commit cbb0817428b90522255712aaf9b4f2ac6c9ab67d parent 76129919574b62d05af23cecedf6c7acee598df4 Author: MTRNord <mtrnord1@gmail.com> Date: Sun, 25 Sep 2022 15:48:25 +0200 Run various new models Diffstat:
111 files changed, 302 insertions(+), 66 deletions(-)
diff --git a/input/MatrixData b/input/MatrixData @@ -5579,4 +5579,18 @@ spam You have won a guaranteed £1000 cash or a £2000 prize. To claim yr prize ham though the graph looks funky :D ham thats not how the optimal graph should look like lol ham but did you really track the error? 🤨 -ham `11 files changed, 447 insertions(+), 10 deletions(-)` lol -\ No newline at end of file +ham `11 files changed, 447 insertions(+), 10 deletions(-)` lol +spam Do you have any kind of Crypto wallet? If yes then this is a great opportunity for you You can make $10,500 Daily from Crypto Currencies Legit investment for more details on how to Apply contact the Company manager now for more details 👇👇👇👇👇👇👇 +ham switch to GNOME 43 runtime wen :) +ham on my low-end phone it takes after about 25 minutes to load and then it freezes forever at the first tap Flutter 👍 +ham But on a recent iphone it will probably be usable +ham this country has a lot of aberrant rules. I'll do with it until it unlocks one day. Difficult to use free applications in a so-called free country. Something to think about... anyway, thank you +ham The form must be filled out by the individual or company that is responsible for each application and is legally binding so the Element foundation can't pre-fill out forms for "you made a matrix client and want to let the French use it". So if one doesn't have the money to hire a French lawyer and authorized translator, the form is unlikely to get filled (or just lied about on the app store). (I believe America and France are the only two countries with this silly rule.) +ham Basrf +ham Based +ham I would test it on my ipod m, but its like 10 years old :) +ham hosting fluffychat web is weird tho, you need files from the build/ dir +ham hey guys I'm trying to build an ARM build of a Flutter app from a Linux distro running under UTM on an M1 Macbook unfortunately, building on ARM seems to yield a blank window, both for the debug build and the release build: how do you guys build for Linux on ARM? +spam Congrats on your new iPhone! Click here to claim your prize... +ham Greg, can you call me back once you get this? +ham I'm writing a bot. I want to be able to provide the bot with the cross signing recovery key from Element, then have it grab all the room keys from the server. 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sha256:dbf7e2f73fd07bc54531519167399dfd61cff2ae04f9bbcfe274ec8ddf9d5bce +size 200607 diff --git a/models/spam_keras_1664113591.3325882/variables/variables.index b/models/spam_keras_1664113591.3325882/variables/variables.index @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ec3febcf23640f79c5f6727bf01dc41915aca2d0662bea88e5199a40f0997323 +size 1636 diff --git a/spam-keras.ipynb b/spam-keras.ipynb @@ -17,12 +17,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "2022-09-25 03:06:15.233931: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "2022-09-25 15:42:34.455982: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2022-09-25 03:06:15.401804: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "2022-09-25 03:06:15.942846: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n", - "2022-09-25 03:06:15.942974: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n", - "2022-09-25 03:06:15.942983: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n" + "2022-09-25 15:42:34.619065: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2022-09-25 15:42:35.129795: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n", + "2022-09-25 15:42:35.129895: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n", + "2022-09-25 15:42:35.129902: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n" ] } ], @@ -32,7 +32,8 @@ "from tensorflow.keras.preprocessing.text import Tokenizer\n", "from tensorflow.keras.preprocessing.sequence import pad_sequences\n", "import tensorflow as tf\n", - "import matplotlib.pyplot as plt\n" + "import matplotlib.pyplot as plt\n", + "from nltk.corpus import stopwords\n" ] }, { @@ -50,11 +51,33 @@ "source": [ "# Read data\n", "data = pd.read_csv('./input/MatrixData', sep='\\t')\n", + "\n", + "\n", + "def remove_stopwords(input_text):\n", + " '''\n", + " Function to remove English stopwords from a Pandas Series.\n", + " \n", + " Parameters:\n", + " input_text : text to clean\n", + " Output:\n", + " cleaned Pandas Series \n", + " '''\n", + " stopwords_list = stopwords.words('english')\n", + " # Some words which might indicate a certain sentiment are kept via a whitelist\n", + " whitelist = [\"n't\", \"not\", \"no\"]\n", + " words = input_text.split()\n", + " clean_words = [word for word in words if (\n", + " word not in stopwords_list or word in whitelist) and len(word) > 1]\n", + " return \" \".join(clean_words)\n", + " \n", "# Convert label to something useful\n", "data.dropna(inplace=True)\n", "def change_labels(x): return 1 if x == \"spam\" else 0\n", "data['label'] = data['label'].apply(change_labels)\n", + "data['message'] = data['message'].apply(\n", + " remove_stopwords)\n", "\n", + "data = data.sample(frac=1).reset_index(drop=True)\n", "sentences = data['message'].tolist()\n", "labels = data['label'].tolist()\n", "\n", @@ -84,14 +107,20 @@ "outputs": [], "source": [ "vocab_size = 1000\n", - "#prev 16\n", - "embedding_dim = 64\n", + "embedding_dim = 16\n", "#max_length = 120\n", "max_length = None\n", "trunc_type = 'post'\n", "padding_type = 'post'\n", - "oov_tok = \"<OOV>\"\n", - "\n", + "oov_tok = \"<OOV>\"" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ "tokenizer = Tokenizer(num_words=vocab_size, oov_token=oov_tok)\n", "tokenizer.fit_on_texts(training_sentences)\n", "word_index = tokenizer.word_index\n", @@ -114,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -125,24 +154,22 @@ "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", - " embedding (Embedding) (None, None, 64) 64000 \n", - " \n", - " dropout (Dropout) (None, None, 64) 0 \n", + " embedding (Embedding) (None, None, 16) 16000 \n", " \n", - " global_average_pooling1d (G (None, 64) 0 \n", + " global_average_pooling1d (G (None, 16) 0 \n", " lobalAveragePooling1D) \n", " \n", - " dropout_1 (Dropout) (None, 64) 0 \n", + " dropout (Dropout) (None, 16) 0 \n", " \n", - " dense (Dense) (None, 6) 390 \n", + " dense (Dense) (None, 6) 102 \n", " \n", - " dropout_2 (Dropout) (None, 6) 0 \n", + " dropout_1 (Dropout) (None, 6) 0 \n", " \n", " dense_1 (Dense) (None, 1) 7 \n", " \n", "=================================================================\n", - "Total params: 64,397\n", - "Trainable params: 64,397\n", + "Total params: 16,109\n", + "Trainable params: 16,109\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] @@ -151,28 +178,28 @@ "name": "stderr", "output_type": "stream", "text": [ - "2022-09-25 03:06:17.122319: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", + "2022-09-25 15:42:37.031499: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", - "2022-09-25 03:06:17.150100: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", + "2022-09-25 15:42:37.068347: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", - "2022-09-25 03:06:17.150155: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", + "2022-09-25 15:42:37.068423: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", - "2022-09-25 03:06:17.151180: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "2022-09-25 15:42:37.069082: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2022-09-25 03:06:17.151737: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", + "2022-09-25 15:42:37.069501: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", - "2022-09-25 03:06:17.151791: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", + "2022-09-25 15:42:37.069558: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", - "2022-09-25 03:06:17.151818: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", + "2022-09-25 15:42:37.069594: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", - "2022-09-25 03:06:17.645774: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", + "2022-09-25 15:42:37.641729: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", - "2022-09-25 03:06:17.645868: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", + "2022-09-25 15:42:37.641835: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", - "2022-09-25 03:06:17.645875: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1700] Could not identify NUMA node of platform GPU id 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n", - "2022-09-25 03:06:17.645913: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", + "2022-09-25 15:42:37.641843: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1700] Could not identify NUMA node of platform GPU id 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n", + "2022-09-25 15:42:37.641890: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:09:00.0/numa_node\n", "Your kernel may have been built without NUMA support.\n", - "2022-09-25 03:06:17.645957: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5904 MB memory: -> device: 0, name: NVIDIA GeForce RTX 2080 SUPER, pci bus id: 0000:09:00.0, compute capability: 7.5\n" + "2022-09-25 15:42:37.641934: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5904 MB memory: -> device: 0, name: NVIDIA GeForce RTX 2080 SUPER, pci bus id: 0000:09:00.0, compute capability: 7.5\n" ] } ], @@ -180,14 +207,12 @@ "model = tf.keras.Sequential([\n", " tf.keras.layers.Embedding(\n", " vocab_size, embedding_dim, input_length=max_length),\n", - " tf.keras.layers.Dropout(.3,),\n", " tf.keras.layers.GlobalAveragePooling1D(),\n", - " tf.keras.layers.Dropout(.3,),\n", + " tf.keras.layers.Dropout(.5,),\n", " tf.keras.layers.Dense(6, activation='relu',\n", " kernel_regularizer=tf.keras.regularizers.l2(0.0001)),\n", - " tf.keras.layers.Dropout(.3,),\n", - " tf.keras.layers.Dense(1, activation='sigmoid',\n", - " kernel_regularizer=tf.keras.regularizers.l2(0.0001))\n", + " tf.keras.layers.Dropout(.5,),\n", + " tf.keras.layers.Dense(1, activation='sigmoid')\n", "])\n", "model.compile(loss=tf.keras.losses.BinaryCrossentropy(),\n", " optimizer='adam', metrics=['accuracy'])\n", @@ -203,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -214,19 +239,22 @@ "# Define the checkpoint directory to store the checkpoints.\n", "checkpoint_dir = './training_checkpoints'\n", "# Define the name of the checkpoint files.\n", - "checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt_{epoch}\")" + "checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt_{epoch}\")\n", + "\n", + "#es_callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=3)" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Average test loss: 0.11202052943408489\n" + "Average train loss: 0.13753025684505701\n", + "Average test loss: 0.07057723039761185\n" ] } ], @@ -239,27 +267,29 @@ " callbacks=[tensorboard_callback, \n", " tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_prefix,\n", " save_weights_only=True),\n", + " #es_callback\n", " ],\n", " validation_data=(testing_padded, testing_labels_final))\n", "\n", - "print(\"Average test loss: \", np.average(history.history['loss']))\n" + "print(\"Average train loss: \", np.average(history.history['loss']))\n", + "print(\"Average test loss: \", np.average(history.history['val_loss']))\n" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "INFO:tensorflow:Assets written to: ./models/spam_keras_1664068239.5405242/assets\n" + "INFO:tensorflow:Assets written to: ./models/spam_keras_1664113591.3325882/assets\n" ] }, { "data": { - "image/png": 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dePbZZ+nQoUO55+bl5ZGXl+f6OT09/cQaWZANzzao0KmNi77c5tH94Bt03NPq1q3LBRdcwGeffcagQYMA+Prrr4mIiOCcc87BbrfTpUsX1/nPPPMM3333HT/++CPjxo1zZ4tFREQ8zqM9N8nJyTgcjjI9L9HR0cTHx5d7mzZt2jB16lR++OEHPvnkE5xOJ3379mXv3r3lnj9x4kRCQ0NdX40buzV+VBvXXXcd33zzjSvIffrpp1xzzTXY7XYyMzN58MEHadeuHWFhYQQHB7Nx40b13IiISK3k0Z6bk9GnTx/69Onj+rlv3760a9eOd955h2eeeabM+RMmTGD8+PGun9PT008s4PgEmh6UCjiQlkNyZj6RdXyJCQmo+GMc67Er6OKLL8ayLGbMmEHPnj3566+/eOWVVwB48MEH+eOPP3jppZdo2bIlAQEBXHnlleTn5596G0VERKoZj4abiIgIvLy8SEhIKHU8ISGBmJiYCt2Hj48PZ5xxBtu2bSv3ej8/P/z8/E6+kTZbhYaGAPC1Y/l44/T2A183hJsT4O/vz+WXX86nn37Ktm3baNOmDd26dQNg4cKFjB49mssuuwwww3q7du2q0vaJiIhUFY8OS/n6+tK9e3dmz57tOuZ0Opk9e3ap3pljcTgcrF27lvr161dWMyvM0ysUX3fddcyYMYOpU6dy3XXXuY63atWKb7/9llWrVrF69WquvfbaMjOrREREaguPz5YaP3487777Lh9++CEbN27kjjvuICsrizFjxgBw4403lio4fvrpp/n999/ZsWMHK1as4Prrr2f37t3ccsstnnoKLsUvpqdWKD733HMJDw9n8+bNXHvtta7jkyZNom7duvTt25eLL76YIUOGuHp1REREahuP19yMGDGCpKQkHn/8ceLj4+natSszZ850FRnHxcVht5dksEOHDjF27Fji4+OpW7cu3bt35++//6Z9+/aeegourp4bDz2+3W5n//6y9UGxsbH8+eefpY7dddddpX7WMJWIiNQWNqsmboR0CtLT0wkNDSUtLY2QkJBS1+Xm5rJz506aNWt2UqseJ2fmsT81h9AAH5rWq2CdTi11qq+liIjI4Y71+X0kjw9L1SaeWqFYRERESijcuJENzw5LiYiIiMKNW9mLem6c6roRERHxGIWbcpxsGZKnp4JXJ6dZKZeIiFQjCjeH8fHxASA7O/ukbl/UcaMPdkpew+LXVEREpKp4fCp4deLl5UVYWBiJiYkABAYGunpjKqIgvwCrMJ9CvMjNza2sZlZrlmWRnZ1NYmIiYWFheHl5ebpJIiJymlG4OULxtg/FAedE5BU6ScrIw9tug4zTe/pzWFhYhbfQEBERcSeFmyPYbDbq169PVFQUBQUFJ3TbTQfSefLHFUQF+/H5bRXbPqI28vHxUY+NiIh4jMLNUXh5eZ3wB7Sffz77MhzkWYVauE5ERMRDVFDsRr7e5uXMK9SmlCIiIp6icONGvl7m5SxwKNyIiIh4isKNG/kV9dzkq+dGRETEYxRu3Kh4WMppQaF6b0RERDxC4caNfLxKXs58hRsRERGPULhxo+KeG9DQlIiIiKco3LiRt91G8YLGCjciIiKeoXDjRjabzTVjStPBRUREPEPhxs2Kh6ZUcyMiIuIZCjdupungIiIinqVw42bFw1IKNyIiIp6hcONmGpYSERHxLIUbN/PVsJSIiIhHKdy4mcKNiIiIZyncuJmr5kbDUiIiIh6hcONm6rkRERHxLIUbN/PRbCkRERGPUrhxMz/NlhIREfEohRs307CUiIiIZyncuJkW8RMREfEshRs30yJ+IiIinqVw42bF4Ua7gouIiHiGwo2b+Xp5ARqWEhER8RSFGzdTQbGIiIhnKdy4WUnNjcPDLRERETk9Kdy4mZ96bkRERDxK4cbNNBVcRETEsxRu3ExTwUVERDxL4cbNSvaWsjzcEhERkdOTwo2bqedGRETEsxRu3KxkKrhmS4mIiHiCwo2bqaBYRETEsxRu3MxPw1IiIiIepXDjZlqhWERExLMUbtxM4UZERMSzFG7cTDU3IiIinqVw42aaCi4iIuJZCjduVhxu8tRzIyIi4hEKN26mYSkRERHPUrhxs8OngluWtmAQERGpago3bla8t5RlQaFT4UZERKSqKdy4WXHNDWhoSkRExBMUbtzs8HBToBlTIiIiVU7hxs287TZsNvO9em5ERESqnsKNm9lsNldRcVa+dgYXERGpago3laB1dB0AVuw+5OGWiIiInH4UbipBv5YRACzcnuzhloiIiJx+FG4qQf/icLMtWWvdiIiIVDGFm0rQvWldfL3tJKTnsT0py9PNEREROa0o3FQCfx8vesbWBUzvjYiIiFQdhZtKUlx3s0DhRkREpEop3FSS4rqbxTtSKNRifiIiIlWmWoSbKVOmEBsbi7+/P71792bp0qUVut0XX3yBzWbj0ksvrdwGnoQODUIJ8fcmI7eQtfvSPN0cERGR04bHw82XX37J+PHjeeKJJ1ixYgVdunRhyJAhJCYmHvN2u3bt4sEHH+Sss86qopaeGC+7zTU0NWdzkodbIyIicvrweLiZNGkSY8eOZcyYMbRv3563336bwMBApk6detTbOBwOrrvuOp566imaN29eha09MYPaRQPwx4YED7dERETk9OHRcJOfn8/y5csZPHiw65jdbmfw4MEsWrToqLd7+umniYqK4uabbz7uY+Tl5ZGenl7qq6oMahuF3QYbD6Sz52B2lT2uiIjI6cyj4SY5ORmHw0F0dHSp49HR0cTHx5d7mwULFvD+++/z7rvvVugxJk6cSGhoqOurcePGp9zuiqob5EvP2HBAvTciIiJVxePDUiciIyODG264gXfffZeIiIgK3WbChAmkpaW5vvbs2VPJrSzt/A4xAPy+ofywJiIiIu7l7ckHj4iIwMvLi4SE0r0aCQkJxMTElDl/+/bt7Nq1i4svvth1zOk006y9vb3ZvHkzLVq0KHUbPz8//Pz8KqH1FXN++2ie+XkDy3Yd4lBWPnWDfD3WFhERkdOBR3tufH196d69O7Nnz3YdczqdzJ49mz59+pQ5v23btqxdu5ZVq1a5vi655BLOOeccVq1aVaVDThXVODyQtjF1cDgt/tx07BlgIiIicuo82nMDMH78eEaNGkWPHj3o1asXkydPJisrizFjxgBw44030rBhQyZOnIi/vz8dO3YsdfuwsDCAMserk/PaR7MpPoO/tiZxRfdGnm6OiIhIrebxcDNixAiSkpJ4/PHHiY+Pp2vXrsycOdNVZBwXF4fdXqNKg8poFV0HgANpuR5uiYiISO1nsyzL8nQjqlJ6ejqhoaGkpaUREhJSJY+5eEcK1/xvMc0jgvjzwYFV8pgiIiK1yYl8ftfsLpEaIrKOKWhOysjzcEtERERqP4WbKhBVFG4y8grJyXd4uDUiIiK1m8JNFQj288bfx7zU6r0RERGpXAo3VcBmsxFVxx+AxAwVFYuIiFQmhZsqorobERGRqqFwU0WK624SFW5EREQqlcfXuak1slIgrmgn83YXlblaPTciIiJVQ+HGXZI2wpfXQb2W5Yabkp4b1dyIiIhUJg1LuUuwWVGZzPL3j4rUsJSIiEiVULhxl+Aoc5mXDvnZZa4uni2lYSkREZHKpXDjLn4h4G0CDFlle2/UcyMiIlI1FG7cxWYr6b0pZ2iquOYmJTMPh/O02s5LRESkSincuJOr7iahzFX1gv2w28BpQUqWem9EREQqi8KNOx0j3HjZbYQHaTq4iIhIZVO4cadjDEuBFvITERGpCgo37nSMnhvQQn4iIiJVQeHGnSrYc6NwIyIiUnkUbtypgj03ielapVhERKSyKNy403FWKXb13GSq50ZERKSyKNy4k2tYKgGssmvZRBatUpyYrnAjIiJSWRRu3CmoKNw48iE3tczVUSHquREREalsCjfu5OMP/qHm+3KGpiKDi2tu8rDK6dkRERGRU6dw427HKCou7rnJKXCQle+oylaJiIicNhRu3O0YRcWBvt4E+3kDmjElIiJSWRRu3O3wouJyaCE/ERGRyqVw424VXetG4UZERKRSKNy423FWKVa4ERERqVwKN+52nJ4bbcEgIiJSuRRu3K3CPTcqKBYREakMCjfudtyeG7NKsXpuREREKofCjbsVh5usZHAUlrlas6VEREQql8KNuwXWA5sdsCArqczVqrkRERGpXAo37mb3gpBG5vtDO8tcXdxzk5KVT4HDWZUtExEROS0o3FSGiFbmMnlLmavCA33xttsASMnMr8pWiYiInBYUbipDRGtzmby1zFV2u42IYM2YEhERqSwKN5XB1XNTNtzAYdPB01V3IyIi4m4KN5XB1XNTdlgKDisqzlS4ERERcTeFm8pQHG5Sd0Nh2QCjnhsREZHKo3BTGYKjwC8ELCcc3FHm6pKeG9XciIiIuJvCTWWw2Y45Y0o9NyIiIpVH4aayHKPuJrJ4CwbV3IiIiLidwk1lOcaMKfXciIiIVB6Fm8pyjLVuDp8tZVlWVbZKRESk1lO4qSyHh5sjAkxxz01+oZP0nLKba4qIiMjJU7ipLHWbgc0L8jMgI77UVf4+XoT4ewNapVhERMTdFG4qi7cv1I0135dTVFw3yBeA1JyCKmyUiIhI7adwU5mKi4pTtpW5KsTfB4CMXIUbERERd1K4qUxhTcxl+r4yV9UpGpbKyFXNjYiIiDsp3FSm0EbmMm1vmauKw026hqVERETcSuGmMoU0NJflhJviYal09dyIiIi4lcJNZQptbC7T9pS5qo6r5kbhRkRExJ0UbipT8bBU+gFwOkpd5RqWUkGxiIiIWyncVKY6MWatG2cBZCaWuiokQD03IiIilUHhpjLZvSCkgfn+iLqbktlS6rkRERFxJ4WbyuYamiodblwFxZotJSIi4lYKN5XtKNPBQ7TOjYiISKU4qXDz4YcfMmPGDNfP//rXvwgLC6Nv377s3r3bbY2rFY4yHVyzpURERCrHSYWbZ599loCAAAAWLVrElClTeOGFF4iIiOD+++93awNrvKP13ARotpSIiEhl8D6ZG+3Zs4eWLVsC8P3333PFFVdw66230q9fPwYOHOjO9tV8rrVuyu+5yc53UOhw4u2lEUIRERF3OKlP1ODgYFJSUgD4/fffOe+88wDw9/cnJyfHfa2rDY7Sc1M8WwogM09DUyIiIu5yUj035513HrfccgtnnHEGW7ZsYdiwYQCsX7+e2NhYd7av5gstqrnJToaCHPAxw3k+XnYCfLzIKXCQnlNIWKCvBxspIiJSe5xUz82UKVPo06cPSUlJfPPNN9SrVw+A5cuXM3LkyJO6v9jYWPz9/enduzdLly496rnffvstPXr0ICwsjKCgILp27crHH398Mk+javiHgW+w+T6t9O7gWqVYRETE/U6q5yYsLIw33nijzPGnnnrqhO/ryy+/ZPz48bz99tv07t2byZMnM2TIEDZv3kxUVFSZ88PDw/n3v/9N27Zt8fX15eeff2bMmDFERUUxZMiQk3k6lctmM0NTSZvMWjcRLV1X1fH3JjEjTzOmRERE3Oikem5mzpzJggULXD9PmTKFrl27cu2113Lo0KETuq9JkyYxduxYxowZQ/v27Xn77bcJDAxk6tSp5Z4/cOBALrvsMtq1a0eLFi2499576dy5c6n2VDtHnTFVvDO4em5ERETc5aTCzUMPPUR6ejoAa9eu5YEHHmDYsGHs3LmT8ePHV/h+8vPzWb58OYMHDy5pkN3O4MGDWbRo0XFvb1kWs2fPZvPmzZx99tkn/kSqita6ERERqTInNSy1c+dO2rdvD8A333zDRRddxLPPPsuKFStcxcUVkZycjMPhIDo6utTx6OhoNm3adNTbpaWl0bBhQ/Ly8vDy8uLNN990zdg6Ul5eHnl5ea6fi0NZlXJNB99T6rCr5kZbMIiIiLjNSfXc+Pr6kp2dDcCsWbM4//zzAVMPUxXhoU6dOqxatYply5bx3//+l/HjxzN37txyz504cSKhoaGur8aNG1d6+8qoE2Muj9wZXD03IiIibndSPTf9+/dn/Pjx9OvXj6VLl/Lll18CsGXLFho1alTh+4mIiMDLy4uEhIRSxxMSEoiJiTnq7ex2u2sRwa5du7Jx40YmTpxY7gKCEyZMKDVUlp6eXvUBJyjCXGYllTocop3BRURE3O6kem7eeOMNvL29+frrr3nrrbdo2NDUlPz6668MHTq0wvfj6+tL9+7dmT17tuuY0+lk9uzZ9OnTp8L343Q6Sw09Hc7Pz4+QkJBSX1UuKNJcZqWUOqyp4CIiIu53Uj03TZo04eeffy5z/JVXXjnh+xo/fjyjRo2iR48e9OrVi8mTJ5OVlcWYMWMAuPHGG2nYsCETJ04EzDBTjx49aNGiBXl5efzyyy98/PHHvPXWWyfzVKpGoFkHiOzkUoeLZ0tpWEpERMR9TircADgcDr7//ns2btwIQIcOHbjkkkvw8vI6ofsZMWIESUlJPP7448THx9O1a1dmzpzpKjKOi4vDbi/pYMrKyuLOO+9k7969BAQE0LZtWz755BNGjBhxsk+l8hX33BRkQ34W+AYBJT03CjciIiLuY7MsyzrRG23bto1hw4axb98+2rRpA8DmzZtp3LgxM2bMoEWLFm5vqLukp6cTGhpKWlpa1Q1RWRb8NwYKc+HeNVC3KQCzNiRwy0f/0LlRKD+O6181bREREamBTuTz+6Rqbu655x5atGjBnj17WLFiBStWrCAuLo5mzZpxzz33nFSjazWb7bC6m5KhKQ1LiYiIuN9JDUvNmzePxYsXEx4e7jpWr149nnvuOfr16+e2xtUqgfXMOjeH1d3U0WwpERERtzupnhs/Pz8yMjLKHM/MzMTXV7tbl6uc6eAli/ip50ZERMRdTircXHTRRdx6660sWbIEy7KwLIvFixdz++23c8kll7i7jbXDMYal8h1OcgscnmiViIhIrXNS4ea1116jRYsW9OnTB39/f/z9/enbty8tW7Zk8uTJbm5iLVE8HfywnptgX29sNvO96m5ERETc46RqbsLCwvjhhx/Ytm2bayp4u3btXKsGSzmKe26ySxbys9ttBPt6k5FXSHpuAZF1/DzUOBERkdqjwuHmeLt9z5kzx/X9pEmTTr5FtdXRtmAI8CEjr1A9NyIiIm5S4XCzcuXKCp1nKx5nkdICi8NN6VWKNWNKRETEvSocbg7vmZGTUE5BMWjGlIiIiLudVEGxnISgw/aXOmxR6BD/4oX81HMjIiLiDgo3VaW456YwF/IzXYe1v5SIiIh7KdxUFd8g8A4w32cdvkqx6blJV8+NiIiIWyjcVKVypoOHBKjnRkRExJ0UbqpSUNmF/NRzIyIi4l4KN1WpnOngmi0lIiLiXgo3Vck1Hbyk50azpURERNxL4aYquaaDl9TcaLaUiIiIeyncVKVyem5UcyMiIuJeCjdVqZyam1DNlhIREXErhZuqVM7mmXUOq7mxDlu5WERERE6Owk1VKifcFBcUOy3Iynd4olUiIiK1isJNVQpvDnZvyDgAiRsB8Pex4203O6lrxpSIiMipU7ipSgF1odUQ8/3qLwCw2WyaMSUiIuJGCjdVrcs15nLNdHCaYaiQgKIZUznquRERETlVCjdVrfUQ8A+DjP2w6y9Aa92IiIi4k8JNVfP2g46Xm++Lhqbq+GmtGxEREXdRuPGEzkVDUxt+hPws187g6eq5EREROWUKN57QuBcER0NBFiRuLLXWjYiIiJwahRtPsNlMuAHISdXO4CIiIm6kcOMpAWHmMueQdgYXERFxI4UbTwmoay5zUzVbSkRExI0UbjylONwc1nOj2VIiIiKnTuHGU/zDzGXOIddsKfXciIiInDqFG09x9dykaraUiIiIGynceMphBcWaLSUiIuI+CjeeUk7NjXpuRERETp3CjaeUM1sqK99BocPpwUaJiIjUfAo3nnJYQXFxzQ1AZp6GpkRERE6Fwo2nHDYs5etlw9/HvBWaMSUiInJqFG48pbig2JEPBTmu3hutdSMiInJqFG48xTcY7KbWRjOmRERE3EfhxlNstlJFxZoxJSIi4h4KN55UqqhYqxSLiIi4g8KNJ2l/KREREbdTuPEk1yrFqdpfSkRExE0UbjzpsJ4b7S8lIiLiHgo3nnR4uPHTbCkRERF3ULjxpOKC4txUQgKKem7y1HMjIiJyKhRuPOmwnpvQonBzMCvfgw0SERGp+RRuPOmwguKm9QIB2Jmc5bn2iIiI1AIKN550WM9N88hgABLS81RULCIicgoUbjzpiGGpyDp+AOxIUu+NiIjIyVK48aTDCooBWkQGAbA9KdMz7REREakFFG48ybW3VBo4HbQoGppSuBERETl5CjeeVFxQDJCbVhJuEjUsJSIicrIUbjzJywd8TaAh5xAtotRzIyIicqoUbjzNVVSc6qq52ZWSRaHD6cFGiYiI1FwKN57mKio+RIPQAAJ8vChwWOw5lOPRZomIiNRUCjeedthCfna7jebFM6YSNTQlIiJyMhRuPM0Vbg4BaMaUiIjIKVK48bSAcHOZnQKUhJtt6rkRERE5KQo3nhYcZS6zkgFoEaWF/ERERE5FtQg3U6ZMITY2Fn9/f3r37s3SpUuPeu67777LWWedRd26dalbty6DBw8+5vnVXlCkucxKAg4flsrCsixPtUpERKTG8ni4+fLLLxk/fjxPPPEEK1asoEuXLgwZMoTExMRyz587dy4jR45kzpw5LFq0iMaNG3P++eezb9++Km65mwRFmMuinptmEUHYbJCWU0BKVr4HGyYiIlIzeTzcTJo0ibFjxzJmzBjat2/P22+/TWBgIFOnTi33/E8//ZQ777yTrl270rZtW9577z2cTiezZ8+u4pa7iavnxoQ5fx8vGtUNADRjSkRE5GR4NNzk5+ezfPlyBg8e7Dpmt9sZPHgwixYtqtB9ZGdnU1BQQHh4eLnX5+XlkZ6eXuqrWjliWApKD02JiIjIifFouElOTsbhcBAdHV3qeHR0NPHx8RW6j4cffpgGDRqUCkiHmzhxIqGhoa6vxo0bn3K73ao43OQcAkcBoOngIiIip8Ljw1Kn4rnnnuOLL77gu+++w9/fv9xzJkyYQFpamutrz549VdzK4wioC7ait+GI6eAKNyIiIifO25MPHhERgZeXFwkJCaWOJyQkEBMTc8zbvvTSSzz33HPMmjWLzp07H/U8Pz8//Pz83NLeSmH3gsAIU3OTlQR1Ylx7TCnciIiInDiP9tz4+vrSvXv3UsXAxcXBffr0OertXnjhBZ555hlmzpxJjx49qqKplevI6eBFu4PvPZRDboHDU60SERGpkTw+LDV+/HjeffddPvzwQzZu3Mgdd9xBVlYWY8aMAeDGG29kwoQJrvOff/55HnvsMaZOnUpsbCzx8fHEx8eTmVmDezmOmA5eL8iXsEAfLAt2JquoWERE5ER4dFgKYMSIESQlJfH4448THx9P165dmTlzpqvIOC4uDru9JIO99dZb5Ofnc+WVV5a6nyeeeIInn3yyKpvuPkf03NhsNlpEBrN89yG2J2XSrn6IBxsnIiJSs3g83ACMGzeOcePGlXvd3LlzS/28a9euym9QVSt3OniQCTeJ6rkRERE5ER4flhJKhqUyy1vrpgYPt4mIiHiAwk11cMyF/BRuREREToTCTXXg2hn8sHATVRJunE5toCkiIlJRCjfVgavnJtl1qHHdAHy8bOQWONmfluOhhomIiNQ8CjfVgWsqeBJYppfG28tO8wjTe7NuX5qnWiYiIlLjKNxUB8U9N4U5kF8yO6pPi3oAzN2cVN6tREREpBwKN9WBbxD4BJrvD6u7OaetqcWZszkRy1LdjYiISEUo3FQXR6xSDNC7WTgBPl4kpOex4UC6hxomIiJSsyjcVBdBxTOmEl2H/H286NdSQ1MiIiInQuGmuihnrRuAgW2KhqY2JR55CxERESmHwk11cfiMqcMU192siDvEoaz8qm6ViIhIjaNwU12Us9YNQMOwANpE18FpwfytGpoSERE5HoWb6uIow1IAg9ub3pvPl8ZVZYtERERqJIWb6iKkgblM2FDmqut6N8XbbmPxjoOs2pNate0SERGpYRRuqovmA8DuA0kbIWlzqasahAVwSVcTft6Zt90TrRMREakxFG6qi4C60OJc8/3678tcffuAFgDMXB/PDu0ULiIiclQKN9VJh0vN5frvylzVOroOg9pGYVnw3oKdVdsuERGRGkThpjppM6xkaCpxU5mrR/eLBbTmjYiIyLEo3FQnAWHQcpD5fsP3Za7u1qQudhscSMslIT23SpsmIiJSUyjcVDcdLjOX5QxNBfl50zq6DgCrNWtKRESkXAo31U3roYANkjZBVkqZq7s2DgPQlHAREZGjULipbgLCILy5+T5+dZmruxSFm9V7U6usSSIiIjWJwk11VL+zuTywpsxVXRqFAbBmTxpOp4VlWRQ4nFXYOBERkepN4aY6iulkLuPLhpvW0cH4+9jJyCtkR3IWj3yzlo5P/MaWhIwqbqSIiEj1pHBTHcV0MZfl9Nx4e9np1DAUgLfnbefLf/aQV+jkx1X7q7KFIiIi1ZbCTXVUPCyVsg3yyq5GXDw09fXyva5j2jFcRETEULipjoKjIDgGsCBhfZmruzYJc31fx98bgLX70jiYlV9FDRQREam+FG6qq+Lem3Lqbop7bgAeGtKGtjF1sCz4S703IiIiCjfVVkzxjKmy08Eb1Q3gmp6NubhLA67t1YQBrSMBmL8lGYDcAgeWZVVZU0VERKoThZvq6hg9Nzabjeeu6MzrI8/A28vO2UXh5q+tSczdnEjP/8xi7EfLq7K1IiIi1YbCTXVV3HOTuBEcBcc8tXvTuvj72EnMyOPmD/8hI6+QWRsTWLs3rQoaKiIiUr0o3FRXdWPBLxQc+WYrhmPw9/HizOb1AHA4LYL9TJHx1IU7K7uVIiIi1Y7CTXVls0Fka/P9wR3HPf2yMxoCcEW3Rnx8cy8Aflq9X7uHi4jIacfb0w2QYwhtDHuXQdre4546vGtDBrSOJCzQF4CesXVZtusQHy3axUND2lZ2S0VERKoN9dxUZ2GNzWXqnoqdXhRsAG7u3wyAt+ft4MxnZzN8ykLiUrLd3kQREZHqRuGmOgstCjdpFQs3hzuvfQzt6ofgcFrEp+eyek8qr87e6uYGioiIVD8KN9XZKYQbL7uNH8f1Y86DA3n7+m4A/LBqHwfSctzZQhERkWpH4aY6C21kLitQc1MeHy87zSKCGNqxPr2bhVPotPhg4S73tU9ERKQaUripzoprbrJTID/rlO7q9gEtAPhsSRzpucdeN0dERKQmU7ipzvxDwS/EfJ+275TuamCbSNpE1yEzr5C7P1vJ/C1JOJzaokFERGofhZvqzjU0FXdKd2Oz2bj/vFYAzNuSxI1Tl3LN/xaRW+A41RaKiIhUKwo31Z2rqPjk6m4ON7RjfX6+uz+j+jQlyNeLZbsO8fgP67TJpoiI1CoKN9Vdcc9NBde6OZ6ODUN5anhH3r6hO3YbTP9nL58vPfp9Z+cX8tmSON74cyt5hSW9PPmFzlLnOZ0Wqdn5bmmjiIjIqdAKxdVdmPt6bg53VqtIHhrSludnbuKJH9fRtn4dujWpC8ChrHyW7ExhwbZkfli1n4zcQgC2JGQyeURXflqznwnfrmVgm0heu+YMnBaMmbaUxTsOMuOe/rSNCXFrW0VERE6Ewk11dwpr3RzP7QOas2ZvKr+ui+fOT1bw4939+H7lPl78bTMFjpKhqibhgexPzeHH1ftJyshj0Y4UAH5ZG090yEbyCp0s3GaOzd6YqHAjIiIepXBT3VViuLHZbLx4VRe2JGSwPSmL8ybNJy3HTBNvFRVMnxb1OKdtFANaRfLdyn088NVqV7A5p00kczYnlVk3Z2XcIbe3U0RE5ESo5qa6K665Sd8PTvfPbAr28+adG3oQ7OdNWk4BPl42nrm0I7/ffzZPD+/IOW2isNttXNG9Ef8e1o4Qf28euaAtU0f35IHzWrvu58JO9QFYGZeqAmURkdOR03n8c6qIem6quzoxYPcGZyFkxENoQ7c/RMuoYP53Y3c+XRzHLWc144yi2psjjT27Obec1QybzQbAuHNbEuDrBcD1Zzbljw0JpGTlE3cwm6b1gtiXmkN4oK/rHBERqQEcBeZzp+h3fbmStoBvYMkf4AtegT//A63Oh373QpMzq6atR6FwU93ZvSCkAaTGmaGpSgg3AH1bRNC3RcRxz7Md9o/dZrNxy1nNXT93aBjCyrhUVsQdIjOvkEunLKRpvSC+ub0voYE+ldJuERE5CqcTCnPAN6jit0lYDx9fDnVj4fpvwC/4iPt0wLznYd4L4BMAV06FwlyY9aS5fvMv5qtpP7jxB/DyzO9+DUvVBKFNzKWbZ0y52xmNTY/Pit2pvL9gJwUOi22Jmdz2yT+lpo4nZ+bx+A/rWLM39aj3lV/o5L2/drAz+dS2nRARqZW2zoI5E00vy9H8eDe80Bz2/lP2up1/wfrvSh/LSIBPr4bMeNizGL67rWSoybIgbgl8eLEJN1hQkA1fXAvf3mbO6Xaj+fLyheAojwUbUM9NzeBa62a3Z9txHN2ahjF1oVkBOT4tFwA/bzuLdxxkwrdreemqzthsNp7/dRNfLd/Ldyv38dXtfcqdXfXegh28MHMzv62P56vb+1b1UxERqb72LYcvRoIjH4IioNfYsuckbYFVnwIW/P5/MObXkmGmQ7vhk8vN7Q/ugLMegNw0+PwaSN8LYU1MGcSmn+G7WyGwHuz+G+LXmNv7BsOFL8PuhbDiI3DkQcvz4KLJZrThnH9DYV5VvRrlUs9NTRDT0VxuneXZdhxH8To5cQezyXc46do4jHdu6I6X3cY3K/by67p49hzM5ruVZp+sjNxCbnx/KXsOZpe6H6fT4vOlZruJZbsOuYKSiMgJsSzYPsd80FdnJ1KIm30Qpo8ywQRgwWQoLGcB1b9fBYomd8Qtgq1/lFw359mS289+Gn66D17vAftXQEA43PA9XPKGuX7tV7DkbRNsvP3hjOvhtvnQ5Rq4+DUY9hJ0Hw1Xvm+CDZha0bpNK/6cKoHCTU3Q4XLABnF/m8RdTdUP9Sc6xM/18+i+sQxsE8VdA82O5E/+uJ6Xf99ModOiZ2xd2kTXITEjj3NemstVb//Nh3/vwrIs/tqWzJ6DOa77+XXdgSp/LiJSCyycDB9fCm/1gfkvVcqMU5wO2PKb6emoiPwsyEkt+Xnt1/DfaJjcCb691QwXHU1uOnx9k6m/DG8OwTGmp2X156XPS98Pq7803zc/x1zOftqEqPi1sKbouk5XmcvlH0BWIoS3gOu+hnotoMsIuHCS+fzpd68JMuM3wvAp5nowPUG9xsLFr5qNnqsRDUvVBKENodlZsHO+SdFnP+jpFpXLZrPRrUldfl0XT2QdP4YVTQ+/85yW/LTmADuTs/h+1X4AHji/DbH1grjlo2Ws25fOsl2HWLbrECmZeWxJyAQgItiX5Mx8Zqw5wJh+zTz2vESkBlr+YUmRq7MQ/nzG9OJc/w34+LvnMfIy4duxpoA2vAXcteTodSYp22HJO2aoyHLCBc+bdcy+ux2cBWbSSGocrPvGtLH5wNK337UAvrvDbKLsHQBXfww758Fvj8KCSdD1OvDyNmFr4WvmPpv0NQW/r3aBhLUw/QYTfLBMaLn8XdOGVZ/BmbfDmXeCd8kfqPS82XzVQDbrNFuUJD09ndDQUNLS0ggJqUEr6a78BH64CyJaw11Ljz1Fz4NmrDnAXZ+t4KlLOjCqb6zr+N/bkrn2vSUA9Iyty/Tb+rhmXsWlZPPtyr1MnrUVME/NsuDDm3oxaupSABZPGERM6LF/IVmW5brPrLxCfl6znws61SfEXzO1RE4r2/+ET64wIaL//RDRBn55CPIz4KwHYdBjp3b/eZmw6y/4878mNBS75HVTUHs4R4HpNZr/IlhH9BzZvMyxDpeb4Z6l/4MtM8EvBG74zvTy7JxvwlPiBnObsKZw+f/MVOv8bNPjk50MQVGmiPfQLsg3fyBy7XRoPQT+fgN+/3fJ49p9YNxS0/tTg5zI57fCTU2Rmw4vtTJT7m6dCw3O8HSLjiojt4A65QSKx39Yx+dL4/jk5t70bl6vzPUvzNzEm3O3A9C9aV2+uaMvV7z1N8t3H+KJi9u7em+y8wuZvyWZ/q0iCPbzZndKFuM+W4mvt51pY3oS7OfN2I+WM2tjAlf3aMQLV3ap3CcsIsdXkGsKYRv3Nj0MJ8qyTK9E8W0z4k0BbPOBMOiJkj/4HIVmGCp5i+nNGD7FXLfxJ/jyerN+y63zTI/4phnQtO+xP+QP7YI6DcDb1zz+b4/CsvdNzwiYUNF6CKz82PSC3L3cFN9u+MEEmwOrSwJQy8HQ5y44sMb0JDkLodnZZijI28+8Rh9fZkoQjmTzMgFoyH/Br07J8aXvwi9H9Ob7BEHXkaYepvh12bXQBLL9q6DNUFMnU8Mo3BxDjQ03AF+NgfXfQu/bTZdmDWNZFnmFTvx9yl/Uz+m0GD99Fd+v2s/b13dnaMcY3l+wk2d+3kCXRqF8c0dfHJbF9e8tYdmuQ8SE+HPLWc14a+52UrJMcdwFHWM4r30046evBsxsrSWPDiIs0LfKnqeIHMGyzOyc7X9C6wvMUIlv4Indxze3mNqWm2ZCdAeY+SgsnmKuG/q8GVYBEzxmjDeFsfeuKl0L8uX1JuSENYWcQ5CXbnoxet5senSCI815Tids/R0WvmqCRr2WZnbQ8g/N72Aw99HqPNMzFFgPXjsDMg5Aw+4mxB3OP8zcvtOVJccOrDa9Mt1Hlw4r2Qfhg2GQtNGEpca9zcJ4rc6DwPDyX5vMREjfB1nJENIQItuUFPfWIgo3x1Cjw82W3+Gzq8DLD26bB1HtPN0it7Msi+TMfCLrmHHf+LRczn5hDvkOJ+e1jybYz9s12+pwraOD2ZmcRYHDwttuo9Bp4etlJ9/h5P8ubMctZzWn0OGk0GkdNVyJSCVZ9y18Pabk50Y9zZDJ0T6sj7R9jikMBtNTc9WH8EqHkuEXmx2u+cz0wrzeHbKS4IIXoPdtpe8nIx7e6AV5aebn4GjITDDf231Mj0ZYU7P+S3rZ3zOu8654FzpcVvr4svdgxgMlP59xvekR8gmE9pdCSP2KPVcwPTh56WaYSVwUbo6hRocby4JPr4Jtf0D9rnDj9zD3ObPWQLdR5j+TT4CnW+l2v62P5+7PV7oWAvSy23j7+u6s3ZfGO/O2079lBK+OPIPvVuzlsR/WA9CpYShX92jEYz+sp1lEEO+N6sHoD5aSk+/go5t6075Bxd/7vEIHft4KRCInJS8D3uhpejU6XgnbZkFuKjTqBaNnmOGeY3E64J2zIWFdybEWg2D7bIhqb34Xrv7MHLfZTZ1NeAu4c3H59731DxNEulwD7YbDrvlmJtGRvS1+odBjNHS5Fpa8BcunmanQV38Mrc8ve7+F+WaBu+wUuGiSGW4St1K4OYYaHW4A0g/Am2eaXw4+QVBw2Aq+QVFw1TSI7eep1lWapTsPcsuHy0jPLeSZSztyw5lmDYUChxMfL7OigWVZPPnjemZtTOSDMT1pGBZA72dnk5lXSKCvF9n5ppgvPMiXL249k9bRpit4z8FsnvxxPa2i6/CvIW2w223kFjj4afV+vl+1j7+3p3BR5wa8dFXnMiEnt8ChniA5PRxYDf9MhV63QXT7it9u5gRY/KZZzv/OJXBoJ0wdYhaN63UrDH3O1Kfs/QfsdtPTUa8lRLaFoEhTTDtjvBleansxrPqk5L4ve8f0oHx7q6mfKa6DueZzaDvsxJ5f/DozpTr7oLltq/NLzxxK2GB+Lp4GXR7LqraTPWoDhZtjqPHhBsy6CN8UTc8La2p6bFZ8ZNY+6HilWUypFkpIz2Vfao5rscCKeOKHdXy4yKwN1L5+CF52G2v3pRER7MttZ7egcXggE75dw6Fs80vxyu6NuLl/M+75fCVbEzNL3Ve/lvVcO6g7nBZP/LiOL5bu4dazm/Pg+SYUuZtlWRQ6LVeAE6kU2/+Euc9Dox7Q9iJT52E/7N/cjrnwxXVmGCikkVnELajspIBSCvPh13+ZNVSgZOYOmNqZz64234c2MdObj+f8/5gC4de6mmAU0hDuXV0y9dpRaH4HOgshotWJPHupIWpUuJkyZQovvvgi8fHxdOnShddff51evXqVe+769et5/PHHWb58Obt37+aVV17hvvvuO6HHqxXhxrLMipF5GWZdAr9g2PyrmTkQ3RHuWOjpFlYbO5OzuOi1v+jQIJR3R/XAsixGvruEjQfSS53XPDKI3SnZOJyWayp6ZB0/RveNpXF4II98s4bsfAfNI4K4fUALFmxL5sfV+123v6RLA14sp2cnr9DB7I2J1AvypUVUMPWCfEttPnr4eeUNfU36fTOvz9nGRZ0bcN/gVrSIDC5zjsgpyUgwvcE5B0uOdbgMrvygaJbRz6ZexpFfMuzT4lwY9LgZ3rGAwU+Y+hCnw6zHsmeJWbr/wGrABuc9ZRaCO9ycZ4v2KMIU3Ha+2uxJlJ9pVhRO2mR6qC0nxHSGW2aZnpPl0+Cne80sqDOur5KXSKqHGhNuvvzyS2688UbefvttevfuzeTJk/nqq6/YvHkzUVFlC6mWLVvG9OnT6d69O/fffz8PP/zw6RluynNwp/mLxssXHj1wclMta6ncAgd+3vZSa+B8t3If363cx/Ldh7i0awMmXt6ZPzclcs8XK3E4Lc5uHcmkq7sQEWy6pVfvSeWmactcs7IAfLxsXNurCZ8uiaPQadGufgjPDO9Aj9iSIsl/fb2a6f+UbHjaqG4A/VtGMLRjDAPbmH/jU+Zs49VZW7nlrGb8a2hb17k7k7M4b9I8Cp3mv6jdBhMv78SInk0q78WS04tlmT+Ktsw09SsxnUzxr7PAzECK6WQKeR350O4SMzPog2Fmp+nDBYRDtxtMIW7qYb0wfiFwxfvl16g4HbDgFfM7q/to8C/n97FlmT2KvP1KD/cUH5PTSo0JN71796Znz5688YbZw8LpdNK4cWPuvvtuHnnkkWPeNjY2lvvuu0/hppjTCRMbml1axy2HiJaebtHROQpNEV+jnqWnQHpATr6DAN+SHpPluw+xLzWHizrVLzPMlJ5bwOdL4pi6cCcZuYVMubYb57SNYsHWZO76bAVpOWZoa2SvJjx1SQdW7Unl6ncWAdAwLID9aTkc/r/tos71aVQ3kLfnbXcde/Wargzv2hCAuz5dwYy1B+gVG06QnxdzNicRVcePvx85F+9jDFNZlsVLv29mxe5U8h1O6gX5MmFYO5pFBAGml+iHVfuZtnAXgb5eTB3TUwsd1hTZB82mhccrwq2IrBT4532Y818TMG6dZ2pplvwPfn3IHPMOMDOL2l1sZijZvWD1F2a3aJsXdLjUrCcTf9hCdgF1TcFvo57Q7qKSjX9FTtGJfH577M/7/Px8li9fzoQJE1zH7HY7gwcPZtGiRW57nLy8PPLySnYnTU9PP8bZNZjdblYvPrDKrI8Q0dJ0+x7aBRe9Ar5Bnm5hiTVfmNWWe90Gw17waFMODzZgFg/s3rT8mp4Qfx9uG9CCm/s3I9/hJNDX/Pfp3yqCPx8YwAszN/PlP3v4fGkcew9lk5BuNvwc2asxEy/vTFZeIct2HWT2xkQ+WxrHz2tK9szq0bQu/+w+xMPfrCGqjj9edhsz1h7AZoOnhnegRWQwfSbOJjEjjz83JXJ+h5ijPqd/dh9iypztpY4t2p7CYxe3Jy4lm+n/7CExo+T/xAPTV/PO9d0rpWZI3OjAanj/fKjfBUb9fOIBZ9Mv8PP9ZtVb/5CSZfjB7OJcXCTca6xZ1n/Tz6bHplEvs0x/8bopXa4xvTxBERDSwCxUt3Cyma7d6SroPOLE17A5AZZlsS81h4ZhAeUO8YqABzfOTE5OxuFwEB0dXep4dHQ08fEV3ICsAiZOnEhoaKjrq3Hjxm6772qneN2bxE2QmWTGs9d8aWYSnMius5Vt/8rSlzWMt5fdFWyK1Qv24/krO/PBmJ4E+Hjx19ZktiRkEh7ky8NFQ01Bft4MbBPFM5d25Ls7+9IqKhibDR67qD1f3taHs1tHklvgZOS7i109Ppef0Yh29UPw9bZzZXfzF/AXy/YA8NPq/bwwcxO5BaWXdP90sSmgHtwuirev70bP2Lpk5BXyr6/X8MacbSRm5BEd4scdA1vg623njw0JvDl3W6n7OJkO3bxCByvjDh33tke2t1h2fiHr96ed1GNXR5ZlsT815/gnVtQfj5sVyvcsgdlPHe/BTRjaMddsFbDxZ7OvUGa82YIgfR9gQXQnGPwU9L275LY2Gwx/w0yxbtgDRn5RdomJ+p1NsAFT0Hv2QzDmF+gxplKDDcDUhbvo//wcHvlmLU5n7fi3Iu5X6wszJkyYwPjx410/p6en196AE1lUr5G00fzlVWzTzzD7STjvaY80q4xks4cUKVs9245KcE6bKD4b25ubpi3jUHYBjw5rV+7qyJ0bhfHrvWdxKLvAtWDha9d0Zfz01SzdeZDMvEJCA3wYf35r121G9GzMO/N3MHdzIm/8uZWXft8CwJaETN6+vhveXnYOZuXzy1rzx8E9g1rRuVEYg9pF88LMTUz7exdnNq/HiJ6NOb99DL7edmLrBfLwN2t56fct/Lh6P50bhbEzOYu1+9I4t00Ub13fDZvNxptzt/HWnO1c0rUBtw8ws8yO9OSPG/h8aRxX92jE81d0Lvev6i+WxvHIt2uZeHknRvYqqR2yLIubpi1j8Y6DdGgQwn2DWzO4XdRR/zLfkZTJI9+u5aZ+sQzteAKLo1URy7L415f/sHb1P9x37XCGdmpQ0RualXEty8wsKn7+2+eYoFK8F9GiNyAgDBLWQ9wSU4RbmGtmT0a2MccP7TS3tfuYolzLAR2vgIGPmtlGdWLMFgTlCahrtnkBj0xtzs4v5OXft3Be+2jOPGyrFofTYuoC87y+/GcPfj52nrqkw2ndg5ORW8DPaw5wadeGZXqiT5TDaeFVS3pwPRZuIiIi8PLyIiEhodTxhIQEYmKO3uV+ovz8/PDzO00Kzw7vufEp+vCJ6WTGwxe+ahagGjjB8+swpBQNmeQcMuP+x5tSWsOc0aQuv91/NrtTsukZe/QVWL297K5gAxAW6MvU0T2xLIuDWfn4ettL7dHVPDKY3s3CWbLzoCvY2Gwwa2MCj363luev6MzXy/eQ73DSqWEonRuFAeDjZeffF7ZnwgXtygw9jejZhB1JWbwzfwdbEjJdO7IDzFwfzy9r42kdHcyk37dQ6LT4dEkcXyzbQ4cGIbSNqcNlZzSiT4t67DlohrsApv+zlxB/H/59YbtSHzoFDqdrc9T/ztjIuW2jiA4xm6H+viGBxTvMbJ31+9MZ+9E/jOkXyxMXdyj3tXvp980s3XmQtXvTaBMT4qonOlmWZfHJkji87bZSoaui0nIKePTbtUTW8ePBIW34bsVeuq37Ly/6zeHbOdug08vHv5Psg2bYaMP35ufYs8w+QhFtSna37jXWXC55G/78T9n7SNnq+qPB4eVPnk8YgblFPeGdroJL38Zp8+L3DfFEOvzoHlr2Llw8+Hvio0W7eX/BTqYv28Nv959NgzDTczR/axL7UnPw97GTV+jko0W7CQ/y5b7BrY9zjyX+2prEZ0vi+L+L2tMwrOYvevr8zE18sjiOXSlZTLjg5FatT88t4OXfNvP50j3cMbAF959X8dezuvJYuPH19aV79+7Mnj2bSy+9FDAFxbNnz2bcuHGealbNFtnGXKZsNX+ZAQx60tTh/PmMGaY6uNN0OXtqpkF+FqSXzB4iZWutCzcAUXX8iapz7F3Mj8Zms1EvuPz3Z2SvJizZaULAVd0bMahdNHd+upzp/+xlw4F0UjLNbK7repf9gD5aTc2EYe24bUALlu5MYf3+dGLrBbF+fzpTF+7kvzM20Dg8kEKnRe9m4fh62/lrazJr9qaxZm8a367Yx+e3nsm3K/bicFo0qhvA3kM5vLdgJ2GBPow7t2S9kV/WHiC+qA4pM6+Q/8zYyOsjz6DQ4eT5mZsAGNWnKf6+XrwzbwcfLNxFxwahDOkYw5Q528jJd/DvC9sRn5bLzHXmAzunwMF9X6zko5t788vaA+QXOrnhzKbHrB+yLIt1+9JZtTeVAa0iaRwewH9nbOS9oh6B1tF16N60LlsTMnj59y3cMbAFXfJXmKX068aWub9Ch5Nxn63gr63JAMzelIBf2m5mepve04tTPiBvz/X4Rbc2O0Pv/ccs+e8TAGc9YBamW/uVGXbKjKfA8sKBHf9df5mVeYv5Bpv9j/xDTBFvynZT6Nv2IjMN2+4NKdsgaROr0gK4bl4oWZYfr5wfxmVNC6DZ2cRnFPDQ18v5a2sy3nYbn409k17NwrEsi03xGSzekcK6fekM79qAs1ubfZbScwvYkZRFu/p18LHb+WXdAT78exeD2kVz+4BjLGh3FGv2pnLbx8u5Y2ALbuxT9vW0LIsvi4ZeM/IKeejr1Xx8U2/sdhufLTEzsa7t1ZQWUUH8+7t1vD1vO2P6NiM08PhF8ZZl8cQP69mRnEV+oZP3R/c84fafjMSMXJbuPEjHBqHEHhHEcwscfLJ4N8M61XeFuIpyOC3X/4WZ6+J5ZGjbY/Zi7UvN4dkZG+naOIxbzmqGzWZj/pYkHvxqtasO79XZW+netC79Wkbwzvzt7E/N4YmLO9S4tbY8Oiw1fvx4Ro0aRY8ePejVqxeTJ08mKyuLMWPMHiQ33ngjDRs2ZOLEiYApQt6wYYPr+3379rFq1SqCg4Np2bIazw6qKqFNTI9NQbYJEHYfaNoHWg02K33+fD+snW7GyC990zNtPLij9M/JW6HJmZ5pSw10QacYfl4TTUSwL/+5tCPeXnZeuLIL//f9WtbtM8Xydfy8ubhLBYdBioQH+TK0Y33XEM+FnR38tj6efak57E/LxdfbzktXdaFxeCBxKdms35/GF8v2MG9LEnd8stw1U2zyiK6s3pvGMz9v4KXftxAS4MONfWKxLIt3/zLv/UWd6/PL2gP8tHo/vZqFcygrnx1JWYQH+fLAkDaE+Pvg5+3Fa7O38uh3a3np980cSDOhyG6z4bQsnBZ0bRzGjqRMVu9No8d//qDAYeovAny8uLpnydBzocPJewt2sulAOna7jU0HMthQtM6Rt91Gl8ZhLN99yHX+Zz//QZc+hdw7N4YNiTnE7vmOLvmvm00Mxy0rU3/yzM8b+GtrMgE+XoQH+bLnYA7Pe3+Pt81JIXZ8bA6yvroZPz8fs3bL4abfSJq9LqFO8/gHA5oyKnUsh6jDfwO/4GxrGTZnoTn37IdcGzumXTmdJTtSWLUnlT1/5xDin03dQF/axLTDP7Qj42asIM8ydXYT5mTQ5Z6z2LgukUe/W+t6rwqdFnd8spw3ru3Gm3O3ucIZwLwtSSx4+Bz8vO2M+WAZy3cfwt/HTnSIP7tTsgFTuN6/ZQQdG5bu/rEsq6iOazt9W9QrtbwBmF67A2m5vDZ7GyN7NSnzobl050F2JmcR6OuFZcHCbSn8768dDO/agD83JQJwbe/GtIgM5uNFu9kUn8HXK/Zyc/9mrNuXxqyNCZzdOpIzGoeV+aBfvvsQO5LNqu6zNyUyb0sSA4pC3NHsSs5i0Y4UVsYdomm9IO4c2KLCw2C7krO4f/oqVsalAtA8IojZDwwodft35+/g5T+2MHtjIp/femK/C1fEHSK56A+a3SnZbE3MdK28fqTluw9x28f/kJyZz4y1B8gpcNChQQi3f7KcAodFs4ggWkUF8/uGBMZPX037BiHM35IEQN8WEQzrVP2Gf4/Fo+FmxIgRJCUl8fjjjxMfH0/Xrl2ZOXOmq8g4Li4O+2GrZO7fv58zzjjD9fNLL73ESy+9xIABA5g7d25VN7/6sdtN701xoW7j3iWzpLqPMit6fnolrPoUul4Lsf2rvo3JR9TZ1MK6m8rk5+3Fe6N6lDp2ZfdGnNs2io8W7eKn1fu5/symBPmd2n9tfx8vHruoHbd/sgKgVJ1Nk3qBNKkXyIA2kVw25W82J2QA0LdFPXrEhtMjNpy07Hxe+3Mbj/+wnpx8B1Ehfqzbl46ft52nh3ckItiPaX/v4rHvS/YLuvvclq4p6fcNasW6fWn8uSmRA2m5xIT4E5+ey+9/L2WQ92p8GMj481qTnlvAuM9WUuCwqBvow6HsAp6fuYkhHWMIDfAhO7+QcZ+tdH0oFvP1stM6Jph1+9JdwebeQa1YN+8bnk58Be8f87jT0ZuXbVczLu9dsGFWv13yDvS/Dxa9Sf7id9hSEEX99GhGekVx+YC+tOnSm3dmb+CKTX8B8GnsswzbOZHIdDMUm+4Vzl9NbufCs/vAzr/Imz+ZUOchsvHHdtZ4LljQngTLToCPF6Oy7ua8tpG8c1Ur7I4cqGM+XGaui+eRb9eQWrSq9tEMahtFXqGTBduSufytv13nd2oYysTLO/HwN2tYvz+dke8uNq+Jt50zm9djw/50kjPz+H7lPqJC/FyvT26Bk90p2QT5etG0XhAbDqTzxI/r+fr2Pq4P6w3703l+5ibmFX0ortuXxu0DW7je10XbU1w9j8mZeczfksSgdqUnlRT32lzSpQEdGoTw2A/ree7XTbw2eysOp0Wv2HBaRpkP8OvPbMr/fb+OTxbv5uIu9Rk1dSkpWflMnrWVmBB/gvy8yC0wm+4+cXF7130Xb8fyzM8b6HvvWUftlViyI4UR/1tc6lh8Wi5PD69Ync9jP6xzBRubDXYkZ7EtMZNWRQHEsizXRsCLdqSwLTHD9dwApi/bw+RZW3jpqi70bRlR5v5/W1d68s0fGxLKDTcLtiZz07Rl5DucNAj1Z39aLpP+2IKX3YbDaXFhp/q8fHUX87q/sYAtCZmuYAMw/Z89NS7ceHyF4qpWa9e5KfbdHSWbyJ3zfzDgodLX/3SfWQ49si3cvqBk6fKqMu9FmPOfksLINhfCyM+qtg1SIZZl8cSP69mfmsPrI7uVW6y4KzmLS95YQHpuIV/ceqar+LN4n6/irS+KXdu7Cc9e1omsvEKen7mJlXGpHEjLoVVUHT68qRe+3iUfMmk5BTz543oa1Q3gzoEtmTx7C+csGsOZ9o187ncV1zzyLjabjbmbE/HxstOjXh5vvD2F2OzVdK5bSEGvO5mwOoLVe1Jp772fK89sRUGdxoQE+DC0Qwx1g3z5Z9dBPl0Sx8DWEQy35uD48V68KJnNlWfzx8/KJckKI9KWSq5XMNPr3MiNqcfo+fQLNWvDND+H2T3f4eOP3+Mt31fZF3IG1ySOIplQvr69D3X8fbhp8jec47WK3xw9CYlsyPakLJqEBzL5mq5c885i8h1Orj+zCc8M70hmXiHP/rKRz5eaD+gm4YH0aV6PllHBZOUXkpiRx7p9aWzYn063JnWZdlNP0nIKGPLKfNJzC7Hb4M6BLblnUCt8ve3sS83hktcXkJKVT98W9Xj2sk7ERgTx3l87+M+MjTSPCCLY35s1e9O47ezmXNatIVsTMunboh4FDotzX55Ldr6D/7uwHQ3CAvhx1X5mrjcftr5edvx87GTkFvL29d0Z2tHUUV7zv0Us3nGQIF8vsvIdXNAxhreu717qPe/131nkFTr57s6+dG0cxnMzN/Hp4jgy80wP1uQRXbn0DFMInZVX6No/rllEEDuTs4iq40dmXqFrL7lijw5ry+RZW8nOd/D+qB489PUaDmblEx7kS0Ghkz4t6vHiVV0IDSj5nfjIN2v4YtkeWkYF06NpXb78Zw+WBTec2ZT/u6jdMTfUXb77EFe89Tfedhu/3nsWz8zYyPwtSTw6rC23nm2G89bsTeWSN0pWlD+8zmzPwWzOf2U+OQUOYkL8+X382aXWo7Isi7NfnMOegzkMaB3JvC1JdGkcxg93ld5bMDu/kPMmzWdfag6D20Xz6jVdmbpgJy//Yer2Luxcn1dHdHWtnbUpPp2r315ESIAPjw5rx52frsBug4WPnEv90NK9lg6nxQszN/FrUcgK8vPm2cs6csYJbJFzImrMIn6eUOvDzcJXzdg9wC2zzV4xh8s+CG/0MDvXDn7K/BValb69zaxzE3sW7PrLrM0zblnVtkHcamdyFvFpufRpUbp2yum0eGvedv7clMjGA+l42238OK5/mZqDMpK2mD2JBk6AJr1dhwuTd+D9hum5LfTyx/u+1WbGT16m+Xf/9+ulVs51Wjb+57iQ1t4JnMsysyjdwAlm2nPCOti/yvSGBNSFec+Z/ZWAH539+aWwB2/4voE3hWTbAjk/91n+5/MK7e0lYe2TwkFY0Z24OOYQYfkH4NBuUwtTvHbMmJnkNuhF16d/x1GQT8FhHeUDWkcSWy+QDxftJrZeILuKhnoAnhnegRv6xPL9yn3cP30VlgUXdIxhZVwq8em52Gxw69nNeeC8NqXCoOt1cjjxsttcPQt/b0/m08Vx3NQ/lu5NSxe470/NYVdyFn1a1HOdn5lXSJ+Js8nINWEiwMeLBQ+fU6YO7K252121UsVsNriocwPGn9eaD//exbS/dzGyVxMmXt6JxTtSuOZ/i/HxsvHODd25ado/+HrZ+evhc5gyZxt/bkokr9BJUkYebaLrMPO+s1xtysl3MGtjAum5BYzs2aRUTdXh+8f5edv56e7+NK4byMo9h7DbbPy9LZnX/ixZ6qB5ZBCzxw/gq3/28q9v1pRqf9uYOkwb04uYUH8cToue/53Fwax8Pr2lN/1aRjD9nz08/M0aLAvqh/pzx8AWXNGtUbk9pTdOXcr8LUmM6NHYLBOxcCdP/bSBM5uH88WtfQB4+qcNTF2401WrFuLvzZJHB+PvY2fMtGXM3VzSe3JNz8Y8d0Vn188b9qcz7LW/8PO28/v9ZzPgxbkALHl0kKtQH0zB8Vtzt9MwLIA/xp9NoK83lmXxxbI9HMzK57azm5dZFDQzr5AAHy+87DaufmcRS3ce5MHzWzOgdRQTf91IhwYh3HBmLM/N3OianVmsZ2xdvrq9b5nXwx1qxCJ+Ukmiihbi8gs161QcKTAcznsGfrjTFBlHtIK2F1Zd+4qHodpcYMLNwZ1mxWJtF1G9bfzJDMlc+iaENYH8bDPEGdaEZpe+ZWYrHVgNy943tSFhjbHbbdx1TkvuOqclTqeFBRWbZvrXy7Bjjtk7bexs12HvddNLvnfkmgL5jlfAN2Mho2ifr/pd+SWvIxmJexnhPZfbvX8uuV9HvlkfZt4LZbcPABN+zn6I/KBrKFyfSGaXboQte42UTndx6JdAJtuu53/8F4DkxkM4e/hUmkQcsddXziHYvcis9dK0D/5A/5YRzNpohsWGdIhm1kZT67G4KJg8c2lHvlm+l+9X7Sc8yJcru5t6oUvPaEih0+Khr1e7/jJuEh7Ic1d0om+LskMUrtfmiA+qvi0ijnp+g7CAMkWswX7e3HBmU96ca4bSbuzbtNwC95v6xzJz3QE2xmfQNqYOnRuFMqpPrGvIZUCbSKb9vYv5W5KwLItXi2bKXd2jMee2jaZDgxDW709n2Kt/ldrWBGBU39hSwz4Bvl5HrSO7/symrnDz7wvbuYZlip9z72bhbDiQ7noPru7RGJvNxtU9G9O5cSiFDouM3ELu+WIlm+IzuOKtv/np7v5sScjgYFY+oQE+9GoW7rptgI8X/5mxgQNpuTxeNGQ2tGMMV3RrxJnN6+Flt7F890Hmb0nCq+j/AMC5baN46qcN/LPrEOm5BQT5evPTGvPv9rGL2vOfGRvYczCHjxfvwttuZ+7mJHy97Dw1vAMTvl3LF8v20Dq6Dhd2rk+Ar5drduLZrSNpWi+IM5qEsTIulVkbE7iud1MAtiZk8O58U+v2xMXtXetz2WzHnhUYfFhYG9GjMUt3HmTa37uYMmc7OQUO/t6ewrt/mQJ8Xy87/7m0Iw3CArhp2jKW7TrE0p0HXa+Zp+gTpbZpPhC6jYImfY4eGLpea9bMWDsdpo8yMzbiFplixys/gNh+5d/uVFmWmc0B0GyAWdq9MAdSd0O9E591IVUkP8sMZ2Ynw6I34YLnYPMvsHuh+WoxCNoOgy+vN/sKxa+Bm34vtYJuhVc/LsyHLb+a7/f9Y9Zrie5g/u2s/twc7z7abJ64/ENzaTnNLKbznoF2F3NuoZMVuw+RlzELvzlPm97Lc/4N+5bDr4+YISOfIGjcE7KSIXUPNO1rpl3Xa8GVwJU9mgA9oOtwGgPLuhXibT8f/syAjHgiLn61/MXqAuqa1+IwQzrEMGtjIi0ig5g84gz+7/t1fLNiL3mFTpqEB9KvRQSdG4YR4OvN4HZRpYb/ruzeCBsw6Y8tXNSlPvcNan3Ka5lUxOh+sXy0aDd2G9x6VvNyz/Hz9uKHcf1xOq1y398zm9VzDX99tjSORTtS8PGycWfRh/2V3Ruxfv8GUrLyqePvzcTLO9EgLIAAHy/axlR8W5ZW0XV4engHMnILueHMpmWut9lsvHRVFy6dspBD2QVc3q1kbZ+2MSV//X97R1+uf38Ju1Oyefn3za5escHtokvV5FzcpQHntY/mq3/28P6CnexKyebbFfv4dsU+6of6E1nHj3X7zGzVK7o1pEk98++kab0gmkcGsSMpiwVbk6nj701SRh5hgT6c0yaKHUlZPD9zE8/+UtIbduc5LRjZqwmb4zOY9vcunv55A0//vKHU8xtStFr5ee2jWRmXyseLdjOsY30clsXdn6+k0GkxqG0U57UvXdtUURd0iuGJH9e7CpfPbB6O0zKF34G+Xvzvhh70b2WC5JU9GvHZkjjenLuNXs3K3wC7qmhY6nTlKIRvx8L6b0sfj+4Et803xclHysuALb+Z6acnM5U8MwleagnY4N/x8N4gMzww8ktoM/SknoZLfpa530peHbWUQ7tg6bsmHAZ69q+USrVoCvz2qPm+Tn24f4NZ7XZTUa9IcIxZcG7FhyW36TPOhIUTtW02fHJ5yc+9b4cLnje9IR8MNdOhH9wCX42Brb+Zc7qMhAtfrtgWI9kHTW9hTMcqWw7B6bT4ac1++rSoR1Qdf3YkZTJo0jwsCx4e2pY7BlbPYB+Xko3dDo3qnvz/qRveX8JfW5Px9bKT73C6aq4ADmblc96keQT7e/PejT1cPT6VJTu/kAKHVaqm5kjFBcQ2m9luJS2ngHdv7HHUYGBZFiviUvl2xV5+Wr2f9KKhPDCF2/+7sXupOpVnft7A+wt2clarCJIy8tgUn8F1vZvw38s6cTArnwtf+4vkzDyahAdyVqtIJgxri5+3F7kFDt6Zt4PZmxJYuy8Ny4IGof50jw3nhSs6E+DrxYG0HC549S9SswtoFRWM07LYnpRFRLAf39/V95Tex+Lhsyu7N2Li5Z3w8bKz8UA6dfy9S93v7pQsznlpLk4LZtzTnw4NjrWQ0olTzc0xKNwcxlEAM8ab4YQ2F5pVT/PSzS6+na4se/5v/zbn9LoVhr144o9X/AEV1gTuW2t6jTZ8D+f/p/Ty7yeqIBem9DLrfNy1pOqKpL8abXZBPtkP8pqgIBde7WKW7S923deml6YwFwLrmfqtYr3vgCVvme+v/ar83aCPvP/f/216Xy54EX550BS8R7Q29Sv+YfDAZrOR44qPoOv1cOkUU9/yx+PQeih0Hen2p13ZXpu9lcU7Unjruu4VWp+lpiouTgbw8bIx58GBpT4McwsceNlt1WoNlbs/X8lPq81wUaCvFyseOw9/n+P3luUWOJi/JYmcAge9moWXKb4FWLgtmeveW+L6OcTfm2/v7EfLKDO86XRaOC3rmBvjpucWYFmUG9K2JmRww/tLXetJNQj159OxZ57yIpcFDidxB7NpHhF03Fli93y+kh9X7+fCzvWZcm23U3rcI53I53f1+RclVc/LBy553fTUDHwY+t5jjs/5rwk+Ryre0mH5h5B+oOz1x1Ncb1OvaGG3iKLLI6eHn6jdC83Q1sHtpki0KjgdZkl8MEN8tdXKj02wCWkEHYsC74zxJtiEN4fL3ik5t/MIM2TV6zbz8/e3H/vfSUGuCUnL3oN/ppp/d5tmmOuGPGvWlclNhWnDTLABs2kjQN2mcPWHNTLYgNka47OxZ9bqYAMwsE3JGjJXdm9cpvfA38erWgUbMLOqAorCzMA2kRUKNmCey/kdYhjetWG5wQagZ2w4YUXv+blto/hj/ABXsAEzfHusYAOmR+lovU+touvw9R196NAghHb1Q5h+e59TDjZgVjlvERlcoenvxT2RSRl5FDg8t6dh9fpXJZ515u0QGGEW2lv1aenr8jJM/QOAI8/MTDlRxfU29YoWXCwOOcXbMZysolkuAOxecGr3VVH7V5kPXjBDa5lJxzq7ZshNM7OODhXNCMqIh/kvme/732fCC5i6GoD2l0Kr8+DMO6FpPxhiFtvkvKfNth/ZKfDdrSYIHi4vA7bNgi9GwrY/TCEvwIJJkJVoiuGbDYAzbjDH9y0HbCY0eWJtJjlpLSKD6dQwlBB/b+6spsNvR6ofGsBjF7UnLNCHUeWsoHwqfL3tfHpLb6aN6cn7o3qUmtXkLo3qBvLz3f355Z7+pzQUdbLa1Q/ht/vOZvptfTwaXBVupIRfHVM/AjD3efOXdbF9y83QQfEH0T9TTTFmRTgKzEyb5UU1GcU9NsXbRexZAlt+L30by4Ldf5dsI3Es20pm1LBr4dHPc6fDAxWU3qi0pvrzP2ao591zzWv/xbWm1yaiNZxxvSlW9w8rOb/DpeZy6ESzI3TxNho+/qYw3ScIds4395mXYf69/HQfPB8Ln1xhXkPvALj+G+h2Y8n9thlqipF7jIHIdmbZgLF/wrAXPL8vmpwQm83G9Nv6MP9f55S72Wp1dW3vJqx6/Hx6H7Zpp7t0aBDKwDZH3xTWHWw2W6Xe//G0OYGC8MqicCOl9bjJDEFk7Id/3i85Hlc0TtzuEmjQzcxy+vGekt6co3EUwgfDzLoluakQ1cFs4AdQv4v5699ZAF9eB1v/KLnd+m/hgwvg5/Hl3WuJtL1mF3RXOxebxzxRe/8p6bGoiB1FQ1JBRd3uNWloyrLM1+FyUmFlUW9ddrJ57fctN7N/Rn5hthzw9oV2F5lzwptDTGeOKqIVXFjU67PoDXixFbx2hqmncRaa3au7XAtjZkCzs2Ho8yW72hcPfwVHwV2LYfTP0NC9Y/dSdQJ8vQgL9D3+iSJupHAjpfn4m/obMOuN5Jml9dlTFG6anAnnFM2c2TwD3uoLn11Tfo0OmBk0e5eCXwhc9Iqp7wkIM9fZbHDFe2b2lSO/aCqxWbuB1V8UPcYvpXuQjlTcg9KgG/iHQn6GmYp8IvatgPcGw8eXlv3QL09eRsnrMaDotdoxr2K3rQqWBWumm7CSn136urgl8EIzeKkVfHy5me1lWeZ9KsgyPSXNB5pz7d4w4pPS0/TPvMvsUj3gkeP3onQZCcNeMsOQhTmmWD2mE4z+Be5bA5e9BQ2LVqf1DYSbZsLoGccvQhYROQ6tcyNldbnW1F6kbDPrmpz9EOwtWkW4cS/T4zLqJ1j6P9j8q1mX5K9JJaGoWG4azHnWfH/uY6ZX6EhePmYI44MLzGOs+Aj63FVSrFuQbepoWg4uv63bZpnLVuebWV9bfjUFxof/pZ+w3uyrVRyqjrTsPcAytUb7Vx6/l2DXAtP7EN7cfIDPfATS4uDQTnOsMmUmmmGepM2mDTa7mR4dGG7a0nKQ6e1a9Yk5/7dHzb5iPW+B3HT47KqSob7ts81X0ibYPNMc6zsOOl1tXpOYjmVrXKLbw7ilFWurzQa9xprH3r/ShJvYs8B+lALNgLqqqRERt1C4kbK8vM2iZ1+PgYWTzV/ueenmQzTK7HtCs7PN19qv4ZubYf4LZtXh+ocNVfw1yQxx1Gtl6ieO+ng+cOYd8PUyMzsnrIkZqiq29Y/S4SYj3vTo1KlfMhzUchD4BZtws2thydTyjT+bIa869U3PQN3Y0o+dcwjWfVPy86YZxw83xTU+zc8xj9moF8T9bdpyKuHm4E4TKhp2MyvvgilcXv25GQIKCDPB5fBp2Yfb+GPJvkY2uwl0aXtKtibwCTI9W43PNEW/O+bC3IlF4Q4zxNbxSjP81OfOk38eR7LZNKwkIlVK4UbK1/5SaP6h+QD89lZzrGH3sqsed7zCrFWz8Sf4/g6zn5WPP8Svg8VF652c/5/jrz3T9iKzZkrGAbNEPpjtIw6sMgsHDn3OfEhu+Q2+ux1yDpbc1j/MDEvZi9oW97eZoZOXATOKCqQzDsBHl5qAUyem5LarvzDTmu0+JlBtmgGDHiu53uk0M3oyE8zwzcafzM8ALc41l80HmMfcPLP83qliB3fAmq/M2jjF0/AbdDW1PvNfhFWfmc1EsZkgGdEaPr6s9HMFMyx07r9NWLEcZgHDA6tg2VQTbHzrwJVTTeDb+jssedu8j/kZZpHGa780QalJbxMkf7jTFIv3uNm8dyIiNZzCjZTPbjfDRe8MMEMuYOptjmSzwYWvmNk1CevMfkMXPG8uHXmmx6X1kOM/nref2Rbi79chq2ha9YUvw9ShZrgnZRus+dKEADC9QV4+5niPm0zoiulsPthz00y9UOpu08sR3tx8eB/aCdMugsveNkvyW5aZ9QVwzgQzhJa00UxNL64z+f3/YPGUI5+0mRbdumhV5XaXmH2Otv5mhsnKG0JbM92EMuuwadHvDTbDaVt/M0NMYAptU3fD1zdDnWgTbCLamHqipE3msYa9UHY13o6XQ//7YcMP0LQ/RBRNt29zgflK3GjqgjpdVXp4rutI8/OW38xwoIhILaAViuXYDqyG9883vRs3fA8tzin/vF0LTGFxfgZgAyxTnHrTr6aWoiKSt5ody8F8oI9bCh8NN70O9VqVLALY+3YzrOLtZwLK4YWtP48vPcsLTJFqaCOYekHRBos26HBZ0Xorf5hekgc2wRfXmSndxSsmb/gBphdNUW452Nwusg30vLns8NPMCbD4TRNObppp1ofZt9yEIN9A+Pl+E7Ca9jeBYstM0wtUrPk5plC7fldT2Ly7aEp7nfowdg6E1K/YaygiUktp+4VjULg5CbsXmWGP3rcfe4bMgdXwyZVmIbaQhnDzHxDa8Ojnl+eDC00B8YCHzYf9ojfhtwkl11/0yrGHfpwOM9Q0d6KpN+k+Bi6ebK7LSjY9McUbMBYr3r9oyf/MMv8NzjBbKvx0nwlrfe+B8585drvzMmFKb0jfa4bHintiDtdtFFw02fSKWZbpidr+p9kIsmnfkvOyUmDqEFM8fOP3qlcREUHh5pgUbirZod1mdeMu15xcce2hXWYIp89dZuglZTu8XjRdePgbZjG5iijMM2GrYfeys3N2LTDDR3UamPVYYvubIa60vfBKh9LnNulrZoYdbYf1w23+FT4v2h4goo3poVkzHRI3mJB14aTyNyQtj6MACnLAX/9GRURA4eaYFG5qoM0zzaykqpgm/MNdpv6kbqxZk2XgoxAcedybuaz4yASrbqPMrCPLMj0wdcrfVVhERCpG4eYYFG5ERERqHu0KLiIiIqcthRsRERGpVRRuREREpFZRuBEREZFaReFGREREahWFGxEREalVFG5ERESkVlG4ERERkVpF4UZERERqFYUbERERqVUUbkRERKRWUbgRERGRWkXhRkRERGoVhRsRERGpVbw93YCqZlkWYLZOFxERkZqh+HO7+HP8WE67cJORkQFA48aNPdwSEREROVEZGRmEhoYe8xybVZEIVIs4nU72799PnTp1sNlsbr3v9PR0GjduzJ49ewgJCXHrfVcHtf35gZ5jbVDbnx/oOdYGtf35gfufo2VZZGRk0KBBA+z2Y1fVnHY9N3a7nUaNGlXqY4SEhNTaf6xQ+58f6DnWBrX9+YGeY21Q258fuPc5Hq/HppgKikVERKRWUbgRERGRWkXhxo38/Px44okn8PPz83RTKkVtf36g51gb1PbnB3qOtUFtf37g2ed42hUUi4iISO2mnhsRERGpVRRuREREpFZRuBEREZFaReFGREREahWFGzeZMmUKsbGx+Pv707t3b5YuXerpJp20iRMn0rNnT+rUqUNUVBSXXnopmzdvLnXOwIEDsdlspb5uv/12D7X4xDz55JNl2t62bVvX9bm5udx1113Uq1eP4OBgrrjiChISEjzY4hMXGxtb5jnabDbuuusuoGa+f/Pnz+fiiy+mQYMG2Gw2vv/++1LXW5bF448/Tv369QkICGDw4MFs3bq11DkHDx7kuuuuIyQkhLCwMG6++WYyMzOr8Fkc3bGeX0FBAQ8//DCdOnUiKCiIBg0acOONN7J///5S91He+/7cc89V8TM5uuO9h6NHjy7T/qFDh5Y6pzq/h3D851je/0ubzcaLL77oOqc6v48V+XyoyO/QuLg4LrzwQgIDA4mKiuKhhx6isLDQbe1UuHGDL7/8kvHjx/PEE0+wYsUKunTpwpAhQ0hMTPR0007KvHnzuOuuu1i8eDF//PEHBQUFnH/++WRlZZU6b+zYsRw4cMD19cILL3ioxSeuQ4cOpdq+YMEC13X3338/P/30E1999RXz5s1j//79XH755R5s7YlbtmxZqef3xx9/AHDVVVe5zqlp719WVhZdunRhypQp5V7/wgsv8Nprr/H222+zZMkSgoKCGDJkCLm5ua5zrrvuOtavX88ff/zBzz//zPz587n11lur6ikc07GeX3Z2NitWrOCxxx5jxYoVfPvtt2zevJlLLrmkzLlPP/10qff17rvvrormV8jx3kOAoUOHlmr/559/Xur66vwewvGf4+HP7cCBA0ydOhWbzcYVV1xR6rzq+j5W5PPheL9DHQ4HF154Ifn5+fz99998+OGHTJs2jccff9x9DbXklPXq1cu66667XD87HA6rQYMG1sSJEz3YKvdJTEy0AGvevHmuYwMGDLDuvfdezzXqFDzxxBNWly5dyr0uNTXV8vHxsb766ivXsY0bN1qAtWjRoipqofvde++9VosWLSyn02lZVs1+/yzLsgDru+++c/3sdDqtmJgY68UXX3QdS01Ntfz8/KzPP//csizL2rBhgwVYy5Ytc53z66+/Wjabzdq3b1+Vtb0ijnx+5Vm6dKkFWLt373Yda9q0qfXKK69UbuPcpLznOGrUKGv48OFHvU1Neg8tq2Lv4/Dhw61zzz231LGa9D4e+flQkd+hv/zyi2W32634+HjXOW+99ZYVEhJi5eXluaVd6rk5Rfn5+SxfvpzBgwe7jtntdgYPHsyiRYs82DL3SUtLAyA8PLzU8U8//ZSIiAg6duzIhAkTyM7O9kTzTsrWrVtp0KABzZs357rrriMuLg6A5cuXU1BQUOr9bNu2LU2aNKmx72d+fj6ffPIJN910U6nNYmvy+3eknTt3Eh8fX+p9Cw0NpXfv3q73bdGiRYSFhdGjRw/XOYMHD8Zut7NkyZIqb/OpSktLw2azERYWVur4c889R7169TjjjDN48cUX3drVXxXmzp1LVFQUbdq04Y477iAlJcV1XW17DxMSEpgxYwY333xzmetqyvt45OdDRX6HLlq0iE6dOhEdHe06Z8iQIaSnp7N+/Xq3tOu02zjT3ZKTk3E4HKXeJIDo6Gg2bdrkoVa5j9Pp5L777qNfv3507NjRdfzaa6+ladOmNGjQgDVr1vDwww+zefNmvv32Ww+2tmJ69+7NtGnTaNOmDQcOHOCpp57irLPOYt26dcTHx+Pr61vmAyM6Opr4+HjPNPgUff/996SmpjJ69GjXsZr8/pWn+L0p7/9h8XXx8fFERUWVut7b25vw8PAa997m5uby8MMPM3LkyFIbEt5zzz1069aN8PBw/v77byZMmMCBAweYNGmSB1tbcUOHDuXyyy+nWbNmbN++nUcffZQLLriARYsW4eXlVaveQ4APP/yQOnXqlBn2rinvY3mfDxX5HRofH1/u/9Xi69xB4UaO6a677mLdunWlalKAUmPcnTp1on79+gwaNIjt27fTokWLqm7mCbngggtc33fu3JnevXvTtGlTpk+fTkBAgAdbVjnef/99LrjgAho0aOA6VpPfv9NdQUEBV199NZZl8dZbb5W6bvz48a7vO3fujK+vL7fddhsTJ06sEcv8X3PNNa7vO3XqROfOnWnRogVz585l0KBBHmxZ5Zg6dSrXXXcd/v7+pY7XlPfxaJ8P1YGGpU5RREQEXl5eZSrBExISiImJ8VCr3GPcuHH8/PPPzJkzh0aNGh3z3N69ewOwbdu2qmiaW4WFhdG6dWu2bdtGTEwM+fn5pKamljqnpr6fu3fvZtasWdxyyy3HPK8mv3+A67051v/DmJiYMkX+hYWFHDx4sMa8t8XBZvfu3fzxxx+lem3K07t3bwoLC9m1a1fVNNDNmjdvTkREhOvfZW14D4v99ddfbN68+bj/N6F6vo9H+3yoyO/QmJiYcv+vFl/nDgo3p8jX15fu3bsze/Zs1zGn08ns2bPp06ePB1t28izLYty4cXz33Xf8+eefNGvW7Li3WbVqFQD169ev5Na5X2ZmJtu3b6d+/fp0794dHx+fUu/n5s2biYuLq5Hv5wcffEBUVBQXXnjhMc+rye8fQLNmzYiJiSn1vqWnp7NkyRLX+9anTx9SU1NZvny565w///wTp9PpCnfVWXGw2bp1K7NmzaJevXrHvc2qVauw2+1lhnJqir1795KSkuL6d1nT38PDvf/++3Tv3p0uXboc99zq9D4e7/OhIr9D+/Tpw9q1a0sF1eKw3r59e7c1VE7RF198Yfn5+VnTpk2zNmzYYN16661WWFhYqUrwmuSOO+6wQkNDrblz51oHDhxwfWVnZ1uWZVnbtm2znn76aeuff/6xdu7caf3www9W8+bNrbPPPtvDLa+YBx54wJo7d661c+dOa+HChdbgwYOtiIgIKzEx0bIsy7r99tutJk2aWH/++af1zz//WH369LH69Onj4VafOIfDYTVp0sR6+OGHSx2vqe9fRkaGtXLlSmvlypUWYE2aNMlauXKla7bQc889Z4WFhVk//PCDtWbNGmv48OFWs2bNrJycHNd9DB061DrjjDOsJUuWWAsWLLBatWpljRw50lNPqZRjPb/8/HzrkksusRo1amStWrWq1P/L4tklf//9t/XKK69Yq1atsrZv32598sknVmRkpHXjjTd6+JmVONZzzMjIsB588EFr0aJF1s6dO61Zs2ZZ3bp1s1q1amXl5ua67qM6v4eWdfx/p5ZlWWlpaVZgYKD11ltvlbl9dX8fj/f5YFnH/x1aWFhodezY0Tr//POtVatWWTNnzrQiIyOtCRMmuK2dCjdu8vrrr1tNmjSxfH19rV69elmLFy/2dJNOGlDu1wcffGBZlmXFxcVZZ599thUeHm75+flZLVu2tB566CErLS3Nsw2voBEjRlj169e3fH19rYYNG1ojRoywtm3b5ro+JyfHuvPOO626detagYGB1mWXXWYdOHDAgy0+Ob/99psFWJs3by51vKa+f3PmzCn33+WoUaMsyzLTwR977DErOjra8vPzswYNGlTmuaekpFgjR460goODrZCQEGvMmDFWRkaGB55NWcd6fjt37jzq/8s5c+ZYlmVZy5cvt3r37m2FhoZa/v7+Vrt27axnn322VDDwtGM9x+zsbOv888+3IiMjLR8fH6tp06bW2LFjy/yRWJ3fQ8s6/r9Ty7Ksd955xwoICLBSU1PL3L66v4/H+3ywrIr9Dt21a5d1wQUXWAEBAVZERIT1wAMPWAUFBW5rp62osSIiIiK1gmpuREREpFZRuBEREZFaReFGREREahWFGxEREalVFG5ERESkVlG4ERERkVpF4UZERERqFYUbETntzZ07F5vNVmY/HBGpmRRuREREpFZRuBEREZFaReFGRDzO6XQyceJEmjVrRkBAAF26dOHrr78GSoaMZsyYQefOnfH39+fMM89k3bp1pe7jm2++oUOHDvj5+REbG8vLL79c6vq8vDwefvhhGjdujJ+fHy1btuT9998vdc7y5cvp0aMHgYGB9O3bl82bN1fuExeRSqFwIyIeN3HiRD766CPefvtt1q9fz/3338/111/PvHnzXOc89NBDvPzyyyxbtozIyEguvvhiCgoKABNKrr76aq655hrWrl3Lk08+yWOPPca0adNct7/xxhv5/PPPee2119i4cSPvvPMOwcHBpdrx73//m5dffpl//vkHb29vbrrppip5/iLiXto4U0Q8Ki8vj/DwcGbNmkWfPn1cx2+55Rays7O59dZbOeecc/jiiy8YMWIEAAcPHqRRo0ZMmzaNq6++muuuu46kpCR+//131+3/9a9/MWPGDNavX8+WLVto06YNf/zxB4MHDy7Thrlz53LOOecwa9YsBg0aBMAvv/zChRdeSE5ODv7+/pX8KoiIO6nnRkQ8atu2bWRnZ3PeeecRHBzs+vroo4/Yvn2767zDg094eDht2rRh48aNAGzcuJF+/fqVut9+/fqxdetWHA4Hq1atwsvLiwEDBhyzLZ07d3Z9X79+fQASExNP+TmKSNXy9nQDROT0lpmZCcCMGTNo2LBhqev8/PxKBZyTFRAQUKHzfHx8XN/bbDbA1AOJSM2inhsR8aj27dvj5+dHXFwcLVu2LPXVuHFj13mLFy92fX/o0CG2bNlCu3btAGjXrh0LFy4sdb8LFy6kdevWeHl50alTJ5xOZ6kaHhGpvdRzIyIeVadOHR588EHuv/9+nE4n/fv3Jy0tjYULFxISEkLTpk0BePrpp6lXrx7R0dH8+9//JiIigksvvRSABx54gJ49e/LMM88wYsQIFi1axBtvvMGbb74JQGxsLKNGjeKmm27itddeo0uXLuzevZvExESuvvpqTz11EakkCjci4nHPPPMMkZGRTJw4kR07dhAWFka3bt149NFHXcNCzz33HPfeey9bt26la9eu/PTTT/j6+gLQrVs3pk+fzuOPP84zzzxD/fr1efrppxk9erTrMd566y0effRR7rzzTlJSUmjSpAmPPvqoJ56uiFQyzZYSkWqteCbToUOHCAsL83RzRKQGUM2NiIiI1CoKNyIiIlKraFhKREREahX13IiIiEitonAjIiIitYrCjYiIiNQqCjciIiJSqyjciIiISK2icCMiIiK1isKNiIiI1CoKNyIiIlKrKNyIiIhIrfL/wuLP5K58qCMAAAAASUVORK5CYII=", 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -285,7 +315,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -293,9 +323,9 @@ "output_type": "stream", "text": [ "['Greg, can you call me back once you get this?', 'Congrats on your new iPhone! Click here to claim your prize...', 'Really like that new photo of you', 'Did you hear the news today? Terrible what has happened...', 'Attend this free COVID webinar today: Book your session now...', 'Are you coming to the party tonight?', 'Your parcel has gone missing', 'Do not forget to bring friends!', 'You have won a million dollars! Fill out your bank details here...', 'Looking forward to seeing you again', 'oh wow https://github.com/MGCodesandStats/tensorflow-nlp/blob/master/spam%20detection%20tensorflow%20v2.ipynb works really good on spam detection. Guess I go with that as the base model then lol :D', 'ayo', 'Almost all my spam is coming to my non-gmail address actually', 'Oh neat I think I found the sizing sweetspot for my data :D', 'would never click on buttons in gmail :D always expecting there to be a bug in gmail that allows js to grab your google credentials :D XSS via email lol. I am too scared for touching spam in gmail']\n", - "1/1 [==============================] - 0s 73ms/step\n", + "1/1 [==============================] - 0s 19ms/step\n", "Greg, can you call me back once you get this?\n", - "[4.9479245e-06]\n", + "[0.00013209]\n", "\n", "\n", "Congrats on your new iPhone! Click here to claim your prize...\n", @@ -303,55 +333,55 @@ "\n", "\n", "Really like that new photo of you\n", - "[0.00084918]\n", + "[2.3931017e-05]\n", "\n", "\n", "Did you hear the news today? Terrible what has happened...\n", - "[0.0015355]\n", + "[4.1954652e-05]\n", "\n", "\n", "Attend this free COVID webinar today: Book your session now...\n", - "[0.9978678]\n", + "[0.9865063]\n", "\n", "\n", "Are you coming to the party tonight?\n", - "[5.155159e-14]\n", + "[6.177529e-09]\n", "\n", "\n", "Your parcel has gone missing\n", - "[2.5171929e-07]\n", + "[0.00260421]\n", "\n", "\n", "Do not forget to bring friends!\n", - "[6.217939e-11]\n", + "[0.00025998]\n", "\n", "\n", "You have won a million dollars! Fill out your bank details here...\n", - "[0.9998441]\n", + "[0.9999875]\n", "\n", "\n", "Looking forward to seeing you again\n", - "[2.6603466e-06]\n", + "[0.00708251]\n", "\n", "\n", "oh wow https://github.com/MGCodesandStats/tensorflow-nlp/blob/master/spam%20detection%20tensorflow%20v2.ipynb works really good on spam detection. Guess I go with that as the base model then lol :D\n", - "[9.0285716e-14]\n", + "[4.980462e-14]\n", "\n", "\n", "ayo\n", - "[0.0138027]\n", + "[0.00903365]\n", "\n", "\n", "Almost all my spam is coming to my non-gmail address actually\n", - "[1.01412886e-16]\n", + "[7.743632e-12]\n", "\n", "\n", "Oh neat I think I found the sizing sweetspot for my data :D\n", - "[2.516162e-12]\n", + "[4.4713904e-08]\n", "\n", "\n", "would never click on buttons in gmail :D always expecting there to be a bug in gmail that allows js to grab your google credentials :D XSS via email lol. I am too scared for touching spam in gmail\n", - "[9.169594e-22]\n", + "[4.2191292e-11]\n", "\n", "\n" ] @@ -394,17 +424,17 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "<IPython.core.display.Image object>" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }
