD9T59rnBHks2yoFfhoqoMQqkuV0mNPGCUg682obqwQqwuK9gau5TuAkMNRPKHKU7cAqqNmbD5EpAjYdqiJXQ4IFtqpdan8qbDwqxf5e2Vb49Pd2CDHI/RBlVfJVRYWcx2l0rNuqwDYoHCb/CcbfCGOvl88ikiQ/1PldZ+i4Cv1tD7V2OLatTXL9oxzFndLHw65ldjSCM8zZ5bbzR8EC23hvR1tBmQqrBSnsFNnbAtsovKTAuble+s93qR+wQ5GL10sfONvwY3TuU3DdLDjpzwKjqo8aq8U1zZ0DA04ViDUM+3O3Bdph1m/SF2xB2qgcW1ewd56xUmiMDe37Kh7V7thqsD2aNSw1mnxTg62WlpbW0arDunjU0aSJWQkMSo7kL3OqMQMd4XSFKyVPce9OAaJDsVZqY7Uso/L+9d3bXgGTEyYqdwiAOZ1Zp9tVVQjPTOw8v1RJLf2hQo9VXqEqTNNiFZBqD3F2OJZbv5JH5xq0Xu0u77gWbm2JDK6DI6BkvThrCdkChs5qvyps0hmK3OBwbJXzPuhs7/1X+ICt2mexVc04b64dellTLLmpKSOt88iFPdZ+VV/XlkjYaM6FMPwS63uOPmisth1br+V+GqxQZLd3KLLhEuD1DUWu3SPgGRbvfZ09bQKcMZnS1qLV3sdurhP4CkuQc/V4YNG/AdOehDBNb6fUFWSFPDuO43VOeyWsODFbcjXPeNAGHBW6q0K0TdMHbH0dWwfYqmOodoCE3TbX2MDuDBcOiZI+BLlP3FG2Y6smdFR+qNOxBQHskBgbDmOtnNMd38KWr+yJrlg/uagKhpuqJJog4AD9We81CDKPs18rgG6qluiFpmrv/F3VDrVdeIJAa+8RHfcdGmM5tnlyTp1VbVYTRs4c2y4dWx2KfDQrMdJNW0gcjQFhndch0NLS0tL6yUqD7WGiIFcAj10ygor6VoqCLBcpZbQ4tqqaLnRcvqW7mn0ffHTjD/vum5fIMirrZ3SvIEe9H7BVoKkG6GBDQWRvKVJTvA7mPuQdfumrpmoZQIf5gq0KRW4QuGttlPdqiwUMm2rsUNeyLR33W7kTnhoDX/9DXnvaoGCFfD+8l73/hGyBkvAEOU8FSuG9ZFDtLxS5rVmWHQqJgWSr+mtAEGDIwH7XUht8nGAckSTnu8u6/rVWVdyIJHFQy7eKS6f6urVJYCFlNFz0MiRa67g684wbq6T/ErIFzpRD6RWK7KiKrMJKA90CFu1ga4XFhsULDKoQ3vbw5Wg5vrOoUpMFtsFh8j2zTSZs1rzlfS3ry6XPIh1A6QvQ6pyCI8WxLd9mr1vrVIQP2DbX0Z4PC7Zjq4ofKbDdu8tujxNslfuYv9jaLt9es9UdKX0IMvFiGALwnhY7LF/l2DodW4DT74WpD9pg22eiPH75F3jrMoFb5/GdcoYvJwzo+PmBktsBtir0vffIju1QLuvAM2DMz/y7qMqxrciz+8+f1ISRsyqyv+JR2rHVQgpIDUiKZEtgtnfKgJaWlpbWUSENtoeRhqZEc+kx6bxfO4LG7HMhe6qEWObOtgqx8MOXr8mbC5s/61isaF8qz4X87yT81WzbN1i3tdrhuApEPG02iDgrPivYTR9vA0VTFbx9pSzJ4k+N1QJb7Y6to1APyABXhYemjwdMgZq8eQIYQWH2oLy+Aj7+NTyQAS+fLu0uXi/u8QsnwYtTYONMCf9U+YKqMmxYnMBgQ6UUzAmxYLuhUuB669fekFu0ylraxQKp2D4CTEuegZdOlWJPpinfV7mEJ94h132bVS27plgq3hoGJGRJ/mo72FbYDqyqSBtphfJ6uZtWju3gc6SvVCEkBZyBoXY4d1O1NzS5I+3rVLNH+kX1+/0pMnmiAC4kSvrKtEJ2DZft2AaH298rWiWwlzxMrlNrk+3cRnUDbJOGSL9UF/mvrKtCdxXYKjAPjpSQaFW4yZljW1cG/xoF8x6y2uEIRY7tAxHJMhEDUuE6dYyAlzvSdmzV+an7RkUxdAa2qWMgY7zd3ymj5FqYHunDxc9Y94/vUuCI26lCxVUIeE/IGYpcVSD9p+4xsF19BcDjfwlnPuJ/X6ExMolTkee/cJSSX8fWT/Eo7dhqWRqQFME3LTkSYfNjiwdqaWlpaR1R0mB7mOkXk/rxZOv5PB7zFxnsYsKat2WgG5UqruOat/27Fl2pZo9Ah2+hH1/tWQulDkdz8+fyeLLlZBat7Pr7TvhQz2v22GuR+jq2QWH2GpZhCTD2BlmDds593vmqBctlTcwOjq0FQU7HVlXDzLAqTVcXCZwHhkgosALbr+6Uvuw3WdoRnyWhwavegN3fQ6+hsl1QmJ3DmzTU+3gqlNgdJYP8qgJxIKdfCIXL7faXbZF1TWMssI3vLyGYCgTz5gp0eVolhLjfSTD0AkgcZIe9KscWpIDU9vl2Ma36crtwVLgFcwoMnYO7JisUOeM4ga4NHwtQN9fJeQa67XursdrOlwRxNZtqJMexoUIAT/ULwIpX7HzekGjoNdj+LCbdzrENCpfcU5AQYrDudQRqq/2EAPuCrcrJThxk5R6bAp2+8nVsFdieeAdc9pZdRde5nFHlDpkE2T6/YzsMA9LGSoh1W6v0bUyG3BfhvaQPwb4/1XmqKIbOQpGVlNsZ20f6LDRW/jVVQfo4/98BO/w3PqvzbX6sgiMAQ/qoqkAcdVeg/Xm7Yxvl9+teComRSbPWRnuyyJ8SBsgxQ+MkxDogSEKRm2okAqXOuid0VWQtSwN6RTKr0fo/Zds3h7YxWlpaWloHVRpsDzNlxoczbWQqLyzI49PabBh5pcBA30niam3+DD76pZ2L2ljlDaL+5PHYrp2//FJPm4BVcz3891x47gRYZ1Xj3fQZJA2D9GNkIOtb2dipmj3e7qByLJWrGhBog0xTrTiRcf3sIkHp4+Hsf8KFL1ptdeRIzX8UZt0hsBsSZRcUcq4ZCjLALdsCGJDuANuqXeJ49RosIFRfIec27GK49HW4eaU8ry4UVzw2E6b9W77f1mQXwnFCONjFn0KiBUYqt9vgnL/E0Tmm5HNGpsjgPGEAxPWRj1xu2L7ADkPOOBaumSGulnMd1ppi2yE77je2gxUQ5OPYWmAbFCoAoa5JW4vcSyHR4vINPkfWu60qkHOMTpPvtFdFrvGGFHeUXDfncRTgAyQPdzi2DrB1Bct5+3NsFbQrsN27qwvH1uGAV++Wa+50DJ1VdJWCw8XNVNCvwDY+CwZOdWznCEV2TgQoJ9apuH5WznuhTEREp8MFL8A5T9rObzvYdtOxVco8Xoo29TsJpvwNzn8Osk6Vz9T97E8KiHsyFFkVkGqqtn5PPgWf2otHdQNsQ2NoDwnvCthHXAHXz7JzpQPdMrFSuEKKweXOlvfVPavB9qjXgKQINpvpNIcmQa4GWy0tLa2jSRpsD0Pdd34OY/vEcct761gz5n743QaY9EdxigwXJA6Gb5+Qgf6nv4MXpnS9hE19uThQYEMXCCQ2VIpr99o0mH6xwFVkb5hxoywls2sJDDpTtk8dLY7xt094V48FcbmeHAmz73EcV4GtBZ8Zx1nLv5gw8zcC2afeZcNJhpU/qFwn1da2Vti5UCCiPFdcxBBrLUx/Oba5cwSU1CC/uki2i06zQzVXT5d81GwH3MT2BUz5fuJg2cdFL8PZT9jOWwfHNk8e3VESCltVYC89U5XvvfxIdJqsxXn1hzDhZim803cSjP+FOOEq51hBOgis7c231rWtsQf4Ub2l8m9giLTTy7FNtL+fkC2uRWuTN3SCuL4t9TYcxGTK/uorYP4jMgHgFYocIW1Q1ZkjkrxhsqHCPoY7ynbiQuMEkJuqvHNswZ7oSBlt9VmBXC/D5b20TVicnKMKpVfFpdQEB/h3bEEAXBUta6/07AOrQaESWttc5w22TrhWiusngJpv5WzHZIi7Gp1qO7bqGqoJEDUZsC/H1hUIo6+WiYch50L26fKIIWvBdib3QXBs1XGUY9sZ2HbXsQW5fl21OTDYjrwAKSTW1mT/bVETS8qx1cv9HPUa0CsSMNgVM9ZnclFLS0tL66cuDbaHocKCA3nhmrH0inTzu3dXUx+aJEVRJtwMN6+Ci18RwJhxk1XQqUYcv87kdFH3rIUlz4uD9PJUePMyG2x2fisFeC54QQaK71wlOX7DrOq6aWOlMvPX/xAYduqbeyQ3M2+uvHa57dBR5dhmTxWw+PR3sP4jmHInZJ0iYdZ9ToDB1vLGsX3E3S3fCouegnXv20BitskAOsAlrk91gbhfKsS3Zo+EAGedIoPmoHCBbl+w/e5p+V7WyfY5qBzNljq78FLOhRI2PGQaHHuTDTq+ocghUQLGpscuLAQQ73DQVH5t30kCW0OmwbWfQP8pAu2bv7D27QTbDHERFQBGJNufnXYP3LhI3OX6cjvc1rkkzEl/lsH/e9dJHjHYlXVVbqO6/rEW2Fblw+x7pSCT031zRwrUKICOTBKY+3Oh3CP1Fd7Fo9QSNmHxsp9GFYocZgNfRZ64pWoSoqpA7teIJO9KuWHxck+2WMXLaopk/wps1RJQ/qTWsgXbsfUFW8OwQq1rZd+G9afRL9ha/bfxE3lUkx1gO4btObbWedZ307H1p0Fnw61rvUO7fRUSDVFptmPcUwqJksmL6kI/YKuqIkd3/F6H/VjbpB0jfd9dudzShyq6QU0sacdWy1JSlJvIkEBySZPfsrMKupaWlpbWT1qB+95E61AoOjSIRy8ewVUvLeGqF5fw0rXHEBvutquEHv87WPCYDMADQ2HLFwK/3/1HXJ6x18l2e9baoZ1GgORBelphwaM2CJVukgFm5Q6YeLMAbFx/yYftP0UKFYHkv0alScGl0k0CW1tmSeXb9R/JYF0N3OOzbLDdu0sAZMy1sPY9aUPKKJhwi3weFgc/+9Q+eVeQwO3GT+ywYqeUIxRqFWvKnGCDxJZZApdZJ1tFlgZIgaXaYhmIx/WTc8qdLSGeTsBxLqXiCxGpY+xwWbCBReUMh8ZBrFUoScEXCHCXbxUoU2Drq/RjxaFU+cyhPmALdmXkSAe8uYIETlWYbl2pwIVzcN9/ioQcb/xE1rW9+FVZexTkGoM9GRGd3rGqrLN/giME5NsB2mqLO8IunOV0hQ0DUseKOxkSZUGvKftxFqqK6CXbRCTJJEh1YUegVNe3vlzgrbpIKvKGWs5fTGbny9xEJtnLIqniV75gC7Lf5lqob5R+j073X4VYVfHd9rUAuhOofUORA91yTep8cmz3B2wNw5646UwTb+64nnBPKDxBliBraz4AocjI3539UWCwhCLXW/n3amKppQEw9q9ftX6SMgyDwb2jWFmfyGkg/4c4/3ZraWlpaf1kpcH2MNbErAT+c8VobnlnNWc8uYB/nDOEkwcnERwYACf+WaA1JlMcrtVvwvKX5YtFq2DU1eLAvjZNQk5BYLJwhbh+tXskZ3fPWgnJHXYJjPu57Z4FXFIWAAAgAElEQVSMuBzm3AvH/J/doJAoGH4xLHxCQoo/uUVClcMTYdwvBT7m3g9YVXsVTCi31B0JV30gbuBxv/YuPOOr+CyBVKWEgeLOeFrsgXNYHFTkCqCqEMQdCyTMUYW2Jg4U6AZpgysQrv5Ilt9xhuyCDNqDwr0d284UEi0TBUWrxUWK62dXhQUBVbNN2hieKMDWGZwEh4mTrCDZ17EFuwqv07FVCosT176qoOM5AVzwogBnpI+jGRJluZnFAmjuCBsM3BaIqkkRsB3bGgtswx3OcFi8vX1AoO3IXfSy3FPfPmGvi+usHFxdYO8nOs0ORfbtfyfYRiQLxDtDkWP95NcqRSRBreVKtzu2fuArOELAtrZUcoKdky1ORaVKXnNro4TXOx3HIJ+qyGCtGezj2HYWivxD1ef4A7u/ztT/ZLuolu9EjcpT7o5jG2VB8f622+WWUOQGn1DkGquw2v64v1o/WeWkRDNvaRx3uJCUFg22WlpaWkeFdCjyYa4zhvXmwxsnEO52ceP0lUx5bC5ltU3i1l35Hpz1qITwtjZKoakLXxJozZtrL5mzcyFg2IPIaU/DFe/CVR/aBWn6neg9KDz2Rilck31Gx0YlDpQCS0WrpIjR7dvgzIch8zj5PCxOYEU5SJXbIdoCtPAEOOeJfRe5UXl3KaNg2lMSdqvA0OnYgjiQTpdy0Nk2NCcMsGHC6TAlDbGXxVEyDCvM1PC/JqpTAS4LqkzZlytQgEsBtsoLDIuXfwFB3iDoq+Rh9nOVfwg22ObNESj0dcnA7oeSjd5hyEpBIR2hVkn1swJDFfI85U55dDpqarmf2mI5ZqDDHVOAuWedVSDLCiMOjRHQCYkS0K8v6+hqqjZHp8tERfVu7yV2wO67mmI7xzcqxe6rzvJr1f6bqqQ4WnuObUTH7YLDrRxbP46xUwEu+3hqXWLnPsAbbMMS7BzbHxKKfDhp0Nn2c997MWQ/QpHTx0kYfeaE/Tu+Szm2FtjWFkv4+N6dXd8DWkeVhqVFsbUlATMgUCZhtbS0tLSOCmmwPQKUkxrNrFsn8exVYyipaeL299bg8TjWox1+CfxyvgDr4HMEMj77vQCtWtcxPFGKDZ3+gITpZp8uA/4Tfg/DL+0Imu4IGHGZ//DOxEHiErc1i2OllDxcHsMSZGDfWCXFnsq27P8AVrUn+wwYdZW0V4UKtxfK6S9tScgW0Fc65nrHfhzrenYWCux1bgNlv8pV7Eq+lZIDAuxiSlmnyGOo5dhGp3YeKgs22IZEezvZoTFSLKuxSq6bv8I87fm+ud75nt2RyrNV7VZhtTkXwZ92wvG/t7dVYKvcMa82WJC6Z61/KHQ6pEEW/Klw7vYljCZYhbJqvKsdgw3ee3faxZ0iU2Qfhqvr9VvVpErVLnFsXW7/jqk70sqx3W1X6u5MKs82aZj3+wrwnf0TnmhP8rQ2CZwdqc5iQpY96eMLtoPPgdPv714BK8PY/3sVZDLF6diCpFBU7tBgq9WunJRoWgmkJizDSmfR0tLS0joapMH2CFGQK4CpOcn89czBzNlcys9eXcYbi3eyvqhKBom9R8hjoBvG/0ryPIdeIOAKMlCP7QPH3eQ9qM4+HS54fv8G2s4wUWcOYmiMHCPcAltMWPlf+Wzw2eyXMo4TuMm5wH5PDVwV3J16N/zfNx3b7gw7czqvXblwSlMfFDe7O1JAqYAeBHgCQ+3w77A4mHQ7nPFw1/vqbe3DWeVXKdYCM1XEq7N2gBSm2h/5OrbnPAG/mCtVoENjvGFchZpW5HZ0gNtDhcsE4n3ldPHU9VPfUY6tmgwAP45tooT5Vu6wwTaqt/Tvz2fD6Gs6P0d1bpU7BWz9ubUg51e1S6IffI/vK5Vnm+wDtv1OlHD73iMcbY8XF3juQ7L/I9WtVRp1peRn+zqzobGSYtCT0K6KR9VX2PdP2WYJYddgq2WpX2IEoUEuCgPTNdhqaWlpHUXSObZHmK45LhNXgMG9n21g/pZSAgy45rg+nD8qldzSWqYM6kXMiXfQOPF2XAEGQbtXwpz7OjpgP0aJg+Qxrl/HcN6pD0noq8opXPm6DPL3d9CZOBD+sMn7PbUP5f65gryd2qkPSeiyU3H9xNELje1exdSIXv7Def1JuZTOcNSRVwropo6WKtbZU7u3P+X8OQtHKcVkivM90E9YOHiDbZ8uloTxJwW2yrENie7Yh0qqOFB5bsdtnO32N4Ggrl3aOMnfho5gG99frldFXsd9GIa0sXKnDZ1qm5SRnZ6e17Erd1hg66dwFEgYsargva/fS9apEnbtG+kQ4PIGdBAob6iU/HPD1b1Q3cNZE26Wf4fCdVbFoxoqJI9+2/+s4memBlutdrkCDIakRLGxNpnB1Qvlngk8wieUtLS0tLT2KQ22R5gMw+CqYzM5f1QqlfXNPDM3l9cX7+TVRTsAyEmNIjYsmAVby4gKCeTVn41itDuKve7eVJXXkRl/AJYDiesnrlP6sR0/G2itC9tYJRBTsFQqth4IZRwrUBDTSaGgY3/V8b3AYHFRgztx6X6MVCitM6RyyLnWuqNIXnC39xUvwOaEVKWT/gJjruvcaWx3jod5F57qjtKOgV5DbYe5K6nje1qkIrFXG5xg6ycPOG0s/GGLQKwCojCfUGQQYFz6nP9Q4Ng+EoqsXHFnLnJXcrq9TbWdg62zf/fl2A44Rf51RwGOyRez7cAXjjrYOpRh1K5gmZyor5BCVnH9YL219JgGWy2HhqdF8/XSDC5wtUrBs+7+XrW0tLS0jlhpsD1CFe4OJNwdyH3nD+PXJ2XxXW45HtPkzx+uJdBlcNOJ/fls7W6uf201Y0PvY8WyIFi/kK9+N5nESHtg3eYxqW1qJTpUBt/1za24Agzcga7ODi0u6eVvd53XGBINN3wFu1fbOag/Vunj4I78/f/epNulUu+BVs6F4lQeKAfu3H9JPq2vkoZ2nY8YFicD/n4n7v8xI3rBTYu6t60zT9Z3mRanY+svFBn8hC9b33EW1Rr/S3HWY/p0/H5splS9VsWdugtYhiHQ0+7YdrIczYDTZWmoyOQflv/ZmdLHWe0IkKWojvRQ5EOpsHi5Ro1Vcv8MOA2WPCufabDVcmhMZiy/X5hDa2gUgWvf02CrpaWldRRIg+1PQCkxoVw4RlyyQclRRIUGkhkfzkVj0rjjw7W0Bcdw/ehY/j1nGz97ZSlJUSH8+qT+hLsDue29NeSV1vHqdeNYsbOS/8zZRr/EcN76xbGEBXdxe2SdvO+GGUbnYa0HUyMu65n99p20/zmtXck3hLW7cgXBdV90PdFwIKRcb1ewn2rAjnVpu5PLDJJHnX4sJDraHd8fTr3L//axfaR4VeGKzl37zhSTKWAb4Oq8fYPOlH8HWlknw50l8PJUKFp55Du2h1JJOfD9O/I8NE5y/Jc8K9XIfQuaaR3VGpMZSzNB5CWcTPamT2Wt4+6ko2hpaWlpHbHSYPsT07A02/HrlxjBu7+0qxZHhATy0KxN7Klq5LLnF9PqMYkNCyYmNIhLnvsOgPF941i2o4JfT1/Jf66UIkw1jS18uX4P64uqiQ4N4srxmWTEd6NqMLBoWxlDU6KJDgva98ZaP1xpY3v+GCpUN3m4fzgLi7PcVD+hyP6UNARu+LL7x1cwuzdf1k3eH8X2Ebc3PAHc+1jKqScU6LbzdrVj+8PlLNYVFidLmAWFS4XmrqqOax116h0dSkp0CLMCJpLd/BFs/QqGTDvUzdLS0tLS6kFpsD2KdN3Evlw3sS8Vdc38/eN19E0I57qJfdlb38xTs7dx0dg0JvRPYPqSndw5Yx0nPDyHirom1MpCCRHBVDW08Nz8PIJdAQQHyr/48GBOG5rE2Mw4hqZG0StS1nKdtW43v3pjJRlxYRzbL47gwABuPSWbBVtLmZzdi7jwYGqbWllfWMW4vnEY+5G7tzK/kqSoEFJjQimpaeS+zzZy/cS+jEj3n3fZ1NpGcVVTt4Fcy49UbmpnEB0WB7UlktPaE1KhpoEhMPKK/f9uc6386z/lQLese4rSYPuj5axAHhonEwbjfq77VMuvxvSJ473tbdwcGgubPtNgq6WlpfUTlwbbo1Bx4cE8dcVor9f/vNQuBnTl+Ex6R4fw2nc7yUmJpleUm+FpMYxMj6G4upEPVhZQ1dBCc6uH5lYPO8rreGZuLh4zF4DESDc5KVGsLawiq1cENY0tfLF2D3XNrby1dBdtHpNekW5+OyWLN5fuYuPuas4dkcIFo1PplxBBUKBBYWUDuyrr2V5WT05KFCcN6kWQKwDTNHl54Q7u/WwD2b0i+fg3E/nN9FUs3VHBnE0lvPnzY8lJ7ZinescHa5m5poh/XTaKs4YfwArRR5Mie0Pm8ZJb7E+hcQJvPeWcxWZKVeGci/a/SJYz//JQVSWOTJZHHYr8w6WKrFUX2ktjdRa6rnXUa0xGDJ+sKaJu5CmEb/kS2lq8K+lraWlpaf2kpMFWy6+mDEpiyqCOOWtJUSHcdGJWh/drGlvYuLuGdYVVrCuqYn1hNW0ekycvG8mg5CjaPCaLcst4Y/FOzsjpzQsL8vjbx+sJDXJxxfgM3lm2i5lrijptT2pMKGcOS2ZV/l6W76xkSO8oNuyu5rTH55NfUc+fzxjEfxft4OqXlnDLyQPYVdlAZEggF41Jo6axlRmrC4kIDuTmt1dhYnL28G7mgWrZCnTDdZ91/vmoq6CutOeOHxwOV39kr/m7P0obK+sbp4yGcb848G3rjnQo8oFR8nAB2zA/az5raTk0IUsqr68Kn8jxje9B/ncHti6ClpaWltZhJcM0zUPdhgOisWPHmsuXLz/UzdDqpkzTJLe0lgh3EMnRIVTWNbOttJa80lpaPSapMaGkxoSSHhfGgq1lPDcvl1W79pISE8KNk7O49Jh0rnxxMSvz93LvtBwuOSadHWV1XPLcd5TUNBESFEBTq4cgVwDuwAAMYNatk7j17dWsyK/kkrHpnDO8N9FhQXxfUMWk7ERaWj20tHlIjHQTE2bDR2NLG4ZBe6Voj8fk9cU7CXcLOGtpdUvbvoY3LpRKvle+d6hbc+RqzgMw7yGpkB7SSYXrAyTDMFaYpnkQEth/ujqU/zebpsm4+79hcp8wHt1+AQw4FS593d5g5WtSTT4m45C0z688Him8eCiX1dLS0tI6jNXV/80abLWOGJmm6ZWHW9vUSm1jK8nRIe3vFVc3UlbbxJDeUeyuauSFBXk0NLdx7sgUJvRPoK6plX/MXM/na3dT39zW6bH6xIe1w/Jn3+8mKDCAS8em09TqYX1RFct2VAIwKVvySctrm3AFGJw0sBdXjM9gS3ENozJiiXB7B0UU7W2gaG8Dw9NiCA7sXsiu+o3uTw6y1mGo4vXwzAQYdDZcNv1Qt+bIVUMlFKw4KMu3aLD98TrU/zff+vYqFmwtY9mkNQTMuQcueU1ybWtL4NEBcMJtcPLfDln7vORpg8dz4MQ7YMy1h7o1WlpaWoeluvq/WYciax0x8gW7CHdgB3BMigohKUpANyUmlH+c470eabg7kEcvHsE903KYs7mEvfUtDE+LZlFuGTGhwYQGuyja28BXG4p5eNZmItyBTM3pzZ7qBp6bn0dkSCCJkW7uOncoe6ob+fT7IuLCgkmKCqG2qZUnv9nKk99sBSDIZZAeG8ax/eM5d0QKy7ZX8Nj/tgAwOTuRV352DOV1zSzYWsrxAxLai24pNbd6+O1bK5m7uZS+CeHM+PVEQoK6WF9Y6/CWDkU+MAqN1WuSanVbE7MSmLG6iM1Z1zN4wwyYc7+AbfE62aC68xSY/VJVAcy4ES56VXLBf4jqK6CmSCbBtLS0tLT2WxpstY5KhQa7OHOYXUTKt+DULyb1o6CygeToEIJc4qy2tHnanyv9aeogr9dfrd/Dxt01DEuLYvmOSraV1PLBigLeXJIPwLSRKWTGhfGv2du47PnFrCuqor65rR2CR6THcPrQZAIDDL5Yt4cv1xczbWQKH68u4qnZ27jt9IEs21FBWmwovaP1moxHlEJjBWp18SgtrYOmEwZIVM28bZUMHnEZfPVXWTJMwWN14YE5UP5i2D4fCpdD9uk/bB91JdZjD9Yq0NLS0voJS4OtlpYfGYZBepz30kC+UOtPpw1N5rShUv1WFd+qamhh+Y4Kmls9TM2Rzwr2NrA6fy9n5PTmwtGpzN9aRn5FHf/bUMxHq+yB1i8n9ePPZw4myBXAf+ZuY21hFfO2lBIVEsgNx/cjLiKYwACD5+fncVz/eC4ek0arx2RUegyB3Wiv1kGUYUDWqZAy6lC3REvrqFFydAiDe0cxe1MJv7rgNAHbrf9zgO1+OLbN9eBplSXHFj8Nx/zcXt+7Zrc8VhXY2zbVQGTHIoydqlaDrZaW1k9QbS3w2R9g2EU9XsBPg62WVg8rOjSIkwd7D27+eclIr9eqemdtUyvbSmoJMCA0yEVWLxk03XXuUIJcAby1NJ8LRqeSW1LL419vaf9+n/gw3lqa3+4MR4YEEhceTEZcGOP7xjE6M5YAwyA+PJgHvthES5tHCmiN0NWhD6ouf/NQt0BL66jTlEGJPDsvj6qwMUTHZEght6pd8mF1kQBoXRnE9YXc2ZA2zgZWpz69FSp3wuTb4ev/B8ERso4yQM0ea3/WxOTse2H9R/D7DR0LQXk8sPY9GHIuBDkib+rKvB+1Dh952mRwHhSy7221tLqj1iZY8xaMuqbnlkk8XFRXCiv/CykjNdhqaR1NinAHMjI9psP74e5AHrhgGH+aOpCYsGBM06S+uY3qxhb2VDUyPC2GjburKaisx2PCwm1lVDcKJD/61RavfUW6A4mPCOa3b63ivRUFlNc2ceqQJFKiQ/l4TSG79zZyz3k5TLRgu7SmieY2D6kxoXg8JgEBuoiVlpbWkaMpg5J4ek4u87aVce6A02DVdHFeg8KgpQ5m3QHrPoTznoH3roVT74GJN3fcUf5iAdiSTfJ606cOsFWOrQW2uxZLvmxtsb2GtdLmz+Eja9mxEZfa73c3FNk0YdmLMOxiCO34/4VWN/T2lZA9FUZf3b3t5z4o1/um73q2XVpHjzZ9Bp/cAgnZkDnhULemZ6WiUcJ79fihNNhqaR1BUssQGYZBuDuQcHdge65tTmp0e66wM3+4aG8D28vqaG71sGlPDWcN601qbCgPfrGRD1cWkh4XxhNfS8GrpCg3YcGBXPniEkakRVPV0MKO8noARqQLPLtdAZw5rDd/OD2bYFeA19JIbR4TlwZfLS2tw0gj02NIiHAzY1Uh5077LWyYKRCZdQps+QI2fgIt9fDB/8kXCv1UcW6qgb075fm2/8njjm+lSndoLNQUy3vVhdDWaoc6l2zsCLarrciN8q3e7yugrS+HPeugaCWMvqZjW/Z8D5/fBqYHxv9y/zrjcNf370p/9aSr42mTyYWmmu6DbckG+ddY3ePLjB1wLX5G2n3inw51Sw6+Nn4CgaGHZ8HByu3yuDf/yAfbqgJoa4a4fv4/V2AbsR+pGT9QGmy1tH7iSokJJSVG4PekQfZs2V/PGsJfzxoCQH55PR7TJC02lOY2D68u2sHXG4rJTorkivEZ1DS2MmdzCZcdk059cxvvrtjFO8t34QowuPOswQxKjuKeTzewvayOydmJ3H3eUKJCgrj/843EhAbx+9MGHpJz19LS0nIFGFw5PoMnv9nK9rOH0PfqD+Hru2Dk5QK2jVUQEAieFnC5oXCVfHHzF/DZbfCLufYgFARo3VHQVA1PHQNDzvPOsS3fCq2N8rp0E/Q/CbYvgNhMcYm3fimfledC3lxoqoXBZ0OtcmpNCWXeMguGXtAxLLpsq73v/dG3T8g5XT9LwqPnPAAJAyTv7UBr53dyDle97x1uvS99dSckDjxwYNvWCi5rqFuRB0ueh4m3yKRA4UqB3IBOVhswTTuMXA3My7fJBMeA0yA4/MC0sbUZZt8Nx/12/3Kyu6vlL0NL408bbPPmyb0clQLNdbDgMTjhD/C/f8hEyYBT5HrOf1SiJA6HtasrrYmyvfk9e5ymWkmxGHJuzx3j09/LxNwv5vj/XEWjRCT2XBssabDV0tIiI94ulBXoCuCmE7O46cQsr23+4IDTy8els2xHJUu3V3DXJxsASIx0c96oVD5aVcCZT1bgDnRRuLcBgP69IjhhQCIvLMjjrGG9O1Sh1tLSOngyDGMq8CTgAl40TfNBn8/dwGvAGKAcuNQ0zR2GYZwKPAgEA83A7aZpzj6ojf+BuvLYDJ6Zm8tL3+Zx73nDBLicA8qT/y7gE5EE8x4SyJxzP1QXSK6syzFc8rTCoLMkF3b3GtjwsQymQRzb3WusDQ1xbj//Iyx9DgaeCVkny/djMgWSvvqbfGfgGd4hyPmLAFPgNc1nucYyK72kxA/Yzn1IQDp9HBRvgLevgGtmiGs17yFxpit3yPEX/Qt6DekZsF33Puz8VuCxz8Tufae5TkK321q8oRLkWjXXQa/B+9hHvYBqoFvc9MeHwQXPyfVa+wEseQYyjrW2rZG+dO6zqQaMAJk8ePUs+PlsAe1ay5Ff9wF89xRM+RtMuq37/dGVdn4Li/4tbtfY67v3nfoKcAWBO1LgfM79MPY6iE7z3q6pVs7FMKRfXUH+9+fxyDa++eA9qZZG+c0lDflx+2lthukXwZjr4MyHpTjcgscgcZDc6+qcK/Jgzr0yqdEV5NeVS9RG/5O9f/cHWpU75PGHgO2WL2HVG7Iut/OaNVbLRMZxv7Hb/v078Nnv4cZFkDTU//5+rCpyJWrF93erdBBDkX/i2cpaWlo9oTGZcfxqcn9euGYsz141hueuHsPXv5vMAxcM44MbJzAsNZpByZE8d/UYxmbGcsvbq5nw4Dc8MzeXy55fzHlPL2TSw3N4eNYmfvn6cp6es43dVQLBxdWN/HfRDspqm/wee3dVA6U1/j/T0tLqWoZhuICngTOAIcDlhmH4jixvACpN08wCHgcest4vA84xTXMYcC3w+sFp9Y9Xr8gQLhqbxptL8lmVXylvRiQD1iBs8DlwzpO2U7jwCQn5DQgSSCveAMGR9nrUCdkCxyf8QdyIljqISpNwvNzZ4vymj5Pc3aXPyYBux0LY+jVEZwjIlm2V9XTry6FwhewnyHIBG6vkMf87+Ndo+OJPMhCuLbXBtnQjrH1fYBYkfHnu/bDwSXm99DlxmnNny0C/pd7eZ02RvN69RgbDBY7w69VvibvrVGuzAFRn8v1s11J5LFgmj1v/JyHgIIPfL/5kb+PxiMOrHKyGCtj2DUy/RMAMYObNkhcLduVpf5p+MXz0K6t/tgi85loukuq39okHvM+7tQlePAU++DkUrYLmWinuY5r2pMP378jjxk86b8P+SvVDVTeXnjJNeOUMmPlbeb3ne1jwqEzA+Kp4PWAKzHXWb43V8Eg//99X2vo1PDcJWhq618Z9yTThgxvguRME0rvabl+qyJPfnQJEFcmw6TMw2+T3BTZIlm3ufF+bv4BHB8Cbl0jl857UvsB22YvyN8NXnjaY9WfYOLNj3235Er7+B+xY0PE4u5b82Bb7l2nKvdVc0/m1rCuVYnvBYf4/P4DSYKulpfWD5QowmJqTzOlDk4kOk1nRoSnRvHLdOF762TGcPjSZF68dyx1nDOKc4Sn89/pxpMSEsLe+mfiIYP4zN5e1BVU88uVmJjw4mxMens3xD83mHzPXc8aTC5i5pohHv9zMyY/N5eqXlnDzW6uY/PBcTnt8Hku3d/GfoZaWVmcaB2wzTTPPNM1m4G1gms8204D/Ws/fB042DMMwTXOVaZpqfZz1QKjl7h4RuuOMQSRHhfCHd9fQ0NwGgcEQ0QtCYiC2r2zUewRgiCsXkSzgumsJbP1KnKVEa+3yhGzH9paUs7rlS9k2KUeANyYDTrsHmqokvLj/iRDXH1obBDjUd+rKoJf32ugsflbckCXPwpMjBC5KNspnDZXwxR8FZte+LxAGsp5uY5U4lAC7lsHadyHnQjnXnYug1Brce1rg3avhxZOhYruE7v7v71L1udRRePCty+D5EwU06yvEaVYh0Stfh8cGyvdriuVzlWOswPbru2QwDhLKu+RZgVvThNVvwCtTYf2H9vG+uF1CtvPmClTnL5Z+2PEtPD5UQAsknPv1C+DRbDknda3aWgR4AHavlkcFNE5HfcMMOQbIhEDpJsmlVRC47kNxcdWkgALc3au9gcRjXccdCyUsdn+kgEMdc9cy+OTWzicSCldIO/MXy+s96+RRTQw4ted7+7nKEc+dI66kc5uGSrsflHZ+J/0I4lTvXuM9KbB9Pnz8a2/Y9bSJE7svLXlOinF5WsXV70xvXwmvnt05MLW1ygQPSHQFyPUDuQ9Avuvx2Odf6l1Q00s7F4nj3+cEmQzqCrpBznf+I/u3bBhIv6rrrSq0O9XaBF/cAd/+s+NnG2bIbwHsc1ZSkQVFjj5V+9+1bP/a2F3VldmpFwqifVVbAuE9H4YMGmy1tLR6WDFhwfxqcn8euXgEk7MTmXXLJObcdiIf3jiBNf84jUV/Ppn5t5/ELScPYFR6LNcf35dXrjuGmNAgbn5rFU/N2UZydAjVDS0syi3jrOG9iQ0P5tqXl7KjrO5Qn56W1pGmVMA5kiqw3vO7jWmarUAVEO+zzYXAStM0O4RPGIbxC8MwlhuGsby09PBZkzUqJIhHLh5BXlkdD82yXB1VkVSFz7kjIXMipIyGn30Ko66CsHgZFKeOcYDtAHlMzqHd9VVg27gXhkyzQ1wn3gr9TrRaYUK/kyC+v92wpBwB3toS77DYgEAZuLqj4Yp3YfyvxGkt3QRJw2Sb+nL5fObNsPI1KWTVVC3Q2FwjDvOGGQIu2WdIGG7+dzaUgg00hStgx3wrH86Eb+4Sh7hkE+R+IwD037PhuckSxjz/EYHJz28X4Fv4BPxnPDwzQZyy8F4Cts31AhvVBQIARRZoFq0UUP3+XXmtHsGG0tzZstKmpCMAACAASURBVF2rBU+Ln5FHlac890GBkdpiAVNPizitBcvsvOg96wQk1Dkr0M2cKEs/vXaeuKUL/gmGS4BDQevenTLp4JRai3zDx/JYtg0eTIflr0jot3JSlVoaBc6VGqsF6L99Al45y3aNqwsFot+/Hla8YgNa0Wq5NkrKNa7ZLVW61SSCP6jYvVruI/X5zkXw+nkw5z57mz1rrUcHBO9eI5MNy16S1/mL5FG1o61VKvquekPuNaW5D0jeuS+Ue9qkD76+S+7zNW9C8jDA8D43p0xT7s0dC8SJd6q1CT66UdzVnVbblOOtQvTVZITZJr/Jdsd2i90+T5uE7qrrXbULolLhjIfkOi19Qfa3+Fn72MUbYPMsgd7t8yWXfOVrcm8Xr4e9u+DBTLm3fc/no1/BoqfkOGab/G2pKpDJoVxHVkfJBrmXdy0TKPd45LimKVXdXW77nFubZDJq9Zt2LqtzsmCv9ee+YKn/fv4x2rPWe4LHWYvAqboSmUQ8CNI5tlpaWgdVzuWCokPF5c2ID+PWU7K9tps0IJH5W0sJCXRxXH/vMfWeqkZOfXweN01fyZCUKPolhpOTEk1GXBiZ8WEY1iB1S3ENlXXNjO/nOybX0tL6oTIMYygSnnyav89N03weeB5g7Nix3YglPHiamJXAzyb04dVFO6Q43iWvdSwe9LNPvfPEbl0noBXXTwbhZZvt6p/uSIHU8m2Qdoy8lzpGCgE1VkHDXoHjQLdAdNlWgdymatk2fgCMvBK+tNzMhGyBK7NNAHjb/yBrCmSfDn0ny+C1qVoKwRSvBVcw3PClAMOWL+DCl+DDn4t7m36s5NvOfUD23f8kgactswQWQmIgPEHaDjIYbqiUwlijrxHXetNnAtsBQZKXuOoNiOoNcX3ks8odUhwqdTSseNW7H4+5QY69+XM5HxDgLFol+wuNEShWoaN7d8qxgyME4IPCBKijHOutb/5cHnPnCJRs+1omEXYulLWBlXLn2CDT2iCvFejUlwsYXPq6hHN+coussdnaIJWyt30tOZaJgwTcV0+X7yUOFndw+GXS7wuflGv3/TsC05/eKts17hWYaKqWHObXzhW36rLpAiJPjYUzH5VrqVzkwBCBnXkPQZUFCvmLITodXj9f9n/5W5BxnLinMZnSX0WrJZxd9Z9TNXsEjDInCPyV50rxLJBQ6jMfkXt/twW0xRvsYlubv5D3NsyQ/lV9qSB8zVvym+hzgvRdXSmc/QSseUfaX7hCrklwhECfK0jy2L/9p9wvxRtgwm9kwsEJtqvflAmGlnrJH22pg4SBcj3Kc+0Joa/uFDgGuSdBQtgb9sr9HBhiu4ggEKrOoa1J+iquH+TNgU9/JxNCV74v1y0mQ3JRU8fI/Ve+TSIe+hwvE1nvXStwHJEk1wPEdS9aJWHlk/8o98Cat+Q7SuW5dlSFeuxzgvTxJzfL8/5T5P0iq4BdU5V8Pu9hufeu+kD6Pe0Yyc2uLpSw6by5sv52vFUbRU0egdxXRoCcR32FTEiV58r99L0VyRFor27RrtYm+Z36rrG77RvZZ8Zx8OzxMPR8+7NOHdtS78m8HpR2bLW0tA5LuQIMThrYqwPUAiRHh3DPtBw27K5mzqYSHp61mWteXsqJj87l+IfmMG9LKXM2lXD+0wu59PnF3DljLe8sy+fiZxfx1tIerkCopXV4qxBId7xOs97zu41hGIFANFJECsMw0oCPgGtM08zt8db2gO44YxAnDkzkLx+t5YF5xXiCfZZv8S1+EhwmA9rgMOh7Alz9kXcRHhWO3GsITPsPXPK6wEF4PEy+XaAWBBaHXwphcQIsgaGQPh7GXGvvKyJJYDMoTGAWpAIvQFCI5AKDDIJD4yDrVAHPK96GO3ZJIai0cRCWABe/Aqlj7TaGJ0huL0gYaEI2DDhdgC11jLi1G2cKNJ96D1z7CQw9T9yjQWfBpNvhljVww1dwwm0CW7uWwJS/iisNcn79ThRHWR1rwWNWv7oEbHevljZPe9qCalP6DqRydHKODMQn3ioD5dXT5fOwBAnddgVL5emNMwVmBpwq8NbWLGCcOkaApXK7nRO91uEGg7hHYXFSdRrDhnI1SK/IE7iJ62s7gir/OmM8nGWFqX7xJwmhTsqRiYI+J8g2X/xRnOsv/yp9tPlzcfRWTxdoW/RvgdqoNDnXgWfK51u+lOsdlSrO+oLHZLIhJkPCct+/XsD87H9Ku3c7wTZfJk4++Dk83E/Cw8s2ywRJdJoAaMl6Oee6Ekco81q5Nm1Ndh6ycqnzFwtYAcT2EQhtbRIATx0DV8+AU+4St/GNC2wo/+JPEs7++W2y/8IVkmcN4pB6WsT5Th0jn5mmhNPPuMmKVCiU7cBe0mrzF/DN3XKe62dINfLI3tKfhoUzeXNlEkX9dgxr0qq+XEK1Q6x1n1U48sZPBL4Nl1yTql0Qk25f78IV9vJea94SF7dsi1yv2hK7b3Ytk2M3VNj56ZtneTvXyjHNuci+Zn2t+8X0SCi/kpr8AZhxo0z0qGtVVQBpY+Tz/MVyXNVnyrGtLpD2tTRKNIO6LwuWy/287WuZIJnxK5no8lVrE7wwRVIUGvZK6PpDfWHLVwLG39xtpzNs+kweg8JkX/8cYqdLKGnHVktLS6trnTcqldOGJhEWHEhpTRM7y+vYUlzLywu3c+3L8h9IVq8IxvWNY/qSfEwTQoICWLGzEo9pcmy/ePonRuzjKFpaPzktAwYYhtEXAdjLgCt8tpmJFIf6DrgImG2apmkYRgzwGXCHaZp+qpocGQoJcvHiNWP5f5+s57l5eXy7tYwLR6dx3cQ+7dEe+6Vhl0iuoDsSRl3Z+XYTHCGqAS648j1xMYLDJZd3wWMCb+GJAriDzhIHaNBZ9veOvVFy2nqPgKs/tMEN7PVVL35F2hOVIs5VQKAAMEiF3/5TBEQSsuG0ewUEvvyrFJsCqcwbECAD+8zjYeBZHSsb9zlejh0cAaOvFTA46zGBjeAICzIjBawLlwuoRaUKAJRuEhcw+3Tpg+J1MgAv2SDwNOY6CRMeej7Mf1gg8/jfiQuVNwdGXQ3LX5LwSyNAzqepRpzT5OHS7rkPyEA75wLJk1WD7+ThEnKr8v1CoiSsvGwLRKbIRINSVKp3ru5xv5bz7j1SJj8m/9F2w89+HIZdLMd8uJ/tLC9+WkC8rVnAaNXrgGHnhV7+pnwnb64ActkW2Y87UuCjuR5GXgGn3g2vnCngMOY6cZYTsuU4DZWWu7wJ3rlKwGfwuTJB0HeyAHreXJkkiO0jRdI2fy6VlMf9XNqSfbq8t2etTIAUrRQAW/e+1ZfhMjHzzd1yn1btgnP/JRM4x98q5zfnPrkecf3l+9EZVvXxXTD9Qgm3BXtprJTRci+vni6TEF/dKaB77Ux4ZIBdoGvAaeKuzr5HXNi8eQJK2VPF9V/xqnyvYJn0GYgLueFj6D1cILG+XM4/6xQ5p7LNMiGy6TPZf1uztLm2WNoNAp3f/lP6NyRa7i8FzGN+JpC85k2ZbHIWE6vdI/dSTZH8fjOVq7tU0gbOf1YgtGi1RFUoVe0SoAx0S5v7HC+/jbpSqcK94FEBWU+LOPZRve3zzZwoURihsdKuxr3yG1dRJAPPgO3z5B6p3CG/+blWMXwVNbBhpkRp9J0kMFu8Tv52vHOVpEE0VAgMF62S66BC2Nua5Tffa7CdU7/mbTj1LuvzVpkEOggVkUGDrZaW1hGssGD5E5YY6SYx0s3YPnGcNyqFd5btIjYsmCmDexEVEsSfpg4iv7yejPgwLn3uO/76kcyWnjgwkSmDelG0t5EhKVGcOyKlq8NpaR3xMk2z1TCM3wBfIsv9vGya5nrDMO4GlpumORN4CXjdMIxtQAUCvwC/AbKAvxuG8XfrvdNM0yw5uGfx4xXoCuCeaTkMTYnm7aX53P3pBjbsrubcESmM7RPb/relWxo4Vf7tr5RbA3DSnTKg7DNJBqWuIAHTC1/w/k7yMLjSch9VrqevnKG7YXHwf1/bxa4Ajr1JBr2J2VaYYYCEEoMAQuoYe9uAABjuk98IAuZXfyRQptzrY/7P/jwoRB4n/1FCJVNHy2D8u6cBE1JGyuf9T5J/aoAe20eWRMo6WV7ftlUG+5HJ8L+/CdiOvU7gtGCZgFtYnAzsQYA/50IpqNVSJxMHx/9OlnkJiRHQcYItCGCVbZE2RjuCGaLTBTA3zhRgi04TR1lp8p8k3Pz7dwUk3ZHyfuYEgcScCwUEci60lpp5QKBk4i0SxhyeKM52QIAdDq7aExor341IEvc8PF6Ab+XrtoOZMgq+f1ueDzxToKV0k+z/1Lu9r1dsJmxHoDgkSvpk4ZMSVgvi4ubOlkkIlSc58RYJp67YLk68Crud95D0d7+T7P0f92txWOP6y3099wEY/wuZSInJFHexqUoqizfXyAROdJp93T67TYD39Ptkoqf3CMnrDQyVCYasU2XixRUsbQS5R8LiBGyzTpH7YfMXEmY+4DSJHBh0toBYRZ78rnqPkNzXnd/JJEddqYBp6SaJYgDbsU0/VtrtaYXT75dCWQusYk7Jw2XSJnGg9P3GT+S30OcEyf+ecqeEuG/+3AbbgmXitLqC4PK3pU3x/eWeTxwsqQSVO6VfSjZK/4fGCswec4NMfKgqyTGZ0i8qv3XAqRa4bpZJo6KVks+rIjySh0mfFy6XfgC7AFXZVsnh/cbKfy5YLuA79Hw5z2/ukskOkMrZbVZZBWexseh0KcCnwHbjTDjl/8kEUH0ZYB6UNWxBg62WltZPTGHBgVw3sa/Xe9GhQQxLk7VzZ/x6IluLa5m3pYTXF+9k7uZSDEMioV7/bgeV9S2kxoRy+bgMTh+a5OXglFQ3Eh0WhDvQJydPS+sIkmmanwOf+7z3d8fzRqADzZimeS9wb4838CDJMAwuH5fBZcek8+hXm3l6Ti7vryggwh3IRWPS+OXkfvSODj04jQkIsAtMHeh1ZX0BuP/JcO6/xYlVyrAG8U5XeV/a15qyIIAx9gYZ/KeMFPdr11I7l1ApdYyEEaeM9n4/LM5+PvIqwIBeQwXW68okXxPEdZ1ypzjGCVlyzkWrZLA99DwZ2Lc22CGSzkF26mgBxLSxAuQRyeK6RafZ68KGJ3bMxTYMmPqAgKdzvdNhF0lY8blPiduecZxAzIr/ivN4wh8kpDNtrJ2/GOWo35Y6WoAnvBec94xALQjcT77d3u7EOwTCmuvlmKqCrgpddyp5uLhqI6+0vzvpdqlOvPZd6DdZ4GjpC3Jewy62JlEcecumCRe/Ko7f4HO9Q/aDw+H6L8VtbGsRl3a0Bc1BIXLtC5ZJ2P13T8k5GoZUAc+eKk50ULgUOAPZPn+R9ENAAIy8XKBs8p9koqT3SAltzTpVQtoHnyMwXV8mMBsUCtd8LH3z+W12Hm9sH5kYmfuA7D8qTVzYAMf1+//t3Xl0XFed4PHvrX1TaV8syZJlybZs2XHi2M6KEychIekQAwFiYNinmYakT7qngQ7T090BmjPdDNPNBNIsARoCGUJPpmkSAjnZacjixHFsx7FjW/Iiy5a1b1Wqve78cV9ZJVuynWipVOn3OaeOXr16qro/vVLd+r27ZS5uuHzmYlMqbhL7J/7WXFjxV5lzoZRprdbavGfq15veDoOHYOXNpoU+s9RUbGyiSz+YlubMxaQ7d5meDAd+a71PrB4XzdfCFatNbwan17yXM3GUNEy8Z0oaJya1SyfMYzf/k2lp3ffwREzlLVnJqAKs6Q/6D5gZpAfazRj95mtMMr3kSjNO+6kvTywFlUlqwZTF5rDW5V48MYZ242fgpe+b/7XqVfO6hi1IYiuEWGA8Tjtr6otZU1/M7Ztb6BqKUOZ3cc9TB3lmfy9NFX4O9IzxJz97hbbaIJVFbg71hbllbS33/f4QFzeW8tNPX4I9axIsIUT+UkrxhRta+cgljXT0hfjljuP87MWjPLDtKFsurOOihhI6B8Yp9jlZW19CQ5kPt9PGo7u7aastZmNT2blf5O3EZjPdSrOVLoEvdpgul7NJKWs8qOU9/zz1cd5S0zqbGY88lapWs2xSRqYVKfM6m7KSvgtuM4ltZvbqTIt6xGqtyv6S3bTJJPVNV5n7JQ1WYltnynX68aezn/ZVevWt5pbZBtOKnj2R0B8/NTGGEiYS6NImk8z7yuALWTNXT6WsySTzMLHEjjs4uTt1xvpPmeQ3Ew+YRP2yz5kbwPvuM+M5+9tNYnR6t3ylJk8UdLrs1uz3nLYG7OJLTGK74iaT5GVf2LjqL01i2/pHE+ucZi7GZCZDqr0I/tiaNfiyOyaSQpvNTM4G5hyFe02LZYbLZ1p9M0sqlTWZbrkHHzeJ14d+bpLymtUTv1OS1Wr/wZ+YxNXuNO+pF+81rf7ZfxulzHh0b4lJtv/UalFeerXpPt25DR67y4yjXZLVSyNbJinccT/sfxQ2/peJ7v+Z/8nMhHWZMhZbiW316sm9NAJVplX++q+a5cGUzTxe0QLHrHHVzdeYibFarjNJ7LbvmkR51RYTa+tN5jh30cREZU2bzCzQvnLTtVunYOm1Zl9Jg0lo6zeY8rx0n2nFrl41MamZjLEVQoi5pZRicZmpSL9000q+dJNpgUim0jz0Shf3v3CUgz0hijwOvv1MO/WlXp7vGOD2B3ZQW+Lllc4htqyt5cOXNOBxSiuuEPmstsRLbYmXdyyr5L9ev5zv/e4Q/7aji4de6cJpVyRSU0/wfN3KKpqrAjy9r5d4Kk1juZ+rl1fSVhvkYG+I69uqqSry0NEXYjSSYFVtkPbeEA9s62TTskretbpmniOdxmwntW9WpvvybNjwn82M09Vtk/dnEoDsL9lVK+G/nZiYGbakwUz0U7zYGrNYPPtfyl3+yfe9paZFNbsb+Jvh9JgEpG7d5InNMmz2yUntlM/hNS2yWp+Z1M7Umg+YbrJ1684cr123zky4VpfVWp9JbDMXJrLd8LUz94FJ9KIjpgU4m7/CjF91+k2XX7sDPmZNPFZijactWWL+/onxya3nme7lYMY6v3jv5LWrMyqXn7mvebNJbH92q0nA33ff5OEH2TLvs/2Pmt4Bp3clB5OUg0ngnV7T2gzmPZ5d5kw3+0VrzYWE/oPmPZG5SABmRuzuXSZBbX/StERv+sKZ7x2lTEv+yz8w44p7XjcJ7pHnzEWEqpXmwkjFcnMxJjOEYPElJrHd9Hkzo3OwzvQamAeS2AohxGkcdhtbNzawdaOp9BKpNE+/0cs7llVwz1Pt3P/CEeLJNEsr/Xzl13v5xuP72dxaxbvaatjcWkXA7SCd1ijFpK7M0UQKp90mrb1CvM3Vl/r46ntWc/ctbZwYjlAd9DAWTbD/5Bidg+MMhONcvaKSJ/f28tMXj/DUG71c2VJBic/FgZNjfOXXe089190Pv47PZWc0mjzjdR58qZPP37CCmqCHx1/voa7Uy5IKP4uCHmpLvDSW+/C7z/yqNhpNUOR2vLXJrhYCuxOWXXfm/tIl5mf2WFqYvNxJ7UWmhc9barUEf3GidWyuKGWSyuxWuTfrY/8+MevvTMsy22ovNBNJTWfVLZPvl7eYceer33f+r7HmA9AyNDGJWoavzCS2izdOtK67A+aWYbOZmbdHj099YQBMq+4H74eGy8+vPIsunJjIacu9cMEHpz9WKXPuT7xqunBPdZGn1EpsMy3KmYs01atMUuwKmJnKs8eP3/oDiIXMdiax9ZaaFuLyZjMRV8aaKcbSA6z9kGmVbbrKtEz7yuFfP2YS25JG0+p7upXvhsf/ykxKd3I3vP9fJlrj55gktkIIcQ5Ou40b2kyryl03tvLFG1YQT6VxO2y8eGiQR3af4PHXT/Lo7m4cNkVdqZee0Sh+l4N3LKvghrYaxuMpvvLrvaxrKOEHH98gya0QecBum+jVUR5wc3mLm+yvtW21xXxuczPhWJIS30Ry9FrXCF1DZsK63752ktFogpaqAOV+Nwd6xqgt8fCOZZXc9W+v8fXHzKyk1UE3zx7oJZpITypDRcBNY7nPXDDTmt7RGPt7xrhsaTmraoMMjycoD7hoqw3istuoLfFyoGeM9t4Qm1ur6BuLsbjMx2/3dNPRG+LLW1bzWtcwTRUBllcHiKfSHDgZ4o2To4RiSS5vrmBFTRFa68JLnCtXwKes8a3TufRzZrbgTOyX3zE/ZVv2zpn9/kyS4rcbpSaPJz4fl90+9X6fNUa58RwJ6ZV/PrFcznSyuzmfi81uktmBdlh7+sTzUyhrNq2oF39imset85tpZW7aZLost1xn/l7BOjPDcXbvApd/ondAudX6nbm4AxOTytWsMf8bU6lfD3dYk0Jluv+XLTUXf7K7n2dbebNJbLd9xywpdrYu7LNMElshhHiTbDaFx5pI5LLmci5rLuerW1azo3OI3+3v43B/mGtaqxgeT/D0G738+06zBl19qZdn9vex6evPkNaaf/zghaxcVESRxymJrhB5ymm3TUpqgVPj+MEkv9n+iIklen7yyQ0c7g/TH4qzvtF0Fe0PxTgxEqVraJyjA+N0DoxzZCDM8Hgcm01RFXRz9YpK/nX7MXZ0DlHudzEQjhNLTk6IlYLv/cehSftcdhtX/sPTaKtXtcdpI5nSJNMT3aztNkVDmY/jQxFW1wXxuRxUFrlZWuGnuthqSdKAArtSpNKaSCJFecD8DRKpNMeHIvSMxtjQVIbLrqgsctM3FuORXd3sPDbMf7q0kRtX11Ad9OB1nXsYh9aalw4PcqBnjA9f0jizz8sGMwY1mUqT1uBy2CY/brOB7SxjfUV+8VnJWGZW5+lkxpXOppv+5/kfe8Wdpht1yeKpHw9UmWWEataY+54g3PT1iceDtSaxnW48eFmTGW+bndgW1ZiZqS/66PmXE7Jaj6dJbEuXmO7I4X543/fnphfANJTWU48ZyTfr16/X27dvz3UxhBBikngyzesnRogm0lzcWMo3nzzAtsODDI3HOdQXBmBphZ93r62lIuDiooZSijwOFpf6sEmym1NKqVe01mdp2hHnInXz3Emm0iilsNsUiVSajr4Q6TQcGQhT4nWyqjbIi4cGqCvx0dEXor7Ui9Nu40fPHeaWtbWcHI1ydGAch03RVlvMqtogLoeN7zzbzrHBCE0VfvZ1j5JIpTk5EuXESPRNlc/rtBNJpCbtK/e7aK4M8NKRQQCcdkVrTRCP08bh/jD1pT5GIwnG4ykuay5nNJLAblPsOznKscEIAO9cVU1ThZ/+sRgdfSGGIwlaa4p4tXOYS5eaC42P7TnJjs4htm5YjE0p0lqTSGn2do8Siia59eJ6Hnqli5HxOH97Sxt2pagp9hD0OHHYFQ67Ag29YzF6x6KU+lyU+930jkW5uLGUWDKNw6Zw2G1nxC3eZh7/azP7811HJ2bRzlfRUWuJrSnaJX91O+z8Ofx1/8Rs26d77EsmkV1588zK0bsPnv+WWRN5uq7b0VGzPNNsjp23nK1ulsRWCCFyYHg8zg//cBiP084ju07wxsmxSY9f3lxOwO1gb/coH9rYQGtNEZc3V0zZuvHMG70Mjcd537r6+Sr+giCJ7cxJ3Vw4IvEU/SGz3EdmibS01thtCrfDzmA4jlKmVbjU78LvsrOvewyloHskis9l55KmMhx2G9sODXB8OML+njH2nhgllkjTUO6jc2Acv9uOy2Hj1c5hygNuUuk0DWV+rm+rZiAU5x8eewOXw0ZlwE19qZcij4M9x0dpqw3y+/Z+4sk01UE3q2uLeeqNXlx2G0qBTSnaaoNEkyn2HB8l4HZQ4nPSNRR5U3+H1poiDveHqSxys2l5Jb2jMRYVe6gv9VJf6iMcS3JiJEKx10lHX4jmygCrFgXpGYvR3htiWVWAhjIfPpcdj9OOz2XnuY4BnjvYz1gsQWtNkE9d2UTA7WA0msBlt50xOWE8mcZuU2e0XCdSptXeKQn3hHC/mZn3rU7MlS+OvQSdL5iW3wInia0QQrzNJVNpukei7O4aoXNwnG8+ecCsrlBbzCtHhwAzBu+6ldXUlXq5ormCYq+TJ/f18LXf7ENr+MINK7h9c8s5XkmcL0lsZ07qZjHbhsfj+N2OKZO3vrEY4ViSxnIfSil6R6MEvU5cdhsa0806ndY8svsEqxYFqSn28MrRIYq9TnpGo4RiKVLpNImURgOVARdVQQ8nhiOMRBKk05r7XzjKBfUlHOwd41BfmNoSDz2jMUYiiTPK43fZCcdTZ+yfSrHXSXnAxaG+MC67Daddnfpdv8tOWcC0GgfcDnZ0DuF3O7hwcQlH+sNUBz0sKvbwzP4+4skUd163nCK3g/oyL0/s7eFIf5glFX7WNZRSWeQm6HES9DpwO+yMRhN0DUVoLPMRTaZ45egQvaMxPnJpA1VFs9/aJsRMSWIrhBB5pnNgHLtdUVfipXskwoGeEN95tp2DPSEGwvFJx75jWQVlfhe/2nmCr713Nb2jMX7zWjfFXid/eWMrG5bkzzqbj+05yZXLKghMMRPsfJPEduakbhaFLHuCrZFIguNDEVwOG4vLvIyMJ6gscnO4P8zJEZNgt1QF6OgLcXwoQjyVZiyaZDSSYGNTGRfUl2C3KV7tHOK3e06STGmqg25SWjMQijMQijEQjjM0HmdNXTHdI1GO9IdpqSqiPxSja2ic1pog4/EkOzqHT5XRYVO0VAU4OjB+Rtfwswm4HdSXerEpRXXQTVWRhz+091MddLOiJki530VaayoCbqqCbvxuBx6HnYGwaZm+dGk5NUEPfaEYY9EElQEPrYuKcNhMwj4SSdDeG+LFQwOU+VysayzhwsWlk1qhE6k0Cmbc5VtrTX8oTmWRjJ0uBJLYCiFEAekdjbKjc4jRaJIV1UWsqSsmpTUf/eE2Xjxkxq5d3lzOob4wJ0ej3NBWzStHhwi4Hdx8QS13XNOCx2knlkxx+wM7aKrwc0VLBb989Th/++42yvyuc5RgbhzuD7P5G8/yp9e08BfXV42MVwAAEixJREFUTzND4zySxHbmpG4WYn6l0prD/WGcdkVHX4hlVUUsLvORSKU52BNieDzOaDTBaCRJLJnC53JQW+LlyIBpKV6/pJRkWvPdZzsYtlqpDw+EOT4U4arllQxHzLJXY9EEypo87Hy57DZSWk/6Hbtt4jmKvU5W1BQxHk+yuNTHc+39KKW4flU1m5ZXkkynCbid9Fljn/0uBxuayij3u+gZjbL96BAvHx7k5GiUW9fVs3XjYtwOO//93/fw85c6ufvdq/jEFU2z/jcX80sSWyGEWAAGQjHueeogWy6qY11DKZF4ir//7T4e2NbJVcsrSWvNM/v7KPY6aasN4nPZeXLf5OUNLlxcwk8/vZEizzQTQsyhX+08zp0P7qS50s9Tf3H1vL/+6SSxnTmpm4UoDNmt05ncQWsYjiRMF/B4kmjCJMpNFX5eOjzIWDRBqd9FidfJ8eEIr3WN4LArir1Ogh4n1cUeLltazng8xfMd/TxrrSrgc9np6A1xUWMpLruNJ/f2MBY7cx3oqTRX+gm4HezqGsHtsOGy2xiLJWmu9NPRF6ax3EeZ30UknuLEcIRrWqu4qKGU7pEo248M0t4Xwu9ysGl5BbesreOSpjKUglgyjcdpn3KNejG/JLEVQogFLPsLyQsdA/xq53G2Hx2ivTfEp61JStr7QlzbWsUXHtpNfamXOza3UORxMjQep6HMx4YlZbgcNrR1tX0uZgP9u1/v5Qd/OAzAE3++iWXVRbP+Gm+GJLYzJ3WzEGKmYskUB3tC+Fx2QrEkJV4X9aVeBsJxth8ZZCyapKLIRVttMdVBD1prth8d4rE9J4klU2xaVslVKyr57rOH6OgLMRiO47Aryvwunn6jl+HxBE67Yk1dMSsXmbWhn9nfy3g8hcthw+OwMRpNmtcMxSkPuLimtYq01vjdDlYtCqK1WQrwypYK4sk0w5E4XYMRDveHSaTTvP/iehmzPEtyltgqpd4F/G/ADvxAa/33pz3uBu4HLgYGgNu01keUUuXAQ8AG4Mda63Ouji2VpxBCnD+tNUcHxmkom7ys0MtHBvmzB3dyfHjyTKGlPicblpTR3heiazDCpc3lXNtaxXvX1RFPptl7YpRir5Myv/nC8VauZt/2vRfoHolybGicO69dxp9dt3zGcc6EJLYzJ3WzEOLtTGtN31iMIo9z0qoDkXiKJ/f18NrxEcbjSSoCbg72hKgIuNjfM8brx0dxOmyEokniqfRZXsFw2hUrFwWJJ9OktabI42Q0kiAUS3L1ikpuXL2IkyNRnnqjh+PDEbZuaOAjlzRIy/AUcpLYKqXswAHgnUAX8DLwIa313qxjPgdcoLX+E6XUVuC9WuvblFJ+4CJgNbBaElshhJg/6bSmvS9EJJ6iPODije4xHn2tm11dw5T7XaxaFOQ/DvZzuD9Mud9FJJFiPGvmz1WLgty8dhFN5X7K/C7WNZbitNt4tXOIREqzsenMyazSac0FX36c915UR0dfiGND4/zu85vPuZZvpg6bi8pfEtuZk7pZCFHIkqk0+3vGcNhsjEUT7OgcIuB2Uux1UlvioanCz9B4ggdf7uS1rhF8Lgc2hWl59jlRSvHE6z2nkuP6Ui9Bj5O93aPYFPjdDupLffit5aESqTSRRAq3w8ZlS8tx2m2sqCmioshNKJqkOuhhWVXgnHVnPjtb3TyX005uBNq11oesQjwIbAH2Zh2zBbjb2n4I+LZSSmmtw8AflFKyboUQQswzm02xPKsbcH2pj+tWVZ9x3K5jw/zTkwcIuB1s3dBANJHi2NA4v3j5GF9/bP+p44q9Tq5preKRXSdIac2HNzZQ4nNycWMpbbXFVAbcdPSFCMWSrK4LcsnSMu74P6/y+/Z+rlpeOW05I/EUn/rxy/hcdu772PqCrsiFEEK8/TjsNtpqi0/dXz/FKgQlPhdfunHltM8xEIrR0WfGFrfVmm7Nv3z1OEcGwgyPJzgxHCGSSBGOJ3HabJT5XQyG49zzdPuUz1cRcNNQ5iXodeJz2UmkNAOhGBubyqkIuPC5HFzTWoXfbefFQ4OU+pysqS/G7bBP+Xz5ZC4T2zrgWNb9LuCS6Y7RWieVUiNAOdB/Pi+glPoM8BmAhoaGmZZXCCHEm7B2cQk//uTGM/Z/8oomhsfjnBiO0jU0zsO7TvDIrhNsbCqjOujhgW2d2BRkJsZ0WeN17TbFxY1lNJT5qAi4+O6zHRR7nfzk+SPccmEtm1dUcXIkyreePsi2w4NorenoCwPw4MvH+PAlDezrHmVFdZEkuUIIIfJCecBNeWBiKSKl4NaL68/5e1Fr+aYdnUNE4imCXidH+sM83zFA31iMwXCcrqEUNgVFHif3/f7QtLNYuxw2Lqwv4aLGEir8bgIeB0Ueh1lf2e+mPOBCAYm0psTrxP82WJJvKm/PUp0nrfX3ge+D6e6U4+IIIYSwlPhclPhcrKoNcn1bDSPjCQIeB3ab4hsfWEsilWbH0SEO9YfpGoqgteZdq2toqQoAcMfmFu5+ZC/vufc5wFy9/uD6enZ3jXC4P8ylS8sZDMf5H+9bwyO7TvC1R/eSSqf5u0f38bmrW7jzumW5DF8IIYSYUx6naWG9vLni1L4NS8r4wPrFUx4/Mp4A4MRIhBcPDTAWTbK+sZSxWJKXDw/y8tEhfvj7wyTPsYST12lnc2slI5EEWnNqpmmAmy+oZWmlH7fDzvHhCIOhOKvrgoxEEthsiuAcr7gwl4ntcSD7L1tv7ZvqmC6llAMoxkwiJYQQooAU+yYqM7tNYbfZubylgstbKqY8/hNXNLG6rpgn9/Xyscsa+dmLR/nnZzuwKfjxJzeyKauL8uYVVdz6nef561+9ztJKPx+9rHHO4xFCCCHySaYeLvY5WbkoOOmxG9pqADPfRTieJBRLMhZNMjyeYCAUYyAcRylw2BTbjwzxXHs/VUEPNgWP7u4mbM2zce8zHYBJfiNWi3JVkZu+UIyvbFnNRy+d2/p5LhPbl4FlSqkmTAK7Ffjwacc8DHwceAF4P/C0LpT1h4QQQszI+iVlp8YrffFdrVzeXEE0kZqU1ALUFHu4/9Mb+d7vOrjzuuWnrhwLIYQQ4vzZbIoij5Mij5NFxVMfc9uGycM/tdakNQyPx3lyXw99YzGGxhNUFbkp8jj5Q3sfK2uCXDrFxJGzba6X+7kJ+CZmuZ8faa2/ppT6CrBda/2wUsoD/BQzA/IgsDVrsqkjQBBwAcPA9dkzKp9OZl4UQggxm2RW5JmTulkIIcRsytWsyGitfwP85rR9f5O1HQU+MM3vLpnLsgkhhBBCCCGEKAy2XBdACCGEEEIIIYSYCUlshRBCCCGEEELkNUlshRBCCCGEEELkNUlshRBCCCGEEELkNUlshRBCCCGEEELkNUlshRBCCCGEEELkNUlshRBCCCGEEELkNUlshRBCCCGEEELkNUlshRBCCCGEEELkNUlshRBCCCGEEELkNUlshRBCCCGEEELkNUlshRBCCCGEEELkNaW1znUZZoVSqg84OktPVwH0z9Jz5YuFGDNI3AvNQox7IcYMsxN3o9a6cjYKs1BJ3TxjCzFmkLgXmoUY90KMGea4bi6YxHY2KaW2a63X57oc82khxgwSd67LMd8WYtwLMWZYuHEXsoV4ThdizCBx57oc820hxr0QY4a5j1u6IgshhBBCCCGEyGuS2AohhBBCCCGEyGuS2E7t+7kuQA4sxJhB4l5oFmLcCzFmWLhxF7KFeE4XYswgcS80CzHuhRgzzHHcMsZWCCGEEEIIIURekxZbIYQQQgghhBB5TRLbLEqpdyml9iul2pVSd+W6PHNJKXVEKfWaUmqnUmq7ta9MKfWEUuqg9bM01+WcKaXUj5RSvUqpPVn7poxTGfdY53+3Umpd7kr+1k0T891KqePW+d6plLop67EvWTHvV0rdkJtSz5xSarFS6hml1F6l1OtKqTut/QV7vs8Sc0Gfb6WURyn1klJqlxX3l639TUqpbVZ8v1BKuaz9but+u/X4klyWX7w5UjdL3Zzvn9UgdbPUzYV/vt8WdbPWWm6mO7Yd6ACWAi5gF7Aq1+Waw3iPABWn7fs6cJe1fRfwD7ku5yzEuQlYB+w5V5zATcBvAQVcCmzLdflnMea7gc9Pcewq673uBpqs/wF7rmN4i3EvAtZZ20XAASu+gj3fZ4m5oM+3dc4C1rYT2Gadw38Ftlr7vwt81tr+HPBda3sr8ItcxyC38z7XUjdL3Zz3n9VnibmgP6utWKRulrp53upmabGdsBFo11of0lrHgQeBLTku03zbAvzE2v4J8J4clmVWaK3/Axg8bfd0cW4B7tfGi0CJUmrR/JR09kwT83S2AA9qrWNa68NAO+Z/Ie9orbu11jus7TFgH1BHAZ/vs8Q8nYI439Y5C1l3ndZNA9cAD1n7Tz/XmffAQ8C1Sik1T8UVMyN1s9TNef9ZDVI3W9tSN0+tIM7326FulsR2Qh1wLOt+F2d/E+Y7DTyulHpFKfUZa1+11rrb2j4JVOemaHNuujgL/T1wh9Wt50dZXdkKMmarO8tFmKuFC+J8nxYzFPj5VkrZlVI7gV7gCcwV7mGtddI6JDu2U3Fbj48A5fNbYvEWFcx79jxJ3WwU7Gf1FAr6szqb1M1AgZ/vXNfNktguXFdqrdcBNwK3K6U2ZT+oTb+Agp8ye6HECXwHaAYuBLqB/5Xb4swdpVQA+H/An2mtR7MfK9TzPUXMBX++tdYprfWFQD3mynZrjoskxGyQupmFEycL4LM6Q+pmqZvngyS2E44Di7Pu11v7CpLW+rj1sxf4JebN15Pp7mH97M1dCefUdHEW7HtAa91jfdikgfuY6OJSUDErpZyYSuQBrfW/WbsL+nxPFfNCOd8AWuth4BngMkyXNYf1UHZsp+K2Hi8GBua5qOKtKbj37NlI3Vy4n9VTWSif1VI3S908X3WzJLYTXgaWWTN3uTCDmB/OcZnmhFLKr5QqymwD1wN7MPF+3Drs48CvclPCOTddnA8DH7Nm5LsUGMnqJpPXThuf8l7M+QYT81ZrZromYBnw0nyXbzZY4zJ+COzTWv9j1kMFe76ni7nQz7dSqlIpVWJte4F3YsYwPQO83zrs9HOdeQ+8H3jaaiEQb39SN0vdnPef1dMp9M9qkLpZ6uZ5rptPn01qId8wM7EdwPQH/6tcl2cO41yKmX1tF/B6JlZMv/angIPAk0BZrss6C7H+HNPdI4Hp1//p6eLEzOZ2r3X+XwPW57r8sxjzT62YdlsfJIuyjv8rK+b9wI25Lv8M4r4S05VpN7DTut1UyOf7LDEX9PkGLgBeteLbA/yNtX8p5stAO/B/Abe132Pdb7ceX5rrGOT2ps631M1SN+f1Z/VZYi7oz2orDqmbpW6et7pZWU8shBBCCCGEEELkJemKLIQQQgghhBAir0liK4QQQgghhBAir0liK4QQQgghhBAir0liK4QQQgghhBAir0liK4QQQgghhBAir0liK0SeUkqllFI7s253zeJzL1FK7Tn3kUIIIYTIkLpZiNxx5LoAQoi3LKK1vjDXhRBCCCHEKVI3C5Ej0mIrRIFRSh1RSn1dKfWaUuolpVSLtX+JUupppdRupdRTSqkGa3+1UuqXSqld1u1y66nsSqn7lFKvK6UeV0p5cxaUEEIIkcekbhZi7kliK0T+8p7W3em2rMdGtNZrgG8D37T2fQv4idb6AuAB4B5r/z3A77TWa4F1wOvW/mXAvVrrNmAYuHWO4xFCCCHyndTNQuSI0lrnugxCiLdAKRXSWgem2H8EuEZrfUgp5QROaq3LlVL9wCKtdcLa3621rlBK9QH1WutY1nMsAZ7QWi+z7v8l4NRa/93cRyaEEELkJ6mbhcgdabEVojDpabbfjFjWdgoZky+EEELMhNTNQswhSWyFKEy3Zf18wdp+HthqbX8E+L21/RTwWQCllF0pVTxfhRRCCCEWEKmbhZhDcpVHiPzlVUrtzLr/mNY6s6xAqVJqN+bK7oesfX8K/ItS6gtAH/BJa/+dwPeVUp/GXP39LNA956UXQgghCo/UzULkiIyxFaLAWON41mut+3NdFiGEEEJI3SzEfJCuyEIIIYQQQggh8pq02AohhBBCCCGEyGvSYiuEEEIIIYQQIq9JYiuEEEIIIYQQIq9JYiuEEEIIIYQQIq9JYiuEEEIIIYQQIq9JYiuEEEIIIYQQIq9JYiuEEEIIIYQQIq/9f3emlgwJePwDAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}],"source":["fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n","fig.subplots_adjust(wspace=0.2)\n","for i, (name, model) in enumerate(models.items()):\n","    ax[i].set_title(f\"{name} Loss\")\n","    ax[i].set_xlabel(\"Epoch\")\n","    ax[i].set_ylabel(\"Loss\")\n","    ax[i].plot(range(len(result[name][f\"Training Loss\"])), result[name][f\"Training Loss\"], label=f\"Training Loss\")\n","    ax[i].plot(range(len(result[name][f\"Validation Loss\"])),  result[name][f\"Validation Loss\"], label=f\"Validation Loss\")\n","    ax[i].legend()\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"xUehFpgh-Bwq"},"source":["#### 2.5 From the observation of the previous question, please determine an appropriate epoch and retrain the two models to avoid overfitting. Also, draw the loss plot of two models and save the last model as ***model1.pth*** and ***model2.pth***. (10%)\n","##### (Note. You should reload the models and the optimizer, otherwise, you will resume from the previous stop.)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["5cda0e2f540c4fb48fb961d3dd513767","8d766705906946f4af94d09b4571a73b","1b08ac5a28964ddb99897d8eb40740ad","e7df8f37b9a7444c9097978539a431de","5bab36319dce4122bf509af0afdee65f","7367237b7cb74c789fc6853294aa800e","fe18617adb9d454999a28365a66acdf7","813f3f1a4d5e48ab907bd6cc9d5ac64e","c02f501b2c80424781ccd844953f44a1","a0eaa0ab537240b1ab1dde6869451998","c6616af00ade4052a55f1da0e5d15b3f","dbad9d5e200f46c184bfdd6cadaa1718","83da630fe487478cac23b09b75da41dd","35d1a7466b6241cc86a5b3ba4f4a64e3","4f7d19d56f00428d912273f4549de012","d24c98cd7a854d69af62911018672381","a398b0d2ca404425a75fd0de673d04dd","ac8435f77fce4b08ae194e5e9ac28d6e","bac14af87e554a939ab4f7de8ae7025f","e923f434d63a4307a45ff920be0588ee","e94d7b0f02ce4991bef226ac45a61dd0","81f4c1bffc084a199c4772f28fef5f31"]},"id":"OT05pN9V-Bwq","executionInfo":{"status":"ok","timestamp":1648931870654,"user_tz":-480,"elapsed":3621,"user":{"displayName":"Rih-Teng Wu","userId":"04870307609359018979"}},"outputId":"d4d2351a-bee3-4ace-b886-742b4240ca0f"},"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/50 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5cda0e2f540c4fb48fb961d3dd513767"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/50 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"dbad9d5e200f46c184bfdd6cadaa1718"}},"metadata":{}}],"source":["models = {\"Model1\" : MLP(input_ch=8, first_layers=256, second_layers=64, third_layers=8, activation=nn.Tanh(), output_act = nn.Tanh()), \n","            \"Model2\" : MLP(input_ch=8, first_layers=64, second_layers=16, third_layers=8, activation=nn.Tanh(), output_act = nn.Identity())}\n","learning_rate = 0.01\n","criterion = nn.MSELoss()\n","max_epoch = 50\n","result = {}\n","\n","for name, model in models.items():\n","    optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9)\n","    pbar = tqdm(range(max_epoch))\n","    result[name] = {\"Training Loss\":[], \"Validation Loss\":[]}\n","    for i in pbar:\n","        training_loss = 0\n","        val_loss = 0\n","        for inputs, label in train_dl:\n","            optimizer.zero_grad()\n","            pred = model(inputs)\n","            loss = criterion(pred, label)\n","            loss.backward()\n","            optimizer.step()\n","            training_loss+=loss.detach().numpy()\n","\n","        for inputs, label in val_dl:\n","            with torch.no_grad():\n","                pred = model(inputs)\n","                loss = criterion(pred, label)\n","                val_loss+=loss.numpy()\n","        result[name][\"Training Loss\"].append(training_loss/len(train_dl))\n","        result[name][\"Validation Loss\"].append(val_loss/len(val_dl)) \n","        \n","    torch.save(model.state_dict(), f\"{name}.pth\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":404},"id":"nI9t0i8P-Bwq","executionInfo":{"status":"ok","timestamp":1648931895662,"user_tz":-480,"elapsed":936,"user":{"displayName":"Rih-Teng Wu","userId":"04870307609359018979"}},"outputId":"8d7434fa-c532-4bbd-e439-d4f879093dd1"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1152x432 with 2 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n","fig.subplots_adjust(wspace=0.2)\n","for i, (name, model) in enumerate(models.items()):\n","    ax[i].set_title(f\"{name} Loss\")\n","    ax[i].set_xlabel(\"Epoch\")\n","    ax[i].set_ylabel(\"Loss\")\n","    ax[i].plot(range(len(result[name][f\"Training Loss\"])), result[name][f\"Training Loss\"], label=f\"Training Loss\")\n","    ax[i].plot(range(len(result[name][f\"Validation Loss\"])),  result[name][f\"Validation Loss\"], label=f\"Validation Loss\")\n","    ax[i].legend()\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"Gj7T-StIJwdw","tags":[]},"source":["#### 2.6 Please load the checkpoints saved from previous question and calculate the mean squared error on test dataset for two models respectively. Also, make a dataframe with target and prediction like below and save it as ***regression.csv*** (5%)\n","|Target|Model1|Model2|\n","|:---:|:---:|:---:|\n","|24.05|26.35|27.04|\n","|21.67|32.78|21.95|"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_edN99DGJwdw","outputId":"794e0746-6cb1-475a-a12d-93e634365c22","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1648932554676,"user_tz":-480,"elapsed":357,"user":{"displayName":"Rih-Teng Wu","userId":"04870307609359018979"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Mean Squared Error (Model1):  102.49895734254744\n","Mean Squared Error (Model2):  115.87375572334807\n"]}],"source":["from sklearn.metrics import mean_squared_error\n","models = {\"Model1\" : MLP(input_ch=8, first_layers=256, second_layers=64, third_layers=8, activation=nn.Tanh(), output_act = nn.Tanh()), \n","            \"Model2\" : MLP(input_ch=8, first_layers=64, second_layers=16, third_layers=8, activation=nn.Tanh(), output_act = nn.Identity())}\n","test_ds = ConcreteDataset(\"/content/gdrive/My Drive/Colab Notebooks/TKU_talk/data/test.csv\")\n","test_dl = DataLoader(test_ds, batch_size=len(test_ds), shuffle=False) # batch_size=len(test_ds) feeds all testing samples in one batch\n","output = {}\n","for name, model in models.items():\n","    for X, y in test_dl:\n","        model.load_state_dict(torch.load(f\"{name}.pth\"))\n","        model.eval()\n","        with torch.no_grad():\n","            pred = model(X)\n","            output[\"Target\"] = test_ds.y_scaler.inverse_transform(y).squeeze()\n","            output[name] = test_ds.y_scaler.inverse_transform(pred).squeeze()\n","        print(f\"Mean Squared Error ({name}): \", mean_squared_error(output[\"Target\"], output[name]))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vRTQYZhyJwdw"},"outputs":[],"source":["out = 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