{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "eBpjBBZc6IvA" }, "source": [ "# Fatima Fellowship Quick Coding Challenge (Pick 1)\n", "\n", "Thank you for applying to the Fatima Fellowship. To help us select the Fellows and assess your ability to do machine learning research, we are asking that you complete a short coding challenge. Please pick **1 of these 5** coding challenges, whichever is most aligned with your interests. \n", "\n", "**Due date: 1 week**\n", "\n", "**How to submit**: Please make a copy of this colab notebook, add your code and results, and submit your colab notebook to the submission link below. If you have never used a colab notebook, [check out this video](https://www.youtube.com/watch?v=i-HnvsehuSw).\n", "\n", "**Submission link**: https://airtable.com/shrXy3QKSsO2yALd3" ] }, { "cell_type": "markdown", "metadata": { "id": "braBzmRpMe7_" }, "source": [ "# 1. Deep Learning for Vision (chosen)" ] }, { "cell_type": "markdown", "metadata": { "id": "1IWw-NZf5WfF" }, "source": [ "**Upside down detector**: Train a model to detect if images are upside down\n", "\n", "* Pick a dataset of natural images (we suggest looking at datasets on the [Hugging Face Hub](https://huggingface.co/datasets?task_categories=task_categories:image-classification&sort=downloads))\n", "* Synthetically turn some of images upside down. Create a training and test set.\n", "* Build a neural network (using Tensorflow, PyTorch, or any framework you like)\n", "* Train it to classify image orientation until a reasonable accuracy is reached\n", "* [Upload the the model to the Hugging Face Hub](https://huggingface.co/docs/hub/adding-a-model), and add a link to your model below.\n", "* Look at some of the images that were classified incorrectly. Please explain what you might do to improve your model's performance on these images in the future (you do not need to impelement these suggestions)\n", "\n", "**Submission instructions**: Please write your code below and include some examples of images that were classified" ] }, { "cell_type": "markdown", "metadata": { "id": "qUXnitbN_nbK" }, "source": [ "# Importing necessary libraries" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "K2GJaYBpw91T" }, "outputs": [], "source": [ "import random\n", "import numpy as np\n", "\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "q0IJIAn51fm0" }, "outputs": [], "source": [ "import tensorflow as tf\n", "from tensorflow.keras.datasets import cifar10\n", "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras import regularizers, optimizers\n", "from tensorflow.keras.layers import Conv2D, MaxPooling2D, BatchNormalization\n", "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, InputLayer\n", "from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint\n", "\n", "from sklearn.model_selection import train_test_split" ] }, { "cell_type": "markdown", "metadata": { "id": "A0umXsXY_vNM" }, "source": [ "# Some helper functions" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "id": "luMDKL9q1tAX" }, "outputs": [], "source": [ "def rot_dataset(dataset, seed=0):\n", " \"\"\"\n", " creats a new dataset where some randomly chosen images are filpped upside down\n", " \"\"\"\n", " (X_train, _), (X_test, _) = dataset\n", " train = np.concatenate((X_train, X_test)) \n", " random.seed(seed)\n", " size = train.shape[0]\n", " rsample = (random.sample(range(size), size//2))\n", " label = np.zeros(size)\n", " \n", " for rnum in rsample:\n", " train[rnum] = np.rot90(np.rot90(train[rnum]))\n", " label[rnum] = 1\n", " \n", " return train, label\n", "\n", "\n", "def plothistory(history):\n", " \"\"\"\n", " Plots the losses and accuracy as graph\n", " \"\"\"\n", " fig, axs = plt.subplots(1,2,figsize=(15,5)) \n", " \n", " axs[0].plot(history.history['accuracy']) \n", " axs[0].plot(history.history['val_accuracy']) \n", " axs[0].set_title('Model Accuracy')\n", " axs[0].set_ylabel('Accuracy') \n", " axs[0].set_xlabel('Epoch')\n", " axs[0].legend(['train', 'validate'], loc='upper left')\n", " \n", " axs[1].plot(history.history['loss']) \n", " axs[1].plot(history.history['val_loss']) \n", " axs[1].set_title('Model Loss')\n", " axs[1].set_ylabel('Loss') \n", " axs[1].set_xlabel('Epoch')\n", " axs[1].legend(['train', 'validate'], loc='upper left')\n", " plt.show()\n", "\n", "\n", "def sample_incorrect(model, test, labels, num_points=5):\n", " \"\"\"\n", " Takes the model, test, labels and randomly samples some examples from incorrectly classified data\n", " \"\"\"\n", " \n", " incorrects = np.nonzero((model.predict(test) > 0.5).astype(\"int32\").reshape((-1,)) != labels)[0]\n", " rsample = random.sample(list(incorrects), num_points)\n", " output = []\n", " for choice in rsample:\n", " output.append(test[choice])\n", " return output\n", "\n", "def sample_correct(model, test, labels, num_points=5):\n", " \"\"\"\n", " Takes the model, test, labels and randomly samples some examples from incorrectly classified data\n", " \"\"\"\n", " \n", " incorrects = np.nonzero((model.predict(test) > 0.5).astype(\"int32\").reshape((-1,)) == labels)[0]\n", " rsample = random.sample(list(incorrects), num_points)\n", " output = []\n", " for choice in rsample:\n", " output.append(test[choice])\n", " return output" ] }, { "cell_type": "markdown", "metadata": { "id": "1_pCVr1F_295" }, "source": [ "# Loading the data" ] }, { "cell_type": "markdown", "metadata": { "id": "ybEqpt9mACn-" }, "source": [ "Using the function to create new dataset with some images flipped" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "iJftRXec16js" }, "outputs": [], "source": [ "X, y = rot_dataset(cifar10.load_data(), seed=1)" ] }, { "cell_type": "markdown", "metadata": { "id": "3ogDvmMJAMMB" }, "source": [ "Splitting the data into training and validation set" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "o-7vinMD19-q" }, "outputs": [], "source": [ "x_train, x_val, y_train, y_val = train_test_split(X, y, test_size=0.20, random_state=0)\n", "\n", "x_train = x_train.astype('float32')\n", "x_val = x_val.astype('float32')\n", "x_train /= 255\n", "x_val /= 255" ] }, { "cell_type": "markdown", "metadata": { "id": "gqPDi2sWAcMP" }, "source": [ "# Model creation " ] }, { "cell_type": "markdown", "metadata": { "id": "4EbBZamWAkOi" }, "source": [ "Defining a convblock for creating the network" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "id": "B57W4Lg12Cpq" }, "outputs": [], "source": [ "class ConvBlock(tf.keras.Model):\n", " def __init__(self, dr, num_filters=16):\n", " super().__init__()\n", " self.conv1 = Conv2D(num_filters, (3, 3), padding='same', kernel_initializer='he_uniform',\n", " kernel_regularizer=regularizers.l2(1e-4))\n", " self.bn1 = BatchNormalization()\n", " self.conv2 = Conv2D(num_filters, (3, 3), padding='same', kernel_initializer='he_uniform',\n", " kernel_regularizer=regularizers.l2(1e-4),)\n", " self.bn2 = BatchNormalization()\n", " self.pool = MaxPooling2D()\n", " self.dr = Dropout(dr)\n", "\n", " def call(self, inputs):\n", " x = tf.keras.activations.relu(self.bn1(self.conv1(inputs)))\n", " x = tf.keras.activations.relu(self.bn2(self.conv2(x)))\n", " return self.dr(self.pool(x))" ] }, { "cell_type": "markdown", "metadata": { "id": "9xVRIgxOAsKB" }, "source": [ "Defining the Sequential model" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "EtXsEoYu2GCb", "outputId": "582ef124-780b-4239-f5ca-502e2502a167" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " conv2d (Conv2D) (None, 32, 32, 16) 448 \n", " \n", " batch_normalization (BatchN (None, 32, 32, 16) 64 \n", " ormalization) \n", " \n", " activation (Activation) (None, 32, 32, 16) 0 \n", " \n", " conv_block (ConvBlock) (None, 16, 16, 16) 4768 \n", " \n", " conv_block_1 (ConvBlock) (None, 8, 8, 32) 14144 \n", " \n", " conv_block_2 (ConvBlock) (None, 4, 4, 64) 55936 \n", " \n", " conv_block_3 (ConvBlock) (None, 2, 2, 128) 222464 \n", " \n", " flatten (Flatten) (None, 512) 0 \n", " \n", " dense (Dense) (None, 64) 32832 \n", " \n", " batch_normalization_9 (Batc (None, 64) 256 \n", " hNormalization) \n", " \n", " dense_1 (Dense) (None, 1) 65 \n", " \n", "=================================================================\n", "Total params: 330,977\n", "Trainable params: 329,857\n", "Non-trainable params: 1,120\n", "_________________________________________________________________\n" ] } ], "source": [ "model = Sequential([\n", " InputLayer(input_shape=(32, 32, 3)),\n", " Conv2D(16, (3, 3), padding='same'),\n", " BatchNormalization(),\n", " Activation('relu'),\n", " ConvBlock(num_filters=16, dr=0.2),\n", " ConvBlock(num_filters=32, dr=0.3),\n", " ConvBlock(num_filters=64, dr=0.4),\n", " ConvBlock(num_filters=128, dr=0.5),\n", " Flatten(),\n", " Dense(64),\n", " BatchNormalization(),\n", " Dense(1)])\n", "model.summary()" ] }, { "cell_type": "markdown", "metadata": { "id": "VvEh0io6A1E6" }, "source": [ "Compiling and then fitting the model" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "7pQG-6zl2Iv5", "outputId": "947733a9-bcab-42a5-8bbd-21f0b9e85cbd" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/50\n", "800/800 [==============================] - 44s 47ms/step - loss: 0.6756 - accuracy: 0.6567 - val_loss: 0.6106 - val_accuracy: 0.6873 - lr: 0.0010\n", "Epoch 2/50\n", "800/800 [==============================] - 29s 37ms/step - loss: 0.5887 - accuracy: 0.7178 - val_loss: 0.5436 - val_accuracy: 0.7452 - lr: 0.0010\n", "Epoch 3/50\n", "800/800 [==============================] - 28s 35ms/step - loss: 0.5422 - accuracy: 0.7430 - val_loss: 0.4992 - val_accuracy: 0.7737 - lr: 0.0010\n", "Epoch 4/50\n", "800/800 [==============================] - 32s 40ms/step - loss: 0.5079 - accuracy: 0.7606 - val_loss: 0.4797 - val_accuracy: 0.7697 - lr: 0.0010\n", "Epoch 5/50\n", "800/800 [==============================] - 28s 35ms/step - loss: 0.4805 - accuracy: 0.7757 - val_loss: 0.4518 - val_accuracy: 0.7874 - lr: 0.0010\n", "Epoch 6/50\n", "800/800 [==============================] - 32s 40ms/step - loss: 0.4583 - accuracy: 0.7904 - val_loss: 0.4932 - val_accuracy: 0.7372 - lr: 0.0010\n", "Epoch 7/50\n", "800/800 [==============================] - 28s 35ms/step - loss: 0.4432 - accuracy: 0.7996 - val_loss: 0.4487 - val_accuracy: 0.8115 - lr: 0.0010\n", "Epoch 8/50\n", "800/800 [==============================] - 33s 42ms/step - loss: 0.4307 - accuracy: 0.8077 - val_loss: 0.4124 - val_accuracy: 0.8268 - lr: 0.0010\n", "Epoch 9/50\n", "800/800 [==============================] - 28s 35ms/step - loss: 0.4177 - accuracy: 0.8158 - val_loss: 0.4239 - val_accuracy: 0.8251 - lr: 0.0010\n", "Epoch 10/50\n", "800/800 [==============================] - 34s 43ms/step - loss: 0.4092 - accuracy: 0.8209 - val_loss: 0.4489 - val_accuracy: 0.8207 - lr: 0.0010\n", "Epoch 11/50\n", "800/800 [==============================] - 28s 35ms/step - loss: 0.4015 - accuracy: 0.8283 - val_loss: 0.3897 - val_accuracy: 0.8263 - lr: 0.0010\n", "Epoch 12/50\n", "800/800 [==============================] - 36s 45ms/step - loss: 0.3933 - accuracy: 0.8335 - val_loss: 0.3868 - val_accuracy: 0.8428 - lr: 0.0010\n", "Epoch 13/50\n", "800/800 [==============================] - 28s 35ms/step - loss: 0.3886 - accuracy: 0.8370 - val_loss: 0.3927 - val_accuracy: 0.8409 - lr: 0.0010\n", "Epoch 14/50\n", "800/800 [==============================] - 28s 35ms/step - loss: 0.3817 - accuracy: 0.8403 - val_loss: 0.4419 - val_accuracy: 0.8339 - lr: 0.0010\n", "Epoch 15/50\n", "800/800 [==============================] - 30s 38ms/step - loss: 0.3748 - accuracy: 0.8469 - val_loss: 0.4171 - val_accuracy: 0.8393 - lr: 0.0010\n", "Epoch 16/50\n", "800/800 [==============================] - 28s 35ms/step - loss: 0.3682 - accuracy: 0.8480 - val_loss: 0.3685 - val_accuracy: 0.8595 - lr: 0.0010\n", "Epoch 17/50\n", "800/800 [==============================] - 32s 40ms/step - loss: 0.3595 - accuracy: 0.8536 - val_loss: 0.3684 - val_accuracy: 0.8395 - lr: 0.0010\n", "Epoch 18/50\n", "800/800 [==============================] - 28s 35ms/step - loss: 0.3590 - accuracy: 0.8532 - val_loss: 0.3855 - val_accuracy: 0.8344 - lr: 0.0010\n", "Epoch 19/50\n", "800/800 [==============================] - 33s 41ms/step - loss: 0.3534 - accuracy: 0.8578 - val_loss: 0.3510 - val_accuracy: 0.8663 - lr: 0.0010\n", "Epoch 20/50\n", "800/800 [==============================] - 28s 35ms/step - loss: 0.3472 - accuracy: 0.8613 - val_loss: 0.3609 - val_accuracy: 0.8493 - lr: 0.0010\n", "Epoch 21/50\n", "800/800 [==============================] - 34s 42ms/step - loss: 0.3440 - accuracy: 0.8654 - val_loss: 0.3661 - val_accuracy: 0.8541 - lr: 0.0010\n", "Epoch 22/50\n", "800/800 [==============================] - 28s 35ms/step - loss: 0.3410 - accuracy: 0.8666 - val_loss: 0.3488 - val_accuracy: 0.8557 - lr: 0.0010\n", "Epoch 23/50\n", "800/800 [==============================] - 36s 45ms/step - loss: 0.3368 - accuracy: 0.8703 - val_loss: 0.3474 - val_accuracy: 0.8565 - lr: 0.0010\n", "Epoch 24/50\n", "800/800 [==============================] - 28s 35ms/step - loss: 0.3361 - accuracy: 0.8695 - val_loss: 0.3395 - val_accuracy: 0.8617 - lr: 0.0010\n", "Epoch 25/50\n", "800/800 [==============================] - 35s 44ms/step - loss: 0.3327 - accuracy: 0.8712 - val_loss: 0.3619 - val_accuracy: 0.8720 - lr: 0.0010\n", "Epoch 26/50\n", "800/800 [==============================] - 28s 35ms/step - loss: 0.3302 - accuracy: 0.8727 - val_loss: 0.3440 - val_accuracy: 0.8748 - lr: 0.0010\n", "Epoch 27/50\n", "800/800 [==============================] - 31s 39ms/step - loss: 0.3300 - accuracy: 0.8742 - val_loss: 0.3609 - val_accuracy: 0.8574 - lr: 0.0010\n", "Epoch 28/50\n", "800/800 [==============================] - 28s 35ms/step - loss: 0.3287 - accuracy: 0.8738 - val_loss: 0.3435 - val_accuracy: 0.8679 - lr: 0.0010\n", "Epoch 29/50\n", "799/800 [============================>.] - ETA: 0s - loss: 0.3240 - accuracy: 0.8767\n", "Epoch 29: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n", "800/800 [==============================] - 33s 42ms/step - loss: 0.3241 - accuracy: 0.8767 - val_loss: 0.3439 - val_accuracy: 0.8671 - lr: 0.0010\n", "Epoch 30/50\n", "800/800 [==============================] - 28s 36ms/step - loss: 0.3007 - accuracy: 0.8888 - val_loss: 0.3115 - val_accuracy: 0.8804 - lr: 5.0000e-04\n", "Epoch 31/50\n", "800/800 [==============================] - 34s 43ms/step - loss: 0.2889 - accuracy: 0.8942 - val_loss: 0.3182 - val_accuracy: 0.8793 - lr: 5.0000e-04\n", "Epoch 32/50\n", "800/800 [==============================] - 28s 35ms/step - loss: 0.2862 - accuracy: 0.8953 - val_loss: 0.3122 - val_accuracy: 0.8783 - lr: 5.0000e-04\n", "Epoch 33/50\n", "800/800 [==============================] - 35s 43ms/step - loss: 0.2803 - accuracy: 0.8983 - val_loss: 0.3178 - val_accuracy: 0.8852 - lr: 5.0000e-04\n", "Epoch 34/50\n", "800/800 [==============================] - 28s 35ms/step - loss: 0.2791 - accuracy: 0.8963 - val_loss: 0.3074 - val_accuracy: 0.8800 - lr: 5.0000e-04\n", "Epoch 35/50\n", "800/800 [==============================] - 33s 41ms/step - loss: 0.2782 - accuracy: 0.9002 - val_loss: 0.3068 - val_accuracy: 0.8873 - lr: 5.0000e-04\n", "Epoch 36/50\n", "800/800 [==============================] - 29s 36ms/step - loss: 0.2753 - accuracy: 0.9006 - val_loss: 0.3098 - val_accuracy: 0.8840 - lr: 5.0000e-04\n", "Epoch 37/50\n", "800/800 [==============================] - 33s 42ms/step - loss: 0.2722 - accuracy: 0.9016 - val_loss: 0.3084 - val_accuracy: 0.8875 - lr: 5.0000e-04\n", "Epoch 38/50\n", "800/800 [==============================] - 28s 35ms/step - loss: 0.2695 - accuracy: 0.9026 - val_loss: 0.3021 - val_accuracy: 0.8868 - lr: 5.0000e-04\n", "Epoch 39/50\n", "800/800 [==============================] - 28s 35ms/step - loss: 0.2679 - accuracy: 0.9036 - val_loss: 0.3182 - val_accuracy: 0.8848 - lr: 5.0000e-04\n", "Epoch 40/50\n", "800/800 [==============================] - 30s 37ms/step - loss: 0.2639 - accuracy: 0.9057 - val_loss: 0.3150 - val_accuracy: 0.8797 - lr: 5.0000e-04\n", "Epoch 41/50\n", "800/800 [==============================] - 28s 35ms/step - loss: 0.2647 - accuracy: 0.9051 - val_loss: 0.2976 - val_accuracy: 0.8877 - lr: 5.0000e-04\n", "Epoch 42/50\n", "800/800 [==============================] - 30s 38ms/step - loss: 0.2645 - accuracy: 0.9044 - val_loss: 0.3136 - val_accuracy: 0.8783 - lr: 5.0000e-04\n", "Epoch 43/50\n", "800/800 [==============================] - 28s 35ms/step - loss: 0.2588 - accuracy: 0.9074 - val_loss: 0.3064 - val_accuracy: 0.8876 - lr: 5.0000e-04\n", "Epoch 44/50\n", "800/800 [==============================] - 33s 42ms/step - loss: 0.2614 - accuracy: 0.9073 - val_loss: 0.3165 - val_accuracy: 0.8722 - lr: 5.0000e-04\n", "Epoch 45/50\n", "800/800 [==============================] - 28s 35ms/step - loss: 0.2559 - accuracy: 0.9079 - val_loss: 0.3007 - val_accuracy: 0.8910 - lr: 5.0000e-04\n", "Epoch 46/50\n", "799/800 [============================>.] - ETA: 0s - loss: 0.2583 - accuracy: 0.9084\n", "Epoch 46: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n", "800/800 [==============================] - 33s 42ms/step - loss: 0.2583 - accuracy: 0.9085 - val_loss: 0.3164 - val_accuracy: 0.8842 - lr: 5.0000e-04\n", "Epoch 47/50\n", "800/800 [==============================] - 28s 35ms/step - loss: 0.2410 - accuracy: 0.9167 - val_loss: 0.2984 - val_accuracy: 0.8966 - lr: 2.5000e-04\n", "Epoch 48/50\n", "800/800 [==============================] - 35s 44ms/step - loss: 0.2369 - accuracy: 0.9166 - val_loss: 0.2953 - val_accuracy: 0.8955 - lr: 2.5000e-04\n", "Epoch 49/50\n", "800/800 [==============================] - 29s 36ms/step - loss: 0.2371 - accuracy: 0.9186 - val_loss: 0.2931 - val_accuracy: 0.8954 - lr: 2.5000e-04\n", "Epoch 50/50\n", "800/800 [==============================] - 29s 36ms/step - loss: 0.2316 - accuracy: 0.9181 - val_loss: 0.2984 - val_accuracy: 0.8930 - lr: 2.5000e-04\n" ] } ], "source": [ "rlr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, min_lr=1e-7, verbose = 1, patience=5)\n", "\n", "opt = optimizers.Nadam(learning_rate=0.001, decay=1e-6)\n", "\n", "model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n", " optimizer =opt,\n", " metrics = ['accuracy'])\n", "\n", "history = model.fit(x_train, y_train, epochs=50, batch_size=60, validation_data=(x_val, y_val), callbacks = [rlr])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "mZoz7FZA2mYq", "outputId": "62a16153-e975-4dce-cc6f-9c7d8fc629f5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "200/200 [==============================] - 2s 8ms/step - loss: 0.2984 - accuracy: 0.8930\n" ] }, { "data": { "text/plain": [ "[0.2983850836753845, 0.8930000066757202]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# final model validation accuracy\n", "\n", "model.evaluate(x_val, y_val, batch_size=60)" ] }, { "cell_type": "markdown", "metadata": { "id": "CWTpV0s2BY1Z" }, "source": [ "Plotting the losses and accuracy" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 350 }, "id": "ygIsP6SI2Vzg", "outputId": "01cd5d77-5130-4591-8c81-9e4226f8584a" }, "outputs": [ { "data": { "image/png": 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7fIc+bK19uypjrSr33nsvbdq04ZZbbgHggQcewOv1MmPGDPbs2UNubi4PP/wwZ5999mHnbd68mTPPPJPly5eTmZnJ1VdfzZIlS+jatSuZmZkHj7vpppuYP38+mZmZTJgwgQcffJBnn32W7du3M2rUKKKiopgxYwbfffcdf//738nOzqZDhw68+eabhIaGVuvPQqSusNaSmJ7N5qT9bEnOYGvKfrYk72dLyn62pewnJSPn4LHtIoMZ3z+GEzo3Y2j7CMKCytYrd2DxlaQiiWFYkJez+rQiPDigqr6eSIUFet180tx8S4D32BI/tY8iItWryhI/Y4wf8DwwGogH5htjvrDWrix02BPAO9bat40xJwH/Ai43xkQAfwcGAhZY6Dt3T1XFW1UuvPBC7rjjjoMN2+TJk/n222+5/fbbady4MUlJSQwdOpSzzjqrxLunL774IsHBwaxatYqlS5fSv3//g/v++c9/EhERQX5+PieffDJLly7l9ttv58knn2TGjBlERUWRlJTEww8/zPTp0wkJCeGxxx7jySef5P7776+Wn4FIbVRQYNmVlsXWZJfUbUrOYEtyxsFkLyPnUI+cx0Cr8Ea0iwzmtB7RtIsMpl1EMN1bNaZdZEiFPj84wEtwhJc2Eep1kLoj8GBJh/yD5R0qSu2jiEj1qsoev8HAemvtRgBjzIfA2UDhxK87cJfv9QzgM9/r04DvrbUpvnO/B8YAH1Q4mmn3ws5lFT69WNG9YOyjRz2kX79+7N69m+3bt5OYmEjTpk2Jjo7mzjvvZNasWXg8HhISEti1axfR0dHFXmPWrFncfvvtAPTu3ZvevXsf3Dd58mReeeUV8vLy2LFjBytXrjxsP8C8efNYuXIlw4cPByAnJ4dhw4YdyzcXqXXyCyzp2Xlk5uSTkeOe9/te78/OZ0dqJttS9rveu5T9xKdkkpN/aK6S12NoGxFMu8hgBsdFEBcV4hK8yBBahzc65j9yRWq1MraRwdbSPiffJYCllf8opY1U+ygiUr2qMvFrDWwr9D4eGFLkmCXAebjhoOcCYcaYyBLObV11oVat888/nylTprBz504uvPBCJk2aRGJiIgsXLsTf35/Y2FiysrLKfd1NmzbxxBNPMH/+fJo2bcpVV11V7HWstYwePZoPPqh43ixSW2Vk5/HWL5t5+acN7MvKO+qxYYFe2kYG06VFGKO7taBtZDBtmgYTGxlCq/AgvKpjJ3JUxrhHgbWVcj21jyIi1aemF3e5B3jOGHMVMAtIAMpcIMgYMxGYCNC2bdujH1xKz1xVuvDCC7n++utJSkrip59+YvLkyTRv3hx/f39mzJjBli1bjnr+CSecwPvvv89JJ53E8uXLWbp0KQD79u0jJCSEJk2asGvXLqZNm8bIkSMBCAsLIy0tjaioKIYOHcott9zC+vXr6dixIxkZGSQkJNC5c+eq/uoi5bYjNZOo0MBSi4ln5+Xz/q9beX7GepLSczi5a3OGdYh0QygD/HwPL8GB7nWLsCDCg/21UIpIccrYRhogYVcaAX4eYqMqNsy5MLWPIiLVpyoTvwSgTaH3Mb5tB1lrt+N6/DDGhALjrbV7jTEJwMgi584s+gHW2leAVwAGDhxYObcfq0CPHj1IS0ujdevWtGzZkksvvZRx48bRq1cvBg4cSNeuXY96/k033cTVV19Nt27d6NatGwMGDACgT58+9OvXj65du9KmTZuDQ1UAJk6cyJgxY2jVqhUzZszgrbfe4uKLLyY7OxuAhx9+WA2b1BppWbl8vng7H83fxrKEVIID/BgcF8HwDlEc1zGSbtGND9aey8sv4JNFCTzzwzoS9mYytH0EL1/elQHtmtbwtxBpGAK9HrJyK6ekg9pHEZHqY2wlDdc44sLGeIG1wMm4hG8+cIm1dkWhY6KAFGttgTHmn0C+tfZ+3+IuC4EDs7QXAQMOzPkrzsCBA+2CBQsO27Zq1Sq6detWmV+r3tPPTKqLtZZFW/fwwW/b+HrpDjJz8+kaHcbZfVuzfW8mP29IYmNiBgARIQEMax9Jj9aNmbIgno1JGfSJacIfT+vK8I6R6sWrChlJ8MuzENoCIjpAZAcIbwfe2rHaqDFmobW25GJtcpjKbCN3pGaSlJ5Dz1aNG9T/e2ofRaQuOFr7WGU9ftbaPGPMrcC3uHIOb1hrVxhjHgIWWGu/wPXq/csYY3FDPW/xnZtijPkHLlkEeOhoSZ+I1H7WWnanZbMhMZ1l8alMWRjPut3phAT4cU6/Vlw4qC19Ypoc9ofkjtRMflmfzM8bkvhlfTJfL9tB5xahvHz5AE7t3qJB/dFZ7ab9GZYXqZVm/CC8jUsEm8aCNwg8HjAet8/j5569gRB3ArTq7/ZLvRLo9WCtJSe/gECvX02HIyIiZVSlc/ystVOBqUW23V/o9RSg2Cqs1to3cDX+RKSOycsvYPqq3azZmcbGpHQ2JmawKSmD9OxDi6/0bRPOY+N7cWbvVoQEFv+rqGWTRowfEMP4ATEHE8eo0ED8PEr4qtTGn1zSd+KfYfANkLIBkjcc/rx9EeTnQkE+2HzfcwGuAo9PWCvoNg66nwVth7nEUOq8A8leTp4SPxGRuqSmF3cRkXomfs9+7vhwMQu2uLKbrcMb0b5ZCBMGxNC+WQjto0Lp2DyU6CZB5bquMYYWjct3jlRAXg5MvccN6zz+TvBvBCGR0GZw2c63FrL2wtpvYdWXsOht+O1lCI6CrmdAt7Ncb2AtGTIq5XegtEl2XgFhNRyLiIiUXb1P/Ky1Gg5WRlU131Majq+X7uDeT5ZiLTx5QR/G9mxJowD1CNQpc5+DpLVwyWSX9JWXMdCoKfS5yD2y02H99y4JXP6xSwTPeBIGXVv5sUu5VaSN9HoMfsaQk1c5C7zUBWofRaQ+qNeJX1BQEMnJyURGavGH0lhrSU5OJihIPSpSfvtz8njwi5V8tGAbfdqE89+L+tE2MrimwxJwwzF3r4KWvUs/du82mPVv6HomdD6tcj4/MBR6nOseuVmwcQa01postUFF20hjDAFeD9kNJPFT+ygi9UW9TvxiYmKIj48nMTGxpkOpE4KCgoiJianpMKSOWZ6Qyu0f/s6mpAxuHtmBO0d3LrUGn1Sjnx6HWY/DiHvgpPtcj1xJvrnXDdUc86+qicU/CLqMrZprS7kdSxuZkpFDTl4BWYkNIxlS+ygi9UG9Tvz8/f2Ji4ur6TBE6iVrLW/+vJlHp62maYg/k64dwnEdo2o6LCksPxcWvQOBjWH2E5C+C858GvyK+dW/9jtY/RWcfD+Et63+WKXaHUsb+eR3a3huxnpW/2PswTl/IiJSu9XrxE9Eqs6787bw0FcrOaVbcx6f0IeIkDq6WMeBuTvVNRw8Lwfyc9wQyPLKzys+aSvJ2m8hfSdc9D5s/90N48xIgglvQEChobi5mTDtjxDVGYbdVv64pMGJjQqhwMLWlP10bF6Bf8siIlLtlPiJSLnt2pfF49+sYUSnKF69YmDNz6HdnwJf3w0RcTDoOmjcqvRzslJhwRsw7yX3OqQZhDaDkOa+Z9/rFt2hzdDKWYUycy+8faaL9/oZENaibOdZC1/cCptmw81zISCkbOctfAvCWkKn09yKmqEtYOof4d1z4OIPITjCHTfnadizGa74QqttSpnERbl/g5uTMpT4iYjUEUr8RKTcHvpyJTn5Bfzj7J41n/RlJME750DiKldL7udnoPs5MPQmiClmEZG0XfDrizD/dcjeB+1HQYsekJHoHqnxrkZdRpKrTwfgH+JKEHQ8GTqcBJEdyh9nTga8fwHsXg0eL/zvyrInWr/8F35/z71e+BYMu6X0c/Zug/XT4YQ/HuolHHy9S2g/uR7eGAOXfwJ52TDnKeg5HtqfWP7vJQ3SwcQvOaOGIxERkbJS4ici5TJjzW6+XraDu0d3JjaqjD1PZZW0Hqb9CXqd70oBlJZUpu2Ed86GPVtc+YGIOPjtVZckLZ8CMYNgyI3Q/WzYu9UlUIvfh4Jct234HdCqb/HXLiiA/ckQ/xus/8ElUWunuX1NY6HjKdDnEogZUPr3ysuGjy6D+Plw/ltu7t3H17rFVM588ujnrp8O0//u6t9l7oGfn4WB17qFUo7m93fdc//LD9/e4xwIjoQPL4HXRkN4G/ALgFP/Wfr3EPEJDw4gPNifjUlK/ERE6golfiJSZpk5+dz/+XI6NAth4ontK/fiCYtg0gSX3Gz4AdZ8DWc+44qHFyc1Ad4e55K/S/8HcSPc9jH/glF/dQnery+5BGvanyEzxfW09b0Ujrut9F47j8cN+ex6hntYCykbXRK44QdY/IHrNTzuNhj1t5ITsYJ818O24Uc46zmXcALsWAK/POsSz/5XFH9u8gaYcg006wbnvOh6It8e55K6wdeXHHt+Hix61/VQFrdQS9wIuHoqvDcBtv0Kp/0LGrc8+s9DpIi4qBA2K/ETEakzlPiJSJn998d1bEvJ5MOJQwn0VmJh9g0zXI9YcARc8y2smQo/PgwvDIWz/gtdxhx+/J4tLgHK3AOXfwpthxy+PzAMhtwAg653xcMXT4KmcW74Z1h0xWI0xiWLkR1gyETIToPv7nPJ27rv4NyXoFW/w8+xFr78A6z8HE575PDet1MegJ3L3NzEZt2gzaDDz81Oc71yxgMXTXKLwcSOgDZD3HDW/leWPEx0/feQth1Of7zk7xPdC677HtZ8AwOvqchPRBq4uMgQ5m5MrukwRESkjLQGs4iUydpdabwyayPj+8cwtH0JvXAVsfwTmHQ+hLeDa76DqE4w/A9u8ZPQ5vDBhfDFbS4RAtcL9ubpbkGWKz4/MukrzONxhcgveAdGP1jxpK84gWEw7hm49GMXy6snw4x/uWGc4JK+7+5zvXMn/OnIeXkeP7e6ZuNWMPly13N5QEEBfHojJK2FCW+6Iazgks8T/gip22DpRyXHtvBtt5BL5zElHwOuN3DIxPKtFCriExsVwo7ULDJz8ms6FBERKQMlfiJSqoICy98+XUZokJe/ndHNbczPg3XfuyGL2ekVu/Bvr7qhjDED3dDDwsMNo3vC9T/C8Xe6OXsvDoclH7mkLy8TrvwSWvc/9i93rDqd4lba7DUBfnoUXj0Jdq2EWU/A3Odg8A1u6GlxgiNcqYWsVJh8hSv1AK7g+uqv4NSHocOow8/peAq07Auz/+P+GxSVmgDrvnVDWv38K/e7SsOVvtsNcy7kwAIvW1I03FNEpC7QbV4RKdWUhfHM37yHx8f3PlSv75dn4YcHDx0U2gIiOkBke99zBze8smksBDU+/ILWwsxHXaLUeSyc/yb4Nzryg72Bbkhk5zHw6Q3w6UT3OVd9Dc27VdG3rYBGTeG8V6DrmfDVnfDyCW4BmT4Xw5hHj75ITYsecPbzMOVqt7BNx1Ng5r+g90Uw9OYjjz/Q6/fRpbDiE+h9weH7f38PbEHJ8wZFKmLRO/DjP+DerRDUBDiU+G1KzKBrdOOjnS0iIrWAEj8ROark9GwembaKwbERnD8wxm1MT4TZT0KHk12CkbLBDcFM3uCKhmckHn6R4EiXADaNdclgajws/dD1So17tvShhm2Hwo0/w4LXXXJVkXIK1aH7WdB2GHz7F7dS5rhn3XDT0vQ8D3YudWUVFk9ycwXHPV1ywtjldGje3fUq9pxw6DMK8t3Q0vajDg0PFakMLfu4553LIXY4wMFVfTeppIOISJ2gxE9EjuqRqavJyM7jn+cWqtk38xE33HLsY25OXlFZ+1wyuGeLKwy+Z5N7TlgIKz5z9fGG/wFOebD0kg0HBIa6c2q70GYw/rXyn3fS/8HuVW7o7IWTiu8BPcDjgRF3uxVLV395aKXQDT+6+X+n/qNisYuUJLqXe9657GDiFxropVlYIJsSlfiJiNQFSvxEpFhrd6XxwW9b+XhRPLeM6kCnFmFux+5Vroj4oOuLT/rADe1s1e/IVS7BzUvL3ufmt8khHj+46APIzz560ndAj3PdkNBZ/3Y1/oxx/12Co6DLGVUerlQdY8wY4BnAD3jNWvtoMcdcADwAWGCJtfaSKg0qLBpCmrue6ULiIkNUxF1EpI5Q4iciB+3LyuWrJTv4aME2lmzbi7+f4aw+rbh1VKEE77v73IqWI++t2If4eZX0lXT4TAUAACAASURBVMTjAU8Zkj5wieKIu+Gzm9zw2lZ9Yc00OO7Wkss8SK1njPEDngdGA/HAfGPMF9balYWO6QT8BRhurd1jjGleLcFF9zoy8YsK4YfVu6vl40VE5Ngo8ROp6/anuEU+lnwI+Tlw3Q8HV3O01rI9NYtl8anE79lPowA/QgK8BAf4ERroJTjQS0iAH4np2UxZGM/UZTvIyi2gc4tQ7jujG+f2a01kaOChz1o/3T1OfVjJW23Q6/xDvX5dxrghtP2vrOmo5NgMBtZbazcCGGM+BM4GVhY65nrgeWvtHgBrbfVkXtG9YO7zbvVZ382F2KgQktKzScvKJSxIq8iKiNRmSvxE6qK8HFeke8kHrgB3Qa6rybZ3K8u+f5fvPcNZmpDKsvhUkjNyynTJsEAv5/WP4YKBbegT0+TQfL4DCvLhu/9zC7QMnlj530nKz8/flbv46k5IXO0KvNfWhW+krFoD2wq9jweKFqvsDGCM+Rk3HPQBa+03RS9kjJkITARo27btsUfWsrf7XZO42r3m0Mqem5P20yumybF/hoiIVBklfiJ1yd6t8Mt/scumYDJTyA6MZHn0+XxhT2BaYiQfFdxKzi8v8FxuCzo1D2NU1+b0iWlCr5hwYiODyc4rICM7j/05+Yeec/Lwejyc2LkZjQL8Sv7s39+F3StdMXRvYMnHSfXqeyn89G9I2w4DrqrpaKR6eIFOwEggBphljOllrd1b+CBr7SvAKwADBw60x/yp0S7ZY+eyIxK/TckZSvxERGo5JX4idUB6dh5LNibQ7YtxhGZuZ6YZxPs5w5md1Ru7z4+OzUM5vlMTduZfybC1j7PyhmYExQ6uvACy0+DHh12pgm5nVd515dh5A+GUv8OvL0G3cTUdjRy7BKBNofcxvm2FxQO/WmtzgU3GmLW4RHB+lUYW0R78Q3zz/C4FoF1kMIBW9hQRqQOU+IlUsw2J6bw0cwNbUvbTqkkQLcMb0apJEK3CG9GySSNahQexPyefBVv2sHBzCgu27GHVjn084vcKw/y2cm/ow9jYEYxq3YTbWjehW8swggN8/ytntYcnXyRo0WtQmYnfnKdcbb6LPyp7+QWpPn0ucg+pD+YDnYwxcbiE7yKg6IqdnwEXA28aY6JwQz83VnlkHj9o0cP1+PkE+fvROryRVvYUEakDlPiJVJO1u9L474/r+WrpdgK9Hnq2asKCLXvYuXQHeQXFj8IKDvCjX9twnu29mTPXzCR72J08ftqtJX9IUGPodynMfx1G/wPCWhx74Hu3uQUdel0AMQOO/XoiUiJrbZ4x5lbgW9z8vTestSuMMQ8BC6y1X/j2nWqMWQnkA3+01iZXS4Ate8PSyVBQ4FahBWKjgtmYpMRPRKS2U+InUsVWbE/luR/XM235ToID/LjhhA5cNyKOKN9qmQUFlqT0bBL2ZrIjNYvtezPxegwDYyPoGh2Gd982eOkxaD2QwFP+VvoHDp4Iv74MC96AUX8p/fh922HxJAgKh8atoXEraBIDwZGud++Hh9xxJ99/DD8FESkra+1UYGqRbfcXem2Bu3yP6hXdC+a/Bnu3QEQc4Ob5fblkR7WHIiIi5aPETwTA+nrcKmEYo7WW3WnZrNyxj/d/3cr3K3cRFujltpM6cs3wOJqGHF5jzeMxNG8cRPPGQRxR7jw/Dz65HrAw4fWDZRqOKrIDdDrVJX4j7jr6Qiz5ufDhJbD99yP3+QW6JHDPJlcvLrzNkceISMNycIGXpYUSv1BSM3NJTMumWZgWfhIRqa2U+IkUFMALQ6D3hXDCPeU6NSM7jxXb97FmVxprd6axZmcaa3alkZqZC0CTRv7ceUpnrhoeS5NGFahx9dNjsO1XGP+6K6NQVkNvhHfPhRWfHn3u14xHXNI34U1oNxz2Jfge2yE13j236utKBoiINO8Gxs/N8+t+NgAD2jUFYO7GZM7q06omoxMRkaNQ4ieStNY9fvkvDL0JAkJKPSUrN583f97MCzPXk5aVB0BYkJcuLcI4o3dLukaH0blFGL1aNyEksIL/m23+GWY/4Zbr7zWhfOe2HwVRXWDeiy6hLa4nc/Mct2hLv8ug53luW1gLaN2/YvGKSP3n3wiiOsOOpQc39WrdhCaN/JmzLlGJn4hILabET2TrXPectdcVRB90XYmH5hdYPl4Uz1Pfr2VHahYndW3OZUPb0jW6MS2bBB1Z9Lyi9qe4IZ5N42Ds4+U/3xgYcgN8fRds+w3aFqn/nLkHPpnohmqNeaxyYhaRhqFlb9g0++BbP49heMdIZq9Lwlpbeb8HRUSkUnlqOgCRGrd1HoQ0g1b9YN5LbuhnEdZafly9i9Ofmc2fpiyleeMgPpw4lDeuGsRJXVvQKrxR5f2xYy18cRuk73bz+gJDK3adPhdBUBNX363o9b+8A9J3wfjXKn59EWmYontB2nbISDq4aUSnZuxIzWJDYnoNBiYiIkejxE9k61xoOxSG3gLJ62D99MN2/751Dxe/Oo9r3lpAdl4+z1/Sn89uPo6h7SMrPxZr3fDL1V+5otytjljupewCQqD/FbDyc0gtVP95yQew8jMY9VdorfIMIlJOhRd48Tm+YxQAs9YmFXeGiIjUAkr8pGHbt90tS952mFuoIKwlzHseay0z1+zm4lfmce4Lv7BuVzoPnd2D7+86kTN6t6yaoUxpO2HS+fDDg9BtnEtEj9Ug34qgC15375M3wNQ/QrvjYfgdx359EWl4onu550KF3NtEBNM+KoQ565X4iYjUVprjJ3VLfp7rEet/OYRFH/v1ts5zz22HgjeA/IHX4TfjH9z45Ht8mxhBdOMg7jujGxcNbktoRRdpKYsVn8JXd0JuFoz9t5tn6KmE+zJN20GX02HBm25lzk8mgscPznvZPYuIlFdwBDRpc9gCLwDHd4rifwviyc7LJ9Cr3y8iIrWNevykbln7Dcx42K3AWRm2zgP/YDKadueNOZs485eOZNoAzsn6nCfO78OsP43iuhHtqy7py9wDH18P/7vKLeRy42wYMrFykr4DhtwImSnw1hmQsADGPeMKtIuIVFR0r8N6/MDN88vMzWfRlr01FJSIiByNEj+pW5Z84J6XfuSKjx8ju3Uuu5v04oT/zOGhr1YS1rQFyR3PY0zBLCZ0CSTAW4X/i2yYAS8cB8s/hpF/hWu/h6hOlf85scdDi56wY4krDdHj3Mr/DBFpWKJ7uznROfsPbhraPgKvxzB7XWINBiYiIiXRUE+pO/anwNpvoXkP2L3CLcLSZWyFL7chfgdxO5fzQd45tGkZzCtXDGBAuwhIjIDnP4SFb8KJfzq2mLPT3MIqRQuj790Cm2a5elgXTara2nnGwOgHYf7rMFalG0SkEkT3AlsAu1dCzEAAwoL86dc2nDnrkzjG35wiIlIFlPhJ3bFsChTkwjnPw3sTYPGkCiV+Wbn5PD9jPctnfcqb3gJ6DjuN28Yeh8fjW7ClWRfoeAr89ioM/wN4A8t24cw9sP13SFgICYvcc/quIgcZCG0OjVu7xVVG3usKIle1jqe4h4hIZTiwwMuOJQcTP3DDPZ+avpaUjBwiQgJqKDgRESmOEj+pO5Z8AC16uRIHvS+E315xvYDBEQcPycrNByDQ6yl25c1ZaxP5v8+XsyV5Py+3TsCmeDj5lDPAU+TYoTfDe+e5YZh9Lyk+HmthxSeuFzJhISSvP7QvqjN0OAmadXXz6Rq3hsat3KqhXv0xJCJ1XHhbVyf0iHl+UTz5/Vp+Xp/EuD6taig4EREpTpUmfsaYMcAzgB/wmrX20SL72wJvA+G+Y+611k41xsQCq4A1vkPnWWtvrMpYpZZLXAPbF8Fpj7j3fS+Bec+7XsAhE7HWMnnBNh74YiWZufn4+xnCgvwJC/ISGuglLMhLgYXfNqUQFxXCpOuGMHzOixDQCwLDjvy8A0nbvBegz8VuuGRhyRvcKpybfoKQ5hAzyB3XeoAbthnUpOp/JiIiNcUYN89v5+Ere/aOCadxkJc565T4iYjUNlWW+Blj/IDngdFAPDDfGPOFtXZlocPuAyZba180xnQHpgKxvn0brLV9qyo+qWMWvw/GD3qd795H93R/dCyeREbfa/jbp8v4bPF2jusQyfGdokjLyiMtK9f37F5nZOdz5ymdueHE9gR5CuDDBTDgyuI/zxgYehN8+QfYPAfiRrjt+bluRdGfHgO/ADjjPzDgmspdhVNEpC6I7u1qhObngZ/7c8LPYxjeMYrZ6xKx1lZNzVMREamQquzxGwyst9ZuBDDGfAicDRRO/CzQ2Pe6CbC9CuORuqogH5ZOdnPUQpsf2t73EvjmXm57ZhIz90Rx1+jO3DKqI35Fh20WJ34x5GW6+n0l6X0h/PCQ6/WLGwHxC+HL22HXcldgfezjbvimiEhDFN0L8rLcMPfmXQ9uHtGpGdOW72RDYgYdm4fWYIAiIlJYVXZTtAa2FXof79tW2APAZcaYeFxv322F9sUZY343xvxkjBlRhXFKbbfpJ0jbDn0vPrjJWssnucPItX6MyprOpOuGcvvJncqW9AFsneue2xwl8fNvBAOvgTXT4PNb4LWTYX8yXDgJLnxPSZ+INGwte7vnYub5ASrrICJSy9T0+LSLgbestTHA6cC7xhgPsANoa63tB9wFvG+MaVz0ZGPMRGPMAmPMgsRENTD11pIP3Zy5zm4Fz/TsPO74aDF3fZ3AkkZDuDRoLsNiyzmnbutcaBoLjVse/bhB14HHC79PgkHXwi2/QrczK/Y9RETqk6jO4BcIO5cctrlNRDBxUSHMWZdUQ4GJiEhxqnKoZwLQptD7GN+2wq4FxgBYa+caY4KAKGvtbiDbt32hMWYD0BlYUPhka+0rwCsAAwcOtFXxJaRmZaXvJWDlF8S3OYsvZm9jQ2IGv21KYUdqJneP7ky/Vrfg+ehS2PADdD6tbBe1FrbOg06jSz82LBou+RCCmkLMgGP7MiIi9YmfPzTvdkSPH8DxHaP4eFE8OXkFBHhr+h6ziIhA1SZ+84FOxpg4XMJ3EVB0XfytwMnAW8aYbkAQkGiMaQakWGvzjTHtgU7AxiqMVWqRbSn7efjrlazcsY+hqdP4t38md6zuxqJVa2nZJIiOzUN54vw+DOsQCXntIDjS1fQra+KXvAH2Jx19fl9hqn8nIlK86F6wZqq7oVZoIZcRnaJ4d94WFm3dw9D2kTUYoIiIHFBliZ+1Ns8YcyvwLa5UwxvW2hXGmIeABdbaL4C7gVeNMXfiFnq5ylprjTEnAA8ZY3KBAuBGa21KVcUqtcfyhFSufms+Wbn5jOzSnNu9C0nPb8dD111NXLNQQgKL/JP1BkCvC9zKckVq+pXowPy+tsMq/wuIiDQk0b3h93dh33Zocmga/7AOkfh5DLPXJSrxExGpJaq0jp+1dipu0ZbC2+4v9HolMLyY8z4GPq7K2KT2mblmNzdPWkTT4ADev24InQJS4JmFMOo+esaEl3xi30vg1xddsfXB15f+QVvnQaMINz9FREQqrvACL4USv7Agf/q1CWfOuiT+WMbBGCIiUrU08F5qhY/mb+XatxcQGxnCJzcfR6cWYa6EA0DvC45+csve0KKXq/VXFlvnumGeqi8lInJsWvQAzBGF3MGVdViakMqejJzqj0tERI6gxE+Ozbbf4MXh8NnNbvXN1KLr9xydtZanvl/Lnz9exvCOUUy+cRgtGge5+SJLPoDYEdC0XekX6nsJbF8Eu1cf/bj03ZCyoezz+0REpGSBYRDRvvjEr3MU1sLPG7S6p4hIbaDET47NjEdgzxY3uf/TG+Cp7vBsf/jyD27oZXrJZTZy8wv405SlPPPDOs4fEMPrVw4k9MAcvm2/uQStz0Vli6PX+a7swpJSev22znPPmt8nIlI5onvBjiMTv96tm9A4yMvstUr8RERqAyV+UnE7l8HGGTDiLvjjRrhhNpz2CER1guWfwJRr4MmuxQ7B3Jayn2vfXsD/Fsbzh5M78fiE3vj7FfrnuOQD8A+G7meXLZbQZtDpVFjyEeTnlXzc1nngDYKWfcv5ZUVEpFgte8PeLW6BrUK8fh6O6xDFnPVJWKuKSyIiNa1KF3eReu6X58A/BAZeDR6Pa/xb9oZht7jka+cS+OEh+OwmyNoHQ29k0dY9vD57E9OW78BjDI+e14uLBrc9/LpZqbDiE+g2zg0jKqu+l7iex0VvucLrxdk6F1oPdKuBiojIsWt7nHve9BP0OPewXSM6R/HNip2s3ZVOl+hy/D4XEZFKpx4/qZjUBFg+BfpfAY2aHrnfzwutB8Alk7Fdz4Rv/syH/76Z8174mVnrErn+hPbM+tOoI5O+bb/BSyMgOw0GXlO+mDqdBm2GwNd3w/+uPuLuMzkZsGOJ5veJiFSmmEEQ1ATWTT9i12k9ovF6DJ/8Hl8DgYmISGFK/KRifn0JbAEMvanEQzKy83hj3nZGbbmKKfkncFHGJL7uPJV5947iL2O70Sq80aGD8/Ng5mPwxhjAwtXTyp+geQPgqqlw0n2w6kt4fgismXZof/wCsPma3yciUpn8vNB+FKyf7hbmKiQqNJCRXZrx2e8J5BdouKeISE1S4ifll7UPFr4F3c8pdsXNpPRsnvh2Dcc9+iMPfbWSZk1CCL3gZQqG3EiPrZMI+eaOw+fh7dkCb50BMx+BnuPhxjkV75Xz88IJf4Trf4TQ5vDBRW7F0axU38IuBtoMqti1RUSkeJ1GQ/pON/e7iPH9Y9i1L5s567XIi4hITdIcPym/Re9A9j447rbDNm9N3s+rszcyecE2cvILOLV7C248sQP92vqGgvZ81A0Lnfkvd/7412Hl525oJsB5r5Zes6+sWvZ2yd9Pj8Gcp2DjTxAQDC16uiFJIiJSeTqe4p7Xf3+oqLvPSd2a06SRP58siufEzs1qIDgREQElflJe+bkw70Vodzy07g/A8oRUXp61ka+Xbsfr8XBuv9ZMPLE9HZqFHn6uMTDyXpd4fXOvK/uwLx7aDIXzXoamsZUbqzcQTr4fupwOn94ISWtLXvRFREQqLiwaonu7eX4j7j5sV6DXj3F9WjJlYTxpWbmEBfnXUJAiIg2bEj8pnxWfuWTtjP+QlpXLP75ayeQF8YQGerl+RHuuOT7OFWA/mqE3QWBjl/yN/Kv7I8GvCv8pxgyEG2fDonehy9iq+xwRkYas02iY8zRk7oVG4YftGt8/hvfmbWXqsh1cOKhtCRcQEZGqpMRPys5a+OUZiOrCPO8A7nlmNtv3ZnLDie25eWRHmjQqx13cfpe68gvGVF28hfk3giETq+ezREQaoo6jYfZ/YONM6HHOYbv6tgmnfbMQPl6YoMRPRKSGaHEXKbtNP8HOZXwVeh4Xv/Ybfh7D/24cxl/Gditf0ndAdSV9IiJS9Q6UdVj//RG7jDGM7x/Db5tT2Jq8vwaCExERJX5SZmk/PkWKCefu1V24dEhbpt4+ggHtImo6LBERqQ0OlHVYd2RZB4Bz+7XGGFTTT0Skhijxk1Ll5Rcw6YtphMXP5CMzlpevHs7D5/QiJFAjhUVEpJCjlHVoFd6I4zpE8smiBGwxiaGIiFQtJX5yVLn5Bdz2we8E/PYC2SaIS255gJFdmtd0WCIiUhsVLutQjPH9Y9iasp/5m/dUY1AiIgJK/OqfZVPgvQkw8zFXuy4no8KXyskr4Nb3F7Fw+SrO8/+FwEFX0CQyuhKDFRGReiUsGqJ7ueGexTitRzTBAX58skjDPUVEqpvG6tUnOftdiYS8bFg/HbBg/Fwx3bbDoO1QaDccQqJKv1ReAbe8v4jNqxbyXeRL+GUaV4ZBRETkaDqOhp+fKbasQ0igl7E9W/L10h08cFYPgvz9aihIEZGGRz1+9cmityEjES75CO7dApd9DCPugoBQWPAmTL4CnuoBM/4FuZklXiY7L5+bJy0kbPX/mBp8P+Gku2tFtK/GLyMiInVSp9Fg811Zh2KMH9CatOw8vl2xs3rjEhFp4NTjV1/kZrk7rO2Oh3bHuW0dTzk03yIvB3YsgV9fhJ8ehcXvw2n/hG7jDiurkJ2Xz+3vzOWUjf/mooCZ0GYEjH8dwlpU+1cSEZE6KGYwBPrKOhSp5wcwNC6S1uGN+HhRAmf3bV0DAYqINEzq8asvFr8HaTvgxD8Wv98bAG0GwYQ34MqvIDAMJl8O754LiWsByMrN5+9vfM4dm2/iIu9MOOGPcPlnSvpERKTs/LzQYSSs/6HYsg4ej+Hcfq2Zsy6RXfuyqj8+EZEGSolffZCXA3OedndZ404s/fi4EXDDLBj7OGxfBC8OI2/aX3nnxUe5L+Em2gfug0s/hpPucw24iIhIeXQc7W5G7lpe7O7z+remwMJnvydUc2AiIg2XEr/6YOmHkLoNTvzTYcM2j8rPC0NugNsWsafz+Xh+fYGJKY+TFdGVwFt/hk6nVG3MIiJSfx2YZrCu+LIO7ZuF0r9tOB8vildNPxGRaqLEr67Lz4PZ/4FW/Q41tGWUl1/Ac7/uYfCys7jK71HWDLifqFunQ5OYKgpWREQahMYtXVmH9cWXdQA4r38Ma3els3jb3moMTESk4VLiV9ctnwJ7Nrv5eGXt7QM2JKYz/qW5PPHdWk7rEc0zd11Dl3F3g59/1cUqIiKlMsaMMcasMcasN8bcW8z+q4wxicaYxb7HdTURZ6k6joat81xZh2Kc0681jYO8vDJrYzUHJiLSMCnxq8sK8mHWE9C8B3QeW7ZTCixvzNnE6c/MZktyBv+9uB/PXdKfpiEBVRysiIiUxhjjBzwPjAW6AxcbY7oXc+hH1tq+vsdr1RpkWZVS1iE00Mvlw9rxzYqdbExMr97YREQaICV+ddnKzyB5HZxwD3hK/0+5IzWTS1/7lYe+WsnwjlF8d8cJjOvTqhoCFRGRMhoMrLfWbrTW5gAfAmfXcEwVU7isQwmuOi4Ofz8Pr87eVI2BiYg0TEr86qqCAtfbF9UZupf+N0FiWjYXvzKPpfF7eWx8L16/ciDNGwdVQ6AiIlIOrYFthd7H+7YVNd4Ys9QYM8UY06Z6QiunUso6ADQLC2TCgBg+XhTP7jSVdhARqUpK/OqqNVNh90oYcQ94/I566L6sXK584zd27cvmnWuHcOGgtphyzAcUEZFa5Usg1lrbG/geeLu4g4wxE40xC4wxCxITE6s1wIM6nebKOqz9psRDJo5oT25+AW/9vLn64hIRaYCU+NVF1sKsx6FpHPQcf9RDs3Lzue6tBazbncZLlw9gQLum1RSkiIhUQAJQuAcvxrftIGttsrU22/f2NWBAcRey1r5irR1orR3YrFmzKgm2VL3Oh+bd4au7IGtfsYfERoUwtmc0787bQlpWbjUHKCLScCjxq4vWfQ87lsCIu49aYD03v4Bb31/E/C0pPHlBX07sXEMNv4iIlNV8oJMxJs4YEwBcBHxR+ABjTMtCb88CVlVjfOXjDYCznoP0nfD9/SUedsMJHUjLyuOD37ZWY3AiIg2LEr+6aMkHENoC+lxU4iEFBZY/T1nK9FW7+cfZPbWIi4hIHWCtzQNuBb7FJXSTrbUrjDEPGWPO8h12uzFmhTFmCXA7cFXNRFtGMQNg6M2w8E3YNLvYQ/q0CWdY+0hen7OJnLyCo18vIwleGw27VlRBsCIi9ZcSv7poXwI061JizT1rLQ9/vYpPfk/gnlM7c9nQdtUcoIiIVJS1dqq1trO1toO19p++bfdba7/wvf6LtbaHtbaPtXaUtXZ1zUZcBqP+Bk1j4cvbIWd/sYfcOLIDu/Zl89nihGL3H7TqC4j/zd0EFRGRMis18TPGjDPGKEGsTdJ2QGh0ibufn7GeN37exDXD47hlVMdqDExERKQYAcFw1n8hZSPMfKTYQ07oFEW3lo15+acNFBQUvwooAGumued106sgUBGR+qssCd2FwDpjzOPGmK5VHZCUwlpI2wlhxSd+783bwhPfreW8fq2574xuWr1TRERqh7gTYMBVMPd5SFh4xG5jDDee2J4NiRn8sHp38dfIToeNP0FQOCSugtT4qo1ZRKQeKTXxs9ZeBvQDNgBvGWPm+paIDqvy6ORImXsgPwfCWh6x66ul2/m/z5dzctfmPDahNx6Pkj4REalFRj/kRqx8fhvk5Ryx+4xeLYlp2oiXftpQ/PkbfoT8bDjpPvd+XcnF4UVE5HBlGsJprd0HTAE+BFoC5wKLjDG3VWFsUpy0ne65SI/frLWJ3PnRYga1i+D5S/vj76fRuSIiUssENYEzn4TdK2DOU0fs9vp5uH5EexZu2cP8zSlHnr9mquvtG3A1NI6B9RruKSJSVmWZ43eWMeZTYCbgDwy21o4F+gB3V214coS0He65UI/foq17uOHdhXRsHsarVw4kyP/oBd1FRERqTJex0HMCzPo37Fp5xO7zB8bQNNifl4v2+uXnwdpvofNprpRRp1Ng48xiew5FRORIZekWGg88Za3t9f/s3XV41FfWwPHvnYm7hwgRSIDgEqA4RQoVKtSou3u71bfddrft7na3W/dCoUapU4N2W9y9uEdIAkmQGMST+/5xJySByAQSYufzPPPMzE/PZLskZ+6952it/6O1zgTQWucDt9R1olJqolJqp1Jqj1LqiRr2RyilFiilNiilNimlzquy70nbeTuVUhMa+LnarhNG/HZl5HHzjDUEeTnz8c0D8XatudKnEEII0WKc+xK4eMGP90J5WbVdbk4O3DA0ij+2Z7IjvUrT95RVUHAEutr+VIgZD8VHIWXlGQxcCCFaL3sSv+eA1RVvlFKuSqkoAK31vNpOUkpZgbeBc4HuwFVKqe4nHPY0pkdRP0yT2nds53a3ve8BTATesV1PHB/x60BqVj7XT1uNk9XCZ7cMJsjTpXljE0IIIezhHgAT/2WKvGz57qTdNw6NwsvFgRd/2Y7WtgqfO+eA1Qlixpr3nUaBxVHW+QkhhJ3sSfy+Bqp2Uy2zbavPIGCP1jpBa12MWR940QnHaMDL1M0clwAAIABJREFU9tob2G97fREwS2tdpLVOBPbYrify0sHFm0NFFq6ftpr84lI+uWUQHf3cmjsyIYQQwn49LwP/WFj+hqlYXYWPmxMPjuvCkt2HWLAz0+zfOcdUBnW21ZZz9oSIs2SdnxBC2MmexM/BlrgBYHvtZMd5YUBKlfeptm1VPQdcq5RKBeYAFcVi7Dm3fTqaTplHB26cvpr9OQV8dONAunXwqv88IYQQoiWxWGDovZC+CRIXn7T7uiGRdAp054Wft1OcscP0AOx6bvWDYsdD5jZp6yCEEHawJ/E7qJS6sOKNUuoi4FAj3f8qYIbWOhw4D/i0Ic3ibW0l1iql1h48eLCRQmrZdG462/Pc2HEgj3evGUB8lF9zhySEEEKcmt5TwD3QjPqdwNFq4enz40g4dIyNf8w0G7uckPjFjDfPe2pdeSKEEMLGniTrTuAppdQ+pVQK8Dhwhx3npQEdq7wPt22r6hbgKwCt9QrABQiw81y01h9oreO11vGBgYF2hNT65RxMYWe+By9e0pOzuwU1dzhCCCHEqXN0gUF3mOmaNVT4PLtrECNiA3Dc8yulwX3A+4TJP0Fx4BUGe2SdnxBC1MeeBu57tdZnYQq0xGmth2qt99hx7TVArFIqWinlhCnW8uMJx+wDxgIopeIwid9B23FTlFLOSqloIJYqBWbaq6/XJONWdJDg0EiuHBjR3OEIIYQQp2/gLeDoBsvfPGmXUornxgTSW+9mkWXgyecqBTHjIGERlJWcgWCFEKL1smtapVLqfOBu4GGl1F+VUn+t7xytdSlwL/AbsB1TvXOrUurvVaaOPgLcppTaCHwB3KiNrZiRwG3Ar8A9Wuuyk+/SfqxLzuLl2StxUmUM6duzucMRQghRD6WUe8XyBaVUF1tfXOm5cyI3P+h3LWz+GnL3n7S7c9ZSLErzyr4YdmXknXx+7HgoyjXtHoQQQtTKngbu7wFXYgqvKOByINKei2ut52itu2itO2utX7Rt+6vW+kfb621a62Fa6z5a675a6/9VOfdF23ldtdZzT+GztRnpOYXc+dk64jzzAbB6hdRzhhBCiBZgMeCilAoD/gdcB8xo1ohaqrPuBl0Gq94/ed+OOZR5dSTFMZrnf95W2d6hQvQosDhIWwchhKiHPSN+Q7XW1wNZWuu/AUOALk0blqhQWFLG7Z+uJb+olBfG+puNnpL4CSFEK6C01vnAZOAdrfXlmP604kR+0RB3IaydDkVVRvWK8yFhAdZu5/HAuK6V7R2qcvGCiCHS1kEIIephT+JXaHvOV0qFAiWAZB5ngNaaJ77dxKbUHF69si/h1hyzw7ND8wYmhBDCHkopNQS4BvjFts3ajPG0bMPuh6IcWP9J5baEBVBaCN3O4/qq7R1Ky6ufGzMOMrbUOFVUCCGEYU/i95NSygf4D7AeSAJmNmVQwvhgcQKz/9zPI+O7cE6PDqZ5O4BHcPMGJoQQwh4PAk8C39vWuHcCFjRzTC1X2ACIHAYr3qks1LJzDjh7Q+Swau0dPl2ZXP3cmHHmWUb9hBCiVnUmfrZF6fO01tla628xa/u6aa3rLe4iTs/CnZn869cdnN8rhHvHxJiNeQfA1deUvxZCCNGiaa0Xaa0v1Fq/ZPt9ekhrfX9zx9WiDb0fclNh62woL4Odv5riLVZTE6eivcPrf+zi8NGiyvOCe4BnqKzzE0KIOtSZ+Gmty4G3q7wv0lrnNHlU7VxRaRn/9/0WugR58p/Le6OUMjuOZsj6PiGEaCWUUjOVUl5KKXdgC7BNKfVoc8fVosWeAwFdYPnrkLoG8g9B18qm7UopnrmgO4Ul5dw7cwMlZeUVOyBmLCQslLYOQghRC3umes5TSl2qjmcfoql9tTaVtOwC/u/8ONycHCp35B2Q9X1CCNF6dNda5wIXA3OBaExlT1EbiwWG3gfpm+F/T5tqnbHjqx3SJdiTf13aixUJh/nrD1srq3xWtHVIXdMMgQshRMtnT+J3B/A1UKSUylVK5Smlcps4rvZh+8+wZ161TYUlZbw9fw/xkb6MiA2ofnxeuoz4CSFE6+Fo69t3MfCj1roE0PWcI3pdAe5BJoGLGg4u3icdMrl/OHeP7swXq/cxfVmS2dhptLR1EEKIOtSb+GmtPbXWFq21k9bay/be60wE16YV5cH3d8L8F6ptnrV6H+m5hTw8vgvVBlnLy03iJ4VdhBCitXgfUxDNHVislIoE5IvT+ji6wOA7zOuu59d62F/O6cqEHsG88Ms20+LBxRs6DoY9kvgJIURN7GngPrKmx5kIrk3bOAuK8+DwXrBNUyksKePthXsZHO3HkM7+1Y/PP2Sa28qInxBCtApa6ze01mFa6/O0kQyc3dxxtQqDbofhD0HvK2o9xGJRvHplX+JCvLhv5gZ2pueZ6p7pmyurYDeW3AOmp6AQQrRi9kz1fLTK4xngJ+C5Joyp7dMaVn9oXhflwLFDAHy2MpmDeUU8dOJoH1T+EpM1fkII0SoopbyVUq8opdbaHv/FjP6J+rh4wbjnwNWnzsPcnByYekM8bk5Wbvl4Ddlho8yOXb81Xiz5R+DtwbDgxca7phBCNAN7pnpOqvIYD/QEspo+tDYscREc2gk9LzXvD+8hv7iU9xbtZViMP2d18j/5nOOJn4z4CSFEK/ERkAdcYXvkAtObNaI2KMTblQ+vj+dgXhG3/lpIuW8n2PJt491g+RvmS9qkpY13TSGEaAb2jPidKBWIa+xA2pXVH4KbP4x8zLw/vIdPVyRz6GgxD43rUvM5eQfMs4z4CSFEa9FZa/2s1jrB9vgb0Km5g2qL+nT04b9X9GHtvmz+sA5HJy1pnOmexw7Bqg/A4ggZW6Ck8PSvKYQQzcSeNX5vKqXesD3eApYA65s+tDYqex/snAP9b4CAWLA4Upy5i/cW7WVkl0Dio/xqPq/iF5gUdxFCiNaiQCk1vOKNUmoYUNCM8bRpF/QO5aFxXXgprRdKl6O3fHf6F132OpQWwNlPQXkppG86/WvaIzvF1AAQQohGZM+I31pgne2xAnhca31tk0bVlq2ZZp4H3gIWK/h1ImXPZrLyS3hoXGzt5+UdMKOEDk5nJk4hhBCn607gbaVUklIqCXgL0yJJNJH7x8YweNAQtpZHsn/pZ5U9/k7F0UwzQ6fX5dDnKrMtdW3jBFqf2XfBRxOh+NiZuZ8Qol2wJ/H7BvhMa/2x1vpzYKVSyq2J42qbSgpg/cfQ7XzwDjebfDvBod2M6RZEvwjf2s89miHr+4QQohXRWm/UWvcBegO9tdb9gDHNHFabppTihYt6khJ+PmHHtvLOd7+fevK39DUoK4ZRj4NXCHiFQdoZSPyK8yFlFRzLhDVTm/5+Qoh2w57Ebx7gWuW9K/BH04TTxm35FgqyYFDlF74bCwIJ1+k8NKZz3efmHZD1fUII0QpprXO11hX9+x5u1mDaAYtFMeGKuwDIX/81z/+8veHJX146rJ0GfaaAv+33c9iAMzPil7LKJJzuQSb5LMpr+nsKIdoFexI/F6310Yo3ttcy4tdQWsOq9yEwDqLMko+cghJ+THXDWZXSy7Oenr556ZL4CSFE66fqP0ScLuUTgY4Ywo2eq/loWQJ/+2lbw5K/pa9CWQmMfLRyW3g8ZCcfb8HUZBIXg8UBLp0KBUdg1XtNez8hRLthT+J3TCnVv+KNUmoAsji94VJWm0Xhg24DW4++aUsT2V4UZPYf3lP7ueVlZqqnhyR+QgjRyp3GojPREKrXZQQWJvFU/zJmLE/i2R+32pf85aTB2unQ92rwi67cHhZvntPWNU3AFRIXm3t1GgVdJsLyN6Egu2nvKYRoF+xJ/B4EvlZKLVFKLQW+BO5t2rDaoNUfgLM39L4SgJz8EqYvTSSqS2+zv67qXccOgi6XET8hhGgFlFJ5SqncGh55QGhzx9dudL8ELA7c5rueO0Z24pMVyTw9ewvl5fUkf0tfAV1WfbQPIKQPKEvTTvcszIH96yF6pHl/9lNm28p3mu6eQoh2w6G+A7TWa5RS3YCutk07tdYlTRtWK7P+E/Pc5yqwOp68Py8dts2GQbeDswcA05YlkldUys0ThsPHXnWP+EnzdiGEaDW01p7NHYMA3P2h09moLd/yxAN/xWJRvLtwLxp48eKeKFXDrNvsFPM7vd914BtZfZ+zBwR1b9oCL8krzBe9FYlfSB+ImwQr3oHBd4JbLS2fhBDCDvb08bsHcNdab9FabwE8lFJ3N31orUTxMfj5YfjxPnh7EGz+BsrLqx+zbobp/zPwVsCs7Zu+LJEJPYKJC/U2C8cP7a79HpL4CSGEEA3X63LISUGlruGxCV25a3RnZq7aV/uavyX/Nc8jHqn5emEDzFTPE3/PN5bExeDgAuEDK7eNfgqKj8LyN5rmnkKIdsOeqZ63aa2PTy7XWmcBtzVdSK1M8gooL4HhD4OjG3x7C7w3HHbONQVdSoth7UcQM/54ZbDpyxLJKyzl/rG2vn3+sXVP9cw7YJ5lqqcQQghhv27ngYMrbP4apRSPTejKrcOjmbE8iRd/OaHaZ1YybPgU+l8PPh1rvl54vJl6eaSJmqsnLoaOg8HRpXJbcHfoOdkUiDt6sGnuK4RoF+xJ/KyqynwIpZQVkC7iFRIXgtXJrAW4YwlcOg1KC+CLKTB1HCx4wRRmGWxaOOQWlvDR0kTO6R5Mj1Bvcw3/GMhJMX3+apKXDijwCDojH0kIIYRoE5w9oetE2DobykpQSvF/58dx49Aopi5N5N+/7axM/pa8DMpqvsitTVMWeDl2GDI2Q/SIk/eNfhJKC2HZa41/XyFEu2FP4vcr8KVSaqxSaizwBTC3acNqRRIWQfggcHIDiwV6XQb3rIZJr0Puflj2OvhGQ+exAMxYlkRu1dE+sI0EajiSWPM98g6Ae0DN6weFEEIIUbtel0P+IfP7GtPk/dlJ3blmcATvLtzLq3/shpXvmrV98TeDd1jt1wrsCk4eTVPgJXmpeY4edfK+gFhTHG7NVMg90Pj3FkK0C/Ykfo8D84E7bY/NVG/o3n4dOwzpm6HT6OrbrY4w4Ea4fwNc8Bpc8j5YLOQWljB1SQLj4oLpGeZdebx/jHmurcDL0QyZ5imEEEKciphx4OINW745vkkpxfMX9eSKAWG4LHoefn3CFFEZ91zd17JYIbRf0xR4SVxsksrQfjXvH/WY6S249JXGv7cQol2oN/HTWpcDq4AkYBAwBtjetGG1EkmLAW167dTE0QXib4KIwQB8bBvte6DqaB8cX/vH4VoKvOQdkMIuQgghxKlwcIa4C2H7T9WWVFh0Kf9yeJ+7HX7ks9KxvB/81+pr62oTNgDSt0BJYePGmbgYIofWPrvHrxP0u8YUjMtJbdx7CyHahVoTP6VUF6XUs0qpHcCbwD4ArfXZWuu3zlSALVrCInDyhND+9R6aV1jC1KWJjO0WRK9w7+o7nT1Nc/baCrzkpcuInxBCCHGqel1mKmPu+tW8Lz4Gs67GsnEm5aOeZFX3p/nnr7v555ztFJaU1X2t8HhT1C19U+PFl3sADu2qbONQm5GPmsJxi19uvHsLIdqNukb8dmBG9y7QWg/XWr8J1POvYTuTuAiihoG13naIfLIimZyCEh4YF1vzAQGxNU/1LCuFo5ky4ieEEEKcqqgR4BFsWi7lH4GPL4Q9f8AFr2I5+wlevbIvVw+O4P3FCUx6cymbUrNrv1ZTFHhJWmKe60v8fCLMWr/N35i/D4QQogHqSvwmAweABUqpD22FXWrodtpOZafAkYST1/fV4GhRKR8uSWBMtyB6h/vUfJB/55oTv2OZgDa/sIQQQgjRcBYr9JgMu/8H084x6/OvsBVzARysFv5xSS+m3zSQvMJSLnlnOS//tpOi0hq+7/YKAa+wxi3wkrgIXHwguFf9x8aOg+I82L+h8e4vhGgXak38tNaztdZTgG7AAuBBIEgp9a5S6pwzFWCLlWiqg9VYfesEHy9PIju/5OS1fVX5x0D+YfNNZFXSvF0IIYQ4fb0uh7JiM4vmuu9NMZcTnN01iN8eGskl/cJ4a8EeLnprGVvSck6+Vlj/xi3wkrjYtHGw2FFzL8o2Kpi4sPHuL4RoF+wp7nJMaz1Taz0JCAc2YCp9tm8JC8E9CILi6jzsWFEpU5ckMLprIH061jLaB5WVPY8kVN9+PPGTNX5CCCHEKQvrDxe8Crf8zyzTqIW3qyMvX96Hj26M58ixYi5+exmv/L6L4tLyKteKh6wkOHbo9OPKSoLsfZUJXX3c/aFDr+PtKYQQwl72tHM4TmudpbX+QGs9tqkCahW0tn07NxJU3bNfP1mRTFZ9o31QmfgdOqGyZ56tX4+M+AkhhBCnTikztTOom12Hj+kWzO8PjeLCPqG8MW8357+xhBV7D5ud4Y24zi9xsXmub31fVdGjIGV1tSqlQghRnwYlfsLm4A7TW6/T6DoPKyotY9rSREbEBtAvwrfua/pGgbKevM4vLx2UBdwDTydiIYQQQjSQt5sjr1zZl2k3xFNQUsZVH67kwVkbOOgZZ343N0rit8TMIArsav850aOgrAhSVp3+/YUQ7YYkfqeiYnpFbf37bOZuTufQ0SJuHdGp/mtaHU3yd1Lid8AkfXZUDhVCCNH6KaUmKqV2KqX2KKWeqOO4S5VSWikVfybja4/GxpnRv/vHxDBnczpj3ljLEfcYyk+3wEsDZhBVEzkULA4y3VMI0SCS+J2KhIXgG23KKtdhxvIkOgW4MyImwL7r+sec3MvvaIas7xNCiHZCKWUF3gbOBboDVymlutdwnCfwACBDPmeIq5OVh8/pym8PjaRfpC+/ZYdxLGEV65KO1H9ybQ7thqPpDZvmCeDsYdYZJkriJ4SwnyR+DVVWCsnL6h3t25iSzZ8p2Vw/JBKLxc5v8fxj4MheKK+ygDzvgKzvE0KI9mMQsEdrnaC1LgZmARfVcNzzwEtA4ZkMTkB0gDsf3zSQnoPG4KmP8pf3v+PJ7zaRU1DS8IsdrxDewMSv4pz9G6Cgjp6DQghRhSR+DbV/AxTl1tvG4eMVSbg7Wbl0QLj91/bvDCX5kLe/clteuoz4CSFE+xEGpFR5n2rbdpxSqj/QUWv9y5kMTFRSStFrsKlz93BcLl+tTWX8K4v4dUt6wy6UuBi8I8xSj4bqNAp0OSQvb/i5Qoh2SRK/hqrom1NH4nfoaBE/bzzApQPC8XRxtP/aFZU9K9b5lZXAsYMy4ieEEAIApZQFeAV4xI5jb1dKrVVKrT148GDTB9feBHYDJw8mBRzgh3uGEeDhzJ2freOuz9aRmWfHQGx5OSQtMf37GrK+r0L4QHBwlemeQgi7SeLXUAmLTP8cd/9aD5m1eh/FZeVcPySqYdcOsLV8qEj8jmaYZ4/ghscphBCiNUoDOlZ5H27bVsET6AksVEolAWcBP9ZU4MXWfileax0fGCiVoRudxQqh/SB1LT3DvPnh3mE8NrEr83ZkMu6/i/hqTQpa69rPz9gCBVmnNs0TwMEZIodIgRchhN2aNPGrrzKZUupVpdSftscupVR2lX1lVfb92JRx2q0435ROrmO0r6SsnM9W7mNEbAAxQR4Nu75nCDi6VRZ4Od68XUb8hBCinVgDxCqlopVSTsAU4PjvQK11jtY6QGsdpbWOAlYCF2qtT7O8pDglYf0hfTOUFOJotXD36Bh+fWAE3UK8eOzbTVwzdRW7MvJqPreif1/UiFO/f/RIOLgd8jJO/RpCiHajyRI/eyqTaa0f0lr31Vr3Bd4Evquyu6Bin9b6wqaKs0FSVkJZcZ39+37flkF6bmHDR/vATPXw71w54nc88ZM1fkII0R5orUuBe4HfgO3AV1rrrUqpvyulWsbvQlEpLB7KS0zyZ9Mp0INZt53Fi5f0ZHNqDue8upiL3l7GpyuSyM4vhvIy2PYjrJlqlnh4h9V+/fpUfBGdtOT0PocQol1oyuZwxyuTASilKiqTbavl+KuAZ5swntOXsAgsjhAxpNZDZixPItzXlTHdgk7tHv4xcGCjeZ13wDzLiJ8QQrQbWus5wJwTtv21lmNHn4mYRC3CbTNsExeZ17a1ehaL4prBkUzo0YHZG9L4Zl0qL/ywgV1z3uQ+l7kElaShfaNRE186vfuH9AEXb9Nmqtdlp3ctIUSb15RTPeutTFZBKRUJRAPzq2x2sS1KX6mUuriW887swvXERWYxtXPNUzi3H8hldeIRrh8SidXeFg4n8o+BrGQoLTYjfsoK7nb2ARRCCCHEmeMVCj6RMP95+Hc0fH4FLP6PmcZZdJQAD2duHeDD3H4r2erzCM9bp5JZ4spdxQ8wOPclXknsSF7hKbSBqGCxmqmiUuBFCGGHphzxa4gpwDda67Iq2yK11mlKqU7AfKXUZq11te7mWusPgA8A4uPj61hB3QgKsmD/nzD6pKWKx32yIgkXRwtXxHes9Zh6+ceALoOsJJP4eQSZf9iFEEII0fLcNBf2zjc1AFJWw+7fzHZlhaDucCQBVXIMh9hzYNgDdAkbwiW7DlKyNpU35u/hs1X7uH9MDFcPjsTJ4RS+j48eBTt+hiOJ4BfduJ9NCNGmNGXiV19lsqqmAPdU3aC1TrM9JyilFgL9gL0nn3qGJC4BdK2FXbLzi/l+QxoX9w3Dx83p1O/jX6WyZ94BWd8nhBBCtGTeYdD/OvMA80Vx6lqTCKauhZDeMOQeCO4BgBNwTo8OnNOjA5tSs/nnnB0899M2pi9P4tEJXTm/VwiqIe0dOtn+LklcfOqJ39rpsOEzuOEncHI7tWsIIVq8ppzqWWdlsgpKqW6AL7CiyjZfpZSz7XUAMIza1waeGYmLwMmjcj7/Cb5am0JhSTk3DI06vfv4dzLPh/eYdg6yvk8IIYRoPVx9IXY8jHkarp8NF79zPOk7Ue9wH2beNpjpNw3E1dHKvTM3cPHby1ix97D99wvoAh4dTn26Z+o6mPMopK2FnXPqP14I0Wo1WeLXgMpkU4BZunqzmzhgrVJqI7AA+JfWunkTv4RFEDkUrCc3ZC8r13y6MplB0X7EhXid3n1cfcEtQEb8hBBCiHZAKcXZXYP45f4R/Oey3mTmFXHVhyu5ecYadqbX0gqi+gXMqF/iYqirb2BNCnPgm5vM3xpeYbBx1ql9CCFEq9Cka/zsqUymtX6uhvOWA72aMrYGKSmEw7trrZi1YEcmKUcKeGJiXOPczz8GMrdB/mEZ8RNCCCHaAatFcXl8Ryb1CWX6siTeWbiHc19fzGUDwnl4fFc6eLvUfnL0SNj0pfnboZbRxZNoDT/eDzmpZp3i7t9g6aumJ6BncON8KCFEi9KkDdzbjBxbcVLfqBp3f7wiiQ5eLpzTo5H+ofSPgf0bzGsP+cdXCCGEaC9cHK3cNbozix89m5uGRTN7w35Gv7yAf/+6g9zaKoBGV1nnZ69102HbbBj7DEQMht5TQJfD5q9P/0MIIVokSfzskZVsnn0iT951rJhlew5xeXw4jtZG+nEGxEB5qXktI35CCCFEu+Pr7sQzF3Rn3iOjmNCjA+8s3Muofy9g+rJEikvLqx/s0xH8OpllKfZI3wJzn4DOY2HoA2ZbYBcI7S/TPYVowyTxs0d2knn2PTnxW7grk3IN4+IacWTOP6bytazxE0IIIdqtjn5uvD6lHz/dO5y4EC/+9tM2xr6ykG/XpVJWXmVNX/QoSF4GZaV1X7D4mFnX5+oDl7wPlip/Cva5CjI2m8RQCNHmSOJnj6xksDqbqlknmLc9kwAPZ3qFeTfe/aolfjLiJ4QQQrR3vcK9+fzWwcy4aSBeLo488vVGJry2mDmbD1Bers06v6LcyqUitZnzKBzaDZM/AI/A6vt6XgoWB9gko35CtEWS+NkjO9lMo7BU/3GVlJWzaNdBxnQLxGJpQM+d+vhGA8r84+vm33jXFUIIIUSrpZRidNcgfrp3OO9c0x+Auz9fz6S3lrK01FZgrq62DhtnwZ+fw8hHodPok/e7+0PsBNj0Vf0jh0KIVqdJq3q2GVnJNa7vW5uURV5hKWO6NXIBFkcXk2iWl52UbAohhBCifbNYFOf1CmFCjw7M3pDGa/N2ce2sBBZ4dKLD+lm4Kguc+NDlsOAfEDEURj1e+8X7TIGdv0DiQogZd8Y+kxCi6UniZ4/sZAjrf9Lm+TsycLJaGB4b0Pj3DOlrpmwIIYQQQtTAalFcOiCcSX1C+WptCnN/P4u7s2fCvL/VfIJnKFw6Fax1/PnXZQK4+JjRwdNJ/LSGo5nSGkKIFkQSv/oU5kJBVo0jfvN2ZDK4kx8ezk3wY7z4HfPtnBBCCCFEHZwcLFx7ViQF/d/i+bl38NmKRDoFuPHvyT3pFepp/p7Q5eDkAQ5OdV/MwRl6ToY/vzB/A7l4NSyYwhyTNK6ZBod2wuUzoMclp/zZhBCNR+YR1ifb1srhhIqeiYeOkXDwGGO7BTXNfZ09waURC8YIIYQQok1zdbLyzEV9mX7rCHJKHLh46kZeWZJOiZMXuPnVn/RV6HMVlBbA9h/tv3n6ZvjpAfhvHMx9DJzcIbgn/PhAZVssIUSzksSvPhX/WJ3QvH3+jkwAxjZmGwchhBBCiNM0NCaAXx8ayUV9Q3lj3m4ueWcZuzPy7L9A+EDTF7C+nn5lpbDpa5g2Ad4bbo7vcQncNh9uXwBTPgc0fHsLlNXSfL6xFB2FL6+FPX80/Ny8DCgtbvyYhGhhZKpnfbKSzPMJUz3nbc+gS7AHHf3cznxMQgghhBB18HJx5JUr+nJO92Ce+n4L57+5lJuGRdHBywUXRyuujlZcHC04O1pxcbAS5OVM50APc7JSZtRvwYuQvQ98Ik6+wdGD8O3NkLjYJIkT/mHOcfOrPMY3Cia9Bt/cbArLjHu26T7wstdh+0+QtBTuWg5eofadl7kDPjwbIs6Ca76VonqiTZPErz7ZyeDsBa6+xzflFpawOvEIt47o1IyBCSGEEELUbWLPEAZE+vHU95t5f1FKywtYAAAgAElEQVRCnccOjvbj1hGdGNstCEvvK0zit+krGPmX6gemroOvroNjh+DCN6HvtbUnTD0vhb0LYOmr0GlUzW0kTldOKix/E6JGQNo6mH03XPtd/Ulc8TH4+gaz/nHvfFj+Bgx/sPHjE6KFkMSvPhWtHFRln74luw5RWq4ZG9dE6/uEEEIIIRpJoKczH14fT0FxGQUlZRQef5RTWFpGUUk5W9JymLE8ids+WUt0gDs3D4vi6o5DsW6cBSMeMX8HaQ3rZpg1fJ4d4Jb/QWjf+gM49yVIWQXf3W5G49wbuRr6H38zydvF75ipnj8/BKvegyF3133eL3+Bgzvhuu9h7Ucw/3mIGg7h8Y0bnxAthIxn1yc7+aTCLvN2ZODj5ki/jj7NFJQQQgghRMO4Olnxc3ci1MeVToEedA/1on+EL0M6+3PbyE4senQ0b17VDy8XB575YSvPp/aGw7s5vGsFlBTAD/fCzw+akbXbF9mX9IEp9HLZdCjIhtl3QXkjVi1PXQebv4Ih95gpqQNugq7nwR/PQcbW2s/b8BlsnGl6GnY+Gy58w7S7+OZmU5lUiDZIEr+6aG2b216Z+JWVaxbuPMjoLoE4WOXHJ4QQQoi2wcFqYVKfUGbfM4xv7hxCduR5FGpH1nz+LGmvjIQ/P4ORj8E1X1dfy2ePDj1hwouw+3+w6t3GCVhr+O0pcA+CEQ+bbUqZ6acu3vDtrVBSePJ5GdvMaF/0SBj1mNnm6guXTTPTRn960FxbiDZGMpe6HDsIJfnVRvz+TMnmyLFixkg1TyGEEEK0QUop4qP8eO3GUZR1OZeJltV45qdya8mjPHLoAhIOF5zahQfeCt0ugN+fhf0bTj/Qrd9DykoY87Rpg1XBPcBM+8zcdnIz+6KjZl2fsydMngoWa+W+joPg7Kdg63ew4dPTj0+IFkYSv7pUtHKoMuI3f0cGVotiVGxgMwUlhBBCCHFmuI99DHpcQtHN84k4azK/bN7PuFcWcd8XG9iZ3oAWEVA5GucRDF/fBGumwo45sP9POJrZsCmgJYXwx7MQ3Av6XXvy/tjxMOh2WPkO7JlntmkNvzwMh3bDpVPBs4Yv8Yc/BNGjYM5jZv2fEG2IFHepSw3N2+dtzyQ+0hdvN8dmCkoIIYQQ4gzp0Asun0Eg8NdIuGt0Z6YtTeTTFUn8tHE/4+KCGBcXzJDO/kT4uaGqFMOrkZufSbpmXgm/PFJ9n8URPEPApyOcdZcZHazteivfMctxrv+x+qhdVeP/btpNzL7bFJXZ+Qts+hJGP2kqjNbEYoXJH8C7w0xyets8cHSt+zMJ0UpI4leX4z38TP+atOwCdqTn8dR53ZovJiGEEEKIZhLo6cwT53bjzlGdmL4siZmr9/HH9kwAQr1dGNI5gCGd/RnS2Z8wn1oSpsgh8HiiWVKTu9888g5AbhrkHjAtGb68FrqcC+f9++Q+gkczYckrpohLbQkcmIRt8ofw4RjTfiJtnRnNG/lo3R/SswNc8h58fhn872k4/78N+AkJ0XJJ4leX7GRwDzTVqID52zMAGCvr+4QQQgjRjvm4OfHQ+C48OC6WvQePsmLvYVYkHGb+jgy+XZ8KQISfGwOj/BgY5Ut8lB+dA90rRwQtVpNgeXaAsP7VL15WAivfhYX/hLcHmxG6s+4Cq2221fwXoLQAxj9ff6AhvWHsX+H3Z8wU00un1j5CWFXseBhyL6x4y1QjtVihtAjKiiufy4pN0/r4mxrwkxOi+UjiV5eKHn4283ZkEuXvRqcA92YMSgghhBCiZVBKERPkSUyQJ9cNiaK8XLMzI48Vew+zMuEwC3ZmHk8E/dydGBDpy8AoXwZG+dEn3AeLpYapnFZHGHY/9LjYrLX7/RkzRfOC18wo3oZPYfCdEBBjX5BD7oWyIogZBx4N6ME89lk4kgjJy8DqBA7OYHUGByfznLvf9P7re43ZJkQLJ4lfXbKTIWwAAPnFpSzfe5hrB0fWP39dCCGEEKIdslgUcSFexIV4cfPwaLTWJBw6xtqkI6xJymJt0hF+32ZmUAV5OnNuzw6c3zuU+Ejfk5NAnwi46gvY8bNJAKeNNyOEzl71T9esHlTDjq/g4ARXzax9/+4/4PNLYecck6QK0cJJ4lebslLIToEekwFYtucwxaXljI1rwDdFQgghhBDtmFKKzoEedA704MqBZq1eZl4hK/YeZu7mdGatSeHjFcm1J4FKQdwk6DQaFvwDVr0H573c8D6CTaHz2eAVDus/lsRPtAqS+NUmNw10GfhGAaaNg4ezAwOjWsA/NEIIIYQQrVSQpwsX9Q3jor5hHCsqZd6OTOZsOnA8CQzwcCLUxxUPZwfzcHHA09kBD5cb8BtxDd39QuhfWoazgx1r9ZqSxWpaSSx6ySwPqlIFXoiWSBK/2pzQymHhzoOMiA3AyUFaHwohhBBCNAZ3Zwcu7BPKhX1COVZUyvwdmSzYkcmR/GKOFpay71g+eYWlHC0yj7JyDSTj6mhlcCc/RsQGMiI2gNggj+ZZilOR+P35uWn+LkQLJolfbao0b88vLuVATiHXDfFu3piEEEIIIdood2cHJvUJZVKf0Br3a63JLSxldeIRluw+yNLdh3h+5zYAgr2cGR4TSL8IH3qGedOtgycujmdgRNCnI8SMhQ2fwajH7asYKkQzkcSvNtnJoCzgHU7aoQKA2vvRCCGEEEKIJqWUwtvVkfHdgxnf3bTWSs3KZ+nuQyzZfYh5VVpJWC2KzoHu9Aj1pkeoF91DvfB1c8LBorBaFI5WC1aLOv7ey9URR+spzurqfz18dT3snW/aQAjRQkniV5usZLNg1+pIalYWAOG+kvgJIYQQQrQU4b5uTBkUwZRBEWitSc0qYOv+HLbuz2Xr/lyW7z3E9xvS6r2OUuDv7kywlzPBXi4EezkT5OlCB28XBkT60iXYs/aTu5wLbgGwboYkfqJFk8SvNtmVi3RTs82IX7ivW3NGJIQQQgghaqGUoqOfGx393JjYM+T49kNHi9hxII+jRSWUlmvKyjUlZZqy8nJKyzUlpeVk5ZeQmVdIRm4RGbmFbErN4fCxIrQ21+gR6sXk/uFc2CeUQE/n6jd2cIK+V5mm83kZ4Bl8Bj+1EPaTxK82Wcmm0SeQllWAk9VCoIdzPScJIYQQQoiWJMDDmeGxDf8brqSsnPScQuZtz+C7DWk8//M2/jFnOyNjA5jcP5zx3YMr1xH2vwGWvwkbv4DhDzbyJxCicUjiV5OSAjiaXjnil5VPiI/LyY1FhRBCCCFEm+RotdDRz40bh0Vz47Bo9mTm8d36NL7fkMZ9X2zA09mBJ8+L4+rBERAQCxFDYf0nMOwBM3dUiBZGehPUJDvFPPuYxC8tu0DW9wkhhBBCtGMxQZ48NrEbyx4fw8xbB9MtxJO//7yVjNxCc0D/6+HIXkhe1ryBClELSfxqckIPv9SsAqnoKYQQQgghsFgUQ2MC+O/lfSkr17z2xy6zo/tF4OxlRv2EaIEk8atJVpJ59omksKSMg3lFhPlIYRchhBBCCGFE+LtxzeBIvlyTwp7MPHByg16Xw7YfoCCr5pO0hl2/wZZvoaykYTcsyILsfacfeFMrKYCV78GeeVB8rLmjEVVI4leTrCSwOoNHMAdyzPC9TPUUQgghhBBV3TcmBjcnB176dafZMOAGKC2Ezd+cfHDiYpg6FmZeAd/cDG/0g1XvQ3F+3Tc5uBN+fgj+GwdvDjDN4lsqreGnB+HXx+GzyfCvSJg2AeY9DwkL6/+sja28DNZMhfTNDT937wI4sKnxY2pGkvjVJDsZfCLAYiE1y/wHGiaJnxBCCCGEqMLfw5k7R3Xi920ZrE06AiF9zGPdxxzvBXFgI3w6GT6eBHnpcOFbcNWX4BUKcx+D13rB4pehILvywlrDnj/gs0vh7UGw4XPodRlEDIEf7oG5T0BZafN86Lqs/hA2zYKRj8G138KQe6C8BJa+Cp9cBC9FwowL4PDepo8l/wh8fjn88gh8egnk1N/P8biEheZn/8lF5n+zNkKqetYkq7KHX1qW6eEna/yEEEIIIcSJbh4ezScrkvnn3B18c+cQVP/rTbKx9XvY8bOZ1unqC+e8AANvA0cXc2KXCZC8HJa+AvOfh2WvQ/zN4NMRVn0Ah3aCRzCc/TTE3wTuASbZ+/0ZWPkOZG6Fyz8GN7/m/QFUSF4Bvz0JXc+D0U+CxXK8NRqFubBvJSQtMWsgv74Bbp0HDna22cg9AF9eA+GDYNRj9X/mjK0w62qT7I1+Epa/BV9eCzfNrfz51+ZIInx9I/hFQ06qSbSv+aZNVGqVEb+aZCeDbxRgKnpaLYoQ73r+IxFCCCGEEO2Om5MDD43vwrrkLP63LQN6XgYOrvDNTbBzLoz4CzywEYbeVz3pUAqihpmRsTsWQ+cxJvn75RFwdIVLPoAHt8CoR03SB2B1gIn/hIveMYnUh2dDxrbm+eBV5R4wyZxPJFzynkn6qnLxgi7nwDnPwyXvm6mXv//VvmuXFsFX10P6Flj9vpkiu/K92tdIbvkOpo6DkkK4aQ6MfgImvw/715spsxUjsTUpOgqzrjHHXPO1Sdb3/GFGMtuAJh3xU0pNBF4HrMBUrfW/Ttj/KnC27a0bEKS19rHtuwF42rbvBa31x00Z63EF2VCYc7yVQ2pWAR28XHCwSo4shBBCCCFOdvmAcKYuSeDfv+5g7IMjcRj/NzODbNgD4Blc/wVC+sAVH8ORBPN3aEjfukeY+l0DAV3MKNbUcSaxiZtkX7Baw+E9sG+FGaU7vAfcA8ErxEw/9Qw1rz1DwTsMnNzrvl5psUn6io7CdbPBxbvu47tOhMF3wap3IXoUdDuv7uPnPg6pq83opn8M/PaUWUO45kOTmHWZaH5W5WUw728mee44GK74BDw7mGt0Ox9GPQGL/gWhfWHwHTX/XGbfBQe3m2TcrxMMjDbFeH5/BqJHQlC3umNt4Zos8VNKWYG3gfFAKrBGKfWj1vr41xJa64eqHH8f0M/22g94FogHNLDOdm4tJZIa0QmtHNKklYMQQgghhKiDg9XC4xO7cfun6/hqbSpX15RY2MOvk/3HdhwIty80UyC/vNZME/WPAScPcPY0rSWcba9LCswI4b4V5jn/kLmGmz8ExpmEM3mpSTqrsjiaJHPEI6b+RU1+ewpSVsFl0yG4u32xj/+b6Xf4w90QsswkmDVZNwPWTYfhD0GPi822638wydj/noYvppjkcdTjsORl2Dvf/BwmvgQOTtWvNepxSN8Evz4JQd0hekT1/Ytfhu0/mmSy8xizTSm46G14dwh8dyvcOv/k67YiTTniNwjYo7VOAFBKzQIuAmobj74Kk+wBTAB+11ofsZ37OzAR+KIJ4zWybIlflebtg6NbyNxpIYQQQgjRIo3vHkx8pC+v/rGLi/uF4uZ0BkppeIXAjXNgzl9g7XTMeEkdfKMgdrwpEhMxBAJiq48sFudD3gHI3W+e960wVUQ3fA59rzYJoG1wBIA/Z5qRt6H3Qc/J9sft4GwSxfdHwne3ww0/gsVa/ZiUNTDnUZOEjXmmcrtSZtQwZiys/QgW/hNmnAdWJ5j0hqmsWhOLxUwznTrWjFDevrAymd05Fxa8AL2vhCH3Vj/PMxgufNOsGVzwoklaW6mm/C8yDEip8j4VGFzTgUqpSCAamF/HubV8FdDIqoz4lZSVcyCnQCp6CiGEOGPsWCZxJ3APUAYcBW6vOptGCNE8lFI8eV43Ln13BR8tTeTeMbFn5saOLnDRW3DBa1B81DyK8szUy6Jc89pihbB4kyjWxckN/DubB0DvK0yyt/RVU5TlzyoJYEG2WTMXNQLGPtfwuANi4PyXzfTKxS/D6Mcr9+VlwFfXgWcIXDrt5KQQwOpopmz2uhzWToNOYyB8QN33dPGCKV+YtZGzroGbf4OcFPj2NgjtB5Ner3mKbbfzof8NZhppzLiTRwtbiZZS1XMK8I3WuqwhJymlbgduB4iIqGX4uaGyksHZG1x9ST+ST7mWip5CCCHODHuWSQAztdbv2Y6/EHgFMytGCNHMBkT6cU73YN5blMBVgyLw97CzamVjsDqAq495NCbvcDj/vzD8YVsC+LEZ6XP2NFNFL5tu7n0q+lxl+uUt+pdJpiKHVq4ZLMyBW36vv4Knmx+MfNT+ewbEwKVTYeaVJulM32yS5ys/M0V1ajPxn5C0FL6/E+5a1vg/5zOgKSuWpAEdq7wPt22ryRSqT+O061yt9Qda63itdXxgYOBphmuTnQy+JolMyzatHMJ93Rrn2kIIIUTdji+T0FoXAxXLJI7TWudWeetOvXO7hBBn0mMTu1FQUsY/5+5A11VBsrXxDjMjdPf/adbROXvCFZ+Cx2n8Da4UXPCKWWL17W2m995vT5kpphe+CR16Nl78VXWZAGOehm2zIXuf+Rze4XWf4+QOkz80U2Dn/KVp4mpiTTnitwaIVUpFY5K2KcDVJx6klOoG+AIrqmz+DfiHUsrX9v4c4MkmjLVSVrKZ74yp6AnSvF0IIcQZY9cyCaXUPcDDgBMwpqYLNcmsGCFEvWKCPLhzVCfeXrAXi4J/XNKrbVWH9w6D8/5jHo3B2RMu+wimnQMfTYBDu8yawV6XNc71azPiEdMqIrgHRA6x75zwAaY9xIIXIbAr9Lqi+prHU6U1JC6GgztqrjjaSJos8dNalyql7sUkcVbgI631VqXU34G1WusfbYdOAWbpKl+JaK2PKKWexySPAH+vKPTSpLQ2I36x44HK5u3Sw08IIURLorV+G3hbKXU1pvXRSdUMtNYfAB8AxMfHt6FhByFavr+c0xUHi4XX5+0mp6CE16f0w8WxhnVqwgjrD+OeNZU6o0ed2prBhlIKxvxfw88b/rBpRD//BfPw7giRw0xPxshhpjKrvc3eC7Jh4yxYMxUO7zZrGgfcaH9j+wZq0jV+Wus5wJwTtv31hPfP1XLuR8BHTRZcTY5mQGlhlYqe+QR5Osv/UYUQQpwpDVkmAWYq6LtNGpEQosGUUjw0vgs+bo787adt3DxjDR9cH4+Hc0spr9ECnXWPqToaPfLU1wyeCVYHuO4HyNxmWlIkLzNN3jfNMvs9QyB8IATFQWA38+wfY4rRVDiwEdZMg81fQ0m+Kbxz8XvQ45ImS/qg5RR3aRmyqvfwS82Sip5CCCHOqHqXSSilYrXWu21vzwd2I4RokW4aFo23qyOPfrOJaz5cyYybBuHr3nr7wDUpi8X+JvTNzWIx6w879DRTM7U2U1STlppE8MBG2PEz6HLb8Y4m+QvqBjmpkLoGHFyh9+UQf4tpKn8GSOJXVfbJPfx6hXk3Y0BCCCHaEzuXSdyrlBoHlABZ1DDNUwjRckzuH46XiyN3z1zP5e+v4NNbBhHiLQMLbYpSZs1fYFcYeIvZVlJoksGDOyBzu3nev8EkfBP/BX2mgKtv3ddtZJL4VXW8eXsE5eWa/dkFnNuznn4nQgghRCOqb5mE1vqBMx6UEOK0jOsezCc3D+LWj9dy2bsr+PD6eOJCPFH2rgUTrY+jC4T0No8WQhK/qrKTwCMYnNzIzCmkpEzLVE8hhBBCCHHazurkz6zbz+KGj1Zz3htLCPBwom9HX/pF+NCvow+9O/rIGkDRpOS/rqqykqsVdgEIl+btQgghhBCiEfQM8+aX+0fw+/YM/tyXzYaULP7YngGY2YJdgjwZHhvATcOipI+0aHSS+FWVnQwdTbukih5+4TLiJ4QQQgghGkkHbxeuOyuS684ygw05+SX8mZrNn/uyWb8vi09WJDFjeRKTeodwx6jOxIV4NW/Aos2QxK9CWSnkpEGvyoqeIM3bhRBCCCFE0/F2c2RUl0BGdQkE4EBOAdOWJPLF6n3M/nM/o7sGcueozgyO9pM1geK0SOJXQZfDxe+aMquYip6+bo64OcmPSAghhBBCnBkh3q48fUF37hsTy6crk5i+LIkpH6ykb0cfpgzsSKiPK4GezgR6OuPn5oTFIsmgsI9kNRUcnKDPlcffpmYVyNxqIYQQQgjRLLzdHLl3TCy3jujE1+tS+XBxAk98t7naMVaLwt/diSAvZ/zdnfFxc8TH1RFvV0e83ZzwcXXEx82RIE8XunbwxMnB0kyfRrQEkvjVIi0rn9ggz+YOQwghhBBCtGMujlauOyuSqwdFkHIkn4NHiziYV/2RmVfI4WPFJB0+RnZ+CbmFJWhd/TrODhb6hPvQP9KXAZG+9I/wwd/DuXk+lGgWkvjVQGtNWnYBo7sGNXcoQgghhBBCYLUoogLciQpwr/fYsnLN0cJSsguKyc4vITWrgPX7sliXnMW0pQm8t8hkhdEB7vSP8GVwtB+DO/kR4ecm6wjbMEn8anD4WDGFJeVS0VMIIYQQQrQ6VovC280RbzdHIv2hT0cfzu8dAkBhSRmb03JYl2wSwfk7Mvh2fSoAHbxcGGRLAgdH+9M50F0SwTZEEr8apFVU9JQefkIIIYQQog1xcbQyMMqPgVF+AJSXa/YcPMqqxCOsSjjMioTD/LhxPwD+7k70i7A1mY/woXe4NJlvzeR/uRqkZUsrByGEEEII0fZZLIouwZ50CfbkurMi0VqTdDif1YmHWZ2YVa3JvEVBl2BP+kX40Cfchwh/N8J93Ojg7SKFY1oBSfxqkJqVDyBVPYUQQgghRLuilCI6wJ3oAHeuHBgBQHZ+MX+mZLNhXzYbUrL5ZdMBvlidUuUcCPZ0IdzXlTBfVyL83BgRG8iASF+s0m6ixZDErwZpWQV4Ojvg7erY3KEIIYQQQgjRrHzcnBjdNeh44cPyck1KVj6pWQWkZRWQmm2e07LzWZecxc+bDvDm/D0EeDgxvnsw5/TowNDO/jg7WJv5k7RvkvjVIC27QKZ5CiGEEEIIUQOLRRHp706kf80VRo8WlbJwZya/bknnxz/388XqFDydHTi7WxBj44KwWhS5BaXkFJQcf+QWlpBXWAqAg0WZh1VhtVhwsCicrBbO7hbI+O4dZBTxFEniVwPTvF0SPyGEEEIIIRrKw9mBC3qHckHvUApLyli+9xC/bcng9+0ZxwvHVHC0KrxdHfFydcTTxRGFaUdRUlZOWbmmrFxTWq7JKyzhy7UpRPq7cevwaC4b0BFXJxlBbAhJ/GqQllXA4Gi/5g5DCCGEEEKIVs3F0cqYbsGM6RbMi2Xl7EjPw8nBgpeLI96ujrg4WuxqGVFWrvltazrvL07gmR+28srvu7huSBTXD4kkoEoj+uLScnam57EpLZtNKTlsO5BLl2BPbhoWRc8w76b8qC2eJH4nyCkoIa+oVKZ6CiGEEEII0YgcrJZTTr6sFsV5vUI4t2cH1iRl8cHiBN6Yt5v3Fu1lcr8wHK0WNqXlsH1/LsVl5QD4uDkS18GLX7cc4Nv1qcRH+nLjsCgm9OiAo7X2KqRaazLzimyJadsZVZTE7wRS0VMIIYQQQoiWSSnFoGg/BkX7sSfzKNOWJvDt+jScrBZ6hnlx47Aoeod70yfch3BfV5RS5BaW8PXaVD5ensS9MzcQ4u3CtWdFctWgCHxcHUk6fIyt+3PZsj+Hbftz2bo/lyPHiukf4cOXdwypM0lsTSTxO4E0bxdCCCGEEKLliwny4J+Te/PspB44WS1Yain64uXiyC3Do7lxaBQLd2YyfVkS//ltJ6/P242jRXGsuAww6w27BHsyLi4ILxdHpi5N5JXfd/H4xG5n8mM1GUn8TpCaJc3bhRBCCCGEaC3snY5ptSjGxgUzNi6Y3Rl5zFy9j/JyTY8wb3qEehEb5FmtEf3RolLeW7SXYZ0DGB4b0FThnzGS+J0gLbsAF0cL/u5OzR2KEEIIIYQQognEBnvy7KQedR7z7KQerE3O4qGv/mTuAyOqFZFpjdrGhNVGlJZVQJiPq13VhYQQQgghhBBtk6uTlbeu7kdOQQl/+Xoj5eW6uUM6LZL4nSA1O58wKewihBBCCCFEu9etgxfPnB/Hwp0H+WhZYnOHc1ok8TtBmjRvF0IIIYQQQthce1YkE3oE89KvO9icmtPc4ZwySfyqOFZUSlZ+iVT0FEIIIYQQQgCmhcRLl/Ym0MOZ+75Yz9Gi0uYO6ZRI4ldFWrap6CkjfkIIIYQQQogKPm5OvDalH/uO5PPM7C3NHc4pkcSviooefpL4CSGEEEIIIaoaFO3HA2O78P2GNN6ct5sjx4qbO6QGkXYOVaRmVzRvl+IuQgghhBBCiOruHRPD+n1Z/H979x9rd33Xcfz5urdlVGja0tIC/UFL2s6wspWJBdfFMHSkukVIXDbINHMukizTMJ1T0ETi4pLNRDen/DFUBiabuOiYjZlABRwL4KBz3crtGJbSpa2Ugm0dZJXR27d/nC/rWXtPf4Rzek7PeT6Sk/P9fs65577v+57vfd/39/v5fs+fbXiKT/3bU1y+bC7rVp3H1W9YwPmzBvvgkY1fm537fsD08TB/5un9GR2SJEmSum98LNzx/p9m4r+/z70Tu7nnid3csn6CW9ZPsHrxbNatOo9LFs5i8lBx8NAhDk4Wk4eKVw4Vk4cOccGsGaxZdk5fPjrOxq/Nrn0HuGD2DMbG/Aw/SZIkSUdLwqqFs1i1cBYfufr1bN3zEvdO7Obeid184l+fPO7Xv37BTH5t7VKuXb2QGWeMn4KIW2z82uzaf8ArekqSJEk6Ycvnn83y+cv50NuWs2v/AXbsbc0inDY2xvhYmDYepo2F8bExNm7fy+ce3s7NX9rMJ+95kuvXLOFXr7iQC05BD2Lj12bnvgNcufLcfochSZIk6TS0cPaMYx5IWjbvLN71U4t47Jm93P7wM3z2q09z20PbWLfqPH597VLevGROz6aB2vg1/u+VSZ5/8WUWzfHCLpIkSZJ6IwmXXzSXyy+ay469P+DvHt3OXY/v4JGtL/DozT/HmdN7M/3Txq8xfXyM+377Z5l5pimRJEmS1HuLz/kJ/vAdF/Phn1/JU8+92LOmD2z8fmR8LKxcMJL4Y7gAAAgKSURBVLPfYUiSJEkaMWe9bhqXLpnT0+/R0w9wT7IuyXeTbE1yU4fnvDvJliQTSb7QNj6ZZFNzW9/LOCVJkiRpmPXsiF+SceBW4O3ATuDxJOurakvbc1YANwNrq2pfkvltL3Ggqlb3Kj5JkiRJGhW9POK3BthaVduq6ofAXcA1RzznN4Bbq2ofQFXt6WE8kiRJkjSSetn4LQR2tK3vbMbarQRWJnk4yX8kWdf22JlJNjbj1/YwTkmSJEkaav2+uMs0YAVwJbAIeCjJJVW1H7iwqnYluQh4IMnmqnq6/YuT3ADcALBkyZJTG7kkSZIknSZ6ecRvF7C4bX1RM9ZuJ7C+ql6pqmeAp2g1glTVruZ+G/DvwKVHfoOquq2qLquqy8491w9elyRJkqSp9LLxexxYkWRZkjOA64Ajr875ZVpH+0gyj9bUz21J5iR5Xdv4WmALkiRJkqST1rOpnlV1MMlvAvcC48DtVTWR5GPAxqpa3zx2dZItwCTw0ar6nyRvAT6b5BCt5vQT7VcDlSRJkiSduJ6e41dVXwG+csTYH7UtF/A7za39OY8Al/QyNkmSJEkaFT39AHdJkiRJUv+lddDt9JfkeeB7XXipecALXXidYWNeOjM3UzMvnZmbzk40NxdWlVf1OkFdqpG+bzszN1MzL52Zm87MzdRec30cmsavW5JsrKrL+h3HoDEvnZmbqZmXzsxNZ+ZmcPm76czcTM28dGZuOjM3U+tGXpzqKUmSJElDzsZPkiRJkoacjd/Rbut3AAPKvHRmbqZmXjozN52Zm8Hl76YzczM189KZuenM3EztNefFc/wkSZIkach5xE+SJEmShpyNXyPJuiTfTbI1yU39jqefktyeZE+SJ9rGzkmyIcl/Nfdz+hljPyRZnOTBJFuSTCS5sRk3N8mZSR5L8q0mN3/cjC9L8vVmu/qHJGf0O9Z+SDKe5JtJ/qVZNy9Aku1JNifZlGRjMzby29MgskYeZo2cmjWyM2vksVkjp9aLGmnjR+sNB9wK/AJwMXB9kov7G1Vf3QGsO2LsJuD+qloB3N+sj5qDwEeq6mLgCuBDzfvE3MDLwFVV9SZgNbAuyRXAJ4FPVdVyYB/wgT7G2E83At9pWzcvh72tqla3XaLa7WnAWCOPcgfWyKlYIzuzRh6bNbKzrtZIG7+WNcDWqtpWVT8E7gKu6XNMfVNVDwF7jxi+BrizWb4TuPaUBjUAqurZqvrPZvlFWn+kFmJuqJaXmtXpza2Aq4B/bMZHMjdJFgHvAP6mWQ/m5VhGfnsaQNbINtbIqVkjO7NGdmaNPGmvaXuy8WtZCOxoW9/ZjOmwBVX1bLO8G1jQz2D6LclS4FLg65gb4EdTNTYBe4ANwNPA/qo62DxlVLerTwO/Bxxq1udiXl5VwH1JvpHkhmbM7WnwWCOPz/dtG2vk0ayRHVkjO+t6jZzWzeg0Gqqqkozs5WCTnA38E/Dhqvp+a+dUyyjnpqomgdVJZgN3Az/Z55D6Lsk7gT1V9Y0kV/Y7ngH01qralWQ+sCHJk+0PjvL2pNPXqL9vrZFTs0YezRp5XF2vkR7xa9kFLG5bX9SM6bDnkpwP0Nzv6XM8fZFkOq2C9vmq+lIzbG7aVNV+4EHgZ4DZSV7dwTSK29Va4JeSbKc1Pe4q4C8wLwBU1a7mfg+tf4TW4PY0iKyRx+f7FmvkibBG/hhr5DH0okba+LU8DqxoriJ0BnAdsL7PMQ2a9cD7muX3Af/cx1j6opl3/rfAd6rqz9seMjfJuc1eTJLMAN5O6/yOB4F3NU8budxU1c1VtaiqltL6u/JAVb2XEc8LQJKzksx8dRm4GngCt6dBZI08vpF/31ojO7NGTs0a2VmvaqQf4N5I8ou05hmPA7dX1cf7HFLfJPl74EpgHvAccAvwZeCLwBLge8C7q+rIk9uHWpK3Al8DNnN4Lvof0DqHYdRz80ZaJxmP09qh9MWq+liSi2jtxTsH+CbwK1X1cv8i7Z9mGsvvVtU7zQs0Obi7WZ0GfKGqPp5kLiO+PQ0ia+Rh1sipWSM7s0YenzXyx/WqRtr4SZIkSdKQc6qnJEmSJA05Gz9JkiRJGnI2fpIkSZI05Gz8JEmSJGnI2fhJkiRJ0pCz8ZMGQJLJJJvabjd18bWXJnmiW68nSdKpZI2UumNavwOQBMCBqlrd7yAkSRpA1kipCzziJw2wJNuT/GmSzUkeS7K8GV+a5IEk305yf5IlzfiCJHcn+VZze0vzUuNJ/jrJRJL7kszo2w8lSVIXWCOlk2PjJw2GGUdMY3lP22P/W1WXAH8FfLoZ+0vgzqp6I/B54DPN+GeAr1bVm4A3AxPN+Arg1qp6A7Af+OUe/zySJHWLNVLqglRVv2OQRl6Sl6rq7CnGtwNXVdW2JNOB3VU1N8kLwPlV9Uoz/mxVzUvyPLCoql5ue42lwIaqWtGs/z4wvar+pPc/mSRJr401UuoOj/hJg686LJ+Ml9uWJ/H8XknScLBGSifIxk8afO9pu3+0WX4EuK5Zfi/wtWb5fuCDAEnGk8w6VUFKktQH1kjpBLlHQxoMM5Jsalu/p6pevVz1nCTfprVH8vpm7LeAzyX5KPA88P5m/EbgtiQfoLXX8oPAsz2PXpKk3rFGSl3gOX7SAGvOX7isql7odyySJA0Sa6R0cpzqKUmSJElDziN+kiRJkjTkPOInSZIkSUPOxk+SJEmShpyNnyRJkiQNORs/SZIkSRpyNn6SJEmSNORs/CRJkiRpyP0/OnxzCqR/wVQAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plothistory(history)" ] }, { "cell_type": "markdown", "metadata": { "id": "qSeLed2JxvGI" }, "source": [ "**Write up**: \n", "* Link to the model on Hugging Face Hub:\n", "* Include some examples of misclassified images. Please explain what you might do to improve your model's performance on these images in the future (you do not need to impelement these suggestions)" ] }, { "cell_type": "markdown", "metadata": { "id": "3N-SPCP7UOLc" }, "source": [ "## Hugging Face Hub model: [flip_detector](https://huggingface.co/numbpy/flip_detector)." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 683 }, "id": "VGa6S606W-Lc", "outputId": "a3562ec0-259f-48fb-bb97-ef0ce8c017ef" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "rows=5\n", "columns=5\n", "\n", "fig, axes = plt.subplots(rows, columns, figsize=(12,12))\n", "axes = axes.ravel()\n", "\n", "incorrect_ones = sample_incorrect(model, x_val, y_val, num_points=rows*columns)\n", "\n", "for i in np.arange(0, columns*rows):\n", " axes[i].imshow(incorrect_ones[i])\n", " axes[i].axis('off')" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 683 }, "id": "jqaneZb2cxOd", "outputId": "3c18534f-e36d-4dd6-88a3-9217903af023" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "rows=5\n", "columns=5\n", "\n", "fig, axes = plt.subplots(rows, columns, figsize=(12,12))\n", "axes = axes.ravel()\n", "\n", "correct_ones = sample_correct(model, x_val, y_val, num_points=rows*columns)\n", "\n", "for i in np.arange(0, columns*rows):\n", " axes[i].imshow(correct_ones[i])\n", " axes[i].axis('off')" ] }, { "cell_type": "markdown", "metadata": { "id": "3cGhWwQ9UkSE" }, "source": [ "## Conclusion\n", "There doesn't seem to be a very clear distinctive difference between correctly classified and misclassified images, although the misclassified ones do seem to contain more unflipped images than the correctly classified ones.
\n", "One of the more recognizable difference is that, in the correctly classified images, the subject of the image is generally very clear and distinctive as compared to the misclassified images where the subject is not clearly distinguishable from its surroundings or as such is either too small or too large which, reduces the network's capacity to clearly distinguish it from its context(surroundings).
\n", "Thus, **it seems that the network is trying to distinguish among the images using the position of the subject to its surroundings.**" ] }, { "cell_type": "markdown", "metadata": { "id": "xo7yZP9ngV8h" }, "source": [ "## Suggestions to improve model accuracy\n", "As previously argued, the network relies on its ability to distinguish the subject and (hence it's orientation) from the surrounding. Hence, anything that helps with that would be helpful to increase the accuracy. In particular, we can do data augmentations such as random crop, changing contrast and image lighting to make the network more robust to slight variations in such things. A much more sophisticated method can be to use visual attention models to focus on the subject more. " ] }, { "cell_type": "markdown", "metadata": { "id": "cnVCcMlhCXDM" }, "source": [ "# Rest of the questions (unsolved)" ] }, { "cell_type": "markdown", "metadata": { "id": "sFU9LTOyMiMj" }, "source": [ "## 2. Deep Learning for NLP\n", "\n", "**Fake news classifier**: Train a text classification model to detect fake news articles!\n", "\n", "* Download the dataset here: https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset\n", "* Develop an NLP model for classification that uses a pretrained language model\n", "* Finetune your model on the dataset, and generate an AUC curve of your model on the test set of your choice. \n", "* [Upload the the model to the Hugging Face Hub](https://huggingface.co/docs/hub/adding-a-model), and add a link to your model below.\n", "* *Answer the following question*: Look at some of the news articles that were classified incorrectly. Please explain what you might do to improve your model's performance on these news articles in the future (you do not need to impelement these suggestions)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "id": "E90i018KyJH3" }, "outputs": [], "source": [ "### WRITE YOUR CODE TO TRAIN THE MODEL HERE" ] }, { "cell_type": "markdown", "metadata": { "id": "kpInVUMLyJ24" }, "source": [ "**Write up**: \n", "* Link to the model on Hugging Face Hub: \n", "* Include some examples of misclassified news articles. Please explain what you might do to improve your model's performance on these news articles in the future (you do not need to impelement these suggestions)" ] }, { "cell_type": "markdown", "metadata": { "id": "jTfHpo6BOmE8" }, "source": [ "## 3. Deep RL / Robotics" ] }, { "cell_type": "markdown", "metadata": { "id": "saB64bbTXWgZ" }, "source": [ "**RL for Classical Control:** Using any of the [classical control](https://github.com/openai/gym/blob/master/docs/environments.md#classic-control) environments from OpenAI's `gym`, implement a deep NN that learns an optimal policy which maximizes the reward of the environment.\n", "\n", "* Describe the NN you implemented and the behavior you observe from the agent as the model converges (or diverges).\n", "* Plot the reward as a function of steps (or Epochs).\n", "Compare your results to a random agent.\n", "* Discuss whether you think your model has learned the optimal policy and potential methods for improving it and/or where it might fail.\n", "* (Optional) [Upload the the model to the Hugging Face Hub](https://huggingface.co/docs/hub/adding-a-model), and add a link to your model below.\n", "\n", "\n", "You may use any frameworks you like, but you must implement your NN on your own (no pre-defined/trained models like [`stable_baselines`](https://stable-baselines.readthedocs.io/en/master/)).\n", "\n", "You may use any simulator other than `gym` _however_:\n", "* The environment has to be similar to the classical control environments (or more complex like [`robosuite`](https://github.com/ARISE-Initiative/robosuite)).\n", "* You cannot choose a game/Atari/text based environment. The purpose of this challenge is to demonstrate an understanding of basic kinematic/dynamic systems." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "id": "CUhkTcoeynVv" }, "outputs": [], "source": [ "### WRITE YOUR CODE TO TRAIN THE MODEL HERE" ] }, { "cell_type": "markdown", "metadata": { "id": "bWllPZhJyotg" }, "source": [ "**Write up**: \n", "* (Optional) link to the model on Hugging Face Hub: \n", "* Discuss whether you think your model has learned the optimal policy and potential methods for improving it and/or where it might fail." ] }, { "cell_type": "markdown", "metadata": { "id": "rbrRbrISa5J_" }, "source": [ "## 4. Theory / Linear Algebra " ] }, { "cell_type": "markdown", "metadata": { "id": "KFkLRCzTXTzL" }, "source": [ "**Implement Contrastive PCA** Read [this paper](https://www.nature.com/articles/s41467-018-04608-8) and implement contrastive PCA in Python.\n", "\n", "* First, please discuss what kind of dataset this would make sense to use this method on\n", "* Implement the method in Python (do not use previous implementations of the method if they already exist)\n", "* Then create a synthetic dataset and apply the method to the synthetic data. Compare with standard PCA.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "TpyqWl-ly0wy" }, "source": [ "**Write up**: Discuss what kind of dataset it would make sense to use Contrastive PCA" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "id": "1CQzUSfQywRk" }, "outputs": [], "source": [ "### WRITE YOUR CODE HERE" ] }, { "cell_type": "markdown", "metadata": { "id": "dlqmZS5Hy6q-" }, "source": [ "## 5. Systems" ] }, { "cell_type": "markdown", "metadata": { "id": "QW_eiDFw1QKm" }, "source": [ "**Inference on the edge**: Measure the inference times in various computationally-constrained settings\n", "\n", "* Pick a few different speech detection models (we suggest looking at models on the [Hugging Face Hub](https://huggingface.co/models?pipeline_tag=automatic-speech-recognition&sort=downloads))\n", "* Simulate different memory constraints and CPU allocations that are realistic for edge devices that might run such models, such as smart speakers or microcontrollers, and measure what is the average inference time of the models under these conditions \n", "* How does the inference time vary with (1) choice of model (2) available system memory (3) available CPU (4) size of input?\n", "\n", "Are there any surprising discoveries? (Note that this coding challenge is fairly open-ended, so we will be considering the amount of effort invested in discovering something interesting here)." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "id": "OYp94wLP1kWJ" }, "outputs": [], "source": [ "### WRITE YOUR CODE HERE" ] }, { "cell_type": "markdown", "metadata": { "id": "yoHmutWx2jer" }, "source": [ "**Write up**: What surprising discoveries do you see?" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [ "cnVCcMlhCXDM" ], "name": "Coding Challenge for Fatima Fellowship (CV)", "provenance": [] }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 1 }