diff --git "a/3. \354\265\234\354\240\201\355\231\224 \353\254\270\354\240\234 \354\204\244\354\240\225.pptx" "b/3. \354\265\234\354\240\201\355\231\224 \353\254\270\354\240\234 \354\204\244\354\240\225.pptx" new file mode 100644 index 0000000..fb8d86c Binary files /dev/null and "b/3. \354\265\234\354\240\201\355\231\224 \353\254\270\354\240\234 \354\204\244\354\240\225.pptx" differ diff --git "a/Euron 23\354\243\274\354\260\250 \355\232\214\354\235\230\353\241\235_\352\263\265\353\252\250\354\240\204\355\214\200.docx" "b/Euron 23\354\243\274\354\260\250 \355\232\214\354\235\230\353\241\235_\352\263\265\353\252\250\354\240\204\355\214\200.docx" new file mode 100644 index 0000000..3e6471e Binary files /dev/null and "b/Euron 23\354\243\274\354\260\250 \355\232\214\354\235\230\353\241\235_\352\263\265\353\252\250\354\240\204\355\214\200.docx" differ diff --git "a/Euron-23\354\243\274\354\260\250-\353\260\234\355\221\234.pptx" "b/Euron-23\354\243\274\354\260\250-\353\260\234\355\221\234.pptx" new file mode 100644 index 0000000..dd6f4ef Binary files /dev/null and "b/Euron-23\354\243\274\354\260\250-\353\260\234\355\221\234.pptx" differ diff --git "a/\352\263\274\354\240\23410_\354\241\260\355\230\204\354\247\200.md" "b/\352\263\274\354\240\23410_\354\241\260\355\230\204\354\247\200.md" new file mode 100644 index 0000000..84081a3 --- /dev/null +++ "b/\352\263\274\354\240\23410_\354\241\260\355\230\204\354\247\200.md" @@ -0,0 +1,208 @@ +4. 최적화 알고리즘
+
+ +1) 미니 배치 경사하강법
+ 머신러닝이 잘 작동되는 모델을 찾기 위해선 많은 훈련을 반복적으로 거쳐야 한다
+ 또 큰 데이터셋에서 훈련하는 것은 매우 느리다
+ 따라서 좋은 최적화 알고리즘을 찾는 것이 중요하다
+
+ 벡터화 : m개의 샘플에 대한 계산을 반복문 없이 진행
+ X = [x^(1), x^(2), ..., x^(m)] X의 차원 : (n_x, m)
+ Y = [y^(1), y^(2), ..., y^(m)] Y의 차원 : (1, m)
+
+ m이 5,000,000이거나 그보다 크다면?
+
+ 전체 훈련 세트에 대한 경사 하강법 구현하려면
+ 작은 한 단계를 밟기 전에 모든 훈련 세트를 처리해야 함
+ 다음 단계를 밟기 전에 다시 오백만 개의 전체 훈련 샘플을 처리해야 함
+
+ 따라서 오백만 개의 거대한 훈련 샘플을 처리하기 전에 경사 하강법을 진행하면 더 빠른 알고리즘을 얻을 수 있다
+ 처음 훈련 세트를 훈련하는 도중에라도 경사 하강의 단계를 진행시키기 위해 미니배치 경사 하강법 어떻게 사용하는지 알아보자!
+
+ 미니배치 : 나누어진 작은 훈련 세트
+
+ ex) 미니배치가 1,000개인 경우
+ x^(1), x^(2), ..., x^(1000) -> 첫 번째 미니배치 X^{1}
+ x^(1001), x^(1002), ..., x^(2000) -> 두 번째 미니배치 X^{2}
+ 총 5000,000개의 훈련 샘플 / 미니배치 1000개 -> 5000개의 미니배치 X^{5000}
+
+ X^{t} Y^{t} : 1000개의 훈련 샘플
+
+ x^(i) : i번째 훈련 샘플
+ z^[l] : l번째 신경망의 z값
+ t번째 미니배치 : X^{t} Y^{t} (n_x, 1000)차원, (1, 1000)차원
+
+ +- 배치 경사 하강법 : 모든 훈련 세트를 동시에 진행시키는 방법
+ 동시에 훈련 샘플의 모든 배치를 진행시킨다
+
+- 미니배치 경사 하강법 : 전체 훈련 세트를 한 번에 진행시키지 않고 하나의 미니배치 X^{t} Y^{t}를 동시에 진행시킨다
+
+ _미니배치 경사 하강법 실행 과정
+ t = 1, ..., 5000인 반복문 (1000인 미니배치 5000개)
+ : 한 단계의 경사 하강법을 구현
+ 벡터화를 사용해 모든 1000개의 샘플을 동시에 진행시킨다
1. X^{t}에 대해 정방향 전파 구현
+ z^[1] = W^[1]X
+ A^[1] = g^[1](Z^[1])
+ ...
+ A^[L] = g^[L](Z^[L])
+ 벡터화된 구현이 오백만 개의 샘플 대신 1000개의 샘플을 진행한다
2. 비용함수 J 계산
+ J^{t} = 1/1000_(i가 1부터 l까지 L(ŷ^(i), y^(i)) + λ/2*1000*(l에 대한 프로베니우스 norm의 제곱의 합)
3. 역전파
+ J^{t}에 대응하는 경사를 계산, 여전히 X^{t}, Y^{t} 사용
4. 가중치 업데이트
+ W^[l] = W^[l] - αW^[l]
+ b^[l] = b^[l] - αb^[l]
+
+ 이것은 미니배치 경사 하강법을 사용한 훈련 세트를 지나는 한번 반복이다
+ (훈련의 한 epoch)
+ 배치 경사 하강법 – 훈련 세트를 거치는 한 반복은 오직 하나의 경사 하강 단계만을 할 수 있게 한다
+ 미니배치 경사 하강법 – 훈련 세트를 거치는 한 반복은 5,000개의 경사 하강 단계를 거치도록 한다,
+ 다른 반복문을 사용해서 훈련 세트를 여러번 거치면 원하는 만큼 거의 수혐할 때까지 훈련 세트를 계속 반복시킨다.
+ 훈련 세트가 많다면 미니배치 경사 하강법이 훨씬 더 빠르게 실행된다
+
+ +2. 미니 배치 경사하강법 이해하기
+ +1) 배치 경사 하강법
+ : 모든 반복에서 전체 훈련 세트를 진행하고 각각의 반복마다 비용이 감소하기를 기대한다
+
+ 비용함수 J가 모든 반복마다 감소해야 한다
+
+2) 미니배치 경사 하강법
+ : 모든 반복에서 어떤 X^{t}와 Y^{t}를 진행시킨다. 즉 모든 반복에서 다른 미니배치로 훈련한다
+
+ 비용함수 J가 전체적인 흐름은 감소하지만 약간의 노이즈가 발생된다
+ 노이즈가 발생하는 이유 : X^{1}와 Y^{1}이 X^{2}와 Y^{2} 보다 쉬운 미니배치라 비용이 낮을 때 (X^{2}와 Y^{2}에 잘못 표시된 샘플이 있다든지 등의 이유로 비용이 약간 더 높은 경우)
+
+ ● 매개변수 고르기
+ 훈련 세트의 크기 : m
+ 미니 배치의 크기 : m
+ -> 배치 경사 하강법이 된다, 하나의 미니배치만을 갖게 된다 X^{1}과 Y^{1}
+ 미니배치의 크기는 전체 훈련 세트와 같다
+ 미니배치의 크기 : 1
+ -> 확률적 경사 하강법이 된다. 각각의 샘플은 하나의 미니배치이다. X^{1}과 Y^{1}은 첫 번째 훈련 샘플과 같다. 한 번에 하나의 훈련 샘플만을 사용한다.
+
+ ● J의 등고선
+ 배치 경사 하강법 : 상대적으로 노이즈가 적고 큰 스텝으로 최솟값으로 나아간다
+ 확률적 경사 하강법 : 대부분의 경우 전역 최솟값으로 가지만 어떤 경우 잘못된 방향으로 간다. 따라서 노이즈가 많을 수 있지만 평균적으로는 좋은 방향으로 가게 된다. 따라서 진동하면서 최솟값의 주변을 돌아다니지만 절대 수렴하지 않는다
+
+ ● 미니배치 크기를 1과 m사이로 설정하는 이유
+ 배치 경사 하강법 – 미니 배치 크기 m
+ 장점 : 매우 큰 훈련 세트를 모든 반복에서 진행
+ 단점 : 한 반복에서 너무 오랜 시간이 걸린다
+ 확률적 경사 하강법 – 미니 배치 크기 1
+ 장점 : 하나의 샘플만 처리한 뒤에 계속 진행할 수 있다. 노이즈도 작은 학습률을 사용해 줄일 수 있다
+ 단점 : 벡터화에서 얻을 수 있는 속도 향상을 잃게 된다. 한번에 하나의 훈련 세트를 진행하기 때문에 각 샘플을 진행하는 방식이 매우 비효율적이다.
+ 따라서 미니배치 크기를 너무 크거나 작지 않게 설정해야 한다
+ 장점 1 : 많은 벡터화를 얻는다
+ 장점 2 : 전체 훈련 세트가 진행되기를 기다리지 않고 진행을 할 수 있다
+
+ ● 1과 m사이 값을 어떻게 선택할까?
+3) 작은 훈련 세트(2000개보다 적은 경우)
+ : 배치 경사 하강법을 사용
+4) 미니배치 크기 64~512개(2^6~2^9)
+ : 2의 제곱수로 구현하기
+5) 미니배치에서 모든 X^{t}와 Y^{t}가 CPU와 GPU 메모리에 맞는지 확인
+
+ +3. 지수 가중 이동 평균
+ 경사 하강법보다 빠른 몇 가지 최적화 알고리즘을 이해하기 위해서는 지수가중평균을 사용할 수 있어야 한다
+
+ 런던 기온 데이터
+ +- 지역 평균이나 이동 평균의 흐름을 계산
+- 일별 기온의 지수가중평균
+ V*0 = 0
+ V_t = 0.9\*V*(t-1)(이전 날의 값) + 0.1*θ_t(해당 날의 기온)
+ 0.9를 β로 바꾸면
+ V_t = β*V\_(t-1) + (1-β)*θ_t (대략적으로 1/(1-β)*일별 기온의 평균)v +- β = 0.9일 때 10일의 기온 평균
+- β = 0.5일 때 2일의 기온 평균
+ β값이 클수록 더 많은 날짜의 기온의 평균을 이용하기 때문에 선이 더 부드러워진다
+ 하지만 더 큰 범위에서 기온을 평균하기 때문에 곡선이 올바른 값에서 멀어진다
+
+ β값이 클수록 이전 값에 많은 가중치를 주고 현재의 기온에는 작은 가중치를 줘 더 느리게 적응한다, 선이 더 부드럽지만 지연되는 시간이 더 크다
+
+ +4. 지수 가중 이동 평균 이해하기
+ V*t = β\*V*(t-1) + (1-β)*θ_t
+
+ v_100 = 0.1*θ*100 + 0.9*v_99
+ v_99 = 0.1*θ_99 + 0.9*v_98
+ v_98 = 0.1*θ_98 + 0.9*v_97
+
+ θ_100의 가중치의 평균
+ v100 = 0.1*θ_100 + 0.1*0.9*θ_99 + 0.1*(0.9)^2*θ_98​​ +⋯
+ 이를 그림으로 표현하면 지수 적으로 감소하는 그래프다. (v*​100을 기준으로 보았을 때), 그 이유는 v_​100은 각각의 요소에 지수적으로 감소하는 요소( 0.1×(0.9)^n )를 곱해서 더한 것이기 때문이다.
+
+ 얼마의 기간이 이동하면서 평균이 구해졌는가? 는 아래의 식으로 대략적으로 구할 수 있다
+ ● β=(1−ε) 라고 정의 하면
+ ● (1−ε)^​n = 1/e를 만족하는 n 이 그 기간이 되는데, 보통 1/εε으로 구할 수 있다.
+ 지수 가중 이동 평균의 장점은 구현시 아주 적은 메모리를 사용한다는 것이다.
+
+5. 지수 가중 이동 평균의 편향보정
+ 편향 보정 -＞초기에 더 나은 추정값을 얻어 평균을 더 정확하게 계산
+ 저번 시간에 따른 지수평균식대로라면 t=1 일때 (1−β) 를 곱한 값이 첫번째 값이 되는데, 이는 우리가 원하는 실제 v1값과 차이가 나게 된다.
+ 따라서 vt/(1−β^t) 를 취해서 초기 값에서 실제값과 비슷해지게 한다. (t는 현재의 온도)
+ +- t가 더 커질수록 β^t는 0에 가까워진다
+- t가 충분히 커지면 편향 보정은 그 효과가 거의 없어진다
+- 초기 단계의 학습에서는 편향 보정은 더 나은 온도의 추정값을 얻을 수 있도록 도와준다
+
+ +5. Momentum 최적화 알고리즘
+ ● Gradient descent example
+ : 최솟값으로 나아가면서 천천히 진동한다
+ 위아래의 진동은 경사 하강법의 속도를 느리게 하고 더 큰 학습률을 사용하는 것을 막는다
+ +- 수직축 : 진동을 막기 위해 학습이 더 느리게 일어나기를 바람
+- 수평축 : 더 빠른 학습을 원함
+
+ ● Momentum 알고리즘
+ : 반복 t에서 보편적인 도함수 dw와 db를 계산하게 된다
+- V_dW = β1\*V_dW + (1−β1)dW
+- w:=w−α\*V_dW
+ ​​경사의 평균을 구하면 수직 방향의 진동이 0에 가까운 값으로 평균이 만들어진다
+- 수직 방향에서는 양수와 음수를 평균하기 때문에 평균이 0이 된다
+- 수평 방향에서 모든 도함수는 오른쪽을 가리키고 있기 때문에 꽤 큰 값을 가진다
+
+ 결국 경사 하강법은 수직 방향에서는 훨씬 더 작은 진동이 있고, 수평 방향에서는 더 빠르게 움직인다
+ 따라서 이 알고리즘은 더 직선의 길을 가거나 진동을 줄일 수 있게 한다!
+ ● 밥그릇 모양의 함수를 최소화 하려고 하면
+ ● 도함수의 항들 : 가속을 제공
+ ● 모멘텀 항들 : 속도를 나타냄
+ Momentum 의 장점은 매 단계의 경사 하강 정도를 부드럽게 만들어준다
+ Momentum 알고리즘에서는 보통 평향 추정을 실행하지 않는다. 이유는 step 이 10 단계정도 넘어가면 이동평균은 준비가 돼서 편향 추정이 더 이상 일어나지 않기 때문이다
+
+ +6. RMSProp 최적화 알고리즘
+ 수직 방향의 학습 속도를 낮추고 수평 방향의 속도를 빠르게 하기 위한 것
+ On iteration t :
+ compute dw, db on current mini-batch
+ s_dw = β*s_dw + (1-β)*dw^2 (요소별 제곱)
+ s_db = β*s_db + (1-β)*db^2
+ w := w - α*dw/√s_dw + ϵ
+ b := b - α*db/√s_db
+
+ w방향(수평방향)에서는 학습률이 빠르길 원함
+ b방향(수직방향)에서는 진동을 줄이고 느리길 원함
+ ​
+ s_dw가 상대적으로 작고 s_db가 상대적으로 커서
+ 수직 방향에서의 도함수 db >> 수평 방향에서의 도함수 dw
+
+ RMSProp 의 장점은 미분값이 큰 곳에서는 업데이트 시 큰 값으로 나눠주기 때문에 기존 학습률 보다 작은 값으로 업데이트 된다. 따라서 진동을 줄이는데 도움이 된다. 반면 미분값이 작은 곳에서는 업데이트시 작은 값으로 나눠주기 때문에 기존 학습률보다 큰 값으로 업데이트 된다. 이는 더 빠르게 수렴하는 효과를 불러온다.
+
+7. Adam 최적화 알고리즘
+ 넓은 범위의 딥러닝 아키텍처에서 잘 작동하는 알고리즘
+ Momentum 과 RMSProp 을 섞은 알고리즘
+ +
+8) 학습률 감쇠
+알고리즘의 속도를 높이는 방법 : 시간에 따라 학습률을 천천히 줄이는 것
+
+● 왜 학습률 감쇠가 필요한가?
+작은 미니배치 일수록 잡음이 심해서 일정한 학습률이라면 최적값에 수렴하기 어려운 현상을 볼 수 있다.
+α가 큰 초기 단계에서는 상대적으로 빠른 학습이 가능하고 α가 작아지면 단계마다 진행 정도가 작아져 최솟값 주변의 밀집된 영역에서 진동한다.
+따라서 α를 천천히 줄여 학습 초기 단계에서는 큰 스텝으로 진행하고 학습이 수렴할수록 작은 스텝으로 진행하도록 한다.
+학습률 감쇠 기법을 사용하는 이유는 점점 학습률을 작게 줘서 최적값을 더 빨리 찾도록 만드는 것이다.
+
+● 학습률 감쇠를 구현하는 방법
diff --git "a/\352\263\274\354\240\23410_\354\272\220\352\270\200 \355\225\204\354\202\254_\354\241\260\355\230\204\354\247\200.ipynb" "b/\352\263\274\354\240\23410_\354\272\220\352\270\200 \355\225\204\354\202\254_\354\241\260\355\230\204\354\247\200.ipynb" new file mode 100644 index 0000000..8b047ca --- /dev/null +++ "b/\352\263\274\354\240\23410_\354\272\220\352\270\200 \355\225\204\354\202\254_\354\241\260\355\230\204\354\247\200.ipynb" @@ -0,0 +1 @@ +{"metadata":{"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import numpy as np \nimport pandas as pd \nimport matplotlib.pyplot as plt\n\nimport os\nprint(os.listdir(\"../input\"))","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-11-13T11:04:13.314057Z","iopub.execute_input":"2023-11-13T11:04:13.314745Z","iopub.status.idle":"2023-11-13T11:04:13.335083Z","shell.execute_reply.started":"2023-11-13T11:04:13.314688Z","shell.execute_reply":"2023-11-13T11:04:13.334066Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"['sample_submission.csv', 'train.csv', 'test.csv']\n","output_type":"stream"}]},{"cell_type":"markdown","source":"
\n## Basics of Pytorch\n### Matrices","metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0"}},{"cell_type":"code","source":"import numpy as np\n\narray = [[1,2,3],[4,5,6]]\nfirst_array = np.array(array) # 2x3 array\nprint(\"Array Type: {}\".format(type(first_array))) # type\nprint(\"Array Shape: {}\".format(np.shape(first_array))) # shape\nprint(first_array)","metadata":{"_uuid":"d60e9b9f706124117b1d7ffcdeefb45b9aca45e6","_cell_guid":"0a70fde1-b9c4-47c5-aed7-1c863b2fd1e1","execution":{"iopub.status.busy":"2023-11-13T11:04:35.830924Z","iopub.execute_input":"2023-11-13T11:04:35.831457Z","iopub.status.idle":"2023-11-13T11:04:35.838219Z","shell.execute_reply.started":"2023-11-13T11:04:35.831408Z","shell.execute_reply":"2023-11-13T11:04:35.837117Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Array Type: \nArray Shape: (2, 3)\n[[1 2 3]\n [4 5 6]]\n","output_type":"stream"}]},{"cell_type":"code","source":"# import pytorch library\nimport torch\n\n# pytorch array\ntensor = torch.Tensor(array)\nprint(\"Array Type: {}\".format(tensor.type)) # type\nprint(\"Array Shape: {}\".format(tensor.shape)) # shape\nprint(tensor)","metadata":{"_uuid":"126ea635dff4e2a6bc9a828dc560863e3be6aa74","_cell_guid":"b383b085-a18f-4c18-a093-428336b6acf6","execution":{"iopub.status.busy":"2023-11-13T11:05:02.586607Z","iopub.execute_input":"2023-11-13T11:05:02.587312Z","iopub.status.idle":"2023-11-13T11:05:03.240289Z","shell.execute_reply.started":"2023-11-13T11:05:02.587238Z","shell.execute_reply":"2023-11-13T11:05:03.239285Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"Array Type: \nArray Shape: torch.Size([2, 3])\ntensor([[1., 2., 3.],\n [4., 5., 6.]])\n","output_type":"stream"}]},{"cell_type":"code","source":"# numpy ones\nprint(\"Numpy {}\\n\".format(np.ones((2,3))))\n\n# pytorch ones\nprint(torch.ones((2,3)))","metadata":{"_uuid":"2d36d68f3b57eef9d8f0ed94885f3376de92b414","_cell_guid":"741468a5-5d91-48d7-95b0-6d02180d0c09","execution":{"iopub.status.busy":"2023-11-13T11:05:11.272509Z","iopub.execute_input":"2023-11-13T11:05:11.272827Z","iopub.status.idle":"2023-11-13T11:05:11.280314Z","shell.execute_reply.started":"2023-11-13T11:05:11.272777Z","shell.execute_reply":"2023-11-13T11:05:11.279237Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"Numpy [[1. 1. 1.]\n [1. 1. 1.]]\n\ntensor([[1., 1., 1.],\n [1., 1., 1.]])\n","output_type":"stream"}]},{"cell_type":"code","source":"# numpy random\nprint(\"Numpy {}\\n\".format(np.random.rand(2,3)))\n\n# pytorch random\nprint(torch.rand(2,3))","metadata":{"_uuid":"1e6b8ce52af8a26ffc39fcd751a834ea7c870a2d","_cell_guid":"a578ff9f-df45-4acd-b5ec-2e26b2690adb","execution":{"iopub.status.busy":"2023-11-13T11:05:18.155805Z","iopub.execute_input":"2023-11-13T11:05:18.156142Z","iopub.status.idle":"2023-11-13T11:05:18.164299Z","shell.execute_reply.started":"2023-11-13T11:05:18.156055Z","shell.execute_reply":"2023-11-13T11:05:18.163247Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"Numpy [[0.4323049 0.35936774 0.49313321]\n [0.57033417 0.37532189 0.38167272]]\n\ntensor([[0.3050, 0.6495, 0.9773],\n [0.8247, 0.8704, 0.2993]])\n","output_type":"stream"}]},{"cell_type":"code","source":"# random numpy array\narray = np.random.rand(2,2)\nprint(\"{} {}\\n\".format(type(array),array))\n\n# from numpy to tensor\nfrom_numpy_to_tensor = torch.from_numpy(array)\nprint(\"{}\\n\".format(from_numpy_to_tensor))\n\n# from tensor to numpy\ntensor = from_numpy_to_tensor\nfrom_tensor_to_numpy = tensor.numpy()\nprint(\"{} {}\\n\".format(type(from_tensor_to_numpy),from_tensor_to_numpy))","metadata":{"_uuid":"c6d3a7b8e0e42fcadecb16264b0563f74d01439a","_cell_guid":"f2cedc86-bd28-4709-906f-e236f4a4dbbe","execution":{"iopub.status.busy":"2023-11-13T11:05:24.441928Z","iopub.execute_input":"2023-11-13T11:05:24.442254Z","iopub.status.idle":"2023-11-13T11:05:24.455358Z","shell.execute_reply.started":"2023-11-13T11:05:24.442198Z","shell.execute_reply":"2023-11-13T11:05:24.452949Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":" [[0.14454371 0.66619438]\n [0.50421057 0.35609975]]\n\ntensor([[0.1445, 0.6662],\n [0.5042, 0.3561]], dtype=torch.float64)\n\n [[0.14454371 0.66619438]\n [0.50421057 0.35609975]]\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Basic Math with Pytorch","metadata":{"_uuid":"42cbe3900b733ab12867612d484d6fffaccd5e31","_cell_guid":"6d7038e6-6aaf-4a1e-9204-406ab21082a2"}},{"cell_type":"code","source":"# create tensor \ntensor = torch.ones(3,3)\nprint(\"\\n\",tensor)\n\n# Resize\nprint(\"{}{}\\n\".format(tensor.view(9).shape,tensor.view(9)))\n\n# Addition\nprint(\"Addition: {}\\n\".format(torch.add(tensor,tensor)))\n\n# Subtraction\nprint(\"Subtraction: {}\\n\".format(tensor.sub(tensor)))\n\n# Element wise multiplication\nprint(\"Element wise multiplication: {}\\n\".format(torch.mul(tensor,tensor)))\n\n# Element wise division\nprint(\"Element wise division: {}\\n\".format(torch.div(tensor,tensor)))\n\n# Mean\ntensor = torch.Tensor([1,2,3,4,5])\nprint(\"Mean: {}\".format(tensor.mean()))\n\n# Standart deviation (std)\nprint(\"std: {}\".format(tensor.std()))","metadata":{"_uuid":"66193cb3c790d13b8328c1c1262e1e3c17230bb8","_cell_guid":"e43af8e7-53ab-40bc-a4f8-4cea941c6df0","execution":{"iopub.status.busy":"2023-11-13T11:05:38.819263Z","iopub.execute_input":"2023-11-13T11:05:38.819945Z","iopub.status.idle":"2023-11-13T11:05:38.840452Z","shell.execute_reply.started":"2023-11-13T11:05:38.819869Z","shell.execute_reply":"2023-11-13T11:05:38.839685Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"\n tensor([[1., 1., 1.],\n [1., 1., 1.],\n [1., 1., 1.]])\ntorch.Size([9])tensor([1., 1., 1., 1., 1., 1., 1., 1., 1.])\n\nAddition: tensor([[2., 2., 2.],\n [2., 2., 2.],\n [2., 2., 2.]])\n\nSubtraction: tensor([[0., 0., 0.],\n [0., 0., 0.],\n [0., 0., 0.]])\n\nElement wise multiplication: tensor([[1., 1., 1.],\n [1., 1., 1.],\n [1., 1., 1.]])\n\nElement wise division: tensor([[1., 1., 1.],\n [1., 1., 1.],\n [1., 1., 1.]])\n\nMean: 3.0\nstd: 1.5811388492584229\n","output_type":"stream"}]},{"cell_type":"code","source":"# import variable from pytorch library\nfrom torch.autograd import Variable\n\n# define variable\nvar = Variable(torch.ones(3), requires_grad = True)\nvar","metadata":{"_uuid":"83e3222b53be71e5fc7207da552ce9e9b90486dd","_cell_guid":"fd8ceaa3-f1e2-4761-924e-00a6daca4a82","execution":{"iopub.status.busy":"2023-11-13T11:05:47.974674Z","iopub.execute_input":"2023-11-13T11:05:47.975155Z","iopub.status.idle":"2023-11-13T11:05:47.983040Z","shell.execute_reply.started":"2023-11-13T11:05:47.975079Z","shell.execute_reply":"2023-11-13T11:05:47.982077Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"tensor([1., 1., 1.], requires_grad=True)"},"metadata":{}}]},{"cell_type":"code","source":"# lets make basic backward propagation\n# we have an equation that is y = x^2\narray = [2,4]\ntensor = torch.Tensor(array)\nx = Variable(tensor, requires_grad = True)\ny = x**2\nprint(\" y = \",y)\n\n# recap o equation o = 1/2*sum(y)\no = (1/2)*sum(y)\nprint(\" o = \",o)\n\n# backward\no.backward() # calculates gradients\n\nprint(\"gradients: \",x.grad)","metadata":{"_uuid":"ff4010e2958a72ce45e43118790f3e20dc2abad6","_cell_guid":"cd73c1cf-d250-48e4-bfb7-ffbe8c03c267","execution":{"iopub.status.busy":"2023-11-13T11:06:04.158957Z","iopub.execute_input":"2023-11-13T11:06:04.159634Z","iopub.status.idle":"2023-11-13T11:06:04.169380Z","shell.execute_reply.started":"2023-11-13T11:06:04.159573Z","shell.execute_reply":"2023-11-13T11:06:04.168143Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":" y = tensor([ 4., 16.], grad_fn=)\n o = tensor(10., grad_fn=)\ngradients: tensor([2., 4.])\n","output_type":"stream"}]},{"cell_type":"markdown","source":"
\n### Linear Regression","metadata":{"_uuid":"6d8fa48e6e641da312175509aae00fea2760cb2c","_cell_guid":"c916b8e5-e078-48de-8bc6-a757022ba65d"}},{"cell_type":"code","source":"# As a car company we collect this data from previous selling\n# lets define car prices\ncar_prices_array = [3,4,5,6,7,8,9]\ncar_price_np = np.array(car_prices_array,dtype=np.float32)\ncar_price_np = car_price_np.reshape(-1,1)\ncar_price_tensor = Variable(torch.from_numpy(car_price_np))\n\n# lets define number of car sell\nnumber_of_car_sell_array = [ 7.5, 7, 6.5, 6.0, 5.5, 5.0, 4.5]\nnumber_of_car_sell_np = np.array(number_of_car_sell_array,dtype=np.float32)\nnumber_of_car_sell_np = number_of_car_sell_np.reshape(-1,1)\nnumber_of_car_sell_tensor = Variable(torch.from_numpy(number_of_car_sell_np))\n\n# lets visualize our data\nimport matplotlib.pyplot as plt\nplt.scatter(car_prices_array,number_of_car_sell_array)\nplt.xlabel(\"Car Price $\")\nplt.ylabel(\"Number of Car Sell\")\nplt.title(\"Car Price$ VS Number of Car Sell\")\nplt.show()","metadata":{"_uuid":"0bed0a61494fab620e639745f0c48b341f665bf8","_cell_guid":"b9a3beb3-9e3c-4502-94c2-fe87ac623ca2","execution":{"iopub.status.busy":"2023-11-13T11:06:14.792070Z","iopub.execute_input":"2023-11-13T11:06:14.792399Z","iopub.status.idle":"2023-11-13T11:06:15.022967Z","shell.execute_reply.started":"2023-11-13T11:06:14.792349Z","shell.execute_reply":"2023-11-13T11:06:15.022251Z"},"trusted":true},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}}]},{"cell_type":"code","source":"# Linear Regression with Pytorch\n\n# libraries\nimport torch \nfrom torch.autograd import Variable \nimport torch.nn as nn \nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\n# create class\nclass LinearRegression(nn.Module):\n def __init__(self,input_size,output_size):\n # super function. It inherits from nn.Module and we can access everythink in nn.Module\n super(LinearRegression,self).__init__()\n # Linear function.\n self.linear = nn.Linear(input_dim,output_dim)\n\n def forward(self,x):\n return self.linear(x)\n \n# define model\ninput_dim = 1\noutput_dim = 1\nmodel = LinearRegression(input_dim,output_dim) # input and output size are 1\n\n# MSE\nmse = nn.MSELoss()\n\n# Optimization (find parameters that minimize error)\nlearning_rate = 0.02 # how fast we reach best parameters\noptimizer = torch.optim.SGD(model.parameters(),lr = learning_rate)\n\n# train model\nloss_list = []\niteration_number = 1001\nfor iteration in range(iteration_number):\n \n # optimization\n optimizer.zero_grad() \n \n # Forward to get output\n results = model(car_price_tensor)\n \n # Calculate Loss\n loss = mse(results, number_of_car_sell_tensor)\n \n # backward propagation\n loss.backward()\n \n # Updating parameters\n optimizer.step()\n \n # store loss\n loss_list.append(loss.data)\n \n # print loss\n if(iteration % 50 == 0):\n print('epoch {}, loss {}'.format(iteration, loss.data))\n\nplt.plot(range(iteration_number),loss_list)\nplt.xlabel(\"Number of Iterations\")\nplt.ylabel(\"Loss\")\nplt.show()","metadata":{"_uuid":"8040e01e2bdc25d6fdbff800262f3afe8b9dac3a","_cell_guid":"2b74a84a-29da-44ed-9b5f-649a5c54b8a9","execution":{"iopub.status.busy":"2023-11-13T11:06:24.089601Z","iopub.execute_input":"2023-11-13T11:06:24.090352Z","iopub.status.idle":"2023-11-13T11:06:24.471258Z","shell.execute_reply.started":"2023-11-13T11:06:24.090266Z","shell.execute_reply":"2023-11-13T11:06:24.470016Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"epoch 0, loss 143.12367248535156\nepoch 50, loss 5.254277229309082\nepoch 100, loss 3.5505359172821045\nepoch 150, loss 2.399244785308838\nepoch 200, loss 1.6212701797485352\nepoch 250, loss 1.0955594778060913\nepoch 300, loss 0.7403160929679871\nepoch 350, loss 0.5002629160881042\nepoch 400, loss 0.3380487561225891\nepoch 450, loss 0.2284337431192398\nepoch 500, loss 0.15436197817325592\nepoch 550, loss 0.10430882126092911\nepoch 600, loss 0.07048650830984116\nepoch 650, loss 0.04763070493936539\nepoch 700, loss 0.03218551725149155\nepoch 750, loss 0.02174883894622326\nepoch 800, loss 0.014696342870593071\nepoch 850, loss 0.009930893778800964\nepoch 900, loss 0.0067106192000210285\nepoch 950, loss 0.004534552805125713\nepoch 1000, loss 0.003064179327338934\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"- Number of iteration is 1001.\n- Loss is almost zero that you can see from plot or loss in epoch number 1000.\n- Now we have a trained model.\n- While usign trained model, lets predict car prices.","metadata":{"_uuid":"6f3c764684ae00949ef3f91f5eb75b7bd11c089d","_cell_guid":"b86efed7-1e47-44ad-9223-59c370a42560"}},{"cell_type":"code","source":"# predict our car price \npredicted = model(car_price_tensor).data.numpy()\nplt.scatter(car_prices_array,number_of_car_sell_array,label = \"original data\",color =\"red\")\nplt.scatter(car_prices_array,predicted,label = \"predicted data\",color =\"blue\")\n\nplt.legend()\nplt.xlabel(\"Car Price $\")\nplt.ylabel(\"Number of Car Sell\")\nplt.title(\"Original vs Predicted values\")\nplt.show()","metadata":{"_uuid":"ec56085a9c6e91f18042f8a79dede663abf09cf0","_cell_guid":"abecf557-4d1f-4e2a-a466-001abb4d27d2","execution":{"iopub.status.busy":"2023-11-13T11:06:36.115945Z","iopub.execute_input":"2023-11-13T11:06:36.116680Z","iopub.status.idle":"2023-11-13T11:06:36.436140Z","shell.execute_reply.started":"2023-11-13T11:06:36.116603Z","shell.execute_reply":"2023-11-13T11:06:36.435174Z"},"trusted":true},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"
\n### Logistic Regression","metadata":{"_uuid":"20b4762eb8607ed428703c2156c5aefe8b49ff3f","_cell_guid":"49344c72-d0ea-4092-96fe-ed5508ae6e0b"}},{"cell_type":"code","source":"# Import Libraries\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nfrom torch.utils.data import DataLoader\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split","metadata":{"_uuid":"1382c63fe24710d3b2840e7dcf172cddbf533743","_cell_guid":"a0bf0fa7-c527-4fd3-b504-02a88fc94798","execution":{"iopub.status.busy":"2023-11-13T11:06:55.463241Z","iopub.execute_input":"2023-11-13T11:06:55.463583Z","iopub.status.idle":"2023-11-13T11:06:56.188701Z","shell.execute_reply.started":"2023-11-13T11:06:55.463516Z","shell.execute_reply":"2023-11-13T11:06:56.187728Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"# Prepare Dataset\n# load data\ntrain = pd.read_csv(r\"../input/train.csv\",dtype = np.float32)\n\n# split data into features(pixels) and labels(numbers from 0 to 9)\ntargets_numpy = train.label.values\nfeatures_numpy = train.loc[:,train.columns != \"label\"].values/255 # normalization\n\n# train test split. Size of train data is 80% and size of test data is 20%. \nfeatures_train, features_test, targets_train, targets_test = train_test_split(features_numpy,\n targets_numpy,\n test_size = 0.2,\n random_state = 42) \n\n# create feature and targets tensor for train set. As you remember we need variable to accumulate gradients. Therefore first we create tensor, then we will create variable\nfeaturesTrain = torch.from_numpy(features_train)\ntargetsTrain = torch.from_numpy(targets_train).type(torch.LongTensor) # data type is long\n\n# create feature and targets tensor for test set.\nfeaturesTest = torch.from_numpy(features_test)\ntargetsTest = torch.from_numpy(targets_test).type(torch.LongTensor) # data type is long\n\n# batch_size, epoch and iteration\nbatch_size = 100\nn_iters = 10000\nnum_epochs = n_iters / (len(features_train) / batch_size)\nnum_epochs = int(num_epochs)\n\n# Pytorch train and test sets\ntrain = torch.utils.data.TensorDataset(featuresTrain,targetsTrain)\ntest = torch.utils.data.TensorDataset(featuresTest,targetsTest)\n\n# data loader\ntrain_loader = DataLoader(train, batch_size = batch_size, shuffle = False)\ntest_loader = DataLoader(test, batch_size = batch_size, shuffle = False)\n\n# visualize one of the images in data set\nplt.imshow(features_numpy[10].reshape(28,28))\nplt.axis(\"off\")\nplt.title(str(targets_numpy[10]))\nplt.savefig('graph.png')\nplt.show()","metadata":{"_uuid":"c6e0d7d3843719091564a580dbe08f67ee0d93ec","_cell_guid":"59cdc9d5-da8f-4d7a-abc5-c62b0008afb0","execution":{"iopub.status.busy":"2023-11-13T11:07:04.093778Z","iopub.execute_input":"2023-11-13T11:07:04.094070Z","iopub.status.idle":"2023-11-13T11:07:09.726802Z","shell.execute_reply.started":"2023-11-13T11:07:04.094029Z","shell.execute_reply":"2023-11-13T11:07:09.725560Z"},"trusted":true},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":"# Create Logistic Regression Model\nclass LogisticRegressionModel(nn.Module):\n def __init__(self, input_dim, output_dim):\n super(LogisticRegressionModel, self).__init__()\n # Linear part\n self.linear = nn.Linear(input_dim, output_dim)\n \n def forward(self, x):\n out = self.linear(x)\n return out\n\n# Instantiate Model Class\ninput_dim = 28*28 # size of image px*px\noutput_dim = 10 # labels 0,1,2,3,4,5,6,7,8,9\n\n# create logistic regression model\nmodel = LogisticRegressionModel(input_dim, output_dim)\n\n# Cross Entropy Loss \nerror = nn.CrossEntropyLoss()\n\n# SGD Optimizer \nlearning_rate = 0.001\noptimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)","metadata":{"_uuid":"7c7a7265a23a8101d5ed0c8826dfec3726d6161d","_cell_guid":"03a25584-c567-4b5e-bae1-7bc9e02184fe","execution":{"iopub.status.busy":"2023-11-13T11:07:19.121038Z","iopub.execute_input":"2023-11-13T11:07:19.121407Z","iopub.status.idle":"2023-11-13T11:07:19.129381Z","shell.execute_reply.started":"2023-11-13T11:07:19.121341Z","shell.execute_reply":"2023-11-13T11:07:19.128056Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"# Traning the Model\ncount = 0\nloss_list = []\niteration_list = []\nfor epoch in range(num_epochs):\n for i, (images, labels) in enumerate(train_loader):\n \n # Define variables\n train = Variable(images.view(-1, 28*28))\n labels = Variable(labels)\n \n # Clear gradients\n optimizer.zero_grad()\n \n # Forward propagation\n outputs = model(train)\n \n # Calculate softmax and cross entropy loss\n loss = error(outputs, labels)\n \n # Calculate gradients\n loss.backward()\n \n # Update parameters\n optimizer.step()\n \n count += 1\n \n # Prediction\n if count % 50 == 0:\n # Calculate Accuracy \n correct = 0\n total = 0\n # Predict test dataset\n for images, labels in test_loader: \n test = Variable(images.view(-1, 28*28))\n \n # Forward propagation\n outputs = model(test)\n \n # Get predictions from the maximum value\n predicted = torch.max(outputs.data, 1)[1]\n \n # Total number of labels\n total += len(labels)\n \n # Total correct predictions\n correct += (predicted == labels).sum()\n \n accuracy = 100 * correct / float(total)\n \n # store loss and iteration\n loss_list.append(loss.data)\n iteration_list.append(count)\n if count % 500 == 0:\n # Print Loss\n print('Iteration: {} Loss: {} Accuracy: {}%'.format(count, loss.data, accuracy))","metadata":{"_uuid":"0cab9c3ec72f73db1b06578fa7a51611141e16da","_cell_guid":"82de08d9-7f3c-4eb9-8a99-9d7a8677799c","execution":{"iopub.status.busy":"2023-11-13T11:07:24.967532Z","iopub.execute_input":"2023-11-13T11:07:24.967887Z","iopub.status.idle":"2023-11-13T11:07:54.275309Z","shell.execute_reply.started":"2023-11-13T11:07:24.967820Z","shell.execute_reply":"2023-11-13T11:07:54.274536Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stdout","text":"Iteration: 500 Loss: 1.8553930521011353 Accuracy: 68%\nIteration: 1000 Loss: 1.6237167119979858 Accuracy: 75%\nIteration: 1500 Loss: 1.3030318021774292 Accuracy: 78%\nIteration: 2000 Loss: 1.205854058265686 Accuracy: 80%\nIteration: 2500 Loss: 1.0430572032928467 Accuracy: 81%\nIteration: 3000 Loss: 0.9426226615905762 Accuracy: 82%\nIteration: 3500 Loss: 0.8949837684631348 Accuracy: 82%\nIteration: 4000 Loss: 0.7554283142089844 Accuracy: 83%\nIteration: 4500 Loss: 0.9816515445709229 Accuracy: 83%\nIteration: 5000 Loss: 0.7963581681251526 Accuracy: 84%\nIteration: 5500 Loss: 0.7545013427734375 Accuracy: 84%\nIteration: 6000 Loss: 0.867022693157196 Accuracy: 84%\nIteration: 6500 Loss: 0.6659964919090271 Accuracy: 84%\nIteration: 7000 Loss: 0.7171083688735962 Accuracy: 85%\nIteration: 7500 Loss: 0.6411340236663818 Accuracy: 85%\nIteration: 8000 Loss: 0.7314873337745667 Accuracy: 85%\nIteration: 8500 Loss: 0.5450932383537292 Accuracy: 85%\nIteration: 9000 Loss: 0.6581491231918335 Accuracy: 85%\nIteration: 9500 Loss: 0.5294022560119629 Accuracy: 85%\n","output_type":"stream"}]},{"cell_type":"code","source":"# visualization\nplt.plot(iteration_list,loss_list)\nplt.xlabel(\"Number of iteration\")\nplt.ylabel(\"Loss\")\nplt.title(\"Logistic Regression: Loss vs Number of iteration\")\nplt.show()","metadata":{"_uuid":"db87c03e9d263f07eb75f82a914d3e966895a6c1","_cell_guid":"924e9606-e155-4e39-89d3-39f941fd52f8","execution":{"iopub.status.busy":"2023-11-13T11:08:05.269002Z","iopub.execute_input":"2023-11-13T11:08:05.269684Z","iopub.status.idle":"2023-11-13T11:08:05.494503Z","shell.execute_reply.started":"2023-11-13T11:08:05.269612Z","shell.execute_reply":"2023-11-13T11:08:05.493463Z"},"trusted":true},"execution_count":18,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"
\n### Artificial Neural Network (ANN)","metadata":{"_uuid":"ea9eba414f2f0f1e63ef564dc0ee708c753ff51f","_cell_guid":"4d38db05-fad0-468c-9000-20caf5465eca"}},{"cell_type":"code","source":"# Import Libraries\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable","metadata":{"_uuid":"cf25ee4b28129a47bac4c9dc7d932295155b79f7","_cell_guid":"6925f8ed-54b7-4d9a-9801-acd65f213bc9","execution":{"iopub.status.busy":"2023-11-13T11:08:36.873883Z","iopub.execute_input":"2023-11-13T11:08:36.874214Z","iopub.status.idle":"2023-11-13T11:08:36.878382Z","shell.execute_reply.started":"2023-11-13T11:08:36.874162Z","shell.execute_reply":"2023-11-13T11:08:36.877472Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"code","source":"# Create ANN Model\nclass ANNModel(nn.Module):\n \n def __init__(self, input_dim, hidden_dim, output_dim):\n super(ANNModel, self).__init__()\n \n # Linear function 1: 784 --> 150\n self.fc1 = nn.Linear(input_dim, hidden_dim) \n # Non-linearity 1\n self.relu1 = nn.ReLU()\n \n # Linear function 2: 150 --> 150\n self.fc2 = nn.Linear(hidden_dim, hidden_dim)\n # Non-linearity 2\n self.tanh2 = nn.Tanh()\n \n # Linear function 3: 150 --> 150\n self.fc3 = nn.Linear(hidden_dim, hidden_dim)\n # Non-linearity 3\n self.elu3 = nn.ELU()\n \n # Linear function 4 (readout): 150 --> 10\n self.fc4 = nn.Linear(hidden_dim, output_dim) \n \n def forward(self, x):\n # Linear function 1\n out = self.fc1(x)\n # Non-linearity 1\n out = self.relu1(out)\n \n # Linear function 2\n out = self.fc2(out)\n # Non-linearity 2\n out = self.tanh2(out)\n \n # Linear function 2\n out = self.fc3(out)\n # Non-linearity 2\n out = self.elu3(out)\n \n # Linear function 4 (readout)\n out = self.fc4(out)\n return out\n\n# instantiate ANN\ninput_dim = 28*28\nhidden_dim = 150 \noutput_dim = 10\n\n# Create ANN\nmodel = ANNModel(input_dim, hidden_dim, output_dim)\n\n# Cross Entropy Loss \nerror = nn.CrossEntropyLoss()\n\n# SGD Optimizer\nlearning_rate = 0.02\noptimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)","metadata":{"_uuid":"cefd0bb2f23b80f30ca65cbb08859ad81ab12e08","_cell_guid":"3472f1c1-5888-4abe-822c-3a493a5f8be5","execution":{"iopub.status.busy":"2023-11-13T11:08:52.342774Z","iopub.execute_input":"2023-11-13T11:08:52.343108Z","iopub.status.idle":"2023-11-13T11:08:52.354165Z","shell.execute_reply.started":"2023-11-13T11:08:52.343043Z","shell.execute_reply":"2023-11-13T11:08:52.353200Z"},"trusted":true},"execution_count":23,"outputs":[]},{"cell_type":"code","source":"# ANN model training\ncount = 0\nloss_list = []\niteration_list = []\naccuracy_list = []\nfor epoch in range(num_epochs):\n for i, (images, labels) in enumerate(train_loader):\n\n train = Variable(images.view(-1, 28*28))\n labels = Variable(labels)\n \n # Clear gradients\n optimizer.zero_grad()\n \n # Forward propagation\n outputs = model(train)\n \n # Calculate softmax and ross entropy loss\n loss = error(outputs, labels)\n \n # Calculating gradients\n loss.backward()\n \n # Update parameters\n optimizer.step()\n \n count += 1\n \n if count % 50 == 0:\n # Calculate Accuracy \n correct = 0\n total = 0\n # Predict test dataset\n for images, labels in test_loader:\n\n test = Variable(images.view(-1, 28*28))\n \n # Forward propagation\n outputs = model(test)\n \n # Get predictions from the maximum value\n predicted = torch.max(outputs.data, 1)[1]\n \n # Total number of labels\n total += len(labels)\n\n # Total correct predictions\n correct += (predicted == labels).sum()\n \n accuracy = 100 * correct / float(total)\n \n # store loss and iteration\n loss_list.append(loss.data)\n iteration_list.append(count)\n accuracy_list.append(accuracy)\n if count % 500 == 0:\n # Print Loss\n print('Iteration: {} Loss: {} Accuracy: {} %'.format(count, loss.data, accuracy))","metadata":{"_uuid":"c91694f3af94e4e1b76ab01489e186718c70ccd3","_cell_guid":"7550e98b-5011-4d09-88ee-97b0ecbc6f19","execution":{"iopub.status.busy":"2023-11-13T11:10:27.103078Z","iopub.execute_input":"2023-11-13T11:10:27.103405Z","iopub.status.idle":"2023-11-13T11:11:35.408814Z","shell.execute_reply.started":"2023-11-13T11:10:27.103356Z","shell.execute_reply":"2023-11-13T11:11:35.408047Z"},"trusted":true},"execution_count":25,"outputs":[{"name":"stdout","text":"Iteration: 500 Loss: 0.06356629729270935 Accuracy: 96 %\nIteration: 1000 Loss: 0.03739963471889496 Accuracy: 96 %\nIteration: 1500 Loss: 0.024180764332413673 Accuracy: 96 %\nIteration: 2000 Loss: 0.046261247247457504 Accuracy: 96 %\nIteration: 2500 Loss: 0.06799810379743576 Accuracy: 96 %\nIteration: 3000 Loss: 0.015149221755564213 Accuracy: 96 %\nIteration: 3500 Loss: 0.03719361871480942 Accuracy: 96 %\nIteration: 4000 Loss: 0.009945693425834179 Accuracy: 96 %\nIteration: 4500 Loss: 0.051873140037059784 Accuracy: 96 %\nIteration: 5000 Loss: 0.027996879070997238 Accuracy: 96 %\nIteration: 5500 Loss: 0.0892927274107933 Accuracy: 96 %\nIteration: 6000 Loss: 0.024367637932300568 Accuracy: 96 %\nIteration: 6500 Loss: 0.037491630762815475 Accuracy: 96 %\nIteration: 7000 Loss: 0.024159817025065422 Accuracy: 96 %\nIteration: 7500 Loss: 0.016099395230412483 Accuracy: 96 %\nIteration: 8000 Loss: 0.08756618201732635 Accuracy: 96 %\nIteration: 8500 Loss: 0.0096824262291193 Accuracy: 96 %\nIteration: 9000 Loss: 0.021723752841353416 Accuracy: 96 %\nIteration: 9500 Loss: 0.006981358397752047 Accuracy: 96 %\n","output_type":"stream"}]},{"cell_type":"code","source":"# visualization loss \nplt.plot(iteration_list,loss_list)\nplt.xlabel(\"Number of iteration\")\nplt.ylabel(\"Loss\")\nplt.title(\"ANN: Loss vs Number of iteration\")\nplt.show()\n\n# visualization accuracy \nplt.plot(iteration_list,accuracy_list,color = \"red\")\nplt.xlabel(\"Number of iteration\")\nplt.ylabel(\"Accuracy\")\nplt.title(\"ANN: Accuracy vs Number of iteration\")\nplt.show()","metadata":{"_uuid":"c5e2e6da7f1ee801e38358dc28d4c99e32d2b761","_cell_guid":"5579a7d6-7766-4d0f-b9d0-584cb4f28321","execution":{"iopub.status.busy":"2023-11-13T11:12:58.416696Z","iopub.execute_input":"2023-11-13T11:12:58.417002Z","iopub.status.idle":"2023-11-13T11:12:58.825804Z","shell.execute_reply.started":"2023-11-13T11:12:58.416955Z","shell.execute_reply":"2023-11-13T11:12:58.825186Z"},"trusted":true},"execution_count":26,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}}]}]} \ No newline at end of file diff --git "a/\352\263\274\354\240\23411_\354\241\260\355\230\204\354\247\200.md" "b/\352\263\274\354\240\23411_\354\241\260\355\230\204\354\247\200.md" new file mode 100644 index 0000000..95aaa75 --- /dev/null +++ "b/\352\263\274\354\240\23411_\354\241\260\355\230\204\354\247\200.md" @@ -0,0 +1,40 @@ +5. 하이퍼파라미터 튜닝
+
+1) 튜닝 프로세스
+다뤄야 할 하이퍼파라미터 :
+- 학습률 (α) : 중요1
+- 모멘텀(Momentum) 알고리즘의 \betaβ : 중요2, 기본값 0.9
+- 은닉 유닛의 수 : 중요2
+- 미니배치 크기 : 중요2
+- 은닉층의 개수 : 중요3
+- 학습률 감쇠(learning rate decay) 정도 : 중요3
+- 아담(Adam) 알고리즘의 β1, β2, ϵ
+
+어떤 값을 탐색할지 어떻게 정하는가?
+- 무작위 접근 방식 : 격자점이 아니라 무작위적으로 접근한다. 어떤 하이퍼파라미터가 문제 해결에 더 중요한지 미리 알 수 없기 때문이다.
+- 정밀화 접근 : 우선 전체 하이퍼파라미터 공간에서 탐색하여 좋은 점을 찾은 후, 그 근방에서 더 정밀하게 탐색하는 과정이다.
+
+2) 적절한 척도 선택하기
+무작위로 뽑는 것이 합리적인 하이퍼파라미터 :
+- 은닉 유닛의 수
+- 은닉층의 수
+
+학습률 α의 경우 :
+- 1 과 0.0001 사이의 값중에 균일하게 무작위 값을 고르게 되면, 90%의 값이 1 과 0.1 사이에 존재하기 때문에, 공평하다고 할 수 없다.
+- 따라서 선형척도대신 로그척도에서 하이퍼파라미터를 찾는 것이 합리적이다.
+- 위 예시에 따르면 0과 -4 사이에 균일하게 무작위로 고르고 10의 지수로 바꿔주는 것이다.
+
+지수 가중 이동 평균에 사용되는 β의 경우 :
+- 0.9 와 0.999 사이의 값을 탐색하는 것은 비합리적이기 때문에 1-β 를 취해준 후, 위의 예시와 마찬가지로 로그척도에서 무작위 값을 선택하여 탐색한다.
+
+왜 선형척도에서 샘플을 뽑은 것이 안좋을까?
+-> 위의 값들은 1에 가까울 수록 알고리즘 결과에 더 큰 영향을 끼치기 때문이다.
+
+3) 하이퍼파라미터 튜닝 실전
+하이퍼파라미터 튜닝 방법 2가지
+1. 모델 돌보기(baby sitting one model) = 판다 접근
+- 컴퓨터의 자원이 많이 필요하지 않거나, 적은 숫자의 모델을 한번에 학습 시킬 수 있을 때 사용한다.
+- 하나의 모델로 매일 성능을 지켜보면서, 학습 속도를 조금 씩 바꾸는 방식이다.
+2. 동시에 여러 모델 훈련(Training many models in parallel) = 캐비어 접근
+- 컴퓨터의 자원이 충분히 많아 여러 모델을 한번에 학습 시킬 수 있을 때 사용한다.
+ diff --git "a/\352\263\274\354\240\23412_\354\241\260\355\230\204\354\247\200.md" "b/\352\263\274\354\240\23412_\354\241\260\355\230\204\354\247\200.md" new file mode 100644 index 0000000..174dad8 --- /dev/null +++ "b/\352\263\274\354\240\23412_\354\241\260\355\230\204\354\247\200.md" @@ -0,0 +1,64 @@ +6. 배치 정규화
+1) 배치 정규화
+배치 정규화는 하이퍼파라미터 탐색을 쉽게 만들어줄 뿐만 아니라, 신경망과 하이퍼파라미터의 상관관계를 줄여준다. 즉 더 많은 하이퍼파라미터가 잘 작동한다.
+
+로지스틱 회귀 등으로 모델을 학습시킬 때 입력 변수들을 정규화하면
+누워있는 학습 등고선이 경사하강법에 적합하도록 둥근 형태로 바뀌어 학습이 빨라진다.
+평균을 계산 -> 입력 변수의 평균을 뺐다
+분산을 계산 -> 하나씩 제곱해준다
+
+은닉층에 대해 w^[3]이나 b^[3]을 빠르게 학습시킬 수 있도록 a^[2]같은 값을 정규화할 수 있나? (다음 층 입력값인 a^[2]가 w^[3]와 b^[3] 학습에 영향을 주기 때문)
+-> 배치 정규화가 하는 일
+-> a^[2]를 정규화할지 z^[2]를 정규화할지
+-> z^[2]를 정규화하는 것이 더 자주 쓰인다
+
+어떻게 배치 정규화를 구현하는가?
+신경망에서 사잇값들이 주어졌다고 할 때 은닉 유닛의 값 z^(1)부터 z^(m)까지 있다고 하자
+이값들에 대한 평균을 계산하고 분산을 계산한다
+그리고 z^(i)에 대해서 정규화를 하여 z^(i)_norm을 얻는다 -> z값에 대해 정규화를 거쳐 표준편차가 1이 되도록 만든다
+
+하지만 은닉 유닛이 항상 평균 0, 표준편차 1을 갖는 것이 좋지만은 않다
+그래서 대신 z~를 계산한다
+γ와 β는 학습과정에서 학습하는 파라미터이다. 둘을 이용하면 z~의 평균을 원하는 대로 설정할 수 있다
+정규화 이후 다시 선형변환하는 이유는 항상 같은 분포 값을 갖지 않게 하기 위함이다.
+
+2) 배치 정규화 적용시키기
+은닉층에서 두 단계로 나뉜다
+1. 선형결합인 z 를 계산하고, 이를 배치 정규화 시킨다
+2. 정규화 된 값들을 활성화 함수를 거쳐 활성화 값 a 를 얻는다
+
+배치정규화에서 사용되는 β^[1], β^[2]는 모멘텀이나 RMSprop 알고리즘에 쓰이는 하이퍼파라미터 β와 다르다
+
+딥러닝 프로그래밍 프레임워크를 사용하면 배치 정규화를 따로 구현할 필요가 없다
+
+● 훈련 집합의 미니 배치에 적용
+첫 번째 미니배치에 대해서 z^[1]을 계산한다
+z^[1]의 평균과 분산을 계산한 뒤에 평균을 빼고 표준편차로 나눠 배치 정규화를 진행한다
+β^[1]와 γ^[1]을 이용해서 값을 조정한다 // z~^[1]을 얻게 되고 활성화 함수를 적용해 a^[1]을 얻게 된다
+그리고 z^[2]를 w^[2]와 b^[2]를 이용해 계산해낸다
+
+이후 두 번째 미니배치로 이동한다
+
+선형결합 단계에서 상수항 b 는 없어진다. 그 이유는 배치 정규화 과정에서 z의 평균을 빼주면 사라지기 때문이다.
+
+3) 배치 정규화가 잘 작동하는 이유는 무엇일까?
+배치 정규화는 입력특성 XX 의 평균을 0, 분산을 1로 만듦으로써 학습 속도를 빠르게 한다.
+배치 정규화가 잘 되는 이유중 하나는 이전 층의 가중치 영향을 덜 받게 하는데 있다. 은닉층 값의 분포 변화를 줄여줘서, 입력 값의 분포를 제한하기 때문이다. 즉, 배치 정규화는 입력값이 바뀌어서 발생하는 문제를 안정화 시킨다. 앞층과 뒷층의 매개변수의 상관 관계를 줄여주기 때문에, 학습속도를 향상시킬 수 있다.
+배치 정규화의 또 다른 효과는 파라미터의 정규화(regularization)이다. 미니배치로 계산한 평균과 분산은 전체 데이터의 일부으로 추정한 것이기 때문에 잡음이 끼어있다.
+드롭아웃의 경우 은닉유닛에 확률에 따라 0 혹은 1을 곱하기 때문에 곱셈 잡음이 있다. 배치 정규화의 경우 곱셈잡음(×1/​σ)과 덧셈 잡음( +(−μ) )이 동시에 있다. 따라서 약간의 정규화 효과가 있다.
+은닉층에 잡음을 추가한다는 것은 이후 은닉층이 하나의 은닉 유닛에 너무 의존하지 않도록 만든다.
+큰 미니배치를 사용시 이 정규화 효과는 상대적으로 약해진다
+
+4) 테스트시의 배치 정규화
+테스트 시에는 배치가 하나이기 때문에 평균과 분산을 계산할 수 없다. 따라서, 학습시에 사용된 미니배치들의 지수 가중 이동 평균을 추정치로 사용한다.
+ + + + + + + + + + + diff --git "a/\352\263\274\354\240\23412_\354\272\220\352\270\200 \355\225\204\354\202\254_\354\241\260\355\230\204\354\247\200-a-single-neuron.ipynb" "b/\352\263\274\354\240\23412_\354\272\220\352\270\200 \355\225\204\354\202\254_\354\241\260\355\230\204\354\247\200-a-single-neuron.ipynb" new file mode 100644 index 0000000..b8d2ee4 --- /dev/null +++ "b/\352\263\274\354\240\23412_\354\272\220\352\270\200 \355\225\204\354\202\254_\354\241\260\355\230\204\354\247\200-a-single-neuron.ipynb" @@ -0,0 +1 @@ +{"metadata":{"jupytext":{"cell_metadata_filter":"-all","formats":"ipynb"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":1480608,"sourceType":"datasetVersion","datasetId":829369}],"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# A Single Neuron #","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\nplt.style.use('seaborn-v0_8-whitegrid')\n# Set Matplotlib defaults\nplt.rc('figure', autolayout=True)\nplt.rc('axes', labelweight='bold', labelsize='large',\n titleweight='bold', titlesize=18, titlepad=10)\n\n# Setup feedback system\nfrom learntools.core import binder\nbinder.bind(globals())\nfrom learntools.deep_learning_intro.ex1 import *","metadata":{"execution":{"iopub.status.busy":"2023-11-27T11:37:20.352408Z","iopub.execute_input":"2023-11-27T11:37:20.353083Z","iopub.status.idle":"2023-11-27T11:37:20.360283Z","shell.execute_reply.started":"2023-11-27T11:37:20.353042Z","shell.execute_reply":"2023-11-27T11:37:20.359047Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\n\nred_wine = pd.read_csv('../input/dl-course-data/red-wine.csv')\nred_wine.head()","metadata":{"execution":{"iopub.status.busy":"2023-11-27T11:37:25.753890Z","iopub.execute_input":"2023-11-27T11:37:25.754271Z","iopub.status.idle":"2023-11-27T11:37:25.783976Z","shell.execute_reply.started":"2023-11-27T11:37:25.754242Z","shell.execute_reply":"2023-11-27T11:37:25.782847Z"},"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":" fixed acidity volatile acidity citric acid residual sugar chlorides \\\n0 7.4 0.70 0.00 1.9 0.076 \n1 7.8 0.88 0.00 2.6 0.098 \n2 7.8 0.76 0.04 2.3 0.092 \n3 11.2 0.28 0.56 1.9 0.075 \n4 7.4 0.70 0.00 1.9 0.076 \n\n free sulfur dioxide total sulfur dioxide density pH sulphates \\\n0 11.0 34.0 0.9978 3.51 0.56 \n1 25.0 67.0 0.9968 3.20 0.68 \n2 15.0 54.0 0.9970 3.26 0.65 \n3 17.0 60.0 0.9980 3.16 0.58 \n4 11.0 34.0 0.9978 3.51 0.56 \n\n alcohol quality \n0 9.4 5 \n1 9.8 5 \n2 9.8 5 \n3 9.8 6 \n4 9.4 5 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
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"},"metadata":{}}]},{"cell_type":"code","source":"red_wine.shape # (rows, columns)","metadata":{"execution":{"iopub.status.busy":"2023-11-27T11:37:29.409545Z","iopub.execute_input":"2023-11-27T11:37:29.410004Z","iopub.status.idle":"2023-11-27T11:37:29.417305Z","shell.execute_reply.started":"2023-11-27T11:37:29.409967Z","shell.execute_reply":"2023-11-27T11:37:29.416053Z"},"trusted":true},"execution_count":16,"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"(1599, 12)"},"metadata":{}}]},{"cell_type":"markdown","source":"# 1) Input shape #","metadata":{}},{"cell_type":"code","source":"# YOUR CODE HERE\ninput_shape = [11]\n\nq_1.check()","metadata":{"lines_to_next_cell":0,"execution":{"iopub.status.busy":"2023-11-27T11:38:18.007849Z","iopub.execute_input":"2023-11-27T11:38:18.008311Z","iopub.status.idle":"2023-11-27T11:38:18.018178Z","shell.execute_reply.started":"2023-11-27T11:38:18.008276Z","shell.execute_reply":"2023-11-27T11:38:18.017010Z"},"trusted":true},"execution_count":20,"outputs":[{"output_type":"display_data","data":{"text/plain":"","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.3333333333333333, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"1_Q1\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct","text/markdown":"Correct"},"metadata":{}}]},{"cell_type":"markdown","source":"# 2) Define a linear model\n","metadata":{}},{"cell_type":"code","source":"from tensorflow import keras\nfrom tensorflow.keras import layers\n\n# YOUR CODE HERE\nmodel = keras.Sequential([\n layers.Dense(units=1, input_shape=[11])\n])\n\nq_2.check()","metadata":{"lines_to_next_cell":0,"execution":{"iopub.status.busy":"2023-11-27T11:39:01.663080Z","iopub.execute_input":"2023-11-27T11:39:01.663492Z","iopub.status.idle":"2023-11-27T11:39:02.001567Z","shell.execute_reply.started":"2023-11-27T11:39:01.663457Z","shell.execute_reply":"2023-11-27T11:39:02.000467Z"},"trusted":true},"execution_count":22,"outputs":[{"output_type":"display_data","data":{"text/plain":"","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.3333333333333333, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"2_Q2\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct","text/markdown":"Correct"},"metadata":{}}]},{"cell_type":"markdown","source":"# 3) Look at the weights","metadata":{}},{"cell_type":"code","source":"w, b = model.weights\n\nq_3.check()","metadata":{"lines_to_next_cell":0,"execution":{"iopub.status.busy":"2023-11-27T11:39:18.614260Z","iopub.execute_input":"2023-11-27T11:39:18.614732Z","iopub.status.idle":"2023-11-27T11:39:18.624894Z","shell.execute_reply.started":"2023-11-27T11:39:18.614695Z","shell.execute_reply":"2023-11-27T11:39:18.623696Z"},"trusted":true},"execution_count":24,"outputs":[{"output_type":"display_data","data":{"text/plain":"","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.3333333333333333, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"3_Q3\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct: Do you see how there's one weight for each input (and a bias)? Notice though that there doesn't seem to be any pattern to the values the weights have. Before the model is trained, the weights are set to random numbers (and the bias to 0.0). A neural network learns by finding better values for its weights.","text/markdown":"Correct: Do you see how there's one weight for each input (and a bias)? Notice though that there doesn't seem to be any pattern to the values the weights have. Before the model is trained, the weights are set to random numbers (and the bias to 0.0). A neural network learns by finding better values for its weights.\n"},"metadata":{}}]},{"cell_type":"markdown","source":"# Optional: Plot the output of an untrained linear model\n","metadata":{}},{"cell_type":"code","source":"import tensorflow as tf\nimport matplotlib.pyplot as plt\n\nmodel = keras.Sequential([\n layers.Dense(1, input_shape=[1]),\n])\n\nx = tf.linspace(-1.0, 1.0, 100)\ny = model.predict(x)\n\nplt.figure(dpi=100)\nplt.plot(x, y, 'k')\nplt.xlim(-1, 1)\nplt.ylim(-1, 1)\nplt.xlabel(\"Input: x\")\nplt.ylabel(\"Target y\")\nw, b = model.weights # you could also use model.get_weights() here\nplt.title(\"Weight: {:0.2f}\\nBias: {:0.2f}\".format(w[0][0], b[0]))\nplt.show()","metadata":{"lines_to_next_cell":0,"execution":{"iopub.status.busy":"2023-11-27T11:39:25.902498Z","iopub.execute_input":"2023-11-27T11:39:25.902960Z","iopub.status.idle":"2023-11-27T11:39:26.836029Z","shell.execute_reply.started":"2023-11-27T11:39:25.902924Z","shell.execute_reply":"2023-11-27T11:39:26.834870Z"},"trusted":true},"execution_count":25,"outputs":[{"name":"stdout","text":"4/4 [==============================] - 0s 3ms/step\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]}]} \ No newline at end of file diff --git "a/\352\263\274\354\240\23412_\354\272\220\352\270\200 \355\225\204\354\202\254_\354\241\260\355\230\204\354\247\200-binary-classification.ipynb" "b/\352\263\274\354\240\23412_\354\272\220\352\270\200 \355\225\204\354\202\254_\354\241\260\355\230\204\354\247\200-binary-classification.ipynb" new file mode 100644 index 0000000..20ef074 --- /dev/null +++ "b/\352\263\274\354\240\23412_\354\272\220\352\270\200 \355\225\204\354\202\254_\354\241\260\355\230\204\354\247\200-binary-classification.ipynb" @@ -0,0 +1 @@ +{"metadata":{"jupytext":{"cell_metadata_filter":"-all","formats":"ipynb"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":1480608,"sourceType":"datasetVersion","datasetId":829369}],"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Binary Classification #","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nplt.style.use('seaborn-v0_8-whitegrid')\n\nplt.rc('figure', autolayout=True)\nplt.rc('axes', labelweight='bold', labelsize='large',\n titleweight='bold', titlesize=18, titlepad=10)\nplt.rc('animation', html='html5')\n\nfrom learntools.core import binder\nbinder.bind(globals())\nfrom learntools.deep_learning_intro.ex6 import *","metadata":{"lines_to_next_cell":0,"execution":{"iopub.status.busy":"2023-11-27T12:03:02.484058Z","iopub.execute_input":"2023-11-27T12:03:02.485055Z","iopub.status.idle":"2023-11-27T12:03:03.002885Z","shell.execute_reply.started":"2023-11-27T12:03:02.485018Z","shell.execute_reply":"2023-11-27T12:03:03.001462Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.compose import make_column_transformer\n\nhotel = pd.read_csv('../input/dl-course-data/hotel.csv')\n\nX = hotel.copy()\ny = X.pop('is_canceled')\n\nX['arrival_date_month'] = \\\n X['arrival_date_month'].map(\n {'January':1, 'February': 2, 'March':3,\n 'April':4, 'May':5, 'June':6, 'July':7,\n 'August':8, 'September':9, 'October':10,\n 'November':11, 'December':12}\n )\n\nfeatures_num = [\n \"lead_time\", \"arrival_date_week_number\",\n \"arrival_date_day_of_month\", \"stays_in_weekend_nights\",\n \"stays_in_week_nights\", \"adults\", \"children\", \"babies\",\n \"is_repeated_guest\", \"previous_cancellations\",\n \"previous_bookings_not_canceled\", \"required_car_parking_spaces\",\n \"total_of_special_requests\", \"adr\",\n]\nfeatures_cat = [\n \"hotel\", \"arrival_date_month\", \"meal\",\n \"market_segment\", \"distribution_channel\",\n \"reserved_room_type\", \"deposit_type\", \"customer_type\",\n]\n\ntransformer_num = make_pipeline(\n SimpleImputer(strategy=\"constant\"), # there are a few missing values\n StandardScaler(),\n)\ntransformer_cat = make_pipeline(\n SimpleImputer(strategy=\"constant\", fill_value=\"NA\"),\n OneHotEncoder(handle_unknown='ignore'),\n)\n\npreprocessor = make_column_transformer(\n (transformer_num, features_num),\n (transformer_cat, features_cat),\n)\n\n# stratify - make sure classes are evenlly represented across splits\nX_train, X_valid, y_train, y_valid = \\\n train_test_split(X, y, stratify=y, train_size=0.75)\n\nX_train = preprocessor.fit_transform(X_train)\nX_valid = preprocessor.transform(X_valid)\n\ninput_shape = [X_train.shape[1]]","metadata":{"lines_to_next_cell":2,"execution":{"iopub.status.busy":"2023-11-27T12:03:06.145348Z","iopub.execute_input":"2023-11-27T12:03:06.145970Z","iopub.status.idle":"2023-11-27T12:03:09.846942Z","shell.execute_reply.started":"2023-11-27T12:03:06.145926Z","shell.execute_reply":"2023-11-27T12:03:09.845118Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"markdown","source":"# 1) Define Model #","metadata":{}},{"cell_type":"code","source":"from tensorflow import keras\nfrom tensorflow.keras import layers\n\nmodel = keras.Sequential([\n layers.BatchNormalization(input_shape=input_shape),\n layers.Dense(256, activation='relu'),\n layers.BatchNormalization(),\n layers.Dropout(0.3),\n layers.Dense(256, activation='relu'),\n layers.BatchNormalization(),\n layers.Dropout(0.3),\n layers.Dense(1, activation='sigmoid'),\n])\n\nq_1.check()","metadata":{"lines_to_next_cell":2,"execution":{"iopub.status.busy":"2023-11-27T12:03:42.508328Z","iopub.execute_input":"2023-11-27T12:03:42.508723Z","iopub.status.idle":"2023-11-27T12:03:58.506155Z","shell.execute_reply.started":"2023-11-27T12:03:42.508692Z","shell.execute_reply":"2023-11-27T12:03:58.504629Z"},"trusted":true},"execution_count":4,"outputs":[{"output_type":"display_data","data":{"text/plain":"","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.3333333333333333, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"1_Q1\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct","text/markdown":"Correct"},"metadata":{}}]},{"cell_type":"markdown","source":"# 2) Add Optimizer, Loss, and Metric #","metadata":{}},{"cell_type":"code","source":"model.compile(\n optimizer='adam',\n loss='binary_crossentropy',\n metrics=['binary_accuracy'],\n)\n\nq_2.check()","metadata":{"lines_to_next_cell":0,"execution":{"iopub.status.busy":"2023-11-27T12:04:11.030398Z","iopub.execute_input":"2023-11-27T12:04:11.030825Z","iopub.status.idle":"2023-11-27T12:04:11.060595Z","shell.execute_reply.started":"2023-11-27T12:04:11.030792Z","shell.execute_reply":"2023-11-27T12:04:11.058936Z"},"trusted":true},"execution_count":6,"outputs":[{"output_type":"display_data","data":{"text/plain":"","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.3333333333333333, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"2_Q2\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct","text/markdown":"Correct"},"metadata":{}}]},{"cell_type":"code","source":"early_stopping = keras.callbacks.EarlyStopping(\n patience=5,\n min_delta=0.001,\n restore_best_weights=True,\n)\nhistory = model.fit(\n X_train, y_train,\n validation_data=(X_valid, y_valid),\n batch_size=512,\n epochs=200,\n callbacks=[early_stopping],\n)\n\nhistory_df = pd.DataFrame(history.history)\nhistory_df.loc[:, ['loss', 'val_loss']].plot(title=\"Cross-entropy\")\nhistory_df.loc[:, ['binary_accuracy', 'val_binary_accuracy']].plot(title=\"Accuracy\")","metadata":{"execution":{"iopub.status.busy":"2023-11-27T12:04:16.292594Z","iopub.execute_input":"2023-11-27T12:04:16.293027Z","iopub.status.idle":"2023-11-27T12:06:42.290607Z","shell.execute_reply.started":"2023-11-27T12:04:16.292996Z","shell.execute_reply":"2023-11-27T12:06:42.289059Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"Epoch 1/200\n175/175 [==============================] - 6s 19ms/step - loss: 0.4827 - binary_accuracy: 0.7719 - val_loss: 0.4312 - val_binary_accuracy: 0.8042\nEpoch 2/200\n175/175 [==============================] - 3s 15ms/step - loss: 0.4253 - binary_accuracy: 0.8015 - val_loss: 0.4025 - val_binary_accuracy: 0.8123\nEpoch 3/200\n175/175 [==============================] - 3s 16ms/step - loss: 0.4106 - binary_accuracy: 0.8093 - val_loss: 0.3956 - val_binary_accuracy: 0.8149\nEpoch 4/200\n175/175 [==============================] - 3s 16ms/step - loss: 0.4019 - binary_accuracy: 0.8126 - val_loss: 0.3895 - val_binary_accuracy: 0.8190\nEpoch 5/200\n175/175 [==============================] - 3s 15ms/step - loss: 0.3973 - binary_accuracy: 0.8147 - val_loss: 0.3876 - val_binary_accuracy: 0.8184\nEpoch 6/200\n175/175 [==============================] - 3s 15ms/step - loss: 0.3927 - binary_accuracy: 0.8171 - val_loss: 0.3835 - val_binary_accuracy: 0.8202\nEpoch 7/200\n175/175 [==============================] - 3s 15ms/step - loss: 0.3891 - binary_accuracy: 0.8193 - val_loss: 0.3820 - val_binary_accuracy: 0.8234\nEpoch 8/200\n175/175 [==============================] - 3s 15ms/step - loss: 0.3852 - binary_accuracy: 0.8213 - val_loss: 0.3794 - val_binary_accuracy: 0.8240\nEpoch 9/200\n175/175 [==============================] - 2s 14ms/step - loss: 0.3834 - binary_accuracy: 0.8235 - val_loss: 0.3777 - val_binary_accuracy: 0.8268\nEpoch 10/200\n175/175 [==============================] - 3s 15ms/step - loss: 0.3798 - binary_accuracy: 0.8241 - val_loss: 0.3741 - val_binary_accuracy: 0.8276\nEpoch 11/200\n175/175 [==============================] - 3s 15ms/step - loss: 0.3775 - binary_accuracy: 0.8262 - val_loss: 0.3734 - val_binary_accuracy: 0.8273\nEpoch 12/200\n175/175 [==============================] - 3s 17ms/step - loss: 0.3742 - binary_accuracy: 0.8280 - val_loss: 0.3726 - val_binary_accuracy: 0.8274\nEpoch 13/200\n175/175 [==============================] - 3s 16ms/step - loss: 0.3711 - binary_accuracy: 0.8287 - val_loss: 0.3689 - val_binary_accuracy: 0.8290\nEpoch 14/200\n175/175 [==============================] - 3s 15ms/step - loss: 0.3695 - binary_accuracy: 0.8301 - val_loss: 0.3657 - val_binary_accuracy: 0.8318\nEpoch 15/200\n175/175 [==============================] - 3s 16ms/step - loss: 0.3679 - binary_accuracy: 0.8311 - val_loss: 0.3637 - val_binary_accuracy: 0.8323\nEpoch 16/200\n175/175 [==============================] - 3s 17ms/step - loss: 0.3663 - binary_accuracy: 0.8294 - val_loss: 0.3620 - val_binary_accuracy: 0.8328\nEpoch 17/200\n175/175 [==============================] - 3s 16ms/step - loss: 0.3638 - binary_accuracy: 0.8326 - val_loss: 0.3628 - val_binary_accuracy: 0.8326\nEpoch 18/200\n175/175 [==============================] - 3s 15ms/step - loss: 0.3617 - binary_accuracy: 0.8341 - val_loss: 0.3623 - val_binary_accuracy: 0.8345\nEpoch 19/200\n175/175 [==============================] - 3s 16ms/step - loss: 0.3609 - binary_accuracy: 0.8339 - val_loss: 0.3640 - val_binary_accuracy: 0.8325\nEpoch 20/200\n175/175 [==============================] - 3s 16ms/step - loss: 0.3597 - binary_accuracy: 0.8347 - val_loss: 0.3598 - val_binary_accuracy: 0.8350\nEpoch 21/200\n175/175 [==============================] - 3s 16ms/step - loss: 0.3580 - binary_accuracy: 0.8348 - val_loss: 0.3581 - val_binary_accuracy: 0.8369\nEpoch 22/200\n175/175 [==============================] - 3s 16ms/step - loss: 0.3570 - binary_accuracy: 0.8353 - val_loss: 0.3570 - val_binary_accuracy: 0.8352\nEpoch 23/200\n175/175 [==============================] - 3s 16ms/step - loss: 0.3561 - binary_accuracy: 0.8362 - val_loss: 0.3572 - val_binary_accuracy: 0.8344\nEpoch 24/200\n175/175 [==============================] - 3s 15ms/step - loss: 0.3544 - binary_accuracy: 0.8367 - val_loss: 0.3566 - val_binary_accuracy: 0.8363\nEpoch 25/200\n175/175 [==============================] - 3s 16ms/step - loss: 0.3527 - binary_accuracy: 0.8375 - val_loss: 0.3546 - val_binary_accuracy: 0.8386\nEpoch 26/200\n175/175 [==============================] - 3s 17ms/step - loss: 0.3524 - binary_accuracy: 0.8379 - val_loss: 0.3540 - val_binary_accuracy: 0.8369\nEpoch 27/200\n175/175 [==============================] - 3s 17ms/step - loss: 0.3510 - binary_accuracy: 0.8376 - val_loss: 0.3575 - val_binary_accuracy: 0.8342\nEpoch 28/200\n175/175 [==============================] - 3s 16ms/step - loss: 0.3500 - binary_accuracy: 0.8386 - val_loss: 0.3548 - val_binary_accuracy: 0.8371\nEpoch 29/200\n175/175 [==============================] - 3s 16ms/step - loss: 0.3479 - binary_accuracy: 0.8387 - val_loss: 0.3525 - val_binary_accuracy: 0.8375\nEpoch 30/200\n175/175 [==============================] - 3s 15ms/step - loss: 0.3470 - binary_accuracy: 0.8405 - val_loss: 0.3537 - val_binary_accuracy: 0.8363\nEpoch 31/200\n175/175 [==============================] - 3s 15ms/step - loss: 0.3462 - binary_accuracy: 0.8404 - val_loss: 0.3569 - val_binary_accuracy: 0.8343\nEpoch 32/200\n175/175 [==============================] - 3s 16ms/step - loss: 0.3448 - binary_accuracy: 0.8406 - val_loss: 0.3536 - val_binary_accuracy: 0.8392\nEpoch 33/200\n175/175 [==============================] - 3s 16ms/step - loss: 0.3453 - binary_accuracy: 0.8419 - val_loss: 0.3521 - val_binary_accuracy: 0.8374\nEpoch 34/200\n175/175 [==============================] - 3s 16ms/step - loss: 0.3433 - binary_accuracy: 0.8421 - val_loss: 0.3538 - val_binary_accuracy: 0.8379\n","output_type":"stream"},{"execution_count":7,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"# 3) Train and Evaluate #","metadata":{}},{"cell_type":"code","source":"q_3.check()","metadata":{"execution":{"iopub.status.busy":"2023-11-27T12:07:05.882563Z","iopub.execute_input":"2023-11-27T12:07:05.882997Z","iopub.status.idle":"2023-11-27T12:07:05.894036Z","shell.execute_reply.started":"2023-11-27T12:07:05.882962Z","shell.execute_reply":"2023-11-27T12:07:05.892498Z"},"trusted":true},"execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":"","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.3333333333333333, \"interactionType\": 1, \"questionType\": 4, \"questionId\": \"3_Q3\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct: \n\nThough we can see the training loss continuing to fall, the early stopping callback prevented any overfitting. Moreover, the accuracy rose at the same rate as the cross-entropy fell, so it appears that minimizing cross-entropy was a good stand-in. All in all, it looks like this training was a success!","text/markdown":"Correct: \n\nThough we can see the training loss continuing to fall, the early stopping callback prevented any overfitting. Moreover, the accuracy rose at the same rate as the cross-entropy fell, so it appears that minimizing cross-entropy was a good stand-in. All in all, it looks like this training was a success!"},"metadata":{}}]}]} \ No newline at end of file diff --git "a/\352\263\274\354\240\23412_\354\272\220\352\270\200 \355\225\204\354\202\254_\354\241\260\355\230\204\354\247\200-deep-neural-networks.ipynb" "b/\352\263\274\354\240\23412_\354\272\220\352\270\200 \355\225\204\354\202\254_\354\241\260\355\230\204\354\247\200-deep-neural-networks.ipynb" new file mode 100644 index 0000000..cf73993 --- /dev/null +++ "b/\352\263\274\354\240\23412_\354\272\220\352\270\200 \355\225\204\354\202\254_\354\241\260\355\230\204\354\247\200-deep-neural-networks.ipynb" @@ -0,0 +1 @@ +{"metadata":{"jupytext":{"cell_metadata_filter":"-all","formats":"ipynb"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":1480608,"sourceType":"datasetVersion","datasetId":829369}],"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Deep Neural Networks #","metadata":{}},{"cell_type":"code","source":"import tensorflow as tf\n\nimport matplotlib.pyplot as plt\n\nplt.style.use('seaborn-v0_8-whitegrid')\n# Set Matplotlib defaults\nplt.rc('figure', autolayout=True)\nplt.rc('axes', labelweight='bold', labelsize='large',\n titleweight='bold', titlesize=18, titlepad=10)\n\nfrom learntools.core import binder\nbinder.bind(globals())\nfrom learntools.deep_learning_intro.ex2 import *","metadata":{"execution":{"iopub.status.busy":"2023-11-27T11:44:52.628582Z","iopub.execute_input":"2023-11-27T11:44:52.628986Z","iopub.status.idle":"2023-11-27T11:44:52.688675Z","shell.execute_reply.started":"2023-11-27T11:44:52.628953Z","shell.execute_reply":"2023-11-27T11:44:52.687235Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\n\nconcrete = pd.read_csv('../input/dl-course-data/concrete.csv')\nconcrete.head()","metadata":{"execution":{"iopub.status.busy":"2023-11-27T11:45:00.068206Z","iopub.execute_input":"2023-11-27T11:45:00.068621Z","iopub.status.idle":"2023-11-27T11:45:00.117652Z","shell.execute_reply.started":"2023-11-27T11:45:00.068592Z","shell.execute_reply":"2023-11-27T11:45:00.116014Z"},"trusted":true},"execution_count":3,"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":" Cement BlastFurnaceSlag FlyAsh Water Superplasticizer CoarseAggregate \\\n0 540.0 0.0 0.0 162.0 2.5 1040.0 \n1 540.0 0.0 0.0 162.0 2.5 1055.0 \n2 332.5 142.5 0.0 228.0 0.0 932.0 \n3 332.5 142.5 0.0 228.0 0.0 932.0 \n4 198.6 132.4 0.0 192.0 0.0 978.4 \n\n FineAggregate Age CompressiveStrength \n0 676.0 28 79.99 \n1 676.0 28 61.89 \n2 594.0 270 40.27 \n3 594.0 365 41.05 \n4 825.5 360 44.30 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
CementBlastFurnaceSlagFlyAshWaterSuperplasticizerCoarseAggregateFineAggregateAgeCompressiveStrength
0540.00.00.0162.02.51040.0676.02879.99
1540.00.00.0162.02.51055.0676.02861.89
2332.5142.50.0228.00.0932.0594.027040.27
3332.5142.50.0228.00.0932.0594.036541.05
4198.6132.40.0192.00.0978.4825.536044.30
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"# 1) Input Shape #","metadata":{}},{"cell_type":"code","source":"input_shape = [8]\n\nq_1.check()","metadata":{"lines_to_next_cell":2,"execution":{"iopub.status.busy":"2023-11-27T11:45:27.563477Z","iopub.execute_input":"2023-11-27T11:45:27.563888Z","iopub.status.idle":"2023-11-27T11:45:27.577280Z","shell.execute_reply.started":"2023-11-27T11:45:27.563855Z","shell.execute_reply":"2023-11-27T11:45:27.575798Z"},"trusted":true},"execution_count":5,"outputs":[{"output_type":"display_data","data":{"text/plain":"","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.3333333333333333, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"1_Q1\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct","text/markdown":"Correct"},"metadata":{}}]},{"cell_type":"markdown","source":"# 2) Define a Model with Hidden Layers #","metadata":{}},{"cell_type":"code","source":"from tensorflow import keras\nfrom tensorflow.keras import layers\n\nmodel = keras.Sequential([\n layers.Dense(512, activation='relu', input_shape=input_shape),\n layers.Dense(512, activation='relu'),\n layers.Dense(512, activation='relu'), \n layers.Dense(1),\n])\n\nq_2.check()","metadata":{"lines_to_next_cell":0,"execution":{"iopub.status.busy":"2023-11-27T11:45:55.279026Z","iopub.execute_input":"2023-11-27T11:45:55.279531Z","iopub.status.idle":"2023-11-27T11:45:55.354804Z","shell.execute_reply.started":"2023-11-27T11:45:55.279501Z","shell.execute_reply":"2023-11-27T11:45:55.353769Z"},"trusted":true},"execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":"","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.3333333333333333, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"2_Q2\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct","text/markdown":"Correct"},"metadata":{}}]},{"cell_type":"markdown","source":"# 3) Activation Layers #","metadata":{}},{"cell_type":"code","source":"model = keras.Sequential([\n layers.Dense(32, input_shape=[8]),\n layers.Activation('relu'),\n layers.Dense(32),\n layers.Activation('relu'),\n layers.Dense(1),\n])\n\nq_3.check()","metadata":{"lines_to_next_cell":0,"execution":{"iopub.status.busy":"2023-11-27T11:46:20.138396Z","iopub.execute_input":"2023-11-27T11:46:20.138833Z","iopub.status.idle":"2023-11-27T11:46:20.214614Z","shell.execute_reply.started":"2023-11-27T11:46:20.138799Z","shell.execute_reply":"2023-11-27T11:46:20.213652Z"},"trusted":true},"execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":"","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.3333333333333333, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"3_Q3\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct","text/markdown":"Correct"},"metadata":{}}]},{"cell_type":"markdown","source":"# Optional: Alternatives to ReLU #","metadata":{}},{"cell_type":"code","source":"activation_layer = layers.Activation('relu')\n\nx = tf.linspace(-3.0, 3.0, 100)\ny = activation_layer(x) # once created, a layer is callable just like a function\n\nplt.figure(dpi=100)\nplt.plot(x, y)\nplt.xlim(-3, 3)\nplt.xlabel(\"Input\")\nplt.ylabel(\"Output\")\nplt.show()","metadata":{"lines_to_next_cell":0,"execution":{"iopub.status.busy":"2023-11-27T11:46:41.681115Z","iopub.execute_input":"2023-11-27T11:46:41.681652Z","iopub.status.idle":"2023-11-27T11:46:42.288013Z","shell.execute_reply.started":"2023-11-27T11:46:41.681609Z","shell.execute_reply":"2023-11-27T11:46:42.286925Z"},"trusted":true},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]}]} \ No newline at end of file diff --git "a/\352\263\274\354\240\23412_\354\272\220\352\270\200 \355\225\204\354\202\254_\354\241\260\355\230\204\354\247\200-dropout-and-batch-normalization.ipynb" "b/\352\263\274\354\240\23412_\354\272\220\352\270\200 \355\225\204\354\202\254_\354\241\260\355\230\204\354\247\200-dropout-and-batch-normalization.ipynb" new file mode 100644 index 0000000..1cd9a26 --- /dev/null +++ "b/\352\263\274\354\240\23412_\354\272\220\352\270\200 \355\225\204\354\202\254_\354\241\260\355\230\204\354\247\200-dropout-and-batch-normalization.ipynb" @@ -0,0 +1 @@ +{"metadata":{"jupytext":{"cell_metadata_filter":"-all","formats":"ipynb"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":1480608,"sourceType":"datasetVersion","datasetId":829369}],"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Dropout and Batch Normalization #","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nplt.style.use('seaborn-v0_8-whitegrid')\n# Set Matplotlib defaults\nplt.rc('figure', autolayout=True)\nplt.rc('axes', labelweight='bold', labelsize='large',\n titleweight='bold', titlesize=18, titlepad=10)\nplt.rc('animation', html='html5')\n\nfrom learntools.core import binder\nbinder.bind(globals())\nfrom learntools.deep_learning_intro.ex5 import *","metadata":{"execution":{"iopub.status.busy":"2023-11-27T11:58:58.204099Z","iopub.execute_input":"2023-11-27T11:58:58.204987Z","iopub.status.idle":"2023-11-27T11:58:58.748007Z","shell.execute_reply.started":"2023-11-27T11:58:58.204933Z","shell.execute_reply":"2023-11-27T11:58:58.746513Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nfrom sklearn.compose import make_column_transformer\nfrom sklearn.model_selection import GroupShuffleSplit\n\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\nfrom tensorflow.keras import callbacks\n\nspotify = pd.read_csv('../input/dl-course-data/spotify.csv')\n\nX = spotify.copy().dropna()\ny = X.pop('track_popularity')\nartists = X['track_artist']\n\nfeatures_num = ['danceability', 'energy', 'key', 'loudness', 'mode',\n 'speechiness', 'acousticness', 'instrumentalness',\n 'liveness', 'valence', 'tempo', 'duration_ms']\nfeatures_cat = ['playlist_genre']\n\npreprocessor = make_column_transformer(\n (StandardScaler(), features_num),\n (OneHotEncoder(), features_cat),\n)\n\ndef group_split(X, y, group, train_size=0.75):\n splitter = GroupShuffleSplit(train_size=train_size)\n train, test = next(splitter.split(X, y, groups=group))\n return (X.iloc[train], X.iloc[test], y.iloc[train], y.iloc[test])\n\nX_train, X_valid, y_train, y_valid = group_split(X, y, artists)\n\nX_train = preprocessor.fit_transform(X_train)\nX_valid = preprocessor.transform(X_valid)\ny_train = y_train / 100\ny_valid = y_valid / 100\n\ninput_shape = [X_train.shape[1]]\nprint(\"Input shape: {}\".format(input_shape))","metadata":{"execution":{"iopub.status.busy":"2023-11-27T11:59:00.592576Z","iopub.execute_input":"2023-11-27T11:59:00.593201Z","iopub.status.idle":"2023-11-27T11:59:16.784231Z","shell.execute_reply.started":"2023-11-27T11:59:00.593159Z","shell.execute_reply":"2023-11-27T11:59:16.782561Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Input shape: [18]\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# 1) Add Dropout to Spotify Model","metadata":{}},{"cell_type":"code","source":"model = keras.Sequential([\n layers.Dense(128, activation='relu', input_shape=input_shape),\n layers.Dropout(0.3),\n layers.Dense(64, activation='relu'),\n layers.Dropout(0.3),\n layers.Dense(1)\n])\n\nq_1.check()","metadata":{"lines_to_next_cell":2,"execution":{"iopub.status.busy":"2023-11-27T11:59:29.812880Z","iopub.execute_input":"2023-11-27T11:59:29.813371Z","iopub.status.idle":"2023-11-27T11:59:30.040910Z","shell.execute_reply.started":"2023-11-27T11:59:29.813334Z","shell.execute_reply":"2023-11-27T11:59:30.039755Z"},"trusted":true},"execution_count":4,"outputs":[{"output_type":"display_data","data":{"text/plain":"","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.25, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"1_Q1\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct","text/markdown":"Correct"},"metadata":{}}]},{"cell_type":"code","source":"model.compile(\n optimizer='adam',\n loss='mae',\n)\nhistory = model.fit(\n X_train, y_train,\n validation_data=(X_valid, y_valid),\n batch_size=512,\n epochs=50,\n verbose=0,\n)\nhistory_df = pd.DataFrame(history.history)\nhistory_df.loc[:, ['loss', 'val_loss']].plot()\nprint(\"Minimum Validation Loss: {:0.4f}\".format(history_df['val_loss'].min()))","metadata":{"execution":{"iopub.status.busy":"2023-11-27T11:59:38.512614Z","iopub.execute_input":"2023-11-27T11:59:38.513247Z","iopub.status.idle":"2023-11-27T11:59:54.658951Z","shell.execute_reply.started":"2023-11-27T11:59:38.513199Z","shell.execute_reply":"2023-11-27T11:59:54.657930Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"Minimum Validation Loss: 0.1921\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"# 2) Evaluate Dropout","metadata":{}},{"cell_type":"code","source":"q_2.check()","metadata":{"execution":{"iopub.status.busy":"2023-11-27T11:59:57.261821Z","iopub.execute_input":"2023-11-27T11:59:57.263275Z","iopub.status.idle":"2023-11-27T11:59:57.275098Z","shell.execute_reply.started":"2023-11-27T11:59:57.263211Z","shell.execute_reply":"2023-11-27T11:59:57.273620Z"},"trusted":true},"execution_count":6,"outputs":[{"output_type":"display_data","data":{"text/plain":"","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.25, \"interactionType\": 1, \"questionType\": 4, \"questionId\": \"2_Q2\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct: \n\n\nFrom the learning curves, you can see that the validation loss remains near a constant minimum even though the training loss continues to decrease. So we can see that adding dropout did prevent overfitting this time. Moreover, by making it harder for the network to fit spurious patterns, dropout may have encouraged the network to seek out more of the true patterns, possibly improving the validation loss some as well).","text/markdown":"Correct: \n\n\nFrom the learning curves, you can see that the validation loss remains near a constant minimum even though the training loss continues to decrease. So we can see that adding dropout did prevent overfitting this time. Moreover, by making it harder for the network to fit spurious patterns, dropout may have encouraged the network to seek out more of the true patterns, possibly improving the validation loss some as well).\n"},"metadata":{}}]},{"cell_type":"code","source":"import pandas as pd\n\nconcrete = pd.read_csv('../input/dl-course-data/concrete.csv')\ndf = concrete.copy()\n\ndf_train = df.sample(frac=0.7, random_state=0)\ndf_valid = df.drop(df_train.index)\n\nX_train = df_train.drop('CompressiveStrength', axis=1)\nX_valid = df_valid.drop('CompressiveStrength', axis=1)\ny_train = df_train['CompressiveStrength']\ny_valid = df_valid['CompressiveStrength']\n\ninput_shape = [X_train.shape[1]]","metadata":{"execution":{"iopub.status.busy":"2023-11-27T12:00:06.817984Z","iopub.execute_input":"2023-11-27T12:00:06.818741Z","iopub.status.idle":"2023-11-27T12:00:06.833990Z","shell.execute_reply.started":"2023-11-27T12:00:06.818701Z","shell.execute_reply":"2023-11-27T12:00:06.832488Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"model = keras.Sequential([\n layers.Dense(512, activation='relu', input_shape=input_shape),\n layers.Dense(512, activation='relu'), \n layers.Dense(512, activation='relu'),\n layers.Dense(1),\n])\nmodel.compile(\n optimizer='sgd', # SGD is more sensitive to differences of scale\n loss='mae',\n metrics=['mae'],\n)\nhistory = model.fit(\n X_train, y_train,\n validation_data=(X_valid, y_valid),\n batch_size=64,\n epochs=100,\n verbose=0,\n)\n\nhistory_df = pd.DataFrame(history.history)\nhistory_df.loc[0:, ['loss', 'val_loss']].plot()\nprint((\"Minimum Validation Loss: {:0.4f}\").format(history_df['val_loss'].min()))","metadata":{"execution":{"iopub.status.busy":"2023-11-27T12:00:17.805338Z","iopub.execute_input":"2023-11-27T12:00:17.805793Z","iopub.status.idle":"2023-11-27T12:00:31.920540Z","shell.execute_reply.started":"2023-11-27T12:00:17.805756Z","shell.execute_reply":"2023-11-27T12:00:31.919177Z"},"trusted":true},"execution_count":9,"outputs":[{"name":"stdout","text":"Minimum Validation Loss: inf\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"# 3) Add Batch Normalization Layers","metadata":{}},{"cell_type":"code","source":"model = keras.Sequential([\n layers.BatchNormalization(input_shape=input_shape),\n layers.Dense(512, activation='relu'),\n layers.BatchNormalization(),\n layers.Dense(512, activation='relu'),\n layers.BatchNormalization(),\n layers.Dense(512, activation='relu'),\n layers.BatchNormalization(),\n layers.Dense(1),\n])\n\nq_3.check()","metadata":{"lines_to_next_cell":0,"execution":{"iopub.status.busy":"2023-11-27T12:00:54.206833Z","iopub.execute_input":"2023-11-27T12:00:54.207352Z","iopub.status.idle":"2023-11-27T12:00:54.387406Z","shell.execute_reply.started":"2023-11-27T12:00:54.207315Z","shell.execute_reply":"2023-11-27T12:00:54.385959Z"},"trusted":true},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":"","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.25, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"3_Q3\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct","text/markdown":"Correct"},"metadata":{}}]},{"cell_type":"code","source":"model.compile(\n optimizer='sgd',\n loss='mae',\n metrics=['mae'],\n)\nEPOCHS = 100\nhistory = model.fit(\n X_train, y_train,\n validation_data=(X_valid, y_valid),\n batch_size=64,\n epochs=EPOCHS,\n verbose=0,\n)\n\nhistory_df = pd.DataFrame(history.history)\nhistory_df.loc[0:, ['loss', 'val_loss']].plot()\nprint((\"Minimum Validation Loss: {:0.4f}\").format(history_df['val_loss'].min()))","metadata":{"execution":{"iopub.status.busy":"2023-11-27T12:01:02.709300Z","iopub.execute_input":"2023-11-27T12:01:02.709744Z","iopub.status.idle":"2023-11-27T12:01:25.259066Z","shell.execute_reply.started":"2023-11-27T12:01:02.709712Z","shell.execute_reply":"2023-11-27T12:01:25.258155Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"Minimum Validation Loss: 4.0833\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"# 4) Evaluate Batch Normalization","metadata":{}},{"cell_type":"code","source":"q_4.check()","metadata":{"lines_to_next_cell":0,"execution":{"iopub.status.busy":"2023-11-27T12:01:27.532884Z","iopub.execute_input":"2023-11-27T12:01:27.534347Z","iopub.status.idle":"2023-11-27T12:01:27.544962Z","shell.execute_reply.started":"2023-11-27T12:01:27.534297Z","shell.execute_reply":"2023-11-27T12:01:27.543725Z"},"trusted":true},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":"","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.25, \"interactionType\": 1, \"questionType\": 4, \"questionId\": \"4_Q4\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct: \n\n\nYou can see that adding batch normalization was a big improvement on the first attempt! By adaptively scaling the data as it passes through the network, batch normalization can let you train models on difficult datasets.","text/markdown":"Correct: \n\n\nYou can see that adding batch normalization was a big improvement on the first attempt! By adaptively scaling the data as it passes through the network, batch normalization can let you train models on difficult datasets.\n"},"metadata":{}}]}]} \ No newline at end of file diff --git "a/\352\263\274\354\240\23412_\354\272\220\352\270\200 \355\225\204\354\202\254_\354\241\260\355\230\204\354\247\200-overfitting-and-underfitting.ipynb" "b/\352\263\274\354\240\23412_\354\272\220\352\270\200 \355\225\204\354\202\254_\354\241\260\355\230\204\354\247\200-overfitting-and-underfitting.ipynb" new file mode 100644 index 0000000..c6d6ab8 --- /dev/null +++ "b/\352\263\274\354\240\23412_\354\272\220\352\270\200 \355\225\204\354\202\254_\354\241\260\355\230\204\354\247\200-overfitting-and-underfitting.ipynb" @@ -0,0 +1 @@ +{"metadata":{"jupytext":{"cell_metadata_filter":"-all","formats":"ipynb"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":1480608,"sourceType":"datasetVersion","datasetId":829369}],"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Overfitting and Underfitting #","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nplt.style.use('seaborn-v0_8-whitegrid')\n# Set Matplotlib defaults\nplt.rc('figure', autolayout=True)\nplt.rc('axes', labelweight='bold', labelsize='large',\n titleweight='bold', titlesize=18, titlepad=10)\nplt.rc('animation', html='html5')\n\nfrom learntools.core import binder\nbinder.bind(globals())\nfrom learntools.deep_learning_intro.ex4 import *","metadata":{"execution":{"iopub.status.busy":"2023-11-27T11:54:42.175641Z","iopub.execute_input":"2023-11-27T11:54:42.176062Z","iopub.status.idle":"2023-11-27T11:54:42.748600Z","shell.execute_reply.started":"2023-11-27T11:54:42.176027Z","shell.execute_reply":"2023-11-27T11:54:42.747047Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nfrom sklearn.compose import make_column_transformer\nfrom sklearn.model_selection import GroupShuffleSplit\n\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\nfrom tensorflow.keras import callbacks\n\nspotify = pd.read_csv('../input/dl-course-data/spotify.csv')\n\nX = spotify.copy().dropna()\ny = X.pop('track_popularity')\nartists = X['track_artist']\n\nfeatures_num = ['danceability', 'energy', 'key', 'loudness', 'mode',\n 'speechiness', 'acousticness', 'instrumentalness',\n 'liveness', 'valence', 'tempo', 'duration_ms']\nfeatures_cat = ['playlist_genre']\n\npreprocessor = make_column_transformer(\n (StandardScaler(), features_num),\n (OneHotEncoder(), features_cat),\n)\n\ndef group_split(X, y, group, train_size=0.75):\n splitter = GroupShuffleSplit(train_size=train_size)\n train, test = next(splitter.split(X, y, groups=group))\n return (X.iloc[train], X.iloc[test], y.iloc[train], y.iloc[test])\n\nX_train, X_valid, y_train, y_valid = group_split(X, y, artists)\n\nX_train = preprocessor.fit_transform(X_train)\nX_valid = preprocessor.transform(X_valid)\ny_train = y_train / 100 # popularity is on a scale 0-100, so this rescales to 0-1.\ny_valid = y_valid / 100\n\ninput_shape = [X_train.shape[1]]\nprint(\"Input shape: {}\".format(input_shape))","metadata":{"execution":{"iopub.status.busy":"2023-11-27T11:54:45.053803Z","iopub.execute_input":"2023-11-27T11:54:45.054649Z","iopub.status.idle":"2023-11-27T11:55:03.904810Z","shell.execute_reply.started":"2023-11-27T11:54:45.054593Z","shell.execute_reply":"2023-11-27T11:55:03.903537Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Input shape: [18]\n","output_type":"stream"}]},{"cell_type":"code","source":"model = keras.Sequential([\n layers.Dense(1, input_shape=input_shape),\n])\nmodel.compile(\n optimizer='adam',\n loss='mae',\n)\nhistory = model.fit(\n X_train, y_train,\n validation_data=(X_valid, y_valid),\n batch_size=512,\n epochs=50,\n verbose=0, # suppress output since we'll plot the curves\n)\nhistory_df = pd.DataFrame(history.history)\nhistory_df.loc[0:, ['loss', 'val_loss']].plot()\nprint(\"Minimum Validation Loss: {:0.4f}\".format(history_df['val_loss'].min()));","metadata":{"lines_to_next_cell":0,"execution":{"iopub.status.busy":"2023-11-27T11:55:14.256827Z","iopub.execute_input":"2023-11-27T11:55:14.257517Z","iopub.status.idle":"2023-11-27T11:55:26.050154Z","shell.execute_reply.started":"2023-11-27T11:55:14.257463Z","shell.execute_reply":"2023-11-27T11:55:26.048384Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"Minimum Validation Loss: 0.1954\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"history_df.loc[10:, ['loss', 'val_loss']].plot()\nprint(\"Minimum Validation Loss: {:0.4f}\".format(history_df['val_loss'].min()));","metadata":{"execution":{"iopub.status.busy":"2023-11-27T11:55:30.274328Z","iopub.execute_input":"2023-11-27T11:55:30.274820Z","iopub.status.idle":"2023-11-27T11:55:30.860938Z","shell.execute_reply.started":"2023-11-27T11:55:30.274785Z","shell.execute_reply":"2023-11-27T11:55:30.859629Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"Minimum Validation Loss: 0.1954\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"# 1) Evaluate Baseline","metadata":{}},{"cell_type":"code","source":"q_1.check()","metadata":{"execution":{"iopub.status.busy":"2023-11-27T11:55:34.024186Z","iopub.execute_input":"2023-11-27T11:55:34.024681Z","iopub.status.idle":"2023-11-27T11:55:34.037538Z","shell.execute_reply.started":"2023-11-27T11:55:34.024639Z","shell.execute_reply":"2023-11-27T11:55:34.036554Z"},"trusted":true},"execution_count":5,"outputs":[{"output_type":"display_data","data":{"text/plain":"","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.25, \"interactionType\": 1, \"questionType\": 4, \"questionId\": \"1_Q1\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct: \n\nThe gap between these curves is quite small and the validation loss never increases, so it's more likely that the network is underfitting than overfitting. It would be worth experimenting with more capacity to see if that's the case.","text/markdown":"Correct: \n\nThe gap between these curves is quite small and the validation loss never increases, so it's more likely that the network is underfitting than overfitting. It would be worth experimenting with more capacity to see if that's the case."},"metadata":{}}]},{"cell_type":"markdown","source":"Now let's add some capacity to our network. We'll add three hidden layers with 128 units each. Run the next cell to train the network and see the learning curves.","metadata":{}},{"cell_type":"code","source":"model = keras.Sequential([\n layers.Dense(128, activation='relu', input_shape=input_shape),\n layers.Dense(64, activation='relu'),\n layers.Dense(1)\n])\nmodel.compile(\n optimizer='adam',\n loss='mae',\n)\nhistory = model.fit(\n X_train, y_train,\n validation_data=(X_valid, y_valid),\n batch_size=512,\n epochs=50,\n)\nhistory_df = pd.DataFrame(history.history)\nhistory_df.loc[:, ['loss', 'val_loss']].plot()\nprint(\"Minimum Validation Loss: {:0.4f}\".format(history_df['val_loss'].min()));","metadata":{"execution":{"iopub.status.busy":"2023-11-27T11:55:38.433989Z","iopub.execute_input":"2023-11-27T11:55:38.434669Z","iopub.status.idle":"2023-11-27T11:55:53.919770Z","shell.execute_reply.started":"2023-11-27T11:55:38.434625Z","shell.execute_reply":"2023-11-27T11:55:53.918294Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"Epoch 1/50\n49/49 [==============================] - 1s 9ms/step - loss: 0.2731 - val_loss: 0.2093\nEpoch 2/50\n49/49 [==============================] - 0s 6ms/step - loss: 0.2025 - val_loss: 0.2023\nEpoch 3/50\n49/49 [==============================] - 0s 6ms/step - loss: 0.1971 - val_loss: 0.2004\nEpoch 4/50\n49/49 [==============================] - 0s 5ms/step - loss: 0.1943 - val_loss: 0.1991\nEpoch 5/50\n49/49 [==============================] - 0s 5ms/step - loss: 0.1921 - val_loss: 0.1986\nEpoch 6/50\n49/49 [==============================] - 0s 6ms/step - loss: 0.1902 - val_loss: 0.1978\nEpoch 7/50\n49/49 [==============================] - 0s 6ms/step - loss: 0.1892 - val_loss: 0.1988\nEpoch 8/50\n49/49 [==============================] - 0s 5ms/step - loss: 0.1885 - val_loss: 0.1987\nEpoch 9/50\n49/49 [==============================] - 0s 5ms/step - loss: 0.1870 - val_loss: 0.1982\nEpoch 10/50\n49/49 [==============================] - 0s 5ms/step - loss: 0.1858 - val_loss: 0.1976\nEpoch 11/50\n49/49 [==============================] - 0s 5ms/step - loss: 0.1851 - val_loss: 0.1983\nEpoch 12/50\n49/49 [==============================] - 0s 6ms/step - loss: 0.1846 - val_loss: 0.1976\nEpoch 13/50\n49/49 [==============================] - 0s 6ms/step - loss: 0.1840 - val_loss: 0.1985\nEpoch 14/50\n49/49 [==============================] - 0s 5ms/step - loss: 0.1835 - val_loss: 0.1975\nEpoch 15/50\n49/49 [==============================] - 0s 5ms/step - loss: 0.1826 - val_loss: 0.1975\nEpoch 16/50\n49/49 [==============================] - 0s 6ms/step - loss: 0.1815 - val_loss: 0.1987\nEpoch 17/50\n49/49 [==============================] - 0s 6ms/step - loss: 0.1812 - val_loss: 0.1974\nEpoch 18/50\n49/49 [==============================] - 0s 5ms/step - loss: 0.1803 - val_loss: 0.1987\nEpoch 19/50\n49/49 [==============================] - 0s 6ms/step - loss: 0.1795 - val_loss: 0.1986\nEpoch 20/50\n49/49 [==============================] - 0s 5ms/step - loss: 0.1792 - val_loss: 0.1988\nEpoch 21/50\n49/49 [==============================] - 0s 5ms/step - loss: 0.1789 - val_loss: 0.1985\nEpoch 22/50\n49/49 [==============================] - 0s 5ms/step - loss: 0.1789 - val_loss: 0.1987\nEpoch 23/50\n49/49 [==============================] - 0s 5ms/step - loss: 0.1775 - val_loss: 0.1983\nEpoch 24/50\n49/49 [==============================] - 0s 5ms/step - loss: 0.1773 - val_loss: 0.1990\nEpoch 25/50\n49/49 [==============================] - 0s 5ms/step - loss: 0.1765 - val_loss: 0.1990\nEpoch 26/50\n49/49 [==============================] - 0s 6ms/step - loss: 0.1758 - val_loss: 0.1991\nEpoch 27/50\n49/49 [==============================] - 0s 6ms/step - loss: 0.1758 - val_loss: 0.1998\nEpoch 28/50\n49/49 [==============================] - 0s 5ms/step - loss: 0.1752 - val_loss: 0.2008\nEpoch 29/50\n49/49 [==============================] - 0s 6ms/step - loss: 0.1746 - val_loss: 0.2007\nEpoch 30/50\n49/49 [==============================] - 0s 5ms/step - loss: 0.1744 - val_loss: 0.1993\nEpoch 31/50\n49/49 [==============================] - 0s 6ms/step - loss: 0.1738 - val_loss: 0.2002\nEpoch 32/50\n49/49 [==============================] - 0s 6ms/step - loss: 0.1727 - val_loss: 0.2010\nEpoch 33/50\n49/49 [==============================] - 0s 6ms/step - loss: 0.1730 - val_loss: 0.2020\nEpoch 34/50\n49/49 [==============================] - 0s 6ms/step - loss: 0.1720 - val_loss: 0.2023\nEpoch 35/50\n49/49 [==============================] - 0s 5ms/step - loss: 0.1724 - val_loss: 0.2027\nEpoch 36/50\n49/49 [==============================] - 0s 5ms/step - loss: 0.1721 - val_loss: 0.2030\nEpoch 37/50\n49/49 [==============================] - 0s 5ms/step - loss: 0.1715 - val_loss: 0.2015\nEpoch 38/50\n49/49 [==============================] - 0s 6ms/step - loss: 0.1711 - val_loss: 0.2005\nEpoch 39/50\n49/49 [==============================] - 0s 6ms/step - loss: 0.1703 - val_loss: 0.2012\nEpoch 40/50\n49/49 [==============================] - 0s 6ms/step - loss: 0.1698 - val_loss: 0.2033\nEpoch 41/50\n49/49 [==============================] - 0s 7ms/step - loss: 0.1699 - val_loss: 0.2021\nEpoch 42/50\n49/49 [==============================] - 0s 7ms/step - loss: 0.1685 - val_loss: 0.2025\nEpoch 43/50\n49/49 [==============================] - 0s 6ms/step - loss: 0.1682 - val_loss: 0.2032\nEpoch 44/50\n49/49 [==============================] - 0s 6ms/step - loss: 0.1676 - val_loss: 0.2022\nEpoch 45/50\n49/49 [==============================] - 0s 6ms/step - loss: 0.1673 - val_loss: 0.2026\nEpoch 46/50\n49/49 [==============================] - 0s 5ms/step - loss: 0.1668 - val_loss: 0.2032\nEpoch 47/50\n49/49 [==============================] - 0s 5ms/step - loss: 0.1665 - val_loss: 0.2035\nEpoch 48/50\n49/49 [==============================] - 0s 5ms/step - loss: 0.1660 - val_loss: 0.2037\nEpoch 49/50\n49/49 [==============================] - 0s 6ms/step - loss: 0.1663 - val_loss: 0.2043\nEpoch 50/50\n49/49 [==============================] - 0s 6ms/step - loss: 0.1658 - val_loss: 0.2050\nMinimum Validation Loss: 0.1974\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"# 2) Add Capacity","metadata":{}},{"cell_type":"code","source":"q_2.check()","metadata":{"execution":{"iopub.status.busy":"2023-11-27T11:56:02.699803Z","iopub.execute_input":"2023-11-27T11:56:02.700264Z","iopub.status.idle":"2023-11-27T11:56:02.711777Z","shell.execute_reply.started":"2023-11-27T11:56:02.700226Z","shell.execute_reply":"2023-11-27T11:56:02.709947Z"},"trusted":true},"execution_count":7,"outputs":[{"output_type":"display_data","data":{"text/plain":"","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.25, \"interactionType\": 1, \"questionType\": 4, \"questionId\": \"2_Q2\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct: \n\nNow the validation loss begins to rise very early, while the training loss continues to decrease. This indicates that the network has begun to overfit. At this point, we would need to try something to prevent it, either by reducing the number of units or through a method like early stopping. (We'll see another in the next lesson!)","text/markdown":"Correct: \n\nNow the validation loss begins to rise very early, while the training loss continues to decrease. This indicates that the network has begun to overfit. At this point, we would need to try something to prevent it, either by reducing the number of units or through a method like early stopping. (We'll see another in the next lesson!)"},"metadata":{}}]},{"cell_type":"markdown","source":"# 3) Define Early Stopping Callback","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras import callbacks\n\nearly_stopping = callbacks.EarlyStopping(\n patience=5,\n min_delta=0.001,\n restore_best_weights=True,\n)\n\nq_3.check()","metadata":{"lines_to_next_cell":0,"execution":{"iopub.status.busy":"2023-11-27T11:56:27.501165Z","iopub.execute_input":"2023-11-27T11:56:27.501617Z","iopub.status.idle":"2023-11-27T11:56:27.511971Z","shell.execute_reply.started":"2023-11-27T11:56:27.501582Z","shell.execute_reply":"2023-11-27T11:56:27.510328Z"},"trusted":true},"execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":"","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.25, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"3_Q3\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct","text/markdown":"Correct"},"metadata":{}}]},{"cell_type":"code","source":"model = keras.Sequential([\n layers.Dense(128, activation='relu', input_shape=input_shape),\n layers.Dense(64, activation='relu'), \n layers.Dense(1)\n])\nmodel.compile(\n optimizer='adam',\n loss='mae',\n)\nhistory = model.fit(\n X_train, y_train,\n validation_data=(X_valid, y_valid),\n batch_size=512,\n epochs=50,\n callbacks=[early_stopping]\n)\nhistory_df = pd.DataFrame(history.history)\nhistory_df.loc[:, ['loss', 'val_loss']].plot()\nprint(\"Minimum Validation Loss: {:0.4f}\".format(history_df['val_loss'].min()));","metadata":{"execution":{"iopub.status.busy":"2023-11-27T11:56:56.009247Z","iopub.execute_input":"2023-11-27T11:56:56.010691Z","iopub.status.idle":"2023-11-27T11:57:00.076833Z","shell.execute_reply.started":"2023-11-27T11:56:56.010645Z","shell.execute_reply":"2023-11-27T11:57:00.075650Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"Epoch 1/50\n49/49 [==============================] - 1s 9ms/step - loss: 0.2289 - val_loss: 0.2047\nEpoch 2/50\n49/49 [==============================] - 0s 6ms/step - loss: 0.1993 - val_loss: 0.2004\nEpoch 3/50\n49/49 [==============================] - 0s 5ms/step - loss: 0.1953 - val_loss: 0.1992\nEpoch 4/50\n49/49 [==============================] - 0s 6ms/step - loss: 0.1921 - val_loss: 0.1975\nEpoch 5/50\n49/49 [==============================] - 0s 6ms/step - loss: 0.1905 - val_loss: 0.1978\nEpoch 6/50\n49/49 [==============================] - 0s 5ms/step - loss: 0.1896 - val_loss: 0.1985\nEpoch 7/50\n49/49 [==============================] - 0s 5ms/step - loss: 0.1884 - val_loss: 0.1994\nEpoch 8/50\n49/49 [==============================] - 0s 6ms/step - loss: 0.1876 - val_loss: 0.1975\nEpoch 9/50\n49/49 [==============================] - 0s 5ms/step - loss: 0.1863 - val_loss: 0.1972\nMinimum Validation Loss: 0.1972\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"# 4) Train and Interpret","metadata":{}},{"cell_type":"code","source":"q_4.check()","metadata":{"execution":{"iopub.status.busy":"2023-11-27T11:57:12.002329Z","iopub.execute_input":"2023-11-27T11:57:12.002902Z","iopub.status.idle":"2023-11-27T11:57:12.015317Z","shell.execute_reply.started":"2023-11-27T11:57:12.002855Z","shell.execute_reply":"2023-11-27T11:57:12.013775Z"},"trusted":true},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":"","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.25, \"interactionType\": 1, \"questionType\": 4, \"questionId\": \"4_Q4\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct: \n\nThe early stopping callback did stop the training once the network began overfitting. Moreover, by including `restore_best_weights` we still get to keep the model where validation loss was lowest.","text/markdown":"Correct: \n\nThe early stopping callback did stop the training once the network began overfitting. Moreover, by including `restore_best_weights` we still get to keep the model where validation loss was lowest."},"metadata":{}}]}]} \ No newline at end of file diff --git "a/\352\263\274\354\240\23412_\354\272\220\352\270\200 \355\225\204\354\202\254_\354\241\260\355\230\204\354\247\200-stochastic-gradient-descent.ipynb" "b/\352\263\274\354\240\23412_\354\272\220\352\270\200 \355\225\204\354\202\254_\354\241\260\355\230\204\354\247\200-stochastic-gradient-descent.ipynb" new file mode 100644 index 0000000..137aa9c --- /dev/null +++ "b/\352\263\274\354\240\23412_\354\272\220\352\270\200 \355\225\204\354\202\254_\354\241\260\355\230\204\354\247\200-stochastic-gradient-descent.ipynb" @@ -0,0 +1 @@ +{"metadata":{"jupytext":{"cell_metadata_filter":"-all","formats":"ipynb"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":1480608,"sourceType":"datasetVersion","datasetId":829369}],"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Stochastic Gradient Descent #","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nfrom learntools.deep_learning_intro.dltools import animate_sgd\nplt.style.use('seaborn-v0_8-whitegrid')\n\nplt.rc('figure', autolayout=True)\nplt.rc('axes', labelweight='bold', labelsize='large',\n titleweight='bold', titlesize=18, titlepad=10)\nplt.rc('animation', html='html5')\n\nfrom learntools.core import binder\nbinder.bind(globals())\nfrom learntools.deep_learning_intro.ex3 import *","metadata":{"execution":{"iopub.status.busy":"2023-11-27T11:49:20.953542Z","iopub.execute_input":"2023-11-27T11:49:20.953978Z","iopub.status.idle":"2023-11-27T11:49:20.961384Z","shell.execute_reply.started":"2023-11-27T11:49:20.953944Z","shell.execute_reply":"2023-11-27T11:49:20.960557Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nfrom sklearn.compose import make_column_transformer, make_column_selector\nfrom sklearn.model_selection import train_test_split\n\nfuel = pd.read_csv('../input/dl-course-data/fuel.csv')\n\nX = fuel.copy()\n\ny = X.pop('FE')\n\npreprocessor = make_column_transformer(\n (StandardScaler(),\n make_column_selector(dtype_include=np.number)),\n (OneHotEncoder(sparse_output=False),\n make_column_selector(dtype_include=object)),\n)\n\nX = preprocessor.fit_transform(X)\ny = np.log(y) # log transform target instead of standardizing\n\ninput_shape = [X.shape[1]]\nprint(\"Input shape: {}\".format(input_shape))","metadata":{"execution":{"iopub.status.busy":"2023-11-27T11:50:06.451321Z","iopub.execute_input":"2023-11-27T11:50:06.451713Z","iopub.status.idle":"2023-11-27T11:50:06.482547Z","shell.execute_reply.started":"2023-11-27T11:50:06.451673Z","shell.execute_reply":"2023-11-27T11:50:06.481416Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"Input shape: [50]\n","output_type":"stream"}]},{"cell_type":"code","source":"fuel.head()\n\npd.DataFrame(X[:10,:]).head()","metadata":{"execution":{"iopub.status.busy":"2023-11-27T11:50:23.330264Z","iopub.execute_input":"2023-11-27T11:50:23.330652Z","iopub.status.idle":"2023-11-27T11:50:23.367788Z","shell.execute_reply.started":"2023-11-27T11:50:23.330624Z","shell.execute_reply":"2023-11-27T11:50:23.366655Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":" 0 1 2 3 4 5 6 \\\n0 0.913643 1.068005 0.524148 0.685653 -0.226455 0.391659 0.43492 \n1 0.913643 1.068005 0.524148 0.685653 -0.226455 0.391659 0.43492 \n2 0.530594 1.068005 0.524148 0.685653 -0.226455 0.391659 0.43492 \n3 0.530594 1.068005 0.524148 0.685653 -0.226455 0.391659 0.43492 \n4 1.296693 2.120794 0.524148 -1.458464 -0.226455 0.391659 0.43492 \n\n 7 8 9 ... 40 41 42 43 44 45 46 47 48 \\\n0 0.463841 -0.447941 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n1 0.463841 -0.447941 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n2 0.463841 -0.447941 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n3 0.463841 -0.447941 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n4 0.463841 -0.447941 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n\n 49 \n0 0.0 \n1 0.0 \n2 0.0 \n3 0.0 \n4 0.0 \n\n[5 rows x 50 columns]","text/html":"
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00.9136431.0680050.5241480.685653-0.2264550.3916590.434920.463841-0.4479410.0...0.00.00.00.00.00.00.00.00.00.0
10.9136431.0680050.5241480.685653-0.2264550.3916590.434920.463841-0.4479410.0...0.00.00.00.00.00.00.00.00.00.0
20.5305941.0680050.5241480.685653-0.2264550.3916590.434920.463841-0.4479410.0...0.00.00.00.00.00.00.00.00.00.0
30.5305941.0680050.5241480.685653-0.2264550.3916590.434920.463841-0.4479410.0...0.00.00.00.00.00.00.00.00.00.0
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5 rows × 50 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"from tensorflow import keras\nfrom tensorflow.keras import layers\n\nmodel = keras.Sequential([\n layers.Dense(128, activation='relu', input_shape=input_shape),\n layers.Dense(128, activation='relu'), \n layers.Dense(64, activation='relu'),\n layers.Dense(1),\n])","metadata":{"execution":{"iopub.status.busy":"2023-11-27T11:50:34.268671Z","iopub.execute_input":"2023-11-27T11:50:34.269113Z","iopub.status.idle":"2023-11-27T11:50:34.674431Z","shell.execute_reply.started":"2023-11-27T11:50:34.269082Z","shell.execute_reply":"2023-11-27T11:50:34.673214Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"markdown","source":"# 1) Add Loss and Optimizer","metadata":{}},{"cell_type":"code","source":"model.compile(\n optimizer='adam',\n loss='mae'\n)\n\nq_1.check()","metadata":{"lines_to_next_cell":0,"execution":{"iopub.status.busy":"2023-11-27T11:50:55.480154Z","iopub.execute_input":"2023-11-27T11:50:55.480576Z","iopub.status.idle":"2023-11-27T11:50:55.505861Z","shell.execute_reply.started":"2023-11-27T11:50:55.480543Z","shell.execute_reply":"2023-11-27T11:50:55.504760Z"},"trusted":true},"execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":"","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.25, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"1_Q1\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct","text/markdown":"Correct"},"metadata":{}}]},{"cell_type":"markdown","source":"# 2) Train Model","metadata":{}},{"cell_type":"code","source":"history = model.fit(\n X, y,\n batch_size=128,\n epochs=200\n)\n\nq_2.check()","metadata":{"lines_to_next_cell":0,"execution":{"iopub.status.busy":"2023-11-27T11:51:23.246500Z","iopub.execute_input":"2023-11-27T11:51:23.246942Z","iopub.status.idle":"2023-11-27T11:51:30.035634Z","shell.execute_reply.started":"2023-11-27T11:51:23.246898Z","shell.execute_reply":"2023-11-27T11:51:30.034482Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"Epoch 1/200\n9/9 [==============================] - 1s 4ms/step - loss: 2.9480\nEpoch 2/200\n9/9 [==============================] - 0s 3ms/step - loss: 1.0748\nEpoch 3/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.5762\nEpoch 4/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.3572\nEpoch 5/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.2495\nEpoch 6/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.1815\nEpoch 7/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.1399\nEpoch 8/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.1214\nEpoch 9/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.1096\nEpoch 10/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0981\nEpoch 11/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0861\nEpoch 12/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0802\nEpoch 13/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0785\nEpoch 14/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0675\nEpoch 15/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0641\nEpoch 16/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0657\nEpoch 17/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0603\nEpoch 18/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0645\nEpoch 19/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0650\nEpoch 20/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0563\nEpoch 21/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0590\nEpoch 22/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0545\nEpoch 23/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0497\nEpoch 24/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0515\nEpoch 25/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0480\nEpoch 26/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0480\nEpoch 27/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0556\nEpoch 28/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0514\nEpoch 29/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0507\nEpoch 30/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0464\nEpoch 31/200\n9/9 [==============================] - 0s 2ms/step - loss: 0.0470\nEpoch 32/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0426\nEpoch 33/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0413\nEpoch 34/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0425\nEpoch 35/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0449\nEpoch 36/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0466\nEpoch 37/200\n9/9 [==============================] - 0s 2ms/step - loss: 0.0397\nEpoch 38/200\n9/9 [==============================] - 0s 2ms/step - loss: 0.0414\nEpoch 39/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0444\nEpoch 40/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0413\nEpoch 41/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0483\nEpoch 42/200\n9/9 [==============================] - 0s 2ms/step - loss: 0.0433\nEpoch 43/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0373\nEpoch 44/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0386\nEpoch 45/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0400\nEpoch 46/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0366\nEpoch 47/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0371\nEpoch 48/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0443\nEpoch 49/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0467\nEpoch 50/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0376\nEpoch 51/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0384\nEpoch 52/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0383\nEpoch 53/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0351\nEpoch 54/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0357\nEpoch 55/200\n9/9 [==============================] - 0s 4ms/step - loss: 0.0393\nEpoch 56/200\n9/9 [==============================] - 0s 5ms/step - loss: 0.0353\nEpoch 57/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0330\nEpoch 58/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0346\nEpoch 59/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0394\nEpoch 60/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0374\nEpoch 61/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0361\nEpoch 62/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0320\nEpoch 63/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0302\nEpoch 64/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0289\nEpoch 65/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0299\nEpoch 66/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0317\nEpoch 67/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0382\nEpoch 68/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0418\nEpoch 69/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0361\nEpoch 70/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0355\nEpoch 71/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0331\nEpoch 72/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0348\nEpoch 73/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0403\nEpoch 74/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0357\nEpoch 75/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0393\nEpoch 76/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0357\nEpoch 77/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0326\nEpoch 78/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0335\nEpoch 79/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0318\nEpoch 80/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0345\nEpoch 81/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0321\nEpoch 82/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0324\nEpoch 83/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0312\nEpoch 84/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0362\nEpoch 85/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0416\nEpoch 86/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0406\nEpoch 87/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0388\nEpoch 88/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0400\nEpoch 89/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0413\nEpoch 90/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0351\nEpoch 91/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0321\nEpoch 92/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0327\nEpoch 93/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0293\nEpoch 94/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0302\nEpoch 95/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0296\nEpoch 96/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0317\nEpoch 97/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0294\nEpoch 98/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0307\nEpoch 99/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0303\nEpoch 100/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0322\nEpoch 101/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0297\nEpoch 102/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0292\nEpoch 103/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0301\nEpoch 104/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0292\nEpoch 105/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0287\nEpoch 106/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0272\nEpoch 107/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0270\nEpoch 108/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0286\nEpoch 109/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0281\nEpoch 110/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0291\nEpoch 111/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0307\nEpoch 112/200\n9/9 [==============================] - 0s 2ms/step - loss: 0.0290\nEpoch 113/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0305\nEpoch 114/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0431\nEpoch 115/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0380\nEpoch 116/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0311\nEpoch 117/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0280\nEpoch 118/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0294\nEpoch 119/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0274\nEpoch 120/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0278\nEpoch 121/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0304\nEpoch 122/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0277\nEpoch 123/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0434\nEpoch 124/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0336\nEpoch 125/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0324\nEpoch 126/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0423\nEpoch 127/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0397\nEpoch 128/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0395\nEpoch 129/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0328\nEpoch 130/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0314\nEpoch 131/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0312\nEpoch 132/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0358\nEpoch 133/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0358\nEpoch 134/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0335\nEpoch 135/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0268\nEpoch 136/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0284\nEpoch 137/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0271\nEpoch 138/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0270\nEpoch 139/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0268\nEpoch 140/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0284\nEpoch 141/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0277\nEpoch 142/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0308\nEpoch 143/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0300\nEpoch 144/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0300\nEpoch 145/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0269\nEpoch 146/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0281\nEpoch 147/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0280\nEpoch 148/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0352\nEpoch 149/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0419\nEpoch 150/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0398\nEpoch 151/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0324\nEpoch 152/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0280\nEpoch 153/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0280\nEpoch 154/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0275\nEpoch 155/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0358\nEpoch 156/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0413\nEpoch 157/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0388\nEpoch 158/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0367\nEpoch 159/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0292\nEpoch 160/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0271\nEpoch 161/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0252\nEpoch 162/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0273\nEpoch 163/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0245\nEpoch 164/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0248\nEpoch 165/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0277\nEpoch 166/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0270\nEpoch 167/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0300\nEpoch 168/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0242\nEpoch 169/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0254\nEpoch 170/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0254\nEpoch 171/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0265\nEpoch 172/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0290\nEpoch 173/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0285\nEpoch 174/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0281\nEpoch 175/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0295\nEpoch 176/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0277\nEpoch 177/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0271\nEpoch 178/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0277\nEpoch 179/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0314\nEpoch 180/200\n9/9 [==============================] - 0s 2ms/step - loss: 0.0363\nEpoch 181/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0377\nEpoch 182/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0410\nEpoch 183/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0320\nEpoch 184/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0369\nEpoch 185/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0310\nEpoch 186/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0339\nEpoch 187/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0403\nEpoch 188/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0352\nEpoch 189/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0350\nEpoch 190/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0302\nEpoch 191/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0289\nEpoch 192/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0277\nEpoch 193/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0261\nEpoch 194/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0297\nEpoch 195/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0279\nEpoch 196/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0253\nEpoch 197/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0267\nEpoch 198/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0254\nEpoch 199/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0247\nEpoch 200/200\n9/9 [==============================] - 0s 3ms/step - loss: 0.0254\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.25, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"2_Q2\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct","text/markdown":"Correct"},"metadata":{}}]},{"cell_type":"markdown","source":"The last step is to look at the loss curves and evaluate the training. Run the cell below to get a plot of the training loss.","metadata":{}},{"cell_type":"code","source":"import pandas as pd\n\nhistory_df = pd.DataFrame(history.history)\n\nhistory_df.loc[5:, ['loss']].plot();","metadata":{"lines_to_next_cell":0,"execution":{"iopub.status.busy":"2023-11-27T11:51:40.429042Z","iopub.execute_input":"2023-11-27T11:51:40.429469Z","iopub.status.idle":"2023-11-27T11:51:41.022787Z","shell.execute_reply.started":"2023-11-27T11:51:40.429439Z","shell.execute_reply":"2023-11-27T11:51:41.021541Z"},"trusted":true},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"# 3) Evaluate Training","metadata":{}},{"cell_type":"code","source":"learning_rate = 0.05\nbatch_size = 32\nnum_examples = 256\n\nanimate_sgd(\n learning_rate=learning_rate,\n batch_size=batch_size,\n num_examples=num_examples,\n\n steps=50, # total training steps (batches seen)\n true_w=3.0, # the slope of the data\n true_b=2.0, # the bias of the data\n)","metadata":{"execution":{"iopub.status.busy":"2023-11-27T11:52:14.638705Z","iopub.execute_input":"2023-11-27T11:52:14.639170Z","iopub.status.idle":"2023-11-27T11:52:31.567359Z","shell.execute_reply.started":"2023-11-27T11:52:14.639140Z","shell.execute_reply":"2023-11-27T11:52:31.566350Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"","text/html":""},"metadata":{}}]},{"cell_type":"markdown","source":"# 4) Learning Rate and Batch Size","metadata":{}},{"cell_type":"code","source":"q_4.check()","metadata":{"lines_to_next_cell":0,"execution":{"iopub.status.busy":"2023-11-27T11:52:56.521769Z","iopub.execute_input":"2023-11-27T11:52:56.522217Z","iopub.status.idle":"2023-11-27T11:52:56.531948Z","shell.execute_reply.started":"2023-11-27T11:52:56.522182Z","shell.execute_reply":"2023-11-27T11:52:56.530801Z"},"trusted":true},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":"","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.25, \"interactionType\": 1, \"questionType\": 4, \"questionId\": \"4_Q4\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct: \n\n\nYou probably saw that smaller batch sizes gave noisier weight updates and loss curves. This is because each batch is a small *sample* of data and smaller samples tend to give noisier estimates. Smaller batches can have an \"averaging\" effect though which can be beneficial.\n\nSmaller learning rates make the updates smaller and the training takes longer to converge. Large learning rates can speed up training, but don't \"settle in\" to a minimum as well. When the learning rate is too large, the training can fail completely. (Try setting the learning rate to a large value like 0.99 to see this.)","text/markdown":"Correct: \n\n\nYou probably saw that smaller batch sizes gave noisier weight updates and loss curves. This is because each batch is a small *sample* of data and smaller samples tend to give noisier estimates. Smaller batches can have an \"averaging\" effect though which can be beneficial.\n\nSmaller learning rates make the updates smaller and the training takes longer to converge. Large learning rates can speed up training, but don't \"settle in\" to a minimum as well. When the learning rate is too large, the training can fail completely. (Try setting the learning rate to a large value like 0.99 to see this.)\n"},"metadata":{}}]}]} \ No newline at end of file diff --git "a/\352\263\274\354\240\23415_\354\241\260\355\230\204\354\247\200.md" "b/\352\263\274\354\240\23415_\354\241\260\355\230\204\354\247\200.md" new file mode 100644 index 0000000..01720fd --- /dev/null +++ "b/\352\263\274\354\240\23415_\354\241\260\355\230\204\354\247\200.md" @@ -0,0 +1,80 @@ +1. Softmax Regression
+이진 분류 – 0과 1 두가지 선택
+
+C = 클래스 수
+첫 번째 단위 –
+출력값 = 입력값 X가 주어졌을 때 기타 클래스가 나올 확률
+출력값 y_hat = (4,1)차원의 벡터
+y_hat의 합은 1이 된다
+
+이런 신경망을 얻기 위한 가장 표준적인 모델은 소프트맥스층을 사용하는 것이다
+
+평소 활성함수 : z^[L] = w^[L]*a^[L-1] + b
+소프트맥스 층의 활성함수 :
+t = e^z^[L]
+a^[L] = e^z^[L] / ti , a_i^[L] = ti / ti
+z^[L] = [5, 2, -1, 3]
+t = [e^5, e^2, e^-1, e^3] = [148.4, 7.4, 0.4, 20.1], ti = 176.3
+a^[L] = t / 176.3
+첫 번째 노드의 값 = e^5 / 176.3 = 0.842, 즉, 클래스 0이 될 확률이 84.2%
+다음 노드의 값 = e^2 / 176.3 = 0.042
+다음 노드의 값 = e^-1 / 176.3 = 0.02
+다음 노드의 값 = e^3 / 176.3 = 0.114, 즉, 클래스 3(병아리)이 될 확률이 11.4%
+
+이 신경망의 출력값 y_hat과도 같은 a^[L]은 (4,1)벡터가 된다. 그 안에는 계산한 이 숫자들이 들어가있다.
+알고리즘은 z^[L]이라는 벡터를 취해서 합이 1이 되는 4개의 확률 값을 내놓는다
+
+
+e를 취해서 임시 변수 t를 얻어서 정규화한 이 과정을 소프트맥스 활성화 함수로 요약할 수 있다
+즉 a^[L]은 z^[L] 벡터에 활성함수 g^[L]을 적용한 것이다
+활성화 함수 g의 특이한 점은 (4,1)벡터를 받아서 (4,1)벡터를 내놓는다는 것이다
+(이전 함수들은 실수를 받아서 실수를 내놨다)
+
+<소프트맥스 분류로 할 수 있는 것>
+학습 세트를 가져와서 비용 함수와 3개의 선택지에 따라 분류하는 소프트맥수 함수를 학습시킨다.
+은닉 유닛이 여러 개인, 더 깊은 신경망을 다룬다면 여러 클래스를 분류하기 위해 더 복잡하고 비선형의 경계도 볼 수 있다
+2. Softmax 분류기 훈련시키기
+z^[L] = [5, 2, -1, 3]
+4개의 클래스를 다루니 z^[L]은 (4,1) 벡터이다.
+z^[L]의 가장 큰 원소가 5였고 가장 큰 확률도 첫 번째 확률이다.
+소프트맥스 <-> 하드맥스

+ +하드맥스는 z벡터를 받아와서 [1, 0, 0, 0] 벡터로 대응시킨다.
+소프트맥스는 z벡터를 받아와서 [0.842, 0.042, 0.002, 0.114] 벡터로 대응시킨다.
+두 클래스만 다루는 로지스틱 회귀를 일반화했다
+소프트맥스에서 C가 2라면 결국 로지스틱 회귀와 같아진다
+(출력층 a^[L]은 C=2에서 출력값 두 개를 모아둔 것이다)
+(확률 합 1이니까 하나만 계산하면 된다 – 로지스틱 회귀가 하나의 출력값을 계산하는 방식과 같다)
+ +<소프트맥스 출력층을 이용해 신경망을 학습하는 법>
+y = [0, 1, 0, 0] -> cat을 의미
+-> y_1 = y_3 = y_4 = 0 / y_2만 유일하게 1
+y_hat = [0.3, 0.2, 0.1, 0.4] -> 신경망이 잘 작동하지 않음
+L(y_hat, y) = - y_j*log(y_hat_j) = -y_2*log(y_hat_2) = -log(y_hat_2)
+y_j가 0이면 합을 고려할 필요 없다
+결국 손실 함수의 값을 작게 만들려고 한다 -> y_hat을 크게 만들려고 한다
+입력값 x가 고양이의 사진이었으니 그에 대응하는 출력값인 확률을 최대한 키워야 한다
+일반적으로 손실 함수는 훈련 세트에서 관측에 따른 클래스가 뭐든 간에 가능한 한 크게 만드는 것이다.
+전체 훈련 세트에 대해 비용 함수 J는 전체 훈련 세트에서 학습 알고리즘의 예측에 대한 손실 함수를 합하는 것이다.
+이 비용 함수를 최소로 하기 위해 경사하강법을 써야 한다

+ +Y = [y^(1), y^(2),,,y^(m)]
+[0,1,0,0]
+[0,0,1,0]
+[1,0,0,0]
+,
+,
+Y_hat도 똑같이
+[0.3,0.2,0.1,0.4]
+,
+,
+(4,m)차원이 된다

+ +
+마지막 층에서 z^[L]의 미분이 (4,1)벡터인 y_hat에서 (4,1) 벡터인 y를 뺀 것과 같다
+클래스가 4개일 때 모두 (4,1)벡터가 된다
+dz^[L] : 비용 함수를 z^[L]에 대해 편미분한 것
+ + + + diff --git "a/\352\263\274\354\240\23416_\354\241\260\355\230\204\354\247\200.md" "b/\352\263\274\354\240\23416_\354\241\260\355\230\204\354\247\200.md" new file mode 100644 index 0000000..e27cd2a --- /dev/null +++ "b/\352\263\274\354\240\23416_\354\241\260\355\230\204\354\247\200.md" @@ -0,0 +1,110 @@ +합성곱 신경망
+1. 컴퓨터비전
+종류
+- image classification : 고양이인지 아닌지
+- object detection : 어느 위치에 차가 있는지. 차가 여러대일 수 있다
+- neural style transfer

+ +문제
+- 입력이 매우 클 수 있다
+1000*1000*3 = 3백만 크기
+첫 은닉층 = 1000개
+전체 가중치 = 1000*3,000,000 크기 행렬

+ +2. 모서리 감지 예시
+어떻게 이미지의 모서리를 감지하는가

+ +1) 이미지에서 수직인 모서리를 감지한다
+2) 수평인 모서리를 감지한다

+ +세로 윤곽선 검출 - 합성곱 연산
+: 원래 이미지와 커널을 원소곱 후 전부 더해준다

+ +딥러닝 프레임워크에서는 Conv2d 함수를 사용한다

+ +3. 더 많은 모서리 감지 예시
+양과 음의 윤곽선의 차이 – 서로 다른 밝기의 전환
+밝은 곳 -> 어두운 곳 – 어 밝 어
+어두운 곳 -> 밝은 곳 – 밝 어 밝

+ +가로배열 필터 -> 수평 모서리 검출
+ +필터 종류
+1) sobel filter – 중간 부분의 픽셀에 더 중점을 둔다
+2) scharr filter – 3, 10 사용
+3) 딥러닝 – 9개 숫자를 고를 필요가 없다. 스스로 학습한다

+ +4. 패딩
+n*n 이미지를 f*f 필터로 합성곱을 한 결과 = n-f+1*n-f+1

+ +단점 2개
+1) 합성곱 연산을 할 때마다 이미지가 축소된다
+-> 수 백개의 층에서 각 층마다 축소된다면 모든 층을 거친 뒤에는 아주 작은 이미지만 남음
+2) 가장자리나 모서리의 픽셀은 결과 이미지에 덜 사용된다

+ +해결
+: 합성곱 연산을 하기 전에 이미지를 덧댄다 (0을 더하는 것이 일반적)
+n+2p-f+1*n+2p-f+1

+ +패딩을 얼마 만큼 할 것인가?
+1) 유효 합성곱 : 패딩이 없는 것
+2) 동일 합성곱 : 패딩을 한 뒤 결과 이미지의 크기가 기존 이미지와 동일
+p = (f-1)/2

+ +컴퓨터비전에서 필터가 거의 홀수인 이유
+1) 만약 f가 짝수라면 패딩이 비대칭이 된다
+2) 만약 홀수 크기의 필터가 있으면 중심 위치가 존재한다
+따라서 3*3 사용이 일반적. 5*5, 7*7도 사용

+ +5. 스트라이드
+스트라이드 : 필터의 이동 횟수

+ +n*n이미지를 f*f 필터로 합성곱을 하고 p의 패딩과 s 스트라이드를 가지면
+(n+2p-f)/s+1*(n+2p-f)/s+1
+만약에 소수점으로 만들었다면 내림을 하게 된다. 보통은 필터에 맞춰서 최대한 크기가 정수가 될수 있도록 패딩과 스트라이드 수치를 맞춘다.

+ +신호처리에서의 교차상관과 합성곱의 관계
+미러링 : 합성곱을 하기 전에 필터를 가로축과 세로축으로 뒤집는 연산을 해주는 것
+지금까지의 합성곱 연산은 미러링 과정을 생략했다 -> 프로그래밍에 영향x여서

+ +6. 입체형 이미지에서의 합성곱
+3D 필터를 사용
+(높이*넓이*채널수) * (높이*넓이*채널수)
+채널 : 색상 또는 입체형 이미지의 깊이, 이미지의 채널수 = 필터의 채널수
+각 채널 별로 필터는 모두 같은 것을 사용할 수도 있고 다른 것을 사용할 수도 있다.
+n-f+1*n-f+1*nc’
+가로와 세로처럼 두 개의 특성 또는 10개의 특성들을 검출할 수 있고 검출하고자 하는 특성의 수만큼 채널을 가지게 된다
+채널의 수 = 마지막 크기, 3D 입체형의 깊이

+ +7. 합성곱 네트워크의 한 계층 구성하기
+합성곱 신경망의 한 계층은 아래와 같이 구성된다
+합성곱 연산 → 편향 추가 → 활성화 함수
+활성화 함수는 비선형성을 적용하기 위함이다. 보통 ReLU 를 많이 사용한다

+ +예시에서 합성곱 연산을 할 때 두 개의 필터를 사용해서 27*2개 가짐

+ +만약 10개의 필터가 있고 각각 3*3*3 크기로 신경망의 한 계층에 있다면 이 층은 몇 개의 매개변수를 가질까?
+-> 각각의 필터는 3*3*3의 크기로 27개의 변수를 가진다. 편향을 더하면 28개의 변수를 가진다. 10개가 있기 때문에 28에 10을 곱하면 280개의 변수를 가진다.
+ +8. 간단한 합성곱 네트워크 예시
+합성곱 신경망의 크기는 깊어질수록 점점 줄어든다
+대부분의 신경망에는 합성곱 층, 풀링 층, 완전 연결 층으로 구성되어 있다

+ +9. 풀링층
+합성곱 신경망에서는 풀링 층을 사용해 표현의 크기를 줄임으로써 계산속도를 줄이고 특징을 더 잘 검출 해낼 수 있다.
+한 특성이 필터의 한 부분에서 검출되면 높은 수를 남기고 그렇지 않으면 작은 수를 남긴다

+ +풀링의 종류
+1) 최대 풀링 -> 이미지의 특징이 펄터의 한 부분에서 검출 되면 높은 수를 남기고 그렇지 않으면 다른 최대값들에 비해 상대적으로 작아져, 특징을 더 잘 남긴다
+2) 평균 풀링
+보통 최대 풀링을 사용한다

+ +10. CNN 예시
+합성곱 신경망의 분야에서는 두 종류의 관습이 있는데, 하나는 합성곱 층과 풀링 층을 하나의 층으로 보고, 다른 하나는 합성곱 층과 풀링 층을 각각의 층으로 간주 하는 것이다. 여기서는 전자의 방법을 사용한다. 풀링 층에 학습해야 할 변수가 없기 때문에 합성곱 층과 풀링 층을 하나로 간주한다.

+ +11. 왜 합성곱을 사용할까요?
+합성곱 신경망을 사용하면 변수를 적게 사용할 수 있다.

+합성곱 신경망이 이렇게 적은 변수를 필요로 하는 이유 :
+1) 변수 공유 - 어떤 한 부분에서 이미지의 특성을 검출하는 필터가 이미지의 다른 부분에서도 똑같이 적용되거나 도움이 된다.
+2) 희소 연결 - 출력값이 이미지의 일부(작은 입력값)에 영향을 받고, 나머지 픽셀들의 영향을 받지 않기 때문에, 과대적합을 방지할 수 있다.
+합성곱 신경망은 이동 불변성을 포착하는데도 용이하다. 이미지가 약간의 변형이 있어도 이를 포착할 수 있다. \ No newline at end of file diff --git "a/\352\263\274\354\240\23417_\354\241\260\355\230\204\354\247\200.docx" "b/\352\263\274\354\240\23417_\354\241\260\355\230\204\354\247\200.docx" new file mode 100644 index 0000000..16bb023 Binary files /dev/null and "b/\352\263\274\354\240\23417_\354\241\260\355\230\204\354\247\200.docx" differ diff --git "a/\352\263\274\354\240\23418_\354\241\260\355\230\204\354\247\200.md" "b/\352\263\274\354\240\23418_\354\241\260\355\230\204\354\247\200.md" new file mode 100644 index 0000000..8c4a6c9 --- /dev/null +++ "b/\352\263\274\354\240\23418_\354\241\260\355\230\204\354\247\200.md" @@ -0,0 +1,94 @@ +자연어 처리의 모든 것
+ +1. 자연어 처리 활용 분야와 트렌드

+ +1) Natural language processing (자연어 처리)

+ +주요 학회 : ACL, EMNLP, NAACL
+학문 분야
+Low-level parsing : Tokenization, stemming - 어미
+Word and phrase level : NER(Named Entity Recognation) - 고유명사 인식, POS(Part-Of-Speech) tagging – 품사, 성분 무엇인지
+Sentence level : 감성 분류(Sentiment Analysis) - 긍정, 부정, 기계 번역(Machine Translation) - 적절한 한글로 번역
+Multi-sentence and paragraph level : 논리적 내포 및 모순관계 예측(Entailment Prediction) - 어제 존이 결혼했다가 참일 경우 어제 최소 한명이 결혼했다가 무조건 참, 독해기반 질의응답(question answering) - 질문을 정확하게 이해하고 답에 해당하는 정보 바로 보여줌, 챗봇(dialog systems), 요약(summarization)

+ +2. Text mining (텍스트 마이닝) - 빅데이터 분석과 관련, 키워드의 빈도수를 분석해 소비자 반응 얻어냄

+ +주요 학회 : KDD, The WebConf(前 WWW), WSDM, CIKM, ICWSM
+학문 분야
+Extract useful information and insights from text and document data
+문서 군집화(Document clustering) - 서로 다른 키워드지만 비슷한 의미들을 그루핑해서 분석할 필요생김 ex) 토픽 모델링
+Highly related to computational social science : 통계적으로 사회과학적 인사이트 산출, 트위터, 페이스북,...

+ +3. Information retrieval (정보 검색) - 구글, 네이버 등의 검색 기술 연구, 검색기술이 성숙해있기 때문에 상대적으로 발전이 느림

+ +주요 학회 : SIGIR, WSDM, CIKM, Recsys
+학문 분야
+Highly related to computational social science
+정보 검색 분야, 추천 시스템 – 비슷한 노래들 자동 추천, 알고리즘 자동 추천

+ +<자연어 처리 분야의 발전 과정>
+느리지만 꾸준히 발전해왔다
+주어진 텍스트 데이터 단어 단위로 분리 -> 벡터로 변경 (워드 임베딩)
+워드 벡터가 시퀀스로 주어지기 때문에 RNN 모델이 적합하다
+RNN 모델로는 LSTM, GRU가 사용해오다가 2017년 구글에서 나온 Transformer모델 사용하게 되었다
+현재 대부분의 자연어처리 모델들은 Transformer모델을 사용하고 있다. Transformer 모델은 주로 사용되던 '기계 번역' 분야를 넘어 현재는 영상/신약개발/시계열 예측 등에서도 다양하게 사용되고 있다. 최근에는 자가지도 학습(self-supervised Learning)이 가능한 BERT, GPT 와 같은 모델의 유행하고 있다. 입력 중 일부를 가리고 앞뒤 문맥을 보고 단어를 맞추게 하는 것

+ +2. 기존의 자연어 처리 기법
+
+ : 단어들의 순서는 전혀 고려하지 않고, 단어들의 출현 빈도(frequency)에만 집중하는 텍스트 데이터의 수치화 표현 방법

+ +John really really loves this movie. Jane really likes this song

+ +1. constructing the vocabulary containing unique words
+ +- vocabulary에 8개 단어를 등록한다. 중복된 단어는 한번만 등록한다

+ +2. encoding unique words to one-hot vectors
+ +- categorical variable -> one-hot vector
+- john: [1 0 0 0 0 0 0 0]
+- 어떤 단어쌍이든지 거리가 루트2
+- 어떤 단어쌍이든지 유사도가 0 (모두가 동일한 관계를 가진다)
+- 단어별로 가방을 준비하고 나타나면 가방에 넣어 그 수를 세어 벡터로 나타낸다

+ +
+: 머신러닝의 주요 알고리즘으로 분류에 있어 준수한 성능을 보여주고 있다
+[Bayes' theorem]
+: 조건부 확률을 계산하는 방법

+ +다른 단어들이 분류하고자 하는 문장에 많이 등장했을지라도, Training data 에서 1번이라도 등장하지 않았다면 모든 단어들의 확률 곱으로 인해 0이 된다. -> regularization으로 해결한다

+ +3. Word Embedding - (1)Word2Vec
+
+ : 각 단어를 좌표공간에 최적의 벡터로 표현하는(임베딩하는) 기법
+ 유사한 단어는 가까이, 유사하지 않은 단어는 멀리 위치하는 것을 '최적의 좌표값'으로 표현할 수 있다.

+ +
+문장 내에서 비슷한 위치에 등장하는 단어는 유사한 의미를 가질 것이다
+Tokenizing -> Vocabulary -> 중심단어를 위주로 학습 데이터를 구축. 중심단어가 study 라고 한다면, (I study), (study I) , (study math)

+ +
+문장의 단어의 갯수만큼에 Input, Output 벡터 사이즈를 입력/출력해준다. 이 때 연산에 사용되는 히든 레이어(hidden layer,은닉 층)의 차원(dim)은 사용자가 파라미터로 지정할 수 있다.
+실제로 Tensorflow나 Pytorch와 같은 프레임워크에서는 임베딩 레이어와의 연산은 0이 아닌 1인 부분, 예를 들어 [0,0,1]의 벡터인 경우는 3번째 원소와 곱해지는 부분의 컬럼(column)만 뽑아서 계산해준다.
+마지막 결과값으로 나온 벡터는 softmax 연산을 통해 가장 큰 값이 1, 나머지는 0으로 출력된다.

+ +
+워드투벡터를 통해 단어를 임베딩하면 queen - king 그리고 woman - man , 마지막으로 aunt - uncle 의 벡터가 비슷한 것을 볼 수 있다. 해당 결과가 의미하는 것은 여성과 남성의 관계성을 잘 학습했다는 것을 의미한다.

+ +
+각 단어별로 나머지 단어들과 유클리디안 거리를 계산한 후 평균값을 낸다
+ +
+Word2Vec은 그 자체로도 의미가 있지만, 뿐만 아니라 다양한 테스크에서 사용되고 있다.
+Machine translation : 단어 유사도를 학습하여 번역 성능을 더 높여준다.
+Sentiment analysis : 감정분석, 긍부정분류를 돕는다.
+Image Captioning : 이미지의 특성을 추출해 문장으로 표현하는 테스크를 돕는다.

+ +4. Word Embedding - (2)GloVe
+
+ : 사전에 미리 각 단어들의 동시 등장 빈도수를 계산하며, 단어간의 내적값과 사전에 계산된 값의 차이를 줄여가는 형태로 학습한다.
+ Word2Vec는 특정한 입출력쌍이 자주 등장한 경우 여러번에 걸쳐 학습되어 내적값이 커진다. 하지만 Glove는 단어쌍이 동시에 등장한 횟수를 미리 계산하고 로그값을 취한 것을 Ground Truth로 사용해 반복계산을 줄일 수 있다. 따라서 Word2Vec보다 더 빠르게 동작하며, 더 적은 데이터에서도 잘 동작한다.

+ +<사전 학습된 Glove 모델>
+사전에 이미 대규모 데이터로 학습된 모델이 오픈소스로 공개되어 있다. 해당 모델은 위키피디아 데이터를 기반으로 하여 6B token만큼 학습 되었으며, 중복 제거 시에도 단어의 개수가 무려 40만개(400k)에 달한다.
+학습된 모델을 나타낼 때 뒤에 붙는 "uncased"는 대문자 소문자를 구분하지 않는다는 의미이며, 반대로 "cased"는 대소문자를 구분한다는 의미이다. 예를 들어 Cat과 cat이 uncased에서는 같은 토큰으로 취급되지만, cased에서는 다른 토큰으로 취급된다. diff --git "a/\352\263\274\354\240\23419_\354\241\260\355\230\204\354\247\200.md" "b/\352\263\274\354\240\23419_\354\241\260\355\230\204\354\247\200.md" new file mode 100644 index 0000000..0817d63 --- /dev/null +++ "b/\352\263\274\354\240\23419_\354\241\260\355\230\204\354\247\200.md" @@ -0,0 +1,46 @@ +자연어 처리의 모든 것
+2. 자연어 처리와 딥러닝

+ +1) Recurrent Neural Network

+ +RNN은 현재 타임스텝에 대해 이전 스텝까지의 정보를 기반으로 예측값을 산출하는 구조의 딥러닝 모델이다.
+h_t-1과 h_t를 결합해 계산한다.
+동일한 rnn모듈 A가 매 타임스텝마다 재귀적으로 호출되면서 A모듈의 출력이 다음 타임스텝의 입력으로 들어간다.

+ +RNN 계산 방법
+1. 변수들에 대하여, h_t = f(h_t-1,x_t)의 함수를 통해 매 타임스텝마다 hidden state를 다시 구해준다.
+2. 이 때, W와 입력값 (x_t, h_t-1)으로 tan_h를 곱해서 h_t를 구해준다.
+3. 구해진 h_t, x_t를 입력으로 y_t 값을 산출하게 된다.

+ +다양한 타입의 RNN 모델
+1. one to one
+: [키, 몸무게, 나이]와 같은 정보를 입력값으로 할 때, 이를 통해 저혈압/고혈압인지 분류하는 형태의 태스크
+2. one to many
+: '이미지 캡셔닝'과 같이 하나의 이미지를 입력값으로 주면 설명글을 생성하는 태스크
+3. many to one
+: 감성 분석과 같이 문장을 넣으면 긍/부정 중 하나의 레이블로 분류하는 태스크
+4. many to many
+: 기계 번역과 같이 입력값을 끝까지 다 읽은 후, 번역된 문장을 출력해주는 태스크
+4. many to many
+: 비디오 분류와 같이 영상의 프레임 레벨에서 예측하는 태스크, 혹은 각 단어의 품사에 대해 태깅하는 POS와 같은 태스크

+ +2) Character-level Language Model
+언어 모델이란 이전에 등장한 문자열을 기반으로 다음 단어를 예측하는 태스크를 말한다. 그중에서도 캐릭터 레벨 언어 모델(character-level Language Model)은 문자 단위로 다음에 올 문자를 예측하는 언어 모델이다.
+예를 들어, 그림과 같이 맨 처음에 "h"가 주어지면 "e"를 예측하고, "e"가 주어지면 "l"을 예측하고, "l"이 주어지면 다음 "l"을 예측하도록 hidden state가 학습돼야한다.
+각 타임스텝별로 output layer를 통해 차원이 4(유니크한 문자의 개수) 벡터를 출력해주는데 이를 logit이라고 부르며, softmax layer를 통과시키면 원-핫 벡터 형태의 출력값이 나오게 된다.

+ +3) Backpropagation through time and Long-Term-Dependency
+BPTT(Backpropagation through time)의 작동 방식
+BPTT란, Backpropagation through time의 줄임말로 RNN에서 타임스텝마다 계산된 weight를 backward propagation을 통해 학습하는 방식을 의미한다.
+BPTT를 통해 gradient를 계산해주면, tanh 로 감싸진 괄호안의 값들 중에 3 값이 속미분되어 나오게 된다. time step이 3이므로 3이 2번 곱해지지만, 만약 길이가 더 길어진다면 미분값은 기하급수적으로 커질 것이고, 만약 속미분되어 나오는 W의 값이 1보다 작다면 미분값은 기하급수적으로 작아질 것이다. 이 계산과정을 통해 Gradient Vanishing/Exploding 문제가 발생하고 이 문제가 Long-Term-Dependency를 일으키게 된다.

+ +4) Long Short-Term Memory (LSTM)
+STM의 중심 아이디어는 단기 기억으로 저장하여 이걸 때에 따라 꺼내 사용함으로 더 오래 기억할 수 있도록 개선하는 것
+Cell state에는 핵심 정보들을 모두 담아두고, 필요할 때마다 Hidden state를 가공해 time step에 필요한 정보만 노출하는 형태로 정보가 전파된다
+LSTM의 특징은 각 time step마다 필요한 정보를 단기 기억으로 hidden state에 저장하여 관리되도록 학습하는 것이다
+오차역전파(backpropagation) 진행시 가중치(W)를 계속해서 곱해주는 연산이 아니라, forget gate를 거친 값에 대해 필요로하는 정보를 덧셈을 통해 연산하여 그레디언트 소실/증폭 문제를 방지한다

+ +GRU : Gated Recurrent Unit
+Cell state, Hidden state를 일원화하여 경량화한 모델
+forget gate 대신 (1-input gate)를 사용하여 h_t를 구할때 가중평균의 형태로 계산한다
+계산량과 메모리 요구량을 LSTM에 비해 줄여준 모델이면서 동시에 성능면에서도 LSTM과 비슷하거나 더 좋은 성능을 내는 모델이다 diff --git "a/\352\263\274\354\240\2341_\354\241\260\355\230\204\354\247\200.md" "b/\352\263\274\354\240\2341_\354\241\260\355\230\204\354\247\200.md" new file mode 100644 index 0000000..8518fd4 --- /dev/null +++ "b/\352\263\274\354\240\2341_\354\241\260\355\230\204\354\247\200.md" @@ -0,0 +1,252 @@ +1. 딥러닝 소개 + +1) 신경망은 무엇인가? + +딥러닝 : 신경망을 학습시키는 것 + +2) 신경망을 이용한 지도학습 + +목적에 따라 적절한 신경망을 사용하면 된다 + +neural network examples +• standard nn +• convolutional nn +• recurrent nn + +structured data +• 행과 열의 형태를 가진 데이터 +• 잘 정의되어 있다 + +unstructured data +• 이미지, 음성, 텍스트,,, +• 구조가 정해져있지 않다 + +3) 왜 딥러닝이 뜨고 있을까요? + +높은 성능을 내기 위한 방법 +1. 충분히 큰 신경망 +2. 많은 양의 데이터 + +따라서 +규모(신경망의 크기 = 많은 hidden unit, 많은 데이터)가 성능 향상에 중요하다 + +훈련할 데이터가 적다면 (small m) +구현방법에 따라 성능이 결정되는 경우가 많다 + +훈련 세트가 크다면 (big m) +large nn이 성능이 좋다 + +• 데이터 +• 계산의 규모 +• 알고리즘 +이 신경망의 발전을 이루게 했다 + +idea -> code -> experiment 의 반복할때 빠른 계산은 딥러닝의 발전에 도움 + + +2. 신경망과 로지스틱 회귀 + +1) 이진 분류 + +forward propagation -> backward propagation + +로지스틱 회귀 +: 이진 분류를 위한 알고리즘 + +64 * 64 사진 +-> 빨간 초록 파란 각각 존재 + +feature vector -> 64 * 64 * 3 + +이진분류 목표: +입력된 사진을 나타내는 feature vector x를 가지고 +y가 0인지 1인지 (고양이사진인지 아닌지) 를 예측한다 + +x : 훈련 샘플들 +y : 출력될 레이블 + +훈련샘플의 갯수 : m +m_train : train 세트의 갯수. m_test : test 세트의 갯수 + +X : n_x * m 행렬 +X.shape -> 행렬의 차원을 알 수 있다 + +Y : 1 * m 행렬 + +2) 로지스틱 회귀 + +:출력될 레이블 y가 0이나 1일 경우 (이진 분류일 경우) 쓰인다 + +y hat : 입력 특성 x가 주어졌을 때 y가 1일 확률 +(사진이 고양이 사진일 확률) + +x : nx 차원상의 벡터 +w : nx 차원상의 벡터 +b : 실수 + +y hat 구하는 방법은? +y hat은 0과 1 사이 값이어야 한다 +-> 시그모이드 함수를 적용 +-> z가 무한대일때 1에 가까워진다 +-> z가 음수일수록 0에 가까워진다 + +—-또 다른 표기법 +x0 = 1 +x가 n_x+1 상의 차원의 벡터 +y hat은 세타의 전치 * x의 시그모이드 + +세타0는 b + +——b와 w를 분리하는 것이 편하기 때문에 쓰지 않을 것임 + + +3) 로지스틱 회귀의 비용함수 + +매개변수들 w와 b를 학습하려면 비용함수를 정의해야 한다 + + +x^(i) : i번째 훈련 샘플에 관한 데이터 + +L : loss funciton +y hat과 y 사이에 오차가 얼마나 큰지 측정 + +최적화를 위해 볼록한 함수를 이용한다 + +왜 이 함수를 쓰는가? +y가 1일 경우 손실값을 줄이려면 +: log(y hat)이 최대한 커져야 -> y hat이 최대한 커져야 (1에 수렴하길 원한다) +y가 0일 경우 손실값을 줄이려면 +: log(1-y hat)이 최대한 커져야 -> y hat이 최대한 작아야(0에 수렴) + +cost function +: 훈련 세트 전체에 대해 얼마나 잘 추출되었는지 측정 +매개변수 w와 b가 train set를 잘 예측하는지 측정 +(y의 예측값이 얼마나 좋은지를 각 훈련 샘플에 대한 참값과 비교해 측정) + +loss function +: 하나의 훈련 샘플에 적용 + +로지스틱 회귀 모델을 학습하는 것 += cost function j를 최소화해주는 매개변수 w, b를 찾는 것 + +로지스틱 회귀는 작은 신경망과 같다 + + +4) 경사하강법 +: 매개변수 w와 b를 훈련 세트에 학습시키는 방법 + +cost function J(w,b)를 가장 작게 만드는 w와 b를 찾아야 한다 + +J(w,b)는 w, b 위의 곡면 +-> 곡면의 높이는 J(w,b)의 값 +-> J의 최솟값에 해당하는 w와 b를 찾아야 한다 + +cost function이 볼록하다 +-> 지역 최적값이 하나 + +1. w, b값을 한 값으로 초기화한다 (보통 0으로 설정) +2. 초기점에서 가장 가파른 내리막 방향으로 한 단계 내려간다 +3. 반복하면 최적값에 도달한다 + +*경사 하강법 +:= 값을 갱신한다는 의미 + +알파 = learning rate +경사 하강법을 반복할 때 한 단계의 크기를 결정한다 + +dw = 미분계수 +왼쪽에서 초기화하던 오른쪽에서 초기화하던 전역 최솟값에 도달! + +변수가 두개면 편미분 기호를 사용한다 + + +5) 미분 + +도함수 +f(a) = 3a +a=2 f(a)=6 +a=2.001 f(a)=6.003 + +도함수 = 기울기 = height/width + +함수의 어느곳이든 기울기가 3이다 + +6) 더 많은 미분 예제 + +함수의 다른 부분이 다른 기울기를 가지는 경우 + +7) 계산 그래프 (computation graph) + +신경망의 계산 +• forward propagation : 신경망의 출력값을 계산 +• backward propagation : 경사나 도함수를 계산 + +J(a,b,c) = 3(a+bc) +1. u = bc +2. v = a + u +3. J = 3v + +특정한 출력값 변수를 최적화하고 싶을때 사용한다 + +왼쪽 -> 오른쪽 으로 계산 + +8) 계산 그래프로 미분하기 + +dJ/dv = ? (dv) + +J = 3v +J의 증가량이 v의 증가량의 세배이기 때문에 3 + +back propagation + +dJ/da = ? (da) + +a를 바꾸면 +-> v가 증가 (dv/da에 의한 양만큼) +-> J가 증가 + +chain rule -> dJ/da = dJ/dv * dv/da + +final output = J +dJ/dv 코드에서 dvar 로 표시한다 + +—————- + +dJ/du = dJ/dv * dv/du +dJ/db = dJ/du * du/db + +9) 로지스틱 회귀의 경사하강법 +(계산 그래프를 사용) + +a : 로지스틱 회귀의 출력값 +y : 참 값 레이블 + +로지스틱 회귀에서의 목적 +: 매개변수 w와 b를 변경해서 손실을 줄이는 것 + +구하고자 하는 것 : 손실 함수의 도함수 +연쇄법칙을 사용해 도함수를 구한다 +1. da (dL/da) +2. dz (dL/dz) +3. dw1 (x1 * dz), dw2, db +4. w1 := w1-알파dw1 + w2 := w2 - 알파dw2 + b := b- 알파db + +10) m개 샘플의 경사하강법 + +J (cost function) : 각 손실의 평균 +J의 도함수 : 각 손실 항 도함수의 평균 + +for i =1 to m +특성이 2개 (n=2)라고 가정 + +dw1, dw2, db는 값을 저장하는데 쓰고 있다 +-> dw1은 w에 대한 전체 J의 도함수와 같다 + +2개의 for문 +• m개의 훈련 샘플 반복 +• 특성을 반복 (n개) + +명시적인 for문 -> 비효율적 +따라서 for문 없애주는 vectorization 사용한다!! diff --git "a/\352\263\274\354\240\2342_\354\241\260\355\230\204\354\247\200.md" "b/\352\263\274\354\240\2342_\354\241\260\355\230\204\354\247\200.md" new file mode 100644 index 0000000..ee1de2f --- /dev/null +++ "b/\352\263\274\354\240\2342_\354\241\260\355\230\204\354\247\200.md" @@ -0,0 +1,158 @@ +3. 파이썬과 벡터화 + +1) 벡터화 + • 코드에서 for문을 없앨 수 있다
+ • 큰 데이터 세트를 학습시킬때 코드 실행시간을 줄일 수 있어 중요하다
+ +w, x : 열 벡터
+벡터화된 구현은 w^Tx를 직접 계산한다
+파이썬에서 명령어 : np.dot(w,x) + b
+ +import numpy as np
+a = np.array([1,2,3,4])
+print a
+ +import time
+a = np.random.rand (1000000) //난수로 이루어진 백만 차원의 배열 생성
+b = np.random.rand (1000000)
+ +tic = time.time() //현재 시간으로 설정
+c = np.dot(a,b)
+toc = time.time()
+print(c)
+print(“Vectorized version:” + str(1000\*(toc-tic)) + “ms”)
+ +//평균적으로 1.5밀리세컨드가 걸린다
+ +c = 0
+tic = time.time()
+for i in range(1000000):
+c += a[i] _ b[i]
+toc = time.time()
+print(c)
+print(“for loop:” + str(100_(toc-tic)) + “ms”)
+ +//평균적으로 500밀리초가 걸린다 (약 300배)
+ +2. 더 많은 벡터화 예제
+ :가능한 for문을 쓰지 말아야 한다
+ +예1)
+u = Av
+ +1. 벡터화 사용하지 않는 경우 - 두개의 for문을 사용한다
+2. 벡터화 사용하는 경우 - u = np.dot(A,v)
+ +예2)
+벡터v의 모든 원소에 지수 연산
+ +1. 벡터화 사용하지 않는 경우 - np.zeros하고 for문 사용한다
+2. 벡터화 사용하는 경우 -
+ import numpy as np
+ u = np.exp(v)
+ +다른 numpy 라이브러리들 (내장 함수들)
+• np.log(v) //원소의 로그값
+• np.abs(v) //절대값
+• np.max(v,0) //v의 원소와 0중에서 더 큰 값 반환
+• v\*\*2 //모든 원소를 제곱한 벡터를 반환
+• 1/v //원소의 역수로 이루어진 벡터를 반환
+
+로지스틱 회귀와 경사 하강법에 적용
+-> 두번째 for문을 제거
+-> dw를 벡터로 만든다
+dw = np.zeros((n_x,1))
+dw += x(i)dz(i)
+dw /= m
+ +3. 로지스틱 회귀의 벡터화
+ +정방향 전파 m번 반복 - for문 없이
+X는 (n_x, m)차원 행렬
+
+1. (1,m) 행렬 만들기
+ Z = np.dot(w.T,X) + b
+
+\*b는 (1,1) 실수이지만 파이썬이 자동으로 (1,m) 벡터로 바꿔준다
+-> Broadcasting
+ +2. a^(1)부터 a^(m)을 동시에 계산
+ +4) 로지스틱 회귀의 경사 계산을 벡터화 하기
+ +1. dZ 정의하기
+ (1,m) 행렬이 된다
+ dZ = A - Y
+ +두번째 for문을 제거한다
+ +<지난 계산>
+dw = 0
+dw += x(1)dz(!)
+dw += x(2)dz(2)
+… //m개의 훈련 샘플에 대한 for문이 남아있다
+dw/=m
+ +db = 0
+db += dz(1)
+db += dz(2)
+…
+db += dz(m)
+db/=m
+ +<벡터화 적용>
+db는 i가 1부터 m까지일때 dz(i)의 합을 m으로 나눈 값
+ +db = 1/m*np.sum(dZ)
+dw = 1/m*X\*dZ^T
+ +<모두 벡터화 적용>
+ +for i in range(1000): -> 바꿀수있는 방법 x
+ +Z = w^T*X + b
+= np.dot(w^T,X) + b
+A = 시그마(Z)
+dZ = A - Y
+dw = (1/m)*X*dZ^T
+db = (1/m)*np.sum(dZ)
+ +w := w - (학습률 알파)dw
+b := b-(학습률 알파)db
+ +5. 파이썬의 브로드캐스팅
+ +네 가지 음식의 carb, protein, fat의 칼로리의 백분율을 구하자
+사과에서 carb의 백분율 = 56/59 = 약 94.9%
+ +행렬의 네 열 안의 수의 합을 구하고 행렬 전체를 나눠서
+백분율을 구한다 - for문 없이!
+ +A - (3,4) 행렬
+ +파이썬 코드
+cal = A.sum(axis = 0)
+print (cal)
+ +percentage = 100\*A/cal.reshape(1,4) // reshape를 이용해 행렬의 차원을 확실시
+print(percentage)
+ +6. 파이썬과 넘파이 벡터
+ +import numpy as np
+a = np.random.randn(5)
+ +print(a)
+print(a.shape)
+print(a.T)
+print(np.dot(a,a.T))
+a = np.random.randn(5,1)
+print(a)
+print(a.T)
+print(np.dot(a,a.T))
+ +1. rank 1 array를 사용하지 말 것. 열 벡터는 (n,1), 행 벡터는 (1,n)을 사용
+ +ex) a = np.random.randn(5)가 아니라 a = np.random.randn(5,1) 혹은 a = np.random.randn(1,5)를 사용
+ +2. assert로 행렬과 배열의 차원을 확인하고, reshape으로 행렬과 벡터를 필요한 차원으로 변환하기
diff --git "a/\352\263\274\354\240\2343_\354\241\260\355\230\204\354\247\200.md" "b/\352\263\274\354\240\2343_\354\241\260\355\230\204\354\247\200.md" new file mode 100644 index 0000000..036d356 --- /dev/null +++ "b/\352\263\274\354\240\2343_\354\241\260\355\230\204\354\247\200.md" @@ -0,0 +1,102 @@ +4. 얕은 신경망 네트워크
+ +1) 신경망 네트워크 개요
+ 신경망을 어떻게 구현하는가?
+ 신경망은 시그모이드 유닛을 쌓아서 만들 수 있다
+ 해당 노드는 z계산 -> a계산
+ 다른 노드는 또다른 z계산 -> a계산
+ x^[i] -> I번 레이어
+ x^(i) -> I번째 훈련샘플
+
+ 로지스틱 회귀에선 z와a를 계산했지만
+ 신경망에서는 z와 a를 여러 번 계산해준다
+ 마지막으로 손실을 계산한다
+
+ 신경망에서도 마찬가지로 da^[2]와 dz^[2]를 계산하고 dW^[2]와 db^[2] 계산한다(역방향 계산)
+
+ +2. 신경망 네트워크의 구성 알아보기
+ ▶ 은닉층이 하나인 신경망
+ 신경망의 입력 층(input layer) a^[0] : x_1, x_2, x_3
+ 신경망의 은닉 층(hidden layer) a^[1] -> 첫 번째 층
+ 신경망의 출력 층(output layer) a^[2] -> 두 번째 층
+ 예측 값 : ŷ
+
+ 지도 학습으로 훈련시키는 신경망에선 훈련 세트가 입력값 X와 출력값 Y로 이루어져 있다
+ 은닉층의 실제 값은 훈련 세트에 기록되어 있지 않다 (은닉층의 값들은 알 수 없다)
+
+ a : 활성값, 신경망의 층들이 다음 층으로 전달해주는 값
+ a^[0] : 입력층의 활성 값
+ a^[1] : 첫 번째 은닉층 -> 그림에서는 4차원 벡터(4개의 은닉 노드가 있기 때문)
+ a_1^[1] : 첫 번째 은닉층에 있는 첫 번째 유닛
+ a_1^[2] : 첫 번째 은닉층에 있는 두 번째 유닛
+
+ 신경망의 층을 셀 때 입력층은 세지 않는다
+ 따라서 2 layer NN
+
+ w : (4,3) 벡터
+ b : (4,1) 벡터
+ (왜냐하면 은닉 노드가 4개, 입력 특성이 3개이기 때문)
+
+3. 신경망 네트워크 출력의 계산
+ ▶ 2층 신경망
+ ▶ 로지스틱 회귀
+ ▶ 신경망
+ 로지스틱 회귀 계산을 반복
+ ai[l]
+ l : 몇 번째 층
+ i : 해당 층에서 몇 번째 노드
+
+4. 많은 샘플에 대한 벡터화
+ a^​[i][j]
+ i : 몇 번째 층인지
+ j : 몇 번째 훈련 샘플인지
+ 행렬Z와 A
+ 가로 : 훈련 샘플의 번호
+ 세로 : 신경망의 노드 (은닉 유닛)
+
+ 행렬 첫 번째행 첫 번째열 바로 아래값
+ = 첫 훈련샘플의 두 번째 은닉유닛의 활성값
+
+ 세로로 움직이면 훈련 샘플은 고정되고 은닉 유닛이 바뀐다
+ 가로로 움직이면 은닉 유닛은 고정되고 훈련 샘플이 바뀐다
+
+5. 벡터화 구현에 대한 설명
+ Z^[1](1) = W^[1]x^(1)+b^[1] - 훈련 샘플 1
+ Z^[1](2) = W^[1]x^(2)+b^[1] - 훈련 샘플 2
+ Z^[1](3) = W^[1]x^(3)+b^[1] - 훈련 샘플 3
+
+ b를 0이라고 가정
+ W^[1]와 x^(1)의 곱은 열 벡터가 된다
+ 행렬 X는 x^(1), x^(2), x^(3)를 모두 가로로 쌓아 만든 것
+
+ W^[1]x^(i) = Z^[1](i)가 있었을 때 훈련 샘플을 다른 열에 채운다면
+ 나오는 결과인 Z도 z가 열로 쌓인다
+
+6. 활성화 함수
+ Sigmoid – 0부터 1까지 나타낸다
+ Tanh – 시그모이드와 비슷하지만 원점을 지나고 비율이 다르다
+ 단점 : z가 굉장히 크거나 작으면 함수의 도함수가 굉장히 작아진다
+
+ ReLU – z가 양수이면 도함수가 1이고 z가 음수이면 도함수가 0이다
+ leakyReLU – z가 음수일 때 도함수가 0인 대신 약간의 기울기를 준다 7)왜 비선형 활성화 함수를 써야하나?
+ 비선형 활성화 함수 : ReLU, Sigmoid, Tanh 등의 함수
+ g(z)=z 라는 선형 활성화 함수를 사용한다고 가정했을 때, 3개의 은닉층을 쌓아도 g(g(g(z)))=z 로 아무런 혜택을 얻지 못했습니다. 따라서 은닉층에는 비선형 활성화 함수를 사용해야 한다
+
+7. 신경망 네트워크와 경사 하강법
+ 역전파 알고리즘에서 여섯 개의 식 유도
+
+ axis = 1
+ keemdims = True
+ np.sum -> 어떤 축 방향으로 더할 때 사용
+
+ 단일층이 아닐 때는 1뿐만 아니라 1, 2, …,m 까지의 계산을 반복하면 된다
+
9)역전파에 대한 이해
+ da = -y/a + (1-y)/(1-a)
+ dz = a-y
+ dw = dzx
+ x는 고정값이기에 dx를 계산하지 않는다
+
10)랜덤 초기화
+
+ 신경망에서 w 의 초기값을 0으로 설정한 후 경사 하강법을 적용할 경우 올바르게 작동하지 않는다. dw 를 계산했을 때 모든 층이 같은 값을 가지게 되기 때문이다.
+ 따라서 np.random.rand()를 이용해 0이 아닌 랜덤한 값을 부여해줘야 한다.
diff --git "a/\352\263\274\354\240\2344_\354\241\260\355\230\204\354\247\200.md" "b/\352\263\274\354\240\2344_\354\241\260\355\230\204\354\247\200.md" new file mode 100644 index 0000000..ef4d7b3 --- /dev/null +++ "b/\352\263\274\354\240\2344_\354\241\260\355\230\204\354\247\200.md" @@ -0,0 +1,122 @@ +5. 심층 신경망 네트워크
+ +1) 더 많은 층의 심층 신경망
+ 로지스틱 회귀 – 한 층의 신경망
+ 은닉층의 개수 값을 변경해 정확도를 평가한다
+ <표기법>
+ •  L   : 네트워크 층의 수
+ •  n ​^^[l ]​​ : l층에 있는 유닛 개수
+ •  a ​[l ]​​ : l층에서의 활성값
+ •  a ​[0 ]​​ : 입력 특징 (X)
+ •  a ​[L ]​​  : 예측된 출력값 ( ​y ​^​​ )
+
+2) 정방향전파와 역방향전파
+ <정방향전파>
+ input : a^[l-1]
+ output : a^[l], cache(z^[l])
+ a^[0] : 한 번에 하나씩 할 경우의 학습 데이터에 대한 입력 특성
+ A^[0] : 전체 학습 세트를 진행할 때의 입력 특성 = 체인에서 첫 번째 정방향 함수에 대한 입력값
+ 과정을 반복하면 왼쪽에서 오른쪽으로 가는 정방향 전파를 계산하는 것
+ <역전파>
+ input : da^[l]
+ output : da^[l-1], dW^[l], db^[l]
+ <요약>
+ 입력X -> ReLU를 활성화 함수로 갖는 첫 번째 층 -> 또다른 ReLU를 활성화 함수로 갖는 두 번째 층 -> sigmoid를 활성화 함수로 갖는 세 번째 층(이진 분류) -> ​y ​^-> L(y ​^,y ​)))))) -> 역전파 과정(도함수 계산)dW^[3], db^[3], (da^[2] 옮김)-> dW^[2], db^[2], (da^[1] 옮김) -> dW^[1], db^[1] (캐시에서 z^[1], z^[2], z^[3]를 옮긴다)
\*주의사항
+ 정방향 반복은 입력 데이터 X로 초기화한다
+ 역방향 반복은 da^[l] (=-y/a+(1-y)/(1-a)로 초기화, 벡터화된 구현이라면 dA^[L]
+
+3) 심층 신경망에서의 정방향전파
+ 첫 번째 층
+ X : z^[1] = W^[1]x + b^[1] (x=a^[0])
+ a^[1] = g^[1](z^[1])
+ 두 번째 층
+ z^[2] = W^[2]a^[1] + b^[2]
+ a^[2] = g^[2](z^[2])
+ 네 번째 층
+ z^[4] = W^[4]a^[2] + b^[4]
+ a^[4] = g^[4](z^[4])
+
+ -> 일반적인 정방향 전파의 수식
+ z^[l] = W^[l]a^[l-1] + b^[l]
+ a^[l] = g^[l](z^[l])
+
+ -> 벡터화된 수식
+ Z^[1] = W^[1]X + b^[1] (X=A^[0])
+ A^[1] = g^[1](Z^[1])
+ Z^[2] = W^[2]A^[1] + b^[2]
+ A^[2] = g^[2](Z^[2])
+
+ Z : Z^[2](1) Z^[2](2) ... Z^[2](m) 이런 식으로 m번째 학습 데이터까지하고 열에 저장
+ A도 마찬가지
+
+ Z^[l] = W^[l]A^[l-1] + b^[l]
+ A^[l] = g^[l](Z^[l])
+ 명시적인 반복문 사용할 수 밖에 없다
+
+4) 행렬의 차원을 알맞게 만들기
+ L=5
+ 4개의 은닉층과 1개의 출력층
+ n^[0]=2, n^[1]=3, n^[2]=5, n^[3]=4, n^[4]=2, n^[5]=1
+
+ Z^[1] = W^[1]X + b^[1]
+
+ z : 첫 번째 은닉층에 대한 활성화 벡터
+ (n^[1],1) (3,1)
+
+ x : 입력 특성
+ (n^[0],1) (2,1)
+
+ W
+ (n^[1],n^[0]) (3,2)
+ W^[l]
+ (n^[l],n^[l-1]) (3,2)
+
+ b^[l]
+ (n^[l],1)
+
+ dW^[l]의 차원 = W^[l]의 차원
+ db^[l]의 차원 = b^[l]의 차원
+
+ Z^[1] = W^[1] X + b^[1]
+ (n^[1],m) (n^[1],n^[0]) (n^[0],m) (n^[1],1) -> 파이썬 브로드캐스팅으로 (n^[1],m) 된다
+
+5) 왜 심층 신경망이 더 많은 특징을 잡아 낼수 있을까요?
#직관 1
+ <얼굴 사진>
+ -> 신경망의 첫 번째 층 : 사진을 보고 모서리가 어디에 있는지 파악한다
+ -> 픽셀을 그룹화해 모서리를 형성한다
+ -> 감지된 모서리와 그룹화된 모서리를 받아서 얼굴의 일부를 형성한다
+ (어떤 뉴런에서는 눈, 다른 뉴런에서는 코 찾기)
+ -> 서로 다른 얼굴의 일부를 모아 서로 다른 종류의 얼굴 감지한다
+
+ 신경망의 초기 층 : 모서리와 같은 간단한 함수 감지
+ 이후의 층 : 복잡한 함수 학습
+ 간단한 것 찾기 -> 모아서 더 복잡한 것 찾기 -> 더 복잡한 것 찾기 (사람의 뇌처럼)
+
\*모서리 탐지기 : 이미지에서 상대적으로 작은 영역을 본다
+ 얼굴 탐지기 : 이미지의 더 넓은 영역을 본다
+
+ <음성 데이터>
+ 낮은 단계의 음성 파형 특징 탐지 (음소 탐지) -> 단어 인식 -> 구나 문장 인식
+
#직관 2
+ 회로 이론 : 로직 게이트의 서로 다른 게이트에서 어떤 종류의 함수를 계산할 수 있을지
+
+ 상대적으로 은닉층의 개수가 작지만 깊은 심층 신경망에서 계산할 수 있는 함수가 있다. 그러나 얕은 네트워크로 같은 함수를 계산하려고 하면, 즉 충분한 은닉층이 없다면 기하급수적으로 많은 은닉 유닛이 계산에 필요하게 된다
+ <예시>
+ 모든 입력 특성에 대한 XOR을 계산한다고 하자
+ x1 XOR x2 XOR x3 XOR ... xn
+ XOR 트리 그리면 결국 y 출력된다
+ 네트워크의 깊이 = O(logn)
+ 노드의 개수 = 네트워크의 게이트 수
+
+ 파라미터나 하이퍼파라미터를 조절해 신경망의 알맞은 깊이를 찾는다
+
+6) 심층 신경망 네트워크 구성하기
+ •  l  번째 층에서 정방향 함수는 이전 층의 활성화 값인 a ​[l −1 ]​​ 을 입력으로 받고, 다음 층으로  a ​[l ]​​ 값을 출력으로 나오게 한다. 이때 선형결합된 값인  z ​[l ]​​ 와 변수  W ​[l ]​​,b ​[l ]​​ 값도 캐시로 저장해둔다.
+ •  l   번째 층에서 역방향 함수는 d a ​[l ]​​ 을 입력으로 받고, d a ​[l ]​​ 를 출력한다. 이때 업데이트를 위한  d W ​[l ]​​ 와 d b ​[l ]​​ 도 함께 출력한다. 이들을 계산하기 위해서 전방향 함수때 저장해두었던 캐시를 쓰게 된다.
+
+ +7) 변수 vs 하이퍼파라미터
+ 변수 : 신경망에서 학습 가능한 W와 b
+ 하이퍼파라미터 종류
-학습률(learning rate, \alphaα )
-반복횟수(numbers of iteration)
-은닉층의 갯수(numbers of hidden layer, L)
-은닉유닛의 갯수(numbers of hidden units)
-활성화 함수의 선택(choice of activation function)
-모멘텀항(momentum term)
-미니배치 크기(mini batch size)
+
+ 매개변수인 하이퍼파라미터를 결정함으로서 최종 모델의 변수를 통제할 수 있다.
+ 하이퍼파라미터는 결정 된것이 없으며, 여러번의 시도를 통해 적합한 하이퍼파라미터 를 찾아야한다.
diff --git "a/\352\263\274\354\240\2345_\354\241\260\355\230\204\354\247\200.md" "b/\352\263\274\354\240\2345_\354\241\260\355\230\204\354\247\200.md" new file mode 100644 index 0000000..0155e93 --- /dev/null +++ "b/\352\263\274\354\240\2345_\354\241\260\355\230\204\354\247\200.md" @@ -0,0 +1,82 @@ +머신러닝 어플리케이션 설정하기
+
+Train/Dev/Test 세트
+신경망을 훈련시킬 때 결정 내려야 할 것
+신경망이 몇 개의 층을 가지는지
+각 층이 몇 개의 hidden unit을 가지는지
+학습률과 활성화 함수는 무엇인지
+(아이디어 -> 코드 -> 실험) 반복하면서 하이퍼파라미터에 대한 선택을 개선한다
+
+훈련 세트: 훈련을 위해 사용되는 데이터
+개발 세트: 다양한 모델 중 어떤 모델이 좋은 성능을 나타내는지 확인
+테스트 세트: 모델이 얼마나 잘 작동하는지 확인
+
+요즘은 훈련 세트로 데이터를 많이 사용하는 것이 트렌드. 두 개의 알고리즘 중 어느 것이 더 좋은지 평가할 정도만 데이터 있으면 된다.
+데이터 백만개일 때 – 98%/1%/1%.
+백만개보다 많을 때 – 99.5%/0.25%/0.25% or 99.5%/0.4%/0.1%.
+훈련 세트는 인터넷에서 긁어온 사진이고 개발과 테스트 세트는 사용자가 핸드폰으로 찍은 사진일 경우(저해상도) - 개발과 테스트 분포가 같은 비율인 것이 좋다.
+테스트 세트는 없어도 괜찮다 – 비편향 추정이 필요 없는 경우에 이 경우에는 보통 훈련/테스트 세트로 부른다
+
+편향/분산
+높은 편향값 -> 데이터의 과소적합
+높은 분산 -> 데이터의 과대적합
+중간 단계의 복잡함 -> 딱 맞는 형태
+특성을 x_1, x_2만 갖는 2차원 예제에서만 데이터를 나타내거나 결정 경계를 시각화할 수 있다
+
+Case 1:
+훈련 세트 오차: 1%
+개발 세트 오차: 11%
+-> 훈련 세트에 과대적합이 되어서 개발 세트가 있는 교차 검증 세트에서 일반화되지 못함
+-> 높은 분산을 갖는다
+
+Case 2:
+훈련 세트 오차: 15%
+개발 세트 오차: 16%
+인간의 오차: 0%
+-> 훈련 데이터에 대해서 잘 맞지 않는다면 과소적합된다
+-> 높은 편향을 갖는다
+-> 반면 합리적인 수준의 개발 세트에서 일반화된다 (1%밖에 나쁘지 않으므로)
+
+Case 3:
+훈련 세트 오차: 15%
+개발 세트 오차: 30%
+-> 훈련 세트에 잘 맞지 않는다
+-> 높은 편향을 갖는다 + 높은 분산을 갖는다
+
+Case 4:
+훈련 세트 오차: 0.5%
+개발 세트 오차: 1%
+-> 낮은 편향을 갖는다 + 낮은 분산을 갖는다
+
+베이지안 오차(최적 오차)가 0%라는 가정 but 최적 오차가 15%라는 가정 -> Case 2는 합당하다
+-> 높은 편향이 아니고 낮은 분산
+
+이미지가 흐릿하여 인간 혹은 시스템도 잘 분류하지 못하는 경우
+-> 베이즈 오차는 훨씬 커질 것이고, 분석에 대한 세부 방식도 달라진다
+
+훈련 세트 오차를 확인함으로써 최소한 훈련 데이터에서 얼마나 알고리즘이 적합한지 감을 잡을 수 있다 / 편향 문제가 있는지 알 수 있다
+
+훈련 세트에서 개발 세트로 갈 때 오차가 얼마나 커지는지에 따라서 분산 문제가 얼마나 나쁜지에 대한 감을 잡을 수 있다 / 훈련 세트에서 개발 세트로 일반화를 잘 하느냐에 따라 분산에 대한 감이 달라진다
+(베이즈 오차가 꽤 작고, 훈련 세트와 개발 세트가 같은 확률 분포에서 왔다는 가정 하)
+
+높은 편향과 높은 분산 -> 그래프에서 많은 굴곡을 가져서 과대적합된다
+
+거의 선형이지만 곡선이나 이차 함수가 필요하기 때문에 높은 편향을 갖는다
+잘못 라벨링된 샘플을 맞추기 위해 너무 많은 굴곡을 갖기 때문에 높은 분산을 갖는다
+머신러닝을 위한 기본 레시피 +
+#1 편향 문제 줄이기
+알고리즘이 높은 편향을 가지는지 평가하기 위해서는 훈련 세트 혹은 훈련 데이터의 성능을 봐야 한다
+높은 편향을 가져서 훈련 세트에도 잘 맞지 않는다면 -> 더 많은 hidden unit을 갖는 네트워크를 선택해야 한다 or 더 오랜 시간 훈련시키거나 다른 발전된 최적화 알고리즘을 사용한다
+(편향 문제를 해결할 때까지)
+베이즈 오차가 그렇게 높지 않은 경우는 크게 훈련하는 경우 최소한 훈련 세트에 대해서는 잘 맞을 것이다
+
+#2 분산 문제 해결하기
+꽤 좋은 훈련 세트 성능에서 꽤 좋은 개발 세트 성능을 일반화할 수 있는가
+높은 분산 문제 -> 데이터를 더 얻어 해결한다 or 과대적합을 줄이기 위해 정규화를 시도한다 or 다른 신경망 아키텍처를 찾는 것을 시도한다
+(낮은 편향과 분산을 찾을 때까지 계속 시도하고 반복한다)
+
+중요한 것
+
+높은 편향이나 분산이냐에 따라 시도해볼 수 있는 방법이 아주 다르다. 그래서 주로 훈련과 개발 세트를 편향이나 분산 문제가 있는지 진단하는데 사용한다
+편향-분산 트레이드오프. 시도할 수 있는 많은 것들이 편향을 증가시키고 분산을 감소시키거나 편향을 감소시키고 분산을 증가시키기 때문이다. 딥러닝 빅데이터 시대에는 더 큰 네트워크를 훈련시키는 것이 대부분 분산을 해치지 않고 편향만 감소시킨다 / 더 많은 데이터를 얻는 것도 대부분 편향을 해치지 않고 분산을 감소시킨다. -> 편향과 분산의 균형을 신경써야 하는 트레이드오프가 훨씬 적다
diff --git "a/\352\263\274\354\240\2348_\354\241\260\355\230\204\354\247\200.md" "b/\352\263\274\354\240\2348_\354\241\260\355\230\204\354\247\200.md" new file mode 100644 index 0000000..dcc661f --- /dev/null +++ "b/\352\263\274\354\240\2348_\354\241\260\355\230\204\354\247\200.md" @@ -0,0 +1,98 @@ +2. 신경망 네트워크의 정규화
+ +1) 정규화
+ 문제 : 높은 분산으로 신경망이 데이터를 과대적합
+ 해결1 : 정규화
+ 해결2 : 더 많은 훈련 데이터 얻는 것 -> 비용이 많이 든다
+
+ 비용함수 J(w,b)에 정규화 매개변수라고 부르는 λ를 추가한다
+ λ : 정규화 매개변수
+ -> 개발 세트 혹은 교차 검증 세트를 주로 사용한다
+ -> 과대적합을 막을 수 있는 최적의 값을 찾아야 한다!
+
+ L1 정규화 - λ/m*(w 절댓값의 합)
+ w는 희소해진다, w 벡터 안에 0이 많아진다 -> 모델을 압축하는데 도움이 된다(메모리가 적게 필요하기 때문)
+
+ L2 정규화 - λ/2m*(w제곱의 norm), 매개변수 벡터 w의 유클리드 norm의 제곱
+ w제곱의 norm : j의 1부터 nx까지 wj^2의 값을 더한 것,
+ w의 전치행렬 * w
+ L2 norm = Frobenius norm
+ *왜 매개변수 w만 정규화할까? 왜 b에 관한 것은 추가하지 않을까?
+ -> w : 꽤 높은 차원의 매개변수 벡터. 높은 분산을 가질 때 많은 매개변수를 갖는다
+ b : 하나의 숫자
+ 따라서 거의 모든 매개변수는 b가 아닌 w에 있다
+
+ L1 정규화보다 L2 정규화를 훨씬 많이 사용한다
+
+ 비용함수 : (훈련 샘플의 m까지의 손실의 합/m )+ (λ/2m*(w제곱의 norm))
+
+ *경사하강법 계산
+ dw^[l] = (from backpropagation)＋(λ/m)*(w^[l])
+ dJ/dw^[l] = dw^[l]
+ w^[l] = w^[l] - αdw^[l]
+
+ *L2 정규화가 weight decay 라고 불리는 이유
+ -> weight 에 1보다 작은 값인 ( 1−​αλ/m ) 가 곱해지기 때문
+
+
+2) 왜 정규화는 과대적합을 줄일 수 있을까요?
+ 왜 정규화가 과대적합 문제를 해결하고 분산을 줄이는데 도움이 될까?
+
+ 비용함수 J = 1부터 m까지 손실의 합 + (λ/2m*(w제곱의 norm))
+ L2 혹은 Frobenius norm을 줄이는 것이 왜 과대적합을 줄일 수 있을까?
+ #1
+ λ를 크게 만들어서 가중치 행렬 w를 0에 상당히 가깝게 설정할 수 있다
+ 많은 hidden unit을 0에 가까운 값으로 설정해서 hidden unit의 영향력을 줄인다
+ -> 훨씬 더 간단하고 작은 신경망이 된다
+ -> 로지스틱 회귀 유닛에 가까워진다
+ -> high bias의 경우와 가깝게 만든다
+ -> λ값을 잘 조정하기!
+
+ *λ값을 아주 크게 -> w는 0에 가까워짐
+ 간단한 네트워크는 과대적합 문제가 덜 일어난다
+
+ #2
+ tanh 활성화 함수를 사용한다고 가정
+ g(z) = tanh(z)
+ -z가 아주 작은 경우 -> tanh의 선형 영역을 사용
+ -z가 더 작아지거나 커지는 경우 -> 선형을 벗어남
+
+ λ가 커질 때 w 작아진다 (비용함수가 커지지 않으려면)
+ w작을 때 z가 상대적으로 작은 값 갖게 되면 g(z)는 거의 1차원 함수 -> 모든 층은 선형 회귀처럼 직선의 함수 갖게 된다 -> 모든 층이 선형이면 전체 네트워크도 선형이다
+ 따라서 선형 활성화 함수를 가진 깊은 네트워크의 경우에도 선형 함수만을 계산할 수 있게 된다
+ ->과대적합된 데이터 세트까지 맞추기 어렵다
+
+ <정리>
+ 정규화 매개변수가 크면 매개변수 w는 매우 작다
+ b의 효과를 무시하면 z의 값은 상대적으로 작다
+ 전체 신경망은 선형 함수로부터 멀지 않은 곳에서 계산된다 -> 간단한 함수 -> 과대적합의 가능성이 줄어든다
+
+
+3) 드롭아웃 정규화
+ 정규화 방법2 - 드롭아웃
+ 드롭아웃 : 신경망의 각각의 층에 대해 노드를 삭제하는 확률을 설정하는 것
+ 삭제할 노드를 랜덤으로 선정 후 삭제된 노드의 들어가는 링크와 나가는 링크를 모두 삭제한다 -> 더 작고 간소화된 네트워크
+
+ 역드롭아웃 : 노드를 삭제후에 얻은 활성화 값에 keep.prop(삭제하지 않을 확률)을 나눠 주는 것
+ -> 기존에 삭제하지 않았을 때 활성화 값의 기대값으로 맞춰주기 위함
+
+
+4) 드롭아웃의 이해
+ +- 랜덤으로 노드를 삭제 시키기 때문에, 하나의 특성에 의존 하지 못하게 만듦으로서 가중치를 다른 곳으로 분산 시키는 효과가 있다
+- keep.prop 확률은 층마다 다르게 설정 할 수 있다
+- 모든 반복에서 잘 정의된 비용함수가 하강하는지 확인하는게 어려워진다. 따라서 우선 드롭아웃을 사용하지 않고, 비용함수가 단조감소인지 확인 후에 사용해야 한다
+
+
+ +5. 다른 정규화 방법들
+ #1 데이터 증식
-이미지 -> 더 많은 훈련 데이터를 사용함으로서 과대적합을 해결
+ 대칭, 확대, 왜곡 혹은 회전 시켜서 새로운 훈련 데이터 만든다
+ 더 많은 정보를 추가해주지는 않지만, 컴퓨터적인 비용이 들지 않고 할 수 있다
+
+ #2 조기종료
+ : 신경망이 개발 세트의 오차 저점 부근, 즉 가장 잘 작동하는 점일 때 훈련을 멈추는 것 + 훈련세트의 오차 : 단조하강함수
+ 조기종료에서는 개발 세트의 오차도 그려준다
+ 개발세트의 오차가 어느 순간 부터 하락 하지 않고 증가하기 시작하는 것이라면 과대적화가 되는 시점이다
+ 단점: 훈련시 훈련 목적인 비용 함수를 최적화 시키는 작업과 과대적합하지 않게 만드는 작업이 있다. 두 작업은 별개의 일이라서 두 개의 다른 방법으로 접근해야 한다. 그러나 조기 종료 두 가지를 섞어 버린다. 따라서 최적의 조건을 찾지 못할 수도 있다
diff --git "a/\352\263\274\354\240\2349_\354\241\260\355\230\204\354\247\200.md" "b/\352\263\274\354\240\2349_\354\241\260\355\230\204\354\247\200.md" new file mode 100644 index 0000000..d3f463c --- /dev/null +++ "b/\352\263\274\354\240\2349_\354\241\260\355\230\204\354\247\200.md" @@ -0,0 +1,190 @@ +3. 최적화 문제 설정
+ +1) 입력값의 정규화
+ 신경망의 훈련을 빠르게 할 수 있는 하나의 기법 : 입력을 정규화 하는 것
+
+ 두 개의 입력 특성이 있는 훈련 세트
+ 입력 특성 x가 2차원
+
+ 입력을 정규화 하는 과정(2단계)
+
+ +1. 평균을 0으로 만들기
+ 0의 평균을 갖게 될 때까지 훈련 세트를 이동한다
+
+2. 분산을 정규화하기 (1로 만들기)
+ 특성 x1이 특성 x2보다 더 큰 분산을 갖고 있다
+ +
+그림으로 나타내면
+ +x1과 x2의 분산은 모두 1과 같다
+ +팁 : 이것을 훈련 데이터를 확대하는데 사용한다면 테스트 세트를 정규화할 때도 같은 μ와 σ를 사용해라 (훈련 세트와 테스트 세트를 같게 정규화하기 위해서)
+ +왜 입력 특성을 정규화하기를 원할까?
+비용함수의 정의 :
+ +정규화되지 않은 입력 특성을 사용했을 때 비용함수 모습은 매우 구부러진 활처럼 가늘고 긴 모양이다
+특성 x1이 1에서 1000, 특성 x2가 0에서 1의 값의 범위처럼 특성들이 매우 다른 크기를 갖고 있다면 매개변수에 대한 비율, 값의 범위는 w1과 w2가 굉장히 다른 값을 갖게 된다
+반면에 특성을 정규화하면 비용함수는 평균적으로 대칭적인 모양을 갖게 된다
+
+왼쪽 함수의 비용함수에 경사 하강법을 실행한다면 매우 작은 학습률을 사용하게 된다
+여기서 경사 하강법은 최종적으로 최솟값에 이르는 길을 찾기 전까지 앞뒤로 왔다 갔다하기 위해 많은 단계가 필요하다
+반면에 원 모양의 등고선의 경우 어디서 시작하든 경사 하강법은 최솟값으로 바로 갈 수 있다
+왔다갔다 하지 않아도 큰 스텝으로 전진할 수 있다
+실전에서는 w가 높은 차원인 벡터이고 2차원에 그리는 것이 모든 직관을 올바르게 전달하지 않는다 그러나 특성이 비슷한 크기를 갖을 때 비용함수가 더 둥글고 최적화하기 쉬운 모습이 된다는 대략적인 직관을 얻을 수 있다
+서로 비슷한 분산으로 비용함수 J를 최적화하기 쉽고 빠르게 만든다
+특성 x1이 0에서 1의 범위이고 x2가 –1에서 1, x3가 1에서 2인 경우에 (상당히 비슷한 범위를 갖을 때) 잘 작동한다
+범위가 너무 다르면 최적화 알고리즘에 방해가 된다
+평균을 0으로 설정하고 모든 특성이 비슷한 크기가 되도록 분산을 설정하면 학습 알고리즘이 빠르게 실행되는 것을 도울 수 있다!
+특성이 비슷한 크기를 갖는다면 이 과정을 중요하지 않다
+
2) 경사소실/경사폭발
+매우 깊은 신경망을 훈련시키는 것의 문제 : 경사소실/경사폭발
+미분값 혹은 기울기가 아주 작아지거나 커질 수 있다
+*매우 깊은 신경망을 훈련시키는 경우
+
+매개변수 w^[1], w^[2], w^[3],...,w^[L] 갖는다
+활성화 함수 g(z)가 선형 활성화 함수를 사용한다고 가정
+b^[i]은 0이라고 가정
+출력 y= w^[L]*w^[L-1]_w^[L-2]_...*w^[3]*w^[2]*w^[1]*x
+w^[1]*x = z^[1] (b^[1]=0)
+a^[1] = g(z^[1]) = z^[1] (선형의 활성화 함수를 사용하기 때문)
+같은 이유로
+a^[2] = g(z^[2]) = g(w^[2]*a^[1])
+w^[2]*w^[1]*x = a^[2]
+ +
+w^[l] 가정
+
+각각의 행렬이 w^[l]과 같다고 가정하면 1.5 * 단위행렬이 된다
+y = 1.5^[L-1] * x
+L의 값이 크면 y의 예측값도 매우 커진다
+매우 깊은 신경망을 갖으면 y의 값은 폭발한다
+반대로 1보다 작은 값으로 교체하면 0.5^[L]
+y = 0.5^[L-1] * x
+
+층의 개수가 L인 함수의 경우
+x1, x2가 각각 1인 경우에 활성화는 1/2, 1/2, 1/4, 1/4, 1/8, 1/8,..., 1/2^L이 된다
+따라서 매우 깊은 네트워크의 경우 활성값은 기하급수적으로 감소한다
+<결론>
+가중치 w^[l]이 단위행렬보다 조금 더 크다면 매우 깊은 네트워크의 경우 활성값 폭발
+가중치 w^[l]이 단위행렬보다 조금 작다면 매우 깊은 네트워크의 경우 활성값 기하급수적감소
+
+비슷한 주장으로 미분값(경사 하강법에서 계산하는 경사가 층의 개수에 대한 함수)도 기하급수적으로 증가하거나 감소한다
+
+현대의 신경망은 보통 L = 150
+이런 깊은 신경망에서 활성값이나 경사가 L에 대한 함수로 기하급수적으로 증가하거나 감소한다면 값들은 아주 커지거나 작아질 수 있다
+-> 훈련을 시키는 것이 어려워진다
+
+3) 심층 신경망의 가중치 초기화
+가중치를 어떻게 초기화시키느냐에 따라 문제 해결할 수 있다!
+
+예제) 단일 뉴런에 대한 가중치 초기화
+
+특성 4개
+깊은 망에서 입력은 a^[l]인 어떤 층이 된다
+z = w1x1 + w2x2+ ... + wnxn + b
+b값은 무시
+z의 값이 너무 크거나 작아지지 않도록 만들어야 한다
+n의 값이 클수록 wi의 값이 작아져야 한다
+
+1) wi의 분산을 1/n으로 설정한다 (n : 입력 특성의 개수)
+실제로 특정 층에 대한 가중치 행렬 w^[l]
+w^[l] = np.random.randn(shape) * np.sort(1/n^[l-1])
+2) ReLU 활성화 함수를 사용하는 경우 wi의 분산을 2/n^[l-1]으로 설정한다
+w^[l] = np.random.randn(shape) * np.sort(2/n^[l-1])
+n^[l-1]을 사용하는 이유 : 층 l은 해당 층의 각 유닛에 대해 n^[l-1]의 입력을 갖는다
+따라서 입력 특성 혹은 활성값의 평균이 대략 0이고 표준 편차 1을 갖는다면 이것 역시 비슷한 크기를 갖게 된다
+3) tanh 활성화 함수를 사용하는 경우 wi의 분산을 1/n^[l-1] 또는 2/n^[l-1]+n^[l]으로 설정한다
+각각의 가중치 행렬 w를 1보다 너무 커지거나 너무 작아지지 않게 설정해서 너무 빨리 폭발하거나 소실되지 않게 한다
+식들은 가중치 행렬의 초기화 분산에 대한 기본 값을 제공할 뿐이다
+이와 같은 분산을 원한다면 분산 매개변수를 하이퍼파라미터로 조정해야한다
+
+4) 기울기의 수치 근사
+깊은 네트워크를 훈련시킬 때 신경망을 더 빨리 훈련시키는 기법
+경사 검사 : 역전파를 맞게 구현했는지 확인하는데 도움을 준다
+<경사의 계산을 수치적으로 근사하는 방법>
+
+f(θ) = θ^3
+1) θ = 1인 경우
+θ+ϵ, θ-ϵ을 구하면 각각 1.01, 0.99
+ϵ = 0.01
+
+작은 삼각형과 큰 삼각형에서 너비 분의 높이 구한다
+
+큰삼각형의 너비 분의 높이 : f(θ+ϵ) - f(θ-ϵ) / 2ϵ
+
+(1.01)^3 - (0.99)^3 / 2(0.01) = 3.0001
+g(θ) = 3θ^2 = 3 (θ=1)
+근사 오차 = 0.0001
+
+작은삼각형의 너비 분의 높이 : f(θ+ϵ) - f(θ) / ϵ
+(1.01)^3 – 1 / 0.01 = 3.0301
+근사 오차 = 0.03 더 크다!!
+
+따라서 도함수를 근사하기 위해 양 쪽의 차이를 이용하는 방법을 사용하면 3에 매우 가까운 값이 나온다
+g(θ)가 f의 도함수에 대한 더 올바른 구현이다
+한 쪽의 차이만을 사용하는 것보다 두 배는 느리게 실행되지만 훨씬 더 정확하다
+
+f​′(θ)=limϵ→inf f(θ+ϵ) - f(θ-ϵ)/2ϵ
+0이 아닌 ϵ에 대해서 근사의 오차는 O(ϵ^2) 이다. ϵ은 굉장히 작은 수이다.
+빅오 표기법은 오차가 상수라는 것을 나타낸다
+​​근사 오차 예시이므로 O(ϵϵ^2)를 1이라고 생각하자
+
+f​′(θ)=limϵ→inf f(θ+ϵ) - f(θ)/ϵ
+0이 아닌 ϵ에 대해서 근사의 오차는 O(ϵ) 이다.
+​​ϵ이 1보다 작은 값이므로 ϵ^2보다 훨씬 큰 값이다 -> 훨씬 덜 정확한 근사이다
+
+f​′(θ)=limϵ→inf f(θ+ϵ) - f(θ-ϵ)/2ϵ처럼 양 쪽 차이를 사용하는 것이 더 정확하다!
+
+5) 경사 검사
+경사 검사 : 시간을 절약하고 역전파의 구현에 대한 버그를 찾는데 도움을 준다
+
+신경망은 매개변수 W^[1], b^[1]부터 W^[L], b^[L]까지 가지고 있다
+1. 매개변수들을 하나의 큰 벡터 θ로 바꾼다
+행렬 W^[1]을 벡터로 크기를 바꾼다
+모든 W행렬을 받아서 벡터로 바꾸고 모두 연결시킨다
+매우 큰 벡터 매개변수 θ를 얻게 된다
+비용 함수는 J(W, b) 에서 J( θ ) 로 변한다
+
+마찬가지로 dW^[1], db^[1],...,dW^[L], db^[L]의 매개변수를 매우 큰 벡터 dθ로 만든다
+dW^[1] 행렬을 벡터로 바꾼다
+db^[1]은 이미 벡터이다
+크기를 바꾸고 연결해 모든 미분값을 매우 큰 벡터 dθ로 바꿀 수 있다
+
+dθ가 비용함수 J( θ )의 기울기인가?
+
+Gradient checking (Grad check)
+J( θ ) = J( θ1, θ2, θ3, ... )
+for each I :
+ dθapprox[i] = J( θ1, θ2, ..., θi + ϵ,...) - J( θ1, θ2, ..., θi - ϵ,...) / 2ϵ ≈ dθ[i]
+ (dθi가 비용함수 J의 도함수라면, 함수 J의 θi에 대한 편미분과 같다)
+ dθapprox ≈ dθ
+두 벡터가 꽤 가까운지 알아봐야 한다 -> 두 벡터의 유클리드 거리 계산
+dθapprox – dθ의 L2 노름을 구한다
+
+벡터가 아주 작거나 큰 경우에 대비해 분모가 이 식을 비율로 바꾼다
+보통 거리가 10^-7보다 작으면 잘 계산되었다고 판단한다
+원소의 차이가 너무 크면 버그가 있을 수 있다
+10^-3보다 큰 값이면 버그가 너무 크니 θ의 개별적인 원소를 신중하게 살펴서
+특정 i에 대해 dθapprox[i]와 dθ[i]의 차이가 심한 값을 추적해서 미분의 계산이 옳지 않은 곳이 있는지 확인해야 한다
+
+6) 경사 검사 시 주의할 점
+1. 훈련에서 경사 검사를 사용하지 말고 디버깅을 위해서만 사용해야 한다
+-모든 I의 값에 대한 dθapprox[i]를 계산하는 것은 매우 느리기 때문이다
+디버깅할 때만 경사 검사를 구현하기 위해 dθ를 계산하는 역전파를 이용해 도함수를 계산하고 dθ에 가까워지게 한다. 과정이 끝나면 경사 검사를 끄고 모든 반복마다 실행되지 않도록 한다.
+2. 알고리즘이 경사 검사에 실패 했다면, 어느 원소 부분에서 실패했는지 찾아본다.
+dθapprox가 dθ에서 매우 먼 경우 서로 다른 I에 대하여 어떤 dθapprox[i]의 값이 dθ[i]의 값과 매우 다른지 확인한다.
+예를 들어 어떤 층에서 θ나 dθ의 값이 대응되는 db^[l]과 매우 멀지만 대응되는 dw^[l]과는 매우 가까운 경우 - θ의 서로 다른 컴포넌트는 b나 w의 다른 컴포넌트에 대응된다. 이 경우에는 db를 어떻게 계산하느냐에 따라서 버그가 발생할 것이다.
+dθapprox가 dθ에서 매우 멀고, 모든 컴포넌트가 dw 혹은 특정한 층의 dw에서 온 것을 발견한다면 버그의 위치를 알아내는데 도움을 받을 수 있다 +3. 경사 검사를 할 때 사용하는 정규화 항을 기억해라.
+
+비용함수 J( θ ) = 1/m * (손실함수의 합) + 정규화 항
+dθ는 θ에 대응하는 J의 경사로 정규화 항을 포함
+4. 경사 검사는 드롭아웃에서는 작동하지 않는다
+드롭아웃은 모든 반복마다 은닉 유닛의 서로 다른 부분집합을 무작위로 삭제하기 때문에 적용하기 쉽지 않다. 비용함수 J는 어떤 반복에서든지 삭제될 수 있는 기하급수적으로 큰 노드의 부분집합으로 정의되기 때문에 비용함수 J를 계산하는 것이 매우 어렵다. 따라서 드롭아웃을 이용한 계산을 이중으로 확인하기 위해 경사 검사를 사용하기는 어렵다
+따라서 교수님은 주로 드롭아웃 없이 경사 검사를 구현한다. 드롭아웃의 keep_prop을 1.0로 설정하고 드롭아웃을 킨다.
+추천하는 방법 : 드롭아웃을 끄고 알고리즘이 최소한 드롭아웃 없이 맞는지 확인하고, 다시 드롭아웃을 킨다.
+5. 거의 일어나지 않지만 가끔 무작위 초기화를 해도 초기에 가까울 때 경사 검사가 잘 되는 경우가 있다. 그러나 경사 하강법을 실행하면 w와 b는 점점 커진다. 역전파의 구현이 w와b가 0에 가까울 때만 맞는 것일 수 있다. w와 b가 커지면 그 값은 더 부정확해진다.
+따라서 무작위적인 초기화에서 경사 검사를 실행하고 네트워크를 잠시 동안 훈련해서 w와 b가 0에서 멀어질 수 있는 시간을 준다. 일정 수의 반복을 훈련한 뒤에 경사 검사를 한번 더 실행시킨다.
diff --git "a/\354\272\220\352\270\200\355\225\204\354\202\25416_\354\241\260\355\230\204\354\247\200.ipynb" "b/\354\272\220\352\270\200\355\225\204\354\202\25416_\354\241\260\355\230\204\354\247\200.ipynb" new file mode 100644 index 0000000..78485f2 --- /dev/null +++ "b/\354\272\220\352\270\200\355\225\204\354\202\25416_\354\241\260\355\230\204\354\247\200.ipynb" @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":1176357,"sourceType":"datasetVersion","datasetId":667852}],"dockerImageVersionId":30096,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nfrom PIL import Image\nimport matplotlib.pyplot as plt\nfrom glob import glob\nimport time\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\n\nfrom torch.utils.data.sampler import SubsetRandomSampler\nfrom torch.utils.data import Dataset\n\nimport torchvision\nimport torchvision.transforms as transforms","metadata":{"execution":{"iopub.status.busy":"2023-12-26T02:07:07.069454Z","iopub.execute_input":"2023-12-26T02:07:07.069889Z","iopub.status.idle":"2023-12-26T02:07:07.077190Z","shell.execute_reply.started":"2023-12-26T02:07:07.069847Z","shell.execute_reply":"2023-12-26T02:07:07.075986Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":"# Preparing Dataset","metadata":{}},{"cell_type":"code","source":"class InvalidDatasetException(Exception):\n \n def __init__(self,len_of_paths,len_of_labels):\n super().__init__(\n f\"Number of paths ({len_of_paths}) is not compatible with number of labels ({len_of_labels})\"\n )","metadata":{"execution":{"iopub.status.busy":"2023-12-26T02:07:16.312197Z","iopub.execute_input":"2023-12-26T02:07:16.312626Z","iopub.status.idle":"2023-12-26T02:07:16.318373Z","shell.execute_reply.started":"2023-12-26T02:07:16.312591Z","shell.execute_reply":"2023-12-26T02:07:16.317127Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"transform = transforms.Compose([transforms.ToTensor()])","metadata":{"execution":{"iopub.status.busy":"2023-12-26T02:07:19.246497Z","iopub.execute_input":"2023-12-26T02:07:19.246932Z","iopub.status.idle":"2023-12-26T02:07:19.252277Z","shell.execute_reply.started":"2023-12-26T02:07:19.246890Z","shell.execute_reply":"2023-12-26T02:07:19.250956Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"class AnimalDataset(Dataset):\n \n def __init__(self,img_paths,img_labels,size_of_images):\n self.img_paths = img_paths\n self.img_labels = img_labels\n self.size_of_images = size_of_images\n if len(self.img_paths) != len(self.img_labels):\n raise InvalidDatasetException(self.img_paths,self.img_labels)\n \n \n def __len__(self):\n return len(self.img_paths)\n \n def __getitem__(self,index):\n PIL_IMAGE = Image.open(self.img_paths[index]).resize(self.size_of_images)\n TENSOR_IMAGE = transform(PIL_IMAGE)\n label = self.img_labels[index]\n \n return TENSOR_IMAGE,label","metadata":{"execution":{"iopub.status.busy":"2023-12-26T02:07:32.747821Z","iopub.execute_input":"2023-12-26T02:07:32.748250Z","iopub.status.idle":"2023-12-26T02:07:32.756829Z","shell.execute_reply.started":"2023-12-26T02:07:32.748214Z","shell.execute_reply":"2023-12-26T02:07:32.755683Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"paths = []\nlabels = []\nlabel_map = {0:\"Cat\",\n 1:\"Dog\",\n 2:\"Wild\"\n }\n\nfor cat_path in glob(\"../input/animal-faces/afhq/train/cat/*\") + glob(\"../input/animal-faces/afhq/val/cat/*\"):\n paths.append(cat_path)\n labels.append(0)\n \nfor dog_path in glob(\"../input/animal-faces/afhq/train/dog/*\") + glob(\"../input/animal-faces/afhq/val/dog/*\"):\n paths.append(dog_path)\n labels.append(1)\n \nfor wild_path in glob(\"../input/animal-faces/afhq/train/wild/*\") + glob(\"../input/animal-faces/afhq/val/wild/*\"):\n paths.append(wild_path)\n labels.append(2)\n \nprint(len(paths))\nprint(len(labels))","metadata":{"execution":{"iopub.status.busy":"2023-12-26T02:07:51.052162Z","iopub.execute_input":"2023-12-26T02:07:51.052630Z","iopub.status.idle":"2023-12-26T02:07:51.614946Z","shell.execute_reply.started":"2023-12-26T02:07:51.052588Z","shell.execute_reply":"2023-12-26T02:07:51.613765Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stdout","text":"16130\n16130\n","output_type":"stream"}]},{"cell_type":"code","source":"dataset = AnimalDataset(paths,labels,(250,250))","metadata":{"execution":{"iopub.status.busy":"2023-12-26T02:07:56.581291Z","iopub.execute_input":"2023-12-26T02:07:56.582073Z","iopub.status.idle":"2023-12-26T02:07:56.586519Z","shell.execute_reply.started":"2023-12-26T02:07:56.582019Z","shell.execute_reply":"2023-12-26T02:07:56.585522Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"markdown","source":"### 2. Preparing Sampler Objects","metadata":{}},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\n\ndataset_indices = list(range(0,len(dataset)))\n\ntrain_indices,test_indices = train_test_split(dataset_indices,test_size=0.2,random_state=42)\nprint(\"Number of train samples: \",len(train_indices))\nprint(\"Number of test samples: \",len(test_indices))","metadata":{"execution":{"iopub.status.busy":"2023-12-26T02:08:08.089360Z","iopub.execute_input":"2023-12-26T02:08:08.089772Z","iopub.status.idle":"2023-12-26T02:08:09.151992Z","shell.execute_reply.started":"2023-12-26T02:08:08.089739Z","shell.execute_reply":"2023-12-26T02:08:09.150799Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"Number of train samples: 12904\nNumber of test samples: 3226\n","output_type":"stream"}]},{"cell_type":"code","source":"train_sampler = SubsetRandomSampler(train_indices)\ntest_sampler = SubsetRandomSampler(test_indices)","metadata":{"execution":{"iopub.status.busy":"2023-12-26T02:08:13.363121Z","iopub.execute_input":"2023-12-26T02:08:13.363566Z","iopub.status.idle":"2023-12-26T02:08:13.369100Z","shell.execute_reply.started":"2023-12-26T02:08:13.363524Z","shell.execute_reply":"2023-12-26T02:08:13.367715Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"markdown","source":"### 3. Preparing Data Loader Objects","metadata":{}},{"cell_type":"code","source":"BATCH_SIZE = 128\ntrain_loader = torch.utils.data.DataLoader(dataset, batch_size=BATCH_SIZE, \n sampler=train_sampler)\nvalidation_loader = torch.utils.data.DataLoader(dataset, batch_size=BATCH_SIZE,\n sampler=test_sampler)","metadata":{"execution":{"iopub.status.busy":"2023-12-26T02:08:20.696534Z","iopub.execute_input":"2023-12-26T02:08:20.697145Z","iopub.status.idle":"2023-12-26T02:08:20.703973Z","shell.execute_reply.started":"2023-12-26T02:08:20.697083Z","shell.execute_reply":"2023-12-26T02:08:20.702916Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"code","source":"dataset[1][0].shape","metadata":{"execution":{"iopub.status.busy":"2023-12-26T02:08:26.200709Z","iopub.execute_input":"2023-12-26T02:08:26.201201Z","iopub.status.idle":"2023-12-26T02:08:26.275642Z","shell.execute_reply.started":"2023-12-26T02:08:26.201143Z","shell.execute_reply":"2023-12-26T02:08:26.274461Z"},"trusted":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"torch.Size([3, 250, 250])"},"metadata":{}}]},{"cell_type":"code","source":"images,labels = next(iter(train_loader))\ntype(labels)","metadata":{"execution":{"iopub.status.busy":"2023-12-26T02:08:28.614410Z","iopub.execute_input":"2023-12-26T02:08:28.615195Z","iopub.status.idle":"2023-12-26T02:08:30.683154Z","shell.execute_reply.started":"2023-12-26T02:08:28.615145Z","shell.execute_reply":"2023-12-26T02:08:30.681917Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"torch.Tensor"},"metadata":{}}]},{"cell_type":"code","source":"images,labels = iter(train_loader).next()\n\nfig, axis = plt.subplots(3, 5, figsize=(15, 10))\nfor i, ax in enumerate(axis.flat):\n with torch.no_grad():\n npimg = images[i].numpy()\n npimg = np.transpose(npimg, (1, 2, 0))\n label = label_map[int(labels[i])]\n ax.imshow(npimg)\n ax.set(title = f\"{label}\")\n ","metadata":{"execution":{"iopub.status.busy":"2023-12-26T02:08:33.489621Z","iopub.execute_input":"2023-12-26T02:08:33.490312Z","iopub.status.idle":"2023-12-26T02:08:37.639631Z","shell.execute_reply.started":"2023-12-26T02:08:33.490254Z","shell.execute_reply":"2023-12-26T02:08:37.638075Z"},"trusted":true},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"# Neural Network Modeling","metadata":{}},{"cell_type":"code","source":"class CNN(nn.Module):\n \n def __init__(self):\n super(CNN,self).__init__()\n # First we'll define our layers\n self.conv1 = nn.Conv2d(3,32,kernel_size=3,stride=2,padding=1)\n self.conv2 = nn.Conv2d(32,64,kernel_size=3,stride=2,padding=1)\n self.batchnorm1 = nn.BatchNorm2d(64)\n \n self.conv3 = nn.Conv2d(64,128,kernel_size=3,stride=2,padding=1)\n self.batchnorm2 = nn.BatchNorm2d(128)\n \n self.conv4 = nn.Conv2d(128,256,kernel_size=3,stride=2,padding=1)\n self.batchnorm3 = nn.BatchNorm2d(256)\n \n self.maxpool = nn.MaxPool2d(2,2)\n \n self.fc1 = nn.Linear(256 * 2 * 2,512)\n self.fc2 = nn.Linear(512,3)\n \n \n def forward(self,x):\n x = F.relu(self.conv1(x))\n x = F.relu(self.conv2(x))\n x = self.batchnorm1(x)\n x = self.maxpool(x)\n x = F.relu(self.conv3(x))\n x = self.batchnorm2(x)\n x = self.maxpool(x)\n x = F.relu(self.conv4(x))\n x = self.batchnorm3(x)\n x = self.maxpool(x)\n x = x.view(-1, 256 * 2 * 2)\n x = self.fc1(x)\n x = self.fc2(x)\n x = F.log_softmax(x,dim=1)\n return x\n \n ","metadata":{"execution":{"iopub.status.busy":"2023-12-26T02:08:52.084275Z","iopub.execute_input":"2023-12-26T02:08:52.084695Z","iopub.status.idle":"2023-12-26T02:08:52.235807Z","shell.execute_reply.started":"2023-12-26T02:08:52.084660Z","shell.execute_reply":"2023-12-26T02:08:52.234601Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")","metadata":{"execution":{"iopub.status.busy":"2023-12-26T02:09:00.373248Z","iopub.execute_input":"2023-12-26T02:09:00.373717Z","iopub.status.idle":"2023-12-26T02:09:00.379859Z","shell.execute_reply.started":"2023-12-26T02:09:00.373676Z","shell.execute_reply":"2023-12-26T02:09:00.378531Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"code","source":"device","metadata":{"execution":{"iopub.status.busy":"2023-12-26T02:09:03.583568Z","iopub.execute_input":"2023-12-26T02:09:03.583976Z","iopub.status.idle":"2023-12-26T02:09:03.590995Z","shell.execute_reply.started":"2023-12-26T02:09:03.583941Z","shell.execute_reply":"2023-12-26T02:09:03.589598Z"},"trusted":true},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"device(type='cpu')"},"metadata":{}}]},{"cell_type":"code","source":"model = CNN().to(device)","metadata":{"execution":{"iopub.status.busy":"2023-12-26T02:09:05.304825Z","iopub.execute_input":"2023-12-26T02:09:05.305423Z","iopub.status.idle":"2023-12-26T02:09:05.330480Z","shell.execute_reply.started":"2023-12-26T02:09:05.305377Z","shell.execute_reply":"2023-12-26T02:09:05.329283Z"},"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"code","source":"criterion = nn.CrossEntropyLoss()\noptimizer = optim.RMSprop(model.parameters(),lr=1e-4)","metadata":{"execution":{"iopub.status.busy":"2023-12-26T02:09:09.325239Z","iopub.execute_input":"2023-12-26T02:09:09.325676Z","iopub.status.idle":"2023-12-26T02:09:09.332695Z","shell.execute_reply.started":"2023-12-26T02:09:09.325640Z","shell.execute_reply":"2023-12-26T02:09:09.331370Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"markdown","source":"# Training Model","metadata":{}},{"cell_type":"code","source":"EPOCH_NUMBER = 5\nTRAIN_LOSS = []\nTRAIN_ACCURACY = []\n\nfor epoch in range(1,EPOCH_NUMBER+1):\n epoch_loss = 0.0\n correct = 0\n total = 0\n for data_,target_ in train_loader:\n # We have to one hot encode our labels.\n target_ =target_.to(device)\n data_ = data_.to(device)\n \n # Cleaning the cached gradients if there are\n optimizer.zero_grad()\n \n # Getting train decisions and computing loss.\n outputs = model(data_)\n loss = criterion(outputs,target_)\n \n # Backpropagation and optimizing.\n loss.backward()\n optimizer.step()\n \n # Computing statistics.\n epoch_loss = epoch_loss + loss.item()\n _,pred = torch.max(outputs,dim=1)\n correct = correct + torch.sum(pred == target_).item()\n total += target_.size(0)\n \n # Appending stats to the lists.\n TRAIN_LOSS.append(epoch_loss)\n TRAIN_ACCURACY.append(100 * correct / total)\n print(f\"Epoch {epoch}: Accuracy: {100 * correct/total}, Loss: {epoch_loss}\")\n ","metadata":{"execution":{"iopub.status.busy":"2023-12-26T02:09:16.501218Z","iopub.execute_input":"2023-12-26T02:09:16.501642Z","iopub.status.idle":"2023-12-26T02:46:57.873681Z","shell.execute_reply.started":"2023-12-26T02:09:16.501605Z","shell.execute_reply":"2023-12-26T02:46:57.872782Z"},"trusted":true},"execution_count":21,"outputs":[{"name":"stdout","text":"Epoch 1: Accuracy: 84.58617482951023, Loss: 44.8067807033658\nEpoch 2: Accuracy: 95.85399876007439, Loss: 12.455018483102322\nEpoch 3: Accuracy: 98.27960322380657, Loss: 5.984365746378899\nEpoch 4: Accuracy: 99.51952882827031, Loss: 2.5103362780064344\nEpoch 5: Accuracy: 99.90700557966522, Loss: 0.9948955184081569\n","output_type":"stream"}]},{"cell_type":"code","source":"plt.subplots(figsize=(6,4))\nplt.plot(range(EPOCH_NUMBER),TRAIN_LOSS,color=\"blue\",label=\"Loss\")\nplt.legend()\nplt.show()\n\nplt.subplots(figsize=(6,4))\nplt.plot(range(EPOCH_NUMBER),TRAIN_ACCURACY,color=\"green\",label=\"Accuracy\")\nplt.legend()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-12-26T02:46:57.875384Z","iopub.execute_input":"2023-12-26T02:46:57.875956Z","iopub.status.idle":"2023-12-26T02:46:58.263990Z","shell.execute_reply.started":"2023-12-26T02:46:57.875916Z","shell.execute_reply":"2023-12-26T02:46:58.262908Z"},"trusted":true},"execution_count":22,"outputs":[{"output_type":"display_data","data":{"text/plain":"
","image/png":"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Q9wYfcLqZ9cP+h4ItWayl0OSkFRAf/88p9MCk3inW/fITEhkcGdBzMifQR9D+2r0xhFooTKXcpk1dZVPDL/ER5Z8Agrt66kdd3WTOg3gct6Xkaz2s2Cjicie1G5yz65OzNXzCQrlMUry16h0As5rcNpTDp9Ev079tdpjCJRLNJl9kYCwwEDHnH3+8LbrwWuBgqBf7n7jRHmlCq0cedGpi6aSlYoi6/Wf0VaShq/P/b3XHHMFRzW8LCg44lIGZS73M2sK8XFngnsAt40s9eA1sBAoLu755lZkwpJKpUutCrEpHmTeG7Jc+ws2MmxrY7lyUFPcs6R55CcmBx0PBE5CJGM3DsDc9x9B4CZzaZ4kex04C/ungfg7msjTimVZkf+Dp5b8hxZoSxCq0LUSqrFhd0uZETGCHo06xF0PBEpp0jKfQkwMbxA9k6gPxACOgF9zGwixQtk3+Du8yJOKhXqy3Vf8nDoYf6+6O9syt3EkY2P5MFfP8gF3S6gXnK9oOOJSITKXe7uvtTM7gDeBrYDCymeY08EGgK9gQzgBTNr7+6+5/3N7HLgcoA2bdqUN4YcBHfnpaUvMSk0iZkrZpKUkMTZXc5mRPoI+rTpo9MYReKI7dW55X8gs9uBbOBM4A53nxXe/g3Q291z9nXf9PR0D4VCFZJD9u2+T+5j1FujaFOvDVcccwWXHn0pTWs3DTqWiJSTmc1391JXs4n0bJkm7r7WzNpQPN/eGygC+gGzzKwTcAiwLpLnkchtydvCbe/fxintT+HN89+kRkKNoCOJSCWK9ETlGeE593zganffZGaPA4+b2RKKz6K5eO8pGal6931yH+t3rufPJ/9ZxS5SDURU7u7ep5Rtu4ALInlcqVjrd6zn7o/vZnDnwVqPVKSaSAg6gFS+Oz66g227tjGh34Sgo4hIFVG5x7lVW1fxt7l/48LuF9KlcZeg44hIFVG5x7nb3r+NwqJCbu17a9BRRKQKqdzj2Lcbv+WRBY8wvOdw2jVoF3QcEalCKvc4dut7t5KUkMSYE8YEHUVEqpjKPU59vvZznl78NNdmXkvzOs2DjiMiVUzlHqdumXULdWrW4cbjdbVlkepI5R6H5q2cx8vLXuaGY28gLTUt6DgiEgCVexwaPXM0jVIbcV3v64KOIiIB0TppcWbWiln8+9t/c88v76FOzTpBxxGRgGjkHkfcndEzR9OqbitGZIwIOo6IBEgj9zjyr6//xX+y/8OUAVO0LJ5INaeRe5wo8iJGzxxNh4YdGNZjWNBxRCRgGrnHiRc+f4HFPy3m2cHPklQjKeg4IhIwjdzjQH5hPrfMuoWjmhzFkK5Dgo4jIlFAI/c4MHXRVJZvWM6rQ18lwfTzWkQ0co95uQW5jJ89nt6tejOg04Cg44hIlIio3M1spJktMbPPzey6vW673szczBpFlFD26+HQw2RvyWbiSRMxs6DjiEiUKHe5m1lXYDiQCXQHBphZh/BtrYFfAj9UREgp3da8rdz+we2c3O5kTmp3UtBxRCSKRDJy7wzMcfcd7l4AzAYGh2+7F7gR0MLYlej+OfeTsyOHiSdNDDqKiESZSMp9CdDHzNLMLBXoD7Q2s4HASndftL87m9nlZhYys1BOTk4EMaqnDTs3cNfHdzHw8IH0atUr6DgiEmXKfbaMuy81szuAt4HtwEKgJvBHiqdkDnT/KcAUgPT0dI3wD9KdH93J1rytWvRaREoV0Ruq7v6Yux/j7icAG4HPgXbAIjP7DmgFLDCzZhEnlRKrt67mgTkP8NujfstRTY8KOo6IRKFIz5ZpEv7chuL59qnu3sTd27p7WyAb6OnuayJOKiUmfjCR/KJ8bj3x1qCjiEiUivSPmGaYWRqQD1zt7psijyT7s2LjCqbMn8KlR19Kh4Ydgo4jIlEqonJ39z4HuL1tJI8v/2v87PEkWAK3nHBL0FFEJIrpL1RjyBc5X/DU4qe4JvMaWtZtGXQcEYliKvcYMnbWWFKTUrn5FzcHHUVEopzKPUaEVoWYsXQG1x97PY1SdUUHEdk/lXuMGDNzDA1TGvL7Y38fdBQRiQEq9xgw+7vZvPXNW/zhF3+gbs26QccRkRigco9yuxe9blGnBVdnXB10HBGJEVqsI8q9sfwNPvrxI7JOzyIlKSXoOCISIzRyj2K7F71u36A9lxx9SdBxRCSGaOQexaZ/MZ2Faxby1FlPcUiNQ4KOIyIxRCP3KFVQVMAts27hyMZHcl7X84KOIyIxRiP3KPXkoif5av1XvDzkZWok1Ag6jojEGI3co1BeQR63vncrGS0yGHj4wKDjiEgM0sg9Ck2eP5kft/zI4wMf16LXIlIuGrlHmW27tjHxg4n0a9uPk9udHHQcEYlRGrlHmQfmPMDa7Wt5ZcgrGrWLSLlp5B5FNu7cyJ0f3ckZnc7g2NbHBh1HRGKYyj2K3PXxXWzO28xtJ90WdBQRiXGRrqE60syWmNnnZnZdeNtdZrbMzBab2ctmVr8igsa7NdvWcP+c+zmv63l0a9ot6DgiEuPKXe5m1hUYDmQC3YEBZtYB+DfQ1d27AV8Bf6iIoPHu9g9uJ68gj/Enjg86iojEgUhG7p2BOe6+w90LgNnAYHd/O/w9wCdAq0hDxrvvN33Pw6GHueToS+iY1jHoOCISByIp9yVAHzNLM7NUoD/Qeq99LgHeKO3OZna5mYXMLJSTkxNBjNinRa9FpKKVu9zdfSlwB/A28CawECjcfbuZjQYKgGf2cf8p7p7u7umNGzcub4yYt2zdMqYumspVGVfRut7ePxtFRMonojdU3f0xdz/G3U8ANlI8x46ZDQMGAOe7u0ecMo7tXvT6D7/QWxMiUnEiPVumSfhzG2Aw8KyZnQbcCJzp7jsijxi/FqxewItfvMio3qNoXKv6/vYiIhUv0r9QnWFmaUA+cLW7bzKzB4GawL/Df2H5ibtfGeHzxKUxM8fQILkB1x97fdBRRCTORFTu7t6nlG0dInnM6uKD7z/gjeVvcMcpd1AvuV7QcUQkzugvVAOwe9HrZrWbcU3mNUHHEZE4pAuHBeDtb97mgx8+4KH+D5GalBp0HBGJQxq5VzF3548z/0jb+m25rOdlQccRkTilkXsVe2npSyxYvYCpg6Zq0WsRqTQauVehwqJCxswaQ+dGnTn/qPODjiMicUwj9yr09OKnWbZuGTPOnaFFr0WkUmnkXkXyCvIY9944jml+DGcdcVbQcUQkzmnkXkUeXfAo32/+nilnTNHyeSJS6TRyrwLbd21nwvsT6HtoX05tf2rQcUSkGtDIvQo8OPdBftr+EzPOnaFRu4hUCY3cK9mm3E3c8dEdnN7xdI5vc3zQcUSkmlC5V7K/fvxXNuZu1KLXIlKlVO6VaO32tdz7yb0MOXIIPZr1CDqOiFQjKvdK9OcP/kxuQS5/6venoKOISDWjcq8kP2z+gUmhSQzrMYxOaZ2CjiMi1YzKvZJMmD0BgLF9xwacRESqI5V7Jfhq/Vc8sfAJRqSPoE29NkHHEZFqKNI1VEea2RIz+9zMrgtva2hm/zazr8OfG1RI0hgy7r1xJCcma9FrEQlMucvdzLoCw4FMoDswwMw6ADcD77p7R+Dd8PfVxqI1i3huyXNc1/s6mtZuGnQcEammIhm5dwbmuPsOdy8AZgODgYHA1PA+U4FBESWMMWNmjaF+cn1uOO6GoKOISDUWSbkvAfqYWZqZpQL9gdZAU3dfHd5nDVDq8NXMLjezkJmFcnJyIogRPT7+8WNe++o1bjr+Juon1w86johUY+Uud3dfCtwBvA28CSwECvfaxwHfx/2nuHu6u6c3bty4vDGihrvzx3f/SNNaTbk289qg44hINRfRG6ru/pi7H+PuJwAbga+An8ysOUD489rIY0a/d759h9nfz2bMCWOodUitoOOISDUX6dkyTcKf21A83/4s8CpwcXiXi4F/RPIcsWD3oteH1juU4T2HBx1HRCTiS/7OMLM0IB+42t03mdlfgBfM7FLge+DcSENGu1eWvUJoVYgnBj5BzcSaQccREYms3N29Tynb1gMnR/K4sWT3otdHNDqCC7pdEHQcERFAi3VE7NnPnuWLnC948ZwXSUzQ4RSR6KDLD0RgV+Euxr03jp7NezK48+Cg44iIlNBQMwKPLXiMFZtWMOn0SSSYfk6KSPRQI5XTjvwdTHh/An3a9OFXh/0q6DgiIj+jkXs5PTT3IVZvW83zv3lei16LSNTRyL0cNudu5i8f/YVfd/g1fQ79nxOGREQCp3Ivh3v+cw8bdm7QotciErVU7gcpZ3sO93xyD+d0OYeezXsGHUdEpFQq94P0lw//wo78HVr0WkSimsr9IGRvyeaheQ9xcfeLOaLREUHHERHZJ5X7QZgwewJFXsS4vuOCjiIisl8q9zJavmE5j336GFemX8mh9Q8NOo6IyH6p3Mto3HvjqJlYkz/2+WPQUUREDkjlXgaLf1rMtM+mMbLXSJrVbhZ0HBGRA1K5l8Ets26hbs26/L/j/l/QUUREykTlfgCfZH/Cq1++yo3H30iDlAZBxxERKROV+wGMnjmaJrWa8Ltevws6iohImUW6huooM/vczJaY2TQzSzazk81sgZktNLMPzaxDRYWtau9++y4zV8xkdJ/R1D6kdtBxRETKrNzlbmYtgd8B6e7eFagBDAWygPPdvQfFC2aPqYCcVW73otet67bmimOuCDqOiMhBifSSv4lAipnlA6nAKsCBuuHb64W3xZxXv3yVuSvn8ugZj2rRaxGJOeUud3dfaWZ3Az8AO4G33f1tM7sMeN3MdgJbgN6l3d/MLgcuB2jTpk15Y1SK3Yted2zYkYt7XBx0HBGRgxbJtEwDYCDQDmgB1DKzC4BRQH93bwU8AdxT2v3dfYq7p7t7euPGjcsbo1I8t+Q5lqxdwoR+E7TotYjEpEjeUD0FWOHuOe6eD7wEHA90d/c54X2eB46LMGOVyi/MZ+x7Y+netDvnHHlO0HFERMolknL/AehtZqlWvM7cycAXQD0z6xTe51RgaYQZq9Tjnz7Otxu/ZeJJE7XotYjErEjm3OeY2XRgAVAAfApMAbKBGWZWBGwELqmIoFVhZ/5O/vT+nziu9XH079g/6DgiIuUW0YSyu48D9r7+7cvhj5gzad4kVm1dxbODn9Wi1yIS0zTvELYlbwt//vDP/PKwX9K3bd+g44iIRETlHnbvf+5l/c71TDxpYtBRREQipnIH1u1Yx1//81cGdx5Meov0oOOIiERM5Q7c8eEdbNu1jQn9JgQdRUSkQlT7cl+5ZSUPznuQC7tfSJfGXYKOIyJSIap9ud/2/m0UFhVya99bg44iIlJhqnW5f7PhGx799FGG9xxOuwbtgo4jIlJhqnW53zr7VpISkhhzQkxelVhEZJ+qbbkvWbuEZxY/w7WZ19K8TvOg44iIVKhqW+63zLqFOjXrcOPxNwYdRUSkwlXLcp+7ci6vLHuFG469gbTUtKDjiIhUuGpZ7qNnjqZRaiOu631d0FFERCpFtVuJYtaKWbzz7Tvc88t7qFOzTtBxREQqRbUaubs7o2eOplXdVozIGBF0HBGRSlOtRu7/+vpf/Cf7P0wZMIXkxOSg44iIVJpqM3Iv8iJGzxxNh4YdGNZjWNBxREQqVbUZub/w+Qss/mkxzw5+lqQaSUHHERGpVBGN3M1slJl9bmZLzGyamSVbsYlm9pWZLTWz31VU2PLKL8znllm3cFSToxjSdUjQcUREKl25R+5m1hL4HdDF3Xea2QvAUMCA1sAR7l5kZk0qJmr5TV00leUblvPq0Fe16LWIVAuRTsskAilmlg+kAquA24DfunsRgLuvjfA5IpJbkMv42ePp3ao3AzoNCDKKiEiVKfcw1t1XAncDPwCrgc3u/jZwGDDEzEJm9oaZdSzt/mZ2eXifUE5OTnljHNDDoYfJ3pLN7SfdrkWvRaTaKHe5m1kDYCDQDmgB1DKzC4CaQK67pwOPAI+Xdn93n+Lu6e6e3rhx4/LG2K+teVu5/YPbOaX9KfRr169SnkNEJBpFMgF9CrDC3XPcPR94CTgOyA5/DfAy0C2yiOV3/5z7ydmRo0WvRaTaiWTO/Qegt5mlAjuBk4EQsAXoB6wA+gJfRRqyPDbs3MBdH9/FoCMGkdkyM4gIIiKBKXe5u/scM5sOLAAKgE+BKUAK8IyZjQK2AZdVRNCDdedHd7I1b6sWvRaRaimis2XcfRwwbq/NecDpkTxupFZvXc0Dcx7g/G7n07VJ1yCjiIgEIi5P+p74wUTyi/K16LWIVFtxV+4rNq5gyvwpXHb0ZRzW8LCg44iIBCLuyn387PHUSKihRa9FpFqLq3L/IucLnlr8FNdkXEPLui2DjiMiEpi4Kvexs8ZSK6kWN/3ipqCjiIgEKm7KPbQqxIylM7j+2OtplNoo6DgiIoGKm3IfM3MMaSlpjDp2VNBRREQCFxeLdcz+bjZvffMWd596N3Vr1g06johI4GJ+5L570esWdVpwVcZVQccREYkKMT9yf2P5G3z040c8fPrDpCSlBB1HRCQqxPTIffei1+0btOeSoy8JOo6ISNSI6ZH79C+ms3DNQp4+62ktei0isoeYHrnXPqQ2Aw8fyNCuQ4OOIiISVWJ65N6/Y3/6d+wfdAwRkagT0yN3EREpncpdRCQORVTuZjbKzD43syVmNs3Mkve47QEz2xZ5RBEROVjlLnczawn8Dkh3965ADWBo+LZ0oEGFJBQRkYMW6bRMIpBiZolAKrDKzGoAdwE3RhpORETKp9zl7u4rgbuBH4DVwGZ3fxu4BnjV3Vfv7/5mdrmZhcwslJOTU94YIiJSikimZRoAA4F2QAuglpldBJwD/O1A93f3Ke6e7u7pjRs3Lm8MEREpRSTnuZ8CrHD3HAAzewkYD6QAy80MINXMlrt7h4iTiohImZm7l++OZr2Ax4EMYCfwdyDk7n/bY59t7l67DI+VA3xfriDQCFhXzvtWJuU6OMp18KI1m3IdnEhyHerupU59lHvk7u5zzGw6sAAoAD4FppTzsco9L2NmIXdPL+/9K4tyHRzlOnjRmk25Dk5l5Yro8gPuPg4Yt5/bDzhqFxGRiqe/UBURiUPxUO7lmgqqAsp1cJTr4EVrNuU6OJWSq9xvqIqISPSKh5G7iIjsReUuIhKHYqbczew0M/vSzJab2c2l3F7TzJ4P3z7HzNpGSa5hZpZjZgvDH5dVUa7HzWytmS3Zx+0WvnLncjNbbGY9oyTXiWa2eY/jNbYKMrU2s1lm9kX4KqcjS9mnyo9XGXMFcbySzWyumS0K5xpfyj5V/nosY65AXo/h565hZp+a2Wul3Fbxx8vdo/6D4itOfgO0Bw4BFgFd9trnKuDh8NdDgeejJNcw4MEAjtkJQE9gyT5u7w+8ARjQG5gTJblOBF6r4mPVHOgZ/roO8FUp/x+r/HiVMVcQx8uA2uGvk4A5QO+99gni9ViWXIG8HsPP/Xvg2dL+f1XG8YqVkXsmsNzdv3X3XcBzFF/XZk8Dganhr6cDJ1v4GggB5wqEu78PbNjPLgOBJ73YJ0B9M2seBbmqnLuvdvcF4a+3AkuBlnvtVuXHq4y5qlz4GOxeqyEp/LH3mRlV/nosY65AmFkr4HTg0X3sUuHHK1bKvSXw4x7fZ/O//8hL9nH3AmAzkBYFuQDODv8qP93MWldyprIqa/YgHBv+1foNMzuyKp84/Ovw0RSP+vYU6PHaTy4I4HiFpxgWAmuBf7v7Po9XFb4ey5ILgnk93kfxZdCL9nF7hR+vWCn3WPZPoK27dwP+zX9/OkvpFlB8vYzuFF9d9JWqemIzqw3MAK5z9y1V9bwHcoBcgRwvdy909x5AKyDTzLpWxfMeSBlyVfnr0cwGAGvdfX5lP9eeYqXcVwJ7/oRtFd5W6j5WvHhIPWB90Lncfb2754W/fRQ4ppIzlVVZjmmVc/ctu3+1dvfXgSQza1TZz2tmSRQX6DPu/lIpuwRyvA6UK6jjtcfzbwJmAaftdVMQr8cD5gro9Xg8cKaZfUfx1O1JZvb0XvtU+PGKlXKfB3Q0s3ZmdgjFbzi8utc+rwIXh7/+DTDTw+9OBJlrr3nZMymeN40GrwIXhc8C6U3xYiv7XWClKphZs91zjWaWSfG/0UothfDzPQYsdfd79rFblR+vsuQK6Hg1NrP64a9TgFOBZXvtVuWvx7LkCuL16O5/cPdW7t6W4o6Y6e4X7LVbhR+viC4cVlXcvcDMrgHeovgMlcfd/XMz+xPFlxl+leIXwVNmtpziN+yGRkmu35nZmRRfOXMDxe/WVzozm0bxmRSNzCyb4gu8JYVzPwy8TvEZIMuBHcD/RUmu3wAjzKyA4ktJD62CH9LHAxcCn4XnawH+CLTZI1cQx6ssuYI4Xs2BqVa8pGYC8IK7vxb067GMuQJ5PZamso+XLj8gIhKHYmVaRkREDoLKXUQkDqncRUTikMpdRCQOqdxFROKQyl1EJA6p3EVE4tD/B1SEt9mhaLe8AAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"# Final Test","metadata":{}},{"cell_type":"code","source":"total_val_loss = 0.0\ntotal_true = 0\ntotal = len(test_sampler)\n\n# When we're not working with gradients and backpropagation\n# we use torch.no_grad() utility.\nwith torch.no_grad():\n model.eval()\n for data_,target_ in validation_loader:\n data_ = data_.to(device)\n target_ = target_.to(device)\n \n outputs = model(data_)\n loss = criterion(outputs,target_).item()\n _,preds = torch.max(outputs,dim=1)\n total_val_loss += loss\n true = torch.sum(preds == target_).item()\n total_true += true\n\nvalidation_accuracy = round(100 * total_true / total,2)\nprint(f\"Validation accuracy: {validation_accuracy}%\")\nprint(f\"Validation loss: {round(total_val_loss,2)}%\")","metadata":{"execution":{"iopub.status.busy":"2023-12-26T02:46:58.266617Z","iopub.execute_input":"2023-12-26T02:46:58.267140Z","iopub.status.idle":"2023-12-26T02:48:18.280207Z","shell.execute_reply.started":"2023-12-26T02:46:58.267089Z","shell.execute_reply":"2023-12-26T02:48:18.279309Z"},"trusted":true},"execution_count":23,"outputs":[{"name":"stdout","text":"Validation accuracy: 95.94%\nValidation loss: 2.87%\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# How to Save A Pytorch Model","metadata":{}},{"cell_type":"code","source":"for param_tensor in model.state_dict():\n print(param_tensor, \"\\t\", model.state_dict()[param_tensor].size())","metadata":{"execution":{"iopub.status.busy":"2023-12-26T02:48:18.282626Z","iopub.execute_input":"2023-12-26T02:48:18.283156Z","iopub.status.idle":"2023-12-26T02:48:18.315372Z","shell.execute_reply.started":"2023-12-26T02:48:18.283102Z","shell.execute_reply":"2023-12-26T02:48:18.314144Z"},"trusted":true},"execution_count":24,"outputs":[{"name":"stdout","text":"conv1.weight \t torch.Size([32, 3, 3, 3])\nconv1.bias \t torch.Size([32])\nconv2.weight \t torch.Size([64, 32, 3, 3])\nconv2.bias \t torch.Size([64])\nbatchnorm1.weight \t torch.Size([64])\nbatchnorm1.bias \t torch.Size([64])\nbatchnorm1.running_mean \t torch.Size([64])\nbatchnorm1.running_var \t torch.Size([64])\nbatchnorm1.num_batches_tracked \t torch.Size([])\nconv3.weight \t torch.Size([128, 64, 3, 3])\nconv3.bias \t torch.Size([128])\nbatchnorm2.weight \t torch.Size([128])\nbatchnorm2.bias \t torch.Size([128])\nbatchnorm2.running_mean \t torch.Size([128])\nbatchnorm2.running_var \t torch.Size([128])\nbatchnorm2.num_batches_tracked \t torch.Size([])\nconv4.weight \t torch.Size([256, 128, 3, 3])\nconv4.bias \t torch.Size([256])\nbatchnorm3.weight \t torch.Size([256])\nbatchnorm3.bias \t torch.Size([256])\nbatchnorm3.running_mean \t torch.Size([256])\nbatchnorm3.running_var \t torch.Size([256])\nbatchnorm3.num_batches_tracked \t torch.Size([])\nfc1.weight \t torch.Size([512, 1024])\nfc1.bias \t torch.Size([512])\nfc2.weight \t torch.Size([3, 512])\nfc2.bias \t torch.Size([3])\n","output_type":"stream"}]},{"cell_type":"markdown","source":"* save and load this state dict.","metadata":{}},{"cell_type":"code","source":"torch.save(model.state_dict(),\"model.pt\")\n","metadata":{"execution":{"iopub.status.busy":"2023-12-26T02:48:18.317100Z","iopub.execute_input":"2023-12-26T02:48:18.317585Z","iopub.status.idle":"2023-12-26T02:48:18.335368Z","shell.execute_reply.started":"2023-12-26T02:48:18.317527Z","shell.execute_reply":"2023-12-26T02:48:18.334350Z"},"trusted":true},"execution_count":25,"outputs":[]},{"cell_type":"markdown","source":"* create a new object from the class and load the state dict.","metadata":{}},{"cell_type":"code","source":"loaded_model = CNN()\nloaded_model.load_state_dict(torch.load(\"model.pt\"))\n","metadata":{"execution":{"iopub.status.busy":"2023-12-26T02:48:18.336825Z","iopub.execute_input":"2023-12-26T02:48:18.337194Z","iopub.status.idle":"2023-12-26T02:48:18.360003Z","shell.execute_reply.started":"2023-12-26T02:48:18.337161Z","shell.execute_reply":"2023-12-26T02:48:18.359170Z"},"trusted":true},"execution_count":26,"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}}]}]} \ No newline at end of file diff --git "a/\354\272\220\352\270\200\355\225\204\354\202\25418_\354\241\260\355\230\204\354\247\200.ipynb" "b/\354\272\220\352\270\200\355\225\204\354\202\25418_\354\241\260\355\230\204\354\247\200.ipynb" new file mode 100644 index 0000000..83a13b7 --- /dev/null +++ "b/\354\272\220\352\270\200\355\225\204\354\202\25418_\354\241\260\355\230\204\354\247\200.ipynb" @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":17777,"databundleVersionId":869809,"sourceType":"competition"}],"dockerImageVersionId":29849,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# 1. Importing the necessary libraries","metadata":{}},{"cell_type":"code","source":"import numpy as np \nimport pandas as pd \n\n# text processing libraries\nimport re\nimport string\nimport nltk\nfrom nltk.corpus import stopwords\n\n# XGBoost\nimport xgboost as xgb\nfrom xgboost import XGBClassifier\n\n# sklearn \nfrom sklearn import model_selection\nfrom sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.metrics import f1_score\nfrom sklearn import preprocessing, decomposition, model_selection, metrics, pipeline\nfrom sklearn.model_selection import GridSearchCV,StratifiedKFold,RandomizedSearchCV\n\n# matplotlib and seaborn for plotting\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# File system manangement\nimport os\n\n# Suppress warnings \nimport warnings\nwarnings.filterwarnings('ignore')","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-01-08T11:41:12.415523Z","iopub.execute_input":"2024-01-08T11:41:12.416181Z","iopub.status.idle":"2024-01-08T11:41:16.022442Z","shell.execute_reply.started":"2024-01-08T11:41:12.416122Z","shell.execute_reply":"2024-01-08T11:41:16.021668Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"markdown","source":"# 2. Reading the datasets","metadata":{}},{"cell_type":"code","source":"# List files available\nprint(os.listdir(\"../input/\"))","metadata":{"execution":{"iopub.status.busy":"2024-01-08T11:41:23.773880Z","iopub.execute_input":"2024-01-08T11:41:23.774387Z","iopub.status.idle":"2024-01-08T11:41:23.786797Z","shell.execute_reply.started":"2024-01-08T11:41:23.774340Z","shell.execute_reply":"2024-01-08T11:41:23.785794Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"['nlp-getting-started']\n","output_type":"stream"}]},{"cell_type":"code","source":"#Training data\ntrain = pd.read_csv('../input/nlp-getting-started/train.csv')\nprint('Training data shape: ', train.shape)\ntrain.head()","metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","execution":{"iopub.status.busy":"2024-01-08T11:41:26.368588Z","iopub.execute_input":"2024-01-08T11:41:26.369109Z","iopub.status.idle":"2024-01-08T11:41:26.436866Z","shell.execute_reply.started":"2024-01-08T11:41:26.369022Z","shell.execute_reply":"2024-01-08T11:41:26.436160Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"Training data shape: (7613, 5)\n","output_type":"stream"},{"execution_count":3,"output_type":"execute_result","data":{"text/plain":" id keyword location text \\\n0 1 NaN NaN Our Deeds are the Reason of this #earthquake M... \n1 4 NaN NaN Forest fire near La Ronge Sask. Canada \n2 5 NaN NaN All residents asked to 'shelter in place' are ... \n3 6 NaN NaN 13,000 people receive #wildfires evacuation or... \n4 7 NaN NaN Just got sent this photo from Ruby #Alaska as ... \n\n target \n0 1 \n1 1 \n2 1 \n3 1 \n4 1 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
idkeywordlocationtexttarget
01NaNNaNOur Deeds are the Reason of this #earthquake M...1
14NaNNaNForest fire near La Ronge Sask. Canada1
25NaNNaNAll residents asked to 'shelter in place' are ...1
36NaNNaN13,000 people receive #wildfires evacuation or...1
47NaNNaNJust got sent this photo from Ruby #Alaska as ...1
\n
"},"metadata":{}}]},{"cell_type":"code","source":"# Testing data \ntest = pd.read_csv('../input/nlp-getting-started/test.csv')\nprint('Testing data shape: ', test.shape)\ntest.head()","metadata":{"execution":{"iopub.status.busy":"2024-01-08T11:41:35.207955Z","iopub.execute_input":"2024-01-08T11:41:35.208505Z","iopub.status.idle":"2024-01-08T11:41:35.247096Z","shell.execute_reply.started":"2024-01-08T11:41:35.208447Z","shell.execute_reply":"2024-01-08T11:41:35.246223Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"Testing data shape: (3263, 4)\n","output_type":"stream"},{"execution_count":4,"output_type":"execute_result","data":{"text/plain":" id keyword location text\n0 0 NaN NaN Just happened a terrible car crash\n1 2 NaN NaN Heard about #earthquake is different cities, s...\n2 3 NaN NaN there is a forest fire at spot pond, geese are...\n3 9 NaN NaN Apocalypse lighting. #Spokane #wildfires\n4 11 NaN NaN Typhoon Soudelor kills 28 in China and Taiwan","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
idkeywordlocationtext
00NaNNaNJust happened a terrible car crash
12NaNNaNHeard about #earthquake is different cities, s...
23NaNNaNthere is a forest fire at spot pond, geese are...
39NaNNaNApocalypse lighting. #Spokane #wildfires
411NaNNaNTyphoon Soudelor kills 28 in China and Taiwan
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"# 3. Basic EDA\n\n## Missing values","metadata":{}},{"cell_type":"code","source":"#Missing values in training set\ntrain.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2024-01-08T11:41:42.516183Z","iopub.execute_input":"2024-01-08T11:41:42.516515Z","iopub.status.idle":"2024-01-08T11:41:42.526557Z","shell.execute_reply.started":"2024-01-08T11:41:42.516466Z","shell.execute_reply":"2024-01-08T11:41:42.525496Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"id 0\nkeyword 61\nlocation 2533\ntext 0\ntarget 0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"#Missing values in test set\ntest.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2024-01-08T11:41:49.289215Z","iopub.execute_input":"2024-01-08T11:41:49.289762Z","iopub.status.idle":"2024-01-08T11:41:49.298814Z","shell.execute_reply.started":"2024-01-08T11:41:49.289701Z","shell.execute_reply":"2024-01-08T11:41:49.298034Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"id 0\nkeyword 26\nlocation 1105\ntext 0\ndtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"## Exploring the Target Column","metadata":{}},{"cell_type":"code","source":"train['target'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-01-08T11:41:59.243787Z","iopub.execute_input":"2024-01-08T11:41:59.244132Z","iopub.status.idle":"2024-01-08T11:41:59.256833Z","shell.execute_reply.started":"2024-01-08T11:41:59.244081Z","shell.execute_reply":"2024-01-08T11:41:59.255705Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"0 4342\n1 3271\nName: target, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"sns.barplot(train['target'].value_counts().index,train['target'].value_counts(),palette='rocket')","metadata":{"execution":{"iopub.status.busy":"2024-01-08T11:42:02.384847Z","iopub.execute_input":"2024-01-08T11:42:02.385244Z","iopub.status.idle":"2024-01-08T11:42:02.599786Z","shell.execute_reply.started":"2024-01-08T11:42:02.385179Z","shell.execute_reply":"2024-01-08T11:42:02.598699Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"* **Exploring the Target Column**\nLet's look at what the disaster and the non disaster tweets look like","metadata":{}},{"cell_type":"code","source":"# A disaster tweet\ndisaster_tweets = train[train['target']==1]['text']\ndisaster_tweets.values[1]","metadata":{"execution":{"iopub.status.busy":"2024-01-08T11:42:04.931422Z","iopub.execute_input":"2024-01-08T11:42:04.931937Z","iopub.status.idle":"2024-01-08T11:42:04.941300Z","shell.execute_reply.started":"2024-01-08T11:42:04.931877Z","shell.execute_reply":"2024-01-08T11:42:04.940090Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"'Forest fire near La Ronge Sask. Canada'"},"metadata":{}}]},{"cell_type":"code","source":"#not a disaster tweet\nnon_disaster_tweets = train[train['target']==0]['text']\nnon_disaster_tweets.values[1]","metadata":{"execution":{"iopub.status.busy":"2024-01-08T11:42:07.022634Z","iopub.execute_input":"2024-01-08T11:42:07.023038Z","iopub.status.idle":"2024-01-08T11:42:07.032297Z","shell.execute_reply.started":"2024-01-08T11:42:07.022970Z","shell.execute_reply":"2024-01-08T11:42:07.031381Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"'I love fruits'"},"metadata":{}}]},{"cell_type":"markdown","source":"## Exploring the 'keyword' column","metadata":{}},{"cell_type":"code","source":"sns.barplot(y=train['keyword'].value_counts()[:20].index,x=train['keyword'].value_counts()[:20],\n orient='h')","metadata":{"execution":{"iopub.status.busy":"2024-01-08T11:42:13.827282Z","iopub.execute_input":"2024-01-08T11:42:13.827781Z","iopub.status.idle":"2024-01-08T11:42:14.201372Z","shell.execute_reply.started":"2024-01-08T11:42:13.827567Z","shell.execute_reply":"2024-01-08T11:42:14.200444Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"train.loc[train['text'].str.contains('disaster', na=False, case=False)].target.value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-01-08T11:42:19.538769Z","iopub.execute_input":"2024-01-08T11:42:19.539202Z","iopub.status.idle":"2024-01-08T11:42:19.573632Z","shell.execute_reply.started":"2024-01-08T11:42:19.539128Z","shell.execute_reply":"2024-01-08T11:42:19.572547Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"1 102\n0 40\nName: target, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"## Exploring the 'location' column","metadata":{}},{"cell_type":"code","source":"# Replacing the ambigious locations name with Standard names\ntrain['location'].replace({'United States':'USA',\n 'New York':'USA',\n \"London\":'UK',\n \"Los Angeles, CA\":'USA',\n \"Washington, D.C.\":'USA',\n \"California\":'USA',\n \"Chicago, IL\":'USA',\n \"Chicago\":'USA',\n \"New York, NY\":'USA',\n \"California, USA\":'USA',\n \"FLorida\":'USA',\n \"Nigeria\":'Africa',\n \"Kenya\":'Africa',\n \"Everywhere\":'Worldwide',\n \"San Francisco\":'USA',\n \"Florida\":'USA',\n \"United Kingdom\":'UK',\n \"Los Angeles\":'USA',\n \"Toronto\":'Canada',\n \"San Francisco, CA\":'USA',\n \"NYC\":'USA',\n \"Seattle\":'USA',\n \"Earth\":'Worldwide',\n \"Ireland\":'UK',\n \"London, England\":'UK',\n \"New York City\":'USA',\n \"Texas\":'USA',\n \"London, UK\":'UK',\n \"Atlanta, GA\":'USA',\n \"Mumbai\":\"India\"},inplace=True)\n\nsns.barplot(y=train['location'].value_counts()[:5].index,x=train['location'].value_counts()[:5],\n orient='h')","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-01-08T11:42:27.233035Z","iopub.execute_input":"2024-01-08T11:42:27.233392Z","iopub.status.idle":"2024-01-08T11:42:27.478931Z","shell.execute_reply.started":"2024-01-08T11:42:27.233341Z","shell.execute_reply":"2024-01-08T11:42:27.477740Z"},"trusted":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"> # 4. Text Data Preprocessing\n\n## 1. Data Cleaning","metadata":{}},{"cell_type":"code","source":"# A quick glance over the existing data\ntrain['text'][:5]","metadata":{"execution":{"iopub.status.busy":"2024-01-08T11:42:43.111776Z","iopub.execute_input":"2024-01-08T11:42:43.112314Z","iopub.status.idle":"2024-01-08T11:42:43.119583Z","shell.execute_reply.started":"2024-01-08T11:42:43.112264Z","shell.execute_reply":"2024-01-08T11:42:43.118470Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"0 Our Deeds are the Reason of this #earthquake M...\n1 Forest fire near La Ronge Sask. Canada\n2 All residents asked to 'shelter in place' are ...\n3 13,000 people receive #wildfires evacuation or...\n4 Just got sent this photo from Ruby #Alaska as ...\nName: text, dtype: object"},"metadata":{}}]},{"cell_type":"code","source":"# Applying a first round of text cleaning techniques\n\ndef clean_text(text):\n '''Make text lowercase, remove text in square brackets,remove links,remove punctuation\n and remove words containing numbers.'''\n text = text.lower()\n text = re.sub('\\[.*?\\]', '', text)\n text = re.sub('https?://\\S+|www\\.\\S+', '', text)\n text = re.sub('<.*?>+', '', text)\n text = re.sub('[%s]' % re.escape(string.punctuation), '', text)\n text = re.sub('\\n', '', text)\n text = re.sub('\\w*\\d\\w*', '', text)\n return text\n\n# Applying the cleaning function to both test and training datasets\ntrain['text'] = train['text'].apply(lambda x: clean_text(x))\ntest['text'] = test['text'].apply(lambda x: clean_text(x))\n\n# Let's take a look at the updated text\ntrain['text'].head()","metadata":{"execution":{"iopub.status.busy":"2024-01-08T11:42:45.480317Z","iopub.execute_input":"2024-01-08T11:42:45.480663Z","iopub.status.idle":"2024-01-08T11:42:45.858262Z","shell.execute_reply.started":"2024-01-08T11:42:45.480611Z","shell.execute_reply":"2024-01-08T11:42:45.857298Z"},"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"0 our deeds are the reason of this earthquake ma...\n1 forest fire near la ronge sask canada\n2 all residents asked to shelter in place are be...\n3 people receive wildfires evacuation orders in...\n4 just got sent this photo from ruby alaska as s...\nName: text, dtype: object"},"metadata":{}}]},{"cell_type":"code","source":"from wordcloud import WordCloud\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=[26, 8])\nwordcloud1 = WordCloud( background_color='white',\n width=600,\n height=400).generate(\" \".join(disaster_tweets))\nax1.imshow(wordcloud1)\nax1.axis('off')\nax1.set_title('Disaster Tweets',fontsize=40);\n\nwordcloud2 = WordCloud( background_color='white',\n width=600,\n height=400).generate(\" \".join(non_disaster_tweets))\nax2.imshow(wordcloud2)\nax2.axis('off')\nax2.set_title('Non Disaster Tweets',fontsize=40);","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-01-08T11:42:53.083180Z","iopub.execute_input":"2024-01-08T11:42:53.083886Z","iopub.status.idle":"2024-01-08T11:42:55.471479Z","shell.execute_reply.started":"2024-01-08T11:42:53.083809Z","shell.execute_reply":"2024-01-08T11:42:55.470708Z"},"trusted":true},"execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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2LTyVS6yr0XQvYAzzlcFkU//OoqtzTU9yRA2YhlddNNN910021X2ALYfG96jl/mOf5cCF1u2yycw/x2j94lhHAsYZ0x//OU+T5MHV47sRY4I0yZoz1lfmqGaz7BU8f4Juq71KPv6wivyYQgMvtHapMGKJtE6NzK3aI8t0c9bTkuhOwCl9wCggQpBSkbalzk119Rtl/wDyypIUQwlOc+dK8f9EYI2fEB7vX1BAkma+6N2DjM3U7kEoIHg7l/n6ZiZxH4Pg8LUmakp32/D3ZPYGeg+OTKgcMjaPu5Hv2PhJA90ft7A2SG0Z+IDdhzlxseQt49VtwYRR+cFMl1CnbtWuLe023X2zSHuaK0HAWez7lN1OfOJfeNMcarPygmxGrQxphtJoppUsauWP4X165RTs7qQHjz370TRT3NuYL1La7/C4CTjTE7wrSnAmtglDq7BLg2ijpn0nDq6G6DMWYtdrDoI2DuPREZRH3uvApsRLSPQ0WkQa5xEUnA5iH3ETBPnyP3J9euNcBZTt+EavsObN/VOruSsJFDgeo4BtjXtetBY8zLofQ7dUzCPx/eb0SkV7hyjcD9nfokmu9JM3+nFEVRFKUleA670LiPC0SkT1MUOvaG204wwAXGmM1BimCMqcUudLfetftAERkZRdVPG2P+F0rA2PQJU1279hWR9GDyuwjNOSaC6MYXlU5fBTu+Ksq2/AkbKOLjzBCy7nZ/aIypirSSZrThfouNlvdxnzFmYgTt+Rx4xLXrVBGJJoXMVcaYFVHI72q4xyppBF+nyz1e+gwbTR3oWLAy3rrcjMPOXvVxozEmaEpLH8aYVwB3Cp2LRCRYyqJbXf8XAycZYwrD6K/CLnTqHltfF65djWBX9TMouxHqMFeUlsNrHIZdIDMKvPmLdzbveT5HunhPS7cbERmNf268R40xWyIpa4zZBLzh2nVCFFU/ZMLkzd4NcBtho4Ms+urO0/c9dlC3zfmcib8z2sd++C+sFMzYOxH/fI73GWNKg8j6YYxZiDVUfQTruytc/1dgpy5Gii/KCOzzt7kXaGrx75OiKIqitCSOc+Zu164E/AM7GsPh2Jy3Pj41xkwNJuxqyw7gH57d0SwG7i0bjI9d/8fRcE2XXY3mHBNBC9pDTtDGp65du82YyMFt95YA90dR1u0wjweOibDccqJwbu6iBMpj7oeICHYGqo+v8Hd+B3OYu8dSK0O8WHD33XrgySBygXD3XQb+gUsAiMhw7KxYH08YYzZ45QJhjNkKuAOOjo+ibY1lV/lOKbsR6jBXlJYj1t+/Ra7/x4jIb2Os3w+xpItIBxHp7t6wU63c9G+oAfBvM8D9gSKMdzLehVfeirL8t67/O4lItwjLfRhlPbsibiMvGTgogIzb+PvKieL5xrUvkpXigznMY9l3w7wRWSISh80FWNcOY8z2SJU7L1QWu3YFuj5Nxf2dOklEmqMORVEURdmdeBm78LWPM0VkcDDhCDjQ8/m1KMq+Rv3L80C6grHEGLM8vBjgf66w6zuKmnNMBHB3kCCOmCEiiSKSIyJdA4yLSlyifR17MhDudp8uIqOaq72RICKJ2LV+fHwe5azj1YA7Ij9Sm/RDY4wJL7brYozZiF3jwEeg8c1Q6mdTlADT8B9LjXX6oA4RScK/T4LNus3F/0XZe1EGZs2gftY0BO67WI67ckWkX5Tlw+H9HbjPmR2kKBGjN4yitByZns9lTdT3OnaxQLBv8d8VkXewi0h+GY2BEwzH+XYW9m3yICKPAPGu9u7jK2y+c5+xcCYwVESewuazXtn41jYat0FQA5SGSCkTCK+B1xN/YzEQq40x28LI7A58gz1/cT4fgc3rDgR0OPuMvK+xC6r6ynijV9wG2RJjzJog9bv7biuQFmXfVbv+j8dO5Vvg2jcY/+/t6ij1g38UVc8oy0bC69RP+0wBJonI69ipld/oFENFURRlb8MYUyMid1I/CzAOG3V+SiNVemfD/RhFWzaLyArqbYBhIhIfgTNrQZjjbrwpEYKlU9hViPWYaA7WWTnQ+TwWWCQiTwNvGWOiuZYBEZE22DWPTsA6PiNNNxKH7Y9AqTNfp36WaxrwnYi8irXhJodLMdgMDHfa4WNNI+ze7YAveChSu3dmlHXsqnxN/T24v4hkeMbj7vHNFGc2zFwR2QzkYSO7R2Fn5Po4AP8+CRZEdCD14zGA9Y3sO19dgfrO60Tf0Yg63PSkoZO7KUzGpkPq4Hw+GZjn/A68a4xZFsO6lD0UdZgrSsuR5fkcUdqPELyJjVL+jfNZsAOBU4BqEZmBfeBOBr6NJse5iAwAnsIanI0hoKFujCkRkSuxBqLvod4feAh4SERWYld+nwJMcvKjNzedXf/H45+XuzHkRCDT1L7fJTDG5IvIbOzAARpGhu9L/X1fiF08E/yjIw4SkWTfoEBEUvCPvgqWpw/8+64NdpX2puDtu86ez5c6W6z0x4L/YF9q+ZzmCdjp3ucBFSLyE/W/A9/F4kWaoiiKouwGTMTm2/XZKL8TkX2NMTMaocsdse1bZDsaFlHvgErEOozDBU6EzAvswZv72jvzc1cjpmMiY4wRkUuxNmOSs7sLdt2ce0RkI/7ji9mR6naCP/4I3IF1aDaGYA7zx4AzqH8hk4jNQz0OKBeRH/G34SJKO9gEvHbvNc7WWCK1e/eIcRH2/rvK+d+3HpM7XZJ7nPSl6/+vsba8T8btMI901q237+7Bf92oaAnUd9465geQaWodjcYYUyEilwNvUz+LpS/wL+BfIrIa/98B78wcRdGULIrSEohINvbNsZtgUbMR4aS2OBV4GP9IWbAP6f2BG7D5xTeLyAcicmQEbR2BfVA31lkOIX5rnMWLTsKu4u2lO9bR9xQ2MmSeiNwgImkBZGNFrJ2YkRjTe5LT0m247Ssi7kGQ28ib5IumMsYsAtY5+1Pxd5CPwaZ38RHKYR5sJkNj8fZdS9wbUWGMqQSOBibQcLZDMvZ7fDPwCbBFRCaKyAEoiqIoyh6Mk+LhDs/uvzZSndu2KQm1SGQQvM7vSOyXaOvYnfCmbmzSmAjAGPMdNj90ICdYe+A0bJ7mWSKyQkT+4ozPguLknH4Om0u+KTZcwHGRMaYMG3X8Cg1tuBTsLM1bsWvubBGR16JcNDZaWsru3VPGRZPw/97WjYOc1CDuvOBfBfnf6yB3f57npH4JxM7ou91hXPQedhbI6gCHuwLnYHO7LxCRBSLyZxGJeTuU3Rd1mCtKyzAK/2lSRYRP2xEWZ2X367FvT+/BTmkLZGAnYiPRvxCRt0SkVQAZX5601/A35DcDD2Cd3IOwD8tUY4y4tyjb/QHQBxtB8THBDaWBTt0LncU5m4NYR+FEdS32ANxGXhz+i9kcHkTO+9ltDLrLGKzx2QAn4ifW+e+9fbdb3BvGmGJjzIXY7+c/CR7xkYIdME4VkWdFJDmInKIoiqLs9hhj3gd+cu06NgZrfTQm1/LeZhuGw7sQZsQR36FwFmIdjA0oepPgUfzdsS9TlonIiSFUXgCMd1cBfI6NIh6NjWJvBSR4xkQRLzJrjCkyxpwHDAEeJLDDH2yqjLOAn0XkCW+u6xixW9i9uyrObO5fXLvc45tR1DuI84FZrmPuMdEBvkAxZ20ld177YNHlsHP6bre4P4wxn2J9I+cBH2D9LoHoD/wdG6TXlEBBZQ9CHeaK0jJ4F8mYFsvFTYwxK4wxdxhjhmPzgx+PzQv9Mw0N+1Ow01QDcQb2AeNjEtDHGHOjMeZ9Y8x8Y8x2b17kYA74MG0uN8a8ZIw5Aeug3w+4DniXhg70LsCnzbA4CPgb06u8LwIasU1ohjbuynyL/wyHIyCiRWqCOczd/88yxuQHqtSJ7nJHbP0Yg7771FONd6B1eRP1tw90LrHCGLPAGPNnY8wg7IyWk7GDr0AD0YuxMzkURVEUZU/mds/nvzVChzudRkaIRRyD4U1VGPEC4nsazkzE4Z7dU2Ol3xhTY4x52xhzOnZMNAS4DBsQ5LXrsoG3QjjL3PdODXCKMeYYY8zjxphpxpi1TtCCNx99Y8ZFc40xfzTGDMBGxJ+CnUU8N4D45dh0LrHGe30ubKLd270Z2rir4x7fDBERXzon9/jmG7cfwBizgvq0kknUz/I+GH8ndahZt96+O66JfRdoNqq7jk0xGHf9J8T5NAljTIUx5hVjzG+xwX77YtMLvU1DB3pH4JMmLgyt7CGow1xRdjJOFOdFnt0fNVd9jkP7E2PMLcaYUdiFVx7AGno+jhGR4wIUP8H1fy0wzhgT7K2smyY5Ao0x1caY6caYR4wxv8MauOfjP0WzNXbBplizyfV/F426jQ5jzA7sixkfPoPwQOoXid1gjPFGPbuNvv1EpLWIZOK/sFaoSArw77veETY5GjZ5PvdphjqaBWPMFmPMe87gayj2RdjTHrFxIuIdtCqKoijKHoMx5gvsy30fh0SSotCDO8eyAL2iLO8ORqkiuvzkexoX4596Lx//WQAxw1jmGGOeMsacgw0mOAmY5xJLxOY49sMJ0nHbli8YY96NsOqmjos2GWPeMcZcb4zZBxsJ+4JH7BIRGRigeFPYbe3eXQj32EWAw5z/g+UvD1TuCM9fsOPyySHq3Rl9566jXWMC5loC5yXaL8aYx4wxp2LXpDgb/7Wv0mncy1RlD0Md5oqy87kc6wD2UYFd/XynYIxZY4y5EbjTcyjQFES3YbjAGBMo/1cgYpoT2Xkr/DJwJOBe4OZ4EQmUhqMp0frTXP/HUW/YKJHjdn4PEJEOhE7HgjFmHeBb1DUeG0VxCP5pVkJFUoB/37URkWERtzgyZgLu2RTevIKxImazTYJWYMwSY8ylwL89h0JNRVYURVGUPYHbPJ+jzWXuXSjUm1IkKE6EaU/XrpkBIpL3CkSkNQ0XkXxxZ10Px3H2PtbW3+A6NEpE2nnEvYEYn0VRVazHRYuMMRcBz7p2CzbdZsAijaxqOgFmjSpR8R1Q6fp8uIik4n9PBBrfuPcd7vkLMMPYlC/BmOb53Bx9tzPqgGYeFxmb0vZ1bPvdgYFHOzOklb0YdZgryk7Eid6837P7GRMkzUQz86Lnc/cAMpmu/yOJLPdxbtStiQBjzGL8p2lmAG0CiFZ4PkfzsPO+5R8fRVnF4o0EPxx/IyqY49ublsVdphr/iLBANGvfGZt6yL1S/dBmishuyv0bLZH8DiiKoijKHoOxC0K6HZ77i0gwZ2MgfvB8PjOKsmfjn6s3ZulHdkOewS6856MMm3Zkp2KM2YJdQ8lNN8/nTM/niMZFzsLqPcMKNo5IbTg/uzJSJ6Axphj40bVrVDNEse/RGGNK8XcsH4FNUembVbHKGLMsQFH3WGq4iPQGhgY5HqjelYBb7zFOAFMs2VljZvf922xjIicVzhTXrhSaODtE2f1Rh7mi7CRE5FDgU/ynHW4A7mqJ9tDQ0KsMION+c907khyNInIIcHRTGhaGSNrtndoazcPuG+ojnQHOEJExwYSVgPyAHfT4OAmbk95HpA5zdyTFT47hHoq3sYvS+risGQx7b369B4PMcmgKTbl/oyWS75OiKIqi7Gl4c5nfQ+SLzn2Df0Ty8SIyMlwhZ9G+P3l2vxxhnXsMIpIqIhOwayW5uccYs7YFmgTh7SFvNG9fwiAiQvSzF6IhUhuuKXal2+4VrN2rPqTocI9vegMXBjlWhzFmE/X56uOwaUjd1z3crFvw77sU7IKWseR7YI7r80ki0hxR5u77N0tEUpqhDh86LlL80B87RWlmRKSXiDyGfQub5zpUDpxpjNkao3puiDJ3mDcKfFEAGfdDsC12delQbeiNNfwjGnCIyGnRODSd6ZHuB/GmQNPRjDFl+Oc7PzhSp6YzDdSdG12At6ONJBaR3s5Lkr0OY0wF/tFXp1G/SM0SY8yahqUAOwD1Tbsb7Gw+wuUv90Vx/MO1Kxn4UESiytsnIoNFJNj06rfwXzTzUOBpEYl4pXgRiReRM50pmYFYjf8Lh4jSAolIpohcGqUhGcnvgKIoiqLsURhjpgPvuXYNwz/aOVTZKvydUXHASyISaNYjAI6T8Vmgs2v3NGPMz0GK7HGISIKInI1d62ac5/CHxMihJyLHOJHdkcqnYRdG91EFLPeIzfF8viICe+teIkxTISK5InKr/BevAAAgAElEQVRxlGsnRWrDefdHk27yNWCh6/MxwOMikhCpAqffz4mmzB6Gdwxzluv/UI5vdzl3mUpsqpdwPIF/nvHzReRu50VORIhIiogEnDnuLFT6F8/u/4nIfoHkQ9TRI4yj3X3/CjZlZyR6fyci+0TRjjbY+9vHdvwDsZS9EHWYK0rjSBCR7gG2fURkrOMI/ruITMFGK1+Ffy7mQuAkY8yUgNobxwPAGhF5WkSODuaMc6I6rgMecu02BM6j/qbn839E5EKv81lEEkXkAuyb5s7YBXsi4TfAXBH5UkT+EGqqmBPl/TV2sU8fr4TQ7U7f0Qt4U0SOF5F+nj7r7C1ojHkF/+vRFpgqIveKSKcQbcwTkfEi8iH24X5siPbt6bgNQLdhFmhhGwCMMduwecK9Zbz6QvEw8IXrcw9ghojcJiK5QcogIp1F5DIR+Ro7KBodpI0GO516h2v3RcCPInJSsMGA4yTfT0Tuww7CXsd/tom7jhr8XzgcICIvisiRItLXc/+6vzOp2AH8ahF5VEQODjbtVkRaicid+OdxrQQmBpJXFEVRlD2Q/6Px+XH/ib8jZwDwvTPT0g8R6QF8QEOn1+WNrHtXIzfAmKiPY/ccJSJXicir2ICAV4FBnvLvAKcbY2pj1J7RWLt9mohc51z/gIjIEGx6HrfMB8YYv0hTJ9jDvRjpAGxQhjd1CyLSU0QmAjc7uyIZF2VgX6isEpGHRWRMsGAMJ0Dib8CNrt3l2KCOQHhTGj4oIteIyL5OW939luEWdGzSs/FfQ+oy4AcROcE7JnS1MUFEDhCRv2MXU3wF2Fsd5j8C7lmy7jFOqPFNsLHUVCc4LCROINHZ+Oeh/z/gKxE5LJjjXESSROQQEXkE+519PEQdbwHPu3blYH8H/yEiXYKVE5G2InKBiLwPLCH0Gkre+/c5x28wzHG2u+9ft//jGGC2iHzjjPFCjeFHY/0MOa7dr8XwN0nZTdlbf7QUpal0wn8l5Wj4Arg8SL6yppIJ/MHZqkVkATbSejvWYd8Z2BfrWHPziDFmlleZMeYTEfkWuwAjTrnngXtF5Ges0zAXGAVkOTK1WAfi+xG2WXDlqxaR9cACYBt2MJEDDMFeczer8I8E9/I4cA71BsbJ+EePuPV0D7D/94A7oj0ZuAW4RUQWYx/uBdgpbtnYFes7hmjP3kawiPBwju+vAG80fxkR5vg0xtSIyJnA54BvenQr7JTYe0RkHva7W4hdAT0HO+jJC6AuWB3znTomOjpw2vwuUCIiv2AjOiqxL3g6AgOx90qkPIZ/VNIFzublRxouJtUWuNrZKp1zXoc950RsBN0IGuYBvKMFp0IriqIoyk7FGDNHRN7A35EdadkysdHSX1NvA/cDJonIMmw6hQqsI3Yk/g4vA1xnjJnJnsE/nS1aCrGpcR53AhJizf7O9pCI5GP7ZCvWrszE2ma9PGUKgBuC6LsVO47z9eURwDIRmY4Nhkh29LlzTU/FzqC8NcI2twOudbYKEZkLrMdeq2RsbvXh1M/c9HGzMSZgNKwxZqmIfEp9IE8O8EiQ+i8EJnjKzxSRc7DR5r4x5H7YWQHFjt27GWv3ZlJv90YTLb/HYoypEpHvaBhINddJvRKMSUAN/kF3EMGsW1fd34jIpcBT1Pv+DnO2Aqfv8p16MoEu2DGt+/7ypvTxcgXQATjO+ZyITT31JxFZgg0e9I2Zs7C/kw0C1kKcw3ciMhM7CwisT+DpIOLHYVPgujnU2Z4UkQ1YP8NW7P2aDeyDPW8367AvF5S9HHWYK8rOoRy7mMxjxphJO6nOBOwDINRUJIM1mP4YQuYM7AO7v2tfewK/Ca4CLjHGfBDkpXUkdCS843kO8BtjTNAHuDFmqojciE3REXWOaWcgdAx2euh1Hh19iSB3IQ3zHe5NTMcaWO5Fkmqxg4ZQfIV/xAzA906al4gwxmwXkbHYlyYXUj+wERqmeglGyL5zXiaNxjrN+7kOpQNjI9BfjH/Eh1f/eyJyP3ATkedUDUQSdmAVKqVQDXCXMSbWuQ0VRVEUZVfnTuB0Gmcr/ioiB2Mdh+50Lr1o6Ij1UQ5caox5Kdr69iCWYJ2yTxpjtu+kOnOxTrNQrAZ+a4xZFeigMeYrEbkBeJB62yyeese8l2nY8dLVjWkw1uG8r7MFoxq41RgTzAHu42JsJH0kNnADHLv0IOB/2DzcPjKoD6wKRRF2HLC38hUNHeYhg4iMMUXOyxjvvRXprFufnuedl3iv4j/GzsJ/vahghPyOGmMqxC6a/Ffgz/j/lvZxtibVgX2p+Qn+M0EaQwdnC8UCrJ9hWxPrUvYANCWLosSOWmzEwkZsWol3gL8BJwB5xphTm9lZPgYb3TET6wALRSU2AvwgY8z1oaYbOW++R2EjXoNN/6rEnu9+xpgJUbT5NqwR+SmRrTY/x5EfYYxZHU7YGPMgNjr9n9gUF1vwrBQfpnyNMeZGbJTE89io95BFgFlYJ/s+xpj7I61rT8OZwjnZs3tmBMbHFOyLFzcRR1K46i83xlyMdRS/jn8KlUDUYKfa/gXoE8l9bIyZg51WfAE2H2e4gUAhNgp9PNA+3CKmxphbsBE8jzltyyf04jObgSOxL8HmE36aeRn22gw3xjTnolSKoiiKsktijFkMNNp57dgCA7DRiOtDiBYDLwL99hJneSXW7lmKTanwDDYSdZAxpq8x5t5mdJb/GztT9B0iS4eyHBvpPiDQjFs3xpiHsY7PUHJLsY7Dg6NYq2oNcDS27QsIb8OVYNOcDDXGhI3uN8asx850GIdN3bKEKJ3Yxphfsff6RcAvEbSxAHgbOB9r9+7NCygGcnJH4vj2ypTgnxooIowxk7Ev8a4B5kVQZAt2jHAa/oFBwfTXGmNudWSfwUZwh2MO8C9gmDHGmwvdq38Rdkx/GdaHsQL7mxrq/v0LcCU2aDFclDzYsdP12O+Udw0DZS9Fmmf2k6IoLYnYxT8HYx+MeUAa1lFcgM23+KsxJpwDMZjesdjIggysEboO+KGpRq/YxZD6Y99Cd8Gm0QDr6FyDdbYGjPjYWThtHIY1Fttg022UYt+KLwHm7cRIGSUKnNziI7H3Vxvs/VuCNegWA/O9+SobUUc2cCA2cqEN9qX0Dux3ZAF2wdNwL7NihohkUf87kIudRluOffGzAJjl5DdUFEVRFCUGiMgw7OzOtthZXluwDtnv93KHYYshIr2xjryu2JmP8Vj7bD3WFlrSSL2DsNG/bbHBHhuAxcaYGTFocw42KKMn1oZLw4453DZc2DzWzYnYRRJHY2ce++zeIvzt3r05qnyXRUTaY1M65mH7rhbbd2uwfbe8KWmSnPzoQ7FBZ22w37tSrC/CN2beaRHczhi+H/V+htbYWSJuP8PKndUeZfdBHeaKoiiKoiiKoiiKoiiKoiiKgqZkURRFURRFURRFURRFURRFURRAHeaKoiiKoiiKoiiKoiiKoiiKAqjDXFEURVEURVEURVEURVEURVEASGjpBjhoInVFURRFURQlHNLSDVB2G3R8oSiKoiiKooQj4PhiV3GYR8TTM3/m4qEjeXzGNI7o3osZG9cxsE0eSfHxLNm+lRHtOlpBgcmrVzCiXUc2l5ZwZPdefLlyGR0yWtWVqTG1rCosAKB3dhsS4uLqdPTIyq6rc/bmjXX7S6urAOp05KSm8sPa1Qxq247KmmpWFRbQO7sN2ampdfUDfL1qOXnpGfTNbkPn1pl8sWIp5w8exvtLFlJcVUlfp/5F2/JJTUgE4Ld9+u/EKxsZP6xYzfjX3gopc8no/bjxsDE7qUWKouwsZm04E4DkhPb0b/tIC7cmOhZsvpKkhDx65dzZ0k0JyM66tuGuw7TVI6mqtc/FDq3OBqB3m3si0l1TWwrAD6uH0Df377TLODXq+hVFUfZkPn3xWz5/eQpF24p5dsZ9AWWKthVz5xmP8NCXt+3k1rU8l46+g7EnjeS8m3/b0k2JiLUlBdw7+zMAZm5bw46qCtqltuLlg8fTPrV1s9U7a9tazpz0PM8cdA5j2/WOuNzLy37ihSXT+PioK0iO361cAM3CurJtAJz9/QNU1db4HRud248HR1zYrPVvqdhM2+S8mOmbWTCDNaWrObHj7/z2f7zhA77Pn8y1ff9EXnK7mNWnKIqyN7BbPS3Lq6u5+ZvPuHa/0bwydxZ92+Qye8smRnXoRGFFBWmJ1tn89uL5XD58FADPz5oBwKrCAmZsWFdXJjslhVEduwDQPTOLuVs2+enwESdSt/9/C+YA1OnYt31H2qVn8NP6teSlpzOqYxe6Z2bx5K8/1dUPMHn1Ss4ZOITnZ81gZIdOVNXU1LXp6pEH8PysGWSmpJCSkEBmcnLzXkRFUZS9DINBSAwvuIcT7joc0PVnqmsL+XX9KS1Sv6Ioyp7MseMOZsjY/txx+kNBZVrnZOx2zvLiglISkuIBSEnbe8Yxt/7yPit25ANw0z5Hk5mUyqayomZ1ljcFA4hAjalt6absEjy44D2ABs5ygKn5i5i8eR6H5A1qlrqLqgr5atPnnNX1vBhrbhggeXyHE1lXtiZmNUxftpbnv57O7FUbKKmoJCstlYFd8rjtlMPpmON/7xeUlPP4pz/w9ZxlbC8po2NOK07Zfx/GH7ovcXG2rQvXbQHg7Idf5dVrz+b6CR9SWV3N3845hrKKau6a+AWtUpL5+3nHM7irv8O/qqaGZ778iQ+mL2BjwQ7atErjmKH9uPq4A0lJ2q3cXIriR/6msQCkpJ1KRqvrWrg1wTGmnPxNB5OZ/ShJyQdEVKZ4x8OUl75Fbrspzdy62LBb/ZKkJCRw55jDeXneTAbk5lFUUc6+7TtSY2rJSUnlp/VrAeidlcOzs6azX4dOZKak8NaieeyorPArs7xgG+ku57hbx4mu6G73/gG59i2wT8f0DevITE4m3vnB9+lz1+/eD7Bs+zbm5W9m7pZNfvtbJSWzdPtWBrRp20xXT9ldmLf9VQZmn8XsbS/SJf0gNpfNISe5DxW1RQB0SR/DmpLvqK4to6q2lKykHmQkdmBNyRT6ZZ7ClvJ5FFQsIye5L0VV1kDyyeWlDmlQ16Dsc9havpCCyhW0TbGGYeukrnXHCitXs7FsOjnJ/SiuWl+nCxG2VywFoKx6G8PaXLwTr1J4pvyyjBXrtnLBiaPCC0dY5vZ/f8TpRw1jaL9OjWrTrEXrmPjFTP561QmNKh+trsZcgz2RgXlPtHQTdgnCXwchIS4LkeYxDbQfFEXZnVmzeAOPXvsi7bvlsnzuGrJyW3PTc5fSuk0GAD9/MZuX7nmHGicwpkvfDlz98DgyMtMi0v/1G1P5ZMJk1q/YzCsLH/Q7tnX9dh6/8WXWr9hMdWU1AMeOO4TTrjkWgIU/L+O5OyZSVlxORlYa1z46ng49Yhe9Goon/vwqY07aF4ADTxi+U+psaWqMYUb+ai7tZ2fVntR1SJgSLc/5vUZxfq+92x708daaqfyQvyikzP3z3mZQZldyk1vFtO61pav5aMP7rCtbywsrnmFo1nBGZI/ksSUP0j29J2Cjz/tk9GVM20N4ddVLlNaUUFVbxWmdz2JjxQZWlazgxI6/Y3uljZJ/edUExrY9hCXFi3h+xdNsr9zGOV3Pp0Nq8PHKO+smkl+xhYqaCn7X+XQ6pXYO2/YPpi/g9tc+o1NOJhccOoIOWa1Zv62In5etoW1mup9saUUV4/79BpsKijnvkOF0zslk1qoNPPzRFJZsyOe+c4/1k6+uqeX+dydx8REjeWnyL/z1za/JSEnmmuMO4vmvp/PP9ybz4tVnAGCcZFs3TPiQaUtWc86Y4fRqn8OyjVt55duZLFi3iWcvP404CZ7B7YoPPyAjKYmjevXmwR++Z8X27bRNT+fEfv25fvRoABLj4+vkNxYX84/vpjB5xQpKqqrol5vL9QceyKHdewCwprAQgEOff467Dj+c84cO86tv2to1nDNxIk//9iSO7NUrYr0+Tnj5vwCc2K8fY7p1555J3zBn0yZSEhI4a58h/HnMnj/D/+3V0zml68iQMsVF/wCgtORF2rafjkhq3TFjStmycSRpGb8no9UNMW1bZcUPbMs/LaRMTtt3SUra036D7ZdRZM8NiNqtHOaXDNsPgMuc6O0aY4h3fggH5bYjIa5+DVPfsaF5Hag1pu4Hs35/ez/dQ/M6NNARbL9bh1u3j6N69PZr29C8DgBcNNQakw8ccRwAg9u2q9v/2PRpHN69J1+tXAZA35zcRl2j3YV5hYsZlNm3pZsRkOnbZrO2bAMAJ3c6JqIysTyfalPBD5vuY2jORSwqfIfs5F5srViIcaXi3FG1luracobkjGd+wRvkpQ6hxtiUQVvKZjMw+2zmbn+ZWmMHVW65gIhQWbuDhLiGg7qq2hLSEtqyqWwmtaaqTletqWJwto2MmLv95ZiceywZO6IXY0f0Ci/YxDLREsJ2i7mu5jkfXSu6+diNr20sb2xFUZRdlHnTlnDto+Pp3Kc9L/71bV65/z0u/+e5FGwp4rFrX+KRb/6P7HaZALzzxOc8e/sbXPdYZKkdDj9zNMMPHchVh/ylwbEHr3ieUccN5Y5Xr67bV1FWCUBleRX/vuG/3P/hn8nITGPKOz/zxI2vcM9b18fgjENjag2/Tp5f5zDfW9heUUKNqSUzKTW8sLJLMb9wDY8s+qjB/jiJo9YVfV9QVcJts17m8f0uIUHiG8g3ls5pXTmq/XH8uPUHzu56ft3+LRWbObPruQB1qVNmFswgPSGDc7uNI79iC2+seYWxbQ8Nqrt1Qmsu6nEJq0pX8vHGD7i4x2UNZJYVLwGgvKacP/S8gs3lm3hz7etc0fvakO0uqajk3re/oWvbLP53w7mkJtU7yC5l/wbyEyZNZ/mmbUy48gz27WUd9yePGkTnnEwe/ug7Thw5gAP7dfMrc8zQvpw+egjVNYZ73/6af15wPMcO68eWohImTJpeJ/fVHBuwNWnech4c/xuOGtKn7lheZgb3vzOJyfOWc9jg0GOgb1asYPLKlVy+3yi6ZWXy3arV/Ofnn2idnATAZftZn1NBeTmnv/E6CXFx3HLwIWSmpPDBooX8/t13ee7k33FI9+50ybS/+wd06cqb8+Y1cJi/PX8+uWlpHNajR51OIKxeLz+sWcOb8+ZxzpChXD5qFKsKCshJjeyl7O7OL9tWhXWYp2XYe7605HnKSl4hLeP3dcdKS+xLh/SMP8S8bYlJI2jb7gcAqquXsH3rOLLbvExCQs86mbj4DjGvt6URSaVt+59auhnNym7lMPcS7xqgex3d7mNuh3Z8iEG9V0e0uoPJRMIlw0eyqrCAPzgvBfZ0Xl/zAfdk/rGlmxGQkTlDGEl0kSKxPJ8ESWZU2+tYVPgOOcl9qKzdQduUfSisXAnAsqKPqawpISk+o65MYeUqtlUsYWvFIlITclm241PSEtpSXlMQtJ4dVWvZXrGUHVXrMaaG5PhMNpXNBCA7uRfbK5ayrWIJm8tnkRTXijjiiIurHxikxGezrOhjAKprK2Jy7l4mvP8jP89dDUCtMXTOy+K2PxzN3KUbeOWj6dx37YkA3PLIB5x7wkgG9+7Am1/Yc/jo23mMHNyVK88cW6fvlwVreOatqcTHxVFWUcW/bzmN1JTEkGWefXsq02avJC+nFdsKS/za9+7Xs/li2iJqaw3D+3fmktMODHtOm7bu4LbHPmRj/g7236dbXZkX3v2Rn+auAmDM8J6ce4I1CB59dTLlldXMWbyeQ0b25puflnD9BYeSGB/fQBdQp+/NL2Y2OJ+5Szfw4vs/ER8fV3cuHXJbc9flxyMCj732LRu2FLF+SyGFxWX88YLDGTO8p1/7RRJYVfAwG3e8TnVtMVmpB9KnzT0kxddPk5y98RwS43IYkPdvv7LVtUVMXT2CfrkPkJdxUt3+qauHMyDvcSqq1wOwuuAJKmo2kJLQkcHtXiQloXOd3uzUMUAcG3a8TFWNjeZIT+pLz+xbaJ3ib0T9vPZwyqvt/dM2/YSg+cED6Q2mE2Bb2SRWbX+I0qql1Jr6ez8r5QD2ad/4l0fRXFvA7/oGu7buaxDuOkRDfulnrNz+Lyqq15GW2IeeObf6zqKB7K7QDwXlU1lV8AillYsw1JCS0Jm8jFPp3HrXmhmjKMquTbuuuXTuY4NuRp8wgseufRGwEd59RnSvc5YDHH7GaK4Y0/Q1G2qqa5nzwyLuftPfAZ6cap06KxesY93Sjfz5+PvrjuW0y/KTPb2ndYbd8fKV7HNgX6Z9MpO7zvk3t024nLEn7cuCn5dxx5mPMnH5I6xbtolX/v4BALO/X0Th1h20aZ/FceMO5szrj6/T+cfj/s7yOaspK6ng7vMe96vv7D+ewLjb63Mpr12ykQl/fYdZUxZSVlJBTrtMTr70CABOufLoOrmK8kr+ccmzTP1kJolJCYw8YjBX/utc0lvvGo7pe2Z9wqdr57OtwtpQ9zk5zH1/j+40gEf3Px2Aa36cyJKizXxy1JUN9Pzp53eYsXU1Xx9b76SsNYZXl//MWytnsqI4HxA6pLXm9O4jALioz+ig7covL2bclJdIikvguTE2mCUn2TrRLvzuv0zdvKJOduEpdwTUcc2PE0lPSOLIjv15ZN43rCzeSm5KBr/pMpirBxxKYly903judmuvPTDvK+Zt30BRVbmfrkf2P41jOg0M2t6WIr+iiJtn/peq2mq//anxSTw44kI+2zCTd9f+WLd/dsEqHljwHjcNbJ5UdW6S4pIb5BjfVL6Rjql2TbTc5LZsrcj3O2486xrnJtuZ6u2S27G1YmvAejaW26CwpcWLeWa5nfnXPqVj2Pb9uHgNxeUV/PHEsX7O8mB8NWcpPdvl1DnLfZx50BAe/ug7Pp+1uIHDvGtb+7vVPsuOcXu3twGE2RmplFZU1QUqfjF7MQCpSYkcMdg/j//ovlbnT0vXhHWYby0t5eVTT+PArl0BOKxHT6avX8dXy5cD9Q7z536ZwcbiYr4aN56uWbaNR/bqxeL8rTz4w/d+ju0zBg/i+k8+YcnWrfRp0waAsupqPl68mPOGDiXe8Ss994tNGxypXh/T1qzhk/MvoFdOTshzizXzFm/gyVe+5d9/ObPFdHdOz2FNyTa6pAc/97g4mxYoLeNSSoqfIi19HEgixlRRWvwU6RlXIBLbWSMAIinEJ3QHoNbY50N8fMe6fY3WuzsHUwVl9wqy2hN7QFEURVEURVEURVEURVEURVGiZreOMN+TSI5P2OPTsACsLl3PO2s/ZXnxKu5bYKNRbh5wBQCC8OWm7/g+/2dqjWFg6z6c2dVGEN+34HH2yezHwh3L2V5ZyO0D7ZTU1PiUgMdS41N4a+0nzC5YAMC+OfsA8NuORwXVlxqfwqcbJzNp81T2yewHwLnd6qNj3l77KXMKF1BrDO1T2nJ57/MDno/vnKSRb88GZZ8DUJfuxFCLEEduygC/zz4GZtm3sWPa3Q5Am+R+DWTccj5aJXZmTPs7nP870ia5H+Kacug7lpPcJ2idBjt1sbnefn4yZT73XGlzdPftnketMWFKwGlH2SlwGWnJLFtbH4lRW2u4+6nPeObOs2ibnRFRmVUbtvP9r8t57u5zwMDZN00AYN0mG7n/2dSFPHHrGYjA1fe9yfzlGxnY0z/dk5cdJRX85/YzQeDiO17lmAP7U1hczqzF63jiNpub7/p/vs2w/vURGaMGdaVr+2yKSyu47rxD+XHOKsYM69lAF8AxB/anW8ccTjtqWIPzAViyegsT/3khiYm2ry+75w1WrNtKl/ZZfPfrct74x3h2lJRz5b0TG0SXA2wt/ZzslDH0y32AalPM8m33Mn/zlQzr8GbI8w7H+qIX66KEe+XcTnxcK4oqZpCS4B/1sq7weVISu9E75+66GQ+rtj/M/M2XsV/nScTH1fftfp2/orq2iDmbxoet36vXrROo01tWtYL5my+nW9Y1tG91NsUVc5m/+VK6Zl1Nl8yGU1+joTmure8aABFdh0goqVzIgs1Xk5f+G/q0+SsV1etZnH+Tc7Th716s+sHdt9H0Q2XNFuZt+j3tW51Jj+ybqDXlFFfMIzEu01v9LkVt5XSqS230alLWYzulzpryLzHVS0jIuHyn1Kcouxu1Na4FE42py0YlAWapmghslkjw6Qk6edUY2nXL5Ynv7w6qo88wG3W5fO4a9jmwL/N+XEqnXu1Y8PMyxp60L8vmrKHviO4ApLVKoU1HG+1487OXkNMuk3nTlvDQNS/Sc58u7Hektan/9tZ1VJZXcUav67jluUsAGHXMUIA6GwNg68YCbjjufnoO7sL/vXQFbTpksWrBOjJzG0b5vffU15x29dE88uWtbFm7nb9f+gyv/etDfn/36ZFfsGbkwt6jOa37cHZUlnPBlJfqor5P7GqvSevElEbrvnXGe7y3ejandB/GJf3GkBQfz+xt62gXZhHRzeU7GDflJVonpvLMQec0aMN/Rp/NtspSnlz4Lf9b8UtIXd9uXMqUTcu4tN8YuqRn88Pm5Ty96HsyElK4pN9BAGyrKOXi718BYFRuN1479CLKa6q469ePWF9ayCdHX9mk69BclNVU8sdfJrCloshvf3pCMg+OuIghWd0Ymt0dwC/K/N21P9EprQ3ndT8kZm1JkHgqPLNzA32926d0ZEWJTdeaX7GFNsm5JMclU1ZTBtRHi/vYUmEX0NxUsaku2ryhTpsWomtaN8Z1/31AmUCs2WrHPj3bRRbZvHZrISN7NcyLnpGSTGZaCmvyCxscS3Mi130LgtZ9dn78jDEgwup825ayyiqG3vhwwPoLS8sD7neTnpRUF13uo0d2Nr9u8L+uU1auYkBubl0UONj+2q9TR16dPZuK6mqSE6xb7djefbgz+WvenDePWw4+GIBPlyymtKqK0wcN9tMJRKzXR//c3J0eXb6rMK9gHZ+tn0NKfGLd9+XVMYHt1fSMP1Ba/CxlZe+QmoC1FOcAACAASURBVHYG5aUTMaaKtIyL/ORKi5+lpOR5AGpq1hEf35n0jCtISz+3TiZ/82GkpPyGjNb+2QSKix6gvPxDcvO+ieo8AukLpqvWlFKw7VIqyr9EJI209HFOufpfjPKydyneYWfv1lSvIC6+E2np55KecSkQeTqpqsoZbN1yIrl535CQ2K9uf3XVfPI3H0mbPDuTKjHRPu82bxhAbW0RYMhu8yLJKUd5NBrn3B6ktORFjCkhOeVI4hN64CXSc/D1l7uvAL/+ijXqMFd2Kl3TOnJVn/Fc/cv/ccsA/ymKm8q3MGXLT9w1+HoE4e55j7C0eBW9M6yRnxiXyI39Lgmo13ts4Y5lLCxaxl2D7fTVe+dbZ/aAVr3p06pHUH3Htj+EtPhU1pSua1DH5C3TuK7vxfRI71I3BS7U+cQKrzM6Eud0YxzYEiI/XzB9zT1N6O/Xn8TLH/4MwPotRZx7wr4cNKyhE7e2NvygdMv2YjIzUho4y0OxdtN2enXJtYaaQI/O9qXWinV2kZ21Gwu45v56Z2ZpeWVYnd06ZNcZgj275LJ2cwH520sY0LNd3WC4f492LF1d7+jOap1GUUk5SYkJJCXGU1lZE1AXwNrNBXTrGNyQ6tctz28gm906jdLyShIT4tl3YBdufsROwz7zmBEBy8dLKv3bPlp3v8RLKnM3XURRxQxaJzc+h2lp1XL27fgpUH8vZqY0TFFVY8oZlPcsifHZdfsS22Txy/oT2VE5h6wU95RlISEukziSwtbv1evWCdTp3VY2mThJcpyyQnbqGLJTD6aw/OcmO8yb59raawBEdB0iYV3RBJIT2tGv7b/wTVTzLfaycEugldxj0w/uvo2mH8qrV1NrKmifcSbpSdYA9L9PdmV27rTF+JQjgSN3ap2Ksjuxec1WVs5bS/dBnZn68a8M2N/mzh0wqhdP/ukVtm0sIKe9dX58M3EaIw4b1OQ6ExLjGTCqN5+++C3HX3Ro3f6y4nJSM1LoPqATxYWlzPl+Efsc1A9jDAVbdpCdV+9o7Tvc2r7L564FYP60pRw37mC+/8A6UJfNXk3fYd0ByM7L5OK7/Bcv69gzj3f/8yVLZ62uc5inpCUTF2+fAYnJ9hmQmp7coP0fvzAZEeHOV66qO96pV7sGcgD9RnTngltPBqBrv44cdtr+zP9xacTXqrnpnG77dntlKUCdM3tAZuhgiXDM3r6Od1fP5vL+Y7l24GF1+4/o0C9omeS4BDaWFXHBlJfokNqaJ0efRVpCw+dscnwCHVJbk5OcHkCLP1srSnhhzPmMzrP3y6Ht+/DL1jVM2ri4zmH+45aVFFZah+2f9zmKLun2eX15/7FcMfUN1pUW0LqJ1yPW1Jpabp/1Cot3rK/bl5Vkr8fDIy6iX2sbpCIINw38Ha0SU/jvisl1sk8s/pQOKfY8j2jf9AVeO6Z2pqByO08t+zejc8cwJHNYQLkhWcOYXzSXp5c/XrfoZ5vkXD7b+DHPLn+SrCTbpjiJqzvP51c8RUFVAed0vYCiqkI+3vABK0uW8966txnUejAH5to0jfOL5vLUMpvab3DmUA7KHRuwDT587/98445ICPbOMOi7xAhV1zrvLbMzUrn91CMCynTMDv2iCaBtWsPvhCANArS2lZWytqiIng892EAeYEdlZZ1jOzkhgRP79+fdhQv489ixxIvw1vz5jOjY0c/Rva3M/oZEqreuzemRj2Obyrc/LeU/r06hVXoKA3r7f6fnLd7AE698C0BZeRUZacncdNlRdHLSgT0/cSpfT11EQnw8rTKS+esNJ5LZKjUi3cF4bL/zIm67SAbpGVdQsuMJUtNOobj4cdJbXYVIfb730pIX2LHjX7TO/BsAiUnDqKr8haKC24Aq0tLHR1xfc1Fa/BytMm8no/WfqKqcQVHBTcQndCE1zQYvVpRPonD7n8nM+jtgz6G6eimF228EU016q2sirisxaV8SEvpQVjqRVpm31+0vK/0fCYkD6hzlPvI6LMCYUjat7+1VVVcOoKT4MVpn3U9i0kgqyyezo+hvxMfXBwZGeg7u/vLvK2jO/lKHubLLsKZ0AxvLt3DPvEfr9pXX1L8d7t868Jcx0LG1pRvoldG1Lsq7Z4Z9e7yqdF2dwzyUvkD8uf9lvLfuczZXbOW3HY9iRPbg8IU8bNpRTEllQ6dqbno6rVMaDjL2NKI9/055mdx+iV14tai4nLNumsDHj19GWkoS24usoVFdU8tSTxR1INpkpVO4o4ythSW0cVZydwIVgtIpL4tla/LrDKfVG6yjvEcna/C0z23FIzedSlycUF1TG5ERuXL9NuvgF1i2Jp8LThxFZkYq3/y8pM6AXLB8Y92LgVUbtgW1H726AC44MfTq26HauL2wlMvPGEO3DtlBZdKTBvm9XMlMsQv9lFQubJLDPDv14JAvbXxkJA/2c5YDJDtR6JU1Wxpdv1dvMJ21phQhEbdVL5KAOIvuNoXmuraxpqRyPpkpo3BndYtV+5qjHzKS9iE1sQdzN42nY+txtMs4haT4vJi0t7kxNTbKqXL7VZiadcQljyWx1Q3UVv1KdfEzJGU/4Ry/goSMPxCXOJzKbTYve1zyaGorZ2BqN5Oc8xK11YupLn4CSMDU5iPxnUjKegjfNawufYma0jeJSz6IxFZ2xoCtJ1AZAKGq6F5MzVq71W4nMfMvxCcfvhOvkKLEhvP+O5GfVq+t+/zbwf3510nHNZDr0rcDbz76KSvmrSGzTStuft6+oGudk8G1j47nrjMfocaJQm/fvS3XPjK+ruxfz3+crRsK2Lx2G7ed/AAD/p+98wyMolob8DPbW5JN742QhCS0UAQEBFHEjr1fFZV7rdi9eq3Xa28o9t6uvQAfVlBQOtI7AQIhvbfNZvvO92OSTTa7m0ZQvO7zZ3dmznnnnJndmXPe85Zxg7nsno6cE09d8wZVJbU01Zr418xnyMxPY1ab4vr2V6/mpds+ZNFbSxHbtEUnXjKJ8+acjEqr4oGP5/D63R9jabHidoucff1JzPhbhwIsK18yPvl07nc47U4O7CjhwY9u5MPHF+K0OyncXszFt58OgMVs4+uXFwOw7oetVJXU4Xa5MTdbOOakvisLC7cVk5Wf5leZ3pV2K/d2Qox6zM0Wn3Kbqyq44adFZIVHYnbYeXjSiZS3NLO9popbxvScT6adQ82NpIYaey54hFldJcVMbo9X3husbieXL3+fVEMkL42/ALX88Kf1eoXKoyxvJ80QwZb6jv+G2dlhGa2SdZyzPcZ5fz1sjyRP717I6toCz3aaPoZn8q8AIFEX6VP++sxTiNUYmbtnES7RjYjIv3dIyp8oTSgjjGmH1R6FoOCWrDu99t2b65vsV0DwSgzazpxM/3mzRhp9x2IXpVzGRfgqGs9IONtnX3ckRkoK6KLqBkam9RzzPCXKSGmdrxW5yWKj2WIlOar/Xn7tdQvKa5iaNwiVon9JWXur+zdqNBg1Gv41xb+XQZja+9l2Qd5QPtq6lRWHihgSFc3akhIeO9Hb+tao0Xg+eyv398Rqc/DUG0t449FLSIgN4/l3Oyyf7Q4nz779M/MekDx/DHo1S9fs5bm3l/Lsv6R4/zOnD2fWeRMQBHjtoxX88OsuLjx9NFabNE4PJLs79puqeKngZwYZopkzZDoLSzYxMznwM1NnmIW55XWaGm5FFFuleOadaGl+HoPherS6cz37FIoMXM5iWkzPHxUKc7V6sqfdCsVg7La1tJrf8yjMW0zPoTPMQqPr+D/LFeno9H+j1fxBnxTmAFr9hbS2vEVI2L+Q5nkuLK3zMYTcFKBG4P9eq1nyktVoz0Kru0jqg2EwNtuvuJwdeTV624eu96v9Xkkyjtz9CirMj3KW7SzkQGU9V5/gPxnonR9ISRcvnjyCUemJfsv0hvX7Sxk72Ndt6kggEwTsbgciotegKlkXT5Q6gvtyb0ImyHCJLs+KOXQ/AOt6LEWXyLq6zR5L8MKWIgBGd1Jy93VAF6uJ4vrBl9PiNHPL5od5a+yTPv3pSe6sT75mf61vEpYHTjqey8b4ty74X6Iv/XeLItc/+gWqNmtotyh6QqekJ0YSExHC3x/+lCijgUGJ0mDX5XLznzckd6Gi8nrMFjtVtSauPmcCqfHh3HPNSdzzwiJUCgVOl4unb5uJTqMKWCctIYJxw9KY/dCnJMSEkhgjTaraV8/PmjaCGx//ErlMskZ49o6z0ai6f6ymJ0Zy/8vfUllr4tgR6R7ldP6QJK5/9HNEUWTCiHSGZUouk8vW7+21LJCsztuvQ9f+dEer1Y4oijz5zk/IZQIWm4MHrz2Z5Dhv5bRc5p2JXSaokQka7C7/yYW8CewJ0J7Isif8l2v7zx2G+7uvXP8yw7VTKGp4ngrTx8QazsNs30ODZTnp4Xf3+9ztHKlrO9DYXXWEyrzfSQpZz5Y8veFI3AeZoCI/fj6lzW9T3vwehxrnEq0/g0Hh9/osvhxtiG5psqmO/BQQsNWehaid2X2ldgQ1qvBXvXa5HbvRRC8FQYWt7gLczn3IFFkAKHSXIwihuJ0FPdYBkMnTcdmWoon+CdHdhL3+kqCyPMj/PDK5wB2v+w9jkH98LvnHB07yed+H3Xsi3vWWfy9KgJjkSB7+wp8Hj0RWfhrP/vivwMdHSWOEkoIK9m8rJjkrntBIAwmDYti/rZhDu8s9SvVnrnub4j2SJe4t864gc2QqKo2KG6YEDvnSHaIo9toqVavvfSiPqSnpPDZ5Ousry3huwyouGjKs50qdqGk18+72TTw08fd/brm7vLM7LNZ7n4zu/k2LqLWaCVFqfJI/9pcoja/1aleL28mxg9HIJY+CebuXcWvuNGxuJ2/vXUOaIZLBIf5DgfxRbG0s8gqxMj1uBP/MPQe9ontl5LnJE8gwxPHg9k+ptjZ5EoU+tvMrPpvoX2H9v8z4zBR0aiUfLt/EKaOyfSyfuzJjZBYvfLvKR8fw2eqtAJw4LLPfbTlphDRu+XHLXj5ZuYUrpvouFPRkFNUXJqel8fG2bWRHRROu6fkZNSw2liFR0fzfnj0cjG1Ao1Bwera3t8jktoSefZH7e1JUWk9MZAgJbYmsJ44exP6iagAOFNdRXF7PDQ995lUnythhsb9280F+XL4bnVZFeVUjk8ZkeOQCAWV3x7w9S7gj9xRe3vszACtr9narMBcEHfqQGzE1PURo2CMIQsc1drvrcLtrUKp89S9K1Ujcpmdxu6qQyf17Q/1eyJXe/xOFMher9XvPttOxB4d9A2ZTgNCNogOEnpP0tqPVnY+p6QnstpWo1Mdhs/6CKDai0fU98bHTKS0Ea7Tei3MKRZaXwrw3fXCLzX7vV/t21/tVWFrLa1+tIi0hkhvOn8SiFTs5Y3L/PP6CCvOjnOPzMjg+r/sMzwPBS9+v5v2bLjji5wFp4DUhajR3b3uCGHUkt7eFRYnVRDM9djIP73oBGTJE3NydcwNqWd/DCGSFpJMbmslDO+YiIpJvzGvb7xvOox2X6OaV/e9TaqnE4pQs22ttDZyffBrx2hge2jEXpUyBWxQ5Ob5jJbhzfwCvPnWmuqXFr7L4r0Jf+y8TBN588CK/xwQBHr7hVL/HHrrO1yKsnXHDUhk3LNVnf3d1rr1gItcy0e+x0ybnctrk3IB1uzIiO5ER2f4Xtq448xiuONPXOnzOJVOYu/dtThk1xeMV0S4jkCy5XBawT4/ffIbf7U++38jYoamce6IUf/TVz1eybV+Fj8Lc5W7x2naLNtyiFZW8cw4GAX8KXEevFL/dc6TCAPVWrkGVR0bEfeyve4DC+v+glseSHHYd8SH+f6t94Y++tr1FKY/A6fa2GnK4GwZE9pG6D3KZgVTjzaSE3UCN+VsONjzOHucchsV9OCDtPlLIFO3vrDbLPUU2btchBFlXRb/Lt67Sd6FdpswDQXqnCrIoEM09tyFQHUGJXDUBe4MUP1Khn9WLHgUJ8udmgMKS/+5EJUjPDI1ew2+Lt5E7ThpP5IwdxNrvt6IP1RLZtoC/fskOrv63ZMGVN16arDtsTiqLfL24ZDIZgiDgcrp9jrWTlpPI4o9XY7faUWkGJjRYZzKM4TRaJSv0DZVl3LL0W8pMzdw6Rhq7TUhM4f4VP9Fos2JzOfnX+ClYnU5e3LSGgvpabl/2PdPTBjMjPZP7V/wE4FU2Pax/C6sCQsDfS53V+9kbqpRCFdRYW4jrIWZ5O+em5jMjMYdLl7/HnevnM2/8BYdt2y3rhYYxVhvCvPGSZel1qz9hwaFthCjVjIlK5Y1Rl6DwE8//j2SEMY01Jz3Rr7ojw9NZeNw9A9yiPychWjV3zZzKv79YwoXPfsyZY3OICTNQ3WRm3b5i7j13GilRHd4afztuFIu37OOmtxdy2ZR8kiPD2FpUyZdrt3HSiCwm5aT1uy3Th0vPpZNGZPLsouXsq6hl1KBE3KJISW0jP28v5O3rziPWODDhS2aPHsO3e/dy0eefMSt/FAmhITRYLGytrCRMreHmCb5GSefl5fHSurWUN5s4NSsLnVLpIxPos9zfC7FTjg4AhVzmdSw+Joz3n77cb92Sigbe+mw1H829Ep1WxbtfrsXhcHrqAgFld4dWoSJF7+sR0h0q9cS2T2/PI1GUdD3+Fxvb9glaP8c6y+h5DN1bAsvq+m4VOz3n3YhiK4bQuzwW5z70QVkOIJNFo9Ycj6X1c1Tq47C0foFacxKyXhq2+W1ClzeTIHQeB/SuD6I70P3qtN3pfr38xUpuvWQKr321GoDVWw8GFeZHAw9/8TOHahqw2B0cOySVG0/u+GOu31/Kyz+uRiGTYbE7ePPa89CplX73A+jUSj5ZuZWF63cyLjOFW0+f5JH16o9rWbmniDijgVpTq1cbvlyzne83F+AS3YzJSOLGk49l26EK3vppPXK5QF1b+fjwUJ649BQKK+t48+ff2FlaxQ1vLQDgxatn9mrAdDhcne7/DzE1ZjxTY8b77O8uPnigY2cnnczZSSf3uo5ckHFTZuAJ/yPD7gx4LFB/OrPqYHGPZf6X+av3/2hnYv4gnnznJ9ZsO4jLJRJm0PhV4LfYtuMW7cjaXnZN1rUA6FU5njIqeTQW5yGfuk3W9Ueo9b8vVS1fkxZ+x2HHLO/Kn+XaGlQ5NFrXIQ3ipEGuybbldzt/O/25D4KgIMYwE4e7nqKGZ49g6wYGt7Ow7ZsLEBCdBcjk1yHiRHS3h6Jy4nYU+FYW/E1A+uO2HLiO6K5DGXIngiLwYnSQIEGOHrLyU/l1/nquvE+yFhsyJoPP5n5HVn6ap0x8WhSblu4EYNKZo7GYrHzw+EKcDt+FOYVSTsKgGH7+bA0Ag/KSQACtQUNEm/XgaVcfzzfv/MIjV7zGRbefhjEqhMpDtVjN0gT42NN7H4akK3aXi8/37GB8QjIABpWK56edRkF9LS9ukt6hLQ47Ro2GRyafSImpiYdXL+XNGWcze/hY5u/bxcOTpBjIi4v2e8IkdC3bH6I1BpZX7cPqcqJpC5fS7JD6vL2hjDBVx+R+XHQaL+6G+Ye2ct2Q7mNJt5MfmUR2WCzPH3Me/1j9Cc/u+Ik7hv4+OSgWHJKshI+Pz2beuPOP+LwxyNHBueOHEh8ewrvLNvDmT79hc7gI12sZkRZPqNbbQlqtVPDODefx0vdr+HrtDhrMFhLCQ7nplIlcNW3MYbWj/ef29OWn8cnKLcxft5MfNu9FqZARFx7C8UMHEaYbOItto0bD/IsuZu6a1cxbu5aaVjNGjYac6GiuGe0/JOHZOTk8uXIFv5WVcvtEX8Or9mdNX+X+XqQlRVJZ00xFTTPx0aGs29ox9xiUEkWL2caWXVKoppG5SYgiNDS3EhGmw2S2EaJXo9OqsDucrNpYyDHDUz1ygYCyuyNJF84TO7+hqKWWB7fNJ8PQ//CKcnkCMlkUDvtm1GrvkDgO+5a249LipSAYcIu+4YWcjt39Orc/eYFkOR37umzvRO4Zc8tQKnNxOgqQy+P71RZ/aPUX01R/EyFh9disSzBGvN4vOQp5GgAOx06v/U5nZ+/53vUh0P1y2Ld0Ot6x2KxVK0mOHRgv4qDCvJcUmYtpcUorP0NCM1EIvpfunnOmopTLcbtFTnrkLW6YISnMRVHkvk9+5MM5FxIT1rHS6Xb739/OxZNGEKJVsa+iw3KwqLqBX3cd4ONbLgYRZj71vudYSV0j327awzvXn48gwDWvfsWO4ipAivG16J4rPTG+rnjpcwqr6hgcH8ljl8zg1EfLefmaswbgSg0M37y3nOTMOMoPVjNsQiZbV+4lY2gScqWcRe/8yjnXSoNbpUrBxmW7yBkziN9+2kFEbCip2Qnkju2YvNdVNvHWw18DMPvBcxBFkTU/bOP0K49j+5p9VBTVkpwZR3VpPa0tFlKzEzyuo0V7KsgYmoS11eYpp1B1tCFtSM8x3NpZ/RdXGB+p/n9Q9BU2t529pgMcEzGStXVSAqtZ6RcwNCybNwo/psJahdVlZ6QxlwtTJIvqr0t/YHvTbtyiSJxGch+9bvDf/B5r39+XNsxKvwCVTMWi8iVeHgfPFrzBGQnTyQpJ5/HdLzMsLJs9pgM02Ju4L/cmtHINX5R8y5ZG6eUSoQqnydHsqf9T1UpW1a7HLYrkhmZ6+vP4bimxrT95vSElLpyX/3V+j+VEXOyuvoHE0CtxiWYK6x8hVD2aUHW+p0y49jhqau+iuFFKJmTUHkurfS8lTW/AEU4U69VW0YnTbcKNHbdoxeFuRCFIz1rBzzO8N7hFG2b7bhLDZiGtag/cJLEv1xaguPGlHq9t+zUAfK5D12sgig6cbhOi6MQltuUIcDciF0K8Yqsnhs6iqmUBBbV3Eme4EJurkpJG6fcn+FGu/tH3oa51Cc22TYSpx6KUR2J3VVFp+qwtDvvRjUwhWVHZG+YgusqRqaciKAYhICLI47HVnY8gi0am7L9rc7t1ur3xDkRnIaJoQnSVozTc3H010Qy4sTfdC4Ic3K2ojM8hKNIOoy1Bghy9JGfF8/q6R/7oZhwWWaPSWffjNvLGt1mYH5NBWWEV0y7oMFa5/eWrmHeb5H1z5ci7MUaFct5NMzwK8K7c9vIsXrnzIwCum/QQIREGrrz/bKZfLM2DYpIiePb7f/Luw19z33nPY7c5iIo3ct4c/0YtveWX4oPMsXzDiJh45oyewPKSIrLCJY+sULWaVoeUM+dAYz2Z4ZKSJjkkjBJTs195ncv1VLY3HB+XyX8Lf+P2377i/PR8mu02PiyUQoN0Tc45JiqF6QlDeHH3L1RbTRwbk45ckLG/uRa3KFkYXhtAkT4pNoMHR57KA5u/Id0QxblpHS7rblGkxWnD5LBSb5PmsQdMtYQqNRiUGo8iv69srJPG9Kck5uEWxaDC/C/EsdmpHJvt663rD4NGzd1nT+Xus6f6HHOZ30SmPoEhidKcfftzt3qOTckd5LV9wbHDOSf3Q+RdvBdkgsClk/O5dHLHOFmSm4TQQ8iYV04/w+/+uad08tB1NyG6ihCUIwjXanl42gk8PM1/ktGuhGu1FMzpYRzVVq43cr+9rPu56ECj1Si5c/Z0bnvkK4yhWo4d3aFbUasUPH7nTF54T4o93mq1I7pFLjhtNKdPG0pORhwZKdFc9c//YtCpGdPJu1urkSyeA8nujpuyp7O1oZghoQmkG6IYEZ5yGD0UMITcjKn5KeRyacFVSiS5GXPLG4QaO0KQqVTHYGn9DI3mRGTyBGwWKZSr3b4BuaLvIY39yQsky25bRav5XVTq43DYN2C1LCS0LTkmgCH0DhrqrqbFJIUp0mhOBdw4nQWIohOt7jwfmT2hUZ9As6DF1PQfBFkoas3x3ZSWYtKLfjxddXrJA6G56V+o1OOlpJ+2tdhsK5HLOkJ39a4P3ver870CvO4XQGJ0GM98uJRDFfX8560fPeF7+0NQYd5Lfqz6meU1kkm/URnGi/lPecXXtjmdPDH/F1ptDtRKOSaLzTPAqWk2E6bX+CjFq5tb/O7vjuLaRjLjo6SBiQCD4zpufmFlPcW1jcx+7SvPPrPNjlalYEhStFdCjEiDjlabb/LFowVRFFn65W9kjUyl1WQlIjaMHesKOfvv0xiUl+RRVH/x0mLOv/EkADYs28Upl01iwZvLvBTma37YSkJadNv3bZx2xWSPlUxNeQNDxw8mIT2aHev2c/4N0/nipcXI2+Jmn/OPE/ji5SVExoV5yrmcbq829JaeFMbfbivAbLMzOCaSJouV44cMYtmeAwyKjmDl3iJGpEirbkMTY3l3xUZmTR7N7vJqNhdXMCQ+mnC9lnWFxeQkxGB3uiipb2JQtOQ+MzKlY8Vuc3E5eytrqTWZOW/sMJbuLuTicSPYUVZFYbW0ODMiOR4RPPLkMhmF1XWMSI7HbLPz8dqtXDlpNE6Xq02m1AaX2+05b+dz9qb/h8PwsCEkaGMxO1u5Il1S+m5t3MXQsGyuGnQBCkGBW3Rz/cb7uCDldAQEfq1Zyy1ZV5OuT/Zx7+nuWG/bsLVxF2Mjeo5Lr5QpuaOTQr3cUsXGhu08PlxKuieKIrdukV4CVdYaVtT8xkNDb0VA4OGdL7C/5RCDDakB5Q006eH/xOaqYE/tbbjcZozaY8mM9FYgxBrOxuYspbJFim1X0vQKelUOQ6LnsqcmcAzWgaSs+V0O1D/q2W4B6oo7LFoyIx8lLqRnz5CuyAQl4bopFNTcTkGNFMNSKTNi1E4mM/I/yGX9d//sy7UFqGz5rNtr2/UagPd16HwNihqepaSpI961tUX6v1a3LAQgJ/pFovTSJEKvyiEn+gWKGp+lxvwdemUWmVGPA7C39p/dtuGPuA8qeQwm21aqWr7E6TahkkcRrp1CmvGOPp/390SmGoNKFcgKS0BlfMHvEVXE2/7lKfM9SUIBr+9Ap2Senc9CwDpO89vI1BNR6KRJnMP0NG7HRuRBhXmQIEctl951Bpfe1aEoHpYgIAAAIABJREFUSs6M44eGt7zKZI1K46Vf7u+1zLxxg3l5eeDY7QBpuUn8+9Puk4+9vsY3Rvpld5/JZXef6bd8ewzzzvhT3A4Oj2RzlRSTvcTURHKIZIWmlMtodTr8lutatj9MjM3g3/mn8d7+tcxZ+wXRmhD+NlhaqBUQeH//Wq/yz487j3f2rWFh8Ta+KtqMXCYjSRfOlYN9PW+7ckH6KA6Z63lwyzck66WwGMdEp/FNyQ7u2jDfq+ypS6TnuFGlZe3pgb1nu+PUJCkn1Lv71vBeWz/UcgVphkhuzDmO6Qk53VU/oswr+NYrZjnAFYOO54r07pQ+vefMXx+jxua9kPLBhDlkhvRtXugPtyjNq/abfmFf8zJqrPuwuBpx9SKxfLphIqcl/Sfg8RrrPvY0/UhJq2RYZHbW4nTb0SnCidUMITP0eDJCJjOQhiA94bJ8AaIUTkmuuxhkMbhaXgNE5LqLQBaOy/Q8gjwOEHC1/he5diYIUqx/V8uryA3XBZQryYzCZW4bF7kbkeuvaDvPKyC2IigykWlnem3LdZcC4DS/h0yRjqAcgdvyDaKrRDJe0J6KoBjiXecoSBA50Ewdn8nU8R0GGX87q8PQZEhGLK/+J3Do1Adv9h86tTeyA/Fz5S5OiMv1KMqXVxdwXEx2D7UCozNcDYKCFpPkcepylSGXJxISdo8nSSWAIfQW3O5qGuuvQxTtqLWS7inU+Bjmllf8yu4Of/ICyQoLfwGr5f8wNT2CIGjRG25Aq+sIo6zWnER45Lu0mKQ5gdk0D1CiUGagN9zY57YBICjR6s7F3PIm+pDr8Odp2tL8JOaW1z2hbRrrrgJBiUKRSVSMFNpMq5euodNVjKnpP4iiBbX2ZELDHvaKV97bPnS+X53vFeB1vwCuP38S2/aVk5UaQ1p8BMMz+/98DirMe8k+U6Hn+5DQTC9lOcBv+0pobrXy7BWn09Rq5fvNHe7RUSF6Gs0Wak1mokKkZAiiGHg/BE5SkRQZxr6KWk/ylaLqjrixGXERxIeH8MY/zkEmE3C63MhkAjuKK5H7dc1uP5eA1eE8qiwE6iqbOGv28fz0+TrsNgeGMB0yuYBcIaO+upmS/ZLlfNLgOL5+/WfyjslAq/efvMXSYuVvd50OSAr2kv1VFO4oZf/2EgBPvVaTlcWfriEsKgSVWlr9/PnL34iKNyKKoqdc5zYkD+5dIoi91bXUmLuPc1VS18i1x4/jg9WbPPuK6xoprK7jmuP8J339fvteMmMj2VlWRX5KAjGhBjYWlREdomdMWiIpkUafOhuLyrjmuLG8tXw9saEGHG1Kb5kg0GyxAaBTKaluNnvkjc9IodliQ6dSkhwRRnZ8FJmxkTz340oATxuMOo3f8/am/4dDqDKEFmcrSqUCVZvFqt3txOF28M7Bz7G6bKhkSsyuVtyiiFwQuGvItSwsW0y1rY4zE6QJ16i2pLBdj43qlCy2t22wtyUH6kr7Qlo77XHJ26m01pCiS/DE+xIEgWSd9JAvaa2g0lrDf3bO85S3uqzdyhsoRsR7J3ZJNd4aoCSAgMv1NwqrJJepnLgYlMh47MetXDX+S8qb7Py6bwc5cdHsqayh0fomtc0G1HIphvfPBYVcMmYEh+obWV5YRH5SPEPjYxke9zEALy1fS5RBR3aMZEkmCAKVDQsIV0ezfN8OGq1WYkMmcKj+v55yCWGhHrkA3+wowGzfTmZ0JHLZc+ysqKWwuoDTh0oDL4UslMlp+7v0S2RH1VUIgorhcZ+gkBkQcWN3VbOv9j6Km14lPbx3k09by+uoDf/AappLTpikDJApMhDdkaQa1+GwLkEUW8G2BKciS7qmjt2I7hoS9dJALVYZhlyZC7hwO/eQH/MMgqDE3volCtUo4rRjCDeeidrwD6n1bjNuZwFyZR6i2IrbeQinfSNu5z7ilXoSYp9BpfX2NLKZP0CuGIzbdRC3sxCHbTVyZR5GZTjDw2cjUwzG7SoFl+RKODL6MWTyZGzmD1DrLydaGUF0zL+RKbIQUOByFoCgReUncWX7/e3MQNyHEPUIv7K7Mm/nCgCiNXqyw2JI1IexpGwvlw0ezZa6MvY21aBVKEnWGz3fM0KkReuNtaXkhsfhFN0UtzQwODSK/MiOHAPfFO+ixWEjK0xavHV0Kgewt6mGXGMs4Wod3xbv4rLM0Rwy1bOxVlocyQ2PI1KtY3llIfmRSQgCfLBvA9dkj8fpdnnOLwB7mqTESTWWFm4eelyP/e4vcvU07E334rb9CqILZEbkhuuP2PmCBAkSpD+ckJrB8pIibvxpUVtc8qkAZIVHUWU2cf2S/+PcrDxPOcCnbFfCVTr2nPNAj+e+MH00F6b7D62g3i6juKyelETJuEVAIK8pDmOrhvPPkuqYWqyUlnvnCKnfbWbXWff7JFK9c+iJ3NklJMuZKcM4M8U3GeqyVQVMmZDltW/eOP9ehk+P9Q5Jc/eGBexukuZhzx1zLlFqaQ7b6rLzf8XbuWXdVyw/9VYi1XofWf3hwlOfpaG+Yw7x/ar7kHcT89jhdmJx2bvs87V+PNowO+v4rkz6TVVZ+h7qQSXX+d3vEp38WvUCuxq/w18OHJOjCpOjiv2mX4nT5nJywgMYlP0PddEXZOrjkSmlcbmz+UGQxSHXX4ogT8TZdC+CIgu57jwERRqOpvuQK0fgsixEpmwzSAoQY7pdrrP5QRRhTyDXXQaAaF+P27oEQZGDTDVKqu9uQbRv8tpuj/ss156O27pEquuuQFAOR66fhbP5AWTqGd51ghxxfqnawwlxHbnDFpVu7lFhrlTmEZdYHvC4Tn8FOv0V3coQhBDCwv0npNTqfJ+bPZ0zkLzOsqJiV3i+a7TdLz6oNSei1gxsOK6QsAcJCQu8EG4I/SeG0H8GPC4hPadDQu8mJPRuryOdlf7Q+z705n4BPPLOYiYMTWNyfgbhIVpe/WoVm3aXcMnJ0rv1+DG998wNKsx7SYOj0fM9Q5/uc3xoShxvLPmN699cQHSonqz4DjcDhVzGQxdM57b3vkGlkONwuXnx6jMJ1Wr87gfQq1Xc98liDlTVY7bZqGxs5rqTJjAoNoJjs1P527xPSYwII7mTYjI50sj5E4ZzzWtfIhNkiKLYqzArMkFgxsgsLp77CYkRoTx35emHc6kGhCvvka7D7IekGIuiW0RoGxjOuudMz/fkwbG4XW5kchnZbfEXz5rtbUHQboHe+fvtL0gWcYOHJXuOhUUamH7BeI/sruftTOc29IZVRT1bV+vUHUkZwrQaFmzaRYvVRk5CDO+t2sjoVEn5olUpKaiqYU9FDdnx0ZgsVvJTEth4qIxQrdozgNap/Cd5iNTrWLBpFzaHkwM19eypqGFXeTUut5twvTTw2FBURo3J7JHXfmxDURmnDs+m1tTKgZp6stt+5+1tOFhb7/e8ven/4RDoTmxvKqDF2crt2bNpcZpZVbvBcyxWE8X1gy+nxWnmls2SVdNbY5/0e6x9f1/boJVraHKYPNsu0cWhVu8XaNdEGLGaKIpby70s28stlQAk6+KJUkdwX+5NyAQZLtHls3jXVd4fRYvNToxBsvLdUFzGrHGjGBIbRWZMJO+u3cis8aN5a/UGdColBrUKi91BbIhUvn0RZ/Gefcw+1nexSKNQcNGo4by5WorbrZDJ/MrrXG72sQkeuQDFDY1cP3kc76/bRKhGg1qpIEzrf9GtHYvjIA2WlYxKWOQVVxzy0CkzsDsr+nGl5KjaLFls5g4rP7ezCFG0oAmZI+0X7agN12NreQW7ZZFUU5mNy7ENQRaOQjUOmSINl2M7otgEgh65IgW5Mhe5QhpM2lreRG2Yja3lVQR5rKeOzbETQTCA2OrbPETslq+QK0ciii3I5LG47L8hyGM89a2mVWhC5nj6oFCNBext/Tjk6YMghIGgQZD5d+vvLUfmPuBxTb84YxSv717DqKgkzyT7gKkOjVxJmFLj9f2bkl0AZIXFsL2+HKNaxzHRKaQavOPmlbc28fchE3h9txTnN1YX4in3dsE6rs4exxt71vD3IROI1YVgdzn5pmQXWWHSpHV7fTk2l4t/5EgJoFyimxxjLFlh0Ty1bann/J3LtJ/rSCEo0lFH9rwQESRIkCADTX5sPPmx3p6MJ6RmcEJqBgDx+hDeOeUcz7H2OOWdUcnlfHiat7LDX7mBoKnZwkdfrfMYRp0xYzgA3y3ZjtXm5MyThzMoNZroyBDKKqQ5p8Pp4stFG0lOkBTqOVnxFOyv4rP56yktb2BEXhIZaTF89OU6HE4XM47PJSYqlPnfbQagxWxj6JAEhucl8fmCDTS3WDj39FE4HG4fGYCXnMHp/hWm1VYTC4q3MfcYyc3/lCTvxPcCAt+V7qTKYhowhflfARE335beR7W1w+AuXJVKfuQFhKtScLgtVFp2srn+Mxxtye+S9aOZFCNZV4cqE1DKfEMwukUX35TeQ4l5IyDdn3idtIASrclELqhotpdTbF6P3d1KpWUXXx66iXNSnydUOXAxkQMhCDpPUnFEhzQOFQyAvNO2HgQdIENQDkNs/QSXSxrnyfXXdC9XdOCyLAC3lLBYUGQgOl3I1RNwNN6GTDkSeVvYiK7bfuXKo0HQgCgi8yMjyMBT0loPwMcH17CjsZTHd3wDgMVlx+Swdlc1yF+YFZsPEBcRyg9rdvP0zTM5WFbHa/dcwN0vS7+foML8CCB0ig1rVPlO9sP1Wj6cE9itPFC8r+7igD1+qf/YfnNOnQj4Jo8AmDk2l5ljvQcvw1PjfZTgXbf/dc7AuKkdKTorp7sqqmW9zKzcE1PPHuMjO5BSvC/KcoBVB3tOZnH5saO8Pjtb/LvcIvJO53ziPOm3IYVBkY4NTYrtlZfA2aO94w0+du4Mz7HcBGmA3B4jrnO53LbQLAC3nDQRmSB4Qr50bkN/+38kyDSk8XXp9zy++2WMqjBS9dKig4jIQzvmopQpcIsiJ8d3JI/o7lhfSdLFEakK5/7tzwDSsyNZ1/0ANFEbxwhjDvdufxqAGHUksW0x1mM10UyPnczDu15AhgwRN3fn3IBapupO5B/CxpIyQjVtXhmCgFwmo6allQO19UQbDCzctpvYEAMtdrvHA+ZArTQo2l1Vw86KagZFRvDu2o2MTklkeEKcR7bV6eSrLTuJ0EnWNGqFwq+8zuUO1NZ75ALoOy3shGjUFNbWMSS2Y6HTH2pFPHKZntKmN0kMnYVcFoLDXUet+UcarWvIifFvfdAdgszbIkgQjNhbv0AUTQidkpcIsqi2/dY2q3IQ3c0oVGNwOQtB1jYxFV0Isgic9nWotGfidtXgckoW2oI8BrvlqzbXVrGjDoIUg9oPoqsStf4a7JYvcNqsCDIjHte8tvpd++By7sfl2IXLsd3rmCALxeXch1Lp/Y7qK0fiPgBYXZJXyJcHtxKp0VHYXMeuhip2NlQSolSzv7mOHGMMNrfT8z3HKD3zmu1WRkUlccBUj07hu2jY4rB55LbTXi5Ga2B+0XZitSFedXKMsTTbpcnAqKgkqi0tvF2wjjHRyYyISKDG2kJhc52n3KioJPY21fB10ba2/nS4cF93wuO8+vM9XvKvn/4Eryzxtvjwx7Y1+xg+oXcDyz2bivjqtaXc+8ZVvSofJEiQIH8FvvpmE2edMpLYaOm9/swri0lNimTC2AxyMuOY+/pP/PMm73mfUiHnhMk5rFjbkfQte3As6alRXHLOMcjlMr5ctJHoSAMJ8UY+/GItN88+geIyaSx1/22neeqddepItu0sZeXa/Vx+4QQfGYCXnH/f5T8MTqRaT6Ra7wkno5EriNYYsLgc7G6s5J19axgcGk12WO88cINI7G1e6qUsj1CncWHa68iFjvFEin4MaYYJfFF0HSIi5a3bMSikcas/ZTnAb7XveZTlBkU0pyY9TIzG1yLX5jKxtPIZCk0raHHW8EPZw5yX+iKyfuaZ6S0uy0JolTxYZZoZCIpBuEzPgqBFpjkVQZGKs+VFZIpBCG3W5IJyFKJD8sYWZP6T+rXLlWlmgGjH7Wybh4qtCIIMUTSDuw7RXY3bvgFBmeO9LZd0M67WzxGdexFsvtesqwyZ+sh59AHYnZIBR+cQu38FknWSruHqwVOotZk4LUnySFAKcjJCfh9PiCB/PsYPTeWas8bzyDuLsdmdKBVy5HIZIbrujeP8EVSY95JIVQSlljIAzE5/VnhB/uwYo0J6LtQPHC4X64vL+lyvs+Jb3o2CvvOx3obUCVTOXzIVf8e61u+uff3tf2+5PO1cwDcUSfv2I8P8h8gItF9ACHisL21o/35zVmDF0T05N/jdf3HKTC4OUGdqzHimxvjGswwk64/iynGjPKGj2n8vt01rW2iJiuh2ceeJM6VFnLz4GFxtIXQ6c+Nx433qB5LXeX+73HbZAFeMG8UrK9YxNTOdZXsPkBkdOCmITNAyNPY9DjU+z46qWTjdJuSyEAyqHHJjXiZSd1LAul1pD5Oi7mQd0/HdTecEnoH2S8ka5cjb3FkB5KqRyBlK++tdG/pPTx25YrAfGaDWB04kpGlzt9Mq213P/dX37YPO+Jx0TuUwz36raR5KzTQc1iXIDd6u4H1hIO9DZ+bkSQnVOv9mnh4nxfr9pWI/x8cP5ufyfbhEt+f7tTlSUrv23+nwCP8x8iLUOs5JG+73N3pGSsciZpGpnoOmOk5OGsIZKXm42v5D7f+Bzv+HO4Ydj0wQyAiN9Dp/1/8dgNagwdpqR6OTFtdcTjeaXg4aP3j6W575+vfJPRAkSJAg/4tYrA50WpUnlIjTKYXn02qUKJUKz3ZvEDo9282tdmKjQ1EpFVx2njQ2jIn0ns8s/mUX9Q1mUpIicLndfmUAPnL8IRdkvDv5b7y46xcA7t/8DQ22VpQyOUl6I6ck5fH37Ik+47Yg3XPQtMpre0zkZV7K8nZiNFmkGSZwsGU1LtHuiUc+OMTXuMfsrGNTvaSMFpBxetKjRGn8h21Uy0OYkfAAXx66gWrrXqqtBexp+pFcY8eiyzUzX+DRV68gNsE33CdAc2MrD875L3M/6D6P0p5tJbz9wmKefnt22552709JEawIewRprCltK43PII07235TggyZplNizi7I9Z3lSjJkmrbxf9s1dTY/giL8DQSZEWfjP8G23GtbYXxKaktoJ0MDdYdxocL4tI+MI60wv/VTyTK23bDumPRkBAE+XreV139ZR5RBz2PnziA7LuqItuOPIkptYPbgqWSFxvVcOMhfHplM4PbnFyCXyfjni4sQBNi4u4RGk6Xvso5A+4IECRIkSJAgQYIECRIkSJAgQYIECRIkSJA/HUEL814yNiKf0jLJSrbAtI/psUd3CJMgRw+byyqwOHrObv6/yl+9/39lulrTdt7urTdEICul7mT3Zn9nrp4wmuKGJq6e4D8xV2dC1fkMi32/x3KHR6C17K77A7lldn61d61zuOvk/a+vNvwDt+sQmjbr+sPhSN4Hf7+Za7LHU9zSwOwh43G63Z7v7fRkTXdGSl63v8X2Y1qFkllZx3jiqXeVKw/wHwq0v5341Cgqi2tZ8rkUQ/esa6YSnSi5Mr94t2SBVnagGmurndFTc/jbHadSVFDB5y8uYd+2Eh684nUAHnr3756QZNvW7OPDZ75DrpBjbbXx+KdSJvvGOhOP/uMd6qubiUkM564XL/eyZgwSJEiQvxqnTx/GW/9diUYjWbhOnZhNSVk9S37dxTeLt3HchEyKy+r5YekOyioaSU2KIDHeyDdLtnHwUC0Ag9KimTBmENkZsbz63q9MnZjFjOPzeO/T1STEhpGdGUdkuG/ccIVcRmlFAxarA1mbt2hXGYCXnO7ICo3hxfEXdFsmSN9osJd4bUeq0wKWjVCncbBlNQBN9sCJBXc1fodblELNZYQcF9C6vB2ZIOeYqCv4pvReALY2fO1lYd4ToUZdj9bl/uk6lhW67Ov47rZ+B65qZNpz6JlOMrpY68u1Z+Myvw0okbclEOy63aN0PzKOJMv2HACk0CwKuYxvt+3h4bOm8+HqTfx421Xsrqjmie9+4d2rzjsi528wWdhRVEFBSQ0HK+spq22itklKxttktmJzOHG7RZQKOSqlnFCdhnCDlvjIUFJiwslOjmb4oHhijIZ+t8HqlvQJzQ4L80s2clxMNumG7sNp9oUDFXV88es21heUUFnfjNMtYtRryE6OYWJeGqdPyPXKOdcTVQ0mvly+nbW7iwAoqW7CbLWj0yhJiAxlWHo8J47KZGx2CkfTMHlfWS2rdhxkd3E1ByvqqDO1YrY6cLd5KOk1KowGLSkx4WQlRTMuJ4WRGQkoDjNM8qqS/ntpTExe7rV9/zUzqK5vITYiBEGA2kYz85dt49pz/Ye17o6gwryXnBQ7jV9qVgLwW/0mSi3lJGn9u14HCdKZVQePbMLLo52/ev+DHP2oFYpuQ7EEGRgEQY1c0f9QLH8karmCzDBpUC6Xyzzfe0ukpnfJz7rGMR8oEtKiqCqtp7q0AYVKTl1lI3HJ0m/+uv9IkyuFUo7b5eaK8Q9x2e2nkJYdz+3PX8bOSQf49/sdixxulzRgfvbWj3huwa1Exnnndakua+CxT25AqVJw57kvULyvktSsI588LEiQP5r1xWVc8+l8HyMBvUrFmxedxZjkRJ86NS1mJr7whte+y8aM5IEZHYY5blHk1/0HWVKwn20VVZQ3NQNgsTvQq1WEaTRkx0QxNiWRmcNyidBpB6xPblFkU2k5ywuL+O1QKdUtZupbWxFFiNLriA0xcGx6CtMyBwGQG9dzTFmn283Ip17C3ikRt0GtYtMd3YeW+2jjVgD+/cNSr/3XTxrHLVOO7fG8M157j4N1DYCULHzXPTf3WGegSE2O5PbrT0JsD5klEwApQanL5faEarnpmmle9a6fNdVH1sxTRuJwuFAqJYXg3XNOxulye5QV/7jCW+kwbfIQJk/IRNkp9nFXGf7kBPn9cIsur22FEDhkmkLoyFsk4gpYrrQtXAvAoJBJvWpHin4sCpkap9tGne0gZmcdeoXv+PilxxYhCALX330agiCw9NutfP/VBspL6vloiW9Yy9VLd/PuvCUYQjVk5SX1qi3+kGlO7XfdzgjKPBTKPK99Xbf7I+NIMjJFGke9eaW0WHDDfxcCoFEq0SgV5Kck4HAF/j30FbcosnFvKcu3HWD1ziIOVtb3qp7N4cTmcGJqtVFW28SOokqv44MTpZAxJ47K5OxJQ4kO670C/e39y3lhzKU8ves7hhmTeXb3D7w01n9YyYk3v4TF5v0uPjYvjZduOttrn8Pp4oX5ko7vk6WbPImZ26lubKG6sYUV2w/w6qLV/POiaZw81jemfWdcbjdvfruOd374DafLN9yWqdVGQWsNBSU1fLl8G3lpsTx4+QwGJ/R/LmpqtQEw5bZX/B6/YeaxXH3KuID1bQ4nC1ft5JNlmzlU1dDtuZrMVprMVg5VNbBi+wHe/n4d4SFazhify6UnjurTPe1MhLbre1xAJqipbv0BgDD1SBSyUByuRprt2xGQkW680a+s4ooGlm3Yh9Xh9Oy7rh/KcviTKsytFjsa7e+b5C5UGcIdWTcB8HTBPJ7a8wI3DJ5Ndkj3q7VBgvxRCS+PFv7q/Q8S5Ejz5sI1RIXpGZQYhUalYNv+crJSYjCGaFm/u5jsFEmBoZDL+PznLVx28mgyEjtiHK7beYjS6iZSYo3ERIR41alvbmVKfga/bNxPfFSoR/aIzOCC8Z+JhPRoCreXYowOQa1Rsmv9QeJSIrHbHLz2wFcAWM12lBoF5mYLbpeIXOHf3KWuqgmA0HC9j7IcIGNoEkqVNLw0RoZgabEdoV4FCXL0EEhZHqJW8/bFZzMysfeLRoW1HYqJzWUV3PftEvbV1Pkt22y10Wy1UdLYxE97C5n7y2pmjRvNnCkTDiuO9PLCIgCeXrqCgupav2VKGpsoaWxiQ0kZ85avAWBCWgr3nHhct0m0FTIZWTFR7Kio8uxrsdkpa2omMSw0YL3Npf6tabeUVfTUHSwOJ4fqGz3b2TG/f5xfQcCvt428HwrqzopuoEclt9JPosCuMnojJ8iRIVQZS4O9Y77UaC8lTOW7wNZ+rJ0QReDkqtXWvZ7vUeqMXrVDJiiIUKV66lZZdnsp2wVB4NUnv0OhkHPtXR3K62mnjSB/XAY3Xvyqj0yrxc68/yxk7of/ID4pnFef/K5XbQniTXsusXpzK0q5nKLaBhZs3uX1znF31fb2kcJy6T0zf+V2ftxQQF3zwOft219W6/l889t1zJyYx/VnHktEiK7Huhq5knq7mWaHlQtSj2FjfVGfzl1QUuO1bXc4ue21Raze2Ts5TWYr/3r7O8pqpXeJPwW03eHkjte/YeWOg71u186iKq548hPmXncmxwxJ6XW9vtC1751Zsf0AT366jPK65n7LbzBZ+GDJRj7/dStXzhjL1acc45Mbrydyop7w2Xeo6U30ykGMjH0HmdCR3NgtWtlWfR1Ot8mvrCff/5kLT8rnh9W7GZuXQmGp/3FMb/hTKswvnvQYk2YM5cSZoxh+TPrv4uprcrZ43JzPTz6L94o+4uFdT5KqS2aQIQ29XNerdlyUfO6RbmqQo4hmq42dnSYEfzX+6v0PEuT3QCaTcfbU4XyyeBN1TWYykqLYXVTJ8MEJRBsNbNkrhRO7ZMYospKjvZTlAKXVTew6WIlcJqDXqrzqxEeGsr2wAqfLzZLfCjyygwrzPxcJadF88cpPXHrbKWj1at5+ZCFX3zeTrav2YWqUJkT3vn4VpsZWfl3YYZUmkwnYrQ5Et+gJxRIeIym0mutbaKhpJjxa2m63nOzrADlIkD87gZTlYRoN715yDkPjAyu1/FFYJynMf9yzj9sWfN8nq0Gr08mrq9axs7KK1y+Y2ef/o8vt5oHvf+aLLTv6VK+dNUXFnPX2R9xz4hSuOCY/YLlh8bFeCnOAPVU13SrMN5Z1mMRNAAAgAElEQVT4V5hvK6/sNpE4wN7qWi9lUl/vSZAgR5L0kIkcMv/m2d5c/zmpBl9lnMlRRaFpBQByQUGy3n8oQZfowOHuSG6nU4T3ui3aTmUtrkavY1+8t4LaqmYefP6SXssrPlBDdFwY8UmS3HHHZXNgb8+LXEG8mTVRutfnv/oxdqeLW6dPYl9VLZMz07j368WoFHJiQvpu2SuKsHx7IR8s3sjm/WUD3exucbndfL1iO8u27OeRWacwITe12/LhKh2z177D/cNmAmB29s0go67ZTF1zK5GhknL+iU+X9VpZ3pmXF0ohkZKjjZw0xtva/P73fuyTsrwdi83B7a8t4uN7LyU52n9i3cNhb6m3wtztlt6H8+av4IMlGwfsPFa7k9cWrWHljoM8Nft0AOIi+u89W2b6iIzwO72U5QAyQUOC4SL21T8KQGqYdzio8FAtU0cPZsPuEs6dNoJH3l7c7zb8KRXm0fFGflqwiZ8WbCIm3sgJM/M5YeYoElOPnEv9tRtv9bv/UGsJh1pL/B7zR1Bh3kFBdS2rDx6ioM1qpqi+gYpmE2a7HbPdgQBolApC1WpiQwwkGcPIiY1mREI8o5IkS52jeWJeZ25l/vZduA5ztffPyl+9/z1hdTpZd6iEdYdK2V9bR1F9A01WG2abHRHQqaQYaUaNhtQIIxlRkYxLSWJcahJ61e/rYTNQNLRa2Fpeyc7KaorqGyhpbKK+tZUGixT7rt09WyGToVMp0atU6FVKDGo1ycYwUiOMpEeEkxYRTkZUBBrFH/8Ky3psLiC53OfGRZMXJ03C8+JiGBoXS3pkeK/jtfeXzvH0MlOiMbXaGD44gS37ygjVadpcv6XnZV2TmaKKetLiIzx16prMnDE5j/W7imm12b3qHJc/iDvm/R/PzDkTl+j2yB4oLA4HOyur2V5RRWFtPRXNzVQ0t9BksWJxOjyudDqVEq1S6qdWqSBUoyEpLJRkYxhJbb+N4fFxnv/N0UiT1cqmNgvJTaUV0n+goYkasxmLw4HF7kAhl6NTKQnTqEkxGkmNkAbNIxLiGZeaRGw/JkMgWZgf2lvJyIlZqDRKyotqiEuJxBCq49N5PwLwwOWvEREbRnpOx/0VZALHnZHPzac/Q2xSJPe+cRWKNkvFm5+6mEf+/g5KlQKX08WD78w+nMsTJMifEn/K8vC2kCjvX3Jut5bWgag2tbDuUKmPslwllzM4OpLINvkOt5vSxmZKG5t8ZCwvLGLuL6u5Y1rvQjGAFCrl2s8XeqzLuyKXyRgcFUGUXo9LdFPb0sqBunofq0a3KPLokl+obmnhzmmT/cryp7AuqK7lhCz/lrA1LWbKmvxbv7XY7BTW1ncbWm13VbXX9rCgwjzIUcSQ0JPYXP85TXZJYVnaupkFJXcwKuJCjKoknG4bVdY9rK15B6coKQnzIy5Ep4jwK6+zshy6D/HSFWUnxZTd7W1hrDdo2LSmkL07y8jK828B74/ORoUK5eHP3R1uaWy4vr6QTfWFFDSXU2Gpp8FhxupyIBdkaOUqQpRaErThJGqlZ0NeWDJDjSmk6XsOHdVbTA4Lv1bvZEN9IQCFLZVUWhqxuGzIBRlhSh3J+ihGGNOYGjuUrJD+jaGn5WR4fXbmp137MVltnDKs+1Ah/vjvTxuZ+9XyngseQRpMFua8tIDHrj6F6aMDh268O+90REQEpN/TIyP6rlfbW1rDhNxUft1WyIJV/VsYbufxT5YyLieVML30n/l02RaWbNzbQ63AmK12Hv3oZ167ZeD1hSU1jbTaHOjUStxukbvf+haAnzbtG/BzAew4WMmspz8F4JWbzyU9zv+zqifsLv/edQAiThxu/6GCVEpJR2AyW3lzwRqKKnoXUsgff7y2oR+88c0t7NtZxtL/28yv323jk9eW8clry8jNl1wYpp81msknD0MfoulBUpCBRtmDK19Fs4lPN2/nq607qW5p6VFei81Oi81OebOJzWUVLNq5B5BcXAGmZw/m4lHDGZHQfYKaI4XL7eZAXQN7qmvYUyWt3Enfa6kxm3st5+HFy3h48bIBb99F+cMl+aecMOCyQeo/4HUNjqb+g3QNBqL/C3fs5s7/+yHg8YyoCL7/e89JX3ZWVvP+b5v4Yc8+rE5nwHJNFlfbp5VDDY0sLyzi3XUbUcrl0u8+fzjjUvsfB/D3oLxZcpNasH0XP+0tZGdFFb1ZPrG7XNgtLhotVs++TV1csZVyOSMTpf/9+NRkxqemMDIxDqU8UCLMI0NiWChlTc2Y7XbWF5exvtjbOkOrVJITG83Q+FiGxsWQFxfLoKiIw3KV78rFJ43y+nS7RWQygdz0OB+ru+vPm+SjwP/7WRMAGJkpTX661pl3uxQvcca4IR7Z/aWsqZnFe/bx8z4pedHGkrJeLaq1hx3ozLZy77iIckEgMzqK/KR4xiYnMWVwGtDxvvgjaLJa+W7XXhbt3MPGkrIef/8upxOb00lDq4Wi+kaQLhMfsgWAnNhozhqWy8yhOX2KUxxi1PHtoec92/P3PuP5/uwC/wYB7bTHOO/KqClDGDVliNe+IaPSuPeNqzzbnb8HCfK/xoYSX2V5lF7H+5dK/5nDyY1xzafzPcryxLBQbjt+IidmZXgWDjtzsK6Bp5eu4Ke9hV773/1tE1cck0+0oXc5FJ5ZutKvsjxKr+PmKcdyam6Wz/O0vtXCwu27eXnlWgCv5/SbazaQGR3JWcNyfWQGUpgHoqt1eVZMFKWNTbTapWu/payiB4W5t3Vd1/O/OvdH5n+2zrM9eVou9z/W8ewrPVTH0sXb2bC2kNoaE40NZtRqBeER0iJmVk4CEyZnMXla7mG9I0URNq8/wNoVe9m+tZiGuhaamySlpd6gITzSwNARyYyflMXYCZmHnSiu/XyAzzmPxPkAtm8+xLIlO9i+uZi6WhMOu5PIqBDSMmKYcmIeE6cOQaXqUFHI/YST+V9DIVNzetKjLCq5B4BmRwWl5k2Umjf5LT/UeCbjogO/X5Uy7/AWTtGKgt6NhRxix9hbJfN+dpxy7hgmn5THY3d8xlPvXE1kdM+Wo8np0VSVN1JV3khsgpGNq/f3qh3+qLe38N+Dv7KobD0ALU6r33Ju0Y3D7aTZ0UpZax3rkc65oFT6j8drw5keN4LrMk/ud1sa7C28U/gzC8vWexT4XXGJbmpszdTYmtlUf4B3DyxlaFgKN2Sdwsjw9H6fuysn5vY/RPBp43N4+f9WY3cEnpP+Hrjcbu5/9wcSIsPIS/O/oHmwpYafK3dhdXW8c2/MPrFP5ykoqWZsdjLPfP7rYbUXpBAtnyzdzLVnTKC2yczLC1cdtszf9hSzpbCckRkD68krilIYnOGD4nn4wyVHTFHemaoGSdf39+e+5IO7LyI+IrAHWSAMqhwONb2BUTMGtbzjd2FzVXOo6Q0Mqhy/9R6cLf2377x8Giu3HOD0Sf3PN/CnVJgDZOYlkpmXyDV3ncqmVftYumgLa37eBcCuzfN59bFFTDghlxPPGsWoCYORHWZMtudH+sbUCeKLIYBiwmSzMW/5Gv67catHyXo4mGzSgPzrbTv5ettOJg9K5e4TphzRxH1NVisF1bVeSuF9tXXYulF6/i8RqP/AX+YadEdRXQNWp9Ov1XOVSXphPLrkF37Yc3gvKIfLxXe7CvhuVwHHpqdwzwlT/pBYnN1RUF3LSyvXsqRAGqAebkw9fzhcLo9yen1xGS+uWItepeLkIZmcPTyXsSlJ/B4Jx5fdcDUmm409VbVeC2e7q2vYX1OHxeFgU2m5l8Jfo1CQExtNXnwseXExnDt8YJMGdZ6sd1WO98bavbsy/VUErC0q4f31m1m2/8AR+T0AuERRugfVNXyyaZtn8WRCWjIzsjM5LTf7d7NAr2+18M66jXy0cStmu33A5O6uqmF31a88/+sqLh41gtnjxxCp7znu4/+zd56BUVXpH36mz6Rn0ntICGkEQpOmCChgAVEEdQV7Xbe4lnV117LVXXXddZu6/u1dQQUVQUXpJfQSCIGE9J5M6mSSTPt/uMkkk+nJJIA7z5fMnDn3njuTmXvP/Z33/b0+fPjwLvbE8sjAAN5ZuZwxYe7bHziib141NSGOV2+4xum5a0xYKP9ZcRW/+nwja48VWNr1RiOrD+dz34WOC331sae0gjfybNOyx0WG887K5Zao+cGo/VTcNn0yl2WmAbDqndVUDIh4f2rD90yKjyUp1DrFfFxEGIre+VLfe3UmmB+qshbMJ8fHEqRQsL9CmAccrqphRe54h9ufHCCYyyUSxrmYN5WXCv0NeiNv/Hcza97fjdlkfe0y6I1oe2s0VJY38f3Xx4hP3MLjf1pOSprnEezHj1bw8gtfU3jCvvVMa0snrS2dlBbX8+WnB0jLiOEnD11OVs7QgidGczxNkzAP/tdzX7Fzy0mb16urmqmuambXtkLik8J45MmrAcjIjiMwUElj/dC9dc8XQuWJXJ0oLGZ/WHI3PSYtUrECo6kHiVhOgDSCGNV4skKuJEblfN4oEUlRSALoNgqfu9agQSmxrTlij05DfySmn9TWGiI1PYY7HljI7+5/j7++cQdyhYxnf72GuuoWWpu1/PreNwFIy4zjtvsXoPKT87PHl/CbH79FcKg/F8wZWvH3tZV5/OvUV3R6aMNhjxpdM1W6oUec7mgo4I/5q2nVe+7xnd9azn37XmFFolDg8OfpVyIRudapTtcJ99yv79hPfVuHVQDG67cNPSJZHejHkhlZfLL9qEfbSSViYsMEATTIX0mwvxJ/pQJddw9tnd1UNrTS1OZ+8BxAj8HIE29u5OMnbrJbT+FP+V9wY/IMvqo6ygXhKRS1e277eqqyga/3F1LVaJuZlRQVarFDKSivd+v4V287wt1XzuDlL3ej7bKe74tEkBobTlxYcO/7M1BQXk9Lh87erix8tuOY1wVzEBYLCivq+Xz3cZd91YF+RIT4ExKgIshPicFopLF38bakVkOHzv3fYVOblvv/vZa3H70Rpdwz+TlN/RiHa29jV+U8AmTpyCSh6I0tdOgLESNjYtRrdrd78uWv6ND1/z++3l3I3x+82qOx+zhvBfM+JBIx0+akM21OOrpO4UPJ21xA3paT5G05ydavjqKOCOTSqydz1cqZhEV6vrIBEKEYOSH2h0SAHauIfeVV/OKz9R5FHHvK9jNl5JW9xy/nX8Qt0xx7Jg6VXSXl3PrBJ17f7/nC//r7dwej2UxRQ5NN1NLGk6d59EvB8qAvEspb7Cop55rX3+OheRdy+3TB2240RGJH6PR6/rp5B+8dODJioqgztD09fHL0OJ8cPU5ccBA/nzOTa+xEtnmbQIWCaYlxTEu0Tk81ms2UNjVTWN9AUaOGM5pmyjUtlLe0cKiqhkO9hco8EcwPlleTXyVMECclxpITd26nlZ+sb+Av321jV0n5qI/dF525rbiUvWWVLMpIG/ExzcCaw/k88/02m4h4b6LTG3g97wBrjuTzq0vmsGKiY6HIhw8fniN2ImD0ieUDr+mxQYG8vWo5iaHe8x4N9/fjv9cvdWuhTwQ8sWge3xQWWR1XXlmlW4L5v7fvsRJg+sZ87YZrHIrlA4kJEiJNX/vRNVz5yjuW869Or+fFHXk8s2SRVX+JWGxZ7O/LFirTNNNtMFiE9IEMjjDPiopAJZNaBPMjTgp/moHChn4xPjM6AqkLS8eqCg0Gg4k//HoNu7cXOu07kMryJn5x9+v889U7SE513/Zh4+eHeOEvX1p8Zd3h9MkaHr7vLX75xFLmLfTsGjCa42maOnj4vrcAIVLfFZVlTTzUK7o+/cJKQtUBlBTXO9/oB0CnQcNn5ULGV49JS1rQfC6N+RUS0dAW+qOUGZRr9wPQ2F1EmMJ1RLPRbKC5u3++FqHsF7dfXXe/5fGs+VnMmt8/v37kafuZaH1ceGk2F17aP9e9/vY5rt9ALyaziaePf8L6au/5LANcFTdtSNutrczjuYK1w7rPMWPm43IhGrlM28Bzk25GJnYuyz2yegMA103LIT3au4FSNy+cwmc7jjl9T+HB/lyUM4acMTFkJESSGhtmt5jwQKqb2tiZX8IHmw9TWuveAkVprYbPdhxjxcUTbV5Ty/2ZH53FvqYSrku6gN8e/cytfQ5kT4EQwd1HdnIUj1w/D4CcMf3FufUGI+9uOsi/1+3A2b+6uV3H+rwCvtx9wqp9wZRx3HfVLJKirBfQe/QG/rt+D29s3Odwn9uOnrGcm4eTsTSY1duOUl7f4vD1tLhwlszMYmZWMqmxjrVPk9lMfkkt63bl8+WeAvQG13VWiqqbeOHTbTx6w3yPjjlYMYmZ8d9S0fYmbd3H6TbUI5eEkRB0CwlBt6KQ2L/OBgcouXf5bIL8h+84ct4L5gOpqRAuwoXHKjlxsAydtpuQ3lS5j/9vK5++uYOVP7mE6++6eFQKhZ4Nyhta2HGylInJMfgr5Ow9XU5GfCQTkmJ4c/MBbp3XXxxk8HNvEKiwFsw/PnyMpzZ+75Woclf0GI386dstlGqaeXLRfK8KhwbzyB//ucz/+vt3l5P1jVaC+X925PHPbbvcsiEZKgaTiWe+28ax3uJZf73qMpc3giNBWXML9635nNMNrm+GRoOq1rYR9w53hUQkIjVcTWq4mvqODvJr6imoa6CosYkDFVXUtru2pRrMyZoGQvyEi39+VS1ZMZFIvDiZ8hZGk4l/bd/Dy7v2npXFk8FclpFGkHJkrVlau7p4aO0Gtp0pHdFxBtLW1c1v1n/Lt4VFPL/08rNqP+PDxw+JAIX9WiEH7Ijl8SHBvLNqudOClUPh/otnefSbDlQomDc2hfUn+gXe47WuI/BO1Nazt7zSqu2eWYKY5GndhGR1KDdNzeX1AdHqX+Sf5BcXz7KI6n30zZf6BHOj2cxpO4EHXQYDJ2qtBdPs6Cj8B/yPiho1aHt67NZ4KdO0WP2/3Cn4adAb+fMTn1jEcplMwuy5GUyalkJYeABmM1RXatjWm918/Gh/PasunZ4/P/kpL797r1sWJt+sP8Lfnv7Cqi0xOZw5l2QxZmwU/v7Cd6CjvYtTJ6v5/ut8S8S1QW/kL7/9DHVYABOnJLsezMF49sZ0NR7gckyzycxvH/nIrlA+cUoyc+ZnERUTjMFgoq6mhbydpzm49wx6vSDAPPXIR4Sq3bMUOt/Z3fAq7Xrhe66UBHNpzCNDFssBEv2nWQTzM+07SQ9a4HKbcu1ei0d6qDyBAKnnNRi8zXMFa12K5SkBUUxRpxKpDCZE5o9IJKJdr6Ois5HT7TUUtArnN4NZ+F7FqtRMC/PcxmR7wwmePbEW86A7O7FIxPQwYXFhTmQ2yf4RqOUBdBp7aOpuY5+mmM11x6jvso1qzms6xVPHPuTpiaucjt031//RdFshebgkRIQwL3cs3x2yzoKOjwjhsmnpzJmQQnZStMeWTLFhQay4eCLL50xk9bYj/G31VnrcEFff//6QXcFc3ruo0KbX8fLpzZR0OM5KcsTA6O7csXH852fXoFLY/s5kUgm3XTYNg9HES1/scrrPp9762ur5yksm89CKi+32lcuk/OzqC2lo0fLlnhN2+7RquzhVJWQ5ZSR4z2+/qMr+56UO9OOnV8/mqlnZbmciT0iJYUJKDDctmMoTb2zgeKnrucbqrUe4bGo6uWPdr38AIJdEkBr6S4+2aWjR8sL7Wyxe5gB/uu9Kj/bRx3kvmOu03Wxef4SNa/ZxOr/fO3b81DHc9uAiLlw4HolEzKHdRbz/0mbeeuEbpFIJy2+3X4TmfGfT0dPcfokwyc0vryMiOICDxVVMSIpxsaV3GGjJsja/gCe+2jSiYqE93jtwBH+5nIfnuV/oyIcPb3Cyvj/l96Wdefxjm/MLrDf5qvcGWdvdw0srrhpV0by4UcPN760Z0SwSTwlWKblsFCKKB9NnF3Wsuo6jNbUcqarlaHWtzWcjEYvJiopkYpxn9RcmJcbw1TGhoExGdMQ5KZY3aLXct/pzjgzyGD+brMjNGdH9lzQ1c/fHaylrdhy5MZJsKSphxZsf8t/rltpYH/jw4cNz7AnmByqquGOQWJ6sDuHtlSuIDhpaQV5HKKVSFmd7XsBtfEyUlWDe1tWN0WRC4mRO8E2htaewWCRi2TCswlbkjrcSzA0mE1+fPM2tF0y2OdbBFA4KPADhemoYEHgjEYsZFxlG4IBFUJPZzNHqWmYmJ9rsc6gFP7dvFuxtxo6L5vGnlxMbb1u07Jrrhej9dav38Z/nN1jaS4rr2b+niGkznYtyVRUa/v3XryzPxWIRd/1sAcuun47IzvV9ziVZ3HzXXP717Fd8/aVQ28JsMvPnpz7l9Y9+gp+/8wUWR+MBdsd0Nh7gcswvPzvAyePWtV3EYhEPP7GUSy+fYNP/muunc3DvGX736McAdGq76dSOXLbWuURV52HL42BZDBKR/UU7d8kMvpy8xjfRm3Scad9OfVchkUrH5xST2ci+xrctz3NCh2Zf4E2+rjnE2sq9Nu19ot6imEnckXopcSrnBQV1RsGFYF9TEd/WHiYrOMFSONJdmns6ePr4JzZi+ZiASB7PXkFWcIKDLeOYHZHJfWmX8caZ73m7ZLNNIMnmunzWVe5lafwFDsdPjxYWLw6WVTE5yTPB0R1uWTSVLUeEOhhzJqSwfM4EZmQmeaVugUgE1108kXFx4dzzgpCx7iwquayumZMV9TZi8R9yhZpKj41fzLa6QpZOWjHkY5JLJfzuloV2xfKB3HbZND7ffdyuhYs9MhIjeeBa1xkU9101i/V5JxxGr+eXCPdQ3hTM7RERHMArDy63iYR3l+SoUF57+HoefvkLduSXOO1rNsMLn27nzUduGNJYOkMFeqMGmUSNSuro99Z7XDFqFsxIJ8hv+IFE561gfuJQOV+v2ce2jcfo0vXgH6hk6SrBC+rKGy4gIcX6yzV5dhq5M1J5+KZX+OqjvB+sYD4mSs3bWw4wOSWOQyXVBKkUiMUiimubOF3dwMne1SqZRGx5nhHnvdXjAIXckh752JffjLpY3scru/eRGxfDpeNsK0n78DFSFPYK5uvyC/j71tETyweytbiEJ77axJ8XLxyV8Vp1Xdz18dpzSiwHWDYh225at7c5XlvPkeoaQRivqeVMo5ByOPjcFxccxMTYaCbERjMxNprsmCi7fveukIolPLRQWAw80zB0/8WRolTTwu0ffkpli3sTy9EgWR1qY5fjLUo1zQCsem81DR1n9zdwpknDyndW895Nwg3EcITzvMbVTA8X9rOv6VOmhS0jr2k1crEKEYLwJkJMi76acEUyXcY2pqjP/s21Dx/eYqBg3leDYrBYnhqu5u2Vy90uqukJk+Jj7UZLu8JeMeD27h5CVI7TkvcPKladGRXhcWT5QFLD1SSGhlA+YAFxX3mVrWAebSsE2PMxH1z0OzVcjUIqJUkdQpBSYbG/OlxV40AwH1zw0/2F6sjoYJ75900EBjm3pVm6Yhr5h8vY+l1/xODu7YUuBfNX/vktXbr+79RtP57PtT+a4XQbuVzKg79eQlWFhvwjgn2GprGDLz89wHU3zRq18QCnY5pNZj58e4dN+633zLMrlvcx+YIUHvu9IIw9+fCHTo/th4RU3C/sNHSfJr/lc8YGXoxSEsRQDBcVkgBmRNzO9rr/YMbM+srHuSL+D0QpM2z6dhs72Fz7PPVdQkCGWpFMdsjQojG9RX1XK8+csLXcUEnk/GniSgBmhru3qKiSCOfSOZFZzIkcmlXjMyc+o6XHep6X6B/Bi9PuIUTm+hogF0u5Z+xCIhRBPFew1ub1fxR+yVR1KnF+9q0wypp655v/9zFRQQFWVl3rf3GrB+/EPuOTo/n22bsBCAlwv6i8J+SOjePh3sjrP3/wvdO+u46X2ojFx1oqmRCSgMlspqG73ar4p6csmZlt8St3hlQi5urZ2fxnnXv39Q+vmOuWjUq0OpDxyTEcK7FvJ1bipoXNUAnsFZKHI5b3IZdK+Os9i7n5mQ85VdngtO/RMzXsPlHGzKwkt/ffpNvOac0f6NSXWdpU0gTGqh8lws9+0dc2bRdrNh22anv0Vs8KxPYx+nn7Pnz48OHDhw8fPnz48OHDhw8fPnz48OHDxznIeRlhfs+SFyjvLf6RnhPPFTdMZ+7lE5ArnadUiCViJs0ay+pXt43GYZ4V5o1PxWQyIxaLGJ8YjclstqQt/WnlZVZ9Bz/3Fr/8fCOAS9/yYJWSqfFxpEUIK6kJIcH4K+TIJRJau7po0XVR0tRMXlnFkNLcn9rwHTOSEhz6UHqCXCIh3N/P4+26DAY6untc9vOXy1HJvP9zHOwpP1TO1/cP3vsM3OFkXSOlmmae2vidy74RAf5MiY8lLSKMmKAg/OUyZBIJOr2e+g4tp3qjrHaXlnvsdf3J0eNkR0eyamquwz512i8BMJq0+MvTEIuUtHYfIlCeafUYRHT0nKTHKBzPmJCfWe3nj99u8TiaOFilZHJcLEnqEOKDgwhUKlBKpcgkQvGYju5u2rq6aevupry5lVP1jRQ3aeg2GNwe44YRtuDo45rX37N6Htb7O8mOjiQnJoqcGCGqfCi/H3u8sfMAGb3Ffho6Onlgweyz7tUOgmc8wMp3Ph52toFKJiU6KJBwfz+UUikSsZiO7h6L3U2TtpNGbafb+7sud2QKYta1d7Dq3TUAHkeXyyUSJsRGkxMTRWJoCMFKJUqZlB6DgfbuHipaWjlZ32CJrHTnPApQ39HBqndXA/DxLTfYeAa7i1oRz76mT4n3yyZAquZ463cESsPpMXVaFUOMVKTQZdISqxr54ro+fIwmfUXsixs13PPROsC6cPe4yHDeuvFayznf22REDa2om9xOEba+ApyOODrIPis9cviZp+mR4VYR5ocGRYkDlvm/Uiqlq/f67k6EeXZvZLoIyIqOZE+p4B9+uMq+DdjJARHmKpmU1HDn9g0Dueunl7qMLu9j8bVTrePNIRQAACAASURBVCLMTx63fc8Dqa7UsGfnKcvz2LhQVqx0HiHeh0gsYtUdc3j05+9a2tat2es0wtzb47ka89CBEhrq2qza1OEBLHMR0Q4w40LBD3r8xERLVPsPnezgxWyv/w8g2KNsqX2BLbUvDOolQi72I0QeT5L/NCaohUh8lcR+pOzE0GXU6U5yqu07tIYmVpf+hBiVMCeKVI1DKpLTpq+jXLuXbqNwr6GUBLMo9olhW8IMl4/Ld1qsVPoQi0Q8N+kWpqhHN4O8VFvP1vrjg45FzJ8nrnIrunwgyxJmkN9azobqg1btOmMP75Ru5dGsZXa3e3rZIrvt3mSkIssHcu0cIbvk/e8PUVbX7LDf8VLb8/lrRdv4x9SVPHfiK3JCEni+YCP/nnbTkI5j4dRxrjv1MnfiWLcizJOjQpmc5n5Ga86YaIcR5hVOinN6g18sE2xjhhtd3odcJuXpOy7n+j+861ID/HDzIbcjzFu69nK0/h6C5BNJU9+ITBJGj7GRBu3X5Nf/jIlR/weAWmVtxXz9gkls3n+aLr372oEjzkvBvKGmhcuvu4DFN0wnJcMzb+5unZ4QLxYP0ZsM2CbfO0YmHnrxDncZmAYy2kLK3R+vtaRGOuKStFRunDKRWWMSkbh5fKWaZl7cuZcv8gswullArkGr5c19B/npha4nZq6YkZTArvvv8Xi7T48e59Evv3HZ76G5sy3i5rrqdSyNXcoX1V9gwkSwLJh4VTwBUiE9tqCtgES/RKp0VWiNWkJloST7J1vaUwP6JxGl2lIAdjbuJDUgFYVYwaGWQ8yPnM8Z7RkqOytRSISUHJ1RR5xKOMnLRDKqdFWkBqQSrYwe1fd/PtPa1cWyN963uqkeiEQk4oqsdFZNzSU3LsbtBMs9pRX8d/c+dpaUue7cy7Pfb+fClCSS1fYvhDq9cAOSHHIfFW1v0WNswF82jrbufHqM9ZbHJnM3ScF3Udb6fzb7OFFXz7r8AreORyaRsCQ7g5VTJpIdHenxucloNlPSpGFPaQU7SsrIK6tE22NfRJyeFM+YMO9MANwlzN+PaydkMz8tBYCMyAirdElvsXLGRDJ6fQydedKOJm1d3dz5kZA2OxSxPESlZH5aKnNSk5kQE0V8SLDLbVp1XZxubOJkXQN7yirIK6uktavLqk+fj/81Od4Xc3uMRn7yyRfUd7i/mJUWEcZtFwiFti/PTHPLbqFv0rmluIR39x9x6xxQ17vAdt+az/ng5uuHZP2TFjgTMyZEiIlRpWPG7NDvs6+fDx8/JAKVCho6tNz54Wc255a44CDeXbXCqc3JcIkN8m4BUUd0dPdYxOo+4kOGP3ZiqPV5vEnbidFstpr3913DMqIiOFwliAaF9bbp3IPF9qyo/jT9CTHRFsH8SJV94WGgJUtmVKTb9x6BQSpmz810qy9ARpa1UNKicX59+P7rfMym/nuahUty3Url72PS1DEoVTKLxUpDXRvVlRq7XusjMZ6rMfftKrJpu3BuJnK5+9ekOZdk/c8I5hnBiyjuEIL6qjuPOehlpsekpb6rkPquQo61CIt5SxKesWu1AiIWxP6aQFkkhzQfYzIbqdEJ++77O5BwRSqL4p4kVO7YH7igLY8aXQnzo4bmQ+zO9jpjD+vseJdflzh71MVygDXlu23aFsdOISXAvXoIg7l37CI21R4F+rQkgY3Vh7g3bZFdET4x7IdRn6bv/u+Gebk88+Fmh/2Kq20LBSslMjQ9Wtr0XVyXdAEHNKVDOgapRMzE1Fi3+4+JUeOvlKPtch68ctGEFI+OIy3e8eJ0fYtnwXKekBARwtJZQ69T4oiUmDCWXZTD6q1HnPbbmV9KfUsHkSGurd/OtPyTcNV8ciL/bdWeGHQbx+p/RkmLsMg4WDB/5q3vuH7hJDbuKmBadiLFlZ4XiO3jvBTM39v6GCoXRU0cccdDl3H7g0Nboes7oW2s3cSupjyqdbUYzJ6tWrw33VZ0+iHhTCxPVofyl8ULmRzv/glq4LbPLlnETVNzuXf1Orej+d7ad4i7Z05DLrGNuDlXEff5w4pEyJAxN2IuX9V8RWaQMGkPkYdwquMUCrEClURFt6kbnVFnaR8omIt6L0qdxk6UEiUhshDLd7ZGV4NcLMdP4keptpQlsUv4pk4Qt9MD0y3b+PAMe5Gg43qjqJ696jKrGz13mZGcwIzkBD7PP8kTGzah07v2bOsyGHhyw3e8vXK53dclYuvJWIA8A4OpnWDFJHSGMstjrf4UNR2fYTJ32ezj7X2HbdrskawO4Z/LFpMxjKg1iUjE2PAwxoaHsWpqLgaTiQMVwk30+hOFbDh5iladcIw/muTYG7OPfY0vEypPocvYip80jABZFB36Opq6T1na9eZOJqlvASC/+WPGh15ns59piXHk19TRpO3kld37eGX3PkCIfEtSh5AVHUVubDST4mPJioqwRNEPlfo2LftLBb/Z9q5ufjp/5rD25w0eW/8NxY2eee31RfjdM3MaV2ale/y5BKuUTE2IY2pCHKum5mIymzleW8/XJ0+z/kQhVa1tXNJbw2IkIkCf+W6bTVSmI1QyKb+6ZA43TJrg8UJRn6B0SVoql6SlsquknF+v/4bqtnaX2x6vree3G7/nL0OsZzBQBHdWHMsnlvv4ISIWibjro7WW7JmBVLe2sb+8ikvTR0648R+l7LjBiwFgv+CppwQorO/TzAgLnfY81rNjIi2CuaZTR0OH1uILf6ZJQ4vO+hjHx/TPo3Ji+wUrTaeO8uYWEnvrN/RtVzcgS29gf1fkTklGKnX//KZQylAoZXR3CXO0jg7bz3Ygg4Xg3Clj3B4LhKjvpDERFJ7oX1AoyK9yKJiPxHjOxiwqtL1G5k5N9mjMrJx4j/qfn5jZ1/Qe+xvfxWgW7iGUkmDCFGPwl6ot11gzZoxmPVpDIw1dRRjNPXQZhbnAxqrfsirlbbtR4SJEzIy4i4zgRRxv+ZJy7X4AOvT1GM0G/KQhRCrTSQ28mLSgeW4VwxQNMyDP1fZb6vLpMFj/fmRiKbelzB/WuEPBaDaxoeagTftVTgp0uiJSGczMcCHCeVt9f1ZKt0nPhuqD/CjJ/Vp7f/9mBw8svNB1x3OM6Rm29SYGUtdsKxiHyv24a8/rPJGzFACtYWgFgVNiwuxmYzlCLBKREhPmMBq8j9xUz+olOROMm9rcz6T1lOVzJni0WOoJNy+YwifbjtoUth2IyWxm8+Eirp/rOliyrfsY2RHP2n0tOmAJxxt+afe10CAVc6eMZX9BBdfOn8gfX3MdwOmI81IwH6pYDsLF3tOqyCBcpJ4/JaxsHGs97qK3faSi8/Lj9goXpSTxr2VLhh1xmRMTxZpbf8T1bwlFYFxZVbTquthUWMQVWY6LgnxV8wUFbccxYyZCEcktyXdYXtvWsIV9mj2YMDEuIIOlccuctnuDYFkwm+s3EyoLpbGnkR2NOwiSBXG64zQAfhI/xIgRIbKI66c7TlvaB2I0C2m4AdIACtsLSfRLpLyznLLOMlQSFTVdNST4JVDTVWOzXd8209XTvfbe/heZkZTASyuuAhhSAa+BXDU+gzFhodzy/hq3LBr2lFWw7Uwpc1KSbV5LCLrF5rEZIyIkBClyBj22jSA1ms18d7rY6fihvTfGb/zoWuKCvRstJxWLmZ4k3EhNT4rnyUXz2FJUwqZTxSxId15kC0CtGEuPsYMo1XjUilS+rX6MBbF/xozJ0l5nJ/pmMO+tug6jycTJ+kYOV9VwqEq4kTxUWUOppoVSTQtfnSgEBBuO7OhIcuNjLSJ6tIeF1eZl9EcvrD10wsp262yw9tgJvi20jSJzhEwi4YGLZ3Fbb/E3b0XJi0WiXgucKB6adyGHKqttBBtvcaiqhvcOOI+e6CMyIIDXb7iGcZFDs1cYzKwxiXx+503cu3qdpcC2Mz49epwrMscxJzXZK+P78PG/wl82bbMSWgdiBh5at4GPbrmejKjh25fYY6Rs6gZjLyNuKFkpg7F3/B3d3XYF85wYaxH7VEOjRTA/WNk/P+3LGsoe0D83zjrT+HBVrUUwP1lnG62e40HBz+RUzwMcpFIxfRKO0eA8Lb3whPU5PCHJfrE/Zwy2i2l2EtU+EuM5G7OsxPbzT0jy7Froaf/zkd0Nr3Kg6QMAJqmFwIzpEbchFTmew+iMLXxadj/NPUJ2Rbu+nkrtIZICHN+zhcoTuTDyPq8cc6n2BJ9U/INWfROL4+4iQhHPl9WvoDN0YOgV/RdF38qups9RSQJo6KokUKZGZ2xnfPBsm+0BIhX9Ue1HWkptxpwbmU2QbGQssJxR1F5D5yBhNlwRRFbw8BZz5kUJ1pEDBXOAA5ozHgnmx6vrh3UcZ4vkaDVBfkraOu0vLHbrDXTrDSgGXEsezV5slfH4x4nXDmnsodiQRIUGcKzEeZ9x8Z6dr9SBjr/PWheODcNh4VT3iuUOhbjwYHLHxnLwtPN7lK1HzrglmItFUstC4mCM5m5EIvsLH/Le7027tov/W7ub0pqhF1E9LxVco9GEWCwa9uqmJ+xu2mcllCslSrKC0gmTq/m2bjN+Ej8uUE/GaDbS2KOhqKPYEpGe6BfP7WNWkewnePUU51dyYn8JqdlxtGq0zFyUw66NR0lMi+ZIr7dc6vh4AkP9ObClgMwpyWjq2pi+YDx53+YTlxLJkZ2nSB0fj6R3deyLN7ex7J75BAb7sfvroyy+5SJOHiyltLCG1Ow4qs40oNN2kzROmChmTfMsZWQ4zEhK4OUVS4cdWdlHTFAgz14l+K/f+v4nTlewAL45VexUMN/TtJM7U35Mol8S5l57nYZu4eKzV7Obh9IfRYSIv516hlJtCf5Sf7vtyf6eRWo4Yk7EHKuLweBUeEep8fbaU/yF/3OyX7LFd/bOMXcCcLTlKBNCJnC45TBXxgiV0BdG9UciDtzGx9AYExbKf5YvGbZQPpCcmCj+tWwxd3z4mcvvPsA/tu6yK5jbQ4TEwWPb70FJk8YS0e2IO6YL9hPeFsvtIRWLuXRcKpeOcy/iLzXwUquFgMvinrdpj1D2p2Lbiy7vQyIWkx0dSXZ0JCunTLS0N2k7OVRVw5GqGgrqGiioa+BQVQ2HBqSNRwUGsP1nd7n9PjefPEOFRvC1C/ZTnlWxvKO7h6c3bXW7f5i/H/9dsZQJse4LFkNBBEPKZHIHM/Dkhk1u/faCVUreXbWCZLV302iDlApe/9EybnlvjdV3yRFPbNjE1/fe6hURzIeP/xUGiuV9WWJFjRrLb1+n13Pvx+tYc/uNXqtRcTawF02u84Lnpz0hPtDBIub4aGvBvKhBw+wxwj3T4QHnuKxe7/KB57KowACiAgMs/68j1TVcNV6wpiiwK5i7H2EeqvZsQdsT9Hoj2g5rQeTahc8Ne79trbpRHc/ZmPYi7MPCPaurofKTI5VJMOid+/Cfr3QZ2zmsEeqOhClSmB3ZZ3/pfG6nkoQwLngBeQ2vW9pa9JUkMTpBTgHSYK5NuJ9qXTHb6teQHTwLlSSAJbH30NxTB8BXNa8TKA0lJ/hCvut6n9nhV7G++v/sbg+wPOEBy/6P2hHMZ4aPnMjnjGOttpZAWcEJQwrAHLwPu+O1lFnd09/4yoe8f/cNzHvWvkuBRmv/93c+oA7ycyiYA3T1WAvmYJ3x+H3tCZYlTvV43Ngwz+9Lw4OdWzrLpBJiPNxvoJ/jRbGuHuE6bDaDN2/1UmPDiAoduWsbwCWT0lwK5kfPVFvqLjojWDGVyrZ3ifRbgFjU73xgNOuobHuHUKX9c95Tdwla4S9vns+Ow2dYfOHQLWjOy7unxTmPc//vr+Gy5dOc9vvXb9dSfqaB5952X4xwxJ6mfZbHIbJgfpv9KBEKYRVpS8MO/KUq7krpj9hs0bfyypk3ATjSkk9+awFpvVYZWz8/SHJGDKeOlqOODObkwVIMeiOdHV2oowTPv/y8YsxmMyvuuxSAta9uAaC6tIHQyCDUUcHk5xVzzV3zAEjJiiM5XYiy6JtUnNhfwrK757H6xU0Y9EZ+dP8iy35GSzCPCPDnX8sWe00s72NGknCRWTo+k8+OnXDad3dpucVl3t5P8sdjf843tV/R2N3AwugryAmeSLVO+JHXd9fx91P9aSBdJh2tuha77d5k4MVg8AXZ0QXa2YXbnvB9WfRl1HfXc3n05W5v48N9RMBfFi9yeIM4HGaPSeLWCybzet4Bl32P1dRxtLrW6yJlqcZ1MZKF6WleHdPbOLKS8JbFRJi/n5WIr9MbOFJdw6ZTxXxbWERNW7vDCMaBrDlxnOVZwoU+2E9JRe8ieauTSaY97v5wrd12iVjMS9dd5dG+AF7L22+TKu8ItZ+Kd1YuZ2y45xFt5xIbC07ZLUpnjxeuvsLrYnkfSqmUF5dfxeJX36HJRQHUmrZ23j9whNt7F7DOBVxFXko8sEHw4WOkUMlkPDB3FjdPmwTA63kHePa77ZbXq9va+cnqz3nnphXnlfXfQOz5sHc4qA/iCYOz4ERAkAPP99SIMFQyqUWoL27qjwTLr6mzPHa0EJobF8PXJ4UszPzq/v6nGqzP1QEKOcke1Dfx8x85W5x2ByLzcHEkLI/UeI7G7Okx2G1Xqjz/TJVKGR0/UMG8uacUY69VZpQqE1dC+UCkg+xXnEWke5tQubDwFKaIoUXfQGN3tSVCvO+1lp56AqWhqCQBSEVyVJIAh9sPRGfsobTDdrErI8hZRLcJTecWdIYy4oJuc9irqXMTnfoiwv0uQyVLplG7gTD/hVZBQoMpbLMV/sb4e559Mph4lWBjJBNLrXzM2/SdVHY2keAn6EwvrroagAS1oBH97YYrrfbziw/WD/tYzhYh/s6tXw1GE+Vawcs80T+MovY6q9d3NxYNSTB3JX7bY7BwP5jIkACPg5hkbsx1DUYjMg/sY1wxIcWz+o9DYXqmc7sdgM5uPacqG8hIdP5bSg19gAM1N7Crcj5hqjnIJGp6jE006bZhNuvJDH/aqv8Df7N/r7sp7xR//cVS99/EAM5LwdxdgtX+lG50nVLvDmWdFZbHC6PnW8RyAIVYTrfJemIYIgvmgbSfAPB4/h/4pPJzsoMyGBc4lpSsOLRtOrKmppCcEcPvb3+VJ1+/k/Vv7yAgWIhSEUvExCSF8+krm8m+IIXAED82rd6Ltr2L43uLCQj2QywRW24qNfVtVBQJJ5Hi45UUHatAHRnEd5/sIzwmhNamkSsc4IzfLppP8AgWRbppaq5Lwby5U0dlSysACXaKyQk2LHeiNWh56vhj/HXiP4ntLX6plofxi7RfIhaJMZqNiEViGrsb7Lafb8jEMkuRTx/eZ0H6WCbFjdxF6acXzmDtsRNoOl3fBH1w8KjXBXN7vqeDiQka2RXsc5G+4mlFjU2cqm+ksKGJ4oYmips0VLe22ZSIlg6wJClrsb8Isb2s1CKYF9U3cfOsyUM6tj8tXgDAf7bncVlmGsnqUCpbWtlS5CLH0A4d3T28sdfW09EeErGYf1yz+LwXywFe3JnnVr8VE8dbIiRHijB/P55YOI9ffOb6ZumV3fv40eQJqGTObdHya+ooamgiNz6GL/ILiQzwJy0ijLiQIDYVFrNy6kSLgPXu/iPcPmOKJfrWGd99LHxua178loqiWpeC+YbaF13u04ePkWRcRBj/vf5qqwypO2dMpbhRwydH+jNOD1XV8Jsvv+W5pZedjcMcNiqZjCClwqoGUbkbC+KuKGu23ofa389hsU2JSERGZIQlY6aoQRBIeoxGTg1YoMyNtz+nGiiYF9Q1WIqLFjVaF43Ljo70KCZUPIKFtY1G5+fA8308R0lYQ4mUlMnOz8UodzAOqIVm8CD4yoyZ4vZtVm1qRbK3Dsslmh7Bn76pu4ZQWRQRingqdUKWfF+EeYjcsRA2ePuBtPZoLRnffYhFYpL8ndlfiVFIY9AZhMLoRlMHFa0vYzbriQy42tKrvmMtMomabmM1RnMHlW2vojOUEqyYRpDSvvDa3GNbNy1G5bmlh80R92oHUcpgKjutz1Wang6LYB7iJ+goK6YKFi7hAdZib2yIZ1kb5xJyl9ZjZlaXC0GrD2VexoMHPmBiaL8YW9jmXi2hwYQ6sUJxhCvBPCzI8326E1BqMJq8KpinJwx/sccVY6LD3CqS6o5gHiDPYErMx5xp+Tv12o0YzTokYn/CVBeSEvIAfjJrh4fH7xDudV9du4dLLxhHQlQI1Q1t7DhyZsjv5wctmEskYrp0w4+SAGjT9xfYSvSzXuGUi+VWr/chEwsf7xUxC3jlzFt8Xfc94wLHMvfqKZiMJsQS4UT5x/d+DMA1d82zVC8X9aYn9PVLz03CbDJb2gc+Brjt0SWW5w/9fRUAY3MSbPpdfefcoX8IHpIbF+OWl/BwGB8TRVpEGKcbbCspD6QvInCwYG7GzPOFf0EqkmLGzLzISwBBRAe4OGIefz/1DCKRGLPZzM/SHiRCEWm3XS4enQJNPs4PRjqaM0AhZ9XUXP65zbZy+2A2nSrmDyaTlTg7XNxZRdf2pmQrfsBWEN8UFlFY38iphkYK6xsp7xUJ7Fl2yCUSxkWEkx0j2LeMj46y8ra+Ze0nTIiyXdg4Wtc/IfSXy3lpS17vY5lH4nmfJ2yLTseMZCESKDoogHf3u1e8dSDr8gvsptzb487pUyx+8+czh6tq3IouV0il3H/x6BRjvSJzHK/t2c+xmjqn/TSdOr48XsiK3PFO+4lEIlq7uvGTyZCIRVw/OYc39x5kckIsepMQ4ZfZ69mcERXulli+c/1h/vrztyzPJVIxYdHBNNW24h+kwmw209kuLMCNnz6WiRednbRrHz4GkhEVYddO7PeXX0J5cwv7yvujDtflFzA2Iox7ZjnPfD1XmRwfa7VwerLeNrrTUwb7h7sKIBgfG2URzPsizAvrGjGY+oXeyXGOIsz7r5tdBgNFDU2kR4ZzprHZegwP7FhGmsFe4HK5lD+9cOOw9xsRZRsYNJLjORpToZAilYoxDFoc7dLp8Q/wLBK6S+feXON8JFyRilgkxWQ2UNy+nerOowDE+jkuXt9p0LCt7t/U6voDxkLk8cSohm454Clms4k1FS/QbmhmcezdhCtiKeo4xEflf8VgFv5fi6JvYVfj525tP5B2g+3Cgb9U4VH0bm3HGuTSaFTSRCpaXyIj4p8AhKrm4CdLsYjj/rJxxAfd7dAHGaBdb5vF5yf1XjS/n8R2X212xlySm2nTBvDsCvuZ4j8UHsrsX4xenjiNm1NmW54/c3xo0fWBQ8h0kUqc30MH+9vWd3CFO3WcXBtAekZi5Mhkvg5EJIKxceEcKa522q+0rtnp630EyMcxIfIlAEzmHsR2ihv3EdabPdDaoWNKpnCvG6kO5ONNh9wayx4/WBVD32Ng/45ThEfbnzh4Sl/xRACVxDpiOkDqj6anmQ6DlgCpbYrH2F4rltPt/QXyxA5+dKJBPj4D+w18bXC/wc9dtY8Gt14wtChIT8mNi3EpmFf0RpgPRoSIX2U87nC7mWEXMjPMtvK0o3YfPgASQ0NGzEN5IDdMmsB/tu/B6MJPubWri7yyCq9GvNpL4x7MsRpB6L041Tv+/uciP/3kC7vtSqmU9MhwsqOjLAL5uIhwp4sW12WP575ptl5sv9uy2fL4ygn9YuKJIRb68ZfL+c36bxmjDqVU04LSRdSxPVYfznerX2RAAPddOMPj/Z+LfHLUvYLfS8dnEhkwetkV98y6wOH3cCAfHT7mUjA3mkyE+qnYV15lVaT7TJOGgtoGjtfWk93rI9zY0cmZJg0pYWqn+/zijX6f+zufXMbVd89HIhWzJOHn3PvHFVx63QyqSxr41yPv063Ts/y+BS7fiw8fZwuZRMJ/ll/Fta+/bzW3/NvmHaSGqbk03b06GucS0xLjrQTzU/WNVLW2AUOrQXK6ocmyfR9TEpxnNA70MW/u1NHa1UV+bf9CYExQINEOstayY6KQisUWcf1YTR1h/n60d1t7dnviXz7SKFUyFAop3d1ChHFPj4GsnIQRi6Ye7fEA/AOUtLZYC3/Nmg6PBHODwYTOS8Fv5yJKSRA5IVdxpPlTjGY9n5YLPt6J/lOJ8cvBTxKKCBF6cxcd+noauk5RrcvH1BuZLhMLc/FLYx7FEzuX4ZAZNJ3MINu56mDhG+CquHsBuC7xIQBWJT9u2Ycj2vT2BHPPstWNpg6U0jjEIgUJwT923NENEb7DYJtRq5J4L0jOnvjebucz8IGVWA5wb9q8Ie3Hz07tjuHip/D8XupsEK0e+bpiIBT/dCWYV9S7n82mN/aL60b6sz4kYkF7HSyi+6nk/On1b0mMDqWirhnlMP4/541g/uwjH9FY1z/5WvP6dr7/wn5UnNlkpqq0keamDq6762KvjO8v9bNEkTf3WIuvofIQyjsrqeisJDPINjKqL/K4zWAbhe6K0rZ3ifZfiFZfir8smTrt9yQF3QDAmdY3SQm+1dK373lbTwHNXYcJkmcAItr1p+g2NJIW6p3K2K7os2BZOMLR5X3kxsa4FG/q3fAJ9uHDW8wbOzoCcbi/HxckJbC71LYgzWB2lZR7VTCPCXKdAvjOPuEcPdKC+eN5X7MidQKfnDnGfeNn8m3FaW5Kn8znpSfo0PcwLjiclp4uLo0fy6bKIgCiVAG8VXiAu7IuwGAycaChiqzQSKZG9kdCl7Q3s7VKSOGaHBGH0WyisKWBrNAoJoQJEW0qmYzMqAhL0c++m/7UiDCH6eeOsCeWA/zkgv72D/YeQW8QFnALahv487JFHo0BgjXLwYpqKlpayY2LYWqiZ9ZMlS2tnKhzT6y/Y/pkVIPSGDfXr2Oqei4A+zSbSQ3IprarglhlMm16DdnBU8lv3cv44Ass23xfv5bZ4UKUiUKs5Ju6NQRJQ4hWJuAvDeR0Rz5xqjEYTHqaeuqIUsQh6k13re0qRyZWEKmIo1RbSJwqGRBR01VGu76FhdErXL4Pk9nMN4VFbe7AowAAIABJREFUbr3n61yI0t5mfloKYf5+Lr3Mj1bXUqppceqrPiE2muzoSKuol77F72eusv6uPThvtlsRX2fyKxmbI0R5XNtblwVAoZKh0wqCVuyYCB5/7W5un/EU7z+/ntufuMblfn34OFuEqJS8cv3VXPfmhxZR1gw8tG4DH91yPRlRzqwDzj2uyBrH37bsxNgrOJvBYjvz8zmeZ8usOWI9JxeLRCxwsZAwOPq7qqXNKkp9kpMgBKVUSkZUhMUuqrC+gTF2znPnUoQ5QFpGLPlH+udvJUV1jMscuWCL0R4vITmc1sPW89OKskbiE923Z6uu1LjudJ4zO/JeDOYejrespy+etFy7j3LtPqfbRauymBstCOzhCue/r8Uvv82izDR+NkrZb8PBYLa1D5K4WAzQ6c9Q1/EpOkMZfrKxRAVcQ1nLv1BKEwhU5OCP/ejsAPl4Spr/Qrj/5QQp7Af62asRNtgyZjiY7QQ92RvzdJ0QHPj6jv3Ut3VYHcHrt13rteNxB21XD+X1zVQ1CtpcQ0sHTW2dtOu60eq66ezW06030NVjoFtvsHkM0N1joMfgujbBz/a96/A1iUjEC1NXenz8Crn3JdCR2OdIEDYEO5qhEOOGMN/UZmt3BFDdsYZOfQljQx+mbyFwe4X94KvkEEHfTAm536r98dsXcuR0NdUNreSkxpCbPnQb4vPjPwvMmJfJns0FHN4tRGlXlTZSVeo4NTo0PJCrb57Nyh/P98r4capY2vSFAJzRljIzrD/tMkEVz5GWfPI0B+wK5rVdQpTl0NZ9RRS3vEp22K8BMOE6La2mYyMB8jRae45jNHeTGnwHxa2vDWn0oTC3Vxzzpv2DM2KDXQt3A70ZffgYafrsLkaDi1OT3RLMD1Q6X+X1lHER4QQrlU69zLedKQXgjb0HuW0EM06y1FGsLj5KVmgU0X6BFuuIsvYWfpYzi9cL+m86ytqFFeq5cSlkhUaSHhLBMwe3MC4kgqOaWivBfGN5IT/O7r9Av3piL3dmXcDLx/dYBPNDD//E4yIvrijSaPi66DRdxn5vy4dmClEV8zNSieqNsjvTMLQbyVMNTby6ez+p4WqW5mTyyZHjXDvR/VTercWlbvVTSKWsyM2xadcZOznVLqQdB0iDKdUWcnHEYjbXf06kMpYz2gKMg26YTGYTCnF/hJEYMTPCLmV7w1ck+6cTLFVT0lFAoCyUFP9MwhXR7NcIkc0ykRyVxJ/DLbuIViZQoSvGYNIzL3Ipm+vXufVejtfW0+xGvYDowACv1wtwhVQsZn5ailtR/9uKS0hWT3Lax50UUXDPlgmgs0NH7Bhbj0Klv4L25v7Jsn+QiklzMtj+5SGfYO7jnCc1XM0Ly67k7g8/s2R56fR67v14HWtuF6wuwv1H58Z0uMQFB3F5ZhpfHi+0tL22Zz8A107M9ijKvKSpmXf3H7FqW5A+lsRQ52ngqeFqVDIZOr1wn1PZ0mZlgeXK0mViXLRFMD/d0ERWlPU5J1ildHkMo83EKclWAvb+PcUjKmCP9nhp6THkDxLMjxwsY6YHtlunT9Z4PK5UbBs132Pynq2Lzmgb8S4VDV1SEYukzIt+kJyQqyhs2wRAjS6f1p5qekwdmDEjFSlQSoIJkccRqUwnOWAm0aqsIY95LhMktbW2sBflPRCVLIUU9W+s2saF/wWz2YBowP8mOvA6qz4xgTe6tHgIlNkej87gvayHTjvfpyA7Yz6yegMA103LIT063Ob1keRMTRO7T5RxpLia/NJaajWeB4EOlacm9HvQv3J6MwtixpPoH0ZVZzPb6wudbOkYV37kQ2G0dK/hIBJBgGp0igOHBLjOCtG02w/0qWr/ALlYzWD1NMp/MYHy/vNeo+57NDqhEPtgwRxgYlosE9OGf407bwTzOZdPYM7lEzAZTVwz7Xfc/asrWHC1fY9gsUSE1Ivm+ACZgeMoaBN+lIeaj7IysT8ibUJINl/WbOT7+m3kBGcxJTQXgA6DcCO4plLw74pWeh7ZYMZEUtD1VHV8SbAim/buk7T2nECCnPaeQtp6TgIgRmp5HihPx2BqJ1SRS3vPaSo71mEyjZ5gPGuM68q43iRQ4fqH3z1AePLhY6QZTcFsepJ74nx+TR1Gk8ltMcwVYpGI2WMS+arglMu+f960lfr2Dh6YOxu5GwVOPEUlkSEXS5BLJBS3NnG8uZ58TS3+0v70q2CFik+Kj9HeI5wLpSIxDTotxa1NZKojaevpZkqE9erz2KAwXj2xF4BpkQlE+gXw6Zl8ov36F+kGi4YlTYIgv6W4hILaejQ6QWQNVanIjIrgwpRkl77PT27+jltyJ7HuZAGzExIpbOoXDV7ZtpdxUcJEWS6VkBLh3A7DHs9/v4PHFlzMC1t2AbC1qMQjwXxnSZlb/eaNHUOAnbRHMya6e4tbdRu7CJGHcaB5G8EyNZmBk3mz9DluTn7Qapt4vxS2NQhehZHKOCvxvER7EpXE3xJRrui1TVNKBLGqvruKGFUysaokdEYtyX7p1HZVsF+zFb3JvZsed9/zRSnJbvXzNvPGuiuYl3LzNOeCubdR+SvR1NnaoqkjgqgssvZeD40IpKlm+AUHffgYDS5KSeLXC+byh2/6bbOq29r5yWph3v/OTStG5Jo3EvzkwhlsKiy2FK7W9UYB3vnhZ7yzaoVL8b+mrd3SX2/sjxqUisX8ePYFjjazIBaJyIyK4GDv4n5layunGvqvfa5s7nLjYnivV6g/3dBkY8U40PLlXGHewvG8/8Y2S4HMz9fsY9kNM1CqRia1f7THmzojlc8+si6Uvf37E9x53yVI3bSC2b65wONxA+zYd3R6QeDsq02jNdjeU9sTVT0lXDmWcOXoZGefy9j7LO195u4gcmMhw5lYDhAksz33DfV47NFptPd9sh1T0muz+6PpE702tjNqNG2s23mc9XkFVDXat7YdDcIV/VZcLT2dTAsTAjOjlEF8WLpnSPv0dqATCDWAznXkUumQCi8PhUA/1/pchwO7LZ2+lOiQJTbtYaqLiQ64yvJcIlZxpuWfQz9INzn3l0J8+PDhw4cPHz58+PDhw4cPHz58+PDhw4ePUeC8iTDvQywRMyYtColEjFwxeoc/PWwKn1YJRbVqumop6jjD2IAUALKC0olTxVKlq+Zvp/5DtDISP4kf1V1CGllX78rhdPVUu/t+o3g7t6VexOvF25H1RsjdlDKbN4q3EyDNIL/kKBNCE6jUnmZs0HUcq2olRO5HTsgvCZL3RxdOjPgzAEHyDMyYECEmWDHe8ni0yI11njbpbYKUrlew3PHIOttoetpZsfMpXpj0U9ZV7WRX03Gmho7j8eybWVOxlQ/KvwMgUhHCrzJvZFxgv3XEybZyNtbu5WhLMXVdzZjNZuL8IlgYLXznlsdfbLXy+UnFNl4sWssns3/PproDfFG9i7quZsLkQtrtFPU47ki5kmCZbRFbH84JUSmJ8Hf+uZ2oqOPD7Ue4ed4UNh05TXiQP2NjwohVB7H5WDHXXziRDQeFjBZtVw9jY8I4Xl7Hwtw0SuubmZbWH1WeHhluVezKET1GI+XNrYwJCx3+m+zlpqm5bkWYA7yWd4CvC4v4+UUzWDI+02N/b2dck5LNNSn9EdLPz7oSgPFqIdL/9kzBQstkNltFFfxy0sWIRSJSg8Mwms02x7QgIc2Sai8RiZgYHmOzjz4atFqe2vAd350SbMPsuRuuyy+A77Zx4Zgkfn/5JcSH2C9KHaZSsSh1LHsqKlg5YSK/2vSN5bWluVmsPXQCAJlEDEMIFlbJZE59rF1xrKbOdSdgrgMv/ytjbP0GzZgRIaK+u4qMwElIRdYRbxmBuaQHClE1IkRkBAqZXBdFXGG1/UBquyoAyAyazIm2/cyPvBqT2YRYJCbBLxUzZvKLa3lnw37EYhErF9nPWgPB/9sdcuNH9/pnGdeFXUEfh6s9T28fLskZsZScrALAoDcg7U2FHZMdz66vDtPa1EFwmBBBVHSsgoDg88PGwocPgJum5VLcpOH9A/02JIeqhN/Zb778lueWXna2Ds0jUsPVPLFoHr9Z/61Ve3GjhsWvvM3P58xkcXaGzZy7RdfF5/kF/GubEOk32KbtoXkXkhVta8lkj5zYKEuE+Z7SCjq6hegzpVRKZrRzX/iB58CGDq3FnqWPc82/HCAxOZwL52Zaoqg1TR08/8d1PPaHaxGLPZ8jmc3Oaxg6Gw/w+phTpqeiDg9A09hfR6qhro3PPt7LipXOvbSLCoXf0O7tnlsu2Iswb+4Zfi2rvn3Y868OlHlWlNKHY4LtRFcbzEaqdRpiVZ5nVQ6XcIWt9WuVrmnY++2zHqzvss2qC5Xb3kem954DD5ZVMTlp6H7MzuiLJH/pi918va/QUtfiXMFfquD3R9eSFBBOmbYJpWRo2TFDOdf9EJAN04GjqlP43sf5ua5D4Y7tTZ+f/WDMmGwyP8apnyBIMcGqTYQUg2nk7YHOO8EcYMGyKcR6UDDEG8Sr4pgXeREA6YFjGePfXzxPhIi7Um7mTwXPozfpqe2yLYaW6JfAFTEL7O5ba+hmbcVB1HJ/FGLhX/Jl5WEilUGUa5u4OmEyeY1nGBcUTbuhi9zQREq1jfhJHacQDRTIR1MsV0ilJHtRkHMHmRvprt4rzTHyvFS0jji/CCaFjGVnYz7PFLzPmY4alsQKk8u1VTt5/uRH/HfaQ5ZtPirfzH7NSaaq05kdPh6D2cTOhmO8XCSkBXcaurlljG1xwN8ff5vC9nLmRExkbmQuR1uEAofrq/dQoq3lX5N/Pgrv+IdFkhsemelxEYyLC2dsTBjfHyti+awc3t16kNwxsZZU5opGYQJ198LpvLv1IGKRiNc27ePRa60rgkvFYlLC1Zyqd1zToY/iJo1XBfMpCXFMT4onr6zSrf6VLa088sXX/GPbbm6ZNolrJ2a7ZankLQYL3QOfOxLwB7fbE8tLNc3c+M7HNA4quigC/ORyzJjR9egt56EdJWUsfe093l21gkw7xeHkEuE60NrdxT/ydlOs6fcqX3voBBkxwjbKIXrwJYQG8/uvN1PSpOGxL74hzYVFzECaO3XUuVlE2V27IOgvcCQTyZkZbv9aaa8IkrPX5kYK6XyN3XXMjRAei0Viq20Ky+tJjA6lXdtlSbe29z8+6cbvCyDnLIky4f5+xAQFWmwRHNHW1U1lS6vDxZqR4IKFOeTnCQVT877JZ/aVvYsdSybzzQe7eODK55h1xUQqTtWSv6eImZePTrqxDx/e4omFcynVNLOrxNqveV1+AWMjwrhn1jQHW55brMgdT1GjhjfyDli1azp1/Hbj9/zhmy2MDVcT7u+PGTON2k6KG5osC8uDuTonk9tnOF6IHEz2ANuULUUllsc5sVEu/WGTQkMI9VNZak0M3B7O3rnZFXf+9FIO7S+ho11YaNj63QlaW3X85MHLSEpxXTy2vraVfbuL+ParI9xy9zwmTXNeZN3ReIBbYw4cD3A6plgs4rqVs3j5H99Ytb/+4neERwQyb6H9AtklxfU89chHAJhNnt/B2bPQKO5wb9HbGUUd9heclRIZCvHI2Np4ky6DgYfXbuD7QuFeTyYRc9HYZJ66fL7VXPztvYd4Z+9hqlvbiAsJ4s6ZQvDVdZP7a9Jc9uKb3D17GtuLy9h8qhg/uZwfTZnATy+eaTMb+zK/kJd35FGmaSEmOJDrJudw24wpDufdgTIVkcpg6rusbUBOtlWdFcE8OziBTyusrT9Kte4VvndGRacwr+yru9RHgFRFvB1BsqzX8nHV/31MVFAAfvL+79z6X9w6rGMxm+Htb/fz8he7AcdC5tnmqQlXc7i5nKrOZnJCEpisTnK9kR3+N+Xy4dHU3c6HZYKV50OZtnYpg3FHn9M7CGgNkKXT0PktcYE3Wtrig1bZ9Gvu2o1SMnJ1OPo4LwXzK6+fflbGvXPMzQ5fSwtI5XfZj7G6Yi0F7afoMfUQ1hv9PV09lavjrkAuti9wh8r9uSp+ktVN+uAoxkm9J4S+6LjskJFZWRwuCSHBvpPQMJGKJfwmaxV6k4FrdjzBlvrDvDfzcaKVwvepw6Djq+o8uk16ywTt/nHXopLIUUj6v2M3JS3gpj1PA7ChJs+uYH6irZQXp/yClADrk82jR15hn+YkJ1pLyQpOHqF3+sMkJsh1EVqJWExTWycldRr8FP0TnpI6DYVVDRRU1lu1g3BOuG72BNbvL+DKqdbV3hOCg90SzCuave8N/PSVC1ny6jt09rhfUKmqtY2nN23l71t3sSQ7gxsm5ZyT0V/u0GM0ctdHa2nUdqKUSrlpqiAGXp41jnER4Rb/2m6DgVMNTXx1opD3Dhyhvbubez5ey9f33oZqkPD910VCROLv5s7n+5IzLL+8P3r+ySXDL2T90LzZHKysJisqkpTwUJfesAMpbHBPOA5WKT0qFNdHqNy1QOAufVHq0cp4h32mZSai1XVT1dDi0NNQpzdQ1eLav1EEJIWO7oLxQJLVoS4FcxDE/9EUzBdcP4OuTiHTbvyMfn/WqfOzuGjJZLZ/cZBPXhQKnYWEB3Lbr5eO2rH58OENJGIx/1y2mBVvfmCpY9HH3zbvIDVMzaXpqWfp6DzjsUvnEBXoz7PfCYW0TAPEcKPJRGF9I4W4vg7cM+sCHpw326N7Akei9qQ4965RE2OjbYTyPsbHnptzjJi4UH7zx2t5/MEPMBqFaM7D+0u468aXGJcZS9b4eNThQgaOQimjS6envU1HVYWG0jP11FT1f9/sRT67Ox5gM6ar8dwZ8+rrp7Np4zFLxDiA0Wjiz09+yobPD3HR/EyiY0Iwmcw01rdxcF8JO7cUYOoVytXhAYSE+HOmyL3MNoCxgbZ1hCo6m+gwdNmNPneXE632g0PGBY68YOMN3t17mNtnTuHj228AoLatg4fXbuCl7Xk8cukcgP9n7zwDoyjXNnztbjbJpvfeGwESQmihN0GK9GJBVBT1iA099nKsRz2f/djxWFBBsIECikjvvYSEkARI770nm83ufj8mbcludtIB9/qTzcw7M+9smXnneZ/nvll7IpYP9hzmhRmTiPTyIDY7l1e2Ch4NDRoNS4a1TGi/tm0PT04Zz8MTRnEqM4eX/tiJt4MdC1p54hy4lM4Lv+/g5RuuY5CXBynFJTy/eTsNag33jTXsbTDIwZ8deWd1lh0vvshk97ZG8j3NIIeANssSyrMMVpyK5Vx5pt7lEQ6+epNAXl/Q9jm+O1CqGnjqf7+z72xKt+zPTCbF3toSG4UF1pbmKCzkKMzlwt/G1wAKCzm/HYqntLK2Q/sf7OjHYMfe9cu7VtDqmdx+5/wmSuurqVOrWBY0kQgHPz69sI3smhLq1PWsCJuORqvh60u7uFQlXIdfjfuJcW4DmOhu2P9KY2AiXQcDvx9v2yUkFD1BYvHzBDk8grlM1+RWq20go+Ir8qu3EuTQ88mdV2XA/ErF38qXx/s91OHtZngNajfzUXf5lS0772lnY7yRiXbpZytkZcqlZnhYOlHVUNscLAdws3BEi5ZKVQ0WFkLQw8G87ftuZWZJkI1Qonq69CJarbaNIcUkt8FtguUAo1wGcrwkkazaQlPAvIM4GzHGauKhWWOQSiQEuguf7dIJQwB4dYkwIOrv01LC3LQOIMijbdaBl73xID3QJgO6O/B1sOf1mVP5529bxd0cW1GrUvHjmTh+PBNHmJsLCyMF5+tZEeFGZW2uFNafOkt6aRlW5nLW3XaT3oxxEKpvIj3difR0Z3ZEOLd+9xN5lVV8fyqW5TG6GXh/XbzAtJBQbC0smBven12pKXjbdjz4fDn3rv9V73KZVMqnN87Ru+5y8kQEZAFCXHq3CqyznE/Lp06pwsrScMVWfmWlqColFxvrNpMfvYm/owOH0zKMtssur+iF3rTg4GLLbU/M0rvumVXLGTdnCCnnsnDzdmL0zMHN8iwmTFxN2Fla8PlN81j09TrKa1tkSbTAY79t5Yc7biLcwP3hSuOumKGMbKwQ+s+OfRxJ1x/Y0Ue0tydPXjeOob4dT+wJdHbEylzeZgI+WqTU1WBvT70BcycrBV4ikhn6iqExwfzng6X8+7mfKS9rGacln88h+XxOrx2vJ44plUp45e2beey+1QA6AfczJ1Kbg/WXY2EpBNVeffsWdv55tkMB8yAbDyykcpSalu+RRqthT348s7z1y6OKYUderN7lEQ5XR/Au0sudlRNHN/8f4urM7IhwTmW2TGZ8su8Id48expxIITEn0NmRzFIhYeDj/Ud1AuajA/24qTHrPNDZkRMZ2aw9HqsTMP9o3xGWDh/M7IhwAPydHLhp6CDWnzxrJGAe0CZgviMvlpX9ZnVahqOz+Fg5425pT36rjPfS+irOlqUx2LH9io722J2v36g92sA+/Zw7L6VoiPoGNQ99uJETyeIqhZuwNDdjcLA3A/yFichQHxf83BzwdLLD3loh2lhyf1xKhwPmJjqPSt0isdM02XmyJIWPh9+NY2MsKa4snZoGJf+OuoXMmiI+TNrKm9G3sSRgHH/mngHg8f7Gnxkb1MblfMwNSMR42Myhsv4cmRWryav6DTuLSCxknkglFtSri6hQnkGlKcfF6jr87O8xepyuck0HzIvzK9j683GWPnBdX3elXZwsro7AkBicrEzao13FxqzlPbSUmSO7bJLEvFG2p0HbUsai1KjYmnOUoyXnyaopoLKhljp1PSpNS0mVBi2yy2asQ2z0Z142lTNWqnr/Jnah8hj7C9dhKbVmrNvN+Fm1lGwmVx6lSJnBaJfFXT5OenUc/tbdn6lgbykue6U7Hbodrdo6yuujuKZnPs+ZA/oB8PSWv6hr6FwZX3JBEW/s3AfAm7v2MybQnwWDBjClX0hzlvaVyNbECwAsjxlqMFh+OQPc3bgrZigf7j/M9qSLbQLmO1IuMS0ktPn/X84nMDkwqMt9fW2WIHXy8f6jTO8fSoCTI1ll5QYz8vQhVo7FtxPZ5X1BYWkV1grzdn+PeSLPua8neVxETtaJyULvLSRSCeNmD2Hc7CHGG5swcYXj7+jARwtnc+f3vwA0e4vUqlTc9+Nv/HzXEtG/076mSXf826WLiM/NZ9eFFA6lZpBfWUVxdQ1SqQRnKyvcbW0YFeDLhEbPikFebbN7xSKVSBjg7saJzGyd5dEiq6AMeTlcDRVsUUMD+OSbe/n2iz3s+ONsc/a3GGQyKTFjQvHxFT9RffnxANHHbDoeIOqYLq62vPPZMgD++3+/c/RA+943AcFuPP3yfACCQty5kNgx7w2ZREp/ex/OlOqObTZmHeUG76HtyrsZIrYsjZQq/UH7rgRNe5NIPb9NO0sLqpRCBVhJTS1F1TVEXlaNMchb2O6jfUcoqKzCzVYIrIVcJucX7u7C9sSLOsuSC4o4nZXDqoPH2hy7QaMxKLU0yT2SD5N/15ErqWqo48eMg9weONHImXY/83xiWHVRV1rot6xjnf7s8+vKOFbc9ncgk0iZ6aVfxurzvcJ7eNfYYZjJuieB8s31u0UHy6USCROigpg7OoJRA/y7rIdtovdpaFA3+040XQefHjif1+I3YCdX8Gj4LNKqC4ktTeP52HUABFiL8yC5nDoRlecWcsPfoVCnZ3CxmkhWxTrKlScpqzsFgFzmiJ3FYDxtFuBm3TseMdd0wDwrrYi1H++84gPm1xI2Foaz9EyI4/IAubHAalVDLStPfUhadR6jXAay0GcCrpb2WMks+d+lLQAkVerPDnLUk5ne18SV7eR6j3vwsRrQZl2YbQxhtt0jybS34DtuD3yzW/bVmr7IMBUbpC+r7bkJkJkD+hHi4swTm//kfH5hl/al1mrZl5LGvpQ07C0tmRfZnyVDorpVf727uFgkGKCMDQro0Hbjgvz5cP9hLhW36JNnlJfx9enTnMnL48U9uwAh0FLZ+DADsOVsIrWNgxCJRMKiofo1QPXhaiMEdMtqaxkZIGQPetjZsObEGdH7KKiqFtXOxebqmAi2s7ZE1TiANESJyIkmB6u+Nf0SO3FWVC3uMzRh4u/Emtu6PhEPEOPvQ8IzK0W3d7WxJvm5R7vl2AA3DOjHDY2T2N1FhKc7EZ7uPDy+faPG7uD722/s9LajA/269b3sbVzd7XjsuTnctnwCh/YnceZ4Kumphc1Z4DXVSswtzLCxscTTxwn/QBcGRQcQPTwQe4eOT8S0Ph7Q5pjdeTwXVyHD/9W3byb2ZBq7/4onPjaD4qJKGlRqnF1sCQh2Y9L1EYyZEI5Zq0CKX4CLod0aZJpndJuAeUJ5JpuyjjPXx3Bmsz7UWg3vnt+kd52rhR2jXcI73L++oLXmtT7qGnWrLx8OtR4fWcpb9qG6bIJFi67CgkarpValYuXE0TpZ502050vgYmHLFI8otuac0ln+xaUdjHENJ9im8xNznWGebwxfp+yivlUi2rbcM9zsP5Z+dh2vpvn0wrY22uUAk9wjcLHQn3CyNU4wwL13Qse+v4Y4lpjBhgNxotoO8HfnpTumEeJ1dVSPmtCPRquluk6JjaLFs2CQgz9vD7mdDZlH+C3rOFGO/oTZefF8xCKdbeVSM2rV9aKPVV5dZ7SNrZHnJkfLUThaCuMOLcL1pje9GZu4pgPmNSI+KBPdy5WcCXqtsjFrP2nVeSzyncCKEF3tV5m0/c/jcomWvqS0Ppe9BWvIqDlHVUMpFjJr5no/jqVMCLydKNnC2bKdBFhHMdl9WfN2P2S8TIB1FFk1CVQ2lLDE/1UAzKUKDhb+SGr1GbRocDT3ZJbXSgqV6Rws/Incuov8kPEyADf6vQC0byooFguz3r+sijV/rDdgrtFdhLm58MudS/ju+Gk+OnBUJ9DbWcrr6vjm+Gm+PX6aMYH+LB85lDGBnTN56Qmagte2HZwsbJpcbF167mfvwP0jRlBQU828cKEcVi6VEebcMkDNr6hq3taQYZIxrM3Nee737QQ6OZJWUqZPKwWCAAAgAElEQVTzAGSMWpU4rXoHRd8Gj8US5O3MmeRspFLD72WdyHO2Nu/bCWNjD8NN1F6hZk4Aaedz2PHjEe5+cUFfd8WECRNXOU0Vb1O/+5p3rp/BCG/DfhYrHp3Gike7rhG8ccdTBtdt/XY/X7wkVB+sT3wbubn+sZubhz3zFo9g3uLuCYwZw81DkHc0dszEE6l89epGJv72z3bXv2lgfRNRQwOIGhogun8Rg/3468gLotsDTPMczAdJW9oEeN5J3ISrpT2jXcRNKmm0Gl6KW09ypX6ZmgW+I9skOukjvvw4ObVpXO+hOzG3s2Ajx4p3c0/Qs7hY9G4Q+HI87W1xtrbibHYeY4NaxtlncwTDVE87W+wsWwJtl4pKdLY/n1eIv1OLbIhUIiHc3ZWLhcV4dEKy9daA8WzLPYNG2xKYV2kaePDE/3hvyF0AhHciWN0ZHOTW3OQ/hu9S9zYv06Lludi1rBqxAmcL8ZJPGzKPsC33dJvlMomU29rJnndoTIqoUzWIfu5rjw82HhDVbtLgEF5fPgOLPpQcNNF9VNQIAfOyeiF55pX4n7CSWVCjVrKy3w34W7tytOgCz55ZC8Bo13BmeQ8lyMaNwjpB0vHZM2uZ6T2Usa6GJwvLqownGznZikv0gb4JlDdxVXzzSworkUglODbqWlZXiguElxaJK6MW1Yf6UtJrhCzdKPsIUVridWqhnxerhBnuMNtgg8af1wpiHHFNdC9p1cJAZoJrlM5ytVZDZk3XXbx7C0dzT+b5PMFPGf9mrOtNeCpCddYPc5qFpdSaAmV6m23NJHIW+j7bZnlc+S7m+TyBh2Vws1aXq4U/c7wf5ZMLCdzk92LPnEwvI/Z3p1T3fKDMTCrlzpihzB80kM8PHwdg7ckzXQ7SaYEDqekcSE1noIcbD48fxaSQrsuUdBUnKwV5lVVklVV0SLe7SUfa6bKsYFcrax4cEUN/F/3yLg4KS+rVwsSHViO+ZLs1r82ayqnMHDLLyhns7ckwP/EPHEqRky59MXHUGZIyCvDzcKSyuq5Zg//yqh6xMkPydrKlegOxE9bKKzhgfjEug18+3WEKmJsw8TelXFmHXCrDyshErph2rQ0pr4TnkwNbThM6WAhEntpznpjre9/A8O+CQmbOrQHj+eLSDp3lKk0DT57+hoW+o1gWNAkwXG0bX57Be4mbSTBgzuihcGSR32i96/SiJ8nhOrf55NYa9x7pDSTAfWNH8N89h/BxsCPSy4Oz2XmsPiJkeT83baJO+8OpGaw9EcvoQD9OZebwx7kkXpqpW9X/0ISRPPjjZkJcnZkaHoJWqyW5oBi1RsPcQf3b7U+wjQf3hkzlswvbdJaX1Vfzj2OfArDYbzS3BU7EXi6u4iG/rpzjxRfYlR9HlGMAdwROErUdwD3B13OkKJkLlS0SQdm1Jaw4vornIhYRpccctDUqTQNfp+zim9TdetffFXRduwayNwwSgpMrvvuV6/oH6yS7LBomvtoUID4tj4R0474Aod4u/OfumT0mv1JTJy4hxUT3UVBaiZezHQ7mQkLiu0OWtWlzd8iUNsvkUjM+GHaX6OPkFBv3S3Kxv/KUDvRxVTzRrpj3AZYKOd/seBKARTGv9HofTpaeYXXa9wA4mTvy1qBXsJS1n0HXlL37dvIHwoxoyL2Mch7e43018ffCpdH4s0BZRmsRk/UZu6hUdb/J45WIr5V+l+ZFvs9xpOgXylT5jHReQIjttfn7MxNpBqzWdMyUsys4KCx5cvI4AO4eOYzvTpxm7clYymq7XvlzLq+Af/z4G0MadU2fv34iER59o1Ea7ePF1vPJ/HL2HBNDxGsZ/nRGMPvRZ4wml8r4+NhR6lpNcDw2agwAwW7ODPYVZ4DWHkN8vRjiK7x/P56O48ZocQ/uSrHB427SV+xphvf3o7pWSXZhmUH5K7G/G1kfB8zbK29uTb2eMuArhRqRCREmTJi4Nnlpzy5mhIRxfXBIl9spzISA0v47e94UzBhVZTWkJ+Zw90sLATiw+ZROwPyTp9cTFOHDng3HqSiuYvz8Ydz8yAzWvLmFihIhASz2QBJz7p7E7p+PYeNgxUtr7gdg7Vtb2L/pFDIzGbYOVjz7pXC+dk5CMOLw1lg2fLIdtVrD+eMpxEwbxHNf3Yvc3IzEE6l8+coGAOqqlVjbK1j57lI8A4RJ+8N/nOHrf/+KjYMVYYMD2pxXe+tP7Izn2zc2oW7Q4BMqZE8//PYSrO2tyEkt5J0HV+Md5EZqQjYatYYnPr2LgP7i9OrFsCxoMgcLEzlfoavRrNZq+DHjID9nHgJggL0vITYeOJjb0KBRU1JfRWxpKtm1Jfp2C4BUIuWlyJuwMRNfTZdadZ51yo8pVxUz3/su3C3bVjw0TfJszPqKGnUVKm09sz2Xsjl3DXcGPMG7yU8yz/tOSpQFWMgUnC49CICZ1IzS+iKmeSwmxKZjwdPW3D4iGrlMyod7j5BTXoG3gx2PXTcWgIWDdZ+1lsUM4Xh6Fm/t2IelXM7do4ex4LI2k8OC+eSmuXy6/yifHTiGmVRKoLMj944R9zx2e+BETpekcvQyve8maZS1aftYn36Agfa+DHYMxMncBju5FWqthhq1koK6cjJrirnQWCGQW9tiOjvA3rdD741cKuOlyJu5++jHOpULmTVFrDi2iuHOwrVoolsEgTZuOJnboNSoKFJWcrLkEjvyYnWMQ1sT6eDfPIFjiMRcIQku2NWJtKLSdtsaY8+ZS6LavXTH9T2qVV5WbTL87G1ySioZ3AvHySwsM9rGz02cke2Le3YywNWNTUmJlNbVMiu0H/cPjyG9rIzlmzcCsOO2OwE4kZPN+0cPsWb+YtLLynh8+58EOjiQWFyEWqPhvWkzAQhzFi/1dVUEzKNGBKG9TGA0INQdZ/f2TcWK8ytIuyDeVbs94soTml9byRRGg+UAFlKhbCncNoy48gROlcaaAuYmup2pHsPYkLWfD5M3kFlTgIVUzpmyi8SVpxLpIGTgxpWl9HEvexZD0jKO5p7M8n6EWnUlqy6u4JF+axrbS2nQ1qNF2y0yLK1RtyeG3EPo08HTR18FMZ2sFKwcP5p7R41gy7lE1pw802Wdc4BTWcIAeNHX61g2YgiPThjd65nNCwYNYOv5ZLYlXuD9vYd4cNxIwHDwskGj4b/7DvNXkmCMdEv0oDZtXti9kzsGR/Nb4nnG+PqRVFzUvO5SYQlO1kJWupW5vENa4WklZQQ4OZBcWKyz/EBKuuiAudigcG9OznSF8qpaTidn42RnODtJ7O+moZMZ/93F5XqihriSpdOqK0wPTyZM/F3RaLUczExnRkhYt7S7kjjyZywRI0OIniBkiX7y9HoaVGodre49vxzn5e8fwEJhToOqZVxn6yDc5x99/3Zev/tzVp98jQcmvUZVWQ02DlbMvGM8Sx6/AYlEwtf//pWdPx4FYP59QrbvZ8/+wH+3P4ODiy3P3fgB8++7Drm5GfVKFR8/tY7/bHgEAGt7K/ZvOsknT//Aq+sfpK5GyQePreW9P5/Cw9+Fz579sblPdTWC5J6h9WVFlXz4+Pe8v+1pHN3s+HXVTgC+eGkDK99bCkDCsUvc9cJ8BsaEsOWrvfz88V88/tGybnvPZRIpr0Yt4eETX5CjJ/jdVFUWX5ZBfJn4LG+ZRMrTAxYYzSi+HBsze27xe4Cs2hR2Fmxkid9DbdqcKz8BgJWZDQt8llNSX8Bv2d8gk5hRp67BydyNjOoLVDaUE2wzsFnXd4nfQ+TVZbIjf4PegPmW+27X26eHJozioQm63gS3DI3ilqFRetu3xkIu4/2FNxhtNzE0kImhnTPHlCDhjcFLefL0N5wo0R/kVWs1nC1L52xZ2wrk7ibIxp13hizj8dPfUNPQIjupRcux4gsAzX/FEmbrxdvRdxhVL3h+9uSOd9gApy5mG20T7utGf7+eS0aqVzVQqzRlmPc2l3KKjDfqIhqNlpRcwxOOTQR4OIne56ak83wxZz4KM3mHnrdO5mbz1JhxDPPyZs3ZM3x+Sqh+f3vqDNH7uCoC5s++d0ubZUvun8y4ae0/4O/94yz/eXx9t/Qhty6v+fUgB/3ZrIYItgkkrjyBjBr9JV0mTHSFEBtvXotczurUP1mXsQu5REaEfSAfDHmI5Eohq+JqDphrtMJDw+ac9ylWZqJU11ChKmSc6y04WxjWo9SiZU3aM8gkcrRoGOY0q3mdBAn97cbydcqj2MvdWej7TLf1t0Hd+9mbDVdJoEwhN2Px4AgWD44gNiePn87E88f5JKqU4k1E9KHRavnq6EmOpGXy8aLZeNu3P5nanUwIDmRqvxC2J13kk4NH2RgnTK5OCQsm3M0VB4UlWgSjzcSCInYkXSSvUsgWWxwVQYx/2++ws0LBtOAQjmRmcuugKJ7a8VfzOj8ne85ktpSDzolqv6S1NetOnuWZqeN58KdNRPu0ZHEldmDywkLkd6i+D34HneFiVhF3zBzO5gPnDEqyWIqchFH18TmLnTgTez6d4bmbP8TKxpLnvmjJ6Hxszjuity/ILDbeyISJa4ikilyWHPwIgGcGzmWRX+/oVncHHx0/wg/xcRTUVOtc/+wsLPhm3iKi3IWs4s3JiXx0/AjpZWV42dpxc0Qky6OH6fhw3PjzehIKC6hRqbjv99+alz8wPIbHRo1tbgMYbQcQvepjKpRCxYoW+GL2fCYH6sq4TVu7mnuGDOdwZgZ/pVzEXCpjvH8AAK9Mug5b8xa9Zn3n2nSeQPO5GuLAltOMnDYIe2dB69jV24nYA0kMndRSGzpqZhQWCkG6s3Ug3aPR9NLR3Q7PAFekMim2jtbUVNZh42DF8Z3x7PrpKFY2luSmFTFyuu5EvErZgLxxf2ZmsmZXx/TzOWRfyufJue/qtHdyFypXM5PzcPV2xMNfOP6I6yNJOZfVvE44D/3rE0+kEjrYH0c3YTw2aZHwvX5g0mvNx3H2dGBgjJCVGzjQm0N/iDcgF4u3won/xdzP46dXc748y/gG7WBjJiQr/DtqCTHOoUZat8XJ3A0AF3NPSuv1B60KlcL4zt3Cu3mbkvoC+tsN4Wz5EaLsR3KhKh6VRhg3e1i2ZEkrZFYoNb1XpdVb+UEKmTnvD13Oqovb+D5tP2pt3yYnRDsG8fmIFTx7Zg0ZNZ0PPk7xEH6nzw5chELWu5K9qbnGx1oTBwf3aB8uieiDie4nMaPn5XrPZxRQL0L+sZ+PfvlRfUwNDmmuGhNbUQvgYWPDMC/hehru4sq2SxdFb9vE1VEzbcKECRMmTJgwYcKECRMmTJgwYcKECRMmTPQwV0WGuT7sHIyXoVvZWBhtI5YKVYuBqJuF+NkQAAe5oM9T0VDZbf0xce3hZG7Lzkm6WR4fDV3Z/Hp16l/N7f475GFKlJWcLLlAiI0XhcpKxrtGs8DCnqzaQpzMbaluUDLNQ5AAavoL8EPGHm7ym4inwoXRLgM4VJSAtcyC7Npi/K2F0qtiZRU/jH6JzJoCLlRmE1+eRqiNN+Wqasa4DmR/YTzjXDuvkdcei/2eb359dPd5LsQLGSFLH3pMb/vWxp3rP9vFtp+EUpvXvrqbOwLfMnicaZ73dUd321DTB4Z6VUql8UZcWUaMUV4eRHl58NzUifyVdIGNcQkcSROqcDSdTFtJyC/g5m9/YM3Sxfg7itNF6w7emTuDhzdsYc/FVHIrhOv8dyfaz5SaHzmAV2Zcp3eduUz4nMqVdfz36GEulbSUtSnkcgorBGdzN7uOmaU8M3U8ADcPGcRdI4c2L391m34DIn1YyMV9hyrrxH0n+5ri8mq+/eM4SlWDQQ1zhRHzuSYqRf4Oewqx77kxM72ukHQqjctVrhKOidPKNGHiSmF1yj5me0fjbGHb1125Yvk18TxfnjrJzzfeQqCDI+vizwLw2v497L5jOY6WQjbuvvQ0nt21ndcmTWGQuwcppaU8vXMbDWoN9w+Pad7f6rkLUaobGPr5J/x3+g1cFyhkN7Y2U149V9D/NtYO4PQ/HqBGJZT7R3z6gcHzeGnPTpZHD2XDjUvIrazk0b/+AOCjY0d4ZuwEg+d6+Xm2R221ktN7z5OakMW694T911XXc2DzKZ0M86bs8suRtZIFk7Z6rUVL9qV81vzfZlYdeBGFjSXfv/M7KqXuOHTZ8/N4ZPr/4R3sjpO7PYPGClI2Wq0Wdz9nPt7zPPrQarU6RpWts96bpVINrJdIdW8E+oZ1CmuLy9r0TMqyk7kNX4y4nz9yTvFVyk4dHWsxKGTmLPYbza0BwhjKTqTB5OUU1wsysUX1uc3Z5pfjbilkQqZXC7IeJfUFOJm74WMVyNHiXSz0uYeU6kRofH+7W1bySkUmkXJ/6AxmeA7l65Sd7M4XfIAatB2v7JNKpIx0DmW0S79O9yfYxoNvR69kTepe1qcfoKpBfGZ/qK0X/wiZyhhX8RWiAHd9/YvBdV/duVD0fjQaLeXVxvvr42Ivep+d4ej5K8Ps9u/G2ZRc1BpNj/ouHU8y/tnaWlkQ5Oksep9N2eWt0ffYVq3SlfmxkuveV1ubgYvlyomedICn376Z4P7GTc+6M2Beq27R1bQ2E68ZC2DT2L66obrb+mOic1w6n0PC6QyCwz0pyC2jtlqJf4g7SCSkJudSWlTF0gf0B7GaOJGXzdkCoRTxrkFD223bnVhIhQvFHO9RfJ++GzOJlBv9JrAufTcKmQVWZhbUqeuxkMqb20TYBxjcX06tUAqVXVOEg7k1UY5BeCuEssrEikzWpu1kZb/5fH7xdwJtPEiszMRT4URsWQrqTgxQeoOb75tMamKe8YY9SEVd75vWlYsMlDkqjD/Y9TYKuRlzI/ozN6I/2eWCo/bPsfH8eCaewqqOXzPzK6u4/+dN/LzsFtGBzq5iaWbG5zfO49e4BL4+dgpAr0a7BMEk9O6Rw5gSZrjU8e1p0wF4eeJkdqWmsGhGiwxYYl4hy8cNA2DTmfNotFqDgV5DtA6WAzw0fpSBlm1xsBRncFVUfXUYDsukUtQaDV7tPBi4itSJL+0GQ9uuINZQtyO69x3lzQ2P6l0+83bBAPiGxr+G2PLNPrZ+d6Db+2XChFiqG5R8krydca79TAHzdjiVl8Mgdw+CHQUN0rn9hODPv3bv4GJJMcO9BLmxD44d5vZB0cxpXB/g4MiSiCjWxsfqBMyt5HJkjUFAC5mZ3om9pmXG2jUhkxq/N0a4ufPoSMFUO9TJmblhQj9P5ua0e66Xn2d7HPsrjrDoAN7a1JL4kXUxn8dnvc1Dby3RCYJ3lKryWqztrFDYWFKvVHF0WxxDJuoG4jKScrlp5XSm3Kx7rw/o701VeS3xh4XgbMSoULRaLWVFlTi62uEb5klBZgn5mcW4+zpzak+Ln5dvmPAcbmh9/2FBfPbMD5Tkl+Pkbs+eX44BMGRCx4KE3YVUImWW9zBmeA0hriydEyWXOFOaCkChsoKK+hoqG2pRyCywN7fC0dyaAXa+DHMOZohjMNZmXY8paNGwLuMjKlSlzPe5i8qGcnbmbyCz5hJ/5v1AP9sohjkJkzRJlbF8l/4+DRoVs7yWIpPI2FT3LU7mrlib2aLpY1mSviLQxo1XBt1ChUoYYx4sTORMaSqXqvLIryujQlVLg1aNXGKGpUyOo7kNXgpH/K2FCYpBDv4MdgrEQd71cZCFVM7y4CksCRjP3oJzzdrlSRXZ5NeVUauux0wiw8HcGh8rZyLt/RnnNoCBHTQbbeLBySN1/q+pV7Ej4RLejh2ToaytF6cb7mzXc2NFgCOmgHmfUF1Xz+mLOQwLM37v6iw7TxvX8Y8O8dYb8O4IzgorCquFWEG5sg57C0u2X+qYh4AYrsqA+YSZbU3S9BEe5ccPB5/rlmNam1lRoRIyBytVHcsUr1ELF/UmE1ATfcferXEEhLqTfC6b2molt9w3iV+/PYiqvoHFd0/gpy/2Gt1HcklxrwbKm1BqhBvc1tzjOJrbYC4146+8k7hY2FOrViJrNAtRalTNbdrD1kzBn7knqG6ow8HcGoWs5fupRctcn9FszztJsK0XVapaIuwDCLP15rmzq3klUr95TE+QcFIwcXnz8fUUZJdy28rriRoZzOp3/iQ3swRlbT3LHptOQJh+/cjDO86xe/MZLCzlFOaWcf8Lc/EL6TkTk/I+CJiLDU46WXcuK6a3aNIeXzl+NA+MHcmf5y/w1bGTxOd2zLz5QmExL2/bzX9mXd8T3TTIvMgBzIsUssWKqmtIKiikrLYOCeBopWCAuxv2CsMB57cPHeTx0WPYcD6Bhf0HYGthwdxw3YfLUDdnVh88CYCrrU2Hg+X62JV8iQVR4rw53G3FZbXnVV4dFVV3zRYCNr/sPmtQw9zDTlzQrDMTPN2J2OOL/Qw7Q1CE/gG4b6hHu+ub8A7Sn3VnwkRvcbT4Yp9r5F4NhDo588eFJHIqK/CytWN7iqALamlmRrBjS9ZYUnERp3Jz+PTE0Tb7aDLu6ogeaXcz6DLtcXtLYSxcWd+SiKDvXC8/z/Y4sPkUU27SDXb5hLjj7udM3KELRI3rfKZrWLQ/gQO8eXjK61jZKZpNRVujqm9g3bt/sPGznShr65l772RmL5+IuaWcF765j1XP/wRAbVUdGo2W+fddx/VLRqOwtuDBt5fw/I0fYO9sy4ipLZWlTdnhhtbbOVnz8Lu38tLST1A3aPBs1GF/+J1bO32u3YFMImWwYyCDHTtnQtlZIuyHE2E/vM3yed53Ms/7zjbL53vf1WbZ8wM+AWC6x03NywbYDWl+bS93ZnngU93RXaP8ef+yXjmOIZqy/Gd4DWGG1xAjrXsWhcyc6Z7RTPeM7tHjDPH3brNsbGgASz5fzz3j2363DCHWyF7dQ0b2yVlCQpGYLGQTPcOfxxJ7LGCeklvMuTTjz+0To7qukW9tbt488T53/RpcrKyZEhhMWnlZl/fdmqsyYC4WmUyKnWP3zI65mDs3B8yTqy4yjfazkFtzsUowXHQyF+8Ea6JnCOrnQXVlHQOi/Ug41eKmbe9sw45fT6FUGpfTsJGb8+HJI1jL5b0aOF8WKAT/WmeUGsoubS/r9Ca/iQBM8xxmsN0iXyELMKBRokWj1SCVSMmoLiDGORy5tPcuHU2VIk++fTNpyXms+2QXcnMzaquVPPP+ErLTivjyzd954ZM7DO5Dbi7jsf+7kQvx2az/bDdPvn1zj/U3r6LKeKNuJqcxM9sYrld4wLw1ZlIpswb2Y9bAfuxPSee9PQeJzxMfON949hx3xQwlzFV8uVd34mJthUugf4e2uVAsmAcdycxgYf8BettYmZuzbIxw3UkpNO5A3pr0Ev0DiP0p6aID5mKDx5eKOta3vuKL344glUpwsrcyeM10t7FGLpMZNfUsramlSlmPjUXvmjc1kVEmboDoJfIz7E7sncSNxaztrrwqGBN/Dz5M2sbu/AQya4TquxsPtJXxODb91ebkBIByVQ2fJu9gT/55ylTVeCocmOczjNsCBfNJqUQ3MFFQV8Hb57dwuOgiEmCsaz8WGjD5VGs1bM4+xbacs1yqKqC8vgZnCxsmuvfnwX7TALBqNIorUlYyc/ebLPaL4YkBs9rs687DqyiuF8Ymv034Z7fIOCyJGMSJnGwmfPMlduYWeNoK15VPb5iLU2M1m0arpVal4p+jxrCof9t7TF8GypuwFlGJpu9cW5+nMZ776l69y//719PNr+//j/5x6dIndT/PN355BIA3f/tn87InP2sbXG3i0O9nqKtW8uWxVwFQ1tZz1/B/MXv5RABCB/vz9pbH22x3/ze/Nr+W3xdNDbCHOqQLQ3TajZ0VzdhZLYHCG1dOb34dPaE/0QYyyr0CXfnf4Zeb/x8YE9J8biZMdJVLJSXcv2Uz2243/GzYFaas/prVCxbiY9ex7O7upqK2juKqjlV0msvNMDeTUd/Q/pi2pLJnKkU/23wY6D3TWBNt+f3oeR6YK1RWOdp277j7279OGm0jk0o7FDB/eaLhuOs/hgqTRc4aBRtPnGNLRgJbHlsGgL+DAztuu5Ofj8ez8cQ5ymtqm9d1hKsmYJ6fXUpFWQ2hA9vOrvUG4XZhpFSnAXC85DT5dYW4W7avZV5aLzy8Hik+0biPjjtqm+heJt4QhUatQSqTEtaY6TbvduGCodVo22ju6SPAwYGcqgqcFH0TfGwd1DEU4BGbdSq+nfBQYyET5F56E//QlmxwGzsFdTVKslIKiD+ZxhuPfA+Ab3D7WYluXo4AeAc4U5DdMe3CjtKkYd2bZJSVi2oX4OTYwz3pGcYF+TM2yJ+fY+N5fftequvrjW6jBT49eJT35s3s+Q52E6HOLsxZt5bsinLmrFurs27TLUJW1tcHTxLuIWRqFVbV8OjUMaJ/x3et20CkZ9tKjLhc8TJGAU7itOFzyiuoqFNiZ3llV1bdPXek0TYyqZRQF2cS8o07y6eWlBLp2XMVLO2RUizu2hbm5tLDPdHlh4Q3sbIVJ+Xj6GqHs2fv+Q+YMNHEVM9IRruGsSMvnh/Tj/Bi5AK8rXQTXVpfa2vU9Sw/8jn5dRUsCRiNt8KRuLJMPkz6i4uVwuTuq1GLm9vXaxq479iX5NaWcXvgODytHDhceIF/xf6ktz8yiZQNGcfxVDiwLGg8dnIFJ0tS+SH9SHM1zNMD5wDgYmHLeLdw/sg5w8rw6Zi3SmrIrinhbFkGK0KnAN2reXy2II/v5i1ipI9+iQGpREJ/F1cuFBfjYWN8ok4mkSIBoxn+Ytt1J8bO9UqlurIWuUXL9+FSfCaObsaDfK8smArAp7uOMi0yFH9nR7JKy9mbmNpjfTVhoju51vTdb3h/dZtlRVU13DtB/6Rre3g625Ge3/6YMT41j1kj9SfvdJbtJ5PZE2vytelrlKoGPt18CIBnl4hPAjZGUmYhW44kGG03OToEB5vuDdQvGh7BiCAf7l/9a4fWieGqCZj//sMxfvpiLx/+/CAhA7yMtv+pmWkAACAASURBVK8sr2XJuNeZe9so7n6i6wGTSa7j2Jq7HQC1Vs07yR/yz7AH8bDUH6grqS/lneSPAKjXCMGd8S6ju9wPE13HkFagmGA5QEJRIfdFj2BDckKn9IOvZtwtez/gqu9z8QlyI2SAF4++sVjPFm3JzWjUa08rxsO3Zys9csorqKlXYWXeO/rZtSoVmaXiMksDna/OgDkI+t+LoyKI9vbktrU/UyxChmbXhRRUajU/rdqDf6gHhbllhEcJD7tpF/IJDvekrlZFbkYxvsFumMllbF57mAV3juPgX/E4udoKHgfAgCHissVrGrUBS2trm8vNjdFkUPr46DE8PnoM/9q9k1cn6R/A3DoyinAPYbK2o4YtiwdHcN+YtgPrjph+Bjo7YWlmRl1D+9U4WuBEZjaTQ4M61McrlQEerqIC5nG5eX0SMC+oqhIlyWJtbo5fLxriAtg5iZeAibk+kpjrI3uwNyZM6CfcTni2SKrIBWCgvQ/BtoZ/y9+l7Ce1qpD/xdzDEKcAAOb4DMXbypEPkwST9hu8BzPSRUiW+T37NOnVRbwQuYC5PkKV0DyfYTx75ge25Z7Ve4xvR6/Q+X+WdzS5taXsyT8PtATMARb6jWB3fgK78xOY5tkiXflHzhmkEgmzfbpXtiC7soKMsjLcbdr/fa+MGc2K338jxMmZ6cGhaNCSXFxEg0bD/HDdYIyZVIq/gyMbExMId3FFgmDY5WZt3al2DeqWe7DY+3FXzvVKZMK8YRz7K45/znwTiUSC3MKMxz5aZnQ7F1vhvSyrqWVEkDBucre34fvD7RuamzBxpaDWanjszz9JKirE2UpIcnt/xsxmP6c9aam8e+gQao2GYCcn/n3dFOwsLJrXAQbXt+aFXTuRIOGlyZOBNr7n3cant83T+V8iEaQ2rTtR1Rjm42o0YL4vLoXHb5yIWRc8FlpzLi2fF1Zv65Z9meg6v+wXxh3jIgMZF9n1Z7W6+gae/3pr84R+e9w8abDO/7PeWc2Wx5axI16Qdnthw3YO/msFuxIuEZ+Vx8ppY4jNyOXdrfupqVdha2nBywum4OvcO88zV03A/OT+ZFw97EWZfQLY2ivwCXLl9OHumcXyUngwxV0w4tiev4fs2lyeOvsiw5wGE27bD0dzeyRIKFdVkFx5kaMlJ5sD5QCjnIcTbNO7emkmeoZ+Ti58EXsCN6vu0Q820XEGDPHn1MFkXnt4DQDDJ4QzfEI46z/dRXJcJgDfvr+NoePCsLFTUFZcxVtPrKekoJL7X5zX3q67jBZILixisLe4a1VXScgrEOX3bGUub9YIv5oJcXHmfzfNY/Hq9Ub19WpVKk5l5WLrYEVpkZD5n3Ba0MxbsGwsP325D2dXWyKGB+Ll54xarSEo3JOAUHcO7zjHjBtH8Ou3B4H2A+YNGg1fHj3JL7HnSCvpeAVD8rO6Rol3RBnWQbQ2N2+eLDiRls3MSPHap/qC5QArxsboXa4PmURCPzcXYnOMZ6XvT0m7ZgLmQ3y8+Tn2nNF2p7JyWDIkqhd6dPlxc0W1i/R0v8ZyrkyY6Bt25ycQaOPaHCxvYpFfTHPAfHtefHPA/FjxJaQSCdM9dX2YpnhGGAyY6yPU1oPjxYLUY5NcHsBIlxC8FY78mnlCJ2C+NSeWEc4huFsaNjbuDC5W1oQ4O3Pdt18Bwr0BwM/egSfHjGNasHDeU4KC+Xz2PD46doRPjh/FTCYlyMGRFcP033femjqNF/fsYubab3FUWPLYqLEs1CPn0l67dw4f4MvTJ3Umdu/7/TfMpFJCnZz5fUnHfHj0nevl53mlYm4pNygJIwZrc3Ne3LAdfxdH0ovKsOwlM/WrmR/SD/B+0pa+7oYOUomUg1Nf7+tu9CqppaV8OnsOIU5OvHtIGMt/cOQIL06aRHFNDc/v2MHGW5bgam3NV6dO8ca+fbwxdWrzOkDv+iakEgmv7NmNXCrjXxMn9vj5+HVjcHBIqDfbTya32yavpJIN++O4cWLXx7SHzqXx3FdbUaqMS9+a6B2a4trPfPEH79w3h5j+fp3eV72qgcdXbeZSTrHRtjH9/YgO0VUM8XK0I7+8itPpgtl2mIcLaUWlJOUWMtDbHWVDA//+bRdf3b0IW4UF2+KSeW3Tbj67c36n+9wRrpqAeVF+Of0H+yHpQIDSy8+ZuOMp3daHpf6CyUZxfSmnSmNp0DZwpPhEs+SKIfrbhXF3YM9oaJnofaLdPYl29yS+sGNGhN3JwcKfGe48C3OpJcXKbFKrz+JhGYSPVT8OF21klMt8KlXFJFUKJkvDnGaSV5fCkaJNjHSZQ4NGRaEyHXfLIFSaOkrr83Cx8KFMJWRQ1qtrcbX0w0HuRlLlUYY5zWxznN4gZlJ/Yia16B+6eNjz8ueCOc7Sh6a2ab/iX3NY8a85OssO7zhHxPBAve17ilNZOQYD5r9nxxLp4IuftRNrUg+xNHA036UcolZdz72hE5uXieVIepaodlFentfMBE+Ehzs3R0ey9mSs0bbJhUXYayFmYjhrP9lJ9ChBf3PnptO4uNmh1WpRWAnZGTKZlJLCSjJTCpsNrYzRoNFw17oNHEnP7PwJXUaIk+EqiNisXMprBTMyRysFao2mw5nml7PvYqpoDXOAkQF+ogLm2xIv8Pz1k5oDKVcisRdySEgVzuWW6w1nYI4PFldhsD8lvU8qj/ZeFFcmPzaoY7r6JkyY0E92TQlDnNomwtiYWWInFzIYs2pavBzy68pxMLfGQqYbcGwvkJ1Qns3PGUeJL8+iWFlJrVpFvbol4NB6slyChHm+w/gkeQfZtaV4Kxw5V55FenUR9zXKsXQXKrWahT9+z3WBwXx+wzwszGTUN3o8/H4hiUe3/cGZfzyIuUwGwKSAICYFiJs8HerpzZZbbutSu8dGjeWxUWON7mPbrcvaLFsZM1rnr6FzbX2eQPO5Xmu8snAqp9NzyCopJ8rPk6EBfSONasJER/G2s2seT08NFsb+z+0UAuGnc3OJdPfAtbEqZV7//sxa853OOkDv+iZWHT9OXlUlq+bM7fmTQfDJAfhk1xHOZOQikcBgPy9WTIrB0apj8hYTo0J464c9RrOB3/l5LwEejowI71wwtVap4sutx/h62zGTZvkVSo1SxYMfbuSeG2JYdv0wzOUdCw+n55fy/NdbRRl9SqUSVi4Y12b5AG93kvIKScoVDGFvGBzO2YxcknILmTd0ABfzikkrLOWOz1sk7Fxtu8enUgxXTcC8tqYeKxtxGphNKKzMqalSGm8oEjOJ8Hb9M+wBdubvZXPunxQp9c+k2MvtmOEhBOhmel6vYxJk4urmu/gzqDRqzhUV8M7kGe22FRs0UXfwLqJBg7lU+D0oNTXYmjmRUXNOJ5BtK3dGrW15sPKwDMJTEYSHZRBHin5lpMs8DhVtwNbMCT/rgTiZe5JaKAQgx7nexNHiTfha9W/eh6HjtEdPnf+VzuG0TO6KMWwIa2UmBGjt5VZsyjpNVUMdtvKOXd+a2HtJXKBsqI9xKauriXmRA0QFzEtqarjtVuHB99F/L2xebsiz4M5HpyGRSvANEmRPmjwODLH+1NnmYLlCLmd6uJBt5u/kgFUPZGIFuzo3/64aOhgs7w7TT4AJwQGsOnTMaLui6hp2Jl/i+n4hRtv2FSnZRe0Gyptws7Ghv7sr5/ML221XWlPLsYwsRvr3ns6tSq1m10VxyQHjggJ6tjNGSDyZSnJsOjUVdWjbue7f8mj791YTJq4EOjpy6Yim7sHCZB49+R3hdl4sCxpPoLUrdnIFX6Xs5ddM/Yk6c32GsurCLjZlnmRF2BT+yD6DnVzBRHf9xoudJbG4iKTiIn65cUmb+9zEgCDeOnSA0rpa3K2vPgmTyzF0rq3PE7gmzlUfF/KL+WrfCYLcnJgd3Z+NJ88xf6j48YIJE31F6zGGtvFq3XQFvvz5tHVbfc+u+sYrthYWHMhI52x+HoPc2/oDdTdP/rgVgEgfD16cK8g27jp/iSd+3MoXyxZ0aF/ujjZMiApm95mL7bZTNai5/4MN3DltOEunDMXeWtyzan5pFb8ejGP97jOUV9cZbBcZ6ElcqrgKSRM9h1qj4bPNh/llXxwLxkUyZUgoQZ7OGArjaLWQkJ7Prwfj2XT4HCojBrJN3HH9MMJ928pZD/R240JeEdLG5/IoX09+Oh5HYWU1Xo52lFTX4u1ox4aVSzt9jl3hqgmYO7rYkJXa/oPq5eRmlWAt0miqI0iQMMV9Ite5TyC7NofU6gwqVVVo0WBjZo2vlQ8BVn7NZZImri2mBobgYW3DpbISo23lIjNOKpUdm9jxVIRwpPg3XMx9KKrPQiG1QYqUQmUG+XVp5NWlYCYxJ69WCKLk1l7CU9HiRmwjd+Js2W5szYSZ96bgu7lUd4a6SJlFXm0KubWXyKhJaD6OWHrq/DvCqCkDGTWldwf3h9IyDBoe3uDdUto222dwm2zUjmSXZ5dXEJstbqBxrWWWhriI06Ivr9P/3TLkWSDWy6CJLQlJAHjb2/Hjspubs1F6ij1JKVg2zv7XqRoY5CN+kN4dpp8A0T5eOFkpKGnMdmmPVYeOMbVfyBUrA2KlMOfLzUexspAbDZzPi+hvNGAO8NOZ+F4NmO9IvtScedQegc6O9Hdv36y8J6itVvLyHZ8BEHsgSdQ2poC5ib5C7LXK19qZ7Jq248BKVR0VKuH36NPKNNTN0p64sizqNQ06ppz5dfpNu9emHcRMKuOzEXdhZdYylqhrMGx67WxhywT3cLbknObe0Mlsz4tnuleUzvG6Ax87OyzNzPgm9hSLBkQglUi4WCK8F28fOkCEm/s1E0A2dK7X2nka4r0/D/DUDRP4cLtgELcvMdUUMDeCvbk1QTZ9Y/5tiL9j4l52RQVJRUX0c3FhxyVBoneol5A8FO3lxct7dlNQXY2btTW/JSYy1t9fZx2gd30TN0dGMjMsjAe3bGHd4sU97nHQJMf48JSW58QIb3cWfLymU/tbMWc0+86mGJW31Gi0fLn1GN9tP0lMfz8GBnjg5WyHjcKiOWu8vLqW8uo6MgpKOZGURWahcW+tWyZHc+8NI5n8+Kem7PNeYuWCcVzIKuSPY4l61xeWV7Fqy2FWbTmMnZUlfu4OuNhZY2luRn2DmpJKYWyTklNMRY3hiRB9RAR4cN+sUXrXDfB255v9pxgaKFQwBbs7k1JQgr1CiE+FuDtTUafkRGo2wwK90WqhpLoGZxurDvWhs1w1AfOIoQHs3hJLUlwW/SJ9jLbPzSwhKTaTQSOCyM4p5e6HvyHAz5naOhUTxvTjzltbsgbn3foxv659AIBX/m8zY0aGcN2E/nqXA1w3QcjUkCDBR+GNj6Lr5WkHj14kLaOIWxeP7PA2gMHtXv6/TSyYNYTIgcbfMxPiqFWpyK+uIqGogGCH9oN2Yo0fxQSeWhNiM4Rgm2gkSAhhKFq0zZlL83xa9JDn+jyis12MsyBXEmE/Xmeby9e3ft20D09FsN5t2qOnzv9KR6VWsyUhUZSWcVekG9adOisqw83V2rrXNNV7C7VG3OjK0qxnb3MXCoUqo4fGjezxYDkIRlw3Dxf0aX893THj4e4w/QRBv3V+5AC+PHrSaNu43Hw2nj3HgkFX5gO2l4s9BSVVokxq5kYO4K3dB4yax/1xPplHJozG16F7NYMN8b/D7cvCNbGwjz6DNW9taQ6UW9spGD93KG7eTkhlV+o0iom/Mw7mwnW8SFnZrunnVI9IPkr+i5MlqQxtJc3yc8bR5teT3Vt+c8Odg9ieG8e23LPM9m6ZnNuZp98boUGjxsbMUidYXq6q4Whx+95MC31HsDPva9amHaRYWdlsMNqdOFoq+HzWPN47cpBPThxD2dCAR2OwaIJ/II+MFD/xf6Vj6FyvtfM0hJW5HH+X3jWKvtqZ7hnNdE/DXjR9hVar5HyWfmmk/j4pSCTipAgb90ZawXxUDVkEuv+JmcylQ32prT9NUcUH1NafQq0WgqsSqQK5zBt3h+ewsZzcof3pY6y/P/87eYLEwkKcGk0//ztjJgCOlpa8PmUq9/z2K2qNBl97B16fMkVnHaB3fWsGuLry1Lhx/GPTJtbfeCPQc88dYe7Ce5xSWEKQqxB/SM4vop9H5xIhQrycWT5jBJ//fkRU+/oGNfvjUtkfJ66y2RDThglV6o8tnoBUIiHEy4UL2UVd2qcJcZRU1vCv26aSXlBqVEKloqaO+NSOJVQZwsPJlvfun4PcTH8ipbejHWlFpdw9cTgAMqkEjVZLuJfw3baUm/HhbXP4z5a9VCvr0Wq13DZ2CAuGtYyxHlmzhcKKKnLLKrnnyw1E+3tx/5SRetcBOuuN8febbjRhwoQJEyZMmDBhwoQJEyZMmDBhwoQJEyb0cNVkmM+5dRS7Np/htZVrefXzZfiHGM76KC2u4o1/rkOt1nDdHGGGNzjQlY/euhWVSs0/Hv2WsSNDCA3W3ce36w/j7eXQnEFubHl3MiYmhDExHdN57cw2JrrOqfwcypVKnBQKGjQazNrREHa1EZdxGp/bcQPR1pneHcn67s1tevL8r3S+OnqKmwZHdtmQ0RDltXWsP31WVNvrw0OuGcPPJnIqKkS1c+qgEU5HqWsQNP77ufWO1EU/dxe+2H8cACdrqw59rvqyywFWjI3pcD9uio7kq6MnRVU4vLFzHzH+vnjb23X4OD1NUkYBfh6OVFbXNWeZG3pPnawUzB4Yzsa4hHb3qdZoeGf3Ad6ff0O39/dyfk9IIj7P+PXT0sysz7L8D/5+ptlE9+Odz+Lu69wn/TBhQgzRjv5YyOS8ff53lgaOxUJqRnmjxMpN/i3ZSEsCRrM9L55HTn7HkoDR+Fg5EVeayYbM40zxiABgjGtYc/tZXtF8l3KAN85tIrumFC+FAydKUkko12/cPcollJMlqbyVsIUxrmHk1ZWzJvUAzhY2lNZXG+z/CJdgfKyc+CZlH2G2noTb9Yx/yVg/f8b6XVtSb4a4Es+1pKaWA6npHMvI4lJxCZll5VQp66lTqbC2aPTJsbTEQWGJn4MDw3y9GebrTT83lw6N5H2c7Hlt025SC0t4/ue/CHHv3ut3TkUlh9MyOJmVQ2pxKVnl5VQ2ngcI0o5W5nI8bG3xc7QnysuTGD8fIj2vLMmTqwGJxAwPx9dQawT5pAZ1EaVV33RqX2pNBTVKYSyqVCViJjNusttEfUMqaQWL0GrrkMs8sbIUrqtabQP1DamYSTuWra6PYCcnvlmwsN02Y/z8+G3JrQbXAQbX71h2Z/PraSEhTAvpuXjMnA++RSJpqayd88G3eDnYoUVLTlkFo0Wa0uvj3lkjuZRTzM7TF7qru+2yYGwkz94q6K83jbWH9/M1ZZj3EiUVNVjIzfjk4YU88IGQaR2f1j1Z5PrwdhEqbT97ZCHOdu3HhA786z6d/7++Z5HO/wN93PnuvhsNbv/+0lmdWieGqyZg3m+QLzfeM4Ef/7eXhxZ9zMQbBjFsbBhuXo5IpBJKCio4e1woEdnx6ymqKmoZMjqEybMHk5PXoqMkl8sIDnSjqKRKJ2B+8OhFUtIKefGpOTrHNbTcEGt+OMKJM2lotVq8PIUStqdWziAhMYf1G47xyrPzAHjh9V+5ecEIBoR7sXHLKbbuiGdolD//uHOCzv7OxGXw1ZqDyGRS6urqefe1m1AozHW2AXS2W/39QY6eTMXNxZaS0pZB9eY/Y9m59zwg6FENjvTlrqXib3AmBEKdnJEiQa1tP1gO4GsvriT/XF6BQc3rq5m/8/lnlJax+vhplrdj/tkV3tt7iAoD+tyXc4sIaZirje1J7ZelNxHkLE7rvLM4WynIq6xq18BQLMs3/ap3uUwi4fPZcwHBZM680RugSuTnfzlpJWX83459ZJWV4+Ngz1NTxuFi3TEduAAnR6b3D2Pr+WSjbctr61jx0ybWLF18xf3Gh/f3o7pWSXZhmajJhwfGxrDpXKJRzcc/ziczvX9YswlsT1BcXcOrf4mT07l5yKAOf8bdRXFeGYPHhQOYguUmrng8FA68Fb2ET5K382bCZqRICLQRTKpaB8wtZHI+j1nOp8k7+DXzBGX1NXgqHLg/bAp3BI1vs18LmZxVMct5O+F31qYeQCKRMM61H1+O/Adz97zdpv1tQWOpUNXyZ24sv2Qew0vhyK2BYwi2cWf5kc8N9l+ChPm+w/kwaRv3hHRd1sBEz3Hr2p84ltF2wuTQw/calHiLzcnjf0dOsOPCJYP3oaaxYUWdksyycuJy8/n9vCCL5efowD0jh7EgckDzWKI9Hpk2hjPpOfT3ciPI1ZHB/l2fgFFrtWw+l8gPp+M4kZXdftuGBuoaGiipqSUhv4A/E4XAno+DPYujBnLrkCjsLTvnV9bknzT03U/0Tv5/u2QRozrpRzL3q7Uk5BcYXK+Qyzn92APIOpHMUlRdw6gPVhlcf+GZRw2skeFks6z5P61W2emAuUxqh5XFcBrUBViaR3Ro27LqH9Fq67CxnIyv69dIrp5wVJ/w/i1dC/a1h1Qi4fXlM3jrRwU/7xOXhNUZzGRSHpw3ltuntn0mHtbPl+93ne6xY5toobRKmPy3tbLgs0eFgPSr321n2wlx/kIdYUioN/+5W0gccrHvecnSnuSqukLd+eg0HJ1t+PaD7WzfeIrtG0/pbSeRSpi+eDj/ePoGHQM3jVZLekYxicm53L98YvPyelUDX3y7n9ExwTpusIaWt8e2XfG88ORsQoPdRWmiAsyfNQRrawtS03Rn1zQaLa+/+wefvL0UF2cbUdtkZJVw6NglPnvvNtDCHSu+BCAnt4wdexJ4/41bAJBI4J/P/UBici7hYdeWtnFvcCArHQ8bW6Lc2n/v7BWWeNrZkltR2W47lVrN+tNnuXfU8O7sZp/zdz///+49RIyfDxHdnAmz52Iq607Fimo70t+XMNfuDVJVKpXUNTT0ima3Pspr6/jhdJzRdhLoce324X4+bD6XyPGMrC5/zv9p1Ez84OgRZoaGEejgQGZ5BbvTUprbXCwo5vbR7ZtTGuPNHft47voJ+DjYk1VWzmt/7eXTG8VNCrfmkfGj+SvxAmoR97rEgkKWff8Lq26a22ffG32UV9VyOjkbJztxwWQ/RwduGhzJ9yJ+f09v2UagkyP93LqeLXU5yoYG7v95kyj/BytzOfeMGtbtfRCLtZ0CSyvzPju+CRMdZYxrmE52uCFszCx5YsAsnhggLqDhbmnPW0OWtFm+//oX2ywzk8hYGT6dleHT26w7OeO1do+j1mqQS2XM8Lr2Jsv/DpzPL8Q1qOU+WV0vGL2+un0PG86eE1XZZYiM0jL+tXUHH+4/8v/snWdgVFXexn93embSeycQEnrvoCBSpNmxYMHe2+76Wld3Xde+1nXV1XXXihVFUVGQpgiClFBDKAkhIb0n09t9P9z0KZkJaeD8viRz5pxzz512z33O/zx/Xjp/PhNTvee4WnvgKLOHDWwWyn/KyWPGYPc+2L6wMfcYT67dSH51x0kBvXGito6XftrCf7bu4A9nTuWq8aP9Fp9D1NICfmpEOMdrXMeTU1bRKcHcaLVxqNx7gnCTzcbh8spOJeL2JsT3VO4UEEiLdR/k0RFWuxTgGBI093cnlhfmVxIdG0KQtiV4pKZaz7rv9pKUGsmUGYNd2jT5lXcXSoWch6+YxYRBKTyxbC0ADcbOBeS4Y1haHA8tmcXQfu7vkcZlJCMTBJ91swCdp1bfcs+gVUt55p6+cQHThqfxype/UFXvefear+g0Kq6fN5Fr5o5HJjs9drefcr9SFyydxuwLxvLLmv3k7CmktkqK7AuN0JE+RLqYT501lNjEtglKco9VcOf/LSMuNpSH/rSQiPCWiYhSIefNl67mrgc+ZvrUUgYNjPda7o0nHrmQj7/4jZLSOi6/WBL/pkxId6nn9CFhXWWVnrCQIBex3BtFJTUMSIuRIuUESEuVbtTzCyo5UVzDvY982qa+0WT1ue+TYU/1fwmSRxGuTkdARq0ll0h1JtXWI1gckrWCVhFDg62wuZ5CUFNu2kekOgMEgRrLUUz2akZH3dAjY/ZEdmUFt46ZyJeHfUu4NyE1iZX73Wcjbs1/ft3BgiGZJPfYZKdn6Kvnvzkrj8kj09i8O4/kWOn3YtOuXKaPH0j/pK4RmM12O7d89jX/W3IRQJcIZ3uKS/nDV9/5fMN093T3GalPhhO19Vz8zkfMHTSQS0YPZ0paao9Zvljsdv741SrK9foO645OSuh2S5ZrJoxhVfYh3tq6g3OGZJIYGtLpvmK00nWp1mRiSrJ0kxYfHML7e1oiL3QqFW9s3Nb4v7JT4rlKoWj+niWHh6H2kISlI/pHRXDdpHG8vdW3pJP7S8u44O1lPL1oLtPT0zp1TH+oaRSTfzx8lPSoSMaluCbozj1RyTULJvDNLwc6tGRp4t6Z01h3JJeyBu+fQaPVxtXLPuftyy5kZGLH8wdf0Vus3Lb8a7KKSnyqf+9ZZ/TqIsWoMwaxb4sUFWi12FCpfUsGHSBAAP8xOqx8enwrCxJHE6bsnV0lAU6Og2UVTB+QBkjC8E2ffw3A0cqqLjtGuV7P0o+/4OFZM1g6frTHeuuzc5k9rMVu4qud2X4L5ha7nb+uXg/AF3vdJ7ntLHqLlSfWbmT1oSO80miD5u/1bkRCnHvBvKJzNhG7i0t8CiTYXVzSKcH8YJlnMX5kQtfNNboHJ07RCIBM9vv7fXrtuVWMGp/GkuunY7VKlo5/uuF/WC12GupNXHfHLC5c4lsiwq5mzrhMpg5LA+CLTfv4eH0WZTXeA948IRMEJg5O5eLpIzh7dIbXwNMQrZrMlBhyCjwvBAXoGuo9LIQsmjyUmaMH8sWmfXz+0x6KKuv87jsqVMv5U4dzxawxRIacXt/tU04wBwgODWLe4gnMW+x7NGqTh7k7BEFApVJw313n8OzL3/P6i1ehVMjdlgMeM7wCJCaE8+Af3rqF/AAAIABJREFU5lPfYGbprVJ091fL7iAoSEVNrXSBsNud5OZ7X3kGiIzQUVdvorrGQGSEdPEXRbz+6CTGh5OXX9F8419YJPmUpaVGExcTyvN/l7x/ZDIBu92JTN4zIpdcUJMZdgH7az5EhpyhEUvYX/MhSpkWlUw6N7vT3KaexVFPhDqdKksODtHK8Iir2F/zYY+M1xuDIqN5e88OYrXBPomEZw8c4JNgXGc2c+vylfzn0gtIOAnRra/RV88/v6iaaWMGkF9UzdHj0vfx+oum8O5X27pMMAeoMBhY8oG0UPXo3JlcOGJop/tauT+HR7//EZPN7lP9WRnpjHcjEnYFdqeTVQcPs+rgYWKCdSwYksmcQQMZl5LUqS2mvnC4oor/+/p7cjqI3GliydiR3TKO1oxMjOdv82fx2A/rufidj7hjmuQHfs7gDJ89/NujU6l4aN2P9A+PIL+2hiBli8CYHBFKaeOOjc5GY4QHaXhp4xYGRkdypKKKiJNYVPjDjKn8lHuMIxW+3chXGAzc+OkKZg6UbrhvmTqBscld57GbX13D1uOFrM45ytbjhYDkKf7MorluBXNdkJr3V23HYLby2VppYWLupMFeI85D1Gr+Pn82t3z2VYcLV7UmM1d88Bl/Omsa10wYc9I5DbYXFPHAt6s5UevbZHZsciJXjuvdKNPrHj6fe+Y/B8Azt/yXu/5xBRExfc/PPkCAUxWj3cL6MmnRb0XhDiwOW7fbsYg1tyHacxCUQxDCX+/y/mtMJl7Z/isb849RqtcjIhIZJF2rRsTG8+bC8zuRiafrmfDfNwCoNBrblP9j9jwWD+lc3ogmQbRcr+fqj7/w+ffeXxxOJ3//cQPBKmmO0TrPRWFVLR9syWJvYSlPrJTEbpPVjt7iX6BVtdHEzZ9/xZ7i7vPJBdheWMTF734MwAdXLKZfRHgHLVoYHh/Ht9mulgQdRYl7wtfF7N1FJSwZ4/881Ztg3tU7WtuTUzQYp7OtiJqRsBWlwnMkvsWWQ3H1fdgdpdgd5YhI9zBFVXdTVHV3m7rBmrNIjVnmpheRWsNyag3LMNtyEEXJ416lSCNMez5RITcjCJ2z5ulJ8g6Xcs2tMwFYt0qyQLFa7Pz3izv5bfMR3v/3hl4TzEGKDgZYOmccV80aS3ZBGVlHitidW0RRZT11BjMNRjNmmx21UoFaKUmJUaFaEqNC6RcXycgBCYwZmESUjzs3AT562L1GdzJsfuXOLu3vxgWTuHGB/zmfvBGiVbPr354slLoegxcrT51GxdI547h69jhyCsvYcuA4BwvKOF5WQ2WdAaPF1mw/GqRWEhEcRL+4CDKTY5k0JJXR6Yko5N2Tt623OSUF8+5i4IBYJo0fwPsfb+GGq890Ww60ea41TlHkDw9+glIpRxRFLlw0pvm5tNRoYqJDuPO+ZURFBtO/MfLb4XDyzEvfc7ywCoPRQlmFFG197RXTSE2O5L675/GXp75CqVRgtzt46i8XoQ1StWkDUFZRz7VXTKNfShQTxqZxx70fkhAXTmK8NGFITAjnvAWj+ePDknAnlws4nSLPPHYxmh6I9nKIFo7Wf4dGHoFcUJHb8ANaRQw2pxEBmdt6OkUcVmcDMZoR1FrzyK1fhd3ZdVuEOsuYuATGxCWQXenbSuiszHTCgzTUmswd1j1cXsmF/1vGn86axgU++gu6wyGKlDXoCWlM/NO07bA36I3zh7avgfvzb5S6RBFZKxGrO7TephuMB75ZzbKde7hh0jhmZab7fH47Cot4/Zdt/HLsuM/HDFarePScmW3K3lizlZhQHelxUUTopBvPbUcLGJIUS3x4COv353L5NElce3fjTq49yzf/9Qq9gfe2Z/He9izCNBom9Utmcr8URidLdiiDYqJRdvK9NFitbD1eyOe797PhSJ7PkfX9oyJYNMx1a2NX8+GO3RyprCJSq6Vcr+fxRk/px9dsIDxIQ6hGg7yDLWmrb7m2zeNnZs9lZ3ExhfV1jIlPYEJSi9CbXVJOWlQEAPVms0+7XNrz1/lns+5QLseqaxidnMDMjM5vr1bJ5bxy4UIue0+6tjR5gnbEhqN5zX8zYqKYO2ggk/qlMCIhDp2qY/sOhyhSVFtHfnUtuVXVZJ0oZkdhEZUGY4dtWzN30iAAco6XM7hfrM/tzhrYnzvPnMyrm7Z2WNfqcPDMup/5eNderpsk7QhYNHSwT37uTYsim48V8OGO3c2vmy9E67S8cuHCXk/4u2/rUUZOlbzcf/k2i9/W7id9eApR8WHIPQQg/Pntm3pyiAECnNLo7RZeyfkBg8NKRkg8r064hoQg3wVDvxEbEC0bAQeizL8kkr7gcDpZsuIzDlVVIpfJ6B8ejlapahalDVZrnxDLAe6eKO3iK6qvp8Zs4sdjudSYOrbK8kZ2eTkOp5Pbv/jGZ7FcIZOhU6vQmy0+RTe35rE1kiA+MjGegdFSwEhKVDg3nzWRinoD544eAkgBYwNjfQ8oqTObuebj5eSU+xep3XQuAAaLFXsHOUOaaLJ/XPLhZyy/ZonPO/48JRA9WlmNQxT9DgLZ2YEvexNZxb4J6+3JLvd8/9ndyVDjwx/D5ijF4aymRv8BotjxAoogKFErB6FWSvMtg2UTNvsJdOopKBVpbepqGuu0xcGJqjuoN36DTNCiVmYC0tzBYsuhvO5Z6k3fkxb7OTLB9135vYHRaCW80Tlg9UopSGPBRePQBKnIGJJIWfHJWRV1JTKZwPC0eIanxXO1G+/xAKcedkfHv6WCAENS4xiSGkis3MTvQjBPSozwGF0OUgR4EzcuPbPDck/IBIHXnvcUxQ5/uf9ct8/9+f8Weuxzwtg0JoxN86vNTUunw1LX8nmzhjNvln+JObqKUZE3IOJsFsdb/98ed/WiNUO8tulJ3totZQXPqargxVkLOqyvVii4evwYXt30q0/9VxtNPLJqLc9v+IWJqcmMTIwnJljXRvS1OhyYbTbqzRYaLBaqDCYqDAaqDEZK6xsoa9DjEEVeXyz5Es/OdLUF6im64vyBNq9BR+cPtHkN3J1/kEbFJ6t2EqzTNK+Irli7h9jIzke3hwdpuHLcKF77ZZvHOnuLS7lnxXfoVCrGJicwNC6W5PAwQjRq1Ao5ZpudCr2hOWJ3S34BRXX1fo/l0bkzXW4WZILA4skj+ODnXYzpL4mwsaHB7Mwr4rqZ47E5HH4fpz11ZjNrDh1lzaGjzWVKuZwBURGkRUaQEh5GtE5LpDYIjVKJUi7D7nBistsx26SIkfIGA4W1deRVVZNdVtFhgsX2yASBv82b1WFS3q7gcS9JF2tNZp8WitpzuKqSt3ZuJz0yigsGD2F59gEWD5UivyYPSG1egDlRU9cpMXR51n4uGdNyLfh4516WjOt8NP7A6Cheu1i6vl3/yZc+39w2caSiiiMVVbz2yzYEIEqnJSE0RPpOyBU4nE70VitGq/T50FutlNY3+H0cdyxfvweb3cHhgnL+eqOrV7A37jxzCrmV0i6uVT4kPz1eU8tjP0jCxBNrNjIiIY6RifH0iwgnQhuERqnA5nDSYLFQVFvP4YpKthdIN951Zt8/R2qFNLX718XnEhfS+zePL97zfpvHDruTw7t9X/wLECCAd2I1ofw46+EeO55o2Qac/HzBE1tOFHCoqhKtUsnKy64iPaKth6+/c4Lu5OoRbe1M8pZ/wg6Tb4KpJ45X1/Lgd2u8RmWPTU7k7IEDmD4gjcTQEMKCpOhakRY7sp0nitl4NI8V+w96nd817Vp89Pt1fHz1pc3l0SE6bj17EoMS/LcNcTid3Lp8pU9i+ajEeOZkDmRyvxQGREW4BLk0WCwcrqhiS34B32UfIreq2mt/FXoDt3z+FZ8tXUKQsmOpY1h8rFsPZYvdTn51Del+JI93iiK7i3yLps+vqqHOZG5+73zBZLNz3IP/u0wQGB7v+8J/ZwjXXd78f53hSxw+COYqRTqJkS2JjQsrr8dmP0F48BLCtBd32L6y/g3qjd8QrJlBUtS/kMta3g+Hs4YTlTdhsPxKWe3fSYh41s8z6pgj9eW8lL2WA7UlGO1W+gVHcnX6ZM5P8X/3XlxCOMdzy2moM3H4gPQ78eenpeSLRoPFYxBBe9YUZ/OH3z7z+/jt+XLmrQwO6x0bn546h6M5JTxy9zL++Oh5TDqz49wkpzMBm/jO8bsQzAP0DVqL3d6Eb0/1+oJYDnDzaMkK6HC17xETN04exxd79lPcQfLL1tSaXIXHU5W+eP4XzR6F0ym2SUjRmWjd1phtdu6ZPpVyvYHPd+/3WtdgtbIp7zib8rpeOFo6YYxb6xdtq90ku45JE7WwIDVymcCx8moOFVdw8EQ5SoWcw8UV5BRVMDjJ/xul9tgcDg6VV3LIzyijzvKHGVOZ3IlETZ1hSlpql/f53OZfeGT6DF78VdrVtCH/WLNg3j86gh/2S+KsVtW53UE/5R5rI5hvzS88KcEcYHKa9Hq/ccl53P3ltz7bBrVHBCoNRr8jxTvLjDHpxEQEk1/i/QbcHQLwwvnzAWkRb+3hXJ/b2p1OsopKfN667StqhYJ/XyItlHal1c3J8PePunZbbIAAAXoZ6+Zu7T6vpgaAsfGJLmI5cNLWVn0dpyjy1f6Dbp/LjIniL3PPZpKHZJ0CNOdumZOZzpzMdO46cwoPfLuaLfkFXo+740QRe0vKGNkqSlkpl/Pmhm1YbC2C+91zp3Z4Dv/Y+As7Cr0vHAyKieahWdOZ1r+f13ohajXjkhMZl5zIHdMmsfJADk+u3eg1ICGnvJLnN27i0TkzPdZpQqdSkRYZQZ4bIT6nvMIvwfxIZZXbnXZhQRrq2o1XBHYXlzLDj5wuh8orPNrxpUVG+LRD71RCFM1UNbyBIKhJjPxnG7EcQC6LID7iCXJLZ1Fr+JS48L8iE7rOP7nc3MCSn9/GaG9ZGMiuLeGhnStwOJ1c1G+Ml9aunHfpBJ548HMEpMhygJg4KafQ0ZwSEpIjumzsAdri8CG6uit46Jb3ePrNa5off/vZb4Q1Wisfzy3nqltdf5PeeuEHFl4ygaTUlh08JYXS71FCSvcmfQ3QMQHBPEAAP9lZWsxvJSeIDtKSGelbEscgpZLnz5/P1cuW96nImJ6ir55/++zNJ2tdYLbbMVitPHbO2dQYTX4JaF3FvMEZPDR7htvnrp4+ts1faLtI8MTl5zSXP3WFf9G2fYUbJ4/n1qkTe+x4713RcXSMv2iVStLC3U+aqw2m5ghzEWl7nb+ecRa7o/l9F5EWb7qKGen9efeKxdyxfGWPid4nQ2l1Az9szUEmE0hL8H9S2iTc/POiRfz5ux9ZsS+7q4foM2EaDa9etKh58aKvMP7szudtCBAgQN9D7GbB3GiXdhOFnGbi38myaOggnj93nt8LBvEhwfz3sgu556vvOgxCeW/7Ll44b37z48e/XsdVU8fw7e4cJqencLSs43wle4pLeee3XV7rzBucwXOLzmmTo8UXZILABcOHMCoxnus/XeHVsuaDHbu5YPhQn2xKRiTEuRXMD5VXsnCIO5sQ9+w6Uey2fP6gDLYcL6SgXXLR3cUlfgnmB734qne3HUtvYLTswOGsRauegELu/p5brRyEIKgRRQtm6wG0at9z3HXEJ8e2txHLW/O/o5v9FszPv2wS/TPiMButTGi0qmtCF6xu9jcP0HUMHJzAJ2v+r8eO115KqK7UExMf5rXNzfe2veeuqdTz1UeS5eNtD3TsZhBA4tWHPmPVh5v5vvCVLu03IJgHCOAnh6oruW3MRD7P2e9XRPL4lCSeXXQO93/zQ6eT9Z3KtD5/6HzCwr5OtdFESngYr160iPu++YFvD7gmEuoOFo+SIpD/vmCOX36Lve1v3FUoZDL+POesXk9w2BWkhIXx2Mb15NVUc/+Pq8mMbIk4WLk7myWTpHPcllfYqQQrC4cNYsl7nzIwJorcimouHt25xGSeGJOUwDc3Xs0D36zm57z8Lu27qzlUUE5qfAQNBnPzb1JnvhMKmYxnzz2HwbHR/GPDL11iF+MPmbHRvH7xuaT6kegsQIAAAfzGUQz2/C7tskTfwP/9+AOlBj2l+gaMjfZs3+ceof+rL7jU//ziyxmf6JrE+ZfC47yzexdZZSU0WCxEa6VI08lJKdw6biKDojwHubRuCzS396VtdzNvsCSsPX/e/E4nVVfIZDy9YC77Ssqavb7dseHosTb3NlHBWmYPG8hveYVcPnkUj36xxutxROAvP6zzOsef1r8fr1xwcvk1+kdG8Obi87nwnWVYPdjNiMALP23m3csv6rC/EfFxfO0mqt9f//WdHgTzofGx6K1WV8Hcz11m2V4Sfo48DQVzi13aUWm0bCe70PU73x6Hs2s9wI/pPS8QFRhqcIhO5IJ/8/CRbqx2Ac4IBBecFuhCNBj0Zv582/vcev8CqirqAemzeyCrgOceXk5ZSR1X3zaT0RMH8MX7m1m1fAePvriEtIGx5B0u5eP//ET+0TIAXnj0SyafNZhps4byzqtrKT1Rjdlk47q755A2sHstmAJIBATzAAH8JESl4t9Zv6G3WXl3XxbnZwwmKsi37V/nDR9MsFrFvV9/36VRnacKTecPnLavQZNgLpfJePH8BYxIiOcf6zcBdIuIplYoeHDW9B4VioPVKoLVquZI595mREIcTy2cy6DY3ruh7Urum3oGu0qKGRYTS3pkJGMTWqw1rp3Wknhnemb/TvV/wYghzEhPo7C2jqSwUKJ0Xbd9tYkonZb/XH4h3+zP4YWNv3i9Qe9NJgxJxWCyUFRR2yWLR9dNGseU/qk8+M0asst8Swx9Mijlcm6ZMoHbpk3sdGLd7iY/p5jI2FAAQiN731M9QIAAnUVEtKztlp7D1GrC1GoGRUaRV1vDoapK4oODGROX4FI3IijIpeyZzT/z5q7tCEBccAiJUSEU1kvRx18dOsh3Rw/zytwFzB/o6mHbvi3Q3L6jtt1NTLCOZxbOBei0WN5EqEbNLVMm8Njq9R7rNFgsHK2sJjNGWqhvSk5fb7Lw+rqt5FXUeD3Gj4ePer32xeh0vHT+/C653mbGRHHHGZN46actHutsPnacwxVVzefjieEexOZDXiK63eEpwnxYXCxGq41vs9sG0ewtKUUEn5PYHvTy2g6PP/0Ec6dTD4BSnoBa1bGgrJD7npT2ZLE7HYii6PubBxw5WEzGkJO3yxsYEssNGdOosRqpsRiptRql/61GGmzmUyIorbvP4aW/r+SHr1t2utz/+EXMWtDWfjL3cCl3L/0Pr7x7IwB/v/8zrFY79/3tAswmGy8/+Q26YDUPPXkxmUM7XrABiEuMoCCvgui4UPbuOEZDbUsSaG2wmvufWkz+0XI+/s9GRk8cwMVLp5F/pKy5zoDMeC6+eirrvtsLwB0PSXkLs/cUYDJYeOjZSykqqOLtF1fz15ev6NyLE8AvfpeCeUnVndQbv3T73MCk/S7+WH0Zo1kS4gorLgMgM6UA4ff5tvYYwSoV148cxw95RzgvY7Df7c/OGMCqm5fy98Zkgb1h29GbnJ0xAKD5NTjdzr8p2VIT100c2+zP+PTan9h8rOs8y2ek9+eh2dMZ4Ie/YleQEh7GT3feyMe79rJ8zwHyq73fQHUHw+JjuaXReuWcwRn+zFf7PKuPHuGcgRnNQvn6Y3mc3X9Alx4jQhtEhNZVdOhKBKRFsrmDB7J8934+2LmbY1U981kRgPGp0uT2opHDmD/EvdDRLz6CtdsPo1V33db/wbExfHHdElbsO8jrm7d53TLeWeQyGecPH8Jt0ybSr49Hlb/2wCdk78gD4P2dTxLVwdbUAAH6PNZdiJY1iNYscBSBWA+i/wmehbAnEYIu8a+RswrR0phs2vIzov0oOKtA1IMQDvJoBOUYUJ+NoJ5Kp271RCPYJWFPtOWA/VDj3xzpudbY9uAs9V1IlsXthnYewwnBIby+4Lzmx//ZtYOnNv/EmLiENuWeWJGTzZu7tpMSGsar8xYxKk5KAOdoFFze2rWd57Zs4k8/fs/IuHiSQkI7bNvU3lvbnuCBmWd2qS/1BcOH8Oz6n73mGckqKm4WmJ+6RLIK+PN5M/kp5xgXjPMuWr69dYfX52+ZMsHtgkdnuWrsaN7Y/Btmu+fz+WzPPh6ZfZbXfobGxSIXhObPTBPF9Q3Umy2EatQeWrZQYZAS1remaZEjMzYai5tI+Hqzhbyqap980p2iyOEK9xHvcpmMod2c8LM3kAnSZyVIPZbkqLd6/Pj9gz0L8ANDYlHI/AtUuPPqt+g3IJY5i0Y1i7eR0SF+j2tASDT3Dpvj9jmnKFJrlQTo/x6RFpNWFGT5fYzuprvP4dZ7z+GSpVPJ+u0Y/3r2O4/17HYHb7wg7X6/9NppfLlsK68+8x26YA3X3nY2n72/mTdfXM0Lb1/v03Hjk8LZvC6bGeeMYMfmI8gVLTsQ+qVL39HgEA0mo39BZ4XHKjmQdZynH5ASpaYOOPkcYwF84/TOmhIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQL4yO8yFFmnOQsQcDil5B42xwmstiO9OqYApw6DI2O46pvPuW1s5xMLJoSG8PpiKWrmQGk5y3bu5oecI11mcZEWGc7oxAS/Mrv3NE2vQXecP/Tea1Bndo0ya4rUeWfJRewoLOLDHbv58XAuNg++i95QyeXMHTyQq8aNZmzyyW/r6ywhajU3T5nAzVMmsL+0jI1Hj/FrfgH7S8q8Ri6dDGmR4czMSOfcoYM8bp89HVibl8s5A1uSAX1xMLvLI8x7Eo1CwVXjR3Pl+NH8dvwEqw8dYX3jzpLiLrRqCQvSMC45kYmpyczOTPfJz7umwYTeZEEURewOyTKpM77w7ZHLZCweNYwLRwxh3ZE8vtx7gJ9z80/alikjJopzhw3m/OFDSAj1PyqpN8jPKSZ9uJSINBBdHqA3cRg/Qa69vPMd2HNx1j0Itj1dNyhfERsQ9W8hGt/zHM0uVoCzAtF2EIwfIcrTEELuR9DM9u9QNbchWn/tgkF3P05R5KVtUgTis7POaRMh3hTde9u4iWzIP8b24hMs27eH+6ee2WHbpvae2vYESWGhnDd8SJf2qVOpGB4fx/bCIo918lrtBPtixz4WTxhBiEbNotHed9Ueq64hy4Mnd1NU+WVjRnRi1J4J1aiZPyTTa8LttYdzO4wwD1IqGBAVyZFKV8/qwxWVjE/p2I7BnR1LerR0D6JRKBgWF4tMEFysJrJOlPh0r5JfXeNxfp0RHYlGcfrJOiqlNBc2W3N65fhL+k/gg9xtGOwWl+fuGDzD7/7e+OhW1ny7m+UfbuF//1oHwLjJ6cxZNIopZw1GpTr591AmCESqdUSqdcQGnRrzxPZ0xTkEadUk91NTXanvsO702dLOmYUXjcdhd/Lac6t4+KnFzJg7nOrKBpZ/6Nn2qT1xiRGs/HgbF189jT3bjyG32Jqfk8l82w+tUCowm9pqIin9o0kfnMCf/nahz2PpTvIOFHHPohd4aeWfePKW/2G12Lj35auwGK288sAn6EI0PPCvawDIHJUKeE7KeWRvAXcvfIF7npPmZ/OWTGnz/L6tR/n8jXXk7MrHpLcQEqEjY2QKtz+xmLjktr+dMrmMwqNlvPnYlxzYnodKo2TIuP7c9OgFJPXvXFT+6ffL6gOhusWE6hY3PzZatlBYvthLiwAAU/uncvjhP/b2MFxICgvt0XHlVFfwwbmLWZ3nPdO8rwyLj+WphXP527xZZBWVsOtEMYfKKymoqaVCb6DeYsHSaoKkUshRKRSEqNXEBuuI1mlJCg9jQGQEA6IiyYyNIkyj6ZKx9QS+nD/Q/Br4cv5Ar70GHYn+41OSGJ+ShNFq49fjBfxWUERuRRX5NbXUmczNvu5NW3DDgzT0iwxnYHQUk/olMyk1Ba1K2e3n4Q/D4+MYHh/HnWdMxiGK5FZUcbSyirxGq5aSugbK9HqqDEbqzRb0Vitmmw2bw4nD6UQQBFRyOWqldEmKCNIQqdWSFBZKWmQ4g2JjGJ0UT2zw6e1/XFBXyztZWewuLeWvGyWPUZPNRoPFdaJ+KiKA9Bnul8xf5s4EoLC2jn3FpRwoLaewto6S+gbK9QaMVhtmux2bw4FKLidIpSSo8WYwSKUkSqslJTyMlIgwUsLDGBQTTUZstN/WPKu2ZHPJ2aPYfrBzCVQ7Qi6TMXfQQOYOGojRamPHCUmk2FlYTG6l9L2vMhgxWq1Y7A7UCjlBSiXhQUGkRoQ1262MTIxnfErSKSOSt8ZqsZHYyUlqgABdiWjdDp0QzEXrZulvze0gmjqo7QOyaFCkgjzFt/qOfJzVN4Cj0L/jOPIRa28H7aUIoX9rLOybuQ46S05lBYX1dYSq1UxO9vx6jolPYHvxCXaWFndJ255g4ZBB3WI3NyIh3qtg3jrwY+exIhZP8E3k/uaAZ1GzyY6xO0TdiSnJXgXzorp6jlXX0D8ywms/IxLi3ArmB8srfBTMXRcLhsa12KRoVUoGREVytN0xdheXsHhUx8nXvSX8PF2DSLTqSchlYVjtuRgtO9Cqx/fo8WM0IXw0/QZezF7LgdpizHYbQ8ITuH3wDCZF+59HaEBmPLf+aR433TOX3zZLwZprv93DPx77CrVawYw5w5mzaBRDRvp4bQjQJSSltFjvxMRJtltNCTXDInSYjFZEp4jgg+AdnxSBw+EkIjqY9MEJ7N+V77FufZ2RZf/eSPaeQsxvbmDslIHMv2gc/dJjqCqvB+DJ+z5l9rmjmTR9ELt+zeXJ+z4FYMIZmcw9f0xnT7lLsNsdvPnYl1xy+yxWvL2R1x7+DF1IENfct5DP31jHW39bAcDzX97T6WOs+2I7L/xpGfEpUVx080xiEiMoO1HN3l+PEhXnGoQjkwk8dPlrjJqWwW1/X0xlcQ3L31zPX695k3+vfwiFwv850O9SMA/QtynRN/D1kYPsKS8lp7qCOrOZeqsFpUxOuEZDmFowP7U/AAAgAElEQVRDcqOH4KjYBEbHxjMhIdmnSdi647ncsGqFX+NZt+R60sNbVq9CVGqWHdjLuHjv0b1600r0ph+QCcGEBV+DWtkyGaqse5zosL+0qa+Uy5mYmszE1GS/xteEw1mL01mPUtF1QnG94VOMlk04nQ3IZCGE6a4gSD3Vr/aCoCFEe36HdU/m/N/bn8UDm1a7lK+6ZClDo3vW08/XCFKtSsmsjHSiZUEcUYZTFWxgSGoc00cO4Oe9ecRGSOLw3twSMlNiGJ3ee9Hk/iAXBDJjo8k8TRJw9iSpYeHcPnEi5UYDFwyWosqUMjmZUT2XxKinSQmXBO8FQwf1yvGvPEdKojptZOcSqPqDVqVk+oA0gOa/vweS0+Ooq+qbSV8D/L4QFKmI9uON//fzrZHjOGLNXdL/rcVyWTiCdimC+kyQxQN2REcBmNcgmj5rrN8SWYagQRb5HigGuXh4e8V+BGf1VeB0l/9BAaqxCPJkEHTgrEG0HwJ7212zovEzcEginRDxLzoUzYNvRXBc5P45yzpE8w8tj+WpCMF3+X4+Qsde0P5wuFo6r3qLhQGvvtBh/RpTy3t4Mm17goVDuyfJaGywzuvzDeaWRfqUqHAKqmpJjep4x9YmLzl6zkrvvmusL2Lx/pKyDgXz4QlxfOlGeD9U7t43vD07T7guQgxr5ys+MiHOVTD3EJXfnoNeBPORCfEenzuVkQlBRIfeQ1nt4xRV30VK9P/QKNvvuhAxWrZhd1QQqj23y8eQERrLG5O7NrmiXC5jynRp3jtl+iD27y7g3y/8wHdf7uC7L3eQOiCGK26YzsxzunZHRgD3aIJaAtFkMil4RqNRNT6WRHKnKCL3YQkzpX80/10pCcTzLxrH/IvGNT83qfE9j44L5fFXrwLgtgcWuPShVCl46t/XuJRfdetMn86nJzlz0WgWXDUNh8PJ648s56HXr2X6uWOoLq/nizc9J5j2BZPewhuPfkFS/xheXXUfGm1LPo8r7jnHbRu7zcGZi0Zzy2MtcxhtsIY3/7aCnF35DJ+Y7vc4AoJ5gD6DwWblL5vW8dXhbJekKyAJkSa9jRJ9AzlV0qRhbb60rX/r0luJV/RM9Gl2ZTkRGg37KkoZHhPnMWu9zV5AqG4JauUISqtuIS7yZRTyBIzmjeg0c13qW+15WKx7USr6o1GNAkTM1p3Y7MUEqSegkCd4rOtw1lCn/x8iIhrVKHSa2YCA3SFN3szW3QSpJiCXSxM3i+0gAHZ7IVrNTATBfcRyqO4yQnWXUVRxKfGR7zW3VSn6IwgaHA4pW7uIFekGzInZug+NagwKuTSBdYoG9KbvUSkzUSlafqRs9nwstgMEqacil0W4LbM7SgBn4znsdemjLyL6mdU762gR154zgXdXb+dERS0AhRW17M6VIpkGJkaRfbzslBHMA5wcMVodd06cxJDok4/Iza97h7Sw6zhS8zIANmc9ScEXUqz/miFRj7itW6L/DodoIFg5EACrs5ZY7dmUGdcSpEii1pxFqGowMkGF3pZLmHoUDqeBgvqPAEgLuxaFPJRyw3pSQ5dQZ9lLg/UIckFDqHoo1aZthKiHIGucfhTUf0Ra2LXYRSN66xFCVYOxiyZMtkJ0qgHIUDQfR6dMO+nXJEDPs+ja6bzxZ0lALCusIi7l9F0ACtC3cVr34jB9Kz0QWhIPqqJXemwjNrwkJdRsjTwZWeQyaDUvAxDkSaCagqCRRBtnzTUgNoqPohnRukNKyOkrohln7R/aieVyBJ2UfEzQ3QKytkkoBQD7McSGpxEtG1u6skjb/9G/hhB8t9fDCqopHp8THUVAK8FcFoEQ1HFQRHfRtAtRq1T6FCDROmnnybTtbjQKBYNju2dnTkcJLOtb7Wrbf6KUVXsOEaRUIDTe73x2p6t4aLBa2Vdc6rFPXyK0O0t6lHchHOBQRSUdSakj4t0L7znlnoXqJix2OwdKy13Kh7Xr050of6SyCoPV2mFy14Plrv039+th7K2pMyzH5ijDKUoL2A5nbfNzpbV/QS4LRyaEIJeFERF8tUv7etMqbPZ8HM4GnM4GnKIBgPL651HK45AJwchkIUTorgRAELomWW1UyM1Y7ceo0X9AXukcNMohKOTSd8PuqMDmOIHDWU+Y9qJuEcy7A5PRysbV+wD47sud5B4qZcqMQdx49xyCtCo2rN7HS4+vpLigmitv8t/6paept5kw2C2ItNwDJwZ1/L3sM3jQcgJ0TNMO0ugEaVG13yBpXhQWFYzJIF1LfI3Ob8/uzYcxNJi44c/ntRHLO2L+lW2DO5ssYcoKqwOCeYBTE32jBcUFXy7jaI3rVriOGBIVQ7yu56waxsUn8e3RHIZExXoUy1sjl4UTqrsUg3k9YborkcnCqax7guSY5c11nM56KmofITLkjkaRexSiaMViO4xCHktJ1Y2kxH7npa4Jh7MBuTwKmSBFjtjshVTVPw1AqO4KymruJT7yNWSyUEqrbyVMtxS5LBxB8M+GwGo7jMW6l1DdZVQ3vAZAmO5KzNYsDOYNhOmuoKz6LhKj3wdAb/qGiJA7KK95gMSod7A1bimuaXiNUO1llFX/gfio17HZj7uUGc0/02CSbmQjQm5r7kMmO/VsCTwRGaLlm1+zsdjsRIfp+ObXbPQmC5nJ0gWowWhm1ICeE8sbjBZufv5zCspquP2CaVw5Z2yPHbsjempsE255GadT5KzR6bxwx3ndcgxv7C0tbSOYL9u3lytHjPSp7b2vrWTj7lxkMoFPnlVR2PAZCcHSDUSteSdF+hWEqDz7kBrthaSH38rxeun7q5bHUmvZgyjaKdV/T7AqgzrrASI047A561HItGgVKYSopaiJYJXkNykiRVYabHnIBTVKeRh2pwG1IpYa807SQqXIiRD1IIJVGeTXvUta2LUcq/svanksEZrxaJWp1Fuzm4/j7Vy3v/kHn16fAD3P/KvPoOCIJKQ8cvm/uOsfSxg5tXsiJwME8IYy8r/+NXDWIJq/dykWQh93EcvboJKuTYLuZkT9q83FoulzBN1NPh9e1L/cLlpcjhD+MoLGfWRVM4r+CBFvQf3fEY0ftOvzdQT1dFCO9nkcfRmdUgr4SAwJ4fPF/tntnEzb7iYjJgpZN4k4QUrvtn6t8+u8fs0FPvWZU17pNtgJJIvBaJ0fuyr8RCmXo1YosNg9588p8SFfypC4GOQyGY52O0UPV1QhgtfY0n0lZS47TAVgSLtFD3eR4E5RZG9JGVP6ebfhcBdhrpRLu0UG+7Crs6zuaewO94saNfoPWz2SuxXMq+pfx2TNcimvMyxv8ziscVexXOiqHFICCRHPEBJ0DjX69zFZd2GxH5aOIYtAqUgjQjOdMO3FXXS87iPvcCnffbmTdav2otZIMtz8C8by2AuXEx3bsiA3aFgSI8b045Wnvu3Tgvkfd0rXl2JTDWHKtt/xtybd2BtDCtDDNAnZzZH5jY+FVtcvp1NE3gnBvPi4tLsnJcM/y6n2vuZKtfRds1k7l2OtzwjmZuteqvXvEBVyC07RiMUm+aCplcMQRSNW+3HUygys9kKcTj1q5SAU8kgM5s1oVMMRRWdL1KyjnOCgmVhsOaiVwwhSjeq2cVtsh6hpeAujZTN2RymCoEGtzCRUeyFhOmmrhafoXU99SG3a9tNRH95wOKsoLL8Ui+1gsy1ISuynyGV9IyHkwz+vAfAoloeq1URqgnCKInUWC3qrpc2k7KxU35PhnZHcj3VLrqfaZKLGbKLabKLGbKTaZGJb8Qn2VniOjmhiWHQsw/y0+RAEJaJT8qLSqEa7rLrLZCHIZdEYzBsJD76huY0gKLBY9+B01gMOj3UV8kRUygEo5AnNlikG84+N7cBg+h6HsxqLbX9j9HZ0c1t/CQ5aQGn1LYToLsZmzwdApczEbM0iOOgctJrpmKzbsNgOABASdAFa9RkYVT9hdxRjMEmRSeHB16FRjcdqP4zJsg2LdbdLmdReEixb96GS9Y59Q3dw3tRhOEWx+cao9f8gXWR8TRLSFeQUlHO4UJqUr95+qE8J5n15bF3JxuP5XDa8ZRvmr4UFPgvmrZELQaSEXNr8uN6SjQwlsnbXE6OtgAbrIUz2QhdhOlY7k6yyuxgT9yqi6MDubCBcPQZRdKCSRVBt3kGCbgEWuzSpMdjyGo+VQ701G4UsBL01lxDVYKpMm1HIQxGQIQjSTZ7FXonBlodaHkOxfiUaeRwiIvLGcbQ/ToBTj89eXY3olK7ZhgYTD1z0MtoQDXEpUR69BP+55sGeHGKA3wmi/TD2Bsl6Q1Ckowi5H4dxOXKt+zxGUuLLdiKgPA5BfYZPxxOCLmojmGPPB2cFyHyIHHbWIxo/btufbmnHYnnr+qF/RrTtgsb5WGPHkmge8ZbP/fRlMqMkoTC/thajzYa2AzG4q9p2N5kxfcPW7khZFf9cs5kBsZH88ZwzWLHzABeOc/XbPlzh2baknw8JuE+WELXKq2BeaTB22IdaoSAjOsolotxks1FQU+v1PHa6SfiZGhFOsLrt/d6QuBgUMpmLuL67qMSrYF5pMLo9hyahvEk490Zm4s4O63ijf9y3J9W+iZTo/3WqXbBmJsGavmdJ4Q+3XfFvhoxI5q4HFzJjjvQ9Uijdv3cDMuOxtkoY2Rept0k2VR9PuxOZnwF4AU4PhC5Y2HXYPdjZNup9TWK8r6iDumZ3SxN9RjBXq4aiUQ5FrRxMVcNbRIXcDEBVw+so5HFo1ZNRKdIwmH8hOvQeqhv+02hTEYfRsg1RdBIdegcAlfWvYbTsICrkZqoaXu82wbxW/x5lNY/QLGQKOkSnEZNlBybLDuoMkil/UvQHzVuHfOkDcOnHWx/eaC2Wa1SjSY6RJt9ymatJfm9wrK6Gb48ecikXgBtGjWfp8DGkhrYdq93p5GhNNQD7K0oZ24GXeGvUcgXp4ZGku5nzvLpzq0+CeWfQm9YQEXKblxoC8ZH/xGrLoaTqZlJiV1Jv+BynqCcy9F5Mlm3N25wEN3UlZM0/LCB9loIbV/lDtZe0PdpJeEgKghKVIoPahv+iC5rd5rmmbX5OpwGh0aNTEFp7qovN0eGORjHf6axv3sbXvsxBRbv2Uh+nG60F8vYRRT0plgMMTo0lIzmaosp6LjhjeI8euyP68ti6Eovd3rxwIgJGW+cmzGlh17V5nBh8LonBrttVtcpURsQ8A0C/0KVt/gKMi5eElYTghYg4EBr9b0NVQ5uF74xIKcJbQJrUjIh5CoAK48/EaGdQYdxA//CbEHE212lqJyBDpxzg8hxAmHpEm+MEkHCIIvsrStlafIJjddWcaKin0mjAZLdjdkiigc3h6NZfy6xr7/Cp3jtPfu1SZmwwcyzbc9K5AAG6A3v9cyhCJTsqu/5FAJyWDR4Fc+zHXIoERcfJ+ZqRJ4EsDJx1bftUdTyfF01ftPVMF1QIOt++cy3IEIL/iFjTNtJPtPyEYM+FPm5x5wvDYmJJDg3lRH09n2Xv49pRvi+kn0zb7iYupG8kOX/ph194YOEMXv1xCwA/5xxzK5gX1Na5lDWRXVZOxtMvddsYfcHk4zxqREKcWwuWQ+WVfgvmrRN+NqGSy8mMiSa7rK29SlYHPubt6zfhixVLgL7Dax/ewsDBXnYntSIiUscT/7yq+fHTb0i5uiaNTmPssBTCQ7W89fEvZB0o5PJzxzNjUka3jNkb8xOlnUqLNj5PsjYSeSvR/I2J1/f4eEBKRAlQXanHoDeTnyt9d4oKq8g7XIo2WE1EZDBqTd9ZIP290LQ4ZLc52iwUVRTXuq3fZOF4Iq+coeO7P9eUJ/qMYC6gwO6swGI7ikIe27y9RyGPB0RkjeKbrFWiHKNle6PwK0chj6TW8DkAomhGqUiizrC8sX3XYzCtpazmYUAkPPgaokLvaTyWA4P5F8prHsJs3QtAceX1pMR+6RIl7rkPXPrx1Ic3WovlQerxJEcv63NWFiuP5OB0s4XvTxPP4K5xk922UchkDG6MCmn621epaXiNOv27aDUzUSuHYXcUU6d/D6vtKFV1kkAVFnwdomihpuFfyGShqJTSDYxS0Y/qhpexO0pwii1RBTZ7gUtdkCLXK2sfx2TdQXTYw4Roz6e0WhKxLNbdOEUjseHPdomnXKjuKgorFpEWt6lNucG0DqstF4ezDLVyCJbG70CbttrLACivvR+jeaO0KKCegFo52KXM5uZmNUD3EqJV88lfXbdi9gX68ti6knMHDeLSzz8hIyqaI1VVXDqs7ywOCK2SxbUWsdsL3U30D7sOo62QtLDr3dZr/dhTHwGxvIV6i4X/7t3BBwd2U23u2QR0neWtX/7ScaUAAXoCmRZBkeZ7faebmzh/A06EUKBFTBTFBh/ShgHWtvMrQX2Wi1+5T4dXT0OURYGz9S5OEdHyM8JpIJjLBIEHpk7nrh++5dktmwhTazh/0BCX4IPDVZV8fTiHpSNHE9do4+ipbdNz3tp2N8Ed+Fn3FFqVkn7RHUeIlzfoO6zTm1jsjo4rIQnmn+/Z71KeU17B3EEDPbbLKnIVzNsn/GxiZEKciwC+p9i7YO4p4efpmuzzdMVXsRxAE6Ri+OjU5sebd0h52+KjQ1nz80GeeeAC8k9U8a+/XcYjL6zsFcH80+O/AvDsmCVEqfvGIt++XVLy4Qdvf79N+bL//MSy//wEwE33zGXx1VNd2gboXuJSJHeLw3sK2gjgG792v/tlzBmZBOnUfPX2Rs46fywqde8scvQZwRwgNuxBQIZaOZCmRH+0u4GODLmpzV+pnqzV/7R73B3bQ0TKax8DREK1FxMX8XSr5+ToNDNIjv2c/BLJc8pk3Um98XPCdFf42IdrP+77aI/QLGa0Fsu16ikkxbzfHL3el9hV5jrB0CgUXD+y70R5dJaIkDuJCLmzTZlCnkhU2ENEhT3kUj8m/ElEbM2LQkHqySSq3kcQFLT+HCsVqS51AdTK4SRGL2t+LAgaEqL+DYAomhqjyqV+kqI/8utckmI+a/NYEOToNGcja3fzFqq7FJ1mTrMoH6q7rPm56LA/t6kbH/lvRNHSHO0uk4W6lLVu766PAAFORy4cPJSz+vWnoK6O5NBQorTd5/3Z3cgENcEqzzeZAXznSE0VN3z/JQX1niP5+iIpAwM39AH6BoI8BXv9YwCI9jzsdfcjKLz46bvbYi5aXMu8IZrb9enL77kD0bq7bZFyvH/HbUaOoBqDaF7bttj6G+iuc9/kFGNRxiCKG+p5dssm/vTj9zy+aQOpYeEYGnMklegbmndqXTp0eIdtgeb23tquyMnmWK2UkLXBaqXBaiG3VtoBu2z/HnaUFBGqUhOsUnFGaj+/dsTqVH0j+jE5MownV27gWEU1jyxfw8A490mba0x9ewHXXXCWOzxFbB8q92w5k1dVTa3J7FLuLsIcYERiPJ/s3temrNpooqCmllQPUeyeBPPhCYEI81ON7VuOcDSnBIvZvYXQtbef7bZ84qg0AK67ZApPv7Eai9WOQiFHLpcRrGu/G7tnGB8l2eL+UnHIJcK8t5J+jpkojWn1jsc6rJueGe9Sb9KZmW3KFl48noUXd/b6G6A1ZywYzXvPfcezd73PxTfPRKlSsPXH/RQdc//7pgsN4ua/Xsg/H/iUu+Y/z+zFE4iKD6eqrI7dvxzmjicvITGt+4Nn+5Rg3lbc9lXo9tame7yUzNYsrHbJpzUy9Ha3dZTyJEK0kvdyneET6gyftBG7femjdT/u+miPJIgLbcRynWY6SdHvurG16BvkNU4sWzM0Khadsm9EVvQkgqBEQNmuzP3r4K5uU7n7+kEnP8BGGoxfozd9R3TYo+2OoUEQVH5FsLuzhjkZu5iOqDVvJVwzmXL918QGn99txwkQ4GQ4XFXJC1s2kx4Zxf3TzmB59gEWD/XDBgBp+fT3wu/hXA02Gzf9sOKUE8sDBOhLKELuw2ndBUjWKoIiHZnKS4CGPNmlSHTk+/6LIza0i+wGQebDApKjFMR2EbvKk0iUqxgEtBXMRXvOafXLefPYCZyR0o9392SxtaiQnMoK1HLpNjchOIShMbHMS88gKcQ1Sr99W6C5vbe27+zZxb7yMrfj2V1awu7Slqhhpyj6JZh3lJizp/jDOdPYfbyYIYmxDIiJYHQ/9+dgsnUumVpfY3BsNEq5vE3iU4AcLx7t7uxYBGBkonsxe1Si+9+A3cWlHgXz7HJXSxaNQkFGjPsFjAB9k0/e2cR7b2yg/8BYjh2V3tMBGXGUFNVgtztYcOE4j22bLDrvf2YFCrmMh//xNYIgsGt/AXX1vbNgFd4q0WexsaZXxhDg1CE2KYInPriVd575hnef+xa5XM7kucP54/NLWDrpMbdt5i2ZQmxSBMv/vZ5PXv0Rq8VGWGQwQ8alERzWdfqWN/qYYH5qYLJIGaJlgg61crDHekHqiYAkmJutexCxIzS+5L720dSPuz7aI5MF4xQNnKi4QhLLg2aTFPV2l1hwdBf1FtdInYTgvmUbE6AtIdrzCdG6is3uyvoaWuUAcqv/RkLIlb09lAB9GEmA7T2v/Oc2/8Ij02fw4q+SZ+iG/GN+C+adyUZ+qvJ7ONePsveQX+fe4681QQolqsbkX0o/k+QECHC6Yqv2nOTcIchRekiAKagmuV4JbDngOA7yfh0eVzSvoWX3KyCPB4UPPpxOV+FBkJ2EMOaurZtj9BVuGjuem8a2RPQtfOt95g3O4K7pU7y2GxoTy3OzfU+K2lHbD3fs5s0t2/nnVQvdtll52VVuy7uC9pYyvcXaA0eZPWxgs1D+U04eMwYPcKlndfhmedLXUcrlDIqJZn9p24WQwpraZh/09osZu9zYsaRHRxGmcR+slhEdhU6lat4B0cTuohLOG+aqCZjtdo5Xu17/h8bHIu8jn5MAvvH9il388dHzmHvuaBaf/SwAry27BZvVznN/XUF8kueo7IfvmAdARVUDsVGhCAJU1Rj46sc93LRkWo+Mvz23ZMzqleMG6H0GDEvi+8JXmh9Pmj2szeOFV09j4dWun8sRkwfy4ld/dClfmftC8/8vvLWWe2+W8uR98s0OzpqcyVMfeQ4wbuKupy/lrqcvdSnPGJnaZmz+EribChAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQhEmHcKR+P2Srk8GrxsaFTIY5r/F0UbTkdtYxvf+2jdT/s+2iMTNBRX3oTZKvmiBalG9enocgCDzepSplEEPpYBuocKwyqCFGnUmDahVfa8r/KOnEK+2ZLNvmMllNfosdjsaNUq4iKDyUyOYfLQfswcMxBdkPvv7UNvrWLN9kMA/PbmPcg7iCAdd9NLAMydMIinb17gto7D6WTiLe5XXS+eMZKHr+pc9EB+aTUrNkmJk7IOn6C4qp4GowVdkIqIYC1p8RFMGtqPGaPTiYtwnyimO8bW9Pp9tWk/OYXlmMw2YiKCmTY8jWvmTSA+MgS1Uo7R4uygJ4nqBiOfrtvNL/uOUVRZh9kqbUuOCA5iWP94Fk0ZyozR6fgTBKRVKkkLPznvP4VCijIuqqjj/TVSMpVf9+dTUasnSKNkcEos550xnHkTB/nc58HjZXzx0z52HT5Bea1kFxAbEcykIalcPmsMAP3i3I+76bP78l3nM2JAAk+8L9kDbMs+TkSIlmvmjefiGSMBeO+HHXy8LosGo5lRA5N44IqZHvt1d66tzxPo1Lm6e1/9fU+Lq+o598H/ArDupVsJDw6iukFK4Pzp+t1syDpKaXUDolMkJjyYkemJXD5rNINTXX1Pv8877HreMhnnZwxhTtpABkfGkBAc3GxB0FepLKll5dsbyPo5h4piKcI1JjGCUWcO4tzrZhCXEtheHqDrUYRLSdYdDf9EFiRdCwV5f0RHIU7LBi8NM0E5Cmx7WhU6EeseRYh4GzzNsR1SdKrY8GKbYiHoEp/GK4r1roUnk4dIcHONFY0gWj2fQw9idzp577csVuw9wPGaOjQKBQOiIrhz+hTOHNBxJH+A7mN9di6zh7XMl7/ame02wtxbpHOQUoGul5OYRmp937o/PD7WJcJcBHKrqhufb2u1suuEa8LOsUmekzvKBIGRCXH8erywTfluD4k/D1dUufVg9+S37i9Gu5WJ3z3tcoxkbQRr5t7jd3+1VmmeM23VPxA97Nb8ZMaNjIxwtbzqiJu2fMDm8lyXcgGBbYseJFjRsa2nxWFnzDdP+H3s9tw95GxuHTTdrzZVlQ3NiTxlcukezmKxoVYruebWmTx4+wdccPkkt22z9kufl6/X7sVkbtFP/vHQRZ0Zfpfwv9yNACwdcCYKQd5r4whwmiFAeWUDlTV6autMBKmVvPb+T4iiyHlzRrJ9z3FOlNQQHxOGwSS5VVx27ng+/HIbNruDeTOGcbyoiuKyOsoq6zl76iDGDk/t4KCe6dt3Vn2eDrbs+5RgxIc6PiYqsdrzsdrz0ajGYLZmUVn3IipFJiHac31q3xuIPp5bgABdQYh6FKHqMT16TKco8vi7PwLwzZYDLs/rTRb0RRZyi6r4flsO+46V8tCV7hO+dAcCAulJUdTqzdTpTdgdvgnFnrDZHbzw6U8s/2mP25+uOr2ZOr2Z/NJqNu7OJTEq1KNg3pVjczidPPyf71m7w1V4LKqo47MNe/hu60FeuP08woODMFpsHfa5ftdR/vrOaoxm14W/8lo95VlH2ZB1lCnD+vH0zQsJ0frmz58SFsZjG9eTV1PN/T+uJjPSfxFRq1by28EC7n39G5fxWfUOth0sYNvBAtbvPMJTNy9AIfe8+OJ0irz42U98sj7L5T09XlrD8dIavvhJWqi9/cKpXDtvgse+8oqr+WDNTnYeOtFcZrTU8dSH64gO13G8tIZ/frGp+blt2ce56+UVfPH3a1Aq3E/GPZ2rVS9tEffnXMHz+3oy72l5jZ7S6gb++OrXzX215nhZDcfLapg/2b1FW3592+3YarmCj8+7lLFxvnvi9ibb1izx6UkAACAASURBVEifj2dvfweTvm1ytLoqPUf3FfLtOz/zp1euZvp5nj08AwToDIKsMfDEWYtM1WLrIcjjcRje99pWFvIgzuomGzfpGiRatyJWLUYIvg1BNRVkYY1PlyOa1yHq/9X4uFUiK3kKgu5GH0fs7jfqZObLnq6dvWvn0CTQ3f75SnYWFnP39ClMSUvBYLOxOe84cSHu5wYBup/Cqlo+2JLF3sJSnli5HgCT1Y7e4jrfAe/BTvOHDOLZhXO9Hm9TxQGONBRz/YA5nR90J6m2NvBM9nKeGy0lwR2eEAftknICHK10Fcz1FivHqlzzcY1L9n5tHpOU6CKYHyyraLa2abJWAzhU7j4h3oguSvipVahIC44mr6HtcYqMtejtFp9E6NZkVUvn5UksB9hdfaJTgnlOnfucAWnBUX6PszcIC9dSXdlAYkokcQnSdePIwRKGj04lSKumtsbgse3bn24G4L6b5xCs6xvnuub/2TvP8Diqsw3fs31XfdUlWy6SLRe5dxs3wIBNMcW0gGmhhZqQAB+kEAIhIYQAobfQO6YYm2psjI17k7tc1Xtbba/z/RitpNXOrlbFtgy6r8uXtTNzzr6zs2XOc97zvBXS5+S67DknNpA+flaolAp+3HQQs9WJIMDHX27jgjPHkpoUw2MvfodSqWDhvNHsOVBBbYM0Xv9y5W6SjdFkpsXz5pINDM9JY3hOGpecM4H/vLyiTzA/3qiUUvaX11uLdAMrf8PpaXOjLAhqFMrWQh6R9tG2n/Z9yJGR9Aox+gVUN/yJBsv/qKi/E7VqADrN6I5P7DjiE0Vq7bYTHUavwep2sbq4kO+LDlNQX0utzUqd3Y5aqSDFEMWwxGRm9hsIwDk5ucRpe76Iqz8GoMM4jlUMIrC9qpylB/ezpbKMKquFRockriQZDKRGRTMtI4uFQ4YxLLF1BUekXr129yHUCiNKhQFNmxUgx5JPftwVIJTnDUrjvBkjyUyKQ6tRUWeyUlBSw8a9RewprOKCmXnHJS4/CoXAh3+9quVxWa2J8+77X5f6EkW494XlrM6Xsj/8wuTccTmMzckgPsZAk9VBWY2J9XsKsdidTM8beFxi+++StQFieZoxhsVnTmRIZhIOt4f8Q+V8sHIHdz27FFUE76e1O49y7wvL8IlSdvClc8cwKD2xJau5rMbE0p/2sOtIBev3FHH381/w3O8uainaE467p5/CtopyRiankG00Mj6986Ko3eXm3heWo1UrueoMSSDKG5yORqXkUFktb327lYq6Jr7fdpCnPl7D7y+dHbKvf767kiWrdwIwKN3IojljyM5IxCeKHCqt5cNV+ZTWSILu00vW4vX6+PXZ8hkyH6zagU6j4t+3SBO5FruTf76zEofLw/OfraOsxsRFs0dz+oQhfLv5AJ+u2UVZrYmfdhcyZ2x2ROfa9jyBiM917c6jACGva6hrCnR4XfcWVfH8Z+totEiFmc6clMvYIZnEGrTUmqzsPlrJtoNljB+SKdve3K7ex/zBQ04asbxwfzl/v/5lAFRqJb+6awGTT88jKSMBRJHaikY2rdjNpy+t5LFbXydjUAo5o/qf4Kj7+DkiKKLwmO6T/lYNQvQUdlwUXTMBIVZqIzb9vXW7Zz9i452SJCRoAR+IISZaFckojK9ChAXYBSEuWGoSQwspHSLXVjBAiELxx4sPtktiyw+HjvLmlYuYOqD1cz8uTIZuH8ee/onx3DhnMjVNVs4dOxwAtUpJTor8BH64DPJ628k13gslRB+qrQvatqeqWlYWHteBYD42M7jwp8fnY29ldfP+1vf/gZrg5wUY3UOCOUBefEaQYC4icsBUxfjEzglN2+tKOjxmR30JV2VP7VS/NQ4zdU6L7L68hJPjfmj0hIFsXHOAvHEDmDpTWvX45MNLOffiSWzdcJgBg0OPTdOSpaLDg7PknQZOBPEaqeinw+tGp+wdhYr7OPmJidJhsTrRqJS43R7sTjcGvRqlUoHb40WvU6NSKdFqVS3Jt1abk7TkWDQaFYsvnMKWnUUY46PQalT4fN1L0O0TzLuAXitlz/lEG07XHrQaeYHL7tzU8rdOPTqgWGekfbTtp30fckTrpRn8lIQHcXmOYnWsoqz2agakfo1K2XM/rJHgE0U+O7iPKquZSouFSqv0r8pqpsZmxRsiu3xJwR6WFARn4spx24Sp/GHyKT0Z9nHDn0XwSv4Wnt22Aas7eKDl9nkpNDVSaGrk6yMHAXho3SquGTWeOyZMw6Du/o+Ty+sNG4NcHG1jAHokjqKmRu5e9TWbyktl91dYzFRYzOyoquD57Rs5fWA2j8w+gxRDFLGayGbadaoBmJ07AEiJPj5FSlduO9Tyd2pCNK/ee2lQhuvpE4dy6wUzqKo3k2o8sUVvo3RdXza7ZHV+i1g+KN3Ik7dLr3G/5OCJvt8xC5vDFZGA3N3YSmsaeXfFNgAyEqUbzrf+9Cvio1vFixl5A5k/ZRhXPfIeTTaHbD9+bE43D7z2DT5RZMTAVF6++xJ0mnbfzcPhwlmjeOC1b1i2bi+b95fw1cb9nD1teMh+/73uJ/4wfQZL9u3louEjuiSU+7HaXSTE6Hn7T1eQ1u49NSG3X8u5llQ38v7327l47hiyUoKv04Y9RS1i+dSRA3jytoUBmd5Thmdx0ezR3P7UJwBsO1DGC5+vZ3reQIYPCP7Nqao38/p9lzFqcOtgsLTaxCvLN3KwtJYxORktVjsTc/uzcvtBTBYHu49WhhTMQ53rhFwpeymSc/VfUyD0dQ1xTYGw1xXg0XdWkhBr4N2/SEXisjOCRQefTwz5eUiPjqa4ydTyOC3q5CmO/d4TX+FtXh3yr09/x7AJgUUPE9PjyR0/kMnz8rjrnH/z4X+/4f6XI83E7aOPyFHF/ROfS7KoEr0lKPTjUGhCr4jxIxiulv5QpCGaH26xXGlBDC5g39wSQXcaQuxD8oU3Q6EItqASvTUIKvkVKB3iq43oOY43H+2QbNumDuwfIJbL4fB4+MPnXwHw/YEjqJUKZmUP5IGzTiVG23oPuGxPAc//tJGi+kYy4qTvyUvGjeLaKRMCbEOO1jXwnx9+YkNRCTaXm5ToKK6aNJZrp4Re4WJ1ufj1e5+iUSl58ZKFQcUff24kxURx86lTyE3vOMEkLTb0aoAGmz2i58tvPMpDe96nxmHirmHnA1Bur2d/UynXDZ5HtdPEv/d9wr/GXss9O15jWGw/yux1jIkfRJI2lq/KtwCgVqiodDTw68FnMMGYw4uHvqLcXo/d6+LmnPkMjk7jn3s/xuaVPrdxakNAHLnJSWhVKpweT8D2w7XBmeS7KoIznhP0egYZw3++xoaYENpZURm0X06oj9JoGJRoDPscnSEvPoOlJflB2wuaOi+Yb6sv7vCY/Hr5sV449psqQ+7Liz85BPMrb5iNoTk7/MIrpDH04YOVvPrfFaRlJvCHv4Yem7o9km5w/2OfM7h/EkLz/eKvL5l+jKMOzVnpYwC4c+ubzE0dgU7R+p14fv+JoZqdlGyvf4Otta8EbBtrvIqJSTd0qp8ttS+zo751ZduEpOsZZ7y6U324fFaKLWsptW2iwXkUq6cat0+amLzt9yJqhR6dMp5YdT+M2hwyDONI1Y9GJfSOlQkdkZEaR73Jxsih6Xyzei+XnDOBl95di16n5tTpuWzZWRTU5qw5I3ntw3VkpMYzLLtnNc8+wbwLaNUj0WrycLp2U2d+lozE54OOcXtKMdu+aHkcGxXoWxhJH+37ad9HeJRkJL5AcfV5ON0FlNVeTVbKZwhCz2cFh8Lt83LX918et+c7mThqauDa5UsAKDQFVz4Ph8Pj4YXtm/j6yAFenn8BQxK67vnqj6M7MQDdjmNtaRE3fPUZdk/HNhh+VhQeZnPFa7x5zkUk6AwdNwCUgh6XtwqN6vhOHrU8v0IRViA+0WJ5dxBFeP3r5sGKSsl/blsoK5S3xdANcb4zfLFub8vs8k0LpRvUtmK5n0HpRi49dSyvfbkpaF9blv60pyVT+M9XzQsWy9tw56KZLFu3V2q3bk9YYfVgnSRqbCgp5qLhI8LGEAm/On18kFjuJzZKxy3nT+e+l77EJ4osW7eXW84Pvun2+5+rlAoeuOYMWVsUnUbFA9dIk7UX/PF1fKLIa19u5l+/OSfo2Gi9NkAsBxg/NBOWS3+fOr7VK1WhEMjOSGTbgTKqG8zH9FzbXlMIf13bX1PoWDB3ebw8csN8WaHcT7jvhtMGZPParm0tjw83Bg/ceyu71h1kxCTJ87a9WN6W3HEDGTU1h13rDx6v0Pr4heFzrcdre6/5gZR17eUF1MZXI2ovaGeB8xRE+xL/BumfzyJ5gQuxCKpBoBkv7dadByr5ib6wKNNAYQRfm8+55wBoZ3a+LwB3QdAmQTW0a331ECKSBQXAjdM6nrR4a/MOfj1VErM/uvYyKpos/OHzr3hu7UbuPU3yEV5zpIg/f7mCvy04jVHpaS1WGX9c/h0er4+bZ0wGoNps4bI3P2BYSjLPXHQuqTHRHKyplfW59mvsFqeL69//lFidlqcvOgftL6TeUnux/OPNu1g0aVTQcRmxsSH7qDLLZwW3J0ETzZ9HXkZBUylvHpVsYE5PGyt7bJm9jjuGnkc/g/Sbuq52H77mXO+/5F3OEUslbxz9Ho1Sjc3r5MFRV1Bqq+XZg8v51YDZCAL8bZRktbSt4TDvF/3Y0rdSoSA3JYmd5YEC7aG6YOF6t4xgPr6D7HKQRPWshHiKGwLHXztl+jssY/mSl5bSo4ZKI0NkaBeEEanlcPu87G4o7/C4CruJaoeZFF3k45594QTzBPnVeXKoFApuzp1Fg8tGg9NGo8sm/e2y0eiy4/F5I+6rs2Rmtd4D6g3SGOgv/7o0orbz54w8JjF1hwKz5Ls/ODqFIqvMxGwfPYqIj53177Cj/h3cvtCrzpxeM06vGZOrhBLrevLr30KtMDAoZi4zUu5C2Qtql4TjrDbv9THDpeSnu286A1GUEotmTJTuq9qvtrjv1rPweH2olAqmjGsdb/zx9vndiueX8Wt/DEiNf5ji6osw2z6nShFDYuzvUCnTAR82xzqqGu7FJ0ozPVr1SOKiLutEHwT1E6qPcCgUMWQmvUlR1QIcrp1U1N9JRuILzXtPrG/hL5n9dTVc+cVHYS1p4rQ6jDo9JqeDBodddrlfoamRiz59l3fPvYS85M4LwD0Rh19o704c+dWV3PS1vFguAIl6SQyPUmuotdsCCsWanA6uXf4JD806PaLnsrj20S/uRqotnyF5ekZm5dIdJgzNZONeaSa0vK6J+176kt8umkl6YujBxcnIkfI6KuqkQmVzxmbLZiufKLYfLAOkQktzx4Yv9jpr9OAOBfMf848AkBgXxdD+4TOvjDEGEuOiqDNZ2X1EvqCTnyGJSZz33juUNZk47713AvYtvfyKEK1CM21k+GJps8Zko1AI+Hwi2w8GZvv4i5duLZCW1k7I7U9KfOgMMv/kyOjsDHYcKmPdnsKWm5a2ZCQFv+/jY1onvAamBmZMRemkbAh7B57y3TlXaL2m0PF17cw19ZPbP5mxOZEP6Npz09jJLDmwh6Zma5ZVxUc41FBHTjcmKo8X5kYbSemRfR8YU+PYsym4oFcfffQEXvMTqOKai70JnfgNbr6f99UvBnezt7EqF0XCM6A8NkUpBc1ERMe3rRtcmyHq113oyYvo3h68WXNis//sLjden7TyJCW644KmozJSuXN260RnTlIi544cxvY2BRefXbOBKyeO5dyRUib+QKP0vXPZ+NG8t21ni2D+/vZdCMDzF5+HQaMOOLY9OpUai9PFde99QnpsDI+fPz8i27aTnaLaRgYkxXOwKlAk/ulAkaxgPjQ5tE1EpdlCtaW5UHh06PuIDL30+9/PkESloyFof9u6V1qFukUs9zMoqtXmJFqtx+51UWytZmdjIQ/sku6pBkSlUG6vp7+h9Te+nyE49lFpqUGCeUmDtMrL5fW2eIzvqpQTzCOzExqbkRYkmLcX4G0uN5VNwQkDPeVf7md4XBpKQYFXDKx3UNAk7xkeit2N5bh8no4PRLJlOSMj8uSQUIK5UlAwPC7Y4iYUSkHBHcND14qyeJw0OKXv/PyGUu7ZsiTivjti0amPkjsykyHDM8gdKU1SDBmeQVJKx79HMyYET74uXbGzx2LrCveO6L118n5uiPj4ruw+iq3rutTe7bNR7zjY68XyUAgCCGGKS/vpqE5VV/jFCeZNtk+xOX7E62vCJ0o/QB5v6xdwac3lKBTxKIUYFIoYovVnEq0/K6gfvXYyGYnPUFF/J42Wt2m0vI1CiELEhdjsYahVSzds/ZLfQJB5c4bqAwjqJ1QfHaFW9Scz6X+U1FyM2fYFtSpJLEqKu7vTfXUWpaDgotzws6GfFOwJEmEHxMYzMT0yYSEv6cRkCneVRqeDq5Z9LCtS94+N45ZxUzhzUA5GfauAZHY5WVF4mBd3bAYkodtPk9PJtV9+wjeXXoNRF3kF+FBx9I+VCpB0FEfbGLoTh9Pr4dZvvwiygjHq9Pxu0gwWDhlOrDZw+dCRxnqWHtzPizs2Y/e4qXfYueO7ZRE9X5RmKGVN/2v2Lz8+A54r5k3g+61SxmRBSQ0rthxg5baDTM8byIKpw5kzNhut+uT/Kt5X3HpT7bfC6C0crZCyc1KNMUTpw3+PDkzreJn6viLpXOtMVibc8ETEcThcHhwuT8jM5T9Mn8Efps/gz6u+56G5p0XcbyiyUsKfi06jIjMpjpLqRgorAwenR8qlQbK/0OqwrMg8/3OzktlxqAy7083RinqG9AsciMoVydSqW7PWY6MCV0Epm7OuO/Kf6865Qus1hc5dV//EQrjrCjBiYOSDOTnSoqJ5/oyF/PqrT3B4PHh8PhYv/5inTz+HiWldF+KPB8aUWCoKI8s8KjtSjTEl7hhH1McvFmVmlzKrW7zL/WK5Ig6F8fXO2ax0Fu2p0EYwF11rEHyNoOjcZLToXAO+4O88oavZ6u2XdIe0owmPTq1C0Tz4rYvA43pUevB3aJxOG1DfoaC6lm2l5by4Tn7S2+PzoVIo2FtZzaiMtBaxPBxqpYJbPlpKeZOZdxZf/IsQywE+2LiTe86exZ1vL2VsVmv2cUGlfAHKvPSUsP1ta57YOGvYkJDHlNuke7VSWy3pzeK5TqnB4pFs8optbWuDBQsnCpltWVEpDI3J4L4Rl7Rsy288Sn7j0ZbHFfbgDG45QdpvJXq0voHc5CSaHE5KGoJX6I7PjMweZFxmBkv37A/YdrRe+qza3R70ahVH6xtkk6byelgw1ynV5MQkBwnkB0zVLcU7hQiS7bbVyduxTEsezPqaIwHb8utLOyWYh7JkyYlJ7lH/7GiVtqWAqNXTte+3UDz05K84fKCSQwWVvPuqVOC+6Eg1UdE6ho7IYOjwDK64IbDOTklFA/3TEzhSHHwftSm/kPNO71116vo4NuyofytILE/VjyLDMIEYdQZqhaTZeH1OHN5GGlxHqXHso97ZmoQyLP742NH+3Dj5VZpOYnOsxWT9IOR+hyuwKrZSkSArmAPEGBai04yh3vw8VsdqPN4KBEGPTj2aGMNC4qOlYnXhhG65PqQ2gf10RSz3o9dOIs34OBV1t1PXJIkAGvUQYg3nd7nPSFApFDx+avglEJ8d2BvkZT4xPbPDdicrf1r9HdW24CU0Z2fn8tjcs2S9wGM0Wi4YOoJzc6QJmEfW/8D/drYuza+xWbnvh2958azIvwTl4vDHAPKe5G3jaB9DV+N4YftmSs2mgG2D4hJ4b+GlpEXJZ6EMjjfy20nTOX/oCC5f+gEVFnNIP/z2eEUbSiEKu/toxwf3EDqNitfuk1aHvP7VZt5dsR2L3cnanUdZu/Mo0XotZ08bzhXzxpOZdPIKRW3tLJLjQmcQnQgstmafyqiOLami9eH93Xw+EXMHHufhcHu8YYVVgKvHjOty/22JxPLGL1D7XyM/JmvgOcpZ2MiR0Oa4tu8JP3KWLm3pamZAd861u9cUOr6uchMFnWVGZhafXnAFv/1+OQX1tVRYzFz82XtMy8xiweChjElJZ6gxEa2yd93ajZ8znK/f+QmAtcu2c8o58u/vHz/fyoEdRZxzzazjGV4fvyREF+6G3wB+SxLp+0YVc2eYNmZE+ycBmwTdmcdWLAcE3TmI5sfB1ywSim5Ey1MIsQ90ohcfouXJ4L41U6CrfuiKdvcp3hLAC4T/bg/qRhDISZJEUb81SziiOhC3faKI3e3mt7Onc+Fo+WQdv9gtIi+uynGktp7pk7I4XFfPo9+v4U9nzImo3cnOPWdL38OXTB7NNTNbfd0f+WKV7PGJBgM5SYmyftsA64uk1WrhBHMvPh7a8z61zibuypXGqBl6I+8Wruavu98lWRsX8XXzMypuAJvrDvDnnW8BMDVpOGdnTGR5+Wb+0px1nq5PCOo3nCBd1NBIbnKSbMFPtVIZcfa3XOFPX/N4Zl9VNeP7ZbQI6O0ZLTOB1F1GJmQECeZ2r4sSqxRDVlTHnulygrlSUHBl9pRgwbwhch9zm8dFsUXeiq4zdiwnmuGj+zN8tFSvweOWrF8OFVSw4ccCflyxlw0/FgQJ5p99m8/tV8/h/sc+ByAvt3VC5lBhx9+dx5JGlzTZubx8OyZX4MTnLUPnnYiQfpaI+Njb0HofIqDg9Iy/MyC64zp+dk89Ry2rKbSsJjum75p0hd41qjoOpBkfJ834eI/1p1YNJDXh0R7vY0PVfeTEXUqCTt67zY9BJ2WI5PYP7xcWa7iIWMNF3Yqzj+6xtrSIZYeDfSQnZ/TjqdPP7jBrxb//LzNOpcHh4NMDe1v2fXP0IN8cPciZg0LfiIaLI9IY/HHIxdDZOGxuNy/tCMwCUiuUvHjWwpBieVsGxsXzvwUXcs5Hb0YsmNvdhZidu0iLPr6fBX8G+U3nTWPxGRP4cuN+lv60hz1HK7HYnXywcgef/LiLG86Zwq/PntIjz+nPDD5eBKzi7GWOT52pja1QCCgEoWXQEtyXiH/XsKwU/u+K0Ms65QhXuNRf9DO/qpIcY/eLOYmIHWYEtSxx7mAQGuFHLOB1k+vxWL01unOuba8p9Px1hcjFmY4YnpjM8kVX8VHBbp7btpESs4l1ZcWsK5MGqQIQ37zKJ9kQhUGlRqNUoj4GWZHvnHtJxwcBl/32LNZ8IU2wPnLDK8w4eyyTThtJUkYCiFBb0cDm7/fw0/IdxCfFcNlvf54T5n2ceJRduQ/2FCMJwm3wdZwR3W0EDULUDYjmR1o2ibb3QDMeQRfZMnix6WFw7w3eEXVT18NSDQn8TRWtiI7vEHTyyUXhWDAiF4D//riegzV1DEnu+iSEQhAYlprMwZq6sAUoAYYmJ/LJzr04PB50HXiRJ0VH8cd5c1iYN5wr3vqQ7CQjl4//5WR0thXLAW45bVrIY0/NGRxSMF+6Zx8A986dKZvZPzN5JDOT5Sc6/j3uuqBtr06+I+Dx9KThTE9qrSWSoo3jX2OvBeC6wcEi0f0jwv9+5SQlolersLuD7UXKGiX7wYLqYLFyRGpyxP72w1OS0alUODzBz7G/uobx/TIokslgj9Pr6B/f8wk2efEZfFIUbN/kz+yORDDfUV8StG1QdBITEwcgILRkqwPsaSzH4/OiUnQ82bbfVBnQti2jQviv90bWfL+XfTtL2LurlCMHpNc1o7+R4aP6cdk1p7SI6W25/eo5ACycJxXYvPy8VjutJ/638tgHHYZ7d0g1QfLi+rO2Zj/z0kbxY/V+Jid1oXZHHyGxemqwe1snjDKjJkYklgPoVUZGxF/AiPgLjlV4P3tOWsG8uK6Ri598h+xUI3aXm3mjhnDLPOlHPL+4gtdXb+WJxcEFxzpi8+FSJmX3LjuBPn4evL4rMCNb2SygPDTz9E4v8fzzjLl8V3gIi6vVz/uV/C0RCdVycXQnBqBLcXxXeCjIimXRsJEMNYb2QWzP8MRkzh86giUFeyI6XqeSKr03ONYRqzsx/p0GnYZFs0ezaPZojlbU8/EP+XyyZjcut4fnPluHQafh8tO6n2FstvXsMsKOaGulUd90HMSEThCt19Bgtkf0mtic7pBiOUjFoKL1Wix2J063J6iAZXfo6aKfFruTWEP4rHp/Jnn7DOiEmMCM8gZLZNe0bVZ5fEzk9kzdpTvn2vaaAj1+XXuaQw115FdXYHYFv59FoMEhXQP//yea1P6JPPzebQD8/fqXWbtsO2uXBQ/KM7NT+NMrN5KYdvKutOmjd+O1vhO8UVCi0IaZIFMEi0SiYzmY0xD0C0GZCcKxWVUlRF0Fzh8QXf5l2D7Exnsganfz/luCM74BPIWI5n8gOoOzgQXD5QjayAbasqjzJFsYX6uQJzY9gKBotu3STAjRMJhrp0jHfrnvANe+9wl3zprG2Mx0nB4P+6trmTawP5lxkXvN3z5zKrd9/AU5yYmckZvTMkl6oKYOj8/H+aMkQfXyCWN4d+tO7liyjJtnTCYxykBpowmby828XPk6J3npqfzrvLO467OvGJAQz/RBWRHH9XPih32HOX+CvLB97shcXtqwWXafxSmNEz7bvZdfjR9zzOLrKZSCwPDUFLaVBieklZqklbH7q4MtMiIp+NnyHAoFeWmpbCktC9pXUCNNPBTKZJjnpR0bO9K8ePnYDzRnnXdkn3LUUkuDK/hecUR8OjFqHQOijRRaWidUnF4P+02VEWWIh7JjkeI+eTLMH773QzKzEllwwQQe/M/lAMTFGzpoJdFWKPdz3cWhJ7COB2a3dJ95e+4ZFFqruT5nLlcNnskdW944oXH93PD4urcKtrOIopdCywqKLauodx7E4anDK7pQKaRxnUGVQrxmMKn6cWRFz0Gv6li7EUUvFfbNlFrWUOvch8VdistnRSlo0CkTSNDk0D9aWl0xKOZMFIK8TL274U221z4HwMiEKxmfdFvE5/VVeFZoyQAAIABJREFUyfXUOna3PJ6X+TRpho6Ljp+0gjnA0PQk3rrlUlweL5c9/S5zR3R/Nuvpb9bx5i2RZU31JHsbXqLStg69SvoRdHhaf1C21jyCxV2Ex2cnzTCNkcbfkF/3BF6fgzqHVOwhM3oupZYVjE26mxT9JNk2ADvrpKWZVk8FVncZLq+Jccn3kG6YyZGmTyixfIOIj2Td+JY2fXSfcksTK4sCl6KdOlB6v+Z2QiD2Y9TpuXz4aF7O39KybXNFGbtrqsIW3gwVR3diADodB8DSg/uDtl2d13mh+OJheREL5gn6bgwSjwGD0o3cfflcLpo9muse/QCzzclb324NKZi3tauw2l1Bfs9t8ft2Hy/aelXnHyrnwlnBRaFOFANSjTSYy6ioa2opHqnXyi/vLqmSX/7altysZLYWlFJU2YDJ4iAuumOrl0jo6aKfRZUNYYVfm9PdUqh1UHqgKJSdIWX6adUqnG4PBcWRLfvcX1zd0m5gWvez5COlO+cKrdfU31dPXteewuPz8eBPK3l7z45OrZroDQybIFWrf3X9g6z+fCs7fzpAXaUkuCVnGhk/exgzzh6HSt05W4c++ugMqri/t3nkQ/QcwedcE76RMr1VBHZtbd4oIlpfQbS+Er6tEAWKeARVLmimIOjPB0XHdTJaUSDE/xux/lfgKWze5kW0viZFYX0TNOMQlFmSaC82IroLwBO8mhG1dF8hxNzfieeXQ4lguBrR8lTrJl8DvvrLm59nFIJ6BAgGEF0gWsBXj+itBtwokr5uaaZvXn33zuKLeXbNRp5bu5EqswWDRk1OUiKTsjonhJ02NJvnL1nIc2s38sJPm1oSQQYlJnDT9NZBcUZsDO9ddQmP//AT17//KU6Pl9SYaK6fGl7sP3PYEO6Y1cgdnyzjw2suY3Di8fuNO94U1wVnNgP8dLAopGA+LCWZMRlp5JeHFjefWbuR+cOHkqA/fhPqXSUvLVVWMC9pzjA/UCMjmEfoX+5nbGaarGB+oFmMl8swH93D/uV+cuPSUCuUuH2BK2r2myIr/BnKv3xEvHRvlpeQGSCYA+yoL41IMA9V8FOjUDE0Nrx/fm/ir/+5nL07iln3w37efvkHAPoPTGLkmCxGjs1i5Jj+GJNiZNu+/vEGAK5ZNBUAm93Fv178jr//4bzjErscnub3ioiI1ePE5nFiUGmx9LD3+y+daHUaSkGNt7m+YbltG1X23aTq83r8uczuMn4ov4dG1+GgfW6fZOdrch3F5DpKkeV7ttQ+yUWDvkCnDH1vU27byPqqh7F5gseSHtGOxWfH4i6nxPojAHsb3+X0jKdkhfic2PPIr3sZn+jmcNMyxiZKK+YUQnjbNrO7NEAsj1KlkWaILHnypBbM/WhUSnLTk6lpki5iXJSOeouNu95aRo3ZSnp8DI9evgBBgBe/38j6g0UAzBkuCZbXzJ7Awco6Xlm1iT1lVdzy2mcAPHPNQhSCENDG3+6a2ZFnUHSE2V1EuXUNp/VrnY37pnhRy9/jku5GIagR8bG86BxGGm8GINUwhRjNAABcXjNjkn5PlW0DKfpJsm18oodyqzQwOCtrCS5fE6vLbybdMBOLu5Riy9fMyXgREFhd/hvqnXswasMX7ewjMtaUFAVlrc4f3PnCU21ZkJ0bIFQDrCg6HFao7uk4FmRLy2k7GwfA1srAG8TUqGiGJUZWWLAtE9MyiVJrsLpdHR/cSxmckciMUYP4euN+qhvM+ERR1sKhrUBeWNnA6OzQAuGq7YeOSayhGJaVgjHWQH2TjRVbD3DLBTNITegdXubjhmay41AZPlFkdb40YXTW5FzZY9fvKZLd3pZZYwaztaAUnyjy0ep8ru8hG52eLvq5dufRsCLy6h2HW4ppjs0JHOT5vcan5w1k1fZDbC0opareTKpR/ka+uFoa1O06ItXhmDpiwDGpVB6K7pwrtF5ToMeva09x23df8PXRgyc6jG6h0amZd+lU5l069USH0scvEEGZ1u5xBl77Rx22U8T9BwBfw/Xg6cRnULSC14roLQPnSkTLEwjRdyFEXRN5H4okFMYP8DXcCO78dju94NqCyBbZpn4E3ekIzecQVLSzCwhRNyK61oBrW/BO9y5E967g7QAK+eSMOJ2O++fN5v55s2X3L7/xKtntt8+axu2zAjMs5+QMYk7OoNDBNzM0JYkXLwlfc+fKiWO5cmKgNeZN0ycFiO8/V2743yeM6hfsk72rNLQYDnDz9Mn85uOlIffXWK3ct/w7Xlh04kS+SMlLkxdiSxtN+ESRgzXB9jMTOpFhDlLhT9gatP1ArSSYFzeYgvb1dMFPP2qFkqGxqexpDJwkOBCxYB5sxwKtgvmo+AyWlewM2JffUMqVdHyvFSrDPDcuNSJLl97CtFm5TJsljT/aephvWnuQ15/9ntLiOr7Z8lfZtiqVdE/92kfrOWPmcP7x3Ddcd8mJzTA/PV1Kjqp3WliQMZZFa54iRq0jJ6bnPfZ/yagELQOjZ3HY/D0APtHDl6V3MDLhYvLiL8Gg6pmaKj7Rzcryu2hySWPhKFUamVHT0auSUApqHM22MA3OQ1Tbd+IVnaTpJ4YVywFi1P2wNycDqxR6UnRjSNAOQauMw+k10eA8QLltY8vxjc7DrK18gHn9ng3qS6eMJyt6DoXm73B4G1tE9gHR4cfNR5q+CnicHbuASI1Cfxmlvvvoo48++uijjz766KOPPvroo48++uijjz766KMDTvoMc58ocriqnl0llfzhHKmid2m9iYpGMy/fcBEalZKrn/+Qw9V1mO1OtheW89pNkuXKb/73KQDjB2UyOiuNRy49k+2F5Tx37fkt/W8vLA9o42/nb9MTWNwlxGlyENrMX8RqpOx3r+hiR+1jeHw2lIIWt8+M2FxdT6tMwOWVloUpVBqUghav6AzZRiGoSdFLSw/WVd4NwJA4aQllk+sIFncJq8tvaYnBczyKGv1C2F4VvKRvemb3/A/HpqZjUKuxtfEB31oRvKzvWMYxNlXKGuhMHOUW6T3b6Az045qY1jUPOpVCQa4xiW0y53ai8flEdhwqZ/zQ8Odmd7rZVyhlcKQkxIQsEDhyYGtWyRvfbObxW+QzdPYUVvHx6p2y+44VCoXAFfPG8/SStThcHu565nOevF3K3kqOl880tzvdiKKIoYOCid3lnGnDef2rTYgiPP+Z5AU7feSAIEub6kYL73wnkzHXjgtnjeaNr7ZQb7bxyrKNDOmXxOwx4S3B9hdXk5EYG9ZGx8/VY7rvYQ/w/sodLJg2nAGpwTP/TVYHz38uvRYKhcC50+W9Ka+dP4nVOw7j9fn46+vf8tQd56NRBWbzOFwe/vb6t4BUHFQQ4Oqzjm+NgO6eq/+aAhFfV/81BSK6rt1h2eGCkz67vI8+TjQe81OBG0Qz+MwdtPIheqTCmYJqMGJnMszbIzqkIp6+OoSY30feTpGAwvguou0tROtz4GuKrJ0yAyH6TslrvSdzpAQ1ioRXEE0PIDq+6Ll+++g1XDQxjxvnTg7a/sgXwb74bTl9SDYT+2XK2oz4+f7gYf76zUr+fMbclnpOxwq7241eHX6pfihGhcjkLjU1UdTQGFSsMzMuluToqE49x7hM+ZVxTQ4nxQ2N1NmCx+Kj049d9u7I+IygDPMym7SC0OJxEq0KvUJle32wJYuAwPA4KV456xW5IqHt8Yo+DpmrZfeNisDOpTexa1sRRw5WcuRAFUcOSuO+ksIadHoNQ0dkcOr80AWFrzxf+jx+9OU27nr4Y578y8Wkp5zYmi835rTW/ziv3wTGJAzA4nEwIu7kui4nA5OTb6HMthWHV/o8ekU3O+vfZXfDhwyIPoWhsQsAyDRMRiF0bdVFuW1jS3Z5hmEqczP+HdJL3Cu6KLOuQx9BdnuMOpMxiTcSq8miX9QpKIXgcX+FbTPfl90JgIiPSvtWGpyHSNAG1xUZGnchhebvADhokpxBOswwN/vt4KTfnMGxZ3cYt5+TWjA/UFHL4uc+ID0+lkcuPYvEaKloQmm9ieEZyS2DemO0AavTxeGqOkb2S8X/2zyiX2pzPzUhxe/2bfztwrXpLFGqTEyuQ4j4WraZ3dKbtdq+GZeviWmpj+LyNVFs+aZNS/mbjHBtnF7Jn3dU4m3EqAe0bI/VDMagSmNWxrMIKPCJHgShbwFCT7GjOnApWbRGQ1pU9+wqBCA73siumtalctuqKhAJvcCkp+PwP09n4thfF+z5BzAwLr7LcQyIi++VgrnH6+WGxz4kvVlUm543kBEDUklPikWrVtJkdXKkoo5l6/ZS1OydHc77e+64HOKj9TRa7Pyw/TA3P/4x504fSXpiLE3NBQ037S/m0zW7iTVocbqVnSr+6fOJLYUPQSp+6HB50Gki+6lYfMYE1u0qZOuBUvYXV3P+HyWf1XkTc8kbnEaMQUuT1UFNo5XdRyvZfqCUJ+84nynDO560CRUb0GF8A9OMLJo9ho9+yKe0RrrRuOKhd1h85kSyMxPxeLzsPlrJ+yt34BPFltc4FAatmn/cuIBbn/wEt8fLXc8sZcrwLKaPGkhSnPR58np9NJhtHKmoY0tBKWU1Jj7861URC6vPbtqIw9s6GPv9tBkRtfMTG6XD7fFy9SPvcelcaUn5mJwMtBoVh8vqeOubLZQ3e3pffto4+iXLf/5GDkrjhnOn8uLS9WzaV8xlD77FJXPGkJ0pLa0/UFLDh6taX1eAK8+YwBgZ25NjRftzbXueQETn6r+mQMjrGuqa+mM4lnywT34CLEqt4YxBOUxK60dOgpHUqGgMKkkc0CqP/y3ev255jZiEKH7z99Ykg/1bj5KYLr3myRmd8W/uo4+eRaFp9/sq6FGo7wndwL0Xn+ke8Bxot0MJqkGgSEAIGkqJiGLzb5WvHnzlIAYWOBetLyLoTgP1WCJGUCNEXYegX4ToXCltc65C9BSArw5EGwjxoExCUI8B7WwEzUyQGZj2CEI0QvzjCJ6bEO2fIrqaC/l6i0FsFvSFOFDEIij7g3o4qHt/scc+JOTEcoCb5nZsn/HQ/NM4/7V3cbYTlNvyzrZ8ypqaePTsMzEaetbPvM5m4/Pd+/hk517OHTmMm6Z1zUJncKIRg0aNzRX4+bW73awvDBaHO1Pw009ydBTpsTFUNAVP3P1w+GjQtqQoA2kxx87uMC8hgw8LA7eJzVVTDpiqGJ8of79e77QG+ZMDZEUbiWoW2YfHpaEUFHjFVr2jzNZIrcMCQJJO/rwOm2tweuXfSyNDFCrtrTz4h/cZMjyD3JEZXHaKVFcrd2QmSSmhixvf+eCHAY9VKiVOl4d/PP8NAvDUA8e/9l4oBkR1viZaH5ERpUrh3P7Ps6ryr9Q6WuuU+EQPR80/cNT8AwA6ZRzZMfPIjTsbo4zYHA6zu7Tl7wExp4YUywGUgoas6DkR9z3KeE3Y/emGSWTHnQPAIZNk61Xj2CkrmKfqxxGvGUyj6wgVNinZyeIuI1otP1FTbc/H4i5rbivdd8WEOFaOk1ow9xf9lEOhCBZ7h6Qn8d2ug/gtnHeXSOLh7GGS150gCDjcngD/4PZt/O38bXqCWM0g0gxTWVl2LVEq6eJFq/sBYNSOZF/Dq6ytuAOdKpl47ZAO+wvVxuOztYjyW2v+joASr2hncsrfiFZnMTj2IlaX34yAEvBxSvpTKIXeVfTsZKXaagl4PCC26+JwWwbGJQQI1Va3C7PTSaxWPgOgN8RRZ5dfudAvpuuz5GlR8r7KPcnCvOEszBveuUbN3yP+YoNLVu9kSZjDz5sxkmvnh76x12vVPHLDfO56dikOl4fN+0vYvD84OyMuWsd/bj2Ppz9ZK7sfpCxagJv//TEWuxOz3YXNEegDv2zdXpat24tSoSDaIA26o3VaJg7rx1+uPiOoT6VCwZN3nM/Db3zHN5sLWgTtL9bt4Yt1kRVmbRtfJLH5nzfaoAkb212Xzqa6wcLqfKmISXldE4++uzLgmFiDjifvWMiryzfx067ggUpbJg7rz/N3LeL+l7+kptHCxn3FbNwnX/DIj1IZWSbVX1Z9z9Vjx/H5/n3M6J9FQYhJpnDMGZvNvIlDueeFZbyyfGPI404bP4Q7LpoZtq8bz52KSqnghc/XU1TZwGPv/xB0jP8386qzJnLbBce3uG5PnevEYf0BOnVdI72m3aXt96ufc7Jz+fusecRpe8/v9I61BQwaEXgT+ruzH+Pi26TP5HV/Ol+uWR99HB8EPV7be9LfzcWrvLyC2vhq8LHuXfjqF0tCdJv2QvSdCIZLpCKboZ6m7QPRiej8EbHpAfC1fpeL1jcQ4jshmPtRxErFQwH050fowHkMUQ1FiLn3xMfRxzHhcHUdL63aRHmjmX4Jsdw4d3JLgloocpISuffUmfzt2/DZ6D8cOsqc515l8YSxXDN5HMlRncvOBqkY9o7yCjYUlrCuWcTeVlaB1yeNd8/tdI+tKASBEakpbCkJzpb/dPe+oG2d9S/3MzYjXVYwX7pnf9C2Y+Vf3tJ/GAG6oCm0YL49RKb4iLjWDHqdUk1ObAoF7fzI8xskke609GGyfYTyLwcYlXByCeYfr7y3023+/ofwtRZOJIfN0r3pm0fXUONoCihI//zk605MUD9j4jT9WJj1EgdNX5Hf8A4mV/DnzuE1safxY/Y0fkyafgxjjFfQPyoyr/soVWsycInlR3JizyVSn++eIFEr6SyHkARzu6c+5LFD4i5gc83j0PyuO9i0lHGJv5E99oi51b88O/acTsfVKwXzKvseUvU9X2xyTFY6E7P7ce2LHyKKcMqwgdL2AdKXuUIQOGv0UC57+j0yE2J5YvE5QW1Aaudv01PkGW8lj1tl952a+b/gc0n8HQBJusCbbf9juTZHTJ+SqpcyA7LjLgZgV/0z1Dp2Eq3OYmDMOQyM6fybqI+OaXIFZvlGaXom4ydapp9GpyOkYN4b4jC75ItzyvURKYYuLrc81mhUSt68/3K+3Sxlp+06UkFZrYkmmxOPx4tOoyItMZbRg9M5d8YIxuZ0PNs5ZcQA3ntgMW9/u5XN+4qpbLCgEGgpxjhz9GAWnzGBpLgoBqUbQwrmbo80mDhcHpwR0h6vz4fJImWwmywO0qpDT1AYtGoeuXEBV8wbz9JmQXv7wVKq6i3YnW7ionQkxOgZ0j+ZWWMGMyZb/mbX7fFFFFvb+MLFplEpefzW8/h6kzQA+XztbgpKanA43SQnRDNz1CCunj+JlPhoBqUbOxTMAcYPzWTpI9eydN1efsw/woGSmoDM9IRoPQPTjUwY2o/TJw5hYJoxovNJ1Os5MzuHDSUlXDF6DPeu+Daidm0Zm5PJ9LyBfPDXxbz5tTT7vn5PETWNFnRaNcOyUlh4Sl7I4qftuW7BZOaOy+H9lTvYtLeIqgYLCoVASnw0k4b15+K5UuZgTubxzy5pf65tzxPo9LmGu65dvabdxdKuqPEwYxL/Pf2ckPZNJwpzg5U4Y+8o9ttHH+3xmp9AFfew9EAIndEHXnym3weK5YAQ/zSCdlbnnlTQIujmASA2tt7ni66NfSJzH72ehz9fxR8WzGRAUgJFtQ08/PkqXr3+og7bLZ4wliN19by9tX2h2kDsbjcvbdjMyxs2MzQ5iWkDs8hOTCBeL2Wdx+t1qBQKLC4XNpcbi8tFuamJo/UNHKlroLC+IcgapScZlZ4qK5jvKKsI2jY+s4uCeWYaX+1vv4oF8suDheJjaccCMCQ2BZ1SjcPrDtpXEKb457Y6+cQCf8FPP6PiM4IEc78tS2cEc71SGjMOjk4OGdPJyN03vs5jL10TsC06ShpLb90lvcafr9iJvU0S0WP3XXjc4mvPn/KlotkXZk1iSF+hz4gQ8XarvYCCoXFnMyRuPiXW9RSYllFiXY9PDO630p5PZVk+mYaJzEy9l2h1+GuUbpiIThmPw9tIqXUt35T+hlHGa8kwTOZ4COdaZWDypFcMvUo+O3Y+2+uew+OTxmeHTcsYa7wRoZ0djVd0U9RcLFWl0DMg+tSgvjqiVwrmDa4idEop81Wt0GNQBQ9IsxLjQ2aXj8lK54nFrcJv279vmDuZG0IsMwO4//y5Qds6anOykB51CttqHgGg0r4On+hFq4xjeHzfDOCxxOp24/H5ArZFqXpGqI5SB/djcjqA4Gzt3hKHxS3/5adTdV301qt65VcZIFlajBzUszcRWSnx3H9leK8ugHt/dSr3/kr+hyE1QRK1tr78ux6NzU93zzs1IbpHYrNZnCw+/V9YzZLg//hbNwLwwu8XyR7vcrjZ/tJGXv/HxYyaODBs3++/9APLP9hEQ52F5NQ4PvvsdnT67n+mNM1WGiang6c2rudwfegZ9vY8fut5uBxurj/3SQb/I4pREwdyXwTvlUgYlG7kvis6f6MB8I8bF/CPGxfgcri5at5j3P0PadJ21MSBDEwztlxrm8XJRVMfCrhej98q79XffvuPH2/j8LKdLF0m/75pf72eb3e9XA5pgHj9uU9y9z8uZtHs0SyaHdpPsiMyEmN77POVHhVDidnU8nhm/4G9TiwHUGtUNNZ25AndRx8nCGUmgmpoh4eJzp/AU9i6QSONATotlrdB0J4SkH2Hrw7wAl3zGu0pdjQc5nfbn2t5/K+xNzLJGDi5WOs0sao6n/W1e6h2NFLnkjIJY1R6MvVJjI4fzLy08QD0N6T0SFyNLgvr6/aysW4fhdYqGl0WrF7pdyFOFUWCJoaRcQOYljSSCcYhqCLwbPWIXhasvh+3TxJYh8X25/mJv+2w3cI1f6HJbW15PCgqjf9NubvDdhf/9DdqndL39qpTH+/w+N5IWlw0IzOlrOaRmakkx0aeBf7neXNxerx8lL+7w2NFoKCmloKazq+oO5aMSosso9ugUTM0pWsJA+M6IbRHGk9XUQoKcuNSya8vDdpX0BQ603ubjH85BAvmeQmZfFwUWCtoh8xztWVfY/Dz+vvtyn2Q3Wul1llBf0Og1cNHJc9wcf/bOt1fT7J3Z2hP91c++AmAu2+c1yKin2j8r//FWR1bNfUh4faFtvzsDAIKsqJmkBU1A7u3nkNNzZ7eTV9S7zwScGyZbQufF9/I2f2fJl4zQK47ANSKaKan/pnVFffjFZ1U23fwfdmdRKszyY6dz+AYyfc7Wt21xGGPz06ZbR3V9p2YXEewe+txec14RQdenwuvKJ9UGSrWgTHzWuxb7N46SqxrgmxiSq1rcDXXqhkQfSoqRectwHqlyhSnzqDa0bp8f0hs8PL6PjpPjHoAszNePNFh/OKQ+ykXA4dNXcYnBvcT6taht8QRCkU3tB+18sQOOPvoeTweL5+9tY4Vn2+nvLgOjVZN/8HJXHnLqUyY0bE1lR9DtJYlG/5MdXkjV817rMPj/e9ktTr8e2rXlkLe+O8K/u+xS8kZkUFjvaVHxHKAf595FgAPzjmVlUePsGh+51ZcRXoOJ4KOYuvs9YqUSK5X22+x3vbanTEoh1d3bm15bHUHZ3/1BrKGppP/0wFWf7aFcbOHo2u2cvI1T9a6nJHFrdH2zlVDfZyctBT79NXibpCW7ErCuWTfqIq5M7CBO1DQETQ9kDQjaJufr23iQs/cg/UkRdYqJhlz8TX7DL9duII3C78L8B324/S6qHWayG88zNtFKwA4PXU8v829CIOya4KO1ePg3aLvWVKyBqdP/vuiztVEnauJQ5YyPi9bRz9DEjdln8spyXlh+1YJSgZHpVNglkSpo9YqRFFECCO6FduqA8RygEJbFTavM+w5NrmtLWL5ycizK9YjCALVTVbufv9L+hnjKK034fVF/p5VCAKPLJhHcnQUz/8k2aX1vnd8eCK1QBmbkd7lAqYj01JQK5W4vR1nnh5rSxaQbFnkBPODTdWIiAjtRnhOr4e9jcEZ9xBoyQLyFir+IqNe0YdSpn7afhmhXq4fh9fGt1XvIyAwPmE2cepE1tR8gd1rYXqSVKMmWZvBT7XLSdKm09+Qg81r5rvKD4hVGzkWGbS7d4S3amyLz+vD4wn9HkhLllZFDc7qPT7h/qzyHQ1FjE0ILcT20YrD2/O/C3qlkVEJUiLxqIRLqbTvZEf9m5RaW20q7d4GVlb8lQsGvIoQpgh4ZtQMzh3wLttrn6XY8gMiPizuMvLrXiG/TrKvyzBMIc94Fan68RHFJ+JjV/1r7Gl4uyUjvCfIjbuoRTAHqQBoe8H8SFP37FiglwrmgqDE4ZU8f9P04W9++uijt2NQq1ErlLh9rT+Cth4SO2zu4Jm4UH62vSWOaJlsdAB7N5ZUerzBA7m2OJybMFv/R7LxhS4/R/u+gB7przPU1N9MTNR16LQdD94dznXotNOPeUzdeZ5Q5yP6RP52xzvs2VbE4ltPY+yUbOw2J9vWHyIxTGGccCgi9JrW6tS8+V3HmWMFO0vIGJDI7PlSEbnMAR1XCY8Uf6QxWi0Lh3XSN5/Iz+FEEGlskV6vSInkeml1kkjbG1+7W8dNYemh/dTYJOFmVfERPD4fKpl6LSeSc6+dzb/veIN/3hxoC7fkuRUB/3fEV5XPdXxQH31ESEuxz/ZFP0Phawh8LPRAnRRvGQFiuSKB3jgMK7ZV4xV9/F/+ywBsqQ+2ipBDbE6c+K5yK4fMZTwz8Y5Oi+altlruyX+JCntkdmxt2/1512uckzGV3+VehEJGdPOTG9u/RTB3el2U2WvpZwht67DHVBi0TRRF9jcVMz4h9OT9IUvvK0TfGYZlSCsFctNbX5u8fl1bNfi7WdOZ2E+yHPy/5d9SbbF00KL3MNCYQIxWi9kZ2hoAulbw049GqWR4ajI7ZSxY2pIeG0NSVHj/+J4gVCFNm8dFibWBrKjAlf+7G8sCxpZ+Mg3xxGkCszmHxqYGWb74/95vqgx67gq7CZMrWGDLiw+2sLR5LTi8Vs5Ku6JZAIdpSWdx1LqPvU2bADg1ZRFj4mewxyQ93tawmgnGuSRp0vm0rOeTCn9/fbBFbldxN4tCwp5IAAAgAElEQVTp9z/2OYP7JyE0Z5r9+pJjP9Zrz4U/PgG0Jt59UbaNFF1si1UOwCezjs0K5hOFQmY1mI/OaxeNro4tP7tLmn40Z2X+m8PmFayufLjFrqXeeYgK23YyDBPCto9RZzIr/RGaXMUcbPqUw01f4fQ24p/yLLdtoNy2gQHRpzIt9Y+oFeFXHv1YcT/Flh8AKTM+I2oaGYapxGsGoVMZ0ShiUCl0FJuluhfrqx+J6DyN2lwSdSMAqHPspcK2CaunssWL3ek1UW5bD0C0OrOl4Gdn6X13akCd8xA6pSSK1DgKSNINDTsT0kcfvZ04rZbaNsUuza7wN16RYpHxAw9XAK43xCFn3wKSj2FXaXI5IjiqJwW43meF0J7GpkdJS/78pHyeLz/azKbVBTz62q8ZM3lwy/bhY+WLDZ0IGhusGHrJksg+mgmT3XWyXy+j3sBb51zMNcs/ptJqocJi5t4fvuGfc85Areg92fCnXTIFhUrBl2+soaKoBpfDg7nRiqZ5MqKnVmH00UdnUGglKymf4xsUujMD9vmcK4MbtPc398lnT3YG0f5x4FOo5P16TzSF1kqeO/h5kFCuVagZFT+YFF08OoWaepeF3aajslnUR62V/Gf/x/xp5BWdet7fbnsOU7tsbpWgZGTcQNL1xhYB3uS2cthSQaE1UGBcVr6BepeZh0ZdE1I0HxrTL+DxEWtFWMF8t4xgDrDXVBReMDcH+16fTJw2IhuAotpGHvvyR8oaTGQmxPGHBeGLhIdi5mAp+/S7m67h+XWbeHPrdmyuY7dSamhyIhePyeOCvBHd6kdAygDfUBTaKgO67l/uZ2xGeoeC+bG2Y/GTF6aQZoGpKkgw31Yn/9qMM/YP2qYUFIyIT5f1PN9RXxokmIcq+CmXYW7UpHBm2uWsrvmcYTHjsXhMmD2NJGszW1bMtMflc6BV6NEodCiOkdb01he/RaHsuG+Px8vV5z0Vcv/8OT1f36+rPDXxqhMdwnFHowiuz+PwNMgcGRqH10Sjq6inQuqQ7JjTqbLvYm/jJy3bqh17OxTM/cRqspiQdCfjEm+l1LqWApN0H1Npk2pjFVlW4vA2cEa/55DTRgrNUpKMXyzXKGI4LfMpknTy38tCmMnuUOTGSR7+6xx7EfFxyLSUMYk3Nj//d/hEaVIjO/Zs2RgjoVcK5qn6PA43STewidqcPrG8j5OezJjYAKG6uKmxZVlid6TXo6bAL+ootZqYEAU/w8XRXfm3M3EY9fLZEWXmpi4/v6mDzA8Aj6eMmrqb8HhL0etmEx97DwCNTU/icK5uOU6vO4O4mN9Qb3oQUbTjdG7BoJ+Pzb4MY/xDgAaPRxoIyfVntr6N1fYZ4EWnndayHaCqdjF67Sk4XJvweqtJTX4PhRC+QF5j0+PYHatQKdPx+mpatod6Hpd7Pybz0zhdO6mqXQxAatIbgCLgXPU6yeoqLuY3OF1bMZmfBpR4vTWoVP1JNj4DCNQ13IPbcwSxufiZXjeH+Nh7wj5P29iAgPhCnU9bvlmyhbFTBgeI5e0pK6rjneekIh47NxdiarCSmBLD/EWTuPSG2WFf01AsmvYwVrMDURR58LnFTJkdLGj88YbX2bezGJtFes+dNfKPAJx6zhjuefSSgGM/e2sdn7+znpoKE6mZ8Sy6bibzF01q2W8xO1g09SHe+l56bd565nvWfrcbUYTzF0/n6ttPB+CmhU+x6NqZ7Nh4hHXf722xC5l4yhBu/dN5RMW0Tk5Fcg7+1667r9vF0x/mL/+9klETB7Jh1T4A/nrb2/zxicuZeUYe+3YU85db3uSjdX+SjQ2QjS8SRFHknedWsuz9jdhtLibPziVzQOAy1T/e8DpARNfLH5u/b7nXru31anutIPB6Afzw5U7ee3EV5cX1pKTHMX/RJC68ekZEg6ZQDDMm8eXFV/Pohh/5uGA3Sw7sYVtVOdeOGs8p/QYwMC6hV/iaz71wEnMvlN7nPq+Pc7PuYOH1Un2Y6/50/okMrY9fOD7HiiDB3Gtb0iKot6DKDngo2pcDIETdBorOr3ISnasQLS8FbtSf3el+jge7Go+yq7E1A06n1HD1oDO4oN8paBWBVkmiKPJd1VaeKFiCwxuYOLGyajuLB57OgKiORT6nz82Du98KEMuVgoKLs2ZzxYDTiFbJe46W2Kp57uBSNtTta9m2rnYPbxR+x7WDzpRtkxsbKJgftlQwKzl0rYq2GebTkkawvlYqZr63KbzocbhNhrla0SuH2xHx769+5P/OnU2/hDhKG0z884vVPHOVfF2RSDBo1Px+zgxumDqRD/N3s3TPPvZVyd8LRopSEBiZnsqMgVJSxelDshmd0XO1g/LSU8MK5gpBYGxm955vXGY6b27Z3mEcx4PB0ckYVBpsnuBkqINN1czLCFz5GMq/fFyifJLLmIR+soJ5fkMpVxC44lTOvzxWrad/VHB9uypHMbtMG/CKHkRElIKSOlcFLp+jRUuqc1Wyuf57Kh0lpDVlkRc3lZVVH5GkzUSt6PmkiqhoHSnp8REfr9GE/q6YMSE7aNvSFTu7FFd36W/ouVW1Jwt6mZqKVW0spCPhUNO3ssU5jyWJ2sCJ3c74hPtRCCqyoue02J2U2zayuuL/8PjsVNm3U27bSIZhalC7IkvgqtI841UhxXIAp7fzWtDAGKmo+paap3D5zBxuWs7oxOsRUHDU/E3zUQLZsQs63befXvkLrkDFlOSbAGh0Re79dCwQRS8Nzj00OHdidh0GwOouxuGtx+Wrx+tz4hNdCIISpaBFIejQKOPQK1PQqaSMhWj1AGI0Q4jVDMGg6ppJfh8nN+NTM8ivbv3Rt7rdVFikL4WM6K5ZTPhEkSONgUL1mJT0sOJ3qDi6EwPQqThyjfLea+1F985wpLHjoog+0URy4qeAQEX1AqIMF+LzNeB0bSItuXXmtar2CnRaqXiJXjsLtSobn89EQvyD2B2r0evm4ROljKr2/QmosNo+IS15CSBQWXMJzv9n77wD26rOPvxc7e2994jjxHbsDJKQEAgJARJIIAHSskeh7PKVtkA/RimU9oPS0pbVFspeoeyVsMNIIAnZ2zvee8mytvT9cWVZsiRbdpwQWj3/2Lo659z3Xkn3nvue9/29th0oFUMpQIKgJDHuX6Paa3eI1xuz5WNSEj8A3DS2iA5Nh6M26H4AlIoy4mP/SmPzZpLin/eOZ7Vt8TvW1g4x8mvwWG22vaQlb0AQFLS0n43dXo5cPpnY6PsQBDmDzu+G5uOINvwKhbww6H6G2wZ47ZNI9EGPZ5DBlO6qA82svnLk4moarYK4JLGo7G1/XE1sgp692w7x0F1vkluYwnELRi/sNpzXvrkDi9nG2bN+G7LNXQ9fiMvl5vlHPmHn5hoefO4qAGQyf0fouy9/y/OPfsr1ty+noDidA7vqefS+d3HaXZx5vn9xnPtufhmARWeWcd5PFtDTaUI6bLxHf/cuqy6dz19fvob2FvH7d/+tr/LyP9Zz5S9PH9MxDJ67wz1vk4rSqD7YTMmsbPZuF+/VaVlx7N9Rx4JTi6k60ExB8ZBzIhzbwuWTt7ez5skvueHOFUydnsm2DZX866EPSUwZKjR818Pidzycz2vQNmBU++67+WW/zwrw+7y2bqjgr3e/xc9+cxYFxek01LbzlzvfxOFw8uOfLjys445Vqbl3wSkszMzh5599QE1vN3d9LS4cqWQy0vVRJGrE1MgopQqtXIFSKkUukR5WnYhg3DV/9AKwEqmEmHHKKEWIMFG4nXU4TU/jsu/A0fsbnzfM4A4sUiuoFuPuU4Hbk73mWdx1dV+OJOqBAId6SBxVuE1PeaLLfdSbZbkIqvE7HY8Gak809x/LfkpRVHbQNoIgcGryLKLkOq+EyyBu3Kxr3sLV+aNrhj5Vvc4vWlwiSLir+OIRHdkgFhj9Q+mVPFz+Jm80fO3d/nztx8yJK2SqIVBTN0ebgkIiw+Yp/Fk9gnSK0WGmztQGiBH2pybP8jrM9/eO/HzqK8mSpwsesfviheeNOMaR4sypkzlz6uTRGwJKmYz0GPG+mh4ThXKC6nsYVEqunDOTK+fMpLG3j811DexsaqGmq5vG3j66zaIMh9nuwOVyoVbI0SoUaBRyErVacuJiyYmNJi8ujhnpKSMGCx0Olx5/D22NXeSHeP/xj28le/LhP9uP5TMJxqCdwRirjRJBYEpUCls7AxeFqoyBixu7QhTtnBEkwhygLMT2YLrp5X2tAdtCRcAnqTKJV6YiICDxFAEuipqDVPB3dy1L8Y+OPi/jRgRBCNBmnwheeH9ssiSnnx2oCV3f3E1GSgzVdYEFcTfvrGXFKeMvTv99c9pHj/HUCReQpgl/UWEsbHx/O9V76rno1sD77T/veJUzLl9IWl54haoTgzh6e211NA5sIU1zXJAeQww4RJmx7V3PhLWv4TjdNqTC+DI02y0H/F5rZYdfmDtVM4fimEvZ0SlK03ZY9gZ1mJvs/gte8aqRsyQ6xrgAASAVxGt/rmEZB3rWYHK00jqwDb0inXbLbgCSNbO8Mi3j4Zh0mO/qXkOcUpyMDji6mJ1w1VGPMu+0bOOQ8U2aTZ/hcJlGbe92u3C57UA/VmcHRqqCtlNJ44lTzyJNeyop2sCHTeuA6DhxuXqQSFOQK+Zis3yISnspAOb+v6PWXRPwv8vZ4m3nsG3D6TiIVF6E01GN221CJitApgj+Y7a7+qnseYZm06cMOJoAAa08g1TtEvKjL/V+ESOMn5kpaTy927+I1NcN4kRkdWGYmprD2NbahNnhn844I3nklMBQdhyODcCY7MgwiBNvnULhJ+UyONZ4qOgeXe9SLssDj/6YXF6Iw1GLw9mKUlGKb4y9QjENm12MVpJI4hBc3UilSgRUuN1Wn7EIGM+NC7ujhpb2H3nHc7v9tRqVYeiPAzgcNZ6xpzBYmEwuFyfTNkf5qPsZjs1e7nesCsU0z/b9KOSFKBTFCJ6bsUQSj8vdj9ttpavnDlzuAQTPdcDl6sONEyHE7SOYbYP2ORwdQY9nEItZ/B45nS5iE0bWjI2J1/OTm/0jyFIz43jr+Y1U7msal8McQDKKJvSgzrVMJkUiEVBrgk9gXnr8c867YgGLlouLJek58bQ0dPHi3z8PcJiXzRG/TysuECcbGTmB6eGTilK5xBPBnOmZ3J18Rin7tgc+0Ix2DMPP3XjPW0FxOtUHxcnQvm2iHUvPO44Nn4gOhar9zRQU++tMjmZbuLz78iYWLp3GaavEtMKMnAS2bqygqW7oWjD4WcHon9dYbCubkzfiZ/XiY5+x4oK5nHxGKSAuIixbPZv312w+LIf5res/ZHdHK+VdHThcgenFFoeDyu5OKsO4Hk4E4TjMAeKTj8yDUIQI4SJIM5HqrsPtbEOiGcpyEJB7in8O76BH0P0Mt/EB/+323bg6zgDFdAT5dJCmgaABnKJz3dUBjnrcdnEBG2eQqFRBjyTqQU8R0GOXq/LEaKxQznJf5sQVMj1GdClu7670bt/dO7JWa79DdIq+07jRb/s56SeM6iz35YZJZ7O7t5YKo+hwc7vdPF/zMX8ovTKgrVSQkKtL5UCf6PCu6g8tt7Ovt9ar0ZurS2GqYShitsfeT7O5kxR1YKSlw+X0OtoBJuuDOwgnEqvTzlmfP8x901cxMy57wsaN0qj420cbyUuMpbKtkxht8Gj/wyEtysDKkqmsLDk8+ZQjgUZ/bP9OB5loO4ujU8NymDcN9NBtG/DbppOJtkwyBI+IL4tND7q93tRFj22AaMVQJvLBYA7zEBrrQIBzfPjrYIxU8+BwGasU4PW3BEbBvvXRTm68dCH/+0dR/rJ48tDxV9YeXnbGfwfBF0J++rvVQbeHQiOLJ15VSMcwB/QXLb/n9LQHiVUGX0jvsBzks+a7gfFFUANs7XiSdst+JkUtI0M7F7U0Joxebg70vsfB3ne9WwQkZGgDHdvjQeVjQyhnvlTiv93i7Ak5ntHeQL3py3HbUxC1igM9awBRKiZKke19T5RjGT8RrZMIESJEiBAhQoQIESJEiBAhQoQIESJEiBCBYzTCvCh6JXFKMVJhMKXmaNFrO8jujv+j0zKyjth4sTg7aOxfh1aWHjTC3O3RFRIEPW73ABJpMjB6URTfdg7bd6h0P8Xc/xi47aj1N2ExPRk0wtzhMvFV0yUYbdV+2/tsFfTZKmgd+JoTUkX5CIkgD+gfITwWpGehlsn9IrHXVlUA448wX1ddEbBtYWbOuOyYSBtGs2NwnXd6Uipf1dd6t9f39VLd00VudKBG2EiUd3XQ3B+YUj0cu6MSUVZEwGbfT5T+RiSSGHrM7+GbKm2z7UBjOMXTPviqtPgeAeMJSJFJ00lOeBmQ4nbbEQKuYeGtU8qkYiqx3b4fcHn2K2auKGQFI+5HQILbbfH0E/enkBf6HavNI+GiMQzqLgdeay3Wr3G5ekiIewKXS1wVFrXJR9jPMNsAr312e1XQ4xlE5YkIFiSCV+oiFOYBG288K6Zgb/r8AK3NPbicLkz9VmafNP601sOlt0u0u7uz30+OBGBySTovPPYZnW19xPnIVBSWjh59NnwsAL1Bjak/nIK3/gyeu8M9bwVFabzyxBc47E6qD4pRer95+CKef+RTHHYnVQeaOf/qhWO2LxwaD3V4I7gHycpP8oswP1KM9nnVVLSyb0cda574IuA9h0OUNpLJxj63WXNg95j7HAtcdfc5aKMmPioxQoSxIEgSkOluQJBPGb0xIGivAGcj7oEXh73jAttW3LatYzdCmokk5jEIFtV+DBGvjOKstHlj6jMvXky19o0wH16UczhrmzYD+OmfyyUyLsk5dUz7FgSBK3OXcquPLMymzgMcMonRqcN11Cfr070R5i3mLsxOq1eCxhffCPlJ+nQSVTFEK3T02MSMvn19h4JGmNeYWnD46NQO100/EgzOYie6CPQdKxbx2f4qaju6Kc1I4aTC0LVl/hN5/KNbcTpc9HX3e+d3j97xGns2Bc8k/74Ybufh2lgUQvaktr8Tp9uF1BOVvacnMDu41BNBHqqmSoJKT4o6imZzYMHg3d2NLEgSNZetTgf1pkCZmZGKkv4ncuOlCwE4a4k45z1/xSzvew89FaRo9QRy2keP8eGp1/FR4wFu3/Yem5f/kk+bDrKru4mbi07myxbxev+XfetxuFzk6uO5Z8YyDHKxttJvd6xlalQy79bvodtm5oz0qVxTeELQff12x1oA7ipbOqHiOHu+reCP1z5FR1M31z9wPpmTU3n90Y/44JkvuPO568iekobbJV5B/3zjMzidLrZ9vpc5p5Xy879d6jdWWexFfNJ0h9+2AUcHb9X9hCzdiaSop6OWxuBwWzA52mgc2ErzwHbwqVyXb1hCZd9HYzoGN26azTtoNu8ABOKU+cQpJxGjzEYljUYu0XiLW1qcPfTY6mgwbaLP7l98ujBqBVrZCEWuu57BoMgmTXv8iAoTZmcn+3te8b6OVxUHbRejFJ8r28yi1n5V3/tk6QL9n0Z7I583/cKj1jE+ohRZJKln0GreRov5O0wOcf4hl2i92uvj5Zh0mCskGsxOUc+42byTfP3io7Lfqt7n2dv1F9xHQYw/UTM/xDviDWjQ+eV0VOKw78Vh340gKHHa9+Ow70UQ5N7/xfbKoXbSRKzm15FIU3C7RnYelPc8GeAs96XbuouavlcByIsKv9p9BH+ilCrOLpjCy/uGinOsrxPP+96ONorix6Yn1WUe4JX9/oU+psYnMis5LUSPke2YKBvCtQNgRX6hn8Mc4Lk9O7j7hPDS/Ad5u2L/6I0AuayA9s5rcTgb0agWe2VVVMrjaWlf5WnlRq1ahFIxC5OnyFeosYCg4+l1F9PSvhrRWewiKf55BGHsziK5fJLHvoU0ty1HJstELssGQCbLDrofwLMvCVrNCpraliKTZpAY9yRKxUyfYxWPE0CpmIU1xIO/QjEdR99fae24CKlUfOBUyH1TZgP3E2gbXvvk8klBj2cQwSOynJWXSNWB0GnSAA/++jXqqsSU5/+5ZyWTpqaiUMm5/txHRzmzRxardehm7/bVrAVvgUil2j9FTakcfTFyJCmRsTJ47g73vBWUpFFf3Ubl/iavNIkhRkNqZhyV+5s4VNlKQdHo14LxMvxZTDZB2qqjMdLn5Xa5sZhtXHrjKSxZGahFOR5H+Q+dojlh6j1HiHCECddZLiJBMPwGFMfhNj4obnIG1+odFWkGgvYyBPWP4QcQfHJyUtmYZQqygxT3HHBYcbldIcfa3HUwYNvcuCkhi3yOxKzYAj9nths3mzvF1PkAh7khAzx+BDduakwtQfXOfQt+TtKL97LJ+gw2eYqM7us9xOKkwOt8Zb+/k+JoSLKopHLWnXLzhI8rCLB46n/3NVwqkxCTYCAmQQx00EdpRunx/eBr5+HaGEr2xOZyUGfqIkcn1qPa1xM4Vw+lUe5LaWw6zY2BDvNdPg7z2v4Ob60sX0qij9y88miwZWMFlQeasVocQd+/7Lrgz8G+jvJBrjjv+Am1bTjpmmhazH1s66ynMCqRWmMn+3tbKY5OodNq4q7tHwDw75OvIEGl45nKTdy/+xPumzFUu+Ld+j38Y/6PUUvlQeUEBQR+t/NDZIKE20uDF2s+HKLj9fzq8Suo2HGIV/78Abf840rOuf5UavcNXafbm0S/o7HbxN0v3cCLD7xLZhDd/2zdSRRGLeeAj8wJgMvtpMb4OTXGz0e0ZWb8T8jTLx6zw9wfN53WCjqtwYMWQ5Gunc2cxOtHbNNs3sr2zr8jFRTEq4qJUeahlsYhEZTYXf30eHyGTaZvcHhqvCRrZpGkLgs63iTDWQCU97yOGxeNpg182HAN2folqKVx2Fx9tJp3cMj4MU63nXzDcgAq+94NOt5oTI4+h1bzNoy2Bgbsoo8gW38KMkE1rvEGOSYd5q2WfVidYsSoShqFy+084pHm+7seobznySO6j0HkEj2xyuDafCrtxQHbdNEPDf0f89eg//u2k8lL8I34FMcN1PEDaOwf/QfbZBLbRBzmh8dlJTNYs3+39+Y/OAW488tPeOWsH6GQhv8dv2fD53763wCXlwRO2sO1Y6JsGIsdS3MLuOPLT7A6hyYML+/bxSXFZWFHmbcNmHhmmCZ7MFTK2ahCaIdH6X9GlP5nAdtjo37j7es7zvBtw9FpVqPTBNdFc+seZH3nHibpVezo2UW0PJo0dSp6uY59vQfI0mbicNlptbaTphZv1IJ0NQ3CfBRuJamqZHb1VJGpqURgJvXuRHpsfaxK9y9m8kX7V/Q7FhCjWEGONotPW9eTpc2k3TYXi7uMNHUqZol4+d/dsZF8XR7JcU/yQfOHLEs5Daf2d3zdfYAsbQ9G+b1Mjylle/dOAAwaPV+2f4NCoqTF0kK0/DTS1Jcjl8j5Z/XTLEs5Fad7LhWuBDI14qTZ5XZysGMraeoU8qNuA24Lef4ATjq9hOce/ZRDlW1k5QdfyNnydblXh7tohica3+agpWH0ArBHkoRkUaM/OlZL+a4GZs4bqk5+cHcDCclR6PSHd+M+XAbP3eGet/ikKFQaJZu/OMjUGUPOhimlGXz7+QG0ehVxSUem4GNqZpxXP32Qusq2EK2PHoJEIHdyCrVVbcQnRY3eIUKECMc0gmoZgspTWNn2HW7renAcwO2oAVcfuAdAkIha5oIGJMkIMk8UrmwSgvKEYz6ifDjFYeiWDyeYk9uNG7PThlYWeM9zuV3s6wvUSJ4WPb4IZokgoTgqm6/b93i37egRI2zPy/QvMF6g94/4ru5vDnCYO90uDvQNadDn6zwOc4OPwzyI/QBVPgU/lVJF0MWECONn8/Ya8nMSiY3W/qDG/iGRpYtFL1dhtAdmMVYZ270O82AR5jPiMgO2DacsNoN1jYEF/nwd8FXGwCKX8SodSeofbiHxV57+imcf/5yc/ERqPHPW3ElJNDd243A4WbZyZsi+e8vFc1NUkILRZOHdT3Yzf1YeUfojl8E3NTqZg71tHOht5cyMYnZ0NXCgp5VVWaXs6GqkOEZ8Vk1Q6QBYkVHCWZ/+02+MU1Ino5Z66j8FqRX0RPlGWs1GHjt+bJri4ZKcJX5X0/ISaa0PHkiakCbqcUtkEh762bP0dfVz5hULg7adn/RLNDJxzB1dz3sju0OhkGg5PvEmACYZluLGhVRQ4HQH+lBCkagqQi2Nxewc+7OaQqKlNPZiSmJ+PKo/VeIJdHO6bbSat9FqHtnHkqY9ngXJ9xIqIz/GoxgyO/GXbG57EDcu2sw7aDPv8GsnIKE07iqmxf4EgAbTBizjONYM7UmopXGYnZ1eh36eYfTC46NxTDrMYxTZ3krFLo68s7ze+O5Rc5YDJKjnBpFpmGhGjwxxua0MOBpHbTdSBHqE8JkcG8/VZbN5fPsmv+3bWpu44eN3eWjxGWjloSOPBldl//DtF7w1LKp6dko650weufLwSHb42gCMakcwG8Zqh06h4IppM3h8+2bvNqvTwVVr3+Lls35EombkyWqP1cLl77+OyT7+9J2jzXdd2zgzdSkAu3r2cHLiiaxr+ZgC/SRiFNEcNJYTLY+mUF9Akkp0FH/VsRG5RIFWquHbri2kq9OoMdVid9k5M3Up7zWtDdiP3eVALVVjdVoxOy3esR0uB2elncm6lo8p1IsP8CbHAEqJf9qVbx+p51rVahUndv1Ok9ceCRLvMSxJWkymJp10dRpr6l/32gmgk+n8jmk0Vl12Al98uJv/veppLrlhMYWlmVitdmoOtjB9bh6JqdGkpMewbYOYCnjCkiLMJhvPPfIJDnvwDCGHXfz9OJ2B0Q2+OD2SGU7HyO1CIXjCns+/+mSeffhjktJiKChO5+Duet54bgPX3HZ4hUfCYbRjGDx34Zy30SgoSuOLdbu57KYl3m2FpRmseeKLoNHl4Z7f0T6vM1bP5tH73qVkVjZTSjPZvbWGHfpoWxYAACAASURBVJuqiInXjes4fG0Lx76RuPj6Rdxz00tk5SUy/5Qi3C43tZWtOB1OFq+YPu5xXzjzvHH3/aFQu7+JT179lit/s2r0xhEiHCYut5tpf3+YAbudK6bP5I4FC/3etzudTPv7I/4L++dcwJy0dL/Hw09rqrjqXVGu7KOLLic/yn/Rv99m49OaKtbX1rC3vZXm/n4sDgdauZxUvYHSpGQAzptazIyU4JGdf/5mA49s+RaA3y4UM28vnhY8qsuXbxvqueCNV72vF+fk8cTys0foATna5FHHHY4QQn5heKbVIG3WHgYcgc648ex7kFxtip/D3Ndx7Uu2NhmFRIbNJX6u1UEKf1b1N3mlYiSChFyd6Bia7ONsr+xvwu4ZQy4ZeqSuNA7tN1+XOu6igi/VfMsrtZtpGhAl8TQyBYVRKfy6+AyvwxLghHV/wGi34MbNw7Mv5KSkQHk1o93C/HW/56NTfsFjBz/jk+Z9uIELc+ZyQ6F/Jvfaxt38s+IL6k1dJKujODdrJpfkzvMex8r1j3BZnpgpvbmjms9aDiCXSJmfkM/tJWeikx/ZoIA3PtjOtZcuPCJO7SM59g8JAYGp0Slsag8s3FvV184pKWK2zvAIc6kgYVrM6BHgpTHBZYr2+jjga/oDHeYjFfz8IbD2zW38/M4VnLq8jHMX3Q/Aoy9ejd3m4IHfvElyWuhijs++IV7/H7htJX996nOKClJ4+Nn1PPi/R26+VByTwsHeNqSChLLYNNbUbKfdYiRNE0V5b2CQSrDr/aCzPBR6uYoNrTXs7m6i5AjI7TR7CqM2VrWRnBVcjmTw/mW32Ln+gQtQqELbLCBhRtwVgChxcrDvfVoGdtBtq8XmCfhVy2IxyNPI0p1Irn4RKmmUX/8oRQZd1vAlk3L0C8nSzRdlWQZ20GWtwuhoxuzoxOGy4nTbkHqy1+QSDTp5MjHKXNI0x5GlnY9MEt41+aSUP1Db/zEtA1vpsVUz4GjF7jLjdjuRSdRo5eL9OV5ZRI5+CcmaQLnnYBRErSJOWcj+njW0mrdjcXYhFRSopHEkqadTEC2+P0iCqnhcBUAlgoz8qOXs7noGAIMikwTV+GSHfTkmHeYut5MWsxjNKAhSElVjSaEMH4tTvBDv7rz/iIwfiiRNcO2mo43DZQ6vnXtg9EbHMDank0N94mSz32bDaLPSZ7N6/zdarfTbrWxpDr548NDmDaTo9OgUSvQKJXqFAp1C4fO/kuyoGKQhHhZ8+cXs+XxZX8PeDv+bzEc1lSx55WmunzGH03MnEaceSqUz2W18WlvN33eIjuV9w/pq5Qr+vHhpSK24cO0YtAEY1Y7hNozXjhtmHs8b5ftoNfV7t1X1dHH6mme4efZ8VuRPwaD0d+Y29xtZV1PBI1u/pdMsfjejlSp6rGPXcj7apKpTWNv8EQX6SSh99DLLjRVeBzSAyuc9jVRNk7mZrJgMMp0ZDDgGmKTPp2Ggka86NmJzBS4YSATBO5bv2L77dLpFh6BepuOgsZwMTTp1Aw0cGqj366OTafmqYyMDDvF6kaCM99rTZBmaKEsFCb32PprMLWRqhuwEaDa3+B3TaChVch589ipefPwzXvz753S1GVGp5WTmJVIyKxuAX9x3Dn/7rVgx/rLT/kR0rJZzL19AbILeb6zH7nuXda9/h80qPtTecpm4OCqTSZkyPZM/PiNm3zz7t495/dkN2Czi+bznZy8ik0nJzBOd/I+9cUPY9gOcddHxSGUSnn/0U9qbe0lMjeaKn5/KaatCR48cLuEew+C5G+m8hUtBSRqbvjhA0XSfCPOyTBoPdbJo+ZAzJ5htgNe+QduCfV7DPyuA01bNpKWxmyceXIvVbGfe4qlc8+szguqGj8Zw2wbtG25buMw9eQp3P3wRL/3jc17553qkMikZ2fGsvuqk0TuPwAnpgZIB/2lU7q7j9cc/iTjMIxwVJIJAUUISW5oa2N/eHvD+7rZWP2c5wPbmJuak+Tt79nn6ahUKcmP8HR6v7d/Lb7/4DFOQjLw+q5U+azsHOsT+a/bu5oqymdxx4sKAtheUlPL41s04XS5e3C0+I4XjMH99v38U57lTRw9qMMiPvLOwzx782SJGMb57EUC0wn/BNNQ+pIKEPF0q+z065sEc63t85FiyNIkoPA7xQsNQ9KzD5aTcKMr0FPlE5Vf7jDdeOZbvOmv5876P+NOsH5GnF+/hndZ+NndUk6jyP0dfn/5rzE4bcz743ajj/nLrq5yRNo3L8xfQZe33alEPsrG9knt2vcOd05ZTHJ1GTX8Hd+98G4fLxZWTTvS2+/1uUbbwkrx5vHDCT2m19HLbttf4Z8UX3Dx1/LIKX2+q5OlXN+Jyio43h9PJ7TctozA/mYbmbh5/9gu27q7jnofeQ6mQsfTkYpafKmZtP/3KRgA+33gQmVSCXqfinl+tIMogRuA+9M9PmJSTyMdf7ae3z8ziE0QHzcXnzh117P9GiqNTgzrM60yifEXjQA89Nv/fWIEhCa1s9Pn+1OgUv0WrQdosRjos/cSrdNT2B0YDF//A5Vg6O4wUl4nXEIlU/O1ZrXaUSjmXXnMyt133PGf/eE7QviqPHGB37wB9JgsrTytj+75xyoSFSXFMCk9XbOK4+EzyDYlUGTuIUoi/p+lx6dy7cx0A7ZZ+ElQ63q3fw/zEsWUJ/ShnOkvTpnDTptd54aRLAq5vh4vL6eaBa/5FV2sv1z9wAX1d/bz4wLvs21yJ5X4rMxYVMec0z+9cEPjT9U9jtzvImpzKpbePvLiskcUzPfZSiL10xHbDWZX1zJiPQyLISdMcR1qYTurxIJOoyTesIN+wYvTGYyRONZUTkn8bVtuFqQ+Mez+Cj3s7Tz8xQWrHpMO801pBlEKcYFidRty4EMIslDcWyrvFlBG7q3+UliISj/h9onoO0cpiohQFyKR65BIdAlJcbisOlxmrsxOLswOTXUzlM9qrMdoqsTq7AGEE/fKji0wSnr6ZXDKxF66jzd6ONla+MbxoU/i8VxWosTicLZdeS8IoEdEgpiI9uXQlq99+hfo+f+22pv4+bv/yY27/8mOilSpi1Gr6rFa6LeagGm4gRmk/tWwV6fqxpf6HsqOpvw/gqNmhlct5eMmZXPzua34PpV0WM3d8+Ql3ffWp12mvkyvotpgDHOPxag0vrljN6WueCRHHdOwwI6bMq+eZpxMLo56eLEbmunF7M2t8aRhopCx6Gtu6d7I8dam3f642O2SfRYkL/V4Pbze4T4BsbZb3gemaPDEVKkuT4dfH9/+3G9/zs8d3vNUZqxAQSFUn++mW5mqzx3aiAJ1BzdW3nsHVtwa/2RUUp/PIv0fWYgO47vblXHf78lHbXfqzJVz6syVsr2/iYGsHHf0mzptZwqcHhiIA9jS1UtneSVl6CidfOhv1icnsbGhGKpF4tw86RV7YvJMrFs3kVwtz2F7fzJTkBJwuF69v30teQixl6Sno9CrW7b2P7fXiw/Ur3+2io9/EosI8b584rYYFt59EWboY3fbUxq1cMU90us//8Qykc+LZ2dCMzelEtyCVe1ZdTZRaxbc1dUxNTkQmlfDCZtG5UtHWiUGt5DtzJ4/8+3p2NjRT3tZJXnICsq5YTFYb2+qbmJERfoTHhdcu4sJr/fUWM3ISWLf3vqDndzTC/bwEicBlNy3xi2wHWHJWcEmoK24O/QAfrm2Dn1c4zD5p8vdafPaHyoDx2F/4jPCfxbQk0WE+6LT25TtPIIVOIdaQ6LfZ2NYS6Fzd1y4GEZQkJgUEDUyKjfPeF/JjY5mTlkF+bBxqmYwmo5HPaqvZ09bqbf/Ujq2UJiezvKDQb5xknY5Tc/NZW1lOeacY8PNdUyOzUkM7kAbsdtZWlgMQqxadHItzRtej1oxhgXu8GEM4s4MV3wyX4dIvZqcVELPufCPAQZRWGXSY1wSJMPfVL/d1kscq9CSqommziIE4+zxjDDrMWy3dGB1DAUmTDeNzmA84bAhAvFJHukZchEnXxFAaE3y8cKPYZ8fncH6O6JDzjVIf5O/l6zk/Zw7L0kQHUqY2jvOyZvHqoS1+DvOpnkjf6yeL9/88fQJnpE1jR3e933huN/zuzc/4YIeoJ6+QSbl68RwumB98seeFNzZxy3WnMTlPlLGxWO3e2h/pKTHcd9vZXP4/z3LXz88kK90/k+Os08SiiJf9aB6CAP94/ks+XL+X1T7azx9/uZ/771iFSinH4ZPBNtrYE41lwMa6V75h08d7OVTegrHHhEavIs1TD2b24iKWX3ICWsP3VzA7VDR344DoMD/YG1jUd0ZceN93uUTK1OgUdnTVB7x3oK+FE1T5QR3mRyIC+WgSFa2hq8NIakYsSSniM3PF/maKyzJRa5T0dJtC9o32fBduvPtVbrlanLMOmMOX9RgPaZpoavo7+enkeUgFAbfbzZRoMco4WqHmdx6t8qs3voLT7SZDG82908fupJwSncyvShZz7cZXefGkS1FJJ8ZFOe+M6cw7IzCz89r/O9/v9Qv3vwPAuTecSumCQpwOF7cs/+OE2BDhaOKm2ijq6gtIyDUsm5BRj0mHeapmOnaXOJHqszcdEWe53dXHIeNbYbUVkJAffTl5URcBoJSGTpcZCZO9HqOtCpU0cILyfSARFGjlGV7HfiiiFUcmwv+/lRSdnjdWXsBP14nfv+2tgRP1Hqtl1IjpJK2Ofy1dSXHC+LQRfe0IZkM4diRpxWiew7Fjdko6j522nGs/fAeb018WwuV20z4gTh7aCZxEJGi0PH/meUyOjSc7Koaa3u5x2XA0CfVQE8zxDbAs5TTarO0sSzktoH+oPuGODQREFwXr4/v/cHtC9RlvCvL3zXd1jVw1/zie2LCFJL0Ou893UiII9FmsaBRyWo0mEvU6vqtr5PicTO/2jBhxAlyYFM+kxDj+9MnXTEqMY09TK9FqFcdlpZEZGx2wT8C737V7yr19rA4HV80PHk3Qb7V5bUjQab1j725q9W6/bO4MCpPEe86kxDgA7zFtq2/i8uNn8uSG77A7nVx74hye/XbbmBzmESJMJKa+8DLfIkSYKKZ55FC6LWZaTf3eeQ2IDmkQpVIAnt6xjW3NoR3m0xID5URKk5K548SFLMjMZlJsXMD7N82dx93rPwXg+V2irufLe3YFOMxBjCgfdIADvLB754gO87WV5Qx4ZOvOLhQLdgfTkB3O0bh/h97H+EMfQgV1BJsD+eqYGx1mOq19xCmHtJF9I8yLhmm6TzFkDjnMez065h4/4fBo9cn64NITo3FC4iTOzpzBRV8/4Y2qXZk5gzPSS1FKxv/4HsrhPkhFXys7uup4siIwHd7hciKTiM7rYJG+Brk6QPP624pDrPlmp9+2+9/5gqVlk4nRBjqDVy4t4+4/vcupJ4rf1xWnlRIXE17Gw7fbRAnRD7/Yh0atoKmllxNm+y8QLZgzyRupK5N+P/PUnRsruP/G5+lu7/Pb3tdloq9LfNbZv7WWN/75OTf/6QLmLin+PsykKIRzusEjEXSwrzXgvXAKfg5SGpMe1GFe3tvKCYn5HAriMC/6gUuyTJuZzaavyimensXcBWJQxV9+9w7LzzuOrd9WkZUbXDIE4OafiNJJbvdQ0fs7b1x6xG3edOYvvP8/d6J/rb15iWLw1xuLgtfK+03ZyPZ9eOp13v+XpBayJDXwvnc0OH6Z6FR/7vdv8cmabzD1mll13eiBNBGOLVoGttJvF+/Bqdq5aGShf09j4Zh0mAsIXjF9jSxwcjkRNPZ/hCsMsX0BCbOT/0yyZuFh71Mrz0ArP/KV0sdCmva0UfXbswyR1OiJJkGj5bWVFwDw1K6tPLr127AlRRRSKT+ZNosbZs4dUWt8LHYcjg0wsuZ5OCzOymPt6kv51Wfr2NYaXHMyWJ/7TlpCsufhtiwp5QfhMB8rcomcNPWxM0E81uyZaOK0Gt7auQ+r3UF1Rxf7W8Sow33NbThcLmI0arYcaqS930SUSolEEPy2n1EsToA7+geo7uiiMDmBPouF6Rmp1HR0oVEE/lbitGIWxeB+ffu0Gft5+putzMxMQ6OQc7C13WvT1rpGrw2Ad2zf7VKJhI5+cQG6ukMsoLK/pZ19zW0k6nW8vWs/SQYdXaYftvRWhGOD23/8MBqditufvMq77Rcr/hR2/7YQBZkiRDhS+Dq593e0+znMt3qc46uLRA3M53Zup8tspq63h8woceHTaLN6M/UGne/DuaIstBSXANw6X4zcfXXvHqxOB7vbAh1RAHPTM5gUF0dFp/g7WVdZTveJJxOjDh6F6ivHMuj0P1bQy4PbPOCJCh8Pw/sORqsPOnl9GS6VUmtqIU5poMcmZh23WYbmk8OLoE41ZPFF2y4ADhr9HX41/UNRt2qpkkxNeLVbhiMRBG4vOZOf5C/g7frtADx28HOeqvyKZ+dfSZxyfPU6RnK2u9xikdYbChdzVkZgVKbvedRIFWHtr76rN2Cb0+WiuccY1GF+2sIijp+Vx9pPRS36a259kd/84kyKJ48876xv6uZfL28A4PmHr0CjVvDMq99gt/tLfqiU36/rY8eGcu68+B84PDVTiufkcdzJU4iO19Pfa2bXxgoANn+2D2PPAPdc9S9ue+QSTjxz/PVPxku6JoYYhYbuYbIrreY+nG4XB3sDr1PhFPwcpCw2nWeDyDiX97XRZTXR7/D/PadqoolV/rC15S+66iQ0WvG6tOrC4wGoqmjhX3/7hOS0GH5591mjjuGbxHQkC37+N5FXIt4Pfvvyjd+zJREOh11dT3n/L4yeuCKyx6TDvNWyD6tHNF8ljcLlnvjCny0D4emc5kZdOC5n+YNvf8nqeZ50tgRxUv3kJ5t5Y9Me/n71KjLj/SMMO4wm7l7zMY9cOaSV9LcPNlDV0klechw/Wzbfb9zBMQ+XSdGX0zKwnj5bZdD3cwyrSdUGrrDd8ej7nLekjNKC0JEtO8sb+ffHO/jd9aFTc6qu+UXQ7V9tq6K6qZNLz5w9yhGMjqnZzMIWUc5Ar1FyxdlzmT55fBEf4RLO+RnUPL+qdBYXFZWytrqC9XXV7Glvo8VkxOZ0EqVUkaDRkh8dy6JsMUpiUVYu0cqJK6gjFQQ/G4BR7ZhoGwDyomN5fdUFbGlq4J3KA2xrbaLV1E+vx4mvksnIMkQzJzWdlQVFlAyLaH9o8TIeWjwxqTcR/ntZVVaEy+32OqH/72z/SPqilESkngg933a+2wF+vng+EkEgNz4Wp9uNVBAoSQ2ehbGqrChgvME+kOTzP9y/8nRvvynJCX59Brls7gy/7T9fLN4/hh/T1JTEgP6Xzg0uaRIhQjgc3FbL8GDOfZvDL2wUIcLRJis6miilil6rhQPt7SzMEiPmqrq76DabMSiVFMR5snTi4jnQ0c625mavw3yfj/Z5KIf5aGg8QQfZ0dEc7OzAZLPhcLmCRoNfMm06d37+CSDW6Hl13x6unhmYhdRo7GNTg+jMLU5MYnLcsZHdOkiUPLjDt8tmZHTRmOB024x+rw3y0NKT2doklFIFVk9hz0MDbcyMLeCg0V8TWC9Tk6n1d3pPjRqqJ9FsFhcvjA4zepmaGtOQw3ySPi1kMdRwSVZHcXXBQkDUC1/x2d94t2Gnt+jmRCIRBAoMyVQa20hSGUbvEAYZcYHPq0qZjPTY4ON3dPUTH6vjR2eJMiqdPSb2HGj0c5hrNQp6egf8ZFP6TRZ0WvG5RKNWYLM52LilkuPKssdkb7CxJwpTn5kHbnweh8OJRCrh5gfPZ/E5/r/dVVctBMQo9N9e+STmfit/ueUVJpdlkXSEZWKCURSdytdt/j4Cp9tF00Av5cMizJPUBlLU4UtzlsYGfxavNLbRMBAYAPVDL/gJkJY5FAiq1oiLTnc98KPvy5wIEf5j2NP1LK3mbQAkqEpI1cydsLGPSYd5jCLbmz7nYuKd5W5cdFq2jdpOIiiYHPPTce3jl2edGLDtylNmU94UWPE5FBsPHOKVmy8YddzDQSbRckLqM1T2PEOz6VNMjiYEBKKUheQazidNN/7CLUDAg3O4LJiRx4IZ450y+/P+V/v4+UUnA1A66di82aplclYVTGVVwdSw+5hsuzFatpBsuGJM+3K4erHYa9EpS4PaAIzJjtHGHQsCMDs1ndmpR3ZBI0KEQda/upFv3tnKr18YiioYqXCtr1Pct510mGPD770wH5hD9RmpfyhbfbePdDxjKdIbIcJoPPDGz4NuX3bJAgDO8PwNxXvPfsna57+ecLsiRBiJ4sQkNtQfYr+PjvlWjxxLUUKidypbmpQsOsxbmji7UJQr3NsuOo1i1GrSDYfnZIxWDQUiON0uZEEkKVcWTuX+DaJcRr/Nxst7dvHTmccFTLdf37/XK26y+hiLLgdIUEYR7XGa99iHaklV9zdzXOz46j9UD9Miz9WlhGwrESTk61K9WuWHTOLnODxifGpUVoCkS4E+A5kgxeEekmwr76tnZmwBdQNt3m3j1S8H+KR5H0a7hSnRqeg8RRT39TTRazeTpQ3Mvna4RFsGC7qPl+sKTuZ/vnuFPF0Cp6RMxeV2U2lsw+F2sTx97PP74ydl8uN5pby/XdQw1yoV/OKMBRjUwYNu/vjYRzS19iD36JbHxWi5cKV/4NX5K2dz/6Pr0OtUrFo2ndMWFlGYn0JelrgodOUvnkOrVTKrNHvM9gYbe6L44MWNdHeIizpnXnJCgLPcl9J5k/jJr1fwyO3/xtxv5d+PfcoNvz9vwmwJl6KYQIc5QJWxnTpTl9+2GbHhR5eDuBiUpDLQavGXpqk2dlDX3xXQviTmh13wM8J/L+0WUWrptk3v86+TJi7y+b+RNvMuGkxfopRGYXP10zKwFYAOi5iVJBXkzEm8dUL3eUw6zBNUQxOlDkv5CC3Hx4C9AYcrdFGFQZI080cseHnTU+/w1ytWcN6DL3DbypNp7OpFo1TQ2NXLv7/ZxV8uEyvM5qeElpW5e83HmKw2ojRiSk11q3iDeOmrHTR193LHyx8yOz+DFcdN5dn1W73jDo55w5OiDnZRRjINnT3MyE3jnLkl/O39DTR09mK22bnpjBNGtEEu0TEl9gamxN4w4vl48s1v+GZ3LYkxerr6hs7fW5/v4qNvD+JyuZlRmM5Pz5nnfa+t08jtj75Hc4eRucVZ3vdu/vNbzJqawa7yJjp6xIny3245B41KwWuf7OC9r/ZyXFEm168eerh+5p1NbN5bh9vtJi1RjFi448pTQ9rb0NbDP17fyI7yBjp7RXt1GiW/vWYpeo2Sp97exOY9ovbggum5XLhsFnsqm3lh7Xf8341DRedue/hdLloqRjo8895mZBIJXb0mkuMN/PaaZQjC0LkBAs7PcJwuIw09fwLPg1C8bpXX0ayUZdDU+zhJ+otp6nsct9tOvHYlmjB05J2ufr8+ClkyTb2Ped4zoVYUkKS7kJa+p1HLc9ApS3G4umnqfVy0W/djTLZ9AFgddVidTcRpzsDu7PR7rZYX0NL3JA5XD0mGywBQy3KHjdtDq/F5nC4jOuV0olQL/GwDUMrSvechXrcKreLYe6D7T6PHaqHO2EOdsYeWAXHS3jZgot1sosNiwmiz0m+30W+zMuCw43S7sbucOFxDD2ByiRSZRIJcIkEukaKUyjAolBgUKgwKJVFKFdFKFUkaHcka8fqZrNWTotGTpNGFpZ96NPnmna10NAVOzCOMTKdlgKqeTmr6umk2GWn2fJ9aB/rpsZrps1nps1mxOOw4XC7sLiduxO+PXCJBJRMjKqMU4vclVqUmRWsgTWsgXR/FpOg4cg2xx9z3JcLI5BYHX+zMmJQ84vuDpOWOT74gQoTDoSRJdJj7Fv4c1C8vShzKDJqWlMyavbvZ0TLkmN3viTAvDaJf7kttTzef1VSzp72Vut5eus1m+m02LA6Ht+j58DouwdDI5ZwzRXTiPbtzO3W9PXx1qJYTs7L92r2xX5zPKaRSlk/+fnRhR6MkWozm/6p9t3fbrp5qfpS5cMxjOd0uv0KdANOickfsM1mf7u1TNyA6zMv7/CPMh+uXAygkMvJ0qX7O9YPGBmbGFtAwMPQdGi77MhbkEikv1XxLw0C31xmeqonmpsJTODl56PN8+MCnPFe9EatT1Kr/ny0ve6VT8nQJ/Puk6wIHH4GFyYX87bgL+GfFFzxZ8SUyiZRsXTw/yR95sXMk7li5iDtWLhq9IXD/HaPLgM4/Lo/5x/kHVQkC3HXzmSP2+/lPTxnX2BPF+neGgvVWXDb6+Vyyejb/+sM7mPutfP7WVq679xwkR1l3PVRU9/qW8oCaAeEW/PSlNDadj5r2+W2zOO1s7qgN25YIESL899Bnq2Vv9wtB35MIcuYn302MMn9C93lMOsz39ryFyy3e+DuslZyc/OsJHd9orw6rXbxq1ojvy6QS+i1W0uIM7K5rptM4wPknlHHKtHwqmkePJN9e0wQC/PGSM9hcWc9z67eSmySmW91x7iL21LXwu/OHIrwvXTgzYNz6DlEb7pazF3plXnbUNmGy2njgkmXUtffwp3e/5K9XrAjrmINxqFlMi/p6RzVP3X0BuOHHv34GEJ3SH35zgMd+vRpBgBvuf4191UPpiH0DVv5++49AgCvufolT5xWSnSIeo0Iu4/9+tjxgf+eeUoZWo6S6wf9YP9iwj3uvO4PJWYkhC/v4kp4Yzb3XLuNXf3mbK84S0zKm5IgPPjsrmthV0cjj/yuu8v38T29QNnn0leuKunb+/cDlYpX3+9ZQ09SJVCIZOjfgd36C4XD14HAZyYi5RTwP0iT6LN/gchcAYHZU0W76NwppEkpZFk29j5Kf8Miotg3vE6dbiU4pyitIBDVOVz+CICdOu5zugY8AaOl7hiT9RShkadR03o5aLk4StcpSkpVXUNt1F2r5JL/XuXEPkKS/mD7rZroHPgZAE3WD37hutx2LvZq8+IfE/Rif9rMNICPmVu955dqDOQAAIABJREFUUEjHVzA0QnAcLhd7u1rZ29nKvq52DnS1Ud7TQZ9t/Nqgg1idDqyjP9MHRSIIpOuiyI2KJTcqljxDLHnRsUyNTcKgUB62bWPF5XSx/bPdZBRGolZGosti5ru2Bna0N7OzvZm9Xa1h1zsYzuD3p98upsF3mEMvLsokEvKiYpmekMrMxDRmJYnO1hzD+Apv/9B5r+oAmYZoShKSx5u89b0RFRue7qjWENHjjHD0KfVIqVR3d2FzOlFIpXzn0S+fnjwUpTz4//6OdgbsdjRy+VDBzxByLC39/dz5+Sd8WhNammgwQ0kiCGHNby+eVgaImupu4KU9O/0c5luaGqjrFQvznZqXT9QES+hNFPPiRce/r8N8c9cB+uwmDPKxaRVv6TpIr93/fnJc3MiR6r6FP5vN4sL58KKdxVE5QftOjcryc5hX9jfSaevD7KOjfjgO85OSJnNS0uiR9jcWLubGwsWjttPLVexafk9Y+z4xqYATkwpCvv/mwuABVtdOPplrJ58c1j7+27AM2KjZL363YhIMpOWMXoxOoZRTMC2TnRsrGOi3UHugmdyioztfDeWkfqNue8C2sRT8HCSYwxzgrXr/QrECAlOjQ2eMTBQWpx2j3eLVT6/sawvZtsXcR0VfGzq5Ep1MidaTCfJ9Zm66cWNy2Oi3WzDarbSZ+0ZsX9nXhkIiQy9XopMrUYdZm+BIcrSP4eqvXuMfC84F4Iy1T/KbmadSb+pFJ1PwdWsNvVYLVpeDX5ctorqvi91dzdxUIi54tQwYuX3LWi7Mn8HrNbtQSGU0mnr5eckCjk/K5rbN72Oy24hWRua2E4VaFodeno7Z0YETO2qp6FdMUs+gKOYiYpSTJnyfkdCtCBEiRIgQIUKECBEiRIgQIUKECBEiRIgQgWM0wjxbdwJamahD1mOrm/DxzY7Qq4W+GBQjr1Bkxcfw0c4KTi0tYFNFHRa7g9QQRUyC0djVS3ZCjHes8aBSiB+hbxHRmtZuttc0cstzHwB4o9bHS0OrGGGelxEvrpoKkJsmfj41jV3Ut/Zw4wOvedsPWMTIQblMSlZyDBKJuNKalx5PY2uPN8K8rGBsqVUP3HQWz3+whab2Pi5aOhOA+WUjp1uGoqaxkyk5Sd5K04XZSVTUd5CfHlgUyeUaivaZnJWIwqOrF2PQMGC20dtvHjo34Hd+gqGUZZAR/Uuae/8BQLT6ZEDA7Xbgxonb7cDp6kcpS0MiKEmNCi+dcngfpTybqg5RT1anKCPJcGngsbnNSAQdAlLcbpt3u0KagERQ4cYV8LrD9BZ2ZztqeR4WtyNgzKExhqKtgh2P73mIVp9MlHr86Z5jYc0zX7Ni9WzUGgUNhzrZsaWGvMmirVNK0nnthY2ce9E8DuxppLaqDaVKzqTCFG87u91Jc4MYiZSZk0BPl4njT5rMhvUHmL/w6Kc9O1wutrU18mVTLQCbWxvY1d6MxRn6s/m+cLndXkmY9Q1DmT4CkGWIoThOzDQojkuiND6F6YmpqKQTe5t67x9iVsSHz6ynZncdVrON7tZelkj9i+6ss76ERCrh7NjLAZh/1nHc8sz1AJj7LZwdezkup4vX257EECdKz/z12if4fM1GAN7sfMpb7OvTl77mncc+pHqXKAE1aUYOq3+5grlnzpzQY5sIXG4337U18mm9qFn5ddMh9nW2MnrM48TjcLk42N3Bwe4OXinf5d2eoY/ixLQcFqfncWJazn+FbIvF4eCWzz9kwGEnQaPl5MxcFmXlsjAjB5XsmJzKeVmz7wE0+vAiXGMSDMSlTExR8wgRwqXEI7vidLup7u4iVW+gtkec+85MGYroLIiLRyOXM2C3s7e9lenJqVR2i/OBYBHmXWYzq159iZZ+UbIqKzqac6YUMT05hQxDFNEqNVq53BthvurVl/zkXkKRGyPOo+dlZLGh/hCfVlfR0t9Psk7UBH99/1DE5nnHoH75IIuTpgPwRNX7dHkKdjpcTp6q/pD/mTy6NMcgbrebp6rX+m0ri8kjTzfyc4avxni7tRejfYBWy1CxQYkgYYohuC7zVEMWbzJUb+GQqZXGgaHMWK1MRZomtBxmhP8uutv7cHueJxPTw3/mT8oYeoZvb+456hHmSWoDCSpxjttuGSqqOygTNIhaqqAwauxFj0tjgsu0DR8/WxeHXj7+TJk1Nd8BUGlsFyOXHVZvBHO/Q/xrtFvGVAPg1drveLX2O+/rwVoHGpnCG3Wul6vQyZR+r5dnTBvzuep3WPnnwa8AMDos9Hvs7XeIf4128XhMDhvuMczYb9n6ht9rqSDei7QypRixLVOik6v8/tfJlFxXeBIqqXxcx2B0iBmqx8oxyAQJRruYUZChi2ZHZxMdFhOzEjKIVqi5d9bp1Pf3cO+2j/lx3vSQdrhw89DxKyjvbeeRPRtQSGQICDw8fyXftIrPf08e2DSmc3a0ONDYznl/eoE7z13M6nnTvm9zRiRNO5807cQXvR6JY/Ipa3/Pu8gkYlqLw2VlVvzlEzq+1dkZVjuNfOSb0pT0RF7ftJu7zj2FbdWNAUVhRiM1xsDWKlEjsbGrd0x9Bwm2z5ykGArTErnnx6H1vcfCoF54VX2HN1X0ULP4gJCTFktynJ6//eocJBIBh9PldZDvrmiitrlLdDgLUFnfwSVnzh6yfYwpS2mJUdx55Wn0mSz86LZnAFj78DXjOqb89Hg+31LBYObrvuoW5pflolEr6O4b8LZzOF1U1Q9NgAePzd+uaL9zA0PnJxhmezldA2txY/dscaNXzqCx9xHU8nykgpp47Soae/+KUpaJVlGCJsRYXeYPsToa0ClnBPSRSxNwOEU77M42+i3foZRn0d6/BrO9nB7zZBJ0q2noeRCJREOs9gwsYcgVCciwOGpxuQcQPAV5LY5DfuNqFSV+fYbbBiD4nYeJc8d1WcWU3HVNe7kgZ3bA+y6X21uZfMBkJS5Bz54d4sLclJKhiVt9bQdKpQy9QeXXLjZeR8n0LABSM2L55suD7N5+CKfj8AotjQWjzcrH9ZWsqy1nQ/MhTHbb6J2OYdxAbV83tX3ig+p7NWJxKLlESml8MnOSM5ibnMnMpDQ0srFN0IYzKL+y7MrFOB1O/nrdk6QXpLD6l/6yVRKpBEEQyJ8upmPX7BlavD24pRKJVILb5ebAlipmny6mx1ftOsQkT/vB69sTt77Iqw++Q8bkVJb+ZDG43Wxet4M7z3qAq/94MeeOorl5tNje3sRrlXv48FDFiDIpxwL1xl5ePLCDFw/sIEqh4rSsSZybX8zs5PGnvx/rbGysY8Ah3jPaB0y8emA3b5TvZfMl1x7zDnNDrC7stnNOLWHOqSWjN4wQYQJJ1RuIU2voNA9Q0dVJt0V8oM8wRJGoHZIGkQgCpUkpfNNQx67WVqJVauwe3fFgDvPHtmzyOsuX5ObzyNIzkUulIe3wrRcSDpeUlrGh/hBOt5t/79vDjbPnYnE4+KDiIADJOj3zM7LGNObRRC4Rr13nZy3i0Yq3vdvfadpISXQ2i5NmhDXOwxVvUWFs9Nt2YdboMiWZmkRUUgUWpw2X28WmzgN+jpo8XQqqEOn9U6L8Hel1A21++uUF+vQxPxdG+M/FbBqS6lGqwp/HqtRD378B4/ik8A6XIo8UyvoWY8g202LTvI7KsY2dityjuW93hdZ7LIo5PP3yN+t2ALCru2GUluNn8NphclgxOay0hmiXb0gcs8PcZLfyZMWRL4g+uGDQZzfTZzeHbHf5pHljdpgfq8eQrY9lbZ343LkscwobWmqxOB3UGLuYFCUGQGboomkwBfrqXD73i4IoUWbJIFdhctioN/WQoxcXvAb/jsS5D77Awaahe4jMU68gWqOmMD2BS06awfEFx+79fKwEO95oT03HY+14j8mnLI0slqnRZwFQ3rsONy6ECVSPcbgGRm8EyISR9fOmZiTyx7e7SI01EKVR43a76TFZ+PtH37CjtonHP/oGgHmTs1hYlMcTn2xiT30Lj6zdyPzCLM46rog3N+3lV8+9T1psFJIRbjTDx503OYtz5gZ/qCzLTuWbg4f4xbPvAbBgSg5nzx5/le/sVPFHPqckmyvveYXUBANpSaITPT0xmpWLSrn+/teQSgRcLjd//sVKb9+ctDjueOx9WjqNzC/NIStl5FV1p9PFvU98SE1TFyaLjZZOI1eefTwZydFc94d/I5dJcbvdnHtK2biPB6A4P4UZhelc+/tXceNm3rQcSvJTcLshMVbPT+99BYC4aB056SNHiGSnxnrPDeB3foKhlheQasgBz+ctIE4U8uP/5nFAixPs3LgHcLsdCELwn6lWUcLUpDV+23z7HOq+l4LEfwIgk0RT3XkLuXF/JDPmf/365MTd5/mNSUHt/4CRF/dg0NexmtMQBP8b5fBxM2KGKhQrZWlBj2fwPAyeg4mgyihefO/b9UFQh/mkKSm88dK3ZGTHU1/bgc6g8i6EHKpup7qilaryFrQ6JXW1HeQWJLP12yq/doMOd4A5JxRw9y9f4c7/O7JVrx0uF5/UV/JaxR6+aKwZcWL5n4Ld5eS7tka+a2vk0V3fopBKmZOcwaL0PBZn5JGpH3skaulJU71/bRY7f73uSWJTYlj6k+AFqQpmiFksbz/2IS6nC4lUwr5vyskrzcLUO8CBTRXMPr0Mt9tNze46ll8ztFC5d2M5rz74DtNOmsrv3/81Ss+DzxUmK7ctvY8nbnuRmUumkVMSPILtSNNvt7GmfBcvl++ksie8heRjjV6bhVcrdvNqxW7youK4sLCMH00qQSv//nUYJ5ItLYEPeYuycolVRXQRI0SYCEqSklhfW0N1dxdtJnHRcGZqoJNmRsqgw7yFdIOYVZqi1xOvCQxt2NgwtNB68/HzQzrLBx+5G40ja7UOZ1FOHml6A43GPl7zOMw/rami3yYuop8zZer3qqcbLuekL+Dbzv1s7SoHxIjx3+97mYN99VycvQS9PHjYSMNAO49VvsM3Hf4ayCvS5jErdnT9b4kgIV+Xxp7eGgA2du71ez9Ywc9B0tTxRMm1Xt10h8vJFo/94B+9HiGCb5aV1Rx+kItlYKitxvD91CIo8uiYr28pD9lmRuz45rFKqYzJHufxnu7GkO1KIgU/IxwhimOTWVMlLqj87ril/8/eeQZGVeVt/DctM5NMem8khBQg9NCLNKUKiKKga28rtt21ra6+7oq9F2xr17WLIEVEeu+hQ4AkpPeeyfT2frhkkmEmvRB0fl8yc+855547mXvn3Of8z/PnQGkeIhHE+wRyuEL4TubVVRPl5YtSKrNHowOcq214dhJfMEEa6eXL/lJhDJCnqW5VX0J8VNw7YwwARrPwnF9YVcPqg2n89b/L+XTxtYyId70q41LkwvMtrBImJXra+fZIwTxQ3ocjld8CoJT4d6pYDmC1te6HSipu/kE0MsCX9U/fCcADs8batz8+33XCk8fnT3ba9+z1TUeBf//QDfbXfl4Kl+02LtOYxdPHNN3xdrJ4wTgWL3BeAjF7fH9mj+9vf3+kXEhqckZcwdgr+9DbJ4AvTx9iYnKDxc0bD13l8hgSiZj/3DPT5b6P/29Ru/r96t/nudx+y5yR3DLHUVAVieDZxbOabOulB+a4fN3UZ9MUF4rNwjbny7Epsbz5toU6QV5XU1T7qbANKaHetzZVo82Ctav+t6VvHW2nOeoTtTTF8DHxpIzqg0gsYsTYeGxWG6JGKwcee0aY8Nm34ywjxyeyd/sZFt463qlcPQW5FYwYG4/Mo/NE/8YUamr5Mu0Qy9JPUKFv3WTfHxWjxcKOgmx2FGTzzL5NxPsJk1nTeyUwN64/Sf5NWyG1l4QUIWLcZDBRkF5EdN9ITu4+S5/Bsejq9JzZL1iXFGYUo9cYSEhpsIn6/fMtANz09AK7WA6g8JJz41PX8MTMF1j7ySbue7tzV1G1RIm2jk9PHuTbM0fsyTf/CGTWVLBk3ybePryLW/oP47b+guWN/x8g2c6Jcmcructj+lyEnrhx88dkcGgYW7OzyK2psUcKNrZjqWdYuCDcnC4vo1+wEFE2KMR1tKC20f21ucTW27IFwbZK13Q0nCskIhF/GTiYV3bvIK+2hsPFRfyW0SBq9WQ7lsaIRCKe7H8DDx56zx6lbbVZ+SlvO8vzd5LsG0uEMhBPiSAY1po1nKsr4lyds31Nsm8s9yW4Hve7ItE7yi6YN04+CjCgGcEcoJ9PL/ZWpNnf7yxvqN+RhJ9toU5vZNKS/6I3CTZ8IhGse+IOACL9W28T2pgzhWVc8+bX9vfeSjlbn74beTOrmSxWK4P/+Xar2g/xVbH5qbva1TdXaA0mdpzJIvWcIG6lFZRSUFlLrU6P2WrFS+6Br6eC+NBABkaHMWNIItGB3Wv9FRDsg0QqxmK2UpLX9CrkCynOaxDkgsMujl1ZU4k/GzM0sP3f9yEBgiDWnGA+wL97rWjc/HkY4B/GczWCm0CUly9+ciVWm40pkQlsLzrHA7tWYLCYeWLoVCK9fPk4bS9/2y2siApTqpA0MSk9Ijian84d5YFdK4hWCdduU2Xr8fGUc81o59/tKwYl8Je3v2fDsbM9QkDuLC6V8+2RgnmocgChyq4b5NloXWSmqGd+PD2ezBphIKCQSPH1UNAvIIT+ASEk+bWcEdxN5+HlkYyXR/tXFlyq1JmaF8wBB+HblQgOsOCmsRTlV7HgprHNlpPLZVx59fB29LRpTlaU8N8T+wFYm32mzcu0/yzUR0VnVFfw3rG9JPkHMad3P+bE9SOmHdHnrkgY1iCAZ53II7pvJGn70rl1yUKMOiPfvfQLINixACQ1EswzjggP4YnDnHMt1Ld75mBmp/SzNVTqdXxwfC//SzvcIz3uO4sao553juzms5OCt+Q9A0dxR/JwlB2087mY5NRUOW2bEBXb/R3pYgoyS9m19jDXPTD9YnfFzZ+MgedF77zaGmoMgvXB8AhnkWZImGBPcK66isxKYbw72IUdC0C0jx+5NULE1JacLG4Y4OwNmlZexmMb17W73wuTB/LWvt0YLRY2nstke042ACMjo+jle+nkA/D38ObdlAd44ugnpNU2ROZbbFaOVZ/jWHXLloHjgwfwVPKNeIhb//zWOBL8Qt/k5iLMAfr7xjgI5o3rd5dgrlJ4cMWgBFanCv2w2bC/vufyUe1qc2WqY8T+7KF9mxXLLxapWQV8s/MwW0+ds0djuqJGq6dGqye3vJrNJzN5e90urhzWl8fnCYFofp5dH7ntoZCROLgXaanZVFfUkZ9ZSlSfkGbrGPUm0o/lAaBUyYntG97l/XTFwBbEarFI1KQXeWsY4i9cK1/j2t9ZIhLTrx3+6G7ctIZolR+75t1vf//woIn2188Mdx6LfjZpodM2gCmR8QCEeXrz6URh1fkrozrHdjPEV7A2lF2Q16tao+e9dbvZfDyTKo0w4R4R4M3VowZy66QUJyvh0po6Xlqxld1ncux5/Cb06+3kW15Wq+GKJZ+waNxgHp8/yWWfbnzneyrUWtb+63Y6eyGbq/O1WK2sPHCKtYfOkFlcQbVWR5C3F5MHCMFDf589Hk+543NeQWUNb63ZReq5fMrVGmwXOACH+KrY9O+WJ2973q+fm0se7/NRNBnVFfSLDkEqElOm05BZU0EfX3cCHDetY9CqJe2q15ZEIc3h4SElJq7lSZ6QcN9OOR5AenUFrx/awbqcppc9umkaIUHkDo6WF/Hx1NYnDGuOqEThAcXTW0nWiTx6D+yFurKOxJQ4THoTtRVqCjNLyDqWi5evJ+F9Qu11NbU6pDIJnj7OEc4+gSpEYhF11V27asBstfJFWioAbx3e9YeKKG+J+nN97dAOvjp9mKdGTGZuXL+L3Kv2Ual3jDz1lSsIV3lfpN50HacPZfH58yvdgrmbbqfegzy9ooI6owFvDzkJAc5jVn+Fklg/f7Krq9icfc6h7oXM69uPXXnCZOqz27aQVVXJyMgoVB5ySurq2JGbzZr0M3ict2oZExXNnvy8NvXbX6nkysQklqed4qdTx+12LJdKdHljfGVevDPsfpbn7+B/2RupM7cu4j5E4c/tvaczLWx4m/MjJXm7FvoCPXwIUzTvO9vfx7W/qrfMk3Bly561ncX8Ecl2kRxg1XnBuz2CudVq49fDpx22XT2i5eAbkUjEmMQYqjU6qjQ6qjU6e9R7V1BQVcttH/zkkD+qLaw5dJqzRUJU6Vf3XodK0fQKkM5iyvzhpKVmA7Dy8+3c99yCZsuv/3Gf3fv8siuHIpZcnATnAXLBovbUVf/pkvZnRQ1w+Ntejh3O4Yev95B2sgCd1oCPrycJSWE88PAMvp94p72cxWJl/dpjbNlwkpysMmprtPgHqhg7IYk7Fk9GqXS29Fvyr2WIRCIefWouH723ke2b09BpjURE+fOfF68lMtr19X60Mp83Tm3ky/G3dujcQpU+9s9/Z2kG/XzDCJS3Pj9MV3D3wceoMTnbiC2Ov4VJwc5OB43PwU3LmC1CsFxRdS1L1+7GS+7BgjEN14jWYOKWd3+gpLqOGycOJSpA0COO5hTx1q87SC8q58W/zLCXN5jN3P7+Moqr1Nw6OYWIAGEF0q7T2Tz+jWPS7GAfLyYlx7EmNY2H5kzAQ+q4ij6/ooaj2UXcP2Nsp4nlZouVomrh++TqfCViMT/tOUaEvy+3Tx2Or6eCAxn5fLdTsNOx2Ww8eU2DvarBbOav/12OVCzh2UXT8fNSsPpgGt/sOAzA32aPY9G41lk8uwVzN53OmSphOeWUqD5sys8g0S+IR4dedkn4KLrpOVhsViI9/fDzaCrlqWuqjIIAWaht2S/sQFY+eZU1xAULAx25VMLh3CL6hgdTXKOmzmAkITQQsUhEekkFfcOD0RlN5J1P0hsXHIBMIubbvUe5dXwKCaHtmxCq0Gt5+eA2lmWcaPfg300DCxM7L8N3/cN3n6GxFKQXkXE4Cw+FjPihvTEbzYglYs6mZpJ9Mo+Eob0dHta9fD0xmyxoarR4+Tp+j2sr6rBZbaj82vb9bgv7S/J5cvfvpF+i/uSdSam2jge3reaHs8d4fuw0Yn2az6fR09CZTA7vo707b6KuJ6FRt82Swo2bziLI05Mwlbc9Seew8PAmx63DwsPJrq6iSqdDBAwICXVZ7uq+/dmRk83qs6cxWMx8ejiVTw+nOpQJUCr575WChUihWt1mwRzg5kFDWZ52inKtMP7xlMmYGZ/Y5nZ6AlKxhOt6TWJWxCh2l59iT/lJztUVUWVUo7MKkwG+Ui/8Pbzp59uL0YH9GBGQZE8g2lZ6eYaglMjRWRxXJ7YUXQ6CJUv9b76t0ditu6LL6xkRF01UoC/5FcLYNLdcGP8eyS5kSGzbvJ93ns2mQt0wkZ8QHkT/KNff78aIRSI+vssxUMFgNpNRXMHCt79tUx9aQ6S/D5cPjGf9sXSHbQBDe0eQFB5MoLcncpmUao2OY7nFrD+Wjs7Y8FtaL5gvXbebJ65ybafamUy7bhQ/f7SF4twKfv16FwmDopl2netJjeN7M/jsxdUAeMhlXLu45SS2lyLfvbOezSuE1YAfb/lXC6WbZuO647zy3CrCw/1YsGgUIWG+FBdVc/RQDoFBjsEFEomYX385RGi4LwtvHIu3j4Kjh3JYuewANquNBx6Z4fIY5WVq/v34j6hUCm6/ZzJms4VD+7MICeve8dh35w7wcPIVF10wXxA1mzKD8HyhNtdxsPIYanNdt/ahQFdMel2WS4H+UiSjuIKBD73ptH3a4ASWP3YTEY1str7YepBzJZV8cd91pPRpWAVy1chkogJ8eevXncwZ3o+xScLE7uqDaeSUVbFk4TTmj2qYBL161AAe+99afjt8xuGY144ZxKbjGWw6nsHMoY45QdakpiEWiZg3sj8doS3nC/Dd3x2tqOcO709RlSCybz6e6SCYn8orJaesmrdum8O4vsJn0D8qlN+PCEGJafllqBSty3fV4wXzCkMGgfL4i90NN23g7mTBFzxHXc1d51+7xXI37eGh/lcwI7JttjJr8wUPyUdTf26xbHGNmuGxkfQ672X4xu87SQgN5GRBCUazhTsuG84n2w8glYi5dVwKn24/SIiPF8NjhR+mXoF+WKxWksKD2iWWW2w2vkw7xJuHd6I2tmwl46ZlwjxVTI5qnbdzfbSOrRWWN4nD4ji+M43MozkkpMQhlUmQyiTEDogmPTWL/LOFjJgx1KFO3xHxpKee42zqOYZOcYycOZsqWLEkuLBr6Sh6i5lXU7fz+alU9wTMBewqymHWyi/414hJ3Nh3aMsVeggKqRRNI9Hcy+OPldS0Hm2t/mJ3wc2fmMGhYY0E86ZtCIaGRbA8TYjijfHzx0fuOjpVLBLx9ozZXB7Xh59OneBkaSm1RgMqmQdRPj5MjO3NHUNS8FcKq5BCvdqW9LOeQaFhDAoN41hJMQCzE5LwlLXOgmqIfx+2THm9XcdtTL1I3BltAaikSqaFpTAtLKVT2msKkUjE2okvtKuup1TB5smvdXKP2o5IBPNS+vPe+j0O21emnmqzYL7yoKMdS2uiy5tCLpUS5O3V7votccfkERw8V8BVw/szZ1g/EsKbzmOzcMxg/j5zPA9+sYrjecUO+37ce5wHZ47DS+78u2q1WNHW6dGo9WhqhQlddU3DhELOmSIkUjFe3ko8VQoUnk3/NnsoZPzrg1t57Nql6LVG3nzkOzb8uI8RU/rjF+SNVq3n2B4hL87eDSfskzD3PbeAyN7Nr3i9sJ/t7eOliFZr5N031hEVFcD7n9+JQtlw77vxtgku67z76e0O76+YOYiS4hp2bT/TpGB+6kQ+i24ayx2LG0S5uS4sOTcVnebNUxvxkSkZ4O94/R2tzOf1kxvQWox4ywQroCVD5hDtJQRubS46zftntmGx2TBbLbyYMp8BfhHkaip57cQGAPaWZfFY6s8oJDLmRQ/m2ljhHvncsbX5BuPWAAAgAElEQVT09Q3j1/zjVBu0zIwawN2JDee/oySdd9K2YLZZiVMF8Z8hV+ItU7A67xg7SzN4OaVhwktt0jNz41I2TfsHcolruXBa2ESH90/rXuWMunsF89+Lt3Kg8sgfRjBvnASz3uIrt6yGX/af5NGv1vLmrVfa7Uo2Hc8gLjTAQSyvZ+G4Qbz1607WHz1rF8z3nc1DLBIxa5hzQuxpgxOdBPOxSTFEBvjy894TLgTz04xO7EWYX8dWul54vrllwqSvq/NtisRw4d64Lz0Pq9Vmt6Gp0wu6SmM7MZEIpO1YqdMjBfOjld/bX1caMpkc/mSH27RhxWipRGcuRWcu6XB7bpqm/saa6Nf5Cfi6izqT4D1cqt1DrTEDtSkDvbkMk1WD2aZBhBSpWIlMrEIpjUAli0YlE5IDBigG4+vRD3E7EnZ2JVabmWrDKSr0qaiNGWjMeejMJZitWsw2LSLESETCQ5tM7I2nLAIvaTT+igEEKIbiff78uhM/j7Yn6/OStW1ZpadHw8AqKTwYtU7P0F4RbErLZEXqSQK9PPGQSVl1OI1QXxU2m82hjkQsplyt5VxZpT1SvTVk1lTw8I61HClzTlrlpv0sSBjYYlKVeqQyCQFhfuSfLcJkMCGTNy0wJKTE8fsXW/EN9CF5TMPAod/IBLKO51KUVUbicEfxe9adU1nz0Qb+9+wy+o9OQO4pfDcNWgPfPL8ckUjEjNs6N6rpbHU5925Zafd3d+OM1mziqT0b2JiXyZuXzb4kkoL6KZQOgnlPnWBbcuuHHaqfm17cciE3btrJR0//xOxbhYf8yDhn/+APZs+1vy4vrGLpI9/wwGt/cSr3l4GD+cvAwa0+7pzEvsxJ7NtiuSgfH849+HCr221MY1HjuuRLz47FTce4akQyH2zY6zBJvu7oWR6fN6nV/uN1egNbTzZ4xUslYq4c1nNtzJKjQtnyf3chEbdOAAn28eKdW+cy86XPHOxiTBYLqVkFXNa34TlHW6fnxhH/tluiNMVL93/l8F4sETP5qhQeedP5vgGQMDCaV5c9yPN//ZzivApO7D/Hif2u/fmVKjkPvHAdk69yPWlU30eg2X666iPQbD8vJQ4fzEJTZ+Cu+6Y6iOVtJS4+hCOp2Q6i24UsuH50k/V1FmEFzL+PrOa7y+4k2sufF443WF0YLGaePfYrn4+7BW+Zgt8LTgLw3LHf+O8Y4f/wcfpOnhkyh2S/CPQWE1KR8L/q5RXAO6ME7+z5Wz7klZRriPN21ll+zTvOB2NuQCGROeRUqDRo+M+RNfww8S6CFCq+ytzLqyfWs2ToXKZH9uetU5uoMxtQSYXnlPWFp5gSltSkWN5TOFad1nKhS4imkmAuGDOAa179mpdWbOWNWwVf9PyKGob3cW0pplLI8fVUkFdeY99WXK3GX6VELnP+n4b6OYvSIhFcM3oAS3/bRUFlDZEBvhzPFcboOWVV3D+z45MUbTlfgJN5Jfyw+yjHc4upqNWiNZowmhvu5YItr3DtpsRFEaDy5IP1e/DzUuLnpWBNahrF1UJQxKPzLmt1P3vkVTA4YJH9dZUhq9myFpsOnbkUvbkUvUWwAtGZS9FbhG3215YybLbWJfusZ1VW50Y1JAf+g3jfWzq1zXp05hLW53aN5+e48E8ACFJ2bmLDzuJ01YecqXL9kB7newMDAx9rVTtmq4as2h/IVa+izpTdbFkbFowWA0ZLNRpTPuW6/Q77JSI5AYqhRHhNJdxrMnLJxZk8KNcdACCvbjWFmk2YrZomy9qwYLUJgozJWovWXEA5B8hRLwfASxZNpNd0YnyuwVPa9Ylnnhw0i0SflpeBXkj9j31rmDPE8UFg9qAkLFYbErGIw7mFzBvW3746wmqzNblS4u/TxrVpFcVnJw/ycup2DH/gxIsXAxGwMKFtdiyTFo5l+dtreXjKM4yYPgSL2UJNWS1/+8AxCUjisN7UVWs4vjON2Xc3LI3tOyqeTd/uwKA1OCX3jB8ay+3PLeLTf33H4uGPM2LGEGw2OLDuMPlni7jlP9eSMKzzJqJWZJ7kX7vXozObWi7shq3555i98ks+mDKPwUEXJ5lWa4n3C6RA3RB9WqXrmdYle9Ydu9hdcOOmSe5ecm2rywZF+LsUy3sihepaDhbmAxDr509KM9Hxbv6YhPt5Myo+mj3pDQlT1TpBAJ8+uHX2POuOnsXQSHyY1D8Of6+ePaHcWrG8nmAfL4ckqfVklVY6COY2q61FsdwVVosVg675XDHxA6L4aMsTrP9hH7t+O0r26SLU1Vo8vRVEns+dNHxSP668aTw+AU1H6Hekj0CL/ewO2ppzwBVFBUJS9F6xrX/WPnu6iNXLUzl9qoCqSg16nRGjUfjuC5H9zv3y9PTAtxkbxUy1YPETrvQl2kuw/ZsUmsiZGiFQM11dSnZdBTfv/MKhXrCiQay8vvcIHjn4M1dGDeS62BSCFW2L4J0a3heFRJg0kIobfKePVuUzwD+CoPPHujJqINds+S8AHmIpMyKT+S3/hD1a/df8E9zbdyI9mTJDBUX6EgI8Lp3k1u2ld0gA8eGB7Et3tGxrbhGxq30iF9/r5rbPH5XM+7/vYcW+k9w/cyy/pgr5LXyUCqYM6DoHEFfnuzMtm/s/XUm/qGDumDKCuNAAfJQKPtkkaHA/7z3h0IanXMbH91zD7e//xI3vfI9MKiY22J/nbxD00mmt/F2EHiqYl+hOUaw7CoBIJMFf7igoZFR/SW7dSvTmUkzW7l364ebSQmduOVrNhpXMmv9xtupTTNb2LYe9EIvNQJluL2W6vRyveIVZsdvt0dvdQbF2O2eqPqTacKrlwq1EY8rjbPUnpNd8TpRqFv3870Mp7bqs5Tf0HtmuegPOZ3NfM/X+Fkq6RnI+qmD24CQHEbw5Qby1Ynmt0cAjO9ayPje95cJu2sy4iJg2ezvf/vz1eCg82PbTHr59cQUKLzm9B/ZyKheVGIFSpUBXp6ff6IYf2b4jE9BrDE4JP+tZ9M+riO4byU+vr+bXjzchEkGfIbHc9twiLrum6UiVtmC12Xjp4DY+OrG/5cJuHCjU1HLt2m95ZdxMrurTMS++rmREeCTb8hoCCIo1aip1WgKUXeeB314mXz2CKQvanmwOYPOyfWxZfqCTe+TGDfz8/gbWfrGN//tyMQCx/SLZt/4YG7/fg8xDSkl+BTf9cy5DJvTlm1fXUJJXQVF2Ga+ueoR964WJoPQjOdz42BzKC6t45+GvWfLdAzx9/VISh8ZSlF3GgDEJ6OoM+AUJosSUa0djNll4bN5rvLH2n112bitOp9nTnV/XDck+57//NaeLy+zvxSKR3c4iJtCPMXG9uGHUEMJ8HKPWFnz4LScL27bKd9X9N5MQ0r78MH825o8Y4CCYg2Cx0lrB/EI7lvkdsGPpySRHhjoJ5jVaR+FZ6lXLSwfMaM152GwmIr0XEOPbYONxrvo9SjTrECFFJvZlUMibyCT+aE25HCm5B7gNgGq9kLMgs/pdUsI+B6BMu4lzVe8TOMXKlZNNDAh+CR+5cN3WGAT9I73yNdJ0HyEt9qF/4DMoZc7jUi8fJb/lvtU5H8oF7Fx7lK0rU1F4ytn1m9CnW/95JVq1np8+2MSIKf154j0hCLAgq4xv3lrH8b2Z1FTUERgqjMNn3DCGhfddbm/TZrPxzVu/8+tXO9FpjYyc0r9Jq5mtKw/x/dL1FGaXExLpz4zrx3D1XZNcJj6tFwbFrZw82b8nk6cf+4H4xDAW3TSWXjFBqHwUfPfVbn5bdbjJelKZpMl9jTvS+ImwsWiNzUakpx8rJt/TZBNzowczMTSRFblHuGH7p7w6/BqGBLQ+J4JC6jrCvilBtJ5FvYfzxKFfmBwmrKAt0FaTEuj8netsjtWk8VvRZtLVWWgtOnxl3iT7JjE3Yhq9PJ0nfrM1eawu3ECONp9CnaDxVBqrWbjH9Wf69ailyMTtX3XQkzBbrJgsDcG/vYL87HkrLkStM1Cr0xMd1PBMHOqn4lhuEUazxSmJZ33U9YUEeXsxObkPqw6eYvH00Xb/79kpSU5tdDYXnu9X2w4hlYj5ZPECB/usxnkpLuS3w2cIUHmy9snb8FEq2t2Xi5Nu2Y0bN27cuHHjxo0bN27cuHHjxo0bN27cuOlh9MgI8wpDOr4ewmyawaLGhhVRI22/zpSN2uja78uNm8a05FevNRdxqPRJKvSHuqwPAYrB3RZdrjOXcLT8OUq0O7rsGDabhTz1agrrNtIv4F4A+vjeiKvlaxcDuVi4rfVWdcwGJ8Cr8yI3z1YLy/Tu2PgzeWrXs8FuOs6ixNZ7ytYjV3pwxwvXc8cL1zdbTiQWsarmS6ftsclRbLD80GzdcfNGMG7eiDb3rTXozCb+tm2Ne9VCBzBaLPx9+xpy1NUA/G3I2IvcI2dmxSXy2v6d9vc2YFdBLnPiW/ZF7m4SBscwfEr7ovWzTxe4I8zddAnX3HsF2afynbZbrTYe+/AOstMK+O6NtQyZ0Je/PHolOo2Bp69f2mK7Rdll3PPCQiJ6C57oddVa3vy74Bk85drRHN15hrGzuy7BsN5s5osjwhhWLpGyMHlglx3rQgK9PPGSe2CxWqnSCjZRJwpKOFFQwtf7jvD+DXMZHdcQpeitkOPn6RzlVaPTY7OBSu7hlJBL0oSXsBtnpg7sg7dSjlrXEC2962wOFWotgd5Nj2nroxQPZxfatwV5ezEhqftzF3UHPp7O1o2NfXABTpY9SbDnJAb7vAuAxeaYkDrKeyFxfvcCItKr3qCwbhUxrbRdzar+mH5BS/CRJ2Ox6RGfl2OsNgOny5cAkBL+BVKxNyWadZyueJ6hYf9t62l2mP2bTvG3lxcSkyisJv7i5TVMvXoEz319D48uWMq5kwXEJUfiqZITFObHP5feTECIDycPCBrNW499T5/+kQyfLNhfblx2gB/f28j9z19Lv+GxHNp+hs9eXE1IpL/DcVO3neadJ37ggReuI3FwL/LPlfL2o99jNltYdP8VTv0MixAsOfJzK0ge6NrTuTHLf9iHVCrmlaU34tkoAaq+gxY1cd5CtHyhroYCbTWRnn7sLs2074/3CaHWpOdgRQ7DA2PO+y0L/uKBcmE1TqleTYjCm1vjx1BuqONwZZ5ThLlKKqfSqCGO1j/nDgmI4oVjv1GmVxOs8GZN/nHGhjTYSEZ7BeAp8eCrzL0AzI4a0GJUekf5Jmc5qwrXI0JEgIcfgXJ/ygwV7Cjbx57yVB5IuJ3RgcMc6mgtOkw2ExHKULylKk7VnsVDLGOov+uVVWLRHyM2OKOognMllQxvlOBz+pBE3v51Fwcy8hkR7/i9/2G3sCrk8oEJ9m2jEqL5/chZfjt8mnkXrB7acKzpZ8hrxw5kw4fpfLXtEOVqwdp3/siuXcnm6nzNVgveCrlDdHm1Rs+es7mumgBgX3ou4f7ebbbuupAeKZhHeA7FZBWyO9eaCh3Ecjdu2oK+GcG81niW3UX3YrCUd2kfQj1dZ+jubEq0OzhY+nizPuWdicWm40TF6wCU6w4yLOQ5ZOKOZUv+I7KjIJvFW34BoM508f0C/6gEKJRMj0loueAfCI3JyG0blrG/xFkEctN23jwsCNIl2jqeHzutSx8VNhad4vLwBlF5W8kZJoY6Z66vJ84vgCti+7Ahu+Hh638nj/RIwbw5z9WW8PLu2X65bv54xPQV8heofD3R1elbKC1gtTaYg8qVHnaxHEDl54nKV/ge11bUsX/9MRY9NKsTe+zIM9s2U6ETnpluHDQYf2X3XUMPTxvP/KGOD95pRaX8e9VGjheU8OiydWz4x+0ozicZ+/zWa1y2M/6V/1JRp+WN62YzISG2q7v9h0UulTJrSF9+2HPUvs1itbLmcBq3XNZ0Xq5Vqc72jXNT+jWZ+PBSpyUbRZvNQpX+IENDG0Rqichxoqdcu50izWokIi905jyCPae0+vjRPjdwvOxhwlVziPS+DrlEEFrrjOlozNkAHCy62V7eQ+LatqSr8ZBLmXrNCMoKhWCCz15czay/jCUuOZLgcD9K8iuJS47EP9iH25+YY68Xcd5L/JfPtpFxIt8umK/5aicT5w5j2kLBsi26TyiHtp2mMMfxOfzbt35n7i0T7MlOI3sHM/PGsaz9epdLwXzo8FiUSg+W/7CPyVck4+HRvLxlNlvwUikcxPLaGh2HDjSfN68lPKVCe08Pns3du7/GX+7JxNAGOySFRMbSUYt46fg6NGbjea90uLnPaK6OESZV/3NkNXmaKmRiCcEKb+5Ime90nNsTxvL04VX4yJTcEDeCudEtBwv5eXjyzNA53Lf3O8w2K9Fe/vxnyByHMgt7D+fRgz8D8PPkv7bvQ2gF28v2AbCqcD0h8iD+lngn8apYAKw2K6sK1/Nd7i+8l/E5fVSxBMsD7HX7+yTS30f4TE+rM/j3iddQSb14KNF1f8+qD1Cky2BiSPNBUe2lyliMv0fb7GknrvivPUAHYGZMEh9MvAqAWq3B7sVttQm5BvLKa1i29zgiEdw3oyGg56bLhrH+SDoPfLqSGycOJTpQsF85ml3Msr3HmDY4kfH9Yu3l5w7vzxdbUnl22WbyK2qJDPABYH9GHifzmrYwHp0QQ3SgH59vPkhShHAv6hflnDS9PVx4vvVJSl2d79ikGA5k5PPiii1M6Neb4io1X2xNJchbeOaoqnPO7bRgzED+/cMGRj/xHiDc+4N9hfLTByfx9yvHIZO0bC3TIwVzg0VNqFIYgAUret7DoJtLB4OlEqvNjFjk+FWvMhxnT9G9mKyuPZs6kzDP1mfhbS+ZNd9wsuJ1bFi7/FiuKNZuY2fhHYwN/xC5JKDlCl1IpUGYMFhXeLLdPuidxbL0Ezy+ex1m68X5v/yZuLrPAGTirvVT6ynUGAVR5+bff+JoedFF7s0fj2/PHMFgMfPK+JlIOiEhlSu2lpx2EMxX5R1pVjAHeGrsZHYX5KIxCX59+4vy2ZSTydSYPl3Sx/bw0c6nCQr3b7lgE6j8PF16lLpx01W0xvdWcT4iVVMrPJDlZzQ8XLpKWjfthnEAbF+VisVswT/Yp8P9PFRUSGpRIX4KBZXnk/6uz0zncLHwGxDh7cPfRl381TH9wkN45/q5TH39E8rrNOzLymNiYtdEKlttFu5JFXyl3xv2ySXvVZujyeL5tP8QpghnyYCX2tXG/BHJDoI5wKrUFgTzQ2lO2y41//JanZ79GULwQHpxOedKK6ms01Kj1aPWGzGazRhMFgxms1M0uTPnJ8Sa+PnXmrLJrF7KmMg1SMVenKv+AKvN6LKKxaZ1qh+umkuQ50QK1Ss4UHg9A0New1c+BLChlAoRlaMjV7TyzLsObz9BWJIrGq4r30AhEloml2I0CGMRncbAio+3snfjCUoLquxJRTVqPSMbrTYrOFdqF8HriUkKdxLMs84Ucio1ix/e2+jUJ7PZgvQC72SVSsHiv13Bmy//yr23fcIVMwcRFOxNeVkdhw9m8eAjM4iIang2TRkZx9FDObz35u+MHBNPaUkNy77bS0Cgippq5/+XK376di+jxycQ3cs5v8K0iP5MixDOu06tZ4JZiOS2WW3UndRwdeEArl7o/Hz66nOreP+pG6hT68nPq6Rv/wiXx54clmT3Gm/MU4Oan5gdExzHmEl3N7lfKZGR4CMIoR1dpd0UNmz8lLfa/v6ePjfZxXIQIsKvipzB4eoTnK7NYGPJdq7vdVWX9KUj1JmFRLP7KlYxI7zpz7StlNbW8Z8fNzhs85J7MKR3OPdOH8OgmHD7drlMymf3LeDd3/awfO8JqjTCuCDC34cHZo7j9inDHdqRy6R8eu8CXv5lK19tS7VPHF7WvzdfPbCQmc9/5rJPIhEsGDOAN9fs5K/T2pefqCkuPN/66HFX53vrpOHUag38eug0P+0+TkSAD7dMGkafMOEavGXpjw5tL993gldXbuevV4wiPjwQsUiMyWwhs6QCgE83H0Cl8GDx9JbzifVIwbzKmINCIiyvkYmVeEovrgB3KSAWyfD26IPRUo3JWoPV1tJg4M+BDSt6Sxme0oYLTmPKY2/xA90ilnvJolHJunZJY0bNV5yseKNLj9Eaao1n2VV4B+Mjv8BD3LbEi51JplpIRPX8sbUXVTD/3+nDPL1nA80ksHbTiSxKHHSxu9AtaExGbl7/E4BbLO9Cfs44gdVm443LZndapHmeppJvsoTlrser8nnh+K8A6CxG6swtR7bG+Pjx2uSZ3LdhNdbzkUkPbf6NFfNvIM6vZ4yTouM7lgh6wpxhTJgzrOWCbty0gdpKDd+8uppT+zPRa9cAMGxyfwJCWzdW6T9SmJRa9u7vvHT3JwSF+zU7sZM8Kh6A5R9s5IpFYzrYe4EjJUW8uHOby33+CiUfXTkPlYeHy/3dTZiPikg/H/Kqasiv+nPb0OVrc0EkIkrZ+sR9HWFAdCgJ5wWE9GJBGDhTWMaZwjJ7dGBjUrMKnBLHDY4Jp3dIz/hNaYndZ3P4bOtBDmTmY+mk4BSRSIqffAgF6mVEeS8EwGzVIBULArLJWotU7INU7IXVZqBMu4VApTBJJpMEYrCUY7LWIhP7UKrZ5NS+wVKKXBJCjO+tGC3lVOsP4ysfgsojAfP5Z9Mq/UH8FcMBG0ZLJR6S7k98K3KxwkAscd72+kPfkHu2hL+9spCEgdF4nBfY75/5motGHd9emEjTZrWh1xq5+ZFZXHGt8zPchWJ5PTPnDiUkzJcfv9nDt1/uwmg04+vrSf8BkaguWLl27Q1jqFPr2bz+BGt+OURYuC/XLBpNbO9g/rHY2XqxKX5fcwS93szseUPJyS63C9xh4X58/7/dLFg0iuU/7icqOoC+/SMQiUUEh/hQmC+IrbU1gsD55afbCAr2BpEIs8niUGfrplMUFVZRWlzLxKlCpH5cn1C++2onOp2JmN7BzL+2cywff8xOZVHv4S0X7AA5mnxKDcIEiZfUk/6+rhMSJ6riOF2bwRl1psv9bSFPe4oV+W9QYyxlYugN9PYaxOaSr6gylmCy6pkSejMhCsE67Jf8tzDbTGjNNcyKuIcgeTTf5SwhQplAlbGYXl79iVQmsqNUEGdLDTmszH+LRJ9R9PPp2G/9skdubHMdlULO4/Mn8fj8Sa0qH+bnzZu3znG5b9+L9zdZz2y1IpNImD2sX5v72BRtPV+pRMxDcybw0BzXzg3H3/iH/bXZYuXFFVu4ftwQ7p/pOpBg79lcUs8VtO7YbeppN+Eri6BUf9L+PsFnmsN+L1kvAhRD2t2+xpTfKhuOjhzDFUpJaKe21xi5JIApUT/b35usaoyWaozWauGvpRqDtQqjRVgCUr+vzpiF2tSxJUg9Hb25xC6Ym6xq9hY/YP8cmqLeBkjlEYunNBKlNOT8cjwxJqsak7WWOlM2daYcbDZLk+10tR1LrvqXNovlHmJfApUpBClSUEhD8ZD44yH2wWzVYbTWfz+qqNQfpUy3D625dTcTALUpiwMljzA27ENEoosT7VtnbvBuPJVfwre7jnLLxBRqtDryKmqICwnA11PBvoxc+kaEIJdJ+Wq74P9582WCUPPV9kPcfNkwAryUbD6ZyaKxg8kpr2bnmWyGxISTHNX8tfzZyYMs2b+5607SjQPDQyKJ9+v+h4nuxmAxc8fG5Rwtu7SEcplYglIqxWixoLdcOpO5KzJP4imT8fyYaS0XbgXRXgHcmSCsOCrTq7kySpjkkYml9PFu3ZLrmXGJvDp5Bv/c+jtmq5Uag55Fq37g7amzGRPZq+UG3Lj5E+IT4MXiFxe53DdqmnAdBkX4s+S7B+zb9VoDivNL9mXnl/g/+/2DTvWXbnrSaZvFLAh3WrWOEZd3jqd4qJeKSG8fyrVarOen4sNVKibG9Gbx8JGEqXqWJZ7OJNzr/Tz/3DZLX+Z8RrSyFzfH3t5tx7zqfHT4q6u327etTD3FYxETncq6smO5ekTX+tN2BlqDiSe+X8emExnNlhOLRKgUcrwVHshlUhQeMtQ6A3kVzT8HJge/QFrFEvJqvwOsRKiuIsZX+B/6ygei8khkX+ECpGJvApUNQplU7EVvv7+yr2ABHpJAQs5btWjNOfYyaeX/RmvKQyySIZeEMCD4jvN9VTA4RMibcKbyJSxWDTas9PK5mUhv13ZGPYEDW9K444k5JI8QIqlNRuHaL8511FoiYoPJOlXosC033dEKQiQWEdcvkpyzxQSF+7WpHykj40gZGddiOalUzF33TeWu+6Y67duw+ymXdZ5+YYHTtlFjE0jqH8G7r68jOiaI2N4N47i8nHKkMgmTL09m1/YzLtvcsO4YANNnDSYyKoC3X13rVKespJakvhFcfd0olr7+GwDjJ5roNyAKhdIDrcbgsu22sDLvKJ+c3Uk/3zDmRXeu9nUhedqG/7/GrGXRnsXNlleb6jp8TLnYk/lRD1Gqz2FH2Y9IRR4YrDquiX6USmMhG4o/Z2Ev4Xf8qihBdD1du4e02j1MCI6myljM9PC7CPBoCL4cHSREvR+v3srMiK6zr+kJaA0mvttxlCuH98XPyzkHSU/EaLZgMJkd/M7rqc/xkVtezdikmFa11yMFc6NVi84u7Dr7MSf43UaC323tbv9o+XNk1y5rsdz48E8vmujXUWRib2Rib7xoPqIhV72Kw2VPd1OvLg6NE38eKVtCnSnbZTmJSEmUagbhXlMJVAjCqVTcfOJHq81IjfEMJdpdlGoF/9sqw0nql/N1pR1LteEUR8tfaHV5f/kAkvz/SojnuFblBejlPQ+AGsNp0qrea3Ui0XLdAU5VvUNywD9aLtwF1JkaBg9JEcEkRQSREBbImkNppPSOpFeQHyfySgj2UXEoq4DbJg2nX6QwyOlb780VGWx/bbIIEyIbj6dzx+SWZ/F/TD/Os38QsVwqFqOQSFFIZSilws+F2WrFYLFgsJgxWMw9wm6mPck+L0Ue2fEbe4ubTm7S3YiABIHv49wAACAASURBVL8gBgaF0stbsOGI9fEjxtufcC9vPKUylFIZ0kaWB1abDZ3ZhM4sLOWt0OvIVVeRXVtNjrqa9OpyjpQVYeghwvo3p4/g56Hg0ZTOuZcHnU/sdHfiRJJ82heNfU1iMuFe3ty/cQ2VOi2lWg1/WfMT1yQlc/vAFPoFXhy/Uzdu/ggc2nqKdV/vpLK4huvb4T2+edk+Nny3G4Arb5uIRNo5FkOzE5KYndC8bVNP4WheEeV1GiRiEYOiOrbq5FJGa9GQq8kmWtm9k5lzUwQ7iDfX7sR83h7j18OneXj2BIfkZwazmd+POiZ7U8ikTB/sOuqzp2C12bjv8184kOmYvyU2WBiHzBqSREpcFL2D/Qn2UXGha9KPe46xZLlz5HdjFNIIhoZ+2MReEQODX22ybqzvHcT63uG4jbvsr4eEftBkXR+5MFkxIvzrZvvXkwjvFcih7WcYN2swujoD/zsv7JrNjs8Hs24cx/v/t4wBo/rQb1gsJ/ZlcmTnWfxDHC2rbnxoBs/e/Rm9EkIZN3MwNquN7DNFWCwWpl7dOdHUnYFC6YFMJsVktiASi7Ccv9YsFqv9umsOvU4YBys9PVB6ejSZMyAgUIVcLsV2PnfG4GExvLxkJX37RzJvQccjwudFD2ZeK3zQOwOtpcFnWiGRE+PZfJLWYHnHg6GCz0ePKyReGK06Kgz55GnS+DlPuIaD5NEYLIIVz9qiD1FKVKhNlQTJhb7JxHIHsfzPgMZgZOOxDGw2G8v2HkdvMnPPtJatS3oKnnIZ45Ji+WLrQTxkEhLDgzFazOSUVvPzvuMA6I1mbrysdStae6RgHqNqCJ0/W7MOG1Z34k837UZnKSZXvQqAQs0Gp/1ikZx435uI97sZmbhtPpNikQf+8oH4ywfS1/8eALTmQvLUqyjUbCJQ0bRnYEew2HQcKHnE7pnXFBKRENkzNPg/RKqmt+tYvvK+jA5bSpluHwdKHmmVlU1m9f8I95xKgKL9NhmDVi1pVz1bIxMUiVhMuVpLVmklINxAAQ5lFeDjKW8xoVFWaSWnC8s4VVBK75AAvtyeyrDekQyMdv0A+HtOOk/sWtfjbVgkIhEJfoI/XXJgKFEqH8K9fIj08iHcy5swL28UEqmD0OkKG1Bt0FGu01Kh11BQV0t2rSB+ZtdWkV4tRJd0VVSxSibMHM/ufWmICB3hv8f3szrL2We0uwn1VDEjJpFRYdGMDutFgKJt0YNikQgvmQde5/93QUovkvwdvRKNFgtHygrZW5zH1oJzHCotdNVUt/Hesb309glgQULnRd3VGLU8lvojWkvDPfzdka1fnjg2shcbrruVR7asY0vuOaw2Gz+dPsFPp0/Q57w9S0pYJH0Dg/GTK/CRy5F1MEv8hUyMbr/dmMXc/MNkZ4mMbty0lWGT+jNsUv+WCzbBlAWjmLKgdT6fz9/xETvXHHbY9ux39zN8Stv8o8+dyOe+qc9z/yvXM/uWrs+bcyEWq40qrSA47M7M5fX1QgDJDSOHEO1/8Sz6LjYna044jEm7C38v4Xd5Yr/ebDohWBpUqLXsOpvDZX0b7ttbT52jTu8YoTptUCIqRc+w9mmKn/edcBDLRSL455xJ/GX8UPv75rgY/5M/Mg+9fgNLn/iR28Y9i1+Qimv+KkTVB1wghE9fOIqSvAo+eX4lBp2JsdMHcs8zVzt5lY++YgD//vROvntnPT+8uxGJTExUXAjX3Xt5t51Ta9i8/ji/rT7M+Il98Q/w4psvhMCymN7BKBQyCvMr+W3NYbLPldG7TwgRUf6s/+0ohflV9IoNZMIkIU/fN5/vIKpXIHIXdVyh0xqprtZSWaHm5LE8ho/qOXlsWkIhkdtfB3r4s2TAo11+zAs1xEB5FGHKOOZGNqwYO169FYAAj3AmhlzPwcrfUJsq7C1ciOR8bjyjrXWJwi81NHojb6zegcZgJCkiiA/vnk+Ef8dzsXQnr948i4827GfZnuOU1NRhs9kI9lGREifkiXjjlitJCG+dV3+PFMxz6nZTaxJsIOQSn0tKLK8sGUVA6L6L3Q03jSis24Ta5NoDy8cjgRGhr7ToM/7iP39k7qJRJA9teemGpzSCJP97SDovoHekraY4XfkBWnPzIpJSGsaosHcA8PXoeLRIsHIUEyK+YHfxYvTm0mbL2rByrOJFJkV+S5NZc1rAYrMS6emHn0fzUf4XUmUUHtoKtcIqlb/NHIdYJHLwY7z5smFYbTZ7woubJjjOMNa/7x0SwPMLhYmG/pEhWK22JkX2kxUl/H37Giy2njcQ95J5cFlkLCNCohgUHE5yQAhKaceTY4kAf7kSf7mSBJyjAOo/i3M1lZysKOFkZQkHSvI5UVHSKZHp8/oIokZnnEtPZmdhDq+kuvau7Q48JBKm9UpgQfwAJkT27rJEmI2PNzIsmpFh0Tw4ZCxZtVUsSz/O8kzBqq1I0/X5Jy7k8d3riPb2ZVRY5/jQvndmC/83aA7estYvb1yXlU5hnZpCdS1FGjWFdbXk1jr7A2dWVzr87Sqy73mk1WU3/SiMi5a9v4G8jOIWBfPfit/vUN/cuLkU+PubN3Hz43PZtfYIAF++sPIi96ht/GvFev61Yr3TdoVMyj0TR/HglI4nIa011bCycDnHqo+gMQvL84PkwYwJHM8VYTMQiyRYm7BGrBdG91bsYkfZVvJ1+VhsFkLkIYwIGM0VodORiV2Lwyarkf2V+zhSnUqOJotasxqZSEawIoShfilMD5uFh4u66eozbChZR542lwqjEDSws3wbO8udf8Nnhc/lqkhnqw2xSIzBqmdN4UoOVR2gyih4HsslcuJVicyJmE8vz5afIeaPGGAXzAHWHEpzEMx/P3rWRZ2en+zzl4MnHd5fN3owN04Y2ur6tdqO21j80Rk/azDjZwlRxz4Bgnf7b7lv2fd/vOVf9teJg3uxdG3L4wGRWMQtj83mlsdmO2y/fIGzV/nIKf0dEob2NK69QYi2tVisSM7ns/jXM1cDIJGI7ZM2d9/nKPIvftDZ4u+x/5uHSCRyWWfU2Hj760efmgvAB++sZ8lL1+Hto+S1F1ZfUoJ5tLIhkWmxvgyD1YBcLG+mRhf0wbMv5+oOsyz3ZQDivVPorRIC/A5WrqXGVIZU5IFSomqyjXrPc7WpgmW5LzPIfwqJ3j1n9UNHCfFVsW3JpW01o1LIm/U8bws9UjD3loXbo8yrjT1n+bmbS5MqwzGX24OUIxgV+naLtis9DbUpi8zab5otIxEpGB32Lj4e8c2WayveHn0YEfIKOwtvx0bzgkeNIY1CzUYivK5o9/Ee6n8FMyLbNnhfmy8stXk0VfD0Fzch7jW1vTmaEsvLdRru3LTcbjPREwhUeHJ5r3im9UpgfEQMckn33+7rhdUEv0AS/AK56rzArTObOFRWyP7iPLYXZHO0vMiexLAtXP8nsGLJU9fwwNZV3T4RIxMLdmTXJw3mgcFjCFZ6devxG9Pbx59HUy7j4WHCoGdF5ineOrKTPHX3JZMzW63cu2Ula+fdSqhn04Po1hKh9CXe23X0UFPc8/ulJabVs+vXI7z2YEMiLYlUTGCYLxXFNXj5KLHZbGjVQpTOgFHxDJ7wx18x4sYNgJePEi8fJVFxbbsX9BQCvTztHqEF1cL92GK18dq1s5jat+MiTq2phhdPL6HCUI6nxJN4byH4Q2fW8kvBMs5pMlCIFWhd2HdabVY+yRKsLw5W7kculhOhjEQkElOgzeeXgmUcrjrIw0lPoJA4T1zurtjJNznCfStQHkSCKgG9RU+eLpd8bS6n1ad4JOkJRBcEhZhtZlRSb/r5JFOoy+ecJpMwRTjxKufAlVivWJfnLRFJeP3MS+Roson2jCFAJQQk5OvyOVp9mFO1J/lXv38TqWzezmBC31iCvL0oVwufz+YTmWgNJjzlMvQmM9vTHHNYRQX6Mjyu+TZ7AhnFFQ7v5wzr26b66SUt5zFz0zXcf2gJ7w57mr0VR3gv41u+GvUy+yuE5/SMuhz+EjOXH/LWsrv8EBKRBJXUk0eS7sBHpqJIX8Y76V8RoQghWysEVlptVv6ReCu9PCOc9jfeV09TbbcXSaPkz9J2roxrabXzhVwxYxDLvt+LVCrptISf3UVvVS+7zUqZoYLNJbuYGT6lXW3VT1hqzDps2JzuxQCJ3iPsQraPLIjrYwQb4okh17ts87a4V5y23dXHOVecRCQEat0Y277V8G4uLXqkYH6s6gcC5cJAS2uuZGTwXZdUlDmA2XSauhohcYS3/xtYLeVo65YiQorVWoZYEoWP/1JAhFb9NkaDkJjFQyGIi56qe6guvwq/oF+oq3kaMKLyfYnq8mvwC/q5iaP2LI7nFJNRVI7CQ0ZkgA8ZxRUkRQZjsVjtr01mC+mFwsClXK1h8YyOZRhuLYLNyDt2y5IL+frDLQAc3J1BcKgPVRUNSSfW/nyQbeuOY7XaGJgSy833Cjf6d55bDUBBTjl6nYmUsfH2fV9/uMVlW+3hbNVHzSYaBRgc9GSni+X1BCiG0Mf3JjJqWs4knl79eYcEcz+PtieL8pJ170y11WbjgW2rL0rUqysGB4VzW/8Uruzdt0VLlYuFUipjXHgM48Jj+MfQ8VQZdOwoyGZr/jkANudnUm1ofplbcmAoAwK7LpFyT0BvMXP35hVUGXQtF+5E5vdJ5uFh4wGIUvWc5fT1k1zXxCczL64f3509yluHd1Gh13bL8Sv0Wu7fuorvZixq17X1wZktDW0ZNTx08Hv6eIcgPj/IX5w0udP62pNY/XlDZOWdT1/NVXdPQSIVMyf6Qe557louv240hVllLH3sWww6Ewvubf9vhhs3brqPh6eNZ/5QIajh+bXC/e3rvUd4b8teJifFtSswoTHL8r+nwlBOkndf7ov/OwpJw5gwR5vNm2decSmWA6wvWcvByv0A9PcZwJ1x96CSColRNeY6Psxcyhn1aZblf8eNMc55scYEjkdn0TLcfxRB8oa8EAW6fF4+/Rzp6jOcUafR19sxCrafTzL9fITPZGPJ75zTZBKvSmxT0s88bS7eUh+e7P+MQyS5warn/Yx3SKs9ydqiVdwVd2+z7UjEYuak9OPzrQcBBJH89DlmDE5ie1oWepOjXd5Vw5NbtDO52FhtNrRGRztKf1XrA59qdXq2p2V3cq/ctJYQeQAVxmpOq88R6xVBoa7ULn73UQlRu9NDx3Nd9ExEiPhfzkq2lu1nboTwPH269hw3x1xFPx9BJ/qtaDu/FGzkwYSbnfZfuK+lti8V4hPDiE+8NHNDiBBxQ8x8AN4++wnf5q7AS+rFhOCRDoJ3nraQneX7mRE2GX8P188h4YoQJCIJBquBTSU7uTy045HEbty4okcK5sl+8wmUC2Kf+FJLuimSYTIeRlv3Nr4BnwibxH5YLeVYTKfwD9mBSORBdfnVmM1nsVlrMRkP4BckJCGtqbgJAJPHKMTiAGzWWqzWSrAZsVnViMUBTR66p5FVUolcJsVHKedodiE3TUrh800HkUpE9tc2bNw+VZj5+2zTgW7pl1TsycjQN5oUy/Ozy9m/XVim+PbXd2PDxt1XvwtAUV4lW9Ye45VPbkMkEvH4X7/g7MkCEpMjufefQnIoqUyC1Wrl5plvcNPiyRTkVLB/+1mnttqKxpQHQEHd782WC1SkEO09p13HaC2J/neSVfs9FlvzyxqrDaeoMaThK+/X5mM8OWgWiT5tF0RV0u4VzN87tpc9RRd3Jcy0XgncPVBY0jg8JPKi9qU9+MuVzI3rx9w44XtitlrZU5TL2uwz/J6bTqULQfT6xPb7418qvH14F2mVzdsfdSaBCk9eGT+TqdE9f3mnVCzmpr5DmRWbxOO71rEhN6NbjnugJJ83D+9sVxLQ/n4RLl//0Tl3Ip/4gYKVzTWNPEjlShk6jfAbEtE7mKc+vZvbR/+bb1//ldv/b/5F6aubPybXJj3MgnuvYOHfZti3ffbsCn56dz2PLL2FqdcJy+tfufdzKoureWm5kLS8tkrD/15ezZ7fBMuUmgoNodEBTP/LOK5ZfDliiePE2fN3fAQiEQ+/Iwg0nzyznJ2rD6GrMwjf8c//SmQHoskriqr58KkfSd0q5LMQiWDE1AEXxbf8Qu6fLAS8rD56mrSiUn44cIzrR7Z/FZjWouFApWDldGPMbQ5iOUCMZywzwmexPP8np7omq5Hfi9ciEwtRgHfE/dUulgN4SVUs6nUTz5x8kt3lO7k2+gYnWwAPsQczwq50ajtSGcWogDFsK9tMjibbSTDvLK6NXuRkuyIXK5gXeQ1ptSc5qz7Tqnbmj0i2C+YA64+lM2NwEhtPNCT7rJ/YmDe851pg1CMWifBRKqjRNgRV5JRXERPk12Jdmw1e+GWrk2+7m+4jTtWLbE0B2ZoCJgQN56w6i2yN4Ec/JUS4D6dWn2Rb6QGUEjkl+nJGBDSM9wM9/OxiOUCsVyT7Ko+63H/hvpbadtN6tpfto1hfak/iqTXrKNSVALCheDtnajPxlCpRShQM8u1Honecve7YQCFRaXlMJd/mrOC9jM/5MvtHQhXB6C16KoxV6C3CNTo5ZFyTfVBKFEwPm8jaos18fO4bVheuRyVVoavvk0XHhykvd8n5u/lz0TPDD924cePGjRs3bty4cePGjRs3bty4cePGjZtupkdGmFttFo5X/QiASCRhkP91F7lHrcdm01FX/Qhyz/mIxI6z3VLZAEQiwW9JLA7CZtVgMZ9F6jGI+sSIUo+BAFjMp5B6DMFo3IFI5AUiOUbjTqQeQ7r1fDqCSiknq0SwXqnVGVhzII0QPxVikcj+2mS2sGr/KQAMFywN7Cr6+t+Lp7Tp6L7CvEpiE4QIIJFYhAgRMeczVeecK6Mwr5In7mmwI9FqDBgNZj58ZS0gZK/2kEvRqPVYrTZ7exe21VZy1YJ/bUve4Yn+d7ar/bYgE3sToZpGnnp1i2X/n73zDo+qTPvwPTWZZNJ776QBIXQQkA6CgCKi2HXtva5tdV3b2j/bWtbK2hUQpSO9956QkN57nyTT5/vjJBOGTJKZNEBzX5eXk3PecmaY8p7f+zy/p1C1rlsR5tdFtC8CYwuDPYQI69XT7u9Wf3s4WlHMu0d39fk8HRHn4cMLY6cx1j/0vF1DXyAVi5kYFM7EoHBeHj+TPSV5LM88xYa8DJr1OhwlUhZEXviRUD3hdHU5n6X0T9YNwNSQKN6ccBlejhdXTQcvRyc+m7aQH84c56X9W2jqhzoCn5zcz/TQaJJ97IsSv9SvzZv7heMrGe8TwyjvcDzkznyQtpkvM3dyY+R4pgf8ud7bTapmAiPa/+45OjvQUNNmp+DsqiB5Uhw7Vx8diDAfoFeJSgwm97RlofTTh3NwcnEk7UiuOcI8J7WIEVNaam00anh83ltUltRy5Z1Cyr5fqDdph7P56uWV5J4u4on/tLfyqCqp5cWbPwHA2c2JW55ZgF5n4Mj20/gGdz9LVKvR8eRV71JRWG3O1PAL9eLQllTeuO+rbo/bW7gpBB/w+6eM45W1W3l38x5mJQ7C09l+az2AbFUWBpMBP0d//Byt2w8kug5lBe0jzLNUmTTqG4lWxgDgInVt1yZQEYRMLENn1FHQlG9uawuecsGDV23oG7s0ESKSPUZaPefvGABAg77eprEifT1JCgvgeF4JAHvO5LX4l+ea24yJEdaQAe4u1oa44EgKC7DwX/9s8wHGRocil3acld7QrOGlXzez9qhtkfkD9A1RylDyGosQI2KQSwR/lO2mWiu8l30cPCluLufH/DW8n/wPFBJHfi5Yh87Ypg84StpnEbcW97V2vvVccbOQrdnZ2APYzrqSzWQ3Ws+uzlTlkKlq+3yaTCaLCPNW5gfOZKhbPOtKt5Jad4b8pkJkIhlecg/CnIMZ45mMj0Pnv5k3hi3CU+7Btoq9lKnLqdLW4NqSTRSjjOi07wAD2MoFKZhXaTJwkwvpuxpDAyaMF5GHuQQP3w3UVd+OpnklAA6KK8zn2rWWxqNpXgstX+h6rZA6JHeZjtjUTHPj5zgoLgeTDnXTTzgpL56KtZkllUxKiGD7qWxumz4Ko8lkTvs79zF0rwijvThKfYlw7XwDxj/Yg9wM4YfVZBSurTBX8FkPi/TB19+NVz++CbFYjF5vQCIWc2hPBg31wsL52TevoaGumW3rT1qMd+5Y9mDCSIFqdZft3OSD8FX0jw98qHKeTYJ5adMOEr0e7YcrEnAQC19rEUrvPptDZxQ85P++a12/F2MEcJE78MTwiVwfl2wurPlnRSISMTEwnImB4TTqtKzLPUOVugkXef9a7/QnBpOJJ3evR2/sfHOst7g5fjgvjJ1upVzOxcOSQUkkePpy6x/LqFb3ree7wWTi0R1rWHfFrTh2s5jutrJ0/BXurCk6znujriO7oZwvx9/GY4d+6lAw37ak7zdD+wKFsyPVZe0LtHr6uFKYWWZxzMPHhaqS2v66tAH+IkQPDTXbmBj0wvdqxrE8pi8eS/oR4cZerzNQmFnGtQ8Lti3LP/qDgoxS3lz5KIPHtYmpM5eMwz/Um69eWcm0q8cyfLJlQMDpQ9ksfnAWALc+e4X5+OW3Xtqj57Dll/0UZZXxyP/dyMzrxpuPz7ruEl676wu2rzzUSe/+Y8noofx48ARZFVW8vXEnr1w5s1vjVGoqAPB16Niaz8vB+jqvWC14ImeqBNuROw/d3OlcjXrrtYUqNRXsqdpFpuoMVZpKmgyNaI069EZhY7avVn/uco92FjGtyFqKzRlNtq8PrhiZaBbMVWot3+w8YmFLsnBUYg+u1jomEzRqtFTUWynIajRRXq9C6eCAk4PM7rEXjRliIZgfzS3mmve+Y8klQlBZQpAfSkc5TRotBVV17M8qYO3RNFRqwfv8ylGJ/HowpZvPrP8wmkwUl9RSWl5HWWUD5RUNlFfWU17ZQF19MxqtHo1GEHw1Gp3wt1aPyWRCKpUgk4qRSCQoHGUonR1a/nPEw10IjPDxcsHHS4mPtwuBfu4E+rtZFLHsC6KUIfxevJkE12hCnQIobC7FRdoWqNFoaMJZIlh56Iw6DlafZJi7/UFX59JoEKwd+2LsLudu0pBXWE1xaS3FJbUUl9VRUlaLqsWSrlmtQ63WCf/X6BCLRSgc5TgpZC3/l+Pr7UJQgAcAwYHuBAd4EBrsibvb+Qly+ffQZ3plnHDnEO6Juqnrhh0gFomZFziDeYH9W/tGYxA+d1sKs1iTl0Z6TSVlzQ3ojEb8FEpClG7MDB3E3LBYPM8JRJKJe8du2gScqCxhT2keKdVlpNcI+lK1pol6rQaJSISzTI67g4JB7t4kevpxWeggoty8emX+/sJkgpc/XItWZ6CmronH75jOJ9/t5LUnr+Dmx5fyyG3TACgpr8NJIWfHgQyLtvuP5eLuqmDWpAT0BiP3P/8jAJ+8cp3N13BBCuaBTsnojMIXW72u+CISy2kpWCDF1eMT6qpvAEAs9gVR+wrsADL5cGQOY6mtXASYkDtOaTk+ApOpgfrq7SjdXgaTFlXds0g9PuqnZ9Jzbp4ygsKqOm6eOgKwFMQ7etzXhLssRNwS5d8RoRE+DB8v+J89cvNn+AV5EBAi7HAGhHgy5+pRPHXnUsQSESajiRc/uIHYwcH88JlQuPW5+7/F00dJREtBjtbxzh3LHmo1p2jWl3bZzt+5/wqXeDgOMX82O4t6V+lyadaXoZD+eQo0/uf4PgAyaqv6fe5ELz8+nrKAUJeu/Rr/bDjL5CyKGXy+L6PP+Tr1MCcqu/689wYPD7uEh5M79gi8mEjyDmDZnOu5YcPPFDfaFn3XXXLqa/jg2J5ueZkDjPeJ5u5Bk3nh+Eo0Bh0ysQSJSIyLzPpaASDc7eL8zIfHBZKTJghYep0eqaxlUzMxmD1rj1FXpcLNSwlA5skClOfpBnCAPy9RQ0P57fOt6HUGclKF96LJZGLywlFs+H43Wo2Ooqxy9Do90UOFaNs9a48REuNvIZa3MveWSXz1ykp2/n64nWAOsPDu6e2O9ZSjO9IRiUVMXjiq3bmJ84dfMIK5RCzmqcsu5Y7/reDXYyksHjmEpJAAu8fRGgVxU96BcAx0KCq3Rn57yIX1drAipNO5XGTtI9B3V+7gu7yl6E16AhRBxLrE4y73QCFxJK3+NCfrjlsZqXfo6Hl1l8uGxfL679vMRT6/3n7YfM5F4cDUwT2rW/LfzfvZmZaLSq2hQa1FpdbQqNHSUTxJZUMjU1/6DACxWISzgxylgxylwgGlg5xpg6O55dIRHc43NTGKWUmD2HD8jPlYRmkVLy7f3OW1Xn/JMJ6+Ygr7MvIpqW2w74n2IUWltaRllJKWIWxspGWUkpFdTlOztoue1tFq9bTWRq1vaKasous+EomYQH83QoM8iYrwJTbKj9hof3y9ey/zwNfBi+LmchYGzUQsEmMymYhQBpvPRyvDCHMO4vHjr+MkUZDkHtcr80YrhXoAfTH2uVRUqTiZWsjx1EJOpBSSk1dpDg60BYMBdLpm6hvagj9SOkiMCA32ZPiQUJKHhDJsSAgeA+unPmdncQ5P7V0PQJGVe43chhpyG2rYWZLLS4c28+iwidyRIGTNS0QiXOUdr/NtIb+hlv+lH2F1bhqlTR1/h+kAtUFPlbqJrLoq1uWl89bRHYz2DealsTOJdffpsO+5LN7wPQfKCiyO+Tkp2X3VPUhFPdNpl2ed4rHda9od/3TylcwKHYRIBM89INQJ3L4/g+37M5BKxaiaNAT6upHa8p1ZXdfIVbOTuXRMjEXbhbOH8e+PNjBrUgJHTuUzaYzt2WStXJCCudpQR4U6DcCiYu7FgKefUKBGJHLE3WuZxTmZ53/Nj13PeuykfAAn5QPtxhKJXPAOaNtB9w7I7u3L7VMcZFKi/C+sXaxgZfsCPta45f6Wmx0rrh4z5g1jxjxLaxwHRxnvLO04+u+W+6dbHctWypv22NTOz6n/WNf8UgAAIABJREFUhC+JSIFSHg5Ag7bz92atJrXfBPMytfDjtSz3CPfFTe718fMaavnPiX29Pq4tLBmUxAtjp+HQzajWAS58ilT1vH1kZ7/M9fTIydw1pHvWRxcqkW6erLj8Bq5a8y1Fqr4Vzf976iALo4XIPHsjNsQiEQ8c+A6pSMwjh35EhIgDlTnUatsXuL3YGT1zCKf2C0VZ9288xSVzhd/PifOGs/GHPTwy903Gz0mi4Ewpp/ZlMu6y7hcKHGAAa0QPCUGvM1CUXc7pQ8J6JSIhmJikUIxGE1knCyjNq8TJxZHACOEmsjSvkiHjrd9YObsqcHF3piS3vQqlUDqaN4B6k8riaty9XJA7to/I9Q706PX5esKE6DAuHRTB9jM5/Gv1FpbdfZ3dwTFysRDcojN2LBjqjNYtuFpF9khnQQi+K8r2BXhFS2T7t3lfYzAZuDn8b1zibbkx2qRv6lPBvLdROsqZPiSG1UeELIuzC2bOTY7DQdqzNeXpogqO5hZ33dAKRqOJhmYNDc0aaBGwQ20o4Pnaktl4OCv4cY9t/w5ODjIenTORa8cLvy9DwwLOq2BuMBg5nlLIrv2Z7DmYRXHp+c+sMhiMFBTVUFBUw+4DWebjrVHMQxOCGDY4hKTEEKIifLod8LZ0dFshxhcHP2RxToSIRwbdYrVfgKMPHw5/3uJYvGsU/0p80Or5s8+16kkdjd0Tqmoa2bzjNJu2C5+vtMz+CXgByC+sJr+wmpUthamHJgQza0oiUyfG4uz0583EPV98dHIfbxzdbnN7rcHAa4e3saNI0PO+mrYIT4fu2ZQBfJZ6gH8f3mbXBsy5HCgv5PLVS/l4spABNz04uss+Nwwa1k4wL2tSsbUwixkh9gvQZ/NrdvtsHy9HJ6a1XJeqScM7n2/GRelIZbWKsCBPggM82Lb3DFPGxXL4pGANpNHqcFE68uL7ay3aujg74uLsSF1DM7sPZ3PzwrF2X+MFqbrUaHIZ4nH1+b6MAf5EKGXCzrKzLLiLlr2PyWTCaGj7YpNI7d+Jq2g+0GUbmViJu0Pvp1V2hptc8OTtSjCv06YR4DylPy6JPJUQ9f1R+rY+EcxfPbjVbMnSX4iAl8bN4Ia45H6dd4D+5+0jO/vFh/u62CS7xfKKinq2bkpl8RJhsXE6tYj4hKAeXcfKFYeIjPRl6LBQi8c9GdvfScmX0xdx1ZpvAVDpuheh1RU6o4Hn924C4LvZ19jV98WkKylT1+OvcEWEiAp1A8vyDnFf3DSb+u8vLiCvrpZoDy8CXVz5IyeTGwcP42hZCelVQlpmgrcPIpGIpSePcsewkbg5OJrb/Z6RhkqnZZCHF1Kx8JuUXl2JQipjfkzvRl3NuGYs6iYh9Xjw2LaF+cipCUycN5ydq46w/CPhdXT3duHWZxb06vwDDBAc5YfC2YGCjFKzBUvs8HAcFHLCYgNIP5pLdVkdUUNCEJ0lAnV2T2rqwJBDJuuddGurdKBPiS5Aa7YnZ1/K7qw8TpeU8+OBE1w3xr6NsFa7lQpNeYdtanTVVo8HKIT6EkXNhXbNCZBSdwIAg8lAhHNkO7EcoEprv7Xi+Wbh6ESzYH42V/aBHUt/IJNI+MeVU7lmXBIrDpzicE4RhVWC9ZdKo0EqFuPhrCDKz4txMaEsGJmAp7It+nZYWIBFhHp/cexUAas2nmDvwSyzJceFTm2dsJG/Y28GO/YKNkeuLo6MTo5g/KgoxoyIwEXZs6jZiw2jycS2Xems2niCoyfyeyRg9iYnUgs5kVrIe59tZsKYaBbOEe4bhyb2r/6h1xn48bNtbF51jIrSOty9lEyaNZibH5iBw1mbvpVlddw4800W3ngJdzx+WbtxHrnhU6orhI2tr9c/Zv6ts3V8gPdf/I3s9BIee/kqPnl9DSlH8pA7SklICuWOx+cQFGZbwMu36Uc7Fcs9HBQEOLtgNJkob1JRrWnLENhTmgfAAzt+x70Hgvk4/7AO32utFq0Bzq64yR0Ri0RUNDdajULXGQ08sON3ANZcfguRrp27H1wWFovnwc1Uqy2Den7ION4jwbysSWV+bc7mqqjB5nuTPYezCfZ357bF4/l1wzEqqlXERvixavMJnrhzBsdPt/zOi2RW2wLMnTqYLXvSMRiMeLrbn4VxQQrmMrETR6r+1/JYMSCeD9BjPB3PT7HUb95Zx8ZfDtBY34zRaCIiLoD/+/VhO0cxUatpv8g9F6UsvN/ti+QS26KamvTdizzpDip93y1AD5QWsCEvo8/Gt4ZYJOK1S2azOGZIj8eqatpMoy6TULeLpxZCX3Eyu4RvNhzijXvmne9LMZNTX8Nv2al9Ps/4gDBeHGvd72/r5lSaGjWERwpRlmKRiJycCqKj/RgUF2AWbCorGshILyU+IYiiwmoO7heikRISgxFLRKScKiQ62p/EIW2L9NMpReaxWn0y09OKGTIkhKyMMvNja2O3jguYxzYYjBQX1RAa7k1CoqW4HuvhzUdTBOH1tk3L+8wPfneJsNDbXpTDpUG2Fxh68OB3Fn+LRWLeH2W7n16xqoHRgSFmmxadQdjEO1xaxO1JQrG4T44e4PakkSR4+RDr6W3RLq+ulgdGjuXL44dxcxRudB2lUtwcej8iyd3bhRufsJ7d9fSnf2Pi/OFkpxTiG+TJ+DnD+iQ6d4C/NiKxiIjEYEpyKsg+JdxcLX5I8CqPTQ4n80QBjXVNxAwNM/cJiPChNM+6MNpY14yqtomAcNtTmnuKd6AHaUdy0Wn1yOSWt28VRdaF477i13tv6LJNhLcHJ//5UJftAHb9vf2aJNI5GolIQom6mApNOT4O7QsHn6637kMdo4zFSeJMqVpI1c5SZRBlY1FPjbFtDekmax/prDFqSGvoek0ObVHyzYbznzk0OiqEU28+0idj/99NtmXv9gUx/l48Od/++gA3ThzOjROH98EVWaJWCwEQG7elsmLNUbLzbPBGuQiob1CzacdpNu04jVgsIikxhGmT4pg8fhCuLt0XBC90DAYjG7el8u2yfRQU1Zzvy+kQrVbPlp1pbNkpODaMSArj1iXjGZrQ98K5yWTi5Ue/5+i+LBZcN47QKF/yssr57bs9ZJ4u5vXP/wYIv8vefm4kjYpk27oT3P7obETits3f0qIaTh/PZ8ldQsBdq1huz/it5GWW8dTtX5I0OpJ7n7mcitI6ln29i+fvW8qnKx9C2knBYICc+mr+ddC65dNY/1AeGzaRUb6Wr+3pmnK+ST/KD2eOmbfXNxb0TEMY7OnHhIBwdpXkAoIV5fTgaCYGhhPnIfxGnltbqVBVx1dph/nq9CELsb25JTjr3eO7eX9i5/fCMrGExVFD+CRlv8Xx7UXZlDQ1EODUPdum33JSrW4AXBM91Px4aFwQK9YfpayyAblMgovSkbgoP95fWkWArxtuLd83RpPJatvWMX5cdYg5k7tn63pBCubRrrZFWF0IrNyfQnF1PfdeJhRa/HDNHgK9XFk41vZ/kL8vXcuSiUkkR3YdTXcos5CR0f0fJX2x4yofdF7mPbw9jaW7nuO9p3/m9mfm88W/uy6SeS6NugIMpq6L2DnLOvdo7AtkYtuEDVv818+lQt2AWCTCy6FtjgadupMeApVq68WbeoM3+8kqA9q8/d+cMIeronsnAsjLaRpeXDzfr381Pjy+p8+LyPo5Kfl46gLzzv25lJfVcc114/jxu70ASKViFl0zhp++3ysI5i14+7ig0wvi687t6Vx7fVux4c8/3UJ4hC/p6cUWgnnKqULzWNdcJ7SPjvEnKsav3WNrY3/+6RYA89iurk4MGRZKUJD1jbtJLQL2E8Mn8e9D2+x4lezn3we3MTEw3OYU5eeGzgeEBV6uqpK9FZl2z+ksE6JosmqqSaks51RFGb5OSlakC5su/s4uSMViKpoayaoRBLXWdq19AXMB3cyaKuK9+k8ABOGGZuK84Uyc1/fCxQB/baKHhpJ3poSClnT5+BHC90PsiAhWfbENvc7ApCtGmttPmj+Cr1/9jRN7zjB0vOUacvXXQpTZJXP7L+sraUIsO347zLYVB5lxrWVx992rj/bbdfQXzlJnRniM4kD1Pr7J/Yp7ox/CUdIWxVrSXMTaEutrarlYztzAefxSIBT3+iLnU+6NfsjCy9yEicyGM9Tp6hjp2ZZtFahouxfLVGWg0jeglApigNqg5pu8r6jV2iaUtY51uj6Fam01nnL7axhdrPx93XreuGz2+b6M80ZTs5bvl+9nectns7Hp4ogm7w5Go4mjJ/M5ejKfdz/ZxGXTBQ3kiftmnecr613Wb0nhy+93U1revoj5hc7h43kcPp7HyKQw7rl1MjGR7Tcge4s9m1PZty2Nf7xzHRNmtN2/evu68vFrq9m3XRDxx00R6n9Mn5/MW88u4/jBHIaNiTS337pGsFuafo4Nrr3jAzQ3aZm1cCR3PznXfMzJ2ZFP31hD2okCBg8P7/Q5/fPAJqvZ5ddED+Xf42ZbXfvHe/jy6thZTAmK4p5tvwKgt6NYc0fcP2Qcg9y9uSE2ucvIcIBgpRvPjZzKCJ8g7t2+st35tblp/HvcLJylndf4u27QMD5tEcxb71QNJhM/Z57goaHdswQ+145lZMumw9k2l/4+rlYLdP76ibDRfueSCRbHrbU1GIw0NWsZN9z2wKazuSAF84sJo8mE0dh/aTgfrt3D1w8u7rf5/iw4S3tmG9BdHJ3kiCVidBodLu5OVHfjR7Yru5NWnGWhdo/dU6Q2CuZag/278Fdu/RiFVM4fM9oi8seufc3ucXqLPSV5HCyzP723uzw/RhC2rYnl2TWvo9YXodYVojPWEO35PF5Owg58Xu1H1Kh3A+ClmEqIm7DTXtzwHaWq5bg7jifS43HzWPWaY+TXfYoICTpDJQ7SIOJ93gJE7eYBLObqKw6nF/Dpb3uRiMU0a3V89NginBxkPPz+SkbFhXA8s5jKOhUfPnIVAE6Ocl79ZhP5ZTU0a3SMSwzn7ivGA/Dlmv0cOC2kSwb7uPP8LTPN89TUN/Hkx6uprFPh7+nKy3fM4XxktufWC6/tyqy+jy5/edxM3DopONPUpGH92uO4ewgpa3K5lD82nMTbx4XcnApyssrJyihD7iAlK6OMjDOlhIZ5sewnYRE1eGgIkVF+NKrUJA623Nz19FKax+qMgvyqdmO3jguYxy7Ir0ahaO/ney63Dx7Furx0jlWUdNm2u6TVVLA2N53LI2yzM/FzbCsyF6BwY2XBEbvmu3JQgvlxlIcnb08TUlkH+/iZozVaF/BPjJ1ofnx2O4DbkkbwwSGhJsPU8Eg252YxqCUavbuU5ldxYs8Zpi0aY5cFmU6rp1mlRqF0bBdBO8AAPSV6SAif/XO5uaisf5jwPo9NDufjzDIQQczQtnXUlXdNY+eqI7xw48dceedUc5+0wzms+2YXE+cPZ+S07m9m67R6qsvqaGpQU5hVZj6ef6YEL393nFwc8fB1Re4gfMdNXzyW5R/9wYdP/kBpvmA95xfixYnd6Zw51j6l+c/A1SFLyFRlkNaQylMnHiXMORwQinrmNeUx0mM02Y2ZVGraR+1O95tNuVqwc9lesYWXUp4jyCkEV6kr9fp6qjWVNBmaGOM1zkIwT3QVxL5gp1AKm/L556mniXWNR2PQkNOYjd6kY17gFawqbi88nEuUMoZI5yiyG7P456mnCXcORywSIhlV+gYu9ZnGJJ/JPXyV7KeqqYkvDx2mtlnNTcOT8XMR1vLfHj1Gg0ZDcmAgY0KC+XT/QUyYuGboUE6Xl5MU4E+wmxuf7D9AiJsbAAW1dRQ31DMnNpY4Hx/e3b0Hf6XyoqtB1lsYDEZWbTzBV9/vpqbu/GcW9Dd6gxEvj97PEvvov1uYPzeZ4A4CJPqSwuIa3vpoI0dO5Pf73L3NoeN53PnYNyy5cjS3LhnfJxZiO/84haNCzvhplgWxk8cJlnzHDwi6Rqugfcn0RD58+Xe2rjl2jmB+jLihIQSFWa5J7R2/lTmLLAtmDxosaENlRTWdCuap1eXsKM6xOBbfEs398tiZXQbKzAiJ5tFhEwHs8j/viLH+oYz1t1/zmRMWy5WRie1Ear3JyOHyIiYFdi4mh7q4M6Glzc6zXo+fMk7wwJDxdtc0SK+t4HSNpeXatTFDO2jdfTbuPM3arae4ctYwc3azvQzckfQRR7OL+N9WoQq5TCqlpKaesbGh3HeZIOJ8sn4fu9Ny8XNTUtXQ9oP60s9CukdehSD8jI8P477LxpNZUsUXmw6QWlDG/f8VFmnv37HA/OZctuck64+mYzQaGREdbJ5nAAFHaf9GzbUSFOGLXmfA0dmBV+79msb6riOkz6XZYFt0dnrNp6TXfGr3+P2BwWR/ZMVo7wirHqExrr74niU2nUt5S9HPjPqOfS+7w/vH9vbqeJ2xMCqRW+KtR1yaTDqqmrYyKmg9emMdx0tvwstpCnUaQXCr1xxhmL/g3Xyy7A7cHEfi6pBEoMv1SERKGnXt08EatWmMCtqAWCTnWOl1NOoycZKGt5sH6FOxvHXz8YUvN/DFU9fia2XRLZdJeePe9qljTyyZgkwqwWg0cfmTn3PXgvGIRLBmbyqv3jmX2FDfdmlfpdUN/OexRcilEu54/SdySqqIDOz/QsUfHBeK+vZ1dPn8yHhmhHZe3MXN3YlZs4dapDKajCbz308+O998/O/PCP8OMYP8zf92YrGIuPhAjEYTYrHl4mnq9ESLsQCuunp0u8choV7txm4dFzD/HRsXaNPzlohEvDVhDnN+/xqtoe/qD3x4fC9zI+Jskgk+Tt9qftygV9uUPWMr5y5au1rE3pksRNXm1dVyx7BRnba1hU0/7+O7t9agqm1i4d22Z7ScPpTNkwvf5dqHZnPz0/O77jDAAHYQkxRKQ20jY2ZY2puFxQYglogRiSDorKg7uaOM11c8wjdvrGL9d8ImdH21Ct9gT256ej5X3zeTnrBn7TFeu+uLdsc/++dy8+NH37vJHE0ud5Tx2opH+PQfv/DrJ4Lnv0gsYvT0Iby16nFuG/1cj67nQsRN5s4z8f9kZdFyTtQdI6MhHQBvBx8WBl3NdL9Z/CfzXauCuQgR14fdDMAw9+Fsq9hMtiqLkuYinKVKfBx8iXcdzDgvy/ulVkH78din+b1oBSfrjnOs5ghOUmfiXOOZH7gQV5krq4t/6/L6RYh4cNBj/Fq4TLh+VQZysbAB4i33wVXW8Vq2L/FycuL6YcM4WFjIpswsFg8VNglyqmt4e66wqfre7j1cPyyJQFcX/rFxE1FensR4C+uj7Opqc5bakAB/bhk5nBc2bSbW25urBicS7uHOcxs3nZfndj45eDSX9z7bTH5h/1okXUiIRSLmTO+5heS53NuyadlfGAxCJPD3Kw6w9Ke9aLX6fp2/LzEYjHy7bB879p7hyQdnMyS+d4MKi/OrUTdrmZNk/Tepoc4ya17hJGf8tAR2/ZHCfc/OFwJyTheTn13B/c+2XwvaO34rfudstrQGZui0nd8XWCtK+XCSEFEtE9u24XBbgrDO/uL0QarU528jbcmgYVafT3Z9dZeCOQjFP8FSMC9urGdncQ6XBkV21M0qv2ZZXoezTM6csN6toQQwc2I8MyfGd92wEwYE8z4kv1KoeP3L328E4IZ3fmDO8DhEIhE7UrL59tElYIIrX1tq7vPUVZMBoZiJ0Whi9oufc+/s8UQHePHy9bM4mlPMh3deYW5f0DLHuiNpfH7f1YhEcOdHy0nJLyMx1K9Xn8+2nByKGhqYEBpKmLulr9//jh8j1subMcGd28UcKy1hmH9Ap2066lOqauD39HTuHDGy607nIBHbb/DfGzzwquC/f/c/F3J8TwaxSfbvCDbrL36/O4PRfjHonVHWaxfcE3spswI7jupaW3QKgCcOLbN7zo44VVXGvtL+iSxI9PLj1Us6TmMUiWS4O44lpfx+AIJchRvCJq1g6eDiMJhWo2mlPJFGbTquDp0X21LK4xGLhFQsudgLg7Gxw3n6kopawU7HTeloVSwHGBbTXiTV6vS89cM2mjRa5DIpqiY1RpMRiUjMW/ct4Jv1BymqrOfGWSOYMLTtBz021Bd5i2+dh6sTjeq+KQ7ZGUWq+j6PLG+NKH9hzPQu206dlmghaAPt/rbGueL4uX/bM1Z3x+6MaHcvHkgaz9t9aKuUVlPB5oJMpod0XXE+wb3tfayQyBkW3/+WWq04tPgd9jSyvJWDm04hEomYtGCEXf3iRkQglUk5ujNtQDAfoNcJjw9iXdnH7Y6LJWJ+zXnXah9nVwV3v7yYu1+2LbPz2S/utPl6Lr1iJJdeYd961ifQg398aX2OFdnWn8PFjqvMjZvCb+vw/AMxj3Y5RqLbEBLd7BPxnCROXBt6A9di3a/905Ff2ziOM9eH3cz1dL2GCnOO4L8jl3baRiaWddmmK35LPU1FYyORnp4Yzqrv4e/Stu5q0ulwlsuRiMXojEbEIhEGowmD0Yje0NbHx9kJR6kUo8lEk06Ps1yOU0u/vwo6nYGPv97O8tWHOy0U/FdgZHI4/r6WG0EbN6ewZdtp5DIJY0ZFMmd2Eu99uJH6BjVarZ677xACcZatOIjSxZH8/Cq8vZTUN6j5x1Pz+Hn5AVatOcaLz19JRLgPe/dnseGPk4BQZLmsvJ5bb5zAsKRQXn97LUajiYOHcxg/NponHmlfSLIrauqaeP41YUPseEr/ZRb3N/lF1Tzw1A/87YYJ3Hj12F4b12Q04ebhzP3/sL6O8wtsXxti2rxhbFl9jAM70pkwI5Eta44jlUmYNLv993Z3xgfaFQO1lbV5aRZ/ezgo7C502eorPjMkhh8yjnfrOnqDYV4BSESidgFatRrbdJrpLc/b38nFopjo9xnH7RLMjSYTv+VY3vsuiEjASdq9f6O+ZkAw7yFSsRj9WZ5GOoMBWUu4f7iv4CvUGt0VHeBtruAdE+gtHBdBlL+wY6/R6Xnj120ANGl0yKUSGpo1ZuHHGtllwi52fkUtd33cFpXSqOl94Sejuorbho/gowP7mRoRyZGSYhJ8fFHIZJwoLWVkYBCbs7OZFil8YDZnZ+PlpOBMVRUKqZRRQcGcKitnmH8AR0tKOFNVSYKPL0P8LIX9s8/5ODub+/grXbqd4CcRde7L1NfIHaSMmhJPYXY5rp7OdvXVGi7+SAUTvVdwz0Pe+eaHUtr7Reu+Pn2418e0hpNUxidTrmhXsONcdIYqIjwew0nWthvs3OLTX1m7gVZ3sQbtSbycJnc5rwjrO+TW5ulLvNyEz0ZtQzNVdY3mv00mzFYp1qJlD6YVUNeo5vV7Lqe+Uc3GA22Lm2AfN56/dRb1jWoWPfc1G9+523yuO8Jrb/NL5sk+jyy/c4gQue3p2HUhJncP+76fLib+ljiSr1MP92l0x+enDtkkmEcovXkrdQPFTTUEOnngp3AlzLn/sxv6gsqSWrz83fAOsH7T0hFyBxm+wR6UF1z8v3kDDDDAABcqUrGYvJpamnS6DtdBi4cM4Z1du1HIZMyJHYSHQsF/9u4j2ssLhcy6qDF7UAz/2buPCE8PHKV/DYkhJ7+Sf725+k9TzLOnzJtpaalQV9fM76uO8sH/XW8u2rhrTwYuLo48/MBMSkrr+LAlY8bLU8nUS+P5culOrl44ivc/Eo4vvmo02TmWr29rVuOzT84jJ7eCb77fS1CgBw0Nal7511X877vdhIbYv6Y6k1XGM6/8SnllQ9eN/wQYTSY++2Yn2bkVPPXQZTj0gh1eQIgn2ekljJ0cZ7O93vCx0Xj5urJ17XEmzEhk58ZTjJ4Yi6t7+3v+7ozfXao1zRQ11lscmxgYYbf9SCuTAiPOq2Aul0hwkztSrbGMwm/S26YbSlqe9zXRQ3nvxG7z8c0FmVQ0N+KjsO0ecl9ZPiVNlp+xswt9Xmj8NX7N+pAIP09+2XPCvNt+MLOAWcmD0Oj0ZJcKHoNGowlEkFFSyW3TR2E0mcgorjTbA+SWC/61BzIE0QfgrVsvp65Jzfqj6ea5RCIRGq0eo8lk/qBG+gmifICHC5/cvRCxWITeYOwTIUit1/P0po08OGYc3584ziBvb06UlXJr8nASfH1J8PFhX2GBuX1ebQ21ajWOEimuDo74K5VoWzYXjpQU87fhI/j00MF2gvnZ5+4aOcrcp2ecf2EM4Lt3N/Dk+zfa1cdg6r1U/YudN0cuIs7Nv9M2vS2YV6ubWZWd1nXDXuDh5EsIcXHrtI3B2IgJIxlVzyNCjMHUTJz3G7g6CGlSbo6jOVZ6A2DCU3Eprg7JmEwG0queokmbhd6kQqMvJsz9AbvnAYjzfgOFLLw3nm47pC2bjf+4eSZ//3gVcqkUvcHA2w8swNWpY9/txAh/vli9n4fe+xVvd2diggULJqPJxF1v/oJcJsFoMrF46rAOxzgfmIDlmaf6dA5PR0WH9j5/NZykMu4dOpaXDmzpszn2leZzpraSQe6dR2u/nbqBJxMvI8jJg6KmGl5PWcf7o9oXqrkYqa9uJCKheym+Lu7OlP+F09kHGGCAAfqauXGxzIyJRiaxDJZ4YtJE8+MoL09emjkdo8lkFknenTcXiVhs9Y6qtcDnG3OEAngXxl1X37J+yyne+uiPP5VdR0/wcHPikjGWAQMlpbWEh3ubxXKAwsJqwlt8qQP83SgtFYIJvTyVuLg4IpdLcXHpPMAjIrxtjaVUOtLcrMXHxxWJRMyb/7eOurpm5l9uX1HmTTtO8/r769H8Bf89N+9Mo7CkhlefuRIAH+/Oaw11xsSZg9mx4SS//7CPq26e0O68qUX/Ovs9IRKLmDI3id+/38fJw7mUl9RaFOjs6fjdJaW6rN2xwZ7dd3CI6eLeoD9QSGVwjmBub9zWtYOS+PDkHnPAl95kZFnWSe4ZbFumwopz7FjiPHxI8rbPgaI/+evkSw0wwAADDDDAAAMMMMAAAwwwwAADDDDAAAMMMEAnDESY95AnLFQsAAAgAElEQVQhYf6MiApi8ZvfIhGLmDY0hoQQP45mF5kjIp/831pKauqZkBBOuK9QcGBcXBg3vfsjQV5uhHi7m8f6/I8DANz/35V4uzozKLCtWKVYJGJW8iCuf+cHAj1defvWy819F40fyh0fLUMiFmM0mfjwjitw7OU0FUeplOcvncJ3J44T7+NDvUbDiEBLP2F3R0eWpwqeRA1aLcFubmRVVxPv40NWdTWnK8o5VV6Or7Mzv55OxV/Z3qf47HNn93GQSEirrCS1ooIEn/NTxNNWfv54M4vvmcaXr6+2OJ5xsqCDHh1jMPW/r/KFypygwV22SfIUfPR3Xfb3XplzZXYqGkPfRxvEenibi4J0RonqZzwU4wl0uR6AnJq3qdccNUd9h7rdRajbXRZ9RCIJcd5vdjhmgu8H7R4X1n/Vbh7AYq7ukt5QQF5jGQmuYTQZNKws3MnVoZOp1wlWGTWelTx2z1jqdI2M805kb2UKoSJfLl3ohd61mTMNheY+AKXqar58+lr2VqbgJHWkuNmN9IZ8XGROXHFTKNEuQSS4hvFzfluhxSGRAbxxj1BYMlNVhNfkBhS+Rgqayjlak0m0SxC+Du7srhQiwBcECQVefs7fyuLQKeytTDFfmzBnJWFOfsglQqpySl0OUcogBrt1bGeztySPgoa6Hr2WXXHX4DE4y86vJdWFxA1xw/js1AFKm1R9Nsc3p4/y0rgZnbZxEEsJchLWA0FOHjiI+3Y51qjT0azXoTMYkEskKGSyPvMKdHF3orq8e+/ruqoG5N30mRxggAEGGMA2zo0ut4aIthR8wFzoszMkvRDVaQ8qbQaZNe/hLI8kxkPwsy9uWEGgy8I+nfe75fv5dOmOPp3jYmP2tMHmTNFW/P3cyM2rtLBWDA31IjWtGICS0jr8/TvPrLWGtehhkQi0Wj0P3jsDBwf71lRrNp3kjQ/W/6U96NMzy7jvqe8BeP/Va/H3tf/fBWDCjEQmzhzM52+vJzejjMHDwzAaTRQXVLNncyqvfyHUpPD2sxx/+vxkln21k6/e3YCLm4LRk2J7dfzukF3XPuMxwtXDSkvbCFW6IxaJzC4TFysBTi5MDY7ij4JM87EfM45z9+CxXWYXqQ161uefsTi2+AK2Y4EBwbxXeHjeRB6eN7HdcT93IZ3ljZvntDv3wNxLeGDuJe2OL33omk7neuqqKVaPzx+dwPzRCbZcbre5o6XY5l0jRwFgOCtN77ZkIeV/YXyC+UtALBLx4YH9TAmPYHN2FnePGs2bM4WUvcG+vhbWMmczLzbO4lxrH4C3Z81u1/5CJDRaSNc5tvsMV9w6yXz82K4zHXXpEJOpNyxp/jq0+v135XVuKyv62DIDhJuSV8bPsulmxEsxhTNVz1PdvBOTSY9M4kGo293t2n25+zC3XTKCsnoVm9OyuG50EqdLK1i69wg3jxtuTlk7WlBCvL8Pw0MDu5wHsDqXvYgRodI3oZA4EKDwIkoZRIRzAJvKBK/4oe5RBCm8WV4g3IwUNVeS11TGtaFTATCYjOY+AEeqM8zt3OVKc//0+ny8HFw5WZtNgmtYh9cTrQwiWhlMtDLIos81oVMsalScTXFzldU5P8sSNskilAGcaSjoVDD/JaNv31vOMjk3xtuXkvpnx0Ei5W+Jo3jl4NauG3eT37JT+cfoKeZimtZwlTvxQdpmolx8yGwox0PeO97x+fV1bC8QqtcfLi0iraqCgoZ6GnXtN16dZXKCXVyJbSn2mewXyMTgMKI9eualHpEYxJFtp0k/kkvs8HCb+xVll1OaX0VkYufFwwcYYIABBrj4MZpM7DmQxZET+TSo1Fx1+XDiYvwxmaCySvC3dXFxxNGh403UzJp3GOT5FFk175mPVTbv6BPBvFXj+uDzzSxbdaTXx7/YmTujfYFGd3cn5swaytPP/YJCISd5WCjz5iRz8HAO/3rlN7Q6PffcLmgbP6842K5/fX0zS7/dTUpqEUu/3c3IERF4ebYPtgOoqlaBSMTrb69FrzcQFurF326ZZLXt2azbfIo3P9jwlxbLWyktF/y6H3zmpxbR3LWLHu0RiUQ88+a1/P7DPjb8epjt608glUnw9Xdn3JR4XNys35+HR/sRFRdA6rF8Lr9mDFKZ9U297o7fHeq17W1xfRXW33+2IJdIUEhlVtfk3aWksZ6T1WWcrikHIL+hlmp1MzWaZhp0GtR6HRqDAbVBj9qgQ2/snbpy1w9KthDM8xpq2Vuax3j/ju+3ATYVZKDSacx/yyUSFkZ2HRB5PhkQzAfoNh1FEZwtgt8xfAT5dXVmsb2jdvacuxgYO0P44M9YNJqpV7Y998M70jvq0iFi0UC03fkis7aKU1Xt/ct6mxmhMYz0tc3zVyELJ8n/fzaP7eeqRGcQRN94fx/zf2//sQuAGF8vThWXtRPM7Z3HHgwmI24yZ07UZTHFN5lqbT35TeVtc0sEH3oXmRMbSw/SqFcT7RLEsoLtDHaLIM411KLP2e3c5Upz/5N1ObjInJCIxOQ2lpLdWEKmqggQRHJrnN0nv6nc3D6joRCZWGoeo6M5o5TC66jSq0l0De/wNVDptKzPs38DzR4WRMZfsBXHzydXRQ/mjcM70PVKfYz21Gs1bCrIYm649egYgGeHzGVraRp5qiqSPEKY5Deo2/MZTCZWZaax9NQRjpaV2NyvUaclvbqS9OpKAH7PFGo1xHn5cF38UK6JH9Kp6N8Rk+aP4Mi203zw5A+8seIRnFw6rj/Qislo4rN/CoXLR05NtHvOAQawhbdPbOc/KUKhqsVRSbw2ei5bizN57dhWihprGeTmw5PDpjLGNxSALUWZvHViGzkNQpRZkLMbt8aO4vrojutCGExGdpXmArC5KIMTVcXkq2pp0GlwkEjxcnQizt2XmcGxLAhL7HKj/H9nDvHC4Y1IRCJSFv8duVhCeq3w2/dt5hH2lOZS1tyA0WTCV+HCKJ9gbhw0kqGeF64f6AADqDU6nnxxOUfPyry9ZHQUcTH+iETw8HM/tRyL5t5bJ3c4jkSkwEnWuUDTGxhNJl5+ew0geF0PYMnQxGBCgzytnpszeyhzZltGkD50X/ssvEcfnAXA88/MB+DfLy0C4IF7p7drO25MlPmxj7cL/35pEV9/s4trFo0mOSkUg8HIw0/80OV1r99yitffX39BRPxKJGLkMilanR6DoXdEze5SWl7HQ8/+yPuvXoufTzdEc7GIBdePY8H14+zq959f7u/18R98fgEPPr+g3fFBiUGsP/lKp31VVoRtRQ/vq5x6QTBX6TR8e+YYq3NOc8qKz3p/MCkwghClGwWqtozSH84c71IwX5Ft6V8+M2QQ7g5d3yecTwYE8z4iOTKI5MjuFb36M+EglRLj1bNotYuZeTdZFqN49M0ldo8hEdv2JZLs8wKejkl2j98fiHr5q6awsYbNpWlkN1TQqNfiLJUT6eLDNP84gp27nyp1Lqtz+6fY571DbSuSYSuZ5VWkl1VwurQCB6mE06UVpJaUkxDga24T5y/YGtWr1SSHBHY0VJ8Q5xpKjEuwORvg9si5iEQiQp18LdrN9B+JyWQyp14aTUbE5/QBCHXytWjXyqKQSy2OPxXfcVHFq0ImWe3zZLzlZ7Z1jGhlkNU5p/oNb3et1tiQd4Zmva7D873BNYMsb1BMJhWq0lGIJIEofTdb7aMqn4rJUIzSX4ieEonaIjUM2v1oGj7EoDuCyaRBLIlA7nQ1cuXfgPaRIAbtMXTNP6PX7MdkKMKEEYk0ApniKuTK21taWb5G2savUdc9j4v/KYyGPDT1/8agPQaYEEuFGySFx7uIpZaFpezB01HBzNBo1uTav4FpK79mpnQqmK/IP8JVoSN6PM/uonye27mJ7NreK5SZVlXB87s28+GRfTw7bjILYuLt6j998VhWfraFrJMFPDbvLe5/fQmJZ93cnktpXiUfP/szBzadQqF05Io7rWfSDTBAb5JZV8WRykLu2rnMHHF1rKqY27b9xMpZt1LcVM9dO38xF7UCyK6v4rmD63EQS1kU2T6FeGdpDk/uW01pc4PVOZv0WppUWgpUtfxReIbP0/azdPK1NkWsGUwmcuqrOFBRwEtH/gBoFymWr6ohX1XDipyTPJ40mXsSxtv8ethLcXU9+87kcyy7mLyKGoqqhcjERrUGtU6Pg1SKUuFAsJcbUf6ejIgKZnxcGO7OnRfzOx+YTHAqv5Q9abmk5AviQ255DTWNzTRptEglYtycHHFzcsTfw5XkyECGRwYxJMwfiQ2ZgX197SfzSjiYWcCpvFLyKmqprG8EoFmrw2A0oZDL8FQqCPZ2IyHEjxFRwYyOCWlnn9GffPHdLlLSS3jwjmmMGhbGjfd9aXF+4pgYAPYezu5UMFfIQkireplGXQ4plc8C4Czr/vqgIz78fMsFJ5S7uSrw8hC+OxwdpDg4SHFwkCGXSdDqDGi1etRqHdW1TdTWNaHW9N2ac97M82+pMGH8IL5cuoONm06hUqlZfNWoTtsfPJrLa+/1n1ge6O9O8uAQIsK8CQpwJyhAuF/1dHdG4ShDdlZEtU5noFmto7q2kaKSWopKasjJF4Ibjp0qpKikps+vt6Ssjoef/YlP374B1y6KsP5ZsXaf5tiNQJKz6ang/lPmCV4/vI3qcwp39jdikYglMcN44+h287ENBWeo0TTj4WD9/VKtbmJHUY7FsWtjzv93R1dc9IL5mcpKBnl7k1VdTZSn9Z3NAQa4UJBI7V+cSkW2pRbJxG4oZR3bP3RFSkoh2dkVAFRXq5g7dxi7d2ewYMFwMjPKSEkpJCraj8GDz1+6vAkT76Rs4uusPVYXOG+lbOTWaOEG8ZGE6Yi6dNLqnI15GT3qbwvjAkIZ5tO7kWDRvl68vrDNvui1K2eZH988ThB05w4RxDyD0YRE3P8ZHZKzxOTOKpmffU7cSZ+OxuhOlXRb+3TWrjOxHGBrYbZd12Qv1iqOi0RKZIoFaJu+x6A9jERuKdgadCcx6jOROS2yEMoBdM0raa55GLE0ApnTNYhwRK/dh7r+ZfTaQzh5fto6i7mPtvET9OodSB0mIXaciQk9evUG1PUvYzIJN/QOLo9YvX69ZhPq2meQOExErrwNk7EKvXqbMIPEvwevjMC1g5L6VDDfXpRDo07boX/8karcHgnmeqORf+/bzhcnDnd7jK4ob2rkoc1r2JibyRuTZ+Mss22RL5GK+de39/Lklf9Hbloxjy94m+AowaYsfmQE7j6uiMUi6qpVZJ0sJPNEPiaTCZlcyrOf3Y5HN6KZBhjAXjLrK3npyCaGeQUxJTCKHSXZ7C/Pp9mg46PUPRyrKsJP4cLVkUnkqwRx4tdcwUbro9Q9VgXzMKU7Feq2+ghOUhkjfUKId/fFw8GJGk0TqTVl7CwVbhjTa8t5ZO9vfDf1epuu+aPUPazOS6V19TPEM4BL/MNxlyuoVDeysTCdfFUtJuDN49tI8PDn0oDI7r9I52A0mthw7Aw/7jzGsZziTts2a3U0a3VU1Kk4ml3Esj0nkUrETEqI4JZpo0gK7/0I+PzKWua9/JXVc6/fNIfZwy03MY1GE8v3nuSbbUfIq+hYgNIbjKi1KspqVZwprmRHivD77e/uwk1ThO/xq8YN6fXaUZ2hUmv4fscxVuw7RUnLZkVnbVVqDfmVtexJy+PzPw7g5uTI/NEJ3DRlBL5u3bcYOFpaQrJ/AHUaNT+lnGRaRBRRHl3fj2/Zlc6iy4ezaJ71bI1WMbG8ovPnFu3xCLXqo7jI43GWCe91d8fetaL7aeWh82rB4ufjSlyMP3Ex/sRG+eHn44avjwsOdr7fVI0aCotrKGwRW3MLqsjIKiMju5zK6u7XdVE6OzD5ko4DBPqL6ChfXn1xkU1ti0treeHNVX0ulifGBnLZtMGMGxmJj7eLzf1kMgkymQRXF0fCQ9oHHlZWq9h/JIcNW1I4nlLQZ3YyRaW1PP/677zz4mLEPbxXXLfqKBvWHKe+ThB6v/zhnt64xD7FUdr+M6bto+xUW3j50BY+T21vXQQQ5CysnZO8A4hx88bPSYmHgwJXuSNOUhkKqVC7aM7qry0sUXrCNTFD+b/ju8wZu1qDgeVZp7g9wfpm1e+5p9Gb2jb6g5VuXBIQ3ivX0pdc1IJ5YV09nx86TLyPDxWNjTw+UYjm7U87j0VrvmPZ3Ov51/7NaI0GXhk3k8XrfuDny5bwffpxAFblnMZgMjHWP4RHk4VrfGbPRnLqq2nS67g0KIJHkydwsKyQz04JHwK5REKRqp6JQeHmPh8c38vO4lwApodEcefg0RytEBasH53Yh0QkprK5kSClG+9eenkPpcIBeoP/+/uPPPzaNRzcdpp3//4jl980gesenGnXGAqpb9eNAJ2xZ8UDT54o5NolQqTzjz/sw9vbBb1e+ALcui2ViHAfzqSXnFfBfGnmXr7M3E2osyeLw0cS4+qLh9yJJr2WtPpSfso5xBcZQrq1l4OSm6PsSwU7myJVPanV5V037CH3Dund6HJ7OR9i+V8do8nE7uK8Pp3jyijrthYy5xvRNn2PtukHFOcI5romwRJDprC84TAZq1HXPo3UYQxOXt/RunRwAJprHkLX/Ct6tRDxKHVs+35zdHsF3J0QidoiDUwuD6Eqm4iu6UdhjA4E8+bap3Dy/Bqpw1kRkm6tdwQ9f89OCArH10lJeR8V/9QZDWwtzObyiDir54OdPclvrCbU2f6Nfq3BwN0bf2NLXt9uurSyJiudgvo6vrl8EW42pk36Bnny4aZn+PLllWz6aR+FWULUZuv/z0YkEjF8cjx3/msRYbEDNhID9A/1WjV6o4FlM25GIhJxe9wYpq7+hKLGOn7LPYVS5sDGuXfir2gTOdQGPesK0shtqKa0qQF/J0sBJFTpwcNDJhHpKggcUwOjrdoa7S7N4ZZtP2Iwmdhblkdai8VKnHvn671VealIxWLeGisUrJ4fZvk9/0TSZB7Y/SsbCwW7r09T9/aaYH4os5CXf9lMTln3s1n0BiNbTmax5WQWU4ZE8ewioS6JTw8EW1tJKyq3EMyzS6v4+9K1ZJRUdnvM0toG3vh1GwBfbznEO7fNY0hYzzd0O8NkgmV7TvDe6l00NHdf9KhrUvPNtiP8vPsEt88YDcBt00bZHXX+n0P7+fzyK3hxx1aS/QN5Zdc2vpzXtX94TU0j4aHeHZ5vFed0+s6tKaqb91HU8DN6U1PbwTpI9vvEtifQBdv3nOHjr7b1yli2IGl5/YcmBDNxbDQTxsR0y0faGkpnB7Pwfi4VVSqOpxRwIqWQo6cKyCuosnncGZMT7BbvzydqtY5nX11Jg6q9P3VvIJGIuWzaYJZcOZqQoN7Lej4bb08lc6cPYe70IZSW1/HL74dZue4YOl3vi7lHTuTzny+38sDtU3s0zmXzkklKDuMfT/zUS1fW9yhlDu2ONfUwO9iazYstLM86ZVUsvzIykTsTRxPvYZte1JsyqZejE7NCYywy8n/MON6hYP7rOXYsi6OH9pteeXhPJpGx/nh42b/euHi+3awQ7ObKTcnDiPfxOW8pcR6OTtRrNVSrm9Ea9TRoNXg6KMhrqOW37FQAfrxsCSLg+vU/cbyyhCTvAP41dhoysQSDycSEXz7hkRZRPK9B2PVdt+BWAK5Y/Q0LIuOp0ag5VFbIT5cJ9gC3bPyF0X4h5utIrS5n68I7kEskLF77PRm1lQxy73ghMkD/UJJXiUgsYvl/t/Lppqd46a4v7RbMHaW2Lb6b9KXduUQz7h5ObNxwEgCNVkd+fhWZmWVkZJQSFeWHSqUm8TyK5QA/5x4mxNmTny+9ExeZpXAzyjucq8NGcPX2/wLwY87BHgnmWwuzenStthDu6sHEoPA+n2eAC4uTlaXU9HEq3dQQ6xYYElkiEvkw9M2rMbm9gEjUunDQo2v+HbEkCKmD5edG17wKk6kRufNtnLtskCkWtAjmWwBLwVwkbh8VIxIpkcji0Gv2tBwxcq4tC4DMcY6lWC707ujp2o0ImBIcyU9nTvTamOeyMT+jQ8E8pbaI9UUncZTIzJkwP07quqCu0WTi/k2rbBLLnWVyoj28iHT3QCmTo5TLcZBIUev1NOq0NGg1ZNfWkFUr3Bg36jq+CThRUcpt61bww7xrkEusF2JqN7+rggfeWMItz8znxG4hWyc7tZCGaiG7wNlNQWiMP4ljo/HtwPt0gAH6kisjhpjr8cjEEqYHxbD0zCEALguJsxDLASYFRLKuQLgxzGmobieYA9yXeEmX817iH8GiyCR+yjoGwOGKQqBrwRzgkSGT2gnlrcjEEl4edRmbizIxmIwcqixAbdD3KIXcaDTx3updLN16qFejGLeezOJghvC8X7vpMiYmdD9D0hbSCivMj/em5fHoV6to6kWLivI6Fbd98DMvXDuDuSPts7GylYZmDU8sXcPetN7bcNfo9PxnrfB7vPt0Lm/fejnerrYXoVZIpVQ1N1Gn0XDDkCT2FxV03Qnw9XHtVJQ9niK8NzryxW4lq/ZD4r3+iVRse/SureQVVPHyO2v6xbLDy8OZK+Yks2C2YK3p3ouFC23Bx0vJ9EnxTJ8kvHdLy+vZfzibfYezOXhMeL9ptXqrfS+fceFbKpzN6x+sJyu3ouuGdnLpeKEWzV03TSI4sG+Ecmv4+7rxwO1TueaKUXz5/W7WbjrZ63P88vthYiL9mN2HNWYO7Mvi6//+P3vnGRhVlf7hZ3pN740EEkJooUkTpCgKSLEAllVce1vr2ruuvf0tay+Iuq6roisiIiBdei+hBEJIL6Qnk8n0/4ebTDJkZjIzmYHozvMlmVvOPZnM3HvO77zv712LxWKlV2oUdz94IVqtktJiYZH2leeXkJQcSX5eBRaLjUeeupi0PoK96KYNuSz6egsWi5WDB4R7x5hxfXny+bkOVjfeEuJEMK9u0fncHkCj0fuFTrPVynM7Vnfa/vq4GcxJ965gpr9vZ1dlDnMQzI/VV7O9spiRsY6aUX5DLXur2ussiUUi5nnZ9+6w5Jut3HDP1D+PYF50ooqy1i9HYkokyamuhV+D2cIXu4UB53UjXBfhCRRDYxLYWFqARiZDYZWwqayQITEJHK2roqBBEL+v/rV9JU1nMmKwmHlm6yp0JhMKiYQGowFLqw9hnzBhYNAWJZ8ZEU1BQx0V+iayo+PtcsHg6HgO1VaSFSHcKAZGxtknsVEqtV+r7wbxHbFYRF5OCfEpkYSEq31KZ9LKUro+CNCZCr1uuyPTpmVja72L2v2bH5oJQN++8Vittm6nY3WXUn0df+k9qpNY3oZSIuOCBGGw9+mxjd261saywEYAg1CUMcj/HutO8W/zN8naMPqGu64dIVdfjb7ufkzNi5FrBCsAc8t6bNYq5CF3caowbTEJonJzzU0u27RanUw+bC0Ym/+DuWUNVstxbNZ6bLZmsHV8PjkXzCXywS6v5S/OTU4PqGC+oeQENpzL/O+MutqnNt/dtZUV+cec7suIiOKijP5M79PX/toT2sbOR2qq+PV4Lj8dO+zUE31neSnPb17LM+PP86rPIeEaxs0YCmD/GSRIT6BfWIzD69SQdpFjaFTn2h5xHQT0BmP3ohMHRybYBfMqDyfgSomUv2ae5faYaKWGrPAYcmorMFutlOrq7RHv3tBWLPy+BT+zLicw2SxNLYJwcNfHi3lozmSuGB+4OjyHioUo/r0nyrhnwU+0uBD/uoPRbOHJr1eQEh1Oth8tZyrrhUyom95dxInKwHkX78kv5dq3v+Xj2+eQEOlZVHOkSs1ffviW588Vijh6Ov+cOnkAX/+wnay+8Zwzpq99u8lk4Yelu1i+RrA/uvNG988blTQRrbyv22N8wWKx8vwbv2AIwOekIzHRIdx09XimTBzgVXT/R9u38/KGDfbXX82bx5gUz+aLnhAfG8pF04dy0fShNOuF/+mm7Xms25TL5h3H7eJ5VkY8fft4FtnaE1iz8QirNvi3PpVGreC+285nysQzO6eLjQ7h4bumce74frz41jKqa7sn7J7K6++tYPCAJJLiw/3aLkBdrY63XvmFf358HZFRWn74ZhsfvbOKvz88w35Mzr4ibrztXAZlp/DTDzv49t+befBxoUDsu28s591Pryc8QsPD9/4bgDlXjO6WWA7QJ7Tzwkd+Qy2TfSxTeFKvs9uXeMPa0uOdAq3mpA/yWiy32Gzo/GTH0sbY+F6kh0WRV9++APrdsf2dBPPF+Y7R5RMSe5Og8ew5U5RfxdvP/UR8YjjHc8sJj9Ty0ItzCQ1XU1okzFeevPNffPLjXQDk7C7gyw/W8NKH11JaVMMnbyxnz7Z8Xn70OxQKwV5yyuxhTL/UM2vMHieYf/HBGrZvOkpaunDzzT9WyahxfbnmVudFoI5WV50RobyNoTEJfJazkwt798NksfDd0f3cNGgk8ZoQErXCh+CLqZchEYkwW62IRSLWleRTZ2jhvckXUWdoYUl++437aGukl8VmQ4Qwgb198BgilCqWnci1T2z3VpVxbnJ7BGHQVqFnMnR8P1668wue/Oh6zCYLJqP3N8lQeT9EiLHhPi2x3th9P95TPZnf37WN24YLqZr7qyqo1DVxvK4WmVjM9UNG8OlewT93dGIy20pLmJGRycJ9u8mMjKa2Rc/1Q/z73YxThXbpHVZnEh4osUrfo02sNhtbyjyLkukOs0+zYG6x6qjVb6Bav4pmo5CybbRUYbLWIEaKXBqLRpZFuGocMZoZSMVhAe2P0VJFpW4xdfqNNJuOYbIIDz0bZqTiMOSSKELkQwhXjSNaPQ2RyP+PrAbDbqqbV9DQsosWcwEmaz0ipMglUcil8YQrBcucSNUkQhT+EftOLXjibyYnu0/Bl6lm09LwD0zNX9sFc5P+v6375nQ63mYVPETl2pucRo0DiCVpnc7RVV2K1ZyLVDkFueYGxJIEEIVgaHjBLsK7QiQOfHTO+MRUZGKJT5opMP0AACAASURBVINXT6g16DlYXcHAKMG/+45t/3J5rFgk5u2RrovSAhyrreatnZs6bY9WqXlo9ATm9BvokyVd2xlZkdFkRUZz54ixfHf4AK9u20C1vtnh2C8O7ObSzAEADIkN2qcE+WMTc0qxTa20PZrM2USuo72Kwdo9Ia1jUSxP2xoWnYxa6rwuQkeile0Rwo0+TI6tVhv3f7YUwCOxPCU6nLRY4Z4dFaImRKWgTtdCVaOOY2XVnKx3b31ltdl4cdFqpGIRc88OTLRqnU7P0bIqHly41KVYHqpWMiBZmH9GhqiJ1Koxmi3U6fQcKq6kqKquy+uYLVYe/Hwpix6aj1bZOTrRW2qb9Nz8nmCX1pVYrpBJGZwaT1yrxU2YRoXNZqNWp6espoEDhRX2AC1XFFXVccv7P/DV368kRNV1/5+ZeK7DwvDrU6Z3eQ7A1fPGkJtXwRMvLUapFASM//vgN5557WcsFqvdE/vSmc79yPNq3wHAYKlib+VdaGUZdFx8T4+4w6N+uOKLb7dw+Fj3MnfdIZdLueKSkVw9dzRKhXdFAPNqanh782a0cjnNrZlhj6xcyc9XX41G3vX9wVvUKqHNtgh0XbOB1RuOsHxtDlMnBS7i2J806YT74FsfOS947yu9U6N55ck5xPWg2iujhvfm83eu44mXfmL3/u4F03XEYDTzyj+X8+Zzl/vV1gPgUE4JmVkJRLZG/543dRC3XvuxwzHRMSEMyhYWhfpkxLFxfbvuYTKZkbaK49K2enF+iKQeEBnXaVtOTWdrQU/JrfPNAmxfh8jsNuamex9YVN7c6I+3pRN/6TuEZztEwC8tOMwzo6Y4FDhdnH/Q4ZzLM7x71ufsLuTuJ2aTnBbN5++u4qsP13LbQxd2eV5iSiRP/t+V3H7Zezz0wjxSenvvwNHjBPOdW/J4e+FNiFoFYJvVxj3Xf+pUMF9y+DCVTTre3bIVtUx2RoTzIdEJrC/N55kxUzBazTy55TfenjQLtVTGVf0EceWqX79BIhJhxcaCKXMYEp3AO3s3c93KRcSqtfYocYBwuRA5e9fanyjRNTA5pY896nxMfAqXL/sabDYmJfdheGyi3cM8SM/kir9N4Yq/TbG/fu27O71uQyJSECJPp8HovgBlkzEfo7UeuR9FzoyISD7Zs5ORCUnsKC8hTKFEIhITplTx/eEcGg3CAMRitRGpUrGttJj+0TE0GAyMSOgcndVdLksdwVf527gzazJh8s4VmKsMTfxWKlSxv7LPKJ+vc6T2ZMAtMwZGxZEe5n3Uly9YbUZKGhZQVP8eFmuz02MsmNCbCtCbCqhqXs7xmhdIDL2GXuF3IhF5Xx19c+EwzNZG+2u5JJbRKZvs/Smoe5OShs+w2ZynRJss1Zgs1eiMuZQ3fYdCmkDviIeJ0cxwery31Lds53jtCzQZOqcu2jDRYi6mxVxMQ4uQnl9Y9w6hihH0jnyYUIXvBaWaTEb2OBn4+JNJXQjmiJTIVHMw6j7DYjqIWJqKuWUFEvkIxNLOafEisTCAlSmnI5G7j25sw6hbgNWci1x7E8rQJxz2GUTdqxDvLzQyOaPjk/k9gH7yG8sK7IL500Mutm//IHctFyQMJFUbRUlzLesrcrts6/XtGzGfInakhYXzr5nzSA7x331fIhJxRf/BjEvuxfyfv+NEfbtIZAPe2CF8jxde2HlxJUiQPxLO/Ent+5wI0x1FAnc2Dc1mISJzTWkeu6qKya2voqpFR71RT4vZjMFi9klw7xPqmXVRx6LTvthJvL30d9YecG9Lp5RLmT9xBNNH9CM93vVYxmaDA4XlLN1xiG837nMr2D6/aDW9YiIY1dd/kbIdufatb+1R7R2ZNrwf887OZlifRLcWnyXV9Xy2egf/3XIAs8X131FW28i3G/dx/XnOfVw9xWq18eDnS7v0jR/ZN4UbzhvJiIxk5FLXEZU6g5G1+/P44NctFLoR/wtO1vLIl8t45+aLXR7TRm51Fa9t2UhGRCQPnn0Ov53IY17/rqMeZVIJLzx2KRu25LJ2Yy7FZbXYbDaGDU5h8rh+TDxbEMxdCXOhikEOP/3JkWMVfPHtZr+320ZaShTPPnwRqU6KOXaFzmjk9iVLEIlELJwzh7wa4bPx8PLlPLh8Oe/MmhVwT2CNWsGsqdnMmvrHsWJ5d8EaAGr8GHU9KCuJl5+8lBCtZ7VdTiehISpee3ouz7z2MwDrN3c9xvSE3fsLWbJiL7On+jcb6NSAPZuT55ZKJT/lmPbfr79lMnfd9BlJKZFERgkBc0OGp3W7XzEqDfHqEMqb2+ez60vz8bWi0tYK3xYwTuo7f249HQ90ZHN5YOY7czMG8+ru9bRYhHGNzmRkVfExZqYJgYGHaivJb2hf8I1Uqjk/xbvMoLikcJLTBLF77OQs/vncEj/1vmt6nGAemxCGDZvd09OGjdh45xPBWVlZLDl8mDCFkoSQED7Yug2Am0aeddo8zbUyObnX3Gd/feSav9t/n5MxyOFnGyqpjO9nXOW0vXiN8CV/Z9LsTvtuzx7D7dmOBQKHxQii5PuT2wc1HX8PcmZZ8d02Js0ejlwhfNVEPmYCRKtGdimY27BysnkzSdppPl3DGef3zsBisyERiRgSF4/VZrNHMHb8HWBgTCzS1u9d2zn+Zk7qcNZXHGXeug+ZlZxNmjYauURCs9lIXuNJfi7eT7RSEPf6hcaxsvRgp5XUCxIHdHmd07EQ5crb2N/oTSfIqbwBvcm7h6TV1kJx/UdUNy9nQOyHqGUZ3eqH0VKJwVyGVBzCgYrraDDs9up8g7mMwyfvptl0lNTwe3zshY0Tta8DUFT/Id6GHzQYdrK37DJ6RzxActjNPvUgp7qik+jpb0bEdp0rKNfMx6j7DFPzd0jkQ7HZ9MjU85weK5FlY+J7zIbNHgvmVrNwv5IpT139N2M1B74+gKeMiE0KqGC+tbyImwcJi3fRivZo1npjM6OihcWJOGUoX+dvddtOha6JFfmOzwC1VMZXMy8jKSQwkU0pIWF8NfMyLvj2Mwd/83WFQoZESWNDwK4dJMjpQCpyPVeQuNnnCovNxrs5G/no0BagXTj3F878VP3NtqNFfLZqh9tjRmf24ukrzifRA9sOkQgGp8YzODWeS8cO5sl/L7fbo5xKm0D84yN/JVzj/UJ9V5wqlidEhvL8VdMYke5Zfn1SVBiPzzuPC4Zmct9nP9PQ7NqW5z8b9vDXySO6NR/95LdtbDvqOttRrZDx/FXTODfbs/GZRiFnxln9mTa8H6/+uI6v1+9xeeyGg/n8uDWHi0e7jyB+edMGnjhnEq9vEWwQ15zI90gwB+GzMWFsJhPGZnp0fEdi1JMA0JnyyK/7EL25FJVU+D/2Dr/F6/basNngtfeWY3GzINIdLpg0gPtvv8AeVe8tj//2GzXNzXwxdy7DEhIYliBkesklEh5avpzPdu7k+hGe2Qz8r7Avp9jvvt4DsxL5v2fneZ0dcDqRyST842FBT3rm1SWs+b37megA73+2jrFnpRPjgxe0KwYMSubdN5ZTU91EZJSW1StyGD7S84LVBSequOKacVww3f+LONN6ZbLw8E7765N6HWtLhHnM5CTn9aJOpW22+UuBb/8DsZPxiC8yy7+OuL7nd4cwuZIZaVl8n3fAvm1pwRG7YL680HHBZk6fQXbNyFOsHe/JNuxasbO3Qd/s37HXmamUGSRIkCBBggQJEiRIkCBBggQJEiRIkCBBgvQwekyE+QuPLkIEVFc2cssV75PSWuiz6EQVvXrHuDxPbzJjs7WQU1nJxN5pAByvqaVv9OmxOggSxB0VRdU8ePk7DBmbwYz544j1sWp2vHoix+v/3eVxxU3L/BphDjhEiotd/A44rBQGIrocYNyyV+y/f5C73ukxJ1uEtKk7tn7tdH/ORU93eZ3dJwNrmQEwLiE14NfQGQ9zoOJajBbXnmlScRgySQRmaz0mSx2nRl3rTQXsLbuMwfFfAqCV++5X2GjYS3nTN06iy0XIJOFIxUIhGYu10WWfC+veQSPLIlrj/ef8aNWjlDd953K/RKxBJo7ChgWTpRqrzVn0mI382lcwWWvoHfGw133IqXEeWecvUkPCCVd0nR4qlmYgkY/G3PIrNmsViBTIlLOcHitTXYyh4VWMuk+QqS9GLHFMl7dZqxCJWmsGiIQISJEkHgCrpYyOCeKGxvexWbv2gT1dZEcH1ofb1b1EI5Xz9N7FpGmjKGiqRilxH6X0a/5RLKekqN4z8uyAR3gnhYRy78hxPLdprX1bWy+W5x/l+uxgJFuQPy6npoF3lzs2/sDyovYIMrFIxMSEdCYk9CEzLJpopYYQuRK1RMayosM8su0Xr9r3JerdG0wWC89+85vbY8ZmpfL2jRe5tf5wRWZiNJ/cMZeb3v2eg0XOfWBrm/S8/MNaXpzvmRe2ryREhPDp3+aSFOW9ndWovim8OH86f/vwvy6PqahrYtPhAs4Z0NnmzBOKqur4ZOU2l/s1SjkL77qczETv/VglYjEPXzoZhVTKwtWuswn+uXQj04b1Qyl3LReoZTLSwgNfc8QVh6uepW/kA6hlqTS3ZlIern6WEfGf+dTe2k1HOHLMd49id8yfN4ab5p/TrTbeuNC5Z+/srCxmZ52ezNU2Ni/bw1ev/ozVYsVstnD/u9eTObR9brP9twN88eJiLGYLKZkJ3PnaVWjD1JTmV/L6HQtJ7BNLfk6xPXL0wQ9uIK1/kr3t799dicVi4dD244yZOoTHPrsFmZvPois+/GI9PjhTuSQ+NpQXHrukR0eXt9E2V3/snguprGok53D3M6h1zQY++mI9j93btX90R/7x2CKqq5qorKgH4OF7/83AwcnMv34CoWEq7n1oBk88+A0Wi5WExAju8cCfug2zycJXC3/n+/9sxWAQMiIvnTeK2XM8y4p1xyV9BjpEmAO8vlsouHtOYm+3mWptLGn17z7WoTCmN0Qr1Z22Ha2rJlbleZT/t8f2sacqcBn0V2UOdYgwX1+Sb8+oXlPiWAvl8r7eZwJUltVz4mgFaX3j2Lz2MP2HCnPR8EihZkttVRNNDXq0oSo2relc2FetVVBX0/TH9jCffZlvfsOZ0VEcr6klIyqSNceFNOEbz/pjTuBGxiUzMi656wOD/GGY//fpzP/7dI7sKWDxgvXYbDbGTctmoBdpRgBRyhHIJcKg3mipd3lchf53ms1lqKV/zmJs9w+84LRcx1lxDX+ilcntvsaBwmyt40DF9Z2EZ6U0mZSw24hSnw+ATBLZ4ZxGappXU9zwMTrj4Q7bG8ipuBGA4Ym/IJP4NjnKq3naoT8aeX9Swm4hQjURqdixSKvRUkmlbglFde9httZ3aidKfR4iL7ywixs+dSqWS8RakkKvJUYz08F2xoaFJsN+Shu+AKBSt4SOiwnF9Z+glWcTo/FuwHgowIK5NwKwXHM1+to7sbXUI1NORSR2XihXJI5AFfEWzbW3oaucikw1G5EkHpu1Gqv5KGbDVrRxwuBRLBGeYTLVpRibFtBS/wRWcx4ikRKzcTMWwzYk8lFYjK6FgNPJ0JjA3itrWpopaqwn5RSP8aeHXMzumkJKmmvJjkhheKT7BbQtpY5p+VKxmMuyvC/44wvz+g3ipS3rO1kJbS8vCQrmQYK08nPhQbtYHtpaj+jzSVcwJMp5PZdAi9++8OOWHLfe1n3iInnz+tk+ieVtaJUK3r/1Uua8/AVVDc79hH/ZeZj5k4YzICUw4ySJWMw7N1/ik1jexvj+aUwb3o9fd7lOsd+TX+qzYP7ess0YTK597v9x5QU+ieUduXPGODYfKeBIyUmn+6sadHy/eT9XTXRdt6VXWDhPrVvN8bpaHvxtOZmRngesmS1WDh4upayyHpPJdfHtmRe4FleU0gRCFUIgR9tPhSTW4z50xGq18em/fvfp3K649oqzuf4v43w+/6lVq/h63z7uHDuWO8eMcXncLYsX81teHs9OmcJfsh3ft5KGBt7dupWtxcUAVDY1IZNIGBwXx8MTJtA/xnWAojO+eetX7n5jPn2HpGLQG5G03hfqqoTApX/e/y/eWvEoEbGh/PeD3/jk6UXc88Y1AORsPcb1T1zKwDEZ/LxgLQCL3lnB/e9eB8D7j/yHt397jPDoEB6b9yaX3DbFJ7F8x54T7D9U4vV5rpDLpbz0xKVEhHUWMHsycrmUFx67hBvv+ZyT1e6LMHvCynUHmT9vDL2SPffRfvL5uW73Dx/Zm+Ejb+i0PbH1Ggu+vs2+bVB2Cq+8Jdgbb1x3BL3eyOff3A6AoUUQzK+57F2/COZDohMYHZfC1or2cfiB1sKfT25dyXOjL+gURNiRgzWVPLF1Zbf64Mxq84sjuzwOvvul4AiPbVnRrT50xfCYJPpHxHKoVpjn6sxG1pUK2uz+6vYCyiNiksjwoYZbSlo0i77YSH5uOWERGh5+SbARVWmEQK0rbpzAnX/5gPBILWMmZVFa5Lg4Mfev43nzH4sJCRXs3mZdPprzZnrmxd9jBPNBQ3sBsGd7Pju35GE6pYJ52/5TGZqQwNCEBHIqKjk/o3seu0GCBAKb1UZjvZ6GWh2hERqWfPE7m1ce4MZHO/vUu0IsktJLK3jTH6v/3PW1bBaO1H7IsJinu9vtHsl1GWcH/BpGi4Vjdb6tAHvKqPiUgEXht3Gs+kmMFkdxNloznczoV1wW8ZSKQ4jVXkSMZgb5tS9R0rDQvs9oESZUR6sfY0Dsez71qaNYnhx2E2kR9yPC+cRbLoklOfQGolST2Vt+BSZLe9Ero6WKk7qlxGo9q9fQbMqjoPb/Om1XyzIYGPcJSmnnhUoREkIUQ+kXIxRvjlKfz+Gqe7HZ2p9NR6sfJVw5GpnE8wf/wQAL5t4IwDLlhbSIn8ZmrUamdj+QlSrPRxv9M4am9zAZfsNmrUEkCkcsTUUR+gBisePEXSIbiDpqAYaG1zE2vQciGRL5WWiif8Bi2o++hwjmUUo1SdpQSpoaAnaNvVVlnQRzgGGRvRgW6XxscypHax3vSSPikzzKJPAHYQolIxOS2VziWKzo1D4FCfK/zNKCQ/bfb+0/FsClWA5QZwxsYXFvsdlgwartbo+5/+KJbqONPSVco+TumeN54t/LXR7z4fKtvHWj5+Nkb7hq4jAyErqfiXzlOUO7FMx9obi6nl93u2530qB0pgzxrmCaM6QSMXfOGMcdH/3o8pjvNu1zK5g/MHY8O8tKGRgTS3pEJCMSXH/mO1JcWsuD//ie4tLaLo91J5hbbC0cOPkQSmkiepPwjDJaqsmrfQeA9Ig7POoPwLJVBygscV9g1Vsuu0gQ7LojloNQH8Fis/lUxLcNjVzOidpa5gwQajmlhodT29LCGxs3cvOPP7L2xhu9mpvMun4SL930MZPnjubCv04gMk4Y5xzeIUST9h2aRkSskAV37rzR3D7pWfu5UQnhDBwj6Da9Bwhj8I1L27NPTQYzUpkwP5BIJU6LQHrCZ19v8uk8V9zwl3H0SfVuYaGnEBGm5oG/TeXBf3zf7basVhsLvt7I0w84z0w9neh0Lchk7c+lY0cFMTvSjx7rz4w6n5k/L8Rscwwc+XfuHo431HDf0HM4KzbZwU+7qKme747t46OcbfZimCDUH2o2m/CG0XEpRCnVVLc027ctL8zlme2/8cCwiailzgPIjtVX8/6BLQ6R31qZgiZT5+LX/uCqzKE8vrVdmP+8NTK/433rir6+FYwVS0Tc/+ylLvfPu3Y8864db399+fWO2TxjJvZjzMR+Pl27xwjmbbz/2jJuvW+aR6uIe8rKOFBRicli4WBlJa9O968VRZAg3eXLN35l59pDjJ4ykJseu4jQ1rSRey5+0+u20kKFlbS8+i+x4boYTWHjYnqFXESU0vUA93+N2patlDcthtb3rX/0Sy6PPd5Q08n6wN+MiU/p+qBuUKffyEmdY6p3mHIkWdFvIBJ1fW8ViaT0iXwck7WWyqbFDvuqm1dQ3byCKLXv0f5x2jn0jnjIo2NVsj70iXyMIyfvc9heo1/tsWCeV/0UVpvj4EAqDmNQ3AIUUs8md9Ga6fQy5VFQ1/7dtVibKGn4nLSIv7s5sx2z1crROtf2OP5gkDeZCyIJiCSIJLFIFV2nCYtlWagi3va4ealiMtKYyU7bcVVgVK65FrnmWo+v4Q+yo+MDKpgfqT3Z7SK/5bpGh9fOBPhAkhISxuZTtp3apyBB/pcpaW7PhBoa3fVzZW914IuLe8PWo4WU1ri+D47J7MW4/ml+u96skQP417pdAE4jnNfnHKeirom4cP+JHgAKmZRbpo72S1tD0hIJVQsLl84KgB4t9e15/+OWHKxW1+PQmy7wLSvbGeP6pxEfLmSXldd1vqfnV9RwrKza7QLDiIREu1D+n5x9XDGw63T7dxesoaZWx503nktW33hkMt+yFuK1M+2/hyo8KzbqDKvVxsL/+FdcHTa4F7dfN8mvbXaHcKWSf192WaftVTod/9yyhcK6OnpHeJ5Bet5lYxh1/mBWfr2Je6e/xMMf3kT/kX0QiTuL7qdOq1Sazgv+HUXxax+/hHumvkRyehyR8WEMGe+90LVzb4Ffo8uz+sZz+cUj/dbemWDMWX2Ydu5Afl2d0+221vx+mL9eJiwO907tXrZLd5h43kC2bjrG3bcsRCQW2e8lDzzmvwXXrIgYHhoxked3rOm0b0t5IfN+/YoIhYoETQgiRFS16Kho7hzJf0mfgSRpQnln/6kjavcoJFLuzD6bp7c5WqZ9dmgn3x07wMjYZBI1wuKUDRt1Bj0HaioobHTMGLtpwCji1Fqe27Haq+t7yiV9BvLizrXoWguer2+NMG9DI5MzI823+VCApRm39DjB/JzzBnBgT2GnVSFnEeZDExJICAkhTqslr8a/K8JB/vzkHiql8PhJsgYn06I3cXBfEemZ8cjlUn76Voh+nHP1WNLSfUvtA4hPieS1RXfZV8nbmDXf+0gDjUxYgU8OmUFR4xI3R9rYWfkIE5O+QuFF5OufmUrdMvpFPYlY1HVEZm5tYAVNcJ5a5U9KGh2zEERISI98xiOxvCN9Ih6nunkVFqvjQ7+4YYHPgrlUHEbvyEe8OidGPZPjkucdoswbDJ5V+tYZc6lr2dJpe6/wOz0Wy9tIDruZkobPHCxiyhq/olf4HYhF8i7Pz6uvwWhxnXbsD3qHej7ZMRt+x2apRKG9HVxE+v8vkBbqeUqpLxzxwz1Fb3KMRolRa7rdpjdEqzqnH5/apyBB/pdRSNqfr7UG99HjBY21rCjODXSXvMJdpDQIArc/EYlg1sj+gHPB3GqzsXTHIa6f4l+BauLAPmiVCr+0JRLBwF7CIvXmwwWd9jfqDdhswnHesHTnIZf70uOjGNQr3rsG3SAWiewLId9v3u/0mN8P5TsI5ifqau2+5bnVjs+39YUFHgnmBw6XcsUlI5k3u3u2XlpZBrk1r6A3l6CSCmPrzMgHUMvSvGpn0/Y8Kk76b+E8JkrLMw/OQuxEPD5TmCwWPt6xg7X5gohV3NBAo8Fgf5Y3GY1etVddXkdUfDiX3n4+NZUNHNyeR/+Rfeh/Vjog2KrUVNQTGRfGmkVbGT7J83tI4ZFSLr9nOudfMdarPnXk28Wu/fl94d5bpvSo/6ev/O36yazbdBR9i3f/71Ox2eCLbwXh96kzGGmuUEh54rk5Ab/OTQNGUW9ocSl21xr0Lp/9U5KFbIpXzp7O2pJ8p8d0xV+zRrD7ZCmLW/3Q22gyGVhTktfl+TcPHMWjIyaTV18dMMFcI5NzUZ8B/DvX+Rx9dlp/l9HwPZkeJ5hvXHuYC2YORSzp2ttva1ExIQoFcVot6ZGBnfAG+fMhFolobGxBpZKz/KfdpKXHknuolNnzRtInUxgAd0csBzh/7ihsNhsWc3tEuEQq5rw5vk8A+kf8jZKmFZ0iZjuiN5ezuex2xiZ8AIDCR8/p7mCzWUAkQoT/fDpXlB7kQF0pt/WbgEoiCJRWm43XclbwQ+FuRIiYny5EDt3eb5L9PKU0gbqWXSgkQhqdRu46ldXXghzekBkeuJV4g7mUmua1Dtsi1ZPRyDO9bksmiSBBeznFDZ86bG9o2UGTMcenAqCx2ouRtRb39BSRSEKoYjjVze0r6wZzqfAZa93virLGrxxet9nRxIdc7lUfAMQiOdGaqZQ3fmvfZrbW02jYS5iy6+90bp1zn1B/oZLKiFV7Go1nwdD4FohkyDXXBbRfPZ20EO8+j96S64esAplEgsXcntJ5usXqZifXk4h7ngdzkCBnioER8ew8KXgDLzq+D4DpKZ0jqQqbarlx/beYrIFdPPWWTYdPuNwnl0qYPDjd79e8YKgQNfr6YudF+TYczPe7YD59uG8p2a6IC3P9zLXabOgMBq8E+uMVNW4j/ccPSPOmex7R5hXvSjDfX1Du8PqrA3t5bPwkAG755ScHG5ZDVZ6NcwxGM7HRzuumeENuzav0i3oElTQZvVn4/h2pfpGhce961c5Py/d2uy8duffW8wnvYT7Xz61dy7/27rVb2F46YACRajUrjh3jvwcPdnF2Z97++78oKziJTCYlMj6My+6cCmDPpr77/+bz9FXvYDFbiU+L4e7Xr/a4bbPJwtevL+W/76/EoDdy8S3nMeuGzhmLrqiu1bF1l2/CpDPOHplO/8w/R32wsFAVc2cN58vvOgcTecv6zUcBqG/QExbq3O7zz8T9wyYwNCaRx1v9wMub3WdaKiRS7hg8lr8NFhZ+xCIRw3ysnSQC3hg/k4ywKP65f5PHAVhxai1PnHUeM1sju9PDougVEt4p+txfXJ05zKVg7kuxT4CU3tF8+L3n9lr+pscJ5gOyU4iNDyMsovUh42ZZ/khVFfvKyxkYK4ia140Yfjq6GORPgsViJSxczf7dBfTpG4+usYWB2SlIJGJqWgtiFJ2oIiXNd3Hzy/9bxorvtqFr0GO12uidlcAb/72nW/1WJo+efwAAIABJREFUSePJiriNgzXubV3qjUfYUCoUVxkZ+wphiv7duq6nNBiPUtj4E8VNv3Beyo/IXBQS9IWfivayu6aIu/ufa9/22bFNfJ63mSR1OGKRmHcPrwWgjzaGaUmCoKuUptBkPEQTQtSOO8E8UA+QNuLVWkLk/oluckZty0Y4xbInWu27XVW0ZnonwRygpnmVT4K5t0Uy21BKO2cZma3CpNJdEdKOIjtAmFIoluTKx70rQuTZlPOtw7YGw3aPBPNA2n4A9OrSpsNKS8OLiEQqzIYNWIw7UYQ+iEgS2AK0PZ3U0MAK5sVN9Vhstm7VLQhTKGkxt2d6nNQ7L5YXKJxdL+w0eagHCfJH4Ir0ofzr6E6sNhtrSo8BcPlvXzIrdQAxKi31hha2nSzk54KDGK0W5vUZwnfH/SvS+UpRVR0Vda4LwWWnJaBRdp1F5S1tdisZ8dEcLeu8sLj3RBktRrNffNPbyE7zr+gVonJ/H2xs9k4w33a0yO3+kRn+t/Trys8995QMgDaxHOAvg7K5aVh7Yb2n13sWuZg9IJld+wq5cEr3ildLRHJUrXVo2n6KRd6NsSurGtm203/i6rhR6Ywf3fPqqv1eWEhiSAgfzHa0qvhqr2/3oWf+7V7AGjaxP8MmPtZpe2LvWD7Z8g/76zYv85d+EOwNNy3djV5nYMH25wAw6I1cd9ZjXgnmv64+4NbWyFu660Hf07jikpH8sHQ3uubueVmbzIJo++vqA394uxpPmZKcwbiLhWKbq4rz+KXgMEfqqqjUN6E3mQhTKMkMj2ZCYm/mZQwmWumYERqt1HDiGs9sSU9FLBJxZ/bZXJk5hO+O7WdTWQFH66upNeixWIW5v0omI1EdSv/IGCYlpjMtNROlxPEZuv6SW3y6vicMiIxlaHQie6ocbef6hcd4ZFfXE+lxgrlCKSNnX+fBgjNLlmuGDeVg5UkGxP5xii8sLd1Go6mZaEUY/UKT2VVzlL4hSRxsKGRS7BCKmoWCcFqpigP1J+irTWJQeNqZ7fSflH4Dk8jISkDSms1gtdrsqVbX334egFMfNm/Yue4wn//+BG898i03PjqbT190Z6XiORnh11DRvJ7qll1uj9OZhO/S+tL59A69nL7hN/o12rzNS73ecJiT+m2U6lZQZ/A+SsFTjjRUcHZMHyQi4X/WYjHx6bHf6RMSw3cTb0YiEjNnrRBV/58T2+2CuUaWgd50AhsWl0Um2yhqrHe7v7v0jQisz1ujYXenbeEq31MaQxRDkIhUWGyOaWYNBvefPeeIfRLZQShIeioWmzDBl+H8M220VGC0VDhsC1EM9en6bShlnSuS64yepdY787PzJ6khXX23bZj1S7BaqxBLElCGPopcG7hB0ze5+6gztBCv0TI4Kp5NZQUMjIqjSq/j/F7CotXygqNMTW1fwMpvqGVdST7DYxKx2Kwcqa1iQKSwKH60tRjv8NgEmkxGPj+0i5sGjqJfRDQfHdjGzYNGUd7cxMrCo8zPGkZOtfC/31lZwoCoOCxWKwWNdWSERTE8tn3Q1vX71j3MVitlugaStb77jvcKCaNC1/752VVRig04HcnBNmBHeWcf0LQALzQECfJHIis8lmdGTOXpncvtdVC2nyxi+0nHOY1EJOKewRO4a9B41pQeo6rl9C5+OcOZJUpH+iYEdtySkRDlVDC3WK3klp70m8gdoVURHepfOyulzP1U2mR1XXPIGYeKKtzu7xXj//tuqMq9wFxe1+jSWqajWA5w9yjPxpt33Xgu9z7xDW9/vJrZ04YQHxuKUuF9qr5UEs6x2rfQytJpMgkLVXIv5zlLV+7rVjHNjshkEu6+eYpf2vI3cRoNu8vK+M8+IQNGIZXyS24uJQ2BDebwFl2D3qGWXd7+IiJivRs/LVt1oOuDPGTooBQy0/0bWPLJ5+vJLxDGtL1To7jxrxP82n5XhGiVTD9vEIuW7PRLe0tW7PufEcxByOgFmJmWZY/cPp1EKzXcNmgMtw0ac9qv7QkWW+fnnq/R5T2BHieY33zPBRw+UEx+a4XbtPRY+me7Xk3/I4nlACarGbVUQYvFiM7cQrQijH11+cjEUr46sYq7+10CwEfHltJbG8/hxqKgYB5AJB2sfzr6knVXKG9DqZYjlogxGUyEhKupqfSPGCtCzFlxL7O+ZD56c3mXx1ttZvLqvyK/4TsSNeeRoDmPGNVoL6O/bejNFTSZTtBozKOqZSdVesEfzmQ9PYOt6pYmUjTt9ksrSg9Sb9Tz0KCpKCXCw+ucOEF8W1K0z35cceOXhCtHIRVpaTS6L3RS3BRgwTyAdiwAjYZ9Dq8lYi3ybkUQi1DJ0mkyOg4+Gwy7wUvJTiVL8chH3jmd7R9sTh7IHWlwsnigknVefPUGZ8K92epZVkJXqXvdpVeX1iIStHHdT8H0FKPVQohcTrPZRKPJQJxay7byYvqERbKtXBCRTq04/2tBLrcNFmyVPsnZzo0DR/LB/q2MT0yj3igUVVNL5aRowxkQEUu/Uxag4tVaTK1pij/nHwYgMyKafVXlRCiUjIpLJu0Un/d4TQgKiRRDhyr2/qaosb5bgvng2Hi2dxCty5oa2VNRxrC4wKcI7ygvcRDr2xgS++dITw4SxF9c1Xc4g6MSWHBYqIOz7WQhVS06FBIpMUoto2JTuDpjBIMiBQ/qYdFJrOwBXubHK9zXgUqPD2w9nIyEaMC5h3puaZXfBPOMAAv//uBYmWtbQLFYRFKk/ws+a7sQzI1mC436FnuB046cWuQzQulZ9t6Lby2jSWdk0ZKdXYp26396wOW+/lFPUNm8mmbTCcIUQwCIUU/yqA9trFjjv0Cf6ecOIj421G/t+ZPHJ03i4RUr+MfatQDEajT8JTubRydOZMpnn53ZznVg4iUj2bZyP/dOfwmxWIxMLuX+d671+PyDuWUUFvuvtt2M87uXBeGMbTtP8NHb1/i9XW+YNTXbb4J5YXEN+w4Wkz0g2S/tBfnjsq2ymP3VjtqUTCzh4j6+Bcz1BHqcYL7oy03s3n6cvllC9NeGVQfJHpHGFdedc4Z75h/EIpE9OnZ/XT4hMhVikQgbNi5KPpuV5cKNKz0kkSaTnkFhaR62bMNsbcZkbcJsbcJkbcJkbezwe9t2QbBp295kKuyy5QM1rwGgksQjE2uRijXIxCFIxVpkYk3rz5DW7VqkYiHFsu1Yf3pY/9FI6h2L2WRBqVHw/O0L0TW0+K1tpSSGMfHv8Hvpdfb/a1dYbUaKm5ZR3LQMAI0sBY00BaU0FqlYg7i1KKTFasBs02Gx6jFbdRgs1TSZCrC48U0/Hailcmy0R4H8t3APITIlUxPbb8JaqTDwrzM227fJxGGEK0ZQqVuG0XISq82EWNQ5ksVstVIZ4CjgjLDA1ls4NaJa5cTKxFtUsrROgrnFqsNsbUQq9nxiIBOf3iK0LebOUbGHT97r8NMfmCweCuZOREd/4qww45lEjAhx6/NuR0UJYXIlErGIc1PSuXnVDwC8P/lih3MywiL5JGc7I2OTiVVp+SEvh3h1CBarlUiFMBHfVlHErN79OanXkVdfjcVm43DNSQ7WVKKQSMipqeRAdQX9WyPTG4wtjIhN5Hh9DRpZZ1sBERCpVFGmC9yCRnfteMYnpbJgn+PE5sUt6/jmoisCGmVutdl4YfNap/smp/YJ4JWDBPE/92VP5L7siS73z+2Tzdw+rqOgxsSmcvzKR91eIzsygTfPvgiAlWU5LCvZj0oi57eygwyOGcqm6iPcuPUTJsRm8uE5lwFQqBNE0kd2LWJH9Qlqjc3EKEOY02sEN2ZO4JrMszhUX8bl699nZlo/+oU6Fn080lDOvHXv8Z8JtzIgLJFPJ17m0fvRRkWd+3tfIKKaPW2/q755Q4RWxeateRw5Ws61V3e2WHjv4zXMvlDIQktOal9YLS2rIzHh9GTUlNW6flZYrTZG3PfWaenHqeiNZkKdDDG2lZZ4VOTzVNJ7x5Deu/tBbyWNi0gKmefTuUUltUIb5f6xYhSLRFx5ac+Nsh0QG8tPVzv3Ec/7+99Pc29cI1fKeGyB79mPm7Z3XQDRU7QaBZPGOdY9WLEqh9XrDiGXSxl9Vh8iIzQO95STVY28/s8VvPTMHB5+6nuyMuMpLasje1Aygwcm88PiXZRX1PPS678AMHRIL6ZNGcTHC9dTVlaH3mDi5msn8skXG3j+yUu44W8Lueu28ygrFwK6pk0Z5Je/rXevaAZmJZJzuLTrgz1g7cYjQcE8CJ8e3N5p27RemfY53B+RHieYb9lwhFc/vBZRa96XzWbj/psX/mkE89lJjqlqVpsNcYcctzRNXId9VrvYcCoHql+lVLcKs1UQYMzWZrs9hr+pNwhRevUc9uFsEVKxCqlYi1qawDmJn/u3cz2cO18QBnG3PnUpezcdpd+Q7ouXHQmVZzAu4WM2l9+OweL9arrOVGS3bfkjkKaNZmNlHndkTWZvTTHbqvK5us9oe3Q5QGWLMLlSdPDrSgm9DrkkCrkkBoU0walYDlDd0oz/HO+cE6PytCijb5hPifaXiLuffuyqDbO13ivB3B998QazJbDZAm24K8DbkUBHmEcqe5ZgflWWo/1N2/Mur76aScmC2CqXOFoknd+rr93ve0hMgsMzcmCU8HyUthabfGDEBPu+/5sww97G6+cIPvmDWo+3txftOkoxUqkOqGB+spu2CxNS0ohUqanRty8Ebisr5p2dW7hzROBSMl/f/ju7K8octqW0euWfneTf51mQIH9G1lfk8tSQ2aSHxPD2od+YlTyED0Zfw7WbFnB9QzlZofGoWxf6Y1WhvDxiHtGKEHbXFPDU3sVkhSUwPrYv/cMSyI5I4buCHTw+eKbDNRYX7SY9JJYBYb75g1Y1NLvd740Hty+4a7+y3n8LzRqFex/222/q7JFcU6tj0Y87uOu2wNtsWKxWapv0XR94BjCanWdgpYaFU1BfR2qYdwsK999+gT+6RW3LDp8F8627jvulD21MPDuTpITAWrwF6Zptfiz2OX50XxQd7GHqG/QsXrqbf75+lX38uXmra4G+tKyOO285l6TE9s/FvXecz6HcMh6+r72m04GDJTTrjTz5yGyKS2p5/9O1SCVidDoDCXFhHDpcRm2d/+27zp/Y32+C+eYd+dx1k1+a8og1lbt5LudLACbEZPPUoGsDej2bzcbTOQvZXnOEsyIyAXhm0HV2zfJM8X3Ret479qP99aXJE/hb34vdnBE48uqrWVl0tNP26/qPOAO98R89TjB3xhn+HAYUsZs/zpVYDqA3V3hkxXHmESLfzdZmj0WlPyNyhZSRk/tTWVJLKP4VDcMUWZyTuJAt5XfRZDrh17Z7GpenncXDu35g4q+v0Wg2oJEquKHveIdjDtYJ4k5H6xajpZrjtf+HFRMxateD9OoW95NGfxCrCoxobLEKfbfZHKtmS0R+EMxdtGG21gOeF6DythBTd7GcJqsgT7AR+EKNET189b7teaeUyLiqn2sv+Y7FMTs+I9uEcmf73OFJsc1ARz5UdfN/LxWLuX7wcF7b9rvD9te3/06tQc/Doyd0WnzoDnqziec2reWrg52Lgd3RKtD7MjQ7vDOf3L0FNDe0YHPjGXvlvdN9aD1IkO5T1tTIuuJ8cmuqqDXoMVmtzM0UIvompfT2uj25RMqs5KGU6+t589BK5qaNJCs0nnhVKKXNtWSFxhOtEBbS7+3fPj7ppYnkq+ObOVRfyvhYwWruirRRPL//Z/7eepxaKsdis/JLyT6uTfe9KF2zweh2v8oHb2lvULtpv9lg8tt12gTz/QeLef6VnymvbOC6+eMYPiSVb77fzpJle3j2ccEas3daNHnHK/ni683kF1Tx0uu/cPaYDCaMy/Rbf06lqcXoNy9tf+OqV/sqylmSexiVVGafr/90ufMo5kCglvWi2VSI2ge7vS1+LPQJML2bBUzd4e3z1p/SSbNJ+A5O/vJTHhk3kYv79fdj6/6lobGF3GPu6wB4w9izHDPpSsvqSEuNdjv+7FhsVKmQOojlrigsrmH/gWL+8eJPAKSmRCESwdrfjzDpnH7s2lOAweh/28CxZ6Xz5oer/NJWSVktJWW1f9pFo6NNJfx+cj8AG6sO2LdlhgSj6tt4dsdqh2fY8Jgk+88rH/mcc0dlctMlvtdV8wcLFm8F4NdNB/n25es8Oud/1ysjSJAgQYIECRIkSJAgQYIECRIkSJAgQYIE6UCPizAfPjqdx+/+isz+Qlph7sFSzhqbcYZ7FSSIf1j4ys88+NZ8v7erkfViUvLX7K16gaLGJX5vv6cwKyWbypZGfi7eR39FAnf0n0yssr0IY0lzHQfqBN/q6zPao61Km74hM+pJxCI5R6qfIVo1GZGoczRmtT7wEeYx6gDZkriIdrD5xWTGVRs9O/3HSufINJUsFQAR7lOzvUElS+vymJqWZszWwNhmteFpsa0zTZK25xXECvR7V+WHe8t1g0fw9aF9lDQ6Zk4s2LeTFfnHuPess5nWJxONzPdo0AaDgZ/zDvPWzs1OC30Oi0tgbj/v/DP1OgPP/PUDAPb+7ryw36kEI8yDnG4ajAae2bSaH4/mYDklynd4rDAnOTXC/MO929hRUUKoXMnrk5x/ZsNkwr2lzTouUi5YZ8nFUoythYabzUKE9xfHN7Gu4gilzXVYbVYazQbOsbT7505NHMRrB39lWakQ5Tan1wg2VORSZ9QzI2mIz3+70Wxxu99dBLg/0ChdP49bTP6LqpRLhXGfRq3gsQdnkn+iii+/3sTwIalcPmck+SdOOhyf3ieWy+eMZOXqg9x9e+AtWYx+/FtPF5/OuqRb52/ffYI1G49QUib4iCclhDNhbCZjRniWzVFv2E950y9IREqHcfDoxEVuzzMYzew54D9LyvAwNSOHpvqtvVNRtj7X9Sb3GRdNRuFeopT6T+L5aJfgSfzmBTP4cv8eLuiTgbob44xAsmPPCb9kaUgkQkzpyGFpDtvj48IoKKzGZmv/uCmVMpp07Vn0RR0LjnqYCdkrOZK+GXE81GHss+73I/z86z7uu/MC9uUUB8T6IyEujNSUKAqKXBcb9oYtO/OZM9OzCPM6YxN37XqbviHJPDHwzBZA9QRn777EjRvE/xrv7d/C2hJHm6uHh7uuGfNHoscJ5n+5YQI5ewspPC4MWkaNz6T/4GCqQ5A/Fl//c4XT7cdyBDG3siWHcv0+AESIGRxxebevKRGpGB7zLEmaqRyofu2M2rOIRXISNJORBMCC44a+47ihr/PU40R1GPtmP9nah/ZHm0KSQKMxx24JojcXIhGrUUjiHM6vNQTeOzJaGRjBXCISJuUikQybrX1AbbV1X6iz2JzbSUjFYd1uO5A461+/6NcBCFG4tgQJBE0m9ynv/sATW5EWSzW/FMwiTNEXq81EsuZc+kfc6Lc+bKl4hIywy4lWnt73t7sE2pKl3tj9gs8amYzXJ0/n6p+/67T4UtxYz31rlvHo+pWckyxM2ofEJtA3Moq0sAhC5XJUUhlKqRS92UyzyUiDwcDx+lqO1QoTpV0VpWwqKXS5sBOhVPHWeTM9srjpyL9e/dkulGtCVUy4aASxSZGIJT17wS3I/w4VzU1c9tPXFDR4V/wvQRPCyhPHALg8azCj4jvPV05N3Xdmt/j4HqEIcl5jJU8PuYgBYYkoJDIuW/++w3EysYRLe43guxOCgDWn1wh+Kt7D2THpxHQIHvCWroSYQLuEdLQwOJVA3CXSUqMB0GoVNLf4z/Klu5y6UPNH4IYl/3V4LRaJ+Hhm1/65VquNF978hRVrDwIQGqLEZoPd+wv5ecU+JrcWWnzy/pl28dIZw+I+8Knf+w8WY/SjxcXkcf3c9rO7xGqEuUN+ba3b4/JqBLE2Xuu/ekn3jD7b/vvYZM9tGM8EO/YW+KWdgf2ERVKtxnEeGxGuZvoFg3n4yUWoVDKGD0ll+gWD+c+ibTz7shCwFh0VgkTs3Z1r0IAkduw6wVPPLwZgzMg+DM3uRUHhauLjwggLUQXMrmnMiN5+E8y37jzOnJnDPTp2Z20uJfoqIuS+P7tOJxkhSZwTk822msNMjhXmOOla3+qG/NGw4fpZnN9Qwzv7N/N93gGH7bPS+jMqrmffLzylxwnmAKFhaqJihC9PSKjyDPcmSBDv+X3ZPi686uxO22UyIbqluuUoYTLhJmKwNmLDishPDklx6vHEqMZQ1PQTefX/AqDR6N/CNq4IlWeSGnIxySEzkJ8BMVWEyKmYo5QmUt+yGwCVNIWq5tWoZWko1I6CeaBFzVC5wq8+w86QicMwWqrsr9sKA3cHi4s2vCn4eSaQOfkMmqzuJxuBosVFwSx/Eqbw7HmpkSVzXtLnWG1mlhfNJTVkFmppXNcn/onx9L3zFZ2f7i1jElN49pwpPLpuhdO8D4PFzG8FQgGqtp/+QCuX8+n0S+gV6v19fePSPahaJ53vrnqUuJQov/UrSJDuYgNuXbHYLpZrZHLGJ6XSKzScj/dtd3vupF59EItEWG02VhfkORXMPWFDpVAk697+5zMsUljwMlrNFDd3fl5dnjaKBceEWga7awpYX5HLc8Mu9em6bSik7sclXXmcdxd37XcsuOcvPK1/ASCVStC3BH7BG0DRRVRwpFbNxaMHnpa+nEqoyvkz8vnJQuS91WbjeF0t6wtPeNTef37czsp1B7nlmglcfOFQNGrhGdGkM/Djst18/OUGADJ+iGX+PNeFrSt1K4nVnO+w7WTzWmLUk9xe/0ie/3yuAc4Z29ev7Z3K0AShaPnGggKqm5uJUncu8r6psJCTOh0KqZTs+Hi/XPeT3TuJ0QjXuihT8C6f+tVCll91rV/a9zdHjvmnvtuAfq6LxM+Yms2MqdkO215+dq7TYz9623nktLPt117dORjs2y9uA+CGv57jsj/dZUA//4m+B7woILqrNtdv1z0diBDxdIALi/ZU3tizga+P7iVKKdwLVFIZFquV8uYmKvWdNYIEdQjPjvZPYWd/40uiRo8TzL/4YA3bNx0lLT0WgPxjlYwa15drbu1cuTxIkJ7K+XNHMsOJYJ6zXRCuE9XDMVqFqN1GfanfxPI2xCIpqSGXkhoipEpW6bdToltJmW41Bot/VpHlkjCilaMAiFGNJkY1Go2sZ64kxmnaK5FX69cSpZrk9Li2tOhAESoP/AKgQproIJi3mAtwvzbcNXon2QoSsRqpuGdHBcgkMZ22Gcz+qQbvLQZL4AVzmdibxRgbOnMJNqzIxELk0qHaT6jQC8VQEtQTAOgXLlhI7Tz5Ak2mAsxWPfHqsQyMFAbxB2s/orx5E6pWwb3F7J/7y+nGu/fOexqN/it6fWX/bORiCY9tWHlaFmLiNFo+nnYx2TG+TcCry+sYek6W0FZQLA/Sw1h2/Ai7K4XnwqSU3rx57kzCWxfQuhLMQ+UK+kZEcaSmil2Vvj9bktVCCvvmk3mcnzAQndnAu0dWY7Z2tkqJU4YyOU6Ivn35wDIUEimT47N8vja4t0QB/xbedIbOTftthToDSUODnoVfbeTAoRL0X20EYOSINGZOG0Lv1Giqqpt46vnFTJsyiLGj0wPWD2UXiwNKuZS7Z413e8zpJl7bPg5MDAnlu4MH3Bzdzs8r9jHrgiFcNXe0w3atRsHVc8dQXilYj/3y2wG3gvnJ5tWdBPOyph+7FMyPHq/0qJ+eIJNKGJQV2EjTs5KSyIiM5FhNDbf+9BP/N306KWHtC9j7Kyp4aPlyAC4dMACN3D/fG5vNFvAME39htlg5UeifMWj/vq4F8z8b/vxbm3QGyirqSYjrOrhiZ80fSzD/X8Zqs3FSr+Ok3nnGeUdC5Qo+O2+efRzVEYPRzFMfLGP9rjxkUjFjs3vzwDXnolU7ZnKs2HyYz37aSnFFHXHRoVw8aTB/mTYCcWvmRlF5LZ/8uIVdh4upaxAy6aMjtFw8aTB/nTXK3o7NBp/8uJnvV+1F32Jk3NA+pMR7X5S2xwnmO7fk8fbCmxC1viE2q417rv/Ur4L5kOjHGRL9uN/aOxOMjHvtTHehx5AVcStZEbee6W44cPH1zj2b7nx+HgAtlnpOthwGIE4VuKrqbSJptGoU0apRDIl+lEZjPrWGfdQZDtNsLqbZXIbRUovF1oLFKtgGiERSpGIVUpEGqViNTByGRpaCVpZGiCwNrTwVrSyNnu5hDdBg2IfeXECLuQyAupZtLgXzQEeYBzq6HCBEMYxGwz77a4u1GYO5DIXU18G8lWZTfufryIfQ0///oYphnbY1GvYCkBBy1WntS8tpEcw9W3jTmYpZVXItjcZ8JiV+jEyspaplL1Ute5mU+BEAG8ruAiBGOYxI5SCGRT+AWCTDhpWlBTMZGHkrjaZCSnUbOC/5c3vbywudR9n0dAL93Ww2+1dwmtNvIEPjErh31S/sO+mfiCpnTElN56VJU4lWdY5m8xRNqAqlOvCiV5AgvrAkTxiLRSrVvDtlNhqZd5/VPmGRHKmp4kS979lLzw4Vghue3fcT01e9QaRCw1/TxxHtwmblyt6CyHjj5oXMSx2JQty96VxsuHv7hia9/xb8vG0/Jsy/NnZjR6fbRe+Y6BBeemYOAHfd5tyjXCaT8Nrzl/m1D65QyWVoFHJ0LiLua5sCbxvoLW9u3WT/vdEoWH15QkVlg9so3jZLjF9W7ne6X28qpKDhS+oN+zhc/SwAFqvw/pitjV1e/5gfBfP+mQkoA+zzLwJemz6d+YsWsau0lMmffkp8SAiWVgu1Sp0gZA2Ki+PhCRMczr1hyX/JjounoL6OUYlJXDFQiI5+dfPvFNbX0Wwy8dDZ55AZFR3QvyHQFBRVY+qiHoOn9M/0T4T+H4H42FAiwtTU1vunjlduXoVbwfx4k7C4fNLgnQVakJ7PoMg4/jnhInqHOhelv125m6unn8WCp66koqaRpz5YxoKftnLXFcI9a8v+EwC8+NlvPHzdFAb0jqNfZPA6AAAgAElEQVSgvJbnP12B2Wzh2tnC2EetkhMToeXZ2y4kOlwYI+zJLeH5T1fQNzWGs7OFGhhLf8/hiyXbePDa8xjcN5Gt+wt491sheyk+yvOgvx4nmMcmhGHDhqhViLFhIzZe+NId3V/Eks9/59KbJmExWzm4M5/0AUlYLFbKCqpIyRAi3Mwmi/21SCTixJEy0gck0aI32rfHJkaweaWwCj5z/jiO7iui8Gg5WcPTOLAtj8a6ZqLiw5l8kWc+TEGCuMJms2G1CMvz8tYBVbXhKEqJ8LmuajlCtLKf36PMnSMiRN6HEHkfevXs4GC/oZalUa1fR5xmBgAmq+sHtCHA0ZryAEexAoQqRlDK5w7b6lo2Eqed51N7DYbdWG2dJ2khTsTonoZK1hu5JAajpb2QV13L5tbfrHBavnMCPSnCvM2SZVP5/TSbywhX9KPBeJwIxQDaFkEiFELqbZ3xKGGKTPZUvYrZ2oxEpMBkbcRms9JkKiJMnuFw7wqVBy76LpAEOsLc5CRStLukh0fywyV/4f/ZO+/wqMq0D9/TMzMpk957IPQuXelNQAQVEUVl115WXT91Lbjq2l1dy7o2bNhFRbGiNFGQ3gmQAAnpvU9v3x8nmWSSmWSSzATUua+Li+Sct01m5pz3/N7n/T0fZB3gnUN7ye2BYNeWwZHR3D5qPNOSe/5+Dp2YyaFtguWEyWh23Af9+Dkb2F8hLKbPSsnoslgOLQmDa43t8xTMiB3IjFjBQkMjV3Fw/iOOc2un/M3x8yBNPACfnHejR32qpC3jvCCx5/ki4kI7tlfLK69hXD/fJTQ8XeH+2hUd8ieZrDYRHRrEqVLXUbJ6kxmDydJpJHpvMiS6RVhUSmWMHO+ZdURIsJKSsjq354tLax3lXKGUJZGmuR6TtYLYwAsAIYcPQKAso8O+DQYzhcXeu18OG9w7u2sHR0fz9RVX8OrOnfx6+jRlWq0jUGJgVBRzMzO5cvhwlG2sfU7X1fLgeVNIDtEAsKdEECwbTSZemj2PvNoaHt+6hdfnLuiV1+ErvLVrQK1SEBN1dudo8jZ90qPZubd9cFR3yDlVxqTxfR2/H67LZW9NNicbiznZWEypvtqp/OG6XKZt+rvb9r6f9DQgJMp2R3NukDqzlq+LhEW8bZWHKTVUo7UaCJKqyAiMZ1LUUGbEjEIq6nzO/37eT7yd+32HZTZMea7TdgA2lQu2sI8eeQ+AlaPvIlUd6xivu7ECHo/XUzaX7+exrPex2YXFNpFIxL39lzIteqTbOoPDY5gQm0xevXDdrDMZ0VtMKKVyIpVqRkTGMSc5k2kJGR2G0g1IjeG6iwQHhtT4cGaN68ehnJbdeW9+uR2AS2YMY9Y4YedcYkwoC6cM4YuNBx2CeXiImlsudb7XJERr+GTdXo7nlTsE8883HGDG2EzmnzcIgJTYMHYcFvIcFJZ5fg84a+64j9/3GSKgqryB65e8QmJTQpaCvEqSUoVt9Wn940kbEEdKZixvPfUNKX1jyD5YQHCoikGj04lLEepsXLPb8fsXKzez6JrJrH51I+HRwU7lLK2ykYvEIhrq9ASo5JhNVlRBARh1vo2o8NNzDm7LZkiri/KxPbl8/r+fuP/N687gqFp477nv+XH1TrT1emw2O6n9YvnPmtuJVg7mVMMGAMIUGb0klv85kYqDSQ65AXHTRDo+0H2CVbObZHfeojN/Sm8QqpyAWKR0Erkrteu6LZhXate5PB6m+n1kvg5VnkdZ4+eO341NOw1q9L8Qquy91+BrwVwESD2MMG9mcPgtbCv9P2JUEwmRZ1Ck3QBNztjVxiMAxKomUq7fhclWz7jopzDZ6slvFD4Tamk8daYT2Gn53jSYvZNwqbfx9WKWu0SaPUUqFnPVoOFcOWg4vxTk8WVOFntKhclnVxMYpoRomJyYxoV9+zMsyntbdJfft4Db5ggPPE9e/ya3PrOU0MizO/+Bnz8PVXohqi4pWNOt+pKmB/XetC34rkiIuk0PimRoaM/Fuj6xHUeWnnAj4HqLjtrPiP1z2ThlxIS7FcwBjhWVMyz17Ek0NzUlrVv1zh3bh9Vr9zBqWApDBzp7/+87VMDqtXsAOH/6ILdtyCURpGpuIEjeNUuik6crvJpAMTO98xwwz6/4gnWf73Z5LipOw7vr7/aor8SQEB6bMaPzgq1QSmUOsRzgZI0gWO4uKeLWH74BICM0zGVdqUTsiGKHJoPHbpgAf7lqK689+S0AFy4bz/X3zutyG51xMq+i80IekBDXvXtBT7jo8VXMGNaHG84f51F5o9nChY++y6PLZjMyI77H/cfHeO81Z7fJD/Bp/ia2Vnpm1dRdlBIFh+tyefjwO1Sb2u8wqTE1sKv6GLuqj/Fd8XYeHXINITLv7l7qCqe1ZWgtBpfjbT1WgG+Lt/OYl8a7rfIIjzeJ5c3f47v7LelQLAeYldSXWUl9OyzjCQPSnK+VQeoAGlvprScKBEvZgznFvPv1znb1LVYbUokYvcHMhz/s4Zd9JymtFOy7rDY7jXojE4a13JPyS2scwnszafHCnOJ3KZhfsHh0p2UkUjHVFQ0UnCwnrX8c2no9A0amUHiq3JFQqpnm38OigtnwxW4iYkKw2+2O4wUnyzl5pAiAE4cLsVpthISpObzzFCKxCHEXhQc/Z4ZVT37Nv9feeaaH4ZY9Px/j3V9X8MK9n3LNfRfw5hNCBm2pKICBGsG6oER/4EwO8U+BzpxLpW49dqyIkJCiucllOavdx4J5L0SYS8UhRAVeQGnDJ45j1fqfaTRlESgf0KW2zNZqShs/bXdcLe9PsKLjm+vZQnzwcifBvJm8mufQBExE5MVV+47wtde0rBuWIkGyFMIDhnKyfjV9Qi4jMmAkm4uvw263E6sSkg+FBwzBaK3laM2b/FryNwKkkWgUQnKrYHkqMaqxbCxajloqTNgDZd1Lenem8bUli68E82ZEwHmJKZyXmOI4Vq3Xsb+8lAq9ljqjgXqjEa3ZhFQsRi2TE6UKJDE4hAHhQlBCeA9sVzri0PYTDBkvfGZ+/WYfO9cfJn1QIuExIUjcJBu8f+W1PhmLHz9tUUikmKzWbgtoVQZBcA8N8G2OEr3VRJVRy/H6UlafFoS3R4Ze6JW2+ydGIRK5F/1PlFS6PuElThS7bl8sEtE3rn0ukj8y/ROj+HG/e2/ffaeKz7hg/sCm9TzalOxzyReftDuvlMpYPmwE5yWluG3jr5dPYO+hfG699yOSE8OJiw7BjhBZnl9YTZ+0qKZyHXu22+yC2GK21VPUsBqASNUU1DL3Qn5RiXetIFISz24rk7b6dnqTOD4wIoqnp8/qsG5yiIafT+cBcFH/gWRVlDuJ72cTJWXeeV/jvCge+4rma7VM6l6vqtcZkDXNsZTyjnf2xcd67zWfLnSOIF+YcC4TIlvsZ09rBRvBT/I3CX0rI7k8xbUlFoBU1LkmV22qZ8Wht6g3awmSCXPZc8L6ER0QitFq5mDtSU40CrpfVv1pnjn2MY8O/muHbS5Jmsqs2HOoN+uoM2upNTXwWNb7nY7FE7ZVHmFX9THHeN2NFeBo/WmePvoRjw25pkd97qo+xiNH3sVqtyFCxP9lCgGEM2PO6VG7XUHZQb4Um92OoSmfyfUXTWDeue01C6lE+Cw8/PoP5BZVcd9fZ9AvRRDhFXIpV65o//60Xd7r6DvjjrNGMB80LMmjcsvvmotILCIxPQqb1YZYIqbvUOe6UxeOcvw8+YIR2G12hyd6M4npUdz57FKnYxkDE5B044/oxzNeuutDik6WYdCZGDllAMvumc+xPbl88sIPAEikEmoq6olOCOOu/y1HJBLx0X++Z+/PRwEYO3MwF900g7xjxXzaVCfnwGn+ecXLADy0ShBBaysbeOyaN6guqyMqPoy7X1nuWEX7/r1f+XnNLqw2O0PG9WHZPfMB+OcVLzN0YiZZO09RXVbHY5/cCoAysGcPQAEqOWKJGLPRTJBGRXW5sP2w3HAEo1VYUQyQhGCzWxH3knD3Z6Sw4T00AaORigJpMB1xW87XopZC0juX3LigqyhtWA2OyF87J6v+yeCYDxCLPN9yfqr6May29tmv44Ov8s5AewG1vB+hyvOo0W9xOt5oOkJuzROkhfVOPgtfL8ZIXEwoN546xYu//UawQlgovmXsWEYnJDArsWURZFTkCsfP/UKXU9c4nTGJzhGLComGqfFvuex3V944vjgSzOy+QuTBzWOcE3itP3mSE1VV3DDaeVF8R0EBQLu+OqrjS8QeTMh7QrMY93XxlwCEyEKIVyZgtVupMJYTGxBHmDyc/bV7mRw1jZONORTqC6g3C/eMQSFDKNIXIhcriAmI5WRjDomqZDIC+zj6KDeUcbj+IOlqYTu6FRtydSFjI5JIVg/mx9LvmRkzB6Dp5/GUGko43CBsgUy0JVNlrEBvNRCvjEcqklJsKCZNnY7RZnTZpyc8d9sqp9+tFhvZ+3+fOxH8/PGIVQfRYDJyorbrUdRWu53dpYUA9An1rWh2rK6EG3e8h0Is468ZwlbkOfHeyYGjUSvJjI/iWKFrS4NDp0up1RrQqL27KFBcLUSGnSqrdnl+QGI0qj+ZhdPoPh3vGNiZk8/yaaM6LONrbjmn5T6fFKzhoUlTnc7rzGZu+v7rDgXz4CAlrz97BR+v2cWW33LYf1j4HiXEabjuynO55ALhNSo6sZ/JrX2NYdH/43jV42gUgoVBdvVTDI9+zW2diqrOPc49RSaTeCQ23vTABSy9aSr1NTrqa7TkZpey8pmO7R58xchYYcHll/w8bvpeCOSakpLKJf3bR/NPTk7l62wh0vWKLz/DarPx8ORpvTfYLlBR1f55pTvEdyMpYG8TIJfy/cMdC75Prt7EjOHC3HzKkI7t9eJjvfeaK6sasdtbFmqGhzrPGffWCAuCzYJ5qDyQWT0UbXdUCVrR9OiR3JYpBCSqJM7BtF8UbuHlHGEO/lvlEXIaCukT5D7IRyqWEKnQEKkQvt9Wu81rgvmGsj1O4+1orADbq7I6HW9HHKg9wT8PvY3ZZkGEiDsyL2F2bO89Y3mCWCSiT5KwQH6qqJKoMPd2bNsO5nLLpecytG/L7gqTxUpRhbPNV0KUhuwC550nuUWu5xsdcdYI5p7SWvgWSzx7wG0rlrvDW2J5Tb2Ob349wi/7TwFwsqgKncGEOkBO36QoLpk+lCkj2z9w5pVUs/jedwC4+ZKJXDV3NGs2H+SzDQfIL6tBLpOSHh/OPVdOIz2h/cRcqzex6rtdbNqdQ3FFHQq5lIHpsVx5/ihG9Xe9INF6rJ6Os7vc+PhipDIpNquNq0Y9wBV3C9uxTh0RJkkrf3sYmVzKXQueJT+7FG2djqydJ3l6zR0APLj0ZQaNzSBzRCp3vnQ1AEd2ruDh92926qe8sJrHV9/m1JZcIXzUN32xk6e+uEPwa7r4BbL3n6bvMMGXUa6Q8cBb3rVyiU+NwmK2EqBW8NhN76CtFzwuQ+Up4NjG6xfLfY1MHIJGMZJy7feYrBXY7GaHRUtrfL2j2u7zHgTU8r4khlxHQd2rjmP1xn0cq7iNzIhnkYg7jiS1263k1jxJufardudCAs4hOnCR18fsSzLCH2Jf8YJ2iaCK6t/BbKsjPex+pGLPoyvsdgu1hq2UNX5OdODFhCrP67SOK0Hbm7jyyP4iK4sVU6YwMs7zaLTntm3jk0vd2xa1ZfmIESikUmr0rpORTU9PZ3p6+4n6c9sEj0FXfbmr40t84THeGknTrjW5WLjunBc5hR9Kv0UjC6VPUCZRiqYcLHZhJ0JOYzazY+byQ6mwhbnUUIJcLEctUbO7egdxygROa3OdxOu9tbuZHTPX8ftPZT8wI3o260q/JVmd6nJcBquBEJnw2c9pOA7YmdXU74DggegsWhSSALZWbnHZpyf868NbulTej5/eZEJ8Etk1lfyYd4Iag97hSe4Jnx0/TLlOSLZ3bkIKAD+8u4Uf3xeSStVXN7JyzxNdHtOeDYdJG5xEaFSLddHwsGS2z/HdAu95A1PdCuZWm42Nh06waKx7e4zusG6f+0hqgAn9U7za3++BgYkxRASrqazXujy//Xg+RVV1xIefOY/lmMAWISMhOBiVzHk+rZLJiFJ3bh+gDJCz/LIJLL9sQrfHIhEFYLJWYbHVkxB8GQA1hl0d1qn0krAKkBQfhtgDnUGukBIVqyGqSVwPiwruNcF87aVXuDx++5jxndYVi0T8Z+b53h6ST6j00kJITNTv3zLOZrez43i+QzDvjNho771ms8VKbb2O0BDf7Fp0x6CQVP7Rf6lby6BFCeexrfII+2qEnDrbq7K6LUB7g47G2zxWoMvjbdvckbo87j/4JkabGREibut7EXPjxvb8BfiAaxcKlkR3v7iW1Phwpozqg91m52RhJVabjTkThKjz+MgQdhw6zdRRfdAahKj017/YiqVN0t9FU4fw71UbGZ6ZwOCMOPYdL2RXVj4A4V34fP7uBPPfA6s37GflV9sJUgmrRekJEURo1BSW1bL7aD67j+bz0LWzOX+Ce3uEylot//lwM59vPMDQvvGkxoVRUF7LwRPFhLvIGF9Zq+XGJz/ldGkNidEaJg5Lo6ZBz+6sfHYczuOeK6ezaMqQDsfanXF6islo5tX7P8WgNSILkKGt0zkSYaY3JUuRNUURaCKC0DcayM8uoc/QJMeFJGNIErlZRWSOcP3g30z64MR2bZWeFm6ixbkV3HfJi46y+saWJE0Dx3ScJKY73Pq44Bt9wz8XcWBbDplNuyEiAlr8lCqNHT8w+Ok5icHLkUvCkUsiUUhjXYrlAJJu+PJ1BZPVt6Jca5I1t1Oj30KjKctxrEr3E3uKZ5MYcgMRKmEbpkzS4g9qtWmp1m+koO51tKajTu1JxMJ1p2/EM/RmskxvECBNok/Ekxwtv7ndufLGNVTr1hMVuJDQgIkoZenIJKGACJtdh9kqeJzpLXnozaeoM+yh3rATa5NHfKR6vkdj8LVgbrHZBG9JIL+2lue2bWNXYSEVWi1BTRHmz82ZQ7BCwQPr15NbU4PebObclBTuGD+e7MpKXtm5k8NlZVzzZUtUw+sLFiAWiVzW6Yz39u/n8yNHGJ+UxN3nChGRrfsBHH019+OqDsDHBw/y9fHjgPAgMDohwTGG/+3Ywdb8fOx2O0kaDU/OnNnlv5+vBfNmf3mTTZjYba38hSCp8IASIBaiNksNJRTo8snX5REkDea3ql8x20wAKCVKSvQlJGiSSFAlobfqSG8jXMcExPJT2Q9kBAoPSCEyDdurtqKRhVGsL6JQX0CBLh+JSOL4+URjNiqJMGkUi8RoLVrH2Kx2G4HSIHIajrnt0xNGTe35HMKPH19xab8hvHtkH1qziZvWr+W1mRcSLFd0Wu+H3Gz+uW09AGqZnEszhWjv2Vedx5BzhTneg5f8p1tj+vr1Dfz1X4udBHNfM3/UAF5ft8Pt+a92HPGqYG6z2/lmd1aHZeaO6po39R8BkQjOH9mPVZv2uDxvs9v56Jf9/N+FZ0cemb+Ndu25vGRgx7sfWkeg9gSZJIw9pVfTP/xhxzGL3fViQzPeikSGP4a42lVMVivfnWj/7HphZv8zMBoBm91OVU3H77unaEI8XzT1hI9+3s8nW/ZTXF2PKkBOZnwk/7hkCqnRzr7xBrOF+979np8PnUIqFTOhfwr3LZ5KoNL5fnTePa/QoDdgt8OL1y/gvEHO9kPLn/+U44Xl6Ixm7nhjrdO5a2aN4ZZ57efvIcHeFbcrKht6XTC/MmVWp/76EyIGOQTovCZrmDNFZ+OdECHcb7s63gBxy07y7IZC7j34OnqrYF11a99FzI/v/PntTHHuCCFY6t+3L+CttTt4Z+1OpBIxybGhXDWvJSJ+xbWzePKd9Sy8801Cmz67l58/inBNoFN78ycNoriynpc+3oLBaGbSyAz+fsUUAN792v18py2/L9XDjx8/fvz48ePHjx8/fvz48ePHjx8/fvz48RH+CHMfcMn04fRJimTiUGHFT9YqqdX73+/mxU+28N73uzuM3F6/M5tApZzVTy4nNqJl9bqithFNUPuVz8fe+pHTpTUsnz+G6xeNR9y0YnUsr4xrH/+Ef7+/kTGDkomPdN7C13qs3Rmnpxz45TgNtTruX3ktDbVafl7Tkilc4sZaJ6V/PL9+sw97k/dr9v48Rs8QVtuat7+ZDOZ2HvWu2kvqGwtAVHwYj31yK2KJGIvZ6lTWE+senfFXquqexGQ+jlgcjDpgOhEh9yMWO0cYfP76Jpf183NKuei6KWTVrsFmFyINq4wnmBRzX6d9F1ZcBIDe+FunZV0RE/YKQaoF3arbFQorLjqrxlip20CEaho682mqDb8S3oF9hsTHyX6NvRhhLhJJGRD1OgdLl2CwFLaMwVLMiaoHOVH1IABSsQaZJBSLrb4pmrq917ZEHMjAqDcACJD+PpM6Rqhm0Sf8MU5UC6/bbm95Lyy2BorrV1Fcv8pd9R4j9dAarCeYrVbkEglJGg3Pn38+13/1FbeMHcvgaOes5P+cMgWZRILVbue8lSu5ffx4+kZE8O/Zs5ny1lusvLB9IjlXdTp7RcuGDSNIoSC7siWhW+t+gHZ9uaqTX1vL2mPH+GDxYkCIol/22WccLC1lSEwMa7KyeGHuXAZERXU7cZ/Zx9/N5qRF8+OE12vHjqjNXzAmIJblqUKyyyRVilOZb0vWMjhkKAfr9jE7Zh42u62d7/owzQin46nqNKc2/pLaYjnW/HOiKslhFSVCxIayHxkfMdFRJ1mV7LAMc9WnHz+/d/qFRXLVwOG8fXgvvxXnM+njN7io70BGRrd4YxY1Cl7bvxTmcay6gnV5OewubUnKdeeoiR5buez66SCr/rUGq9VKYt9Ybn3+KgKbIvGKT5WzcsWn7P/5KE/99TUUSjnTl05gztW+jyZOitQwpm8SO7LzXZ7fn1vMhoMnmDbEO7sx1+7M4kSJe9/4kenxJEee/V7CvuCSCUN4f/Net/ezj385wPkj+zEgMdrl+d5kW2E+Hx0+iM5sdhx7c/5CJiYmd1jvsuvfYM60QcyZNoioCPdetZ3RL/wBcOyvExgU8WSHdbzpYR4W2rn1zB8NuURyRqPJXVFdo8Vm847tpTejrfecKOQ/X27hmb/OIz0mnOoGHTuz84lqEwkLQiT6VdNG8t7/LaG0ppH7Vn3PG+t2cMeFzs+tW566Eb3JzLg7/+uyz//dtBCT2cqkf7zCk1cLdjrNUejuEh6GBHs3qr6iqoG+6b13fVJKFAwP7fzeFBXQck9psOh8OaQO8WS8rccKno9XLRV2reZqS7j7wKtoLYKbws19FrIgvvv2Vz3loyfa5z+7duE4hw1LayYMS2PCMPeJmwekxbDqEddWU60Ri0TcePEEbry4/eueO9FzfdMvmPuA0CClW+/vy2aO4OXPfiWvpGPD+ao6LY/eeL6TWA4Q2eYCm1ssTDa3HswlMTqU6xe2iOUA/VKimX/uQD7bcICvfznMDYucPzDuxurpOD0lc0QKHz//PQ9e/jJhUSGkDojvtE6/kakMHteHuxf+B+x2Rk0dSP9RwpenWdw+b8FIbpv9JNGJ4dz/pnv/8dgUIYnA+Veey70Xv4BYIsZus/HwBzejUHqWBNFiLaS4chn2pozsNquWOu172DERHeq87Xbtu78QoFKQMSiBfsOTiYp3vuglB05ELRXGVGvyJz/zJXXGfUSopnG67jX6hN1Pbu1/iVKfj8jFBhtPMnH3BKPV4tP226KQxjA0djVZ5TfSYNzvsozFVovF5j6zvFwSxcDoNwiUD/TVMHuNmKBLHYL/scrbHXYrvUGAxPeJy8w2QTDvCKPFwiObN6MzmVBIpdQbDNhstg4Xi7pTx5vkVFeTV1vLlZ995nRcaxLsSl5ZsIA3du2ioL6ea0eOZEqa+0mWO0w+tmRpm/C3rVjuitZlZkbPocJYzsxoIWmnO+G67fGu9jM6fKzT763za3RXLM87VkxY07b14LD2D4l+/JxpHhg3lXKdlm9PHafGoGflwd2spCWw481Du53+b82yAcP5y+CRHvVTW1HPS7et4oVNKwiNDmHN/35k5QOfcPtLywGIS4viwQ9u4aYJD3LPm9eT2BTs0VtcP2usW8Ec4LmvtjCmbyKBAZ1b1nRErVbPS99u7bDMtTPGdHj+j0xShIa5o/rz9S7XljVmq5V7Vn3HR3cu7fF70VP+s2Mbj02e7kgu7imhGhVvfvArb3+4lZHDkpk7fTDnju2DTNa1nE5a80nKtT9hbXo2ayYj9Da3dSqrvWfJEqb58wnmZyM1td4TP70pHuuMZkQiEZHBahIiQkiICGFIquvr+qDkaG6aK9hlpMWEc/7Ifuw/VeKyrLgDKw+lXOY4r5AJc8/OkidLJWLUKjlananT1+QJ3swT4AlJ6miP5qhycctc3OLjeX9HeDLe1mMFz8erliqpMtZz74E3aDAL34sbMxawKOHcTmr6cYdfMO9lJBIxYUEqKmobsVptbqOr1Uo5I/t1nCkdcBjXA4zsl+Ay8UhGgiDMHs9znczHk3E2H+suwWGBPPvNXe2O9xuZ2k7obv37pbfN5tLbZrtt98bHnRPGtW2vbdvTLx3L9EvbJzpomzjUFY36dQ6x3Om47ut2gvm7Wx+kqqyO4/vzObb/NFm7c5HKJKRkxjJm2kCO132NRCRMLq12IyPC/9Jp/366h8FSTIXuRxTSaGTiECSiAOx2MyJR+8l9Z2JjT/G1KOcKuSSSobGfUFT/DgW1r3QojrdGLJITH/wXEkNu6jRJ6O8JjVJYNDwnfhOF9Sspqn8Lq63rE+1AxWBiAi9GE+DZar1a5tnCXE8w2ax09uj2W0EBdQYD/503j1qDgW+OHXOcE4lEGCwWbHa702S8ozrdobkfoF1frugTFkZcUBDvXCTsspGIRFhsNke95JAQnpo1i1qDgVnvvGFhS20AACAASURBVMOOG27o8ph8nV8gsIfvv0wsI07Z+UJzT2n2VfcmL9/zMVm7hSToq/Y8RnjMmUtW58ePKyQiES9Pv4AxR/bxwt5tVOk7vydEKNX83zkTWdKvfX4gdxzbdZI+I1IIjRa+A1MXj+Omif/s9ri9zcj0eKYOTmfjoZMuzxdW1XHL61/x2o2LHEJMV9EaTNzwyhduk1oCnNMnkXH9Oo5Q/qNzy9zxbDx4Aq3RtYCVX1HLlc9/wn+vu5C4MO9ft6sbdWw8eIKLx3f8+U4ICqZveESX23/l6cspLq3lx81Z/PRzFg898zVBgQFMn9SfudMHexyderTyEZJCllHS+DXhAcLzXaM5p8M6Wm37Z7nucrZEmJ/IKmb9l3vZv/0klWV1mE0WNOGBZA5O5Lw5g5kwY2Cn/s4dtQl4rV1P2PLDIZ666xNsVhsisYi7nryEKfOGuS1vMJrdnusqIUEBXmtrwoAUFowdyLJnP2ZgcjQXjh3E3HP6ubx+DkyKcfo9WKWg0eC9z2pnhASrvCaY6w3eez88QSM7O76HnuLL8UpEYh44tJIKo/C8H6+M5ML4iT7r78/AWSOYH97vPqIBYNAwIVlige4UZcZCklV9KNLnYrDqiAlIIlQewZE6IfJjfISQ8GtzxddMjpzf7udmdlZvBEBnaSREFkaCKo2cxsPEK1NpNNcxMGQUh+t2MihkNF3lt0N5bNgpJMTILqigqk6L3mDGaLZgtnT+YB4b4dkDZVmrbWVf/nyIL38+5LZsvc7g8njzWLszzj8TFmuZy+M2uw6brQGx2HlLoVwhQx4gQ6GQYbPZMBpsNNYLiQKV0jD6hwhb83Pqv8eOzWXEc2ukEkEskYjDsdpqgbPz/ZFK4s+qMSaFXEOj6SgpITcBEKIYhtiFWA6+FzV7O8K8GRESEoL/SlzQUiq166jW/0yjSci+bbKUYMOEVKxBLolEJUsjTDmVMNVkpGJNt/scl7TPW8MHIElzM0mazhe2PEUiDiRZczsJwddRa/itKUHqYczWKszWKmyYkYhUSJvslgKkSShlqQQrhqNRjkcuiepSf70hmBssFugkyGtoTAwv79jBX9esIUqtpl9kpOOcWCRibt++LPzwQxKChdf98vz5buvozGYeWL+e7MpKh6XJiaoq/m/iRGICA7l73TpOVFfTaDJR3NDA38aNIy001NEP4Ojr5fnzsdpsbussHTqUZU0R5hKRCJvdzsqFC1FIJCxdvRq5RILdbmfZMPcPVB3h6+9mb7z/Zyt5x4pJHyQEAPjFcj9nM1cOHM6SfkP48fQJdhQXkF1TSZ3R4LDG0CgCSNWEMiEumZkpfQiQdu0xStRmV469mxZSvuS+i6ey60QhDXrXQs2+U0Vc/8oXPHLZTJIiuzZHOFFSxYMfruNoofsAHoVMyorF07rU7h+RGE0Qdy2cxEMf/+S2zMnSKpY+9yE3zB7HorGDkEt7FvRR3ahjZ3YB3+89zi9ZuSjlsk4Fc6PVyo3fraVPWLhjEfv2MZ4llYuL0XD1kvFcvWQ8R7NL+OnnLDb+epw13+4jI1WYY50/fTAzJ/cn2IUdKYBcEk6UajrV+h0kBF8GQFblAx32azJ5737vbly9gcVs5eV/CUkd132+u931pLy4lvLiWn5Zd4j+w5K497nLiPTgHtzcrqs2e9Kup2zfdJSnW4nlf3/0og7FcgCjF99Tudx7O0LFIhH3LZ7KX2acw9odWbz6/W+8vX4X79xxKeFBzsFInUWB+xqF3HuyoDffD09QiM/s366r+HK8b+d+T5Wx3vF7kb6CF3M+5++Zi33W5x+ds0YwX/vpTgCqKhqordaSnC48kBfmVRGXEOoQzMUiEXprIwpxADWmSqZEXcCm8q9IUS/Aau/al9NiE8orJCpMNiMGq54QaRi5jUeJDIjjlPYoVnt7P9+OsNnt3P+/b9mwK5sgtbBCOX5wCpNGpBOsDkClkPPsB5todDMRbUbqYTR3a3+7zOQoMhLdr/InRDlPbNuOtTvj9AbHjgg+kHkny1EEyIiJ05B3qoL0PtFUVjQw/rxMtm4+RnSchqyDBaT3jXFEvZcU1pCYEkFZSS16nYnktEgK8ippqNcTHhlMUmqEow5AeWmdo5xcLuWr1Tu5eOk4UtI7F7+kkhiXx8XikHZi+TVTHsditpLSL5Z+w5KZvGCkU/R/mDyDA9UfAKCUhnYqlgPEhL3Y6jc7VlsdVls1Nls1Vms1Vls1OuPPNOi+6rQtX9IyTvdjBHptnEHyAQTJW3yqIlTuH8R6GgXaGbVG14tWvYVYpCQq8EKiAtt7VHeFA9Ufs73iFUaGX82oiOVeGt2ZQSJWEa6aRngHnwtvECz3/bbpWqOeWHXLtei1Be3zAYQqlaxessRtG/+cOtXjOiqxmOfmzHHb1rMdnHPVj0Qsdltn0YABLBrg2m+uo9fjKTVGfY/b6AhfX1vOZkxGM3GpkZ0X9OPnLEAukTAvLZN5aZleb7v/6HReuesDqktrCYvRsGn1dkZMaW93pgpSUltR3+uWLACRIYE8sWwOf3tDmKO58tHed6qIi55axeWThjNnRD8y4zv+fh8tLOebXUf5+Nf9WKwdP1fdd/HUP613eVsWjh3EgbwS1mw/7LZMTaOeJz7byJs/7WT2iExG90lkeFocgFu7lupGHXnlNeSV15BTXMmunAIATpRW0tU1nIv7e8eur3/fWPr3jeXmv0zh06928+EXgi7w4hsbeOXtzcycPICrlownJso5ml4sEoQni62eU7UvA6A1n3Lbj81u7/Qz2BXkXbSQ8RZWq40Hb3iXfb+dcBwTiUUMHJFCxoA4ZHIppQXV7Nmaja7RyNH9+fz9sld5ZtW1xCSGedxuc5tAj9r1lD2/5vD4HR9htdoQiUTc/q9FTL9wRKf1vLkI4s7nuyfEhAZx3ewxLJs6ggv/9Q7f7DzKVdM8s/LqDhKxGJEILDbPP+tSL75uo/HMBIj5gSpjPUqJgksSJ7O6YDN6q5Fvi7eTHhh/Rj3Mf8+cNYL5fY9fDMCj93zKM69dhbgpCsNus/PkA587ylntVtSSYE5pj2K06dlVvZlAaQjlxiKKDHkAFOlzkYhklOjzKdbnIRZJHT/HKVMcbTVvIWr2EMrVHkMpUSMSiekfNIJ38p7hypS/d+l1bNqdw4Zd2SREaXhzhbDSHdpm9fnfH2zsUpsdERXWIo4MTIvhH1dN97hu27H6cpwdUZAnJHeTK2QEBgeQdaiQiy4by6fvbSM6TsPRw4VYrDZ+/ukIKelRZB8tJrgpQdLg4UnEJYSxf3cuS5efyxcf70Amk6BSKzAYTE51APQ6k6PcgkvOIa1PjEdiOUCgchaVdY9itzuLniHqZe3KzrnMOYFBcW6F4+cx0wYSrRxEtHKQ53+kdoiQiDVIxBogzen4mRbMW3A/RvCdYF5tFLb5/lB8hKWpXdsd4mtRS2s2obeYUUq7v7LcaCknQByMVOy9LYN+fI9G4fv3q9rHou8fmWqDb/92Ib3w/p+tJKRHU+fFJGt+/JzNPLrsZapKhK3Q5YXV3H/hs/Qfk8EV9y4gOCyQ2168mocufQGr1UZMSiS3vXB1uzYuvm0Oz9/6DkEaFfOvm8a0JZ5F7HqLcwekcmdTorln1vzssozJYuXtDbt5e8Nu4sKCSY8JByA8SEWQMoB6nYGqBi0nSqoorfXs+3/llJFcOOb3ny/Fm6xYPI2aRuH+tPmwa6scgPK6RlZt2sOqTXscxxQyKSGqANQBcgwmM1qjGZ3R5FXBeGqKc86Qj48c7FY7JWV1fLv+EOs2HqGsop74GCHIa8nCc7Ba7Xzx7V42b8vm3w9dzMB+cY56gyKFBJ/9wh+gQrcZgMGRC932Y/KykCf3YlRuV3j/pfVOYnlEdAgPvnQFfQY5W7c11ut5fsUXbP3pCJVldTxx58c8++H1AEhd7Eho3a67Njtq11WbnnJw5yke+dv7mE0WRCIRf3voQmYu9ExU9mZEs9SLiyDr9+fQaDDRPyEKdYCcowVl1OkMJHdxd05bmoVwq5tEp1KJmMQIDd/uPApA37gIRCIRKoWMiGDXdiCyHu5QaY3R1LuWLH5ayAiM5+HBy4kJCCMjKI5/HnoHO3b+l/MlqeoYhmjSO23jSEEZlz33ISlRoay992rfD7oNRouFc+56qd3xXc/ciqKLO/u8wVkjmDdTWV7fLklVaXGL526SKoMEZSpikYRfKr9jVNgkR/kliTc51bss6WaXPzczLnxGu2N27IgQUW4sol/QcKSirglb2fmCMDp5ZEY7ARqgoKwWrd47/lAAowckOX7elVWAxWrzODq9o7F6e5wdoQ4UIiDy8ypJ75NJY72B9d8fJCIqiHHn9uWhuz/hoacvxWa10dhoYMDgRArzhWSnAU0JOwNaJe4UiUSIm/4GaX2iHXUAjhwscJSTSMTUVDVScLqSxOTO/fekknjiIlZRVfcEJnM2YnEoIepLCQ36W7uyF103pZt/DT895WSD8Ll+7OB3XRbMQ+S+F7Uq9FqSgro3UbLZzXySu4xpsQ+QEuhP3tEbbDoqPKCeKK/m2knndLsdpVSGWiZHa/bddbXGx6LvHxlfR5hHKHueB+CRrc6L2MsGDSc1xPeRmFa7nTXZR9odn5/Rr10yU1fMu/o8Xrn/UwDKCqqITgz3+hj9+DlbeOC9jq3Dhk8ZwPApHfuWj50zjLFzumcv5S2umCREdVqtdv7z9ZYOI4+Lq+sprq53X8DD/u5ccF6P2vgjIhGLee4v8wD450c/uU0E6gqj2UJ5XSPUeXdMebU1pGiEe092VaXTuS35p1ky0DNff7PZypbtOXzz40H2HjyNRCxmwpgM7rl1FiOHpgDQbI99yQUj+PuDq3lp5UZe/fcVjjbKteuJUs9AKg4iNlCwXq3QbSZAGte2OwBMZi8L5mcgwry6ooHP3voFwPG8+/ArV5LWr/2OlMBgJfc+dxl3LHmFnCNFZB8uZP2XewGYfbHznLZtu+7a7Kjdtm26pI3nedY+wZb3oZtWYTIISTJvfvACZl/i+ZzbWxHmYrGo07w6XUEmlfDh5n0UVdZhsdmICwvm1vkTmTykc9GyLf/9ZhsA723cg7Hpc3zHG2uRSsSkxYTz6T+ucCr/yBWzeGK1MG9c/OT7hKgDuHXeBOaPcb1bs6sJdzuity1Z/LQwRJNOTICw22NCxGCWpcxkVd46LHYrDx9+l1dG3UFUwNm9i0smlnDfRVOp0eqpbtDxydYDZ3Q8Z51gPmpcBn+7+g1SMoRkH3knyhg51vmiIhYJX+jhmgntxPWe0tyeTCRnXER7Qb0zYsKFiO/c4up25xr1Rp58d33PBtiG9ARB6J00Ip2f957k2Q82cfuSSU4+VGaLlW0H8xjRL4EgVcv2PHdj9cU4OyLvlCBwjp7Qh99+zWbJlROw2+yImixMHn/+cgCmzByEzWZHLBaROcB5IrRoyRin/1vTXAdw1Gsu95cbpzr68QSVYiKqqG+78vL89DKNlu7bCIV7QdTqjJ4I5qX6w1hsZ9bW5c/GlP7pTv/3hPAAlW8Fc3+Eebfx9WJDeEDPry1vHdrr9PvU5PReEcwlIhErftmA3uIcMZQRGs6wqM4tI+Ysm0h+TikADyz5L7c+cxlDxvf1yVj9+PHjXa6aOpL48GBWfLgOnRcT6wHImhKt371oEosnDPVq238kJE27rh+9fBZDU2J55sufHYLZmeCDwwe4f+JkAK7/bi0jY1ueyY5WVrip5cyLb2zgx81Z1DcYiI0O4ZorzmXu9MFuk2gqA+TMnzmE/7zq/HxaodtIlNr5eb2k8UsiVZNdtuNtqwhvioye8sNnu7A05RmbOEPYkeFO2AYhQOzym6fx0E2rAPjqPUF4bStut223ozbdteuJYN46yC3nSBEP3vAOIOwCB7jpgfnMvbT983xHWL20a0Ii9q4dy6RBaUwalNZpuc/vu7LdsRvOH8cN57fsWL9l3nin/ztjWFocn9xzRecFmxB78bVbLe7fD6nI+TtjsPZOgOaflStTZ3KysZitlYeoNTey4tBbvDjiVhSSs9cqUiwWsWSiMCcwWix+wbwtV1w3mcmzBlFcIIi4lywbT5Ib78tAqe+SR4XKu+e3OXlkH179YhtbD5zi+ieEiKrM5Ciq67TszMonMVrDsL7x7M8u8uZwWfHXWZTXfM7nGw+waXcOfZOFrT8VNY2cKqqiUW/kq39f4ySYtx1rb4zTFRdfLtwMSgpruKTpZ3citrgL4rYndboilvv5fdBodhbMj1b+AxBhsxsRIUYsVtIv/F8u60YE+D7LdqVe1+26BdqdXhzJn4sb3/2SMemJ7Dst2DNVNGh5Y/ki1Ao5D325gdOVNehMZib2TebW6cJk9MPtB/hyjxBZOzYjib/PErKMHygo4fXNu5CKRVQ16ojTBPPU4jltg2baEaVUk99Q23GhHuBrW5E/Mr6PMPf9tcWXxAUGcbLWeXE9q7LcI8H805fWYW/aNqxt0HPPoudRBQUQnRjudvv2iz/+o+eD9uPHA+zg5dCbPx7Th/ZhYGI0/1q9ga1H87zS5vC0eB5sSu6ZFuPfdeIpl0wYwoT+KTz71RbWH8jxWT/9EqK4ZPxgl+eaxXKApYOGcO3wUY7fH9rimZ3nmu/2M2F0OhfMGso5w1M7nT+B8DynahJb9eZ8Tte/R53xIMeqhDm91Sbcxy223rMAs7mxxPAl+7e3WPOMm+Y6WrgtIyf2QREgw2gwk5dTBkBVeT3hrTzhvdFu2zZdoWraWZ6XXcr917yFtqElEOiG++Yx77KxHvXdGrnMO5KWpSmB/Z+R5sUSb9CRVZFG7pz3rVhficVmRSo+M/kA/uiIEHHvgKXcsucF8rSlnGgs4uljH7NiYPtFGj+uOesEcwCd1oi8KVNwYnIERoMZRcDvI/ttaJCS1/6xmJc/+5VDJwRh5sjJEiJDA5l/7iCuWTCW97/f7XUhOlgdwBv3L2HNpoOs236MgznFWCxWwkPUDO0Tx+SRGURonB/Y2461N8bpiuaLanLanycp2OnGX0kOnHimh3FWM2TtI92qZ8d58to/4klO171GfNBSJGIVp2tfw90jsjdsEzqjWNu1bcsF2h0cqvmMKuNJdBbBimhd0QPtyl3bV3hIEbdZuW8wl7K36j0KdbvQWapRSAKJUw5nRLhwowxTpLrst8p4gp0VKynVH8SGlUiFEBE6IvxKJCLXq9J6aw3H634gv3Eb1aZczDZhcUAmVhOhyGCgZiGpQS1brm12Cx+cugS9pZal6Z8QKHWdT8COjfdPXtRpuc5QSKW8cPn8dsfvnz8ZmUSC1WZn+jMruWXaeEQiWDp2KEEBwmvNKatyqnO8pILv/n41cqmEZa9/ysnyKjKiO37oTwgKYXe5766ppdqePSTWVzcikUpQB7e3E/sjY7ZZqTJ0fyHLE+LVHT9Anu1EqQPbCeZHKss9qvv2Y+3zVegaDORm+X5+4cdPZ7x9eA8fHz3ItOR0lg0YRlyg976rP+aeIKuynNvPcY4IfHnvDj45epBV8y4hJaRnXrYVOi33bF7HW+cvAuDRbZu5fOBQr+8+iQ0L5n/XL2T3iULe2bibrcfyuiwWisUixmUms2zSCMb1S/bq+P5MxIUF8+zyeZwqreKDLftZfyCHWm33F32bxerM+CjGZSYxe3gm/RI8m2e1FssBbhs9zk1JZz576wbC3USTu+O8cX0dVi1KWRJpmusxWSuIDbwAAFGTjWqgLMNtG972HDebe19gPXGk5d7ZWRR4M1KphKT0KHJa1c0+VOgkjHuj3bZtukIdFEBVeT0rrn+XhrqWz+1198xlwRXdy9fgrffVbqdL9rZ/JMy9JJgnKgW9J0imosGsQ2c18t7pH1meOsdr/ftxRilR8K/Bf+Wm3c/RYNGzuXw/GYFCboLLkqed4dGd/Zx1gvmqVzeR1cpnetDwJFbc/iFPv3rVGRxV10iJC+OZv13g9vy1F47j2gvbTyhSYsPY+U7Xkoy2Ri6VcOmM4Vw6Y7jHdToaq7tx+uk5frG8c6x2G/EqDRp510TsGpMgfBXrWiJ5jZYy9ObTSMRqdOZT2LEhov1KtkIiJSxA6dNI3Zzaqs4LtUIskhKuyCBckcFp7W/UGHNJDTqPEFmCUzmRi/CcMn0W3xX+HyablqiA/sQoB6E1V3KqYTN5jYJP4Yy4R0gOdJ6glhuy+LrgDiw2A5EB/QiRx1NnEibD3xbeRXKg6+vCkZo17Kl6F7k4kHBFGiqpICDXmYso0u2lSLeXKbH30Td4luO19QuZx96qVRyr/ZZREctdtluo3Y3OUk2ienS3xXKAESntExcZLRae+GYzOqMZuVRCg96IzW5DIup4stw/LhJ5U3RseKAKranzLYWJgb7bFQVwuofR65cOuJsBo9N5du2dXhrR74OChjpsHRn0eoEEH7/3vibSxWJiTo1n17LXf33Q28Px48drbCnIJbumkuyaShZnuo6o7REuImdvHjGGo1WeWVd0lQfGT/ZJu82MykhgVEYClfVath7LY+/JIk6WVjk8zBsNJswWKwqZlCClgoTwENJiwhieGs+4fsmEB/kuMCEpQsOB5+/wWfuuuGXueKf/e5O0mHBWLJ7GfRdPYX9uCftzizhaUE5hlWBaXl7XiNZowmS2IpdJUMnlqAPkhAUqSY4KJSUqlLTocIanCXMjjbrneXxCAzxbcO+qWA6gUsodEeYAckkEqZobCJL387gNhZcFc1MvC+Zmk8VhXQKgCQ/0uG7bsrVVjV5vt3Wb7pBIxDx883tUlgmf0/hk4Vlh/tKuR5Y3o1B47321WKx/SsHc0oGNSldRyN1Hizc/ry6In8D7eT8B8H7eT+yuPs6A4GQCJAoMViPVpgZ0ViNPDLnWa+PyFL3VSKNFT6NFj9ZiQGcx0GBpH1izqXwfKkkAaqnwTyUJIFCqRC31fU60rhKnDOeBgVdy78E3sNltvJn7HQBpgXGMCe/vso5ELEZnNPPqj9tZfyCHslrh+61SyBieFs9Ns8fSL95NsJsdvt4t5Nz4/LdD5JRUYrbaSIrQMGdEJssmjUDhpZ0hzRw8XcIVz3/MdTPHcPWUUfzvh99YfyCH6kYd0Zog5o3sx1+mC7ZRXUkeetYJ5gf35vHv15fzjyY/LKlU0qkNh9VaTm7pWBQyYUXTbjcSqJxLWPDtPh9vafUNhKj/glLhPrlgg+4rKuoeJDVmByJRAFX1T6GQDSRQOa/b/eqN21AqnCdnBtMeahpeJTb8jW6366d3qDMVIBMLk8oS/QHSg/yre674+4AZzI4f2KU63xUeAuCuPZ87jqWG/o3Sxi+x2nQka250KZY3kxio8algfqK2svNCrYhXjSReJWSJ11oqqDHm0jd4ZqdJPy12Iz8VP4jJpmNG3MOkBU12nCvTZ/FtobA4t6HkX1yaugq1tGWHx8+lz2CxGRgbeSNDw5Y4tXu87ns2lz7pss9BoYsIV2SQHDgOcZuEyQeqP2Z7xSscqP7IIZgD9NfMZ1/V+xyv+46REcLCqAjniWpO/Y8AZIac3+Fr7gxXiXx2nCygVmfg+aXzqNMb+O7gcY/a6o7PYWI3ves95XRDjU/bP1vYUVDAmMREr7XX04WGzpCKxcSogzoveBYTomg/+ffU9z0xI8bbw/Hjx2s0L/z0DY3wSU6AXSWF/H3Dd5RoG3nk3Gn0CXW/E+npHb+QX1+H3mzm7rHnkhkW4fLYPZvXAaA1mdC0EijfOLCbD48c4NXZC8gMi2DD6ZN8fvwIcrGEokZB0L7jnAmMjUvkrk0/YLPb2VKQx/SUdJ6aPMvlmNwREaxmweiBLBjdtTmaO05WP0Fc0GUoZSleaa81JmsF2ZX3Myj6dbdl9JZ8lNIkr7bZFV49sZbVBZs9Ln99+nwWJ00BhPnIyPR4RqY7BwU8vn89S9NHkBIU5nS8wtDIvbu+5V/ndu099yaFxTVERwb32ANcLJKRW/sqVruzHWNG6G0uy8u9KKwCmHvZS761qA10aQd+27I6bUtb3mq3dZvueO+l9VSVt+y2LTotXINffnQttz280ON+W+MtSxZoEo4VnZfrDd45vJeVB/ew4dLlHiVZ7wnejTDv/POzLHkmx+sL2FV9DIBj9fkcq893KnOmhOcHD73F3prOLa8ePfJeu2OJqijeGXN22gqOCsvkuvR5vHpiLfamQKHHst7j5ZG3k6hqL3zLJGKu+d9qsgrKyYyPJDZUeJbJKalk8+GT/Hb8NB/ecRl9YiOc6tlsdu557zvW7c8GQCmXkR4TjkQsIqekkhe/3cr6gyd48+aLUSu876V+JL+Mv768mtzyagYkRBMfHsyh06W8sm47+3IFB5BXr1/ksdXzn2/5zI8fP378+PHjx48fP378+PHjx48fP378+HHBWRdhLhaLHSseADabzaPMxzJpMolR3wBgt5s5XTaJYPVipJK4Tmr2DhKxhjrte2gCvbOtpKr+aRIiv/RKW358g8VspeBkGfU1OoI0KhLTo5A1bQUsNxzBaBX8hgMkIdjs1nbe035AI++6l7Ja1j4swGgppc64n5SQ6zFYilHL0t3WTwwK4UBlSZf79ZQTdV2zZOkuJ+s3orVUkBY02Sm6HCBaOYDBoYsB2Fv1LodrPmdM5A0AVBiOU208RZAsliFhi9u1mxkyhyO1a6gwtI/EDpBonDzKWzM49BJ2Vr5Orck5eiBQGkVy4DjyGrdSoN0BQJK6xfLFbNOT2/gLAZJgUnxgZTQ4IYbXNu3khne/JDJITWaMEGlvtdm477MfOVUh+DY3GoyU1NZz07Tu21SlhYR1XqgHlGgbMNusyP7giXP+8+s2Pr7sUq+15+sI83h1MBJPMpqdxQTK219X601GFyX9+Pl90ZyIu6PI754QrlTx3LTzOVRRxst7tvP89LntyuwpFSKetCYT/50xj9y6kTzl6AAAIABJREFUGh7/7WduGDa63bHrh53jcHn578z5/FaUzxsHdgNw7dBRHGtj9WKz23l++lyOVwu7217as52UEA11RgMr5yzkhd2/kRHq23uTJ6SH3XvG+jZZKyiqf5eMsBVnbAy+4L5h08/0ENxy/xNfkhAbymP3Xdijdo5WPkJSyDJKGr8mPECw9Gg0u48MFYtESCViLB5oC55gMPZuhLlK7XwvNuqFiG5PIsKNBrPT7+rAlrZctetplHnrdlu36Y6q8nqUKjmLlp/LF2//4ohu/2H1LtL7xXYr6ac3LVl0ehOB6rMjxLw545bFZkfh46m9Xt/57gBP6ciSpRmpWMITQ65lfdkeNpTtJaexkAazDplYSpBURbwqggHB/lwX3uaSxMmcaChifdkeALQWAysOvcXLI29vF9F/rKiCsEAVH9+51Ml6RWc0c/tba9menc8bP+3k6Sudd4C/vWk36/ZnMz5TeP+eXDYHjVrQdGq1Bu5852t2nSjk2a+28OBi79+nth7LY1BSND8+eI2j3+Lqev7y39VszxZ0iI+3HmDpucM8au+sE8wnzxzE3de/S3GhIFL8/Zq3mbtopIe1BaHdbM0HbIhFgqdWec09mCynsNt1qAImEx58FwAlVdcJtewG7FjRG7eREX8SEFOnfZ8G3VeAFaVinKMOQHX9s2gNm5FKYrHaPPMgDFTOp0H3BSFqZy/26vrn0Rm3AKAOmEFo0I2Oc8WVV6JUTMBg2oXFWkZ85EdYLIVUN7yEwXSQ4sqW7LZxEe8AYLVVUlJ1HVZrGVJpPDFhLwMip9cDOL0mV/2YzMepbngJEVKsVuE1SqUJxIT9F5eGjH6c2LH+CG8++TVxyRGoggLQNRgoyq3g6rvnMmH2EELlKdDkkWz3i+UuuX/I+fQNju5yvUBp+0lOaeNXxAZehNnWQIPxEGEB4xCJXF/+knxsm1Ft0FNt0BEW4NsEo8X6/QAkqse4PJ8cKExI91a9S6FuD82lyg1HAYhTDWtnjdJMjHKwS8G8I8QiCUpJKFpLZbsFogGaC8lr3MrR2q8BZ8E8t3ELFpuBfqFzkYg6n7yfLqtBpZCxN6eIWedkOo6/cpXrh7JQtZIPbnAtvj61eLbbfp5fOs/lzx3RVxPReaEeYLPbKWysIzX4zIsfnfHkZuG+V1hfT2FtHbUGPQ9Om8rU9DRe/m0Hv+adBmBaRhrXnDOK7MpKXt2+E4DDpWVc+7mwYPzaogUurXa6Qn69b61s+ob69n3vDWQuLIjqjIYzMBI/fryLSibDaLV47L3cVRKDhPwFqSGhDluUtpxsym2yq7SIW34Sgn8yNOGcrK1qd6ygvo5UTcs1PqUTG5nm609w06KXzmwiNjAYiUjMPZvXUW3Qs2zg0B68wp5TWP8WxQ0fMzDyv6jlfanSbaJMuwYRcozWIlI0t2G16WkwHQYgRXMrRkspOVUPEht0WbuymoCxHK+8H6tdC4BM3Dyvs3Os8h5sdhNmaxV9wh/CZjeTX/c/tKZsjlXeQ4RKsEiMUM0kt+ZZ9JYCbHY9qZo7Ucv7OtptadM7dOc+9vqx34gMCGRhymCMVguXbXyPL2YsZ+VxIfjgo5N7eWXCxfQNEQIB7t31LVqzEY2iZf7574ObyNcKi8Z6i5m7hkyhT0gk/9zzA7UmPUarhXuHTmtn69KaNXmH+CY/C4VEyqTYdBanDeu0flFJLRfO8Uys6Ai5JJwo1XSq9TtICL4MgKzKBzquI5di8ZI4WFOr9Uo7niKVSVAHBaBtEO6/1ZWCp3CwB57w1RXOieFDwlvquGrXkzbbttu6TXek949jxYuXEx0fSnq/WP71tw8AsNvtvPbEt6T0iWbQqFSP+m7GmwJ3Xb2eqIizw0Zv+aARLB80olf6qqv3nh2pSunZ+yESiZgRM4oZMaM6L9yGKVHDmRLled4+gHPC+rFhynOdlntm2I2dlukKzePsynjPCRNyM3Q23osSz+OiRNfBau64d8Dl3Dvgco/K3rngvHY+5SqFjFvOH8/27Hz2nCx0Omc0W3hn424UUimPXyE8RzeL1sLPAdy7aCqLnl7FVzuzuOvCSSg9sPDpKv9YNMWp37iwYG6fP5G7Vwne7Z/+ngXz8xeNZPjoVE6fEkTa5LRIYhM6f/g3W05TUD4fAJMlh4TIzxGLhUz3kZpHmzJnW8ktGU148P8BImLDW3znymvvI1h1KSDGbMmjQbeGhMjPABFFFYsxmPYjFgsXT61hPYlR3wJ2TpdO9uh1iRARpFpEnfYDmgVnvWkXetMuEiIFr+WiyitQKgTZKkAuXBzFIgWx4Ssd7chl/YgOewF9yS7iIla5+DsUER/zCSKRnMKKhZjM2YhECqfXAzheU4B8mMt+AIymLFJifkUkEryFmtuTyzLx0zGfvrKB57+8HVVgy0qdXmvkgStfY8LsIVjtZir0gmdXtHLQmRrmWc3SVPd5ATpiUKjg4fjNtFscx0QiGXpLPlKRGr2l0K1YDtBH45sos9ZkVVcwMc63q+Zas3ANdZckU93qeKO53PGz3iIsVjYn7HSFUuL+Ab1Au5NTDZuoMp5EZxFEALNNj8VuwmY3u6yTqD6H4P9n76zDozzWPnyv72bjHuJGQnAp7rRQKJQW6u5C3U69p3bq9FBvT6kbtFShpYUCLe7uIQJx13X7/niTTTa7m+wmG6Rf7uvq1TA778xs3snuvL955vfIenG8cZMwdnMlaqnwkH+0TvBqzfTQv3xfXgn1GgPB/iosVmunvMa7i0C5gmg/f0q1HSdF6izHaqu6JJiX5Ffw1r++6dIY7nrl8nZfN1ksrMnJBeCPG6+jTq/nqsXfMTk1hR1FxewoKuLry4XTDTcu+YGz4mIZGBPDqzOEhdeODz/mw7ldi0przbG6ap+15Yq0bt4oORnozM5/uxar94lSS49XsmLRJo7tLaCxTsuwyVlccb/wt62p09FYryUsOgipjxMB9dCDO+L8A6nR67otd8mJeiGxXV5djV08b0tq07qjb3gkr05q2ajdUVrsVLa1pJCtJS0PqAUNde3270qIFQEGi5lnxk5B6UXiq+4iLvAGGo2Om/A2m5WsyNfQGI9yvO5dotSz3V7ftq5IJEckgqzwBQDU6jdTWPcxICIz/BUAKrUrqdSuICHoduICb6Cs8WfSw/5tb7PesBOLTUNWxAJ0pnxya14hLugme7stbfqGW1JncVXiOdSZNA7/1Rob+CBnqctrZif249Ftv3JhUn9WF2czJbY3ADdlCM+SR2pb1nY7KgsRAW+OnsPm8uMsPLKZnZWFaMxG3hwl+EbnN1Tz4p5VXJQ8gGC5kmeHnkuBppbnd63kg7EXuxxDjUHLV8d28O2Ua+1zbWXRkQ6v91PJkUq6HizUnC/HbK0nt/YdADSm3Hav8Vcr0PpIMK+udU4E2N1kDIhn5wYhij73sHAiNim9/QAjs8lCQZ5jkF96X0fP+7btdtSmq3bbtumK/sOSiIoVniNGTcniinmTAfjqnVWYzRaev/dr3vzuDiJjPN+UigzzncBd19B9eaxOV/QGEwaj705LhHuRNLaH0xexSMTZA9JcvpYcKTxrVjU6fgbuziumTqtncHIvQv1dBwemRoehkEoxmM0cLqpgcLJvHUH8FDL6J8Q4lY/LSqZ5SZRbVk2tRucgqrvj1K+S2mCz2oiJC/VIJG9Na0uWkqobMZuLUMj6YbMZqKh9EqtNg0ikwGqtx4YFUau3Xt3wFnJpEgF+5wOC4G4y51FU0RJ1aLU1YjELxxnlskya7d89FY9tQJD6agorLsRPIewCGU1HUcoH0ixiK+UDMJgONf0sCOZKhevoUHco5f3sArdEHC6M23LC6f00vyf7dS76ad1W6/bs78mm41hRb5qj1gXEpMUeA0Ak8ixRQ36pEOlqMp9wei0qZD6B6vbFl9ZU1b9Gdb3jTlxE8PME+9/QwXXzqa6f73E/rQlSX01kyMtO5aI2DykikcgenF9lyEYpER6cKvVHCFdmuI3m7cE7FGLhbzvZv0WkSgy6hZLGH9BbikkOvtPdpUD3RwEDbCk90e2CeQvei1ogbPK5fc3FiQgbVv4sfobchr9QSAKIV48gyX8MAApJIDKRig3lb2K0uorGEdEn+Hy2VLwPwJG6XxkSdi0acyXF2l1EKDMIU7j+wm5LSkwYYpEI82kmljeTGRrZrYL53spSzklI7/T1NeX1/Pb5ui6NoSPBXCaRMDJBSNp5x8+CEHDdUOE771hVFf2jo+yzr19UFIcrKhkY47zw8RX7Kku7rW2A3idhE667aRb9WhOg8Dyiq3lOvff4t5hNLWuG2JSWjbu9m7J59rr3uWf+lZx75ZgujLaHHjxnRkoG+yrL2F5aiNVm6/KJlbZYbTbuW/Ub5dpGnh13NpU6LW/v2Mze8hLmb13PhPgkLsoUAifWFeQzb8UvAExKSOHizH4uy749LERa37liKfGBQUhEImr0Ot7YvomdpcW8sX0j4+KSiFS7jvYs12oQIeLBNb9jslhIDw3jweG+tzzrCmq58D0mFQditboSJa1u6+pNBaikLRGqKmkSAGZrA8eqn0UqDsRoLkclS3Hbv9aUS51+Bwcr7gXAT5bq0G5zm77ET6rET6okRtXynWGxWd0K5lGqAGwISTyXnTjYrgVLgaaW5ACh3SR/QazMaahie0UBd2/60V4vLTCcvIZq0gKFqPR4dTCFGve2ZSc0tfQOinD4u/Hk+omje7Ntdz6zpg1w27Yn9IsQktBnhj1BhfYvAPpHtJ84MiIsgPLKhnbreEp1zcmNMAcYOibdLmxvWnUQgMmz2o+U3L7+KMYm65S4ZOE5JzzKcQOvbbsdtemq3bZtesKVTYJ57uESNq06SF21hmfv/JL5X93qsS2MUilD7adAo+26VZw3kdbTl3zG+WmZjItL4tmNawDYW1GKUiLl8j4DeXjEOABuX/kLR6orWX2psx5x7+pf2VZaxIYrbrGXXbnsWzYUtegi+bc+6LL/21f+glomZ1pSGvO3rSe3roYIPzXnp/bh/rNGe2zR6MvocoDw0K4J5ht/2c6Xz3+P1WLF0rRmfOiTefQeKnxmVxZV8/Y9n1CSU4bJaGb6jZO5+H7htO/bdwsbmamDklizaAP1lQ1MuGQ0lz8iBNoc2pLNwke/Rteoxz/Yj3vfv4VeKS2bQ188t4S1SzYjlUnxD1HzxDf3EhQewBfPLqGusp49fx1k9p1CwuTVX2/AP0TNsz+2OFH8k4gIUruN/pY3JWy2tgmeySkTgpB25RUz4L7/dthHvdb3p1Ujg/xxtZRTK+SEqAURv7pRS2FV/ZkpmD91/zc8t+AKh7Ln/vUtT77i7KXrjrCgRympuhE/5WS0hvVYrLXEhP0Pi7W2yZZEoEH7AwBWay2hQS2+dXJpOlJJLLER3wASbDYTIpEEoykHAKPpMM0LNaM5x+NxiURK/FWzqNN8hULeH4Usk0adEKkOoDfuJizQcbHjSrQSIcZm0zeNoa0g5PzB2Pb9APb31F4/rtpyfD8qFLLedpFfwIrBJERON0evt4fZUuZSKG9GZ9zulWBuMO13KhM2JU4uF944gbtnvU5sSgR+/kq0jXpKT1Rx7YNCJF2Uqj+5DasACFWk9Yjl3Uyj8QjxgYKFUZ1hF0qp+53M1GBBcLXaOic0e8Lm0oJua7uZAJnw5d9odm0bpTG3RB75y1pEK5VUeJBqjg53hd7i/PCT17CW3Ia/CJT14sLE91BKnCNDNpS/6bbNzKAZbK/8CIDDdcsZEnYNOQ2rsWElw8PocoB1e3NRyKUYjGb6JUV7fN3JYlB4DH8Vth/91BV2d9F/PyYpgrm3d7/vaZVWEEAeGDeWlNCWEwsZ4eH8fjTbvs2zt7SUSanCArl5E1JvNvtM2DrRUEuNoXujiQaEn37z0Bu0ZhObip2/p0MUnm2K71l/hLcfXgSATCFl5pVjSOoTy9sPO55kOGtKFupAFRt/29MjmPdw0rgscwAL922nXKvh+6P7uTijv8/anpqcxtRk583ep8dO5umxk53K7z1rtEdlr01ybRfmqs0piULOlhh/IQLz4xlzWLBtI7cMGsao2ATMViuX/by4/TdySnBcF0vEKizWFpFTa8pzW1cpjaXOsM3+b71ZiMiv1q1BJU0gMfguihu+xmAuE64WybDaHL8H/GQp+MuzyAh/0V5Wp99ub7e5zVPN+Ql9+SFvH1qLiVi1e7Ey1i+IbeXC53iBRtgATQ0IIyskmpeHO9rKrSrOZndVUVPdWuLU7iN94/yCyK6vtHstA6QGhnd4/bzrJ/LYf37kg8/XcunsYQQHdc6msKjhe2IDLkIqDiDGXzhpXtiwiLiAy9xe48vo16rq7guAcMe0ucP48u0/0WmNbPjzAADZ+4tI7+c6uttisfL1u6vt/551hetcPG3bba9Nb9rtiOa13UMvX8J9l73H8WNl5Bwq5vXHv+fR+e7vY1siwwPIO9F1wbzGy1MDG4tO8N2RA1yVJegN8waPIL+uhrAuWG9+PH0O1Todb+3cxNeH9rZbd82JXP4uyGPe4BEkBgazvjCf93ZvIVAh5/ZBngVe1tT59qRERBf/xha/8jP3vX8z6UNSMDRtgkhanTycf/P7jDhvCE8veQDAXqc1q7/ZwPM//wuFn8IeqGHUm3jrro95ZcUT+AerWbtkM+/c8wn/WfqI/brzbj6bq56Yi0gk4uPHv2HVV+uYc4/wHBoQ6s/9H97K85cLp5e+yH6L24c9QmONBn8PLYzOJDpjlaLRC/ciKtifjF4RHdYP8fe9HZ5c6l7DVMpb5pHO6Prke1tOO8G8LTarjbIS7xJyyaVpKOXDqNN8RoDfHKrr36C48mokkkgUsiwALNYaymqE3To/xRi7H3h06NvIpEkE+V9NYcWlgjiNjdjwz5HL0pvqT6Cg/Hxk0nhkUu+iRIP8r6Om4V0AlPKhqBSjKKyYC9jwU05GKffEr11MgN8sTpTPQCYRovRiwj50W7vt+wHs70kk6tokVcgHthHMwWASdrs9Ecz1xq0dvL7Dq/EYjI6CuQgpCtnJtzwZO2MgI87uy4nsMhrrhKSfCenRSJt24yw2A2eF33bSx/X/gTK94BG6JH8nd2ROxIaFWsM2ghTCfCzX/E6AvC/iVqcnWqOUSEkNCiW7tvuSc+6uKEFvMaOUePcRLBEJEZ1mW8dHSWPVwzhc9xsFms0u7UyON25uqevX4o8XoRQ800p0e8DhEaiFMt0Bp7Iqg3CyJDlgvEuxvM5Y5Ca6XEApCbInJ82uX0mpbj/H6lchEclJD/RcwA0LUnPR+AEs3XSwW6IFu8qgiO6LlIauR0uHRAZy3rXjfDQa12iMRvuG1JMr/kQiFqE1mXhtxrkM6hXDiLg4rvjmW8DGhORkBvcSfmfN93JGZm/mfPE1cUGBvD17VpfGsrebo8vVMjkpQWd2hPmb2zdRb3B+GMkKd2331Jbv3lmJRCqsPV775QHSByQAOAnmUpmUxIwYjh8p7uKIe+jBc0KUKhZOu5BrflvCUxtWEa5SMynBfeTxP4GpyWnM37qB748epN6g56aB3vvH+gKTVXi+O177FvWGXRyve5sQ5VjkEueH7EDFEArqBOvIQxX3o5BGgZv8P0HKYZQ2LrFHhyulcSCSEKgYSlH91+jNJYhFCqRN1p1+snQMljIOVtxNlFqITg7zm0S1bgMHK+4GIFQ1gWj/ufZ2m9s81UyLy2Tq8ve5p59wernWqOON/cKJnp1Vhbx5YB1jo5O5LGUw3+Xt5u6NPxDnH4xEJGZIeBzrSnO5c+MP9vYmxqQyN3kg60pzuXvjDxisFh4ZOMVt/2FKNRcnD+TGtYtQS+WMikri8tQhHV7/whvL0RlMfLVkC18t2SJYtLgROZZ95f5kaKVuLbEBFzmUVeu2tCuYR/jQvuNEUfdaurlCHaDkmnum8sGLy7A1RXc+c+cXPPnWVWT0j3Ooq2nQs+DJH8g+IGxgJKZFce5FZ3nUrrs2vW3XU1R+cv79ztXcffE7NNbrWLt8L6mZMVxy8wSPro8IDyDvRGWXxgBQXOqd7rSpuIA/Lr6O1Fa5JYj3zoO9LQqJlBj/AEJVHYvuVTotX828hDGxwtpqckIK20uLWXU812PBvKS0fWsvb5BJJQQFdi1P16zbpvLClW8y+YqxnHeT8AwY2mTRYzFb2LfuEM//8rC9vsLP+cTjmNln2cub9Zf8AwUUZZfw4JRn7fXC2lj/bPtjN6u+WodfgIqS3HJGzWrR5mJSogiNDrZHpIslYgJC/dHU6/6RgnlnaBbZByTGMP86z3J8+Rp9O/ZCrUVyTzcEThvBfO+OfD57bzXZh0q4etYCe7nRYGL82X3bvVYiiSQxao1DWVRIi71GfOQvzteIQ0iLdR/lF+h3CYF+rqPaw4IewZvH3wC/Ft89sciPlF4toq6fJByl6hwkkmiksgHoNIIvuVTWj8igBzCb9mMRybHZGjCbspHJhyCRphAR/B8A9FrhgVPb+B4SSTRhamHMRv1KwgNuwGLOxgSopCnI/GZjtQpRpQrlNPTaxUhlfYkIvB2LaTemJtsak3E7UllfwgNuQK9dhEQqbBS4EuWVsoHUs8ihzNgkmHuCrlUEiCuMpmys1nq7H317WKy1mC2OD9pyWSYiUcfHxqXiMKSSaCzWGmy2ru9O7918jL7DUkh14+VWazhuFxVlIj9U0tM/Sd+ZwvFGQeh+98hf3JE5kQrNSiq1q9GbCkAkalcsb2ZQRK9uFcxNVgs7yosYE+PlhptcmE8FjZtJC3D/AAOQ4j+B7fJ4chvWktOwmtSAlsizcv0h9tV8B4BUrKRvcMvx1UhlH0LkSdQY89lb8x0DQhw/B7PrV1Cq2+fUn79UWDzUGI47vWa0alhX1rHlUVbw7KY+VnKkbjkV+iOkBU5GLvY8UiE9NpxP/9hGaIDfaSeWgzC3RHTWKKdjag16jjfUktjNyWu7wrd79zEmUZj7Vw4WInJeW7eenUUlJIWEcNvI4dw20n0Og39PcY6i7Cx7uhiR3xEDwqPP2DTZDUYDC7Zv5KO9rjeuh0Z75jl4eGcemUOFh8dmsdwdYdHBZO9xf+qshx66g8GRvfj5gquY9+cv3PD794yLS+bc5HTSgsNQy+Quj/a6IivMs02kU01WeCQfzWjftuJk0Jw4My30SafXwvwmAaCQRtMvSsg51T9qoVM9d3Uzwl9yWXdwzCKnMrFIzoCoT5zKk4Lvcipz1+6pwk8qY/2slnEGy1X8e8hUl3VfGe68wdwstLfl6SHTPB7DJSmDuCTFMUiqo+ttNhvhof5MGN3b435cYbUZsGFtCggTVlYWlxY+LXQ1+rU15ZUN6PRGVErXzxXNSTQ1DXrhv0bh/ydyWk556nUm1v6+Dz9/BX5q4eSWOkCBn78S/0AVKj/ntmdfNYqj+wpYs2wPAFXl9dx32XtkDU4kvV8scoWUsqIatq87ah9DYIiaR+ZfhlzhXv5p3W7rNoEutespMfGhPDr/Mp687TOsFiufvrGC5IxozhrfsQWurxJ1eiuYZ4ZGOIrlJxm1TG4Xy5tJDgphZ5nnwQdFpTU+G09YqGs7DG84+6pxjJgxmBWf/80944Tvh0e/vIuskb1pPgDe1vq2LQoXfzc2m42oxAje3+FspQtQmF3C5898x8I9r6EKUPHVf37AZGgRWCUSIfhDLGnrDNB9p9K7ysKVW/lx037emzeHhPDufzZMiRJU0uySrm9edZay2kasVhtiseMcadQbqWlsOU0WF9axvginkWA+YGgS8xfewEtPfM9Dz7Qs4sQiESLxmfq42TE2mxGxKACbTYvN1oBYIohOJuMW5Iqx2Gy1iERqbDaN/ee21wP2NqxWYXJazPmIJKHI5CORSJPQNr6Dn/8daBuFhCgG3S9IZBmYTXsQiUPs9TT1L7gsd4fCRRS5wRvBvIMIc7ChN+7CT9nx7rJrOxbPst8G+V9HkP91AFhtGiwWIWLAYq3Gaq3GYq3GZC6gqv4Vj9p7Yd5nhEUFMnbGQKZfMZrgNouzAHks5bqW31NaoOvFbQ/e02h23PCIVJ+LShZPgLz9jbfWDI6I4btsZ1HYl6wqyPFaMO8dOI1dVV9ytH4FNcbjBMsTMFv1GK0aZsY7+oSJRVKm9nqWZQX382fxM+xRLiZIHofWXEWJdg8ikfBlPznmCQJkjpYRE6IfYlnB/Wwqf4ec+tUEyGKoMwo2MlWGHDKCpnOkbrnDNckB49hW+REnNJv45cTdhCvT0ZmFBVihdjtB8jhiVAMo0bk/WhitEo7ChylSOVq/ArCRGXSeV78jGyCXSkntdXomWgxWKMkIieBwjWurHF+wpbTgtBbMJ6am8OSKPwH4Oy8Pi9VGsErJ7SM6l+i3K2wt695j9cMiO06AteTIfvLrvHtAa75uc7Fv7Z1s2NAYTeTWVbO9pAiti2SfzZ6YM1MzPWrTqDMR7OGDrLZRj1R+2ixNe/h/wM0rfqRc00iZtpFKnRYbsLYwj7WFeR1e25b8W/6ZPqY99OBrnnvEfRJXb4hWn8e24ivxl6ehabJO7eU/t91rInwkrDZzvKCazHTX1mvzLhSsCMuL3X/H19doePF+18nWh45N5/n/Xe9ULhKJeOilS4hoio79/uN1WCxWDuzM58DOfKf6KRkxPPr65Xb/cne0brd1m0CX2vWGIWPSueH+c1n46m/YrDZefmgxCxbN67CPlETfjKHIS8E80q9rkcVdtQB11b9IBFYvRNwiL90c2iPRyzyErqgqriGsVwhz7z2P6qb7cXDTUbJG9kYqk9BnZG+Wf7ya824Wos91DTpUAR27JiT1jaexTsO+dYfoP64PNpuN2vJ6Qpq89zW1GvyD1KgCVBj1JjYv28HQc7qWZ+FUc9M5wzladPLE6yEpsQT6Kckvr2F3nrBpM8jHST07wmA2s+1YASONtmBkAAAgAElEQVR6O24k/X2gJVg6NTrMI/9yOI0E82Yuu36cfffm/wdiaBKuTIZtiMTCH6wICdgsiMWhmIybkUgS7T8rVLMdrwd7GyJRCHrtd1ht9UgIRSQWjsSIxeHotd81eZ+DVJaF1VqPTD4MsznHXs9duTsUsixEIhk2W8tDtaeCudWmxWh0rCsW+SGVJjT5xAvojds9E8yNzoK5K0G/I8QiNWKp8OUjI95ebrGUeyyYJ/fpxTMf3cSfP2zjiWveJ7F3DOdfN46MgcIfboxqEDT9jVYajno9xv8PVOgbEItEhClaNhsaTB0nhqjUO/sJipCRX/tuU8JfCUnB89ptY4gHIldX+TXvME+cNcmrKGg/aRjnJ7zJ1oqFVBqOUGXIQSEJIFzhOkInVJHC3KSF7Kr6guOaTVQ1HEMhDiA5YDyDQ68EIFzpfG2Uqh/nJ7zFtsqPKNPtp9qYR0RTH+fFv4ZaGuEkmCslwcxKeIOtFR9Spt9Puf4gaqlwrDozaAZDw65jd/U37QrmzWQFz2Zd2esEyKId7GI84WhBBcH+Sg4eL6VPQqTT7vLpwOheid0qmK8pzOGSdO99eEMiAgk8CUcKk0NC+PLSi7u9n46o1mu7PeHnqJj2I6oBfjx60CG5k6f8lH2o40rdwJzegrVduAdHhQGiE8LJPSBsTNisNrdBEEa9idz9hSRmdK9tUQ89tGZl/rFTPYQeeuihk8T4n0+4ahxacwEqqbB2l0vaPweeGOdbm7Tc4xVuBfPuRCQWcf19QiT/2bOHsPy7rezccIzyklosZgvBof6k94tl7NR+TJg+wOMAxOZ2W7cJdLldb5h7/VhyDxezeuluNA16nrnzcxYsmoc6wH3ulPRWiRu7QnFprT2Roa+eIUQI0c2uqNR1zT+8o0hrTygs9l2EeVpy109aLZj3IaW55UjlUrsVyyUPnm9//cGFt/P23R+x9L0VWK02zrlmgj3pZ3soVHKeXvIg7z/wOdoGHTarjTn3TGfadcIppd7DUknuH88dIx5DHaRi8BTf5TTpLHf+7yf6JURTUFnLkNRY5o7qz5vLNlBYWYfOaOKeWWNJjQ7jia9+B8BotlDdoOXxS6aQEnXyTz4o5VJuPmc4839ey6NNY3rjhvPp3SqQzWaDnblFVDZomDaoa6eM3PHKT3/z/m1ziAgUnmuLq+t589f19tcvGe35RshpJ5gnpUZis9mcMq7+U0V0lfrqNiXNWd+F9yulH823qfXP7q9vbqNNAhy/S12UWwAJUlmLqKxQXeCy3B0ikQyFLAu9cU9L71bBQ9psKUIqcS886o07seHoMaSQ9UUh7+8gmOs89DHvSoR5dyBXyphxxWhmXDGaHWsP88X85TTWaVnw830crP0Ra9MmQ5XhGBOiHztl4zxduXDNe6ikclaec6+9bORvnTsKW9jwBcHK4UhF/jQYnf2325IREkGY0o8qvW+ToLSmTNvI1rICRkZ3LKi1JkyRxvQ4z38Pamk4Y6PuYyz3edVPhDKDGXHuN4huzfjbqSxEnsi02OfdXjMs/HqGhTtHyrgjI2g6rjzU22NAagwrth2ld3zEaSmWA4yJSeTjA9u7rf11RfmYrVakYu++N7/ed3odNe9u1hTmdWtyX7lEclI2304m8QFBPD5qolfXjJk5iEULhEXz5y8v5ep/zXQ6zmo2Wfjf099TU1HPpfd4bgXQQw9dZURMfMeVeuihh9MWmSSEIElIxxWbSE4MRyaVYDJbfNL/3oNFzDjbtbD22Z//8kkfP323jV+WbKOspBY/tYLU9CjueOBc4puiquNTIrjl4fP4cfFWfv5uK+VldYjD1AyZ3IeJ5wnWd+tWH2LNyv2oVIJVxbq/DnHDbZPRagws/mIjw8ek8/hzc+x9HsspY9veAoprhfxDkcnhzJg9hLmXj3RYX7fuMyommIuvFJJ/zpjdEvDy6x/7uO/VS9m9I5/f/jzAyr8PM2xkGnc9OB21v6N16pqV+/n60/UUF1QT3S/WZZ+uSEuORCSCri7rTCaL3Qs9NanjpIWeEOnnz18n8tCbzSilgpZTbxRORO8pLyVY6VkS9e7AZoPs3PKOK3pIWkrXBfPnfmr/7yYqMZznfn7Y5Wt3vnlDu9f2HprC63897fI1kUjEI587W3EBXP1US66El/94wv7za6ueare/rlJYWce/5ky026nszitGqzfyynUzOFFRy+s/r2XBTefzn6takoGv3nuMVXuySZnqmYe9r7lmwlAKKmr5dqMQJHfxa1/QOyaCsAA/qhq0FNfU06AzcN7QTJeC+dJtB6mo19CoN1KvbQmWfOXHvwlUKfBXKQhQKdyK3v0SotAaTMx47mP6JkQhFonYe7wEo9lijzq/ZMwZLJh//v4a/vhlF5pG4ZdjtdpISY9iwSc3neKRnSzaChxSNz9704a7cnfJarxLYqOQDXQQzJsxmA62L5gbnO1YFPL+KOWDHesZd+Iu+aBjf45CqEikRC7rnl0rbxk6PpOh4zMpaPKsS/Qfa4++rTU6ez73AMPDk7G5OE6WHhhJpNK951R5U9LP7PqWL3+ZOIhgxVDKNcsxWiqw2kyIRe4TPYiA0TGJLM3r3gjOX3IPeS2Y/7MR7veB2h8Ri2Re27EAGE0W7p4z1qtrHnv6ezZuPkavmGA++/AmZO1k1/YFo2MSUEqk6C3uk5J0hUaTke3lhadkbu1ae5jB4z2z6jjVrCnM6db2z4qM8yix76CoGPZXllNn6PgEzakkITCIj6fPIVDRcV6Q1lx8x1TWL90FwKI3fmfd0p30Hy3kRsk7VMTbD3/D7nVHKMotJ31gIjOu6d6ksz300JrFs9wnB+wOinSVXLP5Bfu/L0+cwk0pjt91ZquFvyv2sL5iH9mNhVQbhHVNiDyAaFUoo8IEi7kJkQOJUPjWfqva2MDmqgNsqRLWP4XaCmqMDWjMegJkfgTL/MkMTGBYaAYjw7JQStrPCeMtNpuN/XV5bK0+zN7aHCoNddSZNNiwESIXrDTCFUEMDenNqPC+pPl3blPSV/dhQqQgRvr6PpwOtJ4LruYB0KW5YLXZ2Lg1h517T9DQqGfuzCFkpkdjs0FlVQMAAQFKlArPErN5ilQiJikhzGci4a593Zt3Y++u43z41kqefOEiEpMjqKnRsHt7PuERjs9CvyzZzucL/+LOB6aTkdWLQweKePu15VjMVmbNFRL7btmQzb2PCpG4CckRfPz+as4+dwAvvnEl99/2KTnXjCY1PZrtW3JY8NKv3PPweWT0EewUCk9U8foLSzGbLVx+7dh2+wQc+gV4e/5y5l4+kjcX3kBFeT0vP/0TX3+6jpvvPNtep22/rvp0h9pPTq+oYK8tVVxx6KiQ38ZXgvmUxBQ+3b+Tu1Yt47LM/tQbDXy6bycAapnj/LbabDQYDTQYDVQ3RZ/n1FYTKBfWXgFyhV109wUnCqvQ6ow+ay/dBxHmPbSglEkdvMfzy2rYmVvEvz79DYDkqFAadQZeWCLkcwz0U1BRpyH5FESXNyMSwRMXT2FS/1QAFm/Yy978EnLKqgjyUxIfHsyo3gnMHNbH5fVv/LqB8jpnx4DvNracUheLRW4Fc7FIzOd3X8o7v29i1d5j1DTqiAr2Z+awPtx4tpCcWOJFUNk/M2y7hx566KGHHnrooYceeuihhx566KGHHnrooYcevOS0izDfvukYXyy7lwXPLwXg5nunsvCNlad4VD20h1I+iDrN507lBuMB1Mpz3F6nM25zKlPI+jv5jlut9RhNx5DL0t22ZbMZMJocfSgVsn6ITtEUv+6hGS7L41OFXVeNuZycBiHpnQgxwXLvkj/+f+D1s1x7HN+eMYFpvdwn8PytSLDmeWj7EntZfOD1yCVhyCURKKQx7UaXNzO2V/dHmP+ad4Qnh09GJfVt9MyZyt6a7wCoNuSRGTTTfgrDG/JKqgnxV+GnlBEW2LEf97qNR9m4+Rj+/gqKS2r57MsN3HTdeK/79QaVVMbYXkn8WdB93rmrCnI6HWGu0xjYvzkbgKqSOoLC/MkYkkRoU1KcttRU1PPhv78HYM0P21he+m7nBn0SMVktrCvK79Y+piSkeVTvoeHjeOCsseyrKGVd4XHWFx5nR2kxJqtvjot3FblEwkUZ/fjXiHEEK7w/NuwXoOTlHwRrrQX3f8W2Vfspaorsy9lXQM4+IXHpqHMHcN+Cq5H1JP3s4f8RJzRlDv/eWZPN/MOLKdVXO9Ut1VdTqq9md43w3fFh7jIuS5jMlYnnIBd37e+mwaTli+Mr+LlwA2ab68+eGmMDNcYG8jQlLC/ZQog8gKsSz+H82NGIRV2PwdpadYgPc38lt7HY5esluir7//fV5vJp3u8MCUnntrTZpPp3LalYZ+/Dh7nLAHx2H04HOpoLzfMA6PRc0BtMPPzs9+za15K4eszwVDLToxGJ4N4nFzeVpTHv+om+eWOtSE+J8lmEeUlZHWUV9URFuD/92hV0WiMikYjQsABiYkOIiQ0hq1+cU72vPlnLJVeNZsq5gj1MXEIYpUU1fPXJOnukt1wh5ZzpQlRmRVk9H727ivMuHEJqejQRUUGUldSRmh7Nlx+tZfZFZzF5aj97+7HxoZx3wVCW/bjDHu3trk/htXUOEebpGTFce/NEABKTI5g0tR8H9jomLm/br6s+2yM9NconEeaHs4X8NjOn+ibh47i4JF4Ydw4L9+3g9pW/EOnnz/X9BMsakQg+2tdiP/vzsUPct/o3h+unLP7Y/nOIUsWua+/wybgADmX7LpePQi4lrpfn9kjNfPbjFpau3seCxy8iLvqfd1qnK7T1qE+KCqFPXCTPXDHVXvbr9sMkRAi/t9vOHcm36/dQXqfpdJ9946PY+1/BynV71VccrP2N2fGvECRvOdGlkEpZ98qlBMmcv3u15mpWl77GzEzhFNeYzCSv+v/z6Zs7PXYAo9lMoJ+SR+dM4tE5k7rUFpyGgrlSJUcsFmM0CkfVAwJVVFc2nLT+zTYLBZpq8jWVlOpqqTZqqDE0orUYMVqFMTU/xMrFUuRiKWqpgjCFPyFyNb38QkhWhxOjCvEqmd+ZjEI+0GW5wdSe2Ghtslpp29YA5NIUxOJAuxc6gN64o13BXOjLcVF3Kv3LMwY5CuANtVqkMgkqtXCcqkqfTZBM8M00WBuwYUXUc+DDI0Lk7Sea85c62wVoTfnIJWFE+59PnWGXR/1MiU9FLBJ1q8dxnVHP4ux9XNfHu8SW/yRWlTwHQIOpmDKdkAQ4QBbFiIhbO9VeXEQQ+/KEo5QzRrg+6tVMWXk9r/53OUkJ4fz3lct57Y3f+XrxZoYMSmTIoO7dxJqamN6tgvmPOQd5eOgEr33M1y/bxZsPfk1DreNCSyqTctG8s7n20ZakOzabjeVfrOfj//yEpk4HgNLPt8fzu4uVJ45RZ+xeC5Sz41M9risWiRgYGcPAyBjuHDISndnEluJC1hXmA7C2IJ/smiqHa4IUSmRe3l9PxuEvVxCsUJIcHMLwmDgmJ6QQ4de1ZLDNmy3PfjWPE0dL2L3uCBVFNdhsNsJjghk0PpOkzK4JXj30cCZyXNsi1C4v2cLrR77DarO2c0ULZquFL/NXsqsmm5cH3opK4p1dUjNHGgp4bO+H1Bqdj0C3R42xgbeyf2Bz1UGe6nsNAH5S7zfVLDYrC44s4beSzV5fu7Mmm1u3z2de2mzmxHV+s7v5Piwv2QLg8X0wNz0T+uI+nA74ai50NA8++mo9B46UcPfNUzhrUCJX3/Gxw+vjRgjPfJt25HaTYO5b24htu/J9Jq62ZfioNKbNGsQ9N39MRp9eTJs1iCnn9kfeanO5rlZLTbXGbp/STEZWLF98tJaqJi0lIFBlf02uEK4PCha+32UyCUaDoHXk5ZRzcF8hiz7f4HJMZrMVTaPebZ+Avd+w8ICmcsd6AQFKNBqDQ1l7/ZrNVqTS9tc8A/vG8deGI+3W8YR9h4o8qrf8omsBKC6ppVdMi9C7cWsOR7JLuf7KMfayK7IGckWWa83khv5D7T9fmJ7FhelZTnU2bhVsBI+0ErjfO6dlTd56DAsme25puf+wZ+/VEzLSozuVP+raC0eQc7zCZ+M4U7BZbXzw3mpWrdyP2Wxh5Oh07rn/XJRK94F0g5J7senwcR78RNisHZuVzIj0eL5dL9gjl9TUo5BKCfRTUtWg5cMVWzhwopR3ft3I6MxEZo9wH3ToimFhV1JlyHUq15qr2VP9PeOjXHu+n0p8Ld2cdoJ5XEIYZpMFVdND9/MPf0tjQ/c81Oosgl/Txopsdlbns7vmOEfrS7F4uFBtD7lYSlaQ8IUxMCSBYWHJDA9LRSbuXm/cU4FC1huRSIXNpnMoN5rcJ1c0mA5htTpuhIhEiiZRXIRSNgCtoSWTrd64nUC1e5/J0y3h51+/7OSTl5bR96xkpsw9i3ee/B6z0cz1D89k0gVD6eU3BKNVEKQadMU9YrmHvDrsIjKD2s9E31Ywt2Gh1rCNIIUwH8o1vxMg74tY1L6wF65SMziiFzvKfbeQcMWH+7dyVcYgr4XNfwp1xgJqjMeRiVWkBkwGYGTk7SglnYvU2X6kAIVcisHYsT/4My/8TEREIK/+5xJCgv14+rHZPP/KUp598Re++OhmAvy7LwnP1IR0Hhev6LYo4kqdhtWFOUxNcL/R2Jb8Q8W8dNtHWMxWIuME77vEjF5oGnTk7i9g0Ru/o/JXcMld08g7WMRb//qGQ9uFRdS4WcKmzy3PXuS2/dOJxUf3dlypC2SGRJAQ0PkoGZVUxsSEZCYmJANQo9cx+NN3HOq8c84sxsadeaeTEnrHkNA75lQPo4ceTguKdVV2v/D5R77F1upJL0YVRqQiGIVETq2xgVxNiV2gbc2Bunwe3fsh8wfNQ+JlpPfummM8se8jdBaDy9ejlaFEKUNRSmQ0mLTka0rRtqm7rfow9+8WTha9MeQuFGLPTs01Ry8/ue9jtlY5B9lIRGIS1dGEyP2x2mxUG4VAmhPacoffk81m453sn6g01HNL6kyP+m5L832Yf+Rbe5vNnIz7cDrQ3lxwNQ8At3Oho3mwev0RLpo5hItmuQ4YiY0RolTLK+pdvt5V+mb4doN21brD3SaYi8Qi7npwOpddPYYVv+7hi4V/s/iLjfz3g+sICRXEboPBBDgLRc3zWNEkwLkK5msrcNqsNvR6E9fdMpGp57kWeKVScYd9tu4XsCcbdUdH/XYklgOMHJrMGx3W6pjmpJ/llQ1ENgn+7qiu0fDdz9u557az263XnXRlDFt25PlsHMMHJ7t9be22Y6xYfwilXJgTpZX1PHDjFJLjwpzq2mzw3DvLMZks1NQLPu4P3nQ2m3fnERLox7RxfTBbrNzx9GI+eO5yn43/ZLNm9UGWfLvF/u+Vf+wjPiGMK64abS/75sErnK67ffoop7LP7r3UZR+PzJ3EI3Nboqz/Kl2AUhJAjVHIvaCWhqG31DO11xNsqlhIvakEk1XPqIibCFO4vp+Vhhy2V35JlSGPP0teJtl/NKkB41hd+hpGi7bVM7ytqc83MFgbMFuNjIm8lQ3lH3Be3HMsyruZ8VF3UW8qQSb2I7t+NQASkYx6UxkjIq4jzm+wyzGcTE47wfzux4SFzu0PTgdg97Y8Mvp2LqGLK6w2GxsqjvJjwXY2VghHzpsjx32J0Wpmd42QzHF3zXE+y12HWqpgfGQmF8YPY1iY+w+UMw8JCllf9MbtDqVGcz42mw6RSOV0hc5Vwk9Zlt1CRSEf6CCY64w7nOq3pm3CT6GNUyeY//jR37y17AE2rzrAZ6/9xvt//Aur1cbjV7/PpAuGorfUUaHvXruPfyIzYvt1WGdgqHBMcf10IcN2hWYlldrV6E0FIBJ5JJY3My0xvdsF86LGepbmHeLCVO92fFujM5j4z1erAGjQ6nnjzgvsr9U06Hjpm9XUNuromxTtdUJMX/D1qp0s3XSQKUPSuWmGY8buOYn/A+CRD39jwCRhgewv7XzkT1iQmovGD2DppoNYbbZ2T/q8u+Bqh3/LZBKeefwCN7V9S7BCycS4FFaeyO62PhYd3euVYP7Th6uxmK1cfOdUrn9sNiA8qAFUldTy1FXvsuiNP6gpr2fpJ39jMVuJT49m3guXMmhcRre8h+6gRNPAuuL8bu1jdkr7pxu8JUSpIlrtT6nGu6i/Hnro4fTGarPy1H4hutZmsyERibkgbiwXxU0gUul4tF1j1vNr8SY+zfsdAIPVZH9tX20u3xf8zSUJnh8/rjLU8+yBzxwEUqlIwpz48VwYK6wV2o7BYrOyrfowC3N+JU9TYi/PbigE4M2j3/NQpmeJVBfm/ArgJJaHyAO4LvlcJkUORu0iUrnW1MifpTv4In8FjeaWYJ3FJ1aTpI5mavQwp2s6ovk+NIt9nt6H1vcAOncfTjVVTclM25sLruYB4HYudDQPamo0JCWEu329WcQ1mbsexOaK3mlRBAcJJ1Zr67Rdbm/X3hNU12gIDenaaaz2iIgK5MobxjH38pHccNm7/Ll8LxdfKQhnEZFBBIeoOXKwiKEjUuzXHDlYTERUIP5eBICIxCJS06LIz6sgPNJ98Ep7fTaPtzv6bY/YmBBiY0IoKqnp1PVt2bw9l/PPdb1pAHAsr5wvFm0iN7+SF18XbFTGjExDKpWw72Ahz7+2jNLyem64cgyDByTy3/dW0tCgw2i0cPuNEwE4UVhtj0avqGxg/tsreOHfc3j5v8uxWm1s25XH6OFpjB0lrOfbthsYqHIYw5iRaYwf3duj95d3vJIyH25KDR+S1O7rMqmEJ+44F4DDuWV8/uMW/n2Xs42tSARP3SlogX9vFZ6T/tqSzdxpA3nx/RVMG9eHnftPMGG45884pyMH9hc6lR084FzWzNHKSuav30BaWBgPjRvLkv0HuKift/qBjfTASWyu+ASAQaEXs7bsLUp0BzBZtUzr9SS1xkK7qO2KcEUqg0Iv5kj9n0yIuhuAEt1+QMS5sU9RqN3N7upvyW0QToooJYFMjL6XelMJ68reQSKSYbRqCJTHUKo/hM5cQ6zfQGwIn/fn9HqMKkMe26u+7BHMXWGz2TiRV4FWI0R/BwSqKC6oJii4fRuGjrDarPxUsINPctdSpPXNh6i3aMwGlhfvYXnxHlL9I7k5fRLnxPRDxJlv3aKUD3ISzMGKwXQYpdx5ouuNrgTz/q3ac7zGaMrGam1ALHa9y2swOkaYi8WByKWnblNCpVYQGKpm8oVD+XPJVuRNO+zN/68x5tEv5JJTNr5/Ms1RPc3WLZHqc1HJ4gmQey9Iz0ruw0vb/+5WWxaAt/dsZlZyn05Hmb/xwzqGpAsbi3/vyXF47d+f/sGs0VmcM9SzxVN3cMWUIchlUmobdR1X7iLpseF8+sc2QgP8XIrlL7wqHGFbs/Ywj/9rJhPHZTrVKSqu4cob/kevmGC+/sTZGubw0RK+XLSJI0dLqa3TEhzkx4RxGdx640RkUs9PEV2YmtWtgvnfhbmUaBqIUbcfHdPM/s3HCAhWc+0j59uF8mbCYoK5/vHZPHnFO/z04RpUagVXPDaDC26ejFR2Zp2cWpy9t1v/pkXA+SnOx2m7Sp+wyDNeMD+8I4+je46jrdc7RKG15fL7pp/EUfXQw6mlOVpZLBLzdL/rGR3uer2iliq5JGES/YMFceqB3e9haDotC/Bp/h+cHT2MULlnn/kvHfqaOlOL/ZZKouClgbfQL8j9+lkiEjMyLIvBwWk8vu8jdtU4fof9XrKVqdFnMTC4fUuqXTXZLCn426k8WR3D/MHzCJK5Fx6DZf5cFD+BCZEDuW/XO3Zvc4A3ji6hb1ASsSr3Yqw7zFaL3X/b0/vwwO73ALp0H041Lx36GsCrudC81nY3FzqaB5ERgRwvqHL5GsCeJtEoITbUuzfjIWKRiOGDkwBY8dfBLrdntdlYte4wF58/tOPKXrJuzSG0GgNpGdH4+SnIPlJCQ72O2PiW341IBFdcN5ZP//cXUTHBZGT14vCBIr5ftJl5903zus+rb5rAM498S2JSBGMnCutkq81Gfm45FrOVs6cPaLdPwCf9tu3TE0YOTeb7Zb7RejZtz2lXME9LjuTSC89ixZqD3Ht7S3T3xq05qP0UPPHgTPKOV/L5oo1otEYC/ZXcP+8cSsrqeOsDIdBppov2KyobqG/U8+JTc/jsm40kxrdEYbdt998Pn+9yDB69vx3OVhudJShQRe/UqHbrxLTy+Y+PCaHUjVjfqDUw/6NVBPorqagW1r1JsaEEqJX4+ymobdCxYWcu184Z6bPxnwra+pMDSCTutYBX163n8YkTeH3DRgD+ys3rhGAOCkkAUrEQPKho0tZqjCco1u3nj2JBJA9VeJcHq95UQrBcCFoMbvI6rzUWNrUlnIYNlMVQbyolyX8kx+r/Ji1gAoWanZhthqZ6Sa3G6I/J2v26gSecdoL5y0/+QEVZPcFtdmiffKXz4uK2qlxePLCU/MbTxxspp7GcR3Yt5rPcdTzeb7bdvuVMRenWx/ygS8FcZ3CR8FPeWjBvGx1uRW/chZ/SlT+hFWMbv3SlbACcwo0IP7WCssJqouJCufWpCwFhM8igExbUMrEfu6o/E34WqXrE85NEtU44tRCq8izKOkYdwMjoBDaWHO/OYZFTV8XCA9u4rf+Ijiu74JHLJ5NfJiwOWwvmdRo9hRW1TmL5LxsPUFwlLFJumyVEp7z3y0Ziw4M4f3Rf7nn7J87KjGf3sWIq6zS8c88c1Mr2o/IX/raFzQeF39P4AalcM7Xjh4YPlm1m44F8ooL9qWroeoQPCIe/5FIJjTrXx8vNTdFKJpMFq7VzoqlMKsFPJefm6ycQFqpm994TfPHNJkKC1Vx5qeeLt3MS0ghT+lGl9817b4vFZuPdvZt5bpT75MutqSypJX1gAhI3x17TBgiLp4TeMbyw+C7CYs68xDwak5HPDjrnz/Alw6PjiX25hi8AACAASURBVPX3ffKvrPAI1pzw3cPNyUKvNfL0NYKotGe9Z96iPYJ5D2cSv+cdxWqzMSOlaydtLogd61akbU2fQOHh85aUmbyV/YO93GAxsrJ0G5cmTO6wjT21OeysOepQdl/Gxe2K5a1RSOQ8mnUl125+0cnC4+vjf3YomH+RvwIbjt/BzSJte2J5ayIUwbw04BZu3PaKfdNBbzHyZf5KHu7TuWP6FzRF1nt6H25JEU5Gd/Y+nGpczQPwzVxobx5Mm5TFNz9sIzM9mnEjW6JETSYLP/y6kz/WCIFQd900xZu34xUjhwobT74QzAGWLN3B3JlDOuXh3B4ymYQfv91KSVENFrOVqJhgbrhtMqPHO37eXHDJcCRSCV8s/Jvysjoio4O58fYpTJvp/YnrUeN688wrl/L1p+v45jPh2UkiFROfGMalV7f4crvrE/BJv6767IgRQ1P4fplv1nlbd+ZTV68jKND5tHxHJCcKm3b+agU6nYnComqSEgThOyYqiNLyOqdrmoM5IsMDkYhFvPLG79TV65g9YxAHj5S4bLcrrFjjm7kPMGxgYof5+wrLWt5zQUkNMZFBLutt2JFLXHQIN148ih9WCN7cldWCle/Myf1YvekoFouV0CDvA2pXrzpATbUGuVzKrNmnNodYVr9YfvrBMeB0yFD3n7sqmYykEO+TqnpCiDyBCEUaU2L+5VF9iUiG2dpimx0gjaZYK9hd1hsFy66QJqG8OUdZvamEQFk0Ecp0Dtb+xsTo+yjW7kXaNG9OV4vi004wLy+p4/WPbvBJWyarhf8eXs7i/C1Oi7LThUN1xVy78QNuTJvALWmTfJJl/lTgzv7EaHL+IDZbijFbip3KlfKWnWOppBcSSQQWS8smh9643aVgbjTnYrU5Ck6n0o4F4K4XLyE41B+A1CZLIb3WyA2PzALAXxqFzlx9ysZ3pnO0vowlx3eyv6aIepOeQJmSrOBezEkcTFaQszduaeNPyIMiqNL9jdVmIkQ5CpHIs6jYuWn9ul0wB3hz90Zmp2R5HA3shItozZKqelRKOU998gfltY2k9grjoUsnMn14Jje+Kvh03jJzJGKRiE0Hj7PwgYvt18qlUl67bZZHXe/OKWb3sWI+fEDY+LnrrR8Zkh5Lv2T3fvP5ZTWs25vL549eDja46OnPvHm3bsktruKKKd27AEpNieSxh1p8UocOTmLl6oPs3V/glWAuE0u4rPcA3tnrfaIzT1l0dC/zBghj6mhuGXRG/No5Phvc5OMYnRB2RorlAB8f3EGNoXsjFq7K7J7vnz5hvk1SdrL48tVldqFcHahi/OyhRMaGIpac+afreujBbLVy28qfAci/5aFOtdF80vSShIleXTczdhRfn/jTbqkB8HuJZ0Lt922iu9P8Y5kS5d13Z5g8kClRQ1hWvMmhfHv1EaoM9YQpXG8cHmssYk9tjlP55YlTCFe4FlDcEecXwYWx4/iu4C972eqyndyQMp0IhXffUyJEnboHQKfvw6mm7TwA382F9ubBVReP5GhOGU++9LM9wd3r7//JM68tw2KxMnGMIAbPmdl9x/Gb7SPEIpFPTp2VlNWxev1hzh7vW0u2kWN7M3KsZ6dEZ80Zyqw5rgNWxk3uw7jJLWMLCvZjxaYn7f/+ePE8h/ojxqQzYkzHlhft9dnMh1/d5lR29U0TuPqmCU7lnvbrjmGDEgkO8vOJ1Y7JbOGPNQe4ZLZ7myepTIJeb3Qqbxs9nBgfxoEjgv5RUlZHdJNYrFLK7MlPCwqrm64Fo8nCPbefjULuKNe5ikp2N4b2OHS0hFwfJtpsvfHljupaDc+8JVjXVNVqeOCGKVTXafnsh80czCnlf4s3MGJgEkP7xvPDit2UVdbbk9sGqoUcZQMyYlm0bAfTJ3gfWW02WZj/ym8Y9CbUasUpF8wnTcoi+0gpK1fsx2qxMm36gHbHlBAUxDOrVpNXXc3Dv/9Beriz/3tniVH1pUCznd+LngEg0X8EieoRbK/6kjLdYTZXfkKCWvg76BN0LqGKJBrNFfxe9AyZQVNJ8h/Fobrl/F70LIHyGESISfYXnj9PaLbxe9GzWGxGxkTehlgkYb3xPQJl0SglQXYrlq4yIDGGvf+9zydttea0E8yDw9SYjGZk8q4Nrc6o5f6dX7OrOt83A+tGLDYr/8tew8HaIl4cLBj2q6VnVoZ1uTQFsTjAKZGnwYVg3ta/XCRqsiuROlojKGWD0FhWtlznxse8rR0LnNqEnwB7Nx1r9/UeS5bO8+mxjbx+8E+7f2LzQndPTSGL8rZxR+ZEAG7PaFmE2bCRUzOflOB7KWn8HhtWRHgmmJ+XnMHzW1d3u8imNZt4Zssq3p/sQw9tESikEp65bhoiEdzx5g8cK64irVcY4wcKkTWbDx4n0E/JWRnxyGUtn7uD0zw/9ZJbXEVWYhTNa7g+iVEcLaxoVzAvKK8lLTZciEYQQUov33zp+ynlLPxtC34KWbcJ5/UNer78ZiO79pygsqoBjdaI0WgmvIPEQK64KnMw7+8TEr5YusEmxGS18NYe4QH2hdFTfd6+K2xY2VXxb7BZKddtItpvAoMinmJv5YsYrXVYbAb6hd4PgFrm3ZG/rtBoMrJwv/PpJl8SpvTj3MTusT/KCovolna7mw2/7kLV9KDzzqrHiIr33QK/hx5ONY0m70QKV2QGCp+D3gq8UpGEseED+LmoJefPCW0Z1caGdu1AdBYDm6sc1+fndML3G2BYaIaTYA6wu/aYW9F1XYVz0mWRSMS06LM6NYYZvUY4COZmm4W15XuZG+/qVKp7MgMTOnUPgE7dh1ONq3kAvp0L7uaBTCrhhcfnsG7zUf7acJTCkhpsNhuD+8czaUwGE0YLgnkHAatdIjBAiBoe2C+eXftO+KTNr5ZsYcq4Pj4bd01FAwse+ZZnPrrR5euL3hVsPf74div/+exmeiV6b0X0T0MqETNtUhaLf2prFds5fl25r13BPDkhnIqqRv79orBxOm2K63xbo4ansXVnPk+/9AtGo9nuYR4dGcQ332/l2VeWEhEWgFgsoqq6EZEIXvrvb5hMFpISwsnKdJ+otvUYpk3px+jh7Z/waX5fviLAX+mRYD44K44bLx7tVH7f9ZO573rHDUZ3yTwtFisarZHR7SQYdcf+/YUY9F2LyvclIrGIW+dN4dZ5np2keXDcWHYWF5MVGUlqWChDenmfvHhitCAoT+vVslk2M+4FAIaHX+tUf3zUXYyPusupXCKSMTv+VYcyd9HpzT7nrbkudREAIyNaAqWT/FuSmfpLI+zjOtWcdoK51Wzlhrlvk5YhCC3iJk9fbyxZ6oxabty8kNzG8m4ZY3exvuIot24REv98MOKGM0w0F6GQ9Udn2OhQajA5J7bUtfEvV8iEyPJm4bwZpWIoGn2LYK437kQwXHBchRhMp59gfmBby7H5tct2M77VsbQJswb3WLJ0kr9Kj/DqgRUMDk3g9ozxZAX3Iliuotao40BtMe8fWcvbh9cAkBoQwdRego9wSvA9mKx1KKUxRPpNQ9xmrrWHUiLlst4DeG/flo4rd5Hfjx9l0dG9XNbbM5++jkiKCsUGmC0WZFIJeqMZVdNm5EXjhT7+u2QtQWoVV0xxjOJp62HdHumx4azalW0Pcj+YX8q4/u0vZOLCgzhWVGmP6jle5hu/QT+FDJVcRoPWtSWLL3jupV/YvecEl140nD6ZvfBXK+ze6N4Sow5gRpKwWbg0r3sSAX+bLYgTN/YdSmpQ+2Ll1j/3Mz16Xpfq/FD4FCZLPSOi3+BIzQcEyFMo0fyFTBzIgPDH0JqL2FcpLLJGRC/w8t10nnf2bqbOqO+4Yhe4PGMgMnH3eLonBYWgksrQmU+fxb4nVJbUMqgpV0CPWN7DP40GY9e/a3oHxHX62iEh6Q5CLQhJF0eEuY9yPVR/3B500LqdzpDi73yqD+Bw/XG3gvm+WmdrqTT/WK+jy5tJ8IuilyqM4lZe5nvrcrwWzE/2fTjVuJoH4Nu50N48EIlg/KjejB916nLsAMyaOsBngnlOfgVLV+zh/Gnufa99yWVNQlve4ZIOav7/YuY5A3wmmOedqGTz9lxGDktx+bpMJmH+887P8c2idUR4AC89PRfArcf4K89c5PDvT77awGVzhjN4QAIWi5V7HvmGm64Z57Zdd2NwR02txmdWRABnT+iD7CTkM/pj3SF+++sAF04d2K7Xtzt2bM/rhlGdPO5d9huNxqZN+qaUEQvn+DDQrgeXnHaC+aXXj+v0tXqL8BB5x7bPzjixvJmDdUUA3LP9C94bfn23PXh3B0r5ICfB3Gqtx2wpQSppWUTp20SYqxTDXbankjse77Ja6zCac5BL0xzK20axS8ThSCXe77j5knnPzrX/fHjXcYd/AwTJ4tCYhDmqlJyZ1gango+PbSRRHcaHo69CJWnx1A6R+zE2Mo1hYYnM/et9AD7P2WwXzPXmEiq1f2LDgggJarl3DwPX9BnChwe2Ybb65shQezy1eSV9QiMYGO76IdQblHIp1597Frcv+B6r1cbofknEhgsPpMH+QmRNkFqFDRvRoZ2PguqfEsPQ9Dhunv8tNmBM3yQGpMSgNQifyS989SfHiqowWSzklVRz5wVjSI4JZVRWIte9vIjY8CDiInzzdzB+gLCgXbrpIFabrUM/PW+x2WD3nhOMG9Obm64THsYrKhuoqm4kIqJzvtV3DBSOrC3LO9Qt5mHN8/ah9ctZMuNKn/9O2qKSRiESSdhd8QwGaw1JgZdwouFnAuTCIt9PGovW7GzL1Z0crC7nw/3OyaZ9iVIi5fos3yf8akYsEjEuPpEjVZUAqKSeb/ydSvz+j73zDo+qTPvwPX3Se0hPSEKAhN57byqCIgIiKpbVtey6a1/b6q7dte2urutnQ2x0RAVUmvTeQyCk95Dep8/3x0mbZGoyKbhzXxcXmXPe8553ck5O+b3P83u83FC6W6+B4MLF1YozBPMI947bLUV5tN82o7bAqlCbXJVl8lmEqMNj8JSa95Ct0FguUHyxur04GevRuWeeWM8wE8G87Xe0h+4+Dj2Nud+Rs88FS+eB0di10eOOMHVCAt4fuVFd45xM0o++2MvU8Qkd8r1u4t2n1tJQp8bLt8XP//M3t1KYW466XsPKJ64lJsF8FueRXRe4fC6PFQ/PobSoin89s54X/u8u3n5yDQa9kZP7BIu0sTMT+dNrS+zu92oiOjKAQQPCOX8x3yn9ffr1AYuCeVcweXw/Plm9j592nqe2Ts2SGzuWfWOJL9cfQaV2XvDFdbMG22wzZXQ8U0bH22xnjbmTBzJ3csevqSeOXd2CuZ+bkkcnT8RHadk+04Xz6XWC+cDBEVw8n0fm5WIAYuKCGTgk0q5t37ggRPg1ic4dQSaWEOsZTKxnMKFuvgQrvfGSKfGWuTWL11KRBIPRiM6oR63XUa1toFrbQKGqksKGSjJqrpBbX9YpP7ST5Vm8e3E7jydeZ7L8eEYeuWVVxAb7I5dKOJ0tzCgPCAtieEwYn/96gpVTR5KSf4XT2YUMCAvCz8ONI2k5DAgPRtoYsZ9eXMaQqFAaNNrmdiIRpBaWUlpTxwOzW1IisksrAdh/KYth0aHoDAbSisoYEBZEUkRLNeTWHuSt0WgvNQvmRmMDaq1p0S+l3PxNQCEfDkgAffMyleaUTcG8p6PL7aFMncYQ/+UApFX/1GgRcnX613cnFyoLuSl6uIlY3hqlRMbkYEEMX5/dUvAlr2Y1vsoxSEWe1GiSHd5vqIcXN8Qlsv5y+2wGZ6PR67l/13f8sEBIi/JX2vfAHRPiD8B7D5nONE8ZEtssIpujpKqWpdNMI2Ha9mEPd10zhruuMZ38clcIgt5Ld5kv4PfgDRN5EPsL+djD3rMZ5JVU4u2h7BJhWCQCPz8PLl0u4ujxDCoq6/l2/VG8vDr+YjTAT7DamBkZz45c63ZOneHklQI+u3CCu5PMp5ZuK/rAafsyGDUMDnwKiUjIlPKS96VCJaR/1uvycZd236SmzmDg8f3bunzCa0nCEAKUjhcgcoSP5l59kSRDJyZw/rBwXmvUWuSKq0Pod+HCHpwhmHvLOn7dCFa0LwBWo7Pu3VuirjT5bMTINb/aV+jLXqq0dWaX1+tUqA3thZpQt85ln4S5mVpRVGpqMTRb99n3fN3dx6GnaXsegPPPBUvnwdLffcSc6YnMnZZEZHjXFLGzF5lMwtzpiazbYt7601Gqa1T86+NdPPvIdbYbt+HCiSxAsCj6y79u48yhNDZ+/CsXTmbRUKfmL/9cQX5WKZ+8+j3P//dOu/stKaqiprKeF/7vLr7+p5C9HRkf3Ol+u5ra6gbys8voP9jx7I8F84Y6TTC/mFbEgaPpTLTD6sQZxMcG8+pfb7LdsAOUVdSxZfsZp/QV31eYXEuI62OjZc9TU6Pi8uWinh5GpyiureOl3XtQSFsk3H/Od/w648IxXAqdCxcuXLhw4cKFCxcuXLhw4cKFCxcuXLhwQS+MMF+/+iCnjmXQr7Gwwb6dFxgyMoZlNqxa9l25xObcjs0Mx3oGMzt0EGMD4xjkG9FcwKUzNOg1nK4QUg6PlKaxsyiZ/HrHPHq/yTrElOABjA1smc0sqqxhZGw4UQG+vLN1P/1ChIiM5Lxihse0ROxtP5NKv5AAkvOKGRYdRpC3Jycz8xkbLxQWqm5Q466QselYcnM7tU7H3dNG88ke06JoO84LJkl3TxMiwVftPcEdU0by6Z7jJhHmCpn5yG6NLhV3pgGg0pyjdcQ4gJvCfLSjWOSOQtbfJIJcrTkN7jc3f9YbKtDrTe13lPLu8Y2zxgfPb2j++Up+hcnnB/52E36KvpyrWAOAuzTAFV3uACKsRw03BRUbW5lbyMQ++CpGcqVuGxp9CQaj1iEfc4CHhoxnU1pylxRmbEtBXTV37VgPwJdzl+Ipc66dwQ+HL7B2jxBdMKJfOMMcKPDZ2/H2UEIJVNd1nVf1o3+cy3sf/MIzL26kb3QgD/xuOqfO5HA2Oa9z/Y6YxK689E5lJ9niHyf3MSMylr7e/l22D5VesAw5VfI8BqMWL1ksA/0f5Er9QY4XP4HeqCEpwPlVzC3xn3NHSC4r7tJ9yCUSfj/YvL3Y/zorn17In655A4DX7vuEP7y5HL8O2he5cOEoay+d479njiIVi/nzyInM62vql/zQzu871f+VesvWI/biIel4erVSIkckEmFsdd+o01m//9Vou7aIOYDGTBQ5QLWFqGv3TvwOhO1N6z4ZMTbvy1fmaVcf3X0cepqePA8C/D34Ys0hvlhziMSEUObOSGLm5AHNhTi7mwVzhzotwhzg5z0XGJIYwYJ5jr2TFuUKtkLhfYNM/s9Lv8L545m8+scvAYi0M6LXYBDOx6BQHyRSMe8+tZbqCiHq/7oV4zmy80KH+rVGXY1w3n/xz18Qi0XMXDiCguxSEgZHEhLhx9r/+5Vrlozh+68PUV+rYsBQQZsoL6lh9g0jcPNQsOajPdx052Q2rz5IeHQg/QdHsOWrQybrAa5fPp61H/+KTqtjxoLhxPZvsXaaPXUgq749SH5R+0yKjvCvj3cxckgUSuXVnSX33kc7UWt0TunrFidbxXQlp05kYTR0/Tt8VxLn78/8gf3xUVxNdQ6vfnqdYH543yXe/O9KRI2ql9Fo5LF7P7cqmOuNBt5J2ebQfmaEJLIyVvCfHeTb8SIvlnCTyBkfKFiHjA+M508D5nGsLIMvMvZzoCTV7n7eStnKt5MeMrEWcJcLF+r+YUHUNAg3pWHRYaQVl3GpsISLBSXN64ZFh3EyKx9vNwVikQh9Y0q6r4cbJzLyTdpdLi7luxMXUGtNL6J9gwRhZdXeE4zoG06Qtyffn0wh2Mf0AVQmjUIi9kNvMJ0Y0Ghbvq9ae6bNNjFIxJYreivlo0wEc5XGdHtNG3sXAEUvsGRJGh1r9ucmgpVJBCuTAChV238+9BTVGhVbcy4xJjiSg0XZJPn3Ia+2ihqtkIrc3zcIP4Vb87rc2kpqtRoSfAIZFey8v6/+Pn3YfyUNlV6LUtL+gUWl17KvWEj9H+jT8tAU6X0nckkAckkQCmmow2I5QIy3HzfEJbEhrettWQBOlwh2Syt+WsPqOUvwkjvv5jh/XCLzxyU6rb/eREZBGctnmi8w5SzGjo7l68/uM1k2eqTj1drbMtA/mBtiE9mY7rhtkL006LTcvWMjm+ffhrcTz6nWZFWvJ97nDgLdRmM06tlfeDcgYkjgX7pkf5bYmZsOwDun9tto2XlWDBhOmId5EVivy0Sr3oNUPhKMusZll5DIhHuAXpuKVD4Co7EWVd3nuHneh0Tan4baD3Hz/D0ABn0RGtVPKD3uQKc9j05zHKksCSN6DLosJNJ+Qv+taKh5G5E4GKlsAGJJRMv2mlPodRdB5IZEEi38DMJnabxdfVvi5Xv+r90ysURM30RhUu7Q9rMc3XGeuEGRBIT4IJGaD1B45uPf2bU/Fy5s8bdDu5qLZD2196d2gvkP6Rd7YlhORSaSoDG2PLur9Bqr7ev0XS+UWsLS2BRmnukcQWnGqq9eJzyj2iuYdxZHj0NP05PnwX/euJXC4ip++TWFX369wDsf7uDfH+9m3KhY5k5PYnzju5PMwj3C2URHBjBlfD/2HrrstD7f+2gn8bGCZUVign0e/cHhwjv3uaNCYdyi3HIAIuKCiU8M589vLLXZh9JNTl2jH3tehhBUJhKJ0Kh1PPDCjchbCb6O9GsvNVXCvutqVdz557kEBHtz5kg6UfGCGJ+bWYJOqyMvs5Qn3mgpVHnuWCZ7t58jbmAYSnc5UpmEqdcM4dBOQQfomxBish7g500nCAj2JjTKnzX/3cNf3r6luT+JRMzKWybw8jtbnfK9Cooq+Wj1Xv74u5lO6a8n2HvoMnsOtNdOOkJUuD8zp/beGg2t0Wn1HDrQ+/UWW1SpVaw+ddpk2UuzzReSNYdOZ2D9miNs33aGokJhIsnDQ8ngoZEsXzGRhP6m9Qv0egObNhxj+9az5OWV4+4m/N3FxAYx75qhzJln27veFheS89m/7xLJ5/MoyBM0xJpaFRKJGD8/DwIDvRg2Ipqx4+IYmNgzAX69TjA3hy0b2u0FZ8mqK7WrryiPAF4YsohhftFOGJljjA6IZXRALEfL0vnb2c0UNNiOOE+rKWZH0XnmhAon5PwRLRema4f1b545FouFX9Kry+YBgqe5wWBELBYxKLJPu+J3ieHBSBr9zK21A5iRFGfSbnBkiMViegr5UOpVe0yWqVsJ5m0Fbze59Yg8pWIkVXVftOrrPEajFlGj4KnRtb/49QYP89JC67PZFyo3YTAKURdl6jSmhjzdHcPqFCKgRqumj5snR4tzMWLk90lCscL/JB9mQkh08zqNQc8fBk/g05RjThXMb+k7midPbOT3h7/igf5TSfQNw1OqoEarIrmygP9c+pXMWuFa8M8xy5q3K6xdj0TkRh/PBcjEHS8u+diIyWzNukSDznmFUmxxuqSQFT+v5Usni+a/VUqq6vh46xHcFbIuF867gsdGCueYSu+c6A9zZFSV88DuzXw+++bmuhbOJNRjOhfL3ye39nu0hhrifW5z+j5scamilD/+KkSOdmXEvkdj9sdDQ8ZbbKNRbcPN8wEAVLUfAaD0vJeG2g+QKSZjNFYhEnkgkUYhlSUikfZv14dYEgII1x1NwxYksgHotGcRif2Qyschkca037FIidJjBQ21H+DmOap5e70uDURuiMW+zT8DiMW+9vdtgf0/nLLZRq8zkHo62+4+XbjoDKEeXlzWCFGbQe4eNlr3DOY8ve3FiBGtwTRz05x4bLJebLpeIZEzyi/BQuuOEe1hPlLVQ2o+iruz4rK57S3tyxLdfRx6mrbnATj/XLB0HgCE9vHh9iXjuH3JOC5nXOHnPRfYc+AS+w5fxttLOHYzJg3gkftnO2081rhnxWT2H05z2jODVqfnqb9vBODdl5YQGx1kc5tBo4Xgi5/XHeXVP6ymT6Q/YomYxBExnNyXyssPCu/Eo6cPZPS0AXz7/k4AUs/m8sXb2xk5uT/TFgxn/f/t4fWHvyIgxAexREz5lWpEiHjriTXoGgPjouJDuOPRee36nbO4c1HDIRGCJ/0dD89h/Sd7GT2lPyKRCIPegF5vQK8T/k4CQ3xMv/uoGHZsPklBThk3mQmUNLf+h28O0yfMD7lcytJ7p7XbZvbURFavO0xOXnmnvlMTG384xcQx8Ywc2v06Umcpq6jj7Q9/cVp/K5dN6JJaUZ2hqrKe9HQhozQ97QoZacWkpV0hN6cUnc60jlFdnZpZU19xqP/vtz+Gm5v91/Xy8jqW3PieQ/t46pkFzJozyOy624cP56fLl1HrrL8jvvTiZvbsEiaapk4fyHMv3IhWq+fpJ9Zw6mSWSduqqnr2773EoQOXefq5hUydLmiNapWWp59cy5lWz+vVjVlJZ0/ncPZ0DocOXub5F25EJHb8PDh7JocP399J6qVCs+t1Wj1FhZUUFVZy/lwuX67aT9LgCO67fyaJSd0rnPc6wXzE2DieffgrEgYK9iKpFwoYNd56Rd0NOcesrm9ifFA/3hi+DA9pz4pOYwLi+Hbygzx24huOlqXbbL82+0izYN4WsZUTtPW6thc0SSuRxFo7e/trQmlGMG8taqvbCOZKhfWbspvc1K7FaNSg0V1EIRN+Hxqt6e9PKolAIu5c4SBnsGXVPpTuCuIHRTBgeDTBbQraRHtOwkMqPDhVanq3cLCvIJMTJfnMiezHoeIcfORKJCIRlRo169LPAhCodOf4lbzmdR7SrklXmx8xhEtVxXyadoA7S7MAkIol6BpfUsQiEX9OFGZaZ4YOaN4u2uc+ajTJ5FR9gt7YQIL/sx3af6iHF/cOGs17pw927os4yJmSQhZ8v5oPZ9xAfz/LGRku4L7546yul0harn1tH56a0GqF80kma4ls94HbxgAAIABJREFU+vSXYyybMgx3hYyzWYWkFZbRPzyI0uo6pg6K5dfzGdSrtdSphZf2+JAA5DIJ3+49w+0zRpJbWsn0wXHsOpvGjCGW72lhHt48OHQ8b53cZ/d37gj7C7J59tDPvDpxng2TI8fxkfdnbMg/ndyr/RTW1XDXjvXUabs+uu/hYRMA68V5JdI4VLUfIZWPQSQRos3UDRsQS0LBqEck9kerOYzCbSEG/RVBxMaAXpuCTitkG4hECnTaZHTac0hkiRgN1Ujlo9Dr0hGJLYiARhXq+jWIxQHodWnN24vEXuh1aUhliRiNqsb9IYj19vZtgb9//ZBD7V246GpWXbOY79JSALgh3nJm1RNjJpMU4LgdwfnSYt481rnrdWfE4ga9xsSCDsBDat3Wwltm+nctF0v52+C7OjwGR/CSmi+sWa/vnH1JW/sTESK8bPwe2tLdx6GnaXseQPeeC63pFxtMv9hgHrxrGmeS8/jvqr0AbN52utsE85jIAOZMT2L7LudlklZWCbZADz+zhndfWkpcjG3RHOARMxHfKx6e027Z/X+9weT/Jv7+6T0mn79872du+t1Uho6PR68Xnn2fWPaBxX47Q9ZlQbA88PN5tFo9RiMMHBbFNx/uJiouGIUFwVEkEhEaHUBFSQ3evu4U5JTx04bjZF8uJiYhhDFT+5usB5i1cARfvb+TkEh/+g0KJxbTSH6xWMSdt0zkxTc7Z73VhMFo5PnXt/Dhmyt6vFitI6g1Ov7y0kbKK8wX4XWUqAh/ZkwZYLthN7Jh7VH+8/6Onh5Gl/L8jp2sHDGc71JSmBAVRWqp7YDhSxcFQfqTj/a0E8tbo9cbeOO1H+g/MIyQEB/efWubiVhujn2/XmTNt4dZttxy0FBbjEb49OM9fPvVQRydm0w+l8fDD67invtmsPQW6+/7zqTXCebL755C8pkccjJKABgzKYGBViojF6uqOV1hW3Ac6d+Xd0beilzcO76yp1TJP0ffxgNHP+dkeZbVtifLs7iiqiZY2fs9P5VmfMwNhmr0hkpEIhkaXYbJOje5dcFcJu2LROyP3tAyM6zSnG4WzLVt+usN0eUAqw48T1lxFZdO53DxdDYXjmcilUmI6R/K2JlJFDWcQSH2AsBDFsyZ8i8Z7HcLYif45zubiaExTAiNQSISkejfkoXw6cXj3BQrHIemCZS2mQd3DXS+t9mjSbOZGTqAjTmnSKkspFanxkumZLBfOIujR5hYsTShN9RRo0lBoy/DTRbVqf3/fvBYNqQJIlZebVWn+nKEzOpybvhhNa9MmMONcUndtt+riSqNCh+59YiypqglgCsl1WbbFF0RjquPd8vLrt5owF0hTASdySzgtukj+WzHcWRSQYDPKamkQaPl3rljAfhyz0lumTKMhPBA4kMDyCut5ER6Pjq9eZG+Nb8fPIaNaclkVjsnIsYS36aeRaXX8Y9J13ZJpHlPkFldzq3b11JQZ/7YOpME30DuSjRfg6M1cuVchNodEqQ03aMMNNVdlzKIpscxd++nmpd7+plGpXj6viO0lw1u6U9m2R/VzesRk/00bd+g2oVMORON6hcw6pAphfRijeoX3DwftKtvS4ya8du0enJx9RLm6c39w8babDcxPIahQSE227XFGZlfpeqOP0sUNZS1W+ZjRghtjZ/c1KKkVtuA1qBD1g3vSEqJHE+pG7U6UzuQ/Hr7MoUtkd9QYvLZR+6BWOTYfa27j0NP0/Y8gO49F1qj0eg4dDyDX35N4ciJjGaP5X6NlibdxV3LJ7Jzbwpand52Yweoqm7g4We+5blH5zN2ROct/BxlwuxBfPH2dnZuOkFttfC3t+ieqV2yr5h+wsRjREwgIrGoOVDlyX8sRSIRN9vu3vXI3HbbLmsVJR4WFcA9j19jcT1AcJgvf375JnQ6PVIL9j0zJg1gy3YhYO/UuZwOfafW1NSqeOqlDbz/2nJ8fcxPAPYWmpwIXnlnKxcvFzmt3/tXTu110eVNwU6/ZQLd3ZjTL55DubncOmwoT/30s81tigoruXSxkE0bhABjNzc5vn7uFBcJ9ztDK193tUrLmq8PMX1GIr/8LEwcikQQGOSNRq2jqqp9DZJ1a46waPFo5HL77hlvv/kj2348Y3ade6PVkq+vB3q9gcrKOtRq02h6oxH+78NdVFXWce/93WOP1DvU41YYDUaShkaRNNQ+UetIaZrNNj4yN14fscwhsTzlYgGZWaUoFFKiIgNIvpBPfJxw05bLpWz67iRLFo9Gq9U3/+zlqeTAoTQWXj+cnbsv0NAgRCrERAcikYib+5s5XXihlIulvDHiFm7a+x5VGvNFcJo4XJrGgojeby+gsFBwU6fLwYgGWkVfiMU+yGX9bPaplI+gTtUyY6jRtvhNanTpbdr2DsEcQK6QIVfKUChkGAwG1CpD80OKzqCi6XdRVptKhMc4qrQ5+Mm7/yHKFm1viE2fF8YkWlzX1Qzzj2SYf6Td7QtrN+EpTyAs8CboZDytm1TGKxOEaIzbf17Xqb4cpUGn5c97f+RAYTZPj5puNar1f4GixmJrWzIusDn9Av39gnhnynVWt+kX3yKIHDicxopl7WfF9x8UPCwHtPKcTIzsw+rdJ+nbx59Ab09+OJZCsK8nBoOBLUcvUKtS4+VmKpxIxGLKquvJLC5nSlIsD3+8hbfumm/ze8nEEl6bOJdl276hq8vTbE6/QLVGzQfTF6KU9LpHAoc4X1bM7T+vo1xl/X7qDETAS+NnOzDR0PZFrvV2UgvLHenPEu37U3r+HoM+GzfP+zAa9Rj0QtCBm2eTL3/vm7h14aKr6ej91NsJxbdy66/YbmSBHDPbxnpa90se6B1j8tmIkYvVOQz2bV9zpysY5NOXw2UXTJZl1JlPy7aXjFrT7ZPafEd76O7j0NO0PQ+ge88Fg9HI6XO5/LLnAnsOplJXrybQ35NF80cwb7oQGNI3unuzKkOCvbnlpjF8seaQ0/uurlHxxIvruWPpBFbe0r12FrGJYbzwcddmDuTklRPg74lHo+gllZk+S1gStJ2Btb5FInjiD4I4v/Khz5xS8DI3v4KHnvqGt/++hOBAr0731xVotXpe/IcQWe9Mb/5pE/szcYx194eeoG9skFVP7VMnsym50hJMI5WKmTHLsQA0R89hP193Vn19P9WNQnNVZQNVVfVUVTVQXVVPZmYJRw7Z1jKbkDe+p1WrVPzz4CHSy+wLrPr7C5swGAzcc+90Fi8Zg1QmoaBAsIb+y2Pfkp/fYhP96+4UsjKFCeikQRE88fT1hIf7YTQY+fZr4br4yf/taW5fVVnPubO5jBxlW8Nat+ZIO7Hc3UPBosWjmT4jkegY0+u9wWDk0sUCNm04zu6dySYR6Wu/PUL/AWHNFjJdSa97O37+kW/4+7vLTZb9/Ym1PNeqKERrzlTYnim8r99M/OWOzfLn5JajkEvx8lSy+9eL9I0J5FKqMDN3w4IRxMUG0zcmCL3e0PwztMxuFRRWcttyIVV7/cbjeHkpm/trjb/cg/v7zeS1ZOupQmcrcjokmH+27wR3Th7Jscw8csuriG0s4OnjruRIeg4Dw4Ipr2tg+oBYdl/MoLy2nqoGFcHeniSF92luAzA00vaDn1QSglQizCzr9MXNy7X6HJMocWiKLrf9sKCUjzQRzNXaixgRbnY6XV6bto5HxHUF90x/BZ1WT8yAUAYMi2bawpEmljZ+iliqGq1YfOUx5NQdZIjfLZa665UEKHv3rHprfJWjyKr6EKnYC7W+lAA3y0WE7WFKuHBTuDEuiU1dWKDREusvn+eX7DQeGTGJFQOGI+lls/xdyZX6WnbkpvF95kWOFOUCLR7VCX6BbD92CW93BX38vNh7NoPb54w0saCaNF6YpHNzk5NysYBPVu1j5YqJzREwu35NYdvP5xCLRFwzp+XBa+LAGMYPiDabTWGupsOKacL1+g/XT0QsEpFZXM6kgTHI7XzYGhsSyZ1Jo/g0+bjDvyNH2ZWbzk0/fsV7U+YT79vzllYdYd3lc7xwZGe32LAA3D5wBGNC7J+0602IRAok0oTGn6XNP3cVep2h+f7XEZ9DFy66Az9FxwRzZ0SYJ1dndXjbE+Xta/n087JeO2aobxxikRiDsSXj6Uh5SrcJ5kN8Y9sJ5pm1hRSrKuijdNzmIKuuiCKV6TvGIB/HA1C6+zj0NObOA+iec+H9T/ewc28KpeW1KJUypozrx9zpSYwcFu10Ibm2Vs1fX9rEW68ts90YwZv54NF00jI7PoFiCaMRPv/2IMdOZfGn+2bRP95xG6jeRFlFHTv3CoVbL6UV8+UHd+Ph7t/Tw2pHeIhQv+qu5RP5z+e/OqXPnPxyHnjiK157bhHxfbs3E8IW1TUNPP/6Fk6e7XxEfWs8PRT86d7eWfR07Ph4xlqxcX7+mfUmgrlCIeOJv1zfpWMSiUWEh/sRbsG+5+SJTIcE8zevFWoVvjBzBrsyMrhpkH2Cf1FhJdddP5xlt7YEiYWFCWP68+PX8tifvmpeXl3dwLmzufj6efDy60vwbNQuRWIRt6wQtM09u1NIT2vR+c6ezrEpmOfmlPHZx6Z/e9Exgbz8+lJC2tQzaEIsFjEwMZyBieFMnJzAq3//zsRO9a03tzJ0mBBk7evXdRldvU4wb4vRYKTYSgFFW8U+PaVKFkY6LjR7eCjIzikjLi6YuNggamvVJDVWZpVIxJRX1JGTW0ZUZEDzzwBp6cVcTivGTSm32F9bFkaM4IPUHc1G+ubIqC2xuM4eiqpqGBUTTlSAcMM4n1dMsLcnJ7LykTYKRTlllcilEjwVcho0WupUmuY2YJ9gDqCQDQFAp28pLKHV5aDV55q0s+Vf3tJupMlnjfZSs1DeJJwLiJr33dNcc4tp1GpBZsvxGzszCaNRj1ovRMf6ymOI9uycgOvCOkW13xHqeRNaQw016nP4K8cjEnX+8ve3cbM4WpxLfm3X2z+0pUqj4q+Hd/DNpTM8PGwic6IFIfi3KJ6nVZbxS85lfs5J43RJgdWoa5VGi9Fo5GLOFSYMiiGrqIK4sBYR2NNTEDce+cNcXn3zB1Z/c5DvfjxFeKgv5RV1FDc+TN1397TmidAmWr/EWfq5LU3rlDIpN09y7Pr05Mgp7M3PJK2yfaq3s0kuK2b+llU8M2Y6tw0Y3uX7cxaVahVPH/yJrVmXum2ffb39eGpU16Qy/xaZH/EQNz8kZOXc9ewNVts+tfg9ZHIpf//6we4YmgsXLB0wGDEiPOUdK9DoDMG8sNHOI722gDjPMLu30xp0HCwz9VuO9QzDV9beaqM1HlIlI/0SOFbekrH5Q/5BlkfPwl3S9XWepgUP55OMrehbCbVGjGwrPMLKvvMc7m9b4RGTzyKRiElBjr8PFDaUkV5bAGD3cdAahPeQjhyHnsbceQDdcy6s++44w4dEce/tU5g6oV+79+aeRCoR88yfr+XeR1Y73ZqlieRLBdz36GqumzOYlUsnENRLo5TNkZNfzv4jaew7fJmUS4VdWljd2Sy9YTS791/iYppz7EmulNZw32Nfct/tU7h5gWDR19OvYcfPZPPKO1spLa91et8P3DkN/y4UJl1Yp+nU8lIoWDhwIBvOJ9stmi9eMsbs8mHDownu482VYlMtY8ENI5rF8raMGx9vIpjn5dqOdH/v7e1oWmV3eHkpefWNZQT3sc9yeuq0geRml/H5p3ubl9XXqdm0QQgsu7OLLKagFwnmZ09kseo/u7icUsht17/bvFyj1jLFSrpEQb31AzQxOAG3DlQpz8wqYdyYOA4eSmP5snEYDEaTCOHf3TWl2YOr9c9PPS7YAfRrNWO8eNEoVn99sLm/mDbpZQqJjEnB/dmaf9rieAoaKiyusxd3eUshxhPZ+Xi7KRCLRfi4Kdl8UrAUCPTyQNwYjdm6jSM02aLUqVoJ5vpcNBrTaBJb/uUt/Q1HSBEXHlr0hnJUmhPt2smlcYjFveOB46Z7p1tdX6a+jFIizKaVqi4RqOyPyO5UfBeOIhLJaNDlIBV50KDLc4pYDsKL8r+mLmDJtq/RGWx7U3cFFytKuH/3ZiI8hfNpZeIIliUMxVPWe14+HKVMVc/BwmwOFGSzvyDbIa/4+LBAMovK6RsawP5zmdw+e6TZdrNnJBLSx5tv1x0hOaWA1LRiPDwUjBkVy+IbRjJmlHOjq0L9Ha9BoZBI+WD6QhZ+v5oGndap4zGHSq/juUO/sC0rtVkQHhLouKdvd6A3Gll/+Rxvn9pPcb3zXwosIRNLeHfqfNy6qLDx/zoe3m6kHMuw3dCFCyfx+hTHBdrWuEtlDPAPcopotDrrZ14YtNLu9t/lH6BSY3r9mxdi/qW4LbdGzzIRSmt0Dbx9cS3PJt1m9/47Sh+lH1ODh7Gr+KTJ8rW5e7gmdKxDUea59VfYnL/fZNnkwMGEuXUsW2p1luAJa+9x+C7/AECHj0NP0/Y8gO45F9Z9cp9DIvE/3/+FyqoG1Gotty4bT1V1A5dSC1l52yRKSmt4+72fWDBfmOz/6ZdzyGQSiq9Uc+dtk0ys+L5ZexidztCcBW6JuJgg7lw+gY++6LoC7Aajke9/OsvWHeeZNiGBmxeOIjGhd9n4VNeomn2/T5zJ5tjpbPILO65JLNzzHteFD+XeftOcNML2fHR5Dz/mn+G7aQ+3WycWi/jrE9dz7yOrqantXKHhJrRaPf/+ZDcHjgqRwvffOY0B8d3/7FxWUcdn3xzg+5/OOFxM0R4mjonjutmdDEw0qlCVzAJA7vs2YnkHr5PGWlTFEzAaawBQBKxDLLddU+hqJbvSfPDw3qwsuwRzf38PIqMs3xMTkyK4Umyq002YaDn7tK11SnGx9ff0rMwSTp8yrTl528rJdovlTSxdPp4N645SU9Pyt7tls6AJrrhjEjJZ19g+9RrBfMjIGN76+C5ee3YDj794Y/NysUhkNYW3Vqe22u9wv+gOjWfp4jEUFFay9GbhD7mtaCxqNX0osmMqsW1/5sZpTTCvs/E9LXHnZEEsun6Yqb/PHRNH2LQUsLbcGuZ8zLW6LNS6lghAkUhmt9+4WOSBQtYftbblD7m24Scz++09/uW26OM2mIyanQD4K+JdYnkXE+1zL4W1G1HpC+jr+5BT+x4RHMYTI6fwyrE9Tu3XUZpE5ZeO7uadUweYGRnH3OgEpoX3xaOXiudGILOqnFMlhZwuKeBYsZA5cqmipMPe3TqDgZp6FTX1Ku651nqht8FJEQxO6t0p0wm+gbw8YQ6P7P2x2/Z5sDCbBd9/AcA1MQk8NmIycT49b9XSdE5sz0rlHyf3kV7V9ZH3bXl2zHSGBvauF9rOklEpBB4cLsijqK4GvdFAsLsncb7+jAuL7NaCsAo3OTWVdd22PxcunMH2xSud0s++krNszNvHogjrWYcXqrIA+CTD9L7gLlEwK8T8JHFbBvvGMjZgIEfKUpqX7b5yColIzCMDlqAQOzYpWKUV/m53Fp9gkE8sCTbsSG6Lmc2BknMAqA3ChLBar+GpMx/x9vAH8JPbFlNL1JX85ez/oTO0RAFLRRJujZnt0Nhbs6/kLIDdx6HtMQDHjkNP03QeAE49F2ydB46I5UYjnDqTwztv3IKvr2AHeehIusX2BoORZ568nsysElZ/fYhH/hiCVCJh4+YTaLV6br91ol37XX7TWJIvFjYLoV2FXm9g576L7Nx3kagIwc5kyrgEpozvR//4kG6LWDYYjGTnlXEhtZCUS4UkXyogM7v0qoogt4fwEF9eePx6Hn9xvUnBw85y6pyQSX/vI6uZMj6BFYvHMqBf1wvnV0pr2PTjKdZ/f8Ip/uzmiAr359lH5jvhXGz1+xZ1IvBE5Iky5CxGfT6qK5M6Oyib1Gjz8ZKFd/l+LHHHug0MCW1/Lp0ttC9TIspGLYiQUFNLFKlMQkzfIAutwc/fNMugtYBtju82mQa6KpUyrp3vuG4nk0mYPHUAW39o0Uyb9n0xpYDBQ7rGKrPXCOZNLLtzcrOXrD006K1H3cV4dqxYiFwubRcJ3hls9RftYX1fDXrn+7LaYynQER85c0J4vcrUs0ghG4xIZD7Nw3yfI9sI5j/YtV9HMBpVGAzV6I01GAzCjKXBWIPBUI3BUINOX2B2O7X2AlW1nyMWC7NkYrEXYpFXm/89EbW6MQQo+hGgECw0ihrO2jvCxjHVYTA0jstYg95QjcFQi8FYTYP6gMWt61Q7MBhrWsYk9kJiMk5P7C84Z32czh4j0OFx1mkuk1X1AUYMiJCgNzq/IOC9g8aQUl7SI37m5qjTatiSkcKWjBTkEgmTwmKYGBrNkMAQBgX06ZHo2HqdlrTKMlIrSkmtLCWl4gpnS4qo0jgnwqOJjIIyls/s/QWSHWFRXBJnSwr5POWk7cZOZltWKtuzUpkYFs3N/QYzNzqh24uDFtbVsCHtPOsuC+JKdo1lm7au5Pq+A7ljYM+dWw06LdetX838uP4sGziYME/HsxZac7K4gFcP/cqxonyLbfyUbqxIGspDI8ah6Ibjnne5CC9Xuq+L/2Hev7yJlOpsbo2eRYyH6QtyvU7Fj4WH+SxjGwAag6k4cmfstfjI7P/7eWrgcu4//o6J//eO4hOcrEhlQbggKo4NSCTeMwyxqOW5y4iRK6pKcuqLuVidw8mKy80ivs6o5+Uh99jcd5R7Hx5KEAKk3rq4tnl5Tn0x9xx9kzv6zmNGn+F4Stt7y1dr69hRfJIvMrdTozO1s7w79lriPTsvbNh7HNoeA3D8OPQ0Tw0UaodZOxfMnQdg+Vyw5zwwGI0cPJrOybM51NSquGn+CAb0C8FohNIy4T3My0uJUiHj0Yfn8uY72/D0VPLQ/aYeysY2omffxuhHT08lDQ3C+3NGVgn5BRW88Yr5mmjmEItE/PXx+fzxL986zcLDFjl5wu//y/WH+XL9Yby93BjYL4QBjf/CQ/3oE+TVIQsbg8FIWUUdxSVCgE3RlWoKi6vIzCklK6eM7Lyy5lpsv3VGD4/h/pVTef/TPV3S/95Dqew9lErf6ECunTmYiWPiiAhzvD6DJaqqGzh2Kottu85z4nR2l05qeLjLeeWZG5uLuXYKkRvK4P2229lN1xepb9CVk1yxjnHBf+ryfVliyZBBPDC2fRDYi7t227V9UJD1CUpvL9P7bFCQF1KpZa1F2eb6o1Fb12MPHTAtOjt0eDRKZce0iP4DQk0E8ybOnsn53xHMY+KCSb2QT32dcIMbOjIGjUaHwsIvVSGWWhWTvWVXR2FCb5n1YkMyca87VBaRiIXZcakkAp0+z2wbNzv9y5tQKkZSVbfaepsOFPzML1mKWpuMwViD0dgxywOV5oRZi5i2iERKxGJvfDxuxderJU2sXH2ZEDfzKU46fRG5VxZgMAqCs0DHrD9q6jdQU7/B2ggRi4QHfLHYi2C/N/BQ2lfYo/04u2qMLeO0d4w51Z8xMPA1xCIFekM9qeV/Y2Dgax0anzVemziXrOoKTpWYn1jpKTR6Pbty09mVK0TjSEQi+vkGMihQsI2K9PQl3MObME8vQj286ePuiZtUZkc5XmEKp0ajpkLdQKWqgUqNiuL6WvJrq8irrW6Oes+rraKgtrrDUeOO4K6U8/HWI7grZL8p4fy5sTPJrqlkd17321UYgf2N9jhecgVzovoxITSKcSFRhHdStDWH3mgkuayYw0U57MvP4kBh174M2MPQoFDenHxNj45he8ZlMirL+eeJQ/z75GGmRfXlgeFjGRXiuDj06bmTvHJoj00rqQpVA/86cZgf0i6x6rrFRHmbL8zTGlW9hlO/ppgsK2gsnnZo2xmz22jUWo7tTOby2RzGzBpk57dw4eK3wYP9BOH4g8ubMWJkV/FJdhWfJEjhS4ibP+4SBZWaWjLrCs0KtAAj/PpxY7hjkXbeMg9eGnI3T575L2XqFv/Sck0Nn2duB+DzzO2IRWI8pW4oxDIa9Goa9GoT//GOcm3oOACy64pZn9sSWFOpreW91PX8+/JGoj1C8JN7YjRChUYQUbPri9sVqgSYHTKKm6OmdXg8D/a7kQ8ubwbotuNgxEiDXkO9TkWdTkWdroE6fdPPKmp15oM8TlemIRfLcJcq8ZAq8ZAI/7s3/gyCR7k974/ejeK+tXPB3HkAdPhcUKm1PPm3Dc1RuSBYPgzoJ0RU/+m5NY3L4nngzmkMSorg5Rcj+O6HU/y47Qz9E0KpqxPG0CQyN2Eu8zsywp/H/3wNr7/1I6/8bTHubvaJf0qFjNefX8R9j31J0ZXur1dUXdPAkZOZHDmZabLcy1NJYIAn7o3ClVwuRaGQIpdJ0esN6HR6tDo9KpWW6loV1TUN1NSqusSqw1GcXdS1LSK73mAEP/P0rBK27+q6YKfM7FLe/3Q373+6m0B/T4YNiiQ+NpiIUF/CQwUBPSjACzelzMROwmAw0qDSUlldT35BBfmFlWQ11ss7nZxLVk5ptxxLkQiefWR+c+bD/xrl6jROl6+iQp3BvqKXifScRFr1dmaFvQrA5uyVjAv+EzXaQmRidwrqj6PWV6M3ahgT+CDecudkMZsTywEeHGc9k7oJH1/rE7hyhel9wt/fev2NttYnGo35ibbS0hqT/5tITOz4pHaYhQKqWZmm9R6PpOWy7sg56jUtOvEHd1qvo2QJlw+ECxcuXLhw4cKFCxcuXLhw4cKFCxcuXLhwQS+MMP/iw91cONsy2zxoeBTP/elr3vjwDrPt3aVyqxHmMnHXp2o4A1uzrR4dKFza0yjlw6htMB9hrrSz4GdLe+uFHERIUcgcj0rT6QvQG2xX9nUGRqMKvV6FXl9KSuVmO7fRWozSdz5GDEYhit2gr20V0W7Hlj0wTnvHKBbJ0RlqgJrGz4ouGZVCIuWz2Yu5eetXXK7sfm9le9EbjVysKOFiRYnVdgqJFKVEilIqRSGRNkeiagx6tHo9WoMetV6HvjeEq7Ri3uj+AFzMuWJX+61HUlj/61kPPBcFAAAgAElEQVSyiiuoV2sJ8HYnISKI68YNZNaIfu3an88qYsuBZE5ezqewvBqDwUhUH1/mj0vk1lkjTK7ldSoNc5/4iBB/b9a/cLvZ/d/0wiqKyoVzc8eb9+GmMJ9NJRGJ+Pe0BSzZ9g3JZcVm23QHNRo1G9LOsyHtPAARnj4MCwolxluY8Y/x8iPa25cITx88ZHLcpbJ2HthqvY76xiKmVWoVOTWVZFVXkFVTSXplGSeu5FOrdb4NWUcJ8/Dm45mLut2Kpi2bLrfYkhmMRnZlZ3BtbILDEebrLp7nbwd2ObRNZlUFt36/lvU33EIfD+uRJzWVdbz+wGeoG1qO4YEfT5v8bwkPbzfu+MsCh8bmwsXVzoTApqJdRj64/B3GxnysEnUlJWrbFlRDfON4afDddtVUaktfj1A+HPUoL57/nPNVmWbbGIwGqrVdV1vg/viFBCp8+G/69xhbPVPojQYyau3L2lsePZO7Yq+1O7rUHMJxEPbfXcfhh/xDvJu63uGxHilLMfEct9j/lFdxk9j33GvrXHDmefDJV/tJvlTIH383k9HDorntwU9N1k8eKzx/HTqRwa2LxvLqP37E3U1OfYOGB+6dTmiIL2vWHeGl174nMMATscT671wkgtAQH+5YMYnX/7GVF569wW4/Zj9fD97462L++PS3AFRWOd/a0VFqalVOK1rZ3TTotDx+Yg17r1zCTSJnSYxQ3+3+hOnNf785dWV8mLqb42WZVGjqCVJ6cVPUKO6On2LSlxEjH6buZm32Uep1GqYE9yfKw/6aO0/+YR5qtY7dBy7ZbtxJSstr2bE3hR17zf/disUilAoZWp2+V1jjiETwxB/mMXFMnM22utp/o6v/FqPhCjRm7IvE3sj9v0AsExwAVMXDMBqqabrGyv0/QaKYYdKP0VCNqngoyuCDaGvfQd+wrbm91ONOZF6POu8L2oG/Ip5BfstIr/6J8cGPAJBRswNNo/7gJQulRHWBBl05fdyGohB7MyH4UWq0hRwpeY9ZYc7JaH9r3wEenTyRjckXWJSU2Lw80N0+Jw13B+10bLVve+00WtACUpLNWz6Ghvs6NJ7WeHiYt3SurjK1Z3v/50M8t2gm3m6d1316nWB+9mQW//joTp56QCg4JpVK2hXcbE2QwosytWXRTG3D47y3oLIxziClNyVVtbyzYS+P3DSV7CsV5JVU0TdESJHJKCyjql5FsK8nbnIZ04bGsft0GtOHxTf3kZJTzJo9Qjr0ilkjqapraO5DLpNwNqOQhIggisprqFdriA0NwN/LnYPJWQAMiQ1l3/lMgnw8iA0NYFhcmNUxK+VDzXqNA7g5WMlYLo1FIvZDbzBfmVsuG4Coi4TQrmCQn/0eei46h1TsSW71560+219oyFF8FUpWz13K4h+/arYjuVpR63Wo9Tqqeo9uaRdr95xBp9dzKbeEF1fOtdp21U/HeW/jPgZG9+GmyYNBJCKvpJLDF7LpG+JvVjD/4qfjHLqQzbjEaKYNi0On17P7dDrvrN9LvUrDfdePb27roZQzb8wANu47x5n0Aoa2uWamZBeTWVjO9eOFhx9LYnlzfzI5q+fczOKtXwOQUdU9k33WyKutsnmuS8Vi3KUy9EYjDTptj9urOEKA0p0v5y4hyK1n/Wg1ej1HCkwnJRUSKfP6Wq5ib46sqkqe37+zQ2PIrani8T3b+eK6xVbbBYX5sT71HyQfzeDE7gus+/fPRPcXCqXGDjLvL6hQygjrG8TMm8fi38e27YsLF78lmgpmLoqYQqxHGG9e/NbES9oSMrGUZVEzuDV6VqesG/3lXrwz/EF2FJ/k6+wd5NbbN+HcmjA3QaSaFzqWJJ++Dm9/c+Q0hvv14z9p33G6wv4ii4k+MdwXdz2DOrDPtlRp61gUIQhy9h6Hpt+7M45Db8BZ54Kt82DX/kssnj+Cxdebt85rsqu4UlKNj48br/29/X3ntZduNrvt+LGCwBcU6MWrjdu99doyAIYPjWL40Cj7v0wjMZEBvP+a4PX+6F/X9og9y2+Fr7MO8eeBc3mg/wzOVOTy8rnvAQh382Nh5HAA3KUKgpXevDZiCYEKL06VZ/PC2c309w5hUnDLc8+W3NN8mraPZwZfz1C/SA6VpPHuxV8IdbPvOUIiEfP8Y/PRG4zsPZTq/C/rAAaDkfqG3vHSJRLBEw/N47pZg6220zcIAYC6uo9RBGxAJO2Lrv5rtNUvowjag0jcYp+h7HMajPU0FCVZ6q4ZTcWDSNxuQBZ4H0ZDUxBa7wiC9ZFFkFWzB4AYrxkU1h9HZ1BTrcnBVxEDCEJ6rbbQaftMLSsF4FBOjolgbi9yuWP3JZmD7S1RVGT+/fCVv33HK3/7zin7aKKmxlQwD/Pzol+I/ZNn1uh1d3WxWGwyS2EwGNDrLXujxXoGc7Ha8glZrum6iAhnUqKusbo+2iOQ3afTiQzyZfeZNDyUckb0CycySJihuZh7BU+lnAa1FneFjJOX89t5kyZEBJEQIRRCiQ8LYOvRlOY+/rlpP/HhAVzILqZereGea8by9a6TaHR6Vs5piQY/eCGLRZMG8/WukzYFc4UFT3GZNBqJJNjm76QtSvlI6lQ7LKxz3L/cxf8GcX6Pdev+Qtw9WXvtLdyy7dseK074v8y0YXEE+3qSVWRbbNh6NAVfTze+eGoZklZR0HqDAY2F6I6nls/AXSFH2eph4nfXjWPBs5+y+cB5E8Ec4OapQ9m47xyb9p9vJ5j/eESIMpk/3v6HH3+lO1/NXSr0vfXrq2JiRmcwUK1R9/QwHMZbrmD13CXE+vS8d+PJ4gLUelPP3KlRMXjKHYsaeeHAThp0lifow728CfPwoqiultya9ufW3twsfs4UxKw5fePbrW9CKpMydGICQycmsHPdEcbMFl667nq2Y/6BLlx0lH+dPMS50mKujU3ghnjHXzS7g7pWhSuH+cXz+dinOFKWwp4rp8moK6BMXY3GoMVf7k2Imz/jAwTBYWrwUIIUHY/Uao1YJGZOyChm9xnJ+apMTlQI4tHZygxK1ZVUaeuo16tRiGW4SxX0UfgR4R5MglcEI/wSiPbo0+kxxHuG89awB0ityeNg6XlOVVymVF1FhaYGsUiMr1zIbglU+DDctx9jAwYywNtx8dMSHTkOU4OF9w9nHYfegLlzwdx5AHT4XKioqCMmKtDyGBqD5bS6znvlO4vIRu/cD964lUf/uo7M7NIeHtHVybjAOJZEC1HlfT2DOFmeBcCarCPNgnmgwpM/DZzTvE2Uhz9fZR4iparQRDBfk32EeWGDuSFyRHN/B0vSya23P9NXIhHzwhPX88IbW9h76LLtDX7DNEUOP/7gXK6bbV0sBzBoTgrbyYYgkgoTVVK3G9BWPYdRl4aonaOAfaK3WDEBqYeQmSvCdoR7VyIRydAZWrI5AhQJXKoWAkInBj9Gcf0ZpGIRPooYShqSwQtqtIV4ykKdNoaEgEAWrv6KvOoqFq7+ymTdd7fdanN7a8HH5pBInOPaXVvTfVkwbX3UNTo9f/rie+JDApqzvx+YPd7cpjbpdYL5tDmDeOK+VRQ0FvB45J7PuG7RSIvt+/uEsrXAfBEpgPz6no/As4ecOus33QHeodSXaLj/+gl8/vMxPJRy3FtFI4pFIsSNgs/kQbH8+cMtvHnvfJM+JGIxpY1pZE1iUlMfCRFB1DSoGBoXxpn0lvSJmD7+fLlDKGg5LD7cZJ+2cFdMol+E8woghgV+4bS+9p7P4I///Y65I/7C63de67R+7eVSXgl3/3MdAIOjQ/jPg4vMtpNJI5HKznPjS6varZs2JI53f9fx1PVVO0/wzua9/P6acfz+WssXkKax2hqnM4/11U6Yhzdrr13O8u1rSK/qvfYsv0UKyqrZcUJ40Y8JsS50Bnh5kFlYzrmMQobFt9haSMRi3BTmHxb8vdqnv3ko5fQLD+LYpZzm6Ommm3P/yCAGxYTwy4lUHl86DY/GAk16g4Gfjl0ixN+LUQmOVfUO9RCyJNZeewvLt68hq9p85o2LjuOrULJqzs0k+js+udsVnChqf32dGulYROWRglz25LRPs/dVCOmNb824hpnRLS8mZ64U8tju7VyuML2G/d+ZY4B1wbw1sUkdL+7jwkVnOViQw6GCHBp0WrOC+Zx1nwHw0qTZjAl1ToEuR2ldQPLbzOPojQZujR3D+MAkFu/+iO9mPNnlYyhV1fLsqS18OH45g31jGewb2+X7tESCVwQJXhGs7DuvW/fbtpCnTCxlUtBgJgXZFo46yvXhE7g+fEKX9d8ZRCJRh8+F+oo/omnYBIBX8A4k0v4m64ODvMnOtfx8fCZZyKiKCu/5Ceu2BPp78u9Xb+EvL2/ibHJ32VH+dujrGWTyOcE7BICdhS1WJfU6DaszDvBr8SUKGioxGA3U6NRM6WOaVZddW8a14UNMlsV7BTskmANIJWJefGIB7320k83brFvH/VZRyKU8+UfhmjtrykC7thHJhOdAo+pHjPoCRJIw9KpfQKRsFtA7glg2vMPbOhtfeV/qdSXsKnyOft7XEKAcQGXJvwDwlIWikPgABqI8JpBfd4Tdhc+jN2oYHfig08bw6OSJPDp5Is/v2MnfZs10vIMuLrRridoetI1aOMp5ARK9TjC/dtFIho/pS3aG4K8bHRtEqJXKvBODEngnZbvF9cmV+RDt9GE6nbMVOVbXjwuMZ0BjdGLriO8mFk9puVlkFZUzMSkGubT9LN5DN0wEBDGntZg0b3R/DAYjYrGIpGghMmD5DGG21mBoFIDEIgbFhJisc9Fxmi5dTQKaJfr4evHSbfOorBMiX5Jzitl2/GIXj84UEbbH6cKUPu6ebLjuVu7dtYmjRbm2N3DhFDIKylg+077r0/0LxvPAe4Xc9eZaRvePZMGEJGaN7IdCZvnWqNbq2Lz/PPvPZ5JdXElNvYoGtRaNTpjZbs6QavVwsnjqEF5Y9TPbj17kpsZr9aEL2ZRV13PPtWM7/BwT5uHN2mtu4daf1vRq3/yrjUA3D76cu4QBfkG2G3cTGZXtJ/+nRMY41Me/Th5ut0wqFrOq0WJlaHCIybqhwaF8s2Ap16xbRUl9S7be8SJhUj2/pppwL2+b+114z3QkZp5HXLjoDrKqhAnFAf7m/55TK4SAlRrt1ZcF48LF1crc6Yl8s/EYA/qFMHlci/2dVqtn448n+Wm3UCflD/d0QBzqBrw8lbz30lI+++YAX647clVZzfU0bf2OzTw28+zpDWTUlvDXIQtJ9AlDIZGxdN8HZvtrW7ego/XrJBIxj9w/m7iYIN77SLCu01lxOfgt0SfIm5efvoGEOMcyhaTuQnSzQXMC1ZUpiMTeiCShyP0+RCTu+GSXSGTep7onkIhkzI14x2TZ0tiNzT+PDPxd889NPuddxe3De89Egj1Y8uIPD/dzmu1Lc58Rfiaf1xw+a/JZIhIzdWDHAgF6jWB+/rSpYOzp7QZAWWktZaW1DBpmPuUu1jOYSPcAizOJR8vSMWLsVBGYrkal13KkNN3i+j5KH/r72J/WoZBLTQT01lgrLmopXcPRNA4XtukfEcS+Nx6wq627Qsb8MS0zvQcuZHWrYO7IWF2Y4qtQ8tXcJTyxX5jU25Se3MMj+u1TUlXHx1uP4K6Q2RTOB8eGsvHFO/j8p+N8f+gCxy7l8o+1e1g5dzS3zRnZ7npZU6/mrjfXkF5QxpQhsdw6czjBfl54KuW8t3EfF7LNF+OcO7o/b637lU37zzcL5lsPO27HYo5gd0/WX3crv9vpmpjpLE3WK5/NXky0V+9Kr8+sMs0i8FO6Eellv9d3SlkJ+/Oy2y3/3dDR7YTy1gS6ufOHEeNMfM+bXnV352SwImmYzX2PmmHbr9KFi66iUi1EOPkpe88LuKMYgadObEKr11OmruP5YdcBEO7uw4unfwQgvaaE2WEDuSdhUru2cV6B/P6QUPtisF84OXXljAqI5uaYETx7cgv1Og2+cree+nou/gdZcfM4UtOLee6171Aqhezltz/cwYv/+AG93sC0iUJE+qL5vVcgkkjE3LNiMqOGxfD3t3+kpNS6vaoLgYzaEpPPlxqtdVsX69xfcpk/D5zDcH8h6lFj0JFX1z6bMsrDn9TqIpNl6TWO++63ZuE1w4iOFMby3GvfUVXdYGOLq5uhSRH8/amF+PrYV0DSHAbtGRQBXyKWj3PiyFy0JT6g92XcWMPLy/xz11PPLmBgYtdmn/510SwADEYjWSXlHEht/w5kL71GMN+y9igAZSU1VJbXER0nRILkZZURFuFnUTAHWBo9ln+kbDW77oqqmmNlGYwJ6Fn/I2tsKzhDvd5ykYfFUaMdEvxD/W1HfLlw4aJ7kIklvDNFeLkdGhTKS0d3tasv4MJ5TB7cl8ToPpzPKrLdGAjy9eTxpdP446JJ/Hw8lc9/OsZ7G/dRVl3PIzdPMWn7za5TpBeUsWLWyHbrpFb83hQyKfPHJfLNrlOk5pUQEeTLnjPpDI0LIyq488Ksj1yYmHl8/zY2p1/odH//i4wLieK/MwV/bR957xPWiupMi5sn+Fv2fjXHF+dPtVvmrVDwwPAxNre9oV8iLx7Yhb5NVFhyaedeSl246A60BiHCSSxyjidnd/BNxjH2FKW2RF4Cr4+8EYAdBRf5pUCYcP19/8m8OvIGKjUNPHl8I7fFjTXbNq7/ZHIbxaanh8wjykN46T5ZlotIJOLtMYs5UpLFZ2kHu/V7uvjfRSaV8Mozi9h3OJU9B1LJK6zAaDQyfHAk0yf2Z+oEQTDvIScBhxg2KJLP3lvJvz/ZxU+7k3EFm1vnaFkG32YdYVxgHKcrcvipQMgmeGbw9c1tItz9OFiSxqyQJOp0at5P3YnO2D5a9eboMbx6/gdG+Mcw1C+SE+VZHCnNIFDp2akxDmssUL7qX3fy9oc7erwgaFegkEu5Y9l4brlxTIc9q416wZLIqMtBJLYnOr2V7ZVRZ7mZ3TTV5HFGXy6cjZe3+Yn46qqun4Tq49NyDQj19WLTsY4HLfYawfzpV4SU4JeeXMub/72j2Y/baDDy2rMbrG67MHIkH6fvoVJTb3b9J2m/9lrBXK3X8knarxbXe0gVLIpqb8Hiwjm4gudddCcrB45gWGAof/h1i9mCei46z9mMQhKj+3A6LZ/EqD52Z8goZFKuH5/InFEJ3PTXVWw+cL6dKJ5RKNhizBrZz2S53mAgu9i6j/jiKUP4Ztcpthy8wKC+Iag0Oq7vZHR5a2RiCe9Omc/ggBBePb7HNSnjAHcljeLpUdOQinuvoFavNZ1Uj/a2f6KlTqtlS1pKu+XLBw7FS66wub23QkFiYDDnSkwzKFLKSixsYUrWxQL8g4WJfG//zr3EunDhKN5yBeWqBvKuonvuLbGjuTV2DHqjgcW7P6JGq/5/9s46PKor/eOfO56RuLuQhCS4S4tTKC2Flhp137r+upWt7Na7ta17u926ADWcFkpx94SEBEiIu4zL/f1xYZKQiZJAoPN5Hh5mzr3n3DPJ5Mr3vO/35Zmdi/BVaSgz15NoaFwwE4F/7viVhwdMRyNXtrqvRi5F8R4TywGOmKqJ10uRlHH60ytyzcvpjyDAuNEpjBud0v7OvRxfg4ZH753BnPOH8tbHv7s92L205JlBc1hStJvXMpeikSu5LuksAHfBT4CnB17E07t/ZsbvrxKo1nFt4lhC1IYWY82OGUKhqZpXM5dgcdqZFJ7GQ/1m8PGB1d0y18AAHc88MouVa/fzn/dWUF3rWWs63RgyIJYH7ziHqIiA9nduA0EmBbjKFMlYyicdbZUjKGJRGv6OXCN5otvrX8Fh/AjERk9rW/XfAAUyZTLqYM+Br61hr3sSh+lbECUrNWvlXGks1VDUQd+c0Gfy0n0EtnLPX1ra8/dj7yxf735db7FRb+m67V6vEcyPUVFW1yKauqSops0+OoWae/pO41+7Fnjcvrkyj18Lt3N+VO9L63otawlF5taFlhuSxhOg0p3EGf21UCoUHCiu5I2f17A9txC700lKVAg3TJEWKSYMaL7QsnbfIe54dwFTBiXz8o3nexqST5Zv5o2f13DH+WO4edpId3tNg5kJj7zXYv9BiZH8977LuvFTNbL/iCRovPXrWrbnFeF0ukiLCeXm6SNRK1v3ePM01/bmeexn89RV5zAkKZo3f1nLpux86s1WQv30TB6UzG0zRrdaOHb/kXLeX7KBbbmF1BrNrUZorHj2FoJ9T9+/iUEhESyZdT3Pbl7JV/tbL1jspWuMyYjnv0s3Ex8e2KZYLopQXFVHZFDzjJxjBZQ92VeF+ksX/tKqekhstMn675LN1BrbLmySEBHIkOQoft+eQ1WdEZVSwTnDuv8h8caMYQwIDufuP36h2OhND24LX5Wal86awbS45PZ3PsUY7fZm7307IHQfY2FuVov+ckHg2v4dvydK8AtoIZiXNfE1b4u3H/qGfVvyAPjf1mcJCu+4lYwXLydKkn8QVSVHWJSXzR2DR7mLJp9OrCrJJlYfyB19x/P1wS2Umevc297LWs20yHQSjgrfre3r6WoYpfVnS4WUplxoavtZy4sXL+2T2ieMN5+fy+r12Xzw+Z/kH2lZf+RMRKmQM35MCoEBbT+f/TThHgAmh7cdMJLhH8U3Z9/W7nFlgsBdfadwV98pzdoviO5ezWfi2FSGDojl4y/X8uuyXdgdnr2ZezvhoX7ccMUYpk/qd+KDiXasFRcBINNMRuXzAQhqwIrTvBBbzX34hE0CQYXS8ABKwwPtDinIfPGJaFmc/niUvv9C6fuvE/0EXnqY9AzPtitZmUVcMHtojx47I7ox48FHpWRQ3FldHqvXCebDRvfh7us+JL6P9CEPHShl6Kj2o8MviB7CsmIppWd9eU6L7c/u+ZkYbRADA1q3djnZfJb3J98d3tjq9lTfCK5M6J2V088UiqpqufaVbwgw+DAiJYYGi43NOQXc++HPADwxdyoXjemGiwqg81Hx3LXnUt1g5mBJFT+s3dV+pxNg96ESbn7zewAsNgcZcWHEBvuTX1HD7e/M5+yM1gsfnMhc/9xzkFd//BO5IDCsTzRmm51tuYV8/vtW9h8p44O7Lm7RZ19BKde/9h0Ac8b0JzzQwK6DxazY0fi3fOfMsfSNCsFfd/r7bOqUKp4bM41z41N5Yv1yDta1HZ3spePUNJi5blr7WTku0cX5j35MRnwYfWPDCPHTYbTYWLvnEEfKa/jbzNEt+swY1Zevft/GC9/8zqHSatRKBVv2F7D9QCGDk6PYnlPY5jEvHj+QRz9axJ+mg0wclITep+OiZ2cYHhbN0tk38Nj6Zfyc1zKy2AuMi0rgxbHTTxvx7Pj1G582CtMez3dZe1q0TYpL7NRnD9G2fAhu6GCRxENZRSQdTW/2iuVeTjbjouPZXHKEKouJyd9+zPiYBAAidAbkTbJKvsvazYaiE6sD8Y9RE06of2sMDYrl67zNFJtqUckV+Ckl26h8YxWf525kfHgyq0tzGBmSwIjgeI/7tjbuvEPbuX/TD0Tp/JG3YVuzpuwAaX7hBKk7lyVy3oq3+GDMVURpe1ddiFPBjsJiXvltDQCfX3PJKZ6Nl55k3OgUzh6Vwubtkvg3f+F2NmzJO+OKg0ZFBHDu5H7MnDaAgBPwwD4d8DX4cN+tU7jy4pH877v1LFq++7QpChoTFcCVc0YybWJGl+1XjsflyMLl2A+AWj8fhMbfv0w9EepfQnRVI8g7V0jUy5lDdIyUuRYYqKOqqjHIZvu2w4guEaEHrB5eX7KWe6aPpcZkYdbQ7snk7nWC+VW3TGDCtH4UFUgrspdcPYbYBM+V7ZsiIPDCICn69aq177YoAmp12rlt06c82f9CpkV6Loh5srA47byWuZjv8ze1uo9eoeGlIXNRyXrdr6hdHEdXXQ9mFpG1I5/Kkloa6izYrPYWlbG7ygMvXd4t42zJOcKVEwbzwEXj3RGlm7ILuPVtyQbopXmrmDQgCX/9iYu0SrmcGcP6AlI0dU8L5k99vRyLTfL0um/2OK6d3LiS99OGvTz55bIemevyHTlMGJDEi9fNQH1U1CmqquPSF75gU3YBuw5JxV0GxDdG6L67cD1Wu4NXbprJ5IF93O1Pf7OCeWt3A+Dro+asjIQOzwPggfP+zSsL/867j36Dw+bkrpev5MGZL/HSLw/yxgNfUJhbisVkY+gk6YR6zcOzWP71OkryK7j6oQsA+N8LPxEeG8w5V4zt1LE7wtmR8Sy78Abe372Jd3ZtwOSwt9/JS5scLK4iQO+DVqMkqI1MBJkg49pzhrF+32EWb8zEYncQoPchPjyQF88/j6keor/7xoTy+h2zeffndXy6ZBNKhZxBSZF8+vfL2He4tF3BfPLgPgQatFTVm7rVjsUTvio1b4yfyfS4FP61USrYWGpqaKfXmY+/WsPDwyZwecqpvQ/oLHqVmipzYyqw2d4xv8YD1ZVsKWn5vbw8rXOf30fRMjPIZO/Y+cpmtRPZgfs4L156gqvSB/HZ3u1UmI2YHHYWH/TsRbv0UMtgm87SHYL55QnD3K/lgowFk24F4KvxN3jcf915D7Zo87TvDxNv8dj/uaGzOjSvr/M280DG1E4L5l1BROQf2xZgczmpsjXwWP/zSTSE8NxuKW2/2mbE4rRzc/I4BgRE89zuRS3azkTMtf/EavwYjeE+NIb7sTZ8iNX4IaLYgEI1Ch+/fyGTxyCKkj+spe5ZbOZfQbSiUI/Cx/efyBRxrY4vig3YLctxWFbitO/G5SxBFC0IMi0yeRRy5SBU2ktRqIa1OkZXcFhXY6y6AVG0Igg6dIGfoFC3HTBmtyzDZv4Rp207oqscUCKTRwKgUJ+FWncdMkXnnhm6G0GAEUOkOYwYkkBRSQ0/Lt7ByjX7KS2va6d37yUy3J/xY1KYdFZfUvv89cTQ0GAD/3f7OVx18Sjm/bqVZSv39TqrFpkgMKh/DDOnDQRg4tkgsZ4AACAASURBVFmpHjNnTwRBHg2CtCDrMP4XufYSQIboOIC9/mVkyv69SixXHpdZf7osdpwJDB+ZxNLFjVpSeVkdmzfnMWJk91tm55ZKGvCmAwXdJpj3XsNOL168ePHixYsXL168ePHixYsXL168ePHi5STSK8OXo+OCiY4Lbn/H4zAcTTt8b+T13Lrx0xZR5hannUd2fMfiol3cnjKFFN/wbplvR3GIThYX7uL9nN9b9S33kasAeGP41URrT68CPMY6M/M++oNFX20AoLaq56IZuyvCXKdRce/ss5utuo5IiWHyQMnTdvn2bJZtz+bSswd2y/FOFvvyS8kpqiAqSEp/v3rSkGbbZ43K4NvVO9lXUOqp+wkhl8l4/PIp7uhygMhAX6YNSWHe2t3sy5eO2TTCfNfBYmSCwPh+zW1ixvdLdEeYZxaUdXouvoF6GmpN1FU2YLc5MNaZ8Q2SbAhuf/5yFCoFLqeLawY/AsDVD13AxDkjeOD8l7jqwZkIMoFtK/fx75//r9PH7ihKmZw7B47mspQBvLlzPV/v34nddXp6451q/FQaogP92H1QymKYMTKt1X0FAe6Zczb3zDm7U8cY2y+esf3iW7T3iQrmgjEZbfaVy2XIZALBfjpGpbUe5dWdzIhPZVyUFOH0yrY/+Txr+1+yIKhMELg8ZQAPDh1HgPr0s3XyVamaRZiXdTBb4H97d7RoC9fpmRDbuiWXJ5xiy+/M8fVmWiM6KYzaSq+fvpdTQ4DGh29nXs6DfyxmW2nRqZ7OaUe+sYqX9yxnQ/lB/r51Hhq5klkxA7kkXspafDtrFUsL97mLJvspfXh1xCUEqFraMzy9cyGCIPCPAeciILCzSiqM+Mre5ZicNgxKDU8NmkmMLpDnhkjeuL8VZ7KiOJObDcFsrpDsLT4ecx2BaimDTERkc8XBZm3dzY7CYl5e8Scmux2DWs3T508hNsCfw1U1PPzzUgDiAwPYX1aO0yXy8uxzSQ6VPOVX7M/lld/X4KdR0z+ye547nY5srMb/Ya57yt1mtyzH6cjFN2QFpuo73G2N21fgtGdhCF2FIDS3g7OZJPtGc+0TiGLLa4voqsPpqsNpz8Rm+hq17iZ8/J7sls9it67EWHUTiDYEmS+6wM9RqIa0ur/oqsBYdSsO2/GWplacDil75NjPx8f376j17ftgnywiw/25/foJ3H79BHLyylizMYc1Gw+Qk9f555uTha9BQ/80KWNjxOB4hg+OJzryxApFnimEh/pyxw0TufXa8WzYepAlv0v2d+s25Z4yj/PkxFDOmZDO5HFpBPdwkXVBFoA64EMA7PWvYm94B0QrgjwcuXo8CsN9PXr8zmIwNLcns1rsGI1WdLqescf00sicS0Y0izAH+PSjPxg6LKHbLIKOkRQWxKVvfEVRVS2XvvFVs23f3X1Fl8bslYL5iRLh488no2/m3i2fs7e2ZSry6rIsVpdlMShAEiymRw5gbEgKUdruvQBYnFK68o7qw6wuy2J58R4qra0/5PoqfXh16JUA7rmdLuTsLuDZOz6n9DQrbtI3OhSlvGXxy+HJ0s3B8u3Z7M3vflG5p9lzuASAYUc/h6c0rMFJkT0imPeNCSHI0PJBKSJAEqobzC19bx0uFzKZ0GKeSkXj78Zi67xdSeqQeHaszsJHp0GlcbLzzyxSh8Rjs9p575FvMRstqDRKjEdT6VxOEYVKwahpA9i6ch96fy0Dz0pF1Uqh0u4kxEfHU6Om8Ld+I3h/zya+z5EWCsxem5ZWOfaAPj0uhWv6DmZEeAy784oprzUi7wFftBNlY2Y+FbVGrp8+vM2CpN2NXiktxD45cjJX9x3Mv7euZslhz7YEZyLT4pJ5YMjZpPg3X4jPKi7ns7XbuHbsEKwOBzlH0/j6RoRgttkpqKolMSQQtULO9vxi+kaEoFLIyS2rZGBMBEarja82SIV7rztrKMlhQR7HDtD58HtmLnNHDmTzQUkg8jT2kLjIVj9DvF8Ah2obi/JlV1e2uu8xaq0W5u1v6V9+edoA5J1Mza2ztjxvaxQdu4U8/7pxvPsPqUZFaUElYTFB7fTw4qV7SfIPZP6sK8mvq2F/dQUgfaeNdhtPrF0BwA39h5IeFHoqp9kridUF8sbIy7hw5Xv8e+gcEg3Nz6OXxQ/j9r7j3Qtor+1bwc8FO7k2qbEOiEwQeG73YhQyOY/0nw6A1eng6V0LAfh07LUYlBqWFu7lmV2LeWXYHJ7dvQhfpYZySz0JhmAEBJ4YOBOAJ3f+hK/Sh4f6TcdX6cMTA2e2aOsurA4HTy3+nf9edTG+GjVL9mXz9JKVfDj3QgC2FUiLMP83+SyGxkTx1ZadfLR+Cy/OmobZbueJhSv49vrLiQnw49mlq7plTk7bNhzWDah1VyNTpGIzfo7TsR+XIw9Tzf1uoVylnYtc2R+b+Tucth24nEewWxaj8pndbDy5QgoSOiaWyxV9UKhHI1MkIwg+uJyF2C2/4bRL96VW40fIVVJRRZXPBV3+HHbLCozVfzsqlgehD/oSubL1wAPRVUd9xUW4HAePzjMFpc+5R21orDjt0gKxzfwTiDbMdc8B9CrR/BjJiaEkJ4Zy/dyxlFc2sG9/EVk5JWQdkJ7f9h8oocHYsToh3UVwoJ64mCASYoNJSw4nLSXCK453ALlcxtgRSYwdIdlLWCx2du07wvbdBWzbnc/+AyW4XN3vXS8IEBcdxICMaAamRzOwXwyhwSe3Lo9MLRVSVKu7XlDxZBEe3rKOxrYtBzl7fN9TMJu/FolJoQwfkcjmTXnutpzsEt5/5zduv2tqtx7rnuljuWf6WJ5e8DuPXzipW8Y8IwVzgCC1nk9G38Lr+5fy1cF1HvfZUX242f+hGl/S/aKI0wUTow0kWGMgUKVHq1ChkSuRC43inSi6cIgu7C4nDQ4LdXYzldYGSsy1FJgqyakvIa++HJAiy9sjQR/C68M8R5VbnXYaHFacooueLBUSpvHtUr/SI1U8dt2H1FX3Lv+ujhDQijd5cBPv46r60+9zVR6dc0gbHs4BHkTt7iDEz/OKtuyouOnpO5wRG8am7AK2HDjCiJQYd/uGrMPu1+mxnfdB6zs0kR/f/42zZw3FYXOw7Kt1zLljKjv/zKK+xsg/Pvkb9dVGVs3f3KzfedeN58Mnv8cQqGf2LZM7fdwTIUrvy1OjpnDfYMkz/fPM7XydvZNiozdC8xipAcHMSkznkuT+gLTYcIysgjLiwgKoM1pwiWK3e/Z1FZdL5MOFG1Eq5Fw+cdApm0eiXyDvTZrNzopi3tm1gWWHc3r0unIqkAsC0+NSuHXASAD6B3mO6usbEUJaZAh9I0L479qtXDdWipj8ePUWQn11DIuPIjbIn1eXriE5LIi9haUMjY+izmxFq1ISE+hHaoQkHjUVy48fG8DulO4DSmqlv2NPY7clmKcFhbIq/6D7/f7KckqMDYTrWo8g+mTXVozH+YwrZDKuSO98xlStB8Hcp4OC+blXn0V+jiQCPHb5W9z10lwGjGlZI8CLl54m1tefWN/Gh2YReGr97zhcLsZGxTE5tvv9NM90/izL4eeCXegUUpTeEWM1EyNSm+3zcc5aSsx1vDWyMTs0p76MQw3Swt81a/7rbg/R6PmjNJtYXSC3pU7g20ObKbNIfs+DA2MBeHPEFXx7aDPzDm/j+j5jGRwY26Ktu8gpr+RgZTVX/+/7xjnqG+85wgzSOXhoTBQAqWHBLM86AEBuRRXhvgZiAqRszwnJCWSVlp/wnFzOYlTaK/HxkwRhpWYydaVjABGb+UfUumsA8PF7FgCVz7nUlgwDnDhtm+B4wVw16Oj+T6JQj3cL6E3RGO7HXPs4VuNnANhMXxwduyOCecv7MLtlGcbqW0G0I5OHoQv6Brmij4e+jZhrH3WL5Wr93/Dx/cdxY19zdNutNFTMQXTVYK57EYVmartjdzcOuxOFsmVAlidCgvSMH5PC+CbXRVGEotIaiktqKC2vp6yijtLyekrL66irN2Ox2rFaHViP1qqy2hzYrA4QJAFXIZejUMhQqxTodGr0OjUGnRpfg4878jgk2EBIkJ7QEF9iIgPRaVXd/4P4C6LRKBkxJMHtX2+22DiUX8mRomqOFFdTUFRNSWkdRpPVvd1ssWO22HHYnajVSjQaJT4aJRq1Er1OTXioL1ER0rUrMtyfyHB/4qKD8DW0XtTZS3P6DWhZ2+Lzz9YwcnQfVKozVhLtNdx13zRuu+kTjE0WAuf/sJn6egu33zkFg2/HF7odDhfbth5k6eJdTJ8hPdMMH9E8c/bKsd33rH1GfjuqbEay64rJqS+h2mpsv8NRyix17puyk42fUsuHB1a6I9CrrA1U24xU20zYXB0r7nWibJvxTJf6/efh79sVy9U+SrS63ndSb60IadNWoZOCm7M32R20Mfdj0bndTVfGvXXGaLYeKOTBT37livGDCQswsOdQCfPX7yYmRLpBuGhM/06PmzI4nq0r93L7C5djtzl4+6GvefjDm7CZ7Xz96iIev/xNAsP9SMhofhH1DdJjCNSDKBIafWqskY7ZRtw9aAx3DhzNH4UH+TZ7FyuP5GF1npxzQm8iyS+I8xNSmZmQRh//1iNUh/eNxWi2UVhee8rFcpco8sb8P9GolGzYd5hdecXcMXssIf49XzStPQYGR/D+pAvJra3k033bWJC7F6PddqqndUIEarTM6ZPBtWlDiNb7dapviEHPz9szAQjz0yOKIlqVlFmSGhFCvdnC4NhInC4XATofthwqZMaAVCqOLk7mlVeRGOL5XJFXXkVWcTn7ihrTrj2N3RbDw6N4t8l7EXhv+yb+eVbL6ImC+loAPti5pcW2WclphGo7b1uQV9sye8xX3bFr+ndvLkU8Gl1lrDfz0EX/QWvQEBYThELhWVB4Y9nDnZ6jFy+dRQBCtXqKGk7fAnxNEfEkTfYchxoqeTNzFb9Mvt0tmL+7/w9sx9nK6RVq8urL2VNdSL+AqKOTFYnSSvd3Cybe2mz/InMN3xzaRLG5FrVMga9KQ7XNxD+2zwdAp1BjdFh5MGO6u71pW3ciihDl78tPt1zlcfuxc3mzPk36Nv19eMpo7Soqn5nu1zJ5NHJlOk77XmmbtrltpSALRq5IwOk4gNOR3+qYat1NbRxRQOP7KDbTN1I0t213h+cq0FyItVsWY6y+HUQHMnk0+uBvkclj2xzD5cjFZv4ZAIVqiAexvBG5IgWN4f8w1z4GOLE2fITW/4UOz7eruJwuvn3lVxZ++DvlRyr58sDrBEdJ9wW/fbUWtVbFWbOHd2gsQYCocH+iPETFni5sqcjnwS0LaLBbuC9jMlckdq1Y7M/5u/n7lgVt7nNNn5E8OmBarxzfR6MiLSWCtJSI9nf20mOkZ0QTHuFPSXFjtmZebhn33/0Ft94hBcdlZEQjHJcBbDbbqCivp7rayICBbZ+nmlKQX4nJZMNotGAySs9XDUYrxgaL1N5gpSC/okW/xQt3kpNdgvboApZOr0arVaPTSf+0OjVarYqICH/Ump7PgO8uIiMDePDh8/nn4/OatS9fupt1a7KZOq0/w4ZLondMXBB+fj4IgoDZbKOu1kzhkSry8yvZu/sIu3bmY7FIAUGTJnvOSkoM7T795owQzF/PWkp2XTHZ9VIEU1u2J72VHdWH3ZHupxP7d+azY12Ox21jpvXj3MtHkTYkHl0vXQGtajB7bC+vbfwOBTaJQj8mnrtaEdoBKutOfUR6oF6KHq+obf1vobrh1M/zGEOSonj+2nP5+6cL+XDpRlyiSIivjjlj+nP7eWMAyW++s2gNGn4pesf9/ufCtwHQaNW8uuihNvtWFtdwwY0TO33MnkAmCEyMTmRidCJGu43l+QdYdGg/a4sPn/YiZ2volSrGRMQxLiqecVEJxBo69tBQ02Bm36FSBvWJ6uEZdgARlm7OprreRGiAgXvmnM01U7v2wNBTJPkF8czoqTwybDw/H8xkQe4+tpQeafMc15tQyxWMj0pgdlI658Qmd3rB7poxklfqeQNS3Z/5+IWW8wak4nSJbpuf9MhQ5EePc+85Yz32aTp2Ykggz82Z5u7b1titMS4mHn+1hhqrxd322Z5txPr6cW3/IW6LlZzqSm5d+hPQ0s5JLgjcMXhkm8fxhFMUya1uKZhHGzqWlfbpsz+1aDPVWzi4r6VlnhcvJ5twXfcJ5oXF1Tz8r/l8/t6Nre4T5RPMbxNf7ZbjHc/Ejz/m4owM5mRkEGHo3tR8vUJNlc1IIo2WLPV2C75KDTqF2r2Qv7Ikm7GhzSP1L4kfyvSoDO7b/D2fnXUdoRoDfXxDqbNL57MtlYcZFhSHiEiV1Uikjz+fn9VSvH1npGfRurX2tujo7yE5NIh6i5XN+YUMj41CBCqNJoJ17WdpJgUHUlRbR2FNHVH+vqzJ7b7nPNlxEdMyecRRwVyGXNGyfosgOyoeePAo7yiCoEWmSMBpz2r0OhcdILQtJwiyxiABu/kXjNV3Aw5kiiT0Qd8gk7fv7W4z/8SxpQiV9iraWxpSaqYdFczBYV3V7vjdwbw3FvPT28uYcdMkvnyuuQArU8j49qVfOiyYnwl8dmADxSZpAf+tzFVdFsz/SuRkFpOc5hXWewKZTODqa8/ipRd+bdaelVnEvXd+DoBWqyIwSI8gCFgsdurrzG5hNiBAx/c/3tPh411/9ftdmufOHYfZuaP9a8Uzz1/KqDEnN3PmRDlrXCr3PziD119dgtPZGGBqNFr5cf4WfpzfMtCnN3BGCOaf5f15qqfwl2Xd0ub+qIJM4O+vSYb6E2YOPhVT6hRZBWVYbA40x6XibM4ucL/OiGu8kdMdXckrqW7dHmNb7pFunmXnyYiT7Eu25kqChCi2DDbfdajkZE+rVarqTfx73iom9E/ixetnNCsYerJZ8e16fv1kFf1GJZMxqvddiHRKFbOT0pmdlI7D5WJz6RFWFx1iU4n0nd1dWYLNeXoVDY0z+DMoJJKBIdJN4pCQSPoFhXUpWyG7oBx/vYZ9h0tIiw09qV7hxyOTCSx+oa2Ird6DTqlibspA5qYMpNTUwOKjHucrC3LZWFKApRdlNYRr9YyNjGdidCKTYpLQKronwqKtjISmgra8yfeyO7IYOuK3r5DJuDi1Hx/taryZFIGn1q3kvR2bSA4Ios5mZU95aasWOxen9iPRv/MRFznVlR6zWmIMHYvi/2DNE50+phcvJ4sI3cn1fO1JgrVaXlu3jtfXr2dsbCyX9OvH1D59UHVDZPMNyWN4YvvP+Cp9uCJxOBfEDKRfQCQpvqFcsuoD9EopwnxMiOeCwn39wnkgYwp3bvyG/511HRq5kjePWrS8sHsJRocNURS5JmkUF8X1nucHjULB25dewHNL/8Bos+ESRa4bOYQ5g9ou8g1S9Pk/Z0zmxq/mE6jVMiE5odvmJZM1z7QTBOl7LMgCPAvYghR0IoonVhdHEJoHL4g4EdqREwTBF7tFqhVgrL4LcCJXpqMP+hJBFtxm32M4bI3XvmMWMm0hifAKwIHLWYgomhGEni34vfiTVdz37o2MnDG4hWAenx5NQU5xjx6/o1TbpICpaquJMB9fdIqesmJpvLdRCF0/B02KSOG7iTdSbTVJ/2wmFhyW6sdk1514wdSeHr8z3HnNB8QlhjD1/EFMPncAgcHdl5FqPFrbQCmPQ6X8a9riTTt3AHv3HGHRrzs8bjeZbJhMp1c9vtONGecPIjzCj2f/9RO1tb0neLMtzgjB3MupY++Wg83eX3j9uNNCKD+G2WbnpfmrePTSSW4RZOP+fFbuzgXAR6Vk6qBGL78+kcGoFHIyC0rZuD+fkalSao4owifLNwGQXdgyveZk0y8unMTwIPJKJH/IL1dt46qJjVXnF27OZHtu74nu255XREWdkchAX5w9UBilM0y5bDRTLhvd/o69AIVMxuiIWEZHNKaI2ZxOdlWWsKuihKyqMjKryjlQW9krioeGafX08QsiyT+IPn6B9PEPIj0wDP8OWjt0hAFJESzbnE1KTMgpFctPZ8K0eq5Lk84X16UNwep0sLWskK1lRWwrK2RXRQmVlp6/yVHJ5fTxC2JgcAQDQyIYHiZlDST5/TWLRt41dBTzsvdSbWmeGVVmMlJmatt+zk+t4cGRZ3fpuKsLDnps7xPQsd9DTJ/2owe9eDlVXJran76BISR1YjFpzcYDfPb1OlwuEYfDyaP3zSA1Wfqeu0SRF/6zGIDcQ+W4XCKPPXAeCXGSMLhx60E+/uJPnE6R2OhAHrjjHPQ6SWy++taP+fy9G1m9LpuX3lrKz1/exZqNOZw9qqWntCd+mDuXgtpafszM5KfMTO5euBA/jYYL+vblkn79AMgI7Vph04nhqUwMb+5NLiDw72Fz2uy3cMqd7tdTItKYEtEY/dzPX7Ki+uLsG7o0p5NFv4gwvrru0hbtcYH+LLn9umZtQ2Oi+O9VjT+TaWnJTEtr/P3dMrY7IozlrUZ1C4K6nb5t32O7HAexW1bgtO/B5TyMy1UNrnpELIiiFcROZjUKSpz2vZiqbz/aIAV0+Pg+0WGxHMDlbHxmqS/rfCE30VWDIO9ZwbysoILoVmw3ZDIZTnvvCGZ5YptUbHd5USavjJjDedHtL/50heuTR7GnuhCz085D/bte2E+vVDMgoHnW6L4aKeirOwTtnh6/M7z71a0s+2UHP3yxjk/eWsHQUUlMPX8Qo8ennrDPdlHFtQAYtBcRHvhWd0zXI0bL79QZv8Fi24bTVY1c5odCHo3eZzq+2ouRy09tce37H5xBTGwQn32y2h097uXkMmRoAp9/fRvffbuRed9twmzufLZ8SmoE5543kCHD4rt/gsfhFcy9nBClR5qvws26vmsP5KeKqYOSWbwli3WZh0mLCcVktbMpO99dzfrBORPwb2LJolOrmDt+MJ/9toXb3pnP8OQY9BoV2UUVFFZKaWezR2Xw44a9LY71x548KutMNFisHC6rdrcXV9Xx4dKN6DVq9BoVfWNCSY5svIlcvj2bOpOVBotUJCHriFQw6GBJFR8t3YjeR41eo6Z/fDhxoY3VzJ+8Ygq3vCn5RL08/w+WbN1PVJAfh8uqyS4sZ9aoDH7yMM/W5trePE+EkSkx9I0O5as/tvPVH9vd7Vq1kuhgKYrxwtH9uXzcoLZs2b0giYzDQqMYFtp48ycCZaYGDtfXUFBfQ4mpgTKTlE5bbjZSZTFTb7fSYLNSb7dhdTpwuFw4RRcOlwu5IKCQSdEhCpkMpUyGQiZHLZdjUKrxVWvwU6nxVanxU2kI0eoJ1+rdkXvhWgMROgNqec9fcnxUSuZOGsS2nN6zIHS6o5YrGBMRx5iIOHdblcVEVnU5h+qqOdJQR+FRS4Nys5Fqq5kaqxmj3YbN6cQhunC6XMhlMpQyOSqZHJ1SimjyVanxU2sI8dERrjUQqTMQY/Cjj38QsYYAt9WIF0n0fmH8Ody27OdOW+Y8P/4cgn26Vuj5j3zPgvnIyBiP7V68nE6Mj0lgfEznIn+//H4j/3fnNFL7hGGx2lE0ieA+UlTNw/dIPtr906P5cdF2vp6/iUfvm0F1rYlX3l7Ge69cRWCAju9/2sq7n6ziwbsky6bwMD/KK+rZk1VEYnwIBUVV5B4s77BgDhDj58ddo0Zx16hR7Cwp4afMTBZlZ/P5DimiLi0khEv69WNWWhr+mt5pl+ilHdqM1u18Zp7LKYmD5tpHsR+NQvWM4uj4nanVJNBQdR2i2Hyh11RzH/rgX5F1VDxznWjh+54XqyOTwsjemkeUh0XiNT9tJmlAnIdeJxenKLKx3PM1vbsZEhTDynPvPSnHOlNITA7j1vuncfM9U9m0NocVC3fy0j8XoFYrGT9VWtiYev4g0vq3LGDZUeyOQ9002+aIOCitup960w/N2h1OMw5nCRbbFqrq3yTU/0UADNqOFAxuSa29CD9l23V/2uOSy0Zy7nkDWbFsDzu2Hyb3QCkAdbVmzGYbPj4qDAYNvn4+xMQGk9QnjL59O2eVs+KPR09ojifCY0/O5rEnZ7e/41FmXzSM2Rd13DIpqU/YCX8+rU7NdTeM47K5o9i+7TCbN+aSky1di2qqjdTUmHA4nGg0KvR6NRFRAcTEBJGWEcWQIfEEdWP2RXt4BXMvJ0R9k1SKkAh/QiNPr+Ikl44byK0zRvPmL2vZnF2AzeEkIzaMG6eOAGDCgKQWfe6ZdRYhfjoWrN/DtgOFqBRyMuLC+OcV5wDgq1V7FMyf/fY3ympaegeW1jTw9q/r3O+vmTSU+y8c537/0H8XuQX8phwuq+atJv3uvuAsbpjaGLkyMCGST++TImLe+XUd2/OKOFBcQXpMGO/ecRGh/vpWBXNPc21vnl3FbLPz6o+rOVhaxdA+UUQE+kpe8SIYrTZ25hUB8OIPKzFarNw0rfMevJ54d+96rk0dilahYnuFJK5m11SQHhDKwfpqjHYbyf7BaBVKPs7czI1pwwlUa1l+JIerU6To242l+RxuqKaPbzA5tRXU2syEaQ308Q1ma/kR0gPDEICsammRo9zSwL0DzmZ5QQ5TY5o/AD++aSkXJ/Vnft4eIrS+XJEspZx+lbODv2WMIqu6jAO1FcyMT6fIWMeuymKKTHXc0Hc4n2Rt5oa+rUctCUhRw2FaPSPCun6T1VHEhtcQG5qUKDRJ/1yA4PsEgrbzfqMAYvVtiLZ1CIZHEI4ranWM3QeLqTNa8df7uEVaL51jcfEK/qhYD8DIwCFcGHVei30CNVq2NCxjWuRErjB0XyVyL20zLSGZZ8dN5fE/V+DoQIFphUzGY2MmMCOxa+m3VRYzm4qbLz4FaqRF5NTA7lkw9eLldOPC8wbx1Eu/MHVCOjOnDSQosLGQbnCQnv7pjdfZpPgQVq+Xav3syyoiNTmcwABp/6kT0rjxns/c+6YkhZF7qJwDB8uYMi6NfVlFHDjY9QjHgeHhDAwP5x/jx/PJtm0AvRFCXQAAIABJREFUvL95M0+tXMnzq1czOy2Nu0aNIsq3Y/UIvJx5iK5KGiok4crllCxDZIp4VD6XoFANRiaPRZAFIMi0gIL6igtw2ra3MeLxB7AhilWofC4CQJDpsBo/x+Usxlh1PfrgHzpmlSLTgUu6l9b6vwhC5xZ73B7uPcjch2bx+p2fcGivZM257PM/ObxPer16/ib+9f19PT6H9thTXeSuHeCl9yKXyxg9LpXR41LZsyOf915dwsL5WwFYOH8rsQkhXHHjOCZO69fpsZsK5qJox+bIxuEsRhQdKOTSfZ1KmY5M6FyQRWXt8y3E8uNxuWopqboNAEGQo/dp+XzRFiZHFTur5jEu7K5O9fOEXq/ptFDcW9hYMAqbs7mjQUqwtBARpm8768tT32P92+vbE/j4qBgzNpkxYzseGHCy8QrmXk4Ip73xgd0/uPf4QH6Uex3XJryLUub5Jmxcv0R2vNl44/KfW6SbxQLTLmK0A9ocWyYIXDVxSDOLk+M5NnbT8ZY9fXOb42bVraLQtJfJ4ZII/VvJ20RpM9j2etdX59NjJC/zt267sM15Hk97c/XE2PT4VscDuGHq8GaC/jFe+/FP5q/bw6s3zWTSwJae4RV1ktXAOY99yMLNWd0mmDtFEe1R376t5ZIgdFPaCN7fuwG7y8md/ce6heiMwDDSA6Sfpd3VGKVSbKpjZGgscYYA9laXoleqMTvs/Hp4H6n+IeyuLMbqdHJrxigA3tu7AaCFWA6QHhDGD7m7SQsIpdBYy9qSQ4BUVMvssLGnqoRQH2k1dXF+FtemDuOz/ZKnY68r0KhIR1BPRnQdzaRwVYHz0ImNKdoQrb9Jr60roBXBPDEiCJkgSJHxXrG8S5wbMQWlTPIFr3ecfkW0z3Tmpg1gYEg4T61bycaiglYT7M+OjuPvI8fRPySsy8f6MXtfs3MewOyUdKC9kmueqSiu4eePVrJ9dRblRdL5ISQygIFnpzLz+vGExfw17Xa8nF6cMzGDUcOSWPLbHm5/8EueePB8MvpKEW9aTwXKj/6RCsfZhB3/t5vSJ4y8Q+XIZTLS+0by69JdVFa1bbfUFgW1tXy/Zw8L9u2jqF6K0I319+fmYcNwuFx8vmMHS3Jy+PSiixgc4S0091fEUv+WWygHUGrOQRfwHgit1AYRO1/PRBvwNiqfY9GkLlzOEuyW5TjtuzBV34su8D3au6LIZGG4OASAQjUKmcKzV/6pZOKlozEE6PnyuQX4GDT87+l5JPWXLBOf+OYeRpx76oML1pXlneopeOkAZpONVcv2sHD+FnL3lzB6XCo33SXZ2vhoVaxcuofXnv6JooJKrrxpfKfGdrqqsNr3UtPwEQ2mX3CJLS0WBUGFTjOFIL9HUClaBg8ej91xiOr6D1q0y2UBuESTZOfkRrrylVTdTXz4MBTylveoq0vfwOysxeGyMDToSgAUMjVbKr6g0nqQFcUvkqAfQ5LhLFaVvI7VVY/DZWNs6N+osh4CILvuNxQyDQ32UsaF3UOg+tRneHQXsf53YXEUYndWU2Vegd1Z3X4nD32BTvf/K+IVzL2cEDpfH2qrJEHFbj39faDWlH/G3LhXeu14ZyLrsw6jkMs8RvMDBBqkFW6NSkGdqfuiIvoHhvNx5iaS/ILcQvSCvD2EaQ1UWT37M+fWVbKvqpQ9VY0FU4+J7jIE5IIk0KYFhFFnszA0JJr9NeXMy9sNgMXZ+t+Ij0LhtqvwV/lwbmxfAI401LK1vBCb00mtzYLZYUcmCChkMuIMAXyZvZ1Yfe/K7BA000Azrdnjj6skjRNKiRVUkghvWw+a81vdLT2u6+LgyeKP8nWUWyu4OFp6gPy+4CcAQjTBTAgZy29lq1lfsRkXLtIMKVwSMwuAjw5+QYm5FItL8nob6Jfu3vbvrDfJ8OtLdn0u1fYaHul7Lz7ytiOwFhQuZHftPgCGBAzk/Ihz2p37vCO/sLNmL4GqAGrtdV37AZwE9tZl8n2BVHSr0FyIVq5lkP8ALou9GG0P+5ieDNKDQ/nmgssobqhnW2kRpcYGXIiE6/QMC5eiW8N1J56u+EtuVrOiqoIgcG2/ztcp2bhMOge+ePunmBuan8drKxs4sLuAXz9dzf2vX824C4ae2KS9eOlhKqoaCA7Uc+nsYVRVG9mTWegWzNuiX99I3nj/NyqrjAQF6lixKpPhg+Ld21P7hPH9j1sYkBFNQmwwhwsq8TV0LpLW5nSy7MABvt29m/X5+chlMqYkJfH8OdL5fWxcnPvafP2QIVzzww88s2oV8+bO7dRxvJwZOGxrm73XGB5sXSxHbOYl3lHkyqbe9zK0AW/RUDEHp30PdssiLHX/RuP7UJtjKNQjcdg2Hp3zelS9UDAHGDa1P8Om9gdAdIktFslOJU7RxZ+lB071NLy0QV5OKQvnb+G3RbtQa5ScO2sI/3z5coJDm2cBpWZE0X9IHK8/90unBXOA/NJptGWtJIo2GsyLMFpWEB74Lnqfc9scr870PU2f8XSaSYQGvIRCLi3Emm2bKa9+DKt9d5NjmKmuf4MQ/2ePPzpHTDu4MPZVfOTNn28HBV7C/roVjA+7G4C8+jVo5L5MCL+XOnsxf5a+TZqfZIkmF1RMiXiIMks2Wyu/ZGrkqbNI6W4iDFe6X+8szsPu3Nqlvl3p/1fkjBDMb+wz4VRP4S9LZHyQWzCvKu+94klTlpe8QbX1CHbRQrxuGGNDrqHCeoiNFd9Qas5hfsHj7n0vjHkKAYFdNYvIql2FCxcx2gGMDbkGgI0VX3PYuB0REX+VdFGYFnG/x/GOjdUVNlR8xSHjNvf7JP0ohgddzKrSD3CIVgrN+0g2jCW77k8mhd1GrG6Qe85Au/OeFnF/l+bVHRg0agqcLnbmFTE4qXnRFZco8sES6QbZZLUzNr1zPqNtMT4ykbMjEpA18Uh2iWKz98dsTo79n+QbxMtjGsXafoGNPoVXpjQXkZyiiFwQGBAU4Y4Al7Xhxzw7oR+zE1qm1t2cLkXUO8NFt5/z9UfnMyU6ucWcz2SEgHe7+BfUuxgbPIJ/7n2JOdEzpfPLUdH6iaj/o9RSztqKTTye/gACAs9mvkpuwyGS9PFcF385CkGBS5Rucu/a/ggXx1zgPq+oZEruS7m1Q3PYX3+A/fUHeDz9/wB4MesN+hqS6aNv/W+syFzCtupdPNP/UURR5MFd/zyBn0LPUWGt5JX9b2B3NS5QWZxWfi/7A7vo4JbE69vsv7f2T/KN+zg38m8ALC56n1hdOhl+p75GR4OjmjeybyZMHY9TdJDmN5rzki7rseMtuFC6sZ5X8BLDg84jVpve6TEOZRXx7E0fAqBQyrni/hmMmNKP4MgAEEUqimvYtGIPCz74nZfu+C+RCaH06e/1R/fSe3nlrWUUldSgUMoJCtDxjzkdSyv3Nfjw4J3TeOSZ+TidLiLD/fm/OxoXKsND/cgvrOKKi0cikwm4RJE+iR0vkPbUypX8lJlJjcVCjJ8f948dyyX9+hGi03ncX6tUcvmAATz5228dPkZP89iWxXydu43cy/7R6b57q0u4YfU3PD/8PCZF9t707t6E6GqewSDIWrfnsVtWIrqqWt3eUQRBiy7wUxoqZuJylmBpeAuZIhGV9pJW+yh9LsRS/xbgwtLwIUqfOQidtGU52XRWLC811/NT/i4ANpQfJLe+nFqbGbvLhVahJMzHlwR9EEODYxkflkyCoe2MrAN15awpyyWrtpTs2lIO1JVjOy5j7IFN83hg07xWx9g569F26w/trDrCZas+aXOfCeHJvDfGuyjXHrdd8R5p/aO56+HzGD8lA4Wy9XoFiclh2Kydz/iQ6FgdAlG0UVx5CzGhP6JRtR7MYLZucr8WBCVhgW8gb2KD5KMaTnToAgpKp2NzNC7a1Bm/JdjvSQShaWaWwMTw+/m9+CXUcgNnh94BgFre0smgxnbEHTnuq4ygzt4Y0GZQSgFU/qpo6uylHfq8Xrx4wpuv7sWLFy9evHjx4sWLFy9evHjx4sWLFy9evHAaRJi/nXMtMyLuIUHf6BddbSsmQNXotXdHypROjflb6YcMDjiPQFXnKuxur14EwJaqX/BXhWF1mhgVfDF99CM6PEZ2/XpKLXmcHXJl+zufBvQbnkjmtsMA1FWbyNtXRGL6iVUu7mkmhd2OXFAg4uKDA1czNuRqgtXxnBv5IIW513FRzNPN9q+xFZNZu5LL4v4NCHyX/zAl5mzCfVLYW7uC86MeIVTTB7GJG2Vb47XF/ro/KDJnAlBvLyNKm0GheS+F5r1cHveSe795BY8RrZWikeN0QwhQRWN1NjAx7FYOGbfiqwxrMmc6PO9TwfVTh/H3Txfyt7fmMSYtnqhgPwSgss7I9rwiSqol382IQF/um929EZ7HR2Z3Z6S2vMlY3TGuvJUx/irR5WcSCkHB0IAB7KrZh06hJcNXSldWypQUmosotZTxXOZr7v0tLgt2l53PDn2LxWVx+4ubnCZcYmPmQaqhZQ2A1ig0F5Ooi3dHpyfq4sg3HWkzwrzUWk6sNhoBAUEQiPbpnef6bdU7mkWXN2VT5ZZ2I8x7OwHKcG5MehmX6OSdnNsZ6D8ZX2XvLcD59WuLcTqlaKZ/L7iPvkObf8eCIvxJHRLPiKn9uP/8l/nujaU8+uFNp2KqXrx0iOefuMhje1REAJ+/d2Oztv7p0bz6zKXu90MHxfHBoKtbHfvnL+90v/7Ps53LHvli504mJyYyd8AAzo6P71BGlkwQ0Kk8+K73AO9nrueihP6EaE7cLqotnGLHoie9gEwRh8uZ737vsPyOSteyMLvTvg9TzQPdd1x5+NEo8zmIoglT7UPIFHEoVJ6fqeWKPqi0c7CZvsflyMVYdQNa//8gk7eegSG6KrGZF6LUTEYmj2p1v+5CFEWWf7GGX95bTmFuKQIQ2UfKRJ15y2SmXn02Qiv37J/mrOc/+1ZidXqOGK63W6m3l3OgrpzlRVm8wDLuTBvPnWmt23F8d2gb/zuw8YQ/l5eTx9uf30Kfvh2rJxEQqOeZ109UTxJQKZNRyuMQBAUOpxSJbbXvRhSP3Uc7Ka26h7jw1bQWa2tvEjWuVqY3iy4/hkzQEuL/FIUVV7jbXKIJs3UtWs3EZvtG+PTjvOhn2VPzM3trJf1tSOBlyAUlDlejrV+AOo5Ss5SlW2cvxlfZmPlday8CpCh0X1U4Xrx0lV4rmJdacgEIUkWT07DBLZgbHdVsqfqJqeEdSzv3xOSwzhc0bMqQgBkMDZxJg6OSH4+82CnBHLpWKKu3MmHmYL5/f6X7/aKvN3Dn054fJHoDDtHG7yXvYneZUchUWJxGXKILmdB6ylOl7TDVtiK+y3/E3WZzSR7Xs6KfZHPV99TaShgeJKUSJnby+9CUVN/xTA6XUo9+K3lbOr41nzBNCk2/OWGaZMqtBwHwkfthcdahEFQoBCUO0e5xzm3N+0TmfKJMHZzCh3otX6zaxp5DJfy59yCCAH5aDQnhgcwdL1mdXDy2PzpPxbS8eDkNmRI2ni8Of49BoWd6+GR3e5RPJEHqQB7pey8yQYZTdCITZOys2UODw8i9KX+jwSGlUK+v2NxszM5YPsVoo9hYtc29YJbbcIjB/m0XPA5VB5NvOuLuU2QuaXP/U0WNvabVbVaXFbPTjE8Xfcz/LP+WvIYdAKQYRjA6WCqovLzkE+wuKwWmLPr6jiKzbh3TIm4mQTeAbVVL2VO7GhEXcTppoXNCqPSQs6b8e/IadiAiEqAK54Kouzs4E5FqWwkuXKhlUp2HhUXvUGktxO6SiislGQa7j/ND/ovYRSui6OKQcQ+PZHyPgOCemzSiNL9jff4o+5rchm34KoJpcLb+M22P3etySB8u+c0eL5Y3JXVwPP1H9WH3+pwuH8uLl78ya26+mdBW7FdaY1qfPoyNje2hGTXSYLfyyu5VTIzs02OCeUZAOBtn3dsjY5+pqHwuxGH90/3eXPdPnM48FKpRCIIel6sEh2U1NsvPCKhRqMfgsK7rlmPLlf3QBryFseomEO0Yq27CEPwLMoXn4nw+fs/itGfjtO/EYf2TurJRKFVnIVOmIghaRNEMgOgsxunIwmnfD4goVEPhJAjmnz+zgO9e+YWJl45m0twxiCLk7pSCyt64+1NKDpVzzRNzWvT7Km8LL+5e3qxNq1CR5hdOoFqHiEiFpYED9eU02BuLJ44JbdvHfWxoorvOkvtYuZupszcKjlMj+5LkG9LqGApZ+2YEqX7hfDfxRqqtJmpsZmps0u9hZXE2G8oPttvfSyMdFcsBND5K+g2Szt2Zu4+Q1j+ahjozixdsY9T4VGLi2w6k0GomEOr/LEpFy/syp6uGqrqXqGn4FACbIw+j5Td0mqkex3K6Gu8RFfLWrydazXgUcqnOjsN5BACTdX0zwdzirGV58QuoZFpsLhNnhd7m3haojqfBUc6Swn/R1+8cEvSjyDduZknhUzhFG2NDb3UX/TQ7qlhe9DxGZ6Xb8/x4NhaMQhDUjIj+Q5qTq5b8mjepNK3A5ixDLjOgV6WREPAwOlVfj2NUm9dQVP8/6q3bcbjqUcqC8PcZRbTvLehUqR77gIsq0yrKTYupt27D6igBRJTyYHzVg4nyvQGDemCrP8eTzbaiGRht2aQGS8GbofoLPe7nEi1sKBiF09XAwIhv8VWfGTWJeq1gnlO/AYDhQReytvwrAMoseayt+IZy62F+LXqVZP1IUn3HsLT4HczOehyiVAhtUthNbK5cgEaup9J2BL0iELOznllRf2dj5Xx2VC/iopjHCWlSLXdP7e/sq/0DhUxFkn4YA/2ntTNDkXJrPkHqGEBsNoemxweazSHN92wKTHv5pfBl6h0VnBN+O8Hqnr9R7SkS0yMZNSWDDSv2ArD4mw1MuEASOPsN7z6/6e4i37gDi6ueC6Iew+KsJ6tulXubIMhwiDZExGbCU5AqDl9lCJfEPoeADJfoQDgqsPurIpke8QAWZz2f5EkLMbcnf9PmeJ0lRB1Pdt0aaBIJXmLOJlE/kiprgceRm84ZaHfex+Z8qhiWHE2FYR9PB8/FoDBwyHiYFaW/MT1iDA0OySN/a/0mIh0RKGVKcuoPEKeLRSVTuV8rBeXRPtLfbrRPz98ce/HSVQwKPQaFHhGRYHVjJEaYJoQpoeN5NvM1ZIIgeYX3vYskfQILChfxYtabBKj8AIjVRnf5+Mn6RNINKTy972VEEQb59yPZkIjFaeXjg19QYJIKezlEJ4XmYi6PuZAonwgG+GfwxJ4XCFUHE6Zp/SHrVOKvbL0Irk6h7ZBYvrduDUfM+wGotZcTq0unwJRJgTGTaxOk8+pXh/5FrC7D3SdBP4ggdRQWp5Fzwm8ir2E7/spQdtf+wbUJzwICnx+SaloUmXOI9ElmZ83vzIl5kHBNYoezfartJXyc+yAV1iNcm/gcarkkmE+PuEXKnjoaXfl69k1MCL0CELg4ViqotqjoPWYFTEZAoNpW0mRuuOdXZM5BLdOSU7+ZG5NeAVHknQN3dGhunqivMREc0bHCxIFhfuzdlNvlY3nx8lems2I5gE6lOikR5mtLD3ojv3shKu0cHNZVANjMPyOKVqwNH2Llw2b7CbIgdIEf4nIWdZtgDqDUTMXH73HMtf9CdFVjrLoOffBPHr3UBcEHffD3mGsfxWaaB6Idu3UlWFd6GPkYchDU3Tbftvj1gxXc/cb1nHPNuBbbBo5L46NHv2khmDtFkbcz/3C/vyX1LADu6DuuhXe4SxTZV1PMb8X7yawpZUhQ27U+xocnMz68uZf/ooI9zQTz6dEZnBedcXzXTqGRKxgQ0PKZy+SweQXzNrjx4re61O/jH+5s0fbNx6v513+u4N2XlpA2IJoPXlnC02+2zBQ5hkY1hMigzxBaKfArl/kT4v8sLpeROtN3ABjNS1sVzEXR2qRvS6/xRgS0mrGA5F8OzaPTATRyP2ZGP+95XoKSWTEvNWs7Xgw/JphHagcyIvjaNuYiYXOWYnOWA7Cr5HIs9iP4KBPxUSZhsR+i2ryG5CDPtR0OVv+bI7UfAAJqRRg6ZQQWxxHKGn6i3LiIvsGvEayb3qJfpWkl+8qkOkkyQYVaEY1MUGG2H6LcuJAK0xL6hUmLFf6aMe1+hp4mwnAlByqfpLj+a6B1wbzStBynqwEfZfwZI5ZDLxbMDxqlCK5RwZeQXb+OMkseoZpERgRdxN7a3zkn/HZAsjjRyA1Mi7iDmqNG/ytKPkCvCCTdbzx/lP2PEYEXsrzkPQBG/j975x0eVZX+8c+dXjKT3gslBELvAtK7KAL2il1WXVfdVdfVde11Xd217qqsXVBcCwIWpEgH6S1AIKSTkJ7JJNPv/f1xk0kmM6kkEP3l8zw8zJx72sxk5p7znvf9vuGXUlz7RarD5rGwp2wVC3v9o1WGzT3l35Fh3YXNY2F69G2kV233mUPD8QG/ORhUIVwc/wCF9hNsLfmcefEPdsh7Foh9OzPZvSMDV6OkEHfc7//lbS93P30Zx/bnUF5chegReezmxQA89Np1jJnW9gRhHcXS7Ae8n+f8hMcAiNX3Y3vJUr7K/RtGVRgR2vrTeQGBfqZJfJL5B4JrE0XMS/gbIZpYhoZexLLsh2SDsyRySeLTqBQaPs9+EJWgRkJieOjFPuM37m9ewt9oD7H6/iQaB/NZdt3fiUSvoNHE6fuTbtkUsE3DOQNtmjfAkYxCamxOhg9MxOl0o9MGvqF2JNOjprK1dBt6pYGRoSNINCSSoI9na+k2APqZ+hKti2JZ7v9IMMSTac2iwlXhfTw9epq3TUfx1L9WsXrjEb/yVR/IxqNgc/u8VBtz/1P/Y8e+LObPGsqDdwRejDSHWHQe1J7uK6K2gGBEsv4HyfEDeApA0CGoh4JhIYK2FdnUxWKk6g+QHBvAkw+4QSF7PQja8QjGW0DZ+Un5pJqlSJbH/coF4yIE0wNt68xzSu7Pua32eTaIVlCYQRGBoB4M2kkIugubmZADyfYZkv17cGeAVCO31YxGMNwEav+krc1R7qxgVoxvGKLV7iT/mJ5x5nnMGFK/0dEqNDw58KGA/ZyusBJVMI7E1Jb/9g9mFzK4hxyaOD9+DvPj5/hcr6xyYcwdygtTbw3UnKsSF3BV4gKsdicrdqWR65Do17xjOks37yMlNoJRyQk+47eF7/ccY84I2VNjQ9pJJg9o2rNqZNhwPs/9Eqfo9Ls2LaoVf//AQPMEn6SfAMWOHGL1KdRF+8Tq+3DaXr8ZNCrN2D1VqAQ1KoUat+ik2JFDmbOAj7Me8+nfIcoeWFclPcLWkq+pcJ5mXMQlpJhGtTi3OkmWZTnPU+ksJkbXG7fk5IeCd3GKdlS1GyC7x+oTQbW5+H+EaWO9yUubm1u1u5IoXa1kjyAQdQYH+2FRZgqySlpVN/9kEWFRwe0eq5tu/j+TZ7EQbTSiVjYdNZldUUGwTkeIrvUJE5dk7Oax3d+3qu6L513MZT3rPeP+fmAdq/OOkW0tB2DOD+/4tUm/8mGUQr0nq1IQOGEp4dm9P7GrJBetUs2I8HgeGSZLcPY0+Yb7P7xzFctO7vM+f3nsfBb0CHw/fnTX9xytOM2L513MM3tX+/XfuG+A3OoKXjqwnp3FORTbrD5Hm9F62Ti0dV5ro4O6GgoMoXJEq0o3C2fN53hch5DEKgSFEYUyEbV2Ktqg2xAUYYi1XqEdidZ4G6I7E0f1R3jcJ6guv4Og8I9qr/qaKgRBjyHkn2iNv8NpW4bbsQ3RkyfPtzZxoKCMRqnqi0o7DrVuLgrl2ZFjsNc46D8msDTegLEp2G0Ov/IiexWlDjlq0KDS8MeB04DA0egKQWBQaByDQrumHF43bWP6HN/FsyAIOOwuvlqyjaGje9GzdxQqtZLsk8Xs23mSO++f00RPoNWpqSirxlplY+4VozmwO6vZscPM9zVpLG9cr85gbnfubflFAUILB1RadX+f5073yVb121lIkpvDp+U9j1HTnyExn6NRyt75ouSgwrYFrcr3O1dk/QaAvMp30KkSSI181esRLuEhr/Jdssr/wbGSBzBpB6NV+e7Pwg1TiTPfSJh+MiH68QjI92y3WMHR4vsot20mu+I1AEJizr3BPMq4gMzyF7E49gBQ4zqOQe2fWPt07fsSHdR11SbaQ5c0mFvdpVS55JOeL3OfxinWcLxqB1E6/w1ymTPf66EdUqtbVOk6TZAqDJ0iCJWgQRcgq25Dyp0FRGiTWu0FXCfJ4hLtvH/yHoaEzvKZQ8PxAb85hNQaY0M1cVS6ilo1Znv598s/cMefZqPWdN5HHR5t5oVP7uBvN79L0akKbDXyguCJ295jzLQBXHjdOEZM7ItK1fQCvqO5LfmDJq9d2/OfTV6rk0Opo8LyMkplNH30/eilnYlBN4sa+2qc9u+wS9VcFifrcDldR/B4CgCwO7bhdmehVqcwMXwyDocBjWYQDufO2npF6HUzcbnTAdBqRqJW9SbVPIVU8xSs1UvwiBWMNcei06ZSZf2AMeE3MczUFwCX6yiCoMXu2MaoIBNqlZ0oTV/s9s1oVDGEmKKpqv6EPvp+pOhlnUxL9XuYg+5AdB+l2p3OFQl/QK3y/z69u2wLAAePybpfQ/sn8MCLX/PGY1f61e1o0ixHkCSJGk8NSkFBpauSAnuB97pOKd+AkwyJ1HhqSDElc9pe5H3cuE2srvVhbU0xZngvBEGgwmLjdLGFrLzSM+6zs5Gcu5Gs/4KGCxDJgeTYCI6NEHQnQtAfm27vWIdU8SfZGNwQT5Z8vSYLybYMwfwsgn5BJ7yCBqh6IehmIInlIJaBOwfwtLkbqeZjpKoXQAqgay2Wg1iO5D6OIJZBUwZzTy5i+e2+7yuApwDJ9i2SbQWC6SH5MKEFNhZv46fTP5NqSvHTHf98y36umTAMg1bNgewCThSU0i8+kpziCqodTpJjwgkPkj2KNx/NYmjPWAYmRlO3vdpw+CSTB/ZQ1k33AAAgAElEQVRmw+GTRJiNnDxdypAe8ndBr1GTlneawT1iWH8og6mDkll78ATxYWb2ZRbQLz6S4b3iaLhVa1hv+uA+zc4T8JurXqPmUM5phveKp6jS6h1/V4a86c4tqaR3dBgZp0ux1NiJCg6id3SYdz6CAOmnSiitqq7/yMTmPbHDNWHc3+8eluV+CUC+7RRBqiAmRY5nXtxFLX4+TRGl7cmRym3glaQ57jVwlzryIYA2aaQ2iWB1JNf3eBJBUCBK8t+vUGscCtXEMi/+HmyeKt46/nvuT/3Ir4+mmBa9kGU5z5NiGkWm9QB2t5XLkx7C5pHzPhyurD9QPVjxMzZPFTMj6/XbG86tbk6i5EEQFJQ68iiyZ3m93ksc+a2eV2NGTOnPD5/K95bNK/cyYe7wgPU2Lt9N+r5s5t7k753XTTfdtMwT69Zhd7v55PLLm6zz+Nq1GDUa3rzY31miM7gocQCTY5L5Pu8oHx/fxYvnzSXR6BtxohAUfs9v+HkJ46J68PiI2RTUWHj36HZu3SR7JP54we98pCIeHT6T21PHsvV0Fo/v/qHFOR2rLGbhz58G7P/HC+SD0rr+HR43N21YikpQ8OJ5FxOq0fN19kE+TN/JA0OmsrDP2fOi0wc/gT74iYDXDKGvYQh9rcm2QeFLW+xfo5+PRj+/2ToKZQIhcbkt9tXSfBqjD34WffCzLVesRalORa9+rOWKZ5ExFwxj/4YjJPbzN2jvWXeYMXP8733qBn/7NW4nudXywVKSMbTzJtpNl+DaW/3XOs8+/AW33D2DBVeP8Sn//ps97N4hR9/NnOsv1REcauTPiz7g3kfl33Vbtf/hTEP02tYZYdWqniiVkXg8xbg9BS03aAV1kix1eMTyDum3jt6mCT7/t4a6tW5qxKveaHwAhaAlzDCtUW2R7Ip/eZ+lRLzgI58ioCQx+A7KbRuotO+koGopPUMbO3kpSA7zd6RUKULoGfog5bbNWB0HWj3/zkapMBJlnO/1MC+o+sxv/k5PCRX2zYCCKGOtXGWOLLH47uFfcIsSe4rzmZHYh7emLECjVPJz/kle3rMJtySSHBzOc+NklQCzRkuWpZwHNn9HT3MoR8uL8EgS/5o4l36hkX7XAJ/rAP/at4Xvso6iUigI1uh4c8oCwnTtc3Tskgbz41U7GBchG+iGh16IW3KyNPuvjI+8BqWg8upzAoRrEjllOwrg9TCv8w5uLSHqaEqdOcib4NZLZ0hIiHj85tDS+BVOeZ7lzlNe43lnMXH6AA7tyyEs3FcvsE7zqjEfvtzyQrMhCoWAzqDBEKTjijum8f7fv6PGWh/qtWNdGjvWpaHVq+mREkNC7yiCzDp0Bi0KZcu6aE1xYwd6yDePEpPxeizW+tBEtzsLUawhxHwfFuu7SJKDYNPdVFbJoVVuzym02rGoVb0or3wWtToVh3OfTz1BUCDWeQQLBp8RJcmJQmFClGwolTFIyEa+upAlQdChUIT4jONw7kOpjMbu2IEkOb1zMwXJxhGNcyAadSpO10FEscJvzDr2pslGrLeeuIp7nv4ClVJx1pJKDg0ZgkfyoKy9UV2ReBkCgp/he2z4mFqvSQW9jL28jxu26ShmTx7A7MlylMTeQ7n84bHPO6zvzkKqfAgU4QihbyFoxoGgA/cJpKoXkRybkaz/BvVQBG3jBQDgOohU8QfZsKwejmD6E4J6GAhKcMkHPFL1v5HsP9aOE9o6j/V2ImjGgmas9xMVi6fWery3HqlmGZKlNvGuMh7BeJPct3o0KEJBLENyZ4BjHegDnIhLsqFWLLtVPjRQRCKYHpTfW0UwuE8iVb+JZP9JNsrXLgQF3awm5zQpchyTIscFvCZKIobaiI59Wae4YfJI3l+3C5fHw6KZY/hk4x6cbtnoesu00X7tc0vl35WckgqigoOw1Di8/UWag3B55LZOt4cD2QW4PSI/7E2nT2w4abmnaw3m9QgC7M7IxyP6htIHmifgN9frJ42gf0IkqfHyAqZu/MIK2ag7MjmepIgQjuYXYdRpsDldPvNxuN3cMm00762r12ufOii5yfe2jgHmVJ4Y+NcW67WFBEM/ehgH8WHmI0hAn6CRJBhkTcMjlsAh6qGaGEaGXcDHWX9DEBReuZRrejyGSqHho8xHUNZG+4wOa5sxP0KbQKIhlZ1l3zE4ZDKbi5exNPspglTyRjta1xMAm6eKFafeoJdxCEuznwLgkoT7feYGeOd3TY/HiNAm0jtoOO9lPEiIJpqwM0iYdPV9F7BpheyV8tztixl/0TBGTx9IRFwoSFBSUM7OtYfZsmofIREmrr6vaS+qbrrppmkOnT7N9UOb1z09PymJD/a2zlOwIxgYKv92pFXIyeSGhMXRN7h5OS+X6GFOYn/+Nrw+4i5IreWZvbLO897SfEZH1ke5GVUaepvCKbZZWzWnGreTK3sPC9j/3lJ5jVHX/6HyQrKqyvj3+MuZFCM7mgwKi+W7nCOklRcSpD47ch8dhd3jZvpXi/nnxIs4L6Z9kYKzvn6PC3vKEV/3DR/fkdP7VbHkheXex4JCIL5PDIv/+hm71xwkKTUOUZTIrXVA2vdzGg8u/p1fHxG6IPqYIzlhkR0Fb9/yKQBPDr+IsZFdT9q0m85lx+bj3PC7qX7lg4f34N/N2Gh+/9CFSJLkTSr7QMB8cgIgIQj6Ju0AgVAqwvB4ihHF1v2+toSikdySJNY0UfPsERMk2x2FZnLa1VHtPIbdLdtLVAozIboxAeuZtMOotO/E4tjdprnoVPI+UqyVmhYlJwrh3OdwizVd6zWYF1m/oVfon1E0iCYorl6BJHkI0Y9Hq5JtN4/vWAPAiotvJFxnYOHqZdw6cDQapZJSew2PbP2R5XNvIFJv5L20XTy3S5bXeuF82c63qyiPh0ZOZnR0Ah8f3cs7h37h5YkX+V0D/K5f128Y9w4bjwC8uHsDX2cc4taB/vvm1tBlDeazY+s9fVWCBrWgweouJVLbgypXCV/nPcfg4BmkmM4js3o33+Q9j7vWe3Ba9K38Uvq1X782j4XNxUvIr0ljs2inl1FOJDos9AKGhMxiWc7jqBV6ehqHMjy06bD8PeXfcdK6G5uniilRN/nNoanx6xARWZH/D6zuMmbF3NlkvY5gy/ojzLp4WKuN05+9uaZT5uGwuUg/kEv6gZa9ElpDIIP5Ccs39DF3rMerQmFs8DgEa80yRNGCQlEfMq5URGCtWYYk1R8UKAS5nUY9AFG0oNWMxuU66q0nSW6UinAA7I7tGA0N5i0oqMtC7XKfwOk8hNN1EIVgri1Lx6AeiEcs847jcP6CQghGQInQYM5C7Vfc4ynC5T7hHddvTO9rlG+0Uq0DpyhKLXpzdiTKBjeq5gzfDb2RGj7uSGP5rxbJiSL0PVD1rC9T9UMIeRupdAG4jyNZ3whoMBctz8rGclVfFGEf+eo+quWDAyHkdai4G8m+GsnyBELkGuDsRY+0CakKqepF+bG6P4qwT0BoFHGkjEVQDwT9vMBdVL8nP/BkgaBGEfYhqBp4hasHIIS8iVR2NTj3IFXJ2nqCbgZNZZNvjgEJ0Xy8YQ+9osKINAWxYtcRooKDKK+uX1D2ipJDxj/asJsRveLRa9QcLyjmaH4xwQYd3+5Mw2p34BFFQox6dmfIBoB+8ZEczS/mSF4RUwb15t73vuXVW+YhihJVdjtDe8aRUVjq7QtgUn+53ss3zW1xnoDfXBuSWVTmHb+OOqO7IAjekPx+cZHe+ZwoLOHbnWk4XO6AfXYGA4MnemVLAK80C8CEyMuZEOnvvTkzRo4sSDLUy5DVPR4aMo2hIQEOqICbe/+9TXMLUoVyZ0q97uXF8X9osS+90sQjA/4X8Fpzc5sWvRCiF7ZpfoGITgznmaWy5uazt73L5pV72bzS32AXnxzFo4sXER7TLcnSTTftodxmI94cWGu1jqigIMpstrM0o/ZzTbKvN+6QsPrD3LzqCh+DeUf2n1ctHzrX9V9Vq/fcUE9aoHXJELsiUu0CvznZno6m0mlHrZDHM6g6X+Kxjs9X7UatUnLp7GGtqp+dX8ZfX/6WT165qVX11y7d4lcWHhdKzrFT5NQayhuWv/fYMsbP95dde2TIbBZtXYJbFMm2lgFw06aPSQ2O5vKew7kwYRBh2tYbOLv59aI3aEg7kOuXsDPtYC46XdPfnZyTxSz970aKCiqIjgvlmtsmERzi+zejUJgQRUut3rhIa/coXvtGAAc6KVDkbgsoBF85MFE69/cjg7r5BLoNqXamex+7RQubsvylSRri8gT2oHd5yiiq/oZK+w5srixcYgWiZEMUG0cHnD07THMYNf0xa2XbqcWxh5Lq74kKqrclnbZ+BfjKsThF2Umq7vdfrVB470F7ik8xJCKGSL1st1rQewAXfvuBz5gxBpPXIJ4aGsmPOekBrwW6/nPeSb4+eRijWkNuVQUzEgPLZbWGLmkwvzLpSb+yq3s8G/Ax4NUzb8gFsfLmbEHCXwC4IukJAGbG3BFwzKEhs1tM9FlnRA9kTG88h7rxA82hrymwZ2FnMGBoIlExwQSHGNviPP+rpNi2r8MN5uag233+b3yDaaq8DqPhEmQJCSVazTCfehrNYKDeqF2HyXiDz/OIsFcBqLGtBcCgn0GNfTXBpnpDiTloUTNzg9Dgh33GbTxmHTPPl70l737qc/IKK7jj8c9YMKMFgeJuuhSCZrSvsdx7QY1guBbJ8iS4DoFYa7RURMn/e7LBJXuBCsbbmk2SJATJBnM8+UiOzZ3qZX4mSPafQJI9mRXmx/2N5a3pw/al97Ggm+trLG+AYLgeyblHfh8BXGlt1jQHGJ/ak3H9engjO0RJ8onyuH7SCO9jUZS8h1zPXisfIqbGR/q06Z8QhbLBxv6Za+rvc/9ZJC9q5ozoF7AvkI3cE/r3RNNIUivQPIGAc637v1dUmHf8/glRPv1deb7v70zdfAYlRfu9B920jg/W7+amqSMpqrSy/lAGV40fysHsQo4XlqBXq8kqLifSbCQ5Rj681aqVLN20nxunjOR4QYlXVic+zOxt//2eY1Q7ZK+X5JhwVAqFt786jflApI6UPeX+u+1JNizfzYEt6ZQWyoapyPgwRkxOZfxFw1Gpu+jhWzfdAJd+1rKsRUfy1dXXtKl+kEbDqaqqZuuctlrRnkVjaXuJN/oenGkU9XOu24R3Rv+N+z4vMolwnZHXDm8iVKsnVGPgm+yDFNRYvJrqvyb0KjVbrgi8F+4sHt++xuuRPiupeeNSR3LVRW2XyxHasNb47/62HXg3xflRvXl/wkIe27uSzKp66cejlad5Zv8PPH9gNROj+3BZz2FMj+3XvR76DTP3slG8/uIqDu7NJrlvDAgCJ9MLWf/jQS6/rmkZlTdeWMXtf5xFfFI4+TmlvPn8Kl542zfhpVIRiihaABGn6zgaddNrtjpEqRpXrTe1Qgiisa1BbIecSmMje2u01DsbRRsSA7ul+nusUmHA2EiTvTE6lX9+qbKadRwt+SMesRqVIgSzdjjBqjGoar3vcyv/0+r5nE1iTdcBssG8oGqp12Be7Uyn2nkEpSKICEP9PvPPI2TZoQWrPqK3OYwoQxDjYnsAoGhkmAx0LGBQ+/5t1BnbA11reD3TUsYr+zazZsGtGNUaXtu/Faen/Y5XXdJg3k3HodWpORzAq7spSZZfM0HqBKpceZjUCS1XbjdNncY2d0rbcGPS0Bu6bV+/YJMcjeB2Z2E2BYpMaG4OrRt3fq1xfNTgJDLzSumVEE58dEiT9bvpgjRz4xbUQ703JMkly0gJWtlwKTVI5iJozmt+DFWqnCxTtIBzN3RRg3ndAQCKcFCPaL5uIDyF4GngJaQOrL0MICh7+NzsJXc6QjsM5uBrdG5uY1Rn4G6uvbKVXnBN9aVVq7ji/MCHZq2dZ3toOJ/uzeGZERVcL8WTWVSGTq3CbNCiVAhcPm4wn2yUvyfXTBhGv/gI+sSGs+7QCa+szvBecd72uaUVLJoph59+snEPZr3O219r0OjUzLxqLDOvGtsJr7SbbjqXfYUdo+HaWQyLjeV/hw9z84gRfptJh1veLH6VlsbQmLOTBPFM0Cs714jS2v4NKg0fTb6W69Z/wuVrPkSjUNLLFMY/xszjwsTmDSXdyIfpm09lew3mZ4MvvtvD9n1Z5BdWMHJQIg8uqpfe2b43k3c+24JHFOkRJ0fr/XnRTIKM8j3M4xF5+vXvycgpITTYwBP3XkiwqX26t21hdEQPVs64k1W5hwH4KGMHh8rl9adHEvm5MJ2fC9NJNIZy74CpzE1s3/qym67N9bdPITouhBVf7GTrz/I+LS4xjN8/eCEXzGt6DxIVE0xKfzlKJqV/HGGR/g5CWvUgXG7ZqaeyeimRIU+0OJ+qmq+pyx3lEcsps/yTMPP93us253a/NqJY7VfWkMaa5XXR8r8WlA3kbLTKWIbGtk2u1eUp42jJfXjEGuLMC+kd+ojPoYHLU9ZlDeYRRlky8WT5M1gcu6lxncCg7kNRtZzsM9J4oU8EwfEK+QDw94PHcVkf39+sEVFxPLHjJ4psVqL0QSw/mcaEuJ4dMk+L00GwRotRrcHhcbM29wQTz6DvboP5b5yFt09Bbzj3ukdng1JHGtnWn1AJWurc6S9I/PDcTqoDqcs6rW7FiXB7OXy8fjMYajZQYbFRYbExMMU/gaYkwQUL5YQ+1TVOvn3vLsIahH8tXrqFD77YBsCS128hKT7Mp/3S5Tt588MNXD1vFHffNMXnWmZuCZ+v2M3ugzmUlMmaaVqNil6J4cyc2J95s4aiakFm6Kl/rWL1xiO8/LfLGDO8F4fTC1jyzS8cPHqKKqudYLOe1ORobr16PCm9oprt60zZuV9eoHy2fCdpJwpxOt0kxIYwe/JArrx4JJqOTMorNBOOrWjwGTT2ChDLGtRrXl9UrhMhG8zF4rbN72ziKZH/V7bzEE30TcosWR5DsrQyyZRY2b4xuxhxoc2H93fT9cgolBeoddI6WrXSK4UTpNNysqiUfnGRZBaV+bRTKhSUWGrILCrzSuWAr5ROw3LAp79uuummY4g0GukTFt7mdotGj+b6L75g/iefcOvIkSSHh6MUBE6Wl/P+HvlgLKO0lEenTOngGbfMr/nYc2XOYcJ1BtZddBfBGl3LDc4iFqeDIZ++yrYr7+SVvZv5PuuY9/D+lgEjuX9EvbzY0E9fw+K0IwH/nXEZ0xMD5wN5ff82PkvfT1FNNa5aj3uzRsvHs65kaKS8H7DXeuvdt3ElP+WcQKNQMjmhF0+PnYlJI+9XrvhuCWllRVS7nCxa6ytVevfQcTzQYG4dyRUXjuCKC0ewYu1BjmYUesvLK2v4+ztrePf5awkPMfL5Kllb+I2PN/CXO+S8M7mnynnuwfn0jA/j3c+28N4X2/jjLYFlyxqzaORfWqzzzu4XmrymFBTMS5Kjj+clDSbdUsTX2ftZmXuQYru8F8qtLueBnV+xvjCdv4+6BGW3Q8FvCkGAWXOHMWtu62SE6nDYXfz90a+Iig2mILec8jIrH/9nvff6wjumoteOw2pbBUCF9b/oNMMwBZBn9fbpPEBJ5TM+ZaWWl3F7CjAbFyJJVkoq/RP1utyZzc7V6Trm81yl9LcxdGWM6vooGZs7G49kQym0/lCtwr4Fj1iDSmGqTZzpa89w1u1duyB1WurRQZeTV/kup6u+oFfYXyiuXllbfplP/br7x+sHtrI4bSd2t4ubB4zihtQRhGr1PH/+Bdy29kvcokiSKYTnxnVMjsIhEbH0C43k4hUfYtJoGR/X44z66zaY/8Z5+O6P+dd7t7a6/srjHRNedi6YEvvKuZ7Cr55PV9Qn1hNFiYycEnonhvPig/43VEGAvr3lpLV7D+WSkV1MWEj9D9LBo/XJGQ8dO+VnMD+RJRtaU5N9PZ2+/mEf/1y81qudrq/VbLPZXRw8eoqDR0+xat0hXvrrZT4G+qYoLrXy44Y0nn/jB9ye+sSFJWVWNpdZufOGzvWOrjsYaIhBryEzp5S3PtrAtj0niQwLaqJ1e2guVLmhD3TjRXZbF911fXXlxXrd593eOTYOEFOcQV/ddHN2qJNYaSitUyeFs+lIJpMG9OLnwye5dbqc/KahzM89F41HIQhenfy6a4GkdK6fNIJ3ftrh7a9u3IevePWMX8PzX9x7xn10001Hs+zKq5q8plYoMWo0PLdpAz9nygYDARgVH8/5iUn0Dg0lWKdDr1LjFkUsDgdZFeXszM9nY3YWAG5R5OpBg3l2xsx23WnGJCTw99mzeXL9ev66xjcnUZ3H+bMzZzKxx5ltHttDaK0Gc5HN2mLSz67G1tNZxBmCu7Ru+V3rl7MgeQB3DD6PUpucR6RxhNn+6+6hxu1iwMf/bLKfrzMOs/jQTr6aex29zGEsObaPZ35Zz4bLFxGqrTcKfZAmG5sXDT6Pb+YupKDawn0bV/L6/m08MnoKAB/OugKHx83wJa/z+pSLAZiWIBvpNedAFujw8QJSk6MJD5E9WmdPlPOM3PTgR9460ZFmetbuVyaO7sPf3/mp1f1PvMQ3OtNmdZB1WI7yTtt+nLv/dWOgZk3S1xzFQ4Nn8sCg6awrOMa/j24irUI+AFiVe4jU4Ghu7/v/N+nqb53S4iq0OjVBppYP6abOqY8E7TvAXwIEwGy4nNLK5xClGsBDYdldWKqXYNTPRq3qgYAGT62jULV9I1bb8gbyKQIaVTJO9wkqq5dQWb2kybnYXfvweIpRKgP/zlfbV/s8b400TFciSDvQm5jT7s7jdNUy4syt/257apOcqpURBFIHKKlpOrlrVyHWdC15lYspql5JuPECHO5T6NU9MWvrZbB+zEmn2i1LOP586SIAbG4Xk798hxtS5b3FhLieTXqV9zSHsu6Sennh0dEJfDr76oDXGl8XgFcnXdwhrxXak5Gsm2666aabbrrppptuuummm2666aabbrrppptufoP8v/EwL3FUkVaZT6a1mNyaMortFsoccniT1e3A6rbjFj24JRFJkrpIPtp6tsxupQxAI8Ii2pbkTtmCzEVXpsKZwYHS/2DW9GJY+F2ctKykt3nuuZ7Wr4rn/jTP57nbI/L4a6uarF/nHV7nYT56qOy1JIoSh48X0LdXFOmZRRxOL+DCab7aVccz5VPs1D7R3rKtu0/yyrtrkCS45IJh3HD5WK/3tShK7DqQzSvvruFYxmkefuFr3nz2mhalWTbtPMEv+7KYMq4vV8wdSa/EcJwuN5k5pew5lEOPRp7vHcnh9AL+/dFG7/Mp4/py763TiAwLwuF0s37rMf7xzhr2O9qfiMKP5hKwiPXJhFCE+l5r6AkgFoEysIeCl7qQMUVE8/XOJYrakPaGOuRtau/72oTQtxC0rQvN7aabrsiNU0eSV1LJTVMDJ0Nrq158oP72bTrWTItuuvn1Miqu+fvi/T/+wM+ZmSSa5YSSb869mEFRzUu+/W7UaHIq5QS4d61cwWeHDqJUKHh62nT+8J9vArap8xz+16J5ftcuGTCAWX36sDUnh+zKSiRJIjE4mPOT5NxFZm3rk5t1JKMjk9ApVTy9dzW3pY5Fq1BR4bQBcEPKqHb36xZFiuxWqlx20ivlyMWsqjKOVJwmSC2/1gid8Yw00a9OHs7DO1cx5MuXAPl3Mkpv4qLE/jw4ZCogRxicS86PTeLG/rLXXnJw03I+LUl47Ck6xZDIGG8fC5IH8ui2nzheUcp50fXydkNqpVn+NHwCACkh4czvPYDdRfXRpQaV2jueVimbHIzqcycT2jihZ8MEcg0Kfa635Za48NFLm7y27OWV7PrpIDOum9D6DmtRCgpmxvVnWmwqD+z8ku/z0gD4PHN3t4f5b5AV/9vJp4s3Ul4q26kio80sXDSF2c1omEuixPjp9TkVdmxMZ8ykvj51FAozYeY/+sio1Dg2U+PY3OKcTIYFhJv/Qk7RLMQmJCeVyigUgg6XO4eiikeJDf83jX1zrbZvsTv3+5TpteNaHL9roaBn6IMAHC2+l8zyl1ApgokKmkfD11vtTKe4+lvizDegUdavAwwa+XOxu3KocuzDpK2T3xEpqPqc3Mq3ztYLaTc6VSKh+omU2zaSX/lfAKKDfH//qpwONApfU3NaWRGR+l+XZj38xg3mB8pz+P7UATYXHyO/pu1ZfH8LDByayIr/7WTQ0CSEBknUeiZ3rmbzuWB/6VuMiLiPA6VvA3CqZkuTBvOPF2/g4/9uDHitOQQB3vnkDnr07nrhpFkni1h03dutrp86MJ7XFt/SbB2VUkFpedPJOxoau+skVkA2htvtLqZPSKWg2MKhY/UGS5dblgzJzi8jyKglPkY23EoSvP7+eiQJZk8ewP2LZviMpVAInDesJ68+eSXX3fM+h9ML+H79YS6eMbjZ17BlZwZXXTySP9w81Vtm0GsYPsjA8EGJzbY9Uz75ageiJNEzUd54PPmnud5DKa1GxQVTBuJ2i7zw1o8dNqbkOtxkKLfk2ud9LKhTfa4J6hH1CUGdvyDoL2l6EFca1GUJ1zS9gDvnqIeC7Uv5AMCVBuoBbWuvjAdl7d+45zQ490K3wbybdpBbk8cJ60mya3IAyKnJpdJpocZTg81jQyWo0St16JQ6IrURJBkSSTImMixE/n0zKFuWn2oNWpXKK53SWf39d9uTrWqrUCrQaFWUFFSw8v0NrP9qF26Xm96DOjNxdzfddA4bsrL4+kgaSoWCxQtkGbuUVuqQJwXLydXfnbeAaR++z6cH9jMnJYXHr5WTFb7zww5mDk8hKTKU/NJKNh1uXiPWqNEws0+fM3g1HU+cwcxb4y/n5YM/8+TuHxEEgRSzfCh9JgbzHcXZ3PCzrzzA64c38frhTd7nfxk6ndtT25doeNnJfTy3bw2/HzCBfsGRKAQBp+jhhKWEt49s9Rrl7xl45nrc+TWVPLjjW7/yWQmyZMFNKU0nZB8eFXfG42j07V4AACAASURBVINs+F6VdYxT1RbijGZW5xxHp1TRJ9jXuWRohH/i2GCNjiqno0PmEYjm3p/m3ps6BvWN5Z/vraO0vJrwUCOrNx8BYPSQeomiwhILGTklJCdFsHlXBoP7teA80krGzR3BkheWn1EfSkHgztRJXoN5fnUFYq2Bv7WH3Y0Pdqpdnfd5ddN2vv9mD/99fQ2XXTeOfgPjQYKjh/P598s/4Kh1rpp3xWi/dts2HPUxmP+0Yq+fwRwg1HQndudBrDb/71FTaNWDiAp5HoXCTGLkNxSU3orTfdKnjlIRQmzY21Tb11Be9SZW2wryigoJDroZjToFSXJ4rzVEENQE6TtGt/psEmm8CACH+xSZ5S9xrOQBMsqeQa9OwiNW4/AUeKVXooOugAZfO7N2OCH68VTYtrC/8CrM2pEoBA3VznScniKSQn5PSfWP1LhOBBy7yPoNNncWbrEKj1iFrfazKKiS74MWx25UggmlIogQ/QTM2uFNtgV82jfXtjGxpusot22kpOZHQEGU0dduMLdXf9blZQBw6apPUAgCGqWSlyde1Nq3ucvwmzSY7yrN5NWjP3C4Mr/lyr9x9u2SF9W/bDnuU/70P689F9PpVFSCHpO6cw2gkgRLP9rCX55oOknGr5lHXvG9gZZX2ohuJkqhX+/6BXNGdr3B/ECtfnm/5Gj69Y5mz6EcbHYXep2azFzZy9njEemXHO313jhyvIDcU/LB1rUL/BcDdURHmJk+PpVVaw/y3bqDLRrM9To1t13Tdo+OM8XjEdmxNwuAudPlOQaK4Jg5qT+vvLsWp6uDvMxd+wMbhyUXUs1S+bF6qH9iT2UcgnYikmMTUvU7CLo5IATSzZOQqt+obRONoDn7721rEXSzkaqeB8mGaHkKRdj70IbELACC/koAJOvrSLalCIbLQdmc/mudhvy59TZrjiOWYzx35CW/8omR41nU++ZOGXN3+V7+lf5mwGvTo6dwU8/rW93XV3nf8nV+6xf7DZkWNZmbey1sV9u2csJ6kh2lO9ldvpdiR/NJfDySA4foAFclhfbTHKw8DICq1jtjRMgw5sVfRA9D597jOoK4Xq07UD5xMJdlr/3I5lV7kUSJQWP7cNU9sxk1bWAnz7Cbbjqezw8dBOD8xMRWG8obE2syMTGpB2tOZrDkwAHeuEh2+qiotjE6Rf7uR4cE8dnGfc110yU4WVrG3V+v5LvbbvCWTY5NZnKsrGG9bP8hvjwg/87NeucDVi+6CYBnRs3hmVFz/PobHBZLxlV/9SsfH90rYHlTtKV/tyjy1J7VXJ8ykj8N9s93s6Uwk53FOa0euyU2FWbwS3G2X3lrNN91Z+BB35Br+w1jV1E+k/73Dia1llijibenX0KYzvfQ1qg6+57iZ/L+AASb9Dz0u5n8+cWv8Xgk4qLlSJCHfjfTW2f0kJ4s/XYnJ7JLCDHrefK+jjHuFOeVodb4m11coqdN0QkWp9372KTWtTkqLExrJKOqfj1yqPwUV/Ya0UyLbs4mX3y0hbv/fCEzLhrqLTtvQgrxiWF8sljOh9XQYF6QV8bXn27n6ME83nxBjgi321xUW5s6CFEQG/5vyqoGUG55A1GyNjMbBcHGq4kIeQKFIEd7a9T96BGzgWr7BlzukyhrI3GNuukoFCZUyjgqrO8gSS5szp3YynY20z8EGxeiVHScE8fZJiH4dkL1E8i3fEilfTvVzqMoBC1aZSxGfX8iDLPRqfwP3QZGvUNe5WKKq1dQ5diPQtBg1KSSHP4YEYYLcLqLmjSY51s+wOo85Fde5djv8z+AhORj9G6qbV275to2JswwBa0qDof7FCH689GqfJO36pQq3pry27CX/WYM5hISrx6VvTQ/PrmFrieqcm74LRrGmyJIHceu4n9gcWWzvehpgjW9O2Wcn386xA23TyYuPrTlyr8yrrvY11Bt0GvoGd/0jSwhVvaKMgXpyMotxeMRUSoV3oSfKb2iSO0Tw64D2aQdL2Dk4CSvFAv4Jvw8fLwAkA3cvZOaX/wOSY1j1dqDHDle6B2zKVL7xHgTh55NcgvKvUbw1OToJutpNSoS40LIyO6orNgKxPJFCOZHELQTQTCAOwOp6kVwyye9QtBdAVsKpkeQnJeBOwOx7AYE0/0I6mEgKMElH7pJ1W8h2dfU1n8UhLP/3rYaRShC0H2y0dy1B7H0EgSDHFUhaIaBYAKxAkk8Bc4dINkQzE/7dCEY5aTJkv1HcKcjll6OYLwNQTNelmyRrEhiMbhPgn2NV9pGCO66CZRTzX2J0kVSZC/2Kd9Ztosbe16LVtHxIfubi7c1eW1SZNc9dGkrbsnNjtJd/Fi4hszqrDPvT5R/Q34p28Xu8j3MjZvDZQkLEH7FyWf3bz7GstdXs2fDEQRBYMyswVx1z2xSR/Y611Prppt2s69QTsbXN/zMZMp6h8pryz0FBd4yo1bDU0t/okdUKNlFFejUTd933aLI3oIC8iorcXqaTgJ+1eDmnQ06guZseVcOHcSYJDma5PYvAkvPnGucohu7x0WQyv+eaHHZybaWMyGm4363tpxuPnLgbHGgpJBPZl/J2JikM+5LKSgQkP8uz5R2vT+N/ghHD+nh41HekB7xYfzz0cvaMzUAPn7mK7+y6krZy3Td0q2Mnj3U7/rSk7v4Lu8w85KGMDE6mURj03vL/WV5/G3vCu/ziTFtjyIZEZ7IzpL6Q4cvs/cxNbYvU2P9vZG7OfucLqggNUCUXb+B8RQVVPiVxyaEcc1tkygrqWJarZFdrVLSI7m5fbRAmOkego03UG1fjc2xHbfnNJJUjaJWrlOnHobJMB+1qmeA9kqMummAf8StWpVImOmPlFpa3gNp1H0JN/+5xXqdyZjE7Wfch1HTn74RL7SpjULQkhTye5JCfu8t259bwJ8+28xHt0BKxPOkRDwfsO3wuPbfL8+kbWMElAi1puTGciy/NX4zBvNnDy7nq9xd53oaXZKjh/LJPHGanslR9B/82w11Hhp+FyX2A4Rq+xGs6UGEbkjLjdqBKEos+3gr9/3l1xdS0hIDU2JbrhSAvr2j2H0gh+z8MnonRXDwSD4xkWaCTXoGpMhG8cPpssH8RFa9wbx/n3qDeYVFXlSGBhta1AwMC5X1r9weEYvVTmhw03IFIeaOkTJoK5aqei+QkGbmB2A2tc3ruTmEoD8g2b5Aqrgv4LGhEHQngnZqgCuAKhlF6DuIFb8H1z6ksoVNHD0qEcx/Q9DN9rsi2X8A+woQq5DqPBfEKuo8ryXrq0g1n4EQhKAIAsGEEPycn+e35NwONZ/U9lMFohU8skFCqlmC5FgnG7yhvh/Tw/USKnWv13gzSDYk62vgPolkeVTuI9B7owmgoyfIn50i7D3E8j+Aay9S1ctIvBzwnRF0FwYs70oICEyKGM//8nwXTnaPg11lexgf0bF6gla3lX0VBwJeS9DH09vYs0PHO5eUOyt45+R7iNKZGwca45FEluevwuaxs7DHNR3ef2chSRLbvpe9Vpa9vppje7NQqZVMv3IMV949i6S+7bvvdNNNV6LUJq9h2ujw6UfdvamuP4DHr53JvpOnyC+tZHDPWEYkB5aJyCov59ZvviGrvGUZyrNhMHeLEg+u/IFjxSWEGwy8Mm8OofrWrXf2nSrg7+s3UeNyYdZqeWbODJJCZCeNOo/01cdO8Mj3P7HzvjtZk57BzL7JHTp/g0rDxNhk3j22Ha1SRb+QKJweN5lVZSw7uQ+bx8XNfVuWAmkNoiSx7XRWh/TVHHWGa08T96j8agvZlnKiDUEdMp5KoaCnOZSvM+Rogv5hkYCAUaUmqg1jtOf9ycgpIaaNubzOhE1f/+JXZqhd389cOJHrHvb3tpSAfWV57CvLAyBMK685e5siCNEYUAoKqlx2TlaVUGizeNuZ1Tr+OKDtEoFX9xrJhydkI6Hd48Yjidy57TP6mCNJMsqyO9VuB8V2KzVuJz/Pua/Z/kod1eRVV2B12alyO6hyOahyyfuf7cX1BxyZ1lLeTd+CSa3DpNJiVGsxqbWEaAwkm5o+ZCyoqeS0vYoqlx2LyyGP43JwrPK0T739ZXn8N30rJrWOILWWILUWk0pLhC6o2UOIzu6/rUTGBHMsLZ+EHr7OasfS8omMCQ7YJjQ8iGtvn0zvvv4ySc2hVIRgNlyJ2XBlu+cbiDDzfUiSjbKqNwi029JrZd39mLA3UCjMHTp2N2ePKsd+7O4clIogIgz+9oCGLP/5IOt2Hudo1ml6J0QQHKTjhT9c3GybV5ds4NJpQ0iMqf9+5RVVkBAV4lMvr0g+SGpc3pH8JgzmK/L2dhvLm+B/n2xl7y+ZpPSPZdPaNIaM7AnA1Tf9djz66si1ricxaKrXUJ5fvZl4Y+e8ztXf7ef6WyYSEfXb+qF/7j9ylMZffjeL7Xszee4/P3LZ7OHcfFnz2o+pyTHsPpBDZm4JQQYtxWVWZkyQNbIH9pV1FdPSZR3zhlrnqX38b+6tiQ0JlKenKdoarthRNEwm1DjRUGNaSlzaJgQNiohvkaxvI9lXg3gKBD2CejAYbkLQTmq+veY8FBGrkWo+QLKvBU8uIIFC/qwE7TgEw02gasKryn0Eyf5T0/2LlfI/6j9rwfykv1SKO1OefyAkK7jrQ9W8/QTdBfh78wtBdyHoLkCqWSob4gE8+SDZQRECiggEzWgEfTM3b0UUivCl8pzs3yK5DtYmWNWCMhJB2RN00xG0M5ruowsxMXI8X+Yt94vG2lS8tcMN5ttKf8EtBZYcmhzV9t9os9pEqCYUq9uKS3Sd6fQ6lEhtBOeFjWR7afNhqAChmhBMKhNGlQGP5KHKZaXQfrrFCLnVhWvpbezF+Ij2afKeLdwuD+u/2skXr68m94R82KXVa5h/21Quu2sGkXG/vSitbrououSm2H4Yq1v23BZFZ8B6KcH+iTRbQ7BWS0lNDYeLilqu3AwHT8uGmobJOU8UlPLB2l30jg7jotH9Wb5dNj7OH+srX/Tchg0UV1fz6JQpDImJQaM8t9JgWWXlvHXpxSSHh/HPjVt5Y/N2/jaziQP7Bjjcbp5YvY6Prr4cs07L90fTeWr1ehZfKWukJgSbKayysif/FKlREWSVlXO0qLjDDeYAr427hDfTNrM0Yw+nbVWIkkS03sSoyETeGH8p/YI7JidUWkWhNwlqZ/CPPZtYfGgndo98L1609mtUCgUpIRF8P/8mb71IvZGUkAimfrkYkHWzk0yhPDRqEhf0aJ8X8ksTL+SxbfK68IJvPiBUq+PBkZO4rM+gVvfRmvenTg7yH++uQRAEIsODuO3K89s15/bwzu62eZkCftELZY6a2v+blvpJMUfxynmXkmBsu4Eo1hDMy+fJXvQP7PwKm1teQ52wFHPC4ht1aFK3HG34wfHtvJu+pcV62dYyXj601q88Wm9iw5w/Ntnumf0/sLag5UTi+8vy2V/mL8U7OqIHH0+68Zz131bmXzWGN//+HUWFlfQbEI8kSRw7nM8XH2/l1rub3l8U5Jb7GMwDJf08m4QHP4zJeCXWmm9wuo8jSRJqVRxG3Qz02rrvZNv252uPZvDW+u2IkoTLI/LCpbMZFC/v+U5brDy9ah25ZZW4aiOrLh85mFvGywnpn161jv4xUaw6eJTyahtzBvfjd5Pkw879uQX84yc550WNw4VZr+XJeTNICpO/X2+u384Ph9NRKRQE63X886qLCDXIe9Y31m+jvMbGjpN5XD92GCsPHMWs0/LWdfN9+q5xyN+zxn2vPZLBKz9txqzXMji+bQce55p8yweA7F2uCCjhWs/8KYOZP2Uw9770FX+9dSZRYS0fZN57ra8MWmllNZ//uJf7F071KwN8yjuaX73B3Cm6ee1YxyXM+62xfWM6L719E4IgGxkf+N37wLkxmH+5/gBpmYUMTo6lb1IkxRXV5BSWExEiewsPSo4l8QxOh/KrN5EYVP9lyaxa1WkGc7fLwxdLtnHnfc2fqP3ayD8tn9IpBIElK3ax5JWbefjl5a0wmMs3rOy8MjRq+WdlUKpsKA8PNRIVYeJIrcHkZI4sPRJs1hMTWX/gEBEqe5qUV9YgSc17aZXVJiJVKRWYg5r/kT5XmE3186q01C7ym8gdZK3uwKQ7krPW2/oBBNMD7etDEYoQ9EeEoKYXsU3R3nZ+/RiuQTB0oBetqjeC+a9nKGShQNBdALoLfsWCGDJhmlAGBQ/wamXXkWY5SqmzjHBNWBMt287m4q1+ZUpBNuSMD2+70XdG9FRmRMu/9XaPA6u7iiq3lSqXlSq3lRJHiZ/3/NnkotgL/AzmCfp4RoQOY4BZTt7Ww5hEkMrfu87msbGnfD/fnlrFKVuB3/U6vspfztjw81AKHXjY1kHYa5z88MlmvvrPWopPlWMKMXLt/XLkxfzbpmKujRDqppuzhcWVy9r8B7C4clus216D+YDIKDZmZ7E9N5fNObLkwYSk5vJd+LM5J5sdefIcUyPqvS5f+3YzD1w6mTdXyr+lm2uTfjY2mO8pKOD2UaO4eUTX0CSOCzaTHC7fS2b0TeaxH/wNZoFILykls6yc65Z84S2LCqr/3RgYE83RomKOFBUzd0Aqe/MLOFpUHKirM8ak1vKXodOZkhdK+uEMbnj8ija1Lyus4JXb3+aZFQ81W29zO+VYzBotWTe3LGvwwIiJPDCi+cSkLtHDpSs/YXpSH96ZfglapQqn6GFV5lHu27CSA9fdi0apZPUltwRsf9/w8dw3fLxf+aioeL5rYJRvD615f4akyovsj17uOANmSxzamk7fEb3QtFP68bKewxgUGsuaU8fYX5ZHTrUcHVLqsGLzuJAkWS8+1hDMgJAYZsSlMjWm7xk5A02PldchK2fcyacZO9lenEludTk1bicGlYYQjYFeQeEMDfvtRqV3VS65egxKpYIlizdQXibvdUPCjNx4x1TmXt50cuTWJv08m2hUyYSZ7++w/t7dtJOn5s9gYFw0dpcblaJ+/fvXb1YzpW9v3rhmmLfM3ig32MoDR/nP9QvQqVW4PXKEjcPt5qmV6/jg5ssBMOm0/HA4nWdXrefthfIB7VWjB3PXlLEIArzy02a+3XeEG8+vv8cG63U8u2Am9y1bxZo/3sql//4Ei82OVq3y9m3SyYdPDfu2OV08/u0ali66msTQYJ777ucOe686m6LqbymuXolC0JNgXtSuPh7813Jeum8+1z/6MfcvnMqpYguG2t/RU8UWvl5/gBfvuZjkhAiO5xTz3vIdnMwr4al3fmDiiGQSokK8ZYC3fOqoFN76YjOniiqxOVzcdeUEkhMi+NMr3zCgVzR5RbLT3rB+8SyY0rpIu1+9wXxdYRqljuYSFjRNkEo2aIVqjJjUOnRKNSpBiVLR9TagHcW51D0trazm4okD2ZmWQ43DidmgQ6Gon49B277FhtWVx9GKzylxHGZXsZzIzi3ZcYrt+7toLd8t38u1N00kOOTcSH50BnWe0OlZRcRGBWMO0rXoHQ31WuQ5+WXesoaZ5Qf1i2PdlmOczCnxGo/HDPf1UB7UTzaw2+0ujmcV0bdX0147BxskFW1Ov/xckhgXhlqtxOXycCxD9hgb0t/fYu7xiGQ3eN+6+XXw6vFX2Vexj0htJM8Nfg6V8Ou7nU6OnOBnMJeQ2FKyjXlxHSM5dcpWwMkAWt7DQ2WtRZP6zMKldUotOqWWCG29canSVXlODeY9jT0YaO5Pni2fSZETmBI1kSht65KS6ZV6xkeM5bywkbxz8n22l/qHeAMU2YvZV7GfkaFNJ+Q5V9w48q9YyqvR6jVceMNEJi8Y6b2WvjcbURR9InACMWZm58tFdPP/h13Fr2PzlDI68l4itLJRQSl0bNLCS/r3Z2N2FhKw6NvlADw0YSLXDB7SrKe3y+Phs9qEoS9s2uiNL1mQWp+4W69RkxTZskOJw+0m1nT2JChaouH3XJJa708oSbIX+YpbAieDHhQTRXpxCUpBYFhcLMv2H6TIWt0BMz53bO0C+uVHyoo5Wl7MV3Ovx6Cq35NNTejN33dvpNxh6zCplrbSFd6fQNw//WneP/QP4prJVdQSxa5tXNV7NL/v30IEaCs4VLmVAeaxKGoP020eK6WOAhIMKQAcsfxCsUM+lBtoPp8/D57p015CIr1qN6WOAs6PaHk+9w+azv2Dpjdb5/EPf+TJG1vnYPbT7nSmD0/x2gfeHHdVq9o114elxk5OrWzDoJ6+Xrzt6b+zmXfFaOZdMZrK8hocDheR0cFNOpG1Pennr5drzxvK/V98x8VD+nPlqMFEmuRDVI8osisrn7ev95U80ql992XT+yd7y+oiu4+fLiWrtJwb3vvCp25d3wAbj2exYv8RjBoNueWVTEv1jWRKDA0mwmQkKSwYpUIgWK/D6nCSU1bZbN8ZxWXEBJtIDJWldqb068XRws45+D1TDp++DZdYhssj2yvsbllCKjnsUbSq9nnGq5RKrDUO4iKDOZxRSFllNVfMlPc0U0elkJFb/16kJEVy3ZyRfL/1CA/eUC9FVVcGeMsPHD9Fjd3JM7+/iNzCcl77bCMv3Tef/NMV/Om6KSREt90599e3w2/E5uL0VtUTEBgV3ovJUakMCkmgtymKGrv8B6xT9cHhzkSv7o/NdQyDZgAuTxEh+hlU2NYgSjV4RCt6dV9vbzWuo7g8xYTqZ2J17MagGeBTHh/sq/lVWLWYCOOlqBRhVNjWePtWK6OwuY4TpB1BjfMQHrEKAL26HwpB5+3b7s6k0raBIO0IQKCo6kNizLejV/dr9nWPGNObR+/9lL4D4khPy2fU+W1PENISi5+rT0Ay6LzejJ0xMGC9RQvkMP9hKbLRUJSkVp+QV5ZZ2fz9Qe/zcTPlMcKizASpExgUdhP24hJ6muYAoBDUhGg6N4GYw+7iy6XbueXOtmvIdVVGD5a9oR57dRUvPDAfl9uDy9V00qg6YqODCTbpOVVUicPlRqdV06dnvYFoYF/ZYL5h+3FvWf9GciwpvaLo2yuK9Mwilnz9C0/8aW7AsQqLLazdKofPzZna+nDOs41KqWD00J5s3ZXByrWyfvNlFw73OSQC2PjLCWpsgUPDu+ma7C7fzb6KfRiUBoodxSzPX85lCe1PFHWuGBk6nCCVEavb19CwqXhrhxnMN5X4e5eDbKz/LXNnn9sIUgV5Penbilqh5nfJt3DaLh+2ZVZn+9VJsxztkgZzS20EkMPm5LuPNvHdR5va3Mf3hW919LS6+X9Mkf0QA0OvZUBI5xlH5qX2Z9nhQ2zLzcXulj3bnvx5Pf/YuoWxCQn0CQsnXG9Ap1Lh9LgptdnIKCtjW14uVQ5f48bIuDgu6V/vLZgQEcwLX6wnq6iMxz9dTXJs4GTso+Lj2ZaTw+UDA6/DzzanKi0cKy6hX2QEa49nMDwhrlXt+kaGY7E72Jmbz+jEeCSgtLqGCKPsoDIoJpr3d+5hVEI8KZHhZJSWEaKvj+pb88lG1n+21ev1e96c4cy5dRqPXvwi/UYncyrjNIMnphIWG0r6LtlrvDivlFfvXOz1BH/jnvepLLZgr3Fw7cOyp+HhLcd4YeHrnM4p4cYnrmDY1EG899elABScLMJebefW566l56BEXrn9bWqqbJjDWz7AcHjc7C5pOfqhs0k0BaNTqvggbTdXpAxGIQicqCjlpd0bGRwec86M5V3l/elM9pSvxSk6GB02C4AQTSQbi77CI7kZFjoFkyqULSXfYvNUMTb8IoocuSToUwjVyIb6jcVf0SdoKFtKllPqOEVP40ASDClsK1lJhDaOBEMKBfZM9lf8jEEpR/dWuoo5Zc/w9rOx+CsmRV6KWR3+f+ydZ2BUVfqHn+nJpEx6b4QkQELovaqAiKiAgIiKYO+ua//v2vuuddfeVhQVFQSlg9J7TygJqZDeezKT6f8PN22YSTJpEHCeL5nce+65Z24yc8/9nff9vZRpW89wa4+qOsHD/NO1+/DzcEWEiLp6Hd9sOozeaGTm6AEEebvzyZp9iEUirhsjLA6aMbPsz6NkF1cyPDqYPoHe/LIjkdp6LYMiAzEaTcRFBBDso+KbTUIW3+LpI/h0zX60egMjYkLw9XCx6CMuIoDl244T5idYwA2MCKCyVsPSzYcxA3MmxJOaU0xuaRWF5TVMGx7DyH6hnX7v3YnBYETpomgz47pzRT8vTW4YPIDJMX1Yffw0C7/8iXfmX8uQ0MAWNq1ta0rONoplm4FgD3dWP2h7cfZcWQUfbtvPuodvx0Uh59MdB62KaTcG2opbZHya7ejbjGU2vVR8cS3U2kQkRqM/h9EsBD26ymMJUd2Hr0vnnxVDAzzYdjiNKaNiOHw6G63eQKBP162OswrKSUzJ47mPhQWkPkHCfEmhkHZKLIfLQDBPqcpvt02Eqy+vDJrLQA/L1CJ1wyesrG4VLorB1OqOEuB2NwXVnyFuiDzRGrIwmtQEqR6hqOZ/AJjMWgLdH6Cg+lPK1etwlsVQpzthsf18/FwXUVa3GonYDb2xqKlvuSQAo6kKiUiJ1pBHoPv9ABRUf4rRVG3VN4AZA0p5bLtiOcAtd03idGIO2WdLGDU+ukeKfv761c6m10ajqVXB/Hw6kk5Wr9bx0fO/Nv0uVwj/utPmjQTASeJNnOedeCqi7e6zO1jz6xFuWjQO115qC9JRFs8ZbfET4NNXbrbr2JhIP9LPlVBRqaZ/VIBF5HdcQ1G3vYczmrbZ8i9/7O4pPPz8T/y55wxKpZwl88fi1/CwYTKbOX4yh7c//4P6ej3RffyYeVXvFcwBbpsziv1HM8jIEtKFXnp/HY/ccSW+Xq7oDUb2Hs7g7U+3IJWIm9LDHPRuynRlfHPuG4Kcg3im3zMsPbeU9QXrGeA+gFj32PY76EVIxVLG+YxhS6FlmnxhfRHptZkARLlGdrp/M2b2lVpXoPeUezBI1bs/u11FJbNdnKkjSEVS5oUIQs3bKR9Y7U+pti9g4EIz94FLqVemWwAAIABJREFUw8ffwV8Ho1mLi7TzEaD2IAI+v34W9675nQO5zeJenU7H1sxMtmZm2tXP0MBAvpo122KO/Mj140k8m0//UD/6+HsyuI9t4fm5K65g0cqVvLpjBwvj4wlWqXCWXrxHvfF9wvn64FHOlJTg5azk/VkzmvY9vHodxbVCJmhBdQ13/LyKYcFBPDJhDE5SKZ/MvYE3/txJrU6H2WxmychhzBskPF8Eq9zJLKvg3jEjkYhEmMxmBvgJAlFVaQ1rP/uDD3a9gui8AIX8zCIefH8xQQ3zzwPrj1mNuTEqPnHHKd7+8wU8/FRNbZXuzjy77BHOncrhh9dXIVPIUTcUeP/n8r+Rl1bAF09/z01P3gAieO6nx0jYcZqV765r8zodLs1Ga7Rd5+NC4qlw5supN/LesT18fOIAWqOBAKUbk0P68PjQi7fI3VuuT2vkpReiq7evnkpEnO3n8Bi34YQoY1iX9wUA/k7huMu88ZIHsLN4JQvCnmS09zWcq0smufoQUrEMP0VY0/Gl2lwm+d6IvyKMib5zEDcs1sd7TCS5+iAAgU59iHYdio9CGEO4ywD2lP7W1E+pNrdzF+A81h1IAuCGsXGE+Xnw+g9b+X3fafw8XAnxVfH1xkM8OmcCtRodj8wZj6+qeSEmKsiHJdNHIBGLKauuI6uonNfuFL43vvvjKJFBwv/BuSIh0nXvqXOE+Xlw/dhYm30ATB/Rj+0Jzc+fP21PYP7kwQR4ufH6D1uJCPAiLjyAW6cM483l2y66YL525WF++GoXFWXC96OvvzuL7r2C6TfYDpDobNHPS43imlr83FxZMm44pbVqjmfnMyQ0EKlEzJDQQFYePcmCkYOa2tdpdbgo2s4ki/bzpqZey5FzQub6iIhgzGYor1Pj7aqkWqPF3UmBi0KO1mBge0oG46Mi7Bpvy75HRAjBoi377uvrRX5lNXmV1QR7uLM3wzowprcQ5/dFt/fZP8KP33ec4tk7ppKQkouonUxtmVRCvVbf7rbwQC9iIvx4/m7LrJauuGxc8oJ5YX1Vm/t9FG58MfpOfBTWK/w6o+Cp7O9+F2W1K3FRDKKsbhVySQBmjJTW/YrBVIP0vAq+MokPpXW/YjJrUcoHYDBV46oYjkaf2rT9fGrq9wEmjKZqJGJVU99mjEjFXtRoD2Ey1VJat6LpHAppcFPfemMxhTVf4aYYiYt8MHpjCfX6DJxkbRe4MZvMxA0OJW5w71gt7SzeASpEIlHTRDYz2XqhxNhw3XWmGjKqfifYZQLu8ogeHZe6TsuaFYe55Y62fQEvZexd2OgfFcDhROHLfurE/hb7+kX6I5NKOJNR2NzeRvrioAHBvPDYTF7/cCNrtpxgzZYTODdECOn1xiZROTLMh3/93xxksp5bjf1jdzKHE7OordNSp9ZSWt5s8fP3V1YC4O7qhItSjqtSwYSRUUwcbZnBMWhAMHcvnMCXP+4BYNveFLbtTcFFKadea8BoNDF6SATRkf58v+pgj72XS4UTR4SU20EjejY7pCt8mvEpXnIvnoh5AneZOw9GPcjnGZ/zWcZnvBn/Ji7S1v2Zd5f8zkTfWZypPkx/95GcqT6MQqKkXCt8LnydQshVpzFQNY5SbT5OEhey1WcIdOqD2ljNAPdRJFUdJFY12qLfan05Gwu+5drAJZTq8inXFuLrFEJxfQ4aYy3uMmF1XS5WWPUxyXeClWAujFWIDO+KYH66KplyXYXV9gk+Yy0iMRy0TryHIBCpZO5U6ast9pXZuLa9gbtfvPFiD8GBAwv8nAZRoD5ClHv3ZM60hqtczvdz57H8pJAR+eXRw2RXtf2cAjTZqNw1bDiLBw+xsoa87Z3lXBEfycyRAwj0aj0C65nNm6nRall67BhLj1mLwS3JePzxdsfVFSK9vfhmQevfBR/NsZ1F2Eh8gD/Lb7up1f2H/nZ/0+tlC+c1vS7ILCI8NsRKLAdwUsqbxPLzMZuE54tGG8LHPr2Xd+/5HDdPFx58fwkAEXHCs5SLhxJNbT05KXmc2nsGgNcX/geAsAEhFJ4rJqQhUCS4lfO1ZG9h77EbmRgUwcSgiIs9DAt60/WxxXOz37G77WbNMpvb5WJnJCIpxoYC6VqTGg+ZL1KxjEl+c0ms3EmNoQJfRQgmsxERCkwYMZmFaFdjw882w5FtIELc1E9TH11E0yBgKZ3kKBVyJGIRdRodgd5uyGVS7rxmFME+Kh6aNY5vNx9h/EBhzj82Ntxq+H6ezfqNWCQE5hlNpqbnwbp6HR6uzpbvqZ1LoNHpG8YlRm8Q3rO3ygWFTNquXVxPs/G3Y3z94Z/MvXUs/eKCwQxnTufx6bub0GqF/40b5o+0Oq66UsMbz65Ao27OWH71v7desHFfCF5cs5Wc8ipkEjF+bi7cNfeapn1vzLmaV9ZtY/mhREwNf8PZQ+Oain62hpNMyocLb+CtTULwZ51Wh8ls5vaxw5g7LI744ABi/H2Y99mPuDnJGdvX/rokLfuu0wp/l5Z9K+UyXrx+Cvd8twovFyWTY3rvs29P0D/Cnw/ydhLo447K1RmT2UxVrbAA/dXq/ZxIL0Cz+gCj48OZfUU8fUK8Ka6o5f8+XMfMibFMGBLZtA2w2H7wVBb/96GwUD1+cB+um9S1rLtLXjCvN7a9orswYqxNsRwgxKO5SIrS87mGVyZAbOM1+Lu1LHLSvM+MERESXOSDrY5pROV8JWazocXqSXM7pTwOEVKKjCX4uDSm9Vv2ff7rEI+nbJ7nfF54Yjmvvn9L0++vPvMLAM//q/VJaG9EKpXg5qFsSvU+e8Y6Vex0xVImB77D0ZL38HEayLHS/3BF0Ps9PrZVPx/kxpvH4OTcOQ/2y4VGH3OA+H6W0U8ymYToSD+SUgvwbij45uNlO7Vz6oT+DIgK4MffDnMo8RylDSvsTgoZEaHeTBnfn9nTB/eoWA5w9GQ2G7adsrkvNbPIapubq5OVYA6weN4Y+kUKiwM/rz1CclohOr2R8GAvZlwZx03XDWf7vt4ZKXqh+fYjQbh9d+ndF3kkrfPcgOcsfpeKpDwU9VCH+ijXCf8/ZbpCXCTuRLgKkTHe8kDy1OnsLF7F9cF3s7lgGf5OYeRp0vGU+3OuLgkT1g80ydWH8FYEkFR9EIXYmQjXWLzlgRRozqIQK9GZhAmIXOxk1Ue4MpRwZShZast050bv7EXhNyMVd26q0Jody6TL3I6lO2mMiOjrGsmxigSLfWqjGjPmi1qbxIGDS4FRvo+xJe9RDpV8QIxqFgCuskCkou7PDhSLRNw6SIhyu3XQIBILCzleWEB6WRmV9fVoDAacpFJUCgVRXl4MDghkWJAwZ2rtk/z13+az6/RZ3l29C41Oz7ShgkXk7POKfvb39aW/7+WXit8RAiJ8yU7OxWw2W9fgOe93JxcFdVVqAHJSLQNx4sb349Xfn2btZ1vY8PVWwgZYi/Ch/YKJGhIBwJNfP9C0/eTuM5zYJfiqFp5t35N2b3HvFoQvNr39+ry88nF8Q7tWKP1E5U6OVvxBrEoohB7oHMn2op/x1PsT7ByFWCShXFuI3qRFLBITpuzHzuIV+CqERRy5WCgqGOzcl82F3xGnGourVMWx8j8p0mbh7xROPzdr8bBlP3KxglJtHgkV2ynTFuCnCCHKreO2b1OGCdneX60/QLi/F05yGdeNHcDn6w4Q4qMiNtyf9HwxW4+loT8vu3ZAmD8f/LqbqcOiCfG1zNQbFBnEVxsOEhnkhbNceOaeMDCCN5dv49S5AmLD/Zk8qK9FH17uSn7be4r0/DIAooJ9mD1+IB+v2YuzXMa04TGcK+o9wQcrvtvLw09fy9QGexWAUROiCQ714vuGjH5bgvmyz7bxyD+ux9Wta/c0M92TySHqAYnx01tntbovyMOdz87zMG/J8zNbt88dGOzP93fZ1sVEInh73gyb+wAevnJs0+tvlgga3nd3NheGbqtvgKtjo7k6ttkd4Z6J1n/bniZPXUaw0rbNW3fzn6eaF9GDfFWs+eAeAO6fZ1ks+olFV1odK5dK+PDpue1uA7hnzlirbd++0vkFJEeIlwMHDhw4cODAgQMHDhw4cODAgQMHDhw4cMBlEGHuKnOiSqdudf9gz7BW99lG3Mrr1ts1Rn23d4ylN0/L44XtXspZVse37Nve87SG2WSmqKCyw8f1Ftw9myPMSwutU1ylIifqjRXoTDVEq+ZSrDl+QcZVXaVh/W9HmbtwzAU5X29l8pho9qx6stX90QtCMFc6EePh025fBhd46v5p7bb7+OR+fk5P5LupC4hw87TY98JjM3nhsc6nXy9cNJpnHxT8r7bkpJJUXsxjgzsXGTtmWB+Ln41U6+q5fdsv/DDtZisbm44g9jtkV7sNK4+wc9MJTCYz8cMjALj9oSls/PUIJYVV3P6QUOn+m//8QVCYF9PnDOe/r64hL6uUeo2QzTN8XFRTuxNHzrLsk21IJGLqNTre/OIOstKLWPntXp57t9n7/rUnfmLe4vH0HxRq0V9jX+fSi/j5612kJQkRXi88/D0AL//3VkRikdW4G89/Ifky80sOlR/i3sh7GellHQFQrC3mmRPP4KfwA+Bfg/7Val/OEleOVWyn3qjGReKOXNycTmrGzGjv6SRU7CTQOQKNsY4wZX+CnCP5IestFoY/ZdWf1qRhqv9CdpWsRiF2bupPhMjC+qSf23CbfUzyncCyrOUW29RG4b56tOI4o707HvFQb6znSLm1JUCMWxQBTj3rJXw54iX3tNpmMpvQGrU4SS6PGhoOHPQUe4teQ2eqJbnyF5Irf2mz7eJo25kxnWVwQACDA7rmLauQSZk2JJqhkUGsPZjEZxv2A9YR5q9NddQP8PBTMf2OK3nuurdwaqgvNPSqgVx3n/WcMm5sDCveXsMbt/4Xn2AvxBIxVaU1APx7ycco3ZxR12i4/51F5GVYZxbGjYvh6B+JALx6k5DROuraoUxfcgWbvtnOazd/QEAfP8SS1p/ZKrRqkisKW93/V+dSuD5h/YMIsmEzaS8TfITIWJPZ2OQ9DjAn5GGMZmNT8fBY9zFIWmgJ80OfaJrjNWaajfSajsGsRyoSIrCnBy62ONdwL8vPQaiyX1M/jX3MCLyTrtAnQIi2f2XJNYhEoqbEjpduvxqD0YS04fMQ7ueBWCyysKCaN2kQOoMRuVR4z4/OaX7uGhQZyJt3X4tELLZIFnn9zhkW/Z7fx2NzJ1mN8Z+3TMVsNiMWi2i59+XF063aXkiKCirpP9Da575fXDDFbWg4foEeRET5dehc9bqj1Kh/o153BL0hB5OputsizKND2q8x6ODiUqYV7nU/Ze3jiQHXX+TR9G4uecE8xNmzTcHcSXLp2GTIJN2XDnHi6Dm+/Ww7acn5LLpBKBam0xqYZGdBzt6IskVhzdoqjdV+hcSDrXkPMsrvWQD0proLNrYVP+7nhoYCpD1tFXKpsiv/LGtnLmm3XYmmjm+SD/PSqPYF84fix5JcUdwNo+v8GC4VCnLK2b4xkX9/dScikYhn7/0GgNTTeVx57SAeveUzbnvgSsxm2LstiY9/fhCAB5+diVQmwWQS0iZvv+ZdFj14FWazmXeeW8X7392Dt5/9Va1b9tfYV0SUP0++NpfTx4UHzlc+aq4obmvcqafziIkL7q5LYxcGswGD2YCJzhdnnegrpBMO9byiVSuNcT6Cr6ufk5BmazKbEIvElGjziHEb1vQQ1JJJvnMsfjYyytty4t9aH+N8xrA8ZwUGk/VEeXfpvk4J5ofKj6Iz6ay2O+xYOoeLVGlze1f+Hx04+KvgqYi+4EXhu5N1h5LZdPQMZmDG8P6sfm5xu8f8lZlx51XMuNM6Bf+TQ29a/C5TyHh9/f/Z7OP1dc9a/B7aP5gxM4cB4BvizWtrnwHg9hfnWx0L8NT/HrC5/Xz2FZ/j4rom927+StenpVjeiKTFNsl5RfEkNtoDNueJbdFaP11FbKOOgLTF4pFMavu88la2n398W9vb6gMEqw0ry6ZegG+AipSkPELCLTWhlKQ8fAOsi8kv+2w7ABVltbzy5E9ERPo1WUctut/a1gLAYCykqPxR1No93Tx6BxeKPSVn2JB3FJlYSmF9JfdETWWEl1DX8NO0zeSpy6k36ngg5hrEiPhPynreG7aEzQUJZNYWcXXgYL7J2AZARm0Rr55cwUS/WK7wv3R1wp7kkhfMR/r05XRVXqv78zWVxKourLDSGxg0PIJ3v7yDt55fxVMvCSvXYpHIZhGcSwWJtPlmWFdjLZiP8H0SMNPoAjnW/8ULNDIoL61l8zrBX/a6OW0XmPirkV5VxtIzR8itreKJvesZFyBkfdzYN54n9qxDZzJQWq/mtdHT0ZuMfHhiHymVJTyxdz3TQqOYHtYPwKptlMr+BSYz8PzBzVRq69EaDfxj+FVkVpdxsqyQxwZPoEAtrLL+88Amnho62WoMYpGIw8W5PL5nHQXqGl4ZLQjp0Sof7ty2gkHegWTXVDLSP4SF0UP49/GdZNdUojHoeXrYZGI8fG2+Bz/n5gKRn546gMFk4pFB47rjsluRlVlMfnY5/3ffUovt6jotTs5yBo3sw8GdKZjNZoaN7YvCSYZOa+Czf69Ho9Yhlwu3i7qaekwmM+UlNbh7KO0SyxvF9vP7a+xLImn9e8nWuNV11oWVLzXs9Z1ujB6SieRWAnhHaa0PV6kLwz2HcrDssNW+k1WnqdJXoZJZT9TbwpZ/uZNEwWgb0fkO2keMYyHWgYPOMtbv6fYb9WJKq+t48ZZp+Kps135piclsZltmJvtzcqiqr+f2IUMYFBDQJDoW1dSgcnbGWXrJPwJeFuwpzLzYQ+jV9Pbr8/bmf+AdZJ0B5sBBZ5i1YDQf/3sDxYVV9IsNxmw2k3I6jxXL9nHXw9YZRDGxwRY/28NgLCC7eAZGY/cHm3UUY0NAUEeVqXfe2cDIkZEMHhyGh4eS//1vJ4kJ2cybP4qJE/v1yFh7IybMvDxoARm1RXyTsQ1FQ70ptUHLa4MXkqMu5cOUjfx76CLG+/bjw9QNpNcU8P7wO5CKJNwSMRGATQUJPDnghov5Vno9l/xsaU7ocL7L3N1UEfd8DpVmMDXgr7tacvOSCUjaSAW8lKirqW96bSu9sVp3jpy67RhMzWLaYO/7L8jYAH5eJghEM24Yetlc8+4gSuXNa6Onk1hawLvjLS1S3psgRNNuzk5lU3YKD8eP457YUazOPMUro69ut629/JGTiofcmddGTyentpJXDm/l5ujBNtsO8PSzGsOWnFS8nZS8N+E6TpYV8vFJIR36gwnXk1VTyQsjpzZZwhwtyaNOr+OjSbM4W13BG0e38eWVc22+h9v7DUMqFvNN8hF0JiN/GzTexoi6h/BIP/wCVbzx2WLEYjGGhsrwjamQ1900im8/2opIBIsbJmWJhzKpqdLwz3dupqYhq2PHppMAePq4Ul1ZR0VpLZ4+wkO82WzG2UVBZblQqLXxHGfTimz219gXCAt6Oq1g+2I2mZsW92yNu2X6ZiPqmvcQiZwwm+tRuj3ejVeud+Ah73oht7b6mOw7waZgbjKb2Ft6kGsDr7ZxlG1KtWWkVFsXsh3lNQInicLufhw4cODgcqCorpbEwkLK1GrqdHpcFXK8nZXE+/sR4OrW7vFLpo6w+H3VPuHeeeO4eIvtGoOBu1av5mBOcxHnKZGRDAoIaBIlblu5kql9+/LsJGubAgcXnv3F5y72EHo1vf36DJo04GIPwcFlxJybRyORiPnxq51UlAuZ8h5eLiy+/0qumzfCqv3oSTEd6r+w/KFeIZYDDPztVb6duJhRPhE29/+QeYg12Sf4+Yq7Lbbv35eGv587W/88xSuvziPrXCnvvX8rL7+8+i8lmEe6CjZQblInNEYd5+qEAtOJFed4LlGw2YxwEWx65oSOZub2N3g6djbSHsoouZy55AXzUKU3c0JH8Gu29YM+wNq849wXfRXeivajMi5HIvp2zM+qt6KurScvs7nSvJvK2arN4ZJ/EeOxgHM1mwhwHkmlrnuiElxdnaitrW+3XaM//LYtp5g2Y1C3nPtypkan5YVDW1DJnSjS1NLX3XbEeI1OWACxp21rZFaXE93gnR7q6kFOraUHvrmVBbeWhLoKEbZ93D3Ja3G8s0Rm4Z+eUVXG4eJcHt71OyAsGLT1HlIqSsiqruDbqQs69J46SmCoF9fOG8mz9yxFLBFhNgnv+ZWPFqFwkhER5U+9RodIBOEN3xv94kNY/tVOnn94GV4+wkN9nxjBi1UqlfC3F2fz2hPLkcmlGAxGXvrPrYRF+uLjr+KJxV/i5Ssc01p/jX0BiMQiJk0fCMCjt36Gf5Anz717s81xN465JWKxL04ui9CqVwAmHDWtO8ZAVSzeci/KdOVW+/aU7uuQYL6ndD9mG0nUk//idixGs4miemHxKFudS76mgBpDDbWGWmoMdWgManQmPTqzDr1Jj96kR2cSFpH0DT8dOHDQefLVhzhXK6Qh1+hyAXCThxDuMplgl7Hddh5DQ1bVr0mn+eb4cVLLSlttG+npyaLBQwC4eWA8ihaR39kllYT5epBeUGZxzP7kLMBaMH9/714SCgp44corGR8ezvSlS63ONy0qih1nz/YKwbxII2T37Ss6S3JlEedqy8mqKadar6XOoENr1CMVS3CSSHGXOROgFOYUQUoVsR7+xHkGMMwnFLn40hMAyrR1HC7JJrfu0q0t1VM0Xhvgkro+9w5/tt02Xxx96wKMBIxmM4lleRwsySK9uoT06lJK6+uoM+jQGAS7PIVEhotUjpdCSR83b/q6ezPSN4wRPmE4SbpfImrpK36hKdMK4nNiWT4pVcVk1ZaTU1dJuVZNeb0ajVGPzmQQAn+kcpRSOQAuUhkuUgVuMgUhLh70cfOmj5sXEa5ehLt59ch3zw3zR3LD/JFUVajRavX4+qvoDvcYjXY/Gu0Bi21SiT8ql0U4K8YjlQQhFru0cvSFRyVzJr2mxGr7iJGRLLp9Au+8swGt1oBUJkEiEePqeuHq+TQ+4WTXlnOyvIAzVcUUqKvIV1dTqK5BY9ShMeipNxoQi0QopcIzq7NEjrNUilIqJ1DpTqiLB6EunoS4eNDX3ZtwVy+7x3B+bH6EixAUFeMexHMD51ns+yp9K3f2vYqV2fsZ59sPZ4kcWUNEusZobZ/ZUSq0ao6U5nCiPJ/s2gpy6iop1tQ29a026DFjRikRPlv+zq6Eu3oR4ebFMJ8QhvuE4tyLbbQvecEc4PEBMzhenkVmrfWKmdao529HlvHFmLtQSuQXYXQXl/deXcPf/3k9h/el896rv3P9/FEA3Hr3xZ8od4TfvtmN0djs1RoYZi2YKiRehLpcQZH6KNGquRwsfq1bzv30i7P418u/UVdrnw3ET9/uZer0+EvS/uaNzzYD8Ox9V3Pg+Fne+Gwzc6cP5Y653V/QdGtuOuFunjw2eALfpxynsMEWRSYRozboLdoBNtvaS5TKh+MlgnVTTm0loa4qlFIZ1Q1Cdka1pUh4/hgAsmuESfvZ6gpCXT2atp8/iemr8ibOy5+3x13btO23zNOtvodIdy/+NW4GT+7dwP+umoeLrOe+p6bdMJRpNwxtdf+bny+x+N3dQ8l7397TavvhY6MYPjbKanvfQaE8+9Z8/tfgrTft2kEkHD2HWSJm4szBTLpqADnZZYSEerP+92PccOMIUs8UED1YsOu54dZxaNRa3n59LfMXjqH/kDDqjSaiYgIozK/kz80niejjS9yg0KZzSmUD0NR+gljsjUMs7zgiREzwHcfveeus9uWoc8mqyybcxb4i2nts2LEEOPkT43bpegh3llpDLUcrEjhcfpSk6jMO4duBg4uAGRN7Cl8ls2Zz0zaFxB2z2Uyh5hhpVWuIcL2KSQEvI+pi9FV2VRWPblgPwImi9osVZlZU8PIO4V7548kTfHjtTGK8hQX+FXtO8MScSTz+5RoGRwY1HZOSZy0iAKxPTWXx0KEsHtr6fT7cw4P86mq7309HkYravv+Wa9X8cjaB386dIL269YWERoxGA1qjgSpdPTl1FU3bf88SouydJTLG+fdhXp/BXBUUjaSd819IjGYTmdVlJFcVcaayiOTKYs5UCoumJfW1dvfzffoRi589yZ7rHyXA2f66NF2htevTkWsDwnW5UNcGaPX6TJwzyuJ3Ta2Wc6eFbI+kA2k8/IF17YH3Tu7gk+S2vaSfHjSFe/vbt6CXVlXCt2mH2ZSbTKXO2r60JWqDDrVBR0l9LSlVzRqKXCzhqqBobu47jPH+kR22zGiN//toLbdMH44ZM8P7hyESwYqtCXyz9iAA3ioXXrx7OlGhXc+oBDhelsvGnGR2FKSTWVPW/gEN1Oq11Orbf+aXiMQM8gpijF84Y/0iGOYjPJN012KDytN27ZrOUquxnN87K8YQ5L0UsfjCfN47itZksOkgIRaLeO6fK5FIRLz04ipEIkhIyKKqqvWaht1BpU7DzoJ0/sxPZW/hWQCq9e0HVBrNUKUTMq6raG5/2kYxY0+FkqHewQzzDmGYj1D8dbhPqF33tXiPcAAOlqbxj4QfABjn259QpTc56jLuj76acBdf3j+zln/EzSXSVQhmK6mv5h8JP3Bt8HAm+PZv9zyNZNdWsDb7NOtzTpNaZXtOcj7Vpnqq9fUUaqpJLG8uDCsVixnpE8bsCCEIYEbIgKZFq97AZSGYO0vkfDb6Du458DUAWXWWE7CkqjyW7Puc5+NnE+8RaquLy5aC3HJEYhErvt/Hlyse4pWnfgYuHcHcoDey5rs9/PDfPyy2DxgeYdVW0lDkRGeq4WT5V1TpsrplDGYzzJo3kh+X2lccIyerlD07zjDxqksvTS+vSBCFxSIRP649wo/v3cH/vft7jwjmI/1DWJZyjPy6ahQSKSq5sDIco/KlSF3Dgzt/Y27fgYz0F24Y57ctra/joxP7OFFWwLvHdzGuXhf7AAAgAElEQVQ5OJJ5feNtnmtKSBS78jJ5eNdvaI1G/jH8SoJdVHx++iCP7l7TFLXUeEM6fwwg+IL+fc9aijV1vNJGMdDhvsHszj/Lgzt/A+DK4EjGBYbbfA8gFJ0JdfXgscHjeXLfej6ZPKfbJqcXC61Wz7rfjjHlGuHvoa7T4u3jxsnEbGQyKcu/28fDjwte2ga9sBAmEomobbBdcnaWERjkQd8ofyIifUlJzm86Xq8zcuuSCaz6+ZCFYA5mRCI5ZnPHFlMcNDPJdzxr8tbbjA7fXbrPLsE8tSadonrrxeu/WrHPWkMda/M38EfRtl4rkt//xSr2ncli/lghI+r5+VMu8ogcOOgZTlf8SGbNFob5PEA/lVAcWS4WMk91plpSKldxrOxzvCqiiffqfEHNgpoabl7xC4W1lvchZ6mUvl7e+LgocZbK0Bj0lNTVkVFeTr2hudhyWlkZC1euYMVNNxPp6ckTc4S5+rwJg7j9qub6OP9aud3m+Uvr6ojxbjsDTywSoTf1XLFgV5lt261KnYb3T+1kReZxdCZjt51PY9SzNT+VrfmpBClVPBo3iRsjBiG+wAX9KnUaC9E3uaqI9KqSbn2vlyqN1wa4rK/PoudubHXfL++u48gfJ5l6a8fnQo3Xri2yayt4LWEL2/LTOtz/+ehMRjblnmFT7hkGePjzzGBhbjDBP7JL/e5OyERnMCKVSNh8IIV/3jGNn7YcZ9W/7wIgNbuY937cySfPzGunp9YxmEz8ei6R/6UeJMOOBbmuYDSbOF6Wy/GyXD5N3tsUbT7CN4xZ4QO7XfR7qqGW09vnBTfZi0bb7MYgEjkT6PX5BRXLS+trqdBZitq5dZV4yq2fGUrqa/kmbT+Rbj5W+55+eiYlJTX4+rojEkFZWS3r1h3njjt6Rts6VprLsvTDbMxNbsoe6ykqtGq25adZfI495M5cFRTN9JD+TPCPRCGRMsG3f5O47eek4t1hzfOWu6Osve4He0YAMMYnhjE+go1PY4T5f0fcaff4jpYKC4AfJ+1hV2FGx95cGxhMJvYXn2uy4Hrp2CYWRY3gnn5j8VR078JRZ7gsBHMAH4Ub3467D4DnE1eyuzjFYn96TRF37P+CK/wHMCtkOKN9+iIXXzZvv1VEYhHpKYUEBHng5u7c4ajnzKR8Vn65w+72R3el8O+//9jBUVpjMpmoLq8j9WQuddXWK+RXzRpmta2xyOdI3yfJq9tDZMB1XR4HQG1tPXMWjObXnw6irbdP+Phx6Z5LUjBvrBieeq6YQD8V7q5O3VZFfO3MJRa/B7uo+HXGIqt2comEZdNuttpuq+1Lo6bxEq2L142IwMoTHWDplJtstrc1hqtDbfvEnf++AB4bbHtCbOs9/NBwnrEB4YwNCLd53KWGQiHjutnNn9Fffz6Im5sTYrEYs8nM9TcOZ+vmk8T0DyIjrZC0lEKMRhPuHsJN8URCNldOjaO8vJacrDJOnchpOt7Z2Xb0n8GQipPL3Tb39VZOn84lM7OE8gbP95kzh7B3bxqzZg0jJaWArKxSYmODqdfoOX06l75R/qhUzhw/LiwGRkX5E9ui0E9ebjmHDmcSGxuM0Wji3NlS+kb50a9foF3j8VP40s89hjPVKVb79pUdZGHYfCTtRF/aii4Xi8RM8Ok+u4Pezsmq03yU9jlqY9eiXUSIUDR4vpvMJnSmrqdNOnDwVyStai0xqhuI97Qx5xC7Eu91O7WGQtKq13daMDcDj2xYZyGWD/Tz57GxY5kUHoHURu0NvdHIzqxz/PeAkCZ/qriICo2GRzasY+0ttzWJvi3FcoD7Z9j+Pg10cyOtrO1IysO5uUR69lyRQjcbgvlvWSd57fiWdiNeu0q+uopnD69leeYx3hs9q0Pp7Z3hp8xjfHRaCKYp1PRc1P6lSuP1cVwbGHvdMH586/dOHXumyrZgbgY+PyPMuT48vQut0WCzXVdIrixiyU7huX5G6ABeHzETd1nnrC/iowL58EmhptMTHwhBRU5yKU5yQY8ZFBWE3tD59/BnXiqvHt9Mnrqq/cY9QOPiz76is+wrOstLRzdxdUh/bowYxDj/Pl0Ohko6kdN+o/PIyhDE6PC+fhiMBU3bXZ2nI5F0TyS/vfyWncgHSdswmptF538ea/0z4SSR8p/R1nalCQnZrFt3nHpNsybz+hvzu3ewCBkKbyb+ybHS3G7vuyNU6jSsOneCVedOEOcZwO/TLvyzbk5dBS8c3cTubhTJ20Jj0PPFmf18n36Uh2IncHe/MRc1e+yyUozdZYKv9QcjbmNt7nH+e2Yz5bq6pv0ms5lthUlsK0xCLpYSq2qoLOweQB9XX4KcPfFVuKGSK3GTOeEskfeq1L7OMHRUJG/+cyUvvn0zBr0Rva5jK/klhZVs//2Y3e1zMorJyejZYhLjpg8kwoYAVFp/Gh+ngZgxozGUYjS1nyZjD3U19ag8lMycNYxVPx+065iMtEIO7Utn1Dhru4rezMh4QbB94T/reevJWegNRvT6yyf6w8GFYf4tlhkJcxeMtijkCRAeIUQNPPVcc2Xu6H6Cp3lj0dy77rsSkVhEaLi31fE3LrBMfRWJXNDU/AeR2OWSEc5Pnsjl5oVj+Gm5IJb4+Lg1FUoVi4WIe2dnORs3JtInwpfUlALiBobg7S1kRJw8mWMhmO/encrNC4Vrv3LFIebNH8XPPx2wWzAHmOw73qZgXqOvIbHyJMM8h9g8rjGK+mCZdVp0vCoOT7mH1fbLkT2l+/gqc6nFA4EtVDIV/dyiCXYOJMA5AJXUDVepKy5SFxQSBQqxHLm4OTLp+6yf2Fz4Z08P34GDy5JaQyG+TgPbbOPrFEd6tbUllb1sTk/jWIEgSEyPEuynPrp2ps0i1Y3IJBKmRvblyog+ADy6cQMb01JJLilhfWoK1/cTIshue2c5V8RHMnPkAAK93FG52Bas5sTG8uXhwwwKCGBaVPP8U2c08l1CAgCrk5J4/sorO/0+28NV2iyYG0wmXkvYckHsMlqSWJbH9Vu+4pPxQqRqVyNjWyOnttIhBreB4/o0U5JbjkzeOdklo7oMvcmIrIVfttqg44mDv/NHnvV8rafYmJNMYlk+n4yfx0BP++eVjUjEYiqq1UilErILK1i/J4l6nWUgmi0LjvbQGPT888h61mSf6vCxPYnGqOf3rJPsKzrL7usetVo0PZWQbXdfJqMJg6Hj0c3pZ4R7UnhfP0zm5s+iQtb2/bAnuDtmPDdHjmB/sVBj7rFDK7i5zwjCXc5b2BSJ8JQrGeEdRqBSZdXP0qW7eOyxa3rMs7xCq+alY5tYn5PUI/13hZmhcRf0fGbgfykHeP/UDup7YEGuPdQGHW+f2MaGnCTeHjWLGNWFXeRp5LISzBsRIeKGkGFMCYhjZfYhfji7j1KtZXqkzmQgoUKI0mv8aQuxSIREJEZ0kU0SDlzzUqeOW3jHRBbeMbHp9/e+vKNDx3dXdHF3ERTuw9/esB0VfLpiKZMD3+FoyXv4OA3kWOl/uCLo/S6fs9G7fN6tY1mz6ggGOwXkH5fuvuQE88VzRlv8BPj0FetobwcOOoo92S2NQrmtY9o7XuE8GwCD/mQnRndx8PBUsmXzSbQNDwzZ2WWkpxeRliZE3KtUSk4kZtO3rz+1tfXEDQzh5Mkc3BomiecLMaFh3qxccYiB8SF4ebvyxx+n8Gko1movo7xG8N255WiM1pGAu0v2tSqYH60QxBhbUdV/lWKfeZp8vj77nU2xXCISM85nLNP8rwIgwiXsos8rHDj4q+AkUVGrz2+zTa0+H4XE+uHcXtalCMKVq1zOW9OEzLe2xPKWNLZ7c+o09mZnUa3VsiEtrUkw//pv89l1+izvrt6FRqdn2lAh4232GMuH5wdHjeJ0UREPrV2LUibYFL6wdSt/27ABY0Mq+YyYGBYNsf093h24yQXB3Gg28dC+lWzNT+2xc7WF2qDj7t0/AfDxuHlMCbKdJejAQXex7LVVVtvqGnyVty3fx8jpgzvVr9FsIr26lAEe/kKfBh2LdnzPifK2v9N6gnx1FbduX8YXExYw2q9jWbG3XjOCxS//gE5v5MH5E0jPLWFsfB9e/VqoLSGTSvDz7NictbS+jiW7fuBMZc8G63WFeX0G28wweuKeby7oOMQiN4zm8obXF6e4p6tUwbQgIQM/zMWL6cGxjPKJ6FAf/v4q+vTpGeF0T2EmTx9aQ3EH6yhcCKRiMTdGDLpg51MbdDx9aA2bcs9csHO2xumKQuZt/YZ3Rs/i6uB+F/z8l6Vg3ohIJMLPyZ0QpZeVYG4vJrMZk/nSjbDVqHU4K5uj1DpqyTLyiv58vP5xAI7vSeXYnlROHT6Lzk5rku5k3PSBPPbmTbh52PYykoqcqDdWoDPVEK2aS7HmeLect65OiFT38XXj6pmD2fCbfRH3SSdzSTyWxeBhl57NRnJGIWqNjqFxoeh0BpwUvbdysQMHAPV13wJ6DPrTuHp0faHsfBqtSIyt3A8aI6ylHbD6uuaaQZjNZouFyWeeabaSio4OaFpEMJnMiMUi+vULxNwQgXP+gub48dFN7fr3D7Lq2x7kYjljvEeyvXiX1b6EyhPUGupwlVpPtPeV7rfZn5vUtVWR/XLjq8ylGEzWERiecg8ei3mYSJeICz8oBw4cEOoyiaTKnwlUjsTf2fr7qFBzjKTKn4lyn9npcyQ2FPicFB6BStG5yDd3hYLJERGsTUnhZIuCoQqZlGlDohkaGcTag0l8tkH4vj1fMJdJJHw+ezZb0tLYmJZGVmUlJrOZsSoVM2IEwfiamJgeXapzlSowA08fWnPRxPJGGv1mHzuwmp+vXEysZ8BFHY+Dy5vdqw9ZbVO6Cdnv0xZN5Nb/m93pvpMrixjg4U+90cDdu3+6KGJ5I3UGHXfuXs6yybc1FSa0h8nD+jJ5WF+r7TuOpgNQq9EybZR9YliVTng2v3X7d2R0oKDnhUYsErEgsvUizADL1jyGWNL2t7LBYGLxrP90aSxSiT9GkyCYG00V7bTueaYG9bfISLIXvd7ISy+uIiLCp0nXWrx4YjtHtc3XKUKm71uJf9qo4tQ7mBIUg4/ThVnoqNCquX3nDyTbUT/hQqE26Hho7wqeGTyVu/t1f229tri0/UYcOHDgwIEDBw4cOHDgwIEDBw4cOHDgwIGDbuKyjDCv1mtYlrmHn7MOUmvoHh/rS5X/e3gZH/zvrk4fLxKJiBwQBEDkgCDm3nMFep2B00fOcnxPGkd2nSEzqXmV21mpQOnedU8piViEi7szXn7uDBgazvhr4m36lrdEIfFga96DjPJ7FgC9qa7N9vbSaMkCsOC2cWxacxyTyb71x+VLd19SEeZf/rIXgJMpwt908IAQnvzXaj56wbYNzoVEo9Zx+mQOGalFFORVkJ9XTklRNRqNjnqNnnqNDqlUgsJJiIZ3cpah8nAhIFBFQJAH4X18iY0PISTMh17mNOSgG5A7TUcsCcBoSO+R/l2lrgCU68pt7i/TlVm0s5e2IsBbWtSIW9rTtHGMve3aYpLvBJsR5gazgQNlh5jqb+l/qzaqOVll2+tvvM/YdguFXg7kafJJr8202i4RSfh7zCP0cenafUBr0rbfyIEDBzYZ6n0PhZpjbMp9EJU8AgA3WRBgpkafT5UuCy9FDEO87+n0OUrVgvVCmKrzti4Awe7C8WWaZlusdYeS2XT0DGZgxvD+rH6u9cKkImB6dDTTo6O7NI7O4iZz4pvUg/ye1Xv8hDUGPX8/sJo1V9+DQnJZPvo66AV8cfStHuv7TEOk58vHNnG4xH7v655CazRw395f+HXKHYS5dq2I8BXDO2Zfagb+fmA1QK+OLgehfkKIi+0aPi6uQnS1X6B99wx5Jz3wm46XDUCrTwZAq7/4/txPxE3t1HHTr47v1nG8lfgnXzVEmPdmburTdqZCd1Gp07Bo5/e90ubIjPD3Ai5olPllNWvYX5IGwIsnVnXaguVyw6uD/rX2IJNLGTIumiHjorn54SnMjX+uySJg+oJR3Pf8rG4/pz2M8H0S4aMkiERj/V/sln5bCuaBwZ5cefVAtm6yzyf52OGznDmdR/+44PYb9wKOJwmVoD95aQGPvroCqUSM+CKpyzlZpWzdfIojBwQBND2lsN2FCp3OgE4nWCLUVGsoKaomPaXAoo2buzPDRkUy6coBjBoX1SSwXwhys8u4c8En7bYTieC3rc8A4Owsb6d15/hj4wkA3n6l9QrltpBKxaz+8xkUip67fWxel8C7r69tt92W/c83vTYa0tFpt2E2FuLs9ni3jylcKQiexyuOc13gdVb7j1UIVk19XPp0+7kvNFGukQQ7CwuleRrLtN+9pQesBPOjFQkYzLaLwUzyHd8zg+xlHCq3XdRunM+YLovlANX6zs1p9qdm8e2Oo5zKKkJrMBDm48H1IwZw26RhKKTtf4Zzy6pYf/QMB9KyyCgsp0ajRSETFkCCvVSMjg5j8RXD8fewXii68+MVHMnIZdbIOF5deHWb56lS13PVi1+gNxp5+/aZTB/i8Bt20H0oJCquC/2aUxU/kl23E4DCBts+d1kIw7zvJ9ZzARJRx9PDm88hQWswoDN2zcZRZxC+S1t63pZW1/HiLdPwVXVsQfZC0ihEp1eX8s6JbXYd0+jJPNo3nP4e/oS7euIhd8ZZKkNrMlKpVVOp05BUWcSB4nMklOUBgljXUTJqyvgkeQ9/H3hFh49tjWiVL1ODO/9dVaPXcrC49TpajTQKbv09/Dp9LntxknTfnLgr16dGLzx72Xt9etu1KSuoQKFU4KqybSPaUVKqilmTfYoVZxPabSsVixnpE8YwnxCi3X0JdlHhLnPCSSpDbzJSoxOubVZtOUmVhewqzOiUOFahVfPQvpWsnnqXTY/u88nILWXZxiOUVtbSsrbnx0/P69B5fz2byK7CjI4OFxC+p4Z6hzDEO5gwFw9CXDxwlzvhLJHhJJVRbzRQo6unWi8EXFbqNGRWl5FSVUxqVQk5dRUdKky6sO+wVvd9v+7vHRr7NbNb78se3JSzqFELPvvq+h2YTFWIxV1b4L0YjBlrucCyfn37n4nWePfk9m4Vyz0VSkJcVAQphc+cQiJFKhI3fZ/V6Oup1NVztqaMkg74pAcq3ZkY0DPFq1uiMxm5a9fyTovlUe4+jPaLIM7Dn3BXLzwVwvefUirDZDajNugprq8hq7achLJ8DhZndaow9FuJf6KUyril7/BOjbOjXDaC+fq8BF48IXwJmGwU3PqrEjc4lLUrDzNwcJiFf3lE3+6ZWDgrFQSGeZOfVdot/XWd5veYU7eDKPfO+8U1oq6zjOy7+fbxbNt8Envvl8u/3cPL/17Q5XFcCBqjUxvfm8lktjuavjswGk38ufEEa1cdITW5oP0DOkFNtYadf55m55+ncXKWMX3mEOYuFFYpA4JsRwF0F8Gh3ri4KiwWYWxhNsPZDOFmFTvQfn/AjnD6RE6njjMYTKSdKWDg4NBuHlEzGWkd90wzGs5i0CfipOyZbIihnkNRZCnIrMtkVd4qZgfNRiwSHhAOlR9id+luRIiY6NM1H73eQqPQvTx7hcX29NoMSrSl+Cp8mrYdKrMtFvdxiSBU2TP/v72Nonrbk8sRnt0TEVJYX9h+o/NYuv0o761tzhRwUcjJKCzjvbW72Z18Fr92xLfCyhque/ObpnuASARKuZw6rQ6AtIJS0gpKWXskiWWP3kyEn2WU2YLxgzmSkcumhBSenj0ZADdn24LkpuMp6I1GPFycuCre2uPUgYOuIhU7M8T7LoZ4dz7rsi38XV2p1mo5Vdy1qKwTRcL9L8itOeDFXamwSywf98UXbFmyBFd56wvteqORTw8d4nBeHl7OztwxbBhDAtvO4LSHRhH7+i1ftOkB6yKVsyhqBDdFDm0/MtXNGxC8Wx+JndjkW/xN6kG+TTvUJELYyzeph1gSParpIb6rzA6PZ3Z456MdkyuLuH7Ll+22uyJQEIheGnZNp891MejK9Wn0zrX3+vSWa7P28z/54c3fqCiqAsA3RPgfXvTcHKYvntzpfvcWnWVf0dk22wQrVdzdfww3hMWjkref7R3vFch1YXE8PWgKpysK+ezMXjbmJHdoXMmVRXyYtMuuhagXPt/InCsGERXq027b1jCYTLx3akeHjon3CmRJ9GgArgnp36UsE41Bz+HSbHYXZrK3KJPUqpJW2/o5u3FVUOuZPkqXji3QPvTUjKbX9RodTs5y7p33sUUbsVhEYIgnSx6aQnhfPyZNa65zoVRcgVwWjU6fhslcS2nV6/h5/rtDY7iY5OWWExzixdmzltf8yJGzzJzZ8VpJP2ce59PkvV0ak7vMiSuDorgqKIYh3sEEK+1fgKjUaUirKuFEeT77is9yqCQbjcF2jcD5fYZckADG549sILGD9RHkYgkLIoexsO8wYlTtF2Ptjx/Ql0VRQpjr4ZIsvk8/ysacpA75x79yfDPR7r6M9A3r0Hg7w2UhmJ+szOHFE6scQrkNEo4IN9dDe9Mstr/6/i3ddo7I2KCLKpjX6HNwk4VSqbNcbS5UH+wRwTy8jy/jJvVn7077qgYf2JPK2Yxi+nTTIkVPMm1cfwAefuVncgsruf/Fn5g9tecrMptMZjatPc7yb/dSVFDZ4+drpF6j5/eVh1m7ShD8pkyP564Hp+Dl0zNRXCIRRPULJPHouXbbnk3vnYI5QPKp3J4VzFM7Lg5KpEIUr067B6l8VHcPCaVEyeKIxXyZ+SVr89eyvXg7fgo/qvRVTXYs80PmE+x8aWSTtMcEn3EA/JLzK8bz7q0Hyw5zXZAwcdcYNZxqxY5lsu+Enh1kL6JSV2Vze8uFhc5QrhMKM+Vr7P9MnMgSFhs/WLcbgKmDonl2zhX4qVzR6g1sSUzltZXb0Orz2uwnwMONa4f2x8PFmSnxUcSF+uMkl6LWChP6dUeTeGv1DqrU9fx3w17eW2KZeTElPgpfdxdKqutYe0R4CL9lou2HmjVHhP+hmcMHIJNc/hY+Di4/xoeGkVZWxpH8PE42iN7x/v4d6iOxsJCj+cLncmJ4RNP2Yxl53DiufeGxqLaWZQkJJDWI9jNiYrg2xjLC9+09e1h67BgD/f1JKi7mj4wMVt9yC/18uvZd1UhbD7zXh8XxwtDpnRasG0XAxwZO5vbokTywdwVHS+2fy6gNOr7POMojsZfHwjZATlUVb+/dw6HcXErq6qyuf4CrK/vuuRcAo8nEr0lJrEk5Q1pZGRUaDb4uLkzrKyxSPj1hIkqZEEWdVFLC7B9/YNXCW3ho3Vq0BgPvTL8GtUHPP/74AzeFIPb959prGeTfXExVbzTyyaFDrE5OIr+mBh+lCzP7xfD4OGER3tmOzKZLlY3/287Xz//M3Edn0G9EXzCbOXNEsGr79Knv0Wr03HB/52wowPZnq1FEu6//OB6Jm4Rc3Ln7Z5xnAB+OncvuPhk8fvB3KrRqu4/9LHkvN4QNpK97298hErGIeVMGd2p8jWzMTaZYY1/GnVQs5qn4q7iz35huK3TsLJUxKaAvkwKEz0xxfS3b89P4PesUh0uEbIjGv9NNfYYgEXV/ucCt6xPZv+MMz729gC9WPmSxz2w2k5FSyEdvreftL+9A1sLGRSSSEeD5ITkl12M266mq+x6x2AVv938gEl24bOtGfs06TrGmhgf6T2ratqMwldcSN1JSX8M1IXG8OvSGpv/pNWuP88ADU3jxxVXEtcjcz8jo+CJ1UkUhLx7b2KlxR7p5N9mBzAqP7/QCjIfcmZG+YYz0DeOufmMwmEwcL8tlc+4ZNuQkUdwQgS4WiZjXp2ufG3tYcTaBX88lduiYiQF9eX3EtQR1YKGgJSJglG84o3zDSeo/jqcPrwGwK8LdYDLx0L6VrJ8u3N98nXouA++Sv2uZzGaHWN4G3SmMt0bkgCD2NNg7XAzSqlYxzOdv7C54Bl+nZnG3QpvWxlH2U6e2jmC5ZckEuwVzs1mIMv/HKzd2y3h6klkN4viI+DDO5pbRJ8SbYP+ejbrOTC/ivTfW9lhEuT00RlD+sfEEe3aeYdFdk5l782iLrIzuIqa/fYJ5ZnrPVaaura0n+2zrURHtkXwqtxtHY4097/38yAyZYhKyzmfT28VY77H4KHzYWLCR9Np0zqnPoZQoiVfFM81/GvGq7vXVu5i4y4ToxsEegzhWYZnueKj8aJNgfrwi0aYdi0wsY6xP9y9c9FbMrchEElHXplk7bHjJt8fXWw8Dwvyor783b99+LZKGdGmFTMr1I2LRG0289PMf7fb1xq3WUXtKhfBgddO4wZwtquCH3cc5lGYtWkklYm4cM5DPtxxkxX5hjmBLMM8qqeBklrAgcOPogXa+SwcOOk65NpUCtbBAXmcoRoQIpdSPQOUIvBRd8/y+MTaObxOOYzKbuW+t8ND31azZxPq2H3HVKLDfv3YNZkAiFrNwYPP9JMTHg5ySSkJ925+PvbNnD74uLkjFYjakpqK95hrmxMY27V+fmsp9o0bxxPjxVGu1zP3xR748coR3rum5CF2JSMQrw69lQWT3ebB6KZQsu+I2Htu/ii15KXYf9+vZRB6OnWhTQNtTksxYn34APSJ0dTdag4HFq1Yhk4j59/TpeDo5szo5iaXHBbuhp8ZPYNGQ5u9ciVjMjydOEKJy574RI/FwcuJAbg7fJQj3eJPZzCtXTWlqbzCZeHXHdu4bMZL/HTvK89u24qZQ8MT48Xx+RPgcvbZzJ7/cJGTRmoEH161lX3Y2i4cOJcrLm7SyMpYmHOd0kSCAfD9v3kWzemxka0I6U4ZY2jrsOpXJpIFdszxY8f4GHn5/MVNvbQ4WGDVDuP7Bff35/o3VXRLMz0culvDfsXMBumQN1JKJAX1ZPfVObt2+jDy17UCA8zGazbx9chufjW87wzMq1JeE1DyGxHQ+sGRzrv0R8K8Mm8FN3fidYws/J1cWRA5lQeRQCpI/miEAACAASURBVNSCtcSa7FOsyz7NTZEdj3o2m+GP9Qms/eUweTlliEQigkK9uH7eSKZdJ/T31Qd/8Owbc20eLxKJiOofaFEDqSUK+SCCfH6gsOxejKZKKmo+p07zByrXO3B1vhappOvZRvayOisB1xYPbiX1tTx+aCW+Tm5cGzKQtTkniHLz5Z4Y4fP0wAPCd9P11w1h/k2jm4776MP257It0ZmMPHnodwymjmmHThIpj8dfye1RI+2yIOooUrG4SUD/x5BpHGxYgDlTWdxpQdpeSupreTPhzw4d87eBk1u9l3aGWM8AVk8VMgD/eWQ9q861ry2Wa9W8eFRY+Phk/PxuGok1l7xgvrs4hXO1nRN+pA2FyPyc3PFzcsdZIkMukSIXy7rtj98bSE3KR12nZfDwiCZ/5+70bY6MDeq2vjrDMJ+/ARClmsMAj1ubth8pebdb+tfU6ay2RfcPZMTovhw5aJ+H2s6tSdx+92RCwry7ZUw9TbC/R5NQnp1fTliQV7efw2yGH77ZxQ//243R2HsWvDRqHV98+AdHDqTz7Mtz8PB06db+YwbY93nJ7IQtib0knci121LIFsmn2o5M7SyF+UJ2QXuWNQDR/QLabdMTRLtGE32RiqldDCb7TrASzM/WnaNcV4GX3JPDDd7t5zPCcyhKSfekvV8KNC4wnM//s3eWgVFc6x9+VuPuRjwhJGhwp7hTpEALLRXqcuv0/tveW7t191sqlBpSHIoUdwkQEgiECCEJ8RCXzcr/w8SW3WRnkw3Q3jxfspk9M3PWZs75nff9vUWqInxt2vZdLakr5Y9c8yYCGq2WQ+ebPF9nDIhuFMubM7lPV978fTe1avO9gJvTN8yfn/efoqy6BrVGi/yaSdqcQT1Y+udxUnOFLIxT6VfoHax/DdxwXJgAx3TxItzHMlGunXTSHLWuhgO5r5JRsafFNoH2Ixnq/TJySdsK18d4enJbTHdWJCaQWyFEQE7/5ScmhIczLjSMrh4euNvaYS2XU1NXR0FVJecKCtieksL2VKFWS4M37uLYvoS6No27zmbksi3uAtZKOZJmM5RfnjMMirmrd29eGjUKCfDpkSN8FxenJ5jnV1QQVn9sRysrbuvenZ/jzYsqMwcJ8OHAW5kU0M1kW3NRSmW8O2A6Sdu+IbPyqqh9sipLiC/KppeboWj37KlluFsJ1/JJvrFM9euHv+3NO25PzM/nUslVvpw6jeH1GQkxXl5sTk4G4GxBvoE9z7rb9b8zM7t1I7tM+L7uSE3VE8wBJkVEcHuPHmh0Wv61axefTJrMlMhI8isrAfjmRJMl2/aUi+xMS+OLKVOZ0Gyc5GVvz6t7dgOwMy2tMaL9RrE3IdVAMN945Fy7BfO8jAK69jP+2iL7hpB/2bLZ2O8NmG4xobw5/nbO/DRqATN2fNtog2SKP7OTiS++Qk/Xluc4mXlXuf8/K/BwsW9ceAdY9dbdovt2VGTB00GeQR0ull+Lj60jIET7P9B1cJuOsfy/e1j54wFGjevOLRO7o9MJWbefvLWJ3CvCNe77DY+zatlBevYLZvcfZ/SsU7VaHcnnruDiZizaVkudOgudrgY7m/GUVa4AQKVOo6DkJQpKXkImFe4NCnkwUqk9kjbeD33dvjfZ5lJFEfeGN9U5+in1KGqdlu+H3tnoAb4h80yjYN5Ac7Ec4M67zMtm/SrpYKtWOsYIsnfl8yGziXS6Pm4BUomEQZ5BAI1/O5LXT21v9O0Xw//1GsfdEZYPilLUZxO83X8aNjIFP6fGmdynYcF8a1YSE/yjLN4n+BsI5rvzxFf5VUrlDPeMZKhnJN2dAwiyFyZmkr+VPK7Pj1/v5ly8EPkV0zuQl578BYB3vrzLYucI7XZzWBBEOd+BTqdBUr8Q0t31Posct8pIhDkIUeZiBXOdVseK5Yd4+v+mWqRP15NvVx/mlccnW+x41dXCAsTb/17HoX3io4KuNyePp/Pgnf/l9ffnExZhOXE2oqu41fv0NqSYiaU9diwARYXlFOSV4eHlaKEeCaReFG87IXbhoZP20dO5B04KR0rr9IuynC45wzD3wSSUnDW63/+SHQuAl7Vx64X4kgS6O0Ubfa41VFoVH1z4lBqN+AEsQEZBiZ4IHh1gvF9WCjldPJy5mNO+CbyrvU3jY41Oixx9wdzTyZ6R0SHsTBAEwdWHz+gJ5jodbI4TBPNbO6PLO+kg4gq/IKNiD5FOMwl1FCKp7eRe6NBSWZdHWvl2LpSuwabQjQEeT7f5PK+OuoWsslIOXhZEHY1Ox+bk5EYBUwzTunblmcH6xZI/fVCcvaAEuLVbt8ZZzYyoKL4+dkyvjVanQ9nM9ijYxYW8CvHFx8zlqe6jOkQsb8BOruS9AdOYu2uZ6H0O5KUZFcw/73s/m68IE/RVlw/xY/oeeruEMM2/H6O8YrCSXn/bgtYorxXmJ1byps9TAmZHP0Z5CPPhw5mX0eh0yJpFgAc5Cz7z3vbCQkKDdY+bjbAgXlVXh1anQyqRsCX5IrYKBWPD9MXooYFNha+PZGbeMME8q7CEn3efIiE9l7dWNhWmra5VU1FtGBxlLh4BblyIS8M/wnCcfyEuDY8Ayy2+zA3p3aG/qwA7F97pP40HDqwUvc+y5GN8MLDla9XL97Uvi6WotlK0VcycNkR33wxs+v04jy+Zwrip+v3vGRvE0k+EAIo7HxjFwgdHAVBcWIGuWQSUTC6j3+Aw+g0xDOxJyQ5Dp2t9TKnRFgt/VcXteh1iKK+rabTR0Oh0rL18mnG+UY3R1NHOvqy6ZBiU8/hjyxk6LIKxY2NwcbHD0dHGoE1LFNVWml3kM9zJg59GLsDNyrIBdDcLSSV5bMkUp6c2FNnsCLG8ORLg5T4TyKwsEV3g9+34XYz1i+yQ7LC/vGCeUGJa+JEg4dYufXksYixOyv+diDeAM3EZvPffRSx5+EfkcmmHpMG5ezsx/9ExqNUaovsGW/z4pqjTVnG66HNyqo7UL37o8LcbQXe3+y1y/JoWBlExvboQ06sLiafFrXbv3HqGhfcNx9Pr5qtIvXzdMRbO6M8Xv+w3eO58muUina8WV7Lk8Z+AjhWELUVxYQXPPvIjr70332Ke3T5+Ljg42lBeVt1quwbv/LycErx8LGuL017BHOBcYhYjvCw7WE8xw7s88gZntpgiuzqbFxNfbNO+3/czHZlxvZBJpAxxH8SWnG162xNKz+KqdKFWa7ig6G7lRjenjlnlv1mJcYxiffYmg+278/cxynM4fjbiv6/5NQV8lvI16ZWXzO5HaZX+dcXVvuUxj7Od6cghjVbL7sRUDpy/REpOEYXllY3CQq1aTZ1aY/IYc4f0bBTMt5++yHMzRuJkK5z7eGomV66WYa2UM7F3V5PH6qSTtnCp/E8inKYz0PMZg+fs5F542vRAh5b08p3tEswVMhk/zJjJh0cOA/DfE8dFpX03+EY/PXgIi3r3MQjjeeyrdXr/N2SNfHT/NL3tHnZ2XK1uugZcra5GpdVSWlOjJ6JqmwksSpmswywy+rj7tznS0hxi3QPo4+7PyUJxdnHHW4hS7eMaQh9XIcr4majp7MpLYHN2HK8mrOT9pPWM8+nFNL9+RDreHIFC/f39cbO15ZPDR3C1scHZ2oZ1SUnklAsR4/833LDIZEJeHj/FxxOfm0tBVSXVdXX6mUY6nVBwp56G72aDiG5T/7+kWRtd/T4ZJSVU1dUR/tGHLfa5pMa8RWBL4u/uzH3j+1NYWsnkfk1jFLlcRqhP+8Xs6Q+N4/MnfyQ/s4jI2BB0Oh0X6j3MV324mXtfm9vucwA4KKx4rsdo0w3byWjfCMb4RfBntrgFvy1Z53ihZkyLXsIB7bT4LKiuFN02zOGvma1WU11HVHfD2lXdegRQU2NYEHLWQvHXV1Ni+fXGw9qBvBohGGdnznkKaiqYExTb+HydToNKazjG/ODDOzh9OoOffzqERqOlX/8QBgwIbdGGpjlfnjtIlVr84pinjQM/jrjjbyuWA3x8dq+oYptB9q78s9fYDu9PAzKJhLf7T2Xi1q8pUbWumQBkVl5ldXq8Ra3fGvjLC+aFtaYLP/xfzDRmdul3HXpz8yGVSRqtF7RaHRptO3wYWuHOp25cdfK4wvfxsO5FX4+nkSBFh5YLJSs5XfgpfT2ebffx1Wot6joNcoVhIZXbFw3ln//4RfRxVv50iEefnmi68XUmqL6K+4kEIZV/7qSmG1bDtvZSWlLFc48tJyOt7d7ZN4LKilpeeOIn3vx4gcVE8/CuPpw8liaqberFPIsK5mq1lgtJ5lXANkZSYhYjRltWME9NFr84E9H15hbMHeQOTPedfqO7YRFGeAw1EMyTyy/iojD+vRzmPqTDM7dUWhXVmmqqNNVUa2qoVldRVf9/Q6FMY1yuyuTPvN3YymywkdlgIxf+Cv8LIq6NzAaZxLzCWeEO4XhYuVNQqx+xrdKqeDPpfe4JXkhvl56tvi9FqmK25+5kV/4eajT6CxHOCidK6kz7iV5rtdSaDiY3URwsLa+YJ77bQEaB8H662tsS6OFCV7/690mpoLSqhgNJl1o9zsCILgR5ClGKl/KvsvHEORYM7wPAxno7lrE9IrC3VrZ4jE46aQ912mo8rFvPYPCwjia1rG1FwJojk0obI8Tv7NmLFYkJHMq8TEJeHlV1TYKHo5UVPb29GR4YxJzomMZtxnhxnuB7rNPpuJRXzOHzxsdl/fz9eWPvXmrUaqxkMj45cgQHpZI5v/2Gr0OTbdSFwkImR0Y2Pvayt3yxLAnwcu/x182vekFYX9GCeVKJ6bGGjUzJZN9YJvvGklt9lR25Z9iQfYw1mUcId/Dlti6DmeDbu9He80Zgq1CwfNYsbl+1ilm//YZSKiXYxYX3xgtzsmsLvu65lM7i9euJ9vDkwX79CHN1xdHami/rsxBWJCYYnMOcT0+LDlcbG14b3bKY6+do2cxEc3FztGPxxAFE+JmuLWAutz46Hplcxi9vruNqvnC/dvYQXu9dL89myv2WEbnvCu/fWAC3o3m2x2h21gvmphQEtVbL5sxzLAoXH336+aoDPDJHXEZijcZQMG4JO8VfczwxYGg48ScuERCkL/ifPJbGgKGWt9+5kYzwDuf7i4eprFOxOuMk3V18GeAR1Pj85Ypi3K0NhWq5XErfvsGEh3uzd28S27clsHbtCaZN68OwYZEtnq9SrWJl+ukWn78WqUTCZ4NmdWgxyRtJRoWQRbBT5ILYy33GY93GAqdtxcPanie7j2z0KTfFl0kHmRPcy+Ljjr+8YF6tbv3iOcoryqRYXlb+AY4OT1FW/jE21mMor/gaB/sHABofS6VuVNdsBcDebhFqdRo1NbtRKvuCRIZKdRyFIgZ0atSaSyjk4cJzzaio/AGFPJw6dTpKRRR1dedRKGLQaPOwsR5Hdc12dDph9VSrrUShEH70dXVJaDT52NhMaDpPs+0ATo6GUTMAI8fF8NyDP3Als5in7vuOyTP7Gm33V6ZaXUSoY5PViQQpXZ3nsfvK45Y7R7UKB4Vhyk/fAaGEd/Xh4nlxBSu3bjjN7YuG4WrUW+zGMayvkB45eaTw3Ro/rCny4kh8eruPX1Few/OP/WQxsdzKSk5AkAeeXo64utljZa1AoZBRV6dpzAgoKqwgP7eUy5cKUKvb55FeW6vm38+v4ONv7sEvoP1+7pFRvqIF8/SUfAYPb3kAYC6pybnUGolSMJeOKPwp1pLF0ckGb9+OLUbbXhwVjszwE5dGf7Pja+NDmH0IKRVN39myunIOFh42aCtBwnAPy0cUvnX+Ay5XClGBVZpqNDrTkc3GSKlI03sdLaGUKrGR2TDKcxiz/E1/jjKJlFn+0/kq9VuD50rrSvkw+TPcrdyJdBDSZN2tXFFIFFRpqiitKye1Io3cGkMRpyEy/YWoZ3jq9BJU2tYjYxoitxsoqWw5KqO8lUg/rU7XKJaHernx2vxxxHQxtKY6nJxhUjAHuG2wUFD6nXV7WX04gQXD+1CrVvPnGaE4d2exz046Ek+bGEpUrY9limsv4mFtvn1Sq+e1s+OxAQN5bMBAAFQaDZV1ddgrFChk4oVWL+emMaO3iwPrjxi3wnp26FAWrVnDQxuEoqNutrb8PGcOmy5c4FC9Tcyb48bxn717UWk02CuVfHPiBPN79GjrS2yRMX6RxLhcvwJyt/iEI5VI9KLnW6K4tori2ipcrUxnHRfWlvFHzim2XjlJVlURsa6hyCRS3jz3O79m7Oe93osA8LFxae9LaBMbL1zAzdaW3Xffg5N16yLqdydPopBK+Xn2bOyaeZs3X8hpD4FOTiQVFDA6JFTP9udmo6SihiXfbaGqVv9++slD7R+zTXtwDNMeHENpYTm11So8/IU5g8RCAo5UImlTMcm2EurgxihfYdyy68pFk+23ZZ03SzA/f0l8oEzzApGmyK0qJ9De8vW3LMkv3+oXdZdIJfgFuLH00x3EHU2lS5A7Wp2OzPRCTp9I59l/t+/76enyVrv2tzSPRo0kuSyfry7sI9jBnTdj9V/fntxkerkaBqr9uSORAweSsbe3ZsLEHkyb1geNRsuSJStaFcw3ZCSaFV2+IKwvfdwNo/3/LqxOF2qXiAml7e3mz3DvG2OlNTe4N9+cP0xWZYnJtlmVJRzMS2OYhfv6lxfMnZQ2FNW27L03xb+PiKM03dQVimgUiu4oFNH1/zc91umaBhRV1VtwdHgUgNKyN1DIu6JSnUYmdcVKORC53Jg1iY7KqtUolb2oVZ3Awf4Byis+B4RBi1p9CZ1O8OZydPgH5RXfoNPV4ujwKGXln1Fdvb7xPM23t8akW2Pp3S+EjPQCAoM98PG/MQO6jqRKnUdy6Soj2y0XyVxbU4dDCx5Z8+8ayqsvGJ7fGCqVmt9/PcLiRy1XJd2SzBpvOAh78aH2ZQ/otDre/Nda0lLaZ+0S3tWHYaOi6DcolOBQL6RScYPPujoNyUlXOHrwIru2JZKfJ67q+7WUlVbz4tO/8vn392FrJ37QZoyIKPGTyLRUyxb/tIQdC0DKhdwWMy/aQnlZNQV5ZaYbcvNHl/8dGe4x1EBortIYirHdHLvibmX5VNji2mLK1R3ns3stKq0KlVZFWZ3pLLYGhrgPIqH0nNGFBIDC2kIKa8V7hvvZ+PJ816cAcFI4EuUYSXyJYQRgcwI9XFDW+9mq1BrOZebTO9jQPkCj1ZKe13Ik/vns/MbI8pfmjDYqlgMUlonzE53eTxhHfbLlIGl5xZzJyCGvpILKWhVd3J2JDb05LA46+XvS3+Mp/sx+Cju5F4H2gk2FjdwdCRIq1flkVOwhvXw7Y/0+6dB+KGWyNgmJX21puqaU16gorzZeWyfAyYktCxeSmJ9PeW0t/f39sVUo6OphGE376q5d1KjVDAsK4pEBA4wcrX0sCLu+ATr2CiuCHdxILRN3jc2pKmtVMD9RnMKazCPsyz+HlUzBRJ8+vNXrToLthaJvGZUF/DP+J95JWgvAh33uaf+LaAOHLl/G18FBlG+5WqPFwcpKTyy/WlPDwcuWySSdFBHB5uRkfjx9mvtiY4220WFe1HpH8NXmw/xz3mgcbNs3lgf4+T9rufXRCdheM0d0cjdeCLy9xLoHNHo8Xy9mBfUExAnmcYWZFNf7jDf8vu59/Ve+fXE+k5/8r0H7knLTVgsNuFiJ96rek5PCAM9A0w1vIDv/OGN0u5uHA5fTC7icXqC37bvPdwIwZFTbLA+d7O5s034dhYvSluXDFlGrUWN1TeSyDnitzzR8bQy/6yWlVTz9zCQcHJoWCGUyKRPGt77wu9qM6HIHhRVPxowU3f6vhlanY+2l1ucTzbkr/MY5dcilUm4PjeWdMztFtV+RdsrigrnlXdE76aSTTjrppJNOOumkk0466aSTTjrppJNOOunkL8hfPsI80tGHQwUtr3j62JhO25dJ3amo/BGZrOWoxTp1CnV1iQCo6hJQKMIor/gaK2V/FIpuaLVlWCn7oVanIpEYLwyg0eTiYL+YyqqVKJU9qaxajUzqgw4NlVUr0WrLkEr1V9JkMncqq1ai09XonadOfb5xuzGuLURp72BNUWE5RYVCtFxMry4m35e/ChFOc8za3haMFdpoYMiIrgQGe5CRLi6ifdOaOObdOaTFiPWbjYLiCrw92u45uGzpXo4fTmnTvoOGRbDgnuGAEGHeFhQKGdE9AojuEcCiB0ZxYE8S3325mytZ5lcAz84s5osPt/HMi9NMN24FcyKk0y5e/whzRycbykpbj/qoq9OQkpxL12jLRIaa5V9+kxf8/DsyyK0/P2X8ZtISZLinOC/Kvyv3hdyFDh2HCo+06zg9nbvzYOh92MubxhMxTt1MRpjLZVIGRQhRVXvPpbHmaCLzh/YyyMjZlZBKZW3Ln2Vls3vetTYvDeh0sP64cWuIa3GwESL5JvXuypqjiWw7nUxhuWBBd2unHUsnHczh/HdQaSs4VvAhxwoaChIKReIbkEms+CPrATTXFDK+M/zA9etoC3Tr4tX42MZKQc9pLV9nreRyYn1bv0feFhPDzG7dUGk0jUUdLYm3jSODvYxl2nYsPV19RUeY51WXE+2inzmzIuMAa7KOAnC5soAgO0/+ETmFSb6x2Mr1o5ED7Ty4N3QMryeKyzDtKOZ378GSHdvp/rmQcSyVSPC0Eyx8JkdG8NyQoY32P8MCAzmSlckru3czMjiYK+VlLI2Lw8NOuM8UV4uP9jXGhPAIJoZH8Oa+vVwoLKCfnz9anY6MkhK2pwrzgJ9nz8bbvmOir8Xi4+pAmG/7i3wCrP1sO2s/286cpyYx/aFxWLczA9UUI3zCOvT4xhhZf05budKkpYVWp2ssqjveXyjk/cE/bgXA39OZ/zw8Wa/9C58bFktvCRelLY4Ka8rqTBevXJV+mnsiB9zU/tPfrn70RnfhpuDa6HIQ7s49XIzPL/v3C2XD+pPUqprGqffcM4LRY1q2VCuoqeBMsfj6XXeE9cXBDAugvxqJV3PIrRaX1e2ktGZc/W/5RjEruAfvJ+xCI8JybdeVi1Sr67CRW25s85cXzEd4RbUqmF9Vma6obGd3B6ClIeDewX5x43MNjxXyMFxdPm7aSdEd0AAylPRueqxs2VfMyfEFAJyd/l2/pemc+o/1z63/XPPzGO7TwIaVQgGXosJySoorCQzxJCujEN96L7W/k2Ae7jSzw8+hqlW3+JxEAvPuHMLbr6wTdazqahXrVh5j4X2G1etvRr767QD/fmxSm/Y9feISv/6w3+z9PLwceebFafTua9kJl1QqYfgt3Rg8vCvff7WL1b8cNiiSZ4rtm+MbPcXb6i3u4eWIi6sdV4tNX5+uZF+ltqYOK2vLXPjFCOaPPD2BN19ea7LducQsywnmIv3LASLauHjSSduxllnT37UvBwoPGX3eViYsAPZzEWOD9vdFLpHzUOh99HCKYU3WevJrzbMGc1O6MtN/OsM8BhsUCO3uJM5f+Z7RghXCvqQ0LuYU8vxPW3h2+gg8neyp02jYezaNV1f9iVwmRa0xXt8hzNsNqVSCVqvj+90neGn2aKwUwpCxwarlw037OZVmXgHheUN7suZoIkeSL1NYXolUKmFaP8sWD+6kk2txUnTBSXHjxr15lRXE5+ZSVFVFpaoOeyslbja2dPfyFCUeOtvb0CPIh7KqGtYeTsTdURA4g73a7tErl0pF2Xi0hdG+4TfEdsPLRrwQW15naGvzSfIWhnoIdgfPRk2nr2vr4qSNTGmyeHJHsjIxkTf27eXRAQOIcHNHJpWg0mi4WCQEhHx9/Bj2SiVPDBwEwOK+fSmtrWHD+fP8knAGf0dH7u0TS4SbIB7ftnJFu/ojAT6dPJkfT/ux6mwim5KTUUil+Do4MiZESJF3sr5xwUJfbRasjYrKq3jmm42E+Aivu6FA3IOTB5l9zJ8ufsTmb3ex5uM/+P3jP5j77FSmLB6NlU3HFJ0c6BHI7twkLpTl8GDELQbPL03Zy7rMOL7sv4gAu7ZfHwprK3jlzDo+7begUdAc4NGF3TmmA6DiCoV5RoNg7mQvLLrPGNEdNyf9oEJvN/EBWVKJhH4eXdh5xXSRwhJVNU8dWcd/h83FRmb5RcFObhwffbyVmTP7sfPPRHr3CeKSiIDFvTmpory6QbiO3R7aNJf588B5lq0+QlbuVbzcHZk2tgfzpgrjbKlUwtnkHB544WeWf7SI4IAmS8qUSwUsenoZ3727kIgQL5PHawhsqaisZcKdn7Lma6Ge4rcrDrLncDI6YM7kPiyeP5TktDzueXY5P7x/F2FB+pZrDef99p2FRIZ6YYz9ueLqqAEM9w5FeQPvcwBuVnb0dvPnRKFpDUOl1XA4/xK31NdesAR/ecF8ml9v/ntxV4s+5hfLchnkLmY1ti2DRlkLj8UibeFxa+1kLWzX55//mQ3A60tW8e5Xi5BKJei0Ot56aU0b+tmJStWyYA4walwMPy7dC0BOdsu+sA2sXXmM2bcLAzMb2xtfyfuHNS1HQyanty3CWaVS89Hbm80WpLv36sIr78zF3qHjKsDL5VIWPzqG0HAv3n1tA5oWRKOW+PLDbQD0GxSGoo0e3uFdfTh2yPTAU6fVcSmtgEgLRFXnZF+luKh1H2hrGwVDR0ZhZbWR2lYWigCSErNhbru7BUBKsnjB3BLvhTG0Oh1TNv3AueL8Fts4Kq3Yc+v9uFqbLhbWVg4kX+L+H9byxuxx9A3y56PtBzmaepmymlq8HO0ZGy0MAh4dMwhbpeFEoKJWxbf7TrAj8SJZV0uxkgu3+p4B3tw7vB8DQg2L6BRXVLH25Dn2nE/jYl5RY/SxvZWSrr4e3D6wFw9E38MDofo+rWkFxUz5cBlPjhciHhVSBSuPJfDr0XgyCq+ilMsJ93Lj5emjCfdqe0TXOz1fb/O+N4Ih7gMZ7D6A+JIEEkvPkVKRSrGqhEq1sEim1qlRShU4yB3wX9AZVAAAIABJREFUsHIn2C6I7k7d6OYUZSCUN+Bn48vyAUtNnrvBs/zRiYP5dMshtp1OZtvpZOytlVSr1Gi0WgZ3DSTKz5Nvdx43egwXexvuGhnL97tOsOH4OXbEX8Tb2YHiiipKq4ToLntrJUsfnsWDX6+lWiWuaFxXP0+6B3qTkCH83kd0C8HD0XhWXiedWIrBXi9c1/OptcKY4vdzZ/n+1CmSi1qOeg5xcWFhTyHYZl5M98brdXO+3XaMjx+Yzru/76V7kDfvrxWKxX32oH6RtEtXr+Lr6HjDCy7eiOhyAEel+HGjSms4vlkz7Dm8rMUXFO/hHMTSAY+Ibm9J1Fotr+zexcJevXhq8BCjbQ5ezuB4dnbj/3KplCXDhrNk2HCj7dOefKrxcTcPD73/bwkJ0fv/jvpCsXdcUzBWKpGwqHdvFvXubf6L6mC6BXrp/bUE1nZWzHp8ItMfHMuOnw+w6oPNrP5wC/OenUrPEYaLwUHRbS8iKJdKiXL24lBBmRCtZYT7wkZwsdyymakNDPQMEiWYnyrKNrp94mBD7+1XH5hoVh+Ge4eIEswBDudfYsHu5Xw48Fa62N/YGm6fbThIak4RoT5uPDptCN9uO8a6Q4JzwRePziTAo/XrTlZhCf7uhm1a2m4JisoqeeXnHRYphmuMjIpifGydzBZjXZztGDo0gtOnMpg2rQ/vvbvF5D4H8sQLxM3rBBw9fYl3vtrOsw+MJSrMm8vZxbz15TbUauEef+esAURH+BDk78bWPed4aGHTtfWPPYmEBnroieWtHe/OWfq1RF56XyjePW54N26f0Z+rpVXIZIL2FxHiRXSED+u3x/P0/fp18bbsTiQ4wL1Fsdzc92PkDchqMcZI33BRgjnA3tzUTsG8OVYyBS93v5UnTiw3+vyqy8dYEDykcfX4f43C/DK9qXfuFdNibieGmBLMpVIJcxcMBuCjtzebPF5FeQ0b15wA4Lb6/W4ku49eZMYY48Uy5PK2Tbx+/m6/2bYnffoF89r789ssQpvLLeO7o9HqePfV9Wbtl5crFA5dv+o4s28f2KZzR3T1FSWYA6Sl5FlEJBYTXR4S5oVCISM03JtziVmttk1KaP15cxBryeLm7oBbBxVT+iX5dKtiOUCZqpZ3Tu7jrcHtK4Yrhr3n03nvj/1IJRL6BvtTraoj7lI2PxyIAyApJ5/v752tt09BeSWLvllFeuFVurg5M7JrCMWVQgGmI2mZHEzJ4F/TR3Nbf/3f+y9H4vli1xEcrK2I8HbHw0EQMS8XlXA0NZOjqZm8OWc803sbjwYuqLfXeGvzXn47Gk+fQD9Co1y5XFTC6ctXcLfvuAWGmxUJEno596CXc+uFiDqKxWMGEOXnxfK9J0m4nEudWkOwlwvT+kazcERvtp9uvYDXk1OGERPgzU/7TnGpoJjs4lLcHewY3UMYPD8wdiA+Lg5EB3hxIlX8tWDekF4kZGwFOu1YOvn7cbm0lMe3COPAM3mmF4LTrl7llT27Afgl4QyfTppMhJt+4WRrpYLi8irKqmq4bVhP4lKMC1IPbdxIoLMzX01rn21ce+ntdmOK+DoqzBDMNYbjenPEcgA7uRV28huTtq/SaKhRq/UKeDZQVitEz2eUlDAs8OYufHg9GR4TAsArP+9gcFQgfSOE4AEXexs+33iI77cdZ8HoWEb3Ml8gkivlTLx7JOMWDuP9+7/hi6eN6xLbqo1vF0Owg1tjtPep4ku8ePp38mqE+cgLMVMIqS9I2xwdOt5M3ERpXTW1GjVPRY1vjDy/dnsXOzdeObOOSnUtzkrDMdu1FkYtkVLWcsRveWUNFTWq5m5Y+LiLjzKfEdSD9xP2iLJlAYgvvsLEbV83Fi1cHDkIl1aK/XYUh5Iy+OX52xv/v3d8fy5mi7OPKiqr5Ofdp3h+zihR2/8qPHZ0BV3sXPlsoHmRVw0aQXlFDT8uO8Dly0Um9zHHjmW0X0Tj4x9WHmLWxN6MHSYs9vj7uDB9bE/WbRcKiDaI3JNuiWHVpjgeWDAMqUTIztyxP4mFM/U1gtaOd61gHttdyIqbNVFYfAz0088YmTmxN+//908evnMENtaKxuC/HfuTmD+95SKdWp2OxKs5ot+PPu5tX+SzJLFu4vtxUqSwLpa/vGAOMMwzkme7Tebdc4ZCZXZVMR8kbeGZbpON7Pn3p++gMB5ftJSgME8upeQTO9CyVWP/V6hTaUy2GTdZqCL+03f7KCwoN9n+91+FqO4Zt/VHqbyxP8VJI7px69ieRp87c974xKw1CvPLWP3LYbP2CQrx5F9v33bdxPIGxk7swfl6YXjjmjiz9v112QGmzoxtk11KRJR4WxFL+ZiLEczDI4V+RXTzNSmY5+eVUlxYgat7+zwC6+o0ZGaIGzia876ZQ3rZVf5zYreotr9djGdMQBhjAjp21X174kVu6RbK+/MmNUYdZl8tY+anPwFwNDWT+MwcegY0vScvrdlBeuFV7h/Zn8fHDtZbLD6bncfC/67k9Y27GRQWSIBrU82M2wf1ItLHg5Fdgxv9Thv4fn8c7/6xj+/2nWhRMN+aIET8OFgp2fzkIvxcmiZA+WUVuNj9NWo2/N0YGhXE0Kggo89N7BPJxD6t20qN7RnO2J6tR2l894h59UIavpNuDraMiL4xkaid/O+ioz6jrIX0N4mk7WOQnPJy5q1aSW6F/hjQRi4n1NUNdztbbOQKqtV1FFRWklpcTI26Sbi9WFTE/NWrWHXbPEJcmiIiXextWPzpal6aJ0SStVR/IKOkhDt6Gh/LXS/cre3wNMMaxZJYG/HCbQktOqbt/U+bzrNhxD/btJ8lsVUoGB4UxDcnTmAlk9PVwx2VWkN6yVVWJAi1LqrVau7u879tk2aMfQlp+Lg4sOX4eQA+fGAaaTlFLH1yDs8u3dQmwVxdp2H3ikOs/GAzZYXlPPDOHfQcYRhR3R6C7JsEM1elPa/3msW5UkEIXJqyj//0mm2wz57c8zgqbPhnzFSyq67y7rktTPcXvhPXbl8UOgwJ8E6fuRwvSufHtIN6x4pyFieYN9gdFdRU6HmIP/3ROnIKy3C011/Y+mrJbaKOC2AnV3JHWCxfJh003bieWo2a/54X5qTLU05wW3AvFkX0J8Cu46PO03OL+XXPaa4UlfLy8m30Cw9g6kDj42idDl76cSt1ag3F5VX8c95o6jQavvnjKClXinh5uZDdPKJ7KAEeTnrbR3QPZXSvMD7dcJDswlKqa+t4bPpQrhQLCyobj5xDKZeRU1zOg5MH0T8ywKBtmK8br/y8A4CqGhVOHTxuv1xZzPzgvmbv9/ySqQA8/vg4jhxOYfyE7q22r1SryKwQHzA60COo8XHq5UISLlxh+ZqjRtuqNVrkMikTRkTz9c/7iTtzmX49Azl6Op3yilrGDdO/BrR2vIZjNRAd0Xqg3C2DI/nshz3s2J/EtLE9OHwyHYDS8mrGD2/Z6jCj4irVanFZoc5KG9G/kz++2822ZXspKyrnu8T3Re1jDjEuPsJihAjrgotlBdRpNSgsZCXztxDMAeYHDcJVKUTEvZawjipN02Dyl0uHqdKo+EfXCTgq/rcm7QsWj2DkuBiuZBYzZ+EQugS7m96pnVRXCTfKcycukZ1eQHlJFdVVqhYnJ23hvn9OtdixxKBWmxbM5fVC7+zbB/HVx9tNtm/wr/5jwymmz255JfB6MHdSbIvPPbd4rNnH+235QerqTL9nDSgUMv7vtZnYdJDnnynue0SYhB7enyxqsaOB8rJqdm5LYNJ08yckEVFmFP5MsZRgbjoKNCxSGBBHiuxf0tkshoxoXzGQS6n5om1xxPbLHFQaDY/v20CVyAEEwHMHt7Bl2t1423acMCCTSnllxhi9FH0/F0cm9hCiH1YeSyAxK69RME/NL2LfhXQC3Zx5bMxgg8yqaD8vbo2N5tcj8ayNO8vjY5uyW1ztbBgbbXySeOeQ3nyw7QBpBS0POBsKOL43d5KeWA7g6ai/oHIsK4svjwh1Nk7l5FCrVhPk4sKsmGjuju2DrL7fBZWVDPzya36bN5efTp9mZ2oqAEMCA/lkymS+izvJV0eP4ePowHsTJxDj5WVwHoAvjxxr9TwN/HhKiBh5ZecuTj32CJdLSnhn337ic3LR6nSEurny3sSJhLk1TVobBm4rExL4+fQZMkpKqFQZilmpzzxlsK3hfRDTt+/jTvL67j0ce/hB1p1L4tf4M2SXleFlb8+QwC48M2woLjbGxzeVKhXfxZ1kW/JFMkpK0Op0uNvZ0svHh2eGDSXAyamx3cAvv8bX0ZFtd99l9Fjjv/+BK2XCNfLoww+2qWDg6iNnAJjWrxuyDvJQ7qST5qi11Zwo/JzMyn1UqVtfnL0r3HidBlPogMe2bNITy2M8vfjHoEEMDwwy6hdep9GwN+MSnxwRAigS8/O4Wl3NY1s2sfH2BY3X8CVzRqHTNbkwvL5gvNE+2CkUHeZLLpbmot7NTj+3psVACULW8q7cpsLK0c5dcFTYUKqq5FxZFjKJlHtCRrd4vIt5RXyy7SAhnq48OWEoa0+c5da+4mpPtIVPJk3m82NH+TXhDHkVFWh1Orzs7ennJ0T4fz5lKpHuHT/v+6sxKCqQ+ycNbBQHa+vUKOQyZFIpDjbm2UHWVqnY8t1u1nzyBzWVtcx5ajLTHhzbIcU//e2aghz8bAURK9BOsLrLqS4xuk9GZSGhDp6N+1ypLiGjUrgGXrs9u+oqgfbC96WLnaGFnpPSGielNaUqcdHdl8qL9QTz8qpafnp1oUERcnN5uNtQtmYlAZBebl4mc7W6jmUXj7M85QQjfcK4PTSW4d6hHeZEEOztyj/n3UJiRi6vLjR+3W5AIoHX7xKyV3fFp7Dz9EXumzCAO0fHsvl4Ektu0/esv3Z7fNoVqmpUvHXPJC7nl/Dh2n3cOkTI4tPpdLyxaCIpV4pYuvUoVgqZQdu7xvZtdCV4+97JHE/OZPlO8YFkak0uOp3w3VDIg0y2t5O3rQZEw0dlZ2fF6DHRbNt6hvETWs7kPF+SJ9q/XCmV0a0+k0Kr01FTU8fi+UOZOMr4dbxB4HZ1tmVg72D+2JNIv56BbN1zjiF9Q3FytGk8FtDq8ZqL5QBWJoIpFXIZU8d0Z/2OeKaN7cHWPWcB6N8rCDeXlq0OL5SK1xTCHMXfPybeM4qeI7rx4vR3RO9jDjZyBb62TmRVGr/WNUet1ZJcWiA6K8YUfxvBHGC8r/BjiXEO4IOkLezOS2p8bl1mHHvykpjdpT+3eEcT6ejdok/o3wmdDjQaLQ5ONlRW1DRaKER1t3x6RVZaAT9/vJ39W+IBzPaFNgdjgnlhTSLu1jGotOWklq7Hz24ojsogi5xPjGDewOQZffh12QFKS6pEtV/102Emz4hFLr85hYOC4nK6+IqfABUVlvPH+lNmnWP27YMIDPEw3bCDaPCRn3vnED5/f6tZ+274/USbBHNXN3vc3B0oKjQt0KentG4TIoaKihoy0k0fpyHCXKwFzLmE9gvm5hT8DO8AwfyFw1tJKBLfB4Di2mru37WGVRPvMFrh3RJE+XrgZsTKxMepSaSvqGkqXHYkVcgg6B8SgKyFCUmElzD4OXdF/HdKJpXiZm9LflkFGq3WqMhpb6WsP3fr95YNSed5essfBLkIqe9zYmKwlss5mpXJm3v2EpedzRfTp+ndnd/Ys4cgZxcGBgjpiX+mpPLs1m1cKChgfs8e/HT6NC9s28HGOxcYnAcgyMVZ1HmaszM1lZd37GRIYCB39elNcXU1e9PT8XHQF//f3it4CS89Ecf0qCgeGtCfkpoavj52jKzSMl4YOYKRwUGtvg/m9O2xjZtJyM1lQkQ4kyIjOJ6VzW9nErhQWMjq2+cbtC+uqmbebytILS6mt68Pd/XpjQQJFwoLOJhxmTfHN32/7JRKpkVF8duZM5y8coU+vvq/tcS8PFKKipkZLUSutEUsP5GaRVxqNjKplLmDb2wkbCf/O8QVfcmF0jX42MYSZC8InudKVhLuNBW1tpqcquPYyN0Y6PFMm8+xLeUiJ3OENOfxYYIQ+9mkya0uCilkMsaEhDIqSMi0ePyPLfxxMZmkggI2J19gamTTvbW5nuNsb3xxbGJEBAcyMpjXvfWIu44k4AZ7BZvDSzH62THL0/cSaCeMRT/uey9W0qZrXK22jmdPLaNC3bJY+OHWAzw/eQSfbhcWXfZdSO9QwdzByqpVT/JOjCOVSvjHV+sbf5tPf7MRCRKOJ2dSUlFt1rEWhD+BVqNl5hMTmfnoBGw6sP6SWzPxOatKEIozKgU7Cj8b47+7YHsPEkqEuX921VV8bZwJthe+49du97FxJq74UuM2Y/jZOVOqEjdezqvWn99MGBTF9GeW4u/prCeaf/6cYWR8a9jIFLw3QPDVnrvrh8aaEeag1enYdeUiu65cxMfWkVlBPZkV3OO6RJ23REV1LW+t3I2DrRUFJZUEe5u3+Jied5VTqdks+U7w9G6+f0OBWwdbK6pqVUbbZheWEujV9Pq7eJr3XlwpvIvaOmHBMdzftAXKeN9uHMxPZU6QuDl0dgs14o6fSG9VMM8UIbA2EOLo1hiwIpVICAvyID2zEE8308FRU0Z359WPN1NaVs3BE6m8+nSTVtWwIGPO8cQwY3wvflp7jDNJ2RyME4KKXnys9boA5rwfDffzzAtX+OjhpXgHCYtsaWcycPZ0YsmyR3ASaZO6/LXf2ff7UeQKGfYudrz4yxM4uTtwJTWPd+/9Er8wb9IShHmsVqNlybKHCYrWr7kVaO8iSjAHwSO/UzBvBT9bF96PvYPEkix+TNvPn7nCikuJqoqlKXtYmrIHe7k1EY7Cmxhk546frSse1g64WTngILfGXmGNjUyBlVSBQipDKpEgrZ++Sm6AH7pS2raP6u2X11CQV4rzNStNL70tPv1JDEd3nuPNx5dTWy0+QtPSnL36AyN83iOu4APcrWM4WfgxI30/tMixtRrx0fFW1gpunTuAH74WZ++Qn1fKzq1nGD+lV1u716F8u/owrzwu3tJo89qTZkWXOzjaMO8u4wWLjPH7N3uYtXgkezaeorqilsAIb5BAelIOVwvLmThX8AA7/OdZpiwYTFZaAfGHLxLazY+uvVv3cpw4rTfLvt5DRYW46AkQ7FLSUvIICTO/iFBkN18O7btgsl1FRQ35eaV4ejmZbNsSSQlZJpM8FAoZXYKFgbRfgBu2dlZUVda2uk/SWfMte65FrH85QERXy1qyLD13nN9TE40+N9w3GD97IVr61+R4g+fPFOXy/KE/+GhYx2S8XBuZ3YC0mQDT/CPNKREmJ6uOJ7DqeAKtUVZt+B0/kHyJrQnJXMgtbIwYr1LVUVOnpk7T+m/62qhyY1ytrub/tu+gn78/y+fMAtATk57a/Afrk5LYmZLKmLAm+zCFVMZHUyahqu9D7GdfsPn8BfYsvpcAJyfKa2tZcSaBGrUaa7lc7zwAy+fMEnWe5ry4/U++nXkrA7s0DdZ0oCdi16rVLD8tfC+ivTz5YHLTIDXC3Y25v64gv6KCMLemSK2r1cJkvPn7YE7fTl25wtoFt9PVo2mB8e7f17Av/RKnruTQ21f/9/Ha7t2kFhfz3PBhPNBfP5OpTqMxsN+5o1dPfjtzhhVnEgwE87VnhSCEWdHiBKCG9FKdDk6mCdeJ55cLixhzBnfH11W8b2knnbSHzIr9hDlOZojX/zVuu1C6ju4ud+Kg8KVWU8qWrAcoUaXjadO2hZxNF4R7ub1SyVtjhcw8sRkUDe3eHDOWg5czKKutZcvFi3qCuRiWDB/Og+vX8+6BA9wbG4trC1knHYmbBf2BD246xZApTcUjj24X7msDxnXMgsDvmYd4JGISgJ5Y3vD/DP8BfHR+I3cGjzS6v61CQWAHFeDrxHL8e8E48q9W4OUiCD0SCRSWVrL6wBkenmpeXakp949m1j8mYe/U8b7YzX9bGp2O/zu9msJaYdy3JHoKRbUVLE3Zy9mSLD5P/pPBHuFM9e/FoYIUnju5ApVWzZP1XuWAwfZAO3fWZcbx3MkV+Nm6GGS6AXhZ23NOZH8Layv1/l+x4xRvPjIFN6f2F/ru6SqMTz4ZNIsnDq+hTit+3nktOVVlfHZuP5+f20+sewAzgroz0V8IDHAyo5Bwe9mXmE6AhzMPTBrIqv3x5JcI759cLqO61rDmwrXbg71c6Brgyb8XjGt2TKG447UR9MbankzJ5mSz+hjZhaVm9V+rE5+hDfBs93E8cuQ3Pji7k0Vhg3A1ce94/rnfiDQyB7xwvnU/7txq8f0KcdCPqL5n7hD++c46gvzdGDEwHK0W0jMLG4NCxzcr7DsoNgRrKwWf/7gXO1slA3sbWg62drzxRooEm8LTzYGh/cL4+PtdjRHpw/q3bimVVyX+/fC1bRqnnz2UzJNfLgbAP8KHH/69ip/fWMPDHxrPSL2WyYtHs+DFmUgkEr578Td2/ryfmU9Majz2vW/MJ2aIYBO58asdrHp/E89+99A1/RGvg5jzuZvibymYq7RqUsvzSa3Iw9XKuOBQoa7hZP0qasPfm5mTk15v0375OSV8sPQeC/dGn0vJubz+8DLUZoik7cG7i2GaGIBcYk2N5ioqbTnhTrPIrzYvyrk1xPglNWf67H6s+vkQlRWti40N/PbjQcZO6tnuNLW2snzdMRbO6M8Xv+w3eO58mngxU6eDHX+cMevcU2fGtsmKJSejiPmPjmHdd/upU6mZ8+AoVn21Gzdv4WKqrvedr6qowdXTicTj6SYFc6VSzvDR3diy/qRZfTmyP7lNgnlEVx9RgjkIUebtEczF+JcHhXo2ZjpIJBDe1Yf4uEut7nMx6QoajbaxcndbSEkWF63i7euMk7PlJiXbLie36Fs+JiCML0bMaBQUlVIZy84bfi/WpZ0j0MGFJ3sNtVi/GjA3tV5Xf52K8vUk0rv1NLoA16ZJvVan4+nftrAtIRlHG2uGRwRxS5Qg1jrZWmOrVPDmpj2U17R8PZPLTKdVbjp/gaq6Ou6O7W1USJoaFcn6pCR2p6XpicXdvYXflrL+HAFOTpTV1jZaifg6OqIDSmtqsLa31zsPGIpWLZ2nORMiwvXEcsAg4ju/spLaev/haE/9YltR9YJ2Rol+JMSm88LvvaX3wVTfJneN1BPLAUaHhrIv/RLpV6/qCeaVKhWbLyQT7OJiIJYDBmI5QDdPD3r6eLPlQjIv3zKqsaCcRqtl0/nz+Do6MOCa96UlHvrvWuIzrqDR6PQWXCJ83fnH5GGijtFJJ5agWlOEt41+FJtcaoNKWwb4YiVzItp5PmdLfiXCaUabzhFfX+BzeGAQTlZtE1kcrawYERTExgsXSBBRMPRantu2jaq6Or46doyvjh3DTqlE0cJ9JO7hh9vUR1NYsqDekW1n9ATzP38TfIg7SjAvVlW2+rxGp6WkruU2/m5OvLFhN+mFxby4ejthXsbnK53cWKQSCd6u+lGR+xLTeHDyILOPdde/zIuObg/29QVmR3lHMcrbuD/689GTeT5aP8jphZgpRtsa2/5qz5mt9sFRKX4RrqhG/7fSNyqAg/Hp+Hk66Y19zCn6eS3j/CL5aeQCHjv0O/k1FW0+DghBEScKMzlRmMmrJwW/8LF+kcwJ7sVgr2CL2bYUlVexdOtRzmYI1/jPNx5icLdA+kUEsHJfPLnFZSgVchxthftIqI8b+SUVPLt0EwBTB3ZjeEyI3vaGbYeTMhrbDY0OxsXB+OfVM8TXoO30QdGsPywEmT7/7Wb83J30AnRModWaJ1C+ELeOarWKb5IP8E3yAezkSqOe04cnPwfApEk9uf0OwwWtzz7d0ep5zBGIvWz0dcOh/UJ5a8mtLFt9mB9/P4pcLqWLrysLZvY32FcukzJ+eDdWbopj/ox+RjUdc44nllkTe/PEv1cyfZyw2K9UtC7vmiMkN7+fewd64B/RNMcYPDWWjx/5VvSxjm+LZ+cvB7C1tyYnPZ9BU5vsgN19XRvFcoDg7l04uP64kf6Iv/7kVJWJbmuKm04wf2nVdl6ZPZYD5y81/j9/cC8eHDOgxX1+SN1HcnkuyWXChedSZSFaXcfZgfyVcHa1p06lQaHsuEKKn7/0e6tiuVQmxd7RBut624v8Zik1tvbWjWmmOh1UV9Y2ij7NsbW35o4nxtJvZBQBoYZVwAGsZM7szH6Y/p5LAKjTtj7wNQszBXM7eyumzerHr8sOiGqfnVnMvp3nGDm241I3WyPIXxjUn0jIAPQ9zRu2iSE+7hJ5OeJTfSQSIapbLFcyCkk/n0Pu5SJsbJv8AZ3c7fnz9xPU1taRmSpYTaQmZZOSmM3ZE+nYO9oglYkb6AwcGm62YH74QDK3322+AGSOvUhaSh4DhrRefK81zCn42UBklK9Jwby2Vk3axTzC2xj5rdOJ92iP6Go5O5a4/Gwe37fR6GLYlKAoPho2RU+wfmXAWGzkCr5KNCzU8nH8QbxtHZgfcWMtJrychIFejwBv/jW9ZZ/Va9mReJFtCckEuDrz60PzcDVS6Oc/G8VlzLRGQp7wOT+4bkOr7Qor9e2snKz1xScbhULPcqZBSK+rj/ho63mac60fujHcbG0bvyPXCuMN/3va6UdSNfTNVP9a6tu1wjyAc/37U1qjnzWQVFCARqtlQIA4gbuB23v25Pmt29iQdJ75PYU01/2XMiisquKRgQNEm9n5uzlxMaeQ0qoavJ0FcWJ09zAemTgIWyvz7Vw66aStWEmdqNXqT55sZK6UqjJwsxKiuO0UXlTUtR6p1hqFVcJvtotT2xe2Afwchf2Lqs2zhgBh0dTL3p4J4W0fK7QXW3n7f9s5lwpY9/UuLsSl88WS3wCoqaylqtz898QcIhx8WJ6+B4BeLsF4WDUJeYW1ZSxP30O4Q8vjkH+MH8LpjCtE+XoS4uFCr0DLW8gBVKsL+f3SdCKdZtHPw7A+xtZMIQqwWlPMrUGFIc+pAAAgAElEQVSraVju1erqSCheRlr5H1Sp87CWuRLkMJqebg8AQuBRAzqdhtTyzaSX76C0No1abSk2Mjf87YfTx+1h5FLDccLenBeQIGGw10vEFX7K5Ypd1GmrcFAEMNL3LQAcFObdjyzJ5fwSung6k3KlyOC5w0kZzBxy46yMxGBtgd9We3E2I+K6Uq1fz8W5XrzNKbSckAUQ6x7AlgkP8OrJbWy4bDxj1FxU9RHrmzPPsTnzHP52zswP7cNtwb3atCj4y/O3Nz52c7Dl+TmjeH7OKIN2Pzw912CbUi7jy8cMFzKMbW9p0Wd4TAgAXs72fPLQjBbbvrJwnME2sWh15n2uOp0OT2sHxvmKK45rTCwHuGNB61khV1XiLHIB3K0NA20Hx4YwODZE1P6PLhrJo4tGttrG1PHs7aw48Lt4ezgba+G6MHGkOA2prE58Fr1LswWya62WdTqdaNeNrIs5/PjKapbGv4uNgzU/v7GWOlWTK4W1vWHNB2PSmzm/vRKV5cYLN51gnllUilQi4ft9JwDY8MxdPPHjxlYF808umC6w+L+KVqPlntmfEhbpo7fSZSlLlszUfBKPpxt97pYZsUycP5DIngEomhUumBrxfKMn+OebntSLGNdqtBTmlhJ/OIXNvxzmwunLgBAlXJxX1qJYDtDX4xmaJ80P8vpXO19dE22pVzpz3gDWrDhKbY04m5pflx1gxJhoboDjD8P6CpGMk0cKxUHGN6vqfCTe+OdrjL07z5p13qgYf7x8xKev+ga688z7gk/vjHuG6f3VaXVImn3Hn353HgBhMX4Gz7VGj96BSKQSdFrxH/rF8znUVNdhbWPeYNYce5G0i20v/KnRaDl/zrSfXEPBzwZEF/5MzG6zYJ6TXSwUBRaBWF/11kgpFSZK9+5aTa3GMMXx7qhYXuo32mgkyZLYkXja2PPa8Z0GBWRePLINDxs7xgS0ngrXkQwKEzIojqRcNqi23hrnc4UCUGOiQ42K5ZeLSqioFfcZtUZZfYT6vX1btwoIdNG/JhiLtJdKWn5tzc8DtHiua8/TnJYKaDbHVqFgXg9hgv3T6Xhe372HW0JDKKmu4dPDR7CWy1nQW99qq6xZlH5r70NLfXO3Ez9YLK8VzuVobV7xsSldI3lj9x5WJCQ0Cubrzgl2LA3+5WL4121j+NdtY8w6dyeddATOVsEUVCeAc5MY4WHdjaSSlQTYDUMhtSW78jDWsrbbaVjJZNSq1Y3WUW1FVZ+10pbinZ9P7Rh7MHNoq4Vkc3yCPJj35ESK80q5ZbYw/5MrZQRacNHcGE9ETuGJOCFi7tZ9bxFq742z0o6yuipSynORS6V8FHuv3j4P/7CuxeNJpVI+u3OaxftpI3cnwG4Y6eVb6eP+GDJJ09izoi6bghrBuqan2/005Ubp2JvzArlVJ4h0noOzMpgSVTrnS1ZQVCNkPo31/wwJDVmGMpJL12Ev9yHadSFWUkdyq09yoWQV6LT093zWaN+q1AXsyXkOpdSBXm4PoUVNTtUxbOWW8ZJtD6sPnOGpmcN5+psN9AzR/y4lZxfcoF6JR9mGAomW74P43/e1Y+zFM8yP4BeLs9KGDwbOYG5Ib14/vZ2kkrbPmYyRVVnCu2d28cnZfcwM6sG9kQP/UgWOOxqdToVOZ54t78cDzNOiUlPzWfbDPkrLqunVM5CFdw5BLpfh0kqBS4Aajfh+OSqunwWPpfjzwHmC/N2IiRR3fzQ2920JK1nTvSX/ciHpiULwXXBMAIc3naTboAhRx6ksqcLe2RYbB2tUNXUc2XyS2LHmL1Bam1EzzJzP3RQ3nWAulUhIys7H30WIsHCyte6wysX/C8xdZHmbgOYc3KpvvyGRSvjnpwsBGDrReAEGpbUCdYUwoaip1hdhpDIpnn4ujJ3dj7Gz+7F91TE+fVGIYP996V6QSLjvBeOpZSWqVM4UfYWjMphebg+TXXmAEEfjba8HTs62TJ7ehzUrDCNSjZGems+RA8kMGibu4tMRzBpv6KP+4kMTRO8fdyzNrPP1H2Q5cbE1QVysWA5ga2eFn78rWZcNI1BaQqvVkZKcS0xP8yJnnJxt8fJ2Ii/XtE9cWjsKf6Yk54pauLk2wjxCbOHPxCymze7bpr5dT//ynKpyFu5YCUBJrf4Ku1QiYUnsSO6Pbj0t7p5uffG2c+DpA5upVje9pxqdjkf3rufnccJCTaynX7v62hbCvdy4pVsou86l8p9Ne3hu0nCsr0nNq9No2H/hEv1C/HGoF1Ibioim5RcbHLO8ppZ/r9tpkf41FAYdHx5OrF/HCR/NzwN06LleHDUSgNzyCn6IO8nyU6dxsbGhp7c3H06eSKS7vjVOQ98a+mdu38wpVt5gp1JQYV62lbVczq3R3Vh28hRJBQUEOjvzZ2oKfXx9CXL56xT066STBoIdxpJcqi9sRjrNZHPWYlakTUYptadaU0SMyx1tPoeXvT1ltbUk5revSPeZ+iwUXwfLFAW73rRF6DeGi6cj85+eREh064WkLUl350BWDhWE4BUZBzhflk1hbRmuSnvmBg5hXuBQ3K307SNenTW28fGXO48yvns4ge4uZBWXsve8+IATcwl3msHlij1kVuwhyKGpD2nlWxtF71DHJmuOyxV7yao8wAifN+li3xTZaiv34HjBBwBkVR4gwK6pgOikgO/0zhniOIlKdS6XK/fSH+OCeUFNAjEud9LbvcnyJ9JpVjteqeV4aqbw2mYP7cHC0bF6z729qv1ZdP8LmPP7Vl3jK15SXs2WQ+covaZO1EOzxNexMsUAz0A2jFvMlsxzfJK4l9Ry8XM5MdRq1PyaepIVaaeYEhDNY9HDCXboFM7NjS5vC598so3F943CP8CVbVvPsHrVcebNH2hyvxqzBOKbTho1Sk1tHcUlVaRcymf99nheeES8VmOOkNx8kS4g0pfVHwgWPmkJl3HycOSFHx9tfP61uR9RlHOVgswiXpj8Ft0GhrPwJeHaH9E3hOCYLjwy8P+wc7Kl9+gY0X1ojjHLnpYwZ2HAFJYZ1XTSSSeddNJJJ5100kknnXTSSSeddNJJJ5100slfnJtuGWVQeBee/WULn9wlpLDVaTSo1NenmGQn5pN6Vt/mYcaiYS1GljdgZa2gqn51ucaEFcO4Of2xtbfmjUd+BOD3b/YQ0SOA4ZMNvYLji76gj/s/OFP0NQBXqg7e0AhzgNl3DGLDmhOiC6L+8sP+GxphHpcoWOAcPZOBqq5pZe4fdxn6rF1L1uUicq+I9y8H6BkbZFb760VQiIdZEeYAyUlXzI4wB4iI8hUVYZ6dWYRKpUapNP+yLca/XCaTEhymb3nk5e2Ek7MtpSWt+7+dT8wyu08NpF4UV9isoQhpWylV1XDXjpXkVBpGQTgorfh0+DRG+onzqJsUGEmQgwv3715DVkXTZ1ejUXPvrtUArJpwB+HOrRfe7AjemDWOxaVr+O1oPDvOXiTKxwO7+kjy/NL/Z+88A6Oq0j/8TJ/03hPSK4TeexUFFBRRVLAjrlh3dW1r+dvW3teuK3YFUUFUQKr0XkNLTwjpmZRJmfr/cEmZTLuTAnHN8wXmzrn3nrmZufec33nf31tHZmkFtY1NrHvglpYI86l9E3jz9+1sPpnD9R9+R2pYMBV1wt98R1Y+0QG+DImJYF/umU71rX9ICD8cy2BnQUG3Rn23PQ90b4T57kLhmmzMzub1WTOZlZLssH3/c97o5+M6JAcGIpVIOHD2LEazGZkL2XrXDhjA0v0H+P7oMQaEhdKgNzC334WpsdFLL50lwWsGid6WdiUB6hSmhL1IhuZbTGYDqe7z6Ot3rZ0jOGdMVB9OV1Swt+hMS62CdBG1ENpyqLiYfUXCPWVcdEyH+/K/Qm2lln8v+ohGrWXB6f/7akm3nTNQJUT2L0m6RFx7r1Y7AE19A8PjhXFgiI8nX+042PUdPEe4+wg8FeGcrllpEWGeU7uGMHehyLOHvPX7l1e3AbnUjSjPCRbHCXNvzaorrt9nEWFuCz9lAsX1ezEj+NlKbMTepXbid3Q+aB9dDnD7jO6zCzkf1DY0cf9HP/P+Xd0bzS93YIfXHqPJ0vP44Xd+pl9cGNsOZTNleBJ/HMhmeN8+Xd1FJMDMqDQuiUxl7ZmTfHhyB4cqOjd+bY/JbGZl/lFWF2RwbfwQ7uk3Hl8XCqL+r+Fqwc+O4O6mol+6kHF0xdxhPPvMT6L2cyXS2JUI5gvJqexS/vHMclRKBQuuGMHUsSmi9zW6UOdR1ub3LpVJeeCTv9lt+9i399p9TyKR8NBntp/b4fEhfHL0FYtt/cYk88Jvj1i1lUvE/33aZ7h0hh4nmC+aPJxFky1T4j+7o2v8tv+KLP9iOyBYRuRklhATJwhiT74y36ptve4wdY3bUMhC8fO4XNTx89qJXbNFWMAoVK1fu/aWLLYYe0l/plwxlPUrBF/7D575iZFTBB9VpbrVW0kucbugxWRsERjkxUUzBoguInkyo4j9e3IYPCy2m3tmm9c+FVIS77lhotMqy+05sDfXpfYSqYSEpAvvaWiLwGDXK7bn55V36FyJKWH8sfG403Ymk5m87LIOicbHDjkXzPvEBNoU45PTwtm9PdPhvkVnqqjW1OPj63ohnMxT4gTziKgA3D1c82Juy8bCbE5prP9GI0KieHXsLCI8Xfubp/kH8+ulN/HYrrX8mJ3Rsr3Z6uXr04d4fJj4wptdhY+bmi8WX813uw+z+tBJDuSfRX/OUzfQ04NB0eFMTUsg2Lt1ku/v4cZnt87jtbVbOZh/lsMFxYR4C4VvrhjSlzsmj+STP/Z2WjC/LDWVV7Zu45N9+7gsVRjcRbUrkFdeX4+XUolK3vHhSdvzCK9TuuU8ANvyhKLIRrOZGD9fTGazQxu5y1KF+hBtr0N39c1HrWZqQjxrT2fy/u7d3DHCshaMwWTCbDajkFkPQBMC/BkeGcma06cp19ajksuZmXzhFnN76aUzSOxMsiI8RhHh0TVC2RVpfVl68AAms5nFq4SCvh/NnkNaUJDTfVsKFa9aiRmQSaVc069jBQhNZjMbsrPZUVBAdWMj1w8cSP/Q0Ja6GyW1tfi4ueHWyfuLI04WlHHP2z+SHBVEo87AA/MnkRAe4HS/ihotT322jjfuFIrSff7CKu586Vo8fS6MEHWmoRKNTouv0oMIN+e2Cx4qJU98v47oID/yyjWoFd1ZpFFCovdlHKh4nzp9EZ6KcMobM6jR5TPA/zar1rX6QgymBr44bb9Ans5oGVBQ0XicU9UrKG/MoMFYgcHUgNF8bt7WXNyp3eNOIXXvVC2A88HukwWs2HaE+na1WZqLIfZUXBG6ugu9CyJUe/GxVtvIknljyT1bwS2XjWThJUO591X7NQA6i1Qi4eLIFC6OTGF/uRDY83nmHn4rPOHS53CE0Wzi88w9/FxwjCcHC7YYM6PE13rpLGZzA9Xab9Ab8lApUvD2mAfYft41NO3utn7oDa5Zsoqh0ajnq+w93Jwo3LNyc8t47bXfWt4/efJsy+v77rNvSeKKtbPY35jeIHyf8ipuw2w2opCFEBP0mfjzmKrILbuR+BBxon97+qdGsO7Lexy2qdftp1jzHHHByy22u7IoYDC3/k7MHSno18W48rvtypoPPU4wBzhaUIL23ENsWHwkOoPRyoe1F3G0Le5pMJh4/l/f223bqDtGsPffqNJ+D5gQ49hT2ybyNCDUh5BI5wNKVRuRu65GXAXbq2+f1CKYV5TUsG2NUNBm0uzBLW08FeHsLXuZGn0eO0ufxkcpLlq0u7lq4Wh+W3UAk8gikl9/+scFE8wnjRQEkcMnzuDv2yqoDUhx7sd8+sRZl84VGRVg8V3oSfgHWFfJdkaZiChxWySJLKwJkJ1Z0jHB/IhzwTwh2fZxk1KcC+YAx48WMnKs64KaWA/zzhb8nBOXxpw4+4NYTdNBfFXWHv6O8FKqeH3cpbw+rmsLrY1NiiHjuftaXheUaljwxBfEhQfQ0KRn8lDBm7u5zddr9/PztmNMHprELZeOQCmXsWD0IBaMHiT6nHHB/ry1wH5xsiVTRrFkirWwFBfkb9FXR/i6qXll5iXctfJnZi39HBAKTIZ6eVFRX09mRSW7CwrYcOstRPq4vmhl6zwAs5Z+3i3nAZiXLvjwfXv4CLM//7Jlu1ImI9LHm/n9+3Pz0CEtmoKvm1BQqO116K6+ATw5ZTLHS8t45Y9tbMjKZnB4OCqZnILqav7IzeXr+VeRFGg7E+Lagf259+dfqG7MZlpCPF6qji9Y9dLL/zr9goO5ql863x49QnGdEG03+6svuDgxkYviE0gJCiLQ3QO1XE6jXk9ZvZaMsjLWZmayNkt4xprOTUgXDRlKvL/rvrgNBgO3/PADuwpan/lT4uLoHxracg9asHw5U+PjeWi840jizjI0OZJnbr6E3Sfy+er3/Tx+/TTnO7UjOMqfGBfGSF3BzvJTALx6YiUF9a2L7BFu/tydPJPxwfYzbZ6aO40DeUUUVlYzICqMIbHdW8sk3udSDlV+RGbNKgYGLCan9jeUUi+rKHIAzCbUMl+GB//T7vE82xTmPKPdwcaz9+OvSqav/0J8lDGopF4cqVpKZvVKu8eQSnrmuL4t763ewSPzp+Dl/ud6phlM4sS8/67bg8FoYtHFI3hr5TYKy6tp0Om5+7KxJIQH8NbKbQAW2wvKNfyy5wRqpZziqloevmoScaHWi1x6F0R7ZbvFeIPRhNkM2gY99Y063NVK6hqa7OzdtQwOjGz599FGLd/nHmJ5zkFyaq1r93SEqqZ67tmxAoDNZzN5esiM8+KHXVx5N3UNq1teN+oOEOz3gs22hWU9e0GoPeVNWl46uq5FMH/scaH/FRV1BAR4Mn26uEVlVwpF6kRGo1c3CEK9u3IQ4X7Pij5+MzKpX4fF8s6ilom/R7siUG/ZcJyxE1OQulA3zlVciRp35XM6o8ep0G+v3c7B3FbhbUhsBHd88iOfLL7S7j4zwq3tOXqxRi6XUlluP2VGrUimrPZDFLJgxNrb17dJk/QPFFegyN2rtQJxTaW4YmRRCSGE9gmg+JxNxp5NJwBLwXxAwB2UNx7GT5WMjzKaQLVja5jzRXiEHxOn9WPDOZHfGYf255FxpJC09PNX6KiZzbtPAzBzYl9kMtdKHGRniosUbiY8sucWj1O7KZ03akdpSccKniSlhiGRtAbrOCI70/Wq72fPVFFZXue0XWKy7Wh/sUL18aNnXBbMqzX1VDi4J1n0o5snzVpdNkqpPzKpOyrZ+bdScUZiVCAfPTIfncHIjU99BcCEQfEkRwdzzUWDUSrkaOrELUBeSKbGx/Pjwut4b5cQ6bIhK5vKhgZ81Wr6+Pry97FjCPRwPVPB3nkA3tu1u1vOk6/RcOc5Ud7f3Y35A/rjda7QplanZ0dBPs9t2oxcKuWGwZaLF22vQ3ddAxAKEf648Do+3LOXdacz+eLgIaQSCPLwZFpiAsEe9hcHL05MJMDdnYr6eub27bVj6eXPjRkThdptnK0XMk90xhpSfOcRqE4FzNQbylDKvJFL1I4P5ICnJk2msKaabfmCvZ3RbGb1qVOsPnVK9DEuS0nh/tEdK4L32rZtHDx7lscnTWJMdDTTP/3Uqs20hAQ25eR0u2AOgkB2PL+UsABh8e/tH7dRWCaIdHddPrYl6vypz9ahbdLh6yFEkn/xonBf1ZTV8sxN7xOdHG5RvH3BP7vHbvFAVTb3H/gUgL4+UVwRNRI/pQeVujo2lhzl4UNf8OrgmxkRkGhz/7xyDR9v3suZqmoi/HwI8HInJrD7xrpusgAiPcaRXfMrA/xvJa92PbHe05HZEK09FRFU6k4T6THO5vvtOa75GilypkW8jULa+jwymBod7PXnIMzfS1TGQ0/DmbWEXCblq00H0BmMLL5kJIeyi9A26Xjh5hnkl2l49Yct3DR1aEtQYtvts0f2RSmX8fTC6WTkl/DRmt08d4O1LVHbYvfOaB/hOWVYEpU1Wi4ZncrVjyzFy11FfOT5H28Hqj1YnDKaxSmj2VtewPc5h/i18Dh1+q4R71fkHia3rpKPx12Dl6J7F2XqGzdbvK5t+NGuYP5no/3fIy1NWIBcu+YIW46dpKK8FpVagZ+ve8t7tlBJxcud9U4KYhqMpZypepBG/UkAzGYDDfoMvNXTCPIWCh0XVf0LtbIv1fU/YjBW4uN+GcHed7UcQ1O/gsq6L9EZ8kgJ32t5ft1+ijXPYjLXI5MKz80IvxdRyqPRGXIpqLwPlTyWRv1xzGYjUQFvoVYIVpA150T84urnkUl8cFPZ1kddWcipP/d7j0oO58ODL7Zs19Y18emHm5BIJEy7pD+YzSz7eidnCipJH9iHyD7+LP96F7XnAmPnzBvGkUP5TLkoHQ9PFV9/to1rrnd9nNPgQsFSVxZKnNHjBPO92YV8evtV3PqBEAktl0mdplI8M3De+ejan5KnH/yu5f+aKi3BoT72G0vkBHktol4n3nPPZGxdaTaJTNXwbhO5XFEqXmRM6BvRIpjnHC+yej+z5kcSvOf0GKG8LddcP4aNa4+IEkVB8DJ/5pVrurdTNuifLIiSIYHe+Hm7YZVraYdmuxBXCA7pfPRkd6FSuX5r1FSJW/xpj6enmrAIf4oKnUc5ZGeWunx8Mf7l4CDCXKRQndEBH/MskXYsrvSjo7gpotA0HQIg3LNrI8a7EqVcRmKUkOJfpqkjOTrYbtsfNh1m7e6TGM9ltwxJjmTx5UKkxr+X/k5+SRUNTXpG9Ytp2f7fn3exOyO/JSMmMtiXx26+yOp4bY/lKsmBgbw2c4bTdkEeHmTd/3er7d9fZ3lvXDRsKIuGDbV5HkDUuQCuHzTQ4l9nPLL2d8q0wu9+06JbcG+Xfm80jWLke+/z66lTVoJ5c//E9u2mIYO5achgm+/NSkl26J3uq1bzwLixPDDOuWVaW2RSKTKJhGAPD8bERLu0by+99CQM5kbWn7mf4gZLe7woz3EEkgpIWHPmbqI8xjI08M4On0chk/HpnCt4becOAD7Yu0dUVGjzveMfo8dw46DBIkde1qw+dYobBg3ihkH2s4uifX0pqunYAr8r7D1ZyE0vfkOYvzfPL5rJoawitI06nl80g/xSDa8v38L104X7tkQCLyyayZ6TBXyxbh+JA4X7TfO/54sPMtcxNkiwzXp+4EKL966JHsfDh77gk6zf7QrmL/+yhYdmTSDS34fCymqe/3kzb19vP3OrK0jyuZzf6zaSofmaBmMFCd62xy/RXlPIq1vPSc0y0ux6jJtpHvebzAaUUk8LsbzJWE1xfffZOpwvdAYj93+4irgwQTRv1hlun9mzfcxr9I4XK04XlZNfpuGdJYKtak5JFQeyzvDgJ78AEBfq37INsNgOEOYvzMuig/04W2k7mKVaJz4ww1thufi4aI5wfS8d15cBieHU1jeRFnthrTmHBkYxNDCKJwZfzJrC43ybfZDdZXmdPu7+8kKWbF/Of8df61L9GFeRSr0xGVuDoqQS1zOkzwebik9hNJuYEtbqtb2y4LDDffLqbM+LQ8N8qdJoqattRC6TWiym2sLDhUWL8kbHc3m5LJjowP9SXvseAEZTDSE+1lk7Gu0PxAQtRSpxw2y2XOjydb8CT9U4MkssF6TM5iaKqh4lNuhbZFJvquuFheOiqn8REyRk5dY37SHU52E8VMOpqFtKee27RPq/jslcz5mqhwCID16JUt6HIs3jNj9DgEp8QI7Gzu+9tqYBbV0Tt/xtEgHnAmZj44K4asGoloDLy+YO4chBQYPY/scpho2MZ/3aI6T2jexQYCIImRxi8XPhczqjxwnmUonUQlQ0mcw9wrPLEZtLTpBdV8pN8fajNR468C3zY0Yy0O/8Dv6uXNAqaLh7qOgTa99Lsb5pH+7K/mib9uCmTEdixwOrLZ7ebmgqhBt1XbW4h6i3f6tgnn9afMSst1/rfmVnrYtLljUcJMG7Z6YbRccFMXp8Cts2nxDVfvf2TDJPFbvk8S3p8BSrFZVSmLQdOWm5IOHMkqW0uJqmJvFFNQBWrdjHqhX7XOtgD6apSfyqZ3uSUsNECeY5HYgwFyOYS6QS4pNsFyfz8/cgKMSbMicR9KcyijCZzC6lYmWJ/P3LZFLiu9nvXi5xo8lYilrmWpG284nJbCanqIJj2UIW1j1X23/mFJZq+G3nCd578Cqax+p3vLScjJxi0mJDuf+6SSjkMkwmM5fe/xG3zRmNRAKrt2Xw7O0zW4T45oXQ9sdre6y/KgeKihgeJWQCtRfLQRCcFVIZtU3Oa3X0RLbl5VOq1XL7iOHdOuHrpZfu5kDFh5Q1HmV40H2EnyuI+GOepWjYx2M8hdrtnRLMQfjdN0eIXz9gIN8ePcL2gnyOlJRQr28dJ3irVAwIDWV8dAzz+vZr2dYZyrVakgIcR85KJRL0Iq0dOsPQ5EgevnYKN7/0LUaTidziKg5mnuGhDwWRLjbMn6JywcquT4gQhd0nWPh3xEVCqv3r937OkMlp9B+ThE+AF0ufE1LYv3vjN67421TGzBJvOyaG49WFPJF+td33p4cO5Mkj39p9XyWXE+kvBCZF+vugknd/Abkw92F4KSI4VvUFfqpE/FW2F0+jPScR7TmZfeVvodFlEew2ALPZTK2+kAKtEKU6LeJt3OXCsz/cfQQlDfvZU/YKER5j0OqLydB8hVoeQKPReg72Z+LSkefPY7orcSaYxwT78cR103j8i7W8uXg2sSF+pEQG838LLmppcyi7iJRI4W/cdvvGw1kUlgt/17zSKiICbAc1VevEZxgEqD3svtcntGdlGatlcmZHpzM7Op3s2gq+zBQif5fnHEJr6NgYbntJDq8d3cT96ZO6sqsWBPg8SGnVPzGbm5BIVAT6PCxqP7ksEqmk66LfTeYmDEb7gVP/2i/YOLUVzB/c+0OHzvX2W2sJDfNlwoQU+vaNJNRRMGXHMV0AACAASURBVCgQoha/iFDe6DwjWww+bhcjlQgZUxKJOLm1UX+SJn022aWWrhqC80Pz/0PxUAm1HtWK1Jao8iZ9JgqZEFimlAuFdL3VU2nUZdCeUDfxAYuVdhYQQsN9uem2iXz7xQ5GjE5gyPA4aDNPWL/mCJUVWqKihfGI0WgiKSWM1T/up7y0liuvGSm6D21xRTAPdRPnfCGGHieYXzIwmZveX0Z+hXDTXvjut1w1sudFDLdlQkgKE0LEV6c9n6T0iyQ/p4x6bRP12iZOnIsCTbVh9+HlNpHSmndQKRJEieUgiN/NgnnZWQ26Rr1FIU5bhPdpHcyfEhn9Cha/QxrrrR9enopIavWFeCnOv5WJGK65YaxowRzg66VbeexZ+1ZEVnSBnnHXQsH30GwGkwsTqnIXMgX+V3F1waAtSSnhbFp3zGm7muoGystqCQwS/xA4dth55HdEpD9uDlZ7k1PDnQrmDQ06crNKiUsULziLjTCPjg3qUNS/K9ToThDrcwtFdSsxY0Ii0pbqfHG6oJxbn/uGUH9vnlwkFLfx97a/ep5TVElBqYYlL1sWe9E26tDpDbz81SYaGnUoFXLq6hsxmU3IJFJeums2n/+6h6Jy4e+94OIhjB0QZ/N42sY/pxDcVfio1WRXCgtdTQaDVZHO1SdPUVJXx6S4nlFPwxWMZjNv79iJQiYTHXHfSy89ldza9aT6XkWqr/2MVC9FJFqD64vSjgj28OCuESO5a4QwOdQZjWj1ejwVCpvFdjtLmJcXpysqHLbZU1hInN/5Eas81ErGp8exakcG8eEBJEcF8+QNrSLdgdNCpOv+c/82C+jN7FpzmOBIfzYu383jn/2N/FPCYvGLP/2DZ2/5oMsFc7lUis5kP/ihyaRHJrU/NvBxV/Pm2u3EB/uTWVKBn8f5KFYqIdFnDvvL/0N//5sdthsX9gzBmuVk1qwit3YdUokCD3kokR7jAFDKWoWUNL/r0Jlqyaldw6nqH/FUhJHqew2+yjjWFC7u5s/UvcQE+/PqD1soqhC+b+EBPtx3+TiXj3PLAPt+8Pb4+NCLzhvZwVn0q0QiISLAh9svGckTX6zlpVtmseNEHg98LESqju0by+yRfdlxQoigbrvd211NRW09jy79jfIaLQ9dZVvkPVsvvl5TgMpSMC8s1fDyFxsxm8288Y8rqKypZ+uhbC4b10/0Mc8HcV4BPDZoOgD39ZvIt9kH+OjkTso6IKa+f3wbl0Sm0teve4JLvN3n4ek2A4OxGLksFKnE/iJFWyICP0epsJ+Z6CpN+uPkl0yx+/4Ho6+zuf3xATOI97YdzJlZU8bTh36xPtaHt6DVNrFl8wlef+03cvPK+eabJXbPHeouXiDOq6sS3dYREmlH7v1mlPJIEkPX2m1h/fc1t/u3TR/sWG+FuovXEM7Y+b3nZpfxx8bjGAzGlmKgScmhfPif9YyblIpMJqOosJLGBmGe2BxMl5YeScaRQrw7WMi7QCt+sTbkf1kwnzcinZEJUWSVCJPQ+BB/ogIuXLXtFQV7KWmo5m9JrTeBt06uJco9gDlRQ/gubxcrC/czPDCeu5NbB4Hvn97A9jLBDzpE7U2FrvUhtyJ/D2vOHsFoNjHEP7bl2B9lbmJneWtxvfHBKVwf51oKdXteeOx7ykpr8PWz/IG1LQbajEoeS/A5/yWxxCSFtkSJm4wmck6cJXlgH4f7xLYpWlh6porsc/YqcU4sF0rPtP5IZHLrAWtFUwZ5deuQS1Q0q8cXRy0V9TnOB0mpYQwZEce+XeIqSW/ddIKCvHKiosX5u7nqOW6LD78TCsGs3nSMWm0jZjPE9wnkw2fspW8KiPWh/l/GbDJj0BuRK1yfBCelii/kmZNZIlowr6trJC/XuVVOoh07lmaSUsPZusn5Ys/xY2dcEswzRQrmrtix/FGUw91bVtl8b1JkHK+Ote196qlMJLf6U1SyoE6J5W8d3g7AKwf+sPn+nf1Hcf8g171jmz3MxRIb7k9ogBdv/2Nuy0DFYDQhlUrYcSSXmrpGnl8yixptI2t3tf5tI4N9ePyW6dRohSiieY98ypo3brc6XvOx/srcNGQwz2/eAsDcL79mRkoS3io1VQ0N7C08w7a8PEK9vLhr1IgL3FNxmMxmXtzyB2q5nK15eRwoOss/xo0hxLNnpvj20otYGo2V+CodF1OXSCSYzB3PFBODUiazKoTXlVyelsaHe/bQPzSUaQkJLdt1RiOfHRTsFn/IyOCxSd0X8diea6cMZtEry/jmsQXszMjjn++fE+nSY7lstFAb4aftx3jww9VEBPggbSNID56UxrX3z+T1ez9H16hHrhCmrTK5FE/vrhejB/rFsjxfsNOZGNIPlbRVcGg06lmWv53BfvYXQP81ezIbMrLILa9iQJ8wJqScn8VSk9mIVKIg1mu6w3YSpKT4XkWKr/UcsD1SiZzBgXcy2EbGxcLEnTb3mRD2b3EdvsC89sMWHrhyAhEBQmTqmYpqXlq+mdcXu2afM+WaVg9eiQSa6nWseOs3BkxMIyY1ArlSTt5xYTHo4KYM/vbygk71+4zWvljt5abi/bvmAjAsKYphSVEA3D7D2mbG1raNh7MYkhBh8722FNWLD5BqL8w999/fmTu5P5+s3AWAn5c736zZbyGYF1RqiPJ3rvu8+Isw9rp6RH+inehEYo9pC0+FiluSR7IgYShLT+/h3eNbqXXB69wMvHFsMx+MtZ+50lmkEg+U8niX9pFJu3bRtNlv2x5pvrbnmSOD44j1tJ0V5au0HRT0+GPLkcllREb4MWPGAJKcZCCHuSCYn652zV62K1EpkjGaatA27cJDNYJmEdxgLEcus+8QIeybgN4g3Gt0hgKU8ihq2/nbNxPrJb5+Q76dBYSYuCAio/yRSCUt+tOsy4eg1xtRKGSQDmMnJFtpIlKphPGTU0WfX2x/bNHHs+u+4z1OMNc26YgK8L2gInlbLgnvz4Jt77I4cVLL2s3G4gy+GiusZF0VPQJPuYrMulZv4VxtOX+UnuSz0bef22Lmyi1vAlBYX8mvRYf5YOTNSJBw+67/cqz6DHqTgYNVeXw48paW49y15zMG+8fQz7fjEdOlxdW8+pGjiIPOEd83gi2rD7W83r/1lFPBPKFddPv3Hwo/6Adete/Z3Viv49jenJbXXn7WK6gTw14V1ecLybU3jhMtmJtNZr5Zuo0HHp8tqn1XCOa7DuUCsOLtRTz/wVruXDCB/3y5xel+5WW9gjmI9/FvT2JyGBKpBLPJ+f5Zp0sYNirBaTuA40cKRR0zwU7Bz2bEF/4sZOYc2z7L7WlqMlCY7zgSrhlXFhR0JhNVTXY813T2I6J9VQPwVQ2gVic+C6QnUN+k599LfyersByD0UhuUQV3XDmWyGBfrpg4gDteWt4ibJvNZl6793L6xoXyyapd3PvaDwT6erR4opvMZm5/YRlKhazFw3zeFCG6uP3xmo+lVva4YcR5Y9GwoQS6CwP6rw4d5t2du2k0GPBQKonx8+XOUSO5cfBgfN06XkTwfGIGfj5xkor6ekK9vHhownhuteEN30svfzY85MFodDkO25Q0HMJH6Xj82tO5Y/hwjpWUsGTVqhabqMfXr+eeX37BeC5r8JKkJBYO7N6skeSoIJ65WfBn9fNyY/mT1wOw+FLbYlzbqPO2SGVSnlzwDjK5lKdvfL8l0/TQ1pPUVHZN6nxbFidMZ/HudwG4fMsLjApMbin6uaP8JAaTkUf72s9SkEhgSt9W4erHfceYM6R7CyYbTA2c1CwjzutiVDLH1gS9CKgU8haxHCAioGP2Odc+ZDk/e/a6t7j56auZs8T6+/zrJxvZ9/tRpi1wPZK9mSIXoru7g7P1NS4V3WsvzNXVNzJlWFKLYC6RCAuVzZTXavls2wEevdT5gt4/Z4gLPHHlmI5QyeTcljKKy2PSeWTPz2w8m+l8p3NsKDpNZk05Cd7nv8CpPaQy/649nsT1aN650YMIVtvfz0tu2zJm8eLJvPfeBnbuzCI3t5y4ePu1nAAS7USw20Kja6BQqyHSo/t0yIKKu9Abz2AwVZBTdi1uynRCfR5GKlETHfgxZzVPYjTVAcIzO9DrVvw8HAdMSSUehPsLC5a5ZQuQywLwUtuO+O/rKz7b4XRNOSaz2WY9SVsBgoo229q/v2XDcSor6oQioR2gRt9IaYM4vUkmkZLk0/q9yDxbwdurtxEb4s89lwrByD/uOsacEeKezz1upnvbRyv4con4KLruxk2mZFhAHFtKT7akHIwITEAts287UqCtIMErpM2XS0Kcl/BHy64rpaC+gr/t+rSlfb2hiYL6StJ8Iix8qFN9IjhVW9wpwdw/0Au9zohC2T0RLQNHWxa+2bU+g2vunOpwn6AwXxL6RZB5VFgJ2/Cj4GM9eGwiU66wPTn/9OVfqK9r9U2LTrCOYq3R5VKg3YjB1LryOyDgdqt2F5L0gX3oNyCKo4fEWdFsWHuUhbdOIDTc+Y1b0YHI5vaoVcL3WiqVoNMb8fZUU17lfFJSWyve064Xa9zclURGBVCQV+60bY4LhT/FFvx0GmGeEt4yUXW0JpBxRHzhz9ys0hZR1hnJ3VzwEyC3+r8A1OpOkh70fLefzxWign3tRpe7qxQ8fdslNt+bNSaNWWOsvTrVSjkfP2r7ePa2Ozped/HK4U2cqi4nySeQf/Sf6PL+zx1YD8C1CYOI8RImB+9kbOe77IMsnXgN0W2iD547sJ5rE4T0/ua2Yri8b5rFv2Kx9dk6+3nz66pciqgoa9Ty8O7VfDReiDaUSSRsXbzI5fP20ktPJ87rEo5VfUmgOo0+npZCi9Gs54RmOVk1vzI86N4On2Pxqp/wVKp4ZfrFne0uINg87SgoIEdThVouJykgkMHh4Q7d9xQyGe/PmcPa06f59fRp8jQaTGYzo3x8uCQpCYCLk5K6wsHvvPD3N66nrKiKoAg/JBIJlSWCYPjL0j+4/uGuL6aZ6BXGhyOETNv3T69lY8kRGow63OUqRgYksTjhIvp4WIovzRaitth2Kq9bBHO9qZ78uo2AmdPVP2EwN9E/4Ban+/Ui4O2u5j+rtrcUu8w6W4GfV+czFnb9dpDrH59r8730sSm8+8AXnTr+6Zoyu+JVZ5nUP55J/R1HKZ+oFjf/8D9XbM9XaXlN3dRKautb5+gHThbi4abk5FkhqvfdjbvILKngkeVrmJQaz08HMnh7wWVc/tYXPDprEoVV1XiolBRWVfPdLqFg5JsLLiMxRBDmn121kUptA406PbdNGo5aLrc4JsCk1Him9U3g9bXbKKispkGn577pY0kMCeBvS3+kX2QoBZUahsQItbvmDUu3+AxBak8+GDefR/b8zLKcg6KuBwiiuVjB/NXlW7hyfH/6BHePaCuVeiPpYglQKvVEyO4XHzj2zGDH9/AgtRefjFlotf399zewZMlUQsN8KT6r4T//+Z2nn7FvYRvl6YenQkWdyMyAXWV5TgXzQC/7+lK43zMO940KeAsQgpT+OJ5DQbmGBeeGJW7K/sQFr7C531PLTvLMNa1R4x6q4cQGfdPy+qUfBR2obZsgb2urGj+VO6Fu3hQ3OM8WqTfoyKopJ9FH/KKDPToTWQ5wpPKs6G9XvHcASmmrLvbGqq08cPkE3l69vWXb1oycP69gHuQlznvpfHJln+G8c+r3lgHmHcnTHLaPdPcns7bEIto0r04QwuI8gwl18+Wd4TcglUgxmI1IkaKWKVhffAxzm69ChqaQcUFJHerz0w9+B4CmSsvNV75FQnKYRfq8LUuW4upXkUrUmMyNhPr8XdR5EtMjCQj1oaJYGMSePJRPYbbw4IuMs//jGj9zYItg3szL93/Dge2ZTJs7lMi4YCQSyM8sYdXn29i+5qhF2wGjrSNs95S9QJLv1eTW/kao2zA0OnGR3Oeba24cx6P3fSWqrdFo4tvPt3HPgzOdtu2IFUh7+oQJYoveYMRNreDR11ZRp3X+gNHrOu7f3YtAUmqYKME8O0u8x6rYgp/OIsg9PFVE9hEGeo76eKaggtqaBrxEpEqL9S9XKGTEOoke6ApifG4CoE4nPmqkl+5lS3E2P13U8QypRwZZR1fckTaaExrrSZ+ttt2Jrc/Wmc9b1qjlv6f28MRg25GavfTyV6a///VUNp1k09lHkLfxFt1Z+hJNpscxm41Ee04ixde22CWGdVlZADw9eYrNIsCucKi4mCWrV1FUaxlNFevnx8sXXcygMPuL3BJgemIi0xMT7bb5syCRSgiObF3A3L32CAAL/mnbWq0riPcUou9eHCRExetNBhRS+9PlRR8L4kZ6pHXU3pFCceMcV9GbtOwvfxu9qR4/VQJTIl7DQ96zCnAXardR3niCgT1QyH/46slsOpxFXqmQ3p8eG8b4fp23z3HzUJOx8zRRydZj6oxdmajdOldkscGgJ19bRYxn10YHi+VIZZGodm0jO9ty11XjWPLCMs6UabjmX59RW9/ES3ddRnKYoBfcNG4Iqw4c51+XTQZg9aET1DY2EennzeHCs1TU1nPtqIFM65vA6WLLuYjZDLuyC/j0livx92y18mh/TIAD+UVom3S8Mn8GeRUaXvp1C28vuIyCymoenjWRPk5cDiTAs0NnUqjVsKM0V9Q12VqSzW0pju1umvn7la7bNrqCTNod3x8pUqknJlPXZZzLpVJGBVv/LhVKOaFhwt8oNMwXpZNMVwmQ5hvK7rI8UefdcjaLuTEDXOprXaOOT9bvQW80MmtoKtpGHY06A1FBPuzNPIPbuTpcZytrKdbUcuWodBLDAgnx8aLgXLFdjbaBTzbswWyGuSPT8T1XA+OdNdsJ9vFEgsTqPCE+Xhbvi2VoUBQ/5zuvnQawr6KgSwTzzrKvXHzdwwH+ERav3ZQK+gR1fAGqZ1U166WXXnrppZdeeumll1566aWXXnrppZdeeumllwtEj4swHxQTzjc7DjH4XCpMs7dVYqh4g/quJsErhAajrmXlJt5TWDk1mk08cXgFOXWlaA1NFDdouC1xMrGeQYwKTODGHe8DEOHuT6SHsJoX6e7P3Khh3L77v0iRYsbMG0MXku4bxRD/GBbt/BgQElrGBCXS369jfopXLhjt8j4KWRABngup1C5D8E1yvp4ikUiYMHMgKz5uTf9Y/ZWQ7rD4X/a9t2deN4rlH2yipsqy4vf6FXtZv2Kv434q5Uy7cpjVdpXMnyiPiZTU7yPRZy67Sh2nxFwoho2MJyE5jMyTZ0W1X7v6EAtuGU9AoGN/MIWi8z/nfy5qzZ6478ZJ7DtaQGqC86iVpqbuLZT1VyApJZz1vx1x2q4wr6K1qIYDjEYTJzKcR4PExgfj5q502i7tXO0BRxHmZjOcOHZGlMd61mlxkVdxCSFdkj3hDE3jfqp1x/BVDer2c/XimMyacj47tZdCbTUP7FrFqOAYLo9N54GdKwHQmYyUN9bz1NDpfHZqLz5KNVk1FYS4eVGla+D1UbP56MQuvs46AMC7Y+eS5CA6orntu2OF6NLmti8f3kR+nYYGg44HBkwiySeI/9u3loqmehqNev6WNppBARE2j2kGHti50qKvAJ+dEp5vbT9b/4Awq897RWx6y/kBiz78kHuUn/OPoZLKmRAWT/+AcP5zbCunqst4YNcqpkYImWnTI5NtfoaHd69Ga9BZpUv30sv/KlKJgsnhL5BXt5m8uo0A1OoLMZvNhCqGEOM1mWjPidAFZiX51RrCvITx2rb8fPI1GoxmM+FeXoztE02Qh+NM2or6em76cQWaRmuru5yqKhauWM6yqwTrrNQg+/c1k9ncYiVpC5m058ZMnckuJSIumNzjlmOYfRszALh44dguP2eDUYdcIkMhtRxvHKsuIKeulGC1DyMDk5BJLK/b3GFCwcLbJg23OuZzKzd2eT8B3OVBzIv7tVuO/Vdgw6FMpgy0HKduOZrd6SjzWbdN4a17PuXI1pPED+gDSMg+kg/Axm+3c+U9Mzp1fID95YUXLMJ8Z6m4CN3BAbatZPvFh/HRv+aTVViBGTPxEYGoHEQHxwT6sfboaaanJ7EzM58mg4EIP9sFHCUSeHLOVB5bsQ5vNzUPz5qAt536MTllVezLPcM/vvkFgPgg4XqqFXKn0eXNSCUSnhk6g2m/viuqftUJTWt28B9Hslm1IwPlOd/8ospa/nbpKIYlR/H5un18/8dhXr79MhLCAzCZzfzfZ2sxmszszBCu//j+cfxrwVSL7eP7x/HYgmk8/ulv6AxGqmrrefjaKcSGtn5X5DJhPq+Ud0/2kVTihYnur2nm7eXGJ59sJjo6kNzccnx8bRcHbcvI4GjREeYbik7TYNDjJhefKfbT7mME+3gSFejDR7/v5qXrZ/Lcig1oG3U8duVUvtkm1PsbGBtOWlQw//5+I09ebelY8dUfB7lq9ADC/Lx4etl64s9pn7OH9SU6yJenlq23Ok//6DCL98UyITRedIT5prOZzI8TV5+sO9nsQt2A8aGW9lKRgT48v3wjuaWVPPHVWoCW6yuGHieY78oSwu23nLAszvPOTXMuRHdaeHf4TVbbZBIpzwyw7Zm0JHkaS7Bt3XJp5CAujbQWZW6On8DN8RM619Fz5OeUMf0y14QftSKV0pp3kMsCcSX54NKFo/nhv1swm8wMGpPIqKn9nO7j7qnm5gdn8vpD37nUR4Br7pyKnw3xWCYRbmw6Uy1HKj+iWifuxnghuPbGsTz18DJRbfV6I8u+3MHt9zhOte+MT/2hE2cYkNIq/FRV1+Pn486oQbGi9tc1GTt87l4ExBbWNBpN5OeWEZ/oeCEj81QxTY3OFzL69o8Sdd7UvsL3Y83Pjj37jh8VKZifEmctkyTyunSWGt0JFFJfapqO4q1KRUL3i/S92CbBO5Cnhl7MocoiXhpxacv2l0e2+h2uLTzJmsKTmDAzq08arx7Zws3Jw/m//cJA6NaUEZwU6bdpq+3+8kK0eh1vjp5Dbm0l/z64gffGXcmO0jy+nHQtAWrHopekTX+b+7okbQxPDRX8jdt/tvaft+35gZY+PD98Bl9m7uO7Kddb+JjekjKCn3KP8uSQ6Q4/w22pI5FIJLw5+nJ2lubx0Yldoq5RL738+ZEQ7TnxnDDefTy3ZQt7igTLwSaDpV2dQiZj0ZCh/H3UaLs+xEsPHmgRy+P8/JiTmoZaJufXzFMcOHuWer2exzb8DsDyq6+x2PdwcTHPbt5MRmkp9XrHz/+sv4uzXuwMf5QdYVxQqwfwjvIMRgU6r/fwy6dbWPTUlTxz43ukDm+d+OYcE18nRSx1hkb+dehLdldkIpVImNdHCDi6J3kWr59Yxbf521raJnqF8c6w2/CUty422hLKm1k8eUSX9LFj5eTFk1X7GwC5teuQSVREuI8k0ecy1hfdT6A6lVrdGYLdBuIuD2ixWak3CM/MHaUvMiX8ZXaVvUqjUYPR1Ei6/w0AlDYe5o/i/6POUMxA/1sBCHMf0uX9d/X6bD6SZSWYr9qZ0WnBfMGjlxMSHcSq939n+yqhPld4nFBza8lrN3DxDZ2f4+8szeWKmI4Vzesozd7PByvE/f6GBtmfVygVclJjreuQgXB/bGhj8ZkWHszyPUd4Ys5U9uWesSgQaovB0eEMvn423+w6xPK9R7l53FCrYwLEBvmRGh7Ms3Mt59XOjt+eaE9/hgX1YZeIhQSNrsHCf95kNrcURs4squCjX3YxLDmKhdOGkFnUGphUWlVHtbaR1++YzQerdwr9D/W32h4b6o9EAk/fJIwxNx7MZMOB09xySes9KDZsv0ufz1WkUm8w2g7UeunoOkYFxTE2pPV+frqmlEgPP9wc1AW0xd33TGf7tlMUFlaSlhbOyJHOFwAmhCXw5rEtoo7fYNTzc8Ex5sWKL45d16gj3N8LpVzOLVOEZ4KnWoW2UY+yTdCXWiFHKZOjN1rrJg06PR4qJTKpFL3RSINOeIZ7qJS4K5XIpBKr82zJyLZ4Xyzjw+JbwgOc3T+3leRQb9DhLnceXNddFDfUcKRKXKCpVCJhdIilfnX3rDEczCkiJTKYmBDBfnhgrHh9occJ5hdaGP9fYdXyPS4L5mBGKlGhViS7tFdonwBueWgWA0YmkNDPdrSdLaZfNZz80yUW0enOmHTZIObfYdtrdlTIEwAMC7qfM9qtxIV2n8dhZxkzIYU+MYIndH6uc9/q1T/u55obhIgaeyupSqUcicRxUUZ7vLF0I5/8e0HL6388v8LidS/dT3xSKFKpRFQhzNws54K52IKffdPFFRVOFdnuuIgJrdkMOVnixMykFMcFSbsKX/Ugiut+xUuZ3CuW90Bq9U08uU8o2uStVFPaUEe8lxAd4KN0QyWT46O0HU3UEbJqKthbXsDd238EIME7QPCtHHYJD+/5BW+FmscGT7N7zub+tu9rR8/f3If8Og1JPkGiin7Z+gwFdRpizxU1vVBRar30cr5ZU3gnAeoUAlQpBKiFMa63IpKuiChvz9Z8++KJ3mjknd27qG5s5OnJtseym/NyAVDJ5Xx31Xz83QRx9ubBg7nn19WsPnWK/WeFieOBs2ct/Mzv/eUXzMCioUPxd3fvluKArrCt/JiFYL6meI8owXzRU0Iw0iU3jGPuHa3BR+8+/G2X9/GTrPXsrsjk4vBB+Co8+LVIEJVC3fz4sXA39yTPItk7nCOafD7K+p2Ps9ZzT7Lt+UVWaQUfbNxNUVUtkf7e3DZpOAGe9qMflVJxYw2xxeo6QpNRw0mN4MN+SdR7SNoETNXqzzA86F68FML4r1C7zeYxwExx/T6mR76NWubX0lYp9WBc6BNodNkcqvwUEC+Yi702IP76FJZr+HLjAY7kFPP8dxtatjc0Gahr0Ik+nz0kEgkXLRzHRQvHdfpY9th4NhOj2WSV6dCdrDtzEhCy+5yhkMrsRpj/d9UuFs4Yhlxmu+8JwQGU1NRx71c/M2dwGmkRwWSuriTCnRt3DQAAIABJREFUzxtfdzfMZjOa+kb+s34HB/IEYbZet4MxidFMTUvg4eVr8FAp0Tbp+OeM8VbHBJgzOI2JKXFsP53Xsm1CciyXd7A4bz+/MFGCuclsplrXgN+5gqjx4a1jQi83FQ1Ntr9/IX5eyKRSnvp8HZq6BgDmjR+Ar6ebxfZ54wdQ19DE899sxNtdRZlGS0zo+R3jebtfhcFoe+75fd4BgtVeFoL5ZevfZem4GxgeGCPq+Pfc/bnFa5lMyt69OXy2dCvvvmcd2NqW/v7hBKo9KG/UOmzXzIcndjA3ZoDoZ+ilw1J5b81OIgN86BsVwuZjtUQF+DKxbxyfbNiD/Nz97PudQk2+KemJ5JZWsXJPBgXlGuKCA7h8RD/e+nUbbkoFFw1IIipQyHZ4f91OYoL8USsUVueZ2j/R4n2xBKg8GBkcA+DUh7/BoGd1QYZLCwhdzfc5h0VlcgCMDI6xmpt9v+MIc0eluySSt6XHCeYgiCkms8liW09OHeyJVFVoWf7Fdrvv27JsadBlIJP60aA7jJuyn0ui0dxbO7ZqvujRS4lLFQb7Hzy7kpqqepvtfAM8uebOqVx6/RgHK8DCdoXUkxivi8mu+Zk4754pmkskMP/6MQC8+NRPTts3Ner54VshEvDGxZPstlMo5OguQAFOtdr1W8mEqX2JiPTrht5cOGR2BoFiUKnkRMcGiRKSxbQ5fkRcJIjYCPOYWCH1281dSUO9/YnFiWNnMJuF77g9igoraRA5OREbed9ZSrUbUMi80epzz8v5enGNDUWnifYU7hd39xvHl5n7KanvvtTPeO8A0nxDeGGE5TNkSGAkH4ybx5eZ+/ku+yCLUkY67G9H+2rv/BWNWk5Xl2PGUupTSmXUG/ROj7GnrIA9ZUKKeIFW41Kfeunlz4q7PIii+l0c1yzDZBZ+JwqpB/6qpHNCejIBqhS8lZEWgmFHaZ5kT46NIzEgAJVcTk5VFWsyT9NoMPDl4UNcnprK4DDr51uuRvhd9g0KahHLm4/5+IRJrMnMxGAS5kjrc7ItBPPapib+MWYM8/uf3wjU9hQ1VPB9wRaOV+fxxilBjG006tAarG1mHNFWLAe47oGuH9NvKzvO7MjhPJh2OQCjg4QFlQcPfs6cqBHMjxaCVQb5xVGlq2Nr2XG7gvkzP23k/hnjiA70I6+8imd+2sjHt9ovJKuSiRs71+gaXPlILlGrL8JXJURWt//uyyXqFrG8PWaLuEQJo4IfZHvJv1HKvBgedC8Avkohyk8p9cRgsj2/s4fYawPir09koC+3Th9OebWWmcNSW7bL5TLiw7rW+tWgN3aLnWBVUz2bz2YxOfz8FfX9LsdxZmlbRofE4KmwXdx03e5T3HSp/awLpVzGRzdfYbFtw4NCZsI901p1i0cvtT0Pfu8G66BLW8cEWDLFugDnsiXX2u2bPbwV4gM12uoXYoVYiQR0BgMPzp+Eqp31avvtv+w+QVSQL4tnjWTZ5kOUasSJw12Fn9ftdt+r0ze1LBZ0lDfeXNjy/5qaBn7++SCnTp7l4oudP+8kwOzodD4+uVPUubJrK/gu54BoK5JwP2+emn8RBqPJakGof3QYn24UMk4WjB9EVKBPi675zzmW+tljV07FbDYjbRMt/uw1FyORSFrm1e3P0/59sVwVJwTWiilc++mp3VwZO7AbQgycozMZ+Sprn+j2V8ZaF2zdn3WGuaPSbbQWR48TzN9eu50f9hyjtkFYLTaZzSSHBfHlnfMvcM/+9/FQDUFTvwo3Zdp5jbCccsVQACZcNoh9m09y6nABlWU1yGRSfAI8SRscQ/qIeJQq21/XWr1tYfBs/c4eK5gDTLpIsK757KPNFBc5Fy5+Wr4HgHnXjcbD0/ZgRKnqmGBuMJqoaPNgbf86wNex/YBS5Vo6FcD4SamMm5zqvOFfiKTUcFFieG628zYnjzv2L/cP9AQgJEycX5/k3MM7OS2cg3tz7bbT1jVRmF9OVHSg3TbZmeLsWNRuCvrEnJ/K3Cp5IFFeV1NU9yNmTF0imvxVqWo6xL6Su5AiZ1jo+3gpOz+xGxoYxRenhci/ovoaIaLczkRF09TAG8f+YH+5YIvw5tE/GBsax9SIRP6TsY3DlUW8engz48PimBKe2NL2zaN/ADA2NI758QP5oziHO7cJYs/E8ASmhCdw/65VeMhVaA1NPDJwqtP+OuurPQYHRlqcv7kPV8b2Z17cAG7Z/C0eciWjQqK5NmEwiT5BlDTUcue2FVwRK0weJocnWH2GK2P7syz7EHdv/4FID19kFzgCtZdezgfjQoUMRJPZQLUuF4AqXRZVTVlU6/Io0u6kVl+ERCLlunjxPqC2cFco+GbeVQD0C7a0HjhTM4Z5331DcV0d3xw5YlMwr2sS5j/BHp5W7wV5eDAsIoIdBUIU35ESy1ogz02bxtu7duGlUpEUGIibg4izSG/bXsBdQbhbANfFTKVCV8O0UCGiWCGREePhvCaOI3atPQzAtPnWYldHKW7UkO7bWi+qr4/w/3pDE6nellmzaT5RLMu3H4wU5utF3wjhb943IoQgL8dj5wCV4/ebyaqtENWuI3gqwqhuarZBbb8Ua/l8kEvd0JvqAKxsL4Pd+jPZ7UVOVq/gVPVKfJUxuGLv2R6x1wZcuz4B3h4sumQESRFdP7Y0GU18+8rPrP5wA2WFFXyZ+QaBEUKU7/qvtqFyVzJ2jnUNLlf59PTu8yaYH6sqbllkF8P0iBS77/l6utGoM6B24Fv+Z6OkQXwwhKfc9ty9mWptI+//vINDWUU0NO1gZFo049PjkCDhiaVr0RuECP+4sACunjjAYntcWABXjOvHss2HKK6sQaWQ4+XedVmXncVP5c6hykIui+rcYm52dikrf9qPXm9k5qyBXHut+GfB/LjBogVzgJcPb2RKeBJBautnsT3sZU9EBwlzbXeVwmEQsERibQ0ktWG10vY8tt4Xw/RI4bfqp3KnqsnxgubJ6lJ+LchgRpTzDLGu5uus/aJ/Z94Ktc17UFSgL/nlGvoEitM82tPj7ljbTuax7uFbeeL7dQDcP3M8r/zyxwXu1Z8PvwAPlwt/SiQKwnwfpkkv3lS/K5HLZYyYksaIKa79GDcW3U2AynqfiqaMrupat9AcjXz1wjG88cJqp+21dcIkauX3e1rsWdqjUimoq3UtgqeZ+5773u7rz1683uG+KrXrgrnYCOO/EkkpYU49wkGwZHFEXW2j00UYsZHl7UnrF+lQMAchytyRYJ4r0o4l4ZxNzfnAS5FETvVHKKUBfzmx3GTWUW84g1oWjFwqfpJqj7L6LeiMlQBUNO7usGD+00U3t/w/wsOHZVPt34eavb4/Gi8IVU8Mtl3z4YnBF1m9Z6/tPf2s06o/Hn+1406L7G/bz2Zvm63zA1wVN4Cr4iwjKJRSGUsnXmPV1tYxXhzRcxeSe+mlO5FK5HgrBUHUYG6kyVhNg7ECqUFGVzlFLxoy1EoobybC25u/jxrDP9etYW+R7UXt5l40F4RrT7/gkBbBPLfK8jnvplBQ29TE3audjym728PcX+nFwphpxHu6niVWlGN7jLNvg1CkrCsFc53JgKe8VVhykylt/h9AJZVjbJcB3ZZGnYGHvv2NcD9vCio0lNfV85/fdwCwZKp1n93kCrwVamr0jsft+XVVAJQ3agl0Uj/DVdQyPxJ8hGfC70X3o5C4Eeo+hGSfy63aBqnTOVr1JVuKn8BdLgjOEmQ0GTVsLXkaudQDg0nL0MC77QYziaW54J7Y6+PKtekOsRzg+zd/5af/rGXGrZP58rkfLN6TyqV8+9KqLhHMt5fksKcsn2FBfZw37iQvHha/gOgmV3BxlP1AqOkjk7nvtR+YODjBQjSfPaHjkZ8XGrG1ckLdvJGfE0rHpccxLr3VLz/Ez5M3lghj2H9ebRk9//7PO1g4bQjDkqMwnsssWvTKMuQyicX2Ra8sY8ns0fz3AXFj1PPN1LAUvsrew86yHIvFsH8f/g0vJwEln427EYB//P0r6rSNTL8oHX9/T0qKqykprgZg0mTn2lGslz9jQ+LYWpItqs8aXQN3bf+eLyYubPnbdZRJ/eKdNzrPNNte3Zo8kpcOb3DSGp49uI5x54ppetnJIulqShvreOOoeOvmGxKH2cxOOppfzG/7T6JWyC0WJL6+X1xWSY8TzN2UCqRSCbpzq2g+7mrKa7oupcRkNpOjLSO7tpTSxhoAqnRaGk16dEZDuxSznsOj/Wa71H7G5a4XVSmreR+1Mg2DsYww3wfpTGTA+STe+zL6+t1otX1v2SvnvzMd4KKZA/ji4y1UlItbPVvxzS6uuHqETZFaZScK3xnOBHFnePu4OW/Ujrq6jgn7/8skpYqbWJaWVFOvbcLdw/YD65ST6HKAvukdE8xT+zn3Mj+RUcS0GdYpUc3k2pkMt0fs9egK9CYNUhQ0GJxfu/81Suo3cqD0HwwOfpVQD8fFhcUQ5DaO/NplyCVuBLt1n5dnL7300osYsmp+pazxGOWNGVTphKAQs9mEryqOQHUaKT5XEqhOa7Gm6Ay2osbbMiBUiLIuqXM85pPYSX4O82otel/VaGlF8cBvv+GpVPLk5MmEeXldcA/zJpNgf1NraGB10U5GB/alj3uw0/0enfcGSYNirLafOpDbxT3sWmYObI1sa440d0asdwCHKs6Iarum8DjXJQztUN8ckeh9qcW/zczq84nFa5lEwdRw23OrKe22+yijifQQrCfd5cFMCX+5Q30Te32669q4wq+fbOK+d29hxIxBVoJ5TFokBafFFa0Tw7/2/cLKabe6ZF3jKqvyj7GtJMd5w3PM7pPu0KLkVH4ZceEB5BdXdUX3LjjZtRXsKxdXLyrRp2OLNBMHJPDOyu2s3nmc2gZh3rxg6hAiAn0sti+Y2vXFdLuSB9OnE6j2ZEdpNmWNdS3bNboGmoziMuMnTmpdjKntoIZwX/pE0YI5wN7yAv6+60deGznnvNYNOJ9cnzCMT07uoqLJsd5a0lDLY/t+AeD1kdYLql2N0Wzmn7tXOl0wbcZLoeKmZNuWT2/f1rkamT1OMI8J8kNvNOKuFMTAv3/+MzUNnSt20mTUs+bsETaWZLC7PJsG458vsvV8COaBXjehVqYi6XlfC4fYEssB+vk7LgDRU1AoZMy7bhTvvbFWVPtqTT2rf9rPFVdb3xQ6EundFQSH+Li8T1lJTTf05M9NXGIIcrkUg8F+BFMzeTnlpNopsnvqhPNBeUcjzFP6Oi/se+KY48lNXnbPE8yD3IWojr+iJUt5w44uPZ6feiBT+4irRt9LL7300t1sLXkahdSDOK+LGBZ0DwAB6mTkkq5LWVfKZOiMRjyUjsdh6nN+s01G6wJ6YkJ23NvYrDQYLIUGlVzOzUOGcM0F9jBv5svc9Tzb/2bePvUjaT7RvHN6Jc8PuNXpftMXjGH+vZdYbe+Oop8An+du5rezB5xuL2tyPG49lH+Wuy8ajVwmpV6n5/lVm3hq7jSH+/TzCxMtmH9yajfz44f8pay0xF6f5msDXLDrU1pQTmSS7UL1UqkUo9550UyxZNWU88je1bwywjV9QCzZtRUt4pgYpBIJNyQ6jp5/YOHkDvenTt/Eb4UnmNWnL+puXCQQixn498F1otuPDonp0HmSo4J4Y4ntv7G97T0RtUzOkpQJLElp9ewe8NMzvDD0couin/rG3zHqD6P2ss6AuvTSQZ3uxwD/cKZHprCm8ITofX4pyKDRqOeVEXPOW2T1+cRNruDv6RN5dK/zzLSf84UsrxhPf+7t17H6hWJ5av9vbC0Wv7ixOHWMSzUFXOHC33Ha8fgVQtX4h2ZPBGBXZgHpUR3zvNObjHyZs42lOVup1rlWbOSviBkzEuTU6w7irrxwlXA7SlbNT8R7tz48Cuo2kehjXeijJzJzzmC+XrqVao247+nyL3dw2RVDrQrKXCjBPCjEdS/MXsHcGoVCRkxcMJmnip22LchzIJg7iTBXqeTEJ3Xsvurj6054pD9FhZV22+RklqDTGVDa8Ck0GEycKbC/b1uSz6NgXla/kXpDAQqpz19KLIeuF8x76aWXXnoS/fwWUNZ4lKyaX8msEUQgP1UCQeo0AtV9CVL3w0vRueeNv5s7xXW1ZFVWOowyz6kSoitV5wR2pax1HKdpaI0YN5lty+dtt7ePIP/PpZfy9MaN6I1GYvz8cFMo7BbpGhrhfPG7s6hkCjS6OuoMDcyOGM0hTZao/WyJ5cD/s3fe4VFU6x//bG/pvSekQRIgdKRIUVABBQtXEbFde/l57eXavfZr76hXsWFBxS5gp/ceIIT0nk3fzfby+2NSye5m0whgPs/DQ3bmzJmTye7snO953+/Lottdb+8NEapAqs06qs06r7e7Izk8mOveX8mFE0ewfONurpk5ocvzTwqL55Mj270aa6G+lhf2/cFdI3suPJ5oeHt9Wq4NMGDXJyopnMM78ohO7vx8vf7bbSSNjO/T831buK9VPP7P2Ll9llFS3FTH5X99gt7qfcDigvgRPY6i9gaj3cq9277n6T2/cl7CCM6NFxYFMwJ7VxehJziBZ/b8yh/l3lvYzooa2n8DOkEJU/p23agfeGDUGWyszEfXjff372U5LPjlXZ4YN49JYQn9NzgX1FuM/FKazW9lh3lryoX9co4LE0fzfVEWm70oAArw2oF1NNks3Js5q08XKK0OYVHxge0/8VXBHq+OGRYgZK1dnXpKn43jaI47wbwFhVQY2rRhQyirayRQ0z3LhwK9ljt3fkqe3jtvqUHAYN6BWj6SJvM2VPIRx7TwZ19QZtjUQTCvNG4/YQRzhVLGeRdOYNnbf3rVvlqrY82Pe5h7bsfqzQMlmIdHdD/CvKSo/4oYeeIv7U9sq13LyIAJnBHu/v3xYcHLTA09k0SN+wI21eYKnsu+jwhlNBaHmZEBEzkrYiEAD+2/jseGLxX6KnwFgOF+YxkTOKXTvpbtIERVeyOYe7p+OV1EmKemRSGV9lwUThse7VEwt9kc5B6udCnolxbXYLd3HUGv8VEQFRPU4zF2F4UkFL0174S77/UWvTUXo8276LZBeo7D6WRfTQW7q4XP5r6aCop19ZQbdDSYTRjtVuwOB0qpDLVUho9MTpTGj1gfoUBNnG8Aw4PDGRUShZ/cdYRLseEQsWr396ueYnM42FJZzLbKYvbXVlKoE3yTtUY9BpsVh8OJSiYjWKEmwS+QjKBwJkXGcUp4XK99HwcZpC8YG3IjAE6nnVpLDgBa4360piz21X5Ig7UQhdiXEGUGp0f9t0fnGB4WRoVexzs7trNgmJA63l4Mb+Ht7YL4Z7TZuGvNKp6dfWbrnOennMOt7bRNrtOjqw1tgRW+8o4e2+cvX47N4WBbadf39P72MAcIkPlw2643uWOY8FxksHUvY7gou5zPXvqZquJaIuKDuahZSA8M7buCpStPvafP+jp79DAOlWt5bOVvXH/6KUxNTejymFMjkpCLJVgc3kUfv31oI6EqH65I6VqMPxnozvV5+5BQkHWgrs/F9yzg5ZvfoyBL8G9f89E6Cg8IP6/9eiuPrritz8/5eZ6QAZGnq+G/E+YTo+lZUbsW1lXkctvmb6i3GLtu3IxCIuU2L6JNP/55OwtPH9XqX24wWfj6j70smeO9lU69xcj7h7fy/uGtACT5hTAvNp1ZUamkHwPxvNzQyH3bf+hW5Ovk8CEM8Q3ir4N57C+tdFnP4O/I+fGjCVF0Lqhps2zHUHcLDnsJSt87kCqEmnzGhvtxOOrBaUbl9wB2W25rNLrDXo6x4T40Qctoqr0CAIksE4etEKliAnJ1m1d1pNqPB0afwT1bv+/WeIv0dVz658fMik7lmqHC33BsSM+ytT2Rq6tha1Uha0qzAaFmgd3paPUc7w9EwJPj5jH/l3e9Xih7//AW9taW8fT4cxji2/v5+sH6Su7ZJvxNDtR1rYMASMVinhx3duvP/cVxK5i35+VVG3jmYu+jCvbVF3Pj1mU0dfPB7O+Or2oGVY1voJAln5Cikd1hbmel4MTm8P7L/nhgwT8m8MUnmzA0efe+/fzjjZw1f3SHoojKARLMff1UhIT6Uq31vlp4UYEWm9XeKUq+v5keOheZSIbe7v1YPRGliuOWlEexOa28ePh+RviPI1qV0Lp/TeXXhMqFh7gWUfzofe23p6ZF8tO3XZ+3qKDa5faGekNrERR39NSOpYX04TH8tmqfxzY5h8pdCubuxn00qcOiOJZZtY2WQwzxv4oy/XcntSWLEwe59W/TaMmm0XIIQ7vCXDur3AsoSf5XMTSo82TPYC3iz5K5bo+bFPkhgcoxbvcDbK+8kSrDWk6NXolYJGNv9UM0mg/gI08mI/g+AhSZmO1a9lc/BkC1cTMyiT8JfotJ9O9cPLMFvVWY0BQ0fkKNcTMmWyUgQiWNJEQ1mSH+l6KS9m+UZVZtJZ9k72Z10WFqTF1nEDVZLTRZLWiNTeQ31gGFHfaLgOSAEGZEJ3JWXCpjwqJbI0irzSVopP7IxUJKoo/UfSSkNxTq6nk3ayvfFxyk3uzZQ1BnMaOzmCnQ1fFnaR6v79tEoELFuYnpXDt8IpHqgYkkGmSQ9ohEEgLlyQBIRApU0hACFUnUW/IpadpASdOGHvd9dupQfs3L5UhtLecs/xiAmydMJCU4GJVURn5dHe/s3MGm4iIAfBUKvs/OZn1REXH+QtDB/qq2IJ/smmpMNhtKacep2vZ2YnhCQEdxbOUll/R4/P3BLannNWevCnep+9I7Fyb2xBv3fsZVj1xAdGIYpXlVvHHvZwA89dWtfT7WvuD691cyfVgif91/HW/8tpk7lv/I84vneTxGI5UzJzadbws9P1O14AQe37WG7dpibhsxgyTf4F6PuyVrodFqIkDe/ZpE/Ul3rk9L7kV/XB9vrs3MCyfhG+jDJ0+uROWr5MP/fEXSCKEw50Of/YsJc/ove3ubtogzfn6TixLHsCRlXLd/70P1Vbx5cD0/Fh/o9rlvHz6DKHXXgVPf/rWvgziuVsr5ccOBbgnmR5PbWM0rWWt5JWstESo/JoUncEpYPGNDYon3CXKbYdMdbA4Hu2pK+CxvFz8WZ2FzdB30055/ZUzrg1H0LybLDgzmDRjNWwCw2YqxO+pxOHWIxX5IxAFIJTGo5ONRKSahUkyAXl7dG4a5vi4ikQ/qwFew27Ix6V7G6RQ8z0XiADT+T+KwF2NseLiDCN4eh014blb5PYJYmuCyzQUJmWytKvI6irk9v5Ye5tdSYXE7xT+UM6OHMTUikYzACFQS73UYh9NJubGRAl0thxu0bK8uYpu2iFrzwLhixPkE8sLEc7lu/edeV3TcUV3MWave4vyEkVySPJbhga4tqTyxq6aEj3K280NxltvMOnc8MOoMRgb1fzb6cSOYL/1ti9t9B0u9ixIvMQgRjzdt/WBQLO8BNns1YpECmcS7QjXdwWF3oC2vp7HegK7egNUi+C76BqhJH5PQJ+eI9z2DX0quxV+eSIMln6Sjitcc72h8FMy/YByffejdhK28tI4/1uzn9LPaqosPlGAOQtRytTbb6/Y2m4P83CpShnX/5uoNayq/5rBOeMDO8BvLzLCzuzxmdcWXHNTtIUAWhM7qWXQ+GqlIRpQqngZrXatgvr9hB+XGYi5LuKVTe3f7vPXtdhdh3p/+5S24s4JpT06263EUF3qXWTA0/djZsQD4yFMoaFiGQhJ60orlAE6nlcN1r/VZf2KxkgDFCCx2wWbA4qjH5tB3cZRrGi2HyKl7HYNNEPEbzPvZVnEj02O+Z3vFTTRY2iZydpuRQ7UvoJSEEeXT+bOd17CM7LoXASGqtD16ax56ax5Fui8YGfKYy+N7y/6aSp7c8Qcbywu7btwNnEBOfTU59dW8k7WVWbHJvHvaBQAEyiMoMbTdg0cGzOjROXQWM0/v/IvPDu/G3s2H1/bUmY28f3AHnxzezVVpgrfpraOm9GuRskEGccW+2g+ps+RSb8mjwSJ8Jh1OG1KxiiBFCsGKoYwL+T+CFT3P0JiXmsqy3bvYXVFOTo3wPfevn117AMf6+fPxwoVcvOILynQ66owdAzzGR0ezrbSUFzdt5N5Tp7XKEltLS1hXWNDabmxUx+/i9ND+s0ToCflNFfwv92fiNWFckzSPjdVZzIn0PvI3JCqAlExBcEzJjCOoB/VyjiX3nD2dpDBBqLzljMnsLPAue+ufqRP5rnCf1yIFwKqSg6wuOcgpYQlMCItnZGAUwUo1/nIl4ubidFaHHYvdjs5qotFqptFipNrUhNakR2tqotKoo7SpgXKj8LybERjJV6cff/Wf+uP6tFwbwKvr4+21GTd7BONmC/Myp8OJSNw3kR/pgREEylUei3BaHHY+OrKNj45sIy0gnAmhcaQHRhCnCSRQoUYpkeJwOlsjSIua6siqq2BdRR7763pWkHR8aBxXproutHc0MmnnAClRH0bGVBgbWVmwl5UFewGhCGBaQASJvsEkNEfARqr9CFaoCZCrUUqlyMUS7E4HRpsNo91Ko8VIUVM9xXrhmfZwg5Zt1UUYbdYejen8hJEdopB3FZRxz+c/U1an4+bZQpTyhMRYHv/2dxoMJsw2G3fOnUaBtrY1Gr2yQc+jK3/ljSuEwoVPfPcHdXojRquVa2dOIDMukpdWb6CkpgGD1cptZ00lJdy7BRO98Xtqda9jtux128Zu12K3a7FYczCYBNsjuSyVQJ/r8dO0WIT03bxJIksFQCTyA6cBh00IfhFLhe1iSSwOe8lRR7VbxBCpmtsneDzPE+PmUW5sZGM3CtseTU6DlpwGLa8dWIdEJCLOJ5AotT9hKl/UEhlyiRSLw47BJtRPbLKaMdisVJp0FOnrvC54eqw4LSqFO0bM5LlmeytvsDsdrMjfzYr83ST4BHFKWALDgyKI9wkiRKEBBJ90AIPNitakp1Bfy77acjZVFVDSVN+jsf5kpMuVAAAgAElEQVRjyCiWHKNCzyevKjDIIIMMMsgggwwyyCCDDDLIIIMMMsgggwwyyCDd4LgJ8/llXw4XnuK6qrvMhQfg0TicTu7dJVRP19s8pw6fSCgkMjID4o7JuYyWA0jEgRgte1HJh/falkXfYGT1iq3sXJfNwR2FGA2do/7Txybw/Iqbe3WeFob4ziFKPQm9tRSNLAqlpHfp6APBBRefwsovtmI2ebeS/dmHGzjtzBGt1hVKldzzAf1I6rBINq71PsIcYOe2/D6PMM9vym79/6bkhwB4O+9pEn2GEa9OdnlMlVkokpnVuJPbUp/A6XTyzKE7vT6nEycVphKKmnKZH7UEAKvTyk/ln5PhP6Y1HbkFT/sSEsOQy6VYLJ5XnctKarHbHUgkHdc9u/IvB0gbHuPNr+WWIcnhKJQyj+9TdxHmpV561/dX5oE7AhSZmGwV2BwndzFasUjBWQk7Wl/XmnawteJaAEaFPkuE5nSXx4ncPC4oJWFMjvq0w7Yy/U/s1t7d7bFl172EnzyN0WEvUGfeyYGap7E6GtheeTMmexXjwl8HQCb2Y0/1/RisRRTpVnSKEC9s/IxDtc+1vg5WjifObxFqWSwOh5lGyyFy69/BZK9ij/bfyCVBhKgmd3u8rrDY7Ty940/eP7i9W9FwPWV2bErrz2KRBINd1ysf8x1Vpdz41zdUGnqWJeAKi93Om/s3A7C6+DBLZ5xHSkBIn/U/yCBdsb/uE4IUKUSpJzA88FIAghVD8ZPH9llGkUQs5q1z5nPNd9+wr7LSbbv4gADenX8usX7+rFy0mP9uXM/m4mJAiMK6aMQIFqZnMPGdpbyzYzvrCgvJjIjAYLWy+kgOTtoS4RcMS+OXI0cYGhJCXIB33sVF9fVkV1czO9n181Bf8k7uj9yYvID38n8GYEvNQa8izD9+9gdEIqipaOCZ6/5HRFwI5YVaHPZjcVftOQ4n/N+H35EYFsRtZ02lsLqeMQldZ+RlBEYwP3443xbu79b5nMCmqgI2eVmo7UTlRLk+piYzSk1bjZGjo8uLs8uIHdqz7MkQhYZXJ1/A+b++R25j19aGB+srOVjv/j7UF0So/Hh50vleFxuNDgvgi193ccHMTEQiEd/8tY/woP6za9NZzWzVFrJV27eZft6S5BfCI2PO6rBNo5TzzEVzyKmsaXVX0Jss+KuVPHjuaZTUNvD0D3/xj/HDO/XXkvC3NbeY969ZSJCPGoDdhWU0mS08t3guhdX1PPfTWl69bL7HsTkcjVTW3YXe2D0f7xYs1sNU1t2OzvA1ABFBryGRhPWor850/E4WS4XvKrtlF6jAYS9GLIlBJFLjdAj2qi1R6N1BKhbzxuSFXLP+c7Zpi3o9arvTSb6ulnyd+xpfJwLXp02hyWbhzYPdt6gr0NdSoK+F7v85usXc2HQeH+fZ7qwrvt2SxYKJGV61PW4E8wVj090K5jvzu05p+6Z4OwcaTuzCZXKxlMxAQRwfGzSE8cFDGB4Qi6yXJv+HD5RhaDKTOTahVYRzVRxSoxhLveF7VPL0HovlFpOVD19cDcBPn2xyKZJ3l6X/+ZasbW3pMjc+eh7DRneuNF5vyWVvzVv4yYcwKvhG8hp/INGv71Pt+xP/ADVzF4xm5edbvWpfmK9lw1+HmDpDEEgG0pIlI7P7Czvbt+Ry0aV9I1S1UGES0rTiVEmtYnSsKpEyY6FbwbzaLDxURirjECFCJBIRofROVC4zFvFKzsMEyoJZHH8jvlIhZVgqknJ76hO8euQRRvoLE8RYdaLLfS3bAaRSMUOSw8g+UObxvDabg8ry+k6FMQ8f9HxcbHwIuzbkYNALC4vxKRH4BajZsyWXpLQorBYb5cW1xCaGIpVJ+H75Js6/4lQSUtqsmiQSManDItm32/0DRmGeFqtVSHeVtfOpL/ZSMD/WliwWey02hw5w4nTaEImOm6/HPkcsajehE8nabZd22Ndjephia7HXkhn6FDKxL/6KdCqbfqfGtJV6814yQ58mTN1WVCrJ/yr2VT9MvXk/tJOQTPYqDtUKRfvifIVU0eEhD3U4T6ByNJGas1hbcg4WRz1ZNU8yLea7XgtnWmMTV/62gv01/TtJbUEjk3P2kDZxvMKUj1riS5nxCACRyqTW1Hxv+C7/AHes/6m1Sn1/kNdQy3k/fcxbM89lamRCv51nkEHac3HS6mNynjCNhq8XLeaH7EMArMnNJb+uDpvDToyfP6cnJnF+ejqqZl/yUI2GZ2ef6bKvmyZM5IWNGzhUreVQtbbDvkUjhPlSemgo53z0EQ/MmMGVY9pqRaS9/DL3TZ/OZaM6eyb/lpfH43/+eUyKfiolcmLU3V8cS2pe1E/MaHsOS3Xx3H+88eKq9dwzbzqvrhGKT67Nzue8cd5NyB8YdQabqgqpMvZNfZ2TjRPh+jy88AX+s/JO5C7mYrt+z+Lxxa/wVcXSHvVdbW5CI5WzdOqFLPr9A6pNrosCHys0UjnvnrqIMGXnoo3uuPvS03jsf6t55fN1AIxIjuShq13f/050otX+vDftYtTSjoFsyc1WKX5KBQaLYNNRUF1HUpgwl4sJ8qe0rqMlaIuvc8uj9SPnz+LBr37BX6Xk3nOmk6etY2dBKXcuFyzAEsM8F2C0O+op0Z6LxXrYYztvMJjXA1BUNYfYsB+QSvo+2EmmnAWAzfwXTXU3gtOEyu8BxJIYzPq3MNTdjEgSQU+MM3xkCpZNW8wtmwTh/7ey3l+Tk4E7RsxEhIg3Dq4f6KF04uy4DJ6fuACJF3PNomr3di8bDxWeeIL5pae6Lwr20AWzPB7rcDp4N/evXp1fJpbgJ1PhK1MhaRfxmat37Z8uQoRG2lFYcOLEbLdhc3Z/svn82EuYEpqCXNy3f5IPl/7BgT1C5Mrw0fE8eNtyAJ598/JObRXSBEL9rgPA4TQhFim7da7CnAqeuvljCnO8q2zrLckZ0Xzz/rrW1z8u3+RSMN9T8wZjQm5lb43wMFJm2HDCCeYA/1g8ie+/3oHN6t37aPmy9W2CuWrgBPPhmbH4+CjR673P8Ni3q5Daaj1BId4/cHVFlFIQ7vfWb8XZHONZZMgl3W+022OC5cKqeLmpqPWYlqjzLs/XXPTzaESIkIplXBh7DZ8VCe/JW1P/g0Qk7bSvZXsLqWlRXQrmAGWldZ0E8yPZnj9/6SNiqCqv5x9XCcVWVrz7F5mnJBEU5sv+HQUEhfoyfFwCUXHB2O0OEodGdhDLW0gbHuNRMLfbHeQdEYTDoe182d15r7fHP0BN2DH2Ki3Tf0ec38XUGLec1GL58YyvPBWZuC3aKECZSY1JWDwMVU/t0NZHJiwyOZxmrI5GZGLh/VLU+Bl2pxmJWE1asPsod7kkkHi/xeTUv0GTtYAG834CFK4X7b2hrKmRi1Ytp1jfvdoHvWH+kDQ07SZjsephZDWsJ1yZAOC1WP5DgSDu3bbuB688y0NUgidhmEpDiFKDSCSi2thElVGP1tj1JF5vNXP1b1/x3ukLmRx5/ItggwzSHSQiEQuGpQG0/t8TbpowEYPVyv927sDa7LUsFYtZMjKTf0+b7vFYi92OvZuF6fqDKGUwrxxeSXGTlmcPfk6CJsKr4ybN7b/iiP2JWiYjPsS7SP+jCVSoeX3yBVz25ycY7T3zSz6ZORGuj9Vs4z+LX+Hhz29F2hwo8uM7vwPwxp0fcdmD5/e47xaBPMEniOUzL2PJnx8P2OJBgFzFe9MuZlhA9yKKQwN9ePXOC7DbHTgBqeTkdAZO9Q/l7akXEe2iEKqraPyksCD2FAlZuSW1DUQH+qOSy9CbBEE9X9sxYnl0fBSvX76Azzbv4ctt+xkTH0VaVBiPLzyjy7E5nRbKqy/vJJaLRHLUimko5IL/vlyahFjsi0ikwukw4HA2YrEewWTdi9G8Aaez42fQZi+nrHoJMWHfIRZpuhyHO2TKWa0CuVgSiSZoWes+lf/jndprgj/stM031HXtEHcoJFLemPIPAF4/sI7XD6zrVf2ek4XbR8xgiG8Q92//EUs/BtJ4iwi4Zfh0bk4/1etysze88TUZca6fO7KKvNcrjztVwGCxopa3iX5OJ9jsdsC9EPhXVTYVRu8N4+ViKWdFjeSUECHadFRgHBEq1w84E1c97DLaSi2Vs/aMB1weY7ZbKTc1kNMo3Px21RXyZ+Uhj2N8O+d3RgTEEKLo29SkvTsKee7tK7j3xg+RSsUe06aq9R8ikwhvKrMtj1Dfq5GIvXvwK83XcveiN2ms6/sV7ylnjeSV+7/EYhai49f/vJdbnlgIgEze9haWilT4ynpXzPB4ICTMj9lzRvLzd7u8an8ku5xtm3MZf0oSSuXAWbJIJGLGT0rmj1+8T5l0OJz8tnof/7hkUp+NI14jWBQk+aTx+pHHcDqdpPmNIkGTitlhYkXxu5SbirE7bVSaSjk78mLClULK7FDfkbx8+EGCFGGEKPqm+G20KoE0P2Hyt6bia+ZEXthp39HbU720IykvqYPmWjstCxVVlZ4Fu+GZsVTkalmzUrDl8A/2IWtHIT5+KsTN6aMqtfA+kkjE1FbrKc7TEpvYsZhY+oiuI/Bzm8X7FsG8od6AXtf1gsqxji4HSPC/AoBQ9fFfzf5kRSXp+FAjb/7+kYp9Wn9uQSpueyC3O4ytgrnWIERDBCoykXSx6OsjT2r9uaeCeaNFyKK6/NcVXonlAQphTKdExDE5Ip7MkEgCFSoCFSp85AoMNgs6i5kiXT2H66vJqhUWnTaUF1Kk6/gMsSgls8NrCRJmhV9GtfnoYkju2VtTwR3rfwTwOEGI8fHn4tRMzohN8WinktdQy5riHD7P2UN+Y53bdia7jev+XMl38y5jiJ/naKhBBvk7IgLunjKVa8eO40htDVa7nWGhYQQquxfMMpBcnTSXrIYCUnyjiVOHkeGfMNBD6ldigv154rs/yK+u5YEv17RGk3rL6OAYlk69kBs3ftlamHGQNo736/PEt3fx7/nP8swVb3DP+zfwzr8/5ef3/wTgvg9uZOq543vcd625bX6d6BvMF6ddzk0bvySrrm+D1LoiwSeIt6ZeSLJfz23VjraSPFk4N14Qmx8bO6dTZLknpg9LZP3hAu5Y/iNmm507555KVIAf763dzl2f/kS4vw9isZi6JqE49L+/WI1GIafJbOHus6cxJDSIDTmF3PbJDwBMGzrEbWZLvf4djJZtra9FIgVBvjcT4HMNYrGfV+N1OBqo079Dne51nM62z6HZepA63WsE+93j9e9+vNASrXxLxjSmhA/hvm0/kKfzLiP6ZOa8hJEk+4Vy59ZvvbKC6i/CVb48MW4eMyK7ZyV33qThXD3btQ3c0195X9j0uBPMr377S5bffHHra5EI/m/Zd3xww4Vuj/mlfJ/X/S+Mm8B1KacRrPAuolUulroUzD2tbiskMhI0ISRohC+T2ZEjuCt9HuurDrM053eX1jHZjeVcvnEpb00UKnDHqrv3kOUOsUTU6nnlcDixO9xPiAPU85FLBRHMbMvH4TR7ZczS1Gjk/svfcSuWSyRiYhLDiIgLQuOr4vdvdrhs5w6lWk762CHs3pgDgMlg4cCOAgAyJ7V9cHxkUWzXPkejtZDNVf/BX57oqrsTgosum8LqH3bj8PD3as+ny9YJgvkARpgDTJ0xrFuCOcC3K7Zx3kUTkUr79gFqVvi5zAo/t8M2hVjJknj3nvnzIhdBN7LJQhQRLqPLAR4b3pZ2OTfyIq/3gRBh7g1lpW2CVP4R19kwR5M+IgZjnYHZC4SsnhaPRafD2clvEeDKW89wud0bH/S83I7WFKUl3vm6pQ479oL5IAOPVNxxwVjcbBcjc/kQ3/aedGJv/t9Bo1WoYVBt3MRP+Z09IN1hcfSsSvvdG4VIlpx6zw+SEWpfbho5iUUpgijvzmpNI5WjkcqJUPsyIbzjAnCJvoFVRYf5Pv8gZruNzJCON6uNNd8SoUxAbxN+l9PDL+1UI6E9TTYLN/y5ErPdfb0EqUjMNRkTuCVzcmuVe08k+gdxvf9E/pk2jjf3b+b1fZuw2F1HpugsZq77YyU/nH0Fci9q1QwySG/IL6jm3Q/XER8rPF9fe+U0fl6zjzlnjBjgkXkmQKlkXFTXPtjHI4VNlXxTuoFKUx2RyiB8ZWri1H3lc3v8ceuZU9hdWEZaVBiJoYGMiu/+s8zk8CF8efqV/GvT12Q3ePdc93fieL4+Kl+lIJqf8yyXDbsdp9PJc78IwXWpY4b0qm+bw4HOasZXJmS3x2gCWHH6lTy5+xcAlufuaLXu6C/OTxjJI2PO6pYY3J6D+ZWkxIUilYjZd6ScvUfKmDclnQBfVZfH+stVXJI0lhX5u4+LaNf2JPuF8O9Rs5kWkeSx3fS0RKanCdpEuL8Pb1zRNkd9YMFpndq/deV5Lvt588pzO227aVbXgWd2Rx21uldaX4vFfkSHLEcpd+/y4Aqx2J9gvzvRKE+jtHoxIHiiA9Tp3ibA5yok4hO3Ts3YkFh+Pus6Pj6yg1ez1lJvMQ70kAAIV3m3oNHXjAiK5PszruG1rHW8m70J4Jh9BiUiEYuSxnLXiJn4yLpvGepOLAe45oyJXvdz3AnmKnnnCZmnlB2H08G6qq4LDUpFEh7LvICzoroXQaaUyGiydV7FdjgdWBw2ry1URIg4NWwoU0JT+axwMy8fWtVJiC831nPDlmUAvD/pWkKVvY82n3HGcO6+fhllxbXcfvV7zDt/nNu2Nkct9Y3fI5NEEKhxfZN2xaev/UqlCyEsNimMC68/jSlzRqBSt73JuyuYA2ROSmoVzAH2bs5t3t4mmGcG30i1aS+BiqH4y+MJUfY8vX6giYoOZMasDH5f4534vH9PMft2F6EawKKfAJNOTSUwSENdrfeZBlWVDaz5YTdzz+3eF/bJTPyQ0NY6A54Ka5a3E8xb7E884eevIiYuBN95yk4iuCtR3NP2wCAN4RH+VFa4j6o9WsQvK/ZOMB+ICPNBBh6xyLUgKxJ5J6baHDqcPbBEA3A6PRfZdcV3+QdYVdi13+HFqZk8OmF2r0XhGB9/rk4fz9Xp4zHaOt8XJgTNI0KZgNjL6/X8znWU6t0XuRWLRLxw6jzmD0nv9ljlEgn/ypzCyOAIrv1jpVtf9MP11by8ZwN3jRnM7Bikf3l72V/cdM1pvPdhm8Xf5m15x71gfiLz8uGvuT75HGJUIZQYq3k5+2ueH339QA+rXxkVH9Ujobw9yX4hfDv7at7P2cLbhzZRZzb00ehODo7n66P2U/Hk93fzyIUvkTgittdCeXvqLYZWwRxALpa0FpW8cMgoHt/9S58XuBwZFMV9mYJFxvjQ7teqas8j76zig0cuoV5n5IG3fmTW+FQeeWcVL93eteYgF0t4dOwcbs44la8L9rKyYC8ARwYw6nVMSAyXJo9nXmy614VPB5Im42ocjjYbn7CAx7stlrdHKR9DWMCTAFTUCsFoTqcRvXEV/polvRvsACMRibk8ZTwXDhnFysK9fHxkO4cbtF0f2IcoJILGODt6KAsTMpkc3nf3ku4iF0u4fcQMliSPBeDNgxv5In+Xx4Cb3iATSzgvYSQ3pE0mVhPYL+dYfyD/xPMwb8HucFDVoCfMX4gAr9Y1YbG5nwAf0VW5FLSP5va0Od0WywEUYvcRVUabBbm8e5dQLBKxOGESQ/0i+Ne2jzDYLR32lxkFAeymbcv4YPJ1qCS9E0DnnjeW0eMTKczXEj8klMgY9286kyWLML8bqGv6CnDgTfGEmspGvvuwYxXd084VPky3Pv2PDpYpvWHIURGnhYfbUtCsDgO7a16n3LC5OZrOSYxmOgGKVKTd9GE/nrj48qn88ct+vA0YWL5sHbPnDOwigVQmYc6CMSxv5znvDe8v/YOpM9Pw8+86yuDvgFgsIjlVsKfI2lvstl1ZSbsI89yuI23ShscgEkFAUN94xqeNiPEsmB81pvbj9USKl5Y0fUm9aScNFmGBKt7vsmN+/kEAr13pXON0tnn2RvnMJTXAfTbJ0cgk3fPMt9jtPL2j69opd42Zxk0j+s5yqgVX0d4KsYomewOFTVkADPc/1e3xRbp6Pjy00+M5Hp4wq0dieXtmxiTx0qlnc9Nf37ptszRrCxenZhLjc2zrFgzy90KlkBET3fcTr7eOfEmDVY/ZYeHC2DNotOnJ0Qnf25fEz6HaXM9rOZ/zyPDreGT/UlJ94yg3VZPhl8RZkZP5IP97KkxC6rfJbuGKIecQr+ned+CvublU6HRdbgPYX3XsonJDFf6k+grZaKm+MQQrBiZC7kREKhZzzdBJLEkax7dFQib1NwX72FlT0meRxHKxhPTACEYFCRkMp3YRIXs8cfT1abk2QL9cH1fX5pKkWzofJBKhVMuRyCR8++Yh1n29tcPuT3Jf6XyMlzRYTMS6sYdOD4xg+cxL2VtbxsdHtrOmNLvHtjVKiZRZ0UO5OHEME8P6rs6IVCJGKZfy0U/buGjWaBafNZYlD33UrT5ClT5cN2wy1w2bDEChvpY/y3PZqi1kd00plf3o6y4RiRkZFMXMqGTOjB5GUi9saQaCJtMvrT/LpPH4qi/odZ++asGXv6bxOay2AgAMpt9PeMG8BZVUxuKksSxOGktWXQW/lmbza9lhDtULQWp9mdMhFokYFhDOhJA4JoTFMyksAaDDItlAE6YSgnkfHnMmtw6fzg/FWXxTsI89taW9uu9KRCJGB8dwTryQGTwvNp0Aee81oZOu6GcLN82ezJI3PidALQiddU1GHl042237/fXuxaQWJoUksyjhlB6Np2V1xxVGuwV/1D3qd2zQEJ4Zs4hbtn3UWmSwPUd0lTyd9T2PjuzdzcxosBAZE+hRKG9BKRuKVvcOMkkY3lYa3vJbFlZL2+pS6shY7vivYDEh7kOPspij/JNL8tpW+XZUP0+ochTjQu9AhBgnDrLrv2B39auMC72rz8ZwrIlPDGXSqUPZuLbrDAqAHVvyiIjsWbGhvuTsc8fwxUcbsNm8LzjVUG/gjRdWce+j3mc2nOykpgkTZk+CeXlZe0uWriPMM0b2rcd/WkYMf/6S5XZ/i195dVUjIWF+lHVhyRISJkymg4L7rgist+itRwaF8hMcmcS/9TvA4TCjlvUuGsoTXxzZS1mT++hsgMWpo/pFLHdHiTEbo12PWiJ8jhxOu9to8zf2bcbmdH+PHhMazWXD+ibrZ17CML7JOwDAL8U5nfbbHA5e3buRZybP6ZPzDTKIKyIjA3j5zV8pav4eeubFnxkS3zvBw4mTvfU5PJl5MwEyYSK5tdZ9ZmC5qZprk84nSiU80x5szMdoN3NP2hUAlBm1/C/vGx7MuKZb49hcXMzm4uIut/WUFsHAW5blr0aEiGpzI49nfUyEKohyYy0OD/ecvmBB/AgWxJ9cGQMqqYxFicK9eFHiGBosJrZoCzlQV0FOo5YyQwNVRj06qxmz3YYTJ/Jmyy+FRIpKIidIqSZM6UOo0odYnwCSfENI8gshwScIqdj1XK3R2sAHBf/j/1JuP2a/a09ouT4t1wbw6vq0XBugR9cH4KI7zzkmv2MLBpulyzYjg6J4dsJ8nnQ42FVTwmZtIUcatOTpaqgxNaG3WTA328rKxVLUUjnBSjWJvsEk+YUwITSecSGxHvWPniIRizhUUMmqTYd4/yHBftfajbmiK+J9grg8JYjLUwR/+GpTE0cateTqaijR11Nl0lFl1LfaauisZvTN7wWrUyiOLBKJkIklyMUS/GRKgpRqQhTCykScTyAJvkEM8w9nRFAUyn64LseK9oU+1cqZ9DZIpT0a5WnU69/rdB5XlBcKC8SR8T23Hy4vrOnV8T0hIzCCjMAI/jV8euti1P66cvbVlVOoq6PM0EC5sZEGsxGj3YrJbsPudDTfa4QgF7VUjlIixV+uIkbjT6wmkFifAOI0gaQFhPfIdmSg8JcruSRpLJckjUVnNbNNW8TumlLy9TUU6GqpMTXR1HzPMtmtSERiVFIZaqmcCJUfcT6BDPENYlRwNGOCY3ps9eSJk7bo5/ikGFbd80+qGvUAhPhqPFqy5Dd1nR5xRVLPU30VHixXelule0poKpcnncqy3LUu939fsosZ4enMDE/r8Tnuu/kjXnrvKq/aOnES6tu9h/Rtfx7q8PrS287sU6G8heDwjpFn9TX61p+NthqS/NoeWkSIGRawiD/KXKz8n2AsvuJUrwVzgB+/8RwxeCwICfPj7PPH8c0XW7tu3I7f1+wnNS2K8xd57yl1MuONj7fZZKWh3oCfv5qC3K7vhX0tmHtT+BOESPOQML8OnuuuGOqld3t/IBFpyK1/C6lYPSicn6CIEOMjT0JnyaHevI+22I++T5X9ONtzUeYE30AeGN/Zk7I/CVXEIkKEA2EC6k4sb7Ja+C7/gMe+Hp5wep9etQebr8WfpXku7VlW5mVx39iZrUVRBxmkr7nmimnsP1BKSpJQ0DsuJojh6a69wVet2cez//2x9XVqijDheuuNKzq0EyHi5tSLeOXwp/hI1VybdH6nvtoHxSjE8laxHKDYUElWYy7PHFzWui1W7Xpy547N113XrfbHgmQf4bom+bR9pw/z9byAqdebmH/eSx22fbfyVnx8jt97wiXpd5HQzkIuIS2aa/7juubWZy/8yOqP1vPEV7cBEJUY1ml7yzZP+MuVnBE9lDOih/Z43HVVDbx4zQc89vmJP09qj79ceK/09vp4y/wb3Af09Qc2h/fislQsZnxoXK9tVPqSqxZM4tmPfueqBRPx0yjR1uuJDuvbzLIQpYYQpYZTmqNzjxXfLVvHF68LEdwfb3vsmJ7bW2yOtnmiTOLd/M1bpO36sznc2+TUaXV8876ge93wSM+C5Fr66OnxfUGLsH1KWMIxf68dj/jKFJwWlcJpUSlu21RWNvDog19htzsJCVHxxDMLen3eKy9dylP/XQRARETne8lJW/QT4EBpFU1mYUUizM+QScUAACAASURBVN8Hk9WGUuZ6qEVNnivYxmtCGB/c8+KPCokHSxZ71yu9XXFT6izWVh4iT+86RfKVQ6uZFjYUiahnInRQiPc+6GbrEaSSYCQiDVJJaNcHAKX5bTdfpVrOqMnuPyi9QaGSIRKJcDanexibTK37DLZKDjes6HSMwXZsvab6g9S0SMZOTGTHlryBHkq3WPLPafzy0x6a9N1LB3z71V8ICFRz2pknV4RQT2iJMO+KqooGmprMGI2e70dSqdjrYqLekpQagVwuxWLx7GFWmF/N+EnJHTzXXeHt79wfaGTxmOwVyMVBAzaGgUAsalvRtzm8rz1wvBKmnoHOkoPJXkWVYW3ztul9eo79NZUcqvP8/XLP2OmovSiS2Zc4nHYKDQcQN2eIRatcfx//XJiNwYUHeguZIZGdCor2ljhfIftpRnSiyyhzi93ON3lZXJHmfRTrIIN0h7UbDjNtSmoHkXzTllwmTeydDUW6XyIPZVzLj2XrWV2xiRSfOJpsbUXCSgxt2V9HL0LFqsNJ8onl1tTFPT5/qMaNR8NRrPhEKNY1flISCV4Is71haqj3BZePJ0qNtfjLhMxhH2mbUL+3vpC99QWMCUwk3b8t8MA/2IcnvrzNq74X3T6P/KwSr7cPFB8WvIfJYUQjacv0ezXnBRI0iWjNVaT4pDI1dDrLCz8EwGBvwuqwsjBmEV+WfMaNyf/isQMPcnHsEqotWhRiJdtqNyNtDkCrMdcwP/o8hvn2zu7r70j/lvTsf6aPSWL6mLb7bWiADy/c2rmA5SD9g9PZNicXifrWAlUkaouMdjqNOB1OXrj7Mxx2BzvXZTNxVgbzLz+VT1/9hcLD5QA8f+ennDIrgylndbaTPfp4oFMfno4f5Phj/dpshqVFc8ttZx6zc560RT9fW7OR3QXlra/HDonmxve+4b3rFrpsX2327FU1Oqh33lsePcz7QDCXiMRcljiVR/Z+7XJ/YVM1q8v2Mjd6VI/6z8iM5fsvtzE8M65D8b6EpM4Py3JZPAazEKEcqOkcJeOKuuq26x8aFYhU1ruiZu4QiUTI5BIsZkGYc9jbHhtS/f/h8hh32080Lr586gknmPv5q1jyz2ksfeWXrhu3w+Fw8sxj39LYaOLcf4zvp9H1juqqRn76VogunTJjKEkp3YsG85aYOCFVXK1RYGhyv/BQVdkAle59xFtISo1AoejbW75UKiY5NYID+z1P9ooKqjEaLDTUey7MNJAR5o2WQwzxv4oy/Xc4cSDy0pbqREclbRNGa0zbiPE9sW2REvwuoaDhI+xOE/trhCifU2TLUMvcZVc4MdhKUUu9j7b5vSTX4/5QlYbZcf2zeOyJclMewfIoTHYhA8uJs7muR0f+KPX8fTI3vv8i885OGOZSMAfBrmVQMB+kv9i4+QjTpqR22Lbq1/29EswbrU28kP0RKokSo93E1YnnEa4M5quS3wB49tAHBMv9EbsJeknzG8LOukM8deC91m3jgtKZHdEzG8n2/PVrFpUVDWgrGzsUVV/z4x5MRivTT08nISmMLz7eiNPpZO6CMeQerqC8rJ6qigamny4IminDIvnsww3YbHZmnTWSxJTwXo/teOWtnFXorEJAzktj/wnAxups7ty5DKGmn4j/jr6cySHu75FbVu8lZ1cBS+6dT3VZHa/c9lGfRXI/dNErpI5JoDxfy/BJKcy5fBrvP/Y1FYVaTE1mrnz4AuKHRfHcDcL7yWqxUa9t5ObnlxCXGsmLt3yAUW/CL9D1IssRvXBvvjbxJrJ1B1lT8TMAWnMVF8VdQphC+Nvvrt+BRioI6pfEX061WcvnxZ8gFUkx2o2EyEPJa8ql0drIUL9hOHFy1RCh2GupsYSfyr/7WwrmN0y8nze3PDHQwxjkb4pEHIzNXgaA3eE54LS72B1tlpsScTDainp09QYeefcqlr+yhtjkcBLTorjgmhn8/s0OAG581L3WdPTxQKc+PB0/yPFBbbMjxIvP/Ux+vha73UFubiWTJqewaPEkSkvreODeL3j/IyFTbv8+wUbug/fX8d8XhECC6modr760mrLSemw2O3PmjeJCN24Er7y4GpEIbv7XmbSvw3vz2990aisRiXn5mvle/R5/D0VgkEEGGWSQQQYZZJBBBhlkkEEGGWSQQQYZZJBBuuC4izDfnlfCsusv5Oq3vwKEispikXsnzTqL54jFkQG98+7yVPSiydaz6tNHMycqk9cP/4LW5Dpa/ruSXT2OMN+9PR+ArRs6RnT958XO6Z8ipFjtFUgl3hdBMhvb0rrVmv4rVGC12FqjywH8gtqiI1L8T+4VxpGj48kYGeux+OPxyPkXTWTj2mz27S7q1nFOh5M3XljF/t1F3HL3XPz8+zZtrCeYjFY2rz/Mb6v3sW3TERwOIcMhc2xCv52z5baXnBrB3l2FbttVVjR4jEBvoa/9y1tIGxHjRYS5tks7FqDPLWO6g488hYKGZSgkoX+b6HIApSQMP3kajZaDlOq/RykJJVxzOlKxYOdldzRhsmvRyOLwkXkZidmusJuT/i3ydjQKSQgjQh5lt/YeTDbBCmFd6fnE+p5PkHIsMrEfdqcJo62MBstBqg0bCFBmMibsRa/Psb483+P+C5NHIu2hjVpvGKIZidlhoM4q/N6uosudwIbyAo/9nBmf6nF/b5gVl4xUJHZZcHRrZQlGmxXVMbayGeTkpqy8ni+/2c6B7HJefuPX1u1Gk4UmQ++e4/1kGh4Zfn2n7Y+62Abw8pjOhegvie+fYrdVlY1kjIwlZVgkrz23itjmAmnjTkkmY2Qsrz33M6Fhfpx93ljCIvx5+ZmfiI0PJjUtivMumshrzwnRxXlHqggJ9SMyJpBPP1jP/Y9f0C/jPR7YV1/IxfGndtj2Zs4qJocO5anMJTyZ9RUf5f/ZGmHeUKPn/oVt3x0TzhhJRLx3lpY9oTy/iuufWtTqdX5gay5GvYn7/ncdpbmVvPvQCh7+5GbuequtdtXGH3ay4fud6KYORSSCf793HXvWHeKr19Z06r/arCVcKWRNtkSTA8jFig6vK00VRKnantdCFKHUmKsZEZDJjrqtjAsaz8HGA1gdQiZ2pLLNBkktUWOyt9lq/p0oOlg60EPoFTZ7CWU1V4PTjlQSQVTIRwM9pEG6gVQS0RphbrLs6NO+TZZt7c4TSWhoABKpmJfu/ZzG2ibmLZncrf5CIzseD3S7j0EGnqBgIRPpP0/9gy8+20KT3sSVV3fPJvO5p3/glMkpPPr4OECo39aeFp349Vd+QSoVc+P/da4t8eCFswBwOJ0UVAnZEJsOuddWjua4E8zFIjHOdiZdDocTu4eq6nqr5y/dMKVfr8aj9OBh3tW5vUUmljAzPJ0vCre43L+9No96i4EAubrbfbsSxt1hsmYT5ncjtU1fAA68SUAICPZBW14PgK4Lu4XeUF3R0XLCP8g7z8aThcVXTOX+2z8d6GF0C5FYxN0Pn8v1ly7ttpc5wNrfD7BzWx5L/ikU7Z137hgUymMnpNTVNrFt0xG2bjzClo05nW7Qx4rUtEiPgnlVeQPaqsYu+8kY0U+C+XDXRdPaU1RQTUXzfcIdUdGB+PgOXHEvMRKc2DnxXSK7T0bIA2wtvwq700Ruw//IbfhfpzZpwfd0EsybrAXs1t6LzaHH5hDS7mwOHfZ2Pomby69AIlIiFfu0/ssMfcJ78b0HRPnMA5GYfdUPA2B3GCho/ISCxk9ctu/OAokTyKpxXXOkhVOjErzury8RIcJPFoyvNNBtm2JdPfVm988uPjIFCb7uj+8tGqmcBL9AjjR0Tge2OuwcqK1ibFjX95RBBvGWqMgAliyaRE1tE7NPa7OBkEolDIn3PkDkREShkCKTSbHZ2grtBgVrUCikOBxOTCYLao0CiUTc2qb9fgBDk5mwSH/kcimLLp8yIL/HsaLW0kSspk3wPthQwhFdOfdnLEQmljIzfARPZH3Zut+Vh/mW1Xtbf265hn2FQq3oUBi0+HA5WZtzeOqqpQDEpUbS1Gjk9buE7zrfAA01FfXEpkRQWVhNTHPB2+gk17Y6wYpgcvSCX3C1pa1Ox9FLrxHKKPKb2qzJqs1aghUhxKkTWK/9iyXxV5CjO9xqBeop8G2gsFlsPHrJawA47A5Ov2gSp100qVt97Ft/CKlcStqEZAC2/OS+GLjd7sBm7Vzw+kRCb/wZpXwMYQFPDvRQuuS799fy/QfrqCyuReWjIClDsN274bELiE0OZ/2Pu/nz250oNXI2/LSHK+45G4PexIo3fmP8aencd1SB52/+9xffvb8WbWkd4bFBLLz+dM5a7Pn9YtSbeeCyt5DJpTzy3jUo1ULNoD+/3cFnr6yhrEAojhkWHchZiydx/jUzEUv6L9hCpZiEySJY7xrNG7HZSzoU6+wJVlthc3+bWrepFVMRiURYzDZufOR85O3m7lKZBJMXC9Xuju9OHwCVRh2rig+xo7qYw43VVJua0FuFYzVSOQEKFQk+QaT4C88CE0LjmBAa11rUszc4nE62aov4oyyHrVoh6FFr0lNjakIhkRKoUDPEN4gp4UM4I2YosZqAXp/T0/ldnRvot/P3BXa7g717innimYtatx2tBX3+qfDe02p1PPaEawvv8IC2mhyRgUJA2Ldbsrwex3EnmM8ZNZQrl66gqEYQVy5983MuPMW9mb/F4bnYnJ+sd9Gpaon7D0xX0e3dYWxQglvB3OF0sruukBnhaT3q+/CBMgxNZjLHJrQW53MlPErEvlQ1vonDoaNa9z4B6gVdRpsHhPi2CuZVpXUYm8yo+iHSfP/Wjp6rMf1csOh4Y/ykZJKHCl7DR7LLu2h9/BAe4c9dDy7g0ftW4OzBxEGvM/HWy0IUzMfvreXMs0cx/fR0hqZH01fP304nlJcJ0c/ZB0rZv6eYrD3F5OdWdli8GyhSh3mOui7Iq6K8zLMYDZA+sm8rorf2O7zrfvU6E1s3HfHYJjV94KLL4dh7mO+qKiO7tpr04DBGhgoRXW/v3ca1I8ezo7KUQ7VatIYmbh3bJlC8tHMjYSoNQ4OE+7JYJGrto9KgZ3Z8MqsLcjgzoXv+2YGKTKZEryCv/l1qTFsx2bWIEepRyCQB+MgS8ZN1jjq2OZpoMO/vsn+704TdbsJsr24+Tt+t8fWEKM0cQpSCD3CR7gu0xnU0WQqwOhoRixQopeH4yYcRqp5KpPoMr/staKyjyea+fokIGBHcP3UNuqLEmI3RrkctEQIFHE47YlHHuiJdFStNCQjut/G1MDQw1KVgDpBVWzkomA/S5wQFarjs4kkk/c2eHX/+bhewi6kzhlFaXNtp/1nnjOb9pX+gVMqYdloaxYWdP5ez5o7k4/+tJTIqgJS0KJKOfXmGY4aPVIm13bzyx7IdJPlEMMxPuCfJxVL0Ns/BUkq1nKZGoehrSU5Fn47v6Ofe2NRIkkbGcftrV7Zu+33F5lZRfck98/nhvT+pKasjLDaYfRsPA1BRWO2y/xSfoWyoXsfbea8TIg91670/MmAUBxqF7/63815vLfopEUlYYVpOsCIEjdTnmGeYdYcDW4+wbU3b4kba+O4v4j/bvFDxUbaQZfDQBS/0zeD6kSu33obBbuy6IXB14mJmhgylqu5uACy2bJxOGyWWLDSqMwj0vQkAbf39yGUZ6A3fYHfU4KNeAECQr+Ddb7LsoLrhCRyOJiRif8ICn0UmTeiwD3C5vyfs23yEdx//lvvfupL4oZHUaXXsaX7vh0S0iYJbf8viX88sIj4lgmXP/MDpF4zn8Y9v4K6Fr5CXJWQDJGZE8/0H6/j4+Z+58fGFpGbGkb2rkNcfXIHNZufsy6a29idq9wE16E08eOlb+PiruX/plcgVgu6y469DvHLv5/zfUxeRmim4IJTkVvHyXZ9iszpY5CI6tq/QKE+nTvc6AE6nlcq6e4gK+QBRD+VAp9Pa+t5wOtuCyjSqOdRWNSISwfN3fYrNaicuJZzL75hLfGoENc1BkE/c+AGzFo5n4mmd6xkcfTzQqQ93x1cZhTnGc/v+YGXBPhxuJvP1FiP1FiMFulr+LBfmqO8c2iwEskYlc+2wSYwO7tnz6IbKfJ7a/RsH6ytd7rc47OisZor0dfxVnssze37ngiEjuWPEDEKUvQ8K9XT+9ucG+uX83nL0d5rR2DE4seVPJ/Ig+miadccd2/PJPlTO0GGRndq8+XPbgo7eJMzhdEbvgzmPO8H8HxNHcEpyLLmVwoNdUngQscHuVzxsTs+rtZ4ixL1BI5W73ddVwdHuMCZoiMf9e+uLeySYf7j0Dw7sEVa1ho+O58HblgPw7JuXd2oboPbO+L49QzNjyWk26LfZ7Gz9/SDTz+mZfYwn/vh2Z4fXY6b2X8r48crFzZE9//n3l120PL6YPG0oN912Fq89/3Ov+tHrTHz16Wa++nQzQcE+pA2PYWh6FLHxwYSG+RMU4oNSJUOhaPvM26x2rFY7RoMZXaORxgYjNdU6KsobqCirp6SomoJcLUZj7wv49hepaZ1v/O3Zuc2zPQRAeGQAwSG+fTWkDoSE+RES6ku11vP98Kdvdnrc39XCQH9zrC1ZdlSWcfWIcby1Z2urYN7C1ooSbsicyJt7Oi6iKiVSFqdltm6XiSWtfSQFBLG1vASbo2cTVB/ZEEaGdq8Ylb8ig7lDuhbMu2Jc+Bsut8f7LSbez3WWlK88pctzyyVCpHRywHUkB1zXu0E2U6L3vDiV6B+MRub+uaE/CVXEIkKEo1mkOFosByjRey4QnBrQ/xG3qQEh/OhmX4Gua+umQQbpCe98sK7TNolYxBMPn7y2fuddNJHI6EAkLqIW73pQeOb/193zcDqdiMUiTpnaeT/AHfefg83mQCo9ue3KhvvH8WWRMLFuspn4oXQ7twyd17q/wlSHn9RzIFb6hGS+fGU1T1/9NiFRgYglYuqqGvns+R85vLOAD54Qio+NOz2DcbNGdNg+7vQMZi/2Poo/fUISO3/P4okr3gRg/OyRjJo2jB/e/QMAbUktMoUM3wA1wyelsPrj9Tz5z6VExIe4jWS9IuHqTtvuT3+0w2sRIi6Ou9Tl8U+PFMTjc6PbrHtG+rfNCwPlQfxfyu1e/479xc7fD/S6j3d3P9MhKVEkEvGN9h2X19ZutXNu2DW9PuexRioJJyrkAwDqdG/icDYS7HdPp3Y6w0qiQz5EJFJ1EE+dTjNVdf8mJnQFYrEfeuP3aOsfICrk4w77gE77e4qxyYxIJCIo3I+IuGAi4oJJc2GhKVfIOH3heLRl9bz31PfMvWQKiRnRhEYGUFki6FCJGdF8+tJqFt5wOqedL1hCxCSFUV5UzfKXVncQzBUq4dnPoDdx/yVvEhoVwN2vXoZU2vYstvylVcy/Yhozz20rcB49JJQ5S6bw08fr+1UwVylOQSkf22rHYjD9QXn1PwkPfA6JpHuLyTZ7GZV1t2Mwd/xeVStnopSP4svlq7ngmplkTkrGbndw9yJBqJfJpTzxkWvLsvb8tHxTh+MBr/o4WF/JVWs/B4To8p5gddhZU5LN+QkjenT8f/f+wVsHN3brGLvTwRd5u1lbkcfSqQsZHuh57n88n787BARoqK3Ro9OZ8PVVsmFddof9UqmY9IxofvphN2fPHw2AwWBBrW6bZ809R9g+fWYajz28khdeXkJwiE+HfjLi2rKqVHJBJ8ocMhVvOa4E87K6RtbszaGqUU9UoBAhlRHjuRq7RCTG4UE07yoSoCt8ZO4tAsqNXUd1ekuwwgelRIbJ7tr2obDJdTRAV+zdUchzb1/BvTd+iFTq2Q++J0yaPZwfPm77UH7y6i9MPnM4INzQ+oJdG3LY1c6DXSQSMXa6+wr1JytTZwgLJnEJIRQV9Oz9MFDMXziO2hody5et75P+amv0bPjrEBv+OtQn/R3PRMUE4eOrRK/r+b2sv/zLW0gbHsO6Pw72qo+hAxxhjtOOSCTG0seV490RptbwdU4WERofDtcJn+dDtVoO1FQRolLz1eH9mG0dM6hMdhsrDu8jRCXYcykk0tY+TotL4to1K3lz1oJjMv6/KxUGz9HxsT7+x2gknYlSCWnh5cZct20qDJ4nEEGK7lu/dZdgpftzVDT1XSDCIIO0547/EzJJWmwyiktq2bazYABH1L9ExwahVMldiuXtEYk8R2+1cLKL5QDXJs/m5u3vALB9/xFGByYyP2b8/7N3noFRVekffqZmZtJ7L6ST0DuICIiIwroUFRtYFte6rn11/a9tl7WXta9rw94rxYIoiID0UBPSe0J6m8n0/4ebTDKZPkkAdZ4vMOfec8+5k+SW33nf32vZvqPxGFlBfc8qL/x0n80xZH5S/vnRX23ar3vkYq575GK32+3x7A//sGm77C7bYKcnv7nbbv/bnr/SbvvvkX0/up+O74hegbQX/2AVCkdZ1gqZzf4ngzhlDA1a4Zmzw9CFyYntrScEKBcgEgmLSSJRX+CSVp+P3lBCVUPfwqREEu1wW//t3jJ5Tg7zl0/llj8+RebYJOYvn2YRu+X9gqoCQ3qepXuy7oN7/JZlflJ0WkGPaWvqpKWxg8xx1jXxssYl885TX9NU30Z4tPDc13uNfPBPr9BQ3cJjH99kJZYDlObXcGR3KR88953duRsMRps+Q0lEyL1UHV8CPYEVXd0bKaubSaD/MgKUC1HIBJFYLLZ9ljWaWtDqDtCpWUuH+jNMZmunBZHIj8hg4Zo4Y/4o3nxiA99/uovO9m6W/skz3+qB/QGXx+jUa7l260d2hfIQuZIx4XHEKYNQSmVojHpatBqK2hso62jGOCAKPVoZyNw4z9Op7tm9nveLra2ZJCIRWSHC73RWcCRBcgVag4FqdRt7G6usslbr1O1csultPjvrKtKCPMv4vGf3egCH49sbGxiy8b1BpZJzyYrTuP7PrxEa6s+M04SA2Op+dc/uuHsRzzz1DV9+vgeTycz8BWO48KKpNsdKS4/m6mvncO89H/HkMyvw85PyxOdbuG3xLGblpvLelv1cPMu7oN5TRjDfXVLFPz76jj9MyCYtOpyqJiEK6rLn3+fxyxYyOtF+irOfWIbe5Fgwb9e7l3bkiAg/x1GZVWrb9MbBECxT0W20H/1Vq/Yu6kosEVnSGUwmM8Yh9tMbMy2NyNgQiy1LZVE9j/ZEsf/t6UuRygZ30a8sPs7DN1kXFTn93DFExp56PkvDTe/7zEUrT+PRB78YkmM2dPyPUP+lSMXhaHQHaex4HYDIoGsQIaVTuw2lLBeRSEZjx+tEBl2D2aynS7sHpTwHf7/JLkbo44pr5qBQynntxU1DMvffE5nZsW5Fkjsid/Tw2LH0MljBXCQWkZ51cmwseunUF5EctPKEjXde2khMZrPVIuaTs88FICc8ymYbwM0TZti0934ubm1mduII5JLhe9D2Ac3dzq3YAuXDV/zaFbua12M0G6jTCNeKxQm2ok2bzvnC24mIjlc5ydxr0Q7umc2HD0dEDsiyio4KYsN3B0/SbIafGbN+f4ElgyU9MJavzvg7AMe17cQpQ62KJy9OmEqccvhqPPg4MXS2dlG43/2Cb+7ySe1LTrcvvWl4Cvx6wurRfdHhZsx0GdR0GDrp0HfyQtEaarud12hxRK9YbosZqTSRpOiNHm7zHpFYxA2rL+DCG87iu49+4e0nN/BRT8Hnxz+7mdCee0Gvx34vYontwqGlftUAQdXc81nRbxGkslj47safnkVlUT2v/OsLrn2gbzHAbDLTrdax8o5zOesCW8EPGFaxHEApn0xkyIM0tP6fpc1k7qStcw1tnWssbRJxOGJxIGKREpNZg8nUjtHkTPcSExP2AvIe+8bUnHjuf9U2W8VdvOn/Ycl+qrqsdTSlRMaDkxbwx+RRSBxYTHXotew4XgbAppoivq7M58LUcQ73d8THpXk2YvVFqeO5MXcmsSr7NRW7jQbeLdrDYwd+QNejZ3YZdFy79SO+Ols4f4XEtVxrb2xX43cbhaAsZ+O7M7a72BO5AZZfPI3lF0+znvelffUBoqODWf3whXb7vv6WdfbwzNOzmHl637NPUW1fgOmWwyW/fsH82W+28fKqJTb2K+eOz+LhL3/k1T/bN3EPlCmcRpEPp2Be2tlgV9TwlmC5kvpu+4J5g5f2L7Pnj+LOa9+gprKZW1e9xsKeFdahQiqTsOruRTx0U1/q1NYNgidcXWUT1/xjMaMmO7ebsYfRaOK7j3fx8r++RNPV5zEklohZccuCwU/8V8yc+aN485XN1LnhW+2KiICVtHR9ilgcSLBqAQq54AOmkGWh1uUhlUTTqd1JZNAqFPIcFLIsalsfQiHLQqM74JFgDoLY31sx+amH1lrSrHw4J3Nk3OAE82GPMB+c53BScgTKkxx5ozU2UNz6ElKx6oQJ587uHY62DWzv/ayQSrl05NDbYfmwpvcB0xGBQ1AoyFuyAqcSJAunUVvlcB9X81fJhr+wcoATUd7V/Hz48JY33v7Z6nNnl5ZONwuSDxRWfPx2kYmFV+N4ZZjNtmkRvz87yN8i+7fkYzoJ7x9X3G9fyzhZiBARIPUnQOpPrCIapcRxVr23+MmyMZna0Gh3oPSbBpgxGhuRSCKttgE22wdLZFwIl/z1bJZePYerZwuWg5s+2cWya+Z6dIyQiEAK9pczYVa2pb1gfwWRcSH4B/UtFIRGCprRNfcvZe7SSdxx/rMkpkezcIVgsyQSi0jNiae8oI6Ikxj4FxJwFWazhsa2hwH7QadGUxNGNzJuxSIhSj8q9AkClCd3QejHWtvsyltGn8HSFMe1EEF4bj8rXhBZz4rP4oGJCzA4Cca1R526nQf2fmvVdv+Es1mR4Vx3U0ikXJU1lczgKK7c8p7Fb72ko4n3igUr0yszp3g8tjvj94rhzsZ3NfbvhVNGMNcaDHa9yjNiIujSOvYXDvcLcGqNUu1lZHYvsUrHFzSNUUd5VyMjAgZ/UQehmIwjHFm1EqXTwwAAIABJREFUuOLcJRMZPzmV8tIGkkdEEpsw9JERsxaN4/vP9rBzQIRp0aFq7lj+PCOyY5k0O5ussUnEJg5I7zBDR6sQsdfW3EVFYT2Hd5eyed1+S1GI/qy8dQEJqUPzff9akUjEXHjZDJ55dP2gj9XR/TNmTBhN7YiQYjAKBeG0+iK6tDuRiEMQiSSWbVp9EUp5DkZTOyo/7xZf5i8cCwjWMv++99MhEf5/62TYKWDhLkqVnBFpw1voLCM7FqlUjMHg3QtI5siTbMcChCtm0KLdg1Q0PF7vw018gP3oBR9Di87o/CHamRg83Oxt+RapSI7BLDwzzY6yTfN35XE/lNEkDseQOh7DWcagDx+DISvDOotJoZAxKse9xV6JTzD34eM3gdFg4pev95/safxuEIkUxIa/RmPbfZhMnYCZkICrCfK/yGobYLPdW7auz0PdoSF9VCKqQAWFByotWkPcCM80BJFIxEV/OYs3H1tPdEK4UPRzfzmfvfID197vuP5Fxpgkbn/6Mh65cQ1xKRGM74l6vezWc/jn1a+SlBnDaecI78Nmk4myglqMBhNnLvMsEM1bQgNvQCGfzPGWW9EZSrw6hlI+magwodCtXOp50dyhxp4Vy+gwz9+f5WIJcrFnkf5rCnej7mdtMj8hy6VY3p+ZMSNYnDyaT8v6ChG/nC/U07g8Y7LTAKuBYw/l+K7GBiHQ5eED37CxtoAWrZoAmR/Lksdx5+jh8+N3l6LaJi58VAjqbenUWP7fy4d3XubWcU4Zwby2pYPVn9u3aqhtcRxdHaMI5hCOo6ny22sGNa8k/3BEiDBj38pkT3PpkAnmnQbHkS5aL6Ouvv5iH3POHjUsQnl//v7cSv7vipc5ZCcKtjS/ltL8Wrv9juwt48IJ97o1xplLJrL8OvdXhX/LnL1oHO+89hNNjYPzew1SzsVsNiASCZeC2JDeVD0xkbJ0BI8zcb9tYvxk6ZgxImJwaWPZufG89OafeeaxDWz65rebFj0UDEZQHjkqYdij4+RyKWkZMRQc9e5666qwqSc4O1NnXo2d+kJGBK+iuuMzzJi8LvzpSpAUOZ2hj1MdV8LZyYyQDpCGMilsAftbhWcpM2ab3zdXLwEag3eL856g1jsew1nggA8fg6G5pYuFC6wjzb5ct5/zFrrOzBmYJm82m9mzt4xNm45wrLCOhsYOtFoDoaH+xMeFMn26UE9g9hnZhIcF2DukRxQV17NrVynHCusoLW2gtU0Qf7q6tMhkElQqOeHhgaSOiGT0qATOmJWNvyMv5UFw6HAV+/MqOHiwkrr6NtrbNXR2apFKxSgVwmJhZFQQcbEhZGfFMnp0IiOzY93ySB8KXvzvJj76eKdN+yUXT2fVVa49dKs1zQTLhIjJAGlftO2B1nIOtJYxITSVnGD3M/YMeiNL4q9DrxXuC5PPGs0/P77FaZ/Sw5VcN8PaG/2qB4TI5AtvPtdhvy/+u5EX73zXqu35n+4nbUySgx5QfLCC3RsPUdRjT1J2pIq2xg662tXI5DKUgQrCY0IYkZvAqOmZzFw8ySqi1h1++mI3q1daF/Zec/BRopP6CkwX7i9j8yc7ObC1gOa6VlobOyy+0kFhAaSNSWLc7BzOvGgGSg9+rztauig5VEnJwUpKDlVQcrCSioIay8+jP28//AVvP+ye3eWyv5zN1f9a7vY8erlj/moe+/Yej/udKoQGXme3PTLEecF4hXwsCZGfe7zNW2QyCV+8toXaikaMehPRiWFc8bdFAEyf73khxz9edQZSmYS3n9xAQ3ULUQmhXHnXH5i/fJrTfjPPHUvNbeey+trXeeqLW0hMj2baWaO479VVvPfMt3zwrOBjLpGJSUiN5sIb5nl+soNA6TeF5JgtdGm+o63rHTS6nZhM7U77SCRRqPxOIzjgCpTyEyPuu4u/Hbu/Iy11TIl0fA0cCrqNBj4osbZDuX6k+8Wbezl/xBgrwfq4RqiZdKS1zmERTntjD+X4zsbu5ZVjP/N+6R4uTZ3M2LAEmnVdpAeeGsGt3z04NIWWT5k3k7vOm+1w24QUxxEgIwKiAMeFO/LbBieYKyVyohRBDq1Sfm44xvlJQ5Ou0KLrcrjN22fNmqpmrr/sv0yYmsaiZZNIHqbobD+ljAde/RNP3fkhW78+4LqDhyy+6nT+dNeiIT/urxWZTML5l0zjv8/YLxriCb1iucBAkVBs9/+DFct7Ufn7cdf9izn3j+N58alvKS6sG5LjDidKlZx55wgv3SknKNshOiaY4BAVba3O/ZPtMdz+5b2MHBXvtWCeNYQR5jKxY6G7U+84W0kiDqC07VUMpk4q2t8h1n8hcoltSrYrXHlES53Mz4d3fF1+JlKxinmJXw37WAqJc8uSDp17Fg/DQbQiha0Nn+AvFTLj7C3OOIvuBuhyImYPFV0Gx3+Hrubnw4e3/LK7xEYw35tX7pZg7ufX93tZU9PKvx/5iiNHqm32q69vo76+jb37ygB49bXNXLFyJhecP8Vj0bi1Vc1Xa/ex4esD1NXbfwcBwcKwu1tPc3MXhYV1fPPtQZ557jsuXj6NSy+ZPmhPXLMZvt90mPc/2EFJaYPDOWh7RMjWNjWFhXVs3iIUZQ8L8+fJxy8haWCG6RDz/Asb+eSz3VZtvSL5JRdPt9fFhpcKv6ZDL9zDn554FQDbGgu4fe8bPe9hIh4bfzkzItzzh5fKJIzITeTYXiGYqORQpcs+h3cU2bQd3em4kHMvJQf7ji3vEZuTR9q+P7c2tLP+9c1889ZP1Fc02mzvxWjQ0q3W0lLfRlFeOd+9+zPP3/E2F958LhfdthCp3PtrdUVBLdFJEbQ3dfKfv67h56/22Oxj0Am/T13tGmrLGtj65R7eeOAT/vzvi5h/2Uy3xvnbokfd+s5PFEd2FJ7sKfwumHrWKKaeNcrpPjMXjmNmz7U/KMyfDZX/sWz734+2ixoLV8xk4QrHv3fnXXE6511xuk37hTfMsxHCp5yZy5Qzc53O78Qhxl95Nv7KswETWn0BAAZjDSZTKyazGrEoEIk4BJk0GZnUc5vdE8W4iHjymq3fQ/9z6CdyQmOGVTTf11hl9e4XpQzwKrJ9dJj9d+EdxyscitYDxx7q8Z2NbdmnoZTUwAj+Me7k12gYLk6ZN5NzxnlXnCYt0LnVQH13O606NSFylVfHB8gJjnMomG9vKKJdryFI5tmK+0Dqu9to0zkWw7w9/lU3nMnl185hx0/HeOPFTXRrhBfVh55b4dXxnKEKUHDPCyv5/jPhwed/q7+irblzUMcMjwnm2n/8kZnnOPef+j2ycPFE3luzlfa2X3+RtDHjk3nhjVVs/PoAH7+7g9Ji74rODCc5oxM4c8EY5i0YjVJ14m0XMrNj2bXD9UvTQIbbv7yXkaMT+PyjXR73k0jEpGZED9k8/JxYSjgTzMUiOUppAlGqOYhF3v9827TOBXNn8zvRVHd+Q536RyZGPTSsfX5LuLJc6dCfPME8UZVNoirb6T6hfs6fJQamdQ4HXU7+DsNczM+HD2/R6QxWdYfMZtBo3FsgUvRET5eUNnDTzW+jVrv3d97dreell3+gqPg4d/9tkduiuclk5uprXqPJy2donc7Amre2cqywjn8+sAyxF1lmHZ3CvezhR9ay3Y6I6y5SiYTEBM8Xnz3hmee+4/Mv+kRXkQhuvP4sliye6NFxDraWc3Gytej1YuHXzIjM4qGxl/Hvw5/wVumPbgvmAJkTRlgE86baVtqbhJ9pULj9zAN7oqo7gnlRXl8Ry9TRgjgklVkvlpiMJq6feR/Ndiwv3UHXrefth7+gKK+ce9+5ERBqS3lKRX4NaaOTuGnOgzTWuG+d2tmm5skbXqOprpWLbz/5QVSHth1ze1+TwYhB77Mc83EqI8ZPNhLA8u+viUvSJvB24W6M/Qq0tuu7uWTTW8xPyOZPWVOZGDH0QWS7G60X5bKDvbNBVUllKCUyNANsmGvUjq/XA8ce6vGdjd1LY3cXkYrBZ9Kdypw6b+5ekhvs+hd/W2Mh58aN9XqMnJAEfqg/anebzmTg04pdXJE2y+vjA+xucl7QL3AQgrxIJEIsEmE2mwcWeR4WzlwiPKDOWjiWzV/tZ/172ynIq3S7wIpILCItJ44/rDiNuYsn2jzw+RBQKGUsXT6VN17+8WRPZUgQiUWcde5Yzjp3LPt2l/LVJ7vZua0Ine7kWBxIJGJyxyQy9bQMTp8zkpi4k1egBSBjZJzHgrlILCI7d3AFOd1lZK53DyEj0qKQDyJSaSABToouOhMzo1XzaOnezf7jNxOhPJ3EoOVe2bK0uogwD5KfvKKQA6lT/0i3wbPFKW/6/JaIUTn3uK9XuydwvfX9HhZNzSE0QMnB0lqKaprISoikob2LM0ansvlgCVHB/rz/Yx4AK+ZN5Pt9hUQE+5MWG864NO+yMmL9nXvdN2gcZ7oNFY3djseIdvH9+vDhLXPPGMlfbnuHlGTBCqK8oolz3EzP7xWP7/nHxxaxPCBAwbixScTFheKvktPapubQoSoKi+pt+m/8/jDBwSpuuO5Mt8YTi0Wcc84Y3n5nm1V7WJg/GekxJPQI0IGBCgwGI40NHeQdqKS2zromzPYdRXz8yU4uvGCqW+NazrejmxtuehOAqqpmu/vExoQQFRVkmUNzs/B3XVHZRHd33wv3OQvGDJsli9kMzzz3LV98udfSJhKJuOO2c1hwtueBNs26LhL9+zIHj7ZVUdRRyz255yMTS5kTPZrVhz/26JiZ41OsPpccFgSOcbPsC1KHtguCuUgkAhGYTWZaGwSbhNqyBmJTbDMbDXojFQV9kZVZE+xHgYolYs5eMYv3HrPOxgqNDgYgfWwyCenRBIT4Y9Abaaxp5uDWAurKrSPRd2zYz6fPC0Xmzr9pgd2xnFGwp4StX+62EstTcuLJnZZBSGQQJqPJMubeHw7TNsB+8s3VnzHmtCxyp2c4HefGJ1eg7rANKnrnkS9tFiHmXjiNucvdy0SISRZ+Bred+U+39vfhw8fwkx4UwR1j5vJw3vdW7Wbgm6p8vqnKJzM4kmUjxvKHpByilUPzvHm4xfqev6WuhLQPnNsUeUKL1nFA7cCxh3p8R2O/kL+FT8v3c1zTgc5kpKyziexPH7Rs/2retWQEWQv3epOR/xZs5YuKA9Rq2ojwC+CchBz+mjPHJnvXaDbxWXke66oOUdjeQKtOTaRfAHPjsrgt90xUdux3bvrlI0SIeHjiH3n00Hd8U32ELoOOJP8wnpt2IckB3i/c+3LDffjw4cOHDx8+fPjw4cOHDx8+fPjw4cOHD34DEebxqlDC/QJo0jqO6tpcnz+oCPNJYc79mtaU/MSSxEkED8L25bPK3U63h8i8O/ab//2Bn74/wtiJI7jiurmkpHmXpuENMrmUecsmMW/ZJDRqLcfyKik8WEVrUyedbWq6OrrxU8gIDBXOLSjEnxHZsYyanEpAsC8t2x3Ou2AyH76zHXXXybMBGA7GTxrB+Ekj0Gh07NwmpAJv31LAwbwKGuqdFyTxBqVSWKlMzYgmd0wio8YmMnpcMv4Bp040sDc+3yNSo1ANQ+Eve8TEhRAa5k9Ls2cRqoMpaGqPCKXja2V1Z5ulgPJAa5Tj6k2AiPHRz6LWl9NtqEMp9Xxux1rte7z2Eqk8NdLWzJho0OwkUJYyrH1+a8T6O49IyW9pQG8yInNRXHP5rLGs25lPgFJOXUsHK86cyBvf7UbWk95e2dDKzNwUMhOEaNj0uHB+yCti2czRvLNpr9cR5imBzguAH2t17Gk7VBS0OB4jKfDkZvL4+O0y/8xcpk5OpaZWiMKOiQ4mNMS9Z+v9+/vsLsRiEVddMYvzl022mx118GAlDz2yFsDKe/zTz3YzZ/ZIcty85y1dMomPPt5JWGgAZ589mtNmZJCW6vgZ3myGH348whNPfY1G02d79N4HO1i2dDISN60zTCYzD/7rc7uR5cHBSpYtncyC+aOJiLB/LTSbzRQU1PHLrmI2b8nnnAXDY6loNsPTz3zNV2v3W9qkUjF/v+s8Zp/h3JrKEQFSBXpTX1bjupo9pAXEkB0kZOrJxVI6Dc6zyAaSOSDau9dr3F6EeVNNC8crmwBIyIhBFaigYE9fBvLRncV2I8wHFrLMnJDicD6Lr53HJ899Q1hUEGddOpPpC8eTOsqxdZ/ZbGbzJzv5z01voOn3rvHh0+sBWHL9fCRSz+LvtnzWZ9+XMS6Fvzy90iYSvxddt553HvmSD55c1zcnk5l3H/2S1Z/d5nScnKnpdtvXvfajTVtcajST5nleEPKtgqfcsqUx6I1cPvJWj4/vw4cP97k6exrhCuG+/q9939n4ex9ra+Ch/Rt5JO97To9J5cLUccyLyxxUfSlnEeBDgcbgONP+ZI29ID6HSeGC9dffdn9OsFzJ38ecbdmeoLJ+1zAjRIBvP17KirQppAVFUtR+nLeKd3K4tY43Zq6w2OUBSERiPijdQ7wqhKszTyNYrmRnQxnvFO/CbDZz7zj7BbDrNe3csOMDAmUKbs6di8FkYtvxEmJVwYP6Hn71gjnAlPA0NtTkOdy+vaEQg9mIVOSdtcfokASCZEra9fa9otv0Gu7c9z7PT7ncqzF+qD/K3uYyp/tkBsV4fFyAyOhgnl3zZxRK54XKhhulyo+x09MZO93+w8uJZsWqM1ix6oyTPY1BExCg4PONd7rcL7+0no+/2cclCyfR1tlNdX0rKfFhHCs7zoFjNWSmRFLf2EFXt/CSlZoQTmiQih15ZYzKiGPbvhLCQ/1JTQhnTGafxce+o1UAluPJZVLL8cZkxvPO2t1cumiS1TghgcJiyO7DlWSmRCERiymrbiI3PZZurd6ynwgRRRUNNLUJAuxdDywBoLmxk/wj1ZSVNHC8ro262lYa6ttRd2nRavVoe1KBDQYjEokYqUyCn5+MgEAFQcFKQkL9iYoOJjo2hLiEUFJSI4mNF9J0hilreEiYfnom327/x8mehlM+WHfyXwbCFf5IxWIMJlsLKKPZbBEER4dbX1OjVHMt//eXpXo1drtOS3Wn8wWdOBeCq6e0aA9R0fEFTd17UOtrARP+smQSAxeRFnyZja1MWftHVHR8QbuuEKNZi9bYxBcl1kXvzhuxB1G/e5m7fQCrfq7mBti1vTGYuihue5varu/p1FcCJvwk4YQqBOFlZOiN+MtcWwAdbHoUgJK2d0kNvpjR4X9z2ccVGSERSEQiK4/E/uhNRo62NDAm3Pk9e2dBJSazmQ6NlsjgANb9cpSo4ABMZhNf7ThCp0aLRCymqV14GC6ra0bl5/w+3qStQS5RUN4lFEIfFWxbgCo33Hm9gKK2Jiuf5+HAmSg/MvTELer7+H1RVd3Ci6/8YBGxY6KDuXbVbBLjPUvTve2Wc5yKwKNHJ/LEYxcDcP1f1tDWU2fGbDbzymubebJnmytCglW89PwVJCWFu2VpIhLB3Dk5SCRiHvjn55b2tjYNBcfq3Bbq163PY8/eMpv2nJFxPHj/MsLC/F3MQ0R2dizZ2bFc7qRInjs4Om+zGZ58agPrNvS9+8lkEu6/dwnTp3n/njEqOImPK7YD0GXoZm31bm7KWmjZXtfdQpDUs4CepKxYFCo/unusfEqdFKE8/EufV3z62GQCQlQDBPMi5l44zaZf8YEKq88Z4x0HegVHBPLcj/eSmBmLyA1ve5FIxOzzpyKRSVi98gVLe68Xe+G+UrInp7k8jj1yp2Ww+rNbUagcB3bIFTKuvG8ZHS1drH/9R0v7nk2HaW/qdOgFfyLwD1YRlRTh9v69xVh9QKW6hj0tBzjYlk+DVlgkatd3YDAbCZEFEi4PY3RINpNCx5Li734tJqPZyLV77qJd32fjc23aSuZEzRjUfI+0H+OBw09aPotFYl6Y8BChcveFOI2xm70tB9nXeoiyrkraeubYZVDjL1URJAskPSCFscE5TA4bh0z8m5DpTjhLU4T785zYdF46uo13i/fZ1Ocxmc1sri1mc20xsaogVmUJtmUXp03wuNZUm96zRVRPMePYT/lkjZ0aGEFqoHDtU0hlBMoUTIlMcXicjTVH+aH2GM9MvYD58X2LxdHKIFbnfc0Pdcc4M9a6NshHc1ZZfV6cNIYadRsbawocCub7m6v4c9Zp3JrbZ393Seokp+foDr+Jv8QZkRlOBfNOQzd7msqYGuHdDV0sEnN6VBbrqvc73GdXUwn35X3KP0YvtvHhcUZ+Ww0PHvzM5X7jQpPdPmZ/zlk8gWNHalB3aRk7McXiB+3nu2n/rshIiiQjOZLUxAi+3nqUcSMTSIgO4YX3fiI1MYKjJfWoNTquXCI8iH+wYQ86g5EVf5gCwI68UhbPHcMHG/ZYCeb1TcLN3t7x+u+3cXtBX3uG8NIWEepPXn4Vk3KTaO/qRqWQsfbHQ5b9dHoDK/4whbe+2ml1LmERAcyYlcWMWd4VCu7P+rX7WXnJi3a3jcyJ59mXrhj0GD5OLBKRiJTAUIramuxuP9Qk+L0NFMzL2l63/D8l+Eqvxj7YVOdybunB4V4d2xFFrWs4rtlGlHI6Mao5mM0GatWbONz0JAaTmuzQa632D5CNIDloKWazkbzG1QTIkkkPudxqH5FIPOg+7swNsJmf1tjC1pqr6NSXEqYYQ2rwJYgQ0a4rpEGzA4BxEfe6/F4ONz1JSdu7AEMmlgMoJFJSg8MpdCL67qyvdCmYzxw1AoPRhLQnMq2/SN3//zf+8TQAxCIRKTGCsHfp3Al2j1mlKUBj7EQlCeo5jhHxgEX8MD8lyYEhlHe02jsEGoOeorYmMkPcFwE8oUOnpaLDfpE3qUhMbphPMPcxPLz46o/ceM2ZxMYIAkdtXRvP/fd7Vt+31O1j5OTEuxUxHRsrZEpcvvJ0nnn2W0t7Xl45tbWtlu2uSE72/O/wjFnZJCaGUVnZFyFeWOieYG40mnj/gx027THRwTz87wsJCFB4PJ/BoBjwrmLuWah87IkNfP3NAav9/vXgMiY4iFJ2lz+nn8WNu/8HwO5DRYwPTeW8hMmW7Tsaj5EV5Fl2j1giJm1sEod7vMlLnAnm2/sKfqaPTbYRgx0V/+wvmCsDFCRmOL//JGV7nqF0+h8nkZARQ1Wh9XNOUV65V4K5XCHj9v+uciqW9+fSv53H129usaqHdeSXIqadO85Jr+Hl7WNPe7T/gitnD89EfkVUa+p4v+JzdjY71lQatM00aJvJ7yjio8q1TAwdw6XJS4lXug4elIgknB4xhXW1fT7Wmxu2D1ow39xgfV0cG5LjtlhuNBv5pm4zn1ato8NgPwu3Xd9Bu76DKnUNPx7fRrg8lPMTFzI3anCLjv3p6v4OAJkkGbksc8iOe6oS6qfi7nHzuD5nJh+V5vFRyX6K2m2f3WvV7fxzn/DdrCnczepJ5zIjOsXtcQwm62K+uaExTI/yTrezR6aTIp4Dxx7q8Z2N7QlfVx9FKZVxZpx19teMKCFA7ZeGMhvB3B5ZwVHsaCjFaDYjcbCgfkW6e7UoPOE3IZjPjMxEKpJgMDuuPv1F1R6vBXOARfHjnQrmABtq8jjSVs3fchcxJTzNaYSWzmTg44pdvFDwHWqjzuF+vYwPS/F0yoBgyXIkT3g4GzU+mX/cIggIj754ubNuvxn2tORRpanlj3HWRWk21G5kc+N2poZNYEn8Qge94ZnC/3F2zByyAp1HrOxtOcAnVUIRHX+piiXxixgZlOF0DicSiURMU6ua8hrhBUrZE6mYkRxJh1rLmIw4DhyrtuqTHBfGe+v3MDYrHpWLBRZ7xyupaqK4soHC8uNW7XkFwjiBAQrEYjFGk5mQQCX78qus9iuuamT9lsNoh7HoZ0xMMOPGJ9PeLkSAtbVpaG3pwuhmgdpfC41dX1LYKKSuTkrYjkzi/CV8d9UMdMY6YgOvZESY/ah2tb6Q+o53adP+gtZQicnUjVjkh0wShUqWTpBiCuEq4XfeTzr0VcmdkRUa6VAw31BeAMDFmdY2Xd6K5P1Z33NsR6QGh3scueCKMRF3IxWrkIj6hIys0D+zsXIRFR2f2QjSEcpJRDAJk1lLXuNqFJIIkgOdC0be9HFnbmArmB9qepROfSk5YX8lI8T6Z2IyC9kjYpHz69GR5mcpanuT1OBLABgd7joLxxNmxCQ5Fcw/KDzAqpzJDrf3Iu2Xxt3/ecHR/10R6ZeICBEmTD197We8zYobwVsF+xwe5+vyY8MmmH9bWegwOn98ZBz+MttCPj58DAV+MolFLAeIjQn2uOD02WeN8mj/c84ew39f/gGtVrh2mc2w5acCll/oWRFOT8nNSbASzNva7WfIDmTPnjKbwqEAN1w/74SL5X5+UisbGbPZzKOPCzYg33x70NLu7+/Hw6svJHcICpynB8by1Rl/B+C4tp04ZSgi+q7BixOmEqd0bmtlj8zxKRYxvLc4p9FgsrEyObyjn2A+LpnQSOsizaWHK9FqdPgpra+TxQf7BPOMcSluRY57Q87UdBvBvK3JvULXAzlj6RS79jKOCI8NITk7ntLDfQsO9RXDbyHmDFWQZ9kGNzy5cphm8utgV/N+ni18Da3Jte7RHyES/Sh/ybiKKWHjXe4/J+o0K8E8v72I41rhdyXKz/NnG61Jxy9Ne63a5kSe5lZftVHD4wUvcbjN+fvBQJp0Lfy3+G2Othfx59TLhiTavKZR0H4CVUuJCXtu0MdzRFf3Jtq73qdbtxejqQWJOBipJIEA5QKCVOcjkZzYwIhguYJVWVNZlTWVfU3VfFySx7rKI3Tobe1sKzpbuGLzu/x78kLOH+GenXOI3Po6kB0Sxd3j5g3J3D0d+0SP7y4Vnc1oDHpyP7NfKLlNZ/uMcqilhvdKd3OguYbG7k40Rp3FVlUwebG9z/lL5YT5eW+R7YhTUjA3m8FkthasJE68hYLlKqZGpPFzwzGH+2yqO0KrTk2Ilz7jk8NTSfIXIgMruuwLMQDlXY1cv/O7RZCqAAAgAElEQVQNIhWBnB4lrKLEKkOI9AvEZDbTquviaHsNOxuLaXNg8TKQMaFJRCuCXO9ohwN7ynn85Su46/o3kUrFw5pmfSoyMXQsE0NtL3jnxM5DJpbRYfDuQW8gWxq2szJlOYCNuO5oDieaay+aiVgkIjmuL/34rBnZmExmxGIRI9P6Vu6XnzMRwLItp2dbb3svC2ZaezD2Px7AvdedA0BGcpTNOP2jKLNSoiwvRv33G25rgAmTRjBhknXq6l23v8funSXDNubJwIwJM0bLp8FS3f4SFS1P9DumgNGsxmgoo9tQRrNmI80a4YF1VPR7gx7TEyZHJbCuLN/utp9rywBo0HQRqXSeWu4JBpOJ9Q7G7GV6TNKQjdeLn8TWTkAq9idInkmDZidmTHZtT04EruYGWM3PYOqiuutbAmTJNmI5OBfKpSLhoTG/5QUKW18lNfhSRoffMRSnYcOchDTW5O91uL2wtZHdx6uYFHViF4oMZj01mj7BJV6ZYXe/+UmZTgXzdWX53DR2cNFYzo7tiLmJ3gc1+PDhisBAJa+u+YnkJOFZvqyikZBgz94JxnsYweznJ2XC+GS27+iz2ig4VuvRMbwheEAdILXaPYFqX165TVtMdDAzToKdorKfKGwymXnk0bV89/1hq32Cg5U8+tByMlxEVHtCrzAVr7S9f02L8C4qs7+Pea/XeFVhLckj+0R+TZfWKvo8bUwS/oFKlAEKNJ1C2r3RYOLY3jJGn2Y9j5J+EeaZE1O8mqM72LM/6Z2bp8xa4npReSDRyeFWgnlHq2c1c04UaqOal4uf4ebMu072VE4Ztjbu5LnC120sHqL8IsgNFn6fQ2XBiERi2vTtHG0vpFrTtzijM+l5suBlrktfyRmRziNIE1VxpAWkUNxZBgi2Elt6IsTPT1jk8dx3Ne9HY+z7PQ+UBjAxzLXffbdRy32HHqdCbR2QFiQLJCcowyLeKyR+qA0aytXV5HcUWtVR2NKwA4PZwF8zrO0pBoPeUDZkx+qPGQP1zbfSof7Yqt1g1GAw1tGt201zx7NEhTwCQKDqvGGZhzPGh8czPjyee8afxeflB3n56HYqu6wXiY1mM/fsXs+o0FiyQ1yL+6EDBNqG7hN3XRo49oke311MZjNhfiruc2ClEqeyzrrbUl/EddveJyckhqszTyMtKIIgmYKXC7byUZnj9xdX9aO85ZQTzJ/7dhuf7TpMh0ZY9TGZzWTFRvLOjRc57Xdu/FingrnOZOCLqj1cnmrr6ekOYpHI0vefBz93sTc0dHfwacUul/u5w2Uj3FvFtIdYIqI3mMtkMmM0DV4sO5W4//Cj3J97J2vK3sdgNvKnEZfy4JHHuDfnDr6t/4HNx7cxKngkFye5l3L7SdVX5LUeJkwuRJC06Z37Edd3N/Bh1RfkdxTSqhd8MVUSJTem/wmVVOVwDo/mP0tusLCgcqyjmBZ9K3dn34xSouD741vY3rgLEyZGBmZyQeIfvflqbHAkPIudRKI42+Zpn4HtVsUd+kdair2LrvRxYmjWbKS8RfCG9pfnEBN4GUpZGlJxMAZTGxp9EW3dO2jR/EB0wPKTMseZcSkOt/VGt756ZBd3TZw9ZGN+WHSAFq3zRdDZ8d55ozvDaNZS0fEZ9eqtdOrL0RvbMZg1mMw9AonZbG8R/oTgcm4D5temO4bZbCRC6bnfnFQcSEnbuxS0vExK0LJhE8sBZsamEKH0p1Hj+KH0kT2b+WDBJSf0GtbQXcG0cNcvIKfFJhPnH0RNl/37W0FrAzvqKpg2hAs8pe2CDcuWmlK728UiEUtSc4dsPB8+BnLzDWfx845CKnuKWeZkxzF9intCsFQqvITFuWml0p+01CgrwbyoqN7jY3iKzXXHQVbHQA4etLULGT8+2S0P9aHGv59Vx9PPfGMjloeHBfD4oxd5ZVvjLtWaZlp1XYTI/e0K6O5ir6BlyaFKK8G8YHexxW4kNiWSgJ7FnIxxKRzY2rfQeHRXkZVgXlfeSFe/DIKsAUVGhxKxHes1s5u/WwNJH5ficR/VgCwHnUbv1dg+TizVmjpeLn7HSiyP8ovgT6kXMS7EcdZOYUcpr5S+S1mXcF0yY+Z/Je+Sokok2d95QMKcqBkWwRywCObLEhZaZY24w+bj260+nx45FanItYT2aum7VmK5QuLHiuTzmR013WH/Vn07b5Z9xM+NfRrStsbdjAzMYH7M0NRe6y+Ym816dIZjGIy1mM0GpD0ZyHJZDmKRZwvKTW0P2YjlAzGZ2qhrvg4Qah4FKB1n+Q8nKqmMS9ImcOGIcTx3ZCvPHv7JarvBZOKlo9t4evpil8dKD4pgU01fsMqh5uFfFHc09oke312SAkLJb6tnTmwWcjdE7TcKdyATi3nj9JX4S/sWz9XGk3PNP+UE858Lyvnu7lXc94ngJXT7wlk8sf4nF73gzJhcHpULFbTbdPYrxn5WudtrwRzgDwlCGtA7pdso6Tzu9XE8IV4Vxtxo20rq7jJ7/ijuvPYNaiqbuXXVayxcOnjj+1OJQGkAaoOaDkMnepMBtVFDoFQoqjc/eg4qiZJKdY1bx6rR1LG35QD/Gv13ywPgHQfud9onWhHJX9JX8UTBCyxJEC76qf59vlHO5iAXC9GSt2T2WRLUdzfwc+NO/pFzGyJErD76JMWdZaQFpLh1Dj58DDd1HW8DoJJlMDrmY8Qi6xeYIL/JRAdcjMmsRcTwrPS6Ij04nIyQCKe2Ga8c3sWStFyyQtxPCXZEi1bDo3s3O90nTKFiVtzQvsjqTR38VHMFHbpiYlRnkBZ8KQpJNDKxP4ebn6ZVe2RIxxvuuRlMQsaPTOx5YdROfSmHmh4HoEV7BKNZi0Tknjeqp0jFYi5IH82LB229fnvZdbyKlw/v5NpRw2u90B+5RMmWhg+Ri4W/SUfiuVgkYmX2BB7e86PDYz2463vWLrpiyAT/1bs3AdgtxgswLzGdGNXQFsT14aM/677JY9EC77L9wnsia70JIoiLsxbZm1u8i/4qK2sk/1gtZaUN1NYJARrt7Ro6OjRouvXotAZ0OgNanQG93jsru/p620W0zCGM3vYElb9w/V7z1lbWrrO2xAwJVvGfpy6z+W6Hgh2NQvDVk/lfUqnue4aIV4ZxU9ZCZkV5vrAXlxZNQLCKzra+99OSQ5XMuaCvgOehAf7lvWROHCCYD/AxLz5gnRXgrOCnPcqPVnOsp8hr2dEq6soaaW/ppKOlC22XFm23Dl23AV23zhIdP1hUgUpCo7zLnD4RvFj8FNel3cK/jtzD8qQVNGobUEiUlHcJ2aeN2ga0Ji1L4i8kTpnA59UfWrWFyPsWV76pWwsIPtbnxg5NANSvjZeL30Zr6rO/iFZE8kDu7S49wDMCR/BA7u2sPvofjnUI373epOfZotd4bKxgF+lI/D4tYjJvln2EziQIbPXdwt9yQXsx2UHuZ8w061o52GadGeeOH/reloNsafjF8lkulvGPnFtId/E+HyIL4qaMPyFCxNbGvhpeH1Wt5YwoIbLeTzw46zqjqRmt/jCtna/Qqf4Kk9lWNxOJ5Pgr5hEefDdyqevsP72hjJaOl23aJeJQTGY1ZnN/+xNBY6lrvomUmElIJc6L0Q8nUrGYm0fNshQFfbWg72f2U5172ebTo5J5Ob9vUaVZq+ZISx05ocN/7xw49oke310WxOewoeoI7xTv5MoM2wyRgQYrBrOJAJnCSixv1WnYfvzkOACccoK5Ui5DLBahMwip/sEqBY3trh8u5WIp58ULhbDeKt1qtU0qkrAgbgwrUwdXOEHa4wd6V+4fuHbna5i8XFV3F7FIxL2jF9td0XeXc5dMZPzkVMpLG0geEUlsgufee6cyaQEpHGrPRyFWIBMbOdyW77W4XK9tIEmVgAiRJZomQel5YRx3seeLXq2pob77OP8++pSlrds0vBWQffjwhG59GQAhytk2Ynl/xMMkVrrL+WmjeMiJIGgwm/jrlq949+yLCfPzzIeyP3qTkVt+Wkur1vnf6fL0MUidWIt5Q0nbu3ToikkLXsGo8NustondiH4ZTryZm1QsRLN0Gzz3JTWYuhgf9SB6YwcHmx5hX8O9TIp6xPOJu8nVOZN58+heugyOrQ6e2PcTSYEhnJs8+ALF7hKnTCdIJkQIbW34hBkRi+16ma/MnsD/Du+kqdt+gMGR5uO8dOgXrh89ze52T1hbdpSNlUVO97lpjPeZdD58uMOBQ1VeC+YDi096gnKA37RGo8NsNrsVtd3Z2c0nn+7mu+8PUVNjv1DvUNJhx14jJGTo/UDdQaWUs25DHmve3Gqzra1dQ3lF45AL5vtaSrh93xsA5AYnsjRxGqFyf5p1nfxQf4i7897myQlXMTXcvt2VI0QiEeljk9m/5ailrfRQldU+R/plIaT3i0jPmmidmTZQMC/K67NjCQoLIMaNiPvONjWfv/Ad33+wndrSExMA1h9/D72/TzQSkRSNUU2EXySlXcV06NsZFzLJ8j62KvUGjmvr+KTqPeZHL6Lb1G3VdnnKNUhEEjYd/xZDT+2VhbFLTuYpnTSOdZSQ32F9/782bYXbBTMVEj9uTL+S2/IeRN8jfleqa9jTLBT9nRRm/5qukiiZEjbeSnQGoQCoJ4L5Tw2/WEXGp/onkaRyXS/hy5pvrT4vTTjXpVjenytGXMgvzfss59yu72BzgyCMzo8efKR5Rf3ZgON6XWazjk7Nerq6NxIT9iIBynOcHq9d/RH0s+n0V8wlKvQxpJJYADS6XTS0/B9afV/9CbNZQ0vHM0SGrB7UuQwFS1OEYt79BfNWnQaDyeTy3W1SZBIqqQy1oS/6+a2iPTw0efij5+2NfSLHd5f58TmcHZ/Dowe/o6D9OJPCkzBhpqKzmY01Bbxx+gpilH2LqDOjUtnZUMa/8r7mjJh0atXtvFa4nQi/AJq19t9bhpNTTjBPiQxFbzSikgsPp7e+tZZ2ja0pvz0uThFe7N4t24bRbMJf6sfSxMlcMmKG1x7g9pgUPoJV6bN5ufCHITumPa5KO4PJ4d6l8Ot0Bksxo9iEUHQ6A22tapeC+err3/RqvBPNPS8IhVPSA1LZUPc908InYjAZ+LFhG4tiz/LqmFF+EVSoq6xujDWaOic9Boe9VfF4ZRzhfmHcnX0zYpEYo9no8YLJlZe9RGVFE08+s4Ix44SU+q/X57H+q32UlzViNJqITwxjztwcll4wxe2iV9t/LuSbDXkcOVxNe5sapcqPEalCdO7cebksOHccUqnzuer1RtZ9JXhPbfnxKOWljXSptQQFKcnJTWDReeOZNMX57/zA87N3boDH5zdUDMX3dCoj7hE19UbHtRxOBS7JHMezB7bTaaeoSy/5LQ1c/PV7vHnWhUSrbL05XaEx6Llpy5f8WO18xVsplbEq13O/Tld06IVx4/ytr3lms5FOna0XrTWCiDrQT3Ko+ngztyB5BiLENGsPeOy9rpBGkRggeFO26wop7/iUIJnwUpQZerXbx3GXMIWKa0ZN5cn9jjPg9CYjN27+gnsmzeFPbhQB9ZTjmk78ZXJLBIbepAUz1GqE7z49cAKNumqi/GytVVRSGXdPnM3tP693ePxH924m1E9pUyDXEzZXl3DLT+uc7rMsbRSjwk9ehJGP3wdxsSFU17QQH+d54Ihc5n22lJ+ftdhuNoNWa3Apwm/5qYDHn9xAp5se0SIRyOUylAoZWp0Bjcazwnp6vRGdnSLrKtXJWfyuqGziqae/trvNbDbzr39/yTNPX0Za6tAVkHu56DtmRgpZvQ+PW2G17eLk07k7721eK97osWAOgo+5lWDez4vbbDJzdFefEJ7Tzypo5GTr6M7WhnZqyxosBTNL+/meZ7phx7L1yz08fePrVtHuzhCJRMgVMhT+fug0OjRd7r2TO8NPdWoXd47yi2Zvyy4mhE4hv+MIepOO+u4aijuF7INXSp4HIFYRb2nv3wZQrankuLaemzKGtuj4r42N9dbPSNmB6eQEeVYLIFoRyWkRk/nx+DZL24Y6QYNxJJiDEAk+UDDf3rSHK0dcZMnydkWvlUvfMV0v7lepazja3pcxIhNLWRAzx63xegmUBjA2JIfdzXmWtrwWwZZqKARzZ2J5f8xmHbVNfyYx6nMU8okO99No+75nkUhGdNgzSMR9mRZK+WQSoj6jsn4BOkPfAkp71wdEBN+HSHRyrwlak517n1TmVqCTSirj0vSJ/C+/73flk9I8lqaMYXJk4pDO052xT+T47iICnpqyjHdKkvikbB8bqg4jE0uIVQYzNzbTpnjpVRkzaNN3s7byIB+U7iFeFcKVGdNID4rk0s1vnPD5n3KC+b1LzwTgrj/OBuCXokpGJ7qXUhCjFCINLk6ZTqjcnwuSpxAgHZ6q7tdkzKVW08pXVY6N5wfDmTG5XJsx1+v+t//5DR55YSVKlZztWwpY89IPhEcEkJUreH6tvGa23X5bvz7g9Zgng7SAFA62HebKlIvQmw28XvYeN2Wswmg28VLxG1RratEYNTTqBM/KZfGLCJOH8mrp21SqqzGYjVRrarkocQnxyljGhORy76GHLYU4ohXe2zXYm8Oy+EXEKR3/PkcrIpkXdQarjz6FWCTCbDZzR/ZfvEq/Ki9r4MvP9wDw4yZr+4PiwnqKC+v5YdMRHnvqUoKcRHtotQYe+ufnbN1iXeG7o13Dgf1CZMuB/RWsX7uf1Y8sJzTUfiHF6qpm7vnbB1RVNttsa27qZOuWfLZuyefMs0Zxx12LkLp4Qe09P3vn1vuvO+c3VAzV93SqE+Q3CbUunyb1OuJ1q1DJs93qd7iwlv1HqxCLxVy8yPFD11ARKPfjhjHTeGSPc6uUgtYG5n72P24cM50rcyahkLi+LZqBr8sL+OeuTQ59oPtz05gZhCuGPkpP0VNpXmOsA8ZY2gvbXkdnanPaVyyS4ieJoFNfjsmsQ+zGw6onfbyZm0wcRIz/bGq7NlHY+hqZIdZFjkzmvurozgqAjon4O536co62vAAIQnyM/2yX5+cp14+exjcVwgv04Wb7vsQms5l/7trE5yVHuG386YP2sS9tb2FLTSnryvLZfbyKDeddabEVilIk06itItJPuM8XduxmRoRj/8Xz00fzVVk+m50s+Nyz4xuOtTZw+/hZ+Mvcvw/pTUb+e2gnzxz4Gb3J6HC/cIWKeyZ7/6zjw4e75B+rY9PmfBR+Pdf4ngjvl59Z6bKvdhBWFFqtdeSXSGQrog/k628P8tjj62ysx5OTwpk6Jc1S5DIhIYzQEBWBgQoUCnnvKfHMs9/y+ZeOCxPbQyaTIJGIMRqthZTubs+E96GipZ91TVJiOGqNjsbGDkubRqPjnv/7mBeeu5ywsKF5njraVsV9ox3XXjk7Zhz3H/zAq2NnTEix+txU20pnm5qAYBVlR6ssxTOlMglZ/faNiAslomeRp7FGqAVRsLvEIpiXHOwvmFuPMZDv3vmZJ294zcZ3PClLiAKdPH8M6WOTiU+PISQykMDQABQquSUb4vnb3+ar/23y7MR/hSSpUtja+COXJl9FUWcBIrGcaEUciUrBKmdlSt8ifHFnIYnKZKs2tVFNtCKWFcmreLNcsKm4Pu02FJLh0SROZY60W78TTY/wzhr2tPBJVoJ5QYewwKQ3GSyFegeSG5xFpF84Ddq+AB+NsZtdzfs5LcJ5EENJpxDYUaXp84OWiWUu+wE2Fi7ZgRkovfjZp/onWQnmRzucZ+p5hwi5LAOZJBmRSIrBKDzLavUHMZt7711G6pv/SnLMFnAQyKLvJ4L7yXKsxPJexCIVkSEPUt14iaXNZFaj0f6MSuHZgsJA6tTt1KjbmRDh3NveEe8X2+p5GUHua0CrsqbxTtFei7WL0Wzm+p8/5r8zL/BqTvuaqolQCPe1RH/nmVQDxx7K8V2NDbDhrBvcOqZYJGJF2hRWpE1xua9ULOaOUfO4Y9Q8m235S++12+eZqRe4NQ9vOOUE81+KKtl2rBytoe/hdNuxcu46b7bbx7h1pPO0kaFAhIj7Ri8lUKrg3bLtrju4yfJkIUr+9pxzB2XFYjQYUfas4H+45mf+9fQlhEcGcdPl/wMcC+a/NpQSBW9OecHy+c0pz1v+f0P6VQ773ZD+J7vtyxMXszzRdYGHgdyWdb1Nm0QkdjiHO7P/4vBYsyKnM8tFBXB3eP2VzZbIpIsunc6cM3OJiAikqamTtV/u5cvP9lBcWM9jD33FPx+60OFxHln9JVu3FCCVilm8bDKzZo8kJiaYxoYOtv0siEXvv7udY/m13Pf3j3j6+cttfD47Orq545Z3OF7fTkBP0Z7LLp/J1OnpBAYqOH68ne+/PcSnn+zi++8OIRaL+Ns9zovX9Z6fvXMD3D6/oWIovqdfA3FBq2jo/BSjWc3B+guID7qGmMBLkYqdR+0VlB4nKS6U9k4tJrP5hBRDvDpnCl+UHCG/pcHpfl0GHY/s3cyzB7YxIzaZmbEpxPkHEaZQ4tcjoDd3q2nsVrPneDUbK4s4rul0aw5jI2K5epTrhwNvSAxYSEnbOxxsfIhOXRkSsR+Nml00de8jXDGBpm7ngklCwNkUt73D1ppVRKtmYMKIztjC2Ij/G3Qfb+c2JuJu2rQFHG1+jnr1FkL9xiIR+aE2VHNcLbwsnRb3KkFyxym1YpGUKdFPsLlaeCjf0/B3Tpe96bSPN0jFYp6fLXiSLln3ltOirweb6rhi40ckBwoPn6fFpnBabDJpweGE+ikJ9VMiFYvRGPR0GXQ0atSUtDdT1lMs80hzPTuPVzktNJqgzCRBmUmtRniRzA5ybafy9OmLOG/tGgAqO20XMkxmM68f3cP68gIuyRzH2UmZZIc6foko72jl24pjvFeYR0mb7eJof6QiMS/MXjwoSyQfPtzl4QeWed1XMwjRuGtARK5K5Yez2199fRv/eeZbK7E8LMyfO29byBQXGXiDJTBAQeuAyOP2dufFrIcTmUzCpZfM4JKLplFa1shfbxFqqHR3C0LO8YZ2/u++j3nq8Uvx8xv8K61ULLZ4HttDa9Ij8dJazV4xzor8GnKmpnNsT5mlLX1sMvIBNj69UeY/fbEbgGN7S5l9/lQ0nd0cr+oTA51FmB+vbOK5296yEstDo4O59fmrmHzWaK/O6bdKkmoEtd3vEi6PwF8aiNlsIi0gg6Ptgp3EyyXPAjAqeCwzwmdxtP2gVdu4kEmIEBHhF2mxYllT/jJ/Tv2LTXbxraNnc+vo2Sfu5E4gzbpWGrTWzwHpAckO9nbOQLvVXquSos5SRgbZz/gQIeKMyOl8XLXWqn1zw3aXwnev/Ul/JoeNxV/qOvglv8PaNilR5Z29a5DMuq5Ll0G4NmtNWvzEg8/8USlmExWyGpnU9rphNLXS3P4YrZ2vA6AzlNDV/T3+CvtZ/EZTn2WYVOK4YLxKcQZSiSDgGoyCLZVau33QgnmVuo3l379JWlA4CxKELKEzYtMYFx6HxImeVt3VxrOHf+Kj0jybbYtTHBekHUiEwp/Hp/6BG37+xJKD26xVc/EPb3H+iLFcmi4Eio0Mibbrut/Y3cWhllq21ZfxQ00RJR1NvD7rIsC1YN5/bMDh+I7Gdja+O4L574Ffry+ADx8+fPjw4cOHDx8+fPjw4cOHDx8+fPjwMYScchHmD3/5A3f+YTZ+g/ALPFGIRSJuz1nIxLARPHpkHfXdzlPgnZGgCuPO3EXMjPTM18shPeErx47UEBLmT0RPNXLRrzCi1Yd3tLdruOYGIZXlguVTLe3BISpuumUBYrGYzz/ZxfafCzl6uJqRubZFTHZsK2TLj4Ln4u13/YF58/tWW8PCA8jMFtI4Y2JDePzhtRw5XM333x3irLOto1XefH0Lx+vbkUrFPPqUEPWZ2ZMCChAS6k9mViwJSeH854kNfPfNQeacmcuUaY4rc/een71zA9w6v6FiqL6nXwMKaRIjo16joPFG9MZGKlqfpKrteUKVc4gMWEqocg4ibK/fk0Yl0qXRUV3fdkKiy0GIFntpzhLOW7uGdp1r3021Qc/GyiKXBQrdJVLpz0tzliAdRLaQM4L9spka8wz5LS9Q2PoaYpGMMMU4To97g1btUZcR5iPDbkIs8qOm61uOtb6KRKQkSO7cm9XdPt7OTSGJ5IyEdylqXUNt1w+Utn+ICBEKaSQx/nMs+7hCLglhasx/APip5nJ+qfsrZ8S/g1wytNESKYFCZsUrc5excuOHdOmdR6KWd7T2/Lufd4/tH9K57Gpej9FsoE5TCsDihL+67BPqp+SNeUIa4/Jv3nMYwV6v7uSp/Vt5av9Wi71QtCqACIU/ErGYRk0XDZou6tQddvsPRAQ8NGMBU6NPDX9FH799tvx8jFmnWT9jb/+lmOlTHT9n9NLUJGQUGQxGpFLP3k+qq1usPoeHOa+XsW5DnpWNi0wm4fFHLiYlxXUxx166vbSQCQ8PsIkwLy4+8UUhe3nhucstHuUZ6dH84x4ho+f/7v3EEimdn1/Lw4+u5d7/W+w0ct8dxoWO4OMKIbJ0dvQo/Pr5HHcb9XxUsY0Jod5F+UclhhMcEUhbP1sZS4T5vlJLW+502/vpyCkDIsz3lQn9C2qt9ssc7zjCfMOaLWj7+drL/KQ8/MXtJI90/9lY66Ev/q+VCL9IHhr9NAB/jDvf0r4obqnd/e2135x5FwBZgTlW//6eaNTaZpk5syV1hr9URbAskDa99TNGg7aJkTh+bp0dNYNPqtZZ1d450HqUFl2bw8KjRrORnxt327S7418OUNdtfc1cX/s962u/d6uvO3Tou/DzG1yEuUI+gbjwNYgc2BtKxCFEhqzGZOqiXf0hAF2abxxGmJvN2n59A+3uIyBCpRC+x/Yuwd6qv53LYClub+L5I0Kh6OePbEUulpAeFEFKYBhBcgUqqRyd0UCTVk1hWwPF7Y12qzLlhMZwSZpn9qFnJ2Rz6+jZPHHwR0ubwWTi/eJ9FsuXYLmCOHB+tasAACAASURBVFUwQTI/dCYjXQYdtf/P3nkGRHGubfjaDuyy9C5FQBHsvffYNTH2NGMSU0wvJycnJ72cnJPeTEw3saUZNdao0dg79oKiFJEivS3bd78fAwvLLrAgWPJx/dGdmXfed2aXKff7PPdTUUpZPfW2GtM3UGf/zvoGmqX/vzvXnWA+qnN7jqRl4u9p70fXI6rlxK4rZXhwAgMC2rEm8wgrMg5xtiTbpaJoUpGE3v7R3BzWnZtCOtWbMtJYeg+I5R8PfE9hfhkvvi3YUZjNFgz6ulMNAUIi/BrVj1QuRaV2Q+2jRO2jZPPyg/VuLxKLcHOX4+YhR1GZcqgtF/5INWVaTEbnXqduHnIm3zOEuK7hxHWrO9WnlWoUbjJuvqVHnevvmD2Q31cewmqxsnXLKaeCcpUHeky7IDsRuDZjxnbh6y+2UlJcwV9bTtkJwSajmT/WCalOI0d1shPKazPx5h6s+PUAGRcLWPHrgXoF8+Y4vuaiOc7TjYTarQ/dQzeTVfoVOWXLMFlKKKj4g4KKP5BLgglVzyXEczYiUfUtJiJU8LO72jY0UZ4+/HDTDO7a/Eu9BUCbm0B3FT+OmUWIR30PjldOkMcggjwGOSxXy9sR4Vm/rZFEpCDB93ESfB93ub/GtGnq2ORir0aNa2yk8xeRKiF/QtQep+ubk56BYfw05jbu2bK8XtuUliTOsy9qmR/5+kuNahfjJdz3l42exd2bfyG7AdG7QFdh929jkYrEvNV/DNNjb8zrXys3Jnv2nXcQzP/486RLgrnFIjzTX8woJLpt42rbXEixF0/atau/wO3RyponVQwa2L5RYjlAXn7DtTWc0alTG4fxHjl2sY6tW56gymCfKvr3E2y15j04gs+/qL7ub9+RxMIfdnDvnCFX1N+DsWN48MACAG7d8Tb9/ePwkSspNJSzN/8sJouZFzo23Se1ffcoDm4+Yft88WwWAOcOp9mWdezrKP51qFX48/zRdCxmCxnnqgVzv1AffIOdC4AAx3fa+yoPmNCjUWI5QH6tyZ9WWqmPcpP9s5AIUZO8vKvwkLg7COZlpvqftwIUvnT0iuNkDV9xK1Z25u/n5tDRTtscLjpBmcnedtFf4UtnL9dqNmkaGNOVordc+cSVr/rJOsXy2ttVCeY6g2t1+0Si+sV8hSze7rPBVHcdnSvFYDFzuvgyp4ud1xlyRrx3EN8OnulSwc/aPJwwkAiVD/86sBat2VFzKzHoKDG4Vsi7KX0Ddfbfkn3/3bnuBPMtJ88zuVdCkz3irhUKiYxpEX2YFtGHAn05x4sukqbJB6DIoEFrNiARiVFJ3Qhy9yJaFUCCVxjukpapCjxn3ggupRfg7aNEpRZuTga9iSf+Panedt9te75J/Rn0Jv73xBKH5d5+KvqN6kjPIXFExgYRHOGHTF73z64or4z05BxO7E9h54bjZJwXLnC6CgO5mUXc8fioBotBNoZPH/uO0bOHsnnJTiY/MoYjf50itlsU8X2Fh/JfP1jL9Kcnculctm1dUW4JAyb1ZPfvBwlo40f6mUzi+8biphRuEHtXJzLpoVF2bar2V3Of370kzKxqijW2MTz68ZxmO7aYmEAUbnXfDH18lERG+pOWmse5pGyH9WazxVasskvX+icpRGIRbdr4UlJcwflk+5vS2bPZaCujUvr2r99DWCSCPn1jyLhYwInjFzEZzXV+31d6fM1Fc52nGw2p2IsI72dp4/UERdot5JWvoEi7DYM5h7SiN8nTrCA+8DsA5JJAVmw6itFkJjktjxcfHntVx9o9IJTl4+7gob9WklbWsi98PQKEF9DPht5MiFLdwNaOHE/PJjmngA6hAeSVahjWMZptp1II9FKybNcx5gzryebjyQD4e3rQLsQfk9lCRkEJ0YG+yGUSjqZl0yE0AB+lO/vPXyQ+TIjQO34xh9Fd25GWW0Sf2OrI3sU7DjNjQBcUUilnMnNt7QEkEjHns/Nxk8voFR3G1pMXmDWwq9040/OLqdALf+MxQX5kF5dRoTcQE+RnN4Zgb09b+0MXBFHX2bjlUgkXLhfQNTKEyAAf/jp5geGdGha1rjWd/YLZMOkent61jp1ZqQ03aGaMFj1lpkJydELf/orGFfpp7+3P6ol38/C2VRzMbZzo7ip+bh7MH3oL/YNbJ75buTpkZRezfNUhTp/N5uPP/7Qt1+oMaCoaN4l68GBKowRznc7I0VqCc1z7uoMGALvClgDRlRHWrmI0mjl1KrNRbaro2iWC32sVC01Pz+f4iQy6dL5+skGmTe1NxqVC1qytFnCWLN1DRLgfN43s2OT9tvMM4eu+Qk2iL5M38dflE2jNBjykCvr5tefB2NFEKBs3YWK3/1qCeVZqLmaThbQz1dfbmu8LtnbdogAhSMlkMKGr0HMpOYdLyTm2bZx5pNekqmBoFW07Ne7+YNSbOL2/JYoOXn0kEkeNwWyquzh1K01DZ7G/vkrFUgcP98agkDgKsVpzwwLg8MABdoI5wPbcvXUK5tvz9jksGxbQ3+WxV7gwpmuNu2KAS9vJpFFIJAGYzXmYzM3zLl3lYV6F2XLl72URSh9uCmvP1qxkLLWrZbuAh1TQ4+bG9eWB+P64SxqeTKiLiREJdPUN5bPTu1iRdgKz1dJwoxr4uym5NaoznX3rf1a4Xvv/O3LdCebdokIJ8VHjoxSKQFWl1/2weDd+fkqiIv0Ri8WkpuURGxNEQWE5A/rFsmffeVuaY3p6PgnxoWh1Rk6dziQ2JpBOHZtWNbcp+ClUDA++9qlXbSLto8XdPeTEd27+82AxW3j53m84tld4kKp6ELnr6bHces9g5PWImrXxCfDEJ8CTbgPaceeTo9m+9iifvbyC8hItW1cloinT8sqX9zSbtUxM1yg2LdpOdJdINKUV+IX6cHJXksMDa811beJCOLEzCbPJglgsorxIg7vKDb8QITXfaDA5tKm5P32FgXVfb2Hk7ULk5andZ21jaE58/epP+wUICvYiLTXP4QUNoLCg3FZYaeXyg6xsIHugirJaBaJysqsLgYSFOVbNrk1YG2Ebvd5EfkE5wXVEzFzp8TUXzXWeblTEIjl+HuPw8xiH3pRJRskn5Jb/isZwmuT8pwHoGLSEwb1iCfBVkZ5ZfyHAlqKDTwDrJs3hg6NCqt4PSYmYLI17iKgPT7mCJ7oOZE68kMLXVBuWo2lZzB7ak+/+OoSs8lp6saCYwfFRxIX6Exvsx44zgiA6vX8Xvtt6kEAvFT2jw4jw9+bDdbtoF+zHqUuX6RYVSqBaRWKKIJ7IpRK+2XKQf99ad3GdP46es7UH8HRT4CaTonZXEOilwmg2O4zTaDLz4CjBGmnxjsNU6I08OKovi3ccpnvbMNsY7hney9Y+p1j4m3Q27p5twyjV6nFXCPeOG0EsryLAXcniUTNYm5bEO4e3c7GsuOFGTcRb4YZSWj3pfkl7Fq25HA+JMFFjsZoRixo3wRzgruTnsbez8MwhPjy6u1myMsQiEVNihMybF3uNwFvR9AizVlppLKEh3tw5qz8FhRpGjah+NpdKJbSNbFzk9h8bjzN9Wh+XM6XWrD2CwVBtjyISiRgyJK7eNlKp/b3DYm7cfer31YdtzySNpX+/GLy9PBxsWT6Zv5nPP52NvJ6Al6vN44+OIju7mEOJ1ZOT776/npBgbzpeQUZhjEqwjHin+2wAjBYTMnHzHHftopw56flknMvGWGmhExodiE+g40S7rLKgaWyXCJIOCdGYaWcyybxQHXjRvkdUvX3XDj6xmBsnKq35Ziu6Rk4wXa8ovR0LN+Zdo2fTvzMeEvuC3kaLESvWJovmOidCtIcLEet9fbvzXWWxzqrCmZe02aSUpxNdqwhpmUnDkaKTdstEiBga2N/lcSrEcjRUX0N7+Xalnar+Ca3G4CVrauaqCLAiErkjFjVcvLQKidgXszkPi6W84Y1dQCy2v8ZZLU3LVqxJoLuKLwdNJ1dbzt7cNAAO5WWQXl7EJU0xJQYdOrMRg8WMm0SKSqYgzMOLOO9ABgRGMSJMyOy5EqG8JuEqb/7XZyJPdR7KtuwL7Lks3KfOleRRqK+g2KBFIhLjIZUT5K4i2tOPeO8g+gdF0cU3FMkV+os5699Z30CL9P934/p58qnEXSblaFqWw/IgiZSJ47uxfMUhrFYr06f25qdf9tu8BDOzigjwFy4gZeU63N3lrN94grZR/pw9l3NVBfP/b/z2zXabWC4SiXhxwd0A9Lup6VEeVfsaNqk7ETFBPDXtUww6I/u3nOanz7dw26M3XfG4ARQecqRyKTKFjFO7z6LyUSGWiEk/LUR7pJ7I4MKxdLt1fcd155Vp7/PyT0+ScvwiXv6enNiZRExX4aZ74Vg654+k2rWp3eeE+0faPp8/kmobQ3PiLIKiNorKPnVax5crjaZpD8bmWi932hqeh3JFw5ccN/fq86Ap1wHOBfMrPb7mornOU0siFlWLahZrw+O1WCsc2rmCQhpGrN/byMQ+ZJZ+RYlOsMLQmdJYvSULhVyK3mDivumuRTY0N0qZnJd6jwDg3vhe/JCUyMqUU+RdgYVGrJcfM9p14bZ2XfGUX3nVen+1ijWJZwjyUmG2WPj94GnKtXrBJ7qsgtTcQvRG4eV61YFT+KqEh16Pyt96XGgAZVod3aJCOZyaiZe7AkmluGOxWpk5oAtrE88wsad9SuSKfSeJCwuwaw+QW1JOyuUC2ocGkJpbyNmsPM5cyrUbZ2G5/cOuR41rWc0x1Gxfe9ua/ZosFnyU7iReyGRc9/rFpeuViVEdGBcZx58ZyfyUfJzd2WkYzFcewaaSKegXHM7k6I6MDm+HXFItggQowhEhwoJwbWmsWF6FWCTivoTeTIvtzMLTh/g5+XiDNi3O8JQrmBjVgfsT+hDt1fBkaSuttBS+Pkpm39bf5ofdVNIvFrDsp73ceXv997DMymjeJUvt7aB69YwiMKD+zKPAQC8u1bC+OH4iw+XxJSVl8933O1zevjYKhYypU3vz7Xfb7ZanpOTyymsreemFW/DwaNxzgdXKFXuLO0MiEfPqy5N59InFpKUJGb1Go5mXXvmNz+bPJiS4eepVNJdYDo5R4JfT8kg7XR1d3rFf/TVEOvSOsQnm6WcyyU6tvpfWFuNrE9DGz05gP7H7LFB/1nEVZxNTWfTmSpe2vREIbet4HTi4+QQGrQG5e9Mzv7/auJ8Ve0+SX6KxBQd4ugvPhV88PIVOkcFM+e8iRnVrx7xx1QLsgg172Xw0mRXPz+ZMhvCdznp3Kb88dydxYdUZDWcz85jx9hJ+fPZ2EsLrt3a6HlBJlQ7LKkxalFLXxdqaaEyOgUYqacPBUzKxjAF+vQDYfLn6+rg9b5+DYL4n/yAmq30NiASv9gQqXJ9c9ZSpKDRUB0vEqKKYHHZ1M2udIRZ7YrGUVvqNWwDXAnus1sqJCicXcqu18e/XYpH9JIfF2nwBZIHuKm6JFAI0qv69lgS5ezIzuhszo7tdtT7Pncni0bu/oU2kH9/9+shV77+pzH93A6t/FYIOH3pqDFNu69tAi6vHdSeY/2PiEKxWsNRKH1i5qjpF0NdXyaY/T+Hv74nFbGHj5pNoNHqbAOXl5cGxExnERAdQXq6nY0LTIg1Mmq8xlr6F3OdzJG7jmn5QLYTucm8QCTcjt8Bt12QMJYXlLP5wo+3z2Fl9r1gor010QijT7h/Gsk83A/DTZ38ydpbwR+Tjf2X+wCNvH2SL9AawWqx20ev/XDgPgJiukbZ1GWez6DOmGzKFjLjeMcR2j0JSowjUs98+BEBs97YO+wOY/vTEesfQXOhdKPqkr4xCcnd3FOvdazw03nv/MG6+1bXiF6JaN1QPj2oR0ZWop5oCu1JZtwB5pcfXXDTXeWpJpOLqSQe9OQeFtO4JRLOlDJNFEMekTSyQGKiaQWbpV7bPWmMKvt5+3DqqK+u3n8JitV61wp91EaZS8+9ew3m+13CO5mVxMPcSJwpyuFhWTLamjHKjHr3ZZPue3CQyvBVuhCo9iVb70cU/mP7BEbRVN68QOL57nN35qfn/J8YPRCwSMW90P4d1du0tVsRiEZ3Cg5xuExNkn3101xD7WgBV7QG+TEpjSEJbtp9O4b4RvXlz1hgA4tsEOt13zX1V/b/mdjXb1zVugIQ2gTecNVttJCIRYyLaMyaiPRqTgf05GRzLF1JazxTlklleymVtORVGAzqzCYlIjLtUirtUhlruRrjKiwhP4W8wytOHnoFhJPgG1Rn5Eepev+VVY/GSu/Fkt0E83nUgiXmZ7MlO50RBDimlQiRenlaD1mgEEXhIZfi7K4lQeZPgG0jfoHD6BUegkFx3j5mt/D8l6VyOg2C+et1Rbp7QuBfJ7xbuoLxcz+w7BzoVj4+fyOC/b68FhACeKsRiEXPvHdrg/nv2jOJwZVFHgMTDaWzcdIIxo+v2/DebLazfcJwFX25pcnR5FdOm9Gb79iTOX7C3jdt/4AJzH/iW22b1Z9iwDniq6o7sLC6p4OTJSxw4mMLp05l889V9VzSmuvDwUPDWm9N5+NEfKC6usPX9wkvLmf/xXXbPn66wIPkPYj2FNPRRwV2bfbw+QV74hQrZqAVZRWg1eo7uOGNbn9Cv/mt4fJ8YVi0Q3oUcBPPuUfW27TEigaPbT9s+H9l2ms3LdjPq9roLGZpNFjYu3sFX//75bxNdDtB1SLzDspL8Mt598Bue/XJuk0TzdQfPsPivwyx6aiaRgT4s332c91buYO3L9wLgrXQtsyo+XLhGdYkKYfnuE7wwY4Rt3er9p4kJ8bshxHKAIDdHkTlTm017z8ZnDZaZNA6+4iB4lLvC8EBhkrOmYL47/yCzo6bb1ZDbne+YJTw8oHFBPn5yH9I11RNhebqCRrVvKSRiHyyWUsCCwZiMXNZwQIrFqsFoEo5FLFJRW2i3NMFOpbbI7oqX+o3KgpP7mBbTmQB3x8mjupgz5VOyLrl+XkdN6Mqzr9zSlOG14iLX3ZvM/E17WHnwFGVa4cZssVqJCwlg6aOzAJg2RZghtFqtNiGj5v9BqKReFX1a8wW8leZn5/rjNgsSgIl3tkzk6KhpvW2CuUFv4s/lQvXq6Q/VbS3QFOqzeqlaJ3eXM/GB6gj3mmJ5Y/bX0uTlNlz0KbvSLsXfSQqon58KuVyKwWCiIL8cVT0vR/XRpk31w0zmpULata+/QnpmhnCTkMul+NczIXKlx9dcNNd5akk8ajwUlWh3oVb0qnPbYt0uqCxarJQ1zVqqZrFPALHIndjIAJb8fgAfL49rLpbXRITgcd49IPRaD8VGzfNT1/+dfbYtF9fdxqX+a7SfM7wnGfklzBnmOBHk6r5d3q5GvzeyWK4rmIlI7IPC5wvbMqVUzog2MYxoc33Zy5h1G9EVPQCAR9AhRGJHf16xSETvwDb0DmzN1GvlxmX/oRQmjO1it+zwsXSXBPNhQ4Vib8eOZ1BUpOGXX/ezZu0RunWNIDzcD6WHnJJSLSdPXuJcDV/pmsyc3pd27ep//gGYMK4rS5ftpaKGQPn2u+vYsvU0/fvFEljjeaa4pILk5Bz27D1vs56Ty6XceccAvlvYtEhzhULK669N4eFHfrD1UUXO5RI+/PgPPvrkDyIj/fHzU6FSumEwmCgrEyYHMrOKKCqqztry8XFdKGgKwUFevPn6NJ7+xzKb/U1aWj6vv/k7b70pFOh09T1w1aUDzI1pngzWuqgStvdWZiHsXFkt0Dkr+FmT+D7V94/Df52mokyIzAxpG4jKu/7zPHb2EH56b52tDcD7875l26/76DNWmBwIbCNMppfkl3H+WDp7NxyloHKccjcZtz07iR/eWOHKYV7XdOgVTYde0bZo/Sp2/n6IE3vO0WdMF4Ii/JFIxeg0BsqLNRTlllKQLZyLyfNGMWyaffTjsbRsOkYE0TZIeOeZ0Cue//yylZQcQSztEdO44L1Zg7vyn1+28tQtg/FQyDBbLKxPTOLuEa4F5VwPeEpVtHEP4ZK22vv6fHlakwTzC+X2dWGkle8Zsaool9rHVG4X7hFKRoXgZFBmKudY8Sl6+AiTkYWGYs6V2f8m3CVu9PWzDyppiAR1ew4XVdcqOFOa3Kj2LYVC1gmjKR2AEs2PBHi/2mCbsoqVgJAtYbYUUVj6Ib7qZ2zrtQZHv3eLpf6s3dqe5WJRy94jrhXlRj3vH97JiDYxjRLMQ8J8HdwJTEYzOVnFiMQiwsLtJ4n8/BvOsmjlyrjuBPPdZ9PZ/PxcXvlNEEf/MWEI76/f6bBdTYG8dqRmTauGVrG8Zdm7qdrnS+2jJDq+ZUSn4HBf/ILUFFwWRNLEnWeB5hfMXSEoonGel9eK9LQ8yiujm5yJuIUF5WRcFNJY4zo4fm9SmYQuXSM4dDCFPbvP8fDjo1yyQalNbPtg1Gp3Sku17N97nmEj6hZhrVY4UFlUqGOnNvUWeK06vroE6oaOr7lorvPUksgk/qjknSg3nCS77HsCVFNxkzoW8DJbK8go/khoI/ZB7dbbts5iFSL/rZiQNOB9l10qvGhXWbqo5J2xWkuQy6REh98Yfz+tCCikUmKD/Rre8P8JVksRVstlxNIO13oorbTSSiMwGEx22S5WK2hdtGubWCmq3zarP089s5SKCgNarYG9+86zd1/DhRDHjunC3Psaji4HUKvdefaZcbz+5ipq1i47lJhq59ftDIVCxmuv3ErXLhH8sGhXk63fgoO8WPCZYK/46usrOXvOfhLAahVE6SorlGtNQnwo//rnRN74T/U5O3AwhfmVRV4ff3SUS/vRm4208WjZ+12VdcredULBUk1lPRuVt5LwuPqLrAW28cMvxJuC7GI74bshOxYAta+Kp+bP4a05X2Ct8cNK3HqKxK2n6m2rcJfz0pJH6DIwjiX/XYXZdPUsBVuKJz+9h6dHvwVgdy6L80rZtGRXvW01TmoQRQf7senIObKLygjx8eSvExdQyKQ2Ab2xjO7RnvdW7WBDYhJTB3Rm56lUijVaJvZ2jI6/nuniHW8nmO8pOMT4kJH1tHDOnvxEu8/tPIXfvEzcuOjkYYEDWJy23PZ5d/5Bm2C+v+AwVuy9/Qf690beyD46e9k/H2brLnOuLIX2ntGN2k9z467oT7l2HQDF5d/iJu+Gp8fkOrfXG46TX/Km3bKC0vcxmbNRK+/Cai0nv+Q/Du2MpvrvUwbjWbvPUsnfs7jkrqx0TI0suAnw30/vcFiWkZbPfTM+x8NDzne/PtIcw2ulEVx3grm7XIZYLMJQWa3ay8ON/NKm+8u20rLkZFQXSfFtwSheAG9/T5tgnn7OeQRPK9WYTBaW/7wfgDlOXtSWLNple7EYcZNzEXvqjD4cOphCXm4pXy3YyrwGvOONRjNWq9WuMJRYLOKWKb1Y/P1Otv55ilsqs0Q6OJlcWbMqkUuVv6lbptQfRVF1fM6OzdXjay6a4zy1NG28HiMp70FMllJO5EwlTC1YB6ndeiFCRoUxicySL6gwCpEQbbwft/OZM1RWRz+WNQFv96F4ufXHQx6HXBKEWKTAbCmnwniO3PLfKNJuBSBELaSiSsQqktPO4+XpzpkLl4lrG9Q6mdnKDYlJuxKL6TwKr7eu9VBaaaWVRjBiaDyPPbOUqMpCn+kXCxhXj81JFWKxiPjKSXd3dzlfLriHd95bzwkXvMU9POTMmT2YqVN6NcqGbeiQDrz+6lTe+2A9JSWu+bvGRAfy/HMTia60nYmODiS5jmh3VwgKEqzcPvnoLn5ffZhffztAXl7jaxnENZBV2FwMG9qBS5lD7CLrV/0uiGwREX5MvrnhKNF4rzZc1OQB0N+/Zepn1GWdktA3xqXfSIdeMexeYy8exjVQ8LOKwZN78/JSKR8+tpDSAtcK+EV3CufZr+bStqMQZNG2Yzjnj6W71PZ6JiohjHfW/ROA/933JZeu4G8FYPrAzhxNyWTCa9/h6S4nyNuTD+dOwkfl3mDbCr3jxJ1MImFK/04s332CqQM6s+bgGQZ0iMRffWNF444OHsaG7L9sQnRyWSonSs7Q2ct14T9Hl8eeAnurlNFBrk1A1maIf1+Wpa/EbBV0psSi4xgtRmRiGfsLjzhsX2Xl0hiilOHEecZwtuyCbdnS9BW83PFpO/uXq43aYxoFJW9V1qkyk1P4MKWaZSjdxyCTRiJCjtki2DxpdDso1/5ewz5FhFwag8F0nhLNMko0y+rsR2c8itmch0TimLEo7HuT3Wdn1jAZ5SW8nbidA5czyNMK16qaUxnBHp7sm/6w7fO8basQAe8NmsBbh/4CYH36WTRGA1GePnwx/Fbaqn1s2xstZj47vpcVKafI0pQS4Cb8XU2I6sAz3QfjLq2eJDFZLfx2/iSrU09zrjifIr2WAHchqnt0eCz/7DkUpbTaxuntxO38cfEc6WVCJP2Y379zOL7zs59Feg1/C600jutOMI8K8MFoNuMhF36oTy9eS6n2WvumSTCVz8dU8QsAVksOIkkYUo/bkSrnQq1qzxbjMcwVv2I2HBC2N2cCFsTSKCTuU5Aq78NpoQWrBpPmWwDMuj+wmNIBCyKxP2J5d2Se/0AkiXBpxMbS1zBpvkeqvBuZ+tWmHbYLFOSW2P4vlrSsAFYzIqKs+MorKv/dUbjJWLpIiJLQaPSMHtuFwEA1hYXlrPn9MKtXCg/cAwfH1RmB3btvDOMmdGPDuqP89st+ks9lM258N8Ij/ZBJJTZ/zksZBZw8nsH+vef5cP5sotra3yRn3TGAXTvOkpqSy3NPCzfZO+cMpv+Adnh6upGXV8rmjSdZsVz4mxkwqD2DhtQfwVl1fM6ODXDp+JqL5jpPLYmvxygivJ/hYvH7GM35pBW9Wee2Iep7CfG82+k6s7WCgooNFFRsqKc3EcGedxDhXZ221zkulD/3nKVdZECrWN7KDYtZvwOR5Pqx72mllVZcY/TIjvTtHU1WpVVbcJAXPt4NF5+LiQmy43/lhgAAIABJREFUq1USFurDR+/fweEjaWz96zRnkrIoyC9HbzDh7e1BWKgPAwYI1hojhsU32ZJk4IB29Og+j02bT3LgYArnL1ymtFSL0Wi2jScoUE37dsEMGtSe/v1i7QTXjvGhVySYVyGTSZg2tTeTb+nJocRUjh2/yKlTmRQUlFNSqkWrNSCXS1EqhTEFBqoJb+NHp45h9OrZlpCQ5im+6Qp33j6AzMwiNm46Ybd8/mebCQv1oXev+iOxn4ybyMsnfgKgn38ckcrmf0ZrV4dg3lDBzyri+zgK5u26NxxhXkX/Cd3pNiyBLT/u5uDmE1w4fpHSSgsdk8GEu1JBQLgf7bpFMnBiT/qO7WpnLxnfJ+ZvIZgDxHYVCj5+ue9N9q4/wp61hzl/NI3CnBIqynUo3OV4eivx8vckKiGM6M7CO3jvUc4n2k5dvMxXj0ylV7u67ctUbnKb7WwVyVnOMzVmDOrKd38e5EhKJjtOpvDmnWOacpjXlBC3QPr59WBvQfVv9ssLi3mt07P4yX3qaSmgM+uZf/47jJZq+9dAhT/9GmmTUoVa5klPn84cKDwKgNas40RJEjGqKJJKq7OF2rgLUc+xKtf/tmoyPXwib57+2PY5qew8X15YzAMxd9jsZFzFYrVwsSKTKKVjZnBjEIvV+KqfsosKr9DvokJff0YFgKfHZPzU/+Ji7mgslhKn20gkgYhFbhhNF8ktfpEQvwXU1rvKtavRGY7ZLXNX9Lf7rDebmL35Z6RiCe8NGo+3Qph0WnnhJAvPJPLPHkOZ3aG7Q/85FeU8sHUFarlQu+LZ7kMwWS3szEolTFkd1GlFENh3Z6czp0NPYr39SC4W/gYXnknkZOFllo2eZctGk4rELDt3lDYqL+Z16oeXwo19ORcB+CHpMBarlTf6jbbtf0JUB4aGtWV9+lkWJR3m3YHjCVdV1xIDrsrEiUQiRlthYMm3O9i19Qx5uaW4u8vp1C2Cu+4fSmyc88lsvd7Etk0n2b0tieSkbIoLNcgVUkLCfBg4TNBmZtw1AIWbfeZFVbHRWXcP5LZ7Btv6BVzuuz6OHkrlxSd/xGK18tq7M+k9oHnrNtWHqKYIeQ1xGITeJFwY95/PoHN4MD7Khmdom5uqop9iWTxWSxESRWVKn0iBWbcZqzkdqfIBZOrn7doZih7GrN+BRDFE2FzaFqxGzPpNWE3pSFVPIPN80q6N1VKIvmAGVpMwGymW90As7weIsJqSsBgO4xa4C2pYIdRV9NNY+hYmzdctLpYDTOv2ki01Te3jwc+Jr7dIP1arlVm9XqG0SBDKlZ5C5OvyY3WLfjU5WpAJwDdn9zJ/wDSn22zJOkdyaT4PdXCcTd6fm07fwEgnra4/7rnzCzIuFjB1eh8uXRKitffvdZ4yHNsumHc/vB1Pdd1/X2azhS8/28LK3w7gyuXimx8ecCoEFxaW8+K/fuFcUraTVtUMGNSeF16e7HAhrqL28dV1bFD/8e3fe57lP+9HU6FHUy48vFZo9JSWajGbLYjFItReHig95HgoFXgoFQwdFl9nUc/mOk8tTZn+KDlliynTC3UADGYhmkAmCUCt6EmQ5x1OPc6tlR52BZoNFOu2ozGcQW/KxmwtB6sFiViJQhqOWtGLQNVUlHL76uSbdp1BpzeCSMTNIxqO6mullZpYjGcwlAlR3WbDQURIkbiNRK5+BZG4OuXZainAWP45Zv1WLGbBp1IkUiNR9EXu+RIiiX2xrIqcTih8vsRqzsRYPl/oy5KFWBKGm+8SRBLhBUVX9AAWwwGsdRQ4UoZcoCoGQVcwE5EkELG0PcaKJVgtJYilccjVLyKR93Zoa9L9gbHsbSzmS4ilcSi83kBf/BQy1SNI3afbtnPmjW61lFJxuTMK74+Qut9qOweA3Xmo6xzU5WFu0q5GX/wUCq+3kHrMbPT30MK0zri14iq2u3FqWj7fLNpJZLhgufHAPUPYsOmES1Hmrfz/YFHqNhILhfeww4UpxHgG469wrKXzXvc5V3lkrVzvZOQXM+mNhfz+whwiA+sWgj9YtYPf95/mf3ePI7jSuuXrjfsJ8VWz4vnZDts/9c0acorKyMgvZsubD6CQtVys4/PH3yJFc9H2eW707YwKGnLF+y0xlvHc8TcpMlQLrQEKX+5pO4uePl3qbHe+PI2vU5aSpqnO5hGLxLyc8BTxatcmmJxxuOgEbyd9Zvs8OmgobVURfHlhsW3ZXZFTAZgY6pqdkzO+Tf2RTTnb7ZZFKtswJWw8PX0612snU27SkFR6npOlZ9lXkIif3Jf/dH6uyWOpxkJ2wcOUa1e73EIh60SbgOWIxWoMxrNkF9yHwWTv9S4RexPitxCN7k+KyoRz6y7vjZfqHuSydlitetu6mkU/RSIZbUMOIxFXW2Edyr3EtA1L+XL4rYyJaG9bbgX6/DKfPkHhfDbUvsjlvG2r2JB+lnmd+/Fcj/qzD/5IP8dD21ayYNhkxkXaR7cvPJPIawf+5OsRUxgV3vBv7PZNP3GhpID90x1tUr47fYjXD25h4y33Eud9Ze/7VZYsSpWClVvr/x1UCdcx7YORSsWcS8ompn0wSqWC1POXKS3RolBI+WThXADaxtoXQ1+7IpFP/idY9wSFeBMc6k2FRs+FczlYLMIjVefukbz3xd3UTIqq6rdH32g0ZTpbv4DTvmv3O//dDaz+VcgkeeipMUy5rbpOxOkTl/jXo4sxGc289L/p9B/SMhlg1PF+0ZoL0EorrbTSSiuttNJKK6200korrbTSSiuttNJKK1yHliwAqbmFZBQIs5AR/t7XJLq8JlZLMQr/dXbRU1LVY+gLbsak+Qap8g47qxSZ1xvIRR4gsh+3zPo4utyhmLU/O0SYG0tfx2q6gMzzucr9P1RrEEYQOZmJrDUPYix7tzK6fA4y9StNONrG4RektkWYlxZVcOpQKh0bSHlsCqcT02zR5dAyfukjQ9szMrS903UfntzGTyOcW1Rcr2g0et787wwA1q89yh/rj5GelofZYqVNG1+Gj0xgyvQ+DfpoSyRiHn58FGPGd2Ht74c5dvQiebml6PVGlJUFN8PCfOjYqQ1DhsXXGTXt66ti/hf3sHGDkIq1ZdNJLlzIRVuhR+3lQULHMMaM60r/ga5FDVQdn7NjAxo8vuzsYo4cTqtz/xaLleIiDcVF1TUUwsPrLgbVXOeppfFUdMNT0Q1dZRbPuM9/4J3JY+kZFlZvOxFCAVZ/5UT8lRMb3W9eYTlKDwX1ubFM/GIRAGPi2/HY0P51b1hJ7WPoHVH/MbRyY2I1X0RXMA2xoh8Abj7fglWDoey/6IsexM3v1+qNRW5YzTnIVI8gkrStbJ+GofRV9JYK3HwdvQSNFQvBqkPuVXnPFHliMRxEJKn+PSnUrwIWdIV3IZZ1Ru75z1p7sb/OmHV/YZFmCF7nIneMZe+jL7of90Ah9VUkEvwPLcYz6IvmIXWfhNzrv1jNmeiLn8JiukiTqaw9UPM8NHQO7Me+qTK6/DW76PJGfQ+ttHKd8dX323nk/hF8t2inbdm+gyn/byPMyw0GBn31NWV6IcPu51kz6dXAc8DfnT9zqq0C2qoCsVgt5Oqc2w/cO/MzbhrbhdvvGXy1hnfDUVGuZ/bIt9GU6WzL3lvyAB1d9Fy/kfBXK4kO9uPmN78HhNoH4f7ePDFpEAAjuwr2AQ+O7Ud+WQX/+mE9BpOZYZ1j+Pf0ESzccsjpfm8b0o375y9n2sAuzRZdbqksRKg166gw69CatVSYtGjNOrvtsrQ5JJel4iEV9Ax3iRvuEjfcJApEjUj08pJ58mT7+3nr9CfoLQYA8vSFvJP0OQEKXzpWFsn0lXkhFkkoMZaSVHaejIosh33dHnHrFUWXA3Tz7oiP3MsW8X6q9CzFxlLbeolIzOCAvnU1d5k5UTPJ0eVxvPi0bVm65hIfnvsKqUhKpFKw7vGSeSIXy9CZDVSYK7isy6PEaF8vwk/eXBl8YkL8FlBYlkBR6Xws1vpqGYjxUs7C3/tVxJXPrHJZHJHB29HotmM0pSARCzVBlG4jEYs9kUpCKS7/CqvViNZwEG3hwXr2D17Ku+yiywHKDMI9SSGx/72LAKlYUu/+7k9wzOKszfr0s3hIZYyOcNR8BodGAbA3+6JLEeYdfALYk52O2WpF0og6JVeDC+dy8PZR8tkP99ssULQVBl775y8cPpDCsoXCs9AL/5lq127U+C6Ul+kYNqojwaHVlmqp53N56v6FAJw4ks6xxFS6OdH8Du9PcejXWd+1+62L82dzeOGJpRgNZv79n6ktGV1eJ9edYD5/0x52JqXRLlj4AzyXk8+QDlE8OrrxRReaC4n7LQ6pxiKxGqnHHRhL38Ks21TpZV61rg5RTaRCJIvHot8DWLAF+Fs1mLVrEUnbOgrltrbO03ZENSxajGUfYCr/HKnyHmTql109vCsiqn0wF5Mv2z4v+uAP/rdEOAZRM/kUWy1WFn3wh92yyCYWESrQV/Dont/I05UT6qHmg363IgKWnD/Eb2nHGBDYlme7jLBtf64kjy/O7OZkUTb37xR8Db8cNNPma3U9o9cbbd/BhJu7M+FmR7+vxhATG8QTz4y7on2IxSLGTegGYPu3qVQdX1OPbfKUXkye4mg9cqU0x3lqLGaLhYT/CF55704ey82dq4vprD5xhmdX/cHpF55AIq5OKqppGyOTtHyykVrlhtFkxmptvr+dq30MLcFzezbwc/Lxaz2MZmNgSCRLR89q1n0ayj8DsQo37wXCApHglSsX+6IrmIpZvwuJQngpFYmUKHw+q7WHnlhMqZg0C53u32q6gHvAZmo+Eknkfey2qfYtlwr3ckndHqUAVoy4+35ne3YQqb3R5o/DYjxeuX/hmcZY8R0iSTAK74+oTvqToS9+rN7914eo0qrN/jzUfw4E5Jj1O9AVP4Jc/RJSjzvt1jbme2illesNd4WMNmENe+Y6o1wviDwj3vuGMp3wMr9k7gx6RjoXmHVGYTJ3wic/8PbUsfSKuv6EaJVcztFHHyGztJQhX39zVfsu0emQSSR4yOq2JLgWLOr/xLUeAuVlOqRSCW7u9Z8bV7drCV6Z9wMHtp+1fZa7yQgO82H0lJ5Mnj0QSeXzmIdKwfL9L5ObVczdN71z1cd5tTCazdz1wU8M7RTNR3NvRiGTYjCZ2HTkHM8vEur97Hr7YeRSCUo3OW/dNdZhH5P6JDjdt4dC+H5vrmO9q3yb+iMHC4+iNevQmV2rDbc+eyvrs7c6LBchwl3ihofUnclhY12ybengGcsrHZ/m7aTP7MTgPH0h23L3NNheIhJzX9vbGRl05c8YYpGYwf59WZ0lFJ/M0l4mX19oW9/dpzNesisPzJOIxPyrwyMsTv+NDbXOo8lq4kJ52hX30TRE+Ho+jpdyNhrdJrT6fZjMl7FaNYjFwj3STdYNT49bkEmjnLSXoHQbAYxwWCOThuPr+RQFpQ3/vctl7fFT1w4+gT7B4fi5efDR0d34KNzxqfIwTzlFtqaU8b2GO92fUibH163huiTpZUVUmIzELKp7jMUG+2LbxwtyWHL2CMfyssnTllNhEmxl9GbhXi8Utb3+tKEHnhhlJ1q7e8i5+6FhHD6QwokjzmtRKNxkzLp7oMPytrGBjBwrBBis+e0QyWeynQrmzvp1te8qZDJhYiQ9NY/nH1tCRYWBf712K4NHuF4suDm57gTzPefS+fGx22yCpMVq5c7PfrqmgrlYGuN0uUgqzExZTbU8lK06TBW/YNELVXotplSwlmC1VoDVULWRbXOL8QxgRiLv1/jBiQRvPZPme0zlnyL1uO2qieUAgyd0Zce66oiM4/su8PG/hSizR9+YilRW/0xgQ5jNFj57eQXH912wWz54fNcm7S9LU8IPQ+9ALpZw21+LOF+SRzuvAO6M7YWnTMG5kjy77dt7BfBu35tJXJfB14ObVwRqaa6P8gQtx9/9+Foa98pIlb8ev++q9DepBXzLr/YxtHJtsOj3IpH3twm0VUhkXQARFtOpBoVasSQCq7UcMFH70UeiGOqw7EqRyDrbTbRXRatbzfb3GIvxFBJ5X2o65EnkzT+RB/WfAwCLKRld0f3IPf+JTDnHcX0zfA+ttHKtCAnx5uMFf3KxsrbL2x9uoG2kv0ttVQrhN3/ghYfJKi5l5Pvfutzv9T6Zey2i4l7b+hfj2rdjVOzVK9p1ozD/vQ0MGRHPgKH1F753dbuWom9l8bd5L0xCq9GTuPs8Cz/4A73WyO0P2wtpYsn1JyQ1J+cy80nOymfx07Nwl1dPYAxKaMsna3YDUKLREuClavS+NySeJTrYj65tQ65ojIWGYjsP8SvBipUKs5YKs5YyY30RyvbEqKJ4p+tL/HxxNdvy9tgi3Ruik1ccd0RMIVrVfLXEhgcOtAnmVqy2yPeqdc2FRCRhTtQMevh05reMdSSV1V13qy7aebZlQshNzTYm29jE3qg9ZqD2mNGs+/VVP4nVqqWwbD5OyhTirhDOb7DvfMRix4kJpVTO0tGzmLVxGVPXL0EmEbSktmof3h80gQlRzq958gaiz6uwWK34unnwZo1CnbWpWSR0W2YKc7f8Rke/IB7q3JdYLz+85EIm5+cn9vFT8rG6dnNNEYlFDBruKDCHVz73FBdqHNY1REBQ9XnRaJxPvNXVb2P69lAqyM4s4rlHFlNaquUfL9/C8DGd6m3Tklx3gnmIjxq7SRorhHg3v/1GoxA5t4Spehm2Wqu/dKulFEPBdCymc0jcRgIgVd6LSBKMSKzCWPo/LEb76u1Wa2UakJOLRkNUFQk1lr4BIOzbqrOlZLc0fYYnEBjmQ25mdSG0jb8cACD5xCXueHw0/Ud1bHS0udVq5dD2JBZ98AfnT2barfML9qLviKbNtCf4BNsuqH4KDzQmQwMtWvm7sOfQBRb+tAeVUsGcGQPo2rENv6xJ5I+tJxnaX5j8untGwzYgrvLqe2uYMqE7XeKrI1F3HThPWkYBd0698lS/Vlr5/4LVWoRJuxKTdqXz9ebLNT6ZMGqWYtatw2IWbE2slkLB1qwO6swKuxIcCmBW3QPtXxCtlgJEYm/7TcVejejI2cyhEPFS8zzUfw4EQU9f9JCwjdXkvKdGfQ+ttHJ9cf+cIZw8nUm7GKHobUQbXzolND7yWyxuWAB3q5zM3fJM62RubSxWK7vT0xnX/spsFZqL7bmnKDKUM7lNw89lhwrP4yNXEaMSIudEzTzZYLVYOXIwhSENRNG5ul1LonAXJpGCKrM2otoHcyktj82rEh0E8787YX5eKGRSlm0/wi19OyIWiUi5XMina3eTEC5cb1wVy7UG4T5dUFbBucw8ft19nNdur1vYu9Hwlql5MOZOJoeN4UDhUY4WnyJXlw9ASaUtilqmwlfuQ2evDvTw6UyMKqrZxxHqHkR7zxjOldkH5HnJ1HT3bn5hrotXPF284knRXORY8SlOl5wjVy8cd5lJg9asQy6W4S5xI1DhT5h7MO09Y+jk1YEAxVUrqN5s+Hk9j6dyBuUVqzCYkrFarcikoSjdbsJdURUEW/f1c3XqafzclGyf8qBNnG4uIj29OVOYy01tYpFLGhbZvz19EKlYzLLRs1DK7ANGqiLNnXKN5wn9/D2dZiDJFcLzSVUBT2fkZBWzae1RTh3L4HJ2MWWlOgx6IwZDjfeDOprX1a+rfYOgA7701I8U5pdz7yMjGTW+7uLAV4PrRjD/x9L1iESQW1LOrR8uJipAuAGn5hYSE9QCL7ONwVLhfHmlUF6V/gxgrvgei+kcUuVcZOoXHNs4sVap8jK1mnMbPbQqsV7u/R5WSynG0lcxlDyL3PvTRu+rKcgVUp54azov3P2Vw7qUM1m8Me971D4e9BgUR2ynMEKjAvAN9MTNQ45CIcNoMKHTCqJ1cUE5mSn5XDiTSeKOsxTllTnsE+CxN6aiaGIKYlMiaUSI0JlNWKzWG8KKpRXnbNh6isfvG0Hn+OoX5BmTeiKXSSgp1dbTsvkY1CeWQX2ufURV73cXUKYTvAqtwBezbmF4u2i7bUp1enq/+znbnpjLp9v3svFMsrC9Fe7u250nhlVn/Sw6cITFB46SVVJKmLeauf17MaOHY0T54oNHWXrwKJnFpSgVcuKDAnhp7HCi/e0fBnUmE/9YtYGtZ1OQScQMjo3ilXEj8FQoXD6GjWeSWXsyCQ+5nI1nknl6xEDK9Qa+3iP46Q1rF82HU8YDkF5YzKc79nIw/RKFGi2Bnipm9OjEgwPtbTlauUaIvJC6DUGmcqxCD9hFchtKXsOkW4Vc/Vpl5DaIxAGYtMvRlzx/VYYLIHKxprpI7IvVUivqy1LkfGOg9hOq1VLgsIWh5DUAu/NQ/zkQRHyF13+wWvLRl7yIWBaPRDGs1mBd/x5aaeV6pFNCmJ1I/sfmk4wdde2ilm4E0oqK+GTvPvZfukRhRQVBKhUzO3dmXl/7++OiI0cAWHz0KJdKSlHK5SQEBvDKiBHE+ArXhpk//czp3FwqjEYe+n21XftH+vbl6UHNF9npKssv7sFoMbskmK/NTKTQUMYnPQUbTp3OwJsvLAdg/+5k3NxkTJrai7vmDqPm68Jfm06y7PudZGUUEhgsTIiOv6UHU2/vj7gyoOjpB7/nwrkctFoDrz73i63t7XMGM+ehagsCV7dz1m/tPsvLdEwZ9Q5Lfxfqai36ehs7/zqD1Wrl1pl9mfOgc+uDuoiIDmTTisRGtali27pj/PTlNrIuFhAY4sXY6cLva8rdAxFXZmlkphew9LMtnDiYSkmRBr9AT8ZO683MB4Y57G/10r2sWbaXy5nFuCsVxHQIYd4LkwiPDrDrE3Dot2afruCtdOPj+2/m8/V7+XbTQfQmE4FeKgYmRPHwuMYF4iRdErSAhxesRCGTct+o3ozr6dyz98DhVNpFBwLg4610uk0Vz8bNa9Q4WpKqcU8KHcWk0FEO60vLtDz3xgqmvzOpxcZw6mwWJcvC+fmtZ1usD2dEKyOIVkZwa9iVWXf+sXgnm5buprRQwzcH3nBYX1pYziu3zefDjf9y2j5x6ymiO4fjE+A8WLOu9Q31WxO5NAZf9TMuHpE9u7PTCVWqkYqaP0NrfFQH1qYl8UNSIvd3dP6eVzN212ix4ClX2InlRXpBN9iVnVZnP74KwR4mt0JDnPfVr1vWVLuujauP8vHb6zAZzUS2DaBLjyj8Az3xUCo4eigNgAO7k5u935p8+9kWCip1wG2bTnLrzD4o3K6dhdt1I5jfPrBpFhtXA4spGWfzT4KVCoiksXbbAkjcnF0IzVhNKQ5LRdI4QIzFeAQwVy51La1EJBZmriXutwJgNSVhqvgJkzQOqepRl/ZxpfQY3J45z47n+3fXO11fWlTBtjVH2LbmyBX3NeuRm+g78sp83Gpjtlr454E1XCjNp9yoJ6uihMc6Cn5s0Z5+iEUixocnMOXP72ij9GL+gGnN2n8rLUtmTjHfLN3F8dOXKCzSoFTKefmpiaiUinrb/fDLXg4eSwNgYO9Ybpvc22F57XULf9rD/iOpBPh5UlRcnXmyYr3w29+w9SQ9u0Ty0Gzh93XqbBZLftuPRCKmsFhDcICal56aiEgEn3+/DYDs3FKyLxdTUqbjqQdGMqCXc4uoKooqtGQWl9p9rs3BZ+ehNQqz4t3+N7/e/T25fB2TOndgbn/BJqKgQou0RsbI0kPH+GTbXl4eN5zOocEcy8zm9Q1/YbJYuL1X9XX9QPol3tm8g4+nTaRdgB/5mgr2pV0kSO0YdbPkwFHu7d+TX+6dRU5pOf9YtYEFO/fzz5uqfRJdOYZtyam8MfEmYgN8+WDrbiZ3iee7O6YAcMf3v3BmQG/igwNQymUEeap4/9bxBKiUJGZk8uKazcQHBTIkNqre89NKyyNRDMBiOIZYVpWGWfcDtEn/J1K3W5C6T7FbbjGerqNFIxF5gLX5JtjE0gTMhr3UrGtiNhx23rUkEIspzW6ZxbDfYTuT/k8Ah/PQ0DkQy3sgEgdgNp5EX/wYbn5rENfwr2zM99BKK9cLmVl1T0AdSExtdsG871s1JnOtsODOWxgWVz2Zeywjm1lf/cSax2YTG2gfEJSUk8etny3ht3l3kBAqCGHrjp/li+37uVhYTIiXJ9N7dWbOgJ4ASJqpVlB9KOVyglQqPho/nkCVkkOZmTy/cRMJgQEMbSv4l+6/dIn/bt8BwPxJk2jvL9zj92RcJFhVfY9fOHUKBpOZnp9/zscTJjAipvq8yFyI3G8JUjW5zIhwzfYzTh3K4tTtts+rfj7A3McEq4S7HxjG6ROX+OTtdQSH+jB6gvD8c2jfBT7631qeeG4icQmhXEoXoko/eGsNJpOF2+YINlZvfXQHBoOJaWPe5d9vTKXvICECXya1fx90ZbtDlTaWtfut3WcVVaL/iDGdmXHXAIoLNUikjf8+si4W4B/U+GzpxF3JfPLqKh57ZTLtO7fhUmoeH7+0AgCTycysSkHcQynHP0jNc+/NxDfAk1OJ6Xz08gpi4kPpNbi6gN+Jg6l88+4GXvjwdiJjAykqKOfY/gt2Y6vqE3Dot2afrtK/QyT9OwiWIXsOXOBscg73zGj8BFD3aGFCb++7j7Lk532s+/U4E7vEERbqWH9h5fojzJsjjLMhwfx6omrcdY1Z7enOgnfuuMqjurEYe9dgugyM4+VZzgMk1b6qOsVygDXf/MV9r02rUzCva31D/TYXt7fvxnN7NtBx2Ye2ZWKRiCB3FROiOvBcz6HIXLRgqc24yDjGR8bx1qG/SCrKo09QOJZKn9f0siI2Xkxm2ZhZhHgItsdDQtuyL+cirx74k+Fh0WRqSvn6lBB8FeiupFDnPLC2T1Ab3CRSXj+4xVaMVCGRUmzQcXeHHk0ae0uTnVnEx/9bi8lk4ZkXb2bMzfb15sorCzjXJ5g3F6+9N5PVvx4icf8FPvjPGp5/Y0rDjVqI60Yw7xEVxpnMXHa+W8hPAAAgAElEQVSdTaOgvAI/lTArMyguiviwQJf28fNioWjEhFt7sG7lYUaO7czeHWeZNLUXSacySUvJReEm49LFQvz8VERWzjJ37BJe737N2t+Rqu5HJK4xO2TVYqpYCoiRuFWnSonEQpqe1ZLtsB9T+QKslmKH5SKxFxK3UZh1GzGVfwGA1CGCyyQ8eddR/LMKmdcbWEwpGMs+QCSNQ+LmOHPbEsycNwKV2o0vXv8dk9HccINGIpGIuee5CUydO7RJ7bv5CQ8gNcXumv9/v+8t9bZ/pceYJvXbyrUnLNibV56ZyPP/Xcnd0/vTIbbhgrEnzmRyIimTT9+8DYBn31hO14Q2mM0Wu+U116mUCvYcusCX794JVrjrse9s20wZLxQlVSkVpFS+LFVxPjWXZZ/PRSaT8Oi/fyQtI582oT7sOSS88CyZfx9l5TqeeOnnBsVygLc2beetTdsb3M7VbIl+bcO5s3f1DTO61vrPd+xj7oBetkKjbf18yCgq4bOd++0E8wqDEZFIRKCnknAfL8J9vOjexrknY+fQIFsEe2yAH5M6deBwhuM1taFjUEilTO6SQHZpGe9t2cXMHl2IDxau48FenmSWlBIfHIC/SsmzIwfb2kX6erPowBFO5VxuccH8/o59mNT22qVVNzdVxXmaE7nqcbT5E9AVPQiAzOMORCI1FnMmZv1W5OqXEVUWKhJLojAbdldOQAtpnGb9Fky6Dc0yFom8GybtCsy6jYgkIVgtwuRUU727Zcr7MGlXoC9+GqnHLKzmyxjLP8GZGC1RDMWkXY2x/BPE8oFYTWcxln/hsK1YEgVgdx4acw4UXm+gM51DXzQXN//fbVl0jfkeWmnleuHZF36lQx2F4pPO5TR7f/v/Pc9mq9DjDcfJ3K7hIcQE+LLq6Gn+MXqw3bpVR04TF+xvE8t3nU/nldV/8urNI+kcFkxqfiEvrtqMySxkhTw4tOWzoAKUSp4bUuP+6O3N94cPczI31yaYVxgMNnuSQJWScC8vwr286B5qf4/3kMlsmZ4K6fVR9LPUWIGP3DW7DE+pO2XG6gnT7n2imVSjeHx4pD8njqSzevlBm2C+5Nsd3DK9DyMq/VfDwoVo+wm39mTtykM28drNXWbz+pbLJbi726f+V+HKdku+FSYvavdbu88quvWKqty+t+04GkNZiZZDO8+yacUh7nxkZKPaAixbsIWb7+jP8InCOQuL9GPcTOG3vf6n/Tbx2sffk3ufqS6YGRrhx6rFuzl/OtNOMNdWGBCJwDfAk+BwX4LDfYnvFuG0T8Ch35p9XkvunNmPC2mO2eeZ2UUs+H47iccv8vr7awFQyKWMHdGJSWME64IPv/yTdm0D+XPHGUpKtYwY3IG7pgu10hb+KOgl23afRSoVo1K68fpzN+Oldiczu4jnXl/BkgWCndTx05ds23/4xgwW/rib4lItR45fZOrEHmzefgZPlYL/vjjFtu9tu4WCsM72XXPctccMsGnbadZsPEZWTjG/LbSPit+1/zzf/7QHi8WKySRoDf9+arztve7U2Sy++H47Wp0RlVLBs4+OISxYsLzbuU8Q+L5ctANPlRvx7Rp+F2wMmTnFtr5AsAFNOp/DvbOuftbM1l/2sWHRTrJSc1l66l27dVkpuXzz6nKO7kji7Qe+RuEm56bbBjBu9mDbOsDpeldIOpTCt6/+hlajQ+XlwRMfziakbeOiq39OPs6bh7byWJcBxPn4I66MMjeYzSQX5/PFyX2o5Aqe7Nq0cysC5g+9hR+SDvPL+eOsTUuyie9hSjWjwmPxrmED80DHPpQYdPyecpqlZ4/SRuXF/R2Fa2U7b3+mb1jqtJ9QpZovht/Ke0d28MoBIZBFjIhYb7/rVjA/tPcCJpOFuI5hDmI5wOVsRx2zJZh+5wD6D4mjc/dIHpn9NX9tPElsXAjT72w+69zGcN0I5ot2Hmb90aT/a+++A6Sq7oaPf+/0ur33XZa29CoqKEWKXcEuGFGxR2N7eDXGkhifJ4bEWLEXjLHGblREBBREikQ6LMv2xvYyMzv9/ePuzrZZWBAIz/v+Pv+we+8959wZZufe+zu/cw5zRg5mcEo8TQ61B+ORD1cyZ9QgFkw59AfL0aqW2bKhEEdrG3HxdrztX6ilxbUYjHpsdjNajcJZF4zlg3fUzKxDBcwVXQbumrPRmtqDppoo/G1fEvQVoLPdgKLtvAhrzRfgc7yKt+mBzmxyxUTA/QN+z0Y0hgkEPBt7taGPeJiAdyfeliUA+N0r0ejHgmIk6C8l4F6DIfYdNLpBvcp2p8MQ/Rzu2vPwNP4GY9yH/ShzdJx95SmMPmUgSx9We+w3r9lziBL9M+KkAdz80AVkDf5lC54I0V9FpXUMyU0KDakdPCCRgiJ1sb6u27vui4uxkZMZrwZxFcjO6N8Dx8CcxNBq0FGRFpwuD3qdljHD1e+V+//0MQAXnzuuX/X96fzZnDu8c0GUT7fvZvHHX/WrbDijUvv+u6t3uqh1OBmRktht+8jUJJ5es54DLepiQAl2G6flZjFv9DAueeVtRqYkMm/0cM4bMQSjrvdlaERK9xvZCJORVnf4xUUOJtKs3vCY2tuIsXYGcw1aLW6fOheb0+Pl1fWbWbl3PxVNzfiDQVrb3EzN7dk9oKpu/Yg61yq8/jqizZPJiLzhkOdS2vQyMeYpWA3dv49zI2PJjTzyacf2NyzBH2ghEPSQFrGwV/1HqsahBlfjrLNQ+hjxVOP44qD7u9pdu5ghcX86onNRtGmYYj/C26Le+LsbbyMYdKFoEtEaJ6N0WbPDEPkonqb7cNVdjtLewaw1zsQU+xauml/egay33UHQX4u76R4IBlDaFwU3H2HAXKPPwxj9NN6WP9NW/xka3WAMkY/SVtd7ESadeR5Bfxle51sEW59Co8vDGP0kbQ2/7nacIfJRgG7vw+G9BzqM0c/jqj0Hd+MdmKKfB5TD+n8Q4kRx1uyRzL9sUth9Ty5dcUza1Bwi8/vCscN444ct3DlzMhpFwd8+n+dnW3dz/WmdQfBnv13PlSeN5pyR6jU9MzaKSyeM5O2NW4HjEzB3er28vGkz3xQUUN7cTCAYpMXtZlp25/Xx9OxsLh4+DIB5/3iLkUmJXDx8OBcMHRr2Gn8iiTbYKHPW9+vYqrZGLLrO0YkZWb3v83IGJrF29e7Q74UF1ezcVsrbr38ftk6fL4DuCLK5D6awQF1Poq92e7Y5dHhar2MOZc0XW7v9Gx1nZ+Edszl/fv+y9bud795qdm4p4Z0XVoXd7/P50em0uJwePnzte9av2sWBikYC/gCOVjcTeyx8OmHKIGbNHc8dly9l0Ig0Zs0dx/Rzx4Tmz+3aJhC23Y42u3riOfX7orHJSZvbx4JLJpE3JIXlK3fwzZpdGPQ6ThqfQ0y0lW07y3hkyWdUHVA71a+58lTGjMzk8aVf09LiwuPxc9O1U0lNju61DSAtTEZ5h9TkaB659wKuuf11HrjrHEBdk6Gnr1fv4k8PzMVk1Ic62QDOn6N2EFx92SkoihpE/urbHVxyfv8WHI+wmVh82xwe/NMnvPvSDVxz++u0tLZht5k4f84orr5M/Qz0rLvneYc751lT8xg/KpNFdy7rte/N93/knltmMzg3kTa32inZ8X/k8fh4/LkV/O2RS7FZjXy7dg9PPL+Cxx68iLY2L0ueURf4XPrnK0lJiuLJF7/p12vtj/oGB+99upnfLDr8zqJjYfolkxgzdSi3Tnuk176UnAQeWHYzN5/2exa/sIj0Lh0HHfuAsPsPxeP28vQ9b/I/H92FLdLCdx9v4tnF/+AP797e7zp8gQAPbVjBVUPGcteY8EH67yuL2Fhd2m3b0qkX9LsNUBOuFg4dx8Khh3621mk03DtuKrcMbSXCciagpa5FTXC1maZS9KvFoWP9gSY8vkLMBjXYPDU1h6mp4Z8lT0Qd0yTHxvXuRG5zefm5fUqW48VmN/HQny/l9mtf5uWnVzBgUCJjJx7/9/OEuYv5ZPNO3vr15aGVcDvMnzyGy59+q18Bc3uEGgyZMn0oVZWNlBbXUrC3mn17KrHaTJQU1TIgN5HSHhmeB6VYMca8ic/xAj7nOwAEA9Uo2hT0Eb9FZ+2+oI9GPwxDzEv4Wv6Kt3WpWoWiR6MfjzHufYLebXjCBMwVbSLGuE/wOdS5wP1ty/E53wA0KJoENKZZ3TPcD3bKmmgMMS/hrp2Lp/46jHEfH7esr9TseB55bREA+3dWsPKjzXz3xdZui4L2R2xSJKfOHsH0C8YyeFTGoQv8AntbdpNrG4hGOXTQp9BRQLb10Fm+x6o8wLcHVpBqTmOQPfwq0a/+/cZQOw0e9SFgQ/16ZiedddTO4T/p1b/feMzbyMmMY9UPe2kfocXu/CpOHj8ABbpt77rPbjWyv7gmNKyrpLx/D2B9PVg3NKlDvK6fP4WM1P7PDaxRFLRdhjb/0nn3OxYvC6fNqwacey7d0fH+mLpkj2kUhQfPnM71p07gw5938tTqH3hx3Ub+cfWlxFkt3cpbDEcn66zna+9rDYPFH3/Jvtp6HjnnDIYlJ2LS6bjwxfAZAwCJtgsw67NocW8nNWI+FS1vkmA9D53GTknT8wCYdem0+cpx+ytJtl0GQFXrBwSCLuKtZ2LVD6a0+QWCwSDJ9ksB0Guium1zeHbh8pXi9lUQbz2TKFP4oE9m1K3oNBHsrl3M4NhHKGl6nkDQS6LtApraNpFkU6fs0mqslDQ9T7LtIkqbX8UXaCTVfhVGXTJFjX8DNCTZLiBIkLJmdYSEy1dMpHECFn1WtzIBPJQ1vxLabzOoc2x2bduoS6Ko4QmMuiQUFCpK6tiwajcnTR1CcsbhdRJodNkYo58FoLq8IbTQ2J6tpQweae52nCn2rV7l1TLFvbZbkrb3q/1P3vyB7EFJjJiQjTH6mT6PU8/nHWqrmvj05TVcdK06jdDe7U0MHtm7fQCd6Wx0prM7NwT7WDcFBb3tN+htv6Ha2cof16/i/kkDCVi/4OvCfBbkwY+VpRQ3qx1VuVF/odzRTIvHzeCYODQehT1Nn5Knq8Xl81Lc3Ehu1Aj0enWh7tfXbGLRyAkMjolD0cRhSVjPS1s3MdfsIsZk4buyIkqam8iKVB8Okq2PsK6ihGFxCYyJTOGFrRu5fqSaddPx85YDFeypryUvVs2YbXK3UdLcSFZkNFFGM5ury8mLTUBRYHddDTUuB78Zd/wzssT/u/oKlgMsuOw/k610weg8Hv96Lev3l3DKgEzW7isCoNnl5txRnfd2e6tr2VJSwQtrNoStx+cPoDuMuZaPxN1ffMm+ujoenTWT4Ynq9fHcN/7e7RiNovDwDDVYdOPEifxzxw6eWPcDz2/YyLuXX0acxRKu6hPCuJgBfFq+kcuzJhOl73taC3fAyxcVPzE0ojO4HAy3cFkwGMq2DwaCtLV5ufqGaaGM856OdrC8o02gz3Z7ttk1kNxfJ01VP6c3/fZcbBFmrPYj6zANBoK0uTxcddtMZl4Y/lm/Iyj6l3vfo6TgALf//kIG5qViMOm5dV7vURyKRuGW353HJYtO5+sPN/P3Z77hvZfWsOTNG4iOtXVrEwjbbs9geTAIW7aqAfbHH72M6Cj1M93U7OKjz7fw9JIrQ/ec6zYUYLUYuf/ucyhsjzkse3sdDqeHCJuJO2+eSWV1E089/w1nzhzRaxvAow/88ukHpkwaiMmo3k93/Z5Yv1lN6Fu+aicWs4GKqiYmn9T/58KUpChio62kJkeh0ShE2E04nB7sNhPrN+9n+Sp1+rcjqftgLjhrNA8v+ZSZp+dxXnvQPzZa/ZvdX1JLaXkDt933duj4jn1FZXUkxNtD5w5wyoQB7CusIRiEPz7xL7xeHw1NTu66cSblVY18sXIHAAa9lqoDzVx7+amMGZHBX5//mpbWNtxeH7f8aiptHi+vv7uewpIaHn1STTQ5dcIA9Hot23aV84fHPw+VHzsygxf+/h0VVY242rzc0D49Z05GHP/1yD8ZmptMeVUjo/LSOHfWf3aRwyNRtLOc8n3V/Ne5S0LbYhIPZxF7cAd8tPm8WHXhR9g0e9wUtzQwJSW733UGAi0caHoMFA1RlovQ61KpbX4GCBJtu5LWtjV42qc71GvTCARbiLUtorblGYJBD5HWiyDoo67lOTy+/ViMnfcUjY53CQSdRFjOw2KYQH3LSxj0OZgNo2l2foLHV4zXX06E5TwATPqh1DY/SSDoxKgbRIz9xFoYPKN99o0dP5fS1Ogksv17zuX08Pijn1Lbx/qCx1J2bgJ3P3A+j9z7Pn+87588s2wRSSlRhy54FJ0wAfNAMBgKNHXlDwbwBwJhSvR28ZUn9/r5nt+pH9AN6/Yx8dSB/PD9Xi67Sn0Ym3vpwRd40VkXobOqwV+d7dY+5wRfW7ucRFMqNe4qEozJ1HqCJJqXoNeof+yFjr2kGjOx+SPZ3WojK/IbCmuWMypqEjXuCiL00eS37CDNnAVa9QJZqZmAQWcg0ZSqltdlkt0l6P1N9cdMjv8Oo0a9QSl25FPZVkqqOYsadyXuQBtJ9g+w6SLZWPcjWdZBpFty+PaAOnRrWsI57GjezLCIcexo3kyELpqqtjKyrAMxaIxsb9rEqXGz2N60ieGRao/ztqaNxBji1fMxZ5JtHcy3Bz5jWsI5oeO2NW1kRKT6wJyTl0JOXgrX3XcuzQ0O9u0op2x/DY5mF87WNtwuLwaTHotNzdaw2s2kZscxIC+V6PYL2/FQ4iwm2zoAjaKlxFlMQWs+6RZ1HjqtoqHcVYZBY2SQbTDFjiKyrQPY7yigwlVGujmDanc1bf42UszqtC/1nrrQ7zadjT0tu0i3ZBKjjwmV39uymxq3Oswu2ZSCTqMPtatXdFS0VZBjHUCiqbNnt9Sp3qgVOfaTaxtEVVtlqO4KVxkOn4NoQzSDbENC7UQb1ECr0r50RaOnIbSv62vt2ibQrd3/V7naPCxZ+jX7i2tDI1GKy+q4fsFpDBucwuhhafz6/rcgCCeNzWb44BSAzu3Qa9+E0VnctPhNkhMjQ0Pz/P5A6CaquKwOh8tDdU0zCy/rOwvH6fIQbP8+XLJ0ORqNhja3l/tvP+ugWSfHW3KknVirha3lVUzOyQxt31pRRXKEnQhT73nikyPs3DzlJBZOGsucZ1/j4627uPbk/mXPHytr9hVx9xlTGJeu/g17/H5KG5oOUaqT3TCKA45PiDCORquowds2XyURpnHYDcPIr/s9Fn0OMebTiDSNJb/uYYy6ZFLsV2DUprC37gEAjLrEbtss+hzshhGkRfyK/LqH+wyYA2gUIxCkqvWfGHVJmHTplDQ9R4r9Cmqc6voSNkMeWsWMXhtLqv0KmtybqHWuINF2Hr5ACznRd2PQqoFNi17NVE+PWITS3pnYtUxm1M1Y9INC+8ubX29/DZ1tRxhHkWSbh1mfSX7dA6RkxOL3BUjOiA0ttuVyuMnMTaSpwcHJM/JYt2IHianR7NxSzIAhKegNWkoKDjBkVAZOhzrK4NM3f2DuwinYI8zk7yhn8Mh09mwtpSi/GqNJT25eCj+vL2BAnvp3qdVpQ2XanG6K8qsZMCSZNpeXypI60gckoNNpKClQv5OHjMogtUvm4P7dlezdVsbwcVmUFdaE6i7ed4CWRjW4HZsYwcgJOaHziUuKDAVN6qqbQ9sLdlUAhH19HW0G+1p6vovlRflkRUSxvCif+Xmj8bZnj1W0tjAxWQ3oZEVEs6GqjBtHTWTpv39Er9Fy3cjxPPfzBhItNiYmp5EVEY2v/R4rLzaBwTHdMyYXDBvNh/k7sRuMNLa5+LmmCk17h5zNYCTRYmNDZRljElLCnufmqopQmwB2vSFUx/flxQyKjmNrbRVun4+bRp/E0n/3npNdiKOhsKiWl5Z9R2a62ll3/cLTWL9xP2fO6r1A9bEWa7Nw2qAsPtqyk1MGZPLRv9U1kaYNySHaol5DAsEgLo+X22acwoVjh4Wt51gHywFWFxay+LQpjE/tcn1s6vv6mGy3c+ukSVwzbhwzX32ND3fuZNH4zsxVrUaDAqHvnf+0+Vmns6JqK7dsfIF78+YxPKp3kk6xo4b/2fkBFa567hnamclYUtQ7Aasgvyo07YqiURiQm0jR/gPEJRx6bm+NRoOiqPeNR3pcR5tAv9s9Esb2qWA6Oq+PlKJRyBmcTPG+auIOEVzb+N1err1rDsPGZgHg9fioKus7OSU+KZIrbprO3Ksns+isx1n5yRbmLZzSrU3gkO2Cmi19163qSPPHnvwSu83Ir6+fQUVVI9mZcb0SNLLbp7XpWC/J5fJSVl5PVnuyQHJiJFUHmsJuO1pMYTpCSssbeOUfawFY9sw1WMwGXn/nB7ztCTD0eB2u9s6XrrTt3zuabusOBEN1L3vmGoDedf9Cs6cN4+TxA/jim+3cdI+a1PLg3ecwbEgKBCEpMYJXnvhV74LBzufgzteg3tcqCtz/GzWhbM36fFb/kE9udnzoGex3d5zN/pJalr37Aw6Xmwi7ibtuVDs3nnx5Jf9934Vcdv54lq/eyR3XnxGqf90mtdOka3mDQYvT5eGhu8+lrKKBZ15fBcB/33shFVWN3HbtdNKST5xnvCORmBHHs2seOOLyVp2B01JzeGHHBoxaLUNjEnD71Wf0wuZ63s7/GZfPx7V5/RsNAeAPNOAPNpMYcR86bSI1TUuItl2FXpdKZf3/QVG0RFvnA+Dy/ITP76DR8Q46bRIGXSa1zU+SFvscRv0QYiNuRkGHq32tIZtpGhbjBCob7sNqPIUIy/m0uL4EwOuvwGwYTaxxEZUN9wEQMJ+J2TAejcZMINB6xO/TsTJ+0gByBiayP7+a6y59ltHjsnC5vOzZUY7H42PBInVq5DdePPTUr0fTaTPyuPSqU3ln2VoeuvsdnnjlmuO6COgJEzA/c/RgrnjqbeaMHkS0xRxaqO6rn/dy5qjwq0MfjouuPJnKsoZuQfWjJQhsrP+ODMsAGrx1DLAOJc6YxGcV/wAgyZxOqXM/3qCXGQlqAL/QsZdvqj9ibtpCSpwFROqjKXDsxq5Tb2wMGgMWrY0tDetC5bOtne9DgEAoWA5Q6NzL1PizWXngE3xBH7MS57K65l/4gr5Qmz3VuqtD/0bqY3D5HRg0JiL10fiC6sXNF/RS7FDn/fIHfX2eDygUtO7CHww/f3lEtJWxkwcxdvLxmR7mSG2q/5EUcxrFjkIALDoLBo0Bq9ZKlKHzfSlozWdm4hy+qvocX9DP2cnnsaJanfbCHXCHfs+1DSJSH0V+yx5mJ50VKl/vqWegXX3/EoyJfFD2bqjdgfZBOH0OjNru2Rrploz2fzNJt2RQ5CgM1W3UGDFrzbj97m7n2VPXfV1fa19thvPBs18z45JJRMbZ+ak9i6CysIbUnATiUqP595rd5I7MYMj4HN5/+isuar/B7Ph59yY1s6FoVzk5w9NpbXKGytujrezcUEDOiHQURaFwRxkN7TeP8xefF/YcyvZVhdqsKq7F2T41U+aQlG51LJwzhpJNhdgVBWdLG5+8tJIFt8yizeGmaFc5ADnD06nZW8XPy7cyflAyCW0+WhocxHa50Vpw0SQWXBQ+cHn9/ClA7yFkv7vj7N4Ht3tk8fm9fn73k02MH6UGoC88U53//IW/f8e23eVHLWDe9WHVHy5Lqh8U4MbJE3li1TrSoiIYkZLE1vIqXlv/E7+dPbXbsV/tyqfV7SEvKQGb0cCOymqaXG1kxRx5L/HReA0A6dGRrC0oZvaQXBweL0+sWocv0P+1GOzG4VS2vo3bV0VapJotUNXyT7SKEY1iIIj6wGHQxqFRTAQJ4A840SpWFEUb2h9uW9cyB+MPOFHQ4Au0YtKlolGMZETegM0whD21HwLg8paQFnkt1a0f4/HXYtHnEMSPSZdGdtQdlDS9SKz5dKLNk3s+M/UqA92fq3ztN35d265zfotWY0WrsdBzIevKkjoALr9pOh8tW0tMvJ3dP5fg8/pZ/a+tZA1MZO/2MoaPz6alyYXJYiCpPQiRMySZrIFqQKBjzYzS/TUYjDpsEWacrW5iEiLY3j508MKrJ4fKfPDa98y9ejLvvbyG2Hg7wydkk5IRy76dFbQ0qfcdJkv3zJacIcnk5qWQMySZvdvKQnWbLIZQh6/b5SU2MSLsGh5dt6/+lzp8PdzrOxytHg93jp/M0n//SEFjPTvqDrC9Vr2md83MafW4eW/PNuLMFoxaHR/k7yDJaiMY7DxO1/7QW+NyUNBYz4CozhEt68pLCASDNHvc1LgcXDx4OGvL1c5bp9dDpNGEVtGwt6GW3XU17Kw7gE6jCf2cYLGG2gQoamoI1TE0Jp5mj5txiansra/hn3u34/YfnQdrIXp64bXV3LJoOq8s+y607T8VMAeYN3Y4//X+FzQ4XXy7u32RxkvPCe3XKAqDk+LZd6COpDCLYx8t3vbraF/JSemRkXxXVMzsgQNxeLz8bd3aXtfHL/PzaWmfNm1YQiJ2o4Ft1dU0tbnIjup+z6LTaMiMjuajnbsYEq9msymAxWAgwXr8Fy7MsSXyyMgreGjb2yza8CxJZvV808yxKApUuRopddaiUTTcOugsJsV1Psf8e1Mhn7yvjhoeOyGHHdtKWfX1Dm5b3HnPt2DR6Ty8+F0ys+OZPHVoKDmsqOAAfn+AM87szCTV6TSkpMWw4ottZOcmoigKZrOBmB7D4w91XEdQo2e74do8HnzeLvdqvt6fs/m3zuAPt71JxoCVnDpzGMFAkKJ89Xrm9weYcZ56H5ycFsNPa/M5deYwXA4Pbzz1ddhr7vfLt+NsdZObl4LFaiR/ZzktTU5SMmN7tQn0ardrm12NyFM7jf77gbl8/PkWPvtqK3NmDKeopE5dZqzLPZESZkRjZnosO/aoneaV1U0kJUSG3dZfFoshNBo13PQm4bQ62rBZ1Wc9i9mAx+Nj7YZ9TByTBUB0pIW6Bhvru4AAAA60SURBVEdoipWOub8Pp25Le2dKz7p7nnd/z7lDbX0rcTE2Lr1gPPWNDgC27y5n2JAUsjPjaHW4+XlHGaOGpREMQmOTg+goK5npMVTVqFPjVB1Q3+ONW4oAcDjd/PX5FdhtJmrrW8lsP6fs9M7Pit1qxNnmobSigax0tSMkOTGSykN0bnTU0VG+uKyerTvLeGjJpwBkpXW2YTTqj2uw3GI30VjT3OeUK4faH07W0FRam5xsW7eXEacMIhgM0ljb0ufCon15+rTzeHrbD7y192eqnC0E2pNIEs02Jiam8+zpFzAkuv/zout1GSRELqa25VlspukEgk60GhsKOoJ40SjW0BSOimIGgviDrRg0aSiKibiIjmkPe/9N67Tx7dMRhr9+9txvNZ1Ced1tmA1jiLEv7PdrOF60Wg1LnvsVy15YxYa1+axdvQe73cTo8Vn86oZpRMWo1+i/v3R8A+YA19w8nYK9VWxaX8CSP3zCb/8477i1fcIEzBdNm8jEnHTW5ReTX10XyrD43dwZjMr45XNXGwy60CKfR1uTt57T489iY/1qUi1ZoUB2ilkNeLn8TrKsg2j2NrCq5nNyrIMJEuCUuJlsavgOh68Fs9aKRtFg0qpDH6rbykmJzCTFnBkq31W6OYfVNf8iwahmdUXoothU/x2R+hhafc2h4xKNKaE2DRoTFW3qkPByVxEWrY2N9atx+Z0Egn6sOjv7W3eRas6i3FVMmauQ4RHjeblQHVpzbfbdBIKB0PlUtZVR0VZMuauIvIgxvFK4hKuz7zgm7/Gx9n3tatItGaRZMnD5nQywqavON3rrqXRVkhaVQVVbJaXOEkqcRUTqo1hft5YofQwtXd5vAKOmM6t2X+teLFoLGkXTrTyAqUuHR9d2/cEANp2d/JbdTIjpO6O0a92KooQWxejajq79AlDmKqXUWYJeow/t66tN4KDtnnPtVFa+ux5rhJnmevWGZe9PRWi0GiwRZmKTotj+Qz5DxoefY2rnBvXBcO7NM3nvyS+x2M2h8j+t2knW0FTytxTjcXu55PY5vPvElwc9h8SMuFCbXrePy+9WH1Q+XLoCr8cXqkOj0dDa6MRkNZKUGU/OsHSyhqbywbNfM/dmdVjme09+SWxSFCNOHkRKTgIFW0ux2E20OT19vh/HwsnjB7BkqTrf3o8/FeL3B4iwm5k/7+CjYvrjb9+u49X1m2nzdQambnn3E3QaDbnxsXx8/fzDqu+qiWPQazU8tXo9FU3NpEZFcNeMycwb3T0jTq/VsmzDFkobmvAFAqRGRXDn9MnMGHz4QzWP9mv47/Nm8+C/VjDjqVeItVq45uRxxNsO78E9wjiGZvcW9JrODoDKlvcAiLPMwuXtPh1Hsv1iChsfR6tYiLeoi1iZ9Zndtjm9hf1qu6jxKQJBN5lRt6BRzBQ3Po1Jl47dOBwYilmvXos8/hr0mig0ih6Xr5hAUA2yOzx7qXV+RTDoDWU32wzqQmH7Gx4j3jq7V5mOYzr2J9rUrLuubcdbZ1Pc+AwWfTZajYmKkjoK91RSVVqP2dp99MGk6Xn8/tY3eODpBQQCQRzNLvLGZOL3+YmMtrJ9UyGnn6UOwa2vaaF0v7qmQMGuCvbtrMBqN1FScICcIcn8tDYfW4QZTXsWlFarCZWJibfzzSdbiEuIIBgMYm4PVHe0A3Rrq6cdPxWF6lYUJdQGqEH7jvPRG7QU7qlk/+5K9AZdaHvOEPV+5mCvrz9uGn1St3//MvVMAIbHdV9PIMZsYd6g4aHst0Aw2Oc0TfdMmNJr37SMHHyBQCioDjAhqXNKgq71/XXaWaHtHT/nxSaEbbOjDn8wiFZRGBWfdNBzE+KXMhv1pP3CbFgAX3u228E6abvOF9zXcacPysas1/PnL7/D3j4aa/LArG7H3Dp9Ere99Sm5q2KZmZdLIBgkv7ouFNw+b/SRLxj98MqVvLNte2gtjyvefQ+dRsO4lBT+cWnnGgqPzZnN/StWMPWll4m1WFg0fjzx1u4BXL1Gy+s/bQGgpEm9xqdFRHD35Mmckdv7Gv/Y7Nk8tPIbzl72BgBRJhN3T57M3GF5R/x6fonTEvJ4+9S7+KB0PZvq9wFQ4qwhGAwSa7RzccYpnJ82kQG27sGjex48n9Vfq1M3vPj0CkwmPZcsOJVZZ3culnbylME8/OfL+Mer3/HWa9+jbZ8OJT0zjkuv6j391N2/O5+nl3zBjfOfJyLSzMIbpzMzzLQqBzvu5ClqQk7Pdvtq81hZ+sdP+fL9jXjcnfdqi69+CZ1Oy9AxGTz2ujqKe9K0oTz49ALeem4l77ywCq1OS1q2Gpi85LrTQ2XvfHQeTz30EQtnLSEqxsq8a6YQE2ZEsl6v4+M3vqWyrB6/z09iajRX3zGbk6d3fr462gR6tdu1zQ5NzS4e/evngBpodro83HLdNKKjLJw1awSLH3ofs0nP2FGZxMeFHyV98sRcNvxUxEP/8wkej4+brp1KWkpMr20ADY0Olr39A7v3VvHyG98zYVw2Z54xvFt9l184kceeVp+R7FYTF549htnTwo9G6TBkYDI57aPZFt25DKvFyPhRWaH9FrOBBZdMYtGdbxAdZWHyxFzK+7nQX0fdHfOP96y753mHO+c//PVzqg8009jk4s4H3mNwbmJo6pIlzyynoqoRvV4bmm7lirnqM5/RoOOP913A0y99i9PlIRAIcvH54zj7jBGYTQbuukl9zrv7wfeJirRwcvsz6tqNBaQlR7PwslP46Mt/U1OnTjmhhJkuMzOte+dGcnvnhl6nDZuJ37OOzLQYBuYkcu+v5/Q+tq83tQ+P/GopdVVNHCir47fz/sbQiQOYv/hcAB674SWqS+toqmvlvrmPAzBwdCYLu0zzc9GvZ/O325dhj7Jw7nXTmHFp94TSvvaHaxdg/uJzMZj0PPDGzTz/23dwtboJBAJceNMZzL7y8Nb4sRuM3DtuKveOm3qY70p4bu9uml3/Ihj0AEGirJdzoOkxNIqZCPM5ONy913mIslxETfNfMfgzMRlGgn4YZsNIDjQ+gt18Tu9GAI+viEbHW7i9e2h1hb82BwIO/IE6fIEDON0bsZn69xrTs+JYvqF/mfuDhqYc9FiDQXfQ/Ta7iZvvmsPNd/X+nHb46sfe5Q/V7qHavvWeM7n1njP7LKtoFB598sqD1n+sHPuxfEIIIYQQQgghhBBCCCHE/wJKMMy84f8BJ8RJHCuBYCCU/dv1566CBFFQWF79AQDDIsayu/lnZiSef8gyff0erv2D1REI+vu18GW4+g60VZDfup1T42YdsvyJruvr+7zyE0ZEjmJn8zbmJHXvUezr/e6pv8d1/5z07/+iv3UfzTY3fr2NmooGFEWhrlJdzHXM6XlsWb0LS4QJe6SVlkYH46YP490nvmTuzTPR6bWhn0v3VgJqRqei0VCxvzpUPi03kdYmJ4PGZFG0sxxFo1Bdos4R2XVKlq7n4HK0hdpUFIULb1LnkPtw6QqsEeZQHRNmjqC84ABarYbT507glYf/ycwrTqVgawn+9jnMFY0GgkHGTB1KdEIkn7+6OpQNNGdB+NW6hQCobv0IgzaOaLOaSVHa9DKxlumYdRmhOcB7CxIkgNJtupJw2w5fMOhDUfoeRBYMekNDEDt+B02vcw0EPWgUQ9gyPfeHazuIvz0j/fC+pwL+QCh72+8PhObMBHWRsJ6ZO28tXcnEqUPY/P1eLlk0tdcxXX8PV76jHaBbW+H0Vf5wHOz1HU11Liex5hN3wb0jJGnoor9CzxcvvrYGp8vDtu1lAAzMTSQxPoKr5x864/aRz74F4P3NnRnZoM4hPiY9hWXXXgzAEyvW8dq6zaEFsbselxsfy4e3dB/99Kcv17Bs3U9cM1mdj/WuWb0z8VbvKWTp6h/ZXVmDTqshOy6aRVPUtYJmDRt46HdACCFEWFU1zTz8l8/ITIvBoNdhtxkZNjiF3fuqALjmslOpqWvhz0uX86ffzuNvL66gscmJx+vn5qunkp4SjdfrZ/EfP8BqUUcKnTldzZjfva+qW/nH7p/HK2+vpbB9SsKTx6kLV541YwTX3bWMl/5y1X/gHfj/QzDoBUVBCU2uEUSdJuXgz1o9n6WCQQ+KcnhTKHZV1fgQ8RG3o9VEUVF/Jykxjx9xXeKYCPt8IQHzE4wvoA7pqfVUk2BM7lcA80TQ4KklyhD7i4K3JyJvwEuN+wBJpqT/Nf8Xx4PP60enD/9+9DeY1NdxXQNJB6ur6zkc7Liu+/y+QCgA3jOABuGH4AlxKDWOL2jzlZIeeX1oW61zBXbjCIzaxIOUFEeLx+2jsrSOtOz4YxZ8FicM+aIW/dXt+WL7znJK2hcJzEiLYXj7nMRCCCGEEMdSm2c7za7PUdBhN8/GZBh+6ELieJKAuRBCCCGE+F9NAuaiv+T5QgghhBBCHErY5wtJwxJCCCGEEEIIIYQQQgghkIC5EEIIIYQQQgghhBBCCAFA3yuCHV8yvFYIIYQQQghxtMjzhRBCCCGEOCKSYS6EEEIIIYQQQgghhBBCIAFzIYQQQgghhBBCCCGEEAKQgLkQQgghhBBCCCGEEEIIAUjAXAghhBBCCCGEEEIIIYQAJGAuhBBCCCGEEEIIIYQQQgASMBdCCCGEEEIIIYQQQgghAAmYCyGEEEIIIYQQQgghhBCABMyFEEIIIYQQQgghhBBCCEAC5kIIIYQQQgghhBBCCCEEIAFzIYQQQgghhBBCCCGEEAKQgLkQQgghhBBCCCGEEEIIAUjAXAghhBBCCCGEEEIIIYQAJGAuhBBCCCGEEEIIIYQQQgASMBdCCCGEEEIIIYQQQgghAAmYCyGEEEIIIYQQQgghhBCABMyFEEIIIYQQQgghhBBCCEAC5kIIIYQQQgghhBBCCCEEIAFzIYQQQgghhBBCCCGEEAKQgLkQQgghhBBCCCGEEEIIAUjAXAghhBBCCCGEEEIIIYQAJGAuhBBCCCGEEEIIIYQQQgASMBdCCCGEEEIIIYQQQgghAAmYCyGEEEIIIYQQQgghhBAA/F+CYY2iWDY2CQAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"## 2. Tokenization","metadata":{}},{"cell_type":"code","source":"text = \"Are you coming , aren't you\"\ntokenizer1 = nltk.tokenize.WhitespaceTokenizer()\ntokenizer2 = nltk.tokenize.TreebankWordTokenizer()\ntokenizer3 = nltk.tokenize.WordPunctTokenizer()\ntokenizer4 = nltk.tokenize.RegexpTokenizer(r'\\w+')\n\nprint(\"Example Text: \",text)\nprint(\"------------------------------------------------------------------------------------------------\")\nprint(\"Tokenization by whitespace:- \",tokenizer1.tokenize(text))\nprint(\"Tokenization by words using Treebank Word Tokenizer:- \",tokenizer2.tokenize(text))\nprint(\"Tokenization by punctuation:- \",tokenizer3.tokenize(text))\nprint(\"Tokenization by regular expression:- \",tokenizer4.tokenize(text))","metadata":{"execution":{"iopub.status.busy":"2024-01-08T11:43:02.970146Z","iopub.execute_input":"2024-01-08T11:43:02.970831Z","iopub.status.idle":"2024-01-08T11:43:02.981788Z","shell.execute_reply.started":"2024-01-08T11:43:02.970773Z","shell.execute_reply":"2024-01-08T11:43:02.980567Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stdout","text":"Example Text: Are you coming , aren't you\n------------------------------------------------------------------------------------------------\nTokenization by whitespace:- ['Are', 'you', 'coming', ',', \"aren't\", 'you']\nTokenization by words using Treebank Word Tokenizer:- ['Are', 'you', 'coming', ',', 'are', \"n't\", 'you']\nTokenization by punctuation:- ['Are', 'you', 'coming', ',', 'aren', \"'\", 't', 'you']\nTokenization by regular expression:- ['Are', 'you', 'coming', 'aren', 't', 'you']\n","output_type":"stream"}]},{"cell_type":"code","source":"# Tokenizing the training and the test set\ntokenizer = nltk.tokenize.RegexpTokenizer(r'\\w+')\ntrain['text'] = train['text'].apply(lambda x: tokenizer.tokenize(x))\ntest['text'] = test['text'].apply(lambda x: tokenizer.tokenize(x))\ntrain['text'].head()","metadata":{"execution":{"iopub.status.busy":"2024-01-08T11:43:08.251302Z","iopub.execute_input":"2024-01-08T11:43:08.251998Z","iopub.status.idle":"2024-01-08T11:43:08.407380Z","shell.execute_reply.started":"2024-01-08T11:43:08.251934Z","shell.execute_reply":"2024-01-08T11:43:08.406589Z"},"trusted":true},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"0 [our, deeds, are, the, reason, of, this, earth...\n1 [forest, fire, near, la, ronge, sask, canada]\n2 [all, residents, asked, to, shelter, in, place...\n3 [people, receive, wildfires, evacuation, order...\n4 [just, got, sent, this, photo, from, ruby, ala...\nName: text, dtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"## 3. Stopwords Removal","metadata":{}},{"cell_type":"code","source":"def remove_stopwords(text):\n \"\"\"\n Removing stopwords belonging to english language\n \n \"\"\"\n words = [w for w in text if w not in stopwords.words('english')]\n return words\n\n\ntrain['text'] = train['text'].apply(lambda x : remove_stopwords(x))\ntest['text'] = test['text'].apply(lambda x : remove_stopwords(x))\ntrain.head()","metadata":{"execution":{"iopub.status.busy":"2024-01-08T11:43:14.957712Z","iopub.execute_input":"2024-01-08T11:43:14.958095Z","iopub.status.idle":"2024-01-08T11:43:36.595258Z","shell.execute_reply.started":"2024-01-08T11:43:14.958037Z","shell.execute_reply":"2024-01-08T11:43:36.594110Z"},"trusted":true},"execution_count":19,"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":" id keyword location text \\\n0 1 NaN NaN [deeds, reason, earthquake, may, allah, forgiv... \n1 4 NaN NaN [forest, fire, near, la, ronge, sask, canada] \n2 5 NaN NaN [residents, asked, shelter, place, notified, o... \n3 6 NaN NaN [people, receive, wildfires, evacuation, order... \n4 7 NaN NaN [got, sent, photo, ruby, alaska, smoke, wildfi... \n\n target \n0 1 \n1 1 \n2 1 \n3 1 \n4 1 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
idkeywordlocationtexttarget
01NaNNaN[deeds, reason, earthquake, may, allah, forgiv...1
14NaNNaN[forest, fire, near, la, ronge, sask, canada]1
25NaNNaN[residents, asked, shelter, place, notified, o...1
36NaNNaN[people, receive, wildfires, evacuation, order...1
47NaNNaN[got, sent, photo, ruby, alaska, smoke, wildfi...1
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"## 4. Token normalization","metadata":{}},{"cell_type":"code","source":"# Stemming and Lemmatization examples\ntext = \"feet cats wolves talked\"\n\ntokenizer = nltk.tokenize.TreebankWordTokenizer()\ntokens = tokenizer.tokenize(text)\n\n# Stemmer\nstemmer = nltk.stem.PorterStemmer()\nprint(\"Stemming the sentence: \", \" \".join(stemmer.stem(token) for token in tokens))\n\n# Lemmatizer\nlemmatizer=nltk.stem.WordNetLemmatizer()\nprint(\"Lemmatizing the sentence: \", \" \".join(lemmatizer.lemmatize(token) for token in tokens))","metadata":{"execution":{"iopub.status.busy":"2024-01-08T11:43:40.930254Z","iopub.execute_input":"2024-01-08T11:43:40.930793Z","iopub.status.idle":"2024-01-08T11:43:43.077730Z","shell.execute_reply.started":"2024-01-08T11:43:40.930729Z","shell.execute_reply":"2024-01-08T11:43:43.076765Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"Stemming the sentence: feet cat wolv talk\nLemmatizing the sentence: foot cat wolf talked\n","output_type":"stream"}]},{"cell_type":"code","source":"# After preprocessing, the text format\ndef combine_text(list_of_text):\n '''Takes a list of text and combines them into one large chunk of text.'''\n combined_text = ' '.join(list_of_text)\n return combined_text\n\ntrain['text'] = train['text'].apply(lambda x : combine_text(x))\ntest['text'] = test['text'].apply(lambda x : combine_text(x))\ntrain['text']\ntrain.head()","metadata":{"execution":{"iopub.status.busy":"2024-01-08T11:43:47.169808Z","iopub.execute_input":"2024-01-08T11:43:47.170197Z","iopub.status.idle":"2024-01-08T11:43:47.202413Z","shell.execute_reply.started":"2024-01-08T11:43:47.170132Z","shell.execute_reply":"2024-01-08T11:43:47.201269Z"},"trusted":true},"execution_count":21,"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":" id keyword location text \\\n0 1 NaN NaN deeds reason earthquake may allah forgive us \n1 4 NaN NaN forest fire near la ronge sask canada \n2 5 NaN NaN residents asked shelter place notified officer... \n3 6 NaN NaN people receive wildfires evacuation orders cal... \n4 7 NaN NaN got sent photo ruby alaska smoke wildfires pou... \n\n target \n0 1 \n1 1 \n2 1 \n3 1 \n4 1 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
idkeywordlocationtexttarget
01NaNNaNdeeds reason earthquake may allah forgive us1
14NaNNaNforest fire near la ronge sask canada1
25NaNNaNresidents asked shelter place notified officer...1
36NaNNaNpeople receive wildfires evacuation orders cal...1
47NaNNaNgot sent photo ruby alaska smoke wildfires pou...1
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### Getting it all together- A Text Preprocessing Function","metadata":{}},{"cell_type":"code","source":"# text preprocessing function\ndef text_preprocessing(text):\n \"\"\"\n Cleaning and parsing the text.\n\n \"\"\"\n tokenizer = nltk.tokenize.RegexpTokenizer(r'\\w+')\n \n nopunc = clean_text(text)\n tokenized_text = tokenizer.tokenize(nopunc)\n remove_stopwords = [w for w in tokenized_text if w not in stopwords.words('english')]\n combined_text = ' '.join(remove_stopwords)\n return combined_text","metadata":{"execution":{"iopub.status.busy":"2024-01-08T11:43:50.241813Z","iopub.execute_input":"2024-01-08T11:43:50.242195Z","iopub.status.idle":"2024-01-08T11:43:50.248691Z","shell.execute_reply.started":"2024-01-08T11:43:50.242128Z","shell.execute_reply":"2024-01-08T11:43:50.247553Z"},"trusted":true},"execution_count":22,"outputs":[]},{"cell_type":"markdown","source":"# 5. Transforming tokens to a vector\n","metadata":{}},{"cell_type":"code","source":"count_vectorizer = CountVectorizer()\ntrain_vectors = count_vectorizer.fit_transform(train['text'])\ntest_vectors = count_vectorizer.transform(test[\"text\"])\n\n## Keeping only non-zero elements to preserve space \nprint(train_vectors[0].todense())","metadata":{"execution":{"iopub.status.busy":"2024-01-08T11:44:04.321013Z","iopub.execute_input":"2024-01-08T11:44:04.321378Z","iopub.status.idle":"2024-01-08T11:44:04.516768Z","shell.execute_reply.started":"2024-01-08T11:44:04.321314Z","shell.execute_reply":"2024-01-08T11:44:04.515727Z"},"trusted":true},"execution_count":23,"outputs":[{"name":"stdout","text":"[[0 0 0 ... 0 0 0]]\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### TFIDF Features","metadata":{}},{"cell_type":"code","source":"tfidf = TfidfVectorizer(min_df=2, max_df=0.5, ngram_range=(1, 2))\ntrain_tfidf = tfidf.fit_transform(train['text'])\ntest_tfidf = tfidf.transform(test[\"text\"])\n","metadata":{"execution":{"iopub.status.busy":"2024-01-08T11:44:26.083529Z","iopub.execute_input":"2024-01-08T11:44:26.083929Z","iopub.status.idle":"2024-01-08T11:44:26.561113Z","shell.execute_reply.started":"2024-01-08T11:44:26.083844Z","shell.execute_reply":"2024-01-08T11:44:26.559941Z"},"trusted":true},"execution_count":24,"outputs":[]},{"cell_type":"markdown","source":"# 6. Building a Text Classification model\n## Logistic Regression Classifier","metadata":{}},{"cell_type":"code","source":"# Fitting a simple Logistic Regression on Counts\nclf = LogisticRegression(C=1.0)\nscores = model_selection.cross_val_score(clf, train_vectors, train[\"target\"], cv=5, scoring=\"f1\")\nscores","metadata":{"execution":{"iopub.status.busy":"2024-01-08T11:44:29.139596Z","iopub.execute_input":"2024-01-08T11:44:29.139942Z","iopub.status.idle":"2024-01-08T11:44:29.483232Z","shell.execute_reply.started":"2024-01-08T11:44:29.139878Z","shell.execute_reply":"2024-01-08T11:44:29.482168Z"},"trusted":true},"execution_count":25,"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"array([0.59865255, 0.50301464, 0.57118787, 0.5669145 , 0.68888889])"},"metadata":{}}]},{"cell_type":"code","source":"clf.fit(train_vectors, train[\"target\"])","metadata":{"execution":{"iopub.status.busy":"2024-01-08T11:44:32.638265Z","iopub.execute_input":"2024-01-08T11:44:32.638593Z","iopub.status.idle":"2024-01-08T11:44:32.725893Z","shell.execute_reply.started":"2024-01-08T11:44:32.638552Z","shell.execute_reply":"2024-01-08T11:44:32.724812Z"},"trusted":true},"execution_count":26,"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n intercept_scaling=1, l1_ratio=None, max_iter=100,\n multi_class='warn', n_jobs=None, penalty='l2',\n random_state=None, solver='warn', tol=0.0001, verbose=0,\n warm_start=False)"},"metadata":{}}]},{"cell_type":"code","source":"# Fitting a simple Logistic Regression on TFIDF\nclf_tfidf = LogisticRegression(C=1.0)\nscores = model_selection.cross_val_score(clf_tfidf, train_tfidf, train[\"target\"], cv=5, scoring=\"f1\")\nscores","metadata":{"execution":{"iopub.status.busy":"2024-01-08T11:44:34.926176Z","iopub.execute_input":"2024-01-08T11:44:34.926533Z","iopub.status.idle":"2024-01-08T11:44:35.098758Z","shell.execute_reply.started":"2024-01-08T11:44:34.926480Z","shell.execute_reply":"2024-01-08T11:44:35.097691Z"},"trusted":true},"execution_count":27,"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"array([0.57258065, 0.49626866, 0.54277829, 0.46618106, 0.64768683])"},"metadata":{}}]},{"cell_type":"markdown","source":"## Naives Bayes Classifier","metadata":{}},{"cell_type":"code","source":"# Fitting a simple Naive Bayes on Counts\nclf_NB = MultinomialNB()\nscores = model_selection.cross_val_score(clf_NB, train_vectors, train[\"target\"], cv=5, scoring=\"f1\")\nscores","metadata":{"execution":{"iopub.status.busy":"2024-01-08T11:44:46.503837Z","iopub.execute_input":"2024-01-08T11:44:46.504570Z","iopub.status.idle":"2024-01-08T11:44:46.554162Z","shell.execute_reply.started":"2024-01-08T11:44:46.504497Z","shell.execute_reply":"2024-01-08T11:44:46.553045Z"},"trusted":true},"execution_count":28,"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"array([0.63324979, 0.60688666, 0.68718683, 0.64341085, 0.72505092])"},"metadata":{}}]},{"cell_type":"code","source":"clf_NB.fit(train_vectors, train[\"target\"])","metadata":{"execution":{"iopub.status.busy":"2024-01-08T11:44:53.287277Z","iopub.execute_input":"2024-01-08T11:44:53.287658Z","iopub.status.idle":"2024-01-08T11:44:53.298881Z","shell.execute_reply.started":"2024-01-08T11:44:53.287591Z","shell.execute_reply":"2024-01-08T11:44:53.297666Z"},"trusted":true},"execution_count":29,"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"},"metadata":{}}]},{"cell_type":"code","source":"# Fitting a simple Naive Bayes on TFIDF\nclf_NB_TFIDF = MultinomialNB()\nscores = model_selection.cross_val_score(clf_NB_TFIDF, train_tfidf, train[\"target\"], cv=5, scoring=\"f1\")\nscores","metadata":{"execution":{"iopub.status.busy":"2024-01-08T11:44:55.260964Z","iopub.execute_input":"2024-01-08T11:44:55.261296Z","iopub.status.idle":"2024-01-08T11:44:55.309490Z","shell.execute_reply.started":"2024-01-08T11:44:55.261241Z","shell.execute_reply":"2024-01-08T11:44:55.308355Z"},"trusted":true},"execution_count":30,"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"array([0.57590597, 0.57067603, 0.61188811, 0.5962963 , 0.7393745 ])"},"metadata":{}}]},{"cell_type":"markdown","source":"well the naive bayes on TFIDF features scores much better than logistic regression model. ","metadata":{}},{"cell_type":"code","source":"clf_NB_TFIDF.fit(train_tfidf, train[\"target\"])","metadata":{"execution":{"iopub.status.busy":"2024-01-08T11:45:05.626256Z","iopub.execute_input":"2024-01-08T11:45:05.626888Z","iopub.status.idle":"2024-01-08T11:45:05.637294Z","shell.execute_reply.started":"2024-01-08T11:45:05.626830Z","shell.execute_reply":"2024-01-08T11:45:05.636318Z"},"trusted":true},"execution_count":32,"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"},"metadata":{}}]},{"cell_type":"markdown","source":"## XGBoost","metadata":{}},{"cell_type":"code","source":"import xgboost as xgb\nclf_xgb = xgb.XGBClassifier(max_depth=7, n_estimators=200, colsample_bytree=0.8, \n subsample=0.8, nthread=10, learning_rate=0.1)\nscores = model_selection.cross_val_score(clf_xgb, train_vectors, train[\"target\"], cv=5, scoring=\"f1\")\nscores","metadata":{"execution":{"iopub.status.busy":"2024-01-08T11:45:08.351943Z","iopub.execute_input":"2024-01-08T11:45:08.352299Z","iopub.status.idle":"2024-01-08T11:45:43.444735Z","shell.execute_reply.started":"2024-01-08T11:45:08.352244Z","shell.execute_reply":"2024-01-08T11:45:43.443818Z"},"trusted":true},"execution_count":33,"outputs":[{"execution_count":33,"output_type":"execute_result","data":{"text/plain":"array([0.46775956, 0.384689 , 0.4330855 , 0.38900634, 0.53142857])"},"metadata":{}}]},{"cell_type":"code","source":"import xgboost as xgb\nclf_xgb_TFIDF = xgb.XGBClassifier(max_depth=7, n_estimators=200, colsample_bytree=0.8, \n subsample=0.8, nthread=10, learning_rate=0.1)\nscores = model_selection.cross_val_score(clf_xgb_TFIDF, train_tfidf, train[\"target\"], cv=5, scoring=\"f1\")\nscores\n\n","metadata":{"execution":{"iopub.status.busy":"2024-01-08T11:46:04.709199Z","iopub.execute_input":"2024-01-08T11:46:04.709810Z","iopub.status.idle":"2024-01-08T11:46:33.766451Z","shell.execute_reply.started":"2024-01-08T11:46:04.709720Z","shell.execute_reply":"2024-01-08T11:46:33.765355Z"},"trusted":true},"execution_count":34,"outputs":[{"execution_count":34,"output_type":"execute_result","data":{"text/plain":"array([0.47533632, 0.32510288, 0.42830189, 0.40084388, 0.53014354])"},"metadata":{}}]},{"cell_type":"markdown","source":"## Making the submission","metadata":{}},{"cell_type":"code","source":"def submission(submission_file_path,model,test_vectors):\n sample_submission = pd.read_csv(submission_file_path)\n sample_submission[\"target\"] = model.predict(test_vectors)\n sample_submission.to_csv(\"submission.csv\", index=False)","metadata":{"execution":{"iopub.status.busy":"2024-01-08T11:51:08.360136Z","iopub.execute_input":"2024-01-08T11:51:08.360528Z","iopub.status.idle":"2024-01-08T11:51:08.366891Z","shell.execute_reply.started":"2024-01-08T11:51:08.360483Z","shell.execute_reply":"2024-01-08T11:51:08.365540Z"},"trusted":true},"execution_count":35,"outputs":[]},{"cell_type":"code","source":"submission_file_path = \"../input/nlp-getting-started/sample_submission.csv\"\ntest_vectors=test_tfidf\nsubmission(submission_file_path,clf_NB_TFIDF,test_vectors)","metadata":{"execution":{"iopub.status.busy":"2024-01-08T11:51:11.111296Z","iopub.execute_input":"2024-01-08T11:51:11.111634Z","iopub.status.idle":"2024-01-08T11:51:11.698499Z","shell.execute_reply.started":"2024-01-08T11:51:11.111587Z","shell.execute_reply":"2024-01-08T11:51:11.697585Z"},"trusted":true},"execution_count":36,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git "a/\354\272\220\352\270\200\355\225\204\354\202\25419_\354\241\260\355\230\204\354\247\200.ipynb" "b/\354\272\220\352\270\200\355\225\204\354\202\25419_\354\241\260\355\230\204\354\247\200.ipynb" new file mode 100644 index 0000000..96ef32b --- /dev/null +++ "b/\354\272\220\352\270\200\355\225\204\354\202\25419_\354\241\260\355\230\204\354\247\200.ipynb" @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":17777,"databundleVersionId":869809,"sourceType":"competition"}],"dockerImageVersionId":30096,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# NLP Quick Start for newbie😁 - with 9 steps","metadata":{}},{"cell_type":"markdown","source":"## Step 1. Library Import & Data Load","metadata":{}},{"cell_type":"code","source":"import pandas as pd \nimport numpy as np ","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.038537Z","iopub.execute_input":"2024-01-15T12:02:13.039324Z","iopub.status.idle":"2024-01-15T12:02:13.044554Z","shell.execute_reply.started":"2024-01-15T12:02:13.039250Z","shell.execute_reply":"2024-01-15T12:02:13.043395Z"},"trusted":true},"execution_count":65,"outputs":[]},{"cell_type":"code","source":"train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')\ntest_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.045861Z","iopub.execute_input":"2024-01-15T12:02:13.046376Z","iopub.status.idle":"2024-01-15T12:02:13.097889Z","shell.execute_reply.started":"2024-01-15T12:02:13.046325Z","shell.execute_reply":"2024-01-15T12:02:13.096854Z"},"trusted":true},"execution_count":66,"outputs":[]},{"cell_type":"code","source":"train_df.head()","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.099215Z","iopub.execute_input":"2024-01-15T12:02:13.099753Z","iopub.status.idle":"2024-01-15T12:02:13.114503Z","shell.execute_reply.started":"2024-01-15T12:02:13.099708Z","shell.execute_reply":"2024-01-15T12:02:13.113061Z"},"trusted":true},"execution_count":67,"outputs":[{"execution_count":67,"output_type":"execute_result","data":{"text/plain":" id keyword location text \\\n0 1 NaN NaN Our Deeds are the Reason of this #earthquake M... \n1 4 NaN NaN Forest fire near La Ronge Sask. Canada \n2 5 NaN NaN All residents asked to 'shelter in place' are ... \n3 6 NaN NaN 13,000 people receive #wildfires evacuation or... \n4 7 NaN NaN Just got sent this photo from Ruby #Alaska as ... \n\n target \n0 1 \n1 1 \n2 1 \n3 1 \n4 1 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
idkeywordlocationtexttarget
01NaNNaNOur Deeds are the Reason of this #earthquake M...1
14NaNNaNForest fire near La Ronge Sask. Canada1
25NaNNaNAll residents asked to 'shelter in place' are ...1
36NaNNaN13,000 people receive #wildfires evacuation or...1
47NaNNaNJust got sent this photo from Ruby #Alaska as ...1
\n
"},"metadata":{}}]},{"cell_type":"code","source":"train_df.info()","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.115977Z","iopub.execute_input":"2024-01-15T12:02:13.116348Z","iopub.status.idle":"2024-01-15T12:02:13.139948Z","shell.execute_reply.started":"2024-01-15T12:02:13.116286Z","shell.execute_reply":"2024-01-15T12:02:13.138106Z"},"trusted":true},"execution_count":68,"outputs":[{"name":"stdout","text":"\nRangeIndex: 7613 entries, 0 to 7612\nData columns (total 5 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 id 7613 non-null int64 \n 1 keyword 7552 non-null object\n 2 location 5080 non-null object\n 3 text 7613 non-null object\n 4 target 7613 non-null int64 \ndtypes: int64(2), object(3)\nmemory usage: 297.5+ KB\n","output_type":"stream"}]},{"cell_type":"code","source":"train_df.nunique()","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.141575Z","iopub.execute_input":"2024-01-15T12:02:13.142055Z","iopub.status.idle":"2024-01-15T12:02:13.164567Z","shell.execute_reply.started":"2024-01-15T12:02:13.142003Z","shell.execute_reply":"2024-01-15T12:02:13.163291Z"},"trusted":true},"execution_count":69,"outputs":[{"execution_count":69,"output_type":"execute_result","data":{"text/plain":"id 7613\nkeyword 221\nlocation 3341\ntext 7503\ntarget 2\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"test_df.head()","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.167351Z","iopub.execute_input":"2024-01-15T12:02:13.167837Z","iopub.status.idle":"2024-01-15T12:02:13.182706Z","shell.execute_reply.started":"2024-01-15T12:02:13.167787Z","shell.execute_reply":"2024-01-15T12:02:13.181248Z"},"trusted":true},"execution_count":70,"outputs":[{"execution_count":70,"output_type":"execute_result","data":{"text/plain":" id keyword location text\n0 0 NaN NaN Just happened a terrible car crash\n1 2 NaN NaN Heard about #earthquake is different cities, s...\n2 3 NaN NaN there is a forest fire at spot pond, geese are...\n3 9 NaN NaN Apocalypse lighting. #Spokane #wildfires\n4 11 NaN NaN Typhoon Soudelor kills 28 in China and Taiwan","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
idkeywordlocationtext
00NaNNaNJust happened a terrible car crash
12NaNNaNHeard about #earthquake is different cities, s...
23NaNNaNthere is a forest fire at spot pond, geese are...
39NaNNaNApocalypse lighting. #Spokane #wildfires
411NaNNaNTyphoon Soudelor kills 28 in China and Taiwan
\n
"},"metadata":{}}]},{"cell_type":"code","source":"test_df.info()","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.184795Z","iopub.execute_input":"2024-01-15T12:02:13.185361Z","iopub.status.idle":"2024-01-15T12:02:13.204646Z","shell.execute_reply.started":"2024-01-15T12:02:13.185293Z","shell.execute_reply":"2024-01-15T12:02:13.203430Z"},"trusted":true},"execution_count":71,"outputs":[{"name":"stdout","text":"\nRangeIndex: 3263 entries, 0 to 3262\nData columns (total 4 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 id 3263 non-null int64 \n 1 keyword 3237 non-null object\n 2 location 2158 non-null object\n 3 text 3263 non-null object\ndtypes: int64(1), object(3)\nmemory usage: 102.1+ KB\n","output_type":"stream"}]},{"cell_type":"code","source":"test_df.nunique()","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.205987Z","iopub.execute_input":"2024-01-15T12:02:13.206478Z","iopub.status.idle":"2024-01-15T12:02:13.227825Z","shell.execute_reply.started":"2024-01-15T12:02:13.206440Z","shell.execute_reply":"2024-01-15T12:02:13.226464Z"},"trusted":true},"execution_count":72,"outputs":[{"execution_count":72,"output_type":"execute_result","data":{"text/plain":"id 3263\nkeyword 221\nlocation 1602\ntext 3243\ndtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"## Step 2. Data Preprocessing","metadata":{}},{"cell_type":"markdown","source":"### 2-a. Drop Columns","metadata":{}},{"cell_type":"code","source":"train_df.head()","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.229501Z","iopub.execute_input":"2024-01-15T12:02:13.229949Z","iopub.status.idle":"2024-01-15T12:02:13.243448Z","shell.execute_reply.started":"2024-01-15T12:02:13.229905Z","shell.execute_reply":"2024-01-15T12:02:13.242335Z"},"trusted":true},"execution_count":73,"outputs":[{"execution_count":73,"output_type":"execute_result","data":{"text/plain":" id keyword location text \\\n0 1 NaN NaN Our Deeds are the Reason of this #earthquake M... \n1 4 NaN NaN Forest fire near La Ronge Sask. Canada \n2 5 NaN NaN All residents asked to 'shelter in place' are ... \n3 6 NaN NaN 13,000 people receive #wildfires evacuation or... \n4 7 NaN NaN Just got sent this photo from Ruby #Alaska as ... \n\n target \n0 1 \n1 1 \n2 1 \n3 1 \n4 1 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
idkeywordlocationtexttarget
01NaNNaNOur Deeds are the Reason of this #earthquake M...1
14NaNNaNForest fire near La Ronge Sask. Canada1
25NaNNaNAll residents asked to 'shelter in place' are ...1
36NaNNaN13,000 people receive #wildfires evacuation or...1
47NaNNaNJust got sent this photo from Ruby #Alaska as ...1
\n
"},"metadata":{}}]},{"cell_type":"code","source":"train_df.drop(columns=['id','keyword','location'], axis=1, inplace=True)","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.245113Z","iopub.execute_input":"2024-01-15T12:02:13.245930Z","iopub.status.idle":"2024-01-15T12:02:13.256113Z","shell.execute_reply.started":"2024-01-15T12:02:13.245876Z","shell.execute_reply":"2024-01-15T12:02:13.254896Z"},"trusted":true},"execution_count":74,"outputs":[]},{"cell_type":"code","source":"test_df.head()","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.258012Z","iopub.execute_input":"2024-01-15T12:02:13.258671Z","iopub.status.idle":"2024-01-15T12:02:13.276614Z","shell.execute_reply.started":"2024-01-15T12:02:13.258594Z","shell.execute_reply":"2024-01-15T12:02:13.275374Z"},"trusted":true},"execution_count":75,"outputs":[{"execution_count":75,"output_type":"execute_result","data":{"text/plain":" id keyword location text\n0 0 NaN NaN Just happened a terrible car crash\n1 2 NaN NaN Heard about #earthquake is different cities, s...\n2 3 NaN NaN there is a forest fire at spot pond, geese are...\n3 9 NaN NaN Apocalypse lighting. #Spokane #wildfires\n4 11 NaN NaN Typhoon Soudelor kills 28 in China and Taiwan","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
idkeywordlocationtext
00NaNNaNJust happened a terrible car crash
12NaNNaNHeard about #earthquake is different cities, s...
23NaNNaNthere is a forest fire at spot pond, geese are...
39NaNNaNApocalypse lighting. #Spokane #wildfires
411NaNNaNTyphoon Soudelor kills 28 in China and Taiwan
\n
"},"metadata":{}}]},{"cell_type":"code","source":"test_df.drop(columns=['keyword','location'],axis=1, inplace=True)","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.277857Z","iopub.execute_input":"2024-01-15T12:02:13.278218Z","iopub.status.idle":"2024-01-15T12:02:13.287811Z","shell.execute_reply.started":"2024-01-15T12:02:13.278181Z","shell.execute_reply":"2024-01-15T12:02:13.286671Z"},"trusted":true},"execution_count":76,"outputs":[]},{"cell_type":"code","source":"print(train_df.shape, test_df.shape)","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.289421Z","iopub.execute_input":"2024-01-15T12:02:13.290085Z","iopub.status.idle":"2024-01-15T12:02:13.301447Z","shell.execute_reply.started":"2024-01-15T12:02:13.290031Z","shell.execute_reply":"2024-01-15T12:02:13.300379Z"},"trusted":true},"execution_count":77,"outputs":[{"name":"stdout","text":"(7613, 2) (3263, 2)\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### 2-b. Tokenizer","metadata":{}},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.303039Z","iopub.execute_input":"2024-01-15T12:02:13.303708Z","iopub.status.idle":"2024-01-15T12:02:13.312806Z","shell.execute_reply.started":"2024-01-15T12:02:13.303659Z","shell.execute_reply":"2024-01-15T12:02:13.311621Z"},"trusted":true},"execution_count":78,"outputs":[]},{"cell_type":"code","source":"X_train, X_valid, y_train, y_valid = train_test_split(train_df['text'],train_df['target'], test_size=0.2, random_state=111)","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.314211Z","iopub.execute_input":"2024-01-15T12:02:13.314829Z","iopub.status.idle":"2024-01-15T12:02:13.328439Z","shell.execute_reply.started":"2024-01-15T12:02:13.314777Z","shell.execute_reply":"2024-01-15T12:02:13.327233Z"},"trusted":true},"execution_count":79,"outputs":[]},{"cell_type":"code","source":"print(X_train.shape, y_train.shape, X_valid.shape, y_valid.shape)","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.338243Z","iopub.execute_input":"2024-01-15T12:02:13.338682Z","iopub.status.idle":"2024-01-15T12:02:13.344679Z","shell.execute_reply.started":"2024-01-15T12:02:13.338642Z","shell.execute_reply":"2024-01-15T12:02:13.343623Z"},"trusted":true},"execution_count":80,"outputs":[{"name":"stdout","text":"(6090,) (6090,) (1523,) (1523,)\n","output_type":"stream"}]},{"cell_type":"code","source":"from tensorflow.keras.preprocessing.text import Tokenizer","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.347409Z","iopub.execute_input":"2024-01-15T12:02:13.347772Z","iopub.status.idle":"2024-01-15T12:02:13.356740Z","shell.execute_reply.started":"2024-01-15T12:02:13.347736Z","shell.execute_reply":"2024-01-15T12:02:13.355569Z"},"trusted":true},"execution_count":81,"outputs":[]},{"cell_type":"code","source":"vocab_size = 1000\noov_token = \"\"\ntokenizer = Tokenizer(num_words=vocab_size, oov_token=oov_token)","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.358773Z","iopub.execute_input":"2024-01-15T12:02:13.359366Z","iopub.status.idle":"2024-01-15T12:02:13.370841Z","shell.execute_reply.started":"2024-01-15T12:02:13.359211Z","shell.execute_reply":"2024-01-15T12:02:13.369888Z"},"trusted":true},"execution_count":82,"outputs":[]},{"cell_type":"code","source":"tokenizer.fit_on_texts(X_train)","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.372026Z","iopub.execute_input":"2024-01-15T12:02:13.372519Z","iopub.status.idle":"2024-01-15T12:02:13.584163Z","shell.execute_reply.started":"2024-01-15T12:02:13.372481Z","shell.execute_reply":"2024-01-15T12:02:13.583160Z"},"trusted":true},"execution_count":83,"outputs":[]},{"cell_type":"code","source":"X_train = tokenizer.texts_to_sequences(X_train)\nX_valid = tokenizer.texts_to_sequences(X_valid)","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.585402Z","iopub.execute_input":"2024-01-15T12:02:13.585921Z","iopub.status.idle":"2024-01-15T12:02:13.760792Z","shell.execute_reply.started":"2024-01-15T12:02:13.585882Z","shell.execute_reply":"2024-01-15T12:02:13.759794Z"},"trusted":true},"execution_count":84,"outputs":[]},{"cell_type":"code","source":"for i in range(10):\n print(len(X_train[i]))","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.762146Z","iopub.execute_input":"2024-01-15T12:02:13.762841Z","iopub.status.idle":"2024-01-15T12:02:13.771427Z","shell.execute_reply.started":"2024-01-15T12:02:13.762787Z","shell.execute_reply":"2024-01-15T12:02:13.769758Z"},"trusted":true},"execution_count":85,"outputs":[{"name":"stdout","text":"19\n20\n18\n26\n16\n22\n19\n18\n14\n8\n","output_type":"stream"}]},{"cell_type":"code","source":"X_train[0]","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.773735Z","iopub.execute_input":"2024-01-15T12:02:13.774448Z","iopub.status.idle":"2024-01-15T12:02:13.789313Z","shell.execute_reply.started":"2024-01-15T12:02:13.774354Z","shell.execute_reply":"2024-01-15T12:02:13.787853Z"},"trusted":true},"execution_count":86,"outputs":[{"execution_count":86,"output_type":"execute_result","data":{"text/plain":"[132, 1, 9, 324, 12, 16, 679, 13, 4, 2, 3, 1, 616, 160, 1, 4, 2, 3, 1]"},"metadata":{}}]},{"cell_type":"code","source":"for i in range(10):\n print(len(X_valid[i]))","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.790547Z","iopub.execute_input":"2024-01-15T12:02:13.790885Z","iopub.status.idle":"2024-01-15T12:02:13.798230Z","shell.execute_reply.started":"2024-01-15T12:02:13.790852Z","shell.execute_reply":"2024-01-15T12:02:13.797041Z"},"trusted":true},"execution_count":87,"outputs":[{"name":"stdout","text":"19\n26\n26\n23\n12\n11\n20\n15\n11\n10\n","output_type":"stream"}]},{"cell_type":"code","source":"X_valid[0]","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.799605Z","iopub.execute_input":"2024-01-15T12:02:13.799934Z","iopub.status.idle":"2024-01-15T12:02:13.810355Z","shell.execute_reply.started":"2024-01-15T12:02:13.799899Z","shell.execute_reply":"2024-01-15T12:02:13.809139Z"},"trusted":true},"execution_count":88,"outputs":[{"execution_count":88,"output_type":"execute_result","data":{"text/plain":"[73, 15, 231, 507, 18, 751, 397, 922, 175, 5, 1, 4, 2, 3, 1, 4, 2, 3, 1]"},"metadata":{}}]},{"cell_type":"markdown","source":"### 2-c. Pad Sequences","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.preprocessing.sequence import pad_sequences","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.811744Z","iopub.execute_input":"2024-01-15T12:02:13.812520Z","iopub.status.idle":"2024-01-15T12:02:13.821846Z","shell.execute_reply.started":"2024-01-15T12:02:13.812449Z","shell.execute_reply":"2024-01-15T12:02:13.820528Z"},"trusted":true},"execution_count":89,"outputs":[]},{"cell_type":"code","source":"max_length = 120\ntrunc_type = 'post'\npad_type = 'post'","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.823481Z","iopub.execute_input":"2024-01-15T12:02:13.824195Z","iopub.status.idle":"2024-01-15T12:02:13.833899Z","shell.execute_reply.started":"2024-01-15T12:02:13.824139Z","shell.execute_reply":"2024-01-15T12:02:13.832598Z"},"trusted":true},"execution_count":90,"outputs":[]},{"cell_type":"code","source":"X_train_padded = pad_sequences(X_train, maxlen=max_length, truncating=trunc_type, padding=pad_type)\nX_valid_padded = pad_sequences(X_valid, maxlen=max_length, truncating=trunc_type, padding=pad_type)","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.837383Z","iopub.execute_input":"2024-01-15T12:02:13.838140Z","iopub.status.idle":"2024-01-15T12:02:13.898936Z","shell.execute_reply.started":"2024-01-15T12:02:13.838085Z","shell.execute_reply":"2024-01-15T12:02:13.897716Z"},"trusted":true},"execution_count":91,"outputs":[]},{"cell_type":"code","source":"X_train_padded[:2]","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.900515Z","iopub.execute_input":"2024-01-15T12:02:13.900994Z","iopub.status.idle":"2024-01-15T12:02:13.908653Z","shell.execute_reply.started":"2024-01-15T12:02:13.900946Z","shell.execute_reply":"2024-01-15T12:02:13.907458Z"},"trusted":true},"execution_count":92,"outputs":[{"execution_count":92,"output_type":"execute_result","data":{"text/plain":"array([[132, 1, 9, 324, 12, 16, 679, 13, 4, 2, 3, 1, 616,\n 160, 1, 4, 2, 3, 1, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0],\n [826, 1, 140, 26, 1, 7, 6, 1, 23, 156, 1, 71, 181,\n 14, 1, 1, 4, 2, 3, 1, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0]], dtype=int32)"},"metadata":{}}]},{"cell_type":"code","source":"X_valid_padded[:2]","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.910188Z","iopub.execute_input":"2024-01-15T12:02:13.910870Z","iopub.status.idle":"2024-01-15T12:02:13.923470Z","shell.execute_reply.started":"2024-01-15T12:02:13.910819Z","shell.execute_reply":"2024-01-15T12:02:13.922425Z"},"trusted":true},"execution_count":93,"outputs":[{"execution_count":93,"output_type":"execute_result","data":{"text/plain":"array([[ 73, 15, 231, 507, 18, 751, 397, 922, 175, 5, 1, 4, 2,\n 3, 1, 4, 2, 3, 1, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0],\n [ 19, 1, 51, 551, 54, 7, 840, 6, 1, 1, 10, 79, 50,\n 422, 34, 20, 6, 1, 10, 50, 1, 13, 1, 33, 1, 1,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0]], dtype=int32)"},"metadata":{}}]},{"cell_type":"code","source":"print(X_train_padded.shape, X_valid_padded.shape)","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.925437Z","iopub.execute_input":"2024-01-15T12:02:13.926020Z","iopub.status.idle":"2024-01-15T12:02:13.936626Z","shell.execute_reply.started":"2024-01-15T12:02:13.925977Z","shell.execute_reply":"2024-01-15T12:02:13.935676Z"},"trusted":true},"execution_count":94,"outputs":[{"name":"stdout","text":"(6090, 120) (1523, 120)\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### 2-d. Match Data type to numpy.ndarray","metadata":{}},{"cell_type":"code","source":"print(type(X_train_padded), type(X_valid_padded))\nprint(type(y_train), type(y_valid))","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.938100Z","iopub.execute_input":"2024-01-15T12:02:13.938473Z","iopub.status.idle":"2024-01-15T12:02:13.950516Z","shell.execute_reply.started":"2024-01-15T12:02:13.938435Z","shell.execute_reply":"2024-01-15T12:02:13.949160Z"},"trusted":true},"execution_count":95,"outputs":[{"name":"stdout","text":" \n \n","output_type":"stream"}]},{"cell_type":"code","source":"y_train = np.array(y_train)\ny_valid = np.array(y_valid)","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.952376Z","iopub.execute_input":"2024-01-15T12:02:13.952838Z","iopub.status.idle":"2024-01-15T12:02:13.961542Z","shell.execute_reply.started":"2024-01-15T12:02:13.952785Z","shell.execute_reply":"2024-01-15T12:02:13.960464Z"},"trusted":true},"execution_count":96,"outputs":[]},{"cell_type":"code","source":"print(type(X_train_padded), type(X_valid_padded))\nprint(type(y_train), type(y_valid))","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.962926Z","iopub.execute_input":"2024-01-15T12:02:13.963492Z","iopub.status.idle":"2024-01-15T12:02:13.974323Z","shell.execute_reply.started":"2024-01-15T12:02:13.963445Z","shell.execute_reply":"2024-01-15T12:02:13.973423Z"},"trusted":true},"execution_count":97,"outputs":[{"name":"stdout","text":" \n \n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Step 3. Modeling","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Embedding, LSTM, Dense, Bidirectional, Flatten","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.975607Z","iopub.execute_input":"2024-01-15T12:02:13.976122Z","iopub.status.idle":"2024-01-15T12:02:13.983844Z","shell.execute_reply.started":"2024-01-15T12:02:13.976077Z","shell.execute_reply":"2024-01-15T12:02:13.982679Z"},"trusted":true},"execution_count":98,"outputs":[]},{"cell_type":"code","source":"embedding_dim = 16\n# vocab_size = 1000\n# max_length = 120","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.985387Z","iopub.execute_input":"2024-01-15T12:02:13.985823Z","iopub.status.idle":"2024-01-15T12:02:13.995612Z","shell.execute_reply.started":"2024-01-15T12:02:13.985777Z","shell.execute_reply":"2024-01-15T12:02:13.994432Z"},"trusted":true},"execution_count":99,"outputs":[]},{"cell_type":"code","source":"model = Sequential([\n Embedding(vocab_size, embedding_dim, input_length=max_length),\n Bidirectional(LSTM(64, return_sequences=True)),\n Bidirectional(LSTM(64, return_sequences=True)),\n Bidirectional(LSTM(64, dropout=0.5)),\n Dense(32, activation='relu'),\n Dense(16, activation='relu'),\n Dense(1, activation='sigmoid')\n])","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:13.997016Z","iopub.execute_input":"2024-01-15T12:02:13.997454Z","iopub.status.idle":"2024-01-15T12:02:15.594704Z","shell.execute_reply.started":"2024-01-15T12:02:13.997402Z","shell.execute_reply":"2024-01-15T12:02:15.593513Z"},"trusted":true},"execution_count":100,"outputs":[]},{"cell_type":"code","source":"model.summary()","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:15.596486Z","iopub.execute_input":"2024-01-15T12:02:15.596965Z","iopub.status.idle":"2024-01-15T12:02:15.607830Z","shell.execute_reply.started":"2024-01-15T12:02:15.596914Z","shell.execute_reply":"2024-01-15T12:02:15.606637Z"},"trusted":true},"execution_count":101,"outputs":[{"name":"stdout","text":"Model: \"sequential_1\"\n_________________________________________________________________\nLayer (type) Output Shape Param # \n=================================================================\nembedding_1 (Embedding) (None, 120, 16) 16000 \n_________________________________________________________________\nbidirectional_3 (Bidirection (None, 120, 128) 41472 \n_________________________________________________________________\nbidirectional_4 (Bidirection (None, 120, 128) 98816 \n_________________________________________________________________\nbidirectional_5 (Bidirection (None, 128) 98816 \n_________________________________________________________________\ndense_3 (Dense) (None, 32) 4128 \n_________________________________________________________________\ndense_4 (Dense) (None, 16) 528 \n_________________________________________________________________\ndense_5 (Dense) (None, 1) 17 \n=================================================================\nTotal params: 259,777\nTrainable params: 259,777\nNon-trainable params: 0\n_________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Step 4. Model Compile","metadata":{}},{"cell_type":"code","source":"model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:15.609556Z","iopub.execute_input":"2024-01-15T12:02:15.609917Z","iopub.status.idle":"2024-01-15T12:02:15.626802Z","shell.execute_reply.started":"2024-01-15T12:02:15.609883Z","shell.execute_reply":"2024-01-15T12:02:15.625577Z"},"trusted":true},"execution_count":102,"outputs":[]},{"cell_type":"markdown","source":"## Step 5. Callbacks","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:15.628231Z","iopub.execute_input":"2024-01-15T12:02:15.628584Z","iopub.status.idle":"2024-01-15T12:02:15.635484Z","shell.execute_reply.started":"2024-01-15T12:02:15.628551Z","shell.execute_reply":"2024-01-15T12:02:15.634227Z"},"trusted":true},"execution_count":103,"outputs":[]},{"cell_type":"code","source":"filepath = 'my_checkpoint.ckpt'\ncp = ModelCheckpoint(\n filepath=filepath,\n save_weights_only=True,\n save_best_only=True,\n monitor='val_loss',\n verbose=1\n)","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:15.636967Z","iopub.execute_input":"2024-01-15T12:02:15.637312Z","iopub.status.idle":"2024-01-15T12:02:15.646581Z","shell.execute_reply.started":"2024-01-15T12:02:15.637267Z","shell.execute_reply":"2024-01-15T12:02:15.645429Z"},"trusted":true},"execution_count":104,"outputs":[]},{"cell_type":"code","source":"ep = EarlyStopping(\n monitor='val_loss', \n patience=5,\n)","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:15.648274Z","iopub.execute_input":"2024-01-15T12:02:15.648745Z","iopub.status.idle":"2024-01-15T12:02:15.656493Z","shell.execute_reply.started":"2024-01-15T12:02:15.648695Z","shell.execute_reply":"2024-01-15T12:02:15.655315Z"},"trusted":true},"execution_count":105,"outputs":[]},{"cell_type":"markdown","source":"## Step 6. Model Fit","metadata":{}},{"cell_type":"code","source":"epochs=30\nmodel.fit(\n X_train_padded, y_train,\n validation_data = (X_valid_padded, y_valid),\n callbacks=[cp,ep],\n epochs=epochs\n)","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:02:15.658099Z","iopub.execute_input":"2024-01-15T12:02:15.658568Z","iopub.status.idle":"2024-01-15T12:12:15.442624Z","shell.execute_reply.started":"2024-01-15T12:02:15.658517Z","shell.execute_reply":"2024-01-15T12:12:15.441428Z"},"trusted":true},"execution_count":106,"outputs":[{"name":"stdout","text":"Epoch 1/30\n191/191 [==============================] - 70s 318ms/step - loss: 0.6411 - acc: 0.6194 - val_loss: 0.4842 - val_acc: 0.7695\n\nEpoch 00001: val_loss improved from inf to 0.48419, saving model to my_checkpoint.ckpt\nEpoch 2/30\n191/191 [==============================] - 58s 305ms/step - loss: 0.4282 - acc: 0.8143 - val_loss: 0.4646 - val_acc: 0.7820\n\nEpoch 00002: val_loss improved from 0.48419 to 0.46459, saving model to my_checkpoint.ckpt\nEpoch 3/30\n191/191 [==============================] - 59s 310ms/step - loss: 0.3900 - acc: 0.8372 - val_loss: 0.4638 - val_acc: 0.7905\n\nEpoch 00003: val_loss improved from 0.46459 to 0.46376, saving model to my_checkpoint.ckpt\nEpoch 4/30\n191/191 [==============================] - 58s 306ms/step - loss: 0.3797 - acc: 0.8459 - val_loss: 0.4540 - val_acc: 0.7958\n\nEpoch 00004: val_loss improved from 0.46376 to 0.45403, saving model to my_checkpoint.ckpt\nEpoch 5/30\n191/191 [==============================] - 59s 307ms/step - loss: 0.3558 - acc: 0.8542 - val_loss: 0.4480 - val_acc: 0.8024\n\nEpoch 00005: val_loss improved from 0.45403 to 0.44799, saving model to my_checkpoint.ckpt\nEpoch 6/30\n191/191 [==============================] - 59s 309ms/step - loss: 0.3558 - acc: 0.8605 - val_loss: 0.5049 - val_acc: 0.7925\n\nEpoch 00006: val_loss did not improve from 0.44799\nEpoch 7/30\n191/191 [==============================] - 59s 306ms/step - loss: 0.3312 - acc: 0.8675 - val_loss: 0.4720 - val_acc: 0.7853\n\nEpoch 00007: val_loss did not improve from 0.44799\nEpoch 8/30\n191/191 [==============================] - 59s 307ms/step - loss: 0.3041 - acc: 0.8827 - val_loss: 0.4910 - val_acc: 0.7840\n\nEpoch 00008: val_loss did not improve from 0.44799\nEpoch 9/30\n191/191 [==============================] - 59s 309ms/step - loss: 0.2925 - acc: 0.8931 - val_loss: 0.5598 - val_acc: 0.7774\n\nEpoch 00009: val_loss did not improve from 0.44799\nEpoch 10/30\n191/191 [==============================] - 59s 310ms/step - loss: 0.2786 - acc: 0.9025 - val_loss: 0.5600 - val_acc: 0.7833\n\nEpoch 00010: val_loss did not improve from 0.44799\n","output_type":"stream"},{"execution_count":106,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}}]},{"cell_type":"markdown","source":"## Step 7. Model Evaluate & Save","metadata":{}},{"cell_type":"code","source":"model.load_weights(filepath)","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:12:15.443788Z","iopub.execute_input":"2024-01-15T12:12:15.444079Z","iopub.status.idle":"2024-01-15T12:12:15.518203Z","shell.execute_reply.started":"2024-01-15T12:12:15.444049Z","shell.execute_reply":"2024-01-15T12:12:15.517096Z"},"trusted":true},"execution_count":107,"outputs":[{"execution_count":107,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}}]},{"cell_type":"code","source":"model.evaluate(X_valid_padded, y_valid)","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:12:15.519614Z","iopub.execute_input":"2024-01-15T12:12:15.520204Z","iopub.status.idle":"2024-01-15T12:12:19.286211Z","shell.execute_reply.started":"2024-01-15T12:12:15.520164Z","shell.execute_reply":"2024-01-15T12:12:19.285087Z"},"trusted":true},"execution_count":108,"outputs":[{"name":"stdout","text":"48/48 [==============================] - 4s 77ms/step - loss: 0.4480 - acc: 0.8024\n","output_type":"stream"},{"execution_count":108,"output_type":"execute_result","data":{"text/plain":"[0.4479884207248688, 0.8023637533187866]"},"metadata":{}}]},{"cell_type":"code","source":"X_valid[0]","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:12:19.287552Z","iopub.execute_input":"2024-01-15T12:12:19.287971Z","iopub.status.idle":"2024-01-15T12:12:19.295075Z","shell.execute_reply.started":"2024-01-15T12:12:19.287923Z","shell.execute_reply":"2024-01-15T12:12:19.293937Z"},"trusted":true},"execution_count":109,"outputs":[{"execution_count":109,"output_type":"execute_result","data":{"text/plain":"[73, 15, 231, 507, 18, 751, 397, 922, 175, 5, 1, 4, 2, 3, 1, 4, 2, 3, 1]"},"metadata":{}}]},{"cell_type":"code","source":"model.save('./model/basic_nlp.h5')","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:12:19.296514Z","iopub.execute_input":"2024-01-15T12:12:19.297095Z","iopub.status.idle":"2024-01-15T12:12:19.361376Z","shell.execute_reply.started":"2024-01-15T12:12:19.297048Z","shell.execute_reply":"2024-01-15T12:12:19.360420Z"},"trusted":true},"execution_count":110,"outputs":[]},{"cell_type":"markdown","source":"## Step 8. Reload Model","metadata":{}},{"cell_type":"code","source":"import tensorflow as tf","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:12:19.362805Z","iopub.execute_input":"2024-01-15T12:12:19.363446Z","iopub.status.idle":"2024-01-15T12:12:19.367468Z","shell.execute_reply.started":"2024-01-15T12:12:19.363404Z","shell.execute_reply":"2024-01-15T12:12:19.366644Z"},"trusted":true},"execution_count":111,"outputs":[]},{"cell_type":"code","source":"mymodel = tf.keras.models.load_model('./model/basic_nlp.h5')","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:12:19.368772Z","iopub.execute_input":"2024-01-15T12:12:19.369387Z","iopub.status.idle":"2024-01-15T12:12:21.089890Z","shell.execute_reply.started":"2024-01-15T12:12:19.369346Z","shell.execute_reply":"2024-01-15T12:12:21.088719Z"},"trusted":true},"execution_count":112,"outputs":[]},{"cell_type":"code","source":"mymodel.summary()","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:12:21.091556Z","iopub.execute_input":"2024-01-15T12:12:21.091917Z","iopub.status.idle":"2024-01-15T12:12:21.104444Z","shell.execute_reply.started":"2024-01-15T12:12:21.091880Z","shell.execute_reply":"2024-01-15T12:12:21.101445Z"},"trusted":true},"execution_count":113,"outputs":[{"name":"stdout","text":"Model: \"sequential_1\"\n_________________________________________________________________\nLayer (type) Output Shape Param # \n=================================================================\nembedding_1 (Embedding) (None, 120, 16) 16000 \n_________________________________________________________________\nbidirectional_3 (Bidirection (None, 120, 128) 41472 \n_________________________________________________________________\nbidirectional_4 (Bidirection (None, 120, 128) 98816 \n_________________________________________________________________\nbidirectional_5 (Bidirection (None, 128) 98816 \n_________________________________________________________________\ndense_3 (Dense) (None, 32) 4128 \n_________________________________________________________________\ndense_4 (Dense) (None, 16) 528 \n_________________________________________________________________\ndense_5 (Dense) (None, 1) 17 \n=================================================================\nTotal params: 259,777\nTrainable params: 259,777\nNon-trainable params: 0\n_________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Step 9. Predict Test Data ","metadata":{}},{"cell_type":"code","source":"X_test = tokenizer.texts_to_sequences(test_df['text'])","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:12:21.105961Z","iopub.execute_input":"2024-01-15T12:12:21.106405Z","iopub.status.idle":"2024-01-15T12:12:21.185950Z","shell.execute_reply.started":"2024-01-15T12:12:21.106364Z","shell.execute_reply":"2024-01-15T12:12:21.184946Z"},"trusted":true},"execution_count":114,"outputs":[]},{"cell_type":"code","source":"X_test_padded = pad_sequences(X_test, maxlen=max_length, truncating=trunc_type, padding=pad_type)","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:12:21.187336Z","iopub.execute_input":"2024-01-15T12:12:21.188021Z","iopub.status.idle":"2024-01-15T12:12:21.215122Z","shell.execute_reply.started":"2024-01-15T12:12:21.187971Z","shell.execute_reply":"2024-01-15T12:12:21.214184Z"},"trusted":true},"execution_count":115,"outputs":[]},{"cell_type":"code","source":"y_test_raw = model.predict(X_test_padded)","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:12:21.216469Z","iopub.execute_input":"2024-01-15T12:12:21.217157Z","iopub.status.idle":"2024-01-15T12:12:30.802074Z","shell.execute_reply.started":"2024-01-15T12:12:21.217105Z","shell.execute_reply":"2024-01-15T12:12:30.800973Z"},"trusted":true},"execution_count":116,"outputs":[]},{"cell_type":"code","source":"y_test_raw","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:12:30.803942Z","iopub.execute_input":"2024-01-15T12:12:30.804439Z","iopub.status.idle":"2024-01-15T12:12:30.811269Z","shell.execute_reply.started":"2024-01-15T12:12:30.804386Z","shell.execute_reply":"2024-01-15T12:12:30.810401Z"},"trusted":true},"execution_count":117,"outputs":[{"execution_count":117,"output_type":"execute_result","data":{"text/plain":"array([[0.7287144 ],\n [0.6113465 ],\n [0.86930424],\n ...,\n [0.9640878 ],\n [0.80375636],\n [0.21127743]], dtype=float32)"},"metadata":{}}]},{"cell_type":"code","source":"y_test = list(map(lambda x : 1 if x > 0.5 else 0, y_test_raw))","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:12:30.812606Z","iopub.execute_input":"2024-01-15T12:12:30.813131Z","iopub.status.idle":"2024-01-15T12:12:30.828812Z","shell.execute_reply.started":"2024-01-15T12:12:30.813094Z","shell.execute_reply":"2024-01-15T12:12:30.827175Z"},"trusted":true},"execution_count":118,"outputs":[]},{"cell_type":"code","source":"set(y_test)","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:12:30.830383Z","iopub.execute_input":"2024-01-15T12:12:30.830755Z","iopub.status.idle":"2024-01-15T12:12:30.841600Z","shell.execute_reply.started":"2024-01-15T12:12:30.830708Z","shell.execute_reply":"2024-01-15T12:12:30.840235Z"},"trusted":true},"execution_count":119,"outputs":[{"execution_count":119,"output_type":"execute_result","data":{"text/plain":"{0, 1}"},"metadata":{}}]},{"cell_type":"code","source":"y_test[:5]","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:12:30.842971Z","iopub.execute_input":"2024-01-15T12:12:30.843515Z","iopub.status.idle":"2024-01-15T12:12:30.851831Z","shell.execute_reply.started":"2024-01-15T12:12:30.843467Z","shell.execute_reply":"2024-01-15T12:12:30.850900Z"},"trusted":true},"execution_count":120,"outputs":[{"execution_count":120,"output_type":"execute_result","data":{"text/plain":"[1, 1, 1, 0, 1]"},"metadata":{}}]},{"cell_type":"code","source":"test_df['predict'] = y_test","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:12:30.853122Z","iopub.execute_input":"2024-01-15T12:12:30.853515Z","iopub.status.idle":"2024-01-15T12:12:30.864437Z","shell.execute_reply.started":"2024-01-15T12:12:30.853477Z","shell.execute_reply":"2024-01-15T12:12:30.863392Z"},"trusted":true},"execution_count":121,"outputs":[]},{"cell_type":"code","source":"test_df","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:12:30.866207Z","iopub.execute_input":"2024-01-15T12:12:30.866725Z","iopub.status.idle":"2024-01-15T12:12:30.888404Z","shell.execute_reply.started":"2024-01-15T12:12:30.866671Z","shell.execute_reply":"2024-01-15T12:12:30.887165Z"},"trusted":true},"execution_count":122,"outputs":[{"execution_count":122,"output_type":"execute_result","data":{"text/plain":" id text predict\n0 0 Just happened a terrible car crash 1\n1 2 Heard about #earthquake is different cities, s... 1\n2 3 there is a forest fire at spot pond, geese are... 1\n3 9 Apocalypse lighting. #Spokane #wildfires 0\n4 11 Typhoon Soudelor kills 28 in China and Taiwan 1\n... ... ... ...\n3258 10861 EARTHQUAKE SAFETY LOS ANGELES ‰ÛÒ SAFETY FASTE... 1\n3259 10865 Storm in RI worse than last hurricane. My city... 1\n3260 10868 Green Line derailment in Chicago http://t.co/U... 1\n3261 10874 MEG issues Hazardous Weather Outlook (HWO) htt... 1\n3262 10875 #CityofCalgary has activated its Municipal Eme... 0\n\n[3263 rows x 3 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
idtextpredict
00Just happened a terrible car crash1
12Heard about #earthquake is different cities, s...1
23there is a forest fire at spot pond, geese are...1
39Apocalypse lighting. #Spokane #wildfires0
411Typhoon Soudelor kills 28 in China and Taiwan1
............
325810861EARTHQUAKE SAFETY LOS ANGELES ‰ÛÒ SAFETY FASTE...1
325910865Storm in RI worse than last hurricane. My city...1
326010868Green Line derailment in Chicago http://t.co/U...1
326110874MEG issues Hazardous Weather Outlook (HWO) htt...1
326210875#CityofCalgary has activated its Municipal Eme...0
\n

3263 rows × 3 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"test_df[test_df['predict']==1]","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:12:30.890220Z","iopub.execute_input":"2024-01-15T12:12:30.890985Z","iopub.status.idle":"2024-01-15T12:12:30.910125Z","shell.execute_reply.started":"2024-01-15T12:12:30.890933Z","shell.execute_reply":"2024-01-15T12:12:30.909095Z"},"trusted":true},"execution_count":123,"outputs":[{"execution_count":123,"output_type":"execute_result","data":{"text/plain":" id text predict\n0 0 Just happened a terrible car crash 1\n1 2 Heard about #earthquake is different cities, s... 1\n2 3 there is a forest fire at spot pond, geese are... 1\n4 11 Typhoon Soudelor kills 28 in China and Taiwan 1\n5 12 We're shaking...It's an earthquake 1\n... ... ... ...\n3257 10858 The death toll in a #IS-suicide car bombing on... 1\n3258 10861 EARTHQUAKE SAFETY LOS ANGELES ‰ÛÒ SAFETY FASTE... 1\n3259 10865 Storm in RI worse than last hurricane. My city... 1\n3260 10868 Green Line derailment in Chicago http://t.co/U... 1\n3261 10874 MEG issues Hazardous Weather Outlook (HWO) htt... 1\n\n[1023 rows x 3 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
idtextpredict
00Just happened a terrible car crash1
12Heard about #earthquake is different cities, s...1
23there is a forest fire at spot pond, geese are...1
411Typhoon Soudelor kills 28 in China and Taiwan1
512We're shaking...It's an earthquake1
............
325710858The death toll in a #IS-suicide car bombing on...1
325810861EARTHQUAKE SAFETY LOS ANGELES ‰ÛÒ SAFETY FASTE...1
325910865Storm in RI worse than last hurricane. My city...1
326010868Green Line derailment in Chicago http://t.co/U...1
326110874MEG issues Hazardous Weather Outlook (HWO) htt...1
\n

1023 rows × 3 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"submission = test_df[['id','predict']]","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:12:30.911826Z","iopub.execute_input":"2024-01-15T12:12:30.912612Z","iopub.status.idle":"2024-01-15T12:12:30.919730Z","shell.execute_reply.started":"2024-01-15T12:12:30.912556Z","shell.execute_reply":"2024-01-15T12:12:30.918539Z"},"trusted":true},"execution_count":124,"outputs":[]},{"cell_type":"code","source":"submission","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:12:30.921072Z","iopub.execute_input":"2024-01-15T12:12:30.921436Z","iopub.status.idle":"2024-01-15T12:12:30.938444Z","shell.execute_reply.started":"2024-01-15T12:12:30.921398Z","shell.execute_reply":"2024-01-15T12:12:30.937556Z"},"trusted":true},"execution_count":125,"outputs":[{"execution_count":125,"output_type":"execute_result","data":{"text/plain":" id predict\n0 0 1\n1 2 1\n2 3 1\n3 9 0\n4 11 1\n... ... ...\n3258 10861 1\n3259 10865 1\n3260 10868 1\n3261 10874 1\n3262 10875 0\n\n[3263 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
idpredict
001
121
231
390
4111
.........
3258108611
3259108651
3260108681
3261108741
3262108750
\n

3263 rows × 2 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"submission.columns = ['id', 'target']","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:12:30.939682Z","iopub.execute_input":"2024-01-15T12:12:30.940266Z","iopub.status.idle":"2024-01-15T12:12:30.945130Z","shell.execute_reply.started":"2024-01-15T12:12:30.940230Z","shell.execute_reply":"2024-01-15T12:12:30.944205Z"},"trusted":true},"execution_count":126,"outputs":[]},{"cell_type":"code","source":"submission","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:12:30.946256Z","iopub.execute_input":"2024-01-15T12:12:30.946740Z","iopub.status.idle":"2024-01-15T12:12:30.963699Z","shell.execute_reply.started":"2024-01-15T12:12:30.946704Z","shell.execute_reply":"2024-01-15T12:12:30.962730Z"},"trusted":true},"execution_count":127,"outputs":[{"execution_count":127,"output_type":"execute_result","data":{"text/plain":" id target\n0 0 1\n1 2 1\n2 3 1\n3 9 0\n4 11 1\n... ... ...\n3258 10861 1\n3259 10865 1\n3260 10868 1\n3261 10874 1\n3262 10875 0\n\n[3263 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
idtarget
001
121
231
390
4111
.........
3258108611
3259108651
3260108681
3261108741
3262108750
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3263 rows × 2 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"submission.to_csv('./sample_submission.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2024-01-15T12:12:30.964843Z","iopub.execute_input":"2024-01-15T12:12:30.965424Z","iopub.status.idle":"2024-01-15T12:12:30.975660Z","shell.execute_reply.started":"2024-01-15T12:12:30.965383Z","shell.execute_reply":"2024-01-15T12:12:30.974552Z"},"trusted":true},"execution_count":128,"outputs":[]}]} \ No newline at end of file diff --git "a/\354\274\200\352\270\200\355\225\204\354\202\25417_\354\241\260\355\230\204\354\247\200.ipynb" "b/\354\274\200\352\270\200\355\225\204\354\202\25417_\354\241\260\355\230\204\354\247\200.ipynb" new file mode 100644 index 0000000..358f79e --- /dev/null +++ "b/\354\274\200\352\270\200\355\225\204\354\202\25417_\354\241\260\355\230\204\354\247\200.ipynb" @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":14897,"databundleVersionId":1020216,"sourceType":"competition"},{"sourceId":858763,"sourceType":"datasetVersion","datasetId":455251},{"sourceId":25441779,"sourceType":"kernelVersion"},{"sourceId":25470426,"sourceType":"kernelVersion"}],"dockerImageVersionId":29845,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# ResNet-34 PyTorch Starter Kit","metadata":{}},{"cell_type":"markdown","source":"# Part 1","metadata":{}},{"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\nimport os\n\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# Any results you write to the current directory are saved as output.\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import Dataset,DataLoader\nfrom torchvision import transforms,models\nfrom tqdm import tqdm_notebook as tqdm","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"/kaggle/input/bengaliai-cv19/test_image_data_2.parquet\n/kaggle/input/bengaliai-cv19/sample_submission.csv\n/kaggle/input/bengaliai-cv19/class_map.csv\n/kaggle/input/bengaliai-cv19/train_image_data_2.parquet\n/kaggle/input/bengaliai-cv19/train_multi_diacritics.csv\n/kaggle/input/bengaliai-cv19/train_image_data_0.parquet\n/kaggle/input/bengaliai-cv19/train_image_data_3.parquet\n/kaggle/input/bengaliai-cv19/test_image_data_0.parquet\n/kaggle/input/bengaliai-cv19/test_image_data_1.parquet\n/kaggle/input/bengaliai-cv19/train.csv\n/kaggle/input/bengaliai-cv19/test.csv\n/kaggle/input/bengaliai-cv19/train_image_data_1.parquet\n/kaggle/input/bengaliai-cv19/class_map_corrected.csv\n/kaggle/input/bengaliai-cv19/test_image_data_3.parquet\n/kaggle/input/grapheme-resnet-18-naive-learning-2/__results__.html\n/kaggle/input/grapheme-resnet-18-naive-learning-2/saved_weights.pth\n/kaggle/input/grapheme-resnet-18-naive-learning-2/__resultx__.html\n/kaggle/input/grapheme-resnet-18-naive-learning-2/__notebook__.ipynb\n/kaggle/input/grapheme-resnet-18-naive-learning-2/__output__.json\n/kaggle/input/grapheme-resnet-18-naive-learning-2/custom.css\n/kaggle/input/grapheme-resnet-18-naive-learning-2/__results___files/__results___10_1.png\n/kaggle/input/trained400/resnet34_400epochs_saved_weights.pth\n","output_type":"stream"}]},{"cell_type":"code","source":"train = pd.read_csv('/kaggle/input/bengaliai-cv19/train.csv')\ndata0 = pd.read_feather('/kaggle/usr/lib/resize-and-load-with-feather-format-much-faster/train_data_0.feather')\ndata1 = pd.read_feather('/kaggle/usr/lib/resize-and-load-with-feather-format-much-faster/train_data_1.feather')\ndata2 = pd.read_feather('/kaggle/usr/lib/resize-and-load-with-feather-format-much-faster/train_data_2.feather')\ndata3 = pd.read_feather('/kaggle/usr/lib/resize-and-load-with-feather-format-much-faster/train_data_3.feather')","metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","execution":{"iopub.status.busy":"2024-01-01T14:19:19.550225Z","iopub.execute_input":"2024-01-01T14:19:19.550589Z","iopub.status.idle":"2024-01-01T14:19:35.437015Z","shell.execute_reply.started":"2024-01-01T14:19:19.550539Z","shell.execute_reply":"2024-01-01T14:19:35.436109Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"ls /kaggle/usr/lib/resize-and-load-with-feather-format-much-faster/","metadata":{"execution":{"iopub.status.busy":"2024-01-01T14:19:38.899542Z","iopub.execute_input":"2024-01-01T14:19:38.899903Z","iopub.status.idle":"2024-01-01T14:19:39.922158Z","shell.execute_reply.started":"2024-01-01T14:19:38.899843Z","shell.execute_reply":"2024-01-01T14:19:39.921395Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"__notebook__.ipynb custom.css train_data_0.feather\n__output__.json test_data_0.feather train_data_1.feather\n__results__.html test_data_1.feather train_data_2.feather\n\u001b[0m\u001b[01;34m__results___files\u001b[0m/ test_data_2.feather train_data_3.feather\n__resultx__.html test_data_3.feather\n","output_type":"stream"}]},{"cell_type":"code","source":"data_full = pd.concat([data0,data1,data2,data3],ignore_index=True)","metadata":{"execution":{"iopub.status.busy":"2024-01-01T14:19:44.427835Z","iopub.execute_input":"2024-01-01T14:19:44.428132Z","iopub.status.idle":"2024-01-01T14:19:44.734560Z","shell.execute_reply.started":"2024-01-01T14:19:44.428092Z","shell.execute_reply":"2024-01-01T14:19:44.733865Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"class GraphemeDataset(Dataset):\n def __init__(self,df,label,_type='train'):\n self.df = df\n self.label = label\n def __len__(self):\n return len(self.df)\n def __getitem__(self,idx):\n label1 = self.label.vowel_diacritic.values[idx]\n label2 = self.label.grapheme_root.values[idx]\n label3 = self.label.consonant_diacritic.values[idx]\n image = self.df.iloc[idx][1:].values.reshape(64,64).astype(np.float)\n return image,label1,label2,label3","metadata":{"execution":{"iopub.status.busy":"2024-01-01T14:19:51.237519Z","iopub.execute_input":"2024-01-01T14:19:51.237896Z","iopub.status.idle":"2024-01-01T14:19:51.247013Z","shell.execute_reply.started":"2024-01-01T14:19:51.237830Z","shell.execute_reply":"2024-01-01T14:19:51.246233Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"class ResidualBlock(nn.Module):\n def __init__(self,in_channels,out_channels,stride=1,kernel_size=3,padding=1,bias=False):\n super(ResidualBlock,self).__init__()\n self.cnn1 =nn.Sequential(\n nn.Conv2d(in_channels,out_channels,kernel_size,stride,padding,bias=False),\n nn.BatchNorm2d(out_channels),\n nn.ReLU(True)\n )\n self.cnn2 = nn.Sequential(\n nn.Conv2d(out_channels,out_channels,kernel_size,1,padding,bias=False),\n nn.BatchNorm2d(out_channels)\n )\n if stride != 1 or in_channels != out_channels:\n self.shortcut = nn.Sequential(\n nn.Conv2d(in_channels,out_channels,kernel_size=1,stride=stride,bias=False),\n nn.BatchNorm2d(out_channels)\n )\n else:\n self.shortcut = nn.Sequential()\n \n def forward(self,x):\n residual = x\n x = self.cnn1(x)\n x = self.cnn2(x)\n x += self.shortcut(residual)\n x = nn.ReLU(True)(x)\n return x","metadata":{"execution":{"iopub.status.busy":"2024-01-01T14:19:58.573202Z","iopub.execute_input":"2024-01-01T14:19:58.573551Z","iopub.status.idle":"2024-01-01T14:19:58.584969Z","shell.execute_reply.started":"2024-01-01T14:19:58.573491Z","shell.execute_reply":"2024-01-01T14:19:58.584203Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"markdown","source":"## ResNet-18 Model","metadata":{}},{"cell_type":"code","source":"class ResNet18(nn.Module):\n def __init__(self):\n super(ResNet18,self).__init__()\n \n self.block1 = nn.Sequential(\n nn.Conv2d(1,64,kernel_size=2,stride=2,padding=3,bias=False),\n nn.BatchNorm2d(64),\n nn.ReLU(True)\n )\n \n self.block2 = nn.Sequential(\n nn.MaxPool2d(1,1),\n ResidualBlock(64,64),\n ResidualBlock(64,64,2)\n )\n \n self.block3 = nn.Sequential(\n ResidualBlock(64,128),\n ResidualBlock(128,128,2)\n )\n \n self.block4 = nn.Sequential(\n ResidualBlock(128,256),\n ResidualBlock(256,256,2)\n )\n self.block5 = nn.Sequential(\n ResidualBlock(256,512),\n ResidualBlock(512,512,2)\n )\n \n self.avgpool = nn.AvgPool2d(2)\n # vowel_diacritic\n self.fc1 = nn.Linear(512,11)\n # grapheme_root\n self.fc2 = nn.Linear(512,168)\n # consonant_diacritic\n self.fc3 = nn.Linear(512,7)\n \n def forward(self,x):\n x = self.block1(x)\n x = self.block2(x)\n x = self.block3(x)\n x = self.block4(x)\n x = self.block5(x)\n x = self.avgpool(x)\n x = x.view(x.size(0),-1)\n x1 = self.fc1(x)\n x2 = self.fc2(x)\n x3 = self.fc3(x)\n return x1,x2,x3","metadata":{"execution":{"iopub.status.busy":"2024-01-01T14:20:03.160283Z","iopub.execute_input":"2024-01-01T14:20:03.160633Z","iopub.status.idle":"2024-01-01T14:20:03.175818Z","shell.execute_reply.started":"2024-01-01T14:20:03.160573Z","shell.execute_reply":"2024-01-01T14:20:03.175033Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"markdown","source":"## ResNet34 Model","metadata":{}},{"cell_type":"code","source":"class ResNet34(nn.Module):\n def __init__(self):\n super(ResNet34,self).__init__()\n \n self.block1 = nn.Sequential(\n nn.Conv2d(1,64,kernel_size=2,stride=2,padding=3,bias=False),\n nn.BatchNorm2d(64),\n nn.ReLU(True)\n )\n \n self.block2 = nn.Sequential(\n nn.MaxPool2d(1,1),\n ResidualBlock(64,64),\n ResidualBlock(64,64,2)\n )\n \n self.block3 = nn.Sequential(\n ResidualBlock(64,128),\n ResidualBlock(128,128,2)\n )\n \n self.block4 = nn.Sequential(\n ResidualBlock(128,256),\n ResidualBlock(256,256,2)\n )\n self.block5 = nn.Sequential(\n ResidualBlock(256,512),\n ResidualBlock(512,512,2)\n )\n \n self.avgpool = nn.AvgPool2d(2)\n # vowel_diacritic\n self.fc1 = nn.Linear(512,11)\n # grapheme_root\n self.fc2 = nn.Linear(512,168)\n # consonant_diacritic\n self.fc3 = nn.Linear(512,7)\n \n def forward(self,x):\n x = self.block1(x)\n x = self.block2(x)\n x = self.block3(x)\n x = self.block4(x)\n x = self.block5(x)\n x = self.avgpool(x)\n x = x.view(x.size(0),-1)\n x1 = self.fc1(x)\n x2 = self.fc2(x)\n x3 = self.fc3(x)\n return x1,x2,x3","metadata":{"execution":{"iopub.status.busy":"2024-01-01T14:20:06.342511Z","iopub.execute_input":"2024-01-01T14:20:06.342795Z","iopub.status.idle":"2024-01-01T14:20:06.358381Z","shell.execute_reply.started":"2024-01-01T14:20:06.342755Z","shell.execute_reply":"2024-01-01T14:20:06.357297Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"code","source":"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")","metadata":{"execution":{"iopub.status.busy":"2024-01-01T14:20:18.402344Z","iopub.execute_input":"2024-01-01T14:20:18.402819Z","iopub.status.idle":"2024-01-01T14:20:18.472291Z","shell.execute_reply.started":"2024-01-01T14:20:18.402598Z","shell.execute_reply":"2024-01-01T14:20:18.471354Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"markdown","source":"## Training Model\n","metadata":{}},{"cell_type":"code","source":"model = ResNet34().to(device)\noptimizer = optimizer = torch.optim.Adam(model.parameters(), lr=4e-4)\n#scheduler = torch.optim.lr_scheduler.CyclicLR(optimizer, base_lr=1e-4, max_lr=0.05)\ncriterion = nn.CrossEntropyLoss()\nbatch_size=32","metadata":{"execution":{"iopub.status.busy":"2024-01-01T14:20:30.913347Z","iopub.execute_input":"2024-01-01T14:20:30.913655Z","iopub.status.idle":"2024-01-01T14:20:35.338082Z","shell.execute_reply.started":"2024-01-01T14:20:30.913612Z","shell.execute_reply":"2024-01-01T14:20:35.337457Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"epochs = 10\nmodel.train()\nlosses = []\naccs = []\nfor epoch in range(epochs):\n reduced_index =train.groupby(['grapheme_root', 'vowel_diacritic', 'consonant_diacritic']).apply(lambda x: x.sample(5)).image_id.values\n reduced_train = train.loc[train.image_id.isin(reduced_index)]\n reduced_data = data_full.loc[data_full.image_id.isin(reduced_index)]\n train_image = GraphemeDataset(reduced_data,reduced_train)\n train_loader = torch.utils.data.DataLoader(train_image,batch_size=batch_size,shuffle=True)\n \n print('epochs {}/{} '.format(epoch+1,epochs))\n running_loss = 0.0\n running_acc = 0.0\n for idx, (inputs,labels1,labels2,labels3) in tqdm(enumerate(train_loader),total=len(train_loader)):\n inputs = inputs.to(device)\n labels1 = labels1.to(device)\n labels2 = labels2.to(device)\n labels3 = labels3.to(device)\n \n optimizer.zero_grad()\n outputs1,outputs2,outputs3 = model(inputs.unsqueeze(1).float())\n loss1 = criterion(outputs1,labels1)\n loss2 = criterion(outputs2,labels2)\n loss3 = criterion(outputs3,labels3)\n running_loss += loss1+loss2+loss3\n running_acc += (outputs1.argmax(1)==labels1).float().mean()\n running_acc += (outputs2.argmax(1)==labels2).float().mean()\n running_acc += (outputs3.argmax(1)==labels3).float().mean()\n (loss1+loss2+loss3).backward()\n optimizer.step()\n #scheduler.step()\n losses.append(running_loss/len(train_loader))\n accs.append(running_acc/(len(train_loader)*3))\n print('acc : {:.2f}%'.format(running_acc/(len(train_loader)*3)))\n print('loss : {:.4f}'.format(running_loss/len(train_loader)))\ntorch.save(model.state_dict(), 'resnet34_50epochs_saved_weights.pth')","metadata":{"execution":{"iopub.status.busy":"2024-01-01T14:24:57.868555Z","iopub.execute_input":"2024-01-01T14:24:57.868928Z","iopub.status.idle":"2024-01-01T14:31:36.747758Z","shell.execute_reply.started":"2024-01-01T14:24:57.868854Z","shell.execute_reply":"2024-01-01T14:31:36.746766Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stdout","text":"epochs 1/10 \n","output_type":"stream"},{"name":"stderr","text":"/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:15: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\nPlease use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n from ipykernel import kernelapp as app\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=202), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a41a318fe5884e86a7abaf8258eccc8d"}},"metadata":{}},{"name":"stdout","text":"\nacc : 0.66%\nloss : 3.6364\nepochs 2/10 \n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=202), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8457f8ca04b247aa86957084d0dbed7f"}},"metadata":{}},{"name":"stdout","text":"\nacc : 0.69%\nloss : 3.1594\nepochs 3/10 \n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=202), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ffc415c85c994bb1b6ee0b9d6ad3a6b9"}},"metadata":{}},{"name":"stdout","text":"\nacc : 0.73%\nloss : 2.7390\nepochs 4/10 \n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=202), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"689c8d882075426e95331f7297c19424"}},"metadata":{}},{"name":"stdout","text":"\nacc : 0.76%\nloss : 2.4014\nepochs 5/10 \n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=202), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2a4f582e7bcb4354b7f2a3c949ec4cdb"}},"metadata":{}},{"name":"stdout","text":"\nacc : 0.78%\nloss : 2.1855\nepochs 6/10 \n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=202), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"631d4f6069244023b4f6af2160c55333"}},"metadata":{}},{"name":"stdout","text":"\nacc : 0.79%\nloss : 2.0200\nepochs 7/10 \n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=202), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"920afdef6cba4be88a82aa69a8765163"}},"metadata":{}},{"name":"stdout","text":"\nacc : 0.81%\nloss : 1.8457\nepochs 8/10 \n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=202), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2c0820eca9734aab99f835303ee2c220"}},"metadata":{}},{"name":"stdout","text":"\nacc : 0.83%\nloss : 1.6976\nepochs 9/10 \n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=202), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0c98553db1164cdf8124693680d70703"}},"metadata":{}},{"name":"stdout","text":"\nacc : 0.84%\nloss : 1.5551\nepochs 10/10 \n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=202), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ba27cfc4bfc34b0d9ef8eb8f4e495230"}},"metadata":{}},{"name":"stdout","text":"\nacc : 0.85%\nloss : 1.4813\n","output_type":"stream"}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nfig,ax = plt.subplots(1,2,figsize=(15,5))\nax[0].plot(losses)\nax[0].set_title('loss')\nax[1].plot(accs)\nax[1].set_title('acc')","metadata":{"execution":{"iopub.status.busy":"2024-01-01T14:31:41.344964Z","iopub.execute_input":"2024-01-01T14:31:41.345308Z","iopub.status.idle":"2024-01-01T14:31:41.940637Z","shell.execute_reply.started":"2024-01-01T14:31:41.345247Z","shell.execute_reply":"2024-01-01T14:31:41.939572Z"},"trusted":true},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"Text(0.5, 1.0, 'acc')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"# Part 2","metadata":{}},{"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# Any results you write to the current directory are saved as output.\nimport cv2\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import Dataset,DataLoader\nfrom torchvision import transforms,models\nfrom tqdm import tqdm_notebook as tqdm","metadata":{"execution":{"iopub.status.busy":"2024-01-01T14:32:28.104529Z","iopub.execute_input":"2024-01-01T14:32:28.104856Z","iopub.status.idle":"2024-01-01T14:32:28.306004Z","shell.execute_reply.started":"2024-01-01T14:32:28.104801Z","shell.execute_reply":"2024-01-01T14:32:28.305237Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"/kaggle/input/bengaliai-cv19/test_image_data_2.parquet\n/kaggle/input/bengaliai-cv19/sample_submission.csv\n/kaggle/input/bengaliai-cv19/class_map.csv\n/kaggle/input/bengaliai-cv19/train_image_data_2.parquet\n/kaggle/input/bengaliai-cv19/train_multi_diacritics.csv\n/kaggle/input/bengaliai-cv19/train_image_data_0.parquet\n/kaggle/input/bengaliai-cv19/train_image_data_3.parquet\n/kaggle/input/bengaliai-cv19/test_image_data_0.parquet\n/kaggle/input/bengaliai-cv19/test_image_data_1.parquet\n/kaggle/input/bengaliai-cv19/train.csv\n/kaggle/input/bengaliai-cv19/test.csv\n/kaggle/input/bengaliai-cv19/train_image_data_1.parquet\n/kaggle/input/bengaliai-cv19/class_map_corrected.csv\n/kaggle/input/bengaliai-cv19/test_image_data_3.parquet\n/kaggle/input/grapheme-resnet-18-naive-learning-2/__results__.html\n/kaggle/input/grapheme-resnet-18-naive-learning-2/saved_weights.pth\n/kaggle/input/grapheme-resnet-18-naive-learning-2/__resultx__.html\n/kaggle/input/grapheme-resnet-18-naive-learning-2/__notebook__.ipynb\n/kaggle/input/grapheme-resnet-18-naive-learning-2/__output__.json\n/kaggle/input/grapheme-resnet-18-naive-learning-2/custom.css\n/kaggle/input/grapheme-resnet-18-naive-learning-2/__results___files/__results___10_1.png\n/kaggle/input/trained400/resnet34_400epochs_saved_weights.pth\n","output_type":"stream"}]},{"cell_type":"code","source":"test = pd.read_csv('/kaggle/input/bengaliai-cv19/test.csv')","metadata":{"execution":{"iopub.status.busy":"2024-01-01T14:32:32.324664Z","iopub.execute_input":"2024-01-01T14:32:32.324982Z","iopub.status.idle":"2024-01-01T14:32:32.344791Z","shell.execute_reply.started":"2024-01-01T14:32:32.324937Z","shell.execute_reply":"2024-01-01T14:32:32.344062Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"code","source":"class GraphemeDataset(Dataset):\n def __init__(self,df,_type='train'):\n self.df = df\n def __len__(self):\n return len(self.df)\n def __getitem__(self,idx):\n image = self.df.iloc[idx][1:].values.reshape(64,64).astype(float)\n return image","metadata":{"execution":{"iopub.status.busy":"2024-01-01T14:32:35.105456Z","iopub.execute_input":"2024-01-01T14:32:35.105759Z","iopub.status.idle":"2024-01-01T14:32:35.112361Z","shell.execute_reply.started":"2024-01-01T14:32:35.105716Z","shell.execute_reply":"2024-01-01T14:32:35.111399Z"},"trusted":true},"execution_count":22,"outputs":[]},{"cell_type":"code","source":"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\nmodel = ResNet34().to(device)\nmodel.load_state_dict(torch.load('/kaggle/input/trained400/resnet34_400epochs_saved_weights.pth'))","metadata":{"execution":{"iopub.status.busy":"2024-01-01T14:32:37.675715Z","iopub.execute_input":"2024-01-01T14:32:37.676051Z","iopub.status.idle":"2024-01-01T14:32:38.577249Z","shell.execute_reply.started":"2024-01-01T14:32:37.675993Z","shell.execute_reply":"2024-01-01T14:32:38.576348Z"},"trusted":true},"execution_count":23,"outputs":[{"execution_count":23,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}}]},{"cell_type":"code","source":"def Resize(df,size=64):\n resized = {} \n df = df.set_index('image_id')\n for i in tqdm(range(df.shape[0])):\n image = cv2.resize(df.loc[df.index[i]].values.reshape(137,236),(size,size))\n resized[df.index[i]] = image.reshape(-1)\n resized = pd.DataFrame(resized).T.reset_index()\n resized.columns = resized.columns.astype(str)\n resized.rename(columns={'index':'image_id'},inplace=True)\n return resized","metadata":{"execution":{"iopub.status.busy":"2024-01-01T14:32:45.371654Z","iopub.execute_input":"2024-01-01T14:32:45.371993Z","iopub.status.idle":"2024-01-01T14:32:45.380271Z","shell.execute_reply.started":"2024-01-01T14:32:45.371932Z","shell.execute_reply":"2024-01-01T14:32:45.379340Z"},"trusted":true},"execution_count":24,"outputs":[]},{"cell_type":"code","source":"model.eval()\ntest_data = ['test_image_data_0.parquet','test_image_data_1.parquet','test_image_data_2.parquet','test_image_data_3.parquet']\npredictions = []\nbatch_size=1\nfor fname in test_data:\n data = pd.read_parquet(f'/kaggle/input/bengaliai-cv19/{fname}')\n data = Resize(data)\n test_image = GraphemeDataset(data)\n test_loader = torch.utils.data.DataLoader(test_image,batch_size=1,shuffle=False)\n with torch.no_grad():\n for idx, (inputs) in tqdm(enumerate(test_loader),total=len(test_loader)):\n inputs.to(device)\n \n outputs1,outputs2,outputs3 = model(inputs.unsqueeze(1).float().cuda())\n predictions.append(outputs3.argmax(1).cpu().detach().numpy())\n predictions.append(outputs2.argmax(1).cpu().detach().numpy())\n predictions.append(outputs1.argmax(1).cpu().detach().numpy())","metadata":{"execution":{"iopub.status.busy":"2024-01-01T14:32:53.104209Z","iopub.execute_input":"2024-01-01T14:32:53.104547Z","iopub.status.idle":"2024-01-01T14:36:01.499794Z","shell.execute_reply.started":"2024-01-01T14:32:53.104500Z","shell.execute_reply":"2024-01-01T14:36:01.499007Z"},"trusted":true},"execution_count":25,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:4: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\nPlease use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n after removing the cwd from sys.path.\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=3), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"81e40eb081c84160a7c5e4b6d5960469"}},"metadata":{}},{"name":"stdout","text":"\n","output_type":"stream"},{"name":"stderr","text":"/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:11: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\nPlease use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n # This is added back by InteractiveShellApp.init_path()\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=3), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7fc9a0588a4643e4bb2125b0212be778"}},"metadata":{}},{"name":"stdout","text":"\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=3), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"67827d406b8f469bb4f537be9db34c2d"}},"metadata":{}},{"name":"stdout","text":"\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=3), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"199e9fde99614ab9981f3fda99f1330e"}},"metadata":{}},{"name":"stdout","text":"\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=3), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3b5b8cd27df149019fb8eee7cc78a680"}},"metadata":{}},{"name":"stdout","text":"\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=3), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"586f722768444b44bc9fc79945fd68f5"}},"metadata":{}},{"name":"stdout","text":"\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=3), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"eadad35c23f34337b56e337922330b1e"}},"metadata":{}},{"name":"stdout","text":"\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=3), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b5ede7ff2c9f4fa09f4ab22c7abae976"}},"metadata":{}},{"name":"stdout","text":"\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Save Results","metadata":{}},{"cell_type":"code","source":"submission = pd.read_csv('/kaggle/input/bengaliai-cv19/sample_submission.csv')","metadata":{"execution":{"iopub.status.busy":"2024-01-01T14:36:01.501793Z","iopub.execute_input":"2024-01-01T14:36:01.502051Z","iopub.status.idle":"2024-01-01T14:36:01.515250Z","shell.execute_reply.started":"2024-01-01T14:36:01.502003Z","shell.execute_reply":"2024-01-01T14:36:01.514300Z"},"trusted":true},"execution_count":26,"outputs":[]},{"cell_type":"code","source":"submission.target = np.hstack(predictions)\nsubmission.head(10)","metadata":{"execution":{"iopub.status.busy":"2024-01-01T14:36:01.516567Z","iopub.execute_input":"2024-01-01T14:36:01.516875Z","iopub.status.idle":"2024-01-01T14:36:01.531229Z","shell.execute_reply.started":"2024-01-01T14:36:01.516819Z","shell.execute_reply":"2024-01-01T14:36:01.530621Z"},"trusted":true},"execution_count":27,"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":" row_id target\n0 Test_0_consonant_diacritic 0\n1 Test_0_grapheme_root 3\n2 Test_0_vowel_diacritic 0\n3 Test_1_consonant_diacritic 0\n4 Test_1_grapheme_root 93\n5 Test_1_vowel_diacritic 2\n6 Test_2_consonant_diacritic 0\n7 Test_2_grapheme_root 19\n8 Test_2_vowel_diacritic 0\n9 Test_3_consonant_diacritic 0","text/html":"
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row_idtarget
0Test_0_consonant_diacritic0
1Test_0_grapheme_root3
2Test_0_vowel_diacritic0
3Test_1_consonant_diacritic0
4Test_1_grapheme_root93
5Test_1_vowel_diacritic2
6Test_2_consonant_diacritic0
7Test_2_grapheme_root19
8Test_2_vowel_diacritic0
9Test_3_consonant_diacritic0
\n
"},"metadata":{}}]},{"cell_type":"code","source":"submission.to_csv('submission.csv',index=False)","metadata":{"execution":{"iopub.status.busy":"2024-01-01T14:37:55.542700Z","iopub.execute_input":"2024-01-01T14:37:55.543069Z","iopub.status.idle":"2024-01-01T14:37:55.549062Z","shell.execute_reply.started":"2024-01-01T14:37:55.543007Z","shell.execute_reply":"2024-01-01T14:37:55.548216Z"},"trusted":true},"execution_count":29,"outputs":[]}]} \ No newline at end of file