lectures/L22_deeplearning.ipynb

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   "source": [
    "# Deep Learning: II\n",
    "\n",
    "*Davide Gerosa (Milano-Bicocca)* \n",
    "\n",
    "##### Reading/watching\n",
    "\n",
    "- [Textbook](http://press.princeton.edu/titles/10159.html) Chapter 9.\n",
    "- [Neural Networks Demystified](https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs) youtube playlist.\n",
    "\n",
    "##### Dig deeper:\n",
    "\n",
    "- [\"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow\", Aurelien Geron](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/). \n",
    "-  [\"Neural Networks and Deep Learning\"](http://neuralnetworksanddeeplearning.com/index.html). Free online book!\n",
    "- Many many blogs and videos. Deep learning as a million applications that go well beyond astrophysics. There is truly excellent content accessibile out there with just a tiny bit of googling.\n",
    "\n",
    "This course is based on previous work by many people. See [here]((https://github.com/dgerosa/astrostatistics_bicocca_2022/blob/main/README.md) for credits.\n",
    "\n",
    "---"
   ]
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   "source": [
    "Before starting today, let's install the two big Deep Learning packages out there. [Tensorflow](https://www.tensorflow.org/) and [PyTorch](https://pytorch.org/)-- the rivalry essentially boils down to Google versus Facebook. These are the public deep learning implementation of giant companies.\n",
    "\n",
    "In general, I find TensorFlow more customizable and there are excellent tutorials around! Tensorflow is somewhat less readable while coding in Pytorch is a more \"pythonic\" experience (some operations feel more natural). But it's really a matter of taste here... In my research group we joined the Google camp and use TensorFlow. \n"
   ]
  },
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   "cell_type": "code",
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      "Requirement already satisfied: pip in /Users/dgerosa/box/lib/python3.8/site-packages (22.1)\r\n"
     ]
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   ],
   "source": [
    "!pip install --upgrade pip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tensorflow"
   ]
  },
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     ]
    }
   ],
   "source": [
    "!pip3 install tensorflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PyTorch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "!pip3 install torch torchvision"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Keras\n",
    "\n",
    "**[Keras](https://keras.io/)** is a deep learning API. Essentially it is Scikit-Learn for deep neural networks.\n",
    "\n",
    "Keras needs a computational backend to handle the heavy computation.  Three popular (open sources) deep learning libraries are [TensorFlow](https://www.tensorflow.org/), Microsoft Cognitive Toolkit, and [Theano](http://www.deeplearning.net/software/theano/). TensorFlow now comes bundled with a version of Keras and that's what we'll use here (actually TensorFlow 2). PyTorch is another option. If you apply for a data-science job in industry knowing one of these tools might be the most useful thing for you to have learned.\n",
    "\n",
    "In short, for neural networks:\n",
    "\n",
    "> numpy -> tensorflow\n",
    "\n",
    "> sklearn -> keras\n",
    "\n",
    "In the same way that you can build a linear regression algorithm in numpy without using sklearn, you can build a neural network algorithm (not to mention linear regression) in tensorflow without using keras.  But just as sklearn makes our life easier, so too does keras.\n",
    "\n",
    "\n",
    "Crucially, these libraries work with automatic differentiation. We won't call the autodiff modules explicitely but they are [tensorflow.GradientTape](https://www.tensorflow.org/api_docs/python/tf/GradientTape) and [torch.autograd](https://pytorch.org/tutorials/beginner/blitz/autograd_tutorial.html).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Photo-z with deep learning <a class=\"anchor\" id=\"one\"></a>\n",
    "\n",
    "An important problem in galaxy imaging is being able to use observed galaxy color data to estimate its redshift. This is sometimes called \"photo-z\". Now, on your humble computer, you will build a deep neural network that is trained on a sample of SDSS galaxy color data. You'll see that `pytorch` makes this quite easy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina'\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.utils.data as torchdata\n",
    "\n",
    "from astroML.datasets import fetch_sdss_specgals\n",
    "from astroML.utils.decorators import pickle_results\n",
    "\n",
    "from astroML.plotting import setup_text_plots\n",
    "setup_text_plots(fontsize=8, usetex=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in and prepare the SDSS data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fetch and prepare the data\n",
    "data = fetch_sdss_specgals()\n",
    "\n",
    "# put magnitudes into array\n",
    "# normalize to zero mean and unit variance for easier training\n",
    "datanormed = np.zeros((len(data), 6), \n",
    "                      dtype=np.float32)\n",
    "for i, band in enumerate(['u', 'g', 'r', 'i', 'z']):\n",
    "    band = 'modelMag_' + band\n",
    "    datanormed[:, i] = ((data[band] - data[band].mean()) / \n",
    "                        data[band].std())\n",
    "\n",
    "# put redshifts into array\n",
    "datanormed[:, 5] = data['z']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's define a little helper class that will make our network. It is initialized with a given number of neurons in the single hidden layer. These neurons in the hidden layer connect to $5$ input variables (colors) and spits out 1 variable (redshift). You can also see that it uses ReLU activation for the neurons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define structure of neural net\n",
    "class Net(nn.Module):\n",
    "    def __init__(self, nhidden):\n",
    "        super(Net, self).__init__()\n",
    "        self.fc_h = nn.Linear(5, nhidden)\n",
    "        self.fc_o = nn.Linear(nhidden, 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        h = F.relu(self.fc_h(x))\n",
    "        z = self.fc_o(h)\n",
    "        return z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do a train-test split on the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split data into 9:1 train:test\n",
    "dataset = torchdata.TensorDataset(torch.tensor(datanormed[:, 0:5]),\n",
    "                                  torch.tensor(datanormed[:, 5]).view(-1, 1))\n",
    "trainnum = datanormed.shape[0] // 10 * 9\n",
    "traindata, testdata = torchdata.random_split(dataset, \n",
    "                                             [trainnum, datanormed.shape[0] - trainnum])\n",
    "traindataloader = torchdata.DataLoader(traindata, \n",
    "                                       batch_size=128, \n",
    "                                       shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a function to train the neural network. This looks like a lot of code, but the first block is the key. The network will have $4$ neurons in the hidden layer, and will use stochastic gradient descent to train the weights and biases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "@pickle_results('NNphotoz.pkl')\n",
    "def train_NN():\n",
    "    # 4 hidden layers\n",
    "    model = Net(4)\n",
    "    # MSE loss\n",
    "    criterion = torch.nn.MSELoss(reduction='sum') \n",
    "    # stochastic gradient descent\n",
    "    optimizer = torch.optim.SGD(model.parameters(), lr=0.001) \n",
    "    # Adaptive learning rate...\n",
    "    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, \n",
    "                                                           verbose=True, \n",
    "                                                           patience=5, \n",
    "                                                           threshold=1e-3)\n",
    "\n",
    "    min_valid_loss = float('inf')\n",
    "    badepochs = 0\n",
    "    for t in range(100):\n",
    "        train_loss = 0\n",
    "        for i, databatch in enumerate(traindataloader, 0):\n",
    "            photometry, redshifts = databatch\n",
    "            optimizer.zero_grad()\n",
    "            z_pred = model(photometry)\n",
    "            loss = criterion(z_pred, redshifts)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            train_loss += loss.item()\n",
    "\n",
    "        with torch.no_grad():\n",
    "            photometry = testdata[:][0]\n",
    "            redshifts = testdata[:][1]\n",
    "            z_pred = model(photometry)\n",
    "            valid_loss = criterion(z_pred, redshifts)\n",
    "            if t % 10 == 0:\n",
    "                print('Epoch %3i: train loss %0.3e validation loss %0.3e' % (t, \\\n",
    "                        train_loss / len(traindata), valid_loss / len(testdata)))\n",
    "            # stop training if validation loss has not fallen in 10 epochs\n",
    "            if valid_loss > min_valid_loss*(1-1e-3):\n",
    "                badepochs += 1\n",
    "            else:\n",
    "                min_valid_loss = valid_loss\n",
    "                badepochs = 0\n",
    "            if badepochs == 10:\n",
    "                print('Finished training')\n",
    "                break\n",
    "        scheduler.step(valid_loss)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's train this thing. It should take about five minutes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "@pickle_results: computing results and saving to 'NNphotoz.pkl'\n",
      "Epoch   0: train loss 1.279e-03 validation loss 9.864e-04\n",
      "Epoch  10: train loss 8.837e-04 validation loss 8.362e-04\n",
      "Epoch  20: train loss 8.774e-04 validation loss 8.213e-04\n",
      "Epoch  30: train loss 8.689e-04 validation loss 8.115e-04\n",
      "Epoch  40: train loss 6.109e-04 validation loss 6.128e-04\n",
      "Epoch  50: train loss 5.935e-04 validation loss 5.999e-04\n",
      "Epoch  60: train loss 5.860e-04 validation loss 5.966e-04\n",
      "Epoch  70: train loss 5.838e-04 validation loss 5.913e-04\n",
      "Epoch    78: reducing learning rate of group 0 to 1.0000e-04.\n",
      "Epoch  80: train loss 5.790e-04 validation loss 5.901e-04\n",
      "Epoch  90: train loss 5.769e-04 validation loss 5.876e-04\n",
      "Epoch    95: reducing learning rate of group 0 to 1.0000e-05.\n",
      "Finished training\n"
     ]
    }
   ],
   "source": [
    "model = train_NN()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (fc_h): Linear(in_features=5, out_features=4, bias=True)\n",
      "  (fc_o): Linear(in_features=4, out_features=1, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 312,
       "width": 355
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the results\n",
    "with torch.no_grad():\n",
    "    photometry = testdata[:][0]\n",
    "    redshifts = testdata[:][1]\n",
    "    z_pred = model(photometry)\n",
    "    fig = plt.figure(figsize=(5, 5))\n",
    "    fig.subplots_adjust(wspace=0.25,\n",
    "                        left=0.1, right=0.95,\n",
    "                        bottom=0.15, top=0.9)\n",
    "\n",
    "    ax = plt.axes()\n",
    "    H, zs_bins, zp_bins = np.histogram2d(redshifts.numpy().flatten(), \n",
    "                                         z_pred.numpy().flatten(), 151)\n",
    "    ax.imshow(H.T, origin='lower', interpolation='nearest', aspect='auto',\n",
    "               extent=[zs_bins[0], zs_bins[-1], zp_bins[0], zp_bins[-1]],\n",
    "               cmap=plt.cm.binary)\n",
    "    ax.plot([-0.1, 0.4], [-0.1, 0.4], ':k')\n",
    "    rms = np.sqrt(np.mean((z_pred-redshifts).numpy()**2))\n",
    "    \n",
    "    ax.text(0, 0.35, 'RMS error = %0.3f' % np.sqrt(np.mean((z_pred-\n",
    "                                                            redshifts).numpy()**2)))\n",
    "    ax.set_xlim(-0.02, 0.4001)\n",
    "    ax.set_ylim(-0.02, 0.4001)\n",
    "    ax.xaxis.set_major_locator(plt.MultipleLocator(0.1))\n",
    "    ax.yaxis.set_major_locator(plt.MultipleLocator(0.1))\n",
    "    \n",
    "    ax.set_xlabel(r'$z_{\\rm true}$')\n",
    "    ax.set_ylabel(r'$z_{\\rm fit}$')\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not bad at all! Still, this was a relatively simple fully-connected network. Other problems will require more complexity..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Another multilayer perceptron example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's go back to the dataset we used last week, when we classified gravitational-wave sources based on their LIGO detectability. I had previously tackled this problem using a multi-layer perceptron: https://arxiv.org/abs/2007.06585. This was implemented using TensorFlow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import deepdish\n",
    "import os\n",
    "\n",
    "def sperical_to_cartesian(mag,theta,phi):\n",
    "    '''\n",
    "    Convert spherical to cartesian coordinates\n",
    "    '''\n",
    "    coordx = mag * np.cos(phi) * np.sin(theta)\n",
    "    coordy = mag * np.sin(phi) * np.sin(theta)\n",
    "    coordz = mag * np.cos(theta)\n",
    "    return coordx,coordy,coordz\n",
    "\n",
    "massvar = ['mtot','q','z']\n",
    "spinvar = ['chi1x','chi1y','chi1z','chi2x','chi2y','chi2z']\n",
    "intrvar=massvar+spinvar\n",
    "extrvar = ['iota','ra','dec','psi']\n",
    "train_variables=intrvar+extrvar\n",
    "\n",
    "def lookup_limits():\n",
    "    '''\n",
    "    Define the limits in all variables. If you want to change this, please check generate_binaries() and pdet() as well.\n",
    "    '''\n",
    "\n",
    "    limits={\n",
    "        'mtot'  : [2,1000],\n",
    "        'q'     : [0.1,1],\n",
    "        'z'     : [1e-4,4],\n",
    "        'chi1x'  : [-1,1],\n",
    "        'chi1y'  : [-1,1],\n",
    "        'chi1z'  : [-1,1],\n",
    "        'chi2x'  : [-1,1],\n",
    "        'chi2y'  : [-1,1],\n",
    "        'chi2z'  : [-1,1],\n",
    "        'iota'  : [0,np.pi],\n",
    "        'ra'    : [-np.pi,np.pi],\n",
    "        'dec'   : [-np.pi/2,np.pi/2],\n",
    "        'psi'   : [0,np.pi]\n",
    "        }\n",
    "\n",
    "    return limits\n",
    "\n",
    "\n",
    "def store_binaries(filename, N, approximant='IMRPhenomXPHM', noisecurve='design', SNRthreshold=12):\n",
    "    ''' Generate binaries, compute SNR, and store'''\n",
    "\n",
    "    inbinaries = generate_binaries(N)\n",
    "    outbinaries = evaluate_binaries(inbinaries, approximant, noisecurve, SNRthreshold)\n",
    "\n",
    "    deepdish.io.save(filename,outbinaries)\n",
    "    return filename\n",
    "\n",
    "def readsample(filename='sample.h5'):\n",
    "    '''\n",
    "    Read a validation sample that already exists\n",
    "    '''\n",
    "    return deepdish.io.load(filename)\n",
    "\n",
    "def splittwo(binaries):\n",
    "    '''\n",
    "    Split sample into two subsamples of equal size\n",
    "    '''\n",
    "\n",
    "    one={}\n",
    "    two={}\n",
    "    for k in train_variables+['snr','det']:\n",
    "        one[k],two[k] = np.split(binaries[k],2)\n",
    "    one['N'],two['N']= len(one['mtot']),len(two['mtot'])\n",
    "\n",
    "    return one,two\n",
    "\n",
    "\n",
    "def rescale(x,var):\n",
    "    '''\n",
    "    Rescale variable sample x of variable var between -1 and 1\n",
    "    '''\n",
    "\n",
    "    limits=lookup_limits()\n",
    "    if var not in limits:\n",
    "        raise ValueError\n",
    "\n",
    "    return 1-2*(np.array(x)-min(limits[var]))/(max(limits[var])-min(limits[var]))\n",
    "\n",
    "\n",
    "def nnet_in(binaries):\n",
    "    '''\n",
    "    Prepare neural network inputs.\n",
    "    '''\n",
    "\n",
    "    return np.array([rescale(binaries[k],k) for k in train_variables]).T\n",
    "\n",
    "def nnet_out(binaries, which='detnetwork'):\n",
    "    '''\n",
    "    Prepare neural network outputs.\n",
    "    '''\n",
    "\n",
    "    return binaries['det']\n",
    "\n",
    "\n",
    "def trainnetwork(train_binaries,test_binaries,filename='trained.h5'):\n",
    "\n",
    "    if not os.path.isfile(filename):\n",
    "\n",
    "        train_in  = nnet_in(train_binaries)\n",
    "        train_out = nnet_out(train_binaries)\n",
    "        test_in  = nnet_in(test_binaries)\n",
    "        test_out = nnet_out(test_binaries)\n",
    "\n",
    "        # Kernel initializer\n",
    "        my_init = keras.initializers.glorot_uniform(seed=1)\n",
    "        # Define neural network architecture\n",
    "        model = keras.Sequential([\n",
    "            # Input layer, do not change\n",
    "            tf.keras.layers.InputLayer(input_shape=np.shape(train_in[0])),\n",
    "            # Inner layers, can add/change\n",
    "            keras.layers.Dense(32,  activation='tanh',kernel_initializer=my_init),\n",
    "            #keras.layers.Dense(16,  activation='tanh',kernel_initializer=my_init),\n",
    "            #keras.layers.Dense(8,  activation='tanh',kernel_initializer=my_init),\n",
    "            # Output layer, do not change\n",
    "            keras.layers.Dense(1, activation='sigmoid',kernel_initializer=my_init)])\n",
    "\n",
    "        model.compile(\n",
    "            # Optimization algorithm, specify learning rate\n",
    "            optimizer=keras.optimizers.Adam(learning_rate=1e-2),\n",
    "            # Loss function for a binary classifier\n",
    "            loss='binary_crossentropy',\n",
    "            # Diagnostic quantities\n",
    "            metrics=['accuracy'])\n",
    "\n",
    "        # Decrease the learning rate exponentially after the first 10 epochs\n",
    "        def scheduler(epoch, lr):\n",
    "            if epoch < 10:\n",
    "                return lr\n",
    "            else:\n",
    "                return lr * tf.math.exp(-0.05)\n",
    "\n",
    "        # Actual Training\n",
    "        history = model.fit(\n",
    "            # Training inputs\n",
    "            train_in,\n",
    "            # Training outputs\n",
    "            train_out,\n",
    "            # Evaluate test set at each epoch\n",
    "            validation_data=(test_in, test_out),\n",
    "            # Batch size, default is 32\n",
    "            #batch_size=32,\n",
    "            # Number of epochs\n",
    "            epochs=150,\n",
    "            # Store the model with the best validation accuracy\n",
    "            callbacks = [\n",
    "                # Drecrease learning rate\n",
    "                tf.keras.callbacks.LearningRateScheduler(scheduler),\n",
    "                # Store the model with the best validation accuracy\n",
    "                tf.keras.callbacks.ModelCheckpoint(\n",
    "                    filepath=filename,\n",
    "                    save_weights_only=False,\n",
    "                    monitor='val_accuracy',\n",
    "                    mode='max',\n",
    "                    save_best_only=True),\n",
    "                # Save logfiles for tensorboard\n",
    "                tf.keras.callbacks.TensorBoard(log_dir=\"logs\"+filename.split('.h5')[0], histogram_freq=1)],\n",
    "            # Shuffle data at each epoch\n",
    "            shuffle=True)\n",
    "\n",
    "        # Store the last (not necessarily the best) iteration\n",
    "        #model.save(filename)\n",
    "\n",
    "    model = loadnetwork(filename)\n",
    "    return model\n",
    "\n",
    "\n",
    "def loadnetwork(filename,verbose=False):\n",
    "    '''\n",
    "    Load a trained neural network\n",
    "    '''\n",
    "\n",
    "    model = tf.keras.models.load_model(filename)\n",
    "    if verbose:\n",
    "        model.summary()\n",
    "\n",
    "    return model\n",
    "\n",
    "\n",
    "def testnetwork(model,binaries):\n",
    "   '''\n",
    "   Test network on a series of binaries\n",
    "   '''\n",
    "   test_in  = nnet_in(binaries)\n",
    "   test_out = nnet_out(binaries)\n",
    "   model.evaluate(test_in,  test_out, verbose=2)\n",
    "\n",
    "\n",
    "def predictnetwork(model, binaries):\n",
    "    '''\n",
    "    Use a network to predict the detectability of a set of binaries.\n",
    "    '''\n",
    "    # Return the class (0 or 1) that is preferred\n",
    "    predictions = np.squeeze((model.predict(nnet_in(binaries)) > 0.5).astype(\"int32\"))\n",
    "    return predictions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load sample\n",
    "binaries= readsample('../solutions/sample_2e7_design_precessing_higherordermodes_3detectors.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split test/training\n",
    "train_binaries,test_binaries=splittwo(binaries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "312500/312500 [==============================] - 491s 2ms/step - loss: 0.0531 - accuracy: 0.9773 - val_loss: 0.0484 - val_accuracy: 0.9794 - lr: 0.0100\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.engine.sequential.Sequential at 0x13153cac0>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train a neural network\n",
    "trainnetwork(train_binaries,test_binaries,filename='trained.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29608"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# That's going to take ~12 hours on this laptopt (don't have a GPU turned on...). Here is the trained file:\n",
    "# https://github.com/dgerosa/pdetclassifier/blob/master/trained_2e7_design_precessing_higherordermodes_3detectors.h5\n",
    "\n",
    "import requests\n",
    "url = 'https://github.com/dgerosa/pdetclassifier/blob/master/trained_2e7_design_precessing_higherordermodes_3detectors.h5?raw=true'\n",
    "r = requests.get(url, allow_redirects=True)\n",
    "open('trained_2e7_design_precessing_higherordermodes_3detectors.h5', 'wb').write(r.content)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-18 09:09:56.081749: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "# Load trained network\n",
    "model = loadnetwork('trained_2e7_design_precessing_higherordermodes_3detectors.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some downsampling, otherwise the evaluation takes to long for class\n",
    "test_less={}\n",
    "for k in ['mtot', 'q', 'z', 'chi1x', 'chi1y', 'chi1z', 'chi2x', 'chi2y', 'chi2z', 'iota', 'ra', 'dec', 'psi', 'snr', 'det']:\n",
    "    test_less[k]=test_binaries[k][:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 - 0s - loss: 0.0682 - accuracy: 0.9800 - 189ms/epoch - 47ms/step\n"
     ]
    }
   ],
   "source": [
    "testnetwork(model,test_less)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0], dtype=int32)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictnetwork(model, test_less)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Convolutional Neural Networks (CNNs) <a class=\"anchor\" id=\"two\"></a>\n",
    "\n",
    "Recent interest in neural networks surged in 2012 when a team using a deep **[convolutional neural network (CNN)](https://en.wikipedia.org/wiki/Convolutional_neural_network)** acheived record results classifying objects in the [ImageNet](http://image-net.org/) data set.\n",
    "\n",
    "If you naively apply a multi-layer perceptron to an image, the first thing to do would be unravel the array of pixels into a long one-dimensional array (much like we did for the digits images a few weeks ago). That's fine but it's neglecting precious information, namely that pixels next to each other are correlated.\n",
    "\n",
    "The idea behind CNNs is inspired by human visual perception. ***Each neuron in your visual cortex doesn't \"see\" all of what your eye can see at once*** and some neurons are more sensitive to one pattern over another (e.g., horizontal lines vs. vertical lines).  \n",
    "\n",
    "Moreover, the simplest deeply connected neural networks would choke on \"real\" data which has far more than 28x28 pixels, and would require following tens of millions of connections. So we use a combination of \n",
    "- \"convolution\"\n",
    "- \"pooling\" \n",
    "\n",
    "to reduce the dimensionality of the data first.\n",
    "\n",
    "![Ivezic Figure 9.19](https://www.astroml.org/_images/fig_cnn_1.png)\n",
    "\n",
    "### Convolutional Layers\n",
    "\n",
    "In a convolutional layer, each neuron is not connected to each neuron in the previous layer, but only those that are within its \"field of view\" as defined by a kernel (filter).  We slide the kernel over the input layer and the value in the next layer depends only on those pixels.\n",
    "\n",
    "![](https://miro.medium.com/max/4800/1*GcI7G-JLAQiEoCON7xFbhg.gif)\n",
    "\n",
    "Here's another perspective that helps to visualize going from one layer to the next.\n",
    "\n",
    "![Convolution example](https://docs-assets.developer.apple.com/published/09348c5368/a55b1477-4f79-4221-8aa1-ab3ae9f01f89.png)\n",
    "\n",
    "Choosing a filter (kernel) with a certain pattern can help recognize certain types of features (like horizontal aor vertical lines).\n",
    "\n",
    "![vertical filter](https://miro.medium.com/max/1338/1*7IEbBib7Yc7qST5IGQoYXA.jpeg)\n",
    "\n",
    "![horizontal filter](https://miro.medium.com/max/1238/1*PSSAaH2pZbl5bK3Ef_zk4A.jpeg)\n",
    "\n",
    "The purpose of the convolutional layers is really to capture high-level features in the images, like edges. \n",
    "\n",
    "\n",
    "This reminds me of polarized light, which is personally how I visualized CNN in my mind...\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pooling Layers\n",
    "\n",
    "It is common to follow the convolutional layer by a so-called \"pooling layer\", essentially to reduce the amount of data that needs to be processed.  The full architechture of a CNN might look something like this:\n",
    "\n",
    "![CNN Example](https://www.researchgate.net/profile/Xian_Wei2/publication/331986652/figure/fig1/AS:740547106988032@1553571597647/The-classic-structure-of-CNN-It-consists-of-two-modules-Feature-extraction-module-and.ppm)\n",
    "\n",
    "Where the pooling layers are reducing the number of pixels by averaging, summing, taking the max, etc. \"**Max Pool**\" is the most popular, and literally corresponds to taking the maximum of the convolution.\n",
    "\n",
    "![Pooling example](https://miro.medium.com/max/1000/1*ydNsGDxMldAiq7b96GDQwg.jpeg)\n",
    "\n",
    "or another way to see this is...\n",
    "\n",
    "![](https://miro.medium.com/max/792/1*uoWYsCV5vBU8SHFPAPao-w.gif)\n",
    "\n",
    "When we are done with convolution and pooling, the output gets fed into a regular, fully connected neural network that outputs the predictions.\n",
    "\n",
    "- This is clearly much more sophisticated than our basic fully-connected multi-layer perceptron. \"Deep\" networks consist of tens of layers with thousands of neurons. **These large networks have become usable thanks to two breakthroughs: the use of sparse layers, and the power of graphics processing units (GPUs).**\n",
    "\n",
    "\n",
    "- Sparse layers or convolutional layers in a deep network contain a large number of hidden nodes but very few synapses. The sparseness arises from the relatively small size of a typical convolution kernel (15x15 is a large kernel), so a hidden node representing one output of the convolution is connected to only a few input nodes. Compare this the our previous perceptron, in which every hidden node was connected to every input node.\n",
    "\n",
    "\n",
    "- Even though the total number of connections is greatly reduced in the sparse layers, the total number of nodes and connections in a modern deep network is still enormous. Luckily, training these networks turns out to be a great task for GPU acceleration.\n",
    "\n",
    "\n",
    "For further study, there are lots of resources for CNNs online.  For example, see\n",
    "https://medium.com/analytics-vidhya/convolutional-neural-networks-cnn-explained-step-by-step-69137a54e5e7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cells are from Geron, Chapter 10, see \n",
    "https://github.com/ageron/handson-ml2/blob/master/10_neural_nets_with_keras.ipynb\n",
    "\n",
    "We'll start by introducing the **Fashion MNIST data set**. The next cells load the data, define test, trainging, and validation sets; normalize the data; display an example image; list the possible target values ($y$), and show a 4x10 grid of examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n",
      "(10000, 28, 28)\n",
      "(55000, 28, 28)\n",
      "(5000, 28, 28)\n",
      "(10000, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "from tensorflow import keras\n",
    "\n",
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "(X_train_full, y_train_full), (X_test, y_test) = fashion_mnist.load_data()\n",
    "\n",
    "print(X_train_full.shape)\n",
    "print(X_test.shape)\n",
    "\n",
    "X_valid, X_train = X_train_full[:5000] / 255., X_train_full[5000:] / 255.\n",
    "y_valid, y_train = y_train_full[:5000], y_train_full[5000:]\n",
    "X_test = X_test / 255.\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X_valid.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This data set is a huge collection of images of items of clothing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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99mFx1Wgy0e3eAB5JkuSTheXPoeKfVX05Y1L2e81qwuAZM6t9mSTJhwA+zD6u29DKNZYk5f4kSYYifWPzDkj/Azc3y9IbwINm1qGgmDk53+f5Q+Wabl+5t/0P0v/kAsCafKClHGJm95vZ9ExovXJMZQBaWP/wHQBXI9WsWQHFfV2lrssA+DSnrn/K8iwHYNWccurm1zWwA4DuSMNCPfzdEQspp6HtHGFmewK4A+kG9QIvcmsAPGZMyGmTWynPmqiNqvcsUr0jIPWjvpndB/P//jJ9Bf8NA1xa0XFLo/4TJflHbcg/5B9FtBX/aEloDSOKkH+IxlBZB+U9XLOweX6ta587kL5zwwD8H4B3zexGMzvUqrzUOEmSbwH8HGm04c4A3rT0ZVanWpUXFIoGoT6kCcl7Iqk18hrZayF/46yIdmR/VpDvU6Sd08qVLyx9e9oTSDeWnkEq2Ps25k+4jwPwUyz8v0RtnapvCayQJMk8M9sC6bX8CdIJ9pHZz0wz+yOAC5Ps+fAqfFvPyjq+WXiW0gzJft/ZiDIq/rwwX66wMuZvRjJfV/muwmfZcbV2znUhezJ4OIDhZnYa0o3VykbrYQD+mnNoQ9usck0fRBq6UMSXFSN7UuompKLgk5Fq2YzC/P/EbopUsH1h/cOJSNvoGaRPKw0zsw1yNt0rdZ2FdGNhYeRtGtTTrxvKD5H+vfdUSXsEafjrGgAOM7PTkiTJq2tj/oaOSDcpbkcqpH+0md2eJMkDNZTFY8xmIB/JoWiDIhf/pDzBfUHlnm3IuAfQuIfa+ph6Iv+oAfkHAPlHLm3IP1oSWsOIIuQfoiayjcte2cfXcrItbIyrae2TJMk3WXTGkUj9YxOk0XUHA/jIzC5FKoHDxwwzs9EAfoV0Lbxl9vM7M3sYaUj4uIXUQSyI+pAmZHHaUH0E6cbPkkiF7F+soYyPyF4+N9f8tA/pu7OQOsk4ADsmSfIFH2Bmef/xF0SSJAu9kbJwg4sBXGxmGyHVpvwh0oXRH5E+AfKrJqzmomBfpJu/dzeijIo/l/FlIPZnpqifqITgfVy2Uo3FzFYG8E2SJPOqpSdJ8rGZ/RhpqEF7AOvV8fSVa/ptkiSjG3Dc/kg3U79C+gKoaDKQDTRlmAdgR6STn1eRDop/RDoQ5dV1uQbWtUWQhZQegPRJ3E/SPekFqERZdEEqO3F/E1RlOQDHJkny9yxcaV8AV5jZ+kmSzG1gWTzGTEmSpEme/jWzJXM2RThktnLPNmbcq1cf02DkH7Uj/wjIP6rQFvyjBaI1jChC/iFqZTBSaTYA+E+NZdS69qk8dXolgCstDeE/CGlUyNpINVb7Yr6kQOWYEQC+b2YrIX3y9TAA30X6QqQXzKz/opAdW8xQH9KELDYh/0mSfIBUXBlIb9aFYmbfN7OpZlZ5UoB373vmHNMO8x+d5/yDs98PeScRTUeSJCOTJDkd6cbSDdnXPzOnrdqaMLNuSP+L91ySJI3pYCr+2bMgTyVtasHirmO1LzOJgMp/n8Y2rGqN4i6kT3PmkiTJZ0g7bSDdxKwXlWvaNy+DmS1jZkebGT8VOjj7Pa6R/1m9IkmSt5MkmYw0LAYAjjWznQvquoIVaOmZ2f5mdmgj6tRUHIh0M+S7ADbK+dkZqUg5kP5TpSmYnCTJ3zP7x0jDZtZA/lPPRfCYUeRDu5jZj8xsibw8C6HqPYv59/s3mH9/vIVUZ5fTi47lv6EhfcyXmK+xVQ/kH/KPIuQf8o9Wg9Ywogj5h2gEJ2a/ZwO4psYyal37RCRJ8laSJOcjjS6tSI7tb2Y9cvJ/nCTJ9UmS7Ik0Ku9TpOvOH9XyR7Rl1Ic0LYvNhmrG2Ugnq4PNbMeijJa+gOV0AF2RbcRlQrkVYfxqGxQAsFP2+wsA93GR7rene1F9MP9x+3C8mQ02s0X6sp+Wjpn9xcz+5b/Pbs7/yz4uhfk3c0uj8tQHt7OZ2YFmtnH21T5Z+p2NLOe27PcAM8tbIO3s8lZj65zvK4PmN6j9xVm1sonlPHJEVLTq6vl05i3Z7z4Fej57IH0p1gb0XWP7hwVIkmQYUh8xpP/5beey3If5IRl7oApmtjrmSxG0NH4I4OkkSR5MkmR0zs+jmK8/u6/l6wDXhewfHMdkH4ea2ZAGFvEM0jBjIL9NlkI6Jp1QEIK8MBZ2zz5RecI7e+K/MpZVHfcsfblbpU/lvqJir2xmg3LOWSnzgewfHUy1fmz9rB9bNqe8CvIP+UcR8g/5R2vjbGgNI/I5G/IP0QDM7AgAu2YfT06SpNZowprWPmb2P2b2mM+YPbV6Hn3VOcu/ppm9a2YbVDnmOaR6rCG/aDBnQ31I05AkyWL1A2AbAJ8AeA/AgJw8SyJ1jgRpSLVR2i5IQ63nAejpjlsK6SPSCYALXNrfs+8nA1jRpXVG+p+hBMCwnDpNy9I3oe/eQjpxbfbrWuc26pn9rQmAwQ08dhhSTcjVqqR9j9rAqhyXADg7p9xJefXh+uYce3ZR27q8fbK8H9F3XbPvfpt9/m/2uW8jyzEAj2ff/b8qZeyXpc0C0KVK+vAs/Uv2yyxtSQDPZelXL2L/qdTrJwV5fpTlmQ2gfY4fNdgfsmv6WJZ2W5Xjlke6gfsegHb0/fezY74GsJE75jtI36qcAJhU4PcL1BfA6tm5EgBXFvjmBACrVEm/Gml/t1VDr1ETt3GPrF4/LJG3ct8nSENrG93OWdrgvDbJrluSXftqfVHueZGGOiUAPgDQvUr6WVn6IQ28Zj3pOrwAYCmXPgDp09oJgJ1dWn+km+/fANi8Stl3ZsddXyXt2iztHizY7w7KyvwcwAZVjv13duyv6Lt/ZvfJUgV/q/xD/iH/kH/U5B8t+Qdaw+hH/qGfhftBZRxIctK/A+AE6rN/m5NvGErM81H72udspGvIBdazSN8dkWT+vFL2Xc+8+mR/02tZ+o+auw1a64/6kCa6rs1dgSZylo0AvIF0d/wyAHshDaHeBcBJSDcXEqSTseWqHH8c0kncFKRCyhtnZQzPjrsewJLumDUBzMjSX0b6OPVmAA5HKrr7UZZ2J9LJZ1d3/D+y9Ouy407NPv9Pc1/POrbLKtnfvivmT9yPzL7rWLKMykJkdHbsFkifpvhldjN+AWAPyt81K//O7LhLs8+rZOm9ss/TXH1WyH58ffsD6J8d2zH7fKlr2/7ImcwjHZTezPL/CsDmSDutb7N2Xxnp4DNmIdehsBzK1wnAK1m+qwBsj3QQ+1+kHeoHALbJOUfF3/+E9OUWxyO9j3bD/E3fl1Flo66J/eih7NzfIr0XD8jqtSWAQ5BKAnyb/W07uGNr9gcqoyOAEVn6fUi1WgchfQnW6Oy6+vMugfmb2+9m13ILpNpAj2J+/zAtO1/fkvXtm7VlxT8P5/pm5x2WpY3J6rhJdt67s+9PLXmN+i+Ctl0qO9efkC6iNwXQqyB/LwADker5JUgH8v6NaWfMv++PdG3Si9p/q8y/EqRPWVXO2SvnvB1dvc/N0qcifbneIAC7Z231LYDLGnDNlsWC/dT1SO/fvZCOXz/C/PEpb2I9BOmmyBykIWKVMfP27LhHkU163XErYv49eVdWj02Q+visrB0PyDnnodlxI5FO8g5Eqrl0r/xD/iH/kH/U0z9ayw+0htGP/EM/1du+MjadAbcuzdpxNwC/BvB6lvYOgP0LyrkTJef5qG3tcxbmb6Idh3S9vgXScWsy0vHqGMrfI8v/FVJZnMp4sDfSf7pVxpIl63E92+qP+pAmuKbNXYEmdJYlspv8jqxD+QKp9sabSDeWqm4i0fEDs0abinSDazbSTaQDC47pmjX4O1lnMBfAk5mznYP5nV8CtwOPdCPtagAzs7qOz45ZbDoN0H/UqvycXbKMlZCGzN2VdcZfZD8TkS4m+rv8w3LOd0SWPjwnfTDmP2GywE927NkFf0/Pgr9h/cyXPkY6AI0EcHCWVnmS8YIS1yK3HJdvGaQd5AtIO6zPkeqfXYwqT6bScZVrcwRSIelbkP5Hq+Kf54E2GhehHy2DdCC/KLu/pmd/01dI79OnkQ7inaocW7M/VKnDiUif0v0IaR8xCenTMWvn1HtZAL9BurFZ6Y9ey/xoL3e+SSXrO6lkffcF8ADSUI+vkPYzdyEVBi97jZJF0LY9q5x3eAkf9T81tzPy7/vhC7nvhxWUu0D/BmA7pG//fjdrkzlINxaqbh4UXIONqpxvCaT/ZBqJ9L/I85A+Bb3AxNqV1RvA5UgnN18gHcOeAHA0gCUKjlsC6Tg3PDvmC8y/H/oUHGdIFwYTs2swHcC/UOXelX/IP+Qf8o/G+Edr+oHWMPqRf+hnwTYYVqW/rvx8jfSfUK9nPnAI8h/uyS1nIedv0Nony/99pNJiE5D+0+2rzO9uBrB1lWM2Rbq+eynzsa+z308jfeq2VUYftLQf9SH1/bGskkKIFoCZ3YT0vzZbJEnyfDPXZTjSJ1qPTFK9TiGEEEIIIYQQQog2z+L2UiohWi1mtjTSsL0ZSJ8mFUIIIYQQQgghhBAtjCWbuwJCiJQkSb5E+ki7EEIIIYQQQgghhGihaENVCBFhZn0BLI305RoA0NXM+gOYmiTJ3GarmBBCCCGEEEIIIUQLQBqqQogIM5uE9E2LHmmpCiGEEEIIIYQQos2jDVUhhBBCCCGEEEIIIYQoiV5KJYQQQgghhBBCCCGEECXRhqoQQgghhBBCCCGEEEKURBuqQgghhBBCCCGEEEIIURJtqAohhBBCCCGEEEIIIURJtKEqhBBCCCGEEEIIIYQQJdGGqhBCCCGEEEIIIYQQQpRkyWY8d9KM5252kiT+881sUZ5+kZ6sRprUP0aPHh3sTz75JEobO3ZssC+77LJgX3/99VG+3r17N7oeTz31VLDPO++8YJ977rlRviWWWCLYvXr1itLat2/f6Ho42rx/iELkH6KI1uAfgHykOWkNPiL/aD7kH3yiJP9URWuHPfbYI9grrrhisL/++uso32677Rbsn/zkJ7nlffvtt8H+znea9XmcNu8ftfjEY489Fn0+/vjjg73MMssE+/PPP8891z333BOl9enTp+q52Fd8nRbBerfN+0dZHnnkkWDz2hcA1l133WCvvfbauWXMnTu3qg0At956a7AHDx4c7N133z3Kt8IKK5Spbr1oDf4BLMI+pOw96ccHvs/33XffYM+ZMyfK9+CDDwb7/fffj9IeeuihUueupb6NoMEn0BOqQgghhBBCCCGEEEIIURJtqAohhBBCCCGEEEIIIURJrChsoIlpEY+7MxwGDgC33XZbsJ9//vlgf/PNN1G+zp07B5sfkQeAHXbYIdibb755XepZB1rD4+519Y/rrrsu+jxv3rxgr7766lHaOuusE+z//d//Dfbw4cOjfN26dQv2VlttFezlllsuysfHjR8/Pkr74osvgs1hV3/+85+jfK+++mqw33vvvSitR48ewd5nn31QB9qcf4gGIf8QRbQG/wDkI81Ja/AR+Ufz0eb9o5bwxpNPPjn6/Pe//z3YHLLrwzGXXnrpYA8bNixK47ltC6LN+0dZeB174IEHRmkbbrhhsD/44INg+/BrlgMYM2ZMlHb33XcHm9cwRSwCybs27x8sZXfaaadFaW+88UaweS3cs2fPKB+vcbn/8CHbEyZMCLaXE2EmTZqUm8aSJA888EBuvjrRGvwDaMY+ZNasWcH+/ve/H+ynn346ysdjB4f/+3ua01jC0Oe9/PLLg33wwQeXri/vy/nya0Qh/0IIIYQQQgghhBBCCNFUaENVCCGEEEIIIYQQQgghSqINVSGEEEIIIYQQQgghhChJm9RQZQ2YH/3oR8EeMWJElI+1QJZccslgf+c78T40f/78889z0/r27RvsX/3qV1G+o48+ulTd60Rr0A9ptH/ce++9wX700UejtKFDhwZ7+vTpUdoqq6wSbNZTveeee6J8F110UbBZr4a1ZgDgtddeC/Zqq60WpZ1yyinBPvTQQ4P94osvRvnYr5Zffvko7cYbbwz27rvvHuyyekZVaBP+IWpG/iGKaA3+AchHmpPW4CPyj+ZD/lHAz3/+82C/8MILwWbdOwDo0KFDsN95551g8xwXAFZaaaVgf/bZZ1Ea6yieeOKJwea5JhBr5Pk1UhPQJvyjrI7uZZddFn2+5ZZbgj1u3LhgczsDwN577x1s1sr1+wJc3iuvvBKl8Tp5zTXXDPZ+++0X5fvZz36WW/8m8J024R9F8PWeO3dulObXoRVYTxUAll122WCzxqnvP3h/xL8nJC+fPxd/9lq/DdHSLElr8A+gRh8p228888wzwea9CAAYOXJksNu1axfsjh07RvlmzpxZtWzWXfZ4f+T3EPE41b59+yjfWWedFexFsGcmDVUhhBBCCCGEEEIIIYRoKrShKoQQQgghhBBCCCGEECVZbEL+OWQAKA4b6NSpU7A5RGbllVeO8vG1WWqppYLNIQ4AsMQSSwT7m2++yT3vBx98EGwOowHix5zL4tuu6NFuR2t43L3R/vG3v/0t2NOmTYvS1ltvvWB37949twwOefBtxj730EMPBfujjz6K8m222WbBXn311aM0Dsl6++23g/3VV1/lnmvq1KlRGh/HcgC/+MUvUCNtwj9Ezcg/RBGtwT8A+Uhz0hp8RP7RfMg/CB/S/Yc//CHY/fv3DzavMQBgzpw5weawzU8//TTKt8IKKwS7S5cuUdq7775b9TgOCW0G2oR/FIXC//Of/wz2ySefHKVx6D2vV1maDIjXnbxe4DUFANx9993B7tq1a5TG4d+8/vVrruOPPz7Yv/vd79DEtAn/YJ566qno8znnnBNsH2I9ePDgYPv9DIZD9BkO/wdiSTovO8L+wX2JL7tINuCKK64Idu/evXPr2wBag38AdfaRq6++OvrMPuL30HjPi9N4TwSI27RHjx7B9ntho0ePDjaH+APx2MR7H35Pi/dWevXqFaWx9A2zKPfJ9ISqEEIIIYQQQgghhBBClEQbqkIIIYQQQgghhBBCCFESbagKIYQQQgghhBBCCCFESaoLZLQSivRlGK8fwhqqrAfB+pMA0K9fv2CPGTMm2F6DgcvzuhFTpkwJNmuJrLTSSlG+l19+Odgbb7zxAn9DhbJ/swBGjRoV7HXWWSdK+/jjj4PN+i8AsNxyywV76aWXDrbXdWHdD9akYU1dINao+fDDD6O06dOnB9trkzCsK/L+++9HaXzcuHHjcssQQrQcWNuHxxTfR3C+L7/8MkpjDTRO8xrMyyyzTLB5HAKAjh07BpvHr9VWWy237r4PYr20jTbaKPc4IYQQDePxxx+PPvOcj/v6VVddNcrH81ceA9q3b5+bz69heD7M89WXXnopyrfJJpvk/wGiJorWeDfffHOwvSYhr0G++OKLqt8DseYha7J6PcW+ffsG289B2F/Yj7wWr/dhUV+22Wab6PPaa68d7OHDh0dprHPK80GvjZrHvHnzos+8hvbrWF7/8l6Mn1+OGDEi2KzLCQBnn312sK+99tpSdRQL8utf/zr6zHtevt/nvofXIH6/hN8Lw77E+t1A3Nfwu2P8ubmP8vqnfC7/3qEXX3wx2JtuuimaA+3ICSGEEEIIIYQQQgghREm0oSqEEEIIIYQQQgghhBAlaXUh/2VD3rfccstgT548ObcMDrX0YYz8ODQfM2HChCgfh/X70PKePXsGmx9RnjlzZpRvl112Cbb/u7henOYf0fbhHG0dDj3w15vbacaMGVFar169gs3SAF4SgsMjOJ8Pl+GQ2qI24tActoE4DNiXwWFYfG7/uLyXqhDNQ63tkhciDsRhf97/OAxLPtByyGuLI488Mvo8ceLE3DI49Ip9wIdMcSiOPy+H0XBIFofoAMCgQYOCvddee0Vp//73v4N91VVX5dZXpOTdy/7eXZSyPkX9ixD1gOfid955Z5R2wgknBFtz2ZiPPvoo+sxhlzw39CH/PEfldYSfy7IEmZdI48983hdeeCHKp5D/pmX27NnRZ5Yg82HWPI6w1IO/r7jP53XKZ599FuXjcciHhfM6lG0/drEs0AcffBCleQkK0XD8fsDxxx8f7NNOOy1K69+/f7CPPfbYYPswfJYGYJZcMn/ryEtKcZ/B0hSTJk2K8rFUlO+D/vSnP+WeTxTDEh0rrLBClMbzPO8/n3zySbCLxmM+jv3F75Nx23/66adRmp/3VvDSZSw/4+eod9xxR7A55H9RzmX1hKoQQgghhBBCCCGEEEKURBuqQgghhBBCCCGEEEIIUZJWF/Kf9/juqaeeGn3mcIju3btHafzWOQ6V8G8v4xB9fkTev4WZH3H3ZfjH2iustdZa0eeVV1452G+//XaU9uMf/zjY//jHP4KtsKgF4TcFcjgLSy8A8RvofFtw+3K7ezjUnv3IvwXz9ddfDzaHVgFAp06dgs1hDv6ReA7R8mlrrrlmsDkE+NVXX43ybbjhhgv+EWKRUxSCMG7cuOgzv131wgsvDHa7du2ifNxHiNYBh7MstdRSwd58882jfGPGjAk2h7z4zzweDBgwIMrHYTksTwLEoT0cysVSNkAcMur7oJNPPhmiPHl9QFHI/xNPPBHsU045JcrXp0+fYPvQLR77hg4dGuz11luvVJ2AeFxhf/TzGw7r2m677XLLE02LlwzisN///ve/Udphhx0W7Ntuuy3Y9Wi/f/3rX9HnV155Jdi/+MUvorQnn3wy2IMHD270uRcneK7p4XvT38PdunULNo8xfu3AskBeXsD39RWefvrp6PNPf/rT3DqKxjNy5MjoM9/j3LZAvA7l9Sm3sy+DxxqedwKxVIcvg/2Dxx4vbcV18muT7bffHqJx+Hu6aO3KYf687vQh/zxuMLyHAsTzRn8M909cPn8PAEcccUSw11577SiN18miYTzzzDPB9n079w2+n+d5I/cTLN0BxHsfvK/i5WeK9kh4LcTn5fKAuH/x/dCDDz4Y7AsuuADNgZ5QFUIIIYQQQgghhBBCiJJoQ1UIIYQQQgghhBBCCCFKog1VIYQQQgghhBBCCCGEKMlio6H67LPPRp9ZF9Mfw/ofrPPCehJArEnCx2yyySZRvgkTJgSb9UgAYN111w12ly5dgv3ZZ59F+VjHrkOHDlHaa6+9BlGOe+65J9is5+Y1hlir0mvisuYu6354WPeW9YdWX331KB9/njFjRpT2/PPPB5s1Tr12COvqsp4REPs6+9gDDzwQ5ZOGasvn7rvvjj7PmjUr2Oxjb775ZpTv0ksvDbb3P9ZX3GOPPYK95ZZbRvm85o1oWrzGWIXevXtHn7kP8v0R65LxOOd1NFmvyusrsv4Vaxv5PvO9994LtvcVHufEwuG2L9IuZfj689gGxFq6Xm9x+PDhwX7ooYeC7bXDb7zxxmBfcsklUdrll18ebO5fWPsKiLXPfP/i/Uk0HV6Ll5k5c2b0me//Y445Jti+H+I5qtdAY9i3Wd/dn4t9EQCGDBmSW2ZbhDXtvO41w307txEQrzP8mMBMnTo12P49EAzfw2+99VZuPlF/WH8YiO8znht62D+8viUfx/7htbG5n/danewv7B9+XOPPzz33XJQmDdVFC2uZsjavn1esuOKKwWY/8n0Ep/k5Ku+J8B6L11CVDzQNvCdSNG77NN5L4PbmdwsB+f0Qv/fF4+eCrO3q978Y1m/1fd7EiRNzj1tU6AlVIYQQQgghhBBCCCGEKIk2VIUQQgghhBBCCCGEEKIkrS7kn+EQBR8izSFw7dq1i9L4UWR+zNk/8szhERwm6cOpOJRh0KBBURo/Ms+PvnMINxCHUHJYFBCH/U6ZMiXYHJouUjbddNNgv/jii8EeM2ZMlO+WW24J9q677hql8SPns2fPDvbAgQOjfBz6wo/E+1AGfiS+W7duURr7KYfB+MflWaLg9ttvj9KOOuqoYK+wwgrB3myzzSBaPuxj3k+vvvrqYJ944onB9qFbLFvhQ/65/2BJgX/84x9RPpaE2H///aM07/ui6eCwaSAeX5ZeeukojcNCeTz0/Qcf9/7770dpLGfDYTRelob7rgsuuCD/DxALJS/M34dTMhyWWxRO6cPwP/jgg2BzWN1dd90V5dt4442D7aWLOHSbx0cfdsWhXArxbz54XuF56qmnos8chslzWy+jxT7m56jsc+3btw/2+uuvH+Xj+asPL+UwVBGH1HuZlrz+g9cbQNxOPEct6meKQnZ1fzcfL7/8cvSZfaBI4oPHd+83eX7g8/EaxktHsB9wP+DnKuyLPuRf1B9uJ9/O3C9zyL8f93mdwesI33czfmzgz1x+z549c8sQ9YPnf0X4/vzpp58ONs/5/B4Gjyu8DvUyNSuttFKw/V4b+8KoUaOqlgfEa1svmcb7fLwfWCQhUG/0hKoQQgghhBBCCCGEEEKURBuqQgghhBBCCCGEEEIIURJtqAohhBBCCCGEEEIIIURJWrWG6uTJk4Pt9RpYS8hrirGmB2uEeG0Y1p7hfB07dozysR7EJ598EqXNnDkz2KwpwzpT/txeL4k1KljHRBqqC7LXXntVtb2OyP333x/s559/Pkrbcccdg80ahS+88EKUb8CAAcHmdvf6hXxuXw/2MdY969SpU5SP68iaZwBw5plnBtv7lag/rN2Sp2VWhO9nuK9i3/N511lnnWD//ve/j/Kxfgxr1QCxNl3v3r2rlgcAEyZMCPbZZ58dpXm9RdF4vAZQHqxp5zUry/ofa1f5MvL82esceX0tUX+8T3B7sG6Z17tirSmeFwHA0KFDg7333nsH+6GHHory8VjUt2/fKI3nLkXaiV53Vyw6ijTzGK+Nyu3J7efnuZzP9w08R+X5Cev+AsC0adOCvccee+TWUcTXys8ZuC/guafXVmdN/Y8++ijYXmOXy/e6dTwX5TL8uwJE08L6wx4/brz77rvB5vYrmi9w/+81Wfne9+di3+H1jdfZ5D6JfVs0DdyefjxYd911q+bj/QWfxu1ZNL7wMR7uM4YMGZKbz/d3RecTxfD44Odu3FZ+74r3Erjd/F4bv/+H15fvvfdelI/34Yr0T3n/i/sxIPZP/64Q3qt5/fXXg73ttttiUaEnVIUQQgghhBBCCCGEEKIk2lAVQgghhBBCCCGEEEKIkrTqkP+33347N40f//WPMnOILYfI+EfVOfyJQ2R8qAufi+UEgDhUjh9b92VwKA2H6QDx4+/8KPPGG28MUQ4fCv/AAw8E+4QTTojSONyFw2XGjh0b5Rs0aFDV8n2+NdZYI9gdOnSI0h555JFgc6j2W2+9FeXjEI3zzjsvSlOYf9PiwxPYP/ieLgrXZVhuAgBuuummYL/44otR2uWXXx7sp556Kti+j+DPPmxn++23DzaHY3KfA8T9opcDEPXHjxUViqQAvBwMh+937do12ByCC8ThN14yhMc9Hq98/fxxov4UhWSylMzEiROjtJ49ewbbh2u++eabwV5rrbWC7UO3ePyZMWNGlMZlcig4y1EA8TglFi1FvsPzidmzZ0dp3G+wT3hpEA7Z8z7Gc2WWA/D9Fc+3ffkihsdxH/p45JFHBpvnsl5yg8P3uS38/IHv49GjR0dp++23X7CfeeaZqvUTTY8Pey5aux5wwAHBfuKJJ4Lt5e+4DO4//PyPxxA/L+D7fYsttgg2S0gB8f3upSlE/cmbXwJxP8H5unXrFuXLkw3weypcht/b4DTuWxoiG6CQ/9rhuWLRHIH3sYD4vu7Ro0ewvRzD+++/X7UMvy/B48+cOXOiNPYnHld4TQrE6xo/B2Gfefzxx4OtkH8hhBBCCCGEEEIIIYRogWhDVQghhBBCCCGEEEIIIUrSqkP+Ofzdhw/x2wb9GwX57ez8KLkPl+FHnvnxYv8WbX7U2JfBj0dzmENRSCa/ldvXkd/Q+oMf/AAin6K3sfM19aERHArNoVAcDgsAzz33XLAPPfTQYPtH51mawofcrL/++sHmR+K9nAX7kQ+zzMunMInaKes7TFE4BfdHm222WZTGb9v2oXhnn3121bSVV145yuffys2wL3Hf5cMpuHwfksFvmO3evXvuuUR58vzFh8lyCFWfPn2iNO67+DgOwwFiyRo/9nAZXCf/Jmi91bnpKepD+G2qvg/iccWPZw8++GCw77vvvtx83Df4tuY5DtfRj3V+7iIWHUUh9HfeeWew/Zt+uT/gca9Iwsb7KfsSz4F9XzZ16tRge99hSQsRzxk86623XrB5/uDDIPn6s3/w2gaI5wK+DJaE4HnHpEmTonwctunnMaLx+DkZrwPeeOONKO3mm28ONq+TvXSEnwNW8JIyfu3D8Nhz/PHHB/uggw6K8nE4+dy5c3PLE/UhL1wfANZcc82qx7A8GJC/L1G0tvRvd+exgX2WZYo8RXIFomHwmOuvK68TTjzxxCjtmmuuqZrP73/xvhb3BX4Nwvi25/kE9w1DhgyJ8rEk0YgRI6I0nteMGTMm99xNiZ5QFUIIIYQQQgghhBBCiJJoQ1UIIYQQQgghhBBCCCFKog1VIYQQQgghhBBCCCGEKEmrFqpgbQiv+8MaH14LijUfWPfH61tyGaxH4nWhWJvI61MxXIbXseN6eI0K1pbyWjmiNjp16hRsr0fJvjRr1qxgb7LJJlG+kSNHBvuvf/1rsNdZZ50o3+jRo4PtdZBYy4b9iLVQgVjDiDWtPEU6aqJYGzUP3y+w9hv7DmvvAnGb3X777cH+z3/+E+Xbe++9g81avABw1VVXBfuZZ54J9iqrrBLl4z6CdXmB2IfZr3x92e//9a9/RWmHHXZYsFuDhmot7Vwr3K8D+fpP3o/yxoq///3v0ecOHToEu3PnzlEaj4Hct/B4AsTt7uvB8N/i/w7+XPZvWRyph2/xvVakTZmHb1++/327HXLIIcHmPunJJ5+M8nG/1q5duyiN50JdunQJNo9twIL+KWqjrI+xHxWN/ddff32wvc4t+1ItvujhcWXw4MFRGs97vcZajx49ajrf4sqMGTNy01jfevbs2cH280b2iTy9TGDB/oSZOXNmsFm79ZVXXonyTZ8+Pdhe61vUBt8vft3JepR+/OU1JPuHf/8Cl8lleD8q0uPkvJtuummwi/oPv/4VixZuz/Hjxwf7tttui/Lx/LKIIs1TXquwhq90dBcNPB537NgxSmO984svvjhKGz58eLBZk9TPDfP6Bt/X8Dji68HraH5P0JZbbhnl4/7Kr6N5T+f5559Hc6DdFyGEEEIIIYQQQgghhCiJNlSFEEIIIYQQQgghhBCiJK065H/s2LHB9mH9ReEG/Fgyhyv4sImyYQkcduVDIflReD6Xf0Sew3FYkgCIw/R8iJ2oDX4c3YdPcxqHpX388cdRvgEDBlQt+/XXX48+f/e73w02h2oBcdgl+/Cqq64a5eNH4ovCK0QxHDLA7VzUf/h7mvsFDpkYMWJElI/v1RNOOKFqHQDg1VdfDfbDDz8cpT3xxBPB3nbbbavWD4hDftkGgEGDBgWb+yovP8EhOLvsskuU1hrC/JuLovuRfacoLJ5Dcu+8884obd999w22l3zhNuTwTh/+y76+wgorRGnsw1xH72N8rilTpkRpvXv3hsjHh1Byf1O2P7/rrruC7ceHd955J9hffvlllLb66qsHm+cWPnSTQ0O9hBLXv3379sHmuYmoH2XD7fPC/K+++uro84QJE4Ltw+i4j+KxzZftfZhhX+Iy/DjCYXksmQQo5N/DIZJFadwve/kq7lu4jXzb8tzWS45xOw0cODC3jA8//DC3vqI2eJwtWlv6EFqGw7b79esXpXE/n2cDcX/k+3wOKeb7m8cdYEFpJIbXWf44URtF8wqeP7BP+HlcnkzIvHnzcs/lj8mTOvTrblE/WMKFx21/7/r7nOnbt2+wec0wbdq03DK4n/D7Z3xuX8bbb78dbJYU2GijjaJ8PEc4+eSTc8tn+ZlFiZ5QFUIIIYQQQgghhBBCiJJoQ1UIIYQQQgghhBBCCCFK0qpjh1977bVg+/C1ojcZ89tsOWylKOyXH2lviLxAXhiFfwMah+kVPYbNoRHjxo2L0vgRbVE+bM6/9ZbDXtnmNxQCceg2v/nYh0KxBIAPx+Qy2Md8eHDZMJimfqN5a8ffdxX8deMwCR+q9N577wWbw/oPOOCAKN/f/va3YHfr1i3Y6667bpSPwxNeeumlKI1DHjjU1vsD9wsPPfRQlMZhvhwa1qtXrygf+59/Wzf/za3hTd5F94Hvv/PSuAx/DH/2aXz/F4X5c1juWWedFWyWCAGAN998M9g+7Jb7D5Z68D6bF5bj4Xy+7nxuL2vSlkL+866fbxuek/j5CcNzlfvvvz9Ku++++4LNb/32bcOhlj6c6oYbbgg2h/P6sCjuU7wsBI9bHOLpx7qbb7452EceeSREffHzBw5z4zTuT4B4HPH+y+HenFYkU+HTeOxg6ZhRo0ZF+bh8ljkBYmkTEUtwdOjQITcfh3f6eUGeHJnvj7hdfBqH8vMcxEsS6K3d9YfXHH6ewe3J80sglo7i43zIb56Mh5edy3uTNxD3OzfeeGOw/ZyAZUd8H8TSBgr5b3oeeeSRYHNotpek22abbaoe78O5i+QceGxg+6abbory9e/fP9hF8yWxcFgOk9d8fm8pTzIIiOeULE3ofYTL4H7D3+PcX/n+hccYXtNMnDgxyseydx4uk+UkeG0MNG3/oidUhRBCCCGEEEIIIYQQoiTaUBVCCCGEEEIIIYQQQoiSaENVCCGEEEIIIYQQQgghStKqNVRZU8xrDLHGB+spALGORJ4GFRDrLbL+A2s8eLzODWvU8HFeX4L177z+a54+idexk4ZqbXi9OPYdbj/WnwPitmY9OtboBWKNEd/uXtOogvexIg0tEcMaf1OnTo3SWAuUba9JOG/evGBvueWWURrrHP7+978Pttcb+t///d9gs6bV5ZdfnlungQMHRmkbbLBBsNmvvN+0a9euaj4gX//qrbfeyi3Da+2wLuOGG26I1kSRtqUnTx+zVm1i1rm+8soro7S777472Ntvv32wJ02aFOVjXyyqF/+dXjeI+yrWsfJl8LXx/R2nvfLKK1HaPvvsk1vHxQ2+N7hvL/Ir1qkEgDvuuCPYDzzwQLA/++yzKB/fk6x57Nt3/PjxwWa9TCDWpxozZkywV1111Sgfa4l7n2N9Kp4XeT2tDz74INgNue9EPtx/580XAGDHHXcMttdUXHnllYPN/gDEYx+3WZGGqu8PvSZaBa/n269fv2B7H+bzyVfi68P9ABDPWdk/fN+e9z4G37ezX3ltVF4XsR6fRxqq9YfnhkX6h749eY7KurdFWvBl8fXgMeCZZ54J9tprrx3lYw1Vj/c50bTceuutweY+w4/7/I4IHlP8+0SKWHHFFYPNc4c33nijdBmiYfC1LXqfUJGe6FNPPRVs1un2YxGP27z29PsqXIZ/DwePHTwfeeGFF6J8hx9+eG59uV/iOo0cOTLKt8suu+SW0Vj0hKoQQgghhBBCCCGEEEKURBuqQgghhBBCCCGEEEIIUZJWHfLPIQ/+sWYOmfbhSPzYOYcW+TAmDofgsBofHszH+fB8fpy+6Fx8HD8aDeSHeXEYl6gdH9rG/sLt5/PxI+YccuN9kf3Nw+HpfF727WrnFvOZOHFi9Pn5558Ptg9p6NKlS7A5NNbLfTz22GPBfvPNN6O0TTbZJNjXXHNNsP/2t7/l1otDGjgEBiiW6uDQDf5bfLguh+IVyUWwz86aNSvKx2k+RNmHIrd0mjp8lO/xV199NUq7+eabg819uW9nDvPnsDwfdsXtN2fOnCgtL8zSj1E8hvixh/uavLHR8/bbb+emLQ4UhTf7vrmCD2G+/fbbg/34449HaXlh3Cz94+EwOx+queaaawZ77NixURrPE3bYYYdgv/baa7nl+xBSri/3L35s4z7F/80ckt5W4HbyobKc5tuT7z0fxs3svffeweYxYP3114/yjRgxItg85wBi/+O29XNZvg+8rAyH7HI/t95660X5uG/z/dyLL74Y7C222AJtHR5zO3XqFKVNnjw52EWSUjwOcmi2Hx+4Pf09zWMOjze+Tn4OJRoPzx/eeeedKI3lM/w9ze3Jc4uiNUzRmMf9UdH8kqWA1lprrSgf+6nvC2fPng1RX4rmbxwGzdIMvk/OC9f3YwPnK5KoKmLUqFHB9rJikoNpGHmyhb7f958Zluspmpdye/B97deMRftkfBzX6eWXX849r5+H540/fp3blOgJVSGEEEIIIYQQQgghhCiJNlSFEEIIIYQQQgghhBCiJNpQFUIIIYQQQgghhBBCiJK0ag1V1mTwOqOs9eN1FFmTivU+WGMIiDUg+FwrrLBClM9rp+XVkXUoPvjggygf65iwbiIQa0WwHs64ceOifG1Ro6weFGniem0zhrVJWL/D6xSxnlGeHi4Q6yV5HytLkQ7S4gTft17fZZ999gm21wLle5/7iKeeeirKx8f5foE1Wrke48ePj/LxPc7t6bVfWOPF65exFg7Xl7VgAeC9994L9mqrrZZb/pQpU4Lt/y4+l9fkam0aqqzrw9cGAH73u98Fu0ijZ4011gi2bzO+Hv5eHTRoULB33nnnYHv/ePLJJ4PNPuz1hVjb8sMPP4zSuF7s217fjjVffZ/Gvs5pRbpJfvxa3CjqO996661gs07qmDFjonzcN6yyyipRGrcbjx2+7bltuH35XgViXbuePXtGaax3y1qarAUNxG3q/YzrxX+Xv0481xo+fHiU1trmJ0Uap0Vp3E58fWrVfhs9enSwjznmmCiNtbTZF2+66aYo35133hlsr73PWrqs485zTSAef3wa+xzr6XltRNbJ8/0ha/9KQzWeN/IYAMQ+wX29n3vyfVuk0c99kJ+D8Jz16aefDrbX0vSazKLxFOlRcj8zffr0KI3bjG3vH3njnM+Xp5MIxPMfvt+nTp0a5WO/mjBhQpTm5+mi4fj7ke/3u+66K0rr3LlzsNnHijTRuV/32v1FGqp+LlrBz4nuueeeYHsNVemmNgxen/C95d8n5NeRDLexn1MyPP8peucM4+e5PJ9lv/Dvp+Bz+XdScH15/urXsk2JnlAVQgghhBBCCCGEEEKIkmhDVQghhBBCCCGEEEIIIUrS6kL+OQyGHwPnR86BOPTAh8AyPvSlTJoPt+PHkH34OD/2zI9b+0evN91002BPnDgxSuMQLX6kmkMPxYIUhb9zG3I4LJAf3uL9Ie+Rdh8Sw4+3e/9guB4+H5fpw685dHtxDvNn+PpwnwDE19G3Bbcht0v//v2jfBzq4u8z9isub+utt47ycQjttGnTgs3SDr4M31exL7LtfYBDbf3fzOGCeSHiQBxq0a5duyita9euaK384Q9/iD7zWHHiiSdGaSwBMGPGjGD768Hh8B07dozSuA1ZSoJDr4HYhx9++OFg+9AtDsf09zeHU3Fojx+j2Nd9yK/PW6Ho3vG+s7jB98wVV1wRpbHUDl8HL+fCY7y/X7mP4vvaj0UcPsf+4n2E+xTvIxyuxT7tQ/K32mqr3DLmzJkTbA7x9H8z15+lUVo7RfPEInis8GGtM2fODPazzz4bpXGI/ksvvRTsoUOHRvnOO++8YP/5z38O9l/+8pcoH/fthx56aJQ2YMCAYF922WXB9uMeh4P+/Oc/j9L4fuGwfh/2y9JWPhS0SF6pLcI+5yUQeDzieajvszmN72nvz0XzRh47uB6PPvpolG+llVbKLUPUBsvJ+TUB9708RwDKS4blrZG8HBSXX7QO4jp5WSBO8/OHxV1CaFHg9xSY6667LvrM/TBLr/D3QCw/6KUIGZ7zeikXltziMcSfi8eQIskJsXB4/cr4+4znfLfcckuUxmMJX38/9+Q+hMcKf49zPu+rfFyRPB5LzvTo0SNK47+Zj1uUfYueUBVCCCGEEEIIIYQQQoiSaENVCCGEEEIIIYQQQgghStLq4vY4tI1t/9YwfmzYh2TyGxE7dOgQbP9mW6YoJIbTfD340WN+3N2HQvHj0D68dPLkycHmx7A5fE80DL6mPsyS32DLIZc+DIHbkx+JLwpdK/IjLs8/6s6fX3/99SiN3yreVnjssceC7e+XDTbYINgcqgrE9z6HQfIb3QGge/fuwd52222jNA5/4lBeHybF5IVFAHHb+pA9H16RB9fDv4Hzu9/9brD5Wvl6MD4ko9aQ1+aC37ju+0mWVHnzzTejNH7rJYcw+pAj7iN8aAvLzXCYFIc++TLZd7wkRF4/A8S+yLISPsScKRo3WA7B+x73k0VjZWvEv438t7/9bbC973fr1i3YfA/5Mtgv/Jtvi0Kj8uDQJT8Wcf/ifYQlUVi6w8uL8D3DEkT+fByG6vsJDkv1fsb9kn/Db0ukaKx+/PHHg+1DISdNmhRsvid9yD/7lQ9/32+//YJ94403BtuH/f7mN78J9l//+tdgb7bZZlE+Pvfll18epbEfvPLKK8H2/RD3USwvAMT9Bt8fLEUDxOPqf//73yjNh/C1dcaOHZub9qtf/SrYPGfwPps3F/VjP/uV71v4LclHHnlksE8++eTc+on6wH2m7yN4jPd9Po8pPI6zVJFPK+rveOz3PpU3f/V9FR/n/xatZesPt60Pw+f7n8cvlp4CYrkg7uO9xCLPb3zfwuMLp/E4CcTyWDfccEOU5qVuRDE8P+f709+7LP91zz33RGm8N8Zjgp+v5o0//lw8F/DrLl7jcBm8zgJi+S3fl+XVw6+Hm5LWtUoWQgghhBBCCCGEEEKIZkQbqkIIIYQQQgghhBBCCFESbagKIYQQQgghhBBCCCFESVqdhirrIbCmk9f9YT2FtdZaK0pjTSDWl/BaaV7PsILXDWN8PVjngXUiWJ8PiP8Wrw3BWlasgeh1TER52Ac8eVpCef4AAJ9//nluGuvGeH0ZbnfWyPP52He8Hk5b1FBdd911g33OOedEaeutt16wvVZT7969g833mW9z1ozy2pd8P7LOjNdQ5H6CfYc1DX0+rzuVd5zXa+W/xevWFWm05uXz/sx6TL78lsijjz4a7K222ipKY/9gTV0AeOedd4I9a9asYHs9waWXXjrYfjxgbTNuM9/ufI+zHxW1EWuD+zJYl7Jfv35RPtYI5nMBsU4Rl+/PxTqM3k/Lagm3JNjHzzrrrCiN/cDDYzz7RZF2tvcfbmPWpPL3HZ+Lba9vy2l+7PD6etXqAMTt63VBO3fuXOpc7Eve3//whz8E+4ILLqhap5bE6NGjg/3LX/4ySuO+0muws2bcgAEDgr3xxhtH+fi4NddcM0pjjbHTTz892Ndcc02Uj+9Rvvavvvoq8uC5BBD7LWuv+n6e73HWhgWAt956K9hvv/12sDfffPPcc3sf7tOnT26dRQxrThZpX+aNJf579iPfLuwfU6ZMCbbXYBb1h+eefszlccPPQbhfLppP5GkeFmmwl9XT91qLfC6ePwEL9iei8bz77rtVbSCe2/I+wnPPPRfl43e9cBk8HwDieYufw7Af8Jjq3yPDeq2jRo2K0qSh2jBmzpwZbO43/Nyc+4aXX345twzuT/weF88vuR/y/QTf/6zdCsTtzWWwXj8Q94f+3Sncp3D/VfY9JPVAT6gKIYQQQgghhBBCCCFESbShKoQQQgghhBBCCCGEECVpdSH//AgwPxrsw+04pNs/gs6PQPtwrTz4kWcfQsf18GFu/Ngzp/lwiHnz5gXbP1LN8CPbeaF8IqUoFIofHfcSC3z9fWgKw21Yi08BcRuyj3n/YHz4aFukR48ewb7qqquitMmTJwf7ySefjNI41JRDXbwEB3/eYostojQONWAJEh9Kyf7HIQg+3IHDi72/cVsXhXhxyJQPDeNQ8KLwB/Z7H/I1ZMiQ3ONaIscdd1ywn3nmmSht3LhxweZrA8QhunwNuH/2n32fP2fOnGBzGLWH24l9x9eJ29rLvKy66qrB5pBfPzZwn+THF64j+6zPx/XisF4A+PDDD4PdWkL+zzvvvGDzfQzE951P4zApbmt//3Mb+HsyT+bDl8H9EJfn24Z90Idacdtz2/gxpkjGgvssDg/28hGcz//NfG+1BiZMmJCbxj7h55ccFj1y5Mhg33zzzbnl+TkD99M8L/AyHAz7hw/J5H7D+wf78AsvvBBs7x8cku/Hka5du1Yt38vlsKzR66+/HqWVDSUW8bUqmucy3Gb+3uR+x5dXtnzuk7Q2qQ/czxSNUX4Nkxde7+/bvDTvH0XrX583D66/71u8xIxoPHy9+/fvH6Vxe/K9732M57kc8s+SAUAsO/fggw9GaZMmTQo2ryP8uXgcPe200yBqh9uH73Ev0cTzCZZjAIDrr78+2Ly+5PkNEEvdvfbaa1XrAMRzQ54vAMBGG20UbPYlL2fIcm3HHntslMbjD5+rSIqr3mgGI4QQQgghhBBCCCGEECXRhqoQQgghhBBCCCGEEEKURBuqQgghhBBCCCGEEEIIUZJWp6HKWmGsDeF1flgzinXmAGDMmDHBLtJh5c9FWpp87rLaM0U6m/369Ys+33vvvcFeffXVq5YtGgbrBvq2zdPy8rp1X375ZdUyvMZQWW0w1rLx/sG6fUU6mCLWV2UbAIYOHVr1GK/h9PTTTwfba/2whgxry3hdKO5bWKfupJNOivIVaVCxLibrFXqdTdaj83qwrCfN9fB6fKzJ5cvwuq8tHb7nttlmmyiNP/t7afr06cGeMWNGsMePHx/l42vqy2Cf4/vW9zM8vrDtNUjZB9Zff/0ojXUphw0bFuyLLrooysdjoB/n2P+47/JjKusezZw5M0rzmputgbXXXjvYrLsMAL179w62v0/4uvC19PqnPD54+Hqx//hjeBzgc/m+pkiHlX2QbT8usWaw90GuI9t8jwD59w8Q6+76e7Ilsu+++1a1AeDhhx8O9iOPPBKljRo1KtjcTqwfCsRt7e81r8uaB7c135NFOuvs9wCw+eabB3uDDTYItu/zzjnnnNwyWC+Nx8uOHTtG+XgM47EIiP2jW7duufUX8b3K/YKfo/I9zsd4Le4izX4uv8ivymqtivJceeWVwfZjFI8Nxx9/fJTG62TuI7x/8HjA7cffL4y8vKyrDgB33nlnsFkLEWh988vWwHXXXRdsPyfIw68rpk6dGmzWufR7D9z/Dx48OErjvoX7Hd/ncBnPPvtslLbXXnsVV1xE8NyC9ZX9uyCK9iYOPfTQ+lesjvg5KveHRbqxTYmeUBVCCCGEEEIIIYQQQoiSaENVCCGEEEIIIYQQQgghStLqQv45VJsfY/fhJj179sxNmz17drDXWmutYPswK/6cF74LxI+qt2vXLkrjEMoiiQIOj/vBD34QpXHIf1FonygPPx7Oj8QDcSgCPzrvH4/nz/xYuQ+p5bb2bcY+xraXDeCQGO9/ovF4mQ3/uaXjZQTaOkUha3w/+lD1Xr16VbW32mqr0ufmsBruC4pkPFZbbbXS5eex5557BptlAoA4XN9LFHA9uO/zY5QPF2T8+VoDBx10ULC9LBCHZ/twxJdffjnYPHb4EHcOSfLlcygdS0T4tuFzcyi1lyHh+Q7LlQBA9+7dq5bPfwcAbLzxxsF+9dVXo7TOnTsHm+cx7733XpSPQ/r8uLrPPvtgcWHnnXeuahfhw+04RN+3J4fsloXbyIfk14OJEycG289j2P/YB9ZYY40oH/dzPq1Pnz71qGabpmjuWSRBUmv5DM+BiyTSRHl43Gbb48dmbqe8sP5qx+XB6xF/TF4aS0wAwHbbbVfqXKI+jBw5Mth+POB7lfcvfL/ev3//YHMo/7vvvhvlY8k0v3ZiGQE+b9Ec5v/9v/8XpSnkv2Hw3kSRJIzfr2J4vcL9ht+bqIfUS14d/TqOxxUv58fw378o90v0hKoQQgghhBBCCCGEEEKURBuqQgghhBBCCCGEEEIIUZJWF/LPb5nkcLj3338/yrfjjjsGm0OhgPgN1kVvT897+7b/nh9p9yEx/Nh0UZg5Pza90047VT0vED8OXfTGTVEeH8rPfsBp/q2EZd+EyWEO/hh+fJ7DLbwvsr8onEqIYoreXtnU+DcoLyr4jdpDhgxpljq0JrhP3XvvvUsf59/43tL46U9/2txVEFXw/UJz9RO1cs011zR3FURGWYkYHgd57fDRRx9F+Yre8M5pRSH/ov5wm/lQW8bLzbDEDLeZnxflhesXSUd48tJYMsmX6Y9hOQqtb2rDrxl5PenXrhxe361bt2CzFBEQh/bz/oX3xY022ii3Xrw/wnXi8wJxn8YyBAAwefLkYBeFeosFKdrDKJLq4nvUH5dH2fHB3/955/IyaYyXseB+o2wZ9UZPqAohhBBCCCGEEEIIIURJtKEqhBBCCCGEEEIIIYQQJdGGqhBCCCGEEEIIIYQQQpSk1Wmosn4p6yR47YaBAwcG+/nnn4/SXnnllWD369cv2J999lmUj3UdWHfVazLw508//TRKY/0K1onx+ebNmxfsTp06RWmrr756sFnfSBqqtcPX32v2sD4M+5XXEWHNoSLNxiLtGS6jXbt2Vevny/D6V3k0RAdJCCGEEEK0bFiXn+d1/v0OvF7IW4sA8dzWz2V5jroo9ehE+Tm71yvltSyX4dcfebqsRe+H8Osg9kXG+1HR31KkDyvKcfTRR0efR48eHezPP/88SmON0kmTJgXbazPzvsSECROqfg/EOqmsuwoADz/8cLCfe+65vOpHeH3xX/ziF8G+4447SpXRluF7jdve+0HRvgWPEWU1VOu9x1DUD/mxjuu73HLLBXvmzJl1rVMRekJVCCGEEEIIIYQQQgghSqINVSGEEEIIIYQQQgghhChJqwv555AT//gy89ZbbwX76quvjtK6d+8e7A8++CDYPoSey//444+D7R9rXmuttYLtH1HmR+P5sXj/uPLWW2+94B+RwY8ycyjH2LFjc48RxfTv3z/Yr776apTGfsByAD4sJU9yoihcf9lll43SPvzww6r1mz17dvR51VVXDfbaa69d9RghhBBCCNG64XmjXy8ceOCBwb799tuDzaG8QDxnZduXx3zxxRfR5969ewd75513zj2ubFioKI+X7WJYioHXsUC8ZlxmmWWC7eXCOLSaz+XXsRwa7Ne/eeH6XkKP69i+ffsorejvFOWYO3du9HmjjTYK9iabbBKlPf7448FmyQbff3DarbfeGmwf1s/5ODzfH7fNNtsE28sG7L777sE+88wzo7Rhw4ZBlKdPnz7B9vIuTOfOnXPTWoIMR5EkwQorrBB97tGjR7C5j2IpxaZGT6gKIYQQQgghhBBCCCFESbShKoQQQgghhBBCCCGEECXRhqoQQgghhBBCCCGEEEKUxJpRu6SmE48ePTrYv//974P9+uuvR/l22GGHYF900UW1nKrFcM455wSbNSUGDhwY5dtrr73KFmkLz9LsNJtjTpgwIdgzZswI9pw5c6J8rJXKNmsb+c+dOnWK0lhXd/nllw/2aqutFuVjHatFgPxDFCH/EEW0Bv8A5CPNSWvwEflH89Hm/YPXZl63kuE10VNPPRWlTZ8+PdgjRowINr/3AQC22GKLYHt91X333TfY/A4A1k0EFrmGapvwD9YyLdIT9JqTb7/9drB5/cHvAvFl8rl82/Jn74vsL1xGt27donxXXXVVbv25jnXSbmwT/lGWyZMnR59ZO5ffz3HllVdG+VgzmTUqPT/72c+CffTRR0dpb7zxRrAPPvjg3DKmTZsWbK/t2QR6nq3BP4BmnIOUHX8WYxr8R+sJVSGEEEIIIYQQQgghhCiJNlSFEEIIIYQQQgghhBCiJM0Z8i+EEEIIIYQQQgghhBCtCj2hKoQQQgghhBBCCCGEECXRhqoQQgghhBBCCCGEEEKURBuqQgghhBBCCCGEEEIIURJtqAohhBBCCCGEEEIIIURJtKEqhBBCCCGEEEIIIYQQJdGGqhBCCCGEEEIIIYQQQpREG6pCCCGEEEIIIYQQQghREm2oCiGEEEIIIYQQQgghREm0oSqEEEIIIYQQQgghhBAl0YaqEEIIIYQQQgghhBBClEQbqkIIIYQQQgghhBBCCFESbagKIYQQQgghhBBCCCFESbShKoQQQgghhBBCCCGEECXRhqoQQgghhBBCCCGEEEKURBuqQgghhBBCCCGEEEIIURJtqAohhBBCCCGEEEIIIURJtKEqhBBCCCGEEEIIIYQQJWnUhqqZDTezpMafSfX4A8zsYVfu2TWU8X0z+9DMTq5HnWo4/6pm9rWZjWyO87dEWoJvibaNmQ0r4Wufm9lEM7vWzAY2d51F02Fme5nZrWY2JWv3L8xsupk9ZWZ/M7PDzKyzO6bVj08iZiH9wtdm9r6ZjTazq8zsEDNbqrnrLFoG6kNEWcxsSTMbama3ZHOMT8zsMzN7x8z+a2a/NbNtzUwPxixGaHxZ/Cm5tvjQzJ4xs5PMbNkmOu8wlz7JpR9Rj/OKlovWufWjHgPxCAAD3M9ulH5UlfS76nDeCkfXoczDALQDcHhjK2Nmg63hm3p7A1gCwJ2NLGdxo7l9S7RtzkDqU2fSd+xrmwL4MYDZAIYCeMHMDlzUlRRNi5ktZWY3ArgHQA8AZwHYGcCWAI4HMBHAcQCuy36YxWF8EjFF/cJmAH4A4Cak/cMNACaY2f6LupKi5aA+RDQEM9sUwOsArgQwB8DPAWwHYAek/c6XAH4N4AkAU83sgGaqaoAW3z2buy6tHI0viz8LW1tsB+AcAOsAuAjp2mL1Op73spz0XbP0EXU4l2gdaJ1bJ5asQxmfJEkymr8ws3n0cWKV9Ll1OC8AIEmSSXUo80IAHQFcXIcq1cK+2e87m+n8LZVm9S3RtkmSZBqAaWY2iL4b7bKNMLPbAYwBsCaAy8zs3iRJPl+EVRVNyy8BHIx08bpjkiTfUNrLAO4ws2cAXOoPXEzGJ0GU7BceNLPzARwL4C8AbjOzc5Mk+c0irKpoOagPEaUws12Qbp5/AmCzJElGuSzPAbjGzA4G8G8AXZAufG9bpBUVTYLGl8Wfkm38pJndDWA00vt7GIA963TemTnp4wDAzD5pzHlE60Hr3PqhUBEASZI8liTJZkmS3LCoz21myyH9r9DkJElGLurzCyEaR5Ik85AubABgNQBbNGN1RP05Mvt9hdsIYS4HMKkpTt6c45OonSRJvk2S5FKkkRQA8GszO6roGLHYoj5ELBQz6w7gRgDLATi0ymZqIEmSmwD8eRFVTbQwNL4s/iRJMh7pU8gAsIeZrdWc9RFtF61zF05jN1T3yn4aynEANmjkuRcXdgGwPBSq7pFvidbEFLI75+YSrZFe2e+P8zIkSZIA+BvSJ9CECCRJci2A+7OPfzSzds1ZH9EsqA8RZTgXQAcAjydJ8lCJ/H9u2uqIlo7Gl8We18neqLkqIQS0zi2kURuqSZLMy3atG3rcp0mSfJSXbmYrmtmvzGyEmc2yVLh/gpndYKlo//JF5ZtZl0yw+91MTHeMmZ1QJV9FCyr8uPQrvIBzVrffm9n4rF6VtMHZ8Y9lh/dwx07Kqe6Q7PddXKeGlGNmy1oqXP28pULWn5nZG2Z2kZl1qZJ/AeFpM9vGzO61VPD8CzN7y8zONbMViq51U9FY37JUvJ3/xsFmtrGZ3W5mM83s20qb8vFm1tnM/mRmY83sUzP7yMxeyPxxWZd3o6K2ya5rrgA45dvc0hcPjM/89QMze9rMzjOz/jnHLEv3yMdZXceZ2aVm1rtK/pquhygNDy7TOCG7ty4xs5FmNs/MvrT0pSTXmlnh5r+ZrZDdh29l9+X72X26TRX/GtkUf5hAJfypMNwqSZILkyT57cIKs9Y1Pon6cEn2uwOAHwLVXzZkZh2zPnyKmX2VM640qO/PjlnazI629CUX71IfdIeZHWNmq1Q5psHjkshFfYgoxMxWA3Bo9vHmMsckSTIVwAkAHq5S3kAzu87Sl1h9Yela6kEr0L+zdE77fzZ/LfGlmc0ws9vMbNucY7xfTXR+0bPM3yIahcaXxZcvyV5yIf257++HN1WlzGx/M3vA5u8ZTDWzf5vZxi6fX6cs8MKrzDd9ek9Xzk5mdpel69Uvzew9M7vPzIZUqVvpca2JLs/iita5RSRJUvcfAD0BJNnP4AYeuyLS/8h8C+BPAAYjFeL+EYAJWZlnVTluWJZ2KVJNqqORiukOBTA9SzvJHbMCgP5IQ+4TZA8JUPoaWfqlWfpNAF4AcD7Sx513A/BGllYp68js87Tsc+Wnb5U6LwFgJlKx3yVdnUqVA6ATgFeyvFfS9Tod6SR+DoBt3DF9s7JezI67NavHCQAGAdgdwH+ztJcBrNIUftKUvgVg/exvnJblPRup/sdh2d/4MwDfABhGx2yZtcWnSAWaNwewPYB/ZmWMAtCF8i/r2mqSq8Mqzn+GVannUVnaa0gn04Myf/xHNZ+kNh+ZpV+e5d8ewG8AfAZgHoDdG3s99BOu3RF5bZGlL5HdJwnSkM3lKG3F7PsvkPYb2wPYGulLJmYA+BzAnjnlrkLl/hfAd7O2Oj67X2/N0l7M2rZ3c1+rxfEH6T+7EqRj0h8BrFpDGcPQysYn/Sy0TQv7BZd3OQBfZfnvzb7rmV37O8k3xgA4JfON/bI+YhKVU0vfvwSAR7NjrkIaFTMIwPeprKvdMQ0el/RT2P7qQ/SzsPb9XqWNAGzayLKOQzqfm5y11yZIX377eFb+9cjWHO64WeRjO2U+cDSA8Vl5P65yTKX9K3Xf1fnFUs19bVvjDzS+NHsbtIQ2BvBXurc2QHF/Xumjz8jSh+eUeTZy1qRZ+vAs/YgqaUsAuDZLfwLAPln/8kOk659vAPyM8q+S1Wk05u9T9AftKyDV9+4P4E2kY1DUbwD4fXbsSACHIF2bD838OUEqpcN1LD2uNbcPtJSfhfkitM5d+DVsoobpSR3A4AYe+4vsuGtzyv0MwNlV0oZlx30FYAuXtlOWNh2AFdU3p05nZ+lfAzjBpR3KxyHd0EzgNtdyyt02y/uvKmkLLQeAIe3QEgB/qZK+X5Y2C8AaVdKHZ+nfwE3gkL6w7DlUGQyb1WEb6FvZjZ8g3Sjt6tL+i2xAQfqfl/ezvPtVKecvWdpTAL7TkLZCzuCFtIOqTGAX6CSQvokxcd8Z5k+Kz6pyzN5Z2gcAVq/1eugnui5HVOsfsntkAFJdmQRpOMSWLk9loPlFlXL7Ie3P3gMNTpQ+LDv2aQBLuLRNs/s2d9Kkn7q1/8ZZO1X6nc8B3I70jdmrlSyj0patZnzSz0LbtGq/UJD/zSz/5Bzf+BrAXi7t9Epb1dr3I42CSQA8WeWYVZBOeIfRdw0el/Sz0LZXH6KfhbXvueQfDd5wp3J2Qbpx/xGA7i5tacx/kOKCKsfOAvDnKt+vjnSe8jmqrCWyPJW692zua7k4/Gh8af42aO42BtAe6UNRCYCX6PuF9eeVcofnpFf6+2E56cORv6F6HuZvcC3t0rpn/c63AHZzab+o+CfcGjpLXz9L39Z9X9l8HwtgGZe2PNJ1VwLgmIK/c6HjWlv/yfNFaJ1b+qclvpSqX/Z7gbfMJenbTv8JYFzB8S8lSfKc++4JpI3SBWlHVCtzkf7Xjrkd8/WxGsqQ7Het+ql7I92U/QZpJxeRJMkdSJ+qXBXAqQXl/CdJkhfdsV8j/a8QAPzAUrH81sx1Sfo2O+ZQAP+T2acgFVp+LbtunnORXuetAexbpzqtjrRtgCr+DuAaAPe57/YCsB3S/xT/nz8gSZJ7kE6sVgFwTMG5F3Y9RBUslU742sy+RrqwfRXAgUgHhc2TJHnWHfIlgHOy9IgkSd4A8AzS/87u5M7THcAPso+/T9yLTLL79T+N/XvEwkmS5GWkmwiVN18ug/SfVdcAeM/MnsjC3ZYpUVxrGp9Effkg+71aTvrrSZLc6767BMA2mV1r3180p5oL4P8hffKjQi3jkihAfYgowapk52rtluD/kG6ODUuShDXvkCTJlwAuyD7+0sy8Dt4fUEWXNUmS9wHci9Rv929E3UTTofFlMcHM2pnZDkjlVdoj3YD+QfFRTY+ZdQTwq+zj77L+JJD1N1cj7X+8D12LdD3UHcDOVYo/CsBbSZI8SedbGvP3Ns5PkuQLd75PkT6FCgAnF1R9LjSulUbr3NpoiRuq47PfR5jZUWa2FCcmSXJikiTXFxz/kv8iSZKvkHZIQOOEdF/MNhq57M+zjd5a2Bfpf3wfrPH4ysRmdDbhqcYj2e/9Csp5Kuf7x7LfSyB9RL4184z/IkmSWUmSVPyici0frXZwlm+Uy9tYZiL9bx4AXGtmfdw5n0uSxL+Y64Ds94gkST7PKffN7PfggnMv7HqI6mxEPxsgvca/QRqm8JaZnW5mVsmcJMmXSZKcnU0sq/FO9nsd9/13Mb9/fjzn2AXaUDQNSZI8j7S990T6n9rK4uU7SP+p9U8Ar5vZwIUU1ZrGJ1FfbCHp1frkeUmqkwjU3vdX5lS7mtkp5jTokyQ5L0mSP9NXtYxLYiGoDxFNTaY7WPGfqnNZzF8TLAOn6ZskyR8K2jpvriJaBhpfWjFuE+tDpPdvT6QyChslSTKmOeuXsQdSqTsgv3+p6DlvyJq7SZLMRio/AaQSjoFsn2co0s1YZiuk/yQEgCdRnYpP9jGzNXLyaFxrGBtB69wGs8g3VDNB4AV+KMvlSDeulkGqtTHDUvH9fbP/ViyM2TnfVwaIMk8A5FG3zaZMdLs3gEeSJKn2X7oyDMh+TyzIMyn7vaZVEQbPmFntyyRJPkTasQPAug2tXAsjt+3MbCUAPbKPZa7lgII8pUmS5FukGiPfIP2P3ZuWirqfamZr5xxWOff2PAC7wXjvLM+aBafXxmkNJEkymn5eS5Lk8SRJ/g+pbvFXSPVjzuZjzGxVM/utpSL/H1j6MoBKWx2eZVvRnaryX/+5Sf4L/N6rz18lypCk3J8kyVCk/23dAel/x+dmWXoDeNDMOhQU0yrGJ9EkVPwi75+fC2u/Wvv+O5DKuVSeGnnXzG40s0OtyksnaxyXRAnUh4gCuF9YqcYyeG5adS6bzSfmVMkPM1vezE629OVAs7IXi1T6lt9k2fxcRbQMNL60bjain/5In+TskCTJj6pEEzYXFR+ZW7B5NqlK/gpXZr+HmBk/kb8X0qeWr8k5HwBMyPHJWylP3ppX41oD0Dq3NprjCdXeOT8A0v+YIRUN/iXS/zysilRU/U4A75jZ//DOeBW+bZpqA0gHgHoxJPt9ZyPKaJf9/qwgz6dkr5yT5+uc77nsWid4LYWitmtHdplrmXcdG0ySJMOQ+vuNSAWdt0QqtTDOzP5rZn3dIZW6Poh4AOafDZAOREXSBPX05TZPFury7+zjKZWJZPbf99cA/BqpltQRSAeljbKfu7NjfJ9Wud+K/PGrRlZb1EiSJF8nSTI8SZLjkU58r8uSOiJ90VserWV8EnUk6w8q4WWv5WRbWPvV1PdnYVTfRfpymZeQ9i0HI+2vppvZ7/w/q2sYl0QDUR8iHNwvrFVjGTXPZc1sNQAjkIb9A+mLrbbC/L6lEjK7sCchxSJG40vrx21ivZ4kyTvZ5nNLorF7Dg8j1VBdGukTqRWOQio9OD3nfEC8buKfDZH65ADMl9TxaFyrA1rnFrPkoj5hkiQLHYyzcIOLAVxsZhsBOAjpG+TWQPqG1C6Yr+PRWtkX6cT47oVlLKCyo798QR5O+zAnT5EfLJf9boymU0uH/zNS5lrmXcc8lihKTJJkBIDvZ0/K7o10MfVdpC8XeMHM+lNYTqWu3yZJkjd4iOah0h7LAlgPqWj7RUj7q+FJkuztDzCzuTllVe635XLSAWCpgjRRJ8xsZQDfZP/sW4AkST42sx8jDZ9sj7TthWAGY/44UKsmVM19f7YwuxLAldnk9yCkk961AZwGoC/mh3xWjmnIuCQKUB8iSvAI0ocblkT6luMXi7NXpTFz2bOQRqKNA7Cj1ys0s6qRbKJFMBgaX8R8CtecjaBRew5JknxrZlcjfbrxRwD+YmZdkLb7wQXnA4ApkqRrEWidm0NL1FCNSJJkZJIkpyP9j+0N2dc/M6et2pows24ANgHwXJIkjZmkVP4T2bMgTyVtasEj+h2rfZlJBFT+wzS2YVVrPSRJ8jHS/5oB5a6l/w9wRZg7T5IiTyR+gXokSXJ9kiR7In0y4FOk15/1Zirnzv0PrpktY+kLLnYoc17RJCTZ78HZ74aK7Ffut1WyxXg1qt63ou7chVTHKpckST7D/Jcltpr/qIpFxonZ79lYMKytLHXp+5MkeStJkvOR6ln9Kft6fzPrkZO/zLgkilEfIgpJkuQDzH+px0FljjGz75vZVDN7IPuK56Y9c45ph/nh4Zx/cPb7Ib+ZKlo8Gl/aFuFlUDlSiKXWnDVQ8ZGVzax9Tp6eVfIzVyN9mGyAmW2K9GG5D1D94TI+vsgvdzGzH5lZU20ki+ponUu0uA1VM/uLmf3Lf58N8JW3xi2F+ROClkYlfD48iWspB5rZxtlX+2TpdzaynNuy3wMsffteNXZ2eauxdc73lUHzG9T+4qzWQuX67FQtMQuH2tDlrVDR+Fgtp0MflFPmmmb2rplt4NOS9C2+/80+8ksmbsl+9ynQG9oD6QsuFihXNCn9s99fY75Qv7nfnu453z+I+aGd2+fk2apBtRONYZOFSM0A88XzW/KT42XGFVFHzOwIALtmH0/O/oFXCzX1/ZlM0mM+Y/ZU0Xn0Vecsfy3jklg46kPEwjgbqQbmYDPbsSijmS0J4HQAXZE9bJK9ZOXlLEu1N2kD8+e4XyBeANc6V6lQCatlvxhsZq39hbYtGo0vbZLZmH+/VbtOVdecdeA+zNfqrrpWxvx+Z1SSJBN8YhY2/lD28Wikko7XJemLFT3PAKjIAOxR7WTZw3U3ADghcW+JF02C1rk5tLgNVaT/mdoj28DyVP5DMQU5L1JqAVQ21/i/N2sgHayGZJ8rv+9qZDn3AngCaTue5Qsws/2QDnyzMX8zuhq7mNkm7tglAZyafbw2SZJ3FjxsseKPSCey/c3se1XSf400jOJpLNhubyNtr6WQagEFzGwbpBoi1VgCQCcA+/sEM/sO0nAZINW1qnA/gOGZvUCbWvqGzXOR3h/+jYmiiTCz7pive3cHPQ3+dPb7e36z3dIX01X9Z0Y26bg2+3hqlWM3QRoaJRYNawH4cV6imf0I6aRhDuYvTFoiZcYVUQfM7DtmdgLSBSgAnJskSWP65Fr7/hUBbJ2jS1f57lMAlbcI1zIuiYWjPkQUkr18Zn+k9+MNZlb1BajZ/PxapIvbezB/rgCkIdYJgCPMrKc7bimkm7AAcFGSJO9ScmWuspeZreiO64xiTX5gvl/wwy7/BHDGQo4TNaDxpe2SPWBW+cfJdpxmZr2RhtA3xXnfx/ynjk/3T8dm66AjkfY/pyKfysupjkLqI1WjN5Ik+RLz+4/js/I9pyN9107RHoeoA1rnFlNXDdUsRLwb0slVhV5mNgvAzJLh7QnSm2O4mV2I9JHgJQBsjvTG+hLAT5MkSbJzdkU6sVslO75j1oBTkySZa2a9AKyA+ToMlfpU3oDZi+ubHYskSUZnT31WfoD0seTK7vybOf9RGY80bKuvmf0KwFMAfpH9XfdmjzUPBjA2SZJxVY4vVU5Wx8TMDkK6y3+cmS2HNNzjU6T/JToT6dtjhyRJMqPgXJcAuNXM/gTgOaThAr9Ces1fAXBSwbGLhIb6Vja4L40F2/2TJEkWePtpkiTvmtkQpJPTa8xsHaT/KV0WwA+Q/iftNQDfq/geHZtk1+6PAK43s1MBvIX0idbjkU54for5/vNhtkFdKecMS994eA/Sze81kC68+gN4DBTCk53rYKSTn/3N7D6kbwl+D+kb805D6tN78VvzGno9RNS3dKXv+nMWpP3OVkjvkVWQivIfT3lOy9I3BfBfM/sL0rbaGMA5SJ8SWRrz+y325V8g/YfIVgDuN7OLkW76bw7gZKT3bWvXkm4NVPr5y8xse6RPqE9C2m49AHwfqQbYXAAHZqGbAFrf+FT+krRdFtIvLA1gdaSC/Icg1ZiaCuDnSZLcnlPOKtlXHbl9/Xlr7fuRtu1SAB4ys/8DMArp0y0bIl2MJAB+QU82NXhcEgtFfYgoRZIkT5nZ1khf2DPCzK5C+mTYDKSblf0BnIB0g/56AEfznDRJkoeyjba/AnjCzM5Ces+vAeB/kD69dgOA37hTn4c0eq57dtzvkfpoP6RPzlb8q+IjHyTx28fvA3AMgJPM7BKkEW5rA/h7Y69JW0Ljy+I/viykjcuuyf6A9J9Yl2Rr/5FI+4TTAPwN6YbmClm5nyVJMoHO6/v7D5IkmUbrxBWy9K481mTfnY00rH8ogEfM7I9IfbA/gN9mx/48SZIiLd+7AMxCutcwIkmSvBepIUmSYdkm8ZkAnjGzc5Gus1ZDeg8cDuDyJElurBxT47jWJtE6t44kSVK3H6Qi1EnOz9kly1gJ6aB8F1Jdyy+yn4lI9YX6u/zDcs53RJY+PCd9cPZTtb7ZsWcX/D09C/6G9ZFuxn0M4BOkHd3BWdr3s+MvKHEtcstx+ZZB6ugvIBVx/hzphPdiAF0Kyq9cmyOQitHfgvQm+ALppPk8ACvU00cWlW8hnQhWyzt8IefpjPQ/cG8gffPcx0hFl/8HwHILOfZnSP8L+0V2HYchDeHzfnQrHbMpUkHnl5Aupr7Ofj+NdNK8VM65lkGqm/Rc1uZfZn/zPwGsXa/r0ZZ/kN+3VH6+BTAvu1duQ/qfuyWqlLMu0gXMu1n7zs7u6yFVznG2O3YFpE8FjCe/uiUr86jsmP8297VanH+ye23P7D59EmkI0udIN0lmZ/fqWQA6NcCHjsjSh+ekD0YzjE/6KeUPRf3C10gXCq8jferiEOT34bnllPDHhvT9yyCdd9wEYALSce0rpIugmwFsXeWYmsYl/RS2mfoQ/TTEZ5ZAumlxB4B3kI7/nwJ4M+tbtlnI8QMBXJfd519i/rzjwIJjugL4R3a+r7J7/kmkT52d4/xjmDt2ZaRPLs7E/DXEOQCWbO5r2Zp+Cu53jS+Lyc9C2nh4A8o5BOmTqp9n9/dtSLVrj3BljljIeYdl6ZNy0o+ocu79kD7MNSvzkWlI/8GzScm6X5SV/dOS+bcDcCvSddRXSCM5HgJwQJW8Zxdc357N3f4t6WchvphA69zSP5b9MWIRYWY3IRWc3yJJkuebuS7DkepWHJkkybDmrIsQomGY2S8BXAjg+iRJDltYfiGEEEIIIYQQoiXTmta5LVFDdbEl0xvZHWnozgvNXB0hRAvGzDY3s2MLslReAPLsoqiPEEIIIYQQQgjRGBanda42VBchSZJ8mSTJykmSrJHo0WAhRDGbItVH6ukTMk2h/QB8gDSsTwghhBBCCCGEaOksNuvcur6USrQOSgpPCyGan6UAPJq9IOJVpHo2mwP43yz9+7pnhRBCCCGEEEK0IhaLda40VNsgZjYJ6ZtlPdJSFaKFYGbtARwAYF+kb1ntjHTgmQbgYQB/SpLkrearoRBCCCGEEEIIUZ7FaZ2rDVUhhBBCCCGEEEIIIYQoiTRUhRBCCCGEEEIIIYQQoiTaUBVCCCGEEEIIIYQQQoiSaENVCCGEEEIIIYQQQgghSqINVSGEEEIIIYQQQgghhCiJNlSFEEIIIYQQQgghhBCiJEs247mTZjx34P777w/2Hnvs0ejyPvzww+jzww8/HOwDDjigVBlJEl8aM2t0vRx1L7AJaBH+8dRTTwV79OjRUdoyyywT7CWWWCLYffv2jfJ9+umnwf7ggw+itG222aZqWufOnaN8q6yySgNq3WjahH/wfVZ0j3355ZfR58mTJwf722+/DfacOXOifB999FGwv/rqq9zyuYwll4y7ZK7XCiusEOxevXpF+ZZaaqlge9/J4+uvv44++3MX0Cb8Q9RMa/APoIX4yMUXXxzsjz/+OEq76KKLgr3FFlsEe//994/yTZgwIdhLL710lMbjymqrrRbs4447LsrXsWPHhlS7sbQGH2k2/yg7NvGY88gjjwS7W7duUT6eg/i5xCabbLLQOiysHk2A/IP45ptvos883yxi9uzZwf73v/8d7HXXXTfK98YbbwR72rRpUdrvf//70vVchMg/CL6/AeDtt98ONrdnkR8tv/zywX7++eejfHvuuWewH3vssSitX79+wf7Od+Y/o8XjFQAsu+yy+X9A/ZF/1MANN9wQfR41alSwV1xxxao2EPczfg/k/PPPD/ZKK61Ul3rWgdbgH0AL9JE2RIN9RE+oCiGEEEIIIYQQQgghREm0oSqEEEIIIYQQQgghhBAlMR/Wswhp0hNzCNyFF14Ypb300kvBnjhxYrA5HA6IwyE23HDDYHOILgCMHTs22LNmzYrS+Pr26dMn2D7k5ne/+12wV1555SiNz8chFY2gNTzu3iIedf/xj38cbA6pA+I2ZH/r379/lI/DHHxY9eGHHx5sDi334TFbbbVVQ6rdWBZb/ygbSvnggw8Ge8qUKVEaf+bw/3nz5kX5+L7lvoTD84FYDsDXifPyve9DZzbeeONgcz8DAGuttVawe/bsiTyKxgJXr8XWP8ryySefBPu+++6L0jjc7umnnw72wIEDo3zcf0yaNCnYXh5i0003Dfb06dOjNPbT1VdfPdjsDwDQqVOnYHP4HlC3MYVpDf4BNOMYM2LEiGBvu+22wT700EOjfCwrc9lllwX7ySefjPJxGTxXAYBddtkl2FdccUWwf/rTn0b5LrjgglJ1rxOtwUfq6h9+3liP+45lG1599dVgd+jQIcq36qqrBvvzzz+P0nyYZy1ojlp/armmPlR76NChwea5xODBg6N8M2bMCDaPWQBw8sknV7WLkGwZgCb2j3PPPTfYM2fOjNI4BJslPridgXgOMnLkyKo2EMtF/PWvf43SuHyWAzj++OOjfP/973+D/etf/zpK4/GrTrR5/2CmTp0afeaxgmUgzjvvvCgfz0UHDBgQ7H/9619RPm5rv8b97LPPgs39ztprrx3lW2+99YLN8mZNRGvwD6CF7IO0URTyL4QQQgghhBBCCCGEEE2FNlSFEEIIIYQQQgghhBCiJNpQFUIIIYQQQgghhBBCiJIsNhqqzz77bPT5qKOOCjbr0wGxPmW7du2C7bU/WIeqSIOKNWRYBxOI9fS4PK+TusMOOwTba8+wnkidtKpag35Ii9AO+clPfhJsr2vK7cK6U/369YvybbbZZsH22kQbbbRRsFkn1bftOuusU77SjWex8Y+yWl5eR441cd95550ojfWHlltuuWB7fSruF1jX8MUXX4zysYaRZ86cOcHu0qVL1fP6+m6zzTZR2h577FE1rVevXlG+svqyWIz8oyy+bf/0pz8Fu3379lFajx49gj137txgf/3111E+HiteeeWVYH/00UdRviJ9Mda4Yp1UD9efNa0A4KSTTgp2586dc8toAK3BP4BmHGPGjBkT7J122inYPB8BgMMOOyzY3E94zbyPP/442N5frr766qrnvfLKK6N83/ve90rVvU60Bh9pNv944403gv3AAw8E22tk8v1///33B9vPQ3neyOMSAOy+++7BZh9gvwQWnLM2MfKPAi6//PJg33zzzcH2+uzc7i+88EKwvUYhj/2sxQ3E2ob8voj99tsvynf66acHe+mlly7+AxpPm/MPP088+uijg+01k3k84Pv70UcfjfJ179492DwvZQ1WAPjDH/4Q7FtvvTVK4/UO91U777xzlO+OO+7Ire91112HOtPm/OO1116LPj/88MPB/uKLL6I0HgN4bfn6669H+fi9IazL7+e8vD/C75UA4jnNiiuuGGz/PgDuq/z65thjj81Nq5HW4B9AjT6St0/k+xB+t0cRH374YbD5nQ4A8Omnnwab30Pk5xlrrrlmsC+55JIozftTC0EaqkIIIYQQQgghhBBCCNFUaENVCCGEEEIIIYQQQgghSrLkwrM0L0Uhu/PmzQv2tGnTonwcQvn+++/npnFo1fe///0oHz/Gzuf93e9+F+XbZZddgu1Ds1keYI011gi2lxfgsJ0jjzwyN60RYf6iJE899VSwOfxpiy22iPJx+D6H2xa1EYc8+Lz86HydQm/bPEX9B4fyz5o1K8rH9+fAgQOjNA5pOeigg3LLYImIE088MdgcEg7EYRfc5wBxqA6H8/nQPg61GDVqVJTGPsxhmz7kn69NWamEtsJ9990Xfe7Tp0+wffgk+wFfYx/Kz+G6hxxySLB92DfLxrz77rtR2vLLLx9sDt977733onzcd/mQrLvvvjvYP/7xjyGaHg7JKpJduuiii4K9wQYbBLtbt25RPvalCy64IErjfoj7Gg4LFU2Pl9o4+eSTg81SDEDcT3PbFsnDsLTQW2+9FaUts8wywfYhezxH5Tr50G+ev/bv3z9K+/nPfx5sP4aJ2hg/fnywTz311CiNxxIOry8KtWef+uCDD6I0Xkvx2Obp2rVrsFnmCgD23XffYLMfAcCOO+6YW6Yoh++vedzwcwteD3O7swQREM8bOczf3/ujR48Otl+7cv/BcyE/B2HJIx7/RO2wT7DkCxDPPX1oN4fNf/LJJ8EeNGhQlG+TTTYJNsvN+DUu+07Hjh2jNPYlXkcUhe57ia1//OMfweaxRqSUle5syP4Rz0nWX3/9YO+2225RPpaRY19iuToAuPbaa4Ptxw5e4zC+n2jp+18tu3ZCCCGEEEIIIYQQQgjRgtCGqhBCCCGEEEIIIYQQQpREG6pCCCGEEEIIIYQQQghRklatoTpx4sRgP/PMM1G+J554Itjf+973orR99tkn2HvuuWewvc4Ia8OwDtnQoUOjfKzF6LVnuL6sL3HllVdG+bp06RJsr6fFmnyseSOdw6aBfYl1QFhHCIi1zVjL0LcDtzvnA2IfmzRpUrC9L7J2lSiG74sizRXWePIaLqw/5DUtWdeUNQ7XXHPNKB9rBLGGKrfzwurL+sknnHBCsHv37p1bJ6/RyvpoXoMzD/UtMV5DdYcddgi214zi+501y7y+Hevgsu6tv/d5TGGdVADo0KFD1XOtssoqUT7uZ3xbvvrqq8FmnTOvlSaahiI9OfatcePGBXvEiBFRPval9u3bR2nsT9z2XvNZNC1eo5/nlF6TlPvflVZaKdi+/37ggQeCzWPWWmutFeVjbUOvtcfj1oEHHhhsr/nK8x1+9wAAHHPMMcG+/fbbIRrPo48+GmzWKARizX7WUeR5ABDr3rIP+HdOsM7m2muvHaXxuMJjgh9juB5eI08aqo3H697y2O/nazz3ZP1kf09zG/I8w+u1fvnll1XLA4AJEyYEm/s074us/8zzESDWgPXzbZEPryX4HvafvfY2X3+eH/g2Yz9gHU0/Z+F+wev58nzYz20Znnv6OTXPVbiO3hfbKn5On/c+jIas63baaadgs2b/gw8+WFMd//73vwfbr1/PPPPMYJ933nnBbumaqZ7WVVshhBBCCCGEEEIIIYRoRrShKoQQQgghhBBCCCGEECVp8TF9RY8k8yPtW2+9dZT26aefBnvDDTeM0vixZw779SEs/Dg9h8usuuqqUT4OUfD15VAdPm7vvfeO8j300EPB3mqrraI0DjvnkH/RNLz++uvB9m3NcNgEh+VxiAMAfPjhh8H2khAcUsF+qRD/2uHwlqKw5enTpweb+wEAWHfddYPt72lO22677YK96aabRvk4dOHss88O9qmnnhrlu/7664PtQ7L+9re/BZv7IO9j/jimc+fOwV5xxRWD/eyzz0b5ONTP+yn7ZlsJ/3///feD3bNnzyiNw+98WErfvn2DzeGSPtyG23O99dYL9tixY6N8RX1BXggmh+gBC4ZhMRwiPmPGjGB7CQtRPzhkjvsrDqsGYp/hME4vH1E2lC5v7iOaBpaS8W3G95cPXeT5K/sHh/UDwPnnnx/s8ePHVz0GiPuDLbfcMre+ReGULFfk56GTJ08O9muvvRbsAQMG5J5LFMPyDr4tuD15HOG1AhD70QYbbBBs7ueBWELAh1zzGMbn8nBa0XxE1MZVV10Vfeb1hx83WOZpypQpwfah2hyuz2sRH/LPc2UuD4il93bdddeqxwDxGsn7Kc+Bjz32WIhy8DqA5wc+zc/Z/ViUl4/nC+xTPh/3T94XOS/Plf0YxZ/9nJp9neVKvLSNSOFryXNDf835Ov/1r3+N0nj82XnnnXPPxX0Kl8fzTiBeq2y00UZR2p///Odg87q5taEnVIUQQgghhBBCCCGEEKIk2lAVQgghhBBCCCGEEEKIkrTqkP8xY8YE+5VXXonSpk6dGmwOuQbi8Ep+Wym/EROIH4vnt875t5jyo9FDhgyJ0m655ZZgv/POO8F+8cUXo3wcdunf1sdvR7vwwguD3VZCbxc1/Ig8h7ZxuDQQhzZwOMtbb70V5ePwFi8/wbISHGbtQ3ZFPv6NofymUQ9LcHCItH+rZJ4UAxCHLnCIw6233hrl43bnkH8Pv2X9hBNOiNLYD37zm98E24f3cJjezJkzozQO7d1jjz2C7d+8e8011wT7oIMOitI4fIPfKL44w2OIh0NjuV8H8t+47t++zm3LvlgUau9DtTgMqyicikOPfT34fOwrCvlvOjh8mvsT39fkyQEUheb5cDkug/P5t0aL+sN9g28zDsn0bcb+wWl+XsD3dREs++Lf5JwXnu3lcvjcRfMThfzXB5ac8WNunmSIl2LgOQhLFbEUABBLWxX5Io9Tvgyuhx8TRePxsi4bb7xxsPv37x+l3X333cG+5557gr377rtH+a6++upg77nnnsG+9957o3w8p/SSENtvv32wH3300WDvtddeUT72sZEjR+amifKwlJOfO/B97Ptylufgvpznk0A8ZvEawK+F2Tf92oTHl7x+C4jXXH6eyz7H8jIK+a8Otxv7RZEEHu9VeX7wgx/kpvG8lNutaH/qpz/9afSZ99QuvvjiYJ900klRvlqk57zMCR9X7z00PaEqhBBCCCGEEEIIIYQQJdGGqhBCCCGEEEIIIYQQQpREG6pCCCGEEEIIIYQQQghRkhavoephHY9TTz012GuvvXaUb8aMGcH+3e9+F6WxVmXXrl2Dvc4660T5Zs2aFWzWXvzxj38c5WM9Es+WW24Z7KeeeirYrFcIAK+//nqwvfbMqFGjcssX9Ye1XLp16xZsr7HL/sHag4MGDYryXXrppcH2vtKpU6dgd+nSpcYat228Zipr9lx//fVR2tFHHx3sK664Itjdu3eP8rEujIf1zC655JJg+3ZnXRjWpDrxxBOjfKxPs//++0dprEd3xhlnBNvrnLGGkde3e/nll4O99957B9vrtRZpZrYV3VSG9bX99Z42bVqwvaYY6xry/c2aiUDcF7DW3TLLLBPlY606Xwb3O6wH5HWsOJ+nV69ewWZ9Ku/Pon6wBhn7ltd74jS+x73+mGiZsJ6670NZ+9vPC/K0vby2HJfJenpe35LL9z7Gump52mtAPK7k6a4CwNy5c3PTRHn43Q9eP5PbkNvMj1Osl83auf369Yvy9e7dO9h+7uPHt2p18PhxSjSeIh1+z/jx44M9ZcqUYPs5Huvj8rs7vKYpr328tvxmm20WbNZuZU1WYME1umg83A/7e5/vT9+X5+ma+rUU9y08pvjyirQtea7i56UMv4vGr8d43JP2+8LJ01338wfuG5588skojdfKhx9+eO65/Hqlgh+zmJ133jn6zH53//33B9trqOZp+gL57yJYlO8a0hOqQgghhBBCCCGEEEIIURJtqAohhBBCCCGEEEIIIURJWl3IPz/WyyG706dPj/I9+OCDwfbhSfw4tA/zZ1ZZZZVgv/POO8HmcE8A6Nu3b7B9KN6bb74ZbA7J4tBKAOjfv3+wjznmmCiNH8Xmx5z5Woja8SFO/HmttdYKtg+D4TZk6Yjtt98+yvePf/wj2D70lsNq+TF1H3ohyjNkyJBg+7Z96KGHgs1yHKeffnqUr2fPnsH24ZgctsJhWGPHjo3ycZj/aaedFmwO7waAjh07Bvuwww6L0vJCtX0YA/vOSiutFKVx38U++8c//jHK989//jPYI0aMiNJ+9rOfVa3H4sy4ceNy01iKYYsttojS+Ppz+GtR2Can+bGMw3qLQoM5nKdz585RPg4v5jBkAFhvvfWCzXIFoumYN29esDkU1/f7PJ8oG7rkw/EYLmNRhkK1VTiMkWUegDh00Yev8XyCw3J9m3G/wfc/SxUB8VyR/c2fm8v30jE8lvqQbv7b2LdF7bCklA+VZUkzHhN82C/7DktZcZ8PxP7h5SK4DM7npR247+JjxKKH57nsH+wDQHy/81jjQ4NZfoLnEkA8P+E184svvhjl45B/P0ZpLKoN3tvwc0Pu1/093aFDh2BzyLZfL7FP8D3tx4aivQieD3Of5kPFeVzyfwuPKd7/RLFUlL+XmSI5SV4PNjU9evQI9qOPPhpsltoE4j0Y73PsF2X//nqjJ1SFEEIIIYQQQgghhBCiJNpQFUIIIYQQQgghhBBCiJJoQ1UIIYQQQgghhBBCCCFK0upEOFl7hTWovK4pa1fdfffdUdrQoUODvdpqqwXb60KxJtBGG20UbK8zx7o0XjuItUt23nnnYLOuJgCccsopwR45cmSUxvVnvT6uu6gdrwXF+husQ+P1cVkPZs899wy211pl/aE11lgjSsvTLPN6ayIfr5HMGpT+Ov7iF78I9ujRo4PNOqNArEE3c+bMKI01UL3eJfPb3/422KwXdNJJJ+Ue89RTT0Wf+X5fc801g+210viz12FkbSLv6wz3SV6Tpy1qqPL44vWeWNPJjxsMaxx6DVVuM+5bvL4Y6+d5DbT27dtXrZPXDWK9Ia+bdMghhwTb6yuKpoH7FD+uMOwLnM/fn0XkaaoWnVfUBx4r/D3JOnb+vs7TIWW9QiBuW+5P/PFeg5PherEOme/XOM3Pc/nc0rirD7x2WHnllaM0nvNwn+21c7kNeV7gddxZs9D7Kc9L2Y94XgvEvshamqI+NER3lPOyRr8/hueDPJfwlJ3jsO+wzq/Hj19+biTKwfemXxNwH+HHej6O28Jro/Jx3H805P0tXA8el/w6hefbfrzivPwuGpHi2z6P//znP9HnM844I9i+PTbeeONg856X7/dZ7537F68Lz8fxWhaIx7B27doFe6+99orynX/++cHecccdo7Sy753hvrHe2s16QlUIIYQQQgghhBBCCCFKog1VIYQQQgghhBBCCCGEKEmrC/lnOLylf//+Udq9994bbB+ye8cddwSbHxu+4ooronwcDnHxxRcH+/TTT4/ycejWdtttF6Vdc801we7Zs2ewOZwHiEPB/eP5a621VrBnz54dbIX81we+pkAc3suPh/uwX26nzp0755bP4eS+DA6z4dAL/7i8yMeHQr333nvB5jASAFhppZWC/eqrrwbbhxyxZEivXr1y01jqYZNNNony3XDDDcG+8847g33sscdG+Ti8ZcyYMVEah08WhSf4MD2G/bSojIMPPjjYDz30UG6+tgKHufkQVw7R9b7D/TLn82FzeWGWPuz+lVdeCfa6664bpfE4wmE/vp8pavepU6cGm+8d0XRwG9cSeu/7PP6cF+Lv4X5MNA08t+CxB4j7fX/P8/3KY0BR2C+n+fklSwX4sE7uvzic0vsl19GHdPO5fUigqI2isZ/nh2x7/+AxpkjChj/7cF6el7IPeGkH9ud6h1KKYr766qvoM0tEsE9wOC2Q7zs+fJZDtb2MFstH8Pxk8uTJufVViH994PvWz1E51N6HhPP9yW3h71vuPzjNj1dchh832K98HRku0+fjubNC/lPGjRsX7MsuuyxKu/HGG4Nddp7nZRZ43cH4vsHPNSr4MaZPnz7B9pIfvA/C9fBSnjvttFOwt9lmmyjt1FNPDTbPd3bbbbcoX96cyafVgp5QFUIIIYQQQgghhBBCiJJoQ1UIIYQQQgghhBBCCCFK0upC/g899NCq32+44YbR54kTJwbbv3V9q622CnaPHj2Cfc4550T5nn322WDzY85//vOfo3z8yHPv3r2jNH7DIod/HnnkkVG+P/3pT8H2jx3zo9cPPPBAsB9//HGIxjN+/Pjoc1747RtvvBHl82+qy2OHHXYItn/7Lj/6zqEM3mdFPj48hN866N9cym8rfPjhh4PtQ6H4DaVF4Uks1eHD9bfffvtgP/jgg8F+8cUXo3zcz5x44olRGr/lcOutt86tb9EbDjn0go/bY489onwnn3xysO+6667c8hZn+B7kEF0fvsIhSD6ElkNxONxmgw02iPJxmB73Mz4c5s033wy2l5WYNWtWsFnaxvsH3yM+zIVDQTk0zIdW+TJF7fBcoGzIf9GbXL3PMNzeHM5b9BZmUR94vPdjOodMc3gmEI9b3O4+HJvHJp6H8hjo07w8TF7Inh9XWQ7Eh3tzaKgPPxa1wWGLvs9m/+B+38ubsRQV9zMsU+bxclN8bq7ToEGDonwsoeT7Ku6fyr6RWpTHS1txv8DzGC87xOMQ90F+POF72vsHl8Ft68PCRX3g68rt4vtxnpf6OQZ/ZpvvbyD2A87nx6G8OYavB4893o+KQrG5zKK5zuKMD+s/7bTTgu3n6rwe5LHCy9eNHTs293z9+vWrWp7fn+K1EYfre1kglgrx7ctzjZdffjnYXo6R91J4jw8AfvjDHwabZdeGDh0a5fv1r38d7HpL02hkE0IIIYQQQgghhBBCiJJoQ1UIIYQQQgghhBBCCCFKog1VIYQQQgghhBBCCCGEKEmL11BljTgA2HTTTYP905/+NNg33nhjlO9vf/tbsDfbbLMobdq0acGeM2dOsD/88MMoH2t1TJo0Kdhel3H11VfPrf/AgQODzRoS1157bZSPtVa9Hs7BBx8c7N133z33XKI2/PVmvRZuM9ZGBIC11lqrVPmsNeW1x7j8KVOmBLtIE1MUw9eue/fuURrrs7z//vvB9nqCrAXj01j759Zbbw32Sy+9FOU788wzg839zHbbbZdb96uuuir6zPrJ06dPD7bXKeI+iXVd/XHdunULNmtfAbG+1pAhQ3LruDjDfQFr3XptbNYA8ppOnTp1CjZrRvH3QKwtxWX48WXu3Lm55+L25HN5jaKePXsGe/To0VEa/818bt8vSkO1fngtuzy4HVnvyWuicZ9XpGHJGnes7yuaBm6/Ih3mlVdeOUrjMYb1EL0uIadx2/p5M49n3j/Yr9j25+Ly/djBGmhlfVsUw2O81xvceOONg/38888H288v8/RPfV/uNXwZ9hf2YT/P4LmKJ28OIuqD9w8eu1m72c8L/Ocy+bzuIK+nuc8oqw0uGgbPG4t0armP5nUmEI8HReX5camC97cifdU8zVf/bgoeX7zvFL1jYHGGx9LjjjsuSuPr7PVKeRzgNvT9fN5axX/240pePbjf8e3E6xjvV6w1z2PTuuuuG+XjevTt2ze3fO6vzj///CjfGWecEex6a33rCVUhhBBCCCGEEEIIIYQoiTZUhRBCCCGEEEIIIYQQoiQtPuTfP1q+wgorBJsfWz/rrLOifPvtt1+wd9pppyiNw5M41P66666L8m2xxRbB7tq1a7C9NAA/8uwfVedzcWgOlwcATz/9dLDffvvtKO2iiy4Kdr9+/YL9n//8J8q32267QTQcH/rYo0ePYPPj5z7MzT+OngeXMXny5CiNfYcfMV9ppZVKlS0WZMMNNwy2D1vmcKX33nsv2D6sn0MhfKgmh1f88Ic/DLa//84555xg77PPPsE+9NBDo3wvvPBCsG+//fYo7e677w721ltvHezVVlstyvfqq68Ge+21147SOKSHQyZ8aM4bb7wR7Ndffx1tEQ6V7dy5c7B9H8HXn0PqgNg/fBgNw77I4U8zZ86M8nGYDo95QDw+cliX9w8OmfLhO1w++4QPG+Z+UTQOvieZonA29peGhL3l+dlHH31UugxRHp4DcuiZnz/wvbvNNttEaY8++miweY7q58N8L3M4pQ/Z5blFUSg/+4cfE3ke/cwzz0RpXK+i8HGRj28zbk9/v3O4PY8xXiqKfZFD/n3b8hzVjzHsExxWyfMRIPYj37+x5JFC/mvDh9ozn376afSZ5yR8P/p7k+9bHvu9v3Hbeh/j+c7EiRODzWtfUT/y7mk/1+R70M/18+QYinyMfcUfz/7iQ/n53Fy+7++KZAO4zLYU8v/AAw8Em9saANZZZ51g81oWiPtpbjf+HojboEiqgc/t68H9Aa9BvB/weOb9h32V19t+LGKZPu/vLCPAcy3fD/Ga2kvbFfl/GfSEqhBCCCGEEEIIIYQQQpREG6pCCCGEEEIIIYQQQghRkhYf8u/fSDx27Nhg8+PK/vFxlgbwbzUdM2ZMsPkt4D4Ejt9M6R9zZsaPHx9sH27LjxDzW+GfeOKJ3PIuvvji6POaa65Z1Rb1wYeFr7feesHmUBrvYxyKVwSH33DoExA/+s5huv5xeVGeo48+Otj/+te/ojS+xiy/4O99/rzlllvmnotD7adMmRKlcfgDhydceeWVUb6iN6jy29n33XffYL/55ptRvs022yzYvr/jUA4Oi/Ch31zHl19+OUp75513gr0490H8tkkOq3zkkUeifBwi6d92zPcu39/+7dcculQUasLlefkJDuHhfD7kktN838JhNCxFo9DdpqOWcPuicLmiMDjOy23vw0RFfeBQOW4z3y/zHHXAgAFR2r333hvsorft5vlE+/bto3zcH/i5LJdZJAkxcODAYI8YMSJKy3tbuCgPjz1A8VvSWX6F/cOHQXKoNvsf+x5QHO6dJ2nmQ0SLfEBjSdPi+3L+3KFDh2D7tQ77B/cLvjyeN3pJIu4XONRWsmVNQ9GalOE+v1evXlEa9zVcnp8bcl/A5fkw6qJwbv7M4ee+n+G1jve/PFkTL4Hj+6TWDs/H/fyhT58+wZ49e3ZuGXwtt9122yiN16z+fp06dWqw+br6vpzveQ7R9/Xl/sW3G69ruHw/V+H9Oi8HwP7J8x/eMwSAO+64I9gK+RdCCCGEEEIIIYQQQohmQhuqQgghhBBCCCGEEEIIURJtqAohhBBCCCGEEEIIIURJWrzgxM4771z4uQJr/Hl+8pOf5KaNHDky2KyFCsR6Cqy9+MILL0T5WEOCdS183tdeey3YrL0IxNo2Rx11VJTm9UpEffHXd5111gn2xIkTg+11P/L0y7wOB2sYTZs2LUpjnRHWEfEaV6I8gwYNCvZjjz0WpbHuCttFeoJej5L1h1gvz2ugdenSpWp5RVqaXsfm3//+d7C5X2CfAmLdX9bV9PVnP+W6+3P76/H4448He+jQoVhc4b+bdVK9niDft3ztgfyxyGtGsX4ZjyH8PRD3Bb5v4T6J29bXt1u3brllsGYR/81cJ1Ff8jSvirRRWd+sVs3KepQhipk1a1awWXvMa4qxtnrfvn2jtLI6eUyeXma1z2XK8OPZxhtvHOwirTHff4ly+HlB3vzB5+Vx3GvOcf/BWrmsewnE2v6+HtzWfC5ez/h8XsvQlynqC7/vA4jv9zwfAPK1bX2fk1ceEOsasl4m6676Mn3/UZQmYvL6cn/v8zscfF/C+xKsT+rvW77fOc2fi9cYfu3A61pem3itVdb39HNPLoP9z49Rq6yyChYnit7fwfd80X4Bzy287mi/fv1yj2PdXV5n+D4jb33i/ZTbpkgLntcgnTt3jvJxe3s9aNZ85TmIf+cHv1fl8ssvj9L89WkomvkIIYQQQgghhBBCCCFESbShKoQQQgghhBBCCCGEECVp8SH/RfAj4xzKBsSPGw8YMCBK22+//YJ98803B/uKK66I8i277LLB5seLt9566ygfP/7O4V4AMGTIkKp133TTTat+DxSH+Cs0ov7wI+ZA/Nj3lClTgr3NNtvkllHULr179w62f9Tdh2eLhuOlGDg05cQTT4zSTj311GBzGLR/1J/DGnz4CYfIbLXVVsHu2rVrlO/GG28MNkuLcGgCEPcZhx9+eO65WHbEh0LwNfA+xiFZ7du3D7YPzeH7wIfjvPLKK8FenEP+ua05zMiH8bO/+P6aQ5K4DD9GMVwGy4z4OvlwWvYPbjPffvz5/fffj9I4JIZDP9lXRH3JC4EtG95dlM+HZPLnojkDh/ex34qG8eGHHwab71ffL3PIv5+D+DGtgr+v80Jx/TyU57K+DykK08vL58N5eR7DdfeyMvKrfHzbcnv6MYbHY3+/MxyKyyHAzz77bJSP5y4+7JfDLDm8048j3Cf5Onk/EPXl3XffjT6zfBOPNb6f4Xku276P4LmLb9u8OaXPx/7SsWPHKn+FaCjcX/s+n/sMv77h+5HHBp5PArEfsIxUkTSRL4PhNB4ngbgPeuONN6I0L7tWwYd9L24h/0VwG/JaFojlK3nMnTBhQpTvrbfeCjZLLgBAz549g/38888H21/j1VdfPdi8dvH3OPdRXqqBfZDHn1GjRkX5eC3k5fF83gq8bvb19yH/J510UtUyyqInVIUQQgghhBBCCCGEEKIk2lAVQgghhBBCCCGEEEKIkmhDVQghhBBCCCGEEEIIIUrS4jVUvVYH6zh5nZe8fKwFAcTaHQMHDgz27Nmzo3x5+kOsmerxaaw5xDo0BxxwQJSvSIOTtZU4TRqq9cFrRrGOIOueeR3FLbbYItisIeP9kn2C9VSBWCuGtU4233zzKF+Rrm5bx7cf30tes401VC+77LJg++vLWlDjxo2L0liH6qKLLgq217F57LHHgv3www8H22tpshbO6aefHqVNmjQp2L/5zW+C7fWp+BpwnwPEOkusOeT1h7xvMmPHjs1NW5zg68haO143KE+7FIh9ifsP3y9wu3ObeZ/1+lcM+ymf12sw5mkyArF/cJ2KtLBE4yirnZ03xynSMCvSVy2aM7AGm9eDFuXheSP3J143d7311itVBvcvvm3ZP/geZ10zIL6vvQ9wH8JpRX7E82EAmDlzZtW6s+5etXqJ+XiNXW5PP2fge5X7fT9Ocd/O5fsxi/3U+wdr1bGGndfIW2ONNYL93nvv5dZD1B++/zw8z+vSpUuUxj5WNDbwPe39lPuCIh1+rqM0VGuH24z7aP9OBNa59msCzsv5/NyT18KMn5dweX5s8P5Swesqs//5NR2PI9wX5pW9uOA1TxnWJPb3NcO6prvuumuUtskmmwTb65rycewjPXr0iPJx23Ob+j6pSKeb25H3RPwahPWavWYwr5X5vSF+bc8+fvvtt0dp0lAVQgghhBBCCCGEEEKIRYQ2VIUQQgghhBBCCCGEEKIkLT7kvygMoSiNH4X3jw3z48ZjxowJtg9z48fa+TFz/6jxs88+G+zVVlstSttyyy2DzVIDa621VpSPQ2J8fbkeCvOvP8suu2z0+YEHHgg2Px4+Y8aM3DJ8SBbDj75731l//fWD/eqrrwabH7EX9eOCCy4INof8c1gbEIe2+bAXDtf917/+FWwfcsNhb6ecckqwfdu+++67web+wjN9+vRg9+rVK0rjkInPPvss9zgO6/AhN6uuumqwfYiQDwdZXOFrwuPEgAEDonwcguTDVxgehzhsDojDtLn9fGgwl+/L4PKLwvrZv720A/drHCrjZSVE/cgLpStLUTh2rXm934na4P6X52s+PLFv376lyvOhswzfo2z7MYDL8OVxv8FzzSK/6dy5c/SZ59E8f/XSFgr5z8dL8HB7Fq1heK7iZYd4/spjOId6AnE7FYV08/jj1zocCjplypQorUieTTSekSNHRp95jsn3u5+jMrwG9XMa7k+8BBHPmbid/bqK56H9+/eP0rSuLQ+3E0ss+Pkf3/t+Ps99S5GcA/ct3OcXSYb4OSq3LZ/X93fcx3npEp6b8LkW95D/t99+OzeNJSq9zAK3FffFPsSdr6uXZeF7mfv6hx56KMrHYzqvITl0H4hlCXwfwvsi7Ft+Xc57KX7+tMEGGwSb+znvZ+xbo0ePRj3RKCeEEEIIIYQQQgghhBAl0YaqEEIIIYQQQgghhBBClKTFh/zXCr9Z3T+Czo8K8yPF/k1peY+n+1AGDtnt169flMaPVPPj+T7khkMAfTgV16MotFzUhg/B5tAJDnnwIU5MUcgK+8srr7wSpfFj6vyWVIXA1A6HKvrryJ85bHudddaJ8nG7+/BJvqfvvPPOYD/yyCNRPg5x4rCIZ555Jsq31157BXvEiBFR2rbbbhvsnj17Bpv7NyD+m32IA9eXw2q83xeFhRaFtS+u8Ljh+13+7NuCrzH33V42IS+0xV9rLs+/sTIvHNiH9vHf0qlTpyiN3yTKfRBL1Ij6khcWXjY834f3lT2u7JucRe1w2/J9zW/lBRaUmMorg0Px/P3O/Qv7BI9tQNxH+b6M04rGAIbnLQBw3XXXBXu99dYLtg/5F/n48YHvad9nc1vznNL3C9zW3J/7dikKyWcf43w8R/L15dBPoPgt9KLxeCmxjh07BrvsvI7Dp/3b3tk3ffg4H8c+4NfJLIElaoclForm/WuuuWawi9qT1xV+7smh5LyO8HMFHqN8+Dn3GbwH4vsgLoP9F4jl8JgimavFgYkTJ+am8d/Oc3ggXifwfejHfg6997Iy3I7sLx06dIjy8XHcD7EEDBCPF16qgevI5XlpAJ5P+b7M5612XiAem+rdJ+kJVSGEEEIIIYQQQgghhCiJNlSFEEIIIYQQQgghhBCiJNpQFUIIIYQQQgghhBBCiJK0amE81lAo0hY9+uijo8/HHXdcsFl7wuuLHXTQQcEeP358sNdaa60oX58+fYK9yiqrRGmjRo0KNutE3HrrrVG+3XffPdheQ1U0Lb7NWCuG9WVYK7chsM6N155hnU3WvJGGau1wv+C1wd58881g8zX2ei+s+eU1VFnH5ayzzgr2PvvsE+UbPnx41XOddtppUb7vfe97wR40aFCU9rvf/S7YZ555ZrD79++fWyfWWAKA2bNnB5u1mr0WDusssZ4R0Hb6JNbb4X7Aw/dxkR5dkcZTntZqkR5SEexjvv8oGiu5fM4nTc2mgzUui/QLmSKt1XpoqPL45vsXUR7uf9n2157ng14PjNuTbe8r3G8w7du3jz6X7cu4byjSUO3evXv0mevF9fVjp8jHty37i9dAZE3VPH8DYu12bhc/vrOOrh9vOI3L8DrdG264YbDHjh0bpb3zzjsQTYefy/F9zO3k7+m8e9xraXIZa6+9dpTG9zj7ji/Da0iL2uA2437B34+sh1+2b/F6kzw28Hjl3xuQp+UNxL7IOpd+bOC/y2swcx257nnj3+KC1zVluD1GjhwZpbH+Kd+Hvn25DK95zO3Dbejva25vbsOGvIOBxyPWV/X15bb3YxinFfVDfJwfVxuLnlAVQgghhBBCCCGEEEKIkmhDVQghhBBCCCGEEEIIIUrS6kL+y4a28WPDPhyCH4f+4IMPgn3ooYdG+QYPHhxsDmfxoVscbuvDP7faaqtg8+Pz/vH8ddZZZ4G/oUJe+JfCwuuDl3B44403gt2tW7dg+0fiy8LhFj6cin3CP5ouaoPDUebNmxel7bLLLsFed911g+1DC9q1axds3+4cQs9tduedd0b5brnllmBz6AaH8QPA0KFDg80SIQDw2muvBfuCCy4I9owZM6J8HOK5wQYbRGnsfxziwH8/EEtf+PCsvn37oi3AoUavv/56sH1oyIABA4LNYwgQ+w738z6EnsOf2ObxCYh92I9/eX2GD+vn8ZDrDsThQm+//XawWYZG1Bf2i6IQtrJyABxq5ecF/LlIGqlWSRuRD9+vRdIdRSHR3H4+RJf9o0jyIy9sG4h9gv2vaK7NoeRFxynkvzws/wQAs2bNCra/N1mCh8dtH4q7wgorBJvb2UsDsG/6ORMfx37lxzOuB9cdiKWGRH1g2Rgf/soUhWMznObnvCwZ4vuFvBBaH67McluidngM4Gvv70fuI/y8gtuJ+wx/n/JclMsokpQqSuP6+n6G1xx+P4TXZ+xXfh9lcaPo7+P70LcvX3Pu6/39z9fSh+hzXh4DuN/x8HqkaB5adu/Kr294vuPnQuPGjQs2+z7vzwHx3+nHwcaiJ1SFEEIIIYQQQgghhBCiJNpQFUIIIYQQQgghhBBCiJJoQ1UIIYQQQgghhBBCCCFK0upEG1l7oUhfjDVgttxyyyjtl7/8ZbAPOeSQYK+xxhq55bFGhdeGef7554PttWxY54J1pwYNGhTlK9KakrZm09KxY8foM7cFa7d4/TlO8zodDGvbFOl5eJ1GURtXXXVVsM8444worUePHsFm3TCvobraaqsF22tGsdYZa7V4DjrooGB36dIlN9+tt94abK8Lw/Ts2TPYXlONNfi8/lWnTp2C/eqrrwbba+HwNWDNTWDB/mpxha8V6xexhhgQa2r7NuPP3Bb+mjKsV+S1jFjj2Wv+5I2Bfsxgf+nevXuUxvXivor190R9YX8q0pMqq2nJ/uPbnsso0lD1usmiNrgtuC/w80bGa07mtbvva7g9Oc1rsrKus/cBPq7IP7if8GMM90Ney0+UY9NNN40+77PPPsHeddddozTW9+b+w48PeW3hNfe43b0P5Glk+v5i6623DvYRRxwRpfm/TTQenif4NssbK7xuJVOktcp+5fsg9jEun+erAPDII4/knluUh9uC29nPL3m/wbc7z+24rb3frLzyysHmdwUU9f/+nTVcX9Zk7devX5SP18Z+XstzGtZ8LdIOXhwo0lDlft9rZ5eF/cLPT3h9XDRHLbsnV5ai9wRx+X4vhftA9lvvI+zv9dbg1ROqQgghhBBCCCGEEEIIURJtqAohhBBCCCGEEEIIIURJWnUsedFjyPyYr38MecSIEcHmEE8fRsvhvFOnTg32qquuGuXbdtttg+1Dh6+55ppgn3DCCcH2j+BPnDgx2O3bt4/Sih6BFo3Hh7ZyeNy0adOCzY+RA8Dbb78dbA4B9nBIjA/BWm655YJd62P7bZ0LL7ww+nz33XcHe4MNNojSONSWpR04FAWIQ0z43gdiaRD2Ac8999wTbB/+xHBfxaF8nrfeeqtUeTNmzIg+v/nmm8Hm/shft1NOOSXY3tf32muv3PMtTvD92bdv32D/5z//ifJxn+/D4zh0hsOkfL8+e/bsYPN44PsBlqkoClHhcYLDdXz5Po3HQJa68OGjon5wiHdRmHUt+HDesnMGhfzXBw7F5XuSx3qPD9f0bVjBt2VeuJ2XMeJxz4dr5oUH+3Ox1A3LkADAKqusUrW8vL9DLEjv3r2jz2eeeWZuXl4vFIXAsi9yqLYfs/izT+O5EPsYj19APL4de+yxuXUX9YHXmn5dwX7A7Td37twoH8+Bi/oI9h0f8s8+wXMfP6fmuYWoD9zuvh/ndvFtMXny5GBzu/v+g8elon0I9gkvX5MnZ8h9GFB+vsl/V5FE2uJAkaQct71vt7xr5OWgOJ/v94skyvLK4LlsUbh+WZkrDx/n/Z2vAa93vI/wPl/Xrl1zz1ULekJVCCGEEEIIIYQQQgghSqINVSGEEEIIIYQQQgghhChJqw75L3oEnR89njlzZpQ2ZsyYYM+ZMyfY/u107733XrD58Wf/SPvzzz8f7MGDB0dp/Hbsu+66K9ivvfZalI8/33TTTVGawvwXLSzpwG819aGyHIJdFPLPIVnsU0AcKuffJC7KseOOO0afn3zyyWAXhUJxeJIPX/P3OMOhbfzG9O222y7K98ADDwSbwz3322+/KB+/Ade/+fLwww8Pdrdu3arW3eN9jEOt+M3B7L8A8Je//CXYt956a275izMc9syhIl7mhX3go48+itI4HJbvfR/KwiEwHMrv25bfZun9mcvn8dCH9vDY5kPMt9xyy2BzKBjLFYj6wjIO/EZ2H8bk2zGPvLBtIA7lYh/08woveSRqg8cYvvZFMi1FoYt8z3v5Km53tn0fklcnIPaJvPKAYrkRlhjgeYzvG0U+RSGMvj3z/MX7R9m2zcsHxKG4nK8hUiVc33pLnLRViu4t9heWNPOSdDx/5XWyf4M2zx98Gdw/8Xm99JTWsfWB709en3qZrqL7bPz48cHm9iwKCWf8PLTs293zJCaA4vpy/8F/c9G8Z3GgT58+uWl8Hfz1zwvX92N/EUVSMgyPCS1F4of39fya+pe//GWwTz755LqeV0+oCiGEEEIIIYQQQgghREm0oSqEEEIIIYQQQgghhBAl0YaqEEIIIYQQQgghhBBClKTFa6h6jYyyOiysk8q6HQAwZMiQYLNeqddQ7dWrV7DbtWsX7EmTJkX5nnrqqWDvscceUdq8efOCPXfu3GCzhhpQrKElFi2dOnUKNutHeYp0Nhn2We/PrBuz7LLLlq2iIAYOHBh95uvodab4PmYdIdaAA4ALL7ww2D//+c+jNNaS4/t29OjRUb4uXboEm7VlWE8ViLV5fR/EOjZcd6+Rwxo6Xs/3nHPOCfbFF18cbNZ0BmLtVdb2bUvktYXXxma9qqL7ljWpfB+/5pprBpu1W71+GWug+f6INcvYx7zmHvdBXi+Y9WA5X5FOr2gceZpUfL/7fOxLPl8R7HfcT3i/5fmJqJ2pU6cGm+/XovvJ6xLy2FGke8btyXMLr39c5C9cBvchvr8q0sljPbcPP/ww2EW6qyKm6D0QHh6nOJ/XsGOf4HmHL5t907dZnk907do1t36eshqLojzTp08PNt9zALDiiisGm33F6+tz23If5NuLNTO5bJ+XfZj7BCDWaH3jjTeiNK9zKPLh9uT9Ba/zX8RVV10VbNZw9/1H3n6Lb9uiNS7PZ3ku6+e5RXs7PN/m/smv2xY3Nt9882A3RC/25ZdfDvbGG28cbO4zgHgNvPXWW0dp0rpuOBrlhBBCCCGEEEIIIYQQoiTaUBVCCCGEEEIIIYQQQoiStPiQ/6LHwPkRaJ+Pw7YvuuiiKI1DZz/55JNgT5kyJcr37rvvBptDHnxILYfH+lBclgfo3bt3sH243bRp0yBaBtxODz74YLC7d+8e5Zs8eXKp8jgE0IdTcbuvvfbaDaqnqM7QoUODfcYZZ0RpHKrSs2fPYD/22GO55e25557RZ24zvr+9vACH4zA+vJPxPsbh2RyC4UPyBwwYEGyWKgFiH+aQm+HDh+fWw4d7tpXwj7333rvq9z5E+7e//W2w/T390ksvBZv9zff5eWGbRf2MD8nKCwfm8QqIw/J82A+Hgl522WVVyxP1hUM0OZTfzx84jceRhsD9HPuq92kfhilqg8Nqua8v6ve32mqr6POZZ54ZbL4//f3Pc1luTx8emCcNAsSyBOwfXirL90sMy0WwFNKjjz4a5TvyyCNzyxAx3Lf78Zf7D772RSH/vNZhSTQgbmvfB3FoLtfJSxeJRcsmm2wSbF6rAvF9zPe3lwLhdTPPZX04NvuVH4cefvjhqvXwEic83+H1uWgYfP2ffPLJYPsw/CI4hJ7tlsprr70WbPbhOXPmRPl23XXXRVanlgyH+TNrrLFG4WfROPSEqhBCCCGEEEIIIYQQQpREG6pCCCGEEEIIIYQQQghREm2oCiGEEEIIIYQQQgghRElavIZqrXTo0CHYL7/8cpTGmkOsueG15VgDkXXIWCMPiDWzWK8QAJZaaqlgs7bUq6++GuXr1q3bAn9DNYp0Y0V9+O53vxts1sVknwKArl27lipvnXXWCfYee+wRpQ0cODDYrIkkaoev8W233RalHXPMMcH2unV5sPYTAPTp06eq3VLZfffdq37vdTZZJ4v7LbHg9Tj33HOD7XVMWe+J9U/99WadM6/DyrAWmdfJWnHFFavm8z7buXPnYLNGNAC0b98+99yiafjhD38YbNax8u274447BvuSSy4J9qBBg6J8HTt2DPY111wTpR144IHBvvzyy4PN4xIAbLnllqXqLoo5//zzg81zytVXXz33GD+3eOaZZ4L997//Pdjvv/9+lI/1EVlv1+u1cv/l9bFZV5vHs+9973tRPu5rPKxb/vjjjwfbv29AlKdIt3yHHXYINq8rvLY6+wTrDbL2rj+X9w/2MdZhXXfddXPr59Fapf706NEj2GPHjo3SuJ9gjdOtt946ynfwwQcHe+LEicEeNWpUlI/Xybx+BoD77rsv2Kzl79c6e+2114J/hGgwPG7369cv2NyPLwyvsd3QfEX3s08re66iMnlNzlrv/fv3L1W2EIsCPaEqhBBCCCGEEEIIIYQQJdGGqhBCCCGEEEIIIYQQQpTEyj6OLYQQQgghhBBCCCGEEG0dPaEqhBBCCCGEEEIIIYQQJdGGqhBCCCGEEEIIIYQQQpREG6pCCCGEEEIIIYQQQghREm2oCiGEEEIIIYQQQgghREm0oSqEEEIIIYQQQgghhBAl0YaqEEIIIYQQQgghhBBClEQbqkIIIYQQQgghhBBCCFESbagKIYQQQgghhBBCCCFESbShKoQQQgghhBBCCCGEECXRhqoQQgghhBBCCCGEEEKURBuqQgghhBBCCCGEEEIIURJtqAohhBBCCCGEEEIIIURJtKEqhBBCCCGEEEIIIYQQJdGGqhBCCCGEEEIIIYQQQpREG6pCCCGEEEIIIYQQQghREm2oCiGEEEIIIYQQQgghREm0oSqEEEIIIYQQQgghhBAladYNVTPby8xuNbMpZva5mX1hZtPN7Ckz+5uZHWZmnZuzjo3FzIabWUI/Zzd3nRZnzGyYu97887WZvW9mo83sKjM7xMyWau46i5aJmS1pZkPN7BYzm2hmn5jZZ2b2jpn918x+a2bbmpn+MdUC0fgiiqhy7RryM6m56y9aJpqDiHqg+UfrZSF9QOXnQzN7xsxOMrNlm+i8w1z6JJd+RD3O21ZpCXMIM3u4sXNAM/t+5o8n16NONZx/1WxsHNkc52/LlOyrPs/GoGvNbGBz17ml0iwDsZktZWY3ArgHQA8AZwHYGcCWAI4HMBHAcQCuy35aM0cCGADgruauSBvhDKTX+0z6bkD2sxmAHwC4CcCmAG4AMMHM9l/UlRQtGzPbFMDrAK4EMAfAzwFsB2AHpL71JYBfA3gCwFQzO6CZqhqgwa9nc9elOdH4IhrACMwfHyo/u1H6UVXSda1FEZqDiEah+Uerp6gPGIC0Lc8BsA6AiwC8YGar1/G8l+Wk75qlj6jDuURKc88hjq5DmYcBaAfg8MZWxswG17BhvDeAJQDc2chyRMNZWF+1KYAfA5gNYCjSvurARV3J1sCSzXTeXwI4GOlkYMckSb6htJcB3GFmzwC4tDkqV0+SJJkIAGY2t5mr0iZIkmQagGlmNoi+G+2yPWhm5wM4FsBfANxmZucmSfKbRVhV0UIxs12QTk4+AbBZkiSjXJbnAFxjZgcD+DeALkgHntsWaUVFHhpfRFk+8eODmc2jjxOrpM9dFBUTrRPNQURj0Pyj9VOyD3jSzO4GMBpp+w0DsGedzjszJ30cAJjZJ405j4ho1jlEkiST6lDmhQA6Ari4DlWqhX2z33c20/nbLCX7qhFmdjuAMQDWBHCZmd2bJMnni7CqLZ7mChU5Mvt9hVvsMpcDmLRoqiPaGkmSfJskyaVI/3sIAL82s6OKjhGLP2bWHcCNAJYDcGiVxUwgSZKbAPx5EVVNlEfjixCiRaM5iPBo/tG2SJJkPNKn1AFgDzNbqznrI9omSZI8liTJZkmS3LDw3PXFzJZD+uT05CRJRi7q84tyJEkyD+k/8ABgNQBbNGN1WiTNtaHaK/v9cV6GJEkSAH9D+pSREE1CkiTXArg/+/hHM2vXnPURzc65ADoAeDxJkodK5P9z01ZH1IDGF1GGvbKfhnIcgA3qXBfRRtEcRBCaf7Q9Xid7o+aqhKgJzSEazy4AloeklFoDU8hu1e+faAqaa0O1Em5QGN6QJMmFSZL8lr+rJsBsZl0sFfh/NxPPHWNmJxSVbWbLmtmvzGyEmX1sZp+a2Tgzu9TMeucc09XMTjazh8xsppl9ZWZzMmHqH5qZNegqINIdyhUSz/IdaKkQ/Wwz+9LMpln6wpXtq+Stdo06Zn/blKze0iaZzyXZ7w4AfgjUfg1r9KulzexoSwXq383ad4qZ3WFmx5jZKlWO2dzSlxWMz3z+AzN72szOM7P+9b08bQMzWw3AodnHm8sckyTJVAAnAHi4SnkDzew6S18i8YWZzTKzB61Af8bMNjKz/zOz5y0Vif/SzGaY2W1mtm3OMYmZJfTVROe7Pcv8LYsRGl/ml6nxJYckSeZl/3Vv6HGfJknykaUvUeDrMNjMNjaz27P2+7ba9Tazzmb2JzMbm/nFR2b2QuYvy7q8G7lzTHLpRyysbbN8DR4vGurDtV4PAUBzkDaPaf7RVvmS7CXdtePryrqSlZ/hTVUpM9vfzB6w9CV6X5jZVDP7t5lt7PL5MWiBF15lfZdP7+nK2cnM7srGii/N7D0zu8/MhlSp2xWurGFmtqKZ/T7rj76odv3qTWPnEHnp2d9S6cNnZX/PBDO7wdKXqS5fVL6VnLNW8Sfvb6Wvc6UsAI9lh/dwx07Kqe6Q7PddXKeGlGPpmHcS9VufmdkbZnaRmXWpkn+Bl7OZ2TZmdi/5+1tmdq6ZrVB0rdsYvIk6jROy63eJmY00s3k2fw5xrZkV/vPAzFbIrvVb2bV/P2uLbar0LyOb4g+rC0mSLPIfpDdOAuBbAH8EsGoDju0JoD9SrY0EqQ7ey0iFmTdFKvj/bpZ2Uk4ZnQCMzPJcjvRx8+0B/AbAZwDmAdi9ynG3Zsc8DGAfAIMAHADg0ez7awvqPSzLc7b7fgCA/yAVnt8t+9u6UvoSSF+ckmTn2R/py1WOAfBO9v2vS1yjMQBOya7RfgBmAJjUHO2/iHzsiOxvT0rkXQ7AV1n+e2u9hrX4Vda+Ff+5Cul/6wYB+D6VdbU75qjs+//f3pnH2VGU6/8pgRASspF938hCCGvY1yBbgiibiggXwqKoIFd/rhdUUNErKiroRYQLBvACKruCyBq2sAUIJEASErKRfV8Ja//+6D41T705XdNzMpOZM/N8P5/zmbdPVVfXdL1dVd2n36emIJ2A75Md67qi/7M+Zf3gc6XzB2DfLSzrawA+AjAXaQj6KKTC609k5d8KYNsy+y0nfzsSaVjFeQBmZuV9ucw+I7NPqe7H0HcjAWzX2Od2K7ejxpea7zW+1N1/BtC1NDqSb9fsPCwonfvsPJyetd3Xs2t2PO1zIFJx/41IFwHYP/ON67MyXgXQk/K3zo5xdpY+x9ShY5Z+TZY+vkw96zxeVOLDlZyP5vyB5iCN3gbV9IHmH83uU6QPAPB7One7Z+fsmHL7AWibpV+SpU/IKfMy5IwHWfqELH1cmbRtANySpT+JdC4yCukPPXMyP/g65e+Y1Wlqts8N2XZHytMt+2460gX5Ar8A8Its38kAvoB0XDwDaX+XIJVv4jr2Qjju/RXACwB+lvnssQCmNVY/hIJziJx9d0T61vLHAH4NYDTSxQzPBTArK/PSMvuNp2uX56xnAFiIMnNW8qc8fyt8nqms0lxlAcJ+YGiOry1FOifa1tSpUDlIx7xXyPdK5+tipC9XrARwiNlnaFbWi9l+d2T1uBDpWDYGwENZ2svsy831g1r6qqytXs7yzAGwg/HZBMB7mW8cDuBgpIspLgKwCcCncsrtSOU+BGBs1gYXZG1Sui96MWuzwY19rnLPYSM13N5IJ3elDmcTgLuQrjDXpWAZpc7jAwAHmLSjs7SFAJxJc6iZVFxaptxPZ2mrAHQ1aXcgXdFv2zJlPprtd0ot9b3M7HdtdsHvnbPfT7L9HgXwCZPWE+kEOQFwTOSYHwI43qRdjCq94S3oH9HOoUz+6Vn+uZWcw0r9CumvcwmAp8rs0zHrjMbTd9ugZtK7WceCdHXPQv+zPpudu59Sn1T4IVyZco5GOhlaC6CfSWuFmkH852X2XQ7gd2W+7wpgCdK+slfOcUt1H9DY57KR21HjS81+Gl/q7j8DyHdGF8g/J8u7AvSwOkt7qNR/I/11f1mW96Qy5VyVpT1dpi1Go8wDVUq/DGVuoFHBeLElPlyX89HcP9AcpNHboJo+0Pyj2X1q6wMAdEI6PicAXqLvB9SyX6ncCTnpZccDSp+A/Aeql6PmAUYrk9Yv86uPARxr0r5R6r9gxq8sfdcs/VDzfenHmTcBbG/S2iANM04AfCnyf34I4EKT9sXG6oewZQ9US+dxsx/Qs3LfhfnhPEsbj/w565HImbMW9LfC5xm1zFXMvodmeW8uk1ZrOUjHvCezfFeVST8pS1uOMv0WXQcfwfyIhXTR9uey9D83hh9tZZ8dV84HsvOwG1L91CS7Hg80eUoPVL9Rptzhmc8uAT2ELeO3zwDYxqTtm7VNbl/XlD6N2Xj7I/11OzGfj7IL5DyYzjWnEZ4rk9Yqu/ATAANNWmlSuQ5A65yyS7+4XGy+/zxyOkekby4lAO6opb6XZdsO6VspKwDslbNPV9Q8GDgkJ8+1WfpDkWO+WiZtRwB9GtsBG9C/ynYOkfyljnNDJeewUr8C8P3suwdz9vkBd1JIb8pL10qPMvkPQPaGiz519plr6Ny22oJySr+2XZ2TXhrkN9k2RPr20YCc/W7I9rswJ103NDXnQuOLxpdKfWcA+UvZ9jD55yB/Qt8F2UN8AL/J8r2WU04X8quTTNpoVPZAtc7jxZb4cF3OR3P/QHOQRm+DavpA849m98nrAwC0B3AEat4AXwZgBKUPiPUdaKAHqkjfJC3NCU7O2bf0w99k831npG+oJSj/A+yVAGaY71qh5u3JM3KOV+qfZkT+z+XY/Ifo1o3li9iyB6qlOde1OelXI12wzn4/Hvlz1u2QM2ct6G+FzzPq9kD1yizvZi8KFCkH6dvTSfa/bfbjbpandI2Vm4+UroMHcvY9kcrvV9v/U80f6lNK/2/pU/ruPQB/BkVQ0b6tMh/pmFN26YUQ+4NwP9Q8MP10zr4PoEoeqDaWhiqSJHkeaXjDp5A++V6VJX0C6a8W1wN43Tm3Vy1FvVSm7PeRXvjA5sK5p2R/JyVJsimnzOnZ39Gm3L8lSTIhZ5/52d9hscoCgHPuE0gnJ6cCODJJkldysn4KaWf1HtLX7GN1PcQ5t21Onon2iyTVfnmntrq2IGrTJ6ztHFbqVzOzv8c4575rtXGSJLk8SZLf0VdLkf5CDAC3OOeGmPzPJUlSiUi6qAcyXahSn/VYTrZHs7/bw+h8JknyyyRJ5uTsV7iPaelofNH40giUOw/LkyQp+crJ2d+y/UKW71WTd0upZLyo2IcNtZ0PEaI5iNgiNP9ourhUY/pD59yHANYgbZ8BSGU29kyS5I3GrF/GcUjnBEC+/5T0evdwpMmcJMkKpPIkQBqe7nHObYc09PzPpqyDkEbCAMBTOccr9VlDnHO9cvK8mCTJh/xFkiSbIr7clCn1x+Occ+dk586TJMlFSZLcGtm/3Jz1A+TPWetCfZ/nE5D+sPNghfuX5klTkyRZlpOn1N+dFCnn6ZzvH8/+boNU3qClsCd9dkc6X/gR0nD8t5xzFztXs6ZDkiTvJ0lyWZIkq3PKyxs7xqJmLacncvbdbN7TVMm7QdoqJOnj5wcAPJDdrB2CVEfoi0hDjQYDeNA5t0uSJCtzisn7vjSh3N58v1v29/BsYCtHqYH78peZA52KNHR0T6QLCJTOYcm5dswpk8v+c1bGxwDaRfKW6ro9gI2u/JokpS93QPoL4ZIyeXQDUzs7ZX/zOuXazmGlfnU30jDIYwBcAeAHzrkHANwH4N4kSTZwAUmSfOyc+08A/wvgKADTnXPPIdWNvDNJkpkQlcJt3w7p2311ZTeyZ5fLkKQL2qxE6nOcH9nN7AVIfxkdhvRNhpLflP7W1scIaHyBxpetTe55cM61A9A/2yzbL2TMQSpZsVskT2EqHC8q9mGD/KJuaA7SstH8o3mzJ9kJ0h8lFiRJ8nHjVKcsJX9YHXk4Msfkn0XbNyCNtDnROdc5e8gKAMcjnT/clHM8AJhVyxwESPuthWXyVM1Y45wr2z8mSbJzZl6L9OHzHkjP5y+dc/ch7V//lf2gHyOv38ibs9aFejvPLl24cDCA++0YUwdK/lPbnAoA+jrnOub49dJyOyZJssY5twZABwC7VFjHqiNJkqllvn7COXcb0h/9f4bUjy4tJTrnOiPVTD0OabvuiJprN2/sGJ79XZ3kL9JW7p6jSdKoD1SZ7FePCQAmOOe+jzT85QykIQinIxXuLsdHdTxU++zvg0jDW2L4jiu72f0r0hvyuUj1jl5FzS/1+yL9pbG2twwuArAa6VP3gwCMd87tntOhlOq6HGl4SG3k3fzX9Ry1KLJV/AZmm1NystV2DivyqyRJPnLOjUUqwP1VpOLvp2aftc65a5DqofE+451zUwF8C+nE98Ds89/OuUeQhmTNqKUOYnO47Qehshua9mS/G8m3EekNTYfSFy5d5fdJpAP3RKQLS7yNmonQ15D6SJ1Xe2/paHzR+LIViJ2HuvQLAPULW0oF40VFPlwG+UVBNAcR0PyjWZPzkKKpUfKfImMUsPk49QjS+Ut/pPOrq7LvzwHw7yRJ7MNQ9tf9EB9PgPwHZ9U01gyOJSZJst45dwDSa+18pD9snJ19ljrnfgXgyuyFgXI05AP6+jzPJ2Z/79mCMirx19Vl8uT9AFkquwPiLye0CJIkmeec+z+kP7p91zn3yyRJNmRRKk8gfdv8n0jvX+Yh1fMFUl3mE7D52FE6p7H2+yCS1qRolAeqzrkOAD5KkmR9ufQkSdY5576MNBylE4AR9Xj40g3qx3Uc4E5GerP7AVJ9mGCymE1GirAewCeRdkyvIZ04/QrpZCWvrjtUyWBcrYxG+ko/kK6IXQmV+hWyX6hvAHBD1jF9Hqmeyc5I9YOGoiacr7TPJACnZW8+fRrpQ6GxSBckeME5N7IFhtxuKY8iHVi3RbrK4IsVlMG/srXJzVWTtoa+uxTpzcwMAJ9MkuQ93sE5V/ZXVBGi8UXjSxNkS/qFImwTS6zjeFHxWCYqZjQ0B2npaP4hihLt77eAkv8U8R3AjFPZ2+t/RqqneC6Aq5xzPZH2C6dGjgcA81qCHEySJLX+IJFJtvwWwG+dc3si7Y/PAtAL6XyuJ9Ifs6qZE5A+/L1vC8rYIn8lYs/Cdsj+ritaqWZOaW7RGum904tI1wfoiVTn9NN2B+fc6pyySud0h5x0INX/rQoaS0P1XqRv2+SSJMm7SAd2oH6fUJd+BR6al8E5t71z7jznHL+1Mzr7O2MLf3n/3yRJ3k6SZC7S16MB4CvOuaMidW0b0Y6Bc+5k59wXt6BOLZ2Lsr8rsHlISlEq9auAJEneSpLkZ0h/lfx19vXJzrn+OfnXJUlya5Ikn0L6RtpGpL+mnVsuv8gnSZJVSIXdgXQCUyvOudOcc+845/6VfcVvmQzI2ac9asI7Of/o7O/D9mZG1AmNLxpfmhRJkqxD+uYOkNMvmDT7lmLpzZ1WOfsVeuBecLyol7FM1AnNQVo4mn8IgqNXyvX5RX9grSslf+jgnOuUk2dAmfzMn5E+KNvNObcv0geBq1D+wRnvH+u3jnbOneuca6gHyU2WJEkmJ0lyMdIfx2/Lvv66M9qq1YRzrg/SSIjnkiTZkh9qSv4zIJKnlPZORMaiW7kvnXMdUfMW9pt1q1qLoPSW9Ojs7/113L90TjtmL8KUo2zbNEUabVEqAKNcjmAKURKrrs+3JP6e/R3inNs5J89xSBct2Z2+c+avpV9dK5IkyXikr7s7pG8GtDdZ7kfNq9DHlSvDOdcVNaGioo4458Yh1Q4DgO9kN76VUJFfOee+7Zx73GbM3hi5nL7qkeXv65xb7Jzbvcw+zyHVQvP5RZ25DKmW2Wjn3CdjGV2qy3kxgN7IJjqZOPvLWZZyD7EA4Mjs73sIB6At7WNK4Th+f+fcaOdcSxJTL6HxReNLU+PO7O+R5RKzt5D3MHlLlHSkuuTcVO6TU2Yl40WlPiwqQHMQQVwGzT9E+sNK6XyWu47K9vf1wP2okXgoO06hxq9eTZJklk1MkmQegIezzfOQhqr/JVsYyTIRNZqoeXOQ7ZD694VJklRTaH9FOOeucs7dbL/PfuS4ItvcDjU/ijQ1SuHz3A8459xnnXN7Z199Jku/ZwvLKc2TdnPO5T14O8rkLcfBOd+Xfnj8CJUvnNXcGJn9/RA1C6hVOnY8iBqJisNz8hxUp9o1Io35QHUQgC/nJTrnzkXaCCtRM1GsDx5AqqUH1HROfNw2SPUfliJckfCZ7O+w7BV83ucTiPwvtfDl7Fj9kL7i78lWrftltvlf2a8lll8iDf/4VYXHb5E45z7hnLsQ6c0FAPw0SRK7AmVdqNSvdgRwsHOu3K+zpe82AiitALoNgO4oswp05oelG6lJdau+AIAkSRYgPbcbAdzmnCu7OEx2M3ML0sHlH5ld4vtIf7kb59JVd3m/7ZDeBAHAb5IkWUzJpT7meOfcjma/HkhDZGKUHrrwROt6AJfUsl9zRONLisaXpsOvkD4sGemcK/eA+odIz/UzSN+yZt5Gen1vh1Sr0uOcOwThgidMJeNFpT4s6oDmIMKi+YcA/MOz0oPxwzjNOTcYaQh9Qxx3GWreSr/Yvh3rnOuH9AFpAuB7kaJuyP6eg7QPKRsxlOkyl/zjgqx8y8VIF7TarE9rpnQAcJwrL/NU6o/nIWchpSZAqR/gN5x7IZ1nn5htl/7aeU5dy/knUt3nT4AWSCrhnDsJ6Y+HKxD3n6Odc6PMvtuixsdvSZJk/ua7tSyy6/P0bPNueuO3NHZ8zv7g79LFx8o+sM5+fCmNXd8rs+8opBJC1UGSJFv9g/TXqwTpk+lbkWozjUJ6o/AFpBfZx0jDBI4w+/ZGOom4Jyvjmmy7Y5Y+MNtekKWfnW23pTK6IZ3sJUh/kfsU0l/8zkD6ttKGMsfdBqnobgJgMVJR3gOQakc9hlTLI8mOOxLA0IL1HQrgv7K0BOnqzL6+2XHHZ2lvZHUclR33vuz77xU8RyMbo723sm+V/vdL6JyOzD57AzgW6Y3r61nafAAnR8opfA4r9KtLs/xzkeocHpz51fnZdx8D+BLl75/l/wDpQjrHkD/8I0t7DMC2jd0W1fxB+oBiGtK3OP6IdJXSUuf+TaQrmyYA/g+pBqXd/2tIf9Wch7QP2jsrY0K23622jZCuYLooS38Zadjfflmf8DZq+ph7Ml/sbfa/Lkv/S7bf97Ltbzf2+dzKbafxReNLJX7TMfs/jqHzVWrfbmXyD83xhYGRYxyEdHK/EcAPsuv0MKQPHhKkurc9c/b9dpZnHoDTMp86F2lfcQ3CvqFvtk9F40WFPlzn89EcP9AcRHOQLfehPaH5R9V+aukDCvWHAD6b7bcSwJeQLkx5KoBXAPwiS3sxK3OwOa4dD3pn6aU++sUs/RLQfCHLsw3ShxwJgKeQvk24d+YHczK/+notdW+F9MfDBMCLBf7Xn2Z538n6nX0AjEE6N/kYwB9N/m45/+dIANs1Yrt3RB3mEDll/Dnbb2q27wFI++T/h3Tu8B6A48r42j2o45w1+9j6+jGmkvOM9C3F6Vn+bwHYH+kbxh8j7Rc6IJW0eKOW8xAth/J1R3pNJEgf3B+O9Fr5L6Tj3SoAh+QcY0K236+RLnh2AdJ+9likkRalvrBjkbarxg/ifdVIALsBOBRpf744S58EoCuVsRdSPdQEqRb4Z7L2+irSB/+lsaPkm91o347ZOU6Q6sePydrga1mb/DpLm9DY56rWc9lIDbg90oneb5B22AuRhhl8gLTDeAbpJK97mX3HU6PzZ1yWPiEnfXSZOlwE4Lmssd9HOlhcD2DnnHq3BvAjpDee7yG9KZqCNEzneHO8OQXrO6dgfU8A8C+kg9QHmZPei1Q8vug5Shrb4baCb+X+70hfUV+O9EbmRqQPV/IGhYrOYV39Kst/GtKw2llIQ3A/QDqx+BuAg8vssy/Sa+clpCsWfpj9fQbAhXn/kz519qVtkN6I3o30prd0zU/P/KfsIE3774X05uKdzA9WIB2kPxvZpzfSG5P5mR+sRtpHng3gx8YXx5t9OyCdjC3N6joz26dF3dhC44vGl8r8Zlze/wXgsjL5887thFqO0wPpJHEa0v5+HdIb3G+jzMMRs+/XyT+WZG3RM/MRrsMdtE9F40VdfbjS89HcPrHrA5qD6FPcjzT/qNJPLX3AhDqU8wWkDxs2Ze13J1Jt43GmzEm1HHd8lj4nJ31cmWOfhDQkd3nmPwuQPogfVbDuv8nK/mrB/IcBuAPpQ5sPkD5IfhjAKWXyXhY5vwMasd1tu0TnEDlltEP6AP1epD9ovZd9ZmftO9Lkz2vzcVn6hJz00dknd4yp9DwD2BVpX7MO6UPNyQBOzdJOy/b/eYFzkVuOybc90h+aXkA65m1CukbCb5HzA7U5N+OQLsj3d6TzqlL/dTnoZYnm+In4T+nzMdLFbmci7X9OB7BNmXJ2QfrAezHS+UBpvDmxzDEuM/u2RfqjykzUzG3/npV5TrbPQ419rmr7uOyfEUIIIYQQQgghhBCi3nDO/RXp2+8HJEnyfCPXZQLSN1rPTtI1B0QTwzn3/wBcCeDWJElOry1/Y9KYGqpCCCGEEEIIIYQQohmSafKOQSot8kIjV0c0AZxz+zvnvhLJUlqA7NmtUZ8tQQ9UhRBCCCGEEEIIIUS9kiTJ+0mSdEiSpFei8GiRsi+Aq+0CioBfgO8kpDq4f9nK9aoz2zZ2BYQQQgghhBBCCCGEaAicc0ORLpzWNvuqd7Ya/TtJzcr1YuuxHYDHnHO/QLow68dIF7X6ryz9tGpoF2moCiGEEEIIIYQQQohmiXNuDoD+ZZKkpbqVcc51AnAK0oVxhyNdtHU7pIvgPQLg10mSvNV4NSyOHqgKIYQQQgghhBBCCCFEQaShKoQQQgghhBBCCCGEEAXRA1UhhBBCCCGEEEIIIYQoiB6oCiGEEEIIIYQQQgghREH0QFUIIYQQQgghhBBCCCEKogeqQgghhBBCCCGEEEIIUZBtG/HYSSMeu6XjGrsCBWgS/vHuu+96+4477gjSHnvsMW8PHDjQ20uXLg3yLVu2zNs9e/YM0oYNG+btE044wdu9evWqsMb1gvyjIMuXL/f2448/HqS9/fbb3m7VqpW3586dG+Tr3bu3t48++uggbdddd/X2dtttl1uPJKk5Hc41ePPJP0SMavAPQD7SmFSDjzR5//jLX/7i7TFjxni7S5cuQb4NGzZ4++677w7SDj/8cG/37du3vqtYKfKPCvjggw+C7RtuuMHbPJdYt25dkO+QQw7xdvv27Ss6tuYgm9Hk/KMF0SL846OPPvL2Jz4Rvh9X9BpcvXq1t7/zne8Eafvss4+3v/jFL3rb9h98v3r11VcHaTNnzvT2b3/7W29vs802herXQFSDfwDqQxqTOvuI3lAVQgghhBBCCCGEEEKIguiBqhBCCCGEEEIIIYQQQhTEcZjGVkavMjce1fC6e6P5B4dNjRo1yttHHXVUkO/DDz/09iuvvOLtFStWBPk6duzo7eOPPz5I45DxBQsWePvGG28M8rVt27ZI1euLFucfH3/8cbDN4TPz5s0L0o499lhvT5s2zdsdOnQI8nGIPrfzTjvtFOTbuHGjtzdt2pRbxy984Qvevu2223Lz2T69AcLvWpx/iDpRDf4BNKKPXHbZZd7++c9/7u3BgwcH+Tgcj6/r9evXB/lOPfVUb19//fVBGvcbDz74oLcXL14c5GvTpk2Bmtcb1eAjTa4POeaYY4Lt2bNne5vnIywxA4TjmQ3p5tDLiRMn1ks96wH5R0Gee+45b9v2e/rpp73N0lPbbhuqvZ1xxhllbSCUi7BzFyZ2L6k5SMPC7QwA9957r7fvuusubw8ZMiTIt++++3qb56+tW7cO8rGM2ZNPPhmk8dz5s5/9rLfHjh0b5LPHbmCarX/kXWf2GuOw/ClTpgRpK1eu9Ha7du3K7gOEkiEsL8AyZQDw7LPPevvVV18N0q677jpv77///t6291V8n7zXXnsFaQ1w/1sN/gE0kTGmhaKQfyGEEEIIIYQQQgghhGgo9EBVCCGEEEIIIYQQQgghCqIHqkIIIYQQQgghhBBCCFEQaai2TKpBP6RJ+MeFF17obasxx7p1d999t7dZawYIterOOuusIO3+++/3NusU3XTTTRXWuF6QfxC9evUKts8991xv9+zZ09vf+973gnw77rhj2fKs1tG7777rbdZnBYBbb73V26x1N3/+/CBfnz59vB3Tg60n5B8iRjX4B9CIPnLQQQd5+8033/S21WHmvoK1lq2W4aJFi7zNmocA0LVrV2+/99573n7xxReDfIMGDSpU93qiGnykSfQh3NdbDdXtt9/e26xBF+vzu3fvHmyzbt5nPvMZb3/5y1+uc13rEfkH8fbbbwfbjz76qLf/9a9/eXvMmDFBPp57zpw509vWP1hnmecSADB9+nRv77ffft7u27dvkE8aqptRr/5xyy23BNvjx4/3NmtiAmFbcB9h54Yx3WWG56hcHhDq8fIaALbNDzjgAG9fc801uceqJ1qcf7z88svB9uuvv+7tTp06BWnc1uwrdv7RpUsXb7NOKs9ZgNCvzjnnnCCN74OmTp3qbavjzGuP8DwFAEaPHu1t2z9VSDX4B9BE5iAtFGmoCiGEEEIIIYQQQgghREOhB6pCCCGEEEIIIYQQQghRkG1rzyJEy+X999/39tChQ4O02bNne5vDW55//vkg3/Dhw71tQ244xOKtt97assqKBmG33XYLth9++GFvcwimDXnjbQ6x+eCDD4J8HE7F/gYAZ5xxhrc5xG7ixIlBvs9//vPetuF8XI8GCL0TDQz3GbFQ3osuusjbV199dYPWSWwZ3I4cEte6desgH7c99yHcZ9gyWIYECK//GTNmeHv58uVBvq0c8i8KwuG8q1evDtK4bVmSyMrNcGgvS0cAwJo1a7xt5W1E0+COO+4Itvv37+/tQw891NssCwQAhx12mLefeOIJb3PoPgAMGDDA21ZOiCVDOPy/W7duQT4OBW9EKblmxUsvveTtK664Ikhr166dt21IN88xY/M/HofsvQmzww47lN0nls8eiyVmrJzIddddl1umKMaNN94YbO+5557etvMFDqnfbrvtvD1v3rwg34IFC7w9ePBgb++6665Bvvbt23ub5YeAcM7B2Hsd9h2WjgCAe+65x9sswydEU0JvqAohhBBCCCGEEEIIIURB9EBVCCGEEEIIIYQQQgghCqIHqkIIIYQQQgghhBBCCFEQaagKEYE1qVjDDgDeeOMNb8+aNcvbrG0EhJpDkyZNCtI6duzobdayEY3L7rvv7m2rCcT6PqyXa/UPWauuqO7U3XffHaSxz7Eu3qmnnhrkmzx5srd//vOfB2nSUK1uYnp0rLF21113eZt1mwHga1/7mrc/+uijIM3q7omGh/VLWd/S6iuzzhi3m+1ruH9ZtWpVkMbamozVSrS6iqJpsMcee3jbjkVjx4719uuvv+5tq3G3bt263PKfeuqpLa2iaAAWLlzobdtHs5ZuXh8BhPPLxx57zNvHHntskI/7HZ6PAOF8dsmSJd5mfUVAGswNwVVXXZWbxj6xYcOGII3Hg223rbnN57HGwnNNO+dgfVWrtcpzSt6PjwsAXbp08faUKVOCNL5/Yq1OEWfatGne5vMLhOefdbJtGl/fMQ13HnvsPIV90fYf7FdWNzXvWLH+btmyZd5mfWchGhu9oSqEEEIIIYQQQgghhBAF0QNVIYQQQgghhBBCCCGEKIhC/oWIMH36dG9vv/32uWkckjBixIgg3z333ONtG2rFoQ0cniW2Lrfddluw/dZbb3nbhpWwNAOH4dtwKt7mECwbnsXhNz179gzS8sL1u3XrFuTjcD5LTG5ANH1iMg3vvvuut9kvr7/++iDfUUcd5e2hQ4fWY+1EJXAIHvcHNuTOhk2WsKG93E/EyuB869evr0ONRVOE5xOvvvqqt3v16hXkY5+wY4xomnC/YOeG8+bN83bv3r29bcf6lStXevuWW27xdv/+/YN8HIrbvn37II37D5YT4TBtIAz5l7RQ/fD22297255THvutHBnnjYX5c9vyvYgN6Y7BYwr7n60vj1m2TixXopD/4kycONHb9tpfu3att7mPAMK2ZgkzC9/zsq/YY3G4vm3bPL+y8xSuL/dbtgyW2jv88MNz697cuOGGG4Ltc889t85lWMkF3rbPN7i9m0p/zr7FfSNQ2X3NySefHGyff/753rbPaoqgO20hhBBCCCGEEEIIIYQoiB6oCiGEEEIIIYQQQgghREEU8k/YlQ2LvubMr7sXDa+98847g+1TTjmlUL2ayqvXLYUhQ4Z424aF54W62FAoDlewq7hzKNdPfvKTLaqrqBwOhwPClS85lBrID4eKhUywf9hQFw7XsqG8fCwu366CySGAvII4sPnqn6JpY8ehGOxL7Dt2pfdDDjnE26eddlqQFltJWDQMeSvaxsIkuQ+xKy0XTePyY6F+ojpgqSEeH+w8lCUmdtppp9zyNNdsOixZssTbtj15JW2ebx533HFBvjlz5nibpahsSC2PI7bd161b5+2NGzd6u3Pnzrl1r/ReSoSsWLHC223atAnSOOTfzgfzZF5sO7BfcTitDdu2K7czeXNUlsMCwr7KjlEssSWKM2nSJG/vvffeQRr3CywNAABf+MIXvG3vb/Jgn7Dtx9u23dk333vvvdzjch2t7EOHDh28zX1aSwr5P++884Jtnht++ctfLlTGuHHjgu0+ffp420q9sARD3nGB/Pa198lF72u4PJ632DSWCQGAo48+2ts///nPc8u/6aabvP2Pf/wjSPv85z9fqI556A1VIYQQQgghhBBCCCGEKIgeqAohhBBCCCGEEEIIIURB9EBVCCGEEEIIIYQQQgghCiINVaKozo/VgojppnLec88919tvvvlmkO8Xv/iFt1988cWK6sVYjROmqM6rAL7xjW94m7U3gFBzJHa+WXOEtY2AUCNpwIABFdZSbCmzZ88OtlmrxbYt642xrlBMI6boNWfzFd1v/fr13n7uueeCtOOPP75QGaJpYPv7WP9/xRVXeJv1i6xOIutV2X7sb3/7m7dZy8xqYTH2muA6Si+vdvLGi6Lag3b/PD3vGHXR6hVNk7vuusvbXbt29faGDRuCfHz9v/baa0FaJWsAiIaHNSethiWnsdaqpUePHt5+/vnnvX3ggQfm7mP7HPalZcuWeZvXFwBCTWarEy8qgzUEWdcfCMd7q2vYvXt3b3O7WK1Vhq99m+/999/PTeP9eD2AmBavvQ+ShmplsBay1cDs1q2bt//3f/83SNt11129zXqltv9nnV5uWwv7B/slEPYFPKe0x2J9ZuvPXIZdI6Kl8N3vfjfY5vH+5JNPDtI++clPepvvbXncAMLzbK9r1kOOzen5Hpjb3s5RbZsynDem1cs+2KtXryDt/vvv9zb3L08//XSQ74gjjvA23/sAWz4n1uxJCCGEEEIIIYQQQgghCqIHqkIIIYQQQgghhBBCCFEQhfxXQNGwPAA488wzvc1hmP369QvyTZo0ydvf+ta3grRf/vKX3o6FbDAK3aofdtllF2937NgxSOPX2zmkzp772Kvu/Ar7IYccUmk1xRYyf/78YLtTp07e5hAEIAwn4GvfXpt5YdC2v2B/sX1LXpiNrROXwaEPgEL+q43Y+PLGG28EaVOnTvU2h1NxqBYQ+qYNlVm1apW3OazwoYceCvIdffTR3tb4smXY67eEbXseVz744ANvW0kHDvu14cF5/VVeHUT1wOF8HLq5ePHiIB/PXRYsWBCkTZ482dt77713/VZQVMzatWu9becWHALbpk2bsvsAYeg3+8CsWbOCfCNGjMitB/cfLBdx6KGHBvnmzZvn7aFDh+aWJ4pjx3GGw2FtGDSPD3z/EZMTKipbZu9n2Dd5vspyZkAYFm7D0xcuXJh7bBHC1+DAgQO9zecXCNuJ+wgg9Be+17GyEjwfYdv6Ucw/uF7sY3xcIJQts37P/R33R9OmTQvyDR8+HM2JJ554wtt2DDjppJO8/dOf/jRIu/nmm71t5/sMl2nHjrx8ljzZgNhzMdvX5MkGsOwAEN532DJYMpFlTqw/dujQwdtjxowJ0v7617/m1rkIuisSQgghhBBCCCGEEEKIguiBqhBCCCGEEEIIIYQQQhRED1SFEEIIIYQQQgghhBCiINJQrQCrc8ZYLUbWstqwYYO3ly5dGuRjzaHnn38+SGvbtq23WdPzi1/8YpCPdXOsbsbYsWNz6yyKYXVoWOclpu0R061jbRixdeF24msTALp27ertNWvWBGmsRcbtHtOMiRHrT/Ly2X1Yu4Z19UT1EfOjq666KthmPSAea6z2Lutvsr4QEOpmsbbZMcccE+S74YYbvH3OOefk1lHUDW4rq/eUp9Hct2/fIB/rF1oNVd6P+w1pqFYfdn7Jcz5uZ77ebZqdn0yZMsXb0lBtOsydO9fbrI8NAJ07d/Y2z0/s2MG6eKyTanV0uS+wZXTr1s3b3LdYXb133nnH29JQrRyrh1oipn9qr3eGNSdtm+XNPa1GekwzncvgsWzdunW5+9j/ZdGiRbl5RQjr5s+cOdPb++yzT5DvgQce8PZxxx0XpHF78nzQ3uPm3Z9af4jNK9jneD+rk7rzzjt7e8aMGUEaz3dYL57/f6D5aajedddd3t5zzz2DtLffftvb9vofMmRI2TTW5AfC9rVzzzwd0pjWMh/L5otpOXMaa6jy/Y3dtvqqfN/L497hhx8e5HvwwQe9fdFFF+WWXwl6Q1UIIYQQQgghhBBCCCEKogeqQgghhBBCCCGEEEIIURCF/FeAfTWaX2m34fr82nSXLl28vX79+iBfLOSGw/x79+7t7ddeey3Ix68527DOgQMHeru5vRa/tbCv1XP4AvtELDwm9qq72LpwiLSF28mGRXFaLF99w/3M+++/n5tv2bJlDVoPUf9weEys/5g4cWKwvXHjRm9z+I7tq2Llc2geh/9zCBYA/OxnP/P2T37ykyDt1FNP9fYVV1yRW3+RwuFK3IdYqYaVK1d6u0ePHt7ef//9g3wc3tenT58gzYZ5lrDSAKLpw+GeQHgtd+/e3ds2HI5DOVkmANg8/Fs0DbjNbPj0qFGjvL1w4cLcMnhOwmOFDf3kPsjOLVhu4OSTT/a2lULiUE1ROXnz0tj8Mq+PB8I+Ihaqzdj7FM7HEgJAeA/DvmNDvzmfPa69XxX5sNQfyzLZtuUxwF6b999/v7dZ0sPOP3jeyO1uj8X9iZ175tVj1apVQb5TTjnF23PmzAnSeL7D+Xi+2hzh+b4NT+dnTVYmhMcLvh8cMGBAkI+fQ9lrktuNr2V7reb1+zbkn8uw/pP3HMRKTuQ9cwGA/v37e5vD/+2cifuoN998M0jb0n5Ib6gKIYQQQgghhBBCCCFEQfRAVQghhBBCCCGEEEIIIQqikP+C5K1kaLEh/zZssoR99fqNN97wtg3f4Feb+fV8lhAAwtAc+zr9oEGDcuss8uEVBW2IE4dMxl5n5zQbasVlsu/YkE5R/9gVk5nYSutbig2t4O1YSFasThyOE/u/RNMk1rbsA6tXrw7SuM+PhdTxmGLDR7kfYz+yIeEbNmzwNoePApuvMCvicDgVn0u7yu68efO8zW0zduzYIN8Pf/hDb9u2z1uVNW8FX9F0seHd3IZ8vdo5Km/bMEmFajcNWN4DCK/jgw46KEiz/UQJG47N1z6PAXYeyvnsWMT+MWLECG8/8cQTQb6uXbuW3QfYXIJC5MPhzrHV05lOnToF2xy62rZt29z9ioZ0c77YKt8cTrv77rsHaZMmTfK2/V94biGKY58BMMcff3xu2l/+8hdv87MClgYEQj9in7DtF/MdLoPHKxumzjImbLdk+LoYPHhwkMYSXD179gzSeLV6fmZk+3ZuR3sN2rEkD77mue1jIf8x2EdiIf82PJ/HtI4dO3q7c+fOQT4O87fSEvYZT13RG6pCCCGEEEIIIYQQQghRED1QFUIIIYQQQgghhBBCiILogaoQQgghhBBCCCGEEEIURBqqBYlp3L3wwgvevuGGG4K04447ztusXTd8+PAgH2tFsJ4qAKxfv97bU6ZM8bbVhvjggw+8bTWLYrqvIp+bbrrJ29wOQKhNxNoeVsOO06wfsc7In/70J29LQ7XhWbx4cW6abUOGtchYFya2T97+wOaaQ0XqYfVp+Pq2Onui6RPTyuU+3+ocsYZqnl8C4djD+wBhP8b5rM4ea33utddeQdrnPvc5iOKwjiVfy1a3ln1hp5128vbee++dWzZr6QKbt2MJ1tkS1YHVUGb9Mp7/WQ1LvuZt2rvvvluPNRSVYvXb1q5d622rmZqnrWnn+ewTsbkK9zNWO4/9g+85bBmsiWh9ShqqxeFrnM+bPd/sE7169QrSZs+e7W3WV7Xtwu3O41DRuSwQzju4vD59+gT5HnnkEW/bca6ovqLInyvGrmmrec8+wT4WW/+Dn1FwvwKE/Y5tS55/8NzEHovnnlbnu6g/NuTaF41BbGyeMWOGt+2zID4PfM7tdcfn1c4TeZ7A84yGni+wX8TGEdvWfG/E/mn/57zyAGDy5Ml1qqtFb6gKIYQQQgghhBBCCCFEQfRAVQghhBBCCCGEEEIIIQrSaCH/sRDHpgi/8nz//fcHabNmzfL2JZdcEqQ9/PDD3h4wYIC358+fH+Tj17I5HxC+vrxs2TJvb9q0KcjHoTp1CdkQ+fzhD3/wNoc8APnnOHbubag2h+38+c9/9vaNN95Yp3qKusMhJrYPioUW5Ek42HzsB7yPDXWxPsFwOBWXF5MN0LW/dbHnm9vTtlMl/Otf//I2j0NA2CexJIn1xY4dO5atHxCGysT8lMchDu8UdYf7fQ71teG2HD7Xs2dPb9dlzpSXlyUERHUwZ86cYJuv3VWrVnnb9hPsbzyHBDYPBxWNQ0yqo3379kEaz104RNyG23Jfz+XHxiwL+xiH9s6bNy/Ix/2T9T9RHB7HeQy2/sEyHl26dAnS3nrrrbJl2/GF/aDoPblN43kB2ytWrMg9tv1fRHEqeV5iw7m5zxg6dKi3bYg172fD/PPqZH0sbz5s+5yisiDV8LyovuBr8p133gnSFi1a5O1Ro0YFaXltZftl3o5JFTJF74ft/tYvGD42+4WVJ4q1PfsW72flclgSz97H8ByqEvSGqhBCCCGEEEIIIYQQQhRED1SFEEIIIYQQQgghhBCiII0W8t8UX9u2Kyjffvvt3uYwfxuuMHjwYG/bEE8O3+cwDPsqM4f02Ne1uXwOo7CrgvJqZrHX80UcllJgn7CrY+eFyMRCuu1r9RzmwGG5c+fODfL179+/SNVFHeB2rktodl7oXKxPywutsseOhevnhWAA8VVSOcTBrgYr6p9KwvxjbctSIH379g3SOHSLw7NseE1s5UzunzifDQXm/mnmzJm59RW1w2FI3J/YtuH+IBaiz32PHfvtuFWiW7duxSormgxW5omJjQHsYzafQv6bBmvXrg22u3bt6m17DfO9xKBBg7wdkwzhvsXKV/GxrbwAw/NVu7J027Ztyx5L1A2W5IiFXNtQVobHcR7fY7JUTFH5Kls+57P3yeybdo7Ex2Pfia3QLYpj25m3+RyzlAgAdOjQwdvsf7b9YvcteavM2zJisiMtFX7GY0P++Zzb8YGfE/H1ap872W2G243tWDsVlZuL3e9wGVaiIO9eBQjHH+4bbT4eLydPnhykWTmkuqI3VIUQQgghhBBCCCGEEKIgeqAqhBBCCCGEEEIIIYQQBdEDVSGEEEIIIYQQQgghhChIo2moMlZ3Iab5kofNF9tv8eLF3r7jjju8/fLLLwf5WDdi2LBh3l6yZEmQ75VXXvG21Xxh/YYFCxZ42+rhdOnSJbe+fD5YV6l79+5BPtabmD17dpDG2ltWP0mEPPzww97u2bOnt7n9Ylj9KD7fMT/deeedvc2avQDwta99rdCxRXFWrVrlbavnw9d+rM3YtppRvM39gM3HaVbvhTV8O3XqVOa/KF9/ZurUqd6Whmr9ExtruK2LaquOGjUq2Ob+n3WCgHy9IdZes2kWrhdrZllf5DHEjj2ibrDmFbdvTAezT58+uWncbuvXrw/SrM/U9r1outi5G883WdPSXu+si8layID8oKlg10Ro1apVWRsI70G4/ex9Bfct3M9bHTxOs+MZj2GsubfLLrsE+WbMmOHtmEaeiMPzUr7e7djAerZ2/Q9uz5iGotXcLRHTSbTzV/YXno/wmGSPbY+bN4+Rhmr9YH0nT8vU6mmzzjq3X0xH18J9EufL8z1RA6/BM3/+/CDNalgzfE3yObftFJt75qXZ6z9vHZGieqp2vzz9Z1sPOyby/QnXceXKlUE+PqevvfZabj0qQaOeEEIIIYQQQgghhBBCFEQPVIUQQgghhBBCCCGEEKIgTeKda/uabdHQyKL88Y9/DLZvuukmb3OY9U477RTk4xBvDsGO1W/t2rXBNr/uzuUvWrQoyMdhHvZ1+uXLl3ubX3PmMC4AGDRoUNnjAsCKFSu83bt379z6C+CZZ57xNp/jQw45JMj35JNPepvbffjw4UG+uXPnetuGyu67777enjVrlrenTZtW12qLOsJtFgvrt2FSHJLEIQj2uuWQB7Zt/8HH4rAFILzeOc3m4xBiy9KlS3PTRMMSGyvuvfdeb3/hC1/wNodZAXGZBpYX4RBAG77D2zbUisNjOHTQ5uPrQLIxW0ZeyH8sTGro0KG5aRz2a/uhPBkBhXpXH3Z+ydc8+5SVfeBxxIbR2tBc0Tjw/M9iJcF4DtKmTZvc/Xj8ickYxfoglo/heYctg+e5Vr6gb9++uXUUIevWrfM2j8H2mmb5t9WrVwdp3OfHwlg5X55MgMXeW3Lfwv3TkCFDcsuIyQbY8sWWY+8XeOyPyVLl+U4sxD+Wl/1qS8OrWwIsq/Lss88GaVa6Jw9u39h9QYzYvDTPf+w1nrcPEPZzPN7E/MzWnY/NcyE7Z2L5zn/84x9BWr9+/XKPVwS9oSqEEEIIIYQQQgghhBAF0QNVIYQQQgghhBBCCCGEKIgeqAohhBBCCCGEEEIIIURBmoSGqiWmm8DaPKxx+s477wT5WN9y8eLFQdqee+7pbdaFmzp1apCPNT5Yr4I1HmwZVhuCtUpYJ7Vnz55BvsGDB3v71VdfDdJYU2fgwIFlywZC/TurkxXTsxAhM2bM8Pa8efO8/clPfjLIx/qnrHnao0ePIB/78x577BGkderUydtvvvmmt61/iPqHtVXsNc3XvtUqZq2WPA1Lm4+J6UfZfVgnhuth+8iYFo7V3mrpsB6QPY9FNaOsvmge//73v7194YUXBmmso73PPvvkls0+ZvWA2G9Zn8rWN6aJxmOF1dpi2L95TBJ1h3UrYxpXDGvmWbhviOnpse/HdJdF02T27NnBdt7cc+PGjUE+vv65PwFCPU7ReBx55JHBNs/nbf/N/XtMgz1vTIjdY8X8gfuWXXfdNUjjubLVARfF4TbkMdeuu9GnTx9vW91Rbl97vTM8HhS9R7R6irzN8xNenwQIxzzrf6wTz35vtYNFZdjxgOeN7Dvt2rUL8vH4Eps383Zsbpynp1quTAGMGzfO21deeWWQxs8ZeI0cIJyfc1vb+WVsbQ+GfSTW13AfYvuJopqqsfUeYr7F/Rz7re3/+FrgvgYADjvssNzyi6A3VIUQQgghhBBCCCGEEKIgeqAqhBBCCCGEEEIIIYQQBWm0kH9+xfeyyy4L0vh1ZRu6zq8X86vH9lVmzsfh+kD4anMsvIVfL+Z97CvPeWGiQChRwCEPLDsAhOEcQ4YMCdJGjhzpbX6N2r6uzGnLly8P0mJhHyKEw0/41fH9998/yMc+x+ELr7/+epCPQ19smOXBBx/s7euvv97bgwYNqmu1RT2SF+pit2NhEgz7h92H+w8rPZBXJxsuw3XaaaedgrRly5YVqmNzwrYZE5NYyCMWMm/74dGjR3t70qRJZb8HgGHDhnmbw1BiIbmtWrUK0tgneKxs3bp1kI/3s+MXH4/PjR1TeTy0YeWibrBcT8xXi8JzC9s29VG+aBrMnz8/2Oa+Z+XKld624cGdO3f29pIlS4I0O1cUjQPLedUG3z/xGGavde7D88J8gbicCMNziV122SVIO/PMMwuVIeLwmB6Tc2B/mTNnTpBW1D/YD4qGXNsyeL+YZA3LmC1dujRIY9/UvWr9Y+e5eee7e/fuQT6ee7IvxnygqNyUDQGXLOHmHHPMMd7+9re/HaTxtdumTZvcNL63sPcF7Be2f+Ftvq5j7cTlxfoT6xMxSQEm75kcEPonSyBYCUae79jz9uUvfzn32EXQG6pCCCGEEEIIIYQQQghRED1QFUIIIYQQQgghhBBCiII0Wsg/v5571llnBWkvv/yyt6dPnx6kcahAbEXi2MrIHLbCrw3bY/Xu3dvbM2fO9LZ9JZlfh+7fv3+Qxquycug+r/4MhGF6w4cPD9LyVum2q4pzyKddDY1DAkWcT3/6096+5557vD137twgH7fThAkTvG3bhVdgHjp0aJD2zjvveJtfkVd7NTzcnjbkjdvChtBy2ERs1UEOjSgaTmXDKTg0gsNvYmEXtk4TJ04sdOzmREyGpSgcNvLSSy8FaQ8++KC3b7vttiCNQ0w4hOTtt98O8nGIbkxSgPsT2+7sExz+Gwu7io0bMWkAPrYdkxYvXuxtG2IjNof7d/bVmN/admN4dd6ichdWPkI0fWJzT273WOitlcCSJET1we0bu47z5h121W/2K9sHsX/IVxoenl/y+bb3u7wi9TPPPBOk5Y0jsXljbA4Sg/fj+247XrFEAd/3AOEcJNZ3icqwoc15ofw2JJz7iZh/sJ/aPievD4rJNIrNeeqpp4Jtvv6LyizE5OtYagQIfaRov1+0DW3/xM/h2C9sPt62x+Iy2N9tn8fP8ljeEQC6dOlSa91j6A1VIYQQQgghhBBCCCGEKIgeqAohhBBCCCGEEEIIIURB9EBVCCGEEEIIIYQQQgghCtJoGqqsa2r1Ij//+c8XKmPNmjXeZr07W77Ve2MtGqudyLCuUKdOnbxt9SRYo8FqXOVpQ3DdgVADjfcBgO22287brJe04447BvlYN8JqT7C2jYhzwAEHeHu//fbz9o033hjk++Y3v+nttm3bettqkbAOTZ8+fYK0H/7wh95mTZRvfetbda22qCPcLgsXLgzSuI/o3r17kMbty9eq1a1hjRe+9q2mC1/fFi4jT6sK2FznlRkwYEBuWkvg7rvvDrb/+Mc/env58uVBGo8jMT0g1tqx57dbt27efuONN7zduXPnIN+6deu8zWMKaxcBoe/E6sRlxPSu7NiQpwlsx7KYjhL7nzRUa4fnHUV1p2IaqjwXsOWx/1iNNFFdsNaghefRq1atCtLYP6wWI89dRHXAfThf31aXME9X184feE5i+/k8Pc6iun2ibuTp8tvvhw0blltGnu5tTJMwRkznm7f5WHPmzAny8foijz32WJDG82h7/yu2HNbXB8LnD7vssou37djAbRGb/3H/YduPt/PuZ4DN570ixD4n4+cR48aNC9L4XoPvL2Pn2M73K+nP2Q/sWMT9l+3L+FgxDWX2wVg/wXMau4YSz4EfffTR3DIqQSOgEEIIIYQQQgghhBBCFEQPVIUQQgghhBBCCCGEEKIgjRbyz+Hpc+fODdJ424aotW/f3tschm/DLmOvK8+ePdvbHHbJ4fS2DM5nX0nmfDacNy/U177yzK8l21evOTTZlp+HzdezZ09vH3jggYXKaKlwqMrLL7/s7VNOOSXIN2vWrLL5Tj755CDfM888420b9nv66ad7+7777vP2v/71ryDf2LFji1Rd1IGHHnrI23/605+CtM997nPePu+884K0e++919v9+vXzdix0N3bd8n42nIrDGriPsFIlHOLJ/QUQ9rUthZdeesnbl156aZDGEg7cfgAwfPhwb8fCkzj0dtmyZUHa0qVLy+abMWNGkG+nnXbyNodg2tAqDtOxIVkMjzV2fOExxYb9sIQFH9uOQ+xH9nwUDR0UKTwO8DmP9SGxkH9uG+s/XGasDNH0se3H4wqHuVlpAJ7b2jm1fKL64DDOvPBuIH9MsPORWBl5Id2xEOCisgEiDrdfTBrKhrUWhduFfaJoWD8Qji9cx0WLFgX5rERdHnmSB6JyYmHUjJ1f2jlgiZh/2PbjMu0zlliZIs6oUaO8ffHFFwdpP/jBD7zNz8zsNRi7Z8i7Du18ge9Rx4wZU/a4APDCCy94e+bMmUEa309wH2LrwH2NHcN4e968ed4eMmRIkO/OO+9EQ6E3VIUQQgghhBBCCCGEEKIgeqAqhBBCCCGEEEIIIYQQBdEDVSGEEEIIIYQQQgghhChIo4mVsJaG1ThgjRbWLgVCrcDly5d7e8GCBUG+mDbEypUry9pWd4q3WScipnNodW742DEdGta1suWzXiIfK6aTZcvo1asXRDFGjBjh7aFDh3rbaiCy35522mnethqWw4YNy01j7VXWq7FtKxqW888/PzfNajx37NixbD6r6cK6MNwPxPSMbBr3J9xHWH0qpiVqplqeffZZb1uNU9avXL16dZC2YcMGb/O5t9pSrEMa0/nh8crqji5ZssTbfO1zH2/3s/pFeTpHVouT97NpfOyYnif7t/qnLYP1c3msjvUN1i+YvHmGpUOHDkWrKJog3D8B4fUa08jka9n2ZTEtTNE0sTrYJWxbsj42jwG2L4mt75CXT37TMPC1yuNBu3btcvex80FeXyRvHY8Ydq4S01bP24+15IFQn97C/sx68qJ+sG3Gzwr4erfzOvbF2PwjpnvJ9y183JgGpkiJra/B2/z8AQifW/z4xz/29rRp04J83DYx7Vu+Pm2/z9f8tdde623WdAfietDsWzzHiZVh68Fl7rvvvt6++eabkYedb2+pfrPeUBVCCCGEEEIIIYQQQoiC6IGqEEIIIYQQQgghhBBCFKTRQv5j8KvM7du3D9LsthD1TV6YtQ0PnjNnjrd33HFHb3OYLxCGGFsJC5ac6NKli7cPO+ywulVa1BkOp7AhTnn57DaH0th8sdDbPGwoBIfscVgEf18bRf/P5sRxxx3n7dtvvz1Ie/31173N1zcQhnzkhUUBYfgk5wPyQ/RtuL7dLmFDq3jb1iMvBNOGw8TSuL5s2/Bi9ue+ffsGaa+88oq3WeJElIdD/tl/YmG0tm9guN1sGewj3bt3r1M9RdPCSluxhANLiLANhPMMiw3NFU2ftWvXepuvaXvt58m5WDik044/PH9QWG7Dw2HXfO7zpKbKwWM127Gw7bz97badt3Aa19HeL40cOTL3eOxXCxcu9Pbuu+9eqL4ijp33cxvyvIL7FSD0P7btPJT7DztP4Xlk3nGB4r7Zksi7jmtj1KhR3r7vvvu8PWXKlCDfY4895u0333wzSOM25XHEys3wuMKShlZmgtve+g+H3vM9me1rBgwY4O099tgjSBszZoy3+XlMjC0N8be0jLtrIYQQQgghhBBCCCGEqAf0QFUIIYQQQgghhBBCCCEK0iRD/oVoKnBo5vr164M0Xo1y8uTJ3j7ppJOCfA8//LC3u3btGqTxyp3z5s3zdksJzW5Mip7jPn36BNss9cBlxFZFj4VcMzasg8NgeL9BgwbVUusaWuJqvHx+nn766SDt3nvv9fZNN90UpD377LPe5nauj3No25bDTWIr5zYVeOXQGTNmBGlDhgzZ2tWpangV5lgoP/c9sVWeOTTK+hn3S0VDoUTTxK5Ky9vczjYsLy8fEIbpicbDjjF8Hdu5J48XPEewbRtrd6ZomkL+Gx4O+ed2tvNQ5qWXXgq2WWaMZUFi7cf+ZufGsflr3jzmySefDPLdcMMN3rard7OPcd1FcezYwPMKG07PPsEh1rZd8qRA7JyF292GafOcg9vZzmfyJLBaMpXIxsXYbbfdottiy9BTGyGEEEIIIYQQQgghhCiIHqgKIYQQQgghhBBCCCFEQfRAVQghhBBCCCGEEEIIIQoiDVUhIuyzzz7enjZtWpDGujF77rmnt1euXBnkGzZsmLcXLVoUpLGm0bHHHrtFdRUNw/Lly3PTWOPG6hSx1lSrVq28HdNutTpIvN+mTZu8vXjx4iDfunXrvG21iVqihmqME044oawdY+HChcH2ggULvG2v6VmzZnmb/SOmh9SxY0dvs4YaEGqUWe0q9o+YBlXs2HnaWPZYnK9Lly5BWu/evXPLF5vDWtrcvqzLDYQaZlYXs2g+1i2L6bWK6oM177jfsFp4bdq08Tbr5wH1r9MmKiOmoWrnBdwX8zzUlsH7xeYBrNFqxx/uM957772y9RP1R572Pq/nYGHtdwBYsWKFt9u3b+9tO77w2BDzj5jGO2tk8tgzfPjw3H3sHJV1nGNa4aIyli1bFmzz+eZr2s4d+Brn+aXV3eY2s3rPXCbnW7p0aZAvdg8jRDWgN1SFEEIIIYQQQgghhBCiIHqgKoQQQgghhBBCCCGEEAVRyL8QETZs2OBtGwrVunVrb48cOdLbNqSbt7k8IAzv4dBPsXXh0CcgbLMhQ4YEaXPmzPH2mjVrvG3DZWx4VRHYp4DQJzgM5sgjjwzyxUJkrBSBqDu9evWKbgtRF/ianDt3rrdZOgYAJk+e7G0OrbRwWGe/fv2CNA7X3GWXXepaVdGEsGG5eXMLO5dgGSIrUXLIIYfUZxVFhcRC6O38ZPXq1d7m9rTzy1gZDM87rHQMy8pwWC7PXQHNM+oLDqfmdo7NJ0eNGtWQVWpwuH964403vB2TDRAhMSkxlpQCgN12283bPXr08Lb1Me5P1q5dm3ust99+29s777xzkMb9AsvN9OzZM8hn732EqDb0hqoQQgghhBBCCCGEEEIURA9UhRBCCCGEEEIIIYQQoiB6oCqEEEIIIYQQQgghhBAFcVaTaSvSaAcWyBdrajo0Cf/45z//6e0HH3wwSFu/fr23WcfKamm2bdvW2zvttFOQxlpnn/zkJ719xhlnVFjjekH+UZCpU6d6+7nnngvSWBtx06ZN3rYaZdttt11u2t577+3tY445plCdbJ8e02arEPmHiFEN/gE0oo/kzbsa4FptqlTDP9rk+hDWVASA//qv//I26+i+/PLLQb6jjjrK28cee2yQxpp3MZ3erYz8I8LTTz9dUwnqS2z/wfqFbNs5Kuuksg2EcxK2Bw0aVNdq1yfN1j9efPFFb99///3e3meffYJ8xx9/fM2BzHjCernsE7H7/foee2KanrZ/Wrx4sbfHjh1bH3Vqtv7RWCxZssTb3bt3D9Leeecdb/fp02er1WkLqAb/AKrMR5oZdfYRvaEqhBBCCCGEEEIIIYQQBdEDVSGEEEIIIYQQQgghhChIY4b8CyGEEEIIIYQQQgghRFWhN1SFEEIIIYQQQgghhBCiIHqgKoQQQgghhBBCCCGEEAXRA1UhhBBCCCGEEEIIIYQoiB6oCiGEEEIIIYQQQgghREH0QFUIIYQQQgghhBBCCCEKogeqQgghhBBCCCGEEEIIURA9UBVCCCGEEEIIIYQQQoiC6IGqEEIIIYQQQgghhBBCFEQPVIUQQgghhBBCCCGEEKIgeqAqhBBCCCGEEEIIIYQQBdEDVSGEEEIIIYQQQgghhCiIHqgKIYQQQgghhBBCCCFEQfRAVQghhBBCCCGEEEIIIQqiB6pCCCGEEEIIIYQQQghRED1QFUIIIYQQQgghhBBCiILogaoQQgghhBBCCCGEEEIURA9UhRBCCCGEEEIIIYQQoiBN+oGqc+5459wdzrl5zrlNzrn3nHMLnXNPO+f+4Jw73TnXo7HrWRvZ/5HQZ049lTvalJvUR7nVhHyk1nKrykecc7/O6vlKAx5jXKXn2jk3wex7WUPVk465ZzW1YVOgTDvV5TOnsesvmgYaX2ott6rGl62Bc258pG/50Dm3zDk31Tl3o3PuC8657Rq7zqLhUB9Sa7lV1Ydojlr2mC1yjtoU5pnOuUe2tL2dc6c559Y4575TH3Wq4Pids7FxcmMcvzmh+Ufj0SQfqDrntnPO3Q7gHwD6A7gUwFEADgRwAYDZAL4G4C/Zp6nzBIDdAJxTz+W+mJV7bD2X2+SRjxSmanzEObcNgNOzzT2dc7s30KHuQeXn+uxs33vrs0K1MA1V0oYWmnCOa4TDT0J63vjD5/CcMulbs11FE0XjS2GqZnzZilyC9Jz8gL4r9S/7AfgPAH8FsC+A2wDMcs6dvLUrKRoW9SGFqZo+RHPUXFryHLWx55nn1UOZpwNoD+DMLa0M/UAypw67fRrANkj9fkvKEZp/NBrbNnYFcvh/AE4F8CSATyZJ8hGlvQzgbufcRADXNEbl6kqSJOsATHXOdanncjdk5a6vz3KrBPlIsXKryUeOBcBvaoxD2s71SpIkqwGsruRcJ0kyGwCcc6vruVqxY25C9bRhU2JDkiRT+QtzDmeXSV+9NSommjwaX4qVW03jy1YhSZIFABY45/ah76aabA86534G4CsArgJwp3Pup0mS/GgrVlU0LOpDipVbTX2I5qjlj9mS56iNOs9MkmROPZR5JYBuAH5bD1WqhBOyv/c00vGbDZp/NB5N8g1VpL+wAcD/mkkIcy2AOVunOqIJIh9pfpwFYBaAUijV6c65pvqjjxCi+aLxRTQoSZJ8nCTJNah5C+2Hzrn6fvtPNB7qQ5ofmqOKZkeSJI8nSbJfkiS3be1jO+d2AHAMgLlJkkze2sdvqWj+Uf801QeqA7O/6/IyJEmSAPgD0l9/RctDPtKMcM51BPAZpKFvt2RfdwMwtrHqJKqa47NPXfkagIYK4xPVg8YXsVVIkuQWAA9km79yzrVvzPqIekN9SDNCc1RRBs0zt5yjAbSB5LYaBc0/6o+m+kB1Q/b3OC+gSAAATNBJREFUU7FMSZJcmSTJT+z3zrkdM1HvO5xzc1wqAr/eOfeKc+5Hzrkdy+xzhhWMdin/6Zx73aVi8kucc392znXNq1Omm/StTPR3UyYA/A/n3P6x/8U59wnn3Fjn3PXOuTeccxuz/Wc65/7onBsY278FIh9pXj5yKoDWSCertwEovdExrlxmV0aI3TnX06VC24uz8/KGc+7CulbE5Yh6V1DOkc65e51zS51z72e+cb9z7sS6lpVT/unOuWddKia/zjn3lHPupFr2Geycu9Y5Nys7R6ucc084585zqT5Y3n7bOOfOcane1Mps37edc9c553Yuk39Cds4Oz776szmf47bon6+FJEnWJ0lS5/CzJEk2Jkmy1qXi7Vzf0c65vZ1zd2Xt+XH2/Xje3znXw6WLVryZXZ9rnXMvZNd7a5PXLuQwx6TbhSmCY1G+/Z1zf8/6gVKbPuOcu9w5NzJnn9ZZnSZlvrPROTfDOXeNc25wmfwVnY8qRuNL8xpfmjpXZ393QvoWXN4Y1y27Ruc55z7I6TfqdG1n+7Ry6Rgw0aXj5/vZMe52zn3JpQ+T7D517ndaGOpDmlcfojlq3ctv1nPULZ1nRv6XHakPX55d+7Occ7dl57RNrPyifuZqWRDOOfe/Jn18VrdfZNf0ey6cEyYAHs9272/7opzqnpj9vZfrVJdyXDrmfdM593zma+8656Y5537jnOtZJv8c29bOuUOcc//M+rr3nHNvOed+6pxrGzvXzQTNP+qDJEma3AfphZUA+BjArwB0ruP+F2b7z0A62O2L9FfEPyEdBKcC6Gj26QBgJNIwnQRpGM51SH89Pgipds4/srSXAGxT5ritADyY5XkRwEkARiF10LcAXF4qu8y+I7O0NQC+D+BgAKORCgyvAbAawKic/3dAtm/S2G0nH5GPVNieEwE8S9ulc/QegJ1y/p+RSDV3EqQ6ZC8jFWjfF6nw9uIs7Zs5xxxd7lwD6J35x8cAfp4dZ6TJMz7b97Kcsn+RpU8G8AUA+wM4A8Ab2ff/W8E58m0I4EdI32r5dOY/59H/+5Oc/U8EsBHACgBfB7AP0lCbu7L9HgXQrsx+OwJ4OMtzT+bn+yBdWGM5gHcBnGz2GZidtxez/S4pncfs07Gu/389+Rmfw9GRfLtm9VxQaues7U7P/vevI+0nxtM+B2bndiNSQfj9kU7Wr8/KeBVAT8rfGqY/MXXomKVfk6WPL1PPc7K0KQC+SG16HXKudwDdM79MkIacHpPV80dZW64HMGZLz0c1f6DxpVmNL43kQ+OKnhMAOwD4IMv/Tzqndox7A8B3M386CcAibssKr+1tADyW7XMj0jeG9gFwGpX1Z7NPnfudlvZRH9K8+hBojlrkHPk2RAueo6LgPDNn3x0BvJ617a8zH9gPwLlI5SYSAJeW2a/U3tbPzgCwsJyfAWib/a/HoMy1B6AXwjnoXwG8AOBnAA7IzvO0LK1UVqnvWWDO59Aydd4GwNKsvbc1dSpUDtIx75Us7w10vi5G+qPWSgCHmH2Gmra/I6vHhZnfjAHwUJb28pb4QmN9oPnH1j/njd3oOY27d3bySx3SJqQd6pkAuhTY/8LsQupdJu3SrMzf5+w7Okv/EMAfyzT83Cx9TJl9L8vSZgLY0aT1RzqhqG0icmJOnT4G8BoAVyZ9QGM6kXxEPrKFbTk0q9vX6LvTqW0vjOw7PsvzAYADTNrRWdrCnHNSasc55vsjkU7qflzguJeVSSt19m8C2N6ktQEwL0v/Uh3Pk29DpJOaViZ9N9QMikeV8Z13kd6I7VOm7Huy/W4tk/aXLO2eMmn7ZGVuArB7mfQJ2b7jGtvPypzD0QXyz8nyroDpK5BOuMZndg8Ay7K8J5Up56os7WkAnyjih5R+Gco8UEXa1yzP0gaX2e+P9noH4JCu1pw3Kf90lrYKQNdKz0e1f6DxpVydqnJ8aUQfGleXcwJgepZ/rvl+PPnD8Sbt4lJbVnptI32IkQB4qsw+HZHeNI03PlinfqclftSHlK1TVfYh0By16HnybYgWPEfFlj1Q/Ua23y055b6b06YxPzuyFj+LXnuo6RM+tL6O9IFWQttlfTan3EOzvDcX9X2TxyF9aJ8AuKpM+klZ2nIAvSJt/xGAfU3atgCeQ5kHetXwgeYfW/+cN3ajRxp3f6RPnxPz+Si7gM6DGQho3/2Q0zkC6JuVsyynYxlNxxpeJv3mLO3H5vs2SH99TQB8PefYpV955pRJ6wbghzA325Re+mVqZJm0AXW5cJrLRz7SPHwE6RsP74Pe4MjO1bqszi9G9h2f5XmuTForpINAAmBgpB3n0HfHIJ2oXlpLnUvHvazMMUu/Bp+Rs+/3s/QZdTxPvg0BfD4nzx1Z+mPm+9Iv/Pfl7LcXlb0Xfb87fb93zr6lN3HuLZM2Ac3jgWq5yVoXZDfGAH6T5Xstp5wu5IsnmbTN/NCkX4byD1R70P/So8x+ByD7tZm+K01q1gFonXO80hsHF1d6PprDBxpfbHpVji+N6D/j6nJOUHPztsF8Pz77/tUy++wIoE9mV3Rto2Y8ejBnnx8A+AZt17nfaakf9SGbpVdlHwLNUYueJ9+GaMFzVGzZA9Vrs/2uzUm/GsAX6+hn29XiZ9FrDzVz0OXI3iSltNYABsR8NvK/XpnlPaWI75fJ8xnUPOzb7AWALM9k5M9ZS23/QM6+J1L5/erLP7bGB5p/bPVz3lQ1VJEkyfNIO8pPAfg/pE+1gVT39VCkYZSvO+f2KrPvC0mSjM8pd35mdkGqF5HHhiRJppX5fmH2t4f5/lCk4TZAepGWY2LewZIkWZokyU+TJPk4J0up3sPyymhpyEc2o+p8xDnnkIY+PZAkyYrS90mSbEQ6wQKAfZxzu9ZS1Ev2iyRJ3kc6AQA2b4tydRmLdOL16yRJflyg+uU4CEBJs+epnDzTs79DnHO9KjzO0znfP579PdxlGmsu1e48Lvv+sXI7JUnyCtLQGAA4mZJK9uokSV7OOeYj2d8xLl2xszmy2XWZJMnyJElK/lU6T3nndznSkH/Ou6UsBVDS4brFOTfEHPO5JEnsggmnZH8nJUmyKafckn+Ojhy7tvNR9Wh82YyqG1+qDFdLerlrbn2SJO9km5Ve2zOzv8c4575r9fmSJLk8SZLf0VeV9DstEvUhm1F1fYjmqJqjbmVK/fE4l2rCbseJSZJclCTJrZH9y/nZB6iDn0V4MUmSD03Zm5IkmVNheScgfXP4wQr3L7X91CRJluXkeTT7G9Purc1Xt0Eqb9Cc0fxjC2myD1SB9LF6kiQPJElyBtJfPo9A+uvo6izLYAAPOuc2m1A45w50zt2SCTlvcOmiGh8657gz2EzUnViV833JUbY33w8ne17Ovksix4Nzrq9z7rfOudcyYeUPqM6HF6hzi0M+UvU+cgSAfkhDdiz83bhaylmZ831eWwQ45z4F4G6kv7ZuJmJeB3Yjexb7FLXTHZSnb4XHWZrz/dzs7yeQhqkBwBDU/P+zI2XOyf7y/7CbSYvt1wpVdKNUR3IfFDrn2iENhQTqfn4rJrtp/U+kbzsdBWB6Jur+PVdmEQZz7MPL+Wbmn5/O8sR8s9k8OI2h8aXqx5dqouRDeTeGtV1zlV7bdyOV63AArgCw2Dl3u3Pui67MghwV9jstFvUhVd+HaI5aGZqjlsGli+hs9qEs1yL98X17pJqgi1y6wNQJzrlWBQ6xIuf7Qn5WC/U273PpwkGDATyaJMmG2vLnUGr7Ij7T15VZ3CijrK8mSbIGqcQJAOxS18pVGZp/bCFN+oEqkyTJh0mSTEiS5AKEg1s3pFo2HpeuZvcM0l8k/oL0tfA96eOzRg75USStHO3Ifjcnzwd5OzvnDkQqRP0NpALLpyHVftkz+0wqZa1jvVoM8pGq9JGzkN5Y/KNM2qOoeZPidBdZ4RN1bwumG9IJZOltg/Nc+iZAJbQnez+EPlX67IF08NkN6cISdcb+SkywX5X8rX1OumVj9rcDfVfat8h+dt/mRMy/tuT8bhHZG0wHALgd6eIYByJdbGKGc+4h59xQs0uprg+ivG/uifSNqt2Qvj2Qx5Zcb1WJxpeqHF+qguzGobQC+pScbLX5Q0XXdpIkHyFd7Og8pG83tUO6ovn/AVjonPtveyNfQb8joD4E1dmHaI5aAZqj5jI45wMgfesPad/6/5C+0dcZ6QJN9wCY75z7tnMudv3kvR1eH9TnvO/E7O89W1BGfbV9nq9y2e0ieaoazT/qh20b46C14ZzrAOCjrGPZjCRJ1jnnvow0jKYTgBG0b0+kuhwOqXjy+DLlN0S115G9A1K9Hct2Zb4rcR1SR7opSZJxNtE5V+kvOM0S+Uj1+4hLw31OQbqq44acc1760acn0pCLBxqgKjsA+EqSJH9yabjBCQD+1zm3a5Ikq+tY1lqy5zVUCLRzbtucCSuHM5X8jesUhFMYSmlr6Lu1Ji22n923pbAl57cIsZs0JEkyCcBp2Zuyn0Z6Yz4W6YIXLzjnRlJYTqmuHydJUtGNUktA40v1jy9VxmjUXOf/rrCMiq/t7K2PGwDc4NIQus8jfeNuZ6QaZ0NRE9JX2qcu/U6LQ31I9fchmqNWjuao5UmSpNYLNwuZ/i2A3zrn9kTaH58FoBeAXyH1tW81YDW3Bicgffh73xaUUV9tH3sWVvLXdZE81c5oaP6xxTTVN1TvBXBjLEOSJO8CmJFt8i+mByF9rR8A/ln/VcvlTbL75+TpVu5L51xnpCscAlu3ztWMfKT6+SzSiepY5P+idRRS8WkgnVA0BHOTJPlTZn8ZachDLwC/r6As/nUv91cy59zRzrlza3mjIUZZP0EqLg+kvyaWfP8tpL/icXpsX/4fppi02H7vo0Yjp8WQJMk61ISxDYhkLaXZX4BLN6154VxditYjSZJbkyT5FNI+biPSX+TPpWylY8d8c3vn3HnOuSOKHLeZovFFbE0uyv6uAHBThWXUy7WdJMlbSZL8DGlo7K+zr092zpX1qYL9TktEfUj1ozmq5qiNSpIkk5MkuRjAIAC3ZV9/3Rlt1WrCOdcHwCikC2jlSUMUoS5t/07kx4e8Pq0jat5qfbNcnmaC5h/1QFN9oAoAo2p5rR2o0ZHhp+Eux06/cK7fllYsh6dR8+vH6Jw8B+V8H61zRkPVu5qRj4RUm4+cBeCZJEkeTJJkas7nMdSIip8Q0cCpF7LB/UvZ5hnOuRPrWMRE1ISAHVcuQzYRug3pmyeVhtAcnPN9aaB6svRmTPZr9/3Z90fl1Gkv1Gjo3ElJJbuDc26fnGOWyvxXdoPIlN5Q8D7rnNvVOfdZly5E0FwonacjyyU657ogDaPjvCVKmnRdcm5eyp53l2rVLXbO7W7TkiR5DqkuERAuQvD37O+QiN7QcUgXS9ms3BaGxpeQahtfqgLn3DikK3cDwHeyH2gqoaJrOwshfdxmzN4auZy+6pHlr6TfaamoDwmptj5Ec1TNUbcqzrmrnHM32++TJHkPqcYkkL4lHluMrjEpdz5ddj73zr76TJZ+zxaWU2r73ZxzeQ/wjzJ5y1Gbr36EyhfOatJo/lF/NOUHqoOQ/hJXFufcuUgH55WoaUgAeB41Wg9fKLPrV+qrgkySrvj4u2zzoixUxOOc64t0pchy+y5HzS91m9XZOXcMavQtRA3ykZp9q8pHsl+bDkfaudbGddnf7VG+veqVJEnuBTA+2/xT9jCs6L7vA7gk27wg58bnYqS6SFeUSSvKd+wv1M653ZBOVADg5yb/pUhF6cc65/YvU96l2d/bkiSZXPoySZIpqNF5u9TeHGYT2OORvl3wozLllh4W8uTvG0i1b5qTDuevkL41MtI597ky6T9EGlLzDNI3l5i3kZ6n7ZBqAXmcc4cg1MtjtgHQHeGKt6X9PoE0XAao0a0D0nDECZm9mf9l4YQ/RSrS/+ec47YUNL7U7FtV40s14Jz7hEt1Mktj4E+TJNmSa67Sa3tHAAe78rpjpe82Angjsyvpd1oq6kNq9q2qPkRzVM1R0Thz1A4Ajstp01J/PA/5i341NqXz2Ym+64W0fzsx2y79tXPhupbzTwBPIn2WdSkMzrmTkD68W4G4Lx/tnBtl9t0WwPeyzVuSJJkf2b/q0PyjAUiSpMl9ADyMNITiYwC3ItVOGIX0ZvMLSC/Cj5GuYHlEmf1/lu3/HtJO+3AAnwTwR6Ti4kn2OQZpiMp2SMM6RiIVf04ALMi2B2Zldsy2r8nS78m2e9NxWyH9FSMB8CLSi35vAGciXYXudlP2LrTvWKQhPwmAv2XbByG9oNcCWJ+lXZLt25HqfAz9TyMBjGzsNpSPyEdy2m277Pi/Rjp52rd0/nLyDwSwF1L9mtI5Gwmgd/b3nuz7a0r/M+03MjuPSdZmI7PzUVs7dsvO66os/U465sCc43Yz9f5plv4OgPORvmU4Bukk+GMAf6zDOWtdpg1vRTp4HZ/5z7kAFmVpP8kp50SkAusrkYZ4jEKqN3NXtt9jANqV2W9H1Fxv92b1GAXgAqQrP24CcErOMb+Y7TcZwCFIQ+jWAPjnVva7jmXOYcknupXJPzTHf2K+ehDSidtGAD9AuuDDYUgnLAmA1wD0zNn321meeahZyONcAC9j8/6kb7ZP/+z7D5CG/pXa5dNIF9Aotem25ljdkE42EqRvhXwqO94ZSN+S2gDTZ1ZyPqr5A40vVTm+NIUPasamS+w5ydriWKQ/sLyepc0HcHKknHsQjjW557bCa/vSLP9cAF9D+rbOAUjHrbmZn3+J8lfU77S0D9SHVGUfAs1Rx0Nz1IrmqKjjPDOnjD9n+03N9j0AaZ/8/5DOL98DcBzlrw8/K3vtZX5Wrr8YCWC7nPo7pLIKCVKd1/2Rvu38MdI5cQek0gtv1HIeouVQvu5IF8BLkEqsHI70mv0vpOPdKgCH5BxjQrbfr5H2bRdkfnMs0jcdE6Rz8I6N0RdV2H9p/tFI849Gb/ycRtk+a4jfAHgKaXjCpuwErkD6ls+lALpHyjgdaWjDeqQd0GykN7aDyMlKnwFIw1vs9wmACVl543LSx5vjbof04n89q/MqAI9njW2PsdrsewBSgeYV2f+6BOngcDhqLvzSZ1ykzkljt6F8RD6Sc84H5J2/nPz2f/LnNOf7cbXsN7pAO14WOWZeuZeVqfthSFdmXZy11Uqkk76yE7vIOdizzPG2QTrBmozUf9cjDdfbbGA0ZQ0GcC3StyLfQ3pj9iTSFRa3iey3DdKJ2IRsn/cAzEF6vQyJ7OeQDuyzs3OwEMDNiFyXDeR34/Kug5y2mxPzkchxeiCdnE1DemOwDukN1rcB7FDLvl9H+ivse0iv6/FIw0GtP95B++yLtA98KWuXD7O/zwC4EPmT3u2R3rA8h/RG931qz53r63xU6wcaX6pyfGkKH+SPTUl2fS7P2uZGpA/W8q7R3HIK+G5dru3tkf6I81cAs5D2Wx8gfdDyNwAHl9mnon6nJX2gPqQq+xBojqo5aoVzVNRxnplTRjukkg73In2g9B5qrv3xMA+0GsjPklr8LAEwIPI/7Ir0geQ6pA/RJgM4NUs7Ldv/5wXORW45Jt/2AL4J4AWkY94mpG/L/xY5LzGYczMOwC5I335dkp3vmUhDzts2Rj9U6SfiDwk0/2jQj8sqJ4QQQgghhBBCCCFEveGc+yvSVdwPSJLk+UauywSkP/ScnSTJ+Masi6h+mrKGqhBCCCGEEEIIIYSoQpxzrZDKSixC+japEM2GbRu7AkIIIYQQQgghhBCieZGkC6J1aOx6CNEQ6IGqEEIIIYQQQgghhGiWZCvKt0K6KBcA9HbOjQTwTpIkqxutYqKqkYaqEEIIIYQQQgghhGiWOOfmIF0t3iItVVExeqAqhBBCCCGEEEIIIYQQBdGiVEIIIYQQQgghhBBCCFEQPVAVQgghhBBCCCGEEEKIguiBqhBCCCGEEEIIIYQQQhRED1SFEEIIIYQQQgghhBCiIHqgKoQQQgghhBBCCCGEEAXRA1UhhBBCCCGEEEIIIYQoyLaNeOykEY/d0nGNXYECyD8aD/mHiCH/IG6//fZgu3Xr1t5u1aqVtz/++ONC5X3iE5/I3U6S8N/afvvty+bbtGlTkG/MmDGFjl1PVIN/AOpDGpNq8JEG9Y+VK1d6e9myZUHaxIkTvb1+/Xpvf/3rX9/i4/7oRz8KtseOHevtd99919t77rlnkG+nnXba4mPXgRbvHyKK/EPEaPH+wX35xo0bg7Snn37a27169fL2vvvuW+/1WLFihbenTJni7cGDBwf5tt225nFUz549670ehmrwD6AR+xBut7feesvbd999d5DvnHPO8fawYcNyy/v73//u7UmTJgVp559/vrcHDRpU98o2DHX2Eb2hKoQQQgghhBBCCCGEEAXRA1UhhBBCCCGEEEIIIYQoiLMhhFsRhUM0HtXwurv8o/GQf4gYLd4/5s2b5+3LLrssSOvSpYu3OQzfhvIznOZceHp5jLZpHPK/3XbbeZvDhAHgG9/4hrc7d+6cW496ohr8A1Af0phUg4/Uq39cfvnlwfZHH33k7d69ewdp22yzjbevv/56b++xxx5BPg7X55D8HXbYIcj3zW9+09tf+MIXgrQjjzzS26+88kpu/YcPH+5tKwfQALQ4/xB1Qv4hYrQI/+B53uzZs4M0HkM6deoUpH3wwQfe5vGFw/8B4KCDDvL2//zP/3h73bp1Qb6hQ4d6245lHM49ffp0b/fo0SPIt3DhQm9bySous2vXrqgHqsE/gK3Yh3zrW98KtqdOneptvt9Zvnx5kI+3OeS/TZs2QT6+j1mwYEGQtv/++3ub5QCeeOKJIB/7mZVQi91fVYhC/oUQQgghhBBCCCGEEKKh0ANVIYQQQgghhBBCCCGEKIgeqAohhBBCCCGEEEIIIURBtm3sCgghRHMnpoMZ44UXXvD2mjVrvN2qVasg34477ujtvn37BmndunUrfLwSVlu7LnVuCbCmkNV04vPP7cSaiUCoecqaUayLamHtKyDUQ+W0VatWBfmWLVtWdh8hxJZhr2vWrmPNONYGA4B99tnH23369AnSPvzwQ29fdNFF3r799tuDfBMnTvT2iBEjvH3ttdcG+Viv7rzzzgvSeIxhnVT7fy1evLisbcsXQgjR8LDuaNu2bYO0du3aedv25TwHPPvss739i1/8IsjHmtqzZs3y9saNG4N8XP7KlSuDtKVLl3qb71OsDmvHjh29vXr16iBt/vz53q4nDVWB8L7j1ltvDdI6dOjgbdYrtWM938d88Ytf9LbVP2WNX15nAgjbl7nwwguD7YceesjbDaCZusU0vRoJIYQQQgghhBBCCCFEE0UPVIUQQgghhBBCCCGEEKIgCvkXQogGgMNgOAzU8thjj3n7rrvuCtLWrl3r7R122MHb/fv3D/KtX7/e2zZcpn379t7eZZddvH3mmWcG+TisXyH+cTjMhdvFprF0QswHtt22Zii2oSyxtuAwfy6DbSCUixAtl0qlR5g333zT2/fdd1+Q9r3vfa+yilUxsev6qaee8nbPnj2DtDfeeMPbw4YNC9K4P2c5gLFjxwb5Zs6c6e13333X23vvvXeQ72tf+5q3WYbA1v/999/3tg0T5X5uypQpQRqHYXJ5MTkEIYQQdYPn9xx6b6WcuC+3c8oVK1Z4m8eX6667LsjHYf72voIZMmSIt/l+AwjHAA7/5zByIJzLWvkClsDherBMgKg7jzzyiLetBEPr1q29zf5j543Lly/39siRI71tJY7YD6ysGUsP8HGtdFlTR2+oCiGEEEIIIYQQQgghREH0QFUIIYQQQgghhBBCCCEKogeqQgghhBBCCCGEEEIIURBpqAohRAOQpxf397//Pdi+++67vd2qVasgrV+/ft7u1auXtxcvXhzkYy0lq5fEOqz//Oc/vf3vf/87yLfvvvt6+5vf/GbZuosU1iu0ek/t2rUrm8+2C2tcsS6R9RvWlmJ9VgB47733ytYvVoZofOpDy7QS+Fhch9rqMXv2bG9fdNFF3matTwA4++yzvd2tW7eK69nUieljr1y50tsTJ0709q677hrku/nmm709YMCAIG3EiBFlyz/00EODfHz+uT/fY489gnzcD3H9AGCnnXbyNv9f3D/ZMmzavHnzvD1w4EAIwXz3u9/1Nmu3s+YeUFx3XoiWyoYNG7zdpk0bb9vxnOd8nA8Ix3rWU7XXHI9LseuRx4Nly5YFaXlznVh5Vl+V9bt5/JKG6pbx/PPPe9veI9g2KMHzAAAYPHiwty+44AJv23tZvk+y2uq85gNrqC5YsCDIN2fOHG/bOVNTQG+oCiGEEEIIIYQQQgghREH0QFUIIYQQQgghhBBCCCEKopD/gvAryvZ1ZX51vaHDVH7/+997m0NLAWDcuHHetq9r23BTIUTj8OyzzwbbPXr08HaHDh2CNA7pfOmll7w9f/78IN+OO+7obQ4JAoBOnTp5u2/fvrn1mjZtmrdt2E7Xrl1z92uJrFmzxts2tOXDDz/0Nofo2/B8G6JVwoZeb7/99rn14GNzn2/rtGnTptwyxNZna4b5V1KHe++9N9i+8sorvc1zCTvPGDNmjLdffvnlLa1ikyU2z3vhhRe8zXO0I488Msj3y1/+0tsPP/xwkLbXXnt5m/t2Ds8HgP3339/bd955p7e/+tWvFq4v9xUcumn7EBvqx8ycOdPbHPKvsO3qp6g8yYQJE7z9m9/8Jkjr06ePt3v37p1bhu5Tmj51kYrZ0vJt2bNmzfL2ddddF6RdccUV9VqPaoD7VzsW89zThnNb6agS9nyzHAA/97DH4nrYsvOendhrnfez81XerynMnZoLfE9p24PvV7ht+P4GCOUkeL5j8/H8oX379kHaokWLypZh/ZbvnRXyL4QQQgghhBBCCCGEEFWMHqgKIYQQQgghhBBCCCFEQRTyX5BKw/r5tWfery6vrb/99tve/uMf/+ht+4r2Zz7zGW/b0DCtnilE08Cugrxu3Tpvr169OkjjkAfOZ1e35FDbf/7zn0Eah6ezpED37t2DfJxmV64XIdyf8gqkQBiiz/18LCSLw2tsmAv317waps3L+axMgF2VWwjLf/zHf3ibV20HwvnE2rVry9oAcNpppzVQ7aqH//u///P24Ycf7m1enRgIzymHVgLA9OnTvT18+HBvL168OMjHffb3v/99b1uJFj62lZXhfiOWz0oAMNx/se/069cvdx9RHeTdq1hZkKuvvtrbVhbtmmuuKVtGTD6toUPLRUhRaQebxtc+90/22i/afrF8vKL4/fffH6SdfPLJ3mYplOZG3pwvb1X2cnBefo4Qu+bs3JPh/exzCb6nYV/p1atXkI+lyqwP8Bzb9hmicliqx55zvmfIkycDQr/gtrftxNv2foSl7Vq3bu1t69NPPfWUt5viXFNvqAohhBBCCCGEEEIIIURB9EBVCCGEEEIIIYQQQgghCqIHqkIIIYQQQgghhBBCCFEQaagSrHcKhNoQs2fP9vaTTz4Z5DvrrLNyy4zpjhSFy2eNq7PPPjvIx5pcVntCuqlCbF3yNKl22WWXIB/3J8uXLw/SNm3a5O2NGzd6e/To0UG+Aw44wNt33XVXkMb6Q6zDyjYQal61adMGIh/uX20fz1pBnBbTIOSxJ6YrZ9NYs4h1U9lvyu0nqo+YjlUl+oKXX355sM1zHKvR3L9/f2+//PLL3v7qV78a5LvooovqXI9qIHZNWl1T7m+7devm7XfeeSe3DD73lnfffdfbVnuM69WjR4/c+vJ+No3nlNxHWS3uvfbay9ushQeE/sL/izRUmyZWp5v1vGO8+eab3rYalsOGDfP2r371q0Llxe5LpJm6dcnT5iy3zRx22GHenj9/vre7dOkS5ON+ctCgQUFa7969vT1ixAhvWy3U448/3tt/+MMfgrS5c+fm7lfN2P6a24nn6XbOx/cSPXv2zC2Dy7f6p3wNxuYfsfpyHdm2+ZYsWeLtdu3aBWl5euOxcVnUDl+v9tzZdRhKWD/jfDz3sXMEbnvWTLVpjH2ONWfOnLL5mgp6Q1UIIYQQQgghhBBCCCEKogeqQgghhBBCCCGEEEIIUZAWH/LPr7HHXhf/4Q9/6O2XXnopSPvrX//q7ZNPPjlIO+SQQ7w9fPjwQnXad999g20OFdt77729/d///d+FyhONC7+2bkMqOI1fZ+fwbgBYvXp12X0AYNasWd4eOHCgt3fbbbcg36JFi7w9cuTIAjUXWwMO6ezcuXOQxuEUHELVqVOnIN+MGTO8feaZZwZpt9xyi7e7d+/u7TVr1gT5unbtWpdqt2g4RN9e07zNbWvHFw5X4nHIXt92O+9YGzZsyD2WwierH/aDSkPbHn30UW/fe++9QRqPF9bnxo8f7+1vf/vb3m6uIf6W2Pl+4YUXgm0OfeZQ1mnTpgX5ODyOQ1kB4O233/Y2h7L26dMnyMfh+lzHmLyIhfsolg0YOnRokI/noXxcABg7dqy3J06c6O3DDz+8cD1EZeRJC8XSiob4A2GY///8z/94+8QTTwzyHXfccYXLrITY/ym2HO4zbLgu9y3PP/98kLZ06VJvc/9h5Uk47JznKkDY33H/8Y9//CPId+2113p7wYIFQdqf//xnNEfWrl0bbPM9gZ17Mq+//rq3rbzX4MGDvc39v5WvquQ6s2H4PJfYcccdvT19+vQgH0tC9O3bN0jjkH/G/l9WqkjEYUkiDv8HgM985jPeZr/g+0kAaN++vbdjshDsq/w8Awifa/F9qZUGeOutt3LLbwroDVUhhBBCCCGEEEIIIYQoiB6oCiGEEEIIIYQQQgghREH0QFUIIYQQQgghhBBCCCEKIg1V0nyweiSsDcPaVax9BYT6L7/+9a+DtNtuu83brB9y8cUXB/nuvPNOby9ZsiRIa9eunbevvvrqMv9FCuv6xYjprogtx+qIcLtYbbMHHnjA21dccYW3BwwYEOTbtGmTt602EWvgsE6q1cRkzSGrXxPTeRWVkad5aPXnWHPojDPOCNK4LVhPhjVTgVDfzursXXDBBd5mXbzrrrsuyBfTvxEhfB1bnR/WneL2s/pU7BOsWWav79atW5ctDwA++OCD3PJF8yKm4xnTF3zllVe8fcIJJ3h71113DfJxv8T7AMC5557rbdaTt3BfVqnOa7XB80Qg1HvjMZc14gDg8ccf9zbPDYFQ65p1+ZctWxbk69Chg7f5fNs+hLEad1zfV1991dt2LGLs/GT33Xf3NveH9lgtxSe2JjGdw7y0Z599Ntjm/uOee+4J0lgjb8SIEWVtC89JgXAM4zrF5hy27tJNbVh4vso6nZbzzjsv2GY9fy7DXvvcx7Vt2zZI436M14HYc889g3zst6tWrQrSbN7mgtWz5ftJPo8LFy4M8vH6Lbbf5TU6iuop89wzputv4WucNU979eoV5GPd1EmTJgVpu+yyi7f5+YvtZ6ShGsfee/JzBdsXH3300d5+4403cstkv+Dy2rRpk3ssOz85+OCDvc3PMOxziqZ+j6MnJ0IIIYQQQgghhBBCCFEQPVAVQgghhBBCCCGEEEKIgjTt92e3ArHw5m7dunn7F7/4RW4+Duuyr1Tza9R77LGHt/nVdyAMX+AwTgD45Cc/6e2dd945tx58rKKv8YvKyQuztKFJNsyfYR/bsGGDt22Yh319njnggAO8zeHeNuTmscceyy1DYf5bDw6hA4DVq1d7++abbw7SOBSCsT7FbT1nzpwgbfjw4d4eP368t1988cUg3yWXXJJbZxHCIWo21IrTYmGKLOXCkg2zZ88O8u29997ettc0+wGHw9iQrFgfJKof9rPJkycHaQceeKC3R48e7W07przwwgve3m+//YK03/zmN4XqwaGFHOIFhKHgXbp0KVReU4VD723Y4V577eXtadOmefvMM88M8nEI7Pe///0gjcOpuZ2ef/75IN9uu+3mbb7+bYgn19HKC6xZs8bbHMofC9f/wQ9+EKTxWNKzZ09vz5s3L8jH4byi4ZkyZYq3//a3v3nbtgO3O89JgVDO4bXXXvO29XuGQ/xjKIy/6RC7B7j99tu9PXPmzCCN72t5Lhu7Z7HzE+5b+N6V+yYgvNe2/VNzxbYLyzFwmu1reQyxzxT4XrO+nxXY+nI7cbt37tw5yMfzYSuPw37A41ysDxKbw5I+QFwaiPtmvj+x8DyP5Sis/AL7hfVHlqAYNWqUt2+66abcfNZHevTokVvHrYWeogghhBBCCCGEEEIIIURB9EBVCCGEEEIIIYQQQgghCtLiQ/6LEltFt3fv3mXtWBm8ghoQvjZtV1j86U9/WqiO/Oq+lRRgKYL+/fsXKq8aibVTXr5Y6Ai/pl501VG7QuH111/v7T/96U9BGodWclilDdmL/S+cl8Mq586dG+Sry8qMoga7+uGWhqnZUCgOjeCQKQB45JFHvH3ooYd62672zCF8VhbkzTff9Pb8+fO9zas7A/EVFFvi6t0xWLbB+kde6Jz1G+6jWdblX//6V5CPw9z69esXpLEvcfiTPVbREExRO0XHmK0Jh/aOHTs2SBszZoy3Odz7mWeeCfLxisF33XVXoePakLsf//jH3r7iiiuCtGuvvdbb559/fqHymyo8n7IyT7wKMYdPx1ahtn02h9C//vrr3rbh+nz+Y3I/3GfH5GJ4/In183al73POOcfb3CfxfMRu2/9FVEZsbH7ggQe8zeGdgwYNCvKx5NjIkSODtLx5I88lgHAl7qL9opW2Yrmi3/72t0Ha4MGDvf2d73ynUPkiDrctz1tuueWWIB/LlVg5GA7f5fLs2MAh3WzbvDwO2bBhzmelKZorVvaA7/OXLFnibXs9cj7bD9c3PCeyfRD7BPuKDTfn/ViSAAjvZYcMGeJtDjEXtWNlIXjsYKkyIJSM+NGPfuRtK1nH9xbsZ/YZFF/X9lj//ve/vX344YfnHot9hu9rAYX8CyGEEEIIIYQQQgghRFWhB6pCCCGEEEIIIYQQQghRED1QFUIIIYQQQgghhBBCiIJIQzWC1cbL+z6mTZmnQ/X0008H26x/ZbUv77zzTm9/4xvf8DbrpgHAdddd5+2HH344SDv11FO9zTpnzQ3WbuJ2sm3E7RLTjozBZXIbWb1C1rL55S9/GaRxHW+66SZvs6YVEGoOWd2pnj17eps1RqxGGadZ3TerpynyyesXiuqG2XPPOjadO3cO0lhrZsCAAd7u2rVrkI91+9gGQu0aplevXsF2c9ZWrm/4mo5pMLNeoe1n2A+GDRvm7aVLlwb57rvvPm9bjW7WFOPybZ2aitZnU4L779j5iWlnx8Z+1qSzfQa3D7ebLS9Pjzc2vh9wwAFBGo91rL38zjvvBPmsBhszffp0b1966aXeZq1PADjttNO8/de//jVIO+6443LLrzb4/7bXJF/XVvM4D6tJ+tJLL3mbdclGjx6dWwa3sx0DOM32DdyH7LrrroXqa2G/5fpaHcXFixd72+rGisqIad1+73vfq3N5y5cvD7Z5TQCeJ5511llBPr7eDzvssCBtxYoV3n7yySe9PXny5CAf6yNazeHhw4fXUvOWS6U6/3njC2umAqE+rp1PLly40Nt8j2F1EvPuzQCgU6dO3ua5le0/eK0RvmduztjzyPd/3J/yeQPCeQWfN5sWa5eia40wNh/fD7MebKw8ex/01ltveZvvU6wWr4jz6KOPBtusc9qlS5cgbe3atd7muaGd7/DYzz64cePGIB/7mT0Wzy+5TnZs43sjfuYCAEcccQQaG72hKoQQQgghhBBCCCGEEAXRA1UhhBBCCCGEEEIIIYQoiEL+IxQNm4iFUzFTp071drdu3YK0Nm3aeHv//fcP0vjV66FDh3qbX4MHwlexbXjpV77yldx6NSfywjjtq+Oc7/XXXw/SZs2a5W1+1X3OnDlBPj7HHNZgQ6c5VIlDLoEwnOPyyy/39vjx44N8HOZhw0L5FXmuow0j5PANDikEgKOPPhrVBP8vDR3OXN/lc4gUAHzwwQfe5nBJABg5cqS3OZzq97//fZCPQ8vHjRsXpHXv3t3bHOJl+yobFiTy4VCjDz/8MEjja5X7dRtex/tx/8QSHpY8+QYg9AHuE2x9RQpf13W5xjkM0YZWF4V9gf0gJj/zi1/8wtu/+c1vgrRDDz3U2/a65jApPtZnP/vZIB/7D0sIAMALL7zg7XPOOcfbv/rVr4J87HdWDoDPm5WjqTauuuoqb//nf/5nkLZ69WpvjxgxolB5HAppt7ld7FyFw145HNuWx2XEmDlzprcHDhxYaB8A+PrXv+5tHpusfAn7pkL+KyfvPiMW/l8UG4554IEHevuRRx7x9imnnBLk4znrbrvtFqQ99thj3mY5q2OPPTbIN2rUKG/zfAeoXJqrJVAfc1SWHbJSJSw3xfejQDhnjYWZd+zY0dux+9/27duXtW35VjqruWLPFdO3b19vx+RgLHmyZfVBTF6Pbb7vsdh7GJ5TW0k9UZyYTIadD7I0C2OlJTZs2OBtng/bdrJShcz222/v7QcffNDb559/fpDvnnvu8fY+++yTW15joTdUhRBCCCGEEEIIIYQQoiB6oCqEEEIIIYQQQgghhBAFaRExFJWugFgJsbIXLFjgbfu6O4dH2JAKXhmeQz732GOP3GPZ8FIO+21O2LbNW7Xy5ptvDrY5rOmQQw4J0jg8lle9ta+wcxm8MurPf/7zIB+H9d92221BGoe68CrINoyYQ/hYkgAAPve5z3n79ttv9zaH6QDAV7/6VW9ff/31QVq1hfw3dJh/JZICNgwvL/zOhirxseyqpo8//ri3J06c6G3ri7wq5tlnnx2kcSgXh9TZEAwOxbPURyhhc4L7U9uXcxtyeIw9hxzSyCEvdhVNbicrCcF+wH5q+w8bOifC82XD1PLGESAMa+L2sNdk3kq6dpuPZaUavvSlL3n7+eef97Yds3j+wCFYQBjuzX7x7LPPBvm4zBNOOCFI49XCue7vvPNOkI/D3a3MRHMK1eOQZhsi3bVrV2/36NGjUHmXXHJJsG39oAjsi0XHIiD0j7lz53p7ypQpQT4bxs3wuJJXJ2Dz1ZtFPrE5SNHxmGU3eCVlO4fk8ngsAsLwb5Zp+MxnPhPkO/zww7198cUXB2k8P+Z5Kd8TAaEkFksNAJXLq7QE7PjF2zwOWRkFlglhH7ArZr/55pvetnMLLoOv99ixrKzRc889522+59p3332DfHwv9eqrr0IUw85Rt+Z8nucmReehtr9rTnOHxsQ+m7DbDMux8FzOygdx+/L1b/trO64wLAcyadIkb996662F69sU0BuqQgghhBBCCCGEEEIIURA9UBVCCCGEEEIIIYQQQoiC6IGqEEIIIYQQQgghhBBCFKRJaKhWqnHKOjFWI4T1Ghpab5GJaa/9/ve/97bVL2StGKt9ecstt3ib9WVWrFgR5ONzYLUYY/Vq6lj/4O3Y/8X6c0ceeWSQNmbMGG9bnY799tvP20OHDi1Ux759+3rbamSypt3+++8fpE2ePNnbDz/8sLetfhnrYj755JNBmtU0KXHNNdcE26+88oq3hwwZEqQtWrTI21bfqCVSSZ8R0yV6+umnvW317QYPHuztCRMmBGnz5s3zNl/ftn6su8x9BBBqWbF2GusxA8CMGTO8fcABB2z+TwjPjjvu6G2+9gGgT58+3uY+2voH78f9GOsJ2W2rr8pai6wzZdtWGqpx6jI+sk71d77zHW/3798/yMfXvIXHZx5/rrvuuiAfa3DyHMFqU7KfWa0qPta0adO8feihhwb5rrjiCm9b/SvuG3jeZX2V9fqsD7JeeLXBGuYW1vUDwvGf+96YBqTVG+Q+hMd3m481bHkfq3PIZcS0nHnsX7x4cZAvpqHK8LVky+BrxKYV1ZttKcTmIDwHfuGFF7zNOstAOCaceuqp3rY+WxS+Dqxu5z777ONtq5/8xBNPeJt1xS3cB1ldXtYmrnbsHJDJ086NzS/t+MXb3E523s9zxeHDh3vb+hHPd+z9Bs9BeOyx96Cs0X/wwQcHabNnz/Y2+wrfswChXuOgQYOCNNbvtuNSc8LeD5ew/UVePiBsJ34WYf2IfafoHMnm4zkBU4lOuNh65M357Bo/DN9n2Ouf719tH8I+wn1BjK25NlJRqvcpmxBCCCGEEEIIIYQQQmxl9EBVCCGEEEIIIYQQQgghCtLkQ/5tGodK8GvCNsytvuHj2jpxPTi8GwAOOeSQsmnf/e53g3zf+973vG1Duq+++mpv82vyNgRk48aN3m5O4TH2Ve6ir3ZzOP2CBQuCtPnz53vbhp7xK+c//OEPvV30FfOxY8cG2//3f//n7V69egVpnTt39ja3J4d6A5uHFecxfvx4b1977bVBGodk2XAtDgVXyH+IDc/icMpYvzN16lRv33XXXd62oa+vvfaat20oxJIlS7zNIbR8rQNhSJaFJUQ6derkbSsZ8sYbb+SWIfJ55JFHgm0Oob3ooou8bWU8vvzlL3ubw2FsSCTLcVx22WVB2le+8hVvc5//97//PcgnCYfN4T6wLmPMpZde6u2jjz7a29yHAmEb2NBFlgN46623vG3HdA4f53mGDY/m7blz5wZpc+bM8fbvfvc7b3/2s58N8s2cOdPbVpKI68X94bJly4J8HOZl6xgLWW3qPPvss8E2X6877bRTkMZ+kCfHY7HnhvtpPt92LOLyeW5owyzzwnLtsVmWgP0GCNs6Nr/s1q1b2bJtfa00UksM+Y+F5XIfZMeOe+65x9ssI8WyIEA4v7zhhhu8fccddwT5zjvvPG+fc845QdrEiRO9zXNqDs0GgB/84AfeZnmBusDzYxsS3BRCOutCXug+UP99IUt/AMCVV17p7T/96U/eHjZsWJCve/fuZcuw4fQcrs9zUiCUn+Fr2Ib8vv322962bctzHg4bHjlyZJCPx6Xp06cHabxtpdWaE5VcBzYMn9uG/dTeF+b1T7Hwf1sGw+Omzcd1isnjiK1PTKaF281e83ll2HkMp7F0kZ0j8FzL+k9TmF/qDVUhhBBCCCGEEEIIIYQoiB6oCiGEEEIIIYQQQgghREH0QFUIIYQQQgghhBBCCCEK0iQ0VGN6HFYvhLWhNm3a5O1JkyYF+S688EJv33zzzUHa7rvv7u2Ydh3Dmg9WF4u1po444oggjbXrWEsz9j+zhoyF9fmsDgWntWvXLreMaoD1fF5++eUgbfjw4d622pG8zXobVmOIdVJ33nnnII31S1mr0mpfsoYHt+eZZ54Z5PvHP/7hbfY3ABgxYoS3jz/+eG9bzVRua6sVwnpErHdl/y/2b6th1JyI6ZLl6fvErkd7vvO0WridgVBritvd6vnydWthn2CftXTs2NHbVj+PfYl1XVn7Cthcl1UUY/ny5cE2n8d//etf3rZ6QLvuuqu3bZvllb969eogjfUyuX+yvvif//mf3m5O+tpbQuyaZ3guAYTXGl9bVoubtVatLjW398CBA73N/XddYH32Ll26BGl/+9vfvM062q+//nqQj7U1rT9yGuvwWa1vqyPbXOjdu3ewzXML24/y+S+q6/Xqq68G2yeeeGLZfHbuWbT8mK4dw/MMq7UaG6eY888/39us32gpWl5zpqgeotU8veCCC7zNmrUxzj77bG9ff/31QRprc++yyy5B2h//+Edv8xzk3nvvDfKxXqslpifKTJgwwdt2bm/r1dSJ/Z8vvfSSt+39DWv0d+jQwdtWs/aWW27xth1f+HpnXV3WZgaAGTNmeDvWl/CcxvZBPAbycVmfFQh1MW+99dYgjf9nHl+OPPLIIB/f11pd66LjeUuB28I+K+C2jo0N7MOVnt88bVT7vIX1caWh2jAUXQcGCO877LMPJq+t7Bwydiz2LfaDapsjqAcSQgghhBBCCCGEEEKIguiBqhBCCCGEEEIIIYQQQhSk0UL++RV0fsUXCF8v5lAAIAwBaNu2rbdXrVoV5OPXi1955ZUgjUP+Y2H+DIc52DA3Dqk4/fTTg7Tf/e53hcpnBg8enJvGr0DHwkRtuEW1wWGp9pXyZ555xtvWP/j/PvbYY71tw5lffPFFb8+fPz9I4/DJyy+/3NsnnHBCkI/DZRjrz9xmNtR+6NCh3uYQTK4fEIYL29AL9k2+Dqx0BJ+rFStWBGlFw8aqgVhoQdEQScaGScycOdPbTz31VO5x+Ryz/7Vu3TrIx/2YDa14/PHHvc2yFRy6CwALFy70tpUGWLNmjbc5vJhDl4HQj6wP2/BPUcOVV14ZbH/rW9/yNsvN2HN6zDHHFCr/i1/8orc5fBsAbrvtNm9zG1n/YJkUUTvf//73vc3jDRCGnr711lvettc1n3M7VvM4wNeulYuxckV59OrVy9v3339/kMayMjyu2PGBxxU7Z+L68jhiw3I5pLQ5hWByHwqEfSWHNAJAv3796lymneNwP81jf9GQfzsv4nw2/DMvn/VZK22QB9edQ5aBzc+VyIfPlZVvqmQ85mvzzTffzM3H4xcQzk+4P7Ih/jFZqqLSBnztsGRStfOlL30p2L7xxhu9bedhfE/K9w72HnePPfbwtr1n5OufZYLsOeX7Jb6+rYwRl2f7Ba4vz1/ttc4SfbY/5TJYQu/BBx8M8vXo0QN5xO6HWyJ8/m1YfyX3QUzR8G17LDsuMdUW3l2N1CXkn2VEuG+wcxBuU/YzWzanWelD7je4jrafiF3/TYHmM+MVQgghhBBCCCGEEEKIBkYPVIUQQgghhBBCCCGEEKIgjfaOPL8GHltBzL7Gz6ur8grHsRUmv/rVrwbbZ511VuF6luCwiYMPPjhI+9znPuftSkL8LbySIZC/umVsFbWm/mp0bXBI09FHH52bz656zdscesZh/ABw0EEHeXvZsmVBGodKsJ/acP0BAwZ4m0P2rD//x3/8h7crDZ2ePn26t+3r8uwHHDZhj8Wv0tv/maUHqh1uPxt+wmkcSsnh9EAo62FX7+YQLQ7rfe6554J8HKLFq0RPmzYtyMdtwe0MhNIX7Fe8SqyF+0ggDAfmcA0bfsP/pw3vVMh/PlY+Y+TIkd5mORHbL4waNapQ+WvXrvW2leZgX+J+wYb8izgvvPBCsM0rL9u+kccY7kN4PADCdrOhVhwKzdfnk08+GeQbMmSIt3ll7m9+85tBvquvvtrbLEEEhCtnc2il7Rt529aXQ3EXLVrkbRs+zv2rncdUMyzHA4TzKxvayn19DJ4z9O/fP0jjcxcbz/LCXG27xML88/JZOSwbflcEO2d6++23vd2nT586l9ccyFvx3oY38/lm2RcgX36hLiGdzPHHH+9tuxL8b3/7W2936dIlt4xY/1G0HiwvYOVEqg1uo3vuuSdI69u3r7cHDRoUpPG8gMcGO76wLIuVH+P7nVgoNc8Z+Fq1/QX3M1Y+jcc5lr3h+3OL9Y887Jx62LBhuXnZd0TYnrYv521uaysNwGmxcP2YvA+n2fIZloQQWx++joHNr/MSVrqM7ynznkUAoS/F5jGcz8oxxq7/poDeUBVCCCGEEEIIIYQQQoiC6IGqEEIIIYQQQgghhBBCFEQPVIUQQgghhBBCCCGEEKIgjaaheuedd3r7T3/6U5DG2pesIQaE2lKsw2L1GlgjZI899gjSWLOQNQotrFFzwgknePuQQw4J8l1zzTW5ZbBGDdepLhpDrLXF2jhWj8RqW7QEWM+y3HZzob61Q1j7s9phXTYAeOutt7y9ZMmSIO2NN97w9tKlS73NmlZAqF1lfYr17h566CFvs2YqEOqNLViwwNtWG4zzsSYjAAwePLhsmj1W9+7dvW01PVlLiXWKrFYa9x8LFy4M0mIa1S0dqxPI+qWsTWdZvnx5ncu3etKf/vSnvc1jQ3PSRG4oWCf5K1/5SpDGemxWO4w1o9i24y+XYTW8+frla8vqwLFe6xFHHOHtr3/960G+Qw891Nt//etfg7Sdd97Z26xdZXUYWWvVarDzNs87WGMcAF577bWyx612uJ1rg+drMfjcxXTcefyxOmd5WM1rPpadK/P/xvplVhu2qA4rc+yxxwbb3JcV1ZqtBux8PnaN5M317XjwqU99Kvd4zz77rLfHjBlTa9mWH//4x8E2+8R3v/vdIC2mm8rwsWMamexH9twwPD+rRk4//XRv23UPWOfeniueK/KYYucZrHlvr2nWMuX5sb2G8+4nbX25jrZv4TGF55esJQ8Ae++9t7etnxb1W/6/eI4OAM8884y3Nf8J+2/rY3mapzHt7Vgbxa53Phbb9tpnH46NUaJhsBq23Pfw+g92npv33Mn6BPcb1pe47+H97P1wU0dvqAohhBBCCCGEEEIIIURB9EBVCCGEEEIIIYQQQgghCtJoIf8cwmxDSiZPnuztJ554IkjjMD0Ovd24cWPusewryRwic/jhh3v7tttuC/JxqNVnP/tZb//mN7/JPZYNw+cw/0rhEBCuk30Nvn379t7mEGAhmjOTJk0KtjmU5NRTTw3SXn/9dW+/88473l60aFGQj/skew0/99xz3ubQKCvLwH0Sh4GuWbMmyMfhnjbMbZdddvE2hwmzdIEt08oBcJgN1yMWyjdjxoxge7fddsvN29JhGRoAmDJlirdjIeG2nfLgPt+2+5FHHult9vtqD5fcGnDbjB49Okjja3fatGlBGktqcFidPefcN9hrjcvn679Dhw5BvtmzZ3v7kksu8fa3vvWtIN/f//53b9twfZYYYfkCO1fhMEkb+sfyAByuasNQuR9qTvI7Z599dr2Xyb5jZVr4WmY/teG2225bfgofC6W2bcs+weXHwj+LwvPr5owNYYyd/zwef/zxYPuoo47KLY9DYvnY559/fpDv2muv9favf/1rb0+dOjXId8EFF3i7Psb6oiHcFvZnDjOtRnhMseP2rFmzvG3v43heyuOGDaHleam9Nrlv4f7f9vlWfqoEh9YDof/ZMhie41hJGb53t5JjXF/uj+z/zPWwdbTz6paGPVe8HevLi7ZtnkwAUPn1zvCxbfi5Qv4bHpaXAsLrkNvXtoWVxMrLx/5oJUXy5jHcF1rqw+fqG72hKoQQQgghhBBCCCGEEAXRA1UhhBBCCCGEEEIIIYQoiB6oCiGEEEIIIYQQQgghREEaTUN15MiR3r711lsL78eaXfPnz/e21S9jDUDWIAVC/RrWaPj2t78d5Dv99NO9bXXJ8ojpjDB10X946KGHvM0aNX369AnysQ7NwIEDC5cvRLUxffp0b1stJb6+//CHPwRprOvCOoZWO4i1RleuXBmksd5Lt27dvM1aiADw1ltvlbVteawnw8cFQq007lusll5MP8pq45Sw2jdcZkxfVYTYc8VaQdxfW12oovrabdu2zS2DfYnb2Y4NYnNYP+5Tn/pUkMb67AsXLgzSeM7A192cOXOCfDxXsdcgb3OfZOcPV155pbdZT5n7EwDo27evt61eGteR+zmbj/3stddeC9K4z3vmmWe8vd9+++WWYTWDOa2lkKdVB4TztcWLFwdp7GP9+vXzttUa4zL5WLZtY5qeXCaPP3aMKUrsf25O8PU9c+bMII19PaZpztej1ZXkMu38hNdL+NGPfuRtq8F89913e/vJJ5/09nHHHRfkO+GEE7C1iN0jsW6q1SavNr75zW+WtQHg0Ucf9fZVV10VpE2YMMHb1q/ysP1Cniahve/kuYqdW1QCt63V9OT7cNvfcV/DdbRai+wfS5YsCdJ23333CmrcfIitI2PvK+x5LRHrr7k9bX9U9LlHbJ+YxqZoeIpqqNq24fsYbtOYVntMc5zzxfo/aagKIYQQQgghhBBCCCFEFaMHqkIIIYQQQgghhBBCCFGQRgv5rxQOAxkxYkRZGwBGjx69tarU4LT0UAYhLC+//LK3OcTfYsMTOER6xYoV3rahchxOxPmAMAR4w4YN3rYhTiwBwGEReeFY5crgkAcOoe3UqVNuGTaUn/NyqKA9FufjcEYRh88pEIa6cFvbEH+Wc4iRFw5n4TCsonICLRk+R4MHDw7S1q9f723bvnvuuae3+ToZNGhQkK9r1665x+YwfA53svIlnI/7Als2+4jtX9gfuT9kyQMg7A/23XffII3z9uzZ09v2f2YJlEpDxlsKfE55HAHyZRoseSF2NnSzaCgeyxDYcaRo+H7RfHWRJWiK8LzgvvvuC9I45N/K8XBb8/VuQ3Y5FLx3795B2ty5c73Nch+dO3cO8r344ovevuKKK7zN8iFNCZZue/jhh4O00047bWtXp8E48sgjy9oWDsN/9tlngzSWvWJ/AMLxi68r25fwnHLIkCHetjJGPAZaKQYOJ+exx4Z083zH1iNP3sIea6eddip7XAA46KCD0JKJSTYUDcm3+dh37P0Cw/NSW4bt54uUoZD/hiHWhrNnzw62ud1icxCeJ7Btx3PuG+zcIs9Hli9fnnvcpojeUBVCCCGEEEIIIYQQQoiC6IGqEEIIIYQQQgghhBBCFKTqQv6FEILDv+xKgLyCql25kMOOONzJrkjNIQg2zNrKA5Swobacj9NsaA6ncagWEIa+cIiyDe3j0EEbTsHhWmzHVg7+y1/+EqTtv//+3o6FMrdEYiuo2lDeSuDwUdtmHMLDtg0dF5vDIek2FKpVq1bettcT9xXcNrHrwl7XHLoYk3HgVY25fBtOxX2DXVWc/ZP7JOtL/H/aPo7DkbkPfeWVV4J83G821bDirUksjJ3bhf0NCEPv2d+sjALn43Nv88XK4G0ug8tuCKotxN8yYMAAb1966aWNV5EqI9bf/eAHP9iKNWn68Dh+xBFHBGl2u5o57LDDGrsKVY8Nk+fx3d7f8FjP+WyfzGUWDfm35PXzdv4Rk6URDY+9V+b24Laybc3tGxvTeT873+Hy+bjz588vVF5TQW+oCiGEEEIIIYQQQgghREH0QFUIIYQQQgghhBBCCCEKogeqQgghhBBCCCGEEEIIURBpqAohqpqdd945d9vq/ixbtszb06ZN8/a8efOCfKzdsm7duiDNaqWWsLowrH/FNmuVAkDPnj1zy+Dt5cuXe9vqOrIen9VoXbNmjbf5f7FaejENSGly5mP1JlnPktuJ9TCBuCYV06NHj9w09kXWFFJ71Y2BAwfmprFmKBDq4rI2KuuM2v3YJ4BQm4zb0B6L4Ta1vsPXf8eOHXPL4Gve9iE77rijt62+2SOPPFL2WLYvZB+3/i4qg9vTauFxW+TpbQOhthlrlAGhXh3ns77Iur+WvDKEEEI0PFZDNaZJmncPYyk6R43pWeZpccbKsP+LaHjuvffeYJvns7xmR1Ffsvny1nsAwjkDzxuL+mlTQW+oCiGEEEIIIYQQQgghREH0QFUIIYQQQgghhBBCCCEKUl3v0wohhMGGFnCIiQ1F6datW1m7GrD/JxML73n33Xe9zXIAsZAMe97atWtXt8q2YDgknM+jDaG10gx58H52Hw6NYrtt27bFKitqJSbD0alTp61dnUbhhBNOaOwqNGtsmPwzzzzj7REjRnh7wYIFQT6WnGB69+6deyy7D4fys7yAPZYQQoimiQ2jZtkXKxXD8H2ALYND9PNsILz/sJIyLFUUy8dlKuS/YYhJOHzjG98IthctWuTtOXPmeNu2Dd/vVArPO9gfn3766S0ue2uiN1SFEEIIIYQQQgghhBCiIHqgKoQQQgghhBBCCCGEEAXRA1UhhBBCCCGEEEIIIYQoiDRUhRBVjdWfa64U/T+33Tbs1ln/VFqoDQ9r83744YfetlqcrF0YY/Dgwd7u3LlzkMY6We3bty9bByFE0+b8888Ptv/2t795m7VMd9hhhyAf9ymstWx1UleuXOlt2+9wmatXr/b2XnvtFeTr169fbv2FEEI0HjwXBELdVNYxtWy//fa5ZbCuKWtbWv1TXivApvF+MW1Uvm+x45yoH+y9IXPMMcfkbk+ZMsXbjz32WJBvzZo13l61apW3ee0OIPQL9jkg1HznecaYMWNy69sU0RuqQgghhBBCCCGEEEIIURA9UBVCCCGEEEIIIYQQQoiCuCRJGrsOQgghhBBCCCGEEEIIURXoDVUhhBBCCCGEEEIIIYQoiB6oCiGEEEIIIYQQQgghREH0QFUIIYQQQgghhBBCCCEKogeqQgghhBBCCCGEEEIIURA9UBVCCCGEEEIIIYQQQoiC6IGqEEIIIYQQQgghhBBCFEQPVIUQQgghhBBCCCGEEKIgeqAqhBBCCCGEEEIIIYQQBdEDVSGEEEIIIYQQQgghhCiIHqgKIYQQQgghhBBCCCFEQfRAVQghhBBCCCGEEEIIIQqiB6pCCCGEEEIIIYQQQghRED1QFUIIIYQQQgghhBBCiIL8f/fVkxcGg82UAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x345.6 with 40 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 290,
       "width": 682
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's make a list of class names that we can refer to.\n",
    "class_names = [\"T-shirt/top\", \"Trouser\", \"Pullover\", \"Dress\", \"Coat\",\n",
    "               \"Sandal\", \"Shirt\", \"Sneaker\", \"Bag\", \"Ankle boot\"]\n",
    "\n",
    "# Plot 4x10 array of images from the Fashion MNIST database\n",
    "n_rows = 4\n",
    "n_cols = 10\n",
    "plt.figure(figsize=(n_cols * 1.2, n_rows * 1.2))\n",
    "for row in range(n_rows):\n",
    "    for col in range(n_cols):\n",
    "        index = n_cols * row + col\n",
    "        plt.subplot(n_rows, n_cols, index + 1)\n",
    "        plt.imshow(X_train[index], cmap=\"binary\", interpolation=\"nearest\", origin='upper')\n",
    "        plt.axis('off')\n",
    "        plt.title(class_names[y_train[index]], fontsize=12)\n",
    "plt.subplots_adjust(wspace=0.2, hspace=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll use `tensorflow` on this data set, through it's handy API `keras`. Since these are images and we want to preserve contextual information like edges, we'll build a CNN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "keras.backend.clear_session() # Make sure that we are starting a new model and not adding to an earlier one\n",
    "np.random.seed(42) # Set the numpy and tensorflow random seeds so that we all get the same answer\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalize and prepare the data to pass into a network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55000\n",
      "(55000, 28, 28, 1)\n",
      "(5000, 28, 28, 1)\n",
      "(10000, 28, 28, 1)\n"
     ]
    }
   ],
   "source": [
    "(X_train_full, y_train_full), (X_test, y_test) = keras.datasets.fashion_mnist.load_data()\n",
    "X_train, X_valid = X_train_full[:-5000], X_train_full[-5000:]\n",
    "y_train, y_valid = y_train_full[:-5000], y_train_full[-5000:]\n",
    "\n",
    "X_mean = X_train.mean(axis=0, keepdims=True)\n",
    "X_std = X_train.std(axis=0, keepdims=True) + 1e-7\n",
    "X_train = (X_train - X_mean) / X_std\n",
    "X_valid = (X_valid - X_mean) / X_std\n",
    "X_test = (X_test - X_mean) / X_std\n",
    "\n",
    "\n",
    "# Need to reshape for CNN\n",
    "X_train = X_train[:, :, :, np.newaxis]\n",
    "X_valid = X_valid[:,  :, :, np.newaxis]\n",
    "X_test = X_test[:, :, :, np.newaxis]\n",
    "\n",
    "print(len(X_train))\n",
    "print(X_train.shape)\n",
    "print(X_valid.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next cell looks like a lot, but really it is just a sequential declaration of different types of layer in the network. It is a combination of **2D Convolution layers** (feature finding), **Max Pooling layers** (data reduction), folloed by some **Dense layers** (fully-connected layers at the end of the network for classification) and some **Dropout** incorporated to strengthen the overall network and make sure all the neurons are actually pulling their weight."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.Sequential([\n",
    "    keras.layers.InputLayer(input_shape=[28, 28, 1]),\n",
    "    keras.layers.Conv2D(filters=64, kernel_size=7, activation='relu', padding='same'),\n",
    "    keras.layers.MaxPooling2D(pool_size=2),\n",
    "    keras.layers.Conv2D(128, 3, activation='relu', padding='same'),\n",
    "    keras.layers.Conv2D(128, 3, activation='relu', padding='same'),  \n",
    "    keras.layers.MaxPooling2D(pool_size=2),\n",
    "    keras.layers.Conv2D(256, 3, activation='relu', padding='same'),\n",
    "    keras.layers.Conv2D(256, 3, activation='relu', padding='same'),\n",
    "    keras.layers.MaxPooling2D(pool_size=2),\n",
    "    keras.layers.Flatten(),\n",
    "    keras.layers.Dense(units=128, activation='relu'),\n",
    "    keras.layers.Dropout(0.5),\n",
    "    keras.layers.Dense(units=64, activation='relu'),\n",
    "    keras.layers.Dropout(0.5),\n",
    "    keras.layers.Dense(units=10, activation='softmax'),\n",
    "])   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 28, 28, 64)        3200      \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 14, 14, 64)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 14, 14, 128)       73856     \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 14, 14, 128)       147584    \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 7, 7, 128)        0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 7, 7, 256)         295168    \n",
      "                                                                 \n",
      " conv2d_4 (Conv2D)           (None, 7, 7, 256)         590080    \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 3, 3, 256)        0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 2304)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 128)               295040    \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 128)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 64)                8256      \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 64)                0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 10)                650       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,413,834\n",
      "Trainable params: 1,413,834\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**That's right...your CNN has ~1.5 million parameters that you are going to try to optimize on your machine...**\n",
    "\n",
    "We'll only go through one epoch of optimization, but hopefully that will be enough to get us above $50\\%$ accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1719/1719 [==============================] - 254s 147ms/step - loss: 0.7000 - accuracy: 0.7582 - val_loss: 0.3710 - val_accuracy: 0.8646\n"
     ]
    }
   ],
   "source": [
    "#### This may take a while! Hopefully no more than 5 minutes for 1 epoch.\n",
    "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"nadam\", metrics=[\"accuracy\"])\n",
    "clf = model.fit(X_train, y_train, epochs=1, validation_data=(X_valid, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 11s 34ms/step - loss: 0.3932 - accuracy: 0.8555\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.39324241876602173, 0.8554999828338623]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the test set\n",
    "model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predict values for first 3 test objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.   0.   0.   0.   0.   0.   0.   0.01 0.   0.99]\n",
      " [0.   0.   0.97 0.   0.   0.   0.03 0.   0.   0.  ]\n",
      " [0.   1.   0.   0.   0.   0.   0.   0.   0.   0.  ]]\n"
     ]
    }
   ],
   "source": [
    "X_new = X_test[:3]\n",
    "y_proba = model.predict(X_new).round(2)\n",
    "print(y_proba)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, you can see that the output is a probability that the object belongs to each class (which has to sum to 1 across all the classes).  If we just want an \"answer\", we assign it to the class with the highest probability (done here with `predict_classes`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9 2 1]\n",
      "['Ankle boot' 'Pullover' 'Trouser']\n"
     ]
    }
   ],
   "source": [
    "y_pred = np.argmax(model.predict(X_new), axis=-1) #New way\n",
    "print(y_pred)\n",
    "print(np.array(class_names)[y_pred])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot those and see if the predictions make sense."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 518.4x172.8 with 3 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 147,
       "width": 416
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7.2, 2.4))\n",
    "for index, image in enumerate(X_new):\n",
    "    plt.subplot(1, 3, index + 1)\n",
    "    plt.imshow(image, cmap=\"binary\", interpolation=\"nearest\", origin='upper')\n",
    "    plt.axis('off')\n",
    "    plt.title(class_names[y_test[index]], fontsize=12)\n",
    "plt.subplots_adjust(wspace=0.2, hspace=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that after you fit your model, you can save it and reload it at some later time (which is good because some models might take hours or days to train!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save model\n",
    "model.save(\"my_keras_model.h5\")\n",
    "\n",
    "# Reload model\n",
    "model = keras.models.load_model(\"my_keras_model.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's predict the values for 10 random objects. Display them with their actual labels first, then predict and display the predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx10 = np.random.choice(np.arange(len(y_test)), size=10, replace=False)\n",
    "X_new = X_test[idx10]\n",
    "y_pred = np.argmax(model.predict(X_new), axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x360 with 10 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 120,
       "width": 851
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correct answers (y_test)\n",
    "plt.figure(figsize=(15, 5))\n",
    "for index, image in enumerate(X_new):\n",
    "    plt.subplot(1, 10, index + 1)\n",
    "    plt.imshow(image, cmap=\"binary\", interpolation=\"nearest\", origin='upper')\n",
    "    plt.axis('off')\n",
    "    plt.title(class_names[y_test[idx10[index]]], fontsize=12)\n",
    "plt.subplots_adjust(wspace=0.2, hspace=0.5)\n",
    "plt.suptitle('ACTUAL LABELS',y=0.7,fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x360 with 10 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 120,
       "width": 851
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Predicted answers (y_pred)\n",
    "plt.figure(figsize=(15, 5))\n",
    "for index, image in enumerate(X_new):\n",
    "    plt.subplot(1, 10, index + 1)\n",
    "    plt.imshow(image, cmap=\"binary\", interpolation=\"nearest\", origin='upper')\n",
    "    plt.axis('off')\n",
    "    plt.title(class_names[y_pred[index]], fontsize=12)\n",
    "plt.subplots_adjust(wspace=0.2, hspace=0.5)\n",
    "plt.suptitle('PREDICTED LABELS',y=0.7,fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not too shabby!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Autoencoders <a class=\"anchor\" id=\"three\"></a>\n",
    "\n",
    "**Autoencoders are neural networks that copy their input to their output, but after passing the data through a compression bottleneck**. For example if there are 28x28 = 784 inputs, there will also be 784 outputs, but there will be one or more (odd, but symmetric) hidden layers with fewer neuron than that.\n",
    "\n",
    "***Therefore the encoder layers learn a compressed representation of the data, with the decoder layers transforming the encodings back to the full data dimensionality.*** \n",
    "\n",
    "For example see \n",
    "\n",
    "![autoencoder structure](https://miro.medium.com/max/1400/1*44eDEuZBEsmG_TCAKRI3Kw@2x.png)\n",
    "\n",
    "from\n",
    "https://towardsdatascience.com/applied-deep-learning-part-3-autoencoders-1c083af4d798\n",
    "\n",
    "You can think of this as doing **PCA with a neural network** -- breaking our data down into the only the most important features that we actually *need* (finding the intrinsic dimensionality). In fact, if the network uses only linear (or no) activation functions and $L2$ cost function, then we have exactly PCA.  \n",
    "\n",
    "How might this be useful?  Well, for example we can use it to reconstruct MNIST digits that have had noise added to them:\n",
    "\n",
    "![autoencoder example](https://miro.medium.com/max/1400/1*SxwRp9i23OM0Up4sEze1QQ@2x.png)\n",
    "\n",
    "More exciting possibilities are: \n",
    "\n",
    "- Using autoencoders for \"**unsupervised pretraining**\". For example you have data that is only partially labeled (at least not enough to do traditional supervised classification).  We can train an autoencoder on the full data set, then used the encoder part as the base of a regular neural network that is trained on the labeled data that we do have. This is found to be a much more efficient way of initializing weights and biases than starting from random, because you are \"cheating\" a bit by already figuring out some of the data structure using the autoencoder. See Geron Figure 17.6.\n",
    "- **[Anomaly detection](https://scikit-learn.org/stable/modules/outlier_detection.html)** (remember the old problem of figuring out if two samples are extracted from the same distribution?) e.g. https://towardsdatascience.com/anomaly-detection-with-autoencoder-b4cdce4866a6 and https://towardsdatascience.com/anomaly-detection-using-autoencoders-5b032178a1ea. See also\n",
    "https://www.pyimagesearch.com/2020/03/02/anomaly-detection-with-keras-tensorflow-and-deep-learning/ and \n",
    "https://towardsdatascience.com/autoencoder-neural-network-for-anomaly-detection-with-unlabeled-dataset-af9051a048.\n",
    "\n",
    "### Variational Autoencoders\n",
    "\n",
    "More common in astronomy are **variational autoencoders**, partly because the \"latent space\" (i.e. the compressed representation of the data) that results from a standard autoencoder doesn't necessarily map continuously to the data (e.g., if your training data don't represent the possibilities or span of the full data space). \n",
    "\n",
    "We won't go into detail, just realize that these are something that you might try if you were otherwise going to try an autoencoder to tackle your problem. **Instead of the encoder layers compressing the data down to a single point in the \"latent space\", it is mapped to a continuous distribution** (imagine if PCA gave you a probabilistic compression). The prior on this continuous distribution is a Gaussian with mean and variance. ***Therefore the intuition for variational autoencoders is that the mean vector centers the average encoding of the data point, while the variance samples around this average encoding.*** This allows the encoding and decoding to interpolate much more smoothly across the training data. \n",
    "\n",
    "![Ivezic Figure 9.21](https://www.astroml.org/_images/fig_vae_1.png)\n",
    "\n",
    "\n",
    "This one below is an example of a variational autoencoder trained on SDSS spectra using `pytorch`.\n",
    "\n",
    "I won't cover this in class because there won't be time, but I'll leave it here for you as a further worked out example\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astroML.datasets import sdss_corrected_spectra\n",
    "from astroML.plotting import setup_text_plots\n",
    "setup_text_plots(fontsize=8, usetex=False)\n",
    "\n",
    "# Fetch and prepare the data\n",
    "data = sdss_corrected_spectra.fetch_sdss_corrected_spectra()\n",
    "spectra = sdss_corrected_spectra.reconstruct_spectra(data)\n",
    "wavelengths = sdss_corrected_spectra.compute_wavelengths(data)\n",
    "\n",
    "# normalize spectra by integrated flux and subtract out mean, for easier training\n",
    "spectranorms = np.mean(spectra, axis=1)\n",
    "normedspectra = spectra / spectranorms[:, None]\n",
    "meanspectrum = np.mean(normedspectra, axis=0)\n",
    "normedspectra -= meanspectrum[None, :]\n",
    "\n",
    "# split data into 3:1 train:test\n",
    "torch.manual_seed(802)  # seed used for book figure\n",
    "dataset = torchdata.TensorDataset(torch.tensor(normedspectra))\n",
    "trainnum = normedspectra.shape[0] // 4 * 3\n",
    "traindata, testdata = torchdata.random_split(dataset, \n",
    "                                             [trainnum, normedspectra.shape[0] - trainnum])\n",
    "traindataloader = torchdata.DataLoader(traindata, batch_size=128, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now define the structure of the variational autoencoder. \n",
    "- In the top block you can see that the first layer reduces the $1000$-D input down to $250$.\n",
    "- We then pass to a $2$D mean and variance layer from which the compressed \"latent\" parameters can be probabilistically drawn. This means that we compressing down from a 1000 flux channels to a 2D compressed representation.\n",
    "- The decoding layers decompress the sampled latent parameters and scale back up to the full 1000 flux channel dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define structure of variation autoencoder\n",
    "class VAE(nn.Module):\n",
    "    def __init__(self, nhidden=250):\n",
    "        super(VAE, self).__init__()\n",
    "\n",
    "        # encoding layers\n",
    "        self.encode_fc = nn.Linear(1000, nhidden)\n",
    "        self.mu        = nn.Linear(nhidden, 2)\n",
    "        self.logvar    = nn.Linear(nhidden, 2)\n",
    "\n",
    "        # decoding layers\n",
    "        self.decode_fc = nn.Linear(2, nhidden)\n",
    "        self.output    = nn.Linear(nhidden, 1000)\n",
    "\n",
    "    def encode(self, x):\n",
    "        h = F.relu(self.encode_fc(x))\n",
    "        return self.mu(h), self.logvar(h)\n",
    "\n",
    "    def reparameterize(self, mu, logvar):\n",
    "        std = torch.exp(0.5*logvar)\n",
    "        eps = torch.randn_like(std)\n",
    "        return eps.mul(std).add_(mu)\n",
    "\n",
    "    def decode(self, z):\n",
    "        h = F.relu(self.decode_fc(z))\n",
    "        return self.output(h)\n",
    "\n",
    "    def forward(self, x):\n",
    "        mu, logvar = self.encode(x)\n",
    "        z = self.reparameterize(mu, logvar)\n",
    "        return self.decode(z), mu, logvar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The loss is a modified version of the usual MSE, where we use the **Kullback-Leibler divergence** to asses how close the latent parameter distribution is to a Gaussian. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add KL divergence to loss function\n",
    "def VAEloss(criterion, recon_x, x, mu, logvar):\n",
    "    return criterion(recon_x, x) - 0.5 * torch.sum(1 + logvar - \n",
    "                                                   mu.pow(2) - \n",
    "                                                   logvar.exp())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another large block that is essentially just training the network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model():\n",
    "    model = VAE()\n",
    "    criterion = torch.nn.MSELoss(reduction='sum')\n",
    "    optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)\n",
    "    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, \n",
    "                                                           verbose=True, \n",
    "                                                           patience=5, \n",
    "                                                           threshold=1e-3)\n",
    "\n",
    "    min_valid_loss = float('inf')\n",
    "    badepochs = 0\n",
    "    for t in range(1000):\n",
    "        train_loss = 0\n",
    "        for i, databatch in enumerate(traindataloader, 0):\n",
    "            specbatch = databatch[0]\n",
    "            optimizer.zero_grad()\n",
    "            recon, mu, logvar = model(specbatch)\n",
    "            loss = VAEloss(criterion, recon, \n",
    "                           specbatch, mu, logvar)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            train_loss += loss.item()\n",
    "\n",
    "        with torch.no_grad():\n",
    "            testspec = testdata[:][0]\n",
    "            recon, mu, logvar = model(testspec)\n",
    "            valid_loss = VAEloss(criterion, recon, \n",
    "                                 testspec, mu, logvar)\n",
    "            if t % 10 == 0:\n",
    "                print('Epoch %3i: train loss %6.1f validation loss %6.1f' % \\\n",
    "                        (t, train_loss / len(traindata), valid_loss / len(testdata)))\n",
    "            # stop training if validation loss has not fallen in 10 epochs\n",
    "            if valid_loss > min_valid_loss*(1-1e-3):\n",
    "                badepochs += 1\n",
    "            else:\n",
    "                min_valid_loss = valid_loss\n",
    "                badepochs = 0\n",
    "            if badepochs == 10:\n",
    "                print('Finished training')\n",
    "                break\n",
    "        scheduler.step(valid_loss)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<keras.engine.sequential.Sequential object at 0x13052a160>\n"
     ]
    }
   ],
   "source": [
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's train this thing! (Takes less than a minute)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch   0: train loss 2276.2 validation loss 1259.7\n",
      "Epoch  10: train loss  423.4 validation loss  405.2\n",
      "Epoch  20: train loss  233.2 validation loss  309.6\n",
      "Epoch  30: train loss  181.3 validation loss  281.8\n",
      "Epoch  40: train loss  149.2 validation loss  265.0\n",
      "Epoch  50: train loss  125.7 validation loss  255.5\n",
      "Epoch  60: train loss  111.5 validation loss  240.4\n",
      "Epoch  70: train loss  101.1 validation loss  235.0\n",
      "Epoch  80: train loss   94.0 validation loss  228.6\n",
      "Epoch  90: train loss  157.4 validation loss  222.6\n",
      "Epoch 100: train loss   81.6 validation loss  216.3\n",
      "Epoch 110: train loss   77.4 validation loss  213.0\n",
      "Epoch 120: train loss   71.7 validation loss  210.0\n",
      "Epoch   130: reducing learning rate of group 0 to 1.0000e-05.\n",
      "Epoch 130: train loss   75.7 validation loss  209.3\n",
      "Finished training\n"
     ]
    }
   ],
   "source": [
    "model = train_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 9 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 368,
       "width": 600
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot results\n",
    "with torch.no_grad():\n",
    "    # sort latent parameters from most constrained to least constrained\n",
    "    testspec = dataset[:][0]\n",
    "    recon, mu, logvar = model(testspec)\n",
    "    zorder = np.argsort(np.mean(logvar.numpy(), axis=0))\n",
    "\n",
    "    fig = plt.figure(figsize=(10, 6))\n",
    "    fig.subplots_adjust(hspace=0, wspace=0)\n",
    "    parvalues = [-2.,0.,2.]\n",
    "    for i, z1 in enumerate(parvalues):\n",
    "        for j, z2 in enumerate(parvalues):\n",
    "            # get z1 to vary left to right, z2 bottom to top\n",
    "            ax = fig.add_subplot(3, 3, (2-j)*len(parvalues)+i+1)\n",
    "\n",
    "            z = np.zeros((1,2), dtype=np.float32)\n",
    "            z[0, zorder] = z1, z2 # set z1 is more constrained of the two latent parameters\n",
    "            spectrum = model.decode(torch.tensor(z))\n",
    "            ax.plot(wavelengths, meanspectrum+spectrum.numpy()[0,:])\n",
    "            ax.text(6750, 3, '(%i, %i)' % (z1,z2))\n",
    "\n",
    "            ax.set_xlim(3000, 8000)\n",
    "            ax.set_ylim(-1, 4)\n",
    "\n",
    "            if i == 0 and j == 1:\n",
    "                ax.set_ylabel('flux')\n",
    "            else:\n",
    "                ax.yaxis.set_major_formatter(plt.NullFormatter())\n",
    "            if j == 0 and i == 1:\n",
    "                ax.set_xlabel(r'${\\rm wavelength\\ (\\AA)}$')\n",
    "            else:\n",
    "                ax.xaxis.set_major_formatter(plt.NullFormatter())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The spectra above are generated from our variational autoencoder. As mentioned, the encoder was limited to two components (2D compressed latent space). As neurons are progressively activated in the latent space, we generate a smooth transition from spectra consistent with quiscent to star-forming galaxies. The numbers in each panel indicate the activation value of the two neurons in the latent space."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Generative Adversarial Networks (GANs) <a class=\"anchor\" id=\"four\"></a>\n",
    "\n",
    "Finally here's a brief discussion on GANs, which are one of latest innovation in deep learning.\n",
    "\n",
    "GANs are pure evil, see\n",
    "https://thispersondoesnotexist.com.\n",
    "\n",
    "But they are also brilliant, incredibly useful, and relatively new ([Goodfellow et al 2014](https://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf)). The idea builds logically on autoencoders.\n",
    "1. We have a generator (like the decoder part of an autoencoder) that can produce fake data (e.g., an image). \n",
    "2. Then we have a discriminator (a standard binary classifier) that tries to distinguish fake data from real.  \n",
    "3. Then the generator learns to produce more and more accurate images to trick the discriminator -- without ever seeing any real images -- it just has the feedback from the discriminator.\n",
    "4. We are pitting artifical neural networks against themselves to make them stronger. What could possibly go wrong, right?\n",
    "\n",
    "![](https://learning.oreilly.com/library/view/java-deep-learning/9781788997454/assets/2cf8b4f1-7163-4af1-aa4b-6066329d554a.png)\n",
    "\n",
    "For more, see\n",
    "https://towardsdatascience.com/understanding-generative-adversarial-networks-gans-cd6e4651a29"
   ]
  },
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    "Astronomy has seen some interesting uses of autoencoders and GANs in recent years.  For example:\n",
    "    \n",
    "- https://arxiv.org/abs/1702.00403    \n",
    "- https://www.aanda.org/articles/aa/full_html/2017/07/aa30240-16/aa30240-16.html\n",
    "\n",
    "More generally, these are some applications of deep networks to GW detection (my main interest):\n",
    "\n",
    "- https://arxiv.org/pdf/1711.03121.pdf\n",
    "- https://arxiv.org/pdf/1812.02183.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm afraid that is all the time we can spend on deep learning. One of the coolest thing I didn't have time to talk about are the so-called **Masked Autoregressive Flows**, introduced by [Papamakarios et al (2017)](https://arxiv.org/abs/1705.07057). MAF are the latest answer to the long-standing problem of density estimation (remember how tricky it can be to choose the number of bins? Now tackle that problem with the full power of deep learning!).\n",
    "\n",
    "I'm not saying that you are ready for a data science job after these two lectures, but you now know more than the majority of astrophysicists that talk about neural networks, and you have used the two big codes. There are tons of useful videos, tutorials, and online courses that can take you further if you are interested in deep learning.\n",
    "\n",
    "Another tool you may be interested in: https://astronn.readthedocs.io/en/latest/index.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time to get your hands dirty. Galaxies vs quasars with neural networks.\n",
    "\n",
    "Let's go back to our galaxy vs quasar data we've used in a previous exercise. \n",
    "\n",
    "1. Read in SDSS galaxy color data from `solutions/galaxyquasar.csv`. Create arrays for the $(u-g)$, $(g-r)$, $(r-i)$, and $(i-z)$ colors. Also create an array with the class labels where galaxy=$0$ and quasar=$1$. \n",
    "\n",
    "2. Now we're going to fit a neural network classifier. First, scale your data appropriately and do a 30% train/test split.\n",
    "\n",
    "3. Now train the classifier. Use one the package among those we've seen. These include Tensorflow via keras, pytorch, and the [multilayer perceptron classifier](sklearn.neural_network.MLPClassifier) implemented in scikit-learn. My solution uses the latter, but this is an opportunity to pick the one you're most interested in learning. \n",
    "\n",
    "3. Start from a network architecture with a single hidden layer with 5 neurons, using the `adam` solver, the `relu` activation function, and a learninig rate of `0.001`. Plot the resulting ROC curve. \n",
    "\n",
    "4. Now let's optimize the hyperparameters of your network. Explore different hyperparameters and see what fits the data best.  Do you best now to optimize the network architecture. Be creative!\n",
    "\n",
    "5. Is your best result comparable with the simpler classifiers we've seen before? Do we need deep learning here? If yes, which features are captured best?\n",
    "\n",
    "\n",
    "A few tips:\n",
    "\n",
    "- In scikit-learn, remember that you can utilize all availables cores on your machine with `n_jobs=-1`. Print out the classification score for the training data, and the best parameters obtained by the cross validation.\n",
    "- If it takes too long, run the hyperparameter optimization on a subset of the training set. Then retrain the full network using the best hyperparameters only.\n",
    "- On cross validation, for scikit learn we've seen how to use `GridSearchCV` already. For Tensorflow, there's a really cool tool called [Tensorboard](https://www.tensorflow.org/tensorboard) (but don't start learning about it now, it takes a while to get it up and running).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[My solution](https://github.com/dgerosa/astrostatistics_bicocca_2022/blob/main/solutions/S20_galaxyquasar2.ipynb)"
   ]
  },
  {
   "cell_type": "code",
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
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   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "# That's all folks!\n",
    "\n",
    "![](https://imgs.xkcd.com/comics/data_trap.png)\n",
    "\n",
    "Credit: [xkcd 2582](https://xkcd.com/2582/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extra material\n",
    "\n",
    "The repository has two additional lectures on time series data which, unfortunately, I didn't have time to cover. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feedback!\n",
    "\n",
    "Please remember my feedback form\n",
    "\n",
    "https://tinyurl.com/astrostatsfeedback2022"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Many ideas for Master's thesis projects with me and the rest of my group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of the cool things we're thinking about right now... But these are just some ideas, your interests come first (I build a project built for a student who wants to work with me, not find the student for a fixed project)\n",
    "\n",
    "#### From theory... \n",
    "- Spin-spin and spin-orbit couplings in post-Newtonian evolutions of black-hole binaries\n",
    "- Black-hole binaries on eccentric orbits\n",
    "- Exploring the \"irreversibility\" (i.e. entropy) of black hole mergers with gravitational-wave observables\n",
    "\n",
    "#### ... to astro... \n",
    "- Accretion disks around spinning black-holes for LISA\n",
    "- Quasars with velocity offsets hinting at recoiling supermassive black holes\n",
    "- Lives of massive binary stars (tides, mass transfers, common envelope) \n",
    "\n",
    "#### ... and of course some stas / machine learning! \n",
    "- Tackling the LIGO population problem with deep learning\n",
    "- Machine-learning fits to reproduce the mass, spin and recoil of post-merger BHs\n",
    "- Injection and Bayesian recovery of signals from realistic LIGO noise\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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