diff --git a/.idea/02456DeepLearning.iml b/.idea/02456DeepLearning.iml index d0876a7..a816536 100644 --- a/.idea/02456DeepLearning.iml +++ b/.idea/02456DeepLearning.iml @@ -2,7 +2,11 @@ - + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/Project_Default.xml b/.idea/inspectionProfiles/Project_Default.xml index 9cb11bc..7060848 100644 --- a/.idea/inspectionProfiles/Project_Default.xml +++ b/.idea/inspectionProfiles/Project_Default.xml @@ -5,10 +5,17 @@ diff --git a/.idea/misc.xml b/.idea/misc.xml index c3334de..22fb005 100644 --- a/.idea/misc.xml +++ b/.idea/misc.xml @@ -1,4 +1,4 @@ - + \ No newline at end of file diff --git a/2_Feedforward_Python/2.1-EXE-FNN-AutoDif-Nanograd.ipynb b/2_Feedforward_Python/2.1-EXE-FNN-AutoDif-Nanograd.ipynb index f367779..f679ac8 100644 --- a/2_Feedforward_Python/2.1-EXE-FNN-AutoDif-Nanograd.ipynb +++ b/2_Feedforward_Python/2.1-EXE-FNN-AutoDif-Nanograd.ipynb @@ -64,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 2, "metadata": { "id": "Jd4CoEBNzNWS" }, @@ -125,13 +125,7 @@ " return Var((exp(2*self.v)-1)/(exp(2*self.v)+1), lambda: [(self, 4/((exp(-self.v)+exp(self.v))**2))])\n", "\n", " def identity(self):\n", - " return Var(self.v, lambda: [(self, 1.0)])\n", - " \n", - " def exp(self):\n", - " return Var(exp(self.v), lambda: [(self, exp(self.v))])\n", - "\n", - " def log(self):\n", - " return Var(log(self.v), lambda: [(self, self.v ** -1)])\n" + " return Var(self.v, lambda: [(self, 1.0)])\n" ] }, { @@ -145,13 +139,13 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "xk6PeLc3zwPT", - "outputId": "c8d641db-ffe7-4678-bb39-aacef46c0fef" + "outputId": "f310e91c-166c-408a-8eb8-a08d5548c559" }, "outputs": [ { @@ -177,13 +171,13 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "JmKhYgsY0g_o", - "outputId": "d1a8af18-364b-4469-8ecf-382b0062c726" + "outputId": "bbec2bea-8a4b-4fab-de77-ca178e85b30a" }, "outputs": [ { @@ -227,11 +221,9 @@ { "cell_type": "markdown", "source": [ - "**Answer (a)**\n", - "\n", - "The result Var($v$,grad) tells you the value $v$ of that defined object, and grad tells you how much the factor in which the value has changed. For example for $a$ in cell [5], $v=3.0$, and grad$=14$, as $14*a.v=42$, as $f=a*d+a*b$, therefore $a=d+b$. That is $∂f/∂a=d+b=14$. So $a$, $b$ and $d$ are the independent variables in this case, and $c$, $e$, and $f$ the dependent ones. \n", + "**Answer/solution (a)**\n", "\n", - "And for completeness sake: $∂f/∂b=∂f/∂d=a=3$." + "The result Var($v$,grad) tells you the value $v$ of that weight, and grad tells you how much the factor in which the value has changed. For example for a in cell [5], v=3.0, and grad=14, as 14\\*v=42, as a is a factor of f (f=a\\*d+a*b), so d+b. That is $∂f/∂a=d+b=14$. So a, b and d are the independent variables in this case, and c, e, and f the dependent ones. And for completeness sake: $∂f/∂b=∂f/∂d=a=3$." ], "metadata": { "id": "rfg1rpHGAATh" @@ -261,31 +253,38 @@ { "cell_type": "markdown", "source": [ - "**Answer (b)**\n", - "\n", - "The answer provided for exercise (a), assuming it is correct, pretty much describes what happens, and the procedure behind the code, so I refer to there and to the graphviz diagrams of the data schematics for section (b1), (b2), and (b3).\n", - "\n", - "**(b4)** The sequence of calls to backprop is:\n", - "\n", - "1. f.backward() is called\n", - "\n", - "2. f.grad is set to $∂f/∂f=1$\n", - "\n", - "3. Since $f$ is defined as $c+e$, which is stored in f.grad_fn, the backprop moves on to these objects. $c$ is here calculated/shown first in step (4), but $c$ and $e$ could be interchanged, meaning the following steps (4) and (5) could be interchanged regardless.\n", - "\n", - "4. c.grad is set to 1, since $∂f/∂c=1$. $c$ is defined as $c=a*b$, and backprop then moves onto these objects.\n", - "\n", - " 4.1 Starting with $a$, since $∂c/∂a=b=5$, a.grad is set to 5.\n", - "\n", - " 4.2 Now $b$, since $∂c/∂b=a=3$, b.grad is set to 3.\n", - "\n", - "5. e.grad is set to 1, since $∂f/∂e=1$. $e$ is defined as $e=a*d$, and, same as (4), backprop then moves onto these objects.\n", - "\n", - " 5.1 Since $∂e/∂a=d=9$, a.grad would be set to 9, but since a.grad is already set to 5, it therefore becomes a.grad$=d+b=9+5=14$\n", - "\n", - " 5.2 $∂e/∂d=a=3$, so d.grad is set to 3.\n", - "\n", - "Which concludes the f.backprop()\n" + "**Answer/solution (b)**\n", + "\n", + "The answer provided for exercise (a), assuming it is correct, pretty much describes what happens, and the procedure behind the code, so I refer to there.\n", + "\n", + "The sequence of calls to backprop is:\n", + "\\begin{align}\n", + "\\partial_1^{3} &= y_j-t_j\n", + "\\\\\n", + "∂_1^{2}&=\\frac{∂f}{∂c}⋅w_{21}\\cdot h_2(a_1^{2})=w_{21}\\cdot h_2(a_1^{2})\n", + "\\\\\n", + "∂_2^{2}&=\\frac{∂f}{∂e}⋅w_{22}\\cdot h_2(a_2^{2})=w_{22}\\cdot h_2(a_2^{2})\n", + "\\\\\n", + "∂_1^{1}&=\\frac{∂c}{∂a}⋅w_{11}\\cdot h_1(a_1^{1})=5\\cdot w_{11}\\cdot h_1(a_1^{1})\n", + "\\\\\n", + "∂_2^{1}&=\\frac{∂c}{∂b}⋅w_{12}\\cdot h_1(a_2^{1})=3\\cdot w_{12}\\cdot h_1(a_2^{1})\n", + "\\\\\n", + "∂_3^{1}&=\\frac{∂e}{∂a}⋅w_{13}\\cdot h_1(a_3^{1})=9\\cdot w_{13}\\cdot h_1(a_3^{1})\n", + "\\\\\n", + "∂_4^{1}&=\\frac{∂e}{∂d}⋅w_{14}\\cdot h_1(a_4^{1})=3\\cdot w_{14}\\cdot h_1(a_4^{1})\n", + "\\\\\n", + "\\frac{∂f}{∂c}&=1\n", + "\\\\\n", + "\\frac{∂f}{∂e}&=1\n", + "\\\\\n", + "\\frac{∂c}{∂a}&=5\n", + "\\\\\n", + "\\frac{∂c}{∂b}&=3\n", + "\\\\\n", + "\\frac{∂e}{∂a}&=9\n", + "\\\\\n", + "\\frac{∂e}{∂d}&=3\n", + "\\end{align}" ], "metadata": { "id": "iWfEpItIFZqr" @@ -293,7 +292,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 5, "metadata": { "id": "idGr71jYXl26" }, @@ -309,26 +308,26 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 152 }, "id": "KPe30Q2QXzeG", - "outputId": "7e2617ef-374c-420c-c18a-69f2e5423015" + "outputId": "845dae75-0422-469a-c17e-55605b8b890b" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ], - "image/svg+xml": "\n\n\n\n\n\nfirst expression\n\n\n\na\n\na\n\n\n\nf=a*b\n\nf=a*b\n\n\n\na->f=a*b\n\n\ndf/da=b=5\n\n\n\nb\n\nb\n\n\n\nb->f=a*b\n\n\ndf/db=a=3\n\n\n\n" + "image/svg+xml": "\n\n\n\n\n\nfirst expression\n\n\n\na\n\na\n\n\n\nf\n\nf\n\n\n\na->f\n\n\ndf/da=b+d=14\n\n\n\nb\n\nb\n\n\n\nb->f\n\n\ndf/db=a=3\n\n\n\n" }, "metadata": {}, - "execution_count": 70 + "execution_count": 6 } ], "source": [ @@ -337,34 +336,34 @@ "e1.attr(rankdir='LR', size='8,5')\n", "\n", "e1.attr('node', shape='circle')\n", - "e1.edge('a', 'f=a*b', label='df/da=b=5')\n", - "e1.edge('b', 'f=a*b', label='df/db=a=3')\n", + "e1.edge('a', 'f', label='df/da=b+d=14')\n", + "e1.edge('b', 'f', label='df/db=a=3')\n", "\n", "e1" ] }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 237 + "height": 232 }, "id": "0nittR-mZFeX", - "outputId": "e22802f3-50b4-4b4f-bc0d-0c74946f9917" + "outputId": "e590ed50-dec0-47ba-b0d1-3ec491c44bf4" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ], - "image/svg+xml": "\n\n\n\n\n\nsecond expression\n\n\n\na\n\na\n\n\n\nc=a*b\n\nc=a*b\n\n\n\na->c=a*b\n\n\ndc/da=b=5\n\n\n\ne=a*d\n\ne=a*d\n\n\n\na->e=a*d\n\n\nde/da=d=9\n\n\n\nf=c+e\n\nf=c+e\n\n\n\nc=a*b->f=c+e\n\n\ndf/dc=1\n\n\n\nb\n\nb\n\n\n\nb->c=a*b\n\n\ndc/db=a=3\n\n\n\ne=a*d->f=c+e\n\n\ndf/de=1\n\n\n\nd\n\nd\n\n\n\nd->e=a*d\n\n\nde/dd=a=3\n\n\n\n" + "image/svg+xml": "\n\n\n\n\n\nsecond expression\n\n\n\na\n\na\n\n\n\nc\n\nc\n\n\n\na->c\n\n\ndc/da=b=5\n\n\n\ne\n\ne\n\n\n\na->e\n\n\nde/da=d=9\n\n\n\nf\n\nf\n\n\n\nc->f\n\n\ndf/dc=1\n\n\n\nb\n\nb\n\n\n\nb->c\n\n\ndc/db=a=3\n\n\n\ne->f\n\n\ndf/de=1\n\n\n\nd\n\nd\n\n\n\nd->e\n\n\nde/dd=a=3\n\n\n\n" }, "metadata": {}, - "execution_count": 71 + "execution_count": 7 } ], "source": [ @@ -373,12 +372,12 @@ "e2.attr(rankdir='LR', size='8,5')\n", "\n", "e2.attr('node', shape='circle')\n", - "e2.edge('a', 'c=a*b', label='dc/da=b=5')\n", - "e2.edge('b', 'c=a*b', label='dc/db=a=3')\n", - "e2.edge('a', 'e=a*d', label='de/da=d=9')\n", - "e2.edge('d', 'e=a*d', label='de/dd=a=3')\n", - "e2.edge('c=a*b', 'f=c+e', label='df/dc=1')\n", - "e2.edge('e=a*d', 'f=c+e', label='df/de=1')\n", + "e2.edge('a', 'c', label='dc/da=b=5')\n", + "e2.edge('b', 'c', label='dc/db=a=3')\n", + "e2.edge('a', 'e', label='de/da=d=9')\n", + "e2.edge('d', 'e', label='de/dd=a=3')\n", + "e2.edge('c', 'f', label='df/dc=1')\n", + "e2.edge('e', 'f', label='df/de=1')\n", "\n", "e2" ] @@ -396,13 +395,13 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "DCtpJyr-gyX1", - "outputId": "df808b0c-387f-44cf-98ae-59fe43f829dc" + "outputId": "90e15fba-8fec-4646-d2e2-3595f45536f7" }, "outputs": [ { @@ -428,7 +427,7 @@ { "cell_type": "markdown", "source": [ - "**Answer (c)**\n", + "**Answer/solution (c)**\n", "\n", "The gradient is multiplied by 2, as the same gradients are calculated, and are added back to the variable gradient (from code: bp*grad, and bp=2, for the number of backpropagations done)." ], @@ -449,13 +448,13 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "OnyPDQx9lJe0", - "outputId": "f776d31c-5c23-44d7-a864-3f70060db0db" + "outputId": "7c024864-45be-49eb-9711-8363bb23b2f0" }, "outputs": [ { @@ -492,7 +491,7 @@ { "cell_type": "markdown", "source": [ - "**Answer (d)**\n", + "**Answer/solution (d)**\n", "\n", "Even though a is redefined, the old a is still used/saved in f, and can be accessed by grad_fn() calls. What backprop(-1) then does is go back a step. So running the previous code in tandem, it would do two backpropagations, and then undo one/do a forward propagation." ], @@ -517,23 +516,12 @@ "As an example, we could approximate the derivative of the function $f(a)=a^2$ in e.g. the value $a=4$ using the finite difference method. This amounts to inserting the relevant values and approximating the gradient $f'(4)$ with the fraction above. \n" ] }, - { - "cell_type": "markdown", - "source": [ - "**Answer (e)**\n", - "\n", - "Below is the finite difference code snippet, calculating a numerical partial derivative at a point." - ], - "metadata": { - "id": "eIPL2EwAM9kL" - } - }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 17, "metadata": { "id": "9TGil92lSXDN", - "outputId": "8c98fdc6-4669-4a04-e124-a968e159a91a", + "outputId": "8792d165-26e1-426a-e908-e647d5835957", "colab": { "base_uri": "https://localhost:8080/" } @@ -598,7 +586,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 19, "metadata": { "id": "Y6yfMAQ8aduj" }, @@ -612,7 +600,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 20, "metadata": { "id": "4YabfD43ajNh" }, @@ -650,7 +638,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 21, "metadata": { "id": "u1oDngHLapIz" }, @@ -662,10 +650,10 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 22, "metadata": { "id": "Ysfa3FsBavlm", - "outputId": "d3b816c7-00c4-49f3-a649-2d5fb629a347", + "outputId": "63fa02b6-5c7d-465d-d9e5-e844ff13e5f3", "colab": { "base_uri": "https://localhost:8080/", "height": 265 @@ -699,7 +687,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 23, "metadata": { "id": "zac2HHNlgbpm" }, @@ -742,7 +730,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 24, "metadata": { "id": "ij_ieRsAt7Xt" }, @@ -754,22 +742,12 @@ " raise NotImplementedError\n", "\n", " def init_bias(self, n_out):\n", - " raise NotImplementedError\n", - " \n", - " ## Glorot\n", - " def DenseLayer_Glorot_tanh(n_in: int, n_out: int):\n", - " std = 2/(n_in+n_out) # <- replace with proper initialization\n", - " return DenseLayer(n_in, n_out, lambda x: x.tanh(), initializer = NormalInitializer(std))\n", - "\n", - " ## He\n", - " def DenseLayer_He_relu(n_in: int, n_out: int):\n", - " std = 2/n_in # <- replace with proper initialization\n", - " return DenseLayer(n_in, n_out, lambda x: x.relu)" + " raise NotImplementedError" ] }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 25, "metadata": { "id": "eb18N5phuIha" }, @@ -804,7 +782,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 30, "metadata": { "id": "jOLYGnZKuM6W" }, @@ -845,16 +823,7 @@ " node = self.act_fn(node)\n", " out.append(node)\n", "\n", - " return out\n", - " \n", - " def describe(self):\n", - " # return f'Input nodes: {self.n_in}, output nodes: {self.n_out} ||' + f'Weights of input{i}: ' for i in range(len(weights[0]))\n", - " n_out = len(self.weights[0])\n", - " n_in = len(self.weights)\n", - " params = self.parameters()\n", - " description = [[f'Weight from input {j} to node {i}: {params[n_out * j + i]} and bias {params[-(n_out-i)]}' for i\n", - " in range(n_out)] for j in range(n_in)]\n", - " return sum(description, [])" + " return out" ] }, { @@ -879,7 +848,7 @@ { "cell_type": "markdown", "source": [ - "**Answer (f)**\n", + "**Answer/solution (f)**\n", "\n", "The following code snippet is implemented in the previous Var class." ], @@ -890,19 +859,19 @@ { "cell_type": "code", "source": [ - " # def sigmoid(self):\n", - " # return Var(1/(1+exp(-self.v)), lambda: [(self, exp(self.v)/((exp(self.v)+1)**2))])\n", + " def sigmoid(self):\n", + " return Var(1/(1+exp(-self.v)), lambda: [(self, exp(self.v)/((exp(self.v)+1)**2))])\n", "\n", - " # def tanh(self):\n", - " # return Var((exp(2*self.v)-1)/(exp(2*self.v)+1), lambda: [(self, 4/((exp(-self.v)+exp(self.v))**2))])\n", + " def tanh(self):\n", + " return Var((exp(2*self.v)-1)/(exp(2*self.v)+1), lambda: [(self, 4/((exp(-self.v)+exp(self.v))**2))])\n", "\n", - " # def identity(self):\n", - " # return Var(self.v, lambda: [(self, 1.0)])" + " def identity(self):\n", + " return Var(self.v, lambda: [(self, 1.0)])" ], "metadata": { "id": "hSKslIHFcBuM" }, - "execution_count": 83, + "execution_count": 26, "outputs": [] }, { @@ -918,10 +887,10 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 31, "metadata": { "id": "xDEjtePxE7Mv", - "outputId": "2b69ab62-7eb9-4e15-fd57-dd7c7f4d36cc", + "outputId": "4f1c54f2-1f9f-4091-9d56-0768497444ee", "colab": { "base_uri": "https://localhost:8080/" } @@ -931,7 +900,7 @@ "output_type": "stream", "name": "stdout", "text": [ - "[[Var(v=0.0177, grad=0.0000)], [Var(v=0.0357, grad=0.0000)], [Var(v=0.0032, grad=0.0000)], [Var(v=0.0207, grad=0.0000)], [Var(v=0.0263, grad=0.0000)], [Var(v=0.0243, grad=0.0000)], [Var(v=0.0158, grad=0.0000)], [Var(v=0.0050, grad=0.0000)], [Var(v=0.0213, grad=0.0000)], [Var(v=0.0098, grad=0.0000)], [Var(v=0.0274, grad=0.0000)], [Var(v=0.0131, grad=0.0000)], [Var(v=0.0220, grad=0.0000)], [Var(v=0.0150, grad=0.0000)], [Var(v=0.0118, grad=0.0000)], [Var(v=0.0229, grad=0.0000)], [Var(v=0.0422, grad=0.0000)], [Var(v=0.0200, grad=0.0000)], [Var(v=0.0021, grad=0.0000)], [Var(v=0.0069, grad=0.0000)], [Var(v=0.0098, grad=0.0000)], [Var(v=0.0043, grad=0.0000)], [Var(v=0.0308, grad=0.0000)], [Var(v=0.0311, grad=0.0000)], [Var(v=0.0058, grad=0.0000)], [Var(v=0.0155, grad=0.0000)], [Var(v=0.0399, grad=0.0000)], [Var(v=0.0439, grad=0.0000)], [Var(v=0.0047, grad=0.0000)], [Var(v=0.0277, grad=0.0000)], [Var(v=0.0194, grad=0.0000)], [Var(v=0.0067, grad=0.0000)], [Var(v=0.0169, grad=0.0000)], [Var(v=0.0183, grad=0.0000)], [Var(v=0.0212, grad=0.0000)], [Var(v=0.0034, grad=0.0000)], [Var(v=0.0210, grad=0.0000)], [Var(v=0.0080, grad=0.0000)], [Var(v=0.0149, grad=0.0000)], [Var(v=0.0106, grad=0.0000)], [Var(v=0.0075, grad=0.0000)], [Var(v=0.0102, grad=0.0000)], [Var(v=0.0414, grad=0.0000)], [Var(v=0.0127, grad=0.0000)], [Var(v=0.0100, grad=0.0000)], [Var(v=0.0122, grad=0.0000)], [Var(v=0.0266, grad=0.0000)], [Var(v=0.0251, grad=0.0000)], [Var(v=0.0047, grad=0.0000)], [Var(v=0.0257, grad=0.0000)], [Var(v=0.0112, grad=0.0000)], [Var(v=0.0010, grad=0.0000)], [Var(v=0.0280, grad=0.0000)], [Var(v=0.0103, grad=0.0000)], [Var(v=0.0445, grad=0.0000)], [Var(v=0.0391, grad=0.0000)], [Var(v=0.0069, grad=0.0000)], [Var(v=0.0076, grad=0.0000)], [Var(v=0.0146, grad=0.0000)], [Var(v=0.0182, grad=0.0000)], [Var(v=0.0260, grad=0.0000)], [Var(v=0.0128, grad=0.0000)], [Var(v=0.0212, grad=0.0000)], [Var(v=0.0126, grad=0.0000)], [Var(v=0.0092, grad=0.0000)], [Var(v=0.0174, grad=0.0000)], [Var(v=0.0186, grad=0.0000)], [Var(v=0.0328, grad=0.0000)], [Var(v=0.0133, grad=0.0000)], [Var(v=0.0129, grad=0.0000)], [Var(v=0.0051, grad=0.0000)], [Var(v=0.0023, grad=0.0000)], [Var(v=0.0254, grad=0.0000)], [Var(v=0.0149, grad=0.0000)], [Var(v=0.0018, grad=0.0000)], [Var(v=0.0402, grad=0.0000)], [Var(v=0.0092, grad=0.0000)], [Var(v=0.0089, grad=0.0000)], [Var(v=0.0117, grad=0.0000)], [Var(v=0.0419, grad=0.0000)], [Var(v=0.0064, grad=0.0000)], [Var(v=0.0038, grad=0.0000)], [Var(v=0.0191, grad=0.0000)], [Var(v=0.0026, grad=0.0000)], [Var(v=0.0097, grad=0.0000)], [Var(v=0.0042, grad=0.0000)], [Var(v=0.0036, grad=0.0000)], [Var(v=0.0108, grad=0.0000)], [Var(v=0.0197, grad=0.0000)], [Var(v=0.0205, grad=0.0000)], [Var(v=0.0152, grad=0.0000)], [Var(v=0.0062, grad=0.0000)], [Var(v=0.0114, grad=0.0000)], [Var(v=0.0237, grad=0.0000)], [Var(v=0.0163, grad=0.0000)], [Var(v=0.0196, grad=0.0000)], [Var(v=0.0109, grad=0.0000)], [Var(v=0.0018, grad=0.0000)], [Var(v=0.0379, grad=0.0000)], [Var(v=0.0116, grad=0.0000)], [Var(v=0.0249, grad=0.0000)], [Var(v=0.0113, grad=0.0000)], [Var(v=0.0172, grad=0.0000)], [Var(v=0.0121, grad=0.0000)], [Var(v=0.0354, grad=0.0000)]]\n" + "[[Var(v=0.0002, grad=0.0000)], [Var(v=0.0005, grad=0.0000)], [Var(v=0.0000, grad=0.0000)], [Var(v=-0.0086, grad=0.0000)], [Var(v=0.0003, grad=0.0000)], [Var(v=0.0003, grad=0.0000)], [Var(v=0.0002, grad=0.0000)], [Var(v=-0.0021, grad=0.0000)], [Var(v=-0.0089, grad=0.0000)], [Var(v=0.0001, grad=0.0000)], [Var(v=0.0004, grad=0.0000)], [Var(v=-0.0055, grad=0.0000)], [Var(v=0.0003, grad=0.0000)], [Var(v=-0.0063, grad=0.0000)], [Var(v=0.0002, grad=0.0000)], [Var(v=0.0003, grad=0.0000)], [Var(v=0.0005, grad=0.0000)], [Var(v=0.0003, grad=0.0000)], [Var(v=0.0000, grad=0.0000)], [Var(v=0.0001, grad=0.0000)], [Var(v=-0.0041, grad=0.0000)], [Var(v=-0.0018, grad=0.0000)], [Var(v=0.0004, grad=0.0000)], [Var(v=0.0004, grad=0.0000)], [Var(v=-0.0024, grad=0.0000)], [Var(v=-0.0065, grad=0.0000)], [Var(v=0.0005, grad=0.0000)], [Var(v=0.0006, grad=0.0000)], [Var(v=0.0001, grad=0.0000)], [Var(v=0.0004, grad=0.0000)], [Var(v=-0.0081, grad=0.0000)], [Var(v=0.0001, grad=0.0000)], [Var(v=-0.0071, grad=0.0000)], [Var(v=-0.0077, grad=0.0000)], [Var(v=0.0003, grad=0.0000)], [Var(v=-0.0014, grad=0.0000)], [Var(v=-0.0088, grad=0.0000)], [Var(v=-0.0033, grad=0.0000)], [Var(v=-0.0062, grad=0.0000)], [Var(v=-0.0044, grad=0.0000)], [Var(v=-0.0031, grad=0.0000)], [Var(v=-0.0042, grad=0.0000)], [Var(v=0.0005, grad=0.0000)], [Var(v=-0.0053, grad=0.0000)], [Var(v=-0.0042, grad=0.0000)], [Var(v=-0.0051, grad=0.0000)], [Var(v=0.0003, grad=0.0000)], [Var(v=0.0003, grad=0.0000)], [Var(v=-0.0019, grad=0.0000)], [Var(v=0.0003, grad=0.0000)], [Var(v=0.0001, grad=0.0000)], [Var(v=0.0000, grad=0.0000)], [Var(v=0.0004, grad=0.0000)], [Var(v=-0.0043, grad=0.0000)], [Var(v=0.0006, grad=0.0000)], [Var(v=0.0005, grad=0.0000)], [Var(v=-0.0029, grad=0.0000)], [Var(v=-0.0032, grad=0.0000)], [Var(v=0.0002, grad=0.0000)], [Var(v=-0.0076, grad=0.0000)], [Var(v=0.0003, grad=0.0000)], [Var(v=-0.0054, grad=0.0000)], [Var(v=-0.0088, grad=0.0000)], [Var(v=0.0002, grad=0.0000)], [Var(v=0.0001, grad=0.0000)], [Var(v=-0.0073, grad=0.0000)], [Var(v=-0.0078, grad=0.0000)], [Var(v=0.0004, grad=0.0000)], [Var(v=-0.0056, grad=0.0000)], [Var(v=0.0002, grad=0.0000)], [Var(v=-0.0021, grad=0.0000)], [Var(v=-0.0010, grad=0.0000)], [Var(v=0.0003, grad=0.0000)], [Var(v=0.0002, grad=0.0000)], [Var(v=-0.0008, grad=0.0000)], [Var(v=0.0005, grad=0.0000)], [Var(v=-0.0039, grad=0.0000)], [Var(v=-0.0037, grad=0.0000)], [Var(v=-0.0049, grad=0.0000)], [Var(v=0.0005, grad=0.0000)], [Var(v=0.0001, grad=0.0000)], [Var(v=0.0000, grad=0.0000)], [Var(v=-0.0080, grad=0.0000)], [Var(v=-0.0011, grad=0.0000)], [Var(v=-0.0040, grad=0.0000)], [Var(v=-0.0017, grad=0.0000)], [Var(v=-0.0015, grad=0.0000)], [Var(v=-0.0045, grad=0.0000)], [Var(v=-0.0083, grad=0.0000)], [Var(v=-0.0086, grad=0.0000)], [Var(v=0.0002, grad=0.0000)], [Var(v=-0.0026, grad=0.0000)], [Var(v=-0.0048, grad=0.0000)], [Var(v=0.0003, grad=0.0000)], [Var(v=-0.0068, grad=0.0000)], [Var(v=-0.0082, grad=0.0000)], [Var(v=0.0001, grad=0.0000)], [Var(v=0.0000, grad=0.0000)], [Var(v=0.0005, grad=0.0000)], [Var(v=-0.0048, grad=0.0000)], [Var(v=0.0003, grad=0.0000)], [Var(v=-0.0047, grad=0.0000)], [Var(v=-0.0072, grad=0.0000)], [Var(v=0.0002, grad=0.0000)], [Var(v=0.0005, grad=0.0000)]]\n" ] } ], @@ -957,7 +926,7 @@ { "cell_type": "markdown", "source": [ - "**Answer (g)**\n", + "**Answer/solution (g)**\n", "\n", "The following code snippet has been implemented ind the previous DenseLayer Class, as the function forward." ], @@ -968,27 +937,27 @@ { "cell_type": "code", "source": [ - "# def forward(self, single_input: Sequence[Var]) -> Sequence[Var]:\n", - "# # self.weights is a matrix with dimension n_in x n_out. We check that the dimensionality of the input\n", - "# # to the current layer matches the number of nodes in the current layer\n", - "# assert len(self.weights) == len(single_input), \"weights and single_input must match in first dimension\"\n", - "# weights = self.weights\n", - "# out = []\n", - "# # For some given data point single_input, we now want to calculate the resulting value in each node in the current layer\n", - "# # We therefore loop over the (number of) nodes in the current layer:\n", - "# for j in range(len(weights[0])):\n", - "# # Initialize the node value depending on its corresponding parameters.\n", - "# node = self.bias[j] # <- Insert code\n", - "# # We now finish the linear transformation corresponding to the parameters of the currently considered node.\n", - "# for i in range(len(single_input)):\n", - "# node += weights[i][j] * single_input[i] # <- Insert code\n", - "# node = self.act_fn(node)\n", - "# out.append(node)" + "def forward(self, single_input: Sequence[Var]) -> Sequence[Var]:\n", + " # self.weights is a matrix with dimension n_in x n_out. We check that the dimensionality of the input\n", + " # to the current layer matches the number of nodes in the current layer\n", + " assert len(self.weights) == len(single_input), \"weights and single_input must match in first dimension\"\n", + " weights = self.weights\n", + " out = []\n", + " # For some given data point single_input, we now want to calculate the resulting value in each node in the current layer\n", + " # We therefore loop over the (number of) nodes in the current layer:\n", + " for j in range(len(weights[0])):\n", + " # Initialize the node value depending on its corresponding parameters.\n", + " node = self.bias[j] # <- Insert code\n", + " # We now finish the linear transformation corresponding to the parameters of the currently considered node.\n", + " for i in range(len(single_input)):\n", + " node += weights[i][j] * single_input[i] # <- Insert code\n", + " node = self.act_fn(node)\n", + " out.append(node)" ], "metadata": { "id": "Eg210D_gectN" }, - "execution_count": 85, + "execution_count": null, "outputs": [] }, { @@ -1005,9 +974,9 @@ { "cell_type": "markdown", "source": [ - "**Answer (h)**\n", + "**Answer/solution (h)**\n", "\n", - "The following code snippet has been implemented in the DenseLayer class. The intended behaviour, is that a .describe() call would show a string describing the weight from an input to the connected node, and the bias." + "The following code snippet has been implemented in the DenseLayer class." ], "metadata": { "id": "vc2akTHMeqFD" @@ -1015,74 +984,13 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": null, "metadata": { "id": "iac-VwYGFtGm" }, "outputs": [], "source": [ - " def describe(self):\n", - " # return f'Input nodes: {self.n_in}, output nodes: {self.n_out} ||' + f'Weights of input{i}: ' for i in range(len(weights[0]))\n", - " n_out = len(self.weights[0])\n", - " n_in = len(self.weights)\n", - " params = self.parameters()\n", - " description = [[f'Weight from input {j} to node {i}: {params[n_out * j + i]} and bias {params[-(n_out-i)]}' for i\n", - " in range(n_out)] for j in range(n_in)]\n", - " return sum(description, [])" - ] - }, - { - "cell_type": "markdown", - "source": [ - "And for this following code snippet, the neural network is described in full, assuming the input is of the format of the previously used definition of NN, which is a list of DenseLayer class calls." - ], - "metadata": { - "id": "u9-p1YMzm1SC" - } - }, - { - "cell_type": "code", - "source": [ - "def describe_NN(NN):\n", - " for i, j in enumerate(NN):\n", - " if i==0:\n", - " print(f'Layer {i} (input) to {i+1}:')\n", - " elif i==len(NN)-1:\n", - " print(f'Layer {i} to {i+1} (output):')\n", - " else:\n", - " print(f'Layer {i} to {i+1}:')\n", - " for x in j.describe():\n", - " print(x)\n", - "\n", - "describe_NN(NN=NN)" - ], - "metadata": { - "id": "AfDvTME8nJ8x", - "outputId": "78b6829e-5b5d-4104-f120-b1ae5f53a554", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "execution_count": 87, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Layer 0 (input) to 1:\n", - "Weight from input 0 to node 0: Var(v=0.0033, grad=0.0000) and bias Var(v=0.0000, grad=0.0000)\n", - "Weight from input 0 to node 1: Var(v=0.0100, grad=0.0000) and bias Var(v=0.0000, grad=0.0000)\n", - "Weight from input 0 to node 2: Var(v=0.0786, grad=0.0000) and bias Var(v=0.0000, grad=0.0000)\n", - "Weight from input 0 to node 3: Var(v=-0.0527, grad=0.0000) and bias Var(v=0.0000, grad=0.0000)\n", - "Weight from input 0 to node 4: Var(v=0.1468, grad=0.0000) and bias Var(v=0.0000, grad=0.0000)\n", - "Layer 1 to 2 (output):\n", - "Weight from input 0 to node 0: Var(v=-0.1226, grad=0.0000) and bias Var(v=0.0000, grad=0.0000)\n", - "Weight from input 1 to node 0: Var(v=0.0532, grad=0.0000) and bias Var(v=0.0000, grad=0.0000)\n", - "Weight from input 2 to node 0: Var(v=-0.0130, grad=0.0000) and bias Var(v=0.0000, grad=0.0000)\n", - "Weight from input 3 to node 0: Var(v=0.2579, grad=0.0000) and bias Var(v=0.0000, grad=0.0000)\n", - "Weight from input 4 to node 0: Var(v=0.1736, grad=0.0000) and bias Var(v=0.0000, grad=0.0000)\n" - ] - } + "# Insert code here and in the DenseLayer class" ] }, { @@ -1098,39 +1006,11 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": null, "metadata": { - "id": "1FcylHqLTl-Z", - "outputId": "127cc59b-094a-417a-adfc-eae3bc363847", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 282 - } + "id": "1FcylHqLTl-Z" }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "[]" - ] - }, - "metadata": {}, - "execution_count": 88 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - } - } - ], + "outputs": [], "source": [ "x = np.linspace(-6, 6, 100)\n", "\n", @@ -1152,29 +1032,11 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": null, "metadata": { - "id": "oOL2UolJFtHL", - "outputId": "490293ad-632a-4cef-811b-a7866c5edb30", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 341 - } + "id": "oOL2UolJFtHL" }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - } - } - ], + "outputs": [], "source": [ "# Testing all activation layers\n", "\n", @@ -1235,22 +1097,9 @@ "Comment: If you want to be more advanced then try to make a universal initializer taking both the activation function and type (Glorot or He) as argument." ] }, - { - "cell_type": "markdown", - "source": [ - "**Answer (i)**\n", - "\n", - "Below is the code snippet of the attempt at implementing the Glorot and He initialization schemes. These are also found implemented in the Initializer class from before.\n", - "\n", - "A numerical test would simply be to plot the activation values of each layer, which should result in very alike distributions around 0 mean (see figure 6 of the Glorot paper). Additionally, if it holds, the ratio between ${\\mathbf z^i}$ to ${\\mathbf z^{i+1}}$ should be around 0.8, and 0.5 for the more common method ($1/\\sqrt(n)$) (see the Glorot paper section 4.2.2)." - ], - "metadata": { - "id": "zfJAtlNrr9H2" - } - }, { "cell_type": "code", - "execution_count": 90, + "execution_count": null, "metadata": { "id": "Qyk01CgaFtGt" }, @@ -1258,23 +1107,13 @@ "source": [ "## Glorot\n", "def DenseLayer_Glorot_tanh(n_in: int, n_out: int):\n", - " std = 2/(n_in+n_out) # <- replace with proper initialization\n", + " std = 0.0 # <- replace with proper initialization\n", " return DenseLayer(n_in, n_out, lambda x: x.tanh(), initializer = NormalInitializer(std))\n", "\n", "## He\n", "def DenseLayer_He_relu(n_in: int, n_out: int):\n", - " std = 2/n_in # <- replace with proper initialization\n", - " return DenseLayer(n_in, n_out, lambda x: x.relu(), initializer = NormalInitializer(std))\n", - "\n", - "def DenseLayer_init(n_in: int, n_out: int, act_fn, init_type: str=None):\n", - " assert init_type in ['Glorot', 'He'], f\"Choose initialization type. Must be either 'Glorot' or 'He'\"\n", - " if init_type == 'Glorot':\n", - " alpha = 1\n", - " std = 2*alpha/(n_in+n_out)\n", - " elif init_type == 'He':\n", - " alpha = 2\n", - " std = alpha/n_in\n", - " return DenseLayer(n_in, n_out, act_fn, initializer = NormalInitializer(std))" + " std = 0.0 # <- replace with proper initialization\n", + " return DenseLayer(n_in, n_out, lambda x: x.relu(), initializer = NormalInitializer(std))" ] }, { @@ -1290,34 +1129,15 @@ "Hints: Use the [assert](https://www.w3schools.com/python/ref_keyword_assert.asp), the nparray_to_Var and the Var_to_nparray commands. " ] }, - { - "cell_type": "markdown", - "source": [ - "**Answer (j)**\n", - "\n", - "For the unit test a simple 1-2-1 layer network is made, with the ConstantInitializer initializer, and the identity activation function, for both in and out of the hidden layer of 2 nodes. This means the weights will remain 1, and the activation will be the input itself. Therefore the output layer will output half the value of the input, which then will be tested for, seen below." - ], - "metadata": { - "id": "knpMcVWYxpEM" - } - }, { "cell_type": "code", - "execution_count": 91, + "execution_count": null, "metadata": { "id": "k0miqRUAFtHl" }, "outputs": [], "source": [ - "UT_NN = [\n", - " DenseLayer(1, 2, lambda x: x.identity(), initializer=ConstantInitializer()),\n", - " DenseLayer(2, 1, lambda x: x.identity(), initializer=ConstantInitializer())\n", - "]\n", - "\n", - "input_values = Var_to_nparray(x_train[:5])\n", - "test_nn_results = forward(x_train[:5], UT_NN)\n", - "adjusted_test_nn_results = Var_to_nparray([[i[0]*Var(0.5)] for i in test_nn_results])\n", - "assert all(input_values == adjusted_test_nn_results)" + "# Insert code here" ] }, { @@ -1333,7 +1153,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": null, "metadata": { "id": "I2eDYKvAFtHq" }, @@ -1361,7 +1181,7 @@ "id": "SrwSJ2UWFtHu" }, "source": [ - "## Exercise k) Implement cross entropy loss\n", + "## Exercise j) Implement cross entropy loss\n", "\n", "Insert code below to implement cross-entropy loss for general dimensionality of $t$. Use a logits formulation:\n", "$$\n", @@ -1394,33 +1214,19 @@ " return Var(log(self.v), lambda: [(self, self.v ** -1)])" ] }, - { - "cell_type": "markdown", - "source": [ - "**Answer (k)**\n", - "\n", - "Below is the implemented cross_entropy_loss function. With the aforementioned hint, where an expression for log and exp has been implemented in the Var class, these are used for easier calculation." - ], - "metadata": { - "id": "KfTG1cK8GAj-" - } - }, { "cell_type": "code", - "execution_count": 93, + "execution_count": null, "metadata": { "id": "6nMuxyfzFtHv" }, "outputs": [], "source": [ - "def cross_entropy_loss_single(t,h):\n", - " Loss_1 = Var(0.0)\n", - " Loss_2 = Var(0,0)\n", - " for i in range(len(t)):\n", - " Loss_1 += -t[i]*h[i]\n", - " Loss_2 += h[i].exp()\n", - " Loss = Loss_1+Loss_2.log()\n", - " return Loss\n" + "def cross_entropy_loss(t, h):\n", + " \n", + " Loss = Var(0.0)\n", + " # Insert code here\n", + " return Loss" ] }, { @@ -1436,7 +1242,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": null, "metadata": { "id": "iHyfPPI9Qqwu" }, @@ -1464,36 +1270,11 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": null, "metadata": { - "id": "_rGt1bq_Q7uk", - "outputId": "4eb515f6-3afe-4b1e-b411-3fecc0cf96f1", - "colab": { - "base_uri": "https://localhost:8080/" - } + "id": "_rGt1bq_Q7uk" }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Layer 0 \n", - " Weights: [[Var(v=-0.0105, grad=6.0447), Var(v=0.0666, grad=3.9700), Var(v=0.0947, grad=-1.9283), Var(v=0.0453, grad=10.1192), Var(v=0.0194, grad=-5.9956)]] Biases: [Var(v=0.0000, grad=-5.4943), Var(v=0.0000, grad=3.4099), Var(v=0.0000, grad=-1.6562), Var(v=0.0000, grad=8.6917), Var(v=0.0000, grad=-5.1498)]\n", - "Layer 1 \n", - " Weights: [[Var(v=-0.0630, grad=1.0121)], [Var(v=-0.0389, grad=-6.7952)], [Var(v=0.0189, grad=-9.6631)], [Var(v=-0.0992, grad=-4.6256)], [Var(v=0.0588, grad=-1.9818)]] Biases: [Var(v=0.0000, grad=-0.4464)]\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "[None, None]" - ] - }, - "metadata": {}, - "execution_count": 95 - } - ], + "outputs": [], "source": [ "[print('Layer', i, '\\n', NN[i]) for i in range(len(NN))] " ] @@ -1524,7 +1305,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": null, "metadata": { "id": "01ePmzBzRtdh" }, @@ -1555,55 +1336,11 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": null, "metadata": { - "id": "dhAI7eyeznia", - "outputId": "a0314402-c630-4fdb-edba-5e7c7f8f69e3", - "colab": { - "base_uri": "https://localhost:8080/" - } + "id": "dhAI7eyeznia" }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Network before update:\n", - "Layer 0 \n", - " Weights: [[Var(v=0.0270, grad=-5.3320), Var(v=0.0078, grad=-0.9050), Var(v=0.0973, grad=-3.1266), Var(v=-0.0543, grad=11.3716), Var(v=-0.0981, grad=-0.7954), Var(v=-0.0222, grad=5.7626), Var(v=-0.0706, grad=2.7569), Var(v=-0.0704, grad=7.4011), Var(v=-0.1636, grad=-8.2387), Var(v=-0.0771, grad=8.9566), Var(v=0.0967, grad=-0.7338), Var(v=-0.0008, grad=1.3349), Var(v=0.0501, grad=-2.1023), Var(v=-0.0013, grad=-12.6701), Var(v=-0.0332, grad=3.5947)]] Biases: [Var(v=0.0000, grad=-4.5807), Var(v=0.0000, grad=-0.7775), Var(v=0.0000, grad=-2.6861), Var(v=0.0000, grad=-10.3345), Var(v=0.0000, grad=0.7229), Var(v=0.0000, grad=-5.2371), Var(v=0.0000, grad=-2.5055), Var(v=0.0000, grad=-6.7262), Var(v=0.0000, grad=7.4874), Var(v=0.0000, grad=-8.1398), Var(v=0.0000, grad=-0.6304), Var(v=0.0000, grad=-1.2132), Var(v=0.0000, grad=-1.8061), Var(v=0.0000, grad=11.5147), Var(v=0.0000, grad=-3.2669)]\n", - "Layer 1 \n", - " Weights: [[Var(v=-0.0390, grad=-0.0835), Var(v=-0.0476, grad=0.0000), Var(v=0.0922, grad=0.0000), Var(v=-0.0844, grad=0.0000), Var(v=-0.0332, grad=0.0000), Var(v=0.0803, grad=-0.4865), Var(v=-0.0124, grad=0.0000), Var(v=0.0854, grad=0.1142), Var(v=-0.0006, grad=-0.4639), Var(v=-0.0230, grad=0.0000), Var(v=0.1281, grad=0.0000), Var(v=-0.0418, grad=0.0423), Var(v=-0.0746, grad=-0.1277), Var(v=-0.0646, grad=0.0000), Var(v=-0.0117, grad=0.0000), Var(v=-0.0118, grad=-0.1666), Var(v=0.0823, grad=-0.0767), Var(v=-0.2360, grad=0.0000), Var(v=-0.0025, grad=0.0809), Var(v=-0.1359, grad=0.0000), Var(v=-0.0100, grad=0.0000), Var(v=0.1033, grad=0.0000), Var(v=0.0493, grad=-0.3211), Var(v=0.0127, grad=0.2682), Var(v=-0.0448, grad=0.0000), Var(v=0.1625, grad=0.0000), Var(v=-0.1058, grad=0.0000), Var(v=-0.0722, grad=0.0000), Var(v=0.2528, grad=-0.0357), Var(v=-0.0802, grad=0.0000), Var(v=0.0956, grad=0.0000), Var(v=0.0597, grad=0.3610), Var(v=0.0671, grad=-0.0360), Var(v=-0.0841, grad=0.0000), Var(v=-0.1310, grad=-0.0483), Var(v=0.0974, grad=-0.4589), Var(v=-0.0234, grad=0.0000), Var(v=-0.0579, grad=0.0000), Var(v=-0.0931, grad=0.0000), Var(v=-0.0783, grad=0.2941), Var(v=0.0973, grad=0.0000), Var(v=0.1397, grad=0.0000), Var(v=-0.1750, grad=0.0000), Var(v=-0.0723, grad=0.0000), Var(v=0.1620, grad=-0.2388), Var(v=-0.0200, grad=0.0913), Var(v=0.0257, grad=0.1634), Var(v=-0.0211, grad=0.3584), Var(v=0.0384, grad=0.0000), Var(v=0.0985, grad=-0.1399)], [Var(v=0.0676, grad=-0.0242), Var(v=-0.1111, grad=0.0000), Var(v=-0.0885, grad=0.0000), Var(v=-0.0549, grad=0.0000), Var(v=-0.0359, grad=0.0000), Var(v=0.1374, grad=-0.1411), Var(v=-0.0769, grad=0.0000), Var(v=-0.1256, grad=0.0331), Var(v=-0.0267, grad=-0.1345), Var(v=-0.1051, grad=0.0000), Var(v=-0.0182, grad=0.0000), Var(v=0.0981, grad=0.0123), Var(v=0.0551, grad=-0.0370), Var(v=0.1001, grad=0.0000), Var(v=-0.0761, grad=0.0000), Var(v=-0.2327, grad=-0.0483), Var(v=0.0322, grad=-0.0222), Var(v=0.0721, grad=0.0000), Var(v=-0.0382, grad=0.0235), Var(v=-0.0034, grad=0.0000), Var(v=-0.0778, grad=0.0000), Var(v=-0.1352, grad=0.0000), Var(v=-0.0128, grad=-0.0931), Var(v=0.0449, grad=0.0778), Var(v=-0.0361, grad=0.0000), Var(v=0.1519, grad=0.0000), Var(v=0.1159, grad=0.0000), Var(v=-0.0072, grad=0.0000), Var(v=-0.1878, grad=-0.0104), Var(v=-0.1459, grad=0.0000), Var(v=0.0663, grad=0.0000), Var(v=0.0186, grad=0.1047), Var(v=0.1037, grad=-0.0104), Var(v=0.0586, grad=0.0000), Var(v=0.2906, grad=-0.0140), Var(v=0.0672, grad=-0.1331), Var(v=0.0189, grad=0.0000), Var(v=0.1071, grad=0.0000), Var(v=0.0218, grad=0.0000), Var(v=-0.0423, grad=0.0853), Var(v=-0.0765, grad=0.0000), Var(v=-0.0807, grad=0.0000), Var(v=-0.0054, grad=0.0000), Var(v=0.1035, grad=0.0000), Var(v=-0.0362, grad=-0.0693), Var(v=-0.1275, grad=0.0265), Var(v=-0.0164, grad=0.0474), Var(v=0.1468, grad=0.1039), Var(v=-0.0697, grad=0.0000), Var(v=-0.0300, grad=-0.0406)], [Var(v=-0.0806, grad=-0.3006), Var(v=-0.0214, grad=0.0000), Var(v=-0.1360, grad=0.0000), Var(v=0.1583, grad=0.0000), Var(v=-0.0618, grad=0.0000), Var(v=-0.0047, grad=-1.7516), Var(v=-0.0682, grad=0.0000), Var(v=0.0121, grad=0.4113), Var(v=0.0037, grad=-1.6705), Var(v=-0.0587, grad=0.0000), Var(v=-0.1384, grad=0.0000), Var(v=0.0566, grad=0.1522), Var(v=0.0318, grad=-0.4600), Var(v=-0.0418, grad=0.0000), Var(v=0.0360, grad=0.0000), Var(v=0.0610, grad=-0.5998), Var(v=0.0026, grad=-0.2761), Var(v=0.0022, grad=0.0000), Var(v=0.1488, grad=0.2914), Var(v=-0.1206, grad=0.0000), Var(v=-0.0494, grad=0.0000), Var(v=-0.0458, grad=0.0000), Var(v=0.0328, grad=-1.1563), Var(v=-0.1299, grad=0.9657), Var(v=0.1424, grad=0.0000), Var(v=-0.1040, grad=0.0000), Var(v=-0.0916, grad=0.0000), Var(v=-0.1299, grad=0.0000), Var(v=-0.0199, grad=-0.1286), Var(v=-0.0362, grad=0.0000), Var(v=-0.0833, grad=0.0000), Var(v=0.0518, grad=1.2999), Var(v=0.0969, grad=-0.1297), Var(v=0.0509, grad=0.0000), Var(v=0.0627, grad=-0.1738), Var(v=0.2062, grad=-1.6525), Var(v=0.0017, grad=0.0000), Var(v=-0.1117, grad=0.0000), Var(v=-0.2394, grad=0.0000), Var(v=0.0502, grad=1.0591), Var(v=0.1081, grad=0.0000), Var(v=-0.2020, grad=0.0000), Var(v=-0.0903, grad=0.0000), Var(v=0.0227, grad=0.0000), Var(v=0.0791, grad=-0.8600), Var(v=0.0045, grad=0.3288), Var(v=0.1653, grad=0.5884), Var(v=0.0229, grad=1.2906), Var(v=-0.0703, grad=0.0000), Var(v=-0.0175, grad=-0.5036)], [Var(v=0.1077, grad=0.1583), Var(v=-0.1650, grad=-0.3686), Var(v=0.0207, grad=0.3777), Var(v=-0.0313, grad=0.2460), Var(v=0.0248, grad=0.0000), Var(v=0.1004, grad=0.9222), Var(v=0.0362, grad=-1.1007), Var(v=-0.1163, grad=0.0000), Var(v=-0.1572, grad=0.8795), Var(v=-0.1289, grad=0.0000), Var(v=-0.0971, grad=-0.5373), Var(v=0.0182, grad=0.0000), Var(v=-0.0458, grad=0.0000), Var(v=-0.1841, grad=0.0000), Var(v=-0.0308, grad=0.0000), Var(v=0.0002, grad=0.3158), Var(v=-0.0334, grad=0.0000), Var(v=0.0651, grad=0.0000), Var(v=0.0391, grad=0.0000), Var(v=0.0639, grad=-0.3150), Var(v=-0.0647, grad=0.0000), Var(v=-0.1526, grad=0.0000), Var(v=0.0712, grad=0.0000), Var(v=-0.0821, grad=-0.5084), Var(v=0.1065, grad=0.0000), Var(v=-0.0261, grad=-0.1646), Var(v=0.0579, grad=-0.3076), Var(v=0.0927, grad=-0.4522), Var(v=0.0451, grad=0.0000), Var(v=0.0825, grad=0.0000), Var(v=-0.0115, grad=-0.6270), Var(v=0.0978, grad=-0.6844), Var(v=-0.0233, grad=0.0683), Var(v=-0.0133, grad=0.0000), Var(v=0.0604, grad=0.0915), Var(v=-0.1979, grad=0.8700), Var(v=-0.2472, grad=0.0000), Var(v=-0.1374, grad=1.3173), Var(v=-0.0742, grad=0.0000), Var(v=0.1459, grad=-0.5576), Var(v=-0.0657, grad=0.0000), Var(v=-0.0468, grad=-0.0365), Var(v=-0.0223, grad=0.0000), Var(v=0.0151, grad=0.0000), Var(v=0.0227, grad=0.4527), Var(v=-0.0045, grad=-0.1731), Var(v=-0.0171, grad=-0.3098), Var(v=0.2213, grad=-0.6795), Var(v=-0.0094, grad=-0.0099), Var(v=-0.0221, grad=0.2651)], [Var(v=0.1169, grad=0.2858), Var(v=0.1415, grad=-0.6656), Var(v=0.0391, grad=0.6820), Var(v=0.1241, grad=0.4442), Var(v=-0.0812, grad=0.0000), Var(v=0.0831, grad=1.6652), Var(v=-0.0600, grad=-1.9876), Var(v=0.1905, grad=0.0000), Var(v=0.0563, grad=1.5881), Var(v=0.0015, grad=0.0000), Var(v=-0.0601, grad=-0.9702), Var(v=-0.0509, grad=0.0000), Var(v=-0.0399, grad=0.0000), Var(v=-0.1120, grad=0.0000), Var(v=-0.1441, grad=0.0000), Var(v=-0.0181, grad=0.5702), Var(v=-0.0510, grad=0.0000), Var(v=-0.1634, grad=0.0000), Var(v=-0.0509, grad=0.0000), Var(v=0.0092, grad=-0.5688), Var(v=0.0240, grad=0.0000), Var(v=-0.0022, grad=0.0000), Var(v=-0.0530, grad=0.0000), Var(v=0.0803, grad=-0.9181), Var(v=-0.1382, grad=0.0000), Var(v=0.0191, grad=-0.2972), Var(v=0.0939, grad=-0.5555), Var(v=-0.0371, grad=-0.8166), Var(v=-0.0037, grad=0.0000), Var(v=-0.0844, grad=0.0000), Var(v=0.1200, grad=-1.1323), Var(v=-0.1637, grad=-1.2358), Var(v=0.0985, grad=0.1233), Var(v=0.1033, grad=0.0000), Var(v=0.1776, grad=0.1652), Var(v=-0.0982, grad=1.5710), Var(v=0.2131, grad=0.0000), Var(v=0.0437, grad=2.3786), Var(v=-0.0647, grad=0.0000), Var(v=0.0641, grad=-1.0068), Var(v=0.0044, grad=0.0000), Var(v=0.1939, grad=-0.0658), Var(v=-0.0078, grad=0.0000), Var(v=-0.1106, grad=0.0000), Var(v=0.0244, grad=0.8175), Var(v=0.1192, grad=-0.3125), Var(v=-0.0234, grad=-0.5593), Var(v=0.2013, grad=-1.2269), Var(v=0.0029, grad=-0.0179), Var(v=0.0827, grad=0.4787)], [Var(v=-0.0766, grad=0.0647), Var(v=-0.0060, grad=-0.1506), Var(v=0.1090, grad=0.1544), Var(v=-0.0346, grad=0.1005), Var(v=0.0216, grad=0.0000), Var(v=0.1109, grad=0.3769), Var(v=0.0906, grad=-0.4499), Var(v=0.1045, grad=0.0000), Var(v=0.0849, grad=0.3594), Var(v=-0.0064, grad=0.0000), Var(v=0.0639, grad=-0.2196), Var(v=-0.1070, grad=0.0000), Var(v=0.1458, grad=0.0000), Var(v=0.0159, grad=0.0000), Var(v=0.0746, grad=0.0000), Var(v=-0.0602, grad=0.1291), Var(v=0.1271, grad=0.0000), Var(v=0.0182, grad=0.0000), Var(v=-0.0449, grad=0.0000), Var(v=0.0814, grad=-0.1287), Var(v=0.0085, grad=0.0000), Var(v=-0.0313, grad=0.0000), Var(v=-0.0481, grad=0.0000), Var(v=-0.1895, grad=-0.2078), Var(v=-0.0847, grad=0.0000), Var(v=0.0535, grad=-0.0673), Var(v=-0.0452, grad=-0.1257), Var(v=0.1459, grad=-0.1848), Var(v=0.0491, grad=0.0000), Var(v=-0.1025, grad=0.0000), Var(v=0.0874, grad=-0.2563), Var(v=0.1128, grad=-0.2797), Var(v=-0.1252, grad=0.0279), Var(v=0.0693, grad=0.0000), Var(v=-0.1169, grad=0.0374), Var(v=0.0513, grad=0.3556), Var(v=-0.1101, grad=0.0000), Var(v=-0.1668, grad=0.5384), Var(v=-0.2142, grad=0.0000), Var(v=0.0562, grad=-0.2279), Var(v=-0.0225, grad=0.0000), Var(v=-0.0589, grad=-0.0149), Var(v=0.1535, grad=0.0000), Var(v=0.1027, grad=0.0000), Var(v=0.0622, grad=0.1850), Var(v=0.0607, grad=-0.0707), Var(v=0.0794, grad=-0.1266), Var(v=-0.0221, grad=-0.2777), Var(v=-0.0567, grad=-0.0041), Var(v=-0.0852, grad=0.1084)], [Var(v=0.0416, grad=0.2057), Var(v=0.0015, grad=-0.4791), Var(v=-0.0037, grad=0.4909), Var(v=0.0404, grad=0.3198), Var(v=0.0070, grad=0.0000), Var(v=0.0841, grad=1.1987), Var(v=0.1363, grad=-1.4308), Var(v=-0.0965, grad=0.0000), Var(v=0.1132, grad=1.1432), Var(v=0.0670, grad=0.0000), Var(v=-0.1020, grad=-0.6984), Var(v=0.2652, grad=0.0000), Var(v=-0.0303, grad=0.0000), Var(v=-0.1600, grad=0.0000), Var(v=0.0337, grad=0.0000), Var(v=0.1213, grad=0.4105), Var(v=0.0046, grad=0.0000), Var(v=-0.0923, grad=0.0000), Var(v=0.0749, grad=0.0000), Var(v=-0.0612, grad=-0.4094), Var(v=-0.0738, grad=0.0000), Var(v=-0.0346, grad=0.0000), Var(v=-0.0452, grad=0.0000), Var(v=0.0089, grad=-0.6609), Var(v=-0.1595, grad=0.0000), Var(v=0.0669, grad=-0.2139), Var(v=-0.0326, grad=-0.3999), Var(v=0.0631, grad=-0.5878), Var(v=-0.0089, grad=0.0000), Var(v=-0.0287, grad=0.0000), Var(v=0.0414, grad=-0.8151), Var(v=0.0921, grad=-0.8896), Var(v=-0.0499, grad=0.0888), Var(v=-0.1842, grad=0.0000), Var(v=0.0174, grad=0.1189), Var(v=0.0653, grad=1.1309), Var(v=0.0070, grad=0.0000), Var(v=-0.0470, grad=1.7123), Var(v=-0.1138, grad=0.0000), Var(v=0.0452, grad=-0.7248), Var(v=-0.2218, grad=0.0000), Var(v=-0.0098, grad=-0.0474), Var(v=0.0275, grad=0.0000), Var(v=0.1331, grad=0.0000), Var(v=-0.0486, grad=0.5885), Var(v=-0.0724, grad=-0.2250), Var(v=0.0581, grad=-0.4026), Var(v=0.1581, grad=-0.8832), Var(v=-0.0486, grad=-0.0129), Var(v=-0.0549, grad=0.3446)], [Var(v=0.1147, grad=0.2050), Var(v=0.0605, grad=-0.4775), Var(v=0.1119, grad=0.4892), Var(v=-0.0803, grad=0.3187), Var(v=-0.1063, grad=0.0000), Var(v=-0.0623, grad=1.1946), Var(v=-0.0122, grad=-1.4259), Var(v=0.0398, grad=0.0000), Var(v=-0.1108, grad=1.1393), Var(v=-0.0022, grad=0.0000), Var(v=0.1652, grad=-0.6960), Var(v=0.0168, grad=0.0000), Var(v=0.0250, grad=0.0000), Var(v=0.0526, grad=0.0000), Var(v=0.0340, grad=0.0000), Var(v=-0.0346, grad=0.4091), Var(v=-0.1268, grad=0.0000), Var(v=0.0024, grad=0.0000), Var(v=-0.1403, grad=0.0000), Var(v=-0.0894, grad=-0.4080), Var(v=0.0849, grad=0.0000), Var(v=0.0012, grad=0.0000), Var(v=0.0569, grad=0.0000), Var(v=0.0524, grad=-0.6586), Var(v=-0.0440, grad=0.0000), Var(v=0.0366, grad=-0.2132), Var(v=0.1885, grad=-0.3985), Var(v=0.0062, grad=-0.5858), Var(v=0.0642, grad=0.0000), Var(v=-0.1009, grad=0.0000), Var(v=0.1176, grad=-0.8123), Var(v=0.1183, grad=-0.8866), Var(v=0.0209, grad=0.0885), Var(v=0.0628, grad=0.0000), Var(v=0.0368, grad=0.1185), Var(v=0.0321, grad=1.1270), Var(v=-0.0629, grad=0.0000), Var(v=-0.0739, grad=1.7064), Var(v=-0.0167, grad=0.0000), Var(v=-0.0241, grad=-0.7223), Var(v=-0.1795, grad=0.0000), Var(v=-0.2183, grad=-0.0472), Var(v=-0.0204, grad=0.0000), Var(v=-0.2010, grad=0.0000), Var(v=0.1412, grad=0.5865), Var(v=0.1666, grad=-0.2242), Var(v=0.0440, grad=-0.4013), Var(v=-0.1591, grad=-0.8802), Var(v=0.0598, grad=-0.0129), Var(v=-0.1696, grad=0.3434)], [Var(v=0.0272, grad=0.4768), Var(v=0.0856, grad=-1.1105), Var(v=0.0855, grad=1.1378), Var(v=0.0122, grad=0.7411), Var(v=-0.0956, grad=0.0000), Var(v=0.0392, grad=2.7783), Var(v=0.0681, grad=-3.3163), Var(v=-0.0438, grad=0.0000), Var(v=0.0924, grad=2.6496), Var(v=-0.1096, grad=0.0000), Var(v=-0.0508, grad=-1.6187), Var(v=-0.1504, grad=0.0000), Var(v=-0.2555, grad=0.0000), Var(v=0.0402, grad=0.0000), Var(v=-0.0944, grad=0.0000), Var(v=0.0753, grad=0.9514), Var(v=-0.0191, grad=0.0000), Var(v=0.0422, grad=0.0000), Var(v=0.0096, grad=0.0000), Var(v=0.1954, grad=-0.9490), Var(v=-0.1152, grad=0.0000), Var(v=0.0114, grad=0.0000), Var(v=0.0350, grad=0.0000), Var(v=-0.1259, grad=-1.5318), Var(v=-0.1330, grad=0.0000), Var(v=-0.0718, grad=-0.4959), Var(v=0.0931, grad=-0.9268), Var(v=0.0330, grad=-1.3625), Var(v=0.0183, grad=0.0000), Var(v=0.0413, grad=0.0000), Var(v=0.1336, grad=-1.8892), Var(v=-0.0105, grad=-2.0619), Var(v=0.1168, grad=0.2058), Var(v=-0.0290, grad=0.0000), Var(v=0.1305, grad=0.2756), Var(v=0.1792, grad=2.6211), Var(v=-0.1481, grad=0.0000), Var(v=0.0980, grad=3.9687), Var(v=0.0524, grad=0.0000), Var(v=-0.1069, grad=-1.6798), Var(v=0.0281, grad=0.0000), Var(v=0.0606, grad=-0.1098), Var(v=-0.1038, grad=0.0000), Var(v=-0.1044, grad=0.0000), Var(v=-0.0035, grad=1.3640), Var(v=0.0223, grad=-0.5215), Var(v=0.0991, grad=-0.9332), Var(v=-0.1270, grad=-2.0471), Var(v=-0.0048, grad=-0.0299), Var(v=0.1481, grad=0.7987)], [Var(v=-0.1372, grad=0.2247), Var(v=0.0630, grad=-0.5234), Var(v=-0.0098, grad=0.5363), Var(v=0.0718, grad=0.3493), Var(v=0.0264, grad=0.0000), Var(v=0.0274, grad=1.3095), Var(v=0.0528, grad=-1.5631), Var(v=-0.1710, grad=0.0000), Var(v=0.0230, grad=1.2488), Var(v=0.2051, grad=0.0000), Var(v=0.1180, grad=-0.7629), Var(v=0.0829, grad=0.0000), Var(v=-0.0127, grad=0.0000), Var(v=0.0055, grad=0.0000), Var(v=-0.0485, grad=0.0000), Var(v=0.0571, grad=0.4484), Var(v=-0.0885, grad=0.0000), Var(v=0.1506, grad=0.0000), Var(v=0.0030, grad=0.0000), Var(v=0.0494, grad=-0.4473), Var(v=-0.0319, grad=0.0000), Var(v=0.0728, grad=0.0000), Var(v=-0.0476, grad=0.0000), Var(v=0.3303, grad=-0.7220), Var(v=0.0802, grad=0.0000), Var(v=0.1436, grad=-0.2337), Var(v=-0.0410, grad=-0.4368), Var(v=-0.0783, grad=-0.6422), Var(v=-0.1499, grad=0.0000), Var(v=-0.0934, grad=0.0000), Var(v=0.0753, grad=-0.8904), Var(v=0.0143, grad=-0.9718), Var(v=0.1343, grad=0.0970), Var(v=-0.0155, grad=0.0000), Var(v=-0.1649, grad=0.1299), Var(v=-0.0417, grad=1.2354), Var(v=0.1235, grad=0.0000), Var(v=-0.0194, grad=1.8706), Var(v=0.1261, grad=0.0000), Var(v=0.0105, grad=-0.7918), Var(v=0.0048, grad=0.0000), Var(v=-0.0583, grad=-0.0518), Var(v=-0.0776, grad=0.0000), Var(v=0.1760, grad=0.0000), Var(v=-0.0916, grad=0.6429), Var(v=0.0618, grad=-0.2458), Var(v=0.2519, grad=-0.4399), Var(v=-0.0194, grad=-0.9649), Var(v=0.0662, grad=-0.0141), Var(v=0.0297, grad=0.3765)], [Var(v=0.1629, grad=-0.2989), Var(v=0.0293, grad=0.0000), Var(v=0.0401, grad=0.0000), Var(v=-0.2359, grad=0.0000), Var(v=-0.0612, grad=0.0000), Var(v=0.1891, grad=-1.7419), Var(v=-0.1534, grad=0.0000), Var(v=0.0171, grad=0.4090), Var(v=0.0232, grad=-1.6612), Var(v=-0.1340, grad=0.0000), Var(v=0.0635, grad=0.0000), Var(v=-0.0441, grad=0.1514), Var(v=0.0871, grad=-0.4574), Var(v=-0.0035, grad=0.0000), Var(v=-0.1769, grad=0.0000), Var(v=-0.0198, grad=-0.5965), Var(v=0.0836, grad=-0.2745), Var(v=-0.0002, grad=0.0000), Var(v=0.1467, grad=0.2898), Var(v=-0.0882, grad=0.0000), Var(v=-0.0744, grad=0.0000), Var(v=-0.0406, grad=0.0000), Var(v=0.1908, grad=-1.1499), Var(v=0.1451, grad=0.9603), Var(v=-0.2074, grad=0.0000), Var(v=0.0363, grad=0.0000), Var(v=-0.0380, grad=0.0000), Var(v=-0.0220, grad=0.0000), Var(v=0.0936, grad=-0.1279), Var(v=-0.0297, grad=0.0000), Var(v=-0.1777, grad=0.0000), Var(v=0.0028, grad=1.2927), Var(v=0.0517, grad=-0.1290), Var(v=-0.0653, grad=0.0000), Var(v=0.1471, grad=-0.1728), Var(v=-0.0847, grad=-1.6433), Var(v=0.0060, grad=0.0000), Var(v=-0.0106, grad=0.0000), Var(v=0.1054, grad=0.0000), Var(v=0.1582, grad=1.0532), Var(v=-0.2176, grad=0.0000), Var(v=0.0413, grad=0.0000), Var(v=-0.1435, grad=0.0000), Var(v=-0.0754, grad=0.0000), Var(v=-0.0054, grad=-0.8552), Var(v=0.0947, grad=0.3269), Var(v=0.0932, grad=0.5851), Var(v=0.0644, grad=1.2834), Var(v=-0.0374, grad=0.0000), Var(v=0.0085, grad=-0.5008)], [Var(v=0.0856, grad=0.0024), Var(v=0.0248, grad=-0.0056), Var(v=-0.1879, grad=0.0058), Var(v=0.1029, grad=0.0038), Var(v=0.0919, grad=0.0000), Var(v=0.0917, grad=0.0141), Var(v=0.1448, grad=-0.0168), Var(v=0.1129, grad=0.0000), Var(v=0.1504, grad=0.0134), Var(v=-0.0489, grad=0.0000), Var(v=0.0678, grad=-0.0082), Var(v=-0.0069, grad=0.0000), Var(v=-0.0877, grad=0.0000), Var(v=-0.1083, grad=0.0000), Var(v=-0.0020, grad=0.0000), Var(v=0.0155, grad=0.0048), Var(v=0.0805, grad=0.0000), Var(v=0.0901, grad=0.0000), Var(v=-0.0092, grad=0.0000), Var(v=0.2021, grad=-0.0048), Var(v=-0.0734, grad=0.0000), Var(v=-0.0010, grad=0.0000), Var(v=0.0256, grad=0.0000), Var(v=0.0066, grad=-0.0078), Var(v=0.1607, grad=0.0000), Var(v=0.0238, grad=-0.0025), Var(v=-0.2241, grad=-0.0047), Var(v=-0.0550, grad=-0.0069), Var(v=0.0689, grad=0.0000), Var(v=-0.0694, grad=0.0000), Var(v=0.0995, grad=-0.0096), Var(v=-0.0611, grad=-0.0104), Var(v=0.0556, grad=0.0010), Var(v=-0.1233, grad=0.0000), Var(v=-0.0034, grad=0.0014), Var(v=0.0178, grad=0.0133), Var(v=-0.0725, grad=0.0000), Var(v=0.0317, grad=0.0201), Var(v=0.1507, grad=0.0000), Var(v=0.0355, grad=-0.0085), Var(v=-0.0932, grad=0.0000), Var(v=-0.0583, grad=-0.0006), Var(v=-0.1348, grad=0.0000), Var(v=-0.0759, grad=0.0000), Var(v=0.0608, grad=0.0069), Var(v=0.0152, grad=-0.0026), Var(v=0.1039, grad=-0.0047), Var(v=0.1236, grad=-0.0104), Var(v=0.0612, grad=-0.0002), Var(v=-0.0402, grad=0.0040)], [Var(v=-0.0043, grad=-0.1549), Var(v=-0.0887, grad=0.0000), Var(v=0.0046, grad=0.0000), Var(v=0.0104, grad=0.0000), Var(v=-0.1085, grad=0.0000), Var(v=-0.0125, grad=-0.9026), Var(v=-0.0960, grad=0.0000), Var(v=-0.0046, grad=0.2119), Var(v=0.1021, grad=-0.8608), Var(v=0.1655, grad=0.0000), Var(v=-0.0746, grad=0.0000), Var(v=0.0741, grad=0.0784), Var(v=0.0943, grad=-0.2370), Var(v=-0.1892, grad=0.0000), Var(v=0.0310, grad=0.0000), Var(v=-0.0213, grad=-0.3091), Var(v=0.0844, grad=-0.1423), Var(v=-0.0225, grad=0.0000), Var(v=0.0271, grad=0.1501), Var(v=-0.0316, grad=0.0000), Var(v=0.0110, grad=0.0000), Var(v=0.1252, grad=0.0000), Var(v=-0.0530, grad=-0.5959), Var(v=0.0123, grad=0.4976), Var(v=0.0335, grad=0.0000), Var(v=-0.0662, grad=0.0000), Var(v=-0.0812, grad=0.0000), Var(v=-0.0920, grad=0.0000), Var(v=-0.0640, grad=-0.0663), Var(v=0.0720, grad=0.0000), Var(v=0.0958, grad=0.0000), Var(v=0.0447, grad=0.6699), Var(v=0.0496, grad=-0.0669), Var(v=-0.1068, grad=0.0000), Var(v=-0.1616, grad=-0.0895), Var(v=0.1548, grad=-0.8515), Var(v=-0.1337, grad=0.0000), Var(v=-0.1784, grad=0.0000), Var(v=-0.0827, grad=0.0000), Var(v=-0.0730, grad=0.5457), Var(v=0.0414, grad=0.0000), Var(v=0.0625, grad=0.0000), Var(v=-0.0544, grad=0.0000), Var(v=-0.1551, grad=0.0000), Var(v=-0.0934, grad=-0.4431), Var(v=-0.1132, grad=0.1694), Var(v=0.0162, grad=0.3032), Var(v=0.0708, grad=0.6651), Var(v=0.0093, grad=0.0000), Var(v=-0.0151, grad=-0.2595)], [Var(v=-0.1442, grad=0.0037), Var(v=-0.0085, grad=-0.0086), Var(v=0.0616, grad=0.0088), Var(v=0.0511, grad=0.0057), Var(v=-0.0252, grad=0.0000), Var(v=0.1351, grad=0.0215), Var(v=-0.1817, grad=-0.0256), Var(v=0.0773, grad=0.0000), Var(v=-0.0775, grad=0.0205), Var(v=0.0179, grad=0.0000), Var(v=-0.1031, grad=-0.0125), Var(v=0.1922, grad=0.0000), Var(v=0.0916, grad=0.0000), Var(v=0.0438, grad=0.0000), Var(v=-0.2103, grad=0.0000), Var(v=0.0681, grad=0.0073), Var(v=0.0894, grad=0.0000), Var(v=0.0932, grad=0.0000), Var(v=-0.0885, grad=0.0000), Var(v=-0.0386, grad=-0.0073), Var(v=-0.0504, grad=0.0000), Var(v=0.1099, grad=0.0000), Var(v=0.0511, grad=0.0000), Var(v=0.0473, grad=-0.0118), Var(v=0.0257, grad=0.0000), Var(v=-0.0568, grad=-0.0038), Var(v=0.0053, grad=-0.0072), Var(v=-0.0640, grad=-0.0105), Var(v=-0.2315, grad=0.0000), Var(v=0.2141, grad=0.0000), Var(v=-0.1755, grad=-0.0146), Var(v=-0.0448, grad=-0.0159), Var(v=-0.0869, grad=0.0016), Var(v=-0.0541, grad=0.0000), Var(v=0.0064, grad=0.0021), Var(v=0.0045, grad=0.0202), Var(v=0.0537, grad=0.0000), Var(v=0.1196, grad=0.0307), Var(v=-0.0627, grad=0.0000), Var(v=-0.2040, grad=-0.0130), Var(v=0.0612, grad=0.0000), Var(v=0.0496, grad=-0.0008), Var(v=-0.1758, grad=0.0000), Var(v=-0.0410, grad=0.0000), Var(v=-0.0036, grad=0.0105), Var(v=0.1448, grad=-0.0040), Var(v=-0.1003, grad=-0.0072), Var(v=0.0888, grad=-0.0158), Var(v=0.1421, grad=-0.0002), Var(v=-0.1417, grad=0.0062)], [Var(v=-0.0334, grad=0.0968), Var(v=0.0270, grad=-0.2255), Var(v=0.1462, grad=0.2310), Var(v=0.0053, grad=0.1505), Var(v=0.0116, grad=0.0000), Var(v=-0.1051, grad=0.5641), Var(v=0.0301, grad=-0.6733), Var(v=-0.0324, grad=0.0000), Var(v=-0.0020, grad=0.5379), Var(v=-0.0867, grad=0.0000), Var(v=0.1998, grad=-0.3286), Var(v=0.1091, grad=0.0000), Var(v=0.0126, grad=0.0000), Var(v=-0.0555, grad=0.0000), Var(v=-0.0277, grad=0.0000), Var(v=0.1193, grad=0.1932), Var(v=-0.0757, grad=0.0000), Var(v=-0.1403, grad=0.0000), Var(v=0.0205, grad=0.0000), Var(v=-0.0111, grad=-0.1927), Var(v=0.0285, grad=0.0000), Var(v=-0.0325, grad=0.0000), Var(v=-0.0852, grad=0.0000), Var(v=0.1033, grad=-0.3110), Var(v=0.0862, grad=0.0000), Var(v=-0.0380, grad=-0.1007), Var(v=-0.0440, grad=-0.1882), Var(v=-0.0133, grad=-0.2766), Var(v=-0.0352, grad=0.0000), Var(v=0.0441, grad=0.0000), Var(v=0.0170, grad=-0.3835), Var(v=0.0789, grad=-0.4186), Var(v=0.0574, grad=0.0418), Var(v=0.0545, grad=0.0000), Var(v=0.0106, grad=0.0560), Var(v=-0.0294, grad=0.5321), Var(v=-0.0389, grad=0.0000), Var(v=0.1094, grad=0.8057), Var(v=0.1142, grad=0.0000), Var(v=0.0285, grad=-0.3410), Var(v=-0.0152, grad=0.0000), Var(v=-0.0510, grad=-0.0223), Var(v=0.0927, grad=0.0000), Var(v=0.0938, grad=0.0000), Var(v=-0.0316, grad=0.2769), Var(v=0.1158, grad=-0.1059), Var(v=-0.0444, grad=-0.1895), Var(v=0.0741, grad=-0.4156), Var(v=-0.0391, grad=-0.0061), Var(v=0.0538, grad=0.1622)]] Biases: [Var(v=0.0000, grad=-0.0070), Var(v=0.0000, grad=-6.1684), Var(v=0.0000, grad=6.3201), Var(v=0.0000, grad=4.1166), Var(v=0.0000, grad=0.0000), Var(v=0.0000, grad=-0.0408), Var(v=0.0000, grad=-18.4206), Var(v=0.0000, grad=3.6329), Var(v=0.0000, grad=-0.0389), Var(v=0.0000, grad=0.0000), Var(v=0.0000, grad=-8.9910), Var(v=0.0000, grad=1.3447), Var(v=0.0000, grad=-4.0632), Var(v=0.0000, grad=0.0000), Var(v=0.0000, grad=0.0000), Var(v=0.0000, grad=-0.0140), Var(v=0.0000, grad=-2.4386), Var(v=0.0000, grad=0.0000), Var(v=0.0000, grad=2.5739), Var(v=0.0000, grad=-5.2711), Var(v=0.0000, grad=0.0000), Var(v=0.0000, grad=0.0000), Var(v=0.0000, grad=-10.2145), Var(v=0.0000, grad=0.0225), Var(v=0.0000, grad=0.0000), Var(v=0.0000, grad=-2.7543), Var(v=0.0000, grad=-5.1481), Var(v=0.0000, grad=-7.5680), Var(v=0.0000, grad=-1.1361), Var(v=0.0000, grad=0.0000), Var(v=0.0000, grad=-10.4934), Var(v=0.0000, grad=0.0302), Var(v=0.0000, grad=-0.0030), Var(v=0.0000, grad=0.0000), Var(v=0.0000, grad=-0.0040), Var(v=0.0000, grad=-0.0385), Var(v=0.0000, grad=0.0000), Var(v=0.0000, grad=22.0440), Var(v=0.0000, grad=0.0000), Var(v=0.0000, grad=0.0246), Var(v=0.0000, grad=0.0000), Var(v=0.0000, grad=-0.6101), Var(v=0.0000, grad=0.0000), Var(v=0.0000, grad=0.0000), Var(v=0.0000, grad=-0.0200), Var(v=0.0000, grad=0.0076), Var(v=0.0000, grad=0.0137), Var(v=0.0000, grad=0.0300), Var(v=0.0000, grad=-0.1662), Var(v=0.0000, grad=-0.0117)]\n", - "Layer 2 \n", - " Weights: [[Var(v=0.0306, grad=1.1091)], [Var(v=-0.0713, grad=2.7504)], [Var(v=0.0731, grad=3.1399)], [Var(v=0.0476, grad=1.4039)], [Var(v=0.0060, grad=0.0000)], [Var(v=0.1785, grad=0.1147)], [Var(v=-0.2131, grad=2.1854)], [Var(v=-0.0419, grad=-0.3967)], [Var(v=0.1702, grad=0.7581)], [Var(v=-0.0294, grad=0.0000)], [Var(v=-0.1040, grad=0.1930)], [Var(v=-0.0155, grad=-0.4635)], [Var(v=0.0469, grad=-1.4799)], [Var(v=0.0524, grad=0.0000)], [Var(v=-0.0334, grad=0.0000)], [Var(v=0.0611, grad=2.1831)], [Var(v=0.0281, grad=-1.5177)], [Var(v=-0.0804, grad=0.0000)], [Var(v=-0.0297, grad=-2.9921)], [Var(v=-0.0610, grad=2.9585)], [Var(v=0.0096, grad=0.0000)], [Var(v=0.0075, grad=0.0000)], [Var(v=0.1178, grad=-2.0404)], [Var(v=-0.0984, grad=0.8578)], [Var(v=-0.0419, grad=0.0000)], [Var(v=-0.0319, grad=0.6599)], [Var(v=-0.0595, grad=3.1142)], [Var(v=-0.0875, grad=0.7921)], [Var(v=0.0131, grad=-0.9357)], [Var(v=0.0558, grad=0.0000)], [Var(v=-0.1214, grad=4.9822)], [Var(v=-0.1325, grad=-0.1320)], [Var(v=0.0132, grad=1.3479)], [Var(v=0.0263, grad=0.0000)], [Var(v=0.0177, grad=1.8342)], [Var(v=0.1684, grad=-1.0816)], [Var(v=-0.0298, grad=0.0000)], [Var(v=0.2550, grad=0.2801)], [Var(v=0.0243, grad=0.0000)], [Var(v=-0.1079, grad=-1.3258)], [Var(v=-0.0209, grad=0.0000)], [Var(v=-0.0071, grad=0.2720)], [Var(v=-0.0406, grad=0.0000)], [Var(v=0.0532, grad=0.0000)], [Var(v=0.0876, grad=-0.3907)], [Var(v=-0.0335, grad=2.7892)], [Var(v=-0.0600, grad=1.1218)], [Var(v=-0.1315, grad=-0.1610)], [Var(v=-0.0019, grad=0.2415)], [Var(v=0.0513, grad=1.5678)]] Biases: [Var(v=0.0000, grad=-0.2283)]\n", - "\n", - "Network after update:\n", - "Layer 0 \n", - " Weights: [[Var(v=0.0803, grad=-5.3320), Var(v=0.0169, grad=-0.9050), Var(v=0.1285, grad=-3.1266), Var(v=-0.1680, grad=11.3716), Var(v=-0.0901, grad=-0.7954), Var(v=-0.0798, grad=5.7626), Var(v=-0.0982, grad=2.7569), Var(v=-0.1444, grad=7.4011), Var(v=-0.0812, grad=-8.2387), Var(v=-0.1667, grad=8.9566), Var(v=0.1041, grad=-0.7338), Var(v=-0.0142, grad=1.3349), Var(v=0.0711, grad=-2.1023), Var(v=0.1254, grad=-12.6701), Var(v=-0.0692, grad=3.5947)]] Biases: [Var(v=0.0458, grad=-4.5807), Var(v=0.0078, grad=-0.7775), Var(v=0.0269, grad=-2.6861), Var(v=0.1033, grad=-10.3345), Var(v=-0.0072, grad=0.7229), Var(v=0.0524, grad=-5.2371), Var(v=0.0251, grad=-2.5055), Var(v=0.0673, grad=-6.7262), Var(v=-0.0749, grad=7.4874), Var(v=0.0814, grad=-8.1398), Var(v=0.0063, grad=-0.6304), Var(v=0.0121, grad=-1.2132), Var(v=0.0181, grad=-1.8061), Var(v=-0.1151, grad=11.5147), Var(v=0.0327, grad=-3.2669)]\n", - "Layer 1 \n", - " Weights: [[Var(v=-0.0382, grad=-0.0835), Var(v=-0.0476, grad=0.0000), Var(v=0.0922, grad=0.0000), Var(v=-0.0844, grad=0.0000), Var(v=-0.0332, grad=0.0000), Var(v=0.0852, grad=-0.4865), Var(v=-0.0124, grad=0.0000), Var(v=0.0842, grad=0.1142), Var(v=0.0041, grad=-0.4639), Var(v=-0.0230, grad=0.0000), Var(v=0.1281, grad=0.0000), Var(v=-0.0422, grad=0.0423), Var(v=-0.0733, grad=-0.1277), Var(v=-0.0646, grad=0.0000), Var(v=-0.0117, grad=0.0000), Var(v=-0.0101, grad=-0.1666), Var(v=0.0831, grad=-0.0767), Var(v=-0.2360, grad=0.0000), Var(v=-0.0033, grad=0.0809), Var(v=-0.1359, grad=0.0000), Var(v=-0.0100, grad=0.0000), Var(v=0.1033, grad=0.0000), Var(v=0.0525, grad=-0.3211), Var(v=0.0100, grad=0.2682), Var(v=-0.0448, grad=0.0000), Var(v=0.1625, grad=0.0000), Var(v=-0.1058, grad=0.0000), Var(v=-0.0722, grad=0.0000), Var(v=0.2531, grad=-0.0357), Var(v=-0.0802, grad=0.0000), Var(v=0.0956, grad=0.0000), Var(v=0.0561, grad=0.3610), Var(v=0.0675, grad=-0.0360), Var(v=-0.0841, grad=0.0000), Var(v=-0.1306, grad=-0.0483), Var(v=0.1020, grad=-0.4589), Var(v=-0.0234, grad=0.0000), Var(v=-0.0579, grad=0.0000), Var(v=-0.0931, grad=0.0000), Var(v=-0.0813, grad=0.2941), Var(v=0.0973, grad=0.0000), Var(v=0.1397, grad=0.0000), Var(v=-0.1750, grad=0.0000), Var(v=-0.0723, grad=0.0000), Var(v=0.1644, grad=-0.2388), Var(v=-0.0209, grad=0.0913), Var(v=0.0240, grad=0.1634), Var(v=-0.0247, grad=0.3584), Var(v=0.0384, grad=0.0000), Var(v=0.0999, grad=-0.1399)], [Var(v=0.0679, grad=-0.0242), Var(v=-0.1111, grad=0.0000), Var(v=-0.0885, grad=0.0000), Var(v=-0.0549, grad=0.0000), Var(v=-0.0359, grad=0.0000), Var(v=0.1388, grad=-0.1411), Var(v=-0.0769, grad=0.0000), Var(v=-0.1260, grad=0.0331), Var(v=-0.0253, grad=-0.1345), Var(v=-0.1051, grad=0.0000), Var(v=-0.0182, grad=0.0000), Var(v=0.0980, grad=0.0123), Var(v=0.0555, grad=-0.0370), Var(v=0.1001, grad=0.0000), Var(v=-0.0761, grad=0.0000), Var(v=-0.2323, grad=-0.0483), Var(v=0.0324, grad=-0.0222), Var(v=0.0721, grad=0.0000), Var(v=-0.0384, grad=0.0235), Var(v=-0.0034, grad=0.0000), Var(v=-0.0778, grad=0.0000), Var(v=-0.1352, grad=0.0000), Var(v=-0.0119, grad=-0.0931), Var(v=0.0441, grad=0.0778), Var(v=-0.0361, grad=0.0000), Var(v=0.1519, grad=0.0000), Var(v=0.1159, grad=0.0000), Var(v=-0.0072, grad=0.0000), Var(v=-0.1877, grad=-0.0104), Var(v=-0.1459, grad=0.0000), Var(v=0.0663, grad=0.0000), Var(v=0.0175, grad=0.1047), Var(v=0.1038, grad=-0.0104), Var(v=0.0586, grad=0.0000), Var(v=0.2907, grad=-0.0140), Var(v=0.0685, grad=-0.1331), Var(v=0.0189, grad=0.0000), Var(v=0.1071, grad=0.0000), Var(v=0.0218, grad=0.0000), Var(v=-0.0431, grad=0.0853), Var(v=-0.0765, grad=0.0000), Var(v=-0.0807, grad=0.0000), Var(v=-0.0054, grad=0.0000), Var(v=0.1035, grad=0.0000), Var(v=-0.0356, grad=-0.0693), Var(v=-0.1277, grad=0.0265), Var(v=-0.0169, grad=0.0474), Var(v=0.1458, grad=0.1039), Var(v=-0.0697, grad=0.0000), Var(v=-0.0296, grad=-0.0406)], [Var(v=-0.0776, grad=-0.3006), Var(v=-0.0214, grad=0.0000), Var(v=-0.1360, grad=0.0000), Var(v=0.1583, grad=0.0000), Var(v=-0.0618, grad=0.0000), Var(v=0.0128, grad=-1.7516), Var(v=-0.0682, grad=0.0000), Var(v=0.0080, grad=0.4113), Var(v=0.0204, grad=-1.6705), Var(v=-0.0587, grad=0.0000), Var(v=-0.1384, grad=0.0000), Var(v=0.0551, grad=0.1522), Var(v=0.0364, grad=-0.4600), Var(v=-0.0418, grad=0.0000), Var(v=0.0360, grad=0.0000), Var(v=0.0670, grad=-0.5998), Var(v=0.0053, grad=-0.2761), Var(v=0.0022, grad=0.0000), Var(v=0.1459, grad=0.2914), Var(v=-0.1206, grad=0.0000), Var(v=-0.0494, grad=0.0000), Var(v=-0.0458, grad=0.0000), Var(v=0.0444, grad=-1.1563), Var(v=-0.1395, grad=0.9657), Var(v=0.1424, grad=0.0000), Var(v=-0.1040, grad=0.0000), Var(v=-0.0916, grad=0.0000), Var(v=-0.1299, grad=0.0000), Var(v=-0.0186, grad=-0.1286), Var(v=-0.0362, grad=0.0000), Var(v=-0.0833, grad=0.0000), Var(v=0.0388, grad=1.2999), Var(v=0.0982, grad=-0.1297), Var(v=0.0509, grad=0.0000), Var(v=0.0644, grad=-0.1738), Var(v=0.2227, grad=-1.6525), Var(v=0.0017, grad=0.0000), Var(v=-0.1117, grad=0.0000), Var(v=-0.2394, grad=0.0000), Var(v=0.0396, grad=1.0591), Var(v=0.1081, grad=0.0000), Var(v=-0.2020, grad=0.0000), Var(v=-0.0903, grad=0.0000), Var(v=0.0227, grad=0.0000), Var(v=0.0877, grad=-0.8600), Var(v=0.0012, grad=0.3288), Var(v=0.1594, grad=0.5884), Var(v=0.0100, grad=1.2906), Var(v=-0.0703, grad=0.0000), Var(v=-0.0124, grad=-0.5036)], [Var(v=0.1062, grad=0.1583), Var(v=-0.1613, grad=-0.3686), Var(v=0.0169, grad=0.3777), Var(v=-0.0338, grad=0.2460), Var(v=0.0248, grad=0.0000), Var(v=0.0911, grad=0.9222), Var(v=0.0472, grad=-1.1007), Var(v=-0.1163, grad=0.0000), Var(v=-0.1660, grad=0.8795), Var(v=-0.1289, grad=0.0000), Var(v=-0.0917, grad=-0.5373), Var(v=0.0182, grad=0.0000), Var(v=-0.0458, grad=0.0000), Var(v=-0.1841, grad=0.0000), Var(v=-0.0308, grad=0.0000), Var(v=-0.0030, grad=0.3158), Var(v=-0.0334, grad=0.0000), Var(v=0.0651, grad=0.0000), Var(v=0.0391, grad=0.0000), Var(v=0.0670, grad=-0.3150), Var(v=-0.0647, grad=0.0000), Var(v=-0.1526, grad=0.0000), Var(v=0.0712, grad=0.0000), Var(v=-0.0770, grad=-0.5084), Var(v=0.1065, grad=0.0000), Var(v=-0.0245, grad=-0.1646), Var(v=0.0610, grad=-0.3076), Var(v=0.0973, grad=-0.4522), Var(v=0.0451, grad=0.0000), Var(v=0.0825, grad=0.0000), Var(v=-0.0053, grad=-0.6270), Var(v=0.1046, grad=-0.6844), Var(v=-0.0240, grad=0.0683), Var(v=-0.0133, grad=0.0000), Var(v=0.0595, grad=0.0915), Var(v=-0.2066, grad=0.8700), Var(v=-0.2472, grad=0.0000), Var(v=-0.1506, grad=1.3173), Var(v=-0.0742, grad=0.0000), Var(v=0.1515, grad=-0.5576), Var(v=-0.0657, grad=0.0000), Var(v=-0.0464, grad=-0.0365), Var(v=-0.0223, grad=0.0000), Var(v=0.0151, grad=0.0000), Var(v=0.0182, grad=0.4527), Var(v=-0.0028, grad=-0.1731), Var(v=-0.0140, grad=-0.3098), Var(v=0.2281, grad=-0.6795), Var(v=-0.0093, grad=-0.0099), Var(v=-0.0247, grad=0.2651)], [Var(v=0.1140, grad=0.2858), Var(v=0.1482, grad=-0.6656), Var(v=0.0323, grad=0.6820), Var(v=0.1197, grad=0.4442), Var(v=-0.0812, grad=0.0000), Var(v=0.0664, grad=1.6652), Var(v=-0.0401, grad=-1.9876), Var(v=0.1905, grad=0.0000), Var(v=0.0404, grad=1.5881), Var(v=0.0015, grad=0.0000), Var(v=-0.0504, grad=-0.9702), Var(v=-0.0509, grad=0.0000), Var(v=-0.0399, grad=0.0000), Var(v=-0.1120, grad=0.0000), Var(v=-0.1441, grad=0.0000), Var(v=-0.0238, grad=0.5702), Var(v=-0.0510, grad=0.0000), Var(v=-0.1634, grad=0.0000), Var(v=-0.0509, grad=0.0000), Var(v=0.0149, grad=-0.5688), Var(v=0.0240, grad=0.0000), Var(v=-0.0022, grad=0.0000), Var(v=-0.0530, grad=0.0000), Var(v=0.0895, grad=-0.9181), Var(v=-0.1382, grad=0.0000), Var(v=0.0220, grad=-0.2972), Var(v=0.0995, grad=-0.5555), Var(v=-0.0289, grad=-0.8166), Var(v=-0.0037, grad=0.0000), Var(v=-0.0844, grad=0.0000), Var(v=0.1314, grad=-1.1323), Var(v=-0.1513, grad=-1.2358), Var(v=0.0973, grad=0.1233), Var(v=0.1033, grad=0.0000), Var(v=0.1759, grad=0.1652), Var(v=-0.1139, grad=1.5710), Var(v=0.2131, grad=0.0000), Var(v=0.0199, grad=2.3786), Var(v=-0.0647, grad=0.0000), Var(v=0.0742, grad=-1.0068), Var(v=0.0044, grad=0.0000), Var(v=0.1946, grad=-0.0658), Var(v=-0.0078, grad=0.0000), Var(v=-0.1106, grad=0.0000), Var(v=0.0162, grad=0.8175), Var(v=0.1223, grad=-0.3125), Var(v=-0.0178, grad=-0.5593), Var(v=0.2135, grad=-1.2269), Var(v=0.0030, grad=-0.0179), Var(v=0.0779, grad=0.4787)], [Var(v=-0.0772, grad=0.0647), Var(v=-0.0045, grad=-0.1506), Var(v=0.1074, grad=0.1544), Var(v=-0.0356, grad=0.1005), Var(v=0.0216, grad=0.0000), Var(v=0.1071, grad=0.3769), Var(v=0.0951, grad=-0.4499), Var(v=0.1045, grad=0.0000), Var(v=0.0813, grad=0.3594), Var(v=-0.0064, grad=0.0000), Var(v=0.0661, grad=-0.2196), Var(v=-0.1070, grad=0.0000), Var(v=0.1458, grad=0.0000), Var(v=0.0159, grad=0.0000), Var(v=0.0746, grad=0.0000), Var(v=-0.0615, grad=0.1291), Var(v=0.1271, grad=0.0000), Var(v=0.0182, grad=0.0000), Var(v=-0.0449, grad=0.0000), Var(v=0.0827, grad=-0.1287), Var(v=0.0085, grad=0.0000), Var(v=-0.0313, grad=0.0000), Var(v=-0.0481, grad=0.0000), Var(v=-0.1874, grad=-0.2078), Var(v=-0.0847, grad=0.0000), Var(v=0.0541, grad=-0.0673), Var(v=-0.0440, grad=-0.1257), Var(v=0.1477, grad=-0.1848), Var(v=0.0491, grad=0.0000), Var(v=-0.1025, grad=0.0000), Var(v=0.0900, grad=-0.2563), Var(v=0.1156, grad=-0.2797), Var(v=-0.1255, grad=0.0279), Var(v=0.0693, grad=0.0000), Var(v=-0.1173, grad=0.0374), Var(v=0.0477, grad=0.3556), Var(v=-0.1101, grad=0.0000), Var(v=-0.1722, grad=0.5384), Var(v=-0.2142, grad=0.0000), Var(v=0.0585, grad=-0.2279), Var(v=-0.0225, grad=0.0000), Var(v=-0.0588, grad=-0.0149), Var(v=0.1535, grad=0.0000), Var(v=0.1027, grad=0.0000), Var(v=0.0604, grad=0.1850), Var(v=0.0614, grad=-0.0707), Var(v=0.0807, grad=-0.1266), Var(v=-0.0193, grad=-0.2777), Var(v=-0.0566, grad=-0.0041), Var(v=-0.0863, grad=0.1084)], [Var(v=0.0396, grad=0.2057), Var(v=0.0063, grad=-0.4791), Var(v=-0.0086, grad=0.4909), Var(v=0.0372, grad=0.3198), Var(v=0.0070, grad=0.0000), Var(v=0.0721, grad=1.1987), Var(v=0.1506, grad=-1.4308), Var(v=-0.0965, grad=0.0000), Var(v=0.1018, grad=1.1432), Var(v=0.0670, grad=0.0000), Var(v=-0.0950, grad=-0.6984), Var(v=0.2652, grad=0.0000), Var(v=-0.0303, grad=0.0000), Var(v=-0.1600, grad=0.0000), Var(v=0.0337, grad=0.0000), Var(v=0.1172, grad=0.4105), Var(v=0.0046, grad=0.0000), Var(v=-0.0923, grad=0.0000), Var(v=0.0749, grad=0.0000), Var(v=-0.0571, grad=-0.4094), Var(v=-0.0738, grad=0.0000), Var(v=-0.0346, grad=0.0000), Var(v=-0.0452, grad=0.0000), Var(v=0.0155, grad=-0.6609), Var(v=-0.1595, grad=0.0000), Var(v=0.0690, grad=-0.2139), Var(v=-0.0286, grad=-0.3999), Var(v=0.0690, grad=-0.5878), Var(v=-0.0089, grad=0.0000), Var(v=-0.0287, grad=0.0000), Var(v=0.0495, grad=-0.8151), Var(v=0.1010, grad=-0.8896), Var(v=-0.0507, grad=0.0888), Var(v=-0.1842, grad=0.0000), Var(v=0.0162, grad=0.1189), Var(v=0.0540, grad=1.1309), Var(v=0.0070, grad=0.0000), Var(v=-0.0641, grad=1.7123), Var(v=-0.1138, grad=0.0000), Var(v=0.0525, grad=-0.7248), Var(v=-0.2218, grad=0.0000), Var(v=-0.0094, grad=-0.0474), Var(v=0.0275, grad=0.0000), Var(v=0.1331, grad=0.0000), Var(v=-0.0545, grad=0.5885), Var(v=-0.0702, grad=-0.2250), Var(v=0.0621, grad=-0.4026), Var(v=0.1670, grad=-0.8832), Var(v=-0.0485, grad=-0.0129), Var(v=-0.0584, grad=0.3446)], [Var(v=0.1127, grad=0.2050), Var(v=0.0653, grad=-0.4775), Var(v=0.1070, grad=0.4892), Var(v=-0.0835, grad=0.3187), Var(v=-0.1063, grad=0.0000), Var(v=-0.0743, grad=1.1946), Var(v=0.0020, grad=-1.4259), Var(v=0.0398, grad=0.0000), Var(v=-0.1222, grad=1.1393), Var(v=-0.0022, grad=0.0000), Var(v=0.1721, grad=-0.6960), Var(v=0.0168, grad=0.0000), Var(v=0.0250, grad=0.0000), Var(v=0.0526, grad=0.0000), Var(v=0.0340, grad=0.0000), Var(v=-0.0387, grad=0.4091), Var(v=-0.1268, grad=0.0000), Var(v=0.0024, grad=0.0000), Var(v=-0.1403, grad=0.0000), Var(v=-0.0853, grad=-0.4080), Var(v=0.0849, grad=0.0000), Var(v=0.0012, grad=0.0000), Var(v=0.0569, grad=0.0000), Var(v=0.0590, grad=-0.6586), Var(v=-0.0440, grad=0.0000), Var(v=0.0387, grad=-0.2132), Var(v=0.1925, grad=-0.3985), Var(v=0.0121, grad=-0.5858), Var(v=0.0642, grad=0.0000), Var(v=-0.1009, grad=0.0000), Var(v=0.1257, grad=-0.8123), Var(v=0.1271, grad=-0.8866), Var(v=0.0201, grad=0.0885), Var(v=0.0628, grad=0.0000), Var(v=0.0356, grad=0.1185), Var(v=0.0209, grad=1.1270), Var(v=-0.0629, grad=0.0000), Var(v=-0.0910, grad=1.7064), Var(v=-0.0167, grad=0.0000), Var(v=-0.0169, grad=-0.7223), Var(v=-0.1795, grad=0.0000), Var(v=-0.2178, grad=-0.0472), Var(v=-0.0204, grad=0.0000), Var(v=-0.2010, grad=0.0000), Var(v=0.1354, grad=0.5865), Var(v=0.1688, grad=-0.2242), Var(v=0.0480, grad=-0.4013), Var(v=-0.1503, grad=-0.8802), Var(v=0.0599, grad=-0.0129), Var(v=-0.1730, grad=0.3434)], [Var(v=0.0224, grad=0.4768), Var(v=0.0967, grad=-1.1105), Var(v=0.0742, grad=1.1378), Var(v=0.0048, grad=0.7411), Var(v=-0.0956, grad=0.0000), Var(v=0.0114, grad=2.7783), Var(v=0.1013, grad=-3.3163), Var(v=-0.0438, grad=0.0000), Var(v=0.0659, grad=2.6496), Var(v=-0.1096, grad=0.0000), Var(v=-0.0346, grad=-1.6187), Var(v=-0.1504, grad=0.0000), Var(v=-0.2555, grad=0.0000), Var(v=0.0402, grad=0.0000), Var(v=-0.0944, grad=0.0000), Var(v=0.0658, grad=0.9514), Var(v=-0.0191, grad=0.0000), Var(v=0.0422, grad=0.0000), Var(v=0.0096, grad=0.0000), Var(v=0.2049, grad=-0.9490), Var(v=-0.1152, grad=0.0000), Var(v=0.0114, grad=0.0000), Var(v=0.0350, grad=0.0000), Var(v=-0.1106, grad=-1.5318), Var(v=-0.1330, grad=0.0000), Var(v=-0.0669, grad=-0.4959), Var(v=0.1023, grad=-0.9268), Var(v=0.0466, grad=-1.3625), Var(v=0.0183, grad=0.0000), Var(v=0.0413, grad=0.0000), Var(v=0.1525, grad=-1.8892), Var(v=0.0101, grad=-2.0619), Var(v=0.1148, grad=0.2058), Var(v=-0.0290, grad=0.0000), Var(v=0.1278, grad=0.2756), Var(v=0.1530, grad=2.6211), Var(v=-0.1481, grad=0.0000), Var(v=0.0583, grad=3.9687), Var(v=0.0524, grad=0.0000), Var(v=-0.0901, grad=-1.6798), Var(v=0.0281, grad=0.0000), Var(v=0.0617, grad=-0.1098), Var(v=-0.1038, grad=0.0000), Var(v=-0.1044, grad=0.0000), Var(v=-0.0171, grad=1.3640), Var(v=0.0275, grad=-0.5215), Var(v=0.1084, grad=-0.9332), Var(v=-0.1065, grad=-2.0471), Var(v=-0.0045, grad=-0.0299), Var(v=0.1402, grad=0.7987)], [Var(v=-0.1394, grad=0.2247), Var(v=0.0683, grad=-0.5234), Var(v=-0.0152, grad=0.5363), Var(v=0.0683, grad=0.3493), Var(v=0.0264, grad=0.0000), Var(v=0.0143, grad=1.3095), Var(v=0.0685, grad=-1.5631), Var(v=-0.1710, grad=0.0000), Var(v=0.0105, grad=1.2488), Var(v=0.2051, grad=0.0000), Var(v=0.1256, grad=-0.7629), Var(v=0.0829, grad=0.0000), Var(v=-0.0127, grad=0.0000), Var(v=0.0055, grad=0.0000), Var(v=-0.0485, grad=0.0000), Var(v=0.0526, grad=0.4484), Var(v=-0.0885, grad=0.0000), Var(v=0.1506, grad=0.0000), Var(v=0.0030, grad=0.0000), Var(v=0.0539, grad=-0.4473), Var(v=-0.0319, grad=0.0000), Var(v=0.0728, grad=0.0000), Var(v=-0.0476, grad=0.0000), Var(v=0.3375, grad=-0.7220), Var(v=0.0802, grad=0.0000), Var(v=0.1460, grad=-0.2337), Var(v=-0.0366, grad=-0.4368), Var(v=-0.0719, grad=-0.6422), Var(v=-0.1499, grad=0.0000), Var(v=-0.0934, grad=0.0000), Var(v=0.0842, grad=-0.8904), Var(v=0.0240, grad=-0.9718), Var(v=0.1333, grad=0.0970), Var(v=-0.0155, grad=0.0000), Var(v=-0.1662, grad=0.1299), Var(v=-0.0540, grad=1.2354), Var(v=0.1235, grad=0.0000), Var(v=-0.0381, grad=1.8706), Var(v=0.1261, grad=0.0000), Var(v=0.0184, grad=-0.7918), Var(v=0.0048, grad=0.0000), Var(v=-0.0578, grad=-0.0518), Var(v=-0.0776, grad=0.0000), Var(v=0.1760, grad=0.0000), Var(v=-0.0980, grad=0.6429), Var(v=0.0642, grad=-0.2458), Var(v=0.2563, grad=-0.4399), Var(v=-0.0098, grad=-0.9649), Var(v=0.0663, grad=-0.0141), Var(v=0.0259, grad=0.3765)], [Var(v=0.1659, grad=-0.2989), Var(v=0.0293, grad=0.0000), Var(v=0.0401, grad=0.0000), Var(v=-0.2359, grad=0.0000), Var(v=-0.0612, grad=0.0000), Var(v=0.2065, grad=-1.7419), Var(v=-0.1534, grad=0.0000), Var(v=0.0131, grad=0.4090), Var(v=0.0398, grad=-1.6612), Var(v=-0.1340, grad=0.0000), Var(v=0.0635, grad=0.0000), Var(v=-0.0456, grad=0.1514), Var(v=0.0917, grad=-0.4574), Var(v=-0.0035, grad=0.0000), Var(v=-0.1769, grad=0.0000), Var(v=-0.0138, grad=-0.5965), Var(v=0.0864, grad=-0.2745), Var(v=-0.0002, grad=0.0000), Var(v=0.1438, grad=0.2898), Var(v=-0.0882, grad=0.0000), Var(v=-0.0744, grad=0.0000), Var(v=-0.0406, grad=0.0000), Var(v=0.2023, grad=-1.1499), Var(v=0.1355, grad=0.9603), Var(v=-0.2074, grad=0.0000), Var(v=0.0363, grad=0.0000), Var(v=-0.0380, grad=0.0000), Var(v=-0.0220, grad=0.0000), Var(v=0.0949, grad=-0.1279), Var(v=-0.0297, grad=0.0000), Var(v=-0.1777, grad=0.0000), Var(v=-0.0101, grad=1.2927), Var(v=0.0530, grad=-0.1290), Var(v=-0.0653, grad=0.0000), Var(v=0.1489, grad=-0.1728), Var(v=-0.0683, grad=-1.6433), Var(v=0.0060, grad=0.0000), Var(v=-0.0106, grad=0.0000), Var(v=0.1054, grad=0.0000), Var(v=0.1477, grad=1.0532), Var(v=-0.2176, grad=0.0000), Var(v=0.0413, grad=0.0000), Var(v=-0.1435, grad=0.0000), Var(v=-0.0754, grad=0.0000), Var(v=0.0031, grad=-0.8552), Var(v=0.0914, grad=0.3269), Var(v=0.0873, grad=0.5851), Var(v=0.0515, grad=1.2834), Var(v=-0.0374, grad=0.0000), Var(v=0.0135, grad=-0.5008)], [Var(v=0.0856, grad=0.0024), Var(v=0.0249, grad=-0.0056), Var(v=-0.1879, grad=0.0058), Var(v=0.1028, grad=0.0038), Var(v=0.0919, grad=0.0000), Var(v=0.0915, grad=0.0141), Var(v=0.1450, grad=-0.0168), Var(v=0.1129, grad=0.0000), Var(v=0.1502, grad=0.0134), Var(v=-0.0489, grad=0.0000), Var(v=0.0679, grad=-0.0082), Var(v=-0.0069, grad=0.0000), Var(v=-0.0877, grad=0.0000), Var(v=-0.1083, grad=0.0000), Var(v=-0.0020, grad=0.0000), Var(v=0.0155, grad=0.0048), Var(v=0.0805, grad=0.0000), Var(v=0.0901, grad=0.0000), Var(v=-0.0092, grad=0.0000), Var(v=0.2021, grad=-0.0048), Var(v=-0.0734, grad=0.0000), Var(v=-0.0010, grad=0.0000), Var(v=0.0256, grad=0.0000), Var(v=0.0066, grad=-0.0078), Var(v=0.1607, grad=0.0000), Var(v=0.0238, grad=-0.0025), Var(v=-0.2241, grad=-0.0047), Var(v=-0.0550, grad=-0.0069), Var(v=0.0689, grad=0.0000), Var(v=-0.0694, grad=0.0000), Var(v=0.0996, grad=-0.0096), Var(v=-0.0610, grad=-0.0104), Var(v=0.0556, grad=0.0010), Var(v=-0.1233, grad=0.0000), Var(v=-0.0035, grad=0.0014), Var(v=0.0176, grad=0.0133), Var(v=-0.0725, grad=0.0000), Var(v=0.0315, grad=0.0201), Var(v=0.1507, grad=0.0000), Var(v=0.0356, grad=-0.0085), Var(v=-0.0932, grad=0.0000), Var(v=-0.0583, grad=-0.0006), Var(v=-0.1348, grad=0.0000), Var(v=-0.0759, grad=0.0000), Var(v=0.0608, grad=0.0069), Var(v=0.0152, grad=-0.0026), Var(v=0.1039, grad=-0.0047), Var(v=0.1237, grad=-0.0104), Var(v=0.0612, grad=-0.0002), Var(v=-0.0402, grad=0.0040)], [Var(v=-0.0027, grad=-0.1549), Var(v=-0.0887, grad=0.0000), Var(v=0.0046, grad=0.0000), Var(v=0.0104, grad=0.0000), Var(v=-0.1085, grad=0.0000), Var(v=-0.0035, grad=-0.9026), Var(v=-0.0960, grad=0.0000), Var(v=-0.0067, grad=0.2119), Var(v=0.1107, grad=-0.8608), Var(v=0.1655, grad=0.0000), Var(v=-0.0746, grad=0.0000), Var(v=0.0733, grad=0.0784), Var(v=0.0966, grad=-0.2370), Var(v=-0.1892, grad=0.0000), Var(v=0.0310, grad=0.0000), Var(v=-0.0182, grad=-0.3091), Var(v=0.0858, grad=-0.1423), Var(v=-0.0225, grad=0.0000), Var(v=0.0256, grad=0.1501), Var(v=-0.0316, grad=0.0000), Var(v=0.0110, grad=0.0000), Var(v=0.1252, grad=0.0000), Var(v=-0.0471, grad=-0.5959), Var(v=0.0073, grad=0.4976), Var(v=0.0335, grad=0.0000), Var(v=-0.0662, grad=0.0000), Var(v=-0.0812, grad=0.0000), Var(v=-0.0920, grad=0.0000), Var(v=-0.0634, grad=-0.0663), Var(v=0.0720, grad=0.0000), Var(v=0.0958, grad=0.0000), Var(v=0.0380, grad=0.6699), Var(v=0.0503, grad=-0.0669), Var(v=-0.1068, grad=0.0000), Var(v=-0.1607, grad=-0.0895), Var(v=0.1633, grad=-0.8515), Var(v=-0.1337, grad=0.0000), Var(v=-0.1784, grad=0.0000), Var(v=-0.0827, grad=0.0000), Var(v=-0.0785, grad=0.5457), Var(v=0.0414, grad=0.0000), Var(v=0.0625, grad=0.0000), Var(v=-0.0544, grad=0.0000), Var(v=-0.1551, grad=0.0000), Var(v=-0.0890, grad=-0.4431), Var(v=-0.1149, grad=0.1694), Var(v=0.0132, grad=0.3032), Var(v=0.0641, grad=0.6651), Var(v=0.0093, grad=0.0000), Var(v=-0.0125, grad=-0.2595)], [Var(v=-0.1442, grad=0.0037), Var(v=-0.0084, grad=-0.0086), Var(v=0.0615, grad=0.0088), Var(v=0.0510, grad=0.0057), Var(v=-0.0252, grad=0.0000), Var(v=0.1349, grad=0.0215), Var(v=-0.1814, grad=-0.0256), Var(v=0.0773, grad=0.0000), Var(v=-0.0777, grad=0.0205), Var(v=0.0179, grad=0.0000), Var(v=-0.1029, grad=-0.0125), Var(v=0.1922, grad=0.0000), Var(v=0.0916, grad=0.0000), Var(v=0.0438, grad=0.0000), Var(v=-0.2103, grad=0.0000), Var(v=0.0680, grad=0.0073), Var(v=0.0894, grad=0.0000), Var(v=0.0932, grad=0.0000), Var(v=-0.0885, grad=0.0000), Var(v=-0.0385, grad=-0.0073), Var(v=-0.0504, grad=0.0000), Var(v=0.1099, grad=0.0000), Var(v=0.0511, grad=0.0000), Var(v=0.0474, grad=-0.0118), Var(v=0.0257, grad=0.0000), Var(v=-0.0568, grad=-0.0038), Var(v=0.0054, grad=-0.0072), Var(v=-0.0639, grad=-0.0105), Var(v=-0.2315, grad=0.0000), Var(v=0.2141, grad=0.0000), Var(v=-0.1754, grad=-0.0146), Var(v=-0.0447, grad=-0.0159), Var(v=-0.0869, grad=0.0016), Var(v=-0.0541, grad=0.0000), Var(v=0.0064, grad=0.0021), Var(v=0.0043, grad=0.0202), Var(v=0.0537, grad=0.0000), Var(v=0.1193, grad=0.0307), Var(v=-0.0627, grad=0.0000), Var(v=-0.2039, grad=-0.0130), Var(v=0.0612, grad=0.0000), Var(v=0.0496, grad=-0.0008), Var(v=-0.1758, grad=0.0000), Var(v=-0.0410, grad=0.0000), Var(v=-0.0037, grad=0.0105), Var(v=0.1449, grad=-0.0040), Var(v=-0.1002, grad=-0.0072), Var(v=0.0890, grad=-0.0158), Var(v=0.1421, grad=-0.0002), Var(v=-0.1418, grad=0.0062)], [Var(v=-0.0343, grad=0.0968), Var(v=0.0292, grad=-0.2255), Var(v=0.1439, grad=0.2310), Var(v=0.0038, grad=0.1505), Var(v=0.0116, grad=0.0000), Var(v=-0.1108, grad=0.5641), Var(v=0.0369, grad=-0.6733), Var(v=-0.0324, grad=0.0000), Var(v=-0.0074, grad=0.5379), Var(v=-0.0867, grad=0.0000), Var(v=0.2031, grad=-0.3286), Var(v=0.1091, grad=0.0000), Var(v=0.0126, grad=0.0000), Var(v=-0.0555, grad=0.0000), Var(v=-0.0277, grad=0.0000), Var(v=0.1174, grad=0.1932), Var(v=-0.0757, grad=0.0000), Var(v=-0.1403, grad=0.0000), Var(v=0.0205, grad=0.0000), Var(v=-0.0092, grad=-0.1927), Var(v=0.0285, grad=0.0000), Var(v=-0.0325, grad=0.0000), Var(v=-0.0852, grad=0.0000), Var(v=0.1064, grad=-0.3110), Var(v=0.0862, grad=0.0000), Var(v=-0.0370, grad=-0.1007), Var(v=-0.0421, grad=-0.1882), Var(v=-0.0105, grad=-0.2766), Var(v=-0.0352, grad=0.0000), Var(v=0.0441, grad=0.0000), Var(v=0.0209, grad=-0.3835), Var(v=0.0831, grad=-0.4186), Var(v=0.0570, grad=0.0418), Var(v=0.0545, grad=0.0000), Var(v=0.0100, grad=0.0560), Var(v=-0.0347, grad=0.5321), Var(v=-0.0389, grad=0.0000), Var(v=0.1013, grad=0.8057), Var(v=0.1142, grad=0.0000), Var(v=0.0319, grad=-0.3410), Var(v=-0.0152, grad=0.0000), Var(v=-0.0507, grad=-0.0223), Var(v=0.0927, grad=0.0000), Var(v=0.0938, grad=0.0000), Var(v=-0.0344, grad=0.2769), Var(v=0.1168, grad=-0.1059), Var(v=-0.0425, grad=-0.1895), Var(v=0.0783, grad=-0.4156), Var(v=-0.0390, grad=-0.0061), Var(v=0.0522, grad=0.1622)]] Biases: [Var(v=0.0001, grad=-0.0070), Var(v=0.0617, grad=-6.1684), Var(v=-0.0632, grad=6.3201), Var(v=-0.0412, grad=4.1166), Var(v=0.0000, grad=0.0000), Var(v=0.0004, grad=-0.0408), Var(v=0.1842, grad=-18.4206), Var(v=-0.0363, grad=3.6329), Var(v=0.0004, grad=-0.0389), Var(v=0.0000, grad=0.0000), Var(v=0.0899, grad=-8.9910), Var(v=-0.0134, grad=1.3447), Var(v=0.0406, grad=-4.0632), Var(v=0.0000, grad=0.0000), Var(v=0.0000, grad=0.0000), Var(v=0.0001, grad=-0.0140), Var(v=0.0244, grad=-2.4386), Var(v=0.0000, grad=0.0000), Var(v=-0.0257, grad=2.5739), Var(v=0.0527, grad=-5.2711), Var(v=0.0000, grad=0.0000), Var(v=0.0000, grad=0.0000), Var(v=0.1021, grad=-10.2145), Var(v=-0.0002, grad=0.0225), Var(v=0.0000, grad=0.0000), Var(v=0.0275, grad=-2.7543), Var(v=0.0515, grad=-5.1481), Var(v=0.0757, grad=-7.5680), Var(v=0.0114, grad=-1.1361), Var(v=0.0000, grad=0.0000), Var(v=0.1049, grad=-10.4934), Var(v=-0.0003, grad=0.0302), Var(v=0.0000, grad=-0.0030), Var(v=0.0000, grad=0.0000), Var(v=0.0000, grad=-0.0040), Var(v=0.0004, grad=-0.0385), Var(v=0.0000, grad=0.0000), Var(v=-0.2204, grad=22.0440), Var(v=0.0000, grad=0.0000), Var(v=-0.0002, grad=0.0246), Var(v=0.0000, grad=0.0000), Var(v=0.0061, grad=-0.6101), Var(v=0.0000, grad=0.0000), Var(v=0.0000, grad=0.0000), Var(v=0.0002, grad=-0.0200), Var(v=-0.0001, grad=0.0076), Var(v=-0.0001, grad=0.0137), Var(v=-0.0003, grad=0.0300), Var(v=0.0017, grad=-0.1662), Var(v=0.0001, grad=-0.0117)]\n", - "Layer 2 \n", - " Weights: [[Var(v=0.0195, grad=1.1091)], [Var(v=-0.0988, grad=2.7504)], [Var(v=0.0417, grad=3.1399)], [Var(v=0.0336, grad=1.4039)], [Var(v=0.0060, grad=0.0000)], [Var(v=0.1773, grad=0.1147)], [Var(v=-0.2349, grad=2.1854)], [Var(v=-0.0379, grad=-0.3967)], [Var(v=0.1626, grad=0.7581)], [Var(v=-0.0294, grad=0.0000)], [Var(v=-0.1059, grad=0.1930)], [Var(v=-0.0109, grad=-0.4635)], [Var(v=0.0617, grad=-1.4799)], [Var(v=0.0524, grad=0.0000)], [Var(v=-0.0334, grad=0.0000)], [Var(v=0.0393, grad=2.1831)], [Var(v=0.0433, grad=-1.5177)], [Var(v=-0.0804, grad=0.0000)], [Var(v=0.0002, grad=-2.9921)], [Var(v=-0.0906, grad=2.9585)], [Var(v=0.0096, grad=0.0000)], [Var(v=0.0075, grad=0.0000)], [Var(v=0.1382, grad=-2.0404)], [Var(v=-0.1070, grad=0.8578)], [Var(v=-0.0419, grad=0.0000)], [Var(v=-0.0385, grad=0.6599)], [Var(v=-0.0907, grad=3.1142)], [Var(v=-0.0955, grad=0.7921)], [Var(v=0.0225, grad=-0.9357)], [Var(v=0.0558, grad=0.0000)], [Var(v=-0.1712, grad=4.9822)], [Var(v=-0.1311, grad=-0.1320)], [Var(v=-0.0003, grad=1.3479)], [Var(v=0.0263, grad=0.0000)], [Var(v=-0.0006, grad=1.8342)], [Var(v=0.1792, grad=-1.0816)], [Var(v=-0.0298, grad=0.0000)], [Var(v=0.2522, grad=0.2801)], [Var(v=0.0243, grad=0.0000)], [Var(v=-0.0947, grad=-1.3258)], [Var(v=-0.0209, grad=0.0000)], [Var(v=-0.0098, grad=0.2720)], [Var(v=-0.0406, grad=0.0000)], [Var(v=0.0532, grad=0.0000)], [Var(v=0.0915, grad=-0.3907)], [Var(v=-0.0614, grad=2.7892)], [Var(v=-0.0712, grad=1.1218)], [Var(v=-0.1299, grad=-0.1610)], [Var(v=-0.0043, grad=0.2415)], [Var(v=0.0356, grad=1.5678)]] Biases: [Var(v=0.0023, grad=-0.2283)]\n", - "\n", - "Network after zeroing gradients:\n", - "Layer 0 \n", - " Weights: [[Var(v=0.0803, grad=0.0000), Var(v=0.0169, grad=0.0000), Var(v=0.1285, grad=0.0000), Var(v=-0.1680, grad=0.0000), Var(v=-0.0901, grad=0.0000), Var(v=-0.0798, grad=0.0000), Var(v=-0.0982, grad=0.0000), Var(v=-0.1444, grad=0.0000), Var(v=-0.0812, grad=0.0000), Var(v=-0.1667, grad=0.0000), Var(v=0.1041, grad=0.0000), Var(v=-0.0142, grad=0.0000), Var(v=0.0711, grad=0.0000), Var(v=0.1254, grad=0.0000), Var(v=-0.0692, grad=0.0000)]] Biases: [Var(v=0.0458, grad=0.0000), Var(v=0.0078, grad=0.0000), Var(v=0.0269, grad=0.0000), Var(v=0.1033, grad=0.0000), Var(v=-0.0072, grad=0.0000), Var(v=0.0524, grad=0.0000), Var(v=0.0251, grad=0.0000), Var(v=0.0673, grad=0.0000), Var(v=-0.0749, grad=0.0000), Var(v=0.0814, grad=0.0000), Var(v=0.0063, grad=0.0000), Var(v=0.0121, grad=0.0000), Var(v=0.0181, grad=0.0000), Var(v=-0.1151, grad=0.0000), Var(v=0.0327, grad=0.0000)]\n", - "Layer 1 \n", - " Weights: [[Var(v=-0.0382, grad=0.0000), Var(v=-0.0476, grad=0.0000), Var(v=0.0922, grad=0.0000), Var(v=-0.0844, grad=0.0000), Var(v=-0.0332, grad=0.0000), Var(v=0.0852, grad=0.0000), Var(v=-0.0124, grad=0.0000), Var(v=0.0842, grad=0.0000), Var(v=0.0041, grad=0.0000), Var(v=-0.0230, grad=0.0000), Var(v=0.1281, grad=0.0000), Var(v=-0.0422, grad=0.0000), Var(v=-0.0733, grad=0.0000), Var(v=-0.0646, grad=0.0000), Var(v=-0.0117, grad=0.0000), Var(v=-0.0101, grad=0.0000), Var(v=0.0831, grad=0.0000), Var(v=-0.2360, grad=0.0000), Var(v=-0.0033, grad=0.0000), Var(v=-0.1359, grad=0.0000), Var(v=-0.0100, grad=0.0000), Var(v=0.1033, grad=0.0000), Var(v=0.0525, grad=0.0000), Var(v=0.0100, grad=0.0000), Var(v=-0.0448, grad=0.0000), Var(v=0.1625, grad=0.0000), Var(v=-0.1058, grad=0.0000), Var(v=-0.0722, grad=0.0000), Var(v=0.2531, grad=0.0000), Var(v=-0.0802, grad=0.0000), Var(v=0.0956, grad=0.0000), Var(v=0.0561, grad=0.0000), Var(v=0.0675, grad=0.0000), Var(v=-0.0841, grad=0.0000), Var(v=-0.1306, grad=0.0000), Var(v=0.1020, grad=0.0000), Var(v=-0.0234, grad=0.0000), Var(v=-0.0579, grad=0.0000), Var(v=-0.0931, grad=0.0000), Var(v=-0.0813, grad=0.0000), Var(v=0.0973, grad=0.0000), Var(v=0.1397, grad=0.0000), Var(v=-0.1750, grad=0.0000), Var(v=-0.0723, grad=0.0000), Var(v=0.1644, grad=0.0000), Var(v=-0.0209, grad=0.0000), Var(v=0.0240, grad=0.0000), Var(v=-0.0247, grad=0.0000), Var(v=0.0384, grad=0.0000), Var(v=0.0999, grad=0.0000)], [Var(v=0.0679, grad=0.0000), Var(v=-0.1111, grad=0.0000), Var(v=-0.0885, grad=0.0000), Var(v=-0.0549, grad=0.0000), Var(v=-0.0359, grad=0.0000), Var(v=0.1388, grad=0.0000), Var(v=-0.0769, grad=0.0000), Var(v=-0.1260, grad=0.0000), Var(v=-0.0253, grad=0.0000), Var(v=-0.1051, grad=0.0000), Var(v=-0.0182, grad=0.0000), Var(v=0.0980, grad=0.0000), Var(v=0.0555, grad=0.0000), Var(v=0.1001, grad=0.0000), Var(v=-0.0761, grad=0.0000), Var(v=-0.2323, grad=0.0000), Var(v=0.0324, grad=0.0000), Var(v=0.0721, grad=0.0000), Var(v=-0.0384, grad=0.0000), Var(v=-0.0034, grad=0.0000), Var(v=-0.0778, grad=0.0000), Var(v=-0.1352, grad=0.0000), Var(v=-0.0119, grad=0.0000), Var(v=0.0441, grad=0.0000), Var(v=-0.0361, grad=0.0000), Var(v=0.1519, grad=0.0000), Var(v=0.1159, grad=0.0000), Var(v=-0.0072, grad=0.0000), Var(v=-0.1877, grad=0.0000), Var(v=-0.1459, grad=0.0000), Var(v=0.0663, grad=0.0000), Var(v=0.0175, grad=0.0000), Var(v=0.1038, grad=0.0000), Var(v=0.0586, grad=0.0000), Var(v=0.2907, grad=0.0000), Var(v=0.0685, grad=0.0000), Var(v=0.0189, grad=0.0000), Var(v=0.1071, grad=0.0000), Var(v=0.0218, grad=0.0000), Var(v=-0.0431, grad=0.0000), Var(v=-0.0765, grad=0.0000), Var(v=-0.0807, grad=0.0000), Var(v=-0.0054, grad=0.0000), Var(v=0.1035, grad=0.0000), Var(v=-0.0356, grad=0.0000), Var(v=-0.1277, grad=0.0000), Var(v=-0.0169, grad=0.0000), Var(v=0.1458, grad=0.0000), Var(v=-0.0697, grad=0.0000), Var(v=-0.0296, grad=0.0000)], [Var(v=-0.0776, grad=0.0000), Var(v=-0.0214, grad=0.0000), Var(v=-0.1360, grad=0.0000), Var(v=0.1583, grad=0.0000), Var(v=-0.0618, grad=0.0000), Var(v=0.0128, grad=0.0000), Var(v=-0.0682, grad=0.0000), Var(v=0.0080, grad=0.0000), Var(v=0.0204, grad=0.0000), Var(v=-0.0587, grad=0.0000), Var(v=-0.1384, grad=0.0000), Var(v=0.0551, grad=0.0000), Var(v=0.0364, grad=0.0000), Var(v=-0.0418, grad=0.0000), Var(v=0.0360, grad=0.0000), Var(v=0.0670, grad=0.0000), Var(v=0.0053, grad=0.0000), Var(v=0.0022, grad=0.0000), Var(v=0.1459, grad=0.0000), Var(v=-0.1206, grad=0.0000), Var(v=-0.0494, grad=0.0000), Var(v=-0.0458, grad=0.0000), Var(v=0.0444, grad=0.0000), Var(v=-0.1395, grad=0.0000), Var(v=0.1424, grad=0.0000), Var(v=-0.1040, grad=0.0000), Var(v=-0.0916, grad=0.0000), Var(v=-0.1299, grad=0.0000), Var(v=-0.0186, grad=0.0000), Var(v=-0.0362, grad=0.0000), Var(v=-0.0833, grad=0.0000), Var(v=0.0388, grad=0.0000), Var(v=0.0982, grad=0.0000), Var(v=0.0509, grad=0.0000), Var(v=0.0644, grad=0.0000), Var(v=0.2227, grad=0.0000), Var(v=0.0017, grad=0.0000), Var(v=-0.1117, grad=0.0000), Var(v=-0.2394, grad=0.0000), Var(v=0.0396, grad=0.0000), Var(v=0.1081, grad=0.0000), Var(v=-0.2020, grad=0.0000), Var(v=-0.0903, grad=0.0000), Var(v=0.0227, grad=0.0000), Var(v=0.0877, grad=0.0000), Var(v=0.0012, grad=0.0000), Var(v=0.1594, grad=0.0000), Var(v=0.0100, grad=0.0000), Var(v=-0.0703, grad=0.0000), Var(v=-0.0124, grad=0.0000)], [Var(v=0.1062, grad=0.0000), Var(v=-0.1613, grad=0.0000), Var(v=0.0169, grad=0.0000), Var(v=-0.0338, grad=0.0000), Var(v=0.0248, grad=0.0000), Var(v=0.0911, grad=0.0000), Var(v=0.0472, grad=0.0000), Var(v=-0.1163, grad=0.0000), Var(v=-0.1660, grad=0.0000), Var(v=-0.1289, grad=0.0000), Var(v=-0.0917, grad=0.0000), Var(v=0.0182, grad=0.0000), Var(v=-0.0458, grad=0.0000), Var(v=-0.1841, grad=0.0000), Var(v=-0.0308, grad=0.0000), Var(v=-0.0030, grad=0.0000), Var(v=-0.0334, grad=0.0000), Var(v=0.0651, grad=0.0000), Var(v=0.0391, grad=0.0000), Var(v=0.0670, grad=0.0000), Var(v=-0.0647, grad=0.0000), Var(v=-0.1526, grad=0.0000), Var(v=0.0712, grad=0.0000), Var(v=-0.0770, grad=0.0000), Var(v=0.1065, grad=0.0000), Var(v=-0.0245, grad=0.0000), Var(v=0.0610, grad=0.0000), Var(v=0.0973, grad=0.0000), Var(v=0.0451, grad=0.0000), Var(v=0.0825, grad=0.0000), Var(v=-0.0053, grad=0.0000), Var(v=0.1046, grad=0.0000), Var(v=-0.0240, grad=0.0000), Var(v=-0.0133, grad=0.0000), Var(v=0.0595, grad=0.0000), Var(v=-0.2066, grad=0.0000), Var(v=-0.2472, grad=0.0000), Var(v=-0.1506, grad=0.0000), Var(v=-0.0742, grad=0.0000), Var(v=0.1515, grad=0.0000), Var(v=-0.0657, grad=0.0000), Var(v=-0.0464, grad=0.0000), Var(v=-0.0223, grad=0.0000), Var(v=0.0151, grad=0.0000), Var(v=0.0182, grad=0.0000), Var(v=-0.0028, grad=0.0000), Var(v=-0.0140, grad=0.0000), Var(v=0.2281, grad=0.0000), Var(v=-0.0093, grad=0.0000), Var(v=-0.0247, grad=0.0000)], [Var(v=0.1140, grad=0.0000), Var(v=0.1482, grad=0.0000), Var(v=0.0323, grad=0.0000), Var(v=0.1197, grad=0.0000), Var(v=-0.0812, grad=0.0000), Var(v=0.0664, grad=0.0000), Var(v=-0.0401, grad=0.0000), Var(v=0.1905, grad=0.0000), Var(v=0.0404, grad=0.0000), Var(v=0.0015, grad=0.0000), Var(v=-0.0504, grad=0.0000), Var(v=-0.0509, grad=0.0000), Var(v=-0.0399, grad=0.0000), Var(v=-0.1120, grad=0.0000), Var(v=-0.1441, grad=0.0000), Var(v=-0.0238, grad=0.0000), Var(v=-0.0510, grad=0.0000), Var(v=-0.1634, grad=0.0000), Var(v=-0.0509, grad=0.0000), Var(v=0.0149, grad=0.0000), Var(v=0.0240, grad=0.0000), Var(v=-0.0022, grad=0.0000), Var(v=-0.0530, grad=0.0000), Var(v=0.0895, grad=0.0000), Var(v=-0.1382, grad=0.0000), Var(v=0.0220, grad=0.0000), Var(v=0.0995, grad=0.0000), Var(v=-0.0289, grad=0.0000), Var(v=-0.0037, grad=0.0000), Var(v=-0.0844, grad=0.0000), Var(v=0.1314, grad=0.0000), Var(v=-0.1513, grad=0.0000), Var(v=0.0973, grad=0.0000), Var(v=0.1033, grad=0.0000), Var(v=0.1759, grad=0.0000), Var(v=-0.1139, grad=0.0000), Var(v=0.2131, grad=0.0000), Var(v=0.0199, grad=0.0000), Var(v=-0.0647, grad=0.0000), Var(v=0.0742, grad=0.0000), Var(v=0.0044, grad=0.0000), Var(v=0.1946, grad=0.0000), Var(v=-0.0078, grad=0.0000), Var(v=-0.1106, grad=0.0000), Var(v=0.0162, grad=0.0000), Var(v=0.1223, grad=0.0000), Var(v=-0.0178, grad=0.0000), Var(v=0.2135, grad=0.0000), Var(v=0.0030, grad=0.0000), Var(v=0.0779, grad=0.0000)], [Var(v=-0.0772, grad=0.0000), Var(v=-0.0045, grad=0.0000), Var(v=0.1074, grad=0.0000), Var(v=-0.0356, grad=0.0000), Var(v=0.0216, grad=0.0000), Var(v=0.1071, grad=0.0000), Var(v=0.0951, grad=0.0000), Var(v=0.1045, grad=0.0000), Var(v=0.0813, grad=0.0000), Var(v=-0.0064, grad=0.0000), Var(v=0.0661, grad=0.0000), Var(v=-0.1070, grad=0.0000), Var(v=0.1458, grad=0.0000), Var(v=0.0159, grad=0.0000), Var(v=0.0746, grad=0.0000), Var(v=-0.0615, grad=0.0000), Var(v=0.1271, grad=0.0000), Var(v=0.0182, grad=0.0000), Var(v=-0.0449, grad=0.0000), Var(v=0.0827, grad=0.0000), Var(v=0.0085, grad=0.0000), Var(v=-0.0313, grad=0.0000), Var(v=-0.0481, grad=0.0000), Var(v=-0.1874, grad=0.0000), Var(v=-0.0847, grad=0.0000), Var(v=0.0541, grad=0.0000), Var(v=-0.0440, grad=0.0000), Var(v=0.1477, grad=0.0000), Var(v=0.0491, grad=0.0000), Var(v=-0.1025, grad=0.0000), Var(v=0.0900, grad=0.0000), Var(v=0.1156, grad=0.0000), Var(v=-0.1255, grad=0.0000), Var(v=0.0693, grad=0.0000), Var(v=-0.1173, grad=0.0000), Var(v=0.0477, grad=0.0000), Var(v=-0.1101, grad=0.0000), Var(v=-0.1722, grad=0.0000), Var(v=-0.2142, grad=0.0000), Var(v=0.0585, grad=0.0000), Var(v=-0.0225, grad=0.0000), Var(v=-0.0588, grad=0.0000), Var(v=0.1535, grad=0.0000), Var(v=0.1027, grad=0.0000), Var(v=0.0604, grad=0.0000), Var(v=0.0614, grad=0.0000), Var(v=0.0807, grad=0.0000), Var(v=-0.0193, grad=0.0000), Var(v=-0.0566, grad=0.0000), Var(v=-0.0863, grad=0.0000)], [Var(v=0.0396, grad=0.0000), Var(v=0.0063, grad=0.0000), Var(v=-0.0086, grad=0.0000), Var(v=0.0372, grad=0.0000), Var(v=0.0070, grad=0.0000), Var(v=0.0721, grad=0.0000), Var(v=0.1506, grad=0.0000), Var(v=-0.0965, grad=0.0000), Var(v=0.1018, grad=0.0000), Var(v=0.0670, grad=0.0000), Var(v=-0.0950, grad=0.0000), Var(v=0.2652, grad=0.0000), Var(v=-0.0303, grad=0.0000), Var(v=-0.1600, grad=0.0000), Var(v=0.0337, grad=0.0000), Var(v=0.1172, grad=0.0000), Var(v=0.0046, grad=0.0000), Var(v=-0.0923, grad=0.0000), Var(v=0.0749, grad=0.0000), Var(v=-0.0571, grad=0.0000), Var(v=-0.0738, grad=0.0000), Var(v=-0.0346, grad=0.0000), Var(v=-0.0452, grad=0.0000), Var(v=0.0155, grad=0.0000), Var(v=-0.1595, grad=0.0000), Var(v=0.0690, grad=0.0000), Var(v=-0.0286, grad=0.0000), Var(v=0.0690, grad=0.0000), Var(v=-0.0089, grad=0.0000), Var(v=-0.0287, grad=0.0000), Var(v=0.0495, grad=0.0000), Var(v=0.1010, grad=0.0000), Var(v=-0.0507, grad=0.0000), Var(v=-0.1842, grad=0.0000), Var(v=0.0162, grad=0.0000), Var(v=0.0540, grad=0.0000), Var(v=0.0070, grad=0.0000), Var(v=-0.0641, grad=0.0000), Var(v=-0.1138, grad=0.0000), Var(v=0.0525, grad=0.0000), Var(v=-0.2218, grad=0.0000), Var(v=-0.0094, grad=0.0000), Var(v=0.0275, grad=0.0000), Var(v=0.1331, grad=0.0000), Var(v=-0.0545, grad=0.0000), Var(v=-0.0702, grad=0.0000), Var(v=0.0621, grad=0.0000), Var(v=0.1670, grad=0.0000), Var(v=-0.0485, grad=0.0000), Var(v=-0.0584, grad=0.0000)], [Var(v=0.1127, grad=0.0000), Var(v=0.0653, grad=0.0000), Var(v=0.1070, grad=0.0000), Var(v=-0.0835, grad=0.0000), Var(v=-0.1063, grad=0.0000), Var(v=-0.0743, grad=0.0000), Var(v=0.0020, grad=0.0000), Var(v=0.0398, grad=0.0000), Var(v=-0.1222, grad=0.0000), Var(v=-0.0022, grad=0.0000), Var(v=0.1721, grad=0.0000), Var(v=0.0168, grad=0.0000), Var(v=0.0250, grad=0.0000), Var(v=0.0526, grad=0.0000), Var(v=0.0340, grad=0.0000), Var(v=-0.0387, grad=0.0000), Var(v=-0.1268, grad=0.0000), Var(v=0.0024, grad=0.0000), Var(v=-0.1403, grad=0.0000), Var(v=-0.0853, grad=0.0000), Var(v=0.0849, grad=0.0000), Var(v=0.0012, grad=0.0000), Var(v=0.0569, grad=0.0000), Var(v=0.0590, grad=0.0000), Var(v=-0.0440, grad=0.0000), Var(v=0.0387, grad=0.0000), Var(v=0.1925, grad=0.0000), Var(v=0.0121, grad=0.0000), Var(v=0.0642, grad=0.0000), Var(v=-0.1009, grad=0.0000), Var(v=0.1257, grad=0.0000), Var(v=0.1271, grad=0.0000), Var(v=0.0201, grad=0.0000), Var(v=0.0628, grad=0.0000), Var(v=0.0356, grad=0.0000), Var(v=0.0209, grad=0.0000), Var(v=-0.0629, grad=0.0000), Var(v=-0.0910, grad=0.0000), Var(v=-0.0167, grad=0.0000), Var(v=-0.0169, grad=0.0000), Var(v=-0.1795, grad=0.0000), Var(v=-0.2178, grad=0.0000), Var(v=-0.0204, grad=0.0000), Var(v=-0.2010, grad=0.0000), Var(v=0.1354, grad=0.0000), Var(v=0.1688, grad=0.0000), Var(v=0.0480, grad=0.0000), Var(v=-0.1503, grad=0.0000), Var(v=0.0599, grad=0.0000), Var(v=-0.1730, grad=0.0000)], [Var(v=0.0224, grad=0.0000), Var(v=0.0967, grad=0.0000), Var(v=0.0742, grad=0.0000), Var(v=0.0048, grad=0.0000), Var(v=-0.0956, grad=0.0000), Var(v=0.0114, grad=0.0000), Var(v=0.1013, grad=0.0000), Var(v=-0.0438, grad=0.0000), Var(v=0.0659, grad=0.0000), Var(v=-0.1096, grad=0.0000), Var(v=-0.0346, grad=0.0000), Var(v=-0.1504, grad=0.0000), Var(v=-0.2555, grad=0.0000), Var(v=0.0402, grad=0.0000), Var(v=-0.0944, grad=0.0000), Var(v=0.0658, grad=0.0000), Var(v=-0.0191, grad=0.0000), Var(v=0.0422, grad=0.0000), Var(v=0.0096, grad=0.0000), Var(v=0.2049, grad=0.0000), Var(v=-0.1152, grad=0.0000), Var(v=0.0114, grad=0.0000), Var(v=0.0350, grad=0.0000), Var(v=-0.1106, grad=0.0000), Var(v=-0.1330, grad=0.0000), Var(v=-0.0669, grad=0.0000), Var(v=0.1023, grad=0.0000), Var(v=0.0466, grad=0.0000), Var(v=0.0183, grad=0.0000), Var(v=0.0413, grad=0.0000), Var(v=0.1525, grad=0.0000), Var(v=0.0101, grad=0.0000), Var(v=0.1148, grad=0.0000), Var(v=-0.0290, grad=0.0000), Var(v=0.1278, grad=0.0000), Var(v=0.1530, grad=0.0000), Var(v=-0.1481, grad=0.0000), Var(v=0.0583, grad=0.0000), Var(v=0.0524, grad=0.0000), Var(v=-0.0901, grad=0.0000), Var(v=0.0281, grad=0.0000), Var(v=0.0617, grad=0.0000), Var(v=-0.1038, grad=0.0000), Var(v=-0.1044, grad=0.0000), Var(v=-0.0171, grad=0.0000), Var(v=0.0275, grad=0.0000), Var(v=0.1084, grad=0.0000), Var(v=-0.1065, grad=0.0000), Var(v=-0.0045, grad=0.0000), Var(v=0.1402, grad=0.0000)], [Var(v=-0.1394, grad=0.0000), Var(v=0.0683, grad=0.0000), Var(v=-0.0152, grad=0.0000), Var(v=0.0683, grad=0.0000), Var(v=0.0264, grad=0.0000), Var(v=0.0143, grad=0.0000), Var(v=0.0685, grad=0.0000), Var(v=-0.1710, grad=0.0000), Var(v=0.0105, grad=0.0000), Var(v=0.2051, grad=0.0000), Var(v=0.1256, grad=0.0000), Var(v=0.0829, grad=0.0000), Var(v=-0.0127, grad=0.0000), Var(v=0.0055, grad=0.0000), Var(v=-0.0485, grad=0.0000), Var(v=0.0526, grad=0.0000), Var(v=-0.0885, grad=0.0000), Var(v=0.1506, grad=0.0000), Var(v=0.0030, grad=0.0000), Var(v=0.0539, grad=0.0000), Var(v=-0.0319, grad=0.0000), Var(v=0.0728, grad=0.0000), Var(v=-0.0476, grad=0.0000), Var(v=0.3375, grad=0.0000), Var(v=0.0802, grad=0.0000), Var(v=0.1460, grad=0.0000), Var(v=-0.0366, grad=0.0000), Var(v=-0.0719, grad=0.0000), Var(v=-0.1499, grad=0.0000), Var(v=-0.0934, grad=0.0000), Var(v=0.0842, grad=0.0000), Var(v=0.0240, grad=0.0000), Var(v=0.1333, grad=0.0000), Var(v=-0.0155, grad=0.0000), Var(v=-0.1662, grad=0.0000), Var(v=-0.0540, grad=0.0000), Var(v=0.1235, grad=0.0000), Var(v=-0.0381, grad=0.0000), Var(v=0.1261, grad=0.0000), Var(v=0.0184, grad=0.0000), Var(v=0.0048, grad=0.0000), Var(v=-0.0578, grad=0.0000), Var(v=-0.0776, grad=0.0000), Var(v=0.1760, grad=0.0000), Var(v=-0.0980, grad=0.0000), Var(v=0.0642, grad=0.0000), Var(v=0.2563, grad=0.0000), Var(v=-0.0098, grad=0.0000), Var(v=0.0663, grad=0.0000), Var(v=0.0259, grad=0.0000)], [Var(v=0.1659, grad=0.0000), Var(v=0.0293, grad=0.0000), Var(v=0.0401, grad=0.0000), Var(v=-0.2359, grad=0.0000), Var(v=-0.0612, grad=0.0000), Var(v=0.2065, grad=0.0000), Var(v=-0.1534, grad=0.0000), Var(v=0.0131, grad=0.0000), Var(v=0.0398, grad=0.0000), Var(v=-0.1340, grad=0.0000), Var(v=0.0635, grad=0.0000), Var(v=-0.0456, grad=0.0000), Var(v=0.0917, grad=0.0000), Var(v=-0.0035, grad=0.0000), Var(v=-0.1769, grad=0.0000), Var(v=-0.0138, grad=0.0000), Var(v=0.0864, grad=0.0000), Var(v=-0.0002, grad=0.0000), Var(v=0.1438, grad=0.0000), Var(v=-0.0882, grad=0.0000), Var(v=-0.0744, grad=0.0000), Var(v=-0.0406, grad=0.0000), Var(v=0.2023, grad=0.0000), Var(v=0.1355, grad=0.0000), Var(v=-0.2074, grad=0.0000), Var(v=0.0363, grad=0.0000), Var(v=-0.0380, grad=0.0000), Var(v=-0.0220, grad=0.0000), Var(v=0.0949, grad=0.0000), Var(v=-0.0297, grad=0.0000), Var(v=-0.1777, grad=0.0000), Var(v=-0.0101, grad=0.0000), Var(v=0.0530, grad=0.0000), Var(v=-0.0653, grad=0.0000), Var(v=0.1489, grad=0.0000), Var(v=-0.0683, grad=0.0000), Var(v=0.0060, grad=0.0000), Var(v=-0.0106, grad=0.0000), Var(v=0.1054, grad=0.0000), Var(v=0.1477, grad=0.0000), Var(v=-0.2176, grad=0.0000), Var(v=0.0413, grad=0.0000), Var(v=-0.1435, grad=0.0000), Var(v=-0.0754, grad=0.0000), Var(v=0.0031, grad=0.0000), Var(v=0.0914, grad=0.0000), Var(v=0.0873, grad=0.0000), Var(v=0.0515, grad=0.0000), Var(v=-0.0374, grad=0.0000), Var(v=0.0135, grad=0.0000)], [Var(v=0.0856, grad=0.0000), Var(v=0.0249, grad=0.0000), Var(v=-0.1879, grad=0.0000), Var(v=0.1028, grad=0.0000), Var(v=0.0919, grad=0.0000), Var(v=0.0915, grad=0.0000), Var(v=0.1450, grad=0.0000), Var(v=0.1129, grad=0.0000), Var(v=0.1502, grad=0.0000), Var(v=-0.0489, grad=0.0000), Var(v=0.0679, grad=0.0000), Var(v=-0.0069, grad=0.0000), Var(v=-0.0877, grad=0.0000), Var(v=-0.1083, grad=0.0000), Var(v=-0.0020, grad=0.0000), Var(v=0.0155, grad=0.0000), Var(v=0.0805, grad=0.0000), Var(v=0.0901, grad=0.0000), Var(v=-0.0092, grad=0.0000), Var(v=0.2021, grad=0.0000), Var(v=-0.0734, grad=0.0000), Var(v=-0.0010, grad=0.0000), Var(v=0.0256, grad=0.0000), Var(v=0.0066, grad=0.0000), Var(v=0.1607, grad=0.0000), Var(v=0.0238, grad=0.0000), Var(v=-0.2241, grad=0.0000), Var(v=-0.0550, grad=0.0000), Var(v=0.0689, grad=0.0000), Var(v=-0.0694, grad=0.0000), Var(v=0.0996, grad=0.0000), Var(v=-0.0610, grad=0.0000), Var(v=0.0556, grad=0.0000), Var(v=-0.1233, grad=0.0000), Var(v=-0.0035, grad=0.0000), Var(v=0.0176, grad=0.0000), Var(v=-0.0725, grad=0.0000), Var(v=0.0315, grad=0.0000), Var(v=0.1507, grad=0.0000), Var(v=0.0356, grad=0.0000), Var(v=-0.0932, grad=0.0000), Var(v=-0.0583, grad=0.0000), Var(v=-0.1348, grad=0.0000), Var(v=-0.0759, grad=0.0000), Var(v=0.0608, grad=0.0000), Var(v=0.0152, grad=0.0000), Var(v=0.1039, grad=0.0000), Var(v=0.1237, grad=0.0000), Var(v=0.0612, grad=0.0000), Var(v=-0.0402, grad=0.0000)], [Var(v=-0.0027, grad=0.0000), Var(v=-0.0887, grad=0.0000), Var(v=0.0046, grad=0.0000), Var(v=0.0104, grad=0.0000), Var(v=-0.1085, grad=0.0000), Var(v=-0.0035, grad=0.0000), Var(v=-0.0960, grad=0.0000), Var(v=-0.0067, grad=0.0000), Var(v=0.1107, grad=0.0000), Var(v=0.1655, grad=0.0000), Var(v=-0.0746, grad=0.0000), Var(v=0.0733, grad=0.0000), Var(v=0.0966, grad=0.0000), Var(v=-0.1892, grad=0.0000), Var(v=0.0310, grad=0.0000), Var(v=-0.0182, grad=0.0000), Var(v=0.0858, grad=0.0000), Var(v=-0.0225, grad=0.0000), Var(v=0.0256, grad=0.0000), Var(v=-0.0316, grad=0.0000), Var(v=0.0110, grad=0.0000), Var(v=0.1252, grad=0.0000), Var(v=-0.0471, grad=0.0000), Var(v=0.0073, grad=0.0000), Var(v=0.0335, grad=0.0000), Var(v=-0.0662, grad=0.0000), Var(v=-0.0812, grad=0.0000), Var(v=-0.0920, grad=0.0000), Var(v=-0.0634, grad=0.0000), Var(v=0.0720, grad=0.0000), Var(v=0.0958, grad=0.0000), Var(v=0.0380, grad=0.0000), Var(v=0.0503, grad=0.0000), Var(v=-0.1068, grad=0.0000), Var(v=-0.1607, grad=0.0000), Var(v=0.1633, grad=0.0000), Var(v=-0.1337, grad=0.0000), Var(v=-0.1784, grad=0.0000), Var(v=-0.0827, grad=0.0000), Var(v=-0.0785, grad=0.0000), Var(v=0.0414, grad=0.0000), Var(v=0.0625, grad=0.0000), Var(v=-0.0544, grad=0.0000), Var(v=-0.1551, grad=0.0000), Var(v=-0.0890, grad=0.0000), Var(v=-0.1149, grad=0.0000), Var(v=0.0132, grad=0.0000), Var(v=0.0641, grad=0.0000), Var(v=0.0093, grad=0.0000), Var(v=-0.0125, grad=0.0000)], [Var(v=-0.1442, grad=0.0000), Var(v=-0.0084, grad=0.0000), Var(v=0.0615, grad=0.0000), Var(v=0.0510, grad=0.0000), Var(v=-0.0252, grad=0.0000), Var(v=0.1349, grad=0.0000), Var(v=-0.1814, grad=0.0000), Var(v=0.0773, grad=0.0000), Var(v=-0.0777, grad=0.0000), Var(v=0.0179, grad=0.0000), Var(v=-0.1029, grad=0.0000), Var(v=0.1922, grad=0.0000), Var(v=0.0916, grad=0.0000), Var(v=0.0438, grad=0.0000), Var(v=-0.2103, grad=0.0000), Var(v=0.0680, grad=0.0000), Var(v=0.0894, grad=0.0000), Var(v=0.0932, grad=0.0000), Var(v=-0.0885, grad=0.0000), Var(v=-0.0385, grad=0.0000), Var(v=-0.0504, grad=0.0000), Var(v=0.1099, grad=0.0000), Var(v=0.0511, grad=0.0000), Var(v=0.0474, grad=0.0000), Var(v=0.0257, grad=0.0000), Var(v=-0.0568, grad=0.0000), Var(v=0.0054, grad=0.0000), Var(v=-0.0639, grad=0.0000), Var(v=-0.2315, grad=0.0000), Var(v=0.2141, grad=0.0000), Var(v=-0.1754, grad=0.0000), Var(v=-0.0447, grad=0.0000), Var(v=-0.0869, grad=0.0000), Var(v=-0.0541, grad=0.0000), Var(v=0.0064, grad=0.0000), Var(v=0.0043, grad=0.0000), Var(v=0.0537, grad=0.0000), Var(v=0.1193, grad=0.0000), Var(v=-0.0627, grad=0.0000), Var(v=-0.2039, grad=0.0000), Var(v=0.0612, grad=0.0000), Var(v=0.0496, grad=0.0000), Var(v=-0.1758, grad=0.0000), Var(v=-0.0410, grad=0.0000), Var(v=-0.0037, grad=0.0000), Var(v=0.1449, grad=0.0000), Var(v=-0.1002, grad=0.0000), Var(v=0.0890, grad=0.0000), Var(v=0.1421, grad=0.0000), Var(v=-0.1418, grad=0.0000)], [Var(v=-0.0343, grad=0.0000), Var(v=0.0292, grad=0.0000), Var(v=0.1439, grad=0.0000), Var(v=0.0038, grad=0.0000), Var(v=0.0116, grad=0.0000), Var(v=-0.1108, grad=0.0000), Var(v=0.0369, grad=0.0000), Var(v=-0.0324, grad=0.0000), Var(v=-0.0074, grad=0.0000), Var(v=-0.0867, grad=0.0000), Var(v=0.2031, grad=0.0000), Var(v=0.1091, grad=0.0000), Var(v=0.0126, grad=0.0000), Var(v=-0.0555, grad=0.0000), Var(v=-0.0277, grad=0.0000), Var(v=0.1174, grad=0.0000), Var(v=-0.0757, grad=0.0000), Var(v=-0.1403, grad=0.0000), Var(v=0.0205, grad=0.0000), Var(v=-0.0092, grad=0.0000), Var(v=0.0285, grad=0.0000), Var(v=-0.0325, grad=0.0000), Var(v=-0.0852, grad=0.0000), Var(v=0.1064, grad=0.0000), Var(v=0.0862, grad=0.0000), Var(v=-0.0370, grad=0.0000), Var(v=-0.0421, grad=0.0000), Var(v=-0.0105, grad=0.0000), Var(v=-0.0352, grad=0.0000), Var(v=0.0441, grad=0.0000), Var(v=0.0209, grad=0.0000), Var(v=0.0831, grad=0.0000), Var(v=0.0570, grad=0.0000), Var(v=0.0545, grad=0.0000), Var(v=0.0100, grad=0.0000), Var(v=-0.0347, grad=0.0000), Var(v=-0.0389, grad=0.0000), Var(v=0.1013, grad=0.0000), Var(v=0.1142, grad=0.0000), Var(v=0.0319, grad=0.0000), Var(v=-0.0152, grad=0.0000), Var(v=-0.0507, grad=0.0000), Var(v=0.0927, grad=0.0000), Var(v=0.0938, grad=0.0000), Var(v=-0.0344, grad=0.0000), Var(v=0.1168, grad=0.0000), Var(v=-0.0425, grad=0.0000), Var(v=0.0783, grad=0.0000), Var(v=-0.0390, grad=0.0000), Var(v=0.0522, grad=0.0000)]] Biases: [Var(v=0.0001, grad=0.0000), Var(v=0.0617, grad=0.0000), Var(v=-0.0632, grad=0.0000), Var(v=-0.0412, grad=0.0000), Var(v=0.0000, grad=0.0000), Var(v=0.0004, grad=0.0000), Var(v=0.1842, grad=0.0000), Var(v=-0.0363, grad=0.0000), Var(v=0.0004, grad=0.0000), Var(v=0.0000, grad=0.0000), Var(v=0.0899, grad=0.0000), Var(v=-0.0134, grad=0.0000), Var(v=0.0406, grad=0.0000), Var(v=0.0000, grad=0.0000), Var(v=0.0000, grad=0.0000), Var(v=0.0001, grad=0.0000), Var(v=0.0244, grad=0.0000), Var(v=0.0000, grad=0.0000), Var(v=-0.0257, grad=0.0000), Var(v=0.0527, grad=0.0000), Var(v=0.0000, grad=0.0000), Var(v=0.0000, grad=0.0000), Var(v=0.1021, grad=0.0000), Var(v=-0.0002, grad=0.0000), Var(v=0.0000, grad=0.0000), Var(v=0.0275, grad=0.0000), Var(v=0.0515, grad=0.0000), Var(v=0.0757, grad=0.0000), Var(v=0.0114, grad=0.0000), Var(v=0.0000, grad=0.0000), Var(v=0.1049, grad=0.0000), Var(v=-0.0003, grad=0.0000), Var(v=0.0000, grad=0.0000), Var(v=0.0000, grad=0.0000), Var(v=0.0000, grad=0.0000), Var(v=0.0004, grad=0.0000), Var(v=0.0000, grad=0.0000), Var(v=-0.2204, grad=0.0000), Var(v=0.0000, grad=0.0000), Var(v=-0.0002, grad=0.0000), Var(v=0.0000, grad=0.0000), Var(v=0.0061, grad=0.0000), Var(v=0.0000, grad=0.0000), Var(v=0.0000, grad=0.0000), Var(v=0.0002, grad=0.0000), Var(v=-0.0001, grad=0.0000), Var(v=-0.0001, grad=0.0000), Var(v=-0.0003, grad=0.0000), Var(v=0.0017, grad=0.0000), Var(v=0.0001, grad=0.0000)]\n", - "Layer 2 \n", - " Weights: [[Var(v=0.0195, grad=0.0000)], [Var(v=-0.0988, grad=0.0000)], [Var(v=0.0417, grad=0.0000)], [Var(v=0.0336, grad=0.0000)], [Var(v=0.0060, grad=0.0000)], [Var(v=0.1773, grad=0.0000)], [Var(v=-0.2349, grad=0.0000)], [Var(v=-0.0379, grad=0.0000)], [Var(v=0.1626, grad=0.0000)], [Var(v=-0.0294, grad=0.0000)], [Var(v=-0.1059, grad=0.0000)], [Var(v=-0.0109, grad=0.0000)], [Var(v=0.0617, grad=0.0000)], [Var(v=0.0524, grad=0.0000)], [Var(v=-0.0334, grad=0.0000)], [Var(v=0.0393, grad=0.0000)], [Var(v=0.0433, grad=0.0000)], [Var(v=-0.0804, grad=0.0000)], [Var(v=0.0002, grad=0.0000)], [Var(v=-0.0906, grad=0.0000)], [Var(v=0.0096, grad=0.0000)], [Var(v=0.0075, grad=0.0000)], [Var(v=0.1382, grad=0.0000)], [Var(v=-0.1070, grad=0.0000)], [Var(v=-0.0419, grad=0.0000)], [Var(v=-0.0385, grad=0.0000)], [Var(v=-0.0907, grad=0.0000)], [Var(v=-0.0955, grad=0.0000)], [Var(v=0.0225, grad=0.0000)], [Var(v=0.0558, grad=0.0000)], [Var(v=-0.1712, grad=0.0000)], [Var(v=-0.1311, grad=0.0000)], [Var(v=-0.0003, grad=0.0000)], [Var(v=0.0263, grad=0.0000)], [Var(v=-0.0006, grad=0.0000)], [Var(v=0.1792, grad=0.0000)], [Var(v=-0.0298, grad=0.0000)], [Var(v=0.2522, grad=0.0000)], [Var(v=0.0243, grad=0.0000)], [Var(v=-0.0947, grad=0.0000)], [Var(v=-0.0209, grad=0.0000)], [Var(v=-0.0098, grad=0.0000)], [Var(v=-0.0406, grad=0.0000)], [Var(v=0.0532, grad=0.0000)], [Var(v=0.0915, grad=0.0000)], [Var(v=-0.0614, grad=0.0000)], [Var(v=-0.0712, grad=0.0000)], [Var(v=-0.1299, grad=0.0000)], [Var(v=-0.0043, grad=0.0000)], [Var(v=0.0356, grad=0.0000)]] Biases: [Var(v=0.0023, grad=0.0000)]\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "[None, None, None]" - ] - }, - "metadata": {}, - "execution_count": 97 - } - ], + "outputs": [], "source": [ "print('Network before update:')\n", "[print('Layer', i, '\\n', NN[i]) for i in range(len(NN))] \n", @@ -1635,7 +1372,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": null, "metadata": { "id": "woWYpdw6FtIO" }, @@ -1660,7 +1397,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": null, "metadata": { "id": "mdqaqYBVFtIR" }, @@ -1673,43 +1410,12 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": null, "metadata": { "id": "5kfg76GMFtIW", - "scrolled": true, - "outputId": "ef99de4c-d518-4e92-fde4-86337a0d8c64", - "colab": { - "base_uri": "https://localhost:8080/" - } + "scrolled": true }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " 0 ( 0.00%) Train loss: 105.490 \t Validation loss: 103.076\n", - " 10 ( 5.00%) Train loss: 18.750 \t Validation loss: 14.406\n", - " 20 (10.00%) Train loss: 13.837 \t Validation loss: 10.727\n", - " 30 (15.00%) Train loss: 13.282 \t Validation loss: 10.184\n", - " 40 (20.00%) Train loss: 12.894 \t Validation loss: 9.742\n", - " 50 (25.00%) Train loss: 12.654 \t Validation loss: 9.419\n", - " 60 (30.00%) Train loss: 12.483 \t Validation loss: 9.189\n", - " 70 (35.00%) Train loss: 12.352 \t Validation loss: 9.028\n", - " 80 (40.00%) Train loss: 12.245 \t Validation loss: 8.939\n", - " 90 (45.00%) Train loss: 12.143 \t Validation loss: 8.905\n", - " 100 (50.00%) Train loss: 12.071 \t Validation loss: 8.887\n", - " 110 (55.00%) Train loss: 12.013 \t Validation loss: 8.903\n", - " 120 (60.00%) Train loss: 11.959 \t Validation loss: 8.929\n", - " 130 (65.00%) Train loss: 11.907 \t Validation loss: 8.941\n", - " 140 (70.00%) Train loss: 11.890 \t Validation loss: 8.949\n", - " 150 (75.00%) Train loss: 11.882 \t Validation loss: 8.959\n", - " 160 (80.00%) Train loss: 11.877 \t Validation loss: 8.968\n", - " 170 (85.00%) Train loss: 11.875 \t Validation loss: 8.975\n", - " 180 (90.00%) Train loss: 11.873 \t Validation loss: 8.981\n", - " 190 (95.00%) Train loss: 11.872 \t Validation loss: 8.986\n" - ] - } - ], + "outputs": [], "source": [ "train_loss = []\n", "val_loss = []\n", @@ -1742,29 +1448,11 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": null, "metadata": { - "id": "VetyRWFwFtIY", - "outputId": "0506f456-0e1a-4d2b-b06c-7bd642a32c78", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 265 - } + "id": "VetyRWFwFtIY" }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - } - } - ], + "outputs": [], "source": [ "plt.plot(range(len(train_loss)), train_loss);\n", "plt.plot(range(len(val_loss)), val_loss);" @@ -1783,7 +1471,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": null, "metadata": { "id": "HmNi7S-vFtIc" }, @@ -1794,36 +1482,11 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": null, "metadata": { - "id": "7mmJOTSEFtIf", - "outputId": "5151e70c-8063-4e77-fe63-7a2115e9f520", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 314 - } + "id": "7mmJOTSEFtIf" }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Test loss: 9.784\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - } - } - ], + "outputs": [], "source": [ "y_test_np = Var_to_nparray(y_test)\n", "plt.scatter(y_test_np, Var_to_nparray(output_test));\n", @@ -1842,29 +1505,11 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": null, "metadata": { - "id": "ODi0WlmQFtIh", - "outputId": "c425fd3e-07e5-4715-9ad7-4add3ce17d7b", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 279 - } + "id": "ODi0WlmQFtIh" }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - } - } - ], + "outputs": [], "source": [ "x_test_np = Var_to_nparray(x_test)\n", "x_train_np = Var_to_nparray(x_train)\n", @@ -1891,7 +1536,7 @@ "id": "zTBAmjsAFtIk" }, "source": [ - "## Exercise l) Show overfitting, underfitting and just right fitting\n", + "## Exercise k) Show overfitting, underfitting and just right fitting\n", "\n", "Vary the architecture and other things to show clear signs of overfitting (=training loss significantly lower than test loss) and underfitting (=not fitting enoung to training data so that test performance is also hurt).\n", "\n", @@ -1902,736 +1547,15 @@ "_Insert written answer here._\n" ] }, - { - "cell_type": "markdown", - "source": [ - "**Answer (l)**\n", - "\n", - "To show a case of overfitting, underfitting and 'just right' fitting, 3 networks are proposed. All three networks will be 1 input, 8 node hidden layer, and 1 output. With relu and identity activation functions. Same as the previous case.\n", - "\n", - "Other parameters could be used to exemplify these cases, but the parameter being focused for this showcase, is the number of epochs a model will go through during training.\n", - "\n", - "1. **Underfitting** For underfitting, the network will be trained through 10 epochs with a learning rate of 2e-3, meaning the network will barely see the data, which should result in an underfit. On the showcase of the models with the data, at the end, this is mostly noticable at the ends of the plot (green).\n", - "\n", - "2. **Just right** For the just right case, the previous case is used, of 200 epochs with a learning rate of 2e-3.\n", - "\n", - "3. **Overfitting** Finally, for the overfitting case, the network undergoes 5000 epochs with a learning rate of 2e-3. The overfit isn't particularly noticable on the plot showcase. Where it is noticable, however, is in the reported iterations during the training of the model. At some point, the validation error reaches a minimum, but as the model moves on further, the training error keeps decreasing, but the validation error increases from the previous mentioned minimum. This suggests an overfitting model. (Small note: Having ran it a couple of time, the case where there is a minimum in the validation error, whereafter it starts increasing again, is not always the case. There has also been cases where both errors keep falling and then stagnate completely. And one special case, where the error of both training and validation started increasing, from around 9% back up to 14%, this only happened once though.)\n", - "\n", - "Lastly, the training set error, validation set error, and test set error for the three models are shown. It is noticed that the validation and test errors are very much the same. Another rather noteworthy point is the fact that the training error is higher than the validation error and test set error, which is rather odd. As of now, no solution or explanation for this was found.\n", - "\n", - "A general, and could be most important, case, for which it is important to keep the validation and test sets seperate, is the case where you would hyperparameter tune the model. For example tweaking the amount of hidden layers, the amount of nodes of these layers, learning rate, changing activation function(s), as the most obvious ones." - ], - "metadata": { - "id": "yfMqcW5UJnB-" - } - }, { "cell_type": "code", - "execution_count": 124, + "execution_count": null, "metadata": { - "id": "tQZCn2dxFtIl", - "outputId": "eca86341-cd9a-4893-efc2-b926e6c9f653", - "colab": { - "base_uri": "https://localhost:8080/" - } + "id": "tQZCn2dxFtIl" }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " 0 ( 0.00%) Train loss: 101.651 \t Validation loss: 98.367\n", - " 0 ( 0.00%) Train loss: 110.363 \t Validation loss: 109.484\n", - " 10 ( 5.00%) Train loss: 68.967 \t Validation loss: 56.027\n", - " 20 (10.00%) Train loss: 22.709 \t Validation loss: 18.375\n", - " 30 (15.00%) Train loss: 18.154 \t Validation loss: 14.131\n", - " 40 (20.00%) Train loss: 14.308 \t Validation loss: 11.320\n", - " 50 (25.00%) Train loss: 13.157 \t Validation loss: 10.308\n", - " 60 (30.00%) Train loss: 12.731 \t Validation loss: 9.864\n", - " 70 (35.00%) Train loss: 12.554 \t Validation loss: 9.606\n", - " 80 (40.00%) Train loss: 12.442 \t Validation loss: 9.443\n", - " 90 (45.00%) Train loss: 12.380 \t Validation loss: 9.361\n", - " 100 (50.00%) Train loss: 12.336 \t Validation loss: 9.309\n", - " 110 (55.00%) Train loss: 12.300 \t Validation loss: 9.271\n", - " 120 (60.00%) Train loss: 12.268 \t Validation loss: 9.241\n", - " 130 (65.00%) Train loss: 12.242 \t Validation loss: 9.214\n", - " 140 (70.00%) Train loss: 12.219 \t Validation loss: 9.192\n", - " 150 (75.00%) Train loss: 12.199 \t Validation loss: 9.172\n", - " 160 (80.00%) Train loss: 12.179 \t Validation loss: 9.167\n", - " 170 (85.00%) Train loss: 12.163 \t Validation loss: 9.133\n", - " 180 (90.00%) Train loss: 12.150 \t Validation loss: 9.121\n", - " 190 (95.00%) Train loss: 12.138 \t Validation loss: 9.110\n", - " 0 ( 0.00%) Train loss: 105.555 \t Validation loss: 105.648\n", - " 10 ( 0.20%) Train loss: 51.838 \t Validation loss: 39.745\n", - " 20 ( 0.40%) Train loss: 20.735 \t Validation loss: 16.502\n", - " 30 ( 0.60%) Train loss: 16.676 \t Validation loss: 12.558\n", - " 40 ( 0.80%) Train loss: 13.179 \t Validation loss: 10.158\n", - " 50 ( 1.00%) Train loss: 12.191 \t Validation loss: 9.369\n", - " 60 ( 1.20%) Train loss: 12.013 \t Validation loss: 9.121\n", - " 70 ( 1.40%) Train loss: 11.963 \t Validation loss: 9.026\n", - " 80 ( 1.60%) Train loss: 11.957 \t Validation loss: 9.005\n", - " 90 ( 1.80%) Train loss: 11.953 \t Validation loss: 8.998\n", - " 100 ( 2.00%) Train loss: 11.949 \t Validation loss: 8.990\n", - " 110 ( 2.20%) Train loss: 11.947 \t Validation loss: 8.986\n", - " 120 ( 2.40%) Train loss: 11.945 \t Validation loss: 8.973\n", - " 130 ( 2.60%) Train loss: 11.943 \t Validation loss: 8.974\n", - " 140 ( 2.80%) Train loss: 11.942 \t Validation loss: 8.968\n", - " 150 ( 3.00%) Train loss: 11.940 \t Validation loss: 8.976\n", - " 160 ( 3.20%) Train loss: 11.938 \t Validation loss: 8.965\n", - " 170 ( 3.40%) Train loss: 11.937 \t Validation loss: 8.964\n", - " 180 ( 3.60%) Train loss: 11.936 \t Validation loss: 8.963\n", - " 190 ( 3.80%) Train loss: 11.934 \t Validation loss: 8.963\n", - " 200 ( 4.00%) Train loss: 11.933 \t Validation loss: 8.964\n", - " 210 ( 4.20%) Train loss: 11.932 \t Validation loss: 8.965\n", - " 220 ( 4.40%) Train loss: 11.931 \t Validation loss: 8.965\n", - " 230 ( 4.60%) Train loss: 11.929 \t Validation loss: 8.966\n", - " 240 ( 4.80%) Train loss: 11.928 \t Validation loss: 8.966\n", - " 250 ( 5.00%) Train loss: 11.928 \t Validation loss: 8.967\n", - " 260 ( 5.20%) Train loss: 11.927 \t Validation loss: 8.963\n", - " 270 ( 5.40%) Train loss: 11.927 \t Validation loss: 8.966\n", - " 280 ( 5.60%) Train loss: 11.926 \t Validation loss: 8.962\n", - " 290 ( 5.80%) Train loss: 11.926 \t Validation loss: 8.965\n", - " 300 ( 6.00%) Train loss: 11.926 \t Validation loss: 8.961\n", - " 310 ( 6.20%) Train loss: 11.925 \t Validation loss: 8.962\n", - " 320 ( 6.40%) Train loss: 11.925 \t Validation loss: 8.962\n", - " 330 ( 6.60%) Train loss: 11.924 \t Validation loss: 8.962\n", - " 340 ( 6.80%) Train loss: 11.924 \t Validation loss: 8.967\n", - " 350 ( 7.00%) Train loss: 11.923 \t Validation loss: 8.965\n", - " 360 ( 7.20%) Train loss: 11.921 \t Validation loss: 8.976\n", - " 370 ( 7.40%) Train loss: 11.919 \t Validation loss: 8.973\n", - " 380 ( 7.60%) Train loss: 11.918 \t Validation loss: 8.983\n", - " 390 ( 7.80%) Train loss: 11.916 \t Validation loss: 8.981\n", - " 400 ( 8.00%) Train loss: 11.916 \t Validation loss: 8.987\n", - " 410 ( 8.20%) Train loss: 11.915 \t Validation loss: 8.986\n", - " 420 ( 8.40%) Train loss: 11.914 \t Validation loss: 8.992\n", - " 430 ( 8.60%) Train loss: 11.913 \t Validation loss: 8.987\n", - " 440 ( 8.80%) Train loss: 11.912 \t Validation loss: 8.991\n", - " 450 ( 9.00%) Train loss: 11.912 \t Validation loss: 8.997\n", - " 460 ( 9.20%) Train loss: 11.911 \t Validation loss: 8.996\n", - " 470 ( 9.40%) Train loss: 11.908 \t Validation loss: 9.009\n", - " 480 ( 9.60%) Train loss: 11.906 \t Validation loss: 9.008\n", - " 490 ( 9.80%) Train loss: 11.899 \t Validation loss: 9.025\n", - 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"4980 (99.60%) Train loss: 11.892 \t Validation loss: 9.032\n", - "4990 (99.80%) Train loss: 11.892 \t Validation loss: 9.032\n" - ] - } - ], - "source": [ - "params = [(10, 2e-3), (200,2e-3), (5000, 2e-3)]\n", - "NNs = []\n", - "train_losses = []\n", - "val_losses = []\n", - "for (EPOCHS, LEARN_R) in params:\n", - " NN = [\n", - " DenseLayer(1, 8, lambda x: x.relu()),\n", - " DenseLayer(8, 1, lambda x: x.identity())\n", - "]\n", - " train_loss = []\n", - " val_loss = []\n", - "\n", - " for e in range(EPOCHS):\n", - " \n", - " # Forward pass and loss computation\n", - " Loss = squared_loss(y_train, forward(x_train, NN))\n", - "\n", - " # Backward pass\n", - " Loss.backward()\n", - " \n", - " # gradient descent update\n", - " update_parameters(parameters(NN), LEARN_R)\n", - " zero_gradients(parameters(NN))\n", - " \n", - " # Training loss\n", - " train_loss.append(Loss.v)\n", - " \n", - " # Validation\n", - " Loss_validation = squared_loss(y_validation, forward(x_validation, NN))\n", - " val_loss.append(Loss_validation.v)\n", - " \n", - " if e%10==0:\n", - " print(\"{:4d}\".format(e),\n", - " \"({:5.2f}%)\".format(e/EPOCHS*100), \n", - " \"Train loss: {:4.3f} \\t Validation loss: {:4.3f}\".format(train_loss[-1], val_loss[-1]))\n", - " NNs.append(NN)\n", - " train_losses.append(train_loss)\n", - " val_losses.append(val_loss)" - ] - }, - { - "cell_type": "code", - "source": [ - "types = ['Underfitting', 'Just right', 'Overfitting']\n", - "output_tests = []\n", - "for i in range(len(NNs)):\n", - " output_tests.append(forward(x_test, NNs[i]))\n", - " plt.plot(range(len(train_losses[i])), train_losses[i], label=f'{types[i]} - Train loss');\n", - " plt.plot(range(len(val_losses[i])), val_losses[i], label=f'{types[i]} - Validation loss');\n", - "plt.legend()" - ], - "metadata": { - "id": "_4k-YvV8x2RX", - "outputId": "17c45c47-ffae-4d84-cbdf-d07f9f5dc6aa", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 282 - } - }, - "execution_count": 125, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 125 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": { - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "code", - "source": [ - "x_test_np = Var_to_nparray(x_test)\n", - "x_train_np = Var_to_nparray(x_train)\n", - "y_train_np = Var_to_nparray(y_train)\n", - "if D1:\n", - " plt.scatter(x_train_np, y_train_np, label=\"train data\");\n", - " plt.scatter(x_test_np, y_test_np, label=\"test data\");\n", - " for x in range(len(output_tests)):\n", - " plt.scatter(x_test_np, Var_to_nparray(output_tests[x]), label=f\"{types[x]} - test prediction\");\n", - " plt.legend();\n", - " plt.xlabel(\"x\");\n", - " plt.ylabel(\"y\");\n", - "else:\n", - " plt.scatter(x_train_np[:,1], y_train, label=\"train data\");\n", - " for x in range(len(output_tests)):\n", - " plt.scatter(x_test_np[:,1], Var_to_nparray(output_tests[x]), label=f\"{types[x]} - test data prediction\");\n", - " plt.scatter(x_test_np[:,1], y_test_np, label=\"test data\");\n", - " plt.legend();\n", - " plt.xlabel(\"x\");\n", - " plt.ylabel(\"y\");" - ], - "metadata": { - "id": "sRiVGCt8C94-", - "outputId": "b9a64f4e-9b96-429b-9c13-cb90961811a2", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 279 - } - }, - "execution_count": 126, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "code", + "outputs": [], "source": [ - "for x in range(len(output_tests)):\n", - " Lt = squared_loss(y_train, forward(x_train, NNs[x]))\n", - " Lv = squared_loss(y_validation, forward(x_validation, NNs[x]))\n", - " Lte = squared_loss(y_test, forward(x_test, NNs[x]))\n", - " print(f'{types[x]}-model, train loss: {Lt.v}')\n", - " print(f'{types[x]}-model, validation loss: {Lv.v}')\n", - " print(f'{types[x]}-model, test loss: {Lte.v}')" - ], - "metadata": { - "id": "c1E9oXKCPScp", - "outputId": "d7f0c275-be4e-4634-9a49-e21ee2fd8504", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "execution_count": 127, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Underfitting-model, train loss: 15.888463408454204\n", - "Underfitting-model, validation loss: 13.301859521128208\n", - "Underfitting-model, test loss: 13.095934522532687\n", - "Just right-model, train loss: 12.127843695177715\n", - "Just right-model, validation loss: 9.100617252988878\n", - "Just right-model, test loss: 9.266346075856173\n", - "Overfitting-model, train loss: 11.891633050742277\n", - "Overfitting-model, validation loss: 9.032268271984428\n", - "Overfitting-model, test loss: 9.759133662244869\n" - ] - } + "# Insert your code for getting overfitting, underfitting and just right fitting" ] }, { @@ -2655,14 +1579,14 @@ "id": "qsVPul3QFtIo" }, "source": [ - "## Exercise m) optional - Implement backpropagation for classification\n", + "## Exercise l) optional - Implement backpropagation for classification\n", "\n", "Should be possible with very few lines of code. :-)" ] }, { "cell_type": "code", - "execution_count": 109, + "execution_count": null, "metadata": { "id": "oC8QrI2tFtIp" }, @@ -2677,14 +1601,14 @@ "id": "APqhJv3tta1O" }, "source": [ - "## Exercise n) optional - Introduce a NeuralNetwork class\n", + "## Exercise m) optional - Introduce a NeuralNetwork class\n", "\n", "The functions we applied on the neural network (parameters, update_parameters and zero_gradients) can more naturally be included as methods in a NeuralNetwork class. Make such a class and modify the code to use it. " ] }, { "cell_type": "code", - "execution_count": 110, + "execution_count": null, "metadata": { "id": "Dqfnor1ouMLq" }, diff --git a/3_Feedforward_PyTorch/3.4-EXE-FFN-MNIST.ipynb b/3_Feedforward_PyTorch/3.4-EXE-FFN-MNIST.ipynb index 06cb6c6..21dd552 100644 --- a/3_Feedforward_PyTorch/3.4-EXE-FFN-MNIST.ipynb +++ b/3_Feedforward_PyTorch/3.4-EXE-FFN-MNIST.ipynb @@ -2,9 +2,7 @@ "cells": [ { "cell_type": "markdown", - "metadata": { - "id": "YPhAA-75i3tE" - }, + "metadata": {}, "source": [ "# Credits\n", "> This code is a slight modification to a translation (TensorFlow --> PyTorch) of a previous version of the [02456](http://kurser.dtu.dk/course/02456) course material. \n", @@ -15,9 +13,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "id": "-ZKQkztpi3tL" - }, + "metadata": {}, "outputs": [], "source": [ "import torch\n", @@ -37,14 +33,12 @@ }, { "cell_type": "markdown", - "metadata": { - "id": "Y2JOPn3si3tN" - }, + "metadata": {}, "source": [ "# MNIST dataset\n", "MNIST is a dataset that is often used for benchmarking. The MNIST dataset consists of 70,000 images of handwritten digits from 0-9. The dataset is split into a 50,000 images training set, 10,000 images validation set and 10,000 images test set. The images are 28x28 pixels, where each pixel represents a normalised value between 0-255 (0=black and 255=white).\n", "\n", - "![MNIST.Exampel](https://github.com/Jjtrane/02456DeepLearning/blob/main/static_files/mnist.png?raw=1)\n", + "![MNIST.Exampel](../static_files/mnist.png)\n", "\n", "\n", "## Primer\n", @@ -54,8 +48,7 @@ { "cell_type": "markdown", "metadata": { - "collapsed": true, - "id": "4obMAjfSi3tP" + "collapsed": true }, "source": [ "## MNIST\n", @@ -65,164 +58,8 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "id": "Js7rB21-i3tQ", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 423, - "referenced_widgets": [ - "e6fa3c055b8e4e718745b46b8456a3f6", - "d831c920e5704273a43f5e70f90d9a5c", - "5d6d9596dffe48cab28e78ab71db1d70", - "1b7c6701734546b4b2aa02ec1408447f", - "7961cc16c2fc4741b407dc5fcaaed2b4", - "8a13182e84834d97b209596e213f67ca", - "4643b1eb8ffb49f583d93968cac5f569", - "52d3682c639240b1bad6d7e19a680faa", - "fcc8d180e8bc4f8c830e8427201bb12f", - "cc79870eb68340709053f508bff31853", - "144d447754904649b8344f2260d76cec", - "ac737c93c0fc49129aef3b9c82982e53", - "2fc95d91a09e4dd88d2ad86d681cb8da", - "f4bc2b44ec2544a8a35e4e46900a73a9", - "3ad5a77f79894d55a3cd81b97f087e43", - "0b3cde9ea5e5434c8e718989d1326cbe", - "2ef2f1c5ac8f4f4a9e82d54ab77c7632", - "adaac0526f5e4962af2bbd0b7fe9f2aa", - "25110adfd0704142811dd6a00a793502", - "c34d902db1a54945adda83597ac595ab", - "3729d60df0d94322ab295a4da64eec28", - "184ef7ea117041abb05b2a2c0c9ca759", - "2bad45b3a08d4b118aaf6f79660ef11e", - "cb528814ebdf42cabe23c1c77afc1287", - "505a1611c96b4e4aae0f566e1259d29c", - "fcf8919300d84576af68c8879f9fba3b", - "4c183358fd0b423995705ffc8d95ef0f", - "0132d2061b6a4012aa2474602f17139f", - "2b345b36d895448d9a6c8e3639251865", - "f3d4af3c4c5e4a51a0d1b7066a2a2519", - "94cf5e056592492983352a6da24abfe3", - "548692b5258e47db9926854fd0342b89", - "149c27fd434d45de800d8d1c902661a5", - "332435773bb24c49808666b5ce1698b3", - "058e2fe97b5040b8a9731fd62be6e179", - "2d14dc447e1d475a809f73644af7dc66", - "2f6b6232ca7b4482a7bafd3bfed1c11a", - "bedddc18c8064a42a926bd209db4d031", - "c2818625622d4d0db1cd6d53f5a92a03", - "261ba7c94fe74ac681c1f8e32c40a773", - "e8285f8496b94015bd8d790018ee381b", - "d6b3a83f2789421296f3698a9f2f9c34", - "7283c44e1d934be79460d7f0bdf2667f", - "af9b370ac76646c7bfa431ca81896bf4" - ] - }, - "outputId": "1a763d48-f6c4-4fe4-d96a-4c033adaa5bb" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n", - "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./temp/MNIST/raw/train-images-idx3-ubyte.gz\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - " 0%| | 0/9912422 [00:00" - ], - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - } - } - ], + "outputs": [], "source": [ "# plot a few MNIST examples\n", "idx, dim, classes = 0, 28, 10\n", @@ -321,9 +119,7 @@ }, { "cell_type": "markdown", - "metadata": { - "id": "UhJpQD-ii3tT" - }, + "metadata": {}, "source": [ "## Model\n", "\n", @@ -375,35 +171,8 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "id": "DQ7MV-_ji3tV", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 358 - }, - "outputId": "698821b7-dc67-47a6-b652-86d0c8ac2b59" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "softmax should sum to one (approxiamtely): 1.0\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - } - } - ], + "metadata": {}, + "outputs": [], "source": [ "# Illustrate different output units\n", "x = np.linspace(-6, 6, 100)\n", @@ -430,9 +199,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "id": "y1MEp7kIi3tW" - }, + "metadata": {}, "outputs": [], "source": [ "#Hyperparameters\n", @@ -467,9 +234,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "id": "mPUaME1si3tX" - }, + "metadata": {}, "outputs": [], "source": [ "optimizer = optim.SGD(net.parameters(), lr=0.01)\n", @@ -479,22 +244,8 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "id": "VhYJ74ali3tY", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "c78f8d2e-738a-4905-8f9f-e63728cc822e" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "torch.Size([45, 10])\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "# Test the forward pass with dummy data\n", "x = np.random.normal(0, 1, (45, dim*dim)).astype('float32')\n", @@ -504,9 +255,7 @@ }, { "cell_type": "markdown", - "metadata": { - "id": "WWAudRUOi3tY" - }, + "metadata": {}, "source": [ "# Build the training loop\n", "\n", @@ -521,64 +270,8 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "id": "gKgrQlRbi3tZ", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 644 - }, - "outputId": "d4b08d07-1a51-4a73-fe4c-3f472c096f8e" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Epoch 1 : Train Loss 0.228677 , Train acc 0.248000, Valid acc 0.224000\n", - "Epoch 11 : Train Loss 0.108155 , Train acc 0.808000, Valid acc 0.746000\n", - "Epoch 21 : Train Loss 0.075733 , Train acc 0.845000, Valid acc 0.784000\n", - "Epoch 31 : Train Loss 0.061317 , Train acc 0.869000, Valid acc 0.802000\n", - "Epoch 41 : Train Loss 0.052878 , Train acc 0.883000, Valid acc 0.816000\n", - "Epoch 51 : Train Loss 0.047165 , Train acc 0.893000, Valid acc 0.830000\n", - "Epoch 61 : Train Loss 0.042947 , Train acc 0.898000, Valid acc 0.832000\n", - "Epoch 71 : Train Loss 0.039650 , Train acc 0.902000, Valid acc 0.836000\n", - "Epoch 81 : Train Loss 0.036964 , Train acc 0.910000, Valid acc 0.842000\n", - "Epoch 91 : Train Loss 0.034709 , Train acc 0.919000, Valid acc 0.842000\n", - "Epoch 101 : Train Loss 0.032769 , Train acc 0.925000, Valid acc 0.842000\n", - "Epoch 111 : Train Loss 0.031070 , Train acc 0.931000, Valid acc 0.842000\n", - "Epoch 121 : Train Loss 0.029558 , Train acc 0.933000, Valid acc 0.846000\n", - "Epoch 131 : Train Loss 0.028196 , Train acc 0.935000, Valid acc 0.848000\n", - "Epoch 141 : Train Loss 0.026956 , Train acc 0.939000, Valid acc 0.850000\n", - "Epoch 151 : Train Loss 0.025819 , Train acc 0.941000, Valid acc 0.854000\n", - "Epoch 161 : Train Loss 0.024768 , Train acc 0.943000, Valid acc 0.854000\n", - "Epoch 171 : Train Loss 0.023792 , Train acc 0.946000, Valid acc 0.854000\n", - "Epoch 181 : Train Loss 0.022880 , Train acc 0.949000, Valid acc 0.856000\n", - "Epoch 191 : Train Loss 0.022025 , Train acc 0.952000, Valid acc 0.852000\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(Text(0.5, 0, 'Updates'), Text(0, 0.5, 'Acc'))" - ] - }, - "metadata": {}, - "execution_count": 9 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - } - } - ], + "metadata": {}, + "outputs": [], "source": [ "# we could have done this ourselves,\n", "# but we should be aware of sklearn and its tools\n", @@ -668,8 +361,7 @@ { "cell_type": "markdown", "metadata": { - "collapsed": true, - "id": "TEuk3C5fi3tb" + "collapsed": true }, "source": [ "# Assignments\n", @@ -704,327 +396,10 @@ }, { "cell_type": "markdown", - "source": [ - "**Answer**\n", - "\n", - "The above example has been copied, and the assignment addition added to this copy, below.\n", - "\n", - "For Kaiming He initialization, the pytorch call kaiming_normal_ is inserted into the NN initializer.\n", - "\n", - "The extra layer is simply added as a new self variable.\n", - "\n", - "The activation function is changed from ELU to ReLU.\n", - "\n", - "Momentum is specified in the pytorch SGD optimizer itself. Not for the ADAM optimizer though.\n", - "\n", - "The ADAM optimizer has been added as a comment below the SGD optimizer.\n", - "\n", - "L2 weight regularization is added in weight_decay of the optimizer. For both ADAM and SGD.\n", - "\n", - "Dropout is added in the initialization of the NN itself. Note, that since ReLU is used as activation, it does not matter if dropout is applied/called before or after activation, as it would for other activation functions, where it is usually called after.\n", - "\n", - "Batchnorm is added in the initialization of the NN itself." - ], - "metadata": { - "id": "X8WeizDhLbOo" - } - }, - { - "cell_type": "code", - "source": [ - "#Hyperparameters\n", - "num_classes = 10\n", - "num_l1 = 512\n", - "num_l2 = 256\n", - "num_features = x_train.shape[1]\n", - "\n", - "# define network\n", - "class Net(nn.Module):\n", - "\n", - " def __init__(self, num_features, num_hidden1, num_hidden2, num_output):\n", - " super(Net, self).__init__() \n", - " # input layer\n", - " self.W_1 = Parameter(init.kaiming_normal_(torch.Tensor(num_hidden1, num_features)))\n", - " self.b_1 = Parameter(init.constant_(torch.Tensor(num_hidden1), 0))\n", - " self.bn_1 = torch.nn.BatchNorm1d(num_hidden1) # batchnorm\n", - " # hidden layer 1\n", - " self.W_2 = Parameter(init.kaiming_normal_(torch.Tensor(num_hidden2, num_hidden1)))\n", - " self.b_2 = Parameter(init.constant_(torch.Tensor(num_hidden2), 0)) \n", - " self.bn_2 = torch.nn.BatchNorm1d(num_hidden2) # batchnorm\n", - " # hidden layer 2\n", - " self.W_3 = Parameter(init.kaiming_normal_(torch.Tensor(num_output, num_hidden2)))\n", - " self.b_3 = Parameter(init.constant_(torch.Tensor(num_output), 0))\n", - "\n", - " # define activation function in constructor\n", - " self.activation1 = torch.nn.ReLU()\n", - " self.activation2 = torch.nn.ReLU()\n", - "\n", - " # dropout\n", - " self.dropout = torch.nn.Dropout(0.2)\n", - "\n", - " def forward(self, x):\n", - " x = F.linear(x, self.W_1, self.b_1)\n", - " # x = self.bn_1(x)\n", - " # x = self.dropout(x)\n", - " x = self.activation1(x)\n", - " x = F.linear(x, self.W_2, self.b_2)\n", - " x = self.bn_2(x)\n", - " x = self.dropout(x)\n", - " x = self.activation2(x)\n", - " x = F.linear(x, self.W_3, self.b_3)\n", - " return x\n", - "\n", - "\n", - "net = Net(num_features, num_l1, num_l2, num_classes)\n", - "\n", - "optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9, weight_decay=1e-6)\n", - "# optimizer = optim.Adam(net.parameters(), lr=1e-4, weight_decay=1e-6)\n", - "criterion = nn.CrossEntropyLoss()" - ], - "metadata": { - "id": "msKcVeCrJkx2" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# we could have done this ourselves,\n", - "# but we should be aware of sklearn and its tools\n", - "from sklearn.metrics import accuracy_score\n", - "\n", - "# To speed up training we'll only work on a subset of the data\n", - "x_train = mnist_trainset.data[:1000].view(-1, 784).float()\n", - "targets_train = mnist_trainset.targets[:1000]\n", - "\n", - "x_valid = mnist_trainset.data[1000:1500].view(-1, 784).float()\n", - "targets_valid = mnist_trainset.targets[1000:1500]\n", - "\n", - "x_test = mnist_testset.data[:500].view(-1, 784).float()\n", - "targets_test = mnist_testset.targets[:500]\n", - "\n", - "# normalize the inputs\n", - "x_train.div_(255)\n", - "x_valid.div_(255)\n", - "x_test.div_(255)\n", - "\n", - "# setting hyperparameters and gettings epoch sizes\n", - "batch_size = 100\n", - "num_epochs = 200\n", - "num_samples_train = x_train.shape[0]\n", - "num_batches_train = num_samples_train // batch_size\n", - "num_samples_valid = x_valid.shape[0]\n", - "num_batches_valid = num_samples_valid // batch_size\n", - "\n", - "# setting up lists for handling loss/accuracy\n", - "train_acc, train_loss = [], []\n", - "valid_acc, valid_loss = [], []\n", - "test_acc, test_loss = [], []\n", - "cur_loss = 0\n", - "losses = []\n", - "\n", - "get_slice = lambda i, size: range(i * size, (i + 1) * size)\n", - "\n", - "for epoch in range(num_epochs):\n", - " # Forward -> Backprob -> Update params\n", - " ## Train\n", - " cur_loss = 0\n", - " net.train()\n", - " for i in range(num_batches_train):\n", - " optimizer.zero_grad()\n", - " slce = get_slice(i, batch_size)\n", - " output = net(x_train[slce])\n", - " \n", - " # compute gradients given loss\n", - " target_batch = targets_train[slce]\n", - " batch_loss = criterion(output, target_batch)\n", - " batch_loss.backward()\n", - " optimizer.step()\n", - " \n", - " cur_loss += batch_loss \n", - " losses.append(cur_loss / batch_size)\n", - "\n", - " net.eval()\n", - " ### Evaluate training\n", - " train_preds, train_targs = [], []\n", - " for i in range(num_batches_train):\n", - " slce = get_slice(i, batch_size)\n", - " output = net(x_train[slce])\n", - " \n", - " preds = torch.max(output, 1)[1]\n", - " \n", - " train_targs += list(targets_train[slce].numpy())\n", - " train_preds += list(preds.data.numpy())\n", - " \n", - " ### Evaluate validation\n", - " val_preds, val_targs = [], []\n", - " for i in range(num_batches_valid):\n", - " slce = get_slice(i, batch_size)\n", - " \n", - " output = net(x_valid[slce])\n", - " preds = torch.max(output, 1)[1]\n", - " val_targs += list(targets_valid[slce].numpy())\n", - " val_preds += list(preds.data.numpy())\n", - " \n", - "\n", - " train_acc_cur = accuracy_score(train_targs, train_preds)\n", - " valid_acc_cur = accuracy_score(val_targs, val_preds)\n", - " \n", - " train_acc.append(train_acc_cur)\n", - " valid_acc.append(valid_acc_cur)\n", - " \n", - " if epoch % 10 == 0:\n", - " print(\"Epoch %2i : Train Loss %f , Train acc %f, Valid acc %f\" % (\n", - " epoch+1, losses[-1], train_acc_cur, valid_acc_cur))\n", - "\n", - "epoch = np.arange(len(train_acc))\n", - "plt.figure()\n", - "plt.plot(epoch, train_acc, 'r', epoch, valid_acc, 'b')\n", - "plt.legend(['Train Accucary','Validation Accuracy'])\n", - "plt.xlabel('Updates'), plt.ylabel('Acc')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 644 - }, - "id": "Ice69RbxJt5Z", - "outputId": "e78b2bf0-4fd8-4fbe-8925-d54dd7e38d62" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Epoch 1 : Train Loss 0.197556 , Train acc 0.771000, Valid acc 0.702000\n", - "Epoch 11 : Train Loss 0.007183 , Train acc 0.998000, Valid acc 0.868000\n", - "Epoch 21 : Train Loss 0.002803 , Train acc 1.000000, Valid acc 0.876000\n", - "Epoch 31 : Train Loss 0.001540 , Train acc 1.000000, Valid acc 0.882000\n", - "Epoch 41 : Train Loss 0.001089 , Train acc 1.000000, Valid acc 0.882000\n", - "Epoch 51 : Train Loss 0.000856 , Train acc 1.000000, Valid acc 0.882000\n", - "Epoch 61 : Train Loss 0.000679 , Train acc 1.000000, Valid acc 0.882000\n", - "Epoch 71 : Train Loss 0.000592 , Train acc 1.000000, Valid acc 0.884000\n", - "Epoch 81 : Train Loss 0.000459 , Train acc 1.000000, Valid acc 0.878000\n", - "Epoch 91 : Train Loss 0.000445 , Train acc 1.000000, Valid acc 0.882000\n", - "Epoch 101 : Train Loss 0.000364 , Train acc 1.000000, Valid acc 0.882000\n", - "Epoch 111 : Train Loss 0.000298 , Train acc 1.000000, Valid acc 0.882000\n", - "Epoch 121 : Train Loss 0.000286 , Train acc 1.000000, Valid acc 0.884000\n", - "Epoch 131 : Train Loss 0.000347 , Train acc 1.000000, Valid acc 0.884000\n", - "Epoch 141 : Train Loss 0.000221 , Train acc 1.000000, Valid acc 0.884000\n", - "Epoch 151 : Train Loss 0.000218 , Train acc 1.000000, Valid acc 0.884000\n", - "Epoch 161 : Train Loss 0.000229 , Train acc 1.000000, Valid acc 0.882000\n", - "Epoch 171 : Train Loss 0.000173 , Train acc 1.000000, Valid acc 0.884000\n", - "Epoch 181 : Train Loss 0.000182 , Train acc 1.000000, Valid acc 0.884000\n", - "Epoch 191 : Train Loss 0.000152 , Train acc 1.000000, Valid acc 0.882000\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(Text(0.5, 0, 'Updates'), Text(0, 0.5, 'Acc'))" - ] - }, - "metadata": {}, - "execution_count": 37 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": { - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "0hTI9jaIi3tb" - }, + "metadata": {}, "source": [ "You are done for now. [Good job.](https://media1.tenor.com/images/0fd559b07f2174f9b8b7dbde7c5a67ca/tenor.gif)" ] - }, - { - "cell_type": "markdown", - "source": [ - "**Exercise**\n", - "\n", - "The exercise is chosen to keep the theme of the MNIST dataset:\n", - "\n", - "From Michael Nielsen's book, in chapter 3:\n", - "\n", - "There is a way of determining the bitwise representation of a digit by adding an extra layer to the three-layer network above. The extra layer converts the output from the previous layer into a binary representation, as illustrated in the figure below. Find a set of weights and biases for the new output layer. Assume that the first 3 layers of neurons are such that the correct output in the third layer (i.e., the old output layer) has activation at least 0.99, and incorrect outputs have activation less than 0.01.\n", - "\n", - "\n", - "**Answer (book exercise)**\n", - "\n", - "The digits in binary representation are as follow:\n", - "\n", - "0: 0000\n", - "\n", - "1: 0001\n", - "\n", - "2: 0010\n", - "\n", - "3: 0011\n", - "\n", - "4: 0100\n", - "\n", - "5: 0101\n", - "\n", - "6: 0110\n", - "\n", - "7: 0111\n", - "\n", - "8: 1000\n", - "\n", - "9: 1001\n", - "\n", - "\n", - "Since correct activation is (assumed to be) at 0.99 and incorrect outputs at 0.01, it becomes an exercise of choosing the weights, such that the sigmoid function gives the right value of the 4 neurons to each digit.\n", - "\n", - "Counting the neurons from 0 to 3, the output for the digit 1 should be:\n", - "node 1: 0, node 2: 0, node 3: 0, and node 4: 1, all in all the bit 0001.\n", - "\n", - "It is noted that node 3 for all uneven numbers must output 1, and from top to bottom, for node 2, it is first two counts of 0s, then two counts of 1s, then 0s, and so on...\n", - "\n", - "Mapping like this, and noting the weights $w_{ij}$ where $i$ is the input node, and $j$ the output node, it becomes a simple task to create the weight matrix, as it is simply putting the digit bits into matrix form.\n", - "\n", - "Finally, to make the signal clear, a larger weight value is chosen, such as -5 for node off, and 5 for node on.\n", - "\n", - "The weight matrix then becomes:\n", - "\n", - "\\begin{equation*}\n", - "w_{ij} = \n", - "5⋅\n", - "\\begin{pmatrix}\n", - "-1 & -1 & -1 & -1 \\\\\n", - "-1 & -1 & -1 & 1 \\\\\n", - "-1 & -1 & 1 & -1 \\\\\n", - "-1 & -1 & 1 & 1 \\\\\n", - "-1 & 1 & -1 & -1 \\\\\n", - "-1 & 1 & -1 & 1 \\\\\n", - "-1 & 1 & 1 & -1 \\\\\n", - "-1 & 1 & 1 & 1 \\\\\n", - "1 & -1 & -1 & -1 \\\\\n", - "1 & -1 & -1 & 1 \\\\\n", - "\\end{pmatrix}\n", - "\\end{equation*}\n", - "\n", - "And, as mentioned, that since a sigmoid activation function is used, there is no real value to having bias terms for this final digit-to-bit layer.\n" - ], - "metadata": { - 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"_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - } - } } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } \ No newline at end of file diff --git a/4_Convolutional/278328635316093.ipynb b/4_Convolutional/278328635316093.ipynb new file mode 100644 index 0000000..2fae0fd --- /dev/null +++ b/4_Convolutional/278328635316093.ipynb @@ -0,0 +1,1600 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "oZW0gaQO81Sq" + }, + "source": [ + "# CNN on CIFAR-10\n", + "\n", + "In this notebook you need to put what you have learned into practice, and create your own convolutional classifier for the CIFAR-10 dataset.\n", + "\n", + "The images in CIFAR-10 are RGB images (3 channels) with size 32x32 (so they have size 3x32x32). There are 10 different classes. See examples below.\n", + "\n", + "![cifar10](https://github.com/DeepLearningDTU/02456-deep-learning-with-PyTorch/blob/master/static_files/cifar10.png?raw=1)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "htyg7xxN81St" + }, + "source": [ + "## Preliminaries" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "executionInfo": { + "elapsed": 3009, + "status": "ok", + "timestamp": 1662640064042, + "user": { + "displayName": "Ole Winther", + "userId": "12097569893493878263" + }, + "user_tz": -120 + }, + "id": "v3u2GIWr81Su" + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import torch\n", + "from torch import nn\n", + "import torch.nn.functional as F\n", + "import torch.optim as optim\n", + "from torch.utils.data import TensorDataset, DataLoader\n", + "import torchvision\n", + "import torchvision.transforms as transforms\n", + "from torchvision.utils import make_grid\n", + "from sklearn import metrics\n", + "import torch.nn.init as init\n", + "from torch.utils.data import DataLoader, Dataset\n", + "\n", + "sns.set_style(\"whitegrid\")\n", + "\n", + "def accuracy(target, pred):\n", + " return metrics.accuracy_score(target.detach().cpu().numpy(), pred.detach().cpu().numpy())\n", + "\n", + "def compute_confusion_matrix(target, pred, normalize=None):\n", + " return metrics.confusion_matrix(\n", + " target.detach().cpu().numpy(), \n", + " pred.detach().cpu().numpy(),\n", + " normalize=normalize\n", + " )\n", + "\n", + "def show_image(img):\n", + " img = img.detach().cpu()\n", + " img = img / 2 + 0.5 # unnormalize\n", + " with sns.axes_style(\"white\"):\n", + " plt.figure(figsize=(8, 8))\n", + " plt.imshow(img.permute((1, 2, 0)).numpy())\n", + " plt.axis('off')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 108, + "referenced_widgets": [ + "2435970902984fb09c0a257131d742be", + "548c47f6e8fa4a37a939535ef670020c", + "9d6456ca327f4a858e92c13cd417340d", + "67bbc7b98abf41f1927a7362aff86fe7", + "b589c04a1dab4fd6a9c5cdd4c99d6cf9", + "7289f6e93f2e44819780bb1b7918876b", + "39e70cbbd65d4541a04c23c069b001ed", + "0c6f066e4b44478a89c7384f6c284ece", + "aa44e013a906441892f252576e269634", + "07174f985c5b480b84ff77f61dd88f97", + "30ae8a12c6a54ffcb37973b9ca01a07c" + ] + }, + "executionInfo": { + "elapsed": 18641, + "status": "ok", + "timestamp": 1662640112137, + "user": { + "displayName": "Ole Winther", + "userId": "12097569893493878263" + }, + "user_tz": -120 + }, + "id": "QZeTujLC81S3", + "outputId": "a75010e0-f99a-4ce5-eb6a-2df4f6150777" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Files already downloaded and verified\n", + "Files already downloaded and verified\n" + ] + } + ], + "source": [ + "# The output of torchvision datasets are PIL images in the range [0, 1]. \n", + "# We transform them to PyTorch tensors and rescale them to be in the range [-1, 1].\n", + "transform = transforms.Compose(\n", + " [transforms.ToTensor(),\n", + " transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), # subtract 0.5 and divide by 0.5\n", + " ]\n", + ")\n", + "\n", + "batch_size = 64 # both for training and testing\n", + "\n", + "# Load datasets\n", + "train_set = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)\n", + "test_set = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)\n", + "train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=0, drop_last=False)\n", + "test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False, num_workers=0, drop_last=True)\n", + "\n", + "# Map from class index to class name.\n", + "classes = {index: name for name, index in train_set.class_to_idx.items()}" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 452, + "status": "ok", + "timestamp": 1662640116989, + "user": { + "displayName": "Ole Winther", + "userId": "12097569893493878263" + }, + "user_tz": -120 + }, + "id": "JDHkc52L81S9", + "outputId": "2fe8610c-37c6-47c8-99e1-092299c6050f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training data\n", + "Number of points: 50000\n", + "Batch dimension (B x C x H x W): torch.Size([64, 3, 32, 32])\n", + "Number of distinct labels: 10 (unique labels: {0, 1, 2, 3, 4, 5, 6, 7, 8, 9})\n", + "\n", + "Test data\n", + "Number of points: 10000\n", + "Batch dimension (B x C x H x W): torch.Size([64, 3, 32, 32])\n", + "Number of distinct labels: 10 (unique labels: {0, 1, 2, 3, 4, 5, 6, 7, 8, 9})\n" + ] + } + ], + "source": [ + "print(\"Training data\")\n", + "print(\"Number of points:\", len(train_set))\n", + "x, y = next(iter(train_loader))\n", + "print(\"Batch dimension (B x C x H x W):\", x.shape)\n", + "print(f\"Number of distinct labels: {len(set(train_set.targets))} (unique labels: {set(train_set.targets)})\")\n", + "\n", + "print(\"\\nTest data\")\n", + "print(\"Number of points:\", len(test_set))\n", + "x, y = next(iter(test_loader))\n", + "print(\"Batch dimension (B x C x H x W):\", x.shape)\n", + "print(f\"Number of distinct labels: {len(set(test_set.targets))} (unique labels: {set(test_set.targets)})\")\n", + "\n", + "n_classes = len(set(test_set.targets))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xSA1h94681TB" + }, + "source": [ + "### Show example images\n", + "\n", + "Run multiple times to see different examples." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 466 + }, + "executionInfo": { + "elapsed": 2498, + "status": "ok", + "timestamp": 1662640125654, + "user": { + "displayName": "Ole Winther", + "userId": "12097569893493878263" + }, + "user_tz": -120 + }, + "id": "njJy0klP81TD", + "outputId": "decc1858-fd68-4969-8aca-b9194276967b" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get random training images and show them.\n", + "images, labels = iter(train_loader).next()\n", + "show_image(torchvision.utils.make_grid(images))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Wt3BVFMF81TI" + }, + "source": [ + "## Define a convolutional neural network\n", + "\n", + "\n", + "**Assignment 1:** Define a convolutional neural network. \n", + "You may use the code from previous notebooks.\n", + "We suggest that you start with a small network, and make sure that everything is working.\n", + "Once you can train successfully, come back and improve the architecture." + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 456, + "status": "ok", + "timestamp": 1662640362237, + "user": { + "displayName": "Ole Winther", + "userId": "12097569893493878263" + }, + "user_tz": -120 + }, + "id": "_EsKbw3o81TK", + "outputId": "9ea47766-bca0-488e-8166-319efc4506b5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model(\n", + " (net): Sequential(\n", + " (0): Conv2d(3, 64, kernel_size=(5, 5), stride=(1, 1))\n", + " (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (3): Conv2d(64, 128, kernel_size=(5, 5), stride=(1, 1))\n", + " (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (5): ReLU()\n", + " (6): Conv2d(128, 256, kernel_size=(5, 5), stride=(1, 1))\n", + " (7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (8): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (9): ReLU()\n", + " (10): Flatten(start_dim=1, end_dim=-1)\n", + " (11): Linear(in_features=2304, out_features=2000, bias=True)\n", + " (12): ReLU()\n", + " (13): Linear(in_features=2000, out_features=256, bias=True)\n", + " (14): ReLU()\n", + " (15): Linear(in_features=256, out_features=10, bias=True)\n", + " )\n", + ")\n" + ] + } + ], + "source": [ + "\n", + "\n", + "# Define network\n", + "class PrintSize(nn.Module):\n", + " \"\"\"Utility module to print current shape of a Tensor in Sequential, only at the first pass.\"\"\"\n", + " \n", + " first = True\n", + " \n", + " def forward(self, x):\n", + " if self.first:\n", + " print(f\"Size: {x.size()}\")\n", + " self.first = False\n", + " return x\n", + "\n", + "\n", + "class Model(nn.Module):\n", + " def __init__(self, num_classes, channels, n_features):\n", + " super().__init__()\n", + "# self.num_classes = num_classes\n", + " \n", + " activation_fn = nn.ReLU\n", + " \n", + " # self.dropout = nn.Dropout(dropout_rate)\n", + " \n", + " self.net = nn.Sequential(\n", + " # first layer\n", + " nn.Conv2d(channels, 64, kernel_size=5), #channels in and 64 out\n", + " nn.MaxPool2d(2, 2),\n", + " nn.BatchNorm2d(64), #Batchnorm to output\n", + " # second layer\n", + " nn.Conv2d(64, 128, kernel_size=5),\n", + " nn.BatchNorm2d(128), #batcnorm needs to be 2D, because it will be 4D output\n", + " activation_fn(),\n", + " # third layer\n", + " nn.Conv2d(128, 256, kernel_size=5),\n", + " nn.BatchNorm2d(256),\n", + " nn.MaxPool2d(2, 2), # halfs the channels\n", + " activation_fn(),\n", + " nn.Flatten(), #flatterns the dimensions\n", + " # fully connected layer\n", + " nn.Linear(3*3*256, n_features*2), #high, width and depth, calculated by hand<\n", + " activation_fn(),\n", + " nn.Linear(n_features*2, 256),\n", + " activation_fn(),\n", + " nn.Linear(256, num_classes)\n", + " )\n", + " \n", + "\n", + " def forward(self, x):\n", + " \n", + " return self.net(x)\n", + "\n", + "\n", + "model = Model(n_classes, 3, 1000)\n", + "device = torch.device('cpu') # use cuda or cpu\n", + "model.to(device)\n", + "print(model)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7-IUg3sq81TQ" + }, + "source": [ + "## Define a loss function and optimizer\n", + "\n", + "**Assignment 2:** Define the loss function and optimizer.\n", + "You might need to experiment a bit with the learning rate." + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": { + "executionInfo": { + "elapsed": 447, + "status": "ok", + "timestamp": 1662640370072, + "user": { + "displayName": "Ole Winther", + "userId": "12097569893493878263" + }, + "user_tz": -120 + }, + "id": "48AX85QP81TR" + }, + "outputs": [], + "source": [ + "criterion = nn.CrossEntropyLoss() # Your code here!\n", + "# optimizer = optim.SGD(model.parameters(), lr=1e-3, momentum=0.75, weight_decay=0.0) \n", + "optimizer = optim.Adam(model.parameters(), lr=1e-3)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-WneIN7C81TV" + }, + "source": [ + "## Train the network\n", + "\n", + "**Assignment 3:** Finish the training loop below. \n", + "Start by using a small number of epochs (e.g. 2).\n", + "Even with a low number of epochs you should be able to see results that are better than chance.\n", + "When everything is working increase the number of epochs to find out how good your network really is." + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": { + "id": "iWT0ULctWvm1" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Output shape: torch.Size([2, 10])\n", + "Output logits:\n", + "[[ 0.14539252 -0.01011747 -0.11255603 -0.05638197 0.18672742 -0.0269453\n", + " 0.02410119 0.00064606 0.06433716 -0.1947304 ]\n", + " [ 0.07952069 -0.0138033 0.01365957 0.09811385 0.07486036 0.09517626\n", + " 0.1515725 0.07303505 0.00506334 -0.12196079]]\n", + "Output probabilities:\n", + "[[0.11474955 0.09822315 0.08865951 0.09378242 0.1195921 0.0965841\n", + " 0.10164238 0.09928609 0.10581545 0.08166528]\n", + " [0.10318781 0.0939936 0.09661071 0.10512435 0.10270804 0.10481599\n", + " 0.11089708 0.10252074 0.09578378 0.08435796]]\n" + ] + } + ], + "source": [ + "# Test the forward pass with dummy data\n", + "out = model(torch.randn(2, 3, 32, 32, device=device))\n", + "print(\"Output shape:\", out.size())\n", + "print(f\"Output logits:\\n{out.detach().cpu().numpy()}\")\n", + "print(f\"Output probabilities:\\n{out.softmax(1).detach().cpu().numpy()}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [], + "source": [ + "from tqdm import tqdm" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": { + "id": "NkUanRRb81TW", + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 64%|███████████████████████████████████████████████████▎ | 501/782 [01:26<08:35, 1.83s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 500 training accuracy: 0.42834375\n", + " test accuracy: 0.5544\n" + ] + }, + { + "name": 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i, size: range(i * size, (i + 1) * size)\n", + "\n", + "step = 0\n", + "model.train()\n", + "\n", + "train_accuracies = []\n", + "valid_accuracies = []\n", + "\n", + "model.train()\n", + "for epoch in range(num_epochs):\n", + " \n", + " train_accuracies_batches = []\n", + " \n", + " for inputs, targets in tqdm(train_loader):\n", + " inputs, targets = inputs.to(device), targets.to(device)\n", + " \n", + "# # perform one training step\n", + "# cur_loss = 0\n", + " \n", + "# # for i in range(num_batches_train):\n", + "# optimizer.zero_grad()\n", + "# # slce = get_slice(i, batch_size)\n", + "# output = Model(inputs)\n", + " \n", + " # zero gradient\n", + " optimizer.zero_grad()\n", + " \n", + " # # Forward pass, backward and optimizer\n", + " step += 1\n", + " output = model(inputs)\n", + " loss = criterion(output, targets)\n", + " loss.backward()\n", + " optimizer.step()\n", + " \n", + "# # compute gradients\n", + "# gradients = np.gradient(f,edge_order=1)\n", + " \n", + " \n", + " \n", + " # Compute accuracy.\n", + " predictions = output.max(1)[1]\n", + " train_accuracies_batches.append(accuracy(targets, predictions))\n", + " \n", + " if step % validation_every_steps == 0:\n", + " \n", + " # Append average training accuracy to list.\n", + " train_accuracies.append(np.mean(train_accuracies_batches))\n", + " \n", + " train_accuracies_batches = []\n", + " \n", + " # Compute accuracies on validation set.\n", + " valid_accuracies_batches = []\n", + " with torch.no_grad():\n", + " model.eval()\n", + " for inputs, targets in test_loader:\n", + " inputs, targets = inputs.to(device), targets.to(device)\n", + " output = model(inputs)\n", + " loss = criterion(output, targets)\n", + "\n", + " predictions = output.max(1)[1]\n", + "\n", + " # Multiply by len(x) because the final batch of DataLoader may be smaller (drop_last=False).\n", + " valid_accuracies_batches.append(accuracy(targets, predictions) * len(inputs))\n", + "\n", + " model.train()\n", + " \n", + " # Append average validation accuracy to list.\n", + " valid_accuracies.append(np.sum(valid_accuracies_batches) / len(test_set))\n", + " \n", + " print(f\"Step {step:<5} training accuracy: {train_accuracies[-1]}\")\n", + " print(f\" test accuracy: {valid_accuracies[-1]}\")\n", + "\n", + "print(\"Finished training.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(torch.Size([16, 3, 32, 32]), torch.Size([64]))" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inputs.shape, labels.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0qAsbC8I81Ta" + }, + "source": [ + "## Test the network\n", + "\n", + "Now we show a batch of test images and generate a table below with the true and predicted class for each of these images." + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": { + "id": "7LT0RoAC81Tc" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " TRUE PREDICTED\n", + "-----------------------------\n", + " cat cat \n", + " ship ship \n", + " ship ship \n", + " airplane airplane \n", + " frog frog \n", + " frog dog \n", + " automobile automobile \n", + " frog frog \n", + " cat cat \n", + " automobile automobile \n", + " airplane airplane \n", + " truck truck \n", + " dog dog \n", + " horse horse \n", + " truck truck \n", + " ship ship \n", + " dog dog \n", + " horse horse \n", + " ship ship \n", + " frog frog \n", + " horse horse \n", + " airplane airplane \n", + " deer deer \n", + " truck truck \n", + " dog deer \n", + " bird bird \n", + " deer deer \n", + " airplane airplane \n", + " truck automobile \n", + " frog frog \n", + " frog frog \n", + " dog dog \n", + " deer deer \n", + " dog cat \n", + " truck truck \n", + " bird cat \n", + " deer horse \n", + " automobile automobile \n", + " truck truck \n", + " dog dog \n", + " deer deer \n", + " frog frog \n", + " dog dog \n", + " frog frog \n", + " airplane airplane \n", + " truck truck \n", + " cat cat \n", + " truck cat \n", + " horse horse \n", + " frog bird \n", + " truck truck \n", + " ship ship \n", + " airplane bird \n", + " cat cat \n", + " ship ship \n", + " ship ship \n", + " horse horse \n", + " horse cat \n", + " deer deer \n", + " frog deer \n", + " horse horse \n", + " cat dog \n", + " frog frog \n", + " cat truck \n" + ] + } + ], + "source": [ + "inputs, targets = iter(test_loader).next()\n", + "inputs, targets = inputs.to(device), targets.to(device)\n", + "show_image(make_grid(inputs))\n", + "plt.show()\n", + "\n", + "outputs = model(inputs)\n", + "_, predicted = torch.max(outputs.data, 1)\n", + "\n", + "print(\" TRUE PREDICTED\")\n", + "print(\"-----------------------------\")\n", + "for target, pred in zip(targets, predicted):\n", + " print(f\"{classes[target.item()]:^13} {classes[pred.item()]:^13}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ISA6LJJO81Tg" + }, + "source": [ + "We now evaluate the network as above, but on the entire test set." + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": { + "id": "Smv6_BwF81Ti" + }, + "outputs": [], + "source": [ + "# Evaluate test set\n", + "confusion_matrix = np.zeros((n_classes, n_classes))\n", + "with torch.no_grad():\n", + " model.eval()\n", + " test_accuracies = []\n", + " for inputs, targets in test_loader:\n", + " inputs, targets = inputs.to(device), targets.to(device)\n", + " output = model(inputs)\n", + " loss = criterion(output, targets)\n", + "\n", + " predictions = output.max(1)[1]\n", + "\n", + " # Multiply by len(inputs) because the final batch of DataLoader may be smaller (drop_last=True).\n", + " test_accuracies.append(accuracy(targets, predictions) * len(inputs))\n", + " \n", + " confusion_matrix += compute_confusion_matrix(targets, predictions)\n", + "\n", + " test_accuracy = np.sum(test_accuracies) / len(test_set)\n", + " \n", + " model.train()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ylxBAyXwI7Ab" + }, + "source": [ + "Here we report the **average test accuracy** (number of correct predictions divided by test set size)." + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": { + "id": "a5-c_CDAI7Ab" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test accuracy: 0.771\n" + ] + } + ], + "source": [ + "print(f\"Test accuracy: {test_accuracy:.3f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uMfUqY9rI7Ab" + }, + "source": [ + "Here we look a bit more in depth into the performance of the classifier, using the **confusion matrix**. The entry at the i-th row and j-th column indicates the number of samples with true label being the i-th class and predicted label being the j-th class.\n", + "\n", + "We normalize the rows: given all examples of a specific class (row), we can observe here how they are classified by our model. Ideally, we would like the entries on the diagonals to be 1, and everything else 0. This would mean that all examples from that class are classified correctly.\n", + "\n", + "The classes that are harder to classify for our model have lower numbers on the diagonal. We can then see exactly *how* they are misclassified by looking at the rest of the row.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": { + "id": "ShcEZDExI7Ab" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def normalize(matrix, axis):\n", + " axis = {'true': 1, 'pred': 0}[axis]\n", + " return matrix / matrix.sum(axis=axis, keepdims=True)\n", + "\n", + "x_labels = [classes[i] for i in classes]\n", + "y_labels = x_labels\n", + "plt.figure(figsize=(6, 6))\n", + "sns.heatmap(\n", + " ax=plt.gca(),\n", + " data=normalize(confusion_matrix, 'true'),\n", + " annot=True,\n", + " linewidths=0.5,\n", + " cmap=\"Reds\",\n", + " cbar=False,\n", + " fmt=\".2f\",\n", + " xticklabels=x_labels,\n", + " yticklabels=y_labels,\n", + ")\n", + "plt.xticks(rotation=90)\n", + "plt.yticks(rotation=0)\n", + "plt.ylabel(\"True class\")\n", + "plt.xlabel(\"Predicted class\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lw13Y86VI7Ac" + }, + "source": [ + "Here we focus on the diagonal and plot the numbers in a bar plot. This gives us a clearer picture of the accuracy of the model for different classes." + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": { + "id": "xv75wMhVI7Ac" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with sns.axes_style('whitegrid'):\n", + " plt.figure(figsize=(8, 4))\n", + " sns.barplot(x=x_labels, y=np.diag(normalize(confusion_matrix, 'true')))\n", + " plt.xticks(rotation=90)\n", + " plt.title(\"Per-class accuracy\")\n", + " plt.ylabel(\"Accuracy\")\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ocnQOBAl81Tn" + }, + "source": [ + "**Assignment 4:** \n", + "1. Go back and improve performance of the network. By using enough convolutional layers with enough channels (and by training for long enough), you should easily be able to get a test accuracy above 60%, but see how much further you can get it! Can you reach 70%?\n", + "\n", + "2. Briefly describe what you did and any experiments you did along the way as well as what results you obtained.\n", + "Did anything surprise you during the exercise?\n", + "What were the changes that seemed to improve performance the most?\n", + "\n", + "3. Write down key lessons/insights you got during this exercise.\n", + "\n", + "**Answer:**\n", + "\n", + "2. The CNN now consist of 3 convolutional layers, 2 pool layers which minimizes the layer to a half of the output and batchnorm for 2D data. this allowed to model to reach a test accuracy at 77,1%. When the network only had 2 layers, with a 1D batchnorm, it was not able to get above 50% accuracy, once the third layer was implement with a batchnorm for 2D, it increased its performance a lot\n", + "\n", + "3. To make sure you have the right sizes predicted for each layer, do the calculations for the layers by hand before perfoming the fully connected layer" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8Nzefavy81To" + }, + "source": [ + "# Training on GPU\n", + "\n", + "**Optional Assignment:**\n", + "If you have a GPU, we suggest that you try training your model on GPU. For this, you need to move the model to GPU after defining it, which will recursively go over all modules and convert their parameters and buffers to CUDA tensors. You also need to transfer both the inputs and targets to GPU at each training step, before performing the forward pass.\n", + "\n", + "The code for this is already in place: notice the `.to(device)` statements. The only thing left to do is change the definition of `device` from `'cpu'` to `'cuda'`.\n", + "\n", + "If you don't have a GPU, you can do this on [Google Colab](https://research.google.com/colaboratory/).\n", + "\n", + "Use the code below to check if any GPU is avaiable in your current setup. This should print the models of all available GPUs.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": { + "id": "I09rtehbaCRH" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available CUDA devices: []\n" + ] + } + ], + "source": [ + "# Check if we have GPUs available\n", + "print(\"Available CUDA devices:\", [torch.cuda.get_device_name(i) for i in range(torch.cuda.device_count())])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "actFGqQZM9Vd" + }, + "source": [ + "You may not notice any significant speed-up from using a GPU. This is probably because your network is really small. Try increasing the width of your network (number of channels in the convolutional layers) and see if you observe any speed-up on GPU compared to CPU." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b8mEIylU81Tp" + }, + "source": [ + "# Exercise from Michael Nielsen's book\n", + "\n", + "**Assignment 5:** Pick an exercise of your own choice from [Michael Nielsen's book](http://neuralnetworksanddeeplearning.com/).\n", + "\n", + "**Answer:**\n", + "\n", + "Prove equation BP3 and BP4\n", + "\n", + "BP3 $$ \\frac{\\partial C}{\\partial b_j^L}=\\delta_j^L $$\n", + "\n", + "We are given that \n", + "$$ z^L=w^la^l+b^l $$\n", + "\n", + "So when we isolate for $b^l$ this will become:\n", + "$$ b^l=z^l-w^la^{l-1} $$\n", + "\n", + "Then for the derivative it will be:\n", + "$$ \\delta_j^l = \\frac{\\partial C}{\\partial z_j^l}=1$$\n", + "\n", + "Which then makes equal to one, therefore the final result is:\n", + "$$ \\frac{\\partial C}{\\partial b_j^L}=\\delta_j^L $$\n", + "\n", + "\n", + "BP4 $$ \\frac{\\partial C}{\\partial w_{jk}^L}=a_k^{l-1}\\delta_k^l $$\n", + "\n", + "From the same $z_j^l$, we isolate z so we can do the derivative to w and b\n", + "\n", + "$$ z_j^l=w_{jk}^l a_k^{l-1}+b_j^l $$\n", + "\n", + "$$ \\frac{z_j^l}{w_{jk}^l}=\\frac{1}{a_k^{l-1}} \\frac{b_j^l}{a_k^{l-1}} $$\n", + "\n", + "where $\\frac{b_j^l}{a_k^{l-1}}$ is two constant, this will be 1 \n", + "\n", + "$$ \\delta_j^l=\\frac{\\partial C}{\\partial z_j^l}=\\frac{\\partial C}{\\partial w_{jk}^l} \\frac{\\partial w_{jk}^l}{z_k^l} $$\n", + "\n", + "$$ \\delta_j^l=\\frac{\\partial C}{\\partial w_{jk}^l}\\frac{1}{a_k} $$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + 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"\n", - "Convolution neural networks are one of the most successful types of neural networks for image recognition and an integral part of reigniting the interest in neural networks. They are able to extract structural relations in the data, such as spatial in images or temporal in time series.\n", - "\n", - "In this lab, we will experiment with inserting 2D-convolution layers in the fully connected neural networks introduced previously. We will also try to visualize the learned convolution filters and try to understand what kind of features they learn to recognize.\n", + "# Credits\n", "\n", - "If you have not watched Jason Yosinski's [video on visualizing convolutional networks](https://www.youtube.com/watch?v=AgkfIQ4IGaM), you definitely should do so now. If you are unfamiliar with the convolution operation, [Vincent Dumoulin](https://github.com/vdumoulin/conv_arithmetic) has a nice visualization of different convolution variants. For a more in-depth tutorial, please see http://cs231n.github.io/convolutional-networks/ or http://neuralnetworksanddeeplearning.com/chap6.html." + "Originally created for a previous version of the [02456-deep-learning](https://github.com/DeepLearningDTU/02456-deep-learning) course material, but [converted to PyTorch](https://github.com/pytorch/tutorials).\n", + "See repos for credits." ] }, { "cell_type": "markdown", - "metadata": { - "id": "2lMa9SSfjSvl" - }, + "metadata": {}, + "source": [ + "# Dependancies and supporting functions\n", + "Loading dependancies and supporting functions by running the code block below." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], "source": [ - "## Reminder: what are convolutional networks?\n", + "%matplotlib inline\n", + "import matplotlib\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Convolutional Neural networks 101\n", "\n", - "Standard ConvNets are, in many respects, very similar to the dense feedforward networks we saw previously:\n", - " * The network is still organized into layers.\n", - " * Each layer is parameterized by weights and biases.\n", - " * Each layer has an element-wise non-linear transformation (activation function).\n", - " * There are no cycles in the connections (more on this in later labs).\n", + "Convolution neural networks are one of the most succesfull types of neural networks for image recognition and an integral part of reigniting the interest in neural networks. \n", + "They are able to extract structural relations in the data such as spatial in images or temporal in time series.\n", "\n", - "*So what is the difference?*\n", - "The networks we saw previously are called *dense* because each unit receives input from all the units in the previous layer. This is not the case for ConvNets. In ConvNets each unit is only connected to a small subset of the input units. This is called the *receptive field* of the unit.\n", + "In this lab we'll experiment with inserting 2D-convolution layers in the fully connected neural networks introduced in lab 1 (`1_Feedforward`).\n", + "We'll further experiment with stacking of convolution layers, max pooling and strided convolutions which are all important techniques in current convolution neural network architectures.\n", + "Lastly we'll try to visualize the learned convolution filters and try to understand what kind of features they learn to recognize.\n", "\n", - "#### Example\n", - "The input (green matrix) is a tensor of size `1x5x5` -- i.e. it has one \"channel\" (like a grayscale image), and the feature map has size `5x5`. Let us define a `1x3x3` kernel (yellow submatrix). The kernel weights are indicated in red at the bottom right of each element. The computation can be thought of as an elementwise multiplication followed by a sum. Here we use a *stride* of 1, as shown in this animation:\n", + "If you haven't watched Jason Yosinski and colleague [awesome video on visualizing convolutional networks](https://www.youtube.com/watch?v=AgkfIQ4IGaM) you definitely should do so now.\n", "\n", - "\n", + "If you are unfamilar with the the convolution operation https://github.com/vdumoulin/conv_arithmetic have a nice visualization of different convolution variants.\n", + "For a more indepth tutorial please see http://cs231n.github.io/convolutional-networks/ or http://neuralnetworksanddeeplearning.com/chap6.html." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reminder: What are convolutional networks?\n", "\n", - "GIF courtesy of [Stanford](http://deeplearning.stanford.edu/wiki/index.php/Feature_extraction_using_convolution)\n", + "ConvNets are in may respects very similar to the dense feedforward networks we saw previously:\n", + " * The network is still organized into layers\n", + " * Each layer is parameterized by weights and biases\n", + " * Each layer has an element-wise non-linear transformation (activation function)\n", + " * There are no cycles in the connections (more on this in later labs)\n", "\n", - "After having convolved the image, we perform an elementwise non-linear transformation on the *convolved features*.\n", - "In this example, the input is a 2D *feature map* with depth 1.\n" + "*So what is the difference?*\n", + "The networks we saw previously are called *dense* because each unit receives input from all the units in the previous layer.\n", + "This is not the case for ConvNets.\n", + "In ConvNets each unit is only connected to a small subset of the input units.\n", + "This is called the *receptive field* of the unit.\n", + "\n", + "#### Let us look at a quick example.\n", + "The input (green matrix) is `1x5x5` dimensional tensor - i.e. it has one `channel` (like a grayscale image) and the map is of size `5x5`.\n", + "Let us define a `1x3x3` kernel (yellow submatrix).\n", + "The kernel weights are indicated by red in the bottom right of each elment.\n", + "The computation can be thought of as first an elementwise multiplication, and then summing the results.\n", + "Here we use a stride of 1, as shown in this animation:\n", + "\n", + " \n", + "[GIF courtesy of [Stanford](http://deeplearning.stanford.edu/wiki/index.php/Feature_extraction_using_convolution)]\n", + "\n", + "After having convolved the image we perform an elementwise non-linear transformation on the *convolved features*.\n", + "In this example the input is a 2D *feature map* with depth 1.\n" ] }, { "cell_type": "markdown", - "metadata": { - "id": "pOLUjfUZjSvn" - }, + "metadata": {}, "source": [ "# Assignment 1\n", "\n", @@ -55,16 +91,17 @@ "\n", "Perform the following computation, and write the result below.\n", "\n", - "![](https://raw.githubusercontent.com/DeepLearningDTU/02456-deep-learning-with-PyTorch/master/4_Convolutional/images/conv_exe.png)\n", + "![](images/conv_exe.png)\n", "\n", "1. Manually convolve the input, and compute the convolved features. No padding and stride of 1.\n", - " * **Answer:**\n", - "2. Perform `2x2` max pooling on the convolved features. Stride of 2.\n", - " * **Answer:**\n", + "1. Perform `2x2` max pooling on the convolved features. Stride of 2.\n", + "\n", + "**Answer:**\n", + "\n", "\n", "### Assignment 1.2: Output dimensionality\n", "\n", - "Given the following 3D tensor input `(channel, height, width)`, a given amount (`channels_out`) of filters `(channels_in, filter_height, filter_width)`, stride `(height, width)` and padding `(height, width)`, calculate the output dimensionality if it is valid.\n", + "Given the following 3D tensor input `(channel, height, width)` , a given amount (`channels_out`) of filters `(channels_in, filter_height, filter_width)`, stride `(height, width)` and padding `(height, width)`, calculate the output dimensionality if it's valid.\n", "\n", "1. input tensor with dimensionality (1, 28, 28) and 16 filters of size (1, 5, 5) with stride (1, 1) and padding (0, 0)\n", " * **Answer:** \n", @@ -81,176 +118,92 @@ }, { "cell_type": "markdown", - "metadata": { - "id": "fTsZa_xhjSvp" - }, - "source": [ - "# Load packages" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "executionInfo": { - "elapsed": 3086, - "status": "ok", - "timestamp": 1662639635584, - "user": { - "displayName": "Ole Winther", - "userId": "12097569893493878263" - }, - "user_tz": -120 - }, - "id": "oFUsDSEtjSvq" - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import torch\n", - "from torch import nn\n", - "import torch.nn.functional as F\n", - "import torch.optim as optim\n", - "\n", - "from sklearn.metrics import accuracy_score\n", - "from torch.utils.data import TensorDataset, DataLoader\n", - "from torchvision.utils import make_grid\n", - "\n", - "sns.set_style(\"whitegrid\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TpD2ZUGWjSvs" - }, + "metadata": {}, "source": [ - "# Load MNIST data\n", + "# Data: MNIST\n", "\n", "The code below downloads and loads the same MNIST dataset as before.\n", - "Note however that the data has a different shape this time: `(num_samples, num_channels, height, width)`." + "Note however that the data has a different shape this time, namely `(num_samples, num_channels, height, width)`." ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "executionInfo": { - "elapsed": 2461, - "status": "ok", - "timestamp": 1662639650225, - "user": { - "displayName": "Ole Winther", - "userId": "12097569893493878263" - }, - "user_tz": -120 - }, - "id": "2ycuc1r-jSvt", - "outputId": "c4134d21-8eb8-45af-ef51-21b2526bf28b" - }, + "metadata": {}, "outputs": [], "source": [ - "# Download the MNIST dataset, if you have not already.\n", + "## Download the MNIST dataset, if you haven't already\n", + "\n", "!if [ ! -f mnist.npz ]; then wget -N https://www.dropbox.com/s/qxywaq7nx19z72p/mnist.npz; else echo \"mnist.npz already downloaded\"; fi" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "executionInfo": { - "elapsed": 1209, - "status": "ok", - "timestamp": 1662639661481, - "user": { - "displayName": "Ole Winther", - "userId": "12097569893493878263" - }, - "user_tz": -120 - }, - "id": "vB-wt78SjSvw", - "outputId": "2228cd08-f220-4176-9d76-4284c27b21e2" - }, + "metadata": {}, "outputs": [], "source": [ - "# Load the MNIST data. \n", - "\n", - "# Note that we reshape the data from:\n", - "# (nsamples, num_features) = (nsamples, channels * height * width)\n", - "# to:\n", - "# (nsamples, channels, height, width)\n", - "# in order to retain the spatial arrangements of the pixels.\n", + "## LOAD the mnist data. \n", "\n", + "# To speed up training we'll only work on a subset of the data.\n", + "# Note that we reshape the data from \n", + "# (nsamples, num_features) = (nsamples, nchannels*rows*cols)\n", + "# -> (nsamples, nchannels, rows, cols)\n", + "# in order to retain the spatial arrangements of the pixels\n", "data = np.load('mnist.npz')\n", - "channels, height, width = 1, 28, 28\n", - "\n", - "\n", - "def get_data(split, size):\n", - " x = data[f\"X_{split}\"][:size].astype('float32')\n", - " x = x.reshape((-1, channels, height, width))\n", - " targets = data[f\"y_{split}\"][:size].astype('int64')\n", - " return torch.from_numpy(x), torch.from_numpy(targets)\n", + "num_classes = 10\n", + "nchannels, rows, cols = 1, 28, 28\n", "\n", + "x_train = data['X_train'][:1000].astype('float32')\n", + "x_train = x_train.reshape((-1, nchannels, rows, cols))\n", + "targets_train = data['y_train'][:1000].astype('int32')\n", "\n", - "x_train, targets_train = get_data('train', 50000)\n", - "x_valid, targets_valid = get_data('valid', 2000)\n", - "x_test, targets_test = get_data('test', 5000)\n", + "x_valid = data['X_valid'][:500].astype('float32')\n", + "x_valid = x_valid.reshape((-1, nchannels, rows, cols))\n", + "targets_valid = data['y_valid'][:500].astype('int32')\n", "\n", - "num_classes = len(np.unique(targets_train))\n", + "x_test = data['X_test'][:500].astype('float32')\n", + "x_test = x_test.reshape((-1, nchannels, rows, cols))\n", + "targets_test = data['y_test'][:500].astype('int32')\n", "\n", "print(\"Information on dataset\")\n", - "print(\"Shape of x_train:\", x_train.shape)\n", - "print(\"Shape of targets_train:\", targets_train.shape)\n", - "print(\"Shape of x_valid:\", x_valid.shape)\n", - "print(\"Shape of targets_valid:\", targets_valid.shape)\n", - "print(\"Shape of x_test:\", x_test.shape)\n", - "print(\"Shape of targets_test:\", targets_test.shape)" + "print(\"x_train\", x_train.shape)\n", + "print(\"targets_train\", targets_train.shape)\n", + "print(\"x_valid\", x_valid.shape)\n", + "print(\"targets_valid\", targets_valid.shape)\n", + "print(\"x_test\", x_test.shape)\n", + "print(\"targets_test\", targets_test.shape)" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 411 - }, - "executionInfo": { - "elapsed": 1059, - "status": "ok", - "timestamp": 1662639666747, - "user": { - "displayName": "Ole Winther", - "userId": "12097569893493878263" - }, - "user_tz": -120 - }, - "id": "yGmZxin6jSvy", - "outputId": "60b5cadb-5271-4d96-9d8a-4313c4f3989e" - }, + "metadata": {}, "outputs": [], "source": [ - "# Plot a few MNIST examples\n", - "plt.figure(figsize=(7, 7))\n", - "plt.imshow(make_grid(x_train[:100], nrow=10).permute(1, 2, 0))\n", + "## plot a few MNIST examples\n", + "\n", + "idx, dim, classes = 0, 28, 10\n", + "# create empty canvas\n", + "canvas = np.zeros((dim*classes, classes*dim))\n", + "\n", + "# fill with tensors\n", + "for i in range(classes):\n", + " for j in range(classes):\n", + " canvas[i*dim:(i+1)*dim, j*dim:(j+1)*dim] = x_train[idx].reshape((dim, dim))\n", + " idx += 1\n", + "\n", + "# visualize matrix of tensors as gray scale image\n", + "plt.figure(figsize=(6, 6))\n", "plt.axis('off')\n", + "plt.imshow(canvas, cmap='gray')\n", + "plt.title('MNIST handwritten digits')\n", "plt.show()" ] }, { "cell_type": "markdown", - "metadata": { - "id": "xcq0TsjbjSv1" - }, + "metadata": {}, "source": [ "# Define a simple feed forward neural network" ] @@ -258,269 +211,315 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "executionInfo": { - "elapsed": 312, - "status": "ok", - "timestamp": 1662639671015, - "user": { - "displayName": "Ole Winther", - "userId": "12097569893493878263" - }, - "user_tz": -120 - }, - "id": "_Cb9FsjajSv2", - "outputId": "7691e76d-3420-436f-e0c6-9072cdf925f4" - }, + "metadata": {}, "outputs": [], "source": [ - "assert (channels, height, width) == x_train.shape[1:]\n", - "n_features = channels * height * width\n", - "\n", - "\n", - "class PrintSize(nn.Module):\n", - " \"\"\"Utility module to print current shape of a Tensor in Sequential, only at the first forward pass.\"\"\"\n", - " \n", - " first = True\n", - " \n", - " def forward(self, x):\n", - " if self.first:\n", - " print(f\"Size: {x.size()}\")\n", - " self.first = False\n", - " return x\n", - "\n", + "import torch\n", + "from torch.autograd import Variable\n", + "from torch.nn.parameter import Parameter\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "import torch.optim as optim\n", + "import torch.nn.init as init\n", "\n", - "class Model(nn.Module):\n", + "from torch.nn import Linear, Conv2d, BatchNorm2d, MaxPool2d, Dropout2d\n", + "from torch.nn.functional import relu, elu, relu6, sigmoid, tanh, softmax" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# hyperameters of the model\n", + "num_classes = 10\n", + "channels = x_train.shape[1]\n", + "height = x_train.shape[2]\n", + "width = x_train.shape[3]\n", + "num_filters_conv1 = 16\n", + "kernel_size_conv1 = 5 # [height, width]\n", + "stride_conv1 = 1 # [stride_height, stride_width]\n", + "num_l1 = 100\n", + "padding_conv1 = 0\n", + " \n", + "def compute_conv_dim(dim_size):\n", + " return int((dim_size - kernel_size_conv1 + 2 * padding_conv1) / stride_conv1 + 1)\n", + "\n", + "# define network\n", + "class Net(nn.Module):\n", "\n", " def __init__(self):\n", - " super(Model, self).__init__()\n", - " activation_fn = nn.ReLU\n", - "\n", - " self.net = nn.Sequential(\n", - " nn.Flatten(), # from (1, channels, height, width) to (1, channels * height * width)\n", - " nn.Linear(n_features, 128),\n", - " activation_fn(),\n", - " nn.Linear(128, 128),\n", - " activation_fn(),\n", - " nn.Linear(128, num_classes)\n", - " )\n", - "\n", - " def forward(self, x):\n", - " return self.net(x)\n", - "\n", - "\n", - "model = Model()\n", - "print(model)\n", - "\n", - "loss_fn = nn.CrossEntropyLoss()\n", - "optimizer = optim.Adam(model.parameters(), lr=1e-3)\n" + " super(Net, self).__init__()\n", + " # out_dim = (input_dim - filter_dim + 2padding) / stride + 1\n", + " # self.conv_1 = Conv2d(in_channels=channels,\n", + " # out_channels=num_filters_conv1,\n", + " # kernel_size=kernel_size_conv1,\n", + " # stride=stride_conv1)\n", + " \n", + " # self.conv_out_height = compute_conv_dim(height)\n", + " # self.conv_out_width = compute_conv_dim(width)\n", + " \n", + " # add dropout to network\n", + " # self.dropout = Dropout2d(p=0.5)\n", + " # self.l1_in_features = num_filters_conv1 * self.conv_out_height * self.conv_out_width\n", + " self.l1_in_features = channels * height * width\n", + " \n", + " self.l_1 = Linear(in_features=self.l1_in_features, \n", + " out_features=num_l1,\n", + " bias=True)\n", + " self.l_out = Linear(in_features=num_l1, \n", + " out_features=num_classes,\n", + " bias=False)\n", + " \n", + " def forward(self, x): # x.size() = [batch, channel, height, width]\n", + " #x = relu(self.conv_1(x))\n", + " # torch.Tensor.view: http://pytorch.org/docs/master/tensors.html?highlight=view#torch.Tensor.view\n", + " # Returns a new tensor with the same data as the self tensor,\n", + " # but of a different size.\n", + " # the size -1 is inferred from other dimensions \n", + " x = x.view(-1, self.l1_in_features)\n", + " #x = self.dropout(relu(self.l_1(x)))\n", + " x = relu(self.l_1(x))\n", + " return softmax(self.l_out(x), dim=1)\n", + "\n", + "\n", + "net = Net()\n", + "print(net)" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "executionInfo": { - "elapsed": 313, - "status": "ok", - "timestamp": 1662639684841, - "user": { - "displayName": "Ole Winther", - "userId": "12097569893493878263" - }, - "user_tz": -120 - }, - "id": "GPIgjOTFjSv6", - "outputId": "7401463e-3427-4a1a-ec03-620dc002d312" - }, + "metadata": {}, "outputs": [], "source": [ - "# Test the forward pass with dummy data\n", - "out = model(torch.randn(2, 1, 28, 28))\n", - "print(\"Output shape:\", out.size())\n", - "print(f\"Output logits:\\n{out.detach().numpy()}\")\n", - "print(f\"Output probabilities:\\n{out.softmax(1).detach().numpy()}\")" + "criterion = nn.CrossEntropyLoss()\n", + "optimizer = optim.Adam(net.parameters(), lr=0.001)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Test the forward pass with dummy data\n", + "x = np.random.normal(0,1, (5, 1, 28, 28)).astype('float32')\n", + "out = net(Variable(torch.from_numpy(x)))\n", + "out.size(), out" ] }, { "cell_type": "markdown", - "metadata": { - "id": "p5kIR7JTjSv7" - }, + "metadata": {}, "source": [ - "# Train network" + "Notice how the output's probabilities are nicely distributed.\n", + "The built-in nn functions (layers) alreay have a sane initialization of the weights." ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "id": "2eBO7B36jSv7" - }, + "metadata": {}, "outputs": [], "source": [ - "batch_size = 64\n", - "num_epochs = 5\n", - "validation_every_steps = 500\n", - "\n", - "train_set = TensorDataset(x_train, targets_train)\n", - "train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True, drop_last=True)\n", - "valid_set = TensorDataset(x_valid, targets_valid)\n", - "valid_loader = DataLoader(valid_set, batch_size=batch_size, shuffle=False, drop_last=False)\n", - "test_set = TensorDataset(x_test, targets_test)\n", - "test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False, drop_last=False)\n", - "\n", - "step = 0\n", - "model.train()\n", - "\n", - "train_accuracies = []\n", - "valid_accuracies = []\n", - " \n", + "# we could have done this ourselves,\n", + "# but we should be aware of sklearn and it's tools\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "batch_size = 100\n", + "num_epochs = 50\n", + "num_samples_train = x_train.shape[0]\n", + "num_batches_train = num_samples_train // batch_size\n", + "num_samples_valid = x_valid.shape[0]\n", + "num_batches_valid = num_samples_valid // batch_size\n", + "\n", + "train_acc, train_loss = [], []\n", + "valid_acc, valid_loss = [], []\n", + "test_acc, test_loss = [], []\n", + "cur_loss = 0\n", + "losses = []\n", + "\n", + "get_slice = lambda i, size: range(i * size, (i + 1) * size)\n", + "\n", "for epoch in range(num_epochs):\n", - " \n", - " train_accuracies_batches = []\n", - " \n", - " for inputs, targets in train_loader:\n", - " \n", - " # Forward pass.\n", - " output = model(inputs)\n", + " # Forward -> Backprob -> Update params\n", + " ## Train\n", + " cur_loss = 0\n", + " net.train()\n", + " for i in range(num_batches_train):\n", + " slce = get_slice(i, batch_size)\n", + " x_batch = Variable(torch.from_numpy(x_train[slce]))\n", + " output = net(x_batch)\n", " \n", - " # Compute loss.\n", - " loss = loss_fn(output, targets)\n", - " \n", - " # Clean up gradients from the model.\n", + " # compute gradients given loss\n", + " target_batch = Variable(torch.from_numpy(targets_train[slce]).long())\n", + " batch_loss = criterion(output, target_batch)\n", " optimizer.zero_grad()\n", - " \n", - " # Compute gradients based on the loss from the current batch (backpropagation).\n", - " loss.backward()\n", - " \n", - " # Take one optimizer step using the gradients computed in the previous step.\n", + " batch_loss.backward()\n", " optimizer.step()\n", " \n", - " step += 1\n", + " cur_loss += batch_loss \n", + " losses.append(cur_loss / batch_size)\n", + "\n", + " net.eval()\n", + " ### Evaluate training\n", + " train_preds, train_targs = [], []\n", + " for i in range(num_batches_train):\n", + " slce = get_slice(i, batch_size)\n", + " x_batch = Variable(torch.from_numpy(x_train[slce]))\n", " \n", - " # Compute accuracy.\n", - " predictions = output.max(1)[1]\n", - " train_accuracies_batches.append(accuracy_score(targets, predictions))\n", + " output = net(x_batch)\n", + " preds = torch.max(output, 1)[1]\n", " \n", - " if step % validation_every_steps == 0:\n", - " \n", - " # Append average training accuracy to list.\n", - " train_accuracies.append(np.mean(train_accuracies_batches))\n", - " \n", - " train_accuracies_batches = []\n", + " train_targs += list(targets_train[slce])\n", + " train_preds += list(preds.data.numpy())\n", + " \n", + " ### Evaluate validation\n", + " val_preds, val_targs = [], []\n", + " for i in range(num_batches_valid):\n", + " slce = get_slice(i, batch_size)\n", + " x_batch = Variable(torch.from_numpy(x_valid[slce]))\n", " \n", - " # Compute accuracies on validation set.\n", - " valid_accuracies_batches = []\n", - " with torch.no_grad():\n", - " model.eval()\n", - " for inputs, targets in valid_loader:\n", - " output = model(inputs)\n", - " loss = loss_fn(output, targets)\n", - "\n", - " predictions = output.max(1)[1]\n", - "\n", - " # Multiply by len(x) because the final batch of DataLoader may be smaller (drop_last=False).\n", - " valid_accuracies_batches.append(accuracy_score(targets, predictions) * len(inputs))\n", - "\n", - " model.train()\n", - " \n", - " # Append average validation accuracy to list.\n", - " valid_accuracies.append(np.sum(valid_accuracies_batches) / len(x_valid))\n", - " \n", - " print(f\"Step {step:<5} training accuracy: {train_accuracies[-1]}\")\n", - " print(f\" validation accuracy: {valid_accuracies[-1]}\")\n", + " output = net(x_batch)\n", + " preds = torch.max(output, 1)[1]\n", + " val_preds += list(preds.data.numpy())\n", + " val_targs += list(targets_valid[slce])\n", "\n", - "print(\"Finished training.\")" + " train_acc_cur = accuracy_score(train_targs, train_preds)\n", + " valid_acc_cur = accuracy_score(val_targs, val_preds)\n", + " \n", + " train_acc.append(train_acc_cur)\n", + " valid_acc.append(valid_acc_cur)\n", + " \n", + " if epoch % 10 == 0:\n", + " print(\"Epoch %2i : Train Loss %f , Train acc %f, Valid acc %f\" % (\n", + " epoch+1, losses[-1], train_acc_cur, valid_acc_cur))\n", + " \n", + "epoch = np.arange(len(train_acc))\n", + "plt.figure()\n", + "plt.plot(epoch, train_acc, 'r', epoch, valid_acc, 'b')\n", + "plt.legend(['Train Acc', 'Val Acc'])\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('Acc')\n", + "\n", + "### Evaluate test set\n", + "x_batch = Variable(torch.from_numpy(x_test))\n", + "output = net(x_batch)\n", + "preds = torch.max(output, 1)[1]\n", + "print(\"\\nTest set Acc: %f\" % (accuracy_score(list(targets_test), list(preds.data.numpy()))))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Assignment 2\n", + "\n", + "1. Note the performance of the standard feedforward neural network. Add a 2D convolution layer before the dense hidden layer and confirm that it increases the generalization performance of the network (try num_filters=16 and filter_size=5 as a starting point). \n", + " \n", + "2. Notice that the size of the image reduces. This can cause loss of information in convolutional networks that apply many convolutional layers. To avoid such add adequate padding to the convolutional layer.\n", + " \n", + "3. Can the performance be increases even further by stacking more convolution layers ?\n", + " \n", + "4. Maxpooling is a technique for decreasing the spatial resolution of an image while retaining the important features. Effectively this gives a local translational invariance and reduces the computation by a factor of four. In the classification algorithm which is usually desirable. Try to either: \n", + " \n", + " - add a maxpool layer (add arguement kernel_size=2, stride=2) after the convolution layer, or\n", + " - set add stride=2 to the arguments of the convolution layer, make it fit with the kernel size\n", + " \n", + " Verify that this decreases spatial dimension of the image (`print(l_conv_x.size())` or `print(l_maxpool_x.size())` in your forward pass). Does this increase the performance of the network (you may need to stack multiple layers or increase the number of filters to increase performance) ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Visualization of filters\n", + "Convolution filters can be interpreted as spatial feature detectors picking up different image features such as edges, corners etc. Below we provide code for visualization of the filters. The best results are obtained with fairly large filters of size 9 and either 16 or 36 filters. " ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "id": "giiAmxpYjSv-" - }, + "metadata": {}, "outputs": [], "source": [ - "steps = (np.arange(len(train_accuracies), dtype=int) + 1) * validation_every_steps\n", - "\n", - "plt.figure()\n", - "plt.plot(steps, train_accuracies, label='train')\n", - "plt.plot(steps, valid_accuracies, label='validation')\n", - "plt.xlabel('Training steps')\n", - "plt.ylabel('Accuracy')\n", - "plt.legend()\n", - "plt.title(\"Train and validation accuracy\")\n", - "plt.show()\n", - "\n", - "# Evaluate test set\n", - "with torch.no_grad():\n", - " model.eval()\n", - " test_accuracies = []\n", - " for inputs, targets in test_loader:\n", - " output = model(inputs)\n", - " loss = loss_fn(output, targets)\n", - "\n", - " predictions = output.max(1)[1]\n", - "\n", - " # Multiply by len(x) because the final batch of DataLoader may be smaller (drop_last=True).\n", - " test_accuracies.append(accuracy_score(targets, predictions) * len(inputs))\n", - "\n", - " test_accuracy = np.sum(test_accuracies) / len(x_test)\n", - " print(f\"Validation accuracy: {valid_accuracies[-1]:.3f}\")\n", - " print(f\"Test accuracy: {test_accuracy:.3f}\")\n", - " \n", - " model.train()\n" + "# to start with we print the names of the weights in our network\n", + "names_and_vars = {x[0]: x[1] for x in net.named_parameters()}\n", + "print(names_and_vars.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### ERROR - If you get a key error, then you need to define l_conv1 in your model!\n", + "if not 'conv_1.weight' in names_and_vars:\n", + " print(\"You need to go back and define a convolutional layer in the network.\")\n", + "else:\n", + " np_W = names_and_vars['conv_1.weight'].data.numpy() # get the filter values from the first conv layer\n", + " print(np_W.shape, \"i.e. the shape is (channels_out, channels_in, filter_height, filter_width)\")\n", + " channels_out, channels_in, filter_size, _ = np_W.shape\n", + " n = int(channels_out**0.5)\n", + "\n", + " # reshaping the last dimension to be n by n\n", + " np_W_res = np_W.reshape(filter_size, filter_size, channels_in, n, n)\n", + " fig, ax = plt.subplots(n,n)\n", + " print(\"learned filter values\")\n", + " for i in range(n):\n", + " for j in range(n):\n", + " ax[i,j].imshow(np_W_res[:,:,0,i,j], cmap='gray',interpolation='none')\n", + " ax[i,j].xaxis.set_major_formatter(plt.NullFormatter())\n", + " ax[i,j].yaxis.set_major_formatter(plt.NullFormatter())\n", + "\n", + " idx = 1\n", + " plt.figure()\n", + " plt.imshow(x_train[idx,0],cmap='gray',interpolation='none')\n", + " plt.title('Inut Image')\n", + " plt.show()\n", + "\n", + " #visalize the filters convolved with an input image\n", + " from scipy.signal import convolve2d\n", + " np_W_res = np_W.reshape(filter_size, filter_size, channels_in, n, n)\n", + " fig, ax = plt.subplots(n,n,figsize=(9,9))\n", + " print(\"Response from input image convolved with the filters\")\n", + " for i in range(n):\n", + " for j in range(n):\n", + " ax[i,j].imshow(convolve2d(x_train[1,0],np_W_res[:,:,0,i,j],mode='same'),\n", + " cmap='gray',interpolation='none')\n", + " ax[i,j].xaxis.set_major_formatter(plt.NullFormatter())\n", + " ax[i,j].yaxis.set_major_formatter(plt.NullFormatter())" ] }, { "cell_type": "markdown", - "metadata": { - "id": "0l4hf_-ijSv-" - }, + "metadata": {}, "source": [ - "### Assignment 2\n", + "# Assignment 3\n", "\n", - "1. Note the performance of the standard feedforward neural network. Add a [2D convolution layer](https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html) before the first layer. Insert the utility module `PrintSize` to check the size of the tensor at any point in `Sequential`, and notice that the size of the image reduces after the convolution. This can cause loss of information, and can be avoided by using adequate padding in the convolutional layer.\n", - " Does adding a convolutional layer increase the generalization performance of the network (try num_filters=32 and filter_size=5 as a starting point)?\n", - " \n", - "2. Can the performance be increases even further by stacking more convolution layers?\n", + "The visualized filters will likely look most like noise due to the small amount of training data.\n", "\n", - "3. We now have a deeper network than the initial simple feedforward network. What happens if we replace all convolutional layers with linear layers? Is this deep feedforward network performing as well as the convolutional one?\n", + "1. Try to use 10000 traning examples instead and visualise the filters again\n", " \n", - "4. Max-pooling is a technique for decreasing the spatial resolution of an image while retaining the important features. Effectively this gives a local translational invariance and reduces the computation by a factor of four. In the classification algorithm which is usually desirable. You can either: \n", + "2. Dropout is a very usefull technique for preventing overfitting. Try to add a DropoutLayer after the convolution layer and hidden layer. This should increase both performance and the \"visual appeal\" of the filters\n", + " - remember to use `net.train()` and `net.eval()` properly.\n", " \n", - " - add a maxpool layer (see the PyTorch docs, and try with kernel_size=2 and stride=2) after the convolution layer, or\n", - " - add stride=2 to the arguments of the convolution layer directly.\n", - " \n", - " Verify that this decreases the spatial dimension of the image (insert a `PrintSize` module in the `Sequential`). Does this increase the performance of the network? Note that, to increase performance, you may need to stack multiple layers, increase the number of filters, or tune the learning rate.\n", - "\n", - "5. Dropout is a very useful technique for preventing overfitting. Try to add a DropoutLayer after some of the convolution layers. You may observe a higher validation accuracy but lower train accuracy. Can you explain why this might be the case?\n", - " \n", - "6. Batch normalization may help convergence in larger networks as well as generalization performance. Try to insert batch normalization layers into the network.\n" + "3. Batch normalization is a recent innovation for improving generalization performance. Try to insert batch normalization layers into the network to improve performance. \n", + " - remember to use `net.train()` and `net.eval()` properly." ] }, { "cell_type": "markdown", - "metadata": { - "id": "Nxnfgk0JjSwA" - }, + "metadata": {}, "source": [ "Again, if you didn't already, you really should [watch this video](https://www.youtube.com/watch?v=AgkfIQ4IGaM)." ] } ], "metadata": { - "colab": { - "collapsed_sections": [], - "provenance": [] - }, "kernelspec": { "display_name": "Python 3", "language": "python", @@ -536,9 +535,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.7.6" } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } \ No newline at end of file diff --git a/4_Convolutional/4.2-EXE-CNN-CIFAR-10.ipynb b/4_Convolutional/4.2-EXE-CNN-CIFAR-10.ipynb index 0714b5c..5edcd3e 100644 --- a/4_Convolutional/4.2-EXE-CNN-CIFAR-10.ipynb +++ b/4_Convolutional/4.2-EXE-CNN-CIFAR-10.ipynb @@ -26,18 +26,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { - "executionInfo": { - "elapsed": 3009, - "status": "ok", - "timestamp": 1662640064042, - "user": { - "displayName": "Ole Winther", - "userId": "12097569893493878263" - }, - "user_tz": -120 - }, "id": "v3u2GIWr81Su" }, "outputs": [], @@ -80,39 +70,59 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 108, + "height": 101, "referenced_widgets": [ - "2435970902984fb09c0a257131d742be", - "548c47f6e8fa4a37a939535ef670020c", - "9d6456ca327f4a858e92c13cd417340d", - "67bbc7b98abf41f1927a7362aff86fe7", - "b589c04a1dab4fd6a9c5cdd4c99d6cf9", - "7289f6e93f2e44819780bb1b7918876b", - "39e70cbbd65d4541a04c23c069b001ed", - "0c6f066e4b44478a89c7384f6c284ece", - "aa44e013a906441892f252576e269634", - "07174f985c5b480b84ff77f61dd88f97", - "30ae8a12c6a54ffcb37973b9ca01a07c" + "cbe8d751e35049d2b7e2bc8c628557a5", + "6eb4f8e192164faab5a398573da673c6", + "4a0444da2b404d05a56d359c625e15c0", + "898738ee34e74ddf817065add498b42d", + "e22cb288614d4a6ba89ea5db7c36f291", + "2e023cb2b62847e6ad4e5216e3e1e37c", + "12ca2ba5e3bb4c5c9708fd5fa1863efc", + "84c6e7ec5b014bf984e8153af7301e27", + "a25d968ef09747a5bb9ef2232e89d60d", + "2af97bb1b6f54474b8d850e53747b679", + "15e1742dd25e43099bd61e251d1c2531" ] }, - "executionInfo": { - "elapsed": 18641, - "status": "ok", - "timestamp": 1662640112137, - "user": { - "displayName": "Ole Winther", - "userId": "12097569893493878263" - }, - "user_tz": -120 - }, "id": "QZeTujLC81S3", - "outputId": "a75010e0-f99a-4ce5-eb6a-2df4f6150777" + "outputId": "25dd1185-11a5-4be0-fa34-2d6fef68052d" }, - "outputs": [], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n" + ] + }, + { + "output_type": "display_data", + 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\n" + }, + "metadata": {} + } + ], "source": [ "# Get random training images and show them.\n", "images, labels = iter(train_loader).next()\n", @@ -225,27 +243,42 @@ "Once you can train successfully, come back and improve the architecture." ] }, + { + "cell_type": "markdown", + "source": [ + "**Answer**\n", + "\n", + "The neural network is heavily inspired by the example given by pytorch at https://pytorch.org/tutorials/beginner/blitz/neural_networks_tutorial.html\n" + ], + "metadata": { + "id": "h3rtPbbARafq" + } + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "executionInfo": { - "elapsed": 456, - "status": "ok", - "timestamp": 1662640362237, - "user": { - "displayName": "Ole Winther", - "userId": "12097569893493878263" - }, - "user_tz": -120 - }, "id": "_EsKbw3o81TK", - "outputId": "9ea47766-bca0-488e-8166-319efc4506b5" + "outputId": "4c69b49a-3488-4900-dc91-d7ab540b8125" }, - "outputs": [], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Model(\n", + " (conv1): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1), padding=(1, 1))\n", + " (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1), padding=(1, 1))\n", + " (fc1): Linear(in_features=576, out_features=120, bias=True)\n", + " (fc2): Linear(in_features=120, out_features=84, bias=True)\n", + " (fc3): Linear(in_features=84, out_features=10, bias=True)\n", + ")\n" + ] + } + ], "source": [ "class PrintSize(nn.Module):\n", " \"\"\"Utility module to print current shape of a Tensor in Sequential, only at the first pass.\"\"\"\n", @@ -263,17 +296,105 @@ " def __init__(self, num_classes):\n", " super().__init__()\n", " self.num_classes = num_classes\n", - " # Your code here!\n", + "\n", + " self.conv1 = nn.Conv2d(in_channels=3, out_channels=6, kernel_size=5, stride=1, padding=1)\n", + " self.conv2 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5, stride=1, padding=1)\n", + " self.fc1 = nn.Linear(6*6*16, 120)\n", + " self.fc2 = nn.Linear(120, 84)\n", + " self.fc3 = nn.Linear(84, self.num_classes)\n", "\n", " def forward(self, x):\n", - " # Your code here!\n", + " x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))\n", + " x = F.max_pool2d(F.relu(self.conv2(x)), (2, 2))\n", + " x = torch.flatten(x, 1)\n", + " x = F.relu(self.fc1(x))\n", + " x = F.relu(self.fc2(x))\n", + " x = self.fc3(x)\n", + "\n", + " return x\n", + "\n", + "engine = 'cpu' # use cuda or cpu\n", + "model = Model(n_classes)\n", + "device = torch.device(engine) \n", + "model.to(device)\n", + "print(model)" + ] + }, + { + "cell_type": "code", + "source": [ + "# Tester\n", + "class PrintSize(nn.Module):\n", + " \"\"\"Utility module to print current shape of a Tensor in Sequential, only at the first pass.\"\"\"\n", + " \n", + " first = True\n", + " \n", + " def forward(self, x):\n", + " if self.first:\n", + " print(f\"Size: {x.size()}\")\n", + " self.first = False\n", " return x\n", "\n", "\n", + "class Model(nn.Module):\n", + " def __init__(self, num_classes):\n", + " super().__init__()\n", + " self.num_classes = num_classes\n", + "\n", + " self.conv1 = nn.Conv2d(in_channels=3, out_channels=128, kernel_size=5, stride=1, padding='same')\n", + " self.conv2 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=5, stride=1, padding='same')\n", + " # conv_out_dim = (32 - 5 + 2*1)/1 + 1\n", + " self.fc1 = nn.Linear(8**2*256, 120)\n", + " self.fc2 = nn.Linear(120, 84)\n", + " self.fc3 = nn.Linear(84, self.num_classes)\n", + "\n", + " # dropout\n", + " self.dropout = torch.nn.Dropout(0.25)\n", + " # batchnorm\n", + " self.bn = torch.nn.BatchNorm2d(128)\n", + "\n", + " def forward(self, x):\n", + " x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))\n", + " x = self.dropout(x)\n", + " x = self.bn(x)\n", + " x = F.max_pool2d(F.relu(self.conv2(x)), (2, 2))\n", + " x = torch.flatten(x, 1)\n", + " x = F.relu(self.fc1(x))\n", + " x = F.relu(self.fc2(x))\n", + " x = self.fc3(x)\n", + "\n", + " return x\n", + "\n", + "engine = 'cuda' # use cuda or cpu\n", "model = Model(n_classes)\n", - "device = torch.device('cpu') # use cuda or cpu\n", + "device = torch.device(engine) \n", "model.to(device)\n", "print(model)" + ], + "metadata": { + "id": "2JUe_5H8BD-T", + "outputId": "5d6baa5c-7709-40a1-e739-16a2d455ee34", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 28, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Model(\n", + " (conv1): Conv2d(3, 128, kernel_size=(5, 5), stride=(1, 1), padding=same)\n", + " (conv2): Conv2d(128, 256, kernel_size=(5, 5), stride=(1, 1), padding=same)\n", + " (fc1): Linear(in_features=16384, out_features=120, bias=True)\n", + " (fc2): Linear(in_features=120, out_features=84, bias=True)\n", + " (fc3): Linear(in_features=84, out_features=10, bias=True)\n", + " (dropout): Dropout(p=0.25, inplace=False)\n", + " (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + ")\n" + ] + } ] }, { @@ -290,24 +411,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": { - "executionInfo": { - "elapsed": 447, - "status": "ok", - "timestamp": 1662640370072, - "user": { - "displayName": "Ole Winther", - "userId": "12097569893493878263" - }, - "user_tz": -120 - }, "id": "48AX85QP81TR" }, "outputs": [], "source": [ - "loss_fn = None # Your code here!\n", - "optimizer = None # Your code here!\n" + "loss_fn = nn.CrossEntropyLoss()\n", + "optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9, weight_decay=1e-6)\n", + "# optimizer = optim.Adam(model.parameters(), lr=1e-4, weight_decay=1e-6)" ] }, { @@ -326,11 +438,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": { - "id": "iWT0ULctWvm1" + "id": "iWT0ULctWvm1", + "outputId": "f25574a6-af76-4408-be04-119170b5c05d", + "colab": { + "base_uri": "https://localhost:8080/" + } }, - "outputs": [], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Output shape: torch.Size([2, 10])\n", + "Output logits:\n", + "[[ 0.19921987 0.00566714 0.04510935 0.05991147 0.08334892 -0.0131462\n", + " 0.07602413 0.00425645 0.132242 -0.05279249]\n", + " [ 0.19293371 0.01215491 0.0834594 0.12796438 0.02858325 0.01600285\n", + " 0.03425011 0.02679006 0.10685842 -0.01074213]]\n", + "Output probabilities:\n", + "[[0.11534254 0.09504529 0.098869 0.10034336 0.10272293 0.09327389\n", + " 0.10197325 0.09491131 0.10787018 0.08964828]\n", + " [0.11379482 0.09497543 0.10199488 0.1066367 0.0965486 0.09534159\n", + " 0.09709729 0.09637563 0.10440961 0.09282547]]\n" + ] + } + ], "source": [ "# Test the forward pass with dummy data\n", "out = model(torch.randn(2, 3, 32, 32, device=device))\n", @@ -341,14 +475,518 @@ }, { "cell_type": "code", + "execution_count": 31, + "metadata": { + "id": "NkUanRRb81TW", + "outputId": "2d64ee89-84ce-40d4-83e5-6586524bf46f", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch number 1\n", + "Step 500 training accuracy: 0.4493125\n", + " test accuracy: 0.5583\n", + "Epoch number 2\n", + "Step 1000 training accuracy: 0.6277952981651376\n", + " test accuracy: 0.6615\n", + "Step 1500 training accuracy: 0.668\n", + " test accuracy: 0.6924\n", + "Epoch number 3\n", + "Step 2000 training accuracy: 0.7329415137614679\n", + " test accuracy: 0.7107\n", + "Epoch number 4\n", + "Step 2500 training accuracy: 0.7808441558441559\n", + " test accuracy: 0.7303\n", + "Step 3000 training accuracy: 0.78121875\n", + " test accuracy: 0.7471\n", + "Epoch number 5\n", + "Step 3500 training accuracy: 0.8290070564516129\n", + " test accuracy: 0.7429\n", + "Epoch number 6\n", + "Step 4000 training accuracy: 0.878125\n", + " test accuracy: 0.755\n", + "Step 4500 training accuracy: 0.86165625\n", + " test accuracy: 0.7537\n", + "Epoch number 7\n", + "Step 5000 training accuracy: 0.8951907467532467\n", + " test accuracy: 0.758\n", + "Epoch number 8\n", + "Step 5500 training accuracy: 0.9284855769230769\n", + " test accuracy: 0.7565\n", + "Step 6000 training accuracy: 0.9140625\n", + " test accuracy: 0.7451\n", + "Epoch number 9\n", + "Step 6500 training accuracy: 0.9382684426229508\n", + " test accuracy: 0.7528\n", + "Step 7000 training accuracy: 0.9275\n", + " test accuracy: 0.7464\n", + "Epoch number 10\n", + "Step 7500 training accuracy: 0.9475784632034632\n", + " test accuracy: 0.7615\n", + "Epoch number 11\n", + "Step 8000 training accuracy: 0.9565104166666667\n", + " test accuracy: 0.7579\n", + "Step 8500 training accuracy: 0.9500625\n", + " test accuracy: 0.7642\n", + "Epoch number 12\n", + "Step 9000 training accuracy: 0.9643922738693468\n", + " test accuracy: 0.7571\n", + "Epoch number 13\n", + "Step 9500 training accuracy: 0.9698275862068966\n", + " test accuracy: 0.7693\n", + "Step 10000 training accuracy: 0.96328125\n", + " test accuracy: 0.7562\n", + "Epoch number 14\n", + "Step 10500 training accuracy: 0.9727264221556886\n", + " test accuracy: 0.7657\n", + "Epoch number 15\n", + "Step 11000 training accuracy: 0.9696514423076923\n", + " test accuracy: 0.7597\n", + "Step 11500 training accuracy: 0.97346875\n", + " test accuracy: 0.7509\n", + "Epoch number 16\n", + "Step 12000 training accuracy: 0.9814236111111111\n", + " test accuracy: 0.762\n", + "Step 12500 training accuracy: 0.975375\n", + " test accuracy: 0.7661\n", + "Epoch number 17\n", + "Step 13000 training accuracy: 0.9816534323770492\n", + " test accuracy: 0.7575\n", + "Epoch number 18\n", + "Step 13500 training accuracy: 0.9836165048543689\n", + " test accuracy: 0.7592\n", + "Step 14000 training accuracy: 0.97840625\n", + " test accuracy: 0.7542\n", + "Epoch number 19\n", + "Step 14500 training accuracy: 0.984153891509434\n", + " test accuracy: 0.7641\n", + "Epoch number 20\n", + "Step 15000 training accuracy: 0.9827244718309859\n", + " test accuracy: 0.7666\n", + "Step 15500 training accuracy: 0.98578125\n", + " test accuracy: 0.7614\n", + "Highest test accuracy throughout: 0.7693\n", + "Finished training.\n" + ] + } + ], + "source": [ + "num_epochs = 20\n", + "validation_every_steps = 500\n", + "\n", + "step = 0\n", + "model.train()\n", + "\n", + "train_accuracies = []\n", + "valid_accuracies = []\n", + " \n", + "for epoch in range(num_epochs):\n", + " if (epoch+1)%1 == 0:\n", + " print(f\"Epoch number {epoch+1}\")\n", + " train_accuracies_batches = []\n", + " \n", + " for inputs, targets in train_loader:\n", + " inputs, targets = inputs.to(device), targets.to(device)\n", + " \n", + " # Forward pass.\n", + " output = model(inputs)\n", + " \n", + " # Compute loss.\n", + " loss = loss_fn(output, targets)\n", + " \n", + " # Clean up gradients from the model.\n", + " optimizer.zero_grad()\n", + " \n", + " # Compute gradients based on the loss from the current batch (backpropagation).\n", + " loss.backward()\n", + " \n", + " # Take one optimizer step using the gradients computed in the previous step.\n", + " optimizer.step()\n", + " \n", + " # Increment step counter\n", + " step += 1\n", + " \n", + " # Compute accuracy.\n", + " predictions = output.max(1)[1]\n", + " train_accuracies_batches.append(accuracy(targets, predictions))\n", + " \n", + " if step % validation_every_steps == 0:\n", + " \n", + " # Append average training accuracy to list.\n", + " train_accuracies.append(np.mean(train_accuracies_batches))\n", + " \n", + " train_accuracies_batches = []\n", + " \n", + " # Compute accuracies on validation set.\n", + " valid_accuracies_batches = []\n", + " with torch.no_grad():\n", + " model.eval()\n", + " for inputs, targets in test_loader:\n", + " inputs, targets = inputs.to(device), targets.to(device)\n", + " output = model(inputs)\n", + " loss = loss_fn(output, targets)\n", + "\n", + " predictions = output.max(1)[1]\n", + "\n", + " # Multiply by len(x) because the final batch of DataLoader may be smaller (drop_last=False).\n", + " valid_accuracies_batches.append(accuracy(targets, predictions) * len(inputs))\n", + "\n", + " model.train()\n", + " \n", + " # Append average validation accuracy to list.\n", + " valid_accuracies.append(np.sum(valid_accuracies_batches) / len(test_set)) \n", + " print(f\"Step {step:<5} training accuracy: {train_accuracies[-1]}\")\n", + " print(f\" test accuracy: {valid_accuracies[-1]}\")\n", + " \n", + "print(f'Highest test accuracy throughout: {max(valid_accuracies)}')\n", + "print(\"Finished training.\")" + ] + }, + { + "cell_type": "code", + "source": [ + "import itertools\n", + "test_mode = 0\n", + "if test_mode == 1:\n", + " conv_layers = [1]\n", + " out_channels = [16]\n", + " kernel_size = [5]\n", + " padding = [0]\n", + " stride = [1]\n", + " pooling = [0]\n", + " dropout = [0]\n", + " batchnorm = [0]\n", + "else:\n", + " # Only valid amount of conv layers are 1 or 2 at the moment, as it hasn't been\n", + " # parameterised, and is simply implemented with lots of if-statements. Also, \n", + " # the second layer will take the inputs from the following parameters. First \n", + " # layer will be somewhat static.\n", + " conv_layers = [1, 2]\n", + " out_channels = [10, 16, 20]\n", + " kernel_size = [3, 5, 7]\n", + " padding = [0, 1]\n", + " stride = [1]\n", + " pooling = [0, 1]\n", + " dropout = [0, 1]\n", + " batchnorm = [0, 1]\n", + "\n", + "setup_settings = [x for x in itertools.product(conv_layers, out_channels, kernel_size, padding, stride, pooling, dropout, batchnorm)]\n", + "\n", + "total_number_of_setups = len(setup_settings)\n", + "\n", + "setups = []\n", + "setup_scores = []" + ], + "metadata": { + "id": "V0vZJ2i53KLe" + }, "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "test_mode = 0\n", + "\n", + "active_setups = setup_settings[:2] if test_mode == 1 else setup_settings\n", + "\n", + "setups = []\n", + "setup_scores = []" + ], "metadata": { - "id": "NkUanRRb81TW" + "id": "y3VSxall3-4R" }, - "outputs": [], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from math import floor\n", + "\n", + "# To run below for loop in parts, due to how long it takes to run, manually redefine active_setups under 'else' in this cell, and run the loop again (and don't run the previous cell if you want to keep history logged):\n", + "splits = 7\n", + "part = floor(total_number_of_setups / (splits))\n", + "parts = [i*part if i != len(range(1, splits+1)) else i*part + total_number_of_setups-part*splits for i in range(1, splits+1)]\n", + "\n", + "###############\n", + "split = 7\n", + "###############\n", + "\n", + "if test_mode == 1:\n", + " active_setups = setup_settings[:2]\n", + "else:\n", + " if split == 0:\n", + " active_setups = setup_settings[:parts[0]]\n", + " else:\n", + " active_setups = setup_settings[parts[0]:parts[1]]\n", + " floor(total_number_of_setups / (splits-1))\n", + " # active_setups = setup_settings[50:100]\n", + " # 5.5 parts: [:50], [50:100], [100:150] [150:200], [200:250] [250:288]\n", + "\n", + "number_of_setups = len(active_setups)" + ], + "metadata": { + "id": "UAff4vTv60ah" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# HEADS UP! Below cell takes quite a while to run! (Unless in test_mode (=1))\n", + "\n", + "for i, ii in enumerate(active_setups):\n", + " conv_layers, out_channels, kernel_size, padding, stride, pooling, dropout, batchnorm = ii\n", + "# for i, (out_channels, kernel_size, padding, stride, pooling, dropout, batchnorm) in enumerate(itertools.product(conv_layers, out_channels, kernel_size, padding, stride, pooling, dropout, batchnorm)):\n", + " class Model(nn.Module):\n", + " def __init__(self, num_classes):\n", + " super().__init__()\n", + " self.num_classes = num_classes\n", + "\n", + " if conv_layers == 1:\n", + " self.conv1 = nn.Conv2d(in_channels=3, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding)\n", + " conv_out_dim = (32 - kernel_size + 2*padding)/stride + 1\n", + " if pooling == 1:\n", + " conv_out_dim = conv_out_dim/2\n", + " conv_to_linear = (conv_out_dim**2)*out_channels\n", + " else:\n", + " conv_to_linear = (conv_out_dim**2)*out_channels\n", + " elif conv_layers == 2:\n", + " self.conv1 = nn.Conv2d(in_channels=3, out_channels=6, kernel_size=kernel_size, stride=stride, padding=padding)\n", + " self.conv2 = nn.Conv2d(in_channels=6, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding)\n", + " conv_out_dim = (32 - kernel_size + 2*padding)/stride + 1\n", + " if pooling == 1:\n", + " conv_out_dim = conv_out_dim/2\n", + " conv_out_dim = (conv_out_dim - kernel_size + 2*padding)/stride + 1\n", + " conv_out_dim = conv_out_dim/2\n", + " conv_to_linear = (conv_out_dim**2)*out_channels\n", + " else:\n", + " conv_out_dim = (conv_out_dim - kernel_size + 2*padding)/stride + 1\n", + " conv_to_linear = (conv_out_dim**2)*out_channels\n", + "\n", + " conv_to_linear = int(conv_to_linear)\n", + " self.fc1 = nn.Linear(conv_to_linear, 120)\n", + " self.fc2 = nn.Linear(120, 84)\n", + " self.fc3 = nn.Linear(84, self.num_classes)\n", + "\n", + " # dropout\n", + " self.dropout = torch.nn.Dropout(0.2)\n", + " # batchnorm\n", + " self.bn = torch.nn.BatchNorm1d(self.fc1.out_features)\n", + "\n", + " def forward(self, x):\n", + " if conv_layers == 1:\n", + " if pooling == 1:\n", + " x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))\n", + " else:\n", + " x = F.relu(self.conv1(x))\n", + " elif conv_layers == 2:\n", + " if pooling == 1:\n", + " x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))\n", + " x = F.max_pool2d(F.relu(self.conv2(x)), (2, 2))\n", + " else:\n", + " x = F.relu(self.conv1(x))\n", + " x = F.relu(self.conv2(x))\n", + "\n", + " x = torch.flatten(x, 1)\n", + " x = F.relu(self.fc1(x))\n", + " if dropout == 1:\n", + " x = self.dropout(x)\n", + " if batchnorm == 1:\n", + " x = self.bn(x)\n", + " x = F.relu(self.fc2(x))\n", + " x = self.fc3(x)\n", + " return x\n", + "\n", + "\n", + " model = Model(n_classes)\n", + " device = torch.device(engine) # use cuda or cpu\n", + " model.to(device)\n", + "\n", + " loss_fn = nn.CrossEntropyLoss()\n", + " optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9, weight_decay=1e-6)\n", + " # optimizer = optim.Adam(model.parameters(), lr=1e-4, weight_decay=1e-6)\n", + "\n", + " # batch_size = 128\n", + " num_epochs = 15\n", + " validation_every_steps = 500\n", + "\n", + " step = 0\n", + " model.train()\n", + "\n", + " train_accuracies = []\n", + " valid_accuracies = []\n", + " epochs = []\n", + " \n", + " for epoch in range(num_epochs):\n", + " \n", + " train_accuracies_batches = []\n", + " \n", + " for inputs, targets in train_loader:\n", + " inputs, targets = inputs.to(device), targets.to(device)\n", + " \n", + " # Forward pass.\n", + " output = model(inputs)\n", + " \n", + " # Compute loss.\n", + " loss = loss_fn(output, targets)\n", + " \n", + " # Clean up gradients from the model.\n", + " optimizer.zero_grad()\n", + " \n", + " # Compute gradients based on the loss from the current batch (backpropagation).\n", + " loss.backward()\n", + " \n", + " # Take one optimizer step using the gradients computed in the previous step.\n", + " optimizer.step()\n", + " \n", + " # Increment step counter\n", + " step += 1\n", + " \n", + " # Compute accuracy.\n", + " predictions = output.max(1)[1]\n", + " train_accuracies_batches.append(accuracy(targets, predictions))\n", + " \n", + " if step % validation_every_steps == 0:\n", + " \n", + " # Append average training accuracy to list.\n", + " train_accuracies.append(np.mean(train_accuracies_batches))\n", + " \n", + " train_accuracies_batches = []\n", + " \n", + " # Compute accuracies on validation set.\n", + " valid_accuracies_batches = []\n", + " with torch.no_grad():\n", + " model.eval()\n", + " for inputs, targets in test_loader:\n", + " inputs, targets = inputs.to(device), targets.to(device)\n", + " output = model(inputs)\n", + " loss = loss_fn(output, targets)\n", + "\n", + " predictions = output.max(1)[1]\n", + "\n", + " # Multiply by len(x) because the final batch of DataLoader may be smaller (drop_last=False).\n", + " valid_accuracies_batches.append(accuracy(targets, predictions) * len(inputs))\n", + " \n", + " model.train()\n", + " \n", + " # Append average validation accuracy to list.\n", + " valid_accuracies.append(np.sum(valid_accuracies_batches) / len(test_set))\n", + " epochs.append(epoch)\n", + "\n", + " # print(f\"Step {step:<5} training accuracy: {train_accuracies[-1]}\")\n", + " # print(f\" test accuracy: {valid_accuracies[-1]}\")\n", + " # if (epoch+1)%1 == 0:\n", + " # print(f\"Epoch number {epoch+1}\") \n", + " # print(f'steps: {step}')\n", + "\n", + " setups.append((i,) + ii + (epochs[valid_accuracies.index(max(valid_accuracies))]+1, max(valid_accuracies)))\n", + " \n", + " # print(f'---------- Setup {i+1} ----------')\n", + " # print(f'Conv-layer out_channels: {out_channels}')\n", + " # print(f'Conv-layer kernel_size: {kernel_size}')\n", + " # print(f'Conv-layer padding: {padding}')\n", + " # print(f'Conv-layer stride: {stride}')\n", + " # print(f'Conv-layer pooling: {pooling}')\n", + " # print(f'Dropout: {dropout}')\n", + " # print(f'Batchnorm: {batchnorm}')\n", + " # print(f'Highest test accuracy: {max(valid_accuracies)}')\n", + " if (i%5 == 0) and (i != 0):\n", + " print(f'Setups {i} of {number_of_setups}, completed.')\n", + "setup_scores = [x[-1] for x in setups]\n", + "high_score_index = setup_scores.index(max(setup_scores))\n", + "\n", + "print(f'Highest test accuracy throughout: {setups[high_score_index][-1]}')\n", + "print(\"Finished.\")" + ], + "metadata": { + "id": "_QZ8tgboZY3y", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "bfcd2599-8284-4416-abce-b3915061241f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Setups 5 of 41, completed.\n", + "Setups 10 of 41, completed.\n", + "Setups 15 of 41, completed.\n", + "Setups 20 of 41, completed.\n", + "Setups 25 of 41, completed.\n", + "Setups 30 of 41, completed.\n" + ] + } + ] + }, + { + "cell_type": "code", "source": [ - "batch_size = 64\n", - "num_epochs = 2\n", + "# Re-training of highest scoring setup from before. A risk is that the results might differ, if a different minima is hit in the optimizer.\n", + "\n", + "# _, conv_layers, out_channels, kernel_size, padding, stride, pooling, dropout, batchnorm, epochs_final, _ = setups[high_score_index]\n", + "\n", + "conv_layers, out_channels, kernel_size, padding, stride, pooling, dropout, batchnorm, epochs_final = (2, 16, 7, 1, 1, 1, 1, 1, 13)\n", + "\n", + "class Model(nn.Module):\n", + " def __init__(self, num_classes):\n", + " super().__init__()\n", + " self.num_classes = num_classes\n", + "\n", + " self.conv1 = nn.Conv2d(in_channels=3, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding)\n", + " conv_out_dim = (32 - kernel_size + 2*padding)/stride + 1\n", + " if pooling == 1:\n", + " conv_out_dim = conv_out_dim/2\n", + " conv_to_linear = (conv_out_dim**2)*out_channels\n", + " else:\n", + " conv_to_linear = (conv_out_dim**2)*out_channels\n", + " conv_to_linear = int(conv_to_linear)\n", + " self.fc1 = nn.Linear(conv_to_linear, 120)\n", + " self.fc2 = nn.Linear(120, 84)\n", + " self.fc3 = nn.Linear(84, self.num_classes)\n", + "\n", + " # dropout\n", + " self.dropout = torch.nn.Dropout(0.2)\n", + " # batchnorm\n", + " self.bn = torch.nn.BatchNorm1d(self.fc1.out_features)\n", + "\n", + " def forward(self, x):\n", + " if pooling == 1:\n", + " x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))\n", + " else:\n", + " x = F.relu(self.conv1(x))\n", + "\n", + " x = torch.flatten(x, 1)\n", + " x = F.relu(self.fc1(x))\n", + " if dropout == 1:\n", + " x = self.dropout(x)\n", + " if batchnorm == 1:\n", + " x = self.bn(x)\n", + " x = F.relu(self.fc2(x))\n", + " x = self.fc3(x)\n", + " return x\n", + "\n", + "\n", + "model = Model(n_classes)\n", + "device = torch.device(engine) # use cuda or cpu\n", + "model.to(device)\n", + "\n", + "loss_fn = nn.CrossEntropyLoss()\n", + "optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9, weight_decay=1e-6)\n", + "# optimizer = optim.Adam(model.parameters(), lr=1e-4, weight_decay=1e-6)\n", + "\n", + "# batch_size = 128\n", + "num_epochs = epochs_final\n", "validation_every_steps = 500\n", "\n", "step = 0\n", @@ -364,8 +1002,20 @@ " for inputs, targets in train_loader:\n", " inputs, targets = inputs.to(device), targets.to(device)\n", " \n", - " # Forward pass, compute gradients, perform one training step.\n", - " # Your code here!\n", + " # Forward pass.\n", + " output = model(inputs)\n", + " \n", + " # Compute loss.\n", + " loss = loss_fn(output, targets)\n", + " \n", + " # Clean up gradients from the model.\n", + " optimizer.zero_grad()\n", + " \n", + " # Compute gradients based on the loss from the current batch (backpropagation).\n", + " loss.backward()\n", + " \n", + " # Take one optimizer step using the gradients computed in the previous step.\n", + " optimizer.step()\n", " \n", " # Increment step counter\n", " step += 1\n", @@ -398,12 +1048,97 @@ " model.train()\n", " \n", " # Append average validation accuracy to list.\n", - " valid_accuracies.append(np.sum(valid_accuracies_batches) / len(test_set))\n", - " \n", + " valid_accuracies.append(np.sum(valid_accuracies_batches) / len(test_set)) \n", " print(f\"Step {step:<5} training accuracy: {train_accuracies[-1]}\")\n", - " print(f\" test accuracy: {valid_accuracies[-1]}\")\n", - "\n", - "print(\"Finished training.\")" + " print(f\" test accuracy: {valid_accuracies[-1]}\")\n", + " if (epoch+1)%1 == 0:\n", + " print(f\"Epoch number {epoch+1}\") \n", + " print(f'steps: {step}')\n", + " \n", + "print(f'Highest test accuracy throughout: {max(valid_accuracies)}')" + ], + "metadata": { + "id": "vN3rNKqyqE0q", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "9694d64a-010e-4124-e875-77e0c69e399c" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Step 500 training accuracy: 0.42134375\n", + " test accuracy: 0.5188\n", + "Epoch number 1\n", + "steps: 782\n", + "Step 1000 training accuracy: 0.5227924311926605\n", + " test accuracy: 0.5493\n", + "Step 1500 training accuracy: 0.56303125\n", + " test accuracy: 0.5989\n", + "Epoch number 2\n", + "steps: 1564\n", + "Step 2000 training accuracy: 0.6012399655963303\n", + " test accuracy: 0.6194\n", + "Epoch number 3\n", + "steps: 2346\n", + "Step 2500 training accuracy: 0.6366680194805194\n", + " test accuracy: 0.6293\n", + "Step 3000 training accuracy: 0.63309375\n", + " test accuracy: 0.6446\n", + "Epoch number 4\n", + "steps: 3128\n", + "Step 3500 training accuracy: 0.6619623655913979\n", + " test accuracy: 0.6485\n", + "Epoch number 5\n", + "steps: 3910\n", + "Step 4000 training accuracy: 0.6710069444444444\n", + " test accuracy: 0.6444\n", + "Step 4500 training accuracy: 0.672375\n", + " test accuracy: 0.6688\n", + "Epoch number 6\n", + "steps: 4692\n", + "Step 5000 training accuracy: 0.6920657467532467\n", + " test accuracy: 0.6721\n", + "Epoch number 7\n", + "steps: 5474\n", + "Step 5500 training accuracy: 0.6820913461538461\n", + " test accuracy: 0.6626\n", + "Step 6000 training accuracy: 0.70265625\n", + " test accuracy: 0.6697\n", + "Epoch number 8\n", + "steps: 6256\n", + "Step 6500 training accuracy: 0.720030737704918\n", + " test accuracy: 0.6707\n", + "Step 7000 training accuracy: 0.7085625\n", + " test accuracy: 0.6614\n", + "Epoch number 9\n", + "steps: 7038\n", + "Step 7500 training accuracy: 0.7277123917748918\n", + " test accuracy: 0.675\n", + "Epoch number 10\n", + "steps: 7820\n", + "Step 8000 training accuracy: 0.7433159722222222\n", + " test accuracy: 0.6793\n", + "Step 8500 training accuracy: 0.73128125\n", + " test accuracy: 0.6732\n", + "Epoch number 11\n", + "steps: 8602\n", + "Step 9000 training accuracy: 0.7469378140703518\n", + " test accuracy: 0.6745\n", + "Epoch number 12\n", + "steps: 9384\n", + "Step 9500 training accuracy: 0.7583512931034483\n", + " test accuracy: 0.6655\n", + "Step 10000 training accuracy: 0.74303125\n", + " test accuracy: 0.6734\n", + "Epoch number 13\n", + "steps: 10166\n", + "Highest test accuracy throughout: 0.6793\n" + ] + } ] }, { @@ -421,9 +1156,97 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "7LT0RoAC81Tc" + "id": "7LT0RoAC81Tc", + "outputId": "43c1629f-c5b2-4975-e51c-e293214ac035", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + } }, - "outputs": [], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + " TRUE PREDICTED\n", + "-----------------------------\n", + " cat cat \n", + " ship ship \n", + " ship ship \n", + " airplane airplane \n", + " frog frog \n", + " frog frog \n", + " automobile automobile \n", + " frog frog \n", + " cat cat \n", + " automobile automobile \n", + " airplane airplane \n", + " truck truck \n", + " dog cat \n", + " horse horse \n", + " truck truck \n", + " ship deer \n", + " dog cat \n", + " horse frog \n", + " ship ship \n", + " frog frog \n", + " horse horse \n", + " airplane bird \n", + " deer airplane \n", + " truck truck \n", + " dog deer \n", + " bird bird \n", + " deer deer \n", + " airplane airplane \n", + " truck truck \n", + " frog frog \n", + " frog cat \n", + " dog bird \n", + " deer bird \n", + " dog bird \n", + " truck truck \n", + " bird automobile \n", + " deer deer \n", + " automobile airplane \n", + " truck truck \n", + " dog dog \n", + " deer airplane \n", + " frog frog \n", + " dog cat \n", + " frog frog \n", + " airplane deer \n", + " truck truck \n", + " cat dog \n", + " truck truck \n", + " horse horse \n", + " frog bird \n", + " truck automobile \n", + " ship ship \n", + " airplane frog \n", + " cat cat \n", + " ship ship \n", + " ship ship \n", + " horse horse \n", + " horse ship \n", + " deer dog \n", + " frog cat \n", + " horse horse \n", + " cat cat \n", + " frog frog \n", + " cat truck \n" + ] + } + ], "source": [ "inputs, targets = iter(test_loader).next()\n", "inputs, targets = inputs.to(device), targets.to(device)\n", @@ -491,9 +1314,21 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "a5-c_CDAI7Ab" + "id": "a5-c_CDAI7Ab", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "44a4efd4-d409-46fb-a2b1-4af2c855a0ef" }, - "outputs": [], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Test accuracy: 0.675\n" + ] + } + ], "source": [ "print(f\"Test accuracy: {test_accuracy:.3f}\")" ] @@ -515,9 +1350,25 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "ShcEZDExI7Ab" + "id": "ShcEZDExI7Ab", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 441 + }, + "outputId": "7ac4c022-17a6-4803-f32b-815154cb0233" }, - "outputs": [], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], "source": [ "def normalize(matrix, axis):\n", " axis = {'true': 1, 'pred': 0}[axis]\n", @@ -558,9 +1409,25 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "xv75wMhVI7Ac" + "id": "xv75wMhVI7Ac", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 327 + }, + "outputId": "d9b9aa13-de2d-4e02-9495-9af932f66e5e" }, - "outputs": [], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], "source": [ "with sns.axes_style('whitegrid'):\n", " plt.figure(figsize=(8, 4))\n", @@ -584,11 +1451,46 @@ "Did anything surprise you during the exercise?\n", "What were the changes that seemed to improve performance the most?\n", "\n", - "3. Write down key lessons/insights you got during this exercise.\n", - "\n", - "**Answer:**" + "3. Write down key lessons/insights you got during this exercise." ] }, + { + "cell_type": "markdown", + "source": [ + "**Answer**\n", + "\n", + "First, a more qualitative approach to improving the NN was done. Added a padding of one or both of the two convolutional layers. This seemed to improve the results a bit.\n", + "\n", + "The removing of one or both pooling from the convolutional layers was also attempted. Removing it off one of them seemed to do near to nothing for the result. Removing both, however, worsened the results, or at least made it converge slower. The theory here, was that you lose information when pooling, so removing it might improve reults at the cost of time training. It of course also increases the amount of outputs going into the linear layers (whose number of nodes were kept constant, more on why later), which might explain the worse results.\n", + "\n", + "The an ADAM optimizer was also attempted, but the SGD seemed to perform better.\n", + "\n", + "Changed batchsize, from 64 to 128, where with 128 seemed to improve results.\n", + "\n", + "Removing one of the convolutional layers, at first, didn't seem to affect the results, so only one convolutional layer could be the way to go. The setup before does seem do quite well though (or at least assumed), getting a test accuracy around 0.65 consistently.\n", + "\n", + "Changing one thing, might change the effectiveness of any other setting, so, additionally, there was a more quantitative attempt. This was done with a sort of grid-search through the parameters chosen. However, this does of course increase computational time quite a bit, having to train so many networks. The for loops, with all of the different parts, is (almost) more than 300 different setups. Admittedly, with the previous different configurations, only 1/4 of them were tested, because of the limit on use of GPU power by google colab.\n", + "\n", + "The parameters in the grid-search were the convolutional layers, 1 or 2 of them, the an amount of out channels of 10, 16 or 20, for the second convolutional layer, or the only convolutional layer when having just one of therese. The kernel size of 3x3, 5x5, or 7x7, with or without padding, with or without pooling of the singular or both convolutional layers, with or without dropout and/or batchnorm.\n", + "\n", + "Of course more parameters could be included, but the amount of networks quickly increases. Therefore the loss (cross entropy) and activation function (ReLU) was kept the same, and the linear layer part of the network was chosen to be constant, which of course is a limitation, especially when it comes having more convolutional layers and pooling, as mentioned before.\n", + "\n", + "Final average test accuracy high score was around 0.68.\n", + "\n", + "TL;DR:\n", + "\n", + "Final average test accuracy high score was around 0.68.\n", + "\n", + "What seemed to improve the setup the most was batchsize when increased from 64 to 128, adding 1 layer of zero padding, 2x2 pooling, and, to a degree, another convolutional layer (2 total).\n", + "\n", + "One learning is simply the time it takes to train just one setup of these kinds of NN. Add to that extensive testing, and it racks up quite a lot of time \"quickly\". It clearly attests to why these algorithms only saw use somewhat recently, because of the mentioned developments in computing power.\n", + "Also, with only an accuracy below 70%, there is still quite a way left to a high precision model, say above 99%.\n", + "Just the amount of complexity in general, with only such few (if few is the correct word is up for debate) parameters.\n" + ], + "metadata": { + "id": "Flq0gvE3OMQq" + } + }, { "cell_type": "markdown", "metadata": { @@ -611,9 +1513,21 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "I09rtehbaCRH" + "id": "I09rtehbaCRH", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "226f6862-056a-41b7-87c5-7050849bf74d" }, - "outputs": [], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Available CUDA devices: []\n" + ] + } + ], "source": [ "# Check if we have GPUs available\n", "print(\"Available CUDA devices:\", [torch.cuda.get_device_name(i) for i in range(torch.cuda.device_count())])" @@ -638,20 +1552,20 @@ "\n", "**Assignment 5:** Pick an exercise of your own choice from [Michael Nielsen's book](http://neuralnetworksanddeeplearning.com/).\n", "\n", + "Problem:\n", + "\n", + "The idea of convolutional layers is to behave in an invariant way across images. It may seem surprising, then, that our network can learn more when all we've done is translate the input data. Can you explain why this is actually quite reasonable?\n", + "\n", "**Answer:**\n", "\n", + "As an introduction to the problem, a NN, with convolutional layers (so a CNN), is used and trained on the MNIST data. A trick, in the end, is translating the 50.000 images one pixel in each of the four directions, creating 200.000 new images for training, resulting in 250.000 in total.\n", "\n", - "\n" + "The convolutional layers, as mentioned, behave in an invariant way across images. So the improvement comes either from pooling (if this was used), as the precise spatial information is blurred, so to speak, because of the reduction. So a translation can therefore improve these features, as the translated images will result in new pooling features, describing the same things, but moved, resulting in more information.\n", + "\n", + "Another reason could be the fully connected linear layers at the end of the CNN network. As these layers still see spatial position, and more training should hopefully improve this.\n", + "\n", + "A third thing could simply be the fact that the features initially extracted from the 50.000 pictures get exaggerated. For the fully connected layers, this point is simply a repetition of the second point.\n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "qh7KTAdWD_zx" - }, - "outputs": [], - "source": [] } ], "metadata": { @@ -679,10 +1593,98 @@ }, "widgets": { "application/vnd.jupyter.widget-state+json": { - "07174f985c5b480b84ff77f61dd88f97": { + "cbe8d751e35049d2b7e2bc8c628557a5": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_6eb4f8e192164faab5a398573da673c6", + "IPY_MODEL_4a0444da2b404d05a56d359c625e15c0", + "IPY_MODEL_898738ee34e74ddf817065add498b42d" + ], + "layout": "IPY_MODEL_e22cb288614d4a6ba89ea5db7c36f291" + } + }, + "6eb4f8e192164faab5a398573da673c6": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_2e023cb2b62847e6ad4e5216e3e1e37c", + "placeholder": "​", + "style": "IPY_MODEL_12ca2ba5e3bb4c5c9708fd5fa1863efc", + "value": "100%" + } + }, + "4a0444da2b404d05a56d359c625e15c0": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_84c6e7ec5b014bf984e8153af7301e27", + "max": 170498071, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_a25d968ef09747a5bb9ef2232e89d60d", + "value": 170498071 + } + }, + "898738ee34e74ddf817065add498b42d": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_2af97bb1b6f54474b8d850e53747b679", + "placeholder": "​", + "style": "IPY_MODEL_15e1742dd25e43099bd61e251d1c2531", + "value": " 170498071/170498071 [00:03<00:00, 46212988.50it/s]" + } + }, + "e22cb288614d4a6ba89ea5db7c36f291": { "model_module": "@jupyter-widgets/base", - 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"nbformat_minor": 1 -} + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/4_Convolutional/4.3-EXE-CIFAR-10.ipynb b/4_Convolutional/4.3-EXE-CIFAR-10.ipynb index b57aacc..756ebe0 100644 --- a/4_Convolutional/4.3-EXE-CIFAR-10.ipynb +++ b/4_Convolutional/4.3-EXE-CIFAR-10.ipynb @@ -629,4 +629,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file diff --git a/4_Convolutional/441063280140470.ipynb b/4_Convolutional/441063280140470.ipynb new file mode 100644 index 0000000..2b59292 --- /dev/null +++ b/4_Convolutional/441063280140470.ipynb @@ -0,0 +1,1771 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "oZW0gaQO81Sq" + }, + "source": [ + "# CNN on CIFAR-10\n", + "\n", + "In this notebook you need to put what you have learned into practice, and create your own convolutional classifier for the CIFAR-10 dataset.\n", + "\n", + "The images in CIFAR-10 are RGB images (3 channels) with size 32x32 (so they have size 3x32x32). There are 10 different classes. See examples below.\n", + "\n", + "![cifar10](https://github.com/DeepLearningDTU/02456-deep-learning-with-PyTorch/blob/master/static_files/cifar10.png?raw=1)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "htyg7xxN81St" + }, + "source": [ + "## Preliminaries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "v3u2GIWr81Su" + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import torch\n", + "from torch import nn\n", + "import torch.nn.functional as F\n", + "import torch.optim as optim\n", + "from torch.utils.data import TensorDataset, DataLoader\n", + "import torchvision\n", + "import torchvision.transforms as transforms\n", + "from torchvision.utils import make_grid\n", + "from sklearn import metrics\n", + "\n", + "sns.set_style(\"whitegrid\")\n", + "\n", + "def accuracy(target, pred):\n", + " return metrics.accuracy_score(target.detach().cpu().numpy(), pred.detach().cpu().numpy())\n", + "\n", + "def compute_confusion_matrix(target, pred, normalize=None):\n", + " return metrics.confusion_matrix(\n", + " target.detach().cpu().numpy(), \n", + " pred.detach().cpu().numpy(),\n", + " normalize=normalize\n", + " )\n", + "\n", + "def show_image(img):\n", + " img = img.detach().cpu()\n", + " img = img / 2 + 0.5 # unnormalize\n", + " with sns.axes_style(\"white\"):\n", + " plt.figure(figsize=(8, 8))\n", + " plt.imshow(img.permute((1, 2, 0)).numpy())\n", + " plt.axis('off')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 101, + "referenced_widgets": [ + "9c5d8c0f56314bd7924bfc192b295ca2", + "6b5cb30c8984478399bac2441dd19a78", + "7bb0c35e7b76499c9261b271e01c4ffe", + "1563d8b336a143b182a05b36694e2b9e", + "6a3d9577a4bb4226832c9c269992adbc", + "11d9acef5c554770a0b60a05b0ec00ee", + "3a0f3160f499483c9a84b7c6619ed95e", + "69c4e61ab110442181629b5bdaec1aaa", + "3fe5eff08e5740479fcca18bc53fc591", + "85b70b8fe01e4a36a7b847b2f0f7c514", + "05c3194c14ac4775a3fbf04c8ce235e4" + ] + }, + "id": "QZeTujLC81S3", + "outputId": "14cd98ee-53cb-44e8-d65c-88479b01d6e8" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9c5d8c0f56314bd7924bfc192b295ca2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/170498071 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get random training images and show them.\n", + "images, labels = iter(train_loader).next()\n", + "show_image(torchvision.utils.make_grid(images))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Wt3BVFMF81TI" + }, + "source": [ + "## Define a convolutional neural network\n", + "\n", + "\n", + "**Assignment 1:** Define a convolutional neural network. \n", + "You may use the code from previous notebooks.\n", + "We suggest that you start with a small network, and make sure that everything is working.\n", + "Once you can train successfully, come back and improve the architecture." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_EsKbw3o81TK", + "outputId": "c606a90a-349a-42cb-b9c5-7238ee69cf2b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model(\n", + " (conv_part): Sequential(\n", + " (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=same)\n", + " (1): LeakyReLU(negative_slope=0.01)\n", + " (2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=same)\n", + " (4): LeakyReLU(negative_slope=0.01)\n", + " (5): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (7): Dropout2d(p=0.2, inplace=False)\n", + " (8): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=same)\n", + " (9): LeakyReLU(negative_slope=0.01)\n", + " (10): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (11): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=same)\n", + " (12): LeakyReLU(negative_slope=0.01)\n", + " (13): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (14): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (15): Dropout2d(p=0.3, inplace=False)\n", + " (16): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=same)\n", + " (17): LeakyReLU(negative_slope=0.01)\n", + " (18): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (19): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=same)\n", + " (20): LeakyReLU(negative_slope=0.01)\n", + " (21): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (22): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (23): Dropout2d(p=0.4, inplace=False)\n", + " (24): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=same)\n", + " (25): LeakyReLU(negative_slope=0.01)\n", + " (26): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (27): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=same)\n", + " (28): LeakyReLU(negative_slope=0.01)\n", + " (29): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (31): Dropout2d(p=0.4, inplace=False)\n", + " (32): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=same)\n", + " (33): LeakyReLU(negative_slope=0.01)\n", + " (34): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (35): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=same)\n", + " (36): LeakyReLU(negative_slope=0.01)\n", + " (37): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (38): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (39): Dropout2d(p=0.4, inplace=False)\n", + " (40): Flatten(start_dim=1, end_dim=-1)\n", + " )\n", + " (dense_part): Sequential(\n", + " (0): Linear(in_features=512, out_features=10, bias=True)\n", + " )\n", + ")\n" + ] + } + ], + "source": [ + "from torch.nn.modules.flatten import Flatten\n", + "from torch.nn.modules.dropout import Dropout2d\n", + "from torch.nn.modules.batchnorm import BatchNorm2d\n", + "from torch.nn.modules.container import Sequential\n", + "from torch.nn.modules.pooling import MaxPool2d\n", + "from torch.nn.modules.conv import Conv2d\n", + "class PrintSize(nn.Module):\n", + " \"\"\"Utility module to print current shape of a Tensor in Sequential, only at the first pass.\"\"\"\n", + " \n", + " first = True\n", + " \n", + " def forward(self, x):\n", + " if self.first:\n", + " print(f\"Size: {x.size()}\")\n", + " self.first = False\n", + " return x\n", + "\n", + "\n", + "class Model(nn.Module):\n", + " def __init__(self, num_classes):\n", + " super().__init__()\n", + " self.num_classes = num_classes\n", + " \n", + " # activation_fn = nn.ReLU\n", + "\n", + " activation_fn = nn.LeakyReLU\n", + "\n", + " \n", + "\n", + " self.conv_part = nn.Sequential(\n", + " # CONV1\n", + " nn.Conv2d(3,32,3,padding='same'),\n", + " activation_fn(),\n", + " nn.BatchNorm2d(32),\n", + "\n", + " # CONV2\n", + " nn.Conv2d(32,32,3,padding='same'),\n", + " activation_fn(),\n", + " nn.BatchNorm2d(32),\n", + "\n", + " # Pool and Dropout\n", + " nn.MaxPool2d(2),\n", + " nn.Dropout2d(0.2),\n", + "\n", + " # CONV3\n", + " nn.Conv2d(32,64,3,padding='same'),\n", + " activation_fn(),\n", + " nn.BatchNorm2d(64),\n", + "\n", + " # CONV4\n", + " nn.Conv2d(64,64,3,padding='same'),\n", + " activation_fn(),\n", + " nn.BatchNorm2d(64),\n", + "\n", + " # Pool and Dropout\n", + " nn.MaxPool2d(2),\n", + " nn.Dropout2d(0.3),\n", + "\n", + " # CONV5\n", + " nn.Conv2d(64,128, 3, padding='same'),\n", + " activation_fn(),\n", + " nn.BatchNorm2d(128),\n", + "\n", + " # CONV6\n", + " nn.Conv2d(128, 128,3, padding='same'),\n", + " activation_fn(),\n", + " nn.BatchNorm2d(128),\n", + "\n", + " # Pool and Dropout\n", + " nn.MaxPool2d(2),\n", + " nn.Dropout2d(0.4),\n", + "\n", + " # CONV7\n", + " nn.Conv2d(128,256, 3, padding='same'),\n", + " activation_fn(),\n", + " nn.BatchNorm2d(256),\n", + "\n", + " # CONV8\n", + " nn.Conv2d(256, 256,3, padding='same'),\n", + " activation_fn(),\n", + " nn.BatchNorm2d(256),\n", + "\n", + " # Pool and Dropout\n", + " nn.MaxPool2d(2),\n", + " nn.Dropout2d(0.4),\n", + "\n", + " # CONV9\n", + " nn.Conv2d(256,512, 3, padding='same'),\n", + " activation_fn(),\n", + " nn.BatchNorm2d(512),\n", + "\n", + " # CONV10\n", + " nn.Conv2d(512, 512,3, padding='same'),\n", + " activation_fn(),\n", + " nn.BatchNorm2d(512),\n", + "\n", + " # Pool and Dropout\n", + " nn.MaxPool2d(2),\n", + " nn.Dropout2d(0.4),\n", + "\n", + " # Flatten\n", + " nn.Flatten()\n", + "\n", + "\n", + " )\n", + " \n", + " output_dims_conv_part = self.get_conv_part_out_dims(torch.randn(1, 3, 32, 32))[1]\n", + " \n", + "\n", + " \n", + "\n", + " self.dense_part = nn.Sequential(\n", + " \n", + " # Dense1 \n", + " nn.Linear(output_dims_conv_part, 10),\n", + " \n", + " \n", + " )\n", + "\n", + " \n", + " \n", + " def get_conv_part_out_dims(self, input_data):\n", + " '''\n", + " Get the dimensions after flattening the last convolutional layer\n", + " Output is the size of the flattened tensor after pushing it through the concolutional part\n", + " Input is a 4d test tensor (batch_size, channels, height, width). The batch size is not important, can be 1.\n", + " '''\n", + " x = self.conv_part(input_data)\n", + " return x.size()\n", + "\n", + " def forward(self, x):\n", + " x = self.conv_part(x)\n", + " return self.dense_part(x)\n", + "\n", + "\n", + "model = Model(n_classes)\n", + "device = torch.device('cuda') # use cuda or cpu\n", + "model.to(device)\n", + "print(model)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7-IUg3sq81TQ" + }, + "source": [ + "## Define a loss function and optimizer\n", + "\n", + "**Assignment 2:** Define the loss function and optimizer.\n", + "You might need to experiment a bit with the learning rate." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "id": "48AX85QP81TR" + }, + "outputs": [], + "source": [ + "# Loss function\n", + "loss_fn = nn.CrossEntropyLoss() # multiclass classification\n", + "\n", + "# Optimizer\n", + "lr = 1.5*1e-3 #1e-3\n", + "weight_decay = 1e-4 #1e-6\n", + "\n", + "optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay = weight_decay) \n", + "\n", + "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.85) # decrease the learning rate after 30 epochs to 0.85 times the previous lr\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-WneIN7C81TV" + }, + "source": [ + "## Train the network\n", + "\n", + "**Assignment 3:** Finish the training loop below. \n", + "Start by using a small number of epochs (e.g. 2).\n", + "Even with a low number of epochs you should be able to see results that are better than chance.\n", + "When everything is working increase the number of epochs to find out how good your network really is." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iWT0ULctWvm1", + "outputId": "92c2a8cd-7190-41ec-8f97-a9b08edd035f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Output shape: torch.Size([2, 10])\n", + "Output logits:\n", + "[[-0.04192882 2.1142385 2.985258 2.1519063 -0.3508377 -1.3376064\n", + " 0.34588313 -1.8838671 0.82977384 -0.5367922 ]\n", + " [-0.6804791 0.44476533 0.49571776 1.8375667 -0.11586728 -1.0336082\n", + " -0.46363944 -1.5734311 0.5662721 0.69085413]]\n", + "Output probabilities:\n", + "[[0.02227794 0.19243611 0.4597964 0.19982299 0.01635752 0.00609775\n", + " 0.03283216 0.00353127 0.05326607 0.01358184]\n", + " [0.03198998 0.09856016 0.10371219 0.39681438 0.05626285 0.02247253\n", + " 0.03973619 0.01309814 0.11129384 0.12605976]]\n" + ] + } + ], + "source": [ + "# Test the forward pass with dummy data\n", + "out = model(torch.randn(2, 3, 32, 32, device=device))\n", + "print(\"Output shape:\", out.size())\n", + "print(f\"Output logits:\\n{out.detach().cpu().numpy()}\")\n", + "print(f\"Output probabilities:\\n{out.softmax(1).detach().cpu().numpy()}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NkUanRRb81TW", + "outputId": "c274a641-e359-4d70-cc4f-bfb4d63db9ea" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 500 training accuracy: 0.6390697337962963 Epoch 3/200\n", + " test accuracy: 0.6956\n", + "Step 1000 training accuracy: 0.76328125 Epoch 6/200\n", + " test accuracy: 0.7845\n", + "Step 1500 training accuracy: 0.794342041015625 Epoch 8/200\n", + " test accuracy: 0.8061\n", + "Step 2000 training accuracy: 0.82158203125 Epoch 11/200\n", + " test accuracy: 0.8269\n", + "Step 2500 training accuracy: 0.8407411317567568 Epoch 13/200\n", + " test accuracy: 0.8271\n", + "Step 3000 training accuracy: 0.862890625 Epoch 16/200\n", + " test accuracy: 0.8412\n", + "Step 3500 training accuracy: 0.8663504464285714 Epoch 18/200\n", + " test accuracy: 0.8464\n", + "Step 4000 training accuracy: 0.8794921875 Epoch 21/200\n", + " test accuracy: 0.8411\n", + "Step 4500 training accuracy: 0.884038397606383 Epoch 23/200\n", + " test accuracy: 0.8528\n", + "Step 5000 training accuracy: 0.897109375 Epoch 26/200\n", + " test accuracy: 0.8551\n", + "Step 5500 training accuracy: 0.9052734375 Epoch 29/200\n", + " test accuracy: 0.8507\n", + "Step 6000 training accuracy: 0.908203125 Epoch 31/200\n", + " test accuracy: 0.8597\n", + "Step 6500 training accuracy: 0.9197998046875 Epoch 34/200\n", + " test accuracy: 0.8556\n", + "Step 7000 training accuracy: 0.91953125 Epoch 36/200\n", + " test accuracy: 0.8631\n", + "Step 7500 training accuracy: 0.9263822115384616 Epoch 39/200\n", + " test accuracy: 0.8598\n", + "Step 8000 training accuracy: 0.9228515625 Epoch 41/200\n", + " test accuracy: 0.8536\n", + "Step 8500 training accuracy: 0.9294162326388888 Epoch 44/200\n", + " test accuracy: 0.8598\n", + "Step 9000 training accuracy: 0.9269314236111111 Epoch 46/200\n", + " test accuracy: 0.852\n", + "Step 9500 training accuracy: 0.9290506114130435 Epoch 49/200\n", + " test accuracy: 0.8669\n", + "Step 10000 training accuracy: 0.94140625 Epoch 52/200\n", + " test accuracy: 0.8596\n", + "Step 10500 training accuracy: 0.9323032924107143 Epoch 54/200\n", + " test accuracy: 0.8582\n", + "Step 11000 training accuracy: 0.9392903645833334 Epoch 57/200\n", + " test accuracy: 0.8629\n", + "Step 11500 training accuracy: 0.9364642518939394 Epoch 59/200\n", + " test accuracy: 0.8591\n", + "Step 12000 training accuracy: 0.943359375 Epoch 62/200\n", + " test accuracy: 0.8658\n", + "Step 12500 training accuracy: 0.9454666940789473 Epoch 64/200\n", + " test accuracy: 0.8635\n", + "Step 13000 training accuracy: 0.9552001953125 Epoch 67/200\n", + " test accuracy: 0.8659\n", + "Step 13500 training accuracy: 0.9434502180232558 Epoch 69/200\n", + " test accuracy: 0.8674\n", + "Step 14000 training accuracy: 0.9502883184523809 Epoch 72/200\n", + " test accuracy: 0.8641\n", + "Step 14500 training accuracy: 0.9471028645833334 Epoch 74/200\n", + " test accuracy: 0.8704\n", + "Step 15000 training accuracy: 0.9508338341346154 Epoch 77/200\n", + " test accuracy: 0.864\n", + "Step 15500 training accuracy: 0.95654296875 Epoch 80/200\n", + " test accuracy: 0.8671\n", + "Step 16000 training accuracy: 0.9486517137096774 Epoch 82/200\n", + " test accuracy: 0.8655\n", + "Step 16500 training accuracy: 0.9524739583333334 Epoch 85/200\n", + " test accuracy: 0.8667\n", + "Step 17000 training accuracy: 0.9507649739583334 Epoch 87/200\n", + " test accuracy: 0.8654\n", + "Step 17500 training accuracy: 0.9545200892857143 Epoch 90/200\n", + " test accuracy: 0.8644\n", + "Step 18000 training accuracy: 0.9569836128048781 Epoch 92/200\n", + " test accuracy: 0.8678\n", + "Step 18500 training accuracy: 0.9603207236842105 Epoch 95/200\n", + " test accuracy: 0.8673\n", + "Step 19000 training accuracy: 0.9572647758152174 Epoch 97/200\n", + " test accuracy: 0.8698\n", + "Step 19500 training accuracy: 0.9605712890625 Epoch 100/200\n", + " test accuracy: 0.8706\n", + "Step 20000 training accuracy: 0.95703125 Epoch 103/200\n", + " test accuracy: 0.8622\n", + "Step 20500 training accuracy: 0.9612068965517241 Epoch 105/200\n", + " test accuracy: 0.8666\n", + "Step 21000 training accuracy: 0.9596819196428571 Epoch 108/200\n", + " test accuracy: 0.8624\n", + "Step 21500 training accuracy: 0.9588694852941176 Epoch 110/200\n", + " test accuracy: 0.8699\n", + "Step 22000 training accuracy: 0.9630533854166666 Epoch 113/200\n", + " test accuracy: 0.8675\n", + "Step 22500 training accuracy: 0.9592347756410257 Epoch 115/200\n", + " test accuracy: 0.8673\n", + "Step 23000 training accuracy: 0.9623161764705882 Epoch 118/200\n", + " test accuracy: 0.8713\n", + "Step 23500 training accuracy: 0.958984375 Epoch 120/200\n", + " test accuracy: 0.8692\n", + "Step 24000 training accuracy: 0.96875 Epoch 123/200\n", + " test accuracy: 0.8682\n", + "Step 24500 training accuracy: 0.9670440051020408 Epoch 125/200\n", + " test accuracy: 0.8698\n", + "Step 25000 training accuracy: 0.9670500578703703 Epoch 128/200\n", + " test accuracy: 0.8651\n", + "Step 25500 training accuracy: 0.9701171875 Epoch 131/200\n", + " test accuracy: 0.8702\n", + "Step 26000 training accuracy: 0.964691162109375 Epoch 133/200\n", + " test accuracy: 0.8688\n", + "Step 26500 training accuracy: 0.96875 Epoch 136/200\n", + " test accuracy: 0.8663\n", + "Step 27000 training accuracy: 0.9667440878378378 Epoch 138/200\n", + " test accuracy: 0.8677\n", + "Step 27500 training accuracy: 0.9680989583333334 Epoch 141/200\n", + " test accuracy: 0.8668\n", + "Step 28000 training accuracy: 0.9665643601190477 Epoch 143/200\n", + " test accuracy: 0.8691\n", + "Step 28500 training accuracy: 0.96708984375 Epoch 146/200\n", + " test accuracy: 0.8707\n", + "Step 29000 training accuracy: 0.9651554188829787 Epoch 148/200\n", + " test accuracy: 0.867\n", + "Step 29500 training accuracy: 0.9678125 Epoch 151/200\n", + " test accuracy: 0.8721\n", + "Step 30000 training accuracy: 0.9690755208333334 Epoch 154/200\n", + " test accuracy: 0.87\n", + "Step 30500 training accuracy: 0.9726236979166667 Epoch 156/200\n", + " test accuracy: 0.8696\n", + "Step 31000 training accuracy: 0.9727783203125 Epoch 159/200\n", + " test accuracy: 0.8663\n", + "Step 31500 training accuracy: 0.9728515625 Epoch 161/200\n", + " test accuracy: 0.8707\n", + "Step 32000 training accuracy: 0.9722055288461539 Epoch 164/200\n", + " test accuracy: 0.8726\n", + "Step 32500 training accuracy: 0.9730224609375 Epoch 166/200\n", + " test accuracy: 0.8707\n", + "Step 33000 training accuracy: 0.9735243055555556 Epoch 169/200\n", + " test accuracy: 0.8722\n", + "Step 33500 training accuracy: 0.9719184027777777 Epoch 171/200\n", + " test accuracy: 0.867\n", + "Step 34000 training accuracy: 0.9713400135869565 Epoch 174/200\n", + " test accuracy: 0.8714\n", + "Step 34500 training accuracy: 0.9765625 Epoch 177/200\n", + " test accuracy: 0.8689\n", + "Step 35000 training accuracy: 0.972900390625 Epoch 179/200\n", + " test accuracy: 0.8718\n", + "Step 35500 training accuracy: 0.9806315104166666 Epoch 182/200\n", + " test accuracy: 0.87\n", + "Step 36000 training accuracy: 0.9765329071969697 Epoch 184/200\n", + " test accuracy: 0.8735\n", + "Step 36500 training accuracy: 0.9761186079545454 Epoch 187/200\n", + " test accuracy: 0.8685\n", + "Step 37000 training accuracy: 0.9745836759868421 Epoch 189/200\n", + " test accuracy: 0.8726\n", + "Step 37500 training accuracy: 0.97747802734375 Epoch 192/200\n", + " test accuracy: 0.8698\n", + "Step 38000 training accuracy: 0.9761764171511628 Epoch 194/200\n", + " test accuracy: 0.8737\n", + "Step 38500 training accuracy: 0.9751209077380952 Epoch 197/200\n", + " test accuracy: 0.8663\n", + "Step 39000 training accuracy: 0.97607421875 Epoch 199/200\n", + " test accuracy: 0.87\n", + "Finished training.\n" + ] + } + ], + "source": [ + "num_epochs = 200 #150\n", + "validation_every_steps = 500\n", + "\n", + "step = 0\n", + "model.train()\n", + "\n", + "train_accuracies = []\n", + "valid_accuracies = []\n", + " \n", + "for epoch in range(num_epochs):\n", + " \n", + " train_accuracies_batches = []\n", + " \n", + " for inputs, targets in train_loader:\n", + " inputs, targets = inputs.to(device), targets.to(device)\n", + " \n", + " # Forward pass\n", + " output = model(inputs)\n", + "\n", + " # Compute loss\n", + " loss = loss_fn(output, targets)\n", + "\n", + " # Zero gradients\n", + " optimizer.zero_grad()\n", + "\n", + " # Compute gradients\n", + " loss.backward()\n", + "\n", + " # Perform one training step\n", + " optimizer.step()\n", + " \n", + " \n", + " # Increment step counter\n", + " step += 1\n", + " \n", + " # Compute accuracy.\n", + " predictions = output.max(1)[1]\n", + " train_accuracies_batches.append(accuracy(targets, predictions))\n", + " \n", + " if step % validation_every_steps == 0:\n", + " \n", + " # Append average training accuracy to list.\n", + " train_accuracies.append(np.mean(train_accuracies_batches))\n", + " \n", + " train_accuracies_batches = []\n", + " \n", + " # Compute accuracies on validation set.\n", + " valid_accuracies_batches = []\n", + " with torch.no_grad():\n", + " model.eval()\n", + " for inputs, targets in test_loader:\n", + " inputs, targets = inputs.to(device), targets.to(device)\n", + " output = model(inputs)\n", + " loss = loss_fn(output, targets)\n", + "\n", + " predictions = output.max(1)[1]\n", + "\n", + " # Multiply by len(x) because the final batch of DataLoader may be smaller (drop_last=False).\n", + " valid_accuracies_batches.append(accuracy(targets, predictions) * len(inputs))\n", + "\n", + " model.train()\n", + " \n", + " # Append average validation accuracy to list.\n", + " valid_accuracies.append(np.sum(valid_accuracies_batches) / len(test_set))\n", + " \n", + " print(f\"Step {step:<5} training accuracy: {train_accuracies[-1]} Epoch {epoch+1}/{num_epochs}\")\n", + " print(f\" test accuracy: {valid_accuracies[-1]}\")\n", + " scheduler.step()\n", + "print(\"Finished training.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0qAsbC8I81Ta" + }, + "source": [ + "## Test the network\n", + "\n", + "Now we show a batch of test images and generate a table below with the true and predicted class for each of these images." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "7LT0RoAC81Tc", + "outputId": "2c989472-8982-4286-93bb-561568c4a823" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " TRUE PREDICTED\n", + "-----------------------------\n", + " cat cat \n", + " ship ship \n", + " ship ship \n", + " airplane ship \n", + " frog frog \n", + " frog frog \n", + " automobile automobile \n", + " frog frog \n", + " cat cat \n", + " automobile automobile \n", + " airplane airplane \n", + " truck truck \n", + " dog deer \n", + " horse horse \n", + " truck truck \n", + " ship frog \n", + " dog dog \n", + " horse horse \n", + " ship ship \n", + " frog frog \n", + " horse horse \n", + " airplane airplane \n", + " deer deer \n", + " truck truck \n", + " dog deer \n", + " bird bird \n", + " deer truck \n", + " airplane airplane \n", + " truck truck \n", + " frog frog \n", + " frog frog \n", + " dog dog \n", + " deer deer \n", + " dog dog \n", + " truck truck \n", + " bird cat \n", + " deer deer \n", + " automobile automobile \n", + " truck truck \n", + " dog dog \n", + " deer deer \n", + " frog frog \n", + " dog dog \n", + " frog frog \n", + " airplane airplane \n", + " truck truck \n", + " cat cat \n", + " truck truck \n", + " horse horse \n", + " frog frog \n", + " truck truck \n", + " ship ship \n", + " airplane horse \n", + " cat cat \n", + " ship airplane \n", + " ship ship \n", + " horse horse \n", + " horse cat \n", + " deer horse \n", + " frog frog \n", + " horse horse \n", + " cat dog \n", + " frog frog \n", + " cat cat \n", + " frog frog \n", + " bird bird \n", + " automobile automobile \n", + " bird bird \n", + " cat cat \n", + " horse horse \n", + " bird bird \n", + " frog frog \n", + " ship ship \n", + " ship ship \n", + " airplane airplane \n", + " bird bird \n", + " truck truck \n", + " cat cat \n", + " cat cat \n", + " ship ship \n", + " ship ship \n", + " automobile truck \n", + " automobile automobile \n", + " horse horse \n", + " bird bird \n", + " dog dog \n", + " bird bird \n", + " horse cat \n", + " ship ship \n", + " truck truck \n", + " airplane airplane \n", + " cat cat \n", + " ship ship \n", + " frog frog \n", + " deer deer \n", + " frog frog \n", + " frog frog \n", + " airplane airplane \n", + " airplane airplane \n", + " horse horse \n", + " deer horse \n", + " dog dog \n", + " frog frog \n", + " cat frog \n", + " automobile automobile \n", + " automobile automobile \n", + " cat cat \n", + " frog frog \n", + " ship ship \n", + " horse horse \n", + " deer deer \n", + " airplane airplane \n", + " frog frog \n", + " bird bird \n", + " automobile automobile \n", + " cat cat \n", + " airplane airplane \n", + " deer deer \n", + " bird bird \n", + " horse horse \n", + " ship ship \n", + " cat cat \n", + " automobile automobile \n", + " bird bird \n", + " ship ship \n", + " airplane airplane \n", + " ship ship \n", + " cat cat \n", + " dog cat \n", + " bird bird \n", + " deer deer \n", + " automobile automobile \n", + " ship ship \n", + " truck truck \n", + " automobile automobile \n", + " bird bird \n", + " truck truck \n", + " horse horse \n", + " bird bird \n", + " truck truck \n", + " frog frog \n", + " dog dog \n", + " frog frog \n", + " cat deer \n", + " ship ship \n", + " horse horse \n", + " frog frog \n", + " bird dog \n", + " dog cat \n", + " bird cat \n", + " ship deer \n", + " truck truck \n", + " frog frog \n", + " airplane airplane \n", + " airplane airplane \n", + " dog dog \n", + " bird bird \n", + " truck truck \n", + " dog cat \n", + " deer deer \n", + " bird airplane \n", + " automobile automobile \n", + " frog frog \n", + " frog frog \n", + " ship ship \n", + " deer deer \n", + " ship ship \n", + " deer deer \n", + " dog dog \n", + " airplane airplane \n", + " truck truck \n", + " truck truck \n", + " truck truck \n", + " ship ship \n", + " truck truck \n", + " truck truck \n", + " cat cat \n", + " horse horse \n", + " dog dog \n", + " airplane airplane \n", + " airplane airplane \n", + " dog dog \n", + " bird bird \n", + " bird bird \n", + " cat cat \n", + " ship ship \n", + " frog frog \n", + " cat cat \n", + " deer bird \n", + " airplane ship \n", + " dog dog \n", + " ship ship \n", + " airplane deer \n", + " automobile automobile \n", + " horse horse \n", + " bird horse \n", + " ship ship \n", + " ship ship \n", + " horse horse \n", + " ship ship \n", + " dog dog \n", + " automobile automobile \n", + " ship ship \n", + " horse deer \n", + " automobile automobile \n", + " cat cat \n", + " airplane airplane \n", + " dog dog \n", + " horse horse \n", + " truck truck \n", + " horse horse \n", + " deer deer \n", + " dog dog \n", + " truck truck \n", + " ship ship \n", + " airplane airplane \n", + " horse horse \n", + " truck truck \n", + " ship ship \n", + " bird bird \n", + " horse horse \n", + " frog dog \n", + " truck truck \n", + " deer deer \n", + " cat cat \n", + " truck truck \n", + " frog cat \n", + " deer deer \n", + " horse horse \n", + " frog frog \n", + " dog dog \n", + " automobile automobile \n", + " dog cat \n", + " ship ship \n", + " ship ship \n", + " airplane airplane \n", + " deer deer \n", + " airplane airplane \n", + " dog dog \n", + " dog dog \n", + " automobile automobile \n", + " automobile automobile \n", + " ship ship \n", + " truck truck \n", + " airplane airplane \n", + " cat airplane \n", + " automobile automobile \n", + " truck ship \n", + " bird deer \n", + " bird bird \n", + " dog dog \n", + " cat cat \n", + " truck truck \n", + " truck truck \n", + " deer deer \n", + " airplane cat \n" + ] + } + ], + "source": [ + "inputs, targets = iter(test_loader).next()\n", + "inputs, targets = inputs.to(device), targets.to(device)\n", + "show_image(make_grid(inputs))\n", + "plt.show()\n", + "\n", + "outputs = model(inputs)\n", + "_, predicted = torch.max(outputs.data, 1)\n", + "\n", + "print(\" TRUE PREDICTED\")\n", + "print(\"-----------------------------\")\n", + "for target, pred in zip(targets, predicted):\n", + " print(f\"{classes[target.item()]:^13} {classes[pred.item()]:^13}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ISA6LJJO81Tg" + }, + "source": [ + "We now evaluate the network as above, but on the entire test set." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "id": "Smv6_BwF81Ti" + }, + "outputs": [], + "source": [ + "# Evaluate test set\n", + "confusion_matrix = np.zeros((n_classes, n_classes))\n", + "with torch.no_grad():\n", + " model.eval()\n", + " test_accuracies = []\n", + " for inputs, targets in test_loader:\n", + " inputs, targets = inputs.to(device), targets.to(device)\n", + " output = model(inputs)\n", + " loss = loss_fn(output, targets)\n", + "\n", + " predictions = output.max(1)[1]\n", + "\n", + " # Multiply by len(inputs) because the final batch of DataLoader may be smaller (drop_last=True).\n", + " test_accuracies.append(accuracy(targets, predictions) * len(inputs))\n", + " \n", + " confusion_matrix += compute_confusion_matrix(targets, predictions)\n", + "\n", + " test_accuracy = np.sum(test_accuracies) / len(test_set)\n", + " \n", + " model.train()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ylxBAyXwI7Ab" + }, + "source": [ + "Here we report the **average test accuracy** (number of correct predictions divided by test set size)." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "a5-c_CDAI7Ab", + "outputId": "652f80e6-36ea-4b4c-b1ac-fc510e94bd00" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test accuracy: 0.872\n" + ] + } + ], + "source": [ + "print(f\"Test accuracy: {test_accuracy:.3f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uMfUqY9rI7Ab" + }, + "source": [ + "Here we look a bit more in depth into the performance of the classifier, using the **confusion matrix**. The entry at the i-th row and j-th column indicates the number of samples with true label being the i-th class and predicted label being the j-th class.\n", + "\n", + "We normalize the rows: given all examples of a specific class (row), we can observe here how they are classified by our model. Ideally, we would like the entries on the diagonals to be 1, and everything else 0. This would mean that all examples from that class are classified correctly.\n", + "\n", + "The classes that are harder to classify for our model have lower numbers on the diagonal. We can then see exactly *how* they are misclassified by looking at the rest of the row.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 441 + }, + "id": "ShcEZDExI7Ab", + "outputId": "ddeac9f1-eb72-449a-f359-f47fbecac6e0" + }, + "outputs": [ + { + "data": { + "image/png": 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GicEkqPDwcC5dulSidcTGxtK1a9dCt02cOFFTv7OzM7du3SoRh0NHonHp4Y3SzZPAFasKbM/MzGTU+Iko3Tzx/mAIsfHxAKTevsOAj4bRvHU7fGfNfS6Hxi4dmfrHaXz/PovL+C8KbK9cozqjwncy6dejfHkglIr21bS2v2JuzsyY3+k9309vh0NHonFx90Lp5kHg8ifFYQJKNw+8BwzWxAFgybKVKN08cHH34vDRaL0dDMVDHAzLw2AcevRE6eZF4IrVT3CYiNLNq5B5YjjNW7fHd5b+41PjEX0cF69+KD36ELhqbeEeE6ag9OiD9+CPiY1PAODchYt07zeE7v2G4NZ3MPsPHNKr/pcqQT2N6dOnU7du3RKtIzs7G9/ZcwmaP4/Q4I2E7N3HpStXtMps3r4TCwtz9u8MZlC/3vgFLASgXDkTPh/2MeO+GPlcDgojI/os/I4FnT2Z1rgVrfp4YdeogVYZT79vObZ6I9++/h6hvrNxnzlVa7vbN5P4+9BRvR2ys7PxnTWHoAUBhAZvImRvGJcuFxIHc3P279zKoH598AtYAMCly1cIDdtH6JaNBC0MYNrMOWRnZ/9nPcTBsDwMxmG2H0Hz/QkN3qCeJ64WdLCwYP/OLWqHvPPER4z74jOd6y3UY44/QQFzCd20mpCwCC5duabtsTNUFYutGxjUpyd+C34EoF6d2gSvCmTHuuUE/TAXn1l+PHz4UGeHEk1Qw4cPx8PDA1dXVzZt2gRA8+bNNdv37t3LV199xS+//EJkZCRz5syhe/fu3Lhxg99//52ePXvSrVs3Pv30U+7cUT19eMCAAcyYMQMPDw86d+7MuXPnGDFiBJ06dcLf319z7BUrVtC1a1e6du3KypUrNX9/+PAho0ePpnPnzowcOZIHDx5ojvvbb78VeA87duzAy8uL7t274+Pjo/fgAzh3/iI1HRyo7mCPSdmyuLooiTio/cki8uAhenR1BcClgzPRJ0+Sm5tLeVNTWjZ/g3ImJnrXD/DqWy1JvnSFm1evkZ2VxcmNwbzW3VWrjF3jhvwZGQXAnwcO8Xr3LpptNd58A3Mba37fF6G3w7nzF6hZPW8cOhUShyh6dFPHoaMz0SdUcYg4eAhXl06YmJhQ3d6emtUdOHf+wn/WQxwMy8MwHJ5lnjhMj66qcenSoT3RJ08V6zwBcO7C79R0sKe6fTWVR6cORBz6Wdsj6md6uL6v8nBuS/TJX8jNzcX0lVcwNlYtlpGRkYlCodDLoUQT1IwZM9i6dSvBwcGsWbOG1NTCHy3/5ptv4uzszLhx49ixYwc1atRg3LhxjBkzhl27dlG/fn0WLFigKV+2bFm2bt1K7969GT58OD4+PoSEhLBt2zZSU1M5f/48W7du5aeffmLTpk1s3ryZixcvAnD16lX69u3Lnj17MDMzY/369U/0v3z5Mnv27GHDhg3s2LEDIyMjdu3apXc8klKSsbV9/Gh5G2trkpJT8pVJwc7WGgBjY2PMK1Qg9XbxLQ1Qyd6O1JhYzevbsfFUyncJL/bX8zT3cAPgjR7dMLWwwKxyZRQKBV7fTSd4zMTnckhKTsHWJk8cbKxJSkkpUMZOHau8cUhKSSkyhv8lD3EwLA+DcEhJwVY9BzzpOKp5oqBDcZKUchNbm7weVgVjkXITO5u885UZqeqTiV/PX8S11we49R3MtPGjNQlLF0o0Qa1ZswY3Nzd69uxJQkIC169ff6b90tLSSEtL46233gKgR48enDp1SrPd2dkZgPr161OvXj2sra1Vn1qqVycxMZHTp0/TsWNHypcvj5mZGUqlUrO/nZ0dLVq0AMDNzY3Tp08/0SM6Oprz589rzqCio6OJiYnRKxb/JYLHTKRe29ZM+OUw9du2ITU2jpzsbNoO/x/nd+/jdlx80QcRBOGl5vWmjQndtJotK5ewZNVaMjIydD5GiS1YePz4cY4ePcqmTZswNTVlwIABBQT1EQYwUZ++GhkZaX5/9Lqo65z5TzWfduqZm5tLjx49GD16tF6e+bGxsiYx8fHiYEnJydhYW+UrY0VCYjK2NjY8fPiQtHv3qFTRsljqB0iNS6BSdQfN64oO1UjNl3DuJCSyxLM/AOXMzGju6caDO3eo/e5b1HV8l7bDh1KuQgXKmJTl33v32P71VJ0cbKytSEzKE4ekZGysrAqUSUhMKhAHGyurImP4X/IQB8PyMAgHKysSE5OfehzVPJGErY11icwTqjqqkpiU1yOlYCysqpKQlJzHI51KltoedWq9SnlTU/66fJVmjRvq5FBiZ1BpaWlYWlpiamrK5cuXOXv2LABVq1bl8uXL5OTkEB4erilvZmZGeno6AObm5lhYWGjOenbs2EGrVq2eue6WLVsSHh7OgwcPuH//PuHh4bRs2RKA+Ph4zpw5A0BISIjmbKow3n33XcLCwvjnn38AuH37NnFxcTpEQZtmTRpxLSaGmLh4MrOyCA3bj3NbJ60yzm0d2RYSCkBYRCTvtGqp9/Xbwrh+8jTW9WpT5dWalClblla9PTm3c7dWGbMqlTV1vv/1lxxdrrp7Z3n/oUyo2YSJtZoRPGYix1dv1Dk5ATRr0phrN2KIiYtTx2Efzu0ctco4t3Vi2y51HMIfx8G5nSOhYfvIzMwkJi6OazdieK1pEz0iYRge4mBYHobhUNg8kd/BkW0hqnEbFnGg2OcJgGaNG3ItJvaxx74InB1ba3s4tWZb6F6VR2QU77R8E4VCQUxcvOZkIS4hkSvXb2BfzVZnhxI7g3JycmLjxo107tyZWrVq8cYbbwAwevRoPv74YypXrkzTpk25f/8+AF26dGHy5MmsWbOGH374gdmzZzNlyhQePHhA9erVmTlz5jPX3aRJEzw8PPD29gbAy8uLxo0bExsbS61atVi3bh0TJkygbt269OnT54nHqVu3LqNGjWLIkCHk5ORQtmxZfHx8sLe31ysmxsbG+Iwfw9BPR5Kdk4OnWzfq1alNwOIlNG3ciA5tnfByd2Ps5Kko3TyxtLTAf+a3mv2dXd25l55OVlYW4QejWL7oB+rWrq2TQ052NptGjGVk2DaMypTh6PI1JFz8g27TJnL91C+c27WHBu0ccZ85ldzcXP4+dISNnxbPGaR2HMYydLg6Dt27Ua9OHQIWqePQTh2HSVNQunlgaWGB/6zpANSrU4fOnTrSxbMXZcqUweercZQpU+Y/6yEOhuVhOA5jGPrp5+p5oqt6ngikaeOG6nmiG2MnT0Pp5qWeJ77R7K+aJ+7nmydq6ecxdhRDR45ReXTrQr06tQhYsoymjRrQwakNXm6ujJ0yHaVHHywtzPGfPhWA07/+xtJV6zA2NsbISMHUcV9SuWJFnR0Uubm5uTrvJTyd9NulW79ZRT5RWJSqwo+5d1W/3C/eL251prylODxyAMPwEIfH7ZFe+I1jLwyzSnAnqehyJYmlzRM3Gcz/QQmCIAhCXiRBCYIgCAaJJChBEATBIJEEJQiCIBgkkqAEQRAEg0QSlCAIgmCQSIISBEEQDBJJUIIgCIJBIglKEARBMEgkQQmCIAgGiSQoQRAEwSCRZ/EJgiAIBomcQQmCIAgGSYktt/FS84/+a0YVC1XsDeZpzVsr674GTHHicSsR7iQXXbAksbSG1ITSdahkp/p5u5SfXF3RBu7qtyR8sWFhBWn/lK6DeRXVT0MYpwaw+sKTkDMoQRAEwSCRBCUIgiAYJJKgBEEQBINEEpQgCIJgkEiCEgRBEAwSSVCCIAiCQSIJShAEQTBIJEEJgiAIBokkKEEQBMEgkQQlCIIgGCSSoARBEASD5KVKULGxsXTt2rXA3ydOnMilS5eK3H/r1q34+vo+l8OhYydw6f0BSu/+BK5eX2B7ZmYmoyb7ovTuj/fQ4cQmJKrcExJ5rd37dB/4P7oP/B8+c/z1dzgSjYu7F0o3DwKXryrcYfwElG4eeA8YTGx8vGbbkmUrUbp54OLuxeGj0Xo72HRoj/L4z3Q6FU39z0cU2G5qb4/jjmCcD+6nw+FIbDp20GyrP+ozOp2KRnn8Z6yd2+ntAHAo+jguXn1RevQmcNXaAtszMzMZNWEKSo/eeA/+iNh41TP1zl24SPd+g+nebzBufQex/8Ch53PoOQClV18CV68r3GHiNJReffEeMkzj8Ij4xCSat3+fZes2Pp+Ddz+Unn2eHIeJU1B69sF7yMfaceg/hO79h+DWbzD7D+ofB4BDR4/h4tkHZY9eBK5cU7jH1z4oe/TCe9D/NB5Hjp/EY8AQuvX+AI8BQ4g+efr5HDx6o3T3JnDl6ic4TEbp7o33wKGPHY6dwKP/YLr16o9H/8FEnzylv4MBjNFDR6Jx6eGN0s2TwBVPcpiI0s0T7w+GaBxSb99hwEfDaN66Hb6z5updP7xkCepJTJ8+nbp16xb4e3Z2drHWk52dja9fAEHfzSJ0/QpCwiO5dPWaVpnNu/ZgYW7O/s1rGdTLC79FgZptNeyrsWPVUnasWorvuC/0d5g1h6AFAYQGbyJkbxiXLl/Rdti+U+WwcyuD+vXBL2ABAJcuXyE0bB+hWzYStDCAaTPn6BcjIyNenzOTIz37sv9dJxw8e2DeoL5WkYZjRhG7fSeR7ZScGPoJb/jNAsC8QX0cPNwJf68tR7z78sbcWWCkXzfOzs7Gd873BAX4EbppDSFh4Vy6clU7FjtDVbHYupFBfXrit+BHAOrVqU3wqqXsWLeCoB/88Jk1l4cPH+rn4BdAkP9sQjesImRfIX1i524sLCqwf8t6BvXxwm9hoNb2WQELcXz3bZ3r1nKY60/QvLmEblxNyL4ILl3J76COQ/AGBvXuid/CPHFYGciOtcsJCpiLzyw/veKg8XjUHj+tJWRfIe2xIwQLC3P2b9vEoL698Ju/GIBKFS1Z/P0cdm1czawpkxg35Rv9HWb7EfTDd4RuXl94n9ixSxWL7ZvVDoseO/jPYdemtcyaOolxPvp9mDWEMaqKw1yC5s8jNHgjIXv3celKIQ4W5uzfGcygfr3xC1gIQLlyJnw+7GPGfTFSr/efl5cuQT18+JDRo0fTuXNnRo4cyYMHDxgwYAC//fYbAM2bN2fWrFm4ublx5swZgoODcXFxwcvLi19++eW56j538Q9qOthT3b4aJmXL4trRmYjDR7XKRB4+Qo/OnQBwad+W6FO/UJxLdp07f4Ga1R2o7mCvcnDpRES+T72RB6Po0c1V5dDRmegTJ8nNzSXi4CFcXTphYmJCdXt7alZ34Nz5Czo7VG7RnPSrV7l//Qa5WVnEbt2OXWcX7UK5uZQ1NwegrIU5/yaqziTtOrsQu3U7OZmZ3L9xg/SrV6ncorkekYBzF37Xbo9OHYg49LNWmciow/RwfR8AF+d2RJ88TW5uLqavvIKxsWoxgIyMTBQKhX4O+fuE0pmIQ0e0HQ4foUcXtUP7tkSfOq3pE+FRh7GvZke9Wq/qVb/KIV8clIXE4dDPeeLQluiTvxSMQ2YmCvSLA6jbI2/fVHYkIqowj85qj8ft0bhBfWysqgJQr04tMjIyyMzM1MPhorZDp45ERB3Wdog6TI+uaocO7Yk+cUrl0LABNlZWaofa+jsYwBg9d/4iNR3yOigLcThEj65qhw7ORJ9UOZQ3NaVl8zcoZ2Kic735eekS1NWrV+nbty979uzBzMyM9eu1L7Pdv3+f1157jZ07d1KjRg3mz5/Phg0bWL9+/TNdBnwaSSk3sbWx1ry2sapKUkpKgTJ26jLGxmUwNzMj9c5dQHWZz33gR/QfPopTZ8/p55Ccgq2NzWMHG+uCDskp2NnaqB2MMa9QgdTbd0hKScHWNs++1tYkJeu+dMIrdnY8iHt8SeJBfAKmdnZaZX6f7Uf1np50Pv8L721ax6/jJwJgWsi+r+Tb91lJSknRbg9rK5JSbuYrk7c9jDGvYEbqHdUSCb+ev4BrrwG49R3EtPFjNBO1zg7WVvkc8veJFOxsrPI4VCD1zh3S799n6ZoNjPhwoM71ah0/+WYhcSikX1o/KQ4Xce39AW59BzPtq9F6xUFVR772sCnEIznlie3xiLDIgzRuUB8TPSbIAuPD2qpAH1c55B0fhThEHKBxwwbF41AKYzQpJbnI4ySlpGBnm7ctVA7FyUuXoOzs7GjRogUAbpU+/aoAACAASURBVG5unD6tfa26TJkyuLioPs2fO3eOt956i8qVK2NiYkKXLl1euO8jrKtU5sC2DWxfFchXI4czeup07qWnl5pPSePg2YPrGzaxp+mbHO3Vj5Y/LgA9z1JKitebNiF00xq2rAxkyaq1ZGRkvND6FwStZGBvb8zKl3+h9ebn9aaNCd24mi0rlpRKHPLy9+Ur+M1fjO+EcaXssKhUHf6/8NIlqPyXYvK/LleuHGXKlCmRum2sqpKY9HjxvKSUm5pLAnnLJKjLPHyYTVp6OpUsLTAxMaGSpWoRwKYN61PDvhpXb8Tq7mBtRWLS40XrkpKSCzpYW5GQmKR2eEjavXtUqmiJjZUViYl59k1OxsZae99n4d+EBEztq2lem1az40GC9hf/r/bvS9z2nQDcOnmaMuXKUa5KFR4Usu+/CfotBmhjZaXdHskpmstEj8vkbY+HpN1L17TDI+rUepXypqb8dVn7u4pndsjzyVTlkL9PWJGQlJLH4R6VLC359cLv+C34EWf3XqzatIUlq9axdvNW3R2sqxYSh0L6ZfIzxuGK7nFQ1ZGvPZIK8bC2emJ7JCYlM2LcBGZPm0QNB3v9HPKPj+SUAn1c5ZB3fORzGPs1s6f5UMPBoXgcSmGM2lhZF3kcGysrEhLztoXKoTh56RJUfHw8Z86cASAkJERzNlUYr732GidPniQ1NZWsrCz27t37XHU3a9SQa7FxxMQnkJmVRWh4JM5t3tUq4+z4Htv27AMg7EAU77RojkKh4Fbqbc2XnTFx8VyLiaW6ve6Xtpo1acy1GzHExMWpHML24dzOUduhrRPbdoWqHMIjeadVSxQKBc7tHAkN20dmZiYxcXFcuxHDa02b6OyQ+stZKtSuTfkaNVCULYuDhzsJe/dplbkfG4eVk8rLvH49jMqVI+PmTRL27sPBwx0jExPK16hBhdq1uXX6jM4OAM0aN+RaTCwxcfGqWOyLwNmxjXYsnNqwLVTV7mGRB3mn5ZsoFApi4uI1NwPEJSRy5fp17Kvpvnpws0YNVA6P+sT+SJwd39N2cHyPbbvVDgeiNA7rl8wncvsmIrdvYmAvLz4e2I/+3h56OKjjEK+Ow/4InJ1a53NonScOjx1i4vPH4Qb2dvqtotyscUN133zkEf4Ejz1qj4O800rlcTctjY++GMvoT4fR4vXX9Kpf5dAoX58Ix9kpf59wZFuI2iHiAO+0avHYYdQYRo8YRos3nsPBAMZosyaNuBaTpy3C9uPc1imfgyPbQtQOEY8dipOXbsn3WrVqsW7dOiZMmEDdunXp06cPBw4cKLSstbU1I0aMoHfv3pibm9OoUaPnqtvYuAw+X37G0C/Gk52djWfXztSrXYuApSto2rA+HRxb49W1C2N9Z6D07o+lhTn+vpMBOHn2HD8ErcDY2BgjhYJp476gooWFHg7G+Iwfy9DhI8nOycGzezfq1alDwKIlNG3ciA7tnPByd2PspCko3TywtLDAf9Z0AOrVqUPnTh3p4tmLMmXK4PPVOL3ONnOzszk7bgKtt2xAUaYM19dtIO2PP2n09ThunzlLwt59/DZ5Km/O86PusI8gN5fTIz4HIO2PP4nbvpOO0YfIffiQs+O+hpwcnR00sRj7BUNHjlbFopsr9erUImBJEE0bNaSDUxu83FwZO+VblB69VbGYPhWA07+eY+mqdar2MFIwddyXVK745KWrn+ow5nOGfj5W5fCoTwQup2nDBnRwao1Xty6MnTYDpVdflcM3Pnq936c7jGLoyDHqOHRROSxZRtNGDR7HYep0lJ59VP3yW3Uczv7G0tXPHweNx7gvGTryS7Kzc/B0c6VendoE/Khuj7Zt8OrelbFTvkHZo5dWe6z9KZgbMXEsDFrBwqAVACxf4E+VypV0dxj7JUM/+0I1Rt26qh2Wqh0cVQ4+vijdvVUOM1R3663dtIUbMbGFOFTW3aGUx6jKYQxDP1U7uHVTxWGx2qGt2mHyVJRunlhaWuA/81vN/s6u7txLTycrK4vwg1EsX/QDdWvX1tlDkVuct4gJKv6JK936q9jD/eL9slJnyqtO9bdW1u/TdHHhcSsR7iQXXbAksbSGVP0uQxYbldRn27eTnl6upKloA3d1/9K+WLGwgrR/StfBvIrqpyGM0/Tbpetg9uQPNC/dJT5BEAThv4EkKEEQBMEgkQQlCIIgGCSSoARBEASDRBKUIAiCYJBIghIEQRAMEklQgiAIgkEiCUoQBEEwSCRBCYIgCAaJJChBEATBIJEEJQiCIBgk8iw+QRAEwSB56Z5m/kJITy3d+s0qGc4DIG/FP71cSVO5Ghf0eIpycdLkyhWDeVhs7t8nS1VDUa8V/KP7OmbFShUHw3lYrCGMU0NweAJyiU8QBEEwSCRBCYIgCAaJJChBEATBIJEEJQiCIBgkkqAEQRAEg0QSlCAIgmCQSIISBEEQDBJJUIIgCIJBIglKEARBMEgkQQmCIAgGiSQoQRAEwSCRBPUMHD9+nF9++aVYjnXoSDQuPXqidPMicMXqAtszMzMZNX4iSjcvvD8YQmy86ll2qbfvMOCj4TRv3R7fWX7F4OCN0s2TwBWrnuLgWYjDMJq3bofvrLnP5xB9ApdeH6D06kfg6vWFO0yahtKrH94fDiM2IRGA2IREXmvrQvcPhtL9g6H4zP7+uTwqODlRNzycupGRVP3kk0LLWHTpQp2wMOrs3Yv9vHmav9uMH0+dvXupu28ftj4+ejscij6OS88BKL36Erh6XYHtmZmZjJo4DaVXX7yHDCM2Xvu5fvGJSTRv/z7L1m3U2+Hw6V95/+MxdPrflwRu3llg+8nzf+Dx+USauH3A3p9PFNh+7/592g78DN/FBfuTLhw6dgKX3gNReg8gcPWGAtszMzMZNfkblN4D8B76qXa/aNeZ7gM/ovvAj/CZ46+/w9FjuHj0RunuTeDKJ4zRryejdPfGe+BQTXscOXYCj/6D6darPx79BxN98pT+DoYwRg3AQRLUM3DixAnOnDnz3MfJzs7Gd7YfQfP9CQ3eQMjefVy6clWrzObtO7GwsGD/zi0M6tcHv4CFAJQrZ8Lnwz5i3BefFYPDXILmzyM0eKPa4UohDubs3xnMoH698zl8zLgvRj6/w3cBBH0/i9ANKwnZH8Glq9e0HXbtxsLcnP1b1jGotzd+C5dottVwqMaO1UHsWB2E7/gv9RcxMsJu2jSuDx7MZRcXLLt1o1zdulpFTF59larDhnHV25vL779P4jffAGD65puUb9GCy126cOn99zF97TXKv/22zgrZ2dn4+gUQ5D+b0A2rCNkXWTAWO3djYVGB/VvWM6iPF34LA7W2zwpYiOO7utf92CEH38WrWDptHCGL5hAadYxLN+K0ythZVWHmqI/p2va9Qo8RsGYLLZs21NtB5ZGNr98PBH03k9D1ywkJLyQWu/ZgYV6B/ZvXMKiXJ36Llmq21bCvxo5VgexYFYjvuC/0d5jtR9AP3xG6eT0hYeEFx+iOXaq+uX0zg/r2wm/+IgAqVbRksf8cdm1ay6ypkxjn4/scDgYwRkvZAV7yBLV9+3a6deuGm5sbY8eOJTIyEm9vb9zd3Rk0aBA3b94kNjaWjRs3snLlSrp3786pU/p/Kjp3/iI1HRyo7mCPSdmyuLooiTh4SKtM5MHD9OjaBQCXDu2JPnmK3Nxcypua0rL5G5QzMXmu9/xsDofo0dVV7eBM9MmTxetw8Q9qOlSjun01lUNHZyIOHdF2OHyEHl1cVA7t2xJ96heKe2UY09dfJ/P6dbJiYsjNyuJOSAjmSqVWmUq9enFrzRpy7t4FIPsf9VOwc3NRlCuHomxZFCYmKMqW5eHNmzo7qGJh/zgWyifF4n3gUSxOa2IRHnUY+2p21Kv1qs51axz+ukwNOxuq21pjUtaYLk7vEHHstFYZBxsrGtSqgcJIUWD/85eu8s/tu7Ru3kxvBygkFh3bE3H4qFaZyMNH6dG5E1Ay/eLchYvUrJ5nfHTqSETUYW2HqMP06NpZ5dChPdEnVGO0ccMG2FhZAVCvTm0yMjLIzMzU3cEQxqgBOMBLnKD+/vtvFi9ezKpVq9i5cycTJ06kRYsW/PTTT2zfvh1XV1eCgoJwcHCgd+/eDBo0iB07dtCyZUu960xKScHW1lrz2sbamqTklAJl7GxtADA2Nsa8QgVSb9/Ru86CDsnYqo//dAfrEnS4ia113jhYkZRys0AZO5tHDmVUDndUSSI2PhH3D/5H/2Gfc+rsOb09ytrakpXw+HJZVkICxjY2WmVMatWiXK1a1PrpJ2oFB1PByQmAB2fOkH7sGA2OH6fB8ePcO3SIzMuXdXZISknB1tpK81oVi0Law0ZVRtMed+6Qfv8+S9dsYMSHA3WuV+v4/6RiZ1VZ89q2amWS/nm2JWNycnKYHbSOcR/2eS4HUPcLmzyxsHqGfmFm9rhfJCTiPvBj+g//Qu9+kZScgq1N3vFhVXB8JKdgZ5N3jJqRekd7fIRFHKBxwwaY6DFJG8YYLX0HeInXgzp27Bjvv/8+lSurBmbFihX5888/+eKLL0hJSSEzMxMHB4dSthTyY12lMge2b6SSpSXn//iTT8dPJnT9CiqYmZVIfQpjY0xefZWrfftS1taWWhs3cqlzZ4wrV6Zc3br89Z7qklfN1asp36oV90++uPWWFgStZGBvb8zKl39hdeZnfWg4bVu+gW3VKqXmAOp+sW29ul/8xadf+RC6blmJ9Yun8fflK/jNX8TyhfOKLiw8lZc2QRXGt99+y6BBg+jQoQPHjx9nwYIFxXp8GysrEhOTNa+TkpOxyfPp+VGZhMQkbG2sefjwIWn37lGpomUxOliTmJj0DA7J2NrYlJBDVRKT88YhBRurqgXKJCQlY2ttxcOH2SoHSwsUCoXmU2nThg2oYV+Nqzdiadaogc4eWYmJlLWz07wua2fHw6SkAmUenD0LDx+SFRtLxrVrmNSqhdnbb/PgzBly7t8H4F5UFKbNm+ucoGysrEjM88lUFYtC2iMpBVvrPH3C0pJfL/xOWGQUfgt+5O69exgZGVHOxIT+3h66OVSpRELKLc3rxJu3sKlS6Zn2PfvHJU5f/JP1u8O5/++/ZGU9xMy0HKMH9dbJAdT9IilPLFKeoV+kpxfSL+rr3S9srK1ITMo7PlIKjg9rKxKS8o7RdCpZqsZHYlIyI8Z+zexpPtTQ8wOuYYzR0neAl/gS3zvvvMPevXtJTVVdyrh9+zZpaWnYqE/dt2/frilrZmZGenr6c9fZrEkjrsXEEBMXT2ZWFqFh+3Fu66hVxrmtI9tCdgOqywTvtGqJQlHwun/xOjgV4hCqdogsfodGDbkWE0dMfILKITwSZ0ftL9+d27zHtt1hKocDUbzTojkKhYJbqbfJzs4GICYunmsxcVSvZlegjmfhwblzmLz6KmUdHFCULYtl166khYdrlUnbtw8z9c0PZSpVotyrr5J14wZZ8fGqmyLKlAFjY8q//TYZly7p7NCsUQOuxcQ+jsX+QmLh+B7bdu8F1LFo+SYKhYL1S+YTuX0Tkds3MbCXFx8P7KdzcgJoVr821+MTiU1MJjPrIbsPHcP57TefaV+/scM5sCKAyOXzGDekL92dHfVKTqDuF7F5+8UBnNvkj8W7bNuzDyiqX8RS3V73ftGscSNVezwaH/vCcXZqo+3g5Mi2kD0qh4gDvNOqBQqFgrtpaXw0agyjRwyjxRuv6RMClYMhjFEDcABQ5Bb3N8//IbZt28ayZcswMjKicePGdOzYkZkzZ2Jpacnbb7/N+fPnWbNmDVevXmXkyJEYGRkxefLkor+HesqS71E/H2WGnz/ZOTl4unVl2NDBBCwOpGnjhnRo60RGRgZjJ0/j9z/+wtLSAv+Z31DdwR4AZ1d37qXfJysrC3PzCixf9AN1a9cqWEkRS75H/Xwkj0M3tcMSmjZulMdhah6Hb/M5pOdzKGRJ9SKWfI86eowZ8xaqHLp2Ztig/gQELqdpowZ0cGxNRkYmY6fN4Pe//sbSwgL/byZT3b4aYQei+GHpCoyNjTFSGPHZ0EEFJnQtiljyvUK7dthOnozCyIjUzZu5uWgRVqNG8e9vv5EWEQGAzcSJqu+ecnJIWbiQuyEhqjsAfX0xe+stcnNzuXfoEEnTpxdaR1FLvkcdPcYM/wWPYzF4gCoWDRvQwam1qj20YuFDdftqWseYv3QF5cub8mG/JySHIpZ8jzp5lhlL15KTk4Onsi2f9OrOD2u30LReLZzfbsFvf11mxPR53L13HxOTslhVsiRk0WytY2wNP8T5v6/iM+zJ34kVteR71NHjzAhYSHb2o37Rj4ClK1SxcHxP1S98Z/L7X5ewtDDH33eSul8c4oeglep+oeCzoQMLJDcNRSz5HvXzUWZ8H0B2drZqjH44iIAfl9K0UUM6tHVUtYePL7//+ZeqPWb4Ut3BnkVBKwhcuYaaNaprjrV8gT9VKlcuWEkRS76/kDEKT13y/YU6PIGXOkGVGE9JUC+EIhLUi3F4eoJ6YRSRoF4ERSWoF0IRCepFUVSCeiEUkaBeCEUkqBfGUxLUC3V4Ai/tJT5BEATBsJEEJQiCIBgkkqAEQRAEg0QSlCAIgmCQSIISBEEQDBJJUIIgCIJBIglKEARBMEgkQQmCIAgGiSQoQRAEwSCRBCUIgiAYJJKgBEEQBINEnsUnCIIgGCSyHlRJYAgPi71fvCtb6kx59bowqYml61HJFu4kFV2uJLG0YZmFVdHlSpAP76rXWTKEh6Sm3Sy6XIk6VIXbpdwnKqpXqzWEB7UaylxRCHKJTxAEQTBIJEEJgiAIBokkKEEQBMEgkQQlCIIgGCSSoARBEASDRBKUIAiCYJBIghIEQRAMEklQgiAIgkEiCUoQBEEwSIpMUHv27OHevXsALFq0iBEjRnDhwoUSFxMEQRBebopMUIsWLaJChQqcOnWK6OhovLy8mDp16gtQEwRBEF5mikxQZcqUASAqKoqePXvSrl07srKySlysJJg/fz7Lli0rVYdDR6Jx6dETpZsXgStWF9iemZnJqPETUbp54f3BEGLj4wFIvX2HAR8Np3nr9vjO8nt+B3cvlG4eBC5f9QSHCSjdPPAeMFjjALBk2UqUbh64uHtx+Gi0/g7Rx3Hp2R+lV18CV68r3GHiVJReffEe8gmx8Qla2+MTk2je/n2Wrduot4PGw6sfSo8+BK5aW7jHhCkoPfrgPfhjjce5Cxfp3m8I3fsNwa3vYPYfOKS3g31HZzxPR+N99gSvfTGywHYzB3s6h2zD/XAkPY4exKFTxwLbP4i/RtPPhuvtcOjoMVw8eqN09yZw5RP65deTUbp74z1wqCYOR46dwKP/YLr16o9H/8FEnzylt4O2R08CV655ikdPvAf+L5/HELr1GoBH/yFEnzytv0P0cVy8+6H0fEqfmDgFpWcfvId8XHjfbOfCsrUb9Hc4Eo1LD2+Ubp4ErnjSGJ2I0s2zkHliGM1bt8N31ly969fyKMW5osgEZWNjg4+PD7t376Zt27ZkZmaSk5OjV2X/H3j48KHe+2ZnZ+M724+g+f6EBm8gZO8+Ll25qlVm8/adWFhYsH/nFgb164NfwEIAypUz4fNhHzHui8+eyz87OxvfWXMIWhBAaPAmQvaGcenylYIO5ubs37lV7bAAgEuXrxAato/QLRsJWhjAtJlzyM7O1s/Bbx5B/nMI3bCKkH0RXLp6TdthZygWFubs37KeQX288Vu4RGv7rICFOL77ls51F/CY409QwFxCN60mJCyCS1cK8TA3Z//WDQzq0xO/BT8CUK9ObYJXBbJj3XKCfpiLzyw/vfqGwsiI976bxT7P3gS3ak1trx5UbFBfq8wbY7/k6rYdbHd05sDgj3jvu9la29+e8Q2x+yN0rvsRmn75w3eEbl5PSFh4wX65Y5cqDts3M6hvL/zmLwKgUkVLFvvPYdemtcyaOolxPr7P6fGd2mPdEzxC1B4/5fOoyGL/2ezatOa5PLKzs/Gd60/QvLmEblyt6ptP6hPBGxjUuyd+C3/U2j5r3gIc331br/o1DrPnEjR/HqHBG9XzRCFj1MKc/TuDGdSvd7554mPGFfJBRy+PUp4rikxQ8+bNo02bNixbtgwLCwtu377NuHHjdK6otFi8eDEuLi706dOHq1dVnf3GjRt8+OGHeHh40LdvXy5fvgzArVu3+Oyzz/D09MTT05PTp1WfwubPn8/YsWPp3bv3c733c+cvUtPBgeoO9piULYuri5KIg9qfvCMPHqZH1y4AuHRoT/TJU+Tm5lLe1JSWzd+gnImJ3vWrHC5Qs3peh06FOETRo5uryqGjM9EnTpKbm0vEwUO4unTCxMSE6vb21KzuwLnzun8fee7i79R0sKe6fTWVg9KZiEM/azscPkKPLi4qh/ZtiT71C49WhgmPOox9NTvq1aqlTwgee1zI59GpQ0GPqJ/p4fq+ysO5LdEnVR6mr7yCsbFqMYCMjEwUCoVeDlYt3+TulWukXbtOTlYWV4K3U8O1s3ah3FxMzM0BMLG04H7i4yfE13TtTNr166T+8Yde9YPqbFCrT3TqSETUYa0ykVGH6dFV5eXSoT3RJ1T9snHDBthYqZ7UXq9ObTIyMsjMzNTT4/d8Hh2e4PFofLQj+sRptUf9PB619PYo2DcL6ROHCu8TkKdv1n5V57o1Ds80TxyiR1f1GO3gTPTJk8U6T6g8Sn+uKDJBpaSk0K5dO1599VWOHz/O3r17ee2113SuqDQ4f/48u3fvZvv27SxdupTffvsNgMmTJzN58mS2bt3K+PHjmTZtGgDTp09n4MCBBAcHM3/+fCZNmqQ51uXLl1m5ciXff/+93j5JKSnY2lprXttYW5OUnFKgjJ2t6lH8xsbGmFeoQOrt4nscflJyCrY2No8dbKxJSkkpUKYwB5V/nn0L8X8mh5Sb2FrnjYMVSSk3C5Sxs7HO42BG6p07pN+/z9I16xnx4UCd6y3Uwya/R/72KNwD4NfzF3Ht9QFufQczbfxoTcLShfJ2dqTHxmle34+Px6yanVaZX2bOpU4vL3r//iudNm8geuzXKh8zM1774jPOPOcl3wJ9wtqqYL9MTsHOJm+feByHR4RFHKBxwwaY6Dk5qjyKGB/JKU9sj8ceB/X2SEp+xj5h/YS+uXo9I4YO0rle7eMnFznOVPNEXofinSfAMOaKIkfUZ599RnBwMNevX8fHxwdnZ2dGjx7N0qVLda7sRXPq1Ck6duyIqakpAM7OzmRkZHDmzBk+//xzTblHn7SOHj3KpUuXNH+/d+8e6enpmn1feeWVF2gvFMaCoJUM7O2NWfnypa3C600bE7ppNZevXmP8tBk4vfc25cqVK/Z66nj14O91Gzm/YDHWb7WkbeAitr7tyJtfj+X8wiU8VPfR0uTvy1fwm7+I5QvnGYiH/wuve8HSFQzsYxh98/8LRSYoIyMjjI2N2bdvH/3792fAgAG4u7u/CLcSIScnBwsLC3bs2FHotp9++qnQSeZRknsebKysSExM1rxOSk7GxtqqQJmExCRsbax5+PAhaffuUanikxf00tnB2orEpMeLtSUlJWsujeQto3Kw0XJQ+efZtxD/Z3Kwqkpict44pGBjVbVAmYSkZGytH8UhnUqWlvx64SJhkVH4LVjC3Xv3MDJSUM7EhP7eHvp5JOX3yN8eag8bbY+81Kn1KuVNTfnr8lWaNW6ok8P9hATMHOw1r8tXq0Z6vi/d63/QjzCPXgAknzhFmXLleKVKFaxatuDV7t1o5euDiaUl5OaQnZHB74G63QhUoE8kpxTsl9ZWJCQlFRqHxKRkRoz9mtnTfKjh4KBT3QU9ihgf1lZPbA+VxwRmT5ust4eN9TP2ieSCDr9e+J2wA1H4LfiRu2nqvlnOhP7enro5WFkXOc5U80RygTFanBjCXFHkJT5jY2NCQkLYsWMH7dq1A57vRoEXSatWrQgPD+fff//l3r17HDhwAFNTUxwcHNizZw8Aubm5/KG+ft+mTRvWrHl859Dvv/9erD7NmjTiWkwMMXHxZGZlERq2H+e2jlplnNs6si1kN6C6ZPJOq5Z6f79RuENjrt2IISYuTu2wD+d2+R2c2LYrVOUQHqlxcG7nSGjYPjIzM4mJi+PajRhea9pEd4dGDbkWE0tMfILKYX8kzo6ttR0cW7Ntd5jK4UAU77RsjkKhYP2SBURu30Tk9k0M7OXFxwP765WcAJo1Vns8ao99EQU9nFqzLXSvyiMyindavolCoSAmLl4zDuISErly/Qb21Wx1dkg5fQaL2rWoULMGRmXLUtvTnRu792qVuRcbR7W2TgBY1q9HmVde4d+bNwl9vxs/NWvBT81acGHxEs76zdM5Oani0ChfHMJxdmqTLw6ObAtRjRlVv2yBQqHgbloaH40aw+gRw2jxxvNd+i+0PQp4tMkzPg7m8xjL6BGfPJfH476pdtgfgbNTIX2zkD6xPnABkdt/InL7Twzs/ahv6pac4EnzhJO2Q1tHtoWox2hEZLHPEyqP0p8rijyDmjlzJhs3buSTTz6hevXqxMTE4ObmpnNFpUGTJk3o0qUL3bt3p3LlyjRr1gyAuXPnMnXqVBYvXszDhw/p0qULDRs2ZOLEifj6+tKtWzeys7Np2bIlvr7635WUH2NjY3zGj2Hop5+TnZODp1tX6tWpTcDiQJo2bkiHtk54uXdj7ORpKN28sLS0wH/mN5r9nV3duZd+n6ysLMIPRrF80Q/Ura3bjQIqh7EMHT5S5dC9G/Xq1CFg0RKaNm5Eh3ZOeLm7MXbSFJRuHlhaWOA/azoA9erUoXOnjnTx7EWZMmXw+Wqc5t8QdHYYM4qhn49ROXTtQr3atQgIXEbThg3p4NQar25dGDttOkqvvlhamOP/zRSd63kmj7GjGDpS7dGtC/Xq1CJgyTKaNmpAB6c2eLm5MnbKdJQefVQe06cCcPrX31i6ah3GxsYYBiqtEwAAIABJREFUGSmYOu5LKlesqLNDbnY20WO/5v1tP6EoY8RfazZw+48/eXPieG7+cpYbe8I4McGHNvP9afLpx5ALh4c9352chcfhS4Z+9gXZ2dmP++WPS2naqCEd2jri1b0rY318Ubp7q/rEDNW4WLtpCzdiYlkYtIKFQSsAWL7AnyqVK+vp8QVDP/uyCI9vULr3VHtMU3sEF+IxjyqVK+nuMCZfn6hdSJ+YOh2lp7pPfDtV5/dapMP4MQz9VD1G3bqp5wn1GG2rHqOTp6J081TPE99q9lfNE+n55onaenqU7lyhyH10+4lQfKSnlm79ZpXgfvF+Yaoz5dWXG1ITn16upKlkC3eSii5XkljasMxC98sbxcmHd9VfUKf9U6oemFeBtJtFlytRh6pwu5T7REX1DQTpt0vXw6yi4cwVhVDkGdS1a9f4/vvvuXTpEhkZGZq/R0To/38XgiAIglAURX4H9fXXX9OnTx/KlCnD6tWrcXd3/89c4hMEQRD+uxSZoDIyMnj33XcBsLe357PPPiMqKqrExQRBEISXmyIv8ZmYmJCTk0PNmjVZu3YtNjY2mv8NEgRBEISSosgzqAkTJvDgwQMmTZrEhQsX2LFjB7Nnzy5qN0EQBEF4Loo8g3r0WCMzMzNmzpxZ4kKCIAiCAE9JUJ988slTd/zxxx+ful0QBEEQnocnJqghQ4a8SA9BEARB0OKJCeqtt1Rr7dy/f59XXnkFIyPV11XZ2dl6P05fEARBEJ6VIm+SGDRoEA8ePNC8/vfffxk8eHCJSgmCIAjCM/0flJmZmea1mZmZVsISBEEQhJKgyLv4TE1NuXDhAk2aqJ5Ee/78eVkXqSjMdHtAZYnwlOdbvVAq6f6E72LH0qboMiWM5ll4pY15ldI2UD0Lr7SpWPp9AlA9C6+0MZS5ohCKfFjsuXPn+PLLL7G2tiY3N5ebN2/i7+9P06ZNX5SjIAiC8BLyTE8zz8rK4urVqwDUqlWLsmXLlrjYfxp5QrFBPc08N/aPUlVQODSE0j6DUj9NfWcVuyIKlixu/yTAvVul6kCFynAnuehyJYmlell5QxinhuDwBIq8xAdQtmxZ6tevX2w+giAIglAURd4kIQiCIAilgSQoQRAEwSApMkHl5uayY8cOFixYAEB8fDznzp0rcTFBEATh5abIBDV16lTOnj1LaGgooPo/qGnTppW4mCAIgvByU2SCOnfuHFOmTKFcuXIAWFpakpWVVeJigiAIwstNkQnK2NiY7OxsFAoFALdu3dI8l08QBEEQSooibzMfMGAAn376Kf/88w/+/v7s3buXUaNGvQg3QRAE4SWmyATl5uZGkyZNOHbsGLm5uSxatIg6deq8CDdBEAThJabIBBUfH4+pqSnt27fX+lu1atVKVEwQBEF4uSkyQX388cea3zMyMvg/9s48LKrqf8DvAKKogBsgAm64iympZbmjIymbILhkmppZlqWl4o5brln+MNMkNNfUcgdMVBRXci8StRI3dnABEZVlvL8/ZhwZQJFRhvvN8z7PPDpzz3BePuec+dx75nBPfHw89erV067qEwgEAoGgNCh2tUNISIj2sXfvXn799VdatWplCLdS57vvvmPlypUGrfPwsShcvf1QevYh6Kc1hY7n5OQwZsIUlJ598Bs8jPjERADupGcwaMRInNt3Ydb8r1/cobcvSk8fglY9zWEySk8f/AYN1ToArFi5GqWnD669fTlyPEp/h6gTuPZ9D6XvuwSt3VC0w5QZKH3fxW/Yx8QnJukcT0xOwbnrO6zcsElvB4AjJ8/yzvsj6THoI4I2bil0/FR0DD4ffUFzpTd7Dh3TObY9/ACugz/GdfDHbA8/oLfD4eO/49pnAErvfgStXlfoeE5ODmMmBaD07offkA+1sYiOuYDXu0PwencInu++z76Dh/R2sHLpisuJI3Q7dZwGo0cVOt78q5l0jtxH58h9uJw4Ss8rT+5v2HT6FLocPUiXowep1dtTbweAw8ejcPXph9LLl6Cf1hY6npOTw5iJU1F6+eI3+ANtLNTj41OcO7gwa8GiF3OIOoGr77soffoTtGZ90Q6Tp6P06Y/f0BG67TFwKF4Dh+L57hD2HTysv4MMxqgcPEq8HK958+biD3X1RKVSMWvB1wR/93+Ebd1E6J69XL5yRafMrzt2YWFhzr5dWxkysD+LAr8HoHx5U0aP/Aj/Lz5/cYf5CwleGkjY1s2E7gnncmwRDubm7Nu1jSEDB7AoUP1H2pdjrxAWvpewLZsI/j6QmfMWolKp9HNY9H8EL15I2MY1hO6N4PLVa7oOu8LUcdjyM0MG+LHo+xU6x+cHfk/Ht94ocd2FPJas4Md50wldtZSwA0e4fO2GThlb6xrM8x+Ne7dOOq+n383k+3Wb2Lz0a375fhHfr9tERuY9/RwWfktw4CLCfllP6N79XL5yVafMrztD1bHYvpkh7/Zj0XfLAWjoWJ+ta4PZ+fNqgpd8Q8C8r8nLyyuxA0ZGvLZwLr/3HciBtztj59Obyo11770ZM3U6h7ooOdRFydXgVSSF7gbAWtmNKq+14FDn7hzp0QvHUSMxMa9ccofHsZj/DcFLviVsy0ZCw/cVjsWOEHUsdm5Rj48l+cfHCPzHFE6uJXZ43B6b1xEaXkR77ApTj49tmxgyoC+Llv4AaNpjzY/s3PATwUsWETBfv/aQwxiVi0exCeqnn37SPlauXMnYsWOxtrYucUVyYfny5bi6ujJgwADtHdovXrxI37598fDw4NNPPyUjQ3133+joaDw8PPDy8mLBggW4u7u/UN3R5y9Qx94eB3s7TMuVw81VSUSk7lnWgcjDeLu7AeDazYWoU6eQJImKZma0cW5FeVPTF3SIoY5DfoceRTgcwttD49DdhaiTaoeIyMO4ufbA1NQUBzs76jjYE30+puQOFy5Sx94OB7taagelCxGHj+o6HDmGdy9XtUPXzkSdPsvjG+/vP3QEu1q2NKxXT58QPPG49C+17WriUKsmpuXK0atrRyKOn9QpY1/ThsaOdVEodIfK0dPnePv1VlSxMMfSvDJvv96KI6fOltwh5qJueyi7E3GoQCwOH8XbrScAri5diDp1BkmSMKtQARMT9Sx9dnaO9k9BSkrV153JunqN+9dvIOXmkrB9JzV7uj61vJ1PbxK27QDAvHEjbh0/gaRSobr/gLsxF7B26frU9z6L6JgLurHo0b1w3zx0BG/3XgC4dutK1MnT+cZHS8qblter7icOBfpmj26F++ahI3i7vaN2cHn57SGHMSoXj2ITVFZWlvaRk5ND586dWbZsWYkrkgPnz59n9+7d7Nixgx9//JG//voLAH9/f8aNG0dISAiNGjXS3tZp8uTJzJo1i507d2JsbPzC9aekpVKz5pON0mysrUlJTStQJg3bmuoTABMTE8wrV+ZO+su7HX5Kaho1bfI52FiTkpZWqIytxjO/Q0paWrH+z+WQdpOa+U5ybKytSEm7WaiMrU3+OFTiTkYGWffv8+O6nxn1wfslrreQx81b2Fo92TyvplV1Um7eev73Wuv3Xp2fk5ZGTZt8sbCxKro9iogFwJ/nY3Dr+x6eA95n5sRx2g/IklDBtiYPEhK0zx8mJmFmW/RGk2b29lSsXZs0zYf23fMXsO7WBWMzM0yrVaNGh/aY2em3gErdN/PHooi+mZaGrU3hvvmyKNQeJeiboGmPfoPwfHcIMyfo1x5yGKNy8Xhm9FQqFVlZWUyYMKHEP1iOnD59mu7du2NmZgaAi4sLDx48IDMzkzfeUE8XeXt7M3r0aO7evUtWVhbOzs4AuLu7ExkZWVbqAg1Lg1fzfn8/KlWsWNYqsqClU3PCfllP7NVrTJgxh05vt9Pe9aU0sPPxIjEkFB49AiAt8hBVXm9Fh992kXPrFndOnUFSPSq1+uVOS6fmhG1ep26PmXPp9Pabpdoe/3WeegWVl5eHsbExZ8+WfNpCUDQ2VtYkJ6don6ekpmJjbVWgjBVJyerN1PLy8si8d4+qVV7elsw21lYkp+RzSEnFxsqqUJkkjWd+Bxsrq2L9n8vBqgbJqU82jEtJTcPGqkahMkkp+eOQRVVLS/6MucCipStw6d2PNZu3sGLNetb/uq3EDgA2NaqTlO/sODntFjY1nm9LdJsa1UlK1e+9Oj/HyorklHyxSEkruj2KiEV+HOvVpWJFM/6J1f2+5Hl4mJSMmZ2d9nmFWrY8SCp6o0k7by8Stu7Qee3fbwM51EVJVJ/+oIB7sbEldoDHfTN/LIrom1ZWJKUU7psvi0LtUYK+mR/HenWpaKZfe8hhjMrF46kJys/PD4AmTZrw8ccfs2PHDvbu3at9/C/Stm1b9u/fz8OHD7l37x4HDx7EzMwMCwsLTp8+DcDOnTtp27YtFhYWVKpUiT///BOA3bt3v3D9LZo35VpcHHEJieTk5hIWvg+Xzrpfvrt07sj2UPUS/vCIA7Rr20bvueyiHZpx7UYccQkJGoe9uHTpWMChE9tDNA77nzi4dOlIWPhecnJyiEtI4NqNOF5zal5yh6ZNuBYXT1xiktph3wFcOrbXdejYnu27w9UOBw/Rro0zCoWCn1cs5cCOzRzYsZn3+/ny0fvv8Z6fj36xaNKQ6wlJxCelkJOby+6DR3B5+/kWXnRo48yxM+fIyLxHRuY9jp05R4c2ziV3aNZE0x6aPrFvPy6diohF2G8AhB+IpF3b11EoFMQlJGq/hE9ISubKtevY1Sp6au5ZpJ/7g0r161GxtgOKcuWw8/Yi5bfwQuUqN2xAuSpVuHPq9JMXjYwoV7UqABbNmmLRvBlpeq4mbNGswPjYux+XzgX7Zge2axZohEccpF3b1i93fDTT9E2tQwQuHTvoOnTqwPawPWqHA5G0a/OU9riuX3vIYYzKxaPYCdKcnByqVq3KiRMndF7v0aNHiSsra5o3b06vXr3w8vKiWrVqtGjRAoAFCxYwffp0Hjx4gIODA/PmzQNgzpw5TJ06FSMjI9q2bUvlyvqtTnqMiYkJARPGMfzTz1E9ekQfTw8aOtYncPkKnJo1pVvnTvj29mT8tBkoPftgaWnB4nlfad/v4tabe1lZ5Obmsj/yEKuWLaFB/fp6OIxn+CcaBy8PGjo6ErhM49BF4zB1OkpPHywtLFg8fw4ADR0d6dmjO7369MPY2JiAif56fTdnYmJCwLgxDB89Tu3g3ouG9esRGLQSpyZN6NapPb4evRg/cw5K33extDBn8ezpJa6nWA9jY6Z9NoIPJszg0aNH9OnZjYZ1a7Pkpw04NW6Ay9tv8telfxk1fR53793jYNQplq7ZSOiqpVSxMOeT9/rh98lYAD4Z1I8qFuYldzAxIcD/S4Z//iUq1SP6eLqp+8QPwTg1bUK3zh3w9XJn/PTZKL37qdtjzgwAzvwZzY+r12NiYoKRkREzJoylWpUqJXaQVCr+mjCZdr9uRGFszI2fN5H59z80njie9D/+JGWP+oTUztuLhO26V09G5crRIUz9Wm5mJmc/HoWk56oxdSzGMnzUGHUsvNw14yNIMz464uvlwfhpM1F6+arHx9zZ2ve7uHtrxkce+yMPs+r7QBrUL9lCGhMTEwLGf8Hwz8eq+6aHGw0d6xG4QtMenTrg6+nG+OlfofTpX7g91mzQtIeCGf5f6tUechijcvFQSI+XRhWgU6dODB06FEmSUCgU5C+mUCgYOnSoXr/0/xJZWVlUqlQJgKCgIFJTU5k6depzvDG9lM2KoVIVuP/yvjjWi4qaKY87RU8VGYyqNZHiLxVfrhRR2DeBu/p9Uf3SsFBPr+yqblumGp63kuDe7TJ1oHI1yEgtvlxpYqlZiCGHcSoHh6fw1CuoR48ekZWVVSo+/yscOnSIFStWoFKpqFWrFvPnzy9rJYFAIHhleGqCsrKyYtSoF/ujt/91evXqRa9evcpaQyAQCF5JnrpI4ikzfwKBQCAQGISnJqjVq1cbUEMgEAgEAl2emqCq6LH6RCAQCASCl4XYu10gEAgEskQkKIFAIBDIEpGgBAKBQCBLRIISCAQCgSwRCUogEAgEskQkKIFAIBDIkqfei08gEAgEgrKk5Ns9CopH3Cz2yQ0g5XBTzoyU4suVqoMNZJZ8t92Xirlmr6qsO2XrUakq08tXLVOFmdl35DM+5OAhB4enIKb4BAKBQCBLRIISCAQCgSwRCUogEAgEskQkKIFAIBDIEpGgBAKBQCBLRIISCAQCgSwRCUogEAgEskQkKIFAIBDIEpGgBAKBQCBLRIISCAQCgSwRCUogEAgEsuSVSlBr166lZ8+ejB07tswcDh+LwtXbD6VnH4J+WlPoeE5ODmMmTEHp2Qe/wcOIT0wE4E56BoNGjMS5fRdmzf/6xR16+6L09CFo1dMcJqP09MFv0FCtA8CKlatRevrg2tuXI8ej9HeIOoGr77soffoTtGZ90Q6Tp6P06Y/f0BHEJyYBEB1zAa+BQ/EaOBTPd4ew7+BhvR2eeAxE6TOgGI8B+A39qIDHMLwGDsPz3aEv5HH4+O+4+vRH2duPoNVri3aYNA1lbz/83h+udTj2+0l83huKR7/38HlvKFGnTuvvcCwKV+++KD19CfrpKQ4TpqD09C2iX36Cc/uuzJq/SO/6H9OgRzc+++skn184Q4dxYwodt6ztwPt7djDy9FGG7A3Bwq6W9ljL9/rzecxpPo85Tcv3+uvtIIvxIQMHWXhIrxCurq5SUlKSzmu5ubkvv6J7d4p85GXclLq5dJVuXDovZd9JlTzcekn/Rp/RKbN+1Y/StEkTJOneHSl062Zp9KefSNK9O1JWWqJ06shB6efVK6WZUyc/tQ7p3h21Q1Z6kY+8u7fUDn/HSNnpaRqHszpl1v8UrHbISpdCt/4ijR71iSRlpUv/Rp+VPNx6Sdl3UqUb/8RI3Vy6Snl3bxVd12PSUwo98m4lSt26dpFuxJyTstPiJY9ePaV/z/6uU2Z98A/StAnjJSk9RQr9ZYM0+pOPJSk9RbqffF3KvZkgSekpUsrlGKndm29onxf5kCRJSk8u8pF3K0HjcVbKTovL5/GkzPrg5dK0CeMkKT1Z4/GRJKUnS/eTr0m5N+MlKT1ZSrl8XuMRX3RdkiRJd28W+ci7k6J2uPCnlH0rSd0ef5zSKbN+5Qpp2kR/Sbp7Uwr9daM0+tORknT3phRz8piUHHtRku7elP4+e0Lq0P7tp9bzpG/eLvTIy0jT9Mu/pOw7Kfn65ZMy61cFafrlbU2/HClJ925LWWkJ0qkjB6SfVwdr+mXhn6/zkCQpwLRKkY/pFapJt2KvSIsbt5RmVrKSkv78S/rutTd1ypzfsl3aOuxjKcC0ivRTDw/pj/WbpADTKtI8m7rSrStXpXk2daW51nWkW1euSnOt6xRZj6zGR1k6PPaQSyyK4JW5ggoICCA+Pp4PP/yQ1q1bM378ePr374+/vz/x8fEMHjwYDw8P3n//fRI1ZwE3btygb9++eHh4sHjxYpydnV/IIfr8BerY2+Ngb4dpuXK4uSqJiNQ98z4QeRhvdzcAXLu5EHXqFJIkUdHMjDbOrShvavqCDjHUccjv0KMIh0N4e2gcursQdVLtEBF5GDfXHpiamuJgZ0cdB3uiz8eU3CHmInXs7XCwq6V26NGNiMNHdR0OHcHb7R21g0sXok6dQZIkzCpUwMREfRP+7OwcFAqFHlEoicfRfB6diTp19qV6RMdc0G2PHt2JOHSkgMMRvN17qh26dSXq5GkkSaJZk8bYWFkB0NCxPtnZ2eTk5JTc4bn65RG83Xs9cTh1+qX2SwC7tq25HXuFO1evo8rN5fwv22ji0UunjFXTxlyNVMfnauQRGnuo4+Ko7MaViEge3EnnYXoGVyIiadCje4kdZDE+ZOAgF49XJkHNmjULa2tr1qxZw5AhQ4iNjWX16tV8++23fPXVV3h7exMSEoKHhwdfffUVAHPmzGHw4MGEhIRQs2bNF3ZISUulZk0b7XMba2tSUtMKlEnDtqY1ACYmJphXrsyd9Jd3O/yU1DRq2uRzsLEmJS2tUBlbjWd+h5S0tGL9n8shLY2aNtb5fo4VKWk3C5S5ia1N/jhU4k6GOg5/no/Brd8gPN8dwswJ47SJouQeN4vwKNgez/K4gFu/wXi+O5SZE8bq5VGoPaytCveJ1DRsbfK3xxOHx4RHHKRZk8aY6pEo1O2aPw5P65eF+8TLxKKWLRlxCdrnGQmJmNvZ6pRJjo6hWW93AJp6uVPBwgKzalWxsLMlIy5eW+5ufAIWBd77PMhifMjAQS4er0yCKoiLiwsVKlQA4Ny5c7i7qzu9l5cXZ86cAeCPP/7gnXfUZ88eHh5lIyooREun5oRtXseW1UGsWLOe7OzsMvJoRtjmtWxZvaJMPf6NvcKi75Yxa7J/mdRvSPZOnEadju35+MQh6nZqT0Z8ApJKVdZaglLilU1QZmZmBq/Txsqa5OQnm+elpKZiY21VoIwVScnqTf7y8vLIvHePqlWevqFXiR2srUhOyeeQkqqdJspfJknjmd/BxsqqWP/ncrCyIjnlyUaGKalp2FjVKFCmBkkp+eOQRVVL3Tg41qtLRTMz/om9WmKHx3UU9ijYHqXrUag9UtMK9wlrK5JS8rfHE4fklFRGjZ/EgpkB1La3L3H9oGmP5PxxeFq/LNwnXiZ3E5OwdLDTPre0q0VmQpJOmcykZDb3G8wPb3YmIkA90/Ew4y53E5KwdHjy+1vY23G3wHufB1mMDxk4yMXjlU1Q+XF2diYsLAyAkJAQ2rRpA0DLli3Zu3cvgPb4i9CieVOuxcURl5BITm4uYeH7cOncSaeMS+eObA9V1xUecYB2bdu80PcshR2ace1GHHEJCRqHvbh06VjAoRPbQzQO+584uHTpSFj4XnJycohLSODajThec2pecodmTbgWF/8kDnsjcOnYQdehUwe2h+1ROxyIpF2b11EoFMQlJJKXlwdAQlIyV65fx66WftOvRXu0L+DRPp/HoWd43NDLo0WzpgUc9uPSqWAsOrI99De1Q8RB2rVtjUKh4G5mJiPGjGPsqJG0bvWaPiFQOxTZLwv2iY5sD92dz+Hl9kuAxNNnqdbAkSp1a2NcrhxOfX24pPm9H1OxejVtvR39v+Dcmg0AxO6LwLF7VypUsaRCFUscu3cldl9EiR1kMT5k4CAXD4UkSZJe9v+DuLi4sGXLFjZs2EDFihX54IMPAEhISGDSpEncuXOHatWqMW/ePGrVqsW1a9cYP348Dx8+pGPHjoSEhHDkyJFiauGZW74fOnqMuYsWo3r0iD6eHowcPpTA5StwataUbp07kZ2dzfhpM7h46R8sLS1YPO8rHOzVZ5Uubr25l5VFbm4u5uaVWbVsCQ3q1y9cSTFbvh86coy5i75VO3h5MHL4MAKXaRy6aBymTufi3/9gaWHB4vlztA7Lg1exdWcIxsbGTB73JZ07vF10JcVs+X7oWBRzv12idvBwY+SwwQSuCMapaRO6deqgdpj+FRf/+VftMGcGDna12LF7Dz+u2YCJiQlGRgo+/WAI3bt0KrIOoNgt39Ue32k8emk8VuLUtHE+jzkaD/N8HuFFeHQsupJitnw/dPQ4c78NRKVS0cfTnZEfDCHwhx/VsejcUe0QMOtJe8ydhYO9HcuCfyJo9Trq1HbQ/qxVSxdTvVq1wpUUs+X7oaPH8/VLd02/DMKpWZN8/XJmvn45u0C/vF+gX9Yr+pctZsv3hu8oeWfRXIyMjTm3egOHF3xD14BJJJ79g79Df6OZtyfdvwpAkiSuHzlO2OjxqDQLQ5zfH0jHCV8CcHj+N/yx9uci6yhuy3eDjo+neBjE4bGHXGJRBK9UgiopDx48oEKFCigUCsLCwggNDWX58uXFv/EZCcogFJOgDEIxCcpgFJOgDOPw7ARlEIpJUAajmARlCIpLUAahmARlUA85ODwF/ZY/vSLExMQwa9YsJEnCwsKCuXPnlrWSQCAQvDKIBPUM2rRpw65du8paQyAQCF5JxCIJgUAgEMgSkaAEAoFAIEtEghIIBAKBLBEJSiAQCASyRCQogUAgEMgSkaAEAoFAIEtEghIIBAKBLBEJSiAQCASyRCQogUAgEMgScS8+gUAgEMgScauj0kDcLPbJDSDv3S5bj8rV4K5+O4q+NCys5HHDWpCHhwzGx5wKRdzx3YBMeagZF3IYp3JweApiik8gEAgEskQkKIFAIBDIEpGgBAKBQCBLRIISCAQCgSwRCUogEAgEskQkKIFAIBDIEpGgBAKBQCBLRIISCAQCgSwRCUogEAgEskQkKIFAIBDIEpGgBAKBQCBL/jMJKj4+Hnd397LWKJbDx6Jw9fZD6dmHoJ/WFDqek5PDmAlTUHr2wW/wMOITEwG4k57BoBEjcW7fhVnzv35xh96+KD19CFr1NIfJKD198Bs0VOsAsGLlapSePrj29uXI8Sj9HY5H4erTD6WXL0E/rS3aYeJUlF6++A3+gPjEJOBxHD7FuYMLsxYs0rv+Jx6/49pnAErvfgStXle0x6QAlN798Bvyodbj2IlT+Awahkf/wfgMGkbUqTP6O0SdwNV3IEqfAQStWV+0w+TpKH0G4Df0I61DdMwFvAYOw2vgMDzfHcq+g4f/px1A//EBsGLVapSefXD19uPI8d/1dqiv7MbH0ScYGXOat8aNLnTcwsGOgeE7+eD3SIafOoKja3cAjMqVwz1oKR+ePsrwk4ep3am93g5yGKNy8PjPJKgXIS8vzyD1qFQqZi34muDv/o+wrZsI3bOXy1eu6JT5dccuLCzM2bdrK0MG9mdR4PcAlC9vyuiRH+H/xecv7jB/IcFLAwnbupnQPeFcji3Cwdycfbu2MWTgABYFLgXgcuwVwsL3ErZlE8HfBzJz3kJUKpWeDt8QvORbwrZsJDR8H5evXC3gEKKOw84t6jgsyR+HEfiPGaVnBAp4LPyW4MBFhP2yntC9+wt77AxVe2xlIotlAAAgAElEQVTfzJB3+7Hou+UAVK1iyfJvFxKyaS3zp0/Ff/rsF3BYTHDg14RtXktoeASXr1zTddgVpm6PbRsZMqAvi5b+AEBDx/psXRPEzg2rCF7yNQHzF+nVl+XgoPXQc3xcvnKFsPB9hG3ZSPDSQGbO169vKoyMeCdwIZu8+rKi1Vs079uHGk0a65TpMHEcF7fsYGW7LuwYNJx3lqhPlJyHDQbgxzYd+NnNh+7zZ4NCoV8cyniMysXjP5WgVCoVU6dOxc3NjWHDhvHw4UMuXrxI37598fDw4NNPPyUjQ33n3kGDBjFnzhx8fHxYu3Ytv/32G+7u7nh6ejJw4EDtz1uwYAF9+vTBw8ODTZs2vZBf9PkL1LG3x8HeDtNy5XBzVRIRqXvGeSDyMN7ubgC4dnMh6tQpJEmiopkZbZxbUd7U9AUdYqjjkN+hRxEOh/D20Dh0dyHqpNohIvIwbq49MDU1xcHOjjoO9kSfjym5Q8wFXYce3Qs7HDqCt3svtUO3rkSdPJ0vDi0pb1pezwjk97io66HsTsSho7oeh4/i7dZT7eHShahTZ5AkiWaNG2FjVQOAho71yM7OJicnRz8Hezsc7GppYtGNiMMFHA4dxdvtHY1DZ6JOnUWSJMwqVMDERL0hQXZ2Dgo9Pgzl4gAvNj7UfVOp6Zu1qGNvT/T5CyV2qNW2Nbdjr5J+9TqPcnO58Os2Gnn01CkjSRLlLcwBKG9pwb3EZABqNG3MNY3v/bSbPMzIoFZrZz3iUPZjVC4e/6kEdf36dQYOHEhYWBjm5uaEh4fj7+/PuHHjCAkJoVGjRixdulRbPjc3l23btjFs2DCWLVvGypUr2bVrF8uXq8+St2zZgrm5OVu3bmXr1q388ssvxMXF6e2XkpZKzZo22uc21takpKYVKJOGbU1rAExMTDCvXJk76S/vdvgpqWnUtMnnYGNNSlpaoTK2Gs/8DilpacX6P7+D9bMd0tKwtSns8DJJSSvoYVV0LGzyt0cl7mToeoQfiKRZ40aY6nHykJJ2U9fBugiHtJtPdfjz/AXc+g3G892hzJwwVpss/tcc1HXoPz6K7tepJXYwr2VLZnyC9vndhETMa9nqlDny1QKcBvTls8vn6bdjM+FfTgAg9a8YGrn1RGFsjGXd2tg6t8Lc3q7EDnIYo3Lx+E8lKHt7e5o2bQpA8+bNiYuLIzMzkzfeeAMAb29vTp8+rS3fq1cv7f+dnZ2ZOHEiv/zyi/ZS9NixY+zcuRMvLy/8/PxIT0/n+vXrBvyNBHLn39grLPpuObMm+5dJ/S2dmhG2eS1bVq9gxZr1ZGdnv5IOhqRZ3z5Er9vIdw2c2Ny7H56rfgCFgj9Wr+duQiIfHD9Aj6/nEv/7SSQ9p9cEav5TCSr/GayxsTF37959ZnkzMzPt/2fNmsWYMWNISkqiT58+3LlzB0mSmDp1Kjt37mTnzp0cOHCADh066O1nY2VNcvKTDeNSUlOxsbYqUMaKpGT1mV9eXh6Z9+5RtcrTN/QqsYO1Fckp+RxSUrGxsipUJknjmd/BxsqqWP/nd3hydlukg5UVSSmFHV4mNlYFPdKKjkVK/vbIoqql2iM5JZVR/pNZMHMqtfU4U1Y71NB1SC3CwarGUx0e41ivLhXNzPgnVvc7tP8VB3Ud+o+Povu1NSUlMzFJ56rHwq4WmZoFIY9pNeQ9LmzdAUDCiVOYVChPxRrVkVQq9vtPIfjNzvzq9x4VLC25/W9siR3kMEbl4vGfSlAFMTc3x8LCQnvVtHPnTtq2bVtk2Rs3btCyZUtGjx5N1apVSU5OpkOHDmzcuJHc3FwArl69yv379/X2adG8Kdfi4ohLSCQnN5ew8H24dO6kU8alc0e2h4YBEB5xgHZt27zQvH5hh2ZcuxFHXEKCxmEvLl06FnDoxPYQjcP+Jw4uXToSFr6XnJwc4hISuHYjjtecmpfcoVmBOOzdj0vngg4d2B66W+0QcZB2bVu/1DioPZpoYqHx2LcflwIrr1w6tmd72G9qjwORtGv7OgqFgruZmYz4YjxjPx1J65avvZhDXHy+WETg0rGAQ6f2bA/bo3E4RLs2aoe4hETtgoSEpGSuXL+BXa2a/5MO8GLjw6VzJ8LC92n6ZiLX4uJ4zalZiR0ST5+lWoP6WNatjVG5cjTz8+Gf0D06Ze7GxVOvq9qreuNGmJQvz/20m5iYmVGuYkUA6nXrwiNVHjcv/a1HHMp+jMrF4z+/5fuCBQuYPn06Dx48wMHBgXnz5hVZbuHChVy/fh1JkmjXrh1NmjShcePGJCQk4OPjgyRJVK1alWXLluntYmJiQsCEcQz/9HNUjx7Rx9ODho71CVy+AqdmTenWuRO+vT0ZP20GSs8+WFpasHjeV9r3u7j15l5WFrm5ueyPPMSqZUtoUL++Hg7jGf6JxsHLg4aOjgQu0zh00ThMnY7S0wdLCwsWz58DQENHR3r26E6vPv0wNjYmYKI/xsbG+sXBfyzDR41BpXpEHy93TRyCNHHoiK+XB+OnzUTp5auOw9wnq+Rc3L01cchjf+RhVn0fSIP69fT0+JLhn3+p9vB0U3v8EIxT0yZ069wBXy93xk+fjdK7nzoWc2YAsP6XrdyIS+D74J/4PvgnAFYtXUz1alVL7jB+DMM/H6duD49eNHSsR+CKlTg1bUy3Th3w9XRj/PQ5KH0GYGlhrnU48+df/LhmAyYmJhgZKZjh/yXVqlTRLw5l7KD10HN8NHSsT09ld3r59tf0zfF69U1JpSJ8jD8DQrZgZGzMn2s2cPPiJToFTCLpzDn+DdvD/gnT6LX8/3jjs5EgSYSMUK8orWRdgwEhW5AeSWQmJrJz2McvEIeyHaNy8VBIkiTpZS94OlnpZVt/pSpw/+UuKCgxFTXTP/dul61H5WpwV78viV8aFlaQkVJ8udLEUvOFtRw8ZDA+5lSoVqYKUx5qxoUcxqkcHJ7Cf3qKTyAQCAT/u4gEJRAIBAJZIhKUQCAQCGSJSFACgUAgkCUiQQkEAoFAlogEJRAIBAJZIhKUQCAQCGSJSFACgUAgkCUiQQkEAoFAlogEJRAIBAJZIhKUQCAQCGSJuBefQCAQCGSJuIISCAQCgSz5z2+3USbI4Y7RmTfL1sG8hvpfGdy5mqw7ZexQVR5xAHncuVoOsZCDAzDNtGTbs7xsZufckceOA09BXEEJBAKBQJaIBCUQCAQCWSISlEAgEAhkiUhQAoFAIJAlIkEJBAKBQJaIBCUQCAQCWSISlEAgEAhkiUhQAoFAIJAlIkEJBAKBQJaIBCUQCAQCWfLKJigXFxdu3y58i4+IiAiCgoJKrd7DUSdw9R2I0mcAQWvWFzqek5PDmMnTUfoMwG/oR8QnJgEQHXMBr4HD8Bo4DM93h7Lv4GH9HY7/jqtPf5S9+xK0el3RDpOmoezdF7/3P9Q6HPv9JD7vDcOj3yB83htG1Kkz+jsci8LV2w+lZx+CflpTtMOEKSg9++A3eBjxiYkA3EnPYNCIkTi378Ks+V/rXb+uR1+Unr4E/bT2GR6+RXh8gnP7rsyav+glOJRtLA4fi8K1ty9KTx+CVj3NYTJKTx/8Bg3VOgCsWLkapacPrr19OXI86sU95BALPRwAVqxajdKzD67efhw5/rveDg16dGP0+ZOMuXCGjuPHFDpuWduBIXt28OmZowzbF4KFXS3tsVaD+jMm5jRjYk7TalB/vR0OH4/C1acfSq9njI2JU1F6+eI3+APt54S6LT7FuYMLsxa82Nh4ZRPU0+jWrRsjRowolZ+tUqmYtXAxwYFfE7Z5LaHhEVy+ck2nzK+7wrAwN2ffto0MGdCXRUt/AKChY322rgli54ZVBC/5moD5i8jLy9PPYcE3BC/5hrBfNxAavp/LV67qOuwMVTvs+IUh7/Zj0XfLAKhapQrLFy8gZPM65s+Yin/ALP3jsOBrgr/7P8K2biJ0z14uX7mi67BjFxYW5uzbtZUhA/uzKPB7AMqXN2X0yI/w/+Jzveou7LGI4O8WE7Z1o8ajQCx27MLCwoJ9u7YwZOCAAh4j8P/is5fgULaxUKlUzJq/kOClgYRt3UzonnAuxxbhYG7Ovl3bNHFYCsDl2CuEhe8lbMsmgr8PZOa8hahUKv095BALPR0uX7lCWPg+wrZsJHhpIDPn6xcLhZERHoFfs9bDj+9atuO1fn2watpYp8w7C2bxx4ZNfN+6AwfnLET5VQAAZlWr0HXKBFZ06M4P7bvRdcoEKlSx1C8O878heMm3hG3ZSGj4viLGRog6Dju3qOOwpMDYGDOqxPUW5JVIUPfv32fEiBF4enri7u7O7t27AVi/fj3e3t54eHgQGxsLwLZt25g1S/3BO3HiRAICAvDx8cHV1ZWDBw++kEd0zEXq2NvhYFcL03LlcOvRjYjDR3XKHDh0FG+3dwBwdelM1KmzSJKEWYUKmJio7+2bnZ2DQqHQ38HBHgd7uycOh44UcDiCt3svtUO3LkSdPIMkSTRr0ggbKysAGjrWIzs7m5ycnJI7nL9AHft8Dq5KIiJ1rwgPRB7G291N4+BC1KlTSJJERTMz2ji3orypqT6/vh4e+WPRlahTp1+qhxxiEX0+RrdPuPYowuEQ3h4ah+4uRJ1UO0REHsbNtQempqY42NlRx8Ge6PMxenrIIRb6O6hjodTEohZ17O2JPn+hxA72bVtzK/YKd65eR5Wby1+/bKOpRy+dMtZNG3PloHrcXo08QhOPnoD6yis2IpIHd9J5mJ5BbEQkDV27lzwOMRcKfE50LxyHQwXGxsn8Y6Ml5U3Ll7jegrwSCerIkSNYW1uza9cuQkND6dixIwBVq1Zl+/bt9O/fn1WrVhX53oSEBLZs2cKKFSuYPn062dnZenukpN2kpo219rmNtRUpaWmFythqypiYmGBeuRJ3MtR3oP7z/AXc+g3G892hzJwwVpuwSuSQmlbAwZqU1LRCZZ7m8JjwiEiaNWmMqR4fCClpqdSsafNsh7Q0bGvmd6jMnfSXeyfulLQ0atYsJhZpadhqXEvDQw6xUPeJfA421oX7ZWrRcVDH8Nn+z+0hh1i8gEPRcUwtsYOFnS0Z8Qna5xkJiZjXstUpkxwdQ7Pe7gA06+1OBQsLzKpVxaKWLRnx8dpydxMSsCjw3ueh0OdEUX0iLQ1bm9IbG/CKJKhGjRpx/Phxvv76a06fPo25uTkAPXr0AMDJyYmEhIQi39uzZ0+MjIyoW7cuDg4OXClwuW9IWjo1I2zzWrasXsGKNetfKFm+CP/GXmHRd8uYNXl8mdQvELzq7JkwjXqd2vPJyUPU7diejPgEJD2nVuXMK5Gg6tWrx7Zt22jUqBH/93//x9Kl6vnzcuXKAWBkZPTUueKCU2n6Tq0B2FjVIDnlyRlVSmqadsosf5kkTZm8vDwy72VR1VJ3DtmxXl0qmpnxT6zunPBzOVhbFXBIxcbaqlCZpzkkp6QyavxkFsycRm17+xLXD2BjZU1y8pM9s4p0sLIiKTm/wz2q6jGX/mwPK5KTi4mFlRVJGtfS8JBDLNR9Ip9DSmrhfmlddBzUMXy2/3N7yCEWL+BQdBytKSl3E5KwtLfTPre0q0WmZgHCYzKTktnYdzDL3ujM/oCvAHiYcZe7iUlY5huXFnZ23C3w3ueh0OdEUX3CyoqklNIbG/CKJKiUlBTMzMzw8vLigw8+4MKF558X3rNnD48ePeLGjRvExcVRr149vT1aNGvCtbh44hISycnNJWxvBC4d2+uUcenUnu1hewAIP3CIdm1eR6FQEJeQqF0UkZCUzJXrN7CrVfPlOHTqUMChA9tD1d/ThUdE0q5taxQKBXczMxkxZjxjR31M61av6RMCtUPzplyLi3viEL4Pl86ddB06d2R7aJjG4QDt2rZ5oZOD5/foWITH41gcfOkecohFi+bNuHYjjriEBI3DXly6FIxDJ7aHaBz2P3Fw6dKRsPC95OTkEJeQwLUbcbzm1FxPDznEQn8Hl86dCAvfp4lFItfi4njNqVmJHRJOn6V6A0eq1K2NcblytOjrw6XQ33TKVKxeTft7d5rwBWfXbADg8t4IGnTvSoUqllSoYkmD7l25vDei5HFoViAOe/cXMTY6FBgbrV/6GH0ldtT9559/WLhwIUZGRpiYmDBjxgxGjx79XO+1tbXF19eXrKwsZs6cSfny+n/xZ2JiQsD4MQz/fByqR4/o49GLho71CFyxEqemjenWqQO+nm6Mnz4Hpc8ALC3MWTxnBgBn/vyLH9dswMTEBCMjBTP8v6RalSp6OnzB8M++RKVS0cfTnYaO9Qn84UecmjahW+eO+Hq5Mz5gNsrefbG0sGDx3JkArN+8lRtx8Xwf/BPfB/8EwKql/0f1aiXbFdTExISACeMY/unn6jh4eqgdlq/AqVlTunXuhG9vT8ZPm4HSsw+WlhYsnveV9v0ubr25l5VFbm4u+yMPsWrZEhrUr69fLCaMY/inozUemlgsD8KpWRONhwfjp81E6emr8ZhdwON+AY+SncDIIRZqh/EM/0Tj4OVBQ0dHApdpHLpoHKZOR+npo+4T8+cA0NDRkZ49utOrTz+MjY0JmOiPsbFxieqXXyz0c2joWJ+eyu708u2vicV4vWLxSKUidIw/74dtxcjImLNrNpB64RIu0yeReOYPLoX+Rr3OHVDODkBC4tqR44R+rp5uf3Annci5X/Px8QMAHJyzkAd3Sr57sImJCQH+Yxk+agwq1SP6eOUfG001nxOaseGlGRtz840Nd29NW+SxP/Iwq74PLPHYAFBIkiSV+F2vCBMnTqRLly688847JXuj2PJdbPmu4yC2fNcitnx/4oDY8h0QW74LBAKB4H+PV2KKT1/mz59f1goCgUDwyiKuoAQCgUAgS0SCEggEAoEsEQlKIBAIBLJEJCiBQCAQyBKRoAQCgUAgS0SCEggEAoEsEQlKIBAIBLJEJCiBQCAQyBKRoAQCgUAgS0SCEggEAoEsETeLFQgEAoEsEffiKw1kcPdsKe1GmSoorGqr/yOLu2eXfXvIIg4gDw85tIccHEAWHnMqPP1u4oZgysOn301dTPEJBAKBQJaIBCUQCAQCWSISlEAgEAhkiUhQAoFAIJAlIkEJBAKBQJaIBCUQCAQCWSISlEAgEAhkiUhQAoFAIJAlIkEJBAKBQJaIBCUQCAQCWSISlEAgEAhkyX8uQd29e5cNGza8lJ914sQJPvroo5fysx5z+FgUrt59UXr6EvTT2kLHc3JyGDNhCkpPX/wGDyM+MRGAO+kZDBrxCc7tuzJr/qIXcjjy+yneGTCUHv3eJ2jdpiIdvgj4ih793qfvh58Rn5SsPfb35Sv0++hz3N8bjsfgD8nOztHL4fCxKFx7+6L09CFo1ZoiHcZMmIzS0we/QUO1cQBYsXI1Sk8fXHv7cuR4lF7163iUcXvIIRZycNB6yKE9hAP1ld34OPoEI2NO89a40YWOWzjYMTB8Jx/8HsnwU0dwdO0OgFG5crgHLeXD00cZfvIwtTu119vhP5mgNm7cWOj1vLy8MrDRRaVSMWvBIoK/W0zY1o2E7tnL5StXdcr8umMXFhYW7Nu1hSEDB7Ao8HsAypc3ZfTIEfh/8dmLO3z7HT8umkvo+mDC9h/k8tXrOmW2hO7Bwrwyezev4f1+PnyzPBiAvDwV42fPZ+a40YSuD2btd99gYmKsn8P8hQQvDSRs62ZC94RzOfaKTplfd+zCwtycfbu2aeKwFIDLsVcIC99L2JZNBH8fyMx5C1GpVPrHQg7tUcaxkIOD1kMO7SEcUBgZ8U7gQjZ59WVFq7do3rcPNZo01inTYeI4Lm7Zwcp2XdgxaDjvLFEnROdhgwH4sU0Hfnbzofv82aBQ6OXxn0tQ33zzDTdu3MDLy4s+ffrw7rvv8vHHH+Pm5kZ8fDzu7u7asitXruS7774D4Pr16wwZMgRPT0+8vb25cUP3buDR0dH07t270OslIfr8BerY2+Ngb4dpuXK4uSqJiDysU+ZA5BG83XsB4NqtK1GnTiNJEhXNzGjj3IrypqZ61w8QffFvatvXwsHOFtNy5ejVvQsRR4/rlIk4epzePXuoHbp0IurMOSRJ4tip0zR2rE+Tho4AVLW0wNi45Akq+nwMdRzyx6FHEXE4hLeHm9qhuwtRJ08hSRIRkYdxc+2BqakpDnZ21HGwJ/p8jD6hkEd7yCAWcnBQe8ihPYQDQK22rbkde5X0q9d5lJvLhV+30cijp04ZSZIob2EOQHlLC+4lqmdaajRtzDWN7/20mzzMyKBWa2e9PP5zCWrs2LHUrl2bnTt34u/vz4ULF5gyZQrh4eHPfN+4ceMYOHAgu3btYtOmTVhZWWmPnT17lhkzZrBs2TJq166tt1tKWho1a1prn9tYW5OSmlaojG1NGwBMTEwwr1yZO+kvb4uElLSb2Fo/+d1qWtUgJe2mTpnUtFvaMiYmxphXqkR6xl2uxSWgUMAHX07EZ9hIgjds1s8hNY2aNjba5zY21qSkpRUqU1Qc1DHM994iYvjcHnJoDxnEQg4OIJP2EA4AmNeyJTM+Qfv8bkIi5rVsdcoc+WoBTgP68tnl8/TbsZnwLycAkPpXDI3ceqIwNsaybm1snVthbm+nl8d/fj+oFi1a4ODg8Mwy9+7dIyUlBaVSCUD58uW1x2JjYwkICGDlypXY5BvEryJ5eSrORMew5celVKhQniGj/WneuCFvtXm9rNUEAoGBada3D9HrNnIi8Hvs3myL56ofCHr9bf5YvZ7qjRvxwfEDZNyII/73k0h6Tvv+566gClKxYkXt/01MTHj06JH2eXZ2drHvt7KywtTUlIsXL76wi42VFcnJqdrnKamp2OS7mnlcJik5BVB/b5Z57x5Vq1i+cN1Pfn4NkvKdjSWn3cTGqoZOGWur6toyeXkqMrOyqGJpQU3rGrRp2YKqVSwxq1CBzm+9wYV/LpfcwdqK5JQU7fOUlFRsrKwKlSkqDuoY5ntvETF8bg85tIcMYiEHB5BJewgHADITk3SueizsapGZmKRTptWQ97iwdQcACSdOYVKhPBVrVEdSqdjvP4XgNzvzq997VLC05Pa/sXp5/OcSVKVKlcjKyiryWPXq1bl16xZ37twhJyeHyMhIACpXrkzNmjXZv38/oF4h8+DBAwAsLCwICgrim2++4cSJEy/k1qJ5U67FxRGXkEhObi5h4ftw6dxRp4xL545sD90NQHjEQdq1bYNCzy8Yi3Ro0pjrcQnEJyaRk5vL7v2RuLR/S9eh/Vvs+G2v2iHyMO1eb4VCoaDDG23498pVHjx8SF6eilPnonGsW6fkDs2bce1GHHEJCZo47MWlS8E4dGJ7SJjaYf8BbRxcunQkLHwvOTk5xCUkcO1GHK85NdcvFnJoDxnEQg4Oag85tIdwAEg8fZZqDepjWbc2RuXK0czPh39C9+iUuRsXT72unQCo3rgRJuXLcz/tJiZmZpTTXBjU69aFR6o8bl76Wy8PhSRJ0ov9KvJj7Nix/P3335QvX54aNWqwYsUK7bG1a9eydu1abGxscHBwwM7Ojs8++4xr164REBDAnTt3KFeuHIGBgSQmJrJq1SpWrFhBYmIiH374IXPnzqVly5bPFnjGNs6Hjh5n7qLFqB49oo+nOyOHDyVweRBOzZrQrXMnsrOzGT9tJhcv/YOlpQWL583GQXMm4+LWm3tZ98nNzcXcvDKrli2hQf16hSspZsv3Q1EnmBu4nEePHtHHzZWP3x/IkuDVODVphEuHt8nOzsF/9nwu/huLpYU5386YgoOdev55V/h+gtZtQqFQ0OmtNxj/yYdF1lHclu+Hjhxj7qJv1XHw8mDk8GEELluBU7OmdOuiicPU6Vz8+x8sLSxYPH+ONg7Lg1exdWcIxsbGTB73JZ07vP3U37W4LcYN1R7P2mrdILEoZsv3V6495OAAT/UwiIPG42lbvju6dke5aC5Gxsb8uWYDxxZ8S6eASSSdOce/YXuo0aQxvZb/H6aVKoEkETFlBlf3H8SyjgMDQrYgPZLITEwk9OPPuXsj/qnxftaW7//JBFXmPKPzG4RiEpQhKC5BGYxiPhANQjEJyiAUk6AM6iGH9pCDA8jC42kJylA8K0H956b4BAKBQPDfQCQogUAgEMgSkaAEAoFAIEtEghIIBAKBLBEJSiAQCASyRCQogUAgEMgSkaAEAoFAIEtEghIIBAKBLBEJSiAQCASyRCQogUAgEMgSkaAEAoFAIEvEvfgEAoFAIEvEFZRAIBAIZIlIUAKBQCCQJSJBCQQCgUCWiAQlEAgEAlkiEpRAIBAIZIlIUAKBQCCQJSJBCQQCgUCWiAQlEAgEAlkiEpRAIBAIZIlIUDIiISGB48ePA/Dw4UPu3btXxkavJmfOnHmu10oTlUrF2LFjDVqnoHjkMEZzcnK4dOkSf//9Nzk5OQavPy4urtBr0dHRpVKXSan8VEGJ+eWXX9i8eTMZGRns37+f5ORkpk+fzpo1awzmIEkSu3btIi4ujlGjRpGYmMjNmzd57bXXDFL/7NmzUSgUTz0+depUg3h89dVXbN++vdjXShNjY2MSExPJycnB1NTUYPUWxMPDo9Br5ubmODk5MXLkSKpWrVrqDs7OzoX6xWOHiRMn4uDgUOoOII8xGhkZyfTp06lduzaSJBEfH8/MmTPp3LmzwRw+//xzfvjhB2xsbAA4efIks2fPJiQk5KXXJRKUTNiwYQO//vorffv2BaBu3brcvn3boA4zZszAyMiI33//nVGjRlGpUiU+++wztm7dapD6nZycADh79iyXL1+mV69eAOzZswdHR8dSr//cuXOcO3eO27dv89NPP2lfv3fvHiqVqtTrL4iDgwMDBgzAxcWFihUral8fOnSowRw6duyIsbEx7u7uAOzevZsHDx5Qo0YNJk2axA8//FDqDu+//z41a9bUOoSFhXHjxg2aN2/O5MmTWbduXak7gDzG6Pz581m7di116sPq0YcAABSRSURBVNQB4MaNG4wYMcKgCWrmzJl88skn/PDDD1y4cIFvvvmGoKCgUqlLJCiZYGpqqnOmnJeXZ3CH6Ohotm/fTu/evQGwtLQkNzfXYPV7e3sDsHHjRn7++WdMTNTds3///gwcOLDU68/NzeX+/fuoVCqysrK0r1euXJklS5aUev0FqV27tvZMOb+PIYmKitK5cmzcuDHe3t5s3769yKur0uDAgQPs2rVL+7xfv354eXkxfvx4gyTIx8hhjFaqVEmbnEB9ElOpUiWDOrz22mtMnTqVYcOGUb58eVavXk21atVKpS6RoGRC27Zt+eGHH3j48CHHjh3j559/xsXFxaAOJiYmqFQq7XTK7du3MTIy/NeUGRkZ3Lt3jypVqgBw//59MjIySr3eN954gzfeeANvb2/s7OxKvb7iGDVqFAAPHjzAzMysTBxUKhXR0dHaad7o6Gjt1aSxsbFBHMzMzNi9ezfvvPMOoL6iLl++PMAzp4RfNnIYo05OTnz44Yf07NkThULBnj17aNGiBXv37gWgR48epVb3xx9/rPP84cOHmJubM3nyZIBSOVkQ223IhEePHrFlyxaOHj0KQIcOHfDz8zPoANy1axe7d+/mwoULeHt7s2fPHsaMGUPPnj0N5gCwdetWli5dyptvvokkSZw6dYrPPvtMe4VV2ty+fZsff/yRy5cvk52drX197dq1Bqn/MefOnWPKlCncv3+fyMhILl26xKZNm5gxY4bBHKKjo5kyZYr2Cq5SpUrMmTOHBg0aEBkZqZ2GLU3i4uKYM2cO586dA9TfSU2aNAkbGxvOnz9PmzZtSt0B5DFGJ02a9Mzj8+bNK7W6T548+czjb7zxxkuvUyQogQ6xsbH8/vvvSJLEW2+9ZZDvfvLz6NEj/vjjDxwcHPjzzz8BaNmyJVZWVgZzGDZsGD179mTVqlXMnDmT7du3U61aNcaPH28wBwA/Pz+WLFnCyJEj2bFjBwDu7u6EhoYa1AMgMzMTUC9OEEB6ejrJyck0adKkrFUMTlxcHNbW1tqr2IcPH3Lz5k3s7e1fel1iik8mnDlzhqVLl5KYmEheXh6SJKFQKIiIiCj1utPT07X/r169Om5ubjrHHk+1GQIjIyNmzZrFjh076N69u8HqzU96ejp+fn6sXbtWO+3Xp0+fMnGxtbXVeW7oKdfMzEyWLl3KqVOnAPVZ8qeffmrQRJWcnMzs2bM5e/YsAG3atGHKlCnUrFnTYA4AgwYNYvny5eTl5eHj40P16tVxdnbWTnGVJj/++CMffvjhU1e6GmqFK8Do0aPZtGmT9rmRkRGjR48ulcVUIkHJhClTpjBp0iScnJwM/iHk4+ODQqEg/8X04+eGSpL5eeuttwgPD6dHjx4GnT55zOPFGdbW1kRGRmJtbW2Q78AKYmtry9mzZ1EoFOTm5rJ27VqDX9FOnjyZhg0bEhgYCMDOnTuZNGkSS5cuNZjDpEmTcHd31zrs2rWLSZMm6ay0NASZmZlUrlyZX3/9ld69e/P5558bbKHI43Z/vNK1LFGpVDqLRUxNTUtvMZUkkAW+vr5lrSAbWrVqJTVu3Fhq3ry55OzsLLVq1UpydnY2WP0HDhyQ7t69K/3999/Se++9J3l7e0sREREGq/8xt27dkr788kvprbfektq1ayeNHTtWun37tkEdPD09n+u1/7qDJEmSu7u7lJKSIg0dOlT6888/ta+9agwZMkTav3+/9vm+ffukwYMHl0pd4gpKJrz55pssWLCAHj166JydNG/evNTrjo2NxdHRkZiYmCKPG8IhP4+/DC8r9uzZQ+vWrWnUqBHr1q0jPT2dBQsWGHzFVrVq1fjmm28MWmdBKlSowOnTp7ULEc6cOUOFChUM6lClShV27typ/Tuo0NBQg047P+bTTz/lgw8+oHXr1rz22mvExcVRt25dgzpcvXqVVatWkZCQoLPM3ZALeGbOnMm4ceOYPXs2kiRha2vLggULSqUusUhCJgwaNKjQawqFwiAdb9q0acyePbtMHUA+ibJ3797aRQnPeq20uXr1KjNmzODWrVuEhoZy6dIlDhw4wCeffGIwh0uXLuHv76+9pY+FhQXz58836OKAhIQEZs+ezR9//IFCocDZ2ZmpU6dSq1YtgzmoVCrWrVvHkCFDDFZnUXh6etK/f/9CXwWUxdRf/pWdpYVIUALZkD9R5v/uSdJ8F2aoROnp6cm6deuwtLQE1IsmBg0aVCq3cnkW7733Hv7+/gQEBBh8FV/+73ckSeL+/fsAVKxYEYVCYbC7WahUKvz9/cv8ShLA19eXLVu2lKmDj48P27ZtK1OHp33/+Pjv9l4mYopPRkRGRvLvv//q/O1NaTT608jOzubnn3/mzJkzKBQKWrduzYABA7TLSUub2bNnA+oVS/k92rRpw4ABAwziAOpl5v369dP5w9CCf6RoCB48eFDoPoiG+uPYx2fHV69e5a+//qJbt25IkkRISAgtWrQwiAPI556EAK+//jqzZs2iV69eOn84bYgr+8crbbt27cqGDRtQKpU68TDklGf+225lZ2cTGRlJ/fr1S6UucQUlEwICAnj48CEnTpzAz8+P8PBwWrRowdy5cw3mMHr0aCpVqoSnpyegnuu/e/euwW/zM3r0aCpXrqxdIRUaGkpmZqZ2FZchuHz5Mr///jsA7dq1o0GDBgar+zHDhw8nICCA0aNHs337dvbs2cOWLVsIDg42mMPAgQNZsWIFlStXBtT3Jfzoo4/YsGGDwRz8/f2JjY0t03sSQtlOw7u4uBS50vYxhl5pm5+cnBw++OCDUrknoriCkgnnzp0jJCQEDw8PRo0axdChQ/nwww8N6vDvv/+ye/du7fN27doZ5E4BcvRo0KBBmSSl/EyfPp1p06Zx5coVOnbsiP3/t3f/MVWW7x/A36gYwhlKMkiGbf1CpWEHPMz+SCTTMolB7IBU1DA3yGE4ZKBhTIQYtVhBhq5NVqglSUVKJTqDqNE4QKE2SjcbcRAEFflxDiA/zrk/f7DzfDkCmX49z/NM3q+/4DmH5748c1zcz31f9+Xri/z8fFljuHbt2qQtxdeuXZNl7LS0NLz//vuoqqpCfHy8omcSApDtUNqpVFVVARg/rDckJAQajQZFRUX4888/ZV2TnMrQ0BA6Ozsdcm8mKJWw7YyaN28eurq64OHhgatXr8oag7+/P86cOQOtVgsAOHv2rCKLr2qJQykT139Wr16NlStXwmq1wtXVFadOnZJ15hAZGQm9Xo9169YBAE6fPo2oqChZxm5ubkZXVxcWLVqEuLg4Wcb8N2ooWt6/fz82bNiAxsZG1NXVYfPmzcjKykJZWZlsMUys/bJarbh+/TqSkpIcMhYTlEqEhoaiv78fmzdvlgpn9Xq9LGPb/sONjY0hNjZW2h3V0dHhsGfLao5DadOt/xw/flzW9R8A2LJlC0JCQtDY2Ahg/Kw3f39/WcaOjY1FfHw8Ll26ZHeSh1CogFwNRcu2NciamhrExMQgNDQUBQUFso0P2B8KO2fOHCxcuFAqbr/buAalQiMjIxgeHpbtL7P29vZ/fV2uk73VEodaqGH9Rw12796NPXv2KB0GIiIicOzYsVtec6TExER4e3ujtrYW5eXlcHFxgV6vt2tH4kgWiwVhYWGorKyUZTzOoBRmOyZ/Oo48Pt9m4i/+8+fPS38t63Q6WetdZloCuhUl13/URA3JCVBH0XJBQQF++eUXvP7663B3d8eVK1eQnp4u2/izZ8/GQw89hI6ODlnq0JigFFZdXf2vr8uRoGxKSkpQVlYmrTekpaUhJiZmyt1L5HhKrv/QZFlZWdixY8ekomU5zZs3z+53gpeXF7y8vGSNob+/H2FhYVi+fLnddnv2gyKHCg8Px5dffilt5R0cHMTGjRtlL1Cl/9Pc3CzNaIODg2Vb/6HJRkZGUFlZCaPRCJPJJD2Cl7NWUQ30er3drE0Igfz8fIds1OAMSiV6enpQVFQkFacGBQUhKSkJHh4essYxsRBUrqJQmt7jjz8u+1mINLUtW7bA3d0d/v7+dvVYM43FYpnUnPDGjRsOGYsJSiW2b98OnU4nFcVWVFQgJSUFn332mWwxREVFITo62u6RklJ9kIjUpqurC8XFxUqHoZgvvvgCR44cQVtbm91W84GBAQQFBTlkTD7iU4mpzlgLDw+X/fFac3MzfvvtNwDjmyT4SIloXGZmJuLi4rBkyRKlQ1GEyWRCX18fPvjgA6SmpkrX3dzcHHbUEhOUSuTl5WH58uV4/vnnAYyf//bHH39gx44dssbR19eHy5cvw2KxSNf4iIlmMttswWKxoLW1Fb6+vna7K7lG6zhMUCoRGBiIoaEhad3HYrFIO2ScnJykdteOVFBQgPLycjz44IPSNTlPESdSI9bnKYcJSgWEELh8+bKs/W2m8txzz6GiokLxU6OJiABg1q3fQo7m5OSExMREpcOAn58fTCaT0mEQEQHgLj7V8Pf3x7lz5yb1/5FTQkICIiMj4efnB2dnZ+m6IwrwiIhuhY/4VGL9+vUwGo3w8fGxq86WcwE2LCwMGzduhJ+fn1076ZtrHoiI5MAZlEqoob7CxcUFr732mtJhEBEB4AxKcWazGRqNRmrpfDM5Wznn5eVh7ty5WLNmjd1GCW4zJyIlMEEpLDExEZ988onU0tlGiZ43Sra0JiK6GROUivT29qK1tRXDw8PSNa7/ENFMxTUolSgrK8PBgwfR2dmJpUuX4uzZswgMDJQ1QamhpTURkQ3roFTi4MGD+Oqrr+Dj44NDhw6hvLxc9sSQkZEBNzc3FBYWorCwEBqNBm+99ZasMRAR2XAGpRJz587FfffdB2C878wjjzyClpYWWWMwGo3Yu3ev9P3WrVsREREhawxERDZMUCrxwAMPoL+/H2vXrsWmTZvg7u4u+9FHamhpTURkw00SKlRfXw+TyYRVq1bJei7e+fPnkZ6ePqml9dKlS2WLgYjIhgmKJG1tbVi8eLGUoDQajXSNiEhu3CRBkuTkZADjiUmj0QAAtm3bpmRIRDSDcQ2K8Pfff+PixYswmUw4deqUdN1sNtvVZBERyYkJitDS0oKffvoJJpMJ1dXV0nU3Nzfk5OQoGBkRzWRcgyJJU1MTAgMDlQ6DiAgAExRNMF1Rbl5ensyREBHxER9NEBoaKn09PDyM06dPw8vLS7mAiGhG4wyKpmW1WvHyyy+jtLRU6VCIaAbiNnOa1j///IPu7m6lwyCiGYqP+EgSGBgo9aRycnKCp6cn0tLSFI6KiGYqJiiSNDU1TepJNbGJIhGRnJigSDJVTyqtVsuOukSkCK5BkWSqnlTu7u5Kh0VEMxQTFEnU0JOKiMiGj/hIooaeVERENqyDoikp1ZOKiMiGCYqIiFSJa1BERKRKTFBERKRKTFBEDrBs2TJERETghRdeQHJyMoaGhu74Xjt37kRlZSUAYNeuXbh48eK07zUYDPj9999ve4w1a9bg+vXr/+m933zzDbKzs297DKLbxQRF5AAuLi44duwYvvvuOzg7O086cHdsbOyO7pubm4tHH3102tfr6+vR1NR0R/cmUhtuMydyMJ1OhwsXLsBgMKCwsBDu7u5oaWnBDz/8gPz8fNTX12NkZASvvPIKYmNjIYRATk4OamtrsWjRIjg7O0v3evXVV5Geno6AgAD8/PPP+PDDD2GxWODh4YHc3FyUlpZi1qxZOH78ODIzM/Hwww9j9+7d6OjoAABkZGRgxYoV6OnpQWpqKrq6uqDVajHdXqmbxygpKbF7vaqqCvv378fo6CgWLFiA/Px8eHp6or6+Hrm5uQDGj8s6fPgwBgcHkZKSArPZDIvFgqysLOh0Ogd96nRPEER012m1WiGEEKOjo+KNN94Qn3/+uairqxNPPPGEMBqNQgghSktLRVFRkRBCiOHhYfHiiy8Ko9EoTp48KeLj48XY2Jjo7OwUK1asECdOnBBCCBEXFyfOnTsnuru7RUhIiHSvnp4eIYQQH330kThw4IAUx/bt20VDQ4MQQoj29naxfv16IYQQOTk5Yu/evUIIIaqrq4Wfn5/o7u62+zdMN8bXX38t9uzZI4QQore3V1itViGEEEePHhV5eXlCCCESExNFY2OjEEIIs9ksRkdHRXFxsdi3b58QQoixsTFhMpn+vx8z3eM4gyJygBs3biAiIgLA+AxKr9ejqakJAQEBWLx4MQCgtrYWFy5cwMmTJwEAJpMJra2taGhoQFhYGGbPng1vb288+eSTk+5/5swZ6HQ66V4LFiyYMo5ff/3Vbs3KbDZjYGAADQ0N+PjjjwGMN6qcP3/+HY3R2dmJlJQUXL16FSMjI/D19QUABAUF4d1330V4eDieffZZuLm5ISAgABkZGRgbG8PatWuxbNmy//Zh0ozFBEXkALY1qJu5urpKXwsh8Pbbb2PVqlV276mpqblrcVitVhw9elQ6wupue+eddxAfH49nnnkGBoNBSnoJCQlYvXo1ampq8NJLL+HAgQMIDg7G4cOHUVNTg507d2LTpk2IjIx0SFx0b+AmCSKFPPXUUzhy5AhGR0cBAC0tLRgcHERwcDBOnDgBi8WCK1euwGAwTPpZrVaLxsZGtLW1AQB6e3sBAG5ubhgYGLAb49ChQ9L3f/31FwAgODgYFRUVAMYTYl9f338eYyKTyQRvb28AwLfffitdNxqNWLJkCRISEhAQEICWlha0t7fD09MTMTExiI6ORnNz8218WjQTcQZFpJDo6Gi0t7cjKioKQgh4eHhg3759WLduHerq6rBhwwb4+PhAq9VO+tn7778f2dnZePPNN2G1WrFw4UJ8+umnePrpp5GcnIwff/wRmZmZ2LVrF7KzsxEeHg6LxQKdTofs7GwkJSUhNTUVYWFhCAwMnPLMxenGmGjr1q3Ytm0b5s+fj5UrV+LSpUsAgJKSEhgMBjg5OeGxxx5DSEgIvv/+exQXF2POnDlwdXXFe++955gPlu4ZPOqIiIhUiY/4iIhIlZigiIhIlZigiIhIlZigiIhIlZigiIhIlZigiIhIlZigiIhIlf4H3YGCJ8pwiPoAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def normalize(matrix, axis):\n", + " axis = {'true': 1, 'pred': 0}[axis]\n", + " return matrix / matrix.sum(axis=axis, keepdims=True)\n", + "\n", + "x_labels = [classes[i] for i in classes]\n", + "y_labels = x_labels\n", + "plt.figure(figsize=(6, 6))\n", + "sns.heatmap(\n", + " ax=plt.gca(),\n", + " data=normalize(confusion_matrix, 'true'),\n", + " annot=True,\n", + " linewidths=0.5,\n", + " cmap=\"Reds\",\n", + " cbar=False,\n", + " fmt=\".2f\",\n", + " xticklabels=x_labels,\n", + " yticklabels=y_labels,\n", + ")\n", + "plt.xticks(rotation=90)\n", + "plt.yticks(rotation=0)\n", + "plt.ylabel(\"True class\")\n", + "plt.xlabel(\"Predicted class\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lw13Y86VI7Ac" + }, + "source": [ + "Here we focus on the diagonal and plot the numbers in a bar plot. This gives us a clearer picture of the accuracy of the model for different classes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 327 + }, + "id": "xv75wMhVI7Ac", + "outputId": "8f8c60c3-5142-4eee-9f1f-2d84252d463e" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with sns.axes_style('whitegrid'):\n", + " plt.figure(figsize=(8, 4))\n", + " sns.barplot(x=x_labels, y=np.diag(normalize(confusion_matrix, 'true')))\n", + " plt.xticks(rotation=90)\n", + " plt.title(\"Per-class accuracy\")\n", + " plt.ylabel(\"Accuracy\")\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ocnQOBAl81Tn" + }, + "source": [ + "**Assignment 4:** \n", + "1. Go back and improve performance of the network. By using enough convolutional layers with enough channels (and by training for long enough), you should easily be able to get a test accuracy above 60%, but see how much further you can get it! Can you reach 70%?\n", + "\n", + "2. Briefly describe what you did and any experiments you did along the way as well as what results you obtained.\n", + "Did anything surprise you during the exercise?\n", + "What were the changes that seemed to improve performance the most?\n", + "\n", + "3. Write down key lessons/insights you got during this exercise. \n", + "---\n", + "**Answer:** \n", + "\n", + "1. The answer is in the code above. \n", + "\n", + "2. Initially, I started with a simple network architecture. I used 2 convolutional layers, each with a max pooling layer and filters of size 5$\\times$5 and 3$\\times$3 respectively. For the dense part, I used 1 layer of 512 neurons and the output layer of 10 neurons. No batch normalisation or weight decay. This gave average test accuracy around 60\\%.\n", + "\n", + " As I increased the convolutional layers, the performance increased. To overcome the plateau of 80% accuracy, I introduced max pooling and dropout layers after every 2 convolutional layers, as well as include batch normalisation after the activation function of each output.\n", + "\n", + " For the dense part As the layers increased, the performance increase was marginal. But the average accuracy reached 83\\%.\n", + "\n", + " The dense part architecture was challenging to decide on. Even if there were more layers, the performance increase was marginal, if not worse. Probably due to overfitting, or because more training was needed.\n", + "\n", + " The cat class is the most difficult to classify correctly, most often confused with the dog class, and the deer class afterwards.\n", + "\n", + " The Dropout layer decreased the training time by a large margin. This allowed to also increase the batch size to 256, which also increased accuracy. Max pooling also plays a role in decreasing the training time, since it reduces the number of weights after it, by essentially downsampling the feature maps. \n", + "\n", + "3. Dropout layer is essential, GPU is needed for training times to be manageable, max pooling is also essential, but not after each convolutional layer. Moreover, batch normalisation after each convolutional layer increases accuracy. The accuracy can be increased if a lot of training time is allowed and the learning rate is efficiently changed to speed up training. More epochs are also needed as it seems now, with this architecture." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8Nzefavy81To" + }, + "source": [ + "# Training on GPU\n", + "\n", + "**Optional Assignment:**\n", + "If you have a GPU, we suggest that you try training your model on GPU. For this, you need to move the model to GPU after defining it, which will recursively go over all modules and convert their parameters and buffers to CUDA tensors. You also need to transfer both the inputs and targets to GPU at each training step, before performing the forward pass.\n", + "\n", + "The code for this is already in place: notice the `.to(device)` statements. The only thing left to do is change the definition of `device` from `'cpu'` to `'cuda'`.\n", + "\n", + "If you don't have a GPU, you can do this on [Google Colab](https://research.google.com/colaboratory/).\n", + "\n", + "Use the code below to check if any GPU is avaiable in your current setup. This should print the models of all available GPUs.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "I09rtehbaCRH", + "outputId": "a84a7040-6a84-446c-c19f-81a6960beceb" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available CUDA devices: ['Tesla T4']\n" + ] + } + ], + "source": [ + "# Check if we have GPUs available\n", + "print(\"Available CUDA devices:\", [torch.cuda.get_device_name(i) for i in range(torch.cuda.device_count())])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "actFGqQZM9Vd" + }, + "source": [ + "You may not notice any significant speed-up from using a GPU. This is probably because your network is really small. Try increasing the width of your network (number of channels in the convolutional layers) and see if you observe any speed-up on GPU compared to CPU." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b8mEIylU81Tp" + }, + "source": [ + "# Exercise from Michael Nielsen's book\n", + "\n", + "**Assignment 5:** Pick an exercise of your own choice from [Michael Nielsen's book](http://neuralnetworksanddeeplearning.com/).\n", + "\n", + "**Answer:**\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iFC8jrDox5XX" + }, + "source": [ + "#### [Exercise](http://neuralnetworksanddeeplearning.com/chap3.html#exercise_195778):\n", + "> As discussed above, one way of expanding the MNIST training data is to use small rotations of training images. What's a problem that might occur if we allow arbitrarily large rotations of training images? \n", + "--- \n", + "Data augmentation is a way to avoid overfitting, by artificaially increasing the available data set. By allowing rotation (and scaling) of each labeled training data point, an image, it is possible to have more training points. \n", + "\n", + "However, allowing arbitrary rotations might become problematic. Since each image that is rotated is already labeled, the rotated image might now have a different meaning when encoutered in the real world. An example is to have an image of 6, rotate it by 180$^{o}$ and receive an image of a 9. However, this is still labeled as a 6. Moreover, a reflection of a digit might not represent an actual digit. \n", + "\n", + "As a result, allowing arbitrary rotation can hurt the ability of the model to generalise. The effect of the augmentation on the labels must be examined (eg. 6 to 9), as well as the distribution of the images in the data set in the real world (eg. there are no digits represented by a reflection of 3).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "qh7KTAdWD_zx" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "05c3194c14ac4775a3fbf04c8ce235e4": { + "model_module": "@jupyter-widgets/controls", + 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"IPY_MODEL_7bb0c35e7b76499c9261b271e01c4ffe", + "IPY_MODEL_1563d8b336a143b182a05b36694e2b9e" + ], + "layout": "IPY_MODEL_6a3d9577a4bb4226832c9c269992adbc" + } + } + } + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/4_Convolutional/58452389786301.ipynb b/4_Convolutional/58452389786301.ipynb new file mode 100644 index 0000000..ac21622 --- /dev/null +++ b/4_Convolutional/58452389786301.ipynb @@ -0,0 +1,1302 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "oZW0gaQO81Sq" + }, + "source": [ + "# CNN on CIFAR-10\n", + "\n", + "In this notebook you need to put what you have learned into practice, and create your own convolutional classifier for the CIFAR-10 dataset.\n", + "\n", + "The images in CIFAR-10 are RGB images (3 channels) with size 32x32 (so they have size 3x32x32). There are 10 different classes. See examples below.\n", + "\n", + "![cifar10](https://github.com/DeepLearningDTU/02456-deep-learning-with-PyTorch/blob/master/static_files/cifar10.png?raw=1)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "htyg7xxN81St" + }, + "source": [ + "## Preliminaries" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "v3u2GIWr81Su" + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import torch\n", + "from torch import nn\n", + "from torch.nn import init\n", + "import torch.nn.functional as F\n", + "import torch.optim as optim\n", + "from torch.utils.data import TensorDataset, DataLoader\n", + "import torchvision\n", + "import torchvision.transforms as transforms\n", + "from torchvision.utils import make_grid\n", + "from sklearn import metrics\n", + "\n", + "sns.set_style(\"whitegrid\")\n", + "\n", + "def accuracy(target, pred):\n", + " return metrics.accuracy_score(target.detach().cpu().numpy(), pred.detach().cpu().numpy())\n", + "\n", + "def compute_confusion_matrix(target, pred, normalize=None):\n", + " return metrics.confusion_matrix(\n", + " target.detach().cpu().numpy(), \n", + " pred.detach().cpu().numpy(),\n", + " normalize=normalize\n", + " )\n", + "\n", + "def show_image(img):\n", + " img = img.detach().cpu()\n", + " img = img / 2 + 0.5 # unnormalize\n", + " with sns.axes_style(\"white\"):\n", + " plt.figure(figsize=(8, 8))\n", + " plt.imshow(img.permute((1, 2, 0)).numpy())\n", + " plt.axis('off')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QZeTujLC81S3", + "outputId": "bed6d6cc-2671-484f-adea-6ae4949e2aaa" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Files already downloaded and verified\n", + "Files already downloaded and verified\n" + ] + } + ], + "source": [ + "# The output of torchvision datasets are PIL images in the range [0, 1]. \n", + "# We transform them to PyTorch tensors and rescale them to be in the range [-1, 1].\n", + "transform = transforms.Compose(\n", + " [transforms.ToTensor(),\n", + " transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), # subtract 0.5 and divide by 0.5\n", + " ]\n", + ")\n", + "\n", + "batch_size = 64 # both for training and testing\n", + "\n", + "# Load datasets\n", + "train_set = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)\n", + "test_set = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)\n", + "train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=0, drop_last=False)\n", + "test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False, num_workers=0, drop_last=True)\n", + "\n", + "# Map from class index to class name.\n", + "classes = {index: name for name, index in train_set.class_to_idx.items()}" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JDHkc52L81S9", + "outputId": "9ef74b6c-0a88-49d3-84ef-8312d9d99acf" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Training data\n", + "Number of points: 50000\n", + "Batch dimension (B x C x H x W): torch.Size([64, 3, 32, 32])\n", + "Number of distinct labels: 10 (unique labels: {0, 1, 2, 3, 4, 5, 6, 7, 8, 9})\n", + "\n", + "Test data\n", + "Number of points: 10000\n", + "Batch dimension (B x C x H x W): torch.Size([64, 3, 32, 32])\n", + "Number of distinct labels: 10 (unique labels: {0, 1, 2, 3, 4, 5, 6, 7, 8, 9})\n" + ] + } + ], + "source": [ + "print(\"Training data\")\n", + "print(\"Number of points:\", len(train_set))\n", + "x, y = next(iter(train_loader))\n", + "print(\"Batch dimension (B x C x H x W):\", x.shape)\n", + "print(f\"Number of distinct labels: {len(set(train_set.targets))} (unique labels: {set(train_set.targets)})\")\n", + "\n", + "print(\"\\nTest data\")\n", + "print(\"Number of points:\", len(test_set))\n", + "x, y = next(iter(test_loader))\n", + "print(\"Batch dimension (B x C x H x W):\", x.shape)\n", + "print(f\"Number of distinct labels: {len(set(test_set.targets))} (unique labels: {set(test_set.targets)})\")\n", + "\n", + "n_classes = len(set(test_set.targets))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xSA1h94681TB" + }, + "source": [ + "### Show example images\n", + "\n", + "Run multiple times to see different examples." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 466 + }, + "id": "njJy0klP81TD", + "outputId": "37bf00a2-0372-4e55-8df4-0d2e39fe8830" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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RTZ++72Ipd+fhu/Tlx82dzJRgdfdeIrv9Nfv9jmN35GKzYbFYstlsZo8opUiMHX3fU9c1FgOc6NBZRo9Vzq6x0LAFAI1Jdz5uyCNDksf7NhhbnYVABCMGa4SQineoULxDY0jZjnE1JSHFO3WgptBjMSWMFm/LuvqkVU2ZQ8ZQqMgJCI1BRn2VU2YYE4Pue4Tz7c7G7zlzAlEFyUKIEPc9mnuSKH0Y2HdHFsbM8TT1GWOFqvL0fSDEwOHQ07YNTVNjjJxisFJizDkVj8kAm+WqGPEoOmzRAAy3mO6HuOET3FIQ68DXtO2aQRM5ddxcfw2HWy6bFW1TU1tPuK/WzuhQRGhWNZfLDT94+jn/+T/5Mc8+/ZTv/egPePLoE55//RyJsFqvePzkMf/m//6/+OXzr/ibv/8b6qbGN55mXdGkijp4ci5YEYeIr0aGb+ixJIwX1puWqvFUTYWrH6OaGULHYtniK8+Ql6ScSDlhpMIZT2NapE/E48Cbn70iHCKxi1TGE3LiGALVesnlxdNvXS3zEHzsB1OMxBBnGlO1xPbsOHhl7hlEIISCyGlEfZksSCnxjnNDcjTz5n+SR2pVc4kdGClZScaW/IyRhk05zTSakbKQjFicPT1kGV8ThJjKsZy+ayHIeA/zj9GU/jiv6xzgi0FQWSX3twypZxGO4KVQOPoeZSbyzt+/PtH5mcwAaE56YjJKpmsyApJHSjT1WCNYqWH2CD5sFMyJL/OZP0Zp653v3aepZ4/pO9ojvw75WAAs88/gnaeuG5bLFaEfCP3Am7dXbDYb1hdrjNhvHJAy7fQUvwJOC+VEWJz99a6M3sM0z+Y5LNA2NSkN5DyQ0kBK1UyoMJ7boBjJxNiXuJsUUJXZOpmIc33PvxL3M/euzUzze0wgQkHllBMgOSOSUOMwJqMy6oLREMoayJqIOTJGtYoXmabszVQAW0C0+MFiHWKk5CgQxmOOC1tGj3IESutcGfVUQjRT4tg0JydDPaV0h2hWNRgczhZQRRSpWnzd4n2L5KHEsyhepJFiyNeVxyBYk3HulGhmxFD5as6yTzGNQ2tQSeWeJSCE0ZYwaOzI4Qi5OBz97S0hKlkNYjzWCOKEpqnwrsKIG+nq6ZnJPcpb6ekIUqMmkzQzxMTuGDmG4sE+/fQTPvnkKd//wfexy4avXzznyadP+frFL9l3uznZpV00IKZQ3oC3BmcNTe2wvsZ5x5Mnj6nqirqpcK545kPocb48vz6NyU2aEVxxanCkKmB8ZvlsQXfTE3aCTx6JhqiKE3unMuHb5KNBMMZEGAKJMR54MtvveBpomXxp/IzomXLTYjcW1kcwKkzZgYIWi06lpDxnyESMGrKZ0ocncI0j7Vky6ZQCdtaMk9ROl2ZGOqVMLMEQc+IdmUBv5GZn22+8x7vhwjMqcaRndfrSeJ2NM+yPe1K3h2GPGI/Y+gSX95R9+f+HAHe8lvnt96HB/e+evIpCd508vxMAwpQ0M3mCJX6hGM3Y1GFVMBJGxfXtoHYH0L7ls+fXKuP43014mejb344n+PFSjB9vPXVVs15vOOwPDEPg5atXqMBitURkjH3x/oGUMeYs04KR0zPkjvoFmNiMk3WgU6hggtJprVEMn0XboGlAU08fBmIaitI/m/NCxmhiCD3GWWoZjdxxLdwFQCiBjZE6JWNwczz/bHTmog3VRNayrvP03RzBWMyYCDHJRHNmZfQGYhkTFVI2p7BMTMVIEyBnEAdmylAtMfrCHgmiYyxwugORMUFOSRTgkTH+dw5+OWdiinfuS7MgzuGsMHF7TpbU9Yq6XkKXEKtYm9FxDCxC42sq63FuLN0YH6GMhlQYwhgHDFixGCmGbFZIkjFSQlCiEY09OXRoDgzdke3NliFkUMFbV5iI2rFoG4yvEHFkCWU2jform9MsUpROOgZpSZIYUuQYAjf7jt1xoI+Zp58944vf+z4/+uMf8fkPf8CrN6948slj/uzf/u/87Jc/4+Zwja0cq/UKUx1mmtdJSW5cLSra5YJm0fLpp59SVxW19zhTsCCGRKLgRx8G0pQ/oiNjkZXBJ6SyLHKLWDAW3NFjhkSiJDXZ7+BMfDQIemeJ3pLDxHePw5YiUTNHPdXYaCqA5kcKUIE8Kly9Q6coVgQRi7cWZzxOKoJTCh8eRu/QjKURBjeCobV5pHGKx6mpLOgsBieWNNYzhiGQJmvCGqxzJ96NiXYRZrJ8pGhGaCvXzJTwKh/wcMqrGUGsx68fw+018XhLvH1ZQKhtULXFKCBOWuc99NWZRzqpuvc+Tz37/HyF82uzBzW9IrwHSM+skzt3pbjUkRW8NBifMN8wVe7TR9+Uvfnu++9me94xNL6hOP67Upa/btEprXH84bzn6bNnHI7HUg+73WLGmrhPP/0Ub/z5l+8eSyJIKLbUpNQ5e2Z3GIPChhyPx/k1Y0zJ3NZEXTcAZx6FsvSOHA9o3LPbbsk5stk8orYNRoSsid32iqu3r9nFI4+efkpzsULwxTvS0xwRzubTpMcVHIq9FxPEVIht8BJJuXg3OQRiTkRVVASXIGGpKn9K2jHjvNBU6oPFE0JGs5CSABbFQqLUleWAdxmxpfwiu2K4qbflOBlMPrFYUwaocw47gl5/OM5JMDPbNcYMczo3npW+O5R7ripKaW6mdks2609xMfD21V+Tc0/IPfnY4cVg1eKcRZ0rER1KSMfoorBVXabb9wzDwLHrWK83LJct3hswkCWXcCMGKx40Iblj4QakzuSF5XUe0NiholT1knq5pKoqjLcYV7zRQjdDSgUE50dlDV/8wROerB/zyaPH/OCP/4Dl8hEH9awvNlTeUdlAyomf/uxnVO2Cqm745//sv+L7P/g9trc3vL1+w+1hy/Z4w89e/g0xh3EJJ0Sgqgpj4r1HssUoaMikJFSu4dPHT1ltNviq4tj37A8Hbvd73m7fMMSewfRgHLZSxFQs1zUmWtq8Ai2xycO+o62aj17HH1csT0Fxb804ASfLUEY+e8r2gkwaszSLkrjDeE4KT/O8kKYT6MSFj/U0Ior3hhx0pEBL0LWkEJfi0inNeaY/x2NO7GuhFgpIppxKIeZIa9y5OWDOdvuQ/pZ7P5lOdsoaLWUEFlcvMMaCJuJxi1+ssSRUTkXGd+B0/PW+Pr8LLqfX74PI+0Xf/75MHsLklU/RHb3zQIyWLFGbh7Hg/z5wvnutHwK/+7WCdzNCT3gwUbbTH5PX9MFH8lsEwOn8U9LC9HdVVSwWC7qu4+b6muPxyPX1NY+fPJmTv+6Lotzur1ENY7OIQrEZY0/xW1tqSSdvPcdAt79hGPqSFu5cMfxQnjx9RlV5Gm85HnpiCOx2A/v9lv1+S9cdMNZw2F+DrnDWIjGgOQG5JGeYEs8TKdFgsTBX20zzdTa+SiF4qYsLd+4t6+Qtnu62lFiUda0yJobkVF6Xco8lZjjqirEgOiFjTwuDjIb0VLecU8RksDaBSyXb2wi4iaAdvev7y0FkrjtO5zkI1s5AqapzUk65BSWnjhhBGVAcRiBqBjFY61EjzGVFSink7gcsFViDmTJWKXR66QmQx+YekammthhFUzJh8aTJimQlp0COHZ5AtkpbGbxRIgmNQ6mxTT2p32PtEmsXYFJhtlQhJ8zZ8xIRluuW5aqhXdZ4X2rwNJZwjvUWjCHESDwcqLXUdlZVxeXmkmW75HLziEN/y+54i6sT3XBgCD1D6MhamhQ0VUPlK0iCxeOpWPoly3bFF59/n9VmQ1XXdMNQQHC345cvW3bHHdf7t3RZCLnEGK06rDpasy5sixrqQ4cX/9H64aM9wdpbqBxQur5kLfSadw7nHEOIhJQJMY0lEJmkiSwFHBTGhJpEHikwMWMmdAbVhCERiGBKttBy0dDd9oQhEntBqgpny6DHs3rBmNJIrhTQTShJM3GsGYpjDFFyJsS7tMZEQemEnEwZducJNGeAcp8XPIslFgvL4RYXWOcxmgk3r7DtCpN7slTTGSfy9R0nbKI/z8/7IXD8sMc1UWSZUv9bYhgTaJeROkHxPBJpAiSDJZM14HKH0YjRjH6H2pv7cg6Ap3vIJfwzHtbIxGePLgYG5H4i068u7/MgP+RV3gXt9/GYgE5lJ4a6qri8vMQaw+72lv1+z26344vvf5/KezAGsebOQ1VVfvHi71nULXVV4YzBGoevm7mo3HtfMu+cwQsMfcf26itev37Nfr8vReNGwBo2lw2r9SWXlxtevzqyvT7w/Oufsb/dcjjsSAgxdDgLYXVJXdXU3mOd0q4a2mrJarXAi2JMqekzIhgtDM9k1Za0/pIIIwZS7ojpMC8XBYZUvKEJQKF4ZCbnYlxR2EyjZmR8MhY76xByxhqLs1XRJ6PbbUwCElkHYhgIQ0Ckp7YWU3mwBjUGqnoswyhjniW/s26mjN6qLjE5dOpqU4yQnDOV92erUklxS9Y9OmSMXGDEkWwmxTS2wigshhFb4sEpctwfqDVjfVG7Bchl1H+MHbV6Ygo4a0d6uBgaJcQUSXk0DEMgdgdSt6PSDucSrrXsKqUbIqG/JR8skUx39RU2C7Wx2MqVI2pGQ8AMO86NuM1myWa1ZLVqybnQsklLJq4CQygNAFSUQz/M+Rib9YZVs+bzZ9/DGCWTePbkkqvrN7x49Zyr7Vu6vqMfehpd0EpDXTXUvmXZrPjh936fJ4+e8gc//EOadoHznqiZruvYHw58+f99yfPXz/nbn/81V/0b+tTBotRvWmOp6hXWOJz1XCQwQeDtR6mDjwdBOy5EsYYqjxOUU32fdZ5Gi+c1xKHEBG1dwDFEhn2PmRJfJjI0M3sYkQgmIlb55OkTLlYtn20uuPnqmv1Nx+v9gI4pzFVdIyIcj0fiCLx9GAghzm2gSpeHPNMYZrSu309lTiInemcCxLOApkyfvweEMygxxoeqmrqqSE4Ytq/wqw22u4WqRcUVI+D+qafz8y45+T75WCAsdGMewW0CeL37XZmjgmQxxDGJw2rC5x6rAUMiqfn2C/tIKcByXoSts/U7g+A8SX49cp7y/r7XP3Sd5z8/9JnzY6yWy0IxnqX8v3r5kr7rePr0Kda8e/7rty84OkflPZq1hAiadvR6SpswHWNTjRWMZmIciOmAMjAMA1kKvXh78wrvMut1zXLpcWZJd+3Z3xzZXr3iOETEWJ5//QvWzQrvPFVdA8V7quuavrlheHNDtVxgK0/V1FSU0g8jBZiNNahGQhjY7W948/Irrm+uOW+Nl0iIiywWie9//5KmEUK4ZrX21E1FSMU7sDZT11OdYTFWc04Yk/De4GzLz/6u5/Ymcf22ZAuqemLwQCmst5qoneHSWaqqIYthFzJDVoJJZFvGfc5hGMFvokenZgGlJaPBaAHPlBLO+/MnBuGKOET6cKSpv8DaBjWZHA5kBi4ePSLlhhiP9N2bMZEnUInBYjn0PVPs1rgC2qb2tMsFLkYkFc+v6w5ktYg1GGfIWUhJ6bsj9e4W597y6hdfMhz3HLZvOd68Jg17dNgRNJL6PZIdejyQdzfkuinxxbEd5dAdT8ERgcpWhGHgxcuvqOUJ63Vi+fQH7LY3XF1f83c/+QmLRc1ms8baqoC4Crfpls53JVOz8njveFR/wsWzJ/zw2Y9KPaGWUobW11Te0/iWytfUVUNTtVjrsOIZukB36Dn0XXF2YuSTJ59zsXnM55/9Hq9uX3Db3fDi+iuG0BHjgLFSSimcomqQ/jeQGCNSenV6a2dXfn4PcKPRUpPxUVBRTOs59gNdP5BDQKNipaQXFxJJRxpwnIBOMbVh86TlYrXkoqnRRYXpIlf7vgTYZWoDJvPETXNf0jDHALmT8lyun/cowHdkztSZvJHzmsUpTniX3rs3UIgtPQ1r7zl0B7Q/oN0e6zN56twwDZwWj+x0WXeB+r3Oxzed/+7NzBR0AULDuxmI50yvohiyTOkQuVCimhDN7wX+X0lGWmaqEzzFB0cgLK1qfvXzTKf7FqD7Bx2T8WpHLxsRvPc0TTNTaiLC7e0txhgePXpU6tzORZXjbkuyluA9ORf6zceBKezYNA0xBrquo3Ul066uHFkjYsb6qPGz3XHH4eA57Fcllk7CW0U0kUNfun+MiWtd1eKsLy3IfI1znkXVEP2OUCK00jcAACAASURBVN/iNytcXVEtWxrj8GIxrsZ5j688Ipm+P3Jz9ZZXr55zc7t919NySt1mnn7iWC4Nhy7z2WeWy0c1IYQ5bFL5iZVRYizxMuuVuhK8sxz2CpK53SYMBs1mNhJEwIlQGaExhoVzKAZiZD+GRoYp/n83QH62Rk7ZtGIMVhWcA8YOMnJ6XqG7IeaBvt8haYFzbcm8jkdEI1XtS+aqSSQRsim0a2Vtqc/MiqbR6DM6kh4G6z2IlESdkckS4zFqMeJJCVKCGDIpRNLQs79+yXF/y/76LdLtIXVoCuQ+k2LA2rd44/EY0tCStfRRFYQUhjvPqsRHI4d+Tz8caeKAs44YY/HK9juMUZarBSLjc0jCoKVmz4pFUy66PjtqV9O0DdhS9O68o7IOZyyVr3G2wvtq1uFd3xHCUPo9H7sxMSbj65Zlu2axvKBqG7bHLVmU/fGGY7cjaV9qCt24CO4Sft8oHweCIojz5Vk5i58mkRn7SmrGacIaqK1QeaGqPU8+e8Y2DNx0HX/17/+GvhsHSifqsExBUFKOLDYVF08X/JN/+ocsjSAvrqhWjk1s2XUQvSePbYwm7jyOvUHD9PdY8wMjoTZZfWOB7f2C2HddG0XDfrR0LVEapqZn50zlHcWpI5gzdr4Qx/riEUvdM/z0b9DdFcOb5ywWn2GMJYzHmWI8U5zuPcP+D5epA8lIf2YVcmbMNrt3x7OxICQsgYqMwWig0iM291jTEKX+bpfwbUbHRM3mSQmdvzmmV+f8H91+X8ZafFXx6aefstvt2O12vHr1ir7vefToEav16g64qyr756/praFaLpDKIc4SJc1jk9KR4/HIzc0NtbfUdcXjx4+w3lJVpZ5vaoaw317THXa8ffmCuq5wIth+RwVsmiXdIRHDgcNhSzQ3GCAnobaeSjz7YMkxEWIirRuoPGZRsXI1tasw7YqqbWiWLRcXK4Yw8ObNK67ePKcLA7p6xLRQbFVhbcVqA5ePK1Yrwe7gx//Zmh/90SUplUzaruspndIE1CFSYwSadqBdLKmaFbe3exB48RJMrsnJInKgckIllsumoTUGh7CwQmUtj+uKV0PgJia2YksMUiCJkskEjXMtcx45KqNjPqcIWIFcCtHn55Uzr7/6SaF/88C1XmNMjW8rNi20lSImYTVhNNPHQC3C5dMnWOsBQY+R49Bz7ErCkHpKuMF6BEs4BA67PUPf8fjJI9QIMR9RrTDG09RLvHVYEttXP2d/e8Pu+g0Lm7Em4UwiDAeyCFYs2RqwQupKwbzmXCjiCYinuSsRcYAT2kcN7arFSGlqHUJg0bSFOkdxYyMSpXTvilqAMoTSU/p4vMV5x2q14uLiotSfZkNMmSEnboaOKezRDwf60LHbXzH0HTGF0R8purVqV9TNis3mEy4Xn/No9T0eXz7l1c3XvNk+5+32K5J2qAxoKp1wTg3/v1k+2hMskfGMTk16ZWpzdIqJeREW3rJpPG1TsXEWFU8wyubTS/pQvLRSRFysO50D57l0J7+oqVqLxJIkY61gDfTdgd0BjpTkgxACu/1hTPdODDESU8kCnbL2dJzQhbcuCyymD9NrqiXbtbt+SVNXNG2LbXxZOCNF9yGVbhjrG0UQ32CbBdIsy+ToO4bbaxZDD7YG5+ajzSnz7z3waWw/+FjOklnu3Q2neMLp/Q9TreXVJJZoPBmLYcBqKHSofrtp9Y2xs/Mr06m93ZQGP7W6O/9QPi2CX4Mz+D6P+UPX+Q/uTXp2OGOEzcUFfd/TD31pJiGwvd1St83dc6gyXO0JAkMfqB9tcGIwOaNjPWzJfE+42pZ4s3dkwI6JYrUv3lxVVdR1OzefqJzFimJqh2aPcy23XSwx8zywamqqqmLZLjFJkARyG4ihxP7DogJvMbVHh8Sx37PfbsEacJaLzRIRCLEHTXhr6OfxhbZpKcFmS+grYmVY+Me8fenQEAgJUjKEUJFTocCzlq5R1gh1o/hacD5y9abieLDYPOZ8hIAej3gylbEsbIWX4vUdjgcGsbRNTWMs6qsSr1MlGyG64p1lUzLKVTM5nhpJYGRen8aVfp7TA86auXr1iqw9YjO+ilhfYXxDGCw2G5JLEAcIPSZFnDFUzo0+g9K4CnUCzrDtI/0QORwPiC8JHc546nqBs54YlZAi3RBx3tG2nsdPPmWxKFR2jB2VExZthU1DmU8xlGx1EZwVRCM59ifqQrWARXqXFRrtcnxlqepSBlI5S+0tzpSuWP3xQL1usMaUDNw8Nb0bW9dpnvuiDsPAdrud46w61oLHMDKMxjKEA8PQsb19S4gDOad5Y4asmTqksjtREpp6hXWO7A2N33C5UW67N5DC1JENNUKe7vVb5DvRoSJ2PGbZ/iQzArWOiVhSPMFN5VlWFa0YOmdprGf5ZI3Ppa/boq3w3lFXrmSGkTGi1I1jsXBYz9g5otQBGoGhO3DbR25ComnLNiTH47E0484lCWaqKTnvCago2RhsGi3AMU37TAON9zfRq4nj9jV22dLYiGkuyGJLAeq970zfY46zjceyNca3iG/K9Qw9stuWJrd1REw1x7oExjreEfD03XN883M5AeW9SNN8jEnh6lkm6/l3zyWLJeIKJapgNGI1YvSUIv5t8bGPEZ0mzkyBnp7BhA+nGOGvR76tR+Q/WOSdXxBjWK1WXF1dMQwDdVO23bnd7bh89OidpxtvS//IPAS0afDGgjdkTWWN2IxKpqodla/w3mOswzlP5T2LpqVpGtrFgnaxHBWQYE1pN2irCmsaqmrJL1++Lb14TWazWbFsW548eUrqIrELDOxGEEwMjQVnMJWjG3YMQ8/17ZYhRaImbm8W+MrRNBWrttSm7eeoc4kv5hTJydAfHbWztM2G188zr18MhCSoWlRNSaDTUiPorMFYwXuHc2Bc4Oqtp9t7rFriMJD7gBl6amdoraOxbizliBy7DjsCgK8KmAwhkRSSwIAhC0RriJS65NLD9CxeOFLcFnuXQVJld3ON6oCrBSEhUqGpJw2ekGxpJxYDEnpszjhjccaU9o5Rqa0fQVC47gN96LkJR2xTqOZ13eLHLMpu6Oj7xOEw0LSZprasVpc478dmIhlvBWpPHjIpKTGVOV5KPkA1EUNX6rynjHCFnIsnNs/b0WEQwHuDdwYr4J2hdhYjpT3l0GVkNfWXNSUePR40j60yJ9o/hGHeAWQcvqJrk4yt+xwhHhlCx+3tdt45yDs/06QxlR0icoShGnC+ptksqaoF1pfwU8ZCLlkP+TvQRx+fGONs6TofI+RSsKm21LgJI5esidvdlkdeUCfYIWItpXDRZpqlY3m5oG4qxMjYZw9QKZPeKFkS3ZCxKePaJbk6Elzg2B3Y7Tuuj4HVCMbDVPeTJ1+y7Nygc+pJ8cxUhKilibdL6Q71eK5orbVINri4J+/2dOGGdvEE4y1T2fCctnEeHxvfsFImUaDQihkHtiIOA+HqDcftNc7WmOWKsUdxqW2c03A4DzLxnVygb3QXdaZcy3nufvb8fpKUWEoadwBxecCmAWPupr7/KjLVk05dQ34dx/smuX+Ob/NWv5XG/chztYvS/mmK6QG8ePGCZ8+e4dzZ0lNos2V3e+Tlq7fs3rwmVY5mUdPUQl1bvvj8ExbNgnWz5PPv/R6r1ZrLy0djJmmh/rTss8P68hEI9H3PYbcjxoB3LWbdslwqP0x5tLYzbdOUvp3A7W5Ht9vzs+trkih4R1V5nLU03iEr8E3NJ5uaIfSE0GNEcU5oG+GzZxeYqubqTZhYrFKzl2r6zvLlvz8ikiEPiHFjI+y6FDfbUhuYNdIPR4yUeuQwBKwtnnTbXGKSZ9N4+v5Azh3VhaN2CyrblBIKo6hzZCklVLt+YG0qFlXxLNUY1Fk6bxiscKgtwQgRGEzxoqYSialmMIRw53mJCE8eb0ipK965V5xNLJwQjgf6EGkrh1fFq1L7FiOw3e45HnpyUh5dPKZtPG3tOabA0HccXl7TK6ixXNcLLi4WrNY11jaIgSEO6H5PGgJf/lXC1x5XORZt8dacEZplC7QMoeVwuCWEge3Nju6QcHbP1fWRlEHEsl6v8XULddmarACfx7iMpkiIHSEeWdYZQ4I8sLt+hRGofMWhWVLVC3y1GNcyFOapGLcx5hmIi0EBKcd5fZni7ZQORrk0ePdjQlLWjNGyk1AKgWMMDO5I7Dua+kBVtWQyprVQgbOWmA1pvIYP8V3vk48GQR2z9eaU8TEraEr2FxFSHDjsd1wLSIhcLi5wFpwxLNoKaQxVY5Cp0D3HwsFL8TQVJWYl9BETMq0WWkmtoWpq/JCwQ6kZzDC2cJs21DzFARFz2kB2vP6sp70S7gzPGCMoeFksQO89Gg4MuyNt7BBbI7bm5PGV1JaTopySaUrwXMUQMoSohQIOkZwD3f6Kpl3Q8OSeVzL1ABm9k/dlJM4P9/zaefdz9+V8HEa69u7t3y2UKG2Qywa6CoiWLiJmzvg7I1SnZJAPnfrOufQ07uM1GWyx2vJEkU5j8uHD3u/8n3NJo546fJxvplqUq6VtW9xYyjNRNOdgdZ5gNCVbTcc6L8U5v4bp3M456rouymNUmtOid86RUuL29pY3b94QY2QYBr744gsuLy/vXEMXA/v+yNV2y5tjZrBCVTk2y4qLVcMXT57ga8PCNVysLlhflH+WUqMnquQxqWnaj7D0LRVSVo7jPp9DCBxDJIREiAPW11hV+qHnerfjZrvl9W5bSmuMp0mlWXLVCXno0RSwJiGS8ZXFkIoCtmXLG7kXhhEEzYac7BiqEIQCvGLsWNxuyLnohZwhBDf3SIjRkrOQs8GbTGMN682abAI6ePIhIeoBS8xp7CU8NseYmY8MJGoHKjrqmVxAM+RC501zVZmpvGkLqPtGkYiwXC1QLT0axZbeoFYssVS1U6IH43eMBUpPnYQhj+f3owGz8Jald6y9p6YYM76qaeuatmmRqsG4hkxNbYvD4I2yrB1t27DeLAqdmxNVVSNiaNslzhpCGIoRUy+oqhbNdtzfT3hyeYH1Da977sjInxUPbuiJPoyJjQF0IMaMjsBdEgfdbEmP2z+WPISz0JO1pX1lCagVHVIam0vpIpTHDRDGfI+cEmoLzTp1CxMt9bHJdkSBMBywrho9+FMD+qk15MfKR4PgeXd2YHTDzaj/E8Yauj7y+u0Vdncgrtb84Wc/wItQO7hYrshVxtSZfugKfRkSdux7o8aiWvYdPBwCJiQ0CpVA9mXi75Owi0LMWib7+OBLZ3KYMg0nz9TZ0h0mayndgDz2NrxLbJa9/8bd7a2jaVd03S3d7Vsu+z3GNSQ3JTOUBTXFDcpBdJ4EKoYsQpeU45DYHfakcTfpxfVzpKm5yN8jjqUiCBgV8uj56QzcMv87Ua3mdK47XuIHEEPmqOM3ZEYyf4Z5LIQkdjR00lgnGMvyHcskdP78naOVa531ic70rsyvFCnxgVL0nJKZwWY2qybj5J6cg5Rq2fXg5cuXDEMpE9jv9/PvzjnatuWLL75gvS47ujdN8w6QngNjSunOsY7HI9vtdgbaaX+56d9yueTx48esViuqqqJtW6qqmkG373tevXrFn//5n3M8Hmmahh//+MezZziNys1w5M1hx/OrN3ydjqUDkyqfPd4QH19gf/9HtOrZuCWPLp6wfnRJu1lgiKCJEHrKFBfGDKjSO3IMAbx8c831zQ032y2Vrwhh4Pb2lu9/plSVZ7e75fmrl7x++5ZfvnhREqNcxbJpS1enFNG+g5zYLITlomG9bDAKzoL3kCSVNmjnzysLOQsxjo3DZNyxwpp5q6iye3gpGC41ibl4hmLwMgKNSeRYgPfTp4+pHy3Q/sDrrwLHQekGOGjpmDNoRGPEqmLFjvWFkUVlx11nIi7CIEoMidhUpClWNZZWTfPifWU1IsLF5RprFqOyLbHGvk9lL1ERiLnMX2PLrgZSOmPhFJVMyGUDXi+wrg26qOk3a5JtUOuhblhcNLSrGtuuCBE2F5llXXr7hv2Op08fcXGxZvPogkPXcXu7GynyiqZdcFi0hBDwzrFaLVksljze7IsBFBKff/4FGc+bv3s5G51ZE2bc1WIYjvT2QCdHQn8khQ4xgTQMDEelOxxQNSSR084kE+OkZYOFYpDG0rXGlNaB0+bec2u8zLw/7RAG+q4vews6j3MO7xzzXpspkMIRITH0DmdrRNyo+4uTIuZMN3+EfHxijLFjLzw762Adk09k7BRfCzxtFlz6ioXz7PoDnXMENfjKoJVgqtICKSdXKBc1kA2xL33hUoKcPDnCzbGjDpEUAzfHPYehJ8bEMPaVKaUQebqQGQhPvUxlhhI1psQu7w/OSJcyxg8RwS4eodevCV1Hun6OAzaLSw6pZN4mY8nnC0MYAbI0q8bAEBNdP3A4HNEwIAg3X/2E7uaKw8uvIStV3fKDf/onpKol2oqsxYLXOwA4tayatpIaJ+sdUHv3gd8FpzG71XAWrZl6+t/9tKWUQ0QRopRtgCs90uiBo3ZEKdx7lrF0xJjTXDiLc4qcANDk0YOWRFVbqtqzuVjR1gbvDSlGbrY73rw5lniKmkLZ3DNYoCipYRg4Ho/89V//NVdXVxyPxxnIpr/3+z0XFxc0TcPXX3/NYrGgaRoeP37MxcUFjx8/RkQ4HA78/Oc/58WLF9zc3HB1dcXtuDnuzc2Ww+HAzfam1OGNnt9ch6pK27ZcXFywXq9pmoaLiwsePXrEer3ms88+42//9m/58ssv+cu//Et2ux3WWv7iL/6C3W53tp+mEBaOQw3XeSBZASxh6OniwG448upwjS4r0DU/2FTIZY24hPYdGnpyH/FVy2LRFuBLiRyHEtMxws9/+Uv+7ic/5Re//IpnT58SY+R2u8UiXG7WJQ6TFecs6/WKPmb6rFCNTZ6HRBRQEkNWGhwYLRvgxkgXB14NR7L1qP2EaaZ1fU8/DOP8E0TGPeQY60S1NOlOTLtFjK0OQ/Go26ouFCrCzc01x10gHgyfX17ixXM9WI4x0WkitTV9HNgfe0LX4VWpmiW3MRJEiZXBqZTOMmJwqlhVwpDIowdrbUmEMcahWQlDoe+svddkQyHHjGrieNwTUyZmg3cVzbKmsbYYDiGQjcF5z3K9QXZ7Qj+Ubi+Mu+7st5ghcNkaTFth65b64oLFxYpquWDfFb0Ya/jk6QW1FcxwoK1LY2xrWoYYiIdMn/ZUdaCpKhZNg1ksWK1Xs5n7xfeelueZYbFYsu8m6nBkNwiUIq7M7fWWfFuT3Guub67ZdzvWnzbEzpVNjT0lpjO3KhHSFMPWTN8PxJAYwlBa4p0xJcYYnHFMJSkp9sTQ0/cH9ocDMUTqekHbNDhfYs0lXilYUxpsd/0B72KpLyThFBKClUwywr0dOz8o3yExprTOkVHjzbU1Uiwb4wRbWdyipTYG4w09gWhKFpmxBrW5WJWmdC1QbxC1oCU4HZMSUcRB1p5oAn0ODCnRp9JrEJkULuPmjDpPyvla4WyT3PL4jYxtjL5BtNwoVC0YV4qTd1dUVUuVeoL4MV4mowt1olCQsRWcFmCxY19C711JxkmJuN+S+57hZovVTLtcY/7TH6PeIeKZWqmdOrOM3t+E8Pcdvm+NhZ0+N9UiFliZ+9WMcuLQS3Oq0vUiqjLkjKQea3s8EcSSjCmKbMSouf+pFsu++Ht5vgWjJcPXOMOicdStY7OuWNQG72DoE0OneJsYd5Dh1AX97j2FEDgcDlxfX/PVV1/x5s2bmfY0xnA4HDgejxwOh7KB6UiXykgPtm3L06dP+eyzzxARttstX375Jb/4xS+4vr7m6uqqlDXc3rLb7em6jt3udi69mbyDyYuc2qQtFgvquma9Xs+g+MUXX/D8+XOeP3/Odrtlt9+DKl9//fVdOlRAK0f2tmxkzNkc17Hn5dCVf3GsOz14VCKpO5BCTx/A1alsPip2pHMDMQRC6AuY39zw5s0bFu2iJIB1HSGE/5+2N2uSLLuu9L4z3sGHGDKrsqowECgARKPJFqkXmVH/QbTWD+5HyfQgmWgC2QRYpIAqoIasjMgY3P1OZ9LDPtcjslAAC6buCwtkVmRmuPu955y999prr0WMQk57zsQtNck8txFyJuZESYklPgnki+l3ZfuuSe0zSDQm8TAUCThJEldXhFLF9hVF+nUVtRHShNwAcWAQAC3lwpwW7g4P9N7jjeYYC3MpBA2qmq8uObMkaZHMqaBSJinQKtOgaFkFrUGnAkpgWuy6T+Swzev2rtZxz3YWYYlQRD1lmRdiymTt0FrIIqmIJCHKoKwDa4m5EDPEIkz6vOobTyMlRhwFrSJGBxoTcTpjyeRlIcVCTlBCj8JgSyJPEwsLLIXxcWA8TYQcJFnOIqVn6whNCGJvV7Dium5VlblbXSWeYFCqXN48jVAOxHTLw3DHEA6YrsrCWY3KMtCfckRleb4yjyzVdAyBsESWZRaJtrMcoATBqEWuTmnIKRDDXBVzFkJO2FTHR0oCzNlvdj32co6kbKoXtcDfurbo/hydqe8cBLU2oodHqXM1udZjEXTAX2xplcfvHYwjSSse2gW163EXPYpBglEqODzaWBovigFGe1Q2xJjlpqVFBnB1w5u7L7ifFoZSSEajG4uNCp0yKeW6kWog1Gt1Vqun9OSCpxHDzW+21ag9uVKrSNDQ7tFNj9GW4fN/w0wDl/trNhcf0biOx7xaFD2rBLM6BxiTEtebDRfvvcR87/sMD/cMjw/E+ch8d8PDm1vaktlfXsPf/c8i7dT1MmRaIVL5VVHIwmupdmzfIn/4rdfTAIJ89lJrPlW7fiCfV6CfdO5nWgKmLMQCp5CYTyM236GLZrOdma0lKMucV5k6hT33TaocU8myLlJG5cJGNbTe0W8dF9cdXe/YX7Y4HdE5cD8+4MoRz8RcfE2y/nBpllJ4+/YtpRR+85vf8Mknn3A6nfjwww/PgWmt+Pq+Z7fbkVLi888/54svRGLs008/5erqio8++giAh4cHfv3rX3N/f884TedAp7UWmbP6umfIFIHJTP3veZ55e3dXwYBSffMEQus3m3NwDjFiKsnit7/97Xlwfl1DdtejH1uSgRAiKWdU0WyU5cp4XkwT18cHrt5a1C9/Sexa5jJzDBNTThz8hlk5Fu3ZbLfncYlpmjkOI2mZyctMDgvbvhW3E2fp+0587hbR2I0hcHh4ZE5ZHLsrq1GFhXkcSGGhxELrHbs+o4rCFpnNe9nt0b7hPjzp0aYcSCWICLZRZymwUgW0pZUuiinGCCp01vdVoJ1itURr+555nrkdbhnSCaM1SwxSuVmROFsyLLGwJEXMcL8kXAJrMpPOXDiN9xZnpBdtYxENU6PQprILSxKtTKXw1hJjeicXy7lw9/aOUu2SUloEQWo94zRxjCdKiLS+ZdP1XOwuiCHwm99/yTLNaAofXu+lgp4HhsdbIRxawxKPMFpiuGc67dF+w+s3jyxzYJ4Cj19d0lhNk0biFEgxQ/tC2i8x0e48XG4p6ULy56SYpplxHBjHgds3Y9V7tnjXMicL5UkNR1wbZQ0fhnvuhxPDm1sO6Q1Rn/jeTz1t09HsOo43iRRGSs60DeCKVNC1ZTENE8s8s8wzwQpLdTXOlgqwnMEkKMQcOc1HIqVCyJmUA8uiUdqCERNoSZQyytpq/FxIVc5NHlA5Q9rf5frzKkGlSLEOoxck0BhQTuGuW4xRpFkTT1Lx6Zcb1K5D9Q0+SHZhDfRuizee1vU466S8zUIfjiExhonJNpRYmK8WcjTcbB+JaiYUkd9RRVQ1Vpf6NZgp1FkQVylV58VLrc5MdaH45ueq0UXVzWsbinLkAuPbG8oSUHbH9U8M7e4aZd15iDmXSpKRHYtWCmcU2mqStXydBLLVurDbdqTG47Rh3+3YXb+H6/dgG3GbVqsksarWIdV9I1NJSBXmRdWo+Cee1/PW4Rm/rt1FtcKMz3ippfbh6r9fiiLNifg40EVHo3o2vhofK4WP0pNNJUvDHNlzVhdW6qt2ClO0yCR5mWMbhoVpnjkOA9sWvE3c3z0SlkzfdpyWIn0tFOeB/ye0htPpdK7uuq4D4Hg8nskrq/L/OI4cDgfmeeazzz7j9evX3N3dnSu9m5sblFKEEDidTiilaLyvgs71xYw+VwVrWV2qmgtGnweOz8zBAsY8JWPSfwoVwhVlFO8bfvzjj/n5z3/Ozc3N02tpLbNNrFC1pjGW91zPD03PD24HXhwC770+svvNI9YYglmYdo5T7/n66iWHBMdQePXi5Xlc4uF4ZI6Rjz54ybZv+flPPubVq/cpBcZp4r2X72Gt4e7hgX6eWXLkOiXmmFhiEUgNKMbQaS2MbhWkT14MDeLnYAGXFSq9W7pbLVJ8QoATfz0JhKLEEqPId8UlvOMCX7LApvNpOOsTt85jlcY+s0hT0oSixEyOibisJCY5HGOItemkWFQmmQZlPNo6qRDHiWKFTKGzZVXWTUUqF2vk+Xv3bk+wbVpxlo+Bvt2gjBZafpGk1ZiGvm3ZVBcHYwy7/Q613aBKJoeRcTySh0cwYlCgjPgMlrKQx0fmuFDMI3oOuJBROVGGQjCKkmdRzCkGlQLEBCFi0XiVsbpganCZp4mUM9pYMsJWTijmmJgjwFMQTCmBlpaI8QsqF9Qm40vEmEJUiagTTkFWgZAL0zCKAHjO6CqoUbK0yET0O5Cj/F48LWs7QZUqdaYxzmCsob9ouLm543gcOZYTu34L2z25yJiKDVbGKowwl1c4Vc5gjVFr//C/Q09QztFaBa7MiNpjU05jtx7tFGmBbKNUSvse1Tco73GI8oc3mo3f0diGxrVYY4Uck8UvK6zMy6yZfKDvR5ZNwrUtNmTsItmqqvp+ItBdj/IaBPWzeZ+Sn3wCnxhz734udf6/emlLVmLLEQ8PxGkhqd+wuXofq8A33ZkYUVI8Q8XoTqxznMHGgC6ZHARCgiyjIY3GY+3gfQAAIABJREFUas/11Qdsr9/H+o5cZZQohVVsXK8kksIzGHcNDs8g0j/1zM5oW4VZC/WQrz/rHAyfQNIVyAxFM4fC6TSTGCntzJXVJHlU2FTJSBU2U0iF5Gq2X+phZZBB7nXOahgmgU80lAtL38A8B5Ry9F2LPU6Ep07uHwT7qVZra9WnlOL+/p5pEp3BpmmIMTIMAzFGTqcTX331FTc3Nzw+So8v58zNzc1ZIFno2hpvxGg21UxWVV1cpfVZVURp3pHeUhrZeM8eha7BU7RsU4Ukowy1Ny0fffQRf/EXf8Ht7W1N4FjbtvLUi7DsvLVcGM/7tuH9MXI9Rl6WiTYcQRUWF4mvdsyXG+6y4TEkHqdAGzKh7Qh9z+3hkaTgBz/9GS/2F1htabtWCGjzTNf25FwYh5G2aeljICtFCJl5SfhqPZa0RntPKYk5TjjnAStiFBRMzqhQpEd/PlUURom3mypVHawGwoqLokqipEzMUimv7hGlSh/GeUHngkXhjcVUZ41QoTJde/1Up5gc43kAvBQkmCmpbwKJlD1FWYoWWbOYsiTyRQk1X07zMzRorMFYhbPvJs+tb4mqMOdM13Qoo5jzQqm2b41p6LqWrm3OrOTNdoPTGpUihzePhHliGU+0Vu6RtNelvxZDIMSFhMEk6WNaMjoUsY8ioXB16WT0KnFIqhxUkahEwRwCRSm0sRTl6v1FDA++kbSU2pc1KoGJKA+6TzgSxgj5KetC0YWkEjEFxmGhcS1GG+LKGVmhVSWkpJQmcg6EZRKLryQ+kMZqTLF46/HGs932pJuFcTkxLTNGKTrvKUVaatYarHEY52nW/pPsOlAahfl3TsU/vP4MZ3mJ5qwSV4hSeNc6Nlc9zaZFe4XKlv7lFc567GZfX8Jw2e2x2uC0Rc2ZNBcel+GsCJBiIqWV7KIEC55AB4MpIiVklaWxEduuQ9X23DzJIZNDEJptqT0HpfFWoFfrxGrGef9OP0zOsnd9AguZeZ54OByZ3zyQ41vyV/c83j/Qbfe0vmVeZpY6ALoKCSi3QVtP07X0rUITuX9zxzyfCHFiu5H5mv5iz+6D77G9fB8bM/lwIKVHgSyVRhmDcU4qWu+EOas0oZTaV5AexzvB/Bv9wQo6ye+qsWVRllKZqPLP12BqztVhqj5tSzYcArwdEqcwcOVGPsiJnGeBuCSFlEVqPc462rYR9wFv0UaGXbWxPD5MPDwcefP5DcfTCEpzdXlN1wiR4wd/cU3jOxq/YVh+gzqMxDmTVaz2PuuhVjidTmy3Wz7++GPu7u7OvcG1Cry9vT0zPNee4Fr9TdOE9/4P2KGSxDz1d61SUumt6+EZW1Ct93oNlM9WUyk8uZCXwlQz8JwSqhQa53jv5Ut++tOf8rOf/Yx/+Id/OJNjDBXqThDzCkyIPc5V57i+fsmFa+ldh85iadM5zdhZBgNfv36NSnCRNeqTXzFMgdt54XM1s2wbXIIfvnjF+5cvGZcTScuwfLo/oJbM9nHBZst1u6N5/wNi0SwRhrCwhMAwnei8x2jNaZ7Ry4yZZi5yQIeZOAy8ffNINJby44/PSaVVayUIKkmvqigIMVYfwHzuV6/sc63UOXCctUVLYbvdYhBPPIsiKyO9yxjFdLfI96VPWXOLmpQaA+M0cz/P6ONA32uWnBmoUpCtp+s7YSmmSC5RCIYZvLd4+1Qtaa15/71X5DQxTQPOWRmrGA9cbLc45+nbjRxNqWAMNN6x370vpLN5YnzzO6Y4s0wDl+9dCas0BSwZciIuM1NaCDmLXqvTGK8x4l9E8Q2HUyTEzNVFXxnwkTGeCKPi9PDAxUtRA0olkesZorI7r+t5WqT3+uwSYqUwLbOZwBh670la5CADhaQKC5EpnRiXhdNpYNttScZxnEO98YWkZ7JbKHliGO6IcSKWCW0sxhn63RbfNTR9g980aGcxTtNfd4SSmO4jS5y4u3+Ld9JWMFYSyaZp8bsNTV7pfWsmqVmphd/1+u5zgrXXIzjuiucWXOvotx1tu8F6kYbypsMYh9et0I4T5JBrw3qBWbK/lNJKfK2HuAZliEtgHEfevH7D7c0Nh8cDcZFD1xorx7QGbxVda7FKUUKkRGnIhpyZQ+A4z4RcKCpJQCmgHd9aKT8PIqLAYGh8A+2WsCwsRXG4vWF8eJB5oBCIKVbYVXoWmAalLbrxtK1B68Lh7pGYFmKJHN4ecG7GmZG3yjM8HDg9HkTFokBRMjulrRWFD2swTYtpW4xv8LtLvPUU6xiVJhb1JNX2h08Mni+G2vMsNQOnChVLnKyVYoVaC6IaM+MYi6P3Habp8I0jFQtFs7cWXfs4m3aHd56ua2m7Bmu1MPqqI/bj40jKkWlZiNXaaVlgnmFZwF30dG1P323ZbbbMAQ7zSbLxZ4wnpRTX19dcXl6eZ/+89/z4xz8+B7SVxZlS4qOPPuJ0OnF3d8cnn3zCw8PDE/zI02iEwOZyp85EkPONU7VoruhHJTRQCrn2nLXS4hKQpU8tEL3Ac2eGSyn0Xc/HP/6YF9fXVU7s2fqr7YWSS1W9QMgNc8CdZnw5krvI/Q5i15OtJmvNm2nhNi7c3R/YKstWe9TjSDyNDA8HokmkR8fdLz/Bbd+wbPYMVxvm1jFuHN3bATsumGEi5kgiEzcNs3EMxnHMhaAKWRXiEoV8NM+ocUSPE1tvsQVa7UR55bn6CDLEXIwcM+sspTEGuywELQ4z8gzOiDMi9SUMQBkzUCQl98RqCZC2QqbKQNSGmCyziI8SXCDNAbIQbTKFpBRRwZgzj8vCbGapDrVUedYqjJX3S9HkIrNva0C25l3dXOMsWntczlgrw90uBJxpcabB2a2M/eTpjNmUUs4+rMY6cZtpe7rNFqMKKYw4CionppLJShIHp6SqM1raLFhNMgalIpkEKmONwVhPIKAwTNPCJkSMlXucVaIozbK2s5D50RV5Wq/Vvqoo4Xys7PtSq2Mq+SUR0a7gWs1234q8mlWkwJksiY9oCm3joO3J2ZFLe7aXantRe7Heor1BGdlr290WpxuWJpOmRJqiJDs5kUOoUoEOiox0qCJjQJnMSs//dziQ71x/RhAUgdnVVEEpUAaaztHvtvTtHtc0WNPgdCO5WtakFIkhcDocWaaZaZxgqfT5mnFjNL71Uupqy7gsPN6f+M2/fsrr168ZTicaZ9EonHUoU2ic5mLjeXW9ofUWlrnOLSrmJXF3PPLF7S23YyCWBEacjdUf+cRnJ4MiPmfeWTabLebimjBOsAROD/ci1zRWT61SROFgJckUEZNNxmAaW9lqgayhaMXb5Q5vNK01HG/v0MbjvAjUKm1ERaNWgdZ5tHO4vqPb72l3ezY/+hi/3eHaPaVolqJYcnmSXVPPn1f9Fdl4RUnAFCeOpz8XvJVVCQ8Z8zAE5VjwTLTY/oJms6frWigGi5bqzxm8s1xfvEfjG7quw5j150cZEwmRrIRZuKREwUBxzItinArjVDC2wzcyx7Tf7RmXAg/H+u7fDYIffPABL19Kz2tlY7Ztew5oj49PLgZ/8zd/wzAMvH79mq+//prPP/+ceZ7PEKirOo3PE6BvDtGfv1ZijHo2p5hz7SdDrChEjEIPz+fgJ9l1RrHf7fhPf/3XvHzxkqbagZ1f6xswIEBJET3M2DLg306Efct9XnjstPS2iuKrx0dujyduH+4xrsM0CnWayA8Dw9d3wnmwitvjLxmV47X2HH/yIeFyy/L+BdtPv6A7DFwtsUq0ZRanOLUtD/2GR+8p3tNeXGCtA2WY5gk1DJjTwPsvrmidoTOeYFlZDufLWSvMPRTeN1gryeVsZ0IITGp+h9i2LkldBe8zqwCFqkFJ5LtUlcgyShNsrGzCgK4w9KzHs49oleQgqNprDAHLBEYTq+P6GgSdFn86CVqCjhhtsbZ5J9GU92cxpRHyUyl4X7CmxagGo3fEMpCSOIHkIsPnBkVJGesbmm6Dzont7gKtMmHWeArERA5C8jAKDDI3aY1BOS1KXdqcjcilR2vQxuNLpmjNNC4sU0DphXkSODSjmOZ4Jgg63wD2Wcu9upFUc+X1uykHVKa2DRoyiVgWTCPJT9/0tNph0ei8QqYZ1UacLVjn6coFUM7Im0JjjCQamXL2K4TMxcUevXfkC83j7YH7m3tKCpQSSXEhao+1CbIIGpATucTKaRdk7s+BRL97ENQJ7QvNxmKcRRlFKAq78RTjiKFFlYZSHDnJAp1PJ5ZlJi4z43AUMeCcscrL4WENzjbYxrO52BGXwDROfPIv/y9fffElv/7nXwuUpBR2ZyRgKo3KEWcNLy53/OQHL7joHXl4xNoGaxwYz83jkeuvNvzq9T2nmHBNQ6ZgrHvabN92KZn/sSQ2LnL18fdR7Q519UMeb28YHh65++1vGQ+PhOFInk6UlNAponKqmUgmTIGiQWs5yJMqTKOWOcNcCRQKsq4RCLFKQSuykUym1APY+AbbNFy/9z4vXr7HB9/7AS9+9JewueCxu2BQlvgnh0PlMD6fuUVR1mmtNaPhSQsHFFNxqP6Syw9/xM//h5/z4atXfPQXf8FcDKHAuAS0KtWBPDKNM/d3X1cxbFExiShCLjwcHxnDLGtmFNeP4zChTE/MHdeXLVop+sZzse+Yw4z+MlX04d3lvN/v2W63ALx8+ZKrq6uzDJkxhmEYWAect9stm82Gv//7v+fDDz/k17/+9ZkJen9/z8PDw3kwfoUwzToCkTONb86GqzGInmFRVOhe/r60JZQwldfgCFDfgzEGay1/9R//ir/527/lf/3P/5nvf+/7pGdarFQYtaQIKWByVTqJmkNceLNMTIfIOB74ar7lMwYG5wlzYU6SCE1L5rRMPJwS5mrDsm0Yd4ZxHCgx8tIYbIYcI59/+ZplGKBk2gLZaB4e7knTSFoCwSvC5Z5okB69BqUNuRgoirxEdB3IP80LJWhxZUiRxT07UpSiazY0WgK+qQSXzWbDthO5w7hEORi1IuXIEgPH4SjkIq256LxUQFrT2kZ6mtaja/NaqsFqmovA5be3b4mnmVOUkasSqzOEaijakLUhIP1d21pcYzFeM8RRRByKRSuLqhVLKAmbEjue8szpdKDkyBIirm3RqhKksqyPeRQCn6mScJCYpklMiXNGG0PbdzROY9stJcuAfwZQGd1pUCdyGIGEMpZiPabdizxeitKPzYFpPNDkQtMa1hEPEAYuJeOtkgQ0ZlRM6HoWmLJmvv58+MkIgj23vGT75QpCa7SQMCgl4XuLwdOwYbyNDGNEZ4ftDL5z0BmKFvRQo+voS67OOSJQsiYI63pRRs6mjDhHdLueGBVhnCk5oFiwupEiQXsZhSjx3JMvCpHRzDx7Wn/6+s5B0HohouyvOow3sgGCRrcNWXty8aToIInFPakQl0yYIsscSEGgIV0btNoYqXR8i/UO0IzTwu3tW7788jWvX7/h4fGAq4oBIWexBdEy/hCLsBOd1bTeCtZdCqhEt/EspeVi3NIcJpYlYnyDpojo7Ld9wAorUqiLR6Sh2v0Os38P9eqHlH6PuzyQsTQP98ynA+lwB2ERXC8KtTsaRciBlEW5vSDMrrUhnev8whoEVXVUKGvFpoUkJOMooM2AsRazBPQw4kNks7nApYLtdvXzfMunKqtt0rPq5vxHFYgu9Y0oTdFPhIKQC0U7bL+n3V3RbPcoa9HFQMocT0dKDpQcMDhCiByPR2KMQKHxjqwNSWkOx5lhnAlhYYmBFAoxwmlIeB85HA9sWs+87Wm8pW2c6LCW5xVrzUTr+EIpBefcOfitowgrAee5ZuGHH37IL37xC/b7PVdXV9zd3XF7e8vNzc15pnAYBkIIhGXhdDxyOp7OQ/G6GquuBKwUo0hCKc5wqECoqyGrwWhN0zT0m56Liwv+9n/8W/76r/6ajz78iK5rOQ3DO48qZ3Fob2r/ShVwRjNaxa3N3DjFYgonMocUOFIYpkjRvooXaEooEAP4DtN4mheXjPeQQ6Db7rB1Jvd4umccBng4sjSiCtNcXpOGgTjPFFPQ2x324oLsPcl5KWpzlvZyvSe2cSxAiYF5moBMbuTvrlw0o62gRyC+hVbcv2W8qmC9EB6M1UzLdNaTFf9ScZoxdSRlHbFQRkhrK8LgncU3XvRTCwynUeBUpYWRWKpuMGsrQJ+JdcaYCrtWuC1La0LiSJKgn8F/Y05Q1flGq7WMkGhTN3ZFzbJULop8Xh9ydslutE6TsyFnQY+kp5dr31mjjEeZBVKsEcuB9RTj5f2Xai5AIcwT1viq6VxtoZK8ttFS3a7PgyjMSa01zhqW/I1zYx3+RZ2T0FKeEmU5H0sN8OaMkqQciCljisagnxj3tTorZ6qRcD40Twpkz9syRStyFrWkrJL0ALsOkqJkYYbK/VHEFCEpdELIlUXGLJ6C93e7vnMQ3Owa+q3jex+/BAeJwldvJ4rqiWZDyD0FSw4JayxaCXswpUwMEaWMlPTW4poO4zy+bWnaBm0Nj8cjv/v97/nnf/5n/vEf/4nD44Gl0uCz0thlRluHMQKl6hC5PRw4TZfsOsPGGu7uHzidRn6w7XCtYXe1obkfGccggmnWYJx/VhL9kSsulLSQc8BeX2OuPqR88EN2732PbUxc/fQ/shweCacj8+3XxHEgHI+kcSamyJIDp+M983Dg9OZzdJxxRWjFAqNoirHCoHQOk4UNS1RkMoHIylAjZbG3WTLz9MjN24GH370mFsv+Rz/m6v3v1UX27qXe+cYfrohUh5hlxEPsTEw9OHKBMUSUsrh+T/Y9i3LcnyYC0hP6f/7plwzHB+bxiNUNMYqrxziOALy4vkK7BpxnKJ6wJKZxESX4JHDM46MlxYbXe1F86BvLdnvJbtvgvWJazpwe+RSVjPLN6tBae9b6XMcV1r9jjOH6+pq/+7u/QynFPM+M48jxeOSrr77ieDxyd3fHl19+yd3bt/zu08/41a9+xa9/9WvGcThr0q5B8Pnv1yrPOnHSlrGIzG63o+83vHz5gh/96Ef8/Oc/53/5+7/n/fff5+LiAuMMp/HdIBhCxGjF1eXuPOBpjOG+1fxXB5e7ntZpVKMZ2oZjgZs04Y2Xw8i1+DnSnSLOZvyu56PvfUC2jpAC13/9M9pGBq3v/8t/4X44kr9Y+N5PfsrVi2t++B9eEY9H4jByLBPmaov74JqTtRymmU8//5q0zJRU0CSaxrPptozjyP3jidvPP6dF4bcbLn7+JMagtGGVFXN+db8wwpwNgb5pcd7RNJ5xGYk5MoeZxjYYrRCVeQVaEUoiFZmdHeaRZQmM48TlxQUXjaPfbPEU2qHDeIeaROKOXFAruckKicI3XgKwFem+XEcyYkqyL5KoU03zQoqZvt/wwx//mHUOubOm9jsdxgurOC5ztUpKpDhIklgCVvUYo/G2lao/JebomdJEnCMhzMSwMIwj2TmsXu2yPKYOiivn0U1PNg0oLSRD41jUxHA8YJSo65CzaHEuAaUT3snojzhLZIJZ0ErLaJoxDEHB4Wkd5qxQuTLJi65rPq9KfIIUpSIQt9YUFdEqskQh6ThlMTkJgS9Gis6S7Oc1oEoiufb6z24uyJ+lKB6aEowN2nRstjtUaqBEtJ2JeSGXwuPxkY6GFk/rNoQ8EdNCIVUo+7+xn2C38fRbi+vEiDLljG47QuqIqSUUT+NbrvctvVNYCheXPYfHA6fTkel0Qq1N7a4HrQkh8vD2hmEY+fSzT/nyqy/57HefMccZ5RReNxhnwCqSqv0YDapohlS4OY68OQx4p+m2DbFo5pSJCMSjGk8smmkpLCmgTSZ69S1ZgmQpqmpzpvkkYjR+i9p/AP01sWZIxWjSZodqWvzlNf69VxAiJSyUIBsuExnvXjPc3fDZ/x1gPGCXgeuLjbhxaEMyG7Tf8OLDH6KKpiRIk7DcQp7rfFmSnymrhbbr8E1D23dsP/4Yd/2SESPv7Rud4DPOf/btU+dfpYUoyjBkUYHJuZDHkxwEWTZMCoFxGfnN77/g7f09l/s9sQbBf/qXf+H0eMd0fGTT7wFFCIl5FqPMYV6wTYtxLcl25JyIiyQXohKSGEdHipbPPh2ZTreE4Y6//PkvyCnhbUYtUL6Rqf4BC7b+93OSyzddIp4smsqZnNE0DSmlM2Qqyimykdtuw/uvPmQYBxFrT0mYwAq2my3b7Yau7/HVQV5+Rk/Xd7z38j2uX7xgv99xdXXF9dUVL997j+9///viNm/Nt+oaKgV91/L9jz44z+KqSps3Cj6N1bBaG8Yiuj66bTnOgSUspDlhIngLh+kR0kiII4fjkUjmX7/4Cte2FGM5mEzeOPp+wz0LnVpwP3yfrXqFQ5F7g9vv6K6vuDkc+Or11/zLv/6WNC4QM62V1kRRmmANkzOMjWNKGa8UF+ddBVlnohJDVpscSWfiHJmXhRgjc17Qs8IMhhBnAgnbWIqSrH7ORioyFQhBEjZrJqZJglROCcYDQUUCIlY/6UgwmWzF+NVaK309ZXDW4K2VYFhnM0tlB8sMoyifzFMg1fEq0cR8UiqhiENCDIElntDWoo0SBqOqVH6lyEVTij3bwp3HcZTCOFethgrHuxtiWJinI+1uj3FeemaKs6WUqfO5j8NILJCVJuge1Tvy4UTIsIRZ3Omrx6SpwtKiAFNdbqyWcRCqNFNRnOHQAimVM1y5ErpKkca2EJMMmCqagiTMsWS0NVgvUDKVf7C2FfKzJDznLD3yIv1dGY+ugbAUUm2JC4C84HxHa1t08cQ4M4cJ7WUsJy4zKWjSZHDaoLMWOR6d/+A8/FPXdw6Czhtso1GukEMUPzJlyVhS8aRiUMaJhJQXb8Eue9YBoZyC8C+MER+vFDmejtzdveX+/oHffvopt29vubu/I5WMNrpq+NWbruoMdW2uLqVwXCIPw8yu83yw7aAaNFbhH7LWxAwhFeYli8o9T5T7b17rIi9xEcih2aC6K4rfkEpVllGabC3ae+nkFYFnVc5CAUfkmdptR9u3fP3JjkLGqML2cofxlmA0xb/A9ldc/+QXgCYnCNNCSoEYJ1JcZNwjCMSqtKLd9Pi2oet7mpfvo/ut+KKtXeVvVn+lflOtvasn2Rldv5/VE2Qak2z8dU4uxEScF27vHpimkXGeiWjGeeHN7VtOj28Zjw9cxiL3PSuWRYgn7jRgY8a4BK6qyMQZxQJEFBGKJUXL2zKh8ogtMx9++D4ZizVCdv62xfxtYuB/XCD8aYMBT7qF1p7FtFNKZ6KNdY79/kJgtWEkxUhMIu6tteby8pLr62v2+z1t29D1Hfv9jt1ux36/40c/+jEvXrxgv9+z223puo6+70WNRN6IPCb1h+/TO8/lfk+Iq9s59SApPKggYvRK1koEsjKMYWYYF+KccEXhiqZfQMWZFBfGZSEpmO8e0M1IcZZFF7R1NNuOIS88xpnZa7wXmrrdtzTbHe3lFS4ktHVC/AkBFRMFL4dbKSwUFq0I3oojg3k3+84qk5DEOaRA0YWQFTFHIUuVcF6r2kiVp21lIGZYKhxYlGKJ1Qs0RpYlkpIcqHNcYAbtZJ4zEMlaSHxW6WoH5aVvV5+90pUp/exLq9VCyRB0RqvVReLd9VWAYZzJcWJeJqwXeUS/69FrjqM1uiipppJ8FlWQYLIyumu/eRxF+i6GGfFXXeexn2B9lCYVxTDPzLEQlIiMoA1ZL2QUMSfpnyoZ+zCqDkGtvWtVxNYOWP1MKe8SmUpSZP0sELI2X2XRqlXSUemV9yXjIUajnMHkquKiVqqNvP6apD57pTMM+pSkStK7bvlCQhuJPbkxoBJzEoZ/UdT2U6akgkpaPsu6Z/4MPPS7D8t7RfGwsDDnwhTg8LCg9BbrOjTiB2hcxaq1wriGfrORRTOcGMaRw8M9r+/e8ng48MXnX/Dw8MDpdOJ4PFa2nczUAOesSSkhiwgSISTsiOjw/e5WdB2/v+u56He87HeUqJiWxN0pMs6ZEOVmx5AIxG+pBNX59dCKkhO2v8C3PWn7AcV1JAz2bIQiiyKzDpgrijJkLxvGaUVzdYHXC1fXG4JPlMnQvH+J9oZE4eX3f8bmxQ/Y/eRvWLRhLoquNqEpEZUEElV5laeFYmTjaK3JSUZi51rym7PVkVwpZYpOMsxdVdsVBVWi9JusVJgNBq08oEgpEEphSZnHNBOXkXEceMvC4WC4ubsnACElitGYpqEpPburK7xv0XqFIhFtxWpGPJ9GKAuKCWtGNAswMGcH2cLoWI6veXzzW1SZ2eyvabuXeKtZqg3Nf6trVZQJIfDmzRtOpxPzPDNNE9ZYfvGL/4A1QuAYx1EYjNNE3/dYVw1sq1boxcWFiPtaw2a7Yb/f85Of/OSJcboGPPj3N2UR3cmmtZgUhMUYMybKp5+99L/GHFkeFkJKTDFy/3hkGCZO48QXKvNPpnDRdjhlsLMhh4RCsX2dpd+iQXlN3/VcXV1y9+VXfHE48H/8b/87PiOC0hr8xZ72vZfcH0+Mw0hMBd+0mFYg3wlI88LDMLCESOk2NJumqvg8Pa+oMkknsDCEESJnkoz2hjlKRRjDgndWDm4DYV4Eso8TrmnwTUu36YghcTwOhFjXb9PW4Bk5TgdSTIRlAZVpvKFTtf1iPadpEfaqfhIlMjVgaGee9EELNI2cQcsSnklwyedKKfF//fJXpPkIZeajD665uNhxsflQ/nFJUGpvD80yBMISOA0nliREo+t9h9aObrPj7us3hEUS1DkZUtTMYxDIUJlqLqtZAnx+v/A4Je6Xha5taJ3lqt3QGFEbkvExTdt0IlBdtASWXBAuVnV6oRZ6z46NApQsmqDolRVNDYgVSyrlPE4kI26iC9y0Hdp5VHQol8Em2bs1GFpbq01VeQgVMVPFCLEsp6oZvNrg1S+TUSrUYBllxM2DMrAxDlssBk2KmYySxDrNpFT+WK3zB9d3d5Go1UKhiJROKpTxSNv2bJpIrwMmZo6PC8HVai/cAAAgAElEQVQI7XaaJu7u7nl4uOfmzRuOpxMPj4+8fbhnmEbu7x+YxpF5kQW/vsxzUsN6pZhQRkKOlh4oGRiWyGmMxCBmmVprTiOcpsj940SIqQa9dUD8jx2o68C8wvc7yB2KQjZeBK2fHWImpfWdijA3kplEVkaAHBQ5RWIYiWkmloW3j49oZ8haczVPqDBDEL9CbaozMroy3awErbyqfpazFmgusKzNaxD5um8csmrNtCoUk1WiqjrVYDhRihA/vAOtDFZFJIktbFsLQRNVpuRACIF5mZhSYknSD2i7htYrmraRHoP1pKrpmkvAGLGzahtHzkYgn/FAzCPOTmh6jO5ovcebgiLy9vYrYsl8//ojugniXIUB/oxG979XKa6szZcvX3JxcUEIgb4X6P73v/89Wkmi0bbCavRrL6sSctZqsutbuipPttvvzgo2z17oO4XvAoxjEOkwtYpIC88iViWhWOEntBNISyV00TS+gyJGfrEkUomMGaaSIEVSiCgU86DPoH9/fYHJCkJmGGdSCPzm9WtaZfBoUs6Ywwl7OHJcZkke5wVvvfT7S8FMAZsL0xiljxYV0YiYw/NPtoSFOSzE1VlcKSG6lIwuhpAiIQZCDJSS64B6OpNMtBaJohgiORVSzKQo87m6+oYaXccHtKpsRKoCi2HrW5STPRzCQtYiNWa9rXtXEVOqbUcx4o0lQtECtVLOrHbW/V1hQ+s8u03Pbr+l79oqfCD9M4rYfC1LZBrGM/HJ2NpHtg5SJmfLnDXFNnT9Jaq/JGTFFw9fy47XGeczMSnmCF8/LgyhcMqOMQa8iZhiaIzHuEaQrooM5VT9VhXnM3Bdb2dPzOd7pP5pKVkeQZR7qQ1oowQ+LiJBR5F7JaPpGec8znWUxVB0lLnOLGoxEp5sTQ5rT189KWKlin9Koq4qQa9+1XaO1ZVJf54ZLWgdiWkm5YIpRs4J7SjFyJzid7y+exBE3mipup06Zcx0onU7LkygZ0GFwnFZGLUchjc3N7z++mtub295e3vL8XTi/uGB43AixHBm361NUwXnA2hl+uWqUJPSgqbCeFqvRTrTkhjnxBIUqcKzpyXweIrcHyZiWvUx17D1xw+mVcLK9/sKwWiClqFMjYDVolRe4YxaqcpiKuQ1MKLJOYopZRhZ4kjMC/ODYPbOeMLhSN4cYTihGsS9vnr1oYQBVe861HqTynI7D5GrUs1uNd+MEooVCqkiBwVpFpcqC1VEviyEBassxjisThilMEax7zwsltkUyJFUMuMSOC2BmAtt6/C+xesG3wg0Y6w5P8uUEtYKaWW/2RKjYhweOT0cSeGA7QLOGpz29F5jVMGoxP3dG5S1tK2l9bD4yPgHn+7/37UGwVevXp3JNldXVyKrdjqdhXjbtqFpG66vruUZVxuneRG/s6bx9Jueq6sr9vs9zvuzAsy3mbH+UUf7AsMYxExawzqjtZIFVqdupWS2SuMwOmOypm0V1nqMdSwxMMXAHGXkY3UOoCAszizrYHNxgY6Q58QwzgzjSIiFjW1ojUWFRHnUcP+WKUsf2dmGpgFrC1Yh+z9EljkKo7Me/Cbm5x+LJczMi8wE1hCCMVYcE4oh5kxIgRAWyJLdliQjUMYY6UEhc5glL6IqFSLOiXqNQoKg05qitcynrQeq1mx8QzZOEIwYiEqqBePNGs8kcaPgtCEnUa9SSsxn13EA+eJMklTa0rWO917uubxo8M5K+yJLxpaTYhpmjseB4XhAa8Vmv6F3LU3jcdaSyeQYmbPC2Jb24j1Us2WZAp8/VFa5AuMyMRamOfM4JZaspaUyi8dnZxr2ncG4DsIIRaySxJouVyNhnp2C32BmvnPlSq7JhBjljLEKp0QXWWB66U3JPtEUnfHO0eiWjCZkRS4S/FIReTtVmsrslZN7ZXAqCqR8ljDUSsiBzwMgJWG1Jla5NuGfZ4wKIsKeI151ot26lol/xvWdg2AMiThDmjJN0nQGLl54OrvQlVvULDM4yTp+8/sv+ermjn/4p3/kdDoxjaNISKVECPEZ9fYJu10Hzlf8/d3DoqAR52arrWQ6UjKStGUqhi+PibcmYFTkd/cP3C0LX88LoUhGKJYcgl//0QM1S+YT3UYo58qgzqBnpihDVEZ6gPUt6IqbqwLmnG0ZQoI55rPtDCT0oikhMB8HPrv9P3l48Vv+p5ffw19pYtNySolUNE/SZk/wjAJMRoZtlRIDU9ZacCW/PF1d22C6DW1rQSWUKqikKhwameaRw2Hk9u4Nl5sdu7ahVZmshfDQ+4a933HVRN6+/ZpliTijadtO1G3INE7TOM3pcGAYRu7uH5knkcPKCV68eI+ryxfcDQeG41tu33yKtwfaJnPRdvTtBu96lvEoMnTzxMWLV4ynA599+m8cpoZ5qknHH7m+LbD80WBTr2/2CYGatS+iQKHBWsOHH36AdSLfdXtzwxIWnGu4uBCD3levXtF1PV3fV0KF+taf/c3X/bYrRUMMBqWrKLQqQl5Sor6/9miUElkwQQsUznlKyfR1MDzkLJqTUcg8MYhOYw5R5MVS4nQ6EWNkmmce7h8Yp5H7uzu8czgjFVIuIiKttMFax257Qd/vaHyD16uB6jOyCAJpRZvOwY5SOB2Hs96r9x5dHcbXWUtAeomhYK2XfqDJdWpHY10rBLEUmWcJpuM4Sl/cexq/qU4FE0mrOl9oUEps31QsbDcdqmm4G0RoPVeJxnMiqWryXREfqVAyTiv8pqP0LW3bnZ+f0Yb/9NMfYU3CN2BcIlGYQuDweGQYFo6Po6jNGM2L6x1979nte2F+1vGEVbezqILxnu3VS4ZFMzIxqk7GooxhnCbZUznx8vqKruu5evGSu/tHjseB+XDkeIL7o4Z5wVpFj0E3Dywpsrm6ohRqxbRaVuUzEeVp32TG8R5MQStLjLVCS4aSNcZS9ex11X8V1TCZDgmksqCUJ5dAjHN9jgrnhMmqqwgBRdZqihGxSRKnexEsd6z+NDlBMkHgZSMoTIiRrEXkO5YFnAFjydmjtaX1HapMNeHhO2XPf14QXKBEjS3gFfjG4ErExQGVM1lb5pJ4eHvDm6/fcHN7e85Gl7BIn2PFeXg6KFbtRl0zgbUSlEOlgpRKn79Q62IX5YQFxevjgNUi3vr1MHGMiSEJNJFrs3StOt+93oWvZFHq+vXkn3j+PiDyCfJvs6qwt1qx8qdq0GhN13ckHckRdNsJxKjFeSGEWtkVRNj62f8osjnkZWrWpJ7smiREPn/C6p3fWyOSYtttD6r2F1NGZQNZGvajyZQ00zWG/bZj7yAr+UxziiydZmoVG5cYpoG7w4kFTUTuf2MV3ipKWCA7QtfQOUdKhWkM5DBzeLgV0YTpwHh8oLvSNMbT2RaLhphYhoF5npmnic1OBBO+/vo1tnmJ0f7PrgL/VLD5Y7255+MXK3Hm4nKP1loO3yi9QWMsbdtweXlBv+nxzp+z2OfP4E+9h2+7UqYaCZ/zu9XdSgLgeT8I8YEinnRrImlzJuaMzQlnDMkmma+1UZRsTBAFlSiC7yklxnE897tKKYQkM3VAHfyPaCU/yxoPGGJIJNtU9qH0Q1fXDHlba81REZL8dF9zzudWx7n/dk4mnxxSziQipap2aH6yyamQorRcCzEGzixGKzPKtvqXorXo7BapXtdRGrFgkz0mEDfkJHKLVCKSZJ7P5vGeETuUgq71GBXqWSBSeuM88XA4cTyMlKToW0/ft2y2HW0rYzRG6WoMq4mqGgtXQWjnG9I0k0vh+vpK2OC+YTqdKiSbuNjv6bqOq+sr9puWx8cTX0TRVz2eBlSYcFbLDPYo2p/dPtd+XxESXX3e30YgEeF4eY6qVspZijWI5Vx9Py31TCmitZpLQCVVR68ypUiiJq9T/7vuE1VJjPIYnqpSEZuo7/d89NbBejKZVU84s5r3FkQ+T2uNUUZasv89eoLTuAiLZ3Z4Axul2LeGMkbyfMCZSFCGMBve/P5Tfv/7r3h8eJDB4aqqj9ZoVpHspwB4dhtWVXW+HirPM/qzKac2KCMDydY7lNZMufAvNzf1hsOYISpN1AqbZVBdnLaTGOI+u86Vp15xjpXsomsFphBPB6lHBUKpka/2A7US7zeFYOVkhTMa2zree/WScPLE8YjdfIDSPTn3DK9fo41hUh6UJamqulHyu8HtWV9prT7XJuSzP+HdICg6jX3X8+L6ikwQRYoloNIsmL6yjINCq8DVRc+H71/xvX0rh0bOTDmT00IKl8TlmvvHB/7rJ//GmCGhaTc9VoNRmc5qQtjy4ctrrPXkVPjqyxu+fv2G17/7nN//9jOczlzsPN3LD7hoN+zchmUpLPOJw9s7llmUW7YXEyE/cvP4CR//ZcvlxTWnc0/jD68/N9is6wreDYhnWJ6CMZqm8bx69T4pJd68ecM8TwzDiaZp2G43fPDBK4y1T2w5/nQV+KeugvT+Qq4Gs3Am5q39b70OIGvNWTL42evlCkGnlEj2qc0Qolg5rZXXCpPGGBmHAa0UjXMk587WWEsU+yGdBQGRPu+BaQo46+mbnsY3UhU2/ixUsM7gvZsMPN3ftfJ7HlB0pfRr/xTkV8GDUgqHw2P1v5O955xYcq13bpqm8+KwqhGaWNYYaympMISA+v+4e68mS5Isv+/nIsSVmVlZqvX0iNVYA4wgjAJvwAsfYMaPtF+JDzSS4IJmpBlBI5cAl1gxO3Jbl0x5ZUS44MNxj4h7M6uramcW5NKtq29eEeEeHh5+1P/8T9dQWPmsCFLjMt0yrC3oosM58QQYrYU/VCkg4L0Iz+P6dNbI/XFBYXSFd47V6pZXL6+4vbnlww8+4OR0yQdPHzKdWsl5lB1EvFqJ/SqgKKopZT2lLGu69gaC4/d/53POTs9YzBfsdxuIAsqbT2vKUlJz1tuW29UO2g3b1S0XFy9RcUdVWqFWsxYfYfmg6/eqOPCTpRk8CqMk635cPzOi8Sm+aMqhvFhUDvA41+BoUGiUkyK7Eku1BCVeMEeHTgJW7rHwDkdMKq2Uy6BlGCCyyvt8v0jEE3xDUEJkArJXeTR2YhIQyOA7nzhpf8t5gpt1A1hqO2OqYaagRhGMl0u8ucUrjSkrHkxqHp0s+eb1VZ9/o4wwukQd6BBNc5zbBfSbetbSMp1OhMQxaohawBY6QZC9l3SNLiV9owwBQ8SjPGJFhZjijx7v9dGOqrhPiGSxN/wmlWwhotRh9QCxAJP2qyR+WGuP0h4THZ2GUNbMPv2cavGI6uQjwu0KHSPx5BG+mOCC6nvqm8SB7woAdfcaDiOdkZvbW9at43Z9SdNu8K4hNntCu8F3Wzr/mtX6lpcvX/PLn/81q9cv6c7ndM4JWCFGlospj85PuN5csb16yetvfkkoJth6wpNHn4ug17CsxfprG8frV1esN1tMaPDNiv3mmthtiUYROsX+ds/ag9q27PYtu33D65evKKuS+WLG+maLu2253jt+9w/+gPPzGdsvf7sxwawBjwWiWH+G2XTKfD7n9PQkkQcY5vMFWguY4cmTp8zny55YYGCw/81a51rarhXXklXJBSphER+FI1LrkCwwfdc6CSGvUrHIlCYooagKIeD0IBilkoYIt9Z1Ukw3SEWHEGISnFJNgUSyTExV4WPA+RbtxF2L8kKAUQj4Sdxb2RJUPcl5trAzocA4bzMLyMzOY4xUQ9c6XUsS8FVVDQpzuvbgUzmlpDzn9AJtDE47bpo1m2uHWRcErfGdEwCS91giJQm+rwtiKmbskts4t+l0SlkdCsHCWkBib6t1w27fsFo1nJ0+4PHDhzx98pBJXVAWSCgl1QfVOgnYxETjPEzmp2hbcHVzg4qBeW358NGCSWUpC8e8KNCqwGipvSfKcENlPdMqMK8VsYXYgbUTyrKknM5oO09cbWm3O4qqwhQ2FX0+DjflRaSY1EuJu6T7IwRBWSmQtS64BBFuISocLSE6ogoonaxFVVLGOY4dLor1FnQqEE5O2dBiZgZSbUSIQfZuiEKpqScUdsJuvxb3aeigI9krRhDpSuO8xyqoywllW0l+6js+e+8oBCOb9Q5igdsHVG0liTNEovdEH+l2e3yOJxUF87rGKN0vdt2DWZIWoc0IejsqZNqbwDH/ly5YHwhLBQKzzeZzdChMciMJcswkCy8HYeMbLGSVFsD4eu/71UGMTsFhASY1hCrx6OhQoYXoBJZdSnJ9dfaY+sFT9MkZygdCVeONHXHdHZUBiXe32GPXG3fGD23XQoSmDez2t7iuITRbuv0Kt1+BvmG327LfbXj54hl+u2W2nydrIBKUolQPUGdTtqtL1tcX7FbXUEkpJbffELXCEVEIgMm3ntX1JTc3K3bblma3xrXCfRiDoms6tqsdqvO0K81+v5cCuzdbFicKvTQ0+44mtNyshHzYvgd0q5+yt8QEx78b/x2jgDbKskwxIA3kuJyAUnKRVKnpqNK03+3r/SxU8Y74VGaIMKTepFAgMQZ01GT4FSklaehHngrR0gWskAFTeb0EHZL7PVPmgfZSYmwAqUWcs0Ju4FzvrhdaXMmh0ykJO/YuKfGchMi94YY+1y21sYDJc59dpePPhuNSbCoJ0fF3kkQ+WJHBezofxLNjNI6ITzybGbjkExo014ik12EV0cfeWs730Xt/xxJEKcntdZ7dtmXfdhhlWMwnzCYVy8UU4RRIz3OMvSDMHitxFUeUtoQoeanWaOqqZF6XWANWeazN60/jvJP7FH1icfFUpcbVBcRaEvWLArShaxpiiHRNJ6WpCouLsV+zxx4LhcLoApTk30k+scInz1h2ecvaGfKLQ/TiwdIRRvFrGythzxGGE+kvQE9nlXR5qZyikrKVXB8qYlWJMRXGlLggNSeD9xIGyyGCKK7VvO4KUyQhfJyX+Ob2TltMjPDFl8+wOjIxitlnH7M4mbPbrGlXO5r1jt3tlmgtlop5NeNsFimCZt+1IqUnlQQ7Y0wRs3zy4Qb0U5wJTtJ7lVx/KgI+4kPXbwVZ89Q2xQ+VuD/rumA2nxG0om079rtGhKPRdwTG/e0+iO3YOhzGN/wtN9vgcNtb/OoS3+6hqDGzB5TnT9GLB+ysxc4MJkZ0TEz579nUkYV495I8IbZ417FvdrTNHrffsF1ds9tcUVUtwXcopfnF3/yULyLcnE2YL+YCOljMmBaR/YMpf/2X/zevX78iNBu2mzVdvOD68jVt09DsG2IQrdb7yJdffMtqtRECYpPyJq3GNR0Xtxva202KBcp9jETOTqYYCgpTsdrsWbcd19c3XFy84vz87J3ci2PB9zYBNLYAc8v5gD6lvxhjiDHSdh03NzeSEjGZMJlOMX0ledGGjrt7J0ToUQsxFYVNOrY846YXwJK4LWg/r0BrJfG4kXLYj6m3CQNa2wTkkuT2kOJdvTVsTYpBxxw8h2wBRKldmYj6e2FqVWY/AWN0j2gNgSF2J4PpXa/W2p5uznt/wPV63/11zmGMEuvPiOUbQqBpGm5vb1ksBJx0dnYmeYYJBRu8EDwYrSmqkunZCSpb6y7ikBJoVZGYTUKg7TKCUZQDxSCorbV9Xuk41rnrPJvVihevLnBR8hU//+wzJrWhtBDCLinESnL1Uqgk+CgWUxCKMNc5tputpKVoxQ9/8CknyzlWS6FcAa8YIpZIwctX1zRdS1DC3UmIzKYVi/kEa5+y7Rz7puXmeo1b77BK0Wy2lKVFTypRkEb3/87cOw9G9miTcnS1iimxX+55Tv/q+UF9JOiI1/T7sAYmcYp2AdcZUF1y2+t+rzeGlL8YxdOAAWxKC4vMiyXTck5ZzVi7r9m0W1rX9XzBPnl2xVsSwAiKNUZNePuW0bd31rO32z373YY//7+2xOs1r85OWRJRncSadnupZLW/3fPtuuXlesu+aei8aAnaC2oshNCTyA6M5wzyRKk7D3UkClddJLmJs3870lfP9IqQXJV1aSmrguVyQZseuHmixrLmHj9x1gSlQ0ahWgYn7TDGO3uaGqjYVASLp2t3tLuNkMpOp0xOHlNUC7SZ4HMF63TO9ykBmRnij8dxUBUsgg87gjKABKwDnlhYVD2R0iyFQ3Ut1m/Yri7QbUtrZazzsuSjTz7mwcMHzJZLfvCjnzBbnvDVV1+zu7hlu97y+puXtE1H23YCmEpa7c3NhrbtAIN4KhTBRXzncS6yCR1WKaxSlGUh8Y3ZjM7By5dXqLJCac3ZySnrzZ7nr66o3kEIvm9ssI95jMFZaWO31vRIxhijxIoySXdenz2K8N2QqG8ZPVaXWO2JSnLkApHgPFpHgg6JEDr2jBpKCbR/EIKD1SgXJufNXI0h5FJhQ59KSbJ4THYnOrN2xFTfUNaVeEPzlk7fj0rzqHrX7KFrOEYhG++67gCUlgvmZkGYS1qJ5Tm4RK0VAmwhzpZKKxApyyJdv6Np9oJ6dU5KIMXQj89Yw6yYihCMsL3dYLSitJZCCy0Z3guvaBR6PLmvgrpVSieSduHVzdcWQuTZi0tcuwfgbDlnMqmpCoVWAgpRmVDD61yuk/4pjZK7F0Ik+Mjm9payLDl//JCqknUXYhwB46R2PMHw9bMrbldrvA589METTpcLHj4aEswr71mtt9zc7voUD3wgpionoutkbwAppWNoRhXoMZUZoMxRYEhrFBate7cdxhQyRxGc6/C+I1IQQpdyCTOhtiH7sfKa8N6TsRcqG/5aUxUVCkXT7ul8g48d2gxeCDGGZP/s1J7W17S+Q9kCU1SgmneKo7w7MKZpuby64fL5dxSNY3X+gA8mU0olJ9l2nm0IXLvAs/We692ethOi06AUxg8IpdGaGIXS1B2v0gGAwUdyGl1UeTHmnCwO+OlUnYAh0ym6FUDPbDZjt9vedS++cY8a7RhjYXOf5p9codmNq/H4bk+33+IjlOWU6eIhRTEj6jJphkJGmzeVvqujk99nSQxvx6bgMM5IxIeGoDRGJ/SWigRjRAhqjdYBxY6gPc22gd0Gt6goUMzqmo+ePmF+umQ6m/Hxpz+grKdcXK5QF1uaXce3Xz2nS4Kt3flRPifJPeYFsWWQqiIptuRCICqFKS1lXTOdTqjqmqbtuL1acfqwkDqBJyc4F7m53fIo1u+hJry9jQElx2CWDMzIyfFAT5mWAVuKkTX5WwpWyibiiVH4Y2MGC2ix+gIDhVdMbihyPIshJ3EMalDJ7RYReDz5sNGzNiCOBztHUt0klp8fGDVyZ44f1YHUYtg0xy3EQfjl+e6BNCNLMAvBIUY7WONaq7T5CRdnFoIx1ejLQlDZIqU5JfepEWGmU1hmt9qgtVSmsEaAeCrlzaoofLkgh8v9GNbB2J0bY+TyeoXBMakLlosZ00mFNfJUx+BQSvgrY1biE09m2iLE6A6CV2h3OwprOFksRCHQCh9TTnLaG0NQOA/fvbzh4vIKZwLLs3NOH9Scnk1QUVzTNkhhY22tqDbB9/0EH+Sz5H6Wazn0QhldYIzMfyYwiVrQryF5GbS4AFAqZHygFDs3luhEOWnbPSiPIEBGpoQajApBIHtJS0KUvPxsaaUpiwoi7JsdzjWE6ARQhORuusTmFgClG9rQ0Dknhc1NATT3P2xH7Z2FYAAa51lfr/n5F8+4eL1m/egxk7Kgsobnqxtumobnmx2NNngUFAV2pDmnOej9tf0CSStDvEuSaJlm7GBzyq6aHEPMCfZkV0O6sS7zPbpO4NK6RinY7Ta0zf4d9q04+pfbkaZ9T1Nay0PlGnbbDevNDjU9pTp9wvLxp2hVEYOhiMmaVccS+fvbce7k4fgO3bfBr9HGMq1rmhZcCDSdB2uwxQzaDh8dbSvwcaMVBXA6m/Hk7IxZXWOtwcXA2aPHeKWpJ78GDK6N7DeR2WzB8tGSixdr2rbFeWGUCTEKpF5JSSqNoqpLZrOas9M500nFyXJGoaX+2rNnz+hCh7Hwn/4n/4gnH37Io48/wZye43XBi7/4FdHf556+v/1dYoLX19e8evUqpUBMmM3nwjGZhF1ZlpRlyWKxoKwqUGrEr/h+/R43BRRFSSgjYHGJPD0E04PDQoAcCsnJ80ACOhxf+5C/d+Ai7uXgoNwFJ5VE+ioZMSVYxxSjSaJwnPw/zocU5PcIqDKKnSmlWCyWlKVYTNkCHNCdpGuQGFw+p1jewk6iTaRzUtkhW73z+WzkMUpYbgVlKRyn+8bJBt7CZrNOFrDCKIUtrPAypRibtUZYaLrALjG7hBCF7NyYBCQq7lzX+fkpk1JzskwoaQ0xOFCi5HlPSvkyaKwIgYgog0DUSFUH53j8+BHz5ZzFiaTk+BAxiWPUh4jrPLfrHS8uNnzx3QWXNze0yjFdvsBFxe999BSjpCK9MYrptOSDj5+wqQ1h30jCuncE54mq6PERIcRBOUrXNZ/MUMbjg1CV+ehxneAE4tE6MCopjRRMqzlVsRCF2nao2LFtd33SvITQB6WK5IGICWSjjFinnWtZTCbM6gl1PeX65pZXFxfs3EoqVlhwLilWyVoWT8WGRpfsmoZ9dLSdf2cF9d25Q9MEoDWNC2yajovNjrrtKKzm9WbLuutYdw5vAW2wKaePPgaTdc3Bty4tjuJb8UDwZesqjoiUdc/CP1DvxDAc55wk1m63W6wx+BBYbzY0u11i0HiX2cljSC5RdTj2g7jPoOSgIimG4Ol8pJidYqZLqKcEbchFZu5Ck0fnyz3FeOf7sTv0+wSoCi06RipdUVtFCBofAy7mWmCgrIbScrKYUU4s5w8f8ODBKadnJ0wmlZSjsRZlNKVNjBiJ03Q+m7BYzjk9WVKqObvdluvb1wS/xwfF6ekZ2ghCtm1aqqpguZjy4YcPmdQlREd0EhepSsVkUlFVJcvlhPm0oC4gxo5e3XuP9j6CKM9x1wk1XDmd9JbKuPV1AkfpOyg1cnW9ud/3Eo5qDCSRwH+Pkk4xvXs9AUf95edmvG6yDp7TcMRydwkE0/Xu0pDBLopFfwwAACAASURBVDElkDNYmGMBCOD9oWAsysMtpSgKvJeYXb6u45js2BrPcyXCPRXvTbExa3N+me6fEQU9YtYmoTW48vIzLC5bY4xUGEh5ilEJkX/TNrRdR4xSwLuwkuAtgAuP90IEMB7vYl5TWXGdawJZxqlcPDaEZP0pqZiQx9vfj5TmVVgW1ZLJdJJlJDpRnWWDv/WO1WrD8xevWO927FpHpx0XV9dUheHT8zMmhabQKqHuYTYtUcsZvrR0OUUsCOOKCMBsU4zWUASQecjPeQYfhnuEoCKglAElVXCCFpdy1zbs9xvWuzVRCSG6Tq7OqPI5ouQTEpKFnP3vsScxX6+FU3q3W+O1Iyov+bR+yOcdigNIGlFhCmiROOM7tncWglqL1lcUFV4Zdj7ycrXBGIFz3+y3NDHSaGGE0Nln0uffZZ1NBnzA3Dq6C9lXzWA89k0WuOlN+JjM88GalAlumgaz2XBtDGVR4Jzj8vIyJTubN20dB++OrT7Vu6Du2czShqiTtdq1LU3naAOcnT6hWJ4T6pn49WMacbICZT7U6IE9ygscxXuOFYfva8o12CIysRFXSf0xpQL7tqXpOmpjoFSEWUn15IypCnz2g4/58JMPefz0MbPZFF0WUArApbSart0TXIvC8ejhKcuTE87OHlA+XXJ7e8Ovv9jjO7EiPv34A2wd0cZx8fqCqrScnc758Q8/oiw03377Fftuj+ta5nPDYjnn4cMzlouCwjpcc4P3LV0wb73W37RJfpK41IpC3GByu0fumeS6G+ew9Q/i2Ftx3714iwCMiGsvI+5UWnxG2xRDV/0mJp6oQ6trLJjGVuLdeKfqj8v1FzsnvL3eJ2L57J1BIawrw3HHQvA431JrLaXP+mdRhGDXSQ5ojveNQRm9i1mp/tnXWuoA+uBRnUMb1cflegU4b8qJ2MAagzVWvBApKb5HkZpUjDWCjiIH20YAe/umYbPd0nQCpimLIqGDqx6I43yUAq79vMDZ6QwdXQIaDbzDec/yPhJ1ygWMw0adCfC11hRlST2ZMF3M0cb0oKxcx89oOajZNVxeXvHFl1+x2uxonMfryLOXr9jv1vzepx+hZzVFXRC9UKnNZiWlWdLuGi6fv6R1rqdQk+T3rFgdrkTvdxA8rkvBmihuVIlR9gtaEP6pIHdhNK7zqNDRNQ3bzZrV+oqL1QXGWibzKSZI6agkMxGx16X5zMJMGGisFVf1ixcv2WzXbHa36KkjEGm7vEY0ZTHEFy2GUhdMygl6r/5+hGBwHbgOo6GsLdWkYnp6iikMxhqWhWbfdFxer9hs9/iuQ5dlD2YhPxwpvtcv5IO7IJpAdssc3J60oEPwCTXXnyLdl8ME3P0+uWCS7386nXB2eiJujgMtf7Aqs6ku5845jNz57b2bWoypenzAB0OxeMrUTJl98BlmfopXwo6g7gQl7xOtdy3lw/d5kPffaKXgk0dnTOcV5w9OMNWMqCyrpuXLr7/h2+++w6A5fzjjB3/0ObvXj1DNjoWObPcrnr34Bl9GVGEIWqGtYXN7RV0YqkIzmxQ8fHiGsSXGGh4/OKMuFa9elbR7qYX2+NGSovKgO64vvkUj+Z1l0TCpS84flOyqQLuHZg8PHtQ8fbJku33JdvuK+ByirkDXWPuD/n78Ntt4M3fOsdvvePX6FfPFnPOH51RVhfOe1WqVtPTI9fU10+mUqq57T8X9y+H9XKPeS+KvTtab1N72jCIJADiXrbbYIzHHcbZxn/fFl/NrBiS4TvIDY07R6YXRyNvSW2eHqQ3jc+b+XOIIzS0nQRtjJP8whr6Wo/e+txBFGJohhSGx0OQqHUVhqaoy7QGhB9t4GWL/L7uus9AFhKpNG1zbEX1AuZQiosAFjy0LTGmZTSeSxJ2ElOscXYegI4/CDd4JawlqSP/I10o8VEyywGa03kKQnM/JZDJiHBJlKKelkIThs2eveP78gsuLW7wzaFVISarGsY4Nz759CY/OqB+e4RLCOBqITlisZvMFdT3BGEsTBt5QRmPK47q+uUVpyYPWOvNw2p6d2CjdxxhLVTGr5zx5+BGNN+xbx/Obl3i/IRrPfLEULEJMtUQTynRAXYnrXqsUX8RgVcGLixcEF7i5XBFRoBV6l+YnpJQIpXAp3UIrKCvNZr3h17e/5nJ3Qeg8SxZ31ut97Z2F4Hw6w592hPmc8wcPWCyWPHz8WKiASkNdV2w3O55994KLy2u61jGdz2m6lsZ17Jp9cuso4YJMhWCzhZMtL6WEO1FBeh18jZkU98BVNRKC2oj7RhupnFxWJaWVsjiz2ZTZbM6kru+4uuQ0Mbk8x5tHFnrjXw6fHWtRva1rCsr5KbqcYGcnqHJC3sQP3KBZHt67V8ZhXH1sdIiDjMd5N98HpqVmUVtOJhX1fIayBZN9w/Vry0UixZ6WmocP5uzCAr/TsF6xa7a0oaW8rsFoQiqEvF2vqQrDfFrRtVNmyzltK5p0s9/hugatAlaLtWJ1ZFJZbKnQeLSKFCZS2EhVwmJWoKPD4Gn2DqMDdaVwrk0MJwGtHCrVhXuf2Om7tOPNWzZWYQ7ZbDbsdjtAKqFstlIF3nvPar0W5pGyTBYixKjvCJz7BOD3pXoM6MnYx/7GgJOxCzHn+oWE9hv22thbqvn3b7VCe+GRP4DsuREHyyGpwPF13JnHlGaQzx0yRRnDLdRa9wLVp2R3EeISh5R5yOwxNqVhHKJ5e5djsrKyeM4WYD++9JlCBJ4KERVDov+SDdYYi9LqIEFfrifN7XAbDq57oDCWyYox9mEZbfTBGDJK93i+eoq4O/MpifghRDabHbvtnrZxRCx5LxE3dmS3bWibrncTxiTgxX2pqeoaUxRigh174O6syfEFZ6/b4G1TWosvNQgYxuoCq0spnNDu2bQbKfCmPKYoUFEYZKTae0rRUSODA6lpCpKKgdF0TYPrOhq/QxuLUSWZBkIxpFiQdLWoSMQDHV17y26/GxbEO7R3EoJKKf7wD34fpeBkueTzzz/n8ePHfPLZDyjKgqIsmFQVV5dX/M1P/4YvvviS3b7hyYcf8rdffsmXX3/Fz37+M5wXTUCnC8nMEFL5eQhUTyZ1z0JhdIJRl4V8Zot+seSHXDYjQ13XlGVJVVWUZclkUjOdTpnUNQ8eCNO/QvGnf/pv6Nour4KDf4Pb8X123VzlQfzb5XRBNVuCgmAkRyiXMKKPCb69hwRuT2Lw/Syh0u+YKc3DqaGeaJTV1MZwNdOspordvmViWmrToutI4zyvt9dCUBsCV6sLSFW367oGFMtpzWcff8Cj8wf4aPn6m+e8+O5bvvviOc53tN2G4BqUMmxWF5ydPuD8fIYhUCjFYmqZTxWLhWE5W3BTKm504OsvrphPDCqcslyIpa4CFLqEWPPy3ZDOv1FzztG2DU2z5+XLlz0I5vLyklevXhFCSHB5eWTKUuqjWWupqneT0t8nkKSmnkMbenSesTqFG1RKOxkrZbJGQ3D95iJ5g4MFchBnHgnLsUBxbsjdy7Oss/KpBoEzjjMO5zwOIaiBE7S/LuEOVkBhZf6EqJreossCsCgsxgTaNvauZ1l78mzudntALK+2lVimAoIJWCMVPrKFaRL1mXMOn6zTGMWSKa1BWwshUhDQhRVXXRpzSNaYj0gciMxx8uZ72btBkzWojXCSeh9QKTc5lzzTeU6TviDxw0NSAYWQmHcusL7dst00OE9ysZpUDEZeu9bTtR7nPZnSMXrEsrKaajKVtZLGOeZy9SOshVKa5fJcyKmdg1jifaRLruMYY06FhhglhzNqVqsVL26uuN1t2HQrCu0otKcsxIIMSjxy4k0ZEXiHmFiKhC5Qa0MwQvQRgqeaF2hdYFRJYWoJsY0ASllZAiUKrPc0vqWLHSa++375zkLwX/7LfyHVtKuK07NTZtMZs/kyJbJqrNFM6il1PeHzH/8I5z3zxYLPf/QDnj9/xmeffpRcHxKfstpQVQkiX1UpAVKC25N60gvBnibJGOw9MZlxvMOmh8zmMizWYgs5rqpKiqJkv29SsD+v3v5/R20QUwfeA/F13FWgEMVEo4gmm7UZsxkHDXtkC8Y0t+rApxaPXrPYpL/ukaMqf3jnGsLulvZ2z+q1YXNzQVCabdfR3b6mpqWaGCrVsb56jt/e4tuGalJgnNAiFcVAKqDzgnUNdakxumK7D0Tfsd9s2G4d1irmy5K6LNBKU2hHXUSmleH8dEJZaubTAoMjuj3BibY4KTWPHy44XdQUOqJ8KyTI2mBjR4yDpnwc2/pN2rGl9Nlnn7FYLNhsVlhr++R5gPPz895y2W63fP3117x89YrCWs7PH/LJJ5/0Auh9+u9bpIewZzQmkZT7JbE5qyMeT6qKhSDqVA//l98BMQi/LmndxNFaTb+JyRNjtKWuI96nyg4+o+7uPhGxX2pHbnkG4dhr+qNmlBRN7bpOKNLw7Lc7ERZeLLQYPF03WFTZFaqUJJd3nZT8GgAupJKDIuSciVgTBM0cIm3bUVayb5RV3TOaeO8ojKEsJN6Hj5hCCMCJChciLgGkdDLHlSrS6+FWGfqSZMM+JDHbwTsjlmS+n7GPDcbebS0Ub2pkDfapC8k6ijGwPFlyu3PUF2t2XvWJ4CoZDtPZjLIqUalgbQxhSMGI4Hz6PIqBNKap81ENfZEIyVWQmGZw+CD5qgpBuxozeKKc7nCu4/L1FRebFbtuT1QNLnQE5ROYSISyQpSSiIx/8A3ke57XYKr/mtZtiIGoOoIPh1YgosAZY9Cpfq0USo5UpUUH864ZEu8qBOHzH3zOw0cPEzgmMedn6yQiVbCN5cGDc5anJ0QFtrAUhWUxm0qBWSfoq1ycczKpmc1m1FUt0GUtZNt1VSdhZvs4x1BZIguwnFIhk6kUg8DMCcRKofShUHFdd+DeNH6PdQWCDBtcIG9KYB/nMx56ScVqUwhKiRTAV1loxUGYjbcJARAdul1Vdj+QXQc6uUTV0K8e3E5E0K4Z71S4pqXRntura4LSUnvRdbTbLSZGbGFQIbC+WUG3IwaPsgUaoS1SypIHHoOSOm4uYkyB1ioBCWzi0HTYwrJcLAQSrjSFtZRFSVmUnJ2eYK1mWk9RGKJXBK/QqqAsNWenD5jNJihdEKJCBQ3aJoDAoQKw3W6pquree/ObtLquOTs7w1rDfr9ju93ifcC5jrIsE59kYLPZcHu7IsZAXdc45zk9PesTvt+1bbfbwZJSUJaW4ENy6SdLsEjrXqmecUVy6Wy/PmKIo7UCKqHrclXzfq1F+moPmTVG1rrA/60tU9klqSU3WpZy+BtcuYMAFMFcViVji7Usy1QGKPacv0QRjtpms0J+LEnYYC2Jnk2jdUFmo7HWpJJHBoxGKflcnnWNtgUGjdIebYrEW1okijGPdy22KKnKiraVjRUjhaxB4Z3U7QvBY6yAaUzCHxRl1V9XBHxUyarJdF899bkASGIu+qvxJEU3BKLSqCCeqxCFF1Ynl6MgS0OvfitE6ahnC6bzjuliTWwULiichgLPpDRUkym6KHFpTCHqJPiSxR/04G7XUmnehaRERAae6QiulTxL71VPgBH9oIASZN5R4l52neNmtWK729IFR1nK3uWJxMQqo7VJe6JUhJXdVa5ZpxiqjpmLNaKiIcdD+/nuS9rpdGzEuQG4JnEJWfGFKTDvQZum4vcEKv7kT/6k/1v85cOJB5fMuI1THeRrKd8h0NnB2z24a/rzqJHFM/57fLp7+3zfFmmadoiv6IKsjQyn/rv2MVzhf+ymiCjf9e+1SvFXPRK8UazHIb6Yf32P6n9PiyMfbkyosb4sjUIs7JGCkNlEvHMMMd1RsuwoxiWKztg1AxmOFkZs8JlE+bfdxojF40fiTTEwyPlm72cFAj3yMDfX+SF29sZ1OKQEHX8+bn8XS3mItY1PdO/p39p3UQ55gL5nYTk85f1HH/adVT6JRw2LT5RURv87VCL79JW+x3vctvdse2OBfnwPlCKxxuRPw9/5KVf9RWTAzNG19r9LzFgpTcGnGPBwsSIErDF9Kbp8rd8Tfj5oog6N9nX9pnkZj/P4+9jHPN+89P4e9sT+1r/BYBkp0GNZdtzeGRjTtu27/vQfVNOhe/uP/gG2kLTAcCdAnHXM8YPyHgt09IxoY9DvUK3E2KPg/1G3+Rzjx+9ND/FYcPx/oY3Jln+TZov3q4b9D6UZ8//P64pjK/vvdgIGjXL8xT3PolIoo7HfM5Vinb7h+PcZVvie49+oKw+sQ3/vwfvj9lvo792T5Uda/t97O76w33K/h4zwg8n9tm5+W/f3Tf287fxvn4aRJcXIXfoPqd0xGdSBpvp9CMt/aG1ssf19WLf/r7R4CMEf5/Ae+VcPjxtp9HfWuRqv68Nv+6d39ADf50W6M8y3/mI0LPJ1DEcN92sEGOojL8Oecjz+g7ext+1H/SWv2AgZfNjPPWMcT+tBx+P5f9MVH44tHKNH36UdG9vqeOT39XT43Zs/eEu/b/xKvdM6gPcAxnzyT/8F1fyMN4kLdfQK2fUGKWDAnStMC79fO8nNEZstoWtpt9uUalCjrPD8KQJBJcQlKnHrpeMSgk3oh3PA+rBL3zY8/7P/SfIeidy2v8KFfRKM2UU4XtBpMY4ABvHweRhdjhoWQDosaHE4xFSWZFjaWUhltomYNvv0i2OXcEquv/e2KtCq4MT8DgqDwvO75r+h5lr67gtpjlyj+UJSzz36Lw73rcfbRHG8iNcz7zS976l/kHsBlWIkwxwOA40H55N5Fq7R8e96bxFOL/ju9L8mKKlH+dWXr+jajohUVQCkFlm6R+hh/nNOpmLYDLLb5r3aO+w+OrnYcvX2XME8k0tnxUuluFdZlfz4J5/2HJn//D/759T15Htdr4PbqY8CktfsGJCRI0zH4YOgMhOJuNF0TKst0s+l4k3u1EiGMw40pKnvkYBYb7f8r3/27xLLSOBvVy/Ydy1t2zGfnGNNAcHT7nf4rqUqBhBbWVUJDW6Sl8GyODnHlDW2mjBZnmKKCluW6FRtuNnvubp8ze3tNcvFKZPpjMXyZIiv61GGaZRqAy7EARUZhz0qJ6mLOzViFFRaQQi43ZbLX/45kodY8K/+1X8FOG6uX3Px+hnNZo27uKJ6cYO+WvHdt99wq+G6snzwR7+Lrku2zZbJZEZZ1czmS26vL7m5fo3Sun++Tk/PWZ6c8ZOf/C5lNccWNW3r2G6uubl5gWv3aV+rsEWBLUrmyxPxSLiOkBhVOue4urhkvb7l1Yuv8D5XjbF9fE/q+hagzwFofcu//vq/p/ENRE3XCfI4KrCleGyKkn69aW0Fi2GiVH6IUGqN1TYVJpZa8CGDhA786zLHEUmRaH2UNAcHvvNEFyFYYopxtm0r1Tesp6otRWGoCitrOJ3fE3ExUJs5S3vOH8/+2TsJwne0BBXFdEk5Pz347PivYx0tCyayIDxq8c74RCjcrm9pdnua7Z7ypKKanRCtTn74gFcpxQLwXYvrOjbbDWU9pZjUQ3HNe8ohqWZ3sHn5sMeFTYJ2DzdJ9YJQJwE4uBAzQm/4X44bjF4T20NID1RU8SjdLRH9kviLFKmkUtaG786qujuFZNogwwAWUcBUXTJRF6iYZ+HwfBJikf7ztppAg70AjKNrGwT/oGHF9MWBEMy/CVl54LCsSRZ6vZCNd5js8yhjVLSmzT0RI7RNR9O0gO8nJIY0szGx0DPa1PtTh9Tn+4nAONzou2McWyjp71yQ1XtPm8jbj628nJeY145CMZ3OmE6mbxSCQ/w0DMoiR2suDTgLwf7z9F0WghAH9hQ5KOG4knA82jpk6afxxhHfpBIldKwVHqKXYbW7Zds0dJ0Haqwp6dqGbr8juI5pXfVCsIopNUJLCoO1Adu1WG0obEGBQhmLrmcYWxCCp93t2e52rG+u0MpgbIEuK0GSa4XRUc6n5Bl2IdD6TB+IAFFCXos6FTCOkvOqFIVWBOdTcryoU0ppHj48R6tAaSPBb9lqz+bqFUW7Ra1vKW+vIQYao2muH1Eu59R1wclyznQ2Z7E8g7BnvzM9/yraMqktdSV1CSdTKSO0b1uMbmj3mgZQGKp6RjWZ9gLVJ67krgs472m7Dte1KB3YbmfstwkLkThBVQIojfewQOBid8HO7VHK4DonAkwrTIhoGykQXlMBbAmDj4mJ7DtCETSlLSiUIfblu3xC2+Y9ItV+SOsxKEXnBIEeOi9UeQpirGgaxb6BfduBDpg6MLMFhdF0QViAVFqPPkY6ApuwZ+w9elt756K6Q02tvP7fImEPXAL3CMDxZpT+NtFhfMev/uLfc3N5yX7X8I/+85pqMsUF06utgUAB1DqyefWMm6tLfvqLX/LBD3/M409+QL0UdGrOWRr35d0R4XB+uPPDnMfaHzPkogzCYdjARxOSAurpNaphk2YQNnmjYeSuHAyu1JdiKKMiJ0dwT8cmfsz/cWzfaC1lcpLCdaiDJNl28FEWgBmCbw72trz/jWW+9Dqa42w0EyPKDDv1Qf9pTJLnLKM2OnJ4tcOY1L2Sf/yDlMoV1T1KVT7H0PGbQBFvamKRjrf1/PmhAMzvC1v075umGYFdRpaa1ndchvv9/o1a6zFgR+Z5EG4HFxt7X8IdAem1IuTP0qaVVnb/bKkoYEEdM7ZUFLioB49BToPQKgEL4zDGtmkGB0AM3Fw/Z9e0xFCgnSR6r29vBPIeFW656BlaiqbpOUCNtRhr2ex3FGVNUU7YNQ3T+SmnyqJrcK7lu69/zetf/DVXX/8tl2ePePLJZyzmNfPpnKKwzAyUusDqgoChjbCLgYaAi4EuOHJBa4UlxiAsMpCqTEjytlxXFv6CFi0Lk3IdG2zwXPsN+/VrwtVr9LbB7Xbc7vYEozj9+Ck//Kf/hE8//SGn54+YzU+x1tC1O8pCY4uKejoXykcd6botpa+JsQIafNix36/Y77bYoubsfM7J2TmT6RwY6lFutju0c2A0Zw/PmC1nVJMJFy+fcfn6ObvtijExyMHGEGHfONrgsJbE6xnR1hB1EIRmCJCs9aTv9mhmYRMl1RZMO1pMRc9TFyIUO4ghATotAYsJgs8o2XMyM9TW0jSWV21gvwrEzqJLRVUpptZQFaCtFDsOiAUZk/Gy7lZY6nufpfva36Fu990Wj96NH4L0iI1+dc+Di/AhNqstu4vn/PzP/z279Yqzh0+IbYuKgUAuvhkwShHaHevbC/7qf/tfePbN13z78jVVaXn46DFqvkApgQLnnKisoaojTjnZS7ILZHRBueghgzAbNuc4INT688hu228+Kc3OqyzsIj2iTI3mIqacqKiIasAqDzG9COS8omPtZpByd5J382Ucy8xeY0/9HF3HwRyMHRj5h6r/8lCwZv/lwUnyqYbPe+sv5S2NB3lXsYr3LJfhXmmd3XEpSdlHcsX3QfiNx/vuwm/c3ozWHMbUj18N8SJrbT/etyuNh+ca+s68mkNO7NgS/N5THvUbI6PC3qpXoEARVE7vobcSDZqoBNXtsyKhFLpPBZKi0DL8SNRSiHV8pVIOKKIsxCiblvMOYwqsLYjGpLw5Q5lTo4zpWWKUluO87+j2Gzpb4JuGqppQGsPTWvPwfIJzC57t1hTr56ye/YwwWbAvKrq6ZjabU1cTTDGlUIai1ARl8SGy66B1yUWqkpWdqMEy2fw+eLqxOyNC8C0UFdYWVNUEXVRcNC3RKtS0YFaU2M5hbMvNyxcorXBXW07mD3n6wefYoqRtG5TWlDbdVzQvnn9N1+1Y3V5we3tDjJq2a1itrrm8eMF8dsZkOuPk7AHKaLa7Dc+ev+D6+oarq2uur64JQFnVVImIpK4L5os5dfUhX331Ba5t8V5u8oG7GNAeKZIbOoyKUo7KKElJ0UIubozBpiLHBBm3z8sjrdmoNdEn0oHOYwuVynHppM57bM8uFFiaPSdTz48faj77oOZsWdPFGV994/nl3zr+7f9+y+omsHsJ0w9LyhND/djQxoCPDheUgHqiBuUlJeUd2/tVkWD04B1Yehxp4YPtMzrg6Izx8DVGmu2G61fPefnNl3RNw3JxAl6qPoilISZFDI5us2L7/Bu++flP+fbrL7ltOvarG/CtFOBViaUlnz4v4iO8iFKxF4T9cLJsSfQ+MQ6Ze/mYw012ZG2oPF9x9Dq4DA8FICPhpiHa/hilEns+mRW/P3F/7EEJg3tbHMmmNy2K488PUyfGt+/QKhp9CIcSUWWDsHeUiqV35B3Ir/ex4eRxq6Px9UIwRnzM3Jme4LOVVTAu7prvFdxNfbjb5925fJ9jjskbcimebP2Nf6PvKC130zPeTMWW04VH1uTIRSFL5HBcB/cpCcFe1zuwtgd/g0quq6jznUpsJlryuyQeczDAO8tRq5z0HyF6YtQD6XVhJUaaqs0oI7FAk/KDtVb0rvoQ8K4jdB2xaykIGAVTEzGlRk0sq/0W7baE7TWt94SiRPmZjDoEagy2KLGqRBmFVxCcJqRyPIGYstBUevLExeaC5L2NW5AFJxasLdC2oEURywI1qZiVO+gMqlF0uw3NakVzdYsOmqqcUtQ1i+UDuq4Rl2oItK3DaEXXduy2a5rW0bQdTdOw329Zb9Yslk8oqyn1ZMp6K1UWvvzyS16+fM2L56+4vLxEKcV0vmSxmDOdTHny9AGLWcns9ITqRSWKSDdUDTm8MCUw0xDRVkrgFlqlyvKRIjEYaaX6ZPxxjDgLiqgiwUnRYO9SOEiLUAXJoTREKW9l4GEVeTKH3/9A8cNPNOenGqc1C6MxreEviDR7h2vAnhRUtebU1mwCgKcJouGpIIrT9zmQjtu7V5FAIUVDMgfK6AEkP2j03w3h++E3kH8iCyhkBoAY8dsVF1/9il/82b/l5Ze/wpiS8PnviEDLdT/E8KW5eM7zX/6c//Bv/gf+5s//HZvNmsc/+h1Kq6SIbBDgRA639e4kdZfWaXQFg6EDkAVd7w7VjPVfNAAAIABJREFUo9++qQXRBnK9xLyxZa688SzEvFHLzCossRN2Cq1jKs8igjDgCepYen/PMMgaWfq7vy8j4TKy6LJczYZXfjhUHLMUJWXgLf325+t32PxGZneY6dyJzJsIDj06gUzjcd6SAmKQumO3q2vatiGEjqqqqaqaxfxUxjhimhnP1X1C7V7hx1uneHTOsTU4nP/7kvqrKuenSvPOHxBU3+e2HZLrhc5O9IpD4ZlE/+Gx6abHOwJO3NmiY0lc0CiFBZQPROdp2jVRg5nXoAui0qKyxYgJgejS5qfpq5f33aIoiorWe9q2wRQN1lQ8evBArgFFYa2Qh7uOZrfBFxbqCbYoiaQqFuieRIDooFkzCQsKDVe7HZvVLd3tNdOyYDaveHAyYx0rOgw3uz075ym3a5YPNGU1pSyNeJNipAmBFnAo2jjockElBEIMOEQYju5EKjHkxWKymul0ygcffsatKdlXFZvNnr1ucF2kqiuC3/PrP/8/+ehHP8Hags/+6I+pqgnz2QlVkQnBNxgrSePX11es1hs2my3b3Q6tDbaoePLhpzx8+ISyqvjir/6Kn/3s5/zp//g/c3l5yc3NDZv1GhCqtHoyZz6f88d//GP+8T/+ff7wD37E0+un3Fxf8/rVS2Fa0SOkQgSchc6idKCwUGhNpcFYhTYaa2VefIBunyqbMDKCDDR4Ipl6LaCCoyo6CquoJiVVYaiNZaqESvHBack/+UHNR6eOP3iypTQbjL6lZcIHJye4pyecTzU0oE48H5+VPDw/4Y9+9DkvVhc8u73gbza3tF3EB0XQyXp9x/buQjDSs5/EZMb2y0Il//Fo5xiy/QdrUJZ9shGTBmFjRHtH++ob1s++4PLrX6NcS1XVLCclptsS15cUdU3EE33Lq1//lFe//BnP/vaXbDcbojYsHz+hXpxgyjr1lxNLR9qpUkR9/+Tc8eQdaLhHHxxZgUdnSn0PEihr7bHfouT/CgXe0HWCiHKtqAXWamwBxiiMlVw8ZTRBJUqhsTV5D/hHvrpvsz++xMG2z4Ixv6r0/djqVXpAcWaBerAIRnPXA4tUvHctyHiytZb/jUWPCMj7YoIhRtq25fr6is1mTdftWSwWzBcLJvUMYwau1WHW71pab2u9cve238XDv5V6t+MO+3r/sb15PFkgD9af3IpBUGZgmSgqAde27NoG33Z06y04j/KBemKxVUFhQZXCQasBvCe6DtchtUPLIiFhj3JCQxKG2jCvC6qiYlpPe9ouBQSt8NqCCqjoIDigRGkjrETWYmyBLQqUhn275fL6NToGrje30klVUU5nnJwu+fjhkitXsOkUV+s2gTI0XScFtUOMVGUtwhVFoYSjVQUvFkxA6jgizDDRaLzW7EfX5VyLFNZpAUVZTXj60WeUynCrLC9/+SWNColIWxF9YHN7xbNf/5JyMuXRZ5+x361pmx0xMRM1zQ7nPF3n2G5bdvuGtvN0LlLVBZPpksV8SVGUvHz5il/84lf81V/+lFevXrHb7RLjj1S36LoW2KEVrG5FOO52e6HUS4JP3M2HpeVCG4idWP+6ABMiRfJcqBQSycTezsXEUerJ5lfwAiwKMUopLCKGKPdYKXwXxQJXUFeaB1PDZycFT2Ybzmcd82VABQll6SZgYkOpV/zkx/BJWzCbTzifzJlNJixo2erAxmgmqf8GMSv0fQCBN7R3d4cm90ckJGsqJrM3u70yeqw/YngK+ocvJrfiULlaB4fpdrSvvmLz7Asuv/0C7TsmhWZRFaj9Cn/zEuumEB2+23P965/y+lc/5/XXX9B6KJenLJ9+yOTkFFNOBvcXkR4zn3f2e1w2vbCUd6OLHgyZMSxd2OPHJxkfGxOoZQCyyNY+yitSybEUFMFpun2k2XravcdoRVlY6spCoTBVspZVdkuNlY9Da/uem9YP68DaOXJhHrt2x01r1QMhhgDiSAhnay/NyWCRJE7Cfngj1+yRRZm9vbnOWJ7RcM+4IvS18K6vb7i+uaRttnSuJRI4O3koPov0cKv+rt21mqTvI7fhe7Y3M4+87bi7H/ymQjBfSV+/7k5AOKAS8kmlTVDiNo6uWbNZrdiu11y9eAU+YIGnHz5mEqdQWgpToU0hy8p7QtfiWoW2EVOVBH23GHEMArKxxjKflMyqisWkpmtbuq4jeohGEayiDa08W1FKKWljsUWViPMtRVWgFOyaDf6qJXrHzWZNRaSaTKmWC07Olnz8cE6xU9zsAuudFyi/NnRdkzboQGGtCFgkjinpVomZBYl1eWT9a61wRg1CMELX7TFGhFdEUVY1Tz/8DOUF5/+3eDoVsDYRaTvHbn3Ls1//Eq0NP/ln/4y227PbbeisnGe/29K1HV3nuF1tUh3DiA+gTcl0tmQ2X2CM5bvvnvHzn/+Kv/7rn3N1dT1gHlRWVCPOtTQNbDcrtps1281OkNgxP9Mi3Px4iSQhiI6YWmEDFCNPWEzeA+8i3gWiJxXMDX1pKh9DivmLd0FphQ8a5TXeKbyOBB2ZFIoHE80nJ4ZHk4bTSUs9C4TO4DuN2gVM3FNqx+/9TkTbgvPzGWUzA1ex2W2pfMcEqJSmI9CRhOC7uKxSe3chqAPKBFQAbWXXCkk4SOXkXt8+EAnZWOg5NJXGUxHQRGVQ7gqzX9G9+BX711+xvX1FQUTtr7j8xZ9RXX/BZjnn/Ez4J42CyYsvmW0umFqonz5l+fGn/OF/8V9y/uQHaDPDdYEQHT522KLsYw4ofc9mn+JxvduTox0qcwEO0PRjWX8IwIj0RRSjLB4dIygpaCkCVGIceIXbGEyomRUVtW3QSlGagqo0gp7qGmJ0slHFKEAFFbBVvpY46nvcRnjLkcV2EFfKQok4XPJYODEWkOpAiIUjAXqoBpB1pMFizL9NhcnGJev623PU992NnN7tnuvMGa3x3rHbrNEqsj15TJzCZFL01tXYmrzXxZgtUv7jtWPlJRwJwTGo5b45OL7bfYFdRrpPTFW6lYAYYvBE37Fdr2n2e7brDdeXl+y2G26vXrLbbmj3O/abLecPHvDJRx/z8GRGWU+4Wq+pJ6dMbA3tWhCT61uK6hSTrL/Yl4MamoqKaVHzYLHgbLrEakuz2xMJWA2FNVhrsEXBvjFEpSnrGcGWaGOZzefUdU1dVQTvaLuO2/2OnXOo4DkvFQ9Ozzitz6kKxYMZVPvXnGy2qFZxXSzYBmg7mFjhlCy1oUw8xVI83ROixPl0lFQmo4VsuouRVgeiGlUviIGL1y+YTCqUjkSkUqw2SVgXBbvbFW6/o9BSjULCM55XX/6adrvmL/+PH6NqSzCpLycpNa8vXrPb72jarveWxaio6ymPHj0F4OWrV/x3/+2/5i//w1/x/NlziT17yVE1RZWo/Ky4KqOUjtrc3vLyu+94/vKG9XrN9dUFT5+cUU/MaOFHrOsovafSmimKIipMJ+7PqCEaD1Goi0ulwCi5/yohGBJFVY4Da5XdqAXWKAoFJS01joeTyJNK84GxfFBvmVceo8EUBVYbTBkpzhxndcPnHxlW+8Czq8jPvt7x4lLx9WXHjQ/c+sCNk3JLRitCKgH3ru3di+oGRwxOtMdk0w1uvSgbPQeKf3rNkkP171WMEufzgbDb4TZrYrOhCB2zQlNMSxbTmjPrmXQrik2D0juUNSitmdJwUmkenS2xH3/I8tNPOTl/RFlPCUG0nKzxjfOWRNu+7+oUh7v/XfMjjtGPvfBRR4ccWofD5hVH/wA0wSuiUwSHwMK1JfiWGCJtcNRlidEKHzp5QL2khsQM3XuL0ZAFpEpvxqM9JvLOQ89h3Xy78qu4QVRv+PXnzxbgvWPRYhHnXMjUY3bnRj2ywNXo38E4j6aUkatP657guACmWrM0lmnnqNoOU3R0Vkv+/HhRHguQA1TrwRf3fBbf8P3x8cfHvt1afJOr9t7YYB/ju3+IvUWY+EljDOy2O1yzpWs2rG9uaHZ7tpsNq+sb2v2O3eoK1+5xKe8W7yisZjadYMuSV9e3rK5v2O8dE+NQXUdhDJOqQtkiYSnuCkGjLFYrKlNhtU2AipTjqSCqiEubZ+edkN9HQQ5qHMo1WK+posH5lhBaTGiIXYMJjkUZmJnAxGimhWGiI8ZF2ptr2sbj8Ti9xGst2d7aorQVwSTSKe0VY09BspSTK0/do6Bs1rcEL0WlTVFhTEFRVsQY8c7h25bQub4kUy7749s9+/UtF999jT2ZoWe15Dv7QOc69s2etmv7cWQvjrUFk+mM7W7H5eUV3377HZuNkLCHkMenUsHxRH7tPU5D8J6ubdht16xub1mtN9yutjw4m1GWNcoOu4MmSn6kMVgt733n8U4RNKgipN8p7P9D25s9V3ZlZ36/PZ3hjhgygRyYLLLIYkktqUJuRTtabT/433b4oe0HP9iOVksqdUiupqqKRTJnAHc44x79sM+9uEiSqlRH+0QgE4kEcM+w715rfetb33dw9ZByIkHGiQ2cNYLl5BwhpETLRKESCxO4nHkuZoGn68DFXFAXFiUjMcB+B+MY8C4hW5WLrgSVDAwknJfc9J5XHbxx0CboEIwyw/RJJMIkl/Kxx0cHQedHTBgpdH30pTpsjnKqfg4jDCndV4R5n7tn65ESynuEj8R+xO1usLv3YHtmOvHkbMbj83PWs5rnZ3OKFFC0qKbNnl9ScVEoxPkcJ16w+PNfMX/xBRePn+BjgfWJFPLMTEg+N9gPJIAJuv2X788f2/z+2M8+2MK5D35ZBZ0kEckQnCDY7KagJ3uYYLMOpRs987qmkIakNS6MOO8JwoP64aDET52KmMwsJruy4+ncK7vEB1RiIfKG8AARPmkk3n/rQQt+6vEeH/ThmQvi5AmITNOcYE5ITidUjiSE6W7Jk3t3X7A+vNrD8hZSok22x5oJwXVV8XS+4NoForAMqmeclUR1oOyLk2D9MIDck7ruE7UfHve9xQfnd3Ihp9/xx457cF0czyHG+7euEJMbCYfzOsk+prV8+NJpjD9CYinhnKPrskHw2zdvaLc3dLsb9ncb3Gixw5BtcmJAJ4dicukWUBpJXRvOzpaAxPU9b27+gA2CF9drLuYV5/Oa2XJFQLAbOmLKQ+Wn11+KCi0FRha5YkiJGH0WUpcClwLORcbREoceMyWEZSVQeFzTk+ICJZYQLDF46ugIdo9JnscLTU2gcoFVNWeGQjrN9uVbbtuWftYyzp/j6wKhC4QqQVVYL4B4LxN2kgzlL+V+pUJMkPE9aSmlyN3tW4a6oKhK5stzykphjCF4z9D1+HHE9WP245ueh5QKgsf3Da/++TfUTx5TXV3kpxtzJdePPd579AFWFqDQlKVhsVzx/uaOb7/9jm9+/y1DP1KYkmEcQYiJWasggR1zMpOCwlrL0Ld07Ya723dsdx1325arRwvKsqI4iQJSghaSqjQYnfuLQz/ikiAKKGoQSiK1olK5KJFa4byDkEe7lMznIZGZjCgSpQ7MTOR6NvDFY8En5/CLK8eqDszmkaBq2lGz3Qjevrd0rWPOglUF63nEiIFhUDR9wcvO84cBbswCL8ELAS6rMsXgs7vFD0WTf/L4qCCYgFevvmc5Oj77/Et8SBysQw4s0NyHSPfw10QIETFBCvixQ7gRYXsKb8FZxt0Ou3mN37+ndA3P1jUXv/yMy+WamdGsC8XYtzjrGHwgKo00mkIZloXgiZpjqhojVR4QReehXjkpX0xzginmPlxKTJ5tJ9f28QnDB8eHUJX4wf8dgt9hexRIiJLkNX6QBCsQUeUZKBE5P1sRY2IcPWVdYUrDejansYreCYaQG/eZNflDSO3Dsztlhx4yxR9WM+p4fikdE/Rs5Xu6yZ5inJwEjtNy8kG5GY/VYpqIBilJZJx6fYhj0Lu3LE3Tv3KAiAcY6eTIw7cJoSRFWbKoZyxnS3719DlfffIc1Qa2LvD90NJXiqjyOhUPTnL6XT+IdQ+r+9Ov559Mk/dj/l3i5PcdzvJjlRc/JClHBGEym83PQhwe8gQ8pCkwHqrwHLASOXiOdsCOI0Pf0213DH3Pbpehr3EcaJv9JEfYQwgQoUiTq4eQqKhyv3CSozMiUsgEweZ5uv2e3339LbfblvDlC8zPP+Xpo5+hS0PwAeeyman3JwLnCbq+xznHdtfw/OqSymiidSzmM6q6olwscUjGKNi+f0twlq7rECpT3QujURq0EZytlvgYKbsObQJFtFzWgVlRURcKKRbc3Lb8+uXf8e673xFEov58hVCQtKQfB0BhdIlQIIQ8OJ6RSNhsajiR0ybrpETug0rz4HllE+acNCqdFUqKcqDvMpwcrEMET5FAKH3cCZYXS6p5jdvfkkqJ1ZmJmlJOVn10Gb1S94IKRZmVdrTOfqgJwZe//Ip912U1GSFpdjs2t3fsm/3k3AFKGZTW7PYD+3ag6y1p8lVsmpam6ymrGUU9XZQQSK1JRKIWDD4HlbazWapSSaTJkLIiV9X5BgpCTIQQszm6VlkQPilSjKRguSgd1+vE//il4csrwyfnmsulR2hJVIpdP6dpFN99D29vJG0DerR8cj3w2fOeT54YFlYzcwZTJ3SfMCiU8JQ4IpLgwPmISx4X/Ee+Ez+2EkyJu7tbgjTZdy5NgFqKBO+ze3S0CCnQRRaXEykhY8z2Pt6j+z3CD0jbYqInjiNh+4awf0dsN+jk0aVmYZZczGtKpZhJAd5BTPRThpnIpXqBZFlKQgxIawlDT9QZCw7B5TGJ6NCxyMOvx0Tvw8mzexjkQ6PVP3b8ywav6f7vw6aZJCnKPMroBDFI9LHSiZRlASiKIlHNKsrCMF+WuK7D0mXD3lwyZVhU5Pvxo4OhHwRA8aMQ2pEwf4RCc9Q6CQXHKue0PLz/vgfxfnoz/6Cgmujw9wSpwysfgTvuK+aH9+8Hz4oDGWiCiMqS6/mKTy4u+dnVFW7bQ9Px9m5EhgBSkY4WOOl4rYnD/Xvwcvef/FR+kQ6NgHTMMg4Jxv0V/PHM6sPvOKArh5uWSUb3a0uc/FAKgZQy0uG9w3tP2zUMfZchzpvc59tut/Rdh3MWPw7IaJHRoSd/NykUYkoQ1OFFUr4SJRJaJIie6CPODnT7PfvNlma/xtoRqcSxgo0xEFM+r9Nr9C7QDyPeBy7WSwqlmFc16+WKxXLBbH1OEBqbFJUUjH3P2HdUZZktmVJOpqQUVFWR5eicpgyKMikqndsJMUp2bc/7my0v37zCDx2q1JRK0Is8UzqMA0oaqjIgZZoSdYEPDh8cTdvkwCglpZm89xD3ruqnzysFpliDHQeEkAxFy9C3jENPDAGRYr67cpo5BFQhUUaCG0ljT+g6nM7EnJhETvom9OZQpWptsm8nkr4fGMYRUxaYGIhKUZmC4D1d0+ZnEeLE/MwzmD5k38WEQKm83zjnsdbj/AcVk8oenlGBnWb8rI15Bj2JiTnLFAMyupImM+ZEFj05fMSQ1aAU8HipeHYh+PS64OllyaOVYVZJLIo+FTRhxdZptqOksYLWRmjec37hCQrMXFIVktlMYkqJ1AniQbghm/7Gk6TwXyME/tGV4O9++1tWm4avfv4VxpRIqXDB02w3bLe39N2eejHnyYufZfFbEnQ7ZLtDdnvK/hYlE1pLRgz7vuXb7/6Z0reUcchDkUqhErkXEAFh0KokGknserxzxOBRWqMjLInc3bzKEMKjS1g9gdkF+/2O6EdiGDl//IxCzpAiN05BP6gHfoqMcHr8oC8jpk38RwPgPfx5f6hsFBkUwSb6nSd6BShMXaKnTFcpQ13PWCzX1LMKUyjKWtG/2bEbAklO0mviXjXk9NwfnDMngey0SksfflcmBuUYGY9buTr8zjQlCaRjUDtU+wnuN+pDjBOT0SgHLVQyJHLyfYKEPgk6IVtw3t+/B6MTDwPjkWwsBFVVUyrNX10/51dffM4Xnz+nI6FfvuH1rztuR5tn15blCQx+omB0Qs75Y8exGk5wPzt6H/Li8Qo+7heePq6UyK7mMZ3Qu3OFdiAdKQSkPJYwdg3j0LHd3LDZ3NK0e25v3mHHXA26riOFAGFiWQqopESLgFL5/O+d7O+l+pjQHGJEpYgROYt3ztMPHXVdcHG2QsnMERjdgPcbbMyqHWkyWz09fIh03cBut+PZ1SMenZ3zl3/6C66urlifn7M4e4QqSpQpGYaOrmt4+fJb+r6l7zu++eZ3pJSywLZWuBQheEoENRolam42Pbe7Pf/5b/8eQsesGPni2ZrVasVivaDxjrbZsA0dduVRumQ5L3JrPQbe375lu9vw3ZtvJ/1RTV0vUEqjdMGsnqNO4bUEIXg8AawjOE+rtuz373n35lvubt4QYsCIQKkSqciwoPMQ+g4bA6uLc1S0qL4hFCVBGdAmr++Y+1rZ6FuwWK4pqxkhRN68ecsfvvkDX//zP+NTIknBYrEiRE81L9GbvGYKUxKCJxGoqoL5YsZqvaTrLft2wIfIMDqG0Z2Q3yCqPMrgNDgLwYENMld4QmTjXgQhiaynmhJEhxBZBq2qS6QSSAmejrmRXM0K/sNfXPLZdcEXTyJGlYyyovUXdLZk11e8218wuBnufIWRe2b1liH+DWY5Up1HFleBwkuuomb+tUPfJXwbECkgksc7lQ2RQzx+/CSw88Hx0ZXgZvOeIXi+e/UNZVGCENxtbmjbPW2zR6bAelhzNq9YVRUFAtls0WOD8i06dngfGQb47q7hbrvj+5cvebYqmM0MSmbR5RQifnJktkLjRMSLkAc7U4AY8OOAEprKlKjQE9vI+9/9E2P1htEsabphyqgCMQrmyzX1/ByBJNnxYRzgnjZ/Guz+Rer8T93cw1D86WB7UtnJOUjCkAhjwo8x6+eJyDiC9wLpBEb3gEQXJUVVoFCZcZViVkc5KNgwdVynCvOwuf/ICZ2c8P1f96MOh3OeaPMnQ/05IOavHbIqQRYcuJ98kBMleyJKiSxdlBC4YPBREKLA+5DhT5nZYVIkdPJTBndg3YrjZQhERgLjyQD98QzuA4hSORki5t6mSuCSpx07brY37JLCFRV1PeegunMfarN8008XbT/2H+lYTeetYPKUS4nsL/dTv+zDZ/PD15UpoVKapMgm+DUE7ERUGbsmE1i6hma7YRw62t2WfuiwdmQYWlIe4JoSjKwTeqoPKqbAmp3e8/09MPtyXjEF32muLiEYncc6TwSkUahCZ7X+GLDeEeU4CSrca+zeX2YijANu6LFdz6KuuDxf8+zJNWeXj5gvVqiyRmqNMoaqPGMxn1NXNXYc6YcuMzm1oC4MpIS1jrtdy9ubd4SuxQ09225g249sNh2PFoZPL89Zn1fMZhUpDrjW03dbdlbSNjuatmW9OkcIQd93vH3/iv1+w3Z7d+yJCyGPa+3i7JJlVfO0PCTQiXa/QysxVY4aqRWFz8VBPV9mYQHviD735ZJUSC3x40j0Ab8+pyprZutz/DDgUsILQdI6w5JS5N6pgMViCQjev3/P+3fv2G42mXwSItFH2u0WqRLSJMqZwVlPcD4PqyMJMU5kQSbnCYMUEu/jJGx+v0xVLYlIVKlwLiNXUUuMUahCIU1G1XzyuMwuQuEoi8zWLrTMMnkisZ4XnM9mfHrxCNsIvhsCr3/fEzyEkBjijtEbelswxIgLmnb8HtnfYsKOTy9umRc9izqhcCgpqeuUZ1ZJ9K1DTtV2TCqLLtjM9E36I7Nb/hXEmP1+Q+ctr15/R1kVQOLlq2+P5f+irFDR4s/X6LCgEhLVbzG+Q/se4khwnmEIvHz5kvd3G25ub3hUX6JUyQFwCynhpkxfoLBJ4pIgCUkeKgp4O6J0oigLtB9htOxe/p6dfEcra1qXlc+lUSyX56QQESikUDm9eVD9wSEQwgE+/Oj7xzGLzr9twgoOJBggKWIQeAe2S0SXIYaYHBBwQuIdCCkoTYZVTFkyW8zQUWW260GEVtxDgYeX+xfP63haJ4FSZCjjnpjB8bozqWm6Bu4BvqNl0aFgmP4QxwpPcdhmY8ozQYMrcF7gcrKcA6CWYMCICMJPk5S5XoxTIDyeI5Mu6AfEmCkOQZrcAVTCB38MhD44uqHjbn9Hkwpi5SkuPFLqDDMdXuPIGj69iR8kDR8+5sMaOSQIhHzO6SDvJab7dZpAHX6fePDLPnxVGWP+ECmzpmO+lr7vGIeeze0NXbNnt7ljv7llHAaGrs2s7ZTVhZQUaCnzm1oclGrzOpTH+wkh5oFwEtOGJacqX4CY9DpFpsVb53EhILVClwUmgtA5OXPekaQjj0QnMuX34eYTQ5Y6i85Tl4blvGa1zEom9XyBT5NUmpLUdY0UktVyjfOecehJdsDbjuQHonOMzrPZ97x7c8t+s+H2/S2ttfQusCwrzlcLFotLyjrDjqPt8V3E7yPNAH3f0Q89/ZChw81uy7t3r2iaHX4csgOIz6MYYXJjePL4msdnFzz9/MvjBmHHkagFSmqQERk1UmtMUTFbrBCqIKU+VyU+ghZIrTKa5XzunylDNZth+i4TY3xAFgViuh8h5JerJneRu7s7dhPEbZQmhlyRW9thKoWaKcraAIl2GAnRI4IihEwUCTFlNqvKc7QfBkEBqDIHQW0EwiiIiaQcQudeXxbtSISY3SoEAaEcUimMzkotB3zprK64mC+5mF+yu9nxrnPc3AwMQ2K0gd5HfND4qBHGElOiG1pW4j1nZcufXifmpWVWZq1RKQJVmdDZpYmxD6iU2awImRM7P72/Px4N/fggGKKl2Xb851//X8zqAikT25vXJG8hBFZPnjObSy7CnkejYy4FJjTYcY8dW7pxYNc73u4Gfvf1b2j7juuzGXOjUCnw/auX2YYmJur5ClPU1HXF0Fr8OJJGR6kSpdYEZwkigS6Zl4CPNP0dLuxxSXNx/YKinlPPV4g+Z9Bv37ymMCYz1SZo4yEkehoI5QNo9L4SnIbcxf0I/IPqYjIezgoKU8DwEtclhiaQnJmYZ5JxAoOwAAAgAElEQVQUfJ5PCuPkoSbZbe8Yx54YI1WdJbdC0oChKOZ5U0jpOGt3HwtPddoP15SJKKe9pCm0TJDlaR/uFIo8VGUpOw4kSFJP16mmjV7eBzwEIeQF6AN0faQfEt+/GbBe4qJEGDAGyhIerxXzQnJmLFl6NyLQHOSx8jnCAW78UKs0Tj2o6UERleAmBZoUcS5CNzK2He/bHS/HiKgXLJ48QZgSoTQh3kPZMX1Yo/0w+D0gPoksWqCFQBORAnwC+0fUKdLpLT48s6PlRn5d5TzBNTRty/bujq5tud28p20bhqFl7Fqi90Tvp6cgsk6+zDNqMM0BOjdJouXqUkx09UJOhIcUkMpkDUchcmLIpAMppkCpDJ0NvL3ZcPakpahn/PyXv6B6u6HpBp48PmM5K+jtmCFQqUAWCKF4oAMrBLPVmqg01XJNwnPz/hX/x//+nr/4y7/ixedfEKLEFBVVPScWOlslKUUhC5RSPHv+CWO7Y2i33Nxt2XR3/MPvX/NtI9n7c4b1FY8qxaNS8Uvh0Qn+8bvEb29GjLLM0pZyteKXsxlXdUmTAru45/3rW5p+5PX7W4auwzuHEZNtUsj8A+8dfd/znjfgLOmzL6b7KXn67FPm8xnVbInUJdqULJYXtNtbNm9e83fV/0boW5yNRJ15FFLkNSNJ+O0tfSGQvmd3u6ULgh0FxcUFZlazWC9ICaSS08xgpOs9pqhYn10gdUU7WgbnsGOPcz3WtRSFynqowSGn/uY4Wtq2Z7vbHwltSmucD4wnzkAIMEVmCxsp0UYSoiRK8CL3OGVSBCCIhK7yPF9lDLOixEiNTpJSGUpTcFVfsX8z8L/+x695+/KGobfYJAlREaJCxJB70ygkI1oF5nPH//A/rfiLPyn5D/8WLs4jC+lJgyImddQHJZLJhdFllEy6iehUoAnof4U3xMcHwRAY7ch2e8fY5bkPMfasyoLVas5XTx5xtV5zVoLwLTZ43NDibY+zA87n8jzryQUUkapQWZBVgFSK4BOd92y3PciAKiJhHMA75jJxVmtmRk9qmtC6AYTBSEHpLVX0WAyF9CjhSdEyDp6QYHTpqH7+qIj3b9UER5eJYxU4CXA/aEcdZu7uoSW4ZxjmYutQQeXqgCjwNhFs1vXG32f4aYIYlUpkdf17HUet7h3djC6nj2qqOMRJSZYv4GFNdzjdDE3G6ZoOpKBj4I7i+PP3fx5rv9ykn6C/3LCXBDQhTrqBLn89RplhzwAuJJom0naJt5uAm1Q3ylmJjlAmkCIwFhGzFNRSUIisQJ+p1PeBQZAF0NOPzGzC4XnlrHUTPS/v7lgqSW8H7u52tM4hJgX9y/UST2aw+dHfB6CfREPvIfHDujgG5Zjy2IdIpODJRqyaJCaFGgGnqMLhWRwr8fvldPJ54u3L70k+ZPLJbss49DTtHueGrEriRsTUq1MH/U4kaRLIjTH3R0jxnuSCAHlwIBHHujsPh09VP+FYyR5Bbg9i1yCUZv3uhtnS4lXBYrVgtlpBctjgaYdADANKF5RzfV9NTocQgvNHj1meX6Ck5PHFmkJEvvnmO66ffcr64nIa6vZIJTHOTCSbRPAuV2TjCFJTzs+Ye82y6lh5QSUMrRS0VqBjiaDgXaGogqMIHcNtS5F6ymIkKIvSJU+fPCYVNcHU3PWObdtTG8OrN2/Y7fek0WGkpNIFvfNTW2bq/Z8sQykkl4+fMV8sKMtZhg+1YT4/I4XA2HaYqiJqzUFcK8VIcB4RM0lm6Ae42+Ksp+8sXZTsY4FOBt07YggooyjKAkSebdRGZUs76wghYq1ltJYQJ8b0oYcs8r0/JHuF0UiRSNERwsHfLyNMH7LlpwYMMuXBda0Epphk5dRE1pnGW4xRaCUxGpTMxJ3oIASB94K3bcv+ZuD2vaVrwTqBF+Ggzw0hIlNERkddOBaV4IufLfjiU8Nnz+FiPbCoPUZBtBoXNXbMJKjcxZGkACFGhPR5BEXqnMT/95ZNS4ALgWEc2W7u6PCUMvF0UfJidcEXz5/yV1/9nEWVZ4K2N7e0XYvrO4TPDfoQZWZZxUihJBSausgltJIwq+eMcaTre17etfSuwbJDEyklvFiXzGYFupoTCITk2A4di2qBEZLaW2ZJ4mVEC0uMim5I9D5ifaQbLDEGtBBcPnsymdDBg0ruGAgTcCi37t/Qx88f3J14iHcPllKWRAPbJ3yfiKMkRY8PgWHss6qDVGipM+QgIlJojNLUVXWkIdfFgrpYMLgmV2KJ4+jBqV9hhkpPaotAHm7lUBXeVzSHgChSysFwuqh03CgPm+WkFIEgoLAUDDZhfaLt4gTVqswiiwnrYLfztG3kzU2YaNWalamz8sTEYlwYRyWBUiH0pIohyHJyh01nkmv7ENVI0/lncowkCMG7YPmvr17RvnlLIQUv725onKc8m3N+vuTFs8fs2oGmH+jGbEtKPFS1P73qH6i2TGEihkAiImQkuJGAwgNJHXpIH8LqJ/f+g6L7uIJi5Ot/+ids19Hvd9hxIAaPkJN+rMqzewfbG3mo6BPHyjj4XFkrAfrkHnLo76WcUEQkLmaNyYy8xOP6jtNmmmKk6Qe2TUu5WrBYr1CzOZ99+RWr9Tm/+/o3eBewMmJHS1nOuKwXH74xkFLy5JNPqaqSi/MVF2lgf/Oe//Prb3j6yaecX5wznxdUfpndy8us7mTHHts3ODsyWM9ssWa2fsRFccFwa/lZKtlriSXxh/2IlYKNMvjZGY9kzyfRYr/ZUNqWJ+cFVltiEfjli59xcXnBxdkZmyFyu+/4x29e8n//OvJNcOy7nrqYsZjNedvs8SlmZaKymFjv01tFSp48/4LlcoFSxTR6IaiqOT5Y+rahmC9wmxKbsiFtIhJcPAYp33T03YiQG5IqaJPmLhTQZwm6sW2oFzPmqxVCFmhTIxQMg6VpGrresdtt6YYBWWqUzOMJGbDIBMQYsififF5QGAHR4W0geodUghgDIT6EQ/VEyJEha6qiBaE2R0H/OKFFSsn7PqCSyKgggB8zWSaEyLffvWHcR/ptRFBhjCaJ9lhIRKdIPhCjZ17D0+uK//nfP+cv/6zns08GLlYthcm6s8NosM7QDorgsqydTLm3GENESJcVMqXOiX/47xwEAaIdSGOHkYEvnl3xyaML/vrPv+J6veTxYo4aLW634bbd03Qdo7X0XY8bRtzo6AZL7xN7FxEyUZWKWoEmoJKkrkr6JCmDImwdjRt5s7mjVIKZUawqzcrBOilWq0sQHsPAXBuSj1S6p9vtedWP9CSCLgm6orUeFxPWR/phRAvJL588zgvmXzw+7OOcHocodFC+OHazIElEVPhB4kdoNyPEDDNpLTFaUpo6V5YiZ1MASMFisZ5IHCbTlNO0qYqDF1eEyYdPHHHOH69lrAtoEX4kCB4qk3RoXx4hzigkuUaXOGSu+CJ0o2cMib2PDF5gvaAfFXnYyhCSIpEroiZZnI6UlwIfsgBx4xXCZxNV0Xvm2rPWBWIhSaUmxj7fR5k3TiFElj8CnDylcE/V21RZSaEIBPaD5ze79/y+bcAIBiko12v+/E/+lOvrK55fnbFoerZtQdM2WB8nKSt9DCb3L/EhrJzXQVWVKClJsWAmfVawSI4UBTYcntPpzzFV+/IHVeDh/09fo9ttiOOIwlNpSFqSUu4RCkBOYsSHNIWpF5tr4ThZ1OR+pZ5k6PI4TZYnDAjC5IsXhcyUdw11kZUhnfdobTIiE7KIczt6hCoJUfDu9Ws++fznSAW//odfUwiYVwXRB84vH/P46YsJThUPr+t2gxWRdPuaotaEoeP8bMHb19/xN3aHUYH58oz1xTU//+rPWCzPmC+XVIs1tZDMIhRlSVFWUCfi88f86i9ecGY7XrUN8e+/ZfAFtql5meAujmzGHV+tF9SioKkCbwdL8+qW5v/5W/70qy/5q18tWVeReVlxuf6KF9ePef3+jv/0n/7myCpURUk3WpphYD6rWc+X90C9kKxWFyxXq0yKaRtCjBhtmM+W+PVIfXlBd3dLHxXGiWMh4SfmtIT8TAQIFRiEYCcThQaVIv7NnnmX8Nbg+kAIltE57jZbbjcbBuvZ3t3QdS1IRTnT1Audx1Sm+x9iDmZKgRQRosdai/c+9wVJ02zo/VEaTZJyQr4cWkQWZTgmdF4ZpM791lInlIgQcn/Od5Ht9y2MCuE06+Uls7OC6/OCvu2w1tKMJUlnhKpvWmojWc9K/pd/f85nLwy/+rOep2cNMzMgU8Q7GF2kHWfcjTXf7ZfcdLc0o83VrRIkUWH0tDf4DI96/2H6/NPHRwVBAZwtZ1QGnqwW/OLZEz69uuTz68csC8NcK7q2wY8DY99lmrZzjKPNcy39wL7tsUkwCEVRVRglsp8YOQOVSuSswmiK0lDYMGXB2UYFpfImncSxb5ANKQ2oRFE5ZG+Jg8OHQBAu03hDZsJJqaYA8hAD+ylG3z2h4WEVmL96CIJTFOFkGH6KjW5I2C5hx2wCrCVoo7IsVHYYBZhk6PJdLkyJVoZMOpHH3uSxS5cmofKDAPVxdOEDBRSyynuYYN2DWO6DqiSKQ/zOkB6SIDQuKXySdH7q9yVBOwp6D9tBYqPAJ5ktSzKXER8kKSlS0owOfMjU7RACLgRS9LmfED06ZDglBolzCSsnwedp7lGqmKW1psrqQ92HQ3/tvkrLleveWkLbQK0w8zmr9RmPz8+5XK0wEupS42NBVShSCsQH0jUffpI4QOJZh1Exq0uMMciUmOlIJQJaRKSLuBF6HwkTU1Xp3OM9Pdf7pCo9vJBpY01hkiUk5hk20tSznNZWOqyzvH4P852HGUt5khTJCQ5LgqOGYggTcUfmLoxUee3WdY2UgtE6ijKbxHof6Loe65tpvWS39xB8NoIVGYKyo8NZx3y0J9D6B++nEJAiYqIgujyoXZUFXbPDDntkGpkvNrT7nsdXzyhMwersDKENCIVOoHSB0gZkRFeaYlWyHAb6CGdFoHeWISRG36IJOCkp53MKYeiGLVvr2UbH99+/5vH6jH7fsFwXVCZLnX365JrlYs1us2G/3dHsG9rRoYRGSMNyOWc5nz24LKXz8LqUmdiToXCRGcuFQdU1wRR0SKqQn7vzYFMiJoES4FPCkUAlrEyMMiCdn1agZ1QWowd2mx1FXRMk+JCJSmPw2blHJIK32YXGZXbw/d4ijnsoZBg0BD8lV5kUJT94XkpJZJITxJhNE/LWOY0+ybyHqelDJEG0ibHxjI2n3TjSGJE+UekARUBqDwSUTixMiawUUoNbBVZV4vFa8MUXBZ88gUfnA3U5oqQlBPBJYKOi8zV7W3HTKNpR4CJINWlWTb16EhN69K8To/+4ICgEf/2rX7KsC371+c/49OKcs7pEji1927C53dP3Fmsd1jrckD8fB8u+adk3DTfbLegSvVjz+NkL6rLA2BYfenprKbSiUIJFpXhyMWcxK1nMKkSMaClYr2aUpSEicF5QqJK6WjGrF4BklS44L+7Yz/fM5tVE2Ih4BEhNNV/S7Duijw+rwIxh/chVn2xQD+YHD1T+QwAM03aUjVlSFPgu0dyMtPtAdGCMQBSRupphjMHorD6R7UgcPmRXjbKoUMoQQsjK+UYTgiOmPO+TUsijC8eh9JNN58Nh3jAZgYp0hDTJwlhkNqecBA0CMSkiGktNYyWdhXeNJymNKgt6lxhsYrPNoyrSKBYrg/UeOzraLmds0UqCE8Qo8Cky9D3j2FGJiJGRWkUu57CsNLVRhBjoxgBkso9UoFKmhx/adifDJmSIkqOrxWGhl0VFpxR7mRC15unlGV999iVPzx8xK0qGdo/RhnVtuFjP2ewGxiGPoxxdRo4RCzK7N6JlhnyWizlXV5fMZjNKU7AoJJWGu9v33DUd3O7Zv7mlH0ais6xXK8qiZrTpXhg7D75OaMGkRnxcUWCUJCryJobnnr16f+3iOCQ5rU0B94pE+ZzFQVFoSoySSISUGLxHmgzrGaNz0mkkFxfnGKOxzmOKHARTFNzc3OHCK1xILJThiy++wI4D796+4S/+7M9od3vazZa727u81kIeSUonlYUAZkXBamb42dWS3fY91kM5r9i8e02zvcP3LVVZs1h+y6effc5yuaCqasaQcCESYkSZYjIo9gwEXipHG0ccnsuzOcN2wHcdX+p32HJGW57x6FJTDC2//9uX3KXILkXs2685Q/Pzswt+/uefUMwqXIo8PnvGk+tnPL5+wtf/9E/85r/8A2NrqXVivtQ8unrEvK4Qp2jKBJDnfn4kpYOcX+Y3pMWKtprzBsM6BFRMxADD1CdXgEswpoQ34ImMjJmrYDRFWeCajmb0/L+//gfOrh5x8fQaoTSiKBhDwKwXmHnBuL0jRc/QOEwhpj0CZnVBXRdUlUbIhPUW5z0xhjzELg8J1/S8BGilUVESUppUX0DlhURCIFRWjhFKIVJBsgl757j9rqPdWMZbUFGhkuBld5NRCyzLuWSxKPjsyyuWFyXzteHics3lCp6dJz57dMOq7ricbSmER5BoR0XnChpf05unvOkVX78e2XSKMRhMmZB+6mt6SwwQovwBmvPHjo8MgvDv/uRLzhcVTxYLiuDwzRa73zKOA+Mw0I+OwVp2TTP5YHn6weIyMM28KokyG2h2QyBJxfX5M+hu8UMmALiQkBLmRRZnLYTBOz/NPnm0lhRVyfmTa+bLFcuLxyQ01kYQ71mYBVeLhrFIeCI+OcJEn9Wm5HymIeYM6b/1OKXCM4UUMfWY3AB+jIwbj+styQe0NFnpJQp2uz1STb1AnV21s9J/ljcKKaBExBTF1IxOOJ8DyTB20xst3r/+FAjTgSjz4KFVkGZTpSiJSeKjxLqckaYo0BrqIiv3uyDYtoG7xrHrE++b7NhdzDQugXWRfojUM4USCucT4xjo+0CzD9ghMTSR6PJGbVTKUFeRuD6vmJWwqGBRRkqdmJWHGC4QaOTU/9JaT9JLkzqpKjlwcSEHvvwxJQOANJpqtQAduHh8wdNHj3lxdUVdz1BGEVXAaEGhJM+vHlGalpQ0dsxVtVaHGb9cAeYqMLCaz5jVFev1ksVMUxRQ1wWSbD+UpEAZzXxR0f9+x2a7ww8Ds0IyLzUppFxln7qHJBDJcxriEuREJwayS62bgl0mG+Snfaj80n3vcZodFaQH5sNHqV4BIcVpqDr33KrZgsVyjpQCIcIUDDXXz5/Ttj3DMOJ8wMdERGBHj3eBWVmx3TeM3rGaLfCjZXO7za7hSU4V44RUnLxXtuNISJblJhKdy44fXUv0DpUS3kd8GOj8Dd//9r+ipeDxk+dIU2e5MqXQUqKSILhA33W8evuWdr+nH0eSlLRFReMLroPjKllmpefaKbDgo8IVhqihFgohoGlarA/IkOd1gx8RumQxn7Ner7i4vODlvkNbTy0UL54+Y1HPuH318ph4HbRwc781B0FJoGs3tJvXjK5nINGaOpsVhwCxxxHxKRKSwKeETxNuLQVaKUSwEC1GOIwy6OR5//vfM3QtLkaCHRHRY/s9xgiUgWImMkEkpKzdGgWzuuTx4xXLZcVqVVEUKu81MpNctEoYIzHmPhlLKRMgY8q9eVQekcjzwdPaStnvTwRB9ILQJexdJDYK0RtUjIiYodTCCBZzyaNHM774rOLysuD5Z4bFuqCeFyxqKFXHTG2p1QYTLToEXIAxSJreENQ5QV/Rp6ds2oHv3vyBfR9xUWSJzOm9GH0gegg+4mXMSdlHHh/ZExR8dv2IR8uaKkaGbc/Yt4zNntFZrHOMo6MfLd3QM4wW5/KMTZjUm+uywCeFTYJxdAgdkdWS5MdskTKOR3HlQuXmvxEKq7K6eIYTNcVsxvLRFYv1BcuLa5ohC3HbckDPAguhMGokyICXmqSOdClkUpmO6/8bg+CUgB8ky45qJlGQYvYFdH1k7PKwKjEzvATZsqUfsq6iVJKiKNBao5VGG4kxeb5HCjLrSkukTIx+xLoBaycBgGkjhSOWyYckHgAhChDltG+qY++qs4F+zCICVSlQZYY5bIT9ENg0MX+0oCuokHjAe7AuUZGHg2OKeA/jGBk6z9B5up0luZAhvQIWS826Mjy9KFjUkuUsUWiPFHmzD5nMCElO/nF5Q5Yqw2+JRJQfLNGTcZZD4BJaUi3mlLXm6dU1Ty4uuVyfUZgSpCAplzcMJblYV4So6MdIJ0ZSAj0NHgnIsAoBISKXZwtmdcXZeoZWCqMls7rIOpkWkpAoo6jrAmcH+m6P6zu8PSOFOcFDFAqUOD11JtOZDy4rExIOfd/TsHegmR6k5g4txuyQMqVl0zcL7vuBSQqCTzkIiiw4XtUzFsslQkKMjpQcUksuHl0S0i2DzT2VSE4ec9Utqcuam80dfdfyaHUGKYs0C2lOou4HQTDBdtoL5mmk1tnNINghw6QkCImQLNZbbl59z3w+p9neMF9dYiqJkgY1MR2JCe8CbdvlgGwtCYVVmq5QpDRQJXgkIuXoSUPgTCiWRtMWkmJq0fbjiAsREyMpJOIERZfVnFldM18sUJVBCZAoztYrlvWC21cvj9cWw8HAdlKP8Q6ZLO3uhu3tW8axZ0wJa0p6IZHCI63HC0dIiTHlfnmYKi0l80C6sDajED5iZEKnxO79O0JKiLKc7p3Dj12+N1IydVCAhBuyiENZas7P5qzP5tR1HvKPE/tciknObJpHPD0yeTHlFtS0b8Yj6gBHp4ggCEPCtxG3j8RegM3awIcqc7EQPLo0/Pyzmj//N3MeP9ZcP1PMFoaqNhQyz1Ul21L4HhkdyQmsFYxWsG8KRLVE1o/p05KmTdxsLP0Y8RNL+yCTlgkyk7M8/z/IpkFCNTui3bNt2mwR4j1htLhxpBtHNk1LP47suzaf0LQ/C0BJRV0YklREUfCmuaOzLft5Yj0vmV2cc6sEru/puzYzllLAiEi9KBHSEETF+tnPuPzZ51z84hekCO/vdvzhu2+52254dfOOaC3BObZhh1oVVE+WqFIjJITkMviVFHq/egBt/PHjw+89VAz5TeK9wHaJ3fuRMEaMFxgFWiWEdIQg8T7jfUJmtfdAntE6v7qirmrKsiT6SFkVLJYVpgShPG5s6YYt+2bLQe/yvh796bJflQVaVpkyPPXfbm5bNnvHrrUgNYtFga8rUoTRRt7uRzZNou0EzmoiBoRm3/ZEHKb0lLVhuZ4hpGHs94xdYL/tcGOY9u7c15oLxSfnNS+erfjkeYUxDiE7YASy8WYIUzETc59BitwXlpOrRkqCOM1eHo7MhkzH3q4QCaMNT68/5fJ8xWdPnrCosmoRKjuRIwNKOAQBT4EpZiwWZ9ze3GSkgYQpDMZoqtLkkyJwtl5nfzid+ZhKK5bLBU3TEkNAK81MSApTYGRCxNz7GMeOpt1ys7Xo2YJyeUYxDfFGAU4UWVHkR56bnJbboU+cPTAnufEUkfEYGqc+VO6nSn3oH2Y0JeYLZwweG0BVFdV8wfr8grIuMoKAoWt3Wdh5uWS3a7MVj4poYyiriovLC66fXPH800/p3IjSmhefvEAliW1H9r1Fm+pHryWmxG/etrjmjn/s3/OLc8HCwDx58jtAsHM+Q2Za8PKb3xL9yPXVBT//N/+W2ZNPUCJbDZESRTnnfHXOl89e8I+bG4b9LclFFtU1YnnBf/QvqILj8euG5813rOLA9aLGFIqVEvQpYY2iERFrPaUNaJlIPlcSOngKrajnNZWIRBmJxuCDx7p7YfCUIrvNW6JvUFpx++4NXbtjc/eW737/W968fsnL169p+gFR1TRRgnTIGPBOE0MecxCAkGKyRwNTFcjkUNGjRWI1N8xnFcP372lfNdzcvUOsKsbgCNv3tE3mRVS1zD58Esbeo5VhfVZS1xWzsiT5lGcfY8RaR3BuIlTJD5DDxDCMBOmodIX1gRgjjphHJKZ1JwNoJ7j7fo/de+ytJ40CFRJKWtZniotLzb/76xUvPqn4ky9nXJ9pKjM5hBQFRmuCvSGkhpAGkhMEZ3h3I9nuE/2okOoJurpGVZe8uut4c7unbR29t4wx5iocJj1meTR7F5Pi2MceH80O9UNPCILkPMHmGZ5x0p4bBovtB7z3yAhCqJyJxpD1+VLW/pPaILRh6SyJETncYkVJMBovFBQlWmRXg+QteIswmR68vnhKuTxjiJK/+/v/As4h+x4/uVKc6ZCJKcogvSGogxdfzqo9bmoEaz7oMv1EKJn6RZz0n45KKlNWniTJS+IYcV1+I6WQmYchZUFh7wIxSmLUVLMCbRTGqKx7WVbMZjOkVNnt2mhModFGg4p4PPvxlsG3+DQeneXvjx8P5ALQtaHUJUIZQp+z3Wb09CHhUChdMlKwHw12DFgLvTUcUYQkSCHPOXrnkSpR1yVa5eqgbwe6xjJ0Hj9mJRxS7t9mGaPciwwJhJbIYlKqEJkNmz0SybORMW/0UkzzSDKPzcSUFYR+eBwa/5PYsRbMqxmL2QJjChKCzlr8JOpe1wqhU2achvyGntcFYVFlcWnvKUqN1pq6KkjRk6KkLAxSSiIwjhZGAaIhhuxMXpkC7z3C50FrJXJVEFK28vLRokRAG0Ed8kWPKWKNIvDwTSonaFhy0F4VMM3QCjG5R6QsLjD9i3jweDz83zTsLoUiJsEYwKOJSlIUJVEIBmsJKVBWhuVyxmo9oygMQ9dOQ9eWcRghReZVyXJRURaS3faWuqrQWpNSpCg16/Mlo2hQpSFJPcG0D/vtyVk0kcW8wNuGbnAEa3HW41xWMREk8JG2adhvNmxu3tA2O+bDwKKcT0PfBudHClPw/OqKN19X9CHx9nZLtSh5PDdsiyoTq4CyCJQhJz6lmVEbw37s6Zxn0w/4CYUIZLm6+2UWIDpic4eIibossEObHeMPlxUjb19/z52ROGd5//YtzX7Pzc1b3r5+zebujsFmu6hZZdg1AyF5VKURpcrKQIPlKBChFEFJBks1UqkAACAASURBVBI6RFSIOaJNRLhCJawfCfuAk4ExZb89ERJSQbGoEKSpOk2IlAkwbdNmAfSYx15STFPVGvAu91e1HjlAXPn5ialFkd9/WQwiS5NJBCpqvAXfO/rNgO8iyYFRClNKLi4XPLoSPL6WfPYpXF8FLs4cixnTKAVoemSQxNCRnCOMgm4jCCP4QeNChfz/aHuvXjuyLM/vt22Y466lTVNVXdVmNFJjBH3/N2EECdLLAJK6p7ursjKZJC+vOy7cdnpYce69zKoGUsBMJAhmkszDiBMRe+21/q7ULPwlmiUqGZbas7COympMFB2jPomggWSkxVVKUTQYY/7q2vjXjl8vlu97UlSQIIXINAX6MdD3I/0wMg6jWAHNwtECpDinKpeCtU5225UnhqM4GoxfGKMjGU9cvEb5Gl8bSk6ksRdSga3QdcvV++9IyrEbIv/H//qfMUPHa6d5//qMdlFR1wpbLCpr7BTY+8Q2BlKOYvGkhFJr/uol/3KhnR/OJ2uWZ3cVdaKsF4vCSC7gkAlHiU5RRVC7mONM/IiUIpZdC2dw3uG9ZbVe0bYL2sWCME2EEGiXFc5bjDcUnQlp5LG/pYt7IiMn8etzE/tyTPbiOpTCt47KV1hX0+eJPGYOY2RMiqQt2jUE5dj2jsN+IIyKOHlSObneZErSxDGRQ8IaxWrRiJ1TTGwfj+y2UgjjVChpTh5woi9KuTDGQj+JF6wz4j1ojJWFHfVkEECai6BWc3xgQeu5iM48kV8ewsQTsW7tHY1vaH1DRjPExCFODP2IMZorvZJuavbNNNrS1hZWFSEohqHgvSSc1/VsRxXBWiuw/hR5PHSS/h0Sbd1Qe0/jK6YCeZzmHDw9Yyp5flrEsLryhtWQZ4f/SLFGNn1P9w8xzS5gi5ICqaDM34lWYFRB51MRlCJ5WpdPnC2hPsmEoWRFHyBpC8ZiqppYCt3QoycwbsViteD1myusVtzf3AjuNPT03YAqhdWi4WzdUHvFl5ufObt4xXl7QQojzhs2lyu2IWJrL2kdPJumnw4TA94qrs+XlM+PdMcjh+3haY4hZEbpVA77I3X1wOOXz+x3W1Z9z3Jd5sw6Rxw6vLd89/YNP9Utu6R4vN3yTTC8ztBdLknWoG1howurkNGHnsq2xMoRgMMUuD30hFjIeXZGKk9rKSUncpxI+1uUMrRnZ4zdnmjCEx6Yc+bjTz8Qw8B2+8DN51v2+yM3t3fs90eGccI4h9GG1UKx3W6Fbbl0wgVQinL0sr7MDUPOiRADLiVsTKLPS4mYkkyVxkjpBnqVGRTEKJs6jWHhloQU6KdBcDGTSSmw3e7pO7FkY4ZQTph6zhCmgjH18ztV5nWvCCMtzxKpkYwpCoOmjpZ4zEyPE/39IMQ/HFWrWS0tf/jDmrfvC2/eZX7zbeBsHVi10NQZqww6FEoeRR8YjqQxMB0V21tF6DU5eKrmjMpvOKteUVRDyoaLyvJY1zTO4MaCzQmX9ZNNczKyNmojblbG/Ld2jCkzay8Xxilw7I70w8Djbs8YAlOIVL6Zaf2GmIpQ49Mk8i+lWC9XGDPbdQ0dOQyUSoOOaD2R+omoHUF7jsPIOEW6Q0/daOwx88OX/ywU5sdHWiKbRYu+fiXatS5io6EyCacLm6rG6EBMIzfjxKASgYAl43FsXjDvXuIs6gX28txkvSRMPHeClExOim4XSWNGF03btJTZb9HQUAq4umBdhatqVsvFE/FjszmjqirGMaC1pq4bnHMYoykl8eXhjuO442H/mZQDRQmnTDSC6unUnoTzvzi0XaGtA1WRYsc0alKomUbogyLQELNmGDPb+5E4Rbxy6KJRWRGmQGagoDg7a6kXnspZjruOrh/56cd7wlgIo0Jn0fXVzvLN+3Oa2vP4eORxGHn80z1HRs7PDNeXjjfXKxaNZlEVVE4z9V8wuaIKUvGEmJIL2OhRD6drVHPclMgWTpgYRO7uHtk+PDCEgSmMDGMPKbNctvzjf/h7rq/W6GUjRu2qoFWkbTTJW5zzTNPctVspyNZa9nsZ///08TM//PkDIQS+ffuaV1dXnK827O8fxJc2F1rrOF8tiTlydXnB1dU1337/HcumZtO01P/6kSl23Ewd6CUpnJ6t070sT1mO3ljQkM0skSlF0jDmvD/pAwtWW9RTq3h6ZhVdUAxZETFkZUFbwfZyxBTD+zevWawa2kpztqrRGu4/j5Q8EvPIod+yWa54/eqC999cY53mp48/0Y17ks5UzoEuaJWpnMYZ2ZErlCxKp3dLwfm6ofYNl68W3B+O9ENi199RVYKJb8fIlAohgdWJzkbebSd+F4Q1TEnkMEjm4LhjjD1RQ7Ncstqc4dcLPhT4sO/4g/uRi1XFq8uapV2Tj5bbn25Qy3O8rzi7OMcpQzh0jMcDoba0qxYxiY/kFHnc7fjh40fu9ke8s5zHA2Xck018ulc5Z/70xz9SUiBGSdkwxnB2tqGua0IMqKdpSKY7HDgOA0OMZFUklLaxpCSbPPFMBZUTpR8IKUrm376jGwakpc9UJXO36+hLoU+JV9cbLs7X/M1v3vPwuOfj51u88yzaht98+/0cbVVmQbxIoo7HXuzSJsmJZGaAngBobUWGFnKWbrloPA0qFMpUeLzrmXaBcTtRQqGuKy7fXPIPf/eON69a3r/pqKs7muoeXSZyUEydYz+K6SJTJgWBhw7bwnEX2d1P2NhQ+yVvLt+zWl9SNyuWfk0ujpQNcZy47Gu+VQtczhxToGDpTKJziZ2ZiCUTciLF/ITX/prjV5dLGfNlxnFkHEeGcWSchHIbU55HRwatPTFNL6JaBPQVoXFiHHqGriNNI6Y4MBmUQYZ/llGNHLuJKWSGkMEkdJjYbnd0+y3DYU+7biklM8SImiKuKFyBZAvenDL6FGYeFxVdyNLEkv5axXiyGHrBsHvBFPwLs2lmwsEolkclKIiKqq0xxtK0hphl5x9Swcx6JO8rnLVUVYWf46hKiWhlcNYJ/VhlpjjS9QeO416+y9PffXpYy3ONflJuzOO00290faKMgZJhtw0c95E4KdEvTjDGxBQTxyGy303kkKhNwSmDVRo9L76JjDPSyVllybEQp0gcAymAyuL9ZxU4VfBGSSp57Tl2gWFMfLntGAdLmCLOGlLUuHOLUwajZZN0Ei1KrUuCyZeC4a/P9l+yRFPJhHEixYnt4ZExjAzTQGUN4rk4MIw1VeVwyswNvoySUBpXLDnP7hOzbCUEsdU6dD1d17M/HJjGwH61YFE3eGMZhlHOUWnatmFDpljNZr1mvVyyXCxYO8OZM3gzMaoBysAhWYbov8JjzGxFZZAYmhM4KMbUJ5LMLMRRglEKg28ugkbNnXNhSpDQWF8/CSgo8o6u1wvevn2FryxaJ3ISjV9de7RV5JIIKVBUFr1u5XBeUzVe3H0Q1xClZaOgckKlSElRNC4v3y8lJgNNZWjaJa6q0U7yPV2zwDcNsXuUwuA0i9WCxcUG2yxRWiZCMQyUotDpZFOXCDmTnYXa4yrPcVIMIQJRCHWpMGTNMI7cDoE6y3isaRrKlJiGge6wY2g0da3IcURZTymRbuh52O4YY5rT0aHEiVQMUD29auM4ytg8JVKSNcJag68cxurn5zMX1ssGVGE8HEklU7RGmxMcALURMpy3jt29lSKRAzkIVGCifJZRQBLsvaBw1lBXToKHZ4KLc47Ke5qmnuUQGaeeN/tTKOQyMUVR+ao5ReTpvaLIOSaxPytFo4ohD5HUZ8ZjIA6JkqBZLlhv1rz57ltef/Oaq0tP1XzGKE8pVrTiQ6FXkWgSuhRKSMTRECfNw71iPBb6zrKpV3i3pq7PqKs1jV/gbE3Ms4VjjJiYWGbDKlp0LIxaExVECybOk5Hy3O3+2uPXY4LTxEjgcDiwPwoJpus6UhEtVOVrtDZYY8lZQjRjjBirxC6nZPqu5/bmhrube1IMhGWLntmAWM+UFH0o3D8ciBhcu2YkkPPEhw8/i+jZWqp2SdaW2/2BtigpMHPemLOaZbIkncVuqmiSMaLBUonZWfH5PdVqJslonqOEYI5Df0npmw/JJBy7QHcIPD70qGyweDabFevVmqurV3T9kWkaGeM0/0Uy7qqqmvPzy6cgUqW0kBCaCmUzoQTGY8fj7pY+HDiJ5J/OtzwXPqV4VpO/fJBL4cOHLWraMnTQHRLDkBn30HWZfVc4hB1jzBzHSBgGyJlJaVrnqJ1l0dYUnUUvhDB1narQ+QhRxm5qBvRRCUvBUihTBzazbjw5WMKg+fFPe4yButbst4FXVy1/87sNq4WnqQxOg9IZrRPo/NSZyxjhZREsc4q5mP7mWRisS6Q77umOB27uPxNiIJbAq/MzmqrmcDzSHiu00awqjzNaksud7HydrlC6IqdCmBL3D1tubm74+fMXlLEs1meiTyxiE3bsOqzWwv7V8lnXr6/Z5Ave5MxmtWbVtlyuFlwSeZ0HtN4zqj3neiQGeBjNV8Qm5wy6WNFkqfTE+DxhNCDEIfNi42C0AWMoWlO0ZpoCxxA4BIX2nsX6gtx3xBhgDrX93e++5T/9p/9IiAOfPv7IcX+HUorL6ws+3T8QSxT8iYR2GgzYynL1+ooh6dn+UHwa26aRYNisSOOA9vVsCnl6JBXL1Zq2sbSrFe1yRewO+MazfvOW9uyaH3b/hDOWdrnkH/7DH7i62PD66lzy82LkcHhE6Q5tKpq6QeXMEEaGyhCWFW3bsg0jwzRxrC0VCbfd8+UwsTtM/NxFfpvhlXVsmobDdsfuuOfm85+xecvCXFG0oqhMbFseH3f89OGGIRYqJXmCKY2UlMH4pxctFw1FC9SSxHRbafDOUqwE2TJvJN++uqLe7tjvDuyPI1PKeF9xYvKen9csW8f6bM0/3T3QTRNDP6CT5Ak6ZKNjrELHLIXeGLzTeKuIcSTFQMmZtq5omwrvJDS5FIOvK/H2VHqOUzIitAea+vmapABGphjE3zM7yAoTNeNDZjxMHLcjBvnMd99+x+v37/iH//kfeX1Z0bjA8W5PTEumErAUpj4wdQGns2ysQ2HqM+Og+PKpYPDUtmW9es96saauLvBuhTWndzIxxchxuyftOjaTYRgMespkJ/pa52RKSRZdaXkx3v41x68ugofjkZRHjseOcRxmE1fpq5TW5JyIMUhsUJR2PqWEdaKB68eB7W7H5y+37B730lkmhXGSs2V8YCqaIWr640DICj0pMP38XimadsHZeoVbrURToxTZOKYM0xiplKEyljxJ/5bImKSonIHWkEwR55jwjMWcfv76O3smwTz7T4EqmhzhsA3sHga6Q2KzfMuiXrBeShCutYYhjBy7I8PQk0ueXUc0db0UR455R1uKFOpCJMRCTD1THDhOO8bpQMojxZyKsoIyR94UeI4YeuJsvTh/xdWrJb4U4lDYbyOHfaDrB7p+ErA8yNzfmYJ2CZULtQKrAion4pAJKTPNBChfOw6HhQD8FF5frZmGyDREypQxZLzOpOHIlHsWTc2ZyyzWhlpVhJSJMXHz6chhP7A/HFguHG1tab2QV5YLS7NQWFvQRhaVMX99Z0IU66evkz2KxPkYSSEwSqGV5fz8isvLc5arNdZXoI0UrRPmqRy5wDgJxnk4dPz84TOfbj5xc/MZZS3X16/423fvOFtviLO2zVmLnj05UYqoBYdzRWO1odIGEzPq0GGmA6574LLswQ5c2MiuD7iZHXg6pDArdJZui1JQs9OPGDjbJzs5YyQ1HGsJuRBzphsiU1IEHIvzc7StUa5iM6ezl3Dk8vqab37zO1avXkMJ+Nbz+HjLNI6UpChYlPKcnV0zDJH/8l/+H3Lsubw849X715xdvaJdrjHFcvfxZ27uf6R1Bmsh9gec+bqwo5Bg6Eow2KpuqBdrVtdveP+733P17huKq+iPR8a+4/3bay4uLticb4jG8th13N5+YLE8Z7W+pnIVIQR2ux0/33zhTx8+828/fCZhqWzFcr2gbhzGnXO2Cbh+5Kg97cVKGLHGMVUeXVn23ZHtDoa1p2QHx55dH/nzjz/wr3/8gZaBhTcSputbsq7ILwyCizIoNeeqZAHQYtFPnIFnUp2Izpum4vxsTVYH1DCRU2K1aNgsW85NpMk9zQS1jgSniB1CtkI/uf5IhmjGWc3ZuqXyFsjsd3u6rpubDoPSRUILwikwN5PmlHlnQbeeurLzmrp8cbsEUyOL2TxZk2Nh3PaMnbjSrM4WLBYLVusVf/i733N59ZrLq1fUjcKawOry96Txkjg+cL/7ActAZSZar6icZdOsyGoklwnj9tS2YtUuUUaTnqIRFBqLNhUqTpQc2N89sL1/5LB9hDnXMpqJYBJBZ7RTuCCuYjkrvPd/pYr99eNXF8FpmtBpfGLTpSTzZjV7PQrYmsklzAUwPzmtKK0YxpFj37M7HDn2k4gw1YidgxpNToSsGZNmGCdi1hglO/qUhb1lrMfXC0yzFHsfLeSKnLNoEnUikAiz0Jos0TEk0N6QrQL1i07wFz//xfEUeSOsvZQKx31g6App0qyXF6yWKzbrDUrJTZzSwBQnpiCjJmM0qsjCqVDzdzdjX1o6vZAiYzkyhp5+3BPzJOdeBLD+KhPw9FN5psQIjX7+LQWrlafRFSWANxPewf2DYYyaKQnyFgtUKEpl0AUaJaFGRklcrA6CQ+UkmOHYgfUisG2XFYPR9AqKSeii8Ephjdhk1S7QeAPKYgyMITEEkU/EENnvhcE2jpZQKWJyIph3jlwUtmiUES3Vy0Osn4S6fXL3l/2BRlmDsV5YLWSqeQRtrOUUEqtmZ+ucM+M4McbMfj9wd/vIbnvk5083fPx8w82XL1xdX6JnrOdsvSKnRH84PtkCPqchiU2cPtlmKY3JBTWO6MMBvbunzQNGTdSqsEmRfUy8CLGZSUFiaq4KoE4Y4YkMpZ/YsCcLwYxmKplQ4BAK2jhsW1EvVmjrwVYYLW4yZUwsV2tWFxcYX6GVY6UvGNMEpmPYD+Q5BNn7ln2/5fHLPR9WNSFEzq6u8L5muVxDlM6073ph6xlI04DNS16wtoTwozXWaLw1WOuwvmaxPmd9ccnl9TXvjx27hzt2t4lVW7NY1FSLBUUrxjDx8OWGnDR1vZbRdxSx/Xa352G75/HxQLtYsGga2rbG155iQDPi0Cwv19TLGl85ihL2tascQwh0w0g3jCjVSeJLctzf3fPlyx2vl4Y0BVSOwrrVhlOCFwVCSPJ7KT4RvPI8mf6apiZrpLWWRdswTJJi0vWjjKcXNbXqcHlCjRlDwigh66DkmTq9+nnmA2gjOkCthe157NJMTBRDEUomhJFpCpxCAdRcBEFJnJx1KKVFEvRXnsM8s4WEQDOz7A1UC0+zaVidL1leLmk37cxlEKKaay4Yi+SnjtM9sVgwAa893nicv2IaDyh9xNmEryrqZoE+mYc8YQQn20g5l/7QMRw6wjSSmxqcJplCNoWshRmti8bogkb9/zJE+fXj0BgJMcwLkGA32pwCMY2c8IynjMPIFMLMyJNF5+b+lruHR+66I9MYKSlzGDOu0minKDYzRDiOhT4arG84b1u29w8M/UBb1YQgQu+z9oy6rWkrz2G/Zeh7tvsj/aEnpsLZxRo7p5eH6Uh2BWJNWVts81d5JL845tXtNCadmaJhSvTHiS+fHjlbX/P63TW///3vMEaR8kQ/9oQwMkw9qYygMyWUmeqfiUEeylQCSokgW+ksehwVOcR7YplIZZh9AYFsoOgnXaOacUGVniNeRO/z9U0/2zjOao/VMFwqxsFwcXXB4zay3Sce9hBTJmVxnLdG0VQnBK4QxsI0JYYhMoyJmApTmN1qNBinmWrL1IrMwSjwGpaNoakUF2tNVRuc17MWCrHw+oppKw+61lJYnbfY2ssI3Wu0ypTcwP55UXk57z9pBZNRYC1W15xfXBDGgaE7MBw79taw2x+e5CUbZ0g5EsLE//3nD3y5f+CHP31gvxuIsbBeX7DrA10sXL56w+vXb7g8Pxdj4Zy4u7nj46dP3D88kDQoa8SxZs5WA/DWUGPQDzvS7S3h8wfYDGJNHhTN5GlIxPLyWcxolYUZzzz+NPIzaIp2gt9oy4Smj4nbhwPGe4q2PI4j333/nr/92z/w+eNHnPO8fvue+y+fiWPP5ZvvuH71iqpd8/n2AecMbeupVpdgWnb3n+gOif3jCKYijJCS5e5Lh+LAu28npj4x9YFPHz7z8w8/8unjz/z27QqtDYd+i1+uUPpZgiTsT4XKmkpJ92qriut372jaBq0K3333jn1reKCjjHtiZzHnF3hVUCnwx3/9N0IoLDfnlLIhhgO725/ZPTxw2B2IKbFZN3z37QWvz1bEGHl42PL58ycU8M37bzg/29AuGsaYyYsG8hkh9zz0mR8eE29qQ60NXz5/4tPHj/z88TPV9ZqLtkEPR2y1IBtLmDHBnDM/ffhICSOOTNPWklVpmNMLXqwwshfHGMv5+ZqmXTCMgZ9+/ixjS+/IpWIYOw53N4RjocRM0kZ8XmcdYSiFiQJWxqJGZYauY+w7CmKYP44RowvjUHh4QIheJaP1Xt41rambVjZqTow6nP+aQKKNQQVNyZkxTuQMqtU0rcdi8JVFVZmRgQ+ff+TQHSgp8/bVG2zbYExLTlumPlCZisZUnNeOy82G5aLl8vqa29svbPUdJkBTVZyt1jTW4YxFaU9WioiETKeUiWNmf3uk2/coDYMa6BSkWmOMpdZKGN1FpB9jHBntyK89fnURFEcPPWu4FLlorDlptSSWJiPsnJADIYtOJuXMGCL7gzCdUs7zDF4TNbhlhWkrVGtIKeOniIoSJNszMcSJPgSxoMIQEiyurilGUdUVWEuxhjFntocDh34gKGEe5rEnpQFdaZbNGVXTYJt/b4dwenBPD/GpEEoRylnGtGM/0TSe1bpmdVbRjfcUEjEN5DLNO6aCthlbCjHluYxmSg4SV5PFskqodBExeQvEMpI5MUHnc3nRiT51ei8IMvC8U/z6fuV5pJiwLoFKnF846rZic6G46EXvl0vGGiGJOHciBEkzFWNhmgoxQIowDiIRSLmQ0MQotldq3l16o1g0lrrSbJZgPRjLjHHl2QNVpAFCClLzMFPGikqLJ6HWCm31fNW/0J2Vk0/j8/2SYphRJbMwCqwmewPDkUOa+K//HGnamqapeLw8pyAbkv/3zz/yuNtx++WBGBXWelabFakEIPLq6pLlcsEwDMRJkVKkOx7p+55pCthFg/UVvq2pa2H2GuPIoTANAXVzz/C459gFvrQWlOKQCj9ozRenaF88isJYLbNT1bxJKIi+C3HyCCExhoFgKqZSGIqmdTVVU/Hu4oLFsqXrDlQOJPknUHmNxVDSxOdPn9hPgS8PXzg7X/P73/+WqjLkOMffxEiYJrHw0obz9RmrRcNqseJ8fQY5cTw88vHDn/jy5QPb7S27VcC5mliWxDQRX5q9Fuj6CacKXT8xTSO5RNrlOcpoQpJkDFt52rNztMqoEvC6UGl5NvspsN3vuL39zPt376l8zebskvfffIupFmwu3nF1fcb11Rmbswu2j1uOx577uweMMXz7nZ1tAyFGOYd+HOmHjto7ih9YXmRMreZEd4PVhq4PHIdIHzVN4Suj6UJhGAamoSNNgaof0cZS7CwEd4669iKKj5EpiKPMOE7zhCxReUOYAvePB6ItqJjJI4yjGGEXlExrFKDFWSacJkFKICfpPCXGLKZ5ohSC2O/luUMt5Uk/p5SW9HozRyA5CUJ+fgih8o4aR1YapQvZgXJgs8EUwaUpko8a+kRfBu65o384YpXCTAdM2uHSjnfnio2vuF4teHW2xvuK2B+ZjnvG4xGDwWo3//DinmW85GUqoAjENkyTXLv1tKuWvZ4oKjGGiSFEJops1ofIOMn0MJe/oqv6d45fXQSVmd3n55GMKcyArXRJ0u1AzIkpRWKOeFcJcy8kDl3HME7SB5jZ28KCWje4syV6U1NyJIcJn5xgWfcjY46MIZBSIMTMGAJnux3aWRbrhdDIjWFMiV0nzC6sI04T/X4HBPzCwWWDvag4GTx9fXF/7RfmwlMMOWlSgOEYmMZIu6hYrisWa8thuCXlkZgGjJ2/H1NhnGzzs5YiaFSGEmYsUFGISCJEIJdAZiKpOan7iZwjRVCVvxiwPBXC8uJcXx5aJ8nYUhFjM8oU1s6xyJ5UPFNwnEJVtY4onVH65EXJU+FPUVGyIUeJS+m7kWmMDHNqQp6Zd1bLyGvROLzXLNqCNhGlE4WJnCMxncZ7BmOqOY9QEYp50gTGLEVP6XkD8Av3oyd7MTXfJyVaVHJC50SjCs6CqzR3h57ueODT/QPWWSpn2b95TVHixv8vP/7IoRO/TOdqFgvLcr0klwAELs7PaOqaY3eci4QQw/p+YAoBp5cY56maBb42c9qJJYSeNI6kmwd8d6QeEmVqmIzmUy78yRoOTvEbXlzGTIQxp83NSRqB7MqnkuiGyLYbyT6TtCVg0L6iXix59eqanCaOxx3LSopgyQPOFnRWhGng88efGT5+5l/+9C+8e/eG5aLm+vpc8gdzmd0JRGNWO8dysWDZONaLBZvVipIDx/2Bm88/8HD/ma57ZH/MVPUC4ytSmr16T/eKwrGfMCT2R884DqQU8Y2wVscY0AqKcVTLFeSBkuTPWyK5RMYQ2e333N7eSFyRr1mfX/Hum452fc5vfmtYLBvaheDxh0NP349sH3dY58jKiE9nLoQwMk4yAu33Pc5FStXzakhUi4KyFusc3nqGMXIcIl1QVGWm9z9fGNM4cjj2HLse140yEbOG9WpN22ra1swkrnlhDoFxOm2CM5U3DH3gcBhIbYVOGTUJPh1jJhfFpASaMEYIOCmXJ+g/poQYmp0KoDwyISRiVIQ57QPkFdJaWMUxJjGt0EbiwazntOlXKCpvEHsDDVbGoYSCiSKFKkFJVupUf6oCtgAAIABJREFUCF2inwbu+lvG40AeJ9TxgatF4noJy4tXnFeW62XD1WqF1oqPtzcM+x3D4UBrHBaDxWJOxVDZOTS6SBFMgWEaJabAe6r1ChWP5CTpRF2MDCkRp8w4Cakn6ihQ0q88fnURtM5jdcFMEVsEoC0xzd71hZAL/TSx3e+JOaGsxjaerus4HI/0x540BbxSxEqDt/jzJav3V9SXG1hXtKawURkmw3QMuJ92HLuOIQwQoNhMJPDh0595PDywPTxS1w2lFPopUjUNF8YShsDx0PF4v8c0SgBuX9E0Dcu2QYAsuS41p7iL1dzp12c8pijCoBj7yP6xR6FoXMXqokb5iUP4It2cLSibKVqo6UUVsHkeEWpheWXRvhWlKUYRdSSpRCojRc0JEUYoyTxhgKcF5WUlOGGCs7ff6b+/ZqZjfMRWQfaTpjyJSsmgS0FXEpWrCKDEyuwpHxGEhDP/UMVSMiyaSFqnuYMUfPTExtKzIN65Ucgbbv68ucvNOWJSBGVQyqBVpGRJtTA4YoaQIAd58eJMd45fpxU/dYFqboVLKYQYCcNRGIoqctFWvL68wFQDuzFw3PfiyoPm492eXLJku2WHsTMz0xhygd1hT4gBYwyfbr5w/7jFGInsMjPW14+RKcL9hxu0c7imwc5ShaA06dCjjgNXjwcWMbIqhvCQmaxh51q2tkZVK77nORDJOY1FoxPPOGeWwOYAfLp9YD8IqWZ15Vgsa75//ZrKK5wphO1HrE40JnO+2KBNYBjv2d3f0x87Cbi2FcVULPJIfLzhn//P/w3+9nds1itWTc0//sff8O27C/73/+u/sF7UfPPmDdu7L6Rpy8ef/gm/BGUjq+WeVePR370jxAmlDK5ytMuGUE5+tTKuvtvueHzome7/jB2OoBU3+pamOkjXZMzT5rfOexaVpQ+Rb9YVThWOU6S/37LrR/7u736mrmvwS4pv0HVm3SyovBPfTaCqKpbLBZvVklTgZnvkMYB1IyZ23O8HPj5OtMbjEkw3d1jfcLcd+Zvv33CxWfPt2ys+/vgj3UGKb7Wsac1zsZCXrFCYc/6mgQIYpSkxEMYOVSb6YeBwOHBKddCzQTwoFgVS6ujGwKeHHeQkTi+z8URGkWuPaisuLs7wWuMpxMctY5iIYZq7ODmPPLP0U8gzcS6LBd4crOyNwXpPAUKc/XG1FMUXbxfkgFGZyit0seRUiCWgEigMrT8njZrQw+3nm1m6MDF2R0xJ/OH1iu/fvuZ/+M0rfvv6guWiYblcMHVCFvzTP/8r4zSSYoSlknsfA8pZ2ewXg04BTSGWxDgODHGguVoyTBOf4sSfdgfupoGdSgwlM5UCTiwWrTJ4FLb+by2Wnx8un0D1YuT8bF8sNO4wY4ZjkF2RUlrIB9NEP/TPOVYabG0xTcXiekN9tsSvanIjuYAo0E5jjSO/UgzHgaapKH1CK9nBmNqSVWJ33DPFCEUxjuK6EmNkGidiEDC3qRuaZcNys6CqZGT1l42f+mq8eIqKzwn6biSMYoFW12INZL1GmUImPskqJLpGcq0KkgquUHhjUKlAlD/PDLIXArkkErNpsmIugEV0QC8JOU/SjecR4NPfJ+/jX1yT0Rlt5pw5gWVnxYdo68xJlE5EkSinIkiZ//y8tSwgyQaFosMcGwN5BqxFWydpDBoxwVaz5EGuK6EJaJ3JJsv5KlCcxjWnUbrsJ5Q6pW+rp+t8eYh0QGQ3WmtyKUxJ9FAhRkYlLj37PhAwYBVVreewXiRdPWfRqvlaMKxi5L4ow37fkdJECpG7hy3WCGDvrMUZw7JqGGMho+nGACFjokTjFKVIGNIYUJNEAPVWccQSrSFqy6AbBlXhlf9qhH3S0loMs0BCzjVJ1z8leVJ0JThS5S2VBUfE5oRJI5VXNFZjGckpEEYhNVEizirs/MMva3xlcHEg7O8ZyghVRQ4Ra0YqH6l8wrvIZu2xTkPucNbgmsLrNw1WSerBoQtMwdB3z6/Ry0PniRJH4jjSOIN2DoykkXstHrtZKVLR2GTRSgv5ZYCkFYu2JaREiJFu6MR7dx6l51KIKWGTQDR6Zs1WVYWvKkKSKUE/JYgTuu84DlGE+bXDG3ClEGNkHCdKihLn1nqsQopSlC4uhggnDoniKWfSOYc2s4+wkvw+pbJ0vTHMxgsOrRXWmdnJRKLQqirRhCQRXLMmNs73Ha0wbU21WdOenZEBkwL1OKKMImpJPilFJjiyMX2hJ52tw5TSoKHyHu+86LWL5EF672cW5fNdyylLIkPWFCXNgfSIVopi1KSxELvIdOyodOFiYQnG4rTh6rzi6qzl4mxJWzdUvsIYz3Z3z+N2y/ZxL6xaI1645EROQRi6Rc9wzJwOwbwoaHC1Q+XIvu/Zh4ljDAQ7E9K0BqNQRtaLmCLa/ncgxrTLJVVybPdHcgnEJO15mReWMUwM40g/DLRti1KKMQSOgwiNUwxQMtqAXVRU50uuvn+DXtWo2jEZEeBqJTE6TaU5W63ZrBb0h4FxN4oNWyxMEfphYrvdsj92UBRTHxiHgWkaSZO8NNoqNlcrrt6e8+b9FbYVScIvDykkz12YxpCDALKP91sK4L1nsfH42s4C//naeVmgZKdYkMxCrQx17VBJRKLj1MtNM2VOQBYHCTkH+btLAWaT5Gdfm+cCJeOz8vQ3gfhOfjWuAawtMgozCp1O5fPkD5ilsJGQPuMU5zBjbS8wHSnGs1uJn+Z/l8/KWV6aECTTK4ZELAVdBMnTuszFUMTvWj/jmsxFtOR55ztbgum5SmqroZi/MDcoucwOOzXee8nKmyQNIsTIgUKII/0Qccs1uIrlZv00SE45o1OSH64mpYRzwnjOufDl9nHGajP77vM83pZEAe8cb69fCf6jHcchkkpED3luojWoOZlbO+J6g1UZo8TZXxUNeCgWl+uvrssYjSsGWwQ1ferIYyGoTFJgqopVvWK9WdFWHp8HbBrQJVCrgVVVsalbYtwTUmE8SpKJd0os+nxF6xxKVWhdMDaTtzfsD7DVMCnNlAq1H/BWo9jx7u0GazXJjKzXFatzx2p5hbMeZyu2neXufuRf/+lONse/yBOsmFBlpCKwOb+iqhuib1k0FbV3OC0QSggOEzVOK6z2jKGQNLx9dcnucGTfdRy7I0YbnPWUnAlTYB8iua4odYWrhaBStwthHMaEMo5hyowxEO4emDDSDTc1C69pFTgFOUyk8YjXic2yorYKq2VaEsaR0Y2YF0RK6xxVVc+s7JmBaQzeSjLD0HezlrKmqiuBDKzBOtn85Jl057Rht98TURhtGbTYPDqnWZxvuHjzmrOzM2IMdN2RMQT85CVEd978yTRG2pKTfMc6N489ZXfvrMM5R1PVczcYUQqadvXi5YI4ZcKUGFMiqSyTmmKQ/soxHTLTMTAeRuJxx/mm5n/63RsKI9YU3py3fPPunMurDU3d4JxHGcfn+3u+3Hzhy90Dm2XLsq0hJ1kHp4HineQUkmXKeLKHNArjNHVTwThyd9yxHXuOOYI1GGew3oIXX+A0KuJY0F7/5Y7s3zl+XRFUsFxtaIkc+5ExZYYQGcYgy3Mp7A4Hmd2WTMyZFCaO+4798cAQJrDSASmvWL27oL7c0FyvKF7GgwaeGEynelJiZP26YXXViG5tioQpYZTnuB/49LNm99AzDUE6GR3ROlL0yGJpuFws+Zu/f8P56zMWZ0Y0KFlSA54ube6QBHuTUNxpEBbcNAihwjpDvfTYKlNMeJqgn0an8DJlUDqVjBBf+nCgsp5q4cBa8dvQmYV3VMVynLU8Unzy05nl+al8zi58cbyYynCCcn7RMRkF7gSIm1NTJ5+lmMNPi3yQVsLoy7P5sRS501hWPl8mqbPfpSpQ1By2aTBuZqIhxsgpCl4hkSpFdsozxncy0C5AjlGMfIdCSJJzqLQQE6ytnp+HF1/BafcrOYyCRS7blhQkg68yFo0QCYqpQFk575Pvo2zj0crijETVONdId1jKbLh+IvIIfhNTZkyJMQbc9kDdtDjnsdVSDOK1jDHV6RlSULSid/nEY8BlI5uDrCW09Bcu91ZlDAGVRzRpHqnPchQD3789ky5TVdR1xNlMY9T8/2XsDBZNU8TUC7yGVaNZuoQqhVqDN6LlDHmaseeEcnrOjTQ0rkYZx9vv/x5nHLX1HI9BWKy+prm8ZH29oG2EIKdQbBbgGhFjd4+Rx+3wdE1aKf727RpVWhaV4friHF9VQkCZmY9WCf6r1CwPOXVO8/d2Zg1ocbIZh47BGlyrWDY1Ja9EOO4MzopuuKk8l5fnfNksGUPk1dmSul2itOXfpgNDiLQl0lrDqml5c34mqqmS+eMf/4XHhwecyqwby7qpZjvDGmufO3elFE3TYI0mxZqSZbQo8WhSeHI6TTRmdjTMG74MSrFeLcntgnxWOL9YYpTCO8eHzw8M48SmVbx++4aLy0sojpQzy7ZhuWiYQmDoR1IWqVAqQmwqpeCdAzQhq1m6YHBGJifeOZyV7XKapWXOVV+t85XxBB2IMdMP8pm1cZTJUibDcdsTu5E8DPzNN1f85t2G/+V//I4vtz8zTT2rpcZXUGzBeAngRikeH/fcPWyF35FPq5silTJDEwlf8pOBPkajtMU1njZl6v2eyiQMEwq57jLIO6ezkJq0UtjKEMopj/TXHb8uVBdw3lNpOy8AB7QRpueUBF/ph0FAyZwJOUJWdL240ueShVjjNLq11JsF9brFNI4sxgvioj9LEYoWPAST5YtUBlcp7GSwU8IpjzKw7heMvSRaJJ3QJmOchLO2C8fmcsHlqyWriwZbIQBy/MtrO+F/0tkUwhQFnM6Fqva4ylDVhqITRQm7k9m6qpwKUjnNJJ+rU6EQc8Cg8cZivZHU9ZSw2qBRTHPjn4oAwU/95NOi/5L2ctIFqq/+mYVAX1/Y3GGdukvpAAtqNnc8neVzv3kyzz19wKkgnmyIeIZMQRYsRCNqlEQEgSHnRE4Qc6HoTNEF48T4RQnRFpgzwEImTpKBljLCmnVaWJJKOknRuX19nAyXY4wyCoF5NOXRVuzIcoGiLaCfOtcT0eSrNJA5iV2fimDOc4GQxSWnLGPvIjvTMWZ0lOtS2s4EJTXPQ0HPjVCmzLtZ+aGURhf5rpR6GvY8HaJilA3KXKopSnBltGazEA/FjMLYjDEZr5HUDiWRXtqIhMI6iR/KaU6ZL4lKF5zOouNEg9XoylGvLbYy2IXFVjXG1axWl2gMKimmtBN/1Fo0ur5eYGu5QkrGmAKt4eKiJnUdmmdqulJwfbZCqYx3luVygXfuSdoDSozBZzMJpU4GBLOJu1I4O483vRf50SAW+OM0MYUkZKQkT4hYzinapqZpG9Q04TWs2xpf1XxZtNihZ5omFpVn0TQsVytSDsQ48nDfk1OkdoZF7Wlqj7MOYz3aOF6iZ946VMnkeSyplcJXleiXTx7Es0YamOVj6em6KlvJy1Dg8rzCaHDWEotlHCfOl3BxsWG1bul6kWVkKykrMSUqPxJjIOU0e43KNMYaK4S1mEipyPexXFNXXuQY6aROFVa1sf6JQAOycdFqTjOJgs/nqIgjqDGTxggpYlXi3esN715tWC8chy2QxEvWWAW6oOzz9U9jYBzETvP0OqK0MHdnnXmZNbKn703N3bPzBu8U3ha8Eha4JGLIe6OzcDCUZR6X6/8eOkGxJqudYrXecOyFCv2wOzKMPfuuY3fYiV+mQrRgBQ6Hw6xTUeAdflnTXq9ZvbnErRqSLrNfoozbZW86d0Vaeo/EJB4C3qGtwlcGpki7tnzfvkGrwsO94svHPd5ntDIsl2suLte8/faai2/W2NaRTMBpZCVWz6u5OlUIFGGUTvOwH6iriuW6pVlUoDMJ6TaFUamfxg/zzHC2uJrr4fyS51KYSJQ0kqfM+WJFiZm4O2IAi2ZFTciRkGepRMkkCnH2BzzhqxT91JIVxDJLayNCcKMx2n/VHYbjxJgleVs+S85VK8mbO2VmyvxRPzW1T4Jg8lcO/3K1Tym4T7IPhSZnQ04KgiVPhRQ1KSRmTB5fqzk1XgnlG7E/G/rANGamAUDCeo2M97FFXjaVvn5E9Yw1H48HdrvdU/dWlMK4iqJmzOW0OZivC56awa9+7fTvT8Gh8/etVMGo2SvypE2c7+8UZXNzynQoFIpJInMoCSP7N8rsYFEQM4iXw+34i9J+MgM381gNhViX6Yyl4P38guSCdmLTKT870Ba0pMY3qzXt+Tk5BVT5go4duiS8SZgqY6rE2eaKerVmefWK9nKJqw2uSriqxtoa1IY0alIH/fSJaQpUraWp/CxgPqIQITkp4zScLTSdn9jb4XlMrzXf/ea3QCKFAatkkZX6p+enbH4yi3ryREUprNWY+f5Z52kXS46HRw67e36aIv/06ZFtH6iqhnVTsWo8F8tGfGvbiovrS47HjuNhx/nZOYu64tvv3nF4fGB/f8f3b1/RLldUbUs/9EQFfnHGKjuc9qTtPVXd4qsKVzcYX3O6gwpF5Ry6JAIZ66tZLy0MejUXRK2lC/Rujg+LcV7gNdZVc3pF4erCw//H3Zv8SLJl6X2/cweb3GPKyHxjva6qrhbYbIAaCAgQoAEiNECAtvqD+i8iuCG00UoAd4I2IiUC3ezq7hremJkx+GBmd9LiXDP3iMx8L6vZBEXZg+eL8PDB7Nq990zf+b6iDe/t5oaS4bKH1mutM8VZGaUQNpuNfsY1HI8Hxmni/mHHPM/s99r/Ok0zb+/uyTnS9T3/5X/zT7i6vmIz9Hzz27/QjEnb0Pc91jY8cTNFEaeSwQRDng3TlMiHQJnAzLBt4Pqq57/4x3/C5WB4/cM3PN7dEcNMe3ONN6JZIK8KKCUm8hgpY8SJPoc1WN9hG49pGhKK4E1JnTRvGqzvCAm8j1x0hsvOctMI/rFQ5kwMmp8uksjHgGkMvvcYb7HPxbh/5PhoUd3pODIGIYdU9wqnaKPjgZAicwiEnIhASurJOiBboViwL7YMN5dcffGK4fIS03my08/WDNsiUcTqJZYzOQxTm0epUhk4Zaz/7Ksrrm4bXnxajYAR+r6hHzo2lwPSObLImoOX5xETOhHjHJmnSAyZtm/xrcO2kExYi7WSKxqrLGAVPf8TeKNuvsWwiN4KVQU5RnaHSetzwWg/SVFWf2ssrXGEYinVA0rotfquV8NTUh0jjfqMOIy1dG2HdRbEM+3NmlrdPxww404bT9cyX8GYrJJHi5JP7dmEKm1Uli1M1hSLithqvcfU+2TrrStZSEkfcxTmoA2ueV5qgZl5VJFcXRRJ64FJmKZInJV5R6HyinKLFsKs9GFjZR5aDqlIn1LyyhyjUZdGeEuaTs9feGpqWC3hu/8ujoLORWVDquniNdo+6c6pR3vKSytdVqJIXMfPJsMiMCS1VrZEhs8Rv2IbBFWkUGWIgjO51skFcXqPpAjOZZ1zNoHRiDubxDw/cHyzI7UB3ziG64beNzRO2AweNzjc4Oguv8B1VzQXn+C7XhHSZsI4p9Y1D5q5aWG7nUjTgdZNdMz4mJFyj2RtHn3YCcd95O2bPW+/GXl7nyjllGJb1MhKUZcp58I0z+ymmXEOHGNEaiRlKnGCdZretMZgEI6TtjZ8eT0glNoqMvP2MGHGyP1uT2st3/UtXevZ9C1fvxk5HifCNLEv37B5u8drswDtMPD6fs/jlBmmKlVkHC9ffMreP7AznvDpZ1jf0A0bjbCfVSU2wTEVR/aJblCdRWctZ928OKcMNdboIBRn17/ZmpakaD04L20OVRPwMBasOJy3NL7y1obIPGsqyxijjEfjzOPuyDQvkZ+h61o++eQFbeMZNgNXlwONF0qe8c5BKTjrqiNtnkS4sWiNkQRdbglJGI9CIx2+swxt4pObjp99suFq6yEd+f777wmTypY5W5mxRDQbJVkR5jUCrouFRQpNEaxmZR4zopGcrdkTKwaL4J1j07a82l5ycRe4L1n3tawBw5xnxFniDHmA9PHZ0I8zggWYxpHJiVIJFSqARRuAU9Z+jpAqFVRSEUa71KMaQ3u9ob+9YvPJrSJ9rJBl4aTPZ1R7C/hDASrLYeySrqtxY1HxyJtm4DI3XM3tSiTsm0Ybl42lSKl1JJ1871RL60CmpFI6KRX6TY9rBWmUhUCprLTew1K/Wx5LsFH3Q6mb7zJu6NZISYljCthk8NGoUFeuOnHeYa1HigNR6q9iLOIc/bCl1P5CBY5YjHVIlRPquw7vPKUYfn/I6yZ93I+Y6UAWo2S4oLBpMkYK3pZKRqJGsCArO/0pX1HraCXrhM6qK7aIHJRUFEIdhVSElCwxQ0paYF/ANjGgRtCVlbqpJAghE0OB4FRN3gmUZWOPpJIYs3kSwmnT70mHLpdCKoom0zmzGJc1WfyuIVzv3lMjqO+qpq0oaGddvGuqe4n+8/rJK82ZnFBtBsGUk3OnMU8FRBh5FpaCGI+UtKJjMUp44Iym+GxtiRHAm6TtNqRaTigkq3R0Y5zpU4ezA8PllstNR981XFxt8JtWQWmbLzH+CppXUKPdXA4rLUFBofXiVeanmESbjzQ5YMJMnh5UDHWOvPlBuLsf+d3vXzM9Fg6jo5RzxOHpOksxKi00Rd48Hng4juwrtaBZxkwq9ZyviF4sIQRCDLwcOlVPD0URwONMYUZywRRU47FtuNh0vH6YmKZAHI88hDc09we+uL1iMIZNP3C/O8IY2cTMzbZn27dcXV0iGGLMXN7eYoyj6dUhKGfXIUAXHYhj8qmqwnicPe28c0hYZxQ9ug7FaX0qG5A+jBjy0uaQEimo89c5oXUG74rqH1bauFIK1tiKyFdu35ylopg1hdi2novtwLAZ2PYthUJO88pqpNy3766MVJ1ekuCyhWSYInR9T+89VzLz5asr/uTnLxgaYf84cXf3ltZZvFMAkF2oNKH2L9adUGR9wJK2XJDbhoX60dQxYemTFDWunW94MWy48I/0JnJYHPekbVJiCyVZxBly4FRL+onjo/UEdw+PiClM40wcZ4iqGl9JxSghkmMkFwgmkUVw1mA6j7touf3FZ/irgbKFiUmTayWRsqYYnXdqsM5qTc7Zs1OoKCgS3ivQIcWgnrdNWKcRp1IMjUSUS26t2dSbYEx5fmnr0Q8tYPCtA6Mk10969nK11DWaWQAi+uFLuFU0pVDto8WqIKUY7OwoAcZDoTGdQph9i2tbXNMyHgIGR2t7cA4xFpv9Ci4RU3DO0fqG1oApiTwfsXFWjs3i14s67keMnVaRSdDksrYzZEJRg1SsqXIqC/NKebLgS8nrRJJS6vcAWRdLzoUUlz5rUaHSLJC9IkuzIYVSU9/AklSSQgxZPeAMRhLJGtUto9TewkIgrpcFFcKdT7y0xpi1XzHnrITH9fM1jlB5osXclVxO6ep1H6qpuGWe1HEQ1PlRII0u0CysY7TUkhUkJCCWZDOz0fSqS41GOZzqsFIEokXiaW4L4KxRGatSDZARvFWHxUjBWI1MjRVMjpQSiXHE+RbTNgw3F7TbgfZqw/aza9qho99uFcDhGkyzAfMSMS/AXFFoobQqXC0JgyczIkwU8y3FHCjuEXv5QDpM7B8mjncqCvzD7+54/d0bvv/2B/71b77lzcOeb18/8Isv/piLq1eUyxcn5/BsF8pkYkrspwhi6NuGy+1Q212F4zgxzoE3j3v2s+oM5gJkzUD84uW1oko3Lam8ZpoWZXodxd14ADlgXtdafSlIsex2I/Y4c3u14eWLS756ecm/+Je/5s3rB/a/+YZfff4JX7x8wZeffsHjcWYSx+bmE6x1NN1Awuisfbp14JuG6+uhysjV2mytUeccKTkyj5mm0TroonG3zLOy0DGGSAxBxbWjkoIgsB/V6epaS/aO2WfGBQ1KwXuLtT1/vNmupZG21TYI48zKDzof79eMjiZ4imI1csLYRrNN9bCiCyNPhd2bA5YNX372Cz65ueSid/SH3/Fnf/yS/+QffsVvfvtr3nz/LbvHR7afvGJ7ccFmu6FpGwWpOAfFEELV+IuVHNsYnLNr9HdaHMsWqnuUKYI3ysLjxHDRtvzqk1t+d5xxbUN6OxPmTAqJEjWq9Q6iFNwzoOCPHX8AgXZgMln7/WoElFIixUiMcVUvNqLM5wvxq3iH7Rvs4DGtodiKuluantf0FeskE1Np2szZxKsvybko+qDohhCTbmzIsnku0Vml3arIsyVEN+lpimxBTRpjoL5mAY+sjC01uitPVsGZoVhijtUOFpW8EaNsCEUNspOGLLVOaFqyWKZiCKEgOXKcFfCAzaoibuumh6GIrfqMpm6aGo1ITJVNYklA1wW2tCCcy0MB5CWiXYyBktQuQU45u7T1euvf8ura1XlaqOrcJ69/DZhEx68sQU8+pQA1Mj9F50udRO9V/ZwsZ1HU+aif4OAi2lckpqjh4jQX1FDVS16MYP3OotNjbe8BUJFYU5lq6sVWoVod71rzsUKKiRQ1c1BKjYuXMavyOhmFuCwJjmVYTKlsP883VEk4iVjieq4pRYopChyzBlv5Vb3zWNNg7Fap2/qO4eaaZruhudjSXl5hay3LNb2ygpgBuAYua6QGhYkiEzBBuQeOUI6k9AMhPDCNb7l7feTwEHjzzci4e2TcH3n92x94fLvj4e2O3909sBsj94fM8abQ9M8urDoUlIVeDzZdQ9eonI93tUYvMHcNxymQU2GKe47zXI2ZRsfWGRpvEefx1iiyVJZSea7VEiVtCFENZE4qb9R6p3qEcYagPYt945ir/FDftZXg/8Dd454XfbvuCSuQ+vyyNoJ4i7O1t61Q97XqbmXNFGQp5KQ195RUegujde1SU+UqQqa9e0uK31iVY5pCVrJskVrH1xkVQlwdwWbVcq1MK2iWKSXNLKRYo8yllWL5Ge1NPKfQtsbgjGG2Bd91dM0Fn33xORuXGGzi027g1fXAzabn1+PEfJx90uNvAAAgAElEQVQoWWgalbpqfIO3mhr2Tpvt55zIKVbpOM5Snspis9SCpQDVKZban2hFKfystTROEbu32477EGgeRsSpimRvvaKwLUxGEdMfEwXCH2AEpzDhRQd3SfMpL15gmufaqKmADbeEw1IwjccNHbb3SGMoNukEqSkQ9dNlTW0tN8IsQrz1OSMqtaHbmepsGWeIkerNauoyo4tNPWg5QbErhFqMezI4i0yTse5Muq6yoJTliVMN8JQKXf63+KEqiKtjX3QSmIaGRoVns+BNS7IQbCDbjmwsc9IG/5QDKQrOan7be+31s7ZlSUCmkoixEOaAcQVPwqdEKpGQBcrJo9NmYtHaI0VTCwiUU3pRDbetxq/UZ5Y2jWraK4goF52cGv0t31J3IWtqirycjF5Nh+SyfJIuOaoDpAZVNz8lYlfkG2vUVOnTyinFpPdraWPQhmBTFFilpO75bNOtQJ5qjHRMymron6BdC5qvNcpfU/LyGTWFg8LNvXf0ndPSwJjUA0XZc0x1lBTdXFZzr25LHa2K1DXl2aYqBS+BRmakjKRSBZmZMbaSk/sW1zd0G6soy7Zjc3FFd31Buxloby4xbYdpBzDXIA3ZeIzrQbxGfnkLZYOikAOYIyJ3UHbAt0g+UPKR+fiW/e4N92+/47e/zbz+IfJXf7nn29/8hrvXb7j//T1lVqT1LI5sPMkNTLMlhKcyyAuva8lJPXUrvLjotLndyNoXqvdTOEwBg7AbZ47jTK5gXiuFrrH0rcd4T+ctrbPEVFuyclHi8saz6Tse9gfmOTMG2PY9l0NLCYFw3BN2iavOIWYA53h5c8nN5Za39w98//ot3/5wx/D5K5XnQcsZTxS9BMq1SikZo9kSVbNJNRKUagR17seaZYkxQbM0s5w5ZHUbTkmNYMoZY4WQMoTIRWm1hmbUqc8RpmmuXLWn9pBCXrU2lz1tBSCt2RNLzlllwbLl5KbpPG+sY3IO10B/fcnV9hU//9XPCW9/jxnv+KNPLvnixZYXQ0vYT0z7GYOlbwe2wwWtb2lcQ2NV7DeWSE6RlAI5ByX/robWSX1gsEWQSlBCKpDKagAb6/DWY5pC74RPLzv2aab9NijDTNU6RTKGRK69uR97fLQRPIYAJTDPgTlVUuzpyC5MHFMgO1OBFes80c1KC01YUSVk17iapjKK4KmItxRzjeSomljqLZTFYzF29ZRSluol2Np2kIgEIjNRIlJ0M3OiVFeCQFLjKtkQnwV0J1WC8+fPaxrL48Q9dgJf1CZosbS+xTlP3w1YcZhiyNPJ4OyOkZgyQayqLKfIOO4xVo1+zpZEqg3cC+I0M01aE6RGTNY5jqJg9BIsOdW6mDmd8ZyUgAdDVVzO6lEJNUqtgAsxWsVaDeFy93SDEiMsciynUJ31mrROVZ2VlNVZKCykO9WLPkWIZUkQVk958fCNNSvjB+TaU1dIpeF8Pp+nQk8hZ9b7bB25pte1z6+cbt3zQ4ukT38vysm4GNFstXVCxCDjRM6JvjP0naNrBkJ4YI469vpxBSkZSbXhvUxn80RPJNdzi5z16hSQdE+JO3LaY7sG31jctmFzMdBfdFzdXtIMnn7T0m9vcE2PGy4xjUe8xbQWTKOPPABVlDQ3gNavRB4Q+x2UhNIzz5TwhjDd8/abv+HNd2+4++GRv/nL17x9vef7bx747vs90zQzp4nWjmx85s/+0SdIcaQo/Ku/iTyMwj41uOYK3148AVos6fdSo2v1E1Kt6Qr5rE5ksVigc5bbbUvn4ObqkjBPxGlkayOdBKz1/OmXL/ny5kI5NmNkTonOG1rv2HYNbx9HHo8Tv399T+u1V87kwOEY+V2ZuH35Ja+s4xfzzMsu04Z7vnkY8QZevXhB02gkWFDjds7bWYBpVhSs7lEKHNNIR+93StWZE2EcR3VqQlT91Orge+fw3rF/jEzTxN3bB3b7o7ZmtYo4dc7ivZK3j8eROVGV7FOVmCscx+Ma4S19ys46iFEzHcvfSsFaz5LKiSGdFvNyXdlgmo6rFz2vtn/Ktr2mzZE8PuDmB/7o819xuR2Y5sDbxx27MeC7S9rukrbd4sTj6n8GFVOfU2YMgSkkNm2L8w1t09H5poqhexqrxs5ZlfkSsTpPTcZ4aIaOFAxEw+2wZTcmNgXmUDhOmeMRCAUJibzN9DcFfv7k8j54fLQRDCkhWclMxxAV2TXP2qMCijCTGsoas25PC3uIt5bWerzz1fBpaY26GMzSumB4ghSKaGrhySPV3d0YLIueYQJsTYtlbPUuTJZ1DzqlzZ7sP2vwcDp06ypL3e9Z9LeAO/TKHMZ4rG3oGq2/tH5Ack2hSlq/I5Yq/uqUp7JUTbylT8oms9YUlpRFSkmJiVPQdhLnMdYxJz3pGICakjwPmtSh0uvQHkRZe3AMp14stbRLOrR6hMK6MamnW1YXQO+pvu+8yF2W9GrKZ7prNTW5GM3lu0pR52FJhVbKI7M0yi7RdQGbDcxPZ/KSLly/t7rpS9TGcu/qwj/d02croshqB6Wmu5daEgBZWy+EQog6F+bZ0NY0mLPafqJsN/XM1hSubjSnYkd5et7n6FBRBY/OG0Qa2u2gtF5XA5urDf1Fz/b2gqZraHpPs7nGeuUf1UKUgKtrggxlBE5OFMVSsJT8SIk7wjQTYyBMI9Pj9xx3d3z963/D62/e8vaHR37/1zv2D4ndXWI+TIgErofItrdcDA3/0ecvCBEOx8xffn3EBpT2SjzIsy3lFJifSw2e/fHstzp2XWO53vT0refF5ZZ5tEy24MmYrK0z29bjK6BiTomYM43VvsLeq/PbN35VSikl41NSCjjnaNtWkZJG21pSiFASjXdcWG0qX4zgalzOzjxFJd1w1q+UkDmnOic56V3CKjAeYsREbaa3xpC8J2dHioZpmlSZYlYKNRFlpUE4qVDMMyGxGj+KtqRp9Heq+YlILQvIOq7LeJclBVLnek5PiaZjgowa6L5r6ZwhHh5wZaL3hcvtgHOOMUQO08ycEq5plDzCNxjjMMYhUuX1MAqaywoKM8Zjncd6j6skB95bvHVY6zBORdaNNVX6VfcE2zhFRJfCpu257Ga2TcNuSpSciMEo7/AE+EII/w4iwanKrDzs9jzs9xymkfv9njEGpbYRq/Yia/uEYiAKToS2CJdNr+wLvWd8PBLGmXE/1ou0DNutpsWMqdC/utnOiSkEHo77VTKo5IwRgzMNbdtjRVUenBi8cSigQbca4ol4S2q6cjl0UxJ080hrjaq+Wn9aqMzOUgbKsGJRAE9L2w5sN5f03RYRS4oK+MhJjW6ujbLZABa898RxhBjZbC4whlpI9kCNgItyGs7zzDyPhDCDKFrPNy3HKVWSgqj1EatQ6uXICElq02sdt1SjZyofptRspqbnMohZc/bWnmp0S1qTfGKPWc5zaUrNOSMxkpIgOZNDjSyrKsWCAFtykDadNUR7U0WSz87JWEV+Zq/MbksQutQNC6twc4pPF/J6f59HgO9Bwz0xjM+ixpxPT6QxMc8Qw56byy3boadtWyKpEskvKoCceVVLzvVpSrfilZ+c1uWrLTfDlqvrjuH6gqbvaG42uL7BtQ1maLVNwlvwW434bMfStL5S6+UJ5K6eSY9IUw2gZTo+Mh52fP/bRx7e7Pn+d/d8/be/4+33P/BX//r/4vi4Zx5nvNlwubnm5fUn/IPPeq62wlefQeOgbyy//Nkf8d2bA3/z9T2NnREDBU/Mhmd7qjpnRblhc7XXyxpbtmPN3GhNTIxwte252m6gztf56Jh8oS2JMo+Mc6CxFu9UrFakWdxWTUsWeHk5cHspfPnqhtf3O3b7I1IG+qFju93gvVfFjAJ3+6MqO2wutbZkHXGeTzmg50XcUqWyjEDTEIPWu3I+gbb0ZVW8eRyV3eWMUk5BHLpucy7a9F7X+2KcetlgrautETO7vUp4FVQ5felnnWvbWlmyOctaqevrSeaGCcSsaVuNuFgDhcNU83FtYJ4eMIcDh9ev+dm14fObnpvrS+YQuXvc8XZ/ZAyBV1db+qGl71t822CbRjlijadIJmVLyo6CxzUbfDfQ9D3NoCWGxmlE6KyCe0zvMY2DzqFtQ+Cj0qpRLC8uLJGGr26/Z4pvedg/YKxhznD0kEqgX0kBfvr4aCOYKhIwFhjDzGEcOY4jMWnbr62agsbJ6vJZan0gZcocyXOgNEIJEZMKvW/qxmsUaZoKtpSK6Vtg4RaxhdS2mrFaNlWU5dxbNUbOtoqek6IN17WYuqZn6waYo+E1cmYLa8R2tlGdgz5PG5iyj+i5NphVDaHBSYfkluNeVRusqYjIVJjHioxKSiNEjazapsc00FrVOYtxXrUZrVVY+EJAnpISPreNpVBUMHZW/taQCqZVDa51VwGM0XaLmNJaz7BVlFWsR2t2hVJqaqq+cSnci10rF3Vh1w1t9REqq0PtgTJicOJqqkvrl2KMinJyAictx0IQLqLBjN4elZBZItbybO+pb6w4i9qPmMtqDJ++rDx5bo34ZP2Y5ZWnQVuuuGhq89nXKvI5RPaHUVlUjNd3Zm3P0DHKp+/XFcFSYz7fnJ5wbBrDV//wT7m5MHS9wQ0tpnGYocV4rwz7baN9ssaCaVE32UIKkDM5RkoOlDyT4p6YEuOUeXiY2e9nvvvunrdvj9zdjXz/m0emXWB6CJQ5IiXwySef4T+PeJu5udmw6bXVoCfQmcKLzgEZZwylpleta1QM2Quejn4Y6IaB8czZWFiYYswVBbzMmZpVqLV7BeEuEb1ZnZOYZlKOOv6T8mpOuVCaAaynlLy2ICxgq1KUfHvJmG9aT2OVON05h0MoMVU1eIf4DZRCLlZ1S1Mkp2rIlsRFOZs7KGNNyZl5TphqJJd7r716PfM8cTgcNEW/1gj1X9X9s2R36sXNuZyyaMZomScEdrukn3U81qjPPCkJKCGJtoTltNIPVAJtWY2vcAIIUjNCIu7kjBjh8uaa1+Nr/vr4e+5NoCkWLwfeFs/vQ8vd3wpziOwOR/6VvyOazHfW83r3e7bljv6xpfWOpnH03zXMU+D+fsf/k39g3+y5yJl/s5u44C2bQ4OzFmccTjQAaoyjbTzOKSI2lqyPaYKYMSFxiInHeeIv5I7f9zveXo8csiMm0YhQMnn7fOP48PHRfYKxaM9OMo4kjiSWbJ2ikqrMDKLRh0LHC1Ii1ncY05JmUXJTB3kSSjIaPVY+yhIra/mSDhONECU5TAZPR67Ai0VzTYrBZO2ZE6ohhdqjaPHGrxvhAvA4b6LURegxZqHjOF3w6rCWZUHWTxaDlUab1cVijcdKA6iGIRTE6+a/qCykyvDeVCRcSgnvVIm8cRXqk7PWEWvNb20E1zyupgR8A1WhPoRQ0yKG8p77HblASqjMJstleagPxcvkagS1DqYGqeqOlcoDVrR3KZeKsjyDNFsWhozq9bL0fRaiKP9lNicjuPT4PT/Wka+GZnFyAKayeXLD2rYhxkTJEGJlz08LYnRJ9NSPO7/38PT7PxQlng/m+Ws1LYCIreQEQts2FFMIiwaiaPZhjQRYcMOGM5uHCHRdc/Y1gr96ibuwFJ8JjVVKn+IhVWNXKsjBVJaDUqBEiEEbrMNESYGSJ0I8MIfAfj/x+vWOh/sDf/O33/HD65nXb2befL0nTQWZhG3XMnSWF5+9YhgKwyB88klP1wpdW5DjhEuFUrzOAzE8RsehtMwmYruONhv6NOB7ZQA5u50rkCpVSawsYNaUXEUKil7uksFbo5eiNdqUobA4dFU/z+jILelqEVnT8LmIlmGEta3IWUtK9QuKqQQQUHCVXk9WY6Rz6ZRWX4Wfz45CBZ7logLQQs0MqQPZdkNN/83aA2yWOVGnXgzKBmU0EjNSwLjKwoTuLTVjE5OOn7ZUOB2fM0YULQdYRByIChUXUVo/I1LTtHWmGVudg8UJPXP+ES6Ha/blyHx8zUM+4MTQeWXbClmY3vzAHFX54q71ejHeEedAwxE/Ttor6Az+QUghcjyOfGeFuW04WuExRNrDSBPTCSVakSJOtA5qrCEbBUPGUiBE7QfNahSnlHiwltA3iGQaPK4YmmjJxdF3w1Pf9kcOKc9d6LPjz//8z88GqKwbxIlP8j1vPd+F1rFWmZMnO1A5f8G7H8HplU9+euq3f/gq3/+p+u58bu/KQnz1Y+/9sU9eNtZnF15P9oMBx+Lr1jzG8+9/3015un+XJ8/qZ52+wKBMN0+bfM9TI09O871X9m9zfPi63z1+7FyUZPe0scaYTtf08c7e3/txuue8fx385Puf9sE2dSPl9LHnX/T83Wc/L2NxvlLKOqeW6GiN5itQ6fkaNdXwyNlj+fh3ZryYdf2HtHxPTaGLPJ3Lspza+1fYsyXx7nG2NtZuz3Ial49bqx++P3/YnTuPcNN7nq2/nWz42Vh/4Jvetye+87f6ivPMxvnzy++nwX73s99zpsszYk7zMBFXBPYSjS82U/9/ur85nyLMd9Oup7NTf3vJjJw5ovJOlX49K4GnNeT3YjkWR+Z8R9SRV+q1075xbsueHx+dDn3CxbgMyMe+GZ6lts7e+Z658f7pIj/x9z/sM9ZnxHzgL3+XQ975VT7wp2cn8R7T+lPf8uNnnJ90/5wd/z4Mx9/jd54bjv8/HXP8+EL+H3Ys80THbcFDPT/K+f+frNUPvbjOQvshp7X+/r41/6HXv/c7z9f9ux7cx02tv0/3Tg9jfnoergHXf0CHxWE/cnO35qdfsx5/yGvr8ZP97n+oEfrA8dFGMD87oZVPsxpHXTzy72WP/UMPcza655P0ncV87ml8IFSUv8ssf89nLZ10nHk277z+PV+1vo+TQQdOaY+PiMT+v3xo9HLyut9tn3/24uU494rPorbnr/27zNclCvyxzf9jzvFHp847f/v4M12dpHdSCvKBn/Xz1xF5Frm8883l+XtObxN4Uvv9UL/We6Pnjwzw/233mL9D4A6capnAyv7yH/rxvFa/tHbosayfZ16HvG/f+zHX6vnff+QGnGfFPpBp/Kk9d6nfmvfwRL/v+LiaYIH/8zeG3WzANlzlbxl4ywv7O7bbW7abz/gufspd7Ph66mtXgYHQMNtEsAnDQjNlzsbgFEZr4/a5C1rzMGtdB22fkJobR8EsmgevzIM1oloYHko5GetCwJpM5yL/2Yvf4kRz719czXReaau6tlEGi5LZ7fY8PDzyt3/7LQ+Pe364eySXgrGG7XbAW2WmuL5VjbS27QjjSEkJclLUpBRCUiq5XKSCPRQJlqL2szW+4fLyku12y3ff/MBxHDkcDoSi9ZCHw7iKZl5fDjqOOSGivISXl1fcvdV+tc9+9Z9WQlrLn/63/wvt9rq2YTybRGcp3Kd/O09Tnf18/rw5//3DIcNyrTp/nv7/nZ8/OKcz0/6R//t/+6fkqKi4+/KKVBTwtDRJp5gqSKbSwlXwxfIVRqDrHN4rVdM8RQ7HmXEKlYA7r2CMUqgyTVpvFGFFpGr/lWG76dj2Ha9ur6AU5hA4jvpZml7SimZG07dzTCCGGBP7/REBGlv4k9txBXVINyrjCRbnRSMsH5RcwiSKWwteeq2lEHOkSAKTsG1S0VZraV2HE09Lj80tJntMbJDcIKXBcE2Rlkyvyi0lM8Ujx/mOKT6wn78m54mcZuZJiCFzPIbag5kpleAhzIH7xz3HOfJ41OeGduC/+8f/lfbBmcj//PN/wcYfEJvI0ZAjHB8L85QIs3LHxjkTpkKYTSVit6uMdKpgqVxgTIWYCyGhrSl1Xen/l4b2hcFIncNMPulh5tPrMqxgr6U+qKVxpf1Lay1T16EfLvnlf/7f6/yZA//8n/+vHA7HOv3rvZGn81zO/jlfKUvX0MKVer6SFhzeqhJfYGEuXfpRF2fDVjm28xW4bKGaMs4oF21BEXlGMQQUIGId3Nzc8D/+D//TSsj9T//Z/844KmFB8iOuFy4/2RCPkTwlpj1sb19y/dmnNDebyrCUaX2HtY6SZcVDjMe5kttDsR6MUnFYJkyZIDyge5ClhEyaI8eHR+bdkTBO7O535JApMSsourcMtw3bn21oLjylKZQpwBQpSWlSIobjPnDZXvJP/uy//qgg5aMjwcMs7CaLeE8TI7YcCfYtwRgmWsZ0yTF5drFVI1UEEzxzToScEXIloV4TwvrvAphYjOByF+V8arAivTCLEdSbqiJSynywfFZWPaYFtMdiBJ1JRJlZacIE+qGhb0XbDIyqGSgl1pHj8YHD8YEQA02jHJtKWt3QOkvjLZvB0zSWphEVSk1VWd1q3ukwRqY5MYZISAoyCTGe0GJxZm4sszNM444wTaQ0k+viC/OBaVbdsM6pM0GKlKx9RJbC7uGBkJax02vuLm7oL281inpP5Hrq8Tu7G+e/f+hnc244/10bwVSlpE4viMUTi1V2DWzd6CyRRCqGECs0ugKsjIB3hiyeYhxFtFo6p8ycCjEr8GKZEKVAiqKk4KsR1GsVqSw+TeWXtZ1efzZEMqnKATnjlXQ5KYnDVHu7QigcZ0XtPZE7E8W7WCt4EXxrsA6kNeAKxWrjPqB0WFFFW+dkyBLJJiNNRFzWh1fovIjDZofJFhdFmYtKJcoqLYWehCjvqimE0hLwEK22NYhuuirtVaqIa6SkmRBm5jCxn/YcpsDDcWau8/R8zb68OXLRHdSQRyHPMElhHqNKl42RaDJzzszZEjHMxTFnJSEIRcc1VvKEkAomFkxSYpFcKgitnNiJEDm18lREUi4gixRZXuqYVGUL3StSKpSswtoxlRXYFmN+GseUwuPjA4f9YZ3npc6PhUN4aZw3YlgUgovUEykFJ7ay5izOlxpncZpfjjnV1hFts0q5EFNmjrn26wptoxRlZslLFpgrmjzGjPNgHbRtZYYpwjxTcRAR64S+79bryrnw+vU9x8OIEUPpJtwkRB8I+0AcE+MuMYqQNy2bbcF6o8QfpeBLg3WenHTOBAm1/aMQU6xzKSNlxJQR0qMSd9iWNAXiNLPfPRJ2B+bjxOP9I2lOlFi42nSqO7gbkV0kWI9znjxPpONMHIOKBXjHfpwUqPmRx0cbQRGraKPah0cO5PzI8T4wP+44WMdkMtl9RrSqLuF8QTGEtR7xvHFaav9YvYGruwZnCeHF9wFkSYdps7dqx9XeM7SdoqACr+pVyfp9BQsmVj2709FdXzP0iqI67vfM08RhP/L16zu++fo7Xj/u2Wy2/PEvv6LrBmW4N4au9TSNo2n1uowkbN5ggUYs3uvQ/vD6NT+8fWS3f+BhNxJCZJ6nKiNiMBKZAzzuVBcMAdc0OFAPKASO88QUJqap1R68OTDNARHDfjdzOBwQ63k3xXSWX1rGYYFWr6nC81RHWX8/QbCf/swZsu/HM5MfBk89MYBPzu+n81QFISYY54jzKgfjGouNhijCPE26qVNwzmOd0mv13tF4y1zqnDMG5x2SDESItd1CNzDtbdNHqSkanYcpQehaUsqV6V75XZVVJGOcpakisOM0MsdMTBOHw5EUIzkmOu9o/OLV6TV76/Be6Jyj6Qy2EVzvwWdwyoNYCtqrOemGPcWZKR8IZVKDnQWyIaREspDkgDfK1+lpsDlhS8KVgGSH5IgiGrXNwIjF0FBST4lQkuKuVXorkMtYN/Ow9p5y3lKzNO7XQwwMnw/0AwRJSABm6IdMOU6UCabDTDhmpl0mHA0pFmKEcS7KNJJUMidkgw+VCcmU2jRORXQuHLacemKrUYyY1UCmwmpM1ugQQzJSW3kKWXTHMXlx9pT43T9xCqFtPaWoiO75FK9c9ByPx1Xo2brac2ulNuUXuqbVe+4cBqmo10zbd4gxHOdJVUVIkBJzyBzHyON+VpQrhqvLS7abjq5TQ5Zz4c2bt0zTxOEwsd02DIPnxcvtuljv76aKrk5YL1xdbZ9sAXmOpHmmuII3irKddjNhnwhj5Lh7xG2FdhR6G3GNxyE0PtK6lu32ShmsUqH1mRgTcVYZuTkmEpmUjsQyIzJhmg7feNI8k5mAGWOztt20ynBVXObLn73C2sIh3uOOAefg5uaCqTJq3T8E8A574fGuVNvzcccfYAQr/NfoBt9bx5X3quqbJvZ5h89HfIEoDckYDDNSGfbzKeAHzvdlOeV5c6kQ8PzOq1Z4Un1PqSlQ7e9bdPzqjVw8QnNKt564HO2Tz9xcX3Fx0WlawnvMOGG6ga+GLS+/+Bnffv9aufG6DRYlBEgxchwnjtMRsw8YyThbsFkZchzCOE3Mc+Bxv+ftw4HXb3fs99qo2taer5xhnkZ2h1FTbY2jUEiHozaNOsvNzRWbTUcIAYeBWChtppmmmvqDi4stvu2eRXZl3WKfpzyfR33nf/upY13wtZnylDZ936QTRM4N4RIhnKLEJx72+u/T158fISZC1DYRSGSrpAlZBHGObhjIJa/otpWH1irkPE0zIQT1mGtqcQ5Bew0re9GJ3FsJ3p2rmQcWeqxMmANzmLF10zdGW3RyhsNx5DiqBt44zkzjRMkZ7z3DxVZTljYjslvHoGk83qsAa9sbfAPNRpBGFA1vTDVyQvYQU2EMM1P0hDwRiqeYrOTyVls5DB4vG5oy0JkbtUpFMCXpBlRLCoWCkRFrRpyZaUwhGANJSw3FgHPa+5lJ5LiSPukas2Z9KKqi3r0Mh7+dsM1MMZDmRA6Fea98kiVn4mwJkzAfYDxCiJlxDowhM8dCyEoOH0tmzkLMCtVPecEp5No/rGteFTyVnSlX47kYwVyjy+W9ZZnH+eyRgErevpRTDOeaoTXT0ntl+elbctI9yDpdcSkrV2bbNgzDhqat7CidZ//wSAyBoesZ+p6+7SEVYkpMc+Dy5hrrLPvjkZImSp7JMXA8BmBit1PpqbZpudxuub6+YLvdspBGTOMIpTCOE03r6PqGq+sNzmn7VdOoBuF+f0RMfgdoJi7he2g6R7P1UJXsU04ghVefvWD7yehsEwoAACAASURBVCVXNxuV9MpKCrI7Bg4Ydo+7qmxfyFnw1tN3PZ+9uqVpWm5ub2s/aOL+4XvG8cjhsEfmiMTE7AzZ6z7bXw6kaSbPMykfkJJp5omLuWGYHK8meLMLjA9H4lQQJ7SNpzGBpvl4AN3Hy+/WxnWqtlPjPJvWQSjkknBpwsmMrfUVxFMkItloGrQmroossNr6z2rchHMkx+ojl1O31eLprdGfLLn/qoRQtG+oLDNVZFUCoLL9nwMrBPBdix86hdTOGlIb19BtNlzlgjSdqgVEKCGTQ9IevdrEa2TGmoJzwKyrSzIcxyNTCMRcOM6BKUR2+0Mdm0rYbQ0hJiXQTpmLqwtKyYzzhPeJpnj8dsDUWmUJ2oNXpKzsFEih61qabnia/15DrDNHgvcYnSXKO0uT/tTxPgPGuTGUp89r4PnUoJ1HmaeTWd6/pE/f/e6csy6yFIiASQL+RN/mnN7jXHvBFu7OxYkrNR2mHLS1vray6y8sMXosSibadFzq96dal9b+xBVGXtn9Y0qUqvm26L3lnJVQ3XuGvq8GOj65LmONqqk7g/OC84JvDLYxGK/Op2RNaSaqcLVYjAGXLCaXmhqNGp2Kw9Ez5A0tG9qyrc5B7YFT6XBMUYotKxNWZqwJOFMoRlSfMzvt47OCSRbJVSWjLIottZyxqoCcGcFSOPwwY33AiNbSQsx1Q9ciRYpCnIXpKIwThAjHkBlDJqRCSFINVyFhyEVUj3qdMmVd8xrZlWqg85nxO0WCWhM8q6OdO2grzV1Z63ZS/zFPJrXQNJbiYbPtiEF7QX3jyDkSU+DRqbrDMHQ0rcc3jnbwxHlEJNO0lr5vudgM5Fl5RY0Im77Deo8YUYmyWMhR77dWWHQHc9bQeE/XNgx9V1mpIo1zTJWM3lo1zNbpfLLW0rUNJauRfELdVw/rlLi66ZXnuQiEKSmLlLO8uLlic7Vlu+mVJSlrBiGjeqTzNJOippKdbfGt53J7ycXFJZthw6effoZ1SiDxzbeO+/s3lBCYrCEaFVSmFawtNE1DaixphL41tCI413LZtgy+4ZqGqTh2SZl3cA22cSpJ9gdAVz++RcI6VXoXTz9sufJXvOwviPuRMQe6uGOSHZ3ZM8oVyXiypMrsUk2YFAq1RrVQES1ZIaRSHy6btjZyP0UDVs5L7Nn/bTVwQsbWCFA9NzGo9iAKdtBmTJ5u0s4gXt/fbgdc25JCpmkamsbTX16wf9xz9/1bDnc75uOBb377W1IOFJJ6hFXm44dvv2ccJ8Yp0F9s6TYDn//sj2iuZ/rLHb/7+v/gcDhg377lF7/4OReXF/jGV3qwgneeGCM5KFVcGI/EaVROTal0bTUlFvOM956Xty+5vr6g7Qf2Ys5iqKf0TScNsyXz+DT1CR9nAE8TAhay3idPPvmSP+R4milYnnsnnVqgxEgc98R5TyHTDluc96rkXdXRi3Ucx6zpLiwRhxNXFSs0FX0cT9G0pq9FaanUi6LvVJvNGllrPt539F1L3zXqkFTlCEQJDu7uH9dzbttG2fL7lu12g3N6jnd3D4Q4UdpT8G2sQZxgvGAbi20F34qmep3F2QYpFsmWaJVw3tiMDzpnmtQQzcgsI660dHngRX7FbXpFn3ry0XJk5mhmpiERTWZyUbk4JdGUA8UfMWaklEQIMFnPOFbWESwheyQHUlaQRirVANX0srFGGaPqkTP81a8f2JojrW/Yj8o1+RgSFofgSGNRZZQAYUlhZojFkYqCYUJS4NNKPl2V3nXsKluPUJ1hlE0ol1UvcmV9eTa7TkdmQRqsTrjRlGgWIZuniFcRxRI4By9uL9k9REoxbLdbxmnPOB5w3jBsWl7cXilgyqoSiTG6NlNW0d2ub5lzUJm0AilEzQoZW6tDlZEmR8I8IkSVaDMgJZFjJIVQxWUT1ih/qtYBI/Ocef1agXRGDFYumcbA8TiRc6Dx3boViEA3OFyBfqsMXaliHK62F1z0PX/2J39Ms22xg+f73R228bx4+RLfDhQsD/c7pTHMcH17y+3Ll3z1819yMQzK7lU0g5RSYrw/wJiJ7cheHpgFfOO5fXFL321UEmv/hrR/zT/6/JabvuV2MyjNI0Iyjr5c0MgV3cYSWkgD7O+DGtqP3IM+3ggaQzGObBa6pJbGO7AWKwnDjOGAlwds+UTThguZrlkm4IJUrN6+lFWiCBZbuGzMZzUZUBTSGrFUf0hqOvQMMbrQVFlr8E7oe6W2CrPKsfTino5NJdeVylRgjaU4tGZnLW3fagpsDkiIpHlino+M04EYAykNtL6h+A7EYyw4b9lc3HB5c83Ny0+Ic2AzbPjii895fHxknia6vqNpPKV0yhYfIvMU1FiLxTsV6pRc8M7hvKVkiCEyz4GYArYSiMjiG7xrL1YDWDS3UwnENTo+lQU/3mI9bb5/Hq0V3v+b3v13ypDvA8zI6Z3lvdtWIefIdHzk8PgDOQW68QLvHc5VFQkxJHGErITOTTOwPw5qlLyORd9164m4Wr/NubDfH2qUDX3rq0o2GKvN7cPQ0XhXBU0rCjRXRfCkwBFr1HANfYetgCvnG1LKPO4O7I8TkgJcPU2xqc6a1Pup7B7GaN3TGqdGUNwq9eRNAbEYExEpRCxOGi7Kp2zKlhfpBZtjxk0zx4e91s8A+2oLXSG3gSQRkYgwg8wYoyhXK7qactT1lrLDiseKx4gHIkjQdKgRrFGpH3M+lwocRkVoOhcZZ43uDkqshCqkFGLOxEoNCMo4lbLyesaaol4Q4AVNhS64h5OaSE2HChRjFxJXTf/WxJGOWy2PlDrvSq4b82lOP5/XckpOrIdZBI6N6lmy+JSVhWmJxIyBOQQtHQTBGId3DTmrJuA0zap2YxQ4mFMhxaJMXGJx1jN0A0Y8KVlgxxzyabzR7xQBawRnBe8tbefxjcE5jdBSUSOZ48g0BcbjjLGKiF2vyVr+6Je/ZJpHMpHdfiSSsF3Hy5trbi62bDZbxFjyLDB3CB6JHZc3N7Rdx2X3gjjP5Ji4enXL9vKKzg/cv7kjzhM5zjjbYDCkcaJEFSjXPU91H683Gy43V/SNpbkU2mj5B9cd27ZjM1zSXL3A9xdsbr/g8i/+Cv/rv+b+/jfgMk1jOdQ1+bHO+McZQalG0KohNNbjfENjHbnCdIUZyxHPA6YEJEM2DqSK5y7TSwTVA6QiPSvwpXrf70/cwRoFillz/6tBLGZdJEvqy1qhbSybrsEITJJoxNOZpy0Dpco9CUreKkWQGj2WUmhaVQcnJtI4Mo17Yp6ZpiPTNKpkSTJYMta2NK1HXObi6oarFy+5vLmlhMCm7/j8888Yhp7Hxwe6rsV7BbMYUd6//e5QU3iGxrUYA2EeccbS1igxUUhhJpdEKe6Efl3bS04R33INcJZpBlZy1DUnfT4e56t/+as8MX7nygxP059n732e5fzQdzwxhOXMOC+G8NkhkHNkng/s7l8T5yNx3qsRtFadk2KI4ih2g5gW21zQHQbatuPqcoN3Xg1ipWdquqYi6lT8M8ZITpmu9ZUgvNA2mtK6urrUVgYRLLnC63NF3GlE4ZzVLELf0TiHd5aYCjFOPO4OHA4TVp4S/BpOPp4id42C0cQqn6xUZv7iWBntTAFjMcXr95aGQuYFX7Hlgpt4iTt+Sz4c2N+9IUbLnD1uc62GqwPkQJGMlRmRgJWgrUB1mgQLJQtBDFYc1niMODXGlUbwBKyRJw3UBThOVcXERB2DBNOs6MuUC4c5k0omUlTSUcCh6NeclxGtRM/r2l+mwjkN3lIKUUOo6NiybjmmyEnmK7MC8BYxcGqkv9CwPbmI90xi3YoKYuv31PmbS9LykNc9SESRkaqEptGYc54UImFOTOOMb9pKym+UdN8UnFVaSWMcm2GDtQ0F5cccx5l5TtXpqIZcVIXFWYN3lq7z+GapUwo5FmIoTMeReY5MY6Bp7RP2LGsMX371FfvDnsPhgRjuCSTEG15c33B7fUXbdBq5j4k8eTINZbYMfsPFZktuLfNxJM4T24sr2m4DSfj+2+847B7IcaJvBrxvmcOBMM/kCjKzxlYprJabzcDV4Liywgtr+XmT8K6Frmf7s5+xefk5X/7qPya7Dbsp8S93v8FIpjEWSlnR9x9zfHxN0DpK9hSjDxGHs049VOMRJiyPtPyALyNzKeSiHmMxGVuWjhapaN2CkGoUd9pslzRbWX4XbfoWa9VTFI0QqW0SRfShEg3aJuEdbDcNL64HrgaPlUKaHBd9Q+8i5htzIqxMBRIYp1GkNpmrDlYuqrwsncfaAWRGmsQXv/yMcX9NmiLb4QpnVUqpaVX8MpdC03U03pP2e+KsRvP2xTXbTc883/Jw/8j92wceHh7x3uO841ABFIJwOBwpOTGPBz799Ja2uVJUoTEMjaffbtRoG8fuOCNBxQUWu0T1dDUa1KfN+YpeIcTCh+p1p+P9SaR36Mueve155Ld+yrMfntjERXF7NY75/A24xtINLVc3l/zw9cTj41v2uzu8E4yF4/GRnJXVn2aD8QPN5jOO3RW22fD6bcfN1RWf3b7gV7/8irb1ZAr7/cg4TbQSVAU7ZXznWQgILi+2NG1D613d8KGtUj0xRYahVXDD7RWLRJP3XlsjUub13QP7w8ibux0FQ988rTFZaXBi1Lgaq2lYadTwYLHiUVlkBavoORScy2STSYCnxZeBy/nnyOR5+12k7BrC0fP195m3Y+RhnvDpNf3LhivT4IeEtRFjA9YFcokUkzA1Wmq8OpludniBhMXbgWhQnkvRWpVzkZyfgEPXI9XWoFO2RqO5KWZCTogRvFVYpVD7/sQoyrtQ9TbtSlGSqXO07iNa56uZhupAKMqzkEgsznEsubZFQCxQUiHHVJXN353qCyY4Sfl/aXvPHlmy887z9xwTJk2Z67rZpKQZiaIkYHbWYL//qwUWWAxmLVbQaOSaYndfUyZduOP2xXMiqy6p0ZDATgCJeysrqyoz4sR53N9gfmt+Zr3DuqIa5kWBMONUCHGklIXNtsE1QswLIQZF886ZxqvzxTLNTOOMZENXfRGBKg8rOO8JsyEshZIMrW95e99QCozTxOUy0rRQiKQY8b7F2YZNv8W3jp31xByreLat3QzlrUrU2ac1Hmvd1583jhgCbeP49sMbUkqM40QoE4+nhc+PQSvJaSHYBu8tw/HXLM/f03cd0wLn48BwGSnbDbbpabodP/zwD1gn/E//47/XdTBN/J//1//GFGetNqXFiaE3HePjZ47DI7/65Te895n3PjE8fOLzkvkYf+RtaXgTDXc/n/n08SP/8Ld/y4+//kcWFmxvmeeFpv/9Q9vv/0rjVLW9ZmRU4VNrBGMLIgFTZlwZ8BJwJSHFv9wV8oJXvI6x5eXf6yC6vmLV2bl2O+Rr4deX16iLBOtCFm0nWWtovGXXO1oLtofdpqORhenj61xPaka58gz1T1wDs1TLIO9ou4Z+03F7d8O+31JioXUbYsrMcyQl9RgLMTHPEwg0p+aKKBynkWVemOeFcZqYpplliVirLa/drscYQ9u0jMNAXBa8K+x2G3b7DSmEqp2p52iJieEykhGMb/mwLS8Bp6yPl6nqV0o0NViuc73fpTK8/NQ1sL46/iXQyu8c/2I1eK1ReVHI+d1Xvbz8613VGkPbtjR399zfvYEUODx/0QxaCnFeyDmQ0iq4m3BdR1giKQ/keUtrM0PriPNE6wzeWzbVaaD3Us9zZFrSdWO1UoWfv6qCa8urcpZSVuPiFWRjjCHMC+M8cxlGhnEmhghiSIav2CbWOL2fRKrpsVVxd6y2QYuKzRssuRi9OwSMSWgfo6UtG3q5oaMj4wmykOyG5BLR3WF76DpDs+1pOzA2aVvPZBDl8ppSsK5gcjWsqAHRSNZKT0w1q25wErAsJHJ9z2b1jbkeqzi23ksKYCuuysSVom29CgrSdSbkvDqJ6BahQVCuYxVZqzZerZ2y3hV1X1lpLfmllymvXr82QVaBDXjpQJTXa+41duH6XEUDuzUYmms3wFmDsZ4GS9M0+NbhxhWpqN55RgrBVaFos7rT5Br8LE2j+6qggXoaZ6VZWEPbOcQ2iEl1DcT6t7WD5J0m/baHORhizCzhpWPjG0ix0rOu9mbrGhR+/nZLTO3VDzHnzDRN6l5vIIyLWjuFgDQd1gqdg5u9BsRtZ2kl0zmYjCVJIswDJUcoVlHxBXLJxBRIMVKk4BtL71vebzfc9YW7znDfGfqyUKaBeRyUb4shLiNhvDBeRo6HC89PJ5YxskjClkxcMsn8/14JClhVmxBxdRBlaawhOsH6gmHGlJE2nWmZWOyCoalbhaqwy7qo1nYFgohm+rrg14VcB1zGvOoRUdsbRYNwMZTiAKduEnUhUQExxihJ+n7r2HeWXdPRdQ2SHd//51fFi3mFbLu2VzKr+PSqUG+9pW8b2G748PaNbgQ4coTD4cTl8pnLeSaEwFwtVEIIuGrg2XYdx9OJaV4YhpEQEilqwOxF8K3n/t2OTd9ze3vL50+fmcaRHBc+vH/D7e2ex4cnyrQwx8xlnBmXhZ8+P5KL4LsN777L1dKqVoFFTYjXHaPu59p0XoFw12+sAVNenpdrl5r/cjX4X/riX+6Ssq6BV6Evv3puFWovryPEq8MZR9s07O9viZcjfdfw+PQjIURW7HvJUFKmyAQmQbakcCIGR8z3nPPEU448vbklx1vu7u7YtA63bdlt37HMM/M08f0/fyTEpGstxCvCsFSFe5tLdf52WGvJ1efyqkBSIFwmno9nDqcTyxyv9k+q7Kq3nwh4owArZ9QT0xuLo8EWDSymOK0Ei8UUpTpYAwrgz4BnU/bs8x2udOr2svHaGrQ9drLctC13fUP7IUB/oWw+Iy6BqdV3NQS1pWBzqfPmhBgNVk4UjOBNR2sgmcJMqG4hkSwW98oGSQAv4A1K8TBKwrdSmJeModBadU5va2WcixCTIa5JQj1BarZQ6npZCVdSlV/WezW/rLdSGfAlXztQq/tMKUX5Z6KiFiWX6g5fE8dX624Fr39trSUY5zANuM7T9h4jEVKh6RucazEenFP8xDwlYgQjDV0rGCmUEGhcT+N6jFFqlGsd/cbjG69JlGhn5HQ84RtLu2npOkvbtfS9YRwWYgikHCuVVDmqxlu2d57LuDDPiXiM2ral0GEpJeNHV5MLc+38OGv4H/78m8pyWZHWhZjC9TXzGK5Jnu90FZcclchuDM71DENgvAS+jJHTuPBwGgl3W00YvIfqqtK1HcYIuSRu25677Y5ffvuBP/7Q8XZn2KdnlqcTl8Mzl2EgGE+3dbg0wXTk9Hjg4cuBj59OLLPaZ1GEMBTCf1V49NWe8vu+UGgR6RDxZFoyLc5saHyk6wOtc6QkZAKtGVjMgJcN+VqlrdirVdRo3YVfjIpAe+j5mnlpIJQVMHCdHxpyUai4qipUXGj9RSvUPYRI4wzbznG3M6pAMf/WLMY5xFmF+1YptZe2YdG2UIxMl4HjwwPLOCpxNmVyCYg4vDfsti05LvUmE6JTdZxu03B7e8v9m7eMkwbHT5+/cDqeWZbAdttzd3fD7d2e3c6pyaQvvL3fE282lJJoGkssUdUkrCUbyzBODOPMNC/a0a2+YOsxx4VSPc/gRfbrej2rge5aOv6uw8TLvOW1993rupKaOfPq2r58b9WT/VoJdf1duayWNXVmRP06q0tEyZlShDCcX9qqgK/Q8+2m5y/+4t/x3XffscSZzz/9wNPDF8oyg1iMbRGrcG+TRpbpREwgkgklcJHATz86zuc94+Utu+2Grm2JU8c0qnTd6fEzIahd7th0OO/pt3u1EauJWtc17HYbulaNXUOIlKIt8fMwczhdOJwGYqrpnQFnDI3/Wnu09Q2dUyCOo8FVNKsTNRuzq4nzVRxiPdbrbpDgSYvBx4gzsLs1NL2jWzqyu2MpmWAyu1tPbi2T0/ZmxpOkIRPJJRGLKn4UA0kySTSJt97QiGGbBIfF45BSmIPyhY1JtKZ9fbVpTKK5IsL1Dk4500gmWXCNw1to7LouhBkhFl0jZl23VwSoVhFrkzwZwYlyAksdfBfRinZVvFkrwJh09ig56fxQVFgjUq5BJ4uqDOXrqtZbxL4+5aUQ4oLD0/YN7o0nL4VlTLS9x3tDlul6f+22DSVb2nbPduOxpnBwVluAtqsJVEFMy5v9ntY7zseR6AQ6i29UEMK3BvGCGIdzPefTzDQGhlNCJGJsVnpNY+k6LVScS4Q4s7oMLhRKKrTt8nLPVfCcMcKffXdL4xVxrx1qRfSrsIdBJ7Z1T7YvSjrJ6LjKVNBMXArff3pmKYVkhCiRYgw0N8xDYJkC/3b335PmkTQP2JzZeMfP74U33UQXFw4f/zPhciAOB5YQyM2epunoXYvPhh9++JGn45E5haoPr6jpZVoIzcLve/wBQdDqCRBHwZHwpOzIZUV0WaQScZ1MeDNVNXKFV8trZMbai7g+6tMGJH/1DKu8lxFBilESa4XiXFsjvL5ZNDiubu4p58qPUkPUeZm/+lxF1mwyYwr1b6mVSM6ZkhNxmZkuF8bzmTjPODHEou7x1AF823r6vlHCc1SeSkqJru+4ud1xc7OlaTzWwOnUEJZGWwltz27bs+072kZvODXY1Y0mZuWbLUFJ3SFX/tOrkKOGml+3oS7DzCKjQq/rrAXzQm6/StVdT/bXOqJffy9fn73S+q6twfUq8FuAF161O8s1XpZrsCvXn0mvnlPAzxoEIc3DV7/XO0vTaHXtugbnLe8//JxlDixzYDg+6vyprEkTlJiQnLFF23vkmRjOnE5fyFlnIGnZM7cd09gyzzPTNDENp6rTKcS4YF0LxhHFkTBQ58Z6XU31bssK+khFCfNz0GqyVh2GgrdOKx9ZuYKCd15bWcbiTFvbi3X+CNf1z0ufpBb49R4pHkuDKa626wpislarCNttg80RS6JtDNE5FmOrDqslo//XxwsPMFdEaZGiKOYiFKfEfZOFGAOmQAozmPiVfQ3UijznOoJONYppFehEaAw4Kfia+CpXXTs6Ga5tQf268jPlaw3N69eiryvr6wUFD7G2nte+gyY2GgR1nae14SRcfUuBKijwEhDXI6ZISkIpCec14EjxtI3DeUMmVXAO9J1H8HRtV9WpIHQdRjzWuHpKBMHRNZ7WOaKdic5A42i2BuuVQ1psQayhaTwxqMDDcF6u+5C2n3XGmhvdD9s2vWilxkxwSefV6WuuqiBsOk/nnYJ4TK23K2hFxGBscwX4WJPX3KbS5xTMU2IhLZkwNSQRXN9iOks2hjF6xsvCMgbMvpCngXCxmBRpLLzdCF2ZMGGgDM+U5Qx5rJxeNWMPISNzJJ8vTMtMkox1lU2QqozD7zWv0eMPCIIOg0ekI5UNS+w5XBzLCMMlsgRLzEIqM7Z/pnF7Gn7OjFX3ZgOQkZWQWvtsa0Vy3Z6NVna68WWM5Ov3q+0iBkMWUeVIWcnBqKSbqPN1jMLpEng4jMSwMFwSwzwS5jP+lctzyIk5BUpKOCNYEcRalmVhnibCsjBfLpweHgjHE6YU3t2/4zQPOjAOEeccN/ueu9stqyq7KpRo7329HIfDgRItu87SuT0A2+2etm1o24Z5uTDNM6fTBWsVsfh8PL1y114Rg46u0wxyHCe6rqff7l/EeEvh7/7xE8UPX1ViCqyo5bKsReDvthypycA6j5XV+l0MVEduBbDkGrReTDtfnETyNcv8HZk0VL4K1hbUq9dmXcArad3miXd5neHoZt5tOrb7nmlcwG34kz/992y2b3jz9hv+5q//A+PlQppGSjHkACEXtts72u0GY/tKpB44Hv6B4eI4PvU07gZnWwUb1d1QSPU9G/LSIrZlyoVsWrJ4Wu/wzqvB7xLqbBUuw8Q0BZ5PA+MSMMbRtkrNscB207FpBJEH1tq671X+ytRZkvMWby3WFn1cT+AqPHH9EorQyo6+uaG3t5SgqkbzNDEvKsC+2Ts2tqNYSyYy5cQkN8SiPx/zRMiRWDIpQIiq0xlLJBX9i+ts3BVLazLJZpw4ZtfhimGkUZLydR3CeUg0tTJFdH65qIgkXoS2BJ0/1imHUKtQVBC5lWZtkl8D37UNSiGtVaEIRXtDZAxWMmWlYNV2qZNClEw0hZXjmY2KdWdd2tpRzxBqArCqy8irGVOhME8TRSaOB0tnOrzp2GzvWFGpjdV2onfCtu0QHNb0kDM5JW52m3otzdVGyzYdnTG0Rui2Ha2dGWNi/35HJrPEhTkuIMoRbTvlth6fl9q6LvUhOOtw3tO12qnJReXlpETIwrLJTNN4dZ9fjxCVSmZEL5Ke6aA7hwjZLAoeNIbWWqwUvM24ogG9M564nAnTie9kRnxD21raux7bNhTfU2KmhMj0mFlOkeng6JqNIl3jzPnpwHh5wsUzuUxkq0C1IU6cPz3zGDf4Hezf3TGmmdJlNq1VlHHMxMXh2/8GijE6GC/qdCyOKTs+XybG44nz4ZHncybgCE0m2E+k0mKaX2KM1yzJJN2AK/+PNbDJS7V3BcyUcuXlWFOdp63OQ8iGWJ28XwRzqZu6wsmbqm8aQuL5eCHOhclHlrSQ48CbV2CROQbMAnGeMVW01omwLHOdDc3Ml4Hh8ZEmJVrjKClqG1a0fUtJlBxYFm37OOug8Sjh0Ch5N0XidEbSzM1GhWatdWw3u+r8IIRlJC6Zy2liGE9M08LD84GmdfjG0nWt8oBcRkrEkGkrqKNfy8h18zkORFtrtBrsShV4XpVTru3PV+CA9UlTeZkisFwOpGUiLQPeNVjrdUbsHGLXGXFtdV9bqOU6f/m6Ifoy+3nhAaaXYJlfJMuAmjS9HAqxz5wvE5dhUnFxLDf37+m3G3KJPHz+xA+//p44n6HyBktpEOnY7fcgmSSRaTowTyPDcaBrI43vafuNEu+9x9TmvRLCHRhPRGkCjWvZ73t2m56+a6/2XLkYKi8f9gAAIABJREFUSpmJSQnbxlj6rnsJ8qm+n9e5h6DI065BahC0ztQkSu87IxnyGpxr60+EYhNYockdJjhyKMzjTIq5uj9ElbzyFpsVRuZSQyHSSE8oAwVLTo6UHCE7YkiEUAixaKWoK+LaPTDWaaJSCjQFh6HEjMva7pWXj0XjhFYEUwBRjGWMBVOtXpwoj9LW9ZbrebSiG7dbzyuaOJW6diirkHRSseQKvNMOQGKVXryiRovSWrAGI0rPKEUdNKRWoGt1nRRyoPQBo1HQ/NYadt5hXQUUmSpc0Kzt/kzXW7wVvIpfQlGe7zIFyJn9pmdtjp2ngBFL45RMT0naWm4FaR39xpIQbPCUJdfulrYo3apBXMW4S+GapDqnXNZN765aqjk1lft5uapOvYhpFEoMZEnqYiERDYSK7BUxFKdgMyOgokeZTMS0OxWXt5k4HciXLzCPGHb40uLSFhMyaTkjMVBCwAwHXJxoXWGzabHGUJJD8i3eZcZhQxkL8xgZ5oXn6cKP04/8/OY7+m7Ph29/we3HH+k/3rCUs4LjSubdh7fcbd/x+x5/gHZo0RtdIGNZiuU0Ji7nmdPhwnlyRFFlBzbPFLfHuqW2ZV4FORHVQLzeWCtJeB3wF8grO0iRZcaIyoYVQykWqfp+Vbyw3gCmknYNxjoyiZgiwxAoIZF8IJWE5IVX+z8hJWyAZZ4pYYGckJwIy0KYZ6ZxIgwj4/mMdR68ocSIlFwV2TRHzSkSQmUBe1+DY66LP5HCQlompEQ2nafvN3iv2oIlQ8wFKxayEJbM4fnM+TLy5fGZ3X7LdtfTNqguJLnqG2Yap2o1jbNf1XTjtBCNfRUEqUHQsjqDr8fXahhyfW7VXT1/+US4HAnnR/rNDt90SLvB+A7TdJimVyTSSvSWl05pKS9z4PoHAKmQ8JrfS9JsvbwET23DvLRf1yOmQl4iJRUu40SIKi6w3d/i7C05J6zreX4+MaRAikv97JZcLG3bIbZQTGSej6QVYZss0WfWXdxaqzHH6AwG2yK2AeMx1uN9w7bfsOlblaJKUTfVOgPXTUfXtTOGXDIpZUIutQX/9QfzjadpPBSrOo9XgXXdZGW9NjWAFhEwpnLiBB8bTDakJTENc9VCFZYpEnPCb7NSGij46Ml4vNGgS7GQNQimaAnRqD5rrMnK2iesS8mZytJ2BckNtgjFJyQmvmKVC3Sd0EZBMnpessL0qfqu3hZcnZMWKbU9KRijPEFTW+XrCl31Qguluhek675yDVXaZ3+pHOtaNGuFb9YgWr9Rl+I1Man/F1Nv4Ve1Z/1YeK+AHmvBWEXUOq9BFzJtY/BWxctNTfxjLISirfmu9XWEUwg5YsTQNQ0SUwVYJVyjBHbfiM6EDYQSIUIKCbC1+6MPEfOSeKbaHbPQtVZxA0XI0TNPVfqvlKs7/HrkHFGQe0EIWnWSKOK4Sg9KnfHmoDP8EgCL5KxJcbhQliOSRmw2WBI2FyRlUpiQsOhjGTAp4ozycK215CSw6THMtF3HFBZysYxz4ngOfH6eeRcKxvfsdrf02x1N3yOzQ0oAa7jZ33C7u/kXm1z/0vEHVIJVTUIyyXmS2dDefUBMwDjIx8ySIlOZcGamyIVsL2S7JdpNRV8aTMk1M68V31eVoPbYxZh6LxVs1cEzTrOhXCAboSSjKRtacRUc3sGmgX7TsoSZ07BU8qjQN0KRrODS6eVzpVRUlcIYAhBjZHh6Ii2LqqlPAYmRDnB1njENAwk1DDUVhp1LVAWdmpGO80yZZp1dWVEyt7cK8BFLv+nwvsE7VbA3RXj/4T3ddodregr6M9YIXd/T9S33t3tylU/StSts+g05J8Zx4DUdom09xqhOoM4LBGvctfIuZQXM2Lq/rY2mFURjqylA4fOn/8T4/JE8npB+S/LNdfYVMuw//DHdzRt23/yC4ndg/RXp+9tSG9eZIvVmLV8HuuvNmRSibXN89bOFh4cTmQnnFE7uveP2dqe5U4Gf/eJXbPfv2d+85+//5v/g8PSZw/NnhunCHGeMK2y2Gza7HTebbwhuwZcT0xIYxpGYC31IpFTYbm/x7Y7tzVvE78A0FNPS+NVGS+XT2kY3llyrVu8dXdsxLWuWXVhCIedEiAmZAjYVSs+11d+1nq5roXi1jzJG5f+uldXL0GplKZFRuTJj6GzDfMyMDyeOXw7EkMi5AnWkMNmMbDa4TgCPKRZvOoQGykJJDmIDMZNCIYZICIG8VoK2dlxKwaJwfiuCz5Yo4Ngoj7CkdbCBNcJf/UVDVxzzHBFRP8HjMTGMwrysfEd1htBZt9CIkNA9IoTIKo5hqa3KqDL4pZSqoYpu0PbFWzAmtcKa0OCXWaOa/ty1QuTaSFI0Zm2R2IpPWM+5eRnYICLsb3b0e+HDNzdaYYuh86uKjKG1CVuUahLniBRH6xzF6z4WxgubTUPftSBt1cPNjMsIObLpq5OIt4hRFIRvHFu7JcREOc9Mi6JsUygUq6AVI76KRxq1UTNKi8kYUrY42VKyY3ibWJbwFTq0FBhjINXPakwVD6fgFNmBRee3LYly/khaZkKYmeUT1jbs97c0Tthv35H3BdPu8Ps7jO8BA4tKu4mzmHbLLBNjmHTcQyQtE9N4YRou5JgIS2IYIg9PE0/nzDAazqeRx8/P/D//9//L97/+NU+HA1OJtVFiSWkhxq+xH//a8QfMBDUbEhFiFuZiGaOhSIP1G2wTMXHBpFBtRwoLk4pqm3Rt4wDqKXhtxdVqkJdB98vASqoPmN7wCR1gr4Pvq95f3Uhab9i0wra3LN4jpuN2Dxtf2LqgtoNZkOf1l0BNq+uuIlfprDhN5GXBBV3MTSnEHKr0UKpITYN3Ly4FufZTnG+vrVpjrAZBC/1mUzeXWOcNUYug2sZp+w6xDjGWcRxoGledakyVOFIuYgiRUEVqQ8xYZ2i6V2mP6EYsxr9keqKQbVUCKaSy8iJfKr61LSJAYwVTMjZHvn1zQ+wNuF/oYBxt3Y7nC8P5TGMLrRdFVzpHMVUj8GU8dD2uDg3XarFQsC/Xw8h1o4KCiV87eMeUKKI0AWcVTWtE1wkFxBq6zZa3779hnv6U/c0NxsI0XghhYRwGjBi863CuoWkseQtFRnWTiIkQFuzs2O4tvunYbm8Rv6GII5Xa5nK6SaecCSkTs1IfsmSsc7QdtHOoAIpUW21S7XUUAfz6ellXIelFuxkaGeCqtMQ6PYQrncTUgIi2wKSALYbGeIw1KgaPJZfMMsw01oNr1xsPSQpwMBhscfXh61hAkFLUzqdo9SqmIFlR1MYYnRMZvZ+LtUTriK/Sb0X5amtSpGhXyAqtg9wY5b7XjoCUap7Ly1LIaAAr1+dEMQPmZUfSOfkLlarWRGsEe0F8VvNdrX7Segqq/V+laL060y9lobCKcnx1vWoL11pT256WvndYm7WFnSImCzYLBH0vzgnBQg6ZaZwoZSHFifO0kLKQkrIsnRPcxqturM8YayowRL0NlyUxjQvjWFimQqnmAILBWo8YrSZzUnCO8VVzuGhi3ffC7e0Nj48PqsO6nu9SeDicaJxql3oPzupkx1od2ShXVGfb43ghhxGielkWKQoIcj2uaYklIDhSWChl0h5zDOQUkBzIWZ1gDJkUJ7KBUqLeU7EwLYnzEDmcF06XwBSUl5nTzHQ58HwYePjyA8twQtryksTEqI/XLb9/5fgDZ4KAGJZsGIpwnApt8ni/xbSzzu+WBS/ad1+44GTCmECh0fVT35gGwrUKXBfgq+lRDUy5QMkCMZONAmJ0kYKUdFWtMAY2jeFma9luLTFrK+b9rWXjCi2DAjyy4SQrfB9WWFmpXMaUC8fjiTiOlHmmL9AYbVHNOWrVlxNt19J0rYpg+wbfNAzjjIhhu9tjXaMu1CGpy4QpGCPM08RyPOrmWNGFiHKodpuOzdZyc3tDSoHdrsNaVXwPSyCGuSJFI+fLwrJEzuPEdt+zMy8UCQEa32BdQy5ZN0hqYCzaigp1vmSvyFLNlteMeOPAxhkbZt7+0c8xziJvv2NaEiFEpsszTx9/hJ9+w2bb0m9atrstU/Y1k8xXUWU913W2U4Pyb7sNXA/t+7ESdSW88Bz1RkXVhsTQtJ628dfAulaVbdfRdt+y2bacnh+AyMcfv+f4/MBwuaCk4p6b2xts0+BcA2Ix08z5fFaQCyMijqbp2d/cIa6jYFS3UcAZDcgmCLMNpFgdGoyeZ+M80xyY53INgoihaQybrmXjy1cJgkLbBXKlA4lu+KuGLuXqwwLVLVygYpyEnBKmBsBt25NcUTPanAkx8HQ80LmG0mbVm8+CyU4l2XC44vBFFUlsKdhiMLlcK/oVdCVGwW0qjK9r2hRtzSbvseXreec0RYzEF72Vony0vlU1mpxVLzNH9RxfnR5KWaszuQbAa4UpsAqClppslvVkXk+qbipi5IrwTIk6n9e1ub5yJfjkNTcva4jVfSGLgvJe76krpcAY8NbSNp7tzuO9eosul0krxGwoQf+KbyzOCIHMMJ6Z58LFwukyaTcqO7VS6nramxbvVRGo5MK8JNISmObIOC4cjyPzWIgziFQOqWgQtEZIccLYiEjW7TIrH7JpHJu+Id9bfvqp/ereyznz45dHGiu0vqHvHW1jud01dMpzr2IOYIswDyfSMmi+5pRvjSuIb7HtLWm6qAvGcKnvxWJSIaeZEmdiGNTXUBIxXHQ/qgE/JuE8Jg7nwJfnmedTYBJDc2ehTIyXR/7uH37gvJwIacA3HaUIIWbERqT9Onn+147fvxKsqhXgmYMjLMIPnwfeNIX7foPbGEq0ZFdwNLAUsnxCzBbnt2Qx1xv5GvUqUMMgWJMoKZNSeFnMq0lu4SqLhgg2J+I8Mh4PtL6lazo+fPsd37z13N+2iMlarSZLbxcIA89ffo1rPMasOo96TJeRGFUQ+/HzR86HA59++BFJEZMSE8rhyl3P5XhimmZOhyOb3Y7tbsvPvvuW/W7H7a1ls9nqrGi/Z7Pd4ZtW2zk5QgrEuGGeZpzzV2077z3WOXVkFqdE61zYbHr1W0upPpeZQ2ReFi7jxHn6QiyBmBJt17Hbb7/aVNu2JTedqpiIumfsO+VjOVMYQ521Wsv9rqPx1X6kaAtz3zkkzuS5Z9d9AGNZTMv5MjBNC4/njvt3Hzj98r/T+GY9ptnRZt1wVLhgreZfUKLW2muGtrbBU3oBxqzrw5j1vXxtYoOxZLGEDCGBJA1GUjRL3e93NN4phPxmw+VmR55PbFrH48OOp8dHUoLD4Yl+u0Fsg7GWzXZH23aq8hICIUQOT58RYL/fs79/rzcagrNeXSjCTInAnBQwVGO1GIsT4eZmwzI3bObAeZyr6wL0XUNjE6xKHqDUmkYTAmPSC1ChovpSXENgflUJGu0UoOdrjolxmDWwGQWJ5RCxUrjZv6Fx6n2XUsF4S99uaJIjKZcAJeWsdAGrPMGkaO1Mus6wSo76XuQ19alcK7vwat7pbw1N00BrVYIwFdpTIs6FtBTCAimqNBm1+dZUxGTMENG2psrTaUAgKo+q1EqvQEU/aks15cI8q1RZLOpWgdg6w1IwyFXZpihto4IhWU+vkucVGjWHovPO9aivN1nwGFrfVJ5nQ+PUBsjEQg5ZH1ZHPLSG9t5i9w3S3zOcF4bzwvl4AqBphJRmihjancG5jBUhTjBPgc8fD/z9P3zmdJ6YzhPv7m652e745t0dxjZgVYs1l8gyLzqzFIsVuXr8GR8xkjD2hHUzpbxUgtYY/vTbDzij57lrTOVAezrfVApPgwT1Sn14fCDOA41r+GZ/z367o28NLEfG4UjKR9aORSktpUhF3V9YgvoZinFgWqRpMF4Lqn5/C7Zn+f4HnsdnfnqcOY2B5mbDH/3iLX/5Z9+yu3nHd+9u+HL8wtP5mcXkWkEm/ujbb9n2d18l2v/a8Qe0Qw0rbTfXf1MWndPVIb1Yi7WN3kCApAEbj7i4A+f1Z0WtbdbKa4XrGwoxzaTxpDMR6zHdRmcDa5NjVYbJgRJmluEJ13QgW/ZtYd8Z9p1lSbqaY0rEZSCNB56/fKTd9DReaK7ZTyEugZQTp9OB54dHTodnhssFnTKCsxZXFe2XmJnmhYenA5e6sbmmYV4CucDd3T1iLPM802+2OGsQcSgqOWNNixEhxR3TPKvTeEqqLFHRcCuS1FlD0zZsd1vl6BjDHNSXsJ9mQjIMw0g/jNy/vWN/c/vVRVeZqZo0oJWLc4VcAlNcCFlh1E3T0GwNm9axaR0GVdO47T2SHCU4dn0LYpizcNMapnnBOo+bBCZhmqdaoSmKLKOgnVV+Kl/RCbz0ulgToZcAmVfd0FdDe0npWhWsvysVrVtDKkjS97vm6Yo4rhSVtqPkwP7mltPhhnkaCUtgro8rbtVUYjCaPIjozbpMI+PlwPH5C65paUvW1r8VxFiI1WjaOqVWQL2mtd0d1BA1x1C7FqLyg6ywr5dD5cv0HdlqBQZcqSL5VSuyrGAOUSCPAOIMmEgmqW20FLK1KpIpQuM7VSbJ6iogRRMgl1WRJheDrxuil0ySQhCPkwglavUJtTJcSQxoYlsKpboxmFczYBHBbRt8l6DVNS5RN1gjkVirFIMK+0g9L6YAVufRFnVeTDVxXUNurosiV5CRKVTxDK5zy3JdjfVUkfS1dfzyuvWZeGmHyvX3rJ+kvPr/unqrhJxR2tH6elOfN+LIBE186uCzmII0Wu10254QLGY0NH6j3NaqsZqTUhOcgK/oHJuApcAMZlHqyL5pud90vL3dkoohZBUmDykTlwBFtW/NmiwJlLIgkml8wns0AXp1ve72W7wtlJTxTl0pWm80WbcOgyOXibhMhHkmTBPJ6nsGKGkmTYE0LRRz0VGXseQ8kZMCEMfxxLKMuEYQ0yJO+auWTPYtoPKcS7aMQbhMiZgLnTXc7Dbc3fbc3G4QDN3GcDf2BEksMTLPgXc39zi/5fJ7FoN/gKluA6JK5sZ6XNPS77c4m8iykOqNj2kotgEEk57xo4E40N1Eit0S7ZZL7gjFVKslnQaaMpAuD4wP39N1Ha7f47b/hrSOZDMYDK4IJgfm+cDw+E+Yvmcj97zb/oo3W8NNbzmcFTyynAfGy0eGw2e+/+v/nfu7N+z3W769z1dJ03maCGngp9/8hh9/82suxyOURNsocdX4Dts0iPfYpiFfRn769EjhgFjH54cDtzdb3r6941e/+hV93xNT5I9zhvKGxntFUOWsklhdz26z5fPnzxxPJ54eH9lsd2y3BYwjxMg4Ttre7bXlutlsabqOUnX3UoY/v6ga/GWcyCRyhstgfjfW1CNTmHPi8/MDX46fkcaw2W95u3mH3/TkXcNm59l6w8YJ75pWzSkxtKhWZS4JSVtiiPzjxxP/+HlinEam8QxErC+clg1LcpBrC2yF1a8tpVqEGyt1DFteWomv+IKg7RkTJ/r6aQqwhMJSElYydgzELHSNV381gXkOdbYJbWNpmo6b2zc8Pz4yDRPbzZZhGjlfLhi3IpLNdV632+1YloVpmricj4Rl4Hw5chkHbt+854/+zZ9X+ojBWUV0brc93kCOifM4MowX5nnidH4mLKqo79q9qtj4PXMWkkkVaVVvL1MwVTXF2nJNaExeOZPVRPnKoUU3V6vtYbOzmI3BtgU7j5SaaKYUybnQ+w3Z6KaXI9gMbaUTpayOEFqdGzopYIRkIJgVmKR/e1V0KkVnTiQoqVQ3hkqGX7cVEbbv7tluNiRvNWCETOZAlJFo5qrsQtVD1ZloSFkDcS4UJ4SQmUvGGQ16CfsiGGGriW7RCjaLBk3rDclmlpyvaPIiAYOQi8dd0cmFsOIBRM9lqR9X0NZrq5f71V6oVZOzllZ5EcQYCUugsY1SarIQc2FJURMfJ6pnKQkxsNnuKEuGJXHzszuG84kvXz4SxkhoFsoYaGnYOscyR3JwTLSU2/ekrQLfvnm74W7f8eb2LZc5cLiMSB45jZEv40i5s1jnlU9YpfFyvmCtcHtr2W0dS3ipBI0RPrxVCUERgVxRvDnpcFAMFEsIgfHwRF5G4jwypIFhutBNLXY5E89H0uUMfZWXcy0pGFLMjOPIOBxZlpFN7xHbguvxocM3PUYMS+y4DJnDYDiMcBgziULjPW9u33B3s+H2tqPxO/7oF9/Rtp6mj4zTxOPTicMpMsxwGf7rYe3Vav09Dqkpm1UkVN82/OqbX/J+s/B2kziezxyOZ379wyc+fT4wjoEoDtwA7pFlGpDuDWb3M6z/Rk16xeHEYkuBJVKWI/H0A/GSkO0tbHqkf49xW4zxmkkXKEFngH3n6FtL64UcA+fjM3E6cDw9s8RIConp8sgyPeN9onWRzkZey2cbr/30EALLvDDNM4VEzIGQFjrvcdaRAefVwb3f7pmDOpOPc6CZtUIbpolcMuMwYEU4PT/x4cN7zV5TIoZFs2PnOJ1OzOOk7cAQmMYB5zvNYkXAahtRsoIJrLd02y2ICijv7zTrm2YF/ISY+Ju//kRK5XqjApSctFIvsATHYTT8eMj8+vgRWk/304Gbh4H+Zs/9h7e82fXcbRr+ys/sjbA3YPda1W4p9A6csZi3G957R7P1DMdMXBbiMvHj4DgvhjHaSia3tY2bGOZIqoQsk9cgyFetUG2NvoZtfx3OxRhMUWh4SlkRbkhVj/FMS2ReAqfzhf2ux1nh5u49d+8uxAw//vM/UbBsdzfI6vuT69+p7VlbXeBLSoQYyCXz0w/fczg8k2Lk7v4D+5s3dG1P47XFPA8nhsuFH3/zA8/Pj1yGM5fLgRRnYhrpujva7pbd7S/wm3u6xnx19xmrD1YASa2Sc6UmOTQoIPmqtlOHMxQjJGZSE8i+EA6D3tqbjhgSMRQkL9pMFcgmU4yjnRttpYrF4HB1lh+kKOLTZP2epLWcpw5fKyioVEUYlN+ZV+KHq2sv8/Q3Xwh+gKZalVEocyDOgRwicS7aFo4GWyIFdWW4/rm8BthcZbtQ1GzR6j/zIqUvYtQAFwX85AJNWjBiKQmeTUuMQhTBy6q8YzC5ApJy1srJirZNi/IZr7ibV0tS3V7AOVPXiALdYjZItoQcKbIKbDdghGySzlqLwRVLZx14eL4cuYyBp6cLy2LJc+Bwd2T/5p523yFjZLMIt8Vg2obklRTfhIkyRGYMcUmUOcA0kueJZdHrbZxhs++wS8I0gZLUbqlt9ZHzS3QvpXAZBlKoPOwYryMq61VrNEdhfHzg8uWLrq1YGMbMl88H5jly32fMPCLLhKFFXIPxhmWGJSQOxzPLdCGFCSMd3hdsgSVF4hwI0XCaPYdL5qfPjxxOZ0JUYwIxBudanPMY64jJwGIp2WGtJ0fIeSKVhfjb3Kp/5fgDeIKVz2W09982ju9+/h0/uy18s4dPnx4x9gu/+fGJyzBxPA9Y2+DcgnMDMRVsiZhui/H3GNHWoKk3HiWRw0iansn5TM4DefiA+A3GtRjTvLQdjAaS7XbLrmvo2oYYAufTmUuZGS8HbYIYQ5iPpHih8RlnY/Vxe2kBWGcxRW1cFPYf1RolCzEJc7fgrSPmjFiLb1q2uz1mlcMy5jq3jDGxmMA0TTw+PjAOF7pqzGoKDMOgnK7GK/8wBCX6UkgxYq3W7+pkXjuIVZmlkHHeg7EULM7rfKqv6NAlBES+QFU50f2qtldlBVfAEgxTsPzwdGE2UM4TTba4u4W9qEXJm2SZzcRbk3hvErH3eOu5p7AXoXPCZusRCzetsOm3pNmxXITFOZpFuCzCtm3pG89pnDiPqt241BkXpVyrwrIGQmpLtNonGVkxw3UNQgVyqFAz6CapJGpN10NtR8YYsNbSd55+t2O7v2eaFjI/IJLxjdXzWjK/zZVaFfZzqUa5JRMOT1VN3yEV3LGzlmISYQqcDw+cjke+fPoNT48PnM9HpulMyjM5T3T9QN8PGNnSS4Oju1Ik1v6bmIrONdrOpFRgysrRrTJ9KtcH2RaF6a1B0CWiTeR5xBSPaxMpZJalkHPVj6xgEddCTr5K6Zkrf1SkYCVV1ZDawq1rcRU3l2vFrly/FclbclaAyoogKTB+uWDtGdtYbKN/r6zVfyrkUJAEkrTaRVMhqgYaV+NbXrVaaxDUq1bNaEV/UnFVQjHaVnVFgTypZIZoGCKMAVqn5wHMda6oXyo4yVYB0ZIzjq8lBfX2yjVheXGfUSUiVbrJZD3P4rCNowjkkhWJm9Vxw1c3kXkODOPMeZiQ7HAZxqeJ3GZsa5GpYEOhKcLGqNZpypESIqEExmTUlmpJxGkizjMxRm2bG8G3jiRV1CCAczp/dLZ+zlfHOM+UmqimkJTjHBcVjzCGFDLD6cT5eCKGxBIKwxR5ej6zxIS7Lfi04GNQEZBkkBwZ58I8R07DQpgWclzYbVyVXwvEkCghMUXP02h5OkeeDieGURWPYMUQKE6koFiAlCMxWKz1LNGQkkpN5v8mQdCM2NIgZqTzmf2m5Zd/+ld8d295vy98/vi/cny68Pd/9098fDgwL5G+3+F9wftC15wpqaWELxi/x5LBOkyylFwIYSSmkZIulPP3xIvlHI/0fzzh3/wJsvs5RRrA0+1u6G933P/JL+i93pTH0yOffvhPPD38k9Ikdnvu3rwjLg+YPHB7Y6CcmaaB/e6twqoRmm2PK473337Lly+fOFyOTMOgDuUlkeaF2+0eg6P3Hb7v+eVf/qUqxRvL8/MDxig5tm1bvHMsfuL5+YkvYSHMA9vNhk234XI+60l3Sgx1zrLf7zFGidFi7FXLNFfbpDnMhHPiMk8U58gYQixs9rc0TUfXb3CNRRb7OrYTQySVhZTCS6VVCvuu589/9sc8Dg88nJ94+PhrZhLz+cTZWj5noQnC0/mZP1lO/Co+8w839wTfs5GZfUn0ZHYWui10G8P7t7dsc+E2wXdTwYZMMy3cNT073/LT8czzeean54nDRS2KzpfANCsUOqK8ReVxe0fkAAAgAElEQVQ0KhIypQw2/45Sx7Zvyaaj8Wo7dBVXBuYQyEkQPMZ7kjhFMgfY339Lv71nmjNPjx95+PIDzq72QF/ZqF6PeVFLJWsM3gsxRX74x7/l9PiZH/e3fPjZH6mP3OXI0+NnxnHgfD6rs3ZKFcWowW05PxHGC8P5xN2Hf8vd3T3cvuhsqkFrrTheSXTJqoNYCsZV9ZIadJIBsQkxgZQeGI3wVDKfP36mKS3fyo7TCcY5MS2POO/wjSM1e2y3pciGVEEvK49XUDpPKGAlaKJaBCJXbq9R6ZEK7lKCfKlVZnkhNgLQdKIO4b3HblrECoXMMiXinJTzu5QaICpa3DVqqJv1fYgzWFO0lVs0mUpF54C5qDRaKaJQ+wKlJFXmKcqXC8lwmYT/5fuJyxhZpsT//Cd7bntL267jmFWgI9fJcqmhLKswConu1SpZaRXWGTrbYozQdh7rla/Y9m29foJ4nVBSEk3xGgSjY0wTl/PIP/7zP/N8OHI4jMjc4oIwfZlZNpnQCsejel0uIfB8emJaZqYl0DSdokEZGUPiMkc+ns8sUkhbq4LpJEIJJCJZYu0aCKEK1b8O7jkXDscji1fj38opUUT8vFSd0gvnh2dODweWy8T5MvLjl4GbUdjf9IjbsSHTFWitJYdCuMycp8wcEsdzYR4TJUbe3FscAiUzTZElRi5Z+OE58Okw8f1PPzGOk547q5X9MM4cjhdCNjwcz4RFSMHgZIsYh3jHHLtr4Px9jt8zCBYkT5jSYMqZjWvYNR5L5tNPn/j13/7Ef/iP/5Hf/OZHDscDKQWMEaxTqa9+0/Nnf/6nNLs32P03TP6ewIYxWqZhYZkWEgHbJnILfsoIiRwOTM//hMSFJiWazRuazT37xrNrHe+2DdumkMLM+fkz8/jIcPpMKxvCmLgcMmF+oOQJlwYkqxXPu7dv1h2GpmkxvuH+/Tvu3r9jCjOny1mRaClyChdS1Jnb3f6OtmlpbUe/VQPLnCZCmElxIZWqPeq8op6IzJO2Y/RmjFhj8Y3HO6eAmDUAItX2SFt+OWsVkqLyZlLKLP8fbW/WK9mV3fn99nSGGO+UmcwkWWSpBkntbrXdevEA+BsYfrY/q9/80C1YVrfRaEgtlVTFYpHJHO4Uwxn26Ie1I+5NltygAfsQgQuSGTcjzrDXXv/1H6YJ7Ropok6jnUI1Sqj0P1rGS84VqqoQY86kkkDJjfdZv8Hsd/i7jwy7O1LXUm6/J3/2hnBxyV3KNCriVOTu+1uYIrmD5DJHAwdjsAqsgnuV6RSsNPSdojWJq+Mj+w8f6KaIXW5ZtS2/fnPB4AtTyOyOgfv9zGEMjHM6zwRzzWVTJYnUIDnmZ7pOY8zZTeXELj0zUKuOSjpGJZ6MSmNCkXmTbrh6+RrXNjSt47C/IwUPyX8q06B2E4Vneist+i9jmMYjOVctVg74eWQc9iIuj17YwLnaehWB55TKkCMpzhTypzMmqOyQXDvDJ+ztZBB/Ojdap4oMyGfEJPHH1CNqYTAXmp3eU8Y98aPC6BuKdoQq79G50HWWpjMokykEcglgYmXiKrEKU7XbO8GRlUyjUSKDKKfUFilgRZ3IOs+/k6J9saHpG9yiIVWHERUzrgWjDTFLQVMpi0H+j36HNGfqpHOHIhIO0ZcK4eWk/6v/hYJQ+as6j5wVPhTuj5mcNX1jsAV0zuSYzyQZfSJqcYJaT1/pjz+Us6a6xhhijJIO4oxokWu3fXrmbJUhKa3RUUOEyIwvA2Pa83B4YHccmGMS8/UMJRt8LAw+cnscJH90HjkELyb61jEWg4qKFBKjjxzmwO1xJlvoFh1zCBzHkduHLAkaMZOTuG+l4iQ941kVVAq6rqdrLc7ammkpt2aMkTAHHu72THNi8Indbmb0ieg6rn/2c168vGZ96QgPdzw+3uOS2LvNMfN4iPKe40ycRlSeeT1lipLo4/0QOM6F+8Hz/d3Ix90slohZOurWtSxWK66vX7Bcbmj7jm4UY4ESxbJSmwarelKIpPLpJfsvHT9dJ5gndDGY1LOwK5aNJvuR7373O37zt3/Dv/urv5K8PB/BiEbNOXFGubjY8hf/1Z/Rb64w/QX7smRKhsej4u7jwEGP5CbRHKEsDToYcgqEcuTw8HvisEMbRWcz/WbBetGx7XuutytWLfhpz+/Tnjg/4Ic71BLiFNiHET/fUfKMLbOY9prmkwWvaRrapYjSb169Yg6eP/zhW3IqpCDC6XGc2B2OzDGwWqy42lziWsN60zMPPYdDZhrEbcSgwTq0a9BJIAqtQtW9JYyRBITGSTdotDlDOCnF8wKYKqQXo8C3Skf8NNFpTdN32EZjWo1ysgBkPt35lFQXyywarJwSoURMnem+6bZY9Y7Dwz3x/numHNHfvoBXbyiXN+wWa2hawqJH//Ytbj9hL1pir9FOY1pH0YqioXUJp6GzsHaWpYr8bL6H79+h3z/wyz/9V7y8eckXb66IRaQND8fA27sjt7uRx91UF/WT40jBUmg7RwkT//hPnL+dNoZShbtnabSSTjLljA9JduhGYaKhkFAmSVq7slzdvGa5XLHerPn2m39gPOyYBznH541E7S5BNKEShKuw1tEYjZ9H/Dxx2D1UOFU8Fk/3lXgtinheV1mPqlEvhSRpEs9EyvImYYQqLYVbnSBSnkygdZFFhRpNpJSm6CQFiwmzcrhry6HdM+09+w9HPrtu6dotiSzvT5m+N7SdBhPJxZOZKTqhkkHY2icCToU6M2JwXYShbU4FCFULcJ0zKj7RCCql6F5d0q08atkSHm/J84QbEs5YVKOYYhK4MubKPK3pMFUHqSux6gSZK6mCAoee6m4u5xR6KNUmvnaVRROjZvJCsOgby2rR0qmMPW1w6obK1GfvBKtqxMlJFfVJIVQgXXV9pRRQCqw1stmpmxyxyos45dBWHF/yJPdFVB7PwFT2PA4PHEdPjqLrTBlK0fhYOM6Rj/sjwzQwzhOxyGbFNg0hWlJUjGNgnCOHOXJ79OhWYekYg0ePiXh3JBeZ/+WsRFuralzVJ0VQsVitWLTCg+iaRlJNjGYYJobjgA9vmXxi8pm7/UwowHLLm1/9GV9+9SU2R97Nv+Hu3YBhJqWI94GPj57jnHg4zhQ/4lRgP6bzKOn+MLM/Rt7eR767PXJ3mJmfQbrdYsFme8HLz16z3V5inGGYjqJ7zoEwT5TcQjGVna1/cnX7yUXQeo9LUEJizHe832X+t29+z4cfvuHd97+VE95YFo0TqMIU+l7x9def8Ys/+SX/7V/+G9brC5rFiqANocAUS01XD/jDjv39K+7ffcXu4T2HYeDd/Z6P93u8z1y9KqjlCM09w+17xrYjlGvuw5Hx+MDuh7+jDO9pmcBPlJTARNI0k7OXxSMbSfl8dnQZFmiapueXX37N9WJNvD/y9g9/4N0Pb/G+EIkEnfjWv8VZx2b5kd1+z4vLG1bdgowiIri8VYbLF59xeXGNSgldMiHM+HkgpojOQuVt2hbrGrqur0SDTBySQKBe0ucn79ntdrI76zriNBONwbUtjTU1wqUQZs88zk8yBMSCKZPOMoycM1qLiW9UmcX6gpdf/hrrOr77u/+D/e1b9j+8JX7/vdh/GTg2LVPf0/7N/067WuOvrujWS5plz/rmkm65olutuP7sBcvVkqvLLe1K02rNS32Bb0dSF9m2DeuuYdUJRFRK4XLR8HLdMM5rhln8PZXWQEQrhauykMNhx+/+SlNNPsiIDuxkgHxalOQhLlhnzzMaScCODJOnbVqxWFu2dOsLFusNwxx4vH/PPO4qDPts/qiquTuFnAMhyM9cAtZ2WOM4SQXkz9v6K1JVX2tSiTXdQKGMo1tuuX7zK67f/Jr1aoNS33BqcdU5SuHUianzrFCGUwnBJCNKx0ricfL/VQE87UXgolX84n9c8/Ddng9/94HUr8l6pM0d8/HA8XCkd4V1XLDt14TNnmJHgRCjzJyJmRIjBdHuUou5QXR5trZgWQvcGFUiZYFmP5F+KI15+XP0KoBVlGyhGdCLUX5XLijuMV3BLBJOxn8kDzkbYViGLKTTBDEIMzqkOj0scvXLKV80wRQUx6T59qB5nAq3h8DHfeB+yAyh8GJt+MXLllcLT6OkSynVrSZmhc4aVfTZoMCeqTfP1w111qI65wQVKJnZT6iTobYyaKvpmk7sEq3GOtG4ZpdJOXDZrVm+6PlPv3tH/v6Bj9/vmFPLlCOP00jz+MgYPd+//8gwTkx+pl32soGaJalE3GMiPsGc4TgOmKJZhoaQhDwyzifTBUMsmmIcHS0RGa+cDq0Nb15/Qd9a2XCcaLc58+hHDvuZx8cjIWZc26P7FTfX1/zL/+5/4F/863/NxdUN0+PEb397x2/e/T2Pd99RUkCXxG4fmH1mqiQtqzJ/27xn2Vn6zvDDxyPHKfEwFg6TXBetytk68E//7E/45a9+xb/481+JP6lSXF1szs+Pj4kQEtOU2B0nHo+Bf/hh/mfHHH9U237CnwGgMUYMblSmpBmfI3fDnsMUCFhst5AhNAKPGCPeitvtmuvrS7qmwRmNI2M0NErRmMzCQowKrw0LtWRpXrK7WLIfRtTyluXqkRgCm82K3LbERnPnI42ZIO7xx3um4z3Z71F5xmoJ2DWnx7ESMPLZdePTIqhyREUDKdFoy6Lpubm8YdodGR6P7GImZkkEn3Mg+ESKGaMsfgq8vHpR/UcLx3FGWcdiucIoI+zDnEAFSnWFycA0e1zTorXFpXSiHqCMgVJIMQgUWkkZsiPWlJRIIcrMKUZylG4ihUgK4ZMLHmOqn/vJtktrUzuXyuFrOjbXr/Ff/ILFck3nGubjnjAfhcE1e9LsiTmj+wV+f6C53KLXa7Zty0o1rJvEZ9mwxnGjWxrV4LTmYqmIN4XULNlstrRNV1PV85lZmFOEKneQRb08ETFSYpoDh+P0x1BvJUyU+j2o0NzJB1wauSoQPjELtez4U6Hq9QzWtdUtphpxPpVTtDYslmuBpovkSuYUkWVeeg1ja4egzZkccrKi07qQq5m7tpbF+prl5prL69cslluatoVY2SOffLcTp6RUGU+psoQTDJoEQlLwFMBZuw8n8PTFmwZje5Lv6XxA+yNxSsxpzzQfmacF7axIoUORUCaT8GeN3SmXT4gwFdItqsb6nkRNqqZbVN2dSiT1ozmMUqjFFWqRhCnZZ7Id0IySDlIiOmaYJ9Q0okUQiDKFXPWMGBF5l1CLXhJ/0ZKlYwxZ45O8DmNiPxY+7CO/fVQ8zIUPx8zDmDjOsu4sm8JFl+ldxNbsiJROhhy1E1QitzgplE/EmedX6fSP1jIX1EXIMlqrGk/0lOxgaqdZzvepwnWGptO0fcvF9ZLjYeL23QOxBHzyDH5mP44UMsdxkmzK4CnWyfUqgXkW3kCu2EPMp0QLuU6uGr3rOk9NRYGy5GLwqRDTs2eoPjddv6BtjRiwR7GITDExTTPHYSTEQEyCO/XrNdvra15/8QWL1RpjHSHNHIbA7W4gBE3jOhaLlrmMlDmSpkiJBkXiGORm0gZGXzjOmcOU8EmILUoLrGyc4eb6kqurLX3nagKNpPWcxiMJJUVwDNjGoewM7+YfX7h/9vhJRVABy77D5UYW8RQIMXKIhtJesLhuaMMRUkBljyZhjWZ7ueXlzRWvXlzjp5HkRSLgOicWZgbQGVsyJQ2sO8WiuWJr3zDMnv7qIyZONQy0EOiZWPCDDUQfKeGW/fEd0/4enfY4PK2RLC9jDdpYJiSxIBdLkUf+6bwUKN6LnsgH4uTRWfHFqy/QAUwyfPP7zDiPkkWYIylH9oeRh4c9jXV89flA3y9Y9Es+3j0yVCF5Y8RbUFU2HdqgXUNOmYf9gaKMOPWnjLUWawyukRDK5GdCSmLLpW21QrKULOL+6TgwD4MMf60lTIEwP13wUmCeJ4Kizq5kcW6sISdhwMYYsNayvnnF+uaa5CceP3zP4e494+Mdt9/9jvHhI8PtW3jck4YJ7yWYdqUMv2hXXK+2XG+u+eryms16zc36gra1GGuwFw795qsqAZEOd3cQf86YkkgZQiKkzJzEIUVS1SGlzDAG3t0deHh8EJLM6ZKVaul7ps7Lyqi1dAQC4YmIPWfxghW9YULYo+mcToA2Z6/WfNJuUBl/WvPy9RcMxwN8sAz7O1KYySkQswR8LtxCstXQRB8ohbOfZDnNlmyLbZd89uWfsb16xc3rn1NMgybUKJpnDxkn8glPd+kJmlSJXCJJB4pONdlB8MCToRpWoN9XX3dcfdbw8que3W9nDu8eOXz4yNEPzOPEPC9Is0XPGwFGbMEzoEpGlYZiTP17pTkVSFDmaOesEnVa9OW/GZOIJX265iiFXn+OWknRcvoS8ozVE6qMlDzTri4o0w4O93DYo0LEhCx091hgLkKc8fVSa0XWmhhk8/zgHfez4X7SfPNu5N2j53fvRn53gF1UPOKIKWNU4S9fwNUy8nI5smBAl4JVlqAtMcvGRifZ2JyCalBPs8/nR0iBXByoTNNYgT5zpG0tzj1ZBaoCVsm/pyAm4kor+lWLcw0KzRdfXxO95913b/F55DAnHsYF6MI4WfaHI3MIxFIIw0hWSiQF1cVmvV7DNBPCQNsXmk7TWM1qsWK7XFF0ZPKBKURclWuMk1iwCQT+dL1c1+M6CzGSZl8t2yIPDztub28l2DsGYgrcvHnF669+xudff4VzDX7y3N8/8Pb9Lb//4ZY3n11x8eKKn//8cz6+v+V4HNk/HpimsZpue1pj0NaRTSSomX3N5dRaiz2hM7R9wxdffMbLl1coAnV0i6LImuEMTbOgzdD1ifUm4Noj/P09P6UK/nR2qHMoGrSyEAvKrtC6xcQR/ICd3kOc0Wmg0QlnFKtuicHgp5nH46N0MyWzzEuslay9jIiBj7uxJnJDsuIq7gh1t+3IseCco3OORXON94FxGHhx/QUpvuJXv37Dd99+w7sf3nJ/uyPGkRI8betktqQt0xQ5i9PqMYSZ3GSy1XhVmFRi0oX+asub1tJuO6Z5Ypwn7nYPMqCeJsLsSSHy4fGeS2C12dJ2LV3b0TpXd/OZcZ7O3qFPeWYWHwNxSNzf36ONwWjNcrUEpQglY5uWzjqUaeiaVm5eZJFM1UotpYQPgek4MR7nZx1TwQdP0sJAPSVHGJ52oikH/DgyHu+Yxj1+GtndfWCeR3yYiaslrnFcbK9gtcB2HYvtBZeXF2w2a6bLn/HRtex8w/E+spxnNn6kaaT4d0oYZckHZp8IsTDHREgSt+VjZPZRYIznjlQpkVPGx8QcMimMzzXl8vmVQllbd9hQcjyP8Uo1eVdaSAuSGCCEFpRi8r4SIQpoJ/Ib48gpCoR+/ns0F68+Z50Lqxdf8v6733Hc3XN4+EBMGdJEsML0daYBzdkhB0Rm0bmGzdUrLl99yesv/5y236KbNdpqKSwjteBVksvzxrAWuZNZWiwCb0tHmGofEjlNqyxwonRAQRtoOs1y0xKOhW9uf8Ph/UB4jPzi5Vc0WLrR4OICn+FoH4RkUwJGibtLg6ZRYsyc66xNAxR1BglPIUMC1kqs7WlZVRhs+2uarqWgyW0QOJeZnO/I+RHVfUeZHqDtMf09BA/zhB9myhzk21c00hQNUVOi4jBHDnPiu4+Z74+ad4Pmm7vAw5i5i5pRQTaKPmcu+8J1B//95y1fbhQXuqCTqVZr5iwfUvKhURq0EY9YByi0pDQ8AQUYZ7GNCNElzLvegCpTSuK43wlCExNXV1cYZSQDUFXHmKaTuzAXfv7zl/SNuDXdfv9IGCNjDKhjYdCao0/iqao1Bx9IRRJ7+r6j6zuuXr/k/vGew4eBi9WStm1Y9i3EQhgDppN0+37ZcpylY4w+iMPPj9Z5Y8UKr6TEMBw57vZ89+23fHi44+hnmvWKKQzsfODXX/+c1z/7im6xwGAY/cy33/6Bu4c7Ykn8y//6L/jyZ5/zi19+zRwiMaU65pllfRoeGd6/Zff7f0LdHWl7zc+uXhNiwYfI+x/ectGuuXpxw+bqBa7teXh8xDqH0ppSElPwMA5gR6jzxRw8x2HmJ7WB/OQiqE4iGooW1wCZdTiUmdC6hzyj9BEVCpoggl+MaKh2B1zj5EYpmZiCUHC1loyrUjjuR1IR7z9cADIqJ1SFFopRGGNxVtO6luA0moBRTX0YLzgJXB4eDqQo9PZ+1aGtRRlHiKcIotNRmGNERSWUeg1JK7JVuFXPpnMoKwVljh571zGMI+M4MuwPTMPIdJiZ/ISPvgpoZXcinYki5RNVuz5gSqGMIVWXiWEcZRHUQlvWxqCsxbUiCi2lOsOr6qV5Jg7IQyc7UOkan1/zJ61dvYIKhP4tMJfkH07M05Hj8RE/T0zzJMwzY1lsL3BK0aEpmy266+g3ay7WK1Z9T27XjFozRYUaErvk2aVBnO819CWRx4k0zYyzWJxNsRArqzKkhPeREKVAno4URHici5hemRy44Gn90RKrIYjCKQW+PFWPXOIZiiqV4amK7L5RQq8/6d1OELWq1ldPW4jqOLPYoExDs7rC+4hxvXze6UiOcyWNJDgFRtdPcWKtNk3DarPh+uVrNpcv0G5BKhZji2hTf/SIPV9kT59D+kDk2VBCgDol5QmBoxpE8ynUrwrC7gNKKjzudxwOE2kUZqkuCps0JrWQMtoaOY01tseiscpglaGoTKSaZp/uv9P55uxoKuW5yCiC8/lYUegp5UQOEkZqyZINWtjLOYwzJQeUGeV7BzEHyCZRjDjDYPSZnHNyGxJ4XR4NbaBtNKuFweuCr7EzXywVr5eKr7ea6w5aVWQ29qQ+4cyvrtdBVS2iUrKteOYudr4PjTUCvXFi1WpKkdFJCInoIzkmgXWLRpcilmqidD2TuTbbBTEk9l++oHg4Pk7gxSI1e9kkZqXESix5Md43Cts3dKuefr3g6AeU1XS9uBgZo4gh4GeJ6dLaYhvD6H31gj3B2J9+sZISWSWS94RpktDp4Ygyim7REUJCOUcssL26ZnN5KQQ/pHgehyMhRLQxvHr9mteff87Lz15TjNwxYj8Z8MEzPd7xThUe//ANShvaRc/N1z/nYTew2x0Ib9/huiWX1y9Yrbc0XY+PEWWM3IfVoCCTZOmvJT0HT6g5oj/l+MkSiZT82cw2Jk0qlmAWGLvEthsoM2UyZD+zH/aQPH4O+Pl3vHv7jsXFAteITqmxsr8KUSETPIvrltimxTUNTSfFrm8MTddhXYvuFhIJExK6BHKKVDYyxlo2m0ts03N585rffPOeeXrgOI6sb17S9T1KW8b5gTD7T6aC+92Az452tUA7Q6s7QpYBhKLQXXfiVNA6vpomYkyUELn/8JG7Dx/5P//tX7M7PDD/00D5/AtUfkHz5jMWywXaKo7jTm7GEGiqJKIxliyRHChrCV5kIh/v7uvf1fD68y/puk4WmywaP9uIDka3jma5wHUtpnp0hgiok0+QLMBJiw5NSEHILjUnUooM055pHhnGAz4kinb0L16zNo7GOX712QWb5YLNcoVvW4qW6BtXi85hkMy9CNzdDsCA4gGMQRtN11h0iqiUCF7CcOdU3UCyQEcnobr38xkCSbUbc8bKvOlZZRcKd0MxLVBq8rrCKFdh0Iw27UnaJP6glUIo4bgyOzk9jTFrMgZtHD/WLJQCSbV0i0s2myvW12+YxyMf337L3Q+/Z3/7A8nvKUkYvCnJXFNbB0W67eVqyfXNK7746peo9oKiLDGBUgmVPv37tFZoI8QQFNVKrEjvVzI+SxeYVYYSQGf0KUYJhQTjakoWgTJeox4Nj9/c8eG3Bw7vPWHSaNUxRJiTAuXo5gXW9fTNI0EFks5orHShWhFMgykw5/zkp1k5RClnQn3NOTGnSCmnvBhBLQ6PHyheHJls61BGoxsZbpRiyeOC4mfwC7Q/Qixyj2pI2tbFvzJiKTWuJ7Naa5YLxc06M2bHlAzj7JgjjKHwn78b2Y2Z2Wt+fWP5fKNZqUDKmpAMIcvcK6VEKlK8c5VbSJCwsF5J0q2R4gmDQ2bGGtc4+uWC3eOeECIpSaoEpRC8guIwpiEnQ9Eapx1Wi0A8T7Ihk3GIY71d87OvLZv+mmE38/DhkeFxZtzNzMwUBdpolNE0TrHZLnj54pLteonWlqZ1YjdpG1CKIQxM9wNutLxZfkZJhRQT8zxijGW76tl1EyU8c4zJmcOHD1iyjFymCRUDLy62/OnrN7huwd/8X3/LMA64xwduXr3h8koS3J11tE2qG78VVy+uefXmNZvtBSFkiAVtNP2io+taci6MKN4Wxx/ePZBcy2dffMX//L/+L/zbf/c3/N3f/gPpN//E9es3/MW/+Us+/9nXOGc4Djts7bwL4qgVchYzfSWpJrnKiX7q8dPhUH9E5RmUQ5eWgiUXcREoqohZdONQqWE8ZnzwlCETgmc47GkPDU3nWCw6nLWUAuMUOY4eHzKuW9N0PW2/pF0saLqG7XrN9sLQ94bFUkncSCoQAyVG4uzPlkUPDwPHcWJ3PIqoXrcUFRlGufGNKYQ5/BGBJPlIGOXJdo2l0ZqFtRX3TsI2NLLrWyyEyakSNEqzajt2v7yTJIrJc3Wx5nK9ZL3oMFaktlRySoiSPK5VJplCY22NiVHnjmT0noLClMIwTShjhUpe3TwydeZUP1NRIiovSkkH+fx6qRN0KpBMkb6dp/5CiDKuWeAa+fubtqNxjtY5msUS0zRgHK4SQazR5CSaxTh7gWh4irEpIFC50hBF66dyoiRdQ0MLKSd5pSCFSSFOOErIP6kSZ0KOOPVEODkdTWPIxvBkrVZjd5RAmKZGN6kMxkie2ul3S/3WonlTVefoWoyV+zg/tWB11iZdnbGOplthbMuNsvTLLePLL5l2PxCmPfPhnnE4EIPMyoXcpDkcBsZxJoSM60qdL0kX92OkpjpyVt2UeMCmUsHP2jFUvks0D5gAACAASURBVK9UyiyBzlqLgb2qbEqSQieDCg499PiHI/6h47J9STQCnE5Zc4yFwRfMoEFZun6FZhYKPg1FFZzWWG1JOqO05zQzpVSjZyKpBHHtqHKeZ3USgJwtMRT8FGROr7Xc98UAFqMkqBgjP4tKJKwEYGvQJpG1iLyFBFQlI0pLyoURcNsATieKLkQNToPVimgR304rBvWpiC1cUplcI6me2j/pjAyKXN1prAFQghCpp6uVkiS6DOPA6Cfp/GI1cABS0eIVbB1gKdlQiiYkKejqNMktcJhGjseZh/3AOEcyhc3llpz2TD4RdZYN+qbl5fUNbd+IY1NO+DBJRFTJLPoFIYifq2oFkTJKnFUIYNCyETcGqwolRdInmzEJL7da06QG2zoKha26pN9syUqu3/biktbqGqDbSserC9oYthcXXFxdcbHbsVivaZcLTNtWkgtVjlLDlIUmic+afr1mdXnJaruhoAgxst5suH5xw6svXtOvVqLNbNxZL1UQf9ZYCvH0zCrIqSeUAdj988XsR8dPl0ikkZQ0RTkaAgmL95C0AWNoT2QUZ5AEB9kVhnliKBm7h7ZrWK+XOCdF8DjO3N3vOBwnbLOm7Zd0yw3tekO/XDK+UBS9IJeWtpEU6Riq7CF48jwxDEd8jIxJBKPD7MnFonQDemYaAzFC2yiSDzXk83QSEX3SLLCKVT3GSSE8+ViqrkEZXRdrmZVYNJ02rNuO49dfcXjccXjYcXW54WKzZNk1JDI+5rPo3YdIyqUu9LJqm1NeopIA1GEOZAq2FMbZY5zH9cJeFPgZjNGYxlUT53IWk2rzx51MJhNjLYIlVzuwCh0q0MbSKIs1bTXa7WgbKYKmcRStiVlhoiRmG6uk+wuJMM/kUsNGT9CsOhFVdIUnxEBKFZFmlEyVayRijDU1W2Oc3IZKKYiKnETor43CfiJil6TtYsynzLYTh0QJM09Q4yLzjU8sl6Agsx1V5N+NdRgrM4ZPZBJ18K6VQmuLbgy26Wj6NavNDXGeONxtGXYf2d86CjBPIz4cpCssiaHGToWUsdRkiJqcwCdblup6ovLZ7qnUMcHZiiudJoLVTEDLeRTvT8n+I4GKCqKB0KCmJXHfk48zl91LYlFkbaqLTmEMmX4AqyxtEJKPJpCRxS9pjdWOqMVR5mQVJmzZSCJWv8xITIFUNa1PVVABosvNcYJiRDqgNWSHkJBqATQNyThSdqAsqlo0KuOqDrHUHETZSGQlRclTCFmQkBgTPkr0UUgCJ4YCY4FjgbbINDUqCT9Gi+uMztUnRsn8zyD6V1U3WKV2gPnpcpFSInjPMI5M3hNjJgYtSRkoAY+NwzUdShmo8GuMYgwh4nkpvMM4sz+O7I5HUlBoLJvNmmEImONEMgXTKrp1y+ufvWSx7HAW7t5/5Lg7ErwwvrtuweNuR9YZ28psHqMIKZFDwRRFazqslnTEnMRh6fx01VmnPI4FYy3aGJquBeOYfMJYy/bikhfXlyxXa6xrxGqtFW/PzcWW7cUl24tH+uVCNM2nIljlPgLBKlTl8UcM6/WGxfaCdnFC/ALr1ZrL6ytuXr2g7aV56rqu6p5lGJCQUUE671qFdTyFE/fj/0NizJ+8WmFdg2p6UjFMk+ev//rf83CYGabEr37xNZqZafeepVOsr7Zc3rzi8e6Ox4c77u9u0Vrx+PDI5eWWrutYLxaEOULM3H58x0FJhJJZblhfXtO3PXFzAX2mhMB+/8D9wz0fv/8D8/HAuH9kfziIrq46dAh129Ab6C+WzOOIDoFFZ1lse5xRTwGhUH39Ag/3D3x876Fk+rbBGoOxhuxlppdTYjwOlFLYrtc4I8SMn3/xJemlJ8wT16sNfdPgTA0KTTLzezwe2Q2DZNY1DUvrGJMEApeYOA6SavCP3/2AtYbLywuWuz0hF0IQ42CtNZvtktZZFstldUzRdM7RKoPDodQHTrv1cRzwRYTITdOIKXRjiNFLcrqyKFM7eGUqwSSRk+TB7XazTJlKEeaqtSyWi5pqHzgcx3N+m9IykE4JTtEtbWef3F+osgFlhOhfhEBRkgi4tTGyqITIOMo5tsYQCUQCVycU6gQNqxrbo/94kJbOCmroOoGDS67xRjljtBNNWZbZrG4sumvR48nCL4tzT8loVQORnXSeBShK0ubVestq23O4e4vSmaYxhGlkHEYeHx4ZxwljelzT03ULjLWyFS6i11TKnmd4BepcN1ZJh0zVcu0AU/UwPf+jo4jbSxDHIaVlBpgUKmrKZGHsUMMV66YjbgfGmzVzzgQEPpqmwrDzdNnSzBrbtZi+oXWFgSJmzzqitUPpUNMqRKIRUyCoQFSBmCfRooaZ4gN18CdXRBsu3/w3bNY9OXkUE4VAZkKVGcqM6TMlFIo/kuyCFDLajmQzwuzRYSSrRDYRjKYERRwN4yHjh8J3PyS+v4O3j4V/fJy5nQs/DIX7qRXoGc/lMnGzVPxPX8OLReGqB18EwVI8zRRLQVItoJoCKHQtluhC4OlWs9aCVkzB44vYv5m2gVTzH7OwkbV1WNdScmYYhooGCeqknZXO6NjhOsP2ckFqFckXhuOIXTguXm25Dju63nF5s6FfaQoT3739wN3be467kbbpaZqe1vWUchQ0qWnoFz2usRx2U0290NilFdnJlJiHmZxPhUI2su16TdtolPcoa0Xi03TkpNEq8+ZnX9O1htWqob/corURko/VoBPbFzdcvHzJ5XEU022rMa2ctNMI/WS6rlWhbxsuLy5ZXN+wuLgAK3mecfa8evkZN1cv2Ky3hCCEOXs2N5fkkVzRqJopIj9LIsf/H+BQoxKWCGWWWKM4o/0OEzwmZoo/EOPA8f49Xd/S2A6rFc45mlaw6hg9h/0RrVVNS4/4cSSHgIpebkTliVozO8fu/pbbpsUPA/cf3vGwe+D+4Y779z8Q55E4jYRqKxYrLCe7OXWGsvw0opUmOAMlysV61l30yw5lGjKJ/U7iUOYwE6LCeLkg1Bv3uD8I+cUHGtfgjESU5KrfG4ZRnNDnQCAzx0jWwizLRuNzoqSITgGHdJUoYag+HPY8HvYy57KGy2nCNo7GCfFHaw1mIzu1VlLrtdJYK7OunD5lvUorUykMlSEZY8H7QAi+mkYLU00cSmQHe9Jen91P6q6toMR2SVpXgcErs+CMWJaaC0ipM5JELql6pdZ5yfnaiJuLCN9lNnhOYKdCuTkTS3r2teS7is1cqfgKn2725JeffsBJe5jqLC1MKGUxqgVlscZibVvJMU/vzZw6AJllVOVC3WQJ8cWanqZf0vVLwrGlNJmFaWj6DaJe6bi4fiUz7YoHKWPq+f7Ur/REKsk1zf1UBE9FudS5cFFPNtMqP20wznr6pMjewGSIo6YUizINpulwpT4b0aNsQ7FiWqFjwR4LWgsUZmqKp9xFlZqjq1SDRCqBjLxKEa2naAlPKsJn64bpMXaF1pJvV4pHIeQ6YeI0kK3Mkq0FHE1uyKUBk1EpUIKiWCi6kI3YnaUg3cBmAcNc8BEekiHpzEPIMNYOMWvSMeND5g/3wjDdtBpzcolW5WxYX/dY4lxzdv95Wk9+fORShO1cCWvGWvF/zTLBtM5irRHmMIViZFZvjTjNnBjLTbcEA21rmXRgOs4cHvZSzJzM+2xj0VpzPI7E4Ln/+Mh+NzIdAzk5cgqQ7bkJ0MpQsiZF8KMXspPWNEU6PeOqEfuPGiVtG7STIF5lnBATbSPXSkdxTWot/aLjxKlx9XtnCq61uNZh24YQIyF6mixGBqXe26roZ24HTwHaJUWyH0VgT2HR9zSNQyuBn3OqmtFKhsrFnhsfIUil6jiVZd3+CV0g/L8ogimOkGWQGkPEDxM2PNJRMFbD+MB8fOD++2/47M1rTKNJccY6w2K1Js4z+90D9/f3TNOItZaubWXalAs2+TrnUYRJ4VXh/XffMDw+YG3D8XBgf9ix2z8yHR/RpdBazWK1wrmGYhy63mzD8SAnJcsw2GiNLYEUZqzVlPIrqLSa9cWGttMs1z2FxH6fmOeJFAIpRMaHPUYpurbjuD+QYmS0lsY5rJVBf6kL+AMKqw2LfgGNFUZU06BywpTMFCM+SExN33biHWo1j/PIu4c77h4f0FoRU+D65hrXONqmhQqfaGexbUvT9ygr+jZnJLGb8qmBttKSz0aVUqSU8CUyzzM+zKB4NqOp4l7OPLcaTCuZadaIzCLnfIYcu7arN7mI3XKW950Yd8EnfPDEGFmujDDIjK4LTcFqRYjpLOYHKTpGPy2iuc4Pn9+lRmuUlVDnUt+T4bxonbUS1EJRKhRaEirNpPEj2i6w7QVKb8mmpWkkuPgpX6S+v9RUD2uhpKr9O/2JgjENTduzWG44PnxA50y/XnLz4g2rzSVFOdrlFtf1YCqNq3bN5Ufc9JTFcCGnJ5H6KVVD4OFaGCthRoTXEs0jc1FFSUqcdQZLPlqmRwhRmIXKtTjlsFhwI65vKcuWUAykTHlIdMZhTYMjEqkbHIQtiK46xRJJTNSeElUiqkgcjkVS3J8BbIjIoAFOXq9OHFmUqe4/DlSFQK3DqharF+AKJVqJigqB4gPl6MlTJOaEKYnWgbOay5Xii6vCq4Pju31m/TFRcuHjEe6D4TgXpinxtz8UDI7PNo4VsdrQFZyus0ANMVHjyNLTnfDsnvrkmqUsUidkk2pbh6vPUa4EQNsYgbQp4BTry41AejnVbgaWybIoDY3ZcqfvUeWRYf5OUBqtcdW+LCX48O6e8Tjw/u0dfsjkACl4vCvMTQKtKjrgyKGQQuRwP1a2sEbNmbYzdEtNSXV+/wxN0baRUYh1YExFiZyk2WiZQVuraZylpIjSCudask5olbGtwXUO2zQM04gbNc7KelCyEsLbSaRUXZ9ygeAnwnQkHB9R0WOVYrFa0rbiwRyDF6QqnvtxCo5y0mDGULkXHmMM0xT+6Hr9Px0/qQiWUviP//Bb5nkWKnWMkDK27/n81SXbzSXD4wOhGPoXWxoHcdzzd//pI1k70I5Oa5knxVg9NAMlBDprcErRFI+z0uXcec988Nw/PlKU7Jy7pqXvO642HaMJlCKWZNbKYiC05OofGQPei5VYKYJBD9NImicaZz+J2ZjDTNaKyY+4RcvSbmCvmEYIOXAoEyVlTBrFAV8V9vMe7VVNIGik4UqgszAWFyGxvNrSLpe8+OwlVyoTc5YE9kr8aCtEabWmv9hw8/lrXn/+muA9OUSuri/ZbDbcXF/LeXGO6xfX9KuVhPzWjqJQ53Tx0xhlYwypSKhqCEGCMavuzDorxQUZqKckN0zJQNfIQmXl1silisfhXKzk99v6EBeMsRXSQzqSis8bZ4lV8hBiEnKFkuKVyikyibMZtjGmmlLLy6BEI/gc2ahzDpnu1X16eaJinNiSsp2qbWJGshq1ws/C3sxxxrkJpz3LznBUiFt+TnXWKJMdozLO1YWilKoFlJfRBRZL1NVLHm/fkVLBp4TpFvQX15j+AqUtWRvppdXp9xb40ZoaqzmCnBLpOcV/9MmTVN6r0cVSsq7MRSMz5VIoQZFny3zXEe8t8y6RosYoh9ZtZWJr9tMO7Vom29CyRiXDeDcTtKIrmbLQlJSJJROKeM7OOZCyJ+UgnWDtAHUu2CxzLKeFZPNs5SDzULvGBMVTSiSnQVCZEsTEIATwM4wHcthT5jtK9kLX9wZCkm7QU112NKfcRevgYlnYOni5zPziAv7s0vDnK/h4hD8cCh8GzW623I2R746aFwfLjbVYqM+FNDopC6QfE4QoUH/MmZAVyT5/vmQG37SWxbInGyMMTWPom4ZGG8IcIOea5CL3cyiJYkE1iuNhEJ1sSKS8kST6Ymi7Nf0S2mbF7cePPO4eKRmUygz7mbuPe6ZxYjggjlEZxmFEmwlrFJfXW7SzkDTDMBNDYNhNMkvNkEfPYtmg6ClRY9ynUp2sjay5dZZflaEcD3uJqtsfsHSE1hCT2MNF689G6mbypP3AeP/I3btbbEpctg7tfTUU1zK/KxDnwDiNHCbPa9ex7hasnGOz7Lm4WHL94oJ+1VKI+DBLJ1mh5tOc1lgx5yhGtMHGtbKZNpGfevzkTnA/eoZxEjZaDBjg5cUl6/WG66srtD+SS8dWXZGAo09M40C2HcpC42Q+Y4xFKSlMVmuc0TQashYmltWgigTiDmNAoF1FWa5oG4UzLaqTuU6pcUcxJnxCnDzyiUlXzma+mYKPgeBn2d2fF5UihIMs0g/biJNNTGLzlFWhXS/F2DqDPdmamTqbqri0JFc/0f9DEScXW7V1UjzFxeW0wFtrK6QJ2lnavsOUzDyOjMeBru9wzqKM5A92XUfbdbimEehOnXZT4hOa4olwcXpMK4xzEmE/ewkpRHa35yy4gmDt2ZBLnYEVff68KDH3Pe8Za2eYqlVTKdScQARSrGxM9UzLGKIYRBdk0c85nrtLgVefvkBKT1l2zw9drZROjFBQPH+MT7/i9F5VTvl8FjQYtxRGq9aUEoCANeXJqLl+B9Gqi7+Q1vKdnhyi5aVVwbkGFitc0+HniRBnmTUZg+0W0r1V/ej52jwfZZ6+79kP9TmEUxmjnIrnCZqrkQrZ1QW85kVGRZ4V4w7ivkhwbT5lU6oquo8cxj3KTuzmhmW/RGMY5oT2ARXAFEMqYtl3kg+cn6ssJKtS9QQnSzVdpU4oSzp/uUJR0jFKJx2lCJZUIUekJc4KUhGzghQpUZx5SiqUaCixyCsVsVWT4SwgHsXGyulsDTgLjRH/0ZseNi1812neD4q/HxPHCDtfWGtFVpp0inACUpFU+pMgQ4hJitNt/el9qCQgoHfk2jE12tA18lOXRAyZ6KPMTOsYIedESJFhnph8wIeENUuckc1m3/WwUlxc3vD4cCT4x5quISzy6OWVohFySBbCoMnVMQktJJwAYU6EORLmTIlyzawSNCV5gc9/LFflNAoRPB4xZ0eYz95LqIBP+ClAErMGrTOqFsE4B5KPJJ+4v9vRWMOLy7Ww8nMmJjkPBchVJ4wW0pkxDcEnjNG0XUPXN+hKgsv1uVPnxJ3C0xTmhIyI3WGuZMGfevy0IqgU7fqK5GZi8KQ8gILNzWtWl5cstluaeKRXK171r7l93HO7O/B2SExYgrKkaTqTPkqJWKO5WvX0Rkxq90U8Kr2fhAFaqcTG6Eok8Kgyo/LEZi1KpJgi7z/eMowT3gtDKCtNv1jQ9h3r7YYYRLA6zxJ3JF3E0yGDWoXRjqZpUVrRLZbkkkglM8b5SeJQ5zPRB2IIpJBIYyTNkTR6psMRhcItFiwvL9hcX9Fv1zXxQAg1qKpXq8VljoG271lvNzTAeDiyf3yk73uU1tzd37HdbNFWbNWcE2iknApgTKQpEIYThf35k6pqbmEHFFKRVIwQgjABT4ytE60bYZSmFJnn9BQuW4vfqYs9vbz3zH4WT1KqEL2+xzlHigLDYmWQ5VOFL3PGe19RgUzbttja7Z5mg/M8Y4yh1fm5BQnWapStn+nMQHxadEEW/NNE5xkFCmhprlekHIQgNN2R44BWAWsN1jWEWI0aCpTkoQSxvzO1rBaopmsowJqOtjFstheU5Ln/+EAYD8zTnubyVY1ROr3jbLdNseqTeWZMMl86yVdOKQqnryfnWHpci7APczJIIi1oNHHUzHea2+9H0j5g54BWDalIKskYPEOY+e13v6HrFfv4ls2/2qL7DffRk1IiJk9bDHNJhBwIqWY+BvFhlRqtUElBBB0VKhkMDUo1ZG04qVXlTwrIXvdGnKKaSkbKi15S6CjJSkEsNXWj6nHVBMRIiZE0B5HgJHXe5CpTiFZLh1Gg2EzbF756ofg8wK89fHNs+P1e8493iSln7gfPm148P0nmbChB3bQZLZ1mrp1HKZrkGuKzu8k5WC4tL26W+JxQaHrbSgFEMynNkCNTGJiPM2Bo3IphNzIMEz883uGTOMFsux7bOfpOsdxcoV44rref4cx/IHnH7YdbCYhuesrWMjYjKd4yjiMxB7RxNF3LYtHTNGs0inDMxAmiV/ghSyRbytgMTjnSyhB9QOvnMAuUFCrmninZV2cqKDEjsVSWcUzccYQ8V9jekXOsBMLCNCeKaviP//l3fHzY07YLnJYN9zjN5zmeKZkpwebyinZ1Q9Ar/un7HVMotF1D01hSVuwGcHaBNZrGOvFyLYlpnGRT4WVsoo3GaSvB2/pTZOy/dPy0Ilgg+4nkZ8iJzln+b+7epEeSJMvz+8mmqra5e4THlllVmV3V1UAPMEQfyANvvHDAW3+m/lREY8ADjwSG4LBnejgk2AtrycyIjAjfzEwXWXl4omrmsWRFFXouo0CWR7m5mekiIk/ee/+ldZZV65iGnu+PB4Yff8uFzXTXHdM4EaZRVFsQSHKjM03XsN2tEPfqwsoU0TLPoC+3jD5y9AGHoomGzuaFV+I0xDCwvw+07VOapqFbrbi9N+gRBCMkdigXuxXr9YaL3ZaxFwf3cRg42rKAGk4PHXSRMoaqvXptXN0FJ3BCxM6pAhRKJsckUP8kATHHRPaJPHnIogrhWkcuUvq1yuCUJlejz4z044w2YBFNwBCFW6kK682a7ab2qSbParNivVnTugarpQSSU3WcGCbGfU+/Hx9lESdBZ42aM4qKcESJG4DW0vdTlSiudVn6f3Mws4s82eNsLedMrJqfi07m3L8wZskUmSU56+ph7YkOMZc/5+cxB8gl+0SslTCn65oJwzPHUD5WoskMboCaKHwYHBGGlilO3ALMFdl1hGLYPhnBGNL7JGowKaKL9Dmk8iyZs1oI0/W7i0DhN5dXhDhy8+57YvREP+DMnAmbmgmePZ9smDUtShHiecwCPFFVp9JohcqyaSKfAkqu0mgUOR9dNJ15RiwriB1M9+QQycWwP/YMk+d2/0DIGZ/EeimmxH5/z1Qm1q7Q7LboNlF0wkcvohApoVMNdN6gspWTLQliAV9d4StR/2MIiUJxgWIrKEtyDdoRTC2PqjXFrkl2DcrVHpQVZSpVQHmUkrJ0ylEc6cdMZVDQoQhIlXTMULIW31AFSksmujKKC2t41RVaAuEQsE8snVJgJQvPWTJBXelKRUvvPqFIsZb6T5clC66zrLoGHQNKadauQWd5ZjSNiI4ry9GMYvZcGgkqIWNVQzEZlREaWbpHR83UBFq74erqBX/+59/SOcfvf/c7tFK0jYBRvPe8ffeW+/tbhr4HilSL2pauaysIrXA8HJimkdWqWZxkNo1m1TW0XYt6EPNw5i1dKUz9AZ1kzZMNgPSZsw9kHzjuDxxK4FZHjBYgW8xKtFSz9K7v+gNBZ24Pe4boOXqP1eJM0Q8TPiZiymwbTer3TIcDv/uP/w/atTx9ckE6vieFnh/uHuj+6fesL/+Z3XbDqmu52G3Zbdasuobrqy2NtSLVmCNKi4UaC3L8y44vVowhRVQS8dLOWbrW4bRi6I8c93v2b14zNXBtdrJ7nESLsSjJzozRdG3DZrMSwnDJmDiiY0CrVJVADN5YOptQKYuck5YFVeUEKQnYxija1tKtV3StY5rEOLMoIXtebNfsdlueXF3Rt0f85BmcwelT2jwfORZKUti2kVmlECh/yWikXlCkRlCDYBGIb0WQxVgffk7okFE5k0MSRGhJpBgwpZbwfKxqBgWtWuHgaOnpxCL9KKUVq/WK9WaD1QZvLev1mvV6jbWiNkEqRC9I1mHfc7h94LgfHgfBeb6eZRt1/taAVkVqjRDhUUrU5+smYRGxNebjjcNSepiFqnMtgaoK9rGUfKJ2LJr7pZriVk6eUtKznAPhElAXrtwHKvdQvfj00hMAqpTXHP3qj0dz4BSCBJAivQ6jNhTjMBk2lz1oGPsDcRLZLq1mQvocBGVDMX+v8PA1CsNqu2OajiIGnBMxTBhdJFiXc1G2Ou5qZjn/di43FoTgP3PWxJC5PsgyX8tZpltEhsuVS1Rak8IKHfeoJGXH/XBgf+zZH++k/IdsdkpJ9P3AlDxJZ5rNGu1GcYmp7gGkJEjwpNHRLmbBpRgImRKyLPhZne77o+tUKDoUa1iCYGV3FQ8YSq4keduK7lmljyhJnyVgLpDlBFlcDWwWAIpTp+9WRS3no0t1eSDRKsPGwqs1lJCxMeJAQCxKE3Ih1kAw95KzLpU+UOk/+THuVahABmcdGdkMNs5AdbsQv0SN0Y6SNTEUUtKEIgHXmEZKymTS5BlDJg0J32S2q8jLpz/j+YtrnDGIhYZ4n3ZdR0yR3eWK9+9XHPZ7UkpSSWlmj1KwRmENjKMIes9VnJWFxhmaxlVa7Cm6Fwp+GDBVMWsGsOSsKFEEGmLwhDAS84SztdWUCj76BYwYcsJ2YkL10Pccv5uwuqEU6AdPP3pCiFx1ho7AKo/84w83TAm2646X20JnM79990Cy7yjNG548uWSzXvP06pLrp1dc7jYoZditG0rnMFqhqwZsXeT40uPLXCSU4umm5aJRi22IKoXbH37H8diz3x/Zv3nDAU98m9ls1mDFQ27lVuSm4+l2LSaUjeXd+3eMQ48/PlCmI4SJtm25fPqcX/7qa3S3IqPx+Sxg5VRV7jO7ywuMNVBLl/0wEXxeyoXb7VZI5VqRdmvJLGJeTGuNyEBQgH5/JMWJGCaazmKcwTYGrWonJlUppXzSKQQJlDNJvSC6jrpkNAWTYRo9wUfuX7/HFU2jLKqalMaSKonU0W3WOAraOra7LVprNus1V7sruqal0Q2t63DOobIlBSlt3N/fc9gfef39G+5v75mGgNu+hCWrm/3wIEUJLFppuq4TAe0Ymd2452DGGVl8dryXz5Jgpo3IX6WU8F7KZ+IBOJfJBMoMQpxtuoZGtxz7oyCKg6drW0GcasOQxC3AVhULV42GBTgl6NZWRc7rhrYKMpRyyiCXfO/R+nsKkuUsP1FZXNALCrVuUaVBbVe0uxY/PGO9coTjHbF/IGuFKoHoB9qN+VJAcAAAIABJREFUjLlFYIQznGjRPHn5FW61Yn84iBbu6FGqYK1CwCKPg3nKj5sxkw9onyk6oYuIExQsVikhW2sksmctjijCJxLQUHKMNyvSzZZws2ZtL9Bu4jhE7g8H3t/dcnv/Pda2WNfSNvL+OHnu7x5Yux3Pr1/hwx1xKpgiGaCJBesd1oObksyFbKpqU5FAmGuRtpSKGNXwqEsbQUVmqsXcySmzSWAta2ttoNILrFXSC0yGZCp6tWR0RXQrLRD7nAtj/ZlTxilDAkIu5NpSSSmzNolXK8W/+YsVMUoPbduKDVTKRYAgWolsWgUPZV0qB416fmeXVKTnmIIiRy1uF2SmNIoHU4IQTwbasWr7hpxpVEPOCn009MPE4CM5ZcZhz+ubH2jMW7arS9ZNS/QJdODpdVcDJrgmkVPh6klDLh2uCQzDgJypr2uRAmtoNqAaaZTm6vLe2oKziqZVNGuH0m6ZQaUUhmOPig6UwllXxf0Nq1ZKkX/5669kPeXUIpm8X8BtIM8j5cLhr/4CHyN9CNKqyoXoxSFmmjxl2PPw/g1vf/8bfBLxBjuODFaTdOG7m4kBmHTB/HCQjTIKawxda/n21QUvn+148XTHt794ydXFhufXl1zu1vj0YRf388cXA2O2q47cWlEhqFlZmAKdteS2JXdrbITgD7DONFbz1cUVbC5R3ZZd14qqidbc3N3iY+K+H8neQ4psXMNl13Lx7JrVegtKSxOVWoFBmumKQtO1VcECirZVsqhUPprQF1RNgYqYCdZdipAs0zlZXgs0WhARUlLVFUwi7R8tk0xBUiewja7wZY2pwsZFsgYlO0ynHdFEVF8osWYGqjpkk4ladlulbasQr+Hi8lJq/92aVbcVQn5FAoap4NNImGTQ//a3v+f+7p4f37yr7s8NllNAyDmSVGR2AJhv5MK3yadsthgpHUnpVCZ9quWTGCPZzlp9ME2TqNAspd1T1hZjWPg5qRSsc4LcmgOrMcQafBVq+Q5XFWMWD8UsZsCqok7PK2ymZqcLSvNxxfN0VNmrR07nhdNEVafQqIqmWW8x1rIdrhlJjHEko3FaYUhYXaXp5prr8r0K0Oi2Y7274MXPfsE0TbU8bBZbpg9PsJjHq2qMWfRfTcKoOXAX6Z8pqpN6WQBIAmQq6KRJQTHuI/HgCUdLSuKlGGKsuqaemHxdzUX5JedCTJlhmJi8Z/2iQw8bvEf4Wgl0KpjoMBFMdOQYxIk9merBaSrUX/Q+I4WSNYZuua5ZWrvm4cKBLAlVPJSBkntU6lFphBwgB3IIou40BdIQasAtLP5G1SKrJCXlUUlSqkBFIUeqLqhkc7oUGiJXJhEVBCPZXiqKkBShyL9jEeWYVCSQxiz/ng2WzyuiKUIKCFAlynzKpZceMBBCwtmmthak8pWL9O2VNrSNlgDqNVPKpJg47A80WmTPpumIRuQec54q57ZQCLVvPgmwSyVQidnHUiorAjxTGqzTuOxqOVRhbMZag2nE6PeRZm6BECJTLSWmmoQsXNRSe9VawFBGa9F2rcoyWqnlJ0qx7lpijowxSKm/oqtDTLWsveP92mDSxF+uNUMsqORhOOCHgZjF9FfpIpt2WcIISvjbv3+TeTiOvHm358fbI7vtiusnO549vaBgHm+Kf+L44iB4sd3URT7LZIiRCUWjDCvb0PiR1GvK/oDVinXnePLzl7RXz3C7q0UiLObCP/3mt0whcXMYBKxSMnkNrFY8efmMy80lWhmRx8mlTiVZeNRMPC2yU9tcXC1ACFUb66eS2nwXZOLNO5Sbu1P/rHEWZw1azwah8t8M4bNG5MAqD7ku+lJ2NRWWNivpoxNGKdEFtYriMtxnhqFnPI6yK9NKkFRe/MFKSNimoelaXNegjaVzHRaHKoo8CdE3hUAYJvrjkfu7O/7+//x7bm5uubm54xe/+AVPr589el4xRSLiWrngJysd4iQdNwcBCYCm6plKphKX3lywpyA1DAMhhCUzNNXJPRcBuwyDCBhMwdO0LU3bsNvtlv6ir56SGiUBtlJYcgXazOXR4D2qUeIecB4EK3dx4Ql+crTO7/ng1XLyepBAc3rdui2lW1GSR8WJ2N+jCrRW4UhYQ5Wm+6C+XJErqjoK/KL9C/b7B7wPGFOb9NZ8fC5nQbBQM4dYIEcKumbhlSYxm9WeLfTSPy2ooMle4e884U7jHwo+imzYFAIpC6E9ZQE8lIhQirIg8/p+YJwm1ruuipE3jMceHYVEb0LBeIX1iRQ8KokHnUoWldJSxo05EkqkOM2GLaeHFusGdr7YLMGuDFAO6PxASntK3EMaIFQ8wTCRx4l0GEUOLmlUkPMX0rSoMgUvm4FcM8IUIYaZLlVIyci6VRWAklYEDH125KyYkiYW6QcmBFwTc2GKpVotVVrKWRAs1CDoFXHU+CDAoz5MNE4LBzbUFkl1u0BHQhzQJDSWVbOWjUSwJF9IIbF/OOC0hwR+OtI1HcYYfOilAlPnYsmZKfTENJFLpBBRyqCNWlSHchBNXmstrZJSbE4aZ0X6TDcOba2Uts+uawoRpWRt1VUoJKZEY50EvWrjVkoRqlbt7c/VoqZpcLWyw4YKMIzEKFmqqUpcWiuK9/xwucJow7P2ijFlHu5u+e3/+xveDO8opbaMjCZkCe5SeUn4kPjhfeD1+wNKQfuP39G1jt2m49XzJ1xdXvDNz/6MR4vHZ44vDoKXl5e0ztA2Bp2FZlASsuvymTf/3DHevaUvE1cXO7ZXF1w/e4peb6Ht0EqL4Ow4cHt7y83tLcPoMVphrUMb+c8Y96i9PiuWVLLMcj4KoYzNO5k8N3yKEuh2nXWqLoalqIqETGfLmOLqckO3Fq1EbW1VXzmBM6ySEo1K0hhQgKrOzeqM3C1JlZQTSwlkQd+zMg2qkx2VbarhbOuwnahArHdrXNNgnVvEo3MsTONE9JHhMHLcH+n3Pb//7Xf0fc/xeODt27f0Q8/hsOfm9h3owubpzx+Vwme5N2NOPT1j7FLbWf5UVw3PmvEpkJWmHjPQxVq7OD3MKu65vl5KoW1b6VnEyBRr5jFn3bWHGEKQ0qzWS3FsHMWWKNUm/Uy9mIKX+96dAoixRrhNn+h/PjrUp0OklDM/ziIVooCz3j1heLghmZYcE61xbLYiJq6sedzzOmtDVq9rnFvTrFrhOZozn7mlo1TfZB7vwMcwUYJGm0JSBm3ELVBnUdpRpYgQQUJkxjIQMjqu0WGDChcMh4mbt2/x4Z7JTxUUcyTmUDPyTAgjPhzni+b1D9+jM9x/8xc4taYxW3LZopNGBXAhk0LCTGCjpkRLjoYcNTEoxhQJOTKmiZACujWsy/UyDjN1kYba2wqUtIf4hhLfEfb/iTy9JU+voX9DmXrK8UDpPXlK5KOmxCLE7yGRYyFNBbIooghSXzYGQkWoWXCAlBSh9vVA4YM4rIeimYolFk0omiFEQor4RM0EFaGCQoRGlCGWR4ulLg3EjjR1jMeBELPQOjpDtpoUC35I3N/u0VbGuidDEtPxzarDqJbNquPwcCT7hjhqIXunkTBFVJoAxTBMZxWUee5ajG6wOuNmw82iiEGoCtT5JU73ot1ZbMaYLBkjllzs2bgESmEcJxQiBCJWbpXjW4Nd2wjRP6VE27bLujKf3/w7VTe2IgYvEnOl+iBanbHK0G13XL9qwF3yw90dPia+en6BP0xMU+HlqBlSYsqRUYvmcqogwlQkU58ncRgS/Zi4P3hev9vz7OkVv/j6z76oNfjlsmlGdvKNs2jswg8qSVEaOK7W6FH6f861WNfQdCuKayhaLzcGWBCflELbdqxWLZe7C9bdSlQNzsAZNa7U8tBZeKwLmVazvxnLYJ8XQDX/iuoOTRUoPtvMa62wWlGYUZKqiuFmEQQWPO4SFEWuiqUfo+p3lZzJlUZBEhi3OMAI4rLtOppVi3GWdtViGuk/tl1b1VQMqkj5MU6B8TjgR8/+/sjdzR33t/f80z/9M+M44v0kYJQiouSiOvQhJLhmKOoUiwTQwglstPxpLe0uThSyadDLc6sbjTPFGJDynOIEZJmpEUqLwv+8c3Y1eGqtSUrczGbVk5ILaTaJLRXBWoE3ZS4Bnl2VAHrOGuCfPH7yxWX8PO4oKiga3ba06x3riyf4ccR1a6xrJAs8p9c8ioXyfTLezEnMvNTzmGfiWW03a/XoFGOSTFBToAKUsspV4JmaQdW9icAW0V5BbMB3TD2Mx0Df90xhL35tflru5QxgSimTUwQlc3roj+wfHjgee7ZdR2tbdLbobDHZoJMShGgV59ZJzbGMHJAybk7Cw00e8+GSkj3kUTaheCgeyoGSbinhHcW/g3ADQfABeRpJg6eMCXyWtwdIoQhgKYrLvHDuFN5DjKLyklOVCMxS5kwZfKqmv0UzJU3KmlgMsYiXqc8QkngPhuo4lqmONfUR5ixCGHYeVgWmIbB/GHj7Zs/D8UCME4WRrjFYq09C8z6iqiFvNhmyQmE4tmC0Q2F5uJ84HgJ+EMCbziM37+6wVQz+5uZuEaqYg0zwgb4f8d4LqrwAuXAcRlnjtMZa8fWbEdlQhNOnFEplvE9o8/h5aa0xWuQETUV0WpMq4KauU+r0t+qDKHOO9p7X6FmSUBVFDoEYpZesjSMVhbKOksRxRoy8t0yvnqHcioP3HKeJfpykr5oyUwiEmJnCSfe1qkyggJi+XC0G/oggKIoWSoSHlyAlqulaKbZNi3YrilthTIMyDXa9JSgniLSC1MLblqdXVwzDwP7hnpfPrnnx/JpXL6558eyZSDfpqoZiKri/FEotSyh1QjMKa6pmOGq5D/Jlc/kUkGVKCyr10eJYSMGTfEEZUX4pgB8SQz8wDSONou6mdF2qARVrObRqWOZEjhE/eHLMlDibnWqsNrh1x8XFjs3lBa51tadJLZOwzLbsM6mPHO/33N/e0/c9t29v+f77H3j9+g3//t//hwUJ9qtf/RkXF1teXVzTNGLC+2g4FkEWaqMRjn9hShFnLdYYmraVP6vITpQSFZpHgUiCY0qZECPDOFbD4BOyUbK2005RMkZDKhZbkZybdiV/nzLKSS9wqmWbVM2AZ7RqzCcVGbGZOrsmBcYqrJOe0GkfyKncBtXa6WzF4sOQqD4cBqfxYjuuv/qay8sttzc3NN0a261Qbt44fNDbqxs0zvrFUrKRn2oejvCoklHs4xPw0wROo4smKYXOoFNFplKgCLeuRMArdDQ0voOwpQwXvPt+4OH9noeHB/rpvTg7FMR/ru2gF3HwnATdKvfbcDjscdrx9t0N5tmWdrtD5waTIiV6KYsGYTToqFAB0pBrybEw+cSUAmOcxCw7zxl4rYqEO0oMiGC4hzIC7yn+e/LwO/T0O0p4QId7wvBAHibCw0QeIAcFk6q9N+G9lVRLokXWosOIlD5TXlClpSh8LMQMU4IhK6Zc6GMjwJdiRG2niMlzyBIkIwj/EkVUEvxSFg6njmXpdOZc+PHNLff3D9zfHTkMR3JJOCf4AlXXohhEFD4mcXBp2+pWAoKgtA5rHDFYhuPIw21gGnv2euTv+L8ESZ4z9/f7Wj1RYu2mpBQcgwB/Zs5xTpnR11ZP3VgvCPDa25sHYwGOh4nN+pQJKqXYrrZs153Ym9U/nEuh8xzNNQaUun4sur+l0Pf9Eh/aVsqZxpgqkh8Zh4KfBEm633uOY+DuYeT+/Q0pJbrVmm++fsaff/s1SlkeDj13D0fuD0emEOmnqYoMBB6OVTs65UW1KufCMIx0XceXHn9UEAwR8Z/SqmZsmpISMRSGw4Hp2JN9QmuHdh1Rqaq8QFX917Rdx69+9Ut2ux3kzMvn11w/ueLr5y/YbDbookQCTAnKKRV5vyx5ItBbi5HMi8xpNzCfbTqVJilLZlgqQOb8uH17w8EpVC3HKq0pRYvcV0xkY7DG0rUrcXSmiM+gcbXXk4jBE3OQUkQskGq2ZAyqdZimwbpGvP/GIM7LWpRMisqEyRMmz/jQ0x977m7vuLu7YxxGjocDY7/HkHl2fYmxltVmzctXz9jttjx7ds319VO61YZ3D6dmcNs0aNVUUJAodcz9y1jElUMk08TbTJCutkLzJXjkFMghV/d1xWazxtQsUtQfohCXvQTNBSxT6R4y34UoLYAW6SPFnCgBVFN7jTEtdkH2zIUgl4JZtjnyxK1VOCuIzCUInrUNT1P8PBB+Ji989KYa3pTCrlbQWJrVWjwdu+aDEvJ5ZsfyvvOAfRZXT0DV89dtVaipfxsnD1ajs5R9dNVa1SrXbFsyQUr1C8waG9eEe8d4D7dvbjke9oxTD2q2lBKOm/QRTb1/GlXmZ5+JIXDoj7y7vaVtrlC0qHEiey8bu+DxYTr7z1fvQLHoimMFMwVR7i+6nN2XRO7/M9muULqFKmJf/Fvy9APZv5c+YJwgBggZgoIklKeUKsAlZmJU5KyrQopkfqkGr1LHGRXgkooioAkoBmAsGV8KPiPqUHVTnTIiiVbdCGJBynec1p2YCikVbD4bVSXz+vV7Ug788Po9IQn6VQA0PFqT5n6lUkilpRYxEonZcaUkQ4pZgkMKaCUBqtTNsQ9RKlCKKsSuls+eqyV1NyZzS4ZJ/VtOAbDeJqn0SO9UfA7LUrZ3rnprprSsJUoJYKaUXDmV1U6rnCpu51xiETjR2AqKi2FYQG/Ji+uIKE0VkvfkMNI2DqNbLi+vuLjY0nUtVmtifIr3iTFGYkpMIRDTLI+ZxKe0gGtaqXAZzeF45NhPfPd6/6lZ/9HxRwVBrcDHJJwMlQVlGQslCAk3ANk6sutItmWM4oeWigjsygpruLy8gqJ4/+w9V5cXbDdb1qtNdZoQpFnRufrZzSwzxQzM1XVFKUoG8ly2WJ5aqd2mJTJKIJzLQcskLXA4jBgjiC1tXAUyOGn+5owuGq0RmbZSMx9xSWFeOEME7ws+lKrkT81iZPCZokR1IwcxBK0QeNlNZaZxxI8j/d2Boe95uN9zeDgy+YnJBxSyebh+9gzXNGx2Wy6vnrDZbri4fMLT6+d0qzXv93cnwI9OKCWycgkvO1UVF5CAylUBJ8Yls1bF1OxvlhBLlBjFc09XCoOSyZ5SQpckn6NkglDvjy4FNdNFAFs8thhxhNYJQ0KZvJRTkpLFJlZJu3kvU0rG8tj/MU89ufIEP5UFnv7y/OdPH+f54tx1UarQ2Gpp5MdlMyVZ2cchddZL/fAb1dl5nh956k/nV8D3shFQTqOdQpuMsbrWOiq/ta5upgI+nIe0D4SHgePwwBh6AhPG1ay2KHl/KZimCngrgylBAAtJnOuzyez9A/fDrVQ4wh7iREkjQxmZ1MSkPV5HgolElxdUsGo0OhqMkr6Pce6s/Ft4uPuREhvQHbgGkQ66JQ9H8uRRU/U+9B1hSKQpich8rJq4SSoNsYh59CxnGOu48QZK5f9SS5cxK7wyhAyjgSkVQqrk9/p6zsL/qx60MreZVwt12lin+kKzeTRiSlF4n4lp4jRi60iaqwDzzmcpg584pokMStDolLrRK1qsq0qhH+Iybs5rHmkeMEtScBqBKJF2LJ8Yh48ShnqOIo7vzj6iMHjxPc3pdFVaKREqSJkQZyF5agVIOkZuyRYl+BmjCVmRs+h+Cio9MY5TRfRmMOKbOk6eXIUFtGkEG2ItVmus1XStZs3shDMDc2bMsXitugoiMsZy6A/c3O35/s1hWQ9/6lDlJ/7qb/7mb85u4el/PjWpZyI5pZzUNWZyzfk3KGrvByGHzxBc/WGPhsdj6vErj14//4qzX332OL9kuaRTxvCp48O692c+lQ/vpHz2fFXqs19xTg6fg8Ci3zdPsXrPqJmalNsrqb3u4vIZsbws2XL5g/fjw+NxVvXxtZyd+Gc+++MntiwI9YH+9PseH/rs99rYj8/jj/isnz4+qpF+8Ps/5TM/cxSkNzd/w5mY/8f3+hPnUuqYqitezqdx8uFlzP3WT55E3ZzOGb6aUxXOxuTZz8fj/MQPPf90bU+Ls7Pq7FLO72M+26RSx/cHkxk+mlOPXvvEFX24Hpz/zedWuj/4VOs9Umf9sxkI8hPLxsdfoD43js5LBOWjV/4FR91HHzgD3ubDVgPxj4bcZ+fsxx/Po+d9eiBl/sfZsTzyileYgZDq8Qedv+N8eM4Xwflfz+tnOpP5OY9lHx5fnAmWs//55M04QwIuAzB/4i/PfqX1GTz3Uzfpg7d/dsP/07/6yUMu6adH8pfsJj7/2Z+ajj911Aeq1ONF8ScOacN8OHny2b//tOOj933iEv7Anfvse//UczoPHP81HWfCHZ9cLD589aN/qw9+nv2+Liuf+KzT7xbG5+fW508+sNPG9XPPM8TPnO9ZdvNHHX/oLf8FPvJTx3ng+K/piCn/4T/6L3UsA/BfNOz/wePL/QRT+pODwePjX3xv88d9+xm6EUSDk/PS2uffuPzzD529+mRKOJc1PvU9H2yzPthM/OQ27GzXpc6u61PZ60/nux9/7ozenHe8j5a6TzxGVeHkc8llzlhz3eF8UWlCPmhRq/nwWvKsf/rhlX2ctn50j8v823otn6I7SHb9mZOrN7B8Klp84pELmpiPzm2RpTsjKhc+XHw+vdk8Pe4/lFX/xHs/c/xUZvXZ1z/6/MfXZfT5WC71Lz5VIfhD5/bp+/2pMf3Rhr2cZQ+K5fwKojQzlwfPqyvn5zV//qM9ff4QjX36gjkTmb/zo6HJKWdQn5pIHxzn4/HjKfTpdenTs/+Tny5ydcvn59P4/oKxdW7z9fh71NL/XF5dxr4+odPPflLxBJ+6irMK+/I954pRZ9/KLJRybv32U8cX+wn+7d/+LTc3N594VSEQe7MsejNFS3H+APXZL4RhFmNgmSJlniA1WFT051wnkuciygvazPXtT5d5HpU75xulBA3WdR1//dd/TdOIdt8//i//M+HullWBS61olcImgYLrrMmqUBoNW0dZtyQNh8GLpmJWtLoh60I0RbQmY6G9D5iQ0bmQO0VyitAoctsJwMIqdInolFF9pkyRMglGW1mL6hpy48TjLAUInhICYpElDgLF6uqtVxVhViue/Jv/CVXVcp5//Qpj7aLKknNmHAU11bbtAmSJMYoUmVZAXqgMzjlev/6Bf/iHf+Dh/g6tDVdXzyviTOEcBB+I3kNRNG3L5eUlv/nNb7i7u+Nw7Ll8csFmt+H773/H3e0dP755D1WkuHF2aZZPk0cr6SVcXTzh6uoJv/7zX2OswTnL85cvmQn5//v3/4EhjFLCs5ZC4TgOy3Uao3HW0nYO12iUKuQcKiCqoJVls9mwu7wkBBGKHr0XlRcFz148Z71ZsVp15ORlLOXCNHlyEs3Hw2GgH0ZsNVTOIYlnY8xkH3m2vaBRmn//v/0dY+/xU+J4OGIazeZ6x1e/eMn102v+6uu/QitNIfOd+TuK9jROM+WIT4HDOIicpgarpb9qimLbrjFa3tf7kSkF4cOpE2hszv4UMGs/KFUq9lG6KUUp8iykkOuGJ0NJulp0QQiVapALofK0xAGiTvBqL2WKxhRY2w3/zYv/Xp6n0fwP/93PMTpxPB4ZxwGtFE+2O6wVsWOxB1vRNFu0bmXeqyqPkRPjdECELkT4O+eCD4nJF2IshFiw2mK1QRVFiJFxmOiPA74K5+8PR/q+5+5+z/Wz5/zs59/QNg23tzf8H//u3/Hu3VvGceDyas2Ll0/56tUznj19Ip6ZGbpmQyiWf3wXap8w0P7zv6WEUegXWTwIR58p2hGL5rt3D9wcJt7uJ/ooa5LV0FnN2hm+fXXB8wvLs62m00FigDLoXIPPoscL1sYlHgxeESIMUxHerrFk0zCFzDBFVJGS5rq1OIqIwKmEyIyH+vwVphgomtQ9Yfj1/whK+L+//f0/M/QP9Pt7pv0ooLVU1+dcRHBBG7Iy3E0DU0qMMS2o1eBDRaQb1qstOWd8nFhvO1brFS+/fsHPf/ENz1+84uLqgvWqY7PdsFntmMbA//e7H0hK9JSNcWgtVI22XZFS5njsSXoCndi0hotuw9atuLm9p2kaLq8u+ObVC6bjgf/13/7tF228vzgI7vd77u7uPvr9DKNfRG+pXm9zEKQG+VNdRhzGlUh75WqyO287FhD6eRBUqpZOBSU3l/qWIPiZQHgeAOffrdfrRzfGPzzg7+4wpRCtwWklfKOkKUmBURRnKMWKtYiC2I/YiHABVStK9E1BmyTk7mOAIIuKilrusgPaIHJZTpPSRIoJ3WdUzIKMG6P43I0NsXUUrSB4dAioGGUAIoCbrJWozxhDooiExdm+zTbSXC4BjLIibBsjrm1oV0JoJyhCSguPaUZpGgybdoPSmlCRgNaIFFzTNEIN0RGNwiqN90Gc6Cv3M6XE4bCn6SyuNXg/ME49x+MeraqaRGmEExkj9/d7TA28jW1pm5bj8UDbtkArz7dCssc0MaRRxNLRZGDM0nhPOaMTuGJIxuFKJZrrXLPzIgspjlw8Pg6irJMjSQWUUkQd6ePAOIgs1byH81EUK1QKHKZeLGG8SHzF0TN50VSNIVCSZ9u0TGVkKkGCGhMkiGNiF9Zs8mrJbgqFqCeKEVsqXya8CowcMEphtKLo6qmmZOODFoH2oHo8Ho8SxSRlTl6KiGTYPA/Fba6gkEW1AKnKSy08zQwlG1FlSeCjcOamWPAVHTgnrUoVRIVHoYvBltnHsW5oFWw6gzUKksUgNjibztA4odKsNitcs6Zt12jT1eecEd/ByDiJ7VWMGRE0gNRaRi9SiT5knLFY7cRCKBgGA52F4B2DBVMClsQ09FiVyGEgEki+hzRCmiB7nFmxbg0X25arXYfWhpQKrXOMSQOh3teCCUeIg9g35UohmTKpCmYfH+7YP4zcP4wcgtxfq6DTMDWG61XkyrborsXoqQZ/y/z0JAiCUhmbfN10CCCK6kOssiMry4M/sh8CD71HF0VrNU82LRetxTiwJqHwQBAJQjHBAAAgAElEQVRQGnoJgtq2DPOaSeHeHzkMew79PeNhIMck62D1jyRJECzacDeNTDEyTIG2aWS98EGQ5trIHMmZEANjXLOaJrAGZVvGJO5CSUGz2RCKIWmNWz1BY0hoSlEUJQE30Yk+c+twbcI6uNhYNrahVZZwdyDnggmRpDToLy9X/wsUtgUxpytEL6EXSoMCdJmxxWWZaOvWYY3BmJahP5LGePZp9WeZixkZpQy2aWoSmUWCp2qCziLQ87FM//Py5U/sBmYlmEShGAOmBhilSFpjnaMYRcwGFRHD1JxpRmg9tKowtTCZRGcLVhdYGZQtqKzE2oNMChlVPV6KAj8lUvSYVEWhGyd0hlQIPjLWINfF6gGGobRGELkFVD6RQ5OicuPOrsto0ILg6roOpRT92GOdpe1a8pDJIeOjCD2XkjkeHgBRgNntdnIvc6G1HV275snuKdvdlqZxTNO+bnAUt7d3aKVoTMNuc4GfAq9fv6kl9Ey3allvVmwvNjS2o7GOzXpNCCKk2/c9RhvatmG1XmGc5eGw50IrXOuWkpdCFGM0ZiHjl1JoV001GU1k72WhT2J3pY1mvRa4tdEapw2tsbQp40dPyYmVVmzaFm0tjXHcP+y5PzxgHAvZeFEimQqxj4Q+cPfDj/jjwLg/MkZPyBFfEumbkedPrrh6cUUICT9FYrnEJ8/R76HNFPu4Z2usJotn0xLwQZB5xoh0tFWKBktrWrRS+OQl8FX7oMqkXTwM5wK8BEAWqNRcDqxJh/yUIos4BiTISZFi/W+hJIicVqmbVV3j3QLQmTPDszJWqqhy0FjboKECuKzs8k1z2vEbK+jPnCsEX8S1U4xMw1izHCvjuUBQIqrvbKGx4qnYGHDaobadVD/6NZt1x+F4xGjZ9L378XsgMQ4D3UpzebVmmzu++uoFX3/9iq+//oqnT3YoFMEnKBo1FRTj8lwa50BFEhIAo5bzDlhiFJ6i8A/Vgj6lFMYofM/j4Emlw7YtptZGI0It08pAzqgSUUQMvWTvJbPShiaJQumUO4ag+c2bG94+9Lx76DHAxjl+drnhV692dLahbfWcWmAwYoJc1WJCcwoBmcLb3POQj+zjkUk0bmTTmvNZm0f0YCeTq9+kxzkJ4KhKY8mRvt9Lza8U9GDwIXO/n/j9D+/o1i3/6l//JV99/XO+TZq2KRizpl29ImVLTHA49gt2qu+FC9iurnhyuWazcTx70hCngbHvuTkemPwE737EobCfLFd/+vgX6e4WRGwVbSi2ZXd5Tdut2a03mOodaBqDD57jcGTdySSOfsK/fo3vp2WCAotzutZVaaXrePHyK7TKlBz5/vvfiklu7bk8CnFqFhb68IXP34CYi8gmWYUtli1OwGspo1UkF4UqWf7YKrTTqE6oBKloks1ipybY+ipGLYVLKZtmXCkkJQLEKcjvNAbVKkrnyK2RYBsyk4fJKtGujEloJgryWgIyWpP6UYwuY5DvTGcFfAXb7RrtxE191XUi+G1hs9mwWq1ZbVccj0dCllJkinmpocv6WaS8cHmJ3mm6Zs3l5RWb9RpjTRX1FdjzZrMhxsDdwz27ix1t13Icj4Q0cXd/g3OWy4sdCoOfIkYZLi8vaRrxV3v//v0iEDxOAwXxjnONoVu1j8ZZUVkIv1q4ioVSS28KpQzgRL/VGHarDV3TcLld01gjXKYq+SYZlYHKu7TOgTb0xXD0hXTwZJPRVlOcY4oZHxKHh5FwjMRj4Pjmljh60uChNbRNy+ZqRbvtMBvLs6+eiqKKj+yeXJCVUBHa1rG2m9NGTSmsc2RTsEpDmVjqmFoCoTWaVlnWumXdrlEo8giaCYlaZaG46CqEPuvgqkrtmCs1+mzWpJlaUEQlSCo5ugZFMW6eN5GP8dtzw0cCoEbXrPUxz/P29pbGKlLKtO0KoxXTFCo9SKPGgg+BYewxLko5282cTCmJNU2tBgTRmdUFqXzkiFERMqSQiRn8FDgeB+n1FchZk1PAGcPz508XvdsQJo79AaUjX331EmsdL14+4+rJjidPdlxf7QjBc3tzx8P+gX0fKbhlfqm6SdWI47tRYGImFltfMyhj0TahZjNilUEVlKFqCAstCwWxKIZkuHz2c9brHU7B/u4d/eG2ZvAJQ8QkTdKyOQnBEjO8uRt4e5x4PwQabRhCQMcDzy5atitL0zZzSoLOUsHR2QCaYu0pdy8Q9gPpEDBBYZNj9lnMdcfUaCNeqzGKepgSFZiUhEKhqtNHQZFE2oeCwoeETtLCiMeJcZz4z//p/2b/0AMN33xzQdNqxilw//BAP3hCDKQYqx7qSbn1+umOy8sN6s+/QulEzhGzsmgllZjv3r7GzOjdLzj+5CD40RcojXYt7faKq2dfsdlecLXb0RiFM9BsWiY/8nB4oGsayJnhsOdwHBgmj66TcVZtoZLxXbtmtdpwefkUrUWf78cfvxepIGBuZD86n7nZ+sG5fuqmOK0ICqY8c4mqrNBM9yjimabrDrVkhTJKMjq0KPpXl/CiVF2UlSw+SMami9gslcrsFFNhOU+0JjtNbrXoDmoh8yatUfNE1opsILUGTFXSiaJ1qX2We/8BEtc4kWUTE1qhoDRtg20c1lmss/jgq2P6CQyykN1rOdlai8XSNG7xAZybzlIqDWJ/VDLjNHJ5eYlrLJeXl9ztbxj7nrZtaVonFltlRFVJtqYK8DZNg/eBQiGkgApidxVSIOUzGgFzb0sy3YxUGWQ9kmsw1uK0prWWi9WGVdtwuV7jtDiIl+AXDmisLg9r57CuoSiNHxPaJ5RPmEYChlKZNIigwfH+SDgEYh8ZDyPFRwgZ21isETusbrOi3bTsnu4ksw+R65fPKKawjityTrjk4Hh6XtYYsjE1gMk4kuRKApNRGmssjW1oTCPXjYHZ0DYDaDH51aoGvhN5uzYXQJUlCAogZObEFWa6Ram6mVJsOdEgZsByrU4DqnIRKx+2ZttnE49h7ElW1b5xB+iqPlXQierAUCAlTA6VY9YstB+tDNbOak5pLhGJe0USfmoulcgdhId2PB7rgqywppPWiFas1h2uEUPZGD1ta5nGnq7b0LYdT59esVo3rFaOpnGUkmqvPJFLgjkIzt1WpZbsu+hqOTbf/+oeorWRTfFclp7NoGsgVVqDspQMoWi63VN2V9c0xjCGSBkHqP28+V7LeFGoqCmqMITIEBNjkmeqS+Y4eUJKZITDJ9Wegs6lzj8JguaRmwkYZXCmgaagi/B3Ry9uNORC0zgJTsGLeL7W5LbBTxKorGnqaIOUvXCl1RnvEhFYSTFx9/6W9WrLzfsbrq97Um7JGHwIxBSrnZjCKNk/pCw4kr4/YnRmv7/ENBlUIJVERoTibx/uMTHQfLTaf/r4k4PgCd0jUmam2/Lk+Vf8+l//t7z6+S/Z7K7oGsdu3bJbtzx79ZRYEsM0onPCjyOvv/uOq2d/z3e//x0lRFQWXc67u3u8D0w+sNlcsd3tuLx6jtEJyiTu5X5CqSDkXBQpRaFkFMkM5vObdxCf2xVs1w7VNUw+MwWxj3lixMxc1NeSZIJASFGuV0NoIXWyOKUi0l8TgvHbKkOrNEaLFmiqOb3JkbygAmUwZ5XxnQgNKCdAhTwvaCoDE7mxxJXBXzYkLURbqxzaKtSUUbGg42N0YSxiVzL6gZD8ySU+TCgNzjlRd8+R1jqMlddnFLCUKifGaYI0MPmJjKbppSz9sH/HOA2MfqRbreQ7iSQTcNZx/fIpxQQyE8bWwWwVTWOJIXFzcyNl0Fp6HceRYRxEQNhquk1LJuHjxHlKb6y4SGhncUVk3lQsECM6Z662W1Zty2695mq7pXOO1hhRpoiBFNKidmFtI4FmCuCjKITsPXo/sBoTT3dXqCqXNx32pIcDxx9uGPuAHxJ6SBhlaJzDNQ2r7ZpXX73k1c+veHK9ZbddM2dM690GjGKjd0xhhBFUr5ZLa21DNkjZCbnkUgOSzgpXLCvdsWt2rOyKnDKGEZU1JWnJAotGFSm/y83ibNM1iwxmLFWBhEIoAnYgGnIy5KRPSi0igytgGOqeTSELfkEW0+qwYFA4Z3DNuStBoe8fCFahlKXtVmCdKC7FgjGKUhwxS+aZcqJkRdsARi9gqcZ16FVBxUQInuPxwDgGKX8ZJJBnOO57Doee9ze3GG1xruHqol0WX+ckAHbrFavuipwz19fPuLi4YrVa4ZzGhwHvj0zTQIyBbuV43jylPSb+4e3xNBLVEgor2hG0zpgiPVDnHNZmtEkYW1C51DJkQmvZBGuFbG6cJkSYPFy8+pZXP/uGrl3RZ8Xt4AULkEZKGrBGYaQyiisGExPaIZaVBrJRYvtGoVS/066rQtfVj1LOQ3wflT2JG2htePHNL/EpUXLBT5px9Px4856SPEYVnu929P2B+4dbtJb1erfquLl5wE+R9WojhH+l2R8F4KS19B61EclIkhg0T1PP8faB73/zW0pac3H1jOdffcv1yys2u+dcXz8R/1otUVDK4gM/fvcjw3Hgxx9/AJfINvDu9jXRe0pIHA5HXC78sls/SoY+d3xxEJy7DWdjQBZzbUBbnly/4sWrX/D119+y3lzSNC3rTcvV1YarizUXlxvps5ULtCpE71l1DW3r+PaX34rVUsykEPn9777j7u6BN2/ekFJm8J67+wdWraY1dVdVg9rF7grrGsbRM/QHprEXc84yd5J++tC1n2nQAgTI0JNksDlQGwtOSmIiASfZYc6ZZBRZiyuFKxobEzaBCQVlNcoZSAVdDc9EuVTupKgeZAEQpQxRoUNGR0FuVXUjitZSKl1ZvM4kVUS+zSpMozFOS6b5waXGKOo04yQiuwDX108ZBsnYlFIMw8A4jVUkUQLfLIAbQmDynnEaCX5EKcMQIqtOlBnGqWcKA1OcCKMABlJKxNuAUZpxEC3JpnFYp6pwr2HVbsgJpn5aqAJff/0Vx77n4eGB+/2dOCAc9nSrjlVcP1p4Li4vaPDVY08moYkZkzK2wPXFBa1ztK5hZR1WaWyRjKLogu7WAgPPIsQrFl1yvTFE/H5grR3rJ+LqrauJb/NMs+92lAHep3seph67XtE0DavVClaKdtNiXCETCcmTSis9t1QY7+8oWlFaTUwBEz7YgdcWgIBCqPaWNdMqkmGYYrDKyrXUzy0L51ydAmf9/8sUKAKGsSpjSDidyMgcCqmIMF1WlJQpSYuLivjdSkZIBavMj6Fm4xowuS7mgNEnbAB1rHerhnXnaNsNm/UGYyzJO9pmjbOtaBHPtly1pB2jwhRJcgFSiZScUElAWjEXjG0wRWTCrG3kc5NlCqD0Adeu6NoV24srqOL4biWVEfE1tVJGLpbDfmDoJ1ZrRymeXDzOyIarKZaUM80Hmsxam5PgfL1HSktf0pSToTUUjJJKjNwvLXOBsmwqjDG0xnHRbmg3F9jVBc3mgqtX3xBLIT78jjTek4436CgAOG0KWpQZaaym0QpX/zNLtUShzCxjZqBYyEVW8iJVhGjN2TUpXl69xDYNq/Ua63bkDMfjgTAcyGGCaeTm5h1vleb93XtSlErNLJZirKA5RVJzbmGoxaQ7Ro/KBbKUice+592PP/L1z37N5W7Fv/rLP+Pi2RXr7ZrNZrb3mnvXGR8iusDtu1tu3r+Tja0J7O8fpOytCttdQ4tBTX9w+Qf+mEyw8jukES5D3ChJ5TENF5fPePLkBddPX1CMwzjLet2xu9xw9WTLerNCV0iv1YqcIutVy+XVjmmcaJqGFBLTGNhe/SNv3vxIVHD7/hY/Tjw8HGDdoDt7Uh8qsN1cslpv6HvRpJvG4SwAzs4Ep4n5kVpMLRNZJea5IRcGnbFOYZzGbC2qEQ3QNGXpw01VK7OIS7lV0KCxPmNiQaWCao2ILmcoMaFikoFfZKKkEJm1hnWu74kZHTUmzxQQRTGa3BhoDVFnUsniQ2glA8hWifvAo/qv+JtlMiF49vsHCnB1dSmZaU7kJP5/MQYiAvWfaQaz5VGMgRA9/dRTKAwhMKU1zllynvB5wpeJEIRKkHNm8D2UQglyTk3jMJb/n7I37ZHkytL0nruamZsv4REZuZIsssjaeoG6Jc1AkD5JMwKk/6sfMALUPeoZ1Aw06IXFWlgkk8wtVt9suZs+HHOPJLsaYBsRiEQw4OHmZnbPPe95F6wFawvOzChZ0dmOFMWSfD5fsNvt0Eaz2d8TYmB/2LMYF8QYTt2SQtHOW6yS2CkzfXY2ZHwBrzSPz86wWuZ/Jh8dZ6fu21gqZycWpHhxpiJeiyEW8hjJw8isbWkXc8bYY4zBVzULN6NrFvT3A6kLhG7E+Rm+aZjNW5JLuMZgHGQSIQViiXItcmbbTYzWWJFKxEX7fcKPDIrknpjkChwLILJzlwhnI/fPFIybj7KGoqYv6dSO87oyUbV1ydgScVqKYJqeC6MQRuLE/ivvSSVyfvA2LdOzIx2heiiCU+i9UUVYuO972CmoG8+8bZjPVzjXyOLoPM7WaOXZ7iPaFOx0pUrJqCHhvBQTYRsLGY4cJMYnF5Rx2KJJIch1qCoKniEUnN9Sz+Y0TUu7ODuFG9tKGLUxS3pCKRqwHPYHUk6k7DFGujdvZSF3zmJywrp8XFLknU7QJ0rJ7PT4MyTlRB8L0fTzI1te7sXjhG4aTWuN9jW6OZciOFvi5+esn3yA9Y7t68iwsfR5hP5AIUmHaUSH6Y3Ga41DY6f5JDpNLM1j3l8GsqBtPEgk3tdMK6V5tFgzX53x6PIxi9VjkYmEwO7uim57x9W3L3Exk3Z77q+uyDGjnDndH8cQaXUcFRXhemktiEEfwoSyyf8c+4GQbtAkFvOan/70Be16QTWrHu4tENgVaVK2NztCP/L6u5dENRLVyH63k7lpbVgsljTawtuOH3P8yCKoQFco22CNOcEABsR42lX0Xcft7TVff/UHXvzkI9brcz75+CnrsxnLRU1V1RSUQH2qUIrFec1y3VJKpvI1Sgmk8+T5U+7vNvzqz37Fl3/4mndv3/HNH1/Sj4Gh6wnBkJIjl8hsfsb5+SUvZjUvX9a8ea043N1LR/PDs1A/YI0CaUioMbPQln1JxFLYxEBVWbwzuJkFq4GE9VpmaAGc1jhlcXV1imOyacSQIUZy7SiVg16YdSEFitWSZK8k/DeXTJg5cmMmD0ZxyoyxEI/v0GmyhWwLTc6TfkuxSwJ3agVVrfGzh4gTAHJCG0VdV8TYoAq0dcVRghLGkVxVLNuWccouLBNjU1h5Cl85VqsFs5UU/LFIQUNnVIo4FKr402dtiiIOIyUXjPNYK95/RQVkQVakURNHiVKZLxfUdY2rKmzlwWk2/ZZh6DFGU1Se4NCHoyiZAYUwUpkKrzRVKVRKU2nN0jopKKVgnBVNo5YEBUrBawnzDSFAo6d0jI7KVThXsVw/pus7ur4jjj0lapwCbyu89/z5px/z6Pycq+2Wu5CJWhGtpvaJqtas1g1N63CVQRmFsY7aenTdMoTMdhjZ7zrMeJS0TLB4EQgrJtG/iSxOFiuLo9IVXlc45VHFUHIihEKKipw0J4kDR5TmYW7HBFd6Xah0ohS5RuSCyRabEWJVlE1eTg+d6JGdB4gZdJlQDv2QmHC67VSWud3DqkFVV7hKyB/SuWhcVTMOhe1+z//zN78mxCgzYuep65rz83M+/PAD1usVzin6vuNw2KKQOZGpPBEN1vHo/AXNbEZd1ZScePK058UHH08G95ZZ006xY+U9iqzCGE8KmUNz4O72mq47UHIE4zBm8salYJRDYniPZyQfhrVG1isQgktWky+xIQG109RWURmF8VY6xSLaBofA05PMF+ss7XrN+ce/4uNPf87Z4w+pZmsunz4l9Ftuv1nQ37+hv37J2z/+nsP9hnHYYIwSZixQFaiTvK6dEmqUki7beTelkcg9pYvCTPPkqB+8Q1UBm+Dp2Tl/9Zd/RbtaC4GxP/C3/+FrvvriC/7z3/0dm809u+0ODNSzmkeXjxm6zH3ZsdlvZQ6KJqdBNsRocvAUnckhCrlNFZSVzXo6REiREge63T2393cMKbPfdhiTcDbz7OmHpOK4OyRefX3F/dUNu5tbxnggph6fPKszzwcfLbhYzWHMvH3XvT9J+RePH1cEFRjnMU6gMNm9Cg9IW4ee5kspjygVWSxq1ufCslosamYzj5tMqUMU0XJBYbQnZdn9WWsw2mG14+xsJbl0RXPYj5Ssub3eksZAioG6XZOUoU+yo3Pes744Zwg7igq8jZFxGAjD+MAE/BeE9TlkSpo6BiWQRpigIrLIE3QuJ9hU6UK2COFECxxprKTT22JFJF8n9KJBeyuJACljTBJ5A1Mm2mQnVaaZkYKj7lg0qTww+nIR4oBlIitkmV2WNJkq64ms89718s6LaXIpNJWwcRetwFFaKfaHw0lLGZsoTv2LSD8MxBBpZg25LEl5ZD8ohjSyHQrOi5m2Lkzm6FNHEhNpEIhbFSjJiNG6QSalSsEEkSgEOrXaUFU11jtyyczznMePL4kxYK2kTlR1dVplC4VxHBjLSM6JmEYMGoelsY7WeZzSJ0q+wOZHEoM+QVBmymk7JpwIecGQkbipMQaGMOKsdALOmlOnXTnDYj4jW01/6OiBbMFVhqo21I3HeSvRWzmhixNBfMmCOkcY+oQZFKU5ntcEJ6mps0tixK5OcKiSGZdxOOukA8nHDD3p2FDvOeAc28sjjWLivEgvwBROKhsqJkhZZ41KGpKGiQCWJ0lEhhNZ6qglVMhrmEknoY4d0fceMYWeoLFTFqU+ErVk6G6MpEHIdVHkkths7rm+bkgpcnGxYpyiczSiYVVFYX2DsRV1sxRpRRHv08o3mOWDD6pRBchCbCnSS4sbkbyXuq6Zzxd458hTkdUW0rif9MsT4/G9BHaQDbWebgpVjjM1sBQ8ivWyAWPRvqIYoWioHCF6DInzpaedNRjnKdqB9fh6hq8bnK9QShNSoQ+REUO2FWY2x1YN2vUT90ESVdqZp58kGUbL/e+MxTonWX0Tb0OrJOk/BfT0XJjvNQWFMA50h47tZkO7WGKckxljjgxjz74/0IeRVDLWOHluUsZah3eefTcK8mMUzrtT1NIplFdbUhrJJWCUhCKUnNnvN3SHHVaDU5IdE8fA7faK/e6Kw6ZDu4agKu5uXrO5e8f93SuMzRhb8E7jPTinJi7Fj6h+0/GjiqACrK9xjUTpHD0pNArjhHKcywgqUDWKx09XfPDhJZePz2hqR+VEkxJjkt8DGdYbS4haSC2lYHTBe81yOaepG9pmyX43krPl+mrLOEbCGOlCj7q/4jD2KGMw3vLo8SWuUSzPW0LfcX97Jw/OkSCTjsOT7x8xFNKYBWPXMl8bOCa8J2YhY4qmooArFA2xUif9mKmkCNd1jWv1Cf60s1oetiCwm4pAyZSYiH0QCrcCYoakhEaNFL9MJitE6KyUWFIEcMpJTlnM2FgoURhcUkAfbmaFop01VE3FrK5pmkZYnlpTVxXOOq6urydyiARoSnCupjt09H1PTIn5vOFsPeftnWbb7Rg3B+q6wTuLVw2ZTMzi6j72PdswMm9mWG0JfUbUl6IpUkqjjCUNmaCSpJVrQ+0rtHcoo9HWMFtUqAlG3W63U9F8OLf9Yc/IgDeWcRhRaHyzZFnVrGYzJq4rSkkRohTyRFxgYqlpbagqO+kMwTlZpEJObO6vOQw9wzhweXFGXXmaqiKOeSKjwKKtsW3NlSpEMskpqgZxu59VGCeIgURmJUARQmQcE2EsDPuMGQrUnAp8zplEJibZlJFEZ3qMT3La4Y2ncc3UrUsUTjoVweMm7mjO9318vBSmxHRFmGDUdFwrikZnh0pOGIeFUwRQRgphmvS4GeR+zDLnihmZeSkj7kr6+8+Y0Q6KYRwL87me4scAXdAWzs7nkxDe0DQNISSurkQyc39/T9N+RhgFctdKhNpGGdr5iqpqaeo1cRwZhxGjwFrHYt5QciKnwNDviWGUyDDjMNahcJQijMe68nh7Jp11RmZwObMbEikOlDDivRWJ1HuH1pzWwQl5xhrhGBit+PDxinVQPA5HmLRgipiDQKGp4Gw1w9YzkrFkXaFshbMeYwwxiFTg7vaaw74XE4hqhqpnmOoAZKxR1E6xXrVo66hrL/M2JezrupmhXUXBonSRJBgmHsKkK7Xvw0elcNgfuHp3xR/+8AfmyxVtO2MchFyXSCQlSICpHX7KC+y6Huc8TVO4u9/JfNcafDsjhkDX9bJeOk/ta/puSxgjZpo0hxy5vn7H3d01tbc0fkbMsLm64+r1t3z+T/+Fl5ffMF+esX7ylHdvvmJz8453b3/D2fmc1XqOczN8ZdE6EJM9NRk/5vjRM0Ff11jXE8YRoybhcVULL8Zonr54zAc/+Yi//us/5+NPPuD8Yj29KdkFvv7uO/pDT3foePb0Ce18xqJtGUbFMBSur68IzpHqBopArt5rHj+9wFjD/faO6+sttzdb6nbBGHuauiaOI0PXYY1hfX7BfNnS7fZ8+8033G3uxRmlCDPrNNx4/2auK0rtGEMkW9GKqaJIJPIQhU2lLDjQbY3yjspX1L6WxdPak1WXdxbUQ2CmRlM5R9gPDPue/uaOvB/Qh4iJUwc4QHEyi9RdQEUwRXZzSk+LobeUWnOfguzGNSjv0CpDl054/MO9XPj21XdoqxnHkcVigbWGbnegbVtmsxm77W5ijsl5GG1w1hNTBCMLqjMeU2ku7DOq7kDghnYmBTUctoQosFpb1yRbU6mj4D+T1ShxKEah9YQChII3Flt7qictTdOgiiIOgZiEmNKPohOMoyWMowiHTycGw3ZHJNDUM6oEM2tYty2VMxIXNWkPQxxliSrSMXnncdbQ1OJUI8GeAUh4bxjHkZICTmtqK1rDtp7j3WTJZTNZZUoKIlpXE2U7F8asUAnKqNhYg60M2uqTTMbogNEGa8QlyRiHey9pASSANezyFYgAACAASURBVJZICnmSwCmIkpVXdMJpS+XqiVh0gDIwDokYxNlF2xM3hmMZLMdOvYjQOWcIWpFQTC+MUS3GzHDVJWWUIkzeSNRSnhiiPDBET3OQ6fXErU2Bfeg0378P7+87eied1GJ+hrMWYzXNzFE3FX/13/+ZzD6VZt4uiQlurzf8/nd/YLO9Z3N/j68Ns/lscipyFBra2WOqqmF3vyUMA2kc8c5IBp01DP2BMA4c9nfkIoXH1/Vp41ayEknLbCGfmDL4dsWiXVC1c6695bC55fbtK8auZ9eND2uHEmsyradZGAadZYyhiyIXRWUrohERvUJmsjoD1qEAZwsFQx+h6yOd2jL+8Rue/nSDa84IWXN3e8u7qytmM0dRnpgsCS0ZioUpn9Xwk08+ZIgwREVJYnM2hsxs5inOMSov67WVOauanBEefDblMMby2S8+Zr5ccn6+whpIUdi4n332KU+fXPIXf/HnJ+5AKfDu3Tt+/etfc+hfM47jSRZirGNWzYgxoWyFrWq0dTJERlFSIceA1ZpFO2Po9wz9nroyLM5mGOsZdgdK/IjGHdhuD9R14tFSM94WaAr9WcVnP3/Gx599CMZinMLVmjDW9PcB2Pyo2vaji+AxLPH9lKsTzgKcrZZcXJxz+fgR8/mMqvZoLYP5EBNXb95x2O3pDwfOlwtmlROYMckM7e76GmMsddOwXJ1jJmy+ndfENOfi8owxFg79iN5J4TFaEcaeoTswDiN+ZmmahtX6jO12S7tYMBw6UoxCTvkTmwPtLdpbQomCeGhQiPtISZkUC8mJP6Gqa2xT07QLmrqich6saLuMlsTzohBtoBIqskmAs4xWk/Y78mQxpiZm6FHj/v5bEwWiYPSyAzUUrRizFMACuMLU9XCUTp2OUgpX19dklQkhEFLEO8fmbkPXd8y6GSFEjDGEEE5Mt8pX082dqaualEULmItGa8usqplVDdZodoc9qRznCxpjNaZdTCzLwDhGrDM4a1Ak2e0XgUBxmqqVvwmINdM0VxQBvNChc5Lh/sPZFUmnLwkdE15bamOonBXqvoArktmo5HM8zq9l/iIQllIcfxM4ZgRODiWlTMP4o8ZOiAzHSdfxdcwEtyk1Me0S5KDp+4LOSfIAtegYjVKnGB6ZT3GipZ+uWcoCOaYi6elJSYWRNgyjLE47nK3QaoAiBgc5TsQYI3DWyT+pICzYIpB5yoIWpInJKKQbg1UeTY1TLbZEiIEyjShQvKch5IFp+r1BIO+NGr7/81IK1+9uZfHVnrOztcip7PE6HENcBSqbtY10pcrx1VdfEWNgCANVM6NpxO+2KEdWNcbUKOUZ+oE4DKQYsFQkLSfbHwbGoeOwEyKJUkVyCVOcXJI0znosQrowxmHbpUhvmoa6nUv3aR1xCnJ9/1BaobOaNh4THKqmDQaKlKUQHfo0ZWtmHFHYxlpDKgwhwZDoEuQxUo9BgrcnTeFR1uScoRBIRYE6sojl4y5KCeFQHSPVZJaeY6QPGaUC2z6iVBHDBW0EYVDlnxVBpRTLsxXNbIbWevJ6hf1+K76+ZyuqynOMwgOoKs8XX3yBevWGmCLGiDfv2fqcy0fnlALDMIqRCpqQCzsDB1UYDxv57K1hGHr6vmMYelZK4ZxFIZvXxWLO0I84o7FasVoscLrgbeTZ86c8efqIkIP0lToDNdGZ4w37z9b8Hx7/ComEUH2bygmUlzNjd8A4g9cVz54/54MPP+DpsyfUjUBaKUkq8rDv+Pv/8t/Yb+6J48CT1YpGKQalGceBbrfl//tP/5miFVXb8j/9z/8Ly9UKqzWrdU09M0R+Qj1vUE5ze/MVpYzAyOb2ijz2vPn2OevH5yzWc84fPUJpjXWW1y+/Y7/dcn99Q47pe8+uAtyiJo818RBlB14mQWgfiaMMZxMNLCrsYolfLnn05Amz2tNUTmaXZFROxDIQsxjKysAJXDsTd5etZdzcymDcGRiFoTg6TZpE6Km2lCAPsTuyRkOiBHGSiU52gGQwfUbF9B7EW95bpwqff/4burGjlMJHH31E27bc395RVRV1VbFcLgHIKXHoOijiq3qUU/zyl79gt9/z7t270426bGbMfY3RikPM6FAwEYqK+Lqe5rKi3wq50FSeyjv6/Y4cE7qAsdXJtSMV2ZWHMKKMovKOGC0pCauyxExS6XtXrNKGmBRqHFmuW1ZNi7ey2Uo50Y+DhIU6M2nkFNYYnNNoAyFMThQpTEzGzDiK+01JUUyzU6LExNB1UGWcnRHCQE5JInSMJitNU1WQxGXIAJTCfjvSx4GYA84l5q1ntWhYrS8gZ+I4ir9ieu/hLMicuBRyKOSgKFFDMrJY6UJtKhrXMKvm7Pc9JWvCIHZ8uRSUmeBKJfClmuBLpqKbk5lmpMLoVkWhksGbGYYWn1rsOKBHSL0YMih/ZMTIdzUZMmiYFl3NifZIIitFVg/FIqXE3/3drzGqMGsWgGa9Xsn7nDZbzolJelVXrNcjtW+o6wV6shsMoaeqllxcnnNze0fB4/2akivGvnB/fwcxyefvanIsxDRy2HQMQ8dh34NKoBJxc0dIgZAGQUCsI/UHGfVUDXaxJuRMLAo/W9CWQhwP3N28onT999fDSfYgRcScNgEFaRSuNh2vbvd8824DOeGMYlFb2vkM7x1OZXwf8NVIMZ5VralnS1xV41xFZSueP3/O+nzBMNyyv8/c373Duhpb1TDNDLsU+fbdDZsusT2IfCDGxL7rqayhqSxjWPHiUcOz85p67oTFm8X3+f3ED6UVy9UCpTR9f+Cbr35PSpH+sJ9m45a6bqb5nqBI6/WaFy8+4PPf/o6h65m3c372s8/41Z/9Of/m3/4b6rqmoLnf7dl3Ha/eXPH1H37P65ff8MfffU6IkVgKd/f3vH37lq+++ppqtmKB5+XL1+z3G2IsNPUC62qGXvPZp3/FYt5QuYBrBnQ18Pbum9OG37lHROf4scePLoKzqmaoBrqug2mTWlLG+4bVasWHH37Es6fPqKsGq8Sfzigjriba8JNPPmHoD+QQUL5i2w0cwjucEWnC02cviFnMnEtKpBCpaysm0lZzNq95axOEe65e/ZH763f0uy3eVnQovvv6a2IcSOER6/MV9eWMs8U5Ty9fsNtuePXNS67evJsw+enZBgoRZTJNXZH3o3QaOeG1pqpqdCioALZ4alvR1BXzWUVlwakIWcxtTQFtBKd3xgqslYrM77ymbRvW6xWxGNQmoYLMDnUGHQt6LLghUQLYsRC0dAHVmKGLsnOtZY6VUsHsAjoUXBQ3jPKD6j5fLPDJk5JAhFobXOUZRvHqVMZQVxVNU3Nzf0eKAVc5kQzkwus3r9nt91xf37BeramcZRh7llrRNBWPlyvilDS9PezJIbPfHRhyIiQxAmdyqhlDZOwD3S7gFOIK74vYTplCVOM0N1UnFmdOWUToVhwoTvdhXRFjwsaMVcJQ1Cca0bRZ0zLrGfoBSsZoNwnancDjShaxqqoYx1HmcZNlnDBajcA6SuCgw17mesZMejkl81ytCpW3zPxMEI2SiLlQkrzmcOjJY8+43zKfrygJxu7Azc0tNmjK+S8fTi0lue/HRInTXPBkR2ZofU1bNbRVzY0WOGAcI3Gym/PKYDQYIzPY41xPuklFCceuTkNR2CI+pOfzC2Z2iR4ahn1iMIptdCKT0AntoszvODaDCquODPFpBqmRdHdVhPk37b6VUiyXS8ZeNKCff/45TSMs8DxJMXJO1E3NfDHngxfPWCyXXD66pOt2hNhTiri1lJxwfoYyLc1sKZqxYWAcemFm62NiSGIcRkJMpFTgqG0uhcNhBJVRxokAX2uGYSAXTcGI+H63J+IwSjYl1olbkvf/fKkU7pGQknSZyDJKQdaMsbA9jLy726KUYlZ52naOrua4yqMJZA1DEk/aJiuKtuI1O454ZbHG0NQ1OVfimmMcrqpJVYO2jhwyQ0y8vdlyvem5ue+h5JNLkXOWurJoNaLUiqqC83krDFtrOWm/p31LCIG//Y//kcp75u0MbyaT87pGqwpKZrO5F7eenKmqmt1uj9aapp6xWq148vwDXrz4gCdPn/Lo8lIMErSlWQ0M48j87BGPnzxlu7nn01/9ijevXvH1V19y9fY1u13P1btbnn004Geauj2jHzvifsNhgNbPOXv8CWeXj2gbRxmvQUcZa6gRZRJWGb76wxt2N+FPckD+1PHjZ4LG4YxjUP2DFY4qVN6zWC548vQp5xeP8M6jJ6mDUYakElppnjx7SgyjWB1pw6br6LsDy/mcqvI8evSYEAMhJ1RRk2+nwirAKGa1pzIFnXt2d1fsNjfEfkBXmlEP3Fy9w3kheVyeXzKbV6zXF6yW5+x2W6z25ASH7e77MokiUInzlr4LpJJRKYvhsjLCwMxgsVTOU/uKprLi40ckxoGMCFCVNvJAWkssQhYXiEBRV4523hKGzOh3FBcnGyPRFmYFbhQCjI0yeKcUXCrkQfRXSpnpNQv0CR0L9kiM+V6PC03TYLKd/r4R4oTWjGGkO3TM2hZrDdY5UoqMIYhjxwQn3t7fsd/t2W63zOsWm7Owf9sG6wyt82SrSB72+wPjGKUAqkIseSL4CECZYpKZ1xgl89BAViLkBcha4NCjT2WZ5oree7zzJ+RQKWFnaox4r6qCLoWSE0dKpEKinqy1jAwIeVYeZmet5EeaB/agwD79ZAkn7DprxVYuJmG2jWOg8hVGm8l0OpOKOLF4K2QYHTLkKIbCwRCioh8DcQgMh0gcR3LR5BA47Hbo8Z/DoSUmiWM6iuAnwNsoQ+08jffU3k9s00KMYlVVlFD5jRa2Zj6a1eeCymKwnZPMqsSiTMhBzlScNWcs/ZKoLHfuIJuU4iZpfJJCqAp5ughKKeyJfiSjRXVkZykEij4eSrFcLtirwm534N27tzJW0X5in8IwDPja07YtFDi/6KkqTz85thTEhD2XgvUN1rVUTcNh1zGOPSkFjJJNSyFLEQxhmlsVwJwY1mFMQtjwDucbVCl0w178ZFMijAH6nqQP1FV10rhZJxujf4auvSe5OrJFUxG6TMyFboxs9oOYvhtHsUJ+Ua4S3kEWmVNUhSFkQszsDx273Z5ZUZO+Vsg8Mh5yKFdhnbiv5DASUmbXjWwPgU0XCH0nbj8x42tPFSx1nTk78zwaGqJa4DRg0sR6fSiCKSX+8YvPaduW508fM288s6amqT3GyCZwu72n73vGccBazzBKEox1lmY2Y71e07atqAGKoPkKTsSYdqGp2zmPnj5jefGI2fJ3HLqe25sbYsjc3W3YHwLzCNo0ZDxjMIzRMdMNs+UF2rcUDWMsaBVBSULGcY28enfN9mpk/SNr248uguM4EMN48nxEwdD3nD+55JOffcanP/s56/P15PwgX7VzQrdOieXFucxcKPzmN1/w9u1bvvz9H/j0k5/y4vlzPvv0pyjEfdxNi5A3jiQ20wTrWbUNj9ctbW04WAkHUdaCNfRh4PXr19zfbdB4Lh5d8PjpE6pqTlW1XJw/5mx1wf2NzB6PR45CXWfm8X1C5UDuI846rDUEohCBrGcxn7NczPHekCZHlc39tXSXOVE34iDSzheyOJMxZRQjXeOws5q8D6AtqjJolbF9QI8ZpwrVMFHTEclDpmBQ1APkCHGIwoBDUcbpgTRMC9afsE2bGG27w0G+bzZ0XUcYRzbbDcYazs7OaBcLfBhJJU0pEZ43r1+TcqJpGu7fXROd4SePFvRXbxneJXaHgXq+olmcsb/bcoiBXiVU48kaNtt7DgdDZQ06FZz1uHkra4jOJDtOvNEMdZpYkKBCEZLJ1BmV/HBepUDoDqTc4dCEoaPLkW0cJ69Gw/L8EqUghiDglFZYK+SscRjYbLen4ldVlbDo6lqMA1Kc0k0kC2/ezqYieBz4a6zzXO323B86KJHaNZzPG6o4yWh0y7ypuesOfBtH+j4xDIG3r97iqop50zBr/PeLBZPEJCbCGImpkLL4O1ptqE3FWT1n6WfMrEdPXV6MCTWRNGpv0TYL5EsilXSKPVIZua+KFiZphKaZ8eH5I371/AMWvuW7N3vevLuBNPBkPSd7Ralgl3bEEgh6oJgAWkwKjpmhiQkVUogjznuuUkZrfvbzjykpsNt1dJ14euaksaZGKcfd3R2vX73hH//+c/a7LU+fPaKuDdvtHcN4kBQWVYgps1o/QZmaRGZ7uGa/u0ebiLYaZQrduCdO7PGsC8oZnG6m+X7CVWfUsxmL1XJikEZQN8J4t5Y8RopPwlXQmpATh25LziP6PVj+yD7WkxNNQU8LsDoJ4Jkez1gU3s9wzZyqXTDGQtz3aI5mFJGoLEO+IqjfcOhhff6Ii0cXvPjwOZdPLjDaUdUti+UFYdgTD3uRLhwGcio8fvqc5z89w8/P+U//799xc3PD/d09zazFtzW5NvTKsA2ZYAzeaaxGzP+zYRIlk1Lit7//I48vL3h0vub8xRPOz8/44MMXPHokwvmrmzu+ffWOL7/8I30/EkLgcDhwf78hhMDLb77h5ctv+du/+Vua/2uB9R7nK8YgiAVaszx7xHJ1wS9++XNWZ2v++n/8a1SKKBSvXl1zc7uhmu/54zdvuHp3y+1Nx4sXH1E3c27vbvhv//Vv2G3eUbHj05+t+OiTBSkOhAjjAN98/RW7m4GzJz/5QWvwp48fXwSHnjAOJxcCZTTGO9rFgovLS9yUJyXBpu+5ECBdjXdWHMHjyNWbN7z8+mt+/8UXOMSk+KcffyQWW9ZMdkL6dGFSTBitaZqG5WpFPZthK0/a71HWoq1DWUvMmW4YuLm5RRtL07a4uprYm7LgV+6oE5qOabtSUhbzX2sJRYIhbe1JSaEbh228zIJKYb/vKCUKfd/UaCIlRzJanOxjeo9JpqfheZHAXGvRUwbgEfqjTLorpcQPWd4YFMnbyhPrLx27pMzk/DA9bFME0/vHMfqmFMSWDVDa4KsKYyxKG8YQudts6ceBmCKhKFTXMUbJ5gsx0Hc9h7d3DN7xfF6x3d6LBjMb3EHhd5mb2y1JF/TcTySJo5hY0usw4h2plSUXkX+kqXiXAqaykzi7QKXI6hjkqaQrKpxE2e7oKYmCnCT7sUhGmlKKFKPMwyZIXSvFoMWhB6WIIYi107RRe/9Q00D+6J16/JmadKZ5MvCNSVx3JqQer6GtHM5oqsqi65qqa9ls71ElkZN03ykVmsYe7S75wR+XrUx50OYJKcecjLOdsZiJWXfqdKx8HkLOKrIZmCxjSsmnkiTd4yR30pbGVaxnLWrYMw49ab+nVoHz1jJbtUStGCiYXjGUQHSOgT2JQbR3x4DVyfZIMARF+cFp+cpM/piO+bwihChMv6rG2YqqsozjwJu3bxlGYY+P4zgFHsvuvkwMX21qijIMw4Fx3BHC4bQeYaAbOsIgm1Nn/EnLGyZv0apeUNUzqmqJ0kI2sa6Z3I5gHAO+gLMy/yp2snLTImr/4b1y3McodewEhXSipzXvKFG2zqGtFeebFKAknC6EGBhDBKfYHzry6zdo5bi5vub+/jGuMjRtzXLVorWdEiesiPS1Oc3mLi8vWD/7mMcffMr93Yavv/6a7WaHcQ7tPIHIUAp9SiStyUaTlbhZ5fc2LUppztYXzGYLSobrmxtSiawv1qibW1IsfPHbL/n229d89+pagm5zoZsSPHLRxCAByKWMHMYRNUnJQhSIXhlDHDKhi3xpNIpECgeUdtTVjFm7xtqGUgShSTljrGUcO25vd1zf/pbX3/6God/w+HzBGGtKmZ84WYrC+qLBT0YGP+b40UVwGGTQXFWSTaeNwVSexdmKx8+eYKw5QTQPC8wD79FaQ0mBOAy8efmSr373O774h3/AAYbC+D/89TQkdye6vyw60m5rrWlmLWfnFzSLBe7ujqzvUc6ivUN7J9BCDFzdXINWzOYzFquFBMEqz9nZitWiZTjcPkgKEhCL6NaUkCcSYL3FtTURg1nU2HklDM0YGTf9BA9orJ8j2FUk50gsMIQ8mXgL86wURULshZSzaGsELj2FCcsCEixTEgXkIMvKqAtlKqRxWtiIMoOcXKUE3voTRTCmieEXJT7I+GoS4soNP4TEu+sb0lHPZxRDDFil0UWS43ebLW+/e0VXVfSPz3n96h3b7Q7XnKNcBHvgvt9iG89ZW08FHbz3HANxsAqKESi0AJMOUpo/JQkOImTDJshaPCxjEBH/w1Mqm6mcLW4KwSuToFlP84sYJH5lQpPRQI6RU+AzR6bzQxE8ZVIqMRkYwkAYxwnyscxmMzEWzxKuG1Ikl4LRiJuMKrSVY1Z5lssFs1RYDANvr95CTqRxoNt15JgFWqb8oG9Xp/d3LIDH29NojTMCxTsjQGRO6YRgaPRkC6amey4DWj4bZPegtRKiyWT/UlvLoqq4mM8J2xvCMDLcdTTK8mRZc/nBnCHBpsvk4uhLJDQVuzQy5AGlJCKsJEWORwhbbuX8gzxBa6HylqZxaD2fiEkD89bT1DPOL9aMYeD1m7eEIE49IYxTIRSnoZwhJVCqIpXModvRDztCPOCNOPOgFfv+wNiPjP3IqpXnTOMIUd5S1bTUdYv3SwqRgsa4GWEU0lPsBtpVofaeynvIDmcNeWIlfu+KHe+nUk4EGWMKqcDRsPzoCuO8eJuOYST0B0qK1M7IWhIjTjuGQ8ft3Z7D9sCsbbm9uWZ1tuD84pzlaoHWDj0VQI5FcCL/PXv6lE/+7Ff84i//Lfd3G7yv+O0Xv8V4j/aWsQT6lOhSJBo1FUGp0u/7ammtefbiIypnoCi+/e47Nrt7Li4vubrZsdt2/Pq//j03N/dstx0ffvQTlDbEYkjFTuxmddpU7LudzJG1EovEMuWBJkPqEnfvrsTeTSWW8wuads3Z+jlVtUAjjFHIVJXn0N1y2F/x6uU/st19gzGBs+VfkPMFp+zaaU7/4sUZ+zbAq3+5nr1//Ogi2La12BfttxyGDlfXPP/0pzx+8ZzHz1+QSzmRDLTWE4020/e9UGRzYLe55+r1ay7mc375k485cxXri3NWs4bvXn7D2XrN2fn69BrDMDCOInT1kyD98ePH/OTjj8kFun7A+WpypbAYKyvI7nCAqytSinzw4gXzeYtZLYVurx4ieBVgQ6EcAmHfo5XQ9ueXZyxfPGb+7JIuB7Qz1POGrKTIGo3MBuuaxXIunYU1HLoDIUZhG8Y0ZZrFB59Go7HOMHqJHsoxiruK1SSnGe1Etc6yt865MGoolSa7aecXigitu0hOkruHhuLf6z5hcu8w0+1x/JpEAdP7UQgMmabcOgWi3yHjmDIGx0Q9RR59++aa281I32tm9Yw8GtJQePXujnrusYuaRT3HWzNx8qEUJV6nJQlVfZofKW1PDEZJdZB3VzmHVQqVK7rc/UB4JnZvSmeaZkatNG6ii1srDENj/AQViulyKkDOVFWFtY4xJemW3pvngBhYW20JSTSIi/mc7XYrIn80RosuUK6r1BNnDJpCCj3aOSyO2jsq3zALGY8RzSdGHHKKYl5VeKUI5fuhnw/XiaNFC8rI56Wt7KCL1iQljOsxRIn1UtIhFKVI0254jGmKlkl467DHpIdSyFlxOT/nyeqSp+tn8qzaDmM9qjfcDvDFt+84hIHt0LHNimSKxJiZA8pEmVcn+VzUlBibjygF08YO6YJ85VElcdjtoMi6sJytmFUN3lpiTLRNw9OnT/nq6685dIHN/Z4wysyqadfMFo9oFo9JSchA/S5iVEPlLZV1ggDkzK7r6Q8d3WHP2I14W9E0rZBwFFTGk9LAfn9HGAdSCoTY0YdAUbBYrRlTkWi3uqYUjbM1pZrhqx9cL8UJHZBYouNkfgoZL3mSQRVxkzLq5G0cx4FRayH9aU2tzaS9FsG904qz5RxrxMPXaEuxHuMq+fIVvmmoe8m8XD95zHq9pmln/K//7n/j0eUln//mc642t1zf7KiqxMVSQnj7lHFZUAGjNfE9JxxjDB9/8jHkEZ06bjaJvA1c3UT+6Z/+ka+/fslvfvtHzs/P+dkvfsa/+/f/O0obvv7mW778w5ds7zc0viKOgTBKvqgxhmbWcHV1TQgj1lqWiwXLsyWHEQoWdM0Hn/05j5885/LDv6Abavq3B3zVcXf/JW/e/I67m3fEcSSOgWamWSxWPLpY4ZxmGDqyFjs2bSPPPmzpFplvXx9+jELixxfBuqmoasfm/h5KQXvH+uKcxdmKup0JRT0lcWZQ6rTQxhjFzDmODH3PYbvDKs2imWGfPsVWFc4Yur3kztUzgSeOWXZ5ivYR6n7BedEbrc/uWS5WFNQJDhIcFeKY6MeR7WbLfXtHjlEiWaYB7/uHjhkVxItMOaHWLx+tWVye016usSUglk4abZTobbQ4tzeVoakd1lfYqkI7Jy41B9EtxhAISQgU4uUozMSky5G0J9CYVmStJUqH6XkyQj8vVpGcZA4qhVhGTUQaoZrL9/IDHdPxdWSdfxjew6Tz+j5/YQoEl987wo/GCIkgVh4RgsiMIxQY4jSJLFPsTkynJHkwp9c53gfH/04leep4T+/jATSQYq+NPPx/4qSUEqu6o13V0Yjv4VzkxeTUH2j+ZWK15cnx/vj92BVqI0kNWk0MSuNOTFVh/plJViGthVZKROiTIcNpUSxKjIzR2AnOhIIzFm8nXWN83wfngdh0PJcy/fT07wkPTqUQUxKfyve6vOM5F8qDQB6mDlFhs6EYMYKYVy2VqclRk5IhZ8OIYztGrveRV7dbBkZGNdJrsWOzJQMBUcIZ2bwd0fx8TEURicbDOQmaYMgiL5ns6VzlZAOhRHNZeSGNoRQhJvaHXhjQxlLXc5xv0aYmHGemIaFxKDNB7lmYoMMQ2B8O3N/dsSs7nHY0TYubMjSP908piMA+R2IKJKVQ1lK0JqQsm+tmFKJSFkg+/gBpUVoemuNmqhw3mcf5KEe2KBKbNMUaittnGgAAIABJREFUHTdeIaeHuaKe5r9Gne4Ce4wQokwSJYuvGgbn0N5TtXNcP2K1NAjOiE3h06dP2dzf8+mnn5C+TKj7SFULw7OqasakGaICNN5Y8vslQInheeVq2mrJvhtRypNKzaEvbA8BpT3GeVlLnSAqTdswa2eC2KU8NRuTU86UMemsPWmBU4yEGKiaM5xf4utzZstLTL1gP0S6/poU99zcfMP9/Tfsd98xDjvICmeFrDNvWxbLFl9ZQSbQU8yXxntN/LFhgvwriuD6fCWwxbffAXKCn/zsM568eM5sOadQTsbER0hUksOPRWxg6A4ctltUiLTO88FHl2y6PX0MdIeDBL5Wk8/eJKS1E5Ov6zpZqJTi2eNnpCGx3xwHshFrnESbKM0uHhhDYBMCOQTa2YxxHDh7tGY2n+Hev59DRo0Sw+MqSzNr+ejPfoa/PMNeLCgKUgyM3UFcynWhdTCfFeYt1HXBN4Zq1jJbLokx0e337Lc7usOB+xAFXjSG0HUkVTioQHbA5HJyLIj5uCoqUHaap1XSJaYJzlIGCdWdhLkJWYB0ftAJygVi0nWp09Be6cla60S3lG/CmhchvpinycaintU4pXD9gEXRLhbc7ntCCIz9ThZ4ZagmUfwxiSJGTZrYnt87jkUWsew6MkGP2kedFRw9UQFnnEBN75ULYcpptHFigpDFpiqkhA6jnHiR7DyZywixKsWRMEo8qdJaXGMmTaSE+gqK4axj6HoO+46qqkgp0R96ZrMZ2hiKc5hR5BBaSfrG2PeUuRSe/aHDZUPICqcMtato65ZCpGkqaudYtS1B5/dRQwoK8bCeRPMUSfQuSIFB5oUpJ/owMsaANmAdWFdASxeWciQVEQ5bPcGp2uKocMrhrWPdnmNVzbubPWmfiFGxjYZ/+PaWP7695113i5tr5pdWnJlNJhIwkjcinqYJSlDkIE1/Soju871tS8mFu5tbKqdxWkhB1rrpugvpyVYVbVuzWi0oShi1V9d3KIS8s1w+pq7PKNR0h56+6xn7JBIHBTEMpJAJ48hut+fd1RXffPMV/aanpIL3notH55ytlzy5fEIMI93hIDPiAmhDs1pRzxdoZ+nHkXhzC0aj8kjsA/2uZ7frf7BxlAKgpvk6GZRIU4kKAhqUxluD1rKJrpqGGHqKygzjMNnAyUbGOYN3XjbPcUTrIl3h5KFrrUarNd2mxcYFi+fP2WTDsO2m5zlBHnn24QdYq/g//49/T/t/a16+/CNNA0+fnHH56Ixu9OSsqbVmXtdEqvdXDbQpPHvxhL/81Wc089+x3yd8/Zh2+YKz88yzMVPXipACv/v951N6h8E3mjpa3r56TRgG4hjEJjNrDoc4eXoa9vs9t3dX7GPHL/+7n/Po8qc8efoLDqHnMAY+/91v6Ldv6PdvePndf4B8j2LPavYY71qaZsV82bJazXn67BGLlcFVkHNNnnTBcUq1+bHHv8IxBrQukxGtzJD8ZLeVU2E77E+7Ya0l+PRIcrFGk7Vh1sx4dHnJy82Ow/7Aoe+xdYVvKlZnK2YLqewhSFL0GAP9ME6docYrec31+TldP3BxdU3KhcOhk45xchvJMZwc7w+lkFPCXTnQ4g6/bu0DgysJxU2jqeuG9mzJ+YfP8edLzKLhsN0hygUtEoGS8JWiNoWZLSKVKBGjElUlN5jKhhLEjLgzsnvPqdDORU+2f7Riv5MhvgliT1XGMsGgCIwY08ntw2V5iCgFFcSEPGkkZqmIJ2TR5gdXLE+d1xFqnPxJp+5QT2kgx25RyebwqByiKMlTM7VnuZhDEv3fvuvY7nfU7cOMYtk2+FY8Nq2xJ3KG0lKEtZ4cLkp5f4lEsuoyGiUdZM7kKDPPMgpzV70n5hUYVTYOwxAwWbw8ixU5gxrEZk1PpIU8dcdaG0IQTd1QBNbMObNYLE5zweOutdv3lKKoq4blckXOmYM7oLUU9qEfSLEAeurwFGEcQUnUy+6wpyoG4fVO1zIVtNNiVpwSZ4s5pTbfK+4h5ekrTRITca1JSey4Ui6ElFEhMMZAzBHrNdaDc0IBTURCjuQi4vEay1xV1KphZs+wxWGLI28ig96z0XtKGuQZSp4xeFKsSdFJ6vvRYVwVdNaQLBSIyUozHCWCKmTE87S8t5E7XTLJg7Ma9CSXwjBZ62ViHihErANlFCkWNrue89UZTbPgsA8oE0BlNtudLKLXVzgrBuljdyCGkTD0fP3l17x685ovv/kj4178eY3WPL6/4/x8JR6iMTD2e7y1AqHP5hi7xHtFigP9/8/amz3ZlV1nfr89nelOOWIqFIvFQZZazZZkh/3QjvCTn/zgP9oRHY52221b3WpKlESyyKoCEomc7nCmPflh7XuRqJ7KEboRWUQAYCLvPefstda3vmEYCD7SNBVWZ7JP9P3EoX8W7qwUqj1DE9BKpscQIrMfCRqSgWZZcXlt+aluaVZnVJWjaxwmR3ztOIyDSHCy+PfWdc2iWzC5uWRwGqwVqzNtNNbIQNCuzjBGsVo2tGdv6HsPzQVnF9c0TYdWmvV6zV/+5b8gz0+8+/YKP23lezlN9pqUNNk6ph4JVX72TPqwZ7t/5N3NR959uOfQZ7plR+8hqpqkHPt+YHfY8u79d0IaskbQiRA5bPcnsl+YxR/YuZrlaknXtZxdrnn91U+5fPWG5cVPmSbNt3/4Dbf3HximPeN4RxxvSOEJYx+oK01Tn3Fx/gVGW0KMEo5cN9SNRWvhBShcybf9vGn+Ma8fXQQlu0o+qGNsSyramnEcyT4IW84YTKGhpZywxyyvMhkulktM5YiDYhgn1l2Da2rarqWuK4zV+CD7xBTFci1lcLYiZonrabsFi8WSbrmk3R/IOTMMg8AzJ8KEpBsEpZiVYrfbU5WE5bNuc5qGjreARmOriqpr6M42uNUC3dXMh4FwXIKnRFYRqyxOg9VF3J0ipIA1Mk1ZA85AZRWVVcxenPurxpG6hu5sxdA+kPcDUfmiZ0ol8aBAezF/mmhQKFM4o0H8HdORSZpKBt3zYnF6X8VCDJkiJSJJ/qrOSvSe+XjLHP8rf18sqqT4Nm1FmgPzHBimkXEcadpYqmemdjV1XYnRcGHeHokDx18rPmukySUBXZW0B47Q6THnL8r11/qIk36aXlPOzCFgC26bkcKmskAtWWsMwiyWf0UiW0KIhCwHs1Katu1OrL5jJxBiFDamk7DcFMt9FGNJSknkXOzjtbT+IliXejHHgPFyaJ+YxwmBUtGkGOnqBlNXn92DMYlxc0jp1L7kXNiiSYpgjBGPFyF4ThhrsBaMzSQlhT0k2Q1rpWl0xUI3dKZlQYdJDpMsOsxAIihpZmNWzAHiER4NmlRkGpRGTGXZMR+jvEovTIjys584Xs96MXU8D7SSPSKx4O66GA4IcpBVxDiBCzOK2UeMq3FVy9PTHmVabLVkGEf6/sButz3pQ8Mokp9pGHj//Qfef7jl/fs7/BRIUdJgQoxM08Sia1EEoh9Ztg2UBsFahbWKFD1jP3DYD1y/eAFODPGn0TON87P7V5FMR1IRtCGkGa8CMwmvIGlF1VSsNo4XukHXHdponAEVZjSJOQYh58RY7NHEzAKKlZw5pm0Ud54S8Fw1C4yCatVSdZF5hilXdJszqrpGFZP812/e8PjVT1jYzNP9e4ZiSeZ9QBlLXRs8CU8qK4PyrOqIDzPb3UA/eoYpE81M1o6qWWKrlmmYGMeZu7v3pByoaotzFQrFPInRuUaLBVzJN2wXCxbLBZv1hrc/+ZrrN28ZfM1+/8DN+++4uf2ecdgyDrck/xHNgasXDl0vqKslVdUVGPupkLAkLSLlQIgBfZpohYgooZo/7vXjUiQUtI2jaRxCThIpxO/+4XeMo2e73XF5dclyseDy/LwwdeQBskZTOYtJDZQd4eXbN7SX50zjSNd2tG1D3bWYWpeLMJUP0KBcLbEgpiblRO89qnLYtqZqG16+ecU8Tdx9/Ei/7xmHAWcMIWbm4DFNQwbuH+7Z7/csFi2vr/8SreWtW0l7JUUwdY3rOkzXop0QN9LsiVMgzuIBaK0S4bRpyLlmv5txs0BWRouZ9dT3qOypbWLZaXaHRD8GjEu4pubVz74kbAdImWE/QhCBvlFSmNJx35AUeYwkk8haEYp8IilIVvYQKYuTv7bqB9csFaZgmbKQ3aRMZIoQw4narXJhVKQjNR8iSjxQY+Sqq/EKbm4+sn3c0m8PvNhcnhzpTbY4BU1dEZCCdNwJKI5sMdm/cdqflDcS8yk9XUGRe0AOUvB5vsPNRTKTZLfqU8QrYR5XRpONgXkUu7mTUlcxWXMMY0BjySnjJy8MS0GtyNqeYtLbrmGzXNFU1SmJJCFyFVVZLAkXwJiGOXmGKfB4mAgYlotO3PH9yHzYE6YZssZaYVX3+4GLy2sW9Qq1pexCM5MPTD4wx1B8SsuUnyRoJITANM/MeOYwk8g0raOqM8YmZgIxFs9WZVlULV8sr3hZX7JQLfmgcHXGtVEe+qwAi4/Qe7jfit3dOIzSwYey3EqlP0jIvaRkiosled5nRUxKJAwZnrvcKaU5P7vAqsg8DkzjwCSVkaP/5DwHsrIs10uqtmWOGlMtWa4vqNsV/+pf/e/88r/5Z/zZP+/ohwO73RO3t+/Z3t0T5omurhn2I/vtnv/r3/wNH5+euNtuRSOZRbV4/9iz7O6wumK9algtalxVUTc13aJiuWzouppx3PNw95GPH+748u0b3KLDGsN+P/L0dOCoa8nA41SLgb3STLNi9pp9HwQTVZpu2eIWsLqQPXpKkTQNPBz2BB/oD4Po5kqYdy6NrHEOWzkwqux3RV+ntMG5itX6ijgvMcycvdhgqo6ka9AOjEMbCzmzWCy5OL8gDTvGx4/c3m95/+49KsFqscS9eI1SEI0hd3K9tFa8eXuONSumyXL18g2HIfJxG3jz1U/48mtD22n2T7fsHm+lIcqJuhVTfmsMYZK4uxgi2VQ0bcfZ2Tl/8stfcnFxwdX1NaOHw+D5t//2X3Pz7vd8/+1vIAyQPMmP5DhgTEZtfkq9OmPRXfPh4SPDcODx/pZpisQYeHhcUNUeYwNGtxLY7gyzl8n8x75+9CQYy67CGANGbuIPNzfya61YrVfkYrxqjZhti/mro64qTOm+U8pcXF3TDQO73a64czisq2TSzBmjBa7UtsJVDco4YsjMk0ydh8NBXDzqmto5UtfR1DUPd/c8PTwRJ7H5NvINxe0/lLimH7j3H3dhCHAouHLO4iOJ7GBSjiQytdFUVgrnfvQ89jvuH3YsFzUvrteElIgpc3u/Z91aKqtQFLeXAsUenSVM12C6Dg+l2xZ9mCzWFTGXQzwlghLijDf6E8HEcCooKSmRIXxWB/Pp6xM+np+RYygmxpqjbDKX1APKTkojbiRNW6PKNGIRC6+vX74iH70wLQVuccxZdgHo4/dTpxw6ci6RT+UryZRxNAJXuRg8F0strXRJXP80BRqtSehyyMksqbRMgyFGCaIFbBZT5gySGqCk2p0+oiz2alYbwIjG6+g5SiZ4TzIWchIjeIWQPqzGZkutNcqKwCc1mo/39zxtn7hYL7nebKidY9k1jMHje48ZDVSaulZU1lI7x/MLlo9kHY6kCgr8q6CkasfyGYLQwSunMVr0irJQVKigWTUrNmbBmVpTTwqTZ5xSNM7Q1Aaw0jxFGAfPFD33T3v2h7Gka4h84HivHMk5WcnPZwqagIHnmg6rFO6ZlCDnzBwiSQXmIC5MSsvyO5cJN0SR/sw50Q8zu/2An59YdbcMh4Hb2ztevHrFNPUoFbGVoutqhp18n6a2jIfE7Gd2+57DYWScwifSVU6n++nmwz1+WkBccXm+wegKaytJttCKw37LYfvIYfuAnw7kxuGalqZtqdsJHoeCWMBumCEJ7T9msUccPIUFndBxJmZpEkJM4pTlpyK1kakeJWbux+tO8WI9yqTk3Coic4QJ7OolxjgIE9p1KNtiTE1WpthMiYDf2oq6aamaFp8EUZu9eBo3leybVUmkeH699oc7cp5Ic6Jqr2kag+1HrGswxrJcX1A5x3K5ZrU+F2JRltWY1prkw0nbrWxNXXes1hu61TW2WnIYErcf7/l4d8d3f/h7dk83hPmJRhc5TdsxDgW1yo55DDw9PPHU3zPNA/2w4zA+cZgc/bQnKoNDYY10ZSqKCUBI/8Q7wZwFnsopCWNTSyjp7e2tMM6s4euffU0u+0DnRPRubfHdq4/sJdGfGOvEqspYgbFAvPF0BiSGCKVxdUvVdGhtOBwGQgwMQ89+f2CeZ+q6lhtYKa4uLrFauvx+ty8FUGyZQiwM1RSZvf5saZqL72HSIoQPz+2r8pFoIA2As4bKKUzleNxOPGxn3r/7yOWmo3aZYfJMIfP97Q7zaoNd1kA66fJC0es5IxFOpmvxp+VkFimFFvqyL2w7kxJBa2LOeKPl+5Uw00JiFMcb9x8LlT8BkMddxvHX6jQFGnMMoUV8H/NRRiH7VVSm6aQIkqEuUMrP33zB7Gf2hz33414cfpxjP8/PIDlxJ3xeBCnxMyDEHZXyJ01pPh6oQMon38znL2MMEZn0VVYCfZpCykkJXbILURptrBC25gBW2LXmtHTNWC2s0FxSNHS2hUWYxTDbyf10LIK5EJasstQJsJXAtcrx/bd/EB1sv2LlDN1mzdmyYzeMBO+ZJg3ZkF2F1ZbKOmmATleo2MzpT9dR9rRaSFLSMwjcrzLagHMapY+eV6CjwgTDxmw4t0vO1Ao7DpjoaRrNomroWkNUFT4pMWYfEoOfuXvYsz+IUD3bgNiNmZLIkYs7a0F4lDQegnDKnysyTmuc/rzJnHwgqoAPEZzBZLHqirGYD0SYU2IMvkxce7bMVMawXWzZ7bZsd49M8wGlE1WlWa5a+ieHyYGudeyMIBvjODFOM97H03pA7Pjk37q9vSeHIKhBMhhdYUwtpDql6PdbDvtH+v0TftyTli3WLmm7Bd3ogZFjU7nvZ1KQdAltRCM3zuk0LGifSEhTN3svetYwn/RyKX3SF54aH62lYCstjXDKhJK1KmxkS1UtSKYiagumBV2DkdBsjksHJc5GVSmCIUlUmy/8B1m7a4y2JGWPBwM5J562t/iwxw8zX/7kNXXV4axC6xptLN3ynEW3QhPh9ZfMfmS7e+BoHnD8rGNKKFNTVS1dt6Zuz8ja8fC057vv3vH9d3/g3Xd/T/R7VO5xTUPb1FycrXl4gGn2qGwY+4lhGnk4fCDEmZBnBr9l8BXD1J8+A1khBGIKGFMT0z8xHAqIQLcUOQrdd7VasdlsONucCVPqmTQiZ9F/+RAxqgQwaU1VN9iqom5b6XCLnqxuGmmEyKzWF7Kn0Zo5RBEoBy8u4d6XROgarTaoMp2eb85om5bL8wssivv7ez5+vGUqxJqqss8o/MeDB2KlCY3gjIfdFm4Ujzc3dNcr6vP2iNgRs8JULdXC0i4XjDcP3N584OG+57Dvedz2uHZJyor9eKC2oNKSnB0+iC9gVrKbehp7dlPgkBUP2hCyJ/koUJKSyTHpYxEU55OcwDQKY1VJLQ+nXRVoXPzPc4J10cVJpAwc0xNE1iJQK+rZAYeWOCGtsGSWywWruuXrr37CQtUoD//il3/GPE9sD0/cDA/MJhErjZ5lgrXWlF1fKkzALE8fxedTG3JIxX5LQ0jEuXg+Fu2ZVqrsBD+92rpDB8UQJ4wyVNayXp2Le0yIjPtBjmvjmENhWmZNmEXgbpWmco7ONVTO0FTS7XMAlTO27cp0Cv04STG1DpczSRuGaaRpF7SuYo7ysPp5JocRUqCyS/bbe3Sc+Ytf/Tn1b3/Pw27LnAJ4zzzMJD9JxPxnj9+nHeoRurZGpr3KGawRlqEPCWMltksSwjU5AL2hTRVrHG/NhlW22P0elz1OK+pmTVYNo2/kmUqZISluB8WHfeb9/cBhmsk60awMVadQNnLyRyuTG0qaWFVsjHKU5i1lw8JZuupzw/NsNVlVkAy6atGqZPDFucC3nne39/zjN+/4x99+y9O2h2xIfuL6csPXX3/BelOTGYk5om3k7LwjT2vmwVEpRdVEbOt5+/UVy13H425kv9+hgM35Rqb6EAhppB8Uj49l55w0wSumSWDMurKoFJj6Rz58/3tiGFAaFotWEIff3hXYUmpsSoFpnshZGr3Zy975mDOptEGbQvRLiZyCBDo3LZv1RkhQKWGso6oaFssl0yyORrZqUcaRlRG3GGULXNqgbY1xjUD4IiL97BlRymCrmnZzTjMcCK6mXp9xHmXF0tQNoaoEydCf0hZyzjztPnLoM7vHd5DPaZoXOHfB48MTc/A4bWiahrauyGki7x7pb2+4v7tjHEfZAdvSVCoh9FRuwdPjPUopdrsH3n33D9zd/pE834qpgLEsNldcX1/yl3/1Z/z613/L+/fvedh9JBHJOeLTjG0Mm4sVr7/a8Or1mmpRUTVLXLUkq0AMM5MfqLQrJgCeH/P6/5cnqItuLFtJdDdiSlwVMXVKkl9njehjcjakmAha3FhyFmhHI7ZXbbsoFPmMK5lwSpU9nTpyQ8Lp78jkYkrWlBAcpKMXP9PlcgEp0XYtdmsIwdO2TdGBaQ4HSWR+/spWy1fMYus2jUyHnvqsQdGKnVgU1xrjOlxdY6uapqlZdA3xomGcAh8fB+rZYaxGP4dclZEuPwlJJcTIOE3040Q/zyStZfozhUFZqClJF+urpDl5q8QoD5bVJK0+WacVOPU/9RKtkijhj9DncQqUPy+wpZJpRxfigi6kHEWmshZnNFeXZ/Qft/jDjMrQVBXGbJidZ8BzcFFo+VDG0yMk++ksPTLHPhEupPgcp/OMkG1kCkrHZdSn6wUnTaqzEmXVNJ28/xjFMDqW7r9IDPJRapAhlaKa1SdIWCtE4J8tc6qKrrXsYshlJ1hS6Y0VWryxJD8T/Mw89dROY3XFaiF5izlHnNUsuoaLzZqnwxZyRJNFLD0NqB/Q08ullJ9LiWGxNYh+7BOOW5jape9PCqKmyjWtqVnYlpVpWFhNoyIWgfowNSlbcXhJqnwmisfdyP1TTz8FkgJdaWyj0FX5/EsBPNqj5XL/GC0kNeU0Sct6t7FGWJufHRyF3GFMSTtRxUNSpszH7RN39zs+fnxk9jIlaSUpB03b8OaLLzi7OMc6cQOCXJjnlmDEVUjpTNVo3ry9ZLHtaB4O3OQZYzQ//9kXxUXIo/zIoqk5WyyoaodSMA4z0yiFZ7Na87ha0LY1h8Me91TTLlasV6ui2Xt+HqoTcSUWlmcu6wPUUVesC6tbCYEtiduLzpYuJ8Z5hnnGWoerKpqmFFsj+Z3KWCmAxR1GHuKjVlUaydONoU6PWmlkTYldakE7tKtxdYvJWsLAtQFj5X9Ph4UMI/04MIxbPnz4jrqZ6VaZfh7xMVBbTQqO5C05Bcax50jEOiJ9OVGe4YiYZfRsH2ZiCjw+fWD/9IF5eMSqUNZD0C06VmcbLl9es/z+O9zjA7t+WwKRI67StIuay6sNZ+cLVpuGqjZYpzBG+uvjBkkp8+x5+a+/fjQxxlpLVVd03YIpKrJ2qAJ9Nk0DyPL+GMQIUCdJKPBksqlOF5Es7inr9dlpapTOXx6AEILkzAV/EtuDFMq2baRYeY+d5AKa8nMsl0ucsSwWLdZq5nnkzZvXtG0LwLt374oVz6dXcobkDFkn0uiJ48j4tGVxtUAB4zwzThPjNMnIvlpSNTWXlxcCJ5hzfveHG377179hGWoWi5o3Fx1N47BWE3EyqCcPKstSvB943G457A4Yq1G1O8Ga0lULLIoSiyMTAyZF1ByK5kj2OjmLMDuFhPlcdnZUq0iz8MzvVev8+Q1SiiAqy0SYNDrrAkfKV+ssi6qhfltz/+0ND4eBx6ct55sVl5szcuvZxwHigdpoZgUxlClCcWpYNLpEBB1JC4WZWvB7gTEFPhadeiLrowOJ/NAxJHENGWcW65a66Vgs1zgtEoSuGRj6nv12JySiAjNKkf0U+5M4fmBSPNvKUVvDgcwwZqYYSkGS0OAQpLt3riYVY/dxHOkPe3ZP96wXNauu5fWLS8I4QQY/jyybiq/fvuYP3wmF3hCY+j37qFhy9uyaHWFQSU8xWlHbwjA2xZgZgU21yacimHzZA+oF5+2a6+U5K61pnGbdSfJBRuGzIfkEMcsusXzHd7cP/PHmkf0UsK3BtQ679phGWJtyqQQi1ymTDZgKKq2osmFhnBAkarBKU5fEktO9VVLn9clDN4nw3DiyNXzzx1u+e/eBd+/vAEXTNjSu4vLynJevX/Grv/pLVps19aJleBhFT1qMMXKGYRzJOrNY1vzqVz9nu+v5/vt7UjhQVxX/07/8K1KWIjgetlTG0lYN69WSnBWPj1vqtsFay1c/+YJ57DnsnthuH0ugtKL+6ZegPp/atdXYZLBJjLcTCYwEKBsUVVWLrlkbpll22LJRkOtcty27/Z6M+Ki27YLlai26XwXKOoypMLbGmuo0CZa+H0y5Y8o9/LwGym9aXL2galYo22Bsi3OBSos0RHxFCxxaOCRaaa6uXzKFDwzTLXc3/w5rNly93Io5upIG92gJd3Q5stmwWW4ITcDP/kTEq7Q8w8kPPGwf6Ps9dw/fEscdyfe0TZJmSGc252uuXlxy/folq9+f09zdER9uT9D/6qzj/GrF1z//glevLticL1gsqhJ87clBpEFA0W/++Cr44yUSStNUNVdXV3x83DOGhLXm5IJgrcO5Cm1Fj5dyxpflb1YSC2J0waFT+rT8P76OidtZsN3jMvs44BhlsMaRXaayM8TMnGeauqaupADOk2h5tNHUdcV6tcL7uUgDpBDUdf3Zv2sobExt0D7K/RPTaV819nv87Kmc4uJizdXVGbXTrM7BuAVjrGjud8VMvOPsrOPVyzXLpcNWGpQlmbl4ckaMhvPNgnk/UMXA8GSIRBIGryD83CXAAAAgAElEQVTkzEQiYUtnI3ZbJmlMEqpgGP1pxlIRTFLYH1CCYxR8PCmIqXRc5qj3VCf4Wsgl8dRJmvJgqZxoK8tZ1/Hm6opV07LvJzbnLYf9I9/dfkPULzDdNdpp2rrlylT0c8Qpzf1uJ/sto0opLcrFcqMep36tdDHaOuXCS0HOQvn/weAuE5mWaTsiWtJDP7JeLmnrFmule1+dXYh0IUamaWZ32DFOA5lZiAdKMfkJ5xyLxZLZy+43o4VkYGwhg8h+pl4ssEoRlGIIidl7Pt59JPoJaxTXl+c0tWO3e6Lf9wQfGENkvbng5199yfbxjkNfdsKuGDM/I7CJdjGetG1GHaU2SqarI0mlWHHpDHjNol7RLRd8ffGGVhvqDLv7W6LP6NxirOxb+3GHMRZjLE+94nY78I/f3/PNzRPbIVAvO1STUHVANYlsE5EsLNAsUg2jMi6CrhS1syycpSqfk48Cbbtnz5bAaw9UVuKgRPJj0K7h4b7n4b7nj+8+ME6es/NLtv0oDMXXL1Aqsd/tiEnjg0LNmf3jQI6B2lnAYWxDyD0oi7EVRgVqB5tlxflqQVNXbJaORddR1RqVL8sTbwmjYhg8cXtg6HucNTw9Lajqmi++/JLf//YbHh/2bJ9+j1Vg2+4zLoGyFmdb3GIh905MTMELzJ0Sy6YuGl5pvELMzPHIvlY4UzNNM0oNNG3LcrUU/bP3+OBFhqMsWlcoZU962edb/k+9xhFD4tmvQGuH1RUqSlKEChntQMtS8vNvgjR8zaIrLiwzh8MNbR24vvznomENng/v3xHniejnco4Wgk3R3gbviytTJE3+hPisNx2bhULliv1DYgwzbWVQ1qFsy4sX15xfXJRJTuqE1pm6qVisHD//xVvOr1a8/vKSptNoItNwQDEBlrnkMmZgnkeS/6cugll2grLrsSfm0nFhq7S4GhgrEKkqp21W6pSAILCIOKdkxUmaUj5HYR8XTdVz5EzORFWYgpqsjeSHSYtboBEnhJ0UsV46HVcddV6RuVygGMNnCRIKTgeKMqr4G2VJGQ8RYpQEciJt3dK1NW3bYrWm7UCpijQmmramW7Qslh2LRcuibXFOin62MjGnnCR5gEzlFF1jSa3DC6ELtHSRgiQUXV0un1v5WXVWIkn0iRN4mGQ3pE4fdLlkR+usfLzBSxda4MHn3plZFYgSETAXOgu1q9ksFqzahq6uiSmxWncsNx1Bzfg84fOMNprKSqrIshbvwL2Woh4R6C1T/o1jB6LLYkUp0eLlVIqQ7BFTiqSc+KHeJ2Xh7FdNDUoRswjMMwpVDnntMrZp5MGMET1OjNEzR4+fR4ISPZ4PIqDXxpB9KE4tkgtpnPsETxfiDIgzjY+eYZo49AcMia6W+08rxTCMDNPIPHlCvqNpJVqpdhZvDDFH/lOP53NERJWPR7wkdbEY0xwb/+MAq5OiNg2Lesm62+BywsRJ2J2F5GWOMGQKRKVIyvAwBG63A99+3LKfIiFLegZuBpvBJLIWpnMsOtRcJneyTMauTH21kwbHGHH60Z+9JxjHgWgzRonkSCmDwXD38MjNzQPDOCIOcDJBWms4P1sRi4H2PE5YV5PR4ieacyE5yUMTyoqgqmq0m6m8pnaGrmlo6orGabrG0LRWWJRZSDF9Fv9VpZJ4ek4Th8MegMWyQylDmGemaeTpcUvtizH58ewwVqz9nMNUYmOn5xmiR6VI1zhizMwh4a20eDofS6C8T12e7apAoU3bYa2Ts/V0pUu3yKfnOz/773+uAIKYRBgjLkFVgY+tLuxejiuR51WwBAdUBucUKY0oNXNxvkCkIDN37//AMO447LaFXa5KepBwQGL4xA71/SgSKmXYrB2VNTS1ZjSgVRK/Xudwbcf11TVXl1dUTohKRssevGsdZ5sFr19fcnax5OxsScafrlvh0ZKzII3iW52EMf8jXz+OHQocnrY8bXc89hO7/Y7RZ/a7fdkBiljSVTWubqjqYm1VWbQt+0Mnb0xrTQqp7BjgiJ2eCh/5uLSSPy7Ozk5bUAXcEqaKTC5FiI/KGKNwlWW9WjIc1uw2G3a7Hf3hwP39PcZouq7jWUOHncU2jcYUj07Fbr9lediw7JeYHGhax+sX56wXFYvG4uozbO1xk2e6v+Py+ow/+9OvWSyXtJWl1garCkzbragaj7KKcbdDJ0/jIos2Y6LjVid8lgNH7MSKx6V6Ni0XUonYdEGOiXg0YM4l4dv9IJfgeFKWp0P2aCWGSXHasYI6rRYUSqZHEi2J80XLz794Q2MNKnliGPnqp6+4frHBKYvRlomBrllgrEEZzUXT4KKko2+9Zx8Co/cF8VRoY2USdQLI5SSej9F7whwwHlRM6KLzUT/YxQxhxtSaV69fsX98kgPD6GJVVd5v4fAbWwsbtKmo/MgQJraPA04Zggv0ixlXRRKKMUQJwc1gmpbOWqYUhBSFiOi9j2z7gQ/399w9PrLdblktGrrFivvHR8iJnJPopFJkd3/PoltyuLikMZrZaLZDwHqPVuEkIYGjGkYOOpWkW6+com1qlt2C1tXEmJjxcs0zGDRL27GyK/ZPPU5FrCqZfwqcg7N1jXMaayYeJ83DCP94u+XDY8/tEFF1h1CqZjAzVBPZzMUTVpOiwKYaLVpQNG2uaZWls4bGiWxnSolZFaj72cmxPzyhCKhkJN5IObLR/PrX/8DvfvtHvn77lu2u5937B6ZhoGs3vHlzzcOHO1JIfP/NH1mu17TLJRpDU7ecbdZ8GA74eaY/7FmuOhbLFcpMPJk9++3EZrWkqSucM8zziA+xNMAyCWpdi/NTa0EF5rnn5maibSuaxtG1LcFn+sOeh/sdbkhkfVaeLYW1tciCKkejkaT1eUTHSZ5xk5iC3PPeOsiROUyYI1s8gixoI6vlmuVyTV131FVLitLU5ig5k5S9mIiJnj3jz/77/PeOOlyrLW3d8vrlCzod2DtJ8cllzaKMRfEJDlWAqzXLVcP1iw337wYuzlr+4s9/wYuXr1BKMz594O//7oE/3HxLVQkSaCsj01+WAWKePfM0M+1HjLa0TUfK5yhdoZB1jNLSkC/aBdevvuB//B/+JS9evSbkmb/r/o7GVJwvl7x6teEnP73iV//sZ7SLGlyiH3tC8es9Ev1sXQnqhhg9pB9wCf5Lrx8Nh4owOzFOEwqFNeK/6Ccxxj6euX72RXdmqJQ7TRzH3cLxJqLcOMe9FZRu+DRhwjH89PRnOcuEUBLQrRFCTF1XhSyTiEEXLF7A86auZYIsJshN8zkc6mLGRLHp0k6jnGLyY8kzi1TG0FaOZeswRLFkcxK+2jSGzWaN1pamboU1R0L7kbY1NJVGtzWLtmLZOj48zMR5YhomdFD4MYi41EeiLykQKaNjPDWBVhtMVpgsTvnZKemE9SfCjUriQfj8pY1Mzic6G3KznIpjealny4QcBa5UOVFVlmVTc7laoVDMs2d/2FPVRgyNS85fzjKtaK2FLKMV0RqWlSOUaWQ+itVFPFimXNl9UijVqfiOmvKgK63K/fH5zVx1FdjIlCZs46itY3m2IsTEw+6J0Us47hQ8dVML3Oo9w/7APE9iqKwNta1AW+aYuH3asj/0TCWpu2pbqq5lP8/MMTLMgaEfGMeJ27s7tvsDh3FAFx3qOI6FeXxMU4jSyITIbr/n9sMHzjZn1E3D/FF2i3maWDXPHzCZgI+rSqM1bd3Q1Q1NJT6ZMh8kTLa0dsn6omFVr2iMJc0zPgdC8vg5kZRmqDIPU8ImzWxXNN2Gl3bJVt+R7EdunyaGaSDmgK4i2YoTv5wvoj81WQkb22gWVtMaTecsrbXUxmKNeIYSRXeYU/7sVGmass+KGqtrpjHyD//4O/a7PU1tefFyDSpxcyNyHK2hri3X11dYDI11ZB8Y9we+fPsVbdvQto55numHPSeuQNMQktiYqQxt09J1DYuuIzGRkjzPucSeKcR4QZtMyuLCEw4R7w3jaFisKmKcufs4Mk49UWtoN6dnyWmD1Uq0zSqjVMKohCahJBVaEJ1CRtNGY7WGLD+D97I7E66FTD8xpsKoFMH76azLx+cgHQ9jaWzV0SJMfVYLj7/UWlCybtGR5yWOgegrfIQhKDG2eC4XA1L2GKdYrTqcNYxDz3/49/+Ow24vjcE0EueRMI+oJJNcXVXC5s+QlEUlSRdJrjgTxYnbj7dUlcWHnmmW9c/kI1XT8fbNT3jz4gteXL3Ep4lf/vTn4D3vbyxXLzvefLFhUTuskXVEZStM2dFKAk0W+05UyVD9VEN+zOtHF8GjINl7D4WlGb1nmkaGvj/t8IIPmNNk8azApWL1VSaQE/xTPnzypz3Q8fefQ5cZGbePeDMIuUZ0iKJLzElYoKYwUHWGpq6prDt1qFXlTjAggMvFBkoh6ahG4eOEDxLh5MwRXpEim6Mnx4CxNdo6VmpBWzds1mtCEpH18PRI0xrqyqArS1s7FrUjpcg4T6SpxyVDGiNxFjeaFKJAlzGjQkRr0adV1hTGpiIgNkRY80k7FhM6ZPIPUj+1VgW+AlAnFtZnmCnSaKgjVEk+TejOVHR1zdmiI48iTZmmkcWyo6os+Fk8LWMu7EX5t5xW1EazcFZcUJSiMloCg7UWu62Ui0O/7CZyShI7lQpcpOTnT+lTnkL5abFNRdITPnnq2lHVNfWiZb/dse937A4HxnliP/a0rZg35BjRMaEznK83VMZSaYdxNTErdv3Aru+FBOU9HdA5y24aGWbPfhjZbXcMh56bm1sm7wkpcr5qQSmmeT75QMYCK6uc0Fns/B7uH/j6Z19Ttw13+x27YS+d7DNyaD5GMiSBFK2SIthUDbWrOeElOWGyozGGi+UVlXKYrOinkRA8KXjmWcgzzIkwJUxW2HpFt7yiWV6w6Q1PfaCuvmeanoh5QjtIJgoMenSLyULQqVAsnGXpLJ3VdNbRWENlLEaXFUZWokNLz04VpajqCqMTRIOhoj/0fPP7bwnB07U1V1crpmmiqjTWin1YXRvqZkltapwWVug8TayWC9quRauIn0eGoS9SEkNdVeTZYAobuq5qgRibhhCTEJuCNI0pJRRedKVG7B9jDHg/Ms+iv7w8O2eeLSnPeD+SjYX2051ojUQfmSP5g0Q5hlE5ye47C/VLmPUyOEjiRSL4GbLwKipXSdB2iCeWZS4N4OcFMJ1umFzYMaLNPja5x3upwNdaY6yhbmpS12BiB8kxzYk0BNmbfdZjZkKcQSWqukIbxTj0/ObX/4HkPev1hmk4EOaJHAIhSdHWmEJqK9/GQDYZ78Q6LSTPw8N9SWoR83mlND4GmrrjzesvuD6/5vLsCh9Gfvr2K0xOrLrE5sJy9bKhdvmkY7ZatOEodUJdjDKlj4yfDVI/5vXjiTGVFY/PtmXajxI6+vjE+2+/JyfFz3/+C7RWnJ2dlR1VJnqhmiuliHrGWUeqqlPeYAjhNEbnKAt/Bae0iOfhujFG5nlmnEZ8mFFA27Z0i+Y03eWo8VqRY8Iaw2q5QmktO76ytDU/8NisTU1Ulmn0ZJfJ3shB4mXvQxInjNZZiJHgZ5TeU+mMc4rKVYSYqGZJNshAuuhIYSQFT/ITrdNcrlv+YBR9zBwOEfqB2M/kfkKHjE5HmyTR0hkUOoGey4RoDaZ2JKuJlTqZLGelyFaLWemzblCrYpOWkyTJK4lmyYVccaot6lOxVCiSzhil2CxaLlYdl+sVv7v7jnH2nF1c8+J6w6KtCfsnvI/4kBinLFT9rLEkTPa0RDoiM5FoBaqciQLFkPE+lnBYCTPNhYwkN0+JCCryhdNLgXIKZTXKJDFPCAPv7m4Y9j1jP9D7CZ8is0pst3fiJToH3ly94PX1C/7X//l/oTKOHDPf/vFbZh/QruLXf/8bPt4/8Ptvfk+z6GhXS3ZhFqFxhnmcyCFRLTrxLM2Z9bLGGU1lFKNPzCGyO0zUzuIsOCUpA+J+Y6icZrFcMcZI8Pmz60VKEoeVE621rJqWq+Ulm3ZNV7UlHzFhjWNpW9lljoFqIQ2g9w1DNBy8Yu9r0pxQ88SFW7NqFlxcveXdhwe+/7v/l//j//4b9ocdw7yj6Tx1nVDLyFiKuIquFGLFprJ0leVy1bFwlsYYlpWhtobKambE47UfoZ/Krr8+vikZWXSJCuq3B56eHri5ec8vfvEVP/nyNV+8fUHTtGhdc/vwRNvUOCcmETkGsUMce4Z54M3bl9R1xTQeuPt4w3DYYa0UlhQjlXMsugUX54E4P2GUYrfdoW1Ga4OzDgikKCkbSkPb1uz34of78eMti0XFat3img1Nl2gX4OqAsUc7O7ls1uTi2yqDgcoBlWcRkZdiJedWQqkK6wwL1zINucDmGWsdjXGySkDOQ2vtiUAmzaB8js9dn3IWJj6qxDHpz7WZx89dGWGET8FzGAbGw56vv3hNUprVELi537PtA2ouU2DKfPhww/3dA99+c0vvB/p+5vv/83/jr//631DZmspZ/OzZbFbsdgdiiIQpFFmbws++BCLP+DmcSIkpzpANCpEvKKNZLM54+/an/Hf/7X9Pu1oRUAwB/vTP/oI//dM/53H7K/bDe3b999ze/5ZxHhjmwGZzTl23GG2Yc0aFTO00c0xMgyf4SOjzjy5vP942LRYXAP2JVKG1xs8z26cnPnz4gNaarm0xhYEYSyK8Vlpo0fmofzLle8YTqyiGKPTiI3x6OhvSD74EatJGF5adQJ8xFnuzFJmnGT/NwuqsKrRSOGsLc+lzT7mA6KUUZc8YMtln5nGm7weBK1yFLeGd0pkFUvKkZCRJISd0EbkLlFco3OW+tFqVYE1JFvCTJ0+e5APKmgJ7PNPVHaOPjpNcacxVEeFlCuyUQUUxuo7585lJSEVHWPn4MJXJT+mTX6hSRROoJFg25oRWZYJ2AnOkQjpZLFc0dSt5eNbJtK0iwScpPPN88g2trMYqEftbLYQfjZgOFKbF519C2ymfb8nk/Y9gjUxIgZTCSTuXVGaYB+YocGA28g8pLSbPZFBOM6fAYRr5eP/AerGkrVq6xZIqRDCW1eaMzTTT3t6SC8Flil4gI0q+Ye1o64ZxkuR5ga8gnkKky17FOgl8jV5ckXJmGCey1dRNS356wodwurbwaZVplaKpK7qmoauaAv0YMUbwGT9njGgO0IhYf5wDj/1I38/s9yOHfkAbxWJRE1JgGHu++f03fHdzx/c3d/TjE0nNNAtF0ymMU+I4VHaNDjG9rrRh3Tq6yrFsajprZD1gj1CgIoVMCIo5ZGJAeMDH95UlBDnFjFECd9at4+2X11xfbVivOzQCmV5dr2mWNc4amtrRP47M/YFxCMQcyCrSD0/E6JjGnuBHUvRQIrXmaQI9EWMoxgK6NNJCutFKi240RKYpUs7sEzkFIMUMaKx1p3ijrqux5ih+ffYqu6jjdE5Owm9TR3vIwpYMiahkvaGNPUFkxhhMlgn6uJHIWZjtNtvCknzuu1vY0uUsnqZJiIba4Jz7bBV7DDT/VDaP8rVJCm1Vs1x2bHvPFBX0ofz/Eo8POx4ed2y3ByGqKYFIxzEy09PU7en6mpJ5GJMX4wuU2Kilo1yhfD4gXI58VENJY785O+fy6pqXL16itcLHwDDNLJuayjVsNudEtuxHzdHoXnSzTvyknSvEG01lHUolghW4O5tP//Z/7fWji+BcqLtw3NUpnKsI3vP4cM83v/sdMQTOz86wJcgzWI+1FmVKTloZVZ0radAlhFcy6IKEblr72cUPIRB8OMFlx0JojKYuBBz5Xp4QPMEHhr6nPxwYDges1oW96BjzQDwePuU1FDJClUGHTJrlazwMbLdPXF1uaJqGumoKJCw7hBgn8BmT3ang5MJIyiVDEY5FX1w/amcxSjP1E3kMZB9RtTs5vwh7TnQxn3RhZdGtRJyLLgzJWKCzCEGJ88sP2aGfuvBPTNBP4bDl95RCJ9GmWWMISRb3XddRuRqyIiuDdZbz8wvaWmN1AuNQxadwtJ7ZBw79QSzTlKKuHW6fMdFjtSGpkr4TKakZuUz++fm7JOcorMSyI7XFNKA8Scx+IuFRzpUCnRnCQMiBoMVw+xj6arJBO2Ezjslz+/TA//M3/5431y95++oLFt0C0PiUefHyNaZuedrteNw+sT3sCSkebQrouiVN03C+2nD/cM+T98zzRIoGlS0xIgiEUrR1w6JrMGHGZIXPmYfdDtc0NIslMcM0h+c1sGgDJX9u1XasF0uWzYLaVJhsiAHmOdMfEgZXzMkzu8NI7wN32z37fpTd5jjTtjXL84YQJ3bbkd/87Tfc3G+52+5pzyvapaZbW4zJoBPJgM5QAco4XPE3PW9qWmtZ1zWN0VRa05oiRs7iSTl5GGeBxj+TPQH9MGJ0xChLW7WcnS34q7/6E7qmpalqYojUleXFizPe1MXCK0Ruv73n9uaRu7sdzcKx3LQc+jtirJiHkRCG0ohq5mmi7zU5D4SQT8+c0QLTWV1hrMHPGT9PDP1M21qslUlJqU9sU2cr2qYrEUAVq/VKpp0fEFAoh/qxAFJivbQSf9QUvbBDvScq0MZhigkISGB4RNYDlGb3GPAsZDBzQsykSQ6kJKdCCIHDYS+ey9bRtu2J7Hb66VLimPYDGu8jfT/S9z1La1mvz9j1njlpeNzLPi9lbm/uuX984uGxJySNMpqmc4RRbOLGKYqQXxlcJYzpECepa5kTCz7n8Om5PTpUcTQNiShjePnqDV+8ecsXX7wlKJjmkd2hlzNbO5quw/SVOJVZi1NiXdlUDXVVlzBhSwwB5wT21li0Ceg5sGX4L1S0T68fXQQnPxcfTi8/pLMsu072PuPEt7//hnmcqK3lzdvXrNYr/GolBtnWiHluOXitkwBcZ20Rw5diqDXZ22J8LK4g4zRKYrERRqGLFls5XOVO0UjHYrnb7Xi4f+D79+/ZPT2JwL2qgMx2u2UcpxMkSLlE3409Zhr5iVLUXglbNUJjK9bdkhcvXrBedUJ60arcoKmM95EcZ46BR9nPZbBJJzeUrI4uN0JVtlridJRWqMqSjAEr37eeRVpfKZinWHYXRtijZMLsxX/RWSgOM8fC+cPTp3CPTp3uMSLpuHOQ96E+FcUs+9yUJQF80a6p6gVJVby4vsZYw+X5OSmM5DhjulUxIYz47R2j9/R+ouk6aq2YhwPGGTER15ZZGZzRhDARVcJUQkJSSaOVwOY5JsLgIUZySSJHiUl4OTtwlSM4QFuULQt5tJgdmMAQZmFyxiCm4lmVJIbE7Ef+7g+/48PHj3z37ff8yVe/YNEucE1H7Wouzs75k5/9kvcfbrj58IE5J3wMzH5m0y6oXUUVE43WTJVlnISE0w8BXXa6qwy2kE0imSFKMv39MNEtFly/fo01NV3zuauFs5aGSkyKu5ZFW6ORnfE4K572I4f9yOPjgbqS/YuPke000fuZ3u/JNtBuIq9fr2jrmtXacthuGfsRY0fevKh48+qSvLBkE8lqZNl0VEZjkhgFjAC1NG1d5Vgqg4qRcXvA1C3aOfRKE5P42w4hMYZMjNJ86GcWXjln/OzBJqracna+pHIV5xdrgs9En5kOE5OfGecRZSU31FLx8e6W79/d8rf/8Ae++MlLfrZ+S8w9KXlCHAlxBJV4cXUl+mSj2R/25KRw1tFUFa5yLBdLOW+0hZSwtqLrligl+uEUA85l6kpzefWC9Vpi2qxpxOPVjQyhJ6dn7wuYZ/AqkkcvSBCKWtei/MmZueynUoQpelCRaRYpRgrxtJbJ5JMdpPceYw2VqUpWHsUreU/Oma5ThJCYppmn7SNdu6D+/3h7ry3JsvPO77fdMWEys3xbACQIYChyaaTFpafRmnfh44xu9ALShW50o4tZMksihxwSaFNdNl2YY7bVxbdPZGZ1NVA95MxpFCorIiPixDH7c3/T9YToJfjW9TVn4bFCOWEbZCaaeX95yxwKyva4pmW10sDhdL6urw8MY8DZljgKKEcZjWkMRSMScDHW7pkE5VI4zSVDENRmIYO9S3KzqrKMVmyhuvWa/+5v/oa/+N3vONtu8SHTmox5pMQv1B85TO+4uX3PbndNSnLu+n6Dcy3GWpTRONPgmuZUUSujxQy7NfCvHQRldidZjypKssZqJBpzZjwc2N9cc3V5ydn5RvyxjBUtOaNxtcIDMWG0xpK79gEysCgxYs0msUhjRS8gBH2qwkRlXWmDqQLJORcxtfSCVM1K+F4ZCHWW4mcRr1Xu4Ve+SQETA8+soysKk8UMkiS8qLYT25VS1fwXtE8pVQ2+5OqMINn6oplqqnRPWbpDWp8CUs7ppHqRtci2YTU2FpyCldWUkCXG5EojX9RXFDUYn4Z6JzTlw2S1nC7sUpaBuq4Zpzx9lz3KsYp1JoVSdE2DNQ7QtI3DOnEECcmTUCKKoAqFWrlVQWDnWoxRzOOAUgqjNEoLf884x24KqPp9RMlGY1zlsqVE8ZGcFamKMqd7LRVQ2KZBCf8EjBGwVc5EVYiqEHIilVTbuqZm+UbAD7mwOx4oIYHPPF4/IpxFLqxUCq3uePTocXXELowx4IPnOBxpbSPi0Fksr5wxBK3vuhtGi2KKNpJI5Uws4mCSKmCM2XM8DuRMtfK6O2HWgEOjTW2da7E3jjGTQuS4n5knT86JWK+xwzxx8DNTCPgyY3WhsbDZWLrW0nYKP0qLaLPScj9ay+g0GZnXPO4aGm1Is1zYGoWxms5q1s5wboVDectM8VkECNbi/jCHQspUgfTaWn8goK2EPmULjWvompa2bWmazDxlgk4Uf1omiWXiTuMnn/4zVtOvOkF4pkzOsSJJlbh+qKVaqolelWq70+qT+0FrcE6S8lI0KSeiV2gd0DrTdb2MT7Qh1+Tp/p/7W6iGw5kkZHqtRZ5M1YQzz1UqTQy1E0WELoJcL0oLqC2nLPgK72liPCGOcy4nMJr3E67efzEk/DwzjiPWOoyTQsLou6zGnFkAACAASURBVGMv/okiPBDDXSctJxiOE0o7mtWAct3DhmFBbMyyqnrD1c/Tikl2VkVm1wuAboGWFxazWZEsrO9qFtebyjFVWqGtomk71tstX3z5NU8eP8FZR8kRhaJ0LT5O+FiYpwEfRkL0dwAyEGefKOdcn/Aj+rTWnsT6P3H75CBIDStGA1Faea4PGAqNNQzHA0etefvDSx49OhPuXqozslJoagt0waVYY1lt1ifuIKWQ0CS9BA8BPsQsfCrbOubgOUwjoYATS2O5KWJkHiam0eND4vFnL1DWMvmZ0UvmlVIWUv89C5tSCq/DRI4TK6P5UltWCsbrHdPtOeE4SXvCGvGSW+ZpCkGA5SCzTqVBWwECpVKzlqVdVzuXSov6SD2JRRcKSiRelczRrDH0VrPtHDkiepxjEENvq7C9VNDKKBIVlWcBFLp5WAkuScTSc5P5hxPZqyIODMtVFalApSAIt0YpNn1L3xhxZUAQXSlGgSSjaiYGRWd8FKL5erVi06+Bws38Dp2hMSLmS9NBv+LmOOKDZ8pRHECsoV211UdQZOCijyQyIRdOJKZ61tabc6IN+BzFbT1n/CTnOKdc4dcFlCEjVW/XrEg6kefIcL1DhYJJiv/4n/6Rp0+e8t9sz2jbDZ1ztG3D9uKcL3/xNdfHHfvDgffv3zPtj0QfBGmKonONIP1iIAbPk4sL1m3Hum3ZjSPDNDNPI0pr2lYUTXKBVz+8kjmzueNHKAWuqUowWFqjsRhK7hkOAX+YON7c0vUNzz/bcogDh3Hm7fBeKCE5YWzBGY1rLG0PXVtomsyzpy3l3BL6DNGSk+H7XUarhm3T81XXolXh290BV3qyatkay0Zlzl3iq02LKoWraPnmzZ7bcWC1cgyhsB8T0S2BR7JwY+5QglorHj++oHWK1rT0tsdpR9KZZm3IveZsY2qVX3h//QrvZ1LMXDzZ4qO03X/1Z1/ymz/7JcHPpDCjEWeabAuDPwovLC+BGIpKhDKSk+Zw3J8Cn1IGo52osOgVKSfGohGNpkjXd2gjqMXDmBiGkd1hIIRAVpzQoaXAMCFI56LonJUsxranrkvJEzkpUpAAGlISEFalAMXimUMkpMzN7S0JhetWFCAExW4/M/kVPnoOhz1LMA8hMY4jt7ubk1D1OA445zBGHN699+x3O9qmIUwTx90ePwVyUtzejhzGyO0QOXvylHhfDk4p2raXeXxINA6SqtqwDnLSZKPqGldOlacCafUCOnAC3qnazpXZuQRW6yxPnjzji8++4q/+8q/54vMvZY6uDbbONwOaORYub/fEdKQgQTDGyM3NjTirVFu0pmlwzuHaTjo+KTNOgXH6L+AnuFAXrDXoKvdi0ajG0hnFZy9ecPbkCV/96pe8+OJz1psNbdPIBV6KZDu1pZqC8HnmcaqzNGn1dZ0oqbddc6psrNHS1yuFeR653d0yDBPTuprxGpmNUETQ+Xy75Ve/+Jqz1YpGKa7evGXOiWL1SZT31IZSMGvFoAp/SDNdDVT4wHiz4+rVG8bDr3DO0rYOkdSqElZFgpCgtGQfc0onRJiAJBKpFGYfmH1iHjxxjkLXrXMIlSRzy7HUyhCik4uuZEWaQbcO1Tvcxp4qyjRLdhdjrmoxDzOfUqvrnHIVZpGFKte2s7HmLnNX1Zy3nlOrDWerjkYXpuMtlzd7CoXdfodzAki6uDiv4B+p0JVSNE7UYlIIIpmUCgZxX++c4+zigiFH2sOef3r9PTnHk8PFsrqYvhFDYytZrFH21OotgB9noksUo2rFlmUWm0Uhv7c9sWSR8VIGVRR+TuhYpDWaMscwEoaJ2AdCSqh/+Hu2jx/TrnrapmG/P7Db7zjMIyGIh6VWoK3BBw85o3OhNYZQ1T2iD/ilOxLTKRuVWXbAuraCJqQl/bBiovoCFhpt6Oway5rjUKq/38TTFyIY3KwUu6sZnyZCnKqbiMyjSjGkDFMYKSWQ/chaaRoUm1XP5dXM++uJm93Mxbbj+bNHtEbadJeHgC+KQuA8KfCIw0LXYrXGKEdvHNEUmArTwXOzm+gei+6ttYWiM1o/LJn6bk3rFA5BFfp5Zg4TKIdSFqv72lmRKkMoBYZnz56yWT/i13/e8uTJBU+fnjNNlwTvmcZAziKKfhyOUuVkQVcKeEL8SI0RQNE4DpSSaBonszntUEqkvsRt/UCMXjpLxVIwlNLI+uMcs5+JOT1YLGcviaQ2jpgVBMWxJBygS2IcI/OcCaGwG47VXaKIIbfWxOjxIeJj4ubmmjkkYlYnmbvZTzx68kh8Evd7lBJVrlA5q8PxQNeKOUDTOIL3KKV4/eY10zgyjSNPHz+mpMjV+3eM+z3Bi6RbyJE5D3h1QzHSSgS5zfpewFR+irjWYK2qXq4yH3etoRR1es2iTGWtXNumcafukhzPGgTrumWd46uvvuQ3f/Ybnj96wqrtxV6q3t9oTYqBECameY/WmbOzNSGJT2maBbmfstAlQoq42OCSuHaAOWklf+r26WT56sulltkeol9gjcG2lucvnvHkxQu+/sXXbC/OaFoRkFU1yIUgs5pxmpiHEVUgz54UIilKib89O6sK8grjrGgoGhk0p5wI3nM8Hrm53RGC53yzoXStoA+z0CK6tuXpk8cYBXEaOV5fk+YZ5YTY2TVumTABELViVPAuRz4rmbYU+piZDwP7y2um3ZGua8mbVYVu3PXBlxMsiI5KkK6OF3Jis1xAPjLPET960hwwSVCASlUEYxRSudIiKZd0kXlWVBQDqrGYzmFaW9G5ImYsH1v5hR+YSC48zJQX0q4iLxqiWlGwFVlrKWhxq0e4ma11rJoGQ2Eej+xubwgp0RwPrNcdq1XH2dm2VraltnsFjBAmj59mYogV8i8Dd2sM55sNT0ok6kL+QdqeYgBqTudEL/NOXSoC8iEsTyTjMmBEUSPXqrYoVFFiyJyyINbQEqBiEoHxulgmL/52TjlRRnn5kvN5ZLWWedD+sGe32zH4+dRKFvRZBSWVeu3XpCmiKmirPAAn3YVudWp/u2o07czDW09raQ+31uF0gypOugB+QpWJ7fkW2xiwhawiqQRSieQSlzPAchn46FE5ogusXYMzjpVreJ9njqO03lQRAQdd7X92Q0Ipj9OaphEJv1QM+52vnGCxoeqMgVCIY2Q6ePTKUGyFDynutenlCDSuo20UFkOYRmLwDNMRpR1aOxpXxxwVIS4cL8N6vWa9cpxtnrBatXRdQ4oDKUCMgeroy+z9SXM1BlGFSdYJkEzrSq2aSWmm0GAeBEF5LoSRlDzC7UiAQ+kGYwxt20qgvae1WQqECsCxxtZWaWGMiUBBl8TkEz5kQoLhOOBjBLTMTK2AOVIULMQ4jjJjq44OuWS8H1GqYJ1hHCe6rpfvMk/M08g8jszTSOMcvu1Os8CX333LOAykGGkMWBSH/Z44zZXLLQVNTJ5sR3AJWJ3OV9s1zGFGa7COk5C/ToJWN0bXWaCuRHUxDTZukSxTpyvfGgEN5iyoV63FaP35s6f84quvOV+f0dpGzrtmmfUQU8CHmdkPoDJd16CCJGgEsZ9aFGOWRDwphdEOYwWUqT+gwv2x7dMpEiGSQsAYhOicaiZOIpXI+XbN82dP+NUvv6TpWrQVeawUItEH0eUrmTDPvH/7jnkYIUTGw5EwexprefHF5zirefT4jK7vZNGvKLR5HIjjxOHyhn/4u79nvV5RxokXz57Sdx26oqhKziijOFuvaX/xNdc/vEKliNPrqhN5P0NQWN1QlOM2BL4dPKPN/EXXUnYzZX7LN//hPzL+4ku6/3aFM04QmgWq1TglC++tpDp3UxqsIoZMipl58Bz3I7fXB26/ecPw7hp37dFGjD91KJjJg4+opqCMFVSkVbhW46LGtBbdONSiM6rAdXIjh2OgHD3Juw/QoVL9LUhcgBD8qaJvGtG7VErscUqRGcCT83O+enxB3zhK8Ox2t7x585pp9hhrOL/YsN1uWK0FLIQWoE5IicM0Mg4j8zRxfRzw3hNDoDiRYVttVzzvDNkqrDUVwZYwNSBkQBqh8l+xPEhYFHBuVyRTK+ysiFmIybpodDZCP4kJQmEOIyUXHIphGIjeU5Kibxo2mzN611FK4c37N7x6/wa0ol+t+OKLL/jiy8958+6tLJI+MAx7UsyYItZdrW7YHSZUzrTG8vTxEzZ9z7ZfcbXfYcZRwADaiMFp02Odxbad0IiUY36rTufMmjXOac5Wj4nRMI4HXt++ZN0mtr2iOE1SjhiNJBEWVKtEGDkXTCNz4qwy0zSBMnTGsV71rG3DuBvQKM63Wy6evWDVwDBN+Glid/AMt4GnTxRPLgx//nXLNGUubwP/8//2fzP7zFefPeYXz8652PZEAq2x9HbFD7+/JdlMc67ZPLYoe5e0KAVdv2XTN6yajnevvyWliFIFP0/EMJHigdl7pnHAVZlF63qil/Zb9Jnbm8W2TVOyozFbbKeBxGG4JoYJSqbrHQpDKQajLRTF5eUNWke0zmibMTlUHrJ4maZ5JMy3RO9pzh7J/NIoHl1ckLNhvRIwSzrO90a4hVi5vBbBh/mUmYeBkiIlywghJEXE8vbtNdM8CiDtWWG96rHGUKwCbVh1PYXC7fu3+HkSBHuOqCiK2z6A1g3rfs37H15xe33DcLuj1QYVAqqCasZh5H//X/8XhuHA+XaNnv6Ss+0GVQJN69BtQykFHxJhnCtuwZ6uQWlfn1G0oE/9GKvtVcH2DpNtTf7VqfFUSsZmYQroqpG8GB9YI7rJRhts1ZV2zvHrX/85//av/prz1RqNEv53jhStsKuO2/0NN7s3XN28pWkz3ap2zbRwoZXRGCUWfqbSYULKxOwhJlbtlqYBOH5SbPtZ7VDJXmTAqopwiYwWOaB5OHJ79Z7v/vB7XNtgq7VRDCKt9vr7VwyDLJJ+nIheqqLg5QSWmPDTRJgmybCstOxQIqWmARUSZZzJx5FxDrz59iV+f6BtG0rJkm0YTb/qyTESpkn0IGNGW0NBsob7ABKnFA5NVIZdFJzvM2vYKOH3Xb98h3UtT7/a05212EaqphIjJUVUEZJ3qbOyrBQJhQ+JGBLjOLO72XP9+pLhZk8YJgwSK6taKLC4AmSKSjAmdBZ3CJMBHykIJ0hVWHeJBRUKKkgWTP54D/zkFkEhJX0Cw+haUUoVIb6BTin6RoxRp3FCp3jiH+acSXNkGi3WaA6Hgxgku4YQI3OYOYwDx2lgnmZ280gIUdQcWsusM57IcRoYpkGqWbMgVE9J9sm8mcVvW91FdoXi0eqMpAOHWlUkH/DTxNpaOuPI1qCi5zh5TFZVnSaiQ8FmhbKCFJSKKVTfQSHxS7s0Ms8jx+OBFDw5xjr7ixWht6azDY0yNK0YkmqjeHJxTucaWmNo5hkX4qldZW1TBZctts7AJZu+B/hxDus02lqmODGmmWMYsE7TZceSY+WiJBEyimIRcBZZZlZaJLps0RjEt26aM4RICIW+bWhaRzYGrSq521q0g0hhdxjJeeTrp48FvKMUx+PEcUqcrWbmCxHUDijGKXE4TLx5fUNQiWZvea42qPMOTmN3hcKSE4zDLMhCCl3T44wht5ppTChmSlo0bCvQKSRiFFGFRUynb1vapmO7PUPpQsoBELQhKmO1RRwXHMNRUJHTGNiedbRtc1KykutdjLTDnEWas2jI1IoyCt0qZaZpxjpL16k7i9YPOm0LYC9meW1KUZR0pL9X51cywzJa46xBaQSopAzPnz3De3ElCbMIkWgyKQRKKjRNR9N2OOcYjkdurq948+oVYR6ZtlviPONnz+F45LDbMY9HnMoc9juMEos64yy2GvyaLOBEZ40AWmL9Wkpxdr4hIh232zyAlqLC1u5XjHI+KFBNYwF7Gq8sSbbQPZzECCMORG3TcHF+wWfPnvHs8ZNTIpzIwqNVis4YJj8x+YGQPLqok5NL4R4fWmmsa040mFDqvVw55Mbe8S//1Pbp7dAk7YoUPTk6DHWQaWVGNOx2pBTZ73eYRvTwPvv8c1KI+GnmD//8nSCjiiJXCHz0Xn7OGb+Q3KdJJIasQTsDRQx2DKBDRI0zevDMceCH48j1RuxiQok0XUfTtTx++hhywY8Tx3GqavFyB6gPyuQGg9OG2cBtmJhS4DMn320dNZffv0OZhue/uuH8xRlNb0W8epFPExNAySoRIIwvimlOeB857Edu3t3w7tvXHK93pNnjjBGLIaUoIaO02K1QTUUzEe20CPonSGMg+4w2HaaxqEYR5wRzRPmESlUT8YNtMSFegqCg4hQFSRiktVrVKVA4beidpW8cx8MBsyxkWoNWhFnQatrAYb+jS4m2zczBc5wnrg47DtPINM8cp4GYROO11y2TSgzJc72/YXe4FXRfHZbzQEi6VvN1eXnYX1Q825wT8eQ5MvtMGDz+Zs/Z9ozNphfg01y4mhJOW0qGMEw4xMGAthNgQsrkkFiwidaYavxKlTqT+VOpsm4pBbTWbLYbts2K3jZs01aAPa2jbxpp0AURaGi8pxhxTTHGiRLLPQJ3PiF25S/bNBgnCc6UdxzjkSENdKknloaUagtKVfk5q8AqUqoelCqDFhNely2uOHR2HIfMVKQFf75t2W5WTFEcGubJ49otNlkimcvdgetd4FefbYTTWs/5NET2e880S3tvLpphDNzcHHn5/SVzjjTbBtsqnFpRzu/ihMIQQ2YcR6FLAF23xugVioaDnmjsjDUdKc2kkogpyMxsjoQwEHzE+8S63/Do0SM+++xLYvKIgmNFGNckQ2uHNR23N0emcSLGic1WTLCViSzyY1ppUsn4OVOySBOWrEihVLPYmRBgvxfQyUq33OYHl6K0Ruv1mSnSnahep85UsFB1V7cmolUWg2rnyGQxhW46vvz8Cw5HGTuk4PHTgNWqJtmFvl/RtR2Ncxz3By7fv+f7b79hOtxyONsSp4lpmtnvj0yHAyFMzA4Ou1sJglphnaVpWlIuaFukJW8N4kh7t15cXJxRTGQYj4xzhDlBttLGLTBNU513cxqNaXU3g8sncr8o4mijsFqxXYsQ/Feff8EXL17w/MlTjNXkIOv7OM6is2Md0zwyhYFQAhZDxpKLuJmkLIWKjFHugqDOIrwSkyBs7X8JP0GFQhdFogIZMsyzxzW9nCBlSNPMu/2OYZ4owKtvvxP0J5rd7SBcDqUZhoFSCn3XgZZh6jiOHIYDb9++xXtPn6W60RRUEQSoRbExDV+cP2F/PHJ9uOV6HEhKZmjFaJQx3ByOUGc/N7cHWuv4s69/yTTPsuDfI2ittitW44phf2DSmlQK72Nk21iUaciHielyx+W3bzl7tiHnQNdXUruSKjDnQogwpUCI4KfE7urAcT/w7ru37N9csXv1ng6D7lfo1hCNzP5sZ0ljIo2R6XZkSpnjGGhmUbExvmaaKhHLTDTCFRTYc8KGhHUGt3IPElRtqjkud6jWtm2qqs4yqNY4J/6OGlhphSriFv377w7V4qVFNy1r4+jXa5pGQEIYKx5j48TLN2+43N3w+uYSr+VGUCoTEZWhw3zk8OYl7w633NzcMnnJ0oGTKMKdtZM+zZuVMThl7y2o0BrDqulp7XNWaKa2J63PWLuW3jmyMhjdEGk4+igVXNMQp4EYA1NIKGOFl9dZGufYbFaYqmo/hxltFGEeIQq4RVOwSPL2/u1bjqahcy3PXjwXRF7O3O5vyTESp5lx9kLwBXIU9wltHErJrPg4DFAM6/KIpeHrjIgrT+PAfhw4zgIaa4yit7IApVzNcXGUYoXyk8FieKwtW23ZmIY2rSEUwjSzT56cFa3SuDKyUYWLNWSjmM2aP1xG3l/N3F5NuCoWf3ltMKqQYmDdrik54n3h7eWRGAvPn7ScOcvnZz0vtz0+ZlZdx/z9xNXuFr4SOo9Wmi+/+ApDZne9p6SJGEYoQkp3doWmJa9FAON2f8U0DxyOnlVn6RrNPCe8BWcVjx9vWK0apvnAu3dvmOYB1yzMJY33AUqkZI/3wiN89OiCrtpuUcTbEiBETYiOlHuePv+Mtm3wfkfKMyHOXF1dM42B9+8PPH36lL7v2R3vlKxUHQ7mvMg9FqwVPINgNiTh10rx/NlzUgxoXbi4OKftGqZ5ZHt+wdnFYy4ePRKWgdH0XYtRteXeNDgr4uDGaMZx5IcfXvLtN9/www8vmQ837DYrhptrES2YAl1raVwPSjMMI6XA9dWNUFPatkqZyTfw00gxAdTmtG4Ur1g3G77+4kvOtk8IIZMCzJMocO13u1OQu7PTKxXg9rDA6Fcr2sbSdY6nj84525zxyy//jCdPtxhXERZG41oRJfAxME579sMV+/EKdGIOnrQfgVYoXCDJqqrI00ohG2cRSE8pMw4TedKfVAXCzwiCAu2VLynNM8l6miAD4GmcybowR884jhKVY6reUJYYSs25FeM8oxS0rl2oa1CRdsdpYnfYo1tLmzv5xJxExX+UjEdcmmsLImWKUfRdL60dI4TdUrUzQVeFDjF7/NCaR6ZS1aGiPjZnIbtmpUQDdQwcr27ZvbtBqUR5LG1RbRSkQIwF7xODLwSfmXee27c3HG4O7F5dMt7siccJV7OTHKt9bJZ5jloa7IumKgJzELK9DAFl8I1EAvH3QSdEX1QLQOn+ZowmF+HQnMA0FS2ptXABrRGwkIh3y/A6lMQQPGmeaWKgy5lmQcQq4ballDHDBEq0Na8OB26GkaMPRCN8DKNE8T0pgYfHUVrT0zAKelYv+6tqEKyEW8UpGAKVg7lshZgiFst61ZPPz4h9hy0KKol3rJY5bdcRVSBagzKFQEJ7RcxgjKWxDY2ztG3DetVL6ySLtNeS6eragilZAmEphZLE5ipXk9GQo1QvfhKeY5A2WqkSV4J6FPFfWXtE93WxcFxaNt55sg6oFJi6EW8mcJG8CaR1YGw8RRU8mSnPzNkTW09xCV2KzNMMYDOzihRdyASmpIi5MOZM5xKtS+RWHB8OAd4zc+NmzIuCMyLtd2smAWfgSY/BBAWmMK5ndi2crQtRyZxt9aXDpUzfWubgKd39cgmhg1jDdmOYh2vmWVNygJIpOWKt2BtRNONsSUnXABIplZLVNAKQazuDsYUQRhFdSAGHY3HvENkzoOSKDpUu1XLPK3XHky1Zri9jO7puQ9c3xDSSVULr2tJDgGI5Z0j3xw3VvkxOMEvHYtG6XSYuy315ttnUcQ2s1yusFSpD1684OzunaRpxe69C4JqC0eLxaIyuwtuZeZrY3d6yu73FTxNzo2kMTE1DStI61ss6AoSYMFXLcwGopNpX11oTa5KHu/tWVrUno3StxROxZM00efzssVZGFqUUQozCW40RZ1193V0gXG/E2Hi9bjnfrFmvetrOELNnmPdc9I9qpSyjhURkGPaM855xPpBJ5BRqdYckF0pkIgvCDV8y5IXbq5bezqdGQH5WELRoHFCIRQRr97MnophjYYgR0xjMyUwycbO/FsSOaej6jkQhxEBOGadNhfDLZMz0HUFrbsaRb779nt1+z8XjM2xVVL+9vOGHN+/44f17Lvc3+BgIVriCXdfyi69/wXq9ou069uMos0gfGfcj0zTy+2++5Wzbs9msHnTY9vPEYR6JC6EHxZgzE4VZF1oUaZi4/uYVpoXd1Tlf/OZzuvOOZuUYq0zVcJwZh0QYI+ObPZffveV4uWPejSeD3myqeDSJ4ir1I2VKgBKVGGoaiGSiqo13qwSyrQypmolqpXCVcyggSi2VxL140XatVO715hVneS0O33Uzxoq6gg6VBAuHFPHzyDQMMsieR87aNc46jL4H/98PzCEwBc+7GzkfEXuCTkcF2UBSMq4c5rkKmJtqkGxO9AhdRQjUQnKuS0zO1ZSXGiwK7McDqul59viM880apwwX/ZrXL7/n7etXHA9H5mIwXUPjHDYndLI0rSb5GT0WjJXWkLOarnWsuh4q1QAlJr8iwi4zIh89uqJvu7aldx2tbdjv95L0zSOUhFaK1tpacZxwobLgJGnDqcqD1feCe1GFy80t0Y0oBftHA7EEjAr4NrNvIq9tR8bgUexXA+M0MLLHmoTRCpUsXmWiiuxCQGXYABMFnxPXxxvG857D2YpVbzke4M1V4W034NeJi39jaaPDRMX3795zmGd2YaJ7URWfTOagD8zmSPN0hY+FyScuftkI+g+N9Za22Dvv2TpD2vQrzl88wyjP8XDN9dVLoj8Smej7zek6WHWGlDQpzng/knLC2Zau73CuY7XWlBIZ51uMK3TaYYwiRUm4qcfaWE3bCYhDcq27GVXOEANQhG602rS0qzVNa2F0OA2qttG1aVF0TNNImALFbVlussWqbTnTmlItxSQQyrXg6NuWZrOVYOaE45tLxufM2fkFz54/x1gR0W6cY7tZk6Or11pT3SoUMXiO44G3b95wdfmenKI4KARD8pN02SpITARFNCEmlApMkyeExDzNshZo0UdVRaNdI1Y69Z5b28dk7XF6z7pPaKuljZoyIURubraybueMryT/4+EgZuNOwHbL+rrZbOj7lrOzNUsL2ucjl7evULnwdPOMxvY01pF6mNLA1f41V7vX7Ie3mGYippkYZ5omo5QlYyoYTpHyXCtC8F7oMV3ToY39kbXcH9s+HRhDqX3vqghgNLZvaVYr2n6FaTSpRA63O46TVIK2XaFr31tZgyoCbZcLxdSLUxZfWxUHoo9880//zNV2zRdfPK9BsPD+zXsu310yTyO6DltJIrqtSmEeBlZNi+0U665hUiLeu9msaRuH1oXziy3b7epBNThMM8PsiaQTCCNpRSAxlcjKakwppP2R3Tdv8Dd78IH18y3tRc/oR6bjxOHmwHg5Eo+BeDMyXB+Jw4xeslMjfDklzFmKLWSjCL0hi91aFZ8X49kFGFIkfpJKJhlBcFqlcKI8WI8PlPBQOxTuWo2LvuoScBZNQl2pJUrL4DmlxOBnnkNtSAAAIABJREFUxuBJSRBqOgZGH0X5hYqELaJmEVMiZoEsF6VRzsCSQRslGXkxtT0qZGWtdP2zzCoRJ4DlulILeKGKUt9z8y4Ubo57ZiYScNavWTUtq6ZFN45usyEfDvgQOYSM6zcYZYmDR1ckmakAKRSgRPtytzvQWC3tyxQFzda2uKIgZzrj6EpHLLmqtYBPCZ9EzSjXRIRKM1nUS7SRTN4YR4mibtG2HXMU4rJKd9/rnbpmVAdJkkwk24Q9zxwbT2MO6JsDOSpyoiriJMZ+pj1TOAc3by4FgGEUvq2IUQ92A7jCPk3supHX7Z4ygleGsTWEVaK4wr4plKGKPj9ORFuILjNaj1aKqUilYozCNxNFSzt/KgFUwVrNFApMDr6jXouFNM/4Kg246ldYUwh+J2LrMaFtEV3dpsHZgrOwv1kxWaH0tN36dO2UEqRtaAttCymJz6XSiqa1uCqjJyjExT0mkVIgBKnGqz81rXMQxdrs9ZsBpRWrla3zLekolZyAiPczPqlTxQQ1FN7Pzmr11rUOazWNMThjaLT8vWiKpiL70K82bLbnbM/OGYax8l0X66RSEaayLipV9UIPB7SCxjlyV3Bdj+06XNuJUXkJpxJI2u+6JmFGDBDSUiHLMUoxke8lY8ZY/vq3/z0+Hbm8/YHb4TWxeHSOEjhbR/P0cb0vhcPq55ld37LZrCsSXZ/WmLapxsOtJkRPTpHDuKNExThMfPnkl5yvn3C2foz3I8N0y/XhNWPY4fNEHgZCmonRs8JgTINxMkIrGUKKLJqv1ogqlbWunuMfYyR+avvkINh2Pf06oGLGNQWKZnO2YdX3rPoVxohO3Zx8bWtp+r6j6XqapkPXDMhkUYexiJSarotf01iUrCAcD3tKCmxXDdZYSs7sd7d4L/Oatm8wyUBUKCvzHVGYlIzMWUOKGqsV63VPah2QWVeC/f2Sqe9XbLZbMgJBM8DGapqVQ/UNupFsHmXxUZPHjN15QutplWVKiXlIHA+JaR9JYyT7QtIOGvE2pGqO5qq1aXwQqoVRsG1kyJyL/KnI2KXFklOpkjNQqkSQVgrjAiZmbIwkC3q9XqKHXNAYbFWDMNTggz5VYRmRv1Np8QKUxT2nilrNUmnmIkjYxdFC9kpuALmhiyhXLMG1VpRa6doClEBsKSSValu96g7eG/YtKvnqXhBUShwT7ndE50nANqYM6GTIraJhFgFnLLmYk1SV+JwpErZ6TBYctvr+Vc+3LC1sU+Q8lVRl3lRTgUEFbWS/LIVhngFDzpqURK5J49BUaaii5H1LQRWLwmGUI9fA74y0rtLDLi85WjJWrLSMlraaLiQUcxEgVPGQo1w2FING/BKVgSFHloNXTD4tzp1WaAfFdcxOk5XBhyJqJspinUU5KCRiCaQCurW4VuF6BcR6fZrTeQ4lS7XXqAooKlinKQEa2596vKXA4TCRI1idaFtNSpqYHT5FYswVES2i8RkLqhEAmJW1wFjh55YCYUFAFy3t9JJJRcS50QZlWgFrGIso/kVmPwmZPSqShpwVsYC2ilyKUBtmqZBce05WprYjHakIVzIVGaks6ZiCWtGru2uzamlaJYpMjRXPO1urU4WgenMWeUhnHdo2FGXwMREzaNdgYidUgJwwrkFpy/E4UHLi9naPNpa2X6FsQ9P3mLZFuQ6lkgQ8lwCFbjrQjqIt2rYnnz1BFhvQlqzyacQF0i1auQ1GQaM6dLQQA1nnk9B4a8TBRIB2FpMtyWVWVoIgCC1IWANOeOIhUzzEVJimSFYHkoW3794SpkLyiqvjNTf7K/a3t8zTLM40UeT5UlJ4hLdI1iJWnwsxyshLqYIqBrI43oSUCdOnB0FV/oj74N/+7d+efr5vdnvv5fUiOF0J9X9lOar1mQ/u+B899mD9vvfYvZZR+cj7f/C7asnwP3hNududB+8JVfHlwX7Jvp24mw8eXmYLd7Orcn+f7oMZy0Jhvvd1y0cgux8emo9V8cvr7j2nfnwqhGN4esnPaIr/V9t+6gv+8U195Isr7jtj3J3rXMrpeC3PL9ftx67CDx//0YN/4jB+5DScHv/4+5529sFvZJXu3Tcf2bHywd+fup8/dbj/cy+PP3n6FOae2LRZhmT3XrpUEnePqbvh6L1zKNH3Jz6w3P314Df+2PH42C+Wuzvl/tpwtzvl7t6+D/z4OUOnn9gWWzpJGMpJiOG087V7s/BoKSKFeFqH1b3jJ7/wwXV9d1zvP/OjNfl+8lxBWCff0U/YSiWtf8qdff98aW1O9/FyTdy3lLt/KH78XX8qktw9shiEw8NY9uH26TPBD2SePr59ymH4+YsgcBds1QeL4ie85qfuI5DB/c/dHq5H9wLvB5/zsY/9z/v2P/91n3qM/utu/wr7tMg1wUeSsrvPOLWr+Pix+JN78olr3E+9z0++/4fZTN10MR/8ziduf+p3/7VzoZ/5finfzww/9U3vtwh+5i79sd//6HMfJtof/qriwT1+evhffi2X8vAafuDOfm9L90E5Sv0okb/35E8mdJ+6HqT46ZqbDz7gYwn+T2zL76Wf4Daf3vODF/3U+//48Z93kX46RaJm4B/LcGsicDqpC7KqVFX3B9fQKcVa3pe7TOBH94K6AxB8mG3cO7GyIN5VqousG/eSbkENLc/fBfQl8/hXSOw+mg3d9/G7f2z+6Ad+wvV6qrHVvX89uDn0j471T20//6v/vFf8S4+t/rDs/ciatDwhsuTy84N21c/Yr3Lv/5cfl3Mnb313vE9/37tM88fe+MNrQ26W0z/zB//+udtpYfzwnP9EZ+FTt/KRn/7U+9xPmPODICgnbtFOVVq6N0sVpGrC+mkJ9x/fcm3Ll1JO87h/SdgqpdzrGv10V+H+dteZkpPy0023u+tHXvPwun34snLan7vu3B+/zu+vPYsxr1SYuj5397vOubp25wfr1OI9uny2jC/untP3nv9YxPpR1faxHf4j13/52O886BTK/ZNLOX3GTycLD7dPDoL94yswAe8Lm15gzyk1TJNnnGY6s2aYAq+vrnn79j3725HrV3PVjFOYrsE56NvMuBO9y/W65fyip+sct7vAcZ7ZjxM5F5x1nK/PeP70CauuY7o9sF5Z1msnsGnbsW4fYXrDFCf+/p/+jpw9lMhF17LaNmyf9Lx/6cnecdY842p+w5QHPv/yr0QzsxT+8R/+H4ZxqOg90ZrMp/bBw5OyXNQL+VzVc1IqpF9QXo62Fy0+pRWbzZp1v2K9XnP1/n1VfLgh16F9IZ1AK/VDWJwqoBY+p+7Q3f4452ic43y9Zb1a03Yt/Wpb30cx939B1u2DxVadSCB3QV86T/lB22exSincvTwvjy1ti7IkD2V5E0Gl1RujVNQWqNONlyuA4LQYVM3Fku7aH6cEqjrMO53588eHUyB0XRACrrFoY6vQuqpEd808T1VDtuHLZ5/Rth2L7GNR4Iw7LQY+zDI/LBDrefcxnoTQF5UkP3t+ePWKw2FgmhLnZ+dsNlu6rqFfd2wvtjx6dEZE8c31njf7I7fzRCbU2YhhvdpiqhFuoYCP9N/9cLpxv3v5khCCVE5FJtwUSQC0Kj9uzS+LYdUcbVaipm+b5gS+KHCPHrMsUuqkfnM6/wWo82JB24kock6ZmMU5JBFOHpk6yXy7hFzPoQKtmb3HWctvf/1nMovNmb//5ntR5imZtm1ZrdZ8/dUv+e1v/5Kvvvqa80cXvH77ln/+/T9zvt3y+OKcv/o3v6mi3UpEMrTCfLxI+tEWY2SaPf/p93/g+nbH5fWeP//1n/P555/x7Hx1undDTKf9dkrJnPWkX1AeHOxS4PLykn//7/8nUhLAyjaLE45uHIdJgFq6FaRsFiAwq37FZrvBOMPkZ96/f0f1OJGPQOaI69Wq8qkNzx4/Yb3e8OjiCZFCLJnJz2LsG8V3c55nrt6/5/d/+D2Xl++wjRL+bmflvsniPu9cLybBfc/+sON2d8Xf/d3/hTaFR0/O+B/+5m+4uHjMcSfftes6/sd/9++YvOf716948/5SnC+04emzp6zXa2IOXL9/x5vXr/i7/+8f0MbyxZdf8fkXz9lseqAwz4HZhxOuAARlDcILFLCYoevESWWx0ssxEMYjqs5ChR4mPHE/T/gQGMdJkgRtaDtB+uec2F1dcntzxdvXP+AaR+sanmwvPuGK+TmVoM4oU7CtwjSgjXC20Fkkaoog66ZpZBxHhknsXqwTV3I9C0fPugIqkBJMY6HrNFqJUeQ4TByHEUohaIuKhb5pyCESx5m2USjlyFHeq2ta+k1Pnxs2XUOMkhkpZkKMHAZPygJ3nqYj43xkKvODZMJ7sbyJubAEwfTHgiDc8Y6gLsQGbRRN39P1K548fX5CIm43W1arFZt+Rdf07Hc7tHIcDrfM08gwjg/bFacge7/cuX9Dys+CfMvEppOf0z3oGoBuQXcsDvOn91kC0eltC9SAswSoUopYPZXKCa3cuayEmCo/51NApN7Y5f5Rqwv5EkCXIHt6zWmX7mYPpQZnCbICoPpREajEF06bgjal0ip0VcDRqADKCDq1aRu6riHnkwgbjbWnIKhUFrWVUtAV/JAVZC1IWVXRK67U+YXWKKtoVmvWZxe0ztB0Dm0sthHXAeMcxVpSNGQtgvNFG4ozZG0kCBYBQZ2qtlJEP3Gu0mJZnY7hKQh+5HoEwMj3bVWLiw6bgqB1QYQoqmKQ8EUr0jDEOxQiqmZa5mTMWijiNpISMUUSiVi8oJszmKzJIZGq72YpCpRmGEXQuZRySurmaWQO/q6raCyHcaquCRrtepRpiFWIA+Nw7YrGijxfYwzWKJw53R4f7XAs1fs0iaHx7EeG8cjkJ+FtKk3TrbFWKEaH40CmYJ1j3TQ0xlDNcagRiiXLLQW89w8+r23aqgXbCh0hZ+HFpiCC0KmQGmn3aS0AubtEpgh4RBmMsvRtfxIVadqOpmlFklAJJg5lSCYSdCDqSMngmo6u6+n6tdhoOSV0Ciu8xnkWfqCfA94HxvEoXD8fSDkQ0sybt29JCTr3qN5bivPzc5p5pt/taLojRXsKAjZS1mBKAWNOSaXSQnZXRi+TCkzj6KyAwpb7Ouc7cryt6NQlCIoRgWhTzwZ006KMkwu7ZEpOYqIeAtY1AoLSGuta6STkzLDfobXsl3h4/hewUirRCjKzdVSNZ8bRAxpnespkCDMM+0AMIqKKVZjO0XSCgNxsNC9eaLHoGWA6WnLQZKPlZkqFEooYgsfEftrTKMvcTzSqYXum0bYjzDM0jtVmzYvnz4DM4foNcxiJcWI/vGEME5dvZjb2BdoqdvtLDvOIVw+HvcuyrbXg+yhVy/IexPZjgBpVXQGafoVrWtqu5/lnX/D4yVN+97u/pO97mqZlu9nSuoa2aXj/5g23N7e8/O4l33zzz1xdveO7l9ODNsnD5lH1Y/zgsxdysMiLSabEB5BgLWQlSrlDmi4qF6eWYb3BlTJLWKwwbXlFLlSrw7pwU8hVdm2pJqU6VDVgqdMngQRQStWAKQsdRt2rBE/lyul7LdXgQk43+oPV7kFiUJ+r33UZIZ2q3FNQr++tKjqv3AN65XtJQqG6QSRSiifytbMNTdPRdgXTWR4/f8HzZy9QKZGyJ4ZZPOe0PgGqtBLjYasVTmtJJLjXYloOct1Es3SucHnEHopaCZ5C+I+DoHYKZRW+eEwVi0glVwNTU4WNBYCwBMSF55VSqs8JYf1+RyLnXGkw4lbhs6ckua9bHHGOhMlT4tJVUNxWD7vT5VwKaT5SYkA7SQz8PPHu3SWv3rzHdmfYbsXN7sD+MKAQKaz3NwdWnciEbVeaou8loKdE7v7hWIAViWE6cnn5im+++Qfevb9mjor1ZgPa0Let6Kday+t3V6ScWa079MUFpuswFaRy19q+64bcN2lRStFu1rRNi2tb2lXBx8jNbi+UqXEiVQ3Lrm9RVuQAdTXSVUCjNZ1raW3L2WpT9U/FZaGgCDEI5F9rbP2quh5TpRVt3/H0+XP69RoIlBKBxGrdVfnKA69fv+V4GAi+nCrg9fqc3f6GVy/f8v+2/8iLF3v+7V//zd3Y5oN7rQAhBPaHPT7MKKM4Hg+M43iycXLOMY4D3ouTe9+v6LoVTdudzs2yGb0EQVHEWUZWOaVqDlwwrsVYhzJWkrEQxIA9iXuEsRalRDRbawHyXL97i7OOtmnuqu1P3D4xCBa++/YadGRztqJbaXEP3ymGfWA8DDzanDPNiuItcdakaLANNK3o9nVtQ+si82HicJ3xk4hvBy86ivthZh4DZc6o1kn7xkCMmXkKqDZgXMfmTFHcFqtbhuPAD69f4+PMH1694vnTDY+fblmnxA9vrvn993v65wXnoF2Bjovyw4/Ki7oQ1H58WeYSDxee5ULRUopgjOPZZ19yfn7B46fP+PVf/JaLi8c8fvpMKkEtBNgUJBt7+vxznjx9wZdf/pLf/PY3XN+85z/8n/8Hr1+/4t27d9Ue5O6YS68/P/z8OnxVyIId/MyIyHOtzi7u7uCl+/VgrZU2jzpVuvcOwfKJaplf1Ep36fmqIio8SHA7BTxlSBLaagVwt3CoB58rC1mhVLHqchpbnmLQvdiQ68KqP0w+6qwPrVHWVorAPU/CU016/+yVu8+5FyVVrY60mEFKcFfLF9c1CxWP86gyc4ncjAf8m8K74YqVs7ROpMY2sQVjT5Bt6bSpU+vnwYynVhmnwgNq9j5zc3PDNPo7ZQ9VUOQ6k//pIGhag7VCeci6tkGtoWt7rHHoKk6gjfAvlwVF1cetae7tjVTKMUYmL56FPs8nqo4tBj/OTMeJFFINHCJbtt2sH+ynVJwZSiLHmVAKszly9f4NRmvG446r60te//A9V43j5uoRq67l2ZOnbDcbctrirKKxCqszRmsaZ4RisCRP9c8wTuxvb3j76iWHmyv2t9e8vxkYJs9337/k9esfWPc9fdfz8tUPlJI5Ozvjr3/3O7548ZyL7foUakHa975KcU3+JJ8NQNKabIxQFaxCmUTXBEIrek+udbRdh3WWaRiY/YyfJqkereXx5oxtv2HTbzm/eERWijkEQs6VwpNQWWEoqKpYk2LgsL/lOAy8/f8pe88mS67zzvN3XLpr6la1AxqGJCRqFIqZ2J2J3VcbG/v5NzTL2ZlR7IhGIxIEGu3KXZPuuH3xnLxV1QAlKMkKsguNqnsyT57H/c31NX3f42dP0zhJlrS4ObRtw2azxhjD6STmwnXV4Kqaz28/4/bumu++/xbIjEN4sq5c1h1LUFrmoLnIQ9aVRSmhu1VF0cb7mbv7AzF62rbh1avPuLi4pFvJWOg8XsgiQlFXci6mJAR6bY2Ig2ThSIuCTiX/rlIkrcUZZTkTy/PJMZ6f1fF0oi9KZZkkKiI/8/rZleA0BbLyVHUEk9BaMZwU93czh5uZWndMPhJDLirjqgQCOcicsZAT4ykRJkUKYhWUEuQgJqgx5XIGqWLBIXOlEFOZQUWUiri6RSWFn2fSEBnCwL4/sQs1SosCgVaWeYBxnMtbkkgikPaTWcLjzf/03P20AlwCocG4isurZzx78ZLPP/+C119+LbOibs1yEKiUCUlaXatdJ7PO7Y7VuuVit+V2f01WMM4z+/2+ZOlLLbUEwk8+zaMDP8dYWjD6yaGq+PH/WcyHHyIeT/9QMt8zsKj8Dr38FS1EY/nuwswsFRelbESydQmUn94+9dABXJZRMu/SJRJeUYkQQuj/iQf1+GcuicF5LZ+uu1Syy6uTH99H+XNKAT8N+OCJSZEetQUzopGYCidtnAc4ZeY04JuGrqnRakUI/gzEWT6iBOzFQFed1/ij24LE5Rgj4zhyPPaEEGU2XAKg+qSDQbnv2iq0BTWpMhN0IkxRKsGUwNmA4iEIKgSZF3xAIRY1zqby2aRqyCkRUmKcRvF3ixM55jIT1Ez9yHDsibNwSGXGDiE0n6yrtJtTJhPIWRHmkeF4YF/VpDBzv7/jcH+LAsI08fbqCp0hhSCuC0ZJS1QHnNGEyok5bbHRQQnn73C/5+72luuPHxn6E9PQczwe8FFxOPaklOjalrZtefvuHUopLq+u+Pr1a652O/J69bAXEWTr5D1+ljnjucAFQsqELAmhMxaLpnI1dSXOFu2mo64rqrqiP4XSrBBudF3VrJqWVdOxalrapmFJzWMI5/cq5QRBdJNTDOdxUz+cOJ0OzEUKkuzOHZZcuMZ17VitulIpObp2RdN0tE1N23WkDHe3Nxj9yRjl0XOLxX/TWqn2qqo6W7At4vfSWRBBgRB8cYbIJbGSlzciM22dhcRutMZazTRJ0q+VeTCcLtZLYogcz/zj5dJ6wUssHEG5cTGIe0eMInqSfia9A/4NQXD3vEZpS1u1+DwzTZ4f3vZ8fH/g9vpI01YkAoM/knXAOKiSRqVImEYmLKMPhFPAaktTW+q2JhLwhGJQqUnOoBwYq+nqVlQycoaomXrP8fbIbtOSUUxTYrVuMGRpf97ek4NntRFH9lo3fPf2hqwytjIok6jEaOovXD91PP3E38oZYx3das1f/7u/44svv+IXv/wVTbsGrfEhEYInhpnj/R2n45HT4Yhzju16zWq9wtWGzcWKi2cbPv/iC56/+C3/z29+w/F4ZBz686H9kOs+XOrJV0KT5LA8/4VinGvEOFSVgCQE9XMIOrcKH7csFiSZKcUaOVMVZF3OmTlAiBRD3WJNVQJWEnjJQzmXpX36ZN756E5LKH0M1snn+7uI8/4UMlTanJEUZzIGY9tH6L8F3fBApH3ompVKv/wOjSKEkdP+I3fvvmMcTpj1JdX6GW51SU6REKMExzhLJdPvwR9IvUKv1+BXOCLjtMUpdXY1l1JYlwrMnQOgfMRP+mvLt5OAHvb7PdM0lwD6yKx2uT88zG6VzSgDONnbddNQr8RZxSRHVgrvHeIX+XDQSLtzLmWpxpiqHKiZqqrOyd4wTfgwM/pe2qERmDPDqWc49qQ5Ssejaqhc/WSnZiBkTUgS0EQ6SyToTrcVJszMx4bDYc/h+j2n08BN3eCHI3df/ZLnz1/w1de/FFm4nLB5FlCcM9KKdI7NdiOzoJz5h//2X3n39nv+/O0/cbc/cBpGTocT98eRrK/5/u0bnDU4Z+n7ibpu+fLLX/Dv/urXvHohouePUbyT91zf3jH0I7fXNw/3P8O+H/BZoW2NtbXMFtcyp4opcfl8R91IF+z+7prT6YRRhtWqo6lrdt2G2tRU1ol1U5bK2zknHQ5n8eOEHyexnkuRkCOHw4HD4cA4DEX+TDAX5EjOkfv7e5wzbLZrSYpshTUtq25D1625unrGZ/Nrvnj9C/7pn34vQuOfXMvMXMyLMxcXF7x48YL1Zk1WkXnsufnw/nzfQwi0TYPSDV23whpL8DMpSUIUYyhEepHAXAx252k6a6ZS5siLWo/SijB7nKtwruJ0OrH4oy5ptysehTmL6xBKWreJ+DNO8Yfr51eC84i2gVXbUmkLSuHjnpgjWYkLgDWGpnWE5ETWxstwPZvEHMWAVCFadNZajDYyd1h8CpMcwkabkjFVjDGQUsZkB9ERZsc4JoxKGOWw2hFzlKwjKcKk8LrCJfjsAk5hZEqeUyitG/yTmGKLaPBP0WOWVrZSDzMJrTVt1/Hq9Ve8/OxLvv7Fr7i8eibSRSASZtpgnQj4rtYdOUsrYxhPGA2rrgWKRZRzPHv+gm9CpB9Gbm5ueP/hPfu7O4KfIIcnofnp0ZkfguGnT738A9E4RNqg5x/wUP0tnUlZpzqDGmQGKFVIU0mzOEYBTMhsa/H40udqUMBc6tznF7f3h5//6ed+mBU8bp89VKM/+vfKQlPyzMOAiTNGK9rtFlxLttX5Z50BPsv8lCyfLT9U+/3hhv5wy827PxL7I8Qgc0NbEauOTLHfyhJ0Y/ZMaSRg8cZhncIZRUQUaHRR5FBLAlBaWw+AoOXjlZbNJ2tb3D6WVumS2WudMWUG/Rgav8xpFQia0hlsZXFVhXFGZoS6aNQuuYGiVLXpHIcVmVik3ihPd2k7G2NJKmNxhBxKYhDKutTZ521xE/j08jExB5mHGSXdAs9MzkG6OkbR1o7tao3VBlCc7m65qSrwIxddXWTHIPqBeZo4nY6M44hSilcvX+KqCqU0v//dPzKcDkz9gAEaa9mtO8aQ8QXZbJSSn6ch50g/HLk/3HN32PPCX0qLTwtIqh8D17d7Dvs997c3j/Zj5ng8idXTHAkXicrVVLai6dYFlNVKJVjVbDeXNFVLZSsRxdZi36a1JSsl6jkpMccgzyvLjDFOM9HPHA97Zu+Z/Mw8jSiV2a7XcoZaQ+WqYi0lgcv7RN8PYl5Q9IHbtqXrulKlWeY58uz5S/w8/DTmoLTDS17wdE6ayzltNNY5Li93aMO5HSuAFyUAoSygR4WMv3TxHJy9Z5wmbCzoeG3EKilnpnlGey8uFzmfbZqWc2meJ6kSiwWb0paLiwv8PHK8v2WYhp84c/7y9bOD4OwnVI6kHLC6lqptmaOUGYQ2mrp2TKMlqCTGsyqTI3g9YwCHZMvWis5cipkwJ1LIBcouQdBqgzUWVChZu4VoibNldhlnKEgvi9EOZwwKQ06GOFgchmebiiYO9H5k2M/4mBAHzUc3oGQi4UxI/fHBDCUIKtHX22w2fPbZa7761Tc8f/mSpukAMdKV+2GxBZnXNDXBz/i5xvuZcTLMfkYvI0ht2W4vUNpwPPWsNxuUNoTZ058SyYcffZqHqdcnn+/xn88tQs4x73HL98dr/PH3lRK8bO1EcsyTmVUUdZNsWCAb5zhLiTPFQoaFZvHpp82PAmB+dMfP52h+tIk/ie5akUJkHk6YcY9VmcZEchsgr+R3mrz8kocIfI5AD+Cb/nDL8fYd+/ffUSsJJBhNbjakMIHtWOrYRCTmwJAmOdCNxVmF02JIG1PGPmrBCGioUA3KwhZ3iief68kqF1+2x80mchB6AAAgAElEQVRoCeK2HATyc59yEZUCbTXGWmzlcJWVAGhNmZuph5NMqQdQ0Plgg0XD93Gremmp2pQJygkIKj2ggtUCRnrSin66Lh8TPiSp5gySjGVPTgFFLHQnS9eK23rwgXHoOe1vcUSGZztCCUx9fyyAj7fc3t6Sc+bLL7+kqsVX7s/f/glSkBmdUjil6OoarSNzSPiccUaXcYki5Ug/nNgf99wd7hm9B62ptGb0ieMwcX1zz83tR477+/Pacs4Mw8g0BWYfsbam6xRV1VI3Dc7Z4gAhsy270sUPsGYB9iQvupdZQ4hB0KUpoWMkp8g4iaFz8p798cA0jQyjVIRaKTarVfkdBrRinhMpaWYvpufTOKEaKzqmriqBsCEEwYIZ49huLvC+xvv903cMAbHFEH+U1uQz71P2alWJDZJzQn0AwUKAOiOM/TxTWYeyAhhLxfx2moUSpLXBVYtfYCbPUoH6eSrVrj3f94wEwRiCeDYqi7Ga1WrF2K9p21Zi1b+hFPxZQTAD86zFwPRwR9tuMNqxqVf0eiL7AEpmeDU1eR6Ig8wOYookIlUjppXSBvZYl6mqhB8jcRKouoi8ZkxnMThInHlcXVOjsmEaMm3rQBtSCphoqHLN2m14tt1wsV5xvPfUrqLbrFntNpymnvSP/4X3c2Ly4cl7umo6VEoYNTF5ycgkcValqDHyeqdM3XRsLrb83X/4T3z+5ddcPX/Fh7cfCTEx+wAIXaKqaipncc7QteKicbl7RZg9SsH+OFFXYmpZ1x2TiWgdePXqSz579QX/2//yv/Ob//x/89133/I/fvvfyY8OTTnL5Z5Eyvw1aXJ+OjzTWoHRD6DRXFCkjw6+p5XfQ2DKWXAhzihqq2lcQqVI8iNWBQkIJWGRXGyZeUlSghIf7YgqiiE8qfx+dPw/+t0Lp48nB+ujq/i3aZDKLQf0fCD7gaQc2a5R3QbbrWW2oI3o05YAYF3NNB4ZDjd8/Pa3zKc9nTI0RWA7pIgKM/gJTEvKGZ8SBzIfVeb3LhMqDbVhm2c+jxa854UPGBtJSqolkvDsUtIFGJIe1vujFKb8E/Xwv2op2wquPp9vhRIt2uIi4mo5QLp1TdXU1G2DdU7AQvrhYS8SXaqAwJZYG0v3mgTKaIzSZysdFLjKYZXBZM1sLMF6VCjPLGSSfwAn/OjKhQOWpQo3pbk/+MBpGqmGHlvVhDni58iz3TOxAPMTbaVpK0ubepiFc/jtH7/l7n7P+48fWa/X1HXNMJ64vbthmkbGYUSRmRWcTkd8iISs2OwuebbZgKmJORNCQkeYppG3hz3/8P/9d46nA89fvuByt2OzXvP7f/qWP337Z/7zb37D3f0NOc683rTnlc5zYLFqU8bi6pZuu5X9pnUxl5Uzz5kao5z4exah7FnNpJzwOTElX3zxFCEGxmnij99+SwqeGHzpCok35ssXL1h1Hdvt9jy/nOJE0EL5OYPhsrxLKSlcVdF1DZvNir73nE4nrq+vhauc448en7TKA977M983piSI6RzLvo4oLTQw52wxEFYoZc66qtIJlC7fPHtsKi4qJWNOqtjcFYP1EBPHXiznDBDm+YxPGIaBEMW96HQ8EIOncpaYFFUN1jm6ruPq8pJpHs+8xJ9z/awgqIC6cigVSXMm5YDRit3litNp4P7uiEITQ2LoZ/wURTTXOrJP4lsVdbExyWgXxa388eGblrcxU7mKpm5o65Z+Em+xWIAzMRVCdhkAO2OptONyc8lm3dK2NR8+3JMtbFeWly9eEaNnf/8RqzSH4fSk/DdK44whFnNZyPi4gBIWMIXsNqXF0mhKmX0/wt0erauy4XJxRtCEcWbUEoiOVjIka8154FvXjWzUBNZkctYijFu31JXjarPhm1/+FZV1vP/whuPxxDgVdNQ5QhS6QSrjpU9O1WWJWi1KOY9QVXyy78/fWGZpUFmN1UJWj0GUm5fsXStBX+ks9IcpSfWXKd5t6kFcWD0KZkrJfXpot0rvcFG9WNYgKiI//qQK6C425FxjTKafDoTJ4ycvHok6EqeAioFJa06rFXFuMUrLvskZozXj6Y7j3XtCfyDPE0ob4jRL4uM0KkYBcpzbqVLNBTKzUgXEkOljZFCBsaDoUgikQsZfKr+lclqSmIdk4Mdh8PwuZEDlIkJebs0jeslSBRprcFVF1Vhc5XDO4Aqnrojrl8erzvtZAmuGrIVnGQX1lymV30PP/GEjKcUCVlNa9vOi+PKvXQ+F78I7lZ8dQmCaJxaLL2esiEYbxXrd0lhDZbUAc0JgnD0fr6+5vd/z4fqacZ5pmgYfpNqIIeBjkMrXWDyWOWX6yRP3J/opYqtW1lLuhS7+gtMo1BTvPT7E4iyy5+7ujrvbW079SZr+68fi4AIY09pQ1zV1Uwt0nwJqSZEQNbP3aOWKZ6g6+6DG8iW2R0vXJjNNE0M/cDoeBAyTpF1YOYtyhsoarFboktjITY6QBRdgtVRUC2k/pizBI8t7bZ3QE3Ip95fByKf7MJZ5eCYVpKh8JeT7sWRPOS1I0lR8PJd5spZgWfZXjGJTZoowfoaC5ERABkraoTkL0T/nhJ9n+YzanB3rp2lmGkdC8KRoMLbCuhprzBm8o4vz0M+9fnY7dN21zB5OyaPMjHGZy6vPGIaR+xsJgn4O7O8GxkGMRdu6IUWPj4mctDgUTxFdJbRNMv9gmXE8zDmatmG1WrHptpymSIoD8xxxVZSNE+N5Mze2wtUVr56/ou0s1mb2pztym9HW8MVnX1I7iwsjnTO8u7s+95hBgoQtHKIlCIboUcgGl5QFSu+SiObuNDLla24PA+t2e1ZvWeyG5tTLnDOG8iAlIF5sL2jals12I+dQDVY7UlJo46ialnXX8eL5S8zfwtVux3dv/sT333/PPIvqxsPRIn0oOdwh8/RAeiy/lBfZmeLGrMgsw0BBApad/6gSaRuLJWByYjz18kJm4SVaLVSCRV1nCl5euGxRVqF1BlXJkVtmXQ+tJFX6/Locjgv69SFUn0NfkdJ6WJRi+/IZSkfGVU1/95F5HJh6j6sSxkb84ZrQ74nzwLXWNO2KpqrL3DlCmBj21xxufiD0BzmcTM08DOQYMesOE+K5ZbnMR5YpXtDySVXKTD4w5sCIZ/YzwVZkVw6k80xwoQg8qgHzAwjofJWWZCwHD8iMVWmZu4kM2HKwyYFjK0fVNDRdRVUrKicmq3UJgFln0vkhlGo9L74BUhWGggCModT1CiCWv6uRnJyyb6RDoLUqdmhL4vLj9u55WefZLGeXEEBmXNMkaECn0QlJtkzmcrOldlI3Ho8Hjv3A/fHEm7fvuL2TSrC739M0DbvLXkAy1pJB+HauJliYw8R+HLk93ZFzpm1kTte0La6pcdZho1Q2fvYEH/FzYJo89/f33N/dSRUWI5UzD+9d2adaG6x1tF1H23VoawqVIMkZlaWtaJQkFynnQuQO+BQluUIEFRQJYmLsTxz3B06HvbxvSrHpWipraayjdhargOjlfFKKHD3k8OCgEzPTHMvvEyGGEOT9dcXzb+kK5KzJ6cfPbOGJZtS5KpxnT1YJHwLeh0eByRNjQGtF3axBSRYWvNxbsT1KghjXQJS5s48JVTp92riSiGlikM87jVNh5ZSzNyXGcaQfBsI8453GuoaqbrGuJIKVkyAfn56H/9L1M4OgYuteMzMR0i1OCxLpf377Z/Z3I0ZbjCofFlECSFlsa7yxGG2pXIMiMs/S807JYIwlZy3uxSXbNFqJfFSMNHXFtutwSkEIDPPIvA9s1g1Gi3zTMA4M88iffniD5OqBmBOusqxbd/Yqu9q95KtpT9dp3md9hiXokhFmoC4tMaXVuUcPiaQhGgXOUq06vvrlr3j+8hUXu0s23ZrKWCprqY28uConQlnDOAwM40Df9xwPR/r9NXcf31DVHU3T8tnnX1J3Na6pxAjXaIZpZLvbUlWG/+v/+D/5f//bf+UPf/g97969lwNVaUhLlfdTNYUcVEormeOUgzA9an1yDoSFEldI8ILSVby8XBHnE+PBsz/s8SFgi22V1ZrKyFBeaUPkxDB5hv5IDDNaO7RpUKZCK5E2W6qY8+9a+nsKqerLKtIToMUjOblyxcrgmpqmqVjdfoY2hv79D6xNjXPS+hzub/jw/fd8/POfsK4SeSUkMDkChohRkVVXQVZMc6QfJ6IPbFYbQajZSjLVs9aiRhuDcTWpqPprhXA1vZdD1HlR1DhXgudYykM9/hAYnr5hoHlUqyuh9EAsPpzSUlRZZiS2sqx2K9rNlrqpqc3ERZXZNdC6ipgzx2kUX0CdmCMs8jLpk9Y5CsQhpyCNU5Q9pmXeEpIcztHPRB/w44ifZ6KfJJH6S+3QpRrOqVgYJbQSK6hxGCAljvt7LjZbXr24orVQW826q7j+8IH7/T0/vP/A5COjD7z7eM3x2NP3A8dTj1aKm9s7Npstm/Wa3dUVrmqo1zvanSBVX3wxMY4j8zhyf3dLiJHD4cjWCoDo2eWG3faKtmr5/rvv2d/tRYbQWbarju2649gPQqR/lCWKGlHH7vKiVB/6fC918VqMiMwZUziPVGIUtR6lCx0AoRiklEjzTA4zlcl889VrQkmiDGLPZHImBU8KE70/UjU12ljSNNBYw3a7RmlDP0x8/+4DqW5JOXIaB27ubwuB33E4HMlEQvDkLHPZx/uwfNRCTYNpDOwPPZOPWKsYBjHpTWI5SvSQskYpaQs762RW6YIEwRChFuPguqoliAaPsTU5JlIArWq5h7Xl0I8Mp56+76l8pMm5iAIIjSjMkXkKxKBoOk8TArWrIGfiMvT8C0nZT10/uxI02mJ1wumaxmqyiQz9sQxk5UA2RpeMTAieaSH8mkJq1gmUIAul7abObcGsH+DbS7shp4jVisoaOfdLNhlSwifJsHyMkKAfR0Q1IckhYTQgPekQLD4s5GP9qO0mVYoEE1UGulC5ChU9xFLGZzGTdXVFt1rx/Plznj97zsXljnXTSSluNLVS5yCYgnBW5q5mHFuGrsEZzTgMHI4J7yeOwfP2/Tu2uy2b3ZamqcjAOE3noPjFF1/y4eMHjocDd7d3kp2l0kJZQoc6d2zPm/jcYXkURJ4eVk8bIQLsFLRj7RRtpZkijDkwDAPTPGOso6kclRXNRMgYh3AVg8dPA0H7EkQixkaMrTGqYjlDBETzmNCxVKClFfvJ53vUTX3460pjqoZ6tSGcBsYUC6XiobuQ48zcHwjGStasxCvSFkqBtbLWjGIOQoiPSZiktlTRguosLRu1yLMZQUSeO1jpDABIMUoQLAHwcTX5+F4vaNHH1/m5kc//kcCdyOrJoFSAF7WjbipcZTBO4zS0tWbXibvhHAJD8uL3WCpTivaIytLhUEpjiiwbiqKhqc5argvgLcZEmCeiF7BGCDMx+FLtPN1jP3VJp6Lo5WRJ0BbAxPF0pK1qjIbtqqOyCkMk+JFp6Jlmj49CtTHWiUJL2xK8BI5p9thhRGvDahNxSSor51wxMa6onGOqKkII+HnC+/mciGk0KSSmceLtm7fsu3vapiUEz/G4Z55H5mkswKSlV8EZcblarc5KPAuYiKxRZ/zdYn8k+2RJqhpbnX0GY/DyDnmPzglXFGHmGPAhQAionFEponP5WSmSoygRqRRx2tJVFWhN8AGKX2bOguYdxkESfuMEWasLDSk9DYLyvGR/xiRdAqFBRLSPxfC77B1jMcZhtQUj97yumgLYsaCDANVihtqiTJm9e6lim8ZLkhAzzlVnoKT3gb4f6fuBmLOIPrQtKSeCD8zjzDTOKJPpplkqdVu0SdXCNfz5Iuw/OwiGfAKVWDctF+vnKGP5sx8J4w39cMI4jbEV61XH/rhn9IFxEL29qq6kPSNg8oeXJpeZYHnHTaWp6ko4HznRDwfIgcrBptsy+4j3iSlGAXlqx1wcmNOccU6kluo6UFeWcbrn/nCPVgPfvnnHze0Nx/FA2j0Y0DpjJA1O8QwSsdbig8GGmWMvgt4xKS6vdnzxxWt+/atfsdpsaNpGAgIS+HSKZbMmjBXU27przsNyHzzzLDyw3/3+n3n7/gN///d/z8vXn/H6q9f8zd/8NSFF7u57sm9o64pf/eobcobtdse7tx/Y7/cMw8CTQ/Xc7nq4RDlf4MgPgdGcdTxlswOlBaYAoxSbzrJqDY1NBDzRT9zf3XI4CXexruRQWZXWUl07Dv1AP4wc94fzjMPalrpZUdcrVpsdSluUsqTzrLLIsmUFZlFn0efu2tKqfUrchzwGlLFUtWOzuULNiY8pUcUZGzLW1aw2hrp1hFkC5qqtMQqsVmxXFYooLSQyMUNVCOc5wJQiJkcqsoAAipi0etQGPK8hZ0iRECf8NOOdx5aDLhNlX6cloC1KPZyFyD+9znV9Li7jZJROPH6yymhWm45mVbHaVGAzSs1UxrNbNXz+rGXcBw7e46eTgIWMIc8apRxKuQJQEfNgVwA0ykBXGWprmYdIVha0w2dFmiPz4XQOfPM4MU+ReU7kZD7dep8uquy10lqmkKcBHzw/vHlD9p5N5fj1F8+prWZ/8w7CjCZxeXlF1I6kLHZ1UaTletHFnGdubm6YQ2C6u6fuVihjWa82Ah5Kgp60TU3XNGw2G6bZM44j+7u9YBf8wHv/ntuPN/zpf/4RY4XIrYs82Nu3bxmnmbZpyF+/ksRZK3a7CzabDVfPrnBOuKHGGHSJKMHPRfhb4Schko/jUIjiQv8SyoBiOB7w48g8nHBa4SonVY0PzH7ED4PMqXOiLu0+SpJPTDiVaa1h3dQCtDFK6ChFREMbI6bfp74AqoRXF4MnRk/l9JPHlbPMEed5LuOKUqyQqeuGpmmomwaNoWtXbNdb2q6lqmsuNju0swKuWsQVsnRwlBZ6SoyJEBP1akPygTR7bNOU2W7ksD/x4f1H+v7AersmkVitO8LsGU49tzd39KcTyoJrGtr1GleoJVVV0dQ1/t8AD/3Zsml3/R0hzhANnCTgvH71im234rTbodEiw4QSgrAxAtvWBq0MKZT5yKM3IyZfWgERXYnbhFKwqFdpA3EK5AhtVaGSJ/mZsR/JtaN2Dq0NlbN8+epzVqsVbVtzOn5A5cQ4eW6uryFrPt694zQcmMIsijrnw1gQcVlnyAZVqgGtNFY5mkrROUe93vL61Qsu1y0fvvsj16XPv9msWXUdF9sLVt0Kow0ZdW5lJIT7YrIc8MY5trsd3/z1X3H5/CWnqDiNPb/7wx+oasduu+VqvZX2XAzUXc16t+OrX/6Kv/33/54f3rzhu++/l8F5jsJrIxM/4Z09JkYvl9TgDyR5GdXlUl1lKgu1U1QmM572DMcDfX/k7vaG27s9/TgV8q1wj5w1WKsFcZcik/dM80xMCaUsddVS1y0vP/uKplvTdFs0hqylda4XwNFSnWtRuhYwocwszScn7BmWXzoCMUe01SXbDQjKXYmOYCvQ+qYx+HlmDp7TkFm3NbuLFTlFZu+JscdYIZ4f+h41jjg/g3swM11Ga6aMiGXCYc7gl0XRQxYgQSxR5kOpaKHycL//tW7NuVtc9g1KFaFuS91aqtpgdcQacFZxUYPTAT8P+KDIStOutiSnhOeXonw2ZiG8o1HJYJ3DWI2rNZvW0TrHaMTR26fMGBLaWb5++RIfAiEGDocTh8PAIQ7MPzGPfvK8SqF/bvMut6DswRA8w+nE7c01/eEVbtWwbmquLi6wtuIQDB7DjCGbijl41uMkwI0Q2F1dcTqe6Htpk6oMTmUuL6+o6wZdN6QkkgxJWawxdN0KZyqCD0z9gJ9m5nFEGwVFnacfTkzjyPF0kPGKKx+8vFfPn1/RdivathEne6PLmL1UUSGSlDpXZiFEqYpy0a71i9arZh575qFnGk6l7VrEIPyEiZ5MRJuMQWFVliQuZ2zpTChrsRr8NJIVWJXZrVdE48haYxT4eWYKEWtdUV0xBSH8U89OwGAxCpqzqqUrVTc1bVOzXq243F2isqJyoiKTY8QPA/sYCTnhUyTHdHYeUY1DO8uqac/dSl/eb6dNIcErfEj4IO/lNI7UtYMYqZ0T2b6cCd4zTzMqZEgZt9x/DZQ56r/FjutnV4KnqcfHEY3Fek2TPavmCqOgdhaitD+tkexr0Us0VrQTZ+mZyrD//EqIxI0AuiRoCoIwPRmiK4XAYX1kBiFTnvlUwju83F6wvdiw6lp+CAfCNBF84HQ8kWLmOOyZ/EjIgerRa6tUaeVSAmF5S5VxGGXRrqZqWjZXV2y6Fkfi5s13BD+TU+Ty8oKLix06eGqt0FUDiAJCSpmss0D0l8PAGGrnePaipl6tefXhhj999y33Nx95/+EDpMRFtxL4MokpBFzTsLu64ouvvsKnxM39Pf3Qk4ME2VTmLk+uMyDl4dIFlSlAi4eecJZHI/JUOmPIzNPAPA/MYWIcB079kfv7I0pL4uFchTEKY8TGRWk5aE59X4bmIjxdVw1ttxaum6sxtkYkvB7wRiJcLIFQKkmZG5KTiG8/at1K3MyEFPB+FuAR0habc2AKmdpVMih3FmcMrhJ1oXH2jFPAGMvOOKxRpBRJ0QPSdpznGe+FpKxs+1CVIi1jcW6S+dyiviOAFZl1P7hkLAdeemiplr1F+bd++lLLf5+2sktiaZwVUrxVaB2praJxhk1jqY04ucRkycrgmo6sBdLj7PTA81sw6ipRKzDayAyskpZqQ8XkI/0USSSM1bSXO2JKhBRpqgbFPdMohxVLhfuT6ylVsJKe/UNdLC3OlIXgPU8DYZ7IrRUt1lWH0o7sDWNS6KRIylCFgK+acs8Tq/Wa+/oe6xzXHz8yTwP9AVaNk9mvUaQkdB0MKF0kwKwEQUIk9DPBe1RS5ByJKXB3f4f3M7OfQWdifKw0pQrpXRJ/w4J0FxDIgoaUZ5jwc0mQYip7OxP9jEHcUsI84ueROM9CbQFSDhADKgWcloDnjD2jd2OKWCWVpjECCgp+wjgRbV91LVNSBIoEYIrMs8xwtbZYU86+n0D4PiQpCZWFf+rcct8sbdOy3WyKRJsWpKYXV5tBa0Y/M8yTgLkEMYddt1R1hd9sxSUlKwKZylZ0dYNthI4UYjpT5fw8i4xcjFhtiKXKTmX8sohSLNJqCnW2ePu3XD+bJ3g6eeY0U60StbsnsufN7/5EZVq6ZsM3v/wGgMNhz5+/f0u+v+dw3KOipAK7qxXjZFEmkJNFW4tbGbZqRbttZQAf5aW62d8xzjWbrWO1aqldzWbbkHNgHCN+mM+UgxQTwYtqDcaTjOLucItC0dWdqODHKL+39mTCkxmTguK1JlnxMvtp2hV1s6ZZbcg645Pn+vs/8/2pZ9rvqYwEjWbV0LRrVpsrfvnrv+PqxSt++c3fousKZcVOJ2nxaYs5k0MixpmsDNpV/Mf/9B95+foV3735nt/+9n9wd3vD1Pd884uvqauG2+NRXrCUePXllyRr8Upx3x8ZDnupwpbZweMZ3yKWnNKT57gQiXKpAnMGDFij6BoNcWD2I2E6kXOgqSsuL7bEEBkHj48Fdu09VTZU2rBarwSRBQKjDpmp75nTwCnvCSGz2uzYPdtzefWSpu1o1hfn+aBRkhgplOjonMskjclPX9JhHpgRMed3f/4jhw/v2N/c8/7mxM1hxFaG57uOX71WvKgczgpo6Xo/8t3bG3733TWfP7/g11+/4uudIwfP9cc7xpBIWbFeO6p4gsMHsm1JpRLVWZUq1rKo0EbUuSqMqegWxlQyfs7ouZSeJihC8fkpHpOsdRH2lhlqOa2UKZWgI6tAJlIZxbPVist1x9dXKwgTaRrxpsJjyEljtcfqyGpjmYYT4zTQreozCEfbgDGRykTW2tAqw/r5JfvjwHu/R6mErhp2Vy+5uLqk6VqCsvzj7//If/uH3/Lb331blJh++tKPvkqqDkAq3A+tNJvVmi8+/4zL3QWrtgI/s1lfULcK7R33gyf0UxEkD6BcSfoSzlVUdcPu8pLVumMajgynOz7cvqMZGnbpGdbVaOMgejKapAzWNThl2O22tKsG72f64cQwDSInZ5wcsknEvx8fGjEGfvOb/1KoKorXX3zJ9mLHi1evzrSAHFJJsALzcIKUsDrjrAat2PtR5r050d/dEWPEkljXixCJ4bAfGKae3W4rrderK5ml+pn7u7uzao/W+hywn63X0BpM1XCaE3PMzAnmSQKhn2eszdLNqRwievHUJgpKoI6RlIutmBIFLHJmveqo7Wf8/p/+wM3NDd9//4Y8BzSKdbfiOJw4DD05Z+Zxoj+cuPzsBduLC37x1ddMU2AcZz7e3uKMY9V2fP03v6ZuG7HDUxrrKmJIRO+FL5ilO7fgNlLOIuOXRVFGMCBCMZmmSWaNzY+W9ZPXz+YJXj2vCDmTspU2ZFaYehKS6r3n9fy6EMQVu92GqnY0naUfesZpImaP1pm2qwheY51BO8NqK71lazTTPNKPPWrVUDuL0lDVjrZpuLhYk1Nmnj1HLw8lFckdYzQGxI9qkEzOGINyinE+EeZImCJBB4KO1E/W9kAidlWNcYIas7bCWIOfB0IOzGGk1pmqdaTcUBUy+Xq7wdUtVesI44HDreHNd//MendFs15Trzeo5RgopOeQKZBlBdrRrTa8fPkZN7e3+HHkzdt3bDdbYob1bkOYvaBNc8Q6zWbTiejuOEKYfjINP9viPMr0FKpULKlw/hCBc62wGgwRkmiekoVTV1c161XHOM6sul6CIAptLXXt6JqKL798LY7UKVO5lsPhxLE6EkM4Z4P96cDkAzFl1tsdn623LCo8uuh+alU6okVuDfIjGL5cqQSalCLD1NMPPcM4M0yecfa0OqNzpnWaTSOQ8hRzQaBC5UTp6ND3HFyFIaGtEdh2EnRZHk/k/pZsO5KSdpbpjzTzyNU80xuYtCJphY5KSOPlMy3I0EUZZOH1PXQ/Cr/qLwj8LpQSXcA3UApOo3eLfOgAACAASURBVFDLlwZjoHZaKkGjuVityb7CK8e79z13/cS7IbBeQVtlmjritKJqa1ZtVUQEhJ6QyUxzj6kbaq3YrUToHaVRt0fmkOj3t+wuVrTVmu7iko/Xt1xs11JN+38h8/5R3/cxBAhiTmQN2lma9YqqdvR7saVCaQwV2mfQgk5WWmGyIaZwplbpos60Wq2onKJykFOQdU0jGYXLSxeq1KFxJqmi6mJkXhpzLVWyEhWXRUQ9+OkTUJnMM6Vbpbm9u2GYRsZ5Lvw0xcVqg7MWZzVByUO0WtCvRivCNArMf57FagqZWVdGFSWsCrqWymQuNis2mzWb7UaoC/NMTolxnBinmbdv3xX/vMwXX36Jqxt0HYnHgTR5QohUlSOlhuBjcTbRZ0utn57pFustLZqcqbT4TaHr+Gniw4f3fLy+4eb+FhUSVolo9uRnQhSlr2ke2e/vabYddVPJbHQYOJ4G3v3wA1ppmqqh3m3o1huUtiKyXcCUKWfRA02lm1b45bK1HizllqAt1aMv4K6fd/3sduirzxoihtOdxZmGnBTNpudmPHJ3N3AaD6AatElcXW3ZpQueh0vevPuB99cfSGlGG0W3qvGTyDEZZ9m0W5pKUGH9cMSdoKtbURVPgapytG3N7mormcXsuR9GjBYDS62VtFyVIs2eKURSivK9SjHcn5gHTxwDoYoE99RK6fHsrKoX1Q0R6E4pczrsCWFi9iOvdlvadY1ZVzgSlRYVel3VKNfQ+5HT3QdO/YlXX3zNRXxB023OLgKLykfIisEHYsxYZ2jaFc/rmi9PJ97+8IY//ONvudjtiAraiy3DFBiHgRgntM2sNzWrtiX0A/kvBMEFxPFUSUadZ1c5TNIGdgZb2iN6EaWOwm0U3UHNZr3C+8Dh1OOjIDtd09C2EiC/+eUvqeu6KM433N0d2Lf3ogvrA7f7Pf1w4nh9TcrgQ+SzL756uPelJX1uz6TF/6w4ojxaW46RFBUhB8Z5YBh7+mFmnkVQQadEpWBTGXZdhdWGm/2IzlBrzcWqwhnoh56DizRWTEF1EJBL9IE4nkhWyP8xG4JP2GmkjYFXaeZgFL3VnDTYqMBrcjE5FjRMOWiXtlJKxSW7OANkaZH/pevM4Xr8PA0SAK2S+ZDVNLWlMZrGKHarNclHJt3Sn/ZcXx/57nrPi2c1FyvD5kKx7gyrrmG9qrDKYJXFWkOInnenW1xKNFqzW6/p2kRV10zesz/0fPz4jpcvLrFc8Pxyw7Pdmt22wxlF+JdMf0GC/qO1PXwfIomkMlhNs15TOcv9fk/UmogBZcEE0ArjLDoLaM4Hef9DKA1WBatVR2ocaVVz2N8Xl3kRQ5AZf6lEcyJm0DqV6kKQjnJ+aUl+gziqL61E9ThSZCSJydLlur27Rd3fc3N7i7jnWOzXv2C7XlNXHWGU51lZRVNZjIbDaWYaTvSnE01dYY2hMkYSFaupnMOpSGgs2+2G1XbNZruSWZqXBPb+/sA8eb777nuUUtRtTdt2rDZb3BzpQ2aOGRUTdSW/o+8HccAxulDVwo/Qoctzcs6BliRp8dk0WjOGQH868ubtD1zf3nI4HrEJKmNZrzpJUJBxVgyBvj8yTQN+7sgxMM8j/enA+3c/kGLGGcfm5TPWuxFbtcLXddItzFk4pbEAxR7UjNT5OcSFcxkD8yRUp38DQ+LnB8GhFxKvyZYVGypbUf3CUOkb0nyL0plxPnE8XWNyjTM1F+tnfPPFF3z9+hX9aS/zpTAQfCWOFJsGg8Zpxeevr/C+o+87Yiwmi8OETgk/j4x+wFSwvWr51fpzvI/4fiakiYzl6mpHUkd8munW4JyjbVdUBEI1kacTMf+EKPNCXNKKrMGnyP7mGqMz1sKzyzWrdsvldsPlusMpON3fkqYZgqdxgtbbvXjJiOE4Tvzph3f88z/egKn467/9X7l68ZJnL1+iq3oZahGTkEV9ms8tsxfPP0NhOB5H/vj9W95d39FtL4vUkmKee6I/QjpS6USlwWcQ1dQH+IFsYkoQzCycPKU1/jRxOtzz/rs/YjVsVi3PXlzRtTW5MZB8mdpIBmh05vLiQrRejWMOcoAsPpFt17HdbmjqqjiTWw7Pet68+YFhlNZE1TUc+x5zeydw/KI4pMqA3phl3qawURzsZViZi9TUcvZkAmLFoo3Fti26bhinma6CdVXRWc2lU9Qh4O/vmVLm7ubA1sL61YqXV5Z+8BxPE1OfMW3Nbrthd7EG4HCcMNaJIaqaiRlMClg8WyIvSYwR5hjpTYZCKK9iIMdQguAjcEySl/RMWeEBFPKXriXDXchEaEXVNtjGYCpBsjZtxZevP+dl13LVtFxdCNgjY/mH395yvZ8I04RVHc7WggKsGi4uOp7tWlHuiIqqEj6dtLhaKtuSSmAxVrPe1Pg4EfyBP//5d9zc/cBhuOfdm2t8f49VEXumyD3dgw9rzU/+rJQq4ClF29VsL3e8+uo1wVlGpQl1x+E00o8DN6cD4xyZCr+Yck/GsWeaRm5vr0sVlLjYbnDWUDVb2qoVo+JSnYkIwVwg9PK25JREJKCQzo1SNE7a585WzPPEqT+xLzStpQJRWnFxsSk0ASec4hgZxqkAA2GzWbFed3RdQ5wMRmkuN51UoMFTu6JOVDtWXSvuFsZgjRLnndFL5diIEABZsBDjKFJi290VVb2iW235/O1HrLWs1isur55TNS15mFE3BzKa9XrDEvxXbVuc5id5BzGCzVieD2CspWkaXrx4KUhvZ4lJ2rAaONzf86c//pH9/p5x6IkpoJOAsVACwAspYPLDjC54j59ncabIIszvp0Faxy6IJZhRhOiFatfUbHY7qkpTtzVZi5ilsZama2n9jI8z2hgR5J4Eu9D3JzF7UOYvv2CfXD87CPpJ9rctGoCVsVxtthzvZ+47gf6iEujEPI/EEIqqumPdOLar58zR0/ue40HaYsZl8AGVE6tVxTxGkjcEk0jRUOUKlYSgrXTEVtBiaTYtQz9zO06Ci8wJ7SpE4aJwwCpHXa+wDrwbmQ939LNGp0cvqnr0xcN8sKosbW1pG8Pz3YZ113C53bKuK3EKmGuihuQVyQe8nxjHnj5k+mlmnk7MM6RsePfdH5nHHj+PXLz6DIyjeNgLTYCir1gU5Ou6YbfbcXt7w/F04vr6llXTUDlNjBMhTsQ4ktJEzv7HD2pZklqUWB4WeFZ+NxqVAsF7DqFnd9FCLZDyc5uuIFdUzrRti6jlKGIW94jKOayrcFVVtAOlGnfWUFeWuq6k1aUEHISSFrCtGyEXq6X1V7g9SlpV50LhEYL3R4vT0hY0zmGqSgAjBTnnVCaHQH8c0CEWQEuibTuMs6hBWq1hDtKS16qI8YI1mvWqxdbyZbKIouscsFnk4hqtqJOIMa8ohrsoagI2L3PZBQFaiOJZWHKywkXh51+Dh8o9k9anwlUa7TTKCABDAU1Vc7FZc7nqqGtNU9dY1/H8xSWH0XNxc8t2sxJxiXwSvq4RmS+dIUzC89NKs+o25KCJUTw4g84EAspkjFM0rSGniXGE/f0Nfu6pjKIymqDVWcrtpx6ZLCfzKVBLKcX2YsvmYst6s6GfZhQQlCag8VkxhyBgiSS0loU/PAw9w9Bzd3dLypJkdF2DLSbGWmlMEh3beZoLVy8++FxmHnwjizjAUjEqLa7nSxLpg8eZpw3RpqkLWMliYixya0FARlUle0vl4om35NmJOYjIgFEZazSVszSVLa1TU9roIpFny7u6qLxobcprIedESooQMy9evsI5S7fq0NoyTp4PH68ZxokYM64x5T3ImLYYEyQwyhBSYPrEQsdoTV3XYp2UhfoBnK22xmHg9uaG/nRiHAbm4LHagTLFId7j/UxCF3GA0g1JsRjhShAU4JjwVkv3mxiizKuXzlxlcU1VAICgraFqGlrvsdFinSugIC+0mWkSiybj/oVD5On1862UBgF3OAdGRSqXefbyJXOf6e8nnDOgM62puf1wlOHo6Z7PXm5ZrXZ884v/QNKaY/j/KXvPJtmy60zv2e64tGWubQtDcACCQ8VMKDQR/KAfP6HRkNKEKA0lgjANNNB9bZmszDx2O31YJ7NuG4A9p6O6O/pWVWfm2Wfvtd71mok/fPkHDu2BIezJIaCzZbUoaONIO404O7vXm5qqqihKR1F5yIq6tjTbC3b3Lfe398Qc8TnMSXZ6Hqpa6mrBZvWU9XJBGI+E/jXtvWb4VnzWoxZNRMOF1Ty5eML15YrrixXLUosQ3hh0SuLYUDmiUURvRLvXd7y9u+O+7ZliJmqBOI3K/Or//kfKZslie8l//Pv/ldXFJcViTWnmGR2K6KO4cSBC6OfPn/Pq6685HPb89te/5bNPPuLJ9RY/dUzTkWnaM4wPTFMrGwyWb/FHZjhUn1m2Jz5sVZUUesW0XbO/v+H23Ss+fn6JzW4ma+T5ZLJC+0iR9XrNYplYbjaUdYN1Aq2E2ZfQqBliipEYBlIcJE8yG7J2uMJQlSXNYgGmpKgWnAIxlWauSPMZXjrBiczv7hv3y2qw8pDYsqaoF5SLBhNHTPIwRdq24+txpC4rmrrkxfMN26srimrB8OoVOUmkS1EaSIn9w4HJQl0VvPjsRzSbK+rlBXfv35GPLfngZXUpid5SIWNDYq2kW4sqYRnRTMK6PZkg5lOKRJqPQPX4lv4MGpo5sX3lELTWUBQSUYbTZCvVcggOqw3Xl1s+utpQ0LFcOJarNb/4xY9o1ktGRp49vaIqC9q795LRFqWLzyHSH0fGIWKMY3txwWEY6NqRdjqQbCbaiM8jtoRnL7ZzisHEcX+DSobtumZVOuIU6afvh0PPkU/qsRATy7yMtYZPP/2Ej16+ZLvdymwrZprFiqAt2WYi/ew3KvmHIXj8NLJ7uOdwOJwT0o3RbDZLSZNRjcB9WtiLMUiRmVMEzWz/F9FZC8Upi2mCtkKgMdZgnMNZ+bLO8SHjXinFYrGU+RTirGJjJJFFRF9WWAMxTvTtgJgtZqax43i4J4wji6KisAanJJhZouMcKQVBQNTjM1w3Da6ucU3DGBJayf7iQ8YVib/++S9wzlIUjjEE3r57zz/8t3+iWV9SNgvqpTmBXdR1QUqZRbOkbXuGcWB8+OamaJ1juVwQkUMWY+QAmw1QDvsDr77+it3NLd3QE3ygWG1AGcI4MnQdbXeksE7YtVkOQDGVELREz3uGUhkzF3pKi9TKKFEWFHVNVZcsFg0hiWzDFI7Vdk1RloTsqRtJr++7jq5t6bqW9njEueJRBvZvXD/YNu3zz15gXSL7RGMXVEXFarFisz5wcVGxWmi0daztmqYUlk9ZOtYby2rl8PmGHBw2Fnzy5AnjdsW+c4TjhA6KJlVk6xlXNdWiFJ2hslhjhbjhPCprFJopTPggTC9lDMpqRj0KxZkKQ0PpViwXl+TcM049+/5ANODq8hu1geQXCpNyUZU0i4YnFxu2y4pVZXAELBmnpWIUF/WAc9LxXFxfcOwGdocju909U0zYakFZzFWMjhwebnj3/i1Zay6un/Li0x+x2F7hqgZbWKJVRM+sDUxobdhsL8gZbm9uWS9qKqcIfiTHCU0g40l4OB2A366yPyD8PNZDCmUsuqx58fHHXGyXXK4rtqsFhZ2rTCU0nhNxRlupzpTKNHXBctVQlhUxRG5u73h7c0M1u3Is6mquapc0iwVdP9D2A+/f35CNdCoxa6zKEEa0EwiqsJK0kOKplBHQ65R48I33pOegWqUpyopmueLJRx+Thz1MLb7tCUPg3SHwclljVyuunj9nff0x1WJLdXHNOPb0Q8vghYIdhxFnJe7m+Wc/o2qWuKJmd3tDjJ5x6BiD5FpqYyHL6yqbat4YPGo4YHTGHd6hJkuOStwuZi9JGc3OdnHfgghP7+50GaOwTmMKS1lISoTWs6vNnOl42Pf85ndfUWYJuH15VZHMhContldrPrcG5aRjTCFgpyV1lSgdNHVD8J7ejoSQURjKZo0yK5oVmMIRdWDSI+kQMVphgaKw+JhnOzyDyoq6cvRDoJ/+bdf+D+fvOWeMMRTGMg0D7968Zf9wlJT7YkHfzdBfApUSxFMigT+bTngvInThBTx67YgUQz5TrYXohsqzU0wSLsGcfMIcCZZREIQzm5V85nlmr6L1nIX4AUNUQfQTY9+TsvhoMu8N4zTy/iaLa9Q0snSGRVVRXW1lX7CWuqhIIZFDoqjKWecm+1tKCR8DBBGb26KEJLCRjgJTa2UJEaaQ0IVjioHu0PO//e//la9fv+E3X3zJf/hPf8/q8oqUhWCicpa8SWdpKkvwYvmnOJn6fbAnGiNmGzHgxwlyJGvNNA4cD3tu3t/w7uvXjOOI1ZrraomrFb7r6HY79rt7rHECgw6jZCNOXj4TPxGmET+J04+eNY52KEgevArEuWScUkb7QB0kQsoWBZvtBWkVURrKUpCo2/tb2uORru0Z+uE7fqh/6fph7FAF2+2SooSpHyl0SWElGLeqCpbLkqoyWKewlcG5ipigbArqJlNVmdwPM3EAFnVBXWqsXTGpkTwlVASnLYtlQ72s5qo1zx2tpBGrORQ1RC/wSH6E/RInix6FNRXO1hRFTdfu6MeWwQtLzNjimxKJGZMzWuOcpSodi7qkLi2F0eiUMSTRzkWJNokhUMyO5auNPBAPbTtTotOse5u1YzkyDT2HQ8ubr//EMAwY63iCYrFJ1EUhaQ1GS5eZAKWp65qh73k/vJUqp6swypOTVE9qNllO5A+zYr950/i296YCLQ4f9XpN7RSNjtSzZ2lWErWkYCanIP8vLZBZWTqWi5qqqokhcTjs0bMll0JkGUUhh5e27qylur+3iE+IISbJgSQHVA6orEkhEqLH+xEfNCiDMdX3vP4POnelRL9UlSy2F6ROkQb53Kcw0oWAV5ZkC0zdYOslrllzURd4PzCMLYeuF5au9+L9Wlasr57iXCl+slnYot5P+JnZqk1Ea/HDFTmNfBEnGDtodygadC45mYXnlOaZxiMM+l0rtXmpK4SckQ3OGazTWDczi4VpIxKUHHj3/oGvmxqVEoW9IukGXXiquubCWNByQA7dQGx7rJ0wKgp0bUQSlLLGuYq6WVNWmpw0yhmmNKCjwnRaqnTnyGmu1uNJ0DIXkD8gTeIbz9vj8pS53TByPBxJMc92V9K9+Vl7lufOOs8EiJPZPsj3a6uxxj7OXM9/k8JJ0scdIUwyb41S1CWl5ggWWXM5x3MM1mk0oowmazfD9Y+/P6bE5Ce6rgUVBSUo5jUTA22niNNIGAZ0XWHJxLiaPUPtnDgh3jnSaT6a3ecsQMFJ8pZiIoVI8nE2H1DEJB2oD4mMjGOOhwO//s2vefP2PXe7AyklrLMobUkxzskWkk1pXSGdrvnuEXBiKMs2IJZymgRGE4IwWsdhZGg70elaJ5aEWhHGkalt6feHc5pD9BE/yIw6n9JW5pgojBSJQ9sCGp8NSWsxQSnEA3eMkSlEmZlah22sdMtadMoghMlxFJNv7wPW/nnZzrevHwyHlqWlbjSltTjboLVj8p6qLnjx0TXa6jk/rzwnG0TUDAUkVBMZp4l2uENHgzMFT188Y1h1jP3E7rBjua359OOPSUnR9h1v37ym7TsUmb/++CNSBO8Tk5IqMM8OAhpxSejHlqkfubq4Zr3aYp3iq1e/4u7+FcfpgDVrilzg7Qcreq5MrdGU1gjRpbRUBukCz275mYfdgzhTtAMvmoblZs1lWbI8HDBlibIFIcFqc8H72x139/fc7w+iP1stiePA/ZtX3L19y9OXn3D17Dl/+5/+nlJbTOW4vQsSDZM1VVWxXAq80x1bblTi8sJDyhgkb1FhTnkD3+kExRj80ahaEsBPSeAaZ0uWlaHeNhzbB3z0cqdmUkcxhyaHEGiahqIoaJqG58+fstlsWC7XfPbZJ9zc3tEPI37y9H0/6xLzLHMQV5jNaiUPq1IYLbMNqzJh6hn7I2/39xyPBw6HB8rFJc1yw4uXPwIUKn17gxVYXiEDe1fVqM1TVNOA36Kr90z6yNgq7qOCIfGnQ+JoW5bRsF41qGKJLhbUlQcU1hhWqzV1VbNo1oSxZ+wOHLuOtuskIcLL5yxFgXyl6HHO0WxW9F3PNPT0r75ANU+pyg3oBekUPp0fH7U8pwd8eJ0YjtppcbqJMovTFrKV7iWrmWGcNGPIvGpb9vs/8i/Va3766Us++Xjgx58ZfvFXP+N6XfH86TO6/QN9e+SNtRyPd/TDnr5rKYuCp8+eUDdPqOo1l0+fi2Ysa2KO3O/fM77vCP2AH0dMtoQxM46RfTcxecU4wTDP237IdUoneMytDHz5p6/x00Rdlnzy6Y+oqgajHe2+I/nANIxnkf5pXwFNUzdYo8lRDiBjNIVxqATBh9kVZTaBsBZrFOPQEoMQPAorVmfCErVoLZ7DikBKPUQjOYHWzmvuG2+Etuvpjnt2N+9YNI6qciyWS1IOAt5GhSZRWiOOrcFzfNijjew3Ks2FUIZVKcb9wnr1RKBqFvP+hnTetsA5J7PHGLk97jmMA4ex44tf/Zavv/oTX/7+C/7w5R+xRcWPf/JT1hcbiqpisVjRdwN923N/t6cqC3KC5WKJtYZXt6++iWTPFVmIib4feff2PaXVNKVj3MooY1HVLIsFHrHea2qxUby7vWF/c8fDm7ekJPuSNjLrzD4wfvY5YRpJ0yRG7MqgMXzx//0rtqwwRc1yvWaxXPLyk08IOdN2A3o2ALi+uJhZviKJizEQ/MjkA9OcdhGDJ4UPzQ3+8vWDD8GYA0k5XCEVbkwJNevW6qZEW3FuKQqp4FPK9EdPe9cy7I+URoMRw2VroFCZKnn6cWA69jw8eKINlFsrPzuOjNOI1hIPorUGAyYhdlFWfo8x4KzmYlVSKM9gIlVZgRLJxe5hz/7QknIxC0O/v2qVym82E46BU1BtVuBjJIwTh66n70dikrCZrBW2tLhRup7VaskUBBLp+p7dsWU/inuKRlGUDYWzLJuGbn/HMPaUmwsurp6w2l5ITlvK+CDQi5oZa37ydG1mtYAcE1MfSEGTkznH/HwHXJu9LpllGSd46MwUVaIVDHGaZ1CnTmT+mSwssaIsWK/XlGXJYrFgsViId2ApPrEhBGLaMU0TXd8xDQMhREYvn0HXD3THI2HOrIsxz2w9iSLyIXA43EvKRtdx/bzAFfU8J5wPPMXjQ3oi+6gZ2rUWpQO2bDBFIeM4vSCZmtoobOlop4Bqj4wpMQYxWjhRrbUWLdQpGNlOA2mG2crSUTeSyB2zl5yz02GlRc/lnKWuSrGNCmJWXKSJ0reSpxkVMVqiEZq9NDCSxfjNS4y9XWEwQQv6lb1oHDOUlSSva2PpfZo1UZ4wBdrDQJpes997HnYjOjmuLrc8vbqkLhYUtiI9haJwHI6WurbUdSlpCdUGVyzRRc00yiYy+p62Pc7hpUEOI5/o+8AwRfouMgU5BKcp4sPJuPzPX99miJ7e8/3DA9ZKxubPf/Hvuby4wpqC3f0Dx2OHMX72u507pHkxW+tQwHKxmuf5YGdJQ0pJnFdmSu7Jlk8rIc7lmIT0M8On9uyOF2aihsy2waGNGOt/mJ2YMwx9BymzXW+4ulxSVQXVoiQinWsYIyaLPnFhLcVM3gqjJzB7as4dzzB5nJW5prYF2mZsUXLKTc3WEpViSkk0h0ZTLmv2N+/46s1r/uEf/w9JYc+Kv/nl39IsVlw8fcHFZouzVkz3Z/gZ51BKMYwDU86MU/+tVTgz1/2cgNN29MeWerukdA6rNdvNhs8++xSDZhpHco48ef6MsioZ/cS2F7/l4NPZAaooJG9R4qespN27UkKdEzzcP6D0AWULUgxoJOsVlUlJYGJnC1arjcSUxUQfW0bvGaaIz4JyuaLC2E4itX7g9T9goB1JGIwr5uSIiM5SgdW2lAPKWApXyjDaZ/oxcHzbcfv1HeuqoFxY6osCVxucThR+JHc946Hl4dCja8VqWpBSZgwiuCxLQzWbxgokocBEXKEpCoW14ArFZt1g1YDTnnI+iPuh53BsadsR5woR4H5r8zmRFU5VoyYLCy3ZeWAuDuptP9D2I8PgMcbOyfNqHqKLtVCzqDGj53DoaPuefdtzmMQzUOfMZp0prOV6s+aLr77meHtLcBWf/fivcGWBmWFZPqAsa6UEcogePxakEBm6QJwPwQ9tqD68TjpB4JGOn08CbJlfxBSY/Hj2dM3z5yGpGglrxbFnvV6fO9OmkYrPWkNZlSxCw/3Dg0BA7ZHj4cg4jrTdQNcPDOPIOMk8LeWZeegDXT8wTiPj5DkeHximiWGcWG2fEYPokZTSqG/H/nxQxJxCXlPOwro1JcVCYVxD1SyIfYfKiXaciKql9xODn4RE4BxlIdW1MVao9Hl2DwoTMXqqpqSZGuquZ5iOxHOs0gm2VDgrDL9QiCejDooyB4LvKKjxwcxQ1CNBKWdZD9/grmmJmbJO1nTMGR+CzI0UVHU9hzNb+jzOmXFyKOWQOd4P7O577m6P1K7i5fNnaOX4+NkTFs0SdS0buWh1E01TsFktsW6Jdgu0qwnDgX6a5vt4oDscxOA4JIZe3JqGKdIPkcnD6OU1hJAA84178+F1IsZ8ew4aY+bhcEApgQSruuHi8oq6qPnqT68o3A5rrTgtRXHHlV8hWjyjNNTpjBsaZdAILd+cWMTn+/VorZUThDQzeVWakQVmCYYIU2IYEXs8O4dlf1iJZaZhoLSG9WbD0+srqrpAuZnYlBLH+yNOaSrjWMwHoM6J0YdZiK+xzmELw+g9CShNiXFO/qwsz1BwRkYuKQsLQBuFrgru2yN/fP2K//Z//RPXF1s+//QTfvnLv2W52eDqJbZaorVmnCR5RvZQYYAPw0CMMlv98LYkIKTENE508wE49j3mYkVdirHAZrPm088+pakbpnFk9AOrzRZtNOM0kZJYJvoxEOf5ZjaKAri6OAAAIABJREFUoqwEBtbigFVXDT5Icdi2nWgBlaIqCyEXzYVw0uCMJFSs1xuilxDraZxIw8gUMwmDMhKb1rfHOQv2h10/+BBMXhNGzRSkDc0poW2D0tKSaWcwyqIoUdmSfOD2Tc8f/3jPqy/fsCoty8JwURk++/Qlpiw5+JZx8KSQWW0XXFxecHX9VIawKfDi+QZ8gAzOLWXDMZpsMt2qY2UN9bqgrhXlyuPKitXGMR4rjGso6iVNseEQOu7edRgXMOXE9pPnj/CGkuG3MYYYIuMwsd8f0WpJUYq+ahwD3bEjhoA2itXFlqJpwFiOXccwepRWjD6wb3u+fnPL/WGgnzLKVMKAJDMOnlB6Kmd5cbVh33X88Q//Qr9/z5s//ZaPf/TvKJo1y8Ua70eM1aQc6PuO6Ef8mNFEVPIED2QDBE7Eym/cWCNu+KdAV0BIBvOzPHYHdJpw2Z9nq9boM/1+u96y2ay4vLzg+vqaoijOszhAnPj3e+7vd7x795b7+x03799zc3NH1/W0/SDZXynNVbxAYcHHOZpnIowTcZoI04BWmuVyyScff8LVk2dUpSS05/hN0ThqDpkFDseO+7e3/Oof/l90Fn/FJ08vePbkih9//lPC1NMej/z2t78RpxGjuby6oLCWwhqaqqR0jqauqatSZB2rNaVzlGXNpz/9pcRX6T8yhS/R3RFyIOUwJ5oUpBjp5spXKUVVVUydp/QTT6fAmDYMWuj4KD0zRsHOJvHn+1UobKkwBdBH0BFTgMailFDilRLShFgBTqTsBa3IGh8Nd/c9+/3Aw/2Ri82aTz9+wU8+/4QXT67525//nOX2BdvrZ1TVDqMD1kTKxQJbbii2z+gmT3+/44svfkvf7hh76dKSgWHsCEmRsmXynt1h4P5hoO0nfAT+ciN4vj48CMUowTCOEzfv7/j977/EKMcv/t0v2Gy2dP1IvVxz97BjvLvjlBYu87QEKRGNEWezOU5LJcnfO7nunBZ/zpmiKEkx4qeRcZxmDZtEwQUdADMjDxqj7RzwKhIHrR6LU4ViVdcs6oqriw11UaDnDrSY9bK96iitY1FWNNZCiPTHI3VZCTHISi6mKyuKSv5bUgZXliK2LwriNMleG6OgSVlMHrr7e/77r3/Lf/4v/5Vf/epfaYeBv376jP/wP/8vvPzoJTFl3t3eUzQBbQq6QfYIhcI5jZ88bXuYXau+aR6SM3THI6//9Cd+95vfME0Tq2bN9XrFs+tLNJn1eon9/BOev3hOiEFCCWb5xvOXHzH2A1M/CCEmJUIMBDLOWZ48fSoyimkiYefUe8UwTvPBNlDWNfViwbKqRQJlCy6urlivV1xcXJ3npOvlBYd5ZFHXK3bbqzmvcPwztoTff/3gQ1Bj5UuJZg8V5QFBXPLVLNnWmLmVnrh7OPDQduz7AaImeYMKBav7I60d6I4tozWk0nJxdcV2u2HdrNAqo1SEXHP7/p6u7dnnI9v1gmZTgUpUVclyU+Nqhy0UWQWUzudqz2hLWVQkBGfv2p7lssJV5bfe2SPO5n1k1J5xCviQCBF0ktmW1ZqqlLyu7eWGZrnElhVjd8T7SIyZEDOjT+zagX4MhJCxdmY5ZmTYP0navDOKZeW4XhdoJvzhjvs3X9NsB9ZFgbOKsrA4Z+iOnr5rST5gdZZ52pxR9t33Mb8brSTANs3zzwxaCy05Z3FtsAQROit1ZpNqLY7sJ+hTkjnq2eWds9VS3/ccD0fu73fc3t7y8LDncDjQ9b2EmM65h+n02pIQGx7F5FkWapLfV9YVy80Fm+2G5WIhMsUkP3uuv3Pm+HAkH2XY/v7rd9y9ueHmzQ0kOQSrwrGdreoKV+FdQCkrhUT0oMTwvSocxAWxmLMOEeJAVZQYrSlcgStrynqibmrJegyzV20SmGeaPEoFyGp+rYquG2B+Gq4cjKqnDweG4Ijait9ozpiTe/j8D20U+qTHnuUjYkIvBgTCiRFz4XlHkzN1bnUyWmjkKbLbd0w+zqninru7HavlluurDZfbBaWyZ+s1VxSYsgRjmUKkG3rGcZDA1ZTwU8L7RExKWIZATIpxSrT9iA9z1p/5y+SY0+H3fUSnGBL9zCK+2Ig/pnMFVVXTeynSYnxMB9Dzes1K0ADSd2eSZyYqipgkAur0Gk7s1HR6hrICIx3cI0FFYPs0H6xZffCsKairai6eKnJOhBCJKshrUjIHTshBaowUMkVVnZMb0slrNyUKpcUSyFiylq+IxsdM8JFCP5LBHu52vL+/51//8CXv37xlGic220uWqzVFWYkjkw+Sxack19P7PEOrMI0ZHyb64TGO7ZtIdsaPI/vdPbdv35Bi4uKzFVZJIe+nkZwkRd4VFpMU2gnTNmew2lEVJbFZEL2XNIqT8YORQjelRAyBl4l5zquYzt1dh7YWW5Q0TY0rK4wrWTYNVVVJV641RmdoDNk4TFGREe2yip73b/+EH74J8/6l6wcfgo6CUlU449BGYMPSOQm9nALGScaVUoqhm3h4OPLq5oab/QP7oYcIU3BMGfzb9+SUeH+zY/l0w/rZBb/87CVXl1dcrS4oC4vWGY3nzZc73rw+8L7v+MnnL7m63KByEmbm1Qpmim1Mo7iTRyNJxaWlKmqmNNBOe/aHBzarkmVV8P2wjaIfxGevKmuGMTN6KJBqtXEFbqUwZcHzj19QVQ3WOvb3e4YhME4yJ+mnzPuHgW7whJhZOoGJMuC9ZxgmjocjdaWoF5anz56z23Xc7/Z89ev/zvrJC6pFTb3cwLKibirubib2hx3EUTSLzjD5iZhFDP4dBxIlyQwSEXWyW8mYLDZGMY6M/ZGkoazLR7alUpIRWJVst2vW6xWr1Yqqqs6CWT+LUg+HAze3t7x69Yqvvvqa4/HIfn+k70cx0U4JZgKLiH8FcJWN7FSlp3PywmK55ONPPuX6+pq6Wc6Hd+ZDXkzOmXdfvWWYBrp9x6vf/4nd+3vefS3pG1pr1quGi/WasZ+oixJnKuqqYbe74+Hhnu54oC4LFk2JilfUdQUkUizx3s9Qr6WqajDyMC4WNeumxMaCEBJjAB8ywzBnTQbZSCcfud0deXq54HJd87JReA5008C9dYy2pjMlISV0yoA7vzdt5EsMJzLKZGxpMLpAYUlJOmjvJfHCGGHZhiBEGbTEOiUU7RjoRs/dw5GvXr1hs1qQsuXnP/sxf/WTT3FFxlSSm1nUNaasGLKi7Xt2+4fZ91EOnbYdGKdMzAaxeIaQNMMYORwHCatWZs4o/M604XukIN++NCEk/NDx5R/+SF02DH83COGobnh3d880eSbvz5CmNVrQoijr6JSkcl7+M7qj53lymqUAkqSezy4jIURi9FgrrMyTF6i8jzlOKAXUHBF3aneVUqwXCxZNzaJuOBwfmMJEIMAoZtSj9zjrSAqMs1glVmiTH0W4rxVTTIQwUDRLlJbEmmwKMQsIkujuu5FytcAo8Uf++o9f8bvf/4H//I//iC5KqqLkox99xMXVNTFD1w30w8jhcGCKMltEOWKQrL5hHM7GAUU5ZxN+uHXkzNR33L97y6vff4G1lp99/hk6BZKf6IejIDlhhqfJIh+Z5/RFWUBZPlJ050P/cUb7GOW2vX7CKdw4ZklA8VNHiAJZLTcbMTovRGPsipIcM8bpubgocVVDk4WM2F5eslo0vPrqC467u+8UXH/u+uHs0EJovMe2lcw5Z3BFSUqaGLQkV8+sN+0SRZPZPqnY7QrMzmAazZAyD/2e3WQlOaBSqIWhWFlcJdWp9x6jDUYL+LA/dOx2B7zVjAFar6isnYflDqKSKsfaGSqK9P5I6hXmoClKzWpdMV4PmAqCEt3fvNLl4YgS16FJOGPwqxXj0NMfFa7UqBwhRckoW61YbC7RxpKTQtuKfjzw5u0thz5yOI6kpClsSWE1jS3wMTHGQD9OWKPoo6dUAnuU2fBse8H1+pLf/PENw/GGf/2n/8Lzz35KUS/49OVTfPfA1B/Z3XaMwRMmqWJnXugZavxwCzrRh1MSGERl0HFiHI903QNapdnlR8TqzhrWyyXNoqYsS/w0cnsroaU/+clPzgna3nvatuPLL//Em7fvePP2He2x43js2e3284zNkLWZzW3zNzYo2VwktdqHyBQTy/WWJ89e8Onnn1NVJWaOZQJxhvmwzs8+YHJmUZd88slLnj255rNPPhKXea24uFizXa+YUsSkCMbw4uVLwtSh4sjUPhDHzNhq2hxgJZZvwRWYDIOfsGOP1orSF/ixw+lAVSqIlhgq9OhReKk2s2yND8eRYzfx9bsDpECOnm0jhIC1KzDxhkkt6IsrqvUKpR3H1925eDklr58qZo1BGS3i+xzn+BmZG54qcKUlHzJpRTanebcUHfM0mCEHYn/k//znf+b9/S1ffvUV//HvnvDy2ZKffHLB29evGeJ7dr7i7euv2D3s2B+PECf5PEbox8S+lYy4ycPdruXQTkwesnJn0fj5ofrw+lATOW+AWp1m1fnc5QUf6fuRtu05HruZVAH7hyN9N5KSkGG0mvNks8DiRVHOPsJGWKBWZrynV6KyoBfTNLG7v0fBnBIvCEmIQZxikj7P6ozO8yEqn2WMkxQn1I8bp7MoowkpMoUgzjZaRg+ZTLaGMXp27R5ypDSO0lp0YbHz8DDGhEqSRpJnMkoYe0JMtG3H7evXHO/v+fHL51xsVlxfbXn57LmQSqoajyJmxZAV1pW8u7nlUB3OMWk+RpL3VFVxnilKISCdrTIFypxkIvO+QcYZzaIqqEsn0jGdmYaWdq8ZRpndpfwhCyEzdONcVEBZV5RVjTazhEhpdJrDm7U5Iyk5i0uPsU7cqZTC4DDMZhjOIEH2iRhGtMp4BSk6lDYo7UhKk5TGOAkiMM9f8vFnP+a+aWD6fketb18/+BA8Oap3fU/Gooyb8wHVnAKQEQszDzqgbaZaOOpFQbOs2FyVTD6Q2kE0UEZT1wWriyWLdU0iSkZcnHDRkbP8zjQbp5alODmIVa/AP3nOoBNfXIvEJEWUTSQ1MU4tVWlZrCqGTYlxzBj445ViJMdw+o3CPEpJyAwhkK0mJRF4WucoqwpbSmZgihltSzKGYQy07UDfTaQMhStx1rGsaoZJPCiHnAk54pMET8aUIBqapqKoGu4PHbuu56a9p929I4UNrtxQVyWLpmF/r0W3FuP8KQCcKqtvwaHqA02dfBcpeWIYiX7Ezs77WmvxW3SWuiworBj89sMo+qQ8J17MXo/eB8Zx4uFhz/7hwGF/YPKeEMKsz3Gy2OXZOIOZcx/4SOTJ+RwFs1xvWG+2rNYbgQDPr5/vNO2lszil0SiaopCw5q0/E5uaah6qW9GvaWO42G4Yry8p1ET/oEjTSJ5G4jAwGkvfjbKelWYYJmEDKiXve+olVQOByjAnOA7pcJOk03f9yKEduT8MNKWmMJl268gp0aiMo8MaS1NplqsadEH7pn+UCzBvnmeo89HoQKBQGfyquWjRJ12bE9ehLLlUgD4frCKRCUSVuDvssO80MXpePMs0VSZ/csH+4YHDkLjpLYfDA5MfxeUjSbEYoiJEhQ+RwWeGMdJ2I+MUiUlWoTqJzb91nchW354Dfpspeg6ijTPr1YczY3kcJ0IU6bTYoTG7jcxdiDXAKdldTC9kJHJ6BZzht77rMVqRnT3/2Tnx/mRq8KGEQ75BWKPfREPFzHw+WMRVWchMpzpEG03ImW4apbDWnsnIQa2NBuOYQmTwgbTfo41Du5KkDDFlumHk2A8c+p774xFbWDZhyXIpTjVZadrR0/vAbTuglBjTq3Hi9NSllFBz3JBiTmvRQgw6J8x8G55G+ARVWdLU9cwVUKTg8WMvcGh+vLcnQoIfBtHoBQkYM0ajs567dzHyV0qjjBQNPsgIydiCoqxn4pww1rWZCU4pQpR75IEcIyondChmZngka0NWImdRWlGUNcvVlqnvGe9uvrMmv+/6H2CHBvwUeH37htW2YknFigXRBJKdIQWVmDgQSGQbWGwszz/esFw6/ubvPsb7wN3tgdpUlNaxXjQUC4spFbv2lhDFDzMjdF6jYbEqefZ0w+J6w/ZiRV1pdEL4IJ4Zq1coVWPthNaBTdMQx8RwvOXp9YKmuWax0OxvB4Z+xH3wyMYoYumcIk5rkjHk2dbHqUycBsI00B4PLJ88ociAKyHLh75YX1I9dFhX8/7mNftjTwyGzeWGy82WJ5eX3O/3vL29ZQw9GMVAFKH2qLFVzcX2mhfPn/P845e8u7vhV7/7NV+//T23rzSLq0+prOKTl89o93e0x0zf+TOMcQoXTbN5wOnSM7zJCXaMge64w48tpIlmUeOso7CO9aKicpamsPTdkYMfGYLHupKybs6085QSXSfC/93uwN3dPbc3d3K4zZW6pG88bn7SBc5O9EqdQ3FjhqQMpjD86Cd/xdPnL1gs1+T4wfzoW+IPrRQff/QSW4pPaegnYS+OE1ZLGoCEj4rQ1uiMM4rtouCvP96gc0CnyNvXr/jyiy/47Rdfsbvfsx8tq01P1VRsLiYWzUBT95Q6gx+J/YGhH0jBo5UiRNFPoqUoOLYjb94/cLMb+f3rlr4f2D0UFGpis6xZrxbUy8T2uuanL6/YXD7BJ8sffnv3wVai5pBZiFn+3WgjHc+HG7BSWGvkJ3TEaEjF6XsEslNaZlACYQv5g6B4t7/j7v6ORXVAqx/z73/+MW/fvObmvuV9q5A4zcjoPaQ0+3cWBBKRiUPbcTgO3NwdGCcIycz+Pn8BdvpwUX6rI4TTvZbXba2bPR9PM7mED5IIYK3DFXZezoLMJJi1fZk8E5+MNZKIchoDkAlhYhx79vsHjFGUc2afMKG1zKtVEtbhLMsJPmAzwjJMYQ7cPr1W+YcYRUcR1GfN5CeUlQ5eW8M0jHM49UgKge5wpCgchSu4fvKMh4cjd7s9u/0R6woW6wu2V08kb3OxpLm6wtQVt+0D+aip2pqLywvWV5dsLi/56s17bnZ7jE84a6nKguRHsYjj5NOpCN5jjFgX5pyIORBSQNs5lujDvkApiqpme/2EF599To6RoiqIcWLo4mwGLx2dFAizUfzUE0cR0k8mY+yjMbmeu0GlNdYadg8HDm3Lw8ORql6w2V4+dpdZHHScczSLYdZ7KoxyGG1xrkC5AmUtpqjE/MMYsbzTBqsNzWrLNA6Md7fn5+svXT/4EDx2HVF5+qkn9wmvI83xPTqDzomlqcgpMExH9m1mGMSvb3Ox4PJiyeqiQSvN5eUlpSsxSgsk56RS6CaPtY6UA2PoMUlcORar5ZwraCmIpLEjB40fJ7pji6srsVaz5SyoDThryTpjjQxdlTZsL7fo1DEUifBB9aNmE13yI6PMWktZSEJC6h8IIdL3A8EHUkzy0AlHnnq54smz58QYeHt7zxQSt4cHuu6Is9JdtV1H1w+SQZYzd3d3pKpENTXrlx+zvrhkud2CU+AUPo1MIXG/7zjc31FUNdY6Xjx7yn1Z8C5G0W/lExz63Xstcj+By2QOODGNHYpEVThKK443TVnhjEQs3d4cGP1ISJHt1RXNYsVyvcFZSwqBtu24v73j9u6e3f2O/f7Ise1gJns8YvCnzY7Zv3R+nafKPIvspKpqiqrm6slTmsVyrjBnuUaaH7APOvcMdG2PmmTmOQ1zeOYUzoe+mmeDzhmsjlSFodRr1nVJXTYU1hIDhAl+/ccdD/s9t+9es1jcU1YFq81SZj1NRWGE1q6ixzJiDSybilRYFBUxdsTkoXRUG83Gej52a1aVpi4Vx6CZDoH7/kBxVFyEkuLpe5bdSOQUDHu+YzCjG7ImRbieTgLxucVQZE4O0EqDsWCMdJBn03R1QmhOv9egoiH1Hsa5b5kPn3EYaA9H7u4H6sZROEVIgVktwBgUw5Q5HAYe9i2H48DkIyHpGTv5Nw7Bb6zJD2HQx5EECES6aCoWiwpXGCbfc2j3oiOOgZgzNqm5CxQdcp5lN2iRT2kjf67w5zzKGCJjf6BtH4hxJGUt0gcrBssaJZv5HIYsrlOZMPkZotMoayTC6YN1OE5iEjBpwxQFnfExCJchGXSaY6I0BJXIJqMqzUO3Jx4j+6HjsO/Z71tGHymrBuUqqmlCu5JKKcrlkqKpCIsSYxQPw4h2DqcVMUWKqmS1XtHPZBFrNAOJpEDnjNIOpQwhzKHAFpQVAwqlMiFKIoX9gNorqIIFV+EWG6KfmEKE9sigpOjwk2caRylCnKVpGgkecAJRGgMpeRkjzXyI0z6bgyaNA3mcUCGQhoF+v+cUM5dAQgmsgSi5sMZqVJpZu7bAVBXaOpQb5mG6RukCbSzOlqLV/o6F1p+/fnCy/DCNhDxJYOKQmVRk0WsKbSi0psmamD3D1HLoE0Mvz9l61bBaNNRLR+kqGrfCFgWQGIcOM+PzqS0wSjZBH0diilitqJsK6xQ6BDRScSjv8MPEOAzoUg5UpWbmWk5CZtEKY6Qz0dqwWpbkSWFUYJ8+pN0/QgLqvHmIhqcoS4ZhDnYM4ZxbJYIy+f6yrtlcSjL7xa9+w8Ohxft3DGNPMRR0Q003DAzjIKL7GDgeJmqjyDRsLi9ZbDZUyyWqAOUUKXvevHvPOAXub3tJbCgKnlxfo5TieGw5Ho9i/8Sf2YLmcFCtMzFHUpSw3MIoSufEJsxaISGlQPATDw878SzUihd1zWq1YrPZSvfjPe3hwMNux+5OzIu7tmMcRk6ONd9EVk4H3yOULv/1EfpaLhpW2y2bzZaiqmb/xUd47GSR9eHVDQNMIo7240QMEXycY4szyYdz8oI1kaawrAtNZTWlcyjtKKsl6+0Toq5opz1vbx+o25aisCyOLYtFLRC1NRglgcPLylCVlmJZkR0onUgTMGdINJsKXUfMMlJosDrh1cDgPbH3mLGn10dW728pjy1Kl+S8ON+9fOra57UsFMWTtERARyWL9PHzVaDs/BtURtxsHu+FEgxM/jwakRtFzj6VOcE0TvR9x8PugZRqUl1IakOEFBRjgMFHju3Ase05tidGKN85AP9Szf1nSQpZ3pvRmqapJAnCSsB22x0Z/SCsV7R0CwpIeT4ET1Df7CmrpfAj+9mHPRPDxDi0DP2RmMR/2KvZoJoTcWsu0PJcfM3wqWLOPT3piz9oBIXNmFBKsvNiFonGycMw5hnH0HIYYjK61AzHUfTLXU93HOmOoxxWxuJDmM0nosRnFRXGGWJVkP1IN44U44TTCpU82moxmI7q7ECkJ0ms1yef23lsk3JCz8HCKCmaUo7CfOfRXSUrRdYGjMOUDQlFP44MvkenSFXXdK1YtD3qV8Wg21oj61HnOdRYn4v0s49x1GQvZCOTEzl4fNdhjBPWrFJEP8k+TJqtAyUYW6HF4Dx6lHVgHMrMzFrt0KbAuSCknX+TkPV4/eBO0FhNTNCPnuNhDxbW28+ojHRW4ySzo74PHHvxwFxUNdWyoFlU5JDJyqJcxTTIQxSTFoNbY9muK0Kc8H5AqQmVA/iEcYBzErEUBqIfUV7matEqVKlQNtF1d8Q0kfH48IBVFfWiZrFyuFhSLyvRpuWAOjwiMxJjNMNuzsrcK0R8ygQMuqhxVaBpxGTakshTT3aAgag1tjJszJqf/83PqJY1r27eU9RWujqr6VPgoe8Y/YTTGZUMP/vpX/HjH33OX//d36FLi0S7i2tIMS7ZbC5BlVy/WM4uEg7jSq6fXLPeXPD//PM/0x6EhCEL7JssL63lAMwpo1XE6iS6OKsorWFZlaicCW3L4bhjHCfGaaKoKqqm4fL6GYtGjNLbhwPd8cirr1/x1as3vL+54+bmlq4TGrJE0SiUEZjuDPDlR9g5z3OcNDNFXVHw7MVznr54SVXXc4r0oy9k+uCv8wOaM/3Qk4RKIIegj6Q5aTtnEfmeuozJjzgNb97c4rLCInKXlDIhJu5jQm/XfLSqcc5KQoSRGB1TFOiiAKUIGaaqQhWOrlycN8JycU2JYqUU12dHkznkM0XCMNJ3A+2x4353oB8y+3/9HdYYyqLm05f/0/lwOHlwCuvTSFei1Oz3GM7zMAVMPoLK8zxXo40i5Th/dmnuBuf1oC0ZRQgeYwOljnz22TOeXG0Zu8BhP7Dbddw87IkavFL0HlJSpKh5f3/gYd/yp1fvGXyUAxD1eI9J83v4ZtTQ+fozh995np0yhTHUTcmnn3zEi+dPUTnxcH/Pmzev2e3uCUkO3LGq5KBDyEKKdI7AUloLipCl+AshEIKX2fX+geNhT/BeoP0YZq9LgzP2bMou0gg5AE8HnolWOs2Uzmdgzpm+6wGD1g7ttMxkkfR4a5g7S1nrngljM8XS8nRzhfcT96/3rC7WlHqBK5r5GS+pmpqidJSVw5UV1jlU2ZC8lxn21NP2Pe3unUg9lKTZTCngYyAZwBRoV1AVDVoZ+nYgZvHATXokEohqlEIpa8gfHIJAUoqgLUOC3cOB3/3hXxj2d4Sxp1msOB6O7B8eQMHl1SW//Nu/5fnzpzSLRj4hYTtxLovy4zrIIAhLLcL7U+BKSJ6cZ9ZTzuQIXSts7TKVQvBTGpMih4MEJk8hUK1WVKslytVo47CmpB97Jh++d9193/XDDLSBojAY5VitK+JRYkMklVlw2BC0DNCjLEJjNFVdUZYlZVGQdZr98cQOR2WIWZOSIiowOZBjIiVhBGqdMSoR5pHG5CcRCMcJkxVRRbIOpDzKQZYT2kSUAUVEqYRWomXJQXIJpuAZpulbFWvmROHPMyFm6HsOhyOFMywLhS1K1psthRNRbA4eTAFGcr9OOX1XVxcMQ8/LZ1eMQZGSUIq97yEHCqNpSsv1xYrrJ9dcXl9hqhKMIukZztIWY4X91XhIbiGGBFoYl6vFkpcvNG/fvuXeGPa7u8c8PvXhPZt7ByXQIVqcTax+dKHJUQg/0zhJ5z0Tf+qmEfFrljDMcRjo+56+6+n7gXHyKC0GzGXOhBBmtG4W059bQBHDGd/FAAAgAElEQVT4JjVnQWj5vI3VrDdr1psNq9X6sUs4dYHMNm7fgnllaG+k2M6JbGdivi5OQ0i01sRZTqBnH8dJUC4CWT4Lo8jGsNysKKPE6xRFISbMsyuLMA3l8UgpUThxyXFlxYncY/LpPeszW042YfkAUh2w1YguayhrIFNWEp7q7DelOlK0zGeXejwkTuiEVO/idXmCwTV6thE7UWpOPzgfSmqGLLMip0BZWrZFybNn1ywWDYdjRz8GppBRtsBnRT9bUE1TZBg894cjh2PPGNJMhOH8AhUJvuPo8/3Xd4lbMnowaOqqYrtec311xXq1pO9bhqFjGnui9/go/2/y6cBNQr7QYCo3H0CC4JAzPiTatiX4iXHyaCOSF+/FFo0T8Q1g9rWUfLtEThLYIHNw0fupbL/z+q0xiHJu7hKT7Gt67i/jqaBL6VwAnogz2hjq5YLi/+fuTZosS9M7r987neEOPsSQEZlZWalqSSVEN7RhNEuwxowV7HrBig/T30U7Ldi0GWZgBlqghQwME01LKqlLQ1VlZUZGhIe73/EM79SL5z3nXveIzIySQAtOlUdEul+/94zvM/0H1VKrFqWdcONcJYpMTUM1qWQVQFBGYTIS6HLP/lh4fkqR9GKuBAWpW+4ebRDPQ0MoEoXJRISGo+aW+IPrAmJUUO77cRh5d/OO/e1bxuOBqtkwDiNj3wsISWleffMK5yzjOJJyLOAkgzG2SMPpGWwmIJmSxBlFLlVqLIIAKpuZTqFRpT0tuA/vR/b9jjffvmZ/ODD6kctnz7h49ownn3yKdTXYxNj3orL1EfNA+A0qwaZxqErx/JMVxmp8iFTWULuK1lTsQy5DVzlQZwyr5ZJFKyTHrBJGid6f0ZZMIgQlotg5iVFszDLgd6poXybUCCll+r4rRrIDThsikaxHYuyIwWOUwTmxoMkxSRDUYv+SlTgjd/3A7tCX1atc9JxP9I6UCD5w2O9RKuHDwI8/fc6ibrhcL6mbRoKgH1G2LkGwaFtqeP78CVolfvvLz/n1t2+52+zZ70UFweKpasPVesEXn3/Ky5cvePL8KVhNVJmQMw4DymJsTVsvSdGQXUsWCXJ6Ly3E6ydPuLu9pXGO4/YeUwxtH97McxiUTNdobO1QOaFyouuOhHHEDx0Z4U41bctyvWaxWpMpqNAQiwaoZKDdMDL6gKsr4epYufljkpYQnFqg01MlKUihaRSY9NMnz2XIf3kxB4/zOdEDhN58UIqmqshavM2CUuQqYdWJQN0PA94HIbIboe4oVxUXe4VxdkbJPW2l0nPO0TQNzjkZsheQjawTWeaO5VisEerHBEOfuE9+eohRpaoUV4N66WnWnnWQir2pHJU1qKwZt2o+wKkK1DpzataX9nwBQaVyj07XN5Wse5q5oiiO2qK1KFqlZsrzWCwbnl8v+eJHn7J0FXd3O/ZHzxDBtit8zoQhkLNwDe/u9tzcbek6TyhE+anNfspSRCZfZNM+sH0ACQrMVj1OW1arJS8+ec6nn77g8vKC+9tbjoc9Qy/I3OAjQ8iEKJD3nCLWgDOG2mmsKSmB1qJbO0bevrvHjyN1JXrGzjr64hqSUyLHKMpDOQo4RmVSCmSli1hHqYaTIBIfI1ybpiZnTYymSLDJumbQ6KyEBF7AZNIhzaWKlsCzvrjE5gqbHD5Ip61pK9brhQBjakHI55wkYdJaqvow0vea3VHWvZwzi1VR7bKabIq/jJROpU1sSEU4IbuANpIsPujvTsc2PYJZnDKGYeDdzS33r19z3G1BG6wxOG2pGtEh/eUvfoG1hqurS2IKWOdwzgkAqeiEWidB3RhTukEloVHit+m9aBirZHDKYazFaklyc4ygNWPf8fbb1/zln/05725u6PueFz/+ghdffMGiXdK0CzBB3C36/rvuyPe2jw+CTtMuF7T1ipfXousZ3Z5WWxZmQbXUxGbJ1WJFjBFjDM+un1NXUBmAJSRFDorgO3L2pHgkZyP2LdNQOqWiTKGJQEgjKXpq49FOoXTLOIJOYm9kAKsUy9ZhbBZVFFV4i4xFx8YQcyINmdglzGkUAynIV87oFFFJEUfP2B3pdCSmK5J2UNV0MRPGQOUTrtZYLD4bYvAE72EMtNrw089fclFpNruW3eEITyp0vqJtFyxWC569fE67dIyh5/btG5EOChGnDcl7xmPHeOgJvWfowVYNrqpYN6LLqnLmJ59+ypNFy6U1stBrRT5riWoly1IG+ZlWDHEkFA89lRLaGZbNJYvFgrquubi8pF4sxLm5beQUZQnQfYhsh4Fd37MbyqwriA5iKMaxsUDMKeCik8yaLOspZ9rFgrpZ8NkXP2K5WjOJSk/ybgKIkWQo5fyAwwRCPVFGXhOMqJqYiZQPJAyuSrSLzBWXGK1YuGp2A0l51rChKWokYgFVFaThpJwjCVKMCZ8R9C3C7UrkGcJPCb6pxAZjpMVmjZkDpDYaE5y8XiuCeuiOoco1MtbgKkcVHCooQkIQolDcF5ipKlMLfBIbMEYVnVXJzsky8xOSdGY4jtj1ktVijTYt+27gq1+9Yu8jXosUmh8HQvSzU8p224kwsTEYq0g+SqL6uIL4ofnLXB2ff0uedVdbPnn+jH/2+7/P9cUFTiv223uO+y3H/ZbbmzeErMnKULdLIBOCp7IaKlcEmR1V5cg5cDh2vPr2W27e3JBS5OpyzReff8rlxZowDBz2O477HSl6AhFjpN0gnY8wVzDWGVBZyPJF1Wh+trTmJz/+gpw1XR+5327w3pOUF1H6MRBzQhW3lpgNOYnwvYBVFMrVxCAzS1fXOKcxFurG0DSWRV3JmCZlaqdwthL943XNfr2g67ZsNhtxdo8BW9rCCenMHA5HVNJUtqGyDm8d3jg6P6JixilTgsQjsjzMM1Fyplm0/OR3f8q7xYLtuxu++fWvubq65PPPPuPy6SX90PP1N19zc/MWH0aqqiKXZG2+/9UkVi/o3WmZkja3Ku2PYtpeAHvWWqpKgqgtjh83b9/y7/6vP+WrX37Fdrsj5sx+3/Hmmzf0mz11s8BWDck2VM7x6VXz/fdl2T5+Jmh0aUWJrFZMgZGBSlVY5cgpFvueCuUM1tgifC3cPV3QP+hETp6ci+SU1N/SSlYFkoY7a4tFMhHnNKqAUVIsC4AyOCuwW2sNSotPGMVvL+cyL0MVNf73M1aZI6QySE9zhisdxJJJKeHlhCAu1GM/Yhrx9iIladl0Hak/4oee2hiuli2VTiwqjVGGyjjqdkm1aFhdrnBOk3OUbNOLx9YYRStwPHTkmKQVgsZpMR91pS2Sc2bdNth8AZ+8EDh7SrwdhrMaQlo8k3vzdE60kr67q0U8uq1rVssldV2zWl9g65K1WXlwU0iMIdCPnuMw0nsvxOAoA/wJ2jx9TZykc9+/CfmlUsa6hnaxZLFcYt0ZHP48AE4VQ6lgHm/nbb+JNzqJIAtRWo5dKyWGsNbO1WIsQSNlsUNi2ldtJHkqbbpJ0UaV1DiV6lQpZvuqLDszn/MJIHSK3bkQFMqDPv1unhqWp02VFpw5kwWTdtfpqM+lx2agQWnFlZP+YP6SEHm5GDIma2rXsFysiUlx6Dxv3m3YHgcOY6T3Ylbrw8C+6+iHUagSqi4kZxBPyPMgeLrbvmvL+aFQ+AOeoFIs2pbry0s+ffmCnBLdcWC/3TIOAzEExmHAJ4gYtHWA+Ila7ZioVLqQ9fu+53A4stsd6AZfZnwi3Lxarbm8uCDHwHA4lPsrFak00Q+dQFhaT8c2IZSnBvjpWq2WC2mFMnI8WgkcOeFTEYtXudyXoLMlkSTxKdcnGyPgkZyQI0mkFBA+KgXdrGVmajVVZUS6rxZni6fPP5EulLYiPp0TKkYCQivx3hNdIKkA1szP5Gw+mvWcwH3gopWkSwy+V1dXhOMRFSOvv/6Gtm158uSa6+dP2e62fPX1VzOoZ71ei/nxmUxdLtSHlGKptuUZGEZfnpny7CEUJ2tl/alchavEsFtrxe7unpvXb+gOR6IPZG3oDx0xRr791VcisVY1uNUVq9WSl1effs+dedo+Ogha1aCVZghHQd85xXXzBBVrlK8Y+xsR1cZxuXxCXdUo0xFzj0+BytYoA9oGYudRBCygrchEJcQHLKYAyZRedyTjUSawXFwQ40gIIwtbkbMlJkXdijL5pI4umb6XGUpZiHKCoY9gHK5dipnmdL3LDZhzLi29hLaGtl1wdbEWCoF1kDXdsSfngRThmVtSN0uiT/T7HZt3b+l3d8ShJ3ZHLtuKp+sWUyymnK2E12ItuXZkJ3WaK7OoHCL7uw1j19Ht96gMtatYNRfUTYNzlTxoMeKD56KqubAVL9eXkDP9OHL7858XYjFoFdEqEIkoAhDQCupFQ1VVXFxe0TQNy8WSttj0GGtmLUqFIAeHYWCz3XG72fBus2N7PHIoxFiZvZWgUuYsuizO1toTx6eI66asWF8/YbFc0S6XkEUQuuhezUEw5amVlGeD02kbfZirw3GQStBqW6y1dJnjTZFB/ux9QfpNwSGmYnsk1Z3MXCLGQOXsiURcUH4hwVhsYbTW4o9Wgpo85DIbBkWMglAmSusrxEScoPwwz/t0Vjx2PJscvTPnlXEq4C1pyU6L/mmOWlpXYbICQxbgXOyqughJc9mseXb5gpef/Jj9MfHtzY6/+tUrNt3IkDK90WRkkdp1HeMYGXOmNkYku3wA/Cxi/aFW2oe2KWiq8wW3BHxnLS9evOAnP/kt/ul//B/x5ttvuLt9x9e//qpUa4HgPcdu4DgEUHLuM4mmMhijZUShMz54Xr16xf1my+39lpwFBd20CxbtkvVqzZc//pLKGI7bLSGO83mOMaCUma9BztIqnAPg43kmitVyQUowDnFOXFIWycYQE8nk4tiiUFiRMTSKEOQ+ClCCkajWRBI+jlyNV+S2pa0rjDYzhaOqK5bLhbhqpDWL1ZLVr7/m7du3/O3f/BwfPH2QtTbGVDoAgaQDIQsYiJQw2DJvE9UuPV3KsyuWCjgoeNHaXT15gkXR1A2/+pu/4/LykhefveTJi6e424rm75rZ4eTLL78o68G0Lsja4L0vQggiged94HDoCCkXjVsxJM8xzjqvxhjathU/U6N5+/oNb1+9wrqa1XKFrhd03YHjdsff/uxnBTdhuXz5Oc9ffMJPv3z5XgfiQ9tHBkGF0Y1cFDXitBNps6yJEXwYyVkqJmsN6EjAC4BEaZRyjNELACX72UstpCjOBugySFZk4+jGiNaZClED0FZjXE1ScnIpiv3WNBjdorHEmAuFIaKI5Cxcp/1h4LDveHO75c3bLdud5/rZp3NJPrW0jLO0i4UM6S8vub5cc3VxQWNbrLLopMm+tBrikXqxQSlDyKK1l/0AIZBGoRJ0OmON5ur6UsAu2uCcQ1cO3dQEUxGVYSwVnzaO9eUlg6vIEcIgwsy10jB40hDw4uyKBqrJJgkZtGd7upTTwy0cu4A1CmtrrtqX0m6rKtHic1UhDtuT4n5JVkMI9MPAdrfn7n7D3WbL7nAQtfcowS+WNtFJzUQWcWPkWKdqxbmKiVNW1wuMrem6QVqeCVF9KQt9iMLFDMWbT+XI07bMvDJFODzPbUtVvN5SSowPVD8KrrQAIWa7nJzmmd6pIvEMPgjZflKEUZP4cmQYpd2dyVjj0CahjS7kbQmYU7cskVFJUquQpq8yx1OqKNmIjNSDpWcasQHBi0N2KoHVTKjbnB+05UQEWhIGpRRxckbPxZm7bri6WGOUpVEOZxtC0Pzsr37Bt69f88tXNwRjyUaTKguIpqT0Y2TmOMZILrqTOU/SfOet0B/ohk5jjimol2tgnC6dI804jNzd3fGrX/ySu7tbbm9v6ScOaBGCSNHjQ4+hoDhVRSLSD0e6457jYc+rb1/RDyMpinJR2zR8+tmPWCzXoCzL5YqrqyueP3/Gu9sbQgqzT6BI0pk5uQgxYFDi5vEoEKacefP2LTEmNtsj+8NRYPmqBLSYiKQyjyv9HGUkUVcRlaJUMkiv4LDdoMq8+fnTF3MyaSZACbk4vARiBmUMV9fXeB8wxvL1N78iD7L+aQRc54xDq0wYR/rhgI9CkMcYclZEn7CT+MAZAnsageScZjCNNuAzhHI5Q4z0fmAIgkiNSUyGXVUxjh5rTRENP9Gmcm5Pz3hIhJho2+WcIA6j+HWmGGftYYVQMNqm4c2rrzkcD4QUefHJJ1xePWH97AWvvv6Kb7/5mm53L7easbj+SD8O33NTPtw+3kVCWYwyGGWw2mGUDIFTDtIKVQI4cM6QVSJkcfjVVh7IqfTPOeDKxyaEmDq1EigzjRAE3UlR1TdKzESJFpR46Mng35CzJSVDikFai1mjVJQMJEbGMdD3gcN+4Hgc6ftQfl82Zy0ZKbmXyyXLxYL1as1quWLZLOVY0UKcTQqSIoyRsRvoqyNZZdIwoGLEZAhZ5jAheoJWpPUKKglU2poSJGxRSzZMArIZhXM1uc7U9QBxIAdxG8jFuSH58dRbN0bg0TmVh0WfL01FcFmg+spprLNcLBdyg1pLu2gFBekqATaU7G2qVGKMDOPI4Xhktz+y3x/p+0F4TDEWiaMyA5weoLN9m1BhwrmsSFmJE0FSBJ8Ih6Pwl4pSfiqBdeJJ+eISbVTmSeNOD1MJMCLNpub1OKYk7xWmducpi89pYiydZPOm6gSkWoklkNhyHifuWUpJqt4g/nPJgjZJfCStldlhSSBmXE9pAYaYiVm+gFnBR9QzPpShlllnOQ9Zmflc5lI1n/PtUqmWcwH7yT2fiVGAI3WlqFxFZWpcebbGMfLNN295fXPD7e6AaRfoygpSj1iShFlnhhQChKIYlE9NwYewpXz27/cO6SxKqrktZ4uEmDWGGGNxJLljc3dPdzgSojiOGC0iCFohY5QkBzuhaUP0xTd0Rz8MpJSpKkddSZv/6dNnVE1NVpq6aVmt1jy5vma/35JHwQ9MHaPz4C4cXI0yaU6s5kPKmfuNiGjs9x396GUmbPR8nlLKpaEwBUGL1RUpBIISYKEoKCmOXQ9Ftzh4QclPajbimpHLfNJDVlilaVvx+Qwh4qoKnwImRLQtLjoYuW4hMo6DcIVzFp3OUgGI4J3iPAhOF20KWDFGolaEnPDFFWYMgb7v6fqefhjxIdB1PdZa9vsDdVWVdaaAzAqKVBdTXWPApYzWlgnVLJ6jaV4DUqGkTOyC4Vcj/SjjnvXlBc9evODp51/Q9Ufu7245bt7JVGAizqrvy8webh8dBEkRkzVrW6FNVQ4mYasKpyt0eyU3kvbs+yODH9j3G+p6QV23VNZilcWpBkMgaxkSb44jx97jvUgjVdaJyKsKmGFk2YhS+iqCxmEVEnhRgGO77QgpyuxHi+q+1RaVEoSIihqTLQvT8mydWdWpeIPJw/hbX/yYxmoW7YLry0vauhHUWfGc06n41Si4WF7JjWO0fEYvWZJOUpWqpSPWS5a6Yb/fMPqecUw0S4trl2Tj8Gj8EHFtS2VrUBWH3Y7tbg8+YpWmqVr8fiCM4tjgrMNpfYIMp0gulUlMCeXcqSIrz/Nuf6QbEyl69OWSuqpZXz6h647sDx1oTVMjAaospinJ5yut6Pqe+82Wb1+/483bWzbbLX3XE/wos4vHLaISAM9bdcYIjaJqGg7HkZvbDcOrW6nac55nBzGcKsrJqml6EJra8uUnvycISSXmmsqK4LdolorM1zj6EqwiE2VlnkucBbzJKkcrRYpnC1sJMKm0sOAkFRdiLL6tma4PuMpinaVum3nuY81pTuejVIAxy4MsKkQGhSr2UoLDN2fz6Wmu5b0nFONVY+WY5Wfy2tkhnBIEJ/BE3RBzZEyTE7wn+B2+jtSu4en6is1mR46BX371DbvjnsCUkFkB0SQhUFPmszEkfBdIPqO8tJHlsp/Eus/bhe+3DYX/6IzFFCk7bQRdeHFxwWq14sn1JUbB7bu3Erhrg8oBS6K28PLZJeu+Zd8PDEnkwMhwuWi5vLxg2bZYlVk0NZ9/+mnhYFqqumW5WPHjL3/Cbrth7I+0F0uWbcXL59ccuz139/ccpjn6o3geg3RSsg4wjhI8zu6Vb9+8I8bAUFzbUQptbcE2yDnSymK0wyhZk2rXYhDkct/3mMphnCWlW3kOigavjAgUJ4eFxBgG4nFkuVqhaCEHlouGnC+4uLzANpYQPav1kpwyx11PHhIpZFrn6Mee0Y8s1muUlgRa5OfKCGo+fKm8Y/DyfIZA0IZD17M5HOhj5Ha75e+++jVvd1v2+wPv7ja8e3eHtZrt/Ya6rqgqR7toC0q0oS3oa1dVWFuJAXtVS6A0lqZY3GWlS1hU8zORUmSz27PvOkxTc/H0KU9evuDqk094srlnu9uw39zSLBZ88qMfsXz2gourKz6mXQ+/yUzQZJxNJBXRKhaB34gxDqUqrG6BSEhZZLZIuCAZfExByI7GUNuKFIVK4XPGxyCtUlUVfbuKDo+Pkd24L1qANTHqonxQxHozpBgYx4ExeOmbO0dlHEGJoqFWoIy0oBZ1TfDSKh3V/Azz4tlzlpUoqMj8z6LRWKXQWU+koSISXugQUZFDUVzQSLstKQzyu22VyQtwoUFVGu0aVNUQtYjj+iEw5gFtM7a2pDHi+5H7Nzc4Y7lcrkheWsUUV/qpQkkxFhRmLFWOwIfV2QKUc+arr74ti1vg8OSC/cWKVbNgv99x2O/ouiXtouXC+zIDkTZVXexVdvsD292ezWbH8dDR9+McrDhrR07IL1VQkNoY0VssFY9WmhgzXdfz7vaevgtFnIAyKI9zsJk93HJZ3HNCpfoUcDOMo5cForxeK0XtZObiisuFgqKIIl2DxzOPB4arp7cus1S5MSYAR8oZlwR5kvMpCBlrCgAmS3ZuJiSsRmlp5QgZuPhtTmgYOdHvPV8Tom5KAib3DT2BjUqSkctc8hR8ZGaUQiKGNCtrgHCwvI8oRno/sNnDOIrsWc6qJECZFAIqKVIK8yxHFe3SMcVS8Z+fxgnWM1Ez5MAezP3KVlXCh3TOyWynkhb8arUqfpULFsuW5bLF8ITaKe5vvkVFmeteLGvq2rFcthzHUeTJfJCksOw7CXEyVZJopFJJTZq3h8Oe/W7Lk4uXLJZLltcXXFxc0A8Dh3Gcr8dEnTnJz52epwdtaCRhiLl0EZDrRYhi+Ft4ozEldCzAG6TVJ+1quW6plNvWVEX2LwsYxJbrMjkrlOcKVdriKZBTIEYv124aYTNxjWXNc6ZBJTgeMtpCnSs+efkSlGEMgeNuwzhC9A9bh7bojC4Wwm01OlM1DfWipb24ICrF3XZHXyrCYQyiqwu8yTeFYytIZ2Ol4p+wD66S47NWFLmMlYRSHOc1+oxb6Iq0XUyJzXZL1w8SwGE26005FeCWwlUVq9Ua49xc6HzM9hsFQWsSETHUlQc+oLXD2orKLknJgx+pXAUafGzKkD6SEmhTU7uKMQuoIuRMSIGYPM4IQXSxaIj5iB8C+25PWy9pEsSoiEnPQVAlGaSO4yBSbt4T64bkRFzXWTFY1cUmaFlXBC8zj7Hc3QrFy6fPuWjqAuoRcvckpaQmephWQoFVkgHHWJChukhVQenFC6+ltgbVOursGUzA1AuUa6VFlgKHYUANHdp4Li4b4uDx3cCbb15TW4d+GmlMJZYiORdvQFkEQxJFklwMQXOMqMl5YXpIc+bvfvE1PggidPfskqfXl1ys1+y39+y2G/aHJavVknEcZ8FlpWCxaHDOstluub/fcnu3YX840nW9DNrLosjUSjkz/NRaAqDSQtCeFkvvBbp+8+6WvhOYvVZFPSafIQ4nlGg+zbmsPi2+mUw/eAiZ5AQta4wqQuBCaB5HCU6nruHDKmUyZ5WvKQKKigy5tLDmY9Lz70qBcFrs0cKbU1MncNKgLUIQWiec0oQgwCuxN1IzAOOBk0LZrzkATry0UpElVVT5S/A7b4mqsv/Bi0BACknaTDIKFTCCgt73hDByQLhnlBn1mGR0IAEjlDmy0C2s1jNgacoaH8qfzY3jcq4eZd5KSfWnDXVdc3l5KWCs5ZLFYkHT1AX40LBaL7hcOWoLv/47hUTdxHrhWCpLVoZdNzAMnsOxw6IgRsIwkkIkR0lIEolAxLm6OJt4tpst97fv+J0vP2W5XPLi6RWXl5fsDwfUZjMH8onMrQr5fkryPgSOmU+JVuQo45xYDHCNLgjhJO15YxIqR0L2pXIvYvdZhFWsraSBqTNV1WBtJQm/FussVWTJDA5VADsCGhrxfpCbcMYAnMnQNa3Qw8JAhUVZzY9+9BkZzf7Q8SaO5BQZH11Raw2LRct6tRJedPIsFgvCuGb99Jr+uGez23PoxHw5hCT80iRjIj0lEaXroko3Qykt1aA7D4Iii1aXgCg/E55tXUt1GFPi/u6erutRWgttKQSGsYiFp0xGlzHPAo8+A3D98PaRQTCT8pGUbRGVzYginhfehxpQeIyCylQobalyxFVNmQVGjJKeN3pAO8kKGg1XSrNsR4xZsagbVu0CHzrGOKLQRB/wZiQXwrdB0+2P6JxolHiDBSWmjDEE+pi4vbulriwXqwXdoSP5CAmBDocE9tT/Vz6idCDPhGjhjE1rWyxZeU6QiIXY6Wf4vZtQkFoTsrw2+IiuKirXUK8dqqnw2dL5QN8ndvuRt69eM3Q9l+sLKutw2vLi8qnc7AEqK5QUVxWUmFYSfHUJylmVlpqCUjWdB4vXr99x7CNaZTabDctFw2azQRXwhFaRunIslgtUVfy5lKauRI3922++5fbdHW/fvGW3O+CD+C5O6jqqyExNs7NTYDEzQMY5h7WGwQe22x3D8Sic0Cyvm1y2Y4xzUfZgGS0L4fkmwr2Z4CLrZSsGq0AxHaJ2ThbDYsyp1EmSTCkJChMZ3lpT5i6nxRwKAlRpfBJ6SNcPsxahQonyx1TZTTPJCBhNZS1aiZv26L18FsxBVDr12egAACAASURBVCnReXzcqJE1Q2ONZbVa06aItvVcJWtzqrK01jPSriD9hWuagFwmQsZgKyti2DnRjR1VcS231ggvF2ahcqWNtPgxDKU6slhxCVeJqNI00Suz++nfzHMfpR5SCciZvuvph4Gu69jtdjNwasr2F63j00+e8fq3fsTv/tbnpBxZrFr2b3bsD3uRC7Q11jWs2oq2crRNxbHv2e/3HLuBMUSx5kmRFAMx9Hz++edcXT/B2Iq3Nzd0hz0vXrzkxbNrnl4uubq64n6zLWAYufOmhfq8wyFLwuPgnxn8sXS4/Fn2SUkYNFfrK/FJHAPBi3XamMP8OmuNKMkAq+WTkswZtG7xHvbHnqqKVDHgqharpVoyRsyyUwx03YHDYYPRCbLH90f2SpI04SBGdFb0Y4+xhlrVrC8uMMbRtEti6HFGcdxvHwT5qqq5urri6dMnNE1NGDuaynJ1fcli0XDY3rO/v2c4dvjR01/1DN1R2rnDIK3U4BnCKJ2eEElZpOi0GlHqOCeMTEGyqMRYZedzf+6UszscENvYips3bwkhse8Hvv36G96+ecs4jGzvd/zyb35BsA1X19f8/j/54vGK8sHtoyvBlCdNRgN5kiVLKALkHsWA9MGlaspkRMhhMtyNcqFiRLHAaE1bLdAogq3I2UlLKQvhVAML18r3iIQ4ghG+2RhGdEpYJQ+TtVZQRTEQQ5T2KJFjB34Ufck4JMboiY8SBJ2ztFWmVpyaDGsFrJJCOrV5SgdtWrAnDUqTy0Myz/8FtKO0RmNl0YiK0HnGY0+/OZC6EYZAMgOqUdjK4upGqghyIfmrIjdEySxTqXJOlcrkrZbODywjqik+yAN+lBnbt05j1OTGGMRD8Hg8C4IGa0RF5c3rGzb399zd3XE4drML9wlRqU8t0TNKgdJ6Dox1VZWEIpc2pdwHuYA04JRhKwUqqzJ+VfMCoR/dxAIASnNrOCg4iWzn+XxN7uFTRi8LrwBfxJ3c4KwE/tnqpVxiVUSsdQyAYjQBNZHsMwX9yVkQFLI8qQgGlL2JqdBuUprVZOS+yuh0dr/ACWSUobKOnB3KOqnuklSCpec1nyurBIEbdSIn4fJlBOVotPCsdJJn0RqDNU6QwCiSyijMXGlrpQtvMmNUZOJtVc4RlVRXTMr850Hw/B6Ybr6zLZRgHWOcA8k5z7HrLUqJW/j15YLKKKpFi6ksqtczZUolL5+nxC0ixMDoB7pxCoIyu03Ri51QSR6GUeoc5xxX11es1yvqStRMqlpGMFLpnfFaUXNnIqaIjulBOxQm9R6hlKiCKhUzcIvTDmcqVI4klQk5lOCYzxZ4CYJZcTZSsMSspc1abkaZNwsdxGjxkpxa9SGMYt9Enu+sGEZZt6IEZJU1sQhlzx0GpaQNWdqWD7fJQMBS1RU+BIzO4lLhhAjftg1tu2A4doJkHjxjX/7diefgOIqNlCB75TwJ6juVrsdJID+lNKvu+BzmdHRK+jICFkMpEpHjbo9SGp8Tu/t7hu5IihE/DOw2W5IdqJ3jY7eP9xNMfYHgLkENKJWK99eASgNkg6JCIzOcnCI59sIxQhHSgI8j/TjS1s+p7JKL6gmD84zRcxj2qDwyjnvGYYtKiefLJ2gzYvRIN+yKhqZm323FU9C26MpS14LSu729Y7vbgVGMIRI3PdnLXGR7OBaDRnPK4qFYQQnsYCJ+90laSkpp/Bge8N200mR9cgFPMZFQqMwMWDCW0lLR5DGJoktK9Ddbjtsd+2/fsLQVzi1xWJpsqRHZMrJkcKq8J/LsCGQ5yDxBo0AZWbSMzEBiSA8XVaZ5VaIfBOE4Dh1aJTSZ2oq816JtUE48uZSypKSIMfHu5h3Hw47d5p5jJze4935WSpk+6lRJUWgCpwx6sZC218XFEnLGWS1STykS5+B1Rq7XU3AE4RDGMouVTQGLtiIV8SkfZJ8ov6u0pqkUtvjKxXGcW5mmoBENsqhU1koVOS3MZaZn1GmBV16SLuelBT9xSSfyr1S9IvgZYhFcQHzUcobBi71OCPKAz+jTQi86r3zD6Ik+QlQ0tmVS2qHso9KnNmoMAa08rUtFSzKJHmqSxAwoFIEWkadC5uVWguDQ96QMRiWqqj21fae7OpyClWuFfjRqMZY9b3NP16pcfU5Z4ukejOF0/NP+T++NUhwGOAwdbzd31I3l+fUlT6+uaA87hhTY7XYirC/vJulbgjGO9MHjEcNVrxRRW8gZZS1N21I30hJt2oZqteTzzz9nWTtU6Fm0CxZtS12f5m9TN2CeAcZU5vL6ITAGpI2fZaZtlC7KNTVNvcRVLc7UkDxBxXl+D6BtSY5LFySDyA5GWUtGnwhJYZz4J1aVpa1FODwXIv20F+M40HdHWXuUoEZDeUZThpERslgl6SQm0X3XUWVJI5VW4sJwtqmCSBVQW0XVJKgrXEFtN8sl4elz/NiLyEeaOIny1R2P9McjfXckDnspmIgEP+AHz+Z+z/440PeesXgfej+KbFoByc295vOFptxzKSfu391y2O7g1TeMo3gYphAYQhQesXI0j47r+7aPrwSDgBUq8fkUeK/8gJwDQe1Qqpb5FR4IJAJET8yBcdxJ3zxnLDuIEZ01ESM8kWGPyj1GdVh6yXaSwseRMUcCJQgqhYpFPBjoe1FLMCiU07jakfKI05rGNOy3HWP03O52WMBoR12d2lEhRKJ5BM4AyfCm4WpZkISUr4r+3USGDgVtRemBa07aj5nghQeYgUoZlq6G1QWLqnkg0pwpQ/5yuSe/QEpLTWstATeJIMBUhTlbkVTCZvVgDQqFYkBZBDWZHBKTC1yuiz1JBltJ66GuG4zSOK24Xlcs6zUXK8d+f2AcB47Ho9x0PjJOASafWplTeTRl1Slq/Jjo+1xANaF8lchedlaycZhcEOSEpHJWzisLmXWoUn35nIs4Qi4oRCWACaUxStO4GlRBbiKqQSEmvM4MXs5LykkAU1qqmbau57nk4H2h7pTqNEPwaQZFTJ51slLKa7Q2TNXe1B2IKRVuK2Qt1Y1Bs1S1XO2c2W237LY7+sMgbdqy2mlj5/srnc0NBU2YZgCLxhByFBh9uX/GcSxtVkVIiuwTo/YimB6D8D31VFpOWpKTgnTps06LEVM7uHSDmC74pAAj1+FxxQSnwCeP0tlctZyPMWbCceCvf/EV727v+PyTJwydJ5ua5aVoaKas2O2O+DDifZqh+SHJmjIBUVROGE5BPwXParHgYiU6xjmObDZb+sHP8zhBFGcm2S7rHEaJOHbMiqQMVUEvTtdZBNApEpEVVdVwuX6Kcw3aOJldA03T0jRtcdjpCvIzia6tlkQoBI+YINsyQnBl9itdLkFOlrWmCH2HIF2vGBMKIekPQ5SKSkmwpVArUsxgFcZWdP1RBMmzZrfbc9x3D56waZYZY5hpFUop0dRVCuNEx1RbmclPt4H3kuzY/YFmHAnjKHZPKmF04UaGwGHb0/vI6EUvNEbhP3fdEe9FmDsVAF4KSQS2Szdh6vYZI92ekDzaWZxWaFc0c7XBmIp22b53H37X9tFB8HhMGJOorZhyZmG5Qw6QPMp0aJ0w2pKyJyVRNslpIKWRYZS5SkQTxwFnoLeWrB0JzWHXQe4xqscPIicmMkSy6Ns8olQANCkUx42YOYxysWpr8UGTsi0K8YakK2LyhGQIyZZVSxapcu04jNJOmDzuJMMvWnbI4mBSxOZMVRZbYJYJ8yGgdMRE+ZpUSSbEWUhJuJLW0k08O6VnY99ELiTxWPZKMrTgJcNP5FmNfeLnpSQ0D620aDrGRBfP7KEVNLUhJjnWqc1niBIElSijOCviw1aLk5PTE4VAoamItcGniroyjONI2ziGIlA9DOMDWsYM4CyBUDijrkizWaLTMvdwtlQrxR0dJQosc6Z3vthmFm39oK2v0SeVe8ycuUsY0Khcfp6m14IqcPNIJgYJWtP8Khc1i7mlF/WM+vRFsHlGC+ZcZGZPDZtp5yZUHvG0szoLwlhn4WEqZF8E+ZgfPH1aWSpT0dZLadOW+8wYiyoE54k6MrXGK1OV2KPQyggQpAgqKCUtrVnBp2iZKhRW2znpY2r1n7UzMxPAIs+Loh/ifG3h4UxQELhyL6xXq/PbkNVyOXdI5u9PrT4U0Ui7Ninp2HTdyLEbUdlinLQBU2n3my6Qsghi1DVoG9FBnGZiGU2oHLGpOgmiIwGxqWv2+wNh6Li73TD6RFaWtl0JLaEEnaoSjU47WVAliGhc9VDfR+gPCa0cWosBuLWiZaqAEMZSHUprmvyw/V9aNVINplTGY+La4oee41FD8uRY0daCkvRBZNVywSXsdgeOhw4/CgE9hiLsriluOvJZ07WekLLajMSk6A7C/WV+hsXwe9f1HPY7fN9LMCvybTlnEbNOkkCaSR83AwW4V9VerplzGJXQKst5IpFjoq5HoRDFXDjHQgeSIOgZ+k6CYIwF7SxffhC96hDG+Tz6MM6KSic0nMFYx+X1JR+7qfwY9nS2/et//a9PF32uLj80aJSM8Hx2fKoMHvzj/KMfvl1+/3XfuWPlB9OsbH6f995DXiDJbJ5fNsvFQNFp/P7Pmw9LfeDYc/7un31w3x9qKf7gyx+99tHRPfj+uZHk1Bb7oe3Du/1+W4v83f/9Pe8+/f/s9z5qtx68hdHf19qYzufZgXzsyT29xcf/7g/t/6OLkr/nl873Oc2KKu+DZs7fND9+r489n991E/1/sJ0HvWmO/Z3b48umHl3LB9vDhOvhd8+/kedKGk7V57xfpd15QiU/3p8Pf7764MNyekLPf/54WfjYZ2aaS047833LykN+Zv7Bt5eGlj5bKvN7+2ULynrS8/2+NfmDt9RHPuT54R9n3/uO3/2BZ+nxNlGKpu08lj3ePr4dOnc5vmsnftMn6x/+JD44X/O/H98179+g59tv4kD8vXfZb/I+/wjbCajw993Uw399bJD/R98+sGT+Qy7FP/QyPvp9dfbn9236TIv0/0+b/t4E5jfdpmf57/fb8bxbUt5vqkr/ofsED9u+p+/9Zu/2ITrG/1ubxMr0vbf4+fz2B9/vH7Av6uEfZ9/7x38IPl42rbh5o747T5u2XDLaqUp9fLImMOEEsecj78MpcZjmdlC0lB99xnn1pMvaos9OeDxT6nCuYpK9+k23x7/xXXniD20/VIk+eL/8SJB4/uVcZoCy+TNn5QmEMEuZqdPD/96Rz92aPFcm7zk7PD7hHyr18qOfTd8sF0eXfVJI1To7STw6C5MY97RNi+oseZbff/30NbVrJ37dh143qdRMc8Y5WfqeW+KUUJ2y76llqvXHuZgJp/HE0BI4+KlNOL9uvg6PTuV8Pz8+3+ftmO+oQB8tNOcZ+Pw50x9nJc133t+PPuc82Mycy+94yE/XRc3X4Lu2FE+cUnV23z+qJabdnd72wc7+0Gc82ruz/VScGwhb6354Lfzo5sR31P6/QTw48WALfzhPY5SJw/nhbRK3njbrJrPnv2+IO69I/z5tiofvJX89xAbMZ12d/9fDzuIEIPuY7SOd5TO/bf9vVuZA0wiqTk8yXmU+okoHOmTF8TCyOwb+/a873h00971YAmklhPvP1oGLVvHJdcNyUdPUFmvLQ/CgpZA5e7oZfWL0kfvtgW4I7PrAzV5zGDS3nUVkhsTB2ahMpTO//4XlyVrz/EKjtSWomr8c/xkxy5ztv/tX/z3X10+Kxc5HnbNZv/LBxc6PFNnnh7O8doJ6nrdHkLmROl25OVtLqtgmKY0igBLVjv3uwLt396wu1jR1w8VqBSlyPOz4n/7N/ygoq5T4n/+XP2F36LCuZX11wWq94st/8iVffvEZX/zoM549uSjAHC0SZikTioJLjJHu2NH3Aoa5vb3nsD9wc/OWoR/w3tMPfbkqMueTOcVA6kfhXHqP1hljoK4MpEgK4mforBF38/US6yyvfv2G+82e27stu8HPc2DnDKvVin/5X//L2Z/w5cuXLBYty+VSRLZDYL/fz7Dz58+fc3FxwbNnz/mLv/hzhmHg+fPnsw7ipPjinOOzzz5js9nwJ3/yJyxXCxZtw9NnT2dk8G67F7BMW5cMOWOsnV3ovfcFpJJomob1es3v/M5PeUDEL2fp8XZ/f88f/MEfEIKgj3/vn/4eTdvinJiQqjIDHr1w4IahxxhLXctsSmmNq5wAEqJIcVlnC8+zyLONAoJJMZZFW/akXSzKLEf2MeXMMAwMfS92OBcXM1dyHAdQavZ4M4W7CjITP+z3s3WPcxXjMPIX//bfis6pVjz/4iWL1Yr15VVREirC+SWZOWzvi6yco12uUNoUVPOZCEWZO/3lz/6U/eGWrr9juVxijOZw2BDDQEwDSvnyO6CbAeMyzUoQ3Qo5J85YqsqJxjHCp1Py6BGjLJpZKVF4mYBXGbJvGF7/NmQRhP8v/5t/RdMs5PO0Fi1TJZq2E1l8sn4z5XvT36rcEVPiNIO9pp+p08/mF89/fXiRCoU8/n/86Z9we3fLZnPHv/jn/wWff/YFT588e5A8z6tWztzc3PCHf/iHMmOuG/6r//Z/oKrbE0CNKfEua9mD23oSVE8yq00R7/uZJxj9UKTZ4hzEtLbCCdQaTFGW0pNzh4FsS2KVSOOBGI6EcGT0O0DOr8GhlcG5FucanKsxupbrbITetN3c83/+8f/2UVX1x2uHhoGcOrK2pHIQk8q9yqIeIvKKCmJAp4BKPclDGBQxa7TOQi9oIskpVNQQIJvCwTpPYnmU5WfIMUNIqDBC9CTv8b1iGBRDJ4PsnCEmRWUylUvo5NBRk0dN1loAPWdZ82q14uLqqiyQZx/3PSfvQWV09j1ReDl7zXkYnIjE59VXLg/5eTJfFoCEQOPFiy4Col85jIEwKyQ41heXkOJcXc+bcWCS/K0tGIuZHZ4tfhjJIZCsxtVNIasKCF2VAXgYR7rjcUYubm5v6fuOcRzph4FJfqxuapiCYCduGjoEjJH7PDlRwidHqspSV4aqtqQU6PuR7XbDZrPj/n7LfjyZ9FbOlLvqtLWtaEKulit8EP6ZdU5cHgqHqa7rMwms07V8rLhijJktn0Sa6qGFjvxuYhh6xrGHDM2iRRUX8nEcZ3FlrRNhdKjkS7DQBXFq0MZgzEm4GGTROq8667qmbZsZTKSUKO0ok7AxMRmS1pXwOKfqHiXauLaqhAtYwCCkTDbiXiKeeSdB87ZtBVhQAEFTNZzrGuscTdPMoAdXOSbT4ckNfpobTSLlodAgqqqmt/3Z1SrVsVEC0imLnsaVijmz3cS5m2MqQUbGqYUE8kyVIGiKp2hII1m1ch6M2ASF3KP1IGbSWpGU8FJjtrPTCGSUzRin0DaXtSKXPc0owokKyZk7CgrS2VKpFIvFirZdgqKIWUi3adamLR6MWp8FwQK+EveMU6A7152FPAdBHj3TJ8zdo64GsNltuN/d8/U3v+bN2zdstnd88fmPubi44Msf/9Z3tqWHYXjwTs1iTd0s5iD4QM1JPvzs5VORUuQPc0QPhhAGojdEo+YgOAFXpudBGQmCor6kIIuhAFTzMxhNJviM9pGsekTUQWOUCHFLwlgUaHRdZNdEtarvug8e74e2jwqCGTh0mYgICBur5xJbZSWIvHISI9KiHEIhKE88GSAnUCR8Uvho6Lwhac0QKaTdzNSxPhWBpzbN5ETQR8sYM2OM9AEGz0xSzkCOoqywcBoNxJjZHoQekJQnL08VmS7Q+JT0gxvu+3rzU6vtwfdQJWt9//dF3OUh4EEyvvIASsSX4FgydMOJR5aLoknM0PUDNze3J5mvTz+V9qI+l61SPH35krYPog6xqFgsW1brJVVlyGnk9atvUTlhteL5Jy+p6rpI2AVC8Bw2t2w2G25ubnnz6lt22y03b18zDL14go3jzCdaLBYoBcEPMAyoGKgoMHCjGXVRkKksT58+Y3W55urpNfebDdvNnnebeza7A9vuIHZIOUNOQqHx7gRqUorFYslyuWKxWDB6cSa5vLpiu91wPBww1uDjyO39O/pe0G+3t7cl2KmzZ1hxebnleDzKucsCC9/tduRcWrQp4/0gVj3HA5C5ur4ufoKKYRD1fwVUxnFcr7laOBaLVuTCqoqqqqmahqa9ECcU3l+MFCcLqvM2r8jzCQWirqqidCOvSynRDf3s/7hoBRKeJyBKqXgp4upAcQ1xmKKxOgzDnBhMFem5X6FSirZt5+p3krPLhU8bk1SYUqFmqTbz6dkCCbS+WFGplOaFSpVrsdvdk3PCWsNqfY3Stpz7iSJTGl55EkJQBS6fyVpjTcWoIRJIqqeyYCorPo5RMQ4KKrHIkhNj0NqSCqcyBlk1pk5NKtq1Yf50CrfzfT6dKhXcFPim4KWLy8VEu9F6Mmo+EeOngKm1KgXRVAV+uG38MBhKsDyB/TR3d3f81b//GX/8x/87r759xf64x9mKY3fkp7/7+9RVPVOR5vVHTzZdZ9sZgX26hqeE4HRd57+m85YnezKRhZvchcgnqtD8df5Z6oT2hhPzNE/prywF82hNA6lQklLyxGTQUUwOdBYGgPA8H89/v3v76EpwLGZSXiu0z6CLxiC6cHNU8YgUWakQEXUKndEqEZIs8jFLoAxKE7RDJU2MCl2IuHk6mfMDMG2iyhETjEmEfX2U/RrjdH6nlmPEOkWzUEQMfVQMCVTOJAV5eTqu4tv6XlX3+N8f8z19tsDNQVCdLbzqFNentULJizm/qc/f9yy0c+wG9oeOzXZPVdc0bSNiwUXKbL7DFDSrC6gS2hiq2uBqSyIS4kDwB6z2HHZbbt++4a//8s8gQ91UUFTkN/dbdrsdd3f3vH17Q9/1HI+HuUoKIZSHXBMOlQTrlLEEisQtvswjFssVy9WCT16+4Pr5c6xz7HYH3r6+4d3bd9zd3BBiprWK63pBZS3rpmbVVqxWqwfoUGszxiZQkapSgFh3rdctTWP521/8Bdv9Pfebd5AVdVXz4vlnfHLxkvXFFVbXlIYZxlYsl4Z//p/8p1SV0E1AnBx8GAvR29P3i9mdol22s/uFZlWE2hVv37zhsL3l7/7mZ1xfXrBYtPiYi+2P4eLqKe1izeWTF9TNw3aTHJc8isMg/EuhwChCUdkIRSg5KE1d1yQyfhyxzmGdEKr9KGIUKee5xTh1VM6D6zkn9nGFoJSaA6JSQnMhZyHYF/Wbfujn+9s5N6s2jeNA3/enhS6DHweCr0gplpa2aD+ipEWmjSX6Hj8MhBgxk3Yqp8Qnx0D0YzFuFVqMVmANGFURkiVmSCoUnpioHymlqZTFJBFBsLXBWKFeTU+fsUWUmnyC2udcUAOlGjsTIZif72mFylI1KuYoeGrjljvNKGmVmtImV6Va1bokwilL8gsoc9b/nIONKiIVCcrvT8E058xuv+fnP/85f/RHf8SvfvEVu/2OTObP//zPOB72/Of/2b/g0xef8+zJs1lTMyvwo2cYxkfH9f5RnqyWTmuMrM2nxHtq1abSxck5zh0LnSnj1Hw2I5aEfzpSPQ3V1NSNEdqP1la+lCnppj75NJbgmlLGzL6Qk9j9+3zV79o+OggKD0fmB8K9koPQpV0w0aOkYoOQSumr4tkDWIIOmqzEMDcipHhd2lanoCSX4HSK5UaIWTwDU9bEpGZn4vNEReuMNgpXabISojAqC/d36kXOFduHg9r5f3+o9fne6/Ipc3zwOzlLJTh/5nlPXjGRjHnvs8qDKuUzGeEJjT4w+uK7F0rbjvMsTT5DG4NxsmhUTmZgRis0wqUyCqzKaCKH3R3jMAIizJtC4Hg8cjwe2W33HDb3jKPH+7HwIJWIZWZFTlpmPCjJxlSUB5U4BwDrDFVTsVjIzEsZTeoi2UeIkdZatNNYW7FerGjqiqtFw6qpSvvxdE7vNzf4cGS5XGONBRSDF3GFGCO//uaX3N694ebuNYtGXMWNgXbZ0LQN7XqJVhaZPwjv8+riAucUWov9UfAGH3SZIzqaWqyGUAJgmQQL7HxnZm6VYvQjh/2OZVNRV6Xa8HKNjtqQY6RuFmij38tUpWuiSWkkRlElUsgx+dGz3+0koXHV3C4lMZuvWq0JpWKasu+pXY3WD87hSaj7dNPMC1POReZKFhFXKslZSKK0Uaf/ngKl1nr2mjx7Mgh+IISm+DjKTqRCap86GJFcJPmmz5iKDEkipDKZOHL5tI+z+SxoI8+I1jCZ4yqYeaJKgZ0kCCktTiXzuInn+BBNIz+f15X3mkJTJqtOPzyb4yklC/vkZiNfU9uzgANL6zOn8gyrIjRBnmcmj4FpSk/nxwOamDKb7YbXb9/wq69+xX5/YOhHjDXc3d3RNDVfv/qa1WLNk+unp6UvK/wYGMdH4JHH3a95rHMa7pzOgJycebw8n6hJni2XBXdaxT/Q2z2d7VJSlbcpgV7OmS4/ne7povE6BeGzz5DdF+Wjj91+A9k0uViTs8E54XX6n1xWiFmTsiritxmrZcA/jbqzcmRVkZUTY91i1phKViUqHafyafpnylINJuXE0T1qfPT4KDO0KcupXaZ2WiSREMKromg4qu+HCP/Q9n2zwpTzfEHPF5oM5DNUo7Qgzi/aBzogJRBOAS4pcVAYfSSUZCIX/UL1/i+TUxFBNg1t41i0Nau2YeEstRIFiOX1NZ8/v+Zvf/nXvHn9mr/4f/4dh/st47HDmqJPmDM5iKBAZSdLKUWyds58Y5EM06pc/5SJOWLQVNbw5JMr1hcrTK3woUdHTW00zy/WXFrLT6+f0lQ1y6ZluVpROxFJNiSi0vxaiTpQSpH/9Y/+DVVt+fw/0PZeTZJlWXbed9QVriIitSjV1bp7BjMDDAEzEnwhfi4f+ADiATAjDTSQA9oQnO5pUdVV2VVZlSp0eLi84ig+nHPdPbIanBra0M3CMi2E+xXnnr332muv9ewjJpMZRMHp2WnO6OFXf/+3rDa3bJoFWqUh5+PjB/zsx/+Mjz78PwqROQAAIABJREFUEX/9l/9t8nWThhhckpvShiTYmPRuhYxoo9CVST2IrC/pvWe1WlNVdarGvM8u8D2lKRIhKEA9mnFy/xGT0ThLWzU0mxXdas51sAQ+pO3vJlZlVtRvu3ZXiWll6JqW5WLJb37zWyaTCc+ePeNoNqUwht5aSp2saQip0tAZ3hpYwGUe8vYhqYvYQf7uYIB6qORcVuUY7Lq89zu4WOus9K8UMYQ01O1c6kNmwgzcrZhihPVmiSzSIH9Rpv0iBI+MOj8LOkmeeberACPszZPTuxKio3ctvWuxrmOzXuP6gnpkEMJjdAo6RmuMrtIzED2u6xCqSDZnQma9ztRuECQh+iE4heAJEoKQeJGSbud93pveT3zDPpjCLlAIUhWuSNWJVgKlDuDQQxlAcYgA7dnKA2Q0wJBJuD7tEopkIL7ZLkFKrLV88eJzXvzxBd++ek3bdumeBsF6ueZcXPA3/+ffUOiSJ4+fYVQBWSd3vW5YLzfv7Rx/ao+L71VWYhdsDq/HTgs0BiLpWUjBMZ+PuKs1dJA77OUXhvgfASnxO2caTZJgI1WIQgEaoibGZFw8ZA4++pw0fb/X9wuCEeYbD/0gL7TPNHeQ6MFiSAQVgQuSprMp+BB2lUPbR6RyyFVDptMg4mBZcpBzDDChGDIiGBigXe9Zt2B9zpL2jw8CmcVzfWJ/iVS9+hBAeioOAIchyxp6igPOLu4Gqe91kUjSVl3X7QKg9x7rPb3zbLcNUgqqqs5CvgVK7q/a7pxj3F3NxFNLqhJN29I7B1luKR8pd2vQ9J260JRIppMRD+7fYzqd8PTRfUZaUMnI6nqdKhffM64mPH2ikUJzfX7G4vqGxfVF2ixdYnlqmWSXVBYG16bYebtJn+6tjGJQO4OYWHhlaei7hs06EmPPaDSjMBWzekQ9HUNdoXuHUTpZIpXZSsUUCR14r0cyqsbUVaoY63pMjJHxqEoVTwjcP7qHAJrOJlsdL1lvtrw5fU1ve+4dnWB0hUTx8P4Jo6qiGE1om+xaLpL/pfduJ1acqhxP13W8efuOhw8ecu/eSfLN9B5re6q6zFV3yXhyRD2aYcoSaSp0OWY8maa7pBWFNvT2LgyV3C3ELpEIIXl3Xlyc8+bVWy7Oz5PofFXtIcwQ8M6ly5OHv3WmxQ/rYlAwCTHcSZaUSvZGAwwbQkg+bIMCR9i7fA99w4FEVFUVpiiyZFjqT263W0KIdz4jEmnbNUVj6LsWU9RIpZOSVP7FrmtT8M1/q4gEHDFn/wnlEYiosH1H1zY0zRYjC4SQSOtwbPG0ST+VpNJE7JECqrHB6CIRk5RISfZgvIyErOE6hDSE2kGWwzV9r4a5+9TnPUvkyk6SkhGjAVyuVhMJRGqzq/hEjPS2pe22fPniS2KMjEYjPvrwB0zGU7Q2hJDkD1988yVN29LZno+efogA3p2+ZrNdst6s+P1nf+Drly/SPcjB2QeP7zzL5YYXX77go+cf89EHH/Phs48xZtDn/W6Bu1dFHnaY/U4zQGhDlbW3Kxq8NgfRBw8xa6rmei3kklocfsk9fHwIAQ9KNymyJDMCmZss6RiSMpTMbQ0RZQ60Yqds833FQuAfUQkutwHXeAYtSpHpwHvkezjAAMgcrCRNt1cMF5ns0dpAFAMJJkMjMY8EHCQcQ0kdCbkZOmRIEmsD2z7ih37eQYUXYsA6z7Z1CJl1HTOuL5SnjAf76gBZsg+I77/+1Hf2AWj/NsS0+Np23zNxztF0Pdum5XaxQAjBeDxOQXYMVWWQdxYaOb+MO9bokC12fZ+o+gdGtrvrdOe4k9GsUJrZuObByTHHRzMe3ruHDhbhehbW0zUdbbthMhtTzkbUowlVWVLVJbZd07YNsYso75AIiiKx+qRUlPWIru/TklT5afJkv7S0KWijMYVJNHs8IfZINKoWmPGEoq4wgOpdZsxJpB6sdrJ02IAvkR6K6XjCuK6YjSaURUmIkVlV540/8ujkIdHD7WKNx0GM2N5zM7/GuZ5Xr7+mNBVGFUxrSaECPii6tsdZlytbiwvZ9SKv8L63bJuG09N3SCEwWlJX5e4eG63QSlMUNWU1oihqZBZdlga0GAERFz1aaQR3g+DQbztMxIIPzG9uePPmNavliocPHibma/a1JMOCPidxUiX9SSVz5Rr2yWqClPZw4uD0PlS5A1s1SLkLhDvWaP794XtlWeb3D7uf932f5MHuPCYxmfj2be4N9sj8WUMl4LLDBICIAREdwrWEqFNIESpX6QLb98mdoO/xVciVmsWLHk+HUgM73COiQyqRkqrMiA6ktRl88mgUJPap35UkcleKDEl12OF8/9UwmP8RuQpMgZzgca6jtw3GJJ88pdMISIqvnqbZML+95vef/4YQAsfHxxwdnVCWFTrD0F3f8fLVSxarBevtBi0VRmnenb7l4vqUm/kVn33+Je9Oz+i7nkF/N4RECAyh5fWbN7x795aLizOeP/kAMBmx8d8REPhTQfA7cOiu1TNoy8qD7wfILReRe1vRg3dpnQQpshyeSk4hUiUsW+6aXvlZP3zqh0ovFzmDTjIi/z/HobwNDaS27/v63uzQb0+3rBdrBsBPILLenTjYqFIwGkpbGZMAsFQKisz3iT2LRXrflKvneZyg0gXMvRJxUNcNt0Mx2DTlB3v3+TKTlAQ+wLyxzFc97y636XhjulghRooycPKjyPC87muw94JdjPvv7SPm+7+1e48QA7bt6bqO+Xy+69sslktu5rdcXl7Tu0H7M7EMZ7MZP/3pjxlVhrrQw33lEAIWpM3QeU+zbbHOY7RmVNdUZZkglRjvwjUCptMZZVXz9NEDnjx6yHQyoi4V0QUCkrI0uFbgt5ZFv0UZQ3VvwsPnH3Py+BGT2YTNcsl6ueD09IzeWoQyuwsiiBgloTSU1YgQIn3T4awkBo+SyTVCSvJ4gUYSacUWFQRxepSdEQQupGpGCIl0AhUBUSDwOCEZSncpJX/9i19yVBUcFRXqdgNtT18eUfzgQ9STh1y9PufVl1/w23XPZejoRSAaQRM6mtWSv/nP/5EPn37IT374MybTEabUXFxd0m47nEtZu8fho0uzTj7gO0vTtoQYOD6ZYl3L6flbujYZSI/rUZpNVRpj6ryZSm6bns462s4xMoP/pGIsfTYg3L/atkVZyXa73a2d3necvTvl26+/5pMffMr9+ydMJ+NE2BnWZ/BJY9fZzBzNYw/kajKzSodnSkpB37XEYCgLgylLlJIURmc3A09d1fTW0jQNbdsSYrJiSmvR50qKbPuVU4Xcq3nf0Tt6h+1aNstbtK6Q2rBpNtkaKv1cioQy+PUt7eqC7uoli0ZhY8GD5z9IVRyCxXLBZrMBBJPZEePJiCjmbNpI31lKVRJkIIiGyahM608Eou+SJZNSuVIQdNYjo8eJbJsmBFILYnAJeo3pWfU+u+W8lxwrEdE5MR96fgZBIUFExx9e/IZ3p6958/Yls+kRD+4/4s9+8Zc8evSMsqy4uDjld5/9mr//3a/4X/+3/4VI5OGDRxSm4Gc//SU/+fEvmN/e8Pb0Lf/zv/93XN1csd6seffuDY8ePODh/ftcXF7x6tUrPvvsC7bbJglSB5dqWinz7K/l3du3/PGrFzx7+pSf/+zPMdms9uLygrPzs4PEPxLxROH3+584KBfjUCjkIBl96rcGIDoEDqOSBrOPntvLM9aLJfOLS25ubul6i0VS1TVVXfHgyWOOj0+4/+ARdT1N5uxGpV56jLt9O00f6LxTu33hsgu44QDZg+Cyn+j3fH3vSrA0kVAkSn1SOtcUpdkxdQYcJIrszk0Kgl3v6XvPxqV8y8fkk6cl1CUUZYU2GoRhF5L2Xdbc+4q43uKsw/WO1iXyjItiN6Aeo0drSWkE5TSxFVNwyOE0k2eU0d/pod0JbYJsOJqD8EGDOv94/zNSR9haS9d09K2l7zratmG73eB94GaxZLlYsVqtQcg0mOwcQSo6Hzm6WXJvWiNGBaVJHf4oh+owBXvvwGYyTJpJSwQNNcy1vRecBWCKgqIsKEpDUSiMSaanPnqct2glKLSi1JLWOVwE13uklhhTMp7McjIT6Lo+z8QlY2IfAlIZfIgUIaBNkUg6ziNI3nZShtwwDzv4I81KJWut4FL/JUqZoej0UEVBEun1yTLKS0WskttCJLJZbXHLFbdNg7q4RTUdKkZKCUZE7HaLaTpOtj2q1lglsBFuvGcbYXJywsMHj3jy6Am9c1jvWbfbXVIbYhIzt8Glh9m51P/KAuc6G5p23rHtuwS9CUG/2mKUZqQquu2aTVnS+wBCURm1m1v8U69IYnqiUyKkc8+4bzr6rqXvWgqjKY3BmEEIPqEoUqTh46RrGN6DgUQeT8rjEjJly8E7HOBsTyhMeg8RkwiGSFZUIajsuZhmAodAGmNS4hg2Q6VMIuYog3U+3bvDI0i+YtnfzxOdoG1bTGFyIExpriCwvD5HdBvai2+4vLG0VrCZL6kmM8rRmH6bDLKlUDvISyhyU0kSRSLCBcGOYyDEQBwLBJu/n1GhkNc3Qu2qjqESTyzHgYQTDnUj0z3KgvNCkIxupaTUCikCXd/wx6+/5O3pK969e8VkMuV6fk1E8hPbM5se8frNN3z5xy/4/We/ywhRSi0/+/y39H3HaDTm3ekpr96+5vTsjMXils72vPzmJevVEkHIrhSRrrPYPpGSArmSHXgbMbFAF7cLzs7P6Pqece4DX8/nXF5ff2c17skw7O75PgAOqBnEvP+KGJExmRkvFhcsb69YLW5oFgvazYb1fM7p2RnrzZZ1a9FFgSkK7p8+4sHDxzx/9iFPn3xAXY+pRzN0hs2HlbHfdwfeyZ50eACXHWyB8R/RxvpHBMFZHRkTGZnIdKKpqorJ8TSppOfhx7QAxS4Iihi5vFxyfbWmvQk4n8jEWgtqI3l4rLh3f8JkMkYXNVHmMYsh6OQLH7ynXa1YLtcsF2tut5HGwrqTu/MOIVArGI0kzx+N0rByFDublRizhY8s7+hqHuZAQzFLfmgOLml+YHIgzE2A9KAF2rZleZvsWWzf02zWbDYbtk3Lze2atu3ompaiqvARttbShjVr69Hn86SiEkaosUHqgmhS5puOUuG8pe9SAoB3aBF3w92RYaLmbuajSoMuC3Rp0IVE6QR1WG/pbIKOikJSl4q2T/R0u+0wdbKSmUxmqSdGQAmF7Xu6rmez3dJbhzJF+uS8Fp0aAnRM7vEkl++YYXApBEYMPn4K3zuChihj2qhDyGQIT4gCR4MWiqgNHB2lqx8j5xdX2OUt69evMO+uMduOmVZU3YayWVPKAn8953ix4ai+h1eGrQt47xBK8YOPf8inn6Svy+UNXYbXSlOhdCJw9CEpE4UYk5de8CCSZ5uKgt5brHe03mFj8lzYnp5TSs390ZT17Q0xO7HX4ynj2TFDYpoq3u9mYX3bIcqIzma4MUaWfZdGB/qeUiuKQqNNHkDxqfkqlUhjAz5Dqt7tNsBEYnFEVEr+MioyeDraXmONhphMe7WSCJ1stbQXaKnAFNmuanAjSAnpoMAzmWiMMlRViV2u8QdyfQiBNAVCpdm/ZCmVmMcjakRpdv0xES3X795hlze0Z1/y7u0t67WlOnrJ0eMnHD15SteuCdEhpUrr1VlMDoJCqWS6LQVRilQph5iYsfmZ7u0QoAXe576fSlXdroLN92anFkOWIvN3iRaFhEKnZH64dqVJbitts+Y3v/8Vp+dvubq6oK5qJpMpF9dX+Oh49PARX774Pb/53a/5u1//KpOvJN7d8Lf/5T/z7t1rjmZHvHr3jm/fvOH09JS2bZFC8uLFC66uLigLSdP0CATe7S22EiqYzkHKJMRhO8d8PufNm9e0XYPzDiMKzs+vOD27uNMC+k5LaFeT7L+fLm3qgKaI6JHeErst599+w6vXL3n37g0aR/SOYFtev3nJ/PaWi+tbQkjo3eToPk+fPOeHn/4I/2c9x8f3mRx1HN17TDUyqS88PCsincvez/QgIB/u47vj/CeGQwVwMkozWvfGntnUMqoV42NLUUBRiGQkuwNykx2NtZ6Rd5TOcn0TaSLYCEZ5JpXng3uC+/cs02nPdGLyxi52+DCkcQjn4OY6Mq8D88LDtUdsBetWJVYoKYOf1PD0RPIXnygqk2jjAzSESL5kNpZ81om9w3wOtMP/h6rvT4GeMgZECATbErUmCsV63XJ+fsk3X32L1llOqttwcXHJar0m6irReQuTlXYU49EoM2cLmm3L3DXIRaQ/GTOaHjF5ODr4VNg2PYvFmq5psX1i8A2Bgzu/efBSMg9SKXwQWJeCZdd2NE3LmIB3PZv1LcvbNUjD7P49tFQoISmnJ2gh8H2Hty6pldQVQmuapmOx3FBWFePxCEhsQSUCTZvIKGBwfUuwIZE1RHIDTxWgJ1ibqm6hGdhjaQA76xjGmDwr5d7CRgjB7PFTuumMJko243vgPepoCveOkcLw4NlzRsf3MM+f0tiGbd+xnS+YicBJWfLf/ct/jVaG5WrFZrUBIZiMJqky8IG27VKPDYkPKSBLqYkZYpmv1giVVFDGozF1WTMdz/B1qpwZV3z5u1/TLOZ88PwRH/zoZzy8/xcoU5GYkRFp8kD64YOY2ZdFUVBk5qnRZjdA73wiWaTrkL5CDKn/5RPRpe87NpttWquZ0VnXdVbGUYQ8KnB0NNtVd96n+UNjavrcc3YuEHzcudoPs4WHwXsYh2i2W2z2j/PuuwPKMSqcj7Rti14tUbpgMp4wqmtMkWYEN5s5y+u3nP/hJaHZMFUOb4E+cPXtK+bnl5g/vuT4g2dQarxyxOmWaDxObbG2Sw7rUiCERmKwfbJnil7uLlj02V09JH1NIdmp2CCTnFsMAR+S76iMiUkaZSRGeSfNNEpS5OJay5gkAmXEuY62XbNerWi2Lc5GGnpaO+d6ueDm9oa6qjh7+5bzy2t8FAifquuu7Xn1+g3zxS1BSm4WK+aLZZrR9KkybWKHkmvOzs6xnafdtkCygZIZVRkCVjg44u12zfXNBTc3Fygl0KpkuZqz3a4wdxDsg9YWQzESvxMcBUNwdBBazl5/wbs33/Bv/93/xOn5GdfzG37200959PCEjz58wtNnx7Rtx5s3p7x6/Y6LixtWt1d06xUXb16xuDjn6Pge4+PHfPzDn/HoyQf84NNPMnryPoJyyL7Kx5cTgP8vr+9dCYqY9ThVxIiAEg4ZLTJGZPDIkLg6qQJPHmAyBIxwlDr9rYiJNQoJMjUqInHI2COC3kkLDZWWEGnUghgwwlOoQDG8FzAoog+3yyioTaQQjkJIjDhgH+WFLKPfX6t9XT+Un/uM4s4FTQOfwTqC7Wg3C0RVgy5p2pb1esPN/BatE6NRxiQr1luX5s+MTnJWKg9/FgVCaoTQROvYdlvkskH6Fh8F1cmj5CknUjLb95amSf3AQSA3H9ZwEt+5X846bG+TR1vbJ8KKt7Tbhr5tqVWCp5xzBJ+y69KYJGeUr70QCoRCKo3OLE3TO6wLWOsxRao4yrIgaIEMPSE4hAXrA4PHnhpkxHaDsCErmyTNwbQiBjg935OBFXtn6xGYakKUmuK4pZydpDm/6ShT4w1RFYRaEu8/wK2X+K5FB8m0LhlPRkzHs2TiuV7nzia7THq/6e+XRtJ+1HhSY78LjlIblDaM6wmjesx0MsXLxBT1ItJsVqxvrvAnNf1myWa1YDRTmbQybMrvPYh6rxiTxMWzeotIV6frOvo8wpBm5/YPf5SHRKm4k0cbVGiklHfm/LTWd4LbUEW4rAGaxVruEK/uyLzBnffbB0m+o6wSY1Zm8WmdKaUoixGFMWil0vUIAdu1rG8XxLahmhQQE3pg2yYF3N7i7t8Dr2lCSzetUVg0luAc0eUeUUx6vPiEj7gQ0rS6EInZPDzY8uD88jkO3nSpH5WIGCEkiDHN4x6sxKHlc9CMGmYrRdYqDR76ziWGq0is43MEWukkrrBp8X7IVzVlUVCUyZtwtVrRdUmgoCqqZP7t0jPjfaDre/o27QvhQF1nf+H3aBpAb3tW6yXvTl/RdQ1aV9zcXLBZLzieTQ+esO++dvDj4e8IEHi8bVgtr7i+eMvVxRu2qzlds8b1HV2zZbspWC2XjEYlhdE8fviApulwNtCdX+P6llXXcfruFYvFLfXNAo+gaRoeP76fRQXuipCQW0WHJzyMlvyp8uUfen3vINi2EddHulIgpKVzkaZPundaD0OhBxqiQIiStnXICFr59DBahfcC66GzgdW6pestm2W7azbHzNCSQuX3EWw6aFqXZ50E1uZMOCYupZZQimR8u7xesREH8GXaUyAKnKgJk4OsYQiAg0YPEc/BJhWy6kOUbFcbtqtbzt5+zej+Q8rZMbernpv5gqubRaKTC8GkUoQIVT2inEzRdYWuE7sjbU4G7yD6iLCW26tzLuen3JzMePRhy+jhc2bjGqnT+6y3W25ub2l7S+88boB58lyReO++xwjLxZK2sYyLmn6b6OLtdgO+RcSe8ckE6yPW+6TBV1XMZjM677De0naOzvr0kGqDQoOEwkashb53KN1T2Y6njx4g8TSFQypYN5Z2uUUImQxN62ontZcg7wQwO2/T4L7SaWMXKvXmYoCQe2jv9dFUPaOqZxypMb/44XNOpmNU8CwXa5arDctNS9s71laxDhXRlBw/fciPPnrKw5MjNpsVbdvQdR11PcI5x/xmvnvIk0IKu2AoxUAacVgf6aWkrsaMpjOePHia5y9LtvKKtm24XW2RIVBKwYlRtLe3fP7ZFzz79CdMJhNOJmPknTnYtBkXRRpXOIS1BvYmEebzW5bLZeodyr358GFgM1n3cxh9GHRIgTtjO4cuG865JMHWNLtKsK5GOxHxZKJsd783/P0QaIfPSWzGiFbvsV53BAaPJGCkYFxWFNogVXZylwoVIt1yhW86eqOTFFtlkPSoECm8wm0b2rXjYn6F6rbMjkfMntUE3SFVTFK5AaQXCJ/WUud60AGhBXU1zhqfGT4lT+JGj4/gvGVwNNFofEgQqo0WnKTOtyy1ekLuWKWNN0QS2UYZ6nrMeDxDxCuWi01SypGp2lyGNULAfL6i7z3WReqiYHZ8wqeffJL1YzVlVXI0S58/1pdst1vW6w1b26TiANg2LevVJiVwOXgPgV1I7owwbJst55fn/Mf/9B+YTY4oTM3vP/+cvnMcTf98n+BklOEf7qlFZNiyXp3xzWf/N6+++ozbmwuePjlhMqlYbxti8Jy9O+Wbl1/x8P49Tk6O+bOf/5KqGPH4wWNs/yu224au7Xj1+isiEqkrvn75JQ8fPeXpk/s8ePiIo+OTPaI3bNNiSMD2SUz6UeQfGwe/Nzt02YHqJDOrQScxalxSY1EeduOOOyWddJB9SIoQQkWETEOvPnh8EPhgcF4gHBDCYdwBQIpEqogkvdDOKmwwtL2jd0lCTYSAkpGZEVRaIhWsbGRXcbxXKEVpk2zaLgiy76LnXwy7VEfkDSv9srcuqX4UZaK5i+RMMZke88FHn9C1LTF4CuGSqoGUiGpMNJpoFM6n4/GRXS8t2kymaVqCVqjbFWcX14hH96iqkhCgaVq2TbtrFO+ZoIOG6V31j0jk8vSKKDTtukPpVIFtN0sMjkpFJvIDVPAU0ymtX+MFtH2Di4mJam3qN4zqESEUBO/pbE+pgZHkg+dPqWrDeFwxm42Q0TFSWRQheNpComWJUZKyLpEm9aVMVEiSyoy1CWpSecwlxAA+sZ6E9+k+mEF9Im2ov355zm1jeX15y8oLHk4L3r38irPrJVe3GzbLLdb29F2Li5HpZMxf//InPH94n3icro8pSqbasG1b+j6Rfob10HUdISdyvXVoKYllwfHxMcZorHDUbcdodcuDrscohTIKGy1BSsb1mHlhiFqDjHz7+jW/+7/+wOMfnfPRhx/w3//Lf04pTfZu39+vvu/RWu1IZ8P91DmQXV1ecn19xWq14ujkGLMT/k7V3jCzN8Cfg1PGkCgNQUuIlGUPwXbQEjVmr9HqvEdFUMowHo8JIaRh+hwI9xXPfsB7P197uHFE+q7DOZA4qiLNOOpuSyT1YFPiHJHBIkniDFsbmJUVVVlyNFuB0KAKbN8k2L3taS+WsGqJm4ry2GBmGlMaRBTYaCk0iShXlUThs3C+z6LWKUgEIsHvB97ff8ncV/XevocMgVEKI1Pve1CokUJho8+Efo0SGiXSfiUklIWBmAbVnfM463F9IKgCGTWVHjEpp9RVxdFxYr9WVUn/oy6jQR23zYLOdrT9mn5j2aoW632qNg8qoUFgPZKCoXOBtrd88+obqqJERs3r129RqvjOef/XYshwn6WISALn777m6uxbXn71OzbLa3xoGY9LRqMRD2MSgG+7luVqSdu2nL27Yrv4L1lWMyXf906OMUXB5dU1TdOy2awwGjaF4PXLL9AKZkdHO0RvGKyLUe6deQY+x8Hz9P8mavL+6/tXghaEhcYqdCF3DhBDZhXzvEYY9PBEIu5YL3BR5kyfHevKh+T2YH0KnCFTl2PWFoWs8JhPpovQuyS8bZ3HuZjm0WJExkilEgU9Imj8APXEgTSX72wkqvcUY+KexDPkEsBumDfmnqEP+2MsijKxl/JIhjYFR8cndG2ToEXXZsV9RdAlTgnsMEoT0oM3jH6IkOa8XIwIH9l0ltvlmtl0lI9E0vWWrrdE8izlezBADvl3zmmz2mJ9ptfIdN236yWVCoyNZP30PnWhUFWFKDqikPS2SxtDnjVSSqLKOtHwvUtKaYVESQ33oK4No3HJZFIgg8XiGFUGay11qSl02sBVmQKFKg3aRmQA7xw2637uobUAzqcsO8+fHXq4xQhvbtacr1q+Orvl0+cLmkbzhxcveX255Gy+pr1dEqzFW4upSh4/uEf8+X5uKKnQJ1k5t1ljbRJzGKDE3qZB7ohIAVErKi0ZjWpN0eeiAAAgAElEQVRGdYULLdVmTbWcM/aJ1RcF6FGFqyqCqemsY9P1NF3H2eUVv//iDVdeYUPkL//8l0RTYcPd+xWCvxOshn7vEKDmi1vWqxWbzYbje/d2Q+5DYBt6dsNA+/D1fvW2qxTEXghgB8MOf+ND6lWJsLOwGqrGYYB+J+M1EEky4/COVQ9Jh1X4gBKe3vZoXWBth5CSiEnszSH5yWvYR4EpK2qtMtSs8NLQeEt0Pcp73Kal7xM5ScYxRmjkNI1uRRkRJqK0oKqSrqgPmUx0cNnzkTP0wQYLrKG6GJAbEf5EXypGyNVgDEkqMGaJtxiTjrAUKkHpfZ+o/hkmJab+coLfI0pqtDIYZShNTVWOGNczHpyccHw8RfmkIdtbx3K7ZLVd8fr0W5bLLatiu09CxLA3pE1tEOOPmZRkreNmfoOWiujgdjGnLid37uMhPWI446FtQL5ewVui75hfvuX6/DXz6zOi74CIKZO9kVYlne0xjSbESN/2tG3Lar7c6cgJJTFFxWQ2YblZY50l+A6cIdg1m8UlffMMQTgI6kPVsg/Kh1XgnsH6Tx4EI03v8VvP9cLiY8moEgidgqAa+qgZppUy7NpVLkqs14RYQ/QQHL2TtFax7CtMANUnzb9h6t/n8k0e7PZ98Dgn6HvDtuvpbcy4d4LRqGraKFm2qe9HriwGceyY4U6hFVU8vMGZ7Xn4L+lQIxFrPcF6bNcRUahyzKws2EbBpg9cXM1JVnOK0XiKIBB7kzeMSGcd1kYsMQ9DCyQKYjZvkaCnYybmMXp0BNWYs9s1CBhVBXVZslpv6a1HGJ10WX1qhO8o9/n8Dm+7RKRei7X4mGbJnA/YAF2A202Ho2BSa+p7xxDhdrNIMk9aMpmMUVKhs+Cx946qbnZ9vKbZcnQ85eTkmLKI2GbF4jxw3FlMUTCaHqNMidQFvfcIJTBGYVdLQtvRbDbYINAx9dwGYV2Vu4MqDonU3cW8FprG1KiTyNxGWPdYoXHS4KQhBIcsDObohJ9/+hE/+vAp/+Zf/zdUlUYoqCZj1us1t/N5IiD4sBv+Tg+PyBt0ciswSlKVBdWootQK/+05oxd/5Oj1G2TfgUvMwfKTH7IeT/m74pzf/v3fc3H2juXtnJfXS74+u6Gvpqiy4u++fsVHH3+ym68bFtwQQIZAOEA8A8S5frvm+uaGs/Nznn/4IWVZYozZVXSH1dmhUIO1dvc+Q+ArimQkPUClIdxVOQoxEoPDuf2gPCR4FmC73e6G5O9sJkZTlPvKIsbIer0B4fFOM5u0KYGKmuBCGpMwPcE19NtNUldRiqPZCU8fP+KorjmeTOmUpFGK8z/+AXqLH5iZAtymZ90HmusWg6a+XzEeVZg6CWYXRUHfQ7QRG/pdgNMirblC6bzDkdoMuZpyGZIWkGeTD5OxwPXyDB+PGVcTFrdrnE9zcunzutQGqGrq0YjG2aQUVVdA6tc7l7AAoyXPHj/l5OQewpSoaoIwI9ZtZOINVtR4lYS+x2PD46cfEoPj4+efMBr9BqW/4o9ffZV7/nGnySqkIPqw63M655FO0LUpWbVtGjmL5fvB4m4ASWnFMIoVEMKzmJ9yffo1L379v7Ne3iBj9gANAedbrHIo1dL3ae1NqoLJh0+xtufi4pJ3ZxfczJcoU1LVDZPGgpRUdcnRxPDsYc3jByOen3iOqx4TG5CaINMITsiC5TETJ0XIWrwZJo4E7qiu/AOv7xkEBV2QtE5wsYl00VM1AWVC1sMjafEJkYlYORPZiVxLtl2gdxFIs3K9F9xuHHUlKbQYog4EsBnr5QDzdQicDdg+0OW+WIjk2xNwvmPbSoKXDBEhxoDPv+eTMCnSaJ6+FwQhbbZDPyCEgMtfXdtiraNrW3zX5jkoTy8kbYhcXs3pOou3gfFklFhj0mecPmLRu0J0qNpc/uAYwSkBxhBjCaYiqoIYFE3nUgBIWQVlVdH7vWVJejgHxZ7vvpxPM3DW9Qx6fnhPEAIX0/zmeKwYTWcJfowDfyAx3UpTooTK1PEkm2XqOkmKBY+uSkbjMWU9YTIqsKbAbpdMbEDogrB12ChTwF1uEUBhFNp2CGchWBQaLVQajs8qM4PBaBJClogDV/lImm3rmpbNfM7q2FDVmpEuoOto53Nc02LKiqISfPLBU374yXMm4xLne/rOpbw/RMbVIDYg9hZAOVEJORDczOcIYFSaBGH1PfbyBn8zJyzmKUh4j7eOdrNl7gXf9mvObpZcL7b8/ttzVtZjlOR4XDGrDNJaaiF2w+e7E8vr47D3FomYzBo1xhB8SH1Bdyg2fUgJ3xNZhvcC7lRoh39z6CfYdd1BdZflqHIFOBzPUBUO7+O9u1MBDu+z3zWG/lJSnun6FikV0Uu0Smzz4Ae5ur0IhlYqCTMUNfX9JxliDRhT4HRHWZhMGGKHv8YQWM+XuNiDdszKMV7Axm3pe4d3PjFphUSFZKuUoNGYe3qpPz6MckSREC4tZdYc3QdB5x1/++u/5Xh2xIOTh1zd3NJ0LW275fGDh5RFgQ0+mR13HYXORrJC4lyfnNxj5lBIhVaGshwxOzrBC8Gy3bK8WGNFpA+Bshwl0RFp6aOgMIrR8UMeP/mIbecZ11M2bkVnu/1iGshNwz0IAWcdbduhhCT6pKrl7F1G7759FHdFSaqSUx+7Xc+ZX7zmzcs/sF3f4my3K3iGKiitF5cDU+qrD7J99ahiNKrTOJlPCEjXbKnLyKj03Hs24tmDEQ9OKoRb0m9vWN9eQvUwFQ9oiC7fs0D2JEqGyMOcaCbdfd/X94ZDbVBsnKLdwtY6Sg2FcUiZZsNMVvxWuQGcLJUSLdsHwbaT+DCY8UZ6D4utJWAIpYTe7QJfuwtcIldyEDXYPmBbT+c9NibCTGKjRpzvaFuZ/MPifqDShVQN2pCqJV1onhxksKkSJFdTSbrK9wm66axltVnTW5v6R22Ld3mIVmscgqvrGzbrhu264d79Y+qyYDoyu4AXZMkhlJKsO0kmBkLglCQaDZREUxBkQYyats+woBBIpanqEf16m102wo7BebB837tfDuuTFZAYeoYhkaZdjHS9IwrFZHaUGL4CjBTEaCF6pDBZqUGiVQUiaY52rsd5iypKyqpCFSNG0xmuKOjbBV2ICFPQsqVrLNvWcnmzRIRIbRTTIlDg0SHJh5VSgiex9rJ3nhAgckWKMXfgmOh6bLthc3PN+t6ISag4KQy0Lduba/AeKTQa+PTDZ/zo4+dUlWa13tJ1W2KQaKWYTSZMptOkU3oYaGPIeq+OsizwLvngRcB2Hf3lDfb2FrtepczTeZx13DYtlxa+vd5wtdiw2HZ89u0F5bhiNB1zf1pzUhdo2zORSej58BWH+xrZSVnFEJNNUjYKDjEHQdvvTKDjTqw1r2cx2JGFXS/vfXbdLtE70AXt+37fh9TFLgkZ3meYCzwMeOkY/O5zQwzZjWT/kiRkxztH1yWj0+CgrCRFALJ7BDLzDGKCIIMyeFNRHE8S7Oj6JJNnOqjA25QgD55+gsDqZpnUiYJldFKAFKx9j3MpuEkhkTK51gxBUCm/S3qSn2ZKnBHJvzCpVIk8s5YutXOO/+Nv/xOz2YwPnn/E2fk1682G+eKWn/7ghzy6/yARzJyla1uqukKaNIqRnGBSRSoGBrDUlGXF8ck9rlYrFtsNX7/+Nm3vDib3HyYBaQR91gN+8vg+j55+TIiK2fgI23S0IY3HDJDo4DEqhUiVYudpmw6jcl++d7jyT7hI7MRCMnSORIpIiJ7N4pyr02948/Vn2O0qVYeDYfSw5+14Cj5dPUli8AZPXZdMJmOs86w3ac3ZpmVsIpOR4PmTmif3RhxNCmK/oFlesihPqZ/cTwpT0aS+YPSkCd0UBEPIQRCZfvb/RxD8pAisCsc8BkYyUit4MlUUpaQooTZJYd5ohQ0BF6D3Cus1vZVcXDbkxIDKJIHZSSG4PymZjEpi26aeIJLORVyAJgR8SGMSupQ0bU+jehCSzkbaXlLIQF3AJw9gNtaMao2IftfbaruAc5Ft4+kaixf+DuEw5Oy/bS3r5Srh1pstvfNY51m1DZ21bLs+VXw+KYqUowpVGKp6RNf1dP2Wq+ueqiwQ8TjpKEqFE8MikjtVi1RypeFepM7DRoaoa1AlUhuC9HiRvj+ux0ykAnULAppmm/pWIvVMU1/0btdClQVGGop6hPQeEYaNIKJV2nywDmxkNh1htEoBEE2M6dyDDwQfsMESgqCxlourSxaLW96+ecOD+/d5+vQJ9/7FP2c0fYAxJX18SR+v6a43vDu/4vXpJW9enTEdVXz05CHH4wlGGYRzCGEQGHBJPi/BUFlZSIDQyWdykM6VQvBnnzzjw4ctH09KChGRvmcyqSmKTGmPkQdHY/7Fzz/hlx8/4NmDMX23pSoMo+oe0+m9TFdPmbD1ns2mobN+59sHSQA4OIcEtFFZC9KxLhT9k0dcnUwZHz3EA613/Ga+4LKzqLrgJz//MQTPZ59/QecD/WLLH16ecttERHnE0w8/4t7J8Z3nKzhH9BqhJdv1Bu8c2/UaIQSjyZjp7AglDavFChGSugsi0Hap4vDeY0yBMXoXuPre7vp9w/C8lEmarW1b2rZhPB4n9aeioO8TC5TokRKCBKWT7KFUmhASulHXZlet+5CCca7H+I5FFAHrO7Z9AwSKoqQejaDscGqMkh5LTz0tKaclfetZ9T0jFNHUVOMTZAyUruP+ow+w4xV+u83amIHlcpU3PU9vO9pbS7fsGU9GjB5EigeGgTCidYEaXEFI4g42uES6gn3V64c+aiCoSBAevEHnXMOHwOdfvAAR+ezLPyTrqyDwLmLXDeOypu0t3bbh3nRGLwIuJpFx7yzOJ2RGIolR0XSRECRH45rldouIaQRisVpSlRUPnjwjINh2lvVmiw8RUxbU02M++KTir/7qX/HFF7/ji89/i/OWAdIcWMYgsiSqQIYUdJVURB8J7r1gEeIBlBgYhPFcs2CzvORXf/MfmF+8YrN4l5SGZGozKKVABJq2Qwq5u87DyhhGSoJz1JWB4ylF1YNPs9f3Zx2TkWc22uCtY3m7YMOad5dzwtff8IM/h8nRfSYns3StSQzdGG2yosrOEkIoYuyJ/9QuEgK4L2GU1Sm0ChQqMjaCKn/VRu59zUTaxGyW++q7QOwc0adLWodIEQRjEzmJkZGS+FFFcIFgI871acA2V3JRCHRQdBKaQqA7Qethk/VuixCRfcRUgTIGjE5VlieplNg+QOcpxN2GLyQmXKr4tiwWS7abLYvlGhcSWaXpLb336V+biQHeEYTAhARXKSkJ0dP1FvBYOyaZHIv0b94xRBaFjlonmE8mcYAoIUqFkAYtJSqrYAQhCEikNhTGMJ2Ms9JHlwR5pULG7K3m3B3CjJQKqQJaJY96KVJgLIygNJLaKBSBvmnoCwlRp2Ff3+Fc0spMjf2kwO+8Z7lYcnl+zvX1NWenZxBgVI9oO5vGLMoxxtQoZfLslkU4S6mhNCJ79rGfJ4uKGFPmFmIgpS5iHwSdQLqws1+RQvDjZ48IwPrxMW/evqXbbimMoqwqRuMRksijRyd8+slTxnWBiIH1ZotzaQj6ap6cONq+Y9t0WOfouo7W+rQ5eZ9dMyLjUjIaldw7PkKJpMW4CZG2KDFlyfTjH+BioGsazi4X3DY9J6ZkZBTBJQq7CxEhDYtNh1lsOb245na5ocxO8Bycm4giCSEcwKKD24OUyRXcDQ4IMjFYkxB/UnkZHM4H6vghDDr0DAcCkhDkv5F3SDGDWevwuwnylDspt+H9hUx7gQx7RmIMubd/eF4ytRq8sxk6iwgd6boSUyiUikkFxgjqUYnWUI7GyKomFhVBpFlCLQT15Bgj9hLL3id0ZxDpFj4dQ/QRuwq4MlLf1zgiTuQRgjhwDQR3ZLnE0Psb9oi9QVwuuXfnFGOkbR0+pJEKKYu0CSPpmg7pIiEkV5WqLHGuJ7qQ5/z2wvdkFwut0rMcg8P3fVLkya4lTdukpD1Gorf0XYsU0PfjVNEpxZNnz5nPrzh794bNdpnu2QHsHXzYnVVh0l6SEqI/1UrJXItMlhF48JbF/Iz5+SsuT7+lXd8QnUUW+16+yHNaQ+AVsEvA0vUVu3VbmNyHTXkcOkZK49EqHad1FhcC3km66OnoGZ29wgbP+HiUk554YOnnCd6CDClxiW6HUHyf1/euBD8QglhoHs0KWuEIwjM1gZEWjLWiQCA9yD6kgesAbBx20WPXPQ9X6YRVjIxNpDQwsop6VmOA9v4Ev+7wN1u4XWdlEYETKUiooPBGYWvJ1SbQiMgqwtpGrA10Fz1dr6h6TT01YARKC4wEi8B1jqJPfnFzBgAp0vQ9cbPh/PKa6/NLVssVV1dzhEqByqMSfOgyASEGQugJIVBYy3hcUxgJOLq+JUZL102IUaCMTFm0NihjAJkG5nWSR0tBUUAsd8PjSbsxwW+e5OMolU6bfF0xGdeMRiXjySTNkcWIa3vsttmdFaQNMPmZSZRQaCRHRcnxpORonOAug2d1e0UIG8qyYDyuWK+XbLdrFssF49GEk5MH1Mpg+4azt9/y1RdfcH5+wWK5hhCp6zE3N0uUUJwcjSlUSSE0sWmoo+deKRk/OaGuS45nJUokJX8pDTHI5D1JcldIzg3D/FZOIvy+0lBS8G/+2U958OCE6bTgf/y3/56vXr4musDR8REPnzxmUil+9vNP+Vf/6s8oKs2m2XJ+ecvF5Zyb2xVvTq9ZrNbcLBes1g3WWvqsDTr4QRaFoq4MP/34MR998JS/+stfMDIlNgSurUOUNeV0wi/+4q9oesvtxSWv/+a39PMN/8MzQ2y3LDcN56fneF1SjWYUa4uXa8RXr3h9doUsqh38KAQYnfpcfdfv4EdipCgN9bjawaXBxXQ/pcIGn/U397AVxB2cmlw/5J3+HxwSbsodZCaIaBXZu4UndZuuSYbFZVll30CZYVEwxSGMHPHO0/d3nSS0lCgRwVu870A4fNOw2SSiTlEqhHcIEzm5fwS+YDT5gNHJQ/R0ltIiqSl0yeTkMd6UBCJap+Mcj6bc3s5Zrm7pmw0RiZQl/W0S9rj3QYmVkV52SW1JpcpGqdRRj3Ew4E2myiL61P8SQ89OEoLY3SvylYYC7yO2A6WTOJM2Ao1Ah2wgpFOrY7NOzgzBJVEEqXKCEhUiGkb1FKML2u2a7XJJs1jhOst207As16joEz/AtTS2JbgOezRhE1M/9tMf/Qzb9ayXK968/ZqubwgDKc45Nps2B3mYTmqqskIqxdXVzc6RfXdmkd38llQRgiP0K169+A3fvPgNb1/+gULBqDZ5/lFlVmoWGvBpHCUGeaeHLIVEqJiUeJSkKDSm0mipKJXGbTtidPQWovME51k1lsZu2LpbOvX3PG/XPPnwITuiCEmiE5kM32UYfAYtcVCe+h6v7x0Ew4fP0PaI41Ky3S4Ifctxb5m0nkkIKN8lU8uoGE0nKCT+oiduIDYqKcLEJJ5c+ET2V51ArRRKR5wUhK0krBWsNdEOZJI0t+eLgJhoxKjg4aah7zxt61g4Txcj7SYiFgGpHf4o4EaaflYQq4IoNNXsIco4wCDEPuM7u7hEqAVvX59yczNnvd5ye71IlGVTUI5HBAQ2HARB3+Ndj3Oa0ogkROxdMnHVKXNVIm3nSkQIPaFLtHAZDUUxztuMRIXBjhgcacPH556okDgpsJ3BSoGpCkZVSWnuMx7VKCVomi0vvvicd+/e3rFF8X0DAUpZ8+BowrQqeTgdYWRIJseuJxLo+waxSZtX02xomi3O9Zwc32M8njCbTVMPp9A8f/YY73sePriHD5Gjo2PundyjXd9yEzt8V2LbFRJLpTwnE0mlK1RRp56G1ASbFDRAYIXKrKpMHopxx+YNOfIpvYdrAvDy6paLpkOJwOeff8XFxTm/+MmPeXz/iG3b8PjBEZNRwYsXX3J9dcl6tebs7Jqb+YrVpmW56XawVwguJzUhu5dEyqIgeEVn5f9D25v1WJal53nPGvdwhpgjMrO6uqqrmuwm26IkmoJMwIavfeMLX+pvGjBkQBIsAXJbkkn2zOquuSrHGM+4hzX6Yu0TmdUEzCZAHiBQlVmZFXH22Xut9X3f+z4vX3/V0+23kCNPr66wUqKkJWz2dHcrfj38e1Yh8+W+p9nueDIO/OjFt/Qxc+sCZ1XFYBuYFUSYUpkYBh5uXtGad71MJfxWT+xSJQu42ljFvl/Q9buC9YqJfvC8enULQjNbNkXEkQ9V3sF9KCZrhZ1+/20UUqkuDx7Dt+SYnA/zPjmpQMtdOQzl7yk5hcNKMbXcijTrAOwW0038rkVCCEHTVEBNTtXkdik2I1ImT5g6N2wYdg/M24raLFgcn5HqmiwK/1dJjVWa44snMJ/jrca7gRQjlQ0InVG1ZP9qS05TWnzOpDEQ70fUIlE1km0OkJhUr2WMJUSpxnIuaS1FmFN42Y/12qH3O72UlHz8wUf0Y8fgBjabDaN3CGkRVlO3NcdC4aOn8yPWFHGglQrygUwEGomRlpmpWDQzjk7OuewzdTWnmZcg6qOjIz54/3tYrckhEn1JC5nPZ0hVOkvy7IRnp0f8sz/9MXd31zg/EIJju9ux7zpev3rNvtszDB3HF4DwONcjTfo7/sfHgxng3cBufctnv/rPfPKzn/L628/oN2tSXbIcdZXQE3mnbHgKb0so87uYvXL/TBSdlKirEqw+kwWWUJmK+5uBYdC8unEFsi817fEVi/aUqr3g7Pwjjk7OyEFMmYWTgEvGaQMOZT1FIWQkhn/sTVBArBukhqoShZ0ZIguXWXiYh4wMGQUYIZhXGi00cdTgBSLp0lZDopBomSezKshYgTfkvST3ijwaiBVEPYlbSofbi4QMBhkNbdCEkHBBsIgwxMw+ClyGIBIiRLKTRCnwQiNsTdOckvNAim9bIOTM6mFFzIrbuztW6w37fc9msysBr7Yqw3opCDk+LpgpeHIOkDXeVaToEaIkSUsB0Xv81NqKgqnd54s025SIIDEROfTj2VuUaKmcS75ZLokLOFe8hG4kjhYpBVIJuhxwZHwYePHiBS9ePCept6dVRUJLyazSnMxrjtqGk3lDTsWLE2JpNfsQEA5iUgXNFBOgqOsGO8XwxBwRUrBYzri4OKNtaqSQ1HVDWzdENzBkD1GR3AApYnSpppSqqdo5MUtcEPQ+TbCEcv1TPsi48zTnmVqjB5+cUCwOdXvOvFjvkZ0n+pHXm47eRWaLGVcXp2SROT2aQQq8ev2Gr778kvV6w8PDhn3nGMZQVLeiwLARh2zM0jHQStJWCm00WivcOLLfbrm/uy8EE23K3KwbiF3Py+Fb7kPm5RiYkTjTkmXfoxL0IbGwBqzBGwOUlo33A91uQ7dtvlNdHDL/3rYky+igqixVU6G0JgUYXeDm9gFlKi5liRNjCrk+qEQPQp+DuvM7IIVcgnZLp0CT0qF1dEDl8XaWk3mcMZIPG9xbUkfKb6tOOSVU/L5AS06bp5KSwNSKndTGOSZ8dPTjwK7raecWWSuaec2oVZmJTzMjpRJNZRG5xjVtsXDkhEJgjMRWGilVmbUJyKlYm8LOIWuBqQ/VSjlcQiqp8oqp1VlEeFNh8zg3ydMsjd/b3M9Pjtn3lm23o9t3j6pabRS2MsyFZIyZLBNtbEpnKQtSCoSoqaoekxsaVVPbEkgwmx9zfByxVUu7jCyWCxbLBUdHC2ptUBHCMELOGFmqLCElVdPQ1hXnFxfsdu8RgidGx2qzYbffc3H+kvVmw263geqewW14WHUlgeP3XgexVU6RbrtidfuKb7/8W+7efMtudVu0BTFPGLeIkBEzJYkgyv3iJhFeuRvy431QgAIaoYpHUE1kIKMtyrQwBvrRYnURCi1Ov8fi6AnL42csl1fUTVuE/6noB6ScxJEwwVYEoCal/7s4iv//1x+cLL+76zChRx63ND3UHfxgI1kEzSwq7BTmKaVGhhlCG8TRDNGA9GLa/kqasxSCrAS+lohZC9YgrntEMIjYoGbHRY0nDtLQRBKBZCf1j5IkFUg6EuiJBFwOjDnhcsLtE3cZvpXwwkpyVdGcXbH31wxueKs0zJkvf/sF3iceNmu23cgwerr9iJaFRZlEROnSd44HL4wLKC0ZtUJN8tyjWYsPBXJ88/oVcRKWZCWJYSCEjq7bYa3l9PwKpEZISS3LdRFZMCKKFSSDVKU9ZoXAKIVWEn1gpgpAFk9dTeD61Uv6bs97H304nebhYtnS1hU//sEzThpDpSTJDQzB0wdH50dCDMXPmDPWViwXC5aLupAtiLgh0O3ukMqgtMa0Fe998KwYzycTtEgRN/QMW8fNm555XSGB5fwCbRvG0KOqlhAFacyMG88wBPpdh84CObXqQkz4GBlHRwxFWp5TopnNuEgZpYrN5d9+dssmSjZdT9s+4cn5e7z/0R/z4x/9GE3k9Vdf8cmnn/KffvEbvn7+Ld47jhZzvvf0lLpqWG/WDMNI348MU4uwaWqeXJ1ztFxwslyyaFtmTcUXX36BVJJKw+8+/Yz94HBjwiqDUZpqv6f3nvUw8GcfnPKMY/oXCpEjbYxcdB4hNA9SsNvc4TtJHmr6zS3+qP7OA+a8f2f+VyTlowtoq2lnDcujIzbrjofVlr/6619zfvGaH/3oY5bLOW1boY3mkIheNweww3dngYdXyf1TgCIG/1jNFcBDgdHHUFJL6rpGTjMc70u+4gEEnlN4PPWXk7+c5uKHd5Vxvmd0Q2k75wRK0Zhm4l+ODHHLfujYuBEz3CFrycmpYO8KhmzXbUCX0UJWiuQcIQwM45pxnAQ+44gbHDWKkAvj1489A4HtTWJez2ishRjL3DnlIlgTZXE+HAoO70vkQ0oHCBGnGLK3iTVSCi5PlgxtzaJpkD7jvEMZwWLe0Mw0tYemXbCoahbS4FNmdCMxerz3vH51j/aSKmV4bYwAACAASURBVBlOFi2nJ8c8ff8jTi+eEkPEJ8HRcs5i3mKNJrlAGEfGvSM6jx9CgVloSXUyUi3mNMsFy+MjpCoz3GFwRaE6lmvU9wNfPf813zz/LTe3b8qf+71EnSwkPjhct+I3/+9/4PW3n/K7n/9nZPIsGouSDTFloots12vqpqE6rdGm2BNyNsQUcD6Va6IUxmi0KdoJo+sp/Flh69JSHl3AVDOqKMt1my9ZHJ/y53/5v3B0csVycY4fA96N7PdrQgpkSnSefFSkhqmpLxFKEuM/ciUogO+ve5q+o/KSedS0cc4TmaispBYKrU0ZDkuNUFVRPTYSYYtKRhb7Noec4CwALcgospeIpkakQk2RoczEtMgl2y8Xo2ZWhfVHY0FHsg2kMJJjIESHzwGfAyEGqlphq4oqG9yQ0C/fUN2v8C5wk99imXd3D7jBM+y3hJCJIeF6R5ASrxX1UIJhY/aTvyejlUFkRUIRXEmRVwKSnAImRRm7p9JgLz5IpfG7QBoC27sb8hRAmqv6MR5uFyI+ZYZYInwEgkYramsxWhU5+WTjSFNA6+msYnm04Pz89LFlI4Tgj95/SlNZTmc1KgWiH+n7PUNKuFSYqyFlXIjolKdfR4ah5NcN/Y4Druz4/JKqrlmcHE0bdsZ1HdE54uiwyoLxDCh2+57oHCkZbHOMMce4VAKHfTcQYsaFxH4sVJfoI/1YBCQh5YkGFIoAICUWvGsqF8xqSxKGVFkqtUA0hi9Fg5UarcA+/R6n2vKv64ZnX3xGt9vgho7ZbIY1lrapeFht8OGBZV0/HvvVwcsFkItCEIocfrvbI7WiaWu0KSD2lAQxDCgRWFqYX5xSV4aoLarbovuO836kHyMPQ7GroCSDKHmalf67j95Bwfmu8R0kGYWtGqzNOCMJIdHtB968uWOz3mKtpmkrqspQN7aYwK19xK+FENhsNo90mKaZIYUmJ8/DwwMpR46PFwhRTvRj3xf4+ujKTE9PSfUHbNqhf/+O6vTQEjv87FDu6XEc6bue7W6PMAZl324oBeAQQGRspQgi0Lktb958zWz2hFa29OOWftsT/MBOG0TMJDe11JWlndfYOtK0HhFhHB2D80hbLBLdvsfsNbIV2FrgUsYFV6wj08zvwKh9nA0i0frQri5ird8vKqSUWG2ZV5qn5yUFvrKCujVUVmM9oC3aNGhlCy1rsg6kFDmrz4n7jtT11FVRy2uli34hZcIYHgUth+seJEQt8UHRxxJcrcmlXesdabMhrZ8DoNpjbrc9u2FktXogTqkggx8JTmB1w+Kkxij7tsjNGT9u2XZrrp9/yquvfsP99XP80KOlJAno+v7R7D9fzBBC0nd7KtEidemgVJWdDmBx6u6AsHoS4oip0yGnxPlE8LGAG9oMYo6qZ9i6xTZzlLZFOZ49KfvSuUiTNUelyTM9JYOUTwaJfufXf//rD54Jvr/1LHcjRMuxtbRKsVACrQxKFaFH6SVoUKo03I0mm+L9eexAAimVhUdSTlwZYNYUVVJMCFdEAeLwF5j4khRll7BlExQ2kH2AFMhpJGZHyp7oHG0lmRmDTZl9H9mtb9G7HSlLbg+n4gzdw5qxK4t+FEVO7QcHSkyEBYOQmRAG3DgiEMzaOUloskwEN05tiTyZzScF3eSVRGa0LAqwTsLoPP36gURhDZq2ham1tBscY0x0ITEODlKmtZZ529BUlugGfPA454lSU9eW+skZ77/3lNPTE56/eVNO5Ag+fHpFbTRaC4au8DH3Q48XAi/EIx8z5lxYpqkoZYP3pBjYPNwVBaC1HF8ITFUxWxxhSv+ITmpcPzDSoxBgAjWazcOece9orKWqa8zMELstecy4sMfHhI+JIYRSjQ0j26EjpMJUDQlSDPhhwJARtvrOnGJRG4SqyUhM06Aqw0uqInBA8v7ZFe1iyX93dcXZ+Snb+1vevPy2JD9IWVioWfCw2TGbzQDo+g5gaslOm7AsUAcfIuNuTz1fYmtDTOB9JoSM8HssiVoJmpMlZtYShUGtNWanuPKJ9cMesd8SRkdWAqfKvNjqYpZ/fFCnfzkIWQo8QEKZLGN0jTERrUtzZHSe+7uHx/b4fN4wnzcsFjOsLSQYa82jyGa1Wk2eSIMQmpw9fszc3NyScyozZq2QWTCO5V53g6NpKnLWj4SYnDNpasMJNf0Tprgm/XfilNzoGIae/b6jms2mkF0xefMiMQWEyFiriHmkcx1vrr/l+08qmiaj/R63W7HfrtgrixIGIytMpTGmomnaSREaYEpbEV1HVGYiG+1p+grTK0wr8Snggp8OPOrAfSwboeCtX3XCYB1wdrwzIzzcjEoqrLJUyzlKCGpbKFpICrFFWoRuqLWZ2s9FDS7JyPkJ/cMN3cMd3mSUyqVFH1OpZAdHDH6a+RYbV6BoJ4KUjLks3klkaiXx3uOHnuHLXxfLwNF7fP3Qcb/rePXqJQiFUoajowY/JqypWRxX6HfZoTnj+hWrm+d8+9kvuXn5GbvVPd45si5ait3+sAkGjC2h6l3XIYzGClGoQbnkofZ9VwRBj3Nj8XjxSspGIoZICJHKGoyRKJ1JusFUDdo2xWYWI3HaBHnc8GJxckww/pRKGk2inGsPWZB/yOsPrgR/2JxwGWqSqFFJIbLEkwrKLJQTViAwEpD9ACGQYjGFJsApTdASXynUfkDGUCKRtAFjkOcniLLCkNfb4hLNkKoKtKIKGZUjKpeQ12LCFAhdlE7WzNC5QeWM1YJKJWYELjdD8TcFSQwtWyv5bRH+kslsNw/s1xv6zR3N8gxhqmKqlgKhJbttScoQZMx0ktYyY6VAS4Hr9488VFvXZUYhQeoSEdO7vsxFEDw9v2R0hQ06jGFS342Pp1GREpriwdRakGKGsefs6pyrywtkDux2Ox4eVpjFkvOLc/7yL/+cjz/8kLquePW//x+PEuVlrVFCsBsGtv3A4EYGik9RSoGxFTZXVHUzfX/J4AogPKeI1BV1XVSolWnRWHASVRd4OHOLqQN27nHeIUNAzh27wSPqPfO6xiiQRMaH1+w3G9arh2LKFlDNWqJUJGPQZPzoGAdXfHlS0M4bLpcLzk9PHs3eAlhMm8R6N/C01lxo+LFJfNUPPB8CP62Oy/9XHXP5x3/BPDk+3t/T7teYfs/2voR6vnj5hvOzs/LZ3SZevXgDKfLxD56xr4rfzufEmBLdOJKVppUzLi8vmLdzKmPZfPU5282Wh9Wa9f0daejILvDBh+9xefRj/jfT8h9/9gk3/+m/8c2rHTkJlJpPBu93BjK5+O0OVdQhFUApi1I1Us6IySBkTd1odGWQWhIz7DYd4zjyzbjDVoqmsTx9dsHp2Sk/+MEHNJMVw/vSyZByJMbMdjPw6sUtt3c31HXFk6dXaFtM0d67YhURGTf0OEpFpye4t23b4nmliES01rRty267+w4x5gAGH0eH8w6bKzKBkHpcCMRyDJ6aWJI0waa/unvOfr3nuFnwwbPvQbsgnyyw1SnatFT1kmYKZ1ZSEfyAGzu8Cwi7JSpFUhYfHKv+jqHzyI3FXswYRUkvCVE9bny8YyvJqcwgjTQTP1g/etEOn1jOmc32Fav7HTcvdyyOzlGq/BzTWJZMjZAGIS3WqsmyVHi6lVZcHVfsNyt2mweEm7PoeiDithvcfg8uERc1flZBzgz9wHa3pTEVAqjMiNbl2teVIY4dflyRv/wveO8ZLv6Eb+8CL7Yjr775GikrjKl5+vQI5I6qEsxPLOodmEcIjt/8P/+W3eoN188/Yxh29CFws95jtEUKyTgGpFLoytL5xJB6toPjNEMzm9G2DUpJZo1lMWvwIdD3/eNhTE7diUcFtJC0TcPJ6TEpJx5WdyQ1x7bLyRaUisUkBZgyIA8EnDKOTiByMcszqVtRj3Psf7RNEMCgivRdaAhlbjAEx4BgEIKNrhilYE9Gq4TIiZjLqcvFiMuaIDUeiwh7ZIxUpjw8zXzB0dMrkve4ruNht8a5EuGSTUIkTeUcJkZMClSiRMwoq4tpUmjmQlF7R+UDVQSrBFUSVFlPbDlBSpoc3w0zFcxOFygjsMqB1YScCcmVuXhSeD+iZcWsbR+RXmW4nwo1foq20VpRWYtkyh0MvoCc3YitKxpbNhujPG5MSDGFl+a3cFjwE9xZoikCm67v8EP5Ol7MyE1NGBvq5RGnpydcXl4ym89KDuM7R1UxldE+BiKZKCVoU5qLB/FDLu3rg7pQCIVQkiQkPuxJ/UiKMG97DAZvRmQUJJ1KTqDQCFsyB4X0xJhp2llpKKVMGAei6xm2e4bdnrHvGV0oFJ/JeyWBWpVTYCsMVgqMLvl/l0dLjo+P3iKygFoLfJY0SjBXsJCZIxyXaYTkuGrP6aRmlWFMiSAUuT7m/aplnjzz5Sm7MXB7/4CpDM6VJIhCHQrsu24i3Bt2w/i4IcVYgoP7vkPkTNCmCACMoV4s0XWLbmbIuaa+ekJzekJrGy6vt7z37Fte3tySUrEm2KrGVhWHUjBDORH78ChmKd3GQ9sVpvICISUxBXyAYUL6hZAYR0fKEnLk+vqart8TguNoeYSxBufGSfSQub1dsVl3vH75wH6/Yb6Y0e27Eok2/UxKllP9QfAiZSGCpANAu/wxghCPJBnnHD68Y1LOlMDd4GEi2+QccW5PxqCSAiIpBWIKpFxCl0MMrDZrQjewbCsWsyPmR8c0zQlS1ghRPwqItC6VZQbsbIFPCe1GolAF8Rcj0WeSEwX6/47d4aDjeZz/UTyNKuciBsulEpfFDc67byzjiLFnGDforqgl9/0O7wrcP6Qyd5XKUFVl5ogoOgOrFNuVodut6PcbTq/e42IsB4/gPdEXpSNT5U16+8zu+h3Dvuf185ePkUsfVgYjykHCN0d47QlKk0XZOMq8tlRPxhhQNSHWKK8nLGJ5pRS5ffk14/6eOGzpup5hGElIlClr7n4YkXEaTxFRSWKA/b7wZEVOGKMxRlHVbXm26hIsfkjleJdvK6V4jOOKKUIWWFvT1IVdXA6I5XCecuQtD07wlpZ06Ozlx3v8XV/n3/f6gzdBskQkhcCQ/Ej0jnW/5UHBvRG8ns3ZKcm9lNQqI5MgeNh2jl3qGTEkaUkGAh2SxMwseO/yhMunT/njH/+Eoe9Y39/xy/tXrPyW7bArVYoy2LChdp7aBxaixlqDNRWdDCA151iW/YbFbs/CR45NxdN2gW0WhWAgHISARzzm7wkBVz94RhxH3F3Fw2Zk3Y34VGKLRJQ4b5g1NWfHZwgBMQbWmzskBi0yvR8x1tBUDfN2hsiZvh/pnSub19ixqGuO5kuEMozWE5ygtmEi0gyTEC8jGJFSYCuNsAk3wua6Y7u6x8rM5fFHqKZG5szi4oyriwsuLy6wxjCOw3dOPmlSXo7eEYQArZFak0KAFFHyLU7LubLpK3XwlSVuhuvSwvOBVs0QA1TREhqHsZZ6MSvRSEajpEGJEdcPLJdHVLZmf3dPv93Tr+7Z3z3Q9Tu6/ZY+FmVoRhYiS0wsVVUM70oxs4q2tlydHHOyXFDNZuzkNBcU0Jqy6Z4ExZEVLHVmGUfq3PE96Xl61XCXDb/dB/7rqufGw7ei5ez8KR/OGy4ZsO0cITJff/05m43HGou2mpAD692eyhcQ8v1qh5SCdlYX9uXouLm5Lu1tKVHo0lZantGcXDJbLmhPTlg8e0ZzfIIQkifrkT+9XfHLT37HOHqsrWlnc5r5nMddkFKpjVLig5/ijYpuOAYYx4ncIUoWo/NjmYGPDrIq7cBJ4u+95+XzOzKJzz79lIuLC5ZHC54+fTpF6gQ++eRzNus9D/c9SoFzxzw8PKD0W3WkNmWxLqfxIpWME5km9n0BVh0UvFIyDkNJOpigyYetoh/6gjIjgUik7On6Hh30I9KttKA9iWmzlPDwsGbjEyoNfPjxx1x9/5Ll/JQUDN0ukEKBK1S2LNBZCurlMSFn7DiUtrt35ZAwQhwkIhREX54sOmK6ZoeWoyCjhZjg0w4hioLbaI2QhvhO/1pKjxAjOXW4YUWMcLd6YLMd6XvPMPblIKFV8UKKAoeQqswiBYlxKD7Vf1kteNIPhOnn9SE+hmrnSROhtaKdt7x685KXL57zX/7vn2KNZT5fkNuW8+MTThYLhos/IYQRb2eY7R1N5zG2VM1VpVguT0iyIg0bxN6S0zubYIy8/PITRBywKrFZbehcRBlLM1tgreVhU2DhhFCKkmkN2KxX7LeSHBxVZagqi1a6xJYtFgUYnjPmHasOMInByvyaUOaWi3rGYnGM0oacIiE4CH5qTU+K7jxtepPieLrhykEt/RNFKU3TPIgB11YMzZzxj36CaytCU+GcZ7Pb8c3rV9hDsnJasuv2dEOPzAplLXrWsM2KGAJVbRl05MZtuXn9NSFE+r7nm9rQLWcMlaI9OcVWNeH4lPXQ48ceaysqa1nOGkZRWnuzJ+8xm06r3N2Rb9aE57d4EhqQqUSA+EfRbulR/9k//zMqJRiun/P13ZbrbU93/gRPUUotbcVMWZSpkcEhEdRtzeXTS05Oj+n363IztjOGoHGjJ/SBYRiQKfLBxRlnJ2ecn13y6YuX+GHkNMGsWSCN5tYobldr7jZb6vkJ83nD1dURF6fHeDfyN/Zn5BQQKvMnf/YT2naOlJqb1RbbVCU3z40Mff+dD34IBQs1Bk8gk2RJAQhkoisSZ2sVTdNijMX7QLfvYLrRGmuo25a5bXj/9JxZPaOWCrfvGLc7wnZHFpkkKBEy5GIb2e6h6xneXLPdPrDdrdmOe8ZQaCE5xol2L1kqQ6U1M2loq4plU9NaVTYYKbG5xHUd5OoCeHZkGIQhEjmpJAsFod+X6xAjM+VZWs37bcO/OGnYxcS98zRSoPLIi5hIT77PR//zBe/fvaZbP/Dym2/49rNPeLi95v7hnjQJhESKVLbmeLHg4uKcxWLG1fkFi/mCppkxZI0wNaaeY+sGBKzCgL/bcbvtef/pE55cXfI//g9/wU//619xfXtfsHDJI94x8wqgqRrqypJznnL3FDELggsM+54UAtE73Ngj1RSNJPx0LWHeWo6Xc05OFnz91efc39/z1ctXfPrb3yGV5PziuFQ0WbDZ9IDC2AaDoh8kn376CZmPMeZJmburQ/TZxBn1kyhm8hQKKTGqVIoCATFjpCJpPXVLyuc2dB1udAgpSCKVVrAswjExecm0FJhK43MEyuFCRgVD4M31mt3+b/n66xf88Q8fOD664OzsPbStkVk/+iRFhmZ+DFKTpaTf70qXJYvH+LI61+yjIzkIVUaIjJFlBkiGLMv9HEUhGkIk4Uih+Jz14wYoee/iBxzXVxw1ax7ut+z3PV0/IEVFXUfu7otfLsZY2LiSogEQTJjNwr2MMdD1fUHZ9T2q0lSyVLohBYahR0mF946+3/PffvpTvvrqK774/PMCWTCWfd/xw4//iB//6E+oq3PswrCcz7nMM6p2TV01RfAjJSkkBu/Y7QNEWUYbhxU+Z1abHW7oyCnhQkLLkik59h27zRqZM01T0zQVdVUwjExqUSkyc6uBRHQ9m9U9zWzBUd1grEUgHhGIh+9nrKWqG1KMU6hzxJiKtpmjpCLmqd058UKZ8IqPWLgCvONQ0B4qwX/AHviHb4KlIVUegFC3hOMF+Ycfohcz6llNs9qhbm4Y7m8ZptyupBS9MQwpYoXG2grTNKSxJgbPqBVbAsF1uNUdKWWCD+wljFbjZUOqG3JdE41hlJlOJqQxVNaS65pE6Snb7z2jnhUptH79GlG9INxuH5mAMr/NMnx8T0JwtFhQG8Wuu2ceJZ1tWOYWlwutZjbNI/MQJrm04aQ94ezijLPzU4a9RQuJVQa38sSQGUMiS42uGs7PL1jOyylqNxZ/2WwYWNgaIzVu1rAfHaZ36KpicTTn/PKcp5enhOB58/oNQ9+hpOTy6VPm8yXGVET1aorQOMCQ31VD5YIASyVCKVFUWikKUixE/QOSS01BrkYHXDc8th4WVcO8bjieLVg2LZU2yFQid4L3+L57BHRHcunWaUnoRvww4nZbhm5HP+6Lx1LkiY5TLC8igTWSRhtm2jKrTPmyGsnkb0tpSu14ew9qAVbB3AiWRrEwGRkOirNcNvFxILs9cxS1lBxbSxDFfuJyESOZdgG1RS+OucuSYzcgJ4N2TIFEYjk/YjZruXpyyeXVOcvFgqcXV8znS6p6xmpMeBRBaFwsrecxxJKJGEvl1rQNZ2cnLBdztrtuOqzGiW34tiVhjcFoQ/Bl/nSIAQvO41xplaVYTMA5l3s3ywLwFmSMntO2ZcN+qRQpJnbbHf1YxAnDsCm0DqlJUWGrGm3KfMb7kdXDPUP3lBQSUsnHE7UQB+B2fkRxFU+hmFrwh9XnLWHl3Qr38Jwd7jU1BV/LCbWlJmGPUhIxKfoUGlUXZWy/2bPZ7tl3A/PmG9wwYK1m1pxidYNSNaSipFYT4MLWDc6NSPUWilEWO43KkhzLvD0rSuteQBa5fE2CMfm43k2os/hWaCGQLOfn1GZEqzlufI4PJaBYGoWpMg/r9TRulFhjihI8HbxygviY0FDmrcMwlNlZ6UWDKGkVjCPWVozjyG6z4frNa26vr6f2dp4q/29p64aj5TFHZ2e0rUCYyfObBeYxJQX6rqP3HWM/lMPDu6PpDP3ocWPxjcpDtyinAgfxnqayWGuojGZWF79lGF3Z5AExAShijoV2pUusnNa2bFT5gPMrV1hr8x2Qw+H3rK0m32d4HO18NyPw3fSUR+f3d7oQf+jrD68Ep3s7pUg4O8Z/+B7mX/0rzo+WPGlnLK5vUJ99xieffcr9+o5xHIvB98B5q2pMa5nPG1Ke4ybPjHN7xnHP9Zu7CRkmQCR0pVG2pqkbqqoiyI7kBJ4ycwso0Il51dDMTvngn/0ZT54+4+TkhP7mGvGzX9Kt9lRfv0ENI0KoAk7+PYOoGCNpdKxu7onNEnuy5PT0jAGFF4JlduTVmv7rb1lIzaJt+JN/8RGnl2csjheMmwf6zY7t3ZrV7S03D1te391yfjLj7OyMH//kJwz7jrvbB76+eYVYbTjZjtQx0x5ntucLFoslXlTUixPOL5b84IdPODuuESSqumHoC33/Bz/6MVVVlJHZGGJw1FoQQplPPk7OcoE6kyOZPNFtPLvVUKC5qRRYSRxxpI44Pz8vxuwM3XpN7EeeXVyybOcczRcoFDlEhn6P324Y+459t6Xb79jttqxXJXKoriqaZgZCsOu3bIY1a7dDNhatDDolpBinbOiENrK0TkyZVbqxo6IuxA8oIb/i7e2cgYetw9SSq0ry4cJyZCVezks1GyP7refl9XN+/pvfoJTg9OiI/+m//wsWp6fYtuXNPvEwBm7HwKY94Vov+Fu55PLq+zzrNnz/s19j0oAlcHH+hOXRMVdPrzg9PaGuG2rTEhG4mPns5TU3d/e8efWS1XpDzJGqUVSLBlvPWcxnjP3A2PUcL5Zsdx2bYSD6ET8lKhxebdOgVZkxbzebqcJRbDYbdtst3g/4UJ6ZGMssxRhNnOJzrDli3tYcHy2KBSnGAoKOmZgCq/sVWluMrlguzzBKIin0IzdGbu9u6fsecsYaC6nMGQ+ZgyWTs8ymzKR2FJNI6fD5lJT4dwfTUDUt0oCuBPNFg7YSrZlUyJGmMRgrsVYyBl02pyDQMpMrWwKldyP73cgv/vrnzOcNL779HT/+059wfvGUq8sfkJwmjZKxL7NqUxmQmUQkijhxTjWNtOikyQ6iL3BspRRG6gIEIRBznKT9B8tEEWEczN8AQkqeXHyIEBJ3NbLfDYSQ2G4H2sUCpOblm2uktTSV5fx4ToqJ1WpbSFCxbPUCjRSZ7W7H3f0dr1+/ZjZr0LosUrmL0A/MFwtWD/c8/+ZLHm5vcGPH1cVFiQ3Kme1mwxdffM5mu+X07JLZbMbJ8RFD3+PcSLfbcUjV6YeOkPd4cY+qXVHrHp6vnNl2pcU+mx8xsxD8yPX1G4wxNNby5OoC7x1j39GaFoFgDBANxWC/W0+bYALTkLJA6pr5Yok1pmAclZ4QfIV9ChCiJ8aA1oa2nTOfH2GUBRJJabJwj0Hnh42vQEiKxLG0m9++j3+SSvAxt0yJSUofSnjqfE57fMTd/Q1ZJGIG0zSgSwkv8yEaRxJzZhhHpIC60lijCemAEfJYI2hrRdPOAInzYjqFDDgfyJRTTUJgpMIwxZtIaJqGRdtw1DYM2uClJgjJ0ds3wCEe8t2XoEjjBx9ZZcdaa8ZWEWUxf/qYSSEwupGTStLUlvPjI0SK7B8eEH4kDCND1zOOjm7f8fLFa46WH1G3M1RTMe533PdbtvsdcrdlvdqzXh4xNjWvVxs8BmsbxtGxftjw7deB5E9oKoupatp2XtiNxhJTZhy7gvxKEe8zbiyRLfmdN6W0glSMvUbpUjSmQO96+q5nvd2yW60Ydx1yjCzaOcfNjFNTIxMcNTOsNmgKSSZ4R9+XOKJx6NiuHxj6PUO3x6qMVoqmUmQR8Dkz4sAKrLFEASlmYioLZp6sJC5Feu+oZInSzbmoSYUo854+BZK1nOa3Jz2rJSElbjY90QdmVnFcwVE9ozWSb14855e//lv+z3/3HwDBYj7ji8++5F/++T/no48/4umT7zNThhOreLHZMHRb4vVLXmeBJvPeex9xUQvebxXvnV2U6143Ey6sRPPEXCptwoghMtcC0WoQhvmyLS2hDL/+zW948+oNX3/1LZvthtoaFsfHmLolvHMnFotBOSaP48A4jmXeJnQxOQ/DI1Bb5KkFlCC6MOGhMt6NxYrQ7XHeEdO0IVhT8vNEQmtTLBJkQvDs9w4hwBrLYj4vKk1ZRDElrFp8x7P4biTT4XUgxgghcK5YcQ4rkEDQzi1jKO18cmmH+1SEZUoLlC75lVCSUEoMGqCL/ULPK2ISxCARoyP4yM3LG0T8FW+On7P5wQPLxQWz2Qm0GbftW36BRAAAIABJREFUuN/ecn39kt36AXLZcN0wEFxAJFG6Nn2AJIhNJE/rSH5sjYoCwD6851xgFu8shmz3XVF/9yMxlviptl1gdFUM+zGhjMZWhqPjk5JYERL7fck3PCzcWmuMlgz9ns8//90jLAEEWhVLy8nxCdvtmtvrN+QUMUpNhJZidSqzs8TYd0TX4Yg8+L6AtGNkHMaJYaxIYkcWHShHyuFxrls+QwrTUymOlw39doVzA7bSzOctTVURxj2EgM2RsO0RIpHTgFSlh/wYVRUFQzcQgiDnolRPdfUYYJ1SYtbOi3hoWpe11sxmc5qm+AQFBklECUOQuniwJRPisCS9MOVqpknoURIlvktJ+vte/wBhTH784HIIpNEjMxitqOqKEDwhlAVCW4uQEh99eYNCTq2JzOgdSlAWzUbjQ8CHgNaRqlK0rWGxbEAohjHT7x3jGAgxghAYY0kpo2Vpmx0CY8VkHq9MMVKFnBnjW1M8ucxOvnMzH/4T4BN0PrHLiYAgTkT5OEl5nXPIpqayinlTs+229H1HpQRuHOm7rmyC/cjDw4oQE8pakpQMObJ1Pc451DgwdHs2Q88w9NzvKrSdY0zF0I/EMJDilqYSLBczbNVQNzMWiyIZ9iEwjAM+eqJ3eO8Y+pHdhG6abudywhKl9aApkvycNEMu3sbtasWgO3CJp0fnzNAsjk6pW4OVmkoUQcRBDei8w7kB70acH+j7PW7oicHR1AajNdYq+hQJOeIJCCMwsiRKHBR8yMOCIoo5P3pcqshZkLIgpdLuczmwCyM5OE7gnU2wzKl653kRPJWWjEcNuikhua/eXPPZ51/wN3/zC1LKNE3D9e0NaIHUgg+v3mNmLUsj6e723O/vqe++YWtanG0QF09YHDU8O655//S0UFAC9H1f6DqxTJXjo51FcNRYVHZFuFMVhWvKmW+fP+fzT7/g17/6LTknbF1zenJSDjP5uxuKkEwLpcOHaRMT4H3ZWOKUEl5UycBEDzpkRYboGd1I3/eFXDTBlbUw5KxAZJQqmComiXmKobQ+dcIqhZbTwi+mDfAdFR/wdzbBt6g2Hhc2H/x3DmO2UWRfZjtlJgyJgDaypFhMlmLIiFwWw5RB6AwKdGtIAZKbfJzes11tiWPP+u6GLCJPng1kGTF1g88d/bij63eM44DW9rFtGFyAlLHKsB8cRexT2qBSCnIuXyCL2GiCFYiUv7Nu5JxL6zJE9l1feKXKUFcNWQqS92QSUpaFva4bckpUVUXXTbN7UdLilVLUlSGlwO3NG6SacGgUrqY1hjCOdN2OzeqBwoHVGFkIOhmB00Xh7V1PHDtc8rju7efinEdVFVoaEAOIEZR/DOh+5xOd1lBFW2t2K0+IAWM1s7amqSr6zbpEeQFxGBEiIXREGA2qBCPnif/rxkBMEq17/JSGoZTirXiz3MGH5EIlJU3dTK36ilItF9imEFMKj5hU7Ifeaj5UhQeLS0bI9E80E4wJGTMYhV7v0eqa/ne/w3U77jYbfvZXf83zFy8hHLYPMFlNs4BibM0p4YdAVgV9the65AWmkmcmtSEKS++qIg8HXMy4mEhZltlVo4khEWNgP+4RqUesMp//5jdUUmK1xr18QXr+EvnyGpVS8ey5WPhy6bubYMqgtKU+PkfRAhVBFNm1EBlyWYi2uwfk8QVaRnYPt9zd3bDZbjg9WvLwsOb5i1e8uu64X+9BlhnRbnB8+eY12/WKMQbO6xZde1o18vzNLcOmZ//DOcHfE9w1/dChZKK2IIJnsZwjpOLyyRPOLzOXTx0pOsax5/b2mvX9PS+++oKb6zv6buDo6kmJKIJy4koB9pQHTkp0XcMiYoRgf7eirVq+d3TKxyeXnJ2c0dSzR8l0CJ4QPc4P7LotbhwZx45u2NP1e8YYME1Ne7ygbWog45Njv9/RZYe3gqwkSoKVAsRkpTAa7wNuGIvYwkd2wiAy5JDZ7PeMIdAFz2Yc0E3Nh/86FVgwcKEG6lnLxxdnfH47cL1z/MfPbrk8HjiZWb791e/48uvnjMOANoau2/Ozv/kF2/WaX/z8V3zw7H0uzi+Yz+bo57/mvZe/5d98/n/hmyVpfgYX/ystZ6RkccOAkhqEIkePyJGjusIqjVaSi1lLiImQIj//xc95c/2GX37yG548uWK5XCAy9P3Ize093//+M+aLBbPZrGTzvXMPZmCYFonS1SnzXB8G3ISUK4e4Yik5PZrT1BXtrGazXtMPAykWPNau70q7WUq0NdMDLibVb/luQsBiPuPq8pyTk2PmsxlXV1c8ubosbVIBWZbnzfu3as+qqoohfpKoA9R1Uc56X7IIvf8uriqLkqBQgNnFD6tNRVMrbC1LNSgUSmik8CQSnoTSGaFhbg2jkWirWL3qS4tVt/gYWW8dv/ibX/H5Z1+wWM754OMPmM2PuHpyyrNnf44fAm++fs399Q19t2fzsCM1iWVbc7PelbZnYApyllNMlS7doThOs9s4xS8ZzLufVz8UXcPYYayiTpZhcNzcP7Bab6itRitB9CNv3rwq6eopUNcGayVaG+qqpmlqvvfsGW3T0M5mEx0mT2bvSI6Bu9vn9H3PbrtFSknTNJwfH6NMRRKCl2/eELxjsx7QlCBlJac0llSg66ax2MZycpaJCGIQiFRSbR7XeAG1gZw8u+0apKSqG5rWMJ81WKVwu8k+IwTRRbRWNMs5Q4y4mNj3I24YcaMjJsWymXF5dVbWYFGUnof5326/KwczLdGqhAAra6dnfbpnUznASyHJsqR/kEq6vBSqzHGnQqUIoMocNP7hcYL/gBSJSVQic6YKidiNrD75lM31G1bLOc+//pr7zYaYi0w7pYhRGqU1yhiapiknRemobDHdVk3FOBYT75BGtDJUtqWyVSnfpcK7QnzXJmOURivNsO8IGXwMaCFxfc+Lb75G5MT9yxeYv/2U2ZcvOPYBjZz0I4eB7TsPaAYXAgTJdnCMdkbQhsBbv9BBBCBEpqo1dV1y91JwuG5PnNUFCE1GVhZVR3RV42Oi60a6wRFjplKauTYlegSQyqJtg60XDOOa3X6HUSBywrnAixevaR8alifHmGaBqnZ8++IlJI/rt3T7/dQ6G+h2W/b7geXVk8eKqcSaTB4hKRBSoYUkak1UiqO2ZdkueXJ6xqJtqY1Bpsn/k4tnzkdffJ5+ZHQDXb+nH3t88FR1ha0sdV2jdJH290NgCJExJKIUkIpyUEmN0ZloJcKVikmqolQUETylJe59YDV0DN6z957Oe+p3HtKUEr/+9SdUTcX8eIGpj7mqLOp8jqkrpBD0w4jzAaUUZ+dnaKXouo6uH3n+4hWf/O4zUhYsFktsO6edHyOPnpSMx7pBNgZbWayxqMnbVOZgqqj3v+PHTAxjx/1qxYsXz3lz/Yb7u1vOTk+KtYA8vVcxDfo1fT8Qw9818qYcUVJR1ZZMwruAD29jlVKK/H/UvVmTJFl6nveczd1jy7W2rupleqYbIAaGjQCNFEWTzCQzyUxXMpOZjD8HfwV/QZe80IVAEQaIwgAghtQMBoPpru6urjUzY/PlbLr4jntEZnX31EgQCPlMdVZFRnj4cvxb3+99jVbUTc3D+/dYreacnqzYXZ7T9h273Z7ZfM5sNpNsovTrZBTGUFUSqKDg7HTJgweXfPqDj7m8vGA+n7NcLFiUkqj3kiWRJdOb5HAKWUQus63jd6ijWcHjuTMFWKdJSsrDo+ahsRlrFUYVHlMts3hJR1QCrcVpEhLkhNYZUyvswhGNJrRRqhtljXedALrcZ5rl8pr2fMvFvYe4quHBhw+wjWW/3XF2vqR3A0rvJdtNihgVtpRABUl6NHuW5I9WcqwTjgmpZGkFKYskVlUL+4oymmZWU80kaLNaYUtVJQSPs1oMtZKsSyuZ9xxZYcbNjHRuZYRYa01VOZyT0uesqdDOkQrRfsgZP9LjqVIjSJKdd4NnebLgRK+YuRXKVDgv1DZVtby1DrVCkJgpEUMoVQMjVHpKyt/ChqPBKJKWllHXB4YQhSJNO6ra4Oo5y9US6ywxCw1cyuCcVBFiKOAwJYhijSrl1XTE+KJK9jcSzEPWUtWSjLBoCBa2J/ljCivZu23v7AQjEp2oFGlihv1A/6O/5ksb+blJfJ6hM4pkLfu+JcfE+XyFdQ5TORZLaQ4PphM0p6uYLefsdjvatqPvB6xxzGZz6rrBOUdd1/SDF6ZwrXHaYJRm2HdQFBB01vR5z9/97Gc8f/o5c6359Z9/xff6yMdJUylbmvWZmAOhRMOyZdohEFLmarOnPTvHO0NIIoEkFfdxKFczm1fMFxVVJXNEQ7cnxVOZU7GaatlQJ4trFgw+sdnt2XdLbIK5rTmxjmSMgA+aBWZxjlldcHOzYbPb8d69FZQywmd/9znWOT7+9BNMsyKZmhg9Kg9kv6OqrABHYmDoOoZ2z3ENIAQZ2A8hUNWSuTijCpuG4XK54vL0nCf3H3A6F/Tn0ItyeEwZj2cIA33o6UNHN+zYbG/ou46YIsvVino2o24afBAR0E3bsx8CfUpEq9FZYbLGWoMyEGtL3u0EAWgL4bMBnyN9HNgNHW+6He3g2fWekDO5rqe7lVLij//d/4k18OTxPX7/936P9x885JN7l6yj4qqVnlFMGecsT548nphMvnr2jK+ePefP/+KvmM0W/NonnzI7v49OAdVvMLHFVhWL5QI9m6GrBqekVmekrkXKuuhsC5m3D4Grmyt+9nd/y0/+5ie8fv2arm35+OPvi4yR2hUH6GQ4Xmtu1hvpb98qy8sDrCrLfN5gjaa3A9si+xQLMXtlNavFjA+ePOLy4oyLizMhHR8Gvnz2DJRGGYtxInYq4reSdcxmS2EI04rHjx/yvY8+4A9+/3e4d+8edV0To4wn9X3P0IdicA7mYRLd1SKHpvSoHC5rLQQJPMxR0ELpMRnn0LaUFhVYnbAmYZQoWphROUOL5pwAezLJJ0CIkW0N7qQBIxmv1QqTJZjyPtC3Pe2bNfN5zenlCmUC5/cf8Pj7nzI7W9JtO05nsAsbVBfQShOyIgRFlRRohS6glZSClGlzRsWEdUYozdLhxATyD9ZZmqYmxcTZSeDkZMm+bdm0l6QgROS+E2Ba13XTGMCYMecUubm5EULpqsIVztemrjFWSsbWGpwzNE1DVVXiBGcOZRwxMmX5IQSu99uCJJagclSKuZ8vmc0sy/o9nDN4O6CtxtjmFsmGKUK3Y98YMilotu1AThFbypFGCUw7Ae0Q2O4GfBBGmMrVVJXj5PSCalZhnBYSgSjKHqNaSohSNs5jmy2DQsZK0ogKL8c2omqV0RAt6CRI5zzysRY0fPSFcOKgav/LtnfPBKNIwZjgUdZS6cxHbUanAZN7rucVN9awzgaTNQHYxYTFY3MiXV/LfnJi20okNrsRJv1cgA9933H15g3WSuEhxkDXDoQYMc7RW4O2ijYN9DmRUIyKznHb0rzecNkHfr0NPMAwrytMr4TBNUWGFOnSMXhWMVAkW2xNQOMTJF1q1EgGpY3BOsNiWbE8aZgvKi4uTiB5gXfXjvpsxXx2Qlu1pKdfM/hE3wVmsyWzFHDWoZ68R1dX7IcWtbxEzc8ZdEUzn3N+seKHP/w+fbvn6Wdf8MXTp7TdwH6InL284fTePf7Jr31I6NZs3zzj4x98iFXi3Nv9js3N+lYzWFuD1lA3dSG7SNK3bPcMXQs5EnzPdnPNZrXCx0jw4IMswi4NeN9JpvPqOV3X0rV75osFq2bG6uRUShE5sb7Zst5seL3eQl1hq5rlalHIboMghMlUObNarohNxChDu9szdD3d0DOQCAZ6In0Swumcs9y7o77Tq3XL6zev+JM//wt+9Fc/4fGjB/yrf/FP+f4PPuWHDx7y0/undOsLrt57yH/xX/5zLi4ueP3mmj/70z/j888+5y/+8sc8vP+Qf/ZP/ykn730P9ehD0qe/Q+o7ckxC06elv6GdZFFCOt0UYVBhQfHeU5uK5WrJ5eU95ssVV+s1V8+/5uvnL5nNl/zGr31M37U8f/GMp19+ST9E2iHzz37vt+48oCKZVDnLMCRms0qY9u2Wru+4WW9wznL/8oyPP3zM++89YtY4UuilReAanjx5j1evr/j6xUsSMFss+fCjD+naQE6K09MLtJJh+IcPH3J2ekKKgc36mn0pNSulsUbkbrS2GHvQIxSV+UjMGWW0aCsWaLsupVOlFDHlw5wgYKwAGLSVJolSYDI4XWG1xodE1ImkPVkFtAXXWEKXiUFjTE1SmagSC5fpG+kNDpuI9wmnMqY2mDqjhswQMy+eb9ntfsxs/rc8ePgzzi8vWZ2s6Ko515sbnr18TY6gjKHvIk1doaxGJcg+EPyAc1IWVtmQ+gJUsdPtkvaNUpgoTjjbhLORZiYCyKGAUmKMUvoPMjvcdi1+GNi3ewES9QNtPxBioN/2hU2pkG2XizjKFCkUQwg4a+l6zzDs6Qcvs6NasVwuuX/+PjEGrt9c8ez5c3w/MF8uuH95xgeP7uNUhYoKFXMhDjhk7tYYfuOf/Ab77Zr19ZrKyKhBM6tp9ztyTnzy/e8JkjYGvn7+ipv1jhcvXiIyVJrT8xNcLYLNzUL4aBNyrRWZ6AdSrgv9mdiPEDw5UQTHhSt07FceWGFKNoiSTFBblHJoFUkTM04glnlkcYLHSi3fvr17TzCX8kAWh6KTZhk1F9nQZ8eH2fImG2ZJU2HoUMSY0JTpCh/LTGpm6EUTiyBzd1oL60XwMsNWVTJW0fcdUVI3EW1MEQIo73ExMEdxojTzrLkX4eEAD73iHhVLJeJNxwgieaLVrVJUHyIxj1yiRyhYOekJuVfXTpBTTqNUElqgupJh9BzJRoNzZOuLInwixYwzjqZyzJzFn59iiaTNOXszJ7sKH6Q0UtWO+dyJ9qJR+MGz3+159eoNXld4DN2HDxn2e25uruna+9SVLTRXqZRxjm6ss5Cl+T5K3gjAQhwSGuFWvHnDbL5k1gxoXYlGXM74MBIfb+i6vZTIjMY1DfV8jq1qfBjofU87tHR+EH23MhNmq4qUDSlawiAwfmNg1szlGGJiGCmUoPTIdalAyUyaOnTQy/1QVI04qZtNy9Mvv6ZtW+6drzDakPwg6hNas1guOT1ZcXp2SlCa1dkps5czrq6uubq+ZrPZYN2ZzFDNa2y9LOskkkupz5ZRAF0yHBFXRcBYJByW+WzGxdk5JycnvLm6wseEcRXNfMlydUrTNKAy6/Warg9EKnLKb4FMRNKnZFpGi1HRAtCIMbJczjk5WXF+fsqsriRb7HuS0VDmroZh4OZmjTGa1WrJxfkp12+29F3AGlPugaJp5lSV9N2DFxYYrTSuctPw9HhfpAwlyiEpZ0jSB6K8PpGbK1UIuscIvLxeMiwp6Uk075RwfmolUloohIDeSunLGlFkVzqVmbIMKuFGO7GAbvBEInHwOKWxCqyDFCQDarcdYfCljBnxw571puFqu+fVmy1BG7Qu84LFzmqZ3CEH+UdWY9Yvfc3RCSpKaXjsdY92wwhwAwUxOQHXpESoGmIMzGYz5v2cEDz7dsfQD/R9z24vvd++72l7yahCDNIzPyIul3UiVGKDD+IABy/l1bJ+pB8oKiJNqaKcnZ1wcXbGxfm5jDBlMFoUaTC3XYCgtEUmqzKHeVBnpae/WM6Fiaof6EOiGwLdUOy4sQewUwE8KV3o/kbVnDIeJdqOkZG+SyktPcoyipMLCUK+y/6iJmNRyvUFXJNH2x0Lcp533t7ZCWqQHlmMEIXjcKbnPLEN96zh3MCVgq8yfAnc4HnhAzEZclJUlcHnzD4MxE4g4HnwqPkcV1WksbEeA6vVipQzu+1OhDGMNF91LoK5ux0mJmba8l7WXGD5dW94T8+5V2lOTULFAKGHPJCFMwWTNS4fyjWZzNVmR0Tj08hEHlHJTzNCiiSUXucrZnOHdQofepTO2Mrwcn3DNiT6rOhIdDnTB+EPTAkaV3O6qDltDLrO7M7n6IWhfa3p9pqb3Z6YAlWjyXkgM6C1AH/atuN6+zX7oBiSYhgiXdezXt+w2a5J85nMOVnh6jveFos5OUc2uzXtrmXoW/p2j04JVVlUDOzaLW9evmS73rNcnHJ+eQ/XiICrHzybmxtevviKkDqssyzOLlicndPMRH2h7XZcr9dcb9f03kPtKJow2LqeVCpCEIb32lmaekEKQWYLSwlMG4POkqWkrEilJUMuwde4BpXiw+99gKkq3lxvuNluubn5iq++fM5f/vVPePzeQwGqhMjF+UUx5Bm3XHB+/z73Nxu+/sVTnj9/ztMvv6APPXVTUVUVy/mcpnbUtp7IjvVdA1EeMmMstZZejD11rOYrfvbhR3TdwI//r5/y8MlHfPobv83sdE7EcHN9w8uXLwhRMV9eYIwu83cHZ5Gz9Dycq8RhZT1x1RoNjx8/4r0Hlzy4vMCZouWXIqGLhJTZtZ6XL1/zxdMvuP/oPhfnZ3z04Qc8/ewrXr+65osvX8h1jYa6nlM3C4x1guYMIsRbG4urGmLniRmy9yIxVTIR4WRkmvECpp5lzrKPylW3rtnEUauUZK3G0rhalABSFHD8aDSVPJVkeW5ACdlDTpgkbFVWGww1QffsNwPXzzYsrWVlHYtKgc04a6SAkGB91bG5+gUZz7YPtKqm1XNO37/PzErZlZhLdqQwAXSQWDkqiCaDD2gG6jozZvAiFmzQ070UUMYoTG1QKKPQTmPmelo/owPwwU9qDO1exmK2ux3Xmw37tuX65ob9bk/X9Wy3O9Fj9IOwPsXAZjcrAVJZmymRyOy2W7QCZy337t0DBY8eP+TjD57w4ZNHVNWMmIXY3A+djFyVc4op8eVXX+P3N6R+z6KWikTwMgLlKoexmugTPgZeXW3YbFoyjsXyhNnMUTdgHKBlhk8jKkOmEBEYMzCW/wc/MMqFaV1htTzjQv4RSi88Tdyuh+dQ3fqTklQYRyDT26jX797ePRNUEnHmiRFCmBVUVlRJ8zgY7mvNB9mw1g2tyVyTGLTIx6RsGXJinyydsUSTSFqjk0ENhsHMCUQCgToYQQvalYyUas2Jr3CATRlVP6RKmWVWnCrNHMWFyjQqU6nSVE+g0pGKPGCVkICro9eC70gochiYDTt0DlRJ+o9Oa2Yk5i5x+v4DHj24x9myIg57MAbjxIgMfSAMiYU7p9NyTUpPncqI8rW1hqgyyRnM2RnsAzlk3KConMXkmpRkHnGz29H7UFhfFJqIJeC7Nd1uzXazJgwD1DXWGrR1aHPb+GQfSckLAwnSg5st5pDCxMzR+0QIW756+pTKvmC/3rA6O2G2mNN7z267putaZosKW1Tmh74nJXBVLQLEbSezUkBdNzTLJa5pZNbTCoWVcTXRe/zQ0+53+GFg8EGa/XVNHzxKjRFvmiJfKdEdzklrzX/1z3+XV6+vOJs3/O3Pf8H19Q27fcvnL675+npHCj0nyzkP790rMHTLybzm8uKc7fUFf/eTv+HVm9d89vQpl5fnkCr2W6H2MsZQu1qMdeUE9FMyM0ZJm1ii9BiF6Lhkvo8ePOTq+kY04XzPZnvN1Zuv+Oyzz3n+9UsqJzpzs9rJ/o5PLGfWNxti0VUzTdHjQVE5UYY4PVmwmNVSftcKsFR1Lc9JTAzXe1RWzGczHj+8z+XFOQ/vndE4y/1752x3Ozablt4H6mbBYnnK2YUMPscY6PqekDUqZkHoFbi5da5Qo0mGjpIsYxw7EBCcOEKp6twGxjgrHLvWmtLX0QK8UJGsIxkZBclICTHnREwZY5IYxHDYmTHiPI3uWSwlUNlva/ou4duBGBS10cy0nsY9KmvJSXpEyfdo21DXS6xp0LoCpUXj0kecgqSkPxgLiAilyDph1G1DPGbIlDWq0BhtMWUeMmXJZsQYp8P7lJGemnHkSoArdT3De898sWCxWtAPA5fnZyIW3Ht2u50IEHdlhjRGdu1OCPgLiMRZTe0sdSVcnlVVieqHc1xcnnNyeooyFbaZUWnDfKkEhBcCz642U3Es+B6VE01VS/XHK6hqoUYcPE8/+6JglhLL5ZLVyRmr1SlNrTEGUmrlmdAG6xyYEVwlWWpdCyVgKiM6qggaG+vQrgJrQCfAk1JHSl6I1ZP0Dsm5ZHqBMaDIWYBmOeVCxC629123X4E7VI13kTHnz1myWZNgpYo+l3L0RuMVop+noVfgs2bIkS4rBm2JKuO1ePUUIVhHROh2bBKkkDVlUkQpltHissJlqKylTplFysxSxuVMRdGYIpfjyiXOVtOxm1KGOW7H5DKXlmNglgfqrPBJVDMchlpllpXiwckpJ6sFTW3YDnsp8xgZJI/ek4dIbTMNCl3QZhnQSoh5Rfcqy5xc3ZBtTzYRQ8ZogzOuGIJI1/eFQED6H5qMJZJDL2wjw0AKEuoaJ/0bpe0tw5pLczmnOKHLVCUDpyopkk8MhcCg3WwwWVNZh1LS+x1iKByCAa3FGZASvh+IQY6t7/qiXh0FTl2a+1Vd46oa5RzKWBSGvmsJPuJ9ZPBi2YwVAIfuNIQj+N20gG+vZK0Vn37vQx7du6Db7okh8FVd8+XzV+y7lqvtjtgLm/3lxflktJsCOJnPZ4QQ2W53vHz1ihwFhJFioksSlYaQqWKgSlHUQYyhyrZwqgrBbyi9npSzOElrWSwWLOYLyJnge7p2x35zw3a7oWt7msriXMNiOaNy9pazyMAwBCoXMMYi7aBRsFaXIEpUxI2WvqSgbp2UTXMkhAQoaldxslpyslqwmDfUVcV81nB+dsLgI92wR2uLdTXNfIEeLH4QIdqUBfBTVaXEl0ZEp/wZy6PW2qIyf1CVTyVIuN3qlAABLQwhU9ZLmmjKYPxZJKSKE8yUcnAsJVVVVBmUDPRXlSZnQzWv6HxPlyI2iE1ylcaNpWVrJNuTjhTKOGxVY4xFK1uQrRkfEtohxt5oMWwK4VDNb4F5UdxxgmXnRAEJAAAgAElEQVS2bwwYDnJBUtUZSxtTebncf1vOU3q7jqqWwHoxmwmZtg/s93tBgZcxibbruFpvEU7NLOCcyjJvRDS6qivmi0UBGdasTlYsZnOMrbCuLmVri9WKbvBwvWUc3sspoJB1HVIuM3xyAWKM3Fyvpc6pNU1TM5svePTwAUqL/e27wtySpVScEDyJgIZlPYwl/pykPG+MYC+ULvqOUoQmpUFwBVmCo9G2y79HRQnpnU5al4XdJ9+Zw/2u7Z2doNHiQJIa5/vV2EQjk4lFRV2RsBlsEsV5FQukPIuUSDZNuUYyyJ5LtBUqmRVTDpQPZUSB6cFRXcnusjTeVcqQQgG9pKmuL2KkiUIWOiHccgxQZUx1+5QdgZgzOfQ8nlesVktUTgUer3FGM5s5zs9mzGqD1sIRGKqI9kJwG7oBGxRNTnjAKoE3ZygGMaIGBSGhkgYsMQ2kmDHeM5sZFrOl9KcwDD6QsiIr0VtzKtLYyMIpvNU442TmMWeqyqGNI9+5lYI+S1TWYRTEqPBtS+UUVWXZd0FmhKyhnlXoCNHvub7ybLdXoi1nDCfzSsACIRLaFqUtWWk6n9nud2x2G4wt3J0hoXxEVzKQrEwN1mEWM8iGbd4Tsyz407NzYvLCXNLtMUHIEIyW0Rgo6LlbLUHF44ePaD6wfPjwPr/9w0/58tkL/rc/+xG/+OIrnn39kpsb8Elxdb1mvd2x3O+YVVDlSF3os7a7HZ99/jkpJ5qmpqqqglFjQpm17Z4QPJVzpKYuTC0KrQ11VYvihhHZK200KQe6fsd2e8Pu6iX91SWP75/z/v0zHj+6j02R05MVn/7aD/jg8T0Wi2byF0opFosF8/kc55woG8QomagzRBLPX72gaSzV4j0MRVnbF7aWMpxvtKGpG7nnxpBT4vzsjPOzC/7gDzI//o9/w9/87DNu1msu9qcYYwlhX/pRA1obETYdB7aVIoQAOdPU9ZTpDcMwGZ3jfpWgHm8PaFX1SHkcBZ1awA3SzzGSdUUvpAA+lOy9EmRl9ugkRjEqsFUtwbE1Yh+c4tGDFdeVZl1nNuvE3mc2veeiUcyswpk89Y4GRN/P1pWMbhmD1k4mMUImV4gIuHUQMtooXKMZugzJ3Tov4UEtunUU4FAKZf3q0p8bS8ajuHGYyEH6fpgQj3UlJfmmaVjm+S2DLiMgIivX9R3D0NP1Pa/eXEnFqB9w1tDUFYumZtY0WOeom2aa65wVAmvnHLaq5BhNEfk9TpmUBD3eJ7b9QF1Xsrb09GtEB1OSimZumS8dq5UlBGGWs2pRHH8iRHGAvRee05GZdSwZ56RZLFcs61OSsLWLZqOSGcng96QcSTlMM66AjFvgy/tGOu1jAJcnZ32r2PJd26/AGJMmwyvpZ2nalAhCjWkh6fBwJyRqQE8sIeX/ZFQZqpcHQpqiqrC65OLsxsix7Fsdvi8fzJY4vRJp5THiunt8Y83lTkgXgwwjB+8hJcm6nGSfujj6zc2G58+e8vDhiuWi4mRVC/hjbIQzNnILEq7wzSiS6CKajEMeGpszDJJt+UH0uqxRhEoTo7kVUY9Rb4i+SNUIw8doLKdsQh0RGY/XvigxNHVDioYUPD4OVE7hDAQXGZwQDsfKY7JidrKkmTW42pUMWWrrIcQywBuIGWKCzidCDEJoXaR/rLHgI37XccMVuAqqGtcIh+B8uULpTEoeZ6DrdmSiyPZUiTokAWcUQ5rv3Ct5UaGNo5kveP/JY5bLFUNMLOZzrHH4MKDI7LqBzXbPdtdyspQ+SEbAA22759mzZ7x4/RJXC0u+VmP1Ycx8DFUl5eaqcgXNJmjkkXxv3wp9XUyB7faG6HvOT5ZlUBpOVws+fP8xv/Pbv4lJkdVywUff+4iLiwtc3dzKmkY+Ra1F0V5rw2K54PTsjIuL8zK3GchKEcd2REEHa6VYLVdlYN0LlN5VRY5Jgo7Ly0sePLhmvdmz223YbbcTstMYM60nmWcLkxMcDfo4BwjSExyzv/G9oixviLfIebNQdxFQypGVIB8F8KTLsY3XVU9UYEYfUMpppJfLEhxLmbzATzOi6F5b0qLGZEhDInaRTUyi92hAlxJ7VIWlxppiq0ZofibGjAoiozSejy4E9W/ZDaVKdiqlUwqYhCLzI2t3LO0LknIMFERPb2wpMX2XGnuuHCoA43lLsBKo6oqYhMFpsVzSdj3eB3QBEzknFSVBs0tmaY2lqlwZSi+97nJP1Z25TlAo4zBVjXK2EB1kIknep5QgaHMkxcyw83jfiSpKAb+YsdSZExlNTKLXOM3McgictD5k08MwiEJFI9zDmUSIw9Tr46iJdRcwM13zcrtilGtv39G7vTt3aGH1P6AtZZBVIXyGqtRlU45lwYxOCXKRVpKSxHi55T+jtp9O0iBVqMP+dJFQzPLGrKI4xpE/kQgqlb2O/O9pyk6n1HBynNy2rDkTvBcu1CO9KlkkErX6LvD6zRU//cmP+fST93jw4Iyz048O5amiOi/lsUAmoFQSDsycGPoebxXRivEIZQGE0IsTbPc466hrRwimDMxKACDM7HKMXdEeU0qzWCwKctEcFsGdqMdYK7ONeQ5hIAeLzx5rRR/Uu4h3EoH6OuC0Znl+xmK5oGocw36DH0TtPUeZVfOhK73KSO8zxgr/68yVyFo7Bh8YhkC7bclVDXXDyeUlzjmWJyfM53XpVbbEJIw01lpclWmiONTe+hK03OGCzyOPscZWDQ8fzrm8d4/FYkFGMYTIizev6duWfTew3m452e3x8YyYMilL6Wm/b2Vu8OuvqZoaW1mqYhSsrUppqqJpZoXxSMt8klKgNDFIP3C7W9N3LV23Z79dQ/I8uDxnORd9xNPVgo8+eCKlvRyZzWY8fPiIk8tzfLx908ToivIESjLMxWLBxcU597ZbXr34mkQmaSm75TSSTQli8OTkpChPJGYzQX8aY2WYGsX5uQgwb3cdXz77mu12U5TmJTuuqurW3N8IDJvN5zLTFcJ0fGNP8JhXVJygfQvEEEIgq4hRtpRBE8SExqJKf16VgXljiqNQmSFHYopkJQaQDCoyBSDjo26U8BAbA1Yb+jayTz27fqBLCTWAUxKYRyVZkDiYBEqcoKAUgaDQRoBI2gq5dU7CMcrt0ypLQaHzAfSiMsWoH2VypV8q91hAfhjpVU/7mjK/Aw+rKc5KFUBRjIE6VmV9iBkLIRZCDCm3Ss9sJPcw05qSn6UEexTYwNvBs3YVymSsyQy+JcVwINxQY5k3EGJg33bslGazWVPXc5yVZyYjPVHjHBlFLJyfajJW5bvK+eWcGLzHZFhoM5WTYxwmMJFSerIFE3J0KlOPBBYlOLw1WvHL08FfTUViIuqWOrFKQnwdicIPqTVS4TBFN8yQbQ0WlC2RxHg1pnSxnEQ9E5eVEyr6qeyZZZVI+pFFi017X5xxgYDlxAQHy6PjGyG28jk1Nqrv1Ipj8JAzs6pi6Fs2G2jbhLMWrQyf//xLnn7+lB/9+b9H5d+D/AGf/OADcha2i9lszr4d2LcbcrOl6yI69ey3AZtbdtsHzJxCzYr2Ws74rqXdXLPbbGjbHq0dWtecnFVTDyEXh4rKtN2WqyvPs2fPMErAPDmNfZRU+ofHRLiKi3v3mTU1fRhQYSD7gf36DUP0DNFTN4Y0VwwLjzu9ZDab8/7H34dyD/bXr1H7HYE1fXtDGzzr3b4oCSgWzbyoEjicrtFYwBHbHX3f82a7hsUcs1xy//0nrE5POTk9odtv2O82PPvyil3b0/YBYyuUF6OnyJgisZOKmvq4ZYRdJqiEjFMIquzRo/f4F39g+PD9Jxij+cVnn/Ozn/2MZ198hSHz4ftP2O5artdrMhEfEpvNjj/+43/HL37xGb/1Wz/kdLVkNms4PTlhVs1oqhl978UJOlNKUQPr9bUcSU7s94LsTTHy+NEjHt67xyff+0jYdJxj3w00sznf//7HKCcyRsbUmHomPJbH67AI1gIT+CErzZPHj7i8f8lmfSM9mJn0FHOK7DYlyo6ZyjmWyyVPnjzBukhOia4d8IMY+ojBVZb7D+7x5Vdf8eLlc/7sz/6UJ0+esFotWa1WU4Q+kjiPc4AwZjBqAgcNg8D77Z1w+8D0UcyGFjJqEjJ/aDK6yaWPrcXJFT8nmaVnv+9K5QHCKIJb+lNGGyrrcEpQ0Vgn2ZgCf6Lpu8h251mvO0E4d12xHZnsKuyUqeSJ7k2VjE4FkZbKOmKULwF4QiWDZUTzjs+YnBsUJY2UiIoJ1TjeSxCk5ji+oIrht+aATRhFYMfrGmNkt9/J2ivlVflOWzJ2cXRTL5YjlpvRuN7hSB6DqzHTHWWI8tH9Msbw0fc+QuuEtYbBt6L004vW4dD37G42hJwJKaGMJWUYfEQVNCgqUKhVcEBJB9CM/W1Bi+ac0Uoknna7LVlbjFPUbl4CkyQI4lKBRB8hbLMENVoLGQfowotriprONJjHu2y/ghOUhTLKYKWc6ENPlzNdznhb5pVKhKuUHBipR6UKletpAYj2GyU9VqUkUct9yxHiHhVjafzKlmMkB0/2A3roy4UQx6dyFgqmLEz0VQZDwkxN3UPa/FadOCcUCucMOUXCIAYoBotRht1+J4KXXS+wYj+yaUhpwbkKpUxhzfAFvZbJweMH6HshuQ6xxmotow/7PeSI1Vnem6O8p0CmQwhkooxhqBKBIVp+1igRWC1GM2d9qy8z3VjrcHWDqmvwPXHoMUNLaiWzRBmMrZjNlzR1QzObCxdhgaJLBJcZQqT3nt4HAQ9oKXkY6yT7szJQnjP44WgguO9l3g7FbrPF1TWr09MyCCykvjJ/RQEJCGXVBDJQ6q0lrBQoI1yCUpeR91mnOTs9AeA3fu0TnNFs18Ju8/qVjA28eP6C66sr0a0rsOwvvvyafvCCoDs/Ybmcc/+eCAjPm7n0UpzQqO12W7q+Z7O5YT5rhIRAa3mQlWFWRHVr5wQZCIKI0wpTgTKuOHSLMRXHxCpybmpCXI6AE6UVTSXf74yW3k8lbByprFU/eKKKkEovymhSbidnNGoCxhxx1rJYzFks5+ScePXqFZeXFywWc4HeF6WK4wzieG2NhvqYRu349bGHdfvEpD+u8tg/BVScbHQMIypYFRSngJOEqUk4KkdBVYn8c3Go6lbWrI2Q5zuXUEboxLrOsI+ZpDQpZVHQ0FocYkpFtzGXUZSSGRUC7ZQjpEwKGVdGeY6MxgSSkmtcqL6O6L7GLEcVhPI4b6qUmuzQCAUby9Dy3NojwM3B2Y2PtwQlIkws535EJj21l6R6dnzfRsN3bCdyvh205Jzp206qRXpG0yykIrVciIJM2xKHMD3rPgiQKsQkpOhRNAilOpdQIZBVwZEU7k8psYNkVDLClFIQe1JUMxTlHk1JTJrgnmNvcAT7pUJ3Nyp+6IIpuJvsfNf27k6wkF5npfBIueLZ0PI6DrxOAzunhLTXaFKUtNwZi3GVpNi2EhJnfRiUHaG0xhioC+AlR3K3EYeX4hTjkBNx6El9B21b6u9JHJ9SzCpLnTUVmktbM1OKmValRCoXUKsRXn7YVE5SiqmcsMp0kUSZj1KGrpVeXF1Uv1PMGC2oR2cVdbXA6DV+iDgvXI+NK6winafd7+kWjsFXoB1934sxVonl3JW5lkDXB9qup+t6ur4lEzFGHlxBB1q8H0iRIu0k6gJNpUt5YKoNyHkpi3UNrq7wQ0e2Lblr8fuefeuxSWGqGacXDbNG1OVDzKQgpeGhH2jbju12Jw34YSDExKyqqSthhbHGYqxDaYsfAuvNluvrayE5IDP4QNh3fPX5U4YQaJZLhraj7Xravhd2ES1gI2AysMJBqaTre8u5K6yV0mvw4dCXAVarJYvFnP/2v/5XvP/eQzSZv/qrv+Tzzz9ns7nhzZsrdtsdjbMyt2Qcf/u3T/n882d8/vRL7t0/4/R0yUcffcjp8oTT5YpFUaZYrVZcX13TdaLp9/77Twrf5koCumJ7EhnnGvpBHJkybup3jccPlrpq5OE9WooSSBR1giTcnM5I6U4Zw8ytsEYyAWvFCWoS+92OYfCE4RA4xDAi7xxTRJyzOG7nePTeQ66vr3jx8gUfffRhgenXExDmUC5jMshQSpulzKe1pmmaqYc4jkv4cJtAW/pmGqvBVpL9hBQlOyPTtX3hD7Ui+hwUIRqxHcrQWEMk4pMvGaFklcYqtFaiAu8MzjqaaklImWYx4Jyl3XlUsvSDJ4REXXq8mkwKiail+mArV1iqCgOVSlKKi4nQgZtZjinkyBBDIic/ZX7jZkvmNvZYj53h9OGcJ1owyUIPYJpjhzhmlG3bSsZfevTSz1NYZwtV3Yi8TdNXSKxwUAGZRgyScIOO2/E8XUqJL59+RlVVnF3c4+F777FYzjg9a0prpCWHzGvesNsP7LrI4IWcwFVSjVDGYE3GaojDANqUADCXfmIqbRwtRCg5kzXUpSRvbSWZ4NRTlSqfVARLcMBYyjXleonzs9pOcmHCGPNu2zs5wQz877pnbjoMjj61DGTWVWavEjsUVyGQtBaG+JM5Rmv84JE2focKnTg9NLbcRHGCcuObTgxHGDyp6xEi1FQIXpT0O2KUEqGLaKOpqpqh3ZNjpCJSp0STMg/axKlxXFZzmhQwOaOjADL2+XZ53/thinp32y0xRWaLBhtVYd5Y4Yee7Xv3SDlxdXXFj//6p+UmJj7/7Blvrq7puoALA0ppzk5n5FBhlGKz2VBXGecidVWxXm+4fvMGY2rmtcW5Jfuupes71us1fd+xWMx59Og+MSWqqqEq8z8nJycTV2iMER+KHliJio46x1y/fEUcPPOLC2IIBJ/JHlQQqidVeDCNVaSkGYYEviX0HX5oefPyFbvdhvX6ZlISqJyUP42RxZ6UYkiJ3fqKdt9zc73Bx4GQI4HMphMu0dbVdClhZnM0kRgGCY60wgTDtpeMpus6UkxT5KrNyE95MMSr5Smr1ZLO7kufuaDDsjBNPKlq5rMZ9+/f56OPPuTlq5e8uXrF40ePySlzfnaCLqTSbedRGmaN0DzVTcX5xTmrxYLVYsmDe/dYLOacnpzy3hM/RfvLpSA5rZWelkShhgyENBoc0Zgbn59YaO2Vsrimxm42t84r5zSV56rCH1lXFTFEQiclsmAM0TmaZo6QbPtSSqMkAJKBVnWNUmCULdG2ogbQhqwUn3zyffquo2u/x8OHD1ks5rRtO5Xsxn5TziUrVFraFEcOcRyQH0t5Y1l0VC+QTfqH2mictSIzlBMMTPfLlLaJ0UayOCXXwhSgTO2K/lys8Z1HZYVTWvqLqZy3F6eiVMRqzbxuYGVoqoA1jv1e5NikT1ZAJ2UKwvcDKiuiDWXcJU8zoTlliJmh68Fa5u5gD/f7tpQ0c6kMKVxRbzgOIg73dyQiH8tRaYQqjI1uDuxJh+BOKUVdNwfAHLmAP+JEuD2SumuNOPkyTkLpW44yUCkfMlX57ttD5Tln2t2G7Tqyvtny9devqJqK88uFCF2nxLOvXrJe71ivO7a9lEVTAkxGmSRlUkqfEwquQQBJOUOICZ2FgTeljLaFcq9k48ZWJesuPdJCH3e89g5NudGSSwaqjcO6GhcCMWpC5J22d3aCXy4bnHQW6GMkkOgqmQfsFaz7QYbfXU1YLjDa0HfdhCiVn6U2rPVUEhgXzMzJmMWQEqnR0uZL8iYpvxpyFvZwshDn1nVFV2lSCDitqFKiiomh92xsRVvXzJLDpIwOkHKgr+yRQjFHi2uUDwpUwUlGmiXams1nnJ+fYa1lCIkXL6+E9T5ldvseHzLGilF3WnGynMl4RrlRg/fsu44YE10/FAMpEVylRQUbldFKgCFnZ6cslgsy4FxN5WQAdrlcEIMXVKq2ZBQhCnS8mR9QuRlYbzZCf6alvh6Dp217+iHgQ0aPUyQoQjr0UIMXXsw2JQYU0Top6yElVltVkv1VwuiRAK8NoVBdmbrMcimojMGbAWVEZaDrWqwGchKCaaNJxlD1PXUzZz5fgXbUs4CrZuQMi8Xy1gOwXm9IMdH33VTmHgfs5amTysDF+TnvPXpPjHoSsJZWmvPzsxKdC2+l9JkEtCCIXylvpwwRRUjgQ0IpgzIGY0TcYN8OoDwjzF1roTwZZ9wUamKkElUOL6+qgPWe7XZ/9IBltrs9Riv2+5YmRqx1oqwxDPhBSuS6gE+6QcYW2m4nzCMhEmNBcyqhPtW6qLMXfHtWCnQSIvpK1mpVSemtHzyDD1hjJ6XvEajhbIVShTpsQvWJQYxHz84IZGq7jgNcQSDzWkesLhJMOaGznYwwqkKj0cqCDmiTUK6ZdEidrSV5MhmdPCplTAGzpAINMkqjMZAbNAatLU0VZMA9eawZ6KuCMCzluDwCKZTCKofBFES4gG1yNgLiMbmg2Otb96vt+glBOpaPM2D0YRZwvI4j56Uaq1C5GPDpGhwZ+JLNjxRjOUmmnfIY6IkjCyVYLNVBAfIVRzy2LHKpuao8UhEeytXj9R+G4eirFa5egh+I2bDvIz4FdOWpnEhMRRqUBTczzJyajquqCsn3fI7VAm6Ssq0uzqlkrU4qbEoJWbpQLDqMbUALIbgfpB0TvB8P9HZ7ZATJZJkrTylLhSxqcragqm/oe337pvLdZtLR9od/+Ie3bvxIYfVNH7hVtLp7AG99hbr149t3Nv5F8Y3fWh68t16+8/PulvS714unIznuKx5H8PLLW+n67WM6lL3Gs5j2Vf57BwN5uwT4Db2x6bN36vzH1/24r8DYSzl8wXee691jOJj173j/N+z37pp4a12UqPbtv98+p+OoekJQ3v2Gb/jrNMfG6CB/2Vncfs8BcfaNb/uWf3zbL/Kt147RlcAU+efjTx2t77del51847P4bd/+DVft7ffffdOtMt7RG/PbZ3V83W9/83dtt86M45Wqjv77bvt6+0Z807r8zs+9tUTz0Zo4er5Q33Hf//+13XrW75JuftM53rUnR29+N9+j3v7XaCOVeuv3373l23/7llt9y5fd2d69J6jUrQzqrV//ks/+Spt66y/f/g3fsO985+ffx3ZswN/a711H9S3ne7ArdxfBnX//itfrm95/qyn+/2RTdx74X/b2O5+ZXvsln1Lqm/7+7Vu6+5C+w/YrGayjBzx/11P197ylfOdhPhzAL33927a773oXd/T2h77hU+/2Utl+2YW/+/vvWm2/utf5pnX5q+3g257l/A+1NP5BN/UuCcL/h/7/H/KZG7dfgTbt7WxOwQGxNS7eb7w6x5mNvFPKoXp6xsZ6N8jrE6CgXPAxos8lPb4d2Y49AQFU6CPoXT6KlscoQ8QcZauq6t1u/OHw/1FuOWf6vp/+bach5Hf5LIyR/lt3+Vt38C0L9XYYf+v1b6wHqNu/u5vl5Szlx3Gz9psyyqN9jtnfkfEe56P+MW3Sdzuc2DAquP8SB5cp10gdsms1VhPGrPc4YykX+XbF7QCYGEvDI8NMKpnPuC8ZTB+PWf6SpmFlphLfSFauQBiNyuaq6uj7xkrI4Wy+82xvF1S++b1Hp6vefunvb8tZWKfGw7G3Ryb+MW/fWcHJmRwPYKZJ6Hb83J0Kzsjpe7xXWZPH35Zv34RbVZo7N/X/pcM7HJua1qxUmG/7ge/a3n1Yvu/IJLJV6KQwGOqqEURj26GqWgQ9ayd8kyoxqzMomWMZhi1aZ1ylWCxOaZoFq5OH9EPG+0TXrgl+IHjP2cVDlssz7j18n2rWoLXmZnNF227Y79fsdxvIgp7UqWdod/z8P/2Uy8t7XF7e4+NPP0Ebh4+K65stPkZQhuXJGUZb/sO//9GEgvsf//W/5uLevVu2+5sScsVh8kR/y3v+c26bzYY/+qM/EuYFBb/7xHCxrDg/WXIowaVDb4HDObRdJwi/4MvwraBEnXWFB9ROxi4jMkJ+EFo2ci5KAZno47TIJ5FUrUUfrPSQRhTHyFahlSKOgU0W/UOFIhQKrc5n/uQzT0xgjOJf/stLljOLjrrI8GR6WyS7ErhNj9/u2V/d0EePqitOPnyMqhzKSh93dL5jieuQe9y2phP7EJNLnnqPGYXKdx6y0bof8WIenHn5qYBk2e0Tf/Knz4lRej7/y7/5N1xf3zD0HSn4MvZzOLaosgAjNVTLBfWs4ezygtVqSV1XDH7Pbr9lt98IJ2YBnFSLCts4lqdzVAioELk4X7LvO168ec0Hjz/l/sVDfuvXfsjXz5/x5vo1wYH3e/rumirPUcEQdpZh2DEMLS+uXvLmasOb6w3nl+dUjaOaV/R+x7yu+e9++N8UoEvF//A//c+YumHTDlztB7ohsukiPiVCThNfaUyIckWWdXoUHQOqyBqpcl3TlLWqnCZDPXJQjRZYKQFlKEZjWaD16rg/dxx8H8YKGNsIWdip0n7D9j/8W8gJZR0nv/PfoyuRyRol10SLaZxRM+UAD+j0cSxA/jmKt6VxMb6VAx/I//U3GJtDe+pAQXLbSU3B0t2PHm1he82bH/2vkIU16z/+H/+WFAecUdSVo64qzs/Pp323bQsIMnfkZRZ1COn5NU0twszDgFVqup8ZKbV678tcpxICitLnLBf9yIcKU5UpYz8xxsm3jmTsOeVCB1dhVCUKO5sNwXvqxYLv/ebvvFPw++7K8rEgi7SQ2mYSplAbxZgw+ZiwutzelAv1TixOJwsXXfLE6Am+IyZNzGVZa11QgxZlLLrMzKQcGXxHSAHhWTLi8YkoEQAjBU8qs3MhRhRGuOsoOmjFGR9HIkopZosFi9XqjhO8HZ2MpvLYCeoSf7/lLP8zZRwH9JlsMicZMSoebPNIdnAUiQMkK0TlGqHikvumcE5RuSzzyFqVgVVp2PsifTOCeciyyMXgjJmBfEU4Ys44lDP1NAoR0wHdWdWyJGOBwNshozhEp1W9oG4adHDYBALtacn9QO57+hdbuqs1mxevGWLAnaw4fzLD2CW6qQ+K1VAoo6HBM00AACAASURBVO6GM4esKedUfnVkZJIYrZwVOh8jVw/pbFapOMLbM1jTnrIr3K4vGF3t+nrNzfU1fhgk48gCyBirF9kg6b0zhF7hibBRBDz1UJGyp+137PsWa4RNSFkj6hjZwCxRaYN1iufbl3T9npv+iuZmRkgdi8ZitGa5muF15vpqw9XrNzSqJ3nN9sqz26/p+x03+w1t5+m8Z9OucdHhgqMPLdEfQfIUqKpCVw0qiKyQIqFTQufyhzLfW/iQ7/KRjk5Q2jGalAJ+aBn2G0iZ+eoEbSvJzKaqFBOQQis9Pb+jsxrX5xTUMN7G0Q1lNHraVyZBPB79UChboVzNRLRdflIIHjIGhcwyH+yEJqdS/VJCeXN8vIfjGBfKSMJ/IO88zqJVecBu2as8ncp0Db6zwGyP1WcyfugIvieojEo1OkeGri7zixrN+CznMh4l4y2uKuCgFDA6U1lNt2+JZYSkqgSsEv1AVVdYJ/Y9lBnk43s+gYpiEk7RrEXUfQTJZDuNkmgctTXMZwus0XTtnqHfE/3wzhWBd3aC3guDA1oyQZ0zKYmCQEI4/EYybGFRAO8jSUVS9gxDRGsZiu3dQE6KGF8TdU3CMoRQeO4qYtb4kMuQdksIA69ff43Qp2l81PL5wiATi7CkHyJtH3hzvQPdE5Nw0sWS+Qhvnr1tkMbbf6tEMyItDw5dqzFmm9KEkcDpH1ulDYAheNoh4/btdL7jOMrxkHNKuQQ4B2OjtKAdlUaIzgsWXdjvhefQlhk2W6I/ozVGCYfhuAxCEJHkjEC6Dxph4jBG8oQ0jhUcic1mK8ebdQbVlrNSxHxGzCtymqMjwi7UvaG/eU1/3fHmP33N9uUbrr94TsqwePiAsx8sqaoHmHpZKKaKIczHYc1hO9z1fES8UTKGDDmByoqM/eZSz+gA1ZhL5mkXGdDJklMH/LS8nun2e/p9Ry5UhBON1miwjZHno6lRVpFVYLu7IeYeV420e6HcG422GmUVQ+gJOVN5g1uusLMZv/js5wx+R0otX70YePXqC54//QW//7u/zyff/xQfIvtXL3n19CVz1zL0ka++es3V9Rvafo92hno+ZzafE0NPSh4/GAbvqeLBqOYM237AZMu283QhMcRMLBJnMlpQnGC51kKmMFYujp9QIUkIweP3a9Zf/ILkPbNPfxNjBbE8zeuN/mRE7paA9VB+K3flVh92fMeh/KdLBpZRAqabSgJZVEViICshNchKXFYqgSJKqPJ0DthCKUlWxGyPagupOEMmAV/UYUnl8QWlDkYmT78ox3tw1VMgX3avpv99uzN4CwiDkF7HHGmcLahuGdtyzqJUzTAMRdvQCmtUXU9zprvdbrIH6+trhl7Q5qNyBUoxm89ZLBdY59jtdhNBxPH9GAnIx5J/jPGoInNwlOMc74MHj9hud4Tg6Yf2kOm/w/bumWASxfGRwzrlMpSLwliHaxq0s2jnIBlRYsAyBLmoIWnJpKJhGEr/QCeCjiSliGicq6lnK7SqQAtrQwgd3neQCztK0QbLMQuvn1cMnYJoCNEweM2uSyidSPFApprItG2PUv4QfX3DH6AMPx+7wRJVlp8mH33mH6EDzMDgE4OP+JgE+j4yVmgJJORBFkOrjEObDEYyenIxpKWkmZQmZPAD7No9/eDx06LNWKOYNzWX56ecLOfUlaMyYwknHTgRtcZACYZgurqqsGaMj3NZ6ekOghKgiVB3kbxt6V69ZFhfc/35T2lfvqB985qrZy/xg8f7iF0siKGn6wdUSJA0OY2k5+N1UNylmDqYmLLYj359KGtmsgqH7O+WwS7ZW9aHvRXjmVUi646gdxweacghksd+TCEBUCNjC5nsI5iAihGDcF+mnMhRk0JiV+ZwlVYYq6W81Xak3JNVZPeyRe+3mPmcH5ye0VQXLOeOe/83cW/6I0mWZff93maLb7Fm1tJVvXFmMJwRJUAi+EV/vwBCgAhCbLJnpqeX6q4lKzMjY/HFzN6qD/eZu0dUdrMGGFJWCFRkLB7mZs/evffcc89Zv6Z3Cxa255NXl1w2I1P0dNdLbv/Xv0Mrx2Gc+Gptefve8bQ/MJWC6xuaviFpqXd8gr0XlaLTdSw87kbaToGPdCgaq1haS0iCEEy5EFEEkdQUEX11ClXqeG9kADvsBh6++RO//b//L6b9Fu8jr3/xN1z/5GdizcUJFFX1AX0eBE7w5BzPTsnLXD2J3urpRUAZ82wTTiWhqxwj5RSnXJVyjIcndByxcWRZR1ts0zPhCBhG3VKUIC9lrkprpVfm9VRqCNOndfRDslThfB09/5Hz3evjh3omc6fouh5yIviBYRzwYSJEzzgdaBrHer3GWEWrHTEFaa8oZNxGVyWnsyRjTn7bGiibpqFppGc8iy/M8nwz83t2v5gVjFJKdX61JvNVeSelxDiNqKdH2rZnHEdiDNUD1PyFd/38+PE9wbPm9vHSKl3hXRHhVcbUpFqhiq4/ZyhFkzF137VkLLmI+3MGki6U2sPQxsoyVkWUS4IEwaNgal0p8zmIbJiuX5cMMxXE008prHEUCqmIzNbH2ZLzg/ISmjh/gJ4HwiP6oX5cyf0/+5iJRbO826zyDzWJqfNGeYZRlcCPM3zirKg6SKUmfbspRA5TYRgzu4NY8ATvoQRWi14q/1RYLnqWvRMoJMTqCVZePr/PGKyzKvz8c6KMIVDp6ReAw0gm4d97dt99zXh/x9Pvf8dwd8/0uGXY7claUxpH7hpS6xhjwAbRnBW3E3UcyD1erxfX7zlWUKuIMhcD88aTq9QfHJXggZPI7tnrFwmclEJW+eihd3YxZF0rqm6uBMHZDSWTa7yuAuuliLRgjpBFrVFrI0LVpVBSwg8jSgeULjiXab2nt5YvLq9YLzou1guuFzd0rqc1DSvtaH3EpYR1DYvLG9CGwU+46LnoGnaHkbEUdGPQjWEsGZ8zh5DQB097Dq8V8DGjQ0Zn6e8YKf3QqmASxCLKQHPwq6YMx2Cgykz6oQpaB8b9lv2Hd4zbRw6P90Q/ghIU4Zlq5NlNlQqvUnfqGjjeoTkGzkFQnSookeY7/fx85KpZfATUS41VYaKEkfj+a0oY0XGgOINuOxZXtzi3IOiWoh0RTTgtLQnIxwTg1Gop5FMy8Kx6PV+fHzvm6vcvHGevpxS0bStEmRKralcmRs80ibxZ2zY1WM2SrAWdNClHUJBzEgA4y/4/z9/OCJRI8YkYA4pj9T5/v3HCQzDGME3TMRAe5y3PiDqlFGKIjIjQiKgWVYsqoz/2bj96/HhneSOi2LN/g1GKtlscF07TdWSV8dkfF4fGIg6gjkIjWY9xaLtCaYOP0hjHgGpEUSKpRGMNRQW2h3umcUeMnuRDZYsKrCpKDQqs9A9RWYYvLRinsc5itaNrFgBM3stFj/HZwpjBsHm5nMJA+UEG9Vy7pB4/Yp39zz4U0PULFgvHYrEUCadqlxOrvNVUhXrPM7A5gzLa4LRFehuacUr4kNgeImNwDKHwh2+fuLu748OH9+yeHlgvOn7x08/46198ye31JZ+9vkKVCLX/O1d7cwPdGnsky8xMw5MGpfzfGMMU1elBLZnwp6/ZPu54+w9f8fDtV4yP9wxv7yjJkosjX6xgs0TdXlJeXxMuL3gIB8ruAxFPV7NM5Vw1WJ5Tm/nKwWl7OcuyXwS1Wcno/KfnjEgVIT2ol79bqKiH4aX/o1Gikwl1g80F0zhsY8XjrUxkC5NJGCImgfEerQqQMJ07orqj94RpYnf/wEVvWHeWL/sVl67lSjn+/aufcbnacLnYEEcFkxLo75BRKrKwhku1Qi0u0I0Gnfm3r18zxUQoheJaxpw4RM/7/SM7P3I37vnm7gM5mtMmBfio0RYaMkYiPBCFvQyYiHhMIgGwqBM4fWSCz2xUIEWPH/ao6YD1AyoeUHi0LRgrnThzjFnCB3iO2MyksFIrxRog6zkcXRrmwJQLjlKv8+l2RsQyzQK2VPPvpBju3jHdv+HxH/4jTTywyBNJFy6urvns3/1vLD79KSwbvgmKbYBdrIjHvPzKOWuh7kHHb748nleBLz+T9zkn7R+vBwWlmZN/zcXVBX3rGIeGadiTq5h/jF6cb8IkerpNU5NrS1EwTaYGrqEWG7MgyILlUvp382C+EBU5BsFSCn3f0/U9t7c3UKqB7+MjwyDuObOU4NwLnOdsp+qveH//IO9XKZEXtP/KsmkA7XJBAmJJOCW9oPVqQ4iyqcYSyEUkscT/CbJJhBKIOZJLwiiDMmBbhXMVqddKpHWahHMBpQ4UNclmkaobtMrIflVqSi3EAXSFq2xBHBdizXxHGudY9j3X69fiM+e9aG4OI9+o358J7tQKcK7oypxJnm+LP1w+c2Imv3NerZxljf+i4PivG0lF77ySeZS8Rz9NTOPINE3sdjsx+AyBvuuOC7vkTNaJRASkgp+i+LdmZcnakDQMvjBGmJJGuZ6kDA/bA2/efcB7jyKx7B19a7HaVBq+ro7eNfWogVfP2D8K731lk4oE3TN6di58/1//K9zd8/j7b9kPO3HfbnpoN6hujf3kFtYr8s0GLtfkRY/Xmv00EktmmhrxdbSumuNanJUk4XwQXx03pLrRHLGwuZpQKAR+QyESXpTaOD65Xp9n6zObjmIoxT6752KdJZue0gqMQrcabCGbzCzNhVbkWEiAVZaIRmVBXnSpQsVIYtN3Da83K25WC/76+palblmohrLVHA4TUX0gTYWSlFgbzdl6I6iONhZtRWIs60IuWoKUU3Ta0WrHsmsJi8ykE1+sPjD4VG3R6rtWiqQMQ2nEdugob6mE9GAURhfaDLGyPuee2Qwhz9cpA4t1x+c/+QT98y+J+y0/+/ILbm6uuFi0lflbZjBK8o4i/5BkvZznI3Ld9SkAamZNY0hVhSjrLIJOZ0FQiL8ZVWW9dAo0Cm66Bm8nQuP59NqR9hC3A9u7B7bDlodlQ09icbPn0/5zWho0jsfiKKXuhUVSJFl7c8fvuPiO6NTMai3Me+Jf2g3OqTNn96YmGKc1iDwT1YvPqoL3I4dhOqEUFRGMMZB8fUbHEaWK6A/HiPdBJP2CaOI666ptV2151JK/bdvjebhGJBm10kfHlpMFlTBttdZCsIFjr3JGCEEd+4jWGqwzP3pL/fHO8p2T10xiPOqM0K/LVCTQEar1RTyWxkUVUon1QtdIbgragpFCo178jLFFekUI0WZ2gy7MHlg1i1Ri21FmXDZnis5kEiD6gUp5rM10reNitaGxHTF40ugZinlW4Z0tifrv54yqj8Jk6rQsP7r+ZsjtLy7MF8e/djU5Z9A1m6YUUvAE74/BMISA95NouSJkF1UEmk65kGsQ9MkQsii2i+FaIWR1/NC2JWsYQmK7P2A0rJctVi9pnZHRmarwPnNs5RmWDo4xVWxY1b9bFEpJED7fBErJPP7xT/D+Pfs37xiNIRlHWV6hNtewvsJ+9illvURdrMirBTSWpDW+wiohJSH12EDKBWed5FFUYvsseEzt1ZTzSu/8+sq5H/mElVhUsowyUDen2Ut6huqP2/SL8Yq5MtEKsAptNbbRFA1Fl6PhbEZBqjWCsZTKRLQYdNHorKovoqVfLrjdXPDJasVn61va7GiypRwUU/ZMIZK9BBuNq71hi20aSQpcFtcOrVFGHUdetAXrNNYZln2HahR0mo1xbCfPN/fq1NdFen6pGKm3T/ixfGh1CjLlbCillCr7VY493ISi71tevbpG/+RT0mHN69e3rNcrFq0jBH/MV+bnqWR13LxPj8Y8s6iOGqJzH3Aen1C5GgDnmkS+QNfEwaZqW8YJq2GlICpPMR6z1IwRdvvI47DFh5Hh7QKu1rSNwrUbEgsmpXjKhoKhFKliT8nXaW/5AeBUCkev1I9tNOd5eQ2c8w737Ide9CisMeDEFi02lpzt2fquK7VWYdLTA5gYW0lCQohMk1RnpjisAWus8DKyQLtyPvpY2Qn6JEmeOMx4xnE8mhFrJQmCMbLm5uOoL11K9V8sEryt+oFLy186fjwxRj+RZ8ab1iSt2Y2REDxTmmq1AdooYvGS9xqHdqkumIizhc5odD5QokEZRciJLB1D5u6yaWaMuEVli8Kc+ZYVIQjUxyXHkcRAUYmiPKVM5DKAWmKNEgXyEsFnTCy4dAqBAmtwJODP+9V8/Y7715+5JjOE86xQ4P//OcJS4OnhHjUZdPasliucc0ChcQajOnGzrwt2uVpijRVGZ0oCmXovQVBZ9kHhs+YQNf3mlmXbY9qWYgxRgdOapmu4vt2wuVyyWooYtbUWo+3R4ZwMIacj9HkKfKeUI+VMioWSwcfIGM6qqQJ8eEI9HXDaEW9/gtpcY7/4EnW5QG86uosLMI6i5UMsdhoKhaigZEMsCpJi8B6lI0YHXCNO3MvFQiyijNhFKSXsxRl4KgiT0VDosidWdX3XtihjSEaJRmtM9OsLyZSPMMMcUAtKPXdbUMZIwKFwsexpO8dy3cnMVfCYrKvoueWQMslq6Hv6RU/jHCZkyBGVE69vV1xtFnz5+prbZsVSNyyCg5goMcEYpJIpCSs7E5kk8HUyIjxhLESHbmW+Uh0reCg+46eEL4VYRMMzqUKzaOnNYi6LoRSm/SMhRYKyLPqFsIdLqlWvJDxZSXLgisybSvSpQhn1AVPVQ9LZjmX7mq3+X/CTp7z+nG6zobWGqM/6hzMMqqsmqJINOGcRXwgpiV/hXGDNC6xWgkYb1OzVpxXlmaCG9GOVjhW6C0wx8rjdYccndDiQ/IAysLxY8WraYEqijw+4xz9i9Y7GZfLyE3T3Ce+HQiyGhDuN1QgWe/Y5PGMMFWGxPytr/9x+cHy6FMcZSwRZmUlr8xHigWHYsd9tmaYJRWG1usRPXmzeQsE5g7aW1ULs55IP7O4fyVi06+SsVEPjOrq2rcxSsX/KKbIfR3zw+CBi36Y6Tgw+MOxGpmkgJg94gcFzQTth4AYGVHbEUkjZC7tYaSDWAsVTtHlxv/7y8eMrQRfRJkFRwoqqmbIyCdcmJFEVmrxOEqJMU1BOQdIEp9C6YFxC2yTwptZoLQaMmJmgYdC2/qzOx/4KKoqxZinMQvVKy2KUKrI+N8dKU3pgqLpx1XTu5Rxfqh91ZFUeTGpP8Afp1/NjDnRzq+M8COpycquYM9m5QHj2Asxf/MvZ3MsE7uXxkuisZ1KMnjuZqtrwFLLLYkcUI03T1ma3Os75yb4jPmAZTRonxilyvw1k02Fcj7OOvu1Y9Usanbm+XPHzLz/n9qJj2Tk2vaW1FmtMreiBUk7yYGqGxCAFyShLtXvJSUTHU4qEUODMG0wpg+oWlOUF7ouf4C5vMK9fo1YOtXDorpMxDu3kZ2c/u1l0t7JjtVbH8yolQxUpNihxFbcW02isUjilns2buRiwMaA+vCHvtvj7B8z1NarviZdLpoNnjIV+tQY0+QzCmjfbl8l7npHCnIiTxxYhuqyUQpsG3cxjQxETIw7HpbKsTUPnGulZ5YQqiVe2Y43jYsw044gqgRQdZZ7TzKEK2seTTxtabG+0QatJPPq8waYWYy02N5QaqMEino4CY88dVQKUdHpjhcK0+4DOgdKuUHTiDKFkxRZgyqdq+nSTS10ftQKpF8uUBHEi7u+BiLKzs9KZmHcRJwqrlThRGCOfa1lvwnKHEMXxY5pHNmo1qtSZWLMCo2Wf+wHjfvYbBJKxBBQHrQlREfaRx6/vsHGiJaJ8YGEVbWuwJmNMoushLwxlYVnFPL+cpFhKxNt1ngfiy/zAHDstEuzPB/E/fghadaT8zb8p/y8cW1fUaz0OA8NwYBzHIz+gaTpKFsDY+wGlMybPEH0ha5hHeoqStphIaiiygZIs5TgYnyjZU3IgTKWiMlaSHCCmiRgOxDiRy1h5CsIfKLEQSsboBWRD07g6UqVEUF9VYXGl0LHlxx4/OgjaNqBsRqHJsWZrOmEMWKUwZg5ciiYLu1OQM1HWGA91Dkcl9Bw0tXSdUPn42lppXKOqKrqEI1VkJjGTyakIg1QrtJEhfuvkb2lTUBa009hGXCaeGYBWxuT5kZBKMFHNHOAsAP73IM3yUSaaBhEPOO73EiX18x979q9notrHhf7yp55//fzI519VHA0qXfXnQmnatj/+LefC0ZlaVPMLKUYZXk/igDCP+qbHicPhwPv3jyi7oFtkWmNZdgvKKtHqxKc31/zNL3/JzcbRNQpTAilEUkhHL7RzOvcclEqp86QpkWKSmasyO2wHCYL0x7dmbEtZWtL1Fe6XP8Nc3aA3F9CAsjMjVsvAeL3iCQVn0mlH89lUz0hDyALjqxDBWJS1mN7QGM2yEoV0heWbaaQ57Ilf/Yb07j3Tt29ofvYl6vqSyXzGsA9MSaFuX4M2lFlajIKuM2Mv11VSikRldR5G9BTIES6WPau+xfSarZ94H0dsDHTAF8pwYxxL14KV50SXwqVtaKKiuR9QfhBLGdWSVUaGmgTKm4fyBZnWKFP7guqkwtKEDuscberEY9MYlOvRrkWbTuCzqoSSkiQwuHkBF8ant5g8CZTKGmcsSyPJUMyFUEkfWenaNjm7OGp+LqTqsSWTpwP7D98Rw0hWjkhCZfGcE0KeuIJYI96mrTU4o2sQlK5jyhCtJqRC8QkfhekoeXJBkY8QqFLilPKDIJgyxWSyUmAbiipsdeYxap52id/89nsWxXPVwOsuYVcNXd9gnELbQrd06JXDLB2bvQSOmBKTciJzUSQR12fEFVm8ZzjWi57ex45zGPQcYJ1Vo14Gwf1+x3DYMwwjFxcXNK6lbXrIGoVmGA4olYhGozvD0TQZpJUSArkEETXICUskNrXfqpH2WPJiOh4mlHUUOrQRVnPynhT2xDgS006IbE3PGCZiTAQVaWzBqI6u65ilN8fxIAIsKcgon2s/giF//PjxlaDRKCcbuzWSFZSzxWKtDL7aKhuoavZslGRk65tLWdR6Hq1QtYwVw0Wts+xGWaP0PEDKSTqqaHISh+g0A/kUdFREq6QCtfoYAJ1raF2PjgqC0MWJmThNz4JIADynShBkDhBqQvqDi/j8ypaKhb7odBxnCZmrShTm7PvzKzz/99wBe65Z899BO85+8/Sa19fXXK9a1queyU/EGNhutxQqs2rejCnVs4sjicYZS2MtU4yM44QiY3TBqsJ/+y//md3eY5s1jWvpm4bVYkXf9ITRE1rxe2ucRhmHwZKN2DSlaiB8JHvWIXxrjASeto5mqExOPbkk9j6h7sbjRZgyDDHxx8MBc3+HLYlLrehXCzrbs1qs6oY7Jx8Kp3XtAZ0GpKWPUAfza3KkUBDnn0vEKRFUIahCmwIuBpYPj+QPj+SHR4Y//YEyBRY+kb6+Z3q74+0fvqVbrrhcrFgvr8jrFXG9Jp4FQsoPqx/nLG3fYVcLDFKlfDdNPDwFup3ipjWs+gV/e/WaD83IMAW++u5bTHpFny/46e2FVIOl4HcHSIWMkaw8Z8Z4J8+HNTR1jpCYxEi5QMxaTFgBiMfr5Co03FbigrUW03XYtsV2Hf1iIclW02KUwFCK9lhhP7z7A3a4okNhri5olMUg40oGRWel+lRGes655NlvF4CUxYU+o2A8EMcdw35P9hOYhiYFVI7id1jRqQT4YohZkyKYmuhZleaOCxzJMGCMkJL0PA5B7fZWhZnZy/fZkSMlB1CaqHRFkzSsX9Eki779G8L4yDZuWaqBNiu+33pKHzmYifvf/hHvnpjMt4x+ATha3dCsrkmuY1INlIgiQslHmpUqkszJ836CnWVxvTjJGuh+MI1TIVQZTXoOywcfMcpytbmiazuUUkzjVE2aLev1mlIEvRuDqOEUDNaB0ZkcJnor4kY5DZjkKaNHqUYq/3FPTgVVCS3BQ9yDypIkp5LQDhqnuL5cy7yfVhymA6VkrLPyu7mIKXftL/b9EqU92oxiMRYiP/b4F7hI1CxelzoDODOTBLefoUilT3CCKhV+VBpjJQScSANUxZAzCnMRSj7VKFaV0+aulT6yvs5VPPIcJJ8FplNoOc7X5Dlzer4i5qWUn73C8yD14kJ85OKUH/zESV2mMv4E9D32TuevwUkxg/n75SNJzHmh99HS8EWNWa9zKYlU5eRQ8QwKqVepwpAU8WnTWrYGnRWzaljXtiyT4uIi8ebdPeO4hynRNB2x7XGmcDgY7j484MyaUhqcbuvDp5illeW9yeYiTXD5G7PSkFYKayvD0UidHYoGNR0vQNaaUAq74AlPj6icGDKsxw3L1QqlTIV97dHdex60ffkx34FCJUOgKMfsRRI+lUWVqBy2lOGAevM96mGLetyRHrYy56Ybks+izvI4oNeBZh3Ib99CjujGorr2+Y17sTEZBX3XcnGxBkSIYvf4SMyJKWdigd5ZPtuscdpyrw+8/fCBh+2ORmk+uejpGoOz8oyVAoQMWnriPo6orFHJoIp4w6mcjrqdPlWoMIv59JwwGC1GsY3zNNZhrcH5gGsnnJ8gBZxrKG2HbRzFOIptACFQ7J/uhQN5If0lXYfrc4XDhYhC7e3UZPcMRUnH5CWD0QRryM7xtNuRSsL4kYZE5842hlKfr3LyC81KZi1n8lEuWlqPqo4+1eduTkZNRXl0fW79i0hSSoKcoEKoKFGVMdbhmgXG9qAGUtYoZcklcxg9j087fIiYfWRSD0xqwcFeEpTDK4e9mTD9Gre4lGBXz1vP+y1F3ue8H8/EtxrYn5/jnPDVPeHl98oMqZ4OjUIbexolopKEVJY5Yt2Qc6wfCaUkmVS1io/e0xlH11piFMnMHAeSj1IYFRmPMaUKZIhEECmF2urSNTnRdE0l28REilG4I3MCV2ZT5UJRBWss6FzhenV60z/i+NFBUDKtKIFolgaqV/YowYg82yIrpSFpsqpOx9FJuZsrU1SDc3VAu+76ogJTh4EVQH2jReOMrtRxUCYfTTFLVlVFRuCYmDMhZclw6zC+wDY7SQAAIABJREFUVgrjHJkIL4Yo557gDBcoIB0D1fEt/suPZ/fhh5Xg+euaUp6RcfTZ338e3v/8+bzM9vbDHstAKe1xzoaZ+TaPARwzAHkQctXzVFpTvGS/1jTcXK3YXMLF9Sv2g4ww/P6rN1WNR/P69hUPDx1PTx8Yxs+5vb7Afv4KpxT22cNW50e1uEkLilWECFNhL6Xrw1ZhDmOfw7zKGVKAwzTy3bdfMygN2nJxccnlxSU///nP2Ww23NzcsNlsMMYcadXPgiCiZHQk2VXpK7kR0otplcKkjB4OuG+/xn24w/3uDzRTpPGROGYm1zGuGooyxBAw33/APe4xXct+nDA/+wLbKnRzQ7FOGLYvZNpkDcDVZsXf/93f4IEheL7+7hviMFCmCRsL19dr/u6La949Hfjjneb//X3id2/vePOwZbFsuL1c8Oqip1k4yliIU5QgqCIhR4LPoqe76nBV5i5ngSWnkJhCJMRECL72p+SCzGIYzgi02LWWxlmaxjEsepxz9H3LctmTuwV0G1n/OfP++29ZhMTq1S9QGIq2TMxqQzVJ1PJhtKx9o0xFgOReQIEM2iaSDoz+hrvv3vC4G1A391y+vuXVRUP2npQLMZ5cMkqW6iJU02iFmPCitSAAWgZd5ISpaGzGUatHZI4v6TPYEHGt0TpWrkF9poqRoRljsRlKTCjv6ZYaUzLb7Y7904Ns4AmGqBiTYXP7E7LSjCGy+fIXtFevWfz870jtiuR6AmeBsJwEGsosxzZfppdH7fdm6iTsTGw6M0gu+cwZA2hdg9Gw6BfMghums3V0KQKWnAMxBg7DDm0QAWvjKTFTDgOrznG16UlYhmHg4eGJMcj4wnK1PCa9i8VC9oaU2T9uyQWafskwBRKKxqzYTQOPTwPjwVcTXk0hUequneq+4ZwWsRZ12sN+7PHjh+WTRgUjk08VSii5/kFjKEZLg7TiBwqZY1LS3GMM/qgRSYUlopXZsVk/UphAnIJilVyjZHFMNrJoTasrXm+IB08ckCoyywyVSoaSFCkmcg6QJHuYNTE/8u6OxdQxqeKUuJ/U2s+PmmX9mabh3Hs8rtE5O/3BX5aKJ9XPT+2Q2qR/WWVKMV5/Rh2r7vjihUPwBKdJWTRArZZqfJ6FE6af/G6YJEiaysLV0mAFrSWzahoSim5Z+De//JKub3l42vL0tGe3H9kPe3yY2G4fmcLI1cWG/W7g1eWG682S1lTdUuMwRsgGbeOk6kiFZOqsj0L6uFoy7ZR5ofygsH2LyZ7iJ1zTkLWlYAg+8OHDB6Zpous6lsslr169Yr1ec3t7y3q9pu97mU06sgvKMRGIVPUiU9BJyCfrpyfM9gn19nu6P/4J9/DI6v0W0xiwhimNDFYxEemtYqEMK2tRSQLkcNhjDjvsfkd/fS2JYak9tBdHVoFhnHjz5ltMv0BZw+vbazZGs1KK68cnXrUa5Z94ur9n97jHKU3KsJ8C//j7r/mwbni8bPjy8hOa4ihKk1Ik5kCYAvsoG+82DbK2SsbHSMyZMWZSKcKeTFVQQplKLgIzTTitsFqx8I7GSiDsDgPOGrq24WK5wKw38OqEY/gRNqrj9c0rLpZL+r4jJV+dzs/QD8WxqkkUaZsARI/VMog9DiOHpyc+vHnL9mnHOHiGD2+Jtxeoyw5nHAYhhJWKX2ZETSepQjw2/iruUziR32riqZE8qFUZrYSVWpTC2TM3kUIleMRjNSt9aMO4e+Jwd8fw/husf6LjwNX6lk2n6VWPJlfpycx2P7A9TNjwhDaGRWPQ228hPhHxqPUtdnlJu7kiaoNXhqClX4hSAiEenVnkQh7xpbkHX86MDc7WfTkSiZ4LnreNJEfOzBlBrdK1FiF2VdDaVWcHD0RU8rSuYKyhv1jirCL4iciENoXrmw3BB1IqjOMgsK5RLJYtfdex6Hp++pPP0NqQleP+aeAwBqYI233i7nHkiy+/qOeWuX/ITD6BTTX+KJwzFEQmchbj+LHHjw+CsYIDc/ZMrfa0oe5sgt2rTEkFTe3vWA1ZEUZRAhG2V0apQomZbKRSTEmJDl0ulZ2oKAFSlA0y1UpQG4WNpt4Y8EMheiTLzIocIXkIU2IaPaqIGobKAuOml7AGHIGGug6On51yVX4QjE6//Rev2vFn5td9CXPOxdip4jvBpKdM+PR68zDwy0oxnb2HQpVFy1WF3siD6pxFa1WZo4r5v5ISOQnkbIzBGHu8r9oYlLUCOzu4vb4g5cTFZiGDsSHgrLh6bA8j+t09h8OIM0Ixd0ZjliKua6rMntbVLb7kKmM6JyEyC6T0rKKRj1Ju86GtuCNkX4TEYUQ2L6ZU5x491loeHx/x3rNer4XaXcW7lVL1PdaAj0DmutTc0hZsFA3IbrvFfPgA372he/OeZruj3Y+UVUc0RtQ0stCqrIJGCSdkyJmJzJATOkbsNNEkMFbgvpriPFsm2lYtRD/ijMbSsFx2vFouuG0bLkuiKxPZDwyHHdMw0hpLqLDlbrunLRO9cvjFVYWalMCARfQXpwC7ADFIVVRKYgqemDM+z4R7VWnzImNotNRJumSsEtalz4nGWtpgGYyX6nCwlJRolTkmNKBQytHYjvVyRd82dNYSVCKd2XrVWy9IUP16rhlpGvbQtGjVMY4HDvsd2+2WYZyYJs+42+IfPxAeeprFCuU6jFvU5PTE8lRFGLhzcluOd+DsiVfU8RcwlGrTlFDKPFOMkYcsy4wygBboVVMI44Fh+8C0e0DlAzQJaw1t6+iN5ZQCiWVZjJFcJkwxtLqhhB2lBNS9QSePSRNNZwi2o9hWpMrqbOgRCq3to6rizZxPCMRRoc8jFK9q8jfPEZ8Ny6NoGyvTwUYxz3dDwRiFmt17ikz7WqulaMkBqw3OKCgWlcS4oBiPaxzL5ZL9fsBPgcMwkpXIwJUiz3fTWK6uLzHGMoZCUhbTBMLTREYTi2K9vqRrDSp6dtsDvmrnKpWPkmsyXyx7yP8Y2bSDpeSCn2LtHRmMNdWwFHJVY4i1x2CUYnHRSTVHJh5kAc7MPCiUmE49wqKPzc5ci7WSFNNUXQ5Srg4GhpxDzdoKpURKzDRuhUoLwqHh7Tcj+/6ew0oEYa2VGTBnG3I8DxenIFSXB6hzUsvp6x+DJed+0ktSCoDONZOCE/zISYrto5Dm+TfUyxBbQ6I6ohzMHS2AUJ6fg2labGtxTSuzgUZEDmYnCaMqZJQzqjRSoZfKnq3nm1MipETyE1lJtbletJjXN/zVz7/k01cT45R4/emX7PcD//zb3/Fwf8/9wyN37z/w9pNX/OTTW/73f/e3rJcdxhlCitLQphy9C0UZQhawNeqoUiJjCS8WszEkCsM0sk+ZSVu0dtia/W02Gzn3nPn6669JKfHrX/+ai4sL1us1P/3yS66ur3n16hWvX7/GNg2tEUZeVhrfZrpxoIsB+9/+Ab77lvLNn+BpIIfMlFt8tvigWU2FlSooX7DeE1Pgw7TjvnHs25ayWcnM4ejpQ6E1GqPd2YrjuD6uP79CW8Xnn/2Eh/stYfTs37/H6isumyVXdsIPA3f7PQ+7gRAyn65XLExDaww9kcVCsV7JTj9Mgf1uZLUB1ViyyjxNE99tJx68J1ZyVKgWNTMSwnx2lXAgKh4KCzhVsErROxFIb4yhVQqLSIsNhxWrWLic+81K8dmnn/Pq1WsWTUunodMFp2TDEuWRXJNQEbzIJeOz3L8YJr793T+wvrrh8vo133zzR8bH9wS/42l/YL+fyOYOdzgw/eH3bH7yU1affcH1X/1bfExH9MUUJVrCORwfsnny4PT8qwr+SqKWwijfkQFmYnmxDlM4wnqpSMdfF8N2+8Dd3ffsdg8kHXFK8/ZxR0wdetPTKYNTikZlVt0Sqw2P+109r8JV18pol3+gPB5geINRO9LqmuXFJ9zTM2HxupFh8VKZvsdiUNW9oxwJb5UVdkw0j3vPPC4zr0OtuLm9lNEZYL/fklIBDH3Xo43D+wMpRUEMNHX/mHCqwWlNJHMYR8bBs7pyrJcbPv/pT7h7/8Bud2A/BTkHA6MXbdD9bickSaX48PjA6vIVy6sVzcUFzWXL4lVPv+qkvaIb1ksDynO3O2BwFNWQkkYbRdO00qe0/yMUY9AobcGCTPvLYFPJ4tJnq/efQUmvRdVkKWZiTiRfGZ8GYYAqKFWGKcNZBsjRBiOXmu2jSSkfqfYpCaRgVIUlqv5czpCC3AjSCOkec9hWAo7GaCuzPWfOBDP8MWeGkvVyJLDUdfPRICgzk3DMOM+Pj9yAl4H1Y/84505InfYyo3nxt+rib158XVr78oGSecHGiQ6l0XJzckrElCVzmiFpCpCqugvMDDOoBAKtaK1ls1zgbEsI8Ormhr4beHh4JKfIYa/xfuT9wwMpBT7/7DUhbdB2Td+KdF6qcFipmoBGm+NYhzZywRMJY54HjBijmGm6hmUvm8g0xTrIG59ZRc3VnjGGEAKPj4/81nsWb96w2Wx49foVm/WaT69fsb64oV0saFyLHUf05IlPW9jvxa6rRFTJ6GIZJs9UCmqMNGmiUzt208hjDvzjuGewK5JuudCKJmfaKVB8oJgIruFIanixBmKK7PY7FJnGyEbJsMWrAaUD6ExAY3RDqxNrDb3OdFpxsejpOkXXygYfUsLnTNZGmNtGix2hLjRzH0xZdDvD40ivCrEwk/GF2sstAhejJe1yuWCUkE2obiGlZHzwTH46vSWtuH11xfXNBYvlQuTXkFaIrpuyrhhGYRZINjhViGEiD3se3v4BnUcWTpEf3lO2D+TDgc45WNYB/DSRnw5s2w7Vdmz2j0f5M0WGLDOhNnnmsJfzLMIwBwqk8isFlTPKbwXmbByxf3VsicxPV84BleugthbB6JQCKccKQSemFDlo+P79PdPQosKKi66ltxacIgRZt/KYFXIMHA47nLF0rcz0asBMTzTWQbsiO82gM08p4WdptzPkqJwtLXXsx5fTcitnpeILOLSUwmGcoKTaohJRhVISIURUtJQobFydI8sGslHEoAhR7PEa5+h6EcB2TvZd7wNP2x37/SDjbfP4nKpmCUVz9+GRAhz8xOpa0baONAZWncGoJYfdloBm4RZVqtMTwwFwaBLZtEKysRBzgPKc9fqXjh8/J6hMJZjYWmJTS3kJXs7Y+jAZYq54SBK3cdGZq4zPpOo8V+2T1cwk5SQSUQphJlLJOHU+q6hyDIQhCMHCaiWVXUqoLJBeikqq0WrBNMsVxpiFAo2iOWNRmfoh9ONaRdUFdY6AzKHopCEo5+1m4V2OXz7VZy+quR8GL/Xs03PoZp6PnA1BTq7X553F06tPP2CGKdF6LBLmhTXZYI2SAeAciSUTK9QhLiEntwdtah/huEHIlqGVwhnNqu9oLKRiuNpsaFzDq5tr/DSggPtp4GG75XDY8e27O4qGftnSdKIaUkqshN2C1VZEb12Dc06Ml+vDafTzIBjqmmucY7HosdoSw45YN5SjEo3WrNdrmkZecxxH9vs97969OzJGb17dcHN1jf/lX/PFF5pLpVlcduiUYJxIh5E8eYpRRJ1FlagknrxnHyKrKbAKhT7Co058R+C/5B30DU71WGTuz06eMnmwEeXmLf/5UXLBR8/T9oll09EYWJmCGrdMPsLVgmIKEYPTDZ1J2BhpVaLVcLVoca3CNrAbigzWp0xWDmU01moaq+mtImSZGxUWYCcSiHVN5JLYDSNjjBxCxOdKHit1lELJ56nIOc/PskaUU0L0p2dGKW5uLri+umDR96AyKQeMOo7Xo1U+XY/6OiUl4jiQdo88vfuKzkTiokE9vkfttjAc6NsG2zoWiwV2v6cc7tmZFrNYMD68o22dwO4lMG8CJoumrUIqKJmRi+g85yVBzFtzRI9PkkR1S4ZmdRIEqEdOEa0FmhQSlyBU8/hAzJmSIrpk3t5NTPsGlyJsVqSuwWKJQRSH6gNLjIm9DzitcWaNKyLUYKYtxjhss6Ioh7WZEUU4VrLq2eYy8wbmJPYI21IvcqkoUi7CcD3bk/ajF0azMbgibhIpZ0KYoBjZ70rClETXK1LWHDIiSwksLhYyUtOKnCUoptHz+LjjMIx0/ULEC4zCGUndS4GHx52gQlqhtMY1Dj0OLFvNqlvwT2++oxSN3XTiXhNGUhhQRLTKJL9G2UJREPOEKudmwX/5+PFBsNNYI6K3sQ5AKy2ivQVIRmoWo6BtZMMepkdyHaANk6/4uWaC2v8xQonVSlzjZbdFBYmQORW0EsaPUxptxIZGJS86kCGTYySniTRt6YyjMQ5cV292psSq5+iFDq6KwnXdEaJsKHSUZ5CoPvtcvfhccd4jVMfKLZdCqFmds1Wq6TzIvXit53nlD3uFGgkQ4zAek43Nal3h54+VmaevlQLv756Y9obsI6tFS9c5jBJmn3LmpCijVE0UZY5vNthV2lIQeDpmkZhKBZR2pFCIg2cYPMOUUMWRCnROc3t9waJ3dL3mcBjx48Sv//mfuXv4wOAH/vavfsbVxYrNYkkOgRITGl2DoK1anhnvvfSy4vMgWHIi+sBwOPDgI8E6mnbJZnNB2zaM48QwDDw9PXEYxmMv9GKzYbNe89mnnzKOIiD+zTff8PWf/sQ//erXXF7csr645Gd//0t+1rb83DmufvlzzGc3+OGB4T//iunwyJQn/tN2zz8MBybg5/2Cf18uGHPi2xz5f6YnGAL9447bi0u6kmlUQe/u0RZYd0K7fUZNL+ymkd1hx/Zw4Kc3N6ycZZU8bdxji8esO0w2WK1ZtdCUKALoeWQMmXdbaCdL5xx3TwOjzxy84vrqFU3f8+n1DYuF5/Zy4u5xJ/cSw2axpG8aLvoWVxOkKUcJgj5wfxjZT4H3+z3baRKYMctwd9CGYBSmsbT9ks4qusVzpY6cJkoa0OnA/u4NSms2m9do06G1mFxT+1P7779iuH/P4+9/w/t3d2x3e4w6sLm451P/Fc3wzwyjZxcsRS3YF8/uzQea8ABpR9Mr7r+656uvfg1Z2JSdjijBsViuNJfrhldXLZedw9mCNpFZTVjnWpWWjK4zgPgWXM8wLZ89tTK/J5BRqYPklIJzirZ3IjJ/mNiOe/RmgVGGvU+0w0DKAZWbOisne6r0Kg2uEdKRbVvpw5VIfLjDP+5Jb+8xn/6Udrnm9uIKUxT7ohioIyfqrBKcA2GtFE/71mnP0SVTSji+rwKUrNDK4VxLOtwTw4FpOuDHQkqwci2bZcvFqqNfaKagGYZCU/WBe+dEfxfF/faBHKTSbpsWlGG12fDZJ6+4urhgOAx4H/FTIMYqaVcKTvcQNevl5ZHA55r3HEbP+4eB3RAIqbBaLCkpU0Ika9EaHf0eZQ5o2/xwU/0zx48OgjO8MDM/Z8XuGSVIWRrFMUZRjVAFhRF4ICS8HylKo62TweSKgc9zglapSpigKn1II1tXKNO5meYuCiM+RIZxZDiIee7MxpwLNRnHKFUfjyP4/zIDn4c9TtdKoKGcpWeij+eqCDHUhW6Of29C5limEPBBSvDWWQEilaJzEnxmAeRnkMVMRT3D6efzzzkTvOf93XvGaSLnLP1N586a9KfF/bzPWXh/d8/egh9GlsuWvgaIvm/oWkfrav2b52snabiomohkl7xuqtqZ0kzXVSqpIJ5g0zixfdphrKGxhquLNetVx3LZst3u2e0PHLaP7A8HHh6f2O0P9K3jYtlJM1sLieoo5FNEfskHL/JpPj67Z0ZrrNaIHoyGokkhEaYJyKhScNawXC7Fqqv6JoYQRQpK62Oy0rYdOUViSGx3T4zek35TyG0LbUPfdljrCM6JjmpRRKW5z5Gvw8RjLkSluWp6rqwm1r9TfMT4iAuefhxYG027fcA0FvIVP3SkV0KA1yIULF5uBeU9NmesUlglrN2iCyFPpJxkphJHohAAYibnyOQ9IUHBEsZI0J62gU4bSteK80bMxCgCCLok8ZDTBo1h2TX0xbHuO5Ztw37ytBY+7BX7SYS3KeK0MKUIAZzVNNrQnL+vUtjvthj7lqb5HTkP2KalbZY0ncYqTYqBNB6Ihye2f/wnhrvv2f/xn3l688B+nOg/u2KhNMu2YbIB9MSYE/v9xIMvjBlsHrBqQo97DnHgfl+Y9gfIicZSg4yiW2iuNi3b645fvFqw6g2Lpa5jW4VlY45Pk0DDWhCT+28J0/LsnhUqQFyZ8pXgkwPWKdquoe170jgwZsUYEqOPTDHhU8IlhY8JoxVaWYyr418KrLaV5GEIUeTg0hiE6GMU0/078nAgBI/p13RNR0SL6pUqz08xP98jfoBE5fwMDj3tgJWVXqjEQoUzohG8Wra0jUarQAyZFAI6CzLjjBG4tIoq9F2PtgbvA13f0XSw2qxZrlZ0iwXb7Z4QEz4K+cg4Q9+2NK6BohmnyBQi4xR4837HMHkKBq0yShmsMmQVJXnPkaSSzI4qeDkz+ZeOHx8E0aicSV7o01ZrVqtlXQiF3X4vJ3wY5UIaRd/1xCky+on9fo+ylqZXMrelhB1UovzsshcPqMYaxIdQUVRlgRrNctVjjMNoR0qwH6Tn9H16R84B3fRY406jCZna06hBR50G85/fcLnJx2Hu+vXZEdmZeVRAsd8fKDlxtVmjat9qmzOPfuTddoufPLpA3zhIAQN8vrlk6RoWzqGtPZWBWfqas2jyebmoAB8DT7st//jb37Dd7Ug5c/PpJyyNotHuGJRmCPVUy8r9+P3vv4Hk2Sx7Fn1L17W8ur1kuexYLDo2myVdI/NdXStsSe2qOZBS2CpjpXMC5UWaSmVs20hfWGlCSOz3I94/0C97bl9dcXF1RdNaoPDh4YkPH5741a9+xTRNfLj7wIe7Kxqt+OT6UiC5WR2ITC6RVMCnwOFwkKb5mDhmWkDXNCyalrVNDO2CrAyH/cBu/0QmcXN5RbdYcnl9w+Azkw/snx45DAP73RN3iHHoYrHk09efoJRiHA/st3v2w5bv/tN3vKfwxig+/Q//gYXTTD5SgkInQ3ENDyi+LpHvxsA2a9Aj/+eV9CfbGKXPmmAzBa7Z80kYWb5dUlTi8Ok1Ra84A80lD3IWUxra1lByJIWJvD9graZvWhrbM6HINvI0DaQQuWlbOteD1tz7RIgJpsAYAllpbNMyPgzsth5z4bC9ZdU6YMUweh53B0KYSNEzjpnWWdrGcXN9xaJtWbQOyhIfAq9XDW8eLPeHA98/HRhCZgyZ/eQZfWD0HrXp0GdKHblk3n7/hrv33/PdH//ActmzurikW12x1hZtHON+y/DuG3Z/+g0ffvUf8fffkz58z9N3B8aoeX37f3DRLri8+gS/sKQxYVLgm6/e8u3Oo29v8C7gbWD5tOVwUDy9L9x9/4bgPcZa9gXGuoQ2K8ftRU/8+ys+ve34yedrDtNEKZn+diPSi7qQij66juzf/Tf2h4ZSPmHeIXKJqOrFWWoylnOmaSzL9ZLN9RU5BMbDgf0YsAoOk2fVG5qsmVLCKSHrLdYrYWHrymLPhTHN/mWJOI5YV2h6w4dvvmJCk7o1l7/4Gza3n5AwTBQmlY4yu0dZ0We9zBdHzvBiTlBTWbExoBENXu06bGdorOOTmwui3zMNj+weduSUMdmw6VY469g9PmL6HmM0NzfXjEH6gRcXF7R9z2K1YbFcoozhw+Mj0+gJIdH3S5aLJTfX1xjbkori7d2Wtx8e+O7dPb/+zZ8IMdK2ji8+e8Vm0THLW+oCKQfSPPFdHKq4P/++Xxw/3kWi7tMp12ZzvdIz27O1sjkGFWk6h3Wi3VlCJpkkdhpoUswEomT0RtNY6KzhsnN0XUvf9bTdEmMbrGuxldkoihIi3JuLMIvW6x5y4PFJEQ6ehCXETFFRgh51IJpynI17qR0qvLRa6ZwtBvGEE8mpOAYOu4FxHIWFeHFZG7qgY6YpsDYW3VtROJg8U8xMJfH+4YknrWm0YrFc0lhL7xoarev5nAUvhCbvvefrN2/4cH/Pt+/uuL684pPrq8p0nQNpOSrd/H+0vVmPXVmanvescQ9nigjOzKGqugaVqls9SLbR0IUBwYZv/Ft9Z/jSgN0GDBlqSbZ6qKquzjk5BMmIOMMe1uyLtU+QrJaE7Is+CSYzyUxGnLP3Xmt93/e+z0sRNRnh/R+0xORUCvtxdCglub49Lp/jAhhfpNvrvqXtWh5cXbDbbFn3HWvbVv6iFqBNzYE0kIUi5NoqG11gdJ4Wiwmpqj1DoMiM0oLeGsp2zdOHD0nBQyocbva0SuE/eYqSLIT/c6JAYpwcMUUKlR7xkd1HQGwawjQx+0huJcY0XF31xDiTkkNNM36YmF69JdsWYQzrdYsQDZBxs4Nc8PPM9cvXC+kIuq6nX61Y9Ws2fsZEz0kkwjwzv35LN8/IklBGsJOSx0juqHaCOz8himWjJb+62NHsHrNdX/CrAR5Ojgcyg3+JHzL+wRPChSHl9wPjUgr+OOHchOgMdyniY0IOM33f0CnJYThx6wSv94kv72ZmF1nZyKZvaK1GCtAiY1SmWzX4WDhNM6ecKFKjTIEUyF4QSk3Q2M+OojVFSooSCF/Zqeo4YqWgkYKHm55GV4bq1hpsack+chCRQ4nMoT4jk4scTxnVjR89S6mI5f6ELC0xS95ev2QYZqQ0fP8f/h/U4Zr28Bp3/T3ZTRRqvJbKEtxQubVjpHz2x9jtgd6+5upGMMgTLw4O3Sroeux6hVKJrZwwrSIbRdMqxpiZU+HoQYdIOpz48u8yb19YXn47ECyYXtNst1ijMVIgqetMKIUQZ/IHs06AkjwgSUUuFonaU9JG0K1aLh89riK8mIj7W+aYuL490rY1qmqlG2JKDG7k5Oq6YhtLSXmZ1VX3sBSFq6sLVpsN26srpm9fEscZd9jjrq8RsdA9eFjn6DK/pwWdN8B/MHzm/Um/VBflh89XY4AYIQS6xtJ3az599pDoZ0rySOE5HiLjaaRvNU3TcbmHbo8gAAAgAElEQVR7zLrrIRe+mSe0qJQZrRRqoVUdj0dSLjz/9HNOw8jN3Z7RB5q25eLhDms7Gmtp2pbbu5GbuxN/8W//mpfvDry+PXCaJqQSrDrLgwc7VquGlTUkKUhBgWlr+zcnGp1ozfYHtULhH0mMef+JyvtKrlZa8mwnQSAWS4JaWo1187HGLriks6O+zg+1pJbaStBoRWsNbddhbEPTdvcm+Up7qL33mCquJ6SG9aolxZmjV+Qk6uFmSUMQ1Lbm8o0tp6SP74rCe6HC++F8HbAKSQ3idTP7u7tKNGgb4jLMPW9CUlTFpF42sehYhCay+q9yJFLIStFojcigGls9brwX1uRS0xOO48jbuzve3e2ZQkbZhtV6Q8oF7wOlZGIMdalRGqk0Pn3QChGgtSbn6vNLuQ7q/RlkvVxLKattou8a2q7B+cQ0JTZrz2hdbXFZiWk10kiUVcScGefAOHvmEAgx0SztdwqkGKuIJNd5hFaKVdfjhST6mXl2jMPEPDmMrpYIfT/mLIQYSDkvNhqBPhucl3eXlSJLVeHeuSqP1ZJWoaXBukB0gTTN0CZoLbJTCF0pEtro6k/NqYoSBKimtn6FkmhjUCSEKjhZD1FunLA5LXDmwk5LnirNO5VopUCTaURhowWfmZ717oLd+pJH0bMLib4EfDoRbQd3A6UPFGU/ngv7RHaRpCRzCIiYmFJmLuCFZPKR05S5O0Zup8DoI8eUmKVglQtrq+hEoZECpEaWVE3yqaLnYqm+3EQhIitVCdBdh2osuu+WmVLGn0aCD4zewTDSacWutQgKjVKsjCamSpkJyzMUU8IFUVulH7zOwodlXSekxGl/yzA6SoLXX/yGbrzjwu1JwwlyQmoWkLckTBPH/YHrNzd0TUduCnY1sruYOATF9+M7QpLMWaPbNWRHsTMYQRGSxtRDnxHgQlVAO594dzszj4nkBWmtaJNgyhKBRUqzrHlLGo4u1eP5ftFY1r6yMC8FyEzO1XqkjabfbpjHkeZwJE8DJXomH3EJfBGEIqsIJ9bDtiQjcrWJVaV7PZ4LJbBdh2lbpDZVlJQSOSTSNJKGE81uS1TV61rTPZb9b4FBvB+e8EFluIAcfw+bphbCUCGjlKaxmu26J3lIEeZpxKhCYxXWtHTtiu12Td+05JiqDUtVYVpaYtOMqZqBvHwt5z3DOCKUwjQt/XqN0S1Ga4RUhFQYpsCLV+94dXvgej9QJFijMFYtqmSQtrqmSxEIbZEFZCoYFVHin0AYM3tfsUaLik8rSUwZH6ui6nSYSTGTljdtjGIajvjZkVPi8aPHhFyYfCClesJZtZpWFYyq+J6YIiFFNLlWCUZXvBGAUHV2krknlMQwcbntsCpyun1BZaqJxfNWN2h5bnjKatco4uOLnlmCSj+YqtXA0ipuGceR2/0dv/vuK7aPn9D3FlMSIRdiKfRKoVTDprHE6CEIRNJYGhCSlTGIlCghcns6IYC9nnl0cUHftqjCvWLz5B23xyPfvnzJ3337gmnyrC8eYjeXSLvi++9fE6PDuRO3xxNFaTYPnrLb7sgxvPcKIfjs+RNkiTTWcjhVXqFQda4kZDW9phhx3jGHwOwnTqcXGP26YqViRiuBMZLt5YZu1bG73BIyzC7w4tVb3BzwPqN3FmsbtDZM08w4BGJ0SN0gpKVpuroY5sQ8B/aHE69evcFqgdGVHqN13aDqvq5ZrxZsk04ITvfXqwgFymC6nsNw4nC4AwqXfcdF1/CTbkNhZpwDd/PI5Abuju/IjQVrWV9e1RTrRlfIdMm47DkcDsSUSEkiOsNu1TB0hiY48hQoIiNMobGJX/Utj8sFnzQTIOiF5MdWsWsbnq62bD/7lNXlA568fIWeB+QcCPsTXhr8d9fkq8eU9ccPqUoSEQXJg091FjNpy9R0DE2LjIJXh5lvXtzyboiMKeND4TZG1kbzfNXysLPsOlOzEmSNmc5KUrREtpaQPSEExlQIQmLXa/7Zv/ozHn7yjM9/8VOkrgvMmxfXXL94xXdffsW3v/0NcX9kVRJPNxvWjWXTtJREnfWH6vFMWhFywcX3g3eBoNGCTCakgAtHUh4Y9q94ezdz2o+YL37NLkYUYHRE64I2Aq0FLhZef/+OV4eB8tvf8ZNf/orduudRv+YXP++5fDJxPdVWZpaapz/+HIYjRyZeDxqXIz7BGcIwAFPJzEkwHyOXXrJpIVqFSBqv1qy6K7puR8lTXcDRqL7AkBF38/KMFcjxHrxeVdS1GsyiAkJ2nzwhlMQwniBMFO/IwTOWBh0MjIFWZloleHTZs2oMm04TS2W4jj4iRU3l2V5tmF3ki6+/4cXrt7iYaNo1Ocww7bHzCieXtIv1miLlQvbKS8/ufWILlPt1Ip+jtThfr4IoA0qEZSMrOJ958d3fs10prMoc7l6wXq15+rM/oBRThT1p6aaJgtKStm1o2pYXNzfopuXp06e1M1UK333/PadpxsfIwydPaZoWazuMaZBC4ihgW0Sz4uQycyyUpVtjGsVm09JtJM06I5tAMQKswGiLLBqVJSHfVuHSD3z9cLO80JwJ65RFOPKBpnKaJ0JIpCy4YIuUqgpYfMA5z+6Bqb1bBDF4JLm2Hc7UdmVAqjoT8mFpidiFICKXxAkoufqYgq/BryUGiKG226RBCVHpNQVKquIcYKmA/qGfL8dEjvnem0gppJLP5kVOY6gKSBSNNrRKI1NebphCEOI+lDcUQVAK33eUJVJ8EguCTBliqXNVVwQ3c2LvJmIKNTOxJG73B46nE29ubjgcJmKIiHzg+xjYv7mmUYJz+NMcM7rp2ORcxQXh7IGql8hahcw1/UGJhcuo5WJDaEBKQvAUxBKx9B7BlEu94aAQKAwu47JjTntmH3A+4n2iULmcSqklu1AiheUcS+xCwceppgIIidK20mukrp7OXBARtC41ckuc0W7qvZn54zMLMkRWBT5rVzgjuUmevRsxySOHxK7VNFIh12tukmfIiXX0TEXiEwTnmUtgLGDlEsmlJX3fkRE4X0Bl5lQxT0JI2q5HaYlMYEXmcaO5FCsuTC2BFYJnStMh2OSMHU4YKSHMBO+YvOcdhVkkZhKNkJWo86GiNxVyLBSf0FKSpSRKUJeX9I8esG17DlxjX4z0uqa1xYU6EhKAgaIqQ7fUToRpLbpU65JLDp8CPgZcKoi2Zbvb8OknT3ny+Sc8eLADVfmpm77l2fPH/PQXP+V3n3/KzevXfP/b33E7zxyPE9slZ27TNJxipsSEL2nJ9nv/KiVz2L+rcGQhKL5ZxDxweDuyvx3ZHT1jzljgclfvXR8SIVEjjpLHakHbQHr5FaO1vFaafdDczIk4eSiREOD6y6/oSsDkwLoVqKI5+cwUYU6FORZmCg4WGk71kep2zWpzxfOf/jm7zSXrbktK8/I0KUICeXuCX//l/Q2Zc1qWwoVIJWqrSS6H7NXa4nYdw+UKv9ekElGqxSU4ToF5SujssSLSN5osJE3bEFPNxJtmV1mtWvLu7Q3HYeb63R23+wEfM0WNHIdTRQSOR4KxOGtZPX+OaBuS1XUJE2f7B4vN6X11WNedD1umBSkCSkUaqenWDVoJRB4Ruarzt5uWrm1pbVsrfx84DgOTmyipEEsmiRpsYJsG3dT14XK9IZXCm5u7eijRBts0KG1ASo7DQCm1g/X65sDLN1UYVSg0jaFfNaxXlodXK/peoG0iq0hM1bQv0BhhUNpQyozUPzxa/h+xCcr6aebzhlIVoWf8lvce5yO1pV1bBDFUtZwPtWUlEZhcECXem93PykypNAhFLmKRx4NUS3SLqsGKZWlDeB+IIRBDpKT6I8WAMNXzhlLVqH8fUVBzB+Efiobqn+PRumKIzv7HQr3fhykw+4QQmkZpWqXR976bqrqJpW5NDmqsSqPu28NTEtX/CGQDIiaiT0yDo8TENI/k5MnBc3NzyzTNHE8ngg+QK/j3djxxILNqzhWTRGiLMrYqoXKs6r4PXkqdg0DPkcG52gV0BR/LxdfpXLhX3sa4fKa5IE1T56U5MofK05xjYhgnnA+kRLU16GbJaqzXvUITJDIl4jQzTm7hwlLntIvat5zzBDn/ukKeU+hlbXEUeB/Cu3zcMiXaAo9Ny2DBFklmpvEZGzJbk9kISd929ElwSoEmZ/ZIhix4F9ISppooxtZUBK0raUJpikyQHT5X1q1AYppm2eALVuQ6G9Oap3ox6BbYylz9pinDOFTyvqub4OwCt1rgZQJRze369wC/VZepkKKajI0WCC1prx6wefYp23bN6lRozHf02pNITKnefyXXeXkplSWal+fVNqpWmIBPHh8DPtbqqBVV2Pb48UOePH7IaruiiHrqtleXSGUQ0rC5uOT7r7/lcBy5/e4Fx3GPMWCEoLOWxgVCAWJa7rKP24Z+nkjJIyhEFao9R0nm4ciwH+imiMuFUcDFrnZ7Qig19DbV0FvtBMZBefcKh2CMkrvcsg+CMHmkiMiYuP3uBcUKrhroGwFZcjsXhghDLERRuz6FBdK9sHGVaWm7HVfPfslqfUlrV6S0RK5JQUowqRuE+Mv3b+1+EwTBGci9WLgENK2mXzdsdivuGk3JGpkFPkGaIyl4ZJrRxfNwt0YoTd/X4iLFXJWQxkDRzHcH9seBm3e3jC7ic8bFwjgeMcbQjydU16P6DdvdRYWPG4EX9Xro5VqcDfJ1Ayz3Ld2P1nkZ0SVhtGLdW5SCNEbO1rFV3yxdH42LNRBhmEbcYnWJVP1IBtquq7oObVivN8ScefnmHbCkbRiL0jXpZRgGUi6Ytuft7YHX724JMSKlYNU2XOxWbNYNlxc9bSNQJlFkJMtCFAWFQgmLkAktfbVI/MDXD94EY8hIWdBK4kOtfQrlnkCy2e0Qp5HrtzfsDwPeRcbTBELR9Q2x+hiQChqtampy9CijK8PRWBCKmAol1baND6EmXC+0E6j+w/3tHSUntAQ/z3g3U3JEC1DGgDXEmHGpSoyrp9AjpbjPkKt3cuHm7d+T8xuM1st9kpldIGRBzILjlEk+oY2mU5aNNDyQ6p5PJ0RFxfmYORTBXDInlypVJATeHieOs+cwOcrpCG6G04nsHDlFfHCIkpAlY0Ul9V9qw3q7xhpD1xuMBq3BWEljW9p+Q2OrXaJpO5QSjIT3y0+B4XSixBlK5rBIkW0XmZ1Ha4uQqrazfcD72jo4t0eqmnap7JRBNxWYq5REzJX4cXW1wehqRC8pMbuRly8H2rZu1ACn0XEa54UkUyvex5cbGltPgdt1R982rPoGpaoPNZ1nIgsNQ8kPHlIBfqUhO+Jx4BOVeSYFP9teIqXGoPh0Stjg0fNMZwRBKp6qhhcFblCo7QXBVtvDab+v896bG6ytdoiiLKtW0ncr9JzIU+DoZqRzaB9Yp45m1WK6nuQi+ICcPZKCptDHRHn1piqYU2DOmSEVXj3fEa1iJeCB0jTqvXpNCckf/fTHqAIPdg/YbC3GKrCKn//iV3z6/HPuvv6W/ZuBq95gVMMpCPSUsbKhVQ1bKTEl4X3Ctg2NMexsC85RQqgZazktJBNBpzWXVw/ZbLesNyuarlnu52pWltIiVcuf/Omf8Id/+Cv+/F//t/yf//v/wa//6m/47b/9d3RFsNGaRltSFgzFLXPndF9YKK3583/9PyyWHIsx9R7SVvMXf/Hv8KffMM8HlIC2FTgKMQmmk8aHRCqF21wYhsgpwlbVyLE0Bdx0YPYCN2pWa0tnDep2IsjEXic2m4bcak43gducGZXkkx89xyhdWcUvr0kx8P3bd0ih2duWv/6b3yKEJiVw04yQYFuFwDFN86IvWB6x5O9FJrlIRBZVRb7Mt4XMrHaGxj4jhYnD21vefPeaOSU0iqbpQTfEHPmrb96xavY83h148ugSqyXjwbHWnk5LmsayMorPHl/iYsD5wO3+yMlPhGnErlqu7JaHuy0bo8lScBKwJ+EKyPL+gE/J9xF1MpeP90ABba/RMaNT4tHK0lpJbHtyqmusadaEkLkZ9wjdkYVAdx1du6Zkwe0xkrzEA7/85S/RxhBL5u3+jtM4MjhP16/p+74q1NuGprV89eXfcxw8un3E19+95dXrG370/DH9yrLZtFysW4wWKJlp1YwuEd0E5FmElR2RyIRj3cuqYv+Br39UJShE+cDaVtt9Fbosadu2zgeEJISIktVLp41BmbMIoHwgwy2UFBG2mizzcoFqDVm9fbm8Jx1EU7/VUgrjcKjoLK1w84xzjhRjTXMXdaiulMCaTJRUJZ7QSFl9Zu9fhRQGghekWKeHBShxQS1lWZPARbVxhOiZ3MQw1upBL6272XkOp5HXw8AYAsfgmd2Md56748TkI5ML6BQQKSKdQ5ZUT1ZaYrWlMZpN19MYy6rpWPc91lqaVqO0QC1d1So66pYTlEIrhZSCpD+WBLsQKMEjgBATMWZUyhRqm48lqohSK8D7k6JgQahVjqMoguIrhUVJSYypVj9QLSeLRrnKyQMyZGKq9b3zARcCwzhQUkTJQsx9bdc2th5+7g8l9ZqXM87qfGr98CkVgv7Tp6TjwFBu0PsD1gdWEURTA5VNmNE+IIPDotBLtIqNEWJmGAZCtOQlL03JrhLocz2FO+fxCLyI9xL52XtWof7/tkhs22F2W0IplGGCm31tnQJSFFJZglCtImaFTzBZQ1IKmwoiFmR6/76ElPz4Zz+jNx3PLp/RtCC0IBvFs09/wuXVI/ZffosRsGs1rbL0QeBKRkuDFprGgK5Tc4w+E2IEwS956ssimIsgoUm6gWbDECWHubDbVNGW1hK1pLxIUdCamhhht/ziVz9FG8H1t98xvrvjzf6EVZq4UHrI5aNUFSklP/3pL+n7rq4T922fxHr9W5TSuCKW6kHhQkGJGhKfEHgpuE0FVQrHnLk6JhoB0RcOrlZ4LoMtmiQE2jaU4DidZrRWeCEQVtJ1lXPcrevnU1IhqkqRcj6g55HxeMPvfvcfyVkQQsINDiEEtmtoTKwRWGV1f6+yfKaCOqJBfMAQlktotSroznD5+AqlFW5yuNNE8gnvXfV/SgHCMid4d3JkdcJqSZk8ySqSkRRpKvtXG6w19E2iMYr9OONixEoQ0ZOmI/PNW3LXEkOP6hqs1pSl7V4++Ess6vL6O+e/15Sdc4yZnwZ00bRW49FECtPk8QGcLzSr5p4elBG1xWx7BJIsNFo3NE1DoyS3p2EZiVQ0otamenQjSJdoGsM4RW5u7vC+ziQfXFzSNoquUawbW/3JJSIyiFywuoYmiCzwvkJRYk4Uqovgh75++CYo1bIJZpSskUYFsVQICtUbfEhorWsZK2RtMzUtuu3ulUl1llb9KSUGpGhRulYl55mzXHqJMUCINeZHq4XwmQuHu3395rUlhAk3z/gQUDlhKZX5p6upOsa8pFXXqk1+kKcHkNNE9JVjWQkqEiUMS5wkWUSilERjGP1ALJGYPY1doNzGcrs/8uLVNV++fMlxHDlOA8HNJO8IJ1f7A6mw2qxQulY8bWNpG8vVxYbtes1mvebBxSVd07LqV3Rdh16q5OplrxtCYbH3LCHB58UmWX//vgp1Mc/eI5WsoZS5YMuZWl9P61IqjDbEEJaqSy5RSqKGZi7zURHexy+d/9uUAkmBLtSMwpLJJeFCXRhSplbDznE8naAkrJYVQKAEbdfWgEwqbqvkM0rvPNcU9xXh+3tQcvHLn+NOA9fiS/TfnNB7z0WMsE7QGeR0ghCQPqCKhaVlq0JNmbi+8UTbINuWJ1dXtKueZtVyujsyDhPHw54pZoYoSY8yJUbuppFLFxAx0xZN06/RDx5QekN6d0ccJkIo94eDrBJJJPJuTUwKHwVz25KUoXMJfEL69wISKSX//M/+JQ82D/js6iekNJNJBCN48OABfdPwhXfYHHnUG0oSjNFWAz8GIRSdPYcAQ2NqeryVhSwSiVQ1yKUqlpNqSGZF6i55MwriMaEvDNoaWi0xYq7PKRFyRKlC08Of/Nk/47PPn/F3v/uS3/y/f8vX37zg0WpTnymlgfzRuEEpxR/9i3/JarXGOQ+LF3R2A6vVtt6bBTyCUBTDHDGiIDNEKZiRvA61M6GExIpEK6svzOXClGHICV0KrQDbr4hHuD0cKMJRjMJ0isudRfSapqtagnnKeFWTbPARxgOTnPiP//4NMSW8j8x7hxQa265ZrwVtv2H7+M85Ux0qcu18SF+SQfL5+YNMRsm6Nj789Cmr7QZK4fqbF5xuj4yHE9ZYWmux7YqYAm+OE3fjO7QUrI3AtRpnNUVnWqvptGHTNxgtUfKSu3HkNDuGcab4keHGMw4nStvC1SX28WOa1YpZi6VVXYuEMzNVcB4Pvd8whNBATUc57W8RwbB9ckEulpRhfzjiA8QEqlvXQOaYKk9WKtrVlhxLFQqhMLqhXffYuzuMd9impbENRhuCn0gRnITtqsPNmb/5uxfEEOm7ls8/e4rKEWJYEipyVTlXWQSt1igMWqiqCM81LqtkRSn/BFFKs6usPFMTcGpVpTU5C2IUy78bNus1KWU8ke3lZV3MRI3RETlBjrjpBCnSmfrhV3VRXAC6EKInl0TOiWkc8cFTFnJ5zoV5nOopzVqKFHXWEQLaO7Sba7KzqgQCRIVr6wzcmxHuLzl985RNt63tgSW1QH0QcRtTpWvMISFV3SBKyuz3R0IIhBAZh5G7/Z58PCLnCbG/w6ZaDWxsRSE1tmFzcYW2Fmssl7sNfdewW69prK3EBWORspJHhnlCuA8CcO9NjL+fSlhP+KfT6aOqycdMTlXSbtuegkAZWzfPXE/AOXnGfFqSJeqwuqpnl5SPJa8sLgsRZckVk4LoZ6p4V7PdrqsX6FCl8iFmxsmxP544ngaGaaRvLdvdlmfPn/Hs2WO2uw05OHIMOF/pGdWXWAUj51mg/ODAIoTgwcNP4bJA2XHnJG71kuvvv6H1I40vWDdXwzEF5QNFZ2KjWT3a8sAYtt6znz3H4YQ7HmvMzaZnt9qyWq3Y7R7QZkdXPGLVkKKrPMMiEBGKi0zvDuQIoW+QxxF7ijhR475yo3FpwpOwV5eQRUWYNbrShQ4HHrx6RT+5+0NMLpm//uK3KNWyab6j1Za+bfj806foFEkGwu1rbJp4uOvxw4QVgtB3DDHhUuA0ORqj6RpbDzEpchwGZhfwIXKYI1Easm559ss/on/8nPLoOW71hMns8GyYo0fNAamrurrkSBGpBt4GQc66It90wyQ171KhuBr9pHSthJoP7s0QAv/b//q/UHJkOB4xslqdxnHk3/+n3/D99WvkIqIQsXAaMlpWtfhdFpyKgKa5NxF9FXJVUy/nv1jAA8fJk0rmi1LIzjMiSHPEllqtYCXFCEKYEEqz3lrSUZNlgRzZbTq6nWHuPTEJrFXYohHoelATifQhY7NeNc4Iqtq5WGg/+f3zWhHK1V7Rblt+9PPP2W16Tu/2fPU3XzAeB453NwuZqPp2Q6gb1H5MXB8DRikeO82qLex6waUXdFaxaQ3GrLmyK3o7LL5rje5WYBpK17Fqe2S74tbAGCpEoVDuDyqF8ntMVIGWKwoZHz3D5JGqUKTBx8DkS90Ac1VpS21RCEyTULpBKUO3MpBrh7AIuDse2L/4jsNU2/Gb1QpjTE2LkZoYPc7NtG3LdgsPrzpSkQih6FtNDlV7UnGRBYUkCEVIgdMpIoVF0NBqTSyJEAMlVN/rD339cLN8qt4VqcU9o06cUfDlHImTF7GFI8oEqvIny1mZlDMlBFL0kDPZKEJMFBHq7ipqQ9L7aSmVA8NpwHtH8H75GhBmh7G1vSmM5RwJRKmGB7VkTClV5YWlKgcWD87HCjYlW5TsKaVWkOKs9jrfFlIjVKKUuLQP6iPpXaXgzNNMSgmrJJu+pVECHcPyvRSMNbV1ai3deo02Fmst69WKvm1omwatNEoqSq7BpjGl+6+/jB2WB6v+k/ygpXtOiJ6m+aNNMKUlsLZQhScLK/Tcxim5eg2dc1itluTwirISslpLcqkYrjO/sx4SliDfpRUqBCgll0SKiPeVaTnPM9M8M84TPgY6YbGtZbXq6ftuwURVLxQlUThbYd63R8+Hoo9fNSTY2A42a+K0Zr5rKSFQYkRLSaGKVERa2trU9tGmt+ykIJaCC5E0O7xPxFPGKoug2l0qMUdxTh+PpZCKWHjDmTx7shpJMcBUN/KiqIpOUdWZCVBGIVKVTCYpiSzK5nkm2vG+fVZK4WZ/RyqCd2pk26y52Gz59OkD5uMdJc7M+zuim6q3dGkrnk3JOVVsn5ASs4hjYsqM08zsEz4XnNDIdoNZX7B99gnm4hKvBEFKktAUaWpieEpEUjV653K/CWYqCPl4HDicRkbnCQWmmIil1LZz+ch6Tc6Zb778Hd5NnE4nGlV9cOM48ebtG4Z5plkqO50zU6m0FlMKxyKYqHPwupcI3BkvnFkoT7U7kHLBucS7yVN8hX6vlqq8bRRFKfJyXYyWNK1h7GMF/U8SY3UFezQSlUEnsKVFCo22PUJlhO756GYs+f76nQ1WZ0U4WVRz4vJsFlHBD3bVsr3aoZXk4s0FQlavay61jUeCnGobPqVU1aZIkhzZhIQHlNUkDaZIOiRGCowy9xmd1hikNQitsDlD8FV0laruINfz7f3Y6R88XkVTtwVJzBBiqfdQLMQEQpk6rioCHyJZKITQKG3QyuJc1V4IKZm9Y5on3rx9SyJTpMR2TV3bKXSyQQhZ13USUOh7TS61JasWCnhZkJNlSS6paUKJEEq1lwldPZJlAQ2kf6gq/6+9fvAmGFJaPr3qaVNS3G96UkpCCDg3M82em9u7emp//HRZsKvpObkZd9oTl1nV6OTih5loN5t6M6TEcLwluBk/zxxPA7PzzM4RYiHGQgmBq4stF9stctlohShYLegaSdOAVAVEoIiqbFJS1wUvZnw27y9/0hAr5iuVc4sxfXBz10lmjWGqn1zWdcQAACAASURBVKySknkYuH37lnk8cnV1xY9+8vki7c/Ms1tCbata0nvPPM+ExYGvF0VUTLmaRuGDzeXD6m9pr/y+gmtpFX74Y5qmjyvBVEixsBilFuqORoi8eJsmfHDsD3eV1GAt1mqUbmnahgKkaSKEevhQUmIbcx9I23aWpql+UL+YX9+8fYvzkRgzsw8ch5FhmnDBUURP17esNivarmGeR0T2UAJS1PmilJDz0hGQNdH8w3DMUgq3b95SUuH29sDQGqaHl5z4nGmc0NPEcDiw8p7dNLPKAZUjxc2sc48WDT/vO67aluvNlv1pYHIz++OeV9fXQJ31Plt1fLbpaxu9ZHyIzMAsKu5KTyM6ezglckrMKRHRSDSD05VgkZeE9gQ25UoUyUAu6Ogx0b0/6BS4O7xj8CMuR55tnyLlE7b2Zwzff8vrl99y/eVXpDAiqJYClzNHFziOM6PzDLkQyqKUExUEcHs7coyZKDX64VMef/5zHv3opzz5w58wJseLm2suwhM2pUcbVWENKSCiI6cFXi4jSoFF8/W313z99Sv+6j/9Ndev36IE+JwJuUpSrRS0Hyw+OWe+/Ptf46aR2QeMqlFm8+S4OcycXCDJCk0OOTPlTFpaXVnWhIoH6/pM5ZSYoyBEcFOFMCgpWBlFCgXvMt/OY31GU2FlBVpLHu8Ms9J4KemkpF91bC7XJK8Z7cQ4TZTWILuO7XpDRlGyxF5ZmqZhtVtjW0tMim9fnGHzZ+N3Xp7FulrkzP16+J5oXVujiJpf0T/Y0l9tWV1ccPfmhts3N7z5+gXTYWDYH5jdXFmmQpFCNai/uLtju+159PgCfbUi2walawFhXKKh2tFy9nTrynBttGS+e4cv4G0DrcU2Bi/lfRKI/HhZqdcsaQQWbTtygikIXr+t2MYiJKvdA6bZMQwTr97cIrWhXW/pVlu0Nrx8/RVG187Xq+trjqcDL1+9ou27mt3pmuVQoHjw4AopBClmrq+vmZ3nYqvxoZBSQYoIEopaUoRI+OwZ5onMjGw0wlSlaU6OFCLBBXLTQfonqASlrCzJtKgHc64VgBJquSkAodDGknIh5cQwTHRthzUNbjqS3IRzblH+wRQmVN0XmIPDBc80z3WeFiNhDhyOQ33IZ0/MSy86RZSyzHOkt22dUZaMJKFkqsQAXYkocTlhaFWgVJO+/8BSl7Mnp7kuAD6QUqzztJxIS06hEBKtLWkRF5SSefP6mnc3N0yLSGe72aBkbaPGRWWZF6Xj8Xjk9u6Opt9grKVfrcjBo5Wka80iGqiby+9vhL+/AZ5/7fzjP/sq3PuN1NLSTBnaAjFUOPVprNSWEBM5V2Vf63tsyugillNqYXY1iNToCvaNMRJiQC5KzpgKxlTrxDA5ztQnoTS2acjUgFIJkFON60mBFBxKJBTLaU6CIBNDgPOAfpnpfviKqcYlBTJFS4S1iPUWpxvGpuOYC3aeeCdgYw0tsFISN8xMo+MweqJpWLc9rVR407HdCjyGUGCYTkQ/cTwF/DiggmerNFFI9hS+zYE2ghWpRvOEhJgTk5GgNSFntKvDe//ugBMaskRbixKV2jIOx3rC/bByz4IkBFjYPdzx6MlDHlxdMH/zJePNLWF2zG5myhNzrH7cbFtUFlhlcDHik2A+zohYq5AoW9aPr7C7C57+8z9m9fAp/dVDRjz78Y6b2xesux5VJvJnO4TySJnJ0RNzwGdHLDMCsEnz/duv+fK7b7m9eUtyM71RyCzIFGKu5vz0MYULqQRCLV0eoapohEqNUllic6ZD0Eto+hUOOMyOpmnoOsuPf/SwogtjwtiueugmzzRMlJToOsvt7cD+MNIIi1WKlTGU6UCMmt3uEz779BPay0v8fKJpDd2q47PnmdPtnu/Xv+bp80dcPLjk0Y/+YKmiC3YRbUmjEaowjoFvX1y/XzjOqevnfD7E0h1b2mR5gWLfi2ZY3nft7ahNw9Y+oLtas3mwYTycOL654XS7Zx4njncnjmGuArdGYzcdT378nKvPn7LtWxpRyG5mjr6q9WNExMTNMGCGEVMAqUkIblKCvkOsOuyjS7RWdZq5zIjfryswjg6VI1oIGtWTS+Hd7YRQdU1osq62C9tSoiNlQYwwTQ6EZ3/Yc7G7YG1X3Lx9yzzV0VXfdSijmZ2r4y1ROB5PCAQplsr2HQaOw4CUDUoapM2IUpX4vtR70uUJ3UiEMEA9/E8JpE6gEqYVxBKRKfJD6aH/CHVo/bkmRNRrL+UyuznLb5FoY0FUZM48O4yyGF2tDMk7Qqi4r1wKIQSULIvgZmSaZ07jWDMCc6HEwuwi3mfmpRedMuAjk4u4kOhZgi1L/XOkyCiZKnFGSpDVECoFaJXu7+H7C58jOYeK0QoOHwLTNNXAyxCIKSKlxtq2tsNywXvHfr9nOJ2YhpGmObHfHxblaX1v1WpQEUe3t7e8ffuOzWWi7XqkUmRVDdNKUkU/io83v9qz+K9ugu+vzcdiH6jD6hQSWlWLiJQ1r8yFuslPs6uJB6US7TMw+0AbEyZlFIuFIlSvjlx8fSFGcq6hu1WbIwipMM3VJ3puslTf36KilWoh5osl6DNTUmDpONb5wH1KSVoWjAVflz9+/2fEHUogtUZZi+xWJGVxpsFPE4LCXfRslKKn8LCAd4HZR8bbA7nrMWto+56sq9rTCYPPhehHRKlezuJndIxshSIJGCi8TIE+FtoiWaeA8QkzZVwUFJMoQtH4giyScZgpyiKkRaSCSJBlZnYOYapJ+Pw6jxuKge3FhsvLC/rGUoJnPByIvoKqj9Hhi0BQyU0qg16Uu36qz8UgaltYmI7twydsnz7js1/8Cr3ZIruOd8N3THFmmA7c3r6m0YLZ/QGmixiZySKQmPFlxJURKPioebO/5tWbl4zDCWLBKvX+fl/mDPnDM4uArl9V64WyNNpWZWYS6FPNPtAIjIBGSvrdmrnAMRWarmW96fnk+fMKBIiZ9WpFTInjcOL2+gbvHN2qYZo9h6GiGVdNw9V6xfD9iZQktt3x8PEnXH7yjPF4h9aSpjHkS8lpvcaf3nD19AEXV1c8++QzYs6EGDBaUkrd3DORVN5noC4PIec+Efe6cpbqTyzlomCR2UJtIpIXKIRsNV1r6Lcr2lXDfBroV5bVqmHcn0g+ME2eMmdMa1jtVlw9fcj64QVta9Ep4mdBDJLiPCIoVIiMs0eGhHIeqxuKEEyTQziHDJ5mu0KWqjbN5+/zg1cINWtRKCjKklJiHEeUEZUoJCPKqMXkHilCkqm2uVIKfslHVFozThMxBLQxtG2H0mpZWz0xJ4ZhQCsLiPtO4nA80jQZawpJ6roZSk1OgVg8oXgaU0VXEPE+EOZCv9NIWZAGMon4n0nH+C+9fvAmWHJegiiXiy4gpFx3JmKNBNGGvrGs1xumaeS4v6OESJhGDrevoUSU5D5RPEVPiY6SqzHc+YVJ6SLaNFxdPeLi8Wdo2xGFqXiv2fHVb/8WlGaaAzsEajGqFjKImpGVl4X9HJRLzsiSPxJaABX1Fs/ZhRqrFckoSnakKJmGeTHOnmpidIwcj0egziXa1ZrZR373xTf3s7a0GPhzTqRcs/G895zmma5bEUNg1bVYY8jJY43FLPEjUlZqSrWYvK8Ka6SU+C9Xfx9eK+Dm9g43jfRdV+d2UjLHhHMOv6g2tdGsLx5wOOxxMbM/zKRyYpxrTM84jZzG+R6Rp/TMPE3EFGl9pmkjbSjYtjC7REjLrC1VNmfJNcB0t+l5eLHj8dWOi82KVddUiPfiDTRG3S/kZiFmS2Wrcd5kELecF5t+tUYpTVY9c9fg3Iy2E42PTD5wmwW3797w/btbjCg0wBMpaZRGGcFoDHmcKYcBqw2qtbQPNnTtiqIt26sLWhJrMp/6RD8FlIcvXeB6mvjKHVlpw0YZ/sT2XGTLBYImxirw8if2sZCkQusHaNuhdVe5mTkS8ASjsNbUtgZAKbjDgAsO0Vr+4PHP+dmzzzm9uub6my/5/qvfkvyAT5EUy5K/qFh3DUVkYqiHvlZItDKkMdF0DU+fP+Nf/Hf/DY9+9Dn6yXO8BCcSUW6YvEOvnvDF62te7++4eLDiF58/4vmjNcQbYpzwZeIQj7gcccfMF999w9fffQ/pLDGrcAqBpC0SHyP6g/tTa8N//2/+Z5SqMWRt05BC5PsXL/i//u+/5PSbL6qSV1Uv3J/+qz9lRuD+w19zsVvz6OEl/+Z/+h8RKIKvXMphOPL9i6/4UjgO+0AkkUWdYz57vuHR5QWfPnrC/3d3xxQ87443rMZXiDkwuTvSKZBCILlITp525wjqLbfzgf1vXhDTTAwTKbkKsw8RFyMxtuT8xywl/PtK8LwznteVUhaVaKYsIJGybIZCSkSpv1fhAgVEoVkJVv2G51cb7I8+wx0m/tb+mq/Xb3l7PPGzP/4lj58/4tOffII2gihrTFy2PaW0ECIiJWTIiMmBj+RxwscCqbApgskH/LsbQt+i1z3tbs2cMyQJtPfXTGlN8ZJpDoS5tvuHk6dbGWwjQWe0kGhZ6NfrmjKhOxB1fPH0+XNWq9pe142l6zt22y3r1RopJevVind37zgMR169vMY2XfXkKkVjGxrb0DVN9R8XX9d0mfBhIJRAwpGlWIqcymqefWJtDYhCjnEhKP0TJMvXA83Hga4fYsjEYj2oXqmKuhYl4+aB6EaOhz1QFll8lbvm6PBuJgZPKWlBPklCVoiiKELRdiuafkVEY0LAWMNmu8HIwjjPDNOE1tXzknKu9gwXamWUqxhGQP26JX/Uklju2YVwA+fNXStJMQYB5M2GnCIpxftWx2azrvIYId5LwkVVjdYg2FjTK3ImLhtnCJEiVfX6KAW5ZnGNJeGkX6AAchH0VOtC/VljTP1nrfUHn3356Of80RG81IfXhzp/VHUA749HUkykmOppVBmMbRDSVMCvCxQx4UL17YQQFoiwJGWBX1iRIUZ8GNCTQ5sJ27SEGDkNEz7UJOqSE0rWVueDJ4959PiKBw+v6FYdtjEVOFxypbeETDzPUpb2s5L5fIB+fw/C/8/dm/VItmX3fb89nTEicqis4c63+3Y3m6QsThJE+8EGDAg2/GDpwe/8KvwwfPGz9GTYsAETgmcaIimpyRZ7vkONOUTEGfbkh7VPZGRW1b11KTYg+gBVmRlx4sQZ99prrf+AdQ5rK+rGoIhFb1SEs9s5oGMkx8j1zQ3XV1dMwZNM5LRpaPviyTdN5GEUkEsOzDdb9Cjaqqq1VEbTW017s8ftBvzuGsKMygkVBQU6assLYxkVXKVIrw2OTJ3F1mUmUxmNshqsRiFUn8lnZiWCEXkJggpqJw7wVbeiMw1qjvzix3/Dq6fPmPZ7pjAxxcQYk8zCMyJgnkU1o+tbJuVJfsJWhqZxrFY1Xatpm4ypEhhD1BUur+lz5sJHgtKQIl+8vOZk01I3ho0FMFhd0dgekxPWQGNXONVKhpMywsE+5P4SGO+3YpTQCEL0xKiIKSDFbMk4xphxVuErTXJSS9c5keNE8Dc8ffFzKN6aCpimPdvdc3S9p1oFxm0i4kEnTs4dF4/XfPDRe/z4L9dcX17yxbPnmC8Vg3nBPN0I4rXYFGmkf+r3Fj0bUTHKQcBg0ReASsKXLP0OTG0Jgrcj4NFPKTWK7JU+YNJVLmCvAhSU85awRFxW1EnB5CWzAx4+Oufk/Qs+/vQ9+s1K1LCKVJ4koTLGqYP/qiJUTpCURqFjRseEGRNmGlHzzO7qBhsCtVLkyqGOioYZQfSmEIg+gS2qTroiZIWKoKPI5GWN6O+qTIqB3TQRY2IcB6Zx5tpYUtagxaXkxeUlZGialmnOjGMmxYlhH9heD2idMbrmZHMh3oAp4mqF0rPQXYpUu1E1YfZkFTGVKqbZSuQnVQKijJXmm5OFZflWPUEQUmwM6dArW/JpCYLy5+Ei58g0DIRSPkSJoovRWlyV/ch2GBgnsfyxrqaqWwIGQ0VEU7Ut/WrFnMAGi60sZ2cbwjSy2w9c77a4ShFzxqfI7D15nABNjkr0N1FUtdjNZKXvDKyZMrFDHV63xhwcyeu6IqWI97OUIaxIe8FtGVIeDyl95iQ/YxRroRDCwZtw9r4gXIX+keKiwVlcLArpWGtNU9c4a+najrrMjI5RoZT1l+AXQrhzUHOITCFiQ8SVh2QYd7LfKNq2E0SXq1HKCn9rmJl9wgxivisDmiZjSFkfguA8B8ZJZKWUUriqJuXM7P2hHwqJyilMY3n8+IIn7z3k4eML+rWQ04kTIUuvJ4R04CFqpdHGUGkjyMd7ma91Erib7LAGYqipzMw8eebZFz9KxThMXF8P7INnS5KeyGpFvepgv4ftlrzdS9np+hrNrvCcWmxb0zUNzbxFbbfcbF+iwozNiQaN0o5gKp4ai9aS7X5kK06yovWKyEzISdwQrAKrAEH9jX5mVglvzGG8VCjapqHVms3mnE474m7ix3/5b3n+5VeMw8AueGZgQlNbLRq808iMJ+vMerOBvGfaz9SNo+sqNpuKpo44M+PMRKxaZutwxrJyDU/qFWPM7Lc3/OrFNet1i6s19Zmm1jWVcShbC7ISy6Y5o69eQNKHkvyBysItYvjoBmWe9qQc2e72hNaSYmCcrvF+FDpNTDgFc22YGAVkEz3RB4Zh5ic//wtSnPCjeHlKr35GVZHKJPzWE9WM0onTi4pHH5zw0fc/pH98ytW84+dffkVcX3PtK5LaoQt63JUKUuMc2SPapixVF8URDZeUFDndUyBZbIhyKXkug8fy+zLRzgvlaBloiq1bBpNECMAlj00ZFxX7mz3TzV5MBz54yPrJAx6+/4ishDMt8o+yHZ2EU6miZJsRRXRWbHlahynfUe8idqcwGbZXN5hpJmZFdboRKaqj6zX5mThLVcPYImfoWmIO5JhQIZFUJuuEq2vImRQ9N9dbxlGUvoKPxJh58PCCpBw+wedPnxFD5L0nH7DbJ/Y7EfAPYcc8zzx8+ICmaelPe15dPWWcd3SNIWVPzBMohcZi6Zjna0JOWEQopK4NwXu0Lte2yncO65uWd15VetsKYyxGLQP2ArlfeGfSS9le37DfbdlrjyqNgjkKYVvKEJJmxzDjYxbl9JA5qTtOTh7SlIxsTpB1RJuA8l5S/hQ4PzthtzW8ePqM7XZLVYFKAVI8OImHEJgmD0nEt1PpYWqteM1v8XD/ynzWlNLj3V5bvouQXvpy6igbLhqcS2A9XhcoQJvlsxK8YtnfJaBlJKvTSvwGjb7NEGNBHdzvBy7Z4/GSF1VWtfhES/DVxmCscHiGSSgMN9sdfpZsdfmuECqcE46jQsA+0zgxzzMhCD9y2YvZR4Ev51y0GDVNU9H3FatVw8UH56wfrFGt5sbv2YVUwDJCI8kFFp5jIUYbS9f1NK5mCvqIQI9UBpqWbBKxNsQQcPVMFT0+etxQ05/0XFyc8+ThI7bDnu080TSOyhrUPJLnkTQN+OsbwjShtzvyzUQeZ/Yvr2iqim3VEjYrQph5bgO+gsY4vu/WvGoaLivLn403JJPRLfzSeh5h+Eehoc6BZp7Z/uwX0K7I/QZVg9ECl7/ZDaR+OurHZEycePTgPf7TP/zPSUPkpz/9Gf/7//X/oqcr6fv1Z9RVTVV3JDRhnrl8+QxMQDvFqm7oL3rakydsqp7zsxO+94OPOXnvHNNanl8+5dmUeO4T11GTtUU7UfNIxnD98jk/+qstX/2qovrd3+Tx6ZonJ2tqdmibafuGTz58wvWza/5PB8ELbSSHRVHJ0FpD526HlBgjf/nn/yvBj4x+pu/EjunmZuDm+pVUaTT0a82jDxzT/BXjPmDCnsYYGusZps/JJIKKkgmJaRwYEVLY7ie0gfVpzdmDjvOLFWfnp3z42ZpAy4/+/BVhtphc0600moTOmYOGVVog96CMcJpTEp4thTblrNBy7hTYcmLB4atlYq1EvWRBNd9mgEny5IIgFKi/WKopnzCTxw8jV69ueP5qwCd4/L2PWT1c0552gEfFjM1ZVJ+WMSTJP6m8yDi8mGsrlYll/PKdA7vCNS366Utubvb89Bc/otoIZebso985pPBWVxhrUMZQuR6tFZVr2e2u2e/3XF7d0K0aNpsWpRLWRKxbsltQSVG7FtM6KieT46vrG3b7kWny7Ha/pLINznaM+2vGSTAYSl1i3Q7jrslqRNuIcWKyrnSDD4oYasgn9LUlM6PUII5G1nJ1fQMxY0wmB0+2v4ZyqC7ABqMUShdHhgUVBYUHKANtiMLcj9FjVHFbKMLFPmWiD3LBcsZWDc44hv1A1TT06/XBMy9nLzeUhlJBJGmFq5z45GX5HhvlRrtFTR4mXUUhQdCSRJmpFhnSw8AqN81S5pDPHAfB5TjhdUDK8aKWB+Fw+99dx1r7WhlzUUURME2xP0m3aiKKNwNf7tMo7r+/lHJijOV4sjg4GEHjphxIQfqV3s9FsefWTiqmUJwd5KTkYhCakvSFTclkVGn8Z+Q4jJUg23Q1bV/TrRqwmUhgiiNpFueQ6CfIUa5bca9YsiIDqOBJSuH9bbUBFK4S6HrImWAyyQYBHiQNUZO1wThH5SpBIA8TN8MgGUrOROdIVU2saoy2+GkiVzVJ70h2FPF0bfFaM2SFR3FjNKlyqKSpXU1sKmZnuPGK5DSus7wyCps1V5PmwlpsiMRhZEyKMSl23uJrR7PuaPoV9WrF7urmkDGs2oqzzYonF4/45V/9ihcvr3h5s6MiUGvFyjRoJ+Tn4BNeZaakhNrSWDZnpyIgnBxVNvQnK9ana5pWzK6Hq2uubgZe7EauPShb4eqesN+TxoE07Ljcjuxewa/ee4BFcdavivqMoq1rTk7WnJ2f4GpX+r7p4BlqlaIyhsba20KLymQmshpBeaEKZIC5ANhkXKkaQ7syKB3Iyot9Vsmocl4qJen2ub4lOJFixlqFdVrEG1Qmp0jfG/q1IDtjygSfabO0aUhLqCjZbEFxa2MO92EqgvuKXARh7hHPcj4KOHDbF1n6g2Uf822VzCzPc86QInGcCMOEGgbm3Z7LlzeMc4bC9YtJTKCN9rILWR2QR0vQyYBO6eBDmA91KSnFSstGELrKQdXW2HkmxcjN5TUuKE4/OoxWcn6UFu6fFowCujjAxECaRoKHcQRFwLpEneWz1jqMFgUjVfSVU5YqWEoQQ2b2I3TiU6iNoO7rOh6oUDEGXCO+gYtzkVZZgF7akSJou9jBaVylcVajsyIXpG7W6tfDE7TWHhzAFam4AshdmUhYLQrtKYoOQcKIeWlZZz96fEjCX4tykzdNxcXFY9brNV988QUPzs9578ljnj59xjxP5CzprjayrjERPUfmeRSnAUQ7LqeMKjdCTGCjKL9YuzxIYtY6zzM5K87a26xuCTL3ASfHpcZbFNjtcj/oQOkQKEF/yT9VysavB6rj33Uu2Y5Zgld8KwViyfzeRJg//A14L0oMRkNVi85pXRcJNjIh7Jmmmf1uOPCbjBHeny1eYkZnMbxlyVQlszdaUdfCVbLWoowQ6+cwS0PcGbpNw2ot/wa/Re0CgVEsnhSQI8HLw2h1kdhzVRGxVgw5iDBC1IesWgFt09H1KwmswRGjR88OogCsdC3akHmVWJ2ckubAvBvZ73aM0ygUiHIvhNUpIQYqP5BeXpN2A3rdY2JmDvACw5gjX9kaZTVaGSrb8qpxXDnDmDps43BnLXsTeeUTv7qMrJxlHRJx3PFy3vKrm4GnTUP14JwPvvMhH3/2PdrHj3j1Z/+6VFQUHz9+wEePH/Le+QV/8erf8cvPn/EqZCqlqJWh0j3WrjBuzX7aMUbPTMXJuufBxYrv/cZnOFOhkuHVVy/oVxWrk5q2M4QcuXn5Fc+/esrnz17wcvDSR616mqqSAX17xbOXX7Ebtzg7s/v+9zhdVbx/0eKscEIfPb7g6uOB1WYlVIXdQK00lTF01tJXjo2rDlmFUooH752QaZhmjzKaFBOu0qz6ga4W385uY1mdO6zTmKgwHUSd8UnuyYQgvGMIB6ULpRREhY6avtHUnUYz4ycB5K17xfmZpe4zwXu2V4pVA5FEiJGRVPr/RaNYZVxBS4qtlbQXjNZEH1H3gRYpHvqdMtkun0XUhSRIihGe1hqjtAj8Aypl4n5i++KK3YtL2E3s9wMvX15z9t4jmrZl8jO7ZztiCgKis5rGSRVOK4q3q0NgnOWhF0ktqfogwiZCwhWJOGUym/M12inGeeSnP/+CPAY+OiBcwZS6kdYKp4vAhjH0/QpXOfmKmLi5Dgz7gHWKrnfUVUPXVbRtL+C7ecZZRQiZ5AM6G3Sy7PcTVnuMttRNQ9u1aHOKUpFMwifPatVSNw7w5CgCKZW1+JwZ/YSzEesAo3BWgqRJmhiNBFtlRDT8HZdvEQQFzq9yQilTHOMNsfDpLJQe18humNlPkVW7QosMAtqGUhZIVEYEflerhvVqxbrveFU52roS5GC5qkobQlLMQeJtkGSuwJazzFhYSrKKOWTUtMBjtcwUZBQXyTengLs9wSVbEjf6ZT50P8jlw2AFdzOzO55wdwA3+fD+EkyPg9XxzztCaFmkjLJSh1bC/e85XhYqxl1zTGiqmliLgK4zIihe11LWEXyTBVp4eMqwn0o/E7QxRffVYK2UI4SCAqkyxBCLZZIuyhDSz7NW0bQtxko5dLVqOD3t2Zx01M5gjEyNrLYSPLVj1qJFShZlm9kHfIjlfAgoKCRDXtBrCmxxoEhaEaMmRYd1libU+BDwqqieqEhQgWQjVWWxfUXjZ+y+ZS4UkXEeUdETfC3WUdME52vS7PFT4BfbmRgb/IOeyolF+T5kotIYMjl5vFPsrMYQyQZ+1inqZNgbx6Wq+VVW/AyNfvyE/qOP+PD3/xGPvvN97KpHqT8HpN/+B3/w+zx88ISTvmOzbun7hmwsMyLi8NXNIOCdmz3GQt9X/NYPf4fvfvcxT56c8uTxfDQH9AAAIABJREFUKckHwuSZn6yorOH0xOFqUVT5+OGGOG/J0zUrEwkxkdKeOGxRZFYVVCc1+zYxbL/kl59H/swO5N/8ARdnp2L10/Y8efyIDz98H2JmuhnoW0ejNbWCHMWW7LgXEKInI2CppaKgrKbvLOcnMuFpWwU6kVXAuMzZg5Z5HwlzhimhDFgENJfLhCz4RJhFhtEaqK1mf33D8/iMsP8pL1+9YBh2rDaGTGLYe7yXgBByLCAsCHMiLkCTxIHCU9zBREA/lszqaFzNSwWDMj1exgMt/blUqi9kGHcj8zizvbxBF0R92s3sLrfsL7f4nfB1J+95vh9wjaPta87WPZu+Ie4GotXEyhabLU1dGdbrFU0j4+bSPtHGoIwmG134+iIvGWMghUDwkWlc8cHDDY8fbNiHJT8FVMZWIjuolaKqI9rIxCHHkciEcQmVpBJoK0vOiaurAa0mlFYYcyUKX97z8OEGpQSvYI2mri05GZq2pmoqqrpGFVS/0jMpe/Ceqi7WZqoWP9p5JkfBkVSWQmUbmeNW9J6zYdjKNbCVcHLVr8NZ3iiBpZI5aE1apYkxExXonJkmKaNN3jP5QNs0paypJDNUMlNydUVdOfq+o64bnBN1dOcs1gjUVqGKr5zIQJFEqgdtWLwoloxIS+dRNEu9SI8pnYsljCp0LPm9qFEfjiuV8uMSBN+WrR2/dhwAXye159sybHnpPrn9/t/6+Htzvg3CpZR7fz/ulFLfEARRAu5xxhSlmMLHs6r4ERqq2hVV+orr6xvh2xRLJYUqUm5CW9BKS9JvFEFLUF9spFQ5pcYqqsZhnATBrq1YdQ3rvkWbjNWqlDbkXnJOHiCF9I4We5cUF+Tbgh41QF2umTpknxaNNomctGxPW4IOzNlIP5iARpNMJDmDcgYbHdkYbDWjbAWTRYeZ6GXGm5ua1NX4aSaMMzs9yD6aDbp2ck1nj4oJ4wPZWbKD6KRWPwMvq8xXTpQ+xrZhaxyTq1k9fEj96BH94yd0p2eo6m7G9OjRQ842pwWxKNcrAWRxCb8aJ5hm0APnZx3dasVv/NZnfO977/H48Yau1oRxYhpGMZPOMkN2zpIxPDrfME47YhxZtyOzT8xzZrvdk2KSTGPd4VXN1s6kOPHq+oUYt1Y1XTPinOPkZMMHHzxhd7Xj5ZcvqYzGKVUmBUKNOV6kz72AOUpgMZqmNvStJeqMc0CpLmmd6TpHmrJkZD4XVCWkUKgBpdQ/j4J0zkm4isN+IoctfnrBbhClKVdpUkhME0yzLqjkKH1x5DoFAR7KcWg5/0bpQwlTLoS+61W+1GZlECErqfgcKmRqqZAKT29/s+fZly9IkyfPkbSbGG/2DDcD0+Blcwr0PGGc5mRs2Bgtbis5YZLGkHDKUKFpLay0TF7WnYybVlts7VDGoCqHT5GsFE3bEQvCfbFOO1l1VEZzOSh+xW2dS9tQgmAE7UVLWGVQUbz6nC4UN7DOEkMkBEjJl3LshPczMQT6VS193CzbMFbTtJaqqbCViGmIAItHWemn6pQOw3POhhgjfhbvWak0i6CGTzODF5FzhSXFCqt1+duh+DVkgsdSYkKPlDq3s1LKitYwhYCyMMwjlzdbdvut3EE5EkpZK6N5cvaAk5MVTx6dE4PHB89qfYKrW+YoOoXGWtquw7qajGWYRlEgb2qqcaJuIm3X0TQtzgSGSTH7zEzA2lgEvRUpyR2ec5LB857jcE63AeSIKCE/v6GMKReKQ39zaVizbOVeL28pcx4v97etDhBbdUCMpuLRdj94vi07BAFhGJOFQKpLycBoTs/WnJ6d8P77j2m7lr5vefb0OdvdjsvLK6ZJSPQkDo33FIo2rE/YSjoHC29RKYW2CldZ+k1DVVtcbTk5FXDGZtOTkoiJ55xEUkRBpSvqpiE3NTGEQ0aeF6mpJNJOrznLW4OpLFbbUnaKYF0BWyV85eXBCZ5xmggxMPkZmwwpJdq6JoQowufDnnGexPVjHIjeY6fxQCPRZyUgL/eFgpXW3Gy35Jtr4ucBZytOTzf4YSR5zzO/Z+cnGj/z4KPP2HzwCb/3yXdpnKXuOrYEZp8EmHEEjLnZbok+c5Unvvry57x8/gV+HEWUWWmu95fE5Mlp5qOPf5vPvv8B/+y/+69Zry11BeOwJ0Uh3Q/7HdM0sdte027WNHXNw+98yCfDDxiGiXlO4ve5m/mbv/r3vHr+ipeff8H7n3zIg/cestsoohIHkZA0L6/25PiK9x494cmTR/y3//y/4U9P/hXDzQ3+5UtCTFTWMhfMwJ37MIoYskHKghkIGPq2Ye4ic56odILZgzKYrFh3BgZDmDJhF8k6EVXk2csSvH0mzJHgEzc3kf2UMFvJOp3b4dwzhnEgBo9RsN0H/ARVJRMydMJaqTikqNhtxZmgfmw5W2tO16WHhvQErTLEvOarA5YpC/1hcZnP+fAsLPKhZEVCJgXDduT6csfTL17x8stXDDcDcRBpupxSeZ7E69QFQ9fVfNj1PDk75dNH51SqtCYMNE7hjKZrHOvG0FZw0kLtNE3laFY9xjlUZRmCF6m203M5HqXw08h+mLm82nFxccqLbebzH/nSms6YNpNDJISZm+vnKGVo+x7tDG3V0a+VAOOCx5qGlDSuWjPNAyF4xnFPVTeoVjH6TJo9IQrX2LmK84enosesLGRH8JNI6ukRoc7MXF9fQrYoaqnq+EDftuQUmaaB7TQwx4kpS0/R2Ya+W1NZS91YfAqk8GtwkZjnCa0Ec5isuDJ7lSHrApIRx4WUPZlIIopsZUGPhiialUYrmq5htV6xOdmwu9kyxISrW7StiFmBkrKdMVZkeWKRHFIWbapSay+k8sXcVhtc3WDbFUq1BxPYGKdCR8ioZVZztCwgGPl9KYeqO/28fBgI1dHPJeDdBr9lvdvPvZ4p3g+Md3t7paV9NMs8KPLw9RnlghwtR8XJeU/TibdcygFtYHPSc/pgw9nZhpPzNU1TiY6n7zB1JuuZYVBM04yfirpOVuWUKVTiwFFSyhyEcpc+YNM7ur6mbhzrTUfTOYxFdPyySGXpJbNczj1IhVrJtkCLr2BIhDkwF9Hk2+uly3cXAEY2KBJaR7JJKGMFBRscaEOIwv8LQbhHRgWsSTgTpeRTifP17Bq8n3F2wAdP8IHsC+IuFUuncl0XKo/VDhUVfvClN63xXjNkA6amv3hMs96gjGJMIzoZap0xfo+KR9p9KPq2pmtqdDAYnQBPCBMh56JUNFFXln51wg9/8/t89v3voIrBdcoZY1uZIGQNymGrWXQdGzEs9n5CGUfdWlYnbaEPJeIYqVXFL/78r5g2O+Jqw8X776Ebh64sJhuMMlS2ounWdG3Pd7/3GVcvb9jd7PjX/9v/wXC9ZT97fCpGrYcbmvLcSTkxLt6cLEAoBSSizww7cQdXaFTWtFWFqSsuzs4Zhi3X22vCNjEMgeshkkImRQizZpqBXcblmaYxdO2e3W4ixUjtxGzZGKFYNVbUS/ZjxIfMNCZclirUpl1xtrJcbCzOOME7JI9Vmil2fDXdbX3kIv+oSkkkLc92CYSCNIdV35JDZrM+Yb4OMBsmr/B5xkd/wBFoY7FGvttZh1KKmBORWOzhDMqAsqBd4aBq+ZwyVqpkuah4pXLfKAgU7rbRZOvAZbK1wlPVdyV+kqrQTlHXPdpZtHJ03YrKWYwxaJOJccaHGaMbUlRMI+x218zzSNM2pTqkafqelD2z3xHiKPtQLd8EGgOqAp0xlZdypk3srgPzJHJ9qVQXgpcxQytD5SRWSCJWo3VF5Trx0XSKeRBcwLsu7y6g7ScE+qLI2ZG0JuZYjFAFtRdSFD6J2GQSsiqDOoRUpMyUoW4qur5lte6FbzZO2BIEszKgBb6pjSUlRDRbCSkaVRGTJmVVLkoZWI2mrhuafkXMdalPw7CfSzwpArf3ZLi4X9487s8dLSktYJolEB5nfRwFwqNe3yGtv13vLur0bpA8pkrcbje/9fdl3YPq/O0hcfagJ3gpf07zAApOz9ecnq84OVux2jRUlcNWlnZVgQn4VKNsQjvIakJlaei7IvhttSH6WIjDUkLUWlT4jdVUjWZ90lK3FatVizWCJL4F1i1cwHKOs5TJVAFcmUVINkIKER8js8/HMVBUN7RBZw3Y0mcGTNlWjJiUMCGCNpgYUNoWCbyIVkLlSSahtaGOkcpVTK7Be4/VIhIwzzPBFsJ0iHfASraqqULEmZqUIvNuxlhDDOA9kB3OKtanF7i2hRyYwp4qahoFZtpx3PBVQN+1rJoGgqOpDc5JvzUH6Vc7C+tNw5MnF/zmb32f7372CTFFhnFGqczpaY9WDqUsWltc8NiqQjQiogi1K4upHP3mTJB/aOabmbjzDK927Ddbpn7PB3VPe7KmXveyn1mRIjTdmqbt+XD1gHE/EULgZz/+MZP3XA8TIYGJd1P3HDOUyWcq9VBxd1keo0TwmWmf0DmKb6C1dE1HW694ePaYl9mwv9yRRwj7zO4qiApLVihlmb0ixIRLntQb6jQwXXlSTjQbS60VymqqZFkZx2lX8cV+wM+BsJ9ZdTWrpmbTrTjrKy5WFY2tySnhw4hBsfP1nRYHaZmsIkbCJRCqIp1IygdUaN+3qKQ5WW8Ye0+eNMyJlMTw+gCgseIf6so/lLSXgooyQTBW6rNGoWyxbjIKjBPHHm3E4DYCWvSCowKfEwYtCYyxJBPJxpRU5e7zlVjEKE5F6sxUdM26qLhYtE3ENBHCiNE1MWqmMWOcYRoHYprKmKpZn56TCIzzNfvhFSkFtJVzqLJCq4IQrTTazaA0OcLu5obZe1Q2UkFCSq5aGTSGynUI/bYiZQs4XF1jDWiTII1yDm7zla9d3l02TZfOcdbMeU+OkWEaRNQ6K9b2lIkZdKTpM11KDDEXOKtmv52wKlNVGlXNRAaut1fspy1znthszln1a/rVhvbymhg81jagHMrUPD5/JBDbKfDixRXB73G1xTYVRgWC0iinqWojnJIYmccZoxRV3dC0bVFlMYdeDNwGJYFdvx4A77s1LK+9bXkTdWHJ9u5ncHALbHkbGnTJKu87Rxz3At+kJfobv/0J1gbq2rDbD4QQpGkOTH7kl198KftmNNutEFanaRLARIRMhascTdNwdrISRf2uYdgP+HnGz4FU7HZQqgRBR9v3NE2Nqx3eT/hhZJ4mcpZh31lXZNhmZi+cwyWj1Fqz248CEpjFxzEES84XLHezMVLmjXkpW5vS6y19h2zFDDhFdG0lG2iE1yjc0UlKLCGgZgF2ueBE0itFur7Fz75oGYpE3Bw8YV7KrAHTWFzjeO/Je7y4fM5Xz7+UWXbMTDczH7c9j5ue0xeXMFzje3j00TknbeLBdosfRsZ4K7SplOLi7ISzkzXONPzT/+oP+e3f+S6f/OAJ0+QJMXGyWXF2tuHJkwd8+un7uMrw1Rc/K2U0g1UOYxxaWfq+xTqNipEYJEvtqnXph2tePH+OD55p9ozbLVEnPvn+ZwzbLX/zV/+eh99/hEuRdV3h+g2ublitz4hzJPjI6K84vdjwe//kd/k3/+bfwl/8iJ98/v8ImOtI8FwpOK1bnHEoq0hJ6DqVU8xNJNiZ59cJvEZnS9dYauc4O1lRuYa2abjYrDjtKj64OOX9h8+E8rKd8XNinCKff3XJdgiMU+S0r7k47/j4gxPmoWf2gd1+S9vVNI2jb+B00/DgrGVd7bnaTTyrtzw8rVm1jjonmCPTPuJaoXFFLxL8Ppg7Ge6BTX8YaLM0cvPSypAsF4Q/G+NISiNaRVyl2WzWojWcM1P0UPr2hiwKPTmhiuyi1hmU6DIbLahWIc0rKTVbI8/2QonImRyLWpVW6DCjTEaXDNImTdVYbq63zJPnlsyvCGOFaxqcWaPrDmMqrOsZfWA/CcgpMZMJdF2L1g5b1yi7JduAQlO5BlfVaFtjTaZtG7q2ISdfWlSGFA3zmHDWUNeOqt+Q0sTN9XOaZiTOnsa1Ivg/eYiJpJJMbLUtqjAV85wIcQZESCD4QEoT+p3ls79FEExGGqOKREgS8Sc14rXkfU4ZvAno2tOsIRiDSWAquUBVKwTwylhME0h2IuoBqhmDxzQBKk82E6aJ5DkSmdBK8s+svMyCbERXCWMyVWOwfcneGgs2k1Qomn1SunXaYY2lbeoij6bu+AlKqbEEluK2fI8NcQgybwPK3P/7vrLL8XbgrsTZMXL06z5zPxi/Rou4EwSh7SuqWmgR2i0CtV5QkePENIr/nLVOSmopFQ9CDtm31uVZRxTxtVHyICUwaIiJHIVIH0PE7z3aKibvqYMjBglyuVBijFaiboEGLwLqMQYpcWoply/ao7nMqo3RHCMSVMkmhVZUSLqLnF8pdR/kqVCkkq0qbTDWSsAOEWO82LjESFQiErCAo6yWmbixihACxs94I0167WdsTBhtePjggpQj17srxlF6iUYb+rrlpO3p9iNZgbOZnhV1GIjPnzErw8BdWo4tOqpaJ07POpR5wO+lH+KL+saqa2jbmvWqoWs0KXuCH4u6UUWMAuHPOhFThVYCQElJykiuuoXSG6UIClCZqq1Yn6359Aef8Yu/+QmXL18wbbf4/QoVIipGiLEgC0OpBAQgYazCVBZlDT4BhSZ1fK1Oux5npK+XSg+tMoqzlSecJGY/4Zymb0Xmrakd65WUmp015CCTZ107Hpz2+K7ibBWYp8Q4BVSORUw/07eW05OG800Fq4bgA1fXCVdbXGXoG0PfWJxROJOpLawaw6azrBojYCJkYhmKKXY89P3uE88EsJPJctupAgBZgl/SJcdKB35fzrGUUJOIVjiDdQ6f48HLlLz0G7nl42t1CLw5p5KZS5melA/6yIfWjpZ/KklPcxH3Xkq1kkDKsd5R+FGKxnU47Ugx49OMUhE/y2QgLYFIedAeH8Foh1FRxk5DkZkLxEnBHIUPaiK5oITVFJmDOE+EKVJXDpVrQo7ENDHsJ5ko1YUonyH5dMgulRFKFlqEspUSxaaUxATaTzMpJJwNvOvy7qa6tScbj1aa7XDJlHYkNxMQDzP0DE6hN551gmq2RGuxtUDig6+ll5TBNiPZKUKlMXqkSgHVbEkuMJsJ2+/JNuDHCKpFqZohGrSuRH39HDKGple4WpGjwk4VsU5Mag+2wWBotKG1DdaIv9U4zsw+3qEeLMoLh8C0zO7uLW/K8N5GYl8ske5naveztzdt/3i5ny0e9vde1vemAFq1irox1LVBu5p51uzGPVc3V7x4ccXl5TXGOLqup6oatCm6mkkQbsOUSES0jYzeoyzYQLHX8VKGUQBJBLnnme1+T3MjaN+2q6SHrDJdW2OtpnIWCv9vjuVUJ9lGDkLgDzGDNtRVBRlitIzz0peVcrpR5iBOrEp3IS83F3L5BBhkD+fb2lB+OrHpCgFvBcUWzHyQpLLKEp2UP6tYEaKn9nNRygkiLOAjoYp8+vGnNE3N7Cd+8YtfkGKmbzsuNqc8aVecvBAyfKwzddxhthPXP/+KaFvGqoYHDw9VCasSKnuCn+h6Tbc65dPvPUHKkUBI+Hlkv79hP97g5xHCiNYtVkGKXnzalGEOg5SqjfRuTdY47WTSEiJ92+BiGYRXK9T5OY8fPCGoyHbasru6Yr1ZweyJ40gK0rdLoaACKy37MmyZ/YxPYomqlCYV8Mtyb75/9oDKRKY0E0ny/GeFemhZVR1GG5xRdK3l/EEnnpZOsuqcYdpe4ioJYg9OOhnAgRAS0xw4XTsiCB8ZqJxm1VtWVUNOmasrcSFPZPquQysxk05hwBA47TTnvWHdWOY5YbWgTafCE1yerXBvUNACezkEmINzREGiswheLO2XMrsMwRNCwOkKYy11U+GDP8zhclpQtsJ5jlHJoJ8U2QeyEmPcqAzJB3IQwQlV9lNbLah6ozHFSUEjus+qGFZrMo6A0xl3NBZqpThbn5NSYBwnbqYbMR5OHK69MhptIsoEQnqJ0Y6mXuPqjHKZEGaGYcLPmTALKFIRqJ30gv28Z18cXQiJvq052XRM2ROTJ8Q9TZXoeofDopImF2oLRsaypEIBHU2gAtpEUpyZp5nddi98Wde9Nia+bXnHIJi5nl8R9IACbvavmPxYdkqjnWHKS+Mz0Z1qquyIC6dCBZqFMKoNNkecHnGdI4xbgp/YTdeE1JDpcV3E1hlfi1KM1jO7uEclucB2tSXh8VrcoTMJVpCclFhyWsSqLclAYGYOOyYfCEHRVN2h8LkATpZFSUf7TgnyvpPDm9CibyO231GBWW62t2SKh7P9htLo63zEu+/dAdgA4+SJREKKogk6eyYfiak0rbuWtu04f/AAayxKiSzbOE5Coh93bPeZV5fw6qqiaSwnJzXGZKzRrFYrya4qTZ4ycwrc7HZshwGtNXVlBYxgNY8enlHXDrRkkDJj1cQg5pm77b5Atz3rzZqu6+jXayrrSNFyeVm82pAZrNXivLoA8VS20pe5xe6BEpARWUr5ScvAUmlFipoQDcGVPt6U8D4XOHYkRgFjzRFi1Hin8c4So5KHO2hiMPROs2rf4/ys5jsfPsLPHofjoVGcxETcXWI7Rb2qqDsDcyKoPT4GsZ7h4eGKJb8n+UxSBlMEJvzNDj/7ogurC9hCoPxoS183tF1PXTd0tWMhgZqkyNEzzgO2AMSMtqV/ZOhcTUvNOgPZkEJil/f88Pf+AY8/fZ+TdUvTtWjXkqMWGlGcsK5GW81+t2Uc94w3W9J+QE8zvaAWaItg9HJcwzAQdRS+YLmvfYqYHFl3hu98eC7EbCvKMSCekVqZw6AtzgChGC1LJSDlBBrOzvoDp3bRrFUqH7KrfrMuHqhZSsTifsiDB+fCeU2RdeeonaHploltFt5ioX2geI18LfWMfDjO5d67HVUkqAlbWWOyxiiLQlDyESmWJsBoU5I3jRguFR1QNEktYw6lapXIaIyxOFdTVQ2uajDOoqzF1A3KGJJWh6xWKYsyNcr1ECW7MyDlVadRLOhzmGcPKskYUVdipzZ7tNUlSZXARgrsbi5RRhPzwKZuMEYT0hWjD0xTJPsaXcBBu3mSTG0ehJeZEmmemPLINu1JLsk8wgiYJ2s5ExhQViZ02mpsrdkNER892ERdCXYkjBGbK1YrQ/IZo34NFIkcDTkZIKNSJaAi5bDJYLPFJLv0bNFVJTJgxYg3IzJbRhucsC9lRo+V5qbKJQNQ5JCxxpILCkqVhyHGKJyVpHDGCvKJQkTPuXAMBSmUM+UhsmQERBNSkqbx8THlzG67LTZRt1JMCzr0frB7UxDk3vaWdZd1RDKo8BDfoZz6dT3I4+zy/jJN021wzOC98ClT1EyjwftMTjVG91SVQqtA23V0zQm6CILLvjpymqiqeBgkYrKEoJkmjXMiUJBSXRCaGqWy+CLaBYQEKVmUghQ1MVbE6AjRFT81yeCk0paYZsc8Z+Y504aaGGtSrEm6IqXbmzmTef7iJbOfiWkp8yhpoKtj0bej81n+W2gsizBCKj2TFKKAZhahc+8PAhA+Fp/JFA7nIoRADOngq1m5ipP1BlIm+IjOijYG1DwzrTtir3FNDUpsj3xV4ZNjPtJ6zRmev9wyTp6kZKAnJ+I8Ms8CQKGUdY0RHmQIkXEU4vfoA1McAEEkGyP7PU07nBWz5srFQzkssfSTCg83Jna7kehq6s050WqGqBm3nlRq0Vlpqiqgip3RNI6M+wFtKvrVhkdPHqOsY3V2dshuc4brEZzKxKjK/iUBPmHJKHRdoZUia5izBDcflNxbSpSUVEyS7ZQgWOCHQEbrwrdUlImOgMTmpEoJ3habNcmsJIhEkqllsE8BjyFnMQhnefZSkWsrZdAxVocKkZTkG7K2x3fZ7fNc9kctYwHSw2q7DatNRusJY6xk0fNMWM2lXy7I4LqyVKsTdLsmuh7vwBjFoBRGJYxSJGpsqknBoUeNDgploMoZZaSdMM7Cq7QxYKaAGTzEmTB75pvE1R62s7rz1ARl5UnTwtfWKknyscjCxYjYJhmUqWWyqTRJmLtkXYkBr9EoJAhqrUlKkWNAOWHwmQxJi69hNlpQr0aBNWinwCoyLaoS4W9tYqFHVeio0SGgnExsrFWQJ7IVzmmYI6ZavxMoBkDlr0F5/PEf//Gd4WTpAb8dFvJu779peUcgz99iWbaa3/Dam8uQf1+X1y9l5i6k7fDq4YS/6egP73O0Ioex7Wu+/w0vvuU7vmm5m/nevv5NWfTfbslv+vFun+Etx730iuDuCTh6LR+9sagRfe1XvXYrf81F/Nss+Ruew/JGPvr+nJae+u1qx9foLdPFr/uWN7z/phP5dZ89Xu5/5t2u7pu3/KZ9+hafP8zI3r6VwzcsCOry4puPXH3jM3m76r0Vlwk29+/ft95sry9fd1ne8rH7I5F60wbedsm+bih/7RjUnWO+G8vuLt+CLH97Ub7pvP+tBr2/xWf+Lr7h65Cef/8XdVsbvPvq157wu++/+5X5u5xPvO2yvCkL/o9zOfKavPvyG5d0n7rzpuUtwfY/YFx/96952xsLregtx/XmbX3bEeTb3Fi/jtHp72Y7B4rV0UffdStvuybvPHy984rf4tx/3c6/5b03jETfvLzLLqlvWuHty7ub6lKgt6ngv5SouqRj8EcBmFgj0Hvn7CF0ztMMJTUW8Wvxn9Ol5s2h73bEnStlykNZcJFMKtJMi2gtSurqiyN7TLE4vMejWYQ6nB9zpxR1y5R507j/rvdOXppWHE8X3pYZvz6VyW/9sneZfRZ4iL69nNreynL9vVvuzepSmG//0m8/rjcUm+/9+uurN3zrJWeI0+FPbev/iK/XNzwEdzKbTD66Xoti02ur39ukOno+j1dUHL3+jgnh8bN0/P+d77u3keUzt8lavvM5hcLa+rCvTdPw/4cl58w4joe/7dLTVRycHY6fG2mxRFIx5lZaY5y9k7nmxR4uLWV3JVJuqCOl4Q3MAAAgAElEQVTcoVDSDnOCoyrDnVvgTtWjLOp2Zdne3ax5ETKI4d3slN45CDaxI+wnXn3+C+pK0/YNH/7OH/D08hVfPH/K/sWW3dU1z371Bb/5w4d8/PEF/8V/9k+oVE+eNf/jf/8v0K7i/P33Wb//hKth5H/+03/FxYMLTtYnqKZmtV5xdn7Cw9MVpMjN1SVfPX3OdrdHGcfTF8/48quv+NlPfwbAqu9F99BVfPThR/zwt36bjz7+hC8+/wlf/eqX/OSv/5o0azSGunNEB7q2fPzd74htCpnr8SeENADx4DdojCnwZ5jmmUQWn7EFgXUPpBJjFEdmLX0WZy1WiTKK02L5FJOo1+cs2oXaLBfTkJIAaGJ4PSCbIyhryoWwjeiiKihdZIXVNR9c/CfSC9WaB//wv8R2Gw7cR3U/OOeju++odHe7whuW5UE4GiDy6+8fzs1RlnJUPDwMMocWZr59jXuvxWnPy7/8n8QNXBmGD/4puVrLA7hUPMqDpA4PlBzp/Yfs0EM8GnDV3V+4ixh/t6zi9YH2Dd3JfPROBuYb+PG/hCSKNg9//59jm7XA3tXtMaDy65eigICOk/zDhHA5d29Y8r2LcbyXdz6TlzJZ6buWn4cBLN8eeTouh2aI4zVXf/k/iAFuDnz59E+JcRLJtDJRjsWJQAQoCs/ROkxtCl9U0BEqa2w2QpJWurjSJ7JOGKcEB4QoVpEVsRDYU87Sx80JH2dCCmIBppKA97SithViHGQOAK1pFH6o9zPDuCPlwMLP6bszfve3/hlKaaqq4o/+6I9YrVbAoo+6VCkOF+S1u+LXvrzpur/hRtT61jP16dOn/Mmf/IkkL1rz2w8f0zlNbRNPfuM9TGPYDVuUakBVJO34/K9/zs//4sd0/Tknj5/w6T/+HfrNirpyOAIvnn7Oi2dfcv3yGZgK157x6KMf4OoVN2PCz9JXbyqHqxSuESAdKTHuR5qqxmrDfu8Pfqs+7oskncGaGjCE4NnvYBwUpuowLmFqzzjtubl6zo/+7//lnSp93yIT1KSY2V3vqc5WaCz73YzCsF6f8OHFE/ZX16ys5YPHax6dn7DuWvIEs5/YVI6sNMbPpGnEqcwnH70vqLUcudne0G9WnJ6fc3F+gp8Gbi4vMUpTW8fq5JTgPeOw598Ne0H62UoCgFV0/Zqu7ei7hu9++hF9pUnjnnkPCkO7bnixvWQ8mn0DxDiLygGxWAoJLFkXXkomlIuQDiUro81rAJZELmhohY4InwXuqMDEKNvKZFIZ6MTFOpd/sXgLFm3WXBDW9ybJWYmSBCBBMBUvrcO6ClM12Lq9DQYclWOWWvz9v79huQ3P3y4IHgfApS+SD5t4/bXbz2XIdwWZMQ3YljICko9nksflJnV85Aji7yjovTEQLuseZRwSSO9nkLcrvCm3zPkt5/M4O0nHs1SFalbQrkXRSC37yNcGwUWopBygXJnbVOb1r39L0HtTFeJYBpCcDwLnh9Xysg/lvbK+Skf8rLwIK3tyXrSDSzAtakGwUHwKaCZrYopiTpsUmapw0zQpBUEBZ0FICht4uQLqcI0pbjU6R1HIzl4qPiqxmMEJsKqAvLLsQ0wTIXpCnAlxIucASu6/eHS9lFJ0bUvfdWQU4zwTQizHerggt+f2P2C5vU7vsKXjWcrrP6AcvVMW55xQmdr2ziYqrWmsoa0MTWUxtSFTYeoVSjfsrydsBDsn6pWmMYbWOmql0SkzjqLEkzO0/Rq0RVtH37VUXY+qYRrFfb4pYvu2NtSuKjrFlqo2WA0pBWJSxKhwqiYrjdIVOTtAU7c1rlJ0vcZWDdpGlJuYfIXKM++6vHtPUCliTFzd7Dg9PcGYhmdPr2hOWh4+eswf/u4/YN7t+Ml7F5yvEpuVo7GO68sbtq9uuFj3TCHh54Hx5hWm6/jHf/AP+dUvv+Dpsxc8f/mCh+894uLRIx49OGV3dclP5x/z/9H2Xj+WZdmZ32+bY64NH2nLZVVXV3exyaF6BMwA0kDSzIugJ0H6LyUBAz0LhECIpIZsSza7itVVlT7Dx41rj9tGD3ufc++NjKzOHmFOd2VEXHPMNmutb5lvaano5z0e3btPliYoBX/ztw1NE9J6pdakeY+D4/sMR0P6ecJnT37C9YND9gY9FtPQVmm4O+SfvvmKk8uzLSvNOYOzofi35TmFUGOmtUZIARaMqTuWBnQaiqwjVZlUCp3o6E4ReK8RPokUSmYtRFwUAEJFYRJS14WP7aZkrI9ya2Hpzdr127JCCCmovOmQaTj/hmKiLYhdx2o23QXbrqANAfLWsSk0bzuQ7nBf3dqo7Z68rT78pvCO59hIbO0Qjvfr+q/tywpkd/m1CluLn3UF4ebz33ZTAyHrrbMuti8Vbt6z1TA1nrNVHLcV6A8d3VjcoaOsTkCnrLuVt3Pnt8cqmFZB+dx+mu423yEw7xCQ2zeziQbXn+tka+xVt3WqaOS1H/IbLnlEmyQT0R1hHYUeyK5TfkoplIp7TUSXWdveSLS3E/4QRHahNoxCKC1AiEALRihdUF4GesW2g4Ug8GxKgYqtwVpDS8ZMyM1wjNZhj4qY5JPobVHpo7HqvWC1KiiqmmVRRgW/mXH5lol06++71s56lH23r7f3950Ip91c3t+xBEIWvQT6vYz9vd0OnW8ektDpJUl0rG0MYa9sMECoHrOTOX7ZICtHgiDBI+uaOsrDi6szinJF3TSMd3bwhHIjpSFNJSQaKaBWBBIEGbeYVTjrMY2kUQanDI55MF6cQ6cZQipQCWUV1sigp+nlGiEUOpN44UJpiR3gm+Ed43r38V5KUCA4ODxklI+YX05YFgXT52+YqgnpOGewP+RnP3rCINF88PABs6tXnL6+5MXTFwzSPj2d08tSxjsZvf09ZtKTjEb86Gc/DSwiCu5//jkffvwJjz78kMuXzzBFwaeffMrO7j5plocU9W9+z6tXz/HGUhUlF5dXSJXh0OwdHmJMw/npa+rlKdPJDa9fvWY8eshgMGD/8Ih7swlOiW5hB0PbhdqzREdLMnRtQEiEjGn3wgUuUhH82lLKt7g6pQzcpEqmjPoHoRTEO2Y3Jzhrop9c4IXEa4FUGULKWLhrY6eIsCCVClTCwoNUorPyXctc70UUglFhWsEtHpzOtbupHDqEsTW3vGsf3l4Ef/y4Jdw3E0N8+4Ig1HV1iCeiiFAI1QlXT/icvKUtpYi9FqETgp1btP39zpv7gQe4C8798Dc6PPyfc9ylCJUI8ewwRltVrG99O8TQ/dZr25bGH7szv4Ea2/vZUG7d39EAiJMS0v1d/HakBnM2hA6iUhbqlrLYOGdr3ATC+8ixKUTohRcb+K2Ni/C/oGAj8pMqsqGE8iicx9nQT7AlqAiKMHhrhBBYHUIQWPDSR7YhGRhQYqmM1kFRKy1xXqKcIiEF4ZCRYDpJ3u5PJxAxTCJCP0zbYq13TMEda/OHpmp9nrcVYec9oZVj8S3rNl5vV9FamWp8NObjLd0K7cxubnC9FGwGl6D7CaqfI3OPVNDcVIjS05MZmVLYsuDk++9xXmKd42Y15+DeIfcePEQmiqJYcX19hWkqvK3p93O0AtNIsDVVZVjOC0xddXyw1zdvqOoryuKEVGvyJGP/8DFpPibPxsEr4CHVgixVJIlGpR4vJNZrmiZnlbXt1/74Lv2TkKBQCp1kzKdTFqslE1+SNj0qLHVtGCYJg16fQqaURnJ9uYBxQjLMSaPfP0kSvKliA96aNE0Y7eyQHx4z3t0hTVOCayT0LRzv7DDoDzk9OQmK0DqSJEGIilVRkKSBPFfpFGtrilWNsIbZbMZytUKpFUIm1E1DlvUYj3dYVMvOAlJSIpEkSnYryXq3bokSLXGlQrPMtfumFb6yI4T2BMsxTXtIHzaodSFA7G20UUWoZUqURmpNbU3kFjTBEm7dYFFHtLVdkQQsoNfO8vaxEShvxfQic1K4zw2k8JYC7H65jdduv7b5+g8sE9ZON+HbDEm/AajENrjqPhtf21RI/u076J7jLQXYug/bj6zRrXjn82w/mrj9fKIdO7/9wY2z+a33N3DhHWjvXYATYr9OBG2/y/BMbYzp1v23BoPYEHXtW3eM7db1/eaHN99d33BHERg9GyJqTCEjgT4CF2NxQvoNhUlcwxtn3UCSreBu902bDCcim4nbOE9c/bG+NhSRtwl03ne0q1gT348KbqskKO6BzbrddbS4VVZr5K0izZzWEhHr5YRsPUO3iq/FepzbPqnr/949B1vO7Xdupzi//q0V+dZXN39ura/NdeDXQ3JXLe36pD7w5ipBU0tMZRBaoVJwtcPRUE5XoWuKD8XsTVNRXV0iVApSoZWi3xuwO96l8aG2Nkl0vKvW4FAo6bGVoHSWsigoVzV4idaam+mExeqMxfwFmU4YZD2y3hipUrQWpD4YHlp5EiVItURpFxr8eYW3KqD99zzeSwl6PNP5jHrZsKprJrM5N9MbLmtPshwybAxl4XC5Ik9ydob3ECbj5YspdtgHNcQnJbX31Isl16sF1eSGV5MZB8f3ePDoMX6wS5b3aIxlb2+fhRc8+8MfODp+SJ45vvrqa07OTzHGsH9wQOPgzeklO7s9hJBYJygbA3WBNYZFUbKsDJeTl6TpFavGMNob8+DBI757/i02xpp6eQKERJWOnsyHgmJPZEFSkGoVi7s9TRXiHoHEVXVCInSBUqR5n7qoaIwhOmfCEo28gaa2jPeOSPN+aEBZl9SmQsduDQIZXRqRKNqDEC4yaLmYABBomUIjyrf7uAVmDei2/V1yr/vw9qbtQmDxZ7eXOi119yp5+72N5BexEWOCTjlud9sgdFyKpxKRdm1LDWzAvi0EuPk4UiKFonUB3dZIfzwJ813a9x2f/QHseVd1bSuON1VbikSLQC6xxuqRnOKW5RLzQLDyjrn4I8JuC/3d8dW3uWrphkN7ED4wmhiIkygjcotGpdroVO7pssdbwxbARy9FS+UlouJv1zTQuTubxiIIZAzCB2+LsZFdxDuaxnSx+CRTtElbLUcntHshNCeOsji6Zz1O0jXnTtOk86CE7gSxDZT3JEm2NVZOyEDWAXgR2K+VUmuF3w7PrQHuDFiCwSB4l7D2UXlFRqz2tTaUs2GwxIcMF5Uxqc91HwjGRjyFZJvw47Y71DUWKw11ovFWIW1CYlOam4q6LDl/ekoxX1IZR2Yr7LJgcrZgtHvEcGeXT578iKOH99k7PmA2n6GEx5uGJCYbJjrQ5HknWNkC0xTcXF+zXIS76w0yXp++4OLqKWenvyORkn6a4WXOw0dwfO8DEh0MGekdqRKkseLAeRscZchgUL7n8Z5IUJDlPXLdhw8/YDjImS/mDGdLpmXNalnwu3/6PdeHuzwc99gf7NEfSE4vFoh0gMgaTv7wlMPjQ77485/x6XhEYS3PTk6onEY2Am3h7PSCNy/f8OOPHrOzu8tnn33Gzs4Oed7ji59+CVpyNb2hrBqEUBzfu09VGa4n1/y/f/d3DFJFT0uOj/vs7e/z83/zBb/4T7+nLBuKwrCzn5Cn+ZYUTJPAFuEIjCHW2JDFGbkBvQhCVWkZUV1MgnFtpmYSyfkFxsvAbqAVtTUUVRV6KrrgbhGiwUakieojkhGoBU4ssTRIH9hPZLSU26SMkEjjgs/bWYwP/IZSKNI0C0S5ckP4EJGg3Ba32wky7XGHsBTbP+N2YUsHdr+vXZi3lYun5dJvs0/XErhNlw6Gu99CjGE/ty62Te3VfndbAW6eU/hAxxSImTVSKaTOAyps42hRqDuxlVbx9vPf/v2OcbrTLNgYqLuqa2+n3wNIFb0NfuMhbyvOVnBJuk7mbx9vFfxsvMMalolbzwyd0vMbqLz1Wivvkc4ivQmsNuiovDaRW7i32xdd8+XGz4mIGr1ft9WK18ezdo96gbU13llqbxCNxSMw3qPTBITAONCOwIwSz+Oi4lgrwjjvEVgHL5NC6taQTbayjQMzU9QawsdWVsntx9raD9EciHRfGzMnNhC2IJBgxxeC6t/ocbq1JOLa7JiQttdLFzUU2+u2HWMhWwUXQifrRDG7Hus7DuEVzkqa2iN8inQJfuW4Pj3h5mLOm+9e0QiPTyWqViR5wv7BHjuHx/QGI8pixemrF1xevKaXpyidsDsao5IsJEE6j5AhzKMAnMU0JfP5HOsdpU2YL66YL2dc3UxJlKTIcqwzCCxa1jhb4r1HCY+SIswfCkvcFwqS92dNe9+YIPT7fRKRksmEQS9ltVpgrye4iwnLqylvTk6hLhj4ffYGB0iVMV1WDIqarGp4PZmid3bRo10OHj+mso7TWYFM+6H2y0uWixWTyws+ffSA4XDA4PhecJ8Cx/fv8/r8hCTNA8Fyr8/BaIfziyuMsTx//oyD8Zjd0ZDBOOM4H/DBx5/yT797RVnNqOo2+3PbRtAq+JdtE+JyocN5sCQdFqRAekLs0q0TUDyh/5/1NqxuJ0IvxBAgxBM7o7PeFDL2VLPeY0kwPuvoo/zG/1oLtBMscSN4ERRzuGZsLhs7RrcxlW7ORNhAArbEb/feey+RTUG84eoUG+94WGcx+ltf9d33xO3NLjY/5rv7DfvTsf3h9ishDrOdNLJ+msA6X1Mtp4hUonSC6AvEBhflVu0ZGxL/juu96xC3LPI/5btvnyzOiYQ2bro2XzYcaO1tt3kdd4KId89sF3ttjYHbRkuU7K5Ncon6Cu/BNri6wNkalfaQMnTbaLOX2xF8i9DHhxWwnlfiPvRrpbi1ENqxFbQNrV1LcO8DN2ow+ILR51wIg7nuXBHBtj83kpfE1jVkRGKgVItGN+uH44OIUAIlb7lDvW8NlvUe24xLbyHxDWPLRyjaqiQpNtae2FxPP7Ae/do70hqXnTLcnNP2/m6/9c4lImjJO50FbwW29tSrivmbKyZvrllcz2GYoQYDnASVKIajITt7u6S9AcvllOl0SdMUHB/uMxzvsNN/EBBqpMGTtOg8uLvrqmRV3IQSGJVSNaFLfd0YvJNobRBKoJRAC0vjysAPLRzCJ0jRQ5DgRShfE7Hd2vse74cEheBnf/Fn9NMBZ6/OaIopZbVkcH3F3sUNe+dTpLSU1ZzpzPLMKcrSUwCXZUG9mNH/4BOS44csRM6jvQfsDYcM7n9EZS21MSwWC2xtWMgJk8trpLE8vHfMm5NTlkXB3uEx6IzhwTFffPkzer0+P/rsxzx//pzJ9YSTk1MeffAxH37wCb1RQn/vkJoe9x4/IevdsLqZMLmeUZblFuuIdYHctywKcBrpRQjy+rBUVaCoR3rIkThAK6iFxDhHDW1ojn6Sk6kcDIx6Q/o65dovKOsVVV1ghcN4SWFTXk8MatGgnArXFToKobWkEA6kk6FHggCVJBgk0hO4/Qi9jiTyLR+4xKHi1ruTkUtsfvYdx5Y+2zB5u3Osa9U2kV4QQu23fOzxcNtBJ9eCU6yFVhAOHhuFi7xLCNzxEDIqWyskTbmgPPuGYvIVUkJ2/yf0j35MNn4IOicQADfgZKdM//OPd333j5kZt1BYCj6hQ2nrT8VR2hRk4u45Wwu5d107nsu35sraKGnRY6izi2jQBaXWlCVXb55z+fo5dbHkyRdf0ts5IB9lNHYbC/uuu0g8ZChDkEp1nSE6HBTdiK0RtC56XhsYWuvAJyk1aW/IYDji6P5Drm9mzBdzXr36PrghEQjZulzX7tBW/cjYdSQ8q98whlt3o0cqEVtuyW0CEOvapN3uaNVw6HQU3faiNZJbo7YblfVaZ51hK8W2ApUbY7HO/G7nZ8OdzOYy8Z3c8LeGXmyU0qxH/Xay2e3nShBe4o3g6nSKM4bF+SXl2QyzqLm3f8z400fs/eQjeoMC4S2ushw9foBMc1796imvvv2Gs5dPeXC0z9GjD/jkZz/n4INPyXUPaywCiZSgdUJVlpycvOJycopKBYODY8a7OSo9Avk5aaoY9HKO791jb29ELwdT3FAXM+bLa3b3P0EqTZalqOCyAFGT6HoLhf/Q8d6JMUprpFYYZ1lVK+pqxe7eEJ+kZMMxkzffhyaOTvDm9CXzpaH2DbNqSb1w3Ns5oJSaZ6eXiNFrBqMRXiWgJcZZvv36D8yuL5lfX6CaivnRAf1extVkwmK5pPIwmc0pqoblqsR5yWq1Ynpzw+T6mqvLS/b37jMaL5lWgllRczlbcPr6kqaoSJzn4vwKqQxy4LrV0kTiZCA2uRQoZztk46wAFI1PY29mgbAWEUwlPBbrPcZ7yPsgBzQ2Jcv6JJlDzC/wdUVjbEwXV0hSUpWjdIa0GQaNsdHqV6HjgZY6ZELakEjjsKg0QQqBVioQEvvA3iDXWmhjA7SILyYibJuItwTtHceWMN4M5/v1v+0/m3CojXd0sYqwENvGxWvg1YqntcXb/u4FQdEL//bNbaCkLvFlY1MLIOkNGBx/SG2vceWU+vxbpDXY1TX5/qeIJEPqJLpe794lP1Qo//+fb/bt7ysBat2TdeNaG58X27P4rt/frQTjpG4C8u4lETGKR7YZyBKK6TXTi1P+8Mu/YTm7QQj46Ec/RuBRSsQau/UNvG1whYXYJq/gPW3oz0PHCNVykLYuBmMt3gVWqTRRZFnG5z/+gp3dA/aOjjm/vOby6pI3Jy+xzlM3hp7LaEtLNpFnS9iOaM2x9RFqf8M66wr6/WZ+QIv47hrLtRILAY928nyXQ7uxW9ZI0EeXYERD1oQG1U1kufLex670Miqs1jvQLv62jpnoFmg9RXR1yOGycVa3BuPtPbP5SNaGjFlvYFWGfqDFbEU+yBiOB2R799n95DEHH31A1isx5Yqb03N0IlGZJB/30Urgi4rZ5SVSJ6QHr8gOH0I+pKkdsgljN7+ZcTWbsWpWyEyhM4WQgjzXCJli6zHSg0ZwdXGC9CW9rGJxc0ZVzFksp+h0zHB4TJqHIu9gaIg/aY++f1Nd52iMoagKbqYT6nrJvScf0hvvsncs+eXZH2hMSWMFz19dMplWVFZSLGukX/Hg0ScsneT65QkzI+kPRwzGu/SGfcDzq1/9lno5g3rF9OKEyf17HB0dcHl9zXyxZFpWXExumC1XXE9n9IqKy8tLTk9OuLg45+LsnPHOPZLePlZ5HBbvDa4S5Crhwd4OV1cXlNWcD3/yIDRrBeqmwbnQYFVH61TLCiE8FjBG4HyCkwNSmSK8QNgafAPO4EWNdY7GWpBjUDvUNmc4GpMmAnHxCutXNDUkWoJISGSffjogyYfYeoEtNMYEK1F48EqiU4VE0lRVcNN6i3cJQgi0VEGQeIKSxOKE3VJcwbpcux46ZbOl/O6wlO5ARn7zLegQW2u1ti4oNjYf3XW3r+lcfH/DSuuEQ4ssPQjpg9vsrhvczPZrzx1/kQLUcEw23mVl59RXz1h8/7e4YoIevkInQ/TwAJL9kGzUjVmrWtcYNvwttsb0/Y4/XUkqGf5bD/ZGjeeGH+tWCOjdV7xDCGy5bv3tX2O0yAvaUnTlBdeTSy6ff8vv/+b/AqUYjHfx3rYgDiXbLurhTLeJwH0nvANSw6/RoIcuzm6tRak1Nms7ZVRljdYpSZrxs5/9BQeH9+iNdtk9v2R48ppf/fqXOFdS1Q3OOWRMdPEuJt84SVd2ImXbrxZoCSxsWI4yrkJPjGHazj3c/twa3tZs8G1srzUAwjlkfI7bs9BmwIb0t5ApbeomNr0OfSu992itY/2k6rrXtAk8IZNVh5phQZcsFps/BQO0s3D8en+xRp1hibytLIyxaOfR1rJarmiMwfiagw+O2T3YpX/8IbuPH7H/+BF537CaXrO4uEQqj0xheLBDnmdoY1lezbFCYnZfcPjZl6jhHojYK9EYzk6vuLiZsrIFaT8jyxKEVGQ9TZKlKDvEFgZTNLx+9T3LRQ8hryjnlzTVknJVMBjdZ//wY/JogDgvQvLP2ofyR4/3yw71jv/9f/s/cDawPrx5/YqiWvHp+ZLeaETS73N2s8Q1FbOV4atvXnB9PaWyjsJVOOEQakgvHyKV5vX1NSAwzjNfLCjLgp4oOdjtc3ww4ut//h3fvvgDZ5MzvvzyS8b7O/zi17/h7OKSs8srTl+9INMaihWLyTWiadjbGZH3JEKXKC0CTLeGTz//mPFwxMFowHIxoqqWrOpFtygaaxB48jQlVznKK7LKUzVQGEHhj3DpIXL0MSo/RKoEmhrlCryvGKUrdCpIe5rR6Aiteyj67B/t0MsVy7SPfXXGpDkFrxmPRnzy8Udk4z2QjvPXS+qlxlSWNFn7uWxUCrqfg1UI11Bju8C6QtOJPhvaw2xOuRIe1aHAt5fCNnZa26tiY+N0878h5KBFmOtPusimsZbf6wqlrtarey+k9nu3TrTZRIQ+KkLvY3r7rXuXG7tYiLeXucQhXBC2/Xtfko4e4IHy6inF5XeY1YR070Oywx/Rv/95iEkLGZhavA9ZtluGwOb531e5bcHod7y/fWgpSOQaVXV6ecOAuW1QbBs8G1i9O/3d93FXToTwoAmuQdOUmLKkWC54/rtfcPL9NxRXbxgfHjPMNYPBgDzPSJJQlec2IKW6pQRDdmhAPrT3GefYxm7r1jqssWgZKQd7GU3dUJUVT5+9Ik17jEbX/Pf/foVKFpxfLxmM93jw4GP+7b/5b3j69GtOTp6FTOqoFGTs25do8FERCqc6V68Ttos6O+/ABgzRulNb5dgFXt+BLAJO87FvYAc91+7M23MlVRgT6zg9O+Hk5JRf/+bXTG+mzGbzEK7xjjRNSHSCTjSJTiK1XCDwUFrTy3N6vT5ZljEajdjd2+HgYI+DowMSHQrSO/C3sRo2F8BdfUulg3JZMp3O6I8G7B4d8PEXHzO610P1Fd+8uObZv1xR/vM/8t/+D/8WaWqa1Yo3L79HDfocP37E1cExF71d6ixjsHvA/v373CxXLM4ucaphPpuzXCy4vnpGUU9xqSDp9dBpCiShX2UxYXV+gwV2xBsAACAASURBVC0cprBcls9I+543lwOGPU2uNX29Qyi7UBgRM5ARBLXWysc/rgbfDwl6mEyusQayvE/jPU4kGBRCJWRZxvGDRzRViTCGncNjRNqjNjW1rfDCs7+3S38wIktzTOzpVjcG7xsS5bm3v8P+Tp+j/RE3N5fgPXk/pz8cMBoPOTjYY75cBJ+6rWlsw3I+pS5LBJ6jowM+/OgRjz/5GK+C4vbO8sHDxwx6PQZZSl31KMoV3z39dl3sHt0NUobO6sorvE8wPrBOIHcQ+hjZf4LvHeJVgjA1qSrJZUVvZNCJJMkkaTpAqYRUZQx2RmSpYuf4CTfLIfJiEEoZhiPyvY/J+ine1yTZDqgBjc+QwiIFWO9w1iBETOMWPsZOfDevLa2bI/KIitYebRd+iFPALTH4NiCkTUIJXpOgfLvUbcE6+L5xfe9scOOYBpWmCCFCJuEt1+Ta5g8/Yl5GTHGPLs9OVvvtnevvcq9tTBub1/PcdgnLJEf1dskOPsHWS7wpMeUEZhovFTLroXs76N4YW5chDtvbQSgV0jXxHQC4nUm5dS93vPaDW++OzKSYT7XlwmvHZG1UtA/eDthttCri5991ZxGJ3fF+xzjkHaYpKGdXISPw/DWr6SWJEmSJJs9SkiSJxekhy09sTNtbSNCHBLK23q97tva92GvTOofeMG5cRIdVVWEMIDTzxQKV9qkMpP0dlE64d/8hl5dvODtvqQzXijDQHkJQbiE2KdrEn46TsB3O2wrBRxTo30KBm6PXzoQQ7Vp9S+XcMqM8zhguL694+vQp3/7hW77+6mvm8wXL5ZK6rkM2arJGgm3plFKhSYBWmizLyPMeWZYyGAzY29vl+PiAn/z0J4zHI8bDYdxbG5bSOiD7g9mhCI3RGjHIUaMcMVA0wlBVFRdXF1xNl9zMS558/gEZjtlshjElSV2x/9EnJP0B2WgXrUb09w7pDXcoK4O3C6yU3EwmzOdTptMbvChCL8HAgIBpDOWqYLWYM7m+ppzXlIuGhbhi4CX7VlDbLDRbVwlSpSiVrJGvj5PxrkLNO44/oZWSRUiBTCTHDx8ik4zjB4/ZO9hh/3CXz3/yGavlkjcvT/nos59iTU1V3CClR2vFh5/8lMFwzHA0oq7r2OnBsipKrDXcOxqQaIGSns+/+BHeOVKdcLC/Ty/vcXTviF/+6tfUVcHLbxSmrCkXc6rViqzf46df/oR/9x/+O/7Vf/2vaTMsJT7GWTzWBP7AYrXi2YunnRIUUiNEgpQaJTXSKWSTIZ0LtUrqCNH7hHTvX+PzEV4qFBXjYcNoaHn0cAgeqsZRmQItPbt9RZamKKV40Dtk3lyRT84Agx73Ye8+XlV4u0APP8CmN6zkBK+meNmQOENTV9jW6aIVKIlAh7Ry79Ei+M+tDGwjWicbwu+WZdqt8LBOxMafCMdm1N9FQRI6Gq/HsVWCYa15mnpFvZxSL6bsPnyCVOnb1m+7MMXW/ovXje6bKEVad+qGygy1cO9UJ9tWXjBlwr90QhZk0mfwwc+RSULVHzF//vc0s1c005eU8wvy8T32739BeXOKtYb+B3+BzobobMBGlPIWct64CyHuECh/fAPe/oRc69143vUHu183X9xUhBvzfjuRJr68/vUOYNvaH8p7hHMs51dcPvuGr3/zS86ffU+9WrC7txuIK0YjsiwnSUIpj1axu0wUOlZtu6FCtnXola4U28aa9zjrI5m2A50Ez4ELrE2ma+pcw6rg9OycqgGvcrL+Hv3hkA8/esLp6XNeverFLNLgVpY6DqaXoUTJSXAh+cx5sHFgpZUBCRIM55Y2TYhA9ODvTlLemsGgAAWJUt06tt61kYJu3L0HrKUuVvz2V7/gP/39L/jFL3/FdDYPTZpZcw2H72zsmDY2GP+VUq5pHYHRsM/+3i7/6//yP/PkySfsfvHF2mhlbXC0ROjvOrRMoKeRvZzk0RA3UJxVF/h5QbEs+P0/f8fp5YSL6xk7uznjPEXMb6gzTb63xwd/JtDDPQaPPiLfHdDb22F48JCTiwVFVeJkwfXkgtlsQllckmaC3jAB47CuYVkumV/dML254vnTF1yd33B5OeXoY3g43GO8P0QjUSoj6++Q5WPSbBiydWmJ3n/4Gd965vf5kJCC//F/+veMRjuorAcqCQiwPyBNNWmm0VpijeXzJx8Goek9uCLEpYSgl+9Ejr5YABxnMwTJPToLW8djsfdM2JSIUCLhPK9fv+b18xf8/rf/yHI2o6lqZs0E4yx53ePlqxdMpzdYY0izNIpDF6z7mHnmXdjkm0d4XdAYD7ZGubApClexsA2T4gZnp6R6wc6DjMEo4f6B43AvYTwOXbGvJhUvXs3pDzWjoWZ0lGGNpKk8ZxdLvJc8fnRAf6yDm6PfAxPaTT369F8xNztcVY9ZzicYSqRc4pmgfIl1C/AuFIbKBo1FCRuyQZ3A4kJxeNf2JBySkOCwdqvdmtP1AHR/eenQyxWyrtGDAVYpGqk2UFBLluywZYG7usJfnCLufYBIUlS7edk6eRj3VoFujXyLE1uEGfP0WrR5lyJ/6yk2tYbffqvFAV6Q7XyA0j2WFy+w5QzfFKxurqBpSAYKXUzAK4RzQQgqGdqGiahc/4RNdQcYeNddd39rGbKOfZuzJfwditBvPedmUtHbivDtC7WJSeu/t3+XSISTuKammE+5PntNU8zxpkbiSbKUvN8ny7NYg0l0L64zF82ttNW2/ZmxgcNXSh+LymM2qCCwvXgf41wqdpeQaJ0wHo8pygZjGk5OTvEi5cHjJxRlhUNy79441DD3cqwtkLFmVkWGNe8cCBWyVG2C8zEZRngQDqU12FA+1dXVxfgbeFwXZbjLsAkDHNCaC/kDsZNFGxNs/23jdvPlgovzc/7xN7/l5YvnLBdzlBLISMovW5aYqETbcVq7+tZ7zHmPEpK9vT3uHR/x+NEDev0+DpgvVzFDNsYoY6KNVoLE2XXiz601sBIemyiaVHO2nOAXK0xxii8ttg7Icmdnh2ww5uz0jGsgrUv2Hj9EqZzFskHkY4aPP8Kkkjmey1fn1FVo9TbY1RRLQZU4nAnyqSk9k3mJtw2uWrKYnrKcX7GczahNgUwbju7vs7ffC5n8FeRJxsGTJwyGh0iRB2+2c3gi6bYx70S7t4/3U4ICnnz6MYdHR50CRAaXkoiLScegvtvfQYngTJPCBEXkPb6ReBcWoOpq21rKJEEjQosh1/YKhI50uioKri6uuDg95+LkjKaqMY2hqWukVjjvuJlOWC4X1FVFnmXBNdEtqJAxFNb+rYmPi9ZYt16EHipfU7mKolpi/YJqNmV02CORPQ52Jft7itFQcTNzFKVlMm0QWtPLwoI1xlFVjlVh0Ini6HDIcC9FSIW1isYqhEgYH9xj9xj2p31qMwG7wsgZTl2CX2Kay+Ay8CWaZbR2bIf8fVQcmz1Zw9j5btt0K3zj/U1FJWgzMjzKWZRp0K4BCYbIarGB6sDjrcGVBXY+B+dCmYO/hUDiTa6ZOzbe8et7a9tWvZ1IcKtYvrv7zXOJ+P9bbkPPlmJQ6RAQqP4B3jmMqbDFEiscbpHjyyVepNhyhkxTvMvDCX4AAXSvbLj3ws9br68f+q3vtoeUIUPUbZR7dl0RuufdRHl3EAnEX7Z6W9xyEb/VPSIejjbpRmKbhrosKBZznGnAW0Tk102zoAC1UqHKwYEXvlt/t+sE29ZGOI+XbUnNGtG0bC6hBDC83jQNxgTOzyRNqBtLXTdMJhN6w12OrIWqQUiNlJokScmynOVyRVsqERhpghkVWG1UcPVFeBZq9EKiiXMSsFFprF2yIs7BD9GXdZ/dcMu3Rf/xrw0XiKAsSxaLJbP5DGstvV6OTgMbjXO2y5ZtM0ZDd5mIKVx7vqjYtCJJEnb39jg4POTw+BiEpKqagC6d6xShlAqlJFmiu4zUO5WgM1jpqVGYusA2M8rJJb4ErCQZH5P0M4YyYT6fUlhDbg1jHzLfV8uaBoXoDWmEZVWVzOZLUpGRZ5phL6PoaapKUVchAclUUC5W2LrEFDPKxZSqWGDrGiEsaU+ws5PT6yVhbTQpqU4Zjo9JsxFC6MAN6wOIclZgreF9j/dmjMn7Cb1BgusqXSxCtf4C16EACfG1WEzeNLjGYFaGRGvyvAd27RIDYq256Lh6bROQoJYS0zRMrif8n//xP/Iv//wVs+sJaZLSz3P6BweM98YIrZhMJ5ydvuHk1UvGn/8YpaNrIqaDKYJrTW0IX0/ccLbCStORVlsLS1OxbGoqc0VtnlFXPR4+/AkjfY/PPvoEnSocgleXM86mgmmzS6+WLFbw+mxFWRqaxpPmKXujjMPdHlJ66sYzmRvOl4F5vbcz5MuffcyTJ5/wy39YMJmumMyn5HsaLRuKyQmLm6+pFs9R6Us8S4RvQmsXL3AImrqiiYtgrRwsLTtEkJutS6X1y2xYgi3bjPMkWUaaaBoPWB+FmmFjV4MPWYCFgBtvOfQOEa2wbXgRN6wxICRCq+7763Oxvicf0Y2PONF7bhdoBa9QC203lO5bukl09fa2FcwqYfzRnzN/8xXL2SmaClsVvHk9A6EQMmVR/xW948/p3/+cdOd+UCmmCglRUsGGO6814BAE0nWIbvZwf0q+J22FgCQiQefohO8mgt9SpyK+//Zp1mO0cfiNfzZVYDcNIipBGZTBahlcX3VZk8lQO4c1ZHlGbzgg6/dQiSb09mszKsO5zO0iZSkCkQSROahFsTE+KHVIWGnr66q6ZjaZUBRFCJvY0DbMecuLly9YVobaJ3z40eccJhneC3q9Pju7e5TVDKl8iFlKt7EkVKgJ7fqE2o4asM22BKLgbMe9fT2ixh9Qg5uxTdd2iiGw2Di/kRGN52a+ZLEq+eKnf8bHn35G3dRdzLSNgdZ1zXQ6pa5rmsZQ1AZjLXUTGK2stVR1Rb/XZzgc8hd/+ZeMxyNGgwEX11Oubqa8ePMG04QODB6iYSAZ9TIe3DsmTxMGg8FWDN95x7PFBcY7GuE5Gnky4dnJRjQytDF6+OTHyCzHq4Svv/qGuizAW4xIWNWeV6/PKSrDqqyZNWXoz1hUHAwzer2cJx8+YtT3XA7gd7NXlOWKqqi5uTinXi1pFtdIt0S4ilxDNsiR/R73jkfoVHJzOWdn53NGw0/YP/qcND/ENCo0IfAWRwBIdV29Y7bePv6EmKCImCBaVp3VLcBLFK1ffW3Pey94/fKE1y9f8eK75+yMd/joo4/48MMP6fV6tB2APSCzDKFC8FeqGBcwhm++/obvvv2Of/n91yxnc/aGO8GHL6GqS3b2PiDv5SzKBbPphNcvnvP5k0+QKosuBRvNqNBYE7fdn85HZV01NvJvCoyRVMbTWJBco2jQfsXs/JIzjnm6d81494C0P6aaC3yt6emcTFoSGRp35nlGnglINf1coVRwyybSMcws1UBT1ZKb85pBL2Gvr/nyM83r85Tiu4z5qo/Ak0jPzk6FGqb42RRqQ9Ms0TIWh7fJALfcvCIqlm1g1irJmKItAqr0uLjpFSLVWKEjaw4oZ4OBQiDwbp1w3nvmRcmbyZQnxpJ6v6WvvLfra7tADeWt2FJq6wLgWNAbkxY8Piow36HI7WMtkLaeb5NQGk+bkuiFC4wVZGTD+9j9FbacU5w/xZmKogYtHULWuNkp1jnK5Q2HH/wUhKApF6jRfVQ2IhvudV0TbF1hbRMZ8iu8M7imCehDSJJ8hEpzZJpzF83n5iElnQuvfTLRfkncfs7WqNnQPlue4XUN5RqF3h7BYGS04t1JAdZim5qzkzdMJ9d4YxBJ4KX1HtIsJe/10Dru0fU0dae/jQRFLI1YoyWx0UAkGmaRu35VFdRlyapcBZos5cmzBKklSWapzYrF4oary3MePPgI7x1NXeG9Ryu17pwiBV2adcvi1NYIeofHdX071wo8EoNH4y8YiQSoi6Aj37zjqKqa5XLJ5dVlIKA2FuODC9hEFiof1/f11TXzZYFrXbRShPi+UEBCv5/jnKPXyyjLkrKsWBY1UmmSNKPN6LTOkWUZWZbRG+QgPEVZYJoGIQS6aWhbXLVjr6SklqEszEblePvwiY98nZLjwyGZApYDitLivCbpDcgGI5LegIcfNKyWC5azGyonccuS4uySxgsa5ylcjTMGaw2NqbGmRvqG0SAHv0uaaBamYT6d0JQLvClJpI0cWmC0Q2YCPRDgSlytMYUg3d0n0/cwpk9dK0rvwNZ4GhA1dS1omv8C/QRFZO0VtGnQskMYIiKo1rpsN5a3glcvT/jlP/yav/+7v+fe8TH/1c9/Tt4fsre3hzUGG5VSfzxCJwlJu+mcoy4qvv791/z6V7/m6bffM+oN2BvvhM1iGsqqZDgcMBwNSd5IFrMpp29e4ZoakWgELtb7hCQcbxu8bdhyBkYrzjQmZrZJaqOpTWiuq8QNMEVwyuziDW51wHe64N7DTxjvPaS82cGbIbnuk2tLpgVaS5RKUUojE0WqA9WucB6FY5B5zDBhVTiuz2t6RzAaCEafSLRWfPtKcz7JcVZxbyzZGztGOuVi+TW1X2AagUwjuS8tv+NdyMDfIQWDkpCx6aj2DTY2G5Uqx2uNUQorPMIYRFMH93frBo3WsXOORVlyPpsHC9qv66Y8G25nv3Z34oMbNegnuXZ+toZTl40XpKvHEZrqbsHLjZ+tKI2u3+4RW7Sx8SkhEUqjBsf0bAOuppldUq8ctWnwyiGdxTVLqtUcd3XCbi8UX5fLG5SXJCPIRvvRw+BoqoKmWlEWc6RZgmtwTYhVeaFwoyMSv0OWprRNYOMNd/fezZW8pUAEG0hk0x0a326V3Ibia5Mywnk2z//22uimJ86XU+CNpalKzk9eM5tc451DidCDz3tBmmZkeY5KgsJxtr1uewX/VjavkBLhZdfzLzgc1rynAtGh6bKqKKuCsinRSqC0JO8nJBlkBqqmoigXTKcTmiZkUVZVFesDQxcIKdc0acG1FFPlBUEGEAkunA1Z2O3ajLA7TO1mh/iW1PttJdh6UsqyZD5fcHZ2zqooqauGyjkaE9iwjDHdQJvG0DQmUCc6j7EWrYjZn5o8y2LBeBIQeVmSJBVZ3mO0s7umRxQCHTNIhQBrDHVZBYo3IUhc0hXUty5nnKNpBKZpuqL87ckClUuyRDLqpxwe7pMrTZOP0Mua2oDKemSDEYOdPR5YHRCrh8o7ilWNbyb4ROO1xtKEmKzzmKamacrQTilP0GpEohTWNMynN1CvkK4h157ECYSTNNahUkHaA29KrE8wZQ/td0nEEU2VUQqBsAZla4RsEKqhbgLJwvse760EpZMoGwo0vQ0LRsig/AQgfBsKDouqMZabyZx//qd/4a/+6m/4/VdfobTmb//h1/zdL37LaDhkNptFf7Xg8eMH7O3ucnhwwN7uHuVqxXd/+AN//dd/zbOnT5EuuOvqqo49wBTj3ojvv/sWhOdmekNx/wZTLqgWN0hb4J3B1A3OGkxTUVcli9VqTZvmPUVZUtZFQByxx5ltaoRQ5Foz1OC8xZiSYvaa5fUbfvHya7LeHjrfRY5+wujgY44e/ZTPHn/Ozt6AbCC4ua4oVxXag/cSg6JZGhLt2R0LUqlYKMnLZ46bVJImkvv3UtIc0AtW9Q29VPPv/u0RY1kiqxn/9zeXYKdIaYIVKWQgApaENOFNwWcNIbd8zY6DD0rMOcuLp3/ArJa4YgmmQuCRqSbvjUmzHl5AVtf0i5L0ww8Q/T6kaWDjt47p5IyBLbk/zOi7FblNQWakOsynFxmNCdZwkmiUViQ64eT0gqZpyPs74FVQGEiqekVZLunlo8CWI0PKtLjVWL7NfvaAiNa5bAVeFKzGOaxpMNUSKSTD4WHoTo5BIMlGD8n6RzSrOcX1Sxanf8A68EKgshzbeFxVIGbfIpUgrSt8eYBTgsVFSlMXmGaFLeYoX5P4ikw6lPSEnrJxwOeXSHuEEo+x/XsgW160t723SsXEGEmnIDbbeWyRA4SH7zIZt8aH7ithGUTi6pBsEt7c1Kc+njTVgsvzS85fvuTNt19RziYM0tCzTQqBUwmD4YDxzpgsDcjKijY04js0dTtBSygZZcMG0ooJWwGhqE4YJ2mC1H3yXkKiWwYdhxAKiaZsErJ8zOHhLlpLmrri6uqSyfU1s9ks7oN2PNaxQGeDt8eYGusM1hka28bcXEeRCKrjTQ08tq0T1wO3FiLr8X3x8g1vTk757runrTlG43wnDUVsgkzMeXDWYKoF8+k10+k1xXIZXOI6dHpP0oQsy5FSkkhBP5XgDOX0hsYaamtYrBZEcInUilRnpElOnmbkWUa/nwYULQSr5RJrQ0caTUbT1Jg7YoJaKf7iJ58hXIWtZ6Cg0Qnp0RFK3iCKioYEpzJUNqA30tQkqOWCulhhmgZX1WHklSCN7TG98FRNwWxuef7sO/b2RySppFitmE1vuLp8w25P08s0RztDtEnwTUk5n6K0I1FQLQpwA6h2KGc9FlnO5aWhyAp6ackotejEkGQ1TeMx9r8AEnTW4k0T2oYAwaEuuviN8N12CEqwrrm+vOTy4oLLi0vKqkYZy1wtef36hDzPmE5nJIkmSTTz2ZReL2fY73P/+Ji6qnn5/Bk3kwneeQaDAZmO3dod5HnOwb19+oMcJSXHB3s8vH/M7qhPWcxxpsDZJsQkrcXUFXVdUhTl1uQb72JXhjWakiqgXSUFWoYCWg0Y0eCxNE1F6SyiWiEbjfQV/Uzi7AOMS1guYTavaFaGfp4ivMMax2xeohUkKlhwWjq8XbFcKq5E4A+9mayw1QRtCzIkOWDLE6rFa5p6ijUVEhGD9SFu0TLmb7sJ25jGxn8tQbWAclWwuLpicf6GnhQoETZ6Ntoj6Q2ReU5iLMumJjk/gzTBSkmqQ+bt7PqCxfSKYj7l8s0L5pNL6sajRRPDQCk6zdFJAqZGCUGqFYvZBCckSmage6B0cIXNppyePmO8e4gSEluG0hnT1MGavH20klxE115EFAKPKUqm15csJhehwPijBJ3lSK27eA8ypXfwMUIqmtU1tpjjbU3b+TzRnqpYxB5zDrc8B9PQlE28vKcnarRo0L5CY0JmqQ8JFiFd30K2E6zwW7d+G5u1jDHrxIz2GVuluUaEHeL1dG4tERE33q2RoBCYOqADnaVIEdyYIVAVEXccR+kEy8k1l69fUy7nuKYK8cAoxr1Q6DQlyzJkS8IiBW0IsK377HTQraPzBERUFZ5DdoaM94GaUThieyMHwiF8iK0qmZDJjF7eoz8YoHUo7Z/P5iyXC4qiCCHbdszi43nnsS6Q45umwXmD9ZtJJ5slCW0O86bRsTngG3O48ZyNMdR1Q1WbbuKMX09SSA50eNfQyzPSfs7RwQ6r/R2Wi0PKYhW9IwKtNFJJkiRBysAWU5aOpgkhm6IqoarwvqBxBusdwnqMEdS1x5lARJHlGu8MUkJVVh0dmjENxWp1pxIUQjAc5LjGsmyCS9cLSNIM4wV1Y3BFiZgv8SqjXFmqqu7Ks7wPpS2KkCiZZxrvHcYaXN1QNxVX19eU9RKhPFfXVyyWUxpT4lyO88FA95GrVRA6TSRYekJiEZRGYZyisZK6CdfCQk8KhLBIHeKrd8qMdxzvWSzvQ+ZOVYTB2hy4TsgGf7sldGVfrQqeP/2ek9evubq8RCBiU92My8srhBAsFvPQKinL+PZfvqYoVlTFio8ef4gSgvlsRr/XZ39nj2F/EGqGTOAkHe/u8OWffcnjh/fo93JcUzMc9BgMesxvLhE4TFNDTAe2TU3T1JR1s/YFAY331N4jvEDaAAbTLDTZ1VIG09yDlwKTlkgROlkbW2FNSXE1xxen+OKM2c9/TJMoXs2hum6QNXx4P8dhKaqG0/MJAkFd97l/mKFxeHPOzUQwmUjOL2AyW1JOz+nZKUML9uaayfW3zK6fsVwEEoE06eGcDMhFBhJw77fhvxIq9tQLFm7nBpXB1VyUDVcX57z83W+4v7NHJgVmcYPYP0SO98j2j/FSBWH3/Xc4a6iKkuFgQJokNOWC87Nzzk7P+Po3KQ7H9fUVNMtQSC8T7j94xNHhPWZXU5R3ZNKT9ftkwx2EGpON09CP0Vuuz97wu3/4fzh8/Bi8ZXbyiroqkFLy8ad/Gfu/tWuuXYMBYbjwYAggwVFOp7z56iteP/uONE8ZDnJGR4/I00D55WP7mvGjPycb38M2c1ZvvqFZXOGMC2nkqWI+X6KkIk019uo7rFAYuUdvdEh/uMfBOA8NXEyDbQqcqTH1ClsFxVOpAcnQItN+cMfeGYUJz9KWSGy7rz1bpRLxiWXswGBMcLk11qEdodWRMWgNQgmkVqymN9TGsHd0iErSwPJSxzGwNkBQBBjP1evXPP3q99SrGdpbdK7X7kKpSdIeWa8XkGGsDtd+zWjjvae+hUw7VhIf3dutwUKrSMJrIrr3vJcYA87VkRw+klonCSrp0R8MGe/skuYZHri8vGRyPWExn7Gzm3RdGXzMSncejDHYxlA3ZUie8C7W4K59V51C8OGe2zgmQgQ2oR84Qoa2B7HmGRXxdXxgc/LO4mzD7s4hhwf7PHnyEcL5IPBtIAYI1Glh7Vi7rpBdlTBfFVzdzJjOZ8wXSxaVw1YlxoR+ik1pcbagrgxVXeGFJVEhNuytp6lNJK+G0XDYuZO31qGAQT+hLMC4CpxBe0+mNZWpWS4XuKphvqzQkyXOBD7YLFUUOIRrwAUS7kzDzijHeUtVw8KUVE3Ny5MTjK2o6iVPX3zPcn6NpQyKrYHVypNaC9Yg0ChvybxlL0moSLhu0vBZoDYNWIsRhlGSBcOqCY3X784luPt4z6a6sJhekokmtBQSrZEaWAaJFpTD4YTFeriZzvnmm99ydvqCYnWD9xKLpcIFV1N0y5lUY7zl/6PtvZ4sO/L8vk+6464r2xZooMcA40ezs7shkdKSfJEYoX9Vo4XpkgAAIABJREFUilCEzJuCCmq1juSuyHE7mBm4Rvty1xybTg+Zt6q6AexCD3M6qrvaVd1zbmb+3Nf4aWJWVpysDnjv0SOqsky95MmmweqU+tjeeFo3oLRiNptxsFqxnDVIb1Eq7elgE0cmeAshIaSCm4jOpjbhrcNoLzibxGpVOvSy7JDIs7J966moDNooojfXGaN3Ah890+Y1f/l//Zap7HjWrvj+uwvev1vTaIEyJYt5hfxWwXbT8urpc1589CnD9hW/++g3eQCfBIsna3Ftj46WQQX+/b8LENaEsMPSJr+vCSDNZYoiZVu3gYghBP7Tf/h3BDx2GmhmC7TWTFOPVgYlNK9fnhO6gfvLI97DsCorjr/7AfLdR4iTE0ah6Psd7faCup5jioqiTnJZxMjTTz/GzOaUBysef/AhEDi6OMe7hHbzQnF8cofF6oDef4acRmos91YHlPUM6zdcPH/Fph949vwVr55+xvPPP+HTTz5jspZ2e4UpYbGY8+jxT6+D4A0JIuwLQXCe6EeCd/hmjp41HL77Pmdnr7FTz5M//J571nNwOjE7OAE00UeCBt0ccvidf4MIiu71x7jdSxARF/y1+on1+dsJj5EXqN4S4xYvZoToCK7DT11SOZElQ6ywqqF6+HPU/BTM8tbr/pqNKBNCNN4aR93q8N6q/iJ+GJj6kcvLK15ftbT9SKU0w+aK/vKMyoDSElUYdkNHlIKH7z1iNp9TVRXnL17hJwvW885776JNwRdPX/GHX/yCzz76LTI69l1NHxKoSClNWRQ0ZYH2liDTLEqL2+4MqZp9kwpzKxDu05dr66psIJy/xr7hGJFJxCKJnyGkBhTaVBRlTVU1CKGwzrPebGi7lmkakcIgsv9fMpYVeBfx3mVxjpC7Jp7IzUxaxJtOwjWt6vb7JbLjxddc++TcThP7CvymM5Pm2lVZcPrgIY/ff8RqtWC32dB3HUPXJ/Rs3HsuZoyCc+mEiTDYSD85rB2BiCk0q9WKWVim2WLMThc+oESiK4UQCCqBYYzSVFV6DwstOTo+psgqT29QZmKkG1sgUi8WLI7vIFWd6ClnL3nx5At0c4gqG3S1Yz5boI1Ahwk3bPDTCBTU5YLDxZz33ntACI7NbsPvN1s22w2X55fs2jVdv+H88hw77rBjTyUL8BHlRqqYJPxkKZkVcFh5Vkay9RrnS4QpUaXBY5mcxYWJ9WZC64g2AYzC/jFmgnbqmUaFkjIHCHLJmbKfQNaEFJ7JB7p2zdnrF7TtmhAmqmKGVhItInZM5b9SimBHnITgHdJoysJgtKIqS6qipNvtGIeBoW33+FOkSuRwpZLWoNEaJSJSBqRKvffEwr8BxWSDLN6G3Od8Ow2PlUJLlfhDIg350zZNN6x1qr6EUgiSyW1UMEwBO3U8f/aKQVdsmCMfCmalRkcwUoLRHC0FcWx5sj3n8tkn7K6+oL/8Xd43IiHWfEA4m9pWLnBxNqDUhJKWvd/qdQYXZILki1vK8Xkxv3r+GZPtmezAfL7EaIO1HVIUSKHZ7hzlOHKgDDWaZVFx/94D9IMHcHpCO1m6VtEox6xJc8J6vkQXhhAC/cUSGQN1oTk5OAACxf7AFgK0oVksqaqabrlE2ZGZ8JwcHFBUFW2ludp19Lsznnz+j1ydvWa3XbNuJ4bR0rYtzUIna57b176zu/9NjIjg8f0GP24ItiG6SNUU6DLNPzbrDdX5GQhJOVuilEEIlVovQmDKJaY5oGiW+O51ytpjSpAUGbQgUptUCo8KA8JF3ODyPvAEURCNgeKA4AuCaJCze4hynigGYb96b93HrUuJ9LF/j1P1dXOvIiTvNe88Y9vRty3rszMuzjds24FaadrL12zPnjMzMhGwC83gLChJqQT9bEZZVbx48hQ/TUQbmBUKbQqefvwp5y+e0a4vKGIAkmiE8yElACbippExf19T1+iyQhUle4eQCBlhfWt/xTcVPOIbP2UOKpngH/Y3uxdB3gdUSRQSUxQURUlRVWkm6QPDMOCcTbPD6+8ZckUn8+f7FuebnU3xxmepNRuFyEHx1r+7lYx81ZXoDSEBxPZm2CTgmsx7uywMh6sV81lDoTVPz8/YbXd0bZusi2LMgTpeB8L9U5ocTD4wTo5+nJicIwbHHqgoRaK3RCEyCjmkszJ7L0ql0bqg0IbSJCPuJFzy5ZtKGA1JWZbM6gpkgZ8C49Cx222ZmYYgFUEqRqNwPiJlzzS22GlCyNT+NVrR1Inc3g0tXd+xXm+4uLxi167phy3DOOEnh7OeaXLoCFaAESmZ0spTmsCsSK6rScc1yaRJpfEuzVeF9wwyoH1EuXQOhX+mer99feMg6GyLmyDmFHGv0p366vF60woJQzewWZ/xxeef0m0vKXXkW+8co5QmBPjkyReMk2M+WzIOLW4asHZAikivJS+fPWO1WPDw/gOmdsdus+Hp06cUVUk5a6jKEqMN4zDipglnFG7omM0r6qIEkVBfAYnzNgXB6N9wW99fN601KIuSQmtimFKAVRI7pUAvsxWMEJFC5ZF5DEm2Ojgm5bg6/xgxU/zgh3/Kh+8seHy3RNssiWUEh7VnUmv85S/ZPP0HdutnLIotUgukltjR4YRndBapSoTSiFITSW2i4GX6YkEhc0D3PimcRMntMMjLL37POLUIA1evNQpFVUqsFYwWzjcDB6ZAzRe41RxxcMjy0buUp0foZUPdtcT6CE6OqCaL9BHGIbV2BFSrGXLVoMQDBpcg286ccnxwQNM0NAcndG1H17Z88B1QOhkhN1WF0JqhUETZ03bP+fzzv6PdTrhJZ5ECz24IxNJTua8TTlMQEwIYLP3ZR7TPf4kYztGrBzT3f0q9mDEFwXq9Y331EWX5CX/e1MwOjiiXh9jdhjD20K4pTYk6vMd48QmTC1gHwY9ZEclQZIJ4YSoEFhFbus0GWSwxs3voOz9GzE6Jy4f4foewI6qoESpx7xDiVgD48kxwXwmG60UpckAU7EUP+t6yXW85f/aM7dUlzz/7jPOrLW03YkSkW5+zO3/JwiSwVCRzJKXg4vNP0pxJac5evMBbjwS2L75AKsUvfvMRlxfnTLstRW0IKKYY6fpU3ZRB8OTjP9D3HfI3v+Hh48c8/u4HzO4/RGuDz4eOeatgCteGt+muczy6lbSluZ+QkhgSZSAhSWGvXxvQiKiYLw5ZHRxzcHCEdZJhHNi2u4ywTPe8pwbFa4KezH+etEN9SHNC4o0WURR7C6XsLylvgnbqeN0+Jd66ItfCHdbaRDOSKWEqjKIsNIXWHKwW3LtzSnCO16+u+Ju/+n/YdS3DOCKkxFpL13WMY6J8lKZIib5MrqCJ2K/YdgPOB3yUuJDSB11UJJ6rvm7fL+Y1pqgxJh/xyhCkJoo3JDTevISgKCqMDMQgmJUGkFhp8XZknAaO6gJVGWQpWe9eYaeOcbggBEeMINWMxcGcGI7xLjAMI1fnG3732495/vwF1jqESFZ13gqmUTJ2itKPyNpzNCspS0llAlp1HM4mTleetp0YPGhzQK1mVLGg2wxpb0VPgcLYiFEBXYg0WP6G1zcOgt5a7DQRsr4fZGh+VnqQMgcGHwgu4idP33bIGFnWJT/44D2UNvRT4OXFBZPfEaRgn7+56BAu0g8pEBo8XV3Sb6+Y+o66VJhKowvFerumKDSXF5eEdx8moEkW9pUy8W/wgkBIczGVQraz9ivf/pg3pfceLwSGlDEmR/mU2Ump8DF5D06QgkEMOB+ZXGBynlKfMZsd8+13AseLSK1yrisiCs/Lp3/g2Wcf8erpr4hxQ90IhKnIJQbOpYpBqKSCHqMEK4kitV9DkJl3JVEZ5CIzRF1KdX1vMUI7OHbdhMdR6gKjNJGCYfKMk2dRGmJwfHr5mk27Y7W94OMiEIxBGM27d+9yuFxxenTIdhjApwF8QmZGrB3QpJlQ1ALvPcMw8e9/82uGceT99x/TNHPKqibaIXFAxzGhL4F1P/Af/9/f8pvf/ZarC4d3EU3EaIlqREo0RFK1f/tK9xlTJQ5IXVGePEYWFe2nf4PrNvjP/465TR2DtVYURmEKxfOPf0ezXDBfrZibgMIhfQ/ao8qS2eIO48U5u/aCxayiLArmRZF1L0HEZHUTkcj5fdTiIfr4Q5idEnUFEYwp0Vojr93sxX5sfussfXMl7itBeeu8DblCEqT5+OuX53z+yRf0FzeApMvNjn4YKSSoYFmUmoN5AosFkcTYIyTtWREhWFazKmlVxki/u0pqSWObAAhGYZRGG0NRlChdJNCKVGwvXjP2OzqXmon3Tk8xD+9jNHmO96bMXWrnpYD0hqLKdTCMpMwZiLl68Rn0JRIoo9DmWhlmNlsymy8oyxrrEgDC2pTkSkmSD5QxP7n0jFXMhtMxYoVKazijRm9eY0jPOpd8YS8auo/Y3ACAbu5tP1MkrW2tUGZvb5RAdUVhqKqSQkmMNkQXKGc1ZqH58MPvM7opyckJgZ0S13Doe7x3yWIpe516t1c/kfhwQT+kCuqmNZToDiF6XIRgC7QySOUxTuKiR5C6Fo2WrJZ9UuTZg6P2K1IImnqGCBND27E5O8N7wXZnWS7nPHr/fe48eoft0HOx2XC+fsU0tkzDGmvHrAY2o6wrirKk7Xa0XcfLV6/o2i5X+xHrLNaPDIPD20D0AusHgjA0i4YYRhwThzPLooFZqXn2OnLVeexo8f2G2F9Bc5CQvTEySAlGYJTB6JpC/xHaoT4Pl+Mt1KEPEall2ry5h5M6jxFvE89PC1jOar713gOULtn0jvpX/8i67ZLcV87cQgxYHxBjYFAyZbbbiqHbMI0jWguUFkgJbdtijGZ9eZXQSFJSFHtl++y2QJZAEql9myTeckv0jSsH9BASd8gLChkz9SNxFvc/EtIs5C2WZguTT3ZMzjuKYs28XHPvyLKoAoUk9188MThePf2El1/8jsuzT6lrKEoBusjPgVxJR6RKGRuRhPYSqdWRIQTJM00JVM6WQxCIt2YWLkgmD5OL1xWwixLrLdY7Duo6qcJ3G163O4p2wxmebkyL+b/+8U95dP8+jSpoh44QPDoIgnOpxaQFGoGJkqJWeOeZ+oFf/eNveX15ztB33Ln/gOPj03RcCEFUkqgHJh84v1jzyafP+eTTl/RdsjAyGoxJFfeslgyDx8g3p2n7z0UOxlEIoiwoVg8w9Yrx5e+Yts+Zzn5PLQ5QsmYyC6qyQmnB2bMvqK8qhuWM6nSGLgRaOZAaoSVFtSByRT+MrBaz1ELSmkSWDsToUnWDpmjuIFfvIU+/l8jPMUKYMHqfxOyrv30E/PoWTRKd2ldINxAaRapc2qsN5y9e8uQPn+DaDVO7Zf3qBdtdyzhNWCVY1AWzpmLRlEitUmIXEmhjr0oSQkA3FXuhimnqGccRFS2FEsjCoJXG6IKyqpNSTkwc17Hd0m3XXHUj9+7do1tfQXAoURCzs7p8i093PQ98o/UWv+LzLMRNTG1RYtbk2M/qDXXVUJUNWhsQU7YkSjzSxIVLe0MIeT1flGTt2xgJUREJqChzqzStp7SvkgYvuau1N9uNMWRO61e9d1mgTspEBdkPRIVAKIHSmqIoqLRKlZ2QVGWFNppvffs7KUHJNBhrJ7pdS5+pBm6aGIeBcZyw03iNZrUuYHQHbY8LJP88rbNHX8SHVIh4G3E2t1N9yCLhAWFkcu/Zqxy8tbeKoiRMnjB5drs11gV2naNuanTZcHR6zPj6Ff1Zx3p3yTjssOOWsR/wPqB1TzNLye+z589p247zyyuGbkxbKAasnbIyjiP61CrwYSKKSNlIps5iw8CsDtSlpNCKTRfZtp5pGHHdGtfN8IsCj8KjmKRECUUwCiUMSpqv3WtvX98wCEbGcWIsFKXSN+RYKQnO433E3UKsTeOEnSzTOHD3zikHByu+/4MfIaTmcjsw+8u/Q51fYachbSIpKQqFsxNd32GCxU09wfVMNnF6eueQrUHogs1ui/WW3/7+I/7s5z8hytNUAYqElNy1O6RMqu7TOOHsxNB1SCFwbyY/7NUjCDBNI0E6TGESOCb3zYMLTBm1lRygY84ewQewGJAF9BNhc0X78veE8kNkUxH0xNX2ks0XL/mr//t/5/LsCZO/RFERhEIGj7cWZx0EjUZhSpMO2hAZpikFQAEOm2DpQqBUlTQXPTemu/vFLAUf/uA7WOfwXlAVBUpIgrNMY4edenxwmL5ISiFBUhjDYlnjtp5hmrjqthQXBilgmiaEFNSVQYwTInu/oTRRK+aty1TwgvvvvMP89JjDO6dUEeLZBVPuh8kIMb8BCwE//+63eO/RPf7Pv5zYdVtG2xFlqr5Lo1gdlczmzdfOYyQ376UQJaIsOPrxv2V49Tu2H/8Vrj2nCB3NfGAzlOw6zdnZBAS0gtlP3uH4oGG1rOj7K9zQc3X1nG27YbCRUkaMcDjfI1UivA9jIkB7IgfVPUSRslEZkqVXQKZKIi0uvraNdusS3DigBZIp6v5Q984xdAO/+k9/z8tnL3n95Dnj9go77GivXmPzPKyqS3QlKHRJlD4drgLKylyDisJevSQnEEFEah/wtqEuCra7nq4bGWwSlpZCUunUcpYiMG9SZUw1p9SC3/3m1xzcOeb45ITjk1OkFgzmTam4NJa41m9hr17yhih5fkQqI1WFIMNWBAKN0SVl2SBFEtfu+xEpdPbSWzL1A94OebSRBT0QIBVGF6n9GiIx6lxx+gy5ydOzfX6SCagxxvScMkdQxIhVX+YJRiAIgQ3Qu8hVO+Hzgmyq1MkoSsXd03vcOTnhww8+oJ7XaGN4+P47CZQXAsZk94wYE43D+WsgYPCByU5ZNs2x3mzZtT3PX52x3rZ0/UDbj9duHSEIgk/BMgmSCwJJw1lKOD1Y8ejhAw4OjhJ96dbyjBHa3YDtOi7Od2glcD6w3XaMVuExXFxtuFhvudp1rHctfbdj2K3p+47gA2Ux4Fzk4vwSJQu8j4yToxs6nLM4PzFNfeIqTjYB2kLHYuXRpSTIgd51CD9xfHKMkYL1FTx90bJuB7z9gqdqYntxwHz9mNnqPs3iDoOvCVYRbBJPH8btP7vv9tc3rgT3w1rv/TV/5cb7JeTUPG1e55JE2Wq15PTkmLt377BcHhCiYHBJFQUi1k5UpWExr3nv/Q8Yxz6hptabhAyNHkzKdMAzBYcfHC4GRjtxfnXJ+eUFV5tDqsNlysByBRWzYO8e9bVXhX87ocsiSggSylMEGL1AOnnNSfT7RRlCVrvgWhMwIkGC1CCsTZ517RaJRevIMPacnb/g6ZN/ZLc7x/keXSii8Pn77l+S2APLsq9carjcltvaO1GLKNIiFzEFwRCRt2SdBIKD43dTlRRIYB8ibuwIbiDYgat2hzaWwsx5cOcui9mM46MlbdsxjSMnpmaF5mgzZluYAJsOLTKUIUa8EHgh0DKkNaE9D05OGVVkVRSUncX0EyImtFwMKWESUlJWBScHB1T6kPsP3udifcnVZoM0WVydxC0XunmrirjpF15rfcT9sxOIcole3qe8+z3ii18hpx1aKwYXUOOEHbcE75hE4PkXYHcV5b0lzvbJvmpIrgnEgPWe0UrEKJA6uSBMLmA9BCnR5QpdNIkb93ahEG9/kl7vjdA2X4qNin079KZtSIDt1ZqL12c8+cMfWJ9f0m/WjLstbhrwzlEVGqMVB8sZZakRMjLZEeklQgm0UYkuIyUu5qybJABhncuzs0hV16y3A20/cbnZobWmrktKJSiMZNkUzCpDWRa0znF5ccHz8zWx0JzcvcPjx485OT3BDuMbWWbiS2bpLsn1Ht3/eOMQFgkoI2QCeEix5wgqlNQ455lGm5MylZ5ViGlepk1qP+egJsS1d/t1NbqvKqVQSVAfrg2Y9695D+LZC9OnIvIWheLWybGPnj4kTU/nEtAqOWBo5vMZp6cn3Lt3l8ODFabUKdA5mwJsSKYBbkq9pRjSfNF5zzRM1+3QcRyxzmFdsp7r+oFd23K1XrNtO7p+TOcRAil0TjCyEz2pU6eVxGjJajFnPp9RlsWXukcxRnbbjqFtWa87VgfL1CFRDjcGBuvo1y3D6JGyRMgKGJmcYpoyEtcNxLBhHCyVaYjIRFOxIz46Ykjyac6OeGsBhxARU4DS0PcT4xhQCKyvCFbhxshmN9B1HhNaus1rROigKChMhawX6KpKwLKYCrZh+COQ5ZPGZiZDqqRILlTKrPbcmCiTOoW1lhAC9+6d8ujRIx698y6r1RHD5CgGj1YaIVLlVZYHnJ4e8j/89/+Gvmt59eIFv/ovv2S72TIOI6Yq0FLSE7DdQNePyMxbGc7PePbyBUdHS+4cLxO35xaFI4SQNpRSmMLg3Vdph+4zvoTOdECfF6UHhIfgHG60CawnEv8qDdhzK0RGhPYIJvA9brdFCYspIxeblmdPP+PXv/wPDOMVSIspCyAFFyGKJECw18wSAlS8JjSrjJJLITzTBKJkmnz+8yxZd3t4JgTHdz9AmQKig5BMem2/QccRFSbsy3Ni4ymOIn/x53/GvTunLOd14riNI/7JU/TLNeWTSyh0QllendPM5+iioLeWyTvG4BBaI7RClgXv/+gxrGZM55eI9gp2A0ZqXPBMdkohwWiKpmJxeMThasHjb/2A4uU5Xp1RNSURwdi7RJ0Rhps6Yn97t6POfjqTOVqqQS0fUhczfHdOaF/RKEFvJ4Zxwo0bnJsgOD756IzNqqAJxxgmYnDEYUroXGCwCZFr4975ROb2IiAVZXOIKecokeTKw/Xr2f98s8pufhVfWdlqkVBxQWa4fIwE6zl//pJPfv8xv//lL7FDjyYyDX12WwnMmznLxYzj4xXeWSY7MI4DQoLWyQRXotEZEBZDwJEO4m7oEBGU1MzrAyZ7xsV6y+dPX1AazXIx42BWsJhV1AcVi6agKAxnr9Y8v9jy7HzLbz7+Had3TvnZz37GT37yE6qyfCMf2ItICyJayOuE9Pav+/MlhjQ/VUqhSAAZrTRaGbTUTOOEUgPFMFBUZUpcXRqHKF3mSjIF3TTLT/StEALBRyQKKUI2z5aQbZ7S+O/WLDPcgP1uxCi+KstJc8Mkap0I+FIptFI0Vc3J8RGPH7/Hew8fUmc5tHEaspTjjWuOzwm2c4HJepxz9P3INFmmyaYWafZX3HU9u7bnybPnvDo7Z71p6ccp8WSVpqoajDHURZXPQY1SHiM1daE5WC5YzmdUVZXl8G4lLDFydbmh2205v9gyWx2hTYEsYNruaPuJrnNMIaKLOaZYIJXHuS3WauzkGGPP0I0ouWFeL7PxrQbpAU9gxLkBOw3YKaTXVkTKSiIVrDc9tvcYKWn7hug0fSe42myYes9S7mivdripAC1YzRaYgyNKdZgtqSTDMNIPw5c32ddc31w2TWaJLnVr2E/KrkSeuyHAaFgulyil+Yu/+O9YLpYsFkuKquZic8annz1hu9tS1xU///lP+fEPv8/j99/lO995P80oxu/x8//qxymaTxODc1xuNvxP/8v/xslixayZExG07Y4XL19SNRV1U6GNurZiaWazLJVmMToBY4ySeOsQ1iHErWw13nw4l50XpGIkLUzhMwBFiWuEn/fu2nzW2Sm3TKDSFU1Vszo4ppk1GCNoL87YXbykvXqJFi6ddghiNMQQ6HqLFinTBXJLxGcFeJKhrti3eJLlTBreu/y+CLRUFMa8ceyGqYUwEmNIfKSh59WL57QXZ/SbC+ZNhSIgg+Vvbc98vmA2nxO8JzjHbrMljJYwTPhdwAfH6AaqcaCIBVIbRjsx9ANT9NczpvDqMwKCaRiIudKoyxKfq/fdrmeylmG0nNy7y+LwCL06IRZzmgNNWVUIqahWihA9Wt0GVdy8ZW+GwXQlZKBHxIiKkmZ5l6gEYnjNwVxTFYLLd47xPnUEKiXTPd95D7NaUZQVTQTz7Any2ae8fvUUfEuhBLNZhTEJkKTLhkJLWH/EuKsZvGJ2/3vIapaUk7529PdPt0b3GravX73i4uKCX//i1zz99DNeP3/OtNskMjIBRcAYRdksuPfgHidHK1arhhjTYextGgalPZuSqOhDph3IRJSeLG4YqUyJFhJrI6/P13z65DkvXr7kYDlnVhXcPX3A6dGCh/cOMEowTY4nnz/hnQ9+wF/82z+hG1u6vuXl8y/4m3aN0SaR8PM7Y53HBX89LxNK5k6SuHY22L+/wWUQCiTN0vxDITHK8N6776F0TTdE2s2GYRgwRiOCQTIR/P6eNUpoiJLgI25KgUVgiZCS8Jh8ZRzuhuJAFrzei0sLcuWqEs3l9nuVK0ElIt9+9IDDecOju6cUOgH0FquGxbxhtZxTContei43a8ZpIBI5PDrA+eSTOA5DqnKtZ9v29MPEer2j6weGYaLtdvgcBK82Gza7ls+/eMbkIi4nZYkvGFAmUQgKXVIVVZJiK5Mw92LW8PAhBCR7B5Qv7y1J3ay4+6BhMwSGzY6zy0sEBqFLVCwwKUXh+EjSlC2lPuL1iyfsdpd0u9cpeY0TV8NVmouWJfVMIVUkhAHBhJIOG5LMYGE0TW3QWnBxLtDxCIqKq90B1kXawdMPCtc5xmmNsMkDcnbfc1RH7i4F9aokyAKHxE23yLbf4PrmAtrizUUruGnv7EVwxT4QGkPTNNy5c0pVJqRQjAnQ8uz5c6xzNE3NBx98h29/+33efXif+bwBIqGpaMoS6zyjdby6uKCzE5N1rFYNDx7cx5Ql5+cXXF5dUZZF0hzcZ7rxBuV13Rp9ozX11pUTuuvuRr6XmAmo+2oLKTMgJrVP9wR6kQmuIrdljDKU9QyV++12aHFDR7QDUqbsPXqZNDMDeBeRyTj++lnuX+c1RD4/57381g10njdbS2+8YTf3t38O42g5v7jk/MUzjpczlIyI4OiHiaIsqermuh3XjxPOO6xzmbOU2st1cBS2oGnmWDcx+pHJu0REDo7YZ3yI9zcmU36kAAAgAElEQVSHiEzcwklGvBbEqFJtl6tfY0rKoKmcwBRlOnCEJuDR1/qZb71ttzbvm0ExgTCkKlDNMSFabPsSiGijuPPwEQEDQlMqRVE3lMf3McsDTFkhhWARSiwmgRLaDVO/oXQuga5ERGoHwRK7l3hZM/mKOrh9nZ4riFuv9evxMNeXnSbazZb15RWfffYZr1++4uN//C2XZ6/ZXa0R0acpVsziDwJcJoGnNn1IUnwmcbiue64Z5Zhp6Xltpz+T+TBzLnC5vmK92dEPA3VVspg1HC4XrOYzZk2C2gef5qGjnVgu53z3O4+x3rLZrvm8MOlrhySkvE8s94COa0EIIW91bG6dGyR1ExGzw8mtd10Igco6uYmnnDobwfs05ohZnzR3pYSQN1SM/WvwgRCSSL5UCSwj96Lm+/ZnBg/ddnTfi3t/aQ3G9JMgJrGOGChlrly1plnUlEVBVSTj7Zhb3dcKyyGR232WXJuspR8s2+2ObphY73a5NTjR931GiXrabqDrerp+wCMJJLyAz/JwHonyEEMC/1ifZT1kspIaJsv0tbSj9HUkCmkquu2Gtp/Y9hOFSq1kTyTKhJZvqjlaaHDQt7u0x8eWGKbkN+rSuRh8wAcSKICQVAuVTMwCsQ/IJYnmYTB6RVHUDLaknya23Yj3guDAT47gJDFEjPIYZTFqxOhI2MsOKnFLQ/afv76xn6BSEq0UWt/AgN9cGOK6l14UJcYYZrOKfStrt+t5+vw5//Hv/x7vHffu3+Nf/6u/4OhgzqwpU6/Ye0QMzJqGCLgI//jxJ3z86ee8Pj/n8ePH/PgnP+Lhw3f55NNPefH8GYvFnKauSEoRSYbI2RtfMJchxn6aCMEzuTc1NvGpY4hPmp5aKgq1n3VGtDHXQdX7pIouVExVsUiIU2dTv15GjdI19cExsizxIjDuLgjDFu0nvJ9yvFUEkRapd8nRQQl/bfKpdAIAxJAO9D2KzewDPZGQK0f2ChNvmUgqXSJNQQyRuakoTMVZfc7VtuW3H3+KliloK6WJIUnJSSFpsnO4Niqbq44Yoyhye6yuaqqq5N6dk1RxeUfpk0KHjIGySYP/whgMEQ2slksECWFsdIEpKw5OTtDzBbGo+Oxi5HLTAhuk0giROg6R9Gz+Ke+8/XUbgyJVgaxrOP2QyTRsP/3PICOqbPjxf/s/IusjKObZP2+vw5pOTBkjx6tHrB7/CSd373L5xUd89ou/RohU7VoXMXJAKI+/+BXeHOPNu/nglKTJzFdjCb/uijFydXbBbrvjP//9P/Dr//ILLs7O6LZr6sJQFoai0IQgEuraJ7rStGtBeHbthrvjIYvFjNViludYXLfd0kMBrwQ+CmzvQSiaeoG3kc2m42//4Te8Pr/A+8BPf/gBD+6c8P6De8yqlKGPNtD2PbuuR9eGO3cP+f4Hj7h7cp8oYNfv6PqO87ML/tf/+f+4lv3aB+n9nE5KmbtJ+8Ql5lF9piTEDLCMSbCCEJAkDd/deo2pHErPMErilEBnTcmwh1fHQPDZGzBKBEX+faTv0louK8FeAUbKG4cL78L1DHOfwKV/F7+04G5hxlnOGyqtmBUGb1OXqC6LhFaH6xZpXZVMeZbrg8fGVDR3Y2p/bnYt55cbumyf5JzH+YCLCXPriUhToMqaopkxOpcCTEyBSQiFEBryRxQ68T0DhNEyxcDZds1Rd3CtoPMmSjAyDkNKWtFc7Hp23cBuAuVHRJxwGLSpMGXNqpkT64ZF2SAI1M0MHyPODng34ccRIQLIpIATSMpWSmuEMHiXkgYpCkZbI3XDwekdls0xtSnZuResNx3n51f4CQgS5wGp0UZTlhYhtjh3DrFHAEokEwLn/wiVYGEKiqLIjtJZuHlfUt+qRELwGfgiCdHnzRjY7bbs2h3D2PP++4+4c/cOr169oi4ksyod1uLW2gs+Mg4TT5885ennX/Do4UOODw/TfLBrseNAWWhmdc2saZKBqUh0guDDtZlkimXJsiR4h3c3M0yAmCTkEQi01MnBANLXkjEbjqapodJpwxgRc+aZ2qwEmbIQaRBFhVjMmYyii45x3IF3VKrAAy54OjthSZtFhpCEbABpkumnty7PA5OdjEhSlxiVVO6994xiD+3eg07eOnZjnkqJrDQSA1IG7t49Af89ondIkl/hO/fvMKsrysJQlXWim8SADAHp99SAANaxXK0SGb6pU8s5c5hEHr57nTLOuq7RIaACVHV9q2sgUcZQLlf4omYUhqK7pBgdZWnSHDPPehII6e1wcl0ffHnqFvdBMx2uUleoYoapmgRC0hVezUAYRBbpfeuh5ekwaGVoVg+Zdhtm84qy1CgpE4n49F3mx/fRd95BF0tMcYQqmgxmuv0a8yu9BsR8dWiMEbbrNS9fvOQ3v/wl64tL/GQpjcYYhdIiZ9MRF+FyvWPXtpxfbXj9+oz5rGa1qLh37w7vvHOfpi6zvGGSzUpqRJFhnBjGkbYbk4BSVLx88ZqLyw1n5xfMZg137p7w6NEDmtLQjz2X568I0SO1QJcGXRb8/E9/xre/9R7zWUkUEwhBXWmMnuHG8Y0q2Ps0+0rIxUgI+5F3SjoSTCdVijqKDORIPLvUClWIEHCTpTSGWdMwWxzRT5G+7xl3F/TtyOj6XBmmEYEpSrQ01OUyIcSdIx6scH5KAvhT6nBIUndLBcXtk2x/tsGNGs7b6zABeyRFmRRYlFRpnBAjpioTd1Cp3JKOiJjcb0JMibkyBqUNLoLSBh8F7TDhQpJJE4mLc01wD0LiI7gQcSGvVCkxUuVqKlFJpNQolbiCSim0VpSlpqqLxOcdR7Zty2rZvLFWI6loGPqRTZcS02FyTDYg9lVdnHDTiBs7lJ+hdZKZbJqGECNTgL7fMfYdwY4QHdFPIAaEcEjt0WiE0DT1LAXUqqGeN8yahpODOzSmQAbLs09/y/pqzfZqg7cSEWGQgmUpUZUCI5hEpHMe4xyJGGPRZcC8Tez8J65vHAS1TmX+dR85tzFEbnHsuVDXKExiysoy32acBpxPcmAPHj7g+PiIq8tL7p0c3hwOIidfCIL3dLuO9eWa3XrHg3v3WC2WSCEY+h47TRRaU5YFZVGgrjk+6TUE4jUlcE/MfVPDcP93GUlFRo3JvSTaHuCQbs3nryv3GJaUmqX2CiRYu1IIY5B1hVOCKTrGqcN7i0SmFp/Yt4gS31DmFxhD4vPtkwaRM2KV20QSMGJvSCrIKoX5wP/qK+45T6SNZArD0fERhZZMw4CMDhMnPvzOYw4PlsyqkrIoU3bmLIUQ1ELgQnJzGDYth0eH1E2DVBLnHNba1CKXAmU0fXAEIVjMZ+h84ElT3FonEZRCzRYMsgQvUbpFZTcRZRIFJ7iQn+3t6d/++nKWJ27/GgEREdKgdIku6wRdNw1RlURyuzBHwb1Dw76duhdHMPWSol5QlgVFRq3GqCgXR1RHjxDL95HlHFMtCaibV/lVL/mfvCJ2Gul2O87PzgijRRATVafQaC3T7IoIQWJ9EmS/2qRZUdt2rK9Sa6yqaw4O5hijk0xh3rMhRrpxouszSdlHvHO8Pl9zeblmHCeOT444PT3i8GhFtJa23XFxcU7wnqopWR0fMasbPvjgu9y7f5ey0MRsT6VVSiKr4k1+1t5t/cZ1PV63HomBEG/cDFQmzmeRm4QOzW/M3rMy/V1CrMZQMJ/NiK7F24IYh7SXRSKnF6ZiMVswGYtzDq0j/dCy224YRnerCBL57Lk50/Luyvvoyy3t9LepzalUQmSKSEq+Y0SVKQClqid9ERE8QiuIAaeSDXmIkbIo8D5QGIPWyXbstmhCWqHpI7U900fqMGcjcqmRKvHjUjAs0NokOyajKMtksZS4hqml/VXSYi4E+nHk/OqKbnRYH3AOhEv4CBAEbwluYlIQi4K6mVGWBh9nHCqN2dV0ZosbO6KbCG4kBEXEIVVASYNSBYU5QBcNpp5TVg1l3bA6uEMRJvy4o932dLsEtImhSt9ba1RpMFWBKiuCNIxBMfnk1ark3lvyj1AJpgCQuDUu95R1UV733b8qA05XqiR0KTg+XfHDH33Iv/yX/w1KKf72r/+ah3dP4e4dTKETrSFERFAM7Y6PfvVb4ui5e3TKT/7kZ1RViZaSi9dn9Nstq9mMWVlQGU0hM3JPJMdl5yxTJtImoemCGDRSB8R2uj6kREzleGmqmyow8waD2Ad1IHqcj+mDgMxzQOFgso5x9IhFgawayqMloohMtuf12RdcXL1i3W2RRuLzZFHr1BJSYa8WknhhaT/KZPgZImG0eaaQCLVCpra0DD79f5nc4I2q3nj+e8f51KUrKeqSR9/6AUO3Y+h2vHzxPCmrtOfUzYymmbGYNRkAIJFaUxeGVVVByHZUK0tR12ijCcGhY6AgIeLS94Th8gLnPPP5ElkVGFMkOkXmXgkEyOQjOLhI7wJThKgMRTVjz+HVZarsJQFcxz8XVfaHkkDnjkBESIUwNWZ+l7JcIMsVRpd7Dgr71mVe4dwcfBlWL0JGWRr2nnAekEJhpKK9eImZB+rqMDf997Lq//8uIQSL2YyTkyPef+8R26srnJ1QEqqmoiwLrLVYm+gBUZccdCOrk/uURQp23vZU9YLBwugiKIEuC1x+nlIkxSPrJDYo2q7n4vyKfvKoouLhw3vcvXfM8ckKXWrONpd88skn9Nsdy8WcD374Id/74Q9459G7fP9HP6RZzCiMzm17l9drwNo3UXnOe6y3KCvRNpHVo9DEa8upmza+EirNxlXM4JjU7lRCIGPk9YsXPH/2kvVuoGpWzGYLvv2t9xgOGtp2ybNnv8uYAEVV1NT1jNXqkGlMnLvZzNC2W4zS+CDoxzG7sTust9cJshCJ9rT3rUwiGl/tJ5jev/SQwz6pkikIp+Rwr4Ea896+6UDE7Hs5jQPj0DMOPW6aCNamTkjWxnTe40LE+8gwOsbREnxa31JKZNwn8CnpUUqjVYlSBqU0dVVTNyV1VRKCIoQcVIW8ZY2XtsQUBOtdz9Onzynrhohg6Ed0TPzkoqgzV9pztYuUZYUq0/hrWRQcz+4zjj193/LkDx/hJgNxkY+mQMTmIGio6gNkUSKqivlszrKuOKyWtM+f0J+/wl31+N7hQwIOaiMpi5rju3e5c++IO+/+CF2eMqkjrnaGwkjqSrIyFaZ0fNPrm/sJxtQrDySuXFos6cCG3Ju71TvfSyNdD6ZjRGnNfD5nMZ8jhMIok8EhnqI0qf1IZOwndtsdr16+RkTBop4zK+trG6CD5RI7DhAC3iYUaNAycxnjdatl79a8b2p4F7/sjSkkIJPiQkw8Hy2ua9lku0ZMcdHneWLWZbyFkSUikdIgVMrA3NRid1vWmwt2/Y4Bh4pZE+RW0EtqGDK1f6TOGadnL7cWszRSiGkzShKZXMYbTUaR+VVv3FYeOCeHjHy0ZzksbUx+Vp7gLGPXMxaGUGY7EhGTFqlIyhcxz0WE0cgi0SHC5NibpO6VUZKObJLC8wE8Eo/CBZth5zeVWghgfcT6CEIiRJLGktm/UcoMOIoecXs9i9v3+BWAmZscHQBpKsrT7yJNgzB1hrzDNYrrVth6+6tJWSBVidIl0Q3pkPeO2J8jtgbrG4RSxPAoAT24CYD7l3a7gniDJ/jWdztYLahKzc9+9hMuz8+YxgmpoG6a6yDonGOaJjbbLiVek6OuKoxWBDdSVQV1XVLXye2kLEyax5HGGeNoc0vU0e46zs4uktNCCAx9y+pgzmJZc3S0Yjaf4QMYITk+PORP/+xnvPPeI45PjmmaBq10sgHa+/Fl4veXKEi5y+EzAlPKkK1uboS1RT5LiHn0ECJepu5KEJEYPSE4hjGJFszns1QVOcvQtcznM06O5gTf0rZrdtsrfBkJNmZ5uAxuC2RtzZL5fIEuCoSUuJCc5ic/pUpUSIJI6+Ia1PNV15cq/kz0vwb87MF5+WuEhLIOmfs3jCP9OLFtE+1h0/Z0w0Q/Oaa8N2wAG1IC6XyyznIu++0JgciycNd4AiUzhW3f1VKEANPoUuAMkV23YLI2nZO3lmGMkYurHa/PLnn9+iwZ+4rUlauKkkIbEAnU56JAFgoyWEfpVPWWhWJWLxAHM4b1S/p2oB/i9Vlk3YizHus81UzRzGYsT06QEUQIvHz5ivH8nGmzTSbB0eGkQARNYTSLecnRySlHx6dU9R1QK6KYoaShKAzzWcHBwQzTfX3S8vb1zYPgXjIsb2CR6/X9ARxus6T2eoX5wSZOTKoYmqahrhskkqook0be5BCzdAB7An03sL7a8vLFK1CGpq4plE7mmMFzfHzKNAz5/1rcOBEKlal1e+5PQonuWwtBpCAW34qC+yC+V4hQIrtF5PUdskN0CJHg8uxNJ96LyP3IFCQUQhYJlagLbHtBt9lwdXVO2++YcKh8RMssEryXZFPZwkmIpBkCe1pAvHWYRmyeYcSQfbAj4EJqw76lfJ9UuxX7OxFx3+ZLm8QHh3OWMFnG3Y5RSfysQUqdqkGTZyEi64VKkegaWiKMwk83B8S+rZIIxqkV7aJABYmMMqHRMuhB5oAaXGTySag62UIltXupbqgiyWBTvHXY7Fvx+/u9OYXSI8g9CSFS9WYqqrvfT2Lk4rZhn7hZr299lvupKFWidIUyFdaOOB/TTLk7g02P9QeoaklySt9TWchted74Pm+uuS/PDI8OlxTmmHlTcfbqFeMwIpSkmTWUZYl1KQjaydL2AyFGtDLMZylIRm+5Ceg+rX0lkzYlUFdVnicHxuzScXZ2SVPPiDFyeXFGWUqKQrM6WCa7narh+OCQu6cn/PnPf8pyuaSsytzKT/zZuP+RQRZvzz2vOY8+XgdBnRGL17OGfIbEnFA5EZBZrH7vyC6iZbQjs6Li+OSEzXYg+MBuu+benXd55+EdnGt58eIZVxeXeBvwKuCm1EYVkWuaSGEqlktNaSekVNlpPSVqSIFUN3PAa2T0P1vfZzcHuAEOvgERTkIjziZaRD8MtP3Arhu42nRs247L9ZZtOzBMliEHCus8k/PZENczWZsR2+kcEdejHJmS6msKyh64J3PrOykludFzdLBMBrnEN1DWIUbOLja8Orvi7PU5RiUEvJGSuFhCXRORuCxVplRJsB7ajtmsQmmFVoLDgznzWUV7ccjVuuXicgRp0hk6wDB0TNPEkVIslgsePrzPbr2j36x5+uwZbM5h2FFWM4KWBKMJHurScHi85Pj0HscndyirUwINgZrSFMyqgoNlxfFRgzZ/BO3QcF15ZKqEEAkGK24OgPRmQyqVcp9faYQsKUqLkj3eSqZ2RCEwURAGy7gbaE2BDYlL9tEfkq/cr/7we4RKmVs79AmdKhU/+IFh1cz4V//iX3B8eACk9t9eYWFvpbI/zIRMQ2+lAypEYH9ggCk0zitCPqh9BB80qFSZjTYJzQbnKaS+FtSOLqGy5H60lJGGAkGpJK/Pz3n1+edcnl/iQ4fUqVWbHOvTAD0GUFEz2JRBC2Hz602DdKWgrHRW4LnxGvPe3cxhIzg/kVTTbx29t0AR+4NJhJiC3P/X3pk1S3Ik1/mLJZfa7to7GmhgMDM0zojiiCJNpIkmmVFmetZP4j/Tm2R6IkVq46wYLNPo7rvVnpmx6cEjs6pu3wYapCgjZ66bNVC3lqzMyojw8OPHjxuTRYUF2g6dJ3YeEyNYTTSWzkesh8pDQpOSRAzd1qEaR9PKguyco55MxbmliI+KiCGgaVygCQ2bTYPRisoWgCepRBscTbJsg+Q5Ulb9oI/UUtqjz+/bHeUgwyvDAGQASFVuMnnrXd9uCVXUFKMpk5MzmuSIMTF68CmTU2k8fD59ghmdoAvz1hLZIwTvTA/eujBjNNPZmNHoKc+ePpR7psjKKFpgxIysNO1WYESVqMtSiGgHLJ8sSr13raqHo3LkFkPAOY9WhpQSXSOLpFIJUxia1vGTP/pXjMsRVVEwG8sm1TuXpcF2m8P9jW5o3UHkpFVuipQY1I4MdkCew9D3TVi6QSkcoIygTi51pCRz8d/84Y/44NkLPv3k91nO18znC/7n3/41XbNlu1nzg+/9kHE5Zn65YDoeSeTS59qMldIKIkZZTk+PUVrz5Elg9vWM+fyGi+sLNtsNm3YjzcHps8R33sDhv9bIulBYM6w7qUfMJPEnKkVtw3q5ZLvd8PrNG65u5lxez/nVF19ws1hyeXXDpulwPtJmB+iDpGH6iHq9WuM70XEuczedoiyxZYmpSiHS2IQqdNZejXgVhr6p2lp8MoTUr1eH9+r0/AkhGHzr+NXP/47Nekn0DYvZEePxhKOzR9jRlGI0o8pttLZtg1aBGDqKSjOuApPK8+EHJzx5dMTWaS6v1yxXG16+vOH0rGJUn/AX/+FPefLkKU+ePON//d0vuLwcoY2jWY5x2yVudc3UtUxDx3hUMKoLzk6mPDh7yGQ0w283PHl6zqPHz3j86JjSOqxeY/wrFuurO+/aXfYdZNNy7i/P6oQk1qV/ltxw78PQCkQpIdNIvZ/jy69+w+XFFW3T8ubVK5HFaRquLy+FBXXxBk/ChcBXv3nJm8sLWu8oswJMWZV0Tce23dI2DcfHMx5++Jzj4yPqus7wn8Ce/UTvJ/vgPA5qgHYXJgM2ZBLKruVNryQheUqwWgp3U0LqVjIE3BNdUFm+LDg2qzmLmwtClkhSqodPe/ZL2ilTxH4Ny9BljKjcIaHYO+ehRlP1hBEhPKik3o4E065kpZ/KGoT1pi3aFqIP2TNOM3GBvBsPSYR3m07anqQ8BryXydQ6iSjapiEVtbBzgS6zrruQ70WMIlmmRHmDlIhKWF1NjHRB5cycAnYROIPzfv+WKLso8PDvw+fe5Qb7RS3/XgowFlVU2GqCsVIiPHnwIdXRQ8zkFDM5BVO+dcQejFVkSBn2xp268wSUSkK6KgzFoL+Z9t5vBhjVmEhMIupd5AV4LzHP/m92GPeGPlwWgk8phI6UoC6LPG8iaLBFQVXVFKYQxEL1kX/cMTvTDikZxnJ422Wk/vqzE+xJUgqIahdBxp36Yt4YicwbSmO1ZjqbcnxyxPHxLHcLsDx8eM54XKNIjOqaqqzQShN8xOMJ2oG1OfUgObOKirIosWVBWRU0jeiO+uBRAxHMD3XB7+Y75LudNxxKq+EHDxnR6WFQIczEoStE8AKLZvFfUvAE1xJcJwo0LkgZjHM0nRvQrW7biuh0UjlvGTPMZdD5NyYmUgiCekm2Z4dsYWQlyL3X1K310JiC8XjC2dkDLmYvIUa2GynUb7qOI62p65rJ8RHFaESMkTZKdKuV5IS7xtBuAq5Z4EIiRsN6fUXbdJyejDl/cMb5+RkvPnrIbDKmUo6HZzNGtWEyq1jcXLJdz9lcjHGrJX69YDqV9lDPnj7kgw+eMZ0ckdSIB+dTzo4KRkUDYYNrrllvr1leL/dG/zfbd9IOPZDYyYr0MsAV223LarXi9evX+cc0nJ6ecn0z5+r6hv/2V39DDJFRWfG3//2v0UC33bBe3EBKvJovRHqrLFivpGi3KCyz2YSTkxN+8INP+fVnn/PriwvW6yWPHp3xox/9HlVhUCrSthtS8oPjijEIkaDtcs2SDBAXdjkgAB8czncQoySVdQ+PCvQViOgEBYYSg0EgJi9N63PRcsQHx8hGtA74ZsHNxVe8+frXQIfRadgtKgSWUSEI+SZJ765hccpDFYKo1aiQJbtETqqfzJrcA81HjDYUB9FOTtH2slAAOQ+ZtAFbYesxZTtG1xUeRRMTTQiYIFFkFxKpzSrvRg9Jdyk6lnZMm/WWzXJBsCWmqFCmZJODbOMSOiaUVmy9OJcmR+lJJ6KCJkTagOQRk3QWT2lXJp36nci746kh0h1KMPoFif4w+5/75iiwf1UpcV7JVqhyjB2fUhavUKbg6MW/wI4eYMpZdgqBhD9o1rp/uv1DOc/9N9w+FylYHRomkw4PQu9IE1qnPTfniWG3TN/+lfbnrOp3W6l/Pg7fojH0SYyUC/+1UdIwmERH7/j2/vWHy18RY8yqL2+fg0SMvVsXxX+lFMlIyU/yYajPUzoJzJ/z9MZYjLWMpiPqUYlSkdGopCyO+clP/oC2WeFdk+vyRITat41szCaRopB8ktaGuqgZTcZEK41jz8/PIEZGVU1ZSl4JIu1cRKnJ924gyB3c5N0vPjzKuUSVNwx9UbyUY/TMV01pC2Zj0Z3drJeUBmK3obLQdY6mg1WIpLZlub4WlMsFdCTXTRYUMWFjpMRS6CR1zlkXlRBQOkj0rQ0pN91NxpJUAdhcknZ4vzSG6eSI+oMC12yZ31xx8fprlps1nY+Mp1MePnrE4w8+pAnQNi3L2NFuVzQuMp4WrJYtqUvMX/+KLgS6pPjZL3+DMSV//uf/jj/+4z/iBz/4PqPKsLxecvn1r/j42QvKyYeURye8evOam5trrj77jOsvP+f6y88YjyMPH53xB//yx7z46FOOjo6lOTYOYsfm5gvWy2turl4yn19zvfaQyrfG4l32/p3lvaPrDrsR91I+fbI2hMBoNKLIVN/pdJqTr4pPXnxIPRrx+OFD3HaLaxvm11fMRpLT+D5KIMVcWAoyqbSWhOvZ6SknR1M+ev6URw/POTs7IoSObZDu8a6TAs3oXU4KgzHSfgmlpZh8gEjZuwYnShLIhA8kVBZ9jQmJArWmNJbKFkJi8RqsTLiAl76DRUDbgPMLfvPF/+bVy59xffFrjA1CNIlht6alfplOhCgUc9Gl3O0ejUGUFbSiKEqJqjvpZdeTHehZZiESvb91XZGY1bdTjgp7xqlPiqQspqypjk45Go+Y1tJFWpcVuqyI6ySQjPPYshLacc4/gsUr6BK03uMoCEiSXHKr0ATZbSYUPpWixJ8yuUhFknK0ydAlTeulntRkZqpE2rn+8BvGZD8Udw7wMNzrXcNtSPUuQk3+JXffmISmDwqPpjz/mKKaYKsTYf64VQUAABuvSURBVLdG8fZDzLcX4PVISE/S6cfdXWSZwaLP/9KwvqqMLsgHdtBVUnvM30yc0klCKIk+Q76EvBjn68k+dHDYg0gAQlQaIkElUH2vIC2oXhqiuV3ndkihz+X1keHhZYUQCbl3ncCvGl8ElM0aocbKxYZ0wMAczitFVAw43/H61Ut0BN84VBThjgfnp0wnFlLAe0cvw3t1dY3RmpPjGW27JXjH2dkpk8mY2fERXeYt+M4RXICYKK2lLismozH1ppT7EL3Ivt1RTnA4csTpDZC8tIGRf9nJu7al2za4bYtJitlozHQs9XHrzYbnT56x2mxk8+48y+WS5WrJm+sL2q6j7TpCm7VOo6UcTaRx7miCKWpMWaNNNfyeMa8lXZIaRZ+SSAmHVgQyOLxZKSU267VsYpXm/Pwx0+kRZ+cPWTUbQoo8+/AjHj1+wpOnT9k6x2qxpF1e02S+hjUK365ZbNdcvvmK1nW0QVNqxcOHZ/z7f/tnfPLxC05Oj/iv/+U/8/VXX/Hyiy/4i//4n3g8KjgrH+FNTWWnPHz6Me74BPfJh4wmLdPpiMdPHlJbjW8aurSha5a4dsHy+jWbzYb5ssGrI1qdgPU33rPevnMkOOSX1J7Qb5Tu5qISMxmcYFVVTILs5J998JTJeMyTx4/ZLBe0mw11ZTmaTBjVNboU4WSUoiql0NRaO0CYWmlGdcnpyYxRXTIa1VJjFKWpZkqeXPUuOp+oLM8jUJvOnQb07bGcV5NekoyUqSn9Fjk7rL7o1ChxkDpqjBa5Km1yUS8O1y15/fKXzK9fst1co81Oxy5lcsqwBitIKWTsXu05ypxgz5/rWa5B+QHbj4MKh8qszMMBHVMfLWQ69MFuXDYcxpYU9ZiiqjFFSVCF7NC1JSQtO+GQpM9cEpjWYEArfMz/ksKhpUdblKYtIK/JKSZizNeTIa8k2BMOjSe3rckLyMCm21PLudv2Hdnhm/ZJM4eOMt363LuOmXZBpDKoYkI5OqaoZyhTy3kN5RVDefXh96f+t9+dz7uvBYHEpIKdneMMd35ocIep31DtH7yPnvu/ssMCmR6ZydtHZQddE/L8Vr3Di/JLSJQuu8I0RIQ5CswQeg+RHjjBhEi85TnsQ8CEkDvR7GlX5nOJuQB8cPD5PEIMKO+4vr6k0JYi64JWRcXxbExZGYrC4n2L0pLn71ybYemI92mIsK011HVF8l7SN02ba3RFls0aI9Jn2mT9UpVTF28pK9yytEs9pJRlBOMOCs0RYd/I1hqNNlITWJYlVVWhkmJU1XSZCTwuC6ajiqIydM7ROo9rRRHHB0M5mlFUY+xoitIWpe3AYvddh/NSXhGdw0ePi4FkNK7bEkP3dmoIkTtUaLQ1jMcTqqpiNB4zI4BWHJ+eMjuaMR4JEca3jSheZYqCJuLaLd32htXyRljMUTM5esyjB+d88uIjHp6fURSay9cvef31F7x+/QWhXWFCS+E9tZc1o6hGqEqhTiuq8Zay1NSFxjUNwTd0IeDaJa5dslo1bFvPptWo0ZRo/hGcYGELikIKYfcHcL+oFoW05hCKe4/xR6azGUprPv29H9LXBSV/hkqyiJhMftDaiiKGD2w3G0BRVrnIGlBaE9OxKH/kyCr4DlTAmMSoHqHzbjwEoZL70A3K4kVRQlIYn4AsqwZYpSlNgdWFFLQnRXTyf0UW89UGUxp0XYqGaONIeCG2JY3BolH47orrVwsuX31Gt7kmuA3jcSJFRfSSZ0taYQojOptRRG8VstMOSJuqwhQovOD1IZE6KZUozZigPM63NKEFlZtghoS/hdYkJNemeycIOXQKKBJ1VZMIaBXwozFNURBiSeHkWhbBQEjosMs5eu9JXo7tXMSFgs5M2aYSFa1Ez0pKUrTPUGGOHPZht4TC+4hPXmS8uiYriySKogJynWMUSnfZO6X+Gg4iubxdeAv67K833XrvN5k6eKRSpKxPOH7+J1TTKbqw0Dt02D+rO4/dR6I9YKZyBDdUZwyWiF4KkPvFWujvae847BzdgPX2DkQNTgMgZom3/pk+FUC+j7tc8R5cLnHDzsFlT9nXmwbUAQO0f08Ku/dKIfZhNNe3XwPpS0mSbhLBe6yVkpgQJP/lGy+we1FgtDjsXs/TdR0//en/4ebBFYSAbz0KxeXFV5yfn3J6cpzTBYnHjx/y+tVLuqZlvV5JobitCdHRdS2bzYZ129G1HcvFgqoqGNcjfNdKwX5IWa1GQYjSHiq8e6nseQMp5rKjKA23Y84DRp8lyhRUdYUxmrIqJRefEu26ZbttWG+WzOc30rvUO2KKFAaePPkAZStUUaMoSUnTBo0dz7DVGDuZ0bUt7XbN6uKC7XpNs12xXi1omi2L9ZJN29B00l3kqA5s1r9PDO4gJyid5SuMspSFobWSLmpc4vvf+yHHp6csF0sUogpjjEbFFtfOOZ1ZrC1J3ZKrV7/h8vWXNKsrqUW0I/70z/6QH//4J7x4/jEkz3azxK03lFrx+PyUKiVYrthsPoerNXbdkthQTBPVNIFb0mwaLr9cMb8JbNvENpaMJiPqUUVKxzgDqQY7Pce0LajX37LzFPsOrZTkZocQckGmGYq4E2poQNs7vyFPkzI5hV2nAavJy/PejjVFUpY2G2CFGAehXUjSsohE8BLKqyytRIri9JwjeI9zu15S3u8WBJL0vNo36cLss/KD9CQTB5h3gcZijAajcClAkERwyKoygypLgtS1hNTSdWuBHFLAe/neRMIYK7WOSB2eKLnsxx+y602hoyo02miqopDyiaSoKqHqO9dKCNAnGRJDB43eQkqQBcDfYlNmcgy6QJkCl3Sf3ATvpK9df30KuqQgKnwqSJm8EmKQYvfWU3hJt8cIIebXW+mWGJMS4pMS1qvOi69TIqobEjRup9noo7AFY4yS/xyYvvnU+/8PzNH9qG/PianbD+62O6HRvhO8UmhbUI2naFOgBtblrXPpo5aDA+8/lOOJuPPd8zKGSPRxQFUywLY7/u4y973iAHX2Xz6UtexHJuSIDSVjMfXR2169XnbWfbnDsJElz/vs6FKO7gYn2KML+f0u7Oa0IjOz9wqyY0x0zg1riTFW5mzX4Ttp0xaCkHNUQuoy83qwWMwlUuq2LG8WdG3Lq5eJBw/OOT8/4+GDhzjXMZtNmU7HrEk0zZYeEYgpsmm2tM6xaR1t27Kcz6XWMnehqcpK0KyFpesU0XmZR4cp9+FepJR4+fXXLBdLLi8vGEh5e5sKaw0aKWHo2hbvPF3b0nlH6x03iwWbzZqb62s26xUpeMZVIZJ5xrCezwlJE5WlKEbSnqgcUyZHCA0+taJr7Fp8N6drFqwWr9iu17RdQ7td452kitCK4BuB89/WDZRm4kpjDTjVYbSjrhLTccFsUrFZLSCBVonFzQ3r5RyjPbNJidHw1edfs7q5od10hFBQ1WOOzh7x6cc/4OOPPsGi8S5A53hycs75ZIo2ipN6imo73rz+FX67JbgOFxeYVcSOE6qQrj5tG9i2Bc4XbFNJdBWdqdluNiQUyhQYXeX6sfez93aCAzSR+wnuyAg5J6Fz7iDDHyARY8g7o75eSSugECmhnd7lbscYvRdYSN4ASQ+7/JTVZ0JwKBgGV4rQuIbtZkPbNHRdl5uC1rlkQhaAFJM4pb3lKkapvwEoybqo2UVrpVFG8nJJQZcJNq3PtG4FkX7BkAg0hEC76YbchPdIvRMpK0jkzyBrbcrMS3VwPpGqqITwYgX6IeWu4bFXXSGvaXvOcM9CTAMUI8r7DMxXgZ0LlC5Q2uJFIBXlA9oHYe6lPj4Al0QMwGeaWSIRkqL1wh51Phc1R3BZ7Ldz8p4I6BgFJTCIE0zQISUmMSKfz+xCFfww1ow2FOrtibofrx06sfcZ+Orgf+9+S4YyjdyDHvYj37fhrSn/J72rcGMXMR5UEd1yvju4LDKUieRT6RHSdx//Fmktz5cYd8zgwQnmeZD679xzdlkK4vD5fpOXnd1hJLirFU25VMLHXWkBikGKsL+ImCQ3mGLC6Ii1KW9cfVaiUsPYBXBOmq5qpVivlqzHY9p2y2JxxXq5Yj5fsFresFrOsVYQn8l4xGQyEXEB1w3db2JKNE1D2zm2rTiixc0NbS2qPEcnUwprmYzGlLagURp8PNhkDPc9I9YxJV6+es3XL1/yi1/8Ut64WxZRKglPwljKosS1Hc47mu2WbdewbVuu5jdstxtWyyWubTAq8eBkxnRUU5WW68uFNHP2iXo0oahqRrMTYmqxbgztCJ3VlZJfELo5zeaCttnSdY7QbUghoGIkJkUKLSn64V7uX5O1tq+cRqUOjaesNOPaMK4tVufNHInF/Ib1ag446ko2zjcXr1kvV3RbjzYVdXXM4wfPePH8Y54/fSb6o21H3HY8OjrFFJbJ0ZSprWmXWy6+/AyFg+RowwKKAMuIqS1JGXwsCZSEZHHUxFjTuIr5eos2mrouqHUB+h9BMUZgHEVVVZl4kvUitc21gJLgttbuoJiUwMsEMYgz0wohcmSnFjPEIrqBCmtF5UIphS2sREFaEfpkfA8X5aJvoiYRiEEhzEKTIzrp0EDqC6QV0TtcFw524iFFgpImnAGJVAvVj3pZlFwX6VybVSSg78WFUnjn8gIv3wuRqtQitq0VSmWBYAAljTfdAHvlxUkntDaUlRXGtEnUdUlZWFAK50Qtvqo6lILJZIxbN4QUIPgctR7O0tZJolpyazuIbdjCWwOpgFBRWJWbJBdEVeCQOsHgBZ6uC1A9Q1XJfXW+RZEoikL0BZHv8gj9vSo1hZIdvA9qIBqJBGQiKBGDjiiUsRgjHRyil9/Ie09QUSj034ZiytX1I/XW37cf7/9514F3G7PeUfU1e9/69W/54Nvn8G4nHX0cxBhSDv3SntM8BGoHkHMIK+PeoO5jyAMmZ9odJcWd40y5/6G0PO2HRzz8bJ/7Y88Rku9n6qNCcYhtOJxfZVHgfSkRbk43uHyPjY5ZJEJjTUHKqQuUjBeCjD+lwGjFsl1SlwVXF6+5vnrDZr2mbVpublRGhxKnpyc8efyE8wfnlGXJxZsLYZcaI2hRjHSdZzKeUhUVvnEsl3OurlqWq7FsrK3idHYiG1ASaXGz4wwMP3Lv9OHnn33JT3/2c/7qb/4Htiil9IiAtZrSGqq6Fma41lnM24n4QegIwbFxnajBZDTAkAg3czbbJeMi93/MY9D7LWAxakG7KohRsVg2PH54yvPnj3j+vGZ5XKJWHavS03aRZlTQek3nItvWU2tZA6JSpAPWq8LYiq5p2a6XzOeX1LXlh88/oa4KtIqcHM/YblvW6w3LxYKr60t+8/WXXF6/QaXI16/noqnMiMdPHvPiw+f88R/9Ic9Pjpn4jvmvf8Hy+or1/Ia4XhMtzMOar29+yWa+5NXnn3P+YMZsNmb64Ez0fu2IoBUhaby3pFSTUoGiIBhN0opGWXznudousdPj94JBe/sO2qE9m8sc5AT7aFAW9X1RbXlOuj1nIkGfzsnkDokmRU4n9jvpzIZESd8vaToljkenJHVuOQGWUi7G1VBkHVOrbYaU5LXQSzQhEkQmKJTaaYf2vfnQKrOpIjp6Ul/Vp1XG7oNAl1plWHY3EZJgS4NIsM79rIRiPrhgYhScvy9+73/XXEUMSXROtc0knNxkUDQZA6vViqLUlLUW7dHUi26n24FgD6Tlda+/2H1cUYvgblWjjPy+SRsCCkKi89KNOyaRSFLZiWUSYo74DEkbUi7aT0oNtOu+8q//9pTPc7dTPuwZ15OilNnB6neN43049OD5dzi2oWBd3YrU1O71tz+1e+auqXRAK1cZ7kyHEeJb55L23/D2UUVxbEcyQ6WenEkfbL7lAPtjpVtOUL3tAHvIcziVtHOCMTvBXNd96AT7CLiPBPdgVnGCGaPJG9SBudz/AnnSC4okm6Je8UgCbhF1MH0LLb17f79f66PhGAJN03B9fUXTbPC+I8WAdx1tu2W5mKMV0t0+Ron+MsIkotEuzyVP287xnWe9XtN1UpvXbLcUZYGN0iygKivGozHbtgVVHVxX2s0wxuMxR8fHPHjwQOpJjUUhpU2F3YmYg+TwQ8jF69ERg2PrOnxIIo/mPYrIWAdGFioDpvQ5ZOj5ExpbVKQkOqB1KWICwSVcG0lBMRrPwNRUPlL7hPMRFwKdS5ydnubO8qIne3jDDChDwhKCAgyTiRTGF0XJyYnFmDXee6bTKZvtipBgvtqQnMe1iXFZMx1P+N6LT/j4xXNefPgB4wJoVywWc+aXlyznc5abOU4HfBnpllvctiVqTzkpGJ9MmJydE8wUryeCwgVZf5MviCnngTSgImVliMmLA99u6Rsov4+9txM0xlAUhQhR74XRArGlAVLsHSX0otsGa/cnf04ap5h7Q2WY1QkMpJWmzF0HeueZZK7QNzvxzudJJXqkxlrsuCBW41wkysBKCyHDsd5LLVTq2FeMSUqJJJjKMEzyROcp8mDQxhJTxPmILjQ6ZdkytVskU3Z+gvgmUaYpFEaDpCcjKD/sgENIeC+LRVUVQ4RMiihts5q8FpZrbrzZtp7VcsX0aMx5fURdFcLa87nI+BbdWWWnonUPK+8v3KKkYgpFNSqEtZk9VYieFDzbzgksrA2d4GvScFOK6OiSJaiCqAMxaxcCaKT5q0pDjwwGxh8xR6WyubAwRBV9OUFUgiRoYwje51ZOBxd2UBJxKJ92CDty+5V35gf3scZvCvnunlY7/7T/eu9l1d7f6dYHdgeIXmDwnRPc2zwcfFG6/XD33J6fTT0curdRS9mfDZFbdlgp9VF5rhwcIrsoDNE+VNyDXfvIr6eY9cxS95bQtJRQ+BDpWTSyL5PNsVJChClsQSh3zV7T4Nj77Z040e1my29efkWRN1GkKE6w0dxcX9F1LV3XMZnMhmtsu46QEqOmFSTGeV6+fEPXtETvqcoCazTbTYP3nqIqKGpLXdfM0hFdCMRYcNfYUErxwbOn2KKgHo0ga/aSguS0tRrqlPfXTWsMREcKIqHmQqL1iTang3R0WZws4v1OVUeITCknbKSkaHYkwuObRmVNUM1k9oR6KnNLVLCydKUyPH/+EScnJ1hr6NpbF5Q0SpeYckLSJdrWHB2dMh5PqUdjxpMpVXlNDIEnT54QUuAXX1Qs51d0mwbVKaazGc8ePOFP//Wf8OFHj/n0e88oujVuNefi819y+eaS+XzJ2nds6FirLSZAoQ3Tac3xwxMePHnM+OxDWmY0YUrcrKWUpQ2EJsl4yu2ZlArMZgXQMl9sWS6uUQTeMV3fsvdvpZQhBRmg/USIeaedlU5yW4/DxSmvDX2lgBL2qAQE0ilAqQzPZSen9r5HqyIvCHEQ4VVZJ89oO2jmCQ0DUIpm29C2LdvNBq1NdtIejRRm37Z+wovuqdTh6ZxHEGab7LRUzqeE5AdyUMwFsUOHBBUJoUV5KVANSaFURBExRuDiYlzRNE6gnn757wkuKhEJbJqtRF8+QrAUtmA8GlNVVnxqZqMFJ6o9B6FgkhrOQK6N3CeMwOD0gpIOXMNOPWYJPG1IlSjJ+BTRkZwj3CmhGFOQbC4EThLxish3FtX2SUpYcoQAqRe5kKjTqKFerdd87d87LIQxYlOEA1LCEEPcsuxkFLvX9x3bXU7uvWDWt60f83efw+H70luv51/w1se7ECX6jmE4zoBSfMvZAIN+6+A8h3xd3MvtqeG1wclkBxn6SJ/8XP+4jwDj7nM9hJpI+NSTZzKs6m+V6vSRmHMidB+z+ETeUAcfsLkVEf25sVNhygATCsVseoTViq7tsvQfObeoqcoSayy+c1xeXBJDwtqC4+MTUZ9JsFpvKWwpPeyKEtd5mm6Dcx1GK8rCQI60MMJMVcBkNCFRHtyKlJVvjNF8/3vf46MPn/MHP/79vWh7Fynux+1DyQ795jTkOZSy5GAPOfclOOlgc9Wvnf1auT/mZA3eg8LV7jM9fqCUoq5qjk9OGI/HbDfbg2PfXM9RuqSoxoympyQd+NnPf8X3f++HTI6OOT09Zjab8OjxQ0ajLxlPRqBaVpev8OsN9gZePHvKiw+e8qOPHzI5LglxzsXrz1hdXfDVL39K00V8NBw9/YhZVRBry7SqqUzBuKyYHc/Qo5p1avE4nLlGVR5bREYVFOO8wetyXWuUPpJ16mDr6dpXOLerAPg2e28nuNg0OYLooYp8g3Kht5R1KRnQt5iKJHZEJNU7zzzhM1wnNcJyU/uoIqVMJlHC7ArREYLPtSyiwWl6+aWcdEoxst02tG3DdtMMjtsFKeTc4zcAMKlPpHuBsrvddRGx2lAYS8wRaS/LloiE5LBW+oX5kLs7h5A7VEcSLdZKDiBGYcH2rVSMNpRFTWk6IQulKH3fjBH6vRI1DcHHElEnNAVGSd2ltRpbKIgaHT0KcYLWHrZSUr5Bx5yfue0EAR0cOu49I9v14RC56Q2JgAoSnSgU/a01KavaqIROXs4nyaKn0j5hKAk7NG+UNP0GaSfhRUoZbs3jKu2YxWY/wk0JtpekILnRpA7r8Hon2C8/O3+4c963naDqx+dbI35v8L7Hc3cDnXt1jnshTiIR29XwZEqJi6tr1ptmTzEmH/RbnaAcfBDWoS+BTgfRXg+XSt1f/zhlkkwa5kUPeXLLIWbFuyGi6V/3abfokhLNdtdKSaGYTU4w2tKWLc45QVGyEyxswXR8TFlKw+6QG9LunGAiloG+v+C4LjGAip7S6IxKJGw5xRTjjNpIgX7rFBHJc5NFADonW05lNMpUaBvR1u3GSVZTiaqAaEjIhr4oSmDXJzHGyKtXr1gul3kD2f+aw0+3CxTysfux1/8tMG8e64gWs0iP9uiJHsbMwQj4hnV9uHd7aMkd+y0AlosFm+WKq8vLg8+nJE2RjTHUtUWRWG/W0hR9OWExFplAGVeB0mrOTo4Z4QnjMbaG0wcnTE+m+OhZNRuWKXJ5M2e1WHLddIQkBMSRKdG2QtkSippkCoItWXWwDg63bQkqElRGSZJEtQNJqyNLyInkYHANGkd0nUSN72kq3VUxme0v//Ivd298r8n4T8W+cVU7eOkbLv/bbQ/heq/Xb/+E7/sa7Cbqt57Qb7n9sxqH32J7Y++f1/z6ZtufU+89v75tLvXvgcOSkW9787fa7S+963Nvf+FbaNc3HfKbTucfsv78Q0ztNG3f7x7dJWa/53iHJ9g5/dvM7X5j1R/v8PC3H/w97G4eARz6stv2nRRjfhvtH7z4fNvHv+n1v+9rv8v2WzoO7+cX7z/m/5/Ojfc52N0plO90z/6p3d6UvuMpvdvBHFgP7uTveOcbeNfG4f//D/VdJPrv7d7u7d7u7d5+q+wb4dB7u7d7u7d7u7ffZruPBO/t3u7t3u7td9buneC93du93du9/c7avRO8t3u7t3u7t99Zu3eC93Zv93Zv9/Y7a/dO8N7u7d7u7d5+Z+3eCd7bvd3bvd3b76z9XzG7ImzJKPuaAAAAAElFTkSuQmCC\n" + }, + "metadata": {} + } + ], + "source": [ + "# Get random training images and show them.\n", + "images, labels = iter(train_loader).next()\n", + "show_image(torchvision.utils.make_grid(images))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Wt3BVFMF81TI" + }, + "source": [ + "## Define a convolutional neural network\n", + "\n", + "\n", + "**Assignment 1:** Define a convolutional neural network. \n", + "You may use the code from previous notebooks.\n", + "We suggest that you start with a small network, and make sure that everything is working.\n", + "Once you can train successfully, come back and improve the architecture." + ] + }, + { + "cell_type": "code", + "source": [ + "def output_size(h_in=1, kernel=1, stride=1):\n", + " h_out = (h_in - kernel + 1)/stride\n", + " return int(h_out)" + ], + "metadata": { + "id": "2HuRwS5RCI_V" + }, + "execution_count": 21, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "class PrintSize(nn.Module):\n", + " \"\"\"Utility module to print current shape of a Tensor in Sequential, only at the first pass.\"\"\"\n", + " \n", + " first = True\n", + " \n", + " def forward(self, x):\n", + " if self.first:\n", + " print(f\"Size: {x.size()}\")\n", + " self.first = False\n", + " return x\n", + "\n", + "\n", + "class Model(nn.Module):\n", + "\n", + " def __init__(self, numChannels, size_img, out_channels, out_channel2, k, kernel, num_classes):\n", + " super().__init__()\n", + " self.num_classes = num_classes\n", + " self.activation = torch.nn.ReLU()\n", + " self.conv1 = torch.nn.Conv2d(in_channels=numChannels, out_channels=out_channels, kernel_size=kernel)\n", + " self.maxpool1 = torch.nn.MaxPool2d(kernel_size=k)\n", + " self.size1 = output_size(h_in = size_img, kernel=kernel, stride = k)\n", + " self.conv2 = torch.nn.Conv2d(in_channels=out_channels, out_channels=out_channel2, kernel_size=kernel)\n", + " self.maxpool2 = torch.nn.MaxPool2d(kernel_size=k)\n", + " self.size2 = output_size(h_in=self.size1, kernel=kernel, stride=k)\n", + " n_features = (self.size2)**2*out_channel2\n", + " self.net = nn.Sequential(\n", + " nn.Flatten(), # from (1, channels, height, width) to (1, channels * height * width)\n", + " nn.Linear(int(n_features), 512),\n", + " torch.nn.ReLU(nn.BatchNorm1d(512)),\n", + " nn.Linear(512, 128),\n", + " torch.nn.Dropout(0.7),\n", + " torch.nn.ReLU(),\n", + " nn.Linear(128, num_classes))\n", + " self.dropout = torch.nn.Dropout(0.7)\n", + " #softmax\n", + " self.logSoftmax = torch.nn.Softmax(dim=1)\n", + "\n", + " def forward(self, x):\n", + " #print(x.size())\n", + " x = self.conv1(x)\n", + " x = self.activation(x)\n", + " x = self.maxpool1(x)\n", + " x = self.dropout(x)\n", + " x = self.conv2(x)\n", + " x = self.dropout(x)\n", + " x = self.activation(x)\n", + " x = self.maxpool2(x)\n", + " x = self.net(x)\n", + " #x = self.logSoftmax(x)\n", + " return x\n", + "\n", + "\n", + "size_img = 32\n", + "out_channels = 512 #256\n", + "kernel = 5\n", + "out_channel2 = 256 #128\n", + "k = 2\n", + "numChannels = 3\n", + "model = Model(numChannels, size_img, out_channels, out_channel2, k, kernel, n_classes)\n", + "device = torch.device('cuda') # use cuda or cpu\n", + "model.to(device)\n", + "#print(model)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "feYrdKbjNC6V", + "outputId": "e731a1ca-ec74-45cb-9b3c-b1d7c6c84020" + }, + "execution_count": 22, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Model(\n", + " (activation): ReLU()\n", + " (conv1): Conv2d(3, 512, kernel_size=(5, 5), stride=(1, 1))\n", + " (maxpool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (conv2): Conv2d(512, 256, kernel_size=(5, 5), stride=(1, 1))\n", + " (maxpool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (net): Sequential(\n", + " (0): Flatten(start_dim=1, end_dim=-1)\n", + " (1): Linear(in_features=6400, out_features=512, bias=True)\n", + " (2): ReLU(\n", + " inplace=True\n", + " (inplace): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " (3): Linear(in_features=512, out_features=128, bias=True)\n", + " (4): Dropout(p=0.7, inplace=False)\n", + " (5): ReLU()\n", + " (6): Linear(in_features=128, out_features=10, bias=True)\n", + " )\n", + " (dropout): Dropout(p=0.7, inplace=False)\n", + " (logSoftmax): Softmax(dim=1)\n", + ")" + ] + }, + "metadata": {}, + "execution_count": 22 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7-IUg3sq81TQ" + }, + "source": [ + "## Define a loss function and optimizer\n", + "\n", + "**Assignment 2:** Define the loss function and optimizer.\n", + "You might need to experiment a bit with the learning rate." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "id": "48AX85QP81TR" + }, + "outputs": [], + "source": [ + "loss_fn = nn.CrossEntropyLoss()\n", + "optimizer = optim.Adam(model.parameters(), lr = 0.0001, weight_decay=5e-4)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-WneIN7C81TV" + }, + "source": [ + "## Train the network\n", + "\n", + "**Assignment 3:** Finish the training loop below. \n", + "Start by using a small number of epochs (e.g. 2).\n", + "Even with a low number of epochs you should be able to see results that are better than chance.\n", + "When everything is working increase the number of epochs to find out how good your network really is." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "id": "iWT0ULctWvm1", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "5fa77448-92b0-432d-d66e-f1290b113259" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Output shape: torch.Size([2, 10])\n", + "Output logits:\n", + "[[-0.2728909 -0.19642618 -0.23338474 -0.24838322 0.09379937 0.06010289\n", + " 0.2855124 0.06149838 0.20789815 -0.04478795]\n", + " [ 0.399861 0.359628 -0.2728389 -0.21755561 0.13820726 0.04578905\n", + " 0.2852838 -0.12165536 0.4336457 -0.43000287]]\n", + "Output probabilities:\n", + "[[0.07691602 0.08302809 0.0800155 0.07882433 0.11098605 0.10730851\n", + " 0.13443993 0.10745837 0.12440014 0.09662303]\n", + " [0.1344508 0.12914878 0.06861412 0.07251414 0.10349718 0.09436084\n", + " 0.11989556 0.07981263 0.13907076 0.05863515]]\n" + ] + } + ], + "source": [ + "# Test the forward pass with dummy data\n", + "out = model(torch.randn(2, 3, 32, 32, device=device))\n", + "print(\"Output shape:\", out.size())\n", + "print(f\"Output logits:\\n{out.detach().cpu().numpy()}\")\n", + "print(f\"Output probabilities:\\n{out.softmax(1).detach().cpu().numpy()}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "id": "NkUanRRb81TW", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "410e5d02-963a-4b47-9ae8-83ef8adee197" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\r 0%| | 0/20 [00:00" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + " \n", + "Training accuracy 83.59\n", + "Validation accuracy 77.7\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0qAsbC8I81Ta" + }, + "source": [ + "## Test the network\n", + "\n", + "Now we show a batch of test images and generate a table below with the true and predicted class for each of these images." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "id": "7LT0RoAC81Tc", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "b3c6b788-9124-4778-a325-e054420fba9b" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + " TRUE PREDICTED\n", + "-----------------------------\n", + " cat cat \n", + " ship ship \n", + " ship ship \n", + " airplane airplane \n", + " frog frog \n", + " frog frog \n", + " automobile automobile \n", + " frog frog \n", + " cat cat \n", + " automobile automobile \n", + " airplane airplane \n", + " truck truck \n", + " dog dog \n", + " horse horse \n", + " truck truck \n", + " ship ship \n", + " dog dog \n", + " horse truck \n", + " ship ship \n", + " frog frog \n", + " horse horse \n", + " airplane airplane \n", + " deer deer \n", + " truck truck \n", + " dog deer \n", + " bird truck \n", + " deer deer \n", + " airplane deer \n", + " truck truck \n", + " frog frog \n", + " frog cat \n", + " dog dog \n", + " deer bird \n", + " dog cat \n", + " truck truck \n", + " bird ship \n", + " deer horse \n", + " automobile automobile \n", + " truck truck \n", + " dog dog \n", + " deer deer \n", + " frog frog \n", + " dog horse \n", + " frog frog \n", + " airplane ship \n", + " truck truck \n", + " cat cat \n", + " truck truck \n", + " horse horse \n", + " frog frog \n", + " truck truck \n", + " ship ship \n", + " airplane dog \n", + " cat cat \n", + " ship ship \n", + " ship ship \n", + " horse horse \n", + " horse cat \n", + " deer dog \n", + " frog cat \n", + " horse horse \n", + " cat dog \n", + " frog frog \n", + " cat cat \n" + ] + } + ], + "source": [ + "inputs, targets = iter(test_loader).next()\n", + "inputs, targets = inputs.to(device), targets.to(device)\n", + "show_image(make_grid(inputs))\n", + "plt.show()\n", + "\n", + "outputs = model(inputs)\n", + "_, predicted = torch.max(outputs.data, 1)\n", + "\n", + "print(\" TRUE PREDICTED\")\n", + "print(\"-----------------------------\")\n", + "for target, pred in zip(targets, predicted):\n", + " print(f\"{classes[target.item()]:^13} {classes[pred.item()]:^13}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ISA6LJJO81Tg" + }, + "source": [ + "We now evaluate the network as above, but on the entire test set." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "id": "Smv6_BwF81Ti" + }, + "outputs": [], + "source": [ + "# Evaluate test set\n", + "confusion_matrix = np.zeros((n_classes, n_classes))\n", + "with torch.no_grad():\n", + " model.eval()\n", + " test_accuracies = []\n", + " for inputs, targets in test_loader:\n", + " inputs, targets = inputs.to(device), targets.to(device)\n", + " output = model(inputs)\n", + " loss = loss_fn(output, targets)\n", + "\n", + " predictions = output.max(1)[1]\n", + "\n", + " # Multiply by len(inputs) because the final batch of DataLoader may be smaller (drop_last=True).\n", + " test_accuracies.append(accuracy(targets, predictions) * len(inputs))\n", + " \n", + " confusion_matrix += compute_confusion_matrix(targets, predictions)\n", + "\n", + " test_accuracy = np.sum(test_accuracies) / len(test_set)\n", + " \n", + " model.train()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ylxBAyXwI7Ab" + }, + "source": [ + "Here we report the **average test accuracy** (number of correct predictions divided by test set size)." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "id": "a5-c_CDAI7Ab", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "1cacdb2c-05cb-436e-f108-3a071657238c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Test accuracy: 0.7794\n" + ] + } + ], + "source": [ + "print(f\"Test accuracy: {test_accuracy:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uMfUqY9rI7Ab" + }, + "source": [ + "Here we look a bit more in depth into the performance of the classifier, using the **confusion matrix**. The entry at the i-th row and j-th column indicates the number of samples with true label being the i-th class and predicted label being the j-th class.\n", + "\n", + "We normalize the rows: given all examples of a specific class (row), we can observe here how they are classified by our model. Ideally, we would like the entries on the diagonals to be 1, and everything else 0. This would mean that all examples from that class are classified correctly.\n", + "\n", + "The classes that are harder to classify for our model have lower numbers on the diagonal. We can then see exactly *how* they are misclassified by looking at the rest of the row.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "id": "ShcEZDExI7Ab", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 441 + }, + "outputId": "3aaf24f9-0ae3-4118-e5e6-53270bbfad1f" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "def normalize(matrix, axis):\n", + " axis = {'true': 1, 'pred': 0}[axis]\n", + " return matrix / matrix.sum(axis=axis, keepdims=True)\n", + "\n", + "x_labels = [classes[i] for i in classes]\n", + "y_labels = x_labels\n", + "plt.figure(figsize=(6, 6))\n", + "sns.heatmap(\n", + " ax=plt.gca(),\n", + " data=normalize(confusion_matrix, 'true'),\n", + " annot=True,\n", + " linewidths=0.5,\n", + " cmap=\"Reds\",\n", + " cbar=False,\n", + " fmt=\".2f\",\n", + " xticklabels=x_labels,\n", + " yticklabels=y_labels,\n", + ")\n", + "plt.xticks(rotation=90)\n", + "plt.yticks(rotation=0)\n", + "plt.ylabel(\"True class\")\n", + "plt.xlabel(\"Predicted class\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lw13Y86VI7Ac" + }, + "source": [ + "Here we focus on the diagonal and plot the numbers in a bar plot. This gives us a clearer picture of the accuracy of the model for different classes." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "id": "xv75wMhVI7Ac", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 327 + }, + "outputId": "32d48a09-1c05-4875-896f-5b3870613fea" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "with sns.axes_style('whitegrid'):\n", + " plt.figure(figsize=(8, 4))\n", + " sns.barplot(x=x_labels, y=np.diag(normalize(confusion_matrix, 'true')))\n", + " plt.xticks(rotation=90)\n", + " plt.title(\"Per-class accuracy\")\n", + " plt.ylabel(\"Accuracy\")\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ocnQOBAl81Tn" + }, + "source": [ + "**Assignment 4:** \n", + "1. Go back and improve performance of the network. By using enough convolutional layers with enough channels (and by training for long enough), you should easily be able to get a test accuracy above 60%, but see how much further you can get it! Can you reach 70%?\n", + "\n", + "2. Briefly describe what you did and any experiments you did along the way as well as what results you obtained.\n", + "Did anything surprise you during the exercise?\n", + "What were the changes that seemed to improve performance the most?\n", + "\n", + "3. Write down key lessons/insights you got during this exercise.\n", + "\n", + "**Answer:**\n", + "\n", + "1. Running my convolutional neural net for 20 epochs gives me a test accuracy of 77.94% and for what concerns the training and validation accuracy I got 83.59% and 77.70%, respectively. Therefore, we can say the model works fine and I maneged to avoid overfitting in the training set eventually.\n", + "\n", + "2. I tried different models. At first, I built a simple convolutional net with one conv layer, one maxpool and one flatten linear layer. Number of output channels 15 and running for only 2 epochs. I got at that point a test accuracy of around 58%. After this first trial, I added one more convolutional layer and one maxpool and then I played around with the number of output channels each conv layer and the optimizer parameters. The best result got so far was 66.50% of test accuracy. To get increase the perfomance I decided to add a batchnorm and a dropout(0.7) to avoid overfitting. Finilly I run for 20 epochs and increased the output channels and I got the results described in the 1st ansewr.\\\n", + "The changes that boost the most my model were definatelly the increased number of output channels.\n", + "\n", + "3. The key lesson I got in this exercise is the importance of choosing the right size of the convolutional layer, because thanks to it we look for the common pattern in the image. Thus, if the patter we're looking is too small we would search for too long and end up having a big quantity of pattern to compare. Conversely, if it is too big we won't have enough information to detect the images." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8Nzefavy81To" + }, + "source": [ + "# Training on GPU\n", + "\n", + "**Optional Assignment:**\n", + "If you have a GPU, we suggest that you try training your model on GPU. For this, you need to move the model to GPU after defining it, which will recursively go over all modules and convert their parameters and buffers to CUDA tensors. You also need to transfer both the inputs and targets to GPU at each training step, before performing the forward pass.\n", + "\n", + "The code for this is already in place: notice the `.to(device)` statements. The only thing left to do is change the definition of `device` from `'cpu'` to `'cuda'`.\n", + "\n", + "If you don't have a GPU, you can do this on [Google Colab](https://research.google.com/colaboratory/).\n", + "\n", + "Use the code below to check if any GPU is avaiable in your current setup. This should print the models of all available GPUs.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "id": "I09rtehbaCRH", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "3b1cb78c-d168-4873-cc6f-cf8fe9054a23" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Available CUDA devices: ['Tesla T4']\n" + ] + } + ], + "source": [ + "# Check if we have GPUs available\n", + "print(\"Available CUDA devices:\", [torch.cuda.get_device_name(i) for i in range(torch.cuda.device_count())])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "actFGqQZM9Vd" + }, + "source": [ + "You may not notice any significant speed-up from using a GPU. This is probably because your network is really small. Try increasing the width of your network (number of channels in the convolutional layers) and see if you observe any speed-up on GPU compared to CPU." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b8mEIylU81Tp" + }, + "source": [ + "# Exercise from Michael Nielsen's book\n", + "\n", + "**Assignment 5:** Pick an exercise of your own choice from [Michael Nielsen's book](http://neuralnetworksanddeeplearning.com/).\n", + "\n", + "**Answer:**\\\n", + "**Exercise 7 chapter 1**\\\n", + "\"Write out Equation (22) in component form, and verify that it gives the same result as the rule (4) for computing the output of a sigmoid neuron.\"\n", + "\n", + "Equation (22) has the form: $a' = σ(wa + b)$, whereas equation (4) is defined as: $\\frac{1}{1 + \\exp(- \\sum_jw_j*x_j - b)}$.\\\n", + "From equation (3) in the book we also now that: $σ(z) = \\frac{1}{1 + e^{-z}}$. Therefore, keeping that in mind we can rewrite equation (22) as $a' = \\frac{1}{1 + exp(- \\sum_j w_j*a_j - b)}$.\\\n", + "Where, $w$ is a matrix of the weights and $a$ is a vector listing the activations of the second layer of neurons. \n", + "\n" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/5_Recurrent/5_1_EXE_Recurrent_Neural_Networks_Nanograd.ipynb b/5_Recurrent/5_1_EXE_Recurrent_Neural_Networks_Nanograd.ipynb index cd4e8c6..e3b8c3c 100644 --- a/5_Recurrent/5_1_EXE_Recurrent_Neural_Networks_Nanograd.ipynb +++ b/5_Recurrent/5_1_EXE_Recurrent_Neural_Networks_Nanograd.ipynb @@ -1,1849 +1,1908 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "y-CptVs7iACc" - }, - "source": [ - "# Week 5 - Recurrent Neural Networks\n", - "\n", - "In this lab, we will introduce different ways of learning from sequential data.\n", - "\n", - "As a recurring example, we will train neural networks to do language modelling, i.e. predict the next token in a sentence. In the context of natural language processing a token could be a character or a word, but mind you that the concepts introduced here apply to all kinds of sequential data, such as e.g. protein sequences, weather measurements, audio signals, or videos, just to name a few.\n", - "\n", - "To really get a grasp of what is going on inside a recurrent neural network (RNN), we will carry out a substantial part of this exercise in Nanograd rather than PyTorch. \n", - "\n", - "We start off with a simple toy problem, build an RNN using Nanograd, train it, and see for ourselves that it really works. Once we're convinced, you will implement the Long Short-Term Memory (LSTM) cell, also in Nanograd. \n", - "\n", - "This is *not* simple but with the DenseLayer class we already have, it is doable. Having done it yourself will help you understand what happens under the hood of the PyTorch code we will use throughout the course.\n", - "\n", - "To summarize, in this notebook we will show you:\n", - "* How to represent sequences of categorical variables\n", - "* How to build and train an RNN in Nanograd\n", - "* How to build and train an LSTM network in Nanograd\n", - "* How to build and train an LSTM network in PyTorch\n", - "\n", - "\n", - "[Numpy version of the Notebook (previous version)](https://github.com/DeepLearningDTU/02456-deep-learning-with-PyTorch/blob/master/5_Recurrent/OLD-5.1-Numpy-Recurrent-Neural-Networks.ipynb)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "XapO8SLwiACd" - }, - "source": [ - "## Representing tokens or text\n", - "\n", - "In previous labs we mainly considered data $x \\in \\mathbb{R}^d$, where $d$ is the feature space dimension.\n", - "With time sequences our data can be represented as $x \\in \\mathbb{R}^{t \\, \\times \\, d}$, where $t$ is the sequence length. \n", - "This emphasises sequence dependence and that the samples along the sequence are not independent and identically distributed (i.i.d.).\n", - "\n", - "With RNNs, we can model both many-to-one functions: $\\mathbb{R}^{t \\, \\times \\, d} \\rightarrow \\mathbb{R}^c$ and many-to-many functions: $\\mathbb{R}^{t \\, \\times \\, d} \\rightarrow \\mathbb{R}^{t \\, \\times \\, c}$, where $c$ is the amount of classes/output dimensions.\n", - "\n", - "There are several ways to represent sequences. With text, the challenge is how to represent a word as a feature vector in $d$ dimensions, as we are required to represent text with decimal numbers in order to apply neural networks to it.\n", - "\n", - "In this exercise we will use a simple one-hot encoding but for categorical variables that can take on many values (e.g. words in the English language) this may be infeasible. For such scenarios, you can project the encodings into a smaller space by use of embeddings. If you want to learn more about tokens, encodings and embeddings than what is covered in this exercise, we highly recommend [this lecture](https://www.youtube.com/watch?v=kEMJRjEdNzM&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "bdA4LPsFiACe" - }, - "source": [ - "### One-hot encoding over vocabulary\n", - "\n", - "One way to represent a fixed amount of words is by making a one-hot encoded vector, which consists of 0s in all cells with the exception of a single 1 in a cell used uniquely to identify each word.\n", - "\n", - "| vocabulary | one-hot encoded vector |\n", - "| ------------- |--------------------------|\n", - "| Paris | $= [1, 0, 0, \\ldots, 0]$ |\n", - "| Rome | $= [0, 1, 0, \\ldots, 0]$ |\n", - "| Copenhagen | $= [0, 0, 1, \\ldots, 0]$ |\n", - "\n", - "Representing a large vocabulary with one-hot encodings often becomes inefficient because of the size of each sparse vector.\n", - "To overcome this challenge it is common practice to truncate the vocabulary to contain the $k$ most used words and represent the rest with a special symbol, $\\mathtt{UNK}$, to define unknown/unimportant words.\n", - "This often causes entities such as names to be represented with $\\mathtt{UNK}$ because they are rare.\n", - "\n", - "Consider the following text\n", - "> I love the corny jokes in Spielberg's new movie.\n", - "\n", - "where an example result would be similar to\n", - "> I love the corny jokes in $\\mathtt{UNK}$'s new movie." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "KNmyPw7zk2gY" - }, - "source": [ - "## Generating a dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "M9IEA4t2k2gb" - }, - "source": [ - "For this exercise we will create a simple dataset that we can learn from. We generate sequences of the form:\n", - "\n", - "`a b EOS`,\n", - "\n", - "`a a b b EOS`,\n", - "\n", - "`a a a a a b b b b b EOS`\n", - "\n", - "where `EOS` is a special character denoting the end of a sequence. The task is to predict the next token $t_n$, i.e. `a`, `b`, `EOS` or the unknown token `UNK` given a sequence of tokens $\\{ t_{1}, t_{2}, \\dots , t_{n-1}\\}$, and we are to process sequences in a sequential manner. As such, the network will need to learn that e.g. 5 `b`s and an `EOS` token will follow 5 `a`s." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "dcoN-kb7k2gc" - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "# Set seed such that we always get the same dataset\n", - "# (this is a good idea in general)\n", - "np.random.seed(42)\n", - "\n", - "def generate_dataset(num_sequences=2**5):\n", - " \"\"\"\n", - " Generates a number of sequences as our dataset.\n", - " \n", - " Args:\n", - " `num_sequences`: the number of sequences to be generated.\n", - " \n", - " Returns a list of sequences.\n", - " \"\"\"\n", - " samples = []\n", - " \n", - " for _ in range(num_sequences): \n", - " num_tokens = np.random.randint(1, 4)\n", - " sample = ['a'] * num_tokens + ['b'] * num_tokens + ['EOS']\n", - " samples.append(sample)\n", - " \n", - " return samples\n", - "\n", - "\n", - "sequences = generate_dataset()\n", - "\n", - "print('A single sample from the generated dataset:')\n", - "print(sequences[0])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "YMLd3Gzak2gp" - }, - "source": [ - "## Representing tokens as indices" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "S9LSqaJSk2gp" - }, - "source": [ - "To build a one-hot encoding, we need to assign each possible word in our vocabulary an index. We do that by creating two dictionaries: one that allows us to go from a given word to its corresponding index in our vocabulary, and one for the reverse direction. Let's call them `word_to_idx` and `idx_to_word`. The keyword `vocab_size` specifies the maximum size of our vocabulary. If we try to access a word that does not exist in our vocabulary, it is automatically replaced by the `UNK` token or its corresponding index." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "sNY1OOS_k2gy" - }, - "source": [ - "## Exercise a) Sequence to dictionary function \n", - "\n", - "Complete the sequences_to_dicts function below. You will need to fill the word_to_idx and idx_to_word dictionaries so that we can go back and forth between the two representations." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "Smdo70UMk2gr" - }, - "outputs": [], - "source": [ - "from collections import defaultdict\n", - "\n", - "def sequences_to_dicts(sequences):\n", - " \"\"\"\n", - " Creates word_to_idx and idx_to_word dictionaries for a list of sequences.\n", - " \"\"\"\n", - " # A bit of Python-magic to flatten a nested list\n", - " flatten = lambda l: [item for sublist in l for item in sublist]\n", - " \n", - " # Flatten the dataset\n", - " all_words = flatten(sequences)\n", - " \n", - " # Count number of word occurences\n", - " word_count = defaultdict(int)\n", - " for word in flatten(sequences):\n", - " word_count[word] += 1\n", - "\n", - " # Sort by frequency\n", - " word_count = sorted(list(word_count.items()), key=lambda l: -l[1])\n", - "\n", - " # Create a list of all unique words\n", - " unique_words = [item[0] for item in word_count]\n", - " \n", - " # Add UNK token to list of words\n", - " unique_words.append('UNK')\n", - "\n", - " # Count number of sequences and number of unique words\n", - " num_sentences, vocab_size = len(sequences), len(unique_words)\n", - "\n", - " # Create dictionaries so that we can go from word to index and back\n", - " # If a word is not in our vocabulary, we assign it to token 'UNK'\n", - " word_to_idx = defaultdict(lambda: vocab_size-1)\n", - " idx_to_word = defaultdict(lambda: 'UNK')\n", - "\n", - " # Fill dictionaries\n", - " for idx, word in enumerate(unique_words):\n", - " # YOUR CODE HERE!\n", - " word_to_idx[word] =\n", - " idx_to_word[idx] =\n", - "\n", - " return word_to_idx, idx_to_word, num_sentences, vocab_size\n", - "\n", - "\n", - "word_to_idx, idx_to_word, num_sequences, vocab_size = sequences_to_dicts(sequences)\n", - "\n", - "print(f'We have {num_sequences} sentences and {len(word_to_idx)} unique tokens in our dataset (including UNK).\\n')\n", - "print('The index of \\'b\\' is', word_to_idx['b'])\n", - "print(f'The word corresponding to index 1 is \\'{idx_to_word[1]}\\'')\n", - "\n", - "assert idx_to_word[word_to_idx['b']] == 'b', \\\n", - " 'Consistency error: something went wrong in the conversion.'" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "cGSoDRgHk2g1" - }, - "source": [ - "## Partitioning the dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UMTn1iLIk2g1" - }, - "source": [ - "To build our dataset, we need to create inputs and targets for each sequences and partition sentences it into training, validation and test sets. 80%, 10% and 10% is a common distribution, but mind you that this largely depends on the size of the dataset. Since we are doing next-word predictions, our target sequence is simply the input sequence shifted by one word.\n", - "\n", - "We can use PyTorch's `Dataset` class to build a simple dataset where we can easily retrieve (inputs, targets) pairs for each of our sequences." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "9dW7MrPnk2g3" - }, - "outputs": [], - "source": [ - "from torch.utils import data\n", - "\n", - "class Dataset(data.Dataset):\n", - " def __init__(self, inputs, targets):\n", - " self.inputs = inputs\n", - " self.targets = targets\n", - "\n", - " def __len__(self):\n", - " # Return the size of the dataset\n", - " return len(self.targets)\n", - "\n", - " def __getitem__(self, index):\n", - " # Retrieve inputs and targets at the given index\n", - " X = self.inputs[index]\n", - " y = self.targets[index]\n", - "\n", - " return X, y\n", - "\n", - " \n", - "def create_datasets(sequences, dataset_class, p_train=0.8, p_val=0.1, p_test=0.1):\n", - " # Define partition sizes\n", - " num_train = int(len(sequences)*p_train)\n", - " num_val = int(len(sequences)*p_val)\n", - " num_test = int(len(sequences)*p_test)\n", - "\n", - " # Split sequences into partitions\n", - " sequences_train = sequences[:num_train]\n", - " sequences_val = sequences[num_train:num_train+num_val]\n", - " sequences_test = sequences[-num_test:]\n", - "\n", - " def get_inputs_targets_from_sequences(sequences):\n", - " # Define empty lists\n", - " inputs, targets = [], []\n", - " \n", - " # Append inputs and targets s.t. both lists contain L-1 words of a sentence of length L\n", - " # but targets are shifted right by one so that we can predict the next word\n", - " for sequence in sequences:\n", - " inputs.append(sequence[:-1])\n", - " targets.append(sequence[1:])\n", - " \n", - " return inputs, targets\n", - "\n", - " # Get inputs and targets for each partition\n", - " inputs_train, targets_train = get_inputs_targets_from_sequences(sequences_train)\n", - " inputs_val, targets_val = get_inputs_targets_from_sequences(sequences_val)\n", - " inputs_test, targets_test = get_inputs_targets_from_sequences(sequences_test)\n", - "\n", - " # Create datasets\n", - " training_set = dataset_class(inputs_train, targets_train)\n", - " validation_set = dataset_class(inputs_val, targets_val)\n", - " test_set = dataset_class(inputs_test, targets_test)\n", - "\n", - " return training_set, validation_set, test_set\n", - " \n", - "\n", - "training_set, validation_set, test_set = create_datasets(sequences, Dataset)\n", - "\n", - "print(f'We have {len(training_set)} samples in the training set.')\n", - "print(f'We have {len(validation_set)} samples in the validation set.')\n", - "print(f'We have {len(test_set)} samples in the test set.')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4xMMSm7Mk2g9" - }, - "source": [ - "When working with more complex data than what we use in this exercise, creating a PyTorch `DataLoader` on top of the dataset can be beneficial. A data loader is basically a fancy generator/iterator that we can use to abstract away all of the data handling and pre-processing + it's super useful for processing batches of data as well! Data loaders will come in handy later when you start to work on your projects, so be sure to check them out!\n", - "\n", - "For more information on how to use datasets and data loaders in PyTorch, [consult the official guide](https://pytorch.org/tutorials/beginner/data_loading_tutorial.html)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "t-rfgDfZeMQ6" - }, - "source": [ - "## Nanograd utilities" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "oRO5ssg0eQMK" - }, - "source": [ - "We load necessary utility functions for the Nanograd library, which we saw in Lab 2." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "Jd4CoEBNzNWS" - }, - "outputs": [], - "source": [ - "# Copy and pasted from https://github.com/rasmusbergpalm/nanograd/blob/main/nanograd.py\n", - "\n", - "from math import exp, log, tanh\n", - "\n", - "class Var:\n", - " \"\"\"\n", - " A variable which holds a float and enables gradient computations.\n", - " \"\"\"\n", - "\n", - " def __init__(self, val: float, grad_fn=lambda: []):\n", - " assert type(val) == float\n", - " self.v = val\n", - " self.grad_fn = grad_fn\n", - " self.grad = 0.0\n", - "\n", - " def backprop(self, bp):\n", - " self.grad += bp\n", - " for input, grad in self.grad_fn():\n", - " input.backprop(grad * bp)\n", - "\n", - " def backward(self):\n", - " self.backprop(1.0)\n", - "\n", - " def __add__(self: 'Var', other: 'Var') -> 'Var':\n", - " return Var(self.v + other.v, lambda: [(self, 1.0), (other, 1.0)])\n", - "\n", - " def __mul__(self: 'Var', other: 'Var') -> 'Var':\n", - " return Var(self.v * other.v, lambda: [(self, other.v), (other, self.v)])\n", - "\n", - " def __pow__(self, power):\n", - " assert type(power) in {float, int}, \"power must be float or int\"\n", - " return Var(self.v ** power, lambda: [(self, power * self.v ** (power - 1))])\n", - "\n", - " def __neg__(self: 'Var') -> 'Var':\n", - " return Var(-1.0) * self\n", - "\n", - " def __sub__(self: 'Var', other: 'Var') -> 'Var':\n", - " return self + (-other)\n", - "\n", - " def __truediv__(self: 'Var', other: 'Var') -> 'Var':\n", - " return self * other ** -1\n", - "\n", - " def __repr__(self):\n", - " return \"Var(v=%.4f, grad=%.4f)\" % (self.v, self.grad)\n", - " \n", - " def exp(self):\n", - " return Var(exp(self.v), lambda: [(self, exp(self.v))])\n", - " \n", - " def log(self):\n", - " return Var(log(self.v), lambda: [(self, self.v ** -1)])\n", - "\n", - " def relu(self):\n", - " return Var(self.v if self.v > 0.0 else 0.0, lambda: [(self, 1.0 if self.v > 0.0 else 0.0)])\n", - " \n", - " def identity(self):\n", - " return self\n", - "\n", - " def sigmoid(self):\n", - " return Var(0.5) * (Var(1.0) + (Var(0.5) * self).tanh()) # logistic function is a scaled and shifted version of tanh\n", - " \n", - " def tanh(self):\n", - " return Var(tanh(self.v), lambda: [(self, 1-tanh(self.v) ** 2)])" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "9AMqMsiseMfz" - }, - "outputs": [], - "source": [ - "# convert from ndarray to Var\n", - "def nparray_to_Var(x):\n", - " if x.ndim==1:\n", - " y = [[Var(float(x[i]))] for i in range(x.shape[0])] # always work with list of list\n", - " else:\n", - " y = [[Var(float(x[i,j])) for j in range(x.shape[1])] for i in range(x.shape[0])]\n", - " return y\n", - "\n", - "# convert from Var to ndarray \n", - "def Var_to_nparray(x):\n", - " try:\n", - " y = np.zeros((len(x),len(x[0])))\n", - " for i in range(len(x)):\n", - " for j in range(len(x[0])):\n", - " y[i,j] = x[i][j].v\n", - " except TypeError:\n", - " y = np.zeros((len(x)))\n", - " for i in range(len(x)):\n", - " y[i] = x[i].v\n", - "\n", - " return y" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "id": "ij_ieRsAt7Xt" - }, - "outputs": [], - "source": [ - "class Initializer:\n", - "\n", - " def init_weights(self, n_in, n_out):\n", - " raise NotImplementedError\n", - "\n", - " def init_bias(self, n_out):\n", - " raise NotImplementedError" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "id": "eb18N5phuIha" - }, - "outputs": [], - "source": [ - "import random\n", - "\n", - "class NormalInitializer(Initializer):\n", - "\n", - " def __init__(self, mean=0, std=0.1):\n", - " self.mean = mean\n", - " self.std = std\n", - "\n", - " def init_weights(self, n_in, n_out):\n", - " return [[Var(random.gauss(self.mean, self.std)) for _ in range(n_out)] for _ in range(n_in)]\n", - "\n", - " def init_bias(self, n_out):\n", - " return [Var(0.0) for _ in range(n_out)]\n", - "\n", - "class ConstantInitializer(Initializer):\n", - "\n", - " def __init__(self, weight=1.0, bias=0.0):\n", - " self.weight = weight\n", - " self.bias = bias\n", - "\n", - " def init_weights(self, n_in, n_out):\n", - " return [[Var(self.weight) for _ in range(n_out)] for _ in range(n_in)]\n", - "\n", - " def init_bias(self, n_out):\n", - " return [Var(self.bias) for _ in range(n_out)]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Dzmryk72k2g-" - }, - "source": [ - "## One-hot encodings" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "abRN9f8Xk2g_" - }, - "source": [ - "We now create a simple function that returns the one-hot encoded representation of a given index of a word in our vocabulary. Notice that the shape of the one-hot encoding is equal to the entire vocabulary (which can be huge!). Additionally, we define a function to automatically one-hot encode a sentence." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "IZruCIHJk2hB" - }, - "outputs": [], - "source": [ - "def one_hot_encode(idx, vocab_size):\n", - " \"\"\"\n", - " One-hot encodes a single word given its index and the size of the vocabulary.\n", - " \n", - " Args:\n", - " `idx`: the index of the given word\n", - " `vocab_size`: the size of the vocabulary\n", - " \n", - " Returns a 1-D numpy array of length `vocab_size`.\n", - " \"\"\"\n", - " # Initialize the encoded array\n", - " one_hot = np.array([np.zeros(vocab_size)])\n", - " \n", - " # Set the appropriate element to one\n", - " one_hot[0][idx] = 1.0\n", - " return nparray_to_Var(one_hot)\n", - "\n", - "\n", - "def one_hot_encode_sequence(sequence, vocab_size):\n", - " \"\"\"\n", - " One-hot encodes a sequence of words given a fixed vocabulary size.\n", - " \n", - " Args:\n", - " `sentence`: a list of words to encode\n", - " `vocab_size`: the size of the vocabulary\n", - " \n", - " Returns a 3-D numpy array of shape (num words, vocab size, 1).\n", - " \"\"\"\n", - " # Encode each word in the sentence\n", - " encoding = np.array([Var_to_nparray(one_hot_encode(word_to_idx[word], vocab_size)) for word in sequence])\n", - "\n", - " # Reshape encoding s.t. it has shape (num words, vocab size, 1)\n", - " encoding = encoding.reshape(encoding.shape[0], encoding.shape[2], 1)\n", - " return nparray_to_Var(encoding)\n", - "\n", - "test_word = one_hot_encode(word_to_idx['a'], vocab_size)\n", - "print(f'Our one-hot encoding of \\'a\\' has shape {Var_to_nparray(test_word).shape}.')\n", - "\n", - "test_sentence = one_hot_encode_sequence(['a', 'b'], vocab_size)\n", - "print(f'Our one-hot encoding of \\'a b\\' has shape {Var_to_nparray(test_sentence).shape}.')\n", - "\n", - "print(test_word)\n", - "print(test_sentence)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "id": "JT6BqYrU_NxQ" - }, - "outputs": [], - "source": [ - "encoded_training_set_x = []\n", - "encoded_training_set_y = []\n", - "encoded_validation_set_x = []\n", - "encoded_validation_set_y = []\n", - "encoded_test_set_x = []\n", - "encoded_test_set_y = []\n", - "\n", - "for n in range(len(training_set)):\n", - " encoded_training_set_x.append(one_hot_encode_sequence(training_set[n][0], vocab_size))\n", - " encoded_training_set_y.append(one_hot_encode_sequence(training_set[n][1], vocab_size))\n", - "for n in range(len(validation_set)):\n", - " encoded_validation_set_x.append(one_hot_encode_sequence(validation_set[n][0], vocab_size))\n", - " encoded_validation_set_y.append(one_hot_encode_sequence(validation_set[n][1], vocab_size))\n", - "for n in range(len(test_set)):\n", - " encoded_test_set_x.append(one_hot_encode_sequence(test_set[n][0], vocab_size))\n", - " encoded_test_set_y.append(one_hot_encode_sequence(test_set[n][1], vocab_size))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "erI_MXvKk2hG" - }, - "source": [ - "Great! Now that we have our one-hot encodings in place, we can move on to the RNNs!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "MA6bxjGWjeSB" - }, - "source": [ - "# Introduction to Recurrent Neural Networks (RNN)\n", - "\n", - "Reading material: [blog post](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) and (optionally) [this lecture](https://www.youtube.com/watch?v=iWea12EAu6U&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z).\n", - "\n", - "___\n", - "\n", - "A recurrent neural network (RNN) is a type of neural network that has been succesful in modelling sequential data, e.g. language, speech, protein sequences, etc.\n", - "\n", - "A RNN performs its computations in a cyclic manner, where the same computation is applied to every sample of a given sequence.\n", - "The idea is that the network should be able to use the previous computations as some form of memory and apply this to future computations.\n", - "An image may best explain how this is to be understood,\n", - "\n", - "![rnn-unroll image](https://github.com/DeepLearningDTU/02456-deep-learning-with-PyTorch/blob/master/static_files/rnn-unfold.png?raw=1)\n", - "\n", - "\n", - "where it the network contains the following elements:\n", - "\n", - "- $x$ is the input sequence of samples, \n", - "- $U$ is a weight matrix applied to the given input sample,\n", - "- $V$ is a weight matrix used for the recurrent computation in order to pass memory along the sequence,\n", - "- $W$ is a weight matrix used to compute the output of the every timestep (given that every timestep requires an output),\n", - "- $h$ is the hidden state (the network's memory) for a given time step, and\n", - "- $o$ is the resulting output.\n", - "\n", - "When the network is unrolled as shown, it is easier to refer to a timestep, $t$.\n", - "We have the following computations through the network:\n", - "\n", - "- $h_t = f(U\\,{x_t} + V\\,{h_{t-1}})$, where $f$ is a non-linear activation function, e.g. $\\mathrm{tanh}$.\n", - "- $o_t = W\\,{h_t}$\n", - "\n", - "When we are doing language modelling using a cross-entropy loss, we additionally apply the softmax function to the output $o_{t}$:\n", - "\n", - "- $\\hat{y}_t = \\mathrm{softmax}(o_{t})$\n", - "\n", - "\n", - "### Backpropagation through time\n", - "\n", - "We define a loss function\n", - "\n", - "- $E = \\sum_t E_t = \\sum_t E_t(y_t ,\\hat{y}_t ) \\ , $\n", - "\n", - "where $E_t(y_t ,\\hat{y}_t )$ is the cross-entropy function.\n", - "\n", - "Backpropagation through time amounts to computing the gradients of the loss using the same type of clever bookkeeping we applied to the feed-forward network in week 1. This you will do in Exercise D." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GuvwbvsGz9KE" - }, - "source": [ - "## Implementing an RNN\n", - "\n", - "We will implement the forward pass, backward pass, optimization and training loop for an RNN in Nanograd so that you can get familiar with the recurrent nature of RNNs. Later, we will go back to PyTorch." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gfbfcB-NJZuM" - }, - "source": [ - "We define the Nanograd DenseLayer class from [lab 2](https://github.com/DeepLearningDTU/02456-deep-learning-with-PyTorch/blob/master/2_Feedforward_Python/2.1-EXE-FNN-AutoDif-Nanograd.ipynb) with a few additions:\n", - "* the option use_bias to define a layer without bias. This is useful when we define the recurrent layer and\n", - "* a method forward_sequence which is useful when a DenseLayer is used as part of a recurrent neural network" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "id": "TqkVyEEACHKS" - }, - "outputs": [], - "source": [ - "from typing import Sequence\n", - "\n", - "class DenseLayer:\n", - " def __init__(self, n_in: int, n_out: int, act_fn, initializer = NormalInitializer(), use_bias=True):\n", - " self.weights = initializer.init_weights(n_in, n_out)\n", - " self.use_bias = use_bias\n", - " if use_bias:\n", - " self.bias = initializer.init_bias(n_out)\n", - " self.act_fn = act_fn\n", - " \n", - " def __repr__(self): \n", - " return 'Weights: ' + repr(self.weights) + (' Biases: ' + repr(self.bias) if self.use_bias else '')\n", - "\n", - " def parameters(self) -> Sequence[Var]:\n", - " params = []\n", - " for r in self.weights:\n", - " params += r\n", - "\n", - " if self.use_bias:\n", - " params += self.bias\n", - "\n", - " return params\n", - "\n", - " def forward(self, input: Sequence[Var]) -> Sequence[Var]:\n", - " # self.weights is a matrix with dimension n_in x n_out. We check that the dimensionality of the input \n", - " # to the current layer matches the number of nodes in the current layer\n", - " assert len(self.weights) == len(input), \"weights and input must match in first dimension\"\n", - " weights = self.weights\n", - " out = []\n", - " # For some given data point single_input, we now want to calculate the resulting value in each node in the current layer\n", - " # We therefore loop over the (number of) nodes in the current layer:\n", - " for j in range(len(weights[0])): \n", - " # Initialize the node value depending on its corresponding parameters.\n", - " node = self.bias[j] if self.use_bias else Var(0.0)\n", - " # We now finish the linear transformation corresponding to the parameters of the currently considered node.\n", - " for i in range(len(input)):\n", - " node += input[i]*weights[i][j]\n", - " node = self.act_fn(node)\n", - " out.append(node)\n", - "\n", - " return out\n", - " \n", - " def forward_sequence(self, input: Sequence[Sequence[Var]]) -> Sequence[Sequence[Var]]:\n", - " out = []\n", - " for i in range(len(input)): \n", - " node = self.forward(input[i])\n", - " out.append(node)\n", - "\n", - " return out" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "qDKFjjQEM-xX" - }, - "source": [ - "## Exercise b) The RNNLayer class\n", - "\n", - "Complete the RNNLayer class below.\n", - "\n", - "Explain how we reuse the DenseLayer class.\n", - "\n", - "Explain what the forward and the forward_sequence method do." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "id": "IcM1N6PQrT7l" - }, - "outputs": [], - "source": [ - "from typing import Sequence\n", - "\n", - "class RNNLayer:\n", - " def __init__(self, n_in: int, n_hid: int, act_fn, initializer = NormalInitializer(), initializer_hid = NormalInitializer()):\n", - " self.n_hid = n_hid\n", - " self.in_hid_layer = DenseLayer( , , lambda x: x, initializer)\n", - " self.hid_hid_layer = DenseLayer (, , lambda x: x, initializer_hid, use_bias=False) # we already get a bias through in_hid_layer \n", - " self.initial_hid = [Var(0.0) for _ in range(n_hid)]\n", - " self.stored_hid = [Var(0.0) for _ in range(n_hid)]\n", - " self.act_fn = act_fn\n", - " \n", - " def __repr__(self): \n", - " return 'Feed-forward: ' + repr(self.in_hid_layer) + ' Recurrent: ' + repr(self.hid_hid_layer) + ' Initial hidden: ' + repr(self.initial_hid)\n", - "\n", - " def parameters(self) -> Sequence[Var]: \n", - " return self.in_hid_layer.parameters() + self.hid_hid_layer.parameters() + self.initial_hid\n", - "\n", - " def forward_step(self, input: Sequence[Var], input_hid: Sequence[Var]) -> Sequence[Var]:\n", - " in_hids = # contribution from input\n", - " hid_hids = # contribution from hidden state\n", - "\n", - " hids = []\n", - " for i in range(self.n_hid):\n", - " hids.append( )\n", - "\n", - " return hids\n", - " \n", - " def forward_sequence(self, input: Sequence[Sequence[Var]], use_stored_hid = False) -> Sequence[Sequence[Var]]:\n", - " out = []\n", - " if use_stored_hid:\n", - " hid = self.stored_hid\n", - " else:\n", - " hid = self.initial_hid\n", - " # Takes a sequence and loops over each character in the sequence. Note that each character has dimension equal to the embedding dimension\n", - " for i in range(len(input)):\n", - " hid = \n", - " out.append(hid)\n", - " self.stored_hid = hid\n", - " return out" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "VgAU6qPHKJFr" - }, - "source": [ - "Now we can define a network and pass some data through it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "MFkZ5gNG6d7c" - }, - "outputs": [], - "source": [ - "NN = [\n", - " RNNLayer(1, 5, lambda x: x.tanh()),\n", - " DenseLayer(5, 1, lambda x: x.identity())\n", - "]\n", - "\n", - "def forward_batch(input: Sequence[Sequence[Sequence[Var]]], network, use_stored_hid=False):\n", - " \n", - " def forward_single_sequence(x, network, use_stored_hid):\n", - " for layer in network:\n", - " if isinstance(layer, RNNLayer):\n", - " x = layer.forward_sequence(x, use_stored_hid) \n", - " else:\n", - " x = layer.forward_sequence(x)\n", - " return x\n", - "\n", - " output = [ forward_single_sequence(input[n], network, use_stored_hid) for n in range(len(input))]\n", - " return output\n", - "\n", - "print(NN[0])\n", - "x_train =[\n", - " [[Var(1.0)], [Var(2.0)], [Var(3.0)]],\n", - " [[Var(1.0)], [Var(2.0)], [Var(3.0)]]\n", - " ]\n", - "\n", - "output_train = forward_batch(x_train, NN) \n", - "output_train[0][0][0].backward()\n", - "\n", - "print(output_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "yolo5dKrk2hR" - }, - "source": [ - "## Exercise c) Unit test\n", - "\n", - "Make unit tests to make sure that the output and the backward method work as it should.\n", - "\n", - "NOTE: The .backward() call above simply backpropagates a value in the output (and not a loss). Below, we will extend our loss functions to be able to handle backpropagation through time.\n", - "\n", - "Recycling code from [Lab 2](https://github.com/DeepLearningDTU/02456-deep-learning-with-PyTorch/blob/master/2_Feedforward_Python/2.1-EXE-FNN-AutoDif-Nanograd.ipynb) is fine. " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "id": "GhCB1ASwK3X7" - }, - "outputs": [], - "source": [ - "# Insert code here" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4d4_2b6mK5jH" - }, - "source": [ - "## Exercise d) Advanced initialization\n", - "\n", - "How can we use He initialization for the recurrent layer?\n", - "\n", - "Hint: the sum of two unit variance stochastic variables have variance 2.\n", - "\n", - "Insert code for He initialization of the recurrent layer. Again, recycling code from Lab 2 is fine. " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "id": "oRn3mDnzLxu2" - }, - "outputs": [], - "source": [ - "## He\n", - "def DenseLayer_He_tanh(n_in: int, n_out: int):\n", - " std = 0.0 # <- replace with proper initialization \n", - " return DenseLayer(n_in, n_out, lambda x: x.relu(), initializer = NormalInitializer(std))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ozNN9xXML0yc" - }, - "source": [ - "## Exercise e) Sequence loss function\n", - "\n", - "We want to solve a sequence to sequence problem. So you need a sequence loss function. \n", - "\n", - "Implement the function such that the sequence loss can take flexible input dimensions and so that it can take any loss as an argument, such as squared loss and cross entropy. (We recommend using cross entropy below)\n", - "\n", - "We have provided a bit of code to try it out.\n", - "\n", - "Hints: You can get inspiration from the forward_sequence method above. You can copy and paste squared loss and cross entropy from Lab 2. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "bYpEnbeMP4yL" - }, - "outputs": [], - "source": [ - "# Insert code here\n", - "\n", - "def squared_loss_sequence(t, y):\n", - " \n", - " # add check that sizes agree\n", - " \n", - " def squared_loss_single(t, y):\n", - "\n", - " # add check that sizes agree\n", - " \n", - " Loss = Var(0.0)\n", - "\n", - " return Loss\n", - "\n", - " Loss = Var(0.0)\n", - "\n", - " return Loss\n", - "\n", - "def cross_entropy_loss_sequence(t, y):\n", - "\n", - " # add check that sizes agree\n", - "\n", - " def cross_entropy_loss_single(t, y):\n", - "\n", - " # add check that sizes agree\n", - "\n", - " Loss = Var(0.0)\n", - " denominator = Var(0.0)\n", - "\n", - " return Loss\n", - "\n", - " Loss = Var(0.0)\n", - "\n", - " return Loss\n", - "\n", - "def sequence_loss(t: Sequence[Sequence[Var]], y: Sequence[Sequence[Var]], loss_fn=cross_entropy_loss_sequence) -> Var:\n", - " assert len(t) == len(y)\n", - " return loss_fn(t, y)\n", - "\n", - "\n", - "# Test of loss func\n", - "NN = [\n", - " RNNLayer(4, 2, lambda x: x.tanh()),\n", - " DenseLayer(2, 4, lambda x: x.identity())\n", - "]\n", - "\n", - "output_train = forward_batch(encoded_training_set_x[:3], NN)\n", - "print(output_train) \n", - "loss = sequence_loss(output_train, encoded_training_set_y[:3], cross_entropy_loss_sequence)\n", - "print(\"Loss:\", loss)\n", - "loss.backward()\n", - "\n", - "print(\"Output:\", output_train)\n", - "\n", - "print('Network before update:')\n", - "[print('Layer', i, '\\n', NN[i]) for i in range(len(NN))] \n", - "\n", - "def parameters(network):\n", - " params = []\n", - " for layer in range(len(network)):\n", - " params += network[layer].parameters()\n", - " return params\n", - "\n", - "def update_parameters(params, learning_rate=0.01):\n", - " for p in params:\n", - " p.v -= learning_rate*p.grad\n", - "\n", - "def zero_gradients(params):\n", - " for p in params:\n", - " p.grad = 0.0\n", - "\n", - "update_parameters(parameters(NN))\n", - "\n", - "print('\\nNetwork after update:')\n", - "[print('Layer', i, '\\n', NN[i]) for i in range(len(NN))] \n", - "\n", - "zero_gradients(parameters(NN))\n", - "\n", - "print('\\nNetwork after zeroing gradients:')\n", - "[print('Layer', i, '\\n', NN[i]) for i in range(len(NN))] \n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ezSRiVJzk2h5" - }, - "source": [ - "# Backpropagation through time \n", - "\n", - "Since we have automatic differentiation we don't have to code the backpropagation rule by hand. Just to give you a bit of appreciation for have much bookkeeping is necessary we have given the derivation belwo.\n", - "\n", - "We need to compute the partial derivatives\n", - "$\n", - "\\frac{\\partial E}{\\partial W},~\\frac{\\partial E}{\\partial U},~\\frac{\\partial E}{\\partial V}\n", - "$. \n", - "We repeat the definition of the RNN forward pass from above:\n", - "\n", - "- $h_t = f(U\\,{x_t} + V\\,{h_{t-1}})$, where $f$ usually is an activation function, e.g. $\\mathrm{tanh}$.\n", - "- $o_t = W\\,{h_t}$\n", - "- $\\hat{y}_t = \\mathrm{softmax}(o_{t})$\n", - "\n", - "where\n", - "- $U$ is a weight matrix applied to the given input sample,\n", - "- $V$ is a weight matrix used for the recurrent computation in order to pass memory along the sequence,\n", - "- $W$ is a weight matrix used to compute the output of the every timestep (given that every timestep requires an output), and\n", - "- $h$ is the hidden state (the network's memory) for a given time step.\n", - "\n", - "Recall though, that RNNs are recurrent and the weights $W,~U,~V$ are shared across time, i.e. we do not have separate weights for each time step. Therefore, to compute e.g. the partial derivative $\\frac{\\partial E}{\\partial W}$, we need to 1) sum up across time, and 2) apply the chain rule:\n", - "\n", - "$$\\frac{\\partial E}{\\partial W} = \\sum_{t} \\frac{\\partial E}{\\partial o_{t}} \\frac{\\partial o_{t}}{\\partial W}\\,.$$\n", - "To compute$\\frac{\\partial o_{t}}{\\partial W}$ we use the definition of $o_t$ above.\n", - "From week 1 (exercise i) we have that\n", - "$$\\delta_{o,t} \\equiv \\frac{\\partial E}{\\partial o_{t}} = \\frac{\\partial E_t}{\\partial o_{t}} = \\hat{y}_{t} - y_{t}\\,,$$\n", - "where $\\hat{y}_{t}$ is a softmax distribution over model outputs $o_{t}$ at time $t$, and $y_{t}$ is the target label at time $t$. \n", - "\n", - "To compute $\\frac{\\partial E}{\\partial U}$ and $\\frac{\\partial E}{\\partial V}$ we again sum over time and use the chain rule:\n", - "$$\n", - "\\frac{\\partial E}{\\partial U} = \\sum_{t} \\frac{\\partial E}{\\partial h_{t}} \\frac{\\partial h_{t}}{\\partial U} \\ . \n", - "$$\n", - "This leads us to introduce\n", - "$$\n", - "\\delta_{h,t} \\equiv \\frac{\\partial E}{\\partial h_{t}} \\ .\n", - "$$\n", - "The backpropagation through time recursion is derived by realising that a variation of $h_t$ affects 1) the loss at time step $t$ through the feed-forward connection to the output and 2) the future losses through the $h_{t+1}$ dependence of $h_t$. Mathematically, we write this through the chain rule:\n", - "\n", - "$$\n", - "\\delta_{h,t} \\equiv \\frac{\\partial E}{\\partial h_{t}} = \\frac{\\partial E}{\\partial o_{t}} \\frac{\\partial o_t}{\\partial h_{t}} + \\frac{\\partial E}{\\partial h_{t+1}}\n", - "\\frac{\\partial h_{t+1}}{\\partial h_{t}} = \\delta_{o,t} \\frac{\\partial o_t}{\\partial h_{t}} + \\delta_{h,t+1}\n", - "\\frac{\\partial h_{t+1}}{\\partial h_{t}} \\ . \n", - "$$\n", - "\n", - "Like above we can compute $\\frac{\\partial h_{t+1}}{\\partial h_{t}}$ using the definition of the network (shifted one time step). In the code the intermediate steps to compute the $\\delta$ recursions have been precomputed for you. \n", - "\n", - "For more information on backpropagation through time see the [Deep learning book section 10.2.2](https://www.deeplearningbook.org/contents/rnn.html).\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "XIy3OZaQSrVL" - }, - "source": [ - "# Exercise f) Complete the training loop\n", - "\n", - "Complete the training loop above and run the training. You can leave the hyper-parameters and network size unchanged.\n", - "\n", - "Note that despite the small size of the network and dataset, training still takes quite a while. This is an issue with the recurrent structure of Nanograd. Using PyTorch, we would be able to use much larger datasets and models. We will attempt that in the bottom of the notebook. For now, you should get a feel of the recurrent structure of the RNN under the hood." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "id": "MkaqbWmroncY" - }, - "outputs": [], - "source": [ - "# Initialize training hyperparameters\n", - "EPOCHS = 200\n", - "LR = 1e-2 \n", - "LR_DECAY = 0.995" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "-JtM_IQjonfK" - }, - "outputs": [], - "source": [ - "train_loss = []\n", - "val_loss = []\n", - "\n", - "batch_size = 8\n", - "\n", - "for e in range(EPOCHS):\n", - " for b in range(int(np.ceil(len(encoded_training_set_x)/batch_size))):\n", - " # Forward pass and loss computation\n", - " Loss = \n", - "\n", - " # Backward pass\n", - " Loss.backward()\n", - " \n", - " # gradient descent update\n", - " update_parameters(parameters(NN), LR)\n", - " zero_gradients(parameters(NN))\n", - " \n", - " LR = LR * LR_DECAY\n", - "\n", - " # Training loss\n", - " Loss = \n", - " train_loss.append(Loss.v/len(encoded_training_set_y))\n", - " \n", - " # Validation loss\n", - " Loss_validation = \n", - " val_loss.append(Loss_validation.v/len(encoded_validation_set_y))\n", - " \n", - " if e%5==0:\n", - " print(\"{:4d}\".format(e),\n", - " \"({:5.2f}%)\".format(e/EPOCHS*100), \n", - " \"Train loss: {:4.3f} \\t Validation loss: {:4.3f}\".format(train_loss[-1], val_loss[-1]))\n", - " \n", - "# Plot training and validation loss\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "epoch = np.arange(len(train_loss))\n", - "plt.figure()\n", - "plt.plot(epoch, train_loss, 'r', label='Training loss',)\n", - "plt.plot(epoch, val_loss, 'b', label='Validation loss')\n", - "plt.legend()\n", - "plt.xlabel('Epoch'), plt.ylabel('NLL')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "nAI_D6g25pTQ" - }, - "outputs": [], - "source": [ - "# Get first sentence in test set\n", - "inputs, targets = test_set[0]\n", - "\n", - "# One-hot encode input and target sequence\n", - "inputs_one_hot = one_hot_encode_sequence(inputs, vocab_size)\n", - "targets_one_hot = one_hot_encode_sequence(targets, vocab_size)\n", - "\n", - "# Forward pass\n", - "outputs = forward_batch(encoded_test_set_x[:1], NN)\n", - "\n", - "output_sentence = [idx_to_word[np.argmax(output)] for output in Var_to_nparray(outputs[0])]\n", - "\n", - "print('Input sentence:')\n", - "print(inputs)\n", - "\n", - "print('\\nTarget sequence:')\n", - "print(targets)\n", - "\n", - "print('\\nPredicted sequence:')\n", - "print([idx_to_word[np.argmax(output)] for output in Var_to_nparray(outputs[0])])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Nn7QpUZXk2iH" - }, - "source": [ - "## Exercise g) Extrapolation\n", - "\n", - "Now that we have trained an RNN, it's time to put it to test. We will provide the network with a starting sentence and let it `freestyle` from there!\n", - "\n", - "How well does your RNN extrapolate -- does it work as expected? Are there any imperfections? If yes, why could that be?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "4GNsD6HEJ-Gn" - }, - "outputs": [], - "source": [ - "def freestyle(NN, sentence='', num_generate=10):\n", - " \"\"\"\n", - " Takes in a sentence as a string and outputs a sequence\n", - " based on the predictions of the RNN.\n", - " \n", - " Args:\n", - " `params`: the parameters of the network\n", - " `sentence`: string with whitespace-separated tokens\n", - " `num_generate`: the number of tokens to generate\n", - " \"\"\"\n", - " sentence = sentence.split(' ')\n", - " output_sentence = sentence\n", - " sentence_one_hot = one_hot_encode_sequence(sentence, vocab_size)\n", - "\n", - " # Begin predicting\n", - " outputs = forward_batch([sentence_one_hot], NN, use_stored_hid=False)\n", - " output_words = [idx_to_word[np.argmax(output)] for output in Var_to_nparray(outputs[0])]\n", - " word = output_words[-1]\n", - "\n", - " # Append first prediction\n", - " output_sentence.append(word)\n", - "\n", - " # Forward pass - Insert code here!\n", - " if word != 'EOS':\n", - " for i in range(num_generate-1):\n", - " sentence_one_hot = \n", - " outputs = \n", - " output_words = \n", - " word = \n", - " output_sentence.append(word)\n", - " if word == 'EOS':\n", - " break\n", - " \n", - " return output_sentence\n", - "\n", - "\n", - "# Perform freestyle (extrapolation)\n", - "test_examples = ['a a b', 'a a a a b', 'a a a a a a b', 'a', 'r n n']\n", - "for i, test_example in enumerate(test_examples):\n", - " print(f'Example {i}:', test_example)\n", - " print('Predicted sequence:', freestyle(NN, sentence=test_example), end='\\n\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "X44hQ653vNCj" - }, - "source": [ - "# Introduction to the Long Short-Term Memory (LSTM) Cell\n", - "\n", - "Reading material: [Christopher Olah's walk-through](http://colah.github.io/posts/2015-08-Understanding-LSTMs/).\n", - "\n", - "___\n", - "\n", - "\n", - "A vanilla RNN suffers from [the vanishing gradients problem](http://neuralnetworksanddeeplearning.com/chap5.html#the_vanishing_gradient_problem) which gives challenges in saving memory over longer sequences. To combat these issues the gated hidden units were created. The two most prominent gated hidden units are the Long Short-Term Memory (LSTM) cell and the Gated Recurrent Unit (GRU), both of which have shown increased performance in saving and reusing memory in later timesteps. In this exercise, we will focus on LSTM but you would easily be able to go ahead and implement the GRU as well based on the principles that you learn here.\n", - "\n", - "Below is a figure of the LSTM cell:" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5Rgc-g3zwV9f" - }, - "source": [ - "![lstm](https://i.imgur.com/3VkmUCe.png)\n", - "Source: https://arxiv.org/abs/1412.7828" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ytasZ5cqw4W1" - }, - "source": [ - "\n", - "The LSTM cell contains three gates, input, forget, output gates and a memory cell.\n", - "The output of the LSTM unit is computed with the following functions, where $\\sigma = \\mathrm{sigmoid}$.\n", - "We have input gate $i$, forget gate $f$, and output gate $o$ defines as\n", - "\n", - "- $i = \\sigma ( W^i [h_{t-1}, x_t])$\n", - "\n", - "- $f = \\sigma ( W^f [h_{t-1},x_t])$\n", - "\n", - "- $o = \\sigma ( W^o [h_{t-1},x_t])$\n", - "\n", - "where $W^i, W^f, W^o$ are weight matrices applied to a concatenated $h_{t-1}$ (hidden state vector) and $x_t$ (input vector) for each respective gate.\n", - "\n", - "$h_{t-1}$, from the previous time step along with the current input $x_t$ are used to compute the a candidate $g$\n", - "\n", - "- $g = \\mathrm{tanh}( W^g [h_{t-1}, x_t])$\n", - "\n", - "The value of the cell's memory, $c_t$, is updated as\n", - "\n", - "- $c_t = c_{t-1} \\circ f + g \\circ i$\n", - "\n", - "where $c_{t-1}$ is the previous memory, and $\\circ$ refers to element-wise multiplication (hint: element-wise multiplication is computed with the `*` operator in numpy).\n", - "\n", - "The output, $h_t$, is computed as\n", - "\n", - "- $h_t = \\mathrm{tanh}(c_t) \\circ o$\n", - "\n", - "and it is used for both the timestep's output and the next timestep, whereas $c_t$ is exclusively sent to the next timestep.\n", - "This makes $c_t$ a memory feature, and is not used directly to compute the output of the timestep." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "m8_4RWp3k2iQ" - }, - "source": [ - "## Exercise h) Make the LSTMLayer class\n", - "\n", - "Make the LSTM class." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "id": "qdU0yMXQU7d0" - }, - "outputs": [], - "source": [ - "# Insert code here\n", - "\n", - "class LSTMLayer:\n", - " def __init__(self, n_in: int, n_hid: int, act_fn, initializer = NormalInitializer(), initializer_hid = NormalInitializer()):\n", - " self.n_in = n_in\n", - " self.n_hid = n_hid\n", - " self.g_layer = \n", - " self.i_layer = \n", - " self.f_layer = \n", - " self.o_layer = \n", - " self.initial_hid = [Var(0.0) for _ in range(n_hid)]\n", - " self.stored_hid = [Var(0.0) for _ in range(n_hid)]\n", - " self.initial_c = [Var(0.0) for _ in range(n_hid)]\n", - " self.stored_c = [Var(0.0) for _ in range(n_hid)]\n", - " self.act_fn = act_fn\n", - " \n", - " def __repr__(self): \n", - " return 'Feed-forward: ' + repr(self.in_hid_layer) + ' Candidate: ' + repr(self.g_layer) + ' i gate ' + repr(self.i_layer) + ' f gate ' + repr(self.f_layer) + ' o gate ' + repr(self.o_layer) + ' Initial hidden: ' + repr(self.initial_hid)\n", - "\n", - " def parameters(self) -> Sequence[Var]: \n", - " return self.in_hid_layer.parameters() + self.g_layer.parameters() + self.i_layer.parameters() + self.f_layer.parameters() + self.o_layer.parameters() + self.initial_hid\n", - "\n", - " def forward_step(self, input: Sequence[Var], input_hid: Sequence[Var], input_c: Sequence[Var]) -> Sequence[Var]:\n", - " hids = []\n", - " cs = []\n", - " concatenated_input = []\n", - " for val in input_hid:\n", - " concatenated_input.append(val)\n", - " for val in input:\n", - " concatenated_input.append(val)\n", - "\n", - " # Insert code here\n", - "\n", - " return hids, cs\n", - " \n", - " def forward_sequence(self, input: Sequence[Sequence[Var]], use_stored_hid = False) -> Sequence[Sequence[Var]]:\n", - " out = []\n", - " if use_stored_hid:\n", - " hid = self.stored_hid\n", - " c = self.stored_c\n", - " else:\n", - " hid = self.initial_hid\n", - " c = self.initial_c\n", - " # Takes a sequence and loops over each character in the sequence. Note that each character has dimenson equal to the embeddng dimenson\n", - " for i in range(len(input)):\n", - " hid, c = # insert code here\n", - " out.append(hid)\n", - " self.stored_hid = hid\n", - " self.stored_c = c\n", - " return out" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gKu-bfhzk2iY" - }, - "source": [ - "Here is a bit of code to test it out:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "u4AYroqSVRSv" - }, - "outputs": [], - "source": [ - "NN = [\n", - " LSTMLayer(1, 5, lambda x: x.tanh()),\n", - " DenseLayer(5, 1, lambda x: x.identity())\n", - "]\n", - "\n", - "print(NN[0])\n", - "x_train =[[[Var(1.0)], [Var(2.0)], [Var(3.0)]],\n", - " [[Var(1.0)], [Var(2.0)], [Var(3.0)]]]\n", - "\n", - "output_train = forward_batch(x_train, NN) \n", - "output_train[0][0][0].backward()\n", - "\n", - "print(output_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "z4r4mgFsk2ik" - }, - "source": [ - "## Exercise i) LSTM training\n", - "\n", - "Complete the LSTM training loop\n", - "\n", - "Run the training loop. Training time in Nanograd will likely be long, but see if you can find settings to compare your LSTM learning curve (NLL and number of epochs) to the vanilla RNN from earlier. Do you observe any improvements? Motivate your answer.\n", - "\n", - "Finally, below we will implement LSTM in PyTorch. You will notice it is much, much faster!" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "id": "MOAmppJD66tJ" - }, - "outputs": [], - "source": [ - "# Initialize training hyperparameters\n", - "EPOCHS = \n", - "LR = \n", - "LR_DECAY = " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "tiotu2ab66w-" - }, - "outputs": [], - "source": [ - "NN = [\n", - " LSTMLayer(4, 1, lambda x: x.tanh()),\n", - " DenseLayer(1, 4, lambda x: x.identity())\n", - "]\n", - "\n", - "train_loss = []\n", - "val_loss = []\n", - "\n", - "batch_size = 8\n", - "\n", - "for e in range(EPOCHS):\n", - " for b in range(int(np.ceil(len(encoded_training_set_x)/batch_size))):\n", - " # Forward pass and loss computation\n", - " Loss =\n", - " # Backward pass\n", - " Loss.backward()\n", - " \n", - " # gradient descent update\n", - " update_parameters(parameters(NN), LR)\n", - " zero_gradients(parameters(NN))\n", - " \n", - " LR = LR * LR_DECAY\n", - "\n", - " # Training loss\n", - " Loss = \n", - " train_loss.append(Loss.v/len(encoded_training_set_y))\n", - " \n", - " # Validation loss\n", - " Loss_validation = \n", - " val_loss.append(Loss_validation.v/len(encoded_validation_set_y))\n", - " \n", - " if e%5==0:\n", - " print(\"{:4d}\".format(e),\n", - " \"({:5.2f}%)\".format(e/EPOCHS*100), \n", - " \"Train loss: {:4.3f} \\t Validation loss: {:4.3f}\".format(train_loss[-1], val_loss[-1]))\n", - " \n", - "# Plot training and validation loss\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "epoch = np.arange(len(train_loss))\n", - "plt.figure()\n", - "plt.plot(epoch, train_loss, 'r', label='Training loss',)\n", - "plt.plot(epoch, val_loss, 'b', label='Validation loss')\n", - "plt.legend()\n", - "plt.xlabel('Epoch'), plt.ylabel('NLL')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gi51eWgKxyOk" - }, - "source": [ - "## PyTorch implementation of the LSTM\n", - "\n", - "Now that we know how the LSTM cell works, let's see how easy it is to use in PyTorch!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "O6HDdJLuk2ip" - }, - "source": [ - "Definition of our LSTM network. We define a LSTM layer using the [nn.LSTM](https://pytorch.org/docs/stable/nn.html#lstm) class. The LSTM layer takes as argument the size of the input and the size of the hidden state like in our Nanograd implementation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "8UGrvknfk2ip" - }, - "outputs": [], - "source": [ - "import torch\n", - "import torch.nn as nn\n", - "import torch.nn.functional as F\n", - "\n", - "class MyRecurrentNet(nn.Module):\n", - " def __init__(self):\n", - " super(MyRecurrentNet, self).__init__()\n", - " \n", - " # Recurrent layer\n", - " # YOUR CODE HERE!\n", - " self.lstm = \n", - " \n", - " # Output layer\n", - " self.l_out = nn.Linear(in_features=50,\n", - " out_features=vocab_size,\n", - " bias=False)\n", - " \n", - " def forward(self, x):\n", - " # RNN returns output and last hidden state\n", - " x, (h, c) = self.lstm(x)\n", - " \n", - " # Flatten output for feed-forward layer\n", - " x = x.view(-1, self.lstm.hidden_size)\n", - " \n", - " # Output layer\n", - " x = self.l_out(x)\n", - " \n", - " return x\n", - "\n", - "net = MyRecurrentNet()\n", - "print(net)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "J6r3bPwYk2is" - }, - "source": [ - "## Exercise j) Train in PyTorch\n", - "\n", - "Define an LSTM for our recurrent neural network `MyRecurrentNet` above. A single LSTM layer is sufficient. What should the input size and hidden size be? Hint: use the PyTorch documentation.\n", - "\n", - "It's time for us to train our network. In the section below, you will get to put your deep learning skills to use and create your own training loop. You may want to consult previous exercises if you cannot recall how to define the training loop." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# lets redefine the one_hot_encode() and one_hot_encode_sequence() functions\n", - "# to work now with torch.Tensor variables instead of Var.\n", - "def one_hot_encode(idx, vocab_size):\n", - " \"\"\"\n", - " One-hot encodes a single word given its index and the size of the vocabulary.\n", - " \n", - " Args:\n", - " `idx`: the index of the given word\n", - " `vocab_size`: the size of the vocabulary\n", - " \n", - " Returns a 1-D numpy array of length `vocab_size`.\n", - " \"\"\"\n", - " # Initialize the encoded array\n", - " one_hot = np.array([np.zeros(vocab_size)])\n", - " \n", - " # Set the appropriate element to one\n", - " one_hot[0][idx] = 1.0\n", - " return one_hot\n", - "\n", - "\n", - "def one_hot_encode_sequence(sequence, vocab_size):\n", - " \"\"\"\n", - " One-hot encodes a sequence of words given a fixed vocabulary size.\n", - " \n", - " Args:\n", - " `sentence`: a list of words to encode\n", - " `vocab_size`: the size of the vocabulary\n", - " \n", - " Returns a 3-D torch.Tensor of shape (num words, vocab size, 1).\n", - " \"\"\"\n", - " # Encode each word in the sentence\n", - " encoding = np.array([one_hot_encode(word_to_idx[word], vocab_size) for word in sequence])\n", - "\n", - " # Reshape encoding s.t. it has shape (num words, vocab size, 1)\n", - " encoding = encoding.reshape(encoding.shape[0], encoding.shape[2], 1)\n", - " return torch.Tensor(encoding)\n", - "\n", - "# lets test it\n", - "test_word = one_hot_encode(word_to_idx['a'], vocab_size)\n", - "print(f'Our one-hot encoding of \\'a\\' has shape {test_word.shape}.')\n", - "\n", - "test_sentence = one_hot_encode_sequence(['a', 'b'], vocab_size)\n", - "print(f'Our one-hot encoding of \\'a b\\' has shape {test_sentence.shape}.')\n", - "\n", - "print(test_word)\n", - "print(test_sentence)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "2URKsyFDx8xG" - }, - "outputs": [], - "source": [ - "# Hyper-parameters\n", - "num_epochs = 200\n", - "\n", - "# Initialize a new network\n", - "net = MyRecurrentNet()\n", - "\n", - "# Define a loss function and optimizer for this problem\n", - "# YOUR CODE HERE!\n", - "criterion = \n", - "optimizer = \n", - "\n", - "# Track loss\n", - "training_loss, validation_loss = [], []\n", - "\n", - "# For each epoch\n", - "for i in range(num_epochs):\n", - " \n", - " # Track loss\n", - " epoch_training_loss = 0\n", - " epoch_validation_loss = 0\n", - " \n", - " net.eval()\n", - " \n", - " # For each sentence in validation set\n", - " for inputs, targets in validation_set:\n", - " \n", - " # One-hot encode input and target sequence\n", - " inputs_one_hot = one_hot_encode_sequence(inputs, vocab_size)\n", - " targets_idx = [word_to_idx[word] for word in targets]\n", - " \n", - " # Convert input to tensor\n", - " inputs_one_hot = torch.Tensor(inputs_one_hot)\n", - " inputs_one_hot = inputs_one_hot.permute(0, 2, 1)\n", - " \n", - " # Convert target to tensor\n", - " targets_idx = torch.LongTensor(targets_idx)\n", - " \n", - " # Forward pass\n", - " # YOUR CODE HERE!\n", - " outputs = \n", - " \n", - " # Compute loss\n", - " # YOUR CODE HERE!\n", - " loss = \n", - " \n", - " # Update loss\n", - " epoch_validation_loss += loss.detach().numpy()\n", - " \n", - " net.train()\n", - " \n", - " # For each sentence in training set\n", - " for inputs, targets in training_set:\n", - " \n", - " # One-hot encode input and target sequence\n", - " inputs_one_hot = one_hot_encode_sequence(inputs, vocab_size)\n", - " targets_idx = [word_to_idx[word] for word in targets]\n", - " \n", - " # Convert input to tensor\n", - " inputs_one_hot = torch.Tensor(inputs_one_hot)\n", - " inputs_one_hot = inputs_one_hot.permute(0, 2, 1)\n", - " \n", - " # Convert target to tensor\n", - " targets_idx = torch.LongTensor(targets_idx)\n", - " \n", - " # Forward pass\n", - " # YOUR CODE HERE!\n", - " outputs = \n", - " \n", - " # Compute loss\n", - " # YOUR CODE HERE!\n", - " loss = \n", - " \n", - " # Backward pass\n", - " # YOUR CODE HERE!\n", - " # zero grad, backward, step...\n", - " \n", - " # Update loss\n", - " epoch_training_loss += loss.detach().numpy()\n", - " \n", - " # Save loss for plot\n", - " training_loss.append(epoch_training_loss/len(training_set))\n", - " validation_loss.append(epoch_validation_loss/len(validation_set))\n", - "\n", - " # Print loss every 10 epochs\n", - " if i % 10 == 0:\n", - " print(f'Epoch {i}, training loss: {training_loss[-1]}, validation loss: {validation_loss[-1]}')\n", - "\n", - " \n", - "# Get first sentence in test set\n", - "inputs, targets = test_set[1]\n", - "\n", - "# One-hot encode input and target sequence\n", - "inputs_one_hot = one_hot_encode_sequence(inputs, vocab_size)\n", - "targets_idx = [word_to_idx[word] for word in targets]\n", - "\n", - "# Convert input to tensor\n", - "inputs_one_hot = torch.Tensor(inputs_one_hot)\n", - "inputs_one_hot = inputs_one_hot.permute(0, 2, 1)\n", - "\n", - "# Convert target to tensor\n", - "targets_idx = torch.LongTensor(targets_idx)\n", - "\n", - "# Forward pass\n", - "outputs = net.forward(inputs_one_hot).data.numpy()\n", - "\n", - "print('\\nInput sequence:')\n", - "print(inputs)\n", - "\n", - "print('\\nTarget sequence:')\n", - "print(targets)\n", - "\n", - "print('\\nPredicted sequence:')\n", - "print([idx_to_word[np.argmax(output)] for output in outputs])\n", - "\n", - "# Plot training and validation loss\n", - "epoch = np.arange(len(training_loss))\n", - "plt.figure()\n", - "plt.plot(epoch, training_loss, 'r', label='Training loss',)\n", - "plt.plot(epoch, validation_loss, 'b', label='Validation loss')\n", - "plt.legend()\n", - "plt.xlabel('Epoch'), plt.ylabel('NLL')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ydr7Czg_k2iw" - }, - "source": [ - "# Exercise k) Compare PyTorch and Nanograd implementations\n", - "\n", - "Compare the two implementations (in terms of predictive performance, training speed, etc.). Are they similar? How do they differ?\n", - "\n", - "\n", - "Try to play around with the choice of hyper-parameters, optimizer, and hidden dimensions. How much can you improve the negative log-likelihood by these simple changes?" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "M93ORx95k2ix" - }, - "source": [ - "## Exercise l) Other RNN cells (optional)\n", - "\n", - "Aside from the LSTM cell, various other RNN cells exist. The gated recurrent unit (GRU) is a variation of the LSTM cell that uses less gating mechanisms. Try to look it up in the [PyTorch documentation](https://pytorch.org/docs/stable/nn.html#gru) and switch out the LSTM cell in the code above. What do you notice in terms of performance and convergence speed?" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "juN400Ekk2iz" - }, - "source": [ - "## Exercise m) More complex tasks (optional)\n", - "\n", - "Go back and generate a more complex patterned dataset to learn from. Do you see any significant differences between a vanilla RNN and LSTM (implemented in e.g. PyTorch) when you increase the difficulty of the task?" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "v68YEkEBk2iz" - }, - "source": [ - "# It works, now what?" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "NjpqSrSuk2i0" - }, - "source": [ - "In this notebook you have learned how to use embeddings, recurrent neural networks, and the LSTM cell in particular.\n", - "\n", - "As we have already seen, RNNs are excellent for sequential data such as language. But what do we do if we're modelling data with strong dependency in both directions? Like in many things deep learning, we can build powerful models by stacking layers on top of each other; *bi-directional* RNNs consist of two LSTM cells, one for each direction. A sequence is first fed into the forward LSTM cell and the reversed sequence is then used as input to the backward LSTM cell together with the last hidden state from the forward LSTM cell. Follow [this link](https://pdfs.semanticscholar.org/4b80/89bc9b49f84de43acc2eb8900035f7d492b2.pdf) for the original paper from 1997(!).\n", - "\n", - "For even deeper representations, multiple layers of both uni-directional and bi-directional RNNs can be stacked ontop of each other, just like feed-forward and convolutional layers. For more information on this, check out the [LSTM PyTorch documentation](https://pytorch.org/docs/stable/nn.html#lstm). Next week we will also explore ways to combine RNNs with other types of layers for even more expressive function approximators." - ] - } - ], - "metadata": { - "colab": { - "collapsed_sections": [ - "bdA4LPsFiACe", - "cGSoDRgHk2g1", - "Dzmryk72k2g-", - "M93ORx95k2ix" - ], - "name": "5.1-EXE-Recurrent-Neural-Networks-Nanograd.ipynb", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "y-CptVs7iACc" + }, + "source": [ + "# Week 5 - Recurrent Neural Networks\n", + "\n", + "In this lab, we will introduce different ways of learning from sequential data.\n", + "\n", + "As a recurring example, we will train neural networks to do language modelling, i.e. predict the next token in a sentence. In the context of natural language processing a token could be a character or a word, but mind you that the concepts introduced here apply to all kinds of sequential data, such as e.g. protein sequences, weather measurements, audio signals, or videos, just to name a few.\n", + "\n", + "To really get a grasp of what is going on inside a recurrent neural network (RNN), we will carry out a substantial part of this exercise in Nanograd rather than PyTorch. \n", + "\n", + "We start off with a simple toy problem, build an RNN using Nanograd, train it, and see for ourselves that it really works. Once we're convinced, you will implement the Long Short-Term Memory (LSTM) cell, also in Nanograd. \n", + "\n", + "This is *not* simple but with the DenseLayer class we already have, it is doable. Having done it yourself will help you understand what happens under the hood of the PyTorch code we will use throughout the course.\n", + "\n", + "To summarize, in this notebook we will show you:\n", + "* How to represent sequences of categorical variables\n", + "* How to build and train an RNN in Nanograd\n", + "* How to build and train an LSTM network in Nanograd\n", + "* How to build and train an LSTM network in PyTorch\n", + "\n", + "\n", + "[Numpy version of the Notebook (previous version)](https://github.com/DeepLearningDTU/02456-deep-learning-with-PyTorch/blob/master/5_Recurrent/OLD-5.1-Numpy-Recurrent-Neural-Networks.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XapO8SLwiACd" + }, + "source": [ + "## Representing tokens or text\n", + "\n", + "In previous labs we mainly considered data $x \\in \\mathbb{R}^d$, where $d$ is the feature space dimension.\n", + "With time sequences our data can be represented as $x \\in \\mathbb{R}^{t \\, \\times \\, d}$, where $t$ is the sequence length. \n", + "This emphasises sequence dependence and that the samples along the sequence are not independent and identically distributed (i.i.d.).\n", + "\n", + "With RNNs, we can model both many-to-one functions: $\\mathbb{R}^{t \\, \\times \\, d} \\rightarrow \\mathbb{R}^c$ and many-to-many functions: $\\mathbb{R}^{t \\, \\times \\, d} \\rightarrow \\mathbb{R}^{t \\, \\times \\, c}$, where $c$ is the amount of classes/output dimensions.\n", + "\n", + "There are several ways to represent sequences. With text, the challenge is how to represent a word as a feature vector in $d$ dimensions, as we are required to represent text with decimal numbers in order to apply neural networks to it.\n", + "\n", + "In this exercise we will use a simple one-hot encoding but for categorical variables that can take on many values (e.g. words in the English language) this may be infeasible. For such scenarios, you can project the encodings into a smaller space by use of embeddings. If you want to learn more about tokens, encodings and embeddings than what is covered in this exercise, we highly recommend [this lecture](https://www.youtube.com/watch?v=kEMJRjEdNzM&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bdA4LPsFiACe" + }, + "source": [ + "### One-hot encoding over vocabulary\n", + "\n", + "One way to represent a fixed amount of words is by making a one-hot encoded vector, which consists of 0s in all cells with the exception of a single 1 in a cell used uniquely to identify each word.\n", + "\n", + "| vocabulary | one-hot encoded vector |\n", + "| ------------- |--------------------------|\n", + "| Paris | $= [1, 0, 0, \\ldots, 0]$ |\n", + "| Rome | $= [0, 1, 0, \\ldots, 0]$ |\n", + "| Copenhagen | $= [0, 0, 1, \\ldots, 0]$ |\n", + "\n", + "Representing a large vocabulary with one-hot encodings often becomes inefficient because of the size of each sparse vector.\n", + "To overcome this challenge it is common practice to truncate the vocabulary to contain the $k$ most used words and represent the rest with a special symbol, $\\mathtt{UNK}$, to define unknown/unimportant words.\n", + "This often causes entities such as names to be represented with $\\mathtt{UNK}$ because they are rare.\n", + "\n", + "Consider the following text\n", + "> I love the corny jokes in Spielberg's new movie.\n", + "\n", + "where an example result would be similar to\n", + "> I love the corny jokes in $\\mathtt{UNK}$'s new movie." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KNmyPw7zk2gY" + }, + "source": [ + "## Generating a dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "M9IEA4t2k2gb" + }, + "source": [ + "For this exercise we will create a simple dataset that we can learn from. We generate sequences of the form:\n", + "\n", + "`a b EOS`,\n", + "\n", + "`a a b b EOS`,\n", + "\n", + "`a a a a a b b b b b EOS`\n", + "\n", + "where `EOS` is a special character denoting the end of a sequence. The task is to predict the next token $t_n$, i.e. `a`, `b`, `EOS` or the unknown token `UNK` given a sequence of tokens $\\{ t_{1}, t_{2}, \\dots , t_{n-1}\\}$, and we are to process sequences in a sequential manner. As such, the network will need to learn that e.g. 5 `b`s and an `EOS` token will follow 5 `a`s." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "dcoN-kb7k2gc", + "outputId": "f4b47ee8-0d36-40a5-8831-a6124e42b521", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "A single sample from the generated dataset:\n", + "['a', 'a', 'a', 'b', 'b', 'b', 'EOS']\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "# Set seed such that we always get the same dataset\n", + "# (this is a good idea in general)\n", + "np.random.seed(42)\n", + "\n", + "def generate_dataset(num_sequences=2**5):\n", + " \"\"\"\n", + " Generates a number of sequences as our dataset.\n", + " \n", + " Args:\n", + " `num_sequences`: the number of sequences to be generated.\n", + " \n", + " Returns a list of sequences.\n", + " \"\"\"\n", + " samples = []\n", + " \n", + " for _ in range(num_sequences): \n", + " num_tokens = np.random.randint(1, 4)\n", + " sample = ['a'] * num_tokens + ['b'] * num_tokens + ['EOS']\n", + " samples.append(sample)\n", + " \n", + " return samples\n", + "\n", + "\n", + "sequences = generate_dataset()\n", + "\n", + "print('A single sample from the generated dataset:')\n", + "print(sequences[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YMLd3Gzak2gp" + }, + "source": [ + "## Representing tokens as indices" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "S9LSqaJSk2gp" + }, + "source": [ + "To build a one-hot encoding, we need to assign each possible word in our vocabulary an index. We do that by creating two dictionaries: one that allows us to go from a given word to its corresponding index in our vocabulary, and one for the reverse direction. Let's call them `word_to_idx` and `idx_to_word`. The keyword `vocab_size` specifies the maximum size of our vocabulary. If we try to access a word that does not exist in our vocabulary, it is automatically replaced by the `UNK` token or its corresponding index." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sNY1OOS_k2gy" + }, + "source": [ + "## Exercise a) Sequence to dictionary function \n", + "\n", + "Complete the sequences_to_dicts function below. You will need to fill the word_to_idx and idx_to_word dictionaries so that we can go back and forth between the two representations." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "Smdo70UMk2gr", + "outputId": "ab91f68d-7541-417f-91b1-1935a23667c2", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "We have 32 sentences and 4 unique tokens in our dataset (including UNK).\n", + "\n", + "The index of 'b' is 1\n", + "The word corresponding to index 1 is 'b'\n" + ] + } + ], + "source": [ + "from collections import defaultdict\n", + "\n", + "def sequences_to_dicts(sequences):\n", + " \"\"\"\n", + " Creates word_to_idx and idx_to_word dictionaries for a list of sequences.\n", + " \"\"\"\n", + " # A bit of Python-magic to flatten a nested list\n", + " flatten = lambda l: [item for sublist in l for item in sublist]\n", + " \n", + " # Flatten the dataset\n", + " all_words = flatten(sequences)\n", + " \n", + " # Count number of word occurences\n", + " word_count = defaultdict(int)\n", + " for word in flatten(sequences):\n", + " word_count[word] += 1\n", + "\n", + " # Sort by frequency\n", + " word_count = sorted(list(word_count.items()), key=lambda l: -l[1])\n", + "\n", + " # Create a list of all unique words\n", + " unique_words = [item[0] for item in word_count]\n", + " \n", + " # Add UNK token to list of words\n", + " unique_words.append('UNK')\n", + "\n", + " # Count number of sequences and number of unique words\n", + " num_sentences, vocab_size = len(sequences), len(unique_words)\n", + "\n", + " # Create dictionaries so that we can go from word to index and back\n", + " # If a word is not in our vocabulary, we assign it to token 'UNK'\n", + " word_to_idx = defaultdict(lambda: vocab_size-1)\n", + " idx_to_word = defaultdict(lambda: 'UNK')\n", + "\n", + " # Fill dictionaries\n", + " for idx, word in enumerate(unique_words):\n", + " # YOUR CODE HERE!\n", + " word_to_idx[word] = idx\n", + " idx_to_word[idx] = word\n", + "\n", + " return word_to_idx, idx_to_word, num_sentences, vocab_size\n", + "\n", + "\n", + "word_to_idx, idx_to_word, num_sequences, vocab_size = sequences_to_dicts(sequences)\n", + "\n", + "print(f'We have {num_sequences} sentences and {len(word_to_idx)} unique tokens in our dataset (including UNK).\\n')\n", + "print('The index of \\'b\\' is', word_to_idx['b'])\n", + "print(f'The word corresponding to index 1 is \\'{idx_to_word[1]}\\'')\n", + "\n", + "assert idx_to_word[word_to_idx['b']] == 'b', \\\n", + " 'Consistency error: something went wrong in the conversion.'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cGSoDRgHk2g1" + }, + "source": [ + "## Partitioning the dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UMTn1iLIk2g1" + }, + "source": [ + "To build our dataset, we need to create inputs and targets for each sequences and partition sentences it into training, validation and test sets. 80%, 10% and 10% is a common distribution, but mind you that this largely depends on the size of the dataset. Since we are doing next-word predictions, our target sequence is simply the input sequence shifted by one word.\n", + "\n", + "We can use PyTorch's `Dataset` class to build a simple dataset where we can easily retrieve (inputs, targets) pairs for each of our sequences." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "9dW7MrPnk2g3", + "outputId": "ef7c2aa1-48e1-4d07-d81f-71fd279006b1", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "We have 25 samples in the training set.\n", + "We have 3 samples in the validation set.\n", + "We have 3 samples in the test set.\n" + ] + } + ], + "source": [ + "from torch.utils import data\n", + "\n", + "class Dataset(data.Dataset):\n", + " def __init__(self, inputs, targets):\n", + " self.inputs = inputs\n", + " self.targets = targets\n", + "\n", + " def __len__(self):\n", + " # Return the size of the dataset\n", + " return len(self.targets)\n", + "\n", + " def __getitem__(self, index):\n", + " # Retrieve inputs and targets at the given index\n", + " X = self.inputs[index]\n", + " y = self.targets[index]\n", + "\n", + " return X, y\n", + "\n", + " \n", + "def create_datasets(sequences, dataset_class, p_train=0.8, p_val=0.1, p_test=0.1):\n", + " # Define partition sizes\n", + " num_train = int(len(sequences)*p_train)\n", + " num_val = int(len(sequences)*p_val)\n", + " num_test = int(len(sequences)*p_test)\n", + "\n", + " # Split sequences into partitions\n", + " sequences_train = sequences[:num_train]\n", + " sequences_val = sequences[num_train:num_train+num_val]\n", + " sequences_test = sequences[-num_test:]\n", + "\n", + " def get_inputs_targets_from_sequences(sequences):\n", + " # Define empty lists\n", + " inputs, targets = [], []\n", + " \n", + " # Append inputs and targets s.t. both lists contain L-1 words of a sentence of length L\n", + " # but targets are shifted right by one so that we can predict the next word\n", + " for sequence in sequences:\n", + " inputs.append(sequence[:-1])\n", + " targets.append(sequence[1:])\n", + " \n", + " return inputs, targets\n", + "\n", + " # Get inputs and targets for each partition\n", + " inputs_train, targets_train = get_inputs_targets_from_sequences(sequences_train)\n", + " inputs_val, targets_val = get_inputs_targets_from_sequences(sequences_val)\n", + " inputs_test, targets_test = get_inputs_targets_from_sequences(sequences_test)\n", + "\n", + " # Create datasets\n", + " training_set = dataset_class(inputs_train, targets_train)\n", + " validation_set = dataset_class(inputs_val, targets_val)\n", + " test_set = dataset_class(inputs_test, targets_test)\n", + "\n", + " return training_set, validation_set, test_set\n", + " \n", + "\n", + "training_set, validation_set, test_set = create_datasets(sequences, Dataset)\n", + "\n", + "print(f'We have {len(training_set)} samples in the training set.')\n", + "print(f'We have {len(validation_set)} samples in the validation set.')\n", + "print(f'We have {len(test_set)} samples in the test set.')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4xMMSm7Mk2g9" + }, + "source": [ + "When working with more complex data than what we use in this exercise, creating a PyTorch `DataLoader` on top of the dataset can be beneficial. A data loader is basically a fancy generator/iterator that we can use to abstract away all of the data handling and pre-processing + it's super useful for processing batches of data as well! Data loaders will come in handy later when you start to work on your projects, so be sure to check them out!\n", + "\n", + "For more information on how to use datasets and data loaders in PyTorch, [consult the official guide](https://pytorch.org/tutorials/beginner/data_loading_tutorial.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "t-rfgDfZeMQ6" + }, + "source": [ + "## Nanograd utilities" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oRO5ssg0eQMK" + }, + "source": [ + "We load necessary utility functions for the Nanograd library, which we saw in Lab 2." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "Jd4CoEBNzNWS" + }, + "outputs": [], + "source": [ + "# Copy and pasted from https://github.com/rasmusbergpalm/nanograd/blob/main/nanograd.py\n", + "\n", + "from math import exp, log, tanh\n", + "\n", + "class Var:\n", + " \"\"\"\n", + " A variable which holds a float and enables gradient computations.\n", + " \"\"\"\n", + "\n", + " def __init__(self, val: float, grad_fn=lambda: []):\n", + " assert type(val) == float\n", + " self.v = val\n", + " self.grad_fn = grad_fn\n", + " self.grad = 0.0\n", + "\n", + " def backprop(self, bp):\n", + " self.grad += bp\n", + " for input, grad in self.grad_fn():\n", + " input.backprop(grad * bp)\n", + "\n", + " def backward(self):\n", + " self.backprop(1.0)\n", + "\n", + " def __add__(self: 'Var', other: 'Var') -> 'Var':\n", + " return Var(self.v + other.v, lambda: [(self, 1.0), (other, 1.0)])\n", + "\n", + " def __mul__(self: 'Var', other: 'Var') -> 'Var':\n", + " return Var(self.v * other.v, lambda: [(self, other.v), (other, self.v)])\n", + "\n", + " def __pow__(self, power):\n", + " assert type(power) in {float, int}, \"power must be float or int\"\n", + " return Var(self.v ** power, lambda: [(self, power * self.v ** (power - 1))])\n", + "\n", + " def __neg__(self: 'Var') -> 'Var':\n", + " return Var(-1.0) * self\n", + "\n", + " def __sub__(self: 'Var', other: 'Var') -> 'Var':\n", + " return self + (-other)\n", + "\n", + " def __truediv__(self: 'Var', other: 'Var') -> 'Var':\n", + " return self * other ** -1\n", + "\n", + " def __repr__(self):\n", + " return \"Var(v=%.4f, grad=%.4f)\" % (self.v, self.grad)\n", + " \n", + " def exp(self):\n", + " return Var(exp(self.v), lambda: [(self, exp(self.v))])\n", + " \n", + " def log(self):\n", + " return Var(log(self.v), lambda: [(self, self.v ** -1)])\n", + "\n", + " def relu(self):\n", + " return Var(self.v if self.v > 0.0 else 0.0, lambda: [(self, 1.0 if self.v > 0.0 else 0.0)])\n", + " \n", + " def identity(self):\n", + " return self\n", + "\n", + " def sigmoid(self):\n", + " return Var(0.5) * (Var(1.0) + (Var(0.5) * self).tanh()) # logistic function is a scaled and shifted version of tanh\n", + " \n", + " def tanh(self):\n", + " return Var(tanh(self.v), lambda: [(self, 1-tanh(self.v) ** 2)])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "9AMqMsiseMfz" + }, + "outputs": [], + "source": [ + "# convert from ndarray to Var\n", + "def nparray_to_Var(x):\n", + " if x.ndim==1:\n", + " y = [[Var(float(x[i]))] for i in range(x.shape[0])] # always work with list of list\n", + " else:\n", + " y = [[Var(float(x[i,j])) for j in range(x.shape[1])] for i in range(x.shape[0])]\n", + " return y\n", + "\n", + "# convert from Var to ndarray\n", + "def Var_to_nparray(x):\n", + " try:\n", + " y = np.zeros((len(x),len(x[0])))\n", + " for i in range(len(x)):\n", + " for j in range(len(x[0])):\n", + " y[i,j] = x[i][j].v\n", + " except TypeError:\n", + " y = np.zeros((len(x)))\n", + " for i in range(len(x)):\n", + " y[i] = x[i].v\n", + "\n", + " return y" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "ij_ieRsAt7Xt" + }, + "outputs": [], + "source": [ + "class Initializer:\n", + "\n", + " def init_weights(self, n_in, n_out):\n", + " raise NotImplementedError\n", + "\n", + " def init_bias(self, n_out):\n", + " raise NotImplementedError" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "eb18N5phuIha" + }, + "outputs": [], + "source": [ + "import random\n", + "\n", + "class NormalInitializer(Initializer):\n", + "\n", + " def __init__(self, mean=0, std=0.1):\n", + " self.mean = mean\n", + " self.std = std\n", + "\n", + " def init_weights(self, n_in, n_out):\n", + " return [[Var(random.gauss(self.mean, self.std)) for _ in range(n_out)] for _ in range(n_in)]\n", + "\n", + " def init_bias(self, n_out):\n", + " return [Var(0.0) for _ in range(n_out)]\n", + "\n", + "class ConstantInitializer(Initializer):\n", + "\n", + " def __init__(self, weight=1.0, bias=0.0):\n", + " self.weight = weight\n", + " self.bias = bias\n", + "\n", + " def init_weights(self, n_in, n_out):\n", + " return [[Var(self.weight) for _ in range(n_out)] for _ in range(n_in)]\n", + "\n", + " def init_bias(self, n_out):\n", + " return [Var(self.bias) for _ in range(n_out)]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Dzmryk72k2g-" + }, + "source": [ + "## One-hot encodings" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "abRN9f8Xk2g_" + }, + "source": [ + "We now create a simple function that returns the one-hot encoded representation of a given index of a word in our vocabulary. Notice that the shape of the one-hot encoding is equal to the entire vocabulary (which can be huge!). Additionally, we define a function to automatically one-hot encode a sentence." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "IZruCIHJk2hB", + "outputId": "d0b92eb0-8e51-4a09-a37b-4e528b642930", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Our one-hot encoding of 'a' has shape (1, 4).\n", + "Our one-hot encoding of 'a b' has shape (2, 4).\n", + "[[Var(v=1.0000, grad=0.0000), Var(v=0.0000, grad=0.0000), Var(v=0.0000, grad=0.0000), Var(v=0.0000, grad=0.0000)]]\n", + "[[Var(v=1.0000, grad=0.0000), Var(v=0.0000, grad=0.0000), Var(v=0.0000, grad=0.0000), Var(v=0.0000, grad=0.0000)], [Var(v=0.0000, grad=0.0000), Var(v=1.0000, grad=0.0000), Var(v=0.0000, grad=0.0000), Var(v=0.0000, grad=0.0000)]]\n" + ] + } + ], + "source": [ + "def one_hot_encode(idx, vocab_size):\n", + " \"\"\"\n", + " One-hot encodes a single word given its index and the size of the vocabulary.\n", + " \n", + " Args:\n", + " `idx`: the index of the given word\n", + " `vocab_size`: the size of the vocabulary\n", + " \n", + " Returns a 1-D numpy array of length `vocab_size`.\n", + " \"\"\"\n", + " # Initialize the encoded array\n", + " one_hot = np.array([np.zeros(vocab_size)])\n", + " \n", + " # Set the appropriate element to one\n", + " one_hot[0][idx] = 1.0\n", + " return nparray_to_Var(one_hot)\n", + "\n", + "\n", + "def one_hot_encode_sequence(sequence, vocab_size):\n", + " \"\"\"\n", + " One-hot encodes a sequence of words given a fixed vocabulary size.\n", + " \n", + " Args:\n", + " `sentence`: a list of words to encode\n", + " `vocab_size`: the size of the vocabulary\n", + " \n", + " Returns a 3-D numpy array of shape (num words, vocab size, 1).\n", + " \"\"\"\n", + " # Encode each word in the sentence\n", + " encoding = np.array([Var_to_nparray(one_hot_encode(word_to_idx[word], vocab_size)) for word in sequence])\n", + "\n", + " # Reshape encoding s.t. it has shape (num words, vocab size, 1)\n", + " encoding = encoding.reshape(encoding.shape[0], encoding.shape[2], 1)\n", + " return nparray_to_Var(encoding)\n", + "\n", + "test_word = one_hot_encode(word_to_idx['a'], vocab_size)\n", + "print(f'Our one-hot encoding of \\'a\\' has shape {Var_to_nparray(test_word).shape}.')\n", + "\n", + "test_sentence = one_hot_encode_sequence(['a', 'b'], vocab_size)\n", + "print(f'Our one-hot encoding of \\'a b\\' has shape {Var_to_nparray(test_sentence).shape}.')\n", + "\n", + "print(test_word)\n", + "print(test_sentence)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "JT6BqYrU_NxQ" + }, + "outputs": [], + "source": [ + "encoded_training_set_x = []\n", + "encoded_training_set_y = []\n", + "encoded_validation_set_x = []\n", + "encoded_validation_set_y = []\n", + "encoded_test_set_x = []\n", + "encoded_test_set_y = []\n", + "\n", + "for n in range(len(training_set)):\n", + " encoded_training_set_x.append(one_hot_encode_sequence(training_set[n][0], vocab_size))\n", + " encoded_training_set_y.append(one_hot_encode_sequence(training_set[n][1], vocab_size))\n", + "for n in range(len(validation_set)):\n", + " encoded_validation_set_x.append(one_hot_encode_sequence(validation_set[n][0], vocab_size))\n", + " encoded_validation_set_y.append(one_hot_encode_sequence(validation_set[n][1], vocab_size))\n", + "for n in range(len(test_set)):\n", + " encoded_test_set_x.append(one_hot_encode_sequence(test_set[n][0], vocab_size))\n", + " encoded_test_set_y.append(one_hot_encode_sequence(test_set[n][1], vocab_size))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "erI_MXvKk2hG" + }, + "source": [ + "Great! Now that we have our one-hot encodings in place, we can move on to the RNNs!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MA6bxjGWjeSB" + }, + "source": [ + "# Introduction to Recurrent Neural Networks (RNN)\n", + "\n", + "Reading material: [blog post](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) and (optionally) [this lecture](https://www.youtube.com/watch?v=iWea12EAu6U&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z).\n", + "\n", + "___\n", + "\n", + "A recurrent neural network (RNN) is a type of neural network that has been succesful in modelling sequential data, e.g. language, speech, protein sequences, etc.\n", + "\n", + "A RNN performs its computations in a cyclic manner, where the same computation is applied to every sample of a given sequence.\n", + "The idea is that the network should be able to use the previous computations as some form of memory and apply this to future computations.\n", + "An image may best explain how this is to be understood,\n", + "\n", + "![rnn-unroll image](https://github.com/DeepLearningDTU/02456-deep-learning-with-PyTorch/blob/master/static_files/rnn-unfold.png?raw=1)\n", + "\n", + "\n", + "where it the network contains the following elements:\n", + "\n", + "- $x$ is the input sequence of samples, \n", + "- $U$ is a weight matrix applied to the given input sample,\n", + "- $V$ is a weight matrix used for the recurrent computation in order to pass memory along the sequence,\n", + "- $W$ is a weight matrix used to compute the output of the every timestep (given that every timestep requires an output),\n", + "- $h$ is the hidden state (the network's memory) for a given time step, and\n", + "- $o$ is the resulting output.\n", + "\n", + "When the network is unrolled as shown, it is easier to refer to a timestep, $t$.\n", + "We have the following computations through the network:\n", + "\n", + "- $h_t = f(U\\,{x_t} + V\\,{h_{t-1}})$, where $f$ is a non-linear activation function, e.g. $\\mathrm{tanh}$.\n", + "- $o_t = W\\,{h_t}$\n", + "\n", + "When we are doing language modelling using a cross-entropy loss, we additionally apply the softmax function to the output $o_{t}$:\n", + "\n", + "- $\\hat{y}_t = \\mathrm{softmax}(o_{t})$\n", + "\n", + "\n", + "### Backpropagation through time\n", + "\n", + "We define a loss function\n", + "\n", + "- $E = \\sum_t E_t = \\sum_t E_t(y_t ,\\hat{y}_t ) \\ , $\n", + "\n", + "where $E_t(y_t ,\\hat{y}_t )$ is the cross-entropy function.\n", + "\n", + "Backpropagation through time amounts to computing the gradients of the loss using the same type of clever bookkeeping we applied to the feed-forward network in week 1. This you will do in Exercise D." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GuvwbvsGz9KE" + }, + "source": [ + "## Implementing an RNN\n", + "\n", + "We will implement the forward pass, backward pass, optimization and training loop for an RNN in Nanograd so that you can get familiar with the recurrent nature of RNNs. Later, we will go back to PyTorch." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gfbfcB-NJZuM" + }, + "source": [ + "We define the Nanograd DenseLayer class from [lab 2](https://github.com/DeepLearningDTU/02456-deep-learning-with-PyTorch/blob/master/2_Feedforward_Python/2.1-EXE-FNN-AutoDif-Nanograd.ipynb) with a few additions:\n", + "* the option use_bias to define a layer without bias. This is useful when we define the recurrent layer and\n", + "* a method forward_sequence which is useful when a DenseLayer is used as part of a recurrent neural network" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "id": "TqkVyEEACHKS" + }, + "outputs": [], + "source": [ + "from typing import Sequence\n", + "\n", + "class DenseLayer:\n", + " def __init__(self, n_in: int, n_out: int, act_fn, initializer = NormalInitializer(), use_bias=True):\n", + " self.weights = initializer.init_weights(n_in, n_out)\n", + " self.use_bias = use_bias\n", + " if use_bias:\n", + " self.bias = initializer.init_bias(n_out)\n", + " self.act_fn = act_fn\n", + " \n", + " def __repr__(self): \n", + " return 'Weights: ' + repr(self.weights) + (' Biases: ' + repr(self.bias) if self.use_bias else '')\n", + "\n", + " def parameters(self) -> Sequence[Var]:\n", + " params = []\n", + " for r in self.weights:\n", + " params += r\n", + "\n", + " if self.use_bias:\n", + " params += self.bias\n", + "\n", + " return params\n", + "\n", + " def forward(self, input: Sequence[Var]) -> Sequence[Var]:\n", + " # self.weights is a matrix with dimension n_in x n_out. We check that the dimensionality of the input \n", + " # to the current layer matches the number of nodes in the current layer\n", + " assert len(self.weights) == len(input), \"weights and input must match in first dimension\"\n", + " weights = self.weights\n", + " out = []\n", + " # For some given data point single_input, we now want to calculate the resulting value in each node in the current layer\n", + " # We therefore loop over the (number of) nodes in the current layer:\n", + " for j in range(len(weights[0])): \n", + " # Initialize the node value depending on its corresponding parameters.\n", + " node = self.bias[j] if self.use_bias else Var(0.0)\n", + " # We now finish the linear transformation corresponding to the parameters of the currently considered node.\n", + " for i in range(len(input)):\n", + " node += input[i]*weights[i][j]\n", + " node = self.act_fn(node)\n", + " out.append(node)\n", + "\n", + " return out\n", + " \n", + " def forward_sequence(self, input: Sequence[Sequence[Var]]) -> Sequence[Sequence[Var]]:\n", + " out = []\n", + " for i in range(len(input)): \n", + " node = self.forward(input[i])\n", + " out.append(node)\n", + "\n", + " return out" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qDKFjjQEM-xX" + }, + "source": [ + "## Exercise b) The RNNLayer class\n", + "\n", + "Complete the RNNLayer class below.\n", + "\n", + "Explain how we reuse the DenseLayer class.\n", + "\n", + "Explain what the forward and the forward_sequence method do." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "IcM1N6PQrT7l" + }, + "outputs": [], + "source": [ + "from typing import Sequence\n", + "\n", + "class RNNLayer:\n", + " def __init__(self, n_in: int, n_hid: int, act_fn, initializer = NormalInitializer(), initializer_hid = NormalInitializer()):\n", + " self.n_hid = n_hid\n", + " self.in_hid_layer = DenseLayer( , , lambda x: x, initializer)\n", + " self.hid_hid_layer = DenseLayer(, , lambda x: x, initializer_hid, use_bias=False) # we already get a bias through in_hid_layer \n", + " self.initial_hid = [Var(0.0) for _ in range(n_hid)]\n", + " self.stored_hid = [Var(0.0) for _ in range(n_hid)]\n", + " self.act_fn = act_fn\n", + " \n", + " def __repr__(self): \n", + " return 'Feed-forward: ' + repr(self.in_hid_layer) + ' Recurrent: ' + repr(self.hid_hid_layer) + ' Initial hidden: ' + repr(self.initial_hid)\n", + "\n", + " def parameters(self) -> Sequence[Var]: \n", + " return self.in_hid_layer.parameters() + self.hid_hid_layer.parameters() + self.initial_hid\n", + "\n", + " def forward_step(self, input: Sequence[Var], input_hid: Sequence[Var]) -> Sequence[Var]:\n", + " in_hids = # contribution from input\n", + " hid_hids = # contribution from hidden state\n", + "\n", + " hids = []\n", + " for i in range(self.n_hid):\n", + " hids.append( )\n", + "\n", + " return hids\n", + " \n", + " def forward_sequence(self, input: Sequence[Sequence[Var]], use_stored_hid = False) -> Sequence[Sequence[Var]]:\n", + " out = []\n", + " if use_stored_hid:\n", + " hid = self.stored_hid\n", + " else:\n", + " hid = self.initial_hid\n", + " # Takes a sequence and loops over each character in the sequence. Note that each character has dimension equal to the embedding dimension\n", + " for i in range(len(input)):\n", + " hid = \n", + " out.append(hid)\n", + " self.stored_hid = hid\n", + " return out" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VgAU6qPHKJFr" + }, + "source": [ + "Now we can define a network and pass some data through it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "MFkZ5gNG6d7c" + }, + "outputs": [], + "source": [ + "NN = [\n", + " RNNLayer(1, 5, lambda x: x.tanh()),\n", + " DenseLayer(5, 1, lambda x: x.identity())\n", + "]\n", + "\n", + "def forward_batch(input: Sequence[Sequence[Sequence[Var]]], network, use_stored_hid=False):\n", + " \n", + " def forward_single_sequence(x, network, use_stored_hid):\n", + " for layer in network:\n", + " if isinstance(layer, RNNLayer):\n", + " x = layer.forward_sequence(x, use_stored_hid) \n", + " else:\n", + " x = layer.forward_sequence(x)\n", + " return x\n", + "\n", + " output = [ forward_single_sequence(input[n], network, use_stored_hid) for n in range(len(input))]\n", + " return output\n", + "\n", + "print(NN[0])\n", + "x_train =[\n", + " [[Var(1.0)], [Var(2.0)], [Var(3.0)]],\n", + " [[Var(1.0)], [Var(2.0)], [Var(3.0)]]\n", + " ]\n", + "\n", + "output_train = forward_batch(x_train, NN) \n", + "output_train[0][0][0].backward()\n", + "\n", + "print(output_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yolo5dKrk2hR" + }, + "source": [ + "## Exercise c) Unit test\n", + "\n", + "Make unit tests to make sure that the output and the backward method work as it should.\n", + "\n", + "NOTE: The .backward() call above simply backpropagates a value in the output (and not a loss). Below, we will extend our loss functions to be able to handle backpropagation through time.\n", + "\n", + "Recycling code from [Lab 2](https://github.com/DeepLearningDTU/02456-deep-learning-with-PyTorch/blob/master/2_Feedforward_Python/2.1-EXE-FNN-AutoDif-Nanograd.ipynb) is fine. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "GhCB1ASwK3X7" + }, + "outputs": [], + "source": [ + "# Insert code here" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4d4_2b6mK5jH" + }, + "source": [ + "## Exercise d) Advanced initialization\n", + "\n", + "How can we use He initialization for the recurrent layer?\n", + "\n", + "Hint: the sum of two unit variance stochastic variables have variance 2.\n", + "\n", + "Insert code for He initialization of the recurrent layer. Again, recycling code from Lab 2 is fine. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "oRn3mDnzLxu2" + }, + "outputs": [], + "source": [ + "## He\n", + "def DenseLayer_He_tanh(n_in: int, n_out: int):\n", + " std = 0.0 # <- replace with proper initialization \n", + " return DenseLayer(n_in, n_out, lambda x: x.relu(), initializer = NormalInitializer(std))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ozNN9xXML0yc" + }, + "source": [ + "## Exercise e) Sequence loss function\n", + "\n", + "We want to solve a sequence to sequence problem. So you need a sequence loss function. \n", + "\n", + "Implement the function such that the sequence loss can take flexible input dimensions and so that it can take any loss as an argument, such as squared loss and cross entropy. (We recommend using cross entropy below)\n", + "\n", + "We have provided a bit of code to try it out.\n", + "\n", + "Hints: You can get inspiration from the forward_sequence method above. You can copy and paste squared loss and cross entropy from Lab 2. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bYpEnbeMP4yL" + }, + "outputs": [], + "source": [ + "# Insert code here\n", + "\n", + "def squared_loss_sequence(t, y):\n", + " \n", + " # add check that sizes agree\n", + " \n", + " def squared_loss_single(t, y):\n", + "\n", + " # add check that sizes agree\n", + " \n", + " Loss = Var(0.0)\n", + "\n", + " return Loss\n", + "\n", + " Loss = Var(0.0)\n", + "\n", + " return Loss\n", + "\n", + "def cross_entropy_loss_sequence(t, y):\n", + "\n", + " # add check that sizes agree\n", + "\n", + " def cross_entropy_loss_single(t, y):\n", + "\n", + " # add check that sizes agree\n", + "\n", + " Loss = Var(0.0)\n", + " denominator = Var(0.0)\n", + "\n", + " return Loss\n", + "\n", + " Loss = Var(0.0)\n", + "\n", + " return Loss\n", + "\n", + "def sequence_loss(t: Sequence[Sequence[Var]], y: Sequence[Sequence[Var]], loss_fn=cross_entropy_loss_sequence) -> Var:\n", + " assert len(t) == len(y)\n", + " return loss_fn(t, y)\n", + "\n", + "\n", + "# Test of loss func\n", + "NN = [\n", + " RNNLayer(4, 2, lambda x: x.tanh()),\n", + " DenseLayer(2, 4, lambda x: x.identity())\n", + "]\n", + "\n", + "output_train = forward_batch(encoded_training_set_x[:3], NN)\n", + "print(output_train) \n", + "loss = sequence_loss(output_train, encoded_training_set_y[:3], cross_entropy_loss_sequence)\n", + "print(\"Loss:\", loss)\n", + "loss.backward()\n", + "\n", + "print(\"Output:\", output_train)\n", + "\n", + "print('Network before update:')\n", + "[print('Layer', i, '\\n', NN[i]) for i in range(len(NN))] \n", + "\n", + "def parameters(network):\n", + " params = []\n", + " for layer in range(len(network)):\n", + " params += network[layer].parameters()\n", + " return params\n", + "\n", + "def update_parameters(params, learning_rate=0.01):\n", + " for p in params:\n", + " p.v -= learning_rate*p.grad\n", + "\n", + "def zero_gradients(params):\n", + " for p in params:\n", + " p.grad = 0.0\n", + "\n", + "update_parameters(parameters(NN))\n", + "\n", + "print('\\nNetwork after update:')\n", + "[print('Layer', i, '\\n', NN[i]) for i in range(len(NN))] \n", + "\n", + "zero_gradients(parameters(NN))\n", + "\n", + "print('\\nNetwork after zeroing gradients:')\n", + "[print('Layer', i, '\\n', NN[i]) for i in range(len(NN))] \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ezSRiVJzk2h5" + }, + "source": [ + "# Backpropagation through time \n", + "\n", + "Since we have automatic differentiation we don't have to code the backpropagation rule by hand. Just to give you a bit of appreciation for have much bookkeeping is necessary we have given the derivation belwo.\n", + "\n", + "We need to compute the partial derivatives\n", + "$\n", + "\\frac{\\partial E}{\\partial W},~\\frac{\\partial E}{\\partial U},~\\frac{\\partial E}{\\partial V}\n", + "$. \n", + "We repeat the definition of the RNN forward pass from above:\n", + "\n", + "- $h_t = f(U\\,{x_t} + V\\,{h_{t-1}})$, where $f$ usually is an activation function, e.g. $\\mathrm{tanh}$.\n", + "- $o_t = W\\,{h_t}$\n", + "- $\\hat{y}_t = \\mathrm{softmax}(o_{t})$\n", + "\n", + "where\n", + "- $U$ is a weight matrix applied to the given input sample,\n", + "- $V$ is a weight matrix used for the recurrent computation in order to pass memory along the sequence,\n", + "- $W$ is a weight matrix used to compute the output of the every timestep (given that every timestep requires an output), and\n", + "- $h$ is the hidden state (the network's memory) for a given time step.\n", + "\n", + "Recall though, that RNNs are recurrent and the weights $W,~U,~V$ are shared across time, i.e. we do not have separate weights for each time step. Therefore, to compute e.g. the partial derivative $\\frac{\\partial E}{\\partial W}$, we need to 1) sum up across time, and 2) apply the chain rule:\n", + "\n", + "$$\\frac{\\partial E}{\\partial W} = \\sum_{t} \\frac{\\partial E}{\\partial o_{t}} \\frac{\\partial o_{t}}{\\partial W}\\,.$$\n", + "To compute$\\frac{\\partial o_{t}}{\\partial W}$ we use the definition of $o_t$ above.\n", + "From week 1 (exercise i) we have that\n", + "$$\\delta_{o,t} \\equiv \\frac{\\partial E}{\\partial o_{t}} = \\frac{\\partial E_t}{\\partial o_{t}} = \\hat{y}_{t} - y_{t}\\,,$$\n", + "where $\\hat{y}_{t}$ is a softmax distribution over model outputs $o_{t}$ at time $t$, and $y_{t}$ is the target label at time $t$. \n", + "\n", + "To compute $\\frac{\\partial E}{\\partial U}$ and $\\frac{\\partial E}{\\partial V}$ we again sum over time and use the chain rule:\n", + "$$\n", + "\\frac{\\partial E}{\\partial U} = \\sum_{t} \\frac{\\partial E}{\\partial h_{t}} \\frac{\\partial h_{t}}{\\partial U} \\ . \n", + "$$\n", + "This leads us to introduce\n", + "$$\n", + "\\delta_{h,t} \\equiv \\frac{\\partial E}{\\partial h_{t}} \\ .\n", + "$$\n", + "The backpropagation through time recursion is derived by realising that a variation of $h_t$ affects 1) the loss at time step $t$ through the feed-forward connection to the output and 2) the future losses through the $h_{t+1}$ dependence of $h_t$. Mathematically, we write this through the chain rule:\n", + "\n", + "$$\n", + "\\delta_{h,t} \\equiv \\frac{\\partial E}{\\partial h_{t}} = \\frac{\\partial E}{\\partial o_{t}} \\frac{\\partial o_t}{\\partial h_{t}} + \\frac{\\partial E}{\\partial h_{t+1}}\n", + "\\frac{\\partial h_{t+1}}{\\partial h_{t}} = \\delta_{o,t} \\frac{\\partial o_t}{\\partial h_{t}} + \\delta_{h,t+1}\n", + "\\frac{\\partial h_{t+1}}{\\partial h_{t}} \\ . \n", + "$$\n", + "\n", + "Like above we can compute $\\frac{\\partial h_{t+1}}{\\partial h_{t}}$ using the definition of the network (shifted one time step). In the code the intermediate steps to compute the $\\delta$ recursions have been precomputed for you. \n", + "\n", + "For more information on backpropagation through time see the [Deep learning book section 10.2.2](https://www.deeplearningbook.org/contents/rnn.html).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XIy3OZaQSrVL" + }, + "source": [ + "# Exercise f) Complete the training loop\n", + "\n", + "Complete the training loop above and run the training. You can leave the hyper-parameters and network size unchanged.\n", + "\n", + "Note that despite the small size of the network and dataset, training still takes quite a while. This is an issue with the recurrent structure of Nanograd. Using PyTorch, we would be able to use much larger datasets and models. We will attempt that in the bottom of the notebook. For now, you should get a feel of the recurrent structure of the RNN under the hood." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "MkaqbWmroncY" + }, + "outputs": [], + "source": [ + "# Initialize training hyperparameters\n", + "EPOCHS = 200\n", + "LR = 1e-2 \n", + "LR_DECAY = 0.995" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-JtM_IQjonfK" + }, + "outputs": [], + "source": [ + "train_loss = []\n", + "val_loss = []\n", + "\n", + "batch_size = 8\n", + "\n", + "for e in range(EPOCHS):\n", + " for b in range(int(np.ceil(len(encoded_training_set_x)/batch_size))):\n", + " # Forward pass and loss computation\n", + " Loss = \n", + "\n", + " # Backward pass\n", + " Loss.backward()\n", + " \n", + " # gradient descent update\n", + " update_parameters(parameters(NN), LR)\n", + " zero_gradients(parameters(NN))\n", + " \n", + " LR = LR * LR_DECAY\n", + "\n", + " # Training loss\n", + " Loss = \n", + " train_loss.append(Loss.v/len(encoded_training_set_y))\n", + " \n", + " # Validation loss\n", + " Loss_validation = \n", + " val_loss.append(Loss_validation.v/len(encoded_validation_set_y))\n", + " \n", + " if e%5==0:\n", + " print(\"{:4d}\".format(e),\n", + " \"({:5.2f}%)\".format(e/EPOCHS*100), \n", + " \"Train loss: {:4.3f} \\t Validation loss: {:4.3f}\".format(train_loss[-1], val_loss[-1]))\n", + " \n", + "# Plot training and validation loss\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "epoch = np.arange(len(train_loss))\n", + "plt.figure()\n", + "plt.plot(epoch, train_loss, 'r', label='Training loss',)\n", + "plt.plot(epoch, val_loss, 'b', label='Validation loss')\n", + "plt.legend()\n", + "plt.xlabel('Epoch'), plt.ylabel('NLL')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "nAI_D6g25pTQ" + }, + "outputs": [], + "source": [ + "# Get first sentence in test set\n", + "inputs, targets = test_set[0]\n", + "\n", + "# One-hot encode input and target sequence\n", + "inputs_one_hot = one_hot_encode_sequence(inputs, vocab_size)\n", + "targets_one_hot = one_hot_encode_sequence(targets, vocab_size)\n", + "\n", + "# Forward pass\n", + "outputs = forward_batch(encoded_test_set_x[:1], NN)\n", + "\n", + "output_sentence = [idx_to_word[np.argmax(output)] for output in Var_to_nparray(outputs[0])]\n", + "\n", + "print('Input sentence:')\n", + "print(inputs)\n", + "\n", + "print('\\nTarget sequence:')\n", + "print(targets)\n", + "\n", + "print('\\nPredicted sequence:')\n", + "print([idx_to_word[np.argmax(output)] for output in Var_to_nparray(outputs[0])])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Nn7QpUZXk2iH" + }, + "source": [ + "## Exercise g) Extrapolation\n", + "\n", + "Now that we have trained an RNN, it's time to put it to test. We will provide the network with a starting sentence and let it `freestyle` from there!\n", + "\n", + "How well does your RNN extrapolate -- does it work as expected? Are there any imperfections? If yes, why could that be?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4GNsD6HEJ-Gn" + }, + "outputs": [], + "source": [ + "def freestyle(NN, sentence='', num_generate=10):\n", + " \"\"\"\n", + " Takes in a sentence as a string and outputs a sequence\n", + " based on the predictions of the RNN.\n", + " \n", + " Args:\n", + " `params`: the parameters of the network\n", + " `sentence`: string with whitespace-separated tokens\n", + " `num_generate`: the number of tokens to generate\n", + " \"\"\"\n", + " sentence = sentence.split(' ')\n", + " output_sentence = sentence\n", + " sentence_one_hot = one_hot_encode_sequence(sentence, vocab_size)\n", + "\n", + " # Begin predicting\n", + " outputs = forward_batch([sentence_one_hot], NN, use_stored_hid=False)\n", + " output_words = [idx_to_word[np.argmax(output)] for output in Var_to_nparray(outputs[0])]\n", + " word = output_words[-1]\n", + "\n", + " # Append first prediction\n", + " output_sentence.append(word)\n", + "\n", + " # Forward pass - Insert code here!\n", + " if word != 'EOS':\n", + " for i in range(num_generate-1):\n", + " sentence_one_hot = \n", + " outputs = \n", + " output_words = \n", + " word = \n", + " output_sentence.append(word)\n", + " if word == 'EOS':\n", + " break\n", + " \n", + " return output_sentence\n", + "\n", + "\n", + "# Perform freestyle (extrapolation)\n", + "test_examples = ['a a b', 'a a a a b', 'a a a a a a b', 'a', 'r n n']\n", + "for i, test_example in enumerate(test_examples):\n", + " print(f'Example {i}:', test_example)\n", + " print('Predicted sequence:', freestyle(NN, sentence=test_example), end='\\n\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "X44hQ653vNCj" + }, + "source": [ + "# Introduction to the Long Short-Term Memory (LSTM) Cell\n", + "\n", + "Reading material: [Christopher Olah's walk-through](http://colah.github.io/posts/2015-08-Understanding-LSTMs/).\n", + "\n", + "___\n", + "\n", + "\n", + "A vanilla RNN suffers from [the vanishing gradients problem](http://neuralnetworksanddeeplearning.com/chap5.html#the_vanishing_gradient_problem) which gives challenges in saving memory over longer sequences. To combat these issues the gated hidden units were created. The two most prominent gated hidden units are the Long Short-Term Memory (LSTM) cell and the Gated Recurrent Unit (GRU), both of which have shown increased performance in saving and reusing memory in later timesteps. In this exercise, we will focus on LSTM but you would easily be able to go ahead and implement the GRU as well based on the principles that you learn here.\n", + "\n", + "Below is a figure of the LSTM cell:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5Rgc-g3zwV9f" + }, + "source": [ + "![lstm](https://i.imgur.com/3VkmUCe.png)\n", + "Source: https://arxiv.org/abs/1412.7828" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ytasZ5cqw4W1" + }, + "source": [ + "\n", + "The LSTM cell contains three gates, input, forget, output gates and a memory cell.\n", + "The output of the LSTM unit is computed with the following functions, where $\\sigma = \\mathrm{sigmoid}$.\n", + "We have input gate $i$, forget gate $f$, and output gate $o$ defines as\n", + "\n", + "- $i = \\sigma ( W^i [h_{t-1}, x_t])$\n", + "\n", + "- $f = \\sigma ( W^f [h_{t-1},x_t])$\n", + "\n", + "- $o = \\sigma ( W^o [h_{t-1},x_t])$\n", + "\n", + "where $W^i, W^f, W^o$ are weight matrices applied to a concatenated $h_{t-1}$ (hidden state vector) and $x_t$ (input vector) for each respective gate.\n", + "\n", + "$h_{t-1}$, from the previous time step along with the current input $x_t$ are used to compute the a candidate $g$\n", + "\n", + "- $g = \\mathrm{tanh}( W^g [h_{t-1}, x_t])$\n", + "\n", + "The value of the cell's memory, $c_t$, is updated as\n", + "\n", + "- $c_t = c_{t-1} \\circ f + g \\circ i$\n", + "\n", + "where $c_{t-1}$ is the previous memory, and $\\circ$ refers to element-wise multiplication (hint: element-wise multiplication is computed with the `*` operator in numpy).\n", + "\n", + "The output, $h_t$, is computed as\n", + "\n", + "- $h_t = \\mathrm{tanh}(c_t) \\circ o$\n", + "\n", + "and it is used for both the timestep's output and the next timestep, whereas $c_t$ is exclusively sent to the next timestep.\n", + "This makes $c_t$ a memory feature, and is not used directly to compute the output of the timestep." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m8_4RWp3k2iQ" + }, + "source": [ + "## Exercise h) Make the LSTMLayer class\n", + "\n", + "Make the LSTM class." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "qdU0yMXQU7d0" + }, + "outputs": [], + "source": [ + "# Insert code here\n", + "\n", + "class LSTMLayer:\n", + " def __init__(self, n_in: int, n_hid: int, act_fn, initializer = NormalInitializer(), initializer_hid = NormalInitializer()):\n", + " self.n_in = n_in\n", + " self.n_hid = n_hid\n", + " self.g_layer = \n", + " self.i_layer = \n", + " self.f_layer = \n", + " self.o_layer = \n", + " self.initial_hid = [Var(0.0) for _ in range(n_hid)]\n", + " self.stored_hid = [Var(0.0) for _ in range(n_hid)]\n", + " self.initial_c = [Var(0.0) for _ in range(n_hid)]\n", + " self.stored_c = [Var(0.0) for _ in range(n_hid)]\n", + " self.act_fn = act_fn\n", + " \n", + " def __repr__(self): \n", + " return 'Feed-forward: ' + repr(self.in_hid_layer) + ' Candidate: ' + repr(self.g_layer) + ' i gate ' + repr(self.i_layer) + ' f gate ' + repr(self.f_layer) + ' o gate ' + repr(self.o_layer) + ' Initial hidden: ' + repr(self.initial_hid)\n", + "\n", + " def parameters(self) -> Sequence[Var]: \n", + " return self.in_hid_layer.parameters() + self.g_layer.parameters() + self.i_layer.parameters() + self.f_layer.parameters() + self.o_layer.parameters() + self.initial_hid\n", + "\n", + " def forward_step(self, input: Sequence[Var], input_hid: Sequence[Var], input_c: Sequence[Var]) -> Sequence[Var]:\n", + " hids = []\n", + " cs = []\n", + " concatenated_input = []\n", + " for val in input_hid:\n", + " concatenated_input.append(val)\n", + " for val in input:\n", + " concatenated_input.append(val)\n", + "\n", + " # Insert code here\n", + "\n", + " return hids, cs\n", + " \n", + " def forward_sequence(self, input: Sequence[Sequence[Var]], use_stored_hid = False) -> Sequence[Sequence[Var]]:\n", + " out = []\n", + " if use_stored_hid:\n", + " hid = self.stored_hid\n", + " c = self.stored_c\n", + " else:\n", + " hid = self.initial_hid\n", + " c = self.initial_c\n", + " # Takes a sequence and loops over each character in the sequence. Note that each character has dimenson equal to the embeddng dimenson\n", + " for i in range(len(input)):\n", + " hid, c = # insert code here\n", + " out.append(hid)\n", + " self.stored_hid = hid\n", + " self.stored_c = c\n", + " return out" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gKu-bfhzk2iY" + }, + "source": [ + "Here is a bit of code to test it out:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "u4AYroqSVRSv" + }, + "outputs": [], + "source": [ + "NN = [\n", + " LSTMLayer(1, 5, lambda x: x.tanh()),\n", + " DenseLayer(5, 1, lambda x: x.identity())\n", + "]\n", + "\n", + "print(NN[0])\n", + "x_train =[[[Var(1.0)], [Var(2.0)], [Var(3.0)]],\n", + " [[Var(1.0)], [Var(2.0)], [Var(3.0)]]]\n", + "\n", + "output_train = forward_batch(x_train, NN) \n", + "output_train[0][0][0].backward()\n", + "\n", + "print(output_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "z4r4mgFsk2ik" + }, + "source": [ + "## Exercise i) LSTM training\n", + "\n", + "Complete the LSTM training loop\n", + "\n", + "Run the training loop. Training time in Nanograd will likely be long, but see if you can find settings to compare your LSTM learning curve (NLL and number of epochs) to the vanilla RNN from earlier. Do you observe any improvements? Motivate your answer.\n", + "\n", + "Finally, below we will implement LSTM in PyTorch. You will notice it is much, much faster!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "MOAmppJD66tJ" + }, + "outputs": [], + "source": [ + "# Initialize training hyperparameters\n", + "EPOCHS = \n", + "LR = \n", + "LR_DECAY = " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "tiotu2ab66w-" + }, + "outputs": [], + "source": [ + "NN = [\n", + " LSTMLayer(4, 1, lambda x: x.tanh()),\n", + " DenseLayer(1, 4, lambda x: x.identity())\n", + "]\n", + "\n", + "train_loss = []\n", + "val_loss = []\n", + "\n", + "batch_size = 8\n", + "\n", + "for e in range(EPOCHS):\n", + " for b in range(int(np.ceil(len(encoded_training_set_x)/batch_size))):\n", + " # Forward pass and loss computation\n", + " Loss =\n", + " # Backward pass\n", + " Loss.backward()\n", + " \n", + " # gradient descent update\n", + " update_parameters(parameters(NN), LR)\n", + " zero_gradients(parameters(NN))\n", + " \n", + " LR = LR * LR_DECAY\n", + "\n", + " # Training loss\n", + " Loss = \n", + " train_loss.append(Loss.v/len(encoded_training_set_y))\n", + " \n", + " # Validation loss\n", + " Loss_validation = \n", + " val_loss.append(Loss_validation.v/len(encoded_validation_set_y))\n", + " \n", + " if e%5==0:\n", + " print(\"{:4d}\".format(e),\n", + " \"({:5.2f}%)\".format(e/EPOCHS*100), \n", + " \"Train loss: {:4.3f} \\t Validation loss: {:4.3f}\".format(train_loss[-1], val_loss[-1]))\n", + " \n", + "# Plot training and validation loss\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "epoch = np.arange(len(train_loss))\n", + "plt.figure()\n", + "plt.plot(epoch, train_loss, 'r', label='Training loss',)\n", + "plt.plot(epoch, val_loss, 'b', label='Validation loss')\n", + "plt.legend()\n", + "plt.xlabel('Epoch'), plt.ylabel('NLL')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gi51eWgKxyOk" + }, + "source": [ + "## PyTorch implementation of the LSTM\n", + "\n", + "Now that we know how the LSTM cell works, let's see how easy it is to use in PyTorch!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "O6HDdJLuk2ip" + }, + "source": [ + "Definition of our LSTM network. We define a LSTM layer using the [nn.LSTM](https://pytorch.org/docs/stable/nn.html#lstm) class. The LSTM layer takes as argument the size of the input and the size of the hidden state like in our Nanograd implementation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "8UGrvknfk2ip" + }, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "\n", + "class MyRecurrentNet(nn.Module):\n", + " def __init__(self):\n", + " super(MyRecurrentNet, self).__init__()\n", + " \n", + " # Recurrent layer\n", + " # YOUR CODE HERE!\n", + " self.lstm = \n", + " \n", + " # Output layer\n", + " self.l_out = nn.Linear(in_features=50,\n", + " out_features=vocab_size,\n", + " bias=False)\n", + " \n", + " def forward(self, x):\n", + " # RNN returns output and last hidden state\n", + " x, (h, c) = self.lstm(x)\n", + " \n", + " # Flatten output for feed-forward layer\n", + " x = x.view(-1, self.lstm.hidden_size)\n", + " \n", + " # Output layer\n", + " x = self.l_out(x)\n", + " \n", + " return x\n", + "\n", + "net = MyRecurrentNet()\n", + "print(net)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "J6r3bPwYk2is" + }, + "source": [ + "## Exercise j) Train in PyTorch\n", + "\n", + "Define an LSTM for our recurrent neural network `MyRecurrentNet` above. A single LSTM layer is sufficient. What should the input size and hidden size be? Hint: use the PyTorch documentation.\n", + "\n", + "It's time for us to train our network. In the section below, you will get to put your deep learning skills to use and create your own training loop. You may want to consult previous exercises if you cannot recall how to define the training loop." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "rz8fdylUP-34" + }, + "outputs": [], + "source": [ + "# lets redefine the one_hot_encode() and one_hot_encode_sequence() functions\n", + "# to work now with torch.Tensor variables instead of Var.\n", + "def one_hot_encode(idx, vocab_size):\n", + " \"\"\"\n", + " One-hot encodes a single word given its index and the size of the vocabulary.\n", + " \n", + " Args:\n", + " `idx`: the index of the given word\n", + " `vocab_size`: the size of the vocabulary\n", + " \n", + " Returns a 1-D numpy array of length `vocab_size`.\n", + " \"\"\"\n", + " # Initialize the encoded array\n", + " one_hot = np.array([np.zeros(vocab_size)])\n", + " \n", + " # Set the appropriate element to one\n", + " one_hot[0][idx] = 1.0\n", + " return one_hot\n", + "\n", + "\n", + "def one_hot_encode_sequence(sequence, vocab_size):\n", + " \"\"\"\n", + " One-hot encodes a sequence of words given a fixed vocabulary size.\n", + " \n", + " Args:\n", + " `sentence`: a list of words to encode\n", + " `vocab_size`: the size of the vocabulary\n", + " \n", + " Returns a 3-D torch.Tensor of shape (num words, vocab size, 1).\n", + " \"\"\"\n", + " # Encode each word in the sentence\n", + " encoding = np.array([one_hot_encode(word_to_idx[word], vocab_size) for word in sequence])\n", + "\n", + " # Reshape encoding s.t. it has shape (num words, vocab size, 1)\n", + " encoding = encoding.reshape(encoding.shape[0], encoding.shape[2], 1)\n", + " return torch.Tensor(encoding)\n", + "\n", + "# lets test it\n", + "test_word = one_hot_encode(word_to_idx['a'], vocab_size)\n", + "print(f'Our one-hot encoding of \\'a\\' has shape {test_word.shape}.')\n", + "\n", + "test_sentence = one_hot_encode_sequence(['a', 'b'], vocab_size)\n", + "print(f'Our one-hot encoding of \\'a b\\' has shape {test_sentence.shape}.')\n", + "\n", + "print(test_word)\n", + "print(test_sentence)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2URKsyFDx8xG" + }, + "outputs": [], + "source": [ + "# Hyper-parameters\n", + "num_epochs = 200\n", + "\n", + "# Initialize a new network\n", + "net = MyRecurrentNet()\n", + "\n", + "# Define a loss function and optimizer for this problem\n", + "# YOUR CODE HERE!\n", + "criterion = \n", + "optimizer = \n", + "\n", + "# Track loss\n", + "training_loss, validation_loss = [], []\n", + "\n", + "# For each epoch\n", + "for i in range(num_epochs):\n", + " \n", + " # Track loss\n", + " epoch_training_loss = 0\n", + " epoch_validation_loss = 0\n", + " \n", + " net.eval()\n", + " \n", + " # For each sentence in validation set\n", + " for inputs, targets in validation_set:\n", + " \n", + " # One-hot encode input and target sequence\n", + " inputs_one_hot = one_hot_encode_sequence(inputs, vocab_size)\n", + " targets_idx = [word_to_idx[word] for word in targets]\n", + " \n", + " # Convert input to tensor\n", + " inputs_one_hot = torch.Tensor(inputs_one_hot)\n", + " inputs_one_hot = inputs_one_hot.permute(0, 2, 1)\n", + " \n", + " # Convert target to tensor\n", + " targets_idx = torch.LongTensor(targets_idx)\n", + " \n", + " # Forward pass\n", + " # YOUR CODE HERE!\n", + " outputs = \n", + " \n", + " # Compute loss\n", + " # YOUR CODE HERE!\n", + " loss = \n", + " \n", + " # Update loss\n", + " epoch_validation_loss += loss.detach().numpy()\n", + " \n", + " net.train()\n", + " \n", + " # For each sentence in training set\n", + " for inputs, targets in training_set:\n", + " \n", + " # One-hot encode input and target sequence\n", + " inputs_one_hot = one_hot_encode_sequence(inputs, vocab_size)\n", + " targets_idx = [word_to_idx[word] for word in targets]\n", + " \n", + " # Convert input to tensor\n", + " inputs_one_hot = torch.Tensor(inputs_one_hot)\n", + " inputs_one_hot = inputs_one_hot.permute(0, 2, 1)\n", + " \n", + " # Convert target to tensor\n", + " targets_idx = torch.LongTensor(targets_idx)\n", + " \n", + " # Forward pass\n", + " # YOUR CODE HERE!\n", + " outputs = \n", + " \n", + " # Compute loss\n", + " # YOUR CODE HERE!\n", + " loss = \n", + " \n", + " # Backward pass\n", + " # YOUR CODE HERE!\n", + " # zero grad, backward, step...\n", + " \n", + " # Update loss\n", + " epoch_training_loss += loss.detach().numpy()\n", + " \n", + " # Save loss for plot\n", + " training_loss.append(epoch_training_loss/len(training_set))\n", + " validation_loss.append(epoch_validation_loss/len(validation_set))\n", + "\n", + " # Print loss every 10 epochs\n", + " if i % 10 == 0:\n", + " print(f'Epoch {i}, training loss: {training_loss[-1]}, validation loss: {validation_loss[-1]}')\n", + "\n", + " \n", + "# Get first sentence in test set\n", + "inputs, targets = test_set[1]\n", + "\n", + "# One-hot encode input and target sequence\n", + "inputs_one_hot = one_hot_encode_sequence(inputs, vocab_size)\n", + "targets_idx = [word_to_idx[word] for word in targets]\n", + "\n", + "# Convert input to tensor\n", + "inputs_one_hot = torch.Tensor(inputs_one_hot)\n", + "inputs_one_hot = inputs_one_hot.permute(0, 2, 1)\n", + "\n", + "# Convert target to tensor\n", + "targets_idx = torch.LongTensor(targets_idx)\n", + "\n", + "# Forward pass\n", + "outputs = net.forward(inputs_one_hot).data.numpy()\n", + "\n", + "print('\\nInput sequence:')\n", + "print(inputs)\n", + "\n", + "print('\\nTarget sequence:')\n", + "print(targets)\n", + "\n", + "print('\\nPredicted sequence:')\n", + "print([idx_to_word[np.argmax(output)] for output in outputs])\n", + "\n", + "# Plot training and validation loss\n", + "epoch = np.arange(len(training_loss))\n", + "plt.figure()\n", + "plt.plot(epoch, training_loss, 'r', label='Training loss',)\n", + "plt.plot(epoch, validation_loss, 'b', label='Validation loss')\n", + "plt.legend()\n", + "plt.xlabel('Epoch'), plt.ylabel('NLL')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ydr7Czg_k2iw" + }, + "source": [ + "# Exercise k) Compare PyTorch and Nanograd implementations\n", + "\n", + "Compare the two implementations (in terms of predictive performance, training speed, etc.). Are they similar? How do they differ?\n", + "\n", + "\n", + "Try to play around with the choice of hyper-parameters, optimizer, and hidden dimensions. How much can you improve the negative log-likelihood by these simple changes?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "M93ORx95k2ix" + }, + "source": [ + "## Exercise l) Other RNN cells (optional)\n", + "\n", + "Aside from the LSTM cell, various other RNN cells exist. The gated recurrent unit (GRU) is a variation of the LSTM cell that uses less gating mechanisms. Try to look it up in the [PyTorch documentation](https://pytorch.org/docs/stable/nn.html#gru) and switch out the LSTM cell in the code above. What do you notice in terms of performance and convergence speed?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "juN400Ekk2iz" + }, + "source": [ + "## Exercise m) More complex tasks (optional)\n", + "\n", + "Go back and generate a more complex patterned dataset to learn from. Do you see any significant differences between a vanilla RNN and LSTM (implemented in e.g. PyTorch) when you increase the difficulty of the task?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "v68YEkEBk2iz" + }, + "source": [ + "# It works, now what?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NjpqSrSuk2i0" + }, + "source": [ + "In this notebook you have learned how to use embeddings, recurrent neural networks, and the LSTM cell in particular.\n", + "\n", + "As we have already seen, RNNs are excellent for sequential data such as language. But what do we do if we're modelling data with strong dependency in both directions? Like in many things deep learning, we can build powerful models by stacking layers on top of each other; *bi-directional* RNNs consist of two LSTM cells, one for each direction. A sequence is first fed into the forward LSTM cell and the reversed sequence is then used as input to the backward LSTM cell together with the last hidden state from the forward LSTM cell. Follow [this link](https://pdfs.semanticscholar.org/4b80/89bc9b49f84de43acc2eb8900035f7d492b2.pdf) for the original paper from 1997(!).\n", + "\n", + "For even deeper representations, multiple layers of both uni-directional and bi-directional RNNs can be stacked ontop of each other, just like feed-forward and convolutional layers. For more information on this, check out the [LSTM PyTorch documentation](https://pytorch.org/docs/stable/nn.html#lstm). Next week we will also explore ways to combine RNNs with other types of layers for even more expressive function approximators." + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [ + "bdA4LPsFiACe", + "cGSoDRgHk2g1", + "Dzmryk72k2g-", + "M93ORx95k2ix" + ], + "name": "5.1-EXE-Recurrent-Neural-Networks-Nanograd.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/5_Transformers/5_1_EXE_deep_learning_with_transformers.ipynb b/5_Transformers/5_1_EXE_deep_learning_with_transformers.ipynb new file mode 100644 index 0000000..e081080 --- /dev/null +++ b/5_Transformers/5_1_EXE_deep_learning_with_transformers.ipynb @@ -0,0 +1,15920 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "yeoSXsVuzsTf" + }, + "source": [ + "# Week 5 - Deep learning with Transformers" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Vxq_lY1Wz2AK" + }, + "source": [ + "Some preliminary set-up." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 401, + "referenced_widgets": [ + "72691438bd3443e496380c56af496eaa", + "44408e7021a34ed7b5aeeed803f48f30", + "e54ba4ac5c6949d7bcb97eb9729d6ada", + "335a51f7dd5548c3947a6288495c1737", + "2bd6ec97d073442eb08d141c07fd199d", + "7ffad741bcf84ea59f3a41239cd92045", + "38e443a3d6124361b5fc1acaaeaeb93e", + "3b037f3257684895ba25778397aeee19", + "2fa2d24e07cf4c4cba4cd296ba9281b3", + "c278bbf65b6d43c0b82af9a3cab5ef9e", + "3f549e3d727043358247df4eddcc7c07" + ] + }, + "id": "rK63fnkquGHY", + "outputId": "1921863d-7063-46ab-ca22-dc0c11f592ea" + }, + "outputs": [], + "source": [ + "! pip install ipywidgets rich seaborn torch datasets transformers tokenizers sentencepiece sacremoses --quiet\n", + "\n", + "%matplotlib inline\n", + "\n", + "import os\n", + "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n", + "\n", + "import torch\n", + "from torch import nn\n", + "import math\n", + "from functools import partial\n", + "from pathlib import Path\n", + "from tqdm import tqdm\n", + "import rich\n", + "from typing import List, Tuple, Optional, Dict, Any\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import numpy as np\n", + "import transformers\n", + "import tokenizers\n", + "import datasets\n", + "import zipfile\n", + "from huggingface_hub import hf_hub_download\n", + "\n", + "sns.set()\n", + "\n", + "# define the device to use\n", + "DEVICE = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n", + "rich.print(f\"Device: [red]{DEVICE}\")\n", + "\n", + "# control verbosity\n", + "transformers.logging.set_verbosity_error()\n", + "datasets.logging.set_verbosity_error()\n", + "\n", + "# define support functions\n", + "def load_glove_vectors(filename = \"glove.6B.300d.txt\") -> Tuple[List[str], torch.Tensor]:\n", + " \"\"\"Load the GloVe vectors. See: `https://github.com/stanfordnlp/GloVe`\"\"\"\n", + " path = Path(hf_hub_download(repo_id=\"stanfordnlp/glove\", filename=\"glove.6B.zip\"))\n", + " target_file = path.parent / filename\n", + " if not target_file.exists():\n", + " with zipfile.ZipFile(path, 'r') as zip_ref:\n", + " zip_ref.extractall(path.parent)\n", + "\n", + " if not target_file.exists():\n", + " print(f\"Available files:\")\n", + " for p in path.parent.iterdir():\n", + " print(p)\n", + " raise ValueError(f\"Target file `{target_file.name}` can't be found. Check if `{filename}` was properly downloaded.\")\n", + "\n", + " # parse the vocabulary and the vectors\n", + " vocabulary = []\n", + " vectors = []\n", + " with open(target_file, \"r\") as f:\n", + " for l in tqdm(f.readlines(), desc=f\"Parsing {target_file.name}...\" ):\n", + " word, *vector = l.split()\n", + " vocabulary.append(word)\n", + " vectors.append(torch.tensor([float(v) for v in vector]))\n", + " vectors = torch.stack(vectors)\n", + " return vocabulary, vectors" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 105, + "referenced_widgets": [ + "864803498d9d485b854f6dc22bf8e7f7", + "e276dfc72b4f4b42a5a93241c7dcd0b4", + "30caa70b8e2b4ca28af34fb14cd0cf86", + "233c55aac32242f6be5085631989f7c8", + "c1d0a6be0e0b402696f8f759ecf692e2", + "09b9bc4a5757400fb352de17b2eb1cab", + "9ef088f4f6d244738be3a42da4396635", + "eb07e619043940ecb3dd33ec3f38b03c", + "d7522bc3874c4285b33ca65febd3f2d5", + "586aa53e10754cb8948e3636da7aef9c", + "e0ea063acfe245c783f720279a8004cd" + ] + }, + "id": "gtB1kzl_uGHm", + "outputId": "107a3b51-d3f5-4e5f-8894-626e95df4bc8" + }, + "outputs": [], + "source": [ + "# prepare data for the later cells\n", + "glove_vocabulary, glove_vectors = load_glove_vectors()\n", + "rich.print(f\"glove_vocabulary: type={type(glove_vocabulary)}, length={len(glove_vocabulary)}\")\n", + "rich.print(f\"glove_vectors: type={type(glove_vectors)}, shape={glove_vectors.shape}, dtype={glove_vectors.dtype}\")\n", + "\n", + "# add special tokens\n", + "special_tokens = ['<|start|>', '<|unknown|>', '<|pad|>']\n", + "glove_vocabulary = special_tokens + glove_vocabulary\n", + "glove_vectors = torch.cat([torch.randn_like(glove_vectors[:len(special_tokens)]), glove_vectors])\n", + "\n", + "# tokenizer for GloVe\n", + "glove_tokenizer = tokenizers.Tokenizer(tokenizers.models.WordLevel(vocab={v:i for i,v in enumerate(glove_vocabulary)}, unk_token=\"<|unknown|>\"))\n", + "glove_tokenizer.normalizer = tokenizers.normalizers.BertNormalizer(strip_accents=False)\n", + "glove_tokenizer.pre_tokenizer = tokenizers.pre_tokenizers.Whitespace()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eKoXNCEfuGHr" + }, + "source": [ + "# Language Modelling and Transformers\n", + "\n", + "___\n", + "## Content\n", + "\n", + "* I. Text to vectors\n", + "* II. Language models\n", + "* III. Attention mechanism\n", + "* IV. Transformers\n", + "* V. Applications of Transformer-based language models\n", + "\n", + "\n", + "___\n", + "## Introduction\n", + "\n", + "Since its introduction ([\"Attention is All You Need\", Wasrani et al. (2016)](https://arxiv.org/abs/1706.03762)), Transformers have overtaken the field of Machine Learning. Initially applied to translation tasks, Transformers pre-trained on vast amounts of unlabelled data such as BERT and GPT have been acquired as central components in most of the modern natural language processing (NLP) systems. Transformers power question answering (QA) models, machine translation services, search engines and chat bots. Independently of the language applications, the Transformer is also a versatile neural architecture and, therefore, has found success outside the field of NLP. Transformers are rapidly being adopted in image processing ([\"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale\", Dosovitskiy et al. (2021)](https://arxiv.org/abs/2010.11929)), in reinforcement learning ([\"A Generalist Agent\", Reed et al. (2022)](https://arxiv.org/abs/2205.06175)), video generation ([\"VideoGPT: Video Generation using VQ-VAE and Transformers\", Yan et al. (2021)](https://arxiv.org/abs/2104.10157)), and more. In the following sections, we will first introduce the basics of NLP (tokenization, token embeddings, language modelling), introduce the attention mechanism. In the second part, we will study the Transformer architecture and apply it to NLP tasks.\n", + "\n", + "___\n", + "## I. Text to vectors\n", + "\n", + "In the previous labs, we have applied deep learning to processing images encoded as RGB pixels. We found that processing arrays of RGB pixels using convolutional neural network was effective. In NLP, other neural interfaces are required to enable plugging text into neural networks. Raw text cannot trivially be plugged-in neural networks. In this section we show how to convert text units or *tokens* into vectors and introduce the notion of text vector spaces.\n", + "\n", + "### I.a. Tokenization\n", + "\n", + "In [alphabetic languages](https://en.wikipedia.org/wiki/List_of_writing_systems), text can be decomposed into various types of units or *tokens*: characters, syllables, words or even sentences. Each tokenization system comes with vocabulary $\\mathcal{V}$ that references all known symbols. \n", + "\n", + "The choice of tokenizer is a tradeoff between the size of the vocabulary and the number of tokens required to encode a sentence. For instance, character-level tokenizers result in a smaller vocabulary size (only 128 character when using ASCII encoding) than other tokenizers. Word-based tokenizers encode text using fewer tokens than the other tokenizers but require a much larger vocabulary, which still might miss words seen at test time. Sub-words tokenizers such as [WordPiece](https://arxiv.org/abs/2012.15524) and [byte-pair encoding (BPE)](https://arxiv.org/abs/1508.07909) are a tradeoff between character-level and word-level encoding. They have progressively taken over the field as they provide two main advantages: (i) good tradeoff between vocabulary size and encoding length, (ii) open-ended vocabulary. \n", + "\n", + "Below we tokenize one sentence using word-level, character-level and sub-word-level tokenizers. In each case, the output corresponds to a sequence of indexes corresponding to the position of the given token in the vocabulary." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 837, + "referenced_widgets": [ + "f01a9c20f21547b6926e9eb44b8f43af", + "91759f59f8604393a0d72c623b1b97af", + "06a4618bda4949f2ace26450b1d8052e", + "55783e273f894123b18c1ab84fe996a1", + "8f96e5e63c61463181ad8daed8f104e0", + "8befeadf18114bdba529e65426de91e2", + "8ee9eb7d0c8c4f2998dc41835994e442", + "89d19eb4b1a243bf8318b037f3eb1eb2", + "4a41a1e705b549f8b094e5a1432ea389", + "f1e394f5978f44de9d7f539e78699582", + "d1f5bcde8d754af18fdd052c98a6d81b", + "9b39591d155d4d5b9bf7b9c37c928332", + "52a04a50c3bb491dabd849005adfe60b", + "1d9d2cf9cd59425497adf32e4dd59261", + "882871406b0043229ea41e2bc7e20cd9", + "2af41c4c23e24e3db73a9003a32d0097", + "ddd780b848994a3eaea4257b5b3f6dee", + "85fa69e6cb8c4b84b8bcb7c36bf2d8dd", + "9b601f47611b4281b2b2d1fcab39c38b", + "670ce1a5a4a3477e970db3210192ec4a", + "28ac8463e3f14e899b7e16553d991d41", + "71a9ceb690724a8ab688acbf67c68113", + "d667b4d49dfe49bebbf39dba10ada96d", + "ed1c5ae4218e48bc924fc615e416721f", + "0869911afc8549d9bd4ce0e7f97bdc0c", + "242c86c1e834490897262daa0ef5dfcd", + "389af45e82694958b597e0c25529f6c0", + "a303620e1ed142658667b32cee4b7e70", + "0330f9b958f34c46a34e62b47568fa77", + "4bde8be94bb945b7869e4fa261d686dd", + "9b6c07e98b4c4e95bdddc7dac23d69cf", + "971ee1ec3e25467fa861cf30207f25a7", + "15b3668ae54c4fb88b9d67d5756122c4", + "5fd3ce3c4f1e4751b6e1567b1d0cc447", + "c5bcb4f5e0b740f6adaaecbc9b38a8e0", + "36a31d0fa707478285f094582fb2b090", + "8b0b3d0a39df422ab85e98147252c37d", + "20d9a77f209d473bb1d2e751d706913e", + "8a2dcb7fe90040328cacd70146c48202", + "b4ef3d467ecb4d63a3a8e90b9db13fb3", + "b2966a02f9b74b7895c525b6fb21597d", + "67eb4a8763f648c1a4934e076c64bb2b", + "81bd92c195424af2b552038bb2240473", + "fee18278d97f4191a086db8dc185409b", + "b29bd3a6f57d414ea8c2625317daf851", + "2da10880d1da4b7eba255333d171d653", + "74d518f82ecb4021ade1a713de64104b", + "8b54d237a7cc43beaeb6a1edee13714e", + "148fd6dd13ca40199c14bc396a741d83", + "5cb53426cc29476dadb4c2fb88c5b5ad", + "3ce1683b100b4e249a4272220b0b6bed", + "583320fb40344b9bbb1887848173ca45", + "9b421d1eb87a4d9fbde251053fcf6c2c", + "bcd5a9c91e52470c9c285fda8a659a94", + "fa07548194e244b8b655381e073abb9d", + "9cc17f6d1a40454b8db2052160168a12", + "ec288e953b244616befab8429457c77b", + "c6b3661a3447492ab9e5b8d1b3aae05e", + "3586240e502f4255ab0d3d66e79b449e", + "64f9c055090a4861b33f8ed15a858daf", + "2d966f4595464c49be7f5b323cf54b1c", + "17d2e815f3ca46fa82167ccd1eb85b2a", + "f9ac22e242984baeaae8ddae3748003c", + "2de4643e661d448caaa7a1a7e660c6b0", + "588dfe8a747a4cd2a9ca35bba8898808", + "6d3fa316dfa24d20bda288ddfb71b4c8", + "31949d7fd38f4c20b8bb734dbf9aaa3b", + "636cfa48a2c647efa9ada29b42c37a35", + "bc150147b2c845f3aead1c4b1fa09c61", + "a0f74a508afc44ed9c618e1d181807a5", + "689de7097be44956ac7d9a5736b8e5bb", + "57c21a5483ec40a998a7ef66301a41ae", + "4dce807720b442788b75eecf25174e86", + "978de6521f3d4b8b9ac305b9db7a0ff1", + "bfa2bb61bc534200bdb61bc28bc09b53", + "2cb7d45a1a804c109e67e9a6c50976ee", + "2ba33d1e19ac48149fc2c78e1baba2a9", + "31e124e44206472ab49233418434b9ce", + "8d664b21ce974899bb6a7aaaea21a69e", + "e2036efdcaab46719ab2b57baf01e2a2", + "1a511d9f81cd434ea9a4a0baa1ad43f2", + "acfe14fcd0fc484b957fe97cfcb34c4c", + "a8650804aeaf4a37b9732b890791d06d", + "d3f574de1ff448178358f3a4ddf803c9", + "94d4edee3f7a406281f694241eb143ab", + "84ccee50a10d47f392d094a53c5223d9", + "5b85631ac5a446e5884020fb7ba4b96f", + "fc44ba4fc74f472a95cbeab1ea179b89", + "847410262bdd45919fdb4dbb18a5df2c", + "3828d19ebc43415f8ff87568418ec9b2", + "f79a56e0fe024b9682ec79d134201412", + "a5e970c0cf8a48ae8d2cb45058887d6b", + "aab826eacf3b429a8b67bb4c42698154", + "3369c01acc964d9b8b5738e59b05b6b4", + "15280581483b47f18aa57296f25d8334", + "bc51d76c3a854586b7b4832771a112bf", + "4429b3f77f2941468d72ba1b17821d9a", + "d8afe82dd0454aeeb34099fd21ccf726", + "04a785e5695545f8905306feee92b618", + "59add760ed794047bbb548f5c09e64ac", + "ff32978b71364a37a6724a5ee508bed3", + "43f85ac6c8ae4044a0e4443d7da79eb3", + "1e9772f564fa4d4390518664a1595197", + "aedd4dc023644d5cb76f33462ae965c7", + "6835c929cd844a7f882765f68bd8a7e2", + "1de84e67a3b34cddb1c73beae7888833", + "f3e74d9727564ed3ac609ebb3ec74737", + "16ebe063385841faba7f79ae57dad474", + "f34c4b62a07949b68758d6f6ffd1b31f", + "7ce42569074943d3af0ca12b2ac351d7", + "c11372c27bb34a03a26971a77791141a", + "65fd5c5e9b2c4cbdb4fe0c3314ae29bb", + "cefc16b8f9c94fc0aee7ba72a1c4aeb0", + "d35f83b7433e4f1bb857789c9738bf21", + "999d7a37dd8843b1aa9920c0da638c81", + "5c1987d5e4824dda8818f2d2187a0666", + "340c96ed160c42b191291545dfe6848c", + "8f1bf0c58d1e493a8a00e96ce09590da", + "a45abec670d242799e43d5badb75fa99", + "91e3ec446c5d4030853dca426e304f9c", + "718e86634d1343559659a545f36abb42" + ] + }, + "id": "MPEQDuq4uGHz", + "outputId": "4683777f-3247-482d-c852-f7903f307b1b" + }, + "outputs": [], + "source": [ + "# Example sentence with rare English words and non-english words\n", + "sentence = \"It is jubilating to see how élégant my horse has became\"\n", + "rich.print(f\"Input sentence: [bold blue]`{sentence}`\")\n", + "\n", + "# Define multiple tokenizers\n", + "tokenizer_ids = {\n", + " \"Word-level\": glove_tokenizer,\n", + " \"WordPiece\": \"bert-base-cased\",\n", + " \"BPE\": \"distilgpt2\",\n", + " \"Character-level\": \"google/byt5-small\",\n", + " }\n", + "\n", + "# iterate through the tokenizers and decode the input sentences\n", + "for tokenizer_name, tokenizer in tokenizer_ids.items():\n", + " # intialize the tokenizer (either)\n", + " if isinstance(tokenizer, str):\n", + " # init a `transformers.PreTrainedTokenizerFast`\n", + " tokenizer = transformers.AutoTokenizer.from_pretrained(tokenizer)\n", + " vocab_size = tokenizer.vocab_size\n", + " else:\n", + " # use the provided `tokenizers.Tokenizer``\n", + " vocab_size = tokenizer.get_vocab_size()\n", + "\n", + " # Tokenize\n", + " token_ids = tokenizer.encode(sentence, add_special_tokens=False)\n", + " if isinstance(token_ids, tokenizers.Encoding):\n", + " token_ids = token_ids.ids\n", + "\n", + " # Report\n", + " rich.print(f\"[red]{tokenizer_name}[/red]: sentence converted into {len(token_ids)} tokens (vocabulary: {vocab_size} tokens)\")\n", + " rich.print(f\"Tokens:\\n{[tokenizer.decode([t]) for t in token_ids]}\")\n", + " rich.print(f\"Token ids:\\n{[t for t in token_ids]}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DNH3dQ3VuGH1" + }, + "source": [ + "### I.b Embeddings\n", + "\n", + "A tokenizer transforms fragments of text into list of integers that maps a vocabulary. We assign one vector of dimension $d$ to each item in the vocabulary of size $N_\\mathcal{V}$, this results in a matrix $E$ of dimension ${N_\\mathcal{V} \\times d}$. Converting a fragment of text into a sequence of vector representations can be done by tokenizing the text, and then looking up the embedding vector for each token, which is equivalent to *one-hot encoding* the tokens and performing a matrix multiplication using $E$. Given $\\mathbf{t}_1, \\ldots, \\mathbf{t}_L$ the sequence of one-hot encoded tokens, this is equivalent to\n", + "$$\n", + "\\mathbf{w}_i = E \\mathbf{t}_i ,\n", + "$$\n", + "In the code below, we encode the sentence `Hellow world!` using a BPE tokenizer and a set of embedding of dimension `hdim`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 271, + "referenced_widgets": [ + "fae2e3a4035d403dad3baef4802a9258", + "bd052d320db8492c9b3aa1cf29e4a9cf", + "55ed66dd456e4f19ae98ce739da552b8", + "a4befd44d0b5402e8bcb61f64162ff43", + "f08a4af32b364cbaa774b179c87f737a", + "74acdd9bc09c438ab1f48c8c762c4a07", + "f8eaa261c4df44cea66453f6233555f9", + "c59172344f204280a756b106f69ed793", + "e35b435f72d4403e9ac00852fad7eef7", + "380c55c2680a4f85a81cfb266eace834", + "94ac791468ea4909a84c353d5bc7611d", + "713c08d937ca4e31a8b8f6942a487b7a", + "a4424cb16ec849ee96df431f52a57e29", + "5f1eef839eb24e25bbf667d0c23d2378", + "bad437a15b834e3e8ca6ca67b4860b7d", + "836bd677b7b040e2812fa3d3cf39b491", + "3e842b6e81c041419f630f7bbf69e0d2", + "b81956f3a32f4d8cb6dbe07a62867627", + "7ff7ecf2e5f44961becb04faa0a3048b", + "cad503bc0c3346e2abf5b25d355305d7", + "4772a52c663241ba9b5314d887fd47de", + "1c28b4c4a1bd44ddbed1028e3a755bb5", + "cdb2e3a16db04678a4a6d5d23931dacc", + "6c2c388a465f447a976d3427b59bad7a", + "b173b9397ed748b98aa18e9b4b739fca", + "4ffa8b39a34a4433bcce70f713bd7f2a", + "62f64323983d426c8762984fd25bb02e", + "09e77e9a34f24fedbdb90b2112f27347", + "5cef548a19104d2a910071036dccc588", + "b611fcfb99e84693954b41743fbf321a", + "8d2ad2b28f1644f09ff21d95df9bf0c0", + "3a7a4945d0f04ce5ac54621bf1d02698", + "af69668241644ef4b5cb06223a565d9f", + "fc631832218144c4b29f776ebba56193", + "0f938c22289d45d9bdd7929ed1a97626", + "b9474845c2d24c6f91420bb23ca0e149", + "2066c6d9fe2a4d6f876e81fbbf64e653", + "868d87d61432416fb23969b8405cc628", + "67e91a4c9376417dbb9916e83f5ccdd4", + "9ca45bbcbdbe4eab98cb67ed1249d8fb", + "88b39f446a4d46f5a5748d8bdf4311cf", + "7fdf1d094b4e4693acc53477fd2379c2", + "00046c6611c940058bac1aa75da16f95", + "08995f7f72f14dd8a22565ee4ae41215" + ] + }, + "id": "MKD5CuNLuGH2", + "outputId": "f0a4835d-f378-4048-a7c0-409198a91649" + }, + "outputs": [], + "source": [ + "hdim = 5 # embedding dimension\n", + "tokenizer = transformers.AutoTokenizer.from_pretrained(\"bert-base-uncased\") # tokenizer\n", + "sentence = \"Hello World!\" # input text\n", + "embeddings = torch.randn((tokenizer.vocab_size, hdim)) # embedding matrix\n", + "rich.print(f\"Embeddings (shape): {embeddings.shape}\")\n", + "token_ids = tokenizer.encode(sentence, add_special_tokens=False, return_tensors=\"pt\")[0]\n", + "rich.print(f\"Tokens ids (shape): {token_ids.shape}\")\n", + "vectors = torch.nn.functional.one_hot(token_ids, tokenizer.vocab_size).float() @ embeddings # equivalent to a `nn.Linear` layer\n", + "rich.print(f\"Vectors (shape): {vectors.shape}\")\n", + "rich.print(f\"List of tokens and their corresponding vectors:\")\n", + "for t,v in zip(token_ids, vectors):\n", + " token_info = f\"[blue]{tokenizer.decode(t):5}[/blue] (token id: {t:4})\"\n", + " rich.print(f\" * {token_info} -> {v}\")\n", + "\n", + "# NB: in practice, we use the simpler interface `torch.nn.Embedding``\n", + "# embeddings = torch.nn.Embedding(tokenizer.vocab_size, hdim)\n", + "# vectors = embeddings(token_ids)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BZ9GcEgOuGH4" + }, + "source": [ + "### I.c Word vectors\n", + "\n", + "\"Word2vec:\n", + "\n", + "\n", + "Word2vec ([\"Efficient Estimation of Word Representations in Vector Space\", Mikolov et al. (2013)](https://arxiv.org/abs/1301.3781)) converts words into vector representations, which are learned using the Skip-Gram algorithm. Intuitively, The algorithm is based on the idea that words that appear together are related to each other.\n", + "\n", + "The word vector space allows to use the inner product to compare words, and arithmetic operations to manipulate word representations. For instance, in a well-defined word vector space, the concept \"king\" can be translated into \"queen\" by applying a linear transformation and the vector `vec(\"captial\") - vec(\"country\")` was found to correspond to the relative concept `\"capital city of a country\"` (see above illustration (*Image credits: https://www.tensorflow.org/tutorials/word2vec*)).\n", + "\n", + "\n", + "**Experiment** In the first cells, we have downloaded the [GloVe word vectors](ttps://github.com/stanfordnlp/GloVe) from [\"GloVe: Global Vectors for Word Representation\", Jeffrey Pennington et al. (2014)](https://arxiv.org/abs/1902.11004). GloVe are trained using a Skip-Gram objective on a large collection of documents. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 431 + }, + "id": "NQHCpsoXuGH7", + "outputId": "6a26788a-0ab5-431f-bfb7-454b626d67d4" + }, + "outputs": [], + "source": [ + "def word2vec(\n", + " word: str,\n", + " vocabulary:List[str],\n", + " vectors: torch.Tensor\n", + " ) -> Optional[torch.Tensor]:\n", + " \"\"\"Convert a word into a vector\"\"\"\n", + " word = word.lower()\n", + " if word in vocabulary:\n", + " word_idx = vocabulary.index(word)\n", + " return vectors[word_idx]\n", + " else:\n", + " return None\n", + "\n", + "def vec2words(\n", + " vec: torch.Tensor,\n", + " k=5,\n", + " *,\n", + " vocabulary:List[str],\n", + " vectors: torch.Tensor,\n", + " exclude_vecs: List[torch.Tensor] = None,\n", + " ) -> Tuple[List[str], torch.Tensor]:\n", + " \"\"\"Retrieve the nearest word neighbours for an input vector\"\"\"\n", + "\n", + " # compute the similarity between `vec`and all the vectors in `glove_vectors`\n", + " similarity = vectors @ vec\n", + "\n", + " # potentially filter out some vocabulary entries\n", + " if exclude_vecs is not None and len(exclude_vecs):\n", + " mask = None\n", + " for e in exclude_vecs:\n", + " mask_ = (vectors == e[None, :]).all(dim=1)\n", + " if mask is None:\n", + " mask = mask_\n", + " else:\n", + " mask |= mask_\n", + " similarity.masked_fill_(mask=mask, value=-math.inf)\n", + "\n", + " # return the ids of the nearesrt neighbours given the similarity\n", + " nearest_neighbour_ids = torch.argsort(-similarity)[:k]\n", + "\n", + " # retrieve the corresponding words in the `vocabulary``\n", + " return [vocabulary[idx] for idx in nearest_neighbour_ids], similarity[nearest_neighbour_ids]\n", + "\n", + "# register the vocab and vectors args\n", + "glove_args = {'vocabulary':glove_vocabulary, 'vectors':glove_vectors}\n", + "\n", + "# Nearest neighbours\n", + "rich.print(\"[red]Nearest neighbour search:\")\n", + "for word in [\"king\", \"queen\", \"dog\", \"France\"]:\n", + " rich.print(f'Nearest neighbours of the word \"{word}\":')\n", + " word_vec = word2vec(word, **glove_args)\n", + " words, similarities = vec2words(word_vec, k=5, **glove_args, exclude_vecs=[word_vec])\n", + " rich.print(f\"Words: {words}\")\n", + " rich.print(f\"Similarities: {similarities}\")\n", + "\n", + "# Word analogies\n", + "rich.print(\"\\n[red]Vector arithmetic:\")\n", + "cases = [\n", + " [(\"+\", \"king\"), (\"-\", \"man\"), (\"+\", \"woman\")],\n", + " [(\"+\", \"denmark\"), (\"-\", \"france\"), (\"+\", \"paris\")],\n", + " [(\"+\", \"pakistan\"), (\"-\", \"belgium\"), (\"+\", \"brussels\")],\n", + "]\n", + "for operations in cases:\n", + " # current location in the vector space\n", + " location = 0\n", + " rich.print(f\"Vector Translation: [blue]0 {' '.join(f'{d} {v}' for d,v in operations)} = \")\n", + " for sign, word in operations:\n", + " # retrieve the `vec(word)``\n", + " vec = word2vec(word, **glove_args)\n", + " if vec is None:\n", + " raise ValueError(f\"Unknown word `{word}`\")\n", + "\n", + " # parse the direction (+/-)\n", + " direction = {\"+\": 1, \"-\": -1}[sign]\n", + "\n", + " # apply the vector transform to the current location\n", + " location += direction * vec\n", + "\n", + " # return the nearest neighbours of the end location\n", + " exclude_list = [word2vec(w, **glove_args) for _, w in operations]\n", + " words, similarities = vec2words(location, k=5, exclude_vecs=exclude_list, **glove_args)\n", + " rich.print(f\"Words: {words}\")\n", + " rich.print(f\"Similarities: {similarities}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0ZnUwuv0uGIA" + }, + "source": [ + "**Beyond word2vec** The Skip-Gram model allows to learn meaningful word prepresentations and arithmetic in the resulting vector space allow manipulating concepts. Ultimately, we are interested in learning representations that represent larger text fragments such as sentences, paragraphs or documents. Doing so require combining multiple vectors, which can be done by exploiting arithmetic in the vector space, or by combining word-vectors using deep neural networks, such as Transformers!\n", + "\n", + "___\n", + "## II. Language models\n", + "\n", + "We have seen how to encode text into sequences of tokens, seen how to convert tokens into vectors using a set of embeddings and experimented with a GloVe word vector space. In this section we will see how to model text at the sentence, pragraph or even document level using language models.\n", + "\n", + "### II.a Language Modelling\n", + "\n", + "*Figure: Left-to-right language models*\n", + "![Autoregressive left-to-right language model](images/ar-lm.png)\n", + "\n", + "**Autoregressive factorization** Language models aim at grasping the underlying linguistic structure of a fragment of text: whereas word vectors model words independently of each others, a language model tracks the grammatical and semantic relationships between word tokens. Given a piece of text encoded into tokens $\\mathbf{w}_{1:T} = [\\mathbf{w_1}, \\ldots, \\mathbf{w}_T]$ a *left-to-right* language model describes $\\mathbf{w}_{1:T}$ with the following factorization:\n", + "$$\n", + " p_\\theta(\\mathbf{w}_{1:T}) = \\prod_{t=1}^T p_\\theta(\\mathbf{w}_t \\mid \\mathbf{w}_{t}$. This defines a bidirectional language model, which factorizes as\n", + "$$\n", + "L_\\theta(\\mathbf{w}_{1:T}) = \\prod_{t=1}^T p_\\theta(\\mathbf{w}_t \\mid \\mathbf{w}_{-t}) \\ ,\n", + "$$\n", + "where $\\mathbf{w}_{-t}$ represent the set of tokens $\\mathbf{w}_{1:T} \\backslash \\{ \\mathbf{w}_t \\}$. We call it pseudo because this likelihood is not forming a valid distribution (because the graph formed by $\\mathbf{w}_{1:T}$ is not a directed acyclic graph (a DAG)). Bidirectional language models such as [ELMo (\"Deep contextualized word representations\", Peters et al. (2018))](https://arxiv.org/abs/1802.05365), learn token representation contextualized on the whole context.\n", + "\n", + "In the case, of bidirectional language models, the context $\\mathbf{w}_{-t}$ corresponds to the whole sequence of tokens with the predicted element masked out. It is possible to generalize the bidirectional factorization to masking out one or more tokens. In that case, we consider a model $p_\\theta(\\mathbf{w}_m \\mid \\mathbf{w}_{-m})$ where $m$ is a set of indices of the tokens being predicted and $-m$ is the set of the other tokens. This is notably the approach adopted in [\"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding\", Delvin et al. (2018)](https://arxiv.org/abs/1810.04805)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NQT5jAR5uGIF" + }, + "source": [ + "### II.b Recurrent Neural Networks\n", + "\n", + "*Figure: Left-to-right recurrent neural network. We highlight the information flowing from the context \"My horse is\" to the predicted word \"very\".*\n", + "![Recurrent Neural Network](images/recurrent-lm-activated.png)\n", + "\n", + "**Recurrent neural networks (RNNs)** implement a recursive function $f_\\theta$ using neural networks, which makes them a particularly good fit for sequential data. In the general setting, RNNs model the acquired knowledge at time $t$ using an additional variable $\\mathbf{h}_t$ of dimension $d_h$ (*hidden state*). The hidden state at step $t-1$ is updated with the information extracted from the observation $\\mathbf{w}_t$ using a function\n", + "$$\n", + "h_\\theta: (\\mathbf{w}_{t}, \\mathbf{h}_{t-1}) \\rightarrow \\mathbf{h}_{t} \\ ,\n", + "$$\n", + "which can be imlemented using an arbitrary neural network that takes the tuple $(\\mathbf{w}_{t}, \\mathbf{h}_t)$ as input and returns a new hidden state $\\mathbf{h}_{t+1}$. RRNs can be applied to parametrize language models by projecting the hidden state $\\mathbf{t}$ into the vocabulary space using a projection matrix $\\mathbf{F} \\in \\mathcal{R}^{V \\times d_h}$. This results in parameterizing the transition distribution as\n", + "$$\n", + "p_\\theta(\\cdot \\mid \\mathbf{w}_{